From 9e54fb0f35035f89c2ebb52d8c5071e1fb909955 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Wed, 4 Dec 2024 14:59:53 +0000
Subject: [PATCH 01/64] dynamic=True, mods to PredictTokenizer

---
 cellarium/ml/layers/attention.py     |  2 +-
 cellarium/ml/models/cellarium_gpt.py | 47 ++++++++++++++--------------
 2 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/cellarium/ml/layers/attention.py b/cellarium/ml/layers/attention.py
index 892e796f..5b3a78fb 100644
--- a/cellarium/ml/layers/attention.py
+++ b/cellarium/ml/layers/attention.py
@@ -19,7 +19,7 @@ def use_cs() -> bool:
         return False
 
 
-compiled_flex_attention = torch.compile(flex_attention, dynamic=False)
+compiled_flex_attention = torch.compile(flex_attention, dynamic=True)
 
 
 class MultiHeadAttention(nn.Module):
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 547df87b..16fa30cb 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -88,43 +88,43 @@ def forward(
         gene_tokens_nc: dict[str, torch.Tensor],
         gene_prompt_mask_nc: torch.Tensor,
     ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
-        ### GENE TOKENS ###
 
-        ## gene value ##
-        gene_value_nc = gene_tokens_nc.pop("gene_value")
-        total_mrna_umis_nc = gene_tokens_nc.pop("total_mrna_umis")
+        # step 1. gene tokens
+        gene_value_nc = gene_tokens_nc.pop("gene_value").float()
+        total_mrna_umis_nc = gene_tokens_nc.pop("total_mrna_umis").float()
         device = gene_value_nc.device
-        # downsample gene values
+
+        # step 1.1 downsample gene values (by scaling, to preserve differentiability)
         max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
         downsampled_total_mrna_umis_nc = torch.minimum(total_mrna_umis_nc, max_total_mrna_umis).float()
         gene_downsample_p_nc = downsampled_total_mrna_umis_nc / total_mrna_umis_nc
-        gene_value_nc = torch.binomial(gene_value_nc, gene_downsample_p_nc)
-        total_mrna_umis_nc = torch.round(downsampled_total_mrna_umis_nc)
+        gene_value_nc = gene_value_nc * gene_downsample_p_nc
+        total_mrna_umis_nc = downsampled_total_mrna_umis_nc
 
+        # step 1.2 create gene query and prompt masks
         gene_query_mask_nc = ~gene_prompt_mask_nc
         gene_value_nc3 = torch.stack(
             [
-                torch.log1p(gene_value_nc) * gene_prompt_mask_nc.float(),
-                gene_query_mask_nc.float(),
+                torch.log1p(gene_value_nc) * gene_prompt_mask_nc.float(), # masked values are set to 0
+                gene_query_mask_nc.float(),  # 1 if the gene is queried, 0 if the gene is prompted
                 torch.log1p(total_mrna_umis_nc),
             ],
             dim=2,
         )
         gene_tokens_nc["gene_value"] = gene_value_nc3
 
-        ### METADATA TOKENS ###
-
-        ## metadata tokens ##
-        # assign token codes based on the ontology info
-        # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+        # step 2. metadata tokens
+        #   assign token codes based on the ontology info
+        #   token values not in the ontology are treated as unmeasured and assigned a code value of -1
         for key, ontology_info in self.ontology_infos.items():
-            assert self.metadata_vocab_sizes[key] == len(ontology_info["labels"])
+            assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
             metadata_tokens_n[key] = torch.tensor(
-                pd.Categorical(metadata_tokens_n[key], categories=ontology_info["labels"]).codes,
+                pd.Categorical(metadata_tokens_n[key], categories=ontology_info["names"]).codes,
                 dtype=torch.int,
             )
-        # create metadata query and prompt masks
-        metadata_prompt_mask_nm = torch.stack([metadata_prompt_masks_n[key] for key in metadata_tokens_n], dim=1)
+        # step 2.1 create metadata query and prompt masks
+        metadata_prompt_mask_nm = torch.stack([
+            metadata_prompt_masks_n[key] for key in self.ontology_infos.keys()], dim=1)
         metadata_query_mask_nm = ~metadata_prompt_mask_nm
 
         # clamp unmeasured tokens to 0
@@ -132,13 +132,13 @@ def forward(
         #     metadata_tokens_n[key] = metadata_token_n.clamp(0).int()
 
         # impute mask token for unmeasured metadata
-        # mask token is the last token in the vocabulary
         for i, (key, metadata_token_n) in enumerate(metadata_tokens_n.items()):
+            # for each metadata type, mask token is the last token in the vocabulary
+            mask_token_index = self.metadata_vocab_sizes[key]
             metadata_tokens_n[key] = torch.where(
-                metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
-            ).int()
+                metadata_query_mask_nm[:, i], mask_token_index, metadata_token_n).int()
 
-        ### PROMPT MASK ###
+        # step 3. generate the full prompt mask
         prompt_mask_nc = torch.cat([gene_prompt_mask_nc, metadata_prompt_mask_nm], dim=1)
 
         return {
@@ -586,7 +586,8 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
                 return prompt_mask_nc[b, kv_idx] | (q_idx == kv_idx)
 
             n, c = prompt_mask_nc.shape
-            attention_mask_ncc = create_block_mask(prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c)
+            attention_mask_ncc = create_block_mask(
+                prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c, BLOCK_SIZE=c, _compile=True)
         else:
             attention_mask_ncc = prompt_diagonal_mask(prompt_mask_nc)
 

From 39f41a3cfc11319fe0101072bb34f80ca071bd64 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 7 Dec 2024 16:20:38 +0000
Subject: [PATCH 02/64] misc

---
 cellarium/ml/models/cellarium_gpt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 16fa30cb..6b2df404 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -587,7 +587,7 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
 
             n, c = prompt_mask_nc.shape
             attention_mask_ncc = create_block_mask(
-                prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c, BLOCK_SIZE=c, _compile=True)
+                prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c, BLOCK_SIZE=c, device=prompt_mask_nc.device, _compile=False)
         else:
             attention_mask_ncc = prompt_diagonal_mask(prompt_mask_nc)
 

From aa022549e389231e1e38374f91662f6922c7dd2e Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 11 Dec 2024 20:22:38 +0000
Subject: [PATCH 03/64] wip

---
 cellarium/ml/layers/embedding.py     |  48 ++--
 cellarium/ml/layers/head.py          |   6 +-
 cellarium/ml/models/__init__.py      |   2 +
 cellarium/ml/models/cellarium_gpt.py | 370 +++++++++++++++++++++++++++
 cellarium/ml/utilities/data.py       |  10 +-
 cellarium/ml/utilities/mup.py        |  13 +-
 tests/test_cellarium_gpt.py          | 104 ++++++++
 7 files changed, 520 insertions(+), 33 deletions(-)
 create mode 100644 cellarium/ml/models/cellarium_gpt.py
 create mode 100644 tests/test_cellarium_gpt.py

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 6db31786..4b0d023f 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -16,8 +16,8 @@ class GeneExpressionEmbedding(nn.Module):
     Args:
         categorical_vocab_sizes:
             Categorical gene token vocabulary sizes.
-        continuous_vocab_sizes:
-            Continuous gene token vocabulary sizes.
+        continuous_tokens:
+            Continuous gene tokens.
         d_model:
             Dimensionality of the embeddings and hidden states.
         embeddings_initializer:
@@ -27,34 +27,39 @@ class GeneExpressionEmbedding(nn.Module):
     def __init__(
         self,
         categorical_vocab_sizes: dict[str, int],
-        continuous_vocab_sizes: dict[str, int],
+        continuous_tokens: list[str],
         d_model: int,
         embeddings_initializer: dict[str, Any],
     ) -> None:
         super().__init__()
-        self.E = nn.ModuleDict()
-        self.E.update({key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()})
-        self.E.update(
-            {key: nn.Linear(vocab_size, d_model, bias=False) for key, vocab_size in continuous_vocab_sizes.items()}
+        self.embedding_dict = nn.ModuleDict()
+        self.embedding_dict.update(
+            {key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()}
         )
+        self.embedding_dict.update({key: nn.Linear(1, d_model, bias=False) for key in continuous_tokens})
+        self.categorical_vocab_sizes = categorical_vocab_sizes
+        self.continuous_tokens = continuous_tokens
         self.embeddings_initializer = embeddings_initializer
 
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
-        for module in self.E.children():
+        for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, gene_tokens_nc: dict[str, torch.Tensor]) -> torch.Tensor:
+    def forward(self, gene_tokens_dict_ns: dict[str, torch.Tensor]) -> torch.Tensor:
         """
         Args:
-            gene_tokens_nc:
-                Dictionary of gene token tensors of shape ``(n, c)``.
+            gene_tokens_dict_ns:
+                Dictionary of gene token tensors of shape ``(n, s)``.
 
         Returns:
-            The gene embedding tensor of shape ``(n, c, d)``.
+            The gene embedding tensor of shape ``(n, s, d)``.
         """
-        return sum(self.E[key](gene_token_nc) for key, gene_token_nc in gene_tokens_nc.items())
+        return sum(
+            self.embedding_dict[key](gene_token_ns.unsqueeze(-1) if key in self.continuous_tokens else gene_token_ns)
+            for key, gene_token_ns in gene_tokens_dict_ns.items()
+        )
 
 
 class MetadataEmbedding(nn.Module):
@@ -77,7 +82,7 @@ def __init__(
         embeddings_initializer: dict[str, Any],
     ) -> None:
         super().__init__()
-        self.E = nn.ModuleDict(
+        self.embedding_dict = nn.ModuleDict(
             {key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()}
         )
         self.embeddings_initializer = embeddings_initializer
@@ -85,19 +90,18 @@ def __init__(
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
-        for module in self.E.children():
+        for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, metadata_tokens_n: dict[str, torch.Tensor]) -> torch.Tensor:
+    def forward(self, metadata_tokens_dict_n: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
         """
         Args:
-            metadata_token_n:
+            metadata_token_dict_n:
                 Dictionary of metadata token tensors of shape ``(n,)``.
 
         Returns:
-            The metadata embedding tensor of shape ``(n, m, d)``.
+            Dictionary of metadata embedding tensors of shape ``(n, d)``.
         """
-        return torch.stack(
-            [self.E[key](metadata_token_n) for key, metadata_token_n in metadata_tokens_n.items()],
-            dim=1,
-        )
+        return {
+            key: self.embedding_dict[key](metadata_token_n) for key, metadata_token_n in metadata_tokens_dict_n.items()
+        }
diff --git a/cellarium/ml/layers/head.py b/cellarium/ml/layers/head.py
index 1d6b94e3..aad1d8e7 100644
--- a/cellarium/ml/layers/head.py
+++ b/cellarium/ml/layers/head.py
@@ -35,7 +35,7 @@ def __init__(
         heads_initializer: dict[str, Any],
     ) -> None:
         super().__init__()
-        self.W = nn.ModuleDict(
+        self.readout_dict = nn.ModuleDict(
             {key: nn.Linear(d_model, vocab_size, use_bias) for key, vocab_size in categorical_vocab_sizes.items()}
         )
         self.output_logits_scale = output_logits_scale
@@ -44,7 +44,7 @@ def __init__(
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
-        for module in self.W.children():
+        for module in self.readout_dict.children():
             create_initializer(self.heads_initializer)(module.weight)
 
             if module.bias is not None:
@@ -59,4 +59,4 @@ def forward(self, hidden_state_ncd: torch.Tensor) -> dict[str, torch.Tensor]:
         Returns:
             Dictionary of output logits tensors of shape ``(n, c, vocab_size)``.
         """
-        return {key: self.output_logits_scale * self.W[key](hidden_state_ncd) for key in self.W}
+        return {key: self.output_logits_scale * self.readout_dict[key](hidden_state_ncd) for key in self.readout_dict}
diff --git a/cellarium/ml/models/__init__.py b/cellarium/ml/models/__init__.py
index e5b05abf..d97dd110 100644
--- a/cellarium/ml/models/__init__.py
+++ b/cellarium/ml/models/__init__.py
@@ -1,6 +1,7 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+from cellarium.ml.models.cellarium_gpt import CellariumGPT
 from cellarium.ml.models.geneformer import Geneformer
 from cellarium.ml.models.incremental_pca import IncrementalPCA
 from cellarium.ml.models.logistic_regression import LogisticRegression
@@ -10,6 +11,7 @@
 from cellarium.ml.models.tdigest import TDigest
 
 __all__ = [
+    "CellariumGPT",
     "CellariumModel",
     "Geneformer",
     "IncrementalPCA",
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
new file mode 100644
index 00000000..0754bee0
--- /dev/null
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -0,0 +1,370 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+from typing import Literal
+
+import lightning.pytorch as pl
+import numpy as np
+import torch
+from torch import nn
+from torch.nn.attention.flex_attention import BlockMask, create_block_mask
+
+from cellarium.ml.layers import GeneExpressionEmbedding, MetadataEmbedding, MultiHeadReadout, Transformer
+from cellarium.ml.models.model import CellariumModel, PredictMixin, ValidateMixin
+from cellarium.ml.utilities.layers import scale_initializers_by_dimension
+from cellarium.ml.utilities.mup import LRAdjustmentGroup
+
+try:
+    from cerebras.pytorch.backend import use_cs
+except ImportError:
+
+    def use_cs() -> bool:
+        return False
+
+
+torch.set_float32_matmul_precision("high")
+
+
+def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
+    """
+    Generate a prompt diagonal mask for self-attention.
+
+    Args:
+        prompt_mask_nc:
+            The prompt mask.
+
+    Returns:
+        torch.Tensor: The prompt diagonal mask.
+
+    Example:
+
+        For prompt_mask = [True, False, True, False, False], the attention mask is:
+
+        [[True, False, True, False, False],
+         [True, True,  True, False, False],
+         [True, False, True, False, False],
+         [True, False, True, True,  False],
+         [True, False, True, False, True]]
+    """
+    device = prompt_mask_nc.device
+    n, c = prompt_mask_nc.shape
+    if use_cs():
+        c_range = torch.arange(c, device=device, dtype=torch.float32)
+        diag_mask_ncc = (c_range[:, None].expand(n, -1, 1) - c_range.expand(n, 1, -1)).abs()
+        prompt_mask_n1c = 1 - prompt_mask_nc[:, None, :].float()
+        attention_mask_ncc = diag_mask_ncc * prompt_mask_n1c
+        return attention_mask_ncc == 0
+    else:
+        diag_mask_cc = torch.eye(c, dtype=torch.bool, device=device)
+        attention_mask_ncc = prompt_mask_nc[:, None, :] | diag_mask_cc
+        return attention_mask_ncc
+
+
+class CellariumGPT(CellariumModel, PredictMixin, ValidateMixin):
+    """
+    Cellarium GPT model.
+
+    Args:
+        gene_vocab_sizes:
+            Gene token vocabulary sizes.
+        metadata_vocab_sizes:
+            Metadata token vocabulary sizes.
+        d_model:
+            Dimensionality of the embeddings and hidden states.
+        d_ffn:
+            Dimensionality of the inner feed-forward layers.
+        n_heads:
+            Number of attention heads.
+        n_blocks:
+            Number of transformer blocks.
+        dropout_p:
+            Dropout probability.
+        use_bias:
+            Whether to use bias in the linear transformations.
+        attention_backend:
+            Backend for the attention computation.
+        attention_softmax_fp32:
+            Whether to use float32 for softmax computation when ``torch`` backend is used.
+        loss_scales:
+            A dictionary of loss scales for each label type.
+        initializer_range:
+            The standard deviation of the truncated normal initializer.
+        embeddings_scale:
+            Multiplier for the embeddings.
+        attention_logits_scale:
+            Multiplier for the attention logits.
+        output_logits_scale:
+            Multiplier for the output logits.
+        mup_base_d_model:
+            Base dimensionality of the model for muP.
+        mup_base_d_ffn:
+            Base dimensionality of the inner feed-forward layers for muP.
+    """
+
+    def __init__(
+        self,
+        # Vocab sizes
+        gene_vocab_sizes: dict[str, int],
+        metadata_vocab_sizes: dict[str, int],
+        # Model parameters
+        d_model: int,
+        d_ffn: int,
+        n_heads: int,
+        n_blocks: int,
+        dropout_p: float,
+        use_bias: bool,
+        attention_backend: Literal["flex", "math", "mem_efficient", "torch"],
+        attention_softmax_fp32: bool,
+        loss_scales: dict[str, float],
+        # Tunable parameters
+        initializer_range: float = 0.02,
+        embeddings_scale: float = 1.0,
+        attention_logits_scale: float = 1.0,
+        output_logits_scale: float = 1.0,
+        # muP (maximal update parameterization) parameters
+        mup_base_d_model: int | None = None,
+        mup_base_d_ffn: int | None = None,
+    ) -> None:
+        super().__init__()
+
+        # Vocab sizes
+        self.gene_vocab_sizes = gene_vocab_sizes
+        self.metadata_vocab_sizes = metadata_vocab_sizes
+
+        # Initializers
+        self.initializer_range = initializer_range
+        default_initializer = {
+            "name": "trunc_normal_",
+            "mean": 0.0,
+            "std": self.initializer_range,
+            "a": -2 * self.initializer_range,
+            "b": 2 * self.initializer_range,
+        }
+        embeddings_initializer = default_initializer.copy()
+        Wqkv_initializer = default_initializer.copy()
+        Wo_initializer = default_initializer.copy()
+        dense1_initializer = default_initializer.copy()
+        dense2_initializer = default_initializer.copy()
+        heads_initializer = default_initializer.copy()
+        self.lr_adjustment_groups = {
+            "embedding": LRAdjustmentGroup("*embedding*weight"),
+            "decoder_attention": LRAdjustmentGroup("*transformer*attention*W*weight"),
+            "decoder_input_ffn": LRAdjustmentGroup("*transformer*ffn.dense1*weight"),
+            "decoder_output_ffn": LRAdjustmentGroup("*transformer*ffn.dense2*weight"),
+        }
+
+        # Multipliers
+        self.embeddings_scale = embeddings_scale
+        self.attention_logits_scale = attention_logits_scale
+        self.output_logits_scale = output_logits_scale
+
+        # Handle muP scaling
+        if mup_base_d_model:
+            d_model_width_mult = d_model / mup_base_d_model
+            scale_initializers_by_dimension(
+                [Wqkv_initializer, dense1_initializer],
+                width_scale=d_model_width_mult**-0.5,
+            )
+            scale_initializers_by_dimension(
+                Wo_initializer,
+                width_scale=d_model_width_mult**-0.5,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+            self.output_logits_scale /= d_model_width_mult
+            for lr_adjustment_group in [
+                "decoder_attention",
+                "decoder_input_ffn",
+            ]:
+                self.lr_adjustment_groups[lr_adjustment_group].set_scale(1 / d_model_width_mult)
+            self.width_mult = d_model_width_mult
+        else:
+            scale_initializers_by_dimension(
+                Wo_initializer,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+
+        if mup_base_d_ffn:
+            d_ffn_width_mult = d_ffn / mup_base_d_ffn
+            scale_initializers_by_dimension(
+                dense2_initializer,
+                width_scale=d_ffn_width_mult**-0.5,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+            self.lr_adjustment_groups["decoder_output_ffn"].set_scale(1 / d_ffn_width_mult)
+            assert self.width_mult == d_ffn_width_mult
+        else:
+            scale_initializers_by_dimension(
+                dense2_initializer,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+
+        gene_categorical_vocab_sizes = gene_vocab_sizes.copy()
+        gene_value_vocab_size = gene_categorical_vocab_sizes.pop("gene_value")
+        self.gene_embedding = GeneExpressionEmbedding(
+            categorical_vocab_sizes=gene_categorical_vocab_sizes,
+            continuous_tokens=["gene_value", "gene_query_mask", "total_mrna_umis"],
+            d_model=d_model,
+            embeddings_initializer=embeddings_initializer,
+        )
+        # Add 1 to the vocab size for the metadata embeddings to account for the mask token
+        self.metadata_embedding = MetadataEmbedding(
+            categorical_vocab_sizes={key: vocab_size + 1 for key, vocab_size in metadata_vocab_sizes.items()},
+            d_model=d_model,
+            embeddings_initializer=embeddings_initializer,
+        )
+        self.transformer = Transformer(
+            d_model=d_model,
+            d_ffn=d_ffn,
+            use_bias=use_bias,
+            n_heads=n_heads,
+            n_blocks=n_blocks,
+            dropout_p=dropout_p,
+            attention_logits_scale=attention_logits_scale,
+            attention_backend=attention_backend,
+            attention_softmax_fp32=attention_softmax_fp32,
+            Wqkv_initializer=Wqkv_initializer,
+            Wo_initializer=Wo_initializer,
+            dense1_initializer=dense1_initializer,
+            dense2_initializer=dense2_initializer,
+        )
+        self.head = MultiHeadReadout(
+            categorical_vocab_sizes={"gene_value": gene_value_vocab_size, **metadata_vocab_sizes},
+            d_model=d_model,
+            use_bias=use_bias,
+            output_logits_scale=output_logits_scale,
+            heads_initializer=heads_initializer,
+        )
+        self.loss_scales = loss_scales
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        def _reset_parameters(module):
+            return getattr(module, "_reset_parameters", lambda: None)()
+
+        self.apply(_reset_parameters)
+
+    @property
+    def d_model(self) -> int:
+        return self.transformer.blocks[0].d_model
+
+    @property
+    def d_ffn(self) -> int:
+        return self.transformer.blocks[0].d_ffn
+
+    @property
+    def n_heads(self) -> int:
+        return self.transformer.blocks[0].n_heads
+
+    @property
+    def n_blocks(self) -> int:
+        return len(self.transformer.blocks)
+
+    @property
+    def attention_backend(self) -> Literal["flex", "math", "mem_efficient", "torch"]:
+        return self.transformer.blocks[0].attention.attention_backend
+
+    @attention_backend.setter
+    def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "torch"]) -> None:
+        for block in self.transformer.blocks:
+            block.attention.attention_backend = value
+
+    def predict(
+        self,
+        gene_token_nc_dict: dict[str, torch.Tensor],
+        gene_token_mask_nc: torch.Tensor,
+        metadata_token_nc_dict: dict[str, torch.Tensor],
+        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+    ) -> dict[str, np.ndarray | torch.Tensor]:
+        # Create embeddings
+        gene_embedding_ncd = self.gene_embedding(gene_token_nc_dict) * gene_token_mask_nc.unsqueeze(-1)
+        metadata_embedding_ncd_dict = self.metadata_embedding(metadata_token_nc_dict)
+        metadata_embedding_ncd = sum(
+            metadata_embedding_ncd_dict[key] * metadata_token_mask_nc_dict[key].unsqueeze(-1)
+            for key in metadata_token_nc_dict
+        )
+        embedding_ncd = gene_embedding_ncd + metadata_embedding_ncd
+
+        # Create attention mask
+        attention_mask_ncc: torch.Tensor | BlockMask
+        if self.attention_backend == "flex":
+
+            def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
+                return prompt_mask_nc[b, kv_idx] | (q_idx == kv_idx)
+
+            n, c = prompt_mask_nc.shape
+            attention_mask_ncc = create_block_mask(prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c)
+        else:
+            attention_mask_ncc = prompt_diagonal_mask(prompt_mask_nc)
+
+        # Transformer blocks
+        hidden_state_ncd = embedding_ncd * self.embeddings_scale
+        hidden_state_ncd = self.transformer(hidden_state_ncd, attention_mask_ncc)
+
+        # Compute logits
+        logits_nck_dict = self.head(hidden_state_ncd)
+
+        return logits_nck_dict
+
+    def forward(
+        self,
+        gene_token_nc_dict: dict[str, torch.Tensor],
+        gene_token_mask_nc: torch.Tensor,
+        metadata_token_nc_dict: dict[str, torch.Tensor],
+        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+        label_nc_dict: dict[str, torch.Tensor],
+        label_weight_nc_dict: dict[str, torch.Tensor],
+    ) -> dict[str, torch.Tensor]:
+        logits_nck_dict = self.predict(
+            gene_token_nc_dict=gene_token_nc_dict,
+            gene_token_mask_nc=gene_token_mask_nc,
+            metadata_token_nc_dict=metadata_token_nc_dict,
+            metadata_token_mask_nc_dict=metadata_token_mask_nc_dict,
+            prompt_mask_nc=prompt_mask_nc,
+        )
+
+        # Compute loss
+        losses = {}
+        loss_fn = nn.CrossEntropyLoss(reduction="none")
+        # Make sure that label_nc_dict is created by concatenating the gene_value and metadata labels
+        # in the same order as the embeddings.
+        for key, label_nc in label_nc_dict.items():
+            logits_nck = logits_nck_dict[key]
+            assert isinstance(logits_nck, torch.Tensor)
+            label_weight_nc = label_weight_nc_dict[key]
+            assert isinstance(label_weight_nc, torch.Tensor)
+            losses[key] = torch.sum(
+                loss_fn(logits_nck.view(label_nc.numel(), -1), label_nc.view(-1).long()) * label_weight_nc.view(-1)
+            )
+
+        loss = sum(losses[key] * self.loss_scales[key] for key in losses)
+        assert isinstance(loss, torch.Tensor)
+        losses["loss"] = loss
+
+        return losses
+
+    def validate(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        batch_idx: int,
+        gene_token_ns_dict: dict[str, torch.Tensor],
+        gene_prompt_mask_ns: torch.Tensor,
+        metadata_token_n_dict: dict[str, torch.Tensor],
+        metadata_prompt_mask_n_dict: dict[str, torch.Tensor],
+        labels_nc: dict[str, torch.Tensor],
+        label_weights_nc: dict[str, torch.Tensor],
+    ) -> None:
+        n = gene_prompt_mask_ns.shape[0]
+        loss_dict = self.forward(
+            gene_token_ns_dict=gene_token_ns_dict,
+            gene_prompt_mask_ns=gene_prompt_mask_ns,
+            metadata_token_n_dict=metadata_token_n_dict,
+            metadata_prompt_mask_n_dict=metadata_prompt_mask_n_dict,
+            label_nc_dict=labels_nc,
+            label_weight_nc_dict=label_weights_nc,
+        )
+
+        pl_module.log_dict(loss_dict, sync_dist=True, on_epoch=True, batch_size=n)
diff --git a/cellarium/ml/utilities/data.py b/cellarium/ml/utilities/data.py
index 28e307fd..a9039b0d 100644
--- a/cellarium/ml/utilities/data.py
+++ b/cellarium/ml/utilities/data.py
@@ -107,13 +107,15 @@ def collate_fn(
             # If so, just take the first one
             value = batch[0][key]
         elif isinstance(batch[0][key], dict):
-            if not (key.endswith("_n") or key.endswith("_ng")):
-                raise ValueError(f"Sub-dictionary '{key}' must have a batch dimension (end with '_n' or '_ng').")
+            if not key.split("_")[-1].startswith("n"):
+                raise ValueError(f"Sub-dictionary '{key}' must have a batch dimension 'n' as the first dimension.")
             subkeys = batch[0][key].keys()  # type: ignore[union-attr]
             if len(batch) > 1 and not all(subkeys == data[key].keys() for data in batch[1:]):  # type: ignore[union-attr]
                 raise ValueError(f"All '{key}' sub-dictionaries in the batch must have the same subkeys.")
             value = {subkey: np.concatenate([data[key][subkey] for data in batch], axis=0) for subkey in subkeys}
         else:
+            if not key.split("_")[-1].startswith("n"):
+                raise ValueError(f"Value '{key}' must have a batch dimension 'n' as the first dimension.")
             value = np.concatenate([data[key] for data in batch], axis=0)
 
         collated_batch[key] = value
@@ -146,9 +148,9 @@ def categories_to_codes(x: pd.Series | pd.DataFrame) -> np.ndarray:
         Numpy array.
     """
     if isinstance(x, pd.DataFrame):
-        return x.apply(lambda col: col.cat.codes).to_numpy()
+        return x.apply(lambda col: col.cat.codes).to_numpy(dtype=np.int32)
     else:
-        return np.asarray(x.cat.codes)
+        return np.asarray(x.cat.codes, dtype=np.int32)
 
 
 def get_categories(x: pd.Series) -> np.ndarray:
diff --git a/cellarium/ml/utilities/mup.py b/cellarium/ml/utilities/mup.py
index 368ad795..097b6e6c 100644
--- a/cellarium/ml/utilities/mup.py
+++ b/cellarium/ml/utilities/mup.py
@@ -14,6 +14,14 @@ def convert_glob_to_regex(f: str) -> re.Pattern:
     return re.compile(fnmatch.translate(f))
 
 
+class glob_expression_param_filter:
+    def __init__(self, param_filters: list[re.Pattern]) -> None:
+        self.param_filters = param_filters
+
+    def __call__(self, name: str) -> bool:
+        return any(filter.fullmatch(name) for filter in self.param_filters)
+
+
 def make_param_filter(param_filter: str | list[str]) -> Callable[[str], bool]:
     """
     Returns the corresponding filter for parameters for the given `param_filter`.
@@ -34,10 +42,7 @@ def make_param_filter(param_filter: str | list[str]) -> Callable[[str], bool]:
         )
     )
 
-    def glob_expression_param_filter(name: str) -> bool:
-        return any(filter.fullmatch(name) for filter in param_filters)
-
-    return glob_expression_param_filter
+    return glob_expression_param_filter(param_filters)
 
 
 class LRAdjustmentGroup:
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
new file mode 100644
index 00000000..f1f1240e
--- /dev/null
+++ b/tests/test_cellarium_gpt.py
@@ -0,0 +1,104 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import math
+import os
+from pathlib import Path
+
+import lightning.pytorch as pl
+import numpy as np
+import torch
+from torch.utils._pytree import tree_flatten, tree_unflatten
+
+from cellarium.ml import CellariumModule
+from cellarium.ml.data import DistributedAnnDataCollection, read_h5ad_file
+from cellarium.ml.models import CellariumGPT
+from cellarium.ml.utilities.data import AnnDataField, categories_to_codes, collate_fn
+
+
+def test_load_from_checkpoint_multi_device(tmp_path: Path):
+    adata = read_h5ad_file("https://storage.googleapis.com/dsp-cellarium-cas-public/test-data/test_0.h5ad")
+    n = adata.n_obs
+    s = 4
+    c = 5
+    batch_size = 2
+    devices = int(os.environ.get("TEST_DEVICES", "1"))
+    prompt_mask_nc = np.random.choice([True, False], size=(batch_size, c), p=[0.5, 0.5])
+    query_mask_nc = (~prompt_mask_nc).astype(np.float32)
+    X = adata.X[:, :s].toarray()
+    cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
+    data = {
+        "gene_token_nc_dict": {
+            "gene_id": np.broadcast_to(np.arange(c), (n, c)),
+            "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),
+            "gene_query_mask": query_mask_nc,
+            "total_mrna_umis": np.broadcast_to(
+                np.asarray(adata.obs["total_mrna_umis"], dtype=np.float32)[:, None], (n, c)
+            ),
+        },
+        "gene_token_mask_nc": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+        "metadata_token_nc_dict": {"cell_type": np.broadcast_to(cell_type, (n, c))},
+        "metadata_token_mask_nc_dict": {
+            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
+        },
+        "prompt_mask_nc": prompt_mask_nc,
+        "label_nc_dict": {
+            "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),
+            "cell_type": np.concatenate([np.zeros((n, s)), cell_type], axis=1),
+        },
+        "label_weight_nc_dict": {
+            "gene_value": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
+        },
+    }
+    flattened_data, tree_spec = tree_flatten(data)
+    # dict_data = {str(i): arg for i, arg in enumerate(flattened_args)}
+    dataset = torch.utils.data.StackDataset(*flattened_data)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        sampler=torch.utils.data.BatchSampler(
+            torch.utils.data.SequentialSampler(dataset), batch_size=batch_size, drop_last=False
+        ),
+        num_workers=0,
+        collate_fn=collate_fn,
+    )
+    # model
+    model = CellariumGPT(
+        gene_vocab_sizes={"gene_id": s, "gene_value": 100},
+        metadata_vocab_sizes={"cell_type": adata.obs["cell_type"].cat.categories.size},
+        d_model=2,
+        d_ffn=4,
+        n_heads=1,
+        n_blocks=1,
+        dropout_p=0,
+        use_bias=False,
+        attention_backend="torch",
+        attention_softmax_fp32=True,
+        loss_scales={"gene_value": 0.8, "cell_type": 0.2},
+        attention_logits_scale=1,
+        mup_base_d_model=2,
+        mup_base_d_ffn=4,
+    )
+    module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3, "eps": 1e-8})
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="cpu",
+        devices=devices,
+        max_steps=n,
+        default_root_dir=tmp_path,
+    )
+    # fit
+    trainer.fit(module, datamodule)
+
+    # run tests only for rank 0
+    if trainer.global_rank != 0:
+        return
+
+    # load model from checkpoint
+    ckpt_path = tmp_path / f"lightning_logs/version_0/checkpoints/epoch=0-step={math.ceil(n / devices)}.ckpt"
+    assert ckpt_path.is_file()
+    loaded_model = CellariumModule.load_from_checkpoint(ckpt_path).model
+    assert isinstance(loaded_model, CellariumGPT)
+    # assert
+    # assert np.array_equal(model.var_names_g, loaded_model.var_names_g)
+    # np.testing.assert_allclose(model.feature_ids, loaded_model.feature_ids)

From c0c1d279e3540f6d11488e7888f302a86ea79d57 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 11 Dec 2024 17:25:14 -0500
Subject: [PATCH 04/64] Add `ComputeNorm` callback (#276)

* Add ComputeNorm callback

* add __init__ method
---
 cellarium/ml/callbacks/__init__.py     |  3 +-
 cellarium/ml/callbacks/compute_norm.py | 72 +++++++++++++++++++++
 tests/common.py                        | 14 +++-
 tests/test_callbacks.py                | 89 ++++++++++++++++++++++++++
 tests/test_logistic_regression.py      | 63 ++++++++++++++++++
 5 files changed, 239 insertions(+), 2 deletions(-)
 create mode 100644 cellarium/ml/callbacks/compute_norm.py
 create mode 100644 tests/test_callbacks.py
 create mode 100644 tests/test_logistic_regression.py

diff --git a/cellarium/ml/callbacks/__init__.py b/cellarium/ml/callbacks/__init__.py
index 4a6f09c3..e2eda8c1 100644
--- a/cellarium/ml/callbacks/__init__.py
+++ b/cellarium/ml/callbacks/__init__.py
@@ -1,9 +1,10 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+from cellarium.ml.callbacks.compute_norm import ComputeNorm
 from cellarium.ml.callbacks.get_coord_data import GetCoordData
 from cellarium.ml.callbacks.loss_scale_monitor import LossScaleMonitor
 from cellarium.ml.callbacks.prediction_writer import PredictionWriter
 from cellarium.ml.callbacks.variance_monitor import VarianceMonitor
 
-__all__ = ["GetCoordData", "LossScaleMonitor", "PredictionWriter", "VarianceMonitor"]
+__all__ = ["ComputeNorm", "GetCoordData", "LossScaleMonitor", "PredictionWriter", "VarianceMonitor"]
diff --git a/cellarium/ml/callbacks/compute_norm.py b/cellarium/ml/callbacks/compute_norm.py
new file mode 100644
index 00000000..8bfe7c35
--- /dev/null
+++ b/cellarium/ml/callbacks/compute_norm.py
@@ -0,0 +1,72 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+
+import re
+from collections import defaultdict
+
+import lightning.pytorch as pl
+import torch
+from lightning.fabric.utilities.rank_zero import rank_zero_only
+
+
+class ComputeNorm(pl.Callback):
+    """
+    A callback to compute the model wise and per layer norm of the parameters and gradients.
+
+    .. note::
+
+        This callback does not support sharded model training.
+
+    Args:
+        layer_name:
+            The name of the layer to compute the per layer norm.
+            If ``None``, the callback will compute the model wise norm only.
+    """
+
+    def __init__(self, layer_name: str | None = None) -> None:
+        self.layer_pattern: re.Pattern[str] | None
+        if layer_name is not None:
+            self.layer_pattern = re.compile(r".*(" + layer_name + r"\.)(\d+)(\.).*")
+        else:
+            self.layer_pattern = None
+
+    @rank_zero_only
+    def on_before_backward(self, trainer: pl.Trainer, pl_module: pl.LightningModule, loss: torch.Tensor) -> None:
+        """Compute the model wise norm of the parameters."""
+        param_norm = torch.tensor(0.0, device=pl_module.device)
+        for _, param in pl_module.named_parameters():
+            if param.requires_grad:
+                param_norm += torch.pow(torch.norm(param.detach()), 2.0)
+
+        pl_module.log("model_wise_param_norm", torch.sqrt(param_norm).item())
+
+    @rank_zero_only
+    def on_before_optimizer_step(
+        self, trainer: pl.Trainer, pl_module: pl.LightningModule, optimizer: torch.optim.Optimizer
+    ) -> None:
+        """Compute the model wise and per layer norm of the gradients."""
+        model_wise_grad_norm = torch.tensor(0.0, device=pl_module.device)
+        per_layer_grad_norm: dict[str, torch.Tensor] = defaultdict(lambda: torch.tensor(0.0, device=pl_module.device))
+
+        for name, param in pl_module.named_parameters():
+            if param.grad is None:
+                continue
+            param_grad_norm = torch.pow(torch.norm(param.grad), 2.0)
+            model_wise_grad_norm += param_grad_norm
+
+            # get a match if module name contains `*.layer_name.i.*` where i is layer num
+            if self.layer_pattern:
+                match = self.layer_pattern.match(name)
+                if match:
+                    layer_id = match.group(2)
+                    per_layer_grad_norm[layer_id] += param_grad_norm
+
+        pl_module.log("model_wise_grad_norm", torch.sqrt(model_wise_grad_norm).item())
+        if per_layer_grad_norm:
+            pl_module.log_dict(
+                {
+                    f"per_layer_grad_norm/layer_{layer_id}": torch.sqrt(per_layer_grad_norm[layer_id]).item()
+                    for layer_id in per_layer_grad_norm
+                }
+            )
diff --git a/tests/common.py b/tests/common.py
index 14289e11..6391b0dd 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -13,9 +13,17 @@
 class BoringDataset(torch.utils.data.Dataset):
     """A simple dataset for testing purposes."""
 
-    def __init__(self, data: np.ndarray, var_names: np.ndarray | None = None) -> None:
+    def __init__(
+        self,
+        data: np.ndarray,
+        var_names: np.ndarray | None = None,
+        y: np.ndarray | None = None,
+        y_categories: np.ndarray | None = None,
+    ) -> None:
         self.data = data
         self.var_names = var_names
+        self.y = y
+        self.y_categories = y_categories
 
     def __len__(self) -> int:
         return len(self.data)
@@ -24,6 +32,10 @@ def __getitem__(self, idx: int) -> dict[str, np.ndarray]:
         data = {"x_ng": self.data[idx, None]}
         if self.var_names is not None:
             data["var_names_g"] = self.var_names
+        if self.y is not None:
+            data["y_n"] = self.y[idx, None]
+        if self.y_categories is not None:
+            data["y_categories"] = self.y_categories
         return data
 
 
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
new file mode 100644
index 00000000..09f49ad8
--- /dev/null
+++ b/tests/test_callbacks.py
@@ -0,0 +1,89 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+from pathlib import Path
+
+import lightning.pytorch as pl
+import numpy as np
+import pytest
+import torch
+
+from cellarium.ml import CellariumModule
+from cellarium.ml.callbacks import ComputeNorm, LossScaleMonitor
+from cellarium.ml.models import LogisticRegression
+from cellarium.ml.utilities.data import collate_fn
+from tests.common import USE_CUDA, BoringDataset
+
+
+@pytest.mark.skipif(not USE_CUDA, reason="requires_cuda")
+def test_loss_scale_monitor(tmp_path: Path):
+    n, g = 4, 3
+    var_names_g = np.array([f"gene_{i}" for i in range(g)])
+    y_categories = np.array(["a", "b"])
+    y = np.array([0, 1, 0, 1])
+    # dataloader
+    train_loader = torch.utils.data.DataLoader(
+        BoringDataset(
+            np.random.randn(n, g).astype(np.float32),
+            var_names=var_names_g,
+            y=y,
+            y_categories=y_categories,
+        ),
+        collate_fn=collate_fn,
+    )
+    # model
+    model = LogisticRegression(
+        n_obs=n,
+        var_names_g=var_names_g,
+        y_categories=y_categories,
+        log_metrics=False,
+    )
+    module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3})
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="cuda",
+        devices=1,
+        precision="16-mixed",
+        callbacks=[LossScaleMonitor()],
+        max_epochs=1,
+        log_every_n_steps=1,
+        default_root_dir=tmp_path,
+    )
+    # fit
+    trainer.fit(module, train_dataloaders=train_loader)
+
+
+def test_compute_norm(tmp_path: Path):
+    n, g = 4, 3
+    var_names_g = np.array([f"gene_{i}" for i in range(g)])
+    y_categories = np.array(["a", "b"])
+    y = np.array([0, 1, 0, 1])
+    # dataloader
+    train_loader = torch.utils.data.DataLoader(
+        BoringDataset(
+            np.random.randn(n, g).astype(np.float32),
+            var_names=var_names_g,
+            y=y,
+            y_categories=y_categories,
+        ),
+        collate_fn=collate_fn,
+    )
+    # model
+    model = LogisticRegression(
+        n_obs=n,
+        var_names_g=var_names_g,
+        y_categories=y_categories,
+        log_metrics=False,
+    )
+    module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3})
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="cpu",
+        devices=1,
+        callbacks=[ComputeNorm()],
+        max_epochs=1,
+        log_every_n_steps=1,
+        default_root_dir=tmp_path,
+    )
+    # fit
+    trainer.fit(module, train_dataloaders=train_loader)
diff --git a/tests/test_logistic_regression.py b/tests/test_logistic_regression.py
new file mode 100644
index 00000000..44ac54de
--- /dev/null
+++ b/tests/test_logistic_regression.py
@@ -0,0 +1,63 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import math
+import os
+from pathlib import Path
+
+import lightning.pytorch as pl
+import numpy as np
+import torch
+
+from cellarium.ml import CellariumModule
+from cellarium.ml.models import LogisticRegression
+from cellarium.ml.utilities.data import collate_fn
+from tests.common import BoringDataset
+
+
+def test_load_from_checkpoint_multi_device(tmp_path: Path):
+    n, g = 4, 3
+    var_names_g = np.array([f"gene_{i}" for i in range(g)])
+    y_categories = np.array(["a", "b"])
+    y = np.array([0, 1, 0, 1])
+    devices = int(os.environ.get("TEST_DEVICES", "1"))
+    # dataloader
+    train_loader = torch.utils.data.DataLoader(
+        BoringDataset(
+            np.random.randn(n, g).astype(np.float32),
+            var_names=var_names_g,
+            y=y,
+            y_categories=y_categories,
+        ),
+        collate_fn=collate_fn,
+    )
+    # model
+    model = LogisticRegression(
+        n_obs=n,
+        var_names_g=var_names_g,
+        y_categories=y_categories,
+        log_metrics=False,
+    )
+    module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3})
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="cpu",
+        devices=devices,
+        max_epochs=1,
+        default_root_dir=tmp_path,
+    )
+    # fit
+    trainer.fit(module, train_dataloaders=train_loader)
+
+    # run tests only for rank 0
+    if trainer.global_rank != 0:
+        return
+
+    # load model from checkpoint
+    ckpt_path = tmp_path / f"lightning_logs/version_0/checkpoints/epoch=0-step={math.ceil(n / devices)}.ckpt"
+    assert ckpt_path.is_file()
+    loaded_model = CellariumModule.load_from_checkpoint(ckpt_path).model
+    assert isinstance(loaded_model, LogisticRegression)
+    # assert
+    assert np.array_equal(model.var_names_g, loaded_model.var_names_g)
+    assert np.array_equal(model.y_categories, loaded_model.y_categories)

From 89af99705f4b79c6901a59cc529bed3ff2fd7d6e Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 12 Dec 2024 16:03:39 +0000
Subject: [PATCH 05/64] PyTreeDataset

---
 cellarium/ml/data/__init__.py           |  2 ++
 cellarium/ml/data/pytree_dataset.py     | 29 +++++++++++++++++++
 cellarium/ml/utilities/data.py          |  2 --
 tests/dataloader/test_pytree_dataset.py | 38 +++++++++++++++++++++++++
 4 files changed, 69 insertions(+), 2 deletions(-)
 create mode 100644 cellarium/ml/data/pytree_dataset.py
 create mode 100644 tests/dataloader/test_pytree_dataset.py

diff --git a/cellarium/ml/data/__init__.py b/cellarium/ml/data/__init__.py
index 055fb4d2..33d9d8a0 100644
--- a/cellarium/ml/data/__init__.py
+++ b/cellarium/ml/data/__init__.py
@@ -8,6 +8,7 @@
     LazyAnnData,
 )
 from cellarium.ml.data.fileio import read_h5ad_file, read_h5ad_gcs, read_h5ad_local, read_h5ad_url
+from cellarium.ml.data.pytree_dataset import PyTreeDataset
 from cellarium.ml.data.schema import AnnDataSchema
 
 __all__ = [
@@ -16,6 +17,7 @@
     "DistributedAnnDataCollectionView",
     "IterableDistributedAnnDataCollectionDataset",
     "LazyAnnData",
+    "PyTreeDataset",
     "read_h5ad_file",
     "read_h5ad_gcs",
     "read_h5ad_local",
diff --git a/cellarium/ml/data/pytree_dataset.py b/cellarium/ml/data/pytree_dataset.py
new file mode 100644
index 00000000..3762a313
--- /dev/null
+++ b/cellarium/ml/data/pytree_dataset.py
@@ -0,0 +1,29 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import torch
+from torch.utils._pytree import PyTree, tree_any, tree_iter, tree_map
+
+
+class PyTreeDataset(torch.utils.data.Dataset):
+    """
+    A dataset that wraps a PyTree of tensors or ndarrays.
+
+    Args:
+        pytree: A PyTree of tensors or ndarrays.
+    """
+
+    def __init__(self, pytree: PyTree) -> None:
+        self._length = next(tree_iter(pytree)).shape[0]  # type: ignore[call-overload]
+        if tree_any(lambda x: x.shape[0] != self._length, pytree):
+            raise ValueError("All tensors must have the same batch dimension")
+        self.pytree = pytree
+
+    def __getitem__(self, index: int) -> PyTree:
+        return tree_map(lambda x: x[index], self.pytree)
+
+    def __getitems__(self, indices: list[int]) -> list[PyTree]:
+        return [tree_map(lambda x: x[indices], self.pytree)]
+
+    def __len__(self) -> int:
+        return self._length
diff --git a/cellarium/ml/utilities/data.py b/cellarium/ml/utilities/data.py
index 28e307fd..e0e37825 100644
--- a/cellarium/ml/utilities/data.py
+++ b/cellarium/ml/utilities/data.py
@@ -107,8 +107,6 @@ def collate_fn(
             # If so, just take the first one
             value = batch[0][key]
         elif isinstance(batch[0][key], dict):
-            if not (key.endswith("_n") or key.endswith("_ng")):
-                raise ValueError(f"Sub-dictionary '{key}' must have a batch dimension (end with '_n' or '_ng').")
             subkeys = batch[0][key].keys()  # type: ignore[union-attr]
             if len(batch) > 1 and not all(subkeys == data[key].keys() for data in batch[1:]):  # type: ignore[union-attr]
                 raise ValueError(f"All '{key}' sub-dictionaries in the batch must have the same subkeys.")
diff --git a/tests/dataloader/test_pytree_dataset.py b/tests/dataloader/test_pytree_dataset.py
new file mode 100644
index 00000000..abc20314
--- /dev/null
+++ b/tests/dataloader/test_pytree_dataset.py
@@ -0,0 +1,38 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import pytest
+import torch
+from torch.utils._pytree import tree_iter
+
+from cellarium.ml.data import PyTreeDataset
+from cellarium.ml.utilities.data import collate_fn
+
+
+@pytest.mark.parametrize("batch_size", [1, 2, 3])
+def test_pytree_dataset(batch_size):
+    data = {
+        "gene_token_nc_dict": {
+            "gene_id": torch.randint(0, 10, (10, 3)),
+            "gene_value": torch.randint(0, 10, (10, 3)),
+        },
+        "gene_token_mask_nc": torch.randint(0, 10, (10, 3)),
+        "metadata_token_nc_dict": {
+            "cell_type": torch.randint(0, 10, (10, 3)),
+        },
+        "metadata_token_mask_nc_dict": {
+            "cell_type": torch.randint(0, 10, (10, 3)),
+        },
+        "prompt_mask_nc": torch.randint(0, 10, (10, 3)),
+    }
+    dataset = PyTreeDataset(data)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        sampler=torch.utils.data.SequentialSampler(dataset),
+        batch_size=batch_size,
+        collate_fn=collate_fn,
+    )
+    list_batch = list(dataloader)
+    full_batch = collate_fn(list_batch)
+    for x1, x2 in zip(tree_iter(data), tree_iter(full_batch)):
+        assert torch.equal(x1, x2)

From f22c3a6dddca706443b8d4ea9ea7fa5b04d40179 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 12 Dec 2024 17:08:42 +0000
Subject: [PATCH 06/64] fixes

---
 cellarium/ml/data/pytree_dataset.py     | 39 +++++++++++++++++++++----
 tests/dataloader/test_pytree_dataset.py |  1 -
 2 files changed, 34 insertions(+), 6 deletions(-)

diff --git a/cellarium/ml/data/pytree_dataset.py b/cellarium/ml/data/pytree_dataset.py
index 3762a313..08d95d5d 100644
--- a/cellarium/ml/data/pytree_dataset.py
+++ b/cellarium/ml/data/pytree_dataset.py
@@ -7,23 +7,52 @@
 
 class PyTreeDataset(torch.utils.data.Dataset):
     """
-    A dataset that wraps a PyTree of tensors or ndarrays.
+    A dataset that wraps a PyTree of tensors and ndarrays.
+
+    Example::
+
+        import torch
+        from cellarium.ml.data import PyTreeDataset
+        from cellarium.ml.utilities.data import collate_fn
+
+        data = {
+            "gene_token_nc_dict": {
+                "gene_id": torch.randint(0, 10, (10, 3)),
+                "gene_value": torch.randint(0, 10, (10, 3)),
+            },
+            "gene_token_mask_nc": torch.randint(0, 10, (10, 3)),
+            "metadata_token_nc_dict": {
+                "cell_type": torch.randint(0, 10, (10, 3)),
+            },
+            "metadata_token_mask_nc_dict": {
+                "cell_type": torch.randint(0, 10, (10, 3)),
+            },
+            "prompt_mask_nc": torch.randint(0, 10, (10, 3)),
+        }
+        dataset = PyTreeDataset(data)
+        dataloader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=batch_size,
+            collate_fn=collate_fn,
+        )
+        for batch in dataloader:
+            ...
 
     Args:
-        pytree: A PyTree of tensors or ndarrays.
+        pytree: A PyTree of tensors and ndarrays.
     """
 
     def __init__(self, pytree: PyTree) -> None:
-        self._length = next(tree_iter(pytree)).shape[0]  # type: ignore[call-overload]
+        self._length: int = next(tree_iter(pytree)).shape[0]  # type: ignore[call-overload]
         if tree_any(lambda x: x.shape[0] != self._length, pytree):
             raise ValueError("All tensors must have the same batch dimension")
         self.pytree = pytree
 
     def __getitem__(self, index: int) -> PyTree:
-        return tree_map(lambda x: x[index], self.pytree)
+        return tree_map(lambda data: data[index], self.pytree)
 
     def __getitems__(self, indices: list[int]) -> list[PyTree]:
-        return [tree_map(lambda x: x[indices], self.pytree)]
+        return [tree_map(lambda data: data[indices], self.pytree)]
 
     def __len__(self) -> int:
         return self._length
diff --git a/tests/dataloader/test_pytree_dataset.py b/tests/dataloader/test_pytree_dataset.py
index abc20314..4c5ebcea 100644
--- a/tests/dataloader/test_pytree_dataset.py
+++ b/tests/dataloader/test_pytree_dataset.py
@@ -28,7 +28,6 @@ def test_pytree_dataset(batch_size):
     dataset = PyTreeDataset(data)
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        sampler=torch.utils.data.SequentialSampler(dataset),
         batch_size=batch_size,
         collate_fn=collate_fn,
     )

From ff5321ab7b66b8d6b8b0a10d3a32880697050669 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 12 Dec 2024 20:35:45 +0000
Subject: [PATCH 07/64] pass the test

---
 cellarium/ml/models/cellarium_gpt.py | 31 +++++++++----------
 tests/test_cellarium_gpt.py          | 45 ++++++++++++++--------------
 2 files changed, 38 insertions(+), 38 deletions(-)

diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 0754bee0..839d2629 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -22,9 +22,6 @@ def use_cs() -> bool:
         return False
 
 
-torch.set_float32_matmul_precision("high")
-
-
 def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
     """
     Generate a prompt diagonal mask for self-attention.
@@ -66,9 +63,11 @@ class CellariumGPT(CellariumModel, PredictMixin, ValidateMixin):
 
     Args:
         gene_vocab_sizes:
-            Gene token vocabulary sizes.
+            Gene token vocabulary sizes. Must include "gene_value" and "gene_id". Additionally, it can include
+            experimental conditions, such as "assay" and "suspension_type".
         metadata_vocab_sizes:
-            Metadata token vocabulary sizes.
+            Metadata token vocabulary sizes. This can include metadata tokens such as "cell_type", "tissue",
+            "sex", "development_stage", and "disease".
         d_model:
             Dimensionality of the embeddings and hidden states.
         d_ffn:
@@ -158,7 +157,7 @@ def __init__(
         self.attention_logits_scale = attention_logits_scale
         self.output_logits_scale = output_logits_scale
 
-        # Handle muP scaling
+        # Handle muP scaling for Adam and AdamW optimizers
         if mup_base_d_model:
             d_model_width_mult = d_model / mup_base_d_model
             scale_initializers_by_dimension(
@@ -350,19 +349,21 @@ def validate(
         trainer: pl.Trainer,
         pl_module: pl.LightningModule,
         batch_idx: int,
-        gene_token_ns_dict: dict[str, torch.Tensor],
-        gene_prompt_mask_ns: torch.Tensor,
-        metadata_token_n_dict: dict[str, torch.Tensor],
-        metadata_prompt_mask_n_dict: dict[str, torch.Tensor],
+        gene_token_nc_dict: dict[str, torch.Tensor],
+        gene_token_mask_nc: torch.Tensor,
+        metadata_token_nc_dict: dict[str, torch.Tensor],
+        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
         labels_nc: dict[str, torch.Tensor],
         label_weights_nc: dict[str, torch.Tensor],
     ) -> None:
-        n = gene_prompt_mask_ns.shape[0]
+        n = prompt_mask_nc.shape[0]
         loss_dict = self.forward(
-            gene_token_ns_dict=gene_token_ns_dict,
-            gene_prompt_mask_ns=gene_prompt_mask_ns,
-            metadata_token_n_dict=metadata_token_n_dict,
-            metadata_prompt_mask_n_dict=metadata_prompt_mask_n_dict,
+            gene_token_nc_dict=gene_token_nc_dict,
+            gene_token_mask_nc=gene_token_mask_nc,
+            metadata_token_nc_dict=metadata_token_nc_dict,
+            metadata_token_mask_nc_dict=metadata_token_mask_nc_dict,
+            prompt_mask_nc=prompt_mask_nc,
             label_nc_dict=labels_nc,
             label_weight_nc_dict=label_weights_nc,
         )
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
index f1f1240e..60273bed 100644
--- a/tests/test_cellarium_gpt.py
+++ b/tests/test_cellarium_gpt.py
@@ -1,43 +1,44 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
-import math
 import os
 from pathlib import Path
 
 import lightning.pytorch as pl
 import numpy as np
 import torch
-from torch.utils._pytree import tree_flatten, tree_unflatten
 
 from cellarium.ml import CellariumModule
-from cellarium.ml.data import DistributedAnnDataCollection, read_h5ad_file
+from cellarium.ml.data import PyTreeDataset, read_h5ad_file
 from cellarium.ml.models import CellariumGPT
-from cellarium.ml.utilities.data import AnnDataField, categories_to_codes, collate_fn
+from cellarium.ml.utilities.data import categories_to_codes, collate_fn
 
 
 def test_load_from_checkpoint_multi_device(tmp_path: Path):
     adata = read_h5ad_file("https://storage.googleapis.com/dsp-cellarium-cas-public/test-data/test_0.h5ad")
     n = adata.n_obs
-    s = 4
-    c = 5
+    s = 4  # number of subsampled genes
+    c = 5  # context size
     batch_size = 2
     devices = int(os.environ.get("TEST_DEVICES", "1"))
-    prompt_mask_nc = np.random.choice([True, False], size=(batch_size, c), p=[0.5, 0.5])
+    prompt_mask_nc = np.random.choice([True, False], size=(n, c), p=[0.5, 0.5])
     query_mask_nc = (~prompt_mask_nc).astype(np.float32)
     X = adata.X[:, :s].toarray()
+
     cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
     data = {
         "gene_token_nc_dict": {
             "gene_id": np.broadcast_to(np.arange(c), (n, c)),
-            "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),
+            "gene_value": np.concatenate([X, np.zeros((n, 1), dtype=np.float32)], axis=1),
             "gene_query_mask": query_mask_nc,
             "total_mrna_umis": np.broadcast_to(
                 np.asarray(adata.obs["total_mrna_umis"], dtype=np.float32)[:, None], (n, c)
             ),
         },
         "gene_token_mask_nc": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
-        "metadata_token_nc_dict": {"cell_type": np.broadcast_to(cell_type, (n, c))},
+        "metadata_token_nc_dict": {
+            "cell_type": np.broadcast_to(cell_type, (n, c)),
+        },
         "metadata_token_mask_nc_dict": {
             "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
         },
@@ -51,23 +52,19 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
             "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
         },
     }
-    flattened_data, tree_spec = tree_flatten(data)
-    # dict_data = {str(i): arg for i, arg in enumerate(flattened_args)}
-    dataset = torch.utils.data.StackDataset(*flattened_data)
+    dataset = PyTreeDataset(data)
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        sampler=torch.utils.data.BatchSampler(
-            torch.utils.data.SequentialSampler(dataset), batch_size=batch_size, drop_last=False
-        ),
+        batch_size=batch_size,
         num_workers=0,
         collate_fn=collate_fn,
     )
     # model
     model = CellariumGPT(
-        gene_vocab_sizes={"gene_id": s, "gene_value": 100},
+        gene_vocab_sizes={"gene_id": c, "gene_value": 100},
         metadata_vocab_sizes={"cell_type": adata.obs["cell_type"].cat.categories.size},
-        d_model=2,
-        d_ffn=4,
+        d_model=3,
+        d_ffn=6,
         n_heads=1,
         n_blocks=1,
         dropout_p=0,
@@ -84,21 +81,23 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
     trainer = pl.Trainer(
         accelerator="cpu",
         devices=devices,
-        max_steps=n,
+        max_steps=2,
         default_root_dir=tmp_path,
     )
     # fit
-    trainer.fit(module, datamodule)
+    trainer.fit(module, dataloader)
 
     # run tests only for rank 0
     if trainer.global_rank != 0:
         return
 
     # load model from checkpoint
-    ckpt_path = tmp_path / f"lightning_logs/version_0/checkpoints/epoch=0-step={math.ceil(n / devices)}.ckpt"
+    ckpt_path = tmp_path / "lightning_logs/version_0/checkpoints/epoch=0-step=2.ckpt"
     assert ckpt_path.is_file()
     loaded_model = CellariumModule.load_from_checkpoint(ckpt_path).model
     assert isinstance(loaded_model, CellariumGPT)
     # assert
-    # assert np.array_equal(model.var_names_g, loaded_model.var_names_g)
-    # np.testing.assert_allclose(model.feature_ids, loaded_model.feature_ids)
+    assert model.attention_backend == loaded_model.attention_backend
+    assert model.embeddings_scale == loaded_model.embeddings_scale
+    assert model.attention_logits_scale == loaded_model.attention_logits_scale
+    assert model.output_logits_scale == loaded_model.output_logits_scale

From f47193424d37f76b0009c5a6ddb09f11b139ff78 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 12 Dec 2024 20:59:37 +0000
Subject: [PATCH 08/64] fixes

---
 cellarium/ml/layers/embedding.py     | 23 ++++++++++++-----------
 cellarium/ml/models/cellarium_gpt.py |  2 +-
 cellarium/ml/utilities/data.py       |  2 --
 3 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 4b0d023f..4a47015f 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -47,18 +47,18 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, gene_tokens_dict_ns: dict[str, torch.Tensor]) -> torch.Tensor:
+    def forward(self, gene_tokens_nc_dict: dict[str, torch.Tensor]) -> torch.Tensor:
         """
         Args:
-            gene_tokens_dict_ns:
-                Dictionary of gene token tensors of shape ``(n, s)``.
+            gene_tokens_nc_dict:
+                Dictionary of gene token tensors of shape ``(n, c)``.
 
         Returns:
-            The gene embedding tensor of shape ``(n, s, d)``.
+            The gene embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
-            self.embedding_dict[key](gene_token_ns.unsqueeze(-1) if key in self.continuous_tokens else gene_token_ns)
-            for key, gene_token_ns in gene_tokens_dict_ns.items()
+            self.embedding_dict[key](gene_token_nc.unsqueeze(-1) if key in self.continuous_tokens else gene_token_nc)
+            for key, gene_token_nc in gene_tokens_nc_dict.items()
         )
 
 
@@ -93,15 +93,16 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, metadata_tokens_dict_n: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+    def forward(self, metadata_tokens_nc_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
         """
         Args:
-            metadata_token_dict_n:
-                Dictionary of metadata token tensors of shape ``(n,)``.
+            metadata_token_nc_dict:
+                Dictionary of metadata token tensors of shape ``(n, c)``.
 
         Returns:
-            Dictionary of metadata embedding tensors of shape ``(n, d)``.
+            Dictionary of metadata embedding tensors of shape ``(n, c, d)``.
         """
         return {
-            key: self.embedding_dict[key](metadata_token_n) for key, metadata_token_n in metadata_tokens_dict_n.items()
+            key: self.embedding_dict[key](metadata_token_nc)
+            for key, metadata_token_nc in metadata_tokens_nc_dict.items()
         }
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 839d2629..44cd0d69 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -59,7 +59,7 @@ def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
 
 class CellariumGPT(CellariumModel, PredictMixin, ValidateMixin):
     """
-    Cellarium GPT model.
+    CellariumGPT model.
 
     Args:
         gene_vocab_sizes:
diff --git a/cellarium/ml/utilities/data.py b/cellarium/ml/utilities/data.py
index f4565f6d..c6b5f59c 100644
--- a/cellarium/ml/utilities/data.py
+++ b/cellarium/ml/utilities/data.py
@@ -112,8 +112,6 @@ def collate_fn(
                 raise ValueError(f"All '{key}' sub-dictionaries in the batch must have the same subkeys.")
             value = {subkey: np.concatenate([data[key][subkey] for data in batch], axis=0) for subkey in subkeys}
         else:
-            if not key.split("_")[-1].startswith("n"):
-                raise ValueError(f"Value '{key}' must have a batch dimension 'n' as the first dimension.")
             value = np.concatenate([data[key] for data in batch], axis=0)
 
         collated_batch[key] = value

From b44407e20bd6f6bb894f0e38fc57173339ebb074 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Thu, 12 Dec 2024 22:53:50 +0000
Subject: [PATCH 09/64] inference

---
 cellarium/ml/models/cellarium_gpt.py          |   7 +-
 cellarium/ml/utilities/inference/__init__.py  |   0
 .../inference/cellarium_gpt_inference.py      | 475 ++++++++++++++++++
 3 files changed, 481 insertions(+), 1 deletion(-)
 create mode 100644 cellarium/ml/utilities/inference/__init__.py
 create mode 100644 cellarium/ml/utilities/inference/cellarium_gpt_inference.py

diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 6b2df404..0baa75d1 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -26,7 +26,7 @@ def use_cs() -> bool:
         return False
 
 
-torch.set_float32_matmul_precision("high")
+torch.set_float32_matmul_precision("highest")
 
 
 def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
@@ -564,6 +564,11 @@ def token_to_id(self) -> dict[str, int]:
     def vectorized_token_to_id(self):
         return np.vectorize(lambda x: self.token_to_id[x])
 
+    def set_attention_backend(self, attention_backend: str) -> None:
+        self.attention_backend = attention_backend
+        for block in self.transformer.blocks:
+            block.attention.attention_backend = attention_backend
+    
     def predict(
         self,
         gene_tokens_nc: dict[str, torch.Tensor],
diff --git a/cellarium/ml/utilities/inference/__init__.py b/cellarium/ml/utilities/inference/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
new file mode 100644
index 00000000..d390b28e
--- /dev/null
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -0,0 +1,475 @@
+import os
+import torch
+import numpy as np
+import typing as t
+import scanpy as sc
+import pandas as pd
+from scanpy import AnnData
+from tqdm.notebook import tqdm
+
+from cellarium.ml import CellariumModule, CellariumPipeline
+from cellarium.ml.models.cellarium_gpt import PredictTokenizer
+
+
+def load_gene_info_table(gene_info_tsv_path: str, included_gene_ids: list[str]) -> t.Tuple[pd.DataFrame, dict, dict]:
+    gene_info_df = pd.read_csv(gene_info_tsv_path, sep="\t")
+
+    gene_symbol_to_gene_id_map = dict()
+    for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):
+        if gene_symbol != float('nan'):
+            gene_symbol_to_gene_id_map[gene_symbol] = gene_id
+
+    gene_id_to_gene_symbol_map = {
+        gene_id: gene_symbol for gene_symbol, gene_id in gene_symbol_to_gene_id_map.items()}
+    for gene_id in included_gene_ids:
+        if gene_id not in gene_id_to_gene_symbol_map:
+            gene_id_to_gene_symbol_map[gene_id] = gene_id
+
+    return gene_info_df, gene_symbol_to_gene_id_map, gene_id_to_gene_symbol_map
+
+
+class CellariumGPTInferenceContext:
+
+    MAX_TOTAL_MRNA_UMIS = 100_000
+    ASSAY_VOCAB_SIZE = 19
+    GENE_ID_VOCAB_SIZE = 36_601
+    GENE_VALUE_VOCAB_SIZE = 2_001
+    SUSPENSION_TYPE_VOCAB_SIZE = 2
+
+    CELL_TYPE_VOCAB_SIZE = 890
+    DEVELOPMENT_STAGE_VOCAB_SIZE = 191
+    DISEASE_VOCAB_SIZE = 350
+    SEX_VOCAB_SIZE = 2
+    TISSUE_VOCAB_SIZE = 822
+
+    DEFAULT_METADATA_PROMPT_MASKS_DICT = {
+        "cell_type": False,
+        "tissue": False,
+        "development_stage": False,
+        "disease": False,
+        "sex": False,
+    }
+
+    def __init__(
+            self,
+            cellarium_gpt_ckpt_path: str,
+            ref_adata_path: str,
+            gene_info_tsv_path: str,
+            device: torch.device,
+            attention_backend: str = "mem_efficient"):
+        
+        # load an anndata extract as reference
+        self._adata = sc.read_h5ad(ref_adata_path)
+
+        # get gene ontology infos from the reference anndata
+        self.gene_ontology_infos = self._get_gene_ontology_infos(self._adata)
+
+        # load the model
+        self.device = device
+        self.gpt_pipeline = CellariumModule.load_from_checkpoint(cellarium_gpt_ckpt_path, map_location=device)
+
+        # inject gene categories
+        self.gpt_pipeline.model.gene_categories = np.asarray(self._adata.var_names)
+        
+        # change attention backend to memory efficient
+        self.gpt_pipeline.model.set_attention_backend(attention_backend)
+
+        # gene info related
+        self.model_var_names = np.asarray(self._adata.var_names)
+        self.model_var_names_set = set(self.model_var_names)
+        self.var_name_to_index_map = {var_name: i for i, var_name in enumerate(self.model_var_names)}
+        self.gene_info_df, self.gene_symbol_to_gene_id_map, self.gene_id_to_gene_symbol_map = \
+            load_gene_info_table(gene_info_tsv_path, self.model_var_names)
+        
+        # get the metadata ontology infos from the TrainTokenizer, which is the first step of the pipeline
+        self.metadata_ontology_infos = self.gpt_pipeline.pipeline[0].ontology_infos
+
+        # rewire the pipeline with a PredictTokenizer
+        self.predict_tokenizer = self._instantiate_predict_tokenizer()
+
+        self.gpt_pipeline.pipeline = CellariumPipeline([
+            self.predict_tokenizer,
+            self.gpt_pipeline.model,
+        ])
+
+
+    def _instantiate_predict_tokenizer(self) -> PredictTokenizer:
+        return PredictTokenizer(
+            max_total_mrna_umis=self.MAX_TOTAL_MRNA_UMIS,
+            gene_vocab_sizes={
+                "assay": self.ASSAY_VOCAB_SIZE,
+                "gene_id": self.GENE_ID_VOCAB_SIZE,
+                "gene_value": self.GENE_VALUE_VOCAB_SIZE,
+                "suspension_type": self.SUSPENSION_TYPE_VOCAB_SIZE,
+            },
+            metadata_vocab_sizes={
+                "cell_type": self.CELL_TYPE_VOCAB_SIZE,
+                "tissue": self.TISSUE_VOCAB_SIZE,
+                "development_stage": self.DEVELOPMENT_STAGE_VOCAB_SIZE,
+                "disease": self.DISEASE_VOCAB_SIZE,
+                "sex": self.SEX_VOCAB_SIZE,
+            },
+            ontology_infos=self.metadata_ontology_infos,
+        )    
+            
+    def _get_gene_ontology_infos(self, adata: AnnData) -> dict:
+        gene_ontology_infos = dict()
+
+        gene_ontology_infos["assay_ontology_term_id"] = dict()
+        gene_ontology_infos["assay_ontology_term_id"]["names"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
+        # just because we are lazy
+        gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
+
+        gene_ontology_infos["suspension_type"] = dict()
+        gene_ontology_infos["suspension_type"]["names"] = list(adata.obs['suspension_type'].cat.categories)
+        gene_ontology_infos["suspension_type"]["labels"] = list(adata.obs['suspension_type'].cat.categories)
+
+        return gene_ontology_infos
+
+
+    def generate_tokens_from_adata(
+            self,
+            adata: sc.AnnData,
+            obs_index: int | list[int] | None,
+            query_var_names: list[str],
+            query_total_mrna_umis: float | None = None,
+            metadata_prompt_masks_dict: dict[str, bool] | None = None,
+        ) -> tuple[dict, dict]:
+        """
+
+        .. note::
+        All variables in the AnnData are treated as prompts.
+
+
+        """
+        
+        # slice the anndata
+        if isinstance(obs_index, int):
+            obs_index = [obs_index]
+
+        # save obs before slicing
+        if obs_index is not None:
+            adata = adata[obs_index]
+
+        # generate gene ids and masks
+        n_cells = len(adata)
+        adata_var_names = adata.var_names
+        assert all([var_name in self.var_name_to_index_map for var_name in adata_var_names])
+        assert all([var_name in self.var_name_to_index_map for var_name in query_var_names])
+        prompt_var_index = [self.var_name_to_index_map[var_name] for var_name in adata_var_names]
+        query_var_index = [self.var_name_to_index_map[var_name] for var_name in query_var_names]
+        n_prompt_vars = len(prompt_var_index)
+        n_query_vars = len(query_var_index)
+        n_total_vars = n_prompt_vars + n_query_vars
+
+        # cpu device for intermediate
+        cpu_device = torch.device("cpu")
+        
+        # gene id
+        gene_ids_nc = torch.tensor(
+            prompt_var_index + query_var_index,
+            dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
+        
+        # gene prompt mask
+        gene_prompt_mask_nc = torch.tensor(
+            [1] * n_prompt_vars + [0] * n_query_vars,
+            dtype=torch.bool, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
+        
+        # gene value
+        try:
+            prompt_X_ng = np.asarray(adata.X.todense())
+        except AttributeError:
+            prompt_X_ng = adata.X
+        prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=cpu_device)
+        query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=cpu_device)
+        gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)
+
+        # total mrna umis
+        prompt_total_mrna_umis_nc = torch.tensor(
+            adata.obs["total_mrna_umis"].values,
+            dtype=torch.float32, device=cpu_device)[:, None].expand(n_cells, n_prompt_vars)
+        if query_total_mrna_umis is None:
+            # the same as prompt
+            query_total_mrna_umis_nc = torch.tensor(
+                adata.obs["total_mrna_umis"].values,
+                dtype=torch.float32, device=cpu_device)[:, None].expand(n_cells, n_query_vars)
+        else:
+            query_total_mrna_umis_nc = torch.tensor(
+                [query_total_mrna_umis] * n_cells,
+                dtype=torch.float32, device=cpu_device)[:, None].expand(n_cells, n_query_vars)
+        total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)
+
+        # convert assay and suspension_type to codes
+        assay_nc = torch.tensor(
+            pd.Categorical(
+                adata.obs["assay_ontology_term_id"].values,
+                categories=self.gene_ontology_infos["assay_ontology_term_id"]["names"]).codes,
+            dtype=torch.int64, device=cpu_device)[:, None].expand(n_cells, n_total_vars)
+        suspension_type_nc = torch.tensor(
+            pd.Categorical(
+                adata.obs["suspension_type"].values,
+                categories=self.gene_ontology_infos["suspension_type"]["names"]).codes,
+            dtype=torch.int64, device=cpu_device)[:, None].expand(n_cells, n_total_vars)
+
+        gene_tokens_dict = {
+            "assay": assay_nc,  # categorical
+            "suspension_type": suspension_type_nc,  # categorical
+            "gene_id": gene_ids_nc,  # categorical
+            "gene_value": gene_value_nc,  # continuous
+            "total_mrna_umis": total_mrna_umis_nc,  # continuous
+        }
+
+        # metadata prompt masks
+        if metadata_prompt_masks_dict is None:
+            metadata_prompt_masks_dict = self.DEFAULT_METADATA_PROMPT_MASKS_DICT
+        expanded_metadata_prompt_masks_dict = dict()
+        for key in self.metadata_ontology_infos.keys():  # note: key order is important ...
+            expanded_metadata_prompt_masks_dict[key] = torch.tensor(
+                [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=cpu_device)
+        
+        # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes
+        metadata_tokens_dict = {
+            "cell_type": adata.obs["cell_type_ontology_term_id"].values,  # categorical
+            "tissue": adata.obs["tissue_ontology_term_id"].values,  # categorical
+            "development_stage": adata.obs["development_stage_ontology_term_id"].values,  # categorical
+            "disease": adata.obs["disease_ontology_term_id"].values,  # categorical
+            "sex": adata.obs["sex_ontology_term_id"].values,  # categorical
+        }
+
+        # where to find each thing in the context?
+        context_indices = dict()
+        context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()
+        context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()
+        offset = 0
+        for metadata_key in self.metadata_ontology_infos.keys():
+            context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset
+            offset += 1
+
+        # return gene_tokens_dict, metadata_tokens_dict
+        tokenizer_output = self.predict_tokenizer(
+            metadata_tokens_n=metadata_tokens_dict,
+            metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,
+            gene_tokens_nc=gene_tokens_dict,
+            gene_prompt_mask_nc=gene_prompt_mask_nc,
+        )
+
+        return tokenizer_output, context_indices
+
+
+    def get_marginal_mean_std(
+            self,
+            adata: sc.AnnData,
+            query_var_names: list[str],
+            query_total_mrna_umis: float | None = None,
+            prompt_gene_values_g: torch.Tensor | None = None,
+            metadata_prompt_masks_dict: dict[str, bool] | None = None,
+        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
+        assert len(adata) == 1, "Only a single cell is allowed"
+
+        if metadata_prompt_masks_dict is None:
+            metadata_prompt_masks_dict = self.DEFAULT_METADATA_PROMPT_MASKS_DICT
+
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=adata,
+            obs_index=None,
+            query_var_names=query_var_names,
+            query_total_mrna_umis=query_total_mrna_umis,
+            metadata_prompt_masks_dict=metadata_prompt_masks_dict,
+        )
+
+        # convert to cuda
+        tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
+        
+        # get a reference to prompt gene values
+        FIRST_CELL_DIM = 0
+        GENE_VALUE_DIM = 0
+        prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][
+            FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]
+        
+        # this is the "source", in case it is provided, e.g., for Jacobian calculation
+        if prompt_gene_values_g is None:
+            prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()
+        
+        # inject back to tokens_dict to re-establish the reference for Jacobian calculation
+        tokens_dict['gene_tokens_nc']['gene_value'][
+            FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)
+
+        # get model predictions
+        logits_dict = self.gpt_pipeline.model.predict(
+            gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+            metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+            prompt_mask_nc=tokens_dict["prompt_mask_nc"],
+        )
+
+        # note: we use `q` to denote query genes
+        gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]
+        gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)
+        MAX_COUNTS = gene_logits_qk.shape[-1]
+        log_counts_1_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).log()
+        log_counts_2_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).pow(2).log()
+        gene_mom_1_q = torch.logsumexp(gene_logits_qk + log_counts_1_k[None, :], dim=-1).exp()
+        gene_mom_2_q = torch.logsumexp(gene_logits_qk + log_counts_2_k[None, :], dim=-1).exp()
+        gene_marginal_means_q = gene_mom_1_q
+        gene_marginal_std_q = torch.clamp(gene_mom_2_q - gene_mom_1_q.pow(2), 0.).sqrt()
+
+        return gene_marginal_means_q, gene_marginal_std_q
+
+    def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) -> dict[str, np.ndarray]:
+        metadata_prediction_chunks_dict = []
+        for i in tqdm(range(0, len(adata), chunk_size)):
+            first = i
+            last = min(len(adata), i + chunk_size)
+            if last == first:
+                continue
+            metadata_prediction_dict = self.predict_metadata(adata[first:last])
+            metadata_prediction_chunks_dict.append(metadata_prediction_dict)
+        metadata_prediction_dict = dict()
+        for key in self.metadata_ontology_infos.keys():
+            metadata_prediction_dict[key] = np.concatenate([
+                chunk[key] for chunk in metadata_prediction_chunks_dict], axis=0)   
+        return metadata_prediction_dict
+
+
+    def predict_metadata(self, adata: sc.AnnData) -> dict[str, np.ndarray]:
+        # tokenize the given anndata: no query genes, just metadata (which will be included as query by default)
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=adata,
+            obs_index=None,
+            query_var_names=[],
+        )
+
+        # convert to cuda
+        tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
+
+        # get model predictions
+        metadata_prediction_dict = dict()
+        with torch.inference_mode():
+            logits_dict = self.gpt_pipeline.model.predict(
+                gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+                metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+                prompt_mask_nc=tokens_dict["prompt_mask_nc"])
+            for metadata_key in self.metadata_ontology_infos.keys():
+                metadata_logits_nk = logits_dict[metadata_key][:, context_indices[f'query_{metadata_key}'], :]
+                metadata_logits_nk = metadata_logits_nk - torch.logsumexp(metadata_logits_nk, dim=1, keepdim=True)
+                metadata_probs_nk = torch.exp(metadata_logits_nk)
+                metadata_prediction_dict[metadata_key] = metadata_probs_nk.cpu().numpy()
+
+        return metadata_prediction_dict
+
+    def convert_adata_to_metacell(
+            self,
+            adata: sc.AnnData,
+            target_total_mrna_umis: float | None = None,
+        ) -> sc.AnnData:
+        
+        # make a metacell
+        X_meta_g = np.asarray(adata.X.sum(0))
+
+        # set total mrna umis to the mean of the dataset
+        if target_total_mrna_umis is None:
+            target_total_mrna_umis = adata.obs['total_mrna_umis'].mean()
+        else:
+            assert target_total_mrna_umis > 0
+        X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
+
+        # make a metacell anndata
+        adata_meta = adata[0, :].copy()
+        adata_meta.X = X_meta_g
+        adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
+
+        return adata_meta
+
+
+    def compute_jacobian(
+            self,
+            adata: sc.AnnData,
+            prompt_gene_ids: list[str],
+            query_gene_ids: list[str],
+            jacobian_point: t.Literal["actual", "marginal_mean"],
+            query_chunk_size: int = 500,
+            convert_to_metacell: bool = True
+        ):
+
+        if not convert_to_metacell:
+            assert len(adata) == 1, "The provided AnnData has more than one cell. Please set `convert_to_metacell` to True."
+            adata_meta = adata.copy()
+            print("Total mRNA UMIs in the AnnData: ", adata_meta.obs['total_mrna_umis'].values[0])
+        else:
+            print(f"The provided AnnData has {len(adata)} cells and will be converted to a metacell ...")
+            adata_meta = self.convert_adata_to_metacell(adata)
+            print("Total mRNA UMIs in the AnnData after metacell conversion: ", adata_meta.obs['total_mrna_umis'].values[0])
+
+
+        # subset to prompt gene ids
+        adata_meta = adata_meta[:, prompt_gene_ids].copy()
+
+        # query var names
+        print(f"Number of prompt genes: {len(prompt_gene_ids)}")
+        print(f"Number of query genes: {len(query_gene_ids)}")
+
+        with torch.inference_mode():
+            print("Calculating marginal mean and std ...")
+            prompt_marginal_mean_p, prompt_marginal_std_p = self.get_marginal_mean_std(
+                adata=adata_meta,
+                query_var_names=prompt_gene_ids,
+            )
+            query_marginal_mean_q, query_marginal_std_q = self.get_marginal_mean_std(
+                adata=adata_meta,
+                query_var_names=query_gene_ids,
+            )
+
+        # jacobian point
+        adata_meta.layers['original'] = adata_meta.X.copy()
+        if jacobian_point == "actual":
+            print("Calculating the Jacobian at the actual metacell point ...")
+        elif jacobian_point == "marginal_mean":
+            # inject into the adata
+            print("Calculating the Jacobian at the marginal mean point ...")
+            adata_meta.X = prompt_marginal_mean_p.cpu().numpy()[None, :]
+        else:
+            raise ValueError
+
+        def yield_chunks(lst, n):
+            for i in range(0, len(lst), n):
+                yield lst[i:i + n]
+
+        query_var_names_chunks = list(yield_chunks(query_gene_ids, query_chunk_size))
+        jacobian_chunks = []
+
+        print("Calculating the Jacobian ...")
+
+        prompt_gene_values_g = torch.tensor(
+            adata_meta.X[0], device=self.gpt_pipeline.device, dtype=torch.float32)
+
+        for query_var_names_chunk in tqdm(query_var_names_chunks):
+
+            def _wrapped_snap_to_marginal_mean_manifold(prompt_gene_values_g: torch.Tensor) -> torch.Tensor:
+                gene_marginal_mean_q, _ = self.get_marginal_mean_std(
+                    adata=adata_meta,
+                    query_var_names=query_var_names_chunk,
+                    prompt_gene_values_g=prompt_gene_values_g)
+                return gene_marginal_mean_q
+
+            chunk_jacobian_qg = torch.autograd.functional.jacobian(
+                func=_wrapped_snap_to_marginal_mean_manifold, 
+                inputs=prompt_gene_values_g,
+                create_graph=False,
+                vectorize=False)
+
+            jacobian_chunks.append(chunk_jacobian_qg)
+
+        jacobian_qp = torch.cat(jacobian_chunks, dim=0)
+
+        return {
+            'adata_obs': adata_meta.obs,
+            'jacobian_point': jacobian_point,
+            'query_var_names': query_gene_ids,
+            'prompt_var_names': prompt_gene_ids,
+            'jacobian_qp': jacobian_qp,
+            'adata_meta_gene_values_p': np.asarray(adata_meta.layers['original']).flatten(),
+            'prompt_marginal_mean_p': prompt_marginal_mean_p,
+            'prompt_marginal_std_p': prompt_marginal_std_p,
+            'query_marginal_mean_q': query_marginal_mean_q,
+            'query_marginal_std_q': query_marginal_std_q,
+        }

From 7277d76b140f9ed83fddd76ae8a06e954ab6bed0 Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Fri, 3 Jan 2025 11:14:43 -0500
Subject: [PATCH 10/64] Fix issue with mypy in lightning 2.5.0.post0 (#282)

* Fix issue with mypy in lightning 2.5.0.post0

* Yerdos' suggested fix
---
 cellarium/ml/core/module.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index 7fc0941c..918e55b8 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -9,7 +9,7 @@
 import lightning.pytorch as pl
 import numpy as np
 import torch
-from lightning.pytorch.utilities.types import STEP_OUTPUT, OptimizerLRSchedulerConfig
+from lightning.pytorch.utilities.types import STEP_OUTPUT, OptimizerLRScheduler
 
 from cellarium.ml.core.datamodule import CellariumAnnDataDataModule
 from cellarium.ml.core.pipeline import CellariumPipeline
@@ -290,7 +290,7 @@ def validation_step(self, batch: dict[str, Any], batch_idx: int) -> None:
         batch["batch_idx"] = batch_idx
         self.module_pipeline.validate(batch)
 
-    def configure_optimizers(self) -> OptimizerLRSchedulerConfig | None:
+    def configure_optimizers(self) -> OptimizerLRScheduler:
         """Configure optimizers for the model."""
         optim_fn = self.hparams["optim_fn"]
         optim_kwargs = self.hparams["optim_kwargs"] or {}
@@ -346,11 +346,14 @@ def configure_optimizers(self) -> OptimizerLRSchedulerConfig | None:
         else:
             optimizer = optim_fn(self.model.parameters(), **optim_kwargs)
 
-        optim_config: OptimizerLRSchedulerConfig = {"optimizer": optimizer}
         if scheduler_fn is not None:
-            scheduler = scheduler_fn(optim_config["optimizer"], **scheduler_kwargs)
-            optim_config["lr_scheduler"] = {"scheduler": scheduler, "interval": "step"}
-        return optim_config
+            scheduler = scheduler_fn(optimizer, **scheduler_kwargs)
+            return {
+                "optimizer": optimizer,
+                "lr_scheduler": {"scheduler": scheduler, "interval": "step"},
+            }
+        else:
+            return {"optimizer": optimizer}
 
     def on_train_epoch_start(self) -> None:
         """

From 3677c229a1632599f633ca1c03bb249cf7a73dfb Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Fri, 3 Jan 2025 16:13:31 -0500
Subject: [PATCH 11/64] PredictWriter can choose predict() key (#267)

* Configurable PredictWriter key and error messages

* Lint

* Add key to docstring
---
 cellarium/ml/callbacks/prediction_writer.py | 34 ++++++++++++++++-----
 1 file changed, 26 insertions(+), 8 deletions(-)

diff --git a/cellarium/ml/callbacks/prediction_writer.py b/cellarium/ml/callbacks/prediction_writer.py
index 0db8d461..7b7553e7 100644
--- a/cellarium/ml/callbacks/prediction_writer.py
+++ b/cellarium/ml/callbacks/prediction_writer.py
@@ -13,7 +13,7 @@
 
 def write_prediction(
     prediction: torch.Tensor,
-    ids: np.ndarray,
+    obs_names_n: np.ndarray,
     output_dir: Path | str,
     postfix: int | str,
 ) -> None:
@@ -23,7 +23,7 @@ def write_prediction(
     Args:
         prediction:
             The prediction to write.
-        ids:
+        obs_names_n:
             The IDs of the cells.
         output_dir:
             The directory to write the prediction to.
@@ -33,7 +33,7 @@ def write_prediction(
     if not os.path.exists(output_dir):
         os.makedirs(output_dir, exist_ok=True)
     df = pd.DataFrame(prediction.cpu())
-    df.insert(0, "db_ids", ids)
+    df.insert(0, "obs_names_n", obs_names_n)
     output_path = os.path.join(output_dir, f"batch_{postfix}.csv")
     df.to_csv(output_path, header=False, index=False)
 
@@ -54,12 +54,20 @@ class PredictionWriter(pl.callbacks.BasePredictionWriter):
         prediction_size:
             The size of the prediction. If ``None``, the entire prediction will be
             written. If not ``None``, only the first ``prediction_size`` columns will be written.
+        key:
+            PredictionWriter will write this key from the output of `predict()`.
     """
 
-    def __init__(self, output_dir: Path | str, prediction_size: int | None = None) -> None:
+    def __init__(
+        self,
+        output_dir: Path | str,
+        prediction_size: int | None = None,
+        key: str = "x_ng",
+    ) -> None:
         super().__init__(write_interval="batch")
         self.output_dir = output_dir
         self.prediction_size = prediction_size
+        self.key = key
 
     def write_on_batch_end(
         self,
@@ -71,14 +79,24 @@ def write_on_batch_end(
         batch_idx: int,
         dataloader_idx: int,
     ) -> None:
-        x_ng = prediction["x_ng"]
+        if self.key not in batch.keys():
+            raise ValueError(
+                f"PredictionWriter callback specified the key '{self.key}' as the relevant output of `predict()`,"
+                " but the key is not present. Specify a different key as an input argument to the callback, or"
+                " modify the output keys of `predict()`."
+            )
+        prediction_np = prediction[self.key]
         if self.prediction_size is not None:
-            x_ng = x_ng[:, : self.prediction_size]
+            prediction_np = prediction_np[:, : self.prediction_size]
 
+        if "obs_names_n" not in batch.keys():
+            raise ValueError(
+                "PredictionWriter callback requires the batch_key 'obs_names_n'. Add this to the YAML config."
+            )
         assert isinstance(batch["obs_names_n"], np.ndarray)
         write_prediction(
-            prediction=x_ng,
-            ids=batch["obs_names_n"],
+            prediction=prediction_np,
+            obs_names_n=batch["obs_names_n"],
             output_dir=self.output_dir,
             postfix=batch_idx * trainer.world_size + trainer.global_rank,
         )

From 40550108880c016460933a714b976f25bfe3d54f Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 8 Jan 2025 23:02:58 -0500
Subject: [PATCH 12/64] `PyTreeDataset` (#278)

* PyTreeDataset

* fixes

* increase rtol

* test github actions

* ignore

* ignore ppca

* fix

* remove tanh

* revert
---
 cellarium/ml/data/__init__.py           |  2 +
 cellarium/ml/data/pytree_dataset.py     | 58 +++++++++++++++++++++++++
 cellarium/ml/utilities/data.py          |  2 -
 tests/dataloader/test_pytree_dataset.py | 37 ++++++++++++++++
 tests/test_mup.py                       |  2 +-
 5 files changed, 98 insertions(+), 3 deletions(-)
 create mode 100644 cellarium/ml/data/pytree_dataset.py
 create mode 100644 tests/dataloader/test_pytree_dataset.py

diff --git a/cellarium/ml/data/__init__.py b/cellarium/ml/data/__init__.py
index 055fb4d2..33d9d8a0 100644
--- a/cellarium/ml/data/__init__.py
+++ b/cellarium/ml/data/__init__.py
@@ -8,6 +8,7 @@
     LazyAnnData,
 )
 from cellarium.ml.data.fileio import read_h5ad_file, read_h5ad_gcs, read_h5ad_local, read_h5ad_url
+from cellarium.ml.data.pytree_dataset import PyTreeDataset
 from cellarium.ml.data.schema import AnnDataSchema
 
 __all__ = [
@@ -16,6 +17,7 @@
     "DistributedAnnDataCollectionView",
     "IterableDistributedAnnDataCollectionDataset",
     "LazyAnnData",
+    "PyTreeDataset",
     "read_h5ad_file",
     "read_h5ad_gcs",
     "read_h5ad_local",
diff --git a/cellarium/ml/data/pytree_dataset.py b/cellarium/ml/data/pytree_dataset.py
new file mode 100644
index 00000000..08d95d5d
--- /dev/null
+++ b/cellarium/ml/data/pytree_dataset.py
@@ -0,0 +1,58 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import torch
+from torch.utils._pytree import PyTree, tree_any, tree_iter, tree_map
+
+
+class PyTreeDataset(torch.utils.data.Dataset):
+    """
+    A dataset that wraps a PyTree of tensors and ndarrays.
+
+    Example::
+
+        import torch
+        from cellarium.ml.data import PyTreeDataset
+        from cellarium.ml.utilities.data import collate_fn
+
+        data = {
+            "gene_token_nc_dict": {
+                "gene_id": torch.randint(0, 10, (10, 3)),
+                "gene_value": torch.randint(0, 10, (10, 3)),
+            },
+            "gene_token_mask_nc": torch.randint(0, 10, (10, 3)),
+            "metadata_token_nc_dict": {
+                "cell_type": torch.randint(0, 10, (10, 3)),
+            },
+            "metadata_token_mask_nc_dict": {
+                "cell_type": torch.randint(0, 10, (10, 3)),
+            },
+            "prompt_mask_nc": torch.randint(0, 10, (10, 3)),
+        }
+        dataset = PyTreeDataset(data)
+        dataloader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=batch_size,
+            collate_fn=collate_fn,
+        )
+        for batch in dataloader:
+            ...
+
+    Args:
+        pytree: A PyTree of tensors and ndarrays.
+    """
+
+    def __init__(self, pytree: PyTree) -> None:
+        self._length: int = next(tree_iter(pytree)).shape[0]  # type: ignore[call-overload]
+        if tree_any(lambda x: x.shape[0] != self._length, pytree):
+            raise ValueError("All tensors must have the same batch dimension")
+        self.pytree = pytree
+
+    def __getitem__(self, index: int) -> PyTree:
+        return tree_map(lambda data: data[index], self.pytree)
+
+    def __getitems__(self, indices: list[int]) -> list[PyTree]:
+        return [tree_map(lambda data: data[indices], self.pytree)]
+
+    def __len__(self) -> int:
+        return self._length
diff --git a/cellarium/ml/utilities/data.py b/cellarium/ml/utilities/data.py
index 28e307fd..e0e37825 100644
--- a/cellarium/ml/utilities/data.py
+++ b/cellarium/ml/utilities/data.py
@@ -107,8 +107,6 @@ def collate_fn(
             # If so, just take the first one
             value = batch[0][key]
         elif isinstance(batch[0][key], dict):
-            if not (key.endswith("_n") or key.endswith("_ng")):
-                raise ValueError(f"Sub-dictionary '{key}' must have a batch dimension (end with '_n' or '_ng').")
             subkeys = batch[0][key].keys()  # type: ignore[union-attr]
             if len(batch) > 1 and not all(subkeys == data[key].keys() for data in batch[1:]):  # type: ignore[union-attr]
                 raise ValueError(f"All '{key}' sub-dictionaries in the batch must have the same subkeys.")
diff --git a/tests/dataloader/test_pytree_dataset.py b/tests/dataloader/test_pytree_dataset.py
new file mode 100644
index 00000000..4c5ebcea
--- /dev/null
+++ b/tests/dataloader/test_pytree_dataset.py
@@ -0,0 +1,37 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import pytest
+import torch
+from torch.utils._pytree import tree_iter
+
+from cellarium.ml.data import PyTreeDataset
+from cellarium.ml.utilities.data import collate_fn
+
+
+@pytest.mark.parametrize("batch_size", [1, 2, 3])
+def test_pytree_dataset(batch_size):
+    data = {
+        "gene_token_nc_dict": {
+            "gene_id": torch.randint(0, 10, (10, 3)),
+            "gene_value": torch.randint(0, 10, (10, 3)),
+        },
+        "gene_token_mask_nc": torch.randint(0, 10, (10, 3)),
+        "metadata_token_nc_dict": {
+            "cell_type": torch.randint(0, 10, (10, 3)),
+        },
+        "metadata_token_mask_nc_dict": {
+            "cell_type": torch.randint(0, 10, (10, 3)),
+        },
+        "prompt_mask_nc": torch.randint(0, 10, (10, 3)),
+    }
+    dataset = PyTreeDataset(data)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=collate_fn,
+    )
+    list_batch = list(dataloader)
+    full_batch = collate_fn(list_batch)
+    for x1, x2 in zip(tree_iter(data), tree_iter(full_batch)):
+        assert torch.equal(x1, x2)
diff --git a/tests/test_mup.py b/tests/test_mup.py
index e3759fb9..1360f774 100644
--- a/tests/test_mup.py
+++ b/tests/test_mup.py
@@ -456,7 +456,7 @@ def configure_optimizers(self) -> torch.optim.Optimizer:
 
 @pytest.mark.parametrize("implementation", ["sp", "mup_mu_linear", "mup_cerebras"])
 @pytest.mark.parametrize("bias", [True, False])
-@pytest.mark.parametrize("nonlin", [F.relu, F.tanh])
+@pytest.mark.parametrize("nonlin", [F.relu])
 @pytest.mark.parametrize(
     "optimizer,lr,input_mult,output_mult",
     [("sgd", 0.1, 2**-4, 2**5), ("adam", 0.01, 2**-3, 2**-4), ("adamw", 0.01, 2**-3, 2**-4)],

From a8547670f95a94999240855f7d599848c845af37 Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Fri, 10 Jan 2025 17:53:26 -0500
Subject: [PATCH 13/64] Ruff upgrade to v0.9.1 slightly changed assertions and
 f-strings (#284)

---
 cellarium/ml/callbacks/variance_monitor.py         |  6 +++---
 cellarium/ml/models/incremental_pca.py             |  6 +++---
 cellarium/ml/models/onepass_mean_var_std.py        | 12 ++++++------
 .../ml/preprocessing/highly_variable_genes.py      |  2 +-
 cellarium/ml/utilities/distributed.py              |  2 +-
 cellarium/ml/utilities/testing.py                  |  2 +-
 tests/dataloader/test_datamodule.py                | 14 +++++++-------
 tests/dataloader/test_dataset.py                   |  2 +-
 tests/dataloader/test_distributed_anndata.py       |  2 +-
 9 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/cellarium/ml/callbacks/variance_monitor.py b/cellarium/ml/callbacks/variance_monitor.py
index 42789e3b..cb8a5dac 100644
--- a/cellarium/ml/callbacks/variance_monitor.py
+++ b/cellarium/ml/callbacks/variance_monitor.py
@@ -29,9 +29,9 @@ def on_train_start(self, trainer: pl.Trainer, pl_module: pl.LightningModule) ->
             AssertionError: If ``pl_module.model`` is not a ``ProbabilisticPCA`` instance.
             MisconfigurationException: If ``Trainer`` has no ``logger``.
         """
-        assert isinstance(
-            pl_module.model, ProbabilisticPCA
-        ), "VarianceMonitor callback should only be used in conjunction with ProbabilisticPCA"
+        assert isinstance(pl_module.model, ProbabilisticPCA), (
+            "VarianceMonitor callback should only be used in conjunction with ProbabilisticPCA"
+        )
 
         if not trainer.loggers:
             raise pl.utilities.exceptions.MisconfigurationException(
diff --git a/cellarium/ml/models/incremental_pca.py b/cellarium/ml/models/incremental_pca.py
index 6439d46a..85b0d535 100644
--- a/cellarium/ml/models/incremental_pca.py
+++ b/cellarium/ml/models/incremental_pca.py
@@ -96,7 +96,7 @@ def forward(self, x_ng: torch.Tensor, var_names_g: np.ndarray) -> dict[str, torc
         other_X_size = x_ng.size(0)
         total_X_size = self_X_size + other_X_size
         assert k <= min(other_X_size, g), (
-            f"Rank of svd_lowrank (n_components): {k}" f" must be less than min(n_obs, n_vars): {min(other_X_size, g)}"
+            f"Rank of svd_lowrank (n_components): {k} must be less than min(n_obs, n_vars): {min(other_X_size, g)}"
         )
 
         # compute SVD of new data
@@ -132,10 +132,10 @@ def forward(self, x_ng: torch.Tensor, var_names_g: np.ndarray) -> dict[str, torc
     def on_train_start(self, trainer: pl.Trainer) -> None:
         if trainer.world_size > 1:
             assert isinstance(trainer.strategy, DDPStrategy), (
-                "Distributed and Incremental PCA requires that " "the trainer uses the DDP strategy."
+                "Distributed and Incremental PCA requires that the trainer uses the DDP strategy."
             )
             assert trainer.strategy._ddp_kwargs["broadcast_buffers"] is False, (
-                "Distributed and Incremental PCA requires that " "broadcast_buffers is set to False."
+                "Distributed and Incremental PCA requires that broadcast_buffers is set to False."
             )
 
     def on_train_epoch_end(self, trainer: pl.Trainer) -> None:
diff --git a/cellarium/ml/models/onepass_mean_var_std.py b/cellarium/ml/models/onepass_mean_var_std.py
index 42176458..b62690f7 100644
--- a/cellarium/ml/models/onepass_mean_var_std.py
+++ b/cellarium/ml/models/onepass_mean_var_std.py
@@ -101,12 +101,12 @@ def forward(self, x_ng: torch.Tensor, var_names_g: np.ndarray) -> dict[str, torc
 
     def on_train_start(self, trainer: pl.Trainer) -> None:
         if trainer.world_size > 1:
-            assert isinstance(
-                trainer.strategy, DDPStrategy
-            ), "OnePassMeanVarStd requires that the trainer uses the DDP strategy."
-            assert (
-                trainer.strategy._ddp_kwargs["broadcast_buffers"] is False
-            ), "OnePassMeanVarStd requires that broadcast_buffers is set to False."
+            assert isinstance(trainer.strategy, DDPStrategy), (
+                "OnePassMeanVarStd requires that the trainer uses the DDP strategy."
+            )
+            assert trainer.strategy._ddp_kwargs["broadcast_buffers"] is False, (
+                "OnePassMeanVarStd requires that broadcast_buffers is set to False."
+            )
 
     def on_train_epoch_end(self, trainer: pl.Trainer) -> None:
         # no need to merge if only one process
diff --git a/cellarium/ml/preprocessing/highly_variable_genes.py b/cellarium/ml/preprocessing/highly_variable_genes.py
index 20b0400c..bc3a558d 100644
--- a/cellarium/ml/preprocessing/highly_variable_genes.py
+++ b/cellarium/ml/preprocessing/highly_variable_genes.py
@@ -109,7 +109,7 @@ def get_highly_variable_genes(
             n_top_genes = len(dispersion_norm)
         disp_cut_off = dispersion_norm[n_top_genes - 1]
         gene_subset = np.nan_to_num(df["dispersions_norm"].values) >= disp_cut_off
-        logging.debug(f"the {n_top_genes} top genes correspond to a " f"normalized dispersion cutoff of {disp_cut_off}")
+        logging.debug(f"the {n_top_genes} top genes correspond to a normalized dispersion cutoff of {disp_cut_off}")
     else:
         dispersion_norm[np.isnan(dispersion_norm)] = 0  # similar to Seurat
         gene_subset = np.logical_and.reduce(
diff --git a/cellarium/ml/utilities/distributed.py b/cellarium/ml/utilities/distributed.py
index 992ce7ba..56f7c05e 100644
--- a/cellarium/ml/utilities/distributed.py
+++ b/cellarium/ml/utilities/distributed.py
@@ -57,7 +57,7 @@ def get_rank_and_num_replicas() -> tuple[int, int]:
             num_replicas = 1
             rank = 0
     if rank >= num_replicas or rank < 0:
-        raise ValueError(f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas-1}]")
+        raise ValueError(f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]")
     return rank, num_replicas
 
 
diff --git a/cellarium/ml/utilities/testing.py b/cellarium/ml/utilities/testing.py
index caf3478e..a7ef5a81 100644
--- a/cellarium/ml/utilities/testing.py
+++ b/cellarium/ml/utilities/testing.py
@@ -96,7 +96,7 @@ def assert_arrays_equal(
         ValueError: If the arrays are not equal.
     """
     if not np.array_equal(a1, a2):
-        raise ValueError(f"`{a1_name}` must match `{a2_name}`. " f"Got {a1} != {a2}")
+        raise ValueError(f"`{a1_name}` must match `{a2_name}`. Got {a1} != {a2}")
 
 
 def assert_slope_equals(data: pd.Series, slope: float, loglog: bool = False, atol: float = 1e-4) -> None:
diff --git a/tests/dataloader/test_datamodule.py b/tests/dataloader/test_datamodule.py
index 0427ce80..8923872f 100644
--- a/tests/dataloader/test_datamodule.py
+++ b/tests/dataloader/test_datamodule.py
@@ -100,7 +100,7 @@ def _check_transform_lists_match(iterable_modules1, iterable_modules2, message):
 
     # ensure loading from a checkpoint manually results in the correct location of transforms
     print(
-        "Checking whether we can load a module from a checkpoint " "using CellariumModule.load_from_checkpoint()... ",
+        "Checking whether we can load a module from a checkpoint using CellariumModule.load_from_checkpoint()... ",
         end="",
     )
     ckpt_path = str(tmp_path / "lightning_logs/version_0/checkpoints/epoch=0-step=1.ckpt")
@@ -130,9 +130,9 @@ def _check_transform_lists_match(iterable_modules1, iterable_modules2, message):
 
     print("\nFull loaded module ---------")
     print(loaded_module)
-    assert (
-        loaded_module._cpu_transforms_in_module_pipeline
-    ), "Upon manual loading, flag for CPU transforms should be True"
+    assert loaded_module._cpu_transforms_in_module_pipeline, (
+        "Upon manual loading, flag for CPU transforms should be True"
+    )
     print("    ... ✓")
 
     # ensure loading from a checkpoint at lightning `fit` time results in the correct location of transforms
@@ -166,9 +166,9 @@ def _check_transform_lists_match(iterable_modules1, iterable_modules2, message):
 
     print("\nFull loaded module ---------")
     print(trainer.model)
-    assert (
-        trainer.model._cpu_transforms_in_module_pipeline
-    ), "After Trainer.fit() checkpoint restart, flag for CPU transforms should be True"
+    assert trainer.model._cpu_transforms_in_module_pipeline, (
+        "After Trainer.fit() checkpoint restart, flag for CPU transforms should be True"
+    )
     print("    ... ✓")
 
 
diff --git a/tests/dataloader/test_dataset.py b/tests/dataloader/test_dataset.py
index 6bbcf491..8d3a4899 100644
--- a/tests/dataloader/test_dataset.py
+++ b/tests/dataloader/test_dataset.py
@@ -67,7 +67,7 @@ def dadc(tmp_path: Path, obs: pd.DataFrame, request: pytest.FixtureRequest) -> D
         sliced_adata.write(os.path.join(tmp_path, f"adata.00{i}.h5ad"))
 
     # distributed anndata
-    filenames = str(os.path.join(tmp_path, f"adata.{{000..00{len(limits)-1}}}.h5ad"))
+    filenames = str(os.path.join(tmp_path, f"adata.{{000..00{len(limits) - 1}}}.h5ad"))
 
     dadc = DistributedAnnDataCollection(
         filenames,
diff --git a/tests/dataloader/test_distributed_anndata.py b/tests/dataloader/test_distributed_anndata.py
index fb97349a..dad39235 100644
--- a/tests/dataloader/test_distributed_anndata.py
+++ b/tests/dataloader/test_distributed_anndata.py
@@ -110,7 +110,7 @@ def test_init_dat(dat: DistributedAnnDataCollection):
 @pytest.mark.parametrize("last_shard_size", [1, 2, 3, None])
 def test_init_shard_size(adatas_path: Path, num_shards: int, last_shard_size: int | None):
     shard_size = 2
-    filenames = str(os.path.join(adatas_path, f"adata.{{000..{num_shards-1:03}}}.h5ad"))
+    filenames = str(os.path.join(adatas_path, f"adata.{{000..{num_shards - 1:03}}}.h5ad"))
     dadc = DistributedAnnDataCollection(
         filenames,
         shard_size=shard_size,

From fa013087906beebdc5343baee95ac9460b77e27c Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 14 Jan 2025 18:03:13 +0000
Subject: [PATCH 14/64] token_type_nc

---
 cellarium/ml/layers/__init__.py      |  5 +-
 cellarium/ml/layers/embedding.py     | 72 +++++++---------------------
 cellarium/ml/models/cellarium_gpt.py | 61 ++++++++---------------
 tests/test_cellarium_gpt.py          | 12 ++---
 4 files changed, 44 insertions(+), 106 deletions(-)

diff --git a/cellarium/ml/layers/__init__.py b/cellarium/ml/layers/__init__.py
index 189275a4..da43b264 100644
--- a/cellarium/ml/layers/__init__.py
+++ b/cellarium/ml/layers/__init__.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from cellarium.ml.layers.attention import MultiHeadAttention
-from cellarium.ml.layers.embedding import GeneExpressionEmbedding, MetadataEmbedding
+from cellarium.ml.layers.embedding import TokenEmbedding
 from cellarium.ml.layers.ffn import PositionWiseFFN
 from cellarium.ml.layers.head import MultiHeadReadout
 from cellarium.ml.layers.mu_linear import MuLinear
@@ -10,8 +10,7 @@
 from cellarium.ml.layers.transformer import Transformer, TransformerBlock
 
 __all__ = [
-    "GeneExpressionEmbedding",
-    "MetadataEmbedding",
+    "TokenEmbedding",
     "MuLinear",
     "MultiHeadAttention",
     "MultiHeadReadout",
diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 4a47015f..231fc517 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -9,15 +9,15 @@
 from cellarium.ml.utilities.layers import create_initializer
 
 
-class GeneExpressionEmbedding(nn.Module):
+class TokenEmbedding(nn.Module):
     """
-    Gene embedding.
+    Gene and metadata tokens embedding.
 
     Args:
         categorical_vocab_sizes:
-            Categorical gene token vocabulary sizes.
+            Categorical token vocabulary sizes.
         continuous_tokens:
-            Continuous gene tokens.
+            Continuous tokens.
         d_model:
             Dimensionality of the embeddings and hidden states.
         embeddings_initializer:
@@ -47,62 +47,22 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, gene_tokens_nc_dict: dict[str, torch.Tensor]) -> torch.Tensor:
+    def forward(self, token_nc_dict: dict[str, torch.Tensor], token_type_nc: torch.Tensor) -> dict[str, torch.Tensor]:
         """
         Args:
-            gene_tokens_nc_dict:
-                Dictionary of gene token tensors of shape ``(n, c)``.
+            token_nc_dict:
+                Dictionary of token tensors of shape ``(n, c)``.
+            token_type_nc:
+                Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
+                tokens are present in the input tensor element-wise. For example, if the input tensor has 3 tokens,
+                the token type tensor should be a 3-bit binary number. The token type tensor is used to select
+                the embeddings for the tokens in the input tensor.
 
         Returns:
-            The gene embedding tensor of shape ``(n, c, d)``.
+            Dictionary of embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
-            self.embedding_dict[key](gene_token_nc.unsqueeze(-1) if key in self.continuous_tokens else gene_token_nc)
-            for key, gene_token_nc in gene_tokens_nc_dict.items()
+            self.embedding_dict[key](token_nc.unsqueeze(-1) if key in self.continuous_tokens else token_nc)
+            * (token_type_nc >> i & 1).unsqueeze(-1)
+            for i, (key, token_nc) in enumerate(token_nc_dict.items())
         )
-
-
-class MetadataEmbedding(nn.Module):
-    """
-    Metadata embedding.
-
-    Args:
-        categorical_vocab_sizes:
-            Categorical metadata token vocabulary sizes.
-        d_model:
-            Dimensionality of the embeddings and hidden states.
-        initializer:
-            Initializer for the embeddings.
-    """
-
-    def __init__(
-        self,
-        categorical_vocab_sizes: dict[str, int],
-        d_model: int,
-        embeddings_initializer: dict[str, Any],
-    ) -> None:
-        super().__init__()
-        self.embedding_dict = nn.ModuleDict(
-            {key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()}
-        )
-        self.embeddings_initializer = embeddings_initializer
-
-        self._reset_parameters()
-
-    def _reset_parameters(self) -> None:
-        for module in self.embedding_dict.children():
-            create_initializer(self.embeddings_initializer)(module.weight)
-
-    def forward(self, metadata_tokens_nc_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-        """
-        Args:
-            metadata_token_nc_dict:
-                Dictionary of metadata token tensors of shape ``(n, c)``.
-
-        Returns:
-            Dictionary of metadata embedding tensors of shape ``(n, c, d)``.
-        """
-        return {
-            key: self.embedding_dict[key](metadata_token_nc)
-            for key, metadata_token_nc in metadata_tokens_nc_dict.items()
-        }
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 44cd0d69..937e2a9c 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -9,7 +9,7 @@
 from torch import nn
 from torch.nn.attention.flex_attention import BlockMask, create_block_mask
 
-from cellarium.ml.layers import GeneExpressionEmbedding, MetadataEmbedding, MultiHeadReadout, Transformer
+from cellarium.ml.layers import MultiHeadReadout, TokenEmbedding, Transformer
 from cellarium.ml.models.model import CellariumModel, PredictMixin, ValidateMixin
 from cellarium.ml.utilities.layers import scale_initializers_by_dimension
 from cellarium.ml.utilities.mup import LRAdjustmentGroup
@@ -129,6 +129,7 @@ def __init__(
         # Vocab sizes
         self.gene_vocab_sizes = gene_vocab_sizes
         self.metadata_vocab_sizes = metadata_vocab_sizes
+        self.token_types = {"gene": 0} | {key: i + 1 for i, key in enumerate(metadata_vocab_sizes)}
 
         # Initializers
         self.initializer_range = initializer_range
@@ -199,18 +200,14 @@ def __init__(
 
         gene_categorical_vocab_sizes = gene_vocab_sizes.copy()
         gene_value_vocab_size = gene_categorical_vocab_sizes.pop("gene_value")
-        self.gene_embedding = GeneExpressionEmbedding(
-            categorical_vocab_sizes=gene_categorical_vocab_sizes,
+        self.token_embedding = TokenEmbedding(
+            categorical_vocab_sizes=gene_categorical_vocab_sizes
+            # Add 1 to the vocab size for the metadata embeddings to account for the mask token
+            | {key: vocab_size + 1 for key, vocab_size in metadata_vocab_sizes.items()},
             continuous_tokens=["gene_value", "gene_query_mask", "total_mrna_umis"],
             d_model=d_model,
             embeddings_initializer=embeddings_initializer,
         )
-        # Add 1 to the vocab size for the metadata embeddings to account for the mask token
-        self.metadata_embedding = MetadataEmbedding(
-            categorical_vocab_sizes={key: vocab_size + 1 for key, vocab_size in metadata_vocab_sizes.items()},
-            d_model=d_model,
-            embeddings_initializer=embeddings_initializer,
-        )
         self.transformer = Transformer(
             d_model=d_model,
             d_ffn=d_ffn,
@@ -270,20 +267,12 @@ def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "tor
 
     def predict(
         self,
-        gene_token_nc_dict: dict[str, torch.Tensor],
-        gene_token_mask_nc: torch.Tensor,
-        metadata_token_nc_dict: dict[str, torch.Tensor],
-        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        token_nc_dict: dict[str, torch.Tensor],
+        token_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
     ) -> dict[str, np.ndarray | torch.Tensor]:
         # Create embeddings
-        gene_embedding_ncd = self.gene_embedding(gene_token_nc_dict) * gene_token_mask_nc.unsqueeze(-1)
-        metadata_embedding_ncd_dict = self.metadata_embedding(metadata_token_nc_dict)
-        metadata_embedding_ncd = sum(
-            metadata_embedding_ncd_dict[key] * metadata_token_mask_nc_dict[key].unsqueeze(-1)
-            for key in metadata_token_nc_dict
-        )
-        embedding_ncd = gene_embedding_ncd + metadata_embedding_ncd
+        embedding_ncd = self.token_embedding(token_nc_dict, token_type_nc)
 
         # Create attention mask
         attention_mask_ncc: torch.Tensor | BlockMask
@@ -308,19 +297,15 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
 
     def forward(
         self,
-        gene_token_nc_dict: dict[str, torch.Tensor],
-        gene_token_mask_nc: torch.Tensor,
-        metadata_token_nc_dict: dict[str, torch.Tensor],
-        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        token_nc_dict: dict[str, torch.Tensor],
+        token_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
         logits_nck_dict = self.predict(
-            gene_token_nc_dict=gene_token_nc_dict,
-            gene_token_mask_nc=gene_token_mask_nc,
-            metadata_token_nc_dict=metadata_token_nc_dict,
-            metadata_token_mask_nc_dict=metadata_token_mask_nc_dict,
+            token_nc_dict=token_nc_dict,
+            token_type_nc=token_type_nc,
             prompt_mask_nc=prompt_mask_nc,
         )
 
@@ -349,23 +334,19 @@ def validate(
         trainer: pl.Trainer,
         pl_module: pl.LightningModule,
         batch_idx: int,
-        gene_token_nc_dict: dict[str, torch.Tensor],
-        gene_token_mask_nc: torch.Tensor,
-        metadata_token_nc_dict: dict[str, torch.Tensor],
-        metadata_token_mask_nc_dict: dict[str, torch.Tensor],
+        token_nc_dict: dict[str, torch.Tensor],
+        token_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
-        labels_nc: dict[str, torch.Tensor],
-        label_weights_nc: dict[str, torch.Tensor],
+        label_nc_dict: dict[str, torch.Tensor],
+        label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> None:
         n = prompt_mask_nc.shape[0]
         loss_dict = self.forward(
-            gene_token_nc_dict=gene_token_nc_dict,
-            gene_token_mask_nc=gene_token_mask_nc,
-            metadata_token_nc_dict=metadata_token_nc_dict,
-            metadata_token_mask_nc_dict=metadata_token_mask_nc_dict,
+            token_nc_dict=token_nc_dict,
+            token_type_nc=token_type_nc,
             prompt_mask_nc=prompt_mask_nc,
-            label_nc_dict=labels_nc,
-            label_weight_nc_dict=label_weights_nc,
+            label_nc_dict=label_nc_dict,
+            label_weight_nc_dict=label_weight_nc_dict,
         )
 
         pl_module.log_dict(loss_dict, sync_dist=True, on_epoch=True, batch_size=n)
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
index 60273bed..c6ced9cd 100644
--- a/tests/test_cellarium_gpt.py
+++ b/tests/test_cellarium_gpt.py
@@ -23,25 +23,23 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
     devices = int(os.environ.get("TEST_DEVICES", "1"))
     prompt_mask_nc = np.random.choice([True, False], size=(n, c), p=[0.5, 0.5])
     query_mask_nc = (~prompt_mask_nc).astype(np.float32)
+    token_type_nc = np.full((n, c), fill_value=15, dtype=np.int64)  # 0111 to select gene tokens
+    token_type_nc[:, s:] = 16  # 1000 to select cell type token
     X = adata.X[:, :s].toarray()
 
     cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
     data = {
-        "gene_token_nc_dict": {
+        "token_nc_dict": {
             "gene_id": np.broadcast_to(np.arange(c), (n, c)),
             "gene_value": np.concatenate([X, np.zeros((n, 1), dtype=np.float32)], axis=1),
             "gene_query_mask": query_mask_nc,
             "total_mrna_umis": np.broadcast_to(
                 np.asarray(adata.obs["total_mrna_umis"], dtype=np.float32)[:, None], (n, c)
             ),
-        },
-        "gene_token_mask_nc": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
-        "metadata_token_nc_dict": {
             "cell_type": np.broadcast_to(cell_type, (n, c)),
         },
-        "metadata_token_mask_nc_dict": {
-            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
-        },
+        # use random numbers between 0 and 31
+        "token_type_nc": token_type_nc,
         "prompt_mask_nc": prompt_mask_nc,
         "label_nc_dict": {
             "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),

From 78eb7b1b0f9da7459b733f21dc366ffbaca8198d Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 14 Jan 2025 18:20:15 +0000
Subject: [PATCH 15/64] clean up

---
 cellarium/ml/layers/embedding.py     | 10 +++++-----
 cellarium/ml/models/cellarium_gpt.py | 17 ++++++++++++++---
 2 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 231fc517..bdd0c110 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -47,19 +47,19 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, token_nc_dict: dict[str, torch.Tensor], token_type_nc: torch.Tensor) -> dict[str, torch.Tensor]:
+    def forward(self, token_nc_dict: dict[str, torch.Tensor], token_type_nc: torch.Tensor) -> torch.Tensor:
         """
         Args:
             token_nc_dict:
                 Dictionary of token tensors of shape ``(n, c)``.
             token_type_nc:
                 Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
-                tokens are present in the input tensor element-wise. For example, if the input tensor has 3 tokens,
-                the token type tensor should be a 3-bit binary number. The token type tensor is used to select
-                the embeddings for the tokens in the input tensor.
+                tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
+                value correspond to the token index in the input tensor. A value of 1 indicates that the token is
+                included, while a 0 means that the token is not included.
 
         Returns:
-            Dictionary of embedding tensor of shape ``(n, c, d)``.
+            Embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
             self.embedding_dict[key](token_nc.unsqueeze(-1) if key in self.continuous_tokens else token_nc)
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 937e2a9c..0fb6daed 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -129,7 +129,6 @@ def __init__(
         # Vocab sizes
         self.gene_vocab_sizes = gene_vocab_sizes
         self.metadata_vocab_sizes = metadata_vocab_sizes
-        self.token_types = {"gene": 0} | {key: i + 1 for i, key in enumerate(metadata_vocab_sizes)}
 
         # Initializers
         self.initializer_range = initializer_range
@@ -199,10 +198,10 @@ def __init__(
             )
 
         gene_categorical_vocab_sizes = gene_vocab_sizes.copy()
-        gene_value_vocab_size = gene_categorical_vocab_sizes.pop("gene_value")
+        gene_value_vocab_size = gene_categorical_vocab_sizes.pop("gene_value")  # used for the readout head
         self.token_embedding = TokenEmbedding(
             categorical_vocab_sizes=gene_categorical_vocab_sizes
-            # Add 1 to the vocab size for the metadata embeddings to account for the mask token
+            # Add 1 to the vocab size for the metadata tokens to account for the mask token
             | {key: vocab_size + 1 for key, vocab_size in metadata_vocab_sizes.items()},
             continuous_tokens=["gene_value", "gene_query_mask", "total_mrna_umis"],
             d_model=d_model,
@@ -303,6 +302,18 @@ def forward(
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
+        """
+        Args:
+            token_nc_dict:
+                Dictionary of token tensors of shape ``(n, c)``. The dictionary must include "gene_id",
+                "gene_value", "gene_query_mask", "total_mrna_umis", and metadata tokens such as "assay",
+                "suspension_type", "cell_type", "tissue", "sex", "development_stage", and "disease".
+            token_type_nc:
+                Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
+                tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
+                value correspond to the token index in the input tensor. A value of 1 indicates that the token is
+                included, while a 0 means that the token is not included.
+        """
         logits_nck_dict = self.predict(
             token_nc_dict=token_nc_dict,
             token_type_nc=token_type_nc,

From 2c0e56036384f705dcd0cb027cabfa32c8f01018 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 14 Jan 2025 18:28:54 +0000
Subject: [PATCH 16/64] rename

---
 cellarium/ml/layers/embedding.py     | 12 ++++++------
 cellarium/ml/models/cellarium_gpt.py | 26 +++++++++++++-------------
 tests/test_cellarium_gpt.py          |  6 +++---
 3 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index bdd0c110..cbf410ef 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -47,12 +47,12 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, token_nc_dict: dict[str, torch.Tensor], token_type_nc: torch.Tensor) -> torch.Tensor:
+    def forward(self, token_value_nc_dict: dict[str, torch.Tensor], embedding_type_nc: torch.Tensor) -> torch.Tensor:
         """
         Args:
-            token_nc_dict:
+            token_value_nc_dict:
                 Dictionary of token tensors of shape ``(n, c)``.
-            token_type_nc:
+            embedding_type_nc:
                 Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
                 tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
                 value correspond to the token index in the input tensor. A value of 1 indicates that the token is
@@ -62,7 +62,7 @@ def forward(self, token_nc_dict: dict[str, torch.Tensor], token_type_nc: torch.T
             Embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
-            self.embedding_dict[key](token_nc.unsqueeze(-1) if key in self.continuous_tokens else token_nc)
-            * (token_type_nc >> i & 1).unsqueeze(-1)
-            for i, (key, token_nc) in enumerate(token_nc_dict.items())
+            self.embedding_dict[key](token_value_nc.unsqueeze(-1) if key in self.continuous_tokens else token_value_nc)
+            * (embedding_type_nc >> i & 1).unsqueeze(-1)
+            for i, (key, token_value_nc) in enumerate(token_value_nc_dict.items())
         )
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 0fb6daed..f226ea7a 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -266,12 +266,12 @@ def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "tor
 
     def predict(
         self,
-        token_nc_dict: dict[str, torch.Tensor],
-        token_type_nc: torch.Tensor,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        embedding_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
     ) -> dict[str, np.ndarray | torch.Tensor]:
         # Create embeddings
-        embedding_ncd = self.token_embedding(token_nc_dict, token_type_nc)
+        embedding_ncd = self.token_embedding(token_value_nc_dict, embedding_type_nc)
 
         # Create attention mask
         attention_mask_ncc: torch.Tensor | BlockMask
@@ -296,27 +296,27 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
 
     def forward(
         self,
-        token_nc_dict: dict[str, torch.Tensor],
-        token_type_nc: torch.Tensor,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        embedding_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
         """
         Args:
-            token_nc_dict:
+            token_value_nc_dict:
                 Dictionary of token tensors of shape ``(n, c)``. The dictionary must include "gene_id",
                 "gene_value", "gene_query_mask", "total_mrna_umis", and metadata tokens such as "assay",
                 "suspension_type", "cell_type", "tissue", "sex", "development_stage", and "disease".
-            token_type_nc:
+            embedding_type_nc:
                 Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
                 tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
                 value correspond to the token index in the input tensor. A value of 1 indicates that the token is
                 included, while a 0 means that the token is not included.
         """
         logits_nck_dict = self.predict(
-            token_nc_dict=token_nc_dict,
-            token_type_nc=token_type_nc,
+            token_value_nc_dict=token_value_nc_dict,
+            embedding_type_nc=embedding_type_nc,
             prompt_mask_nc=prompt_mask_nc,
         )
 
@@ -345,16 +345,16 @@ def validate(
         trainer: pl.Trainer,
         pl_module: pl.LightningModule,
         batch_idx: int,
-        token_nc_dict: dict[str, torch.Tensor],
-        token_type_nc: torch.Tensor,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        embedding_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> None:
         n = prompt_mask_nc.shape[0]
         loss_dict = self.forward(
-            token_nc_dict=token_nc_dict,
-            token_type_nc=token_type_nc,
+            token_value_nc_dict=token_value_nc_dict,
+            embedding_type_nc=embedding_type_nc,
             prompt_mask_nc=prompt_mask_nc,
             label_nc_dict=label_nc_dict,
             label_weight_nc_dict=label_weight_nc_dict,
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
index c6ced9cd..f69222bb 100644
--- a/tests/test_cellarium_gpt.py
+++ b/tests/test_cellarium_gpt.py
@@ -23,8 +23,8 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
     devices = int(os.environ.get("TEST_DEVICES", "1"))
     prompt_mask_nc = np.random.choice([True, False], size=(n, c), p=[0.5, 0.5])
     query_mask_nc = (~prompt_mask_nc).astype(np.float32)
-    token_type_nc = np.full((n, c), fill_value=15, dtype=np.int64)  # 0111 to select gene tokens
-    token_type_nc[:, s:] = 16  # 1000 to select cell type token
+    embedding_type_nc = np.full((n, c), fill_value=15, dtype=np.int64)  # 0111 to select gene tokens
+    embedding_type_nc[:, s:] = 16  # 1000 to select cell type token
     X = adata.X[:, :s].toarray()
 
     cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
@@ -39,7 +39,7 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
             "cell_type": np.broadcast_to(cell_type, (n, c)),
         },
         # use random numbers between 0 and 31
-        "token_type_nc": token_type_nc,
+        "embedding_type_nc": embedding_type_nc,
         "prompt_mask_nc": prompt_mask_nc,
         "label_nc_dict": {
             "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),

From ad665dd886adf23267df420abd57689638b71c67 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 14 Jan 2025 18:33:47 +0000
Subject: [PATCH 17/64] fix docstring

---
 cellarium/ml/layers/embedding.py     | 10 +++++-----
 cellarium/ml/models/cellarium_gpt.py | 25 +++++++++++++------------
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index cbf410ef..7ad1ff7d 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -51,12 +51,12 @@ def forward(self, token_value_nc_dict: dict[str, torch.Tensor], embedding_type_n
         """
         Args:
             token_value_nc_dict:
-                Dictionary of token tensors of shape ``(n, c)``.
+                Dictionary of token value tensors of shape ``(n, c)``.
             embedding_type_nc:
-                Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
-                tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
-                value correspond to the token index in the input tensor. A value of 1 indicates that the token is
-                included, while a 0 means that the token is not included.
+                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
+                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
+                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
+                while a 0 means that the token is excluded.
 
         Returns:
             Embedding tensor of shape ``(n, c, d)``.
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index f226ea7a..7127d65e 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -270,6 +270,19 @@ def predict(
         embedding_type_nc: torch.Tensor,
         prompt_mask_nc: torch.Tensor,
     ) -> dict[str, np.ndarray | torch.Tensor]:
+        """
+        Args:
+            token_value_nc_dict:
+                Dictionary of token value tensors of shape ``(n, c)``.
+            embedding_type_nc:
+                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
+                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
+                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
+                while a 0 means that the token is excluded.
+
+        Returns:
+            Dictionary of logits tensors of shape ``(n, c, k)``.
+        """
         # Create embeddings
         embedding_ncd = self.token_embedding(token_value_nc_dict, embedding_type_nc)
 
@@ -302,18 +315,6 @@ def forward(
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
-        """
-        Args:
-            token_value_nc_dict:
-                Dictionary of token tensors of shape ``(n, c)``. The dictionary must include "gene_id",
-                "gene_value", "gene_query_mask", "total_mrna_umis", and metadata tokens such as "assay",
-                "suspension_type", "cell_type", "tissue", "sex", "development_stage", and "disease".
-            embedding_type_nc:
-                Token type tensor of shape ``(n, c)``. Token type uses the bit representation to indicate which
-                tokens are present in the input tensor element-wise. Digit positions (from the right) in the binary
-                value correspond to the token index in the input tensor. A value of 1 indicates that the token is
-                included, while a 0 means that the token is not included.
-        """
         logits_nck_dict = self.predict(
             token_value_nc_dict=token_value_nc_dict,
             embedding_type_nc=embedding_type_nc,

From 71ee80688783613566beee05e4b773bedea11ea9 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 14 Jan 2025 19:06:19 +0000
Subject: [PATCH 18/64] fix test

---
 tests/test_cellarium_gpt.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
index f69222bb..a14f7feb 100644
--- a/tests/test_cellarium_gpt.py
+++ b/tests/test_cellarium_gpt.py
@@ -29,7 +29,7 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
 
     cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
     data = {
-        "token_nc_dict": {
+        "token_value_nc_dict": {
             "gene_id": np.broadcast_to(np.arange(c), (n, c)),
             "gene_value": np.concatenate([X, np.zeros((n, 1), dtype=np.float32)], axis=1),
             "gene_query_mask": query_mask_nc,
@@ -38,7 +38,6 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
             ),
             "cell_type": np.broadcast_to(cell_type, (n, c)),
         },
-        # use random numbers between 0 and 31
         "embedding_type_nc": embedding_type_nc,
         "prompt_mask_nc": prompt_mask_nc,
         "label_nc_dict": {

From a80538e9624a67a4eddec137f65134de8e8e7763 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 15 Jan 2025 17:07:28 +0000
Subject: [PATCH 19/64] TrainTokenizer

---
 cellarium/ml/core/module.py                   |  10 +
 cellarium/ml/layers/embedding.py              |  15 +-
 cellarium/ml/models/cellarium_gpt.py          |  22 +-
 .../ml/transforms/cellarium_gpt_tokenizer.py  | 259 ++++++++++++++++++
 4 files changed, 288 insertions(+), 18 deletions(-)
 create mode 100644 cellarium/ml/transforms/cellarium_gpt_tokenizer.py

diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index 918e55b8..a1d512c5 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -355,6 +355,16 @@ def configure_optimizers(self) -> OptimizerLRScheduler:
         else:
             return {"optimizer": optimizer}
 
+    def configure_gradient_clipping(
+        self,
+        optimizer,
+        optimizer_idx: int | None = None,
+        gradient_clip_val: int | float | None = None,
+        gradient_clip_algorithm: str | None = None,
+    ) -> None:
+        assert gradient_clip_algorithm in ("norm", None), gradient_clip_algorithm
+        self.trainer.strategy.model.clip_grad_norm_(gradient_clip_val)
+
     def on_train_epoch_start(self) -> None:
         """
         Calls the ``set_epoch`` method on the iterable dataset of the given dataloader.
diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 7ad1ff7d..453c1789 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -47,22 +47,23 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, token_value_nc_dict: dict[str, torch.Tensor], embedding_type_nc: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+    ) -> torch.Tensor:
         """
         Args:
             token_value_nc_dict:
                 Dictionary of token value tensors of shape ``(n, c)``.
-            embedding_type_nc:
-                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
-                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
-                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
-                while a 0 means that the token is excluded.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
 
         Returns:
             Embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
             self.embedding_dict[key](token_value_nc.unsqueeze(-1) if key in self.continuous_tokens else token_value_nc)
-            * (embedding_type_nc >> i & 1).unsqueeze(-1)
+            * token_mask_nc_dict[key].unsqueeze(-1)
             for i, (key, token_value_nc) in enumerate(token_value_nc_dict.items())
         )
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 7127d65e..92713fa9 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -22,6 +22,9 @@ def use_cs() -> bool:
         return False
 
 
+torch.set_float32_matmul_precision("high")
+
+
 def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
     """
     Generate a prompt diagonal mask for self-attention.
@@ -267,24 +270,21 @@ def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "tor
     def predict(
         self,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
     ) -> dict[str, np.ndarray | torch.Tensor]:
         """
         Args:
             token_value_nc_dict:
                 Dictionary of token value tensors of shape ``(n, c)``.
-            embedding_type_nc:
-                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
-                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
-                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
-                while a 0 means that the token is excluded.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
 
         Returns:
             Dictionary of logits tensors of shape ``(n, c, k)``.
         """
         # Create embeddings
-        embedding_ncd = self.token_embedding(token_value_nc_dict, embedding_type_nc)
+        embedding_ncd = self.token_embedding(token_value_nc_dict, token_mask_nc_dict)
 
         # Create attention mask
         attention_mask_ncc: torch.Tensor | BlockMask
@@ -310,14 +310,14 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
     def forward(
         self,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
         logits_nck_dict = self.predict(
             token_value_nc_dict=token_value_nc_dict,
-            embedding_type_nc=embedding_type_nc,
+            token_mask_nc_dict=token_mask_nc_dict,
             prompt_mask_nc=prompt_mask_nc,
         )
 
@@ -347,7 +347,7 @@ def validate(
         pl_module: pl.LightningModule,
         batch_idx: int,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
@@ -355,7 +355,7 @@ def validate(
         n = prompt_mask_nc.shape[0]
         loss_dict = self.forward(
             token_value_nc_dict=token_value_nc_dict,
-            embedding_type_nc=embedding_type_nc,
+            token_mask_nc_dict=token_mask_nc_dict,
             prompt_mask_nc=prompt_mask_nc,
             label_nc_dict=label_nc_dict,
             label_weight_nc_dict=label_weight_nc_dict,
diff --git a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
new file mode 100644
index 00000000..10332409
--- /dev/null
+++ b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
@@ -0,0 +1,259 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import numpy as np
+import pandas as pd
+import torch
+
+
+class CellariumGPTTrainTokenizer(torch.nn.Module):
+    """
+    Tokenizer for the Cellarium GPT model.
+
+    Args:
+        context_len:
+            Context length.
+        gene_downsample_fraction:
+            Fraction of genes to downsample.
+        min_total_mrna_umis:
+            Minimum total mRNA UMIs.
+        max_total_mrna_umis:
+            Maximum total mRNA UMIs.
+        gene_vocab_sizes:
+            Gene token vocabulary sizes.
+        metadata_vocab_sizes:
+            Metadata token vocabulary sizes.
+        ontology_infos_path:
+            Path to ontology information.
+        prefix_len:
+            Prefix length. If ``None``, the prefix length is sampled.
+        metadata_prompt_token_list:
+            List of metadata tokens to prompt. If ``None``, the metadata prompt tokens are sampled.
+        obs_names_rng:
+            Cell IDs are used as random seeds for shuffling gene tokens.
+            If ``None``, gene tokens are shuffled without a random seed.
+    """
+
+    def __init__(
+        self,
+        context_len: int,
+        gene_downsample_fraction: float,
+        min_total_mrna_umis: int,
+        max_total_mrna_umis: int,
+        gene_vocab_sizes: dict[str, int],
+        metadata_vocab_sizes: dict[str, int],
+        ontology_downsample_p: float,
+        ontology_infos_path: str,
+        prefix_len: int | None = None,
+        metadata_prompt_token_list: list[str] | None = None,
+        obs_names_rng: bool = False,
+    ) -> None:
+        super().__init__()
+        self.context_len = context_len
+        self.gene_downsample_fraction = gene_downsample_fraction
+        self.min_total_mrna_umis = min_total_mrna_umis
+        self.max_total_mrna_umis = max_total_mrna_umis
+        self.gene_vocab_sizes = gene_vocab_sizes
+        self.metadata_vocab_sizes = metadata_vocab_sizes
+        self.ontology_infos = torch.load(ontology_infos_path, weights_only=True)
+        self.ontology_downsample_p = ontology_downsample_p
+        self.prefix_len = prefix_len
+        self.metadata_prompt_token_list = metadata_prompt_token_list
+        self.obs_names_rng = obs_names_rng
+
+    def forward(
+        self,
+        metadata_token_n_dict: dict[str, torch.Tensor],
+        gene_token_n_dict: dict[str, torch.Tensor],
+        gene_token_ng_dict: dict[str, torch.Tensor],
+        obs_names_n: np.ndarray | None = None,
+    ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
+        ### GENE TOKENS ###
+        n, g = gene_token_ng_dict["gene_value"].shape
+        m = len(metadata_token_n_dict)
+        c = self.context_len
+        # gene context length
+        j = c - m
+        device = gene_token_ng_dict["gene_value"].device
+
+        ## gene measurement tokens (assay, suspension type, etc.) ##
+        gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
+
+        ## gene id ##
+        gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
+
+        if self.obs_names_rng:
+            rng_n = [torch.Generator(device=device) for _ in range(n)]
+            [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
+            shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
+        else:
+            shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
+        shuffle_idx_nj = shuffle_idx_ng[:, :j]
+
+        for key, gene_token_ng in gene_token_ng_dict.items():
+            gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
+
+        ## gene value ##
+        gene_value_nj = gene_token_nj_dict.pop("gene_value")
+        total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+        # downsample gene values
+        max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+        downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+        if self.gene_downsample_fraction > 0:
+            gene_downsample_p_nj = torch.minimum(
+                torch.rand((n, j), device=device) / self.gene_downsample_fraction,
+                torch.tensor(1.0, device=device),
+            )
+            downsampled_total_mrna_umis_nj = torch.lerp(
+                torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
+                downsampled_total_mrna_umis_nj,
+                gene_downsample_p_nj,
+            )
+        gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+        gene_value_nj = torch.binomial(gene_value_nj, gene_downsample_p_nj)
+        total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
+        if self.prefix_len is not None:
+            prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
+        else:
+            # sample prefix length
+            # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
+            max_prefix_len = j - 1
+            prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
+            prefix_len_weights[0] = 1 / 10
+            prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
+        # create prompt and query masks
+        gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
+        gene_prompt_mask_nj = ~gene_query_mask_nj
+        if "measured_genes_mask" in gene_token_nj_dict:
+            measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
+            gene_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
+            gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
+
+        gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
+        gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
+        gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
+
+        gene_token_value_nc_dict = {
+            key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device)], dim=1)
+            for key, gene_token_nj in gene_token_nj_dict.items()
+        }
+        gene_token_mask_nc = torch.cat(
+            [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
+            dim=1,
+        )
+        gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
+
+        # gene label
+        gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
+        gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
+
+        ### METADATA TOKENS ###
+
+        ## metadata tokens ##
+        # assign token codes based on the ontology info
+        # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+        for key, ontology_info in self.ontology_infos.items():
+            assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
+            metadata_token_n_dict[key] = torch.tensor(
+                pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
+                dtype=torch.int,
+            )
+        # create metadata query and prompt masks
+        if self.metadata_prompt_token_list is not None:
+            metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
+            for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
+                if metadata_token in self.metadata_prompt_token_list:
+                    metadata_prompt_mask_nm[:, metadata_token_idx] = True
+        else:
+            metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
+            metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
+            shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
+            metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
+        metadata_query_mask_nm = ~metadata_prompt_mask_nm
+        metadata_measured_mask_nm = torch.stack(
+            [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
+        ).bool()
+        metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
+        metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
+        # clamp unmeasured tokens to 0
+        for key, metadata_token_n in metadata_token_n_dict.items():
+            metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
+        # metadata labels
+        metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
+        if self.ontology_downsample_p != 0:
+            # downsample metadata based on ontology
+            for key, ontology_info in self.ontology_infos.items():
+                if "shortest_distances_matrix" not in ontology_info:
+                    continue
+                metadata_token_n = metadata_token_n_dict[key]
+                shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
+                ontology_weights = (
+                    self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
+                )
+                metadata_token_n_dict[key] = (
+                    torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
+                )
+        # impute mask token for unmeasured metadata
+        # mask token is the last token in the vocabulary
+        for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
+            metadata_token_n_dict[key] = torch.where(
+                metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
+            ).int()
+
+        block_metadata_token_nm = torch.block_diag(
+            *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
+        )
+        metadata_token_value_nc_dict = {
+            key: torch.cat(
+                [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+        block_metadata_token_mask_nm = torch.block_diag(
+            *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
+        )
+        metadata_token_mask_nc_dict = {
+            key: torch.cat(
+                [
+                    torch.zeros((n, j), dtype=torch.bool, device=device),
+                    block_metadata_token_mask_nm[n * i : n * (i + 1)],
+                ],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+
+        ### PROMPT MASK ###
+        prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
+
+        ### LABELS ###
+        block_label_nc = torch.block_diag(
+            gene_label_nj,
+            *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
+        )
+        label_nc_dict = {
+            key: block_label_nc[n * i : n * (i + 1)]
+            for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+        }
+
+        ### LABEL WEIGHTS ###
+        block_label_weight_nc = (
+            torch.block_diag(
+                gene_query_mask_nj / torch.maximum(gene_query_mask_nj.sum(dim=-1, keepdim=True), torch.tensor(1.0)),
+                *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
+            )
+            / n
+        )
+        label_weight_nc_dict = {
+            key: block_label_weight_nc[n * i : n * (i + 1)]
+            for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+        }
+
+        return {
+            "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
+            "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
+            "prompt_mask_nc": prompt_mask_nc,
+            "label_nc_dict": label_nc_dict,
+            "label_weight_nc_dict": label_weight_nc_dict,
+        }

From 699956da7bd6684d431f4ddf386238b20288b890 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 15 Jan 2025 19:44:09 +0000
Subject: [PATCH 20/64] token_mask

---
 cellarium/ml/layers/embedding.py     | 15 ++++++++-------
 cellarium/ml/models/cellarium_gpt.py | 23 ++++++++++++-----------
 tests/test_cellarium_gpt.py          | 10 +++++++---
 3 files changed, 27 insertions(+), 21 deletions(-)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 7ad1ff7d..453c1789 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -47,22 +47,23 @@ def _reset_parameters(self) -> None:
         for module in self.embedding_dict.children():
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, token_value_nc_dict: dict[str, torch.Tensor], embedding_type_nc: torch.Tensor) -> torch.Tensor:
+    def forward(
+        self,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+    ) -> torch.Tensor:
         """
         Args:
             token_value_nc_dict:
                 Dictionary of token value tensors of shape ``(n, c)``.
-            embedding_type_nc:
-                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
-                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
-                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
-                while a 0 means that the token is excluded.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
 
         Returns:
             Embedding tensor of shape ``(n, c, d)``.
         """
         return sum(
             self.embedding_dict[key](token_value_nc.unsqueeze(-1) if key in self.continuous_tokens else token_value_nc)
-            * (embedding_type_nc >> i & 1).unsqueeze(-1)
+            * token_mask_nc_dict[key].unsqueeze(-1)
             for i, (key, token_value_nc) in enumerate(token_value_nc_dict.items())
         )
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 7127d65e..be3cf454 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -123,8 +123,12 @@ def __init__(
         # muP (maximal update parameterization) parameters
         mup_base_d_model: int | None = None,
         mup_base_d_ffn: int | None = None,
+        # float32 matmul precision
+        float32_matmul_precision: Literal["medium", "high", "highest"] = "high",
     ) -> None:
         super().__init__()
+        # Set float32 matrix multiplication precision globally
+        torch.set_float32_matmul_precision(float32_matmul_precision)
 
         # Vocab sizes
         self.gene_vocab_sizes = gene_vocab_sizes
@@ -267,24 +271,21 @@ def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "tor
     def predict(
         self,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
     ) -> dict[str, np.ndarray | torch.Tensor]:
         """
         Args:
             token_value_nc_dict:
                 Dictionary of token value tensors of shape ``(n, c)``.
-            embedding_type_nc:
-                Embedding type tensor of shape ``(n, c)``. Embedding type uses the bit representation to indicate which
-                tokens are present in the embedding element-wise. Digit positions (from the right) in the binary value
-                correspond to the token index in the input dict. A value of 1 indicates that the token is included,
-                while a 0 means that the token is excluded.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
 
         Returns:
             Dictionary of logits tensors of shape ``(n, c, k)``.
         """
         # Create embeddings
-        embedding_ncd = self.token_embedding(token_value_nc_dict, embedding_type_nc)
+        embedding_ncd = self.token_embedding(token_value_nc_dict, token_mask_nc_dict)
 
         # Create attention mask
         attention_mask_ncc: torch.Tensor | BlockMask
@@ -310,14 +311,14 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
     def forward(
         self,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
     ) -> dict[str, torch.Tensor]:
         logits_nck_dict = self.predict(
             token_value_nc_dict=token_value_nc_dict,
-            embedding_type_nc=embedding_type_nc,
+            token_mask_nc_dict=token_mask_nc_dict,
             prompt_mask_nc=prompt_mask_nc,
         )
 
@@ -347,7 +348,7 @@ def validate(
         pl_module: pl.LightningModule,
         batch_idx: int,
         token_value_nc_dict: dict[str, torch.Tensor],
-        embedding_type_nc: torch.Tensor,
+        token_mask_nc_dict: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor,
         label_nc_dict: dict[str, torch.Tensor],
         label_weight_nc_dict: dict[str, torch.Tensor],
@@ -355,7 +356,7 @@ def validate(
         n = prompt_mask_nc.shape[0]
         loss_dict = self.forward(
             token_value_nc_dict=token_value_nc_dict,
-            embedding_type_nc=embedding_type_nc,
+            token_mask_nc_dict=token_mask_nc_dict,
             prompt_mask_nc=prompt_mask_nc,
             label_nc_dict=label_nc_dict,
             label_weight_nc_dict=label_weight_nc_dict,
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
index a14f7feb..ac3d97ac 100644
--- a/tests/test_cellarium_gpt.py
+++ b/tests/test_cellarium_gpt.py
@@ -23,8 +23,6 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
     devices = int(os.environ.get("TEST_DEVICES", "1"))
     prompt_mask_nc = np.random.choice([True, False], size=(n, c), p=[0.5, 0.5])
     query_mask_nc = (~prompt_mask_nc).astype(np.float32)
-    embedding_type_nc = np.full((n, c), fill_value=15, dtype=np.int64)  # 0111 to select gene tokens
-    embedding_type_nc[:, s:] = 16  # 1000 to select cell type token
     X = adata.X[:, :s].toarray()
 
     cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
@@ -38,7 +36,13 @@ def test_load_from_checkpoint_multi_device(tmp_path: Path):
             ),
             "cell_type": np.broadcast_to(cell_type, (n, c)),
         },
-        "embedding_type_nc": embedding_type_nc,
+        "token_mask_nc_dict": {
+            "gene_id": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "gene_value": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "gene_query_mask": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "total_mrna_umis": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
+        },
         "prompt_mask_nc": prompt_mask_nc,
         "label_nc_dict": {
             "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),

From ab6e5a23a2b78214a31f9a8afb17d32343f2b475 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 15 Jan 2025 20:29:46 +0000
Subject: [PATCH 21/64] remove matmul precision

---
 cellarium/ml/models/cellarium_gpt.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index be3cf454..b04a2bdd 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -123,12 +123,8 @@ def __init__(
         # muP (maximal update parameterization) parameters
         mup_base_d_model: int | None = None,
         mup_base_d_ffn: int | None = None,
-        # float32 matmul precision
-        float32_matmul_precision: Literal["medium", "high", "highest"] = "high",
     ) -> None:
         super().__init__()
-        # Set float32 matrix multiplication precision globally
-        torch.set_float32_matmul_precision(float32_matmul_precision)
 
         # Vocab sizes
         self.gene_vocab_sizes = gene_vocab_sizes

From 090161e2baa1857d07850c7c18fea8dd09a056ef Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 17 Jan 2025 17:53:22 -0500
Subject: [PATCH 22/64] Update README.rst (#287)

* Update README.rst

* Update README.rst (#288)

* Update README.rst

* Update description

---------

Co-authored-by: Yerdos Ordabayev <yordabay@broadinstitute.org>
---
 README.rst | 142 +++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 99 insertions(+), 43 deletions(-)

diff --git a/README.rst b/README.rst
index 06458860..8df23d8b 100644
--- a/README.rst
+++ b/README.rst
@@ -1,67 +1,123 @@
-*Cellarium ML: distributed single-cell data analysis.*
+.. image:: https://cellarium.ai/wp-content/uploads/2024/07/cellarium-logo-medium.png
+   :alt: Cellarium Logo
+   :width: 180
+   :align: center
 
----------
+**Cellarium ML: a machine learning framework for single-cell biology**
+======================================================================
 
 Cellarium ML is a PyTorch Lightning-based library for distributed single-cell data analysis.
-It provides a set of tools for training deep learning models on large-scale single-cell datasets,
-including distributed data loading, model training, and evaluation. Cellarium ML is designed to be
-modular and extensible, allowing users to easily define custom models, data transformations,
+It provides tools for training deep learning models on large-scale single-cell datasets,
+including distributed data loading, model training, and evaluation. Designed to be modular
+and extensible, Cellarium ML allows users to easily define custom models, data transformations,
 and training pipelines.
 
-Code organization
------------------
+-------------------------------------------------------------------------------
+
+**Code Organization**
+----------------------
 
 The code is organized as follows:
 
-- ``cellarium/ml/callbacks``: Contains custom PyTorch Lightning callbacks.
-- ``cellarium/ml/core``: Includes essential Cellarium ML components:
-  - ``CellariumModule``: A PyTorch Lightning Module tasked with defining and configuring the model, training step, and optimizer.
-  - ``CellariumAnnDataDataModule``: A PyTorch Lightning DataModule designed for setting up a multi-GPU DataLoader for a collection of AnnData objects.
-  - ``CellariumPipeline``: A Module List that pipes the input data through a series of transforms and a model.
-- ``cellarium/ml/data``: Contains Distributed AnnData Collection and multi-GPU Iterable Dataset implementations.
-- ``cellarium/ml/lr_schedulers``: Contains custom learning rate schedulers.
-- ``cellarium/ml/models``: Features Cellarium ML models:
-  - Models must subclass ``CellariumModel`` and implement the ``.reset_parameters`` method.
-  - The ``.forward`` method should return a dictionary containing the computed loss under the ``loss`` key.
-  - Optionally, hooks such as ``.on_train_start``, ``.on_train_epoch_end``, and ``.on_train_batch_end`` can be implemented to be triggered by the ``CellariumModule`` during training phases.
-- ``cellarium/ml/preprocessing``: Provides pre-processing functions.
-- ``cellarium/ml/transforms``: Contains data transformation modules:
-  - Each transform is a subclass of ``torch.nn.Module``.
-  - The ``.forward`` method should output a dictionary where the keys correspond to the input arguments of subsequent transforms and the model.
-- ``cellarium/ml/utilities``: Contains utility functions for various submodules.
-- ``cellarium/ml/cli.py``: Implements the ``cellarium-ml`` CLI. Models must be registered here to be accessible via the CLI.
+.. code-block:: text
+
+   cellarium/
+   └── ml/
+       ├── "callbacks"        # Custom PyTorch Lightning callbacks
+       ├── "core"             # Essential components
+       │   ├── "CellariumModule"              # PyTorch Lightning Module for model, training step, and optimizer
+       │   ├── "CellariumAnnDataDataModule"   # DataModule for multi-GPU DataLoader for AnnData objects
+       │   └── "CellariumPipeline"            # Pipeline for data transformations and model inference
+       ├── "data"             # Distributed AnnData Collection and multi-GPU Iterable Datasets
+       ├── "lr_schedulers"    # Custom learning rate schedulers
+       ├── "models"           # Cellarium ML models
+       ├── "preprocessing"    # Pre-processing functions
+       ├── "transforms"       # Data transformation modules
+       ├── "utilities"        # Utility functions for various submodules
+       └── "cli.py"           # Implements the "cellarium-ml" CLI. Models must be registered here
+
+Important Notes
+~~~~~~~~~~~~~~~
+
+``cellarium/ml/models/*``  
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- Models must subclass ``CellariumModel`` and implement the following:  
+- ``reset_parameters``: Initializes model parameters.  
+- ``forward``: Returns a dictionary containing the computed loss under the ``loss`` key.  
+
+Optional hooks for training include:  
+
+- ``on_train_start``: Called at the start of training.  
+- ``on_train_epoch_end``: Triggered at the end of each epoch.  
+- ``on_train_batch_end``: Triggered at the end of each batch.  
+
+``cellarium/ml/transforms/*``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- All transforms must subclass ``torch.nn.Module``.
+- The ``forward`` method must output a dictionary where keys correspond to the input arguments for subsequent transforms or the model.  
+
+``cellarium/ml/cli.py``
+~~~~~~~~~~~~~~~~~~~~~~~
+- Models must be registered here to be accessible via the command-line interface (``cellarium-ml`` CLI).
+
+
+
+-------------------------------------------------------------------------------
+
+**Installation**
+-----------------
+
+To install via pip:
+
+.. code-block:: bash
+
+   pip install cellarium-ml
+
+To install the developer version from source:
+
+.. code-block:: bash
+
+   git clone https://github.com/cellarium-ai/cellarium-ml.git
+   cd cellarium-ml
+   make install  # runs pip install -e .[dev]
 
-Installation
-------------
+**API Documentation and Tutorials**
+-----------------------------------
 
-To install from the pip::
+For detailed API documentation and tutorials, visit:  
+`Cellarium ML Documentation <https://cellarium-ai.github.io/cellarium-ml/>`_
 
-   $ pip install cellarium-ml
+-------------------------------------------------------------------------------
 
-To install the developer version from the source::
+**For Developers**
+-------------------
 
-   $ git clone https://github.com/cellarium-ai/cellarium-ml.git
-   $ cd cellarium-ml
-   $ make install               # runs pip install -e .[dev]
+To run the tests:
 
-For developers
---------------
+.. code-block:: bash
 
-To run the tests::
+   make test-examples                   # runs single-device cli example tests
+   make test-dataloader                 # runs single-device dataloader related tests
+   TEST_DEVICES=2 make test-dataloader  # runs multi-device dataloader related test
+   make test                            # runs single-device (all other) tests
+   TEST_DEVICES=2 make test             # runs multi-device (all other) tests
 
-   $ make test                  # runs single-device tests
-   $ TEST_DEVICES=2 make test   # runs multi-device tests
+To format the code automatically:
 
-To automatically format the code::
+.. code-block:: bash
 
-   $ make format               # runs ruff formatter and fixes linter errors
+   make format                # runs ruff formatter and fixes linter errors
 
-To run the linters::
+To run the linters:
 
-   $ make lint                  # runs ruff linter and checks for formatter errors
+.. code-block:: bash
 
-To build the documentation::
+   make lint                  # runs ruff linter and checks for formatter errors
 
-   $ make docs                  # builds the documentation at docs/build/html
+To build the documentation:
 
+.. code-block:: bash
 
+   make docs                  # builds the documentation at docs/build/html

From 143e17a436c1530cf2ff37bfefc6ea7013c4fe58 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Sun, 19 Jan 2025 09:53:50 +0000
Subject: [PATCH 23/64] predict

---
 cellarium/ml/cli.py                           |  11 ++
 cellarium/ml/transforms/__init__.py           |  11 +-
 .../ml/transforms/cellarium_gpt_tokenizer.py  | 112 +++++++++++++++++-
 3 files changed, 131 insertions(+), 3 deletions(-)

diff --git a/cellarium/ml/cli.py b/cellarium/ml/cli.py
index e57ccfc9..8e097461 100644
--- a/cellarium/ml/cli.py
+++ b/cellarium/ml/cli.py
@@ -312,6 +312,17 @@ def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
     return NewLightningCLI
 
 
+@register_model
+def cellarium_gpt(args: ArgsType = None) -> None:
+    r"""
+    CLI to run the :class:`cellarium.ml.models.CellariumGPT` model.
+    Args:
+        args: Arguments to parse. If ``None`` the arguments are taken from ``sys.argv``.
+    """
+    cli = lightning_cli_factory("cellarium.ml.models.CellariumGPT")
+    cli(args=args)
+
+
 @register_model
 def geneformer(args: ArgsType = None) -> None:
     r"""
diff --git a/cellarium/ml/transforms/__init__.py b/cellarium/ml/transforms/__init__.py
index 375c9460..fbe1075b 100644
--- a/cellarium/ml/transforms/__init__.py
+++ b/cellarium/ml/transforms/__init__.py
@@ -1,10 +1,19 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+from cellarium.ml.transforms.cellarium_gpt_tokenizer import CellariumGPTPredictTokenizer, CellariumGPTTrainTokenizer
 from cellarium.ml.transforms.divide_by_scale import DivideByScale
 from cellarium.ml.transforms.filter import Filter
 from cellarium.ml.transforms.log1p import Log1p
 from cellarium.ml.transforms.normalize_total import NormalizeTotal
 from cellarium.ml.transforms.z_score import ZScore
 
-__all__ = ["DivideByScale", "Filter", "Log1p", "NormalizeTotal", "ZScore"]
+__all__ = [
+    "CellariumGPTTrainTokenizer",
+    "CellariumGPTPredictTokenizer",
+    "DivideByScale",
+    "Filter",
+    "Log1p",
+    "NormalizeTotal",
+    "ZScore",
+]
diff --git a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
index 10332409..a06b0763 100644
--- a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
+++ b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
@@ -134,7 +134,7 @@ def forward(
         gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
 
         gene_token_value_nc_dict = {
-            key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device)], dim=1)
+            key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
             for key, gene_token_nj in gene_token_nj_dict.items()
         }
         gene_token_mask_nc = torch.cat(
@@ -175,7 +175,8 @@ def forward(
         ).bool()
         metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
         metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
-        # clamp unmeasured tokens to 0
+        # clamp unmeasured tokens to 0 in order to avoid error during embedding
+        # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
         for key, metadata_token_n in metadata_token_n_dict.items():
             metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
         # metadata labels
@@ -257,3 +258,110 @@ def forward(
             "label_nc_dict": label_nc_dict,
             "label_weight_nc_dict": label_weight_nc_dict,
         }
+
+
+class CellariumGPTPredictTokenizer(torch.nn.Module):
+    def __init__(
+        self,
+        max_total_mrna_umis: int,
+        gene_vocab_sizes: dict[str, int],
+        metadata_vocab_sizes: dict[str, int],
+        ontology_infos_path: str,
+    ) -> None:
+        super().__init__()
+        self.max_total_mrna_umis = max_total_mrna_umis
+        self.gene_vocab_sizes = gene_vocab_sizes
+        self.metadata_vocab_sizes = metadata_vocab_sizes
+        self.ontology_infos = torch.load(ontology_infos_path, weights_only=True)
+
+    def forward(
+        self,
+        gene_token_nj_dict: dict[str, torch.Tensor],  # set query genes to neg value?
+        metadata_token_n_dict: dict[str, torch.Tensor],  # set query metadata to -1?
+    ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
+        ### GENE TOKENS ###
+
+        ## gene value ##
+        gene_value_nj = gene_token_nj_dict.pop("gene_value")
+        total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+        device = gene_value_nj.device
+        n, j = gene_value_nj.shape
+        m = len(metadata_token_n_dict)
+        # downsample library size to max_total_mrna_umis
+        max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+        downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+        gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+        gene_value_nj = torch.binomial(gene_value_nj, gene_downsample_p_nj)
+
+        gene_prompt_mask_nj = gene_value_nj >= 0
+        gene_query_mask_nj = ~gene_prompt_mask_nj
+        gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
+        gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
+        gene_token_nj_dict["total_mrna_umis"] = torch.log1p(downsampled_total_mrna_umis_nj)
+
+        gene_token_value_nc_dict = {
+            key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
+            for key, gene_token_nj in gene_token_nj_dict.items()
+        }
+        gene_token_mask_nc = torch.cat(
+            [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
+            dim=1,
+        )
+        gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
+
+        ### METADATA TOKENS ###
+
+        ## metadata tokens ##
+        # assign token codes based on the ontology info
+        # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+        for key, ontology_info in self.ontology_infos.items():
+            assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
+            metadata_token_n_dict[key] = torch.tensor(
+                pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
+                dtype=torch.int,
+            )
+        # create metadata query and prompt masks
+        metadata_prompt_mask_nm = torch.stack(
+            [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
+        ).bool()
+        metadata_query_mask_nm = ~metadata_prompt_mask_nm
+
+        # impute mask token for unmeasured metadata
+        # mask token is the last token in the vocabulary
+        for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
+            metadata_token_n_dict[key] = torch.where(
+                metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
+            ).int()
+
+        block_metadata_token_nm = torch.block_diag(
+            *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
+        )
+        metadata_token_value_nc_dict = {
+            key: torch.cat(
+                [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+        block_metadata_token_mask_nm = torch.block_diag(
+            *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
+        )
+        metadata_token_mask_nc_dict = {
+            key: torch.cat(
+                [
+                    torch.zeros((n, j), dtype=torch.bool, device=device),
+                    block_metadata_token_mask_nm[n * i : n * (i + 1)],
+                ],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+
+        ### PROMPT MASK ###
+        prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
+
+        return {
+            "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
+            "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
+            "prompt_mask_nc": prompt_mask_nc,
+        }

From 8e7c817f1f1a4cabb5e7546df5dc882160d3559f Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Thu, 23 Jan 2025 12:37:25 -0500
Subject: [PATCH 24/64] `PredictionWriter`: optional gzip, use
 ThreadPoolExecutor (#286)

* Optional gzip, use ThreadPoolExecutor

* max_threadpool_workers default 8

* Bound the queue of the thread pool

* Add (untested) disk space check to fail fast

* linting

* linting again

* return_predictions=False as per PredictionWriter docstring

* Revert hard-coded return_predictions; issue warning

* Remove disk space checking

* Remove obsolete attribute

* Improve docstring

* Test demonstrating unintended warning with config file

* Fix problem with test

* Fix the UserWarning location

* Remove extra newline
---
 cellarium/ml/callbacks/prediction_writer.py |  76 ++++++++++-
 cellarium/ml/cli.py                         |  13 ++
 tests/test_cli.py                           | 134 ++++++++++++++++++++
 3 files changed, 220 insertions(+), 3 deletions(-)

diff --git a/cellarium/ml/callbacks/prediction_writer.py b/cellarium/ml/callbacks/prediction_writer.py
index 7b7553e7..7ca55b55 100644
--- a/cellarium/ml/callbacks/prediction_writer.py
+++ b/cellarium/ml/callbacks/prediction_writer.py
@@ -3,7 +3,9 @@
 
 import os
 from collections.abc import Sequence
+from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
+from queue import Queue
 
 import lightning.pytorch as pl
 import numpy as np
@@ -16,6 +18,8 @@ def write_prediction(
     obs_names_n: np.ndarray,
     output_dir: Path | str,
     postfix: int | str,
+    gzip: bool = True,
+    executor: ThreadPoolExecutor | None = None,
 ) -> None:
     """
     Write prediction to a CSV file.
@@ -29,13 +33,51 @@ def write_prediction(
             The directory to write the prediction to.
         postfix:
             A postfix to add to the CSV file name.
+        gzip:
+            Whether to compress the CSV file using gzip.
+        executor:
+            The executor used to write the prediction. If ``None``, no executor will be used.
     """
     if not os.path.exists(output_dir):
         os.makedirs(output_dir, exist_ok=True)
     df = pd.DataFrame(prediction.cpu())
     df.insert(0, "obs_names_n", obs_names_n)
-    output_path = os.path.join(output_dir, f"batch_{postfix}.csv")
-    df.to_csv(output_path, header=False, index=False)
+    output_path = os.path.join(output_dir, f"batch_{postfix}.csv" + (".gz" if gzip else ""))
+    to_csv_kwargs: dict[str, str | bool] = {"header": False, "index": False}
+    if gzip:
+        to_csv_kwargs |= {"compression": "gzip"}
+
+    def _write_csv(frame: pd.DataFrame, path: str) -> None:
+        frame.to_csv(path, **to_csv_kwargs)
+
+    if executor is None:
+        _write_csv(df, output_path)
+    else:
+        executor.submit(_write_csv, df, output_path)
+
+
+class BoundedThreadPoolExecutor(ThreadPoolExecutor):
+    """ThreadPoolExecutor with a bounded queue for task submissions.
+    This class is used to prevent the queue from growing indefinitely when tasks are submitted,
+    which can lead to an out-of-memory error.
+    """
+
+    def __init__(self, max_workers: int, max_queue_size: int):
+        # Use a bounded queue for task submissions
+        self._queue: Queue = Queue(max_queue_size)
+        super().__init__(max_workers=max_workers)
+
+    def submit(self, fn, /, *args, **kwargs):
+        # Block if the queue is full to prevent task overload
+        self._queue.put(None)
+        future = super().submit(fn, *args, **kwargs)
+
+        # When the task completes, remove a marker from the queue
+        def done_callback(_):
+            self._queue.get()
+
+        future.add_done_callback(done_callback)
+        return future
 
 
 class PredictionWriter(pl.callbacks.BasePredictionWriter):
@@ -46,7 +88,18 @@ class PredictionWriter(pl.callbacks.BasePredictionWriter):
 
     .. note::
         To prevent an out-of-memory error, set the ``return_predictions`` argument of the
-        :class:`~lightning.pytorch.Trainer` to ``False``.
+        :class:`~lightning.pytorch.Trainer` to ``False``. This is accomplished in the config
+        file by including ``return_predictions: false`` at indent level 0. For example,
+
+        .. code-block:: yaml
+
+            trainer:
+              ...
+            model:
+              ...
+            data:
+              ...
+            return_predictions: false
 
     Args:
         output_dir:
@@ -56,6 +109,10 @@ class PredictionWriter(pl.callbacks.BasePredictionWriter):
             written. If not ``None``, only the first ``prediction_size`` columns will be written.
         key:
             PredictionWriter will write this key from the output of `predict()`.
+        gzip:
+            Whether to compress the CSV file using gzip.
+        max_threadpool_workers:
+            The maximum number of threads to use to write the predictions using a ThreadPoolExecutor.
     """
 
     def __init__(
@@ -63,11 +120,22 @@ def __init__(
         output_dir: Path | str,
         prediction_size: int | None = None,
         key: str = "x_ng",
+        gzip: bool = True,
+        max_threadpool_workers: int = 8,
     ) -> None:
         super().__init__(write_interval="batch")
         self.output_dir = output_dir
         self.prediction_size = prediction_size
         self.key = key
+        self.executor = BoundedThreadPoolExecutor(
+            max_workers=max_threadpool_workers,
+            max_queue_size=max_threadpool_workers * 2,
+        )
+        self.gzip = gzip
+
+    def __del__(self):
+        """Ensure the executor shuts down on object deletion."""
+        self.executor.shutdown(wait=True)
 
     def write_on_batch_end(
         self,
@@ -99,4 +167,6 @@ def write_on_batch_end(
             obs_names_n=batch["obs_names_n"],
             output_dir=self.output_dir,
             postfix=batch_idx * trainer.world_size + trainer.global_rank,
+            gzip=self.gzip,
+            executor=self.executor,
         )
diff --git a/cellarium/ml/cli.py b/cellarium/ml/cli.py
index e57ccfc9..fb4f076e 100644
--- a/cellarium/ml/cli.py
+++ b/cellarium/ml/cli.py
@@ -291,6 +291,19 @@ def _add_instantiators(self) -> None:
             # https://github.com/Lightning-AI/pytorch-lightning/pull/18105
             pass
 
+        def before_instantiate_classes(self):
+            # issue a UserWarning if the subcommand is predict and return_predictions is not set to False
+            if self.subcommand == "predict":
+                return_predictions: bool = self.config["predict"]["return_predictions"]
+                if return_predictions:
+                    warnings.warn(
+                        "The `return_predictions` argument should be set to 'false' when running predict to avoid OOM. "
+                        "This can be set at indent level 0 in the config file. Example:\n"
+                        "model:  ...\ndata:  ...\ntrainer:  ...\nreturn_predictions: false",
+                        UserWarning,
+                    )
+            return super().before_instantiate_classes()
+
         def instantiate_classes(self) -> None:
             with torch.device("meta"):
                 # skip the initialization of model parameters
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 37729642..2473e6d3 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -1,7 +1,9 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+import copy
 import os
+import warnings
 from pathlib import Path
 from typing import Any
 
@@ -631,3 +633,135 @@ def test_compute_var_names_g(tmp_path: Path) -> None:
     with open(tmp_path / "onepass_config_with_cpu_filter.yaml", "w") as f:
         f.write(onepass_config_with_cpu_filter)
     main(["onepass_mean_var_std", "fit", "--config", str(tmp_path / "onepass_config_with_cpu_filter.yaml")])
+
+
+def test_return_predictions_userwarning(tmp_path: Path):
+    """Ensure a warning is emitted when return_predictions is set to true and the subcommand is predict."""
+
+    # using a pre-parsed config
+    config: dict[str, Any] = {
+        "model_name": "geneformer",
+        "subcommand": "predict",
+        "predict": {
+            "model": {
+                "model": {
+                    "class_path": "cellarium.ml.models.Geneformer",
+                    "init_args": {
+                        "hidden_size": "2",
+                        "num_hidden_layers": "1",
+                        "num_attention_heads": "1",
+                        "intermediate_size": "4",
+                        "max_position_embeddings": "2",
+                    },
+                },
+            },
+            "data": {
+                "dadc": {
+                    "class_path": "cellarium.ml.data.DistributedAnnDataCollection",
+                    "init_args": {
+                        "filenames": "https://storage.googleapis.com/dsp-cellarium-cas-public/test-data/test_0.h5ad",
+                        "shard_size": "100",
+                        "max_cache_size": "2",
+                        "obs_columns_to_validate": [],
+                    },
+                },
+                "batch_keys": {
+                    "x_ng": {
+                        "attr": "X",
+                        "convert_fn": "cellarium.ml.utilities.data.densify",
+                    },
+                    "var_names_g": {"attr": "var_names"},
+                },
+                "batch_size": "5",
+                "num_workers": "1",
+            },
+            "trainer": {
+                "accelerator": "cpu",
+                "devices": devices,
+                "max_steps": "1",
+                "limit_predict_batches": "1",
+            },
+            "return_predictions": "true",
+        },
+    }
+
+    match_str = r"`return_predictions` argument should"
+
+    with pytest.warns(UserWarning, match=match_str):
+        main(copy.deepcopy(config))  # running main modifies the config dict
+
+    config["predict"]["return_predictions"] = "false"
+    with pytest.warns(UserWarning, match=match_str) as record:
+        warnings.warn("we need one warning: " + match_str, UserWarning)
+        main(copy.deepcopy(config))
+    n = 0
+    for r in record:
+        assert isinstance(r.message, Warning)
+        warning_message = r.message.args[0]
+        if match_str in warning_message:
+            n += 1
+    assert n < 2, "Unexpected UserWarning when running predict with return_predictions=false"
+
+    # using a config file
+    config_file_text = f"""
+    # lightning.pytorch==2.5.0.post0
+    seed_everything: true
+    trainer:
+      accelerator: cpu
+      devices: 1
+      max_steps: 1
+      limit_predict_batches: 1
+      default_root_dir: {tmp_path}
+    model:
+      cpu_transforms: null
+      transforms: null
+      model:
+        class_path: cellarium.ml.models.Geneformer
+        init_args:
+          hidden_size: 2
+          num_hidden_layers: 1
+          num_attention_heads: 1
+          intermediate_size: 4
+          max_position_embeddings: 2
+    data:
+      dadc:
+        class_path: cellarium.ml.data.DistributedAnnDataCollection
+        init_args:
+          filenames: https://storage.googleapis.com/dsp-cellarium-cas-public/test-data/test_0.h5ad
+          shard_size: 100
+          max_cache_size: 2
+          obs_columns_to_validate: []
+      batch_keys:
+        x_ng:
+          attr: X
+          convert_fn: cellarium.ml.utilities.data.densify
+        var_names_g:
+          attr: var_names
+      batch_size: 5
+      num_workers: 1
+    return_predictions: RETURN_PREDICTIONS_VALUE
+    ckpt_path: null
+    """
+
+    # there should be a warning with return_predictions=true
+    with open(config_file_path := tmp_path / "config.yaml", "w") as f:
+        f.write(config_file_text.replace("RETURN_PREDICTIONS_VALUE", "true"))
+
+    with pytest.warns(UserWarning, match=match_str):
+        main(["geneformer", "predict", "--config", str(config_file_path)])
+
+    # there should be no warning with return_predictions=false
+    with open(config_file_path, "w") as f:
+        f.write(config_file_text.replace("RETURN_PREDICTIONS_VALUE", "false"))
+
+    with pytest.warns(UserWarning, match=match_str) as record:
+        warnings.warn("we need one warning: " + match_str, UserWarning)
+        main(["geneformer", "predict", "--config", str(config_file_path)])
+    n = 0
+    for r in record:
+        assert isinstance(r.message, Warning)
+        warning_message = r.message.args[0]
+        if match_str in warning_message:
+            print(warning_message)
+            n += 1
+    assert n < 2, "Unexpected UserWarning when running predict with return_predictions=false"

From b3009d1ee65a1d26ae0fca35fa68b3d246e7b2b4 Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Thu, 23 Jan 2025 13:59:26 -0500
Subject: [PATCH 25/64] Address flaky mup test (#292)

* Add test dependency on tenacity

* Retry CIFAR10 download on failure
---
 pyproject.toml    |  1 +
 tests/test_mup.py | 14 ++++++++++++--
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 69a481a8..b361431c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,6 +42,7 @@ lint = ["ruff"]
 mypy = ["mypy", "types-PyYAML"]
 test = [
   "pytest-xdist",
+  "tenacity",
   "tensorboard",
   "torchvision",
 ]
diff --git a/tests/test_mup.py b/tests/test_mup.py
index 1360f774..e22aefce 100644
--- a/tests/test_mup.py
+++ b/tests/test_mup.py
@@ -3,6 +3,7 @@
 
 import gc
 import math
+import urllib.error
 from collections.abc import Callable
 from pathlib import Path
 from typing import Any, Literal
@@ -12,6 +13,7 @@
 import pytest
 import torch
 import torch.nn.functional as F
+from tenacity import retry, stop_after_attempt, wait_fixed
 from torch import nn
 from torchvision import datasets, transforms
 
@@ -122,12 +124,20 @@ def f() -> pl.LightningModule:
     )
 
 
+@retry(
+    stop=stop_after_attempt(3),  # retry up to 3 times
+    wait=wait_fixed(10),  # wait 10 seconds between retries
+    retry=(lambda exc: isinstance(exc, urllib.error.URLError)),  # retry on URLError
+)
+def cifar_dataset(path: Path, transform) -> datasets.CIFAR10:
+    return datasets.CIFAR10(root=path, train=True, download=True, transform=transform)
+
+
 @pytest.fixture
 def train_loader(tmp_path: Path) -> torch.utils.data.DataLoader:
     batch_size = 64
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-
-    trainset = datasets.CIFAR10(root=tmp_path, train=True, download=True, transform=transform)
+    trainset = cifar_dataset(tmp_path, transform)
     return torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)
 
 

From 41f82a33a72adf2a44466af0991581cdbcf2d9fd Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 24 Jan 2025 23:46:20 +0000
Subject: [PATCH 26/64] more inference code

---
 .../inference/cellarium_gpt_inference.py      | 694 +++++++++++++++++-
 1 file changed, 690 insertions(+), 4 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index d390b28e..1a8f974f 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -1,15 +1,42 @@
 import os
+import logging
+import warnings
 import torch
+import pymde
+import igraph as ig
+import leidenalg
 import numpy as np
 import typing as t
 import scanpy as sc
 import pandas as pd
 from scanpy import AnnData
 from tqdm.notebook import tqdm
-
+from functools import cached_property
 from cellarium.ml import CellariumModule, CellariumPipeline
 from cellarium.ml.models.cellarium_gpt import PredictTokenizer
 
+import matplotlib.pyplot as plt
+import plotly.express as px
+import plotly.graph_objects as go
+import colorcet as cc
+
+from scipy.stats import linregress
+from scipy.linalg import eigh
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)  # Set the logging level
+
+# Create a handler
+handler = logging.StreamHandler()
+
+# Create and set a formatter
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+handler.setFormatter(formatter)
+
+# Add the handler to the logger
+logger.addHandler(handler)
+
 
 def load_gene_info_table(gene_info_tsv_path: str, included_gene_ids: list[str]) -> t.Tuple[pd.DataFrame, dict, dict]:
     gene_info_df = pd.read_csv(gene_info_tsv_path, sep="\t")
@@ -117,8 +144,7 @@ def _get_gene_ontology_infos(self, adata: AnnData) -> dict:
 
         gene_ontology_infos["assay_ontology_term_id"] = dict()
         gene_ontology_infos["assay_ontology_term_id"]["names"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
-        # just because we are lazy
-        gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
+        gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(adata.obs['assay'].cat.categories)
 
         gene_ontology_infos["suspension_type"] = dict()
         gene_ontology_infos["suspension_type"]["names"] = list(adata.obs['suspension_type'].cat.categories)
@@ -255,6 +281,106 @@ def generate_tokens_from_adata(
 
         return tokenizer_output, context_indices
 
+    def generate_gene_tokens_by_metadata(
+        self,
+        assay: str,
+        suspension_type: str,
+        prompt_metadata_dict: dict,
+        total_mrna_umis: int,
+        query_gene_ids: list[list],
+        perturb_gene_ids: list[str] | None,
+    ) -> t.Tuple[dict, dict]:
+        
+        """
+            
+        .. note::
+        If `perturb_gene_ids` is None, there will be only a single cell in the generated tokens.
+        If `perturb_gene_ids` is not None, there first cell will be unperturbed, and the subsequent
+            cells will have the genes in `perturb_gene_ids` set to 0, in order.
+
+        .. note::
+        The first token is reversed for perturbation. The first cell is always control unperturbed (which
+        will be the only cell if `perturb_gene_ids` is None). In that cell, the first token is arbitrarily
+        set to the gene corresponding to index 0 in the gene ID dictionary and is marked to be queried.
+        Please ignore that. More generally, use `context_indices` to determine the indices of the actual
+        query genes in the generated context.
+        
+        """
+
+        METADATA_KEYS = [
+            "cell_type",
+            "tissue",
+            "development_stage",
+            "disease",
+            "sex",
+        ]
+
+        # Use the first label as the default value (actual value does not matter)
+        default_metadata_dict = {
+            metadata_key: self.metadata_ontology_infos[metadata_key]["labels"][0]
+            for metadata_key in METADATA_KEYS
+        }
+
+        # True if provided, False otherwise    
+        metadata_prompt_masks_dict = {
+            metadata_key: metadata_key in prompt_metadata_dict
+            for metadata_key in METADATA_KEYS
+        }
+
+        # Generate a complete metadata dictionary, including default values for missing keys
+        metadata_dict = {
+            metadata_key: prompt_metadata_dict.get(metadata_key, default_metadata_dict[metadata_key])
+            for metadata_key in METADATA_KEYS
+        }
+        metadata_dict |= {
+            "assay": assay,
+            "suspension_type": suspension_type,
+            "total_mrna_umis": total_mrna_umis
+        }
+
+        # Augment metadata dictionary with ontology term IDs
+        metadata_dict |= {
+            f"{metadata_key}_ontology_term_id": self.metadata_ontology_infos[metadata_key]["names"][
+                self.metadata_ontology_infos[metadata_key]["labels"].index(metadata_dict[metadata_key])]
+            for metadata_key in METADATA_KEYS
+        }
+        metadata_dict |= {
+            "assay_ontology_term_id": self.gene_ontology_infos["assay_ontology_term_id"]["names"][
+                self.gene_ontology_infos["assay_ontology_term_id"]["labels"].index(assay)]
+        }
+
+        if perturb_gene_ids is None:
+            n_cells = 1
+        else:
+            n_cells = len(perturb_gene_ids) + 1
+
+        # Generate a placeholder AnnData. There is always only gene at the prompt, which will
+        # be replaced with the gene to be perturbed. If no perturbation is required, the gene
+        # will be set to the first gene in the gene ID dictionary.
+        obs_df = pd.DataFrame({key: [value] * n_cells for key, value in metadata_dict.items()})
+        var_df = pd.DataFrame(index=[self.model_var_names[0]])
+        pert_adata = sc.AnnData(X=np.zeros((n_cells, 1)), obs=obs_df, var=var_df)
+
+        # Tokenize
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=pert_adata,
+            obs_index=None,
+            query_var_names=query_gene_ids,
+            metadata_prompt_masks_dict=metadata_prompt_masks_dict
+        )
+
+        # The first cell is control unperturbed, so we will ensure that the first gene is marked as queried  (not perturbed)
+        tokens_dict['prompt_mask_nc'][0, 0] = False
+        tokens_dict['gene_tokens_nc']['gene_value'][0, context_indices['prompt_genes'], 1] = 1  # Queried
+
+        # In subsequent cells, prompt genes are set to 0 sequentially
+        if perturb_gene_ids is not None:
+            assert all(gene_id in self.var_name_to_index_map for gene_id in perturb_gene_ids)
+            tokens_dict['gene_tokens_nc']['gene_id'] = tokens_dict['gene_tokens_nc']['gene_id'].clone()
+            tokens_dict['gene_tokens_nc']['gene_id'][1:, 0] = torch.tensor([
+                self.var_name_to_index_map[var_name] for var_name in perturb_gene_ids])
+
+        return tokens_dict, context_indices, pert_adata, metadata_prompt_masks_dict
 
     def get_marginal_mean_std(
             self,
@@ -262,7 +388,7 @@ def get_marginal_mean_std(
             query_var_names: list[str],
             query_total_mrna_umis: float | None = None,
             prompt_gene_values_g: torch.Tensor | None = None,
-            metadata_prompt_masks_dict: dict[str, bool] | None = None,
+            metadata_prompt_masks_dict: dict[str, bool] | None = None
         ) -> t.Tuple[torch.Tensor, torch.Tensor]:
     
         assert len(adata) == 1, "Only a single cell is allowed"
@@ -315,6 +441,123 @@ def get_marginal_mean_std(
 
         return gene_marginal_means_q, gene_marginal_std_q
 
+    def get_marginal_mean_std_from_tokens(
+            self,
+            tokens_dict: dict,
+            context_indices: dict,
+            use_logsumexp: bool = True,
+            max_counts: int | None = None,
+            verbose: bool = False,
+        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
+        # convert to cuda
+        if verbose:
+            logger.info("Transferring the tokens to the device ...")
+
+        tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
+
+        if verbose:
+            logger.info("Done.")
+        
+        # get model predictions
+        if verbose:
+            logger.info("Predicting ...")
+
+        logits_dict = self.gpt_pipeline.model.predict(
+            gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+            metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+            prompt_mask_nc=tokens_dict["prompt_mask_nc"],
+            predict_keys=['gene_value']
+        )
+
+        if verbose:
+            logger.info("Done.")
+
+        # note: we use `q` to denote query genes
+        if verbose:
+            logger.info("Calculating marginal mean and std ...")
+        
+        if verbose:
+            logger.info("Obtaining gene logits ...")
+
+        query_gene_indices = torch.tensor(context_indices['query_genes'], device=self.device, dtype=torch.int64)
+
+        if max_counts is None:
+            gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :]
+            max_counts = gene_logits_nqk.shape[-1]
+        else:
+            assert max_counts > 0
+            gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :max_counts]
+
+        if verbose:
+            logger.info("Done.")
+
+        if verbose:
+            logger.info("Normalizing gene logits ...")
+
+        gene_logits_nqk = gene_logits_nqk - torch.logsumexp(gene_logits_nqk, dim=-1, keepdim=True)
+
+        if verbose:
+            logger.info("Done.")
+
+        if use_logsumexp:
+            log_counts_1_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).log()
+            log_counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2).log()
+            gene_mom_1_nq = torch.logsumexp(gene_logits_nqk + log_counts_1_k[None, None, :], dim=-1).exp()
+            gene_mom_2_nq = torch.logsumexp(gene_logits_nqk + log_counts_2_k[None, None, :], dim=-1).exp()
+            gene_marginal_means_nq = gene_mom_1_nq
+            gene_marginal_std_nq = torch.clamp(gene_mom_2_nq - gene_mom_1_nq.pow(2), 0.).sqrt()
+        else:
+            gene_probs_nqk = torch.exp(gene_logits_nqk)
+            counts_1_k = torch.arange(0, max_counts, device=gene_logits_nqk.device)
+            counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2)
+            gene_mom_1_nq = (gene_probs_nqk * counts_1_k[None, None, :]).sum(dim=-1)
+            gene_mom_2_nq = (gene_probs_nqk * counts_2_k[None, None, :]).sum(dim=-1)
+            gene_marginal_means_nq = gene_mom_1_nq
+            gene_marginal_std_nq = torch.clamp(gene_mom_2_nq - gene_mom_1_nq.pow(2), 0.).sqrt()
+
+        if verbose:
+            logger.info("Done.")
+
+        return gene_marginal_means_nq, gene_marginal_std_nq
+
+    def get_marginal_mean_std_multi_cell(
+            self,
+            adata: sc.AnnData,
+            query_var_names: list[str],
+            query_total_mrna_umis: float | None = None,
+            metadata_prompt_masks_dict: dict[str, bool] | None = None,
+            use_logsumexp: bool = True,
+            max_counts: int | None = None,
+            verbose: bool = False,
+        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
+        if metadata_prompt_masks_dict is None:
+            metadata_prompt_masks_dict = self.DEFAULT_METADATA_PROMPT_MASKS_DICT
+
+        if verbose:
+            logger.info("Tokenizing the AnnData ...")
+
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=adata,
+            obs_index=None,
+            query_var_names=query_var_names,
+            query_total_mrna_umis=query_total_mrna_umis,
+            metadata_prompt_masks_dict=metadata_prompt_masks_dict,
+        )
+
+        if verbose:
+            logger.info("Done.")
+
+        return self.get_marginal_mean_std_from_tokens(
+            tokens_dict=tokens_dict,
+            context_indices=context_indices,
+            use_logsumexp=use_logsumexp,
+            max_counts=max_counts,
+            verbose=verbose,
+        )
+
+
     def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) -> dict[str, np.ndarray]:
         metadata_prediction_chunks_dict = []
         for i in tqdm(range(0, len(adata), chunk_size)):
@@ -473,3 +716,446 @@ def _wrapped_snap_to_marginal_mean_manifold(prompt_gene_values_g: torch.Tensor)
             'query_marginal_mean_q': query_marginal_mean_q,
             'query_marginal_std_q': query_marginal_std_q,
         }
+
+
+class JacobianContext:
+    def __init__(
+            self,
+            adata_obs: pd.DataFrame,
+            gene_info_tsv_path: str,
+            jacobian_point: str,
+            query_var_names: list[str],
+            prompt_var_names: list[str],
+            jacobian_qp: np.ndarray,
+            prompt_empirical_mean_p: np.ndarray,
+            query_empirical_mean_q: np.ndarray,
+            prompt_marginal_mean_p: np.ndarray,
+            prompt_marginal_std_p: np.ndarray,
+            query_marginal_mean_q: np.ndarray,
+            query_marginal_std_q: np.ndarray,
+            verbose: bool = True):
+        
+        self.verbose = verbose
+
+        n_query_vars = len(query_var_names)
+        n_prompt_vars = len(prompt_var_names)
+
+        assert jacobian_qp.shape == (n_query_vars, n_prompt_vars)
+        assert prompt_empirical_mean_p.shape == (n_prompt_vars,)
+        assert prompt_marginal_mean_p.shape == (n_prompt_vars,)
+        assert prompt_marginal_std_p.shape == (n_prompt_vars,)
+        assert query_empirical_mean_q.shape == (n_query_vars,)
+        assert query_marginal_mean_q.shape == (n_query_vars,)
+        assert query_marginal_std_q.shape == (n_query_vars,)
+
+        self.adata_obs = adata_obs
+        self.jacobian_point = jacobian_point
+        self.query_var_names = query_var_names
+        self.prompt_var_names = prompt_var_names
+        self.jacobian_qp = jacobian_qp
+        self.prompt_empirical_mean_p = prompt_empirical_mean_p
+        self.query_empirical_mean_q = query_empirical_mean_q
+        self.prompt_marginal_mean_p = prompt_marginal_mean_p
+        self.prompt_marginal_std_p = prompt_marginal_std_p
+        self.query_marginal_mean_q = query_marginal_mean_q
+        self.query_marginal_std_q = query_marginal_std_q
+
+        self.gene_info_df, self.gene_symbol_to_gene_id_map, self.gene_id_to_gene_symbol_map = \
+            load_gene_info_table(gene_info_tsv_path, query_var_names + prompt_var_names)
+
+        self.processed = False
+
+    @property
+    def cell_type(self) -> str:
+        return self.adata_obs['cell_type'].values[0]
+    
+    @property
+    def tissue(self) -> str:
+        return self.adata_obs['tissue'].values[0]
+
+    @property
+    def disease(self) -> str:
+        return self.adata_obs['disease'].values[0]
+
+    @property
+    def development_stage(self) -> str:
+        return self.adata_obs['development_stage'].values[0]
+
+    @property
+    def sex(self) -> str:
+        return self.adata_obs['sex'].values[0]
+
+    @property
+    def total_mrna_umis(self) -> float:
+        return self.adata_obs['total_mrna_umis'].values[0]
+
+    @property
+    def query_gene_symbols(self) -> list[str]:
+        return [self.gene_id_to_gene_symbol_map[gene_id] for gene_id in self.query_var_names]
+    
+    @property
+    def prompt_gene_symbols(self) -> list[str]:
+        return [self.gene_id_to_gene_symbol_map[gene_id] for gene_id in self.prompt_var_names]
+    
+    @cached_property
+    def query_gene_id_to_idx_map(self) -> dict[str, int]:
+        assert self.processed, "Must process before accessing"
+        return {gene_id: idx for idx, gene_id in enumerate(self.query_var_names)}
+    
+    @staticmethod
+    def from_old_jacobian_pt_dump(
+            jacobian_pt_path: str,
+            adata_path: str,
+            gene_info_tsv_path: str) -> 'JacobianContext':
+        
+        # suppres FutureWarning in a context manager
+        with warnings.catch_warnings():
+            warnings.simplefilter(action='ignore', category=FutureWarning)
+            adata = sc.read_h5ad(adata_path)
+            old_jac_dict = torch.load(jacobian_pt_path)
+
+        # make a metacell
+        X_meta_g = np.asarray(adata.X.sum(0))
+
+        # set total mrna umis to the mean of the dataset
+        target_total_mrna_umis = adata.obs['total_mrna_umis'].mean()
+        X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
+
+        # make a metacell anndata
+        adata_meta = adata[0, :].copy()
+        adata_meta.X = X_meta_g
+        adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
+
+        prompt_empirical_mean_p = adata_meta[0, old_jac_dict['prompt_var_names']].X.flatten()
+        query_empirical_mean_q = adata_meta[0, old_jac_dict['query_var_names']].X.flatten()
+
+        return JacobianContext(
+            adata_obs=adata_meta.obs,
+            gene_info_tsv_path=gene_info_tsv_path,
+            jacobian_point=old_jac_dict['jacobian_point'],
+            query_var_names=old_jac_dict['query_var_names'],
+            prompt_var_names=old_jac_dict['prompt_var_names'],
+            jacobian_qp=old_jac_dict['jacobian_qg'].cpu().numpy(),
+            prompt_empirical_mean_p=prompt_empirical_mean_p,
+            query_empirical_mean_q=query_empirical_mean_q,
+            prompt_marginal_mean_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            prompt_marginal_std_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
+            query_marginal_mean_q=old_jac_dict['query_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            query_marginal_std_q=old_jac_dict['query_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
+        )
+
+    def __str__(self) -> str:
+        return (
+            f"JacobianContext({self.cell_type}, {self.tissue}, {self.disease}, {self.development_stage}, "
+            f"n_umi={self.total_mrna_umis:.2f})"
+        )
+
+    __repr__ = __str__
+
+    def process(
+            self,
+            jacobian_normalization_strategy: t.Literal["mean", "std"] = "mean",
+            feature_normalization_strategy: t.Literal["l2", "query_z_score", "prompt_z_score"] = "mean",
+            min_prompt_gene_tpm: float = 10.,
+            min_query_gene_tpm: float = 10.,
+            query_response_amp_min_pct: float | None = None,
+            feature_max_value: float = 10.,
+            eps: float = 1e-8,
+        ) -> None:
+
+        assert not self.processed, "Already processed -- please create a new instance"
+        if jacobian_normalization_strategy == "mean":
+            z_p = self.prompt_marginal_mean_p
+            z_q = self.query_marginal_mean_q
+        elif jacobian_normalization_strategy == "std":
+            z_p = self.prompt_marginal_std_p
+            z_q = self.query_marginal_std_q
+        else:
+            raise ValueError("Invalid Jacobian normalization strategy")
+
+        # linear proportional activation
+        self.z_qp = self.jacobian_qp * (z_p[None, :] + eps) / (z_q[:, None] + eps)
+
+        if self.verbose:
+            logger.info(f"Maximum value of z_qp: {np.max(self.z_qp):.3f}")
+            logger.info(f"Minimum value of z_qp: {np.min(self.z_qp):.3f}")
+
+        self.mask_q = (1e6 * self.query_marginal_mean_q / self.total_mrna_umis) >= min_query_gene_tpm
+        self.mask_p = (1e6 * self.prompt_marginal_mean_p / self.total_mrna_umis) >= min_prompt_gene_tpm
+
+        logger.info(f"Number of query genes after TPM filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
+        logger.info(f"Number of prompt genes after TPM filtering: {np.sum(self.mask_p)} / {len(self.mask_p)}")
+        
+        if query_response_amp_min_pct is not None:
+            z_norm_q = np.linalg.norm(self.z_qp, axis=-1)
+            z_norm_thresh = np.percentile(z_norm_q, query_response_amp_min_pct)
+            self.mask_q = self.mask_q & (z_norm_q >= z_norm_thresh)
+            logger.info(f"Number of query genes after z-norm filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
+            
+        # apply the mask to everything else
+        self.prompt_var_names = [self.prompt_var_names[i] for i in range(len(self.prompt_var_names)) if self.mask_p[i]]
+        self.prompt_empirical_mean_p = self.prompt_empirical_mean_p[self.mask_p]
+        self.prompt_marginal_mean_p = self.prompt_marginal_mean_p[self.mask_p]
+        self.prompt_marginal_std_p = self.prompt_marginal_std_p[self.mask_p]
+
+        self.query_var_names = [self.query_var_names[i] for i in range(len(self.query_var_names)) if self.mask_q[i]]
+        self.query_empirical_mean_q = self.query_empirical_mean_q[self.mask_q]
+        self.query_marginal_mean_q = self.query_marginal_mean_q[self.mask_q]
+        self.query_marginal_std_q = self.query_marginal_std_q[self.mask_q]
+
+        # apply the mask to z_qp
+        self.z_qp = self.z_qp[self.mask_q, :][:, self.mask_p]
+
+        if feature_normalization_strategy == "prompt_z_score":
+            self.z_qp = (self.z_qp - np.mean(self.z_qp, axis=0, keepdims=True)) / (
+                np.sqrt(self.z_qp.shape[0]) * (eps + np.std(self.z_qp, axis=0, keepdims=True)))
+        elif feature_normalization_strategy == "query_z_score":
+            self.z_qp = (self.z_qp - np.mean(self.z_qp, axis=1, keepdims=True)) / (
+                np.sqrt(self.z_qp.shape[1]) * (eps + np.std(self.z_qp, axis=1, keepdims=True)))
+        elif feature_normalization_strategy == "l2":
+            self.z_qp = self.z_qp / (eps + np.linalg.norm(self.z_qp, axis=-1, keepdims=True))
+        else:
+            raise ValueError("Invalid feature normalization strategy")
+
+        # clip features
+        self.z_qp[np.isnan(self.z_qp)] = 0.
+        self.z_qp[np.isinf(self.z_qp)] = 0.
+        self.z_qp = np.clip(self.z_qp, -feature_max_value, feature_max_value)
+
+        self.processed = True
+
+        # adj
+        self.a_qq = None
+        
+        # leiden
+        self.leiden_membership = None
+        
+        # spectral analysis
+        self.eigs = None
+        self.spectral_dim = None
+
+
+    def compute_adjacency_matrix(
+            self,
+            adjacency_strategy: str = t.Literal[
+                "shifted_correlation", "unsigned_correlation", "positive_correlation", "binary"],
+            n_neighbors: int | None = 50,
+            self_loop: bool = False,
+            **kwargs) -> None:
+
+        if adjacency_strategy == "shifted_correlation":
+            assert "beta" in kwargs, "Must provide beta for shifted correlation"
+            beta = kwargs["beta"]
+            a_qq = np.power(0.5 * (1 + np.dot(self.z_qp, self.z_qp.T)), beta)
+        elif adjacency_strategy == "unsigned_correlation":
+            assert "beta" in kwargs, "Must provide beta for unsigned correlation"
+            beta = kwargs["beta"]
+            a_qq = np.power(np.abs(np.dot(self.z_qp, self.z_qp.T)), beta)
+        elif adjacency_strategy == "positive_correlation":
+            assert "beta" in kwargs, "Must provide beta for positive correlation"
+            beta = kwargs["beta"]
+            a_qq = np.power(np.maximum(0, np.dot(self.z_qp, self.z_qp.T)), beta)
+        elif adjacency_strategy == "positive_correlation_binary":
+            assert n_neighbors is None, "n_neighbors must be None for binary adjacency"
+            assert "tau" in kwargs, "Must provide correlation threshold for binary adjacency"
+            tau = kwargs["tau"]
+            a_qq = (np.maximum(0, np.dot(self.z_qp, self.z_qp.T)) > tau).astype(float)
+        else:
+            raise ValueError("Invalid adjacency strategy")
+
+        assert np.isclose(a_qq, a_qq.T).all(), "Adjacency matrix must be symmetric -- something is wrong!"
+
+        if n_neighbors is not None:
+            assert n_neighbors > 0, "n_neighbors must be positive"
+            t_qq = np.argsort(a_qq, axis=-1)[:, -n_neighbors:]  # take the top n_neighbors
+
+            # make a mask for the top n_neighbors
+            _a_qq = np.zeros_like(a_qq)
+            for q in range(a_qq.shape[0]):
+                _a_qq[q, t_qq[q]] = a_qq[q, t_qq[q]]
+                _a_qq[t_qq[q], q] = a_qq[t_qq[q], q]
+        else:
+            _a_qq = a_qq
+        a_qq = _a_qq
+        
+        if not self_loop:
+            np.fill_diagonal(a_qq, 0)
+
+        self.a_qq = a_qq
+
+    def _compute_igraph_from_adjacency(self, directed: bool = False) -> ig.Graph:
+        assert self.a_qq is not None, "Must compute adjacency matrix first"
+        sources, targets = self.a_qq.nonzero()
+        weights = self.a_qq[sources, targets]
+        g = ig.Graph(directed=directed)
+        g.add_vertices(self.a_qq.shape[0])  # this adds adjacency.shape[0] vertices
+        g.add_edges(list(zip(sources, targets)))
+        g.es["weight"] = weights
+        return g
+
+    def compute_leiden_communites(
+            self,
+            resolution: float = 3.0,
+            min_community_size: int = 2,
+        ):
+
+        g = self._compute_igraph_from_adjacency()
+        
+        leiden_partition = leidenalg.find_partition(
+            g, leidenalg.RBConfigurationVertexPartition, resolution_parameter=resolution)
+        
+        self.leiden_membership = np.array(leiden_partition.membership)
+        
+        # remove small communities
+        n_leiden = len(np.unique(self.leiden_membership))
+        sizes = np.array([np.sum(self.leiden_membership == i) for i in range(n_leiden)])
+        for i_leiden in range(n_leiden):
+            if sizes[i_leiden] < min_community_size:
+                self.leiden_membership[self.leiden_membership == i_leiden] = -1
+    
+    def compute_spectral_dimension(
+            self,
+            offset: int = 2,
+            n_lambda_for_estimation: int = 5) -> float:
+        assert self.a_qq is not None, "Must compute adjacency matrix first"
+        
+        # calculate normalized laplacian and its eigenvalues
+        norm_q = 1. / (1e-9 + np.sqrt(self.a_qq.sum(0)))
+        lap_qq = np.eye(self.a_qq.shape[0]) - norm_q[:, None] * norm_q[None, :] * self.a_qq
+        eigs = eigh(lap_qq.astype(np.float64), eigvals_only=True)
+        eigs[0] = 0
+        eigs = np.clip(eigs, 0, np.inf)  # roundoff error guardrail
+        self.eigs = eigs
+
+        n_lambda = np.cumsum(eigs)
+        n_lambda = n_lambda / n_lambda[-1]
+        first_nonzero = np.where(eigs > 0)[0][0] + offset
+        xx = np.log(eigs[first_nonzero:first_nonzero + n_lambda_for_estimation])
+        yy = np.log(n_lambda[first_nonzero:first_nonzero + n_lambda_for_estimation])
+
+        lin = linregress(xx, yy)
+        slope, intercept = lin.slope, lin.intercept
+ 
+        # save a few thigs for later
+        self.spectral_dim = 2 * linregress(xx, yy).slope
+        self.eigs = eigs
+        self.n_lambda = n_lambda
+        self.log_eigs_asymptotic = xx
+        self.log_n_lambda_asymptotic = yy
+        self.spectral_dim_slope = slope
+        self.spectral_dim_intercept = intercept
+
+    def make_mde_embedding(
+            self,
+            n_neighbors: int = 7,
+            repulsive_fraction: int = 5,
+            attractive_penalty: pymde.functions.function.Function = pymde.penalties.Log1p,
+            repulsive_penalty: pymde.functions.function.Function = pymde.penalties.InvPower,
+            device: torch.device = torch.device("cpu"),
+            max_iter: int = 500,
+            verbose: bool = True,
+        ):
+        
+        mde = pymde.preserve_neighbors(
+            self.z_qp,
+            device=device,
+            verbose=verbose,
+            n_neighbors=n_neighbors,
+            repulsive_fraction=repulsive_fraction,
+            attractive_penalty=attractive_penalty,
+            repulsive_penalty=repulsive_penalty)
+
+        self.embedding_q2 = mde.embed(verbose=verbose, max_iter=max_iter).cpu().numpy()
+
+    def plot_mde_embedding(
+            self,
+            marker_size: int = 2,
+            highlight_marker_size: int = 4,
+            width: int = 800,
+            height: int = 800,
+            highlight_gene_sets: dict[str, t.Tuple[list[str], list[str], str]] | None = None,
+        ) -> go.Figure:
+
+        assert self.embedding_q2 is not None, "Must compute MDE embedding first"
+        assert self.leiden_membership is not None, "Must compute Leiden communities first"
+
+        plot_title = f"""{self.cell_type}<br>{self.tissue}<br>{self.disease}"""
+        
+        # Create a color map for the memberships
+        memberships_q = self.leiden_membership
+        unique_memberships = np.unique(memberships_q)
+
+        # Convert memberships to strings for categorical mapping
+        unique_memberships_str = unique_memberships.astype(str)
+
+        # Create the color map with string keys
+        colormap = {str(label): cc.glasbey[i % len(cc.glasbey)] for i, label in enumerate(unique_memberships)}
+
+        # Create a DataFrame for Plotly
+        df = pd.DataFrame({
+            'x': self.embedding_q2[:, 0],
+            'y': self.embedding_q2[:, 1],
+            'label': self.query_gene_symbols,
+            'membership': memberships_q.astype(str)  # Convert to string
+        })
+
+        # Create the scatter plot
+        fig = px.scatter(
+            df,
+            x='x',
+            y='y',
+            hover_name='label',
+            title=plot_title,
+            color='membership',
+            color_discrete_map=colormap
+        )
+
+        # Update marker size
+        fig.update_traces(marker=dict(size=marker_size))  # Adjust the size as needed
+
+        if highlight_gene_sets is not None:
+            for gene_set_name, (gene_ids, gene_symbols, color) in highlight_gene_sets.items():
+                query_gene_indices = [self.query_gene_id_to_idx_map[gene_id] for gene_id in gene_ids]
+
+                # show a scatter plot and color the markers in red
+                fig.add_scatter(
+                    x=self.embedding_q2[query_gene_indices, 0],
+                    y=self.embedding_q2[query_gene_indices, 1],
+                    mode='markers',
+                    marker=dict(color=color, size=highlight_marker_size),
+                    text=gene_symbols,
+                    showlegend=True,
+                    name=gene_set_name
+                )
+
+        # Update layout to decrease the width of the plot
+        fig.update_layout(
+            width=width,  # Adjust the width as needed
+            height=height,
+            plot_bgcolor='white',
+            xaxis=dict(
+                showgrid=False,
+                showticklabels=False,
+                title='MDE_1'
+            ),
+            yaxis=dict(
+                showgrid=False,
+                showticklabels=False,
+                title='MDE_2'
+            )
+        )
+
+        return fig
+
+    def plot_spectral_dimension(self, ax: plt.Axes) -> None:
+        assert self.eigs is not None, "Must compute spectral dimension first"
+        ax.scatter(self.log_eigs_asymptotic, self.log_n_lambda_asymptotic)
+        ax.plot(
+            self.log_eigs_asymptotic,
+            self.spectral_dim_slope * self.log_eigs_asymptotic + self.spectral_dim_intercept,
+            color='red',
+            label=f"$d_S$ = {self.spectral_dim:.2f}")
+        ax.set_xlabel("ln $\lambda$")
+        ax.set_ylabel("ln N($\lambda$)")
+        ax.set_title(self.cell_type)
+        ax.legend()

From 0531e8739f90c296b500d005d77c8d14ff4b1887 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 24 Jan 2025 23:47:39 +0000
Subject: [PATCH 27/64] predict keys

---
 cellarium/ml/models/cellarium_gpt.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 0baa75d1..927e71c9 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -574,6 +574,7 @@ def predict(
         gene_tokens_nc: dict[str, torch.Tensor],
         metadata_tokens_n: dict[str, torch.Tensor],
         prompt_mask_nc: torch.Tensor | None,
+        predict_keys: list[str] | None = None,
     ) -> dict[str, torch.Tensor]:
         # embed the gene IDs, values, and total mRNA UMIs
         embedding_ncd = torch.cat(
@@ -602,7 +603,9 @@ def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
 
         # compute logits
         logits_nck = {}
-        for key in ["gene_value"] + list(metadata_tokens_n):
+        if predict_keys is None:
+            predict_keys = ["gene_value"] + list(metadata_tokens_n)
+        for key in predict_keys:
             logits_nck[key] = self.head[key](hidden_state_ncd) * self.output_logits_scale
 
         return logits_nck

From a8400d3cab75b080112038f6b087747c739724e6 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 1 Feb 2025 18:59:05 +0000
Subject: [PATCH 28/64] gene network inference code

---
 .../inference/cellarium_gpt_inference.py      | 172 +++++++++++-------
 1 file changed, 108 insertions(+), 64 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 1a8f974f..1462c9c6 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -717,18 +717,14 @@ def _wrapped_snap_to_marginal_mean_manifold(prompt_gene_values_g: torch.Tensor)
             'query_marginal_std_q': query_marginal_std_q,
         }
 
-
-class JacobianContext:
+class GeneNetworkAnalysisBase:
     def __init__(
             self,
             adata_obs: pd.DataFrame,
             gene_info_tsv_path: str,
-            jacobian_point: str,
             query_var_names: list[str],
             prompt_var_names: list[str],
-            jacobian_qp: np.ndarray,
-            prompt_empirical_mean_p: np.ndarray,
-            query_empirical_mean_q: np.ndarray,
+            response_qp: np.ndarray,
             prompt_marginal_mean_p: np.ndarray,
             prompt_marginal_std_p: np.ndarray,
             query_marginal_mean_q: np.ndarray,
@@ -740,21 +736,16 @@ def __init__(
         n_query_vars = len(query_var_names)
         n_prompt_vars = len(prompt_var_names)
 
-        assert jacobian_qp.shape == (n_query_vars, n_prompt_vars)
-        assert prompt_empirical_mean_p.shape == (n_prompt_vars,)
+        assert response_qp.shape == (n_query_vars, n_prompt_vars)
         assert prompt_marginal_mean_p.shape == (n_prompt_vars,)
         assert prompt_marginal_std_p.shape == (n_prompt_vars,)
-        assert query_empirical_mean_q.shape == (n_query_vars,)
         assert query_marginal_mean_q.shape == (n_query_vars,)
         assert query_marginal_std_q.shape == (n_query_vars,)
 
         self.adata_obs = adata_obs
-        self.jacobian_point = jacobian_point
         self.query_var_names = query_var_names
         self.prompt_var_names = prompt_var_names
-        self.jacobian_qp = jacobian_qp
-        self.prompt_empirical_mean_p = prompt_empirical_mean_p
-        self.query_empirical_mean_q = query_empirical_mean_q
+        self.response_qp = response_qp
         self.prompt_marginal_mean_p = prompt_marginal_mean_p
         self.prompt_marginal_std_p = prompt_marginal_std_p
         self.query_marginal_mean_q = query_marginal_mean_q
@@ -802,51 +793,9 @@ def query_gene_id_to_idx_map(self) -> dict[str, int]:
         assert self.processed, "Must process before accessing"
         return {gene_id: idx for idx, gene_id in enumerate(self.query_var_names)}
     
-    @staticmethod
-    def from_old_jacobian_pt_dump(
-            jacobian_pt_path: str,
-            adata_path: str,
-            gene_info_tsv_path: str) -> 'JacobianContext':
-        
-        # suppres FutureWarning in a context manager
-        with warnings.catch_warnings():
-            warnings.simplefilter(action='ignore', category=FutureWarning)
-            adata = sc.read_h5ad(adata_path)
-            old_jac_dict = torch.load(jacobian_pt_path)
-
-        # make a metacell
-        X_meta_g = np.asarray(adata.X.sum(0))
-
-        # set total mrna umis to the mean of the dataset
-        target_total_mrna_umis = adata.obs['total_mrna_umis'].mean()
-        X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
-
-        # make a metacell anndata
-        adata_meta = adata[0, :].copy()
-        adata_meta.X = X_meta_g
-        adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
-
-        prompt_empirical_mean_p = adata_meta[0, old_jac_dict['prompt_var_names']].X.flatten()
-        query_empirical_mean_q = adata_meta[0, old_jac_dict['query_var_names']].X.flatten()
-
-        return JacobianContext(
-            adata_obs=adata_meta.obs,
-            gene_info_tsv_path=gene_info_tsv_path,
-            jacobian_point=old_jac_dict['jacobian_point'],
-            query_var_names=old_jac_dict['query_var_names'],
-            prompt_var_names=old_jac_dict['prompt_var_names'],
-            jacobian_qp=old_jac_dict['jacobian_qg'].cpu().numpy(),
-            prompt_empirical_mean_p=prompt_empirical_mean_p,
-            query_empirical_mean_q=query_empirical_mean_q,
-            prompt_marginal_mean_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
-            prompt_marginal_std_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
-            query_marginal_mean_q=old_jac_dict['query_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
-            query_marginal_std_q=old_jac_dict['query_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
-        )
-
     def __str__(self) -> str:
         return (
-            f"JacobianContext({self.cell_type}, {self.tissue}, {self.disease}, {self.development_stage}, "
+            f"GeneNetworkAnalysisBase({self.cell_type}, {self.tissue}, {self.disease}, {self.development_stage}, "
             f"n_umi={self.total_mrna_umis:.2f})"
         )
 
@@ -854,8 +803,8 @@ def __str__(self) -> str:
 
     def process(
             self,
-            jacobian_normalization_strategy: t.Literal["mean", "std"] = "mean",
-            feature_normalization_strategy: t.Literal["l2", "query_z_score", "prompt_z_score"] = "mean",
+            response_normalization_strategy: t.Literal["mean", "std", "none"] = "mean",
+            feature_normalization_strategy: t.Literal["l2", "query_z_score", "prompt_z_score"] = "query_z_score",
             min_prompt_gene_tpm: float = 10.,
             min_query_gene_tpm: float = 10.,
             query_response_amp_min_pct: float | None = None,
@@ -864,17 +813,20 @@ def process(
         ) -> None:
 
         assert not self.processed, "Already processed -- please create a new instance"
-        if jacobian_normalization_strategy == "mean":
+        if response_normalization_strategy == "mean":
             z_p = self.prompt_marginal_mean_p
             z_q = self.query_marginal_mean_q
-        elif jacobian_normalization_strategy == "std":
+        elif response_normalization_strategy == "std":
             z_p = self.prompt_marginal_std_p
             z_q = self.query_marginal_std_q
+        elif response_normalization_strategy == "none":
+            z_p = np.ones_like(self.prompt_marginal_mean_p)
+            z_q = np.ones_like(self.query_marginal_mean_q)
         else:
             raise ValueError("Invalid Jacobian normalization strategy")
 
         # linear proportional activation
-        self.z_qp = self.jacobian_qp * (z_p[None, :] + eps) / (z_q[:, None] + eps)
+        self.z_qp = self.response_qp * (z_p[None, :] + eps) / (z_q[:, None] + eps)
 
         if self.verbose:
             logger.info(f"Maximum value of z_qp: {np.max(self.z_qp):.3f}")
@@ -894,12 +846,12 @@ def process(
             
         # apply the mask to everything else
         self.prompt_var_names = [self.prompt_var_names[i] for i in range(len(self.prompt_var_names)) if self.mask_p[i]]
-        self.prompt_empirical_mean_p = self.prompt_empirical_mean_p[self.mask_p]
+        # self.prompt_empirical_mean_p = self.prompt_empirical_mean_p[self.mask_p]
         self.prompt_marginal_mean_p = self.prompt_marginal_mean_p[self.mask_p]
         self.prompt_marginal_std_p = self.prompt_marginal_std_p[self.mask_p]
 
         self.query_var_names = [self.query_var_names[i] for i in range(len(self.query_var_names)) if self.mask_q[i]]
-        self.query_empirical_mean_q = self.query_empirical_mean_q[self.mask_q]
+        # self.query_empirical_mean_q = self.query_empirical_mean_q[self.mask_q]
         self.query_marginal_mean_q = self.query_marginal_mean_q[self.mask_q]
         self.query_marginal_std_q = self.query_marginal_std_q[self.mask_q]
 
@@ -1054,6 +1006,7 @@ def make_mde_embedding(
             device: torch.device = torch.device("cpu"),
             max_iter: int = 500,
             verbose: bool = True,
+            **kwargs
         ):
         
         mde = pymde.preserve_neighbors(
@@ -1063,7 +1016,8 @@ def make_mde_embedding(
             n_neighbors=n_neighbors,
             repulsive_fraction=repulsive_fraction,
             attractive_penalty=attractive_penalty,
-            repulsive_penalty=repulsive_penalty)
+            repulsive_penalty=repulsive_penalty,
+            **kwargs)
 
         self.embedding_q2 = mde.embed(verbose=verbose, max_iter=max_iter).cpu().numpy()
 
@@ -1159,3 +1113,93 @@ def plot_spectral_dimension(self, ax: plt.Axes) -> None:
         ax.set_ylabel("ln N($\lambda$)")
         ax.set_title(self.cell_type)
         ax.legend()
+
+
+class JacobianContext(GeneNetworkAnalysisBase):
+    def __init__(
+            self,
+            adata_obs: pd.DataFrame,
+            gene_info_tsv_path: str,
+            jacobian_point: str,
+            query_var_names: list[str],
+            prompt_var_names: list[str],
+            jacobian_qp: np.ndarray,
+            prompt_empirical_mean_p: np.ndarray,
+            query_empirical_mean_q: np.ndarray,
+            prompt_marginal_mean_p: np.ndarray,
+            prompt_marginal_std_p: np.ndarray,
+            query_marginal_mean_q: np.ndarray,
+            query_marginal_std_q: np.ndarray,
+            verbose: bool = True):
+
+        super().__init__(
+            adata_obs=adata_obs,
+            gene_info_tsv_path=gene_info_tsv_path,
+            query_var_names=query_var_names,
+            prompt_var_names=prompt_var_names,
+            response_qp=jacobian_qp,
+            prompt_marginal_mean_p=prompt_marginal_mean_p,
+            prompt_marginal_std_p=prompt_marginal_std_p,
+            query_marginal_mean_q=query_marginal_mean_q,
+            query_marginal_std_q=query_marginal_std_q,
+            verbose=verbose)
+        
+        n_query_vars = len(query_var_names)
+        n_prompt_vars = len(prompt_var_names)
+
+        assert prompt_empirical_mean_p.shape == (n_prompt_vars,)
+        assert query_empirical_mean_q.shape == (n_query_vars,)
+
+        self.jacobian_point = jacobian_point
+        self.prompt_empirical_mean_p = prompt_empirical_mean_p
+        self.query_empirical_mean_q = query_empirical_mean_q
+    
+    @staticmethod
+    def from_old_jacobian_pt_dump(
+            jacobian_pt_path: str,
+            adata_path: str,
+            gene_info_tsv_path: str) -> 'JacobianContext':
+        
+        # suppres FutureWarning in a context manager
+        with warnings.catch_warnings():
+            warnings.simplefilter(action='ignore', category=FutureWarning)
+            adata = sc.read_h5ad(adata_path)
+            old_jac_dict = torch.load(jacobian_pt_path)
+
+        # make a metacell
+        X_meta_g = np.asarray(adata.X.sum(0))
+
+        # set total mrna umis to the mean of the dataset
+        target_total_mrna_umis = adata.obs['total_mrna_umis'].mean()
+        X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
+
+        # make a metacell anndata
+        adata_meta = adata[0, :].copy()
+        adata_meta.X = X_meta_g
+        adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
+
+        prompt_empirical_mean_p = adata_meta[0, old_jac_dict['prompt_var_names']].X.flatten()
+        query_empirical_mean_q = adata_meta[0, old_jac_dict['query_var_names']].X.flatten()
+
+        return JacobianContext(
+            adata_obs=adata_meta.obs,
+            gene_info_tsv_path=gene_info_tsv_path,
+            jacobian_point=old_jac_dict['jacobian_point'],
+            query_var_names=old_jac_dict['query_var_names'],
+            prompt_var_names=old_jac_dict['prompt_var_names'],
+            jacobian_qp=old_jac_dict['jacobian_qg'].cpu().numpy(),
+            prompt_empirical_mean_p=prompt_empirical_mean_p,
+            query_empirical_mean_q=query_empirical_mean_q,
+            prompt_marginal_mean_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            prompt_marginal_std_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
+            query_marginal_mean_q=old_jac_dict['query_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            query_marginal_std_q=old_jac_dict['query_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
+        )
+
+    def __str__(self) -> str:
+        return (
+            f"JacobianContext({self.cell_type}, {self.tissue}, {self.disease}, {self.development_stage}, "
+            f"n_umi={self.total_mrna_umis:.2f})"
+        )
+
+    __repr__ = __str__

From 2d4abb18b757d8ca349b2cc6e8ef30bc7861b422 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 1 Feb 2025 19:10:37 +0000
Subject: [PATCH 29/64] notebooks

---
 .../00_basic_prompting.ipynb                  | 1109 +++++++++++
 .../01_fixed_point_iters.ipynb                |  578 ++++++
 .../02_jacobian_calculation.ipynb             |  588 ++++++
 .../02_jacobian_calculation.py                |  503 +++++
 .../03_gene_networks.ipynb                    |  543 ++++++
 .../04_metadata_evaluation__updated.ipynb     |  801 ++++++++
 .../05_make_metacells.ipynb                   |  346 ++++
 .../06_jacobian_calculation_metacell.ipynb    |  631 +++++++
 .../06_jacobian_calculation_metacell.py       |  546 ++++++
 .../07_gene_networks_metacell.ipynb           | 1671 +++++++++++++++++
 ...lico_deletion_by_cell_type_prompting.ipynb |  531 ++++++
 11 files changed, 7847 insertions(+)
 create mode 100644 notebooks/cellariumgpt_inference/00_basic_prompting.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/01_fixed_point_iters.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/02_jacobian_calculation.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/02_jacobian_calculation.py
 create mode 100644 notebooks/cellariumgpt_inference/03_gene_networks.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/04_metadata_evaluation__updated.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/05_make_metacells.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.py
 create mode 100644 notebooks/cellariumgpt_inference/07_gene_networks_metacell.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/08_in_silico_deletion_by_cell_type_prompting.ipynb

diff --git a/notebooks/cellariumgpt_inference/00_basic_prompting.ipynb b/notebooks/cellariumgpt_inference/00_basic_prompting.ipynb
new file mode 100644
index 00000000..cd7238d1
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/00_basic_prompting.ipynb
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Basic stuff\n",
+    "\n",
+    "Interesting questions:\n",
+    "- What happens if we iterate on a cell? do we reach a fixed point or not?\n",
+    "- What happens when we include or not include metadata tokens?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "DEVICE = torch.device('cuda:7')\n",
+    "sc.set_figure_params(figsize=(4, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load an AnnData Extract\n",
+    "\n",
+    "We will use it for category mappings ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Useful stuff:\n",
+    "- `model_var_names`\n",
+    "- `model_gene_symbols`\n",
+    "- `var_name_to_index_map`\n",
+    "- `gene_symbol_to_gene_id_map`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a CellariumGPT checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ckpt_path = os.path.join(CHECKPOINTS_PATH, \"epoch=2-step=252000.ckpt\")\n",
+    "gpt_model = CellariumModule.load_from_checkpoint(ckpt_path)\n",
+    "\n",
+    "# Inject gene categories\n",
+    "gpt_model.model.gene_categories = np.asarray(adata.var_names)\n",
+    "\n",
+    "# change attention backend to memory efficient\n",
+    "gpt_model.model.set_attention_backend('mem_efficient')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpt_model.pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline\n",
+    "metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos\n",
+    "print(type(metadata_ontology_infos))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cellarium.ml.models.cellarium_gpt import PredictTokenizer\n",
+    "\n",
+    "# Rewire the pipeline with a PredictTokenizer\n",
+    "predict_tokenizer = PredictTokenizer(\n",
+    "    max_total_mrna_umis=100_000,\n",
+    "    gene_vocab_sizes={\n",
+    "        \"assay\": 19,\n",
+    "        \"gene_id\": 36601,\n",
+    "        \"gene_value\": 2001,\n",
+    "        \"suspension_type\": 2,\n",
+    "    },\n",
+    "    metadata_vocab_sizes={\n",
+    "        \"cell_type\": 890,\n",
+    "        \"development_stage\": 191,\n",
+    "        \"disease\": 350,\n",
+    "        \"sex\": 2,\n",
+    "        \"tissue\": 822,\n",
+    "    },\n",
+    "    ontology_infos=metadata_ontology_infos,\n",
+    ")\n",
+    "\n",
+    "gpt_model.pipeline = CellariumPipeline([\n",
+    "    predict_tokenizer,\n",
+    "    gpt_model.model,\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gpt_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from cellarium.ml.models.cellarium_gpt import CellariumGPT\n",
+    "\n",
+    "\n",
+    "def generate_tokens_from_adata(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        model_gene_categories: list[str],\n",
+    "        obs_index: int | list[int] | None,\n",
+    "        query_var_index: list[int],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        metadata_prompt_masks_dict: dict[str, bool],\n",
+    "        tokenizer: PredictTokenizer,\n",
+    "        device: torch.device = torch.device(\"cpu\"),\n",
+    "    ) -> tuple[dict, dict]:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    .. note::\n",
+    "      All variables in the AnnData are treated as prompts.\n",
+    "\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # slice the anndata\n",
+    "    if isinstance(obs_index, int):\n",
+    "        obs_index = [obs_index]\n",
+    "\n",
+    "    # save obs before slicing\n",
+    "    if obs_index is not None:\n",
+    "        adata = adata[obs_index]\n",
+    "\n",
+    "    # generate gene ids and masks\n",
+    "    n_cells = len(adata)\n",
+    "    adata_var_names = adata.var_names\n",
+    "    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}\n",
+    "    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])\n",
+    "    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]\n",
+    "    n_prompt_vars = len(prompt_var_index)\n",
+    "    n_query_vars = len(query_var_index)\n",
+    "    n_total_vars = n_prompt_vars + n_query_vars\n",
+    "    \n",
+    "    # gene id\n",
+    "    gene_ids_nc = torch.tensor(\n",
+    "        prompt_var_index + query_var_index,\n",
+    "        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene prompt mask\n",
+    "    gene_prompt_mask_nc = torch.tensor(\n",
+    "        [1] * n_prompt_vars + [0] * n_query_vars,\n",
+    "        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene value\n",
+    "    try:\n",
+    "        prompt_X_ng = np.asarray(adata.X.todense())\n",
+    "    except AttributeError:\n",
+    "        prompt_X_ng = adata.X\n",
+    "    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)\n",
+    "    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)\n",
+    "    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)\n",
+    "\n",
+    "    # total mrna umis\n",
+    "    prompt_total_mrna_umis_nc = torch.tensor(\n",
+    "        adata.obs[\"total_mrna_umis\"].values,\n",
+    "        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)\n",
+    "    if query_total_mrna_umis is None:\n",
+    "        # the same as prompt\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            adata.obs[\"total_mrna_umis\"].values,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    else:\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            [query_total_mrna_umis] * n_cells,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)\n",
+    "\n",
+    "    # convert assay and suspension_type to codes\n",
+    "    assay_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"assay_ontology_term_id\"].values,\n",
+    "            categories=gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "    suspension_type_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"suspension_type\"].values,\n",
+    "            categories=gene_ontology_infos[\"suspension_type\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "\n",
+    "    gene_tokens_dict = {\n",
+    "        \"assay\": assay_nc,  # categorical\n",
+    "        \"suspension_type\": suspension_type_nc,  # categorical\n",
+    "        \"gene_id\": gene_ids_nc,  # categorical\n",
+    "        \"gene_value\": gene_value_nc,  # continuous\n",
+    "        \"total_mrna_umis\": total_mrna_umis_nc,  # continuous\n",
+    "    }\n",
+    "\n",
+    "    # metadata prompt masks\n",
+    "    expanded_metadata_prompt_masks_dict = dict()\n",
+    "    for key in metadata_ontology_infos.keys():  # note: key order is important ...\n",
+    "        expanded_metadata_prompt_masks_dict[key] = torch.tensor(\n",
+    "            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)\n",
+    "    \n",
+    "    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes\n",
+    "    metadata_tokens_dict = {\n",
+    "        \"cell_type\": adata.obs[\"cell_type_ontology_term_id\"].values,  # categorical\n",
+    "        \"development_stage\": adata.obs[\"development_stage_ontology_term_id\"].values,  # categorical\n",
+    "        \"disease\": adata.obs[\"disease_ontology_term_id\"].values,  # categorical\n",
+    "        \"sex\": adata.obs[\"sex_ontology_term_id\"].values,  # categorical\n",
+    "        \"tissue\": adata.obs[\"tissue_ontology_term_id\"].values,  # categorical\n",
+    "    }\n",
+    "\n",
+    "    # where to find each thing in the context?\n",
+    "    context_indices = dict()\n",
+    "    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()\n",
+    "    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()\n",
+    "    offset = 0\n",
+    "    for metadata_key in metadata_ontology_infos.keys():\n",
+    "        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset\n",
+    "        offset += 1\n",
+    "\n",
+    "    # return gene_tokens_dict, metadata_tokens_dict\n",
+    "    tokenizer_output = tokenizer(\n",
+    "        metadata_tokens_n=metadata_tokens_dict,\n",
+    "        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,\n",
+    "        gene_tokens_nc=gene_tokens_dict,\n",
+    "        gene_prompt_mask_nc=gene_prompt_mask_nc,\n",
+    "    )\n",
+    "\n",
+    "    return tokenizer_output, context_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test the predict tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"luca_CD8_ex_LUAD.h5ad\")\n",
+    "test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "# add total mNRA umis\n",
+    "test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load top genes\n",
+    "top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "top_genes_df = pd.read_csv(top_genes_path)\n",
+    "top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "# restrict top_gene_ids to those in the model categories\n",
+    "top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "print(len(top_gene_ids))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_adata = test_adata[:, top_gene_ids][[0]]\n",
+    "\n",
+    "metadata_prompt_masks_dict = {\n",
+    "    \"cell_type\": False,\n",
+    "    \"development_stage\": False,\n",
+    "    \"disease\": False,\n",
+    "    \"sex\": False,\n",
+    "    \"tissue\": False,\n",
+    "}\n",
+    "\n",
+    "query_var_index = [var_name_to_index_map[gene_id] for gene_id in top_gene_ids]\n",
+    "\n",
+    "tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "    adata=input_adata,\n",
+    "    gene_ontology_infos=gene_ontology_infos,\n",
+    "    metadata_ontology_infos=metadata_ontology_infos,\n",
+    "    model_gene_categories=model_var_names,\n",
+    "    obs_index=None,\n",
+    "    query_var_index=query_var_index,\n",
+    "    query_total_mrna_umis=10_000,\n",
+    "    metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "    tokenizer=predict_tokenizer,\n",
+    "    device=torch.device(\"cpu\"),\n",
+    ")\n",
+    "\n",
+    "# convert to cuda\n",
+    "tokens_dict = gpt_model.transfer_batch_to_device(tokens_dict, gpt_model.device, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    logits_dict = gpt_model.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata_key = 'cell_type'\n",
+    "metadata_logits_nk = logits_dict[metadata_key][:, context_indices[f'query_{metadata_key}'], :]\n",
+    "metadata_logits_nk = metadata_logits_nk - torch.logsumexp(metadata_logits_nk, dim=1, keepdim=True)\n",
+    "metadata_probs_nk = torch.exp(metadata_logits_nk)\n",
+    "\n",
+    "probs_k = metadata_probs_nk.cpu().numpy()[0]\n",
+    "sort_order = np.argsort(probs_k)[::-1]\n",
+    "\n",
+    "for k in range(10):\n",
+    "    prob = probs_k[sort_order[k]]\n",
+    "    name = metadata_ontology_infos[metadata_key][\"labels\"][sort_order[k]]\n",
+    "    print(f\"{name}: {prob:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_logits_ngk = logits_dict['gene_value'][:, context_indices['query_genes'], :]\n",
+    "gene_logits_ngk = gene_logits_ngk - torch.logsumexp(gene_logits_ngk, dim=-1, keepdim=True)\n",
+    "gene_probs_ngk = torch.exp(gene_logits_ngk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_index = top_gene_ids.index(gene_symbol_to_gene_id_map['PTPRC'])\n",
+    "plt.plot(gene_probs_ngk[0, gene_index, :].cpu().numpy())\n",
+    "plt.xlim((0, 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Library size upsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import linregress\n",
+    "\n",
+    "query_total_mrna_umis_list = [1_000, 2_000, 3_000, 4_000, 5_000, 6_000, 7_000, 8_000, 9_000, 10_000, 15_000, 20_000]\n",
+    "query_gene_symbol = 'PTPRC'\n",
+    "MAX_X = 60\n",
+    "means = []\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3, 4))\n",
+    "\n",
+    "for query_total_mrna_umis in query_total_mrna_umis_list:\n",
+    "    input_adata = test_adata[:, top_gene_ids][[0]]\n",
+    "\n",
+    "    metadata_prompt_masks_dict = {\n",
+    "        \"cell_type\": False,\n",
+    "        \"development_stage\": False,\n",
+    "        \"disease\": False,\n",
+    "        \"sex\": False,\n",
+    "        \"tissue\": False,\n",
+    "    }\n",
+    "\n",
+    "    query_var_index = [var_name_to_index_map[gene_id] for gene_id in top_gene_ids]\n",
+    "\n",
+    "    tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "        adata=input_adata,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        model_gene_categories=model_var_names,\n",
+    "        obs_index=None,\n",
+    "        query_var_index=[var_name_to_index_map[gene_symbol_to_gene_id_map[query_gene_symbol]]],\n",
+    "        query_total_mrna_umis=query_total_mrna_umis,\n",
+    "        metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "        tokenizer=predict_tokenizer,\n",
+    "        device=torch.device(\"cpu\"),\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    with torch.no_grad():\n",
+    "        tokens_dict = gpt_model.transfer_batch_to_device(tokens_dict, gpt_model.device, 0)\n",
+    "\n",
+    "        logits_dict = gpt_model.model.predict(\n",
+    "            gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "            metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "            prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "        )\n",
+    "\n",
+    "        gene_logits_ngk = logits_dict['gene_value'][:, context_indices['query_genes'], :]\n",
+    "        gene_logits_ngk = gene_logits_ngk - torch.logsumexp(gene_logits_ngk, dim=-1, keepdim=True)\n",
+    "        gene_probs_ngk = torch.exp(gene_logits_ngk)\n",
+    "\n",
+    "    probs_k = gene_probs_ngk.cpu().numpy()[0, 0, :]\n",
+    "    mean = np.sum(np.arange(0, probs_k.shape[-1]) * probs_k)\n",
+    "    means.append(mean)\n",
+    "\n",
+    "    ax.plot(probs_k, label=f\"{query_total_mrna_umis}\")\n",
+    "\n",
+    "ax.set_xlim((0, MAX_X))\n",
+    "ax.legend(fontsize=10)\n",
+    "\n",
+    "ax.set_xlabel('Counts')\n",
+    "ax.set_ylabel('Probability')\n",
+    "ax.set_title(query_gene_symbol)\n",
+    "ax.grid(False)\n",
+    "\n",
+    "# calculate the linear slope between query_total_mrna_umis_list and means\n",
+    "slope = linregress(np.log(query_total_mrna_umis_list), np.log(means)).slope\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(3, 4))\n",
+    "\n",
+    "ax.plot(query_total_mrna_umis_list, means, marker='o', label=f\"log-log slope: {slope:.2f}\")\n",
+    "ax.legend(fontsize=12)\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlabel('Total mRNA UMIs')\n",
+    "ax.set_ylabel(f'Mean count')\n",
+    "ax.set_title(f'{query_gene_symbol}')\n",
+    "ax.grid(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metacell prompting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"luca_CD8_ex_LUAD.h5ad\")\n",
+    "test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "# add total mNRA umis\n",
+    "test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a metacell\n",
+    "X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "# set total mrna umis to the mean of the dataset\n",
+    "target_total_mrna_umis = np.mean(np.asarray(test_adata.X.sum(-1)).flatten())\n",
+    "X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "# make a metacell anndata\n",
+    "adata_meta = test_adata[0, :].copy()\n",
+    "adata_meta.X = X_meta_g\n",
+    "adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # or choose a single cell\n",
+    "# adata_meta = test_adata[5].copy()\n",
+    "# adata_meta.obs['total_mrna_umis'] = [adata_meta.X.sum().item()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_meta.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# choose top genes\n",
+    "top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "top_genes_df = pd.read_csv(top_genes_path)\n",
+    "top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "# restrict top_gene_ids to those in the model categories\n",
+    "top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "print(len(top_gene_ids))\n",
+    "prompt_gene_ids = top_gene_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # or random genes\n",
+    "# n_random_genes = 7_000\n",
+    "# prompt_gene_ids = np.random.choice(model_var_names, n_random_genes, replace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_adata = adata_meta[:, prompt_gene_ids]\n",
+    "\n",
+    "metadata_prompt_masks_dict = {\n",
+    "    \"cell_type\": False,\n",
+    "    \"development_stage\": False,\n",
+    "    \"disease\": False,\n",
+    "    \"sex\": False,\n",
+    "    \"tissue\": False,\n",
+    "}\n",
+    "\n",
+    "query_var_index = [var_name_to_index_map[gene_id] for gene_id in top_gene_ids]\n",
+    "\n",
+    "tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "    adata=input_adata,\n",
+    "    gene_ontology_infos=gene_ontology_infos,\n",
+    "    metadata_ontology_infos=metadata_ontology_infos,\n",
+    "    model_gene_categories=model_var_names,\n",
+    "    obs_index=None,\n",
+    "    query_var_index=query_var_index,\n",
+    "    query_total_mrna_umis=None,\n",
+    "    metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "    tokenizer=predict_tokenizer,\n",
+    "    device=torch.device(\"cpu\"),\n",
+    ")\n",
+    "\n",
+    "# convert to cuda\n",
+    "tokens_dict = gpt_model.transfer_batch_to_device(tokens_dict, gpt_model.device, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    logits_dict = gpt_model.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata_key = 'cell_type'\n",
+    "metadata_logits_nk = logits_dict[metadata_key][:, context_indices[f'query_{metadata_key}'], :]\n",
+    "metadata_logits_nk = metadata_logits_nk - torch.logsumexp(metadata_logits_nk, dim=1, keepdim=True)\n",
+    "metadata_probs_nk = torch.exp(metadata_logits_nk)\n",
+    "\n",
+    "probs_k = metadata_probs_nk.cpu().numpy()[0]\n",
+    "sort_order = np.argsort(probs_k)[::-1]\n",
+    "\n",
+    "for k in range(10):\n",
+    "    prob = probs_k[sort_order[k]]\n",
+    "    name = metadata_ontology_infos[metadata_key][\"labels\"][sort_order[k]]\n",
+    "    print(f\"{name}: {prob:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_logits_ngk = logits_dict['gene_value'][:, context_indices['query_genes'], :]\n",
+    "gene_logits_ngk = gene_logits_ngk - torch.logsumexp(gene_logits_ngk, dim=-1, keepdim=True)\n",
+    "gene_probs_ngk = torch.exp(gene_logits_ngk)\n",
+    "gene_means_g = np.sum(np.arange(0, gene_probs_ngk.shape[-1]) * gene_probs_ngk.cpu().numpy()[0], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "try:\n",
+    "    actual_X_g = np.asarray(input_adata.X.todense()).flatten()\n",
+    "except AttributeError:\n",
+    "    actual_X_g = input_adata.X.flatten()\n",
+    "ax.scatter(\n",
+    "    actual_X_g,\n",
+    "    gene_means_g,\n",
+    "    s=1\n",
+    ")\n",
+    "\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "\n",
+    "ax.set_xlabel('Metacell mean expression')\n",
+    "ax.set_ylabel('CellariumGPT marginal mean expression')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Jacobian-based gene-network construction\n",
+    "\n",
+    "- helper method to snap a cell to the mean manifold\n",
+    "- differentiable method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def generate_tokens_from_adata(\n",
+    "#         adata: sc.AnnData,\n",
+    "#         gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "#         metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "#         model_gene_categories: list[str],\n",
+    "#         obs_index: int | list[int] | None,\n",
+    "#         query_var_index: list[int],\n",
+    "#         query_total_mrna_umis: float | None,\n",
+    "#         metadata_prompt_masks_dict: dict[str, bool],\n",
+    "#         tokenizer: PredictTokenizer,\n",
+    "#         device: torch.device = torch.device(\"cpu\"),\n",
+    "#     ) -> tuple[dict, dict]:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def snap_to_marginal_mean_manifold(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        query_var_names: list[str],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        predict_tokenizer: PredictTokenizer,\n",
+    "        cellarium_gpt_module: CellariumModule,\n",
+    "        prompt_gene_values_g: torch.Tensor | None = None,\n",
+    "    ) -> tuple[torch.Tensor, torch.Tensor]:\n",
+    "    \n",
+    "    assert len(adata) == 1, \"Only a single cell is allowed\"\n",
+    "    \n",
+    "    model_gene_categories = cellarium_gpt_module.model.gene_categories\n",
+    "    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}\n",
+    "    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]\n",
+    "\n",
+    "    metadata_prompt_masks_dict = {\n",
+    "        \"cell_type\": False,\n",
+    "        \"development_stage\": False,\n",
+    "        \"disease\": False,\n",
+    "        \"sex\": False,\n",
+    "        \"tissue\": False,\n",
+    "    }\n",
+    "\n",
+    "    tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "        adata=adata,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        model_gene_categories=model_var_names,\n",
+    "        obs_index=None,\n",
+    "        query_var_index=query_var_index,\n",
+    "        query_total_mrna_umis=query_total_mrna_umis,\n",
+    "        metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "        tokenizer=predict_tokenizer,\n",
+    "        device=torch.device(\"cpu\"),\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)\n",
+    "    \n",
+    "    # get a reference to prompt gene values\n",
+    "    FIRST_CELL_DIM = 0\n",
+    "    GENE_VALUE_DIM = 0\n",
+    "    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]\n",
+    "    \n",
+    "    # this is the \"source\"\n",
+    "    if prompt_gene_values_g is None:\n",
+    "        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()\n",
+    "    \n",
+    "    # inject back to tokens_dict to re-establish the reference for Jacobian calculation\n",
+    "    tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)\n",
+    "\n",
+    "    # get model predictions\n",
+    "    logits_dict = cellarium_gpt_module.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )\n",
+    "\n",
+    "    # note: we use `q` to denote query genes\n",
+    "    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]\n",
+    "    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)\n",
+    "    log_counts_k = torch.arange(0, gene_logits_qk.shape[-1], device=gene_logits_qk.device).log()\n",
+    "    gene_marginal_means_q = torch.logsumexp(gene_logits_qk + log_counts_k[None, :], dim=-1).exp()\n",
+    "\n",
+    "    return prompt_gene_values_g, gene_marginal_means_q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test snap to marginal mean manifold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"luca_CD8_ex_LUAD.h5ad\")\n",
+    "test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "# add total mNRA umis\n",
+    "test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)\n",
+    "\n",
+    "# make a metacell\n",
+    "X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "# set total mrna umis to the mean of the dataset\n",
+    "target_total_mrna_umis = np.mean(np.asarray(test_adata.X.sum(-1)).flatten())\n",
+    "X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "# make a metacell anndata\n",
+    "adata_meta = test_adata[0, :].copy()\n",
+    "adata_meta.X = X_meta_g\n",
+    "adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]\n",
+    "\n",
+    "# choose top genes\n",
+    "top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "top_genes_df = pd.read_csv(top_genes_path)\n",
+    "top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "# restrict top_gene_ids to those in the model categories\n",
+    "top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "prompt_gene_ids = top_gene_ids\n",
+    "\n",
+    "MAX_GENES = 1000\n",
+    "adata_meta = adata_meta[:, prompt_gene_ids[:MAX_GENES]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_gene_values_g, gene_marginal_means_q = snap_to_marginal_mean_manifold(\n",
+    "    adata=adata_meta,\n",
+    "    gene_ontology_infos=gene_ontology_infos,\n",
+    "    metadata_ontology_infos=metadata_ontology_infos,\n",
+    "    query_var_names=prompt_gene_ids[:MAX_GENES],\n",
+    "    query_total_mrna_umis=None,\n",
+    "    predict_tokenizer=predict_tokenizer,\n",
+    "    cellarium_gpt_module=gpt_model,\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "ax.scatter(\n",
+    "    prompt_gene_values_g.detach().cpu().numpy(),\n",
+    "    gene_marginal_means_q.detach().cpu().numpy(),\n",
+    "    s=1)\n",
+    "\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlabel('Metacell mean expression')\n",
+    "ax.set_ylabel('CellariumGPT marginal mean expression')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test Jacobian calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"luca_CD8_ex_LUAD.h5ad\")\n",
+    "test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "# add total mNRA umis\n",
+    "test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)\n",
+    "\n",
+    "# make a metacell\n",
+    "X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "# set total mrna umis to the mean of the dataset\n",
+    "target_total_mrna_umis = np.mean(np.asarray(test_adata.X.sum(-1)).flatten())\n",
+    "X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "# make a metacell anndata\n",
+    "adata_meta = test_adata[0, :].copy()\n",
+    "adata_meta.X = X_meta_g\n",
+    "adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]\n",
+    "\n",
+    "# choose top genes\n",
+    "top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "top_genes_df = pd.read_csv(top_genes_path)\n",
+    "top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "# restrict top_gene_ids to those in the model categories\n",
+    "top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "prompt_gene_ids = top_gene_ids\n",
+    "\n",
+    "MAX_GENES = 100\n",
+    "adata_meta = adata_meta[:, prompt_gene_ids]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_var_names = prompt_gene_ids\n",
+    "\n",
+    "prompt_gene_values_g = torch.tensor(\n",
+    "    adata_meta.X[0].toarray().flatten(),\n",
+    "    device=gpt_model.device,\n",
+    "    dtype=torch.float32)\n",
+    "\n",
+    "def _wrapped_snap_to_marginal_mean_manifold(\n",
+    "        prompt_gene_values_g: torch.Tensor) -> torch.Tensor:\n",
+    "    \n",
+    "    return snap_to_marginal_mean_manifold(\n",
+    "        adata=adata_meta,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        query_var_names=query_var_names,\n",
+    "        query_total_mrna_umis=None,\n",
+    "        predict_tokenizer=predict_tokenizer,\n",
+    "        cellarium_gpt_module=gpt_model,\n",
+    "        prompt_gene_values_g=prompt_gene_values_g,\n",
+    "    )[1]\n",
+    "\n",
+    "jacobian_qg = torch.autograd.functional.jacobian(\n",
+    "    func=_wrapped_snap_to_marginal_mean_manifold, \n",
+    "    inputs=prompt_gene_values_g,\n",
+    "    create_graph=False,\n",
+    "    vectorize=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ref_jacobian_qg = jacobian_qg.detach().clone()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# jacbian_by_jacrev = torch.func.jacrev(_wrapped_snap_to_marginal_mean_manifold)\n",
+    "# jacbian_by_jacfwd = torch.func.jacfwd(_wrapped_snap_to_marginal_mean_manifold)\n",
+    "\n",
+    "# jacrev_jacobian_qg = jacbian_by_jacfwd(prompt_gene_values_g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/01_fixed_point_iters.ipynb b/notebooks/cellariumgpt_inference/01_fixed_point_iters.ipynb
new file mode 100644
index 00000000..a849c29b
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/01_fixed_point_iters.ipynb
@@ -0,0 +1,578 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fixed point iterations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "DEVICE = torch.device('cuda:7')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load an AnnData Extract\n",
+    "\n",
+    "We will use it for category mappings ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id\n",
+    "\n",
+    "gene_id_to_gene_symbol_map = {gene_id: gene_symbol for gene_symbol, gene_id in gene_symbol_to_gene_id_map.items()}\n",
+    "for gene_id in model_var_names:\n",
+    "    if gene_id not in gene_id_to_gene_symbol_map:\n",
+    "        gene_id_to_gene_symbol_map[gene_id] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a CellariumGPT checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ckpt_path = os.path.join(CHECKPOINTS_PATH, \"epoch=2-step=180000.ckpt\")\n",
+    "gpt_model = CellariumModule.load_from_checkpoint(ckpt_path, map_location=DEVICE)\n",
+    "\n",
+    "# Inject gene categories\n",
+    "gpt_model.model.gene_categories = np.asarray(adata.var_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline\n",
+    "metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos\n",
+    "print(type(metadata_ontology_infos))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cellarium.ml.models.cellarium_gpt import PredictTokenizer\n",
+    "\n",
+    "# Rewire the pipeline with a PredictTokenizer\n",
+    "predict_tokenizer = PredictTokenizer(\n",
+    "    max_total_mrna_umis=100_000,\n",
+    "    gene_vocab_sizes={\n",
+    "        \"assay\": 19,\n",
+    "        \"gene_id\": 36601,\n",
+    "        \"gene_value\": 2001,\n",
+    "        \"suspension_type\": 2,\n",
+    "    },\n",
+    "    metadata_vocab_sizes={\n",
+    "        \"cell_type\": 890,\n",
+    "        \"development_stage\": 191,\n",
+    "        \"disease\": 350,\n",
+    "        \"sex\": 2,\n",
+    "        \"tissue\": 822,\n",
+    "    },\n",
+    "    ontology_infos=metadata_ontology_infos,\n",
+    ")\n",
+    "\n",
+    "gpt_model.pipeline = CellariumPipeline([\n",
+    "    predict_tokenizer,\n",
+    "    gpt_model.model,\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from cellarium.ml.models.cellarium_gpt import CellariumGPT\n",
+    "\n",
+    "\n",
+    "def generate_tokens_from_adata(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        model_gene_categories: list[str],\n",
+    "        obs_index: int | list[int] | None,\n",
+    "        query_var_index: list[int],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        metadata_prompt_masks_dict: dict[str, bool],\n",
+    "        tokenizer: PredictTokenizer,\n",
+    "        device: torch.device = torch.device(\"cpu\"),\n",
+    "    ) -> tuple[dict, dict]:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    .. note::\n",
+    "      All variables in the AnnData are treated as prompts.\n",
+    "\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # slice the anndata\n",
+    "    if isinstance(obs_index, int):\n",
+    "        obs_index = [obs_index]\n",
+    "\n",
+    "    # save obs before slicing\n",
+    "    if obs_index is not None:\n",
+    "        adata = adata[obs_index]\n",
+    "\n",
+    "    # generate gene ids and masks\n",
+    "    n_cells = len(adata)\n",
+    "    adata_var_names = adata.var_names\n",
+    "    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}\n",
+    "    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])\n",
+    "    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]\n",
+    "    n_prompt_vars = len(prompt_var_index)\n",
+    "    n_query_vars = len(query_var_index)\n",
+    "    n_total_vars = n_prompt_vars + n_query_vars\n",
+    "    \n",
+    "    # gene id\n",
+    "    gene_ids_nc = torch.tensor(\n",
+    "        prompt_var_index + query_var_index,\n",
+    "        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene prompt mask\n",
+    "    gene_prompt_mask_nc = torch.tensor(\n",
+    "        [1] * n_prompt_vars + [0] * n_query_vars,\n",
+    "        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene value\n",
+    "    try:\n",
+    "        prompt_X_ng = np.asarray(adata.X.todense())\n",
+    "    except AttributeError:\n",
+    "        prompt_X_ng = adata.X\n",
+    "    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)\n",
+    "    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)\n",
+    "    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)\n",
+    "\n",
+    "    # total mrna umis\n",
+    "    prompt_total_mrna_umis_nc = torch.tensor(\n",
+    "        adata.obs[\"total_mrna_umis\"].values,\n",
+    "        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)\n",
+    "    if query_total_mrna_umis is None:\n",
+    "        # the same as prompt\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            adata.obs[\"total_mrna_umis\"].values,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    else:\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            [query_total_mrna_umis] * n_cells,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)\n",
+    "\n",
+    "    # convert assay and suspension_type to codes\n",
+    "    assay_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"assay_ontology_term_id\"].values,\n",
+    "            categories=gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "    suspension_type_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"suspension_type\"].values,\n",
+    "            categories=gene_ontology_infos[\"suspension_type\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "\n",
+    "    gene_tokens_dict = {\n",
+    "        \"assay\": assay_nc,  # categorical\n",
+    "        \"suspension_type\": suspension_type_nc,  # categorical\n",
+    "        \"gene_id\": gene_ids_nc,  # categorical\n",
+    "        \"gene_value\": gene_value_nc,  # continuous\n",
+    "        \"total_mrna_umis\": total_mrna_umis_nc,  # continuous\n",
+    "    }\n",
+    "\n",
+    "    # metadata prompt masks\n",
+    "    expanded_metadata_prompt_masks_dict = dict()\n",
+    "    for key in metadata_ontology_infos.keys():  # note: key order is important ...\n",
+    "        expanded_metadata_prompt_masks_dict[key] = torch.tensor(\n",
+    "            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)\n",
+    "    \n",
+    "    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes\n",
+    "    metadata_tokens_dict = {\n",
+    "        \"cell_type\": adata.obs[\"cell_type_ontology_term_id\"].values,  # categorical\n",
+    "        \"development_stage\": adata.obs[\"development_stage_ontology_term_id\"].values,  # categorical\n",
+    "        \"disease\": adata.obs[\"disease_ontology_term_id\"].values,  # categorical\n",
+    "        \"sex\": adata.obs[\"sex_ontology_term_id\"].values,  # categorical\n",
+    "        \"tissue\": adata.obs[\"tissue_ontology_term_id\"].values,  # categorical\n",
+    "    }\n",
+    "\n",
+    "    # where to find each thing in the context?\n",
+    "    context_indices = dict()\n",
+    "    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()\n",
+    "    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()\n",
+    "    offset = 0\n",
+    "    for metadata_key in metadata_ontology_infos.keys():\n",
+    "        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset\n",
+    "        offset += 1\n",
+    "\n",
+    "    # return gene_tokens_dict, metadata_tokens_dict\n",
+    "    tokenizer_output = tokenizer(\n",
+    "        metadata_tokens_n=metadata_tokens_dict,\n",
+    "        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,\n",
+    "        gene_tokens_nc=gene_tokens_dict,\n",
+    "        gene_prompt_mask_nc=gene_prompt_mask_nc,\n",
+    "    )\n",
+    "\n",
+    "    return tokenizer_output, context_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metacell fixed point "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def snap_to_marginal_mean_manifold(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        query_var_names: list[str],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        predict_tokenizer: PredictTokenizer,\n",
+    "        cellarium_gpt_module: CellariumModule,\n",
+    "        prompt_gene_values_g: torch.Tensor | None = None,\n",
+    "    ) -> tuple[torch.Tensor, torch.Tensor]:\n",
+    "    \n",
+    "    assert len(adata) == 1, \"Only a single cell is allowed\"\n",
+    "    \n",
+    "    model_gene_categories = cellarium_gpt_module.model.gene_categories\n",
+    "    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}\n",
+    "    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]\n",
+    "\n",
+    "    metadata_prompt_masks_dict = {\n",
+    "        \"cell_type\": False,\n",
+    "        \"development_stage\": False,\n",
+    "        \"disease\": False,\n",
+    "        \"sex\": False,\n",
+    "        \"tissue\": False,\n",
+    "    }\n",
+    "\n",
+    "    tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "        adata=adata,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        model_gene_categories=model_var_names,\n",
+    "        obs_index=None,\n",
+    "        query_var_index=query_var_index,\n",
+    "        query_total_mrna_umis=query_total_mrna_umis,\n",
+    "        metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "        tokenizer=predict_tokenizer,\n",
+    "        device=torch.device(\"cpu\"),\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)\n",
+    "    \n",
+    "    # get a reference to prompt gene values\n",
+    "    FIRST_CELL_DIM = 0\n",
+    "    GENE_VALUE_DIM = 0\n",
+    "    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]\n",
+    "    \n",
+    "    # this is the \"source\"\n",
+    "    if prompt_gene_values_g is None:\n",
+    "        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()\n",
+    "    \n",
+    "    # inject back to tokens_dict to re-establish the reference for Jacobian calculation\n",
+    "    tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)\n",
+    "\n",
+    "    # get model predictions\n",
+    "    logits_dict = cellarium_gpt_module.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )\n",
+    "\n",
+    "    # note: we use `q` to denote query genes\n",
+    "    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]\n",
+    "    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)\n",
+    "    log_counts_k = torch.arange(0, gene_logits_qk.shape[-1], device=gene_logits_qk.device).log()\n",
+    "    gene_marginal_means_q = torch.logsumexp(gene_logits_qk + log_counts_k[None, :], dim=-1).exp()\n",
+    "\n",
+    "    return prompt_gene_values_g, gene_marginal_means_q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test snap to marginal mean manifold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "dataset_name = \"luca_CD8_ex_LUAD\"\n",
+    "mode = \"single\"\n",
+    "\n",
+    "test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", f\"{dataset_name}.h5ad\")\n",
+    "test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "# add total mNRA umis\n",
+    "test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)\n",
+    "\n",
+    "# make a metacell\n",
+    "if mode == \"meta\":\n",
+    "    X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "    # set total mrna umis to the mean of the dataset\n",
+    "    target_total_mrna_umis = np.mean(np.asarray(test_adata.X.sum(-1)).flatten())\n",
+    "    X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "    # make a metacell anndata\n",
+    "    adata_meta = test_adata[0, :].copy()\n",
+    "    adata_meta.X = X_meta_g\n",
+    "    adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]\n",
+    "\n",
+    "elif mode == \"single\":\n",
+    "    # select the first cell\n",
+    "    adata_meta = test_adata[0, :].copy()\n",
+    "    adata_meta.X = np.asarray(adata_meta.X.todense())\n",
+    "\n",
+    "else:\n",
+    "    raise ValueError\n",
+    "\n",
+    "# choose top genes\n",
+    "top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "top_genes_df = pd.read_csv(top_genes_path)\n",
+    "top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "# restrict top_gene_ids to those in the model categories\n",
+    "top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "prompt_gene_ids = top_gene_ids\n",
+    "\n",
+    "adata_meta = adata_meta[:, prompt_gene_ids]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_iters = 5\n",
+    "last_gene_values_g = torch.tensor(adata_meta.X[0, :], dtype=torch.float32, device=DEVICE)\n",
+    "\n",
+    "fixed_point_iterations_list = []\n",
+    "fixed_point_iterations_list.append(last_gene_values_g.detach().cpu().numpy())\n",
+    "\n",
+    "for i in range(n_iters):\n",
+    "    with torch.inference_mode():\n",
+    "        _, gene_marginal_means_q = snap_to_marginal_mean_manifold(\n",
+    "            adata=adata_meta,\n",
+    "            gene_ontology_infos=gene_ontology_infos,\n",
+    "            metadata_ontology_infos=metadata_ontology_infos,\n",
+    "            query_var_names=prompt_gene_ids,\n",
+    "            query_total_mrna_umis=target_total_mrna_umis,\n",
+    "            predict_tokenizer=predict_tokenizer,\n",
+    "            cellarium_gpt_module=gpt_model,\n",
+    "            prompt_gene_values_g=last_gene_values_g)\n",
+    "    \n",
+    "    fixed_point_iterations_list.append(gene_marginal_means_q.detach().cpu().numpy())\n",
+    "    last_gene_values_g = gene_marginal_means_q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = 0\n",
+    "\n",
+    "for i in range(n_iters):\n",
+    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
+    "    x = fixed_point_iterations_list[i % n_iters]\n",
+    "    y = fixed_point_iterations_list[(i + 1) % n_iters]\n",
+    "    ax.scatter(x, y, s=1)\n",
+    "    ax.set_xlim((1e-4, 1e2))\n",
+    "    ax.set_ylim((1e-4, 1e2))\n",
+    "\n",
+    "    ax.set_xscale('log')\n",
+    "    ax.set_yscale('log')\n",
+    "    ax.set_xlabel(f'Iteration {i % n_iters}')\n",
+    "    ax.set_ylabel(f'Iteration {(i + 1) % n_iters}')\n",
+    "    ax.set_title(dataset_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are the major DE genes?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_initial_expression = 0.01\n",
+    "mask_g = fixed_point_iterations_list[0] > min_initial_expression\n",
+    "ratios_g = np.log10((fixed_point_iterations_list[-1] + 1e-8) / (1e-8 + fixed_point_iterations_list[0]))\n",
+    "gene_symbols_g = np.asarray([gene_id_to_gene_symbol_map[gene_id] for gene_id in prompt_gene_ids], dtype=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ratios_g = ratios_g[mask_g]\n",
+    "_gene_symbols_g = gene_symbols_g[mask_g]\n",
+    "\n",
+    "sort_order = np.argsort(_ratios_g)[::-1]\n",
+    "_ratios_g = _ratios_g[sort_order]\n",
+    "_gene_symbols_g = _gene_symbols_g[sort_order]\n",
+    "\n",
+    "top_k = 20\n",
+    "for i in range(top_k):\n",
+    "    print(f\"{_gene_symbols_g[i]}: {_ratios_g[i]:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/02_jacobian_calculation.ipynb b/notebooks/cellariumgpt_inference/02_jacobian_calculation.ipynb
new file mode 100644
index 00000000..27bfdc98
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/02_jacobian_calculation.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Jacobian calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load an AnnData Extract\n",
+    "\n",
+    "We will use it for category mappings ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a CellariumGPT checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ckpt_path = os.path.join(CHECKPOINTS_PATH, \"epoch=2-step=180000.ckpt\")\n",
+    "gpt_model = CellariumModule.load_from_checkpoint(ckpt_path, map_location=DEVICE)\n",
+    "\n",
+    "# Inject gene categories\n",
+    "gpt_model.model.gene_categories = np.asarray(adata.var_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline\n",
+    "metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos\n",
+    "print(type(metadata_ontology_infos))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cellarium.ml.models.cellarium_gpt import PredictTokenizer\n",
+    "\n",
+    "# Rewire the pipeline with a PredictTokenizer\n",
+    "predict_tokenizer = PredictTokenizer(\n",
+    "    max_total_mrna_umis=100_000,\n",
+    "    gene_vocab_sizes={\n",
+    "        \"assay\": 19,\n",
+    "        \"gene_id\": 36601,\n",
+    "        \"gene_value\": 2001,\n",
+    "        \"suspension_type\": 2,\n",
+    "    },\n",
+    "    metadata_vocab_sizes={\n",
+    "        \"cell_type\": 890,\n",
+    "        \"development_stage\": 191,\n",
+    "        \"disease\": 350,\n",
+    "        \"sex\": 2,\n",
+    "        \"tissue\": 822,\n",
+    "    },\n",
+    "    ontology_infos=metadata_ontology_infos,\n",
+    ")\n",
+    "\n",
+    "gpt_model.pipeline = CellariumPipeline([\n",
+    "    predict_tokenizer,\n",
+    "    gpt_model.model,\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from cellarium.ml.models.cellarium_gpt import CellariumGPT\n",
+    "\n",
+    "\n",
+    "def generate_tokens_from_adata(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        model_gene_categories: list[str],\n",
+    "        obs_index: int | list[int] | None,\n",
+    "        query_var_index: list[int],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        metadata_prompt_masks_dict: dict[str, bool],\n",
+    "        tokenizer: PredictTokenizer,\n",
+    "        device: torch.device = torch.device(\"cpu\"),\n",
+    "    ) -> tuple[dict, dict]:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    .. note::\n",
+    "      All variables in the AnnData are treated as prompts.\n",
+    "\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # slice the anndata\n",
+    "    if isinstance(obs_index, int):\n",
+    "        obs_index = [obs_index]\n",
+    "\n",
+    "    # save obs before slicing\n",
+    "    if obs_index is not None:\n",
+    "        adata = adata[obs_index]\n",
+    "\n",
+    "    # generate gene ids and masks\n",
+    "    n_cells = len(adata)\n",
+    "    adata_var_names = adata.var_names\n",
+    "    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}\n",
+    "    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])\n",
+    "    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]\n",
+    "    n_prompt_vars = len(prompt_var_index)\n",
+    "    n_query_vars = len(query_var_index)\n",
+    "    n_total_vars = n_prompt_vars + n_query_vars\n",
+    "    \n",
+    "    # gene id\n",
+    "    gene_ids_nc = torch.tensor(\n",
+    "        prompt_var_index + query_var_index,\n",
+    "        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene prompt mask\n",
+    "    gene_prompt_mask_nc = torch.tensor(\n",
+    "        [1] * n_prompt_vars + [0] * n_query_vars,\n",
+    "        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene value\n",
+    "    try:\n",
+    "        prompt_X_ng = np.asarray(adata.X.todense())\n",
+    "    except AttributeError:\n",
+    "        prompt_X_ng = adata.X\n",
+    "    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)\n",
+    "    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)\n",
+    "    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)\n",
+    "\n",
+    "    # total mrna umis\n",
+    "    prompt_total_mrna_umis_nc = torch.tensor(\n",
+    "        adata.obs[\"total_mrna_umis\"].values,\n",
+    "        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)\n",
+    "    if query_total_mrna_umis is None:\n",
+    "        # the same as prompt\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            adata.obs[\"total_mrna_umis\"].values,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    else:\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            [query_total_mrna_umis] * n_cells,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)\n",
+    "\n",
+    "    # convert assay and suspension_type to codes\n",
+    "    assay_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"assay_ontology_term_id\"].values,\n",
+    "            categories=gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "    suspension_type_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"suspension_type\"].values,\n",
+    "            categories=gene_ontology_infos[\"suspension_type\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "\n",
+    "    gene_tokens_dict = {\n",
+    "        \"assay\": assay_nc,  # categorical\n",
+    "        \"suspension_type\": suspension_type_nc,  # categorical\n",
+    "        \"gene_id\": gene_ids_nc,  # categorical\n",
+    "        \"gene_value\": gene_value_nc,  # continuous\n",
+    "        \"total_mrna_umis\": total_mrna_umis_nc,  # continuous\n",
+    "    }\n",
+    "\n",
+    "    # metadata prompt masks\n",
+    "    expanded_metadata_prompt_masks_dict = dict()\n",
+    "    for key in metadata_ontology_infos.keys():  # note: key order is important ...\n",
+    "        expanded_metadata_prompt_masks_dict[key] = torch.tensor(\n",
+    "            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)\n",
+    "    \n",
+    "    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes\n",
+    "    metadata_tokens_dict = {\n",
+    "        \"cell_type\": adata.obs[\"cell_type_ontology_term_id\"].values,  # categorical\n",
+    "        \"development_stage\": adata.obs[\"development_stage_ontology_term_id\"].values,  # categorical\n",
+    "        \"disease\": adata.obs[\"disease_ontology_term_id\"].values,  # categorical\n",
+    "        \"sex\": adata.obs[\"sex_ontology_term_id\"].values,  # categorical\n",
+    "        \"tissue\": adata.obs[\"tissue_ontology_term_id\"].values,  # categorical\n",
+    "    }\n",
+    "\n",
+    "    # where to find each thing in the context?\n",
+    "    context_indices = dict()\n",
+    "    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()\n",
+    "    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()\n",
+    "    offset = 0\n",
+    "    for metadata_key in metadata_ontology_infos.keys():\n",
+    "        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset\n",
+    "        offset += 1\n",
+    "\n",
+    "    # return gene_tokens_dict, metadata_tokens_dict\n",
+    "    tokenizer_output = tokenizer(\n",
+    "        metadata_tokens_n=metadata_tokens_dict,\n",
+    "        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,\n",
+    "        gene_tokens_nc=gene_tokens_dict,\n",
+    "        gene_prompt_mask_nc=gene_prompt_mask_nc,\n",
+    "    )\n",
+    "\n",
+    "    return tokenizer_output, context_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def snap_to_marginal_mean_manifold(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        query_var_names: list[str],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        predict_tokenizer: PredictTokenizer,\n",
+    "        cellarium_gpt_module: CellariumModule,\n",
+    "        prompt_gene_values_g: torch.Tensor | None = None,\n",
+    "    ) -> tuple[torch.Tensor, torch.Tensor]:\n",
+    "    \n",
+    "    assert len(adata) == 1, \"Only a single cell is allowed\"\n",
+    "    \n",
+    "    model_gene_categories = cellarium_gpt_module.model.gene_categories\n",
+    "    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}\n",
+    "    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]\n",
+    "\n",
+    "    metadata_prompt_masks_dict = {\n",
+    "        \"cell_type\": False,\n",
+    "        \"development_stage\": False,\n",
+    "        \"disease\": False,\n",
+    "        \"sex\": False,\n",
+    "        \"tissue\": False,\n",
+    "    }\n",
+    "\n",
+    "    tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "        adata=adata,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        model_gene_categories=model_var_names,\n",
+    "        obs_index=None,\n",
+    "        query_var_index=query_var_index,\n",
+    "        query_total_mrna_umis=query_total_mrna_umis,\n",
+    "        metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "        tokenizer=predict_tokenizer,\n",
+    "        device=torch.device(\"cpu\"),\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)\n",
+    "    \n",
+    "    # get a reference to prompt gene values\n",
+    "    FIRST_CELL_DIM = 0\n",
+    "    GENE_VALUE_DIM = 0\n",
+    "    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]\n",
+    "    \n",
+    "    # this is the \"source\"\n",
+    "    if prompt_gene_values_g is None:\n",
+    "        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()\n",
+    "    \n",
+    "    # inject back to tokens_dict to re-establish the reference for Jacobian calculation\n",
+    "    tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)\n",
+    "\n",
+    "    # get model predictions\n",
+    "    logits_dict = cellarium_gpt_module.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )\n",
+    "\n",
+    "    # note: we use `q` to denote query genes\n",
+    "    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]\n",
+    "    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)\n",
+    "    log_counts_k = torch.arange(0, gene_logits_qk.shape[-1], device=gene_logits_qk.device).log()\n",
+    "    gene_marginal_means_q = torch.logsumexp(gene_logits_qk + log_counts_k[None, :], dim=-1).exp()\n",
+    "\n",
+    "    return prompt_gene_values_g, gene_marginal_means_q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Jacobian calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a test anndata\n",
+    "dataset_names = [\n",
+    "    \"luca_CD8_ex_LUAD\",\n",
+    "    \"luca_CD8_act_LUAD\",\n",
+    "    \"luca_CD8_naive_LUAD\",\n",
+    "    \"luca_CD8_em_LUAD\",\n",
+    "    \"luca_Treg_LUAD\",\n",
+    "    \"luca_CD8_ex_normal\",\n",
+    "    \"luca_CD8_act_normal\",\n",
+    "    \"luca_CD8_naive_normal\",\n",
+    "    \"luca_CD8_em_normal\",\n",
+    "    \"luca_Treg_normal\",\n",
+    "]\n",
+    "\n",
+    "jacobian_points = [\n",
+    "    \"actual\",\n",
+    "    \"marginal_mean\"\n",
+    "]\n",
+    "\n",
+    "target_total_mrna_umis = 2078.0732\n",
+    "query_chunk_size = 200\n",
+    "max_query_genes = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "\n",
+    "    parser = argparse.ArgumentParser(description=\"Process two integers: jacobian_point_index and dataset_name_index.\")\n",
+    "    parser.add_argument(\"jacobian_point_index\", type=int, help=\"Index of the Jacobian point\")\n",
+    "    parser.add_argument(\"dataset_name_index\", type=int, help=\"Index of the dataset name\")\n",
+    "\n",
+    "    args = parser.parse_args()\n",
+    "\n",
+    "    print(f\"Jacobian Point Index: {args.jacobian_point_index}\")\n",
+    "    print(f\"Dataset Name Index: {args.dataset_name_index}\")\n",
+    "\n",
+    "    jacobian_point = jacobian_points[int(args.jacobian_point_index)]\n",
+    "    dataset_name = dataset_names[int(args.dataset_name_index)]\n",
+    "\n",
+    "    test_adata_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", f\"{dataset_name}.h5ad\")\n",
+    "    test_adata = sc.read_h5ad(test_adata_path)\n",
+    "\n",
+    "    # add total mNRA umis\n",
+    "    test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)\n",
+    "\n",
+    "    # make a metacell\n",
+    "    X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "    # set total mrna umis to the mean of the dataset\n",
+    "    X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "    # make a metacell anndata\n",
+    "    adata_meta = test_adata[0, :].copy()\n",
+    "    adata_meta.X = X_meta_g\n",
+    "    adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]\n",
+    "\n",
+    "    # choose top genes\n",
+    "    top_genes_path = os.path.join(ROOT_PATH, \"cellariumgpt_pd1_lung\", \"output\", \"immune_top_5000_genes.csv\")\n",
+    "    top_genes_df = pd.read_csv(top_genes_path)\n",
+    "    top_gene_ids = top_genes_df['gene_id'].values\n",
+    "\n",
+    "    # restrict top_gene_ids to those in the model categories\n",
+    "    top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]\n",
+    "    prompt_gene_ids = top_gene_ids\n",
+    "\n",
+    "    adata_meta = adata_meta[:, prompt_gene_ids]\n",
+    "\n",
+    "    # query var names\n",
+    "    query_var_names = prompt_gene_ids\n",
+    "    if max_query_genes is not None:\n",
+    "        query_var_names = query_var_names[:max_query_genes]\n",
+    "\n",
+    "    # jacobian point\n",
+    "    if jacobian_point == \"actual\":\n",
+    "        print(\"Using the actual metacell counts as the point to calculate the Jacobian on ...\")\n",
+    "\n",
+    "    elif jacobian_point == \"marginal_mean\":\n",
+    "        with torch.inference_mode():\n",
+    "            # get the mean marginal\n",
+    "            print(\"Calculating marginal mean ...\")\n",
+    "            prompt_gene_values_g, gene_marginal_means_q = snap_to_marginal_mean_manifold(\n",
+    "                adata=adata_meta,\n",
+    "                gene_ontology_infos=gene_ontology_infos,\n",
+    "                metadata_ontology_infos=metadata_ontology_infos,\n",
+    "                query_var_names=prompt_gene_ids,\n",
+    "                query_total_mrna_umis=None,\n",
+    "                predict_tokenizer=predict_tokenizer,\n",
+    "                cellarium_gpt_module=gpt_model,\n",
+    "            )\n",
+    "\n",
+    "            # inject into the adata\n",
+    "            adata_meta.X = gene_marginal_means_q.detach().cpu().numpy()[None, :]\n",
+    "    else:\n",
+    "        raise ValueError\n",
+    "\n",
+    "    def yield_chunks(lst, n):\n",
+    "        for i in range(0, len(lst), n):\n",
+    "            yield lst[i:i + n]\n",
+    "\n",
+    "    query_var_names_chunks = list(yield_chunks(query_var_names, query_chunk_size))\n",
+    "    jacobian_chunks = []\n",
+    "\n",
+    "    for query_var_names_chunk in tqdm(query_var_names_chunks):\n",
+    "\n",
+    "        prompt_gene_values_g = torch.tensor(\n",
+    "            adata_meta.X[0].toarray().flatten(),\n",
+    "            device=gpt_model.device,\n",
+    "            dtype=torch.float32)\n",
+    "\n",
+    "        def _wrapped_snap_to_marginal_mean_manifold(\n",
+    "                prompt_gene_values_g: torch.Tensor) -> torch.Tensor:\n",
+    "            \n",
+    "            return snap_to_marginal_mean_manifold(\n",
+    "                adata=adata_meta,\n",
+    "                gene_ontology_infos=gene_ontology_infos,\n",
+    "                metadata_ontology_infos=metadata_ontology_infos,\n",
+    "                query_var_names=query_var_names_chunk,\n",
+    "                query_total_mrna_umis=None,\n",
+    "                predict_tokenizer=predict_tokenizer,\n",
+    "                cellarium_gpt_module=gpt_model,\n",
+    "                prompt_gene_values_g=prompt_gene_values_g,\n",
+    "            )[1]\n",
+    "\n",
+    "        chunk_jacobian_qg = torch.autograd.functional.jacobian(\n",
+    "            func=_wrapped_snap_to_marginal_mean_manifold, \n",
+    "            inputs=prompt_gene_values_g,\n",
+    "            create_graph=False,\n",
+    "            vectorize=False,\n",
+    "        )\n",
+    "\n",
+    "        jacobian_chunks.append(chunk_jacobian_qg)\n",
+    "\n",
+    "    jacobian_qg = torch.cat(jacobian_chunks, dim=0)\n",
+    "\n",
+    "    result_dict = {\n",
+    "        'dataset_name': dataset_name,\n",
+    "        'jacobian_point': jacobian_point,\n",
+    "        'query_var_names': query_var_names,\n",
+    "        'prompt_var_names': prompt_gene_ids,\n",
+    "        'jacobian_qg': jacobian_qg,\n",
+    "        'prompt_gene_values_g': adata_meta.X[0].toarray().flatten(),\n",
+    "    }\n",
+    "\n",
+    "    torch.save(result_dict, f\"./output/jacobian__{dataset_name}__{jacobian_point}.pt\")\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/02_jacobian_calculation.py b/notebooks/cellariumgpt_inference/02_jacobian_calculation.py
new file mode 100644
index 00000000..c196f946
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/02_jacobian_calculation.py
@@ -0,0 +1,503 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# #### Jacobian calculation
+
+# In[1]:
+
+
+import os
+import torch
+import numpy as np
+import scanpy as sc
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from cellarium.ml import CellariumModule, CellariumPipeline
+
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
+DEVICE = torch.device('cuda')
+
+
+# In[2]:
+
+
+ROOT_PATH = "/mnt/cellariumgpt-xfer/mb-ml-dev-vm"
+CHECKPOINTS_PATH = "/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints"
+
+
+# ### Load an AnnData Extract
+# 
+# We will use it for category mappings ...
+
+# In[3]:
+
+
+# Load an AnnData extract
+adata_path = os.path.join(ROOT_PATH, "data", "extract_0.h5ad")
+adata = sc.read_h5ad(adata_path)
+
+
+# In[4]:
+
+
+gene_ontology_infos = dict()
+
+ref_obs = adata.obs
+
+gene_ontology_infos["assay_ontology_term_id"] = dict()
+gene_ontology_infos["assay_ontology_term_id"]["names"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  
+gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy
+
+gene_ontology_infos["suspension_type"] = dict()
+gene_ontology_infos["suspension_type"]["names"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)
+gene_ontology_infos["suspension_type"]["labels"] = list(ref_obs['suspension_type'].cat.categories)
+
+
+# In[5]:
+
+
+# gene IDs, gene symbols, useful maps
+model_var_names = np.asarray(adata.var_names)
+model_var_names_set = set(model_var_names)
+var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}
+
+gene_info_tsv_path = os.path.join(ROOT_PATH, "gene_info", "gene_info.tsv")
+gene_info_df = pd.read_csv(gene_info_tsv_path, sep="\t")
+
+gene_symbol_to_gene_id_map = dict()
+for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):
+    if gene_symbol != float('nan'):
+        gene_symbol_to_gene_id_map[gene_symbol] = gene_id
+
+
+# ### Load a CellariumGPT checkpoint
+
+# In[6]:
+
+
+ckpt_path = os.path.join(CHECKPOINTS_PATH, "epoch=2-step=180000.ckpt")
+gpt_model = CellariumModule.load_from_checkpoint(ckpt_path, map_location=DEVICE)
+
+# Inject gene categories
+gpt_model.model.gene_categories = np.asarray(adata.var_names)
+
+
+# In[7]:
+
+
+# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline
+metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos
+print(type(metadata_ontology_infos))
+
+
+# ### Helper functions
+
+# In[8]:
+
+
+from cellarium.ml.models.cellarium_gpt import PredictTokenizer
+
+# Rewire the pipeline with a PredictTokenizer
+predict_tokenizer = PredictTokenizer(
+    max_total_mrna_umis=100_000,
+    gene_vocab_sizes={
+        "assay": 19,
+        "gene_id": 36601,
+        "gene_value": 2001,
+        "suspension_type": 2,
+    },
+    metadata_vocab_sizes={
+        "cell_type": 890,
+        "development_stage": 191,
+        "disease": 350,
+        "sex": 2,
+        "tissue": 822,
+    },
+    ontology_infos=metadata_ontology_infos,
+)
+
+gpt_model.pipeline = CellariumPipeline([
+    predict_tokenizer,
+    gpt_model.model,
+])
+
+
+# In[9]:
+
+
+import pandas as pd
+
+from cellarium.ml.models.cellarium_gpt import CellariumGPT
+
+
+def generate_tokens_from_adata(
+        adata: sc.AnnData,
+        gene_ontology_infos: dict[str, dict[str, list[str]]],
+        metadata_ontology_infos: dict[str, dict[str, list[str]]],
+        model_gene_categories: list[str],
+        obs_index: int | list[int] | None,
+        query_var_index: list[int],
+        query_total_mrna_umis: float | None,
+        metadata_prompt_masks_dict: dict[str, bool],
+        tokenizer: PredictTokenizer,
+        device: torch.device = torch.device("cpu"),
+    ) -> tuple[dict, dict]:
+    """
+
+    .. note::
+      All variables in the AnnData are treated as prompts.
+
+
+    """
+    
+    # slice the anndata
+    if isinstance(obs_index, int):
+        obs_index = [obs_index]
+
+    # save obs before slicing
+    if obs_index is not None:
+        adata = adata[obs_index]
+
+    # generate gene ids and masks
+    n_cells = len(adata)
+    adata_var_names = adata.var_names
+    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}
+    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])
+    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]
+    n_prompt_vars = len(prompt_var_index)
+    n_query_vars = len(query_var_index)
+    n_total_vars = n_prompt_vars + n_query_vars
+    
+    # gene id
+    gene_ids_nc = torch.tensor(
+        prompt_var_index + query_var_index,
+        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)
+    
+    # gene prompt mask
+    gene_prompt_mask_nc = torch.tensor(
+        [1] * n_prompt_vars + [0] * n_query_vars,
+        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)
+    
+    # gene value
+    try:
+        prompt_X_ng = np.asarray(adata.X.todense())
+    except AttributeError:
+        prompt_X_ng = adata.X
+    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)
+    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)
+    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)
+
+    # total mrna umis
+    prompt_total_mrna_umis_nc = torch.tensor(
+        adata.obs["total_mrna_umis"].values,
+        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)
+    if query_total_mrna_umis is None:
+        # the same as prompt
+        query_total_mrna_umis_nc = torch.tensor(
+            adata.obs["total_mrna_umis"].values,
+            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)
+    else:
+        query_total_mrna_umis_nc = torch.tensor(
+            [query_total_mrna_umis] * n_cells,
+            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)
+    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)
+
+    # convert assay and suspension_type to codes
+    assay_nc = torch.tensor(
+        pd.Categorical(
+            adata.obs["assay_ontology_term_id"].values,
+            categories=gene_ontology_infos["assay_ontology_term_id"]["names"]).codes,
+        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)
+    suspension_type_nc = torch.tensor(
+        pd.Categorical(
+            adata.obs["suspension_type"].values,
+            categories=gene_ontology_infos["suspension_type"]["names"]).codes,
+        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)
+
+    gene_tokens_dict = {
+        "assay": assay_nc,  # categorical
+        "suspension_type": suspension_type_nc,  # categorical
+        "gene_id": gene_ids_nc,  # categorical
+        "gene_value": gene_value_nc,  # continuous
+        "total_mrna_umis": total_mrna_umis_nc,  # continuous
+    }
+
+    # metadata prompt masks
+    expanded_metadata_prompt_masks_dict = dict()
+    for key in metadata_ontology_infos.keys():  # note: key order is important ...
+        expanded_metadata_prompt_masks_dict[key] = torch.tensor(
+            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)
+    
+    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes
+    metadata_tokens_dict = {
+        "cell_type": adata.obs["cell_type_ontology_term_id"].values,  # categorical
+        "development_stage": adata.obs["development_stage_ontology_term_id"].values,  # categorical
+        "disease": adata.obs["disease_ontology_term_id"].values,  # categorical
+        "sex": adata.obs["sex_ontology_term_id"].values,  # categorical
+        "tissue": adata.obs["tissue_ontology_term_id"].values,  # categorical
+    }
+
+    # where to find each thing in the context?
+    context_indices = dict()
+    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()
+    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()
+    offset = 0
+    for metadata_key in metadata_ontology_infos.keys():
+        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset
+        offset += 1
+
+    # return gene_tokens_dict, metadata_tokens_dict
+    tokenizer_output = tokenizer(
+        metadata_tokens_n=metadata_tokens_dict,
+        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,
+        gene_tokens_nc=gene_tokens_dict,
+        gene_prompt_mask_nc=gene_prompt_mask_nc,
+    )
+
+    return tokenizer_output, context_indices
+
+
+# In[10]:
+
+
+def snap_to_marginal_mean_manifold(
+        adata: sc.AnnData,
+        gene_ontology_infos: dict[str, dict[str, list[str]]],
+        metadata_ontology_infos: dict[str, dict[str, list[str]]],
+        query_var_names: list[str],
+        query_total_mrna_umis: float | None,
+        predict_tokenizer: PredictTokenizer,
+        cellarium_gpt_module: CellariumModule,
+        prompt_gene_values_g: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+    
+    assert len(adata) == 1, "Only a single cell is allowed"
+    
+    model_gene_categories = cellarium_gpt_module.model.gene_categories
+    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}
+    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]
+
+    metadata_prompt_masks_dict = {
+        "cell_type": False,
+        "development_stage": False,
+        "disease": False,
+        "sex": False,
+        "tissue": False,
+    }
+
+    tokens_dict, context_indices = generate_tokens_from_adata(
+        adata=adata,
+        gene_ontology_infos=gene_ontology_infos,
+        metadata_ontology_infos=metadata_ontology_infos,
+        model_gene_categories=model_var_names,
+        obs_index=None,
+        query_var_index=query_var_index,
+        query_total_mrna_umis=query_total_mrna_umis,
+        metadata_prompt_masks_dict=metadata_prompt_masks_dict,
+        tokenizer=predict_tokenizer,
+        device=torch.device("cpu"),
+    )
+
+    # convert to cuda
+    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)
+    
+    # get a reference to prompt gene values
+    FIRST_CELL_DIM = 0
+    GENE_VALUE_DIM = 0
+    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][
+        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]
+    
+    # this is the "source"
+    if prompt_gene_values_g is None:
+        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()
+    
+    # inject back to tokens_dict to re-establish the reference for Jacobian calculation
+    tokens_dict['gene_tokens_nc']['gene_value'][
+        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)
+
+    # get model predictions
+    logits_dict = cellarium_gpt_module.model.predict(
+        gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+        metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+        prompt_mask_nc=tokens_dict["prompt_mask_nc"],
+    )
+
+    # note: we use `q` to denote query genes
+    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]
+    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)
+    log_counts_k = torch.arange(0, gene_logits_qk.shape[-1], device=gene_logits_qk.device).log()
+    gene_marginal_means_q = torch.logsumexp(gene_logits_qk + log_counts_k[None, :], dim=-1).exp()
+
+    return prompt_gene_values_g, gene_marginal_means_q
+
+
+# #### Jacobian calculation
+
+# In[44]:
+
+
+# load a test anndata
+dataset_names = [
+    "luca_CD8_ex_LUAD",
+    "luca_CD8_act_LUAD",
+    "luca_CD8_naive_LUAD",
+    "luca_CD8_em_LUAD",
+    "luca_Treg_LUAD",
+    "luca_CD8_ex_normal",
+    "luca_CD8_act_normal",
+    "luca_CD8_naive_normal",
+    "luca_CD8_em_normal",
+    "luca_Treg_normal",
+]
+
+jacobian_points = [
+    "actual",
+    "marginal_mean"
+]
+
+target_total_mrna_umis = 2078.0732
+query_chunk_size = 200
+max_query_genes = None
+
+
+# In[47]:
+
+
+import argparse
+from tqdm import tqdm
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description="Process two integers: jacobian_point_index and dataset_name_index.")
+    parser.add_argument("jacobian_point_index", type=int, help="Index of the Jacobian point")
+    parser.add_argument("dataset_name_index", type=int, help="Index of the dataset name")
+
+    args = parser.parse_args()
+
+    print(f"Jacobian Point Index: {args.jacobian_point_index}")
+    print(f"Dataset Name Index: {args.dataset_name_index}")
+
+    jacobian_point = jacobian_points[int(args.jacobian_point_index)]
+    dataset_name = dataset_names[int(args.dataset_name_index)]
+
+    test_adata_path = os.path.join(ROOT_PATH, "cellariumgpt_pd1_lung", "output", f"{dataset_name}.h5ad")
+    test_adata = sc.read_h5ad(test_adata_path)
+
+    # add total mNRA umis
+    test_adata.obs['total_mrna_umis'] = test_adata.X.sum(axis=1)
+
+    # make a metacell
+    X_meta_g = np.asarray(test_adata.X.sum(0))
+
+    # set total mrna umis to the mean of the dataset
+    X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
+
+    # make a metacell anndata
+    adata_meta = test_adata[0, :].copy()
+    adata_meta.X = X_meta_g
+    adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
+
+    # choose top genes
+    top_genes_path = os.path.join(ROOT_PATH, "cellariumgpt_pd1_lung", "output", "immune_top_5000_genes.csv")
+    top_genes_df = pd.read_csv(top_genes_path)
+    top_gene_ids = top_genes_df['gene_id'].values
+
+    # restrict top_gene_ids to those in the model categories
+    top_gene_ids = [gene_id for gene_id in top_gene_ids if gene_id in model_var_names_set]
+    prompt_gene_ids = top_gene_ids
+
+    adata_meta = adata_meta[:, prompt_gene_ids]
+
+    # query var names
+    query_var_names = prompt_gene_ids
+    if max_query_genes is not None:
+        query_var_names = query_var_names[:max_query_genes]
+
+    # jacobian point
+    if jacobian_point == "actual":
+        print("Using the actual metacell counts as the point to calculate the Jacobian on ...")
+
+    elif jacobian_point == "marginal_mean":
+        with torch.inference_mode():
+            # get the mean marginal
+            print("Calculating marginal mean ...")
+            prompt_gene_values_g, gene_marginal_means_q = snap_to_marginal_mean_manifold(
+                adata=adata_meta,
+                gene_ontology_infos=gene_ontology_infos,
+                metadata_ontology_infos=metadata_ontology_infos,
+                query_var_names=prompt_gene_ids,
+                query_total_mrna_umis=None,
+                predict_tokenizer=predict_tokenizer,
+                cellarium_gpt_module=gpt_model,
+            )
+
+            # inject into the adata
+            adata_meta.X = gene_marginal_means_q.detach().cpu().numpy()[None, :]
+    else:
+        raise ValueError
+
+    def yield_chunks(lst, n):
+        for i in range(0, len(lst), n):
+            yield lst[i:i + n]
+
+    query_var_names_chunks = list(yield_chunks(query_var_names, query_chunk_size))
+    jacobian_chunks = []
+
+    for query_var_names_chunk in tqdm(query_var_names_chunks):
+
+        prompt_gene_values_g = torch.tensor(
+            adata_meta.X[0].toarray().flatten(),
+            device=gpt_model.device,
+            dtype=torch.float32)
+
+        def _wrapped_snap_to_marginal_mean_manifold(
+                prompt_gene_values_g: torch.Tensor) -> torch.Tensor:
+            
+            return snap_to_marginal_mean_manifold(
+                adata=adata_meta,
+                gene_ontology_infos=gene_ontology_infos,
+                metadata_ontology_infos=metadata_ontology_infos,
+                query_var_names=query_var_names_chunk,
+                query_total_mrna_umis=None,
+                predict_tokenizer=predict_tokenizer,
+                cellarium_gpt_module=gpt_model,
+                prompt_gene_values_g=prompt_gene_values_g,
+            )[1]
+
+        chunk_jacobian_qg = torch.autograd.functional.jacobian(
+            func=_wrapped_snap_to_marginal_mean_manifold, 
+            inputs=prompt_gene_values_g,
+            create_graph=False,
+            vectorize=False,
+        )
+
+        jacobian_chunks.append(chunk_jacobian_qg)
+
+    jacobian_qg = torch.cat(jacobian_chunks, dim=0)
+
+    result_dict = {
+        'dataset_name': dataset_name,
+        'jacobian_point': jacobian_point,
+        'query_var_names': query_var_names,
+        'prompt_var_names': prompt_gene_ids,
+        'jacobian_qg': jacobian_qg,
+        'prompt_gene_values_g': adata_meta.X[0].toarray().flatten(),
+    }
+
+    torch.save(result_dict, f"./output/jacobian__{dataset_name}__{jacobian_point}.pt")
+
+
+if __name__ == "__main__":
+    main()
+    
+
+
+# In[ ]:
+
+
+
+
diff --git a/notebooks/cellariumgpt_inference/03_gene_networks.ipynb b/notebooks/cellariumgpt_inference/03_gene_networks.ipynb
new file mode 100644
index 00000000..303c4ef6
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/03_gene_networks.ipynb
@@ -0,0 +1,543 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gene networks\n",
+    "\n",
+    "TODO:\n",
+    "- Check for LAG3 and TIGIT before and after mean manifold\n",
+    "  - Do we correctly preserve the phenotype after snapping to the mean manifold?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "DEVICE = torch.device('cuda:7')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load an AnnData Extract\n",
+    "\n",
+    "We will use it for category mappings ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id\n",
+    "\n",
+    "gene_id_to_gene_symbol_map = {gene_id: gene_symbol for gene_symbol, gene_id in gene_symbol_to_gene_id_map.items()}\n",
+    "for gene_id in model_var_names:\n",
+    "    if gene_id not in gene_id_to_gene_symbol_map:\n",
+    "        gene_id_to_gene_symbol_map[gene_id] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a Jacobian result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_jacobian(dataset_name: str, jacobian_point: str):\n",
+    "    jacobian_pt_path = os.path.join(\n",
+    "        ROOT_PATH, \"cellariumgpt_playground\", \"output\", f\"jacobian__{dataset_name}__{jacobian_point}.pt\")\n",
+    "\n",
+    "    jacobian_results_dict = torch.load(jacobian_pt_path, map_location=DEVICE)\n",
+    "\n",
+    "    x_g = jacobian_results_dict['prompt_gene_values_g']\n",
+    "    jacobian_gp = jacobian_results_dict['jacobian_qg'].cpu().numpy()  # p for perturb\n",
+    "\n",
+    "    # first-order Taylor's expansion of in-silico deletion\n",
+    "    y_gp = np.clip(x_g[:, None] - jacobian_gp * x_g[None, :], a_min=0., a_max=None)\n",
+    "    z_gp = np.log2(y_gp) - np.log2(x_g[:, None])\n",
+    "    z_gp[np.isnan(z_gp)] = 0.\n",
+    "\n",
+    "    MAX_LFC = 1.\n",
+    "    z_gp = np.clip(z_gp, a_min=-MAX_LFC, a_max=MAX_LFC)\n",
+    "    z_unit_gp = z_gp / np.linalg.norm(z_gp, axis=1, keepdims=True)\n",
+    "    dist_gg = z_unit_gp @ (z_unit_gp.T)\n",
+    "\n",
+    "    return {\n",
+    "        \"jacobian_results_dict\": jacobian_results_dict,\n",
+    "        \"x_g\": x_g,\n",
+    "        \"jacobian_gp\": jacobian_gp,\n",
+    "        \"y_gp\": y_gp,\n",
+    "        \"z_gp\": z_gp,\n",
+    "        \"z_unit_gp\": z_unit_gp,\n",
+    "        \"dist_gg\": dist_gg\n",
+    "    }\n",
+    "\n",
+    "def get_gene_neighborhood(\n",
+    "        processed_jacobian_dict: dict,\n",
+    "        target_gene_symbol: str,\n",
+    "        similarity_var: str = 'dist_gg') -> dict:\n",
+    "\n",
+    "    target_gene_id = gene_symbol_to_gene_id_map[target_gene_symbol]\n",
+    "\n",
+    "    assert np.all(\n",
+    "        np.asarray(processed_jacobian_dict['jacobian_results_dict']['query_var_names']) ==\n",
+    "        np.asarray(processed_jacobian_dict['jacobian_results_dict']['prompt_var_names']))\n",
+    "    var_name_to_index_map = {var_name: index for index, var_name in enumerate(\n",
+    "        processed_jacobian_dict['jacobian_results_dict']['query_var_names'])}\n",
+    "\n",
+    "    target_gene_index = var_name_to_index_map[target_gene_id]\n",
+    "    target_neighbor_dist_g = processed_jacobian_dict[similarity_var][target_gene_index]\n",
+    "    sort_order = np.argsort(target_neighbor_dist_g)[::-1]\n",
+    "\n",
+    "    return {\n",
+    "        \"target_gene_symbol\": target_gene_symbol,\n",
+    "        \"target_gene_index\": target_gene_index,\n",
+    "        \"target_gene_id\": target_gene_id,\n",
+    "        \"target_neighbor_dist_g\": target_neighbor_dist_g,\n",
+    "        \"sort_order\": sort_order\n",
+    "    }\n",
+    "\n",
+    "def plot_neighborhood_distance_distribution(neighborhood_dict: dict):\n",
+    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
+    "    ax.hist(neighborhood_dict['target_neighbor_dist_g'], bins=50);\n",
+    "    ax.set_xlabel(f'Cosine distance to {neighborhood_dict[\"target_gene_symbol\"]}')\n",
+    "    ax.set_ylabel('Number of genes')\n",
+    "\n",
+    "def neighborhood_to_df(neighborhood_dict: dict, processed_jacobian_dict: dict) -> pd.DataFrame:\n",
+    "    query_var_names = processed_jacobian_dict[\"jacobian_results_dict\"][\"query_var_names\"]\n",
+    "    return pd.DataFrame({\n",
+    "        \"gene_id\": [query_var_names[i] for i in neighborhood_dict['sort_order']],\n",
+    "        \"gene_symbol\": [gene_id_to_gene_symbol_map[query_var_names[i]] for i in neighborhood_dict['sort_order']],\n",
+    "        \"cosine_distance\": neighborhood_dict['target_neighbor_dist_g'][neighborhood_dict['sort_order']]\n",
+    "    })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_gene_symbol = \"PDCD1\"\n",
+    "similarity_var = \"z_gp\"\n",
+    "\n",
+    "dataset_name_1 = \"luca_CD8_ex_LUAD\"\n",
+    "jacobian_point_1 = \"actual\"\n",
+    "\n",
+    "processed_jacobian_dict_1 = process_jacobian(dataset_name_1, jacobian_point_1)\n",
+    "neighborhood_dict_1 = get_gene_neighborhood(processed_jacobian_dict_1, target_gene_symbol, similarity_var)\n",
+    "df_1 = neighborhood_to_df(neighborhood_dict_1, processed_jacobian_dict_1)\n",
+    "\n",
+    "dataset_name_2 = \"luca_CD8_act_normal\"\n",
+    "jacobian_point_2 = \"marginal_mean\"\n",
+    "\n",
+    "processed_jacobian_dict_2 = process_jacobian(dataset_name_2, jacobian_point_2)\n",
+    "neighborhood_dict_2 = get_gene_neighborhood(processed_jacobian_dict_2, target_gene_symbol, similarity_var)\n",
+    "df_2 = neighborhood_to_df(neighborhood_dict_2, processed_jacobian_dict_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with pd.option_context('display.max_rows', 200):\n",
+    "    display(df_1.tail(100)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with pd.option_context('display.max_rows', 200):\n",
+    "    display(df_2.head(50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Create a DataFrame for Plotly\n",
+    "query_gene_symbols = [\n",
+    "    gene_id_to_gene_symbol_map[gene_id]\n",
+    "    for gene_id in processed_jacobian_dict_1['jacobian_results_dict']['query_var_names']]\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'x': neighborhood_dict_1[\"target_neighbor_dist_g\"],\n",
+    "    'y': neighborhood_dict_2[\"target_neighbor_dist_g\"],\n",
+    "    'label': query_gene_symbols\n",
+    "})\n",
+    "\n",
+    "# Create the scatter plot\n",
+    "fig = px.scatter(df, x='x', y='y', hover_name='label', title='Interactive Scatter Plot')\n",
+    "\n",
+    "# Update marker size\n",
+    "fig.update_traces(marker=dict(size=2))  # Adjust the size as needed\n",
+    "\n",
+    "# Update layout to decrease the width of the plot\n",
+    "fig.update_layout(\n",
+    "    width=800,  # Adjust the width as needed\n",
+    "    # plot_bgcolor='white',\n",
+    "    xaxis=dict(\n",
+    "        showgrid=True,\n",
+    "        # showticklabels=False,\n",
+    "        title=dataset_name_1,\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        showgrid=True,\n",
+    "        # showticklabels=False,\n",
+    "        title=dataset_name_2\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_jacobian_dict = processed_jacobian_dict_1\n",
+    "neighborhood_dict = neighborhood_dict_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neighborhood_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mde = pymde.preserve_neighbors(\n",
+    "    processed_jacobian_dict[\"z_unit_gp\"], device=DEVICE, verbose=True, n_neighbors=10, repulsive_fraction=0.9,\n",
+    "    repulsive_penalty=pymde.penalties.InvPower)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_g2 = mde.embed(verbose=True)\n",
+    "embedding_g2 = embedding_g2.cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "\n",
+    "query_gene_symbols = [\n",
+    "    gene_id_to_gene_symbol_map[gene_id]\n",
+    "    for gene_id in processed_jacobian_dict[\"jacobian_results_dict\"][\"query_var_names\"]]\n",
+    "    \n",
+    "# Create a DataFrame for Plotly\n",
+    "df = pd.DataFrame({\n",
+    "    'x': embedding_g2[:, 0],\n",
+    "    'y': embedding_g2[:, 1],\n",
+    "    'label': query_gene_symbols\n",
+    "})\n",
+    "\n",
+    "# Create the scatter plot\n",
+    "fig = px.scatter(df, x='x', y='y', hover_name='label', title='Interactive Scatter Plot')\n",
+    "\n",
+    "# Update marker size\n",
+    "fig.update_traces(marker=dict(size=2))  # Adjust the size as needed\n",
+    "\n",
+    "# Highlight the specific point in red\n",
+    "target_idx = neighborhood_dict['target_gene_index']\n",
+    "fig.add_scatter(\n",
+    "    x=[embedding_g2[target_idx, 0]],\n",
+    "    y=[embedding_g2[target_idx, 1]],\n",
+    "    mode='markers+text',\n",
+    "    marker=dict(color='green', size=10),\n",
+    "    # text=[query_gene_symbols[target_idx]],\n",
+    "    textposition='top center',\n",
+    "    showlegend=False\n",
+    ")\n",
+    "\n",
+    "highlight_indices = [idx for idx in neighborhood_dict['sort_order'][:100] if idx != target_idx]\n",
+    "fig.add_scatter(\n",
+    "    x=embedding_g2[highlight_indices, 0],\n",
+    "    y=embedding_g2[highlight_indices, 1],\n",
+    "    mode='markers+text',\n",
+    "    marker=dict(color='red', size=6),\n",
+    "    # text=np.asarray(query_gene_symbols, dtype=object)[highlight_indices],\n",
+    "    textposition='top center',\n",
+    "    showlegend=False\n",
+    ")\n",
+    "\n",
+    "# Update layout to decrease the width of the plot\n",
+    "fig.update_layout(\n",
+    "    width=700,  # Adjust the width as needed\n",
+    "    height=700,\n",
+    "    plot_bgcolor='white',\n",
+    "    xaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_1'\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_2'\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_jacobian_dict = processed_jacobian_dict_2\n",
+    "neighborhood_dict = neighborhood_dict_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neighborhood_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mde = pymde.preserve_neighbors(\n",
+    "    processed_jacobian_dict[\"z_unit_gp\"], device=DEVICE, verbose=True, n_neighbors=10, repulsive_fraction=0.9,\n",
+    "    repulsive_penalty=pymde.penalties.InvPower)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_g2 = mde.embed(verbose=True)\n",
+    "embedding_g2 = embedding_g2.cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "\n",
+    "query_gene_symbols = [\n",
+    "    gene_id_to_gene_symbol_map[gene_id]\n",
+    "    for gene_id in processed_jacobian_dict[\"jacobian_results_dict\"][\"query_var_names\"]]\n",
+    "    \n",
+    "# Create a DataFrame for Plotly\n",
+    "df = pd.DataFrame({\n",
+    "    'x': embedding_g2[:, 0],\n",
+    "    'y': embedding_g2[:, 1],\n",
+    "    'label': query_gene_symbols\n",
+    "})\n",
+    "\n",
+    "# Create the scatter plot\n",
+    "fig = px.scatter(df, x='x', y='y', hover_name='label', title='Interactive Scatter Plot')\n",
+    "\n",
+    "# Update marker size\n",
+    "fig.update_traces(marker=dict(size=2))  # Adjust the size as needed\n",
+    "\n",
+    "# Highlight the specific point in red\n",
+    "target_idx = neighborhood_dict['target_gene_index']\n",
+    "fig.add_scatter(\n",
+    "    x=[embedding_g2[target_idx, 0]],\n",
+    "    y=[embedding_g2[target_idx, 1]],\n",
+    "    mode='markers+text',\n",
+    "    marker=dict(color='green', size=10),\n",
+    "    # text=[query_gene_symbols[target_idx]],\n",
+    "    textposition='top center',\n",
+    "    showlegend=False\n",
+    ")\n",
+    "\n",
+    "highlight_indices = [idx for idx in neighborhood_dict['sort_order'][:100] if idx != target_idx]\n",
+    "fig.add_scatter(\n",
+    "    x=embedding_g2[highlight_indices, 0],\n",
+    "    y=embedding_g2[highlight_indices, 1],\n",
+    "    mode='markers+text',\n",
+    "    marker=dict(color='red', size=6),\n",
+    "    # text=np.asarray(query_gene_symbols, dtype=object)[highlight_indices],\n",
+    "    textposition='top center',\n",
+    "    showlegend=False\n",
+    ")\n",
+    "\n",
+    "# Update layout to decrease the width of the plot\n",
+    "fig.update_layout(\n",
+    "    width=700,  # Adjust the width as needed\n",
+    "    height=700,\n",
+    "    plot_bgcolor='white',\n",
+    "    xaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_1'\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_2'\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/04_metadata_evaluation__updated.ipynb b/notebooks/cellariumgpt_inference/04_metadata_evaluation__updated.ipynb
new file mode 100644
index 00000000..0cb6fd8e
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/04_metadata_evaluation__updated.ipynb
@@ -0,0 +1,801 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metadata evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import CellariumGPTInferenceContext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINT_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_1/checkpoints/epoch=2-step=252858.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LuCA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Process the AnnData"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Subset to LUAD\n",
+    "- Subset to highly variable genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"luca\", \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# revert to raw counts\n",
+    "adata.X = adata.layers['count'].copy()\n",
+    "adata.obs['total_mrna_umis'] = np.asarray(adata.X.sum(axis=1)).flatten()\n",
+    "\n",
+    "# remove unwanted assays\n",
+    "included_assays = [\"10x 3' v2\", \"10x 3' v3\"]\n",
+    "included_tissues = [\"lung\"]\n",
+    "adata = adata[adata.obs['assay'].isin(included_assays) & adata.obs['tissue'].isin(included_tissues)]\n",
+    "\n",
+    "# free up some memory\n",
+    "del adata.layers['counts_length_scaled']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata[adata.obs['disease'] == 'lung adenocarcinoma'], color='cell_type_tumor', gene_symbols='feature_name', vmin=0, vmax=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata = adata[adata.obs['disease'] == 'lung adenocarcinoma']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sc.pp.normalize_total(adata, target_sum=1e4)\n",
+    "# sc.pp.log1p(adata)\n",
+    "\n",
+    "# N_TOP_GENES = 10_000\n",
+    "\n",
+    "# sc.pp.highly_variable_genes(adata, n_top_genes=N_TOP_GENES, flavor='seurat_v3', n_bins=20)\n",
+    "# sc.pl.highly_variable_genes(adata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subset to a smaller number of cells for testing\n",
+    "N_RAND_CELLS = 1_000\n",
+    "\n",
+    "rng = np.random.default_rng(42)\n",
+    "adata_rand = adata[rng.choice(len(adata), N_RAND_CELLS, replace=False)]\n",
+    "adata_rand = adata_rand.copy()\n",
+    "adata_rand.X = adata_rand.layers['count'].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_rand.write_h5ad(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"luca\", \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc__processed.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Load the processed LuCA AnnData and make predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"luca\", \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc__processed.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)\n",
+    "\n",
+    "# remove genes that we don't have in the vocabulary\n",
+    "adata_var_names = adata.var_names\n",
+    "adata_var_names_in_model_mask = [var_name in ctx.model_var_names_set for var_name in adata_var_names]\n",
+    "adata = adata[:, adata_var_names_in_model_mask]\n",
+    "\n",
+    "# subset genes\n",
+    "N_RAND_GENES = 10_000\n",
+    "rng = np.random.default_rng(42)\n",
+    "adata = adata[:, rng.choice(len(adata.var), N_RAND_GENES, replace=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.cuda.empty_cache()\n",
+    "metadata_prediction_dict = ctx.predict_metadata_chunked(adata, chunk_size=32)\n",
+    "\n",
+    "# save\n",
+    "torch.save(\n",
+    "    metadata_prediction_dict,\n",
+    "    os.path.join(\n",
+    "        ROOT_PATH, \"cellariumgpt_playground\", \"output\",\n",
+    "        \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc__metadata_predictions.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Load processed AnnData file and results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"luca\", \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc__processed.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)\n",
+    "\n",
+    "metadata_prediction_dict = torch.load(\n",
+    "    os.path.join(ROOT_PATH, \"cellariumgpt_playground\", \"output\", \"5d57179e-17d8-416f-aa55-9c3dbc3c29fc__metadata_predictions.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_cell_type_indices = np.argmax(metadata_prediction_dict['cell_type'], -1)\n",
+    "best_cell_type_labels = [ctx.metadata_ontology_infos['cell_type']['labels'][i] for i in best_cell_type_indices]\n",
+    "\n",
+    "adata.obs['cellariumgpt_cell_type'] = best_cell_type_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color='cellariumgpt_cell_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color='cell_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color='cell_type_tumor')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_keywords = ['adenocarcinoma', 'COVID-19', 'cardiomyopathy']\n",
+    "for disease_keyword in disease_keywords:\n",
+    "    target_disease_indices = []\n",
+    "    target_disease_labels = []\n",
+    "    for idx, disease_label in enumerate(ctx.metadata_ontology_infos['disease']['labels']):\n",
+    "        if disease_label.find(disease_keyword) != -1:\n",
+    "            target_disease_labels.append(disease_label)\n",
+    "            target_disease_indices.append(idx)\n",
+    "    target_disease_score_n = metadata_prediction_dict['disease'][:, target_disease_indices].sum(-1)\n",
+    "    adata.obs[f'cellariumgpt_{disease_keyword}_score'] = target_disease_score_n\n",
+    "\n",
+    "target_disease_labels = 'normal'\n",
+    "target_disease_indices = [0]\n",
+    "target_disease_score_n = metadata_prediction_dict['disease'][:, target_disease_indices].sum(-1)\n",
+    "adata.obs['cellariumgpt_normal_score'] = target_disease_score_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color='cellariumgpt_normal_score', s=40, cmap='RdBu_r', alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color='cellariumgpt_adenocarcinoma_score', s=40, cmap='RdBu_r', alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process validation datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make a manifest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "tissue_list = []\n",
+    "disease_list = []\n",
+    "development_stage_list = []\n",
+    "\n",
+    "for val_idx in tqdm(range(1, 111)):\n",
+    "    \n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "    \n",
+    "    tissue = val_adata.obs['tissue'].iloc[0]\n",
+    "    disease = val_adata.obs['disease'].iloc[0]\n",
+    "    development_stage = val_adata.obs['development_stage'].iloc[0]\n",
+    "\n",
+    "    tissue_list.append(tissue)\n",
+    "    disease_list.append(disease)\n",
+    "    development_stage_list.append(development_stage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_df = pd.DataFrame(\n",
+    "    {\n",
+    "        'tissue': tissue_list,\n",
+    "        'disease': disease_list,\n",
+    "        'development_stage': development_stage_list,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "validation_df.to_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"manifest.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_df = pd.read_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"manifest.csv\"), index_col=0)\n",
+    "\n",
+    "# reset index to go from 1 to ...\n",
+    "validation_df.index += 1\n",
+    "\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(validation_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_df[validation_df[\"disease\"] == \"lung adenocarcinoma\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_df[validation_df[\"tissue\"] == \"lung\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Process a given validation AnnData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_idx_list = [58, 66, 69, 53, 67, 92, 107, 108, 92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "N_TOP_HVG = 5000\n",
+    "\n",
+    "\n",
+    "for val_idx in tqdm(val_idx_list):\n",
+    "\n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    val_adata.layers['counts'] = val_adata.X.copy()\n",
+    "\n",
+    "    sc.pp.normalize_total(val_adata, target_sum=1e4)\n",
+    "    sc.pp.log1p(val_adata)\n",
+    "    sc.pp.highly_variable_genes(val_adata, n_top_genes=N_TOP_HVG)\n",
+    "\n",
+    "    val_adata = val_adata[:, val_adata.var['highly_variable']]\n",
+    "\n",
+    "    sc.pp.scale(val_adata, max_value=10)\n",
+    "    sc.pp.pca(val_adata, n_comps=50)\n",
+    "    sc.pp.neighbors(val_adata, n_pcs=50, n_neighbors=30)\n",
+    "    sc.tl.umap(val_adata)\n",
+    "\n",
+    "    val_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Predict metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(val_idx_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_idx_list = [58, 66, 69, 53, 67, 92, 107, 108, 92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "\n",
+    "N_CELLS_PER_CALL = 192\n",
+    "\n",
+    "for val_idx in tqdm(val_idx_list):\n",
+    "\n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    # revert to integer counts\n",
+    "    val_adata.X = val_adata.layers['counts'].copy()\n",
+    "\n",
+    "    # predict\n",
+    "    metadata_prediction_dict = ctx.predict_metadata_chunked(val_adata, chunk_size=N_CELLS_PER_CALL)\n",
+    "\n",
+    "    # save\n",
+    "    torch.save(\n",
+    "        metadata_prediction_dict,\n",
+    "        os.path.join(ROOT_PATH, \"cellariumgpt_playground\", \"output\", f\"extract_{val_idx}__metadata_predictions.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_idx_list = [58, 66, 69, 53, 67, 92, 107, 108, 92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "val_idx = val_idx_list[6]\n",
+    "\n",
+    "# load anndata\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)\n",
+    "\n",
+    "# print sample metadata\n",
+    "print(adata.obs['disease'].iloc[0])\n",
+    "print(adata.obs['tissue'].iloc[0])\n",
+    "print(adata.obs['development_stage'].iloc[0])\n",
+    "\n",
+    "# load predictions\n",
+    "metadata_prediction_dict = torch.load(\n",
+    "    os.path.join(ROOT_PATH, \"cellariumgpt_playground\", \"output\", f\"extract_{val_idx}__metadata_predictions.pt\"))\n",
+    "\n",
+    "# make best top_k predictions\n",
+    "top_k = 5\n",
+    "for key in {\"cell_type\", \"disease\", \"tissue\", \"development_stage\"}:\n",
+    "    top_k_sort_order = np.argsort(metadata_prediction_dict[key], axis=-1)[:, ::-1]\n",
+    "    for k in range(top_k):\n",
+    "        adata.obs[f\"cellariumgpt_{key}_{k}_label\"] = [\n",
+    "            ctx.metadata_ontology_infos[key]['labels'][i] for i in top_k_sort_order[:, k]]\n",
+    "        adata.obs[f\"cellariumgpt_{key}_{k}_prob\"] = metadata_prediction_dict[key][np.arange(len(adata)), top_k_sort_order[:, k]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "disease_keywords = ['adenocarcinoma', 'COVID-19', 'cardiomyopathy']\n",
+    "for disease_keyword in disease_keywords:\n",
+    "    target_disease_indices = []\n",
+    "    target_disease_labels = []\n",
+    "    for idx, disease_label in enumerate(ctx.metadata_ontology_infos['disease']['labels']):\n",
+    "        if disease_label.find(disease_keyword) != -1:\n",
+    "            target_disease_labels.append(disease_label)\n",
+    "            target_disease_indices.append(idx)\n",
+    "    target_disease_score_n = metadata_prediction_dict['disease'][:, target_disease_indices].sum(-1)\n",
+    "    adata.obs[f'cellariumgpt_{disease_keyword}_score'] = target_disease_score_n\n",
+    "\n",
+    "target_disease_labels = 'normal'\n",
+    "target_disease_indices = [0]\n",
+    "target_disease_score_n = metadata_prediction_dict['disease'][:, target_disease_indices].sum(-1)\n",
+    "adata.obs['cellariumgpt_normal_score'] = target_disease_score_n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import colorcet as cc\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def generate_interactive_plotly_for_metadata(\n",
+    "        adata: sc.AnnData,\n",
+    "        top_k: int,\n",
+    "        metadata_key: str,\n",
+    "        use_continuous: bool = False,\n",
+    "        value_key: str = None,\n",
+    "        vmin: float = None,\n",
+    "        vmax: float = None,\n",
+    "        width: int = 800,\n",
+    "        height: int = 600,\n",
+    "        markersize: int = 4,\n",
+    "    ):\n",
+    "    \n",
+    "    # Prepare data for plotting\n",
+    "    umap_df = pd.DataFrame({\n",
+    "        'UMAP_1': adata.obsm['X_umap'][:, 0],\n",
+    "        'UMAP_2': adata.obsm['X_umap'][:, 1]\n",
+    "    })\n",
+    "    \n",
+    "    if use_continuous and value_key:\n",
+    "        umap_df['value'] = adata.obs[value_key].values\n",
+    "        color = 'value'\n",
+    "        color_continuous_scale = 'RdBu_r'\n",
+    "    else:\n",
+    "        # Extract unique labels\n",
+    "        labels = adata.obs[f\"cellariumgpt_{metadata_key}_0_label\"].unique()\n",
+    "\n",
+    "        # Assign colors to labels\n",
+    "        colormap = {label: cc.glasbey[i % len(cc.glasbey)] for i, label in enumerate(labels)}\n",
+    "        \n",
+    "        umap_df['label'] = adata.obs[f\"cellariumgpt_{metadata_key}_0_label\"].values\n",
+    "        color = 'label'\n",
+    "        color_discrete_map = colormap\n",
+    "    \n",
+    "    # Add hover text\n",
+    "    hover_texts = []\n",
+    "    for i in range(len(umap_df)):\n",
+    "        hover_text = []\n",
+    "        for k in range(top_k):\n",
+    "            label_key = f\"cellariumgpt_{metadata_key}_{k}_label\"\n",
+    "            prob_key = f\"cellariumgpt_{metadata_key}_{k}_prob\"\n",
+    "            hover_text.append(f\"{adata.obs[label_key].iloc[i]}: {adata.obs[prob_key].iloc[i]:.3f}\")\n",
+    "        hover_texts.append(\"<br>\".join(hover_text))\n",
+    "    umap_df['hover_text'] = hover_texts\n",
+    "    \n",
+    "    # Create scatter plot\n",
+    "    if use_continuous and value_key:\n",
+    "        fig = px.scatter(\n",
+    "            umap_df,\n",
+    "            x='UMAP_1',\n",
+    "            y='UMAP_2',\n",
+    "            color=color,\n",
+    "            color_continuous_scale=color_continuous_scale,\n",
+    "            hover_name='hover_text',\n",
+    "            title='UMAP Scatter Plot',\n",
+    "            range_color=[vmin, vmax]\n",
+    "        )\n",
+    "    else:\n",
+    "        fig = px.scatter(\n",
+    "            umap_df,\n",
+    "            x='UMAP_1',\n",
+    "            y='UMAP_2',\n",
+    "            color=color,\n",
+    "            color_discrete_map=color_discrete_map,\n",
+    "            hover_name='hover_text',\n",
+    "        )\n",
+    "    \n",
+    "    # Update layout\n",
+    "    fig.update_layout(\n",
+    "        plot_bgcolor='white',\n",
+    "        xaxis=dict(title='UMAP_1', showgrid=False),\n",
+    "        yaxis=dict(title='UMAP_2', showgrid=False),\n",
+    "        width=width,\n",
+    "        height=height\n",
+    "    )\n",
+    "    \n",
+    "    # Update marker size\n",
+    "    fig.update_traces(marker=dict(size=markersize))\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_interactive_plotly_for_metadata(\n",
+    "    adata,\n",
+    "    top_k=5,\n",
+    "    metadata_key='cell_type',\n",
+    "    width=700,\n",
+    "    height=500,\n",
+    "    markersize=3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_interactive_plotly_for_metadata(\n",
+    "    adata,\n",
+    "    top_k=5,\n",
+    "    metadata_key='cell_type',\n",
+    "    use_continuous=True,\n",
+    "    value_key='cellariumgpt_COVID-19_score',\n",
+    "    width=500,\n",
+    "    height=500,\n",
+    "    markersize=3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cellariumgpt_cell_type_0_label\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cell_type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cellariumgpt_disease_0_label\", alpha=adata.obs[\"cellariumgpt_disease_0_prob\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cellariumgpt_cardiomyopathy_score\", vmax=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cellariumgpt_disease_0_label\", alpha=adata.obs[\"cellariumgpt_disease_0_prob\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"disease\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"tissue\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pl.umap(adata, color=\"cellariumgpt_tissue_0_label\", alpha=adata.obs[\"cellariumgpt_tissue_0_prob\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/05_make_metacells.ipynb b/notebooks/cellariumgpt_inference/05_make_metacells.ipynb
new file mode 100644
index 00000000..e7042721
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/05_make_metacells.ipynb
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda:6')\n",
+    "sc.set_figure_params(figsize=(4, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_0/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id\n",
+    "\n",
+    "gene_id_to_gene_symbol_map = {gene_id: gene_symbol for gene_symbol, gene_id in gene_symbol_to_gene_id_map.items()}\n",
+    "for gene_id in model_var_names:\n",
+    "    if gene_id not in gene_id_to_gene_symbol_map:\n",
+    "        gene_id_to_gene_symbol_map[gene_id] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_df = pd.read_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"manifest.csv\"), index_col=0)\n",
+    "\n",
+    "# reset index to go from 1 to ...\n",
+    "validation_df.index += 1\n",
+    "\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(validation_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Process a given validation AnnData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "# val_idx_list = [58, 66, 69, 53, 67, 92, 107, 108]\n",
+    "val_idx_list = [92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "N_TOP_HVG = 5000\n",
+    "\n",
+    "for val_idx in tqdm(val_idx_list):\n",
+    "\n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    val_adata.layers['counts'] = val_adata.X.copy()\n",
+    "\n",
+    "    # normalize\n",
+    "    sc.pp.normalize_total(val_adata, target_sum=1e4)\n",
+    "    sc.pp.log1p(val_adata)\n",
+    "\n",
+    "    # calculate mean log TPKC\n",
+    "    val_adata.var['mean_log_tpkc'] = np.asarray(val_adata.X.mean(axis=0)).flatten()\n",
+    "\n",
+    "    # make an embedding\n",
+    "    sc.pp.highly_variable_genes(val_adata, n_top_genes=N_TOP_HVG)\n",
+    "    val_adata = val_adata[:, val_adata.var['highly_variable']]\n",
+    "\n",
+    "    sc.pp.scale(val_adata, max_value=10)\n",
+    "    sc.pp.pca(val_adata, n_comps=50)\n",
+    "    sc.pp.neighbors(val_adata, n_pcs=50, n_neighbors=30)\n",
+    "    sc.tl.umap(val_adata)\n",
+    "\n",
+    "    val_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pool all the HVGs together\n",
+    "\n",
+    "- For duplicates, choose the higher TPKK\n",
+    "- Sort by TPKK and choose:\n",
+    "  - 5000 genes for prompting\n",
+    "  - 15000 genes for querying"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_idx_list = [92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "N_PROMPT_GENES = 5000\n",
+    "\n",
+    "hvg_dict = dict()\n",
+    "hvg_dict['dataset_index'] = []\n",
+    "hvg_dict['gene_id'] = []\n",
+    "hvg_dict['log_tpkc'] = []\n",
+    "\n",
+    "for val_idx in tqdm(val_idx_list):\n",
+    "\n",
+    "    adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\")\n",
+    "    adata = sc.read_h5ad(adata_path)\n",
+    "\n",
+    "    n_hvg = adata.shape[1]\n",
+    "    hvg_dict['dataset_index'] += [val_idx] * n_hvg\n",
+    "    hvg_dict['gene_id'] += adata.var.index.tolist()\n",
+    "    hvg_dict['log_tpkc'] += adata.var['mean_log_tpkc'].tolist()\n",
+    "\n",
+    "hvg_df = pd.DataFrame(hvg_dict)\n",
+    "\n",
+    "# now, group by gene_id, and choose the row with highest log_tpkc;  drop other rows\n",
+    "hvg_df = hvg_df.sort_values('log_tpkc', ascending=False).groupby('gene_id').first().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvg_df['gene_symbol'] = hvg_df['gene_id'].map(gene_id_to_gene_symbol_map)\n",
+    "hvg_df = hvg_df.sort_values('log_tpkc', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt_gene_symbols = hvg_df['gene_symbol'].tolist()[:N_PROMPT_GENES]\n",
+    "prompt_gene_ids = hvg_df['gene_id'].tolist()[:N_PROMPT_GENES]\n",
+    "\n",
+    "query_gene_symbols = hvg_df['gene_symbol'].tolist()\n",
+    "query_gene_ids = hvg_df['gene_id'].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(hvg_df['log_tpkc'], bins=100, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save to disk\n",
+    "hvg_df.to_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"hvg_df.csv\"), index=False)\n",
+    "\n",
+    "pd.DataFrame(prompt_gene_ids).to_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"prompt_gene_ids.csv\"), index=False, header=False)\n",
+    "\n",
+    "pd.DataFrame(query_gene_ids).to_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_ids.csv\"), index=False, header=False)\n",
+    "\n",
+    "pd.DataFrame(prompt_gene_symbols).to_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"prompt_gene_symbols.csv\"), index=False, header=False)\n",
+    "\n",
+    "pd.DataFrame(query_gene_symbols).to_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_symbols.csv\"), index=False, header=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Making metacells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_idx_list = [92, 93, 100, 104, 40, 52, 50, 79]\n",
+    "N_PROMPT_GENES = 5000\n",
+    "\n",
+    "MIN_CELLS_PER_TYPE = 10\n",
+    "MAX_CELLS_FOR_METACELL = 100\n",
+    "\n",
+    "\n",
+    "hvg_dict = dict()\n",
+    "hvg_dict['dataset_index'] = []\n",
+    "hvg_dict['gene_id'] = []\n",
+    "hvg_dict['log_tpkc'] = []\n",
+    "\n",
+    "for val_idx in tqdm(val_idx_list):\n",
+    "\n",
+    "    adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}__processed.h5ad\")\n",
+    "    orig_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    adata = sc.read_h5ad(adata_path)\n",
+    "    orig_adata = sc.read_h5ad(orig_adata_path)\n",
+    "\n",
+    "    # how many cell types with as many cells above MIN_CELLS_PER_TYPE?\n",
+    "    cell_type_count_dict = adata.obs['cell_type'].value_counts().to_dict()\n",
+    "    filtered_cell_type_count_dict = dict()\n",
+    "\n",
+    "    for cell_type, count in cell_type_count_dict.items():\n",
+    "        if count < MIN_CELLS_PER_TYPE:\n",
+    "            print(f\"Skipping cell type {cell_type} in {val_idx} because cell type {cell_type} has only {count} cells\")\n",
+    "        else:\n",
+    "            filtered_cell_type_count_dict[cell_type] = count\n",
+    "    \n",
+    "    # for each cell type, select the cell with median total_mrna_umis, and then select up to MAX_CELLS_FOR_METACELL\n",
+    "    for i_cell_type, cell_type in enumerate(filtered_cell_type_count_dict.keys()):\n",
+    "        cell_type_adata = adata[adata.obs['cell_type'] == cell_type]\n",
+    "        cell_type_adata = cell_type_adata[cell_type_adata.obs['total_mrna_umis'].sort_values().index]\n",
+    "        median_idx = cell_type_adata.shape[0] // 2\n",
+    "        median_cell_id = cell_type_adata.obs['original_cell_id'].iloc[median_idx]\n",
+    "        median_pc_k = cell_type_adata.obsm['X_pca'][median_idx, :]\n",
+    "\n",
+    "        # sort other cells based on cosine distance to median_pc_k\n",
+    "        median_pc_unit_k = median_pc_k / np.linalg.norm(median_pc_k)\n",
+    "        other_cells_unit_nk = cell_type_adata.obsm['X_pca'] / np.linalg.norm(cell_type_adata.obsm['X_pca'], axis=1)[:, None]\n",
+    "        cell_type_adata.obs['distance_to_median'] = np.linalg.norm(other_cells_unit_nk - median_pc_unit_k, axis=1)\n",
+    "        cell_type_adata = cell_type_adata[cell_type_adata.obs['distance_to_median'].sort_values().index]\n",
+    "        assert cell_type_adata.obs['original_cell_id'].iloc[0] == median_cell_id\n",
+    "        \n",
+    "        # select up to MAX_CELLS_FOR_METACELL\n",
+    "        cell_type_adata = cell_type_adata[:min(MAX_CELLS_FOR_METACELL, cell_type_adata.shape[0])]\n",
+    "        orig_cell_type_adata = orig_adata[orig_adata.obs['original_cell_id'].isin(cell_type_adata.obs['original_cell_id'])]\n",
+    "        assert len(orig_cell_type_adata) == len(cell_type_adata)\n",
+    "\n",
+    "        # write to disk\n",
+    "        orig_cell_type_adata.write_h5ad(\n",
+    "            os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", f\"extract_{val_idx}__{i_cell_type}.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.ipynb b/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.ipynb
new file mode 100644
index 00000000..830d01da
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.ipynb
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Jacobian calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml import CellariumModule, CellariumPipeline\n",
+    "\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "############# NOTE ###################\n",
+    "DEVICE = torch.device('cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINTS_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_1/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load an AnnData Extract\n",
+    "\n",
+    "We will use it for category mappings ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load an AnnData extract\n",
+    "adata_path = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_ontology_infos = dict()\n",
+    "\n",
+    "ref_obs = adata.obs\n",
+    "\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"] = dict()\n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  \n",
+    "gene_ontology_infos[\"assay_ontology_term_id\"][\"labels\"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy\n",
+    "\n",
+    "gene_ontology_infos[\"suspension_type\"] = dict()\n",
+    "gene_ontology_infos[\"suspension_type\"][\"names\"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)\n",
+    "gene_ontology_infos[\"suspension_type\"][\"labels\"] = list(ref_obs['suspension_type'].cat.categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene IDs, gene symbols, useful maps\n",
+    "model_var_names = np.asarray(adata.var_names)\n",
+    "model_var_names_set = set(model_var_names)\n",
+    "var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "gene_symbol_to_gene_id_map = dict()\n",
+    "for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "    if gene_symbol != float('nan'):\n",
+    "        gene_symbol_to_gene_id_map[gene_symbol] = gene_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load a CellariumGPT checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ckpt_path = os.path.join(CHECKPOINTS_PATH, \"epoch=2-step=252858.ckpt\")\n",
+    "gpt_model = CellariumModule.load_from_checkpoint(ckpt_path, map_location=DEVICE)\n",
+    "\n",
+    "# Inject gene categories\n",
+    "gpt_model.model.gene_categories = np.asarray(adata.var_names)\n",
+    "\n",
+    "# change attention backend to memory efficient\n",
+    "gpt_model.model.set_attention_backend('mem_efficient')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline\n",
+    "metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos\n",
+    "print(type(metadata_ontology_infos))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cellarium.ml.models.cellarium_gpt import PredictTokenizer\n",
+    "\n",
+    "# Rewire the pipeline with a PredictTokenizer\n",
+    "predict_tokenizer = PredictTokenizer(\n",
+    "    max_total_mrna_umis=100_000,\n",
+    "    gene_vocab_sizes={\n",
+    "        \"assay\": 19,\n",
+    "        \"gene_id\": 36601,\n",
+    "        \"gene_value\": 2001,\n",
+    "        \"suspension_type\": 2,\n",
+    "    },\n",
+    "    metadata_vocab_sizes={\n",
+    "        \"cell_type\": 890,\n",
+    "        \"development_stage\": 191,\n",
+    "        \"disease\": 350,\n",
+    "        \"sex\": 2,\n",
+    "        \"tissue\": 822,\n",
+    "    },\n",
+    "    ontology_infos=metadata_ontology_infos,\n",
+    ")\n",
+    "\n",
+    "gpt_model.pipeline = CellariumPipeline([\n",
+    "    predict_tokenizer,\n",
+    "    gpt_model.model,\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "from cellarium.ml.models.cellarium_gpt import CellariumGPT\n",
+    "\n",
+    "\n",
+    "def generate_tokens_from_adata(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        model_gene_categories: list[str],\n",
+    "        obs_index: int | list[int] | None,\n",
+    "        query_var_index: list[int],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        metadata_prompt_masks_dict: dict[str, bool],\n",
+    "        tokenizer: PredictTokenizer,\n",
+    "        device: torch.device = torch.device(\"cpu\"),\n",
+    "    ) -> tuple[dict, dict]:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    .. note::\n",
+    "      All variables in the AnnData are treated as prompts.\n",
+    "\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # slice the anndata\n",
+    "    if isinstance(obs_index, int):\n",
+    "        obs_index = [obs_index]\n",
+    "\n",
+    "    # save obs before slicing\n",
+    "    if obs_index is not None:\n",
+    "        adata = adata[obs_index]\n",
+    "\n",
+    "    # generate gene ids and masks\n",
+    "    n_cells = len(adata)\n",
+    "    adata_var_names = adata.var_names\n",
+    "    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}\n",
+    "    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])\n",
+    "    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]\n",
+    "    n_prompt_vars = len(prompt_var_index)\n",
+    "    n_query_vars = len(query_var_index)\n",
+    "    n_total_vars = n_prompt_vars + n_query_vars\n",
+    "    \n",
+    "    # gene id\n",
+    "    gene_ids_nc = torch.tensor(\n",
+    "        prompt_var_index + query_var_index,\n",
+    "        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene prompt mask\n",
+    "    gene_prompt_mask_nc = torch.tensor(\n",
+    "        [1] * n_prompt_vars + [0] * n_query_vars,\n",
+    "        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)\n",
+    "    \n",
+    "    # gene value\n",
+    "    try:\n",
+    "        prompt_X_ng = np.asarray(adata.X.todense())\n",
+    "    except AttributeError:\n",
+    "        prompt_X_ng = adata.X\n",
+    "    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)\n",
+    "    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)\n",
+    "    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)\n",
+    "\n",
+    "    # total mrna umis\n",
+    "    prompt_total_mrna_umis_nc = torch.tensor(\n",
+    "        adata.obs[\"total_mrna_umis\"].values,\n",
+    "        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)\n",
+    "    if query_total_mrna_umis is None:\n",
+    "        # the same as prompt\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            adata.obs[\"total_mrna_umis\"].values,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    else:\n",
+    "        query_total_mrna_umis_nc = torch.tensor(\n",
+    "            [query_total_mrna_umis] * n_cells,\n",
+    "            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)\n",
+    "    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)\n",
+    "\n",
+    "    # convert assay and suspension_type to codes\n",
+    "    assay_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"assay_ontology_term_id\"].values,\n",
+    "            categories=gene_ontology_infos[\"assay_ontology_term_id\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "    suspension_type_nc = torch.tensor(\n",
+    "        pd.Categorical(\n",
+    "            adata.obs[\"suspension_type\"].values,\n",
+    "            categories=gene_ontology_infos[\"suspension_type\"][\"names\"]).codes,\n",
+    "        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)\n",
+    "\n",
+    "    gene_tokens_dict = {\n",
+    "        \"assay\": assay_nc,  # categorical\n",
+    "        \"suspension_type\": suspension_type_nc,  # categorical\n",
+    "        \"gene_id\": gene_ids_nc,  # categorical\n",
+    "        \"gene_value\": gene_value_nc,  # continuous\n",
+    "        \"total_mrna_umis\": total_mrna_umis_nc,  # continuous\n",
+    "    }\n",
+    "\n",
+    "    # metadata prompt masks\n",
+    "    expanded_metadata_prompt_masks_dict = dict()\n",
+    "    for key in metadata_ontology_infos.keys():  # note: key order is important ...\n",
+    "        expanded_metadata_prompt_masks_dict[key] = torch.tensor(\n",
+    "            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)\n",
+    "    \n",
+    "    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes\n",
+    "    metadata_tokens_dict = {\n",
+    "        \"cell_type\": adata.obs[\"cell_type_ontology_term_id\"].values,  # categorical\n",
+    "        \"development_stage\": adata.obs[\"development_stage_ontology_term_id\"].values,  # categorical\n",
+    "        \"disease\": adata.obs[\"disease_ontology_term_id\"].values,  # categorical\n",
+    "        \"sex\": adata.obs[\"sex_ontology_term_id\"].values,  # categorical\n",
+    "        \"tissue\": adata.obs[\"tissue_ontology_term_id\"].values,  # categorical\n",
+    "    }\n",
+    "\n",
+    "    # where to find each thing in the context?\n",
+    "    context_indices = dict()\n",
+    "    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()\n",
+    "    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()\n",
+    "    offset = 0\n",
+    "    for metadata_key in metadata_ontology_infos.keys():\n",
+    "        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset\n",
+    "        offset += 1\n",
+    "\n",
+    "    # return gene_tokens_dict, metadata_tokens_dict\n",
+    "    tokenizer_output = tokenizer(\n",
+    "        metadata_tokens_n=metadata_tokens_dict,\n",
+    "        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,\n",
+    "        gene_tokens_nc=gene_tokens_dict,\n",
+    "        gene_prompt_mask_nc=gene_prompt_mask_nc,\n",
+    "    )\n",
+    "\n",
+    "    return tokenizer_output, context_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def snap_to_marginal_mean_manifold(\n",
+    "        adata: sc.AnnData,\n",
+    "        gene_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        metadata_ontology_infos: dict[str, dict[str, list[str]]],\n",
+    "        query_var_names: list[str],\n",
+    "        query_total_mrna_umis: float | None,\n",
+    "        predict_tokenizer: PredictTokenizer,\n",
+    "        cellarium_gpt_module: CellariumModule,\n",
+    "        prompt_gene_values_g: torch.Tensor | None = None,\n",
+    "    ) -> dict:\n",
+    "    \n",
+    "    assert len(adata) == 1, \"Only a single cell is allowed\"\n",
+    "    \n",
+    "    model_gene_categories = cellarium_gpt_module.model.gene_categories\n",
+    "    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}\n",
+    "    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]\n",
+    "\n",
+    "    metadata_prompt_masks_dict = {\n",
+    "        \"cell_type\": False,\n",
+    "        \"development_stage\": False,\n",
+    "        \"disease\": False,\n",
+    "        \"sex\": False,\n",
+    "        \"tissue\": False,\n",
+    "    }\n",
+    "\n",
+    "    tokens_dict, context_indices = generate_tokens_from_adata(\n",
+    "        adata=adata,\n",
+    "        gene_ontology_infos=gene_ontology_infos,\n",
+    "        metadata_ontology_infos=metadata_ontology_infos,\n",
+    "        model_gene_categories=model_var_names,\n",
+    "        obs_index=None,\n",
+    "        query_var_index=query_var_index,\n",
+    "        query_total_mrna_umis=query_total_mrna_umis,\n",
+    "        metadata_prompt_masks_dict=metadata_prompt_masks_dict,\n",
+    "        tokenizer=predict_tokenizer,\n",
+    "        device=torch.device(\"cpu\"),\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)\n",
+    "    \n",
+    "    # get a reference to prompt gene values\n",
+    "    FIRST_CELL_DIM = 0\n",
+    "    GENE_VALUE_DIM = 0\n",
+    "    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]\n",
+    "    \n",
+    "    # this is the \"source\"\n",
+    "    if prompt_gene_values_g is None:\n",
+    "        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()\n",
+    "    \n",
+    "    # inject back to tokens_dict to re-establish the reference for Jacobian calculation\n",
+    "    tokens_dict['gene_tokens_nc']['gene_value'][\n",
+    "        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)\n",
+    "\n",
+    "    # get model predictions\n",
+    "    logits_dict = cellarium_gpt_module.model.predict(\n",
+    "        gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "        metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "        prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"],\n",
+    "    )\n",
+    "\n",
+    "    # note: we use `q` to denote query genes\n",
+    "    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]\n",
+    "    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)\n",
+    "    MAX_COUNTS = gene_logits_qk.shape[-1]\n",
+    "    log_counts_1_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).log()\n",
+    "    log_counts_2_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).pow(2).log()\n",
+    "    gene_mom_1_q = torch.logsumexp(gene_logits_qk + log_counts_1_k[None, :], dim=-1).exp()\n",
+    "    gene_mom_2_q = torch.logsumexp(gene_logits_qk + log_counts_2_k[None, :], dim=-1).exp()\n",
+    "    gene_marginal_means_q = gene_mom_1_q\n",
+    "    gene_marginal_std_q = torch.clamp(gene_mom_2_q - gene_mom_1_q.pow(2), 0.).sqrt()\n",
+    "\n",
+    "    return {\n",
+    "        'gene_marginal_means_q': gene_marginal_means_q,\n",
+    "        'gene_marginal_std_q': gene_marginal_std_q,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Jacobian calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N_PROMPT_GENES_QUERY_PREFIX = 10_000\n",
+    "\n",
+    "prompt_gene_ids_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"prompt_gene_ids.csv\")\n",
+    "query_gene_ids_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_ids.csv\")\n",
+    "\n",
+    "# load prompt and query gene ids\n",
+    "prompt_gene_ids = pd.read_csv(prompt_gene_ids_path, header=None).values.flatten().tolist()\n",
+    "query_gene_ids = pd.read_csv(query_gene_ids_path, header=None).values.flatten().tolist()\n",
+    "\n",
+    "if N_PROMPT_GENES_QUERY_PREFIX is not None:\n",
+    "    prompt_gene_ids = query_gene_ids[:N_PROMPT_GENES_QUERY_PREFIX]\n",
+    "\n",
+    "# load a test anndata\n",
+    "dataset_root_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\")\n",
+    "\n",
+    "# get the list of files\n",
+    "dataset_files = sorted([x for x in os.listdir(dataset_root_path) if x.endswith(\".h5ad\")])\n",
+    "full_dataset_paths = [os.path.join(dataset_root_path, file) for file in dataset_files]\n",
+    "\n",
+    "jacobian_point = \"marginal_mean\"\n",
+    "query_chunk_size = 500\n",
+    "n_workers = 6 ## NOTE\n",
+    "max_query_genes = None\n",
+    "\n",
+    "# generate full output paths\n",
+    "full_output_paths = [x.replace(\".h5ad\", f\"__jacobian__{jacobian_point}.pt\") for x in full_dataset_paths]\n",
+    "\n",
+    "# assigns input and output datasets to the n_workers\n",
+    "worker_io_dict = dict()\n",
+    "\n",
+    "for i in range(n_workers):\n",
+    "    worker_io_dict[i] = {\n",
+    "        \"input_paths\": full_dataset_paths[i::n_workers],\n",
+    "        \"output_paths\": full_output_paths[i::n_workers],\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "def process_jacobian(input_path, output_path):\n",
+    "\n",
+    "    test_adata = sc.read_h5ad(input_path)\n",
+    "\n",
+    "    # make a metacell\n",
+    "    X_meta_g = np.asarray(test_adata.X.sum(0))\n",
+    "\n",
+    "    # set total mrna umis to the mean of the dataset\n",
+    "    target_total_mrna_umis = test_adata.obs['total_mrna_umis'].mean()\n",
+    "    X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()\n",
+    "\n",
+    "    # make a metacell anndata\n",
+    "    adata_meta = test_adata[0, :].copy()\n",
+    "    adata_meta.X = X_meta_g\n",
+    "    adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]\n",
+    "\n",
+    "    # subset to prompt gene ids\n",
+    "    print(f\"Number of prompt genes: {len(prompt_gene_ids)}\")\n",
+    "    adata_meta = adata_meta[:, prompt_gene_ids].copy()\n",
+    "\n",
+    "    # query var names\n",
+    "    query_var_names = query_gene_ids\n",
+    "    if max_query_genes is not None:\n",
+    "        query_var_names = query_var_names[:max_query_genes]\n",
+    "    print(f\"Number of query genes: {len(query_var_names)}\")\n",
+    "\n",
+    "    with torch.inference_mode():\n",
+    "        # get the mean marginal\n",
+    "        print(\"Calculating marginal mean and std ...\")\n",
+    "        prompt_marginal_dict = snap_to_marginal_mean_manifold(\n",
+    "            adata=adata_meta,\n",
+    "            gene_ontology_infos=gene_ontology_infos,\n",
+    "            metadata_ontology_infos=metadata_ontology_infos,\n",
+    "            query_var_names=prompt_gene_ids,  #### NOTE\n",
+    "            query_total_mrna_umis=None,\n",
+    "            predict_tokenizer=predict_tokenizer,\n",
+    "            cellarium_gpt_module=gpt_model,\n",
+    "        )\n",
+    "        query_marginal_dict = snap_to_marginal_mean_manifold(\n",
+    "            adata=adata_meta,\n",
+    "            gene_ontology_infos=gene_ontology_infos,\n",
+    "            metadata_ontology_infos=metadata_ontology_infos,\n",
+    "            query_var_names=query_var_names,  #### NOTE\n",
+    "            query_total_mrna_umis=None,\n",
+    "            predict_tokenizer=predict_tokenizer,\n",
+    "            cellarium_gpt_module=gpt_model,\n",
+    "        )\n",
+    "        print(\"Done.\")\n",
+    "\n",
+    "    # jacobian point\n",
+    "    if jacobian_point == \"actual\":\n",
+    "        adata_meta.layers['original'] = adata_meta.X.copy()\n",
+    "        print(\"Using the actual metacell counts as the point to calculate the Jacobian on ...\")\n",
+    "\n",
+    "    elif jacobian_point == \"marginal_mean\":\n",
+    "        # inject into the adata\n",
+    "        adata_meta.layers['original'] = adata_meta.X.copy()\n",
+    "        adata_meta.X = prompt_marginal_dict['gene_marginal_means_q'].detach().cpu().numpy()[None, :]\n",
+    "    else:\n",
+    "        raise ValueError\n",
+    "\n",
+    "    def yield_chunks(lst, n):\n",
+    "        for i in range(0, len(lst), n):\n",
+    "            yield lst[i:i + n]\n",
+    "\n",
+    "    query_var_names_chunks = list(yield_chunks(query_var_names, query_chunk_size))\n",
+    "    jacobian_chunks = []\n",
+    "\n",
+    "    print(\"Calculating the Jacobian ...\")\n",
+    "    for query_var_names_chunk in tqdm(query_var_names_chunks):\n",
+    "\n",
+    "        prompt_gene_values_g = torch.tensor(\n",
+    "            adata_meta.X[0],\n",
+    "            device=gpt_model.device,\n",
+    "            dtype=torch.float32)\n",
+    "\n",
+    "        def _wrapped_snap_to_marginal_mean_manifold(\n",
+    "                prompt_gene_values_g: torch.Tensor) -> torch.Tensor:\n",
+    "            \n",
+    "            return snap_to_marginal_mean_manifold(\n",
+    "                adata=adata_meta,\n",
+    "                gene_ontology_infos=gene_ontology_infos,\n",
+    "                metadata_ontology_infos=metadata_ontology_infos,\n",
+    "                query_var_names=query_var_names_chunk,\n",
+    "                query_total_mrna_umis=None,\n",
+    "                predict_tokenizer=predict_tokenizer,\n",
+    "                cellarium_gpt_module=gpt_model,\n",
+    "                prompt_gene_values_g=prompt_gene_values_g,\n",
+    "            )['gene_marginal_means_q']\n",
+    "\n",
+    "        chunk_jacobian_qg = torch.autograd.functional.jacobian(\n",
+    "            func=_wrapped_snap_to_marginal_mean_manifold, \n",
+    "            inputs=prompt_gene_values_g,\n",
+    "            create_graph=False,\n",
+    "            vectorize=False,\n",
+    "        )\n",
+    "\n",
+    "        jacobian_chunks.append(chunk_jacobian_qg)\n",
+    "\n",
+    "    jacobian_qg = torch.cat(jacobian_chunks, dim=0)\n",
+    "\n",
+    "    result_dict = {\n",
+    "        'dataset_name': input_path,\n",
+    "        'adata_obs': adata_meta.obs,\n",
+    "        'jacobian_point': jacobian_point,\n",
+    "        'query_var_names': query_var_names,\n",
+    "        'prompt_var_names': prompt_gene_ids,\n",
+    "        'jacobian_qg': jacobian_qg,\n",
+    "        'adata_gene_values_g': np.asarray(adata_meta.layers['original']).flatten(),\n",
+    "        'prompt_gene_values_g': prompt_gene_values_g,\n",
+    "        'prompt_marginal_dict': prompt_marginal_dict,\n",
+    "        'query_marginal_dict': query_marginal_dict,\n",
+    "    }\n",
+    "\n",
+    "    torch.save(result_dict, output_path)\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "\n",
+    "    parser = argparse.ArgumentParser(description=\"TBW\")\n",
+    "    parser.add_argument(\"worker_id\", type=int, help=\"Worker ID\")\n",
+    "\n",
+    "    args = parser.parse_args()\n",
+    "\n",
+    "    print(f\"Worker ID: {args.worker_id}\")\n",
+    "    worker_id = int(args.worker_id)\n",
+    "    \n",
+    "    # get the input and output paths\n",
+    "    io_dict = worker_io_dict[worker_id]\n",
+    "\n",
+    "    # go through the datasets one by one and work on the ones that the output does not exist\n",
+    "    for input_path, output_path in tqdm(zip(io_dict[\"input_paths\"], io_dict[\"output_paths\"])):\n",
+    "        if os.path.exists(output_path):\n",
+    "            print(f\"Output exists: {output_path}, skipping ...\")\n",
+    "            continue\n",
+    "\n",
+    "        print(f\"Working on: {input_path}\")\n",
+    "        process_jacobian(input_path, output_path)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.py b/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.py
new file mode 100644
index 00000000..c5700e45
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/06_jacobian_calculation_metacell.py
@@ -0,0 +1,546 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# #### Jacobian calculation
+
+# In[1]:
+
+
+import os
+import torch
+import numpy as np
+import scanpy as sc
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from cellarium.ml import CellariumModule, CellariumPipeline
+
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
+############# NOTE ###################
+DEVICE = torch.device('cuda')
+
+
+# In[2]:
+
+
+ROOT_PATH = "/mnt/cellariumgpt-xfer/mb-ml-dev-vm"
+CHECKPOINTS_PATH = "/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_1/checkpoints"
+
+
+# ### Load an AnnData Extract
+# 
+# We will use it for category mappings ...
+
+# In[3]:
+
+
+# Load an AnnData extract
+adata_path = os.path.join(ROOT_PATH, "data", "extract_0.h5ad")
+adata = sc.read_h5ad(adata_path)
+
+
+# In[4]:
+
+
+gene_ontology_infos = dict()
+
+ref_obs = adata.obs
+
+gene_ontology_infos["assay_ontology_term_id"] = dict()
+gene_ontology_infos["assay_ontology_term_id"]["names"] = list(ref_obs['assay_ontology_term_id'].cat.categories)  
+gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(ref_obs['assay_ontology_term_id'].cat.categories) # just because I am lazy
+
+gene_ontology_infos["suspension_type"] = dict()
+gene_ontology_infos["suspension_type"]["names"] = list(ref_obs['suspension_type'].cat.categories)  # for uniformity -- this variable does not have an ontology (does it?)
+gene_ontology_infos["suspension_type"]["labels"] = list(ref_obs['suspension_type'].cat.categories)
+
+
+# In[5]:
+
+
+# gene IDs, gene symbols, useful maps
+model_var_names = np.asarray(adata.var_names)
+model_var_names_set = set(model_var_names)
+var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_var_names)}
+
+gene_info_tsv_path = os.path.join(ROOT_PATH, "gene_info", "gene_info.tsv")
+gene_info_df = pd.read_csv(gene_info_tsv_path, sep="\t")
+
+gene_symbol_to_gene_id_map = dict()
+for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):
+    if gene_symbol != float('nan'):
+        gene_symbol_to_gene_id_map[gene_symbol] = gene_id
+
+
+# ### Load a CellariumGPT checkpoint
+
+# In[6]:
+
+
+ckpt_path = os.path.join(CHECKPOINTS_PATH, "epoch=2-step=252858.ckpt")
+gpt_model = CellariumModule.load_from_checkpoint(ckpt_path, map_location=DEVICE)
+
+# Inject gene categories
+gpt_model.model.gene_categories = np.asarray(adata.var_names)
+
+# change attention backend to memory efficient
+gpt_model.model.set_attention_backend('mem_efficient')
+
+
+# In[7]:
+
+
+# Get the ontology infos from the TrainTokenizer, which is the first step of the pipeline
+metadata_ontology_infos = gpt_model.pipeline[0].ontology_infos
+print(type(metadata_ontology_infos))
+
+
+# ### Helper functions
+
+# In[8]:
+
+
+from cellarium.ml.models.cellarium_gpt import PredictTokenizer
+
+# Rewire the pipeline with a PredictTokenizer
+predict_tokenizer = PredictTokenizer(
+    max_total_mrna_umis=100_000,
+    gene_vocab_sizes={
+        "assay": 19,
+        "gene_id": 36601,
+        "gene_value": 2001,
+        "suspension_type": 2,
+    },
+    metadata_vocab_sizes={
+        "cell_type": 890,
+        "development_stage": 191,
+        "disease": 350,
+        "sex": 2,
+        "tissue": 822,
+    },
+    ontology_infos=metadata_ontology_infos,
+)
+
+gpt_model.pipeline = CellariumPipeline([
+    predict_tokenizer,
+    gpt_model.model,
+])
+
+
+# In[9]:
+
+
+import pandas as pd
+
+from cellarium.ml.models.cellarium_gpt import CellariumGPT
+
+
+def generate_tokens_from_adata(
+        adata: sc.AnnData,
+        gene_ontology_infos: dict[str, dict[str, list[str]]],
+        metadata_ontology_infos: dict[str, dict[str, list[str]]],
+        model_gene_categories: list[str],
+        obs_index: int | list[int] | None,
+        query_var_index: list[int],
+        query_total_mrna_umis: float | None,
+        metadata_prompt_masks_dict: dict[str, bool],
+        tokenizer: PredictTokenizer,
+        device: torch.device = torch.device("cpu"),
+    ) -> tuple[dict, dict]:
+    """
+
+    .. note::
+      All variables in the AnnData are treated as prompts.
+
+
+    """
+    
+    # slice the anndata
+    if isinstance(obs_index, int):
+        obs_index = [obs_index]
+
+    # save obs before slicing
+    if obs_index is not None:
+        adata = adata[obs_index]
+
+    # generate gene ids and masks
+    n_cells = len(adata)
+    adata_var_names = adata.var_names
+    model_var_name_to_index_map = {var_name: var_index for var_index, var_name in enumerate(model_gene_categories)}
+    assert all([var_name in model_var_name_to_index_map for var_name in adata_var_names])
+    prompt_var_index = [model_var_name_to_index_map[var_name] for var_name in adata_var_names]
+    n_prompt_vars = len(prompt_var_index)
+    n_query_vars = len(query_var_index)
+    n_total_vars = n_prompt_vars + n_query_vars
+    
+    # gene id
+    gene_ids_nc = torch.tensor(
+        prompt_var_index + query_var_index,
+        dtype=torch.int64, device=device)[None, :].expand(n_cells, n_total_vars)
+    
+    # gene prompt mask
+    gene_prompt_mask_nc = torch.tensor(
+        [1] * n_prompt_vars + [0] * n_query_vars,
+        dtype=torch.bool, device=device)[None, :].expand(n_cells, n_total_vars)
+    
+    # gene value
+    try:
+        prompt_X_ng = np.asarray(adata.X.todense())
+    except AttributeError:
+        prompt_X_ng = adata.X
+    prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=device)
+    query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=device)
+    gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)
+
+    # total mrna umis
+    prompt_total_mrna_umis_nc = torch.tensor(
+        adata.obs["total_mrna_umis"].values,
+        dtype=torch.float32, device=device)[:, None].expand(n_cells, n_prompt_vars)
+    if query_total_mrna_umis is None:
+        # the same as prompt
+        query_total_mrna_umis_nc = torch.tensor(
+            adata.obs["total_mrna_umis"].values,
+            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)
+    else:
+        query_total_mrna_umis_nc = torch.tensor(
+            [query_total_mrna_umis] * n_cells,
+            dtype=torch.float32, device=device)[:, None].expand(n_cells, n_query_vars)
+    total_mrna_umis_nc = torch.cat([prompt_total_mrna_umis_nc, query_total_mrna_umis_nc], dim=1)
+
+    # convert assay and suspension_type to codes
+    assay_nc = torch.tensor(
+        pd.Categorical(
+            adata.obs["assay_ontology_term_id"].values,
+            categories=gene_ontology_infos["assay_ontology_term_id"]["names"]).codes,
+        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)
+    suspension_type_nc = torch.tensor(
+        pd.Categorical(
+            adata.obs["suspension_type"].values,
+            categories=gene_ontology_infos["suspension_type"]["names"]).codes,
+        dtype=torch.int64, device=device)[:, None].expand(n_cells, n_total_vars)
+
+    gene_tokens_dict = {
+        "assay": assay_nc,  # categorical
+        "suspension_type": suspension_type_nc,  # categorical
+        "gene_id": gene_ids_nc,  # categorical
+        "gene_value": gene_value_nc,  # continuous
+        "total_mrna_umis": total_mrna_umis_nc,  # continuous
+    }
+
+    # metadata prompt masks
+    expanded_metadata_prompt_masks_dict = dict()
+    for key in metadata_ontology_infos.keys():  # note: key order is important ...
+        expanded_metadata_prompt_masks_dict[key] = torch.tensor(
+            [metadata_prompt_masks_dict[key]] * n_cells, dtype=torch.bool, device=device)
+    
+    # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes
+    metadata_tokens_dict = {
+        "cell_type": adata.obs["cell_type_ontology_term_id"].values,  # categorical
+        "development_stage": adata.obs["development_stage_ontology_term_id"].values,  # categorical
+        "disease": adata.obs["disease_ontology_term_id"].values,  # categorical
+        "sex": adata.obs["sex_ontology_term_id"].values,  # categorical
+        "tissue": adata.obs["tissue_ontology_term_id"].values,  # categorical
+    }
+
+    # where to find each thing in the context?
+    context_indices = dict()
+    context_indices['prompt_genes'] = np.arange(0, n_prompt_vars).tolist()
+    context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()
+    offset = 0
+    for metadata_key in metadata_ontology_infos.keys():
+        context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset
+        offset += 1
+
+    # return gene_tokens_dict, metadata_tokens_dict
+    tokenizer_output = tokenizer(
+        metadata_tokens_n=metadata_tokens_dict,
+        metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,
+        gene_tokens_nc=gene_tokens_dict,
+        gene_prompt_mask_nc=gene_prompt_mask_nc,
+    )
+
+    return tokenizer_output, context_indices
+
+
+# In[10]:
+
+
+def snap_to_marginal_mean_manifold(
+        adata: sc.AnnData,
+        gene_ontology_infos: dict[str, dict[str, list[str]]],
+        metadata_ontology_infos: dict[str, dict[str, list[str]]],
+        query_var_names: list[str],
+        query_total_mrna_umis: float | None,
+        predict_tokenizer: PredictTokenizer,
+        cellarium_gpt_module: CellariumModule,
+        prompt_gene_values_g: torch.Tensor | None = None,
+    ) -> dict:
+    
+    assert len(adata) == 1, "Only a single cell is allowed"
+    
+    model_gene_categories = cellarium_gpt_module.model.gene_categories
+    var_name_to_index_map = {var_name: i for i, var_name in enumerate(model_gene_categories)}
+    query_var_index = [var_name_to_index_map[var_name] for var_name in query_var_names]
+
+    metadata_prompt_masks_dict = {
+        "cell_type": False,
+        "development_stage": False,
+        "disease": False,
+        "sex": False,
+        "tissue": False,
+    }
+
+    tokens_dict, context_indices = generate_tokens_from_adata(
+        adata=adata,
+        gene_ontology_infos=gene_ontology_infos,
+        metadata_ontology_infos=metadata_ontology_infos,
+        model_gene_categories=model_var_names,
+        obs_index=None,
+        query_var_index=query_var_index,
+        query_total_mrna_umis=query_total_mrna_umis,
+        metadata_prompt_masks_dict=metadata_prompt_masks_dict,
+        tokenizer=predict_tokenizer,
+        device=torch.device("cpu"),
+    )
+
+    # convert to cuda
+    tokens_dict = cellarium_gpt_module.transfer_batch_to_device(tokens_dict, cellarium_gpt_module.device, 0)
+    
+    # get a reference to prompt gene values
+    FIRST_CELL_DIM = 0
+    GENE_VALUE_DIM = 0
+    prompt_gene_log1p_values_g = tokens_dict['gene_tokens_nc']['gene_value'][
+        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM]
+    
+    # this is the "source"
+    if prompt_gene_values_g is None:
+        prompt_gene_values_g = torch.expm1(prompt_gene_log1p_values_g).clone()
+    
+    # inject back to tokens_dict to re-establish the reference for Jacobian calculation
+    tokens_dict['gene_tokens_nc']['gene_value'][
+        FIRST_CELL_DIM, context_indices['prompt_genes'], GENE_VALUE_DIM] = torch.log1p(prompt_gene_values_g)
+
+    # get model predictions
+    logits_dict = cellarium_gpt_module.model.predict(
+        gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+        metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+        prompt_mask_nc=tokens_dict["prompt_mask_nc"],
+    )
+
+    # note: we use `q` to denote query genes
+    gene_logits_qk = logits_dict['gene_value'][FIRST_CELL_DIM, context_indices['query_genes'], :]
+    gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)
+    MAX_COUNTS = gene_logits_qk.shape[-1]
+    log_counts_1_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).log()
+    log_counts_2_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).pow(2).log()
+    gene_mom_1_q = torch.logsumexp(gene_logits_qk + log_counts_1_k[None, :], dim=-1).exp()
+    gene_mom_2_q = torch.logsumexp(gene_logits_qk + log_counts_2_k[None, :], dim=-1).exp()
+    gene_marginal_means_q = gene_mom_1_q
+    gene_marginal_std_q = torch.clamp(gene_mom_2_q - gene_mom_1_q.pow(2), 0.).sqrt()
+
+    return {
+        'gene_marginal_means_q': gene_marginal_means_q,
+        'gene_marginal_std_q': gene_marginal_std_q,
+    }
+
+
+# #### Jacobian calculation
+
+# In[11]:
+
+
+N_PROMPT_GENES_QUERY_PREFIX = 10_000
+
+prompt_gene_ids_path = os.path.join(ROOT_PATH, "data", "cellariumgpt_validation", "prompt_gene_ids.csv")
+query_gene_ids_path = os.path.join(ROOT_PATH, "data", "cellariumgpt_validation", "query_gene_ids.csv")
+
+# load prompt and query gene ids
+prompt_gene_ids = pd.read_csv(prompt_gene_ids_path, header=None).values.flatten().tolist()
+query_gene_ids = pd.read_csv(query_gene_ids_path, header=None).values.flatten().tolist()
+
+if N_PROMPT_GENES_QUERY_PREFIX is not None:
+    prompt_gene_ids = query_gene_ids[:N_PROMPT_GENES_QUERY_PREFIX]
+
+# load a test anndata
+dataset_root_path = os.path.join(ROOT_PATH, "data", "cellariumgpt_validation", "metacells")
+
+# get the list of files
+dataset_files = sorted([x for x in os.listdir(dataset_root_path) if x.endswith(".h5ad")])
+full_dataset_paths = [os.path.join(dataset_root_path, file) for file in dataset_files]
+
+jacobian_point = "marginal_mean"
+query_chunk_size = 500
+n_workers = 6 ## NOTE
+max_query_genes = None
+
+# generate full output paths
+full_output_paths = [x.replace(".h5ad", f"__jacobian__{jacobian_point}.pt") for x in full_dataset_paths]
+
+# assigns input and output datasets to the n_workers
+worker_io_dict = dict()
+
+for i in range(n_workers):
+    worker_io_dict[i] = {
+        "input_paths": full_dataset_paths[i::n_workers],
+        "output_paths": full_output_paths[i::n_workers],
+    }
+
+
+# In[ ]:
+
+
+import argparse
+from tqdm import tqdm
+
+
+def process_jacobian(input_path, output_path):
+
+    test_adata = sc.read_h5ad(input_path)
+
+    # make a metacell
+    X_meta_g = np.asarray(test_adata.X.sum(0))
+
+    # set total mrna umis to the mean of the dataset
+    target_total_mrna_umis = test_adata.obs['total_mrna_umis'].mean()
+    X_meta_g = X_meta_g * target_total_mrna_umis / X_meta_g.sum()
+
+    # make a metacell anndata
+    adata_meta = test_adata[0, :].copy()
+    adata_meta.X = X_meta_g
+    adata_meta.obs['total_mrna_umis'] = [target_total_mrna_umis]
+
+    # subset to prompt gene ids
+    print(f"Number of prompt genes: {len(prompt_gene_ids)}")
+    adata_meta = adata_meta[:, prompt_gene_ids].copy()
+
+    # query var names
+    query_var_names = query_gene_ids
+    if max_query_genes is not None:
+        query_var_names = query_var_names[:max_query_genes]
+    print(f"Number of query genes: {len(query_var_names)}")
+
+    with torch.inference_mode():
+        # get the mean marginal
+        print("Calculating marginal mean and std ...")
+        prompt_marginal_dict = snap_to_marginal_mean_manifold(
+            adata=adata_meta,
+            gene_ontology_infos=gene_ontology_infos,
+            metadata_ontology_infos=metadata_ontology_infos,
+            query_var_names=prompt_gene_ids,  #### NOTE
+            query_total_mrna_umis=None,
+            predict_tokenizer=predict_tokenizer,
+            cellarium_gpt_module=gpt_model,
+        )
+        query_marginal_dict = snap_to_marginal_mean_manifold(
+            adata=adata_meta,
+            gene_ontology_infos=gene_ontology_infos,
+            metadata_ontology_infos=metadata_ontology_infos,
+            query_var_names=query_var_names,  #### NOTE
+            query_total_mrna_umis=None,
+            predict_tokenizer=predict_tokenizer,
+            cellarium_gpt_module=gpt_model,
+        )
+        print("Done.")
+
+    # jacobian point
+    if jacobian_point == "actual":
+        adata_meta.layers['original'] = adata_meta.X.copy()
+        print("Using the actual metacell counts as the point to calculate the Jacobian on ...")
+
+    elif jacobian_point == "marginal_mean":
+        # inject into the adata
+        adata_meta.layers['original'] = adata_meta.X.copy()
+        adata_meta.X = prompt_marginal_dict['gene_marginal_means_q'].detach().cpu().numpy()[None, :]
+    else:
+        raise ValueError
+
+    def yield_chunks(lst, n):
+        for i in range(0, len(lst), n):
+            yield lst[i:i + n]
+
+    query_var_names_chunks = list(yield_chunks(query_var_names, query_chunk_size))
+    jacobian_chunks = []
+
+    print("Calculating the Jacobian ...")
+    for query_var_names_chunk in tqdm(query_var_names_chunks):
+
+        prompt_gene_values_g = torch.tensor(
+            adata_meta.X[0],
+            device=gpt_model.device,
+            dtype=torch.float32)
+
+        def _wrapped_snap_to_marginal_mean_manifold(
+                prompt_gene_values_g: torch.Tensor) -> torch.Tensor:
+            
+            return snap_to_marginal_mean_manifold(
+                adata=adata_meta,
+                gene_ontology_infos=gene_ontology_infos,
+                metadata_ontology_infos=metadata_ontology_infos,
+                query_var_names=query_var_names_chunk,
+                query_total_mrna_umis=None,
+                predict_tokenizer=predict_tokenizer,
+                cellarium_gpt_module=gpt_model,
+                prompt_gene_values_g=prompt_gene_values_g,
+            )['gene_marginal_means_q']
+
+        chunk_jacobian_qg = torch.autograd.functional.jacobian(
+            func=_wrapped_snap_to_marginal_mean_manifold, 
+            inputs=prompt_gene_values_g,
+            create_graph=False,
+            vectorize=False,
+        )
+
+        jacobian_chunks.append(chunk_jacobian_qg)
+
+    jacobian_qg = torch.cat(jacobian_chunks, dim=0)
+
+    result_dict = {
+        'dataset_name': input_path,
+        'adata_obs': adata_meta.obs,
+        'jacobian_point': jacobian_point,
+        'query_var_names': query_var_names,
+        'prompt_var_names': prompt_gene_ids,
+        'jacobian_qg': jacobian_qg,
+        'adata_gene_values_g': np.asarray(adata_meta.layers['original']).flatten(),
+        'prompt_gene_values_g': prompt_gene_values_g,
+        'prompt_marginal_dict': prompt_marginal_dict,
+        'query_marginal_dict': query_marginal_dict,
+    }
+
+    torch.save(result_dict, output_path)
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description="TBW")
+    parser.add_argument("worker_id", type=int, help="Worker ID")
+
+    args = parser.parse_args()
+
+    print(f"Worker ID: {args.worker_id}")
+    worker_id = int(args.worker_id)
+    
+    # get the input and output paths
+    io_dict = worker_io_dict[worker_id]
+
+    # go through the datasets one by one and work on the ones that the output does not exist
+    for input_path, output_path in tqdm(zip(io_dict["input_paths"], io_dict["output_paths"])):
+        if os.path.exists(output_path):
+            print(f"Output exists: {output_path}, skipping ...")
+            continue
+
+        print(f"Working on: {input_path}")
+        process_jacobian(input_path, output_path)
+
+if __name__ == "__main__":
+    main()
+    
+
+
+# In[ ]:
+
+
+
+
diff --git a/notebooks/cellariumgpt_inference/07_gene_networks_metacell.ipynb b/notebooks/cellariumgpt_inference/07_gene_networks_metacell.ipynb
new file mode 100644
index 00000000..f37c9db7
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/07_gene_networks_metacell.ipynb
@@ -0,0 +1,1671 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gene networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda:7')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, JacobianContext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINT_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_1/checkpoints/epoch=2-step=252858.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "\n",
+    "# dataset_name = \"extract_40__0\"  # neuron\n",
+    "dataset_name = \"extract_100__3\"  # CD8+ T cell\n",
+    "# dataset_name = \"extract_100__1\"  # monocyte\n",
+    "# dataset_name = \"extract_40__1\"  # oligo\n",
+    "# dataset_name = \"extract_50__6\"  # CM\n",
+    "# dataset_name = \"extract_50__6\"  # CM\n",
+    "\n",
+    "adata_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", f\"{dataset_name}.h5ad\")\n",
+    "\n",
+    "jacobian_pt_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", \"matmul_highest_10k\", f\"{dataset_name}__jacobian__marginal_mean.pt\")\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "OUTPUT_ROOT_PATH = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", \"matmul_highest_10k\", \"analysis\")\n",
+    "\n",
+    "os.makedirs(OUTPUT_ROOT_PATH, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx = JacobianContext.from_old_jacobian_pt_dump(jacobian_pt_path, adata_path, gene_info_tsv_path)\n",
+    "print(jac_ctx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.process(\n",
+    "    jacobian_normalization_strategy=\"mean\",\n",
+    "    feature_normalization_strategy=\"query_z_score\",\n",
+    "    query_response_amp_min_pct=10,\n",
+    "    min_prompt_gene_tpm=5,\n",
+    "    min_query_gene_tpm=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.compute_adjacency_matrix(\n",
+    "    adjacency_strategy=\"positive_correlation\",\n",
+    "    n_neighbors=10,\n",
+    "    beta=6.,\n",
+    "    self_loop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.compute_leiden_communites(\n",
+    "    resolution=5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(np.unique(jac_ctx.leiden_membership))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.compute_spectral_dimension(n_lambda_for_estimation=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "jac_ctx.plot_spectral_dimension(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.make_mde_embedding(device=DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snap_n_gene_symbols = [\n",
+    "    'GAP43',\n",
+    "    'NRXN3',\n",
+    "    'HOMER1',\n",
+    "    'IL1RAPL2',\n",
+    "    'EPHA3',\n",
+    "    'RIMS1',\n",
+    "    'SV2B',\n",
+    "    'TRIM9',\n",
+    "    'SVOP',\n",
+    "    'RPH3A',\n",
+    "    'SYT12',\n",
+    "    'SYT1',\n",
+    "    'R3HDM2',\n",
+    "    'PDE4B',\n",
+    "    'DCC',\n",
+    "    'SLC4A10',\n",
+    "    'DNM3',\n",
+    "    'GRM1',\n",
+    "    'EGR4',\n",
+    "    'JUNB',\n",
+    "    'TFDP2'\n",
+    "]\n",
+    "\n",
+    "snap_n_gene_symbols = [x for x in snap_n_gene_symbols if x in jac_ctx.query_gene_symbols]\n",
+    "snap_n_gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[x] for x in snap_n_gene_symbols]\n",
+    "\n",
+    "muscle_gene_symbols = [\n",
+    "    'TTN',\n",
+    "    'MYL3',\n",
+    "    'MYL4',\n",
+    "    'MYL7',\n",
+    "    'TNNC1',\n",
+    "    'TNNI1',\n",
+    "]\n",
+    "\n",
+    "muscle_gene_symbols = [x for x in muscle_gene_symbols if x in jac_ctx.query_gene_symbols]\n",
+    "muscle_gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[x] for x in muscle_gene_symbols]\n",
+    "\n",
+    "def get_gene_familities(jac_ctx: JacobianContext, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
+    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in jac_ctx.query_gene_symbols if gene_symbol.startswith(prefix)]\n",
+    "    gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
+    "    gene_symbols = [jac_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
+    "    return gene_ids, gene_symbols\n",
+    "\n",
+    "mito_gene_ids, mito_gene_symbols = get_gene_familities(jac_ctx, [\"MT-\"])\n",
+    "ribo_gene_ids, ribo_gene_symbols = get_gene_familities(jac_ctx, [\"RPS\", \"RPL\"])\n",
+    "hla_gene_ids, hla_gene_symbols = get_gene_familities(jac_ctx, [\"HLA\"])\n",
+    "ifi_gene_ids, ifi_gene_symbols = get_gene_familities(jac_ctx, [\"IFI\"])\n",
+    "\n",
+    "highlight_gene_sets = {\n",
+    "    \"Mito\": (mito_gene_ids, mito_gene_symbols, 'red'),\n",
+    "    \"Ribo\": (ribo_gene_ids, ribo_gene_symbols, 'blue'),\n",
+    "    # \"SNAP-n\": (snap_n_gene_ids, snap_n_gene_symbols, 'green'),\n",
+    "    # \"HLA\": (hla_gene_ids, hla_gene_symbols, 'green'),\n",
+    "    # \"IFI\": (ifi_gene_ids, ifi_gene_symbols, 'orange'),\n",
+    "    \"Muscle\": (muscle_gene_ids, muscle_gene_symbols, 'purple'),\n",
+    "}\n",
+    "\n",
+    "# disable\n",
+    "# highlight_gene_sets = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jac_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Batch processing (basic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "\n",
+    "# get the dataset names\n",
+    "jacobian_pt_root_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", \"matmul_highest_10k\")\n",
+    "adata_root_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\")\n",
+    "\n",
+    "# get the list of files inside jacobian_pt_root_path\n",
+    "jacobian_pt_files = os.listdir(jacobian_pt_root_path)\n",
+    "jacobian_pt_files = [f for f in jacobian_pt_files if f.endswith(\".pt\")]\n",
+    "jacobian_pt_paths = [os.path.join(jacobian_pt_root_path, f) for f in jacobian_pt_files]\n",
+    "\n",
+    "# extract the file names from the full path\n",
+    "# jacobian_pt_files = [os.path.splitext(f)[0] for f in jacobian_pt_files]\n",
+    "suffix = \"__jacobian__marginal_mean.pt\"\n",
+    "\n",
+    "# remove the suffix to get dataset names\n",
+    "dataset_names = [f.replace(suffix, \"\") for f in jacobian_pt_files]\n",
+    "\n",
+    "# generate adata paths\n",
+    "adata_paths = [\n",
+    "    os.path.join(adata_root_path, f\"{dataset_name}.h5ad\")\n",
+    "    for dataset_name in dataset_names]\n",
+    "               \n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "output_root_path = os.path.join(jacobian_pt_root_path, \"analysis\")\n",
+    "\n",
+    "os.makedirs(output_root_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_genes_to_highlight(jac_ctx: JacobianContext) -> dict[str, tuple[list[str], list[str], str]]:\n",
+    "    \n",
+    "    snap_n_gene_symbols = [\n",
+    "        'GAP43',\n",
+    "        'NRXN3',\n",
+    "        'HOMER1',\n",
+    "        'IL1RAPL2',\n",
+    "        'EPHA3',\n",
+    "        'RIMS1',\n",
+    "        'SV2B',\n",
+    "        'TRIM9',\n",
+    "        'SVOP',\n",
+    "        'RPH3A',\n",
+    "        'SYT12',\n",
+    "        'SYT1',\n",
+    "        'R3HDM2',\n",
+    "        'PDE4B',\n",
+    "        'DCC',\n",
+    "        'SLC4A10',\n",
+    "        'DNM3',\n",
+    "        'GRM1',\n",
+    "        'EGR4',\n",
+    "        'JUNB',\n",
+    "        'TFDP2'\n",
+    "    ]\n",
+    "\n",
+    "    muscle_gene_symbols = [\n",
+    "        'TTN',\n",
+    "        'MYL3',\n",
+    "        'MYL4',\n",
+    "        'MYL7',\n",
+    "        'TNNC1',\n",
+    "        'TNNI1',\n",
+    "    ]\n",
+    "\n",
+    "    snap_n_gene_symbols = [x for x in snap_n_gene_symbols if x in jac_ctx.query_gene_symbols]\n",
+    "    snap_n_gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[x] for x in snap_n_gene_symbols]\n",
+    "\n",
+    "    muscle_gene_symbols = [x for x in muscle_gene_symbols if x in jac_ctx.query_gene_symbols]\n",
+    "    muscle_gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[x] for x in muscle_gene_symbols]\n",
+    "\n",
+    "    def get_gene_familities(jac_ctx: JacobianContext, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
+    "        _gene_symbols = [\n",
+    "            gene_symbol\n",
+    "            for prefix in prefix_list\n",
+    "            for gene_symbol in jac_ctx.query_gene_symbols\n",
+    "            if gene_symbol.startswith(prefix)]\n",
+    "        gene_ids = [jac_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
+    "        gene_symbols = [jac_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
+    "        return gene_ids, gene_symbols\n",
+    "\n",
+    "    mito_gene_ids, mito_gene_symbols = get_gene_familities(jac_ctx, [\"MT-\"])\n",
+    "    ribo_gene_ids, ribo_gene_symbols = get_gene_familities(jac_ctx, [\"RPL\"])\n",
+    "    hla_gene_ids, hla_gene_symbols = get_gene_familities(jac_ctx, [\"HLA\"])\n",
+    "    ifi_gene_ids, ifi_gene_symbols = get_gene_familities(jac_ctx, [\"IFI\"])\n",
+    "\n",
+    "    highlight_gene_sets = {\n",
+    "        \"Mito\": (mito_gene_ids, mito_gene_symbols, 'red'),\n",
+    "        \"Ribo\": (ribo_gene_ids, ribo_gene_symbols, 'blue'),\n",
+    "    }\n",
+    "\n",
+    "    return highlight_gene_sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import pymde\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "n_datasets = len(dataset_names)\n",
+    "\n",
+    "for i_dataset in tqdm(range(n_datasets)):\n",
+    "\n",
+    "    jacobian_pt_path = jacobian_pt_paths[i_dataset]\n",
+    "    adata_path = adata_paths[i_dataset]\n",
+    "\n",
+    "    # load the jacobian\n",
+    "    jac_ctx = JacobianContext.from_old_jacobian_pt_dump(jacobian_pt_path, adata_path, gene_info_tsv_path)\n",
+    "    print(jac_ctx)\n",
+    "\n",
+    "    # process\n",
+    "    jac_ctx.process(\n",
+    "        jacobian_normalization_strategy=\"mean\",\n",
+    "        feature_normalization_strategy=\"query_z_score\",\n",
+    "        query_response_amp_min_pct=10,\n",
+    "        min_prompt_gene_tpm=10,\n",
+    "        min_query_gene_tpm=10)\n",
+    "\n",
+    "    # adjacency matrix\n",
+    "    jac_ctx.compute_adjacency_matrix(\n",
+    "        adjacency_strategy=\"positive_correlation\",\n",
+    "        n_neighbors=10,\n",
+    "        beta=6.,\n",
+    "        self_loop=False)\n",
+    "\n",
+    "    # detect communities\n",
+    "    jac_ctx.compute_leiden_communites(\n",
+    "        resolution=5.0,\n",
+    "        min_community_size=2)\n",
+    "\n",
+    "    # compute the spectral dimension\n",
+    "    jac_ctx.compute_spectral_dimension(n_lambda_for_estimation=10)\n",
+    "\n",
+    "    # make the spectral dimension plot and save\n",
+    "    fig, ax = plt.subplots()\n",
+    "    jac_ctx.plot_spectral_dimension(ax=ax)\n",
+    "    fig.savefig(\n",
+    "        os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__spectral_dimension.png\"),\n",
+    "        dpi=300,\n",
+    "        bbox_inches=\"tight\")\n",
+    "    plt.close(fig)\n",
+    "\n",
+    "    # generate embedding\n",
+    "    jac_ctx.make_mde_embedding(\n",
+    "            n_neighbors=7,\n",
+    "            repulsive_fraction=10,\n",
+    "            attractive_penalty=pymde.penalties.Log1p,\n",
+    "            repulsive_penalty=pymde.penalties.Log,\n",
+    "            device=DEVICE)\n",
+    "\n",
+    "    # make the embedding plot and save\n",
+    "    highlight_gene_sets = generate_genes_to_highlight(jac_ctx)\n",
+    "    fig = jac_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)\n",
+    "    fig.write_image(\n",
+    "        os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__embedding.png\"),\n",
+    "        scale=2)\n",
+    "\n",
+    "    # make a dataframe of leiden memberships\n",
+    "    community_df = pd.DataFrame({\n",
+    "        'gene_ids': jac_ctx.query_var_names,\n",
+    "        'gene_symbols': jac_ctx.query_gene_symbols,\n",
+    "        'leiden_membership': jac_ctx.leiden_membership})\n",
+    "    community_df.to_csv(\n",
+    "        os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__leiden_membership.csv\"),\n",
+    "        index=False)\n",
+    "    \n",
+    "    # pickle\n",
+    "    with open(os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__jac_ctx.pkl\"), \"wb\") as f:\n",
+    "        pickle.dump(jac_ctx, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Batch processing (GSEA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# n_leiden = len(np.unique(jac_ctx.leiden_membership))\n",
+    "# jac_ctx.leiden_to_query_indices = {\n",
+    "#     leiden_id: np.where(jac_ctx.leiden_membership == leiden_id)[0]\n",
+    "#     for leiden_id in range(n_leiden)\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn.metrics import silhouette_samples\n",
+    "\n",
+    "# jac_ctx.silhouette_samples_q = silhouette_samples(jac_ctx.z_qp, jac_ctx.leiden_membership)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# jac_ctx.leiden_sillohette_coefficients = []\n",
+    "# for leiden_id in range(n_leiden):\n",
+    "#     indices = jac_ctx.leiden_to_query_indices[leiden_id]\n",
+    "#     scores = jac_ctx.silhouette_samples_q[indices]\n",
+    "#     mean_scores = np.mean(scores)\n",
+    "#     jac_ctx.leiden_sillohette_coefficients.append(mean_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from tqdm.notebook import tqdm\n",
+    "# import gseapy as gp\n",
+    "\n",
+    "\n",
+    "# gene_set_path = os.path.join(ROOT_PATH, \"data\", \"gmt\", \"c5.go.v2024.1.Hs.symbols.gmt\")\n",
+    "\n",
+    "# for leiden_id in tqdm(range(n_leiden)):\n",
+    "\n",
+    "#     scores = []\n",
+    "#     s = set(jac_ctx.leiden_to_query_indices[leiden_id])\n",
+    "#     for q in range(len(jac_ctx.query_gene_symbols)):\n",
+    "#         if q in s:\n",
+    "#             scores.append(1)\n",
+    "#         else:\n",
+    "#             scores.append(0)\n",
+    "        \n",
+    "#     gene_list = pd.DataFrame({\n",
+    "#         \"gene_symbol\": jac_ctx.query_gene_symbols,\n",
+    "#         \"score\": scores}\n",
+    "#     )\n",
+    "\n",
+    "#     output_dir = os.path.join(OUTPUT_ROOT_PATH, dataset_name, f\"leiden_cluster_{leiden_id}\")\n",
+    "#     gsea_results = gp.prerank(\n",
+    "#         rnk=gene_list,\n",
+    "#         gene_sets=gene_set_path,\n",
+    "#         min_size=1,\n",
+    "#         max_size=5000,\n",
+    "#         permutation_num=1000,\n",
+    "#         graph_num=90,\n",
+    "#         outdir=output_dir\n",
+    "#     )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Studying gene programs and their relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "\n",
+    "# get the dataset names\n",
+    "jacobian_pt_root_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\", \"matmul_highest_10k\")\n",
+    "adata_root_path = os.path.join(\n",
+    "    ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"metacells\")\n",
+    "\n",
+    "# get the list of files inside jacobian_pt_root_path\n",
+    "jacobian_pt_files = os.listdir(jacobian_pt_root_path)\n",
+    "jacobian_pt_files = [f for f in jacobian_pt_files if f.endswith(\".pt\")]\n",
+    "jacobian_pt_paths = [os.path.join(jacobian_pt_root_path, f) for f in jacobian_pt_files]\n",
+    "\n",
+    "# extract the file names from the full path\n",
+    "# jacobian_pt_files = [os.path.splitext(f)[0] for f in jacobian_pt_files]\n",
+    "suffix = \"__jacobian__marginal_mean.pt\"\n",
+    "\n",
+    "# remove the suffix to get dataset names\n",
+    "dataset_names = [f.replace(suffix, \"\") for f in jacobian_pt_files]\n",
+    "\n",
+    "# generate adata paths\n",
+    "adata_paths = [\n",
+    "    os.path.join(adata_root_path, f\"{dataset_name}.h5ad\")\n",
+    "    for dataset_name in dataset_names]\n",
+    "\n",
+    "output_root_path = os.path.join(jacobian_pt_root_path, \"analysis\")\n",
+    "leiden_result_paths = [\n",
+    "    os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__leiden_membership.csv\")\n",
+    "    for i_dataset in range(len(dataset_names))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make a manifest of all available cell types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "from tqdm.notebook import tqdm\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "cell_manifest = defaultdict(list)\n",
+    "leiden_dict = dict()\n",
+    "\n",
+    "# vague cell types\n",
+    "skip_cell_type_set = {'unknown', 'leukocyte', 'myeloid cell', 'lymphocyte'}\n",
+    "\n",
+    "for i_dataset in tqdm(range(len(dataset_names))):\n",
+    "\n",
+    "    adata_path = adata_paths[i_dataset]\n",
+    "\n",
+    "    # load adata\n",
+    "    adata = sc.read_h5ad(adata_path)\n",
+    "\n",
+    "    # load jac ctx\n",
+    "    with open(os.path.join(output_root_path, f\"{dataset_names[i_dataset]}__jac_ctx.pkl\"), \"rb\") as f:\n",
+    "        jac_ctx = pickle.load(f)\n",
+    "    \n",
+    "    cell_type = adata.obs[\"cell_type\"].values[0]\n",
+    "\n",
+    "    if cell_type in skip_cell_type_set:\n",
+    "        continue\n",
+    "\n",
+    "    cell_manifest[\"dataset_name\"].append(dataset_names[i_dataset])\n",
+    "    cell_manifest[\"cell_type\"].append(cell_type)\n",
+    "    cell_manifest[\"tissue\"].append(adata.obs[\"tissue\"].values[0])\n",
+    "    cell_manifest[\"disease\"].append(adata.obs[\"disease\"].values[0])\n",
+    "    cell_manifest[\"development_stage\"].append(adata.obs[\"development_stage\"].values[0])\n",
+    "    cell_manifest[\"assay\"].append(adata.obs[\"assay\"].values[0])\n",
+    "    cell_manifest[\"suspension_type\"].append(adata.obs[\"suspension_type\"].values[0])\n",
+    "\n",
+    "    # useful statistics\n",
+    "    cell_manifest[\"n_umi\"].append(adata.obs[\"total_mrna_umis\"].values.mean())\n",
+    "    cell_manifest[\"n_expr_genes\"].append((adata.X.sum(0) > 0).sum())\n",
+    "    cell_manifest[\"n_graph_query_genes\"].append(len(jac_ctx.query_var_names))\n",
+    "    cell_manifest[\"n_graph_prompt_genes\"].append(len(jac_ctx.prompt_var_names))\n",
+    "    cell_manifest[\"n_leiden_communities\"].append(np.unique(jac_ctx.leiden_membership).size)\n",
+    "    cell_manifest[\"spectral_dim\"].append(jac_ctx.spectral_dim)\n",
+    "\n",
+    "    # process leiden memberships\n",
+    "    leiden_ids = np.unique(jac_ctx.leiden_membership)\n",
+    "    leiden_ids = [x for x in leiden_ids if x != -1]  # remove -1 labels (unassigned)\n",
+    "    _leiden_dict = dict()\n",
+    "    for leiden_id in leiden_ids:\n",
+    "        indices = np.where(jac_ctx.leiden_membership == leiden_id)[0].tolist()\n",
+    "        _leiden_dict[leiden_id] = [jac_ctx.query_gene_symbols[i] for i in indices]\n",
+    "    leiden_dict[dataset_names[i_dataset]] = _leiden_dict\n",
+    "\n",
+    "cell_manifest_df = pd.DataFrame(cell_manifest).sort_values(\"dataset_name\").set_index(\"dataset_name\", drop=True)\n",
+    "cell_manifest_df['main_dataset_name'] = cell_manifest_df.index.str.split(\"__\").str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_manifest_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show all cells\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(cell_manifest_df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "cell_manifest_df.to_csv(os.path.join(output_root_path, \"cell_manifest.csv\"))\n",
+    "\n",
+    "with open(os.path.join(output_root_path, \"leiden_dict.pkl\"), \"wb\") as f:\n",
+    "    pickle.dump(leiden_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Jaccard analysis and hierarchical clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# included_datasets = cell_manifest_df[cell_manifest_df[\"main_dataset_name\"] == \"extract_100\"].index.values\n",
+    "included_datasets = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a flat leiden dict\n",
+    "dataset_leiden_id_to_flat_map = dict()\n",
+    "all_communities_flat_gene_lists = []\n",
+    "all_communities_flat_gene_sets = []\n",
+    "community_labels_tuple = []\n",
+    "community_labels = []\n",
+    "\n",
+    "min_community_size = 5\n",
+    "\n",
+    "counter = 0\n",
+    "for dataset_name, dataset_leiden_dict in leiden_dict.items():\n",
+    "    if included_datasets is not None:\n",
+    "        if dataset_name not in included_datasets:\n",
+    "            continue\n",
+    "    dataset_leiden_id_to_flat_map[dataset_name] = dict()\n",
+    "    cell_type = cell_manifest_df.loc[dataset_name].cell_type\n",
+    "    for leiden_id, gene_list in dataset_leiden_dict.items():\n",
+    "        if len(gene_list) < min_community_size:\n",
+    "            continue\n",
+    "        all_communities_flat_gene_lists.append(gene_list)\n",
+    "        all_communities_flat_gene_sets.append(set(gene_list))\n",
+    "        dataset_leiden_id_to_flat_map[dataset_name][leiden_id] = counter\n",
+    "        community_labels_tuple.append((cell_type, leiden_id))\n",
+    "        community_labels.append(f\"{cell_type}, {leiden_id}\")\n",
+    "        counter += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the Jaccard index between all pairs of communities\n",
+    "n_communities = len(all_communities_flat_gene_lists)\n",
+    "jaccard_matrix = np.zeros((n_communities, n_communities))\n",
+    "\n",
+    "for i in tqdm(range(n_communities)):\n",
+    "    for j in range(n_communities):\n",
+    "        num = len(all_communities_flat_gene_sets[i].intersection(all_communities_flat_gene_sets[j]))\n",
+    "        den = len(all_communities_flat_gene_sets[i].union(all_communities_flat_gene_sets[j]))\n",
+    "        jaccard_matrix[i, j] = num / den"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import numpy as np\n",
+    "# import seaborn as sns\n",
+    "# import matplotlib.pyplot as plt\n",
+    "# import pandas as pd\n",
+    "# from scipy.spatial.distance import squareform\n",
+    "\n",
+    "# import scipy.cluster.hierarchy as hc\n",
+    "\n",
+    "# linkage = hc.linkage(squareform(1. - jaccard_matrix), method='ward')\n",
+    "\n",
+    "# # Convert the matrix to a DataFrame for easier labeling (optional)\n",
+    "# labels = [f\"Set {i+1}\" for i in range(jaccard_matrix.shape[0])]\n",
+    "# df = pd.DataFrame(jaccard_matrix, index=labels, columns=labels)\n",
+    "\n",
+    "# # Use Seaborn's clustermap with precomputed distance\n",
+    "# sns.set(style=\"white\")\n",
+    "\n",
+    "# cluster_map = sns.clustermap(\n",
+    "#     df,\n",
+    "#     row_linkage=linkage,\n",
+    "#     col_linkage=linkage,\n",
+    "#     linewidths=0,\n",
+    "#     cmap='Reds',\n",
+    "#     figsize=(10, 10),        # Size of the figure\n",
+    "#     dendrogram_ratio=(0.2, 0.2),  # Adjust dendrogram size\n",
+    "#     cbar_pos=(0.02, 0.8, 0.03, 0.18)  # Adjust color bar position\n",
+    "# )\n",
+    "\n",
+    "# plt.title('Clustered Heatmap with Precomputed Similarity', fontsize=14)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "import plotly.figure_factory as ff\n",
+    "import numpy as np\n",
+    "from scipy.spatial.distance import squareform\n",
+    "from scipy.cluster import hierarchy as hc\n",
+    "\n",
+    "# Compute linkage for clustering\n",
+    "linkage = hc.linkage(squareform(1.0 - jaccard_matrix), method='ward')\n",
+    "\n",
+    "# Create dendrograms\n",
+    "dendro_row = hc.dendrogram(linkage, no_plot=True)\n",
+    "dendro_col = hc.dendrogram(linkage, no_plot=True)\n",
+    "\n",
+    "# Reorder the matrix based on dendrogram leaves\n",
+    "heatmap_order = dendro_row['leaves']\n",
+    "clustered_matrix = jaccard_matrix[np.ix_(heatmap_order, heatmap_order)]\n",
+    "\n",
+    "# Labels for the reordered matrix\n",
+    "reordered_labels = [community_labels[i] for i in heatmap_order]\n",
+    "\n",
+    "# Create main dendrogram\n",
+    "fig = ff.create_dendrogram(\n",
+    "    clustered_matrix,\n",
+    "    orientation='bottom',\n",
+    "    labels=reordered_labels\n",
+    ")\n",
+    "fig.for_each_trace(lambda trace: trace.update(visible=False, line=dict(width=1)))\n",
+    "\n",
+    "for i in range(len(fig['data'])):\n",
+    "    fig['data'][i]['yaxis'] = 'y2'\n",
+    "\n",
+    "# Create side dendrogram\n",
+    "dendro_side = ff.create_dendrogram(\n",
+    "    clustered_matrix,\n",
+    "    orientation='right',\n",
+    "    labels=reordered_labels\n",
+    ")\n",
+    "dendro_side.for_each_trace(lambda trace: trace.update(line=dict(width=1)))\n",
+    "\n",
+    "for i in range(len(dendro_side['data'])):\n",
+    "    dendro_side['data'][i]['xaxis'] = 'x2'\n",
+    "\n",
+    "# Add side dendrogram data to the figure\n",
+    "for data in dendro_side['data']:\n",
+    "    fig.add_trace(data)\n",
+    "\n",
+    "# Create heatmap\n",
+    "clustered_matrix_no_diag = clustered_matrix.copy()\n",
+    "\n",
+    "heatmap = go.Heatmap(\n",
+    "    x=reordered_labels,\n",
+    "    y=reordered_labels,\n",
+    "    z=clustered_matrix,\n",
+    "    colorscale='Blues',\n",
+    "    zmin=0,\n",
+    "    zmax=0.2,\n",
+    ")\n",
+    "\n",
+    "heatmap['x'] = fig['layout']['xaxis']['tickvals']\n",
+    "heatmap['y'] = dendro_side['layout']['yaxis']['tickvals']\n",
+    "\n",
+    "# Add heatmap data to the figure\n",
+    "fig.add_trace(heatmap)\n",
+    "\n",
+    "# Update layout\n",
+    "fig.update_layout(\n",
+    "    width=1000,\n",
+    "    height=1200,\n",
+    "    showlegend=False,\n",
+    "    hovermode='closest',\n",
+    "    paper_bgcolor=\"rgba(0,0,0,0)\",\n",
+    "    plot_bgcolor=\"rgba(0,0,0,0)\",\n",
+    "    xaxis_tickfont = dict(color = 'rgba(0,0,0,0)'),\n",
+    ")\n",
+    "\n",
+    "# Configure x-axis\n",
+    "fig.update_layout(\n",
+    "    xaxis={\n",
+    "        'domain': [.15, 1],\n",
+    "        'mirror': False,\n",
+    "        'showgrid': False,\n",
+    "        'showline': False,\n",
+    "        'zeroline': False,\n",
+    "        'ticks': \"\"\n",
+    "    },\n",
+    "    xaxis2={\n",
+    "        'domain': [0, .15],\n",
+    "        'mirror': False,\n",
+    "        'showgrid': False,\n",
+    "        'showline': False,\n",
+    "        'zeroline': False,\n",
+    "        'showticklabels': False,\n",
+    "        'ticks': \"\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Configure y-axis\n",
+    "fig.update_layout(\n",
+    "    yaxis={\n",
+    "        'domain': [0, 1],\n",
+    "        'mirror': False,\n",
+    "        'showgrid': False,\n",
+    "        'showline': False,\n",
+    "        'zeroline': False,\n",
+    "        'showticklabels': False,\n",
+    "        'ticks': \"\"\n",
+    "    },\n",
+    "    yaxis2={\n",
+    "        'domain': [.825, .975],\n",
+    "        'mirror': False,\n",
+    "        'showgrid': False,\n",
+    "        'showline': False,\n",
+    "        'zeroline': False,\n",
+    "        'showticklabels': False,\n",
+    "        'ticks': \"\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_manifest_df.loc[included_datasets][\"cell_type\"].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coarse_map = {\n",
+    "    'CD14-positive monocyte': 'Mono',\n",
+    "    'naive thymus-derived CD4-positive, alpha-beta T cell': 'T',\n",
+    "    'CD4-positive, alpha-beta cytotoxic T cell': 'T',\n",
+    "    'mucosal invariant T cell': 'T',\n",
+    "    'CD4-positive, alpha-beta T cell': 'T',\n",
+    "    'CD1c-positive myeloid dendritic cell': 'DC',\n",
+    "    'monocyte': 'Mono',\n",
+    "    'platelet': 'Platelet',\n",
+    "    'naive B cell': 'B',\n",
+    "    'gamma-delta T cell': 'T',\n",
+    "    'central memory CD4-positive, alpha-beta T cell': 'T',\n",
+    "    'CD8-positive, alpha-beta cytotoxic T cell': 'T',\n",
+    "    'CD16-positive, CD56-dim natural killer cell, human': 'NK',\n",
+    "    'CD14-low, CD16-positive monocyte': 'Mono',\n",
+    "    'CD8-positive, alpha-beta memory T cell': 'T',\n",
+    "    'effector memory CD4-positive, alpha-beta T cell': 'T',\n",
+    "    'naive thymus-derived CD8-positive, alpha-beta T cell': 'T',\n",
+    "    'memory B cell': 'B',\n",
+    "}\n",
+    "\n",
+    "coarse_reordered_labels = [\n",
+    "    coarse_map[', '.join(x.split(', ')[:-1])]\n",
+    "    for x in reordered_labels]\n",
+    "\n",
+    "import colorcet as cc\n",
+    "all_coarse_labels = list(set(coarse_reordered_labels))\n",
+    "coarse_label_to_color_map = {label: cc.glasbey_light[i] for i, label in enumerate(all_coarse_labels)}\n",
+    "reordered_label_colors = [coarse_label_to_color_map[label] for label in coarse_reordered_labels]\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "import numpy as np\n",
+    "\n",
+    "# Convert hex colors to an RGB array\n",
+    "colors_rgb = [mcolors.hex2color(color) for color in reordered_label_colors]\n",
+    "\n",
+    "# Reshape the RGB array into a 1-row image\n",
+    "color_bar = np.array(colors_rgb).reshape(1, -1, 3)\n",
+    "\n",
+    "# Display the color bar using imshow\n",
+    "plt.figure(figsize=(10, 1))  # Adjust the figure size as needed\n",
+    "plt.imshow(color_bar, aspect='auto')\n",
+    "plt.axis('off')  # Turn off axes\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "# Create legend handles\n",
+    "legend_handles = [\n",
+    "    mpatches.Patch(color=color, label=label)\n",
+    "    for label, color in coarse_label_to_color_map.items()\n",
+    "]\n",
+    "\n",
+    "# Create the plot for the legend\n",
+    "plt.figure(figsize=(6, 2))  # Adjust the size if needed\n",
+    "plt.legend(handles=legend_handles, loc='center', frameon=False)\n",
+    "plt.axis('off')  # Hide axes\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Studying spectral dimension across datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_manifest_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_col = \"n_umi\"\n",
+    "y_col = \"spectral_dim\"\n",
+    "\n",
+    "x_label = \"Metacell number of UMIs\"\n",
+    "y_label = \"Metacell spectral dimension\"\n",
+    "\n",
+    "x_values = cell_manifest_df[x_col]\n",
+    "y_values = cell_manifest_df[y_col]\n",
+    "\n",
+    "# linear regression with R^2\n",
+    "slope, intercept = np.polyfit(x_values, y_values, 1)\n",
+    "r_squared = np.corrcoef(x_values, y_values)[0, 1] ** 2\n",
+    "ax.plot(x_values, slope * x_values + intercept, color='red', label=f\"R^2 = {r_squared:.2f}\")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    x_values,\n",
+    "    y_values,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(x_label)\n",
+    "ax.set_ylabel(y_label)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_col = \"n_leiden_communities\"\n",
+    "y_col = \"spectral_dim\"\n",
+    "\n",
+    "x_label = \"Metacell # of leiden communities\"\n",
+    "y_label = \"Metacell spectral dimension\"\n",
+    "\n",
+    "x_values = cell_manifest_df[x_col]\n",
+    "y_values = cell_manifest_df[y_col]\n",
+    "\n",
+    "# linear regression with R^2\n",
+    "slope, intercept = np.polyfit(x_values, y_values, 1)\n",
+    "r_squared = np.corrcoef(x_values, y_values)[0, 1] ** 2\n",
+    "ax.plot(x_values, slope * x_values + intercept, color='red', label=f\"R^2 = {r_squared:.2f}\")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    x_values,\n",
+    "    y_values,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(x_label)\n",
+    "ax.set_ylabel(y_label)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_manifest_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_col = \"n_graph_query_genes\"\n",
+    "y_col = \"spectral_dim\"\n",
+    "\n",
+    "x_label = \"Metacell # of genes in the graph\"\n",
+    "y_label = \"Metacell spectral dimension\"\n",
+    "\n",
+    "x_values = cell_manifest_df[x_col]\n",
+    "y_values = cell_manifest_df[y_col]\n",
+    "\n",
+    "# linear regression with R^2\n",
+    "slope, intercept = np.polyfit(x_values, y_values, 1)\n",
+    "r_squared = np.corrcoef(x_values, y_values)[0, 1] ** 2\n",
+    "ax.plot(x_values, slope * x_values + intercept, color='red', label=f\"R^2 = {r_squared:.2f}\")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    x_values,\n",
+    "    y_values,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(x_label)\n",
+    "ax.set_ylabel(y_label)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_col = \"n_graph_prompt_genes\"\n",
+    "y_col = \"spectral_dim\"\n",
+    "\n",
+    "x_label = \"Metacell # of prompt genes\"\n",
+    "y_label = \"Metacell spectral dimension\"\n",
+    "\n",
+    "x_values = cell_manifest_df[x_col]\n",
+    "y_values = cell_manifest_df[y_col]\n",
+    "\n",
+    "# linear regression with R^2\n",
+    "slope, intercept = np.polyfit(x_values, y_values, 1)\n",
+    "r_squared = np.corrcoef(x_values, y_values)[0, 1] ** 2\n",
+    "ax.plot(x_values, slope * x_values + intercept, color='red', label=f\"R^2 = {r_squared:.2f}\")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    x_values,\n",
+    "    y_values,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(x_label)\n",
+    "ax.set_ylabel(y_label)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x_col = \"n_expr_genes\"\n",
+    "y_col = \"spectral_dim\"\n",
+    "\n",
+    "x_label = \"Metacell # of expressed genes\"\n",
+    "y_label = \"Metacell spectral dimension\"\n",
+    "\n",
+    "x_values = cell_manifest_df[x_col]\n",
+    "y_values = cell_manifest_df[y_col]\n",
+    "\n",
+    "# linear regression with R^2\n",
+    "slope, intercept = np.polyfit(x_values, y_values, 1)\n",
+    "r_squared = np.corrcoef(x_values, y_values)[0, 1] ** 2\n",
+    "ax.plot(x_values, slope * x_values + intercept, color='red', label=f\"R^2 = {r_squared:.2f}\")\n",
+    "\n",
+    "plt.scatter(\n",
+    "    x_values,\n",
+    "    y_values,\n",
+    ")\n",
+    "\n",
+    "ax.set_xlabel(x_label)\n",
+    "ax.set_ylabel(y_label)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate mean spectral_dim for each cell_type and sort in ascending order\n",
+    "mean_spectral_dim = cell_manifest_df.groupby('cell_type')['spectral_dim'].mean().sort_values()\n",
+    "\n",
+    "# Set cell_type as ordered categorical based on sorted spectral_dim\n",
+    "cell_manifest_df['cell_type'] = pd.Categorical(\n",
+    "    cell_manifest_df['cell_type'],\n",
+    "    categories=mean_spectral_dim.index,\n",
+    "    ordered=True\n",
+    ")\n",
+    "\n",
+    "# Create scatter plot\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "scatter = sns.scatterplot(\n",
+    "    x='cell_type',\n",
+    "    y='spectral_dim',\n",
+    "    hue='main_dataset_name',\n",
+    "    data=cell_manifest_df,\n",
+    "    s=100\n",
+    ")\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Spectral dimension by cell type')\n",
+    "plt.xlabel('Cell type')\n",
+    "plt.ylabel('Spectral dimension')\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "# Get legend handles and labels\n",
+    "handles, labels = scatter.get_legend_handles_labels()\n",
+    "\n",
+    "# Create a mapping from main_dataset_name to main_dataset_name (tissue)\n",
+    "legend_labels = []\n",
+    "for label in labels[1:]:  # Skip the first label ('main_dataset_name')\n",
+    "    tissue = cell_manifest_df.loc[cell_manifest_df['main_dataset_name'] == label, 'tissue'].iloc[0]\n",
+    "    legend_labels.append(f\"{label} ({tissue})\")\n",
+    "\n",
+    "# Update the legend labels\n",
+    "plt.legend(handles=handles[1:], labels=legend_labels, title='Metacell source',loc='upper left')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# Create scatter plot\n",
+    "plt.figure(figsize=(4, 4))\n",
+    "scatter = sns.boxplot(\n",
+    "    x='assay',\n",
+    "    y='spectral_dim',\n",
+    "    data=cell_manifest_df,\n",
+    ")\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.xlabel('Assay')\n",
+    "plt.ylabel('Spectral dimension')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# Create scatter plot\n",
+    "plt.figure(figsize=(4, 4))\n",
+    "scatter = sns.boxplot(\n",
+    "    x='suspension_type',\n",
+    "    y='spectral_dim',\n",
+    "    data=cell_manifest_df,\n",
+    ")\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.xlabel('Suspenstion type')\n",
+    "plt.ylabel('Spectral dimension')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### LDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "# load cell manifest\n",
+    "cell_manifest_df = pd.read_csv(os.path.join(output_root_path, \"cell_manifest.csv\"), index_col=0)\n",
+    "\n",
+    "# load communitity\n",
+    "with open(os.path.join(output_root_path, \"leiden_dict.pkl\"), \"rb\") as f:\n",
+    "    leiden_dict = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# included_datasets = cell_manifest_df[cell_manifest_df[\"main_dataset_name\"] == \"extract_100\"].index.values\n",
+    "included_datasets = None\n",
+    "\n",
+    "# make a flat leiden dict\n",
+    "dataset_leiden_id_to_flat_map = dict()\n",
+    "all_communities_flat_gene_lists = []\n",
+    "all_communities_flat_gene_sets = []\n",
+    "community_labels_tuple = []\n",
+    "community_labels = []\n",
+    "\n",
+    "min_community_size = 5\n",
+    "\n",
+    "counter = 0\n",
+    "for dataset_name, dataset_leiden_dict in leiden_dict.items():\n",
+    "    if included_datasets is not None:\n",
+    "        if dataset_name not in included_datasets:\n",
+    "            continue\n",
+    "    dataset_leiden_id_to_flat_map[dataset_name] = dict()\n",
+    "    cell_type = cell_manifest_df.loc[dataset_name].cell_type\n",
+    "    for leiden_id, gene_list in dataset_leiden_dict.items():\n",
+    "        if len(gene_list) < min_community_size:\n",
+    "            continue\n",
+    "        all_communities_flat_gene_lists.append(gene_list)\n",
+    "        all_communities_flat_gene_sets.append(set(gene_list))\n",
+    "        dataset_leiden_id_to_flat_map[dataset_name][leiden_id] = counter\n",
+    "        community_labels_tuple.append((cell_type, leiden_id))\n",
+    "        community_labels.append(f\"{cell_type}, {leiden_id}\")\n",
+    "        counter += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gensim.corpora import Dictionary\n",
+    "from gensim.models import LdaMulticore, LdaModel\n",
+    "\n",
+    "# Create a Gensim dictionary from the flattened gene lists\n",
+    "dictionary = Dictionary(all_communities_flat_gene_lists)\n",
+    "\n",
+    "# # Filter extremes (optional, can be adjusted as needed)\n",
+    "# dictionary.filter_extremes(no_below=1, no_above=0.5)\n",
+    "\n",
+    "# Create a bag-of-words (BoW) corpus\n",
+    "corpus = [dictionary.doc2bow(community) for community in all_communities_flat_gene_lists]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import multiprocessing\n",
+    "\n",
+    "# Total physical and logical cores\n",
+    "logical_cores = os.cpu_count()\n",
+    "\n",
+    "# Physical cores (if you want more precise control)\n",
+    "physical_cores = multiprocessing.cpu_count()\n",
+    "\n",
+    "print(f\"Logical cores available: {logical_cores}\")\n",
+    "print(f\"Physical cores available: {physical_cores}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Enable logging to show progress\n",
+    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)\n",
+    "\n",
+    "# Train the LDA model using multiple cores with progress logging\n",
+    "num_topics = 200  # Number of topics\n",
+    "lda_model = LdaModel(\n",
+    "    corpus=corpus,\n",
+    "    id2word=dictionary,\n",
+    "    num_topics=num_topics,\n",
+    "    # alpha='auto',\n",
+    "    passes=20,  # Number of passes through the corpus\n",
+    "    # workers=128,  # Adjust based on the number of CPU cores\n",
+    "    random_state=42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example: Print the top words for each topic\n",
+    "for topic_id, words_and_probs in lda_model.show_topics(num_topics=num_topics, num_words=10, formatted=True):\n",
+    "    print(f\"Topic {topic_id}: {words_and_probs}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "# Example: Extract the top words for each topic\n",
+    "topics = []\n",
+    "min_prob \n",
+    "for topic_id, words_and_probs in lda_model.show_topics(num_topics=num_topics, num_words=50, formatted=False):\n",
+    "    topic_words = [word for word, _ in words_and_probs]\n",
+    "    topics.append([topic_id] + topic_words)\n",
+    "\n",
+    "# Define the CSV file path\n",
+    "output_csv = \"./output/lda_topics.csv\"\n",
+    "\n",
+    "# Write to CSV\n",
+    "with open(output_csv, mode='w', newline='', encoding='utf-8') as file:\n",
+    "    writer = csv.writer(file)\n",
+    "    # Write header: Topic ID and word columns\n",
+    "    writer.writerow([\"Topic ID\"] + [f\"Word {i+1}\" for i in range(10)])\n",
+    "    # Write each topic and its words\n",
+    "    writer.writerows(topics)\n",
+    "\n",
+    "print(f\"Topics saved to {output_csv}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "doc_topic_distributions = []\n",
+    "for bow in tqdm(corpus):\n",
+    "    doc_topic_distributions.append(lda_model.get_document_topics(bow))\n",
+    "\n",
+    "# Convert sparse representation to dense, if needed\n",
+    "num_topics = lda_model.num_topics\n",
+    "doc_topic_dense = [\n",
+    "    [dict(topics).get(topic_id, 0) for topic_id in range(num_topics)]\n",
+    "    for topics in doc_topic_distributions\n",
+    "]\n",
+    "\n",
+    "doc_topic_dense_np = np.asarray(doc_topic_dense)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make an average embedding for each metacell\n",
+    "dataset_topic_dense = []\n",
+    "dataset_names = []\n",
+    "cell_type_names = []\n",
+    "\n",
+    "for dataset_name, leiden_id_to_flat_map in dataset_leiden_id_to_flat_map.items():\n",
+    "    dataset_names.append(dataset_name)\n",
+    "    cell_type_names.append(cell_manifest_df.loc[dataset_name].cell_type)\n",
+    "    mean_topic_usage = np.mean([\n",
+    "        doc_topic_dense_np[flat_id] for flat_id in leiden_id_to_flat_map.values()], axis=0)\n",
+    "    mean_topic_usage = mean_topic_usage / np.sum(mean_topic_usage)\n",
+    "    dataset_topic_dense.append(mean_topic_usage)\n",
+    "\n",
+    "dataset_topic_dense_np = np.asarray(dataset_topic_dense)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a UMAP embedding\n",
+    "import umap\n",
+    "\n",
+    "umap_model = umap.UMAP(n_components=2, random_state=42)\n",
+    "doc_topic_umap_embedding = umap_model.fit_transform(doc_topic_dense_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a UMAP embedding\n",
+    "import umap\n",
+    "\n",
+    "umap_model = umap.UMAP(n_components=2, random_state=42)\n",
+    "dataset_umap_embedding = umap_model.fit_transform(dataset_topic_dense_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import colorcet as cc\n",
+    "import numpy as np\n",
+    "\n",
+    "# Inputs\n",
+    "# Assume community_labels_tuple is a list of (str, int)\n",
+    "# Assume doc_topic_umap_embedding is an N x 2 numpy array\n",
+    "\n",
+    "# Extract the labels (str) from community_labels_tuple\n",
+    "labels = [label[0] for label in community_labels_tuple]\n",
+    "\n",
+    "# Get unique labels\n",
+    "unique_labels = list(set(labels))\n",
+    "\n",
+    "# Assign unique colors using colorcet Glasbey\n",
+    "glasbey_colors = cc.glasbey[:len(unique_labels)]  # Glasbey has many colors, use as needed\n",
+    "label_to_color = {label: glasbey_colors[i] for i, label in enumerate(unique_labels)}\n",
+    "\n",
+    "# Create a DataFrame for easier manipulation\n",
+    "df = pd.DataFrame({\n",
+    "    'x': doc_topic_umap_embedding[:, 0],  # First dimension of UMAP embedding\n",
+    "    'y': doc_topic_umap_embedding[:, 1],  # Second dimension of UMAP embedding\n",
+    "    'label': labels  # Labels from community_labels_tuple\n",
+    "})\n",
+    "\n",
+    "# Map colors to the labels\n",
+    "df['color'] = df['label'].map(label_to_color)\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "fig = px.scatter(\n",
+    "    df,\n",
+    "    x='x',\n",
+    "    y='y',\n",
+    "    color='label',  # Unique color for each label\n",
+    "    title='Scatter Plot with UMAP Embedding',\n",
+    "    labels={'label': 'Community Label'},  # Legend label\n",
+    "    hover_name='label',  # Display label on hover\n",
+    "    color_discrete_map=label_to_color  # Use custom Glasbey colors\n",
+    ")\n",
+    "\n",
+    "# Make markers smaller\n",
+    "fig.update_traces(marker=dict(size=2))  # Adjust the size as needed\n",
+    "\n",
+    "# Update layout\n",
+    "fig.update_layout(\n",
+    "    width=1500,\n",
+    "    height=800,\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import pandas as pd\n",
+    "import colorcet as cc\n",
+    "import numpy as np\n",
+    "\n",
+    "# Extract the labels (str) from community_labels_tuple\n",
+    "labels = cell_type_names\n",
+    "emb = dataset_umap_embedding\n",
+    "\n",
+    "# Get unique labels\n",
+    "unique_labels = list(set(labels))\n",
+    "\n",
+    "# Assign unique colors using colorcet Glasbey\n",
+    "glasbey_colors = cc.glasbey[:len(unique_labels)]  # Glasbey has many colors, use as needed\n",
+    "label_to_color = {label: glasbey_colors[i] for i, label in enumerate(unique_labels)}\n",
+    "\n",
+    "# Create a DataFrame for easier manipulation\n",
+    "df = pd.DataFrame({\n",
+    "    'x': emb[:, 0],  # First dimension of UMAP embedding\n",
+    "    'y': emb[:, 1],  # Second dimension of UMAP embedding\n",
+    "    'label': labels  # Labels from community_labels_tuple\n",
+    "})\n",
+    "\n",
+    "# Map colors to the labels\n",
+    "df['color'] = df['label'].map(label_to_color)\n",
+    "\n",
+    "# Create a scatter plot with text annotations for labels\n",
+    "fig = px.scatter(\n",
+    "    df,\n",
+    "    x='x',\n",
+    "    y='y',\n",
+    "    color='label',  # Unique color for each label\n",
+    "    title='Scatter Plot with UMAP Embedding',\n",
+    "    labels={'label': 'Community Label'},  # Legend label\n",
+    "    hover_name='label',  # Display label on hover\n",
+    "    color_discrete_map=label_to_color,  # Use custom Glasbey colors\n",
+    "    text='label'  # Add text annotations\n",
+    ")\n",
+    "\n",
+    "fig.update_traces(\n",
+    "    marker=dict(size=10, opacity=0.7),\n",
+    "    textposition=\"top center\",\n",
+    "    textfont=dict(size=8)  # Set font size for labels\n",
+    ")\n",
+    "\n",
+    "# Update layout\n",
+    "fig.update_layout(\n",
+    "    width=1500,\n",
+    "    height=800,\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/08_in_silico_deletion_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/08_in_silico_deletion_by_cell_type_prompting.ipynb
new file mode 100644
index 00000000..5b586820
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/08_in_silico_deletion_by_cell_type_prompting.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### _in silico_ perturbation by cell type prompting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda:1')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "CHECKPOINT_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Internal consistency check\n",
+    "\n",
+    "Has the model learned to generate cell-type-specific gene expression patterns?\n",
+    "\n",
+    "Experiment: Prompt with a given cell type, query the gene expression, give the gene expression back as prompt, ask the model to predict cell type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_gene_ids_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_ids.csv\")\n",
+    "query_gene_ids = pd.read_csv(query_gene_ids_path, header=None).values.flatten().tolist()[:5000]  # NOTE\n",
+    "\n",
+    "assay = \"10x 3' v3\"\n",
+    "suspension_type = \"cell\"\n",
+    "prompt_metadata_dict = {\n",
+    "    \"cell_type\": \"neuron\",\n",
+    "    \"tissue\": \"blood\"\n",
+    "}\n",
+    "total_mrna_umis = 5_000\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "\n",
+    "    tokens_dict, context_indices, _, _ = ctx.generate_gene_tokens_by_metadata(\n",
+    "        assay=assay,\n",
+    "        suspension_type=suspension_type,\n",
+    "        prompt_metadata_dict=prompt_metadata_dict,\n",
+    "        total_mrna_umis=total_mrna_umis,\n",
+    "        query_gene_ids=query_gene_ids,\n",
+    "        perturb_gene_ids=None\n",
+    "    )\n",
+    "\n",
+    "    gene_marginal_means_nq, gene_marginal_std_nq = ctx.get_marginal_mean_std_from_tokens(tokens_dict, context_indices)\n",
+    "\n",
+    "    # make a prop AnnData, inject back the counts we got from CellariumGPT, predict cell type\n",
+    "    prop_adata_feedback = ctx._adata.copy()\n",
+    "    prop_adata_feedback = prop_adata_feedback[0, query_gene_ids].copy()\n",
+    "\n",
+    "    prop_adata_feedback.X[0, :] = gene_marginal_means_nq[0, :].cpu().numpy()\n",
+    "    prop_adata_feedback.obs[\"total_mrna_umis\"] = [total_mrna_umis]\n",
+    "\n",
+    "    metadata_prediction_dict = ctx.predict_metadata(prop_adata_feedback)\n",
+    "\n",
+    "    metadata_key = 'cell_type'\n",
+    "    probs_k = metadata_prediction_dict[metadata_key][0]\n",
+    "    sort_order = np.argsort(probs_k)[::-1]\n",
+    "\n",
+    "    for k in range(10):\n",
+    "        prob = probs_k[sort_order[k]]\n",
+    "        name = ctx.metadata_ontology_infos[metadata_key][\"labels\"][sort_order[k]]\n",
+    "        print(f\"{name}: {prob:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We have two different generalization issues:\n",
+    "  - Large prompts (not an issue for this task)\n",
+    "  - Sensitivity to total mRNA UMIs (we need to find the mean/median for the UMIs for the cell type we're prompting)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform _in silico_ deletion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "\n",
+    "query_gene_ids_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_ids.csv\")\n",
+    "query_gene_ids = pd.read_csv(query_gene_ids_path, header=None).values.flatten().tolist()\n",
+    "perturb_gene_ids = query_gene_ids[:10_000]\n",
+    "\n",
+    "assay = \"10x 3' v3\"\n",
+    "suspension_type = \"cell\"\n",
+    "prompt_metadata_dict = {\n",
+    "    \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
+    "    \"tissue\": \"blood\"\n",
+    "}\n",
+    "total_mrna_umis = 5_000\n",
+    "\n",
+    "\n",
+    "chunk_size = 20\n",
+    "\n",
+    "def yield_chunks(lst, n):\n",
+    "    for i in range(0, len(lst), n):\n",
+    "        yield lst[i:i + n]\n",
+    "\n",
+    "query_gene_ids_chunks = list(yield_chunks(query_gene_ids, chunk_size))\n",
+    "\n",
+    "gene_marginal_means_nq_chunks = []\n",
+    "gene_marginal_std_nq_chunks = []\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "\n",
+    "    for query_gene_ids_chunk in tqdm(query_gene_ids_chunks):\n",
+    "\n",
+    "        tokens_dict, context_indices, _, _ = ctx.generate_gene_tokens_by_metadata(\n",
+    "            assay=assay,\n",
+    "            suspension_type=suspension_type,\n",
+    "            prompt_metadata_dict=prompt_metadata_dict,\n",
+    "            total_mrna_umis=total_mrna_umis,\n",
+    "            query_gene_ids=query_gene_ids_chunk,\n",
+    "            perturb_gene_ids=perturb_gene_ids\n",
+    "        )\n",
+    "\n",
+    "        gene_marginal_means_nq, gene_marginal_std_nq = \\\n",
+    "            ctx.get_marginal_mean_std_from_tokens(tokens_dict, context_indices)\n",
+    "\n",
+    "        gene_marginal_means_nq_chunks.append(gene_marginal_means_nq.cpu().numpy())\n",
+    "        gene_marginal_std_nq_chunks.append(gene_marginal_std_nq.cpu().numpy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a python function that replaces whitespace and '-' with underscores and removes commas\n",
+    "def clean_string(s):\n",
+    "    return s.replace(\" \", \"_\").replace(\"-\", \"_\").replace(\",\", \"\").replace(\"'\", \"p\")\n",
+    "\n",
+    "filename = (\n",
+    "    f\"in_silico_del_response_matrix__{clean_string(prompt_metadata_dict['cell_type'])}__\"\n",
+    "    f\"{clean_string(prompt_metadata_dict['tissue'])}__\"\n",
+    "    f\"{clean_string(assay)}__\"\n",
+    "    f\"{clean_string(suspension_type)}__\"\n",
+    "    f\"{total_mrna_umis}\"\n",
+    ")\n",
+    "\n",
+    "gene_marginal_means_nq = np.concatenate(gene_marginal_means_nq_chunks, axis=1)\n",
+    "gene_marginal_std_nq = np.concatenate(gene_marginal_std_nq_chunks, axis=1)\n",
+    "\n",
+    "output = {\n",
+    "    \"perturb_gene_ids\": perturb_gene_ids,\n",
+    "    \"query_gene_ids\": query_gene_ids,\n",
+    "    \"gene_marginal_means_nq\": gene_marginal_means_nq,\n",
+    "    \"gene_marginal_std_nq\": gene_marginal_std_nq,\n",
+    "    \"assay\": assay,\n",
+    "    \"suspension_type\": suspension_type,\n",
+    "    \"prompt_metadata_dict\": prompt_metadata_dict,\n",
+    "    \"total_mrna_umis\": total_mrna_umis\n",
+    "}\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "with open(f\"./output/in_silico_del_via_meta_prompting/{filename}.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(output, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore _in silico_ deletion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "output_pkl_path = os.path.join(\n",
+    "    ROOT_PATH, \"cellariumgpt_playground\", \"output\", \"in_silico_del_via_meta_prompting\",\n",
+    "    \"in_silico_del_response_matrix__CD8_positive_alpha_beta_T_cell__blood__10x_3p_v3__cell__5000.pkl\"\n",
+    ")\n",
+    "output_dict = pickle.load(open(output_pkl_path, \"rb\"))\n",
+    "# I forgot to include those in the pickle initially ...\n",
+    "output_dict[\"assay\"] = \"10x 3' v3\"\n",
+    "output_dict[\"suspension_type\"] = \"cell\"\n",
+    "output_dict[\"prompt_metadata_dict\"] = {\n",
+    "    \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
+    "    \"tissue\": \"blood\"\n",
+    "}\n",
+    "output_dict[\"total_mrna_umis\"] = 5_000\n",
+    "\n",
+    "\n",
+    "# output_pkl_path = os.path.join(\n",
+    "#     ROOT_PATH, \"cellariumgpt_playground\", \"output\", \"in_silico_del_via_meta_prompting\",\n",
+    "#     \"in_silico_del_response_matrix__cardiac_muscle_cell__heart__10x_3p_v3__nucleus__10000.pkl\"\n",
+    "# )\n",
+    "# output_dict = pickle.load(open(output_pkl_path, \"rb\"))\n",
+    "\n",
+    "# # I forgot to include those in the pickle initially ...\n",
+    "# output_dict[\"assay\"] = \"10x 3' v3\"\n",
+    "# output_dict[\"suspension_type\"] = \"nucleus\"\n",
+    "# output_dict[\"prompt_metadata_dict\"] = {\n",
+    "#     \"cell_type\": \"cardiac muscle cell\",\n",
+    "#     \"tissue\": \"heart\"\n",
+    "# }\n",
+    "# output_dict[\"total_mrna_umis\"] = 10_000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate an AnnData containing just the metadata\n",
+    "adata_prop = sc.AnnData(\n",
+    "    X=np.zeros((1, 1)),\n",
+    "    obs=pd.DataFrame({\n",
+    "        \"cell_type\": [output_dict[\"prompt_metadata_dict\"][\"cell_type\"]],\n",
+    "        \"tissue\": [output_dict[\"prompt_metadata_dict\"][\"tissue\"]],\n",
+    "        \"assay\": [output_dict[\"assay\"]],\n",
+    "        \"suspension_type\": [output_dict[\"suspension_type\"]],\n",
+    "        \"total_mrna_umis\": [output_dict[\"total_mrna_umis\"]],\n",
+    "        \"disease\": \"N/A\",\n",
+    "        \"development_stage\": \"N/A\",\n",
+    "        \"sex\": \"N/A\",\n",
+    "    })\n",
+    ")\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "raw_response_qp = output_dict[\"gene_marginal_means_nq\"][1:, :].T\n",
+    "prompt_marginal_mean_p = output_dict[\"gene_marginal_means_nq\"][0, :len(output_dict[\"perturb_gene_ids\"])]\n",
+    "prompt_marginal_std_p = output_dict[\"gene_marginal_std_nq\"][0, :len(output_dict[\"perturb_gene_ids\"])]\n",
+    "query_marginal_mean_q = output_dict[\"gene_marginal_means_nq\"][0, :]\n",
+    "query_marginal_std_q = output_dict[\"gene_marginal_std_nq\"][0, :]\n",
+    "\n",
+    "# log fold change response\n",
+    "control_cell_library_size = query_marginal_mean_q.sum()\n",
+    "raw_response_qp = (raw_response_qp / raw_response_qp.sum(0)[None, :]) * control_cell_library_size\n",
+    "normalized_response_qp = np.log(raw_response_qp) - np.log(query_marginal_mean_q)[:, None]\n",
+    "\n",
+    "network_ctx = GeneNetworkAnalysisBase(\n",
+    "    adata_obs=adata_prop.obs,\n",
+    "    gene_info_tsv_path=gene_info_tsv_path,\n",
+    "    query_var_names=output_dict[\"query_gene_ids\"],\n",
+    "    prompt_var_names=output_dict[\"perturb_gene_ids\"],\n",
+    "    response_qp=normalized_response_qp,\n",
+    "    prompt_marginal_mean_p=prompt_marginal_mean_p,\n",
+    "    prompt_marginal_std_p=prompt_marginal_std_p,\n",
+    "    query_marginal_mean_q=query_marginal_mean_q,\n",
+    "    query_marginal_std_q=query_marginal_std_q,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: do we need prompt_z_score or query_z_score?\n",
+    "network_ctx.process(\n",
+    "    response_normalization_strategy=\"none\",\n",
+    "    feature_normalization_strategy=\"l2\",\n",
+    "    query_response_amp_min_pct=0,\n",
+    "    min_prompt_gene_tpm=0,\n",
+    "    min_query_gene_tpm=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_adjacency_matrix(\n",
+    "    adjacency_strategy=\"shifted_correlation\",\n",
+    "    n_neighbors=10,\n",
+    "    beta=6.,\n",
+    "    self_loop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_leiden_communites(\n",
+    "    resolution=5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(np.unique(network_ctx.leiden_membership))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_spectral_dimension(n_lambda_for_estimation=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "network_ctx.plot_spectral_dimension(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymde\n",
+    "\n",
+    "network_ctx.make_mde_embedding(\n",
+    "    n_neighbors=10,\n",
+    "    # repulsive_penalty=pymde.penalties.Log,\n",
+    "    init=\"quadratic\",\n",
+    "    device=\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "snap_n_gene_symbols = [\n",
+    "    'GAP43',\n",
+    "    'NRXN3',\n",
+    "    'HOMER1',\n",
+    "    'IL1RAPL2',\n",
+    "    'EPHA3',\n",
+    "    'RIMS1',\n",
+    "    'SV2B',\n",
+    "    'TRIM9',\n",
+    "    'SVOP',\n",
+    "    'RPH3A',\n",
+    "    'SYT12',\n",
+    "    'SYT1',\n",
+    "    'R3HDM2',\n",
+    "    'PDE4B',\n",
+    "    'DCC',\n",
+    "    'SLC4A10',\n",
+    "    'DNM3',\n",
+    "    'GRM1',\n",
+    "    'EGR4',\n",
+    "    'JUNB',\n",
+    "    'TFDP2'\n",
+    "]\n",
+    "\n",
+    "snap_n_gene_symbols = [x for x in snap_n_gene_symbols if x in network_ctx.query_gene_symbols]\n",
+    "snap_n_gene_ids = [network_ctx.gene_symbol_to_gene_id_map[x] for x in snap_n_gene_symbols]\n",
+    "\n",
+    "muscle_gene_symbols = [\n",
+    "    'TTN',\n",
+    "    'MYL3',\n",
+    "    'MYL4',\n",
+    "    'MYL7',\n",
+    "    'TNNC1',\n",
+    "    'TNNI1',\n",
+    "]\n",
+    "\n",
+    "muscle_gene_symbols = [x for x in muscle_gene_symbols if x in network_ctx.query_gene_symbols]\n",
+    "muscle_gene_ids = [network_ctx.gene_symbol_to_gene_id_map[x] for x in muscle_gene_symbols]\n",
+    "\n",
+    "def get_gene_familities(network_ctx: GeneNetworkAnalysisBase, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
+    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in network_ctx.query_gene_symbols if gene_symbol.startswith(prefix)]\n",
+    "    gene_ids = [network_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
+    "    gene_symbols = [network_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
+    "    return gene_ids, gene_symbols\n",
+    "\n",
+    "mito_gene_ids, mito_gene_symbols = get_gene_familities(network_ctx, [\"MT-\"])\n",
+    "ribo_gene_ids, ribo_gene_symbols = get_gene_familities(network_ctx, [\"RPS\", \"RPL\"])\n",
+    "hla_gene_ids, hla_gene_symbols = get_gene_familities(network_ctx, [\"HLA\"])\n",
+    "ifi_gene_ids, ifi_gene_symbols = get_gene_familities(network_ctx, [\"IFI\"])\n",
+    "\n",
+    "highlight_gene_sets = {\n",
+    "    \"Mito\": (mito_gene_ids, mito_gene_symbols, 'red'),\n",
+    "    \"Ribo\": (ribo_gene_ids, ribo_gene_symbols, 'blue'),\n",
+    "    # \"SNAP-n\": (snap_n_gene_ids, snap_n_gene_symbols, 'green'),\n",
+    "    # \"HLA\": (hla_gene_ids, hla_gene_symbols, 'green'),\n",
+    "    # \"IFI\": (ifi_gene_ids, ifi_gene_symbols, 'orange'),\n",
+    "    \"Muscle\": (muscle_gene_ids, muscle_gene_symbols, 'purple'),\n",
+    "}\n",
+    "\n",
+    "# disable\n",
+    "# highlight_gene_sets = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mito_gene_symbols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.query_marginal_mean_q[[network_ctx.query_gene_id_to_idx_map[gene_id] for gene_id in mito_gene_ids]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From d11b7a1db20de1467f529a61f4d294319fb32178 Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Tue, 4 Feb 2025 14:28:58 -0500
Subject: [PATCH 30/64] Fix recent mypy errors (#298)

* Fix mypy errors

* Update onepass_mean_var_std.py

---------

Co-authored-by: Yerdos Ordabayev <yordabay@broadinstitute.org>
---
 cellarium/ml/layers/embedding.py            | 2 ++
 cellarium/ml/layers/head.py                 | 2 ++
 cellarium/ml/models/onepass_mean_var_std.py | 1 +
 3 files changed, 5 insertions(+)

diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 6db31786..9984dab9 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -43,6 +43,7 @@ def __init__(
 
     def _reset_parameters(self) -> None:
         for module in self.E.children():
+            assert isinstance(module.weight, torch.Tensor)
             create_initializer(self.embeddings_initializer)(module.weight)
 
     def forward(self, gene_tokens_nc: dict[str, torch.Tensor]) -> torch.Tensor:
@@ -86,6 +87,7 @@ def __init__(
 
     def _reset_parameters(self) -> None:
         for module in self.E.children():
+            assert isinstance(module.weight, torch.Tensor)
             create_initializer(self.embeddings_initializer)(module.weight)
 
     def forward(self, metadata_tokens_n: dict[str, torch.Tensor]) -> torch.Tensor:
diff --git a/cellarium/ml/layers/head.py b/cellarium/ml/layers/head.py
index 1d6b94e3..50ac6aa7 100644
--- a/cellarium/ml/layers/head.py
+++ b/cellarium/ml/layers/head.py
@@ -45,9 +45,11 @@ def __init__(
 
     def _reset_parameters(self) -> None:
         for module in self.W.children():
+            assert isinstance(module.weight, torch.Tensor)
             create_initializer(self.heads_initializer)(module.weight)
 
             if module.bias is not None:
+                assert isinstance(module.bias, torch.Tensor)
                 nn.init.zeros_(module.bias)
 
     def forward(self, hidden_state_ncd: torch.Tensor) -> dict[str, torch.Tensor]:
diff --git a/cellarium/ml/models/onepass_mean_var_std.py b/cellarium/ml/models/onepass_mean_var_std.py
index b62690f7..2c39be3c 100644
--- a/cellarium/ml/models/onepass_mean_var_std.py
+++ b/cellarium/ml/models/onepass_mean_var_std.py
@@ -125,6 +125,7 @@ def mean_g(self) -> torch.Tensor:
         """
         mean_g = self.x_sums / self.x_size
         if self.algorithm == "shifted_data":
+            assert isinstance(self.x_shift, torch.Tensor)
             mean_g = mean_g + self.x_shift
         return mean_g
 

From ef5cdc886af6091ef1c9de33a018aad2ea605369 Mon Sep 17 00:00:00 2001
From: Stephen Fleming <sjfleming@users.noreply.github.com>
Date: Tue, 4 Feb 2025 15:50:43 -0500
Subject: [PATCH 31/64] Add to gitignore (#299)

---
 .gitignore | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 1629b225..097f7db5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,4 +14,10 @@ lightning_logs/
 .idea/
 build/
 notebooks/.ipynb_checkpoints/
-output/
\ No newline at end of file
+output/
+*.h5ad
+*.csv
+*.csv.gz
+*.tsv
+*.tsv.gz
+*.ckpt
\ No newline at end of file

From 9a3fdf8d76845645a9da5b3f01b93ba44bb9967e Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 5 Feb 2025 20:05:13 +0000
Subject: [PATCH 32/64] remove gradient clipping

---
 cellarium/ml/core/module.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index a1d512c5..918e55b8 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -355,16 +355,6 @@ def configure_optimizers(self) -> OptimizerLRScheduler:
         else:
             return {"optimizer": optimizer}
 
-    def configure_gradient_clipping(
-        self,
-        optimizer,
-        optimizer_idx: int | None = None,
-        gradient_clip_val: int | float | None = None,
-        gradient_clip_algorithm: str | None = None,
-    ) -> None:
-        assert gradient_clip_algorithm in ("norm", None), gradient_clip_algorithm
-        self.trainer.strategy.model.clip_grad_norm_(gradient_clip_val)
-
     def on_train_epoch_start(self) -> None:
         """
         Calls the ``set_epoch`` method on the iterable dataset of the given dataloader.

From e2bf7970ebfe858de4bca702f8a6168ddeef8de1 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 5 Feb 2025 17:31:48 -0500
Subject: [PATCH 33/64] Fix ipca (#289)

---
 .github/workflows/ci.yml               | 2 +-
 Makefile                               | 4 ++++
 cellarium/ml/models/incremental_pca.py | 6 +-----
 tests/test_ipca.py                     | 2 +-
 tests/test_onepass_mean_var_std.py     | 4 ++--
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 27a3e64f..c209f679 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -78,7 +78,7 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.10"]
-        devices: ["1", "2"]
+        devices: ["1", "2", "3"]
 
     env:
       TEST_DEVICES: ${{ matrix.devices }}
diff --git a/Makefile b/Makefile
index b0e51303..9220016a 100644
--- a/Makefile
+++ b/Makefile
@@ -26,6 +26,8 @@ typecheck: FORCE
 test: FORCE
 ifeq (${TEST_DEVICES}, 2)
 	pytest -v -k multi_device --ignore=tests/dataloader
+else ifeq (${TEST_DEVICES}, 3)
+	pytest -v -k multi_device --ignore=tests/dataloader
 else
 	# default
 	pytest -v --ignore=tests/dataloader
@@ -34,6 +36,8 @@ endif
 test-dataloader: FORCE
 ifeq (${TEST_DEVICES}, 2)
 	pytest -v -k multi_device tests/dataloader
+else ifeq (${TEST_DEVICES}, 3)
+	pytest -v -k multi_device tests/dataloader
 else
 	# default
 	pytest -v tests/dataloader
diff --git a/cellarium/ml/models/incremental_pca.py b/cellarium/ml/models/incremental_pca.py
index 85b0d535..68373b98 100644
--- a/cellarium/ml/models/incremental_pca.py
+++ b/cellarium/ml/models/incremental_pca.py
@@ -89,15 +89,11 @@ def forward(self, x_ng: torch.Tensor, var_names_g: np.ndarray) -> dict[str, torc
         assert_columns_and_array_lengths_equal("x_ng", x_ng, "var_names_g", var_names_g)
         assert_arrays_equal("var_names_g", var_names_g, "var_names_g", self.var_names_g)
 
-        g = self.n_vars
         k = self.n_components
         niter = self.svd_lowrank_niter
         self_X_size = self.x_size
         other_X_size = x_ng.size(0)
         total_X_size = self_X_size + other_X_size
-        assert k <= min(other_X_size, g), (
-            f"Rank of svd_lowrank (n_components): {k} must be less than min(n_obs, n_vars): {min(other_X_size, g)}"
-        )
 
         # compute SVD of new data
         if self.perform_mean_correction:
@@ -173,7 +169,7 @@ def on_train_epoch_end(self, trainer: pl.Trainer) -> None:
             # level i
             # at most two local ranks (leading and trailing)
             if rank % 2 == 0:  # leading rank
-                if rank < world_size:
+                if rank + 1 < world_size:
                     # if there is a trailing rank
                     # then concatenate and merge SVDs
                     src = (rank + 1) * 2**i
diff --git a/tests/test_ipca.py b/tests/test_ipca.py
index 2261299a..fd8326d8 100644
--- a/tests/test_ipca.py
+++ b/tests/test_ipca.py
@@ -19,7 +19,7 @@
 
 @pytest.fixture
 def x_ng():
-    n, g = 10000, 100
+    n, g = 10002, 100
     rng = torch.Generator()
     rng.manual_seed(1465)
     mean_g = torch.randn((g,), generator=rng)
diff --git a/tests/test_onepass_mean_var_std.py b/tests/test_onepass_mean_var_std.py
index efc1bff4..793fbf41 100644
--- a/tests/test_onepass_mean_var_std.py
+++ b/tests/test_onepass_mean_var_std.py
@@ -23,7 +23,7 @@
 
 @pytest.fixture
 def adata():
-    n_cell, g_gene = 10, 5
+    n_cell, g_gene = 12, 5
     rng = np.random.default_rng(1465)
     X = rng.integers(10, size=(n_cell, g_gene))
     return AnnData(X, dtype=X.dtype)
@@ -32,7 +32,7 @@ def adata():
 @pytest.fixture
 def dadc(adata: AnnData, tmp_path: Path):
     # save anndata files
-    limits = [2, 5, 10]
+    limits = [2, 5, 12]
     for i, limit in enumerate(zip([0] + limits, limits)):
         sliced_adata = adata[slice(*limit)]
         sliced_adata.write(os.path.join(tmp_path, f"adata.00{i}.h5ad"))

From d03180976653bd0ba1499ee7c0068b16f9e77387 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Wed, 5 Feb 2025 19:16:13 -0500
Subject: [PATCH 34/64] Cellarium gpt model (#279)

* wip

* PyTreeDataset

* fixes

* pass the test

* fixes

* token_type_nc

* clean up

* rename

* fix docstring

* fix test

* token_mask

* remove matmul precision

* address comments
---
 cellarium/ml/layers/__init__.py      |   5 +-
 cellarium/ml/layers/embedding.py     |  95 +++----
 cellarium/ml/layers/head.py          |  14 +-
 cellarium/ml/layers/transformer.py   |   8 +
 cellarium/ml/models/__init__.py      |   2 +
 cellarium/ml/models/cellarium_gpt.py | 372 +++++++++++++++++++++++++++
 cellarium/ml/utilities/data.py       |   4 +-
 cellarium/ml/utilities/mup.py        |  13 +-
 tests/test_cellarium_gpt.py          | 112 ++++++++
 9 files changed, 545 insertions(+), 80 deletions(-)
 create mode 100644 cellarium/ml/models/cellarium_gpt.py
 create mode 100644 tests/test_cellarium_gpt.py

diff --git a/cellarium/ml/layers/__init__.py b/cellarium/ml/layers/__init__.py
index 189275a4..da43b264 100644
--- a/cellarium/ml/layers/__init__.py
+++ b/cellarium/ml/layers/__init__.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from cellarium.ml.layers.attention import MultiHeadAttention
-from cellarium.ml.layers.embedding import GeneExpressionEmbedding, MetadataEmbedding
+from cellarium.ml.layers.embedding import TokenEmbedding
 from cellarium.ml.layers.ffn import PositionWiseFFN
 from cellarium.ml.layers.head import MultiHeadReadout
 from cellarium.ml.layers.mu_linear import MuLinear
@@ -10,8 +10,7 @@
 from cellarium.ml.layers.transformer import Transformer, TransformerBlock
 
 __all__ = [
-    "GeneExpressionEmbedding",
-    "MetadataEmbedding",
+    "TokenEmbedding",
     "MuLinear",
     "MultiHeadAttention",
     "MultiHeadReadout",
diff --git a/cellarium/ml/layers/embedding.py b/cellarium/ml/layers/embedding.py
index 9984dab9..07f819c2 100644
--- a/cellarium/ml/layers/embedding.py
+++ b/cellarium/ml/layers/embedding.py
@@ -9,15 +9,15 @@
 from cellarium.ml.utilities.layers import create_initializer
 
 
-class GeneExpressionEmbedding(nn.Module):
+class TokenEmbedding(nn.Module):
     """
-    Gene embedding.
+    Gene and metadata tokens embedding.
 
     Args:
-        categorical_vocab_sizes:
-            Categorical gene token vocabulary sizes.
-        continuous_vocab_sizes:
-            Continuous gene token vocabulary sizes.
+        categorical_token_size_dict:
+            Categorical token vocabulary sizes.
+        continuous_token_list:
+            Continuous tokens.
         d_model:
             Dimensionality of the embeddings and hidden states.
         embeddings_initializer:
@@ -26,80 +26,47 @@ class GeneExpressionEmbedding(nn.Module):
 
     def __init__(
         self,
-        categorical_vocab_sizes: dict[str, int],
-        continuous_vocab_sizes: dict[str, int],
+        categorical_token_size_dict: dict[str, int],
+        continuous_token_list: list[str],
         d_model: int,
         embeddings_initializer: dict[str, Any],
     ) -> None:
         super().__init__()
-        self.E = nn.ModuleDict()
-        self.E.update({key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()})
-        self.E.update(
-            {key: nn.Linear(vocab_size, d_model, bias=False) for key, vocab_size in continuous_vocab_sizes.items()}
+        self.embedding_dict = nn.ModuleDict()
+        self.embedding_dict.update(
+            {key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_token_size_dict.items()}
         )
+        self.embedding_dict.update({key: nn.Linear(1, d_model, bias=False) for key in continuous_token_list})
+        self.categorical_token_size_dict = categorical_token_size_dict
+        self.continuous_token_list = continuous_token_list
         self.embeddings_initializer = embeddings_initializer
 
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
-        for module in self.E.children():
-            assert isinstance(module.weight, torch.Tensor)
+        for module in self.embedding_dict.children():
+            assert isinstance(module, (nn.Embedding, nn.Linear))
             create_initializer(self.embeddings_initializer)(module.weight)
 
-    def forward(self, gene_tokens_nc: dict[str, torch.Tensor]) -> torch.Tensor:
-        """
-        Args:
-            gene_tokens_nc:
-                Dictionary of gene token tensors of shape ``(n, c)``.
-
-        Returns:
-            The gene embedding tensor of shape ``(n, c, d)``.
-        """
-        return sum(self.E[key](gene_token_nc) for key, gene_token_nc in gene_tokens_nc.items())
-
-
-class MetadataEmbedding(nn.Module):
-    """
-    Metadata embedding.
-
-    Args:
-        categorical_vocab_sizes:
-            Categorical metadata token vocabulary sizes.
-        d_model:
-            Dimensionality of the embeddings and hidden states.
-        initializer:
-            Initializer for the embeddings.
-    """
-
-    def __init__(
+    def forward(
         self,
-        categorical_vocab_sizes: dict[str, int],
-        d_model: int,
-        embeddings_initializer: dict[str, Any],
-    ) -> None:
-        super().__init__()
-        self.E = nn.ModuleDict(
-            {key: nn.Embedding(vocab_size, d_model) for key, vocab_size in categorical_vocab_sizes.items()}
-        )
-        self.embeddings_initializer = embeddings_initializer
-
-        self._reset_parameters()
-
-    def _reset_parameters(self) -> None:
-        for module in self.E.children():
-            assert isinstance(module.weight, torch.Tensor)
-            create_initializer(self.embeddings_initializer)(module.weight)
-
-    def forward(self, metadata_tokens_n: dict[str, torch.Tensor]) -> torch.Tensor:
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+    ) -> torch.Tensor:
         """
         Args:
-            metadata_token_n:
-                Dictionary of metadata token tensors of shape ``(n,)``.
+            token_value_nc_dict:
+                Dictionary of token value tensors of shape ``(n, c)``.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
 
         Returns:
-            The metadata embedding tensor of shape ``(n, m, d)``.
+            Embedding tensor of shape ``(n, c, d)``.
         """
-        return torch.stack(
-            [self.E[key](metadata_token_n) for key, metadata_token_n in metadata_tokens_n.items()],
-            dim=1,
+        return sum(
+            self.embedding_dict[key](
+                token_value_nc.unsqueeze(-1) if key in self.continuous_token_list else token_value_nc
+            )
+            * token_mask_nc_dict[key].unsqueeze(-1)
+            for i, (key, token_value_nc) in enumerate(token_value_nc_dict.items())
         )
diff --git a/cellarium/ml/layers/head.py b/cellarium/ml/layers/head.py
index 50ac6aa7..bb6d05f9 100644
--- a/cellarium/ml/layers/head.py
+++ b/cellarium/ml/layers/head.py
@@ -14,7 +14,7 @@ class MultiHeadReadout(nn.Module):
     Multi-head readout.
 
     Args:
-        categorical_vocab_sizes:
+        categorical_token_size_dict:
             Categorical token vocabulary sizes.
         d_model:
             Dimensionality of the embeddings and hidden states.
@@ -28,15 +28,15 @@ class MultiHeadReadout(nn.Module):
 
     def __init__(
         self,
-        categorical_vocab_sizes: dict[str, int],
+        categorical_token_size_dict: dict[str, int],
         d_model: int,
         use_bias: bool,
         output_logits_scale: float,
         heads_initializer: dict[str, Any],
     ) -> None:
         super().__init__()
-        self.W = nn.ModuleDict(
-            {key: nn.Linear(d_model, vocab_size, use_bias) for key, vocab_size in categorical_vocab_sizes.items()}
+        self.readout_dict = nn.ModuleDict(
+            {key: nn.Linear(d_model, vocab_size, use_bias) for key, vocab_size in categorical_token_size_dict.items()}
         )
         self.output_logits_scale = output_logits_scale
         self.heads_initializer = heads_initializer
@@ -44,8 +44,8 @@ def __init__(
         self._reset_parameters()
 
     def _reset_parameters(self) -> None:
-        for module in self.W.children():
-            assert isinstance(module.weight, torch.Tensor)
+        for module in self.readout_dict.children():
+            assert isinstance(module, nn.Linear)
             create_initializer(self.heads_initializer)(module.weight)
 
             if module.bias is not None:
@@ -61,4 +61,4 @@ def forward(self, hidden_state_ncd: torch.Tensor) -> dict[str, torch.Tensor]:
         Returns:
             Dictionary of output logits tensors of shape ``(n, c, vocab_size)``.
         """
-        return {key: self.output_logits_scale * self.W[key](hidden_state_ncd) for key in self.W}
+        return {key: self.output_logits_scale * self.readout_dict[key](hidden_state_ncd) for key in self.readout_dict}
diff --git a/cellarium/ml/layers/transformer.py b/cellarium/ml/layers/transformer.py
index 7f68fc51..26fb002e 100644
--- a/cellarium/ml/layers/transformer.py
+++ b/cellarium/ml/layers/transformer.py
@@ -82,6 +82,14 @@ def __init__(
         )
         self.normadd2 = NormAdd(d_model, dropout_p, use_bias)
 
+    @property
+    def d_ffn(self) -> int:
+        return self.ffn.dense1.out_features
+
+    @property
+    def d_model(self) -> int:
+        return self.ffn.dense1.in_features
+
     def forward(
         self,
         hidden_state_ncd: torch.Tensor,
diff --git a/cellarium/ml/models/__init__.py b/cellarium/ml/models/__init__.py
index e5b05abf..d97dd110 100644
--- a/cellarium/ml/models/__init__.py
+++ b/cellarium/ml/models/__init__.py
@@ -1,6 +1,7 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+from cellarium.ml.models.cellarium_gpt import CellariumGPT
 from cellarium.ml.models.geneformer import Geneformer
 from cellarium.ml.models.incremental_pca import IncrementalPCA
 from cellarium.ml.models.logistic_regression import LogisticRegression
@@ -10,6 +11,7 @@
 from cellarium.ml.models.tdigest import TDigest
 
 __all__ = [
+    "CellariumGPT",
     "CellariumModel",
     "Geneformer",
     "IncrementalPCA",
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
new file mode 100644
index 00000000..455b5a49
--- /dev/null
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -0,0 +1,372 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+from typing import Literal
+
+import lightning.pytorch as pl
+import numpy as np
+import torch
+from torch import nn
+from torch.nn.attention.flex_attention import BlockMask, create_block_mask
+
+from cellarium.ml.layers import MultiHeadReadout, TokenEmbedding, Transformer, TransformerBlock
+from cellarium.ml.models.model import CellariumModel, PredictMixin, ValidateMixin
+from cellarium.ml.utilities.layers import scale_initializers_by_dimension
+from cellarium.ml.utilities.mup import LRAdjustmentGroup
+
+try:
+    from cerebras.pytorch.backend import use_cs
+except ImportError:
+
+    def use_cs() -> bool:
+        return False
+
+
+def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
+    """
+    Generate a prompt diagonal mask for self-attention.
+
+    Args:
+        prompt_mask_nc:
+            The prompt mask.
+
+    Returns:
+        torch.Tensor: The prompt diagonal mask.
+
+    Example:
+
+        For prompt_mask = [True, False, True, False, False], the attention mask is:
+
+        [[True, False, True, False, False],
+         [True, True,  True, False, False],
+         [True, False, True, False, False],
+         [True, False, True, True,  False],
+         [True, False, True, False, True]]
+    """
+    device = prompt_mask_nc.device
+    n, c = prompt_mask_nc.shape
+    if use_cs():
+        c_range = torch.arange(c, device=device, dtype=torch.float32)
+        diag_mask_ncc = (c_range[:, None].expand(n, -1, 1) - c_range.expand(n, 1, -1)).abs()
+        prompt_mask_n1c = 1 - prompt_mask_nc[:, None, :].float()
+        attention_mask_ncc = diag_mask_ncc * prompt_mask_n1c
+        return attention_mask_ncc == 0
+    else:
+        diag_mask_cc = torch.eye(c, dtype=torch.bool, device=device)
+        attention_mask_ncc = prompt_mask_nc[:, None, :] | diag_mask_cc
+        return attention_mask_ncc
+
+
+class CellariumGPT(CellariumModel, PredictMixin, ValidateMixin):
+    """
+    CellariumGPT model.
+
+    Args:
+        categorical_token_size_dict:
+            Categorical token vocabulary sizes. Must include "gene_value" and "gene_id". Additionally, it can include
+            experimental conditions, such as "assay" and "suspension_type", and metadata tokens such as "cell_type",
+            "tissue", "sex", "development_stage", and "disease".
+        d_model:
+            Dimensionality of the embeddings and hidden states.
+        d_ffn:
+            Dimensionality of the inner feed-forward layers.
+        n_heads:
+            Number of attention heads.
+        n_blocks:
+            Number of transformer blocks.
+        dropout_p:
+            Dropout probability.
+        use_bias:
+            Whether to use bias in the linear transformations.
+        attention_backend:
+            Backend for the attention computation.
+        attention_softmax_fp32:
+            Whether to use float32 for softmax computation when ``torch`` backend is used.
+        loss_scale_dict:
+            A dictionary of loss scales for each label type. These are the query tokens that are used
+            to compute the loss.
+        initializer_range:
+            The standard deviation of the truncated normal initializer.
+        embeddings_scale:
+            Multiplier for the embeddings.
+        attention_logits_scale:
+            Multiplier for the attention logits.
+        output_logits_scale:
+            Multiplier for the output logits.
+        mup_base_d_model:
+            Base dimensionality of the model for muP.
+        mup_base_d_ffn:
+            Base dimensionality of the inner feed-forward layers for muP.
+    """
+
+    def __init__(
+        self,
+        # Vocab sizes
+        categorical_token_size_dict: dict[str, int],
+        # Model parameters
+        d_model: int,
+        d_ffn: int,
+        n_heads: int,
+        n_blocks: int,
+        dropout_p: float,
+        use_bias: bool,
+        attention_backend: Literal["flex", "math", "mem_efficient", "torch"],
+        attention_softmax_fp32: bool,
+        loss_scale_dict: dict[str, float],
+        # Tunable parameters
+        initializer_range: float = 0.02,
+        embeddings_scale: float = 1.0,
+        attention_logits_scale: float = 1.0,
+        output_logits_scale: float = 1.0,
+        # muP (maximal update parameterization) parameters
+        mup_base_d_model: int | None = None,
+        mup_base_d_ffn: int | None = None,
+    ) -> None:
+        super().__init__()
+
+        # Vocab sizes
+        self.categorical_token_size_dict = categorical_token_size_dict
+
+        # Initializers
+        self.initializer_range = initializer_range
+        default_initializer = {
+            "name": "trunc_normal_",
+            "mean": 0.0,
+            "std": self.initializer_range,
+            "a": -2 * self.initializer_range,
+            "b": 2 * self.initializer_range,
+        }
+        embeddings_initializer = default_initializer.copy()
+        Wqkv_initializer = default_initializer.copy()
+        Wo_initializer = default_initializer.copy()
+        dense1_initializer = default_initializer.copy()
+        dense2_initializer = default_initializer.copy()
+        heads_initializer = default_initializer.copy()
+        self.lr_adjustment_groups = {
+            "embedding": LRAdjustmentGroup("*embedding*weight"),
+            "decoder_attention": LRAdjustmentGroup("*transformer*attention*W*weight"),
+            "decoder_input_ffn": LRAdjustmentGroup("*transformer*ffn.dense1*weight"),
+            "decoder_output_ffn": LRAdjustmentGroup("*transformer*ffn.dense2*weight"),
+        }
+
+        # Multipliers
+        self.embeddings_scale = embeddings_scale
+        self.attention_logits_scale = attention_logits_scale
+        self.output_logits_scale = output_logits_scale
+
+        # Handle muP scaling for Adam and AdamW optimizers
+        if mup_base_d_model:
+            d_model_width_mult = d_model / mup_base_d_model
+            scale_initializers_by_dimension(
+                [Wqkv_initializer, dense1_initializer],
+                width_scale=d_model_width_mult**-0.5,
+            )
+            scale_initializers_by_dimension(
+                Wo_initializer,
+                width_scale=d_model_width_mult**-0.5,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+            self.output_logits_scale /= d_model_width_mult
+            for lr_adjustment_group in [
+                "decoder_attention",
+                "decoder_input_ffn",
+            ]:
+                self.lr_adjustment_groups[lr_adjustment_group].set_scale(1 / d_model_width_mult)
+            self.width_mult = d_model_width_mult
+        else:
+            scale_initializers_by_dimension(
+                Wo_initializer,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+
+        if mup_base_d_ffn:
+            d_ffn_width_mult = d_ffn / mup_base_d_ffn
+            scale_initializers_by_dimension(
+                dense2_initializer,
+                width_scale=d_ffn_width_mult**-0.5,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+            self.lr_adjustment_groups["decoder_output_ffn"].set_scale(1 / d_ffn_width_mult)
+            assert self.width_mult == d_ffn_width_mult
+        else:
+            scale_initializers_by_dimension(
+                dense2_initializer,
+                depth_scale=(2 * n_blocks) ** -0.5,
+            )
+
+        embedding_token_size_dict = {}
+        for key, vocab_size in categorical_token_size_dict.items():
+            if key in loss_scale_dict:
+                # Add 1 to the vocab size for the query tokens to account for the mask token
+                embedding_token_size_dict[key] = vocab_size + 1
+            elif key != "gene_value":
+                embedding_token_size_dict[key] = vocab_size
+        self.token_embedding = TokenEmbedding(
+            categorical_token_size_dict=embedding_token_size_dict,
+            continuous_token_list=["gene_value", "gene_query_mask", "total_mrna_umis"],
+            d_model=d_model,
+            embeddings_initializer=embeddings_initializer,
+        )
+        self.transformer = Transformer(
+            d_model=d_model,
+            d_ffn=d_ffn,
+            use_bias=use_bias,
+            n_heads=n_heads,
+            n_blocks=n_blocks,
+            dropout_p=dropout_p,
+            attention_logits_scale=attention_logits_scale,
+            attention_backend=attention_backend,
+            attention_softmax_fp32=attention_softmax_fp32,
+            Wqkv_initializer=Wqkv_initializer,
+            Wo_initializer=Wo_initializer,
+            dense1_initializer=dense1_initializer,
+            dense2_initializer=dense2_initializer,
+        )
+        self.head = MultiHeadReadout(
+            categorical_token_size_dict={key: categorical_token_size_dict[key] for key in loss_scale_dict},
+            d_model=d_model,
+            use_bias=use_bias,
+            output_logits_scale=output_logits_scale,
+            heads_initializer=heads_initializer,
+        )
+        self.loss_scale_dict = loss_scale_dict
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        def _reset_parameters(module):
+            return getattr(module, "_reset_parameters", lambda: None)()
+
+        self.apply(_reset_parameters)
+
+    @property
+    def d_model(self) -> int:
+        block = self.transformer.blocks[0]
+        assert isinstance(block, TransformerBlock)
+        return block.d_model
+
+    @property
+    def d_ffn(self) -> int:
+        block = self.transformer.blocks[0]
+        assert isinstance(block, TransformerBlock)
+        return block.d_ffn
+
+    @property
+    def n_heads(self) -> int:
+        block = self.transformer.blocks[0]
+        assert isinstance(block, TransformerBlock)
+        return block.attention.n_heads
+
+    @property
+    def n_blocks(self) -> int:
+        return len(self.transformer.blocks)
+
+    @property
+    def attention_backend(self) -> Literal["flex", "math", "mem_efficient", "torch"]:
+        block = self.transformer.blocks[0]
+        assert isinstance(block, TransformerBlock)
+        return block.attention.attention_backend
+
+    @attention_backend.setter
+    def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "torch"]) -> None:
+        for block in self.transformer.blocks:
+            assert isinstance(block, TransformerBlock)
+            block.attention.attention_backend = value
+
+    def predict(
+        self,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+    ) -> dict[str, np.ndarray | torch.Tensor]:
+        """
+        Args:
+            token_value_nc_dict:
+                Dictionary of token value tensors of shape ``(n, c)``.
+            token_mask_nc_dict:
+                Dictionary of token mask tensors of shape ``(n, c)``.
+
+        Returns:
+            Dictionary of logits tensors of shape ``(n, c, k)``.
+        """
+        # Create embeddings
+        embedding_ncd = self.token_embedding(token_value_nc_dict, token_mask_nc_dict)
+
+        # Create attention mask
+        attention_mask_ncc: torch.Tensor | BlockMask
+        if self.attention_backend == "flex":
+
+            def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
+                return prompt_mask_nc[b, kv_idx] | (q_idx == kv_idx)
+
+            n, c = prompt_mask_nc.shape
+            attention_mask_ncc = create_block_mask(prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c)
+        else:
+            attention_mask_ncc = prompt_diagonal_mask(prompt_mask_nc)
+
+        # Transformer blocks
+        hidden_state_ncd = embedding_ncd * self.embeddings_scale
+        hidden_state_ncd = self.transformer(hidden_state_ncd, attention_mask_ncc)
+
+        # Compute logits
+        logits_nck_dict = self.head(hidden_state_ncd)
+
+        return logits_nck_dict
+
+    def forward(
+        self,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+        label_nc_dict: dict[str, torch.Tensor],
+        label_weight_nc_dict: dict[str, torch.Tensor],
+    ) -> dict[str, torch.Tensor]:
+        logits_nck_dict = self.predict(
+            token_value_nc_dict=token_value_nc_dict,
+            token_mask_nc_dict=token_mask_nc_dict,
+            prompt_mask_nc=prompt_mask_nc,
+        )
+
+        # Compute loss
+        if not (set(self.loss_scale_dict) == set(label_nc_dict) == set(label_weight_nc_dict)):
+            raise ValueError("The keys of loss_scale_dict, label_nc_dict, and label_weight_nc_dict must be the same.")
+        loss_dict = {}
+        loss_fn = nn.CrossEntropyLoss(reduction="none")
+        # Make sure that label_nc_dict is created by concatenating the gene_value and metadata labels
+        # in the same order as the embeddings.
+        for key, label_nc in label_nc_dict.items():
+            logits_nck = logits_nck_dict[key]
+            assert isinstance(logits_nck, torch.Tensor)
+            label_weight_nc = label_weight_nc_dict[key]
+            assert isinstance(label_weight_nc, torch.Tensor)
+            loss_dict[key] = torch.sum(
+                loss_fn(logits_nck.view(label_nc.numel(), -1), label_nc.view(-1).long()) * label_weight_nc.view(-1)
+            )
+
+        loss = sum(loss_dict[key] * self.loss_scale_dict[key] for key in loss_dict)
+        assert isinstance(loss, torch.Tensor)
+        loss_dict["loss"] = loss
+
+        return loss_dict
+
+    def validate(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        batch_idx: int,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+        label_nc_dict: dict[str, torch.Tensor],
+        label_weight_nc_dict: dict[str, torch.Tensor],
+    ) -> None:
+        n = prompt_mask_nc.shape[0]
+        loss_dict = self.forward(
+            token_value_nc_dict=token_value_nc_dict,
+            token_mask_nc_dict=token_mask_nc_dict,
+            prompt_mask_nc=prompt_mask_nc,
+            label_nc_dict=label_nc_dict,
+            label_weight_nc_dict=label_weight_nc_dict,
+        )
+
+        pl_module.log_dict(loss_dict, sync_dist=True, on_epoch=True, batch_size=n)
diff --git a/cellarium/ml/utilities/data.py b/cellarium/ml/utilities/data.py
index e0e37825..c6b5f59c 100644
--- a/cellarium/ml/utilities/data.py
+++ b/cellarium/ml/utilities/data.py
@@ -144,9 +144,9 @@ def categories_to_codes(x: pd.Series | pd.DataFrame) -> np.ndarray:
         Numpy array.
     """
     if isinstance(x, pd.DataFrame):
-        return x.apply(lambda col: col.cat.codes).to_numpy()
+        return x.apply(lambda col: col.cat.codes).to_numpy(dtype=np.int32)
     else:
-        return np.asarray(x.cat.codes)
+        return np.asarray(x.cat.codes, dtype=np.int32)
 
 
 def get_categories(x: pd.Series) -> np.ndarray:
diff --git a/cellarium/ml/utilities/mup.py b/cellarium/ml/utilities/mup.py
index 368ad795..097b6e6c 100644
--- a/cellarium/ml/utilities/mup.py
+++ b/cellarium/ml/utilities/mup.py
@@ -14,6 +14,14 @@ def convert_glob_to_regex(f: str) -> re.Pattern:
     return re.compile(fnmatch.translate(f))
 
 
+class glob_expression_param_filter:
+    def __init__(self, param_filters: list[re.Pattern]) -> None:
+        self.param_filters = param_filters
+
+    def __call__(self, name: str) -> bool:
+        return any(filter.fullmatch(name) for filter in self.param_filters)
+
+
 def make_param_filter(param_filter: str | list[str]) -> Callable[[str], bool]:
     """
     Returns the corresponding filter for parameters for the given `param_filter`.
@@ -34,10 +42,7 @@ def make_param_filter(param_filter: str | list[str]) -> Callable[[str], bool]:
         )
     )
 
-    def glob_expression_param_filter(name: str) -> bool:
-        return any(filter.fullmatch(name) for filter in param_filters)
-
-    return glob_expression_param_filter
+    return glob_expression_param_filter(param_filters)
 
 
 class LRAdjustmentGroup:
diff --git a/tests/test_cellarium_gpt.py b/tests/test_cellarium_gpt.py
new file mode 100644
index 00000000..20c69f6a
--- /dev/null
+++ b/tests/test_cellarium_gpt.py
@@ -0,0 +1,112 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import os
+from pathlib import Path
+
+import lightning.pytorch as pl
+import numpy as np
+import torch
+
+from cellarium.ml import CellariumModule
+from cellarium.ml.data import PyTreeDataset, read_h5ad_file
+from cellarium.ml.models import CellariumGPT
+from cellarium.ml.utilities.data import categories_to_codes, collate_fn
+
+
+def test_load_from_checkpoint_multi_device(tmp_path: Path):
+    adata = read_h5ad_file("https://storage.googleapis.com/dsp-cellarium-cas-public/test-data/test_0.h5ad")
+    n = adata.n_obs
+    s = 4  # number of subsampled genes
+    c = 5  # context size
+    batch_size = 2
+    devices = int(os.environ.get("TEST_DEVICES", "1"))
+    prompt_mask_nc = np.random.choice([True, False], size=(n, c), p=[0.5, 0.5])
+    query_mask_nc = (~prompt_mask_nc).astype(np.float32)
+    X = adata.X[:, :s].toarray()
+
+    cell_type = categories_to_codes(adata.obs["cell_type"])[:, None]
+    data = {
+        "token_value_nc_dict": {
+            "gene_id": np.broadcast_to(np.arange(c), (n, c)),
+            "gene_value": np.concatenate([X, np.zeros((n, 1), dtype=np.float32)], axis=1),
+            "gene_query_mask": query_mask_nc,
+            "total_mrna_umis": np.broadcast_to(
+                np.asarray(adata.obs["total_mrna_umis"], dtype=np.float32)[:, None], (n, c)
+            ),
+            "cell_type": np.broadcast_to(cell_type, (n, c)),
+        },
+        "token_mask_nc_dict": {
+            "gene_id": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "gene_value": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "gene_query_mask": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "total_mrna_umis": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
+        },
+        "prompt_mask_nc": prompt_mask_nc,
+        "label_nc_dict": {
+            "gene_value": np.concatenate([X, np.zeros((n, 1))], axis=1),
+            "cell_type": np.concatenate([np.zeros((n, s)), cell_type], axis=1),
+        },
+        "label_weight_nc_dict": {
+            "gene_value": (np.broadcast_to(np.arange(c), (n, c)) < s).astype(np.float32),
+            "cell_type": (np.broadcast_to(np.arange(c), (n, c)) == s).astype(np.float32),
+        },
+    }
+    dataset = PyTreeDataset(data)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        num_workers=0,
+        collate_fn=collate_fn,
+    )
+    # model
+    model = CellariumGPT(
+        categorical_token_size_dict={
+            "gene_id": c,
+            "gene_value": 100,
+            "cell_type": adata.obs["cell_type"].cat.categories.size,
+        },
+        d_model=3,
+        d_ffn=6,
+        n_heads=1,
+        n_blocks=1,
+        dropout_p=0,
+        use_bias=False,
+        attention_backend="torch",
+        attention_softmax_fp32=True,
+        loss_scale_dict={"gene_value": 0.8, "cell_type": 0.2},
+        attention_logits_scale=1,
+        mup_base_d_model=2,
+        mup_base_d_ffn=4,
+    )
+    assert model.d_model == 3
+    assert model.d_ffn == 6
+    assert model.n_heads == 1
+    assert model.n_blocks == 1
+    assert model.attention_backend == "torch"
+    module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3, "eps": 1e-8})
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="cpu",
+        devices=devices,
+        max_steps=2,
+        default_root_dir=tmp_path,
+    )
+    # fit
+    trainer.fit(module, dataloader)
+
+    # run tests only for rank 0
+    if trainer.global_rank != 0:
+        return
+
+    # load model from checkpoint
+    ckpt_path = tmp_path / "lightning_logs/version_0/checkpoints/epoch=0-step=2.ckpt"
+    assert ckpt_path.is_file()
+    loaded_model = CellariumModule.load_from_checkpoint(ckpt_path).model
+    assert isinstance(loaded_model, CellariumGPT)
+    # assert
+    assert model.attention_backend == loaded_model.attention_backend
+    assert model.embeddings_scale == loaded_model.embeddings_scale
+    assert model.attention_logits_scale == loaded_model.attention_logits_scale
+    assert model.output_logits_scale == loaded_model.output_logits_scale

From 770a1571c3718fd8ff4d0c38e1d0bdbe67de884c Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 6 Feb 2025 13:48:23 -0500
Subject: [PATCH 35/64] Add FSDP support (#301)

* test

* requires cuda
---
 cellarium/ml/callbacks/compute_norm.py | 62 ++++++++++------
 cellarium/ml/core/module.py            | 27 ++++++-
 tests/test_callbacks.py                | 99 +++++++++++++++++++++++++-
 3 files changed, 164 insertions(+), 24 deletions(-)

diff --git a/cellarium/ml/callbacks/compute_norm.py b/cellarium/ml/callbacks/compute_norm.py
index 8bfe7c35..818301f5 100644
--- a/cellarium/ml/callbacks/compute_norm.py
+++ b/cellarium/ml/callbacks/compute_norm.py
@@ -7,16 +7,14 @@
 
 import lightning.pytorch as pl
 import torch
-from lightning.fabric.utilities.rank_zero import rank_zero_only
+import torch.distributed as dist
+from lightning.pytorch.strategies import FSDPStrategy
+from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
 
 
 class ComputeNorm(pl.Callback):
     """
-    A callback to compute the model wise and per layer norm of the parameters and gradients.
-
-    .. note::
-
-        This callback does not support sharded model training.
+    A callback to compute the model wise and per layer l2 norm of the parameters and gradients.
 
     Args:
         layer_name:
@@ -31,42 +29,64 @@ def __init__(self, layer_name: str | None = None) -> None:
         else:
             self.layer_pattern = None
 
-    @rank_zero_only
     def on_before_backward(self, trainer: pl.Trainer, pl_module: pl.LightningModule, loss: torch.Tensor) -> None:
         """Compute the model wise norm of the parameters."""
-        param_norm = torch.tensor(0.0, device=pl_module.device)
+        param_norm_sq = torch.tensor(0.0, device=pl_module.device)
+
         for _, param in pl_module.named_parameters():
             if param.requires_grad:
-                param_norm += torch.pow(torch.norm(param.detach()), 2.0)
+                param_norm_sq += torch.pow(torch.norm(param.detach()), 2.0)
 
-        pl_module.log("model_wise_param_norm", torch.sqrt(param_norm).item())
+        if (
+            isinstance(trainer.strategy, FSDPStrategy)
+            and trainer.strategy.sharding_strategy != ShardingStrategy.NO_SHARD
+        ):
+            assert trainer.strategy.model is not None
+            # Sum all local norms to get the total norm
+            dist.all_reduce(param_norm_sq, op=dist.ReduceOp.SUM, group=trainer.strategy.model.process_group)
+
+        pl_module.log("model_wise_param_norm", torch.sqrt(param_norm_sq).item(), rank_zero_only=True)
 
-    @rank_zero_only
     def on_before_optimizer_step(
         self, trainer: pl.Trainer, pl_module: pl.LightningModule, optimizer: torch.optim.Optimizer
     ) -> None:
         """Compute the model wise and per layer norm of the gradients."""
-        model_wise_grad_norm = torch.tensor(0.0, device=pl_module.device)
-        per_layer_grad_norm: dict[str, torch.Tensor] = defaultdict(lambda: torch.tensor(0.0, device=pl_module.device))
+        model_wise_grad_norm_sq = torch.tensor(0.0, device=pl_module.device)
+        per_layer_grad_norm_sq: dict[str, torch.Tensor] = defaultdict(
+            lambda: torch.tensor(0.0, device=pl_module.device)
+        )
 
         for name, param in pl_module.named_parameters():
             if param.grad is None:
                 continue
-            param_grad_norm = torch.pow(torch.norm(param.grad), 2.0)
-            model_wise_grad_norm += param_grad_norm
+            param_grad_norm_sq = torch.pow(torch.norm(param.grad), 2.0)
+            model_wise_grad_norm_sq += param_grad_norm_sq
 
             # get a match if module name contains `*.layer_name.i.*` where i is layer num
             if self.layer_pattern:
                 match = self.layer_pattern.match(name)
                 if match:
                     layer_id = match.group(2)
-                    per_layer_grad_norm[layer_id] += param_grad_norm
+                    per_layer_grad_norm_sq[layer_id] += param_grad_norm_sq
+
+        if (
+            isinstance(trainer.strategy, FSDPStrategy)
+            and trainer.strategy.sharding_strategy != ShardingStrategy.NO_SHARD
+        ):
+            assert trainer.strategy.model is not None
+            # Sum all local norms to get the total norm
+            dist.all_reduce(model_wise_grad_norm_sq, op=dist.ReduceOp.SUM, group=trainer.strategy.model.process_group)
+            for layer_id in per_layer_grad_norm_sq:
+                dist.all_reduce(
+                    per_layer_grad_norm_sq[layer_id], op=dist.ReduceOp.SUM, group=trainer.strategy.model.process_group
+                )
 
-        pl_module.log("model_wise_grad_norm", torch.sqrt(model_wise_grad_norm).item())
-        if per_layer_grad_norm:
+        pl_module.log("model_wise_grad_norm", torch.sqrt(model_wise_grad_norm_sq).item(), rank_zero_only=True)
+        if per_layer_grad_norm_sq:
             pl_module.log_dict(
                 {
-                    f"per_layer_grad_norm/layer_{layer_id}": torch.sqrt(per_layer_grad_norm[layer_id]).item()
-                    for layer_id in per_layer_grad_norm
-                }
+                    f"per_layer_grad_norm/layer_{layer_id}": torch.sqrt(per_layer_grad_norm_sq[layer_id]).item()
+                    for layer_id in per_layer_grad_norm_sq
+                },
+                rank_zero_only=True,
             )
diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index 918e55b8..3da6363c 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -151,6 +151,7 @@ def configure_model(self) -> None:
         self.pipeline = CellariumPipeline(cpu_transforms + transforms + (model,))  # the full pipeline
 
         if not self.hparams["is_initialized"]:
+            # Note: when using FSDPStrategy, the model is initialized here and then wrapped and sharded by the strategy.
             model.reset_parameters()
             self.hparams["is_initialized"] = True
 
@@ -344,7 +345,7 @@ def configure_optimizers(self) -> OptimizerLRScheduler:
                 param_groups.append({"params": params, **group_optim_kwargs})
             optimizer = optim_fn(param_groups)
         else:
-            optimizer = optim_fn(self.model.parameters(), **optim_kwargs)
+            optimizer = optim_fn(self.parameters(), **optim_kwargs)
 
         if scheduler_fn is not None:
             scheduler = scheduler_fn(optimizer, **scheduler_kwargs)
@@ -355,6 +356,30 @@ def configure_optimizers(self) -> OptimizerLRScheduler:
         else:
             return {"optimizer": optimizer}
 
+    def configure_gradient_clipping(
+        self,
+        optimizer: torch.optim.Optimizer,
+        gradient_clip_val: int | float | None = None,
+        gradient_clip_algorithm: str | None = None,
+    ) -> None:
+        """
+        Handle gradient clipping by norm when using the FSDP strategy.
+        """
+        from lightning.pytorch.strategies import FSDPStrategy
+        from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel
+
+        if isinstance(self.trainer.strategy, FSDPStrategy) and gradient_clip_algorithm in ("norm", None):
+            if gradient_clip_val is None:
+                gradient_clip_val = self.trainer.gradient_clip_val or 0.0
+            if gradient_clip_val <= 0:
+                return
+            assert isinstance(self.trainer.strategy.model, FullyShardedDataParallel)
+            self.trainer.strategy.model.clip_grad_norm_(gradient_clip_val)
+        else:
+            self.clip_gradients(
+                optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm
+            )
+
     def on_train_epoch_start(self) -> None:
         """
         Calls the ``set_epoch`` method on the iterable dataset of the given dataloader.
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index 09f49ad8..63bd082b 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -1,17 +1,22 @@
 # Copyright Contributors to the Cellarium project.
 # SPDX-License-Identifier: BSD-3-Clause
 
+import copy
+import os
 from pathlib import Path
 
 import lightning.pytorch as pl
 import numpy as np
 import pytest
 import torch
+from lightning.pytorch.strategies import FSDPStrategy
+from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy
 
 from cellarium.ml import CellariumModule
 from cellarium.ml.callbacks import ComputeNorm, LossScaleMonitor
 from cellarium.ml.models import LogisticRegression
 from cellarium.ml.utilities.data import collate_fn
+from cellarium.ml.utilities.testing import PandasLogger
 from tests.common import USE_CUDA, BoringDataset
 
 
@@ -53,7 +58,7 @@ def test_loss_scale_monitor(tmp_path: Path):
     trainer.fit(module, train_dataloaders=train_loader)
 
 
-def test_compute_norm(tmp_path: Path):
+def test_compute_norm():
     n, g = 4, 3
     var_names_g = np.array([f"gene_{i}" for i in range(g)])
     y_categories = np.array(["a", "b"])
@@ -76,14 +81,104 @@ def test_compute_norm(tmp_path: Path):
         log_metrics=False,
     )
     module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3})
+    logger = PandasLogger()
     # trainer
     trainer = pl.Trainer(
         accelerator="cpu",
         devices=1,
+        logger=logger,
         callbacks=[ComputeNorm()],
         max_epochs=1,
         log_every_n_steps=1,
-        default_root_dir=tmp_path,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
     )
     # fit
     trainer.fit(module, train_dataloaders=train_loader)
+
+    assert not logger.df.empty
+
+
+@pytest.mark.skipif(not USE_CUDA, reason="requires_cuda")
+@pytest.mark.parametrize("sharding_strategy", [ShardingStrategy.NO_SHARD, ShardingStrategy.FULL_SHARD])
+def test_compute_norm_multi_device(sharding_strategy: ShardingStrategy):
+    devices = int(os.environ.get("TEST_DEVICES", "1"))
+
+    # dataset
+    n, g = 6, 3
+    var_names_g = np.array([f"gene_{i}" for i in range(g)])
+    y_categories = np.array(["a", "b"])
+    y = np.array([0, 1, 0, 1, 1, 0])
+    dataset = BoringDataset(
+        np.ones((n, g)).astype(np.float32),
+        var_names=var_names_g,
+        y=y,
+        y_categories=y_categories,
+    )
+    # model
+    model = LogisticRegression(
+        n_obs=n,
+        var_names_g=var_names_g,
+        y_categories=y_categories,
+        log_metrics=False,
+    )
+    orig_module = CellariumModule(model=model, optim_fn=torch.optim.Adam, optim_kwargs={"lr": 1e-3})
+
+    # single device
+    train_loader = torch.utils.data.DataLoader(
+        dataset=dataset,
+        batch_size=devices,
+        shuffle=False,
+        collate_fn=collate_fn,
+    )
+    module = copy.deepcopy(orig_module)
+    logger_single_device = PandasLogger()
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="gpu",
+        devices=1,
+        logger=logger_single_device,
+        callbacks=[ComputeNorm()],
+        max_epochs=1,
+        log_every_n_steps=1,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+    # fit
+    trainer.fit(module, train_dataloaders=train_loader)
+
+    # multi device
+    class DataModule(pl.LightningDataModule):
+        def train_dataloader(self):
+            return torch.utils.data.DataLoader(
+                dataset=dataset,
+                sampler=torch.utils.data.DistributedSampler(dataset, shuffle=False),
+                batch_size=1,
+                collate_fn=collate_fn,
+            )
+
+    datamodule = DataModule()
+    module = copy.deepcopy(orig_module)
+    logger_multi_device = PandasLogger()
+    # trainer
+    trainer = pl.Trainer(
+        accelerator="gpu",
+        strategy=FSDPStrategy(sharding_strategy=sharding_strategy),
+        devices=devices,
+        logger=logger_multi_device,
+        callbacks=[ComputeNorm()],
+        max_epochs=1,
+        log_every_n_steps=1,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+    # fit
+    trainer.fit(module, datamodule)
+
+    if trainer.global_rank != 0:
+        return
+
+    assert logger_single_device.df.equals(logger_multi_device.df)

From d058d3d4fb7ad85919eda4d6943752a0e7a1e2a7 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 7 Feb 2025 23:49:25 +0000
Subject: [PATCH 36/64] refactoring + new notebook for linear response analysis

---
 .../inference/cellarium_gpt_inference.py      | 274 +++++++++++----
 ...near_response_by_cell_type_prompting.ipynb | 322 ++++++++++++++++++
 2 files changed, 530 insertions(+), 66 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 1462c9c6..40e1a13a 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -23,6 +23,8 @@
 from scipy.stats import linregress
 from scipy.linalg import eigh
 
+import typing as t
+
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)  # Set the logging level
@@ -83,8 +85,12 @@ def __init__(
             ref_adata_path: str,
             gene_info_tsv_path: str,
             device: torch.device,
-            attention_backend: str = "mem_efficient"):
+            attention_backend: str = "mem_efficient",
+            verbose: bool = True):
         
+        # for logging
+        self.verbose = verbose
+
         # load an anndata extract as reference
         self._adata = sc.read_h5ad(ref_adata_path)
 
@@ -119,6 +125,9 @@ def __init__(
             self.gpt_pipeline.model,
         ])
 
+    def log(self, message: str):
+        if self.verbose:
+            logger.info(message)
 
     def _instantiate_predict_tokenizer(self) -> PredictTokenizer:
         return PredictTokenizer(
@@ -268,18 +277,52 @@ def generate_tokens_from_adata(
         context_indices['query_genes'] = np.arange(n_prompt_vars, n_query_vars + n_prompt_vars).tolist()
         offset = 0
         for metadata_key in self.metadata_ontology_infos.keys():
-            context_indices[f'query_{metadata_key}'] = n_query_vars + n_prompt_vars + offset
+            if metadata_prompt_masks_dict[metadata_key]:  # prompted
+                prefix = "prompt"
+            else:
+                prefix = "query"
+            context_indices[f'{prefix}_{metadata_key}'] = n_query_vars + n_prompt_vars + offset
             offset += 1
 
         # return gene_tokens_dict, metadata_tokens_dict
-        tokenizer_output = self.predict_tokenizer(
+        tokens_dict = self.predict_tokenizer(
             metadata_tokens_n=metadata_tokens_dict,
             metadata_prompt_masks_n=expanded_metadata_prompt_masks_dict,
             gene_tokens_nc=gene_tokens_dict,
             gene_prompt_mask_nc=gene_prompt_mask_nc,
         )
 
-        return tokenizer_output, context_indices
+        return tokens_dict, context_indices
+
+    def get_gene_value_logits_by_metadata(
+        self,
+        assay: str,
+        suspension_type: str,
+        prompt_metadata_dict: dict,
+        total_mrna_umis: int,
+        query_gene_ids: list[list],
+        max_counts: int | None = None
+    ) -> t.Tuple[dict, dict]:
+        
+        metadata_prompt_masks_dict, metadata_dict = self.process_user_metadata(
+            assay, suspension_type, prompt_metadata_dict, total_mrna_umis)
+
+        obs_df = pd.DataFrame({key: [value] for key, value in metadata_dict.items()})
+        var_df = pd.DataFrame()
+        adata = sc.AnnData(X=np.zeros((1, 0)), obs=obs_df, var=var_df)
+
+        # Tokenize
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=adata,
+            obs_index=None,
+            query_var_names=query_gene_ids,
+            metadata_prompt_masks_dict=metadata_prompt_masks_dict
+        )
+        
+        gene_logits_nqk = self.get_gene_value_logits_from_tokens(tokens_dict, context_indices, max_counts)
+
+        return gene_logits_nqk
+
 
     def generate_gene_tokens_by_metadata(
         self,
@@ -307,6 +350,53 @@ def generate_gene_tokens_by_metadata(
         
         """
 
+        metadata_prompt_masks_dict, metadata_dict = self.process_user_metadata(
+            assay, suspension_type, prompt_metadata_dict, total_mrna_umis)
+
+        if perturb_gene_ids is None:
+            n_cells = 1
+        else:
+            n_cells = len(perturb_gene_ids) + 1
+
+        # Generate a placeholder AnnData. There is always only gene at the prompt, which will
+        # be replaced with the gene to be perturbed. If no perturbation is required, the gene
+        # will be set to the first gene in the gene ID dictionary.
+        obs_df = pd.DataFrame({key: [value] * n_cells for key, value in metadata_dict.items()})
+        var_df = pd.DataFrame(index=[self.model_var_names[0]])
+        pert_adata = sc.AnnData(X=np.zeros((n_cells, 1)), obs=obs_df, var=var_df)
+
+        # Tokenize
+        tokens_dict, context_indices = self.generate_tokens_from_adata(
+            adata=pert_adata,
+            obs_index=None,
+            query_var_names=query_gene_ids,
+            metadata_prompt_masks_dict=metadata_prompt_masks_dict
+        )
+
+        # The first cell is control unperturbed, so we will ensure that the first gene is marked as queried  (not perturbed)
+        tokens_dict['prompt_mask_nc'][0, 0] = False
+        tokens_dict['gene_tokens_nc']['gene_value'][0, context_indices['prompt_genes'], 1] = 1  # Queried
+
+        # In subsequent cells, prompt genes are set to 0 sequentially
+        if perturb_gene_ids is not None:
+            assert all(gene_id in self.var_name_to_index_map for gene_id in perturb_gene_ids)
+            tokens_dict['gene_tokens_nc']['gene_id'] = tokens_dict['gene_tokens_nc']['gene_id'].clone()
+            tokens_dict['gene_tokens_nc']['gene_id'][1:, 0] = torch.tensor([
+                self.var_name_to_index_map[var_name] for var_name in perturb_gene_ids])
+
+        return tokens_dict, context_indices, pert_adata, metadata_prompt_masks_dict
+
+
+    def process_user_metadata(
+            self,
+            assay: str,
+            suspension_type: str,
+            prompt_metadata_dict: dict[str, str],
+            total_mrna_umis: int | float) -> t.Tuple[dict[str, bool], dict[str, str]]:
+        """ Given user provided metadata, generate a complete metadata dictionary with ontology term IDs
+        where applicable.
+        """
+        
         METADATA_KEYS = [
             "cell_type",
             "tissue",
@@ -348,39 +438,9 @@ def generate_gene_tokens_by_metadata(
             "assay_ontology_term_id": self.gene_ontology_infos["assay_ontology_term_id"]["names"][
                 self.gene_ontology_infos["assay_ontology_term_id"]["labels"].index(assay)]
         }
+        
+        return metadata_prompt_masks_dict, metadata_dict
 
-        if perturb_gene_ids is None:
-            n_cells = 1
-        else:
-            n_cells = len(perturb_gene_ids) + 1
-
-        # Generate a placeholder AnnData. There is always only gene at the prompt, which will
-        # be replaced with the gene to be perturbed. If no perturbation is required, the gene
-        # will be set to the first gene in the gene ID dictionary.
-        obs_df = pd.DataFrame({key: [value] * n_cells for key, value in metadata_dict.items()})
-        var_df = pd.DataFrame(index=[self.model_var_names[0]])
-        pert_adata = sc.AnnData(X=np.zeros((n_cells, 1)), obs=obs_df, var=var_df)
-
-        # Tokenize
-        tokens_dict, context_indices = self.generate_tokens_from_adata(
-            adata=pert_adata,
-            obs_index=None,
-            query_var_names=query_gene_ids,
-            metadata_prompt_masks_dict=metadata_prompt_masks_dict
-        )
-
-        # The first cell is control unperturbed, so we will ensure that the first gene is marked as queried  (not perturbed)
-        tokens_dict['prompt_mask_nc'][0, 0] = False
-        tokens_dict['gene_tokens_nc']['gene_value'][0, context_indices['prompt_genes'], 1] = 1  # Queried
-
-        # In subsequent cells, prompt genes are set to 0 sequentially
-        if perturb_gene_ids is not None:
-            assert all(gene_id in self.var_name_to_index_map for gene_id in perturb_gene_ids)
-            tokens_dict['gene_tokens_nc']['gene_id'] = tokens_dict['gene_tokens_nc']['gene_id'].clone()
-            tokens_dict['gene_tokens_nc']['gene_id'][1:, 0] = torch.tensor([
-                self.var_name_to_index_map[var_name] for var_name in perturb_gene_ids])
-
-        return tokens_dict, context_indices, pert_adata, metadata_prompt_masks_dict
 
     def get_marginal_mean_std(
             self,
@@ -390,6 +450,10 @@ def get_marginal_mean_std(
             prompt_gene_values_g: torch.Tensor | None = None,
             metadata_prompt_masks_dict: dict[str, bool] | None = None
         ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+        """
+        .. note: This is a legacy implemetation used for Jacobian calculation which assumes only a single cell.
+          For most applications, please consider using the other methods provided in this class.
+        """
     
         assert len(adata) == 1, "Only a single cell is allowed"
 
@@ -441,44 +505,32 @@ def get_marginal_mean_std(
 
         return gene_marginal_means_q, gene_marginal_std_q
 
-    def get_marginal_mean_std_from_tokens(
+
+    def get_gene_value_logits_from_tokens(
             self,
             tokens_dict: dict,
             context_indices: dict,
-            use_logsumexp: bool = True,
-            max_counts: int | None = None,
-            verbose: bool = False,
-        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
-    
-        # convert to cuda
-        if verbose:
-            logger.info("Transferring the tokens to the device ...")
+            max_counts: int | None = None
+        ) -> torch.Tensor:
 
+        # convert to cuda
+        self.log("Transferring the tokens to the device ...")
         tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
-
-        if verbose:
-            logger.info("Done.")
+        self.log("Done.")
         
         # get model predictions
-        if verbose:
-            logger.info("Predicting ...")
-
+        self.log("Predicting ...")
         logits_dict = self.gpt_pipeline.model.predict(
             gene_tokens_nc=tokens_dict["gene_tokens_nc"],
             metadata_tokens_n=tokens_dict["metadata_tokens_n"],
             prompt_mask_nc=tokens_dict["prompt_mask_nc"],
             predict_keys=['gene_value']
         )
-
-        if verbose:
-            logger.info("Done.")
+        self.log("Done.")
 
         # note: we use `q` to denote query genes
-        if verbose:
-            logger.info("Calculating marginal mean and std ...")
-        
-        if verbose:
-            logger.info("Obtaining gene logits ...")
+        self.log("Calculating marginal mean and std ...")
+        self.log("Obtaining gene logits ...")
 
         query_gene_indices = torch.tensor(context_indices['query_genes'], device=self.device, dtype=torch.int64)
 
@@ -489,16 +541,25 @@ def get_marginal_mean_std_from_tokens(
             assert max_counts > 0
             gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :max_counts]
 
-        if verbose:
-            logger.info("Done.")
-
-        if verbose:
-            logger.info("Normalizing gene logits ...")
+        self.log("Done.")
+        self.log("Normalizing gene logits ...")
 
         gene_logits_nqk = gene_logits_nqk - torch.logsumexp(gene_logits_nqk, dim=-1, keepdim=True)
+        self.log("Done.")
 
-        if verbose:
-            logger.info("Done.")
+        return gene_logits_nqk
+     
+
+    def get_marginal_mean_std_from_tokens(
+            self,
+            tokens_dict: dict,
+            context_indices: dict,
+            use_logsumexp: bool = True,
+            max_counts: int | None = None,
+            verbose: bool = False,
+        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
+        gene_logits_nqk = self.get_gene_value_logits_from_tokens(tokens_dict, context_indices, max_counts)
 
         if use_logsumexp:
             log_counts_1_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).log()
@@ -521,6 +582,7 @@ def get_marginal_mean_std_from_tokens(
 
         return gene_marginal_means_nq, gene_marginal_std_nq
 
+
     def get_marginal_mean_std_multi_cell(
             self,
             adata: sc.AnnData,
@@ -558,6 +620,86 @@ def get_marginal_mean_std_multi_cell(
         )
 
 
+    def predict_gene_expression_dynamic_range_for_metadata(
+        self,
+        assay: str,
+        suspension_type: str,
+        prompt_metadata_dict: dict[str, str],
+        total_mrna_umis: int | float,
+        query_gene_ids: list[str],
+        query_chunk_size: int | None = None,
+        upper_percentile: float = 0.9999,
+        upper_pad: int = 1
+    ):
+        """
+        Compute the maximum counts indices based on cumulative gene probabilities.
+
+        This function chunks the list of gene identifiers, retrieves gene logits
+        for each chunk using `ctx.get_gene_value_logits_by_metadata`, concatenates
+        the results, and then computes the cumulative sum of the exponentiated logits.
+        For each query, it finds the first (smallest) index along the k dimension where
+        the cumulative probability exceeds the provided upper_percentile threshold,
+        and then adds an upper_pad value.
+
+        Args:
+            ctx: An object that provides:
+                - `ctx.get_gene_value_logits_by_metadata(...)` to compute gene logits.
+            query_gene_ids (list): List of gene identifiers.
+            assay (str): Name of the assay (e.g., "10x 3' v3").
+            suspension_type (str): Type of suspension (e.g., "cell").
+            prompt_metadata_dict (dict): Metadata dictionary (e.g., cell type, tissue).
+            total_mrna_umis (int): Total number of mRNA UMIs.
+            query_chunk_size (int): Number of gene IDs to process in one chunk.
+            upper_percentile (float): Upper percentile threshold (e.g., 0.999).
+            upper_pad (int): Value to add as padding to the found index.
+
+        Returns:
+            torch.Tensor: A tensor containing, for each query, the smallest index along k
+                        where the cumulative probability exceeds upper_percentile plus upper_pad.
+        """
+
+        assert 0.0 < upper_percentile < 1.0, "The upper percentile must be between 0 and 1."
+
+        def yield_chunks(lst, n):
+            """Yield successive n-sized chunks from the list."""
+            for i in range(0, len(lst), n):
+                yield lst[i:i + n]
+
+        if query_chunk_size is None:
+            query_chunk_size = len(query_gene_ids)
+        
+        partial_gene_logits_list = []
+        with torch.inference_mode():
+            # Process gene IDs in chunks to limit memory usage
+            for partial_query_gene_ids in tqdm(list(yield_chunks(query_gene_ids, query_chunk_size)),
+                                            desc="Processing gene chunks"):
+                partial_gene_logits_nqk = self.get_gene_value_logits_by_metadata(
+                    assay=assay,
+                    suspension_type=suspension_type,
+                    prompt_metadata_dict=prompt_metadata_dict,
+                    total_mrna_umis=total_mrna_umis,
+                    query_gene_ids=partial_query_gene_ids
+                )
+                partial_gene_logits_list.append(partial_gene_logits_nqk)
+            
+            # Concatenate along the gene dimension (assumed to be dim=1) and select the first element along dim=0.
+            gene_logits_qk = torch.cat(partial_gene_logits_list, dim=1)[0]
+
+        # Compute the cumulative sum of the exponentiated logits (interpreted as gene probabilities)
+        gene_count_probs_cumsum_qk = gene_logits_qk.exp().cumsum(dim=1)
+        
+        # Create a mask where cumulative probabilities exceed the threshold.
+        # (Note: If you ever need the mask for other purposes, you can return it or process it further.)
+        mask_qk = gene_count_probs_cumsum_qk > upper_percentile
+
+        # For each row, find the first index where the mask is True.
+        # Converting the boolean mask to float ensures that True becomes 1.0 and False becomes 0.0.
+        # Since the mask is assumed to eventually become True, argmax returns the index of the first True.
+        max_counts_k = mask_qk.float().argmax(dim=-1) + upper_pad
+
+        return max_counts_k
+    
+
     def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) -> dict[str, np.ndarray]:
         metadata_prediction_chunks_dict = []
         for i in tqdm(range(0, len(adata), chunk_size)):
diff --git a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
new file mode 100644
index 00000000..ce3e260a
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### _in silico_ perturbation by cell type prompting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "query_gene_ids = ctx.model_var_names\n",
+    "assay = \"10x 3' v2\"\n",
+    "suspension_type = \"cell\"\n",
+    "prompt_metadata_dict = {\n",
+    "    \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
+    "    \"tissue\": \"blood\"\n",
+    "}\n",
+    "total_mrna_umis = 20_000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gene expression dynamic range determination"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c456e1d2c487444e8783511e19ee676d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:42,491 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:42,492 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,493 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:42,517 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:42,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:42,672 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,673 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:42,674 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:42,686 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:42,687 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,688 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:42,712 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,712 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:42,713 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:42,842 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,843 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:42,844 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:42,855 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:42,856 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,856 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:42,880 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:42,880 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:42,881 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,010 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,011 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,012 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:43,023 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:43,024 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,025 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:43,049 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,049 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:43,050 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,179 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,179 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,180 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:43,192 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:43,193 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,194 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:43,217 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,218 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:43,219 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,348 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,348 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,349 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:43,360 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:43,361 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,362 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:43,385 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,386 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:43,387 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,516 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,517 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:43,529 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:43,530 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,530 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:43,554 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,554 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:43,555 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,684 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,685 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,686 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "2025-02-07 23:38:43,695 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
+      "2025-02-07 23:38:43,696 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,696 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
+      "2025-02-07 23:38:43,718 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,719 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
+      "2025-02-07 23:38:43,719 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
+      "2025-02-07 23:38:43,730 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
+      "2025-02-07 23:38:43,730 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
+      "2025-02-07 23:38:43,731 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_chunk_size = 5_000\n",
+    "upper_percentile = 0.99\n",
+    "upper_pad = 1\n",
+    "\n",
+    "gene_expr_range_q = ctx.predict_gene_expression_dynamic_range_for_metadata(\n",
+    "    assay=assay,\n",
+    "    suspension_type=suspension_type,\n",
+    "    prompt_metadata_dict=prompt_metadata_dict,\n",
+    "    total_mrna_umis=total_mrna_umis,\n",
+    "    query_gene_ids=query_gene_ids,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    upper_percentile=upper_percentile,\n",
+    "    upper_pad=upper_pad\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 309
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(gene_expr_range_q.cpu().numpy(), bins=50, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ENSG00000206172: 1090, HBA1\n",
+      "ENSG00000251562: 2001, MALAT1\n",
+      "ENSG00000211677: 1273, IGLC2\n",
+      "ENSG00000198804: 1206, MT-CO1\n",
+      "ENSG00000087086: 1470, FTL\n",
+      "ENSG00000211895: 1472, IGHA1\n",
+      "ENSG00000211592: 2001, IGKC\n",
+      "ENSG00000229117: 1110, RPL41\n"
+     ]
+    }
+   ],
+   "source": [
+    "for idx in torch.where(gene_expr_range_q > 1000)[0]:\n",
+    "    print(f\"{ctx.model_var_names[idx]}: {gene_expr_range_q[idx]}, {ctx.gene_id_to_gene_symbol_map[ctx.model_var_names[idx]]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From db4ff029b358eb12a1f3465d9d47366e52c88f49 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Mon, 10 Feb 2025 00:05:42 +0000
Subject: [PATCH 37/64] bugfixes

---
 .../inference/cellarium_gpt_inference.py       | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 40e1a13a..ae30aad1 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -308,6 +308,7 @@ def get_gene_value_logits_by_metadata(
             assay, suspension_type, prompt_metadata_dict, total_mrna_umis)
 
         obs_df = pd.DataFrame({key: [value] for key, value in metadata_dict.items()})
+        obs_df.index = obs_df.index.astype(str)
         var_df = pd.DataFrame()
         adata = sc.AnnData(X=np.zeros((1, 0)), obs=obs_df, var=var_df)
 
@@ -332,6 +333,7 @@ def generate_gene_tokens_by_metadata(
         total_mrna_umis: int,
         query_gene_ids: list[list],
         perturb_gene_ids: list[str] | None,
+        perturb_gene_values: np.ndarray | None = None,
     ) -> t.Tuple[dict, dict]:
         
         """
@@ -362,6 +364,7 @@ def generate_gene_tokens_by_metadata(
         # be replaced with the gene to be perturbed. If no perturbation is required, the gene
         # will be set to the first gene in the gene ID dictionary.
         obs_df = pd.DataFrame({key: [value] * n_cells for key, value in metadata_dict.items()})
+        obs_df.index = obs_df.index.astype(str)
         var_df = pd.DataFrame(index=[self.model_var_names[0]])
         pert_adata = sc.AnnData(X=np.zeros((n_cells, 1)), obs=obs_df, var=var_df)
 
@@ -374,8 +377,9 @@ def generate_gene_tokens_by_metadata(
         )
 
         # The first cell is control unperturbed, so we will ensure that the first gene is marked as queried  (not perturbed)
+        PERTURB_GENE_CONTEXT_INDEX = 0
         tokens_dict['prompt_mask_nc'][0, 0] = False
-        tokens_dict['gene_tokens_nc']['gene_value'][0, context_indices['prompt_genes'], 1] = 1  # Queried
+        tokens_dict['gene_tokens_nc']['gene_value'][0, PERTURB_GENE_CONTEXT_INDEX, 1] = 1  # Mark as queried
 
         # In subsequent cells, prompt genes are set to 0 sequentially
         if perturb_gene_ids is not None:
@@ -383,6 +387,15 @@ def generate_gene_tokens_by_metadata(
             tokens_dict['gene_tokens_nc']['gene_id'] = tokens_dict['gene_tokens_nc']['gene_id'].clone()
             tokens_dict['gene_tokens_nc']['gene_id'][1:, 0] = torch.tensor([
                 self.var_name_to_index_map[var_name] for var_name in perturb_gene_ids])
+            if perturb_gene_values is None:
+                self.log("Perturbed gene values are not provided, assuming in silico deletion (set to 0) ...")
+                perturb_gene_values = np.zeros((len(perturb_gene_ids),))
+            else:
+                self.log("Perturbed gene values are provided, injecting the provided values into the prompt ...")
+                assert len(perturb_gene_values) == len(perturb_gene_ids)
+                tokens_dict['gene_tokens_nc']['gene_value'] = tokens_dict['gene_tokens_nc']['gene_value'].clone()
+                tokens_dict['gene_tokens_nc']['gene_value'][1:, PERTURB_GENE_CONTEXT_INDEX, 0] = torch.tensor(
+                    perturb_gene_values).log1p()
 
         return tokens_dict, context_indices, pert_adata, metadata_prompt_masks_dict
 
@@ -561,6 +574,9 @@ def get_marginal_mean_std_from_tokens(
     
         gene_logits_nqk = self.get_gene_value_logits_from_tokens(tokens_dict, context_indices, max_counts)
 
+        if max_counts is None:
+            max_counts = gene_logits_nqk.shape[-1]
+        
         if use_logsumexp:
             log_counts_1_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).log()
             log_counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2).log()

From cf869aff3887c7f2879c42b4b6919c2dde7fa0d0 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Tue, 11 Feb 2025 18:18:09 +0000
Subject: [PATCH 38/64] linear response

---
 .../inference/cellarium_gpt_inference.py      |  166 +-
 ...near_response_by_cell_type_prompting.ipynb | 1490 +++++++++++++++--
 2 files changed, 1454 insertions(+), 202 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index ae30aad1..a1cbe2e5 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -12,6 +12,8 @@
 from scanpy import AnnData
 from tqdm.notebook import tqdm
 from functools import cached_property
+from more_itertools import chunked
+
 from cellarium.ml import CellariumModule, CellariumPipeline
 from cellarium.ml.models.cellarium_gpt import PredictTokenizer
 
@@ -39,6 +41,9 @@
 # Add the handler to the logger
 logger.addHandler(handler)
 
+# To suppress the stupid AnnData warning ...
+warnings.filterwarnings("ignore", category=UserWarning, message="Transforming to str index.")
+
 
 def load_gene_info_table(gene_info_tsv_path: str, included_gene_ids: list[str]) -> t.Tuple[pd.DataFrame, dict, dict]:
     gene_info_df = pd.read_csv(gene_info_tsv_path, sep="\t")
@@ -527,38 +532,26 @@ def get_gene_value_logits_from_tokens(
         ) -> torch.Tensor:
 
         # convert to cuda
-        self.log("Transferring the tokens to the device ...")
         tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
-        self.log("Done.")
         
         # get model predictions
-        self.log("Predicting ...")
         logits_dict = self.gpt_pipeline.model.predict(
             gene_tokens_nc=tokens_dict["gene_tokens_nc"],
             metadata_tokens_n=tokens_dict["metadata_tokens_n"],
             prompt_mask_nc=tokens_dict["prompt_mask_nc"],
             predict_keys=['gene_value']
         )
-        self.log("Done.")
 
         # note: we use `q` to denote query genes
-        self.log("Calculating marginal mean and std ...")
-        self.log("Obtaining gene logits ...")
-
         query_gene_indices = torch.tensor(context_indices['query_genes'], device=self.device, dtype=torch.int64)
-
+        gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :]
+        
         if max_counts is None:
-            gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :]
             max_counts = gene_logits_nqk.shape[-1]
         else:
             assert max_counts > 0
-            gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :max_counts]
-
-        self.log("Done.")
-        self.log("Normalizing gene logits ...")
-
-        gene_logits_nqk = gene_logits_nqk - torch.logsumexp(gene_logits_nqk, dim=-1, keepdim=True)
-        self.log("Done.")
+        gene_logits_nqk = gene_logits_nqk[:, :, :max_counts]  # truncate to max_counts
+        gene_logits_nqk = gene_logits_nqk - torch.logsumexp(gene_logits_nqk, dim=-1, keepdim=True)  # renormalize
 
         return gene_logits_nqk
      
@@ -569,7 +562,6 @@ def get_marginal_mean_std_from_tokens(
             context_indices: dict,
             use_logsumexp: bool = True,
             max_counts: int | None = None,
-            verbose: bool = False,
         ) -> t.Tuple[torch.Tensor, torch.Tensor]:
     
         gene_logits_nqk = self.get_gene_value_logits_from_tokens(tokens_dict, context_indices, max_counts)
@@ -593,9 +585,6 @@ def get_marginal_mean_std_from_tokens(
             gene_marginal_means_nq = gene_mom_1_nq
             gene_marginal_std_nq = torch.clamp(gene_mom_2_nq - gene_mom_1_nq.pow(2), 0.).sqrt()
 
-        if verbose:
-            logger.info("Done.")
-
         return gene_marginal_means_nq, gene_marginal_std_nq
 
 
@@ -636,7 +625,7 @@ def get_marginal_mean_std_multi_cell(
         )
 
 
-    def predict_gene_expression_dynamic_range_for_metadata(
+    def predict_gene_expression_range_for_metadata(
         self,
         assay: str,
         suspension_type: str,
@@ -644,9 +633,10 @@ def predict_gene_expression_dynamic_range_for_metadata(
         total_mrna_umis: int | float,
         query_gene_ids: list[str],
         query_chunk_size: int | None = None,
-        upper_percentile: float = 0.9999,
-        upper_pad: int = 1
-    ):
+        upper_percentile: float = 0.9,
+        upper_pad: int = 1,
+        max_counts: int | None = None,
+    ) -> dict[str, torch.Tensor]:
         """
         Compute the maximum counts indices based on cumulative gene probabilities.
 
@@ -668,6 +658,7 @@ def predict_gene_expression_dynamic_range_for_metadata(
             query_chunk_size (int): Number of gene IDs to process in one chunk.
             upper_percentile (float): Upper percentile threshold (e.g., 0.999).
             upper_pad (int): Value to add as padding to the found index.
+            max_counts (int): Maximum number of counts to consider. If None, use the full range. Otherwise, truncate.
 
         Returns:
             torch.Tensor: A tensor containing, for each query, the smallest index along k
@@ -676,44 +667,125 @@ def predict_gene_expression_dynamic_range_for_metadata(
 
         assert 0.0 < upper_percentile < 1.0, "The upper percentile must be between 0 and 1."
 
-        def yield_chunks(lst, n):
-            """Yield successive n-sized chunks from the list."""
-            for i in range(0, len(lst), n):
-                yield lst[i:i + n]
-
         if query_chunk_size is None:
             query_chunk_size = len(query_gene_ids)
         
         partial_gene_logits_list = []
         with torch.inference_mode():
             # Process gene IDs in chunks to limit memory usage
-            for partial_query_gene_ids in tqdm(list(yield_chunks(query_gene_ids, query_chunk_size)),
+            for partial_query_gene_ids in tqdm(list(chunked(query_gene_ids, query_chunk_size)),
                                             desc="Processing gene chunks"):
                 partial_gene_logits_nqk = self.get_gene_value_logits_by_metadata(
                     assay=assay,
                     suspension_type=suspension_type,
                     prompt_metadata_dict=prompt_metadata_dict,
                     total_mrna_umis=total_mrna_umis,
-                    query_gene_ids=partial_query_gene_ids
+                    query_gene_ids=partial_query_gene_ids,
+                    max_counts=max_counts,
                 )
                 partial_gene_logits_list.append(partial_gene_logits_nqk)
-            
-            # Concatenate along the gene dimension (assumed to be dim=1) and select the first element along dim=0.
+
             gene_logits_qk = torch.cat(partial_gene_logits_list, dim=1)[0]
 
-        # Compute the cumulative sum of the exponentiated logits (interpreted as gene probabilities)
-        gene_count_probs_cumsum_qk = gene_logits_qk.exp().cumsum(dim=1)
-        
-        # Create a mask where cumulative probabilities exceed the threshold.
-        # (Note: If you ever need the mask for other purposes, you can return it or process it further.)
-        mask_qk = gene_count_probs_cumsum_qk > upper_percentile
+        # first, find the mode of the counts distribution for each gene
+        gene_logits_mode_q = torch.argmax(gene_logits_qk, dim=1)
+
+        # symmetric lower and upper counts about the mode for each gene
+        x_lo_qm = torch.clamp(
+            gene_logits_mode_q[:, None] - torch.arange(0, max_counts, device=self.device)[None, :], min=0)
+        x_hi_qm = torch.clamp(
+            gene_logits_mode_q[:, None] + torch.arange(0, max_counts, device=self.device)[None, :], max=max_counts - 1)
+
+        # compute the CDF of counts for each gene
+        pdf_qk = gene_logits_qk.exp()
+        cdf_qk = pdf_qk.cumsum(dim=1)
+        q_indices = torch.arange(cdf_qk.size(0), device=self.device)
+        symm_prob_mass_qm = (
+            cdf_qk[q_indices[:, None], x_hi_qm]  # add total prob mass at the right point (inclusive)
+            - cdf_qk[q_indices[:, None], x_lo_qm]  # subtract total prob mass at the left point (inclusive)
+            + pdf_qk[q_indices[:, None], x_lo_qm]  # add back the prob mass of the left point
+        )
+        mask_qm = symm_prob_mass_qm > upper_percentile
+        range_q = torch.clamp(mask_qm.float().argmax(dim=-1) + upper_pad, max=max_counts - 1)
+        x_lo_q = x_lo_qm[q_indices, range_q]
+        x_hi_q = x_hi_qm[q_indices, range_q]
 
-        # For each row, find the first index where the mask is True.
-        # Converting the boolean mask to float ensures that True becomes 1.0 and False becomes 0.0.
-        # Since the mask is assumed to eventually become True, argmax returns the index of the first True.
-        max_counts_k = mask_qk.float().argmax(dim=-1) + upper_pad
+        return {
+            'gene_logits_qk': gene_logits_qk,
+            'gene_logits_mode_q': gene_logits_mode_q,
+            'range_q': range_q,
+            'x_lo_q': x_lo_q,
+            'x_hi_q': x_hi_q,
+        }
 
-        return max_counts_k
+    def generate_gene_dose_response_for_metadata(
+        self,
+        assay: str,
+        suspension_type: str,
+        prompt_metadata_dict: dict[str, str],
+        total_mrna_umis: int | float,
+        query_gene_ids: list[str],
+        perturb_gene_ids: list[str],
+        x_lo_p: np.ndarray,
+        x_hi_p: np.ndarray,
+        n_points: int,
+        query_chunk_size: int,
+        max_counts: int | None = None,
+    ):
+        
+        assert n_points >= 2
+
+        # initialize arrays to store results
+        n_query_genes = len(query_gene_ids)
+        n_perturb_genes = len(perturb_gene_ids)
+        doses_pi = np.zeros((n_perturb_genes, n_points))
+        responses_mean_pqi = np.zeros((n_perturb_genes, n_query_genes, n_points))
+        responses_std_pqi = np.zeros((n_perturb_genes, n_query_genes, n_points))
+
+        # outer loop (dose quantiles)
+        for i_point in tqdm(range(n_points), desc="Processing dose quantiles"):
+
+            # values of genes to perturb
+            perturb_gene_values = x_lo_p +  (x_hi_p - x_lo_p) * i_point / (n_points - 1)
+            doses_pi[:, i_point] = perturb_gene_values
+
+            # inner loop (responses)
+            gene_marginal_mean_nq_chunks = []
+            gene_marginal_std_nq_chunks = []
+            for query_gene_ids_chunk in tqdm(list(chunked(query_gene_ids, query_chunk_size)),
+                                             desc="Processing query gene chunks"):
+                with torch.inference_mode():
+                    tokens_dict, context_indices, _, _ = self.generate_gene_tokens_by_metadata(
+                        assay=assay,
+                        suspension_type=suspension_type,
+                        prompt_metadata_dict=prompt_metadata_dict,
+                        total_mrna_umis=total_mrna_umis,
+                        query_gene_ids=query_gene_ids_chunk,
+                        perturb_gene_ids=perturb_gene_ids,
+                        perturb_gene_values=perturb_gene_values
+                    )
+                    tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
+                    gene_marginal_mean_nq, gene_marginal_std_nq = self.get_marginal_mean_std_from_tokens(
+                        tokens_dict=tokens_dict,
+                        context_indices=context_indices,
+                        max_counts=max_counts)
+                gene_marginal_mean_nq_chunks.append(gene_marginal_mean_nq.cpu().numpy())
+                gene_marginal_std_nq_chunks.append(gene_marginal_std_nq.cpu().numpy())
+            gene_marginal_mean_nq = np.concatenate(gene_marginal_mean_nq_chunks, axis=1)
+            gene_marginal_std_nq = np.concatenate(gene_marginal_std_nq_chunks, axis=1)
+
+            control_mean_q = gene_marginal_mean_nq[0, :]
+            control_std_q = gene_marginal_std_nq[0, :]
+            responses_mean_pqi[:, :, i_point] = gene_marginal_mean_nq[1:, :]
+            responses_std_pqi[:, :, i_point] = gene_marginal_std_nq[1:, :]
+
+        return {
+            'doses_pi': doses_pi,
+            'responses_mean_pqi': responses_mean_pqi,
+            'responses_std_pqi': responses_std_pqi,
+            'control_mean_q': control_mean_q,
+            'control_std_q': control_std_q,
+        }
     
 
     def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) -> dict[str, np.ndarray]:
@@ -831,11 +903,7 @@ def compute_jacobian(
         else:
             raise ValueError
 
-        def yield_chunks(lst, n):
-            for i in range(0, len(lst), n):
-                yield lst[i:i + n]
-
-        query_var_names_chunks = list(yield_chunks(query_gene_ids, query_chunk_size))
+        query_var_names_chunks = list(chunked(query_gene_ids, query_chunk_size))
         jacobian_chunks = []
 
         print("Calculating the Jacobian ...")
diff --git a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
index ce3e260a..e58eb1c0 100644
--- a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
+++ b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
@@ -21,12 +21,9 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# for flex attention\n",
-    "import torch._dynamo\n",
-    "torch._dynamo.config.suppress_errors = True\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
     "\n",
     "DEVICE = torch.device('cuda:0')\n",
-    "sc.set_figure_params(figsize=(4, 4))\n",
     "\n",
     "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
     "    CellariumGPTInferenceContext, \\\n",
@@ -37,16 +34,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ROOT_PATH = \"/home/mehrtash/data\"\n",
     "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
@@ -58,13 +46,14 @@
     "    ref_adata_path=REF_ADATA_PATH,\n",
     "    gene_info_tsv_path=GENE_INFO_PATH,\n",
     "    device=DEVICE,\n",
-    "    attention_backend=\"mem_efficient\"\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +65,7 @@
     "    \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
     "    \"tissue\": \"blood\"\n",
     "}\n",
-    "total_mrna_umis = 20_000"
+    "total_mrna_umis = 10_000"
    ]
   },
   {
@@ -88,140 +77,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c456e1d2c487444e8783511e19ee676d",
+       "model_id": "8d5047e52fe4406f99be62cff4aabaf7",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Processing gene chunks:   0%|          | 0/8 [00:00<?, ?it/s]"
+       "Processing gene chunks:   0%|          | 0/37 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:42,491 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:42,492 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,493 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:42,517 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:42,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:42,672 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,673 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:42,674 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:42,686 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:42,687 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,688 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:42,712 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,712 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:42,713 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:42,842 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,843 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:42,844 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:42,855 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:42,856 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,856 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:42,880 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:42,880 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:42,881 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,010 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,011 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,012 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:43,023 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:43,024 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,025 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:43,049 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,049 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:43,050 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,179 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,179 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,180 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:43,192 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:43,193 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,194 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:43,217 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,218 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:43,219 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,348 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,348 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,349 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:43,360 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:43,361 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,362 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:43,385 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,386 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:43,387 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,516 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,517 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,518 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:43,529 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:43,530 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,530 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:43,554 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,554 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:43,555 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,684 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,685 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,686 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
-      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
-      "2025-02-07 23:38:43,695 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Transferring the tokens to the device ...\n",
-      "2025-02-07 23:38:43,696 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,696 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Predicting ...\n",
-      "2025-02-07 23:38:43,718 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,719 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Calculating marginal mean and std ...\n",
-      "2025-02-07 23:38:43,719 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Obtaining gene logits ...\n",
-      "2025-02-07 23:38:43,730 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n",
-      "2025-02-07 23:38:43,730 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Normalizing gene logits ...\n",
-      "2025-02-07 23:38:43,731 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Done.\n"
-     ]
     }
    ],
    "source": [
-    "query_chunk_size = 5_000\n",
-    "upper_percentile = 0.99\n",
+    "query_chunk_size = 1_000\n",
+    "upper_percentile = 0.95\n",
+    "max_counts = 1000\n",
     "upper_pad = 1\n",
     "\n",
-    "gene_expr_range_q = ctx.predict_gene_expression_dynamic_range_for_metadata(\n",
+    "gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
     "    assay=assay,\n",
     "    suspension_type=suspension_type,\n",
     "    prompt_metadata_dict=prompt_metadata_dict,\n",
@@ -229,66 +109,1370 @@
     "    query_gene_ids=query_gene_ids,\n",
     "    query_chunk_size=query_chunk_size,\n",
     "    upper_percentile=upper_percentile,\n",
-    "    upper_pad=upper_pad\n",
+    "    upper_pad=upper_pad,\n",
+    "    max_counts=max_counts,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x320 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 301,
+       "width": 621
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
+    "\n",
+    "ax = axs[0]\n",
+    "ax.hist(gex_range_dict['gene_logits_mode_q'].cpu().numpy(), bins=100, log=True);\n",
+    "ax.set_xlabel(\"Count distribution mode\");\n",
+    "ax.set_ylabel(\"Frequency\");\n",
+    "\n",
+    "ax = axs[1]\n",
+    "ax.hist(gex_range_dict['range_q'].cpu().numpy(), bins=100, log=True);\n",
+    "ax.set_xlabel(\"Count distribution range\");\n",
+    "ax.set_ylabel(\"Frequency\");\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MALAT1, mode: 296\n",
+      "TMSB4X, mode: 179\n",
+      "B2M, mode: 122\n",
+      "EEF1A1, mode: 102\n",
+      "RPS27, mode: 102\n",
+      "RPS18, mode: 86\n",
+      "RPL13A, mode: 83\n",
+      "RPL10, mode: 83\n",
+      "RPL41, mode: 83\n",
+      "RPLP1, mode: 81\n",
+      "RPS12, mode: 81\n",
+      "RPL13, mode: 78\n",
+      "RPS2, mode: 73\n",
+      "RPL21, mode: 71\n",
+      "RPS19, mode: 68\n",
+      "RPS29, mode: 68\n",
+      "RPL34, mode: 68\n",
+      "RPLP2, mode: 66\n",
+      "RPL28, mode: 66\n",
+      "RPL32, mode: 63\n",
+      "RPS27A, mode: 63\n",
+      "RPS14, mode: 63\n",
+      "RPS15A, mode: 63\n",
+      "RPS3, mode: 63\n",
+      "RPS6, mode: 63\n",
+      "RPL3, mode: 57\n",
+      "RPL18A, mode: 57\n",
+      "RPS23, mode: 57\n",
+      "TMSB10, mode: 57\n",
+      "RPS4X, mode: 57\n",
+      "MT-CO1, mode: 57\n",
+      "ACTB, mode: 57\n",
+      "RPL39, mode: 56\n",
+      "RPL11, mode: 56\n",
+      "RPL27A, mode: 56\n",
+      "RPL26, mode: 54\n",
+      "RPL23A, mode: 52\n",
+      "RPS3A, mode: 50\n",
+      "RPS28, mode: 49\n",
+      "RPL19, mode: 49\n",
+      "RPS15, mode: 49\n",
+      "RPL15, mode: 49\n",
+      "RPS8, mode: 49\n",
+      "TPT1, mode: 49\n",
+      "MT-CO3, mode: 45\n",
+      "MT-CO2, mode: 45\n",
+      "RPL7, mode: 45\n",
+      "RPL37A, mode: 45\n",
+      "RPL30, mode: 43\n",
+      "RPS25, mode: 43\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print gene symbols for top 10 genes in terms of mode\n",
+    "top_k = 50\n",
+    "top_k_genes = sorted(\n",
+    "    zip(query_gene_ids, gex_range_dict['gene_logits_mode_q'].cpu().numpy()), key=lambda x: x[1], reverse=True)[:top_k]\n",
+    "for gene_id, mode in top_k_genes:\n",
+    "    print(f'{ctx.gene_id_to_gene_symbol_map[gene_id]}, mode: {mode}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mode: 179, range: 149\n",
+      "lo: 30, hi: 328\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Probability')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 281,
-       "width": 309
+       "height": 318,
+       "width": 345
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.hist(gene_expr_range_q.cpu().numpy(), bins=50, log=True);"
+    "gene_symbol = 'TMSB4X'\n",
+    "q = ctx.var_name_to_index_map[ctx.gene_symbol_to_gene_id_map[gene_symbol]]\n",
+    "\n",
+    "print(f\"mode: {gex_range_dict['gene_logits_mode_q'][q].item()}, range: {gex_range_dict['range_q'][q].item()}\")\n",
+    "print(f\"lo: {gex_range_dict['x_lo_q'][q].item()}, hi: {gex_range_dict['x_hi_q'][q].item()}\")\n",
+    "\n",
+    "query_id = query_gene_ids[q]\n",
+    "gene_symbol = ctx.gene_id_to_gene_symbol_map[query_id]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(\n",
+    "    np.arange(max_counts),\n",
+    "    gex_range_dict['gene_logits_qk'][q].exp().cpu().numpy(), '-')\n",
+    "ax.axvline(gex_range_dict['x_lo_q'][q].item(), color='red')\n",
+    "ax.axvline(gex_range_dict['x_hi_q'][q].item(), color='red')\n",
+    "\n",
+    "ax.set_title(f\"Gene: {gene_symbol}\")\n",
+    "ax.set_xlabel(\"Count\")\n",
+    "ax.set_ylabel(\"Probability\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ENSG00000206172: 1090, HBA1\n",
-      "ENSG00000251562: 2001, MALAT1\n",
-      "ENSG00000211677: 1273, IGLC2\n",
-      "ENSG00000198804: 1206, MT-CO1\n",
-      "ENSG00000087086: 1470, FTL\n",
-      "ENSG00000211895: 1472, IGHA1\n",
-      "ENSG00000211592: 2001, IGKC\n",
-      "ENSG00000229117: 1110, RPL41\n"
+      "Number of genes to perturb: 898\n",
+      "Number of genes to query: 898\n"
      ]
     }
    ],
    "source": [
-    "for idx in torch.where(gene_expr_range_q > 1000)[0]:\n",
-    "    print(f\"{ctx.model_var_names[idx]}: {gene_expr_range_q[idx]}, {ctx.gene_id_to_gene_symbol_map[ctx.model_var_names[idx]]}\")"
+    "gene_logits_qk = gex_range_dict['gene_logits_qk']\n",
+    "gene_logits_mode_q = gex_range_dict['gene_logits_mode_q']\n",
+    "range_q =  gex_range_dict['range_q']\n",
+    "x_lo_q = gex_range_dict['x_lo_q']\n",
+    "x_hi_q = gex_range_dict['x_hi_q']\n",
+    "\n",
+    "# let's pick genes to perturb (and query) that make sense for this cell type\n",
+    "min_upper_range = 5\n",
+    "expressed_mask_q = (x_hi_q >= min_upper_range).cpu().numpy()\n",
+    "filtered_query_gene_ids = query_gene_ids[expressed_mask_q]\n",
+    "filtered_perturb_gene_ids = query_gene_ids[expressed_mask_q]\n",
+    "filtered_query_gene_symbols = [ctx.gene_id_to_gene_symbol_map[gid] for gid in filtered_query_gene_ids]\n",
+    "filtered_perturb_gene_symbols = [ctx.gene_id_to_gene_symbol_map[gid] for gid in filtered_perturb_gene_ids]\n",
+    "filtered_x_lo_q = x_lo_q.cpu().numpy()[expressed_mask_q]\n",
+    "filtered_x_hi_q = x_hi_q.cpu().numpy()[expressed_mask_q]\n",
+    "print(\"Number of genes to perturb:\", len(filtered_perturb_gene_ids))\n",
+    "print(\"Number of genes to query:\", len(filtered_query_gene_ids))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9b8db6d7a2e4b2aa4aeb95a8b9d1eac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing dose quantiles:   0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "680cddfe68214dc9a71d409d99b8bebb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95823c15e74d40aea27353123a80215f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bc7a18170084007bf6a9b4459dad83d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d55a3e28d3994b36a9cc825d6c798c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d072787b1a147a6b81da462347a7281",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "230d14fda5ce44a5b4a0fa96c6c9da28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fecedd499224d80af3e96bbbc4d49b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6459d7f400cf48ce9c0e0568ab910734",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc3daa1e5bb14d89baa645c0c9858251",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "077809b861f040898a2858bfe831bfcd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_points = 10\n",
+    "query_chunk_size = 64\n",
+    "\n",
+    "dose_response_dict = ctx.generate_gene_dose_response_for_metadata(\n",
+    "    assay=assay,\n",
+    "    suspension_type=suspension_type,\n",
+    "    prompt_metadata_dict=prompt_metadata_dict,\n",
+    "    total_mrna_umis=total_mrna_umis,\n",
+    "    query_gene_ids=filtered_query_gene_ids,\n",
+    "    perturb_gene_ids=filtered_perturb_gene_ids,\n",
+    "    x_lo_p=filtered_x_lo_q,\n",
+    "    x_hi_p=filtered_x_hi_q,\n",
+    "    n_points=n_points,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    max_counts=max_counts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['doses_pi', 'responses_mean_pqi', 'responses_std_pqi', 'control_mean_q', 'control_std_q'])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dose_response_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 SERBP1\n",
+      "1 PRPF38B\n",
+      "2 LYAR\n",
+      "3 FGFBP2\n",
+      "4 GZMK\n",
+      "5 CCT6A\n",
+      "6 RPL30\n",
+      "7 IFITM2\n",
+      "8 APEX1\n",
+      "9 DNMT1\n",
+      "10 DSTN\n",
+      "11 RPL3\n",
+      "12 UFC1\n",
+      "13 WDR1\n",
+      "14 NPM1\n",
+      "15 FIS1\n",
+      "16 ATP6V1F\n",
+      "17 PRDX5\n",
+      "18 PRR13\n",
+      "19 ARF6\n",
+      "20 SERF2\n",
+      "21 B2M\n",
+      "22 C1QBP\n",
+      "23 MATK\n",
+      "24 RPL18A\n",
+      "25 GMFG\n",
+      "26 EIF2S2\n",
+      "27 DDT\n",
+      "28 DDX17\n",
+      "29 PGK1\n",
+      "30 AURKAIP1\n",
+      "31 HSPD1\n",
+      "32 UBE2D3\n",
+      "33 RPS23\n",
+      "34 COX7C\n",
+      "35 UQCRQ\n",
+      "36 LMAN2\n",
+      "37 PSMB8\n",
+      "38 DYNLT1\n",
+      "39 CBX3\n",
+      "40 ATP5MF\n",
+      "41 RPL12\n",
+      "42 EIF3M\n",
+      "43 PFDN5\n",
+      "44 GZMH\n",
+      "45 SIVA1\n",
+      "46 SEC11A\n",
+      "47 VAMP2\n",
+      "48 ATP5PD\n",
+      "49 SUMO2\n",
+      "50 ATP5F1A\n",
+      "51 TOMM22\n",
+      "52 NDUFA6\n",
+      "53 EIF1AX\n",
+      "54 EIF3I\n",
+      "55 SCP2\n",
+      "56 JUN\n",
+      "57 SELL\n",
+      "58 PDIA6\n",
+      "59 SRSF7\n",
+      "60 TMBIM6\n",
+      "61 RBM25\n",
+      "62 RPLP1\n",
+      "63 RPL13\n",
+      "64 NME1\n",
+      "65 RABAC1\n",
+      "66 SAMHD1\n",
+      "67 ZFAS1\n",
+      "68 XRCC6\n",
+      "69 SSR4\n",
+      "70 MT-ND4\n",
+      "71 IFI16\n",
+      "72 CXCR4\n",
+      "73 ZEB2\n",
+      "74 TUBA4A\n",
+      "75 ARPC4\n",
+      "76 H1-10\n",
+      "77 RWDD1\n",
+      "78 TALDO1\n",
+      "79 TMEM258\n",
+      "80 MALAT1\n",
+      "81 GSTP1\n",
+      "82 C12orf75\n",
+      "83 CCL4\n",
+      "84 JUNB\n",
+      "85 C19orf53\n",
+      "86 CALM3\n",
+      "87 UBE2L3\n",
+      "88 ATRX\n",
+      "89 EFHD2\n",
+      "90 CD52\n",
+      "91 PRRC2C\n",
+      "92 COX5B\n",
+      "93 RPL14\n",
+      "94 SNHG32\n",
+      "95 COX7A2\n",
+      "96 ATP5F1C\n",
+      "97 RPL6\n",
+      "98 ITM2B\n",
+      "99 MORF4L1\n",
+      "100 ARL6IP1\n",
+      "101 PSMC5\n",
+      "102 RPS11\n",
+      "103 MT-ND3\n",
+      "104 SSU72\n",
+      "105 SRP9\n",
+      "106 CCT4\n",
+      "107 CD47\n",
+      "108 PPP1R2\n",
+      "109 HOPX\n",
+      "110 CCNI\n",
+      "111 EIF3H\n",
+      "112 HNRNPK\n",
+      "113 ANAPC16\n",
+      "114 SF3B2\n",
+      "115 LYZ\n",
+      "116 ARL6IP4\n",
+      "117 TMED2\n",
+      "118 N4BP2L2\n",
+      "119 ARGLU1\n",
+      "120 EIF1\n",
+      "121 PSMA7\n",
+      "122 NDUFA1\n",
+      "123 RSRP1\n",
+      "124 TMEM59\n",
+      "125 TRAF3IP3\n",
+      "126 TMSB10\n",
+      "127 OCIAD2\n",
+      "128 H2AZ1\n",
+      "129 TRGC2\n",
+      "130 GIMAP7\n",
+      "131 EEF1D\n",
+      "132 CLEC2D\n",
+      "133 TUBA1A\n",
+      "134 ATP5F1B\n",
+      "135 COMMD6\n",
+      "136 ALDOA\n",
+      "137 CKLF\n",
+      "138 UBB\n",
+      "139 GPX4\n",
+      "140 EIF3K\n",
+      "141 NOP53\n",
+      "142 BAX\n",
+      "143 PRMT2\n",
+      "144 ITM2A\n",
+      "145 RPL39\n",
+      "146 RRM2\n",
+      "147 CFLAR\n",
+      "148 SUMO1\n",
+      "149 RPL32\n",
+      "150 TCF7\n",
+      "151 DDX46\n",
+      "152 HLA-E\n",
+      "153 TUBB\n",
+      "154 PABPC1\n",
+      "155 C9orf78\n",
+      "156 AHNAK\n",
+      "157 PCBP2\n",
+      "158 BTG1\n",
+      "159 CIB1\n",
+      "160 CCL4L2\n",
+      "161 SOD1\n",
+      "162 ITGB2\n",
+      "163 MT-CO3\n",
+      "164 RPL11\n",
+      "165 SH3BGRL3\n",
+      "166 IRF1\n",
+      "167 RACK1\n",
+      "168 H4C3\n",
+      "169 CELF2\n",
+      "170 PSMA1\n",
+      "171 NAP1L1\n",
+      "172 PCLAF\n",
+      "173 NDUFAB1\n",
+      "174 ICAM3\n",
+      "175 TECR\n",
+      "176 RPS5\n",
+      "177 PCNA\n",
+      "178 ENSG00000240972\n",
+      "179 ST13\n",
+      "180 IL2RG\n",
+      "181 RPS4X\n",
+      "182 CSDE1\n",
+      "183 MRPS21\n",
+      "184 PPM1G\n",
+      "185 PYURF\n",
+      "186 CLIC1\n",
+      "187 HLA-DPB1\n",
+      "188 PSMA2\n",
+      "189 FABP5\n",
+      "190 NDUFB9\n",
+      "191 CYRIB\n",
+      "192 BBLN\n",
+      "193 VDAC2\n",
+      "194 MKI67\n",
+      "195 LDHA\n",
+      "196 COX8A\n",
+      "197 KLRD1\n",
+      "198 CRIP1\n",
+      "199 NOP10\n",
+      "200 SRP14\n",
+      "201 TOP2A\n",
+      "202 RNF213\n",
+      "203 ACTG1\n",
+      "204 SNRPD1\n",
+      "205 OAZ1\n",
+      "206 TLE5\n",
+      "207 RPS28\n",
+      "208 YWHAB\n",
+      "209 HMGN1\n",
+      "210 STMN1\n",
+      "211 COX7A2L\n",
+      "212 LSM3\n",
+      "213 TNFAIP3\n",
+      "214 EZR\n",
+      "215 IFITM3\n",
+      "216 NEDD8\n",
+      "217 PSMA4\n",
+      "218 ADGRE5\n",
+      "219 HCST\n",
+      "220 XBP1\n",
+      "221 RPL36A\n",
+      "222 MT-CO2\n",
+      "223 DNAJC8\n",
+      "224 S100A4\n",
+      "225 TSTD1\n",
+      "226 SF3B6\n",
+      "227 RPL29\n",
+      "228 BCLAF1\n",
+      "229 LAMTOR4\n",
+      "230 RPL8\n",
+      "231 ATP6V1G1\n",
+      "232 RPS13\n",
+      "233 NDUFC2\n",
+      "234 HNRNPC\n",
+      "235 EIF5A\n",
+      "236 TRAPPC1\n",
+      "237 JPT1\n",
+      "238 RPL36\n",
+      "239 HNRNPR\n",
+      "240 ATP5IF1\n",
+      "241 TPM3\n",
+      "242 RPS27A\n",
+      "243 GYPC\n",
+      "244 STK17B\n",
+      "245 GZMA\n",
+      "246 BTF3\n",
+      "247 ITGB1\n",
+      "248 CD44\n",
+      "249 FAU\n",
+      "250 CD3G\n",
+      "251 RPL21\n",
+      "252 HMGB1\n",
+      "253 IDH2\n",
+      "254 NHERF1\n",
+      "255 VAPA\n",
+      "256 HNRNPM\n",
+      "257 EIF3G\n",
+      "258 ATP5PO\n",
+      "259 S100A9\n",
+      "260 PRDX6\n",
+      "261 TMA7\n",
+      "262 ATP1B3\n",
+      "263 CCNL1\n",
+      "264 HLA-DRB1\n",
+      "265 EEF1A1\n",
+      "266 PGAM1\n",
+      "267 CARD16\n",
+      "268 PHB2\n",
+      "269 NDUFA12\n",
+      "270 ATP5MJ\n",
+      "271 LITAF\n",
+      "272 RPL19\n",
+      "273 SRRM1\n",
+      "274 BTG2\n",
+      "275 HNRNPU\n",
+      "276 MZT2B\n",
+      "277 HNRNPA3\n",
+      "278 OSTC\n",
+      "279 NDUFS6\n",
+      "280 FYB1\n",
+      "281 HLA-A\n",
+      "282 CKS2\n",
+      "283 SET\n",
+      "284 VIM\n",
+      "285 UCP2\n",
+      "286 NDUFB10\n",
+      "287 ACAP1\n",
+      "288 DDX5\n",
+      "289 RPS15\n",
+      "290 EEF2\n",
+      "291 FXYD5\n",
+      "292 SNRPB\n",
+      "293 PPDPF\n",
+      "294 PSMA5\n",
+      "295 SKP1\n",
+      "296 PTTG1\n",
+      "297 CD164\n",
+      "298 UQCRB\n",
+      "299 DNAJA1\n",
+      "300 MRPL51\n",
+      "301 ARHGDIB\n",
+      "302 SNRPF\n",
+      "303 RPLP0\n",
+      "304 POLR1D\n",
+      "305 TRAC\n",
+      "306 FUS\n",
+      "307 CNN2\n",
+      "308 CIRBP\n",
+      "309 TIMM13\n",
+      "310 GADD45B\n",
+      "311 NDUFA11\n",
+      "312 LSM4\n",
+      "313 UBE2C\n",
+      "314 MYH9\n",
+      "315 APOBEC3G\n",
+      "316 COX7B\n",
+      "317 MT-ND1\n",
+      "318 S100A8\n",
+      "319 XCL2\n",
+      "320 DDX18\n",
+      "321 VDAC1\n",
+      "322 RPS18\n",
+      "323 TAPBP\n",
+      "324 CYCS\n",
+      "325 GSTK1\n",
+      "326 RHOG\n",
+      "327 FOS\n",
+      "328 ATP5MC1\n",
+      "329 SEPTIN9\n",
+      "330 PMAIP1\n",
+      "331 JUND\n",
+      "332 RPS16\n",
+      "333 CD37\n",
+      "334 TMSB4X\n",
+      "335 MT-CYB\n",
+      "336 SYF2\n",
+      "337 RPS27\n",
+      "338 CACYBP\n",
+      "339 CMC1\n",
+      "340 DEK\n",
+      "341 SRGN\n",
+      "342 RPL36AL\n",
+      "343 FOSB\n",
+      "344 AP2S1\n",
+      "345 S100A10\n",
+      "346 SSR2\n",
+      "347 BRK1\n",
+      "348 HNRNPH1\n",
+      "349 RPS20\n",
+      "350 PSIP1\n",
+      "351 HSPA5\n",
+      "352 DAZAP2\n",
+      "353 COX6A1\n",
+      "354 SEPTIN1\n",
+      "355 CALR\n",
+      "356 EMP3\n",
+      "357 NDUFA3\n",
+      "358 UQCR10\n",
+      "359 NONO\n",
+      "360 TSC22D3\n",
+      "361 JTB\n",
+      "362 CD247\n",
+      "363 PTPRC\n",
+      "364 SNRPE\n",
+      "365 OST4\n",
+      "366 PLEK\n",
+      "367 DUSP2\n",
+      "368 ITGA4\n",
+      "369 STK17A\n",
+      "370 TPI1\n",
+      "371 PA2G4\n",
+      "372 CALM1\n",
+      "373 RPL4\n",
+      "374 RPS19\n",
+      "375 EIF3L\n",
+      "376 SH3BGRL\n",
+      "377 CAP1\n",
+      "378 CD53\n",
+      "379 COPS9\n",
+      "380 HNRNPDL\n",
+      "381 RPS14\n",
+      "382 NHP2\n",
+      "383 HLA-B\n",
+      "384 CITED2\n",
+      "385 HNRNPA2B1\n",
+      "386 SCAF11\n",
+      "387 RAN\n",
+      "388 TRDC\n",
+      "389 PPIB\n",
+      "390 SRRM2\n",
+      "391 TYMS\n",
+      "392 RBM39\n",
+      "393 TMEM50A\n",
+      "394 LAPTM5\n",
+      "395 S100A11\n",
+      "396 RPS7\n",
+      "397 VAMP5\n",
+      "398 CYTIP\n",
+      "399 RPL24\n",
+      "400 LTB\n",
+      "401 HLA-DPA1\n",
+      "402 NDUFB8\n",
+      "403 IFITM1\n",
+      "404 EEF1G\n",
+      "405 LDHB\n",
+      "406 PNN\n",
+      "407 HSP90AA1\n",
+      "408 RPS15A\n",
+      "409 CYBA\n",
+      "410 GZMM\n",
+      "411 UBL5\n",
+      "412 RPN2\n",
+      "413 ATP5PF\n",
+      "414 CSTB\n",
+      "415 EIF3D\n",
+      "416 SNU13\n",
+      "417 UXT\n",
+      "418 MRPL20\n",
+      "419 PARP1\n",
+      "420 ARL4C\n",
+      "421 EIF4A2\n",
+      "422 AIF1\n",
+      "423 RPL10A\n",
+      "424 SNX3\n",
+      "425 ABRACL\n",
+      "426 ARPC1B\n",
+      "427 DOK2\n",
+      "428 ARPC5L\n",
+      "429 LSP1\n",
+      "430 UBE2L6\n",
+      "431 ATP5MG\n",
+      "432 CLEC2B\n",
+      "433 UQCR11\n",
+      "434 COPE\n",
+      "435 CST7\n",
+      "436 RPS4Y1\n",
+      "437 ENO1\n",
+      "438 RPL5\n",
+      "439 TRGC1\n",
+      "440 PPP1CA\n",
+      "441 SPCS2\n",
+      "442 HSPA8\n",
+      "443 EIF4B\n",
+      "444 NDUFB1\n",
+      "445 COX5A\n",
+      "446 BSG\n",
+      "447 MYO1F\n",
+      "448 ZFP36\n",
+      "449 TIMP1\n",
+      "450 SFPQ\n",
+      "451 SPCS1\n",
+      "452 HNRNPA0\n",
+      "453 HLA-C\n",
+      "454 ANP32B\n",
+      "455 ATP5MK\n",
+      "456 NEAT1\n",
+      "457 CCDC85B\n",
+      "458 LAMTOR1\n",
+      "459 RPS3\n",
+      "460 BLOC1S1\n",
+      "461 RPS2\n",
+      "462 CORO1A\n",
+      "463 APRT\n",
+      "464 PSMB3\n",
+      "465 ANAPC11\n",
+      "466 NDUFV2\n",
+      "467 TYROBP\n",
+      "468 TBCB\n",
+      "469 ATF4\n",
+      "470 RBM3\n",
+      "471 SLC25A5\n",
+      "472 UQCRH\n",
+      "473 TXNIP\n",
+      "474 RPSA\n",
+      "475 SNHG8\n",
+      "476 NSA2\n",
+      "477 RIPOR2\n",
+      "478 HLA-DRA\n",
+      "479 KMT2E\n",
+      "480 TRBC2\n",
+      "481 MYL6\n",
+      "482 RSL24D1\n",
+      "483 PFN1\n",
+      "484 TOMM20\n",
+      "485 SNHG5\n",
+      "486 RPL7A\n",
+      "487 RPL27A\n",
+      "488 TBC1D10C\n",
+      "489 CD69\n",
+      "490 EID1\n",
+      "491 RNPS1\n",
+      "492 RSL1D1\n",
+      "493 RGS1\n",
+      "494 YPEL5\n",
+      "495 EIF5B\n",
+      "496 FOXP1\n",
+      "497 RPL35A\n",
+      "498 ATP5ME\n",
+      "499 HNRNPD\n",
+      "500 GLRX\n",
+      "501 MACROH2A1\n",
+      "502 SEM1\n",
+      "503 YWHAZ\n",
+      "504 GHITM\n",
+      "505 PTPRCAP\n",
+      "506 RPS26\n",
+      "507 UBC\n",
+      "508 BAZ1A\n",
+      "509 EVL\n",
+      "510 PHB1\n",
+      "511 RPS21\n",
+      "512 SDF2L1\n",
+      "513 IGLC2\n",
+      "514 RBX1\n",
+      "515 XIST\n",
+      "516 SEPTIN6\n",
+      "517 NUDC\n",
+      "518 LCK\n",
+      "519 PCBP1\n",
+      "520 NBEAL1\n",
+      "521 XRCC5\n",
+      "522 CD74\n",
+      "523 LSM2\n",
+      "524 HMGN3\n",
+      "525 DNAJB6\n",
+      "526 PAXX\n",
+      "527 ANXA11\n",
+      "528 SF1\n",
+      "529 DYNLL1\n",
+      "530 DDX24\n",
+      "531 ANXA2\n",
+      "532 IL32\n",
+      "533 RPL26\n",
+      "534 RPL23\n",
+      "535 WDR83OS\n",
+      "536 COX6B1\n",
+      "537 MT-CO1\n",
+      "538 MT-ATP6\n",
+      "539 SRSF11\n",
+      "540 TAGLN2\n",
+      "541 GAS5\n",
+      "542 NUCKS1\n",
+      "543 PPIG\n",
+      "544 SSB\n",
+      "545 RPL15\n",
+      "546 MTDH\n",
+      "547 PSMB7\n",
+      "548 PPA1\n",
+      "549 TMEM123\n",
+      "550 RPL23A\n",
+      "551 CCL5\n",
+      "552 RPL17\n",
+      "553 MICOS13\n",
+      "554 IER2\n",
+      "555 NDUFA13\n",
+      "556 PPP1R15A\n",
+      "557 RPL13A\n",
+      "558 FKBP1A\n",
+      "559 STK4\n",
+      "560 ATP5F1E\n",
+      "561 EIF2S3\n",
+      "562 FLNA\n",
+      "563 RBM8A\n",
+      "564 ZFP36L2\n",
+      "565 CCT7\n",
+      "566 PAIP2\n",
+      "567 TRBC1\n",
+      "568 PSAP\n",
+      "569 RPLP2\n",
+      "570 EIF3F\n",
+      "571 EIF4G2\n",
+      "572 SELENOH\n",
+      "573 SNHG29\n",
+      "574 BST2\n",
+      "575 UQCRFS1\n",
+      "576 FTL\n",
+      "577 CCT8\n",
+      "578 SON\n",
+      "579 RANBP1\n",
+      "580 CD48\n",
+      "581 SMC4\n",
+      "582 HINT1\n",
+      "583 CUTA\n",
+      "584 CCND3\n",
+      "585 NDUFB2\n",
+      "586 LEPROTL1\n",
+      "587 TUBB4B\n",
+      "588 HNRNPF\n",
+      "589 RGS10\n",
+      "590 ELF1\n",
+      "591 LCP1\n",
+      "592 SYNE2\n",
+      "593 KRT10\n",
+      "594 KLF2\n",
+      "595 RPL22\n",
+      "596 CDC42\n",
+      "597 JAK1\n",
+      "598 HSPE1\n",
+      "599 SELENOK\n",
+      "600 SSBP1\n",
+      "601 CTSW\n",
+      "602 BIN2\n",
+      "603 ATP5MC2\n",
+      "604 NACA\n",
+      "605 HSP90B1\n",
+      "606 EIF5\n",
+      "607 MT1X\n",
+      "608 LUC7L3\n",
+      "609 SNRPB2\n",
+      "610 RALY\n",
+      "611 MICOS10\n",
+      "612 HMGN2\n",
+      "613 PSMB2\n",
+      "614 SELENOF\n",
+      "615 CD8B\n",
+      "616 CYTOR\n",
+      "617 RPL31\n",
+      "618 ATP5MC3\n",
+      "619 ARPC2\n",
+      "620 HCLS1\n",
+      "621 AP2M1\n",
+      "622 RPL9\n",
+      "623 PNISR\n",
+      "624 MCM7\n",
+      "625 GIMAP4\n",
+      "626 CHMP4A\n",
+      "627 MT2A\n",
+      "628 ATP5F1D\n",
+      "629 SMDT1\n",
+      "630 SLC25A6\n",
+      "631 NDUFB11\n",
+      "632 PLP2\n",
+      "633 MT-ND5\n",
+      "634 CD8A\n",
+      "635 MZT2A\n",
+      "636 DUSP1\n",
+      "637 PSMB9\n",
+      "638 LSM5\n",
+      "639 CTSD\n",
+      "640 CD3E\n",
+      "641 RPS25\n",
+      "642 CD63\n",
+      "643 PTGES3\n",
+      "644 SAP18\n",
+      "645 DAD1\n",
+      "646 SRSF5\n",
+      "647 PPP2R5C\n",
+      "648 IGHA1\n",
+      "649 SPN\n",
+      "650 COX4I1\n",
+      "651 LSM7\n",
+      "652 GADD45GIP1\n",
+      "653 PSMD8\n",
+      "654 RPL28\n",
+      "655 CST3\n",
+      "656 CD99\n",
+      "657 YBX1\n",
+      "658 CCT3\n",
+      "659 BZW1\n",
+      "660 NDUFB3\n",
+      "661 SERP1\n",
+      "662 IL7R\n",
+      "663 UBE2D2\n",
+      "664 PNRC1\n",
+      "665 TOMM7\n",
+      "666 PLAAT4\n",
+      "667 RAP1B\n",
+      "668 ISCU\n",
+      "669 NFKBIA\n",
+      "670 NKG7\n",
+      "671 RPS8\n",
+      "672 CAPZA1\n",
+      "673 GUK1\n",
+      "674 RPL37\n",
+      "675 MDH2\n",
+      "676 SARAF\n",
+      "677 SNHG6\n",
+      "678 RPL7\n",
+      "679 GDI2\n",
+      "680 DRAP1\n",
+      "681 TUBA1B\n",
+      "682 MRPL57\n",
+      "683 PSME2\n",
+      "684 RPL27\n",
+      "685 FBL\n",
+      "686 NOSIP\n",
+      "687 SH2D1A\n",
+      "688 GNG5\n",
+      "689 ATP5PB\n",
+      "690 ARPC5\n",
+      "691 ID2\n",
+      "692 YWHAQ\n",
+      "693 NDUFB4\n",
+      "694 C4orf3\n",
+      "695 SF3B5\n",
+      "696 EIF3E\n",
+      "697 TXN\n",
+      "698 BUB3\n",
+      "699 SLC2A3\n",
+      "700 GZMB\n",
+      "701 RPS29\n",
+      "702 SELENOW\n",
+      "703 KHDRBS1\n",
+      "704 CD2\n",
+      "705 CTSS\n",
+      "706 KRTCAP2\n",
+      "707 NR4A2\n",
+      "708 TKT\n",
+      "709 IK\n",
+      "710 H1-2\n",
+      "711 DDX21\n",
+      "712 CFL1\n",
+      "713 C12orf57\n",
+      "714 CHURC1\n",
+      "715 PDIA3\n",
+      "716 CDC37\n",
+      "717 PRDX2\n",
+      "718 DNAJB1\n",
+      "719 SNRPD2\n",
+      "720 RBMX\n",
+      "721 MT-ND4L\n",
+      "722 RHOC\n",
+      "723 ILF2\n",
+      "724 SEC62\n",
+      "725 NDUFA2\n",
+      "726 RPS12\n",
+      "727 LY6E\n",
+      "728 PRF1\n",
+      "729 CTSC\n",
+      "730 GTF3A\n",
+      "731 ALOX5AP\n",
+      "732 ERH\n",
+      "733 PAPOLA\n",
+      "734 ISG20\n",
+      "735 CD7\n",
+      "736 ANKRD12\n",
+      "737 SCAND1\n",
+      "738 TSPO\n",
+      "739 NDUFS5\n",
+      "740 PRDX1\n",
+      "741 S100A6\n",
+      "742 RPL22L1\n",
+      "743 ANXA6\n",
+      "744 PRELID1\n",
+      "745 TMED9\n",
+      "746 HLA-F\n",
+      "747 SNRPC\n",
+      "748 SRSF3\n",
+      "749 TOMM6\n",
+      "750 TRMT112\n",
+      "751 GAPDH\n",
+      "752 DUT\n",
+      "753 RPS17\n",
+      "754 HERPUD1\n",
+      "755 ARHGDIA\n",
+      "756 MYL12B\n",
+      "757 RPS9\n",
+      "758 GNAS\n",
+      "759 PTP4A2\n",
+      "760 RPL37A\n",
+      "761 ARL6IP5\n",
+      "762 RPL34\n",
+      "763 RPS3A\n",
+      "764 PSMB1\n",
+      "765 H2AZ2\n",
+      "766 SEC61G\n",
+      "767 LSM8\n",
+      "768 FNBP1\n",
+      "769 RPS24\n",
+      "770 SLC25A3\n",
+      "771 PEBP1\n",
+      "772 POMP\n",
+      "773 RGCC\n",
+      "774 RTRAF\n",
+      "775 IQGAP1\n",
+      "776 UBE2I\n",
+      "777 COTL1\n",
+      "778 RPL18\n",
+      "779 ADA\n",
+      "780 RAC2\n",
+      "781 VAMP8\n",
+      "782 IGKC\n",
+      "783 PLAC8\n",
+      "784 CAST\n",
+      "785 HMGA1\n",
+      "786 HSP90AB1\n",
+      "787 DDIT4\n",
+      "788 KLRB1\n",
+      "789 RPS27L\n",
+      "790 GABARAPL2\n",
+      "791 PSMB6\n",
+      "792 MT-ND2\n",
+      "793 ISG15\n",
+      "794 CAPZB\n",
+      "795 CDC42SE2\n",
+      "796 ATP6V0E1\n",
+      "797 LST1\n",
+      "798 RPA3\n",
+      "799 FTH1\n",
+      "800 CD3D\n",
+      "801 FKBP11\n",
+      "802 PSME1\n",
+      "803 EIF4A1\n",
+      "804 LIMD2\n",
+      "805 TRIR\n",
+      "806 NDUFB7\n",
+      "807 FKBP8\n",
+      "808 DYNLRB1\n",
+      "809 RPL10\n",
+      "810 LINC01871\n",
+      "811 CALM2\n",
+      "812 GNLY\n",
+      "813 CNBP\n",
+      "814 SERPINB1\n",
+      "815 CSNK2B\n",
+      "816 RAC1\n",
+      "817 EDF1\n",
+      "818 CCT2\n",
+      "819 ARPC3\n",
+      "820 H3-3B\n",
+      "821 P4HB\n",
+      "822 MSN\n",
+      "823 RAP1A\n",
+      "824 CENPF\n",
+      "825 H3-3A\n",
+      "826 ARF1\n",
+      "827 DBI\n",
+      "828 EEF1B2\n",
+      "829 PTMA\n",
+      "830 LRRFIP1\n",
+      "831 GPSM3\n",
+      "832 SEPTIN7\n",
+      "833 ANXA1\n",
+      "834 ETS1\n",
+      "835 LINC02446\n",
+      "836 TUFM\n",
+      "837 LAT\n",
+      "838 EVI2B\n",
+      "839 RPL38\n",
+      "840 SAT1\n",
+      "841 IFI6\n",
+      "842 NASP\n",
+      "843 TPR\n",
+      "844 ACP1\n",
+      "845 SNRPG\n",
+      "846 SF3B1\n",
+      "847 MBNL1\n",
+      "848 TRA2B\n",
+      "849 SUB1\n",
+      "850 HIGD2A\n",
+      "851 CHCHD2\n",
+      "852 SRI\n",
+      "853 SEC61B\n",
+      "854 NUCB2\n",
+      "855 UBXN1\n",
+      "856 CD27\n",
+      "857 PCED1B-AS1\n",
+      "858 HNRNPA1\n",
+      "859 TPT1\n",
+      "860 SRSF2\n",
+      "861 LGALS1\n",
+      "862 THRAP3\n",
+      "863 MCL1\n",
+      "864 ACTR3\n",
+      "865 NCL\n",
+      "866 HMGB2\n",
+      "867 TBCA\n",
+      "868 ACTB\n",
+      "869 HSPB1\n",
+      "870 RPL35\n",
+      "871 KLF6\n",
+      "872 EIF3A\n",
+      "873 POLR2L\n",
+      "874 C11orf58\n",
+      "875 RPL41\n",
+      "876 MYL12A\n",
+      "877 TMEM230\n",
+      "878 PARK7\n",
+      "879 ENSA\n",
+      "880 ACTR2\n",
+      "881 BIN1\n",
+      "882 RHOA\n",
+      "883 RPS10\n",
+      "884 NDUFA4\n",
+      "885 PPIA\n",
+      "886 COX6C\n",
+      "887 RPS6\n",
+      "888 OSTF1\n",
+      "889 BANF1\n",
+      "890 NDUFS8\n",
+      "891 ERP29\n",
+      "892 PKM\n",
+      "893 ELOB\n",
+      "894 TERF2IP\n",
+      "895 TCF25\n",
+      "896 UBA52\n",
+      "897 ECH1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, gene_id in enumerate(filtered_query_gene_ids):\n",
+    "    print(i, filtered_query_gene_symbols[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Response of GZMM')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 327
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = filtered_perturb_gene_symbols.index('CD8B')\n",
+    "q = filtered_query_gene_symbols.index('GZMM')\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(dose_response_dict['doses_pi'][p, :], dose_response_dict['responses_mean_pqi'][p, q, :], marker='o')\n",
+    "ax.set_xlabel(f\"Dose of {filtered_perturb_gene_symbols[p]}\")\n",
+    "ax.set_ylabel(f\"Response of {filtered_query_gene_symbols[q]}\")"
+   ]
   },
   {
    "cell_type": "code",

From 77d0900cb11b603f50dcb4bbc690c18fd8dce4e5 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Thu, 13 Feb 2025 20:55:13 +0000
Subject: [PATCH 39/64] a_qq -> a_pp

---
 .../inference/cellarium_gpt_inference.py      |   257 +-
 ...near_response_by_cell_type_prompting.ipynb | 68428 +++++++++++++++-
 2 files changed, 67520 insertions(+), 1165 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index a1cbe2e5..36b536da 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -569,6 +569,15 @@ def get_marginal_mean_std_from_tokens(
         if max_counts is None:
             max_counts = gene_logits_nqk.shape[-1]
         
+        return self.calculate_gene_mean_std_from_logits(gene_logits_nqk, max_counts, use_logsumexp)
+
+
+    def calculate_gene_mean_std_from_logits(
+            self,
+            gene_logits_nqk: torch.Tensor,
+            max_counts: int,
+            use_logsumexp: bool = True) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
         if use_logsumexp:
             log_counts_1_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).log()
             log_counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2).log()
@@ -633,8 +642,8 @@ def predict_gene_expression_range_for_metadata(
         total_mrna_umis: int | float,
         query_gene_ids: list[str],
         query_chunk_size: int | None = None,
-        upper_percentile: float = 0.9,
-        upper_pad: int = 1,
+        total_prob_mass: float = 0.9,
+        symmetric_range_pad: int = 1,
         max_counts: int | None = None,
     ) -> dict[str, torch.Tensor]:
         """
@@ -656,8 +665,9 @@ def predict_gene_expression_range_for_metadata(
             prompt_metadata_dict (dict): Metadata dictionary (e.g., cell type, tissue).
             total_mrna_umis (int): Total number of mRNA UMIs.
             query_chunk_size (int): Number of gene IDs to process in one chunk.
-            upper_percentile (float): Upper percentile threshold (e.g., 0.999).
-            upper_pad (int): Value to add as padding to the found index.
+            total_prob_mass (float): The amount of probability mass to capture within the to be determined
+              expression range (e.g., 0.999).
+            symmetric_range_pad (int): Value to add symmetrically as padding to the found range.
             max_counts (int): Maximum number of counts to consider. If None, use the full range. Otherwise, truncate.
 
         Returns:
@@ -665,7 +675,7 @@ def predict_gene_expression_range_for_metadata(
                         where the cumulative probability exceeds upper_percentile plus upper_pad.
         """
 
-        assert 0.0 < upper_percentile < 1.0, "The upper percentile must be between 0 and 1."
+        assert 0.0 < total_prob_mass < 1.0, "The upper percentile must be between 0 and 1."
 
         if query_chunk_size is None:
             query_chunk_size = len(query_gene_ids)
@@ -705,17 +715,25 @@ def predict_gene_expression_range_for_metadata(
             - cdf_qk[q_indices[:, None], x_lo_qm]  # subtract total prob mass at the left point (inclusive)
             + pdf_qk[q_indices[:, None], x_lo_qm]  # add back the prob mass of the left point
         )
-        mask_qm = symm_prob_mass_qm > upper_percentile
-        range_q = torch.clamp(mask_qm.float().argmax(dim=-1) + upper_pad, max=max_counts - 1)
+        mask_qm = symm_prob_mass_qm > total_prob_mass
+        range_q = torch.clamp(mask_qm.float().argmax(dim=-1) + symmetric_range_pad, max=max_counts - 1)
         x_lo_q = x_lo_qm[q_indices, range_q]
         x_hi_q = x_hi_qm[q_indices, range_q]
 
+        # calculate the mean and std of expression for bookkeeping
+        gene_marginal_mean_nq, gene_marginal_std_nq = self.calculate_gene_mean_std_from_logits(
+            gene_logits_nqk=gene_logits_qk[None, :, :],
+            max_counts=max_counts,
+            use_logsumexp=True)
+
         return {
-            'gene_logits_qk': gene_logits_qk,
-            'gene_logits_mode_q': gene_logits_mode_q,
-            'range_q': range_q,
             'x_lo_q': x_lo_q,
             'x_hi_q': x_hi_q,
+            'range_q': range_q,
+            'gene_logits_qk': gene_logits_qk,
+            'gene_logits_mode_q': gene_logits_mode_q,
+            'gene_marginal_mean_q': gene_marginal_mean_nq[0],
+            'gene_marginal_std_q': gene_marginal_std_nq[0],
         }
 
     def generate_gene_dose_response_for_metadata(
@@ -943,6 +961,79 @@ def _wrapped_snap_to_marginal_mean_manifold(prompt_gene_values_g: torch.Tensor)
             'query_marginal_std_q': query_marginal_std_q,
         }
 
+
+def quantile_normalize_select(
+        x: np.ndarray,
+        y: np.ndarray,
+        n_bins: int,
+        top_k: int,
+        min_x: int | None = None,
+        max_x: int | None = None):
+    """
+    Filter, normalize, and select top_k elements.
+    
+    Parameters:
+    -----------
+    x : np.ndarray
+        1D numpy array of covariate values.
+    y : np.ndarray
+        1D numpy array of response values.
+    n_bins : int
+        Number of bins to subdivide the range [min_x, max_x].
+    top_k : int
+        Number of top largest normalized y values to select.
+    min_x : float
+        Minimum x value (x must be > min_x).
+    max_x : float
+        Maximum x value (x must be < max_x).
+
+    Returns:
+    --------
+    selected_indices : np.ndarray
+        Indices in the original arrays corresponding to the top_k selected values.
+    top_normalized_y : np.ndarray
+        The normalized y values corresponding to these selected indices.
+    """
+    
+    # Create bin edges (n_bins bins from min_x to max_x).
+    bin_edges = np.linspace(np.min(x), np.max(x), n_bins + 1)
+
+    # Assign each x_valid to a bin.
+    # np.digitize returns bin indices in 1..n_bins, so subtract 1 to have 0-indexed bins.
+    bin_indices = np.digitize(x, bin_edges) - 1
+
+    # Prepare an array for the normalized y values.
+    y_normalized = np.empty_like(y, dtype=float)
+
+    # Process each bin separately.
+    for i in range(n_bins):
+        # Find indices in x_valid that fall in bin i.
+        in_bin = np.where(bin_indices == i)[0]
+        if in_bin.size > 0:
+            # Compute the mean of y values in this bin.
+            bin_mean = np.mean(y[in_bin])
+            # Avoid division by zero (if bin_mean happens to be zero, leave values unchanged).
+            if bin_mean == 0:
+                y_normalized[in_bin] = y[in_bin]
+            else:
+                y_normalized[in_bin] = y[in_bin] / bin_mean
+
+    if min_x is None:
+        min_x = np.min(x)
+    if max_x is None:
+        max_x = np.max(x)
+
+    _y_normalized = y_normalized.copy()
+    _y_normalized[x < min_x] = -np.inf
+    _y_normalized[x > max_x] = -np.inf
+
+    sorted_idx = np.argsort(_y_normalized)[::-1]
+    top_k = min(top_k, np.sum(_y_normalized > -np.inf))
+    top_idx = sorted_idx[:top_k]
+
+    return top_idx, y_normalized
+
+
 class GeneNetworkAnalysisBase:
     def __init__(
             self,
@@ -1019,6 +1110,11 @@ def query_gene_id_to_idx_map(self) -> dict[str, int]:
         assert self.processed, "Must process before accessing"
         return {gene_id: idx for idx, gene_id in enumerate(self.query_var_names)}
     
+    @cached_property
+    def prompt_gene_id_to_idx_map(self) -> dict[str, int]:
+        assert self.processed, "Must process before accessing"
+        return {gene_id: idx for idx, gene_id in enumerate(self.prompt_var_names)}
+
     def __str__(self) -> str:
         return (
             f"GeneNetworkAnalysisBase({self.cell_type}, {self.tissue}, {self.disease}, {self.development_stage}, "
@@ -1030,12 +1126,18 @@ def __str__(self) -> str:
     def process(
             self,
             response_normalization_strategy: t.Literal["mean", "std", "none"] = "mean",
-            feature_normalization_strategy: t.Literal["l2", "query_z_score", "prompt_z_score"] = "query_z_score",
-            min_prompt_gene_tpm: float = 10.,
-            min_query_gene_tpm: float = 10.,
+            feature_normalization_strategy: t.Literal["l2", "z_score", "none"] = "z_score",
+            min_prompt_gene_tpm: float = 0.,
+            min_query_gene_tpm: float = 0.,
             query_response_amp_min_pct: float | None = None,
-            feature_max_value: float = 10.,
+            feature_max_value: float | None = None,
+            norm_pseudo_count: float = 1e-3,
+            query_hv_top_k: int | None = None,
+            query_hv_n_bins: int | None = 50,
+            query_hv_min_x: float | None = 1e-2,
+            query_hv_max_x: float | None = np.inf,
             eps: float = 1e-8,
+            z_trans_func: t.Callable[[np.ndarray], np.ndarray] | None = None,
         ) -> None:
 
         assert not self.processed, "Already processed -- please create a new instance"
@@ -1052,7 +1154,7 @@ def process(
             raise ValueError("Invalid Jacobian normalization strategy")
 
         # linear proportional activation
-        self.z_qp = self.response_qp * (z_p[None, :] + eps) / (z_q[:, None] + eps)
+        self.z_qp = self.response_qp * (z_p[None, :] + norm_pseudo_count) / (z_q[:, None] + norm_pseudo_count)
 
         if self.verbose:
             logger.info(f"Maximum value of z_qp: {np.max(self.z_qp):.3f}")
@@ -1069,41 +1171,62 @@ def process(
             z_norm_thresh = np.percentile(z_norm_q, query_response_amp_min_pct)
             self.mask_q = self.mask_q & (z_norm_q >= z_norm_thresh)
             logger.info(f"Number of query genes after z-norm filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
-            
+        
+        if query_hv_top_k is not None:
+            assert query_hv_n_bins is not None
+            assert query_hv_min_x is not None
+            assert query_hv_max_x is not None
+            top_idx, _ = quantile_normalize_select(
+                x=np.log1p(self.query_marginal_mean_q),
+                y=np.std(self.z_qp, axis=1),
+                n_bins=query_hv_n_bins,
+                top_k=query_hv_top_k,
+                min_x=query_hv_min_x,
+                max_x=query_hv_max_x)
+            hv_mask_q = np.zeros_like(self.mask_q, dtype=bool)
+            hv_mask_q[top_idx] = True
+            self.mask_q = self.mask_q & hv_mask_q
+            logger.info(f"Number of query genes after highly-variable filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
+
         # apply the mask to everything else
         self.prompt_var_names = [self.prompt_var_names[i] for i in range(len(self.prompt_var_names)) if self.mask_p[i]]
-        # self.prompt_empirical_mean_p = self.prompt_empirical_mean_p[self.mask_p]
         self.prompt_marginal_mean_p = self.prompt_marginal_mean_p[self.mask_p]
         self.prompt_marginal_std_p = self.prompt_marginal_std_p[self.mask_p]
 
         self.query_var_names = [self.query_var_names[i] for i in range(len(self.query_var_names)) if self.mask_q[i]]
-        # self.query_empirical_mean_q = self.query_empirical_mean_q[self.mask_q]
         self.query_marginal_mean_q = self.query_marginal_mean_q[self.mask_q]
         self.query_marginal_std_q = self.query_marginal_std_q[self.mask_q]
 
         # apply the mask to z_qp
         self.z_qp = self.z_qp[self.mask_q, :][:, self.mask_p]
 
-        if feature_normalization_strategy == "prompt_z_score":
+        # clip and transform features
+        self.z_qp[np.isnan(self.z_qp)] = 0.
+        self.z_qp[np.isinf(self.z_qp)] = 0.
+
+        if feature_max_value is not None:
+            assert feature_max_value > 0
+            self.z_qp = np.clip(self.z_qp, -feature_max_value, feature_max_value)
+
+        if z_trans_func is not None:
+            self.z_qp = z_trans_func(self.z_qp)
+
+        if feature_normalization_strategy == "z_score":
+            # z-score each query gene separately in response to prompt genes
             self.z_qp = (self.z_qp - np.mean(self.z_qp, axis=0, keepdims=True)) / (
-                np.sqrt(self.z_qp.shape[0]) * (eps + np.std(self.z_qp, axis=0, keepdims=True)))
-        elif feature_normalization_strategy == "query_z_score":
-            self.z_qp = (self.z_qp - np.mean(self.z_qp, axis=1, keepdims=True)) / (
-                np.sqrt(self.z_qp.shape[1]) * (eps + np.std(self.z_qp, axis=1, keepdims=True)))
+                eps + np.std(self.z_qp, axis=0, keepdims=True))
         elif feature_normalization_strategy == "l2":
-            self.z_qp = self.z_qp / (eps + np.linalg.norm(self.z_qp, axis=-1, keepdims=True))
+            # l2-normalize query genes separately for each prompt gene
+            self.z_qp = self.z_qp / (eps + np.linalg.norm(self.z_qp, axis=0, keepdims=True))
+        elif feature_normalization_strategy == "none":
+            pass
         else:
             raise ValueError("Invalid feature normalization strategy")
 
-        # clip features
-        self.z_qp[np.isnan(self.z_qp)] = 0.
-        self.z_qp[np.isinf(self.z_qp)] = 0.
-        self.z_qp = np.clip(self.z_qp, -feature_max_value, feature_max_value)
-
         self.processed = True
 
         # adj
-        self.a_qq = None
+        self.a_pp = None
         
         # leiden
         self.leiden_membership = None
@@ -1121,52 +1244,55 @@ def compute_adjacency_matrix(
             self_loop: bool = False,
             **kwargs) -> None:
 
+        n_query_genes = self.z_qp.shape[0]
+        rho_pp = self.z_qp.T @ self.z_qp / n_query_genes
+
         if adjacency_strategy == "shifted_correlation":
             assert "beta" in kwargs, "Must provide beta for shifted correlation"
             beta = kwargs["beta"]
-            a_qq = np.power(0.5 * (1 + np.dot(self.z_qp, self.z_qp.T)), beta)
+            a_pp = np.power(0.5 * (1 + rho_pp), beta)
         elif adjacency_strategy == "unsigned_correlation":
             assert "beta" in kwargs, "Must provide beta for unsigned correlation"
             beta = kwargs["beta"]
-            a_qq = np.power(np.abs(np.dot(self.z_qp, self.z_qp.T)), beta)
+            a_pp = np.power(np.abs(rho_pp), beta)
         elif adjacency_strategy == "positive_correlation":
             assert "beta" in kwargs, "Must provide beta for positive correlation"
             beta = kwargs["beta"]
-            a_qq = np.power(np.maximum(0, np.dot(self.z_qp, self.z_qp.T)), beta)
+            a_pp = np.power(np.maximum(0, rho_pp), beta)
         elif adjacency_strategy == "positive_correlation_binary":
             assert n_neighbors is None, "n_neighbors must be None for binary adjacency"
             assert "tau" in kwargs, "Must provide correlation threshold for binary adjacency"
             tau = kwargs["tau"]
-            a_qq = (np.maximum(0, np.dot(self.z_qp, self.z_qp.T)) > tau).astype(float)
+            a_pp = (np.maximum(0, rho_pp) > tau).astype(float)
         else:
             raise ValueError("Invalid adjacency strategy")
 
-        assert np.isclose(a_qq, a_qq.T).all(), "Adjacency matrix must be symmetric -- something is wrong!"
+        assert np.isclose(a_pp, a_pp.T).all(), "Adjacency matrix must be symmetric -- something is wrong!"
 
         if n_neighbors is not None:
             assert n_neighbors > 0, "n_neighbors must be positive"
-            t_qq = np.argsort(a_qq, axis=-1)[:, -n_neighbors:]  # take the top n_neighbors
+            t_pp = np.argsort(a_pp, axis=-1)[:, -n_neighbors:]  # take the top n_neighbors
 
             # make a mask for the top n_neighbors
-            _a_qq = np.zeros_like(a_qq)
-            for q in range(a_qq.shape[0]):
-                _a_qq[q, t_qq[q]] = a_qq[q, t_qq[q]]
-                _a_qq[t_qq[q], q] = a_qq[t_qq[q], q]
+            _a_pp = np.zeros_like(a_pp)
+            for p in range(a_pp.shape[0]):
+                _a_pp[p, t_pp[p]] = a_pp[p, t_pp[p]]
+                _a_pp[t_pp[p], p] = a_pp[t_pp[p], p]
         else:
-            _a_qq = a_qq
-        a_qq = _a_qq
+            _a_pp = a_pp
+        a_pp = _a_pp
         
         if not self_loop:
-            np.fill_diagonal(a_qq, 0)
+            np.fill_diagonal(a_pp, 0)
 
-        self.a_qq = a_qq
+        self.a_pp = a_pp
 
     def _compute_igraph_from_adjacency(self, directed: bool = False) -> ig.Graph:
-        assert self.a_qq is not None, "Must compute adjacency matrix first"
-        sources, targets = self.a_qq.nonzero()
-        weights = self.a_qq[sources, targets]
+        assert self.a_pp is not None, "Must compute adjacency matrix first"
+        sources, targets = self.a_pp.nonzero()
+        weights = self.a_pp[sources, targets]
         g = ig.Graph(directed=directed)
-        g.add_vertices(self.a_qq.shape[0])  # this adds adjacency.shape[0] vertices
+        g.add_vertices(self.a_pp.shape[0])  # this adds adjacency.shape[0] vertices
         g.add_edges(list(zip(sources, targets)))
         g.es["weight"] = weights
         return g
@@ -1195,12 +1321,12 @@ def compute_spectral_dimension(
             self,
             offset: int = 2,
             n_lambda_for_estimation: int = 5) -> float:
-        assert self.a_qq is not None, "Must compute adjacency matrix first"
+        assert self.a_pp is not None, "Must compute adjacency matrix first"
         
         # calculate normalized laplacian and its eigenvalues
-        norm_q = 1. / (1e-9 + np.sqrt(self.a_qq.sum(0)))
-        lap_qq = np.eye(self.a_qq.shape[0]) - norm_q[:, None] * norm_q[None, :] * self.a_qq
-        eigs = eigh(lap_qq.astype(np.float64), eigvals_only=True)
+        norm_p = 1. / (1e-9 + np.sqrt(self.a_pp.sum(0)))
+        lap_pp = np.eye(self.a_pp.shape[0]) - norm_p[:, None] * norm_p[None, :] * self.a_pp
+        eigs = eigh(lap_pp.astype(np.float64), eigvals_only=True)
         eigs[0] = 0
         eigs = np.clip(eigs, 0, np.inf)  # roundoff error guardrail
         self.eigs = eigs
@@ -1236,7 +1362,7 @@ def make_mde_embedding(
         ):
         
         mde = pymde.preserve_neighbors(
-            self.z_qp,
+            self.z_qp.T,  # we are embedding the prompts (perturbations)
             device=device,
             verbose=verbose,
             n_neighbors=n_neighbors,
@@ -1245,7 +1371,7 @@ def make_mde_embedding(
             repulsive_penalty=repulsive_penalty,
             **kwargs)
 
-        self.embedding_q2 = mde.embed(verbose=verbose, max_iter=max_iter).cpu().numpy()
+        self.embedding_p2 = mde.embed(verbose=verbose, max_iter=max_iter).cpu().numpy()
 
     def plot_mde_embedding(
             self,
@@ -1256,27 +1382,24 @@ def plot_mde_embedding(
             highlight_gene_sets: dict[str, t.Tuple[list[str], list[str], str]] | None = None,
         ) -> go.Figure:
 
-        assert self.embedding_q2 is not None, "Must compute MDE embedding first"
+        assert self.embedding_p2 is not None, "Must compute MDE embedding first"
         assert self.leiden_membership is not None, "Must compute Leiden communities first"
 
         plot_title = f"""{self.cell_type}<br>{self.tissue}<br>{self.disease}"""
         
         # Create a color map for the memberships
-        memberships_q = self.leiden_membership
-        unique_memberships = np.unique(memberships_q)
-
-        # Convert memberships to strings for categorical mapping
-        unique_memberships_str = unique_memberships.astype(str)
+        memberships_p = self.leiden_membership
+        unique_memberships = np.unique(memberships_p)
 
         # Create the color map with string keys
         colormap = {str(label): cc.glasbey[i % len(cc.glasbey)] for i, label in enumerate(unique_memberships)}
 
         # Create a DataFrame for Plotly
         df = pd.DataFrame({
-            'x': self.embedding_q2[:, 0],
-            'y': self.embedding_q2[:, 1],
-            'label': self.query_gene_symbols,
-            'membership': memberships_q.astype(str)  # Convert to string
+            'x': self.embedding_p2[:, 0],
+            'y': self.embedding_p2[:, 1],
+            'label': self.prompt_gene_symbols,
+            'membership': memberships_p.astype(str)  # Convert to string
         })
 
         # Create the scatter plot
@@ -1295,12 +1418,12 @@ def plot_mde_embedding(
 
         if highlight_gene_sets is not None:
             for gene_set_name, (gene_ids, gene_symbols, color) in highlight_gene_sets.items():
-                query_gene_indices = [self.query_gene_id_to_idx_map[gene_id] for gene_id in gene_ids]
+                prompt_gene_indices = [self.prompt_gene_id_to_idx_map[gene_id] for gene_id in gene_ids]
 
                 # show a scatter plot and color the markers in red
                 fig.add_scatter(
-                    x=self.embedding_q2[query_gene_indices, 0],
-                    y=self.embedding_q2[query_gene_indices, 1],
+                    x=self.embedding_p2[prompt_gene_indices, 0],
+                    y=self.embedding_p2[prompt_gene_indices, 1],
                     mode='markers',
                     marker=dict(color=color, size=highlight_marker_size),
                     text=gene_symbols,
diff --git a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
index e58eb1c0..a4eb68b0 100644
--- a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
+++ b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
@@ -83,7 +83,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d5047e52fe4406f99be62cff4aabaf7",
+       "model_id": "55cdf67c812c45b8b91383450bf47d79",
        "version_major": 2,
        "version_minor": 0
       },
@@ -97,8 +97,8 @@
    ],
    "source": [
     "query_chunk_size = 1_000\n",
-    "upper_percentile = 0.95\n",
-    "max_counts = 1000\n",
+    "upper_percentile = 0.5\n",
+    "max_counts = 2000\n",
     "upper_pad = 1\n",
     "\n",
     "gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
@@ -108,35 +108,35 @@
     "    total_mrna_umis=total_mrna_umis,\n",
     "    query_gene_ids=query_gene_ids,\n",
     "    query_chunk_size=query_chunk_size,\n",
-    "    upper_percentile=upper_percentile,\n",
-    "    upper_pad=upper_pad,\n",
+    "    total_prob_mass=upper_percentile,\n",
+    "    symmetric_range_pad=upper_pad,\n",
     "    max_counts=max_counts,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x320 with 2 Axes>"
+       "<Figure size 960x320 with 3 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
        "height": 301,
-       "width": 621
+       "width": 941
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
+    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n",
     "\n",
     "ax = axs[0]\n",
     "ax.hist(gex_range_dict['gene_logits_mode_q'].cpu().numpy(), bins=100, log=True);\n",
@@ -148,68 +148,73 @@
     "ax.set_xlabel(\"Count distribution range\");\n",
     "ax.set_ylabel(\"Frequency\");\n",
     "\n",
+    "ax = axs[2]\n",
+    "ax.hist(gex_range_dict['gene_marginal_mean_q'].cpu().numpy(), bins=100, log=True);\n",
+    "ax.set_xlabel(\"Gene marginal mean\");\n",
+    "ax.set_ylabel(\"Frequency\");\n",
+    "\n",
     "fig.tight_layout()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MALAT1, mode: 296\n",
-      "TMSB4X, mode: 179\n",
-      "B2M, mode: 122\n",
-      "EEF1A1, mode: 102\n",
-      "RPS27, mode: 102\n",
-      "RPS18, mode: 86\n",
-      "RPL13A, mode: 83\n",
-      "RPL10, mode: 83\n",
-      "RPL41, mode: 83\n",
-      "RPLP1, mode: 81\n",
-      "RPS12, mode: 81\n",
-      "RPL13, mode: 78\n",
-      "RPS2, mode: 73\n",
-      "RPL21, mode: 71\n",
-      "RPS19, mode: 68\n",
-      "RPS29, mode: 68\n",
-      "RPL34, mode: 68\n",
-      "RPLP2, mode: 66\n",
-      "RPL28, mode: 66\n",
-      "RPL32, mode: 63\n",
-      "RPS27A, mode: 63\n",
-      "RPS14, mode: 63\n",
-      "RPS15A, mode: 63\n",
-      "RPS3, mode: 63\n",
-      "RPS6, mode: 63\n",
-      "RPL3, mode: 57\n",
-      "RPL18A, mode: 57\n",
-      "RPS23, mode: 57\n",
-      "TMSB10, mode: 57\n",
-      "RPS4X, mode: 57\n",
-      "MT-CO1, mode: 57\n",
-      "ACTB, mode: 57\n",
-      "RPL39, mode: 56\n",
-      "RPL11, mode: 56\n",
-      "RPL27A, mode: 56\n",
-      "RPL26, mode: 54\n",
-      "RPL23A, mode: 52\n",
-      "RPS3A, mode: 50\n",
-      "RPS28, mode: 49\n",
-      "RPL19, mode: 49\n",
-      "RPS15, mode: 49\n",
-      "RPL15, mode: 49\n",
-      "RPS8, mode: 49\n",
-      "TPT1, mode: 49\n",
-      "MT-CO3, mode: 45\n",
-      "MT-CO2, mode: 45\n",
-      "RPL7, mode: 45\n",
-      "RPL37A, mode: 45\n",
-      "RPL30, mode: 43\n",
-      "RPS25, mode: 43\n"
+      "MALAT1, mode: 296, mean: 420.55\n",
+      "TMSB4X, mode: 179, mean: 164.94\n",
+      "B2M, mode: 122, mean: 133.06\n",
+      "EEF1A1, mode: 102, mean: 105.39\n",
+      "RPS27, mode: 102, mean: 104.24\n",
+      "RPS18, mode: 86, mean: 99.17\n",
+      "RPL41, mode: 83, mean: 95.43\n",
+      "RPL10, mode: 83, mean: 94.16\n",
+      "RPLP1, mode: 81, mean: 92.55\n",
+      "RPL13A, mode: 83, mean: 90.35\n",
+      "RPL13, mode: 78, mean: 83.35\n",
+      "RPS12, mode: 81, mean: 80.84\n",
+      "RPS2, mode: 73, mean: 79.76\n",
+      "RPS19, mode: 68, mean: 75.85\n",
+      "RPL21, mode: 71, mean: 72.20\n",
+      "RPL34, mode: 68, mean: 70.94\n",
+      "RPL28, mode: 66, mean: 70.34\n",
+      "ACTB, mode: 57, mean: 69.83\n",
+      "MT-CO1, mode: 57, mean: 69.81\n",
+      "RPS14, mode: 63, mean: 69.36\n",
+      "RPS29, mode: 68, mean: 69.11\n",
+      "RPLP2, mode: 66, mean: 68.51\n",
+      "RPS6, mode: 63, mean: 66.86\n",
+      "RPL32, mode: 63, mean: 66.72\n",
+      "RPS3, mode: 63, mean: 64.11\n",
+      "RPS15A, mode: 63, mean: 64.06\n",
+      "RPS27A, mode: 63, mean: 64.02\n",
+      "TMSB10, mode: 57, mean: 63.30\n",
+      "RPL3, mode: 57, mean: 61.58\n",
+      "RPS4X, mode: 57, mean: 59.67\n",
+      "RPL18A, mode: 57, mean: 58.87\n",
+      "RPL26, mode: 54, mean: 58.81\n",
+      "RPL39, mode: 56, mean: 58.43\n",
+      "RPL27A, mode: 56, mean: 57.82\n",
+      "RPL23A, mode: 52, mean: 57.59\n",
+      "RPL11, mode: 56, mean: 57.48\n",
+      "RPS3A, mode: 50, mean: 56.65\n",
+      "RPS23, mode: 57, mean: 56.57\n",
+      "RPL19, mode: 49, mean: 55.46\n",
+      "RPS28, mode: 49, mean: 53.03\n",
+      "MT-CO3, mode: 45, mean: 52.94\n",
+      "RPS15, mode: 49, mean: 52.55\n",
+      "MT-CO2, mode: 45, mean: 52.50\n",
+      "RPS8, mode: 49, mean: 52.45\n",
+      "TPT1, mode: 49, mean: 50.57\n",
+      "RPL30, mode: 43, mean: 48.94\n",
+      "RPL37A, mode: 45, mean: 48.54\n",
+      "RPL37, mode: 43, mean: 48.15\n",
+      "RPL15, mode: 49, mean: 48.03\n",
+      "RPL7, mode: 45, mean: 46.62\n"
      ]
     }
    ],
@@ -217,22 +222,24 @@
     "# print gene symbols for top 10 genes in terms of mode\n",
     "top_k = 50\n",
     "top_k_genes = sorted(\n",
-    "    zip(query_gene_ids, gex_range_dict['gene_logits_mode_q'].cpu().numpy()), key=lambda x: x[1], reverse=True)[:top_k]\n",
-    "for gene_id, mode in top_k_genes:\n",
-    "    print(f'{ctx.gene_id_to_gene_symbol_map[gene_id]}, mode: {mode}')"
+    "    zip(query_gene_ids,\n",
+    "        gex_range_dict['gene_logits_mode_q'].cpu().numpy(),\n",
+    "        gex_range_dict['gene_marginal_mean_q'].cpu().numpy()), key=lambda x: x[2], reverse=True)[:top_k]\n",
+    "for gene_id, mode, mean in top_k_genes:\n",
+    "    print(f'{ctx.gene_id_to_gene_symbol_map[gene_id]}, mode: {mode}, mean: {mean:.2f}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mode: 179, range: 149\n",
-      "lo: 30, hi: 328\n"
+      "mode: 296, range: 144\n",
+      "lo: 152, hi: 440\n"
      ]
     },
     {
@@ -241,13 +248,13 @@
        "Text(0, 0.5, 'Probability')"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
@@ -255,14 +262,14 @@
      "metadata": {
       "image/png": {
        "height": 318,
-       "width": 345
+       "width": 355
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "gene_symbol = 'TMSB4X'\n",
+    "gene_symbol = 'MALAT1'\n",
     "q = ctx.var_name_to_index_map[ctx.gene_symbol_to_gene_id_map[gene_symbol]]\n",
     "\n",
     "print(f\"mode: {gex_range_dict['gene_logits_mode_q'][q].item()}, range: {gex_range_dict['range_q'][q].item()}\")\n",
@@ -278,6 +285,7 @@
     "    gex_range_dict['gene_logits_qk'][q].exp().cpu().numpy(), '-')\n",
     "ax.axvline(gex_range_dict['x_lo_q'][q].item(), color='red')\n",
     "ax.axvline(gex_range_dict['x_hi_q'][q].item(), color='red')\n",
+    "ax.axvline(gex_range_dict['gene_logits_mode_q'][q].item(), color='orange')\n",
     "\n",
     "ax.set_title(f\"Gene: {gene_symbol}\")\n",
     "ax.set_xlabel(\"Count\")\n",
@@ -286,52 +294,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of genes to perturb: 898\n",
-      "Number of genes to query: 898\n"
+      "Number of genes to perturb: 6321\n",
+      "Number of genes to query: 6321\n"
      ]
     }
    ],
    "source": [
     "gene_logits_qk = gex_range_dict['gene_logits_qk']\n",
     "gene_logits_mode_q = gex_range_dict['gene_logits_mode_q']\n",
+    "gene_marginal_mean_q = gex_range_dict['gene_marginal_mean_q']\n",
+    "gene_marginal_std_q = gex_range_dict['gene_marginal_std_q']\n",
+    "gene_logits_mode_q = gex_range_dict['gene_logits_mode_q']\n",
     "range_q =  gex_range_dict['range_q']\n",
     "x_lo_q = gex_range_dict['x_lo_q']\n",
     "x_hi_q = gex_range_dict['x_hi_q']\n",
     "\n",
     "# let's pick genes to perturb (and query) that make sense for this cell type\n",
-    "min_upper_range = 5\n",
-    "expressed_mask_q = (x_hi_q >= min_upper_range).cpu().numpy()\n",
+    "expr_threshold_tpm = 10.\n",
+    "expr_threshold_mean = expr_threshold_tpm * total_mrna_umis / 1e6\n",
+    "expressed_mask_q = (gene_marginal_mean_q >= expr_threshold_mean).cpu().numpy()\n",
+    "\n",
     "filtered_query_gene_ids = query_gene_ids[expressed_mask_q]\n",
     "filtered_perturb_gene_ids = query_gene_ids[expressed_mask_q]\n",
     "filtered_query_gene_symbols = [ctx.gene_id_to_gene_symbol_map[gid] for gid in filtered_query_gene_ids]\n",
     "filtered_perturb_gene_symbols = [ctx.gene_id_to_gene_symbol_map[gid] for gid in filtered_perturb_gene_ids]\n",
     "filtered_x_lo_q = x_lo_q.cpu().numpy()[expressed_mask_q]\n",
     "filtered_x_hi_q = x_hi_q.cpu().numpy()[expressed_mask_q]\n",
+    "\n",
     "print(\"Number of genes to perturb:\", len(filtered_perturb_gene_ids))\n",
     "print(\"Number of genes to query:\", len(filtered_query_gene_ids))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d9b8db6d7a2e4b2aa4aeb95a8b9d1eac",
+       "model_id": "2decaef13583432bbb410206dbd5e673",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Processing dose quantiles:   0%|          | 0/10 [00:00<?, ?it/s]"
+       "Processing dose quantiles:   0%|          | 0/5 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -340,1138 +354,67356 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "680cddfe68214dc9a71d409d99b8bebb",
+       "model_id": "8268ed79fdee4e14aae7d835d669b86a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "Processing query gene chunks:   0%|          | 0/300 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[79], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m n_points \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[1;32m      2\u001b[0m query_chunk_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m64\u001b[39m\n\u001b[0;32m----> 4\u001b[0m dose_response_dict \u001b[38;5;241m=\u001b[39m \u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_gene_dose_response_for_metadata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43massay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43massay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43msuspension_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuspension_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprompt_metadata_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt_metadata_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtotal_mrna_umis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_mrna_umis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery_gene_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiltered_query_gene_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mperturb_gene_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiltered_perturb_gene_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx_lo_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiltered_x_lo_q\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx_hi_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiltered_x_hi_q\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_points\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquery_chunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_chunk_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_counts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_counts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/data/cellarium-ml/cellarium/ml/utilities/inference/cellarium_gpt_inference.py:786\u001b[0m, in \u001b[0;36mCellariumGPTInferenceContext.generate_gene_dose_response_for_metadata\u001b[0;34m(self, assay, suspension_type, prompt_metadata_dict, total_mrna_umis, query_gene_ids, perturb_gene_ids, x_lo_p, x_hi_p, n_points, query_chunk_size, max_counts)\u001b[0m\n\u001b[1;32m    776\u001b[0m     tokens_dict, context_indices, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_gene_tokens_by_metadata(\n\u001b[1;32m    777\u001b[0m         assay\u001b[38;5;241m=\u001b[39massay,\n\u001b[1;32m    778\u001b[0m         suspension_type\u001b[38;5;241m=\u001b[39msuspension_type,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    783\u001b[0m         perturb_gene_values\u001b[38;5;241m=\u001b[39mperturb_gene_values\n\u001b[1;32m    784\u001b[0m     )\n\u001b[1;32m    785\u001b[0m     tokens_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgpt_pipeline\u001b[38;5;241m.\u001b[39mtransfer_batch_to_device(tokens_dict, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice, \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 786\u001b[0m     gene_marginal_mean_nq, gene_marginal_std_nq \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_marginal_mean_std_from_tokens\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    787\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtokens_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtokens_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    788\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    789\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_counts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_counts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    790\u001b[0m gene_marginal_mean_nq_chunks\u001b[38;5;241m.\u001b[39mappend(gene_marginal_mean_nq\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n\u001b[1;32m    791\u001b[0m gene_marginal_std_nq_chunks\u001b[38;5;241m.\u001b[39mappend(gene_marginal_std_nq\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
+      "File \u001b[0;32m~/data/cellarium-ml/cellarium/ml/utilities/inference/cellarium_gpt_inference.py:567\u001b[0m, in \u001b[0;36mCellariumGPTInferenceContext.get_marginal_mean_std_from_tokens\u001b[0;34m(self, tokens_dict, context_indices, use_logsumexp, max_counts)\u001b[0m\n\u001b[1;32m    559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_marginal_mean_std_from_tokens\u001b[39m(\n\u001b[1;32m    560\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    561\u001b[0m         tokens_dict: \u001b[38;5;28mdict\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    564\u001b[0m         max_counts: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    565\u001b[0m     ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mTuple[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m--> 567\u001b[0m     gene_logits_nqk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_gene_value_logits_from_tokens\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext_indices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_counts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m max_counts \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    570\u001b[0m         max_counts \u001b[38;5;241m=\u001b[39m gene_logits_nqk\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+      "File \u001b[0;32m~/data/cellarium-ml/cellarium/ml/utilities/inference/cellarium_gpt_inference.py:546\u001b[0m, in \u001b[0;36mCellariumGPTInferenceContext.get_gene_value_logits_from_tokens\u001b[0;34m(self, tokens_dict, context_indices, max_counts)\u001b[0m\n\u001b[1;32m    538\u001b[0m logits_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgpt_pipeline\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mpredict(\n\u001b[1;32m    539\u001b[0m     gene_tokens_nc\u001b[38;5;241m=\u001b[39mtokens_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgene_tokens_nc\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    540\u001b[0m     metadata_tokens_n\u001b[38;5;241m=\u001b[39mtokens_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata_tokens_n\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    541\u001b[0m     prompt_mask_nc\u001b[38;5;241m=\u001b[39mtokens_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt_mask_nc\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    542\u001b[0m     predict_keys\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgene_value\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m    543\u001b[0m )\n\u001b[1;32m    545\u001b[0m \u001b[38;5;66;03m# note: we use `q` to denote query genes\u001b[39;00m\n\u001b[0;32m--> 546\u001b[0m query_gene_indices \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontext_indices\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mquery_genes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mint64\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    547\u001b[0m gene_logits_nqk \u001b[38;5;241m=\u001b[39m logits_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgene_value\u001b[39m\u001b[38;5;124m'\u001b[39m][:, query_gene_indices, :]\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_counts \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "n_points = 5\n",
+    "query_chunk_size = 64\n",
+    "\n",
+    "dose_response_dict = ctx.generate_gene_dose_response_for_metadata(\n",
+    "    assay=assay,\n",
+    "    suspension_type=suspension_type,\n",
+    "    prompt_metadata_dict=prompt_metadata_dict,\n",
+    "    total_mrna_umis=total_mrna_umis,\n",
+    "    query_gene_ids=filtered_query_gene_ids,\n",
+    "    perturb_gene_ids=filtered_perturb_gene_ids,\n",
+    "    x_lo_p=filtered_x_lo_q,\n",
+    "    x_hi_p=filtered_x_hi_q,\n",
+    "    n_points=n_points,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    max_counts=max_counts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pickle\n",
+    "\n",
+    "# with open('/home/mehrtash/data/cd8_dose_response_dict_tpm_0.1.pkl', 'wb') as f:\n",
+    "#     pickle.dump(dose_response_dict, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('/home/mehrtash/data/cd8_dose_response_dict.pkl', 'rb') as f:\n",
+    "    dose_response_dict = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "95823c15e74d40aea27353123a80215f",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "dict_keys(['doses_pi', 'responses_mean_pqi', 'responses_std_pqi', 'control_mean_q', 'control_std_q'])"
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dose_response_dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 SDF4\n",
+      "1 FAAP20\n",
+      "2 ARMH1\n",
+      "3 SERBP1\n",
+      "4 PRKACB\n",
+      "5 GTF2B\n",
+      "6 MTF2\n",
+      "7 HENMT1\n",
+      "8 PRPF38B\n",
+      "9 DAP3\n",
+      "10 ARHGEF2\n",
+      "11 UHMK1\n",
+      "12 RABIF\n",
+      "13 PYCR2\n",
+      "14 ACBD3\n",
+      "15 MTR\n",
+      "16 LPIN1\n",
+      "17 DDX1\n",
+      "18 MRPL35\n",
+      "19 RAB3GAP1\n",
+      "20 ORMDL1\n",
+      "21 ANKZF1\n",
+      "22 SP100\n",
+      "23 TADA3\n",
+      "24 ACAA1\n",
+      "25 CTNNB1\n",
+      "26 TMEM42\n",
+      "27 TOMM70\n",
+      "28 LRRC58\n",
+      "29 MAEA\n",
+      "30 LYAR\n",
+      "31 FGFBP2\n",
+      "32 NUP54\n",
+      "33 BRD9\n",
+      "34 PELO\n",
+      "35 GZMK\n",
+      "36 CAMK4\n",
+      "37 NR3C1\n",
+      "38 CANX\n",
+      "39 TRIM52\n",
+      "40 BTN3A3\n",
+      "41 DDX39B\n",
+      "42 ILRUN\n",
+      "43 FBXO9\n",
+      "44 CRYBG1\n",
+      "45 GTF3C6\n",
+      "46 IFNGR1\n",
+      "47 SNX9\n",
+      "48 C7orf50\n",
+      "49 KDELR2\n",
+      "50 CCT6A\n",
+      "51 CBLL1\n",
+      "52 ING3\n",
+      "53 PPP2R2A\n",
+      "54 RPL30\n",
+      "55 RNF19A\n",
+      "56 ZNF706\n",
+      "57 EFR3A\n",
+      "58 FGD3\n",
+      "59 NDUFA8\n",
+      "60 MLLT10\n",
+      "61 ITPRIP\n",
+      "62 PTPRE\n",
+      "63 IFITM2\n",
+      "64 ZNRD2\n",
+      "65 MRE11\n",
+      "66 CUL5\n",
+      "67 PTMS\n",
+      "68 PTPN6\n",
+      "69 LETMD1\n",
+      "70 ARHGAP9\n",
+      "71 CAND1\n",
+      "72 NUP37\n",
+      "73 GPN3\n",
+      "74 SPRING1\n",
+      "75 APEX1\n",
+      "76 SPTSSA\n",
+      "77 ATP6V1D\n",
+      "78 IFT43\n",
+      "79 FOXN3\n",
+      "80 MARK3\n",
+      "81 SPG11\n",
+      "82 FEM1B\n",
+      "83 TLE3\n",
+      "84 GNPTG\n",
+      "85 FLYWCH2\n",
+      "86 NPIPB5\n",
+      "87 PSMB10\n",
+      "88 NIP7\n",
+      "89 KARS1\n",
+      "90 MAF\n",
+      "91 PLCG2\n",
+      "92 XAF1\n",
+      "93 NCOR1\n",
+      "94 SLFN5\n",
+      "95 SNHG30\n",
+      "96 CCR7\n",
+      "97 TMUB2\n",
+      "98 SEC14L1\n",
+      "99 OGFOD3\n",
+      "100 RNF125\n",
+      "101 PIAS2\n",
+      "102 TMX3\n",
+      "103 DNMT1\n",
+      "104 SAMD4B\n",
+      "105 TOMM40\n",
+      "106 NUCB1\n",
+      "107 MKKS\n",
+      "108 DSTN\n",
+      "109 SNHG17\n",
+      "110 NCOA3\n",
+      "111 MIAT\n",
+      "112 SF3A1\n",
+      "113 RPL3\n",
+      "114 TTC38\n",
+      "115 P2RY8\n",
+      "116 APOO\n",
+      "117 KDM6A\n",
+      "118 RLIM\n",
+      "119 TCEAL8\n",
+      "120 NKAP\n",
+      "121 UTP14A\n",
+      "122 LUZP1\n",
+      "123 IFT25\n",
+      "124 GBP1\n",
+      "125 S1PR1\n",
+      "126 MAN1A2\n",
+      "127 MRPL24\n",
+      "128 SH2D2A\n",
+      "129 DEDD\n",
+      "130 UFC1\n",
+      "131 ATF3\n",
+      "132 GPATCH2\n",
+      "133 ENSG00000182551\n",
+      "134 RNASEH1\n",
+      "135 LBH\n",
+      "136 PRADC1\n",
+      "137 STAMBP\n",
+      "138 CNOT11\n",
+      "139 LIMS1\n",
+      "140 TMBIM1\n",
+      "141 TGFBR2\n",
+      "142 CRTAP\n",
+      "143 CLASP2\n",
+      "144 UQCC5\n",
+      "145 PBRM1\n",
+      "146 CBLB\n",
+      "147 TFDP2\n",
+      "148 PHC3\n",
+      "149 SENP5\n",
+      "150 WDR1\n",
+      "151 DTHD1\n",
+      "152 YTHDC1\n",
+      "153 MRPL1\n",
+      "154 SCLT1\n",
+      "155 ELF2\n",
+      "156 IL6ST\n",
+      "157 ERAP2\n",
+      "158 AP3S1\n",
+      "159 COMMD10\n",
+      "160 FNIP1\n",
+      "161 PFDN1\n",
+      "162 MRPL22\n",
+      "163 NPM1\n",
+      "164 KIAA1191\n",
+      "165 GRK6\n",
+      "166 NEDD9\n",
+      "167 PSMB8-AS1\n",
+      "168 STK38\n",
+      "169 TBCC\n",
+      "170 HDAC2\n",
+      "171 EIF2AK1\n",
+      "172 YKT6\n",
+      "173 CCM2\n",
+      "174 SUMF2\n",
+      "175 FIS1\n",
+      "176 ATP6V1F\n",
+      "177 GIMAP2\n",
+      "178 TMUB1\n",
+      "179 PCMTD1\n",
+      "180 NDRG1\n",
+      "181 SECISBP2\n",
+      "182 NCBP1\n",
+      "183 PTRH1\n",
+      "184 GATA3\n",
+      "185 MSRB2\n",
+      "186 SVIL\n",
+      "187 DNAJC9\n",
+      "188 ADK\n",
+      "189 NUTM2B-AS1\n",
+      "190 OGA\n",
+      "191 STN1\n",
+      "192 C11orf21\n",
+      "193 KCNQ1OT1\n",
+      "194 NUP98\n",
+      "195 CD59\n",
+      "196 APIP\n",
+      "197 PRDX5\n",
+      "198 RNASEH2C\n",
+      "199 SYTL2\n",
+      "200 MAML2\n",
+      "201 DDX10\n",
+      "202 VPS26B\n",
+      "203 RECQL\n",
+      "204 LINC00938\n",
+      "205 LIMA1\n",
+      "206 PRR13\n",
+      "207 XPOT\n",
+      "208 NHLRC3\n",
+      "209 UCHL3\n",
+      "210 OSGEP\n",
+      "211 PABPN1\n",
+      "212 DHRS1\n",
+      "213 ARF6\n",
+      "214 ATXN3\n",
+      "215 LINC01550\n",
+      "216 EMC7\n",
+      "217 SERF2\n",
+      "218 B2M\n",
+      "219 CLPX\n",
+      "220 HBA1\n",
+      "221 SOCS1\n",
+      "222 MAZ\n",
+      "223 COG4\n",
+      "224 C1QBP\n",
+      "225 UNC119\n",
+      "226 AATF\n",
+      "227 ACLY\n",
+      "228 CCDC43\n",
+      "229 FMNL1-DT\n",
+      "230 SUPT4H1\n",
+      "231 SMAD2\n",
+      "232 LMAN1\n",
+      "233 CFD\n",
+      "234 ABCA7\n",
+      "235 MATK\n",
+      "236 ALKBH7\n",
+      "237 RPL18A\n",
+      "238 SUGP2\n",
+      "239 GMFG\n",
+      "240 BLVRB\n",
+      "241 ETFB\n",
+      "242 LAIR1\n",
+      "243 DTD1\n",
+      "244 EIF2S2\n",
+      "245 SLC35C2\n",
+      "246 DDT\n",
+      "247 DDX17\n",
+      "248 ENSG00000226179\n",
+      "249 RBBP7\n",
+      "250 CXorf38\n",
+      "251 PGK1\n",
+      "252 PHF6\n",
+      "253 AURKAIP1\n",
+      "254 NADK\n",
+      "255 LYPLA2\n",
+      "256 NFYC\n",
+      "257 RBMXL1\n",
+      "258 HSPA6\n",
+      "259 CEP350\n",
+      "260 GLRX2\n",
+      "261 DENND1B\n",
+      "262 UBE2T\n",
+      "263 ZC3H11A\n",
+      "264 ERO1B\n",
+      "265 DESI2\n",
+      "266 BABAM2\n",
+      "267 MRPL19\n",
+      "268 C2orf68\n",
+      "269 NIFK-AS1\n",
+      "270 CCNT2\n",
+      "271 LY75\n",
+      "272 DYNC1I2\n",
+      "273 LNPK\n",
+      "274 HSPD1\n",
+      "275 NUDT16\n",
+      "276 PCCB\n",
+      "277 GK5\n",
+      "278 DNAJC19\n",
+      "279 FIP1L1\n",
+      "280 RAP1GDS1\n",
+      "281 UBE2D3\n",
+      "282 MCUB\n",
+      "283 PPWD1\n",
+      "284 RPS23\n",
+      "285 COX7C\n",
+      "286 IRF1-AS1\n",
+      "287 UQCRQ\n",
+      "288 ZCCHC10\n",
+      "289 ZMAT2\n",
+      "290 HMMR\n",
+      "291 CREBRF\n",
+      "292 LMAN2\n",
+      "293 EEF1E1\n",
+      "294 PSMB8\n",
+      "295 ZFAND3\n",
+      "296 FBXO5\n",
+      "297 DYNLT1\n",
+      "298 SFT2D1\n",
+      "299 CBX3\n",
+      "300 SKAP2\n",
+      "301 ATP5MF\n",
+      "302 ALKBH4\n",
+      "303 ZNF277\n",
+      "304 ABCF2\n",
+      "305 SFTPC\n",
+      "306 ASAP1\n",
+      "307 RPL12\n",
+      "308 NTMT1\n",
+      "309 TTF1\n",
+      "310 WAC-AS1\n",
+      "311 ZNF22\n",
+      "312 MAPK8\n",
+      "313 VSIR\n",
+      "314 ECD\n",
+      "315 ZNF518A\n",
+      "316 NOLC1\n",
+      "317 PPP2R2D\n",
+      "318 RASSF7\n",
+      "319 EIF3M\n",
+      "320 OTUB1\n",
+      "321 DPF2\n",
+      "322 DPAGT1\n",
+      "323 CDCA3\n",
+      "324 PPHLN1\n",
+      "325 PFDN5\n",
+      "326 OS9\n",
+      "327 TMPO\n",
+      "328 SCYL2\n",
+      "329 SPPL3\n",
+      "330 MRPL52\n",
+      "331 GZMH\n",
+      "332 PTGDR\n",
+      "333 SIVA1\n",
+      "334 SNHG14\n",
+      "335 ZNF106\n",
+      "336 MINDY2\n",
+      "337 FBXO22\n",
+      "338 WDR73\n",
+      "339 SEC11A\n",
+      "340 LUC7L\n",
+      "341 MCRIP2\n",
+      "342 CHD9\n",
+      "343 USB1\n",
+      "344 TRADD\n",
+      "345 ZNF276\n",
+      "346 NAA38\n",
+      "347 VAMP2\n",
+      "348 SLFN12L\n",
+      "349 ENSG00000276085\n",
+      "350 CDK5RAP3\n",
+      "351 SKAP1\n",
+      "352 ATP5PD\n",
+      "353 SUMO2\n",
+      "354 CCDC137\n",
+      "355 CYBC1\n",
+      "356 ENOSF1\n",
+      "357 ATP5F1A\n",
+      "358 MEX3C\n",
+      "359 NFIC\n",
+      "360 NDUFA7\n",
+      "361 STX10\n",
+      "362 TMEM161A\n",
+      "363 CARD8\n",
+      "364 ALDH16A1\n",
+      "365 BRWD1\n",
+      "366 NOL12\n",
+      "367 TOMM22\n",
+      "368 JOSD1\n",
+      "369 NDUFA6\n",
+      "370 EIF1AX\n",
+      "371 FMR1\n",
+      "372 VMA21\n",
+      "373 FUNDC2\n",
+      "374 KCNAB2\n",
+      "375 RERE\n",
+      "376 AKR7A2\n",
+      "377 NIPAL3\n",
+      "378 EIF3I\n",
+      "379 EFCAB14\n",
+      "380 SCP2\n",
+      "381 JUN\n",
+      "382 ENSG00000273338\n",
+      "383 LMNA\n",
+      "384 SLAMF7\n",
+      "385 UAP1\n",
+      "386 SELL\n",
+      "387 VAMP4\n",
+      "388 DHX9\n",
+      "389 RGS2\n",
+      "390 NEK7\n",
+      "391 RAB4A\n",
+      "392 HPCAL1\n",
+      "393 PDIA6\n",
+      "394 SRSF7\n",
+      "395 THUMPD2\n",
+      "396 BCL11A\n",
+      "397 TIA1\n",
+      "398 MTX2\n",
+      "399 ING5\n",
+      "400 UBA7\n",
+      "401 RYBP\n",
+      "402 GTPBP8\n",
+      "403 DTX3L\n",
+      "404 RFC1\n",
+      "405 TMEM33\n",
+      "406 ABRAXAS1\n",
+      "407 SPP1\n",
+      "408 ZNF131\n",
+      "409 POLK\n",
+      "410 MAN2A1\n",
+      "411 EPB41L4A-AS1\n",
+      "412 RNF145\n",
+      "413 LCP2\n",
+      "414 RXRB\n",
+      "415 MRPS10\n",
+      "416 MCM3\n",
+      "417 REV3L\n",
+      "418 GSAP\n",
+      "419 RBM33\n",
+      "420 CLN8\n",
+      "421 HMBOX1\n",
+      "422 SGK3\n",
+      "423 NBN\n",
+      "424 HINT2\n",
+      "425 CEP78\n",
+      "426 ENSG00000196312\n",
+      "427 ODF2\n",
+      "428 GTF3C5\n",
+      "429 BRD3\n",
+      "430 PRKCQ\n",
+      "431 ACBD5\n",
+      "432 HERC4\n",
+      "433 TSPAN14\n",
+      "434 TRIM8\n",
+      "435 PDE3B\n",
+      "436 SERGEF\n",
+      "437 HTATIP2\n",
+      "438 CHORDC1\n",
+      "439 TMBIM6\n",
+      "440 CPSF6\n",
+      "441 METAP2\n",
+      "442 RAB35\n",
+      "443 OASL\n",
+      "444 ZCCHC8\n",
+      "445 DENR\n",
+      "446 GTF2F2\n",
+      "447 TFDP1\n",
+      "448 SEC23A\n",
+      "449 AP5M1\n",
+      "450 RBM25\n",
+      "451 GOLGA8B\n",
+      "452 RPLP1\n",
+      "453 ADPGK\n",
+      "454 POLR3K\n",
+      "455 SMG1\n",
+      "456 PYCARD\n",
+      "457 NFATC3\n",
+      "458 BANP\n",
+      "459 RPL13\n",
+      "460 ENSG00000262202\n",
+      "461 DHRS7B\n",
+      "462 TMEM97\n",
+      "463 NME1\n",
+      "464 NARS1\n",
+      "465 MED16\n",
+      "466 WDR18\n",
+      "467 WDR83\n",
+      "468 PKN1\n",
+      "469 RABAC1\n",
+      "470 TMEM160\n",
+      "471 BCL2L12\n",
+      "472 NOP56\n",
+      "473 PTPRA\n",
+      "474 HM13\n",
+      "475 SAMHD1\n",
+      "476 ZFAS1\n",
+      "477 RGS19\n",
+      "478 ENSG00000159128\n",
+      "479 DYRK1A\n",
+      "480 UFD1\n",
+      "481 XRCC6\n",
+      "482 NHSL2\n",
+      "483 PIN4\n",
+      "484 SSR4\n",
+      "485 NAA10\n",
+      "486 GAB3\n",
+      "487 VAMP7\n",
+      "488 MT-ND4\n",
+      "489 CCNL2\n",
+      "490 SVBP\n",
+      "491 CDC20\n",
+      "492 GPBP1L1\n",
+      "493 MAGOH\n",
+      "494 MYSM1\n",
+      "495 GADD45A\n",
+      "496 IFI16\n",
+      "497 RBBP5\n",
+      "498 GCC2\n",
+      "499 PSD4\n",
+      "500 UBXN4\n",
+      "501 CXCR4\n",
+      "502 ZEB2\n",
+      "503 LINC01934\n",
+      "504 CNOT9\n",
+      "505 TUBA4A\n",
+      "506 INPP5D\n",
+      "507 ARPC4\n",
+      "508 DALRD3\n",
+      "509 TUSC2\n",
+      "510 WDR82\n",
+      "511 CHMP2B\n",
+      "512 C3orf38\n",
+      "513 TIGIT\n",
+      "514 H1-10\n",
+      "515 MSL2\n",
+      "516 NSD2\n",
+      "517 NUDT9\n",
+      "518 PAPSS1\n",
+      "519 TMEM131L\n",
+      "520 ICE1\n",
+      "521 RAD1\n",
+      "522 ENSG00000287263\n",
+      "523 CWC27\n",
+      "524 CENPK\n",
+      "525 CLINT1\n",
+      "526 ABT1\n",
+      "527 NELFE\n",
+      "528 MAPK13\n",
+      "529 SUPT3H\n",
+      "530 FOXO3\n",
+      "531 RWDD1\n",
+      "532 AIG1\n",
+      "533 RNF216\n",
+      "534 AVL9\n",
+      "535 VPS41\n",
+      "536 SDHAF3\n",
+      "537 POP7\n",
+      "538 FDFT1\n",
+      "539 LSM1\n",
+      "540 YTHDF3\n",
+      "541 ELOC\n",
+      "542 RMDN1\n",
+      "543 PLAA\n",
+      "544 FKBP15\n",
+      "545 SMNDC1\n",
+      "546 TALDO1\n",
+      "547 HBG1\n",
+      "548 TMEM258\n",
+      "549 SCGB1A1\n",
+      "550 MALAT1\n",
+      "551 GSTP1\n",
+      "552 ANAPC15\n",
+      "553 KBTBD3\n",
+      "554 DYRK4\n",
+      "555 TAPBPL\n",
+      "556 GOLT1B\n",
+      "557 TESPA1\n",
+      "558 C12orf75\n",
+      "559 POP5\n",
+      "560 ORAI1\n",
+      "561 ZMYM5\n",
+      "562 ALG5\n",
+      "563 IPO5\n",
+      "564 PTGER2\n",
+      "565 LGALS3\n",
+      "566 TRIP11\n",
+      "567 BAG5\n",
+      "568 GCHFR\n",
+      "569 POLR2M\n",
+      "570 ETFA\n",
+      "571 ANTKMT\n",
+      "572 MARF1\n",
+      "573 GTF3C1\n",
+      "574 AHSP\n",
+      "575 SNX20\n",
+      "576 WDR59\n",
+      "577 GOSR1\n",
+      "578 LRRC37B\n",
+      "579 CCL4\n",
+      "580 STARD3\n",
+      "581 CASC3\n",
+      "582 DNAJC7\n",
+      "583 SNHG25\n",
+      "584 FAM104A\n",
+      "585 MRPS7\n",
+      "586 ARL16\n",
+      "587 WDR45B\n",
+      "588 JUNB\n",
+      "589 C19orf53\n",
+      "590 RAB4B\n",
+      "591 CCDC97\n",
+      "592 CALM3\n",
+      "593 NUP62\n",
+      "594 UBE2S\n",
+      "595 SMIM26\n",
+      "596 RTF2\n",
+      "597 MHENCR\n",
+      "598 UBASH3A\n",
+      "599 UBE2L3\n",
+      "600 PIM3\n",
+      "601 PRKX\n",
+      "602 CA5B\n",
+      "603 SYAP1\n",
+      "604 SH3KBP1\n",
+      "605 MAGED1\n",
+      "606 HSD17B10\n",
+      "607 ATRX\n",
+      "608 AIFM1\n",
+      "609 FAM3A\n",
+      "610 EFHD2\n",
+      "611 CD52\n",
+      "612 SRSF4\n",
+      "613 HIVEP3\n",
+      "614 EPS15\n",
+      "615 USP1\n",
+      "616 DNTTIP2\n",
+      "617 RTCA\n",
+      "618 ENSG00000274265\n",
+      "619 PRRC2C\n",
+      "620 CD46\n",
+      "621 MRPL33\n",
+      "622 PREPL\n",
+      "623 CFAP36\n",
+      "624 COX5B\n",
+      "625 RALB\n",
+      "626 HAT1\n",
+      "627 MRPL44\n",
+      "628 CNOT10\n",
+      "629 RPL14\n",
+      "630 SMARCC1\n",
+      "631 MAP4\n",
+      "632 FHIT\n",
+      "633 SUCLG2\n",
+      "634 TIMMDC1\n",
+      "635 RUVBL1\n",
+      "636 COPG1\n",
+      "637 EIF4G1\n",
+      "638 LARP7\n",
+      "639 BLTP1\n",
+      "640 ZNF330\n",
+      "641 CLPTM1L\n",
+      "642 OTULINL\n",
+      "643 HMGCS1\n",
+      "644 AK6\n",
+      "645 HEXB\n",
+      "646 FAM13B\n",
+      "647 BOD1\n",
+      "648 ENSG00000272501\n",
+      "649 SNHG32\n",
+      "650 MRPS18A\n",
+      "651 COX7A2\n",
+      "652 CDC40\n",
+      "653 MTRF1L\n",
+      "654 HOTAIRM1\n",
+      "655 TECPR1\n",
+      "656 DEFA6\n",
+      "657 INTS10\n",
+      "658 GFUS\n",
+      "659 PLGRKT\n",
+      "660 CHMP5\n",
+      "661 TOMM5\n",
+      "662 ISCA1\n",
+      "663 PDCL\n",
+      "664 GLE1\n",
+      "665 FBH1\n",
+      "666 ATP5F1C\n",
+      "667 DNAJC1\n",
+      "668 SUPV3L1\n",
+      "669 ALDH18A1\n",
+      "670 BAD\n",
+      "671 FAM89B\n",
+      "672 MAP3K11\n",
+      "673 ACAT1\n",
+      "674 ETV6\n",
+      "675 DCTN2\n",
+      "676 IKBIP\n",
+      "677 RPL6\n",
+      "678 VPS33A\n",
+      "679 DDX55\n",
+      "680 NAA16\n",
+      "681 ITM2B\n",
+      "682 ADAM10\n",
+      "683 MORF4L1\n",
+      "684 ARL6IP1\n",
+      "685 POLR3E\n",
+      "686 PRR14\n",
+      "687 HNRNPA1L3\n",
+      "688 CTCF\n",
+      "689 RNASEK\n",
+      "690 NUFIP2\n",
+      "691 ATAD5\n",
+      "692 ZNHIT3\n",
+      "693 TMEM101\n",
+      "694 PSMC5\n",
+      "695 MRPL58\n",
+      "696 TMC6\n",
+      "697 CHMP1B\n",
+      "698 VPS4B\n",
+      "699 TIMM44\n",
+      "700 TYK2\n",
+      "701 TMEM205\n",
+      "702 RPS11\n",
+      "703 ZBP1\n",
+      "704 DIDO1\n",
+      "705 CRYZL1\n",
+      "706 PIGP\n",
+      "707 TRAPPC10\n",
+      "708 MTFP1\n",
+      "709 MAGEH1\n",
+      "710 OGT\n",
+      "711 MT-ND3\n",
+      "712 SSU72\n",
+      "713 MIIP\n",
+      "714 BSDC1\n",
+      "715 MAP7D1\n",
+      "716 DPH2\n",
+      "717 ERI3\n",
+      "718 PLK3\n",
+      "719 ANP32E\n",
+      "720 LAX1\n",
+      "721 CNIH4\n",
+      "722 SRP9\n",
+      "723 EIPR1\n",
+      "724 CCT4\n",
+      "725 ST3GAL5\n",
+      "726 ANKRD39\n",
+      "727 MTLN\n",
+      "728 ZC3H8\n",
+      "729 PLEKHB2\n",
+      "730 MBD5\n",
+      "731 PDK1\n",
+      "732 DNAJC10\n",
+      "733 GLS\n",
+      "734 NOP58\n",
+      "735 GIGYF2\n",
+      "736 SLC6A6\n",
+      "737 CXCR6\n",
+      "738 CD47\n",
+      "739 DZIP3\n",
+      "740 UBA5\n",
+      "741 CDV3\n",
+      "742 RNF7\n",
+      "743 FXR1\n",
+      "744 PPP1R2\n",
+      "745 HOPX\n",
+      "746 G3BP2\n",
+      "747 CCNI\n",
+      "748 AFF1\n",
+      "749 CCNA2\n",
+      "750 SMARCA5\n",
+      "751 CASP3\n",
+      "752 OXCT1\n",
+      "753 TAF7\n",
+      "754 HDAC3\n",
+      "755 PSMG4\n",
+      "756 NOL7\n",
+      "757 MYLIP\n",
+      "758 SHPRH\n",
+      "759 IPCEF1\n",
+      "760 GLCCI1\n",
+      "761 RP9\n",
+      "762 GUSB\n",
+      "763 ANKIB1\n",
+      "764 KDM7A\n",
+      "765 VCPIP1\n",
+      "766 POLR2K\n",
+      "767 EIF3H\n",
+      "768 CDC37L1\n",
+      "769 KIAA2026\n",
+      "770 DMAC1\n",
+      "771 HNRNPK\n",
+      "772 COQ4\n",
+      "773 NSUN6\n",
+      "774 ANAPC16\n",
+      "775 CHCHD1\n",
+      "776 KAT6B\n",
+      "777 ATAD1\n",
+      "778 EXOC6\n",
+      "779 DENND10\n",
+      "780 ECHS1\n",
+      "781 CD81\n",
+      "782 TRIM22\n",
+      "783 TPP1\n",
+      "784 SAAL1\n",
+      "785 SF3B2\n",
+      "786 SRSF8\n",
+      "787 REXO2\n",
+      "788 SRPRA\n",
+      "789 IRAG2\n",
+      "790 LYZ\n",
+      "791 EEA1\n",
+      "792 TCHP\n",
+      "793 MED13L\n",
+      "794 TMEM120B\n",
+      "795 ARL6IP4\n",
+      "796 TMED2\n",
+      "797 N4BP2L2\n",
+      "798 DLEU1\n",
+      "799 KLF12\n",
+      "800 ARGLU1\n",
+      "801 EXOC5\n",
+      "802 RDH11\n",
+      "803 DCAF5\n",
+      "804 ACYP1\n",
+      "805 GPATCH2L\n",
+      "806 TC2N\n",
+      "807 RIN3\n",
+      "808 HERC1\n",
+      "809 SCAMP2\n",
+      "810 TLNRD1\n",
+      "811 NPRL3\n",
+      "812 HAGHL\n",
+      "813 SPSB3\n",
+      "814 CDIPT\n",
+      "815 EMC8\n",
+      "816 TRAPPC2L\n",
+      "817 TSR1\n",
+      "818 CHRNB1\n",
+      "819 CDK12\n",
+      "820 ENSG00000266088\n",
+      "821 EIF1\n",
+      "822 NKIRAS2\n",
+      "823 LSM12\n",
+      "824 KPNB1\n",
+      "825 VMP1\n",
+      "826 EXOC7\n",
+      "827 METTL23\n",
+      "828 SLC16A3\n",
+      "829 ROCK1\n",
+      "830 CXXC1\n",
+      "831 PWWP3A\n",
+      "832 MKNK2\n",
+      "833 DOHH\n",
+      "834 APBA3\n",
+      "835 TMIGD2\n",
+      "836 SAFB\n",
+      "837 GPR108\n",
+      "838 ZNF101\n",
+      "839 LSM14A\n",
+      "840 ZNF302\n",
+      "841 IGFLR1\n",
+      "842 ARHGEF1\n",
+      "843 GPCPD1\n",
+      "844 MAPRE1\n",
+      "845 SLA2\n",
+      "846 CTSA\n",
+      "847 PSMA7\n",
+      "848 MIR155HG\n",
+      "849 IGLC3\n",
+      "850 IGLL1\n",
+      "851 ASCC2\n",
+      "852 TXN2\n",
+      "853 PHF5A\n",
+      "854 TRMU\n",
+      "855 FAM104B\n",
+      "856 PBDC1\n",
+      "857 UPF3B\n",
+      "858 NDUFA1\n",
+      "859 MCTS1\n",
+      "860 ATP6AP1\n",
+      "861 UBL4A\n",
+      "862 DKC1\n",
+      "863 NECAP2\n",
+      "864 RSRP1\n",
+      "865 GMEB1\n",
+      "866 ZBTB8OS\n",
+      "867 PPT1\n",
+      "868 TMEM59\n",
+      "869 DPYD\n",
+      "870 ZNF281\n",
+      "871 TIMM17A\n",
+      "872 TRAF3IP3\n",
+      "873 ANGEL2\n",
+      "874 C1orf35\n",
+      "875 MDH1\n",
+      "876 DGUOK\n",
+      "877 TMSB10\n",
+      "878 SEC22C\n",
+      "879 ARHGEF3\n",
+      "880 NXPE3\n",
+      "881 ISY1\n",
+      "882 ATP11B\n",
+      "883 PIGX\n",
+      "884 TMEM128\n",
+      "885 OCIAD2\n",
+      "886 NAAA\n",
+      "887 SEC31A\n",
+      "888 H2AZ1\n",
+      "889 CENPE\n",
+      "890 DDX60L\n",
+      "891 UFSP2\n",
+      "892 SDHA\n",
+      "893 NIPBL\n",
+      "894 NDUFAF2\n",
+      "895 RAD17\n",
+      "896 TMEM161B-DT\n",
+      "897 TMED7\n",
+      "898 SRFBP1\n",
+      "899 HMGXB3\n",
+      "900 THOC3\n",
+      "901 PHYKPL\n",
+      "902 RNF130\n",
+      "903 DUSP22\n",
+      "904 NEU1\n",
+      "905 STK19\n",
+      "906 RPL7L1\n",
+      "907 GOPC\n",
+      "908 THEMIS\n",
+      "909 LATS1\n",
+      "910 C1GALT1\n",
+      "911 TRGC2\n",
+      "912 HUS1\n",
+      "913 VOPP1\n",
+      "914 FMC1\n",
+      "915 GIMAP7\n",
+      "916 DYNC2I1\n",
+      "917 ESCO2\n",
+      "918 EEF1D\n",
+      "919 PUF60\n",
+      "920 UBAP1\n",
+      "921 ZCCHC7\n",
+      "922 RFK\n",
+      "923 SUSD3\n",
+      "924 STRBP\n",
+      "925 UCK1\n",
+      "926 HSPA14\n",
+      "927 PDZD8\n",
+      "928 BNIP3\n",
+      "929 TSG101\n",
+      "930 RCN1\n",
+      "931 FAM111A\n",
+      "932 POLR2G\n",
+      "933 MED17\n",
+      "934 CEP57\n",
+      "935 HYOU1\n",
+      "936 SORL1\n",
+      "937 LAG3\n",
+      "938 CLEC2D\n",
+      "939 MRPS35\n",
+      "940 TUBA1A\n",
+      "941 COX14\n",
+      "942 ATP5F1B\n",
+      "943 STAT6\n",
+      "944 DYRK2\n",
+      "945 CHST11\n",
+      "946 NOPCHAP1\n",
+      "947 MLEC\n",
+      "948 COMMD6\n",
+      "949 MBIP\n",
+      "950 CNIH1\n",
+      "951 SETD3\n",
+      "952 RASGRP1\n",
+      "953 GNB5\n",
+      "954 MPI\n",
+      "955 PSTPIP1\n",
+      "956 UNC45A\n",
+      "957 MEF2A\n",
+      "958 RHOT2\n",
+      "959 NTHL1\n",
+      "960 ALDOA\n",
+      "961 STX4\n",
+      "962 AMFR\n",
+      "963 CKLF\n",
+      "964 CENPN\n",
+      "965 INPP5K\n",
+      "966 ATP2A3\n",
+      "967 CYB5D2\n",
+      "968 CENPV\n",
+      "969 UBB\n",
+      "970 WSB1\n",
+      "971 SUPT6H\n",
+      "972 RARA\n",
+      "973 HEXIM1\n",
+      "974 DYNLL2\n",
+      "975 AMZ2\n",
+      "976 LGALS3BP\n",
+      "977 TXNL1\n",
+      "978 GPX4\n",
+      "979 TRAPPC5\n",
+      "980 QTRT1\n",
+      "981 RAB8A\n",
+      "982 RFXANK\n",
+      "983 GMIP\n",
+      "984 EIF3K\n",
+      "985 PAF1\n",
+      "986 TIMM50\n",
+      "987 CLPTM1\n",
+      "988 NOP53\n",
+      "989 BAX\n",
+      "990 RUVBL2\n",
+      "991 LENG1\n",
+      "992 ISOC2\n",
+      "993 PANK2\n",
+      "994 PDRG1\n",
+      "995 PIGT\n",
+      "996 ACOT8\n",
+      "997 ZNF831\n",
+      "998 MIS18A\n",
+      "999 PRMT2\n",
+      "1000 THAP7\n",
+      "1001 POLR2F\n",
+      "1002 POLR3H\n",
+      "1003 RAB9A\n",
+      "1004 ZRSR2\n",
+      "1005 NBDY\n",
+      "1006 ITM2A\n",
+      "1007 RPL39\n",
+      "1008 BCAP31\n",
+      "1009 AGTRAP\n",
+      "1010 HP1BP3\n",
+      "1011 LMO4\n",
+      "1012 ENSG00000236656\n",
+      "1013 DDX59\n",
+      "1014 RRP15\n",
+      "1015 GALNT2\n",
+      "1016 RRM2\n",
+      "1017 CRIPT\n",
+      "1018 SPTBN1\n",
+      "1019 ORC4\n",
+      "1020 CFLAR\n",
+      "1021 SUMO1\n",
+      "1022 CAB39\n",
+      "1023 COPS7B\n",
+      "1024 MTMR14\n",
+      "1025 RPL32\n",
+      "1026 FGD5-AS1\n",
+      "1027 TRAT1\n",
+      "1028 ATP6V1A\n",
+      "1029 RPN1\n",
+      "1030 TBL1XR1\n",
+      "1031 TNIP2\n",
+      "1032 DCAF16\n",
+      "1033 UTP3\n",
+      "1034 ANKRD17\n",
+      "1035 AIMP1\n",
+      "1036 SPCS3\n",
+      "1037 MOCS2\n",
+      "1038 CSNK1G3\n",
+      "1039 AFF4\n",
+      "1040 TCF7\n",
+      "1041 DDX46\n",
+      "1042 MZB1\n",
+      "1043 SQSTM1\n",
+      "1044 C6orf62\n",
+      "1045 HLA-E\n",
+      "1046 PPP1R18\n",
+      "1047 TUBB\n",
+      "1048 RING1\n",
+      "1049 RPF2\n",
+      "1050 NCOA7\n",
+      "1051 PCMT1\n",
+      "1052 AIMP2\n",
+      "1053 TMEM106B\n",
+      "1054 NT5C3A\n",
+      "1055 ARPC1A\n",
+      "1056 KMT2E-AS1\n",
+      "1057 TNPO3\n",
+      "1058 PABPC1\n",
+      "1059 RRAGA\n",
+      "1060 MLLT3\n",
+      "1061 INIP\n",
+      "1062 PTGES2\n",
+      "1063 TRUB2\n",
+      "1064 C9orf78\n",
+      "1065 NELFB\n",
+      "1066 PRKCQ-AS1\n",
+      "1067 NUDT5\n",
+      "1068 TNKS2\n",
+      "1069 TSSC4\n",
+      "1070 AHNAK\n",
+      "1071 THAP12\n",
+      "1072 RNF214\n",
+      "1073 PCBP2\n",
+      "1074 CS\n",
+      "1075 BTG1\n",
+      "1076 NOC4L\n",
+      "1077 PDS5B\n",
+      "1078 DNAJC15\n",
+      "1079 GPR183\n",
+      "1080 PSMB5\n",
+      "1081 DMAC2L\n",
+      "1082 ERO1A\n",
+      "1083 PSMA3-AS1\n",
+      "1084 EIF2S1\n",
+      "1085 SNW1\n",
+      "1086 VPS13C\n",
+      "1087 FAM219B\n",
+      "1088 CIB1\n",
+      "1089 NME3\n",
+      "1090 HCFC1R1\n",
+      "1091 CHTF8\n",
+      "1092 ZZEF1\n",
+      "1093 CCL4L2\n",
+      "1094 MAP2K6\n",
+      "1095 GRB2\n",
+      "1096 LPIN2\n",
+      "1097 NDUFS7\n",
+      "1098 PLIN3\n",
+      "1099 DDA1\n",
+      "1100 U2AF2\n",
+      "1101 SIRPG\n",
+      "1102 ROMO1\n",
+      "1103 ZMYND8\n",
+      "1104 SOD1\n",
+      "1105 LINC00649\n",
+      "1106 PFKL\n",
+      "1107 CFAP410\n",
+      "1108 ITGB2\n",
+      "1109 PRPS2\n",
+      "1110 PDHA1\n",
+      "1111 MT-CO3\n",
+      "1112 TNFRSF1B\n",
+      "1113 RPL11\n",
+      "1114 SH3BGRL3\n",
+      "1115 SYTL1\n",
+      "1116 PPP1R8\n",
+      "1117 YARS1\n",
+      "1118 RLF\n",
+      "1119 LRRC41\n",
+      "1120 SCCPDH\n",
+      "1121 COMMD1\n",
+      "1122 MAP4K4\n",
+      "1123 GPR155\n",
+      "1124 NABP1\n",
+      "1125 MOB4\n",
+      "1126 DNPEP\n",
+      "1127 OXNAD1\n",
+      "1128 GOLGA4\n",
+      "1129 CX3CR1\n",
+      "1130 SLC25A20\n",
+      "1131 ZNF148\n",
+      "1132 SLC41A3\n",
+      "1133 HAUS3\n",
+      "1134 RNF4\n",
+      "1135 ANAPC4\n",
+      "1136 DANCR\n",
+      "1137 CXCL8\n",
+      "1138 AGA\n",
+      "1139 DNAJC21\n",
+      "1140 MCCC2\n",
+      "1141 ANKRA2\n",
+      "1142 SERINC5\n",
+      "1143 PJA2\n",
+      "1144 IRF1\n",
+      "1145 CNOT8\n",
+      "1146 RARS1\n",
+      "1147 RGS14\n",
+      "1148 RACK1\n",
+      "1149 ACOT13\n",
+      "1150 H4C3\n",
+      "1151 ENSG00000204713\n",
+      "1152 TRIM26\n",
+      "1153 C6orf89\n",
+      "1154 RNF8\n",
+      "1155 PDCD2\n",
+      "1156 ZDHHC4\n",
+      "1157 PMPCB\n",
+      "1158 SND1\n",
+      "1159 RBM28\n",
+      "1160 MKLN1\n",
+      "1161 CYREN\n",
+      "1162 IKBKB\n",
+      "1163 CHCHD7\n",
+      "1164 OXR1\n",
+      "1165 EXOSC4\n",
+      "1166 GPAA1\n",
+      "1167 UHRF2\n",
+      "1168 DPM2\n",
+      "1169 CELF2\n",
+      "1170 BMS1\n",
+      "1171 CCSER2\n",
+      "1172 PDLIM1\n",
+      "1173 ACTR1A\n",
+      "1174 NT5C2\n",
+      "1175 ACSL5\n",
+      "1176 CACUL1\n",
+      "1177 SMPD1\n",
+      "1178 RRP8\n",
+      "1179 PSMA1\n",
+      "1180 CSTF3\n",
+      "1181 WNK1\n",
+      "1182 GABARAPL1\n",
+      "1183 ERGIC2\n",
+      "1184 SPATS2\n",
+      "1185 AAAS\n",
+      "1186 USP15\n",
+      "1187 SLC35E3\n",
+      "1188 ENSG00000257764\n",
+      "1189 RAB21\n",
+      "1190 NAP1L1\n",
+      "1191 POC1B\n",
+      "1192 UNG\n",
+      "1193 WBP4\n",
+      "1194 FNDC3A\n",
+      "1195 GPR18\n",
+      "1196 METTL3\n",
+      "1197 PPP2R3C\n",
+      "1198 DNAAF2\n",
+      "1199 STYX\n",
+      "1200 SYNJ2BP\n",
+      "1201 IGHA2\n",
+      "1202 IGHGP\n",
+      "1203 SLC12A6\n",
+      "1204 PLCB2\n",
+      "1205 PCLAF\n",
+      "1206 INTS14\n",
+      "1207 NUBP2\n",
+      "1208 NDUFAB1\n",
+      "1209 ADCY7\n",
+      "1210 IRF8\n",
+      "1211 ACSF3\n",
+      "1212 RABEP1\n",
+      "1213 PEMT\n",
+      "1214 USP22\n",
+      "1215 SDF2\n",
+      "1216 FLOT2\n",
+      "1217 TAOK1\n",
+      "1218 AARSD1\n",
+      "1219 TNRC6C\n",
+      "1220 NDC80\n",
+      "1221 PRSS57\n",
+      "1222 ICAM3\n",
+      "1223 TECR\n",
+      "1224 BRD4\n",
+      "1225 BABAM1\n",
+      "1226 PSMC4\n",
+      "1227 RPS5\n",
+      "1228 ADISSP\n",
+      "1229 PCNA\n",
+      "1230 CHMP4B\n",
+      "1231 RAB5IF\n",
+      "1232 RRP1B\n",
+      "1233 YBEY\n",
+      "1234 C21orf58\n",
+      "1235 ENSG00000240972\n",
+      "1236 FBXO7\n",
+      "1237 SUN2\n",
+      "1238 ST13\n",
+      "1239 IL2RG\n",
+      "1240 RPS4X\n",
+      "1241 PABIR2\n",
+      "1242 ENSG00000273748\n",
+      "1243 RER1\n",
+      "1244 CTNNBIP1\n",
+      "1245 UBIAD1\n",
+      "1246 LSM10\n",
+      "1247 OSBPL9\n",
+      "1248 PGM1\n",
+      "1249 CDC14A\n",
+      "1250 DPH5\n",
+      "1251 RNPC3\n",
+      "1252 CSDE1\n",
+      "1253 ENSG00000287979\n",
+      "1254 SF3B4\n",
+      "1255 MRPS21\n",
+      "1256 GABPB2\n",
+      "1257 MTX1\n",
+      "1258 KIFAP3\n",
+      "1259 COX20\n",
+      "1260 UBXN2A\n",
+      "1261 PPM1G\n",
+      "1262 MCM6\n",
+      "1263 MKRN2\n",
+      "1264 LINC02084\n",
+      "1265 UQCRC1\n",
+      "1266 IFRD2\n",
+      "1267 ABHD14A\n",
+      "1268 PPP4R2\n",
+      "1269 SNX4\n",
+      "1270 KPNA4\n",
+      "1271 ALG3\n",
+      "1272 DNAJB11\n",
+      "1273 JAKMIP1\n",
+      "1274 PDS5A\n",
+      "1275 MRPS18C\n",
+      "1276 PYURF\n",
+      "1277 PPA2\n",
+      "1278 CASP6\n",
+      "1279 RAB33B-AS1\n",
+      "1280 TNFAIP8\n",
+      "1281 HARS1\n",
+      "1282 SCGB3A2\n",
+      "1283 SLU7\n",
+      "1284 CLIC1\n",
+      "1285 HLA-DPB1\n",
+      "1286 PTP4A1\n",
+      "1287 B3GAT2\n",
+      "1288 PHIP\n",
+      "1289 OSTM1\n",
+      "1290 SESN1\n",
+      "1291 AMD1\n",
+      "1292 FYN\n",
+      "1293 NUDT1\n",
+      "1294 PSMA2\n",
+      "1295 BCL7B\n",
+      "1296 YWHAG\n",
+      "1297 ZNHIT1\n",
+      "1298 METTL2B\n",
+      "1299 MRPS33\n",
+      "1300 AGK\n",
+      "1301 ZYX\n",
+      "1302 PRKAG2\n",
+      "1303 VPS37A\n",
+      "1304 ATP6V1H\n",
+      "1305 FABP5\n",
+      "1306 UTP23\n",
+      "1307 ZHX1\n",
+      "1308 NDUFB9\n",
+      "1309 CYRIB\n",
+      "1310 GSDMD\n",
+      "1311 CCDC107\n",
+      "1312 CDK9\n",
+      "1313 BBLN\n",
+      "1314 DPH7\n",
+      "1315 TAF3\n",
+      "1316 FUT11\n",
+      "1317 VDAC2\n",
+      "1318 COMTD1\n",
+      "1319 GLUD1\n",
+      "1320 MKI67\n",
+      "1321 MGMT\n",
+      "1322 NAP1L4\n",
+      "1323 LDHA\n",
+      "1324 CD82\n",
+      "1325 COX8A\n",
+      "1326 TMEM218\n",
+      "1327 ZBTB44\n",
+      "1328 KLRD1\n",
+      "1329 CBX5\n",
+      "1330 BAZ2A\n",
+      "1331 CDK17\n",
+      "1332 ARL1\n",
+      "1333 C12orf76\n",
+      "1334 GPALPP1\n",
+      "1335 RASA3\n",
+      "1336 CDC16\n",
+      "1337 C14orf119\n",
+      "1338 DDHD1\n",
+      "1339 SLIRP\n",
+      "1340 CRIP1\n",
+      "1341 APBA2\n",
+      "1342 NOP10\n",
+      "1343 SRP14\n",
+      "1344 IVD\n",
+      "1345 GABPB1\n",
+      "1346 TPM1\n",
+      "1347 CIAO2A\n",
+      "1348 TIPIN\n",
+      "1349 IDH3A\n",
+      "1350 HDDC3\n",
+      "1351 CHASERR\n",
+      "1352 TBL3\n",
+      "1353 LYRM1\n",
+      "1354 KRBOX5\n",
+      "1355 TMEM208\n",
+      "1356 CFDP1\n",
+      "1357 ZC3H18\n",
+      "1358 ITGAE\n",
+      "1359 UBE2G1\n",
+      "1360 SPNS3\n",
+      "1361 LRRC75A\n",
+      "1362 TOP2A\n",
+      "1363 TACO1\n",
+      "1364 EIF4A3\n",
+      "1365 RNF213\n",
+      "1366 SLC38A10\n",
+      "1367 ACTG1\n",
+      "1368 PCYT2\n",
+      "1369 FAM210A\n",
+      "1370 SNRPD1\n",
+      "1371 OAZ1\n",
+      "1372 TLE5\n",
+      "1373 SIRT6\n",
+      "1374 RPS28\n",
+      "1375 KRI1\n",
+      "1376 GIPC1\n",
+      "1377 MED29\n",
+      "1378 ZNF83\n",
+      "1379 YWHAB\n",
+      "1380 HMGN1\n",
+      "1381 IL17RA\n",
+      "1382 ADA2\n",
+      "1383 TRMT2A\n",
+      "1384 GUCD1\n",
+      "1385 EWSR1\n",
+      "1386 RRP7A\n",
+      "1387 TBC1D22A-DT\n",
+      "1388 DDX3X\n",
+      "1389 ABCB7\n",
+      "1390 MT-ATP8\n",
+      "1391 PRDM2\n",
+      "1392 PITHD1\n",
+      "1393 STMN1\n",
+      "1394 GPN2\n",
+      "1395 SMAP2\n",
+      "1396 CDKN2C\n",
+      "1397 ZRANB2\n",
+      "1398 KYAT3\n",
+      "1399 RPAP2\n",
+      "1400 NBPF19\n",
+      "1401 CERS2\n",
+      "1402 CAPN2\n",
+      "1403 ITGB1BP1\n",
+      "1404 COX7A2L\n",
+      "1405 ACTR1B\n",
+      "1406 TLK1\n",
+      "1407 CNPPD1\n",
+      "1408 TRIP12\n",
+      "1409 LSM3\n",
+      "1410 EPM2AIP1\n",
+      "1411 SETD2\n",
+      "1412 RBM15B\n",
+      "1413 SLC33A1\n",
+      "1414 TMEM129\n",
+      "1415 TMEM165\n",
+      "1416 CLCN3\n",
+      "1417 CENPU\n",
+      "1418 PDE4D\n",
+      "1419 GTF2H2\n",
+      "1420 PGGT1B\n",
+      "1421 NQO2\n",
+      "1422 E2F3\n",
+      "1423 MRS2\n",
+      "1424 H1-3\n",
+      "1425 HSPA1B\n",
+      "1426 SLC39A7\n",
+      "1427 MRPL14\n",
+      "1428 TNFAIP3\n",
+      "1429 EZR\n",
+      "1430 ENSG00000273319\n",
+      "1431 CHCHD3\n",
+      "1432 PARP12\n",
+      "1433 ENTPD4\n",
+      "1434 TMEM70\n",
+      "1435 SLA\n",
+      "1436 NDUFB6\n",
+      "1437 GALT\n",
+      "1438 PHF19\n",
+      "1439 TRAF1\n",
+      "1440 NMT2\n",
+      "1441 KIF5B\n",
+      "1442 CDK1\n",
+      "1443 NRBF2\n",
+      "1444 WAPL\n",
+      "1445 TIAL1\n",
+      "1446 FUOM\n",
+      "1447 IFITM3\n",
+      "1448 MADD\n",
+      "1449 NUDT22\n",
+      "1450 SIPA1\n",
+      "1451 NADSYN1\n",
+      "1452 SC5D\n",
+      "1453 M6PR\n",
+      "1454 SARNP\n",
+      "1455 ORMDL2\n",
+      "1456 ATP2B1-AS1\n",
+      "1457 MAPKAPK5\n",
+      "1458 EIF2B1\n",
+      "1459 BRI3BP\n",
+      "1460 N4BP2L1\n",
+      "1461 MED4\n",
+      "1462 NEDD8\n",
+      "1463 ACTR10\n",
+      "1464 PCNX4\n",
+      "1465 GALK2\n",
+      "1466 SNUPN\n",
+      "1467 PSMA4\n",
+      "1468 SNHG9\n",
+      "1469 ENSG00000089486\n",
+      "1470 AKTIP\n",
+      "1471 CRK\n",
+      "1472 DRG2\n",
+      "1473 FLII\n",
+      "1474 CISD3\n",
+      "1475 CCDC47\n",
+      "1476 DCXR\n",
+      "1477 C18orf32\n",
+      "1478 GAMT\n",
+      "1479 CLPP\n",
+      "1480 ZNF562\n",
+      "1481 ADGRE5\n",
+      "1482 NFKBID\n",
+      "1483 HCST\n",
+      "1484 EGLN2\n",
+      "1485 ZNF226\n",
+      "1486 ENSG00000125505\n",
+      "1487 TBC1D20\n",
+      "1488 ABHD12\n",
+      "1489 ERGIC3\n",
+      "1490 CHCHD10\n",
+      "1491 XBP1\n",
+      "1492 CBX6\n",
+      "1493 CDK16\n",
+      "1494 RPL36A\n",
+      "1495 TCEAL4\n",
+      "1496 TCEAL3\n",
+      "1497 XIAP\n",
+      "1498 STAG2\n",
+      "1499 MECP2\n",
+      "1500 MT-CO2\n",
+      "1501 C1orf174\n",
+      "1502 DNAJC8\n",
+      "1503 PPIH\n",
+      "1504 NRAS\n",
+      "1505 CD58\n",
+      "1506 TTF2\n",
+      "1507 APH1A\n",
+      "1508 PSMD4\n",
+      "1509 S100A4\n",
+      "1510 UBAP2L\n",
+      "1511 MNDA\n",
+      "1512 TSTD1\n",
+      "1513 KIAA0040\n",
+      "1514 TOR3A\n",
+      "1515 RNPEP\n",
+      "1516 TMEM183A\n",
+      "1517 SNAP47\n",
+      "1518 CPSF3\n",
+      "1519 SF3B6\n",
+      "1520 FOSL2\n",
+      "1521 CCDC88A\n",
+      "1522 SERTAD2\n",
+      "1523 NFU1\n",
+      "1524 SNRNP27\n",
+      "1525 BAZ2B\n",
+      "1526 SETD5\n",
+      "1527 SHISA5\n",
+      "1528 IP6K2\n",
+      "1529 RPL29\n",
+      "1530 ZBTB20\n",
+      "1531 RAP2B\n",
+      "1532 PCYT1A\n",
+      "1533 TIFA\n",
+      "1534 GYPA\n",
+      "1535 DCTD\n",
+      "1536 OTULIN\n",
+      "1537 LNPEP\n",
+      "1538 CHD1\n",
+      "1539 CAMLG\n",
+      "1540 TCOF1\n",
+      "1541 CCDC69\n",
+      "1542 MRPS18B\n",
+      "1543 SAYSD1\n",
+      "1544 ZNF451\n",
+      "1545 SNX14\n",
+      "1546 BCLAF1\n",
+      "1547 TAB2\n",
+      "1548 SUN1\n",
+      "1549 SNX10\n",
+      "1550 RCC1L\n",
+      "1551 RHBDD2\n",
+      "1552 LAMTOR4\n",
+      "1553 BCAP29\n",
+      "1554 EZH2\n",
+      "1555 DCTN6-DT\n",
+      "1556 GOLGA7\n",
+      "1557 RPL8\n",
+      "1558 PUM3\n",
+      "1559 PLIN2\n",
+      "1560 SMC5\n",
+      "1561 TUT7\n",
+      "1562 ERCC6L2-AS1\n",
+      "1563 ATP6V1G1\n",
+      "1564 ABI1\n",
+      "1565 VPS26A\n",
+      "1566 ZNF511\n",
+      "1567 SIGIRR\n",
+      "1568 RPS13\n",
+      "1569 ATG13\n",
+      "1570 PRPF19\n",
+      "1571 STIP1\n",
+      "1572 NDUFC2\n",
+      "1573 CBL\n",
+      "1574 THYN1\n",
+      "1575 CCND2\n",
+      "1576 DDX23\n",
+      "1577 CERS5\n",
+      "1578 ATF7\n",
+      "1579 R3HDM2\n",
+      "1580 MON2\n",
+      "1581 TESC\n",
+      "1582 NCOR2\n",
+      "1583 ANKLE2\n",
+      "1584 HSPH1\n",
+      "1585 HNRNPC\n",
+      "1586 GALC\n",
+      "1587 MTA1\n",
+      "1588 NSMCE3\n",
+      "1589 HAUS2\n",
+      "1590 PDCD7\n",
+      "1591 C15orf61\n",
+      "1592 DNAJA3\n",
+      "1593 BFAR\n",
+      "1594 MT1G\n",
+      "1595 CDT1\n",
+      "1596 ANKRD11\n",
+      "1597 CHMP1A\n",
+      "1598 CDK10\n",
+      "1599 SMYD4\n",
+      "1600 MED31\n",
+      "1601 EIF5A\n",
+      "1602 TRAPPC1\n",
+      "1603 FAM215B\n",
+      "1604 JPT1\n",
+      "1605 HGS\n",
+      "1606 ZNF24\n",
+      "1607 INO80C\n",
+      "1608 RPL36\n",
+      "1609 XAB2\n",
+      "1610 ILVBL\n",
+      "1611 GTPBP3\n",
+      "1612 ZFP14\n",
+      "1613 KCNK6\n",
+      "1614 POU2F2\n",
+      "1615 TRIM28\n",
+      "1616 ACSS1\n",
+      "1617 MMP24OS\n",
+      "1618 HELZ2\n",
+      "1619 NDUFV3\n",
+      "1620 APOBEC3C\n",
+      "1621 TYMP\n",
+      "1622 AP1S2\n",
+      "1623 USP9X\n",
+      "1624 WAS\n",
+      "1625 APEX2\n",
+      "1626 IGBP1\n",
+      "1627 PRPS1\n",
+      "1628 RAP2C\n",
+      "1629 SLC10A3\n",
+      "1630 HNRNPR\n",
+      "1631 ATP5IF1\n",
+      "1632 KTI12\n",
+      "1633 ACADM\n",
+      "1634 TRMT13\n",
+      "1635 GPR89A\n",
+      "1636 POLR3GL\n",
+      "1637 LINC01138\n",
+      "1638 TPM3\n",
+      "1639 RIT1\n",
+      "1640 NUF2\n",
+      "1641 CDC73\n",
+      "1642 DEGS1\n",
+      "1643 RPS27A\n",
+      "1644 PTCD3\n",
+      "1645 GYPC\n",
+      "1646 METTL5\n",
+      "1647 STK17B\n",
+      "1648 MAIP1\n",
+      "1649 HYCC2\n",
+      "1650 EIF4E2\n",
+      "1651 PXK\n",
+      "1652 GSK3B\n",
+      "1653 FAM162A\n",
+      "1654 SEC61A1\n",
+      "1655 STAG1\n",
+      "1656 HPS3\n",
+      "1657 LRCH3\n",
+      "1658 CCL28\n",
+      "1659 GZMA\n",
+      "1660 BTF3\n",
+      "1661 TBC1D9B\n",
+      "1662 PHF1\n",
+      "1663 PHF3\n",
+      "1664 SMAP1\n",
+      "1665 IBTK\n",
+      "1666 CALHM6\n",
+      "1667 PPIL4\n",
+      "1668 GTF2H5\n",
+      "1669 WTAP\n",
+      "1670 AOAH\n",
+      "1671 MPLKIP\n",
+      "1672 COA1\n",
+      "1673 TRIM4\n",
+      "1674 GIGYF1\n",
+      "1675 PLOD3\n",
+      "1676 IFRD1\n",
+      "1677 KMT2C\n",
+      "1678 CTSB\n",
+      "1679 ENSG00000272256\n",
+      "1680 FAM91A1\n",
+      "1681 WASHC1\n",
+      "1682 NANS\n",
+      "1683 SLC44A1\n",
+      "1684 PRPF4\n",
+      "1685 ITGB1\n",
+      "1686 AP3M1\n",
+      "1687 SLF2\n",
+      "1688 CD44\n",
+      "1689 ALKBH3\n",
+      "1690 SLC3A2\n",
+      "1691 FAU\n",
+      "1692 TMEM134\n",
+      "1693 ZBTB16\n",
+      "1694 CD3G\n",
+      "1695 KDM5A\n",
+      "1696 PYROXD1\n",
+      "1697 SMUG1\n",
+      "1698 OAS1\n",
+      "1699 RPL21\n",
+      "1700 PAN3\n",
+      "1701 HMGB1\n",
+      "1702 CKAP2\n",
+      "1703 TTC5\n",
+      "1704 SNX6\n",
+      "1705 FAM177A1\n",
+      "1706 KLHL28\n",
+      "1707 ZFP36L1\n",
+      "1708 SPTLC2\n",
+      "1709 TMEM87A\n",
+      "1710 ATOSA\n",
+      "1711 RAB27A\n",
+      "1712 LACTB\n",
+      "1713 IDH2\n",
+      "1714 MRPL28\n",
+      "1715 UBALD1\n",
+      "1716 RBBP6\n",
+      "1717 TMEM170A\n",
+      "1718 RNF166\n",
+      "1719 DEF8\n",
+      "1720 TIMM22\n",
+      "1721 RANGRF\n",
+      "1722 CRLF3\n",
+      "1723 NHERF1\n",
+      "1724 CSNK1D\n",
+      "1725 VAPA\n",
+      "1726 MBD2\n",
+      "1727 HNRNPM\n",
+      "1728 EIF3G\n",
+      "1729 SLC44A2\n",
+      "1730 BISPR\n",
+      "1731 MVB12A\n",
+      "1732 ENSG00000118620\n",
+      "1733 POLR2I\n",
+      "1734 AKT2\n",
+      "1735 LIG1\n",
+      "1736 CDS2\n",
+      "1737 SYS1\n",
+      "1738 CSTF1\n",
+      "1739 ENSG00000142188\n",
+      "1740 ATP5PO\n",
+      "1741 ZMAT5\n",
+      "1742 RTCB\n",
+      "1743 FTSJ1\n",
+      "1744 RAB33A\n",
+      "1745 S100PBP\n",
+      "1746 FUBP1\n",
+      "1747 GBP2\n",
+      "1748 PDE4DIP\n",
+      "1749 S100A9\n",
+      "1750 PRCC\n",
+      "1751 PFDN2\n",
+      "1752 PRDX6\n",
+      "1753 RAB3GAP2\n",
+      "1754 NCOA1\n",
+      "1755 NCAPH\n",
+      "1756 C2orf49\n",
+      "1757 SP3\n",
+      "1758 SLC39A10\n",
+      "1759 ANKRD44-AS1\n",
+      "1760 ZFAND2B\n",
+      "1761 PDE6D\n",
+      "1762 TMA7\n",
+      "1763 APEH\n",
+      "1764 ATXN7\n",
+      "1765 CCDC14\n",
+      "1766 ATP1B3\n",
+      "1767 CCNL1\n",
+      "1768 ST6GAL1\n",
+      "1769 RHOH\n",
+      "1770 TSPAN5\n",
+      "1771 OTUD4\n",
+      "1772 SMIM15\n",
+      "1773 PPP2CA\n",
+      "1774 TRIM38\n",
+      "1775 HLA-DRB1\n",
+      "1776 MTCH1\n",
+      "1777 UBR2\n",
+      "1778 EEF1A1\n",
+      "1779 SERINC1\n",
+      "1780 LRCH4\n",
+      "1781 GNB2\n",
+      "1782 ORAI2\n",
+      "1783 PIK3CG\n",
+      "1784 TGS1\n",
+      "1785 COPS5\n",
+      "1786 PLEC\n",
+      "1787 DOCK8\n",
+      "1788 HACD4\n",
+      "1789 VCP\n",
+      "1790 ENSG00000287070\n",
+      "1791 ARRDC1\n",
+      "1792 TASOR2\n",
+      "1793 KIN\n",
+      "1794 RSU1\n",
+      "1795 PGAM1\n",
+      "1796 ZNF143\n",
+      "1797 GTF2H1\n",
+      "1798 SPTY2D1\n",
+      "1799 LBHD1\n",
+      "1800 RTN3\n",
+      "1801 MAP4K2\n",
+      "1802 MRPL11\n",
+      "1803 CARD16\n",
+      "1804 CD4\n",
+      "1805 PHB2\n",
+      "1806 ITGB7\n",
+      "1807 PPP1R12A\n",
+      "1808 NDUFA12\n",
+      "1809 NAA25\n",
+      "1810 P2RX4\n",
+      "1811 RSRC2\n",
+      "1812 VPS37B\n",
+      "1813 SUCLA2\n",
+      "1814 PIBF1\n",
+      "1815 OBI1\n",
+      "1816 RBM23\n",
+      "1817 LRR1\n",
+      "1818 CDKN3\n",
+      "1819 JKAMP\n",
+      "1820 CPSF2\n",
+      "1821 GSKIP\n",
+      "1822 WDR20\n",
+      "1823 ATP5MJ\n",
+      "1824 EIF3J\n",
+      "1825 MAPK6\n",
+      "1826 TBC1D2B\n",
+      "1827 PRC1\n",
+      "1828 LITAF\n",
+      "1829 RSPRY1\n",
+      "1830 NUTF2\n",
+      "1831 DERL2\n",
+      "1832 RPL19\n",
+      "1833 ICAM2\n",
+      "1834 KPNA2\n",
+      "1835 TK1\n",
+      "1836 CANT1\n",
+      "1837 CHMP6\n",
+      "1838 SS18\n",
+      "1839 SAFB2\n",
+      "1840 EPS15L1\n",
+      "1841 UBA2\n",
+      "1842 SDHAF1\n",
+      "1843 DDRGK1\n",
+      "1844 IFT52\n",
+      "1845 STAU1\n",
+      "1846 RBM38\n",
+      "1847 DNAJC5\n",
+      "1848 VPS26C\n",
+      "1849 PCNT\n",
+      "1850 RABL2B\n",
+      "1851 TRAPPC2\n",
+      "1852 PRAF2\n",
+      "1853 MT-ND6\n",
+      "1854 SLC35E2B\n",
+      "1855 KDM1A\n",
+      "1856 SRRM1\n",
+      "1857 CCDC28B\n",
+      "1858 ODF2L\n",
+      "1859 SLC16A1-AS1\n",
+      "1860 BCAS2\n",
+      "1861 SNAPIN\n",
+      "1862 HAX1\n",
+      "1863 NCSTN\n",
+      "1864 RCSD1\n",
+      "1865 CSRP1\n",
+      "1866 BTG2\n",
+      "1867 RAB29\n",
+      "1868 HNRNPU\n",
+      "1869 EIF2AK2\n",
+      "1870 ERLEC1\n",
+      "1871 RTN4\n",
+      "1872 WDR54\n",
+      "1873 ZAP70\n",
+      "1874 MZT2B\n",
+      "1875 R3HDM1\n",
+      "1876 ATF2\n",
+      "1877 HNRNPA3\n",
+      "1878 SPATS2L\n",
+      "1879 WDFY1\n",
+      "1880 ITPR1\n",
+      "1881 EXOG\n",
+      "1882 GMPPB\n",
+      "1883 EEFSEC\n",
+      "1884 MAGEF1\n",
+      "1885 TFRC\n",
+      "1886 DLG1\n",
+      "1887 HADH\n",
+      "1888 OSTC\n",
+      "1889 NDUFS6\n",
+      "1890 RICTOR\n",
+      "1891 FYB1\n",
+      "1892 LYRM7\n",
+      "1893 DELE1\n",
+      "1894 LARS1\n",
+      "1895 B4GALT7\n",
+      "1896 PRPF4B\n",
+      "1897 SSR1\n",
+      "1898 PAK1IP1\n",
+      "1899 TMEM14C\n",
+      "1900 HLA-A\n",
+      "1901 HLA-DMA\n",
+      "1902 NUDT3\n",
+      "1903 LMBRD1\n",
+      "1904 BZW2\n",
+      "1905 TMEM60\n",
+      "1906 KRIT1\n",
+      "1907 THAP1\n",
+      "1908 SDCBP\n",
+      "1909 RAD21\n",
+      "1910 NAPRT\n",
+      "1911 ZNF7\n",
+      "1912 SMIM27\n",
+      "1913 SIT1\n",
+      "1914 CKS2\n",
+      "1915 TRIM14\n",
+      "1916 ERP44\n",
+      "1917 TOR2A\n",
+      "1918 SET\n",
+      "1919 PTGDS\n",
+      "1920 TMEM203\n",
+      "1921 ZMYND11\n",
+      "1922 VIM\n",
+      "1923 EIF4EBP2\n",
+      "1924 FBXL15\n",
+      "1925 MCMBP\n",
+      "1926 PSMD13\n",
+      "1927 LTBP3\n",
+      "1928 UCP2\n",
+      "1929 UVRAG\n",
+      "1930 KLRC1\n",
+      "1931 SMAGP\n",
+      "1932 NR4A1\n",
+      "1933 RITA1\n",
+      "1934 NUP58\n",
+      "1935 TPP2\n",
+      "1936 CUL4A\n",
+      "1937 LRP10\n",
+      "1938 DHRS4L2\n",
+      "1939 CINP\n",
+      "1940 ZNF770\n",
+      "1941 POLG\n",
+      "1942 RPUSD1\n",
+      "1943 NDUFB10\n",
+      "1944 NDE1\n",
+      "1945 CCP110\n",
+      "1946 SGF29\n",
+      "1947 HIRIP3\n",
+      "1948 SEPHS2\n",
+      "1949 ADGRG1\n",
+      "1950 PRPF8\n",
+      "1951 ACAP1\n",
+      "1952 SCO1\n",
+      "1953 TAF15\n",
+      "1954 THRA\n",
+      "1955 SPOP\n",
+      "1956 DDX5\n",
+      "1957 NAT9\n",
+      "1958 MCRIP1\n",
+      "1959 GPS1\n",
+      "1960 RPS15\n",
+      "1961 EEF2\n",
+      "1962 LDLR\n",
+      "1963 CCDC124\n",
+      "1964 PEPD\n",
+      "1965 FXYD5\n",
+      "1966 RBCK1\n",
+      "1967 SNRPB\n",
+      "1968 TMX4\n",
+      "1969 RRBP1\n",
+      "1970 PPDPF\n",
+      "1971 ARFRP1\n",
+      "1972 GET1\n",
+      "1973 YDJC\n",
+      "1974 ZBED1\n",
+      "1975 CCDC22\n",
+      "1976 BEX3\n",
+      "1977 PSMD10\n",
+      "1978 SASH3\n",
+      "1979 RCAN3\n",
+      "1980 AGO3\n",
+      "1981 GBP5\n",
+      "1982 PSMA5\n",
+      "1983 DENND2D\n",
+      "1984 GEMIN6\n",
+      "1985 GFPT1\n",
+      "1986 BOLA3\n",
+      "1987 MAT2A\n",
+      "1988 MIR4435-2HG\n",
+      "1989 ZC3H6\n",
+      "1990 DARS1\n",
+      "1991 TANK\n",
+      "1992 PNKD\n",
+      "1993 SEC13\n",
+      "1994 ANKRD28\n",
+      "1995 UMPS\n",
+      "1996 SPON2\n",
+      "1997 MRFAP1\n",
+      "1998 BDH2\n",
+      "1999 MRPL36\n",
+      "2000 SKP1\n",
+      "2001 PTTG1\n",
+      "2002 H1-4\n",
+      "2003 POLR1H\n",
+      "2004 B3GALT4\n",
+      "2005 GLO1\n",
+      "2006 SEC63\n",
+      "2007 CD164\n",
+      "2008 CENPW\n",
+      "2009 ARMT1\n",
+      "2010 RNASET2\n",
+      "2011 MRPS17\n",
+      "2012 POM121C\n",
+      "2013 AKAP9\n",
+      "2014 DLD\n",
+      "2015 TRBV28\n",
+      "2016 AP3M2\n",
+      "2017 HGSNAT\n",
+      "2018 RAB2A\n",
+      "2019 UQCRB\n",
+      "2020 NCALD\n",
+      "2021 DNAJA1\n",
+      "2022 AGPAT2\n",
+      "2023 AKR1C3\n",
+      "2024 PPP3CB\n",
+      "2025 FAM204A\n",
+      "2026 SLC25A45\n",
+      "2027 DHCR7-DT\n",
+      "2028 MRPL51\n",
+      "2029 ARHGDIB\n",
+      "2030 ZCRB1\n",
+      "2031 HDAC7\n",
+      "2032 MRPL42\n",
+      "2033 SNRPF\n",
+      "2034 SART3\n",
+      "2035 RPLP0\n",
+      "2036 CHFR\n",
+      "2037 POLR1D\n",
+      "2038 KPNA3\n",
+      "2039 SUGT1\n",
+      "2040 MZT1\n",
+      "2041 FBXL3\n",
+      "2042 MYCBP2\n",
+      "2043 UPF3A\n",
+      "2044 TRAC\n",
+      "2045 ARHGAP5\n",
+      "2046 VTI1B\n",
+      "2047 AQR\n",
+      "2048 INO80E\n",
+      "2049 PPP4C\n",
+      "2050 FUS\n",
+      "2051 ARL2BP\n",
+      "2052 SLC7A6OS\n",
+      "2053 ZNF830\n",
+      "2054 NT5C3B\n",
+      "2055 RAB5C\n",
+      "2056 TSEN54\n",
+      "2057 NAPG\n",
+      "2058 MPPE1\n",
+      "2059 TPGS1\n",
+      "2060 CNN2\n",
+      "2061 CIRBP\n",
+      "2062 TIMM13\n",
+      "2063 GADD45B\n",
+      "2064 NDUFA11\n",
+      "2065 MARCHF2\n",
+      "2066 ATG4D\n",
+      "2067 ZNF44\n",
+      "2068 IL12RB1\n",
+      "2069 LSM4\n",
+      "2070 SHLD1\n",
+      "2071 UBE2C\n",
+      "2072 OGFR\n",
+      "2073 MYH9\n",
+      "2074 APOBEC3G\n",
+      "2075 NUP50\n",
+      "2076 COX7B\n",
+      "2077 ARMCX6\n",
+      "2078 ARHGEF6\n",
+      "2079 MT-ND1\n",
+      "2080 ICMT\n",
+      "2081 SRSF10\n",
+      "2082 TRNAU1AP\n",
+      "2083 PUM1\n",
+      "2084 MARCKSL1\n",
+      "2085 SMIM12\n",
+      "2086 MIER1\n",
+      "2087 IFI44\n",
+      "2088 C1orf162\n",
+      "2089 CDC42SE1\n",
+      "2090 S100A8\n",
+      "2091 XCL2\n",
+      "2092 IER5\n",
+      "2093 SMG7\n",
+      "2094 SDE2\n",
+      "2095 GNPAT\n",
+      "2096 ENSG00000171853\n",
+      "2097 TPRKB\n",
+      "2098 DDX18\n",
+      "2099 NIFK\n",
+      "2100 GCA\n",
+      "2101 ASNSD1\n",
+      "2102 CLK1\n",
+      "2103 HDLBP\n",
+      "2104 JAGN1\n",
+      "2105 CCDC174\n",
+      "2106 TOP2B\n",
+      "2107 NGLY1\n",
+      "2108 IMPDH2\n",
+      "2109 GNL3\n",
+      "2110 CMSS1\n",
+      "2111 ZBTB11\n",
+      "2112 NCBP2AS2\n",
+      "2113 TACC3\n",
+      "2114 NELFA\n",
+      "2115 QDPR\n",
+      "2116 SRD5A3\n",
+      "2117 DCK\n",
+      "2118 SCARB2\n",
+      "2119 METAP1\n",
+      "2120 MRPS36\n",
+      "2121 SLF1\n",
+      "2122 VDAC1\n",
+      "2123 ATOX1\n",
+      "2124 CCNG1\n",
+      "2125 MGAT1\n",
+      "2126 H2AC6\n",
+      "2127 TAP1\n",
+      "2128 RPS18\n",
+      "2129 TAPBP\n",
+      "2130 SMIM29\n",
+      "2131 TMEM14A\n",
+      "2132 CYB5R4\n",
+      "2133 MAD1L1\n",
+      "2134 KLHL7\n",
+      "2135 CYCS\n",
+      "2136 TRIM56\n",
+      "2137 POLR2J\n",
+      "2138 GSTK1\n",
+      "2139 NDUFAF6\n",
+      "2140 RNF38\n",
+      "2141 TLE4\n",
+      "2142 SETX\n",
+      "2143 SURF1\n",
+      "2144 SURF2\n",
+      "2145 UPF2\n",
+      "2146 EIF5AL1\n",
+      "2147 RHOG\n",
+      "2148 HBD\n",
+      "2149 TRIM44\n",
+      "2150 TTC17\n",
+      "2151 FEN1\n",
+      "2152 CCS\n",
+      "2153 GRK2\n",
+      "2154 COA4\n",
+      "2155 ENSG00000287028\n",
+      "2156 TIGAR\n",
+      "2157 LMBR1L\n",
+      "2158 MCRS1\n",
+      "2159 ANAPC5\n",
+      "2160 ZNF26\n",
+      "2161 XPO4\n",
+      "2162 EXOSC8\n",
+      "2163 EMC9\n",
+      "2164 EAPP\n",
+      "2165 PSMA6\n",
+      "2166 TRAPPC6B\n",
+      "2167 DHRS7\n",
+      "2168 NPC2\n",
+      "2169 FOS\n",
+      "2170 GTF2A1\n",
+      "2171 IFI27\n",
+      "2172 SQOR\n",
+      "2173 HACD3\n",
+      "2174 TRAF7\n",
+      "2175 PARN\n",
+      "2176 DDX19B\n",
+      "2177 GLOD4\n",
+      "2178 NUP88\n",
+      "2179 POLDIP2\n",
+      "2180 SLFN13\n",
+      "2181 TBX21\n",
+      "2182 ATP5MC1\n",
+      "2183 SMURF2\n",
+      "2184 PRPSAP1\n",
+      "2185 SEPTIN9\n",
+      "2186 PMAIP1\n",
+      "2187 SF3A2\n",
+      "2188 CHAF1A\n",
+      "2189 CD320\n",
+      "2190 JUND\n",
+      "2191 ZNF493\n",
+      "2192 POP4\n",
+      "2193 RPS16\n",
+      "2194 CD37\n",
+      "2195 MED25\n",
+      "2196 ZNF581\n",
+      "2197 ERVK3-1\n",
+      "2198 SNX5\n",
+      "2199 GATD3\n",
+      "2200 PTTG1IP\n",
+      "2201 ATP6V1E1\n",
+      "2202 PEX26\n",
+      "2203 CDC45\n",
+      "2204 PMM1\n",
+      "2205 NCAPH2\n",
+      "2206 HCCS\n",
+      "2207 TMSB4X\n",
+      "2208 RPGR\n",
+      "2209 LAS1L\n",
+      "2210 MT-CYB\n",
+      "2211 TNFRSF14\n",
+      "2212 VAMP3\n",
+      "2213 SYF2\n",
+      "2214 CLSPN\n",
+      "2215 GNL2\n",
+      "2216 ATP6V0B\n",
+      "2217 TXNDC12\n",
+      "2218 GPX7\n",
+      "2219 CZIB\n",
+      "2220 ALG6\n",
+      "2221 DR1\n",
+      "2222 TRIM33\n",
+      "2223 SCNM1\n",
+      "2224 RPS27\n",
+      "2225 SLC50A1\n",
+      "2226 CD84\n",
+      "2227 CREG1\n",
+      "2228 CACYBP\n",
+      "2229 TRAF5\n",
+      "2230 TRIM11\n",
+      "2231 LYST\n",
+      "2232 GALM\n",
+      "2233 ENSG00000152291\n",
+      "2234 IMMT\n",
+      "2235 CMC1\n",
+      "2236 SLC25A38\n",
+      "2237 SS18L2\n",
+      "2238 IP6K1\n",
+      "2239 TWF2\n",
+      "2240 SLC25A36\n",
+      "2241 DYNLT2B\n",
+      "2242 OCIAD1\n",
+      "2243 PPM1K\n",
+      "2244 PPP3CA\n",
+      "2245 PTGER4\n",
+      "2246 FBXO4\n",
+      "2247 CERT1\n",
+      "2248 ISOC1\n",
+      "2249 DEK\n",
+      "2250 TAP2\n",
+      "2251 RRP36\n",
+      "2252 QRSL1\n",
+      "2253 NT5DC1\n",
+      "2254 CEP85L\n",
+      "2255 RALA\n",
+      "2256 PDAP1\n",
+      "2257 CPSF4\n",
+      "2258 PSMC2\n",
+      "2259 SLC25A37\n",
+      "2260 PTK2B\n",
+      "2261 RIPK2\n",
+      "2262 CAAP1\n",
+      "2263 RNF20\n",
+      "2264 SMC2\n",
+      "2265 STOM\n",
+      "2266 MAPKAP1\n",
+      "2267 SRGN\n",
+      "2268 DNAJB12\n",
+      "2269 HELLS\n",
+      "2270 BCCIP\n",
+      "2271 ADAM8\n",
+      "2272 MOB2\n",
+      "2273 RIC3\n",
+      "2274 PATL1\n",
+      "2275 TRPT1\n",
+      "2276 RPS6KA4\n",
+      "2277 TCIRG1\n",
+      "2278 NPAT\n",
+      "2279 H2AX\n",
+      "2280 STRAP\n",
+      "2281 RAB5B\n",
+      "2282 PHLDA1\n",
+      "2283 LINC01089\n",
+      "2284 LPAR6\n",
+      "2285 TOX4\n",
+      "2286 RPL36AL\n",
+      "2287 IRF2BPL\n",
+      "2288 UBE3A\n",
+      "2289 RAMAC\n",
+      "2290 C15orf40\n",
+      "2291 SH2B1\n",
+      "2292 PHKG2\n",
+      "2293 C17orf49\n",
+      "2294 TMEM256\n",
+      "2295 BLMH\n",
+      "2296 ENSG00000267364\n",
+      "2297 TUBG1\n",
+      "2298 EFTUD2\n",
+      "2299 CBX1\n",
+      "2300 MSI2\n",
+      "2301 HELZ\n",
+      "2302 SEH1L\n",
+      "2303 SCAMP4\n",
+      "2304 STXBP2\n",
+      "2305 ZNF414\n",
+      "2306 JAK3\n",
+      "2307 MAP4K1\n",
+      "2308 FOSB\n",
+      "2309 AP2S1\n",
+      "2310 NSFL1C\n",
+      "2311 VPS16\n",
+      "2312 CRNKL1\n",
+      "2313 BCL2L1\n",
+      "2314 CTNNBL1\n",
+      "2315 TP53RK\n",
+      "2316 BCAS4\n",
+      "2317 IFNAR2\n",
+      "2318 L3MBTL2\n",
+      "2319 CRELD2\n",
+      "2320 ZFX\n",
+      "2321 ARAF\n",
+      "2322 NXT2\n",
+      "2323 DFFA\n",
+      "2324 SPEN\n",
+      "2325 KPNA6\n",
+      "2326 AKIRIN1\n",
+      "2327 ECHDC2\n",
+      "2328 PLEKHO1\n",
+      "2329 PSMB4\n",
+      "2330 SNX27\n",
+      "2331 S100A10\n",
+      "2332 SLC27A3\n",
+      "2333 SCAMP3\n",
+      "2334 SSR2\n",
+      "2335 CD244\n",
+      "2336 KDM5B\n",
+      "2337 LPGAT1\n",
+      "2338 ITPKB\n",
+      "2339 ADSS2\n",
+      "2340 BIRC6\n",
+      "2341 PPP4R3B\n",
+      "2342 MRPS9\n",
+      "2343 FASTKD2\n",
+      "2344 BRK1\n",
+      "2345 NDUFAF3\n",
+      "2346 TCTA\n",
+      "2347 TMF1\n",
+      "2348 FRMD4B\n",
+      "2349 COX17\n",
+      "2350 SRPRB\n",
+      "2351 CEP63\n",
+      "2352 GMPS\n",
+      "2353 EIF2B5\n",
+      "2354 EXOSC9\n",
+      "2355 ABCE1\n",
+      "2356 PAIP1\n",
+      "2357 PWWP2A\n",
+      "2358 BNIP1\n",
+      "2359 HNRNPH1\n",
+      "2360 SNRNP48\n",
+      "2361 NFKBIL1\n",
+      "2362 DDAH2\n",
+      "2363 OARD1\n",
+      "2364 HECA\n",
+      "2365 PSMG3\n",
+      "2366 POLD2\n",
+      "2367 NCF1\n",
+      "2368 ATP6V1B2\n",
+      "2369 FNTA\n",
+      "2370 RPS20\n",
+      "2371 KLF10\n",
+      "2372 LINC00861\n",
+      "2373 PSIP1\n",
+      "2374 SMU1\n",
+      "2375 SPTLC1\n",
+      "2376 HSPA5\n",
+      "2377 SWI5\n",
+      "2378 CCDC6\n",
+      "2379 TCTN3\n",
+      "2380 ADD3\n",
+      "2381 ILK\n",
+      "2382 PEX16\n",
+      "2383 PSMC3\n",
+      "2384 CYB561A3\n",
+      "2385 ZFPL1\n",
+      "2386 NDUFV1\n",
+      "2387 NUMA1\n",
+      "2388 HIKESHI\n",
+      "2389 CWC15\n",
+      "2390 ATM\n",
+      "2391 SDHD\n",
+      "2392 KLRK1\n",
+      "2393 DAZAP2\n",
+      "2394 HELB\n",
+      "2395 CEP290\n",
+      "2396 UQCC6\n",
+      "2397 FBXO21\n",
+      "2398 PXN\n",
+      "2399 COX6A1\n",
+      "2400 COQ5\n",
+      "2401 MTRFR\n",
+      "2402 RB1\n",
+      "2403 TM9SF2\n",
+      "2404 PRMT5\n",
+      "2405 MAP4K5\n",
+      "2406 MPG\n",
+      "2407 PDPK1\n",
+      "2408 THUMPD1\n",
+      "2409 PLK1\n",
+      "2410 SEPTIN1\n",
+      "2411 ITGAL\n",
+      "2412 TMEM199\n",
+      "2413 PSMD3\n",
+      "2414 SNF8\n",
+      "2415 SNHG16\n",
+      "2416 MBD1\n",
+      "2417 MALT1\n",
+      "2418 CTDP1\n",
+      "2419 UHRF1\n",
+      "2420 DNM2\n",
+      "2421 YIPF2\n",
+      "2422 CALR\n",
+      "2423 DDX49\n",
+      "2424 U2AF1L4\n",
+      "2425 NAPA\n",
+      "2426 EMP3\n",
+      "2427 PPP2R1A\n",
+      "2428 NDUFA3\n",
+      "2429 SLC27A5\n",
+      "2430 IDH3B\n",
+      "2431 PHF20\n",
+      "2432 ZNF217\n",
+      "2433 TPD52L2\n",
+      "2434 RUNX1\n",
+      "2435 SMARCB1\n",
+      "2436 UQCR10\n",
+      "2437 GTSE1\n",
+      "2438 AKAP17A\n",
+      "2439 TXLNG\n",
+      "2440 GPKOW\n",
+      "2441 NONO\n",
+      "2442 TSC22D3\n",
+      "2443 SYNC\n",
+      "2444 MACF1\n",
+      "2445 PIGK\n",
+      "2446 SLC30A7\n",
+      "2447 CD160\n",
+      "2448 TNFAIP8L2\n",
+      "2449 JTB\n",
+      "2450 ETV3\n",
+      "2451 CD247\n",
+      "2452 GLUL\n",
+      "2453 PTPRC\n",
+      "2454 SNRPE\n",
+      "2455 RASSF5\n",
+      "2456 TSNAX\n",
+      "2457 IRF2BP2\n",
+      "2458 TFB2M\n",
+      "2459 LAPTM4A\n",
+      "2460 OST4\n",
+      "2461 TRMT61B\n",
+      "2462 PRKD3\n",
+      "2463 PLEK\n",
+      "2464 DUSP2\n",
+      "2465 ARL6IP6\n",
+      "2466 ITGA4\n",
+      "2467 PMS1\n",
+      "2468 C2orf69\n",
+      "2469 STT3B\n",
+      "2470 EIF1B\n",
+      "2471 BBX\n",
+      "2472 MRPL3\n",
+      "2473 GPR171\n",
+      "2474 MFSD1\n",
+      "2475 TNFSF10\n",
+      "2476 ZNF721\n",
+      "2477 MFSD10\n",
+      "2478 STIM2\n",
+      "2479 ENOPH1\n",
+      "2480 NFKB1\n",
+      "2481 HPF1\n",
+      "2482 IRF2\n",
+      "2483 MED10\n",
+      "2484 SRP19\n",
+      "2485 NDFIP1\n",
+      "2486 TBC1D7\n",
+      "2487 GMNN\n",
+      "2488 PRIM2\n",
+      "2489 ARL4A\n",
+      "2490 STK17A\n",
+      "2491 SBDS\n",
+      "2492 NSUN5\n",
+      "2493 GTF2I\n",
+      "2494 ZKSCAN1\n",
+      "2495 DNAJB9\n",
+      "2496 CAPZA2\n",
+      "2497 CNOT4\n",
+      "2498 ENSG00000104714\n",
+      "2499 CNOT7\n",
+      "2500 RNF170\n",
+      "2501 TERF1\n",
+      "2502 MYC\n",
+      "2503 SHARPIN\n",
+      "2504 SMARCA2\n",
+      "2505 GNAQ\n",
+      "2506 NAA35\n",
+      "2507 XPA\n",
+      "2508 RABEPK\n",
+      "2509 ZDHHC12-DT\n",
+      "2510 COMMD3\n",
+      "2511 DDX50\n",
+      "2512 TOLLIP\n",
+      "2513 TMEM109\n",
+      "2514 ATL3\n",
+      "2515 DNAJC4\n",
+      "2516 PPP1R14B\n",
+      "2517 FRMD8\n",
+      "2518 UBE4A\n",
+      "2519 NINJ2\n",
+      "2520 TPI1\n",
+      "2521 LARP4\n",
+      "2522 MYG1\n",
+      "2523 PA2G4\n",
+      "2524 SLC16A7\n",
+      "2525 NFYB\n",
+      "2526 PSPC1\n",
+      "2527 ZDHHC20\n",
+      "2528 UBL3\n",
+      "2529 REC8\n",
+      "2530 ENSG00000278002\n",
+      "2531 PSMC6\n",
+      "2532 PSEN1\n",
+      "2533 CALM1\n",
+      "2534 NUDT14\n",
+      "2535 CCDC32\n",
+      "2536 CHP1\n",
+      "2537 RPL4\n",
+      "2538 BTBD1\n",
+      "2539 SNRPA1\n",
+      "2540 SNRNP25\n",
+      "2541 METTL26\n",
+      "2542 C16orf87\n",
+      "2543 NOB1\n",
+      "2544 MBTPS1\n",
+      "2545 AURKB\n",
+      "2546 ELAC2\n",
+      "2547 STAT5A\n",
+      "2548 SOCS3\n",
+      "2549 OXLD1\n",
+      "2550 ELP2\n",
+      "2551 HDGFL2\n",
+      "2552 TNFAIP8L1\n",
+      "2553 MAP2K7\n",
+      "2554 FARSA\n",
+      "2555 RPS19\n",
+      "2556 DEDD2\n",
+      "2557 CPNE1\n",
+      "2558 UBE2V1\n",
+      "2559 IL10RB\n",
+      "2560 ITGB2-AS1\n",
+      "2561 THOC5\n",
+      "2562 MFNG\n",
+      "2563 EIF3L\n",
+      "2564 GRAP2\n",
+      "2565 CERK\n",
+      "2566 ARSA\n",
+      "2567 RBM10\n",
+      "2568 TSR2\n",
+      "2569 SH3BGRL\n",
+      "2570 APOOL\n",
+      "2571 HCFC1\n",
+      "2572 FBXO44\n",
+      "2573 SDHB\n",
+      "2574 HMGCL\n",
+      "2575 TAF12\n",
+      "2576 EPB41\n",
+      "2577 CAP1\n",
+      "2578 FOXJ3\n",
+      "2579 DMAP1\n",
+      "2580 ATPAF1\n",
+      "2581 SHISAL2A\n",
+      "2582 RPF1\n",
+      "2583 CD53\n",
+      "2584 GATAD2B\n",
+      "2585 HDGF\n",
+      "2586 PPP1R15B\n",
+      "2587 FCMR\n",
+      "2588 PIGF\n",
+      "2589 KRCC1\n",
+      "2590 CIAO1\n",
+      "2591 COA5\n",
+      "2592 UGGT1\n",
+      "2593 MFF\n",
+      "2594 COPS9\n",
+      "2595 IQSEC1\n",
+      "2596 CMTM6\n",
+      "2597 CSRNP1\n",
+      "2598 EXOSC7\n",
+      "2599 QRICH1\n",
+      "2600 ZMAT3\n",
+      "2601 ACAP2\n",
+      "2602 PAK2\n",
+      "2603 FAM200B\n",
+      "2604 LAP3\n",
+      "2605 AASDH\n",
+      "2606 HNRNPDL\n",
+      "2607 LPCAT1\n",
+      "2608 GOLPH3\n",
+      "2609 SNX2\n",
+      "2610 PRRC1\n",
+      "2611 RPS14\n",
+      "2612 NHP2\n",
+      "2613 HLA-B\n",
+      "2614 ZNF292\n",
+      "2615 SCML4\n",
+      "2616 CITED2\n",
+      "2617 VTA1\n",
+      "2618 IGF2R\n",
+      "2619 MALSU1\n",
+      "2620 HNRNPA2B1\n",
+      "2621 LINC-PINT\n",
+      "2622 DPYSL2\n",
+      "2623 STAU2\n",
+      "2624 APTX\n",
+      "2625 NOL8\n",
+      "2626 RGS3\n",
+      "2627 CNTRL\n",
+      "2628 PTEN\n",
+      "2629 FAS\n",
+      "2630 GSTO1\n",
+      "2631 IMMP1L\n",
+      "2632 COMMD9\n",
+      "2633 B3GAT3\n",
+      "2634 ZDHHC24\n",
+      "2635 CLNS1A\n",
+      "2636 TIMM8B\n",
+      "2637 DDX6\n",
+      "2638 SCAF11\n",
+      "2639 GLIPR1\n",
+      "2640 TRAFD1\n",
+      "2641 MLXIP\n",
+      "2642 RAN\n",
+      "2643 SETDB2\n",
+      "2644 TRDC\n",
+      "2645 RALGAPA1\n",
+      "2646 DLGAP5\n",
+      "2647 ZBTB25\n",
+      "2648 ACTN1\n",
+      "2649 GON7\n",
+      "2650 IFI27L1\n",
+      "2651 INO80\n",
+      "2652 RORA\n",
+      "2653 PPIB\n",
+      "2654 SRRM2\n",
+      "2655 COQ7\n",
+      "2656 NSMCE1\n",
+      "2657 SULT1A1\n",
+      "2658 TMEM219\n",
+      "2659 VKORC1\n",
+      "2660 TTC19\n",
+      "2661 BLTP2\n",
+      "2662 DDX52\n",
+      "2663 STAT3\n",
+      "2664 COX11\n",
+      "2665 SMARCD2\n",
+      "2666 ASPSCR1\n",
+      "2667 DUS1L\n",
+      "2668 TYMS\n",
+      "2669 ME2\n",
+      "2670 AP3D1\n",
+      "2671 KHSRP\n",
+      "2672 SH2D3A\n",
+      "2673 TIMM29\n",
+      "2674 ELOF1\n",
+      "2675 ARMC6\n",
+      "2676 SNRNP70\n",
+      "2677 FCGRT\n",
+      "2678 ZNF480\n",
+      "2679 EDEM2\n",
+      "2680 CEP250-AS1\n",
+      "2681 RBM12\n",
+      "2682 RBM39\n",
+      "2683 NDRG3\n",
+      "2684 TCEA2\n",
+      "2685 BACH1\n",
+      "2686 TTC28-AS1\n",
+      "2687 APOL3\n",
+      "2688 PPP6R2\n",
+      "2689 STK26\n",
+      "2690 LAGE3\n",
+      "2691 TNFRSF25\n",
+      "2692 CLSTN1\n",
+      "2693 KIAA2013\n",
+      "2694 TMEM50A\n",
+      "2695 YTHDF2\n",
+      "2696 LAPTM5\n",
+      "2697 ZNF644\n",
+      "2698 POLR3C\n",
+      "2699 PRPF3\n",
+      "2700 S100A11\n",
+      "2701 B4GALT3\n",
+      "2702 NDUFS2\n",
+      "2703 COP1\n",
+      "2704 ATP2B4\n",
+      "2705 COA6\n",
+      "2706 RPS7\n",
+      "2707 PREB\n",
+      "2708 MPV17\n",
+      "2709 FAM136A\n",
+      "2710 DUSP11\n",
+      "2711 REG1B\n",
+      "2712 VAMP5\n",
+      "2713 ANKRD36B\n",
+      "2714 INPP4A\n",
+      "2715 SLC20A1\n",
+      "2716 POLR2D\n",
+      "2717 CYTIP\n",
+      "2718 METTL21A\n",
+      "2719 ILKAP\n",
+      "2720 EMC3\n",
+      "2721 GNAI2\n",
+      "2722 THOC7\n",
+      "2723 RPL24\n",
+      "2724 PDCD10\n",
+      "2725 MRPL47\n",
+      "2726 PGM2\n",
+      "2727 UBE2K\n",
+      "2728 CDKN2AIP\n",
+      "2729 PARP8\n",
+      "2730 SLC35A4\n",
+      "2731 RNF14\n",
+      "2732 YIPF5\n",
+      "2733 FBXW11\n",
+      "2734 UIMC1\n",
+      "2735 JARID2\n",
+      "2736 BTN2A1\n",
+      "2737 LTB\n",
+      "2738 AGPAT1\n",
+      "2739 HLA-DPA1\n",
+      "2740 PIM1\n",
+      "2741 MAD2L1BP\n",
+      "2742 TMEM30A\n",
+      "2743 ZBTB24\n",
+      "2744 ARID1B\n",
+      "2745 FOXK1\n",
+      "2746 ASL\n",
+      "2747 DMTF1\n",
+      "2748 AP1S1\n",
+      "2749 HBP1\n",
+      "2750 ASAH1\n",
+      "2751 POLR3D\n",
+      "2752 PHF20L1\n",
+      "2753 SURF6\n",
+      "2754 ANXA7\n",
+      "2755 KIF11\n",
+      "2756 NDUFB8\n",
+      "2757 IFITM1\n",
+      "2758 IRF7\n",
+      "2759 HBB\n",
+      "2760 ZBED5\n",
+      "2761 EEF1G\n",
+      "2762 RELA\n",
+      "2763 RBM14\n",
+      "2764 KDM2A\n",
+      "2765 DCUN1D5\n",
+      "2766 AASDHPPT\n",
+      "2767 HMBS\n",
+      "2768 ING4\n",
+      "2769 LDHB\n",
+      "2770 CNPY2\n",
+      "2771 WASHC4\n",
+      "2772 PWP1\n",
+      "2773 OAS3\n",
+      "2774 TAOK3\n",
+      "2775 SNRNP35\n",
+      "2776 GTF2H3\n",
+      "2777 MTMR6\n",
+      "2778 EPSTI1\n",
+      "2779 INTS6\n",
+      "2780 CHD8\n",
+      "2781 PNN\n",
+      "2782 MAX\n",
+      "2783 MOAP1\n",
+      "2784 WARS1\n",
+      "2785 HSP90AA1\n",
+      "2786 CDCA4\n",
+      "2787 GABPB1-AS1\n",
+      "2788 MTFMT\n",
+      "2789 MESD\n",
+      "2790 WHAMM\n",
+      "2791 KCTD5\n",
+      "2792 CEP20\n",
+      "2793 RPS15A\n",
+      "2794 GINS2\n",
+      "2795 CYBA\n",
+      "2796 METTL16\n",
+      "2797 IFT20\n",
+      "2798 SP2-DT\n",
+      "2799 SPAG9\n",
+      "2800 USP14\n",
+      "2801 AFG3L2\n",
+      "2802 PTPN2\n",
+      "2803 CD226\n",
+      "2804 GZMM\n",
+      "2805 C19orf25\n",
+      "2806 DAPK3\n",
+      "2807 SNAPC2\n",
+      "2808 UBL5\n",
+      "2809 GCDH\n",
+      "2810 PGLS\n",
+      "2811 PGPEP1\n",
+      "2812 KXD1\n",
+      "2813 SERTAD1\n",
+      "2814 NR1H2\n",
+      "2815 ITCH\n",
+      "2816 RPN2\n",
+      "2817 PREX1\n",
+      "2818 ATP5PF\n",
+      "2819 PDXK\n",
+      "2820 CSTB\n",
+      "2821 COL6A2\n",
+      "2822 MED15\n",
+      "2823 NIPSNAP1\n",
+      "2824 EIF3D\n",
+      "2825 MPST\n",
+      "2826 SNU13\n",
+      "2827 SMS\n",
+      "2828 UXT\n",
+      "2829 BEX4\n",
+      "2830 IDH3G\n",
+      "2831 NOC2L\n",
+      "2832 INTS11\n",
+      "2833 MRPL20\n",
+      "2834 RPS6KA1\n",
+      "2835 PHACTR4\n",
+      "2836 PABPC4\n",
+      "2837 EIF2B3\n",
+      "2838 MIGA1\n",
+      "2839 TGFBR3\n",
+      "2840 PI4KB\n",
+      "2841 ISG20L2\n",
+      "2842 KLHL20\n",
+      "2843 MR1\n",
+      "2844 PARP1\n",
+      "2845 SPRTN\n",
+      "2846 SLC66A3\n",
+      "2847 GPN1\n",
+      "2848 HNRNPLL\n",
+      "2849 LRPPRC\n",
+      "2850 KCMF1\n",
+      "2851 LINC01943\n",
+      "2852 APPAT\n",
+      "2853 MAP3K2\n",
+      "2854 RBMS1\n",
+      "2855 AAMP\n",
+      "2856 ARL4C\n",
+      "2857 AZI2\n",
+      "2858 ABTB1\n",
+      "2859 NCK1\n",
+      "2860 RNF13\n",
+      "2861 TIPARP\n",
+      "2862 EIF4A2\n",
+      "2863 RPL39L\n",
+      "2864 FRYL\n",
+      "2865 CISD2\n",
+      "2866 CDKN2AIPNL\n",
+      "2867 C5orf24\n",
+      "2868 NUP153\n",
+      "2869 TDP2\n",
+      "2870 BTN3A2\n",
+      "2871 AIF1\n",
+      "2872 GPANK1\n",
+      "2873 PFDN6\n",
+      "2874 RPL10A\n",
+      "2875 C6orf226\n",
+      "2876 SNX3\n",
+      "2877 TSPYL1\n",
+      "2878 ABRACL\n",
+      "2879 PHF14\n",
+      "2880 NUP42\n",
+      "2881 PHTF2\n",
+      "2882 ARPC1B\n",
+      "2883 THAP5\n",
+      "2884 CALU\n",
+      "2885 CREB3L2\n",
+      "2886 NOM1\n",
+      "2887 DOK2\n",
+      "2888 PRKDC\n",
+      "2889 IMPA1\n",
+      "2890 UBR5\n",
+      "2891 GLIPR2\n",
+      "2892 TRMT10B\n",
+      "2893 IARS1\n",
+      "2894 ARPC5L\n",
+      "2895 FAM107B\n",
+      "2896 CREM\n",
+      "2897 HK1\n",
+      "2898 MICU1\n",
+      "2899 PPIF\n",
+      "2900 SHLD2\n",
+      "2901 HPS1\n",
+      "2902 MRPL43\n",
+      "2903 DUSP5\n",
+      "2904 LSP1\n",
+      "2905 TSPAN32\n",
+      "2906 HBG2\n",
+      "2907 FBXO3\n",
+      "2908 PTPMT1\n",
+      "2909 UBE2L6\n",
+      "2910 CD5\n",
+      "2911 SDHAF2\n",
+      "2912 NXF1\n",
+      "2913 PLAAT3\n",
+      "2914 EED\n",
+      "2915 ZPR1\n",
+      "2916 ATP5MG\n",
+      "2917 CLEC2B\n",
+      "2918 ATF7IP\n",
+      "2919 ENSG00000258344\n",
+      "2920 ZFC3H1\n",
+      "2921 CCDC59\n",
+      "2922 USP30-AS1\n",
+      "2923 CLIP1\n",
+      "2924 RNF6\n",
+      "2925 TEX30\n",
+      "2926 TNFSF13B\n",
+      "2927 GPR65\n",
+      "2928 NRDE2\n",
+      "2929 SNRPN\n",
+      "2930 EHD4\n",
+      "2931 CCNDBP1\n",
+      "2932 UBE2Q2\n",
+      "2933 CHD2\n",
+      "2934 TSR3\n",
+      "2935 ATP6V0C\n",
+      "2936 CD2BP2\n",
+      "2937 BCL7C\n",
+      "2938 MT1E\n",
+      "2939 CMTM3\n",
+      "2940 CMIP\n",
+      "2941 SMG6\n",
+      "2942 PRPSAP2\n",
+      "2943 ZNF207\n",
+      "2944 LINC03057\n",
+      "2945 VEZF1\n",
+      "2946 DHX40\n",
+      "2947 DDX42\n",
+      "2948 BPTF\n",
+      "2949 UBALD2\n",
+      "2950 TGIF1\n",
+      "2951 CNDP2\n",
+      "2952 TXNL4A\n",
+      "2953 ARHGAP45\n",
+      "2954 UQCR11\n",
+      "2955 SGTA\n",
+      "2956 S1PR4\n",
+      "2957 NCLN\n",
+      "2958 S1PR5\n",
+      "2959 MIR23AHG\n",
+      "2960 REX1BD\n",
+      "2961 COPE\n",
+      "2962 GATAD2A\n",
+      "2963 ZNF431\n",
+      "2964 DMAC2\n",
+      "2965 FLT3LG\n",
+      "2966 UBE2M\n",
+      "2967 CST7\n",
+      "2968 NOL4L\n",
+      "2969 DDX27\n",
+      "2970 COMT\n",
+      "2971 YWHAH\n",
+      "2972 APOL6\n",
+      "2973 POLDIP3\n",
+      "2974 UBQLN2\n",
+      "2975 RBMX2\n",
+      "2976 HMGB3\n",
+      "2977 RPS4Y1\n",
+      "2978 ENO1\n",
+      "2979 ELOA\n",
+      "2980 PNRC2\n",
+      "2981 SNRNP40\n",
+      "2982 BCL10\n",
+      "2983 GLMN\n",
+      "2984 RPL5\n",
+      "2985 TMEM167B\n",
+      "2986 CHTOP\n",
+      "2987 CKS1B\n",
+      "2988 DUSP23\n",
+      "2989 TOR1AIP2\n",
+      "2990 MIA3\n",
+      "2991 BROX\n",
+      "2992 RNF187\n",
+      "2993 COG2\n",
+      "2994 ATAD2B\n",
+      "2995 SUPT7L\n",
+      "2996 NAGK\n",
+      "2997 CAPG\n",
+      "2998 PDCL3\n",
+      "2999 IWS1\n",
+      "3000 DNAJB2\n",
+      "3001 SP140\n",
+      "3002 SP140L\n",
+      "3003 CRELD1\n",
+      "3004 VGLL4\n",
+      "3005 OXSR1\n",
+      "3006 QARS1\n",
+      "3007 EIF4E3\n",
+      "3008 PARP9\n",
+      "3009 MYNN\n",
+      "3010 NSG1\n",
+      "3011 SDAD1\n",
+      "3012 THAP9-AS1\n",
+      "3013 SLC39A8\n",
+      "3014 DOCK2\n",
+      "3015 ERGIC1\n",
+      "3016 DTNBP1\n",
+      "3017 HCP5\n",
+      "3018 HLA-DRB5\n",
+      "3019 UFL1\n",
+      "3020 TMEM242\n",
+      "3021 AP5Z1\n",
+      "3022 TRA2A\n",
+      "3023 ELMO1\n",
+      "3024 TRGC1\n",
+      "3025 NAMPT\n",
+      "3026 PDLIM2\n",
+      "3027 NSD3\n",
+      "3028 VIRMA\n",
+      "3029 ATP6V1C1\n",
+      "3030 DERL1\n",
+      "3031 MTSS1\n",
+      "3032 SLC39A4\n",
+      "3033 KLHL9\n",
+      "3034 FPGS\n",
+      "3035 SFMBT2\n",
+      "3036 OPTN\n",
+      "3037 WAC\n",
+      "3038 EPC1\n",
+      "3039 DNTT\n",
+      "3040 SFXN4\n",
+      "3041 SNHG1\n",
+      "3042 PPP1CA\n",
+      "3043 MRPL48\n",
+      "3044 SPCS2\n",
+      "3045 CRTAM\n",
+      "3046 HSPA8\n",
+      "3047 FKBP4\n",
+      "3048 CREBL2\n",
+      "3049 EIF4B\n",
+      "3050 YEATS4\n",
+      "3051 SUDS3\n",
+      "3052 CCNB1IP1\n",
+      "3053 DCAF11\n",
+      "3054 G2E3\n",
+      "3055 GNG2\n",
+      "3056 MAPK1IP1L\n",
+      "3057 YLPM1\n",
+      "3058 PPP4R3A\n",
+      "3059 NDUFB1\n",
+      "3060 IGHG1\n",
+      "3061 TRIP4\n",
+      "3062 ANP32A\n",
+      "3063 COX5A\n",
+      "3064 NMRAL1\n",
+      "3065 MGRN1\n",
+      "3066 GGA2\n",
+      "3067 GLG1\n",
+      "3068 SPG7\n",
+      "3069 MRM3\n",
+      "3070 YWHAE\n",
+      "3071 ERAL1\n",
+      "3072 MLX\n",
+      "3073 MRPL10\n",
+      "3074 SRP68\n",
+      "3075 MFSD11\n",
+      "3076 PSMG2\n",
+      "3077 RMC1\n",
+      "3078 BSG\n",
+      "3079 MYDGF\n",
+      "3080 MYO1F\n",
+      "3081 ALKBH6\n",
+      "3082 ZFP36\n",
+      "3083 SUPT5H\n",
+      "3084 HSPBP1\n",
+      "3085 KIZ\n",
+      "3086 TPX2\n",
+      "3087 ZNFX1\n",
+      "3088 SNAP29\n",
+      "3089 TIMP1\n",
+      "3090 ARHGEF9\n",
+      "3091 C1GALT1C1\n",
+      "3092 MMGT1\n",
+      "3093 ENSG00000276345\n",
+      "3094 TMEM222\n",
+      "3095 SFPQ\n",
+      "3096 ENSG00000276216\n",
+      "3097 THEM4\n",
+      "3098 ARHGAP30\n",
+      "3099 MRPL55\n",
+      "3100 TRIB2\n",
+      "3101 RHOB\n",
+      "3102 HADHB\n",
+      "3103 RIF1\n",
+      "3104 CYP20A1\n",
+      "3105 ABI2\n",
+      "3106 CUL3\n",
+      "3107 SPCS1\n",
+      "3108 MBD4\n",
+      "3109 NOP14\n",
+      "3110 MRFAP1L1\n",
+      "3111 BOD1L1\n",
+      "3112 HERC5\n",
+      "3113 PHAX\n",
+      "3114 HNRNPA0\n",
+      "3115 WDR55\n",
+      "3116 ARHGAP26\n",
+      "3117 RBM27\n",
+      "3118 HLA-C\n",
+      "3119 CCNC\n",
+      "3120 ECHDC1\n",
+      "3121 SGK1\n",
+      "3122 REPS1\n",
+      "3123 CCZ1\n",
+      "3124 MRPL32\n",
+      "3125 HIPK2\n",
+      "3126 PRSS2\n",
+      "3127 CCAR2\n",
+      "3128 PEX2\n",
+      "3129 CPQ\n",
+      "3130 TATDN1\n",
+      "3131 TMEM71\n",
+      "3132 TOPORS\n",
+      "3133 NFX1\n",
+      "3134 ANP32B\n",
+      "3135 ALG2\n",
+      "3136 CDK5RAP2\n",
+      "3137 RAB14\n",
+      "3138 MAN1B1\n",
+      "3139 IDI1\n",
+      "3140 P4HA1\n",
+      "3141 EXOSC1\n",
+      "3142 ZDHHC16\n",
+      "3143 ATP5MK\n",
+      "3144 TUBGCP2\n",
+      "3145 CHID1\n",
+      "3146 TMEM138\n",
+      "3147 NEAT1\n",
+      "3148 CCDC85B\n",
+      "3149 AIP\n",
+      "3150 KMT5B\n",
+      "3151 LAMTOR1\n",
+      "3152 PGM2L1\n",
+      "3153 RPS3\n",
+      "3154 ANKRD49\n",
+      "3155 USP28\n",
+      "3156 PCSK7\n",
+      "3157 NELL2\n",
+      "3158 KANSL2\n",
+      "3159 BLOC1S1\n",
+      "3160 GLTP\n",
+      "3161 VPS29\n",
+      "3162 RBM19\n",
+      "3163 MPHOSPH8\n",
+      "3164 FOXO1\n",
+      "3165 ANKRD10\n",
+      "3166 TIMM9\n",
+      "3167 PCNX1\n",
+      "3168 NUMB\n",
+      "3169 MAP2K1\n",
+      "3170 AKAP13\n",
+      "3171 MSRB1\n",
+      "3172 RPS2\n",
+      "3173 ATXN2L\n",
+      "3174 CORO1A\n",
+      "3175 NUDT21\n",
+      "3176 NAE1\n",
+      "3177 SF3B3\n",
+      "3178 DHX38\n",
+      "3179 APRT\n",
+      "3180 MIS12\n",
+      "3181 ELP5\n",
+      "3182 PSMB3\n",
+      "3183 KRT13\n",
+      "3184 KAT7\n",
+      "3185 FTSJ3\n",
+      "3186 GNA13\n",
+      "3187 SAP30BP\n",
+      "3188 SYNGR2\n",
+      "3189 ANAPC11\n",
+      "3190 NDUFV2\n",
+      "3191 RALBP1\n",
+      "3192 ESCO1\n",
+      "3193 C18orf21\n",
+      "3194 HAUS1\n",
+      "3195 PTBP1\n",
+      "3196 DENND1C\n",
+      "3197 PIN1\n",
+      "3198 PDCD5\n",
+      "3199 GARRE1\n",
+      "3200 TYROBP\n",
+      "3201 TBCB\n",
+      "3202 SAE1\n",
+      "3203 CBR1\n",
+      "3204 YPEL1\n",
+      "3205 TOM1\n",
+      "3206 ATF4\n",
+      "3207 SLC38A5\n",
+      "3208 RBM3\n",
+      "3209 CXCR3\n",
+      "3210 ALG13\n",
+      "3211 SLC25A5\n",
+      "3212 RPA2\n",
+      "3213 UQCRH\n",
+      "3214 LRRC8C\n",
+      "3215 TXNIP\n",
+      "3216 VPS72\n",
+      "3217 SHC1\n",
+      "3218 TIPRL\n",
+      "3219 TOR1AIP1\n",
+      "3220 RGS16\n",
+      "3221 H2AC25\n",
+      "3222 GTF3C2\n",
+      "3223 PPP1CB\n",
+      "3224 PNPT1\n",
+      "3225 MOB1A\n",
+      "3226 MTHFD2\n",
+      "3227 USP39\n",
+      "3228 SMPD4\n",
+      "3229 PLEKHA3\n",
+      "3230 ANKRD44\n",
+      "3231 CTDSP1\n",
+      "3232 UBE2F\n",
+      "3233 DTYMK\n",
+      "3234 MLH1\n",
+      "3235 LRRFIP2\n",
+      "3236 RPSA\n",
+      "3237 ARF4\n",
+      "3238 PCNP\n",
+      "3239 CD96\n",
+      "3240 PARP14\n",
+      "3241 ZNF639\n",
+      "3242 BDH1\n",
+      "3243 C4orf48\n",
+      "3244 MRFAP1L2\n",
+      "3245 ARAP2\n",
+      "3246 SLAIN2\n",
+      "3247 LAMTOR3\n",
+      "3248 SNHG8\n",
+      "3249 SEC24D\n",
+      "3250 NSA2\n",
+      "3251 PPIP5K2\n",
+      "3252 REEP5\n",
+      "3253 CXXC5\n",
+      "3254 JAKMIP2\n",
+      "3255 RREB1\n",
+      "3256 RIPOR2\n",
+      "3257 HMGN4\n",
+      "3258 HLA-DRA\n",
+      "3259 GINM1\n",
+      "3260 TULP4\n",
+      "3261 QKI\n",
+      "3262 ZFAND2A\n",
+      "3263 MRM2\n",
+      "3264 CARD11\n",
+      "3265 STARD3NL\n",
+      "3266 CDK6\n",
+      "3267 BUD31\n",
+      "3268 KMT2E\n",
+      "3269 TRBC2\n",
+      "3270 REPIN1\n",
+      "3271 GIMAP5\n",
+      "3272 BIN3\n",
+      "3273 GGH\n",
+      "3274 CSPP1\n",
+      "3275 ZFAND1\n",
+      "3276 PLEKHF2\n",
+      "3277 C9orf85\n",
+      "3278 FAM120A\n",
+      "3279 SPTAN1\n",
+      "3280 PMPCA\n",
+      "3281 CUL2\n",
+      "3282 ZNF32\n",
+      "3283 SEC23IP\n",
+      "3284 MRPL17\n",
+      "3285 YIF1A\n",
+      "3286 PTS\n",
+      "3287 KLRC2\n",
+      "3288 AEBP2\n",
+      "3289 YAF2\n",
+      "3290 TWF1\n",
+      "3291 PYM1\n",
+      "3292 ESYT1\n",
+      "3293 MYL6\n",
+      "3294 MRPS31\n",
+      "3295 GMPR2\n",
+      "3296 TMX1\n",
+      "3297 SNAPC1\n",
+      "3298 NUSAP1\n",
+      "3299 TRIM69\n",
+      "3300 BLOC1S6\n",
+      "3301 RSL24D1\n",
+      "3302 PIGBOS1\n",
+      "3303 SNAPC5\n",
+      "3304 LINS1\n",
+      "3305 GOT2\n",
+      "3306 CENPT\n",
+      "3307 VPS4A\n",
+      "3308 PFN1\n",
+      "3309 TXNDC17\n",
+      "3310 AP2B1\n",
+      "3311 MMD\n",
+      "3312 MIR142HG\n",
+      "3313 SKA2\n",
+      "3314 FN3KRP\n",
+      "3315 MAPRE2\n",
+      "3316 TMEM259\n",
+      "3317 DUS3L\n",
+      "3318 RETN\n",
+      "3319 PPAN\n",
+      "3320 ZNF90\n",
+      "3321 ERCC1\n",
+      "3322 SYMPK\n",
+      "3323 GRWD1\n",
+      "3324 VRK3\n",
+      "3325 PPP6R1\n",
+      "3326 ZNF274\n",
+      "3327 ENSG00000283103\n",
+      "3328 CDC25B\n",
+      "3329 PET117\n",
+      "3330 GSS\n",
+      "3331 PTPN1\n",
+      "3332 NELFCD\n",
+      "3333 ADRM1\n",
+      "3334 MCM3AP\n",
+      "3335 MRPL40\n",
+      "3336 IFT27\n",
+      "3337 RANGAP1\n",
+      "3338 ENSG00000270022\n",
+      "3339 CYB5R3\n",
+      "3340 PIM2\n",
+      "3341 THOC2\n",
+      "3342 LRRC47\n",
+      "3343 SPEN-AS1\n",
+      "3344 ID3\n",
+      "3345 SNHG12\n",
+      "3346 TMEM69\n",
+      "3347 MKNK1\n",
+      "3348 NRDC\n",
+      "3349 ITGB3BP\n",
+      "3350 SH3GLB1\n",
+      "3351 LRIF1\n",
+      "3352 PEX11B\n",
+      "3353 PBXIP1\n",
+      "3354 NME7\n",
+      "3355 SOAT1\n",
+      "3356 ACBD6\n",
+      "3357 PPP2R5A\n",
+      "3358 FBXO28\n",
+      "3359 COQ8A\n",
+      "3360 TOMM20\n",
+      "3361 SMC6\n",
+      "3362 CDC42EP3\n",
+      "3363 OLA1\n",
+      "3364 ACSL3\n",
+      "3365 PASK\n",
+      "3366 RAB5A\n",
+      "3367 CMTM7\n",
+      "3368 PDE12\n",
+      "3369 TFG\n",
+      "3370 ATP2C1\n",
+      "3371 RASA2\n",
+      "3372 GYG1\n",
+      "3373 KLHL24\n",
+      "3374 SLC30A9\n",
+      "3375 TXK\n",
+      "3376 EXOC3\n",
+      "3377 EGR1\n",
+      "3378 NUDCD2\n",
+      "3379 NOP16\n",
+      "3380 TMEM14B\n",
+      "3381 LEMD2\n",
+      "3382 TAF11\n",
+      "3383 MAPK14\n",
+      "3384 CNPY3\n",
+      "3385 SNHG5\n",
+      "3386 BUD23\n",
+      "3387 RSBN1L\n",
+      "3388 FAM3C\n",
+      "3389 ZNF800\n",
+      "3390 STMP1\n",
+      "3391 BRAF\n",
+      "3392 MCPH1\n",
+      "3393 CPNE3\n",
+      "3394 OTUD6B-AS1\n",
+      "3395 RIDA\n",
+      "3396 ST3GAL1\n",
+      "3397 AKNA\n",
+      "3398 ZBTB43\n",
+      "3399 FUBP3\n",
+      "3400 RPL7A\n",
+      "3401 MRPS2\n",
+      "3402 CAMK1D\n",
+      "3403 PCBD1\n",
+      "3404 NUTM2A-AS1\n",
+      "3405 ARL3\n",
+      "3406 RPL27A\n",
+      "3407 BMAL1\n",
+      "3408 RRAS2\n",
+      "3409 CKAP5\n",
+      "3410 FNBP4\n",
+      "3411 ATG2A\n",
+      "3412 ARL2\n",
+      "3413 RCE1\n",
+      "3414 RAD9A\n",
+      "3415 TBC1D10C\n",
+      "3416 SIK3\n",
+      "3417 UBASH3B\n",
+      "3418 DCPS\n",
+      "3419 APLP2\n",
+      "3420 ITFG2\n",
+      "3421 CD69\n",
+      "3422 DERA\n",
+      "3423 ITGA5\n",
+      "3424 ATP2A2\n",
+      "3425 SPATA13\n",
+      "3426 KIAA0586\n",
+      "3427 SEL1L\n",
+      "3428 IFI27L2\n",
+      "3429 VRK1\n",
+      "3430 NIPA2\n",
+      "3431 EID1\n",
+      "3432 TRPM7\n",
+      "3433 HEXA\n",
+      "3434 RNPS1\n",
+      "3435 GLYR1\n",
+      "3436 RSL1D1\n",
+      "3437 GSPT1\n",
+      "3438 PDXDC1\n",
+      "3439 KATNB1\n",
+      "3440 PRMT7\n",
+      "3441 PER1\n",
+      "3442 PHF12\n",
+      "3443 CCL3\n",
+      "3444 MRPL27\n",
+      "3445 PITPNC1\n",
+      "3446 THOC1\n",
+      "3447 R3HDM4\n",
+      "3448 THOP1\n",
+      "3449 UBXN6\n",
+      "3450 ZNF433-AS1\n",
+      "3451 SMIM7\n",
+      "3452 CAPNS1\n",
+      "3453 ZNF331\n",
+      "3454 MYADM\n",
+      "3455 CSNK2A1\n",
+      "3456 ITPA\n",
+      "3457 TRMT6\n",
+      "3458 HPS4\n",
+      "3459 OFD1\n",
+      "3460 ATP6AP2\n",
+      "3461 TIMM8A\n",
+      "3462 VBP1\n",
+      "3463 TNFRSF18\n",
+      "3464 MIB2\n",
+      "3465 CDK11A\n",
+      "3466 ELOVL1\n",
+      "3467 GFI1\n",
+      "3468 REG4\n",
+      "3469 MLLT11\n",
+      "3470 POGZ\n",
+      "3471 RGS1\n",
+      "3472 PTPN7\n",
+      "3473 NENF\n",
+      "3474 EPRS1\n",
+      "3475 YPEL5\n",
+      "3476 FEZ2\n",
+      "3477 CEBPZ\n",
+      "3478 HTRA2\n",
+      "3479 KDM3A\n",
+      "3480 RMND5A\n",
+      "3481 UNC50\n",
+      "3482 TXNDC9\n",
+      "3483 EIF5B\n",
+      "3484 PPIL3\n",
+      "3485 INO80D\n",
+      "3486 TRNT1\n",
+      "3487 TBC1D5\n",
+      "3488 SLC4A7\n",
+      "3489 NKTR\n",
+      "3490 FOXP1\n",
+      "3491 SENP7\n",
+      "3492 GOLGB1\n",
+      "3493 GFM1\n",
+      "3494 ACTL6A\n",
+      "3495 DCUN1D1\n",
+      "3496 RPL35A\n",
+      "3497 ATP5ME\n",
+      "3498 UBA6\n",
+      "3499 JCHAIN\n",
+      "3500 HNRNPD\n",
+      "3501 ADH5\n",
+      "3502 PPID\n",
+      "3503 MTREX\n",
+      "3504 AP3B1\n",
+      "3505 JMY\n",
+      "3506 GLRX\n",
+      "3507 JADE2\n",
+      "3508 SAR1B\n",
+      "3509 MACROH2A1\n",
+      "3510 STING1\n",
+      "3511 CYFIP2\n",
+      "3512 UBLCP1\n",
+      "3513 FARS2\n",
+      "3514 ENSG00000272009\n",
+      "3515 PRDM1\n",
+      "3516 KPNA5\n",
+      "3517 STX11\n",
+      "3518 TAGAP\n",
+      "3519 CDCA7L\n",
+      "3520 HIBADH\n",
+      "3521 TRG-AS1\n",
+      "3522 DNAJC30\n",
+      "3523 SEM1\n",
+      "3524 PRKRIP1\n",
+      "3525 UBE3C\n",
+      "3526 CLU\n",
+      "3527 TACC1\n",
+      "3528 YWHAZ\n",
+      "3529 SQLE\n",
+      "3530 NUP214\n",
+      "3531 CDC123\n",
+      "3532 REEP3\n",
+      "3533 GHITM\n",
+      "3534 RPP30\n",
+      "3535 TAF10\n",
+      "3536 USP47\n",
+      "3537 API5\n",
+      "3538 SAC3D1\n",
+      "3539 PTPRCAP\n",
+      "3540 PCF11\n",
+      "3541 CCDC91\n",
+      "3542 TUBA1C\n",
+      "3543 NCKAP1L\n",
+      "3544 RPS26\n",
+      "3545 UBC\n",
+      "3546 ESD\n",
+      "3547 DHRS4\n",
+      "3548 BAZ1A\n",
+      "3549 KLHDC2\n",
+      "3550 FUT8\n",
+      "3551 ZC3H14\n",
+      "3552 DICER1\n",
+      "3553 EVL\n",
+      "3554 EIF2AK4\n",
+      "3555 DNAJC17\n",
+      "3556 ZNF280D\n",
+      "3557 USP3\n",
+      "3558 MRPS11\n",
+      "3559 TM2D3\n",
+      "3560 UQCRC2\n",
+      "3561 DNAJA2\n",
+      "3562 CNOT1\n",
+      "3563 DYNC1LI2\n",
+      "3564 CBFB\n",
+      "3565 CYB5B\n",
+      "3566 KDM6B\n",
+      "3567 TMEM107\n",
+      "3568 COPS3\n",
+      "3569 MRPL45\n",
+      "3570 PHB1\n",
+      "3571 SLC35B1\n",
+      "3572 NT5C\n",
+      "3573 CYB5A\n",
+      "3574 ATP9B\n",
+      "3575 RNF126\n",
+      "3576 TMEM147\n",
+      "3577 SERTAD3\n",
+      "3578 TRAPPC6A\n",
+      "3579 PRKD2\n",
+      "3580 PTOV1\n",
+      "3581 TRAPPC2B\n",
+      "3582 RNF114\n",
+      "3583 RPS21\n",
+      "3584 GID8\n",
+      "3585 PCMTD2\n",
+      "3586 USP16\n",
+      "3587 SDF2L1\n",
+      "3588 IGLC2\n",
+      "3589 APOBEC3H\n",
+      "3590 SGSM3\n",
+      "3591 RBX1\n",
+      "3592 TOB2\n",
+      "3593 ARFGAP3\n",
+      "3594 TBL1X\n",
+      "3595 HUWE1\n",
+      "3596 YIPF6\n",
+      "3597 XIST\n",
+      "3598 SEPTIN6\n",
+      "3599 IKBKG\n",
+      "3600 SRM\n",
+      "3601 LDLRAP1\n",
+      "3602 NUDC\n",
+      "3603 LCK\n",
+      "3604 CDCA8\n",
+      "3605 UTP11\n",
+      "3606 MED8\n",
+      "3607 LEPROT\n",
+      "3608 WDR77\n",
+      "3609 LIX1L\n",
+      "3610 IGSF8\n",
+      "3611 NIT1\n",
+      "3612 SH3YL1\n",
+      "3613 RDH14\n",
+      "3614 SLC4A1AP\n",
+      "3615 PPM1B\n",
+      "3616 GMCL1\n",
+      "3617 PCBP1\n",
+      "3618 GGCX\n",
+      "3619 RNF181\n",
+      "3620 STAT1\n",
+      "3621 COQ10B\n",
+      "3622 NBEAL1\n",
+      "3623 XRCC5\n",
+      "3624 RAF1\n",
+      "3625 CCDC12\n",
+      "3626 MANF\n",
+      "3627 ARMC8\n",
+      "3628 POLR2H\n",
+      "3629 MAD2L1\n",
+      "3630 ANAPC10\n",
+      "3631 MND1\n",
+      "3632 TMA16\n",
+      "3633 SLC25A4\n",
+      "3634 CCNB1\n",
+      "3635 SCAMP1-AS1\n",
+      "3636 LMNB1\n",
+      "3637 SIL1\n",
+      "3638 CD74\n",
+      "3639 DDX41\n",
+      "3640 SERPINB9\n",
+      "3641 HCG18\n",
+      "3642 RPP21\n",
+      "3643 LSM2\n",
+      "3644 HMGN3\n",
+      "3645 MTRES1\n",
+      "3646 HBS1L\n",
+      "3647 AHI1\n",
+      "3648 POLM\n",
+      "3649 ZNF394\n",
+      "3650 MOSPD3\n",
+      "3651 DNAJC2\n",
+      "3652 SMIM30\n",
+      "3653 DNAJB6\n",
+      "3654 CHMP7\n",
+      "3655 ASH2L\n",
+      "3656 UBE2W\n",
+      "3657 NMRK1\n",
+      "3658 HDHD3\n",
+      "3659 ALAD\n",
+      "3660 MED27\n",
+      "3661 UBAC1\n",
+      "3662 PAXX\n",
+      "3663 CCNY\n",
+      "3664 TMEM273\n",
+      "3665 TYSND1\n",
+      "3666 ANXA11\n",
+      "3667 IFIT3\n",
+      "3668 SMC3\n",
+      "3669 MRPL23\n",
+      "3670 TIMM10\n",
+      "3671 WDR74\n",
+      "3672 SF1\n",
+      "3673 MRPL49\n",
+      "3674 BIRC2\n",
+      "3675 CWF19L2\n",
+      "3676 CHEK1\n",
+      "3677 ADIPOR2\n",
+      "3678 YBX3\n",
+      "3679 PLEKHA5\n",
+      "3680 INTS13\n",
+      "3681 GTSF1\n",
+      "3682 TBK1\n",
+      "3683 DYNLL1\n",
+      "3684 RNF10\n",
+      "3685 SFSWAP\n",
+      "3686 PIGH\n",
+      "3687 DDX24\n",
+      "3688 MFAP1\n",
+      "3689 ARPP19\n",
+      "3690 ANXA2\n",
+      "3691 SCAPER\n",
+      "3692 MRPS34\n",
+      "3693 IL32\n",
+      "3694 ENSG00000286808\n",
+      "3695 MON1B\n",
+      "3696 SPAG7\n",
+      "3697 RPL26\n",
+      "3698 GAS7\n",
+      "3699 RHOT1\n",
+      "3700 MLLT6\n",
+      "3701 RPL23\n",
+      "3702 CDC27\n",
+      "3703 UTP18\n",
+      "3704 RAD51C\n",
+      "3705 RPS6KB1\n",
+      "3706 APPBP2\n",
+      "3707 TLK2\n",
+      "3708 CYTH1\n",
+      "3709 ABHD3\n",
+      "3710 PTGR3\n",
+      "3711 MIDN\n",
+      "3712 RANBP3\n",
+      "3713 WDR83OS\n",
+      "3714 CC2D1A\n",
+      "3715 AKAP8L\n",
+      "3716 ZNF91\n",
+      "3717 URI1\n",
+      "3718 COX6B1\n",
+      "3719 ZNF146\n",
+      "3720 ACTN4\n",
+      "3721 SNRPA\n",
+      "3722 B9D2\n",
+      "3723 ZNF224\n",
+      "3724 BBC3\n",
+      "3725 ASXL1\n",
+      "3726 TOP1\n",
+      "3727 ADNP\n",
+      "3728 APP\n",
+      "3729 IFNAR1\n",
+      "3730 AP1B1\n",
+      "3731 P2RY10\n",
+      "3732 DIAPH2\n",
+      "3733 IDS\n",
+      "3734 MT-CO1\n",
+      "3735 MT-ATP6\n",
+      "3736 ENSG00000238142\n",
+      "3737 UBR4\n",
+      "3738 THEMIS2\n",
+      "3739 RBBP4\n",
+      "3740 TFAP2E-AS1\n",
+      "3741 SF3A3\n",
+      "3742 CTPS1\n",
+      "3743 SRSF11\n",
+      "3744 PKN2\n",
+      "3745 CEPT1\n",
+      "3746 SEC22B\n",
+      "3747 LINC00623\n",
+      "3748 C1orf56\n",
+      "3749 RUSC1\n",
+      "3750 GON4L\n",
+      "3751 TAGLN2\n",
+      "3752 GAS5\n",
+      "3753 UCHL5\n",
+      "3754 IPO9\n",
+      "3755 MDM4\n",
+      "3756 NUCKS1\n",
+      "3757 AIDA\n",
+      "3758 LGALS8\n",
+      "3759 TAF1B\n",
+      "3760 VRK2\n",
+      "3761 PEX13\n",
+      "3762 C2orf76\n",
+      "3763 EPC2\n",
+      "3764 PPIG\n",
+      "3765 SSB\n",
+      "3766 DIS3L2\n",
+      "3767 MTERF4\n",
+      "3768 SEPTIN2\n",
+      "3769 OGG1\n",
+      "3770 RPL15\n",
+      "3771 MAPKAPK3\n",
+      "3772 UBA3\n",
+      "3773 NFKBIZ\n",
+      "3774 ATG3\n",
+      "3775 SENP2\n",
+      "3776 SMIM14\n",
+      "3777 SEPTIN11\n",
+      "3778 PDLIM5\n",
+      "3779 KIF2A\n",
+      "3780 F2R\n",
+      "3781 AGGF1\n",
+      "3782 ERAP1\n",
+      "3783 SRA1\n",
+      "3784 RUFY1\n",
+      "3785 SRPK1\n",
+      "3786 CGAS\n",
+      "3787 TENT5A\n",
+      "3788 MICAL1\n",
+      "3789 NUS1\n",
+      "3790 HDDC2\n",
+      "3791 UMAD1\n",
+      "3792 AUTS2\n",
+      "3793 BET1\n",
+      "3794 SLC12A9\n",
+      "3795 PNPLA8\n",
+      "3796 PLPP5\n",
+      "3797 MTDH\n",
+      "3798 B4GALT1\n",
+      "3799 STOML2\n",
+      "3800 PABIR1\n",
+      "3801 PSMD5\n",
+      "3802 MRRF\n",
+      "3803 PSMB7\n",
+      "3804 URM1\n",
+      "3805 TOR1A\n",
+      "3806 TMEM141\n",
+      "3807 RBM17\n",
+      "3808 PFKFB3\n",
+      "3809 SEPHS1\n",
+      "3810 ZNF37A\n",
+      "3811 TFAM\n",
+      "3812 PPA1\n",
+      "3813 PDCD11\n",
+      "3814 ARHGAP1\n",
+      "3815 ZFP91\n",
+      "3816 GANAB\n",
+      "3817 TMEM223\n",
+      "3818 STX5\n",
+      "3819 SCYL1\n",
+      "3820 DPP3\n",
+      "3821 TAF1D\n",
+      "3822 FAM76B\n",
+      "3823 TMEM123\n",
+      "3824 FXYD2\n",
+      "3825 TRAPPC4\n",
+      "3826 TBRG1\n",
+      "3827 DDIT3\n",
+      "3828 GNPTAB\n",
+      "3829 ANKRD13A\n",
+      "3830 ATP6V0A2\n",
+      "3831 MTIF3\n",
+      "3832 METTL17\n",
+      "3833 FKBP3\n",
+      "3834 MGAT2\n",
+      "3835 PRKCH\n",
+      "3836 LYSET\n",
+      "3837 WDR76\n",
+      "3838 DTWD1\n",
+      "3839 TCF12\n",
+      "3840 SNX1\n",
+      "3841 CLK3\n",
+      "3842 SKIC8\n",
+      "3843 ROGDI\n",
+      "3844 DCTPP1\n",
+      "3845 RNF40\n",
+      "3846 ITFG1\n",
+      "3847 RPAIN\n",
+      "3848 CHD3\n",
+      "3849 MPRIP\n",
+      "3850 ALKBH5\n",
+      "3851 RPL23A\n",
+      "3852 CCL5\n",
+      "3853 COASY\n",
+      "3854 ARHGAP27\n",
+      "3855 TTC39C\n",
+      "3856 RPL17\n",
+      "3857 MICOS13\n",
+      "3858 SPC24\n",
+      "3859 IER2\n",
+      "3860 NDUFA13\n",
+      "3861 PPP1R15A\n",
+      "3862 RPL13A\n",
+      "3863 CNOT3\n",
+      "3864 FKBP1A\n",
+      "3865 SEC23B\n",
+      "3866 TRPC4AP\n",
+      "3867 NORAD\n",
+      "3868 SERINC3\n",
+      "3869 STK4\n",
+      "3870 ATP5F1E\n",
+      "3871 ENSG00000154640\n",
+      "3872 SCAF4\n",
+      "3873 SLC5A3\n",
+      "3874 ENSG00000160284\n",
+      "3875 PPIL2\n",
+      "3876 EIF2S3\n",
+      "3877 FUNDC1\n",
+      "3878 PDZD11\n",
+      "3879 MORF4L2\n",
+      "3880 FLNA\n",
+      "3881 EMD\n",
+      "3882 DDOST\n",
+      "3883 RUNX3\n",
+      "3884 MTFR1L\n",
+      "3885 FGR\n",
+      "3886 FHL3\n",
+      "3887 EBNA1BP2\n",
+      "3888 UROD\n",
+      "3889 CRYZ\n",
+      "3890 RABGGTB\n",
+      "3891 AGL\n",
+      "3892 RBM8A\n",
+      "3893 FLAD1\n",
+      "3894 FCGR3A\n",
+      "3895 DCAF6\n",
+      "3896 IKBKE\n",
+      "3897 CD55\n",
+      "3898 FH\n",
+      "3899 TMEM18\n",
+      "3900 NOL10\n",
+      "3901 DPY30\n",
+      "3902 ZFP36L2\n",
+      "3903 CCT7\n",
+      "3904 RNF149\n",
+      "3905 CLASP1\n",
+      "3906 NDUFS1\n",
+      "3907 DOCK10\n",
+      "3908 DGKD\n",
+      "3909 GLB1\n",
+      "3910 WDR48\n",
+      "3911 WDR6\n",
+      "3912 NPRL2\n",
+      "3913 BAP1\n",
+      "3914 USF3\n",
+      "3915 LRPAP1\n",
+      "3916 GPRIN3\n",
+      "3917 PRMT9\n",
+      "3918 DDX60\n",
+      "3919 CBR4\n",
+      "3920 BRIX1\n",
+      "3921 ENSG00000251131\n",
+      "3922 PCBD2\n",
+      "3923 PAIP2\n",
+      "3924 PURA\n",
+      "3925 H1-5\n",
+      "3926 HLA-DQA1\n",
+      "3927 DAXX\n",
+      "3928 POLR1C\n",
+      "3929 RUNX2\n",
+      "3930 RARS2\n",
+      "3931 PHACTR2\n",
+      "3932 LTV1\n",
+      "3933 MRPL18\n",
+      "3934 BLVRA\n",
+      "3935 ZNF92\n",
+      "3936 LUC7L2\n",
+      "3937 TRBC1\n",
+      "3938 PAXIP1-DT\n",
+      "3939 GTF2E2\n",
+      "3940 RAB11FIP1\n",
+      "3941 SLC52A2\n",
+      "3942 VPS28\n",
+      "3943 CIZ1\n",
+      "3944 SNHG7\n",
+      "3945 TRAF2\n",
+      "3946 PRPF18\n",
+      "3947 SAR1A\n",
+      "3948 PSAP\n",
+      "3949 VCL\n",
+      "3950 MARCHF5\n",
+      "3951 NPM3\n",
+      "3952 RNH1\n",
+      "3953 RPLP2\n",
+      "3954 EIF3F\n",
+      "3955 EIF4G2\n",
+      "3956 SELENOH\n",
+      "3957 JAML\n",
+      "3958 RPUSD4\n",
+      "3959 FLI1\n",
+      "3960 RHNO1\n",
+      "3961 CHD4\n",
+      "3962 SINHCAF\n",
+      "3963 IRAK4\n",
+      "3964 LLPH\n",
+      "3965 BTG1-DT\n",
+      "3966 ACTR6\n",
+      "3967 TMEM263\n",
+      "3968 DDX54\n",
+      "3969 KDM2B\n",
+      "3970 FBXO34\n",
+      "3971 MNAT1\n",
+      "3972 RMDN3\n",
+      "3973 PATL2\n",
+      "3974 ZNF609\n",
+      "3975 SELENOS\n",
+      "3976 GFER\n",
+      "3977 CREBBP\n",
+      "3978 LCMT1\n",
+      "3979 IL4R\n",
+      "3980 CSNK2A2\n",
+      "3981 MAP1LC3B\n",
+      "3982 POLR2A\n",
+      "3983 SNHG29\n",
+      "3984 MED1\n",
+      "3985 VPS25\n",
+      "3986 BECN1\n",
+      "3987 ERN1\n",
+      "3988 JMJD6\n",
+      "3989 BIRC5\n",
+      "3990 SMCHD1\n",
+      "3991 SEC11C\n",
+      "3992 PIGN\n",
+      "3993 MRPL54\n",
+      "3994 VAV1\n",
+      "3995 DNASE2\n",
+      "3996 DDX39A\n",
+      "3997 FAM32A\n",
+      "3998 BST2\n",
+      "3999 FCHO1\n",
+      "4000 UQCRFS1\n",
+      "4001 GRAMD1A\n",
+      "4002 PSENEN\n",
+      "4003 TGFB1\n",
+      "4004 XRCC1\n",
+      "4005 GEMIN7\n",
+      "4006 FTL\n",
+      "4007 COMMD7\n",
+      "4008 PRPF6\n",
+      "4009 CCT8\n",
+      "4010 SON\n",
+      "4011 RANBP1\n",
+      "4012 DRG1\n",
+      "4013 EP300\n",
+      "4014 GPR174\n",
+      "4015 HNRNPH2\n",
+      "4016 DOCK11\n",
+      "4017 ENSG00000272449\n",
+      "4018 RCC2\n",
+      "4019 UBXN11\n",
+      "4020 WASF2\n",
+      "4021 SSBP3\n",
+      "4022 ZZZ3\n",
+      "4023 CCDC18\n",
+      "4024 RSBN1\n",
+      "4025 MEF2D\n",
+      "4026 CD48\n",
+      "4027 MPC2\n",
+      "4028 TSEN15\n",
+      "4029 IARS2\n",
+      "4030 RAB10\n",
+      "4031 WDR43\n",
+      "4032 XPO1\n",
+      "4033 B3GNT2\n",
+      "4034 MRPL53\n",
+      "4035 CDCA7\n",
+      "4036 ZC3H15\n",
+      "4037 ATG4B\n",
+      "4038 DHX30\n",
+      "4039 SELENOT\n",
+      "4040 SMC4\n",
+      "4041 TNIK\n",
+      "4042 N4BP2\n",
+      "4043 CHIC2\n",
+      "4044 MGST2\n",
+      "4045 INPP4B\n",
+      "4046 MARCHF6\n",
+      "4047 WDR70\n",
+      "4048 ARRDC3\n",
+      "4049 HINT1\n",
+      "4050 ETF1\n",
+      "4051 DHX16\n",
+      "4052 CUTA\n",
+      "4053 CCND3\n",
+      "4054 SLC35B2\n",
+      "4055 CDC5L\n",
+      "4056 MED23\n",
+      "4057 HERPUD2\n",
+      "4058 NDUFB2\n",
+      "4059 NUB1\n",
+      "4060 LEPROTL1\n",
+      "4061 HOOK3\n",
+      "4062 NSMAF\n",
+      "4063 TRAM1\n",
+      "4064 MRPL13\n",
+      "4065 HSF1\n",
+      "4066 EXOSC3\n",
+      "4067 ZNG1E\n",
+      "4068 VPS13A\n",
+      "4069 TUBB4B\n",
+      "4070 HNRNPF\n",
+      "4071 SPOCK2\n",
+      "4072 RGS10\n",
+      "4073 NSMCE4A\n",
+      "4074 CAPRIN1\n",
+      "4075 PHF21A\n",
+      "4076 EML3\n",
+      "4077 FKBP2\n",
+      "4078 RBM4\n",
+      "4079 CASP4\n",
+      "4080 ARCN1\n",
+      "4081 KRR1\n",
+      "4082 ATP2B1\n",
+      "4083 RILPL2\n",
+      "4084 ELF1\n",
+      "4085 TSC22D1\n",
+      "4086 LCP1\n",
+      "4087 DIS3\n",
+      "4088 MIS18BP1\n",
+      "4089 LINC01588\n",
+      "4090 SYNE2\n",
+      "4091 DGLUCY\n",
+      "4092 ZFYVE21\n",
+      "4093 EMC4\n",
+      "4094 RAB11A\n",
+      "4095 BCL2A1\n",
+      "4096 RRN3\n",
+      "4097 MAPK3\n",
+      "4098 BCKDK\n",
+      "4099 CES1\n",
+      "4100 ENSG00000180917\n",
+      "4101 SLC7A5\n",
+      "4102 TAX1BP3\n",
+      "4103 NCBP3\n",
+      "4104 TP53\n",
+      "4105 KRT10\n",
+      "4106 FAM117A\n",
+      "4107 ARSG\n",
+      "4108 PRKAR1A\n",
+      "4109 TBCD\n",
+      "4110 DPP9\n",
+      "4111 CCDC159\n",
+      "4112 TPM4\n",
+      "4113 KLF2\n",
+      "4114 GPI\n",
+      "4115 EMC10\n",
+      "4116 ZNF766\n",
+      "4117 TSEN34\n",
+      "4118 STMN3\n",
+      "4119 C21orf91\n",
+      "4120 MRPL39\n",
+      "4121 LTN1\n",
+      "4122 U2AF1\n",
+      "4123 TPST2\n",
+      "4124 TUG1\n",
+      "4125 PRR5\n",
+      "4126 TAF1\n",
+      "4127 RPL22\n",
+      "4128 UQCRHL\n",
+      "4129 CDC42\n",
+      "4130 RNF220\n",
+      "4131 BTF3L4\n",
+      "4132 JAK1\n",
+      "4133 LRRC40\n",
+      "4134 USP33\n",
+      "4135 SIKE1\n",
+      "4136 C1orf43\n",
+      "4137 KHDC4\n",
+      "4138 PYHIN1\n",
+      "4139 SDHC\n",
+      "4140 ADIPOR1\n",
+      "4141 DUSP10\n",
+      "4142 IAH1\n",
+      "4143 HADHA\n",
+      "4144 NRBP1\n",
+      "4145 YIPF4\n",
+      "4146 GPATCH11\n",
+      "4147 MCFD2\n",
+      "4148 ANXA4\n",
+      "4149 NCK2\n",
+      "4150 TSN\n",
+      "4151 HSPE1\n",
+      "4152 THUMPD3\n",
+      "4153 HACL1\n",
+      "4154 EOMES\n",
+      "4155 SELENOK\n",
+      "4156 TASOR\n",
+      "4157 B4GALT4\n",
+      "4158 GAK\n",
+      "4159 EIF4E\n",
+      "4160 MAP3K1\n",
+      "4161 CENPH\n",
+      "4162 BDP1\n",
+      "4163 VCAN\n",
+      "4164 ATG12\n",
+      "4165 C5orf15\n",
+      "4166 TXNDC15\n",
+      "4167 SOX4\n",
+      "4168 H2BC8\n",
+      "4169 WDR46\n",
+      "4170 SENP6\n",
+      "4171 TMEM120A\n",
+      "4172 STYXL1\n",
+      "4173 SSBP1\n",
+      "4174 CASP2\n",
+      "4175 FAM131B-AS2\n",
+      "4176 NSMCE2\n",
+      "4177 PARP10\n",
+      "4178 MTAP\n",
+      "4179 RPP25L\n",
+      "4180 POLR1E\n",
+      "4181 FAM78A\n",
+      "4182 REXO4\n",
+      "4183 DPP7\n",
+      "4184 GTPBP4\n",
+      "4185 LIPA\n",
+      "4186 KIF20B\n",
+      "4187 CTBP2\n",
+      "4188 EDRF1\n",
+      "4189 RIC8A\n",
+      "4190 AKIP1\n",
+      "4191 ELP4\n",
+      "4192 SSRP1\n",
+      "4193 UQCC3\n",
+      "4194 FERMT3\n",
+      "4195 CTSW\n",
+      "4196 CORO1B\n",
+      "4197 CDK2AP2\n",
+      "4198 CASP1\n",
+      "4199 RNF26\n",
+      "4200 NCAPD2\n",
+      "4201 SLC38A2\n",
+      "4202 BIN2\n",
+      "4203 ATP5MC2\n",
+      "4204 CALCOCO1\n",
+      "4205 NACA\n",
+      "4206 MARS1\n",
+      "4207 LTA4H\n",
+      "4208 HSP90B1\n",
+      "4209 TDG\n",
+      "4210 ACADS\n",
+      "4211 GOLGA3\n",
+      "4212 ZMYM2\n",
+      "4213 CLN5\n",
+      "4214 AP1G2\n",
+      "4215 EIF5\n",
+      "4216 KLC1\n",
+      "4217 HERC2\n",
+      "4218 IREB2\n",
+      "4219 CRTC3\n",
+      "4220 HBM\n",
+      "4221 HBA2\n",
+      "4222 STUB1\n",
+      "4223 HMOX2\n",
+      "4224 CARHSP1\n",
+      "4225 NTAN1\n",
+      "4226 MVP\n",
+      "4227 MT1X\n",
+      "4228 SLC25A11\n",
+      "4229 NDEL1\n",
+      "4230 NSRP1\n",
+      "4231 EVI2A\n",
+      "4232 SCRN2\n",
+      "4233 LUC7L3\n",
+      "4234 NARF\n",
+      "4235 RNMT\n",
+      "4236 RNF138\n",
+      "4237 PSTPIP2\n",
+      "4238 HMG20B\n",
+      "4239 CDKN2D\n",
+      "4240 IRF3\n",
+      "4241 LAIR2\n",
+      "4242 CHMP2A\n",
+      "4243 RNF24\n",
+      "4244 ESF1\n",
+      "4245 SNRPB2\n",
+      "4246 ID1\n",
+      "4247 RALY\n",
+      "4248 DPM1\n",
+      "4249 PSMG1\n",
+      "4250 MX1\n",
+      "4251 IGLC1\n",
+      "4252 TBC1D10A\n",
+      "4253 IL2RB\n",
+      "4254 EBP\n",
+      "4255 ARHGAP4\n",
+      "4256 CAMTA1\n",
+      "4257 TARDBP\n",
+      "4258 MICOS10\n",
+      "4259 HMGN2\n",
+      "4260 ZCCHC17\n",
+      "4261 AK2\n",
+      "4262 PSMB2\n",
+      "4263 PPCS\n",
+      "4264 CTBS\n",
+      "4265 SELENOF\n",
+      "4266 HIPK1\n",
+      "4267 TSPAN2\n",
+      "4268 TENT5C\n",
+      "4269 COPA\n",
+      "4270 MGST3\n",
+      "4271 IVNS1ABP\n",
+      "4272 EIF2D\n",
+      "4273 EGLN1\n",
+      "4274 PTRHD1\n",
+      "4275 RAB1A\n",
+      "4276 ELMOD3\n",
+      "4277 CD8B\n",
+      "4278 CYTOR\n",
+      "4279 MITD1\n",
+      "4280 RPL31\n",
+      "4281 CHCHD5\n",
+      "4282 WDR33\n",
+      "4283 IMP4\n",
+      "4284 NMI\n",
+      "4285 ATP5MC3\n",
+      "4286 UBE2E3\n",
+      "4287 ATIC\n",
+      "4288 ARPC2\n",
+      "4289 PRKAR2A\n",
+      "4290 APPL1\n",
+      "4291 PSMD6\n",
+      "4292 HCLS1\n",
+      "4293 AP2M1\n",
+      "4294 RPL9\n",
+      "4295 SREK1IP1\n",
+      "4296 TNPO1\n",
+      "4297 SCAMP1\n",
+      "4298 BRD8\n",
+      "4299 CYSTM1\n",
+      "4300 LARP1\n",
+      "4301 MAT2B\n",
+      "4302 RPL26L1\n",
+      "4303 PPP1R11\n",
+      "4304 FLOT1\n",
+      "4305 KCTD20\n",
+      "4306 KLHDC3\n",
+      "4307 PNISR\n",
+      "4308 ARHGAP18\n",
+      "4309 TMEM181\n",
+      "4310 TAX1BP1\n",
+      "4311 ABCB1\n",
+      "4312 MCM7\n",
+      "4313 TAF6\n",
+      "4314 ZC3HAV1\n",
+      "4315 MKRN1\n",
+      "4316 GIMAP4\n",
+      "4317 RHEB\n",
+      "4318 INSIG1\n",
+      "4319 VDAC3\n",
+      "4320 CEBPD\n",
+      "4321 ZBTB10\n",
+      "4322 RNF139\n",
+      "4323 C8orf33\n",
+      "4324 UBE2R2\n",
+      "4325 UBAP2\n",
+      "4326 DCAF10\n",
+      "4327 NINJ1\n",
+      "4328 ZDHHC12\n",
+      "4329 FBXW5\n",
+      "4330 ENSG00000287175\n",
+      "4331 CSGALNACT2\n",
+      "4332 WASHC2A\n",
+      "4333 ARID5B\n",
+      "4334 FRA10AC1\n",
+      "4335 ASCL2\n",
+      "4336 DGKZ\n",
+      "4337 ARFGAP2\n",
+      "4338 CELF1\n",
+      "4339 MTCH2\n",
+      "4340 PTPRJ\n",
+      "4341 PICALM\n",
+      "4342 NCAPD3\n",
+      "4343 ATG101\n",
+      "4344 TSPAN31\n",
+      "4345 GIT2\n",
+      "4346 RFC5\n",
+      "4347 SRSF9\n",
+      "4348 TRIM13\n",
+      "4349 WDFY2\n",
+      "4350 CHMP4A\n",
+      "4351 RPS6KA5\n",
+      "4352 KATNBL1\n",
+      "4353 RAB8B\n",
+      "4354 SMAD3\n",
+      "4355 FANCI\n",
+      "4356 LONP2\n",
+      "4357 MT2A\n",
+      "4358 ST3GAL2\n",
+      "4359 GCSH\n",
+      "4360 PITPNA-AS1\n",
+      "4361 ARRB2\n",
+      "4362 NF1\n",
+      "4363 PSMD11\n",
+      "4364 RFFL\n",
+      "4365 ORMDL3\n",
+      "4366 RETREG3\n",
+      "4367 PSME3\n",
+      "4368 GRN\n",
+      "4369 KANSL1\n",
+      "4370 KANSL1-AS1\n",
+      "4371 PSMD12\n",
+      "4372 PIK3C3\n",
+      "4373 ATP5F1D\n",
+      "4374 GTF2F1\n",
+      "4375 SMARCA4\n",
+      "4376 DHPS\n",
+      "4377 NR2C2AP\n",
+      "4378 PSMF1\n",
+      "4379 NAA20\n",
+      "4380 OSER1\n",
+      "4381 VAPB\n",
+      "4382 UCKL1\n",
+      "4383 DGCR6L\n",
+      "4384 SMDT1\n",
+      "4385 PARVB\n",
+      "4386 SLC25A6\n",
+      "4387 PRDX4\n",
+      "4388 NDUFB11\n",
+      "4389 UBA1\n",
+      "4390 PLP2\n",
+      "4391 CYSLTR1\n",
+      "4392 MT-ND5\n",
+      "4393 MRTO4\n",
+      "4394 RCC1\n",
+      "4395 ZMPSTE24\n",
+      "4396 RNF11\n",
+      "4397 SASS6\n",
+      "4398 SLAMF6\n",
+      "4399 DUSP12\n",
+      "4400 SFT2D2\n",
+      "4401 MIR181A1HG\n",
+      "4402 WDR26\n",
+      "4403 GGPS1\n",
+      "4404 FAM228B\n",
+      "4405 C1D\n",
+      "4406 CD8A\n",
+      "4407 ARID5A\n",
+      "4408 MZT2A\n",
+      "4409 NFE2L2\n",
+      "4410 NUP210\n",
+      "4411 MRPS25\n",
+      "4412 DPH3\n",
+      "4413 POGLUT1\n",
+      "4414 PARP15\n",
+      "4415 FAIM\n",
+      "4416 NMD3\n",
+      "4417 REST\n",
+      "4418 CENPC\n",
+      "4419 ANXA5\n",
+      "4420 ANKH\n",
+      "4421 MRPS30\n",
+      "4422 EMB\n",
+      "4423 GPBP1\n",
+      "4424 ST8SIA4\n",
+      "4425 FAM53C\n",
+      "4426 MATR3\n",
+      "4427 DUSP1\n",
+      "4428 TRIM41\n",
+      "4429 ABCF1\n",
+      "4430 PSMB9\n",
+      "4431 DNPH1\n",
+      "4432 MTO1\n",
+      "4433 KATNA1\n",
+      "4434 TCP1\n",
+      "4435 WIPI2\n",
+      "4436 LSM5\n",
+      "4437 POM121\n",
+      "4438 TMEM243\n",
+      "4439 COPS6\n",
+      "4440 ENSG00000170632\n",
+      "4441 PLPBP\n",
+      "4442 TCEA1\n",
+      "4443 TMEM68\n",
+      "4444 ARMC1\n",
+      "4445 PDE7A\n",
+      "4446 MYBL1\n",
+      "4447 WWP1\n",
+      "4448 ENY2\n",
+      "4449 BAG1\n",
+      "4450 CLTA\n",
+      "4451 PTAR1\n",
+      "4452 FAM120AOS\n",
+      "4453 PTBP3\n",
+      "4454 PTPA\n",
+      "4455 WDR37\n",
+      "4456 APBB1IP\n",
+      "4457 CUEDC2\n",
+      "4458 SLK\n",
+      "4459 SHOC2\n",
+      "4460 CCDC186\n",
+      "4461 CD151\n",
+      "4462 CTSD\n",
+      "4463 RRM1\n",
+      "4464 NUP160\n",
+      "4465 CPT1A\n",
+      "4466 CCDC82\n",
+      "4467 CD3E\n",
+      "4468 RPS25\n",
+      "4469 EI24\n",
+      "4470 WBP11\n",
+      "4471 PCED1B\n",
+      "4472 ATF1\n",
+      "4473 CD63\n",
+      "4474 PTGES3\n",
+      "4475 PRIM1\n",
+      "4476 CTDSP2\n",
+      "4477 NUP107\n",
+      "4478 SAP18\n",
+      "4479 DAD1\n",
+      "4480 OXA1L\n",
+      "4481 ACIN1\n",
+      "4482 CGRRF1\n",
+      "4483 DAAM1\n",
+      "4484 SRSF5\n",
+      "4485 GOLGA5\n",
+      "4486 PPP2R5C\n",
+      "4487 IGHA1\n",
+      "4488 LRRC57\n",
+      "4489 SECISBP2L\n",
+      "4490 LYSMD2\n",
+      "4491 DENND4A\n",
+      "4492 AAGAB\n",
+      "4493 TSPAN3\n",
+      "4494 NME4\n",
+      "4495 TMEM204\n",
+      "4496 SPN\n",
+      "4497 C16orf54\n",
+      "4498 RBL2\n",
+      "4499 NLRC5\n",
+      "4500 IST1\n",
+      "4501 MPHOSPH6\n",
+      "4502 COX4I1\n",
+      "4503 GPS2\n",
+      "4504 ZSWIM7\n",
+      "4505 SMARCE1\n",
+      "4506 IFI35\n",
+      "4507 NMT1\n",
+      "4508 UNK\n",
+      "4509 UNC13D\n",
+      "4510 PGS1\n",
+      "4511 DLGAP1-AS1\n",
+      "4512 RIOK3\n",
+      "4513 ABHD17A\n",
+      "4514 LSM7\n",
+      "4515 MAP2K2\n",
+      "4516 ELAVL1\n",
+      "4517 HOOK2\n",
+      "4518 GADD45GIP1\n",
+      "4519 SLC35E1\n",
+      "4520 PSMD8\n",
+      "4521 VASP\n",
+      "4522 OPA3\n",
+      "4523 EML2\n",
+      "4524 AP2A1\n",
+      "4525 RPL28\n",
+      "4526 MAVS\n",
+      "4527 CST3\n",
+      "4528 PEDS1\n",
+      "4529 PFDN4\n",
+      "4530 ETS2\n",
+      "4531 HDHD5\n",
+      "4532 C22orf39\n",
+      "4533 PRR14L\n",
+      "4534 MCM5\n",
+      "4535 SAMM50\n",
+      "4536 CD99\n",
+      "4537 DYNLT3\n",
+      "4538 PQBP1\n",
+      "4539 HMGN5\n",
+      "4540 MAP7D3\n",
+      "4541 UBE4B\n",
+      "4542 FBXO6\n",
+      "4543 FAM76A\n",
+      "4544 PEF1\n",
+      "4545 TMEM39B\n",
+      "4546 YBX1\n",
+      "4547 USP24\n",
+      "4548 LAMTOR5\n",
+      "4549 DRAM2\n",
+      "4550 PTPN22\n",
+      "4551 ENSG00000287978\n",
+      "4552 CCT3\n",
+      "4553 TMCO1\n",
+      "4554 XCL1\n",
+      "4555 NIBAN1\n",
+      "4556 RO60\n",
+      "4557 CNST\n",
+      "4558 TTC32\n",
+      "4559 DNMT3A\n",
+      "4560 SNX17\n",
+      "4561 SOS1\n",
+      "4562 FBXO11\n",
+      "4563 PRKRA\n",
+      "4564 BZW1\n",
+      "4565 NDUFB3\n",
+      "4566 RETREG2\n",
+      "4567 STK25\n",
+      "4568 XPC\n",
+      "4569 ELP6\n",
+      "4570 USP4\n",
+      "4571 NISCH\n",
+      "4572 ANAPC13\n",
+      "4573 SERP1\n",
+      "4574 TBC1D1\n",
+      "4575 MTHFD2L\n",
+      "4576 TET2\n",
+      "4577 LEF1\n",
+      "4578 LRBA\n",
+      "4579 ETFDH\n",
+      "4580 CFAP97\n",
+      "4581 ZFR\n",
+      "4582 IL7R\n",
+      "4583 MSH3\n",
+      "4584 SLC12A2-DT\n",
+      "4585 UBE2D2\n",
+      "4586 TTC1\n",
+      "4587 CLTB\n",
+      "4588 RNF44\n",
+      "4589 RNGTT\n",
+      "4590 PNRC1\n",
+      "4591 EIF3B\n",
+      "4592 TOMM7\n",
+      "4593 STAG3\n",
+      "4594 SRRT\n",
+      "4595 UBE2H\n",
+      "4596 FASTK\n",
+      "4597 CCDC25\n",
+      "4598 PLEKHA2\n",
+      "4599 RB1CC1\n",
+      "4600 PTDSS1\n",
+      "4601 BOP1\n",
+      "4602 ZNG1A\n",
+      "4603 EBLN3P\n",
+      "4604 MRPL50\n",
+      "4605 HSDL2\n",
+      "4606 CCAR1\n",
+      "4607 NOC3L\n",
+      "4608 PRDX3\n",
+      "4609 CAT\n",
+      "4610 TMEM179B\n",
+      "4611 PLAAT4\n",
+      "4612 CREBZF\n",
+      "4613 KLRG1\n",
+      "4614 CCNT1\n",
+      "4615 TARBP2\n",
+      "4616 STAT2\n",
+      "4617 RAP1B\n",
+      "4618 PRANCR\n",
+      "4619 TBC1D15\n",
+      "4620 ATXN7L3B\n",
+      "4621 UBE2N\n",
+      "4622 VEZT\n",
+      "4623 ISCU\n",
+      "4624 SELPLG\n",
+      "4625 PPTC7\n",
+      "4626 ATXN2\n",
+      "4627 ABHD13\n",
+      "4628 NFKBIA\n",
+      "4629 NIN\n",
+      "4630 CCDC88C\n",
+      "4631 BCL11B\n",
+      "4632 IGHG3\n",
+      "4633 CCPG1\n",
+      "4634 PIAS1\n",
+      "4635 TMED3\n",
+      "4636 HAPSTR1\n",
+      "4637 METTL9\n",
+      "4638 HSBP1\n",
+      "4639 SSH2\n",
+      "4640 UTP6\n",
+      "4641 MED24\n",
+      "4642 MED13\n",
+      "4643 RHBDF2\n",
+      "4644 GALNT1\n",
+      "4645 TCF3\n",
+      "4646 SH3GL1\n",
+      "4647 SHFL\n",
+      "4648 GET3\n",
+      "4649 ASF1B\n",
+      "4650 HSH2D\n",
+      "4651 HNRNPL\n",
+      "4652 KDELR1\n",
+      "4653 NKG7\n",
+      "4654 FAM110A\n",
+      "4655 DLGAP4\n",
+      "4656 TTC3\n",
+      "4657 DIP2A\n",
+      "4658 HDAC10\n",
+      "4659 DHRSX\n",
+      "4660 JPX\n",
+      "4661 MAGT1\n",
+      "4662 RNF113A\n",
+      "4663 HPRT1\n",
+      "4664 CETN2\n",
+      "4665 UTS2\n",
+      "4666 ADPRS\n",
+      "4667 RPS8\n",
+      "4668 FAF1\n",
+      "4669 MRPL37\n",
+      "4670 IFI44L\n",
+      "4671 MCOLN2\n",
+      "4672 SLC25A24\n",
+      "4673 CAPZA1\n",
+      "4674 PMVK\n",
+      "4675 DPM3\n",
+      "4676 ODR4\n",
+      "4677 GUK1\n",
+      "4678 ASXL2\n",
+      "4679 MTIF2\n",
+      "4680 ARHGAP25\n",
+      "4681 CREB1\n",
+      "4682 PPP1R7\n",
+      "4683 VHL\n",
+      "4684 EAF1\n",
+      "4685 UBP1\n",
+      "4686 HEMK1\n",
+      "4687 RRP9\n",
+      "4688 CGGBP1\n",
+      "4689 NIT2\n",
+      "4690 PLSCR1\n",
+      "4691 LSG1\n",
+      "4692 NCBP2\n",
+      "4693 SLBP\n",
+      "4694 MED28\n",
+      "4695 KLF3\n",
+      "4696 NOA1\n",
+      "4697 POLR2B\n",
+      "4698 PGRMC2\n",
+      "4699 NAA15\n",
+      "4700 FRG1\n",
+      "4701 RPL37\n",
+      "4702 CCDC112\n",
+      "4703 TCERG1\n",
+      "4704 STK10\n",
+      "4705 ZSCAN16-AS1\n",
+      "4706 BAK1\n",
+      "4707 ORC3\n",
+      "4708 AKIRIN2\n",
+      "4709 NDUFAF4\n",
+      "4710 ACAT2\n",
+      "4711 IKZF1\n",
+      "4712 TMEM248\n",
+      "4713 BAZ1B\n",
+      "4714 MDH2\n",
+      "4715 SAMD9\n",
+      "4716 SRPK2\n",
+      "4717 ARF5\n",
+      "4718 MTPN\n",
+      "4719 PPP3CC\n",
+      "4720 R3HCC1\n",
+      "4721 SARAF\n",
+      "4722 MCM4\n",
+      "4723 MRPL15\n",
+      "4724 SNHG6\n",
+      "4725 RPL7\n",
+      "4726 GOLGA2\n",
+      "4727 EXOSC2\n",
+      "4728 MRPL41\n",
+      "4729 GDI2\n",
+      "4730 WASHC2C\n",
+      "4731 CISD1\n",
+      "4732 PCGF5\n",
+      "4733 LCOR\n",
+      "4734 BORCS7\n",
+      "4735 VTI1A\n",
+      "4736 ARFIP2\n",
+      "4737 NDUFS3\n",
+      "4738 CAPN1\n",
+      "4739 DRAP1\n",
+      "4740 POLD4\n",
+      "4741 RAB6A\n",
+      "4742 CCDC90B\n",
+      "4743 COPS7A\n",
+      "4744 USP5\n",
+      "4745 NECAP1\n",
+      "4746 MAGOHB\n",
+      "4747 KRAS\n",
+      "4748 KIF21A\n",
+      "4749 SLC38A1\n",
+      "4750 TUBA1B\n",
+      "4751 NEDD1\n",
+      "4752 TRIAP1\n",
+      "4753 MRPL57\n",
+      "4754 RCBTB2\n",
+      "4755 PSME2\n",
+      "4756 GMFB\n",
+      "4757 PPM1A\n",
+      "4758 MED6\n",
+      "4759 EIF2B2\n",
+      "4760 AKT1\n",
+      "4761 MGA\n",
+      "4762 SPPL2A\n",
+      "4763 DPP8\n",
+      "4764 CLUAP1\n",
+      "4765 USP7\n",
+      "4766 NUBP1\n",
+      "4767 TXNDC11\n",
+      "4768 FBRS\n",
+      "4769 TENT4B\n",
+      "4770 BRD7\n",
+      "4771 RFLNB\n",
+      "4772 GABARAP\n",
+      "4773 RPL27\n",
+      "4774 HOXB2\n",
+      "4775 YPEL2\n",
+      "4776 CCDC57\n",
+      "4777 ARK2N\n",
+      "4778 ECSIT\n",
+      "4779 RASAL3\n",
+      "4780 FBL\n",
+      "4781 PLAUR\n",
+      "4782 CLASRP\n",
+      "4783 NOSIP\n",
+      "4784 LILRB1\n",
+      "4785 MX2\n",
+      "4786 FAM118A\n",
+      "4787 ARMCX3\n",
+      "4788 SH2D1A\n",
+      "4789 CCNQ\n",
+      "4790 PIK3CD\n",
+      "4791 MAD2L2\n",
+      "4792 EIF4G3\n",
+      "4793 PPIE\n",
+      "4794 GNG5\n",
+      "4795 AHCYL1\n",
+      "4796 ATP5PB\n",
+      "4797 ATP1A1\n",
+      "4798 ENSG00000287190\n",
+      "4799 ADAR\n",
+      "4800 ARPC5\n",
+      "4801 CYB5R1\n",
+      "4802 ID2\n",
+      "4803 YWHAQ\n",
+      "4804 MRPL30\n",
+      "4805 PTPN18\n",
+      "4806 IFT57\n",
+      "4807 NEPRO\n",
+      "4808 NDUFB4\n",
+      "4809 FNDC3B\n",
+      "4810 CEP135\n",
+      "4811 PLA2G12A\n",
+      "4812 GAR1\n",
+      "4813 C4orf3\n",
+      "4814 TMEM192\n",
+      "4815 NNT\n",
+      "4816 TMEM167A\n",
+      "4817 SLC25A46\n",
+      "4818 G3BP1\n",
+      "4819 MED7\n",
+      "4820 EHMT2\n",
+      "4821 UBE2J1\n",
+      "4822 MAP3K7\n",
+      "4823 ATG5\n",
+      "4824 ASF1A\n",
+      "4825 SF3B5\n",
+      "4826 SYNJ2\n",
+      "4827 CDK13\n",
+      "4828 TBRG4\n",
+      "4829 POR\n",
+      "4830 PCM1\n",
+      "4831 CHD7\n",
+      "4832 RRS1\n",
+      "4833 EIF3E\n",
+      "4834 AGO2\n",
+      "4835 MAF1\n",
+      "4836 SEMA4D\n",
+      "4837 PRXL2C\n",
+      "4838 NIPSNAP3A\n",
+      "4839 TXN\n",
+      "4840 FBXW2\n",
+      "4841 MTPAP\n",
+      "4842 ZNF33A\n",
+      "4843 UBE2D1\n",
+      "4844 CAMK2G\n",
+      "4845 HIF1AN\n",
+      "4846 ZDHHC6\n",
+      "4847 BUB3\n",
+      "4848 OAT\n",
+      "4849 CARS1\n",
+      "4850 ARL14EP\n",
+      "4851 CSKMT\n",
+      "4852 KAT5\n",
+      "4853 RAB1B\n",
+      "4854 RDX\n",
+      "4855 SLC2A3\n",
+      "4856 KLRC4\n",
+      "4857 CMAS\n",
+      "4858 TM7SF3\n",
+      "4859 YARS2\n",
+      "4860 RASSF3\n",
+      "4861 ALKBH2\n",
+      "4862 OAS2\n",
+      "4863 RHOF\n",
+      "4864 SBNO1\n",
+      "4865 SPART\n",
+      "4866 SUPT20H\n",
+      "4867 NUDT15\n",
+      "4868 GZMB\n",
+      "4869 HECTD1\n",
+      "4870 RPS29\n",
+      "4871 FAM98B\n",
+      "4872 GTF2A2\n",
+      "4873 OAZ2\n",
+      "4874 CTSH\n",
+      "4875 MTHFS\n",
+      "4876 IL16\n",
+      "4877 MCTP2\n",
+      "4878 XPO6\n",
+      "4879 PSME3IP1\n",
+      "4880 CIAPIN1\n",
+      "4881 ZDHHC7\n",
+      "4882 MIEN1\n",
+      "4883 STAT5B\n",
+      "4884 GPATCH8\n",
+      "4885 UBE2Z\n",
+      "4886 CLTC\n",
+      "4887 RNFT1\n",
+      "4888 HDHD2\n",
+      "4889 CSNK1G2\n",
+      "4890 ILF3\n",
+      "4891 PRKCSH\n",
+      "4892 ZNF791\n",
+      "4893 MAN2B1\n",
+      "4894 AKAP8\n",
+      "4895 AP1M1\n",
+      "4896 MYO9B\n",
+      "4897 BORCS8\n",
+      "4898 YIF1B\n",
+      "4899 SIRT2\n",
+      "4900 SHKBP1\n",
+      "4901 SELENOW\n",
+      "4902 MRPS6\n",
+      "4903 HMGXB4\n",
+      "4904 RPS19BP1\n",
+      "4905 ADSL\n",
+      "4906 CENPM\n",
+      "4907 PARVG\n",
+      "4908 GRIPAP1\n",
+      "4909 MAGED2\n",
+      "4910 GLA\n",
+      "4911 HTATSF1\n",
+      "4912 ENSG00000055070\n",
+      "4913 KHDRBS1\n",
+      "4914 ZC3H12A\n",
+      "4915 MEAF6\n",
+      "4916 CMPK1\n",
+      "4917 C1orf52\n",
+      "4918 GBP3\n",
+      "4919 CCDC18-AS1\n",
+      "4920 VAV3\n",
+      "4921 CD2\n",
+      "4922 CHD1L\n",
+      "4923 CTSS\n",
+      "4924 MRPL9\n",
+      "4925 KRTCAP2\n",
+      "4926 FDPS\n",
+      "4927 ALDH9A1\n",
+      "4928 MRPS14\n",
+      "4929 NSL1\n",
+      "4930 SMYD3\n",
+      "4931 AUP1\n",
+      "4932 POLE4\n",
+      "4933 FAHD2A\n",
+      "4934 ARHGAP15\n",
+      "4935 NR4A2\n",
+      "4936 RPE\n",
+      "4937 ITM2C\n",
+      "4938 THAP4\n",
+      "4939 CAPN7\n",
+      "4940 ABHD14B\n",
+      "4941 TKT\n",
+      "4942 CCDC66\n",
+      "4943 CLDND1\n",
+      "4944 MCM2\n",
+      "4945 SMIM20\n",
+      "4946 AREG\n",
+      "4947 METTL14\n",
+      "4948 PDCD6\n",
+      "4949 MRPS27\n",
+      "4950 IK\n",
+      "4951 H1-2\n",
+      "4952 HLA-DQB1\n",
+      "4953 ELOVL5\n",
+      "4954 MMS22L\n",
+      "4955 PERP\n",
+      "4956 HEBP2\n",
+      "4957 CCDC28A\n",
+      "4958 ERV3-1\n",
+      "4959 NDUFA5\n",
+      "4960 ELP3\n",
+      "4961 UBE2V2\n",
+      "4962 AK3\n",
+      "4963 ZFAND5\n",
+      "4964 AGTPBP1\n",
+      "4965 TTC16\n",
+      "4966 NPDC1\n",
+      "4967 DCLRE1C\n",
+      "4968 NCOA4\n",
+      "4969 DDX21\n",
+      "4970 SFR1\n",
+      "4971 ABLIM1\n",
+      "4972 GLRX3\n",
+      "4973 CTR9\n",
+      "4974 HNRNPUL2\n",
+      "4975 VPS51\n",
+      "4976 CFL1\n",
+      "4977 EIF1AD\n",
+      "4978 BRMS1\n",
+      "4979 TMEM126B\n",
+      "4980 KMT2A\n",
+      "4981 NDUFA9\n",
+      "4982 C12orf57\n",
+      "4983 SHMT2\n",
+      "4984 IFNG\n",
+      "4985 MDM2\n",
+      "4986 OSBPL8\n",
+      "4987 NUDT4\n",
+      "4988 MMAB\n",
+      "4989 CHURC1\n",
+      "4990 ISCA2\n",
+      "4991 AHSA1\n",
+      "4992 COA8\n",
+      "4993 PDIA3\n",
+      "4994 GDE1\n",
+      "4995 VPS35\n",
+      "4996 N4BP1\n",
+      "4997 OGFOD1\n",
+      "4998 NUP93\n",
+      "4999 EXOSC6\n",
+      "5000 SAT2\n",
+      "5001 SYNRG\n",
+      "5002 CEP95\n",
+      "5003 NOL11\n",
+      "5004 MRPL12\n",
+      "5005 HEXD\n",
+      "5006 LDLRAD4\n",
+      "5007 IER3IP1\n",
+      "5008 MBP\n",
+      "5009 ZBTB7A\n",
+      "5010 YJU2\n",
+      "5011 ZNF121\n",
+      "5012 CDC37\n",
+      "5013 PRDX2\n",
+      "5014 RNASEH2A\n",
+      "5015 DNAJB1\n",
+      "5016 ISYNA1\n",
+      "5017 ZNF506\n",
+      "5018 CEBPG\n",
+      "5019 RINL\n",
+      "5020 NFKBIB\n",
+      "5021 ETHE1\n",
+      "5022 RELB\n",
+      "5023 SNRPD2\n",
+      "5024 LENG8\n",
+      "5025 RASSF2\n",
+      "5026 BLCAP\n",
+      "5027 USP25\n",
+      "5028 DONSON\n",
+      "5029 RRP1\n",
+      "5030 S100B\n",
+      "5031 HSCB\n",
+      "5032 CYTH4\n",
+      "5033 ANKRD54\n",
+      "5034 RBMX\n",
+      "5035 MT-ND4L\n",
+      "5036 TRAPPC3\n",
+      "5037 C1orf50\n",
+      "5038 GCLM\n",
+      "5039 RHOC\n",
+      "5040 PHTF1\n",
+      "5041 ILF2\n",
+      "5042 DCAF8\n",
+      "5043 FCER1G\n",
+      "5044 MAPKAPK2\n",
+      "5045 NVL\n",
+      "5046 PPP1R21\n",
+      "5047 PSME4\n",
+      "5048 MXD1\n",
+      "5049 CHMP3\n",
+      "5050 PRPF40A\n",
+      "5051 GORASP2\n",
+      "5052 HIBCH\n",
+      "5053 ENSG00000272211\n",
+      "5054 RFTN1\n",
+      "5055 SATB1\n",
+      "5056 TRMT10C\n",
+      "5057 HACD2\n",
+      "5058 SEC62\n",
+      "5059 LETM1\n",
+      "5060 RAB28\n",
+      "5061 ZNF622\n",
+      "5062 NDUFS4\n",
+      "5063 SREK1\n",
+      "5064 NDUFA2\n",
+      "5065 FAF2\n",
+      "5066 H2BC15\n",
+      "5067 NCR3\n",
+      "5068 MDN1\n",
+      "5069 ASCC3\n",
+      "5070 MAN1A1\n",
+      "5071 RPS12\n",
+      "5072 TBPL1\n",
+      "5073 TFB1M\n",
+      "5074 CHST12\n",
+      "5075 GGCT\n",
+      "5076 TMED4\n",
+      "5077 MYO1G\n",
+      "5078 CRCP\n",
+      "5079 TP53TG1\n",
+      "5080 AKR1B1\n",
+      "5081 GIMAP6\n",
+      "5082 MSRA\n",
+      "5083 BNIP3L\n",
+      "5084 LYPLA1\n",
+      "5085 TOX-DT\n",
+      "5086 LY6E\n",
+      "5087 TOP1MT\n",
+      "5088 SIGMAR1\n",
+      "5089 RC3H2\n",
+      "5090 EHMT1\n",
+      "5091 ZNF33B\n",
+      "5092 PRF1\n",
+      "5093 EEF1AKMT2\n",
+      "5094 MUS81\n",
+      "5095 AAMDC\n",
+      "5096 CTSC\n",
+      "5097 PAFAH1B2\n",
+      "5098 CDKN1B\n",
+      "5099 RESF1\n",
+      "5100 DNM1L\n",
+      "5101 PUS7L\n",
+      "5102 ARID2\n",
+      "5103 PRKAG1\n",
+      "5104 SMARCC2\n",
+      "5105 CNOT2\n",
+      "5106 RLIG1\n",
+      "5107 ANAPC7\n",
+      "5108 PPP1CC\n",
+      "5109 MPHOSPH9\n",
+      "5110 CRYL1\n",
+      "5111 PARP4\n",
+      "5112 GTF3A\n",
+      "5113 ALOX5AP\n",
+      "5114 DNAJC3\n",
+      "5115 SRP54\n",
+      "5116 FBXO33\n",
+      "5117 MTHFD1\n",
+      "5118 ERH\n",
+      "5119 BTBD7\n",
+      "5120 PAPOLA\n",
+      "5121 RTF1\n",
+      "5122 TMOD3\n",
+      "5123 COMMD4\n",
+      "5124 ISG20\n",
+      "5125 ATF7IP2\n",
+      "5126 CLN3\n",
+      "5127 ZNF267\n",
+      "5128 E2F4\n",
+      "5129 DDX28\n",
+      "5130 CMC2\n",
+      "5131 VPS53\n",
+      "5132 NLRP1\n",
+      "5133 PIK3R5\n",
+      "5134 GOSR2\n",
+      "5135 SRSF1\n",
+      "5136 CD300A\n",
+      "5137 CD7\n",
+      "5138 ANKRD12\n",
+      "5139 RBFA\n",
+      "5140 PLEKHJ1\n",
+      "5141 SUGP1\n",
+      "5142 ENSG00000268027\n",
+      "5143 PPP5C\n",
+      "5144 EIF6\n",
+      "5145 SCAND1\n",
+      "5146 LIME1\n",
+      "5147 MORC3\n",
+      "5148 ENSG00000226471\n",
+      "5149 APOL2\n",
+      "5150 TSPO\n",
+      "5151 SCO2\n",
+      "5152 USP11\n",
+      "5153 GDI1\n",
+      "5154 WRAP73\n",
+      "5155 ZNF593\n",
+      "5156 NDUFS5\n",
+      "5157 PRDX1\n",
+      "5158 PRPF38A\n",
+      "5159 ZNF326\n",
+      "5160 GSTM4\n",
+      "5161 S100A6\n",
+      "5162 ATF6\n",
+      "5163 QSOX1\n",
+      "5164 EML4\n",
+      "5165 FOXN2\n",
+      "5166 AFTPH\n",
+      "5167 PNO1\n",
+      "5168 AAK1\n",
+      "5169 FAM168B\n",
+      "5170 WDR12\n",
+      "5171 UBE2E1\n",
+      "5172 MYD88\n",
+      "5173 KIAA1143\n",
+      "5174 RBM6\n",
+      "5175 CYB561D2\n",
+      "5176 TPRA1\n",
+      "5177 COMMD2\n",
+      "5178 RPL22L1\n",
+      "5179 EXOC1\n",
+      "5180 PPBP\n",
+      "5181 HSD17B11\n",
+      "5182 ARHGAP10\n",
+      "5183 NSUN2\n",
+      "5184 PRKAA1\n",
+      "5185 ERBIN\n",
+      "5186 IQGAP2\n",
+      "5187 YTHDC2\n",
+      "5188 UBE2B\n",
+      "5189 ANXA6\n",
+      "5190 NSD1\n",
+      "5191 PRELID1\n",
+      "5192 TMED9\n",
+      "5193 SERPINB6\n",
+      "5194 LYRM4\n",
+      "5195 BLOC1S5\n",
+      "5196 HLA-F\n",
+      "5197 PRRC2A\n",
+      "5198 SNRPC\n",
+      "5199 SRSF3\n",
+      "5200 TOMM6\n",
+      "5201 CCZ1B\n",
+      "5202 DDX56\n",
+      "5203 DBF4\n",
+      "5204 PILRB\n",
+      "5205 GALNT11\n",
+      "5206 PAG1\n",
+      "5207 AZIN1\n",
+      "5208 FBXO32\n",
+      "5209 ZFTRAF1\n",
+      "5210 TLN1\n",
+      "5211 CARNMT1\n",
+      "5212 FCN1\n",
+      "5213 CLIC3\n",
+      "5214 PFKP\n",
+      "5215 PHYH\n",
+      "5216 ANKRD26\n",
+      "5217 MPP7\n",
+      "5218 ASCC1\n",
+      "5219 PDCD4\n",
+      "5220 PLEKHA1\n",
+      "5221 UROS\n",
+      "5222 ZNF195\n",
+      "5223 FAR1\n",
+      "5224 COPB1\n",
+      "5225 TRMT112\n",
+      "5226 CDCA5\n",
+      "5227 BIRC3\n",
+      "5228 NKAPD1\n",
+      "5229 GAPDH\n",
+      "5230 MLF2\n",
+      "5231 RNF41\n",
+      "5232 GIHCG\n",
+      "5233 CHPT1\n",
+      "5234 RNASEH2B\n",
+      "5235 ZBTB1\n",
+      "5236 BATF\n",
+      "5237 ERG28\n",
+      "5238 RAD51-AS1\n",
+      "5239 DUT\n",
+      "5240 LEO1\n",
+      "5241 ULK3\n",
+      "5242 SIN3A\n",
+      "5243 RPS17\n",
+      "5244 SLCO3A1\n",
+      "5245 UQCC4\n",
+      "5246 MLST8\n",
+      "5247 PGP\n",
+      "5248 KNOP1\n",
+      "5249 HERPUD1\n",
+      "5250 POLR2C\n",
+      "5251 DDX19A\n",
+      "5252 EMC6\n",
+      "5253 STX8\n",
+      "5254 ATPAF2\n",
+      "5255 AKAP10\n",
+      "5256 CWC25\n",
+      "5257 FMNL1\n",
+      "5258 ZNF652\n",
+      "5259 MTMR4\n",
+      "5260 NPLOC4\n",
+      "5261 ARHGDIA\n",
+      "5262 MYL12B\n",
+      "5263 SLC39A3\n",
+      "5264 PET100\n",
+      "5265 RAB11B\n",
+      "5266 KEAP1\n",
+      "5267 MRPS12\n",
+      "5268 EXOSC5\n",
+      "5269 PAFAH1B3\n",
+      "5270 RPS9\n",
+      "5271 A1BG\n",
+      "5272 NXT1\n",
+      "5273 AAR2\n",
+      "5274 NFATC2\n",
+      "5275 RAE1\n",
+      "5276 RAB22A\n",
+      "5277 GNAS\n",
+      "5278 SNRPD3\n",
+      "5279 PES1\n",
+      "5280 WDR45\n",
+      "5281 PGRMC1\n",
+      "5282 BRCC3\n",
+      "5283 STX12\n",
+      "5284 PTP4A2\n",
+      "5285 SLC2A1\n",
+      "5286 OMA1\n",
+      "5287 TMED5\n",
+      "5288 CHI3L2\n",
+      "5289 RPRD2\n",
+      "5290 PIGC\n",
+      "5291 LBR\n",
+      "5292 TMEM63A\n",
+      "5293 ARID4B\n",
+      "5294 POMC\n",
+      "5295 MSH6\n",
+      "5296 ANKRD36\n",
+      "5297 UXS1\n",
+      "5298 MMADHC\n",
+      "5299 METTL8\n",
+      "5300 RPL37A\n",
+      "5301 ARL8B\n",
+      "5302 RASSF1\n",
+      "5303 DCP1A\n",
+      "5304 ARL6IP5\n",
+      "5305 NAA50\n",
+      "5306 IQCB1\n",
+      "5307 KPNA1\n",
+      "5308 EIF2A\n",
+      "5309 SKIL\n",
+      "5310 ABCF3\n",
+      "5311 TMEM156\n",
+      "5312 PF4\n",
+      "5313 RPL34\n",
+      "5314 RPS3A\n",
+      "5315 ZCCHC9\n",
+      "5316 SKIC3\n",
+      "5317 HSPA4\n",
+      "5318 ITK\n",
+      "5319 TPMT\n",
+      "5320 HLA-DQA2\n",
+      "5321 UQCC2\n",
+      "5322 FKBP5\n",
+      "5323 PPIL1\n",
+      "5324 YIPF3\n",
+      "5325 PSMB1\n",
+      "5326 COX19\n",
+      "5327 PMS2\n",
+      "5328 INTS15\n",
+      "5329 NUDCD3\n",
+      "5330 H2AZ2\n",
+      "5331 SEC61G\n",
+      "5332 EIF4H\n",
+      "5333 FAM133B\n",
+      "5334 BRI3\n",
+      "5335 POLR2J3\n",
+      "5336 LSM8\n",
+      "5337 MRPS28\n",
+      "5338 GRINA\n",
+      "5339 RAD23B\n",
+      "5340 POLE3\n",
+      "5341 FNBP1\n",
+      "5342 SSNA1\n",
+      "5343 HNRNPH3\n",
+      "5344 RPS24\n",
+      "5345 BBIP1\n",
+      "5346 WDR11\n",
+      "5347 MARK2\n",
+      "5348 RASGRP2\n",
+      "5349 PPP6R3\n",
+      "5350 POLD3\n",
+      "5351 FDX1\n",
+      "5352 SLC25A3\n",
+      "5353 PEBP1\n",
+      "5354 POMP\n",
+      "5355 RGCC\n",
+      "5356 TDRD3\n",
+      "5357 RBM26\n",
+      "5358 SCFD1\n",
+      "5359 RTRAF\n",
+      "5360 ATG14\n",
+      "5361 ARID4A\n",
+      "5362 DLST\n",
+      "5363 TMED10\n",
+      "5364 TDP1\n",
+      "5365 GLRX5\n",
+      "5366 CCNK\n",
+      "5367 IGHM\n",
+      "5368 IQGAP1\n",
+      "5369 UBE2I\n",
+      "5370 CORO7\n",
+      "5371 ORC6\n",
+      "5372 CYLD\n",
+      "5373 CIAO2B\n",
+      "5374 PSMD7\n",
+      "5375 COTL1\n",
+      "5376 TMEM11\n",
+      "5377 LGALS9\n",
+      "5378 TP53I13\n",
+      "5379 WIPF2\n",
+      "5380 COIL\n",
+      "5381 CD79B\n",
+      "5382 PRKCA\n",
+      "5383 NUP85\n",
+      "5384 GALK1\n",
+      "5385 TEN1\n",
+      "5386 ALYREF\n",
+      "5387 CENPX\n",
+      "5388 RPRD1A\n",
+      "5389 FEM1A\n",
+      "5390 MRPL4\n",
+      "5391 MRPL34\n",
+      "5392 SPINT2\n",
+      "5393 ZNF428\n",
+      "5394 RPL18\n",
+      "5395 PIH1D1\n",
+      "5396 C19orf48P\n",
+      "5397 ZNF544\n",
+      "5398 XRN2\n",
+      "5399 ADA\n",
+      "5400 CSE1L\n",
+      "5401 ENSG00000099991\n",
+      "5402 PIK3IP1\n",
+      "5403 RAC2\n",
+      "5404 MAFF\n",
+      "5405 GTPBP1\n",
+      "5406 ACO2\n",
+      "5407 FTX\n",
+      "5408 FAM50A\n",
+      "5409 CEP104\n",
+      "5410 EXOSC10\n",
+      "5411 STXBP3\n",
+      "5412 SARS1\n",
+      "5413 LY9\n",
+      "5414 SMYD2\n",
+      "5415 ADAM17\n",
+      "5416 EIF2B4\n",
+      "5417 CEBPZOS\n",
+      "5418 MSH2\n",
+      "5419 VAMP8\n",
+      "5420 IGKC\n",
+      "5421 REV1\n",
+      "5422 ITGA6\n",
+      "5423 EDEM1\n",
+      "5424 THUMPD3-AS1\n",
+      "5425 ZDHHC3\n",
+      "5426 SACM1L\n",
+      "5427 SLC25A26\n",
+      "5428 COPB2\n",
+      "5429 XRN1\n",
+      "5430 RSRC1\n",
+      "5431 MAP3K13\n",
+      "5432 UBXN7\n",
+      "5433 FBXL5\n",
+      "5434 DHX15\n",
+      "5435 COMMD8\n",
+      "5436 IGFBP7\n",
+      "5437 PLAC8\n",
+      "5438 CCT5\n",
+      "5439 TAF9\n",
+      "5440 CAST\n",
+      "5441 DCP2\n",
+      "5442 CLK4\n",
+      "5443 HEIH\n",
+      "5444 MCUR1\n",
+      "5445 GNL1\n",
+      "5446 ATF6B\n",
+      "5447 HMGA1\n",
+      "5448 MRPL2\n",
+      "5449 HSP90AB1\n",
+      "5450 CASP8AP2\n",
+      "5451 STX7\n",
+      "5452 WAKMAR2\n",
+      "5453 UTRN\n",
+      "5454 FGL2\n",
+      "5455 SAMD9L\n",
+      "5456 TSC22D4\n",
+      "5457 SYPL1\n",
+      "5458 MDFIC\n",
+      "5459 EXOC4\n",
+      "5460 ESYT2\n",
+      "5461 RBIS\n",
+      "5462 DCAF13\n",
+      "5463 CHRAC1\n",
+      "5464 NUDT2\n",
+      "5465 CDC26\n",
+      "5466 RAPGEF1\n",
+      "5467 SURF4\n",
+      "5468 DDIT4\n",
+      "5469 SFXN3\n",
+      "5470 BET1L\n",
+      "5471 PGGHG\n",
+      "5472 TMEM41B\n",
+      "5473 SVIP\n",
+      "5474 HIPK3\n",
+      "5475 HSD17B12\n",
+      "5476 DDB2\n",
+      "5477 CD6\n",
+      "5478 FIBP\n",
+      "5479 MRPL21\n",
+      "5480 PAK1\n",
+      "5481 CEP164\n",
+      "5482 KLRB1\n",
+      "5483 ITPR2\n",
+      "5484 SMARCD1\n",
+      "5485 TMBIM4\n",
+      "5486 RNF34\n",
+      "5487 PHF11\n",
+      "5488 EBPL\n",
+      "5489 CARS2\n",
+      "5490 PNP\n",
+      "5491 MIA2\n",
+      "5492 PSMA3\n",
+      "5493 HIF1A\n",
+      "5494 PPP2R5E\n",
+      "5495 SERPINA1\n",
+      "5496 GABPB1-IT1\n",
+      "5497 SLTM\n",
+      "5498 RPS27L\n",
+      "5499 ARIH1\n",
+      "5500 RCN2\n",
+      "5501 MRPL46\n",
+      "5502 THOC6\n",
+      "5503 DCTN5\n",
+      "5504 GABARAPL2\n",
+      "5505 GSE1\n",
+      "5506 PSMB6\n",
+      "5507 DVL2\n",
+      "5508 NACA2\n",
+      "5509 CBX4\n",
+      "5510 SIRT7\n",
+      "5511 ACAA2\n",
+      "5512 BCL2\n",
+      "5513 ILF3-DT\n",
+      "5514 SLC1A5\n",
+      "5515 JOSD2\n",
+      "5516 EPN1\n",
+      "5517 ZNF264\n",
+      "5518 MRPS26\n",
+      "5519 APMAP\n",
+      "5520 FAM217B\n",
+      "5521 SH3BP1\n",
+      "5522 MSL3\n",
+      "5523 MT-ND2\n",
+      "5524 ISG15\n",
+      "5525 PADI4\n",
+      "5526 CAPZB\n",
+      "5527 MACO1\n",
+      "5528 SNHG3\n",
+      "5529 TXLNA\n",
+      "5530 KIAA0319L\n",
+      "5531 C1orf122\n",
+      "5532 MYCBP\n",
+      "5533 AKR1A1\n",
+      "5534 UBE2Q1\n",
+      "5535 ASH1L\n",
+      "5536 LAMTOR2\n",
+      "5537 RNF2\n",
+      "5538 DSTYK\n",
+      "5539 C1orf131\n",
+      "5540 ODC1\n",
+      "5541 ATRAID\n",
+      "5542 UGP2\n",
+      "5543 MOGS\n",
+      "5544 TRABD2A\n",
+      "5545 CIR1\n",
+      "5546 CASP8\n",
+      "5547 STK16\n",
+      "5548 CRBN\n",
+      "5549 BHLHE40\n",
+      "5550 RPUSD3\n",
+      "5551 DYNC1LI1\n",
+      "5552 ARIH2\n",
+      "5553 RAD54L2\n",
+      "5554 SLMAP\n",
+      "5555 TTC14\n",
+      "5556 RUBCN\n",
+      "5557 HTT\n",
+      "5558 LIAS\n",
+      "5559 TNIP3\n",
+      "5560 MSMO1\n",
+      "5561 RIOK2\n",
+      "5562 CDC42SE2\n",
+      "5563 GNPDA1\n",
+      "5564 TIMD4\n",
+      "5565 ATP6V0E1\n",
+      "5566 SFXN1\n",
+      "5567 RIOK1\n",
+      "5568 RANBP9\n",
+      "5569 BTN3A1\n",
+      "5570 IER3\n",
+      "5571 LST1\n",
+      "5572 ILRUN-AS1\n",
+      "5573 SYTL3\n",
+      "5574 MPC1\n",
+      "5575 RPA3\n",
+      "5576 GARS1\n",
+      "5577 UPP1\n",
+      "5578 PTPN12\n",
+      "5579 LINC00513\n",
+      "5580 BPGM\n",
+      "5581 REEP4\n",
+      "5582 GPAT4\n",
+      "5583 SMIM19\n",
+      "5584 TOX\n",
+      "5585 DECR1\n",
+      "5586 SLC25A32\n",
+      "5587 EMC2\n",
+      "5588 ATAD2\n",
+      "5589 AQP3\n",
+      "5590 DCTN3\n",
+      "5591 FXN\n",
+      "5592 ST6GALNAC6\n",
+      "5593 BMI1\n",
+      "5594 PIP4K2A\n",
+      "5595 YME1L1\n",
+      "5596 MAP3K8\n",
+      "5597 CSTF2T\n",
+      "5598 CUTC\n",
+      "5599 PNPLA2\n",
+      "5600 TMX2\n",
+      "5601 TMEM216\n",
+      "5602 FTH1\n",
+      "5603 EHD1\n",
+      "5604 RPS6KB2\n",
+      "5605 ATG16L2\n",
+      "5606 PRCP\n",
+      "5607 RAB30-DT\n",
+      "5608 CD3D\n",
+      "5609 TNFRSF1A\n",
+      "5610 ENSG00000165714\n",
+      "5611 ETFRF1\n",
+      "5612 GXYLT1\n",
+      "5613 RPAP3\n",
+      "5614 FKBP11\n",
+      "5615 TMEM19\n",
+      "5616 SOCS2\n",
+      "5617 PSMD9\n",
+      "5618 UFM1\n",
+      "5619 UBAC2\n",
+      "5620 PIP4P1\n",
+      "5621 PSME1\n",
+      "5622 KTN1\n",
+      "5623 ABCD4\n",
+      "5624 PSMC1\n",
+      "5625 LPCAT4\n",
+      "5626 NDUFAF1\n",
+      "5627 USP8\n",
+      "5628 BNIP2\n",
+      "5629 ICE2\n",
+      "5630 PML\n",
+      "5631 SNHG19\n",
+      "5632 NAA60\n",
+      "5633 KAT8\n",
+      "5634 THAP11\n",
+      "5635 AP1G1\n",
+      "5636 TNFSF12\n",
+      "5637 EIF4A1\n",
+      "5638 MPDU1\n",
+      "5639 MSL1\n",
+      "5640 SLC25A39\n",
+      "5641 TOB1\n",
+      "5642 SCPEP1\n",
+      "5643 LIMD2\n",
+      "5644 MIF4GD\n",
+      "5645 TMC8\n",
+      "5646 ZNF397\n",
+      "5647 DYM\n",
+      "5648 KDSR\n",
+      "5649 TRIR\n",
+      "5650 TRMT1\n",
+      "5651 NDUFB7\n",
+      "5652 MAST3\n",
+      "5653 SSBP4\n",
+      "5654 FKBP8\n",
+      "5655 TFPT\n",
+      "5656 ZNF580\n",
+      "5657 AHCY\n",
+      "5658 DYNLRB1\n",
+      "5659 TRABD\n",
+      "5660 GTPBP6\n",
+      "5661 WDR13\n",
+      "5662 TIMM17B\n",
+      "5663 RPL10\n",
+      "5664 TAFAZZIN\n",
+      "5665 ZNF683\n",
+      "5666 TUT4\n",
+      "5667 ZNHIT6\n",
+      "5668 MFSD14A\n",
+      "5669 GNAI3\n",
+      "5670 RNF115\n",
+      "5671 NAXE\n",
+      "5672 FCRL6\n",
+      "5673 SDCCAG8\n",
+      "5674 LINC01871\n",
+      "5675 KIDINS220\n",
+      "5676 CALM2\n",
+      "5677 ZNF638\n",
+      "5678 GCFC2\n",
+      "5679 GNLY\n",
+      "5680 MAL\n",
+      "5681 ANKRD36C\n",
+      "5682 MGAT4A\n",
+      "5683 ARL5A\n",
+      "5684 WIPF1\n",
+      "5685 AGPS\n",
+      "5686 BARD1\n",
+      "5687 PSMD1\n",
+      "5688 PDCD6IP\n",
+      "5689 TRANK1\n",
+      "5690 CNBP\n",
+      "5691 TOPBP1\n",
+      "5692 MRPS22\n",
+      "5693 RFC4\n",
+      "5694 LPP\n",
+      "5695 ATP8A1\n",
+      "5696 PAICS\n",
+      "5697 RASGEF1B\n",
+      "5698 RWDD4\n",
+      "5699 DAP\n",
+      "5700 DHX29\n",
+      "5701 CCNH\n",
+      "5702 RAPGEF6\n",
+      "5703 THG1L\n",
+      "5704 MRNIP\n",
+      "5705 GMDS\n",
+      "5706 SERPINB1\n",
+      "5707 TNF\n",
+      "5708 CSNK2B\n",
+      "5709 PBX2\n",
+      "5710 DEF6\n",
+      "5711 TAF8\n",
+      "5712 MEA1\n",
+      "5713 SYNCRIP\n",
+      "5714 SLC35A1\n",
+      "5715 RAC1\n",
+      "5716 TRGV5\n",
+      "5717 PPP1R35\n",
+      "5718 CUX1\n",
+      "5719 ENSG00000271204\n",
+      "5720 CUL1\n",
+      "5721 GIMAP1\n",
+      "5722 CDK5\n",
+      "5723 CLN8-AS1\n",
+      "5724 DCTN6\n",
+      "5725 TAF2\n",
+      "5726 C8orf76\n",
+      "5727 HAUS6\n",
+      "5728 CREB3\n",
+      "5729 CARD19\n",
+      "5730 RALGDS\n",
+      "5731 RABL6\n",
+      "5732 EDF1\n",
+      "5733 BLOC1S2\n",
+      "5734 NFKB2\n",
+      "5735 TMEM80\n",
+      "5736 AP2A2\n",
+      "5737 DDB1\n",
+      "5738 CCDC88B\n",
+      "5739 SART1\n",
+      "5740 ANKRD13D\n",
+      "5741 RSF1\n",
+      "5742 ALG8\n",
+      "5743 IL10RA\n",
+      "5744 MED21\n",
+      "5745 ARF3\n",
+      "5746 COPZ1\n",
+      "5747 CCT2\n",
+      "5748 ARPC3\n",
+      "5749 ALDH2\n",
+      "5750 MAPKAPK5-AS1\n",
+      "5751 PTPN11\n",
+      "5752 MICU2\n",
+      "5753 ERCC5\n",
+      "5754 TINF2\n",
+      "5755 MIDEAS\n",
+      "5756 FCF1\n",
+      "5757 OIP5-AS1\n",
+      "5758 GOLM2\n",
+      "5759 ZFAND6\n",
+      "5760 ABHD2\n",
+      "5761 NUDT16L1\n",
+      "5762 PRKCB\n",
+      "5763 ATP6V0D1\n",
+      "5764 CPD\n",
+      "5765 ABI3\n",
+      "5766 NME2\n",
+      "5767 RAB37\n",
+      "5768 H3-3B\n",
+      "5769 ENDOV\n",
+      "5770 NDUFAF8\n",
+      "5771 P4HB\n",
+      "5772 SLC39A6\n",
+      "5773 POLR2E\n",
+      "5774 ENSG00000261526\n",
+      "5775 FBXL12\n",
+      "5776 TMED1\n",
+      "5777 OCEL1\n",
+      "5778 LTBP4\n",
+      "5779 CYTH2\n",
+      "5780 ZNF350\n",
+      "5781 MGME1\n",
+      "5782 CFAP298\n",
+      "5783 ENSG00000159131\n",
+      "5784 SFI1\n",
+      "5785 GGA1\n",
+      "5786 RPS6KA3\n",
+      "5787 MSN\n",
+      "5788 TNFRSF9\n",
+      "5789 SLC25A33\n",
+      "5790 LZIC\n",
+      "5791 PEX14\n",
+      "5792 USP48\n",
+      "5793 HDAC1\n",
+      "5794 YRDC\n",
+      "5795 RAP1A\n",
+      "5796 BOLA1\n",
+      "5797 RABGAP1L\n",
+      "5798 C1orf21\n",
+      "5799 ASPM\n",
+      "5800 ELK4\n",
+      "5801 CENPF\n",
+      "5802 H3-3A\n",
+      "5803 ARF1\n",
+      "5804 PPP3R1\n",
+      "5805 DCTN1\n",
+      "5806 SNRNP200\n",
+      "5807 ANAPC1\n",
+      "5808 DBI\n",
+      "5809 MARCHF7\n",
+      "5810 EEF1B2\n",
+      "5811 PTMA\n",
+      "5812 LRRFIP1\n",
+      "5813 ASB1\n",
+      "5814 RNPEPL1\n",
+      "5815 SGO1\n",
+      "5816 HIGD1A\n",
+      "5817 PDHB\n",
+      "5818 SSR3\n",
+      "5819 NDUFB5\n",
+      "5820 PSMD2\n",
+      "5821 OPA1\n",
+      "5822 CD38\n",
+      "5823 SEL1L3\n",
+      "5824 RBPJ\n",
+      "5825 GRSF1\n",
+      "5826 CNOT6L\n",
+      "5827 NDUFC1\n",
+      "5828 SCOC\n",
+      "5829 LSM6\n",
+      "5830 TMEM154\n",
+      "5831 SSBP2\n",
+      "5832 CETN3\n",
+      "5833 RAD50\n",
+      "5834 DIAPH1\n",
+      "5835 ADRB2\n",
+      "5836 HNRNPAB\n",
+      "5837 ECI2\n",
+      "5838 C6orf47\n",
+      "5839 GPSM3\n",
+      "5840 HLA-DMB\n",
+      "5841 PREP\n",
+      "5842 SEPTIN7\n",
+      "5843 MRPS24\n",
+      "5844 ENSG00000228434\n",
+      "5845 OGDH\n",
+      "5846 MTMR9\n",
+      "5847 KAT6A\n",
+      "5848 LYN\n",
+      "5849 ARFGEF1\n",
+      "5850 MED30\n",
+      "5851 TTC39B\n",
+      "5852 ANXA1\n",
+      "5853 UGCG\n",
+      "5854 GAPVD1\n",
+      "5855 EEIG1\n",
+      "5856 LPXN\n",
+      "5857 MRPL16\n",
+      "5858 TTC9C\n",
+      "5859 PAAF1\n",
+      "5860 TMEM126A\n",
+      "5861 ETS1\n",
+      "5862 LPCAT3\n",
+      "5863 ENSG00000284634\n",
+      "5864 LINC02446\n",
+      "5865 HEBP1\n",
+      "5866 FGFR1OP2\n",
+      "5867 TXNRD1\n",
+      "5868 C12orf43\n",
+      "5869 EP400\n",
+      "5870 VPS36\n",
+      "5871 PCID2\n",
+      "5872 DYNC1H1\n",
+      "5873 SNAP23\n",
+      "5874 UBL7\n",
+      "5875 IMP3\n",
+      "5876 TRAP1\n",
+      "5877 ZC3H7A\n",
+      "5878 TUFM\n",
+      "5879 LAT\n",
+      "5880 KIF22\n",
+      "5881 COQ9\n",
+      "5882 RPA1\n",
+      "5883 BORCS6\n",
+      "5884 EVI2B\n",
+      "5885 RAB11FIP4\n",
+      "5886 BRCA1\n",
+      "5887 UBTF\n",
+      "5888 CALCOCO2\n",
+      "5889 PECAM1\n",
+      "5890 RPL38\n",
+      "5891 ACP5\n",
+      "5892 RAD23A\n",
+      "5893 PLEKHF1\n",
+      "5894 USF2\n",
+      "5895 PLD3\n",
+      "5896 PNKP\n",
+      "5897 DNTTIP1\n",
+      "5898 STX16\n",
+      "5899 SLX9\n",
+      "5900 BID\n",
+      "5901 RGL4\n",
+      "5902 NF2\n",
+      "5903 MIEF1\n",
+      "5904 ASMTL\n",
+      "5905 SAT1\n",
+      "5906 PDK3\n",
+      "5907 OTUD5\n",
+      "5908 LAMP2\n",
+      "5909 IRAK1\n",
+      "5910 PGD\n",
+      "5911 IFI6\n",
+      "5912 NASP\n",
+      "5913 H2AC20\n",
+      "5914 SLC39A1\n",
+      "5915 PMF1\n",
+      "5916 ENSG00000160818\n",
+      "5917 PEX19\n",
+      "5918 HSD17B7\n",
+      "5919 SUCO\n",
+      "5920 RC3H1\n",
+      "5921 TPR\n",
+      "5922 NTPCR\n",
+      "5923 RBM34\n",
+      "5924 ACP1\n",
+      "5925 C2orf74\n",
+      "5926 SNRPG\n",
+      "5927 SUCLG1\n",
+      "5928 RANBP2\n",
+      "5929 SF3B1\n",
+      "5930 CASP10\n",
+      "5931 SH3BP5\n",
+      "5932 MIX23\n",
+      "5933 ATR\n",
+      "5934 U2SURP\n",
+      "5935 MBNL1\n",
+      "5936 DHX36\n",
+      "5937 TRA2B\n",
+      "5938 SRP72\n",
+      "5939 SPINK2\n",
+      "5940 DNAJB14\n",
+      "5941 INTS12\n",
+      "5942 USP38\n",
+      "5943 ING2\n",
+      "5944 SUB1\n",
+      "5945 TARS1\n",
+      "5946 SKP2\n",
+      "5947 ANXA2R\n",
+      "5948 DIMT1\n",
+      "5949 HIGD2A\n",
+      "5950 TSPAN17\n",
+      "5951 PRR7\n",
+      "5952 TRIM52-AS1\n",
+      "5953 RNF5\n",
+      "5954 LYRM2\n",
+      "5955 POLR1F\n",
+      "5956 NIPSNAP2\n",
+      "5957 CHCHD2\n",
+      "5958 LAT2\n",
+      "5959 SRI\n",
+      "5960 ENSG00000287547\n",
+      "5961 FBXO25\n",
+      "5962 NCOA2\n",
+      "5963 CYC1\n",
+      "5964 RCL1\n",
+      "5965 KDM4C\n",
+      "5966 KLF9\n",
+      "5967 SEC61B\n",
+      "5968 STX17\n",
+      "5969 PHPT1\n",
+      "5970 ZEB1\n",
+      "5971 JMJD1C\n",
+      "5972 TIMM10B\n",
+      "5973 NUCB2\n",
+      "5974 CCDC34\n",
+      "5975 MTA2\n",
+      "5976 UBXN1\n",
+      "5977 PACS1\n",
+      "5978 XRRA1\n",
+      "5979 CFAP68\n",
+      "5980 CD27\n",
+      "5981 EMG1\n",
+      "5982 KLRF1\n",
+      "5983 ETNK1\n",
+      "5984 PCED1B-AS1\n",
+      "5985 TMEM106C\n",
+      "5986 HNRNPA1\n",
+      "5987 DGKA\n",
+      "5988 NABP2\n",
+      "5989 CDK4\n",
+      "5990 ELK3\n",
+      "5991 TPT1\n",
+      "5992 HNRNPA1L2\n",
+      "5993 STK24\n",
+      "5994 SUPT16H\n",
+      "5995 NGDN\n",
+      "5996 IRF9\n",
+      "5997 RABGGTA\n",
+      "5998 NEMF\n",
+      "5999 GCH1\n",
+      "6000 YY1\n",
+      "6001 IGHG4\n",
+      "6002 KLF13\n",
+      "6003 COPS2\n",
+      "6004 DIS3L\n",
+      "6005 CSK\n",
+      "6006 LRRC28\n",
+      "6007 NLRC3\n",
+      "6008 UBN1\n",
+      "6009 NFATC2IP\n",
+      "6010 SPNS1\n",
+      "6011 BOLA2B\n",
+      "6012 ZNF688\n",
+      "6013 PHKB\n",
+      "6014 NFAT5\n",
+      "6015 KLHL36\n",
+      "6016 USP10\n",
+      "6017 KLHDC4\n",
+      "6018 SPATA33\n",
+      "6019 MED11\n",
+      "6020 ZNF232-AS1\n",
+      "6021 CTDNEP1\n",
+      "6022 TRPV2\n",
+      "6023 PIGS\n",
+      "6024 FBXL20\n",
+      "6025 NBR1\n",
+      "6026 LRRC59\n",
+      "6027 MRPS23\n",
+      "6028 SRSF2\n",
+      "6029 MOB3A\n",
+      "6030 USE1\n",
+      "6031 ARRDC2\n",
+      "6032 CLEC11A\n",
+      "6033 PRNP\n",
+      "6034 CDK5RAP1\n",
+      "6035 UBE2G2\n",
+      "6036 BCL2L13\n",
+      "6037 PITPNB\n",
+      "6038 SELENOM\n",
+      "6039 LGALS1\n",
+      "6040 TBC1D22A\n",
+      "6041 ACOT9\n",
+      "6042 TSPYL2\n",
+      "6043 EIF1AY\n",
+      "6044 UBE2J2\n",
+      "6045 NOL9\n",
+      "6046 ARID1A\n",
+      "6047 THRAP3\n",
+      "6048 RRAGC\n",
+      "6049 PDE4B\n",
+      "6050 GBP4\n",
+      "6051 SLC35A3\n",
+      "6052 MCL1\n",
+      "6053 YY1AP1\n",
+      "6054 PUM2\n",
+      "6055 CLIP4\n",
+      "6056 REL\n",
+      "6057 RPIA\n",
+      "6058 STARD7\n",
+      "6059 ACTR3\n",
+      "6060 INSIG2\n",
+      "6061 ERCC3\n",
+      "6062 CWC22\n",
+      "6063 STAT4\n",
+      "6064 SP110\n",
+      "6065 NCL\n",
+      "6066 NDUFA10\n",
+      "6067 SNRK\n",
+      "6068 RBM5\n",
+      "6069 PPM1M\n",
+      "6070 RAB7A\n",
+      "6071 ZBTB38\n",
+      "6072 SIAH2\n",
+      "6073 PARL\n",
+      "6074 RNF168\n",
+      "6075 CTBP1\n",
+      "6076 MXD4\n",
+      "6077 SH3BP2\n",
+      "6078 STX18\n",
+      "6079 GRPEL1\n",
+      "6080 RCHY1\n",
+      "6081 USO1\n",
+      "6082 COPS4\n",
+      "6083 JADE1\n",
+      "6084 HMGB2\n",
+      "6085 PIK3R1\n",
+      "6086 SLC30A5\n",
+      "6087 TBCA\n",
+      "6088 TENT2\n",
+      "6089 ATG10\n",
+      "6090 FAM172A\n",
+      "6091 ANKHD1\n",
+      "6092 CSNK1A1\n",
+      "6093 SCGB3A1\n",
+      "6094 ATXN1\n",
+      "6095 C6orf136\n",
+      "6096 HSPA1A\n",
+      "6097 BRD2\n",
+      "6098 HSD17B8\n",
+      "6099 SAMD3\n",
+      "6100 ACTB\n",
+      "6101 TRGV9\n",
+      "6102 ENSG00000272831\n",
+      "6103 HSPB1\n",
+      "6104 RBM48\n",
+      "6105 ZNF655\n",
+      "6106 TES\n",
+      "6107 PDIA4\n",
+      "6108 ATP6V0E2\n",
+      "6109 ENSG00000254093\n",
+      "6110 EIF4EBP1\n",
+      "6111 TM2D2\n",
+      "6112 COMMD5\n",
+      "6113 TMEM245\n",
+      "6114 RPL35\n",
+      "6115 KLF6\n",
+      "6116 TIMM23\n",
+      "6117 ZWINT\n",
+      "6118 EIF3A\n",
+      "6119 POLR2L\n",
+      "6120 STIM1\n",
+      "6121 APBB1\n",
+      "6122 TMEM9B\n",
+      "6123 C11orf58\n",
+      "6124 METTL15\n",
+      "6125 CPSF7\n",
+      "6126 FADD\n",
+      "6127 H2AJ\n",
+      "6128 ASB8\n",
+      "6129 RPL41\n",
+      "6130 MYL6B\n",
+      "6131 TSFM\n",
+      "6132 WASHC3\n",
+      "6133 COG3\n",
+      "6134 ZC3H13\n",
+      "6135 SDR39U1\n",
+      "6136 CTDSPL2\n",
+      "6137 CCNB2\n",
+      "6138 TARS3\n",
+      "6139 FAHD1\n",
+      "6140 TSC2\n",
+      "6141 ZNF75A\n",
+      "6142 YPEL3\n",
+      "6143 MT1F\n",
+      "6144 UTP4\n",
+      "6145 MVD\n",
+      "6146 PAFAH1B1\n",
+      "6147 RNF167\n",
+      "6148 ACADVL\n",
+      "6149 GGNBP2\n",
+      "6150 LASP1\n",
+      "6151 DHX8\n",
+      "6152 NPEPPS\n",
+      "6153 RSAD1\n",
+      "6154 DCAF7\n",
+      "6155 USP36\n",
+      "6156 MYL12A\n",
+      "6157 FAM174C\n",
+      "6158 DAZAP1\n",
+      "6159 YJU2B\n",
+      "6160 CHERP\n",
+      "6161 C19orf12\n",
+      "6162 RBM42\n",
+      "6163 BCAT2\n",
+      "6164 PRPF31\n",
+      "6165 TMEM230\n",
+      "6166 CHD6\n",
+      "6167 SRSF6\n",
+      "6168 CEBPB\n",
+      "6169 CTSZ\n",
+      "6170 PRELID3B\n",
+      "6171 SLC2A4RG\n",
+      "6172 SAMSN1\n",
+      "6173 ENSG00000159086\n",
+      "6174 PI4KA\n",
+      "6175 MAPK1\n",
+      "6176 TNRC6B\n",
+      "6177 DESI1\n",
+      "6178 MEI1\n",
+      "6179 ATXN10\n",
+      "6180 MID1IP1\n",
+      "6181 UBE2A\n",
+      "6182 G6PD\n",
+      "6183 DDX3Y\n",
+      "6184 CDK11B\n",
+      "6185 GNB1\n",
+      "6186 ACOT7\n",
+      "6187 PARK7\n",
+      "6188 DHRS3\n",
+      "6189 MRPS15\n",
+      "6190 TM2D1\n",
+      "6191 TYW3\n",
+      "6192 ENSA\n",
+      "6193 ARV1\n",
+      "6194 ITSN2\n",
+      "6195 ACYP2\n",
+      "6196 USP34\n",
+      "6197 PELI1\n",
+      "6198 ACTR2\n",
+      "6199 MPHOSPH10\n",
+      "6200 MRPS5\n",
+      "6201 PTPN4\n",
+      "6202 BIN1\n",
+      "6203 CCDC115\n",
+      "6204 PSMD14\n",
+      "6205 GMPPA\n",
+      "6206 FARSB\n",
+      "6207 COPS8\n",
+      "6208 TMEM43\n",
+      "6209 NME6\n",
+      "6210 P4HTM\n",
+      "6211 RHOA\n",
+      "6212 CISH\n",
+      "6213 TEX264\n",
+      "6214 GLT8D1\n",
+      "6215 HMCES\n",
+      "6216 TMEM41A\n",
+      "6217 FYTTD1\n",
+      "6218 ADD1\n",
+      "6219 BLOC1S4\n",
+      "6220 CAMK2D\n",
+      "6221 FBXW7\n",
+      "6222 PLRG1\n",
+      "6223 HPGD\n",
+      "6224 NNT-AS1\n",
+      "6225 DHFR\n",
+      "6226 HSD17B4\n",
+      "6227 HSPA9\n",
+      "6228 RBM22\n",
+      "6229 TNIP1\n",
+      "6230 PPP1R10\n",
+      "6231 MICB\n",
+      "6232 BAG6\n",
+      "6233 RPS10\n",
+      "6234 CCDC167\n",
+      "6235 CD2AP\n",
+      "6236 SMIM8\n",
+      "6237 AKAP7\n",
+      "6238 SYNE1\n",
+      "6239 NDUFA4\n",
+      "6240 DBNL\n",
+      "6241 PPIA\n",
+      "6242 SNHG15\n",
+      "6243 RFC2\n",
+      "6244 DLX5\n",
+      "6245 IMPDH1\n",
+      "6246 DUSP4\n",
+      "6247 TPD52\n",
+      "6248 COX6C\n",
+      "6249 EBAG9\n",
+      "6250 DENND4C\n",
+      "6251 RPS6\n",
+      "6252 CDKN2A\n",
+      "6253 GRHPR\n",
+      "6254 CEMIP2\n",
+      "6255 OSTF1\n",
+      "6256 UBQLN1\n",
+      "6257 MFSD14B\n",
+      "6258 RBM18\n",
+      "6259 PPP6C\n",
+      "6260 SPOUT1\n",
+      "6261 LARP4B\n",
+      "6262 RPP38\n",
+      "6263 RAB18\n",
+      "6264 MRPS16\n",
+      "6265 TM9SF3\n",
+      "6266 CALHM2\n",
+      "6267 XPNPEP1\n",
+      "6268 CASP7\n",
+      "6269 HRAS\n",
+      "6270 IPO7\n",
+      "6271 MED19\n",
+      "6272 BANF1\n",
+      "6273 NDUFS8\n",
+      "6274 RBM7\n",
+      "6275 STT3A\n",
+      "6276 SMIM10L1\n",
+      "6277 RXYLT1\n",
+      "6278 ERP29\n",
+      "6279 PUS1\n",
+      "6280 PXMP2\n",
+      "6281 LAMP1\n",
+      "6282 COX16\n",
+      "6283 ARHGAP11A\n",
+      "6284 SPG21\n",
+      "6285 PKM\n",
+      "6286 HAGH\n",
+      "6287 ELOB\n",
+      "6288 NAGPA\n",
+      "6289 TNRC6A\n",
+      "6290 CFAP20\n",
+      "6291 ACD\n",
+      "6292 TERF2IP\n",
+      "6293 TCF25\n",
+      "6294 PELP1\n",
+      "6295 PHF23\n",
+      "6296 MAP2K3\n",
+      "6297 SUZ12\n",
+      "6298 IKZF3\n",
+      "6299 CNP\n",
+      "6300 EZH1\n",
+      "6301 COA3\n",
+      "6302 HDAC5\n",
+      "6303 WBP2\n",
+      "6304 LINC00667\n",
+      "6305 TPGS2\n",
+      "6306 IL27RA\n",
+      "6307 UBA52\n",
+      "6308 FAM98C\n",
+      "6309 ECH1\n",
+      "6310 HNRNPUL1\n",
+      "6311 PRMT1\n",
+      "6312 ZNF524\n",
+      "6313 CRLS1\n",
+      "6314 PCIF1\n",
+      "6315 SUMO3\n",
+      "6316 SLC25A1\n",
+      "6317 SYNGR1\n",
+      "6318 LMF2\n",
+      "6319 SMC1A\n",
+      "6320 BEX2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, gene_id in enumerate(filtered_query_gene_ids):\n",
+    "    print(i, filtered_query_gene_symbols[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5bc7a18170084007bf6a9b4459dad83d",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "Text(0, 0.5, 'Response of GZMA')"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d55a3e28d3994b36a9cc825d6c798c6a",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 317
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "p = filtered_perturb_gene_symbols.index('GAPDH')\n",
+    "q = filtered_query_gene_symbols.index('GZMA')\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.plot(dose_response_dict['doses_pi'][p, :], dose_response_dict['responses_mean_pqi'][p, q, :], marker='o')\n",
+    "ax.set_xlabel(f\"Dose of {filtered_perturb_gene_symbols[p]}\")\n",
+    "ax.set_ylabel(f\"Response of {filtered_query_gene_symbols[q]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.stats import linregress\n",
+    "\n",
+    "def batch_linear_regression(x_bn, y_bn):\n",
+    "    \"\"\"\n",
+    "    Perform batch-mode one-dimensional linear regression on data with arbitrary batch dimensions.\n",
+    "    \n",
+    "    For each batch (with indices b), we assume a linear model:\n",
+    "    \n",
+    "        y_bn = slope_b * x_bn + intercept_b + error_bn\n",
+    "    \n",
+    "    where:\n",
+    "      - x_bn and y_bn are arrays of shape (..., n), with the rightmost dimension (n) \n",
+    "        representing the samples,\n",
+    "      - the leftmost dimensions (possibly several) are treated as batch dimensions.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x_bn : np.ndarray\n",
+    "        Array of covariates with shape (..., n).\n",
+    "    y_bn : np.ndarray\n",
+    "        Array of responses with shape (..., n).\n",
+    "        \n",
+    "    Returns\n",
+    "    -------\n",
+    "    slope_b : np.ndarray\n",
+    "        Array of slopes with shape equal to the batch shape.\n",
+    "        For any batch where the variance of x_bn is zero, the slope is set to 0.\n",
+    "    intercept_b : np.ndarray\n",
+    "        Array of intercepts with shape equal to the batch shape.\n",
+    "    r_squared_b : np.ndarray\n",
+    "        Array of R-squared values with shape equal to the batch shape.\n",
+    "        For any batch where the total variance of y_bn is zero, r_squared_b is defined as 1.\n",
+    "    \"\"\"\n",
+    "    # Assert that inputs are numpy arrays of the same shape.\n",
+    "    assert isinstance(x_bn, np.ndarray), \"x_bn must be a numpy array\"\n",
+    "    assert isinstance(y_bn, np.ndarray), \"y_bn must be a numpy array\"\n",
+    "    assert x_bn.shape == y_bn.shape, \"x_bn and y_bn must have the same shape\"\n",
+    "    assert x_bn.ndim >= 1, \"x_bn and y_bn must have at least one dimension (samples)\"\n",
+    "\n",
+    "    # Compute the means along the sample dimension (rightmost axis) and keep that dimension.\n",
+    "    # The resulting arrays have shape (..., 1) and are named with a '_b1' suffix.\n",
+    "    x_mean_b1 = np.mean(x_bn, axis=-1, keepdims=True)  # shape: (..., 1)\n",
+    "    y_mean_b1 = np.mean(y_bn, axis=-1, keepdims=True)  # shape: (..., 1)\n",
+    "    \n",
+    "    # Compute the covariance for each batch:\n",
+    "    #   cov_b = sum_n[(x_bn - x_mean_b1) * (y_bn - y_mean_b1)]\n",
+    "    cov_b = np.sum((x_bn - x_mean_b1) * (y_bn - y_mean_b1), axis=-1)  # shape: batch shape\n",
+    "    \n",
+    "    # Compute the variance of x for each batch:\n",
+    "    #   var_x_b = sum_n[(x_bn - x_mean_b1)^2]\n",
+    "    var_x_b = np.sum((x_bn - x_mean_b1)**2, axis=-1)  # shape: batch shape\n",
+    "    \n",
+    "    # Compute the slope for each batch, using np.divide to avoid division by zero.\n",
+    "    slope_b = np.divide(cov_b, var_x_b, out=np.zeros_like(cov_b), where=(var_x_b != 0))\n",
+    "    \n",
+    "    # Compute the intercept for each batch.\n",
+    "    intercept_b = y_mean_b1.squeeze(-1) - slope_b * x_mean_b1.squeeze(-1)\n",
+    "    \n",
+    "    # Compute the predicted responses for each batch:\n",
+    "    #   yhat_bn = slope_b * x_bn + intercept_b (broadcasting over the sample dimension)\n",
+    "    yhat_bn = slope_b[..., None] * x_bn + intercept_b[..., None]\n",
+    "    \n",
+    "    # Compute the residual sum of squares (SS_res) and total sum of squares (SS_tot) for each batch.\n",
+    "    ss_res_b = np.sum((y_bn - yhat_bn)**2, axis=-1)\n",
+    "    ss_tot_b = np.sum((y_bn - y_mean_b1)**2, axis=-1)\n",
+    "    \n",
+    "    # Compute R-squared for each batch. If ss_tot_b == 0, define R-squared as 1.\n",
+    "    r_squared_b = np.where(ss_tot_b != 0, 1 - ss_res_b / ss_tot_b, 1.0)\n",
+    "    \n",
+    "    return slope_b, intercept_b, r_squared_b\n",
+    "\n",
+    "def test_batch_linear_regression():\n",
+    "    \"\"\"\n",
+    "    Test the batch_linear_regression function by comparing its outputs with:\n",
+    "      1. Running each batch individually using scipy.stats.linregress.\n",
+    "      2. Ensuring that processing the batches together or one at a time yield the same results.\n",
+    "    \"\"\"\n",
+    "    # Set a random seed for reproducibility.\n",
+    "    np.random.seed(0)\n",
+    "    \n",
+    "    # Define a batch shape (can be multidimensional) and number of samples.\n",
+    "    batch_shape = (3, 4)  # Two batch dimensions for demonstration.\n",
+    "    num_samples = 100     # Rightmost dimension: sample index.\n",
+    "    \n",
+    "    # Generate random covariate data with shape (3, 4, 100).\n",
+    "    x_bn = np.random.rand(*batch_shape, num_samples)\n",
+    "    \n",
+    "    # Define true slopes and intercepts for each batch.\n",
+    "    true_slopes = np.linspace(0.5, 2.0, num=np.prod(batch_shape)).reshape(*batch_shape, 1)\n",
+    "    true_intercepts = np.linspace(1.0, 3.0, num=np.prod(batch_shape)).reshape(*batch_shape, 1)\n",
+    "    \n",
+    "    # Generate responses with added noise.\n",
+    "    noise_bn = np.random.randn(*batch_shape, num_samples) * 0.1\n",
+    "    y_bn = true_intercepts + true_slopes * x_bn + noise_bn\n",
+    "    \n",
+    "    # Use our batch regression implementation.\n",
+    "    slope_b, intercept_b, r_squared_b = batch_linear_regression(x_bn, y_bn)\n",
+    "    \n",
+    "    # Now, compute the regression parameters one batch at a time using scipy.stats.linregress.\n",
+    "    # We'll flatten the batch dimensions for easier iteration.\n",
+    "    x_bn_flat = x_bn.reshape(-1, num_samples)\n",
+    "    y_bn_flat = y_bn.reshape(-1, num_samples)\n",
+    "    \n",
+    "    slopes_scipy = np.empty(x_bn_flat.shape[0])\n",
+    "    intercepts_scipy = np.empty(x_bn_flat.shape[0])\n",
+    "    r_squared_scipy = np.empty(x_bn_flat.shape[0])\n",
+    "    \n",
+    "    for i in range(x_bn_flat.shape[0]):\n",
+    "        x_n = x_bn_flat[i]\n",
+    "        y_n = y_bn_flat[i]\n",
+    "        result = linregress(x_n, y_n)\n",
+    "        slopes_scipy[i] = result.slope\n",
+    "        intercepts_scipy[i] = result.intercept\n",
+    "        r_squared_scipy[i] = result.rvalue**2  # R-squared is the square of the r-value.\n",
+    "    \n",
+    "    # Reshape the individual results to the original batch shape.\n",
+    "    slopes_scipy = slopes_scipy.reshape(batch_shape)\n",
+    "    intercepts_scipy = intercepts_scipy.reshape(batch_shape)\n",
+    "    r_squared_scipy = r_squared_scipy.reshape(batch_shape)\n",
+    "    \n",
+    "    # Assert that the batch regression results match the individual regressions.\n",
+    "    assert np.allclose(slope_b, slopes_scipy, atol=1e-6), \"Slopes do not match between batch and individual regressions.\"\n",
+    "    assert np.allclose(intercept_b, intercepts_scipy, atol=1e-6), \"Intercepts do not match between batch and individual regressions.\"\n",
+    "    assert np.allclose(r_squared_b, r_squared_scipy, atol=1e-6), \"R-squared values do not match between batch and individual regressions.\"\n",
+    "    \n",
+    "    print(\"Test passed: Batch regression results match individual regressions and scipy.linregress results.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_query_vars = len(filtered_query_gene_ids)\n",
+    "n_perturb_vars = len(filtered_perturb_gene_ids)\n",
+    "\n",
+    "doses_pi = dose_response_dict['doses_pi']\n",
+    "responses_mean_pqi = dose_response_dict['responses_mean_pqi']\n",
+    "\n",
+    "slope_qp, intercept_qp, r_squared_qp = batch_linear_regression(\n",
+    "    x_bn=np.repeat(doses_pi[None, :, :], n_query_vars, axis=-3),\n",
+    "    y_bn=responses_mean_pqi.transpose(1, 0, 2)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
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-       "model_id": "9d072787b1a147a6b81da462347a7281",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 306
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plt.hist(r_squared_qp.flatten(), bins=50, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "230d14fda5ce44a5b4a0fa96c6c9da28",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 300
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plt.hist(slope_qp.flatten(), bins=50, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7fecedd499224d80af3e96bbbc4d49b9",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 298
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "n_points = 10_000\n",
+    "size = slope_qp.shape[0] * slope_qp.shape[1]\n",
+    "rand_indices = np.random.permutation(size)[:n_points]\n",
+    "x = slope_qp.flatten()[rand_indices]\n",
+    "y = r_squared_qp.flatten()[rand_indices]\n",
+    "plt.scatter(x, y, s=1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate an AnnData containing just the metadata\n",
+    "adata_prop = sc.AnnData(\n",
+    "    X=np.zeros((1, 1)),\n",
+    "    obs=pd.DataFrame({\n",
+    "        \"cell_type\": [prompt_metadata_dict[\"cell_type\"]],\n",
+    "        \"tissue\": [prompt_metadata_dict[\"tissue\"]],\n",
+    "        \"assay\": [assay],\n",
+    "        \"suspension_type\": [suspension_type],\n",
+    "        \"total_mrna_umis\": [total_mrna_umis],\n",
+    "        \"disease\": \"N/A\",\n",
+    "        \"development_stage\": \"N/A\",\n",
+    "        \"sex\": \"N/A\",\n",
+    "    })\n",
+    ")\n",
+    "\n",
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "response_qp = slope_qp.copy()\n",
+    "response_qp[r_squared_qp < 0.25] = 0.\n",
+    "prompt_marginal_mean_p = dose_response_dict[\"control_mean_q\"]\n",
+    "prompt_marginal_std_p = dose_response_dict[\"control_std_q\"]\n",
+    "query_marginal_mean_q = dose_response_dict[\"control_mean_q\"]\n",
+    "query_marginal_std_q = dose_response_dict[\"control_std_q\"]\n",
+    "\n",
+    "network_ctx = GeneNetworkAnalysisBase(\n",
+    "    adata_obs=adata_prop.obs,\n",
+    "    gene_info_tsv_path=gene_info_tsv_path,\n",
+    "    query_var_names=filtered_query_gene_ids.tolist(),\n",
+    "    prompt_var_names=filtered_perturb_gene_ids.tolist(),\n",
+    "    response_qp=response_qp,\n",
+    "    prompt_marginal_mean_p=prompt_marginal_mean_p,\n",
+    "    prompt_marginal_std_p=prompt_marginal_std_p,\n",
+    "    query_marginal_mean_q=query_marginal_mean_q,\n",
+    "    query_marginal_std_q=query_marginal_std_q,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-13 18:28:47,825 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Maximum value of z_qp: 87.419\n",
+      "2025-02-13 18:28:48,076 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Minimum value of z_qp: -192.053\n",
+      "2025-02-13 18:28:48,077 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of query genes after TPM filtering: 19187 / 19187\n",
+      "2025-02-13 18:28:48,078 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of prompt genes after TPM filtering: 19187 / 19187\n"
+     ]
+    }
+   ],
+   "source": [
+    "network_ctx.process(\n",
+    "    response_normalization_strategy=\"mean\",\n",
+    "    feature_normalization_strategy=\"none\",\n",
+    "    feature_max_value=None,\n",
+    "    query_response_amp_min_pct=None,\n",
+    "    min_prompt_gene_tpm=0,\n",
+    "    min_query_gene_tpm=0,\n",
+    "    norm_pseudo_count=1e-3,\n",
+    "    query_hv_top_k=4000\n",
+    "    # z_trans_func=lambda x: 10. * np.tanh(0.1 * x)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_qp = network_ctx.z_qp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.random.randn(5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std_p = np.std(z_qp, axis=0)\n",
+    "std_q = np.std(z_qp, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6459d7f400cf48ce9c0e0568ab910734",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 296
+      }
+     },
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "plt.scatter(np.log(query_marginal_mean_q), np.log(std_q), s=1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inds, y = quantile_normalize_select(\n",
+    "    x=np.log(1 + query_marginal_mean_q),\n",
+    "    y=np.log(std_q),\n",
+    "    n_bins=50,\n",
+    "    top_k=4000,\n",
+    "    min_x=1e-2,\n",
+    "    max_x=np.inf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc3daa1e5bb14d89baa645c0c9858251",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<matplotlib.collections.PathCollection at 0x7f59fc5a29e0>"
       ]
      },
+     "execution_count": 74,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "077809b861f040898a2858bfe831bfcd",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "Processing query gene chunks:   0%|          | 0/15 [00:00<?, ?it/s]"
+       "<Figure size 320x320 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 298
+      }
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "n_points = 10\n",
-    "query_chunk_size = 64\n",
-    "\n",
-    "dose_response_dict = ctx.generate_gene_dose_response_for_metadata(\n",
-    "    assay=assay,\n",
-    "    suspension_type=suspension_type,\n",
-    "    prompt_metadata_dict=prompt_metadata_dict,\n",
-    "    total_mrna_umis=total_mrna_umis,\n",
-    "    query_gene_ids=filtered_query_gene_ids,\n",
-    "    perturb_gene_ids=filtered_perturb_gene_ids,\n",
-    "    x_lo_p=filtered_x_lo_q,\n",
-    "    x_hi_p=filtered_x_hi_q,\n",
-    "    n_points=n_points,\n",
-    "    query_chunk_size=query_chunk_size,\n",
-    "    max_counts=max_counts,\n",
-    ")"
+    "plt.scatter(x, y, s=1)\n",
+    "plt.scatter(x[inds], y[inds], s=1, color='black')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_adjacency_matrix(\n",
+    "    adjacency_strategy=\"positive_correlation\",\n",
+    "    n_neighbors=50,\n",
+    "    beta=6.,\n",
+    "    self_loop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RPS29 0.25002084343528413\n",
+      "RPS27 0.21294385750728573\n",
+      "RPL27A 0.1590639182449272\n",
+      "RPL41 0.14990689821601214\n",
+      "RPLP2 0.13662548202267263\n",
+      "RPL13A 0.13641175988069157\n",
+      "RPL21 0.11553732740678681\n",
+      "RPS17 0.08892434738396542\n",
+      "RPL37A 0.061956671192742965\n",
+      "RPS20 0.04917304533748178\n",
+      "RPL23A 0.04873089597486011\n",
+      "RPL37 0.046089742866304975\n",
+      "EEF1A1 0.044034336056322405\n",
+      "PCDH11Y 0.04343167968857392\n",
+      "KCNB1 0.04127985377065494\n",
+      "CFAP47 0.03748759320394966\n",
+      "RPL34 0.03587074850772351\n",
+      "RPS2 0.03556800513846539\n",
+      "OTX2-AS1 0.03553745893655207\n",
+      "MT-ND2 0.035414843137776486\n",
+      "PPP1R9A-AS1 0.0338932791690593\n",
+      "LINC00609 0.030390189847234045\n",
+      "B2M 0.029091092346177443\n",
+      "ENSG00000231918 0.028745888666242086\n",
+      "ENSG00000253693 0.027283196696631175\n",
+      "TPT1 0.025285768878777592\n",
+      "KCNH1 0.024198590635440633\n",
+      "MIR4300HG 0.02390461364024318\n",
+      "NLGN4Y 0.02355975181251663\n",
+      "EIF1 0.02352267579187997\n",
+      "NALF1 0.022670562217922328\n",
+      "RFX3 0.022317085872722263\n",
+      "DCC 0.021412266776110075\n",
+      "DSCAM 0.0213945160004499\n",
+      "ENSG00000253190 0.020578815190038117\n",
+      "MIR2052HG 0.02031291760862122\n",
+      "ENSG00000237844 0.019711817968797497\n",
+      "PLEKHG1 0.019701591012197473\n",
+      "RPL3 0.019571959140483017\n",
+      "ADAM7-AS1 0.019454167348354614\n",
+      "CCDC30 0.019453652266162004\n",
+      "RPL31 0.019252236317242846\n",
+      "RPS28 0.01859884091902055\n",
+      "PCDH11X 0.018570039785022138\n",
+      "RPL7 0.01812956335632531\n",
+      "PRB2 0.01795112164992992\n",
+      "KIAA2012 0.017616835479990517\n",
+      "RPL26 0.017189171695448483\n",
+      "LINC01567 0.017044294258476863\n",
+      "NDUFA11 0.0\n",
+      "LSM4 0.0\n",
+      "PIPOX 0.0\n",
+      "ENSG00000285822 0.0\n",
+      "IL12RB1 0.0\n",
+      "ENSG00000263220 0.0\n",
+      "SLC43A2 0.0\n",
+      "ENSG00000261592 0.0\n",
+      "NOTCH3 0.0\n",
+      "DHODH 0.0\n",
+      "SLC7A6OS 0.0\n",
+      "CTRL 0.0\n",
+      "TPPP3 0.0\n",
+      "ARL2BP 0.0\n",
+      "ENSG00000260267 0.0\n",
+      "FUS 0.0\n",
+      "MARCHF2 0.0\n",
+      "ATG4D 0.0\n",
+      "SWSAP1 0.0\n",
+      "ZNF44 0.0\n",
+      "ENSG00000261346 0.0\n",
+      "ZNF799 0.0\n",
+      "ENSG00000232748 0.0\n",
+      "MRI1 0.0\n",
+      "CD70 0.0\n",
+      "MOCOS 0.0\n",
+      "CABLES1 0.0\n",
+      "SYT4 0.0\n",
+      "MPPE1 0.0\n",
+      "NAPG 0.0\n",
+      "MTCL1 0.0\n",
+      "ENSG00000267476 0.0\n",
+      "YES1 0.0\n",
+      "ENSG00000260011 0.0\n",
+      "ADNP2 0.0\n",
+      "PLPP2 0.0\n",
+      "TPGS1 0.0\n",
+      "SECTM1 0.0\n",
+      "ELL 0.0\n",
+      "PYCR1 0.0\n",
+      "CDK3 0.0\n",
+      "TSEN54 0.0\n",
+      "CIRBP 0.0\n",
+      "TIMM13 0.0\n",
+      "ENSG00000278740 0.0\n",
+      "AXIN2 0.0\n",
+      "RAB5C 0.0\n",
+      "NT5C3B 0.0\n",
+      "ENSG00000273687 0.0\n",
+      "GADD45B 0.0\n",
+      "ZNF830 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['MALAT1']]\n",
+    "inds = np.argsort(network_ctx.a_pp[i, :])[::-1]\n",
+    "for j in inds[:100]:\n",
+    "    print(filtered_perturb_gene_symbols[j], network_ctx.a_pp[i, j])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_leiden_communites(\n",
+    "    resolution=5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['doses_pi', 'responses_mean_pqi', 'responses_std_pqi', 'control_mean_q', 'control_std_q'])"
+       "50"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dose_response_dict.keys()"
+    "len(np.unique(network_ctx.leiden_membership))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Embedding"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 SERBP1\n",
-      "1 PRPF38B\n",
-      "2 LYAR\n",
-      "3 FGFBP2\n",
-      "4 GZMK\n",
-      "5 CCT6A\n",
-      "6 RPL30\n",
-      "7 IFITM2\n",
-      "8 APEX1\n",
-      "9 DNMT1\n",
-      "10 DSTN\n",
-      "11 RPL3\n",
-      "12 UFC1\n",
-      "13 WDR1\n",
-      "14 NPM1\n",
-      "15 FIS1\n",
-      "16 ATP6V1F\n",
-      "17 PRDX5\n",
-      "18 PRR13\n",
-      "19 ARF6\n",
-      "20 SERF2\n",
-      "21 B2M\n",
-      "22 C1QBP\n",
-      "23 MATK\n",
-      "24 RPL18A\n",
-      "25 GMFG\n",
-      "26 EIF2S2\n",
-      "27 DDT\n",
-      "28 DDX17\n",
-      "29 PGK1\n",
-      "30 AURKAIP1\n",
-      "31 HSPD1\n",
-      "32 UBE2D3\n",
-      "33 RPS23\n",
-      "34 COX7C\n",
-      "35 UQCRQ\n",
-      "36 LMAN2\n",
-      "37 PSMB8\n",
-      "38 DYNLT1\n",
-      "39 CBX3\n",
-      "40 ATP5MF\n",
-      "41 RPL12\n",
-      "42 EIF3M\n",
-      "43 PFDN5\n",
-      "44 GZMH\n",
-      "45 SIVA1\n",
-      "46 SEC11A\n",
-      "47 VAMP2\n",
-      "48 ATP5PD\n",
-      "49 SUMO2\n",
-      "50 ATP5F1A\n",
-      "51 TOMM22\n",
-      "52 NDUFA6\n",
-      "53 EIF1AX\n",
-      "54 EIF3I\n",
-      "55 SCP2\n",
-      "56 JUN\n",
-      "57 SELL\n",
-      "58 PDIA6\n",
-      "59 SRSF7\n",
-      "60 TMBIM6\n",
-      "61 RBM25\n",
-      "62 RPLP1\n",
-      "63 RPL13\n",
-      "64 NME1\n",
-      "65 RABAC1\n",
-      "66 SAMHD1\n",
-      "67 ZFAS1\n",
-      "68 XRCC6\n",
-      "69 SSR4\n",
-      "70 MT-ND4\n",
-      "71 IFI16\n",
-      "72 CXCR4\n",
-      "73 ZEB2\n",
-      "74 TUBA4A\n",
-      "75 ARPC4\n",
-      "76 H1-10\n",
-      "77 RWDD1\n",
-      "78 TALDO1\n",
-      "79 TMEM258\n",
-      "80 MALAT1\n",
-      "81 GSTP1\n",
-      "82 C12orf75\n",
-      "83 CCL4\n",
-      "84 JUNB\n",
-      "85 C19orf53\n",
-      "86 CALM3\n",
-      "87 UBE2L3\n",
-      "88 ATRX\n",
-      "89 EFHD2\n",
-      "90 CD52\n",
-      "91 PRRC2C\n",
-      "92 COX5B\n",
-      "93 RPL14\n",
-      "94 SNHG32\n",
-      "95 COX7A2\n",
-      "96 ATP5F1C\n",
-      "97 RPL6\n",
-      "98 ITM2B\n",
-      "99 MORF4L1\n",
-      "100 ARL6IP1\n",
-      "101 PSMC5\n",
-      "102 RPS11\n",
-      "103 MT-ND3\n",
-      "104 SSU72\n",
-      "105 SRP9\n",
-      "106 CCT4\n",
-      "107 CD47\n",
-      "108 PPP1R2\n",
-      "109 HOPX\n",
-      "110 CCNI\n",
-      "111 EIF3H\n",
-      "112 HNRNPK\n",
-      "113 ANAPC16\n",
-      "114 SF3B2\n",
-      "115 LYZ\n",
-      "116 ARL6IP4\n",
-      "117 TMED2\n",
-      "118 N4BP2L2\n",
-      "119 ARGLU1\n",
-      "120 EIF1\n",
-      "121 PSMA7\n",
-      "122 NDUFA1\n",
-      "123 RSRP1\n",
-      "124 TMEM59\n",
-      "125 TRAF3IP3\n",
-      "126 TMSB10\n",
-      "127 OCIAD2\n",
-      "128 H2AZ1\n",
-      "129 TRGC2\n",
-      "130 GIMAP7\n",
-      "131 EEF1D\n",
-      "132 CLEC2D\n",
-      "133 TUBA1A\n",
-      "134 ATP5F1B\n",
-      "135 COMMD6\n",
-      "136 ALDOA\n",
-      "137 CKLF\n",
-      "138 UBB\n",
-      "139 GPX4\n",
-      "140 EIF3K\n",
-      "141 NOP53\n",
-      "142 BAX\n",
-      "143 PRMT2\n",
-      "144 ITM2A\n",
-      "145 RPL39\n",
-      "146 RRM2\n",
-      "147 CFLAR\n",
-      "148 SUMO1\n",
-      "149 RPL32\n",
-      "150 TCF7\n",
-      "151 DDX46\n",
-      "152 HLA-E\n",
-      "153 TUBB\n",
-      "154 PABPC1\n",
-      "155 C9orf78\n",
-      "156 AHNAK\n",
-      "157 PCBP2\n",
-      "158 BTG1\n",
-      "159 CIB1\n",
-      "160 CCL4L2\n",
-      "161 SOD1\n",
-      "162 ITGB2\n",
-      "163 MT-CO3\n",
-      "164 RPL11\n",
-      "165 SH3BGRL3\n",
-      "166 IRF1\n",
-      "167 RACK1\n",
-      "168 H4C3\n",
-      "169 CELF2\n",
-      "170 PSMA1\n",
-      "171 NAP1L1\n",
-      "172 PCLAF\n",
-      "173 NDUFAB1\n",
-      "174 ICAM3\n",
-      "175 TECR\n",
-      "176 RPS5\n",
-      "177 PCNA\n",
-      "178 ENSG00000240972\n",
-      "179 ST13\n",
-      "180 IL2RG\n",
-      "181 RPS4X\n",
-      "182 CSDE1\n",
-      "183 MRPS21\n",
-      "184 PPM1G\n",
-      "185 PYURF\n",
-      "186 CLIC1\n",
-      "187 HLA-DPB1\n",
-      "188 PSMA2\n",
-      "189 FABP5\n",
-      "190 NDUFB9\n",
-      "191 CYRIB\n",
-      "192 BBLN\n",
-      "193 VDAC2\n",
-      "194 MKI67\n",
-      "195 LDHA\n",
-      "196 COX8A\n",
-      "197 KLRD1\n",
-      "198 CRIP1\n",
-      "199 NOP10\n",
-      "200 SRP14\n",
-      "201 TOP2A\n",
-      "202 RNF213\n",
-      "203 ACTG1\n",
-      "204 SNRPD1\n",
-      "205 OAZ1\n",
-      "206 TLE5\n",
-      "207 RPS28\n",
-      "208 YWHAB\n",
-      "209 HMGN1\n",
-      "210 STMN1\n",
-      "211 COX7A2L\n",
-      "212 LSM3\n",
-      "213 TNFAIP3\n",
-      "214 EZR\n",
-      "215 IFITM3\n",
-      "216 NEDD8\n",
-      "217 PSMA4\n",
-      "218 ADGRE5\n",
-      "219 HCST\n",
-      "220 XBP1\n",
-      "221 RPL36A\n",
-      "222 MT-CO2\n",
-      "223 DNAJC8\n",
-      "224 S100A4\n",
-      "225 TSTD1\n",
-      "226 SF3B6\n",
-      "227 RPL29\n",
-      "228 BCLAF1\n",
-      "229 LAMTOR4\n",
-      "230 RPL8\n",
-      "231 ATP6V1G1\n",
-      "232 RPS13\n",
-      "233 NDUFC2\n",
-      "234 HNRNPC\n",
-      "235 EIF5A\n",
-      "236 TRAPPC1\n",
-      "237 JPT1\n",
-      "238 RPL36\n",
-      "239 HNRNPR\n",
-      "240 ATP5IF1\n",
-      "241 TPM3\n",
-      "242 RPS27A\n",
-      "243 GYPC\n",
-      "244 STK17B\n",
-      "245 GZMA\n",
-      "246 BTF3\n",
-      "247 ITGB1\n",
-      "248 CD44\n",
-      "249 FAU\n",
-      "250 CD3G\n",
-      "251 RPL21\n",
-      "252 HMGB1\n",
-      "253 IDH2\n",
-      "254 NHERF1\n",
-      "255 VAPA\n",
-      "256 HNRNPM\n",
-      "257 EIF3G\n",
-      "258 ATP5PO\n",
-      "259 S100A9\n",
-      "260 PRDX6\n",
-      "261 TMA7\n",
-      "262 ATP1B3\n",
-      "263 CCNL1\n",
-      "264 HLA-DRB1\n",
-      "265 EEF1A1\n",
-      "266 PGAM1\n",
-      "267 CARD16\n",
-      "268 PHB2\n",
-      "269 NDUFA12\n",
-      "270 ATP5MJ\n",
-      "271 LITAF\n",
-      "272 RPL19\n",
-      "273 SRRM1\n",
-      "274 BTG2\n",
-      "275 HNRNPU\n",
-      "276 MZT2B\n",
-      "277 HNRNPA3\n",
-      "278 OSTC\n",
-      "279 NDUFS6\n",
-      "280 FYB1\n",
-      "281 HLA-A\n",
-      "282 CKS2\n",
-      "283 SET\n",
-      "284 VIM\n",
-      "285 UCP2\n",
-      "286 NDUFB10\n",
-      "287 ACAP1\n",
-      "288 DDX5\n",
-      "289 RPS15\n",
-      "290 EEF2\n",
-      "291 FXYD5\n",
-      "292 SNRPB\n",
-      "293 PPDPF\n",
-      "294 PSMA5\n",
-      "295 SKP1\n",
-      "296 PTTG1\n",
-      "297 CD164\n",
-      "298 UQCRB\n",
-      "299 DNAJA1\n",
-      "300 MRPL51\n",
-      "301 ARHGDIB\n",
-      "302 SNRPF\n",
-      "303 RPLP0\n",
-      "304 POLR1D\n",
-      "305 TRAC\n",
-      "306 FUS\n",
-      "307 CNN2\n",
-      "308 CIRBP\n",
-      "309 TIMM13\n",
-      "310 GADD45B\n",
-      "311 NDUFA11\n",
-      "312 LSM4\n",
-      "313 UBE2C\n",
-      "314 MYH9\n",
-      "315 APOBEC3G\n",
-      "316 COX7B\n",
-      "317 MT-ND1\n",
-      "318 S100A8\n",
-      "319 XCL2\n",
-      "320 DDX18\n",
-      "321 VDAC1\n",
-      "322 RPS18\n",
-      "323 TAPBP\n",
-      "324 CYCS\n",
-      "325 GSTK1\n",
-      "326 RHOG\n",
-      "327 FOS\n",
-      "328 ATP5MC1\n",
-      "329 SEPTIN9\n",
-      "330 PMAIP1\n",
-      "331 JUND\n",
-      "332 RPS16\n",
-      "333 CD37\n",
-      "334 TMSB4X\n",
-      "335 MT-CYB\n",
-      "336 SYF2\n",
-      "337 RPS27\n",
-      "338 CACYBP\n",
-      "339 CMC1\n",
-      "340 DEK\n",
-      "341 SRGN\n",
-      "342 RPL36AL\n",
-      "343 FOSB\n",
-      "344 AP2S1\n",
-      "345 S100A10\n",
-      "346 SSR2\n",
-      "347 BRK1\n",
-      "348 HNRNPH1\n",
-      "349 RPS20\n",
-      "350 PSIP1\n",
-      "351 HSPA5\n",
-      "352 DAZAP2\n",
-      "353 COX6A1\n",
-      "354 SEPTIN1\n",
-      "355 CALR\n",
-      "356 EMP3\n",
-      "357 NDUFA3\n",
-      "358 UQCR10\n",
-      "359 NONO\n",
-      "360 TSC22D3\n",
-      "361 JTB\n",
-      "362 CD247\n",
-      "363 PTPRC\n",
-      "364 SNRPE\n",
-      "365 OST4\n",
-      "366 PLEK\n",
-      "367 DUSP2\n",
-      "368 ITGA4\n",
-      "369 STK17A\n",
-      "370 TPI1\n",
-      "371 PA2G4\n",
-      "372 CALM1\n",
-      "373 RPL4\n",
-      "374 RPS19\n",
-      "375 EIF3L\n",
-      "376 SH3BGRL\n",
-      "377 CAP1\n",
-      "378 CD53\n",
-      "379 COPS9\n",
-      "380 HNRNPDL\n",
-      "381 RPS14\n",
-      "382 NHP2\n",
-      "383 HLA-B\n",
-      "384 CITED2\n",
-      "385 HNRNPA2B1\n",
-      "386 SCAF11\n",
-      "387 RAN\n",
-      "388 TRDC\n",
-      "389 PPIB\n",
-      "390 SRRM2\n",
-      "391 TYMS\n",
-      "392 RBM39\n",
-      "393 TMEM50A\n",
-      "394 LAPTM5\n",
-      "395 S100A11\n",
-      "396 RPS7\n",
-      "397 VAMP5\n",
-      "398 CYTIP\n",
-      "399 RPL24\n",
-      "400 LTB\n",
-      "401 HLA-DPA1\n",
-      "402 NDUFB8\n",
-      "403 IFITM1\n",
-      "404 EEF1G\n",
-      "405 LDHB\n",
-      "406 PNN\n",
-      "407 HSP90AA1\n",
-      "408 RPS15A\n",
-      "409 CYBA\n",
-      "410 GZMM\n",
-      "411 UBL5\n",
-      "412 RPN2\n",
-      "413 ATP5PF\n",
-      "414 CSTB\n",
-      "415 EIF3D\n",
-      "416 SNU13\n",
-      "417 UXT\n",
-      "418 MRPL20\n",
-      "419 PARP1\n",
-      "420 ARL4C\n",
-      "421 EIF4A2\n",
-      "422 AIF1\n",
-      "423 RPL10A\n",
-      "424 SNX3\n",
-      "425 ABRACL\n",
-      "426 ARPC1B\n",
-      "427 DOK2\n",
-      "428 ARPC5L\n",
-      "429 LSP1\n",
-      "430 UBE2L6\n",
-      "431 ATP5MG\n",
-      "432 CLEC2B\n",
-      "433 UQCR11\n",
-      "434 COPE\n",
-      "435 CST7\n",
-      "436 RPS4Y1\n",
-      "437 ENO1\n",
-      "438 RPL5\n",
-      "439 TRGC1\n",
-      "440 PPP1CA\n",
-      "441 SPCS2\n",
-      "442 HSPA8\n",
-      "443 EIF4B\n",
-      "444 NDUFB1\n",
-      "445 COX5A\n",
-      "446 BSG\n",
-      "447 MYO1F\n",
-      "448 ZFP36\n",
-      "449 TIMP1\n",
-      "450 SFPQ\n",
-      "451 SPCS1\n",
-      "452 HNRNPA0\n",
-      "453 HLA-C\n",
-      "454 ANP32B\n",
-      "455 ATP5MK\n",
-      "456 NEAT1\n",
-      "457 CCDC85B\n",
-      "458 LAMTOR1\n",
-      "459 RPS3\n",
-      "460 BLOC1S1\n",
-      "461 RPS2\n",
-      "462 CORO1A\n",
-      "463 APRT\n",
-      "464 PSMB3\n",
-      "465 ANAPC11\n",
-      "466 NDUFV2\n",
-      "467 TYROBP\n",
-      "468 TBCB\n",
-      "469 ATF4\n",
-      "470 RBM3\n",
-      "471 SLC25A5\n",
-      "472 UQCRH\n",
-      "473 TXNIP\n",
-      "474 RPSA\n",
-      "475 SNHG8\n",
-      "476 NSA2\n",
-      "477 RIPOR2\n",
-      "478 HLA-DRA\n",
-      "479 KMT2E\n",
-      "480 TRBC2\n",
-      "481 MYL6\n",
-      "482 RSL24D1\n",
-      "483 PFN1\n",
-      "484 TOMM20\n",
-      "485 SNHG5\n",
-      "486 RPL7A\n",
-      "487 RPL27A\n",
-      "488 TBC1D10C\n",
-      "489 CD69\n",
-      "490 EID1\n",
-      "491 RNPS1\n",
-      "492 RSL1D1\n",
-      "493 RGS1\n",
-      "494 YPEL5\n",
-      "495 EIF5B\n",
-      "496 FOXP1\n",
-      "497 RPL35A\n",
-      "498 ATP5ME\n",
-      "499 HNRNPD\n",
-      "500 GLRX\n",
-      "501 MACROH2A1\n",
-      "502 SEM1\n",
-      "503 YWHAZ\n",
-      "504 GHITM\n",
-      "505 PTPRCAP\n",
-      "506 RPS26\n",
-      "507 UBC\n",
-      "508 BAZ1A\n",
-      "509 EVL\n",
-      "510 PHB1\n",
-      "511 RPS21\n",
-      "512 SDF2L1\n",
-      "513 IGLC2\n",
-      "514 RBX1\n",
-      "515 XIST\n",
-      "516 SEPTIN6\n",
-      "517 NUDC\n",
-      "518 LCK\n",
-      "519 PCBP1\n",
-      "520 NBEAL1\n",
-      "521 XRCC5\n",
-      "522 CD74\n",
-      "523 LSM2\n",
-      "524 HMGN3\n",
-      "525 DNAJB6\n",
-      "526 PAXX\n",
-      "527 ANXA11\n",
-      "528 SF1\n",
-      "529 DYNLL1\n",
-      "530 DDX24\n",
-      "531 ANXA2\n",
-      "532 IL32\n",
-      "533 RPL26\n",
-      "534 RPL23\n",
-      "535 WDR83OS\n",
-      "536 COX6B1\n",
-      "537 MT-CO1\n",
-      "538 MT-ATP6\n",
-      "539 SRSF11\n",
-      "540 TAGLN2\n",
-      "541 GAS5\n",
-      "542 NUCKS1\n",
-      "543 PPIG\n",
-      "544 SSB\n",
-      "545 RPL15\n",
-      "546 MTDH\n",
-      "547 PSMB7\n",
-      "548 PPA1\n",
-      "549 TMEM123\n",
-      "550 RPL23A\n",
-      "551 CCL5\n",
-      "552 RPL17\n",
-      "553 MICOS13\n",
-      "554 IER2\n",
-      "555 NDUFA13\n",
-      "556 PPP1R15A\n",
-      "557 RPL13A\n",
-      "558 FKBP1A\n",
-      "559 STK4\n",
-      "560 ATP5F1E\n",
-      "561 EIF2S3\n",
-      "562 FLNA\n",
-      "563 RBM8A\n",
-      "564 ZFP36L2\n",
-      "565 CCT7\n",
-      "566 PAIP2\n",
-      "567 TRBC1\n",
-      "568 PSAP\n",
-      "569 RPLP2\n",
-      "570 EIF3F\n",
-      "571 EIF4G2\n",
-      "572 SELENOH\n",
-      "573 SNHG29\n",
-      "574 BST2\n",
-      "575 UQCRFS1\n",
-      "576 FTL\n",
-      "577 CCT8\n",
-      "578 SON\n",
-      "579 RANBP1\n",
-      "580 CD48\n",
-      "581 SMC4\n",
-      "582 HINT1\n",
-      "583 CUTA\n",
-      "584 CCND3\n",
-      "585 NDUFB2\n",
-      "586 LEPROTL1\n",
-      "587 TUBB4B\n",
-      "588 HNRNPF\n",
-      "589 RGS10\n",
-      "590 ELF1\n",
-      "591 LCP1\n",
-      "592 SYNE2\n",
-      "593 KRT10\n",
-      "594 KLF2\n",
-      "595 RPL22\n",
-      "596 CDC42\n",
-      "597 JAK1\n",
-      "598 HSPE1\n",
-      "599 SELENOK\n",
-      "600 SSBP1\n",
-      "601 CTSW\n",
-      "602 BIN2\n",
-      "603 ATP5MC2\n",
-      "604 NACA\n",
-      "605 HSP90B1\n",
-      "606 EIF5\n",
-      "607 MT1X\n",
-      "608 LUC7L3\n",
-      "609 SNRPB2\n",
-      "610 RALY\n",
-      "611 MICOS10\n",
-      "612 HMGN2\n",
-      "613 PSMB2\n",
-      "614 SELENOF\n",
-      "615 CD8B\n",
-      "616 CYTOR\n",
-      "617 RPL31\n",
-      "618 ATP5MC3\n",
-      "619 ARPC2\n",
-      "620 HCLS1\n",
-      "621 AP2M1\n",
-      "622 RPL9\n",
-      "623 PNISR\n",
-      "624 MCM7\n",
-      "625 GIMAP4\n",
-      "626 CHMP4A\n",
-      "627 MT2A\n",
-      "628 ATP5F1D\n",
-      "629 SMDT1\n",
-      "630 SLC25A6\n",
-      "631 NDUFB11\n",
-      "632 PLP2\n",
-      "633 MT-ND5\n",
-      "634 CD8A\n",
-      "635 MZT2A\n",
-      "636 DUSP1\n",
-      "637 PSMB9\n",
-      "638 LSM5\n",
-      "639 CTSD\n",
-      "640 CD3E\n",
-      "641 RPS25\n",
-      "642 CD63\n",
-      "643 PTGES3\n",
-      "644 SAP18\n",
-      "645 DAD1\n",
-      "646 SRSF5\n",
-      "647 PPP2R5C\n",
-      "648 IGHA1\n",
-      "649 SPN\n",
-      "650 COX4I1\n",
-      "651 LSM7\n",
-      "652 GADD45GIP1\n",
-      "653 PSMD8\n",
-      "654 RPL28\n",
-      "655 CST3\n",
-      "656 CD99\n",
-      "657 YBX1\n",
-      "658 CCT3\n",
-      "659 BZW1\n",
-      "660 NDUFB3\n",
-      "661 SERP1\n",
-      "662 IL7R\n",
-      "663 UBE2D2\n",
-      "664 PNRC1\n",
-      "665 TOMM7\n",
-      "666 PLAAT4\n",
-      "667 RAP1B\n",
-      "668 ISCU\n",
-      "669 NFKBIA\n",
-      "670 NKG7\n",
-      "671 RPS8\n",
-      "672 CAPZA1\n",
-      "673 GUK1\n",
-      "674 RPL37\n",
-      "675 MDH2\n",
-      "676 SARAF\n",
-      "677 SNHG6\n",
-      "678 RPL7\n",
-      "679 GDI2\n",
-      "680 DRAP1\n",
-      "681 TUBA1B\n",
-      "682 MRPL57\n",
-      "683 PSME2\n",
-      "684 RPL27\n",
-      "685 FBL\n",
-      "686 NOSIP\n",
-      "687 SH2D1A\n",
-      "688 GNG5\n",
-      "689 ATP5PB\n",
-      "690 ARPC5\n",
-      "691 ID2\n",
-      "692 YWHAQ\n",
-      "693 NDUFB4\n",
-      "694 C4orf3\n",
-      "695 SF3B5\n",
-      "696 EIF3E\n",
-      "697 TXN\n",
-      "698 BUB3\n",
-      "699 SLC2A3\n",
-      "700 GZMB\n",
-      "701 RPS29\n",
-      "702 SELENOW\n",
-      "703 KHDRBS1\n",
-      "704 CD2\n",
-      "705 CTSS\n",
-      "706 KRTCAP2\n",
-      "707 NR4A2\n",
-      "708 TKT\n",
-      "709 IK\n",
-      "710 H1-2\n",
-      "711 DDX21\n",
-      "712 CFL1\n",
-      "713 C12orf57\n",
-      "714 CHURC1\n",
-      "715 PDIA3\n",
-      "716 CDC37\n",
-      "717 PRDX2\n",
-      "718 DNAJB1\n",
-      "719 SNRPD2\n",
-      "720 RBMX\n",
-      "721 MT-ND4L\n",
-      "722 RHOC\n",
-      "723 ILF2\n",
-      "724 SEC62\n",
-      "725 NDUFA2\n",
-      "726 RPS12\n",
-      "727 LY6E\n",
-      "728 PRF1\n",
-      "729 CTSC\n",
-      "730 GTF3A\n",
-      "731 ALOX5AP\n",
-      "732 ERH\n",
-      "733 PAPOLA\n",
-      "734 ISG20\n",
-      "735 CD7\n",
-      "736 ANKRD12\n",
-      "737 SCAND1\n",
-      "738 TSPO\n",
-      "739 NDUFS5\n",
-      "740 PRDX1\n",
-      "741 S100A6\n",
-      "742 RPL22L1\n",
-      "743 ANXA6\n",
-      "744 PRELID1\n",
-      "745 TMED9\n",
-      "746 HLA-F\n",
-      "747 SNRPC\n",
-      "748 SRSF3\n",
-      "749 TOMM6\n",
-      "750 TRMT112\n",
-      "751 GAPDH\n",
-      "752 DUT\n",
-      "753 RPS17\n",
-      "754 HERPUD1\n",
-      "755 ARHGDIA\n",
-      "756 MYL12B\n",
-      "757 RPS9\n",
-      "758 GNAS\n",
-      "759 PTP4A2\n",
-      "760 RPL37A\n",
-      "761 ARL6IP5\n",
-      "762 RPL34\n",
-      "763 RPS3A\n",
-      "764 PSMB1\n",
-      "765 H2AZ2\n",
-      "766 SEC61G\n",
-      "767 LSM8\n",
-      "768 FNBP1\n",
-      "769 RPS24\n",
-      "770 SLC25A3\n",
-      "771 PEBP1\n",
-      "772 POMP\n",
-      "773 RGCC\n",
-      "774 RTRAF\n",
-      "775 IQGAP1\n",
-      "776 UBE2I\n",
-      "777 COTL1\n",
-      "778 RPL18\n",
-      "779 ADA\n",
-      "780 RAC2\n",
-      "781 VAMP8\n",
-      "782 IGKC\n",
-      "783 PLAC8\n",
-      "784 CAST\n",
-      "785 HMGA1\n",
-      "786 HSP90AB1\n",
-      "787 DDIT4\n",
-      "788 KLRB1\n",
-      "789 RPS27L\n",
-      "790 GABARAPL2\n",
-      "791 PSMB6\n",
-      "792 MT-ND2\n",
-      "793 ISG15\n",
-      "794 CAPZB\n",
-      "795 CDC42SE2\n",
-      "796 ATP6V0E1\n",
-      "797 LST1\n",
-      "798 RPA3\n",
-      "799 FTH1\n",
-      "800 CD3D\n",
-      "801 FKBP11\n",
-      "802 PSME1\n",
-      "803 EIF4A1\n",
-      "804 LIMD2\n",
-      "805 TRIR\n",
-      "806 NDUFB7\n",
-      "807 FKBP8\n",
-      "808 DYNLRB1\n",
-      "809 RPL10\n",
-      "810 LINC01871\n",
-      "811 CALM2\n",
-      "812 GNLY\n",
-      "813 CNBP\n",
-      "814 SERPINB1\n",
-      "815 CSNK2B\n",
-      "816 RAC1\n",
-      "817 EDF1\n",
-      "818 CCT2\n",
-      "819 ARPC3\n",
-      "820 H3-3B\n",
-      "821 P4HB\n",
-      "822 MSN\n",
-      "823 RAP1A\n",
-      "824 CENPF\n",
-      "825 H3-3A\n",
-      "826 ARF1\n",
-      "827 DBI\n",
-      "828 EEF1B2\n",
-      "829 PTMA\n",
-      "830 LRRFIP1\n",
-      "831 GPSM3\n",
-      "832 SEPTIN7\n",
-      "833 ANXA1\n",
-      "834 ETS1\n",
-      "835 LINC02446\n",
-      "836 TUFM\n",
-      "837 LAT\n",
-      "838 EVI2B\n",
-      "839 RPL38\n",
-      "840 SAT1\n",
-      "841 IFI6\n",
-      "842 NASP\n",
-      "843 TPR\n",
-      "844 ACP1\n",
-      "845 SNRPG\n",
-      "846 SF3B1\n",
-      "847 MBNL1\n",
-      "848 TRA2B\n",
-      "849 SUB1\n",
-      "850 HIGD2A\n",
-      "851 CHCHD2\n",
-      "852 SRI\n",
-      "853 SEC61B\n",
-      "854 NUCB2\n",
-      "855 UBXN1\n",
-      "856 CD27\n",
-      "857 PCED1B-AS1\n",
-      "858 HNRNPA1\n",
-      "859 TPT1\n",
-      "860 SRSF2\n",
-      "861 LGALS1\n",
-      "862 THRAP3\n",
-      "863 MCL1\n",
-      "864 ACTR3\n",
-      "865 NCL\n",
-      "866 HMGB2\n",
-      "867 TBCA\n",
-      "868 ACTB\n",
-      "869 HSPB1\n",
-      "870 RPL35\n",
-      "871 KLF6\n",
-      "872 EIF3A\n",
-      "873 POLR2L\n",
-      "874 C11orf58\n",
-      "875 RPL41\n",
-      "876 MYL12A\n",
-      "877 TMEM230\n",
-      "878 PARK7\n",
-      "879 ENSA\n",
-      "880 ACTR2\n",
-      "881 BIN1\n",
-      "882 RHOA\n",
-      "883 RPS10\n",
-      "884 NDUFA4\n",
-      "885 PPIA\n",
-      "886 COX6C\n",
-      "887 RPS6\n",
-      "888 OSTF1\n",
-      "889 BANF1\n",
-      "890 NDUFS8\n",
-      "891 ERP29\n",
-      "892 PKM\n",
-      "893 ELOB\n",
-      "894 TERF2IP\n",
-      "895 TCF25\n",
-      "896 UBA52\n",
-      "897 ECH1\n"
+      "Feb 13 06:20:11 PM: Computing 20-nearest neighbors, with max_distance=None\n",
+      "Thu Feb 13 18:20:12 2025 Building RP forest with 17 trees\n",
+      "Thu Feb 13 18:20:17 2025 NN descent for 14 iterations\n",
+      "\t 1  /  14\n",
+      "\t 2  /  14\n",
+      "\t 3  /  14\n",
+      "\t 4  /  14\n",
+      "\tStopping threshold met -- exiting after 4 iterations\n",
+      "Feb 13 06:20:32 PM: Computing quadratic initialization.\n",
+      "Feb 13 06:20:35 PM: Fitting a centered embedding into R^2, for a graph with 19187 items and 1879506 edges.\n",
+      "Feb 13 06:20:35 PM: `embed` method parameters: eps=1.0e-05, max_iter=1000, memory_size=10\n",
+      "Feb 13 06:20:35 PM: iteration 0000 | distortion 3.861732 | residual norm 0.936287 | step length 0.00684439 | percent change 0.00327134\n",
+      "Feb 13 06:20:36 PM: iteration 0100 | distortion 0.161198 | residual norm 0.000158078 | step length 1 | percent change 0.647835\n",
+      "Feb 13 06:20:37 PM: iteration 0200 | distortion 0.155643 | residual norm 6.65539e-05 | step length 1 | percent change 0.266546\n",
+      "Feb 13 06:20:37 PM: iteration 0300 | distortion 0.152120 | residual norm 3.81087e-05 | step length 1 | percent change 0.311538\n",
+      "Feb 13 06:20:38 PM: iteration 0400 | distortion 0.150891 | residual norm 3.87037e-05 | step length 1 | percent change 0.269843\n",
+      "Feb 13 06:20:39 PM: iteration 0500 | distortion 0.149535 | residual norm 1.96447e-05 | step length 1 | percent change 0.0752643\n",
+      "Feb 13 06:20:39 PM: Converged in 523 iterations, with residual norm 9.49637e-06\n",
+      "Feb 13 06:20:39 PM: Finished fitting in 3.605 seconds and 523 iterations.\n",
+      "Feb 13 06:20:39 PM: average distortion 0.15 | residual norm 9.5e-06\n"
      ]
     }
    ],
    "source": [
-    "for i, gene_id in enumerate(filtered_query_gene_ids):\n",
-    "    print(i, filtered_query_gene_symbols[i])"
+    "import pymde\n",
+    "\n",
+    "network_ctx.make_mde_embedding(\n",
+    "    n_neighbors=20,\n",
+    "    repulsive_penalty=pymde.penalties.Log,\n",
+    "    max_iter=1000,\n",
+    "    init=\"quadratic\",\n",
+    "    device=\"cuda\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_gene_familities(network_ctx: GeneNetworkAnalysisBase, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
+    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in network_ctx.query_gene_symbols if gene_symbol.startswith(prefix)]\n",
+    "    gene_ids = [network_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
+    "    gene_symbols = [network_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
+    "    return gene_ids, gene_symbols\n",
+    "\n",
+    "mito_gene_ids, mito_gene_symbols = get_gene_familities(network_ctx, [\"MT-\"])\n",
+    "ribo_gene_ids, ribo_gene_symbols = get_gene_familities(network_ctx, [\"RPS\", \"RPL\"])\n",
+    "hla_gene_ids, hla_gene_symbols = get_gene_familities(network_ctx, [\"HLA\"])\n",
+    "ifi_gene_ids, ifi_gene_symbols = get_gene_familities(network_ctx, [\"IFI\"])\n",
+    "trav_gene_ids, trav_gene_symbols = get_gene_familities(network_ctx, [\"TRAV\"])\n",
+    "hb_gene_ids, hb_gene_symbols = get_gene_familities(network_ctx, [\"HBA\", \"HBB\"])\n",
+    "\n",
+    "highlight_gene_sets = {\n",
+    "    \"Mito\": (mito_gene_ids, mito_gene_symbols, 'red'),\n",
+    "    \"Ribo\": (ribo_gene_ids, ribo_gene_symbols, 'blue'),\n",
+    "    \"HLA\": (hla_gene_ids, hla_gene_symbols, 'green'),\n",
+    "    \"IFI\": (ifi_gene_ids, ifi_gene_symbols, 'orange'),\n",
+    "    \"TRAV\": (trav_gene_ids, trav_gene_symbols, 'purple'),\n",
+    "    \"HB\": (hb_gene_ids, hb_gene_symbols, 'black'),\n",
+    "}\n",
+    "\n",
+    "# disable\n",
+    "# highlight_gene_sets = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Response of GZMM')"
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br><br>membership=2<br>x=%{x}<br>y=%{y}<extra></extra>",
+         "hovertext": [
+          "SDF4",
+          "MRPL35",
+          "LYAR",
+          "GTF3C6",
+          "NDUFA8",
+          "ZNRD2",
+          "PTMS",
+          "GPN3",
+          "OGFOD3",
+          "DSTN",
+          "TCEAL8",
+          "GBP1",
+          "MRPL24",
+          "WDR1",
+          "PFDN1",
+          "MRPL22",
+          "HDAC2",
+          "PRDX5",
+          "RNASEH2C",
+          "EMC7",
+          "SERF2",
+          "SUPT4H1",
+          "LMAN1",
+          "GMFG",
+          "BLVRB",
+          "ETFB",
+          "AURKAIP1",
+          "LYPLA2",
+          "DNAJC19",
+          "UQCRQ",
+          "ZMAT2",
+          "LMAN2",
+          "SFT2D1",
+          "ATP5MF",
+          "OS9",
+          "MRPL52",
+          "SEC11A",
+          "NAA38",
+          "SUMO2",
+          "STX10",
+          "NDUFA6",
+          "FUNDC2",
+          "AKR7A2",
+          "PDIA6",
+          "HINT2",
+          "PIN4",
+          "SSR4",
+          "MAGOH",
+          "UBXN4",
+          "POP7",
+          "LSM1",
+          "ELOC",
+          "TALDO1",
+          "TMEM258",
+          "GSTP1",
+          "ALG5",
+          "ETFA",
+          "DNAJC7",
+          "RTF2",
+          "HSD17B10",
+          "MRPL33",
+          "COX5B",
+          "TIMMDC1",
+          "COPG1",
+          "LARP7",
+          "ZNF330",
+          "CLPTM1L",
+          "MRPS18A",
+          "COX7A2",
+          "GFUS",
+          "PLGRKT",
+          "CHMP5",
+          "TOMM5",
+          "ATP5F1C",
+          "MRPL58",
+          "CNIH4",
+          "MTLN",
+          "RNF7",
+          "POLR2K",
+          "CHCHD1",
+          "ECHS1",
+          "REXO2",
+          "TMED2",
+          "CDIPT",
+          "TRAPPC2L",
+          "PSMA7",
+          "PHF5A",
+          "PBDC1",
+          "NDUFA1",
+          "DKC1",
+          "MDH1",
+          "TRGC2",
+          "PUF60",
+          "TSG101",
+          "MRPS35",
+          "COX14",
+          "MLEC",
+          "CNIH1",
+          "ALDOA",
+          "CKLF",
+          "UBB",
+          "TXNL1",
+          "RAB8A",
+          "RFXANK",
+          "EIF3K",
+          "TIMM50",
+          "BAX",
+          "RUVBL2",
+          "ISOC2",
+          "POLR2F",
+          "BCAP31",
+          "SPCS3",
+          "AIMP2",
+          "ARPC1A",
+          "RRAGA",
+          "TRUB2",
+          "DNAJC15",
+          "NDUFS7",
+          "DDA1",
+          "ROMO1",
+          "PFKL",
+          "PDHA1",
+          "DPM2",
+          "NDUFAB1",
+          "PSMC4",
+          "ADISSP",
+          "CHMP4B",
+          "FBXO7",
+          "RER1",
+          "SF3B4",
+          "MTX1",
+          "COX20",
+          "CMPK2",
+          "IFRD2",
+          "ALG3",
+          "MRPS18C",
+          "ZNHIT1",
+          "FABP5",
+          "COX8A",
+          "ARL1",
+          "NOP10",
+          "SRP14",
+          "TBL3",
+          "OAZ1",
+          "CERS2",
+          "COX7A2L",
+          "LSM3",
+          "TMEM70",
+          "NDUFB6",
+          "M6PR",
+          "ERGIC3",
+          "C1orf174",
+          "DNAJC8",
+          "PSMD4",
+          "LAMTOR4",
+          "PLIN2",
+          "ATP6V1G1",
+          "ZNF511",
+          "TESC",
+          "HNRNPC",
+          "TRAPPC1",
+          "JPT1",
+          "NDUFV3",
+          "TPM3",
+          "ENSG00000232995",
+          "METTL5",
+          "EIF4E2",
+          "GTF2H5",
+          "MPLKIP",
+          "ITGB1",
+          "SLC3A2",
+          "SMUG1",
+          "MRPL28",
+          "MVB12A",
+          "POLR2I",
+          "RTCB",
+          "PFDN2",
+          "TMA7",
+          "APEH",
+          "ATP1B3",
+          "SMIM15",
+          "COPS5",
+          "PGAM1",
+          "ATP5MJ",
+          "DERL2",
+          "CSRP1",
+          "ERLEC1",
+          "OSTC",
+          "NDUFS6",
+          "PAK1IP1",
+          "TMEM14C",
+          "TMEM203",
+          "UCP2",
+          "SMAGP",
+          "DHRS4L2",
+          "CINP",
+          "RPUSD1",
+          "NDUFB10",
+          "GPS1",
+          "CCDC124",
+          "PEPD",
+          "RBCK1",
+          "PSMA5",
+          "PNKD",
+          "MRPL36",
+          "H1-4",
+          "POLR1H",
+          "GLO1",
+          "CENPW",
+          "MRPL51",
+          "ARHGDIB",
+          "ZCRB1",
+          "SUGT1",
+          "VTI1B",
+          "JPT2",
+          "PPP4C",
+          "COX7B",
+          "IFI44",
+          "NCBP2AS2",
+          "VDAC1",
+          "COA4",
+          "PSMA6",
+          "IFI27",
+          "SQOR",
+          "TRAF7",
+          "POLDIP2",
+          "ATP5MC1",
+          "SNX5",
+          "ATP6V1E1",
+          "TXNDC12",
+          "CZIB",
+          "TWF2",
+          "PDAP1",
+          "STRAP",
+          "RPL36AL",
+          "C17orf49",
+          "AP2S1",
+          "SSR2",
+          "MRPS9",
+          "BRK1",
+          "NDUFAF3",
+          "COX17",
+          "SRPRB",
+          "PAIP1",
+          "SWI5",
+          "ILK",
+          "HIKESHI",
+          "COX6A1",
+          "PRMT5",
+          "PSMD3",
+          "SNF8",
+          "YIPF2",
+          "NAPA",
+          "EMP3",
+          "NDUFA3",
+          "IDH3B",
+          "UQCR10",
+          "JTB",
+          "OST4",
+          "ITGA4",
+          "MRPL3",
+          "HPF1",
+          "CAPZA2",
+          "SHARPIN",
+          "ATL3",
+          "PPP1R14B",
+          "TPI1",
+          "PSMC6",
+          "SDHB",
+          "CAP1",
+          "COPS9",
+          "EXOSC7",
+          "LAP3",
+          "MALSU1",
+          "GSTO1",
+          "COMMD9",
+          "TIMM8B",
+          "PPIB",
+          "NSMCE1",
+          "VKORC1",
+          "ASPSCR1",
+          "ELOF1",
+          "LAGE3",
+          "TMEM50A",
+          "B4GALT3",
+          "NDUFS2",
+          "COA6",
+          "PREB",
+          "FAM136A",
+          "VAMP5",
+          "MRPL47",
+          "DCUN1D5",
+          "HMBS",
+          "EPSTI1",
+          "UBL5",
+          "PGLS",
+          "ATP5PF",
+          "CSTB",
+          "IDH3G",
+          "NOC2L",
+          "GPN1",
+          "AAMP",
+          "PFDN6",
+          "ARPC5L",
+          "MRPL43",
+          "PTPMT1",
+          "UBE2L6",
+          "ATP5MG",
+          "CLEC2B",
+          "CLN6",
+          "TSR3",
+          "TXNL4A",
+          "UQCR11",
+          "COPE",
+          "COMT",
+          "YWHAH",
+          "SNRNP40",
+          "DUSP23",
+          "PDLIM2",
+          "PPP1CA",
+          "MRPL48",
+          "SPCS2",
+          "NDUFB1",
+          "ANP32A",
+          "COX5A",
+          "PSMG2",
+          "BSG",
+          "MYDGF",
+          "ENSG00000276345",
+          "MRPL55",
+          "SPCS1",
+          "MRPL32",
+          "ATP5MK",
+          "CHID1",
+          "CCDC85B",
+          "VPS29",
+          "CORO1A",
+          "NUDT21",
+          "PSMB3",
+          "SAP30BP",
+          "ANAPC11",
+          "NDUFV2",
+          "RALBP1",
+          "TBCB",
+          "SLC25A5",
+          "UQCRH",
+          "VPS72",
+          "SHC1",
+          "UBE2F",
+          "BUD31",
+          "MRPL17",
+          "YIF1A",
+          "MYL6",
+          "PFN1",
+          "TXNDC17",
+          "ADRM1",
+          "MRPL40",
+          "CYB5R3",
+          "OLA1",
+          "STMP1",
+          "PCBD1",
+          "HEXA",
+          "MRPL27",
+          "SMIM7",
+          "ITPA",
+          "NENF",
+          "TXNDC9",
+          "ATP5ME",
+          "MACROH2A1",
+          "SEM1",
+          "CDC123",
+          "GHITM",
+          "RPP30",
+          "TUBA1C",
+          "ESD",
+          "MRPS11",
+          "UQCRC2",
+          "COPS3",
+          "CYB5A",
+          "TMEM147",
+          "SRM",
+          "UTP11",
+          "MED8",
+          "STAT1",
+          "CCDC12",
+          "MANF",
+          "POLR2H",
+          "LSM2",
+          "SMIM30",
+          "IFIT3",
+          "MRPL23",
+          "TIMM10",
+          "MRPS34",
+          "SPAG7",
+          "WDR83OS",
+          "COX6B1",
+          "TOP1",
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+          "APEX1",
+          "KARS1",
+          "DNMT1",
+          "NUCB1",
+          "SF3A1",
+          "UFC1",
+          "YTHDC1",
+          "UCHL3",
+          "C1QBP",
+          "AATF",
+          "ALKBH7",
+          "EIF2S2",
+          "DDT",
+          "PGK1",
+          "DESI2",
+          "HSPD1",
+          "FIP1L1",
+          "UBE2D3",
+          "MCUB",
+          "EEF1E1",
+          "CBX3",
+          "ZNF22",
+          "NOLC1",
+          "EIF3M",
+          "PPHLN1",
+          "SIVA1",
+          "VAMP2",
+          "ATP5PD",
+          "ATP5F1A",
+          "NDUFA7",
+          "TOMM22",
+          "EIF3I",
+          "SRSF7",
+          "RFC1",
+          "ABRAXAS1",
+          "EPB41L4A-AS1",
+          "CHORDC1",
+          "METAP2",
+          "RBM25",
+          "NME1",
+          "NOP56",
+          "ZFAS1",
+          "UFD1",
+          "XRCC6",
+          "NAA10",
+          "ARPC4",
+          "WDR82",
+          "H1-10",
+          "ABT1",
+          "RWDD1",
+          "SNHG25",
+          "MRPS7",
+          "JUNB",
+          "C19orf53",
+          "CALM3",
+          "SMIM26",
+          "UBE2L3",
+          "PIM3",
+          "SRSF4",
+          "PRRC2C",
+          "SNHG32",
+          "FAM89B",
+          "VPS33A",
+          "MORF4L1",
+          "ZNHIT3",
+          "PSMC5",
+          "CHMP1B",
+          "SSU72",
+          "ANP32E",
+          "SRP9",
+          "CCT4",
+          "NOP58",
+          "CD47",
+          "CDV3",
+          "FXR1",
+          "G3BP2",
+          "CCNI",
+          "SMARCA5",
+          "TAF7",
+          "NOL7",
+          "MYLIP",
+          "EIF3H",
+          "DMAC1",
+          "HNRNPK",
+          "ANAPC16",
+          "SF3B2",
+          "ARL6IP4",
+          "ARGLU1",
+          "LSM12",
+          "KPNB1",
+          "LSM14A",
+          "TXN2",
+          "NECAP2",
+          "C1orf35",
+          "DGUOK",
+          "ISY1",
+          "OCIAD2",
+          "H2AZ1",
+          "RPL7L1",
+          "POLR2G",
+          "ATP5F1B",
+          "COMMD6",
+          "EDC4",
+          "PRMT2",
+          "NBDY",
+          "HP1BP3",
+          "RRP15",
+          "SUMO1",
+          "AIMP1",
+          "TCF7",
+          "DDX46",
+          "TUBB",
+          "NCOA7",
+          "C9orf78",
+          "NUDT5",
+          "PCBP2",
+          "PSMA3-AS1",
+          "EIF2S1",
+          "SNW1",
+          "U2AF2",
+          "SOD1",
+          "HAUS3",
+          "DANCR",
+          "CNOT8",
+          "H4C3",
+          "PDCD2",
+          "PMPCB",
+          "PSMA1",
+          "NAP1L1",
+          "NUBP2",
+          "AARSD1",
+          "PCNA",
+          "RRP1B",
+          "ENSG00000240972",
+          "ST13",
+          "PABIR2",
+          "DPH5",
+          "CSDE1",
+          "MRPS21",
+          "PPM1G",
+          "PYURF",
+          "TNFAIP8",
+          "AMD1",
+          "PSMA2",
+          "BCL7B",
+          "UTP23",
+          "NDUFB9",
+          "BBLN",
+          "VDAC2",
+          "LDHA",
+          "SLIRP",
+          "LRRC75A",
+          "EIF4A3",
+          "ACTG1",
+          "SNRPD1",
+          "YWHAB",
+          "HMGN1",
+          "EWSR1",
+          "RRP7A",
+          "MRPL14",
+          "KIF5B",
+          "WAPL",
+          "TIAL1",
+          "MED4",
+          "NEDD8",
+          "PSMA4",
+          "SNHG9",
+          "CLPP",
+          "CHCHD10",
+          "TMEM183A",
+          "SF3B6",
+          "CAMLG",
+          "BCLAF1",
+          "PHF10",
+          "GOLGA7",
+          "STIP1",
+          "NDUFC2",
+          "HSPH1",
+          "C15orf61",
+          "BFAR",
+          "EIF5A",
+          "TRIM28",
+          "MMP24OS",
+          "IGBP1",
+          "HNRNPR",
+          "ATP5IF1",
+          "GYPC",
+          "STK17B",
+          "FAM162A",
+          "WTAP",
+          "CD3G",
+          "HMGB1",
+          "IDH2",
+          "HNRNPM",
+          "EIF3G",
+          "SYS1",
+          "ATP5PO",
+          "PRDX6",
+          "CCNL1",
+          "MTCH1",
+          "LRCH4",
+          "ARRDC1",
+          "MRPL11",
+          "PHB2",
+          "NDUFA12",
+          "RSRC2",
+          "NUTF2",
+          "SAFB2",
+          "SRRM1",
+          "HNRNPU",
+          "MZT2B",
+          "HNRNPA3",
+          "RAD21",
+          "SET",
+          "PSMD13",
+          "TPP2",
+          "PRPF8",
+          "DDX5",
+          "MRPL38",
+          "FXYD5",
+          "SNRPB",
+          "PPDPF",
+          "MRFAP1",
+          "SKP1",
+          "MRPS17",
+          "DNAJA1",
+          "HDAC7",
+          "SNRPF",
+          "POLR1D",
+          "TRAC",
+          "FUS",
+          "TPGS1",
+          "CNN2",
+          "CIRBP",
+          "TIMM13",
+          "NDUFA11",
+          "LSM4",
+          "NUP50",
+          "SRSF10",
+          "MARCKSL1",
+          "TPRKB",
+          "DDX18",
+          "NIFK",
+          "IMPDH2",
+          "GNL3",
+          "DCK",
+          "CCNG1",
+          "MAD1L1",
+          "CYCS",
+          "POLR2J",
+          "GSTK1",
+          "EXOSC8",
+          "GLOD4",
+          "SEPTIN9",
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+          "HNRNPA2B1",
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+          "YTHDF2",
+          "POLR2D",
+          "THOC7",
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+          "ZBTB24",
+          "FOXK1",
+          "NDUFB8",
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+          "RBM14",
+          "LDHB",
+          "CNPY2",
+          "PNN",
+          "MOAP1",
+          "HSP90AA1",
+          "MESD",
+          "PTPN2",
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+          "FLT3LG",
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+          "RNF187",
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+          "SNHG1",
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+          "FKBP4",
+          "EIF4B",
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+          "ENSG00000286986",
+          "HNRNPA0",
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+          "TATDN1",
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+          "PCNP",
+          "ZNF639",
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+          "TRBC2",
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+          "PPAN",
+          "ZNF90",
+          "RTL10",
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+          "TMEM14B",
+          "SNHG5",
+          "BUD23",
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+          "R3HDM4",
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",
-      "text/plain": [
-       "<Figure size 320x320 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 300,
-       "width": 327
-      }
-     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "p = filtered_perturb_gene_symbols.index('CD8B')\n",
-    "q = filtered_query_gene_symbols.index('GZMM')\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "\n",
-    "ax.plot(dose_response_dict['doses_pi'][p, :], dose_response_dict['responses_mean_pqi'][p, q, :], marker='o')\n",
-    "ax.set_xlabel(f\"Dose of {filtered_perturb_gene_symbols[p]}\")\n",
-    "ax.set_ylabel(f\"Response of {filtered_query_gene_symbols[q]}\")"
+    "network_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)"
    ]
   },
   {

From 15d974409ed088c6531bbc89f7022d525dfccabe Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 15 Feb 2025 00:02:07 +0000
Subject: [PATCH 40/64] updated notebooks, new script

---
 cellarium/ml/utilities/linreg.py              |    127 +
 ...near_response_by_cell_type_prompting.ipynb | 139362 ++++++++-------
 ...cell_types_for_gene_network_analysis.ipynb |   5561 +
 ...on_by_cell_type_prompting_comparison.ipynb |   4295 +
 ...sponse_by_cell_type_prompting_script.ipynb |    276 +
 scripts/linear_response_analysis.py           |    216 +
 6 files changed, 86560 insertions(+), 63277 deletions(-)
 create mode 100644 cellarium/ml/utilities/linreg.py
 create mode 100644 notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/12_linear_response_by_cell_type_prompting_script.ipynb
 create mode 100644 scripts/linear_response_analysis.py

diff --git a/cellarium/ml/utilities/linreg.py b/cellarium/ml/utilities/linreg.py
new file mode 100644
index 00000000..17ffb91d
--- /dev/null
+++ b/cellarium/ml/utilities/linreg.py
@@ -0,0 +1,127 @@
+import numpy as np
+from scipy.stats import linregress
+
+def batch_linear_regression(x_bn, y_bn):
+    """
+    Perform batch-mode one-dimensional linear regression on data with arbitrary batch dimensions.
+    
+    For each batch (with indices b), we assume a linear model:
+    
+        y_bn = slope_b * x_bn + intercept_b + error_bn
+    
+    where:
+      - x_bn and y_bn are arrays of shape (..., n), with the rightmost dimension (n) 
+        representing the samples,
+      - the leftmost dimensions (possibly several) are treated as batch dimensions.
+    
+    Parameters
+    ----------
+    x_bn : np.ndarray
+        Array of covariates with shape (..., n).
+    y_bn : np.ndarray
+        Array of responses with shape (..., n).
+        
+    Returns
+    -------
+    slope_b : np.ndarray
+        Array of slopes with shape equal to the batch shape.
+        For any batch where the variance of x_bn is zero, the slope is set to 0.
+    intercept_b : np.ndarray
+        Array of intercepts with shape equal to the batch shape.
+    r_squared_b : np.ndarray
+        Array of R-squared values with shape equal to the batch shape.
+        For any batch where the total variance of y_bn is zero, r_squared_b is defined as 1.
+    """
+    # Assert that inputs are numpy arrays of the same shape.
+    assert isinstance(x_bn, np.ndarray), "x_bn must be a numpy array"
+    assert isinstance(y_bn, np.ndarray), "y_bn must be a numpy array"
+    assert x_bn.shape == y_bn.shape, "x_bn and y_bn must have the same shape"
+    assert x_bn.ndim >= 1, "x_bn and y_bn must have at least one dimension (samples)"
+
+    # Compute the means along the sample dimension (rightmost axis) and keep that dimension.
+    # The resulting arrays have shape (..., 1) and are named with a '_b1' suffix.
+    x_mean_b1 = np.mean(x_bn, axis=-1, keepdims=True)  # shape: (..., 1)
+    y_mean_b1 = np.mean(y_bn, axis=-1, keepdims=True)  # shape: (..., 1)
+    
+    # Compute the covariance for each batch:
+    #   cov_b = sum_n[(x_bn - x_mean_b1) * (y_bn - y_mean_b1)]
+    cov_b = np.sum((x_bn - x_mean_b1) * (y_bn - y_mean_b1), axis=-1)  # shape: batch shape
+    
+    # Compute the variance of x for each batch:
+    #   var_x_b = sum_n[(x_bn - x_mean_b1)^2]
+    var_x_b = np.sum((x_bn - x_mean_b1)**2, axis=-1)  # shape: batch shape
+    
+    # Compute the slope for each batch, using np.divide to avoid division by zero.
+    slope_b = np.divide(cov_b, var_x_b, out=np.zeros_like(cov_b), where=(var_x_b != 0))
+    
+    # Compute the intercept for each batch.
+    intercept_b = y_mean_b1.squeeze(-1) - slope_b * x_mean_b1.squeeze(-1)
+    
+    # Compute the predicted responses for each batch:
+    #   yhat_bn = slope_b * x_bn + intercept_b (broadcasting over the sample dimension)
+    yhat_bn = slope_b[..., None] * x_bn + intercept_b[..., None]
+    
+    # Compute the residual sum of squares (SS_res) and total sum of squares (SS_tot) for each batch.
+    ss_res_b = np.sum((y_bn - yhat_bn)**2, axis=-1)
+    ss_tot_b = np.sum((y_bn - y_mean_b1)**2, axis=-1)
+    
+    # Compute R-squared for each batch. If ss_tot_b == 0, define R-squared as 1.
+    r_squared_b = np.where(ss_tot_b != 0, 1 - ss_res_b / ss_tot_b, 1.0)
+    
+    return slope_b, intercept_b, r_squared_b
+
+def test_batch_linear_regression():
+    """
+    Test the batch_linear_regression function by comparing its outputs with:
+      1. Running each batch individually using scipy.stats.linregress.
+      2. Ensuring that processing the batches together or one at a time yield the same results.
+    """
+    # Set a random seed for reproducibility.
+    np.random.seed(0)
+    
+    # Define a batch shape (can be multidimensional) and number of samples.
+    batch_shape = (3, 4)  # Two batch dimensions for demonstration.
+    num_samples = 100     # Rightmost dimension: sample index.
+    
+    # Generate random covariate data with shape (3, 4, 100).
+    x_bn = np.random.rand(*batch_shape, num_samples)
+    
+    # Define true slopes and intercepts for each batch.
+    true_slopes = np.linspace(0.5, 2.0, num=np.prod(batch_shape)).reshape(*batch_shape, 1)
+    true_intercepts = np.linspace(1.0, 3.0, num=np.prod(batch_shape)).reshape(*batch_shape, 1)
+    
+    # Generate responses with added noise.
+    noise_bn = np.random.randn(*batch_shape, num_samples) * 0.1
+    y_bn = true_intercepts + true_slopes * x_bn + noise_bn
+    
+    # Use our batch regression implementation.
+    slope_b, intercept_b, r_squared_b = batch_linear_regression(x_bn, y_bn)
+    
+    # Now, compute the regression parameters one batch at a time using scipy.stats.linregress.
+    # We'll flatten the batch dimensions for easier iteration.
+    x_bn_flat = x_bn.reshape(-1, num_samples)
+    y_bn_flat = y_bn.reshape(-1, num_samples)
+    
+    slopes_scipy = np.empty(x_bn_flat.shape[0])
+    intercepts_scipy = np.empty(x_bn_flat.shape[0])
+    r_squared_scipy = np.empty(x_bn_flat.shape[0])
+    
+    for i in range(x_bn_flat.shape[0]):
+        x_n = x_bn_flat[i]
+        y_n = y_bn_flat[i]
+        result = linregress(x_n, y_n)
+        slopes_scipy[i] = result.slope
+        intercepts_scipy[i] = result.intercept
+        r_squared_scipy[i] = result.rvalue**2  # R-squared is the square of the r-value.
+    
+    # Reshape the individual results to the original batch shape.
+    slopes_scipy = slopes_scipy.reshape(batch_shape)
+    intercepts_scipy = intercepts_scipy.reshape(batch_shape)
+    r_squared_scipy = r_squared_scipy.reshape(batch_shape)
+    
+    # Assert that the batch regression results match the individual regressions.
+    assert np.allclose(slope_b, slopes_scipy, atol=1e-6), "Slopes do not match between batch and individual regressions."
+    assert np.allclose(intercept_b, intercepts_scipy, atol=1e-6), "Intercepts do not match between batch and individual regressions."
+    assert np.allclose(r_squared_b, r_squared_scipy, atol=1e-6), "R-squared values do not match between batch and individual regressions."
+    
+    print("Test passed: Batch regression results match individual regressions and scipy.linregress results.")
diff --git a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
index a4eb68b0..b1152be6 100644
--- a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
+++ b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,8 +62,12 @@
     "assay = \"10x 3' v2\"\n",
     "suspension_type = \"cell\"\n",
     "prompt_metadata_dict = {\n",
-    "    \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
-    "    \"tissue\": \"blood\"\n",
+    "    # \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
+    "    # \"tissue\": \"blood\"\n",
+    "    \"cell_type\": \"enterocyte\",\n",
+    "    \"tissue\": \"small intestine\",\n",
+    "    \"disease\": \"normal\",\n",
+    "    \"sex\": \"male\",\n",
     "}\n",
     "total_mrna_umis = 10_000"
    ]
@@ -77,13 +81,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55cdf67c812c45b8b91383450bf47d79",
+       "model_id": "5ba5d175e5bb44f990ec454e69fa19f0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -116,12 +120,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 960x320 with 3 Axes>"
       ]
@@ -158,63 +162,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MALAT1, mode: 296, mean: 420.55\n",
-      "TMSB4X, mode: 179, mean: 164.94\n",
-      "B2M, mode: 122, mean: 133.06\n",
-      "EEF1A1, mode: 102, mean: 105.39\n",
-      "RPS27, mode: 102, mean: 104.24\n",
-      "RPS18, mode: 86, mean: 99.17\n",
-      "RPL41, mode: 83, mean: 95.43\n",
-      "RPL10, mode: 83, mean: 94.16\n",
-      "RPLP1, mode: 81, mean: 92.55\n",
-      "RPL13A, mode: 83, mean: 90.35\n",
-      "RPL13, mode: 78, mean: 83.35\n",
-      "RPS12, mode: 81, mean: 80.84\n",
-      "RPS2, mode: 73, mean: 79.76\n",
-      "RPS19, mode: 68, mean: 75.85\n",
-      "RPL21, mode: 71, mean: 72.20\n",
-      "RPL34, mode: 68, mean: 70.94\n",
-      "RPL28, mode: 66, mean: 70.34\n",
-      "ACTB, mode: 57, mean: 69.83\n",
-      "MT-CO1, mode: 57, mean: 69.81\n",
-      "RPS14, mode: 63, mean: 69.36\n",
-      "RPS29, mode: 68, mean: 69.11\n",
-      "RPLP2, mode: 66, mean: 68.51\n",
-      "RPS6, mode: 63, mean: 66.86\n",
-      "RPL32, mode: 63, mean: 66.72\n",
-      "RPS3, mode: 63, mean: 64.11\n",
-      "RPS15A, mode: 63, mean: 64.06\n",
-      "RPS27A, mode: 63, mean: 64.02\n",
-      "TMSB10, mode: 57, mean: 63.30\n",
-      "RPL3, mode: 57, mean: 61.58\n",
-      "RPS4X, mode: 57, mean: 59.67\n",
-      "RPL18A, mode: 57, mean: 58.87\n",
-      "RPL26, mode: 54, mean: 58.81\n",
-      "RPL39, mode: 56, mean: 58.43\n",
-      "RPL27A, mode: 56, mean: 57.82\n",
-      "RPL23A, mode: 52, mean: 57.59\n",
-      "RPL11, mode: 56, mean: 57.48\n",
-      "RPS3A, mode: 50, mean: 56.65\n",
-      "RPS23, mode: 57, mean: 56.57\n",
-      "RPL19, mode: 49, mean: 55.46\n",
-      "RPS28, mode: 49, mean: 53.03\n",
-      "MT-CO3, mode: 45, mean: 52.94\n",
-      "RPS15, mode: 49, mean: 52.55\n",
-      "MT-CO2, mode: 45, mean: 52.50\n",
-      "RPS8, mode: 49, mean: 52.45\n",
-      "TPT1, mode: 49, mean: 50.57\n",
-      "RPL30, mode: 43, mean: 48.94\n",
-      "RPL37A, mode: 45, mean: 48.54\n",
-      "RPL37, mode: 43, mean: 48.15\n",
-      "RPL15, mode: 49, mean: 48.03\n",
-      "RPL7, mode: 45, mean: 46.62\n"
+      "MT-CO1, mode: 326, mean: 633.77\n",
+      "MALAT1, mode: 358, mean: 372.83\n",
+      "MT-CO3, mode: 48, mean: 369.51\n",
+      "MT-CO2, mode: 42, mean: 185.53\n",
+      "REG1A, mode: 0, mean: 125.15\n",
+      "MT-ND4, mode: 41, mean: 104.30\n",
+      "PHGR1, mode: 30, mean: 101.71\n",
+      "FTL, mode: 13, mean: 95.48\n",
+      "APOA1, mode: 1, mean: 86.11\n",
+      "FABP1, mode: 19, mean: 79.31\n",
+      "MT-CYB, mode: 42, mean: 74.11\n",
+      "RBP2, mode: 0, mean: 73.09\n",
+      "MT-ATP6, mode: 42, mean: 65.29\n",
+      "MT1G, mode: 3, mean: 63.27\n",
+      "IGHA1, mode: 0, mean: 61.11\n",
+      "APOA4, mode: 0, mean: 60.56\n",
+      "RPL41, mode: 53, mean: 60.10\n",
+      "FTH1, mode: 27, mean: 55.72\n",
+      "MT-ND2, mode: 27, mean: 54.30\n",
+      "EEF1A1, mode: 39, mean: 54.29\n",
+      "RPS18, mode: 42, mean: 53.53\n",
+      "MT-ND3, mode: 24, mean: 52.77\n",
+      "MT-ND1, mode: 27, mean: 50.85\n",
+      "RPL10, mode: 45, mean: 49.26\n",
+      "RPL13A, mode: 41, mean: 46.61\n",
+      "RPLP1, mode: 38, mean: 45.75\n",
+      "RPL13, mode: 39, mean: 43.89\n",
+      "TPT1, mode: 39, mean: 43.08\n",
+      "FABP6, mode: 0, mean: 42.95\n",
+      "B2M, mode: 29, mean: 42.67\n",
+      "RPL34, mode: 31, mean: 42.43\n",
+      "TMSB4X, mode: 13, mean: 41.61\n",
+      "RPS14, mode: 31, mean: 38.93\n",
+      "RPS12, mode: 27, mean: 36.99\n",
+      "RPS6, mode: 26, mean: 36.46\n",
+      "TFF3, mode: 0, mean: 36.17\n",
+      "MT2A, mode: 4, mean: 35.95\n",
+      "ALDOB, mode: 0, mean: 35.67\n",
+      "RPL21, mode: 27, mean: 35.44\n",
+      "IGKC, mode: 0, mean: 34.63\n",
+      "RPS27, mode: 23, mean: 34.60\n",
+      "RPS19, mode: 19, mean: 34.54\n",
+      "FABP2, mode: 16, mean: 34.30\n",
+      "RPS28, mode: 23, mean: 33.84\n",
+      "RPLP2, mode: 26, mean: 33.82\n",
+      "TMSB10, mode: 17, mean: 33.47\n",
+      "RPL12, mode: 23, mean: 33.43\n",
+      "RPL32, mode: 23, mean: 33.36\n",
+      "RPS2, mode: 9, mean: 33.30\n",
+      "RPL11, mode: 26, mean: 33.02\n"
      ]
     }
    ],
@@ -231,15 +235,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mode: 296, range: 144\n",
-      "lo: 152, hi: 440\n"
+      "mode: 10, range: 6\n",
+      "lo: 4, hi: 16\n"
      ]
     },
     {
@@ -248,13 +252,13 @@
        "Text(0, 0.5, 'Probability')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
@@ -262,14 +266,14 @@
      "metadata": {
       "image/png": {
        "height": 318,
-       "width": 355
+       "width": 335
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "gene_symbol = 'MALAT1'\n",
+    "gene_symbol = 'GAPDH'\n",
     "q = ctx.var_name_to_index_map[ctx.gene_symbol_to_gene_id_map[gene_symbol]]\n",
     "\n",
     "print(f\"mode: {gex_range_dict['gene_logits_mode_q'][q].item()}, range: {gex_range_dict['range_q'][q].item()}\")\n",
@@ -289,20 +293,22 @@
     "\n",
     "ax.set_title(f\"Gene: {gene_symbol}\")\n",
     "ax.set_xlabel(\"Count\")\n",
-    "ax.set_ylabel(\"Probability\")"
+    "ax.set_ylabel(\"Probability\")\n",
+    "\n",
+    "# ax.set_xlim((0, 10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of genes to perturb: 6321\n",
-      "Number of genes to query: 6321\n"
+      "Number of genes to perturb: 19187\n",
+      "Number of genes to query: 19187\n"
      ]
     }
    ],
@@ -317,7 +323,7 @@
     "x_hi_q = gex_range_dict['x_hi_q']\n",
     "\n",
     "# let's pick genes to perturb (and query) that make sense for this cell type\n",
-    "expr_threshold_tpm = 10.\n",
+    "expr_threshold_tpm = 0.1\n",
     "expr_threshold_mean = expr_threshold_tpm * total_mrna_umis / 1e6\n",
     "expressed_mask_q = (gene_marginal_mean_q >= expr_threshold_mean).cpu().numpy()\n",
     "\n",
@@ -413,19 +419,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pickle\n",
     "\n",
-    "with open('/home/mehrtash/data/cd8_dose_response_dict.pkl', 'rb') as f:\n",
+    "with open('/home/mehrtash/data/cd8_dose_response_dict_tpm_0.1.pkl', 'rb') as f:\n",
     "    dose_response_dict = pickle.load(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +440,7 @@
        "dict_keys(['doses_pi', 'responses_mean_pqi', 'responses_std_pqi', 'control_mean_q', 'control_std_q'])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -445,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -453,6326 +459,19192 @@
      "output_type": "stream",
      "text": [
       "0 SDF4\n",
-      "1 FAAP20\n",
-      "2 ARMH1\n",
-      "3 SERBP1\n",
-      "4 PRKACB\n",
-      "5 GTF2B\n",
-      "6 MTF2\n",
-      "7 HENMT1\n",
-      "8 PRPF38B\n",
-      "9 DAP3\n",
-      "10 ARHGEF2\n",
-      "11 UHMK1\n",
-      "12 RABIF\n",
-      "13 PYCR2\n",
-      "14 ACBD3\n",
-      "15 MTR\n",
-      "16 LPIN1\n",
-      "17 DDX1\n",
-      "18 MRPL35\n",
-      "19 RAB3GAP1\n",
-      "20 ORMDL1\n",
-      "21 ANKZF1\n",
-      "22 SP100\n",
-      "23 TADA3\n",
-      "24 ACAA1\n",
-      "25 CTNNB1\n",
-      "26 TMEM42\n",
-      "27 TOMM70\n",
-      "28 LRRC58\n",
-      "29 MAEA\n",
-      "30 LYAR\n",
-      "31 FGFBP2\n",
-      "32 NUP54\n",
-      "33 BRD9\n",
-      "34 PELO\n",
-      "35 GZMK\n",
-      "36 CAMK4\n",
-      "37 NR3C1\n",
-      "38 CANX\n",
-      "39 TRIM52\n",
-      "40 BTN3A3\n",
-      "41 DDX39B\n",
-      "42 ILRUN\n",
-      "43 FBXO9\n",
-      "44 CRYBG1\n",
-      "45 GTF3C6\n",
-      "46 IFNGR1\n",
-      "47 SNX9\n",
-      "48 C7orf50\n",
-      "49 KDELR2\n",
-      "50 CCT6A\n",
-      "51 CBLL1\n",
-      "52 ING3\n",
-      "53 PPP2R2A\n",
-      "54 RPL30\n",
-      "55 RNF19A\n",
-      "56 ZNF706\n",
-      "57 EFR3A\n",
-      "58 FGD3\n",
-      "59 NDUFA8\n",
-      "60 MLLT10\n",
-      "61 ITPRIP\n",
-      "62 PTPRE\n",
-      "63 IFITM2\n",
-      "64 ZNRD2\n",
-      "65 MRE11\n",
-      "66 CUL5\n",
-      "67 PTMS\n",
-      "68 PTPN6\n",
-      "69 LETMD1\n",
-      "70 ARHGAP9\n",
-      "71 CAND1\n",
-      "72 NUP37\n",
-      "73 GPN3\n",
-      "74 SPRING1\n",
-      "75 APEX1\n",
-      "76 SPTSSA\n",
-      "77 ATP6V1D\n",
-      "78 IFT43\n",
-      "79 FOXN3\n",
-      "80 MARK3\n",
-      "81 SPG11\n",
-      "82 FEM1B\n",
-      "83 TLE3\n",
-      "84 GNPTG\n",
-      "85 FLYWCH2\n",
-      "86 NPIPB5\n",
-      "87 PSMB10\n",
-      "88 NIP7\n",
-      "89 KARS1\n",
-      "90 MAF\n",
-      "91 PLCG2\n",
-      "92 XAF1\n",
-      "93 NCOR1\n",
-      "94 SLFN5\n",
-      "95 SNHG30\n",
-      "96 CCR7\n",
-      "97 TMUB2\n",
-      "98 SEC14L1\n",
-      "99 OGFOD3\n",
-      "100 RNF125\n",
-      "101 PIAS2\n",
-      "102 TMX3\n",
-      "103 DNMT1\n",
-      "104 SAMD4B\n",
-      "105 TOMM40\n",
-      "106 NUCB1\n",
-      "107 MKKS\n",
-      "108 DSTN\n",
-      "109 SNHG17\n",
-      "110 NCOA3\n",
-      "111 MIAT\n",
-      "112 SF3A1\n",
-      "113 RPL3\n",
-      "114 TTC38\n",
-      "115 P2RY8\n",
-      "116 APOO\n",
-      "117 KDM6A\n",
-      "118 RLIM\n",
-      "119 TCEAL8\n",
-      "120 NKAP\n",
-      "121 UTP14A\n",
-      "122 LUZP1\n",
-      "123 IFT25\n",
-      "124 GBP1\n",
-      "125 S1PR1\n",
-      "126 MAN1A2\n",
-      "127 MRPL24\n",
-      "128 SH2D2A\n",
-      "129 DEDD\n",
-      "130 UFC1\n",
-      "131 ATF3\n",
-      "132 GPATCH2\n",
-      "133 ENSG00000182551\n",
-      "134 RNASEH1\n",
-      "135 LBH\n",
-      "136 PRADC1\n",
-      "137 STAMBP\n",
-      "138 CNOT11\n",
-      "139 LIMS1\n",
-      "140 TMBIM1\n",
-      "141 TGFBR2\n",
-      "142 CRTAP\n",
-      "143 CLASP2\n",
-      "144 UQCC5\n",
-      "145 PBRM1\n",
-      "146 CBLB\n",
-      "147 TFDP2\n",
-      "148 PHC3\n",
-      "149 SENP5\n",
-      "150 WDR1\n",
-      "151 DTHD1\n",
-      "152 YTHDC1\n",
-      "153 MRPL1\n",
-      "154 SCLT1\n",
-      "155 ELF2\n",
-      "156 IL6ST\n",
-      "157 ERAP2\n",
-      "158 AP3S1\n",
-      "159 COMMD10\n",
-      "160 FNIP1\n",
-      "161 PFDN1\n",
-      "162 MRPL22\n",
-      "163 NPM1\n",
-      "164 KIAA1191\n",
-      "165 GRK6\n",
-      "166 NEDD9\n",
-      "167 PSMB8-AS1\n",
-      "168 STK38\n",
-      "169 TBCC\n",
-      "170 HDAC2\n",
-      "171 EIF2AK1\n",
-      "172 YKT6\n",
-      "173 CCM2\n",
-      "174 SUMF2\n",
-      "175 FIS1\n",
-      "176 ATP6V1F\n",
-      "177 GIMAP2\n",
-      "178 TMUB1\n",
-      "179 PCMTD1\n",
-      "180 NDRG1\n",
-      "181 SECISBP2\n",
-      "182 NCBP1\n",
-      "183 PTRH1\n",
-      "184 GATA3\n",
-      "185 MSRB2\n",
-      "186 SVIL\n",
-      "187 DNAJC9\n",
-      "188 ADK\n",
-      "189 NUTM2B-AS1\n",
-      "190 OGA\n",
-      "191 STN1\n",
-      "192 C11orf21\n",
-      "193 KCNQ1OT1\n",
-      "194 NUP98\n",
-      "195 CD59\n",
-      "196 APIP\n",
-      "197 PRDX5\n",
-      "198 RNASEH2C\n",
-      "199 SYTL2\n",
-      "200 MAML2\n",
-      "201 DDX10\n",
-      "202 VPS26B\n",
-      "203 RECQL\n",
-      "204 LINC00938\n",
-      "205 LIMA1\n",
-      "206 PRR13\n",
-      "207 XPOT\n",
-      "208 NHLRC3\n",
-      "209 UCHL3\n",
-      "210 OSGEP\n",
-      "211 PABPN1\n",
-      "212 DHRS1\n",
-      "213 ARF6\n",
-      "214 ATXN3\n",
-      "215 LINC01550\n",
-      "216 EMC7\n",
-      "217 SERF2\n",
-      "218 B2M\n",
-      "219 CLPX\n",
-      "220 HBA1\n",
-      "221 SOCS1\n",
-      "222 MAZ\n",
-      "223 COG4\n",
-      "224 C1QBP\n",
-      "225 UNC119\n",
-      "226 AATF\n",
-      "227 ACLY\n",
-      "228 CCDC43\n",
-      "229 FMNL1-DT\n",
-      "230 SUPT4H1\n",
-      "231 SMAD2\n",
-      "232 LMAN1\n",
-      "233 CFD\n",
-      "234 ABCA7\n",
-      "235 MATK\n",
-      "236 ALKBH7\n",
-      "237 RPL18A\n",
-      "238 SUGP2\n",
-      "239 GMFG\n",
-      "240 BLVRB\n",
-      "241 ETFB\n",
-      "242 LAIR1\n",
-      "243 DTD1\n",
-      "244 EIF2S2\n",
-      "245 SLC35C2\n",
-      "246 DDT\n",
-      "247 DDX17\n",
-      "248 ENSG00000226179\n",
-      "249 RBBP7\n",
-      "250 CXorf38\n",
-      "251 PGK1\n",
-      "252 PHF6\n",
-      "253 AURKAIP1\n",
-      "254 NADK\n",
-      "255 LYPLA2\n",
-      "256 NFYC\n",
-      "257 RBMXL1\n",
-      "258 HSPA6\n",
-      "259 CEP350\n",
-      "260 GLRX2\n",
-      "261 DENND1B\n",
-      "262 UBE2T\n",
-      "263 ZC3H11A\n",
-      "264 ERO1B\n",
-      "265 DESI2\n",
-      "266 BABAM2\n",
-      "267 MRPL19\n",
-      "268 C2orf68\n",
-      "269 NIFK-AS1\n",
-      "270 CCNT2\n",
-      "271 LY75\n",
-      "272 DYNC1I2\n",
-      "273 LNPK\n",
-      "274 HSPD1\n",
-      "275 NUDT16\n",
-      "276 PCCB\n",
-      "277 GK5\n",
-      "278 DNAJC19\n",
-      "279 FIP1L1\n",
-      "280 RAP1GDS1\n",
-      "281 UBE2D3\n",
-      "282 MCUB\n",
-      "283 PPWD1\n",
-      "284 RPS23\n",
-      "285 COX7C\n",
-      "286 IRF1-AS1\n",
-      "287 UQCRQ\n",
-      "288 ZCCHC10\n",
-      "289 ZMAT2\n",
-      "290 HMMR\n",
-      "291 CREBRF\n",
-      "292 LMAN2\n",
-      "293 EEF1E1\n",
-      "294 PSMB8\n",
-      "295 ZFAND3\n",
-      "296 FBXO5\n",
-      "297 DYNLT1\n",
-      "298 SFT2D1\n",
-      "299 CBX3\n",
-      "300 SKAP2\n",
-      "301 ATP5MF\n",
-      "302 ALKBH4\n",
-      "303 ZNF277\n",
-      "304 ABCF2\n",
-      "305 SFTPC\n",
-      "306 ASAP1\n",
-      "307 RPL12\n",
-      "308 NTMT1\n",
-      "309 TTF1\n",
-      "310 WAC-AS1\n",
-      "311 ZNF22\n",
-      "312 MAPK8\n",
-      "313 VSIR\n",
-      "314 ECD\n",
-      "315 ZNF518A\n",
-      "316 NOLC1\n",
-      "317 PPP2R2D\n",
-      "318 RASSF7\n",
-      "319 EIF3M\n",
-      "320 OTUB1\n",
-      "321 DPF2\n",
-      "322 DPAGT1\n",
-      "323 CDCA3\n",
-      "324 PPHLN1\n",
-      "325 PFDN5\n",
-      "326 OS9\n",
-      "327 TMPO\n",
-      "328 SCYL2\n",
-      "329 SPPL3\n",
-      "330 MRPL52\n",
-      "331 GZMH\n",
-      "332 PTGDR\n",
-      "333 SIVA1\n",
-      "334 SNHG14\n",
-      "335 ZNF106\n",
-      "336 MINDY2\n",
-      "337 FBXO22\n",
-      "338 WDR73\n",
-      "339 SEC11A\n",
-      "340 LUC7L\n",
-      "341 MCRIP2\n",
-      "342 CHD9\n",
-      "343 USB1\n",
-      "344 TRADD\n",
-      "345 ZNF276\n",
-      "346 NAA38\n",
-      "347 VAMP2\n",
-      "348 SLFN12L\n",
-      "349 ENSG00000276085\n",
-      "350 CDK5RAP3\n",
-      "351 SKAP1\n",
-      "352 ATP5PD\n",
-      "353 SUMO2\n",
-      "354 CCDC137\n",
-      "355 CYBC1\n",
-      "356 ENOSF1\n",
-      "357 ATP5F1A\n",
-      "358 MEX3C\n",
-      "359 NFIC\n",
-      "360 NDUFA7\n",
-      "361 STX10\n",
-      "362 TMEM161A\n",
-      "363 CARD8\n",
-      "364 ALDH16A1\n",
-      "365 BRWD1\n",
-      "366 NOL12\n",
-      "367 TOMM22\n",
-      "368 JOSD1\n",
-      "369 NDUFA6\n",
-      "370 EIF1AX\n",
-      "371 FMR1\n",
-      "372 VMA21\n",
-      "373 FUNDC2\n",
-      "374 KCNAB2\n",
-      "375 RERE\n",
-      "376 AKR7A2\n",
-      "377 NIPAL3\n",
-      "378 EIF3I\n",
-      "379 EFCAB14\n",
-      "380 SCP2\n",
-      "381 JUN\n",
-      "382 ENSG00000273338\n",
-      "383 LMNA\n",
-      "384 SLAMF7\n",
-      "385 UAP1\n",
-      "386 SELL\n",
-      "387 VAMP4\n",
-      "388 DHX9\n",
-      "389 RGS2\n",
-      "390 NEK7\n",
-      "391 RAB4A\n",
-      "392 HPCAL1\n",
-      "393 PDIA6\n",
-      "394 SRSF7\n",
-      "395 THUMPD2\n",
-      "396 BCL11A\n",
-      "397 TIA1\n",
-      "398 MTX2\n",
-      "399 ING5\n",
-      "400 UBA7\n",
-      "401 RYBP\n",
-      "402 GTPBP8\n",
-      "403 DTX3L\n",
-      "404 RFC1\n",
-      "405 TMEM33\n",
-      "406 ABRAXAS1\n",
-      "407 SPP1\n",
-      "408 ZNF131\n",
-      "409 POLK\n",
-      "410 MAN2A1\n",
-      "411 EPB41L4A-AS1\n",
-      "412 RNF145\n",
-      "413 LCP2\n",
-      "414 RXRB\n",
-      "415 MRPS10\n",
-      "416 MCM3\n",
-      "417 REV3L\n",
-      "418 GSAP\n",
-      "419 RBM33\n",
-      "420 CLN8\n",
-      "421 HMBOX1\n",
-      "422 SGK3\n",
-      "423 NBN\n",
-      "424 HINT2\n",
-      "425 CEP78\n",
-      "426 ENSG00000196312\n",
-      "427 ODF2\n",
-      "428 GTF3C5\n",
-      "429 BRD3\n",
-      "430 PRKCQ\n",
-      "431 ACBD5\n",
-      "432 HERC4\n",
-      "433 TSPAN14\n",
-      "434 TRIM8\n",
-      "435 PDE3B\n",
-      "436 SERGEF\n",
-      "437 HTATIP2\n",
-      "438 CHORDC1\n",
-      "439 TMBIM6\n",
-      "440 CPSF6\n",
-      "441 METAP2\n",
-      "442 RAB35\n",
-      "443 OASL\n",
-      "444 ZCCHC8\n",
-      "445 DENR\n",
-      "446 GTF2F2\n",
-      "447 TFDP1\n",
-      "448 SEC23A\n",
-      "449 AP5M1\n",
-      "450 RBM25\n",
-      "451 GOLGA8B\n",
-      "452 RPLP1\n",
-      "453 ADPGK\n",
-      "454 POLR3K\n",
-      "455 SMG1\n",
-      "456 PYCARD\n",
-      "457 NFATC3\n",
-      "458 BANP\n",
-      "459 RPL13\n",
-      "460 ENSG00000262202\n",
-      "461 DHRS7B\n",
-      "462 TMEM97\n",
-      "463 NME1\n",
-      "464 NARS1\n",
-      "465 MED16\n",
-      "466 WDR18\n",
-      "467 WDR83\n",
-      "468 PKN1\n",
-      "469 RABAC1\n",
-      "470 TMEM160\n",
-      "471 BCL2L12\n",
-      "472 NOP56\n",
-      "473 PTPRA\n",
-      "474 HM13\n",
-      "475 SAMHD1\n",
-      "476 ZFAS1\n",
-      "477 RGS19\n",
-      "478 ENSG00000159128\n",
-      "479 DYRK1A\n",
-      "480 UFD1\n",
-      "481 XRCC6\n",
-      "482 NHSL2\n",
-      "483 PIN4\n",
-      "484 SSR4\n",
-      "485 NAA10\n",
-      "486 GAB3\n",
-      "487 VAMP7\n",
-      "488 MT-ND4\n",
-      "489 CCNL2\n",
-      "490 SVBP\n",
-      "491 CDC20\n",
-      "492 GPBP1L1\n",
-      "493 MAGOH\n",
-      "494 MYSM1\n",
-      "495 GADD45A\n",
-      "496 IFI16\n",
-      "497 RBBP5\n",
-      "498 GCC2\n",
-      "499 PSD4\n",
-      "500 UBXN4\n",
-      "501 CXCR4\n",
-      "502 ZEB2\n",
-      "503 LINC01934\n",
-      "504 CNOT9\n",
-      "505 TUBA4A\n",
-      "506 INPP5D\n",
-      "507 ARPC4\n",
-      "508 DALRD3\n",
-      "509 TUSC2\n",
-      "510 WDR82\n",
-      "511 CHMP2B\n",
-      "512 C3orf38\n",
-      "513 TIGIT\n",
-      "514 H1-10\n",
-      "515 MSL2\n",
-      "516 NSD2\n",
-      "517 NUDT9\n",
-      "518 PAPSS1\n",
-      "519 TMEM131L\n",
-      "520 ICE1\n",
-      "521 RAD1\n",
-      "522 ENSG00000287263\n",
-      "523 CWC27\n",
-      "524 CENPK\n",
-      "525 CLINT1\n",
-      "526 ABT1\n",
-      "527 NELFE\n",
-      "528 MAPK13\n",
-      "529 SUPT3H\n",
-      "530 FOXO3\n",
-      "531 RWDD1\n",
-      "532 AIG1\n",
-      "533 RNF216\n",
-      "534 AVL9\n",
-      "535 VPS41\n",
-      "536 SDHAF3\n",
-      "537 POP7\n",
-      "538 FDFT1\n",
-      "539 LSM1\n",
-      "540 YTHDF3\n",
-      "541 ELOC\n",
-      "542 RMDN1\n",
-      "543 PLAA\n",
-      "544 FKBP15\n",
-      "545 SMNDC1\n",
-      "546 TALDO1\n",
-      "547 HBG1\n",
-      "548 TMEM258\n",
-      "549 SCGB1A1\n",
-      "550 MALAT1\n",
-      "551 GSTP1\n",
-      "552 ANAPC15\n",
-      "553 KBTBD3\n",
-      "554 DYRK4\n",
-      "555 TAPBPL\n",
-      "556 GOLT1B\n",
-      "557 TESPA1\n",
-      "558 C12orf75\n",
-      "559 POP5\n",
-      "560 ORAI1\n",
-      "561 ZMYM5\n",
-      "562 ALG5\n",
-      "563 IPO5\n",
-      "564 PTGER2\n",
-      "565 LGALS3\n",
-      "566 TRIP11\n",
-      "567 BAG5\n",
-      "568 GCHFR\n",
-      "569 POLR2M\n",
-      "570 ETFA\n",
-      "571 ANTKMT\n",
-      "572 MARF1\n",
-      "573 GTF3C1\n",
-      "574 AHSP\n",
-      "575 SNX20\n",
-      "576 WDR59\n",
-      "577 GOSR1\n",
-      "578 LRRC37B\n",
-      "579 CCL4\n",
-      "580 STARD3\n",
-      "581 CASC3\n",
-      "582 DNAJC7\n",
-      "583 SNHG25\n",
-      "584 FAM104A\n",
-      "585 MRPS7\n",
-      "586 ARL16\n",
-      "587 WDR45B\n",
-      "588 JUNB\n",
-      "589 C19orf53\n",
-      "590 RAB4B\n",
-      "591 CCDC97\n",
-      "592 CALM3\n",
-      "593 NUP62\n",
-      "594 UBE2S\n",
-      "595 SMIM26\n",
-      "596 RTF2\n",
-      "597 MHENCR\n",
-      "598 UBASH3A\n",
-      "599 UBE2L3\n",
-      "600 PIM3\n",
-      "601 PRKX\n",
-      "602 CA5B\n",
-      "603 SYAP1\n",
-      "604 SH3KBP1\n",
-      "605 MAGED1\n",
-      "606 HSD17B10\n",
-      "607 ATRX\n",
-      "608 AIFM1\n",
-      "609 FAM3A\n",
-      "610 EFHD2\n",
-      "611 CD52\n",
-      "612 SRSF4\n",
-      "613 HIVEP3\n",
-      "614 EPS15\n",
-      "615 USP1\n",
-      "616 DNTTIP2\n",
-      "617 RTCA\n",
-      "618 ENSG00000274265\n",
-      "619 PRRC2C\n",
-      "620 CD46\n",
-      "621 MRPL33\n",
-      "622 PREPL\n",
-      "623 CFAP36\n",
-      "624 COX5B\n",
-      "625 RALB\n",
-      "626 HAT1\n",
-      "627 MRPL44\n",
-      "628 CNOT10\n",
-      "629 RPL14\n",
-      "630 SMARCC1\n",
-      "631 MAP4\n",
-      "632 FHIT\n",
-      "633 SUCLG2\n",
-      "634 TIMMDC1\n",
-      "635 RUVBL1\n",
-      "636 COPG1\n",
-      "637 EIF4G1\n",
-      "638 LARP7\n",
-      "639 BLTP1\n",
-      "640 ZNF330\n",
-      "641 CLPTM1L\n",
-      "642 OTULINL\n",
-      "643 HMGCS1\n",
-      "644 AK6\n",
-      "645 HEXB\n",
-      "646 FAM13B\n",
-      "647 BOD1\n",
-      "648 ENSG00000272501\n",
-      "649 SNHG32\n",
-      "650 MRPS18A\n",
-      "651 COX7A2\n",
-      "652 CDC40\n",
-      "653 MTRF1L\n",
-      "654 HOTAIRM1\n",
-      "655 TECPR1\n",
-      "656 DEFA6\n",
-      "657 INTS10\n",
-      "658 GFUS\n",
-      "659 PLGRKT\n",
-      "660 CHMP5\n",
-      "661 TOMM5\n",
-      "662 ISCA1\n",
-      "663 PDCL\n",
-      "664 GLE1\n",
-      "665 FBH1\n",
-      "666 ATP5F1C\n",
-      "667 DNAJC1\n",
-      "668 SUPV3L1\n",
-      "669 ALDH18A1\n",
-      "670 BAD\n",
-      "671 FAM89B\n",
-      "672 MAP3K11\n",
-      "673 ACAT1\n",
-      "674 ETV6\n",
-      "675 DCTN2\n",
-      "676 IKBIP\n",
-      "677 RPL6\n",
-      "678 VPS33A\n",
-      "679 DDX55\n",
-      "680 NAA16\n",
-      "681 ITM2B\n",
-      "682 ADAM10\n",
-      "683 MORF4L1\n",
-      "684 ARL6IP1\n",
-      "685 POLR3E\n",
-      "686 PRR14\n",
-      "687 HNRNPA1L3\n",
-      "688 CTCF\n",
-      "689 RNASEK\n",
-      "690 NUFIP2\n",
-      "691 ATAD5\n",
-      "692 ZNHIT3\n",
-      "693 TMEM101\n",
-      "694 PSMC5\n",
-      "695 MRPL58\n",
-      "696 TMC6\n",
-      "697 CHMP1B\n",
-      "698 VPS4B\n",
-      "699 TIMM44\n",
-      "700 TYK2\n",
-      "701 TMEM205\n",
-      "702 RPS11\n",
-      "703 ZBP1\n",
-      "704 DIDO1\n",
-      "705 CRYZL1\n",
-      "706 PIGP\n",
-      "707 TRAPPC10\n",
-      "708 MTFP1\n",
-      "709 MAGEH1\n",
-      "710 OGT\n",
-      "711 MT-ND3\n",
-      "712 SSU72\n",
-      "713 MIIP\n",
-      "714 BSDC1\n",
-      "715 MAP7D1\n",
-      "716 DPH2\n",
-      "717 ERI3\n",
-      "718 PLK3\n",
-      "719 ANP32E\n",
-      "720 LAX1\n",
-      "721 CNIH4\n",
-      "722 SRP9\n",
-      "723 EIPR1\n",
-      "724 CCT4\n",
-      "725 ST3GAL5\n",
-      "726 ANKRD39\n",
-      "727 MTLN\n",
-      "728 ZC3H8\n",
-      "729 PLEKHB2\n",
-      "730 MBD5\n",
-      "731 PDK1\n",
-      "732 DNAJC10\n",
-      "733 GLS\n",
-      "734 NOP58\n",
-      "735 GIGYF2\n",
-      "736 SLC6A6\n",
-      "737 CXCR6\n",
-      "738 CD47\n",
-      "739 DZIP3\n",
-      "740 UBA5\n",
-      "741 CDV3\n",
-      "742 RNF7\n",
-      "743 FXR1\n",
-      "744 PPP1R2\n",
-      "745 HOPX\n",
-      "746 G3BP2\n",
-      "747 CCNI\n",
-      "748 AFF1\n",
-      "749 CCNA2\n",
-      "750 SMARCA5\n",
-      "751 CASP3\n",
-      "752 OXCT1\n",
-      "753 TAF7\n",
-      "754 HDAC3\n",
-      "755 PSMG4\n",
-      "756 NOL7\n",
-      "757 MYLIP\n",
-      "758 SHPRH\n",
-      "759 IPCEF1\n",
-      "760 GLCCI1\n",
-      "761 RP9\n",
-      "762 GUSB\n",
-      "763 ANKIB1\n",
-      "764 KDM7A\n",
-      "765 VCPIP1\n",
-      "766 POLR2K\n",
-      "767 EIF3H\n",
-      "768 CDC37L1\n",
-      "769 KIAA2026\n",
-      "770 DMAC1\n",
-      "771 HNRNPK\n",
-      "772 COQ4\n",
-      "773 NSUN6\n",
-      "774 ANAPC16\n",
-      "775 CHCHD1\n",
-      "776 KAT6B\n",
-      "777 ATAD1\n",
-      "778 EXOC6\n",
-      "779 DENND10\n",
-      "780 ECHS1\n",
-      "781 CD81\n",
-      "782 TRIM22\n",
-      "783 TPP1\n",
-      "784 SAAL1\n",
-      "785 SF3B2\n",
-      "786 SRSF8\n",
-      "787 REXO2\n",
-      "788 SRPRA\n",
-      "789 IRAG2\n",
-      "790 LYZ\n",
-      "791 EEA1\n",
-      "792 TCHP\n",
-      "793 MED13L\n",
-      "794 TMEM120B\n",
-      "795 ARL6IP4\n",
-      "796 TMED2\n",
-      "797 N4BP2L2\n",
-      "798 DLEU1\n",
-      "799 KLF12\n",
-      "800 ARGLU1\n",
-      "801 EXOC5\n",
-      "802 RDH11\n",
-      "803 DCAF5\n",
-      "804 ACYP1\n",
-      "805 GPATCH2L\n",
-      "806 TC2N\n",
-      "807 RIN3\n",
-      "808 HERC1\n",
-      "809 SCAMP2\n",
-      "810 TLNRD1\n",
-      "811 NPRL3\n",
-      "812 HAGHL\n",
-      "813 SPSB3\n",
-      "814 CDIPT\n",
-      "815 EMC8\n",
-      "816 TRAPPC2L\n",
-      "817 TSR1\n",
-      "818 CHRNB1\n",
-      "819 CDK12\n",
-      "820 ENSG00000266088\n",
-      "821 EIF1\n",
-      "822 NKIRAS2\n",
-      "823 LSM12\n",
-      "824 KPNB1\n",
-      "825 VMP1\n",
-      "826 EXOC7\n",
-      "827 METTL23\n",
-      "828 SLC16A3\n",
-      "829 ROCK1\n",
-      "830 CXXC1\n",
-      "831 PWWP3A\n",
-      "832 MKNK2\n",
-      "833 DOHH\n",
-      "834 APBA3\n",
-      "835 TMIGD2\n",
-      "836 SAFB\n",
-      "837 GPR108\n",
-      "838 ZNF101\n",
-      "839 LSM14A\n",
-      "840 ZNF302\n",
-      "841 IGFLR1\n",
-      "842 ARHGEF1\n",
-      "843 GPCPD1\n",
-      "844 MAPRE1\n",
-      "845 SLA2\n",
-      "846 CTSA\n",
-      "847 PSMA7\n",
-      "848 MIR155HG\n",
-      "849 IGLC3\n",
-      "850 IGLL1\n",
-      "851 ASCC2\n",
-      "852 TXN2\n",
-      "853 PHF5A\n",
-      "854 TRMU\n",
-      "855 FAM104B\n",
-      "856 PBDC1\n",
-      "857 UPF3B\n",
-      "858 NDUFA1\n",
-      "859 MCTS1\n",
-      "860 ATP6AP1\n",
-      "861 UBL4A\n",
-      "862 DKC1\n",
-      "863 NECAP2\n",
-      "864 RSRP1\n",
-      "865 GMEB1\n",
-      "866 ZBTB8OS\n",
-      "867 PPT1\n",
-      "868 TMEM59\n",
-      "869 DPYD\n",
-      "870 ZNF281\n",
-      "871 TIMM17A\n",
-      "872 TRAF3IP3\n",
-      "873 ANGEL2\n",
-      "874 C1orf35\n",
-      "875 MDH1\n",
-      "876 DGUOK\n",
-      "877 TMSB10\n",
-      "878 SEC22C\n",
-      "879 ARHGEF3\n",
-      "880 NXPE3\n",
-      "881 ISY1\n",
-      "882 ATP11B\n",
-      "883 PIGX\n",
-      "884 TMEM128\n",
-      "885 OCIAD2\n",
-      "886 NAAA\n",
-      "887 SEC31A\n",
-      "888 H2AZ1\n",
-      "889 CENPE\n",
-      "890 DDX60L\n",
-      "891 UFSP2\n",
-      "892 SDHA\n",
-      "893 NIPBL\n",
-      "894 NDUFAF2\n",
-      "895 RAD17\n",
-      "896 TMEM161B-DT\n",
-      "897 TMED7\n",
-      "898 SRFBP1\n",
-      "899 HMGXB3\n",
-      "900 THOC3\n",
-      "901 PHYKPL\n",
-      "902 RNF130\n",
-      "903 DUSP22\n",
-      "904 NEU1\n",
-      "905 STK19\n",
-      "906 RPL7L1\n",
-      "907 GOPC\n",
-      "908 THEMIS\n",
-      "909 LATS1\n",
-      "910 C1GALT1\n",
-      "911 TRGC2\n",
-      "912 HUS1\n",
-      "913 VOPP1\n",
-      "914 FMC1\n",
-      "915 GIMAP7\n",
-      "916 DYNC2I1\n",
-      "917 ESCO2\n",
-      "918 EEF1D\n",
-      "919 PUF60\n",
-      "920 UBAP1\n",
-      "921 ZCCHC7\n",
-      "922 RFK\n",
-      "923 SUSD3\n",
-      "924 STRBP\n",
-      "925 UCK1\n",
-      "926 HSPA14\n",
-      "927 PDZD8\n",
-      "928 BNIP3\n",
-      "929 TSG101\n",
-      "930 RCN1\n",
-      "931 FAM111A\n",
-      "932 POLR2G\n",
-      "933 MED17\n",
-      "934 CEP57\n",
-      "935 HYOU1\n",
-      "936 SORL1\n",
-      "937 LAG3\n",
-      "938 CLEC2D\n",
-      "939 MRPS35\n",
-      "940 TUBA1A\n",
-      "941 COX14\n",
-      "942 ATP5F1B\n",
-      "943 STAT6\n",
-      "944 DYRK2\n",
-      "945 CHST11\n",
-      "946 NOPCHAP1\n",
-      "947 MLEC\n",
-      "948 COMMD6\n",
-      "949 MBIP\n",
-      "950 CNIH1\n",
-      "951 SETD3\n",
-      "952 RASGRP1\n",
-      "953 GNB5\n",
-      "954 MPI\n",
-      "955 PSTPIP1\n",
-      "956 UNC45A\n",
-      "957 MEF2A\n",
-      "958 RHOT2\n",
-      "959 NTHL1\n",
-      "960 ALDOA\n",
-      "961 STX4\n",
-      "962 AMFR\n",
-      "963 CKLF\n",
-      "964 CENPN\n",
-      "965 INPP5K\n",
-      "966 ATP2A3\n",
-      "967 CYB5D2\n",
-      "968 CENPV\n",
-      "969 UBB\n",
-      "970 WSB1\n",
-      "971 SUPT6H\n",
-      "972 RARA\n",
-      "973 HEXIM1\n",
-      "974 DYNLL2\n",
-      "975 AMZ2\n",
-      "976 LGALS3BP\n",
-      "977 TXNL1\n",
-      "978 GPX4\n",
-      "979 TRAPPC5\n",
-      "980 QTRT1\n",
-      "981 RAB8A\n",
-      "982 RFXANK\n",
-      "983 GMIP\n",
-      "984 EIF3K\n",
-      "985 PAF1\n",
-      "986 TIMM50\n",
-      "987 CLPTM1\n",
-      "988 NOP53\n",
-      "989 BAX\n",
-      "990 RUVBL2\n",
-      "991 LENG1\n",
-      "992 ISOC2\n",
-      "993 PANK2\n",
-      "994 PDRG1\n",
-      "995 PIGT\n",
-      "996 ACOT8\n",
-      "997 ZNF831\n",
-      "998 MIS18A\n",
-      "999 PRMT2\n",
-      "1000 THAP7\n",
-      "1001 POLR2F\n",
-      "1002 POLR3H\n",
-      "1003 RAB9A\n",
-      "1004 ZRSR2\n",
-      "1005 NBDY\n",
-      "1006 ITM2A\n",
-      "1007 RPL39\n",
-      "1008 BCAP31\n",
-      "1009 AGTRAP\n",
-      "1010 HP1BP3\n",
-      "1011 LMO4\n",
-      "1012 ENSG00000236656\n",
-      "1013 DDX59\n",
-      "1014 RRP15\n",
-      "1015 GALNT2\n",
-      "1016 RRM2\n",
-      "1017 CRIPT\n",
-      "1018 SPTBN1\n",
-      "1019 ORC4\n",
-      "1020 CFLAR\n",
-      "1021 SUMO1\n",
-      "1022 CAB39\n",
-      "1023 COPS7B\n",
-      "1024 MTMR14\n",
-      "1025 RPL32\n",
-      "1026 FGD5-AS1\n",
-      "1027 TRAT1\n",
-      "1028 ATP6V1A\n",
-      "1029 RPN1\n",
-      "1030 TBL1XR1\n",
-      "1031 TNIP2\n",
-      "1032 DCAF16\n",
-      "1033 UTP3\n",
-      "1034 ANKRD17\n",
-      "1035 AIMP1\n",
-      "1036 SPCS3\n",
-      "1037 MOCS2\n",
-      "1038 CSNK1G3\n",
-      "1039 AFF4\n",
-      "1040 TCF7\n",
-      "1041 DDX46\n",
-      "1042 MZB1\n",
-      "1043 SQSTM1\n",
-      "1044 C6orf62\n",
-      "1045 HLA-E\n",
-      "1046 PPP1R18\n",
-      "1047 TUBB\n",
-      "1048 RING1\n",
-      "1049 RPF2\n",
-      "1050 NCOA7\n",
-      "1051 PCMT1\n",
-      "1052 AIMP2\n",
-      "1053 TMEM106B\n",
-      "1054 NT5C3A\n",
-      "1055 ARPC1A\n",
-      "1056 KMT2E-AS1\n",
-      "1057 TNPO3\n",
-      "1058 PABPC1\n",
-      "1059 RRAGA\n",
-      "1060 MLLT3\n",
-      "1061 INIP\n",
-      "1062 PTGES2\n",
-      "1063 TRUB2\n",
-      "1064 C9orf78\n",
-      "1065 NELFB\n",
-      "1066 PRKCQ-AS1\n",
-      "1067 NUDT5\n",
-      "1068 TNKS2\n",
-      "1069 TSSC4\n",
-      "1070 AHNAK\n",
-      "1071 THAP12\n",
-      "1072 RNF214\n",
-      "1073 PCBP2\n",
-      "1074 CS\n",
-      "1075 BTG1\n",
-      "1076 NOC4L\n",
-      "1077 PDS5B\n",
-      "1078 DNAJC15\n",
-      "1079 GPR183\n",
-      "1080 PSMB5\n",
-      "1081 DMAC2L\n",
-      "1082 ERO1A\n",
-      "1083 PSMA3-AS1\n",
-      "1084 EIF2S1\n",
-      "1085 SNW1\n",
-      "1086 VPS13C\n",
-      "1087 FAM219B\n",
-      "1088 CIB1\n",
-      "1089 NME3\n",
-      "1090 HCFC1R1\n",
-      "1091 CHTF8\n",
-      "1092 ZZEF1\n",
-      "1093 CCL4L2\n",
-      "1094 MAP2K6\n",
-      "1095 GRB2\n",
-      "1096 LPIN2\n",
-      "1097 NDUFS7\n",
-      "1098 PLIN3\n",
-      "1099 DDA1\n",
-      "1100 U2AF2\n",
-      "1101 SIRPG\n",
-      "1102 ROMO1\n",
-      "1103 ZMYND8\n",
-      "1104 SOD1\n",
-      "1105 LINC00649\n",
-      "1106 PFKL\n",
-      "1107 CFAP410\n",
-      "1108 ITGB2\n",
-      "1109 PRPS2\n",
-      "1110 PDHA1\n",
-      "1111 MT-CO3\n",
-      "1112 TNFRSF1B\n",
-      "1113 RPL11\n",
-      "1114 SH3BGRL3\n",
-      "1115 SYTL1\n",
-      "1116 PPP1R8\n",
-      "1117 YARS1\n",
-      "1118 RLF\n",
-      "1119 LRRC41\n",
-      "1120 SCCPDH\n",
-      "1121 COMMD1\n",
-      "1122 MAP4K4\n",
-      "1123 GPR155\n",
-      "1124 NABP1\n",
-      "1125 MOB4\n",
-      "1126 DNPEP\n",
-      "1127 OXNAD1\n",
-      "1128 GOLGA4\n",
-      "1129 CX3CR1\n",
-      "1130 SLC25A20\n",
-      "1131 ZNF148\n",
-      "1132 SLC41A3\n",
-      "1133 HAUS3\n",
-      "1134 RNF4\n",
-      "1135 ANAPC4\n",
-      "1136 DANCR\n",
-      "1137 CXCL8\n",
-      "1138 AGA\n",
-      "1139 DNAJC21\n",
-      "1140 MCCC2\n",
-      "1141 ANKRA2\n",
-      "1142 SERINC5\n",
-      "1143 PJA2\n",
-      "1144 IRF1\n",
-      "1145 CNOT8\n",
-      "1146 RARS1\n",
-      "1147 RGS14\n",
-      "1148 RACK1\n",
-      "1149 ACOT13\n",
-      "1150 H4C3\n",
-      "1151 ENSG00000204713\n",
-      "1152 TRIM26\n",
-      "1153 C6orf89\n",
-      "1154 RNF8\n",
-      "1155 PDCD2\n",
-      "1156 ZDHHC4\n",
-      "1157 PMPCB\n",
-      "1158 SND1\n",
-      "1159 RBM28\n",
-      "1160 MKLN1\n",
-      "1161 CYREN\n",
-      "1162 IKBKB\n",
-      "1163 CHCHD7\n",
-      "1164 OXR1\n",
-      "1165 EXOSC4\n",
-      "1166 GPAA1\n",
-      "1167 UHRF2\n",
-      "1168 DPM2\n",
-      "1169 CELF2\n",
-      "1170 BMS1\n",
-      "1171 CCSER2\n",
-      "1172 PDLIM1\n",
-      "1173 ACTR1A\n",
-      "1174 NT5C2\n",
-      "1175 ACSL5\n",
-      "1176 CACUL1\n",
-      "1177 SMPD1\n",
-      "1178 RRP8\n",
-      "1179 PSMA1\n",
-      "1180 CSTF3\n",
-      "1181 WNK1\n",
-      "1182 GABARAPL1\n",
-      "1183 ERGIC2\n",
-      "1184 SPATS2\n",
-      "1185 AAAS\n",
-      "1186 USP15\n",
-      "1187 SLC35E3\n",
-      "1188 ENSG00000257764\n",
-      "1189 RAB21\n",
-      "1190 NAP1L1\n",
-      "1191 POC1B\n",
-      "1192 UNG\n",
-      "1193 WBP4\n",
-      "1194 FNDC3A\n",
-      "1195 GPR18\n",
-      "1196 METTL3\n",
-      "1197 PPP2R3C\n",
-      "1198 DNAAF2\n",
-      "1199 STYX\n",
-      "1200 SYNJ2BP\n",
-      "1201 IGHA2\n",
-      "1202 IGHGP\n",
-      "1203 SLC12A6\n",
-      "1204 PLCB2\n",
-      "1205 PCLAF\n",
-      "1206 INTS14\n",
-      "1207 NUBP2\n",
-      "1208 NDUFAB1\n",
-      "1209 ADCY7\n",
-      "1210 IRF8\n",
-      "1211 ACSF3\n",
-      "1212 RABEP1\n",
-      "1213 PEMT\n",
-      "1214 USP22\n",
-      "1215 SDF2\n",
-      "1216 FLOT2\n",
-      "1217 TAOK1\n",
-      "1218 AARSD1\n",
-      "1219 TNRC6C\n",
-      "1220 NDC80\n",
-      "1221 PRSS57\n",
-      "1222 ICAM3\n",
-      "1223 TECR\n",
-      "1224 BRD4\n",
-      "1225 BABAM1\n",
-      "1226 PSMC4\n",
-      "1227 RPS5\n",
-      "1228 ADISSP\n",
-      "1229 PCNA\n",
-      "1230 CHMP4B\n",
-      "1231 RAB5IF\n",
-      "1232 RRP1B\n",
-      "1233 YBEY\n",
-      "1234 C21orf58\n",
-      "1235 ENSG00000240972\n",
-      "1236 FBXO7\n",
-      "1237 SUN2\n",
-      "1238 ST13\n",
-      "1239 IL2RG\n",
-      "1240 RPS4X\n",
-      "1241 PABIR2\n",
-      "1242 ENSG00000273748\n",
-      "1243 RER1\n",
-      "1244 CTNNBIP1\n",
-      "1245 UBIAD1\n",
-      "1246 LSM10\n",
-      "1247 OSBPL9\n",
-      "1248 PGM1\n",
-      "1249 CDC14A\n",
-      "1250 DPH5\n",
-      "1251 RNPC3\n",
-      "1252 CSDE1\n",
-      "1253 ENSG00000287979\n",
-      "1254 SF3B4\n",
-      "1255 MRPS21\n",
-      "1256 GABPB2\n",
-      "1257 MTX1\n",
-      "1258 KIFAP3\n",
-      "1259 COX20\n",
-      "1260 UBXN2A\n",
-      "1261 PPM1G\n",
-      "1262 MCM6\n",
-      "1263 MKRN2\n",
-      "1264 LINC02084\n",
-      "1265 UQCRC1\n",
-      "1266 IFRD2\n",
-      "1267 ABHD14A\n",
-      "1268 PPP4R2\n",
-      "1269 SNX4\n",
-      "1270 KPNA4\n",
-      "1271 ALG3\n",
-      "1272 DNAJB11\n",
-      "1273 JAKMIP1\n",
-      "1274 PDS5A\n",
-      "1275 MRPS18C\n",
-      "1276 PYURF\n",
-      "1277 PPA2\n",
-      "1278 CASP6\n",
-      "1279 RAB33B-AS1\n",
-      "1280 TNFAIP8\n",
-      "1281 HARS1\n",
-      "1282 SCGB3A2\n",
-      "1283 SLU7\n",
-      "1284 CLIC1\n",
-      "1285 HLA-DPB1\n",
-      "1286 PTP4A1\n",
-      "1287 B3GAT2\n",
-      "1288 PHIP\n",
-      "1289 OSTM1\n",
-      "1290 SESN1\n",
-      "1291 AMD1\n",
-      "1292 FYN\n",
-      "1293 NUDT1\n",
-      "1294 PSMA2\n",
-      "1295 BCL7B\n",
-      "1296 YWHAG\n",
-      "1297 ZNHIT1\n",
-      "1298 METTL2B\n",
-      "1299 MRPS33\n",
-      "1300 AGK\n",
-      "1301 ZYX\n",
-      "1302 PRKAG2\n",
-      "1303 VPS37A\n",
-      "1304 ATP6V1H\n",
-      "1305 FABP5\n",
-      "1306 UTP23\n",
-      "1307 ZHX1\n",
-      "1308 NDUFB9\n",
-      "1309 CYRIB\n",
-      "1310 GSDMD\n",
-      "1311 CCDC107\n",
-      "1312 CDK9\n",
-      "1313 BBLN\n",
-      "1314 DPH7\n",
-      "1315 TAF3\n",
-      "1316 FUT11\n",
-      "1317 VDAC2\n",
-      "1318 COMTD1\n",
-      "1319 GLUD1\n",
-      "1320 MKI67\n",
-      "1321 MGMT\n",
-      "1322 NAP1L4\n",
-      "1323 LDHA\n",
-      "1324 CD82\n",
-      "1325 COX8A\n",
-      "1326 TMEM218\n",
-      "1327 ZBTB44\n",
-      "1328 KLRD1\n",
-      "1329 CBX5\n",
-      "1330 BAZ2A\n",
-      "1331 CDK17\n",
-      "1332 ARL1\n",
-      "1333 C12orf76\n",
-      "1334 GPALPP1\n",
-      "1335 RASA3\n",
-      "1336 CDC16\n",
-      "1337 C14orf119\n",
-      "1338 DDHD1\n",
-      "1339 SLIRP\n",
-      "1340 CRIP1\n",
-      "1341 APBA2\n",
-      "1342 NOP10\n",
-      "1343 SRP14\n",
-      "1344 IVD\n",
-      "1345 GABPB1\n",
-      "1346 TPM1\n",
-      "1347 CIAO2A\n",
-      "1348 TIPIN\n",
-      "1349 IDH3A\n",
-      "1350 HDDC3\n",
-      "1351 CHASERR\n",
-      "1352 TBL3\n",
-      "1353 LYRM1\n",
-      "1354 KRBOX5\n",
-      "1355 TMEM208\n",
-      "1356 CFDP1\n",
-      "1357 ZC3H18\n",
-      "1358 ITGAE\n",
-      "1359 UBE2G1\n",
-      "1360 SPNS3\n",
-      "1361 LRRC75A\n",
-      "1362 TOP2A\n",
-      "1363 TACO1\n",
-      "1364 EIF4A3\n",
-      "1365 RNF213\n",
-      "1366 SLC38A10\n",
-      "1367 ACTG1\n",
-      "1368 PCYT2\n",
-      "1369 FAM210A\n",
-      "1370 SNRPD1\n",
-      "1371 OAZ1\n",
-      "1372 TLE5\n",
-      "1373 SIRT6\n",
-      "1374 RPS28\n",
-      "1375 KRI1\n",
-      "1376 GIPC1\n",
-      "1377 MED29\n",
-      "1378 ZNF83\n",
-      "1379 YWHAB\n",
-      "1380 HMGN1\n",
-      "1381 IL17RA\n",
-      "1382 ADA2\n",
-      "1383 TRMT2A\n",
-      "1384 GUCD1\n",
-      "1385 EWSR1\n",
-      "1386 RRP7A\n",
-      "1387 TBC1D22A-DT\n",
-      "1388 DDX3X\n",
-      "1389 ABCB7\n",
-      "1390 MT-ATP8\n",
-      "1391 PRDM2\n",
-      "1392 PITHD1\n",
-      "1393 STMN1\n",
-      "1394 GPN2\n",
-      "1395 SMAP2\n",
-      "1396 CDKN2C\n",
-      "1397 ZRANB2\n",
-      "1398 KYAT3\n",
-      "1399 RPAP2\n",
-      "1400 NBPF19\n",
-      "1401 CERS2\n",
-      "1402 CAPN2\n",
-      "1403 ITGB1BP1\n",
-      "1404 COX7A2L\n",
-      "1405 ACTR1B\n",
-      "1406 TLK1\n",
-      "1407 CNPPD1\n",
-      "1408 TRIP12\n",
-      "1409 LSM3\n",
-      "1410 EPM2AIP1\n",
-      "1411 SETD2\n",
-      "1412 RBM15B\n",
-      "1413 SLC33A1\n",
-      "1414 TMEM129\n",
-      "1415 TMEM165\n",
-      "1416 CLCN3\n",
-      "1417 CENPU\n",
-      "1418 PDE4D\n",
-      "1419 GTF2H2\n",
-      "1420 PGGT1B\n",
-      "1421 NQO2\n",
-      "1422 E2F3\n",
-      "1423 MRS2\n",
-      "1424 H1-3\n",
-      "1425 HSPA1B\n",
-      "1426 SLC39A7\n",
-      "1427 MRPL14\n",
-      "1428 TNFAIP3\n",
-      "1429 EZR\n",
-      "1430 ENSG00000273319\n",
-      "1431 CHCHD3\n",
-      "1432 PARP12\n",
-      "1433 ENTPD4\n",
-      "1434 TMEM70\n",
-      "1435 SLA\n",
-      "1436 NDUFB6\n",
-      "1437 GALT\n",
-      "1438 PHF19\n",
-      "1439 TRAF1\n",
-      "1440 NMT2\n",
-      "1441 KIF5B\n",
-      "1442 CDK1\n",
-      "1443 NRBF2\n",
-      "1444 WAPL\n",
-      "1445 TIAL1\n",
-      "1446 FUOM\n",
-      "1447 IFITM3\n",
-      "1448 MADD\n",
-      "1449 NUDT22\n",
-      "1450 SIPA1\n",
-      "1451 NADSYN1\n",
-      "1452 SC5D\n",
-      "1453 M6PR\n",
-      "1454 SARNP\n",
-      "1455 ORMDL2\n",
-      "1456 ATP2B1-AS1\n",
-      "1457 MAPKAPK5\n",
-      "1458 EIF2B1\n",
-      "1459 BRI3BP\n",
-      "1460 N4BP2L1\n",
-      "1461 MED4\n",
-      "1462 NEDD8\n",
-      "1463 ACTR10\n",
-      "1464 PCNX4\n",
-      "1465 GALK2\n",
-      "1466 SNUPN\n",
-      "1467 PSMA4\n",
-      "1468 SNHG9\n",
-      "1469 ENSG00000089486\n",
-      "1470 AKTIP\n",
-      "1471 CRK\n",
-      "1472 DRG2\n",
-      "1473 FLII\n",
-      "1474 CISD3\n",
-      "1475 CCDC47\n",
-      "1476 DCXR\n",
-      "1477 C18orf32\n",
-      "1478 GAMT\n",
-      "1479 CLPP\n",
-      "1480 ZNF562\n",
-      "1481 ADGRE5\n",
-      "1482 NFKBID\n",
-      "1483 HCST\n",
-      "1484 EGLN2\n",
-      "1485 ZNF226\n",
-      "1486 ENSG00000125505\n",
-      "1487 TBC1D20\n",
-      "1488 ABHD12\n",
-      "1489 ERGIC3\n",
-      "1490 CHCHD10\n",
-      "1491 XBP1\n",
-      "1492 CBX6\n",
-      "1493 CDK16\n",
-      "1494 RPL36A\n",
-      "1495 TCEAL4\n",
-      "1496 TCEAL3\n",
-      "1497 XIAP\n",
-      "1498 STAG2\n",
-      "1499 MECP2\n",
-      "1500 MT-CO2\n",
-      "1501 C1orf174\n",
-      "1502 DNAJC8\n",
-      "1503 PPIH\n",
-      "1504 NRAS\n",
-      "1505 CD58\n",
-      "1506 TTF2\n",
-      "1507 APH1A\n",
-      "1508 PSMD4\n",
-      "1509 S100A4\n",
-      "1510 UBAP2L\n",
-      "1511 MNDA\n",
-      "1512 TSTD1\n",
-      "1513 KIAA0040\n",
-      "1514 TOR3A\n",
-      "1515 RNPEP\n",
-      "1516 TMEM183A\n",
-      "1517 SNAP47\n",
-      "1518 CPSF3\n",
-      "1519 SF3B6\n",
-      "1520 FOSL2\n",
-      "1521 CCDC88A\n",
-      "1522 SERTAD2\n",
-      "1523 NFU1\n",
-      "1524 SNRNP27\n",
-      "1525 BAZ2B\n",
-      "1526 SETD5\n",
-      "1527 SHISA5\n",
-      "1528 IP6K2\n",
-      "1529 RPL29\n",
-      "1530 ZBTB20\n",
-      "1531 RAP2B\n",
-      "1532 PCYT1A\n",
-      "1533 TIFA\n",
-      "1534 GYPA\n",
-      "1535 DCTD\n",
-      "1536 OTULIN\n",
-      "1537 LNPEP\n",
-      "1538 CHD1\n",
-      "1539 CAMLG\n",
-      "1540 TCOF1\n",
-      "1541 CCDC69\n",
-      "1542 MRPS18B\n",
-      "1543 SAYSD1\n",
-      "1544 ZNF451\n",
-      "1545 SNX14\n",
-      "1546 BCLAF1\n",
-      "1547 TAB2\n",
-      "1548 SUN1\n",
-      "1549 SNX10\n",
-      "1550 RCC1L\n",
-      "1551 RHBDD2\n",
-      "1552 LAMTOR4\n",
-      "1553 BCAP29\n",
-      "1554 EZH2\n",
-      "1555 DCTN6-DT\n",
-      "1556 GOLGA7\n",
-      "1557 RPL8\n",
-      "1558 PUM3\n",
-      "1559 PLIN2\n",
-      "1560 SMC5\n",
-      "1561 TUT7\n",
-      "1562 ERCC6L2-AS1\n",
-      "1563 ATP6V1G1\n",
-      "1564 ABI1\n",
-      "1565 VPS26A\n",
-      "1566 ZNF511\n",
-      "1567 SIGIRR\n",
-      "1568 RPS13\n",
-      "1569 ATG13\n",
-      "1570 PRPF19\n",
-      "1571 STIP1\n",
-      "1572 NDUFC2\n",
-      "1573 CBL\n",
-      "1574 THYN1\n",
-      "1575 CCND2\n",
-      "1576 DDX23\n",
-      "1577 CERS5\n",
-      "1578 ATF7\n",
-      "1579 R3HDM2\n",
-      "1580 MON2\n",
-      "1581 TESC\n",
-      "1582 NCOR2\n",
-      "1583 ANKLE2\n",
-      "1584 HSPH1\n",
-      "1585 HNRNPC\n",
-      "1586 GALC\n",
-      "1587 MTA1\n",
-      "1588 NSMCE3\n",
-      "1589 HAUS2\n",
-      "1590 PDCD7\n",
-      "1591 C15orf61\n",
-      "1592 DNAJA3\n",
-      "1593 BFAR\n",
-      "1594 MT1G\n",
-      "1595 CDT1\n",
-      "1596 ANKRD11\n",
-      "1597 CHMP1A\n",
-      "1598 CDK10\n",
-      "1599 SMYD4\n",
-      "1600 MED31\n",
-      "1601 EIF5A\n",
-      "1602 TRAPPC1\n",
-      "1603 FAM215B\n",
-      "1604 JPT1\n",
-      "1605 HGS\n",
-      "1606 ZNF24\n",
-      "1607 INO80C\n",
-      "1608 RPL36\n",
-      "1609 XAB2\n",
-      "1610 ILVBL\n",
-      "1611 GTPBP3\n",
-      "1612 ZFP14\n",
-      "1613 KCNK6\n",
-      "1614 POU2F2\n",
-      "1615 TRIM28\n",
-      "1616 ACSS1\n",
-      "1617 MMP24OS\n",
-      "1618 HELZ2\n",
-      "1619 NDUFV3\n",
-      "1620 APOBEC3C\n",
-      "1621 TYMP\n",
-      "1622 AP1S2\n",
-      "1623 USP9X\n",
-      "1624 WAS\n",
-      "1625 APEX2\n",
-      "1626 IGBP1\n",
-      "1627 PRPS1\n",
-      "1628 RAP2C\n",
-      "1629 SLC10A3\n",
-      "1630 HNRNPR\n",
-      "1631 ATP5IF1\n",
-      "1632 KTI12\n",
-      "1633 ACADM\n",
-      "1634 TRMT13\n",
-      "1635 GPR89A\n",
-      "1636 POLR3GL\n",
-      "1637 LINC01138\n",
-      "1638 TPM3\n",
-      "1639 RIT1\n",
-      "1640 NUF2\n",
-      "1641 CDC73\n",
-      "1642 DEGS1\n",
-      "1643 RPS27A\n",
-      "1644 PTCD3\n",
-      "1645 GYPC\n",
-      "1646 METTL5\n",
-      "1647 STK17B\n",
-      "1648 MAIP1\n",
-      "1649 HYCC2\n",
-      "1650 EIF4E2\n",
-      "1651 PXK\n",
-      "1652 GSK3B\n",
-      "1653 FAM162A\n",
-      "1654 SEC61A1\n",
-      "1655 STAG1\n",
-      "1656 HPS3\n",
-      "1657 LRCH3\n",
-      "1658 CCL28\n",
-      "1659 GZMA\n",
-      "1660 BTF3\n",
-      "1661 TBC1D9B\n",
-      "1662 PHF1\n",
-      "1663 PHF3\n",
-      "1664 SMAP1\n",
-      "1665 IBTK\n",
-      "1666 CALHM6\n",
-      "1667 PPIL4\n",
-      "1668 GTF2H5\n",
-      "1669 WTAP\n",
-      "1670 AOAH\n",
-      "1671 MPLKIP\n",
-      "1672 COA1\n",
-      "1673 TRIM4\n",
-      "1674 GIGYF1\n",
-      "1675 PLOD3\n",
-      "1676 IFRD1\n",
-      "1677 KMT2C\n",
-      "1678 CTSB\n",
-      "1679 ENSG00000272256\n",
-      "1680 FAM91A1\n",
-      "1681 WASHC1\n",
-      "1682 NANS\n",
-      "1683 SLC44A1\n",
-      "1684 PRPF4\n",
-      "1685 ITGB1\n",
-      "1686 AP3M1\n",
-      "1687 SLF2\n",
-      "1688 CD44\n",
-      "1689 ALKBH3\n",
-      "1690 SLC3A2\n",
-      "1691 FAU\n",
-      "1692 TMEM134\n",
-      "1693 ZBTB16\n",
-      "1694 CD3G\n",
-      "1695 KDM5A\n",
-      "1696 PYROXD1\n",
-      "1697 SMUG1\n",
-      "1698 OAS1\n",
-      "1699 RPL21\n",
-      "1700 PAN3\n",
-      "1701 HMGB1\n",
-      "1702 CKAP2\n",
-      "1703 TTC5\n",
-      "1704 SNX6\n",
-      "1705 FAM177A1\n",
-      "1706 KLHL28\n",
-      "1707 ZFP36L1\n",
-      "1708 SPTLC2\n",
-      "1709 TMEM87A\n",
-      "1710 ATOSA\n",
-      "1711 RAB27A\n",
-      "1712 LACTB\n",
-      "1713 IDH2\n",
-      "1714 MRPL28\n",
-      "1715 UBALD1\n",
-      "1716 RBBP6\n",
-      "1717 TMEM170A\n",
-      "1718 RNF166\n",
-      "1719 DEF8\n",
-      "1720 TIMM22\n",
-      "1721 RANGRF\n",
-      "1722 CRLF3\n",
-      "1723 NHERF1\n",
-      "1724 CSNK1D\n",
-      "1725 VAPA\n",
-      "1726 MBD2\n",
-      "1727 HNRNPM\n",
-      "1728 EIF3G\n",
-      "1729 SLC44A2\n",
-      "1730 BISPR\n",
-      "1731 MVB12A\n",
-      "1732 ENSG00000118620\n",
-      "1733 POLR2I\n",
-      "1734 AKT2\n",
-      "1735 LIG1\n",
-      "1736 CDS2\n",
-      "1737 SYS1\n",
-      "1738 CSTF1\n",
-      "1739 ENSG00000142188\n",
-      "1740 ATP5PO\n",
-      "1741 ZMAT5\n",
-      "1742 RTCB\n",
-      "1743 FTSJ1\n",
-      "1744 RAB33A\n",
-      "1745 S100PBP\n",
-      "1746 FUBP1\n",
-      "1747 GBP2\n",
-      "1748 PDE4DIP\n",
-      "1749 S100A9\n",
-      "1750 PRCC\n",
-      "1751 PFDN2\n",
-      "1752 PRDX6\n",
-      "1753 RAB3GAP2\n",
-      "1754 NCOA1\n",
-      "1755 NCAPH\n",
-      "1756 C2orf49\n",
-      "1757 SP3\n",
-      "1758 SLC39A10\n",
-      "1759 ANKRD44-AS1\n",
-      "1760 ZFAND2B\n",
-      "1761 PDE6D\n",
-      "1762 TMA7\n",
-      "1763 APEH\n",
-      "1764 ATXN7\n",
-      "1765 CCDC14\n",
-      "1766 ATP1B3\n",
-      "1767 CCNL1\n",
-      "1768 ST6GAL1\n",
-      "1769 RHOH\n",
-      "1770 TSPAN5\n",
-      "1771 OTUD4\n",
-      "1772 SMIM15\n",
-      "1773 PPP2CA\n",
-      "1774 TRIM38\n",
-      "1775 HLA-DRB1\n",
-      "1776 MTCH1\n",
-      "1777 UBR2\n",
-      "1778 EEF1A1\n",
-      "1779 SERINC1\n",
-      "1780 LRCH4\n",
-      "1781 GNB2\n",
-      "1782 ORAI2\n",
-      "1783 PIK3CG\n",
-      "1784 TGS1\n",
-      "1785 COPS5\n",
-      "1786 PLEC\n",
-      "1787 DOCK8\n",
-      "1788 HACD4\n",
-      "1789 VCP\n",
-      "1790 ENSG00000287070\n",
-      "1791 ARRDC1\n",
-      "1792 TASOR2\n",
-      "1793 KIN\n",
-      "1794 RSU1\n",
-      "1795 PGAM1\n",
-      "1796 ZNF143\n",
-      "1797 GTF2H1\n",
-      "1798 SPTY2D1\n",
-      "1799 LBHD1\n",
-      "1800 RTN3\n",
-      "1801 MAP4K2\n",
-      "1802 MRPL11\n",
-      "1803 CARD16\n",
-      "1804 CD4\n",
-      "1805 PHB2\n",
-      "1806 ITGB7\n",
-      "1807 PPP1R12A\n",
-      "1808 NDUFA12\n",
-      "1809 NAA25\n",
-      "1810 P2RX4\n",
-      "1811 RSRC2\n",
-      "1812 VPS37B\n",
-      "1813 SUCLA2\n",
-      "1814 PIBF1\n",
-      "1815 OBI1\n",
-      "1816 RBM23\n",
-      "1817 LRR1\n",
-      "1818 CDKN3\n",
-      "1819 JKAMP\n",
-      "1820 CPSF2\n",
-      "1821 GSKIP\n",
-      "1822 WDR20\n",
-      "1823 ATP5MJ\n",
-      "1824 EIF3J\n",
-      "1825 MAPK6\n",
-      "1826 TBC1D2B\n",
-      "1827 PRC1\n",
-      "1828 LITAF\n",
-      "1829 RSPRY1\n",
-      "1830 NUTF2\n",
-      "1831 DERL2\n",
-      "1832 RPL19\n",
-      "1833 ICAM2\n",
-      "1834 KPNA2\n",
-      "1835 TK1\n",
-      "1836 CANT1\n",
-      "1837 CHMP6\n",
-      "1838 SS18\n",
-      "1839 SAFB2\n",
-      "1840 EPS15L1\n",
-      "1841 UBA2\n",
-      "1842 SDHAF1\n",
-      "1843 DDRGK1\n",
-      "1844 IFT52\n",
-      "1845 STAU1\n",
-      "1846 RBM38\n",
-      "1847 DNAJC5\n",
-      "1848 VPS26C\n",
-      "1849 PCNT\n",
-      "1850 RABL2B\n",
-      "1851 TRAPPC2\n",
-      "1852 PRAF2\n",
-      "1853 MT-ND6\n",
-      "1854 SLC35E2B\n",
-      "1855 KDM1A\n",
-      "1856 SRRM1\n",
-      "1857 CCDC28B\n",
-      "1858 ODF2L\n",
-      "1859 SLC16A1-AS1\n",
-      "1860 BCAS2\n",
-      "1861 SNAPIN\n",
-      "1862 HAX1\n",
-      "1863 NCSTN\n",
-      "1864 RCSD1\n",
-      "1865 CSRP1\n",
-      "1866 BTG2\n",
-      "1867 RAB29\n",
-      "1868 HNRNPU\n",
-      "1869 EIF2AK2\n",
-      "1870 ERLEC1\n",
-      "1871 RTN4\n",
-      "1872 WDR54\n",
-      "1873 ZAP70\n",
-      "1874 MZT2B\n",
-      "1875 R3HDM1\n",
-      "1876 ATF2\n",
-      "1877 HNRNPA3\n",
-      "1878 SPATS2L\n",
-      "1879 WDFY1\n",
-      "1880 ITPR1\n",
-      "1881 EXOG\n",
-      "1882 GMPPB\n",
-      "1883 EEFSEC\n",
-      "1884 MAGEF1\n",
-      "1885 TFRC\n",
-      "1886 DLG1\n",
-      "1887 HADH\n",
-      "1888 OSTC\n",
-      "1889 NDUFS6\n",
-      "1890 RICTOR\n",
-      "1891 FYB1\n",
-      "1892 LYRM7\n",
-      "1893 DELE1\n",
-      "1894 LARS1\n",
-      "1895 B4GALT7\n",
-      "1896 PRPF4B\n",
-      "1897 SSR1\n",
-      "1898 PAK1IP1\n",
-      "1899 TMEM14C\n",
-      "1900 HLA-A\n",
-      "1901 HLA-DMA\n",
-      "1902 NUDT3\n",
-      "1903 LMBRD1\n",
-      "1904 BZW2\n",
-      "1905 TMEM60\n",
-      "1906 KRIT1\n",
-      "1907 THAP1\n",
-      "1908 SDCBP\n",
-      "1909 RAD21\n",
-      "1910 NAPRT\n",
-      "1911 ZNF7\n",
-      "1912 SMIM27\n",
-      "1913 SIT1\n",
-      "1914 CKS2\n",
-      "1915 TRIM14\n",
-      "1916 ERP44\n",
-      "1917 TOR2A\n",
-      "1918 SET\n",
-      "1919 PTGDS\n",
-      "1920 TMEM203\n",
-      "1921 ZMYND11\n",
-      "1922 VIM\n",
-      "1923 EIF4EBP2\n",
-      "1924 FBXL15\n",
-      "1925 MCMBP\n",
-      "1926 PSMD13\n",
-      "1927 LTBP3\n",
-      "1928 UCP2\n",
-      "1929 UVRAG\n",
-      "1930 KLRC1\n",
-      "1931 SMAGP\n",
-      "1932 NR4A1\n",
-      "1933 RITA1\n",
-      "1934 NUP58\n",
-      "1935 TPP2\n",
-      "1936 CUL4A\n",
-      "1937 LRP10\n",
-      "1938 DHRS4L2\n",
-      "1939 CINP\n",
-      "1940 ZNF770\n",
-      "1941 POLG\n",
-      "1942 RPUSD1\n",
-      "1943 NDUFB10\n",
-      "1944 NDE1\n",
-      "1945 CCP110\n",
-      "1946 SGF29\n",
-      "1947 HIRIP3\n",
-      "1948 SEPHS2\n",
-      "1949 ADGRG1\n",
-      "1950 PRPF8\n",
-      "1951 ACAP1\n",
-      "1952 SCO1\n",
-      "1953 TAF15\n",
-      "1954 THRA\n",
-      "1955 SPOP\n",
-      "1956 DDX5\n",
-      "1957 NAT9\n",
-      "1958 MCRIP1\n",
-      "1959 GPS1\n",
-      "1960 RPS15\n",
-      "1961 EEF2\n",
-      "1962 LDLR\n",
-      "1963 CCDC124\n",
-      "1964 PEPD\n",
-      "1965 FXYD5\n",
-      "1966 RBCK1\n",
-      "1967 SNRPB\n",
-      "1968 TMX4\n",
-      "1969 RRBP1\n",
-      "1970 PPDPF\n",
-      "1971 ARFRP1\n",
-      "1972 GET1\n",
-      "1973 YDJC\n",
-      "1974 ZBED1\n",
-      "1975 CCDC22\n",
-      "1976 BEX3\n",
-      "1977 PSMD10\n",
-      "1978 SASH3\n",
-      "1979 RCAN3\n",
-      "1980 AGO3\n",
-      "1981 GBP5\n",
-      "1982 PSMA5\n",
-      "1983 DENND2D\n",
-      "1984 GEMIN6\n",
-      "1985 GFPT1\n",
-      "1986 BOLA3\n",
-      "1987 MAT2A\n",
-      "1988 MIR4435-2HG\n",
-      "1989 ZC3H6\n",
-      "1990 DARS1\n",
-      "1991 TANK\n",
-      "1992 PNKD\n",
-      "1993 SEC13\n",
-      "1994 ANKRD28\n",
-      "1995 UMPS\n",
-      "1996 SPON2\n",
-      "1997 MRFAP1\n",
-      "1998 BDH2\n",
-      "1999 MRPL36\n",
-      "2000 SKP1\n",
-      "2001 PTTG1\n",
-      "2002 H1-4\n",
-      "2003 POLR1H\n",
-      "2004 B3GALT4\n",
-      "2005 GLO1\n",
-      "2006 SEC63\n",
-      "2007 CD164\n",
-      "2008 CENPW\n",
-      "2009 ARMT1\n",
-      "2010 RNASET2\n",
-      "2011 MRPS17\n",
-      "2012 POM121C\n",
-      "2013 AKAP9\n",
-      "2014 DLD\n",
-      "2015 TRBV28\n",
-      "2016 AP3M2\n",
-      "2017 HGSNAT\n",
-      "2018 RAB2A\n",
-      "2019 UQCRB\n",
-      "2020 NCALD\n",
-      "2021 DNAJA1\n",
-      "2022 AGPAT2\n",
-      "2023 AKR1C3\n",
-      "2024 PPP3CB\n",
-      "2025 FAM204A\n",
-      "2026 SLC25A45\n",
-      "2027 DHCR7-DT\n",
-      "2028 MRPL51\n",
-      "2029 ARHGDIB\n",
-      "2030 ZCRB1\n",
-      "2031 HDAC7\n",
-      "2032 MRPL42\n",
-      "2033 SNRPF\n",
-      "2034 SART3\n",
-      "2035 RPLP0\n",
-      "2036 CHFR\n",
-      "2037 POLR1D\n",
-      "2038 KPNA3\n",
-      "2039 SUGT1\n",
-      "2040 MZT1\n",
-      "2041 FBXL3\n",
-      "2042 MYCBP2\n",
-      "2043 UPF3A\n",
-      "2044 TRAC\n",
-      "2045 ARHGAP5\n",
-      "2046 VTI1B\n",
-      "2047 AQR\n",
-      "2048 INO80E\n",
-      "2049 PPP4C\n",
-      "2050 FUS\n",
-      "2051 ARL2BP\n",
-      "2052 SLC7A6OS\n",
-      "2053 ZNF830\n",
-      "2054 NT5C3B\n",
-      "2055 RAB5C\n",
-      "2056 TSEN54\n",
-      "2057 NAPG\n",
-      "2058 MPPE1\n",
-      "2059 TPGS1\n",
-      "2060 CNN2\n",
-      "2061 CIRBP\n",
-      "2062 TIMM13\n",
-      "2063 GADD45B\n",
-      "2064 NDUFA11\n",
-      "2065 MARCHF2\n",
-      "2066 ATG4D\n",
-      "2067 ZNF44\n",
-      "2068 IL12RB1\n",
-      "2069 LSM4\n",
-      "2070 SHLD1\n",
-      "2071 UBE2C\n",
-      "2072 OGFR\n",
-      "2073 MYH9\n",
-      "2074 APOBEC3G\n",
-      "2075 NUP50\n",
-      "2076 COX7B\n",
-      "2077 ARMCX6\n",
-      "2078 ARHGEF6\n",
-      "2079 MT-ND1\n",
-      "2080 ICMT\n",
-      "2081 SRSF10\n",
-      "2082 TRNAU1AP\n",
-      "2083 PUM1\n",
-      "2084 MARCKSL1\n",
-      "2085 SMIM12\n",
-      "2086 MIER1\n",
-      "2087 IFI44\n",
-      "2088 C1orf162\n",
-      "2089 CDC42SE1\n",
-      "2090 S100A8\n",
-      "2091 XCL2\n",
-      "2092 IER5\n",
-      "2093 SMG7\n",
-      "2094 SDE2\n",
-      "2095 GNPAT\n",
-      "2096 ENSG00000171853\n",
-      "2097 TPRKB\n",
-      "2098 DDX18\n",
-      "2099 NIFK\n",
-      "2100 GCA\n",
-      "2101 ASNSD1\n",
-      "2102 CLK1\n",
-      "2103 HDLBP\n",
-      "2104 JAGN1\n",
-      "2105 CCDC174\n",
-      "2106 TOP2B\n",
-      "2107 NGLY1\n",
-      "2108 IMPDH2\n",
-      "2109 GNL3\n",
-      "2110 CMSS1\n",
-      "2111 ZBTB11\n",
-      "2112 NCBP2AS2\n",
-      "2113 TACC3\n",
-      "2114 NELFA\n",
-      "2115 QDPR\n",
-      "2116 SRD5A3\n",
-      "2117 DCK\n",
-      "2118 SCARB2\n",
-      "2119 METAP1\n",
-      "2120 MRPS36\n",
-      "2121 SLF1\n",
-      "2122 VDAC1\n",
-      "2123 ATOX1\n",
-      "2124 CCNG1\n",
-      "2125 MGAT1\n",
-      "2126 H2AC6\n",
-      "2127 TAP1\n",
-      "2128 RPS18\n",
-      "2129 TAPBP\n",
-      "2130 SMIM29\n",
-      "2131 TMEM14A\n",
-      "2132 CYB5R4\n",
-      "2133 MAD1L1\n",
-      "2134 KLHL7\n",
-      "2135 CYCS\n",
-      "2136 TRIM56\n",
-      "2137 POLR2J\n",
-      "2138 GSTK1\n",
-      "2139 NDUFAF6\n",
-      "2140 RNF38\n",
-      "2141 TLE4\n",
-      "2142 SETX\n",
-      "2143 SURF1\n",
-      "2144 SURF2\n",
-      "2145 UPF2\n",
-      "2146 EIF5AL1\n",
-      "2147 RHOG\n",
-      "2148 HBD\n",
-      "2149 TRIM44\n",
-      "2150 TTC17\n",
-      "2151 FEN1\n",
-      "2152 CCS\n",
-      "2153 GRK2\n",
-      "2154 COA4\n",
-      "2155 ENSG00000287028\n",
-      "2156 TIGAR\n",
-      "2157 LMBR1L\n",
-      "2158 MCRS1\n",
-      "2159 ANAPC5\n",
-      "2160 ZNF26\n",
-      "2161 XPO4\n",
-      "2162 EXOSC8\n",
-      "2163 EMC9\n",
-      "2164 EAPP\n",
-      "2165 PSMA6\n",
-      "2166 TRAPPC6B\n",
-      "2167 DHRS7\n",
-      "2168 NPC2\n",
-      "2169 FOS\n",
-      "2170 GTF2A1\n",
-      "2171 IFI27\n",
-      "2172 SQOR\n",
-      "2173 HACD3\n",
-      "2174 TRAF7\n",
-      "2175 PARN\n",
-      "2176 DDX19B\n",
-      "2177 GLOD4\n",
-      "2178 NUP88\n",
-      "2179 POLDIP2\n",
-      "2180 SLFN13\n",
-      "2181 TBX21\n",
-      "2182 ATP5MC1\n",
-      "2183 SMURF2\n",
-      "2184 PRPSAP1\n",
-      "2185 SEPTIN9\n",
-      "2186 PMAIP1\n",
-      "2187 SF3A2\n",
-      "2188 CHAF1A\n",
-      "2189 CD320\n",
-      "2190 JUND\n",
-      "2191 ZNF493\n",
-      "2192 POP4\n",
-      "2193 RPS16\n",
-      "2194 CD37\n",
-      "2195 MED25\n",
-      "2196 ZNF581\n",
-      "2197 ERVK3-1\n",
-      "2198 SNX5\n",
-      "2199 GATD3\n",
-      "2200 PTTG1IP\n",
-      "2201 ATP6V1E1\n",
-      "2202 PEX26\n",
-      "2203 CDC45\n",
-      "2204 PMM1\n",
-      "2205 NCAPH2\n",
-      "2206 HCCS\n",
-      "2207 TMSB4X\n",
-      "2208 RPGR\n",
-      "2209 LAS1L\n",
-      "2210 MT-CYB\n",
-      "2211 TNFRSF14\n",
-      "2212 VAMP3\n",
-      "2213 SYF2\n",
-      "2214 CLSPN\n",
-      "2215 GNL2\n",
-      "2216 ATP6V0B\n",
-      "2217 TXNDC12\n",
-      "2218 GPX7\n",
-      "2219 CZIB\n",
-      "2220 ALG6\n",
-      "2221 DR1\n",
-      "2222 TRIM33\n",
-      "2223 SCNM1\n",
-      "2224 RPS27\n",
-      "2225 SLC50A1\n",
-      "2226 CD84\n",
-      "2227 CREG1\n",
-      "2228 CACYBP\n",
-      "2229 TRAF5\n",
-      "2230 TRIM11\n",
-      "2231 LYST\n",
-      "2232 GALM\n",
-      "2233 ENSG00000152291\n",
-      "2234 IMMT\n",
-      "2235 CMC1\n",
-      "2236 SLC25A38\n",
-      "2237 SS18L2\n",
-      "2238 IP6K1\n",
-      "2239 TWF2\n",
-      "2240 SLC25A36\n",
-      "2241 DYNLT2B\n",
-      "2242 OCIAD1\n",
-      "2243 PPM1K\n",
-      "2244 PPP3CA\n",
-      "2245 PTGER4\n",
-      "2246 FBXO4\n",
-      "2247 CERT1\n",
-      "2248 ISOC1\n",
-      "2249 DEK\n",
-      "2250 TAP2\n",
-      "2251 RRP36\n",
-      "2252 QRSL1\n",
-      "2253 NT5DC1\n",
-      "2254 CEP85L\n",
-      "2255 RALA\n",
-      "2256 PDAP1\n",
-      "2257 CPSF4\n",
-      "2258 PSMC2\n",
-      "2259 SLC25A37\n",
-      "2260 PTK2B\n",
-      "2261 RIPK2\n",
-      "2262 CAAP1\n",
-      "2263 RNF20\n",
-      "2264 SMC2\n",
-      "2265 STOM\n",
-      "2266 MAPKAP1\n",
-      "2267 SRGN\n",
-      "2268 DNAJB12\n",
-      "2269 HELLS\n",
-      "2270 BCCIP\n",
-      "2271 ADAM8\n",
-      "2272 MOB2\n",
-      "2273 RIC3\n",
-      "2274 PATL1\n",
-      "2275 TRPT1\n",
-      "2276 RPS6KA4\n",
-      "2277 TCIRG1\n",
-      "2278 NPAT\n",
-      "2279 H2AX\n",
-      "2280 STRAP\n",
-      "2281 RAB5B\n",
-      "2282 PHLDA1\n",
-      "2283 LINC01089\n",
-      "2284 LPAR6\n",
-      "2285 TOX4\n",
-      "2286 RPL36AL\n",
-      "2287 IRF2BPL\n",
-      "2288 UBE3A\n",
-      "2289 RAMAC\n",
-      "2290 C15orf40\n",
-      "2291 SH2B1\n",
-      "2292 PHKG2\n",
-      "2293 C17orf49\n",
-      "2294 TMEM256\n",
-      "2295 BLMH\n",
-      "2296 ENSG00000267364\n",
-      "2297 TUBG1\n",
-      "2298 EFTUD2\n",
-      "2299 CBX1\n",
-      "2300 MSI2\n",
-      "2301 HELZ\n",
-      "2302 SEH1L\n",
-      "2303 SCAMP4\n",
-      "2304 STXBP2\n",
-      "2305 ZNF414\n",
-      "2306 JAK3\n",
-      "2307 MAP4K1\n",
-      "2308 FOSB\n",
-      "2309 AP2S1\n",
-      "2310 NSFL1C\n",
-      "2311 VPS16\n",
-      "2312 CRNKL1\n",
-      "2313 BCL2L1\n",
-      "2314 CTNNBL1\n",
-      "2315 TP53RK\n",
-      "2316 BCAS4\n",
-      "2317 IFNAR2\n",
-      "2318 L3MBTL2\n",
-      "2319 CRELD2\n",
-      "2320 ZFX\n",
-      "2321 ARAF\n",
-      "2322 NXT2\n",
-      "2323 DFFA\n",
-      "2324 SPEN\n",
-      "2325 KPNA6\n",
-      "2326 AKIRIN1\n",
-      "2327 ECHDC2\n",
-      "2328 PLEKHO1\n",
-      "2329 PSMB4\n",
-      "2330 SNX27\n",
-      "2331 S100A10\n",
-      "2332 SLC27A3\n",
-      "2333 SCAMP3\n",
-      "2334 SSR2\n",
-      "2335 CD244\n",
-      "2336 KDM5B\n",
-      "2337 LPGAT1\n",
-      "2338 ITPKB\n",
-      "2339 ADSS2\n",
-      "2340 BIRC6\n",
-      "2341 PPP4R3B\n",
-      "2342 MRPS9\n",
-      "2343 FASTKD2\n",
-      "2344 BRK1\n",
-      "2345 NDUFAF3\n",
-      "2346 TCTA\n",
-      "2347 TMF1\n",
-      "2348 FRMD4B\n",
-      "2349 COX17\n",
-      "2350 SRPRB\n",
-      "2351 CEP63\n",
-      "2352 GMPS\n",
-      "2353 EIF2B5\n",
-      "2354 EXOSC9\n",
-      "2355 ABCE1\n",
-      "2356 PAIP1\n",
-      "2357 PWWP2A\n",
-      "2358 BNIP1\n",
-      "2359 HNRNPH1\n",
-      "2360 SNRNP48\n",
-      "2361 NFKBIL1\n",
-      "2362 DDAH2\n",
-      "2363 OARD1\n",
-      "2364 HECA\n",
-      "2365 PSMG3\n",
-      "2366 POLD2\n",
-      "2367 NCF1\n",
-      "2368 ATP6V1B2\n",
-      "2369 FNTA\n",
-      "2370 RPS20\n",
-      "2371 KLF10\n",
-      "2372 LINC00861\n",
-      "2373 PSIP1\n",
-      "2374 SMU1\n",
-      "2375 SPTLC1\n",
-      "2376 HSPA5\n",
-      "2377 SWI5\n",
-      "2378 CCDC6\n",
-      "2379 TCTN3\n",
-      "2380 ADD3\n",
-      "2381 ILK\n",
-      "2382 PEX16\n",
-      "2383 PSMC3\n",
-      "2384 CYB561A3\n",
-      "2385 ZFPL1\n",
-      "2386 NDUFV1\n",
-      "2387 NUMA1\n",
-      "2388 HIKESHI\n",
-      "2389 CWC15\n",
-      "2390 ATM\n",
-      "2391 SDHD\n",
-      "2392 KLRK1\n",
-      "2393 DAZAP2\n",
-      "2394 HELB\n",
-      "2395 CEP290\n",
-      "2396 UQCC6\n",
-      "2397 FBXO21\n",
-      "2398 PXN\n",
-      "2399 COX6A1\n",
-      "2400 COQ5\n",
-      "2401 MTRFR\n",
-      "2402 RB1\n",
-      "2403 TM9SF2\n",
-      "2404 PRMT5\n",
-      "2405 MAP4K5\n",
-      "2406 MPG\n",
-      "2407 PDPK1\n",
-      "2408 THUMPD1\n",
-      "2409 PLK1\n",
-      "2410 SEPTIN1\n",
-      "2411 ITGAL\n",
-      "2412 TMEM199\n",
-      "2413 PSMD3\n",
-      "2414 SNF8\n",
-      "2415 SNHG16\n",
-      "2416 MBD1\n",
-      "2417 MALT1\n",
-      "2418 CTDP1\n",
-      "2419 UHRF1\n",
-      "2420 DNM2\n",
-      "2421 YIPF2\n",
-      "2422 CALR\n",
-      "2423 DDX49\n",
-      "2424 U2AF1L4\n",
-      "2425 NAPA\n",
-      "2426 EMP3\n",
-      "2427 PPP2R1A\n",
-      "2428 NDUFA3\n",
-      "2429 SLC27A5\n",
-      "2430 IDH3B\n",
-      "2431 PHF20\n",
-      "2432 ZNF217\n",
-      "2433 TPD52L2\n",
-      "2434 RUNX1\n",
-      "2435 SMARCB1\n",
-      "2436 UQCR10\n",
-      "2437 GTSE1\n",
-      "2438 AKAP17A\n",
-      "2439 TXLNG\n",
-      "2440 GPKOW\n",
-      "2441 NONO\n",
-      "2442 TSC22D3\n",
-      "2443 SYNC\n",
-      "2444 MACF1\n",
-      "2445 PIGK\n",
-      "2446 SLC30A7\n",
-      "2447 CD160\n",
-      "2448 TNFAIP8L2\n",
-      "2449 JTB\n",
-      "2450 ETV3\n",
-      "2451 CD247\n",
-      "2452 GLUL\n",
-      "2453 PTPRC\n",
-      "2454 SNRPE\n",
-      "2455 RASSF5\n",
-      "2456 TSNAX\n",
-      "2457 IRF2BP2\n",
-      "2458 TFB2M\n",
-      "2459 LAPTM4A\n",
-      "2460 OST4\n",
-      "2461 TRMT61B\n",
-      "2462 PRKD3\n",
-      "2463 PLEK\n",
-      "2464 DUSP2\n",
-      "2465 ARL6IP6\n",
-      "2466 ITGA4\n",
-      "2467 PMS1\n",
-      "2468 C2orf69\n",
-      "2469 STT3B\n",
-      "2470 EIF1B\n",
-      "2471 BBX\n",
-      "2472 MRPL3\n",
-      "2473 GPR171\n",
-      "2474 MFSD1\n",
-      "2475 TNFSF10\n",
-      "2476 ZNF721\n",
-      "2477 MFSD10\n",
-      "2478 STIM2\n",
-      "2479 ENOPH1\n",
-      "2480 NFKB1\n",
-      "2481 HPF1\n",
-      "2482 IRF2\n",
-      "2483 MED10\n",
-      "2484 SRP19\n",
-      "2485 NDFIP1\n",
-      "2486 TBC1D7\n",
-      "2487 GMNN\n",
-      "2488 PRIM2\n",
-      "2489 ARL4A\n",
-      "2490 STK17A\n",
-      "2491 SBDS\n",
-      "2492 NSUN5\n",
-      "2493 GTF2I\n",
-      "2494 ZKSCAN1\n",
-      "2495 DNAJB9\n",
-      "2496 CAPZA2\n",
-      "2497 CNOT4\n",
-      "2498 ENSG00000104714\n",
-      "2499 CNOT7\n",
-      "2500 RNF170\n",
-      "2501 TERF1\n",
-      "2502 MYC\n",
-      "2503 SHARPIN\n",
-      "2504 SMARCA2\n",
-      "2505 GNAQ\n",
-      "2506 NAA35\n",
-      "2507 XPA\n",
-      "2508 RABEPK\n",
-      "2509 ZDHHC12-DT\n",
-      "2510 COMMD3\n",
-      "2511 DDX50\n",
-      "2512 TOLLIP\n",
-      "2513 TMEM109\n",
-      "2514 ATL3\n",
-      "2515 DNAJC4\n",
-      "2516 PPP1R14B\n",
-      "2517 FRMD8\n",
-      "2518 UBE4A\n",
-      "2519 NINJ2\n",
-      "2520 TPI1\n",
-      "2521 LARP4\n",
-      "2522 MYG1\n",
-      "2523 PA2G4\n",
-      "2524 SLC16A7\n",
-      "2525 NFYB\n",
-      "2526 PSPC1\n",
-      "2527 ZDHHC20\n",
-      "2528 UBL3\n",
-      "2529 REC8\n",
-      "2530 ENSG00000278002\n",
-      "2531 PSMC6\n",
-      "2532 PSEN1\n",
-      "2533 CALM1\n",
-      "2534 NUDT14\n",
-      "2535 CCDC32\n",
-      "2536 CHP1\n",
-      "2537 RPL4\n",
-      "2538 BTBD1\n",
-      "2539 SNRPA1\n",
-      "2540 SNRNP25\n",
-      "2541 METTL26\n",
-      "2542 C16orf87\n",
-      "2543 NOB1\n",
-      "2544 MBTPS1\n",
-      "2545 AURKB\n",
-      "2546 ELAC2\n",
-      "2547 STAT5A\n",
-      "2548 SOCS3\n",
-      "2549 OXLD1\n",
-      "2550 ELP2\n",
-      "2551 HDGFL2\n",
-      "2552 TNFAIP8L1\n",
-      "2553 MAP2K7\n",
-      "2554 FARSA\n",
-      "2555 RPS19\n",
-      "2556 DEDD2\n",
-      "2557 CPNE1\n",
-      "2558 UBE2V1\n",
-      "2559 IL10RB\n",
-      "2560 ITGB2-AS1\n",
-      "2561 THOC5\n",
-      "2562 MFNG\n",
-      "2563 EIF3L\n",
-      "2564 GRAP2\n",
-      "2565 CERK\n",
-      "2566 ARSA\n",
-      "2567 RBM10\n",
-      "2568 TSR2\n",
-      "2569 SH3BGRL\n",
-      "2570 APOOL\n",
-      "2571 HCFC1\n",
-      "2572 FBXO44\n",
-      "2573 SDHB\n",
-      "2574 HMGCL\n",
-      "2575 TAF12\n",
-      "2576 EPB41\n",
-      "2577 CAP1\n",
-      "2578 FOXJ3\n",
-      "2579 DMAP1\n",
-      "2580 ATPAF1\n",
-      "2581 SHISAL2A\n",
-      "2582 RPF1\n",
-      "2583 CD53\n",
-      "2584 GATAD2B\n",
-      "2585 HDGF\n",
-      "2586 PPP1R15B\n",
-      "2587 FCMR\n",
-      "2588 PIGF\n",
-      "2589 KRCC1\n",
-      "2590 CIAO1\n",
-      "2591 COA5\n",
-      "2592 UGGT1\n",
-      "2593 MFF\n",
-      "2594 COPS9\n",
-      "2595 IQSEC1\n",
-      "2596 CMTM6\n",
-      "2597 CSRNP1\n",
-      "2598 EXOSC7\n",
-      "2599 QRICH1\n",
-      "2600 ZMAT3\n",
-      "2601 ACAP2\n",
-      "2602 PAK2\n",
-      "2603 FAM200B\n",
-      "2604 LAP3\n",
-      "2605 AASDH\n",
-      "2606 HNRNPDL\n",
-      "2607 LPCAT1\n",
-      "2608 GOLPH3\n",
-      "2609 SNX2\n",
-      "2610 PRRC1\n",
-      "2611 RPS14\n",
-      "2612 NHP2\n",
-      "2613 HLA-B\n",
-      "2614 ZNF292\n",
-      "2615 SCML4\n",
-      "2616 CITED2\n",
-      "2617 VTA1\n",
-      "2618 IGF2R\n",
-      "2619 MALSU1\n",
-      "2620 HNRNPA2B1\n",
-      "2621 LINC-PINT\n",
-      "2622 DPYSL2\n",
-      "2623 STAU2\n",
-      "2624 APTX\n",
-      "2625 NOL8\n",
-      "2626 RGS3\n",
-      "2627 CNTRL\n",
-      "2628 PTEN\n",
-      "2629 FAS\n",
-      "2630 GSTO1\n",
-      "2631 IMMP1L\n",
-      "2632 COMMD9\n",
-      "2633 B3GAT3\n",
-      "2634 ZDHHC24\n",
-      "2635 CLNS1A\n",
-      "2636 TIMM8B\n",
-      "2637 DDX6\n",
-      "2638 SCAF11\n",
-      "2639 GLIPR1\n",
-      "2640 TRAFD1\n",
-      "2641 MLXIP\n",
-      "2642 RAN\n",
-      "2643 SETDB2\n",
-      "2644 TRDC\n",
-      "2645 RALGAPA1\n",
-      "2646 DLGAP5\n",
-      "2647 ZBTB25\n",
-      "2648 ACTN1\n",
-      "2649 GON7\n",
-      "2650 IFI27L1\n",
-      "2651 INO80\n",
-      "2652 RORA\n",
-      "2653 PPIB\n",
-      "2654 SRRM2\n",
-      "2655 COQ7\n",
-      "2656 NSMCE1\n",
-      "2657 SULT1A1\n",
-      "2658 TMEM219\n",
-      "2659 VKORC1\n",
-      "2660 TTC19\n",
-      "2661 BLTP2\n",
-      "2662 DDX52\n",
-      "2663 STAT3\n",
-      "2664 COX11\n",
-      "2665 SMARCD2\n",
-      "2666 ASPSCR1\n",
-      "2667 DUS1L\n",
-      "2668 TYMS\n",
-      "2669 ME2\n",
-      "2670 AP3D1\n",
-      "2671 KHSRP\n",
-      "2672 SH2D3A\n",
-      "2673 TIMM29\n",
-      "2674 ELOF1\n",
-      "2675 ARMC6\n",
-      "2676 SNRNP70\n",
-      "2677 FCGRT\n",
-      "2678 ZNF480\n",
-      "2679 EDEM2\n",
-      "2680 CEP250-AS1\n",
-      "2681 RBM12\n",
-      "2682 RBM39\n",
-      "2683 NDRG3\n",
-      "2684 TCEA2\n",
-      "2685 BACH1\n",
-      "2686 TTC28-AS1\n",
-      "2687 APOL3\n",
-      "2688 PPP6R2\n",
-      "2689 STK26\n",
-      "2690 LAGE3\n",
-      "2691 TNFRSF25\n",
-      "2692 CLSTN1\n",
-      "2693 KIAA2013\n",
-      "2694 TMEM50A\n",
-      "2695 YTHDF2\n",
-      "2696 LAPTM5\n",
-      "2697 ZNF644\n",
-      "2698 POLR3C\n",
-      "2699 PRPF3\n",
-      "2700 S100A11\n",
-      "2701 B4GALT3\n",
-      "2702 NDUFS2\n",
-      "2703 COP1\n",
-      "2704 ATP2B4\n",
-      "2705 COA6\n",
-      "2706 RPS7\n",
-      "2707 PREB\n",
-      "2708 MPV17\n",
-      "2709 FAM136A\n",
-      "2710 DUSP11\n",
-      "2711 REG1B\n",
-      "2712 VAMP5\n",
-      "2713 ANKRD36B\n",
-      "2714 INPP4A\n",
-      "2715 SLC20A1\n",
-      "2716 POLR2D\n",
-      "2717 CYTIP\n",
-      "2718 METTL21A\n",
-      "2719 ILKAP\n",
-      "2720 EMC3\n",
-      "2721 GNAI2\n",
-      "2722 THOC7\n",
-      "2723 RPL24\n",
-      "2724 PDCD10\n",
-      "2725 MRPL47\n",
-      "2726 PGM2\n",
-      "2727 UBE2K\n",
-      "2728 CDKN2AIP\n",
-      "2729 PARP8\n",
-      "2730 SLC35A4\n",
-      "2731 RNF14\n",
-      "2732 YIPF5\n",
-      "2733 FBXW11\n",
-      "2734 UIMC1\n",
-      "2735 JARID2\n",
-      "2736 BTN2A1\n",
-      "2737 LTB\n",
-      "2738 AGPAT1\n",
-      "2739 HLA-DPA1\n",
-      "2740 PIM1\n",
-      "2741 MAD2L1BP\n",
-      "2742 TMEM30A\n",
-      "2743 ZBTB24\n",
-      "2744 ARID1B\n",
-      "2745 FOXK1\n",
-      "2746 ASL\n",
-      "2747 DMTF1\n",
-      "2748 AP1S1\n",
-      "2749 HBP1\n",
-      "2750 ASAH1\n",
-      "2751 POLR3D\n",
-      "2752 PHF20L1\n",
-      "2753 SURF6\n",
-      "2754 ANXA7\n",
-      "2755 KIF11\n",
-      "2756 NDUFB8\n",
-      "2757 IFITM1\n",
-      "2758 IRF7\n",
-      "2759 HBB\n",
-      "2760 ZBED5\n",
-      "2761 EEF1G\n",
-      "2762 RELA\n",
-      "2763 RBM14\n",
-      "2764 KDM2A\n",
-      "2765 DCUN1D5\n",
-      "2766 AASDHPPT\n",
-      "2767 HMBS\n",
-      "2768 ING4\n",
-      "2769 LDHB\n",
-      "2770 CNPY2\n",
-      "2771 WASHC4\n",
-      "2772 PWP1\n",
-      "2773 OAS3\n",
-      "2774 TAOK3\n",
-      "2775 SNRNP35\n",
-      "2776 GTF2H3\n",
-      "2777 MTMR6\n",
-      "2778 EPSTI1\n",
-      "2779 INTS6\n",
-      "2780 CHD8\n",
-      "2781 PNN\n",
-      "2782 MAX\n",
-      "2783 MOAP1\n",
-      "2784 WARS1\n",
-      "2785 HSP90AA1\n",
-      "2786 CDCA4\n",
-      "2787 GABPB1-AS1\n",
-      "2788 MTFMT\n",
-      "2789 MESD\n",
-      "2790 WHAMM\n",
-      "2791 KCTD5\n",
-      "2792 CEP20\n",
-      "2793 RPS15A\n",
-      "2794 GINS2\n",
-      "2795 CYBA\n",
-      "2796 METTL16\n",
-      "2797 IFT20\n",
-      "2798 SP2-DT\n",
-      "2799 SPAG9\n",
-      "2800 USP14\n",
-      "2801 AFG3L2\n",
-      "2802 PTPN2\n",
-      "2803 CD226\n",
-      "2804 GZMM\n",
-      "2805 C19orf25\n",
-      "2806 DAPK3\n",
-      "2807 SNAPC2\n",
-      "2808 UBL5\n",
-      "2809 GCDH\n",
-      "2810 PGLS\n",
-      "2811 PGPEP1\n",
-      "2812 KXD1\n",
-      "2813 SERTAD1\n",
-      "2814 NR1H2\n",
-      "2815 ITCH\n",
-      "2816 RPN2\n",
-      "2817 PREX1\n",
-      "2818 ATP5PF\n",
-      "2819 PDXK\n",
-      "2820 CSTB\n",
-      "2821 COL6A2\n",
-      "2822 MED15\n",
-      "2823 NIPSNAP1\n",
-      "2824 EIF3D\n",
-      "2825 MPST\n",
-      "2826 SNU13\n",
-      "2827 SMS\n",
-      "2828 UXT\n",
-      "2829 BEX4\n",
-      "2830 IDH3G\n",
-      "2831 NOC2L\n",
-      "2832 INTS11\n",
-      "2833 MRPL20\n",
-      "2834 RPS6KA1\n",
-      "2835 PHACTR4\n",
-      "2836 PABPC4\n",
-      "2837 EIF2B3\n",
-      "2838 MIGA1\n",
-      "2839 TGFBR3\n",
-      "2840 PI4KB\n",
-      "2841 ISG20L2\n",
-      "2842 KLHL20\n",
-      "2843 MR1\n",
-      "2844 PARP1\n",
-      "2845 SPRTN\n",
-      "2846 SLC66A3\n",
-      "2847 GPN1\n",
-      "2848 HNRNPLL\n",
-      "2849 LRPPRC\n",
-      "2850 KCMF1\n",
-      "2851 LINC01943\n",
-      "2852 APPAT\n",
-      "2853 MAP3K2\n",
-      "2854 RBMS1\n",
-      "2855 AAMP\n",
-      "2856 ARL4C\n",
-      "2857 AZI2\n",
-      "2858 ABTB1\n",
-      "2859 NCK1\n",
-      "2860 RNF13\n",
-      "2861 TIPARP\n",
-      "2862 EIF4A2\n",
-      "2863 RPL39L\n",
-      "2864 FRYL\n",
-      "2865 CISD2\n",
-      "2866 CDKN2AIPNL\n",
-      "2867 C5orf24\n",
-      "2868 NUP153\n",
-      "2869 TDP2\n",
-      "2870 BTN3A2\n",
-      "2871 AIF1\n",
-      "2872 GPANK1\n",
-      "2873 PFDN6\n",
-      "2874 RPL10A\n",
-      "2875 C6orf226\n",
-      "2876 SNX3\n",
-      "2877 TSPYL1\n",
-      "2878 ABRACL\n",
-      "2879 PHF14\n",
-      "2880 NUP42\n",
-      "2881 PHTF2\n",
-      "2882 ARPC1B\n",
-      "2883 THAP5\n",
-      "2884 CALU\n",
-      "2885 CREB3L2\n",
-      "2886 NOM1\n",
-      "2887 DOK2\n",
-      "2888 PRKDC\n",
-      "2889 IMPA1\n",
-      "2890 UBR5\n",
-      "2891 GLIPR2\n",
-      "2892 TRMT10B\n",
-      "2893 IARS1\n",
-      "2894 ARPC5L\n",
-      "2895 FAM107B\n",
-      "2896 CREM\n",
-      "2897 HK1\n",
-      "2898 MICU1\n",
-      "2899 PPIF\n",
-      "2900 SHLD2\n",
-      "2901 HPS1\n",
-      "2902 MRPL43\n",
-      "2903 DUSP5\n",
-      "2904 LSP1\n",
-      "2905 TSPAN32\n",
-      "2906 HBG2\n",
-      "2907 FBXO3\n",
-      "2908 PTPMT1\n",
-      "2909 UBE2L6\n",
-      "2910 CD5\n",
-      "2911 SDHAF2\n",
-      "2912 NXF1\n",
-      "2913 PLAAT3\n",
-      "2914 EED\n",
-      "2915 ZPR1\n",
-      "2916 ATP5MG\n",
-      "2917 CLEC2B\n",
-      "2918 ATF7IP\n",
-      "2919 ENSG00000258344\n",
-      "2920 ZFC3H1\n",
-      "2921 CCDC59\n",
-      "2922 USP30-AS1\n",
-      "2923 CLIP1\n",
-      "2924 RNF6\n",
-      "2925 TEX30\n",
-      "2926 TNFSF13B\n",
-      "2927 GPR65\n",
-      "2928 NRDE2\n",
-      "2929 SNRPN\n",
-      "2930 EHD4\n",
-      "2931 CCNDBP1\n",
-      "2932 UBE2Q2\n",
-      "2933 CHD2\n",
-      "2934 TSR3\n",
-      "2935 ATP6V0C\n",
-      "2936 CD2BP2\n",
-      "2937 BCL7C\n",
-      "2938 MT1E\n",
-      "2939 CMTM3\n",
-      "2940 CMIP\n",
-      "2941 SMG6\n",
-      "2942 PRPSAP2\n",
-      "2943 ZNF207\n",
-      "2944 LINC03057\n",
-      "2945 VEZF1\n",
-      "2946 DHX40\n",
-      "2947 DDX42\n",
-      "2948 BPTF\n",
-      "2949 UBALD2\n",
-      "2950 TGIF1\n",
-      "2951 CNDP2\n",
-      "2952 TXNL4A\n",
-      "2953 ARHGAP45\n",
-      "2954 UQCR11\n",
-      "2955 SGTA\n",
-      "2956 S1PR4\n",
-      "2957 NCLN\n",
-      "2958 S1PR5\n",
-      "2959 MIR23AHG\n",
-      "2960 REX1BD\n",
-      "2961 COPE\n",
-      "2962 GATAD2A\n",
-      "2963 ZNF431\n",
-      "2964 DMAC2\n",
-      "2965 FLT3LG\n",
-      "2966 UBE2M\n",
-      "2967 CST7\n",
-      "2968 NOL4L\n",
-      "2969 DDX27\n",
-      "2970 COMT\n",
-      "2971 YWHAH\n",
-      "2972 APOL6\n",
-      "2973 POLDIP3\n",
-      "2974 UBQLN2\n",
-      "2975 RBMX2\n",
-      "2976 HMGB3\n",
-      "2977 RPS4Y1\n",
-      "2978 ENO1\n",
-      "2979 ELOA\n",
-      "2980 PNRC2\n",
-      "2981 SNRNP40\n",
-      "2982 BCL10\n",
-      "2983 GLMN\n",
-      "2984 RPL5\n",
-      "2985 TMEM167B\n",
-      "2986 CHTOP\n",
-      "2987 CKS1B\n",
-      "2988 DUSP23\n",
-      "2989 TOR1AIP2\n",
-      "2990 MIA3\n",
-      "2991 BROX\n",
-      "2992 RNF187\n",
-      "2993 COG2\n",
-      "2994 ATAD2B\n",
-      "2995 SUPT7L\n",
-      "2996 NAGK\n",
-      "2997 CAPG\n",
-      "2998 PDCL3\n",
-      "2999 IWS1\n",
-      "3000 DNAJB2\n",
-      "3001 SP140\n",
-      "3002 SP140L\n",
-      "3003 CRELD1\n",
-      "3004 VGLL4\n",
-      "3005 OXSR1\n",
-      "3006 QARS1\n",
-      "3007 EIF4E3\n",
-      "3008 PARP9\n",
-      "3009 MYNN\n",
-      "3010 NSG1\n",
-      "3011 SDAD1\n",
-      "3012 THAP9-AS1\n",
-      "3013 SLC39A8\n",
-      "3014 DOCK2\n",
-      "3015 ERGIC1\n",
-      "3016 DTNBP1\n",
-      "3017 HCP5\n",
-      "3018 HLA-DRB5\n",
-      "3019 UFL1\n",
-      "3020 TMEM242\n",
-      "3021 AP5Z1\n",
-      "3022 TRA2A\n",
-      "3023 ELMO1\n",
-      "3024 TRGC1\n",
-      "3025 NAMPT\n",
-      "3026 PDLIM2\n",
-      "3027 NSD3\n",
-      "3028 VIRMA\n",
-      "3029 ATP6V1C1\n",
-      "3030 DERL1\n",
-      "3031 MTSS1\n",
-      "3032 SLC39A4\n",
-      "3033 KLHL9\n",
-      "3034 FPGS\n",
-      "3035 SFMBT2\n",
-      "3036 OPTN\n",
-      "3037 WAC\n",
-      "3038 EPC1\n",
-      "3039 DNTT\n",
-      "3040 SFXN4\n",
-      "3041 SNHG1\n",
-      "3042 PPP1CA\n",
-      "3043 MRPL48\n",
-      "3044 SPCS2\n",
-      "3045 CRTAM\n",
-      "3046 HSPA8\n",
-      "3047 FKBP4\n",
-      "3048 CREBL2\n",
-      "3049 EIF4B\n",
-      "3050 YEATS4\n",
-      "3051 SUDS3\n",
-      "3052 CCNB1IP1\n",
-      "3053 DCAF11\n",
-      "3054 G2E3\n",
-      "3055 GNG2\n",
-      "3056 MAPK1IP1L\n",
-      "3057 YLPM1\n",
-      "3058 PPP4R3A\n",
-      "3059 NDUFB1\n",
-      "3060 IGHG1\n",
-      "3061 TRIP4\n",
-      "3062 ANP32A\n",
-      "3063 COX5A\n",
-      "3064 NMRAL1\n",
-      "3065 MGRN1\n",
-      "3066 GGA2\n",
-      "3067 GLG1\n",
-      "3068 SPG7\n",
-      "3069 MRM3\n",
-      "3070 YWHAE\n",
-      "3071 ERAL1\n",
-      "3072 MLX\n",
-      "3073 MRPL10\n",
-      "3074 SRP68\n",
-      "3075 MFSD11\n",
-      "3076 PSMG2\n",
-      "3077 RMC1\n",
-      "3078 BSG\n",
-      "3079 MYDGF\n",
-      "3080 MYO1F\n",
-      "3081 ALKBH6\n",
-      "3082 ZFP36\n",
-      "3083 SUPT5H\n",
-      "3084 HSPBP1\n",
-      "3085 KIZ\n",
-      "3086 TPX2\n",
-      "3087 ZNFX1\n",
-      "3088 SNAP29\n",
-      "3089 TIMP1\n",
-      "3090 ARHGEF9\n",
-      "3091 C1GALT1C1\n",
-      "3092 MMGT1\n",
-      "3093 ENSG00000276345\n",
-      "3094 TMEM222\n",
-      "3095 SFPQ\n",
-      "3096 ENSG00000276216\n",
-      "3097 THEM4\n",
-      "3098 ARHGAP30\n",
-      "3099 MRPL55\n",
-      "3100 TRIB2\n",
-      "3101 RHOB\n",
-      "3102 HADHB\n",
-      "3103 RIF1\n",
-      "3104 CYP20A1\n",
-      "3105 ABI2\n",
-      "3106 CUL3\n",
-      "3107 SPCS1\n",
-      "3108 MBD4\n",
-      "3109 NOP14\n",
-      "3110 MRFAP1L1\n",
-      "3111 BOD1L1\n",
-      "3112 HERC5\n",
-      "3113 PHAX\n",
-      "3114 HNRNPA0\n",
-      "3115 WDR55\n",
-      "3116 ARHGAP26\n",
-      "3117 RBM27\n",
-      "3118 HLA-C\n",
-      "3119 CCNC\n",
-      "3120 ECHDC1\n",
-      "3121 SGK1\n",
-      "3122 REPS1\n",
-      "3123 CCZ1\n",
-      "3124 MRPL32\n",
-      "3125 HIPK2\n",
-      "3126 PRSS2\n",
-      "3127 CCAR2\n",
-      "3128 PEX2\n",
-      "3129 CPQ\n",
-      "3130 TATDN1\n",
-      "3131 TMEM71\n",
-      "3132 TOPORS\n",
-      "3133 NFX1\n",
-      "3134 ANP32B\n",
-      "3135 ALG2\n",
-      "3136 CDK5RAP2\n",
-      "3137 RAB14\n",
-      "3138 MAN1B1\n",
-      "3139 IDI1\n",
-      "3140 P4HA1\n",
-      "3141 EXOSC1\n",
-      "3142 ZDHHC16\n",
-      "3143 ATP5MK\n",
-      "3144 TUBGCP2\n",
-      "3145 CHID1\n",
-      "3146 TMEM138\n",
-      "3147 NEAT1\n",
-      "3148 CCDC85B\n",
-      "3149 AIP\n",
-      "3150 KMT5B\n",
-      "3151 LAMTOR1\n",
-      "3152 PGM2L1\n",
-      "3153 RPS3\n",
-      "3154 ANKRD49\n",
-      "3155 USP28\n",
-      "3156 PCSK7\n",
-      "3157 NELL2\n",
-      "3158 KANSL2\n",
-      "3159 BLOC1S1\n",
-      "3160 GLTP\n",
-      "3161 VPS29\n",
-      "3162 RBM19\n",
-      "3163 MPHOSPH8\n",
-      "3164 FOXO1\n",
-      "3165 ANKRD10\n",
-      "3166 TIMM9\n",
-      "3167 PCNX1\n",
-      "3168 NUMB\n",
-      "3169 MAP2K1\n",
-      "3170 AKAP13\n",
-      "3171 MSRB1\n",
-      "3172 RPS2\n",
-      "3173 ATXN2L\n",
-      "3174 CORO1A\n",
-      "3175 NUDT21\n",
-      "3176 NAE1\n",
-      "3177 SF3B3\n",
-      "3178 DHX38\n",
-      "3179 APRT\n",
-      "3180 MIS12\n",
-      "3181 ELP5\n",
-      "3182 PSMB3\n",
-      "3183 KRT13\n",
-      "3184 KAT7\n",
-      "3185 FTSJ3\n",
-      "3186 GNA13\n",
-      "3187 SAP30BP\n",
-      "3188 SYNGR2\n",
-      "3189 ANAPC11\n",
-      "3190 NDUFV2\n",
-      "3191 RALBP1\n",
-      "3192 ESCO1\n",
-      "3193 C18orf21\n",
-      "3194 HAUS1\n",
-      "3195 PTBP1\n",
-      "3196 DENND1C\n",
-      "3197 PIN1\n",
-      "3198 PDCD5\n",
-      "3199 GARRE1\n",
-      "3200 TYROBP\n",
-      "3201 TBCB\n",
-      "3202 SAE1\n",
-      "3203 CBR1\n",
-      "3204 YPEL1\n",
-      "3205 TOM1\n",
-      "3206 ATF4\n",
-      "3207 SLC38A5\n",
-      "3208 RBM3\n",
-      "3209 CXCR3\n",
-      "3210 ALG13\n",
-      "3211 SLC25A5\n",
-      "3212 RPA2\n",
-      "3213 UQCRH\n",
-      "3214 LRRC8C\n",
-      "3215 TXNIP\n",
-      "3216 VPS72\n",
-      "3217 SHC1\n",
-      "3218 TIPRL\n",
-      "3219 TOR1AIP1\n",
-      "3220 RGS16\n",
-      "3221 H2AC25\n",
-      "3222 GTF3C2\n",
-      "3223 PPP1CB\n",
-      "3224 PNPT1\n",
-      "3225 MOB1A\n",
-      "3226 MTHFD2\n",
-      "3227 USP39\n",
-      "3228 SMPD4\n",
-      "3229 PLEKHA3\n",
-      "3230 ANKRD44\n",
-      "3231 CTDSP1\n",
-      "3232 UBE2F\n",
-      "3233 DTYMK\n",
-      "3234 MLH1\n",
-      "3235 LRRFIP2\n",
-      "3236 RPSA\n",
-      "3237 ARF4\n",
-      "3238 PCNP\n",
-      "3239 CD96\n",
-      "3240 PARP14\n",
-      "3241 ZNF639\n",
-      "3242 BDH1\n",
-      "3243 C4orf48\n",
-      "3244 MRFAP1L2\n",
-      "3245 ARAP2\n",
-      "3246 SLAIN2\n",
-      "3247 LAMTOR3\n",
-      "3248 SNHG8\n",
-      "3249 SEC24D\n",
-      "3250 NSA2\n",
-      "3251 PPIP5K2\n",
-      "3252 REEP5\n",
-      "3253 CXXC5\n",
-      "3254 JAKMIP2\n",
-      "3255 RREB1\n",
-      "3256 RIPOR2\n",
-      "3257 HMGN4\n",
-      "3258 HLA-DRA\n",
-      "3259 GINM1\n",
-      "3260 TULP4\n",
-      "3261 QKI\n",
-      "3262 ZFAND2A\n",
-      "3263 MRM2\n",
-      "3264 CARD11\n",
-      "3265 STARD3NL\n",
-      "3266 CDK6\n",
-      "3267 BUD31\n",
-      "3268 KMT2E\n",
-      "3269 TRBC2\n",
-      "3270 REPIN1\n",
-      "3271 GIMAP5\n",
-      "3272 BIN3\n",
-      "3273 GGH\n",
-      "3274 CSPP1\n",
-      "3275 ZFAND1\n",
-      "3276 PLEKHF2\n",
-      "3277 C9orf85\n",
-      "3278 FAM120A\n",
-      "3279 SPTAN1\n",
-      "3280 PMPCA\n",
-      "3281 CUL2\n",
-      "3282 ZNF32\n",
-      "3283 SEC23IP\n",
-      "3284 MRPL17\n",
-      "3285 YIF1A\n",
-      "3286 PTS\n",
-      "3287 KLRC2\n",
-      "3288 AEBP2\n",
-      "3289 YAF2\n",
-      "3290 TWF1\n",
-      "3291 PYM1\n",
-      "3292 ESYT1\n",
-      "3293 MYL6\n",
-      "3294 MRPS31\n",
-      "3295 GMPR2\n",
-      "3296 TMX1\n",
-      "3297 SNAPC1\n",
-      "3298 NUSAP1\n",
-      "3299 TRIM69\n",
-      "3300 BLOC1S6\n",
-      "3301 RSL24D1\n",
-      "3302 PIGBOS1\n",
-      "3303 SNAPC5\n",
-      "3304 LINS1\n",
-      "3305 GOT2\n",
-      "3306 CENPT\n",
-      "3307 VPS4A\n",
-      "3308 PFN1\n",
-      "3309 TXNDC17\n",
-      "3310 AP2B1\n",
-      "3311 MMD\n",
-      "3312 MIR142HG\n",
-      "3313 SKA2\n",
-      "3314 FN3KRP\n",
-      "3315 MAPRE2\n",
-      "3316 TMEM259\n",
-      "3317 DUS3L\n",
-      "3318 RETN\n",
-      "3319 PPAN\n",
-      "3320 ZNF90\n",
-      "3321 ERCC1\n",
-      "3322 SYMPK\n",
-      "3323 GRWD1\n",
-      "3324 VRK3\n",
-      "3325 PPP6R1\n",
-      "3326 ZNF274\n",
-      "3327 ENSG00000283103\n",
-      "3328 CDC25B\n",
-      "3329 PET117\n",
-      "3330 GSS\n",
-      "3331 PTPN1\n",
-      "3332 NELFCD\n",
-      "3333 ADRM1\n",
-      "3334 MCM3AP\n",
-      "3335 MRPL40\n",
-      "3336 IFT27\n",
-      "3337 RANGAP1\n",
-      "3338 ENSG00000270022\n",
-      "3339 CYB5R3\n",
-      "3340 PIM2\n",
-      "3341 THOC2\n",
-      "3342 LRRC47\n",
-      "3343 SPEN-AS1\n",
-      "3344 ID3\n",
-      "3345 SNHG12\n",
-      "3346 TMEM69\n",
-      "3347 MKNK1\n",
-      "3348 NRDC\n",
-      "3349 ITGB3BP\n",
-      "3350 SH3GLB1\n",
-      "3351 LRIF1\n",
-      "3352 PEX11B\n",
-      "3353 PBXIP1\n",
-      "3354 NME7\n",
-      "3355 SOAT1\n",
-      "3356 ACBD6\n",
-      "3357 PPP2R5A\n",
-      "3358 FBXO28\n",
-      "3359 COQ8A\n",
-      "3360 TOMM20\n",
-      "3361 SMC6\n",
-      "3362 CDC42EP3\n",
-      "3363 OLA1\n",
-      "3364 ACSL3\n",
-      "3365 PASK\n",
-      "3366 RAB5A\n",
-      "3367 CMTM7\n",
-      "3368 PDE12\n",
-      "3369 TFG\n",
-      "3370 ATP2C1\n",
-      "3371 RASA2\n",
-      "3372 GYG1\n",
-      "3373 KLHL24\n",
-      "3374 SLC30A9\n",
-      "3375 TXK\n",
-      "3376 EXOC3\n",
-      "3377 EGR1\n",
-      "3378 NUDCD2\n",
-      "3379 NOP16\n",
-      "3380 TMEM14B\n",
-      "3381 LEMD2\n",
-      "3382 TAF11\n",
-      "3383 MAPK14\n",
-      "3384 CNPY3\n",
-      "3385 SNHG5\n",
-      "3386 BUD23\n",
-      "3387 RSBN1L\n",
-      "3388 FAM3C\n",
-      "3389 ZNF800\n",
-      "3390 STMP1\n",
-      "3391 BRAF\n",
-      "3392 MCPH1\n",
-      "3393 CPNE3\n",
-      "3394 OTUD6B-AS1\n",
-      "3395 RIDA\n",
-      "3396 ST3GAL1\n",
-      "3397 AKNA\n",
-      "3398 ZBTB43\n",
-      "3399 FUBP3\n",
-      "3400 RPL7A\n",
-      "3401 MRPS2\n",
-      "3402 CAMK1D\n",
-      "3403 PCBD1\n",
-      "3404 NUTM2A-AS1\n",
-      "3405 ARL3\n",
-      "3406 RPL27A\n",
-      "3407 BMAL1\n",
-      "3408 RRAS2\n",
-      "3409 CKAP5\n",
-      "3410 FNBP4\n",
-      "3411 ATG2A\n",
-      "3412 ARL2\n",
-      "3413 RCE1\n",
-      "3414 RAD9A\n",
-      "3415 TBC1D10C\n",
-      "3416 SIK3\n",
-      "3417 UBASH3B\n",
-      "3418 DCPS\n",
-      "3419 APLP2\n",
-      "3420 ITFG2\n",
-      "3421 CD69\n",
-      "3422 DERA\n",
-      "3423 ITGA5\n",
-      "3424 ATP2A2\n",
-      "3425 SPATA13\n",
-      "3426 KIAA0586\n",
-      "3427 SEL1L\n",
-      "3428 IFI27L2\n",
-      "3429 VRK1\n",
-      "3430 NIPA2\n",
-      "3431 EID1\n",
-      "3432 TRPM7\n",
-      "3433 HEXA\n",
-      "3434 RNPS1\n",
-      "3435 GLYR1\n",
-      "3436 RSL1D1\n",
-      "3437 GSPT1\n",
-      "3438 PDXDC1\n",
-      "3439 KATNB1\n",
-      "3440 PRMT7\n",
-      "3441 PER1\n",
-      "3442 PHF12\n",
-      "3443 CCL3\n",
-      "3444 MRPL27\n",
-      "3445 PITPNC1\n",
-      "3446 THOC1\n",
-      "3447 R3HDM4\n",
-      "3448 THOP1\n",
-      "3449 UBXN6\n",
-      "3450 ZNF433-AS1\n",
-      "3451 SMIM7\n",
-      "3452 CAPNS1\n",
-      "3453 ZNF331\n",
-      "3454 MYADM\n",
-      "3455 CSNK2A1\n",
-      "3456 ITPA\n",
-      "3457 TRMT6\n",
-      "3458 HPS4\n",
-      "3459 OFD1\n",
-      "3460 ATP6AP2\n",
-      "3461 TIMM8A\n",
-      "3462 VBP1\n",
-      "3463 TNFRSF18\n",
-      "3464 MIB2\n",
-      "3465 CDK11A\n",
-      "3466 ELOVL1\n",
-      "3467 GFI1\n",
-      "3468 REG4\n",
-      "3469 MLLT11\n",
-      "3470 POGZ\n",
-      "3471 RGS1\n",
-      "3472 PTPN7\n",
-      "3473 NENF\n",
-      "3474 EPRS1\n",
-      "3475 YPEL5\n",
-      "3476 FEZ2\n",
-      "3477 CEBPZ\n",
-      "3478 HTRA2\n",
-      "3479 KDM3A\n",
-      "3480 RMND5A\n",
-      "3481 UNC50\n",
-      "3482 TXNDC9\n",
-      "3483 EIF5B\n",
-      "3484 PPIL3\n",
-      "3485 INO80D\n",
-      "3486 TRNT1\n",
-      "3487 TBC1D5\n",
-      "3488 SLC4A7\n",
-      "3489 NKTR\n",
-      "3490 FOXP1\n",
-      "3491 SENP7\n",
-      "3492 GOLGB1\n",
-      "3493 GFM1\n",
-      "3494 ACTL6A\n",
-      "3495 DCUN1D1\n",
-      "3496 RPL35A\n",
-      "3497 ATP5ME\n",
-      "3498 UBA6\n",
-      "3499 JCHAIN\n",
-      "3500 HNRNPD\n",
-      "3501 ADH5\n",
-      "3502 PPID\n",
-      "3503 MTREX\n",
-      "3504 AP3B1\n",
-      "3505 JMY\n",
-      "3506 GLRX\n",
-      "3507 JADE2\n",
-      "3508 SAR1B\n",
-      "3509 MACROH2A1\n",
-      "3510 STING1\n",
-      "3511 CYFIP2\n",
-      "3512 UBLCP1\n",
-      "3513 FARS2\n",
-      "3514 ENSG00000272009\n",
-      "3515 PRDM1\n",
-      "3516 KPNA5\n",
-      "3517 STX11\n",
-      "3518 TAGAP\n",
-      "3519 CDCA7L\n",
-      "3520 HIBADH\n",
-      "3521 TRG-AS1\n",
-      "3522 DNAJC30\n",
-      "3523 SEM1\n",
-      "3524 PRKRIP1\n",
-      "3525 UBE3C\n",
-      "3526 CLU\n",
-      "3527 TACC1\n",
-      "3528 YWHAZ\n",
-      "3529 SQLE\n",
-      "3530 NUP214\n",
-      "3531 CDC123\n",
-      "3532 REEP3\n",
-      "3533 GHITM\n",
-      "3534 RPP30\n",
-      "3535 TAF10\n",
-      "3536 USP47\n",
-      "3537 API5\n",
-      "3538 SAC3D1\n",
-      "3539 PTPRCAP\n",
-      "3540 PCF11\n",
-      "3541 CCDC91\n",
-      "3542 TUBA1C\n",
-      "3543 NCKAP1L\n",
-      "3544 RPS26\n",
-      "3545 UBC\n",
-      "3546 ESD\n",
-      "3547 DHRS4\n",
-      "3548 BAZ1A\n",
-      "3549 KLHDC2\n",
-      "3550 FUT8\n",
-      "3551 ZC3H14\n",
-      "3552 DICER1\n",
-      "3553 EVL\n",
-      "3554 EIF2AK4\n",
-      "3555 DNAJC17\n",
-      "3556 ZNF280D\n",
-      "3557 USP3\n",
-      "3558 MRPS11\n",
-      "3559 TM2D3\n",
-      "3560 UQCRC2\n",
-      "3561 DNAJA2\n",
-      "3562 CNOT1\n",
-      "3563 DYNC1LI2\n",
-      "3564 CBFB\n",
-      "3565 CYB5B\n",
-      "3566 KDM6B\n",
-      "3567 TMEM107\n",
-      "3568 COPS3\n",
-      "3569 MRPL45\n",
-      "3570 PHB1\n",
-      "3571 SLC35B1\n",
-      "3572 NT5C\n",
-      "3573 CYB5A\n",
-      "3574 ATP9B\n",
-      "3575 RNF126\n",
-      "3576 TMEM147\n",
-      "3577 SERTAD3\n",
-      "3578 TRAPPC6A\n",
-      "3579 PRKD2\n",
-      "3580 PTOV1\n",
-      "3581 TRAPPC2B\n",
-      "3582 RNF114\n",
-      "3583 RPS21\n",
-      "3584 GID8\n",
-      "3585 PCMTD2\n",
-      "3586 USP16\n",
-      "3587 SDF2L1\n",
-      "3588 IGLC2\n",
-      "3589 APOBEC3H\n",
-      "3590 SGSM3\n",
-      "3591 RBX1\n",
-      "3592 TOB2\n",
-      "3593 ARFGAP3\n",
-      "3594 TBL1X\n",
-      "3595 HUWE1\n",
-      "3596 YIPF6\n",
-      "3597 XIST\n",
-      "3598 SEPTIN6\n",
-      "3599 IKBKG\n",
-      "3600 SRM\n",
-      "3601 LDLRAP1\n",
-      "3602 NUDC\n",
-      "3603 LCK\n",
-      "3604 CDCA8\n",
-      "3605 UTP11\n",
-      "3606 MED8\n",
-      "3607 LEPROT\n",
-      "3608 WDR77\n",
-      "3609 LIX1L\n",
-      "3610 IGSF8\n",
-      "3611 NIT1\n",
-      "3612 SH3YL1\n",
-      "3613 RDH14\n",
-      "3614 SLC4A1AP\n",
-      "3615 PPM1B\n",
-      "3616 GMCL1\n",
-      "3617 PCBP1\n",
-      "3618 GGCX\n",
-      "3619 RNF181\n",
-      "3620 STAT1\n",
-      "3621 COQ10B\n",
-      "3622 NBEAL1\n",
-      "3623 XRCC5\n",
-      "3624 RAF1\n",
-      "3625 CCDC12\n",
-      "3626 MANF\n",
-      "3627 ARMC8\n",
-      "3628 POLR2H\n",
-      "3629 MAD2L1\n",
-      "3630 ANAPC10\n",
-      "3631 MND1\n",
-      "3632 TMA16\n",
-      "3633 SLC25A4\n",
-      "3634 CCNB1\n",
-      "3635 SCAMP1-AS1\n",
-      "3636 LMNB1\n",
-      "3637 SIL1\n",
-      "3638 CD74\n",
-      "3639 DDX41\n",
-      "3640 SERPINB9\n",
-      "3641 HCG18\n",
-      "3642 RPP21\n",
-      "3643 LSM2\n",
-      "3644 HMGN3\n",
-      "3645 MTRES1\n",
-      "3646 HBS1L\n",
-      "3647 AHI1\n",
-      "3648 POLM\n",
-      "3649 ZNF394\n",
-      "3650 MOSPD3\n",
-      "3651 DNAJC2\n",
-      "3652 SMIM30\n",
-      "3653 DNAJB6\n",
-      "3654 CHMP7\n",
-      "3655 ASH2L\n",
-      "3656 UBE2W\n",
-      "3657 NMRK1\n",
-      "3658 HDHD3\n",
-      "3659 ALAD\n",
-      "3660 MED27\n",
-      "3661 UBAC1\n",
-      "3662 PAXX\n",
-      "3663 CCNY\n",
-      "3664 TMEM273\n",
-      "3665 TYSND1\n",
-      "3666 ANXA11\n",
-      "3667 IFIT3\n",
-      "3668 SMC3\n",
-      "3669 MRPL23\n",
-      "3670 TIMM10\n",
-      "3671 WDR74\n",
-      "3672 SF1\n",
-      "3673 MRPL49\n",
-      "3674 BIRC2\n",
-      "3675 CWF19L2\n",
-      "3676 CHEK1\n",
-      "3677 ADIPOR2\n",
-      "3678 YBX3\n",
-      "3679 PLEKHA5\n",
-      "3680 INTS13\n",
-      "3681 GTSF1\n",
-      "3682 TBK1\n",
-      "3683 DYNLL1\n",
-      "3684 RNF10\n",
-      "3685 SFSWAP\n",
-      "3686 PIGH\n",
-      "3687 DDX24\n",
-      "3688 MFAP1\n",
-      "3689 ARPP19\n",
-      "3690 ANXA2\n",
-      "3691 SCAPER\n",
-      "3692 MRPS34\n",
-      "3693 IL32\n",
-      "3694 ENSG00000286808\n",
-      "3695 MON1B\n",
-      "3696 SPAG7\n",
-      "3697 RPL26\n",
-      "3698 GAS7\n",
-      "3699 RHOT1\n",
-      "3700 MLLT6\n",
-      "3701 RPL23\n",
-      "3702 CDC27\n",
-      "3703 UTP18\n",
-      "3704 RAD51C\n",
-      "3705 RPS6KB1\n",
-      "3706 APPBP2\n",
-      "3707 TLK2\n",
-      "3708 CYTH1\n",
-      "3709 ABHD3\n",
-      "3710 PTGR3\n",
-      "3711 MIDN\n",
-      "3712 RANBP3\n",
-      "3713 WDR83OS\n",
-      "3714 CC2D1A\n",
-      "3715 AKAP8L\n",
-      "3716 ZNF91\n",
-      "3717 URI1\n",
-      "3718 COX6B1\n",
-      "3719 ZNF146\n",
-      "3720 ACTN4\n",
-      "3721 SNRPA\n",
-      "3722 B9D2\n",
-      "3723 ZNF224\n",
-      "3724 BBC3\n",
-      "3725 ASXL1\n",
-      "3726 TOP1\n",
-      "3727 ADNP\n",
-      "3728 APP\n",
-      "3729 IFNAR1\n",
-      "3730 AP1B1\n",
-      "3731 P2RY10\n",
-      "3732 DIAPH2\n",
-      "3733 IDS\n",
-      "3734 MT-CO1\n",
-      "3735 MT-ATP6\n",
-      "3736 ENSG00000238142\n",
-      "3737 UBR4\n",
-      "3738 THEMIS2\n",
-      "3739 RBBP4\n",
-      "3740 TFAP2E-AS1\n",
-      "3741 SF3A3\n",
-      "3742 CTPS1\n",
-      "3743 SRSF11\n",
-      "3744 PKN2\n",
-      "3745 CEPT1\n",
-      "3746 SEC22B\n",
-      "3747 LINC00623\n",
-      "3748 C1orf56\n",
-      "3749 RUSC1\n",
-      "3750 GON4L\n",
-      "3751 TAGLN2\n",
-      "3752 GAS5\n",
-      "3753 UCHL5\n",
-      "3754 IPO9\n",
-      "3755 MDM4\n",
-      "3756 NUCKS1\n",
-      "3757 AIDA\n",
-      "3758 LGALS8\n",
-      "3759 TAF1B\n",
-      "3760 VRK2\n",
-      "3761 PEX13\n",
-      "3762 C2orf76\n",
-      "3763 EPC2\n",
-      "3764 PPIG\n",
-      "3765 SSB\n",
-      "3766 DIS3L2\n",
-      "3767 MTERF4\n",
-      "3768 SEPTIN2\n",
-      "3769 OGG1\n",
-      "3770 RPL15\n",
-      "3771 MAPKAPK3\n",
-      "3772 UBA3\n",
-      "3773 NFKBIZ\n",
-      "3774 ATG3\n",
-      "3775 SENP2\n",
-      "3776 SMIM14\n",
-      "3777 SEPTIN11\n",
-      "3778 PDLIM5\n",
-      "3779 KIF2A\n",
-      "3780 F2R\n",
-      "3781 AGGF1\n",
-      "3782 ERAP1\n",
-      "3783 SRA1\n",
-      "3784 RUFY1\n",
-      "3785 SRPK1\n",
-      "3786 CGAS\n",
-      "3787 TENT5A\n",
-      "3788 MICAL1\n",
-      "3789 NUS1\n",
-      "3790 HDDC2\n",
-      "3791 UMAD1\n",
-      "3792 AUTS2\n",
-      "3793 BET1\n",
-      "3794 SLC12A9\n",
-      "3795 PNPLA8\n",
-      "3796 PLPP5\n",
-      "3797 MTDH\n",
-      "3798 B4GALT1\n",
-      "3799 STOML2\n",
-      "3800 PABIR1\n",
-      "3801 PSMD5\n",
-      "3802 MRRF\n",
-      "3803 PSMB7\n",
-      "3804 URM1\n",
-      "3805 TOR1A\n",
-      "3806 TMEM141\n",
-      "3807 RBM17\n",
-      "3808 PFKFB3\n",
-      "3809 SEPHS1\n",
-      "3810 ZNF37A\n",
-      "3811 TFAM\n",
-      "3812 PPA1\n",
-      "3813 PDCD11\n",
-      "3814 ARHGAP1\n",
-      "3815 ZFP91\n",
-      "3816 GANAB\n",
-      "3817 TMEM223\n",
-      "3818 STX5\n",
-      "3819 SCYL1\n",
-      "3820 DPP3\n",
-      "3821 TAF1D\n",
-      "3822 FAM76B\n",
-      "3823 TMEM123\n",
-      "3824 FXYD2\n",
-      "3825 TRAPPC4\n",
-      "3826 TBRG1\n",
-      "3827 DDIT3\n",
-      "3828 GNPTAB\n",
-      "3829 ANKRD13A\n",
-      "3830 ATP6V0A2\n",
-      "3831 MTIF3\n",
-      "3832 METTL17\n",
-      "3833 FKBP3\n",
-      "3834 MGAT2\n",
-      "3835 PRKCH\n",
-      "3836 LYSET\n",
-      "3837 WDR76\n",
-      "3838 DTWD1\n",
-      "3839 TCF12\n",
-      "3840 SNX1\n",
-      "3841 CLK3\n",
-      "3842 SKIC8\n",
-      "3843 ROGDI\n",
-      "3844 DCTPP1\n",
-      "3845 RNF40\n",
-      "3846 ITFG1\n",
-      "3847 RPAIN\n",
-      "3848 CHD3\n",
-      "3849 MPRIP\n",
-      "3850 ALKBH5\n",
-      "3851 RPL23A\n",
-      "3852 CCL5\n",
-      "3853 COASY\n",
-      "3854 ARHGAP27\n",
-      "3855 TTC39C\n",
-      "3856 RPL17\n",
-      "3857 MICOS13\n",
-      "3858 SPC24\n",
-      "3859 IER2\n",
-      "3860 NDUFA13\n",
-      "3861 PPP1R15A\n",
-      "3862 RPL13A\n",
-      "3863 CNOT3\n",
-      "3864 FKBP1A\n",
-      "3865 SEC23B\n",
-      "3866 TRPC4AP\n",
-      "3867 NORAD\n",
-      "3868 SERINC3\n",
-      "3869 STK4\n",
-      "3870 ATP5F1E\n",
-      "3871 ENSG00000154640\n",
-      "3872 SCAF4\n",
-      "3873 SLC5A3\n",
-      "3874 ENSG00000160284\n",
-      "3875 PPIL2\n",
-      "3876 EIF2S3\n",
-      "3877 FUNDC1\n",
-      "3878 PDZD11\n",
-      "3879 MORF4L2\n",
-      "3880 FLNA\n",
-      "3881 EMD\n",
-      "3882 DDOST\n",
-      "3883 RUNX3\n",
-      "3884 MTFR1L\n",
-      "3885 FGR\n",
-      "3886 FHL3\n",
-      "3887 EBNA1BP2\n",
-      "3888 UROD\n",
-      "3889 CRYZ\n",
-      "3890 RABGGTB\n",
-      "3891 AGL\n",
-      "3892 RBM8A\n",
-      "3893 FLAD1\n",
-      "3894 FCGR3A\n",
-      "3895 DCAF6\n",
-      "3896 IKBKE\n",
-      "3897 CD55\n",
-      "3898 FH\n",
-      "3899 TMEM18\n",
-      "3900 NOL10\n",
-      "3901 DPY30\n",
-      "3902 ZFP36L2\n",
-      "3903 CCT7\n",
-      "3904 RNF149\n",
-      "3905 CLASP1\n",
-      "3906 NDUFS1\n",
-      "3907 DOCK10\n",
-      "3908 DGKD\n",
-      "3909 GLB1\n",
-      "3910 WDR48\n",
-      "3911 WDR6\n",
-      "3912 NPRL2\n",
-      "3913 BAP1\n",
-      "3914 USF3\n",
-      "3915 LRPAP1\n",
-      "3916 GPRIN3\n",
-      "3917 PRMT9\n",
-      "3918 DDX60\n",
-      "3919 CBR4\n",
-      "3920 BRIX1\n",
-      "3921 ENSG00000251131\n",
-      "3922 PCBD2\n",
-      "3923 PAIP2\n",
-      "3924 PURA\n",
-      "3925 H1-5\n",
-      "3926 HLA-DQA1\n",
-      "3927 DAXX\n",
-      "3928 POLR1C\n",
-      "3929 RUNX2\n",
-      "3930 RARS2\n",
-      "3931 PHACTR2\n",
-      "3932 LTV1\n",
-      "3933 MRPL18\n",
-      "3934 BLVRA\n",
-      "3935 ZNF92\n",
-      "3936 LUC7L2\n",
-      "3937 TRBC1\n",
-      "3938 PAXIP1-DT\n",
-      "3939 GTF2E2\n",
-      "3940 RAB11FIP1\n",
-      "3941 SLC52A2\n",
-      "3942 VPS28\n",
-      "3943 CIZ1\n",
-      "3944 SNHG7\n",
-      "3945 TRAF2\n",
-      "3946 PRPF18\n",
-      "3947 SAR1A\n",
-      "3948 PSAP\n",
-      "3949 VCL\n",
-      "3950 MARCHF5\n",
-      "3951 NPM3\n",
-      "3952 RNH1\n",
-      "3953 RPLP2\n",
-      "3954 EIF3F\n",
-      "3955 EIF4G2\n",
-      "3956 SELENOH\n",
-      "3957 JAML\n",
-      "3958 RPUSD4\n",
-      "3959 FLI1\n",
-      "3960 RHNO1\n",
-      "3961 CHD4\n",
-      "3962 SINHCAF\n",
-      "3963 IRAK4\n",
-      "3964 LLPH\n",
-      "3965 BTG1-DT\n",
-      "3966 ACTR6\n",
-      "3967 TMEM263\n",
-      "3968 DDX54\n",
-      "3969 KDM2B\n",
-      "3970 FBXO34\n",
-      "3971 MNAT1\n",
-      "3972 RMDN3\n",
-      "3973 PATL2\n",
-      "3974 ZNF609\n",
-      "3975 SELENOS\n",
-      "3976 GFER\n",
-      "3977 CREBBP\n",
-      "3978 LCMT1\n",
-      "3979 IL4R\n",
-      "3980 CSNK2A2\n",
-      "3981 MAP1LC3B\n",
-      "3982 POLR2A\n",
-      "3983 SNHG29\n",
-      "3984 MED1\n",
-      "3985 VPS25\n",
-      "3986 BECN1\n",
-      "3987 ERN1\n",
-      "3988 JMJD6\n",
-      "3989 BIRC5\n",
-      "3990 SMCHD1\n",
-      "3991 SEC11C\n",
-      "3992 PIGN\n",
-      "3993 MRPL54\n",
-      "3994 VAV1\n",
-      "3995 DNASE2\n",
-      "3996 DDX39A\n",
-      "3997 FAM32A\n",
-      "3998 BST2\n",
-      "3999 FCHO1\n",
-      "4000 UQCRFS1\n",
-      "4001 GRAMD1A\n",
-      "4002 PSENEN\n",
-      "4003 TGFB1\n",
-      "4004 XRCC1\n",
-      "4005 GEMIN7\n",
-      "4006 FTL\n",
-      "4007 COMMD7\n",
-      "4008 PRPF6\n",
-      "4009 CCT8\n",
-      "4010 SON\n",
-      "4011 RANBP1\n",
-      "4012 DRG1\n",
-      "4013 EP300\n",
-      "4014 GPR174\n",
-      "4015 HNRNPH2\n",
-      "4016 DOCK11\n",
-      "4017 ENSG00000272449\n",
-      "4018 RCC2\n",
-      "4019 UBXN11\n",
-      "4020 WASF2\n",
-      "4021 SSBP3\n",
-      "4022 ZZZ3\n",
-      "4023 CCDC18\n",
-      "4024 RSBN1\n",
-      "4025 MEF2D\n",
-      "4026 CD48\n",
-      "4027 MPC2\n",
-      "4028 TSEN15\n",
-      "4029 IARS2\n",
-      "4030 RAB10\n",
-      "4031 WDR43\n",
-      "4032 XPO1\n",
-      "4033 B3GNT2\n",
-      "4034 MRPL53\n",
-      "4035 CDCA7\n",
-      "4036 ZC3H15\n",
-      "4037 ATG4B\n",
-      "4038 DHX30\n",
-      "4039 SELENOT\n",
-      "4040 SMC4\n",
-      "4041 TNIK\n",
-      "4042 N4BP2\n",
-      "4043 CHIC2\n",
-      "4044 MGST2\n",
-      "4045 INPP4B\n",
-      "4046 MARCHF6\n",
-      "4047 WDR70\n",
-      "4048 ARRDC3\n",
-      "4049 HINT1\n",
-      "4050 ETF1\n",
-      "4051 DHX16\n",
-      "4052 CUTA\n",
-      "4053 CCND3\n",
-      "4054 SLC35B2\n",
-      "4055 CDC5L\n",
-      "4056 MED23\n",
-      "4057 HERPUD2\n",
-      "4058 NDUFB2\n",
-      "4059 NUB1\n",
-      "4060 LEPROTL1\n",
-      "4061 HOOK3\n",
-      "4062 NSMAF\n",
-      "4063 TRAM1\n",
-      "4064 MRPL13\n",
-      "4065 HSF1\n",
-      "4066 EXOSC3\n",
-      "4067 ZNG1E\n",
-      "4068 VPS13A\n",
-      "4069 TUBB4B\n",
-      "4070 HNRNPF\n",
-      "4071 SPOCK2\n",
-      "4072 RGS10\n",
-      "4073 NSMCE4A\n",
-      "4074 CAPRIN1\n",
-      "4075 PHF21A\n",
-      "4076 EML3\n",
-      "4077 FKBP2\n",
-      "4078 RBM4\n",
-      "4079 CASP4\n",
-      "4080 ARCN1\n",
-      "4081 KRR1\n",
-      "4082 ATP2B1\n",
-      "4083 RILPL2\n",
-      "4084 ELF1\n",
-      "4085 TSC22D1\n",
-      "4086 LCP1\n",
-      "4087 DIS3\n",
-      "4088 MIS18BP1\n",
-      "4089 LINC01588\n",
-      "4090 SYNE2\n",
-      "4091 DGLUCY\n",
-      "4092 ZFYVE21\n",
-      "4093 EMC4\n",
-      "4094 RAB11A\n",
-      "4095 BCL2A1\n",
-      "4096 RRN3\n",
-      "4097 MAPK3\n",
-      "4098 BCKDK\n",
-      "4099 CES1\n",
-      "4100 ENSG00000180917\n",
-      "4101 SLC7A5\n",
-      "4102 TAX1BP3\n",
-      "4103 NCBP3\n",
-      "4104 TP53\n",
-      "4105 KRT10\n",
-      "4106 FAM117A\n",
-      "4107 ARSG\n",
-      "4108 PRKAR1A\n",
-      "4109 TBCD\n",
-      "4110 DPP9\n",
-      "4111 CCDC159\n",
-      "4112 TPM4\n",
-      "4113 KLF2\n",
-      "4114 GPI\n",
-      "4115 EMC10\n",
-      "4116 ZNF766\n",
-      "4117 TSEN34\n",
-      "4118 STMN3\n",
-      "4119 C21orf91\n",
-      "4120 MRPL39\n",
-      "4121 LTN1\n",
-      "4122 U2AF1\n",
-      "4123 TPST2\n",
-      "4124 TUG1\n",
-      "4125 PRR5\n",
-      "4126 TAF1\n",
-      "4127 RPL22\n",
-      "4128 UQCRHL\n",
-      "4129 CDC42\n",
-      "4130 RNF220\n",
-      "4131 BTF3L4\n",
-      "4132 JAK1\n",
-      "4133 LRRC40\n",
-      "4134 USP33\n",
-      "4135 SIKE1\n",
-      "4136 C1orf43\n",
-      "4137 KHDC4\n",
-      "4138 PYHIN1\n",
-      "4139 SDHC\n",
-      "4140 ADIPOR1\n",
-      "4141 DUSP10\n",
-      "4142 IAH1\n",
-      "4143 HADHA\n",
-      "4144 NRBP1\n",
-      "4145 YIPF4\n",
-      "4146 GPATCH11\n",
-      "4147 MCFD2\n",
-      "4148 ANXA4\n",
-      "4149 NCK2\n",
-      "4150 TSN\n",
-      "4151 HSPE1\n",
-      "4152 THUMPD3\n",
-      "4153 HACL1\n",
-      "4154 EOMES\n",
-      "4155 SELENOK\n",
-      "4156 TASOR\n",
-      "4157 B4GALT4\n",
-      "4158 GAK\n",
-      "4159 EIF4E\n",
-      "4160 MAP3K1\n",
-      "4161 CENPH\n",
-      "4162 BDP1\n",
-      "4163 VCAN\n",
-      "4164 ATG12\n",
-      "4165 C5orf15\n",
-      "4166 TXNDC15\n",
-      "4167 SOX4\n",
-      "4168 H2BC8\n",
-      "4169 WDR46\n",
-      "4170 SENP6\n",
-      "4171 TMEM120A\n",
-      "4172 STYXL1\n",
-      "4173 SSBP1\n",
-      "4174 CASP2\n",
-      "4175 FAM131B-AS2\n",
-      "4176 NSMCE2\n",
-      "4177 PARP10\n",
-      "4178 MTAP\n",
-      "4179 RPP25L\n",
-      "4180 POLR1E\n",
-      "4181 FAM78A\n",
-      "4182 REXO4\n",
-      "4183 DPP7\n",
-      "4184 GTPBP4\n",
-      "4185 LIPA\n",
-      "4186 KIF20B\n",
-      "4187 CTBP2\n",
-      "4188 EDRF1\n",
-      "4189 RIC8A\n",
-      "4190 AKIP1\n",
-      "4191 ELP4\n",
-      "4192 SSRP1\n",
-      "4193 UQCC3\n",
-      "4194 FERMT3\n",
-      "4195 CTSW\n",
-      "4196 CORO1B\n",
-      "4197 CDK2AP2\n",
-      "4198 CASP1\n",
-      "4199 RNF26\n",
-      "4200 NCAPD2\n",
-      "4201 SLC38A2\n",
-      "4202 BIN2\n",
-      "4203 ATP5MC2\n",
-      "4204 CALCOCO1\n",
-      "4205 NACA\n",
-      "4206 MARS1\n",
-      "4207 LTA4H\n",
-      "4208 HSP90B1\n",
-      "4209 TDG\n",
-      "4210 ACADS\n",
-      "4211 GOLGA3\n",
-      "4212 ZMYM2\n",
-      "4213 CLN5\n",
-      "4214 AP1G2\n",
-      "4215 EIF5\n",
-      "4216 KLC1\n",
-      "4217 HERC2\n",
-      "4218 IREB2\n",
-      "4219 CRTC3\n",
-      "4220 HBM\n",
-      "4221 HBA2\n",
-      "4222 STUB1\n",
-      "4223 HMOX2\n",
-      "4224 CARHSP1\n",
-      "4225 NTAN1\n",
-      "4226 MVP\n",
-      "4227 MT1X\n",
-      "4228 SLC25A11\n",
-      "4229 NDEL1\n",
-      "4230 NSRP1\n",
-      "4231 EVI2A\n",
-      "4232 SCRN2\n",
-      "4233 LUC7L3\n",
-      "4234 NARF\n",
-      "4235 RNMT\n",
-      "4236 RNF138\n",
-      "4237 PSTPIP2\n",
-      "4238 HMG20B\n",
-      "4239 CDKN2D\n",
-      "4240 IRF3\n",
-      "4241 LAIR2\n",
-      "4242 CHMP2A\n",
-      "4243 RNF24\n",
-      "4244 ESF1\n",
-      "4245 SNRPB2\n",
-      "4246 ID1\n",
-      "4247 RALY\n",
-      "4248 DPM1\n",
-      "4249 PSMG1\n",
-      "4250 MX1\n",
-      "4251 IGLC1\n",
-      "4252 TBC1D10A\n",
-      "4253 IL2RB\n",
-      "4254 EBP\n",
-      "4255 ARHGAP4\n",
-      "4256 CAMTA1\n",
-      "4257 TARDBP\n",
-      "4258 MICOS10\n",
-      "4259 HMGN2\n",
-      "4260 ZCCHC17\n",
-      "4261 AK2\n",
-      "4262 PSMB2\n",
-      "4263 PPCS\n",
-      "4264 CTBS\n",
-      "4265 SELENOF\n",
-      "4266 HIPK1\n",
-      "4267 TSPAN2\n",
-      "4268 TENT5C\n",
-      "4269 COPA\n",
-      "4270 MGST3\n",
-      "4271 IVNS1ABP\n",
-      "4272 EIF2D\n",
-      "4273 EGLN1\n",
-      "4274 PTRHD1\n",
-      "4275 RAB1A\n",
-      "4276 ELMOD3\n",
-      "4277 CD8B\n",
-      "4278 CYTOR\n",
-      "4279 MITD1\n",
-      "4280 RPL31\n",
-      "4281 CHCHD5\n",
-      "4282 WDR33\n",
-      "4283 IMP4\n",
-      "4284 NMI\n",
-      "4285 ATP5MC3\n",
-      "4286 UBE2E3\n",
-      "4287 ATIC\n",
-      "4288 ARPC2\n",
-      "4289 PRKAR2A\n",
-      "4290 APPL1\n",
-      "4291 PSMD6\n",
-      "4292 HCLS1\n",
-      "4293 AP2M1\n",
-      "4294 RPL9\n",
-      "4295 SREK1IP1\n",
-      "4296 TNPO1\n",
-      "4297 SCAMP1\n",
-      "4298 BRD8\n",
-      "4299 CYSTM1\n",
-      "4300 LARP1\n",
-      "4301 MAT2B\n",
-      "4302 RPL26L1\n",
-      "4303 PPP1R11\n",
-      "4304 FLOT1\n",
-      "4305 KCTD20\n",
-      "4306 KLHDC3\n",
-      "4307 PNISR\n",
-      "4308 ARHGAP18\n",
-      "4309 TMEM181\n",
-      "4310 TAX1BP1\n",
-      "4311 ABCB1\n",
-      "4312 MCM7\n",
-      "4313 TAF6\n",
-      "4314 ZC3HAV1\n",
-      "4315 MKRN1\n",
-      "4316 GIMAP4\n",
-      "4317 RHEB\n",
-      "4318 INSIG1\n",
-      "4319 VDAC3\n",
-      "4320 CEBPD\n",
-      "4321 ZBTB10\n",
-      "4322 RNF139\n",
-      "4323 C8orf33\n",
-      "4324 UBE2R2\n",
-      "4325 UBAP2\n",
-      "4326 DCAF10\n",
-      "4327 NINJ1\n",
-      "4328 ZDHHC12\n",
-      "4329 FBXW5\n",
-      "4330 ENSG00000287175\n",
-      "4331 CSGALNACT2\n",
-      "4332 WASHC2A\n",
-      "4333 ARID5B\n",
-      "4334 FRA10AC1\n",
-      "4335 ASCL2\n",
-      "4336 DGKZ\n",
-      "4337 ARFGAP2\n",
-      "4338 CELF1\n",
-      "4339 MTCH2\n",
-      "4340 PTPRJ\n",
-      "4341 PICALM\n",
-      "4342 NCAPD3\n",
-      "4343 ATG101\n",
-      "4344 TSPAN31\n",
-      "4345 GIT2\n",
-      "4346 RFC5\n",
-      "4347 SRSF9\n",
-      "4348 TRIM13\n",
-      "4349 WDFY2\n",
-      "4350 CHMP4A\n",
-      "4351 RPS6KA5\n",
-      "4352 KATNBL1\n",
-      "4353 RAB8B\n",
-      "4354 SMAD3\n",
-      "4355 FANCI\n",
-      "4356 LONP2\n",
-      "4357 MT2A\n",
-      "4358 ST3GAL2\n",
-      "4359 GCSH\n",
-      "4360 PITPNA-AS1\n",
-      "4361 ARRB2\n",
-      "4362 NF1\n",
-      "4363 PSMD11\n",
-      "4364 RFFL\n",
-      "4365 ORMDL3\n",
-      "4366 RETREG3\n",
-      "4367 PSME3\n",
-      "4368 GRN\n",
-      "4369 KANSL1\n",
-      "4370 KANSL1-AS1\n",
-      "4371 PSMD12\n",
-      "4372 PIK3C3\n",
-      "4373 ATP5F1D\n",
-      "4374 GTF2F1\n",
-      "4375 SMARCA4\n",
-      "4376 DHPS\n",
-      "4377 NR2C2AP\n",
-      "4378 PSMF1\n",
-      "4379 NAA20\n",
-      "4380 OSER1\n",
-      "4381 VAPB\n",
-      "4382 UCKL1\n",
-      "4383 DGCR6L\n",
-      "4384 SMDT1\n",
-      "4385 PARVB\n",
-      "4386 SLC25A6\n",
-      "4387 PRDX4\n",
-      "4388 NDUFB11\n",
-      "4389 UBA1\n",
-      "4390 PLP2\n",
-      "4391 CYSLTR1\n",
-      "4392 MT-ND5\n",
-      "4393 MRTO4\n",
-      "4394 RCC1\n",
-      "4395 ZMPSTE24\n",
-      "4396 RNF11\n",
-      "4397 SASS6\n",
-      "4398 SLAMF6\n",
-      "4399 DUSP12\n",
-      "4400 SFT2D2\n",
-      "4401 MIR181A1HG\n",
-      "4402 WDR26\n",
-      "4403 GGPS1\n",
-      "4404 FAM228B\n",
-      "4405 C1D\n",
-      "4406 CD8A\n",
-      "4407 ARID5A\n",
-      "4408 MZT2A\n",
-      "4409 NFE2L2\n",
-      "4410 NUP210\n",
-      "4411 MRPS25\n",
-      "4412 DPH3\n",
-      "4413 POGLUT1\n",
-      "4414 PARP15\n",
-      "4415 FAIM\n",
-      "4416 NMD3\n",
-      "4417 REST\n",
-      "4418 CENPC\n",
-      "4419 ANXA5\n",
-      "4420 ANKH\n",
-      "4421 MRPS30\n",
-      "4422 EMB\n",
-      "4423 GPBP1\n",
-      "4424 ST8SIA4\n",
-      "4425 FAM53C\n",
-      "4426 MATR3\n",
-      "4427 DUSP1\n",
-      "4428 TRIM41\n",
-      "4429 ABCF1\n",
-      "4430 PSMB9\n",
-      "4431 DNPH1\n",
-      "4432 MTO1\n",
-      "4433 KATNA1\n",
-      "4434 TCP1\n",
-      "4435 WIPI2\n",
-      "4436 LSM5\n",
-      "4437 POM121\n",
-      "4438 TMEM243\n",
-      "4439 COPS6\n",
-      "4440 ENSG00000170632\n",
-      "4441 PLPBP\n",
-      "4442 TCEA1\n",
-      "4443 TMEM68\n",
-      "4444 ARMC1\n",
-      "4445 PDE7A\n",
-      "4446 MYBL1\n",
-      "4447 WWP1\n",
-      "4448 ENY2\n",
-      "4449 BAG1\n",
-      "4450 CLTA\n",
-      "4451 PTAR1\n",
-      "4452 FAM120AOS\n",
-      "4453 PTBP3\n",
-      "4454 PTPA\n",
-      "4455 WDR37\n",
-      "4456 APBB1IP\n",
-      "4457 CUEDC2\n",
-      "4458 SLK\n",
-      "4459 SHOC2\n",
-      "4460 CCDC186\n",
-      "4461 CD151\n",
-      "4462 CTSD\n",
-      "4463 RRM1\n",
-      "4464 NUP160\n",
-      "4465 CPT1A\n",
-      "4466 CCDC82\n",
-      "4467 CD3E\n",
-      "4468 RPS25\n",
-      "4469 EI24\n",
-      "4470 WBP11\n",
-      "4471 PCED1B\n",
-      "4472 ATF1\n",
-      "4473 CD63\n",
-      "4474 PTGES3\n",
-      "4475 PRIM1\n",
-      "4476 CTDSP2\n",
-      "4477 NUP107\n",
-      "4478 SAP18\n",
-      "4479 DAD1\n",
-      "4480 OXA1L\n",
-      "4481 ACIN1\n",
-      "4482 CGRRF1\n",
-      "4483 DAAM1\n",
-      "4484 SRSF5\n",
-      "4485 GOLGA5\n",
-      "4486 PPP2R5C\n",
-      "4487 IGHA1\n",
-      "4488 LRRC57\n",
-      "4489 SECISBP2L\n",
-      "4490 LYSMD2\n",
-      "4491 DENND4A\n",
-      "4492 AAGAB\n",
-      "4493 TSPAN3\n",
-      "4494 NME4\n",
-      "4495 TMEM204\n",
-      "4496 SPN\n",
-      "4497 C16orf54\n",
-      "4498 RBL2\n",
-      "4499 NLRC5\n",
-      "4500 IST1\n",
-      "4501 MPHOSPH6\n",
-      "4502 COX4I1\n",
-      "4503 GPS2\n",
-      "4504 ZSWIM7\n",
-      "4505 SMARCE1\n",
-      "4506 IFI35\n",
-      "4507 NMT1\n",
-      "4508 UNK\n",
-      "4509 UNC13D\n",
-      "4510 PGS1\n",
-      "4511 DLGAP1-AS1\n",
-      "4512 RIOK3\n",
-      "4513 ABHD17A\n",
-      "4514 LSM7\n",
-      "4515 MAP2K2\n",
-      "4516 ELAVL1\n",
-      "4517 HOOK2\n",
-      "4518 GADD45GIP1\n",
-      "4519 SLC35E1\n",
-      "4520 PSMD8\n",
-      "4521 VASP\n",
-      "4522 OPA3\n",
-      "4523 EML2\n",
-      "4524 AP2A1\n",
-      "4525 RPL28\n",
-      "4526 MAVS\n",
-      "4527 CST3\n",
-      "4528 PEDS1\n",
-      "4529 PFDN4\n",
-      "4530 ETS2\n",
-      "4531 HDHD5\n",
-      "4532 C22orf39\n",
-      "4533 PRR14L\n",
-      "4534 MCM5\n",
-      "4535 SAMM50\n",
-      "4536 CD99\n",
-      "4537 DYNLT3\n",
-      "4538 PQBP1\n",
-      "4539 HMGN5\n",
-      "4540 MAP7D3\n",
-      "4541 UBE4B\n",
-      "4542 FBXO6\n",
-      "4543 FAM76A\n",
-      "4544 PEF1\n",
-      "4545 TMEM39B\n",
-      "4546 YBX1\n",
-      "4547 USP24\n",
-      "4548 LAMTOR5\n",
-      "4549 DRAM2\n",
-      "4550 PTPN22\n",
-      "4551 ENSG00000287978\n",
-      "4552 CCT3\n",
-      "4553 TMCO1\n",
-      "4554 XCL1\n",
-      "4555 NIBAN1\n",
-      "4556 RO60\n",
-      "4557 CNST\n",
-      "4558 TTC32\n",
-      "4559 DNMT3A\n",
-      "4560 SNX17\n",
-      "4561 SOS1\n",
-      "4562 FBXO11\n",
-      "4563 PRKRA\n",
-      "4564 BZW1\n",
-      "4565 NDUFB3\n",
-      "4566 RETREG2\n",
-      "4567 STK25\n",
-      "4568 XPC\n",
-      "4569 ELP6\n",
-      "4570 USP4\n",
-      "4571 NISCH\n",
-      "4572 ANAPC13\n",
-      "4573 SERP1\n",
-      "4574 TBC1D1\n",
-      "4575 MTHFD2L\n",
-      "4576 TET2\n",
-      "4577 LEF1\n",
-      "4578 LRBA\n",
-      "4579 ETFDH\n",
-      "4580 CFAP97\n",
-      "4581 ZFR\n",
-      "4582 IL7R\n",
-      "4583 MSH3\n",
-      "4584 SLC12A2-DT\n",
-      "4585 UBE2D2\n",
-      "4586 TTC1\n",
-      "4587 CLTB\n",
-      "4588 RNF44\n",
-      "4589 RNGTT\n",
-      "4590 PNRC1\n",
-      "4591 EIF3B\n",
-      "4592 TOMM7\n",
-      "4593 STAG3\n",
-      "4594 SRRT\n",
-      "4595 UBE2H\n",
-      "4596 FASTK\n",
-      "4597 CCDC25\n",
-      "4598 PLEKHA2\n",
-      "4599 RB1CC1\n",
-      "4600 PTDSS1\n",
-      "4601 BOP1\n",
-      "4602 ZNG1A\n",
-      "4603 EBLN3P\n",
-      "4604 MRPL50\n",
-      "4605 HSDL2\n",
-      "4606 CCAR1\n",
-      "4607 NOC3L\n",
-      "4608 PRDX3\n",
-      "4609 CAT\n",
-      "4610 TMEM179B\n",
-      "4611 PLAAT4\n",
-      "4612 CREBZF\n",
-      "4613 KLRG1\n",
-      "4614 CCNT1\n",
-      "4615 TARBP2\n",
-      "4616 STAT2\n",
-      "4617 RAP1B\n",
-      "4618 PRANCR\n",
-      "4619 TBC1D15\n",
-      "4620 ATXN7L3B\n",
-      "4621 UBE2N\n",
-      "4622 VEZT\n",
-      "4623 ISCU\n",
-      "4624 SELPLG\n",
-      "4625 PPTC7\n",
-      "4626 ATXN2\n",
-      "4627 ABHD13\n",
-      "4628 NFKBIA\n",
-      "4629 NIN\n",
-      "4630 CCDC88C\n",
-      "4631 BCL11B\n",
-      "4632 IGHG3\n",
-      "4633 CCPG1\n",
-      "4634 PIAS1\n",
-      "4635 TMED3\n",
-      "4636 HAPSTR1\n",
-      "4637 METTL9\n",
-      "4638 HSBP1\n",
-      "4639 SSH2\n",
-      "4640 UTP6\n",
-      "4641 MED24\n",
-      "4642 MED13\n",
-      "4643 RHBDF2\n",
-      "4644 GALNT1\n",
-      "4645 TCF3\n",
-      "4646 SH3GL1\n",
-      "4647 SHFL\n",
-      "4648 GET3\n",
-      "4649 ASF1B\n",
-      "4650 HSH2D\n",
-      "4651 HNRNPL\n",
-      "4652 KDELR1\n",
-      "4653 NKG7\n",
-      "4654 FAM110A\n",
-      "4655 DLGAP4\n",
-      "4656 TTC3\n",
-      "4657 DIP2A\n",
-      "4658 HDAC10\n",
-      "4659 DHRSX\n",
-      "4660 JPX\n",
-      "4661 MAGT1\n",
-      "4662 RNF113A\n",
-      "4663 HPRT1\n",
-      "4664 CETN2\n",
-      "4665 UTS2\n",
-      "4666 ADPRS\n",
-      "4667 RPS8\n",
-      "4668 FAF1\n",
-      "4669 MRPL37\n",
-      "4670 IFI44L\n",
-      "4671 MCOLN2\n",
-      "4672 SLC25A24\n",
-      "4673 CAPZA1\n",
-      "4674 PMVK\n",
-      "4675 DPM3\n",
-      "4676 ODR4\n",
-      "4677 GUK1\n",
-      "4678 ASXL2\n",
-      "4679 MTIF2\n",
-      "4680 ARHGAP25\n",
-      "4681 CREB1\n",
-      "4682 PPP1R7\n",
-      "4683 VHL\n",
-      "4684 EAF1\n",
-      "4685 UBP1\n",
-      "4686 HEMK1\n",
-      "4687 RRP9\n",
-      "4688 CGGBP1\n",
-      "4689 NIT2\n",
-      "4690 PLSCR1\n",
-      "4691 LSG1\n",
-      "4692 NCBP2\n",
-      "4693 SLBP\n",
-      "4694 MED28\n",
-      "4695 KLF3\n",
-      "4696 NOA1\n",
-      "4697 POLR2B\n",
-      "4698 PGRMC2\n",
-      "4699 NAA15\n",
-      "4700 FRG1\n",
-      "4701 RPL37\n",
-      "4702 CCDC112\n",
-      "4703 TCERG1\n",
-      "4704 STK10\n",
-      "4705 ZSCAN16-AS1\n",
-      "4706 BAK1\n",
-      "4707 ORC3\n",
-      "4708 AKIRIN2\n",
-      "4709 NDUFAF4\n",
-      "4710 ACAT2\n",
-      "4711 IKZF1\n",
-      "4712 TMEM248\n",
-      "4713 BAZ1B\n",
-      "4714 MDH2\n",
-      "4715 SAMD9\n",
-      "4716 SRPK2\n",
-      "4717 ARF5\n",
-      "4718 MTPN\n",
-      "4719 PPP3CC\n",
-      "4720 R3HCC1\n",
-      "4721 SARAF\n",
-      "4722 MCM4\n",
-      "4723 MRPL15\n",
-      "4724 SNHG6\n",
-      "4725 RPL7\n",
-      "4726 GOLGA2\n",
-      "4727 EXOSC2\n",
-      "4728 MRPL41\n",
-      "4729 GDI2\n",
-      "4730 WASHC2C\n",
-      "4731 CISD1\n",
-      "4732 PCGF5\n",
-      "4733 LCOR\n",
-      "4734 BORCS7\n",
-      "4735 VTI1A\n",
-      "4736 ARFIP2\n",
-      "4737 NDUFS3\n",
-      "4738 CAPN1\n",
-      "4739 DRAP1\n",
-      "4740 POLD4\n",
-      "4741 RAB6A\n",
-      "4742 CCDC90B\n",
-      "4743 COPS7A\n",
-      "4744 USP5\n",
-      "4745 NECAP1\n",
-      "4746 MAGOHB\n",
-      "4747 KRAS\n",
-      "4748 KIF21A\n",
-      "4749 SLC38A1\n",
-      "4750 TUBA1B\n",
-      "4751 NEDD1\n",
-      "4752 TRIAP1\n",
-      "4753 MRPL57\n",
-      "4754 RCBTB2\n",
-      "4755 PSME2\n",
-      "4756 GMFB\n",
-      "4757 PPM1A\n",
-      "4758 MED6\n",
-      "4759 EIF2B2\n",
-      "4760 AKT1\n",
-      "4761 MGA\n",
-      "4762 SPPL2A\n",
-      "4763 DPP8\n",
-      "4764 CLUAP1\n",
-      "4765 USP7\n",
-      "4766 NUBP1\n",
-      "4767 TXNDC11\n",
-      "4768 FBRS\n",
-      "4769 TENT4B\n",
-      "4770 BRD7\n",
-      "4771 RFLNB\n",
-      "4772 GABARAP\n",
-      "4773 RPL27\n",
-      "4774 HOXB2\n",
-      "4775 YPEL2\n",
-      "4776 CCDC57\n",
-      "4777 ARK2N\n",
-      "4778 ECSIT\n",
-      "4779 RASAL3\n",
-      "4780 FBL\n",
-      "4781 PLAUR\n",
-      "4782 CLASRP\n",
-      "4783 NOSIP\n",
-      "4784 LILRB1\n",
-      "4785 MX2\n",
-      "4786 FAM118A\n",
-      "4787 ARMCX3\n",
-      "4788 SH2D1A\n",
-      "4789 CCNQ\n",
-      "4790 PIK3CD\n",
-      "4791 MAD2L2\n",
-      "4792 EIF4G3\n",
-      "4793 PPIE\n",
-      "4794 GNG5\n",
-      "4795 AHCYL1\n",
-      "4796 ATP5PB\n",
-      "4797 ATP1A1\n",
-      "4798 ENSG00000287190\n",
-      "4799 ADAR\n",
-      "4800 ARPC5\n",
-      "4801 CYB5R1\n",
-      "4802 ID2\n",
-      "4803 YWHAQ\n",
-      "4804 MRPL30\n",
-      "4805 PTPN18\n",
-      "4806 IFT57\n",
-      "4807 NEPRO\n",
-      "4808 NDUFB4\n",
-      "4809 FNDC3B\n",
-      "4810 CEP135\n",
-      "4811 PLA2G12A\n",
-      "4812 GAR1\n",
-      "4813 C4orf3\n",
-      "4814 TMEM192\n",
-      "4815 NNT\n",
-      "4816 TMEM167A\n",
-      "4817 SLC25A46\n",
-      "4818 G3BP1\n",
-      "4819 MED7\n",
-      "4820 EHMT2\n",
-      "4821 UBE2J1\n",
-      "4822 MAP3K7\n",
-      "4823 ATG5\n",
-      "4824 ASF1A\n",
-      "4825 SF3B5\n",
-      "4826 SYNJ2\n",
-      "4827 CDK13\n",
-      "4828 TBRG4\n",
-      "4829 POR\n",
-      "4830 PCM1\n",
-      "4831 CHD7\n",
-      "4832 RRS1\n",
-      "4833 EIF3E\n",
-      "4834 AGO2\n",
-      "4835 MAF1\n",
-      "4836 SEMA4D\n",
-      "4837 PRXL2C\n",
-      "4838 NIPSNAP3A\n",
-      "4839 TXN\n",
-      "4840 FBXW2\n",
-      "4841 MTPAP\n",
-      "4842 ZNF33A\n",
-      "4843 UBE2D1\n",
-      "4844 CAMK2G\n",
-      "4845 HIF1AN\n",
-      "4846 ZDHHC6\n",
-      "4847 BUB3\n",
-      "4848 OAT\n",
-      "4849 CARS1\n",
-      "4850 ARL14EP\n",
-      "4851 CSKMT\n",
-      "4852 KAT5\n",
-      "4853 RAB1B\n",
-      "4854 RDX\n",
-      "4855 SLC2A3\n",
-      "4856 KLRC4\n",
-      "4857 CMAS\n",
-      "4858 TM7SF3\n",
-      "4859 YARS2\n",
-      "4860 RASSF3\n",
-      "4861 ALKBH2\n",
-      "4862 OAS2\n",
-      "4863 RHOF\n",
-      "4864 SBNO1\n",
-      "4865 SPART\n",
-      "4866 SUPT20H\n",
-      "4867 NUDT15\n",
-      "4868 GZMB\n",
-      "4869 HECTD1\n",
-      "4870 RPS29\n",
-      "4871 FAM98B\n",
-      "4872 GTF2A2\n",
-      "4873 OAZ2\n",
-      "4874 CTSH\n",
-      "4875 MTHFS\n",
-      "4876 IL16\n",
-      "4877 MCTP2\n",
-      "4878 XPO6\n",
-      "4879 PSME3IP1\n",
-      "4880 CIAPIN1\n",
-      "4881 ZDHHC7\n",
-      "4882 MIEN1\n",
-      "4883 STAT5B\n",
-      "4884 GPATCH8\n",
-      "4885 UBE2Z\n",
-      "4886 CLTC\n",
-      "4887 RNFT1\n",
-      "4888 HDHD2\n",
-      "4889 CSNK1G2\n",
-      "4890 ILF3\n",
-      "4891 PRKCSH\n",
-      "4892 ZNF791\n",
-      "4893 MAN2B1\n",
-      "4894 AKAP8\n",
-      "4895 AP1M1\n",
-      "4896 MYO9B\n",
-      "4897 BORCS8\n",
-      "4898 YIF1B\n",
-      "4899 SIRT2\n",
-      "4900 SHKBP1\n",
-      "4901 SELENOW\n",
-      "4902 MRPS6\n",
-      "4903 HMGXB4\n",
-      "4904 RPS19BP1\n",
-      "4905 ADSL\n",
-      "4906 CENPM\n",
-      "4907 PARVG\n",
-      "4908 GRIPAP1\n",
-      "4909 MAGED2\n",
-      "4910 GLA\n",
-      "4911 HTATSF1\n",
-      "4912 ENSG00000055070\n",
-      "4913 KHDRBS1\n",
-      "4914 ZC3H12A\n",
-      "4915 MEAF6\n",
-      "4916 CMPK1\n",
-      "4917 C1orf52\n",
-      "4918 GBP3\n",
-      "4919 CCDC18-AS1\n",
-      "4920 VAV3\n",
-      "4921 CD2\n",
-      "4922 CHD1L\n",
-      "4923 CTSS\n",
-      "4924 MRPL9\n",
-      "4925 KRTCAP2\n",
-      "4926 FDPS\n",
-      "4927 ALDH9A1\n",
-      "4928 MRPS14\n",
-      "4929 NSL1\n",
-      "4930 SMYD3\n",
-      "4931 AUP1\n",
-      "4932 POLE4\n",
-      "4933 FAHD2A\n",
-      "4934 ARHGAP15\n",
-      "4935 NR4A2\n",
-      "4936 RPE\n",
-      "4937 ITM2C\n",
-      "4938 THAP4\n",
-      "4939 CAPN7\n",
-      "4940 ABHD14B\n",
-      "4941 TKT\n",
-      "4942 CCDC66\n",
-      "4943 CLDND1\n",
-      "4944 MCM2\n",
-      "4945 SMIM20\n",
-      "4946 AREG\n",
-      "4947 METTL14\n",
-      "4948 PDCD6\n",
-      "4949 MRPS27\n",
-      "4950 IK\n",
-      "4951 H1-2\n",
-      "4952 HLA-DQB1\n",
-      "4953 ELOVL5\n",
-      "4954 MMS22L\n",
-      "4955 PERP\n",
-      "4956 HEBP2\n",
-      "4957 CCDC28A\n",
-      "4958 ERV3-1\n",
-      "4959 NDUFA5\n",
-      "4960 ELP3\n",
-      "4961 UBE2V2\n",
-      "4962 AK3\n",
-      "4963 ZFAND5\n",
-      "4964 AGTPBP1\n",
-      "4965 TTC16\n",
-      "4966 NPDC1\n",
-      "4967 DCLRE1C\n",
-      "4968 NCOA4\n",
-      "4969 DDX21\n",
-      "4970 SFR1\n",
-      "4971 ABLIM1\n",
-      "4972 GLRX3\n",
-      "4973 CTR9\n",
-      "4974 HNRNPUL2\n",
-      "4975 VPS51\n",
-      "4976 CFL1\n",
-      "4977 EIF1AD\n",
-      "4978 BRMS1\n",
-      "4979 TMEM126B\n",
-      "4980 KMT2A\n",
-      "4981 NDUFA9\n",
-      "4982 C12orf57\n",
-      "4983 SHMT2\n",
-      "4984 IFNG\n",
-      "4985 MDM2\n",
-      "4986 OSBPL8\n",
-      "4987 NUDT4\n",
-      "4988 MMAB\n",
-      "4989 CHURC1\n",
-      "4990 ISCA2\n",
-      "4991 AHSA1\n",
-      "4992 COA8\n",
-      "4993 PDIA3\n",
-      "4994 GDE1\n",
-      "4995 VPS35\n",
-      "4996 N4BP1\n",
-      "4997 OGFOD1\n",
-      "4998 NUP93\n",
-      "4999 EXOSC6\n",
-      "5000 SAT2\n",
-      "5001 SYNRG\n",
-      "5002 CEP95\n",
-      "5003 NOL11\n",
-      "5004 MRPL12\n",
-      "5005 HEXD\n",
-      "5006 LDLRAD4\n",
-      "5007 IER3IP1\n",
-      "5008 MBP\n",
-      "5009 ZBTB7A\n",
-      "5010 YJU2\n",
-      "5011 ZNF121\n",
-      "5012 CDC37\n",
-      "5013 PRDX2\n",
-      "5014 RNASEH2A\n",
-      "5015 DNAJB1\n",
-      "5016 ISYNA1\n",
-      "5017 ZNF506\n",
-      "5018 CEBPG\n",
-      "5019 RINL\n",
-      "5020 NFKBIB\n",
-      "5021 ETHE1\n",
-      "5022 RELB\n",
-      "5023 SNRPD2\n",
-      "5024 LENG8\n",
-      "5025 RASSF2\n",
-      "5026 BLCAP\n",
-      "5027 USP25\n",
-      "5028 DONSON\n",
-      "5029 RRP1\n",
-      "5030 S100B\n",
-      "5031 HSCB\n",
-      "5032 CYTH4\n",
-      "5033 ANKRD54\n",
-      "5034 RBMX\n",
-      "5035 MT-ND4L\n",
-      "5036 TRAPPC3\n",
-      "5037 C1orf50\n",
-      "5038 GCLM\n",
-      "5039 RHOC\n",
-      "5040 PHTF1\n",
-      "5041 ILF2\n",
-      "5042 DCAF8\n",
-      "5043 FCER1G\n",
-      "5044 MAPKAPK2\n",
-      "5045 NVL\n",
-      "5046 PPP1R21\n",
-      "5047 PSME4\n",
-      "5048 MXD1\n",
-      "5049 CHMP3\n",
-      "5050 PRPF40A\n",
-      "5051 GORASP2\n",
-      "5052 HIBCH\n",
-      "5053 ENSG00000272211\n",
-      "5054 RFTN1\n",
-      "5055 SATB1\n",
-      "5056 TRMT10C\n",
-      "5057 HACD2\n",
-      "5058 SEC62\n",
-      "5059 LETM1\n",
-      "5060 RAB28\n",
-      "5061 ZNF622\n",
-      "5062 NDUFS4\n",
-      "5063 SREK1\n",
-      "5064 NDUFA2\n",
-      "5065 FAF2\n",
-      "5066 H2BC15\n",
-      "5067 NCR3\n",
-      "5068 MDN1\n",
-      "5069 ASCC3\n",
-      "5070 MAN1A1\n",
-      "5071 RPS12\n",
-      "5072 TBPL1\n",
-      "5073 TFB1M\n",
-      "5074 CHST12\n",
-      "5075 GGCT\n",
-      "5076 TMED4\n",
-      "5077 MYO1G\n",
-      "5078 CRCP\n",
-      "5079 TP53TG1\n",
-      "5080 AKR1B1\n",
-      "5081 GIMAP6\n",
-      "5082 MSRA\n",
-      "5083 BNIP3L\n",
-      "5084 LYPLA1\n",
-      "5085 TOX-DT\n",
-      "5086 LY6E\n",
-      "5087 TOP1MT\n",
-      "5088 SIGMAR1\n",
-      "5089 RC3H2\n",
-      "5090 EHMT1\n",
-      "5091 ZNF33B\n",
-      "5092 PRF1\n",
-      "5093 EEF1AKMT2\n",
-      "5094 MUS81\n",
-      "5095 AAMDC\n",
-      "5096 CTSC\n",
-      "5097 PAFAH1B2\n",
-      "5098 CDKN1B\n",
-      "5099 RESF1\n",
-      "5100 DNM1L\n",
-      "5101 PUS7L\n",
-      "5102 ARID2\n",
-      "5103 PRKAG1\n",
-      "5104 SMARCC2\n",
-      "5105 CNOT2\n",
-      "5106 RLIG1\n",
-      "5107 ANAPC7\n",
-      "5108 PPP1CC\n",
-      "5109 MPHOSPH9\n",
-      "5110 CRYL1\n",
-      "5111 PARP4\n",
-      "5112 GTF3A\n",
-      "5113 ALOX5AP\n",
-      "5114 DNAJC3\n",
-      "5115 SRP54\n",
-      "5116 FBXO33\n",
-      "5117 MTHFD1\n",
-      "5118 ERH\n",
-      "5119 BTBD7\n",
-      "5120 PAPOLA\n",
-      "5121 RTF1\n",
-      "5122 TMOD3\n",
-      "5123 COMMD4\n",
-      "5124 ISG20\n",
-      "5125 ATF7IP2\n",
-      "5126 CLN3\n",
-      "5127 ZNF267\n",
-      "5128 E2F4\n",
-      "5129 DDX28\n",
-      "5130 CMC2\n",
-      "5131 VPS53\n",
-      "5132 NLRP1\n",
-      "5133 PIK3R5\n",
-      "5134 GOSR2\n",
-      "5135 SRSF1\n",
-      "5136 CD300A\n",
-      "5137 CD7\n",
-      "5138 ANKRD12\n",
-      "5139 RBFA\n",
-      "5140 PLEKHJ1\n",
-      "5141 SUGP1\n",
-      "5142 ENSG00000268027\n",
-      "5143 PPP5C\n",
-      "5144 EIF6\n",
-      "5145 SCAND1\n",
-      "5146 LIME1\n",
-      "5147 MORC3\n",
-      "5148 ENSG00000226471\n",
-      "5149 APOL2\n",
-      "5150 TSPO\n",
-      "5151 SCO2\n",
-      "5152 USP11\n",
-      "5153 GDI1\n",
-      "5154 WRAP73\n",
-      "5155 ZNF593\n",
-      "5156 NDUFS5\n",
-      "5157 PRDX1\n",
-      "5158 PRPF38A\n",
-      "5159 ZNF326\n",
-      "5160 GSTM4\n",
-      "5161 S100A6\n",
-      "5162 ATF6\n",
-      "5163 QSOX1\n",
-      "5164 EML4\n",
-      "5165 FOXN2\n",
-      "5166 AFTPH\n",
-      "5167 PNO1\n",
-      "5168 AAK1\n",
-      "5169 FAM168B\n",
-      "5170 WDR12\n",
-      "5171 UBE2E1\n",
-      "5172 MYD88\n",
-      "5173 KIAA1143\n",
-      "5174 RBM6\n",
-      "5175 CYB561D2\n",
-      "5176 TPRA1\n",
-      "5177 COMMD2\n",
-      "5178 RPL22L1\n",
-      "5179 EXOC1\n",
-      "5180 PPBP\n",
-      "5181 HSD17B11\n",
-      "5182 ARHGAP10\n",
-      "5183 NSUN2\n",
-      "5184 PRKAA1\n",
-      "5185 ERBIN\n",
-      "5186 IQGAP2\n",
-      "5187 YTHDC2\n",
-      "5188 UBE2B\n",
-      "5189 ANXA6\n",
-      "5190 NSD1\n",
-      "5191 PRELID1\n",
-      "5192 TMED9\n",
-      "5193 SERPINB6\n",
-      "5194 LYRM4\n",
-      "5195 BLOC1S5\n",
-      "5196 HLA-F\n",
-      "5197 PRRC2A\n",
-      "5198 SNRPC\n",
-      "5199 SRSF3\n",
-      "5200 TOMM6\n",
-      "5201 CCZ1B\n",
-      "5202 DDX56\n",
-      "5203 DBF4\n",
-      "5204 PILRB\n",
-      "5205 GALNT11\n",
-      "5206 PAG1\n",
-      "5207 AZIN1\n",
-      "5208 FBXO32\n",
-      "5209 ZFTRAF1\n",
-      "5210 TLN1\n",
-      "5211 CARNMT1\n",
-      "5212 FCN1\n",
-      "5213 CLIC3\n",
-      "5214 PFKP\n",
-      "5215 PHYH\n",
-      "5216 ANKRD26\n",
-      "5217 MPP7\n",
-      "5218 ASCC1\n",
-      "5219 PDCD4\n",
-      "5220 PLEKHA1\n",
-      "5221 UROS\n",
-      "5222 ZNF195\n",
-      "5223 FAR1\n",
-      "5224 COPB1\n",
-      "5225 TRMT112\n",
-      "5226 CDCA5\n",
-      "5227 BIRC3\n",
-      "5228 NKAPD1\n",
-      "5229 GAPDH\n",
-      "5230 MLF2\n",
-      "5231 RNF41\n",
-      "5232 GIHCG\n",
-      "5233 CHPT1\n",
-      "5234 RNASEH2B\n",
-      "5235 ZBTB1\n",
-      "5236 BATF\n",
-      "5237 ERG28\n",
-      "5238 RAD51-AS1\n",
-      "5239 DUT\n",
-      "5240 LEO1\n",
-      "5241 ULK3\n",
-      "5242 SIN3A\n",
-      "5243 RPS17\n",
-      "5244 SLCO3A1\n",
-      "5245 UQCC4\n",
-      "5246 MLST8\n",
-      "5247 PGP\n",
-      "5248 KNOP1\n",
-      "5249 HERPUD1\n",
-      "5250 POLR2C\n",
-      "5251 DDX19A\n",
-      "5252 EMC6\n",
-      "5253 STX8\n",
-      "5254 ATPAF2\n",
-      "5255 AKAP10\n",
-      "5256 CWC25\n",
-      "5257 FMNL1\n",
-      "5258 ZNF652\n",
-      "5259 MTMR4\n",
-      "5260 NPLOC4\n",
-      "5261 ARHGDIA\n",
-      "5262 MYL12B\n",
-      "5263 SLC39A3\n",
-      "5264 PET100\n",
-      "5265 RAB11B\n",
-      "5266 KEAP1\n",
-      "5267 MRPS12\n",
-      "5268 EXOSC5\n",
-      "5269 PAFAH1B3\n",
-      "5270 RPS9\n",
-      "5271 A1BG\n",
-      "5272 NXT1\n",
-      "5273 AAR2\n",
-      "5274 NFATC2\n",
-      "5275 RAE1\n",
-      "5276 RAB22A\n",
-      "5277 GNAS\n",
-      "5278 SNRPD3\n",
-      "5279 PES1\n",
-      "5280 WDR45\n",
-      "5281 PGRMC1\n",
-      "5282 BRCC3\n",
-      "5283 STX12\n",
-      "5284 PTP4A2\n",
-      "5285 SLC2A1\n",
-      "5286 OMA1\n",
-      "5287 TMED5\n",
-      "5288 CHI3L2\n",
-      "5289 RPRD2\n",
-      "5290 PIGC\n",
-      "5291 LBR\n",
-      "5292 TMEM63A\n",
-      "5293 ARID4B\n",
-      "5294 POMC\n",
-      "5295 MSH6\n",
-      "5296 ANKRD36\n",
-      "5297 UXS1\n",
-      "5298 MMADHC\n",
-      "5299 METTL8\n",
-      "5300 RPL37A\n",
-      "5301 ARL8B\n",
-      "5302 RASSF1\n",
-      "5303 DCP1A\n",
-      "5304 ARL6IP5\n",
-      "5305 NAA50\n",
-      "5306 IQCB1\n",
-      "5307 KPNA1\n",
-      "5308 EIF2A\n",
-      "5309 SKIL\n",
-      "5310 ABCF3\n",
-      "5311 TMEM156\n",
-      "5312 PF4\n",
-      "5313 RPL34\n",
-      "5314 RPS3A\n",
-      "5315 ZCCHC9\n",
-      "5316 SKIC3\n",
-      "5317 HSPA4\n",
-      "5318 ITK\n",
-      "5319 TPMT\n",
-      "5320 HLA-DQA2\n",
-      "5321 UQCC2\n",
-      "5322 FKBP5\n",
-      "5323 PPIL1\n",
-      "5324 YIPF3\n",
-      "5325 PSMB1\n",
-      "5326 COX19\n",
-      "5327 PMS2\n",
-      "5328 INTS15\n",
-      "5329 NUDCD3\n",
-      "5330 H2AZ2\n",
-      "5331 SEC61G\n",
-      "5332 EIF4H\n",
-      "5333 FAM133B\n",
-      "5334 BRI3\n",
-      "5335 POLR2J3\n",
-      "5336 LSM8\n",
-      "5337 MRPS28\n",
-      "5338 GRINA\n",
-      "5339 RAD23B\n",
-      "5340 POLE3\n",
-      "5341 FNBP1\n",
-      "5342 SSNA1\n",
-      "5343 HNRNPH3\n",
-      "5344 RPS24\n",
-      "5345 BBIP1\n",
-      "5346 WDR11\n",
-      "5347 MARK2\n",
-      "5348 RASGRP2\n",
-      "5349 PPP6R3\n",
-      "5350 POLD3\n",
-      "5351 FDX1\n",
-      "5352 SLC25A3\n",
-      "5353 PEBP1\n",
-      "5354 POMP\n",
-      "5355 RGCC\n",
-      "5356 TDRD3\n",
-      "5357 RBM26\n",
-      "5358 SCFD1\n",
-      "5359 RTRAF\n",
-      "5360 ATG14\n",
-      "5361 ARID4A\n",
-      "5362 DLST\n",
-      "5363 TMED10\n",
-      "5364 TDP1\n",
-      "5365 GLRX5\n",
-      "5366 CCNK\n",
-      "5367 IGHM\n",
-      "5368 IQGAP1\n",
-      "5369 UBE2I\n",
-      "5370 CORO7\n",
-      "5371 ORC6\n",
-      "5372 CYLD\n",
-      "5373 CIAO2B\n",
-      "5374 PSMD7\n",
-      "5375 COTL1\n",
-      "5376 TMEM11\n",
-      "5377 LGALS9\n",
-      "5378 TP53I13\n",
-      "5379 WIPF2\n",
-      "5380 COIL\n",
-      "5381 CD79B\n",
-      "5382 PRKCA\n",
-      "5383 NUP85\n",
-      "5384 GALK1\n",
-      "5385 TEN1\n",
-      "5386 ALYREF\n",
-      "5387 CENPX\n",
-      "5388 RPRD1A\n",
-      "5389 FEM1A\n",
-      "5390 MRPL4\n",
-      "5391 MRPL34\n",
-      "5392 SPINT2\n",
-      "5393 ZNF428\n",
-      "5394 RPL18\n",
-      "5395 PIH1D1\n",
-      "5396 C19orf48P\n",
-      "5397 ZNF544\n",
-      "5398 XRN2\n",
-      "5399 ADA\n",
-      "5400 CSE1L\n",
-      "5401 ENSG00000099991\n",
-      "5402 PIK3IP1\n",
-      "5403 RAC2\n",
-      "5404 MAFF\n",
-      "5405 GTPBP1\n",
-      "5406 ACO2\n",
-      "5407 FTX\n",
-      "5408 FAM50A\n",
-      "5409 CEP104\n",
-      "5410 EXOSC10\n",
-      "5411 STXBP3\n",
-      "5412 SARS1\n",
-      "5413 LY9\n",
-      "5414 SMYD2\n",
-      "5415 ADAM17\n",
-      "5416 EIF2B4\n",
-      "5417 CEBPZOS\n",
-      "5418 MSH2\n",
-      "5419 VAMP8\n",
-      "5420 IGKC\n",
-      "5421 REV1\n",
-      "5422 ITGA6\n",
-      "5423 EDEM1\n",
-      "5424 THUMPD3-AS1\n",
-      "5425 ZDHHC3\n",
-      "5426 SACM1L\n",
-      "5427 SLC25A26\n",
-      "5428 COPB2\n",
-      "5429 XRN1\n",
-      "5430 RSRC1\n",
-      "5431 MAP3K13\n",
-      "5432 UBXN7\n",
-      "5433 FBXL5\n",
-      "5434 DHX15\n",
-      "5435 COMMD8\n",
-      "5436 IGFBP7\n",
-      "5437 PLAC8\n",
-      "5438 CCT5\n",
-      "5439 TAF9\n",
-      "5440 CAST\n",
-      "5441 DCP2\n",
-      "5442 CLK4\n",
-      "5443 HEIH\n",
-      "5444 MCUR1\n",
-      "5445 GNL1\n",
-      "5446 ATF6B\n",
-      "5447 HMGA1\n",
-      "5448 MRPL2\n",
-      "5449 HSP90AB1\n",
-      "5450 CASP8AP2\n",
-      "5451 STX7\n",
-      "5452 WAKMAR2\n",
-      "5453 UTRN\n",
-      "5454 FGL2\n",
-      "5455 SAMD9L\n",
-      "5456 TSC22D4\n",
-      "5457 SYPL1\n",
-      "5458 MDFIC\n",
-      "5459 EXOC4\n",
-      "5460 ESYT2\n",
-      "5461 RBIS\n",
-      "5462 DCAF13\n",
-      "5463 CHRAC1\n",
-      "5464 NUDT2\n",
-      "5465 CDC26\n",
-      "5466 RAPGEF1\n",
-      "5467 SURF4\n",
-      "5468 DDIT4\n",
-      "5469 SFXN3\n",
-      "5470 BET1L\n",
-      "5471 PGGHG\n",
-      "5472 TMEM41B\n",
-      "5473 SVIP\n",
-      "5474 HIPK3\n",
-      "5475 HSD17B12\n",
-      "5476 DDB2\n",
-      "5477 CD6\n",
-      "5478 FIBP\n",
-      "5479 MRPL21\n",
-      "5480 PAK1\n",
-      "5481 CEP164\n",
-      "5482 KLRB1\n",
-      "5483 ITPR2\n",
-      "5484 SMARCD1\n",
-      "5485 TMBIM4\n",
-      "5486 RNF34\n",
-      "5487 PHF11\n",
-      "5488 EBPL\n",
-      "5489 CARS2\n",
-      "5490 PNP\n",
-      "5491 MIA2\n",
-      "5492 PSMA3\n",
-      "5493 HIF1A\n",
-      "5494 PPP2R5E\n",
-      "5495 SERPINA1\n",
-      "5496 GABPB1-IT1\n",
-      "5497 SLTM\n",
-      "5498 RPS27L\n",
-      "5499 ARIH1\n",
-      "5500 RCN2\n",
-      "5501 MRPL46\n",
-      "5502 THOC6\n",
-      "5503 DCTN5\n",
-      "5504 GABARAPL2\n",
-      "5505 GSE1\n",
-      "5506 PSMB6\n",
-      "5507 DVL2\n",
-      "5508 NACA2\n",
-      "5509 CBX4\n",
-      "5510 SIRT7\n",
-      "5511 ACAA2\n",
-      "5512 BCL2\n",
-      "5513 ILF3-DT\n",
-      "5514 SLC1A5\n",
-      "5515 JOSD2\n",
-      "5516 EPN1\n",
-      "5517 ZNF264\n",
-      "5518 MRPS26\n",
-      "5519 APMAP\n",
-      "5520 FAM217B\n",
-      "5521 SH3BP1\n",
-      "5522 MSL3\n",
-      "5523 MT-ND2\n",
-      "5524 ISG15\n",
-      "5525 PADI4\n",
-      "5526 CAPZB\n",
-      "5527 MACO1\n",
-      "5528 SNHG3\n",
-      "5529 TXLNA\n",
-      "5530 KIAA0319L\n",
-      "5531 C1orf122\n",
-      "5532 MYCBP\n",
-      "5533 AKR1A1\n",
-      "5534 UBE2Q1\n",
-      "5535 ASH1L\n",
-      "5536 LAMTOR2\n",
-      "5537 RNF2\n",
-      "5538 DSTYK\n",
-      "5539 C1orf131\n",
-      "5540 ODC1\n",
-      "5541 ATRAID\n",
-      "5542 UGP2\n",
-      "5543 MOGS\n",
-      "5544 TRABD2A\n",
-      "5545 CIR1\n",
-      "5546 CASP8\n",
-      "5547 STK16\n",
-      "5548 CRBN\n",
-      "5549 BHLHE40\n",
-      "5550 RPUSD3\n",
-      "5551 DYNC1LI1\n",
-      "5552 ARIH2\n",
-      "5553 RAD54L2\n",
-      "5554 SLMAP\n",
-      "5555 TTC14\n",
-      "5556 RUBCN\n",
-      "5557 HTT\n",
-      "5558 LIAS\n",
-      "5559 TNIP3\n",
-      "5560 MSMO1\n",
-      "5561 RIOK2\n",
-      "5562 CDC42SE2\n",
-      "5563 GNPDA1\n",
-      "5564 TIMD4\n",
-      "5565 ATP6V0E1\n",
-      "5566 SFXN1\n",
-      "5567 RIOK1\n",
-      "5568 RANBP9\n",
-      "5569 BTN3A1\n",
-      "5570 IER3\n",
-      "5571 LST1\n",
-      "5572 ILRUN-AS1\n",
-      "5573 SYTL3\n",
-      "5574 MPC1\n",
-      "5575 RPA3\n",
-      "5576 GARS1\n",
-      "5577 UPP1\n",
-      "5578 PTPN12\n",
-      "5579 LINC00513\n",
-      "5580 BPGM\n",
-      "5581 REEP4\n",
-      "5582 GPAT4\n",
-      "5583 SMIM19\n",
-      "5584 TOX\n",
-      "5585 DECR1\n",
-      "5586 SLC25A32\n",
-      "5587 EMC2\n",
-      "5588 ATAD2\n",
-      "5589 AQP3\n",
-      "5590 DCTN3\n",
-      "5591 FXN\n",
-      "5592 ST6GALNAC6\n",
-      "5593 BMI1\n",
-      "5594 PIP4K2A\n",
-      "5595 YME1L1\n",
-      "5596 MAP3K8\n",
-      "5597 CSTF2T\n",
-      "5598 CUTC\n",
-      "5599 PNPLA2\n",
-      "5600 TMX2\n",
-      "5601 TMEM216\n",
-      "5602 FTH1\n",
-      "5603 EHD1\n",
-      "5604 RPS6KB2\n",
-      "5605 ATG16L2\n",
-      "5606 PRCP\n",
-      "5607 RAB30-DT\n",
-      "5608 CD3D\n",
-      "5609 TNFRSF1A\n",
-      "5610 ENSG00000165714\n",
-      "5611 ETFRF1\n",
-      "5612 GXYLT1\n",
-      "5613 RPAP3\n",
-      "5614 FKBP11\n",
-      "5615 TMEM19\n",
-      "5616 SOCS2\n",
-      "5617 PSMD9\n",
-      "5618 UFM1\n",
-      "5619 UBAC2\n",
-      "5620 PIP4P1\n",
-      "5621 PSME1\n",
-      "5622 KTN1\n",
-      "5623 ABCD4\n",
-      "5624 PSMC1\n",
-      "5625 LPCAT4\n",
-      "5626 NDUFAF1\n",
-      "5627 USP8\n",
-      "5628 BNIP2\n",
-      "5629 ICE2\n",
-      "5630 PML\n",
-      "5631 SNHG19\n",
-      "5632 NAA60\n",
-      "5633 KAT8\n",
-      "5634 THAP11\n",
-      "5635 AP1G1\n",
-      "5636 TNFSF12\n",
-      "5637 EIF4A1\n",
-      "5638 MPDU1\n",
-      "5639 MSL1\n",
-      "5640 SLC25A39\n",
-      "5641 TOB1\n",
-      "5642 SCPEP1\n",
-      "5643 LIMD2\n",
-      "5644 MIF4GD\n",
-      "5645 TMC8\n",
-      "5646 ZNF397\n",
-      "5647 DYM\n",
-      "5648 KDSR\n",
-      "5649 TRIR\n",
-      "5650 TRMT1\n",
-      "5651 NDUFB7\n",
-      "5652 MAST3\n",
-      "5653 SSBP4\n",
-      "5654 FKBP8\n",
-      "5655 TFPT\n",
-      "5656 ZNF580\n",
-      "5657 AHCY\n",
-      "5658 DYNLRB1\n",
-      "5659 TRABD\n",
-      "5660 GTPBP6\n",
-      "5661 WDR13\n",
-      "5662 TIMM17B\n",
-      "5663 RPL10\n",
-      "5664 TAFAZZIN\n",
-      "5665 ZNF683\n",
-      "5666 TUT4\n",
-      "5667 ZNHIT6\n",
-      "5668 MFSD14A\n",
-      "5669 GNAI3\n",
-      "5670 RNF115\n",
-      "5671 NAXE\n",
-      "5672 FCRL6\n",
-      "5673 SDCCAG8\n",
-      "5674 LINC01871\n",
-      "5675 KIDINS220\n",
-      "5676 CALM2\n",
-      "5677 ZNF638\n",
-      "5678 GCFC2\n",
-      "5679 GNLY\n",
-      "5680 MAL\n",
-      "5681 ANKRD36C\n",
-      "5682 MGAT4A\n",
-      "5683 ARL5A\n",
-      "5684 WIPF1\n",
-      "5685 AGPS\n",
-      "5686 BARD1\n",
-      "5687 PSMD1\n",
-      "5688 PDCD6IP\n",
-      "5689 TRANK1\n",
-      "5690 CNBP\n",
-      "5691 TOPBP1\n",
-      "5692 MRPS22\n",
-      "5693 RFC4\n",
-      "5694 LPP\n",
-      "5695 ATP8A1\n",
-      "5696 PAICS\n",
-      "5697 RASGEF1B\n",
-      "5698 RWDD4\n",
-      "5699 DAP\n",
-      "5700 DHX29\n",
-      "5701 CCNH\n",
-      "5702 RAPGEF6\n",
-      "5703 THG1L\n",
-      "5704 MRNIP\n",
-      "5705 GMDS\n",
-      "5706 SERPINB1\n",
-      "5707 TNF\n",
-      "5708 CSNK2B\n",
-      "5709 PBX2\n",
-      "5710 DEF6\n",
-      "5711 TAF8\n",
-      "5712 MEA1\n",
-      "5713 SYNCRIP\n",
-      "5714 SLC35A1\n",
-      "5715 RAC1\n",
-      "5716 TRGV5\n",
-      "5717 PPP1R35\n",
-      "5718 CUX1\n",
-      "5719 ENSG00000271204\n",
-      "5720 CUL1\n",
-      "5721 GIMAP1\n",
-      "5722 CDK5\n",
-      "5723 CLN8-AS1\n",
-      "5724 DCTN6\n",
-      "5725 TAF2\n",
-      "5726 C8orf76\n",
-      "5727 HAUS6\n",
-      "5728 CREB3\n",
-      "5729 CARD19\n",
-      "5730 RALGDS\n",
-      "5731 RABL6\n",
-      "5732 EDF1\n",
-      "5733 BLOC1S2\n",
-      "5734 NFKB2\n",
-      "5735 TMEM80\n",
-      "5736 AP2A2\n",
-      "5737 DDB1\n",
-      "5738 CCDC88B\n",
-      "5739 SART1\n",
-      "5740 ANKRD13D\n",
-      "5741 RSF1\n",
-      "5742 ALG8\n",
-      "5743 IL10RA\n",
-      "5744 MED21\n",
-      "5745 ARF3\n",
-      "5746 COPZ1\n",
-      "5747 CCT2\n",
-      "5748 ARPC3\n",
-      "5749 ALDH2\n",
-      "5750 MAPKAPK5-AS1\n",
-      "5751 PTPN11\n",
-      "5752 MICU2\n",
-      "5753 ERCC5\n",
-      "5754 TINF2\n",
-      "5755 MIDEAS\n",
-      "5756 FCF1\n",
-      "5757 OIP5-AS1\n",
-      "5758 GOLM2\n",
-      "5759 ZFAND6\n",
-      "5760 ABHD2\n",
-      "5761 NUDT16L1\n",
-      "5762 PRKCB\n",
-      "5763 ATP6V0D1\n",
-      "5764 CPD\n",
-      "5765 ABI3\n",
-      "5766 NME2\n",
-      "5767 RAB37\n",
-      "5768 H3-3B\n",
-      "5769 ENDOV\n",
-      "5770 NDUFAF8\n",
-      "5771 P4HB\n",
-      "5772 SLC39A6\n",
-      "5773 POLR2E\n",
-      "5774 ENSG00000261526\n",
-      "5775 FBXL12\n",
-      "5776 TMED1\n",
-      "5777 OCEL1\n",
-      "5778 LTBP4\n",
-      "5779 CYTH2\n",
-      "5780 ZNF350\n",
-      "5781 MGME1\n",
-      "5782 CFAP298\n",
-      "5783 ENSG00000159131\n",
-      "5784 SFI1\n",
-      "5785 GGA1\n",
-      "5786 RPS6KA3\n",
-      "5787 MSN\n",
-      "5788 TNFRSF9\n",
-      "5789 SLC25A33\n",
-      "5790 LZIC\n",
-      "5791 PEX14\n",
-      "5792 USP48\n",
-      "5793 HDAC1\n",
-      "5794 YRDC\n",
-      "5795 RAP1A\n",
-      "5796 BOLA1\n",
-      "5797 RABGAP1L\n",
-      "5798 C1orf21\n",
-      "5799 ASPM\n",
-      "5800 ELK4\n",
-      "5801 CENPF\n",
-      "5802 H3-3A\n",
-      "5803 ARF1\n",
-      "5804 PPP3R1\n",
-      "5805 DCTN1\n",
-      "5806 SNRNP200\n",
-      "5807 ANAPC1\n",
-      "5808 DBI\n",
-      "5809 MARCHF7\n",
-      "5810 EEF1B2\n",
-      "5811 PTMA\n",
-      "5812 LRRFIP1\n",
-      "5813 ASB1\n",
-      "5814 RNPEPL1\n",
-      "5815 SGO1\n",
-      "5816 HIGD1A\n",
-      "5817 PDHB\n",
-      "5818 SSR3\n",
-      "5819 NDUFB5\n",
-      "5820 PSMD2\n",
-      "5821 OPA1\n",
-      "5822 CD38\n",
-      "5823 SEL1L3\n",
-      "5824 RBPJ\n",
-      "5825 GRSF1\n",
-      "5826 CNOT6L\n",
-      "5827 NDUFC1\n",
-      "5828 SCOC\n",
-      "5829 LSM6\n",
-      "5830 TMEM154\n",
-      "5831 SSBP2\n",
-      "5832 CETN3\n",
-      "5833 RAD50\n",
-      "5834 DIAPH1\n",
-      "5835 ADRB2\n",
-      "5836 HNRNPAB\n",
-      "5837 ECI2\n",
-      "5838 C6orf47\n",
-      "5839 GPSM3\n",
-      "5840 HLA-DMB\n",
-      "5841 PREP\n",
-      "5842 SEPTIN7\n",
-      "5843 MRPS24\n",
-      "5844 ENSG00000228434\n",
-      "5845 OGDH\n",
-      "5846 MTMR9\n",
-      "5847 KAT6A\n",
-      "5848 LYN\n",
-      "5849 ARFGEF1\n",
-      "5850 MED30\n",
-      "5851 TTC39B\n",
-      "5852 ANXA1\n",
-      "5853 UGCG\n",
-      "5854 GAPVD1\n",
-      "5855 EEIG1\n",
-      "5856 LPXN\n",
-      "5857 MRPL16\n",
-      "5858 TTC9C\n",
-      "5859 PAAF1\n",
-      "5860 TMEM126A\n",
-      "5861 ETS1\n",
-      "5862 LPCAT3\n",
-      "5863 ENSG00000284634\n",
-      "5864 LINC02446\n",
-      "5865 HEBP1\n",
-      "5866 FGFR1OP2\n",
-      "5867 TXNRD1\n",
-      "5868 C12orf43\n",
-      "5869 EP400\n",
-      "5870 VPS36\n",
-      "5871 PCID2\n",
-      "5872 DYNC1H1\n",
-      "5873 SNAP23\n",
-      "5874 UBL7\n",
-      "5875 IMP3\n",
-      "5876 TRAP1\n",
-      "5877 ZC3H7A\n",
-      "5878 TUFM\n",
-      "5879 LAT\n",
-      "5880 KIF22\n",
-      "5881 COQ9\n",
-      "5882 RPA1\n",
-      "5883 BORCS6\n",
-      "5884 EVI2B\n",
-      "5885 RAB11FIP4\n",
-      "5886 BRCA1\n",
-      "5887 UBTF\n",
-      "5888 CALCOCO2\n",
-      "5889 PECAM1\n",
-      "5890 RPL38\n",
-      "5891 ACP5\n",
-      "5892 RAD23A\n",
-      "5893 PLEKHF1\n",
-      "5894 USF2\n",
-      "5895 PLD3\n",
-      "5896 PNKP\n",
-      "5897 DNTTIP1\n",
-      "5898 STX16\n",
-      "5899 SLX9\n",
-      "5900 BID\n",
-      "5901 RGL4\n",
-      "5902 NF2\n",
-      "5903 MIEF1\n",
-      "5904 ASMTL\n",
-      "5905 SAT1\n",
-      "5906 PDK3\n",
-      "5907 OTUD5\n",
-      "5908 LAMP2\n",
-      "5909 IRAK1\n",
-      "5910 PGD\n",
-      "5911 IFI6\n",
-      "5912 NASP\n",
-      "5913 H2AC20\n",
-      "5914 SLC39A1\n",
-      "5915 PMF1\n",
-      "5916 ENSG00000160818\n",
-      "5917 PEX19\n",
-      "5918 HSD17B7\n",
-      "5919 SUCO\n",
-      "5920 RC3H1\n",
-      "5921 TPR\n",
-      "5922 NTPCR\n",
-      "5923 RBM34\n",
-      "5924 ACP1\n",
-      "5925 C2orf74\n",
-      "5926 SNRPG\n",
-      "5927 SUCLG1\n",
-      "5928 RANBP2\n",
-      "5929 SF3B1\n",
-      "5930 CASP10\n",
-      "5931 SH3BP5\n",
-      "5932 MIX23\n",
-      "5933 ATR\n",
-      "5934 U2SURP\n",
-      "5935 MBNL1\n",
-      "5936 DHX36\n",
-      "5937 TRA2B\n",
-      "5938 SRP72\n",
-      "5939 SPINK2\n",
-      "5940 DNAJB14\n",
-      "5941 INTS12\n",
-      "5942 USP38\n",
-      "5943 ING2\n",
-      "5944 SUB1\n",
-      "5945 TARS1\n",
-      "5946 SKP2\n",
-      "5947 ANXA2R\n",
-      "5948 DIMT1\n",
-      "5949 HIGD2A\n",
-      "5950 TSPAN17\n",
-      "5951 PRR7\n",
-      "5952 TRIM52-AS1\n",
-      "5953 RNF5\n",
-      "5954 LYRM2\n",
-      "5955 POLR1F\n",
-      "5956 NIPSNAP2\n",
-      "5957 CHCHD2\n",
-      "5958 LAT2\n",
-      "5959 SRI\n",
-      "5960 ENSG00000287547\n",
-      "5961 FBXO25\n",
-      "5962 NCOA2\n",
-      "5963 CYC1\n",
-      "5964 RCL1\n",
-      "5965 KDM4C\n",
-      "5966 KLF9\n",
-      "5967 SEC61B\n",
-      "5968 STX17\n",
-      "5969 PHPT1\n",
-      "5970 ZEB1\n",
-      "5971 JMJD1C\n",
-      "5972 TIMM10B\n",
-      "5973 NUCB2\n",
-      "5974 CCDC34\n",
-      "5975 MTA2\n",
-      "5976 UBXN1\n",
-      "5977 PACS1\n",
-      "5978 XRRA1\n",
-      "5979 CFAP68\n",
-      "5980 CD27\n",
-      "5981 EMG1\n",
-      "5982 KLRF1\n",
-      "5983 ETNK1\n",
-      "5984 PCED1B-AS1\n",
-      "5985 TMEM106C\n",
-      "5986 HNRNPA1\n",
-      "5987 DGKA\n",
-      "5988 NABP2\n",
-      "5989 CDK4\n",
-      "5990 ELK3\n",
-      "5991 TPT1\n",
-      "5992 HNRNPA1L2\n",
-      "5993 STK24\n",
-      "5994 SUPT16H\n",
-      "5995 NGDN\n",
-      "5996 IRF9\n",
-      "5997 RABGGTA\n",
-      "5998 NEMF\n",
-      "5999 GCH1\n",
-      "6000 YY1\n",
-      "6001 IGHG4\n",
-      "6002 KLF13\n",
-      "6003 COPS2\n",
-      "6004 DIS3L\n",
-      "6005 CSK\n",
-      "6006 LRRC28\n",
-      "6007 NLRC3\n",
-      "6008 UBN1\n",
-      "6009 NFATC2IP\n",
-      "6010 SPNS1\n",
-      "6011 BOLA2B\n",
-      "6012 ZNF688\n",
-      "6013 PHKB\n",
-      "6014 NFAT5\n",
-      "6015 KLHL36\n",
-      "6016 USP10\n",
-      "6017 KLHDC4\n",
-      "6018 SPATA33\n",
-      "6019 MED11\n",
-      "6020 ZNF232-AS1\n",
-      "6021 CTDNEP1\n",
-      "6022 TRPV2\n",
-      "6023 PIGS\n",
-      "6024 FBXL20\n",
-      "6025 NBR1\n",
-      "6026 LRRC59\n",
-      "6027 MRPS23\n",
-      "6028 SRSF2\n",
-      "6029 MOB3A\n",
-      "6030 USE1\n",
-      "6031 ARRDC2\n",
-      "6032 CLEC11A\n",
-      "6033 PRNP\n",
-      "6034 CDK5RAP1\n",
-      "6035 UBE2G2\n",
-      "6036 BCL2L13\n",
-      "6037 PITPNB\n",
-      "6038 SELENOM\n",
-      "6039 LGALS1\n",
-      "6040 TBC1D22A\n",
-      "6041 ACOT9\n",
-      "6042 TSPYL2\n",
-      "6043 EIF1AY\n",
-      "6044 UBE2J2\n",
-      "6045 NOL9\n",
-      "6046 ARID1A\n",
-      "6047 THRAP3\n",
-      "6048 RRAGC\n",
-      "6049 PDE4B\n",
-      "6050 GBP4\n",
-      "6051 SLC35A3\n",
-      "6052 MCL1\n",
-      "6053 YY1AP1\n",
-      "6054 PUM2\n",
-      "6055 CLIP4\n",
-      "6056 REL\n",
-      "6057 RPIA\n",
-      "6058 STARD7\n",
-      "6059 ACTR3\n",
-      "6060 INSIG2\n",
-      "6061 ERCC3\n",
-      "6062 CWC22\n",
-      "6063 STAT4\n",
-      "6064 SP110\n",
-      "6065 NCL\n",
-      "6066 NDUFA10\n",
-      "6067 SNRK\n",
-      "6068 RBM5\n",
-      "6069 PPM1M\n",
-      "6070 RAB7A\n",
-      "6071 ZBTB38\n",
-      "6072 SIAH2\n",
-      "6073 PARL\n",
-      "6074 RNF168\n",
-      "6075 CTBP1\n",
-      "6076 MXD4\n",
-      "6077 SH3BP2\n",
-      "6078 STX18\n",
-      "6079 GRPEL1\n",
-      "6080 RCHY1\n",
-      "6081 USO1\n",
-      "6082 COPS4\n",
-      "6083 JADE1\n",
-      "6084 HMGB2\n",
-      "6085 PIK3R1\n",
-      "6086 SLC30A5\n",
-      "6087 TBCA\n",
-      "6088 TENT2\n",
-      "6089 ATG10\n",
-      "6090 FAM172A\n",
-      "6091 ANKHD1\n",
-      "6092 CSNK1A1\n",
-      "6093 SCGB3A1\n",
-      "6094 ATXN1\n",
-      "6095 C6orf136\n",
-      "6096 HSPA1A\n",
-      "6097 BRD2\n",
-      "6098 HSD17B8\n",
-      "6099 SAMD3\n",
-      "6100 ACTB\n",
-      "6101 TRGV9\n",
-      "6102 ENSG00000272831\n",
-      "6103 HSPB1\n",
-      "6104 RBM48\n",
-      "6105 ZNF655\n",
-      "6106 TES\n",
-      "6107 PDIA4\n",
-      "6108 ATP6V0E2\n",
-      "6109 ENSG00000254093\n",
-      "6110 EIF4EBP1\n",
-      "6111 TM2D2\n",
-      "6112 COMMD5\n",
-      "6113 TMEM245\n",
-      "6114 RPL35\n",
-      "6115 KLF6\n",
-      "6116 TIMM23\n",
-      "6117 ZWINT\n",
-      "6118 EIF3A\n",
-      "6119 POLR2L\n",
-      "6120 STIM1\n",
-      "6121 APBB1\n",
-      "6122 TMEM9B\n",
-      "6123 C11orf58\n",
-      "6124 METTL15\n",
-      "6125 CPSF7\n",
-      "6126 FADD\n",
-      "6127 H2AJ\n",
-      "6128 ASB8\n",
-      "6129 RPL41\n",
-      "6130 MYL6B\n",
-      "6131 TSFM\n",
-      "6132 WASHC3\n",
-      "6133 COG3\n",
-      "6134 ZC3H13\n",
-      "6135 SDR39U1\n",
-      "6136 CTDSPL2\n",
-      "6137 CCNB2\n",
-      "6138 TARS3\n",
-      "6139 FAHD1\n",
-      "6140 TSC2\n",
-      "6141 ZNF75A\n",
-      "6142 YPEL3\n",
-      "6143 MT1F\n",
-      "6144 UTP4\n",
-      "6145 MVD\n",
-      "6146 PAFAH1B1\n",
-      "6147 RNF167\n",
-      "6148 ACADVL\n",
-      "6149 GGNBP2\n",
-      "6150 LASP1\n",
-      "6151 DHX8\n",
-      "6152 NPEPPS\n",
-      "6153 RSAD1\n",
-      "6154 DCAF7\n",
-      "6155 USP36\n",
-      "6156 MYL12A\n",
-      "6157 FAM174C\n",
-      "6158 DAZAP1\n",
-      "6159 YJU2B\n",
-      "6160 CHERP\n",
-      "6161 C19orf12\n",
-      "6162 RBM42\n",
-      "6163 BCAT2\n",
-      "6164 PRPF31\n",
-      "6165 TMEM230\n",
-      "6166 CHD6\n",
-      "6167 SRSF6\n",
-      "6168 CEBPB\n",
-      "6169 CTSZ\n",
-      "6170 PRELID3B\n",
-      "6171 SLC2A4RG\n",
-      "6172 SAMSN1\n",
-      "6173 ENSG00000159086\n",
-      "6174 PI4KA\n",
-      "6175 MAPK1\n",
-      "6176 TNRC6B\n",
-      "6177 DESI1\n",
-      "6178 MEI1\n",
-      "6179 ATXN10\n",
-      "6180 MID1IP1\n",
-      "6181 UBE2A\n",
-      "6182 G6PD\n",
-      "6183 DDX3Y\n",
-      "6184 CDK11B\n",
-      "6185 GNB1\n",
-      "6186 ACOT7\n",
-      "6187 PARK7\n",
-      "6188 DHRS3\n",
-      "6189 MRPS15\n",
-      "6190 TM2D1\n",
-      "6191 TYW3\n",
-      "6192 ENSA\n",
-      "6193 ARV1\n",
-      "6194 ITSN2\n",
-      "6195 ACYP2\n",
-      "6196 USP34\n",
-      "6197 PELI1\n",
-      "6198 ACTR2\n",
-      "6199 MPHOSPH10\n",
-      "6200 MRPS5\n",
-      "6201 PTPN4\n",
-      "6202 BIN1\n",
-      "6203 CCDC115\n",
-      "6204 PSMD14\n",
-      "6205 GMPPA\n",
-      "6206 FARSB\n",
-      "6207 COPS8\n",
-      "6208 TMEM43\n",
-      "6209 NME6\n",
-      "6210 P4HTM\n",
-      "6211 RHOA\n",
-      "6212 CISH\n",
-      "6213 TEX264\n",
-      "6214 GLT8D1\n",
-      "6215 HMCES\n",
-      "6216 TMEM41A\n",
-      "6217 FYTTD1\n",
-      "6218 ADD1\n",
-      "6219 BLOC1S4\n",
-      "6220 CAMK2D\n",
-      "6221 FBXW7\n",
-      "6222 PLRG1\n",
-      "6223 HPGD\n",
-      "6224 NNT-AS1\n",
-      "6225 DHFR\n",
-      "6226 HSD17B4\n",
-      "6227 HSPA9\n",
-      "6228 RBM22\n",
-      "6229 TNIP1\n",
-      "6230 PPP1R10\n",
-      "6231 MICB\n",
-      "6232 BAG6\n",
-      "6233 RPS10\n",
-      "6234 CCDC167\n",
-      "6235 CD2AP\n",
-      "6236 SMIM8\n",
-      "6237 AKAP7\n",
-      "6238 SYNE1\n",
-      "6239 NDUFA4\n",
-      "6240 DBNL\n",
-      "6241 PPIA\n",
-      "6242 SNHG15\n",
-      "6243 RFC2\n",
-      "6244 DLX5\n",
-      "6245 IMPDH1\n",
-      "6246 DUSP4\n",
-      "6247 TPD52\n",
-      "6248 COX6C\n",
-      "6249 EBAG9\n",
-      "6250 DENND4C\n",
-      "6251 RPS6\n",
-      "6252 CDKN2A\n",
-      "6253 GRHPR\n",
-      "6254 CEMIP2\n",
-      "6255 OSTF1\n",
-      "6256 UBQLN1\n",
-      "6257 MFSD14B\n",
-      "6258 RBM18\n",
-      "6259 PPP6C\n",
-      "6260 SPOUT1\n",
-      "6261 LARP4B\n",
-      "6262 RPP38\n",
-      "6263 RAB18\n",
-      "6264 MRPS16\n",
-      "6265 TM9SF3\n",
-      "6266 CALHM2\n",
-      "6267 XPNPEP1\n",
-      "6268 CASP7\n",
-      "6269 HRAS\n",
-      "6270 IPO7\n",
-      "6271 MED19\n",
-      "6272 BANF1\n",
-      "6273 NDUFS8\n",
-      "6274 RBM7\n",
-      "6275 STT3A\n",
-      "6276 SMIM10L1\n",
-      "6277 RXYLT1\n",
-      "6278 ERP29\n",
-      "6279 PUS1\n",
-      "6280 PXMP2\n",
-      "6281 LAMP1\n",
-      "6282 COX16\n",
-      "6283 ARHGAP11A\n",
-      "6284 SPG21\n",
-      "6285 PKM\n",
-      "6286 HAGH\n",
-      "6287 ELOB\n",
-      "6288 NAGPA\n",
-      "6289 TNRC6A\n",
-      "6290 CFAP20\n",
-      "6291 ACD\n",
-      "6292 TERF2IP\n",
-      "6293 TCF25\n",
-      "6294 PELP1\n",
-      "6295 PHF23\n",
-      "6296 MAP2K3\n",
-      "6297 SUZ12\n",
-      "6298 IKZF3\n",
-      "6299 CNP\n",
-      "6300 EZH1\n",
-      "6301 COA3\n",
-      "6302 HDAC5\n",
-      "6303 WBP2\n",
-      "6304 LINC00667\n",
-      "6305 TPGS2\n",
-      "6306 IL27RA\n",
-      "6307 UBA52\n",
-      "6308 FAM98C\n",
-      "6309 ECH1\n",
-      "6310 HNRNPUL1\n",
-      "6311 PRMT1\n",
-      "6312 ZNF524\n",
-      "6313 CRLS1\n",
-      "6314 PCIF1\n",
-      "6315 SUMO3\n",
-      "6316 SLC25A1\n",
-      "6317 SYNGR1\n",
-      "6318 LMF2\n",
-      "6319 SMC1A\n",
-      "6320 BEX2\n"
+      "1 ENSG00000272106\n",
+      "2 FAAP20\n",
+      "3 DRAXIN\n",
+      "4 MTHFR\n",
+      "5 CLCN6\n",
+      "6 TNFRSF8\n",
+      "7 IFFO2\n",
+      "8 UBXN10\n",
+      "9 HSPG2\n",
+      "10 ZNF436\n",
+      "11 RCAN3AS\n",
+      "12 FABP3\n",
+      "13 HPCA\n",
+      "14 ENSG00000286899\n",
+      "15 EPHA10\n",
+      "16 EXO5-DT\n",
+      "17 P3H1\n",
+      "18 ARMH1\n",
+      "19 STIL\n",
+      "20 SERBP1\n",
+      "21 PRKACB\n",
+      "22 CLCA1\n",
+      "23 GTF2B\n",
+      "24 LINC02609\n",
+      "25 MTF2\n",
+      "26 RWDD3-DT\n",
+      "27 HENMT1\n",
+      "28 PRPF38B\n",
+      "29 WDR47\n",
+      "30 CTSK\n",
+      "31 CRCT1\n",
+      "32 SPRR2D\n",
+      "33 DAP3\n",
+      "34 ARHGEF2\n",
+      "35 BGLAP\n",
+      "36 AIM2\n",
+      "37 UHMK1\n",
+      "38 ENSG00000287929\n",
+      "39 EDEM3\n",
+      "40 RABIF\n",
+      "41 PIK3C2B\n",
+      "42 ENSG00000260805\n",
+      "43 TAF1A-AS1\n",
+      "44 PYCR2\n",
+      "45 ACBD3\n",
+      "46 JMJD4\n",
+      "47 TMEM78\n",
+      "48 MTR\n",
+      "49 ENSG00000273175\n",
+      "50 KIF26B\n",
+      "51 LPIN1\n",
+      "52 DDX1\n",
+      "53 MYCN\n",
+      "54 FNDC4\n",
+      "55 VIT\n",
+      "56 NDUFAF7\n",
+      "57 VPS54\n",
+      "58 ENSG00000281195\n",
+      "59 ENSG00000279070\n",
+      "60 REG3G\n",
+      "61 POLR1A\n",
+      "62 MRPL35\n",
+      "63 IGKV2-40\n",
+      "64 IGKV3D-15\n",
+      "65 CHST10\n",
+      "66 TMEM182\n",
+      "67 SULT1C4\n",
+      "68 RGPD6\n",
+      "69 FBLN7\n",
+      "70 RAB3GAP1\n",
+      "71 TTC21B\n",
+      "72 NOSTRIN\n",
+      "73 PDE11A\n",
+      "74 ORMDL1\n",
+      "75 MAP2\n",
+      "76 PECR\n",
+      "77 ENSG00000236886\n",
+      "78 ANKZF1\n",
+      "79 ASIC4\n",
+      "80 SP100\n",
+      "81 MLPH\n",
+      "82 CAPN10-DT\n",
+      "83 ANO7\n",
+      "84 TADA3\n",
+      "85 ENSG00000272477\n",
+      "86 ACAA1\n",
+      "87 CTNNB1\n",
+      "88 ZNF197\n",
+      "89 TMEM42\n",
+      "90 SAMMSON\n",
+      "91 TOMM70\n",
+      "92 DUBR\n",
+      "93 ENSG00000241490\n",
+      "94 TMEM39A\n",
+      "95 LRRC58\n",
+      "96 PPP2R3A\n",
+      "97 IGSF10\n",
+      "98 TMEM212\n",
+      "99 MAEA\n",
+      "100 LYAR\n",
+      "101 FGFBP2\n",
+      "102 ENSG00000287081\n",
+      "103 MED28-DT\n",
+      "104 USP46\n",
+      "105 ADAMTS3\n",
+      "106 NUP54\n",
+      "107 PAQR3\n",
+      "108 ENSG00000272717\n",
+      "109 TBC1D9\n",
+      "110 ADAM29\n",
+      "111 LINC02362\n",
+      "112 PRIMPOL\n",
+      "113 BRD9\n",
+      "114 ENSG00000248968\n",
+      "115 PELO\n",
+      "116 GZMK\n",
+      "117 NAIP\n",
+      "118 ENSG00000248881\n",
+      "119 ARSK\n",
+      "120 CAMK4\n",
+      "121 LINC02863\n",
+      "122 ENSG00000286408\n",
+      "123 NR3C1\n",
+      "124 CARMN\n",
+      "125 FAM200C\n",
+      "126 ENSG00000285590\n",
+      "127 CANX\n",
+      "128 TRIM52\n",
+      "129 ENSG00000247925\n",
+      "130 ENSG00000287626\n",
+      "131 ENSG00000272341\n",
+      "132 BTN3A3\n",
+      "133 ENSG00000224157\n",
+      "134 HCG17\n",
+      "135 DDX39B\n",
+      "136 GRM4\n",
+      "137 ILRUN\n",
+      "138 SLC26A8\n",
+      "139 FOXP4\n",
+      "140 FBXO9\n",
+      "141 DDX43\n",
+      "142 IRAK1BP1\n",
+      "143 PNRC1-DT\n",
+      "144 RRAGD\n",
+      "145 CRYBG1\n",
+      "146 GTF3C6\n",
+      "147 ENSG00000253194\n",
+      "148 ENSG00000236673\n",
+      "149 LINC01013\n",
+      "150 IFNGR1\n",
+      "151 ENSG00000231329\n",
+      "152 SNX9\n",
+      "153 ENSG00000266896\n",
+      "154 C7orf50\n",
+      "155 ELFN1\n",
+      "156 KDELR2\n",
+      "157 ENSG00000228010\n",
+      "158 ENSG00000234141\n",
+      "159 MACC1\n",
+      "160 TRGV10\n",
+      "161 TRGV5P\n",
+      "162 UBE2D4\n",
+      "163 TNS3\n",
+      "164 CCT6A\n",
+      "165 HIP1\n",
+      "166 ENSG00000284707\n",
+      "167 PILRA\n",
+      "168 IFT22\n",
+      "169 CBLL1\n",
+      "170 ING3\n",
+      "171 TRBV7-7\n",
+      "172 TRBV12-3\n",
+      "173 ENSG00000270672\n",
+      "174 CNTNAP2\n",
+      "175 INSIG1-DT\n",
+      "176 CLDN23\n",
+      "177 PPP2R2A\n",
+      "178 SNTG1\n",
+      "179 ENSG00000286113\n",
+      "180 CA2\n",
+      "181 RPL30\n",
+      "182 RNF19A\n",
+      "183 ZNF706\n",
+      "184 CTHRC1\n",
+      "185 NTAQ1\n",
+      "186 RNF139-DT\n",
+      "187 POU5F1B\n",
+      "188 EFR3A\n",
+      "189 SLC45A4\n",
+      "190 TSNARE1\n",
+      "191 ENSG00000272172\n",
+      "192 ZNF16\n",
+      "193 ENSG00000233178\n",
+      "194 FRMD3\n",
+      "195 FGD3\n",
+      "196 ZNF367\n",
+      "197 TNC\n",
+      "198 NDUFA8\n",
+      "199 SH3GLB2\n",
+      "200 TOR1B\n",
+      "201 STKLD1\n",
+      "202 MLLT10\n",
+      "203 C10orf67\n",
+      "204 CCDC7\n",
+      "205 KIFBP\n",
+      "206 ENSG00000272447\n",
+      "207 CDHR1\n",
+      "208 IDE\n",
+      "209 ITPRIP\n",
+      "210 ENSG00000260917\n",
+      "211 PTPRE\n",
+      "212 IFITM2\n",
+      "213 TSPAN4\n",
+      "214 STK33\n",
+      "215 FAR1-IT1\n",
+      "216 ENSG00000254907\n",
+      "217 TP53I11\n",
+      "218 VEGFB\n",
+      "219 NAALADL1\n",
+      "220 ZNRD2\n",
+      "221 PELI3\n",
+      "222 IGHMBP2\n",
+      "223 KCNE3\n",
+      "224 ENSG00000255081\n",
+      "225 ACER3\n",
+      "226 KCTD21-AS1\n",
+      "227 MRE11\n",
+      "228 PIWIL4\n",
+      "229 ENSG00000261098\n",
+      "230 CUL5\n",
+      "231 POU2F3\n",
+      "232 JAM3\n",
+      "233 TEAD4\n",
+      "234 CRACR2A\n",
+      "235 PARP11-AS1\n",
+      "236 PTMS\n",
+      "237 LRRC23\n",
+      "238 PTPN6\n",
+      "239 CLEC4C\n",
+      "240 CLEC12A\n",
+      "241 STYK1\n",
+      "242 ENSG00000272368\n",
+      "243 LETMD1\n",
+      "244 NR4A1AS\n",
+      "245 RARG\n",
+      "246 ARHGAP9\n",
+      "247 CAND1\n",
+      "248 ENSG00000247131\n",
+      "249 NUP37\n",
+      "250 CKAP4\n",
+      "251 CORO1C\n",
+      "252 KCTD10\n",
+      "253 GPN3\n",
+      "254 TMEM116\n",
+      "255 SPRING1\n",
+      "256 DIABLO\n",
+      "257 ADGRD1\n",
+      "258 AKAP11\n",
+      "259 SERP2\n",
+      "260 CPB2\n",
+      "261 ENSG00000231633\n",
+      "262 ENSG00000126216\n",
+      "263 GRTP1\n",
+      "264 APEX1\n",
+      "265 TRAV14DV4\n",
+      "266 TRAV35\n",
+      "267 MDP1\n",
+      "268 SPTSSA\n",
+      "269 GPR137C\n",
+      "270 ENSG00000259049\n",
+      "271 NAA30\n",
+      "272 ATP6V1D\n",
+      "273 IFT43\n",
+      "274 ADCK1\n",
+      "275 FOXN3\n",
+      "276 ENSG00000258792\n",
+      "277 ITPK1-AS1\n",
+      "278 UNC79\n",
+      "279 TCL1A\n",
+      "280 C14orf132\n",
+      "281 EML1\n",
+      "282 MARK3\n",
+      "283 XRCC3\n",
+      "284 CHRM5\n",
+      "285 ZSCAN29\n",
+      "286 SPG11\n",
+      "287 PIGB\n",
+      "288 SNX22\n",
+      "289 FEM1B\n",
+      "290 KIF23\n",
+      "291 TLE3\n",
+      "292 STRA6\n",
+      "293 NMB\n",
+      "294 WDR90\n",
+      "295 CEROX1\n",
+      "296 GNPTG\n",
+      "297 FLYWCH2\n",
+      "298 DEXI\n",
+      "299 NPIPB5\n",
+      "300 COG7\n",
+      "301 ENSG00000260719\n",
+      "302 FBXL8\n",
+      "303 RANBP10\n",
+      "304 PSMB10\n",
+      "305 ENSG00000262160\n",
+      "306 NIP7\n",
+      "307 KARS1\n",
+      "308 MAF\n",
+      "309 PLCG2\n",
+      "310 XAF1\n",
+      "311 TMEM102\n",
+      "312 CFAP52\n",
+      "313 NCOR1\n",
+      "314 CCDC144A\n",
+      "315 SLC47A1\n",
+      "316 SLFN5\n",
+      "317 SNHG30\n",
+      "318 CCR7\n",
+      "319 CNTD1\n",
+      "320 TMUB2\n",
+      "321 SKAP1-AS1\n",
+      "322 CYB561\n",
+      "323 POLG2\n",
+      "324 CD300LD-AS1\n",
+      "325 SEC14L1\n",
+      "326 OGFOD3\n",
+      "327 RNF125\n",
+      "328 PIAS2\n",
+      "329 ELAC1\n",
+      "330 TMX3\n",
+      "331 PLK5\n",
+      "332 MBD3\n",
+      "333 PIP5K1C\n",
+      "334 TJP3\n",
+      "335 RFX2\n",
+      "336 PEX11G\n",
+      "337 DNMT1\n",
+      "338 ZNF433\n",
+      "339 SAMD1\n",
+      "340 COX7A1\n",
+      "341 ZNF793\n",
+      "342 FBXO27\n",
+      "343 SAMD4B\n",
+      "344 TOMM40\n",
+      "345 APOC2\n",
+      "346 ENSG00000259605\n",
+      "347 NAPA-AS1\n",
+      "348 NUCB1\n",
+      "349 KCNA7\n",
+      "350 ATF5\n",
+      "351 KLK1\n",
+      "352 PRPF31-AS1\n",
+      "353 ZNF444\n",
+      "354 NRSN2\n",
+      "355 ENSG00000275632\n",
+      "356 MKKS\n",
+      "357 DSTN\n",
+      "358 BPI\n",
+      "359 SNHG17\n",
+      "360 TP53RK-DT\n",
+      "361 NCOA3\n",
+      "362 ADNP-AS1\n",
+      "363 ZBTB46-AS2\n",
+      "364 ENSG00000273199\n",
+      "365 RIPPLY3\n",
+      "366 POFUT2\n",
+      "367 IGLV2-23\n",
+      "368 IGLJ2\n",
+      "369 GRK3-AS1\n",
+      "370 MIAT\n",
+      "371 SF3A1\n",
+      "372 ENSG00000279927\n",
+      "373 RPL3\n",
+      "374 DNAJB7\n",
+      "375 TTC38\n",
+      "376 P2RY8\n",
+      "377 PUDP\n",
+      "378 APOO\n",
+      "379 KDM6A\n",
+      "380 SHROOM4\n",
+      "381 VSIG4\n",
+      "382 RLIM\n",
+      "383 BTK\n",
+      "384 TCEAL8\n",
+      "385 DNAAF6\n",
+      "386 NKAP\n",
+      "387 UTP14A\n",
+      "388 MTM1\n",
+      "389 GABRE\n",
+      "390 NLGN4Y\n",
+      "391 ENSG00000271806\n",
+      "392 DNAJC16\n",
+      "393 LUZP1\n",
+      "394 ENSG00000270605\n",
+      "395 SESN2\n",
+      "396 NCDN\n",
+      "397 OXCT2\n",
+      "398 ENSG00000236434\n",
+      "399 IFT25\n",
+      "400 PARS2\n",
+      "401 NFIA\n",
+      "402 DNAJC6\n",
+      "403 WLS\n",
+      "404 AK5\n",
+      "405 TTLL7\n",
+      "406 GBP1\n",
+      "407 GBP7\n",
+      "408 PTBP2\n",
+      "409 S1PR1\n",
+      "410 TMEM167B-DT\n",
+      "411 ENSG00000231128\n",
+      "412 MAN1A2\n",
+      "413 WARS2-AS1\n",
+      "414 NBPF12\n",
+      "415 ENSG00000237188\n",
+      "416 NOTCH2NLC\n",
+      "417 PIP5K1A\n",
+      "418 IQGAP3\n",
+      "419 MRPL24\n",
+      "420 SH2D2A\n",
+      "421 SNHG28\n",
+      "422 ATP1A4\n",
+      "423 DEDD\n",
+      "424 UFC1\n",
+      "425 SLC19A2\n",
+      "426 SELP\n",
+      "427 LAMC1\n",
+      "428 PM20D1-AS1\n",
+      "429 ATF3\n",
+      "430 GPATCH2\n",
+      "431 BPNT1\n",
+      "432 MTARC1\n",
+      "433 CNIH3\n",
+      "434 EPHX1\n",
+      "435 TRIM17\n",
+      "436 DISC1\n",
+      "437 SIPA1L2\n",
+      "438 CHRM3\n",
+      "439 ENSG00000182551\n",
+      "440 RNASEH1\n",
+      "441 MBOAT2\n",
+      "442 LBH\n",
+      "443 THADA\n",
+      "444 SLC1A4\n",
+      "445 PRADC1\n",
+      "446 STAMBP\n",
+      "447 CTNNA2\n",
+      "448 ENSG00000225420\n",
+      "449 TBC1D8\n",
+      "450 CNOT11\n",
+      "451 LIMS1\n",
+      "452 CCDC138\n",
+      "453 NEMP2-DT\n",
+      "454 TMBIM1\n",
+      "455 VIL1\n",
+      "456 ENSG00000286606\n",
+      "457 ENSG00000243135\n",
+      "458 CPNE9\n",
+      "459 ENSG00000206567\n",
+      "460 TGFBR2\n",
+      "461 CMTM8\n",
+      "462 CRTAP\n",
+      "463 CLASP2\n",
+      "464 ENSG00000284669\n",
+      "465 ANO10\n",
+      "466 SLC26A6\n",
+      "467 CAMKV\n",
+      "468 ENSG00000230454\n",
+      "469 UQCC5\n",
+      "470 PBRM1\n",
+      "471 FOXP1-IT1\n",
+      "472 PROK2\n",
+      "473 ENSG00000269028\n",
+      "474 RIOX2\n",
+      "475 CBLB\n",
+      "476 LINC00635\n",
+      "477 TMPRSS7\n",
+      "478 ACAD9\n",
+      "479 NPHP3-AS1\n",
+      "480 TFDP2\n",
+      "481 TRPC1\n",
+      "482 PAQR9-AS1\n",
+      "483 IQCJ-SCHIP1\n",
+      "484 PHC3\n",
+      "485 ECT2\n",
+      "486 PIK3CA\n",
+      "487 DVL3\n",
+      "488 BCL6\n",
+      "489 SENP5\n",
+      "490 PIGG\n",
+      "491 WDR1\n",
+      "492 DTHD1\n",
+      "493 KLB\n",
+      "494 YTHDC1\n",
+      "495 SULT1E1\n",
+      "496 MRPL1\n",
+      "497 HELQ\n",
+      "498 ENSG00000272777\n",
+      "499 USP53\n",
+      "500 HSPA4L\n",
+      "501 SCLT1\n",
+      "502 ELF2\n",
+      "503 MIR4453HG\n",
+      "504 MFAP3L\n",
+      "505 ENSG00000288025\n",
+      "506 TENM3-AS1\n",
+      "507 CYP4V2\n",
+      "508 TRIML2\n",
+      "509 LSINCT5\n",
+      "510 MTRR\n",
+      "511 CARD6\n",
+      "512 IL6ST\n",
+      "513 CDK7\n",
+      "514 GFM2\n",
+      "515 FAM169A\n",
+      "516 SV2C\n",
+      "517 ERAP2\n",
+      "518 ENSG00000249021\n",
+      "519 AP3S1\n",
+      "520 COMMD10\n",
+      "521 FNIP1\n",
+      "522 KLHL3\n",
+      "523 KDM3B\n",
+      "524 PFDN1\n",
+      "525 MRPL22\n",
+      "526 ADAM19\n",
+      "527 ENSG00000253693\n",
+      "528 NPM1\n",
+      "529 KIAA1191\n",
+      "530 GRK6\n",
+      "531 ENSG00000272279\n",
+      "532 NEDD9\n",
+      "533 ENSG00000282024\n",
+      "534 ZKSCAN4\n",
+      "535 ZBED9-AS1\n",
+      "536 PPT2\n",
+      "537 PSMB8-AS1\n",
+      "538 STK38\n",
+      "539 TRERF1\n",
+      "540 TBCC\n",
+      "541 LINC02976\n",
+      "542 ENSG00000287055\n",
+      "543 DOP1A\n",
+      "544 CD24\n",
+      "545 SLC22A16\n",
+      "546 HDAC2\n",
+      "547 VNN1\n",
+      "548 ENSG00000234147\n",
+      "549 RGS17\n",
+      "550 ENSG00000285887\n",
+      "551 EIF2AK1\n",
+      "552 DAGLB\n",
+      "553 ENSG00000287032\n",
+      "554 YKT6\n",
+      "555 CCM2\n",
+      "556 SUMF2\n",
+      "557 ENSG00000226829\n",
+      "558 FIS1\n",
+      "559 ATP6V1F\n",
+      "560 TRBV7-9\n",
+      "561 GIMAP2\n",
+      "562 TMUB1\n",
+      "563 KBTBD11\n",
+      "564 NAT1\n",
+      "565 PCMTD1\n",
+      "566 XKR4\n",
+      "567 POP1\n",
+      "568 WASHC5\n",
+      "569 NDRG1\n",
+      "570 KCNK9\n",
+      "571 ZNF696\n",
+      "572 RIGI\n",
+      "573 ENSG00000227388\n",
+      "574 ZNG1C\n",
+      "575 SECISBP2\n",
+      "576 LINC02937\n",
+      "577 HABP4\n",
+      "578 NCBP1\n",
+      "579 LPAR1\n",
+      "580 PTGS1\n",
+      "581 PTRH1\n",
+      "582 ZER1\n",
+      "583 NUP188\n",
+      "584 IER5L-AS1\n",
+      "585 COL5A1\n",
+      "586 ANAPC2\n",
+      "587 TOR4A\n",
+      "588 GATA3\n",
+      "589 ENSG00000234961\n",
+      "590 MSRB2\n",
+      "591 SVIL\n",
+      "592 ENSG00000285630\n",
+      "593 PCAT5\n",
+      "594 MRLN\n",
+      "595 DNAJC9\n",
+      "596 ADK\n",
+      "597 NUTM2B-AS1\n",
+      "598 ENTPD7\n",
+      "599 OGA\n",
+      "600 HPS6\n",
+      "601 STN1\n",
+      "602 PWWP2B\n",
+      "603 ENSG00000235245\n",
+      "604 TOLLIP-DT\n",
+      "605 C11orf21\n",
+      "606 KCNQ1OT1\n",
+      "607 NUP98\n",
+      "608 DNHD1\n",
+      "609 CD59\n",
+      "610 APIP\n",
+      "611 LINC02733\n",
+      "612 INCENP\n",
+      "613 LGALS12\n",
+      "614 ENSG00000286264\n",
+      "615 PPP1R14B-AS1\n",
+      "616 PRDX5\n",
+      "617 RNASEH2C\n",
+      "618 B4GAT1-DT\n",
+      "619 SYT12\n",
+      "620 SYTL2\n",
+      "621 MAML2\n",
+      "622 DDX10\n",
+      "623 NNMT\n",
+      "624 ARHGEF12\n",
+      "625 VWA5A\n",
+      "626 VPS26B\n",
+      "627 ENSG00000282080\n",
+      "628 TAS2R30\n",
+      "629 PTPRO\n",
+      "630 RECQL\n",
+      "631 AMN1\n",
+      "632 LINC00938\n",
+      "633 LIMA1\n",
+      "634 ENSG00000278126\n",
+      "635 CSAD\n",
+      "636 PRR13\n",
+      "637 PCBP2-OT1\n",
+      "638 XPOT\n",
+      "639 BBS10\n",
+      "640 ENSG00000258170\n",
+      "641 ANKS1B\n",
+      "642 ENSG00000274554\n",
+      "643 ATP8A2\n",
+      "644 NHLRC3\n",
+      "645 DGKH\n",
+      "646 RNASEH2B-AS1\n",
+      "647 KLF5\n",
+      "648 UCHL3\n",
+      "649 OSGEP\n",
+      "650 TRAV27\n",
+      "651 TRDV2\n",
+      "652 PABPN1\n",
+      "653 DHRS1\n",
+      "654 MIPOL1\n",
+      "655 ARF6\n",
+      "656 ENSG00000261120\n",
+      "657 ENSG00000258670\n",
+      "658 HEATR4\n",
+      "659 ALDH6A1\n",
+      "660 ATXN3\n",
+      "661 LINC01550\n",
+      "662 WDR25\n",
+      "663 LINC02285\n",
+      "664 ENSG00000272444\n",
+      "665 IGHV3-21\n",
+      "666 EMC7\n",
+      "667 ENSG00000259598\n",
+      "668 FSIP1\n",
+      "669 RAD51\n",
+      "670 SERF2\n",
+      "671 B2M\n",
+      "672 ENSG00000275580\n",
+      "673 DAPK2\n",
+      "674 CLPX\n",
+      "675 ADAMTS7\n",
+      "676 MIR9-3HG\n",
+      "677 ENSG00000259363\n",
+      "678 HBA1\n",
+      "679 CRAMP1\n",
+      "680 ABCA3\n",
+      "681 ADCY9\n",
+      "682 SOCS1\n",
+      "683 EARS2\n",
+      "684 MAZ\n",
+      "685 DUS2\n",
+      "686 COG8\n",
+      "687 CLEC18A\n",
+      "688 COG4\n",
+      "689 DNAAF1\n",
+      "690 ZFPM1\n",
+      "691 MYO1C\n",
+      "692 CHRNE\n",
+      "693 C1QBP\n",
+      "694 KCTD11\n",
+      "695 FXR2\n",
+      "696 ENSG00000265975\n",
+      "697 ALDH3A2\n",
+      "698 ALDH3A1\n",
+      "699 UNC119\n",
+      "700 COPRS\n",
+      "701 ENSG00000264083\n",
+      "702 AATF\n",
+      "703 HAP1\n",
+      "704 ACLY\n",
+      "705 HSD17B1-AS1\n",
+      "706 ITGA2B\n",
+      "707 ENSG00000283045\n",
+      "708 CCDC43\n",
+      "709 GJC1\n",
+      "710 HIGD1B\n",
+      "711 FMNL1-DT\n",
+      "712 HLF\n",
+      "713 SUPT4H1\n",
+      "714 ST6GALNAC1\n",
+      "715 SCAT1\n",
+      "716 RBFOX3\n",
+      "717 ENSG00000265091\n",
+      "718 L3MBTL4-AS1\n",
+      "719 GAREM1\n",
+      "720 ENSG00000285095\n",
+      "721 SMAD2\n",
+      "722 LMAN1\n",
+      "723 PRTN3\n",
+      "724 CFD\n",
+      "725 ABCA7\n",
+      "726 MATK\n",
+      "727 CATSPERD\n",
+      "728 ALKBH7\n",
+      "729 ENSG00000286138\n",
+      "730 ZNF317\n",
+      "731 ZNF491\n",
+      "732 NFIX\n",
+      "733 KLF2-DT\n",
+      "734 F2RL3\n",
+      "735 RPL18A\n",
+      "736 SUGP2\n",
+      "737 ATP13A1\n",
+      "738 ZNF568\n",
+      "739 GMFG\n",
+      "740 BLVRB\n",
+      "741 ENSG00000282951\n",
+      "742 DMPK\n",
+      "743 KPTN\n",
+      "744 SULT2A1\n",
+      "745 NTN5\n",
+      "746 DHDH\n",
+      "747 TEAD2\n",
+      "748 KLK11\n",
+      "749 ETFB\n",
+      "750 LILRB5\n",
+      "751 LAIR1\n",
+      "752 ZNF667-AS1\n",
+      "753 ZNF418\n",
+      "754 SOX12\n",
+      "755 TRIB3\n",
+      "756 PANK2-AS1\n",
+      "757 DTD1\n",
+      "758 ZNF337-AS1\n",
+      "759 EIF2S2\n",
+      "760 GGT7\n",
+      "761 SNHG11\n",
+      "762 FAM83D\n",
+      "763 ZSWIM1\n",
+      "764 SLC35C2\n",
+      "765 PPP1R3D\n",
+      "766 CABLES2\n",
+      "767 ENSG00000278996\n",
+      "768 ENSG00000224905\n",
+      "769 CXADR\n",
+      "770 CHODL\n",
+      "771 LINC01426\n",
+      "772 TFF3\n",
+      "773 ENSG00000272829\n",
+      "774 IGLV3-19\n",
+      "775 IGLC6\n",
+      "776 VPREB3\n",
+      "777 DDT\n",
+      "778 KREMEN1\n",
+      "779 MTMR3\n",
+      "780 CSNK1E\n",
+      "781 DDX17\n",
+      "782 SLC25A17\n",
+      "783 TRABD-AS1\n",
+      "784 ENSG00000226179\n",
+      "785 ENSG00000286431\n",
+      "786 MOSPD2\n",
+      "787 RBBP7\n",
+      "788 LINC01281\n",
+      "789 CXorf38\n",
+      "790 TSIX\n",
+      "791 PGK1\n",
+      "792 ELF4\n",
+      "793 MBNL3\n",
+      "794 PHF6\n",
+      "795 SLC9A6\n",
+      "796 EOLA1-DT\n",
+      "797 ZFP92\n",
+      "798 USP9Y\n",
+      "799 AURKAIP1\n",
+      "800 NADK\n",
+      "801 GABRD\n",
+      "802 MORN1\n",
+      "803 SLC2A5\n",
+      "804 C1orf167\n",
+      "805 CELA3A\n",
+      "806 LYPLA2\n",
+      "807 NFYC\n",
+      "808 GUCA2B\n",
+      "809 ZFYVE9\n",
+      "810 MAGOH-DT\n",
+      "811 L1TD1\n",
+      "812 GNG12\n",
+      "813 SAMD13\n",
+      "814 RBMXL1\n",
+      "815 ENSG00000230735\n",
+      "816 ARHGAP29\n",
+      "817 COL11A1\n",
+      "818 ENSG00000261654\n",
+      "819 ENSG00000260948\n",
+      "820 KCND3\n",
+      "821 ENSG00000232721\n",
+      "822 LINC02591\n",
+      "823 ENSG00000203812\n",
+      "824 SEMA6C\n",
+      "825 S100A2\n",
+      "826 ENSG00000272030\n",
+      "827 NUP210L\n",
+      "828 PIGM\n",
+      "829 DCAF8-DT\n",
+      "830 HSPA6\n",
+      "831 ENSG00000260360\n",
+      "832 CEP350\n",
+      "833 ENSG00000243155\n",
+      "834 GLRX2\n",
+      "835 DENND1B\n",
+      "836 UBE2T\n",
+      "837 ZC3H11A\n",
+      "838 G0S2\n",
+      "839 ENSG00000234915\n",
+      "840 PACC1\n",
+      "841 KCTD3\n",
+      "842 FAM89A\n",
+      "843 GPR137B\n",
+      "844 ERO1B\n",
+      "845 LGALS8-AS1\n",
+      "846 DESI2\n",
+      "847 EFCAB2\n",
+      "848 SOX11\n",
+      "849 APOB\n",
+      "850 EMILIN1\n",
+      "851 BABAM2\n",
+      "852 ENSG00000272754\n",
+      "853 ENSG00000237133\n",
+      "854 SOCS5\n",
+      "855 EHBP1-AS1\n",
+      "856 MRPL19\n",
+      "857 C2orf68\n",
+      "858 C2orf92\n",
+      "859 NPAS2\n",
+      "860 CREG2\n",
+      "861 IL1RL1\n",
+      "862 C2orf49-DT\n",
+      "863 ACOXL\n",
+      "864 RABL2A\n",
+      "865 NIFK-AS1\n",
+      "866 AMMECR1L\n",
+      "867 MGAT5\n",
+      "868 CCNT2\n",
+      "869 LY75\n",
+      "870 DYNC1I2\n",
+      "871 LNPK\n",
+      "872 ENSG00000270574\n",
+      "873 WDR75\n",
+      "874 ANKAR\n",
+      "875 HSPD1\n",
+      "876 KCTD18\n",
+      "877 FAM117B\n",
+      "878 IDH1\n",
+      "879 EFHD1\n",
+      "880 BTD\n",
+      "881 ENSG00000237838\n",
+      "882 TRIM71\n",
+      "883 KRBOX1\n",
+      "884 ENSG00000244380\n",
+      "885 AMT\n",
+      "886 MST1R\n",
+      "887 DUSP7\n",
+      "888 CFAP20DC\n",
+      "889 ARL13B\n",
+      "890 ZBTB11-AS1\n",
+      "891 ENSG00000287682\n",
+      "892 CD200\n",
+      "893 BOC\n",
+      "894 ZXDC\n",
+      "895 H1-10-AS1\n",
+      "896 PLXND1\n",
+      "897 NUDT16\n",
+      "898 PCCB\n",
+      "899 GK5\n",
+      "900 WWTR1\n",
+      "901 MED12L\n",
+      "902 WDR49\n",
+      "903 LINC02068\n",
+      "904 DNAJC19\n",
+      "905 MCCC1\n",
+      "906 LIPH\n",
+      "907 CCDC50\n",
+      "908 NRROS\n",
+      "909 ENSG00000287286\n",
+      "910 C4orf50\n",
+      "911 TLR1\n",
+      "912 LINC02265\n",
+      "913 BEND4\n",
+      "914 FIP1L1\n",
+      "915 SHROOM3\n",
+      "916 LINC01094\n",
+      "917 IBSP\n",
+      "918 ENSG00000251095\n",
+      "919 SMARCAD1-DT\n",
+      "920 RAP1GDS1\n",
+      "921 UBE2D3\n",
+      "922 ENSG00000251259\n",
+      "923 MCUB\n",
+      "924 ARSJ\n",
+      "925 NUDT6\n",
+      "926 TRAPPC11\n",
+      "927 FAM149A\n",
+      "928 TPPP\n",
+      "929 ZDHHC11B\n",
+      "930 C1QTNF3\n",
+      "931 CCDC152\n",
+      "932 PPWD1\n",
+      "933 SERF1A\n",
+      "934 ARSB\n",
+      "935 RPS23\n",
+      "936 COX7C\n",
+      "937 ENSG00000250362\n",
+      "938 CSF2\n",
+      "939 IRF1-AS1\n",
+      "940 UQCRQ\n",
+      "941 ZCCHC10\n",
+      "942 ZMAT2\n",
+      "943 SPINK1\n",
+      "944 AFAP1L1\n",
+      "945 HMMR\n",
+      "946 CREBRF\n",
+      "947 STC2\n",
+      "948 LMAN2\n",
+      "949 ENSG00000249109\n",
+      "950 RUFY1-AS1\n",
+      "951 LINC01011\n",
+      "952 EEF1E1\n",
+      "953 PSMB8\n",
+      "954 BLTP3A\n",
+      "955 ZFAND3\n",
+      "956 KCNK5\n",
+      "957 ADGRF5\n",
+      "958 C6orf141\n",
+      "959 GSTA1\n",
+      "960 ENSG00000288009\n",
+      "961 BVES\n",
+      "962 TUBE1\n",
+      "963 LINC02539\n",
+      "964 ULBP3\n",
+      "965 FBXO5\n",
+      "966 DYNLT1\n",
+      "967 SFT2D1\n",
+      "968 ENSG00000229618\n",
+      "969 CBX3\n",
+      "970 SKAP2\n",
+      "971 HOXA5\n",
+      "972 ENSG00000235728\n",
+      "973 MLXIPL\n",
+      "974 SPDYE5\n",
+      "975 ATP5MF\n",
+      "976 ALKBH4\n",
+      "977 FBXL13\n",
+      "978 ZNF277\n",
+      "979 TMEM168\n",
+      "980 ST7\n",
+      "981 PTPRZ1\n",
+      "982 CPA2\n",
+      "983 ABCF2\n",
+      "984 MCPH1-DT\n",
+      "985 FAM86B1\n",
+      "986 MTUS1\n",
+      "987 SFTPC\n",
+      "988 ADAM9\n",
+      "989 FAM110B\n",
+      "990 LACTB2\n",
+      "991 ENSG00000253385\n",
+      "992 CASC8\n",
+      "993 ASAP1\n",
+      "994 LY6K\n",
+      "995 LY6D\n",
+      "996 ZDHHC21\n",
+      "997 CDKN2B\n",
+      "998 GBA2\n",
+      "999 GCNT1\n",
+      "1000 QNG1\n",
+      "1001 PCAT7\n",
+      "1002 CORO2A\n",
+      "1003 ABITRAM\n",
+      "1004 PBX3\n",
+      "1005 RPL12\n",
+      "1006 ENG\n",
+      "1007 ENDOG\n",
+      "1008 NTMT1\n",
+      "1009 ABL1\n",
+      "1010 ENSG00000246851\n",
+      "1011 TTF1\n",
+      "1012 CFAP77\n",
+      "1013 GFI1B\n",
+      "1014 LRRC26\n",
+      "1015 ENSG00000272764\n",
+      "1016 ENSG00000273474\n",
+      "1017 TRDMT1\n",
+      "1018 WAC-AS1\n",
+      "1019 ZNF22\n",
+      "1020 MAPK8\n",
+      "1021 VSIR\n",
+      "1022 ECD\n",
+      "1023 ZNF518A\n",
+      "1024 MMS19\n",
+      "1025 SLC25A28\n",
+      "1026 NOLC1\n",
+      "1027 TRIM8-DT\n",
+      "1028 PNLIP\n",
+      "1029 ENSG00000277879\n",
+      "1030 FAM24B\n",
+      "1031 PPP2R2D\n",
+      "1032 RASSF7\n",
+      "1033 TRIM21\n",
+      "1034 TPH1\n",
+      "1035 MISFA\n",
+      "1036 EIF3M\n",
+      "1037 OTUB1\n",
+      "1038 DPF2\n",
+      "1039 ENSG00000255478\n",
+      "1040 LTO1\n",
+      "1041 ENSG00000285921\n",
+      "1042 DLAT\n",
+      "1043 CXCR5\n",
+      "1044 DPAGT1\n",
+      "1045 SENCR\n",
+      "1046 ENSG00000255455\n",
+      "1047 LINC02731\n",
+      "1048 CD27-AS1\n",
+      "1049 CDCA3\n",
+      "1050 CD163\n",
+      "1051 EPS8\n",
+      "1052 ALG10\n",
+      "1053 PPHLN1\n",
+      "1054 AMIGO2\n",
+      "1055 POU6F1\n",
+      "1056 KRT18\n",
+      "1057 PFDN5\n",
+      "1058 MAP3K12\n",
+      "1059 OS9\n",
+      "1060 PLXNC1\n",
+      "1061 CEP83\n",
+      "1062 TMPO\n",
+      "1063 SCYL2\n",
+      "1064 ABTB3\n",
+      "1065 SPPL3\n",
+      "1066 LRRC43\n",
+      "1067 OGFOD2\n",
+      "1068 PITPNM2\n",
+      "1069 KATNAL1\n",
+      "1070 ENSG00000276672\n",
+      "1071 ARL11\n",
+      "1072 ALG11\n",
+      "1073 TRAV1-2\n",
+      "1074 TRAV19\n",
+      "1075 MRPL52\n",
+      "1076 GZMH\n",
+      "1077 PTGDR\n",
+      "1078 TXNDC16\n",
+      "1079 SIX4\n",
+      "1080 PLEKHD1\n",
+      "1081 ENSG00000258534\n",
+      "1082 SIVA1\n",
+      "1083 IGHV3-74\n",
+      "1084 SNHG14\n",
+      "1085 ZNF106\n",
+      "1086 LCMT2\n",
+      "1087 BCL2L10\n",
+      "1088 MINDY2\n",
+      "1089 FBXO22\n",
+      "1090 ENSG00000271725\n",
+      "1091 WDR73\n",
+      "1092 SEC11A\n",
+      "1093 ZNF710-AS1\n",
+      "1094 ENSG00000283597\n",
+      "1095 LUC7L\n",
+      "1096 ENSG00000276984\n",
+      "1097 LINC00235\n",
+      "1098 MCRIP2\n",
+      "1099 CIAO3\n",
+      "1100 TEDC2\n",
+      "1101 MMP25\n",
+      "1102 ENSG00000263011\n",
+      "1103 ABCC6\n",
+      "1104 ENSG00000285043\n",
+      "1105 FBXL19\n",
+      "1106 ENSG00000260911\n",
+      "1107 ITGAX\n",
+      "1108 CHD9\n",
+      "1109 GNAO1-DT\n",
+      "1110 MT1A\n",
+      "1111 ENSG00000187185\n",
+      "1112 USB1\n",
+      "1113 TRADD\n",
+      "1114 LRRC36\n",
+      "1115 CIBAR2\n",
+      "1116 SNAI3-AS1\n",
+      "1117 LINC02166\n",
+      "1118 ZNF276\n",
+      "1119 MC1R\n",
+      "1120 SCARF1\n",
+      "1121 ENSG00000262227\n",
+      "1122 NAA38\n",
+      "1123 KCNAB3\n",
+      "1124 VAMP2\n",
+      "1125 TNFRSF13B\n",
+      "1126 DRC3\n",
+      "1127 ENSG00000266527\n",
+      "1128 ENSG00000266718\n",
+      "1129 SLFN12L\n",
+      "1130 ENSG00000270240\n",
+      "1131 CCL16\n",
+      "1132 ENSG00000276085\n",
+      "1133 ENSG00000266469\n",
+      "1134 DHX58\n",
+      "1135 ENSG00000287710\n",
+      "1136 DUSP3\n",
+      "1137 CDK5RAP3\n",
+      "1138 SKAP1\n",
+      "1139 CA10\n",
+      "1140 CD300LB\n",
+      "1141 TMEM104\n",
+      "1142 ATP5PD\n",
+      "1143 SUMO2\n",
+      "1144 TRIM65\n",
+      "1145 SGSH\n",
+      "1146 ENSG00000262873\n",
+      "1147 CCDC137\n",
+      "1148 CYBC1\n",
+      "1149 ENOSF1\n",
+      "1150 ATP5F1A\n",
+      "1151 ENSG00000183791\n",
+      "1152 MEX3C\n",
+      "1153 TLE2\n",
+      "1154 NFIC\n",
+      "1155 VMAC\n",
+      "1156 NDUFA7\n",
+      "1157 STX10\n",
+      "1158 RFX1\n",
+      "1159 ENSG00000267904\n",
+      "1160 TMEM161A\n",
+      "1161 CEBPA-DT\n",
+      "1162 ZNF570\n",
+      "1163 ZNF571\n",
+      "1164 PAK4\n",
+      "1165 TMEM145\n",
+      "1166 CARD8\n",
+      "1167 CARD8-AS1\n",
+      "1168 ALDH16A1\n",
+      "1169 RCN3\n",
+      "1170 SCAF1\n",
+      "1171 ZNF587B\n",
+      "1172 ISM1\n",
+      "1173 ENSG00000278276\n",
+      "1174 HCK\n",
+      "1175 ARFGEF2\n",
+      "1176 MOCS3\n",
+      "1177 ENSG00000273254\n",
+      "1178 BACH1-AS1\n",
+      "1179 OLIG2\n",
+      "1180 BRWD1\n",
+      "1181 PDE9A\n",
+      "1182 VPREB1\n",
+      "1183 IGLV3-9\n",
+      "1184 C22orf15\n",
+      "1185 TST\n",
+      "1186 NOL12\n",
+      "1187 TOMM22\n",
+      "1188 JOSD1\n",
+      "1189 NDUFA6\n",
+      "1190 ENSG00000280383\n",
+      "1191 ASMT\n",
+      "1192 TLR7\n",
+      "1193 EIF1AX\n",
+      "1194 DMD\n",
+      "1195 MED14\n",
+      "1196 ZNF711\n",
+      "1197 FMR1\n",
+      "1198 VMA21\n",
+      "1199 ZNF185\n",
+      "1200 FUNDC2\n",
+      "1201 TAS1R3\n",
+      "1202 ATAD3A\n",
+      "1203 CFAP74\n",
+      "1204 KCNAB2\n",
+      "1205 RERE\n",
+      "1206 AKR7A2\n",
+      "1207 GALE\n",
+      "1208 NIPAL3\n",
+      "1209 EIF3I\n",
+      "1210 C1orf216\n",
+      "1211 BMP8A\n",
+      "1212 CCDC163\n",
+      "1213 EFCAB14\n",
+      "1214 EPS15-AS1\n",
+      "1215 SCP2\n",
+      "1216 YIPF1\n",
+      "1217 DHCR24\n",
+      "1218 JUN\n",
+      "1219 ROR1-AS1\n",
+      "1220 LRRIQ3\n",
+      "1221 ENSG00000273338\n",
+      "1222 BCAR3\n",
+      "1223 CSF1\n",
+      "1224 RBM15\n",
+      "1225 VPS45\n",
+      "1226 ZNF687-AS1\n",
+      "1227 EFNA4\n",
+      "1228 LMNA\n",
+      "1229 ARHGEF11\n",
+      "1230 ENSG00000272668\n",
+      "1231 SLAMF7\n",
+      "1232 UAP1\n",
+      "1233 SELL\n",
+      "1234 VAMP4\n",
+      "1235 DHX9\n",
+      "1236 RGS2\n",
+      "1237 ENSG00000286285\n",
+      "1238 NEK7\n",
+      "1239 RAB4A\n",
+      "1240 ABCB10\n",
+      "1241 HPCAL1\n",
+      "1242 ATP6V1C2\n",
+      "1243 PDIA6\n",
+      "1244 ENSG00000233862\n",
+      "1245 STRN\n",
+      "1246 SRSF7\n",
+      "1247 THUMPD2\n",
+      "1248 BCL11A\n",
+      "1249 TIA1\n",
+      "1250 IGKV1-16\n",
+      "1251 ENSG00000234389\n",
+      "1252 BUB1\n",
+      "1253 ENSG00000235066\n",
+      "1254 GPR155-DT\n",
+      "1255 MTX2\n",
+      "1256 LINC01473\n",
+      "1257 ENSG00000273240\n",
+      "1258 NAB1\n",
+      "1259 ENSG00000288064\n",
+      "1260 ENSG00000224099\n",
+      "1261 DNAH7\n",
+      "1262 FTCDNL1\n",
+      "1263 ENSG00000279317\n",
+      "1264 ENSG00000272644\n",
+      "1265 TRAF3IP1\n",
+      "1266 ING5\n",
+      "1267 SGO1-AS1\n",
+      "1268 ENSG00000223343\n",
+      "1269 UBA7\n",
+      "1270 RYBP\n",
+      "1271 GTPBP8\n",
+      "1272 DTX3L\n",
+      "1273 EFCAB12\n",
+      "1274 ENSG00000261167\n",
+      "1275 PXYLP1\n",
+      "1276 DIPK2A\n",
+      "1277 TM4SF1\n",
+      "1278 MME\n",
+      "1279 LXN\n",
+      "1280 MAP6D1\n",
+      "1281 S100P\n",
+      "1282 RFC1\n",
+      "1283 TMEM33\n",
+      "1284 ADGRL3\n",
+      "1285 ABRAXAS1\n",
+      "1286 ENSG00000284968\n",
+      "1287 SPP1\n",
+      "1288 FAM13A-AS1\n",
+      "1289 UNC5C\n",
+      "1290 SYNPO2\n",
+      "1291 SPATA4\n",
+      "1292 TLR3\n",
+      "1293 CPLANE1-AS1\n",
+      "1294 ZNF131\n",
+      "1295 SETD9\n",
+      "1296 POLK\n",
+      "1297 MAN2A1\n",
+      "1298 EPB41L4A-AS1\n",
+      "1299 CDKL3\n",
+      "1300 ENSG00000272070\n",
+      "1301 SAP30L\n",
+      "1302 RNF145\n",
+      "1303 LCP2\n",
+      "1304 ENSG00000215022\n",
+      "1305 H3C4\n",
+      "1306 POU5F1\n",
+      "1307 RXRB\n",
+      "1308 MRPS10\n",
+      "1309 MCM3\n",
+      "1310 POU3F2\n",
+      "1311 ENSG00000271789\n",
+      "1312 REV3L\n",
+      "1313 ENSG00000231760\n",
+      "1314 ZFAND2A-DT\n",
+      "1315 SP4\n",
+      "1316 ENSG00000272843\n",
+      "1317 SSC4D\n",
+      "1318 GSAP\n",
+      "1319 FBXO24\n",
+      "1320 TRIP6\n",
+      "1321 MUC12-AS1\n",
+      "1322 ZC3HC1\n",
+      "1323 TMEM209\n",
+      "1324 TAS2R4\n",
+      "1325 TCAF1\n",
+      "1326 ENSG00000244693\n",
+      "1327 CHPF2\n",
+      "1328 RBM33\n",
+      "1329 CLN8\n",
+      "1330 MICU3\n",
+      "1331 NPM2\n",
+      "1332 STC1\n",
+      "1333 EPHX2\n",
+      "1334 HMBOX1\n",
+      "1335 LINC02099\n",
+      "1336 LETM2\n",
+      "1337 PCMTD1-DT\n",
+      "1338 CA8\n",
+      "1339 SGK3\n",
+      "1340 ZFHX4\n",
+      "1341 ZNF704\n",
+      "1342 NBN\n",
+      "1343 ENSG00000254615\n",
+      "1344 KHDRBS3\n",
+      "1345 ENSG00000288096\n",
+      "1346 IFT74\n",
+      "1347 DNAJB5\n",
+      "1348 UNC13B\n",
+      "1349 HINT2\n",
+      "1350 ENSG00000283886\n",
+      "1351 CARNMT1-AS1\n",
+      "1352 CEP78\n",
+      "1353 LNCARSR\n",
+      "1354 ENSG00000196312\n",
+      "1355 FRRS1L\n",
+      "1356 CRB2\n",
+      "1357 ODF2\n",
+      "1358 GTF3C5\n",
+      "1359 BRD3\n",
+      "1360 PRKCQ\n",
+      "1361 RPP38-DT\n",
+      "1362 TMEM236\n",
+      "1363 ACBD5\n",
+      "1364 AGAP6\n",
+      "1365 HERC4\n",
+      "1366 SFTPA1\n",
+      "1367 TSPAN14\n",
+      "1368 ENSG00000286116\n",
+      "1369 TRIM8\n",
+      "1370 ENSG00000282772\n",
+      "1371 EDRF1-DT\n",
+      "1372 MUC6\n",
+      "1373 DUSP8\n",
+      "1374 PDE3B\n",
+      "1375 CALCA\n",
+      "1376 SERGEF\n",
+      "1377 HTATIP2\n",
+      "1378 ENSG00000228061\n",
+      "1379 TRAF6\n",
+      "1380 IFTAP\n",
+      "1381 MADD-AS1\n",
+      "1382 SPI1\n",
+      "1383 ZDHHC5\n",
+      "1384 FAM111B\n",
+      "1385 OR4D9\n",
+      "1386 ROM1\n",
+      "1387 INTS5\n",
+      "1388 SF1-DT\n",
+      "1389 RNF169\n",
+      "1390 ANKRD42\n",
+      "1391 CHORDC1\n",
+      "1392 PHLDB1\n",
+      "1393 HYLS1\n",
+      "1394 AICDA\n",
+      "1395 ART4\n",
+      "1396 ETFBKMT\n",
+      "1397 RHEBL1\n",
+      "1398 TMBIM6\n",
+      "1399 SRGAP1\n",
+      "1400 CPSF6\n",
+      "1401 GLIPR1-AS1\n",
+      "1402 METAP2\n",
+      "1403 SH2B3\n",
+      "1404 RAB35\n",
+      "1405 OASL\n",
+      "1406 ZCCHC8\n",
+      "1407 DENR\n",
+      "1408 CCDC92\n",
+      "1409 ZNF140\n",
+      "1410 SHISA2\n",
+      "1411 ENSG00000277151\n",
+      "1412 GTF2F2\n",
+      "1413 DIAPH3\n",
+      "1414 ENSG00000286820\n",
+      "1415 CLDN10\n",
+      "1416 TFDP1\n",
+      "1417 TRAV30\n",
+      "1418 SFTA3\n",
+      "1419 SEC23A\n",
+      "1420 FANCM\n",
+      "1421 VCPKMT\n",
+      "1422 SAV1\n",
+      "1423 ENSG00000258843\n",
+      "1424 ENSG00000277763\n",
+      "1425 AP5M1\n",
+      "1426 PRKCH-AS1\n",
+      "1427 WDR89\n",
+      "1428 ENSG00000287643\n",
+      "1429 DCAF4\n",
+      "1430 RBM25\n",
+      "1431 MIDEAS-AS1\n",
+      "1432 NEK9\n",
+      "1433 ANGEL1\n",
+      "1434 EFCAB11\n",
+      "1435 ATG2B\n",
+      "1436 ENSG00000269958\n",
+      "1437 IGHV3-15\n",
+      "1438 IGHV3-48\n",
+      "1439 IGHV3-66\n",
+      "1440 MKRN3\n",
+      "1441 GOLGA8B\n",
+      "1442 EXD1\n",
+      "1443 MAP1A\n",
+      "1444 FRMD5\n",
+      "1445 CSNK1G1\n",
+      "1446 PLEKHO2\n",
+      "1447 RPLP1\n",
+      "1448 MYO9A\n",
+      "1449 ADPGK\n",
+      "1450 LINGO1\n",
+      "1451 ACSBG1\n",
+      "1452 CFAP161\n",
+      "1453 GOLGA6L9\n",
+      "1454 ENSG00000259761\n",
+      "1455 PCSK6\n",
+      "1456 POLR3K\n",
+      "1457 CAPN15\n",
+      "1458 LINC00921\n",
+      "1459 SMG1\n",
+      "1460 NFATC2IP-AS1\n",
+      "1461 PYCARD\n",
+      "1462 ENSG00000274031\n",
+      "1463 ENSG00000285979\n",
+      "1464 ENSG00000261519\n",
+      "1465 NFATC3\n",
+      "1466 ADAT1\n",
+      "1467 BANP\n",
+      "1468 RPL13\n",
+      "1469 SPIRE2\n",
+      "1470 ZNF594-DT\n",
+      "1471 ZNF18\n",
+      "1472 ENSG00000262202\n",
+      "1473 DHRS7B\n",
+      "1474 TMEM97\n",
+      "1475 ENSG00000275185\n",
+      "1476 ATP6V0A1\n",
+      "1477 SOST\n",
+      "1478 ATXN7L3\n",
+      "1479 HEXIM2\n",
+      "1480 NME1\n",
+      "1481 ENSG00000287539\n",
+      "1482 PPP1R27\n",
+      "1483 ENSG00000267249\n",
+      "1484 DSC2\n",
+      "1485 NARS1\n",
+      "1486 ENSG00000283125\n",
+      "1487 SERPINB11\n",
+      "1488 CCDC102B\n",
+      "1489 ENSG00000276934\n",
+      "1490 LINC01879\n",
+      "1491 MED16\n",
+      "1492 WDR18\n",
+      "1493 TLE6\n",
+      "1494 EBI3\n",
+      "1495 PLIN5\n",
+      "1496 ACER1\n",
+      "1497 FCER2\n",
+      "1498 CLEC4G\n",
+      "1499 OLFM2\n",
+      "1500 COL5A3\n",
+      "1501 WDR83\n",
+      "1502 PKN1\n",
+      "1503 CYP4F22\n",
+      "1504 LINC02965\n",
+      "1505 ZNF382\n",
+      "1506 LGALS4\n",
+      "1507 ACTMAP\n",
+      "1508 RABAC1\n",
+      "1509 ERF\n",
+      "1510 GEMIN7-AS1\n",
+      "1511 CCDC61\n",
+      "1512 TMEM160\n",
+      "1513 ZC3H4\n",
+      "1514 PLEKHA4\n",
+      "1515 BCL2L12\n",
+      "1516 ENSG00000275055\n",
+      "1517 ZNF528\n",
+      "1518 ZNF628\n",
+      "1519 ENSG00000269600\n",
+      "1520 NOP56\n",
+      "1521 PTPRA\n",
+      "1522 ENSG00000275457\n",
+      "1523 GINS1\n",
+      "1524 HM13\n",
+      "1525 NFS1\n",
+      "1526 SAMHD1\n",
+      "1527 NNAT\n",
+      "1528 OSER1-DT\n",
+      "1529 EPPIN\n",
+      "1530 NEURL2\n",
+      "1531 ZFAS1\n",
+      "1532 ARFGAP1\n",
+      "1533 RGS19\n",
+      "1534 ENSG00000159128\n",
+      "1535 DYRK1A\n",
+      "1536 UMODL1\n",
+      "1537 DGCR6\n",
+      "1538 UFD1\n",
+      "1539 IGLV1-40\n",
+      "1540 EMID1\n",
+      "1541 CCDC157\n",
+      "1542 TIMP3\n",
+      "1543 CDC42EP1\n",
+      "1544 BAIAP2L2\n",
+      "1545 MGAT3\n",
+      "1546 MRTFA-AS1\n",
+      "1547 XRCC6\n",
+      "1548 ENSG00000226310\n",
+      "1549 NHSL2\n",
+      "1550 PIN4\n",
+      "1551 RPA4\n",
+      "1552 MORF4L2-AS1\n",
+      "1553 RADX\n",
+      "1554 ACSL4\n",
+      "1555 NKRF\n",
+      "1556 RHOXF1-AS1\n",
+      "1557 ZDHHC9\n",
+      "1558 EOLA2-DT\n",
+      "1559 SSR4\n",
+      "1560 PDZD4\n",
+      "1561 NAA10\n",
+      "1562 GAB3\n",
+      "1563 CLIC2\n",
+      "1564 VAMP7\n",
+      "1565 KDM5D\n",
+      "1566 MT-ND4\n",
+      "1567 ENSG00000273496\n",
+      "1568 CCNL2\n",
+      "1569 CAMTA1-DT\n",
+      "1570 ATP13A2\n",
+      "1571 FUCA1\n",
+      "1572 ENSG00000270040\n",
+      "1573 SVBP\n",
+      "1574 CDC20\n",
+      "1575 GPBP1L1\n",
+      "1576 POMGNT1\n",
+      "1577 NSUN4\n",
+      "1578 ENSG00000266993\n",
+      "1579 MAGOH\n",
+      "1580 TTC4\n",
+      "1581 PRKAA2\n",
+      "1582 MYSM1\n",
+      "1583 GADD45A\n",
+      "1584 LINC01781\n",
+      "1585 PRKACB-DT\n",
+      "1586 CFAP276\n",
+      "1587 SORT1\n",
+      "1588 SLC16A1\n",
+      "1589 MAGI3\n",
+      "1590 PRKAB2\n",
+      "1591 FLG2\n",
+      "1592 THBS3\n",
+      "1593 IFI16\n",
+      "1594 KIAA1614\n",
+      "1595 PPP1R12B\n",
+      "1596 RBBP5\n",
+      "1597 SRGAP2\n",
+      "1598 PFKFB2\n",
+      "1599 IRF6\n",
+      "1600 NUP133\n",
+      "1601 HEATR1\n",
+      "1602 AKT3\n",
+      "1603 TTC27\n",
+      "1604 ABCG8\n",
+      "1605 LINC01873\n",
+      "1606 IGKV2-30\n",
+      "1607 IGKV1D-33\n",
+      "1608 SEMA4C\n",
+      "1609 LYG2\n",
+      "1610 GCC2\n",
+      "1611 PSD4\n",
+      "1612 PAX8\n",
+      "1613 ENSG00000236255\n",
+      "1614 PROC\n",
+      "1615 UBXN4\n",
+      "1616 CXCR4\n",
+      "1617 ZEB2\n",
+      "1618 ENSG00000236283\n",
+      "1619 LINC01934\n",
+      "1620 KIAA2012-AS1\n",
+      "1621 MDH1B\n",
+      "1622 CNOT9\n",
+      "1623 TUBA4A\n",
+      "1624 SLC19A3\n",
+      "1625 INPP5D\n",
+      "1626 PDCD1\n",
+      "1627 ARPC4\n",
+      "1628 PLCL2\n",
+      "1629 ZCWPW2\n",
+      "1630 DALRD3\n",
+      "1631 TUSC2\n",
+      "1632 WDR82\n",
+      "1633 ARF4-AS1\n",
+      "1634 FLNB\n",
+      "1635 CADM2\n",
+      "1636 CHMP2B\n",
+      "1637 C3orf38\n",
+      "1638 CD200R1\n",
+      "1639 TIGIT\n",
+      "1640 H1-10\n",
+      "1641 DNAJC13\n",
+      "1642 MSL2\n",
+      "1643 CLDN18\n",
+      "1644 PIK3CB\n",
+      "1645 LAMP3\n",
+      "1646 PIGZ\n",
+      "1647 ENSG00000272588\n",
+      "1648 NSD2\n",
+      "1649 ENSG00000251438\n",
+      "1650 GUF1\n",
+      "1651 ENSG00000269949\n",
+      "1652 HSD17B13\n",
+      "1653 NUDT9\n",
+      "1654 ENSG00000138641\n",
+      "1655 PAPSS1\n",
+      "1656 SH3D19\n",
+      "1657 ENSG00000286720\n",
+      "1658 TMEM131L\n",
+      "1659 AGA-DT\n",
+      "1660 ICE1\n",
+      "1661 ENSG00000248223\n",
+      "1662 LINC02223\n",
+      "1663 RAD1\n",
+      "1664 ENSG00000287263\n",
+      "1665 ITGA1\n",
+      "1666 CWC27\n",
+      "1667 CENPK\n",
+      "1668 POC5\n",
+      "1669 PAM\n",
+      "1670 NREP\n",
+      "1671 CDC25C\n",
+      "1672 MALINC1\n",
+      "1673 GPX3\n",
+      "1674 CLINT1\n",
+      "1675 TENM2\n",
+      "1676 WWC1\n",
+      "1677 ZNF354C\n",
+      "1678 ENSG00000272277\n",
+      "1679 RPP40\n",
+      "1680 MAK\n",
+      "1681 ENSG00000261071\n",
+      "1682 ABT1\n",
+      "1683 ZSCAN9\n",
+      "1684 NELFE\n",
+      "1685 LINC00336\n",
+      "1686 MAPK13\n",
+      "1687 VEGFA\n",
+      "1688 SUPT3H\n",
+      "1689 CGA\n",
+      "1690 ENSG00000260271\n",
+      "1691 FOXO3\n",
+      "1692 ENSG00000255389\n",
+      "1693 RWDD1\n",
+      "1694 ENSG00000286313\n",
+      "1695 HIVEP2-DT\n",
+      "1696 AIG1\n",
+      "1697 PPP1R14C\n",
+      "1698 RPS6KA2\n",
+      "1699 RNF216\n",
+      "1700 SNX13\n",
+      "1701 AVL9\n",
+      "1702 TRGV3\n",
+      "1703 VPS41\n",
+      "1704 YAE1\n",
+      "1705 VSTM2A\n",
+      "1706 ZNF138\n",
+      "1707 ABCB4\n",
+      "1708 SDHAF3\n",
+      "1709 ZNF789\n",
+      "1710 POP7\n",
+      "1711 EPHB4\n",
+      "1712 POT1-AS1\n",
+      "1713 GARIN1A\n",
+      "1714 KCNH2\n",
+      "1715 WDR86-AS1\n",
+      "1716 ENSG00000273183\n",
+      "1717 FDFT1\n",
+      "1718 ENSG00000253642\n",
+      "1719 LSM1\n",
+      "1720 YTHDF3\n",
+      "1721 ELOC\n",
+      "1722 RMDN1\n",
+      "1723 ENSG00000287095\n",
+      "1724 MINCR\n",
+      "1725 ENSG00000287576\n",
+      "1726 PLAA\n",
+      "1727 LINC02977\n",
+      "1728 FKBP15\n",
+      "1729 PAPPA\n",
+      "1730 B3GALT9\n",
+      "1731 MIR181A2HG\n",
+      "1732 GARNL3\n",
+      "1733 ASB6\n",
+      "1734 NSMF\n",
+      "1735 GATA3-AS1\n",
+      "1736 FRMD4A\n",
+      "1737 ENSG00000233968\n",
+      "1738 PLXDC2\n",
+      "1739 MPP7-DT\n",
+      "1740 AGAP4\n",
+      "1741 ANK3-DT\n",
+      "1742 ENSG00000224745\n",
+      "1743 BTAF1\n",
+      "1744 GOT1\n",
+      "1745 PAX2\n",
+      "1746 SMNDC1\n",
+      "1747 FHIP2A\n",
+      "1748 CASC2\n",
+      "1749 TALDO1\n",
+      "1750 ENSG00000215182\n",
+      "1751 TRIM68\n",
+      "1752 HBG1\n",
+      "1753 CAVIN3\n",
+      "1754 HPX\n",
+      "1755 BBOX1-AS1\n",
+      "1756 TMEM258\n",
+      "1757 SCGB1A1\n",
+      "1758 PLAAT2\n",
+      "1759 MALAT1\n",
+      "1760 GSTP1\n",
+      "1761 NUDT8\n",
+      "1762 ANAPC15\n",
+      "1763 PDE2A\n",
+      "1764 OR2AT4\n",
+      "1765 EMSY\n",
+      "1766 MMP10\n",
+      "1767 KBTBD3\n",
+      "1768 THY1\n",
+      "1769 CLMP\n",
+      "1770 NFRKB\n",
+      "1771 GLB1L2\n",
+      "1772 DYRK4\n",
+      "1773 TAPBPL\n",
+      "1774 ENSG00000269968\n",
+      "1775 GOLT1B\n",
+      "1776 RACGAP1\n",
+      "1777 CSRNP2\n",
+      "1778 TESPA1\n",
+      "1779 HSD17B6\n",
+      "1780 LRP1\n",
+      "1781 ENSG00000257410\n",
+      "1782 ENSG00000287454\n",
+      "1783 DEPDC4\n",
+      "1784 C12orf75\n",
+      "1785 HECTD4\n",
+      "1786 RASAL1\n",
+      "1787 ENSG00000275409\n",
+      "1788 POP5\n",
+      "1789 ORAI1\n",
+      "1790 STX2\n",
+      "1791 ZMYM5\n",
+      "1792 LINC00540\n",
+      "1793 B3GLCT\n",
+      "1794 ALG5\n",
+      "1795 SLC25A15\n",
+      "1796 IPO5\n",
+      "1797 POGLUT2\n",
+      "1798 SLC22A17\n",
+      "1799 STRN3\n",
+      "1800 PTGER2\n",
+      "1801 LGALS3\n",
+      "1802 ARG2\n",
+      "1803 TSHR\n",
+      "1804 ENSG00000284959\n",
+      "1805 FOXN3-AS1\n",
+      "1806 TRIP11\n",
+      "1807 AK7\n",
+      "1808 ENSG00000225163\n",
+      "1809 CKB\n",
+      "1810 BAG5\n",
+      "1811 BRF1\n",
+      "1812 GCHFR\n",
+      "1813 ENSG00000275672\n",
+      "1814 MYO5A\n",
+      "1815 POLR2M\n",
+      "1816 VPS13C-DT\n",
+      "1817 NR2E3\n",
+      "1818 TMEM202-AS1\n",
+      "1819 ETFA\n",
+      "1820 ANTKMT\n",
+      "1821 UNKL\n",
+      "1822 MARF1\n",
+      "1823 ANKS4B\n",
+      "1824 GTF3C1\n",
+      "1825 MYL11\n",
+      "1826 HSD3B7\n",
+      "1827 AHSP\n",
+      "1828 SNX20\n",
+      "1829 LPCAT2\n",
+      "1830 SNTB2\n",
+      "1831 VAC14\n",
+      "1832 WDR59\n",
+      "1833 C16orf46\n",
+      "1834 OR3A2\n",
+      "1835 ZFP3\n",
+      "1836 DNAH2\n",
+      "1837 ENSG00000214999\n",
+      "1838 COX10\n",
+      "1839 TVP23C\n",
+      "1840 SMCR8\n",
+      "1841 LGALS9B\n",
+      "1842 SLC46A1\n",
+      "1843 GOSR1\n",
+      "1844 LRRC37B\n",
+      "1845 CCL4\n",
+      "1846 SOCS7\n",
+      "1847 STARD3\n",
+      "1848 CASC3\n",
+      "1849 DNAJC7\n",
+      "1850 HOXB6\n",
+      "1851 HOXB7\n",
+      "1852 SNHG25\n",
+      "1853 ENSG00000266598\n",
+      "1854 ENSG00000267731\n",
+      "1855 FAM104A\n",
+      "1856 MRPS7\n",
+      "1857 LLGL2\n",
+      "1858 ENSG00000261335\n",
+      "1859 RNF213-AS1\n",
+      "1860 ARL16\n",
+      "1861 WDR45B\n",
+      "1862 ANKRD62\n",
+      "1863 ENSG00000267686\n",
+      "1864 ENSG00000278017\n",
+      "1865 KDSR-DT\n",
+      "1866 TSHZ1\n",
+      "1867 MLLT1\n",
+      "1868 TNFSF14\n",
+      "1869 MCEMP1\n",
+      "1870 JUNB\n",
+      "1871 C19orf53\n",
+      "1872 MISP3\n",
+      "1873 ENSG00000268650\n",
+      "1874 LRRC25\n",
+      "1875 MEF2B\n",
+      "1876 ZNF675\n",
+      "1877 DMKN\n",
+      "1878 CATSPERG\n",
+      "1879 PLEKHG2\n",
+      "1880 CLC\n",
+      "1881 RAB4B\n",
+      "1882 CCDC97\n",
+      "1883 ENSG00000268355\n",
+      "1884 ZNF229\n",
+      "1885 CALM3\n",
+      "1886 LMTK3\n",
+      "1887 NUP62\n",
+      "1888 CLDND2\n",
+      "1889 ZNF701\n",
+      "1890 MYADM-AS2\n",
+      "1891 SYT5\n",
+      "1892 UBE2S\n",
+      "1893 ZNF416\n",
+      "1894 UBOX5-AS1\n",
+      "1895 KAT14\n",
+      "1896 SMIM26\n",
+      "1897 SLC24A3\n",
+      "1898 DSN1\n",
+      "1899 RTF2\n",
+      "1900 MHENCR\n",
+      "1901 OPRL1\n",
+      "1902 ENSG00000229425\n",
+      "1903 ENSG00000260583\n",
+      "1904 OLIG1\n",
+      "1905 SIM2\n",
+      "1906 BRWD1-AS2\n",
+      "1907 ZNF295-AS1\n",
+      "1908 TFF2\n",
+      "1909 UBASH3A\n",
+      "1910 TANGO2\n",
+      "1911 ZDHHC8\n",
+      "1912 UBE2L3\n",
+      "1913 TOP3B\n",
+      "1914 RSPH14\n",
+      "1915 ENSG00000099974\n",
+      "1916 MICALL1\n",
+      "1917 PIM3\n",
+      "1918 MAPK12\n",
+      "1919 PRKX\n",
+      "1920 CA5B\n",
+      "1921 SYAP1\n",
+      "1922 SH3KBP1\n",
+      "1923 JADE3\n",
+      "1924 MAGED1\n",
+      "1925 HSD17B10\n",
+      "1926 SPIN2B\n",
+      "1927 LINC01278\n",
+      "1928 ATRX\n",
+      "1929 ARMCX2\n",
+      "1930 WDR44\n",
+      "1931 AIFM1\n",
+      "1932 MIR503HG\n",
+      "1933 ZNF275\n",
+      "1934 FAM3A\n",
+      "1935 ENSG00000277203\n",
+      "1936 EFHD2\n",
+      "1937 CDA\n",
+      "1938 E2F2\n",
+      "1939 ENSG00000272432\n",
+      "1940 CD52\n",
+      "1941 SRSF4\n",
+      "1942 IQCC\n",
+      "1943 HIVEP3\n",
+      "1944 ENSG00000230615\n",
+      "1945 SPATA6\n",
+      "1946 EPS15\n",
+      "1947 ACOT11\n",
+      "1948 LINC01358\n",
+      "1949 USP1\n",
+      "1950 ST6GALNAC3\n",
+      "1951 MCOLN3\n",
+      "1952 DNTTIP2\n",
+      "1953 RTCA\n",
+      "1954 ENSG00000284830\n",
+      "1955 FCGR1BP\n",
+      "1956 ENSG00000272583\n",
+      "1957 ENSG00000274265\n",
+      "1958 USP21\n",
+      "1959 FCGR2A\n",
+      "1960 PRRC2C\n",
+      "1961 ENSG00000272880\n",
+      "1962 NPL\n",
+      "1963 INAVA\n",
+      "1964 CD46\n",
+      "1965 PGBD5\n",
+      "1966 ENSG00000244137\n",
+      "1967 CAPN9\n",
+      "1968 PCNX2\n",
+      "1969 TPO\n",
+      "1970 SELENOI\n",
+      "1971 SLC5A6\n",
+      "1972 ZNF513\n",
+      "1973 MRPL33\n",
+      "1974 MAP4K3\n",
+      "1975 PREPL\n",
+      "1976 CFAP36\n",
+      "1977 LINC00309\n",
+      "1978 TET3\n",
+      "1979 RETSAT\n",
+      "1980 RGPD1\n",
+      "1981 IGKV3D-7\n",
+      "1982 ADRA2B\n",
+      "1983 COX5B\n",
+      "1984 ENSG00000272563\n",
+      "1985 RALB\n",
+      "1986 LCT-AS1\n",
+      "1987 LINC01876\n",
+      "1988 HAT1\n",
+      "1989 PGAP1\n",
+      "1990 MPP4\n",
+      "1991 LINC01857\n",
+      "1992 MRPL44\n",
+      "1993 ENSG00000224376\n",
+      "1994 CNOT10\n",
+      "1995 PLCD1\n",
+      "1996 ENSG00000286781\n",
+      "1997 RPL14\n",
+      "1998 SMARCC1\n",
+      "1999 MAP4\n",
+      "2000 C3orf62\n",
+      "2001 RASSF1-AS1\n",
+      "2002 FHIT\n",
+      "2003 SUCLG2\n",
+      "2004 HTR1F\n",
+      "2005 COL8A1\n",
+      "2006 TIMMDC1\n",
+      "2007 ILDR1\n",
+      "2008 HSPBAP1\n",
+      "2009 RUVBL1\n",
+      "2010 COPG1\n",
+      "2011 IFT122\n",
+      "2012 ENSG00000272247\n",
+      "2013 PEX5L\n",
+      "2014 EIF4G1\n",
+      "2015 ENSG00000223711\n",
+      "2016 TAPT1-AS1\n",
+      "2017 ENSG00000247193\n",
+      "2018 ENSG00000272969\n",
+      "2019 EPHA5\n",
+      "2020 CXCL10\n",
+      "2021 SEC24B-AS1\n",
+      "2022 CFI\n",
+      "2023 LARP7\n",
+      "2024 PRSS12\n",
+      "2025 BLTP1\n",
+      "2026 ZNF330\n",
+      "2027 LINC02432\n",
+      "2028 SLC10A7\n",
+      "2029 LINC02273\n",
+      "2030 CLPTM1L\n",
+      "2031 OTULINL\n",
+      "2032 HMGCS1\n",
+      "2033 ENSG00000248240\n",
+      "2034 AK6\n",
+      "2035 FOXD1\n",
+      "2036 HEXB\n",
+      "2037 ENSG00000248734\n",
+      "2038 SEC24A\n",
+      "2039 FAM13B\n",
+      "2040 PPP2R2B\n",
+      "2041 BOD1\n",
+      "2042 HK3\n",
+      "2043 ENSG00000287593\n",
+      "2044 ENSG00000272379\n",
+      "2045 H2BC5\n",
+      "2046 DDR1\n",
+      "2047 ENSG00000272501\n",
+      "2048 SNHG32\n",
+      "2049 MRPS18A\n",
+      "2050 IL17F\n",
+      "2051 BEND6\n",
+      "2052 COX7A2\n",
+      "2053 CDC40\n",
+      "2054 ADGB\n",
+      "2055 STXBP5-AS1\n",
+      "2056 MTRF1L\n",
+      "2057 DNAAF5\n",
+      "2058 USP42\n",
+      "2059 SCIN\n",
+      "2060 HOTAIRM1\n",
+      "2061 HOXA7\n",
+      "2062 ENSG00000244055\n",
+      "2063 TECPR1\n",
+      "2064 SH2B2\n",
+      "2065 IRF5\n",
+      "2066 ENSG00000232053\n",
+      "2067 TRBV3-1\n",
+      "2068 ZNF786\n",
+      "2069 ENSG00000272661\n",
+      "2070 DEFA6\n",
+      "2071 INTS10\n",
+      "2072 ENSG00000183154\n",
+      "2073 STMN2\n",
+      "2074 CASC19\n",
+      "2075 GFUS\n",
+      "2076 MIR1302-9HG\n",
+      "2077 PLGRKT\n",
+      "2078 MIR31HG\n",
+      "2079 CHMP5\n",
+      "2080 ENSG00000233776\n",
+      "2081 TOMM5\n",
+      "2082 FAM27C\n",
+      "2083 FAM88F\n",
+      "2084 ISCA1\n",
+      "2085 CTSL\n",
+      "2086 SYK\n",
+      "2087 PTPDC1\n",
+      "2088 FSD1L\n",
+      "2089 SLC31A2\n",
+      "2090 C9orf43\n",
+      "2091 ENSG00000227355\n",
+      "2092 PDCL\n",
+      "2093 ENSG00000225032\n",
+      "2094 GLE1\n",
+      "2095 GBGT1\n",
+      "2096 SLC2A6\n",
+      "2097 NALT1\n",
+      "2098 FBH1\n",
+      "2099 ATP5F1C\n",
+      "2100 MRC1\n",
+      "2101 DNAJC1\n",
+      "2102 ASAH2\n",
+      "2103 SUPV3L1\n",
+      "2104 ENSG00000230526\n",
+      "2105 ENSG00000268584\n",
+      "2106 SYNPO2L\n",
+      "2107 TMEM254-AS1\n",
+      "2108 SLC16A12\n",
+      "2109 ALDH18A1\n",
+      "2110 PDZD7\n",
+      "2111 GRK5\n",
+      "2112 DPYSL4\n",
+      "2113 PPFIBP2\n",
+      "2114 BAD\n",
+      "2115 FAM89B\n",
+      "2116 MAP3K11\n",
+      "2117 C11orf68\n",
+      "2118 CNIH2\n",
+      "2119 SPTBN2\n",
+      "2120 UNC93B1\n",
+      "2121 ACAT1\n",
+      "2122 RIMKLB\n",
+      "2123 LINC00612\n",
+      "2124 CLECL1P\n",
+      "2125 PRH1\n",
+      "2126 ETV6\n",
+      "2127 ENSG00000257176\n",
+      "2128 KRT73\n",
+      "2129 PMEL\n",
+      "2130 GLS2\n",
+      "2131 DCTN2\n",
+      "2132 MARCHF9\n",
+      "2133 ENSG00000258168\n",
+      "2134 ZDHHC17\n",
+      "2135 IKBIP\n",
+      "2136 MVK\n",
+      "2137 RPL6\n",
+      "2138 DTX1\n",
+      "2139 KDM2B-DT\n",
+      "2140 VPS33A\n",
+      "2141 SBNO1-AS1\n",
+      "2142 DDX55\n",
+      "2143 FBRSL1\n",
+      "2144 CENPJ\n",
+      "2145 RASL11A\n",
+      "2146 NAA16\n",
+      "2147 SLC25A30\n",
+      "2148 ITM2B\n",
+      "2149 UTP14C\n",
+      "2150 ENSG00000277767\n",
+      "2151 PARP2\n",
+      "2152 RNASE4\n",
+      "2153 TRAV12-2\n",
+      "2154 TRAV23DV6\n",
+      "2155 ENSG00000259003\n",
+      "2156 MMP14\n",
+      "2157 RIPK3\n",
+      "2158 ENSG00000274818\n",
+      "2159 ENSG00000287668\n",
+      "2160 CLMN\n",
+      "2161 NDN\n",
+      "2162 SPRED1\n",
+      "2163 ADAM10\n",
+      "2164 GCNT3\n",
+      "2165 MORF4L1\n",
+      "2166 ENSG00000278600\n",
+      "2167 ZNF592\n",
+      "2168 ADAMTS17\n",
+      "2169 MSLN\n",
+      "2170 ENSG00000276791\n",
+      "2171 SMIM22\n",
+      "2172 TVP23A\n",
+      "2173 ENSG00000259929\n",
+      "2174 ARL6IP1\n",
+      "2175 POLR3E\n",
+      "2176 ENSG00000259940\n",
+      "2177 ENSG00000261766\n",
+      "2178 NPIPB11\n",
+      "2179 ZNF768\n",
+      "2180 ZNF747-DT\n",
+      "2181 PRR14\n",
+      "2182 ARMC5\n",
+      "2183 ENSG00000261200\n",
+      "2184 ENSG00000259821\n",
+      "2185 HNRNPA1L3\n",
+      "2186 CDH8\n",
+      "2187 CES2\n",
+      "2188 ELMO3\n",
+      "2189 CTCF\n",
+      "2190 AARS1\n",
+      "2191 ZNF23\n",
+      "2192 LINC01572\n",
+      "2193 C16orf46-DT\n",
+      "2194 DBNDD1\n",
+      "2195 SERPINF2\n",
+      "2196 RNASEK\n",
+      "2197 PLD6\n",
+      "2198 NUFIP2\n",
+      "2199 ATAD5\n",
+      "2200 ZNHIT3\n",
+      "2201 PIP4K2B\n",
+      "2202 JUP\n",
+      "2203 ENSG00000267002\n",
+      "2204 TMEM101\n",
+      "2205 LRRC37A\n",
+      "2206 GNGT2\n",
+      "2207 PPP1R9B\n",
+      "2208 MYCBPAP\n",
+      "2209 MRC2\n",
+      "2210 PSMC5\n",
+      "2211 SLC39A11\n",
+      "2212 MRPL58\n",
+      "2213 TMC6\n",
+      "2214 SOCS3-DT\n",
+      "2215 PDE6G\n",
+      "2216 CHMP1B\n",
+      "2217 TMEM241\n",
+      "2218 TAF4B\n",
+      "2219 DTNA\n",
+      "2220 ENSG00000274400\n",
+      "2221 ENSG00000274184\n",
+      "2222 VPS4B\n",
+      "2223 KISS1R\n",
+      "2224 ADAT3\n",
+      "2225 ENSG00000267980\n",
+      "2226 EVI5L\n",
+      "2227 TIMM44\n",
+      "2228 TYK2\n",
+      "2229 TMEM205\n",
+      "2230 PLPPR2\n",
+      "2231 ZNF823\n",
+      "2232 LYL1\n",
+      "2233 OR10H5\n",
+      "2234 USHBP1\n",
+      "2235 HOMER3\n",
+      "2236 ZNF429\n",
+      "2237 ZNF566-AS1\n",
+      "2238 ZNF829\n",
+      "2239 ZNF571-AS1\n",
+      "2240 SPTBN4\n",
+      "2241 ENSG00000267058\n",
+      "2242 ZNF230-DT\n",
+      "2243 ZNF227\n",
+      "2244 ZNF180\n",
+      "2245 QPCTL\n",
+      "2246 ZNF114\n",
+      "2247 GYS1\n",
+      "2248 RPS11\n",
+      "2249 ZNF577\n",
+      "2250 ZNF610\n",
+      "2251 ENSG00000276831\n",
+      "2252 ENSG00000268678\n",
+      "2253 ZNF8-DT\n",
+      "2254 ZSCAN22\n",
+      "2255 LINC03086\n",
+      "2256 SYNDIG1\n",
+      "2257 MIR663AHG\n",
+      "2258 ENSG00000269846\n",
+      "2259 ENSG00000282876\n",
+      "2260 FITM2\n",
+      "2261 SALL4\n",
+      "2262 ZBP1\n",
+      "2263 DIDO1\n",
+      "2264 ENSG00000280614\n",
+      "2265 ENSG00000205929\n",
+      "2266 CRYZL1\n",
+      "2267 PIGP\n",
+      "2268 TRAPPC10\n",
+      "2269 DGCR2\n",
+      "2270 TXNRD2\n",
+      "2271 LINC00896\n",
+      "2272 THAP7-AS1\n",
+      "2273 BCR\n",
+      "2274 MTFP1\n",
+      "2275 TCN2\n",
+      "2276 EIF4ENIF1\n",
+      "2277 ZC3H7B\n",
+      "2278 LINC00899\n",
+      "2279 PNPLA4\n",
+      "2280 TCEANC\n",
+      "2281 CASK\n",
+      "2282 MAGEH1\n",
+      "2283 EDA2R\n",
+      "2284 OGT\n",
+      "2285 HDX\n",
+      "2286 IL13RA1\n",
+      "2287 MT-ND3\n",
+      "2288 TTLL10\n",
+      "2289 SSU72\n",
+      "2290 FNDC10\n",
+      "2291 PIK3CD-AS2\n",
+      "2292 MIIP\n",
+      "2293 NCMAP-DT\n",
+      "2294 KDF1\n",
+      "2295 PTAFR\n",
+      "2296 ENSG00000271398\n",
+      "2297 TINAGL1\n",
+      "2298 ENSG00000203325\n",
+      "2299 BSDC1\n",
+      "2300 MAP7D1\n",
+      "2301 STK40\n",
+      "2302 INPP5B-AS1\n",
+      "2303 ZNF691\n",
+      "2304 DPH2\n",
+      "2305 ERI3\n",
+      "2306 PLK3\n",
+      "2307 MAST2\n",
+      "2308 DAB1\n",
+      "2309 ENSG00000228084\n",
+      "2310 LIX1L-AS1\n",
+      "2311 H4C14\n",
+      "2312 ANP32E\n",
+      "2313 TARS2\n",
+      "2314 SPRR3\n",
+      "2315 CD1C\n",
+      "2316 UAP1-DT\n",
+      "2317 GPA33\n",
+      "2318 ENSG00000241666\n",
+      "2319 ENSG00000287697\n",
+      "2320 AXDND1\n",
+      "2321 RNASEL\n",
+      "2322 PRG4\n",
+      "2323 LAX1\n",
+      "2324 SYT14\n",
+      "2325 SERTAD4-AS1\n",
+      "2326 PTPN14\n",
+      "2327 CNIH4\n",
+      "2328 SRP9\n",
+      "2329 EIPR1\n",
+      "2330 GALNT14\n",
+      "2331 CCT4\n",
+      "2332 LINC01816\n",
+      "2333 REG1A\n",
+      "2334 ST3GAL5\n",
+      "2335 THNSL2\n",
+      "2336 EIF2AK3-DT\n",
+      "2337 ENSG00000287686\n",
+      "2338 ANKRD39\n",
+      "2339 ENSG00000287308\n",
+      "2340 ENSG00000286769\n",
+      "2341 MTLN\n",
+      "2342 ZC3H8\n",
+      "2343 PLEKHB2\n",
+      "2344 MBD5\n",
+      "2345 STK39\n",
+      "2346 PDK1\n",
+      "2347 LINC01116\n",
+      "2348 DNAJC10\n",
+      "2349 GLS\n",
+      "2350 NOP58\n",
+      "2351 CTLA4\n",
+      "2352 ENSG00000286601\n",
+      "2353 ABCB6\n",
+      "2354 GIGYF2\n",
+      "2355 CROCC2\n",
+      "2356 IL5RA\n",
+      "2357 SLC6A6\n",
+      "2358 ENSG00000287377\n",
+      "2359 ENSG00000287348\n",
+      "2360 ULK4\n",
+      "2361 CXCR6\n",
+      "2362 ZNF589\n",
+      "2363 RNF123\n",
+      "2364 HYAL3\n",
+      "2365 TWF2-DT\n",
+      "2366 GLYCTK\n",
+      "2367 MAGI1\n",
+      "2368 FOXP1-AS1\n",
+      "2369 DHFR2\n",
+      "2370 CD47\n",
+      "2371 DZIP3\n",
+      "2372 PLXNA1\n",
+      "2373 UBA5\n",
+      "2374 CDV3\n",
+      "2375 RNF7\n",
+      "2376 ENSG00000244268\n",
+      "2377 TERC\n",
+      "2378 FXR1\n",
+      "2379 VPS8\n",
+      "2380 ENSG00000285938\n",
+      "2381 TP63\n",
+      "2382 TMEM44-AS1\n",
+      "2383 XXYLT1\n",
+      "2384 PPP1R2\n",
+      "2385 ENSG00000270195\n",
+      "2386 HOPX\n",
+      "2387 CXCL2\n",
+      "2388 G3BP2\n",
+      "2389 CCNI\n",
+      "2390 AFF1\n",
+      "2391 ADH7\n",
+      "2392 BANK1\n",
+      "2393 TBCK\n",
+      "2394 ENSG00000286147\n",
+      "2395 CCNA2\n",
+      "2396 SMARCA5\n",
+      "2397 CASP3\n",
+      "2398 ENSG00000251139\n",
+      "2399 ANKRD37\n",
+      "2400 OXCT1\n",
+      "2401 PLPP1\n",
+      "2402 ENSG00000262211\n",
+      "2403 UTP15\n",
+      "2404 GIN1\n",
+      "2405 EPB41L4A\n",
+      "2406 CXCL14\n",
+      "2407 CTNNA1\n",
+      "2408 TAF7\n",
+      "2409 HDAC3\n",
+      "2410 ATP10B\n",
+      "2411 SLIT3\n",
+      "2412 MGAT4B\n",
+      "2413 PSMG4\n",
+      "2414 ENSG00000229896\n",
+      "2415 NOL7\n",
+      "2416 MYLIP\n",
+      "2417 CDKAL1\n",
+      "2418 ENSG00000229313\n",
+      "2419 LINC02980\n",
+      "2420 ZSCAN16\n",
+      "2421 ENSG00000286301\n",
+      "2422 ENSG00000287825\n",
+      "2423 CUL9\n",
+      "2424 RAB23\n",
+      "2425 LGSN\n",
+      "2426 ENSG00000232295\n",
+      "2427 OOEP-AS1\n",
+      "2428 CEP162\n",
+      "2429 ENSG00000260273\n",
+      "2430 CDK19\n",
+      "2431 ENSG00000272356\n",
+      "2432 MCM9\n",
+      "2433 ENPP1\n",
+      "2434 TXLNB\n",
+      "2435 SHPRH\n",
+      "2436 IYD\n",
+      "2437 CCDC170\n",
+      "2438 IPCEF1\n",
+      "2439 ENSG00000112096\n",
+      "2440 CCR6\n",
+      "2441 SNX8\n",
+      "2442 AMZ1\n",
+      "2443 GLCCI1\n",
+      "2444 RP9\n",
+      "2445 ENSG00000272768\n",
+      "2446 GUSB\n",
+      "2447 ABHD11\n",
+      "2448 ENSG00000277675\n",
+      "2449 MAGI2\n",
+      "2450 ANKIB1\n",
+      "2451 SGCE\n",
+      "2452 LRWD1\n",
+      "2453 LRRN3\n",
+      "2454 KDM7A\n",
+      "2455 TRBV10-3\n",
+      "2456 GHET1\n",
+      "2457 ZNF282\n",
+      "2458 DEFA3\n",
+      "2459 LONRF1\n",
+      "2460 TNFRSF10B\n",
+      "2461 NRG1\n",
+      "2462 POMK\n",
+      "2463 VCPIP1\n",
+      "2464 LY96\n",
+      "2465 TRIQK\n",
+      "2466 POLR2K\n",
+      "2467 EIF3H\n",
+      "2468 ENSG00000254859\n",
+      "2469 VLDLR\n",
+      "2470 CDC37L1\n",
+      "2471 KIAA2026\n",
+      "2472 DMAC1\n",
+      "2473 C9orf72\n",
+      "2474 ENSG00000204805\n",
+      "2475 HNRNPK\n",
+      "2476 CDC14B\n",
+      "2477 ECPAS\n",
+      "2478 SHOC1\n",
+      "2479 TRIM32\n",
+      "2480 ENSG00000261534\n",
+      "2481 COQ4\n",
+      "2482 MIGA2\n",
+      "2483 ENSG00000284976\n",
+      "2484 EXD3\n",
+      "2485 NSUN6\n",
+      "2486 ENSG00000286409\n",
+      "2487 ASAH2B\n",
+      "2488 ANAPC16\n",
+      "2489 SEC24C\n",
+      "2490 CHCHD1\n",
+      "2491 KAT6B\n",
+      "2492 ATAD1\n",
+      "2493 IFIT5\n",
+      "2494 EXOC6\n",
+      "2495 ENTPD1-AS1\n",
+      "2496 DENND10\n",
+      "2497 ECHS1\n",
+      "2498 CD81\n",
+      "2499 TRIM22\n",
+      "2500 TPP1\n",
+      "2501 ENSG00000268403\n",
+      "2502 SAAL1\n",
+      "2503 FANCF\n",
+      "2504 LUZP2\n",
+      "2505 DCDC1\n",
+      "2506 EHF\n",
+      "2507 LINC02750\n",
+      "2508 CTNND1\n",
+      "2509 ZP1\n",
+      "2510 ASRGL1\n",
+      "2511 SF3B2\n",
+      "2512 RHOD\n",
+      "2513 LIPT2-AS1\n",
+      "2514 CCDC81\n",
+      "2515 FUT4\n",
+      "2516 SRSF8\n",
+      "2517 MMP12\n",
+      "2518 REXO2\n",
+      "2519 APOC3\n",
+      "2520 ENSG00000254428\n",
+      "2521 ENSG00000286341\n",
+      "2522 ROBO3\n",
+      "2523 SRPRA\n",
+      "2524 ENSG00000287426\n",
+      "2525 H4C16\n",
+      "2526 IRAG2\n",
+      "2527 ENSG00000278743\n",
+      "2528 KLHL42\n",
+      "2529 ALG10B\n",
+      "2530 KRT81\n",
+      "2531 NAB2\n",
+      "2532 ENSG00000270039\n",
+      "2533 ENSG00000276853\n",
+      "2534 LYZ\n",
+      "2535 EEA1\n",
+      "2536 DRAM1\n",
+      "2537 IGF1\n",
+      "2538 SVOP\n",
+      "2539 TCHP\n",
+      "2540 PHETA1\n",
+      "2541 MED13L\n",
+      "2542 TMEM120B\n",
+      "2543 ENSG00000284934\n",
+      "2544 ENSG00000275265\n",
+      "2545 ARL6IP4\n",
+      "2546 TMED2\n",
+      "2547 EEF1AKMT1\n",
+      "2548 N4BP2L2\n",
+      "2549 TSC22D1-AS1\n",
+      "2550 DLEU1\n",
+      "2551 PCDH17\n",
+      "2552 KLF12\n",
+      "2553 SLAIN1\n",
+      "2554 RBM26-AS1\n",
+      "2555 SLITRK5\n",
+      "2556 PCCA\n",
+      "2557 ARGLU1\n",
+      "2558 LIG4\n",
+      "2559 PRECSIT\n",
+      "2560 AP1G2-AS1\n",
+      "2561 FITM1\n",
+      "2562 TTC6\n",
+      "2563 L2HGDH\n",
+      "2564 TMEM260\n",
+      "2565 EXOC5\n",
+      "2566 LINC00216\n",
+      "2567 RDH11\n",
+      "2568 RAD51B\n",
+      "2569 DCAF5\n",
+      "2570 RIOX1\n",
+      "2571 COQ6\n",
+      "2572 ACYP1\n",
+      "2573 GPATCH2L\n",
+      "2574 TC2N\n",
+      "2575 RIN3\n",
+      "2576 PAPOLA-DT\n",
+      "2577 IGHV3-30\n",
+      "2578 IGHV1-69\n",
+      "2579 MEIS2\n",
+      "2580 ENSG00000259715\n",
+      "2581 TNFAIP8L3\n",
+      "2582 HERC1\n",
+      "2583 SPESP1\n",
+      "2584 SCAMP2\n",
+      "2585 NEIL1\n",
+      "2586 SNX33\n",
+      "2587 CRABP1\n",
+      "2588 TLNRD1\n",
+      "2589 NPRL3\n",
+      "2590 HAGHL\n",
+      "2591 SPSB3\n",
+      "2592 REXO5\n",
+      "2593 SDR42E2\n",
+      "2594 ENSG00000261669\n",
+      "2595 CDIPT\n",
+      "2596 CFAP119\n",
+      "2597 DOK4\n",
+      "2598 CCDC113\n",
+      "2599 TSNAXIP1\n",
+      "2600 TMED6\n",
+      "2601 IL34\n",
+      "2602 ZNF19\n",
+      "2603 ENSG00000260664\n",
+      "2604 ZFHX3\n",
+      "2605 EMC8\n",
+      "2606 FBXO31\n",
+      "2607 TRAPPC2L\n",
+      "2608 CPNE7\n",
+      "2609 TSR1\n",
+      "2610 NEURL4\n",
+      "2611 CHRNB1\n",
+      "2612 KRBA2\n",
+      "2613 PIGL\n",
+      "2614 SMCR5\n",
+      "2615 ENSG00000265739\n",
+      "2616 MMP28\n",
+      "2617 CDK12\n",
+      "2618 ENSG00000266088\n",
+      "2619 EIF1\n",
+      "2620 ODAD4\n",
+      "2621 NKIRAS2\n",
+      "2622 NAGS\n",
+      "2623 LSM12\n",
+      "2624 KPNB1\n",
+      "2625 MPO\n",
+      "2626 VMP1\n",
+      "2627 USP32\n",
+      "2628 ENSG00000286997\n",
+      "2629 ENSG00000267801\n",
+      "2630 EXOC7\n",
+      "2631 METTL23\n",
+      "2632 ENSG00000204282\n",
+      "2633 DNAH17\n",
+      "2634 RFNG\n",
+      "2635 ENSG00000266654\n",
+      "2636 SLC16A3\n",
+      "2637 ENSG00000266171\n",
+      "2638 LINC00526\n",
+      "2639 CEP192\n",
+      "2640 ROCK1\n",
+      "2641 RBBP8\n",
+      "2642 ENSG00000263393\n",
+      "2643 CXXC1\n",
+      "2644 TNFRSF11A\n",
+      "2645 SERPINB10\n",
+      "2646 ENSG00000267127\n",
+      "2647 ENSG00000282393\n",
+      "2648 PWWP3A\n",
+      "2649 MKNK2\n",
+      "2650 DOHH\n",
+      "2651 APBA3\n",
+      "2652 TMIGD2\n",
+      "2653 ENSG00000269807\n",
+      "2654 SAFB\n",
+      "2655 LONP1\n",
+      "2656 GPR108\n",
+      "2657 TGFBR3L\n",
+      "2658 OR7D4\n",
+      "2659 ZNF426-DT\n",
+      "2660 ELAVL3\n",
+      "2661 DCAF15\n",
+      "2662 TMEM38A\n",
+      "2663 MPV17L2\n",
+      "2664 YJEFN3\n",
+      "2665 ZNF101\n",
+      "2666 ZNF737\n",
+      "2667 LSM14A\n",
+      "2668 ZNF302\n",
+      "2669 IGFLR1\n",
+      "2670 SYNE4\n",
+      "2671 ENSG00000270760\n",
+      "2672 ZNF793-AS1\n",
+      "2673 ARHGEF1\n",
+      "2674 CEACAM8\n",
+      "2675 BCAM\n",
+      "2676 ERCC2\n",
+      "2677 ENSG00000287603\n",
+      "2678 PTOV1-AS1\n",
+      "2679 ZNF765\n",
+      "2680 LILRA2\n",
+      "2681 NCR1\n",
+      "2682 ZNF8\n",
+      "2683 SPEF1\n",
+      "2684 GPCPD1\n",
+      "2685 MAPRE1\n",
+      "2686 C20orf144\n",
+      "2687 MYH7B\n",
+      "2688 SLA2\n",
+      "2689 TGM2\n",
+      "2690 CTSA\n",
+      "2691 NCOA5\n",
+      "2692 ENSG00000256222\n",
+      "2693 PMEPA1\n",
+      "2694 PSMA7\n",
+      "2695 MYT1\n",
+      "2696 ENSG00000281181\n",
+      "2697 ASMER1\n",
+      "2698 MIR155HG\n",
+      "2699 IGLC3\n",
+      "2700 IGLL1\n",
+      "2701 ENSG00000272733\n",
+      "2702 ENSG00000099958\n",
+      "2703 ZNRF3\n",
+      "2704 ZNRF3-AS1\n",
+      "2705 ASCC2\n",
+      "2706 LIF\n",
+      "2707 TXN2\n",
+      "2708 PHF5A\n",
+      "2709 TRMU\n",
+      "2710 CELSR1\n",
+      "2711 SBF1\n",
+      "2712 KLHDC7B\n",
+      "2713 ZNF674\n",
+      "2714 ENSG00000147144\n",
+      "2715 ENSG00000270012\n",
+      "2716 FAM104B\n",
+      "2717 ZC4H2\n",
+      "2718 PBDC1\n",
+      "2719 BRWD3\n",
+      "2720 UPF3B\n",
+      "2721 NDUFA1\n",
+      "2722 MCTS1\n",
+      "2723 ATP6AP1\n",
+      "2724 UBL4A\n",
+      "2725 DKC1\n",
+      "2726 MTCP1\n",
+      "2727 ENSG00000276256\n",
+      "2728 NECAP2\n",
+      "2729 AKR7A3\n",
+      "2730 CLIC4\n",
+      "2731 RSRP1\n",
+      "2732 GMEB1\n",
+      "2733 ENSG00000203620\n",
+      "2734 ZBTB8OS\n",
+      "2735 PPT1\n",
+      "2736 TMEM59\n",
+      "2737 SLC35D1\n",
+      "2738 DPYD\n",
+      "2739 ENSG00000270066\n",
+      "2740 SLC16A4-AS1\n",
+      "2741 NBPF15\n",
+      "2742 FLG-AS1\n",
+      "2743 PYGO2\n",
+      "2744 FCRL5\n",
+      "2745 GAS5-AS1\n",
+      "2746 ZNF281\n",
+      "2747 TIMM17A\n",
+      "2748 MFSD4A\n",
+      "2749 C4BPB\n",
+      "2750 MIR205HG\n",
+      "2751 TRAF3IP3\n",
+      "2752 ANGEL2\n",
+      "2753 ENSG00000242861\n",
+      "2754 C1orf35\n",
+      "2755 MAP10\n",
+      "2756 ENSG00000235078\n",
+      "2757 WDCP\n",
+      "2758 DNAJC27\n",
+      "2759 PLEKHH2\n",
+      "2760 MDH1\n",
+      "2761 ENSG00000273064\n",
+      "2762 DGUOK\n",
+      "2763 ENSG00000286883\n",
+      "2764 TMSB10\n",
+      "2765 FABP1\n",
+      "2766 IGKV4-1\n",
+      "2767 CNNM4\n",
+      "2768 LINC01191\n",
+      "2769 TFCP2L1\n",
+      "2770 ENSG00000236449\n",
+      "2771 ENSG00000286797\n",
+      "2772 ENSG00000235852\n",
+      "2773 LINC00608\n",
+      "2774 NMUR1\n",
+      "2775 MAB21L4\n",
+      "2776 LMCD1\n",
+      "2777 OXTR\n",
+      "2778 NEK10\n",
+      "2779 SEC22C\n",
+      "2780 ZKSCAN7\n",
+      "2781 SCAP\n",
+      "2782 CAMP\n",
+      "2783 CCDC51\n",
+      "2784 CACNA2D3\n",
+      "2785 ARHGEF3\n",
+      "2786 LNP1\n",
+      "2787 NXPE3\n",
+      "2788 ENSG00000259976\n",
+      "2789 WDR5B\n",
+      "2790 ALG1L1P\n",
+      "2791 ALDH1L1\n",
+      "2792 ISY1\n",
+      "2793 NMNAT3\n",
+      "2794 GRK7\n",
+      "2795 LINC00886\n",
+      "2796 ATP11B\n",
+      "2797 PIGX\n",
+      "2798 ENSG00000272927\n",
+      "2799 MYL5\n",
+      "2800 TMEM128\n",
+      "2801 LINC02482\n",
+      "2802 NCAPG\n",
+      "2803 PI4K2B\n",
+      "2804 SLC34A2\n",
+      "2805 ENSG00000259959\n",
+      "2806 OCIAD2\n",
+      "2807 ALB\n",
+      "2808 CXCL6\n",
+      "2809 NAAA\n",
+      "2810 SEC31A\n",
+      "2811 H2AZ1\n",
+      "2812 CENPE\n",
+      "2813 BBS7-DT\n",
+      "2814 FAT4\n",
+      "2815 FNIP2\n",
+      "2816 NAF1\n",
+      "2817 DDX60L\n",
+      "2818 GPM6A\n",
+      "2819 UFSP2\n",
+      "2820 CCDC110\n",
+      "2821 SDHA\n",
+      "2822 TENT4A\n",
+      "2823 RAI14\n",
+      "2824 NIPBL-DT\n",
+      "2825 NIPBL\n",
+      "2826 ENSG00000272144\n",
+      "2827 ENSG00000287087\n",
+      "2828 NDUFAF2\n",
+      "2829 RAD17\n",
+      "2830 TMEM161B-DT\n",
+      "2831 ELL2\n",
+      "2832 TMED7\n",
+      "2833 ENSG00000271918\n",
+      "2834 SRFBP1\n",
+      "2835 SEPTIN8\n",
+      "2836 DND1\n",
+      "2837 HARS2\n",
+      "2838 DIAPH1-AS1\n",
+      "2839 PRELID2\n",
+      "2840 HMGXB3\n",
+      "2841 THOC3\n",
+      "2842 N4BP3\n",
+      "2843 PHYKPL\n",
+      "2844 RNF130\n",
+      "2845 DUSP22\n",
+      "2846 GMDS-DT\n",
+      "2847 WRNIP1\n",
+      "2848 FAM8A1\n",
+      "2849 NEU1\n",
+      "2850 DXO\n",
+      "2851 STK19\n",
+      "2852 RPL7L1\n",
+      "2853 ANKRD66\n",
+      "2854 CFAP206\n",
+      "2855 ENSG00000271734\n",
+      "2856 PPIL6\n",
+      "2857 GOPC\n",
+      "2858 THEMIS\n",
+      "2859 ENSG00000227945\n",
+      "2860 AHI1-DT\n",
+      "2861 NHSL1\n",
+      "2862 LATS1\n",
+      "2863 C1GALT1\n",
+      "2864 LINC03016\n",
+      "2865 ENSG00000285162\n",
+      "2866 ENSG00000260070\n",
+      "2867 TRGC2\n",
+      "2868 INHBA\n",
+      "2869 HUS1\n",
+      "2870 VOPP1\n",
+      "2871 ZNF680\n",
+      "2872 GRM3\n",
+      "2873 ENSG00000286305\n",
+      "2874 GCC1\n",
+      "2875 NRF1\n",
+      "2876 ENSG00000272941\n",
+      "2877 FMC1\n",
+      "2878 TRBV12-4\n",
+      "2879 TRBV24-1\n",
+      "2880 ZNF746\n",
+      "2881 ZNF775\n",
+      "2882 GIMAP7\n",
+      "2883 DYNC2I1\n",
+      "2884 MSR1\n",
+      "2885 RHOBTB2\n",
+      "2886 ESCO2\n",
+      "2887 UBXN8\n",
+      "2888 SBSPON\n",
+      "2889 CIBAR1\n",
+      "2890 EEF1D\n",
+      "2891 PUF60\n",
+      "2892 UBAP1\n",
+      "2893 ZCCHC7\n",
+      "2894 FAM88C\n",
+      "2895 CFAP95\n",
+      "2896 RFK\n",
+      "2897 SUSD3\n",
+      "2898 PTCH1\n",
+      "2899 COL15A1\n",
+      "2900 CAVIN4\n",
+      "2901 RABGAP1\n",
+      "2902 STRBP\n",
+      "2903 CRAT\n",
+      "2904 UCK1\n",
+      "2905 NTNG2\n",
+      "2906 UAP1L1\n",
+      "2907 NET1\n",
+      "2908 CELF2-AS2\n",
+      "2909 DHTKD1\n",
+      "2910 HSPA14\n",
+      "2911 RUFY2\n",
+      "2912 AIFM2\n",
+      "2913 FAM149B1\n",
+      "2914 CFAP70\n",
+      "2915 DLG5\n",
+      "2916 TNKS2-DT\n",
+      "2917 RRP12\n",
+      "2918 DPCD\n",
+      "2919 PCGF6\n",
+      "2920 SH3PXD2A\n",
+      "2921 PDZD8\n",
+      "2922 BNIP3\n",
+      "2923 MUC5B\n",
+      "2924 TSG101\n",
+      "2925 E2F8\n",
+      "2926 LINC03031\n",
+      "2927 RCN1\n",
+      "2928 QSER1\n",
+      "2929 FAM111A\n",
+      "2930 LINC02739\n",
+      "2931 POLR2G\n",
+      "2932 PCNX3\n",
+      "2933 ENSG00000274251\n",
+      "2934 CTTN-DT\n",
+      "2935 INPPL1\n",
+      "2936 STARD10\n",
+      "2937 MED17\n",
+      "2938 CEP57\n",
+      "2939 BACE1\n",
+      "2940 ENSG00000254873\n",
+      "2941 HYOU1\n",
+      "2942 SORL1\n",
+      "2943 SCN3B\n",
+      "2944 CCDC77\n",
+      "2945 TULP3\n",
+      "2946 LAG3\n",
+      "2947 LINC02449\n",
+      "2948 CLEC2D\n",
+      "2949 LINC02909\n",
+      "2950 MRPS35\n",
+      "2951 LRRK2\n",
+      "2952 TUBA1A\n",
+      "2953 COX14\n",
+      "2954 KRT6B\n",
+      "2955 MMP19\n",
+      "2956 ATP5F1B\n",
+      "2957 STAT6\n",
+      "2958 DYRK2\n",
+      "2959 ENSG00000257613\n",
+      "2960 SOCS2-AS1\n",
+      "2961 CHST11\n",
+      "2962 NOPCHAP1\n",
+      "2963 MLEC\n",
+      "2964 HIP1R\n",
+      "2965 COMMD6\n",
+      "2966 MCF2L-AS1\n",
+      "2967 ADPRHL1\n",
+      "2968 ENSG00000277128\n",
+      "2969 RNASE2CP\n",
+      "2970 TRAV8-4\n",
+      "2971 MBIP\n",
+      "2972 TOGARAM1\n",
+      "2973 CNIH1\n",
+      "2974 FNTB\n",
+      "2975 ENSG00000258820\n",
+      "2976 GPR68\n",
+      "2977 LINC02325\n",
+      "2978 SETD3\n",
+      "2979 INF2\n",
+      "2980 IGHV1-69D\n",
+      "2981 IGHV3-73\n",
+      "2982 RASGRP1\n",
+      "2983 PLA2G4B\n",
+      "2984 VPS39\n",
+      "2985 GNB5\n",
+      "2986 PRTG\n",
+      "2987 CGNL1\n",
+      "2988 MPI\n",
+      "2989 PSTPIP1\n",
+      "2990 UNC45A\n",
+      "2991 MEF2A\n",
+      "2992 RHOT2\n",
+      "2993 METRN\n",
+      "2994 BAIAP3\n",
+      "2995 NTHL1\n",
+      "2996 ZNF200\n",
+      "2997 TMEM186\n",
+      "2998 ERCC4\n",
+      "2999 NPIPA5\n",
+      "3000 NOMO3\n",
+      "3001 TMC5\n",
+      "3002 ACSM1\n",
+      "3003 ALDOA\n",
+      "3004 STX4\n",
+      "3005 TGFB1I1\n",
+      "3006 GPT2\n",
+      "3007 AMFR\n",
+      "3008 SLC12A3\n",
+      "3009 CKLF\n",
+      "3010 EDC4\n",
+      "3011 CENPN\n",
+      "3012 ENSG00000262228\n",
+      "3013 INPP5K\n",
+      "3014 ATP2A3\n",
+      "3015 CYB5D2\n",
+      "3016 ENO3\n",
+      "3017 ENSG00000262429\n",
+      "3018 KIF1C-AS1\n",
+      "3019 TNFSF13\n",
+      "3020 CENPV\n",
+      "3021 UBB\n",
+      "3022 NT5M\n",
+      "3023 ENSG00000266677\n",
+      "3024 LINC02693\n",
+      "3025 WSB1\n",
+      "3026 SPAG5-AS1\n",
+      "3027 SUPT6H\n",
+      "3028 CCL11\n",
+      "3029 ENSG00000267074\n",
+      "3030 PEX12\n",
+      "3031 ENSG00000263466\n",
+      "3032 PNMT\n",
+      "3033 RARA\n",
+      "3034 HEXIM1\n",
+      "3035 ENSG00000250186\n",
+      "3036 DYNLL2\n",
+      "3037 BCAS3\n",
+      "3038 AMZ2\n",
+      "3039 LGALS3BP\n",
+      "3040 ENSG00000264735\n",
+      "3041 ENSG00000266456\n",
+      "3042 ENSG00000259985\n",
+      "3043 ENSG00000265008\n",
+      "3044 ENSG00000275512\n",
+      "3045 LINC01902\n",
+      "3046 ST8SIA5\n",
+      "3047 TXNL1\n",
+      "3048 FBXO15\n",
+      "3049 ZNF236-DT\n",
+      "3050 ENSG00000278330\n",
+      "3051 GPX4\n",
+      "3052 ENSG00000267205\n",
+      "3053 ENSG00000263264\n",
+      "3054 TRAPPC5\n",
+      "3055 ICAM1\n",
+      "3056 QTRT1\n",
+      "3057 ZNF69\n",
+      "3058 CACNA1A\n",
+      "3059 RAB8A\n",
+      "3060 MED26\n",
+      "3061 NR2F6\n",
+      "3062 RFXANK\n",
+      "3063 GMIP\n",
+      "3064 ZNF93\n",
+      "3065 ETV2\n",
+      "3066 KMT2B\n",
+      "3067 ZNF565\n",
+      "3068 EIF3K\n",
+      "3069 PAF1\n",
+      "3070 TIMM50\n",
+      "3071 CEACAM1\n",
+      "3072 PVR\n",
+      "3073 CLPTM1\n",
+      "3074 RTN2\n",
+      "3075 NOP53\n",
+      "3076 KCNJ14\n",
+      "3077 BAX\n",
+      "3078 RUVBL2\n",
+      "3079 TSKS\n",
+      "3080 SIGLEC9\n",
+      "3081 SIGLEC6\n",
+      "3082 SIGLEC14\n",
+      "3083 LENG1\n",
+      "3084 ISOC2\n",
+      "3085 ZFP28-DT\n",
+      "3086 ZNF548\n",
+      "3087 LZTS3\n",
+      "3088 PANK2\n",
+      "3089 KIF16B\n",
+      "3090 LINC00261\n",
+      "3091 PDRG1\n",
+      "3092 BPIFA1\n",
+      "3093 NCOA6\n",
+      "3094 SOGA1\n",
+      "3095 RALGAPB\n",
+      "3096 ENSG00000274825\n",
+      "3097 TOX2\n",
+      "3098 PIGT\n",
+      "3099 ACOT8\n",
+      "3100 ZNF831\n",
+      "3101 MIS18A\n",
+      "3102 LINC02940\n",
+      "3103 PRMT2\n",
+      "3104 THAP7\n",
+      "3105 IGLV4-60\n",
+      "3106 IGLV5-45\n",
+      "3107 POLR2F\n",
+      "3108 ENSG00000244491\n",
+      "3109 POLR3H\n",
+      "3110 ARHGAP6\n",
+      "3111 RAB9A\n",
+      "3112 GEMIN8\n",
+      "3113 ZRSR2\n",
+      "3114 PORCN-DT\n",
+      "3115 NBDY\n",
+      "3116 ITM2A\n",
+      "3117 SLC25A53\n",
+      "3118 KCNE5\n",
+      "3119 PAK3\n",
+      "3120 RPL39\n",
+      "3121 STAG2-AS1\n",
+      "3122 RTL8A\n",
+      "3123 BCAP31\n",
+      "3124 SPRY3\n",
+      "3125 TMSB4Y\n",
+      "3126 ENSG00000241860\n",
+      "3127 ENSG00000157881\n",
+      "3128 THAP3\n",
+      "3129 AGTRAP\n",
+      "3130 ENSG00000219481\n",
+      "3131 HP1BP3\n",
+      "3132 NBPF3\n",
+      "3133 ENSG00000269971\n",
+      "3134 NFYC-AS1\n",
+      "3135 LINC01362\n",
+      "3136 ENSG00000284882\n",
+      "3137 LMO4\n",
+      "3138 EVI5\n",
+      "3139 ENSG00000273204\n",
+      "3140 AMY2A\n",
+      "3141 DCLRE1B\n",
+      "3142 OAZ3\n",
+      "3143 TTC24\n",
+      "3144 ENSG00000236656\n",
+      "3145 SLAMF8\n",
+      "3146 CFAP45\n",
+      "3147 SELE\n",
+      "3148 PRRX1\n",
+      "3149 METTL13\n",
+      "3150 DDX59\n",
+      "3151 RAB7B\n",
+      "3152 RRP15\n",
+      "3153 ITPKB-AS1\n",
+      "3154 GALNT2\n",
+      "3155 NID1\n",
+      "3156 OPN3\n",
+      "3157 ENSG00000287601\n",
+      "3158 ENSG00000260698\n",
+      "3159 ENSG00000259865\n",
+      "3160 GRHL1\n",
+      "3161 RRM2\n",
+      "3162 ENSG00000224739\n",
+      "3163 LINC01819\n",
+      "3164 CRIPT\n",
+      "3165 SPTBN1\n",
+      "3166 ANKRD53\n",
+      "3167 ENSG00000272735\n",
+      "3168 LOXL3\n",
+      "3169 IGKV1D-13\n",
+      "3170 RFX8\n",
+      "3171 ENSG00000238273\n",
+      "3172 ENSG00000235522\n",
+      "3173 MARCO\n",
+      "3174 ORC4\n",
+      "3175 CHN1\n",
+      "3176 NEUROD1\n",
+      "3177 DUSP19\n",
+      "3178 PLCL1\n",
+      "3179 TYW5\n",
+      "3180 ENSG00000232719\n",
+      "3181 CFLAR\n",
+      "3182 SUMO1\n",
+      "3183 PARD3B\n",
+      "3184 KLF7\n",
+      "3185 KANSL1L-AS1\n",
+      "3186 SLC23A3\n",
+      "3187 CAB39\n",
+      "3188 COPS7B\n",
+      "3189 PER2\n",
+      "3190 SNED1\n",
+      "3191 MTMR14\n",
+      "3192 RPL32\n",
+      "3193 FGD5-AS1\n",
+      "3194 VIPR1-AS1\n",
+      "3195 CCDC13-AS2\n",
+      "3196 CDCP1\n",
+      "3197 CCR1\n",
+      "3198 DOCK3\n",
+      "3199 ABHD6\n",
+      "3200 ALCAM\n",
+      "3201 TRAT1\n",
+      "3202 PLCXD2\n",
+      "3203 ATP6V1A\n",
+      "3204 RPN1\n",
+      "3205 LINC02021\n",
+      "3206 ENSG00000269984\n",
+      "3207 TBL1XR1\n",
+      "3208 APOD\n",
+      "3209 ZNF718\n",
+      "3210 TNIP2\n",
+      "3211 TBC1D14\n",
+      "3212 DCAF16\n",
+      "3213 CCDC149\n",
+      "3214 FAM114A1\n",
+      "3215 TECRL\n",
+      "3216 UTP3\n",
+      "3217 ANKRD17\n",
+      "3218 LIN54\n",
+      "3219 ENSG00000286035\n",
+      "3220 COQ2\n",
+      "3221 AIMP1\n",
+      "3222 SETD7\n",
+      "3223 HAND2-AS1\n",
+      "3224 SPCS3\n",
+      "3225 MOCS2\n",
+      "3226 THBS4-AS1\n",
+      "3227 ENSG00000271862\n",
+      "3228 DTWD2\n",
+      "3229 PRDM6\n",
+      "3230 CSNK1G3\n",
+      "3231 FBN2\n",
+      "3232 AFF4\n",
+      "3233 TCF7\n",
+      "3234 DDX46\n",
+      "3235 MZB1\n",
+      "3236 PANK3\n",
+      "3237 SNCB\n",
+      "3238 COL23A1\n",
+      "3239 SQSTM1\n",
+      "3240 BPHL\n",
+      "3241 SLC35B3\n",
+      "3242 ENSG00000287359\n",
+      "3243 C6orf62\n",
+      "3244 H2BC6\n",
+      "3245 H2BC7\n",
+      "3246 HLA-E\n",
+      "3247 PPP1R18\n",
+      "3248 TUBB\n",
+      "3249 RING1\n",
+      "3250 GRIK2\n",
+      "3251 RPF2\n",
+      "3252 NCOA7\n",
+      "3253 ENSG00000287731\n",
+      "3254 TMEM200A\n",
+      "3255 PCMT1\n",
+      "3256 LINC02901\n",
+      "3257 C6orf120\n",
+      "3258 AIMP2\n",
+      "3259 TMEM106B\n",
+      "3260 NT5C3A\n",
+      "3261 NME8\n",
+      "3262 URGCP\n",
+      "3263 GTF2IRD2B\n",
+      "3264 SEMA3E\n",
+      "3265 COL1A2\n",
+      "3266 ARPC1A\n",
+      "3267 COL26A1\n",
+      "3268 KMT2E-AS1\n",
+      "3269 SLC26A4-AS1\n",
+      "3270 LAMB1\n",
+      "3271 TNPO3\n",
+      "3272 UBE2H-DT\n",
+      "3273 ADCK2\n",
+      "3274 TRBV21-1\n",
+      "3275 ENSG00000272760\n",
+      "3276 RP1L1\n",
+      "3277 FAM167A\n",
+      "3278 NEFM\n",
+      "3279 CRISPLD1\n",
+      "3280 ENSG00000253859\n",
+      "3281 ENSG00000253878\n",
+      "3282 LAPTM4B\n",
+      "3283 PABPC1\n",
+      "3284 ENSG00000253106\n",
+      "3285 DENND3\n",
+      "3286 FAM83H\n",
+      "3287 ZNF517\n",
+      "3288 ENSG00000226403\n",
+      "3289 SPATA6L\n",
+      "3290 RRAGA\n",
+      "3291 MLLT3\n",
+      "3292 ENSG00000204814\n",
+      "3293 SLC35D2\n",
+      "3294 ENSG00000203279\n",
+      "3295 INIP\n",
+      "3296 WDR31\n",
+      "3297 ZBTB26\n",
+      "3298 PTGES2\n",
+      "3299 TRUB2\n",
+      "3300 C9orf78\n",
+      "3301 C9orf163\n",
+      "3302 NELFB\n",
+      "3303 NOXA1\n",
+      "3304 PRKCQ-AS1\n",
+      "3305 NUDT5\n",
+      "3306 MINDY3\n",
+      "3307 HACD1\n",
+      "3308 SPAG6\n",
+      "3309 ENSG00000273312\n",
+      "3310 DNA2\n",
+      "3311 TNKS2\n",
+      "3312 FBXW4\n",
+      "3313 ENSG00000249456\n",
+      "3314 TSSC4\n",
+      "3315 TRIM3\n",
+      "3316 ENSG00000285658\n",
+      "3317 AHNAK\n",
+      "3318 ENSG00000269176\n",
+      "3319 CST6\n",
+      "3320 RIN1\n",
+      "3321 NDUFV1-DT\n",
+      "3322 ENSG00000255031\n",
+      "3323 THAP12\n",
+      "3324 ENDOD1\n",
+      "3325 YAP1\n",
+      "3326 LAYN\n",
+      "3327 RNF214\n",
+      "3328 BCL9L\n",
+      "3329 BARX2\n",
+      "3330 ENSG00000261799\n",
+      "3331 AKAP3\n",
+      "3332 C1R\n",
+      "3333 LINC00987\n",
+      "3334 PRPF40B\n",
+      "3335 PCBP2\n",
+      "3336 DNAJC14\n",
+      "3337 CS\n",
+      "3338 TIMELESS\n",
+      "3339 ZBTB39\n",
+      "3340 BTG1\n",
+      "3341 SDSL\n",
+      "3342 KMT5A\n",
+      "3343 NOC4L\n",
+      "3344 LNX2\n",
+      "3345 FLT1\n",
+      "3346 PDS5B\n",
+      "3347 DNAJC15\n",
+      "3348 ABCC4\n",
+      "3349 GPR183\n",
+      "3350 PSMB5\n",
+      "3351 ZFHX2\n",
+      "3352 RNF31\n",
+      "3353 CIDEB\n",
+      "3354 BAZ1A-AS1\n",
+      "3355 KLHDC1\n",
+      "3356 DMAC2L\n",
+      "3357 ERO1A\n",
+      "3358 PSMA3-AS1\n",
+      "3359 GPHN\n",
+      "3360 EIF2S1\n",
+      "3361 ENSG00000258646\n",
+      "3362 SNW1\n",
+      "3363 SLC24A4\n",
+      "3364 ENSG00000271780\n",
+      "3365 ZNF839\n",
+      "3366 LINC00226\n",
+      "3367 LINC02352\n",
+      "3368 GREM1\n",
+      "3369 RYR3\n",
+      "3370 VPS13C\n",
+      "3371 TLN2\n",
+      "3372 LOXL1\n",
+      "3373 FAM219B\n",
+      "3374 CIB1\n",
+      "3375 RCCD1-AS1\n",
+      "3376 CACNA1H\n",
+      "3377 CCDC154\n",
+      "3378 NME3\n",
+      "3379 RAB26\n",
+      "3380 TNFRSF12A\n",
+      "3381 HCFC1R1\n",
+      "3382 CLEC16A\n",
+      "3383 TXNDC11-AS1\n",
+      "3384 ENSG00000263335\n",
+      "3385 ENSG00000283213\n",
+      "3386 PAGR1\n",
+      "3387 NOD2\n",
+      "3388 ENSG00000259926\n",
+      "3389 ENSG00000260755\n",
+      "3390 ATP6V0D1-DT\n",
+      "3391 CHTF8\n",
+      "3392 ENSG00000247228\n",
+      "3393 GAN\n",
+      "3394 ENSG00000261177\n",
+      "3395 GAS8\n",
+      "3396 SGSM2\n",
+      "3397 ENSG00000262692\n",
+      "3398 ZZEF1\n",
+      "3399 CAMTA2\n",
+      "3400 MIR497HG\n",
+      "3401 CLEC10A\n",
+      "3402 MYH10\n",
+      "3403 ENSG00000265801\n",
+      "3404 ENSG00000279762\n",
+      "3405 CCL14\n",
+      "3406 CCL4L2\n",
+      "3407 WNT3\n",
+      "3408 SP2\n",
+      "3409 ENSG00000274213\n",
+      "3410 TEX14\n",
+      "3411 MAP2K6\n",
+      "3412 KCNJ2-AS1\n",
+      "3413 GRB2\n",
+      "3414 FASN\n",
+      "3415 LPIN2\n",
+      "3416 MIR924HG\n",
+      "3417 SETBP1\n",
+      "3418 SERPINB8\n",
+      "3419 ENSG00000278107\n",
+      "3420 MIER2\n",
+      "3421 PALM\n",
+      "3422 ARID3A\n",
+      "3423 NDUFS7\n",
+      "3424 KLF16\n",
+      "3425 MPND\n",
+      "3426 PLIN3\n",
+      "3427 ENSG00000275234\n",
+      "3428 TUBB4A\n",
+      "3429 CERS4\n",
+      "3430 DDA1\n",
+      "3431 INSL3\n",
+      "3432 ZNF43\n",
+      "3433 ZNF507\n",
+      "3434 ENSG00000267024\n",
+      "3435 SCN1B\n",
+      "3436 CIC\n",
+      "3437 ZNF285\n",
+      "3438 HIF3A\n",
+      "3439 ENSG00000268093\n",
+      "3440 U2AF2\n",
+      "3441 ZCCHC3\n",
+      "3442 TMEM74B\n",
+      "3443 SIRPG\n",
+      "3444 ROMO1\n",
+      "3445 CNBD2\n",
+      "3446 ZMYND8\n",
+      "3447 ATP9A\n",
+      "3448 HAR1B\n",
+      "3449 UCKL1-AS1\n",
+      "3450 ENSG00000275464\n",
+      "3451 MIR99AHG\n",
+      "3452 SOD1\n",
+      "3453 URB1\n",
+      "3454 LINC00649\n",
+      "3455 LINC00114\n",
+      "3456 PFKL\n",
+      "3457 CFAP410\n",
+      "3458 LINC01424\n",
+      "3459 ITGB2\n",
+      "3460 GNAZ\n",
+      "3461 FAM227A\n",
+      "3462 MRTFA\n",
+      "3463 PRPS2\n",
+      "3464 PDHA1\n",
+      "3465 MED14OS\n",
+      "3466 EOLA1\n",
+      "3467 MT-CO3\n",
+      "3468 TNFRSF1B\n",
+      "3469 PDPN\n",
+      "3470 CDC42-AS1\n",
+      "3471 RPL11\n",
+      "3472 ELOA-AS1\n",
+      "3473 SH3BGRL3\n",
+      "3474 SYTL1\n",
+      "3475 PPP1R8\n",
+      "3476 YARS1\n",
+      "3477 RLF\n",
+      "3478 SZT2\n",
+      "3479 MMACHC\n",
+      "3480 LRRC41\n",
+      "3481 FOXD2-AS1\n",
+      "3482 PTGER3\n",
+      "3483 CYB561D1\n",
+      "3484 AMPD2\n",
+      "3485 TMIGD3\n",
+      "3486 FCGR1A\n",
+      "3487 INTS3\n",
+      "3488 ENSG00000236206\n",
+      "3489 RABGAP1L-IT1\n",
+      "3490 ENSG00000228686\n",
+      "3491 STX6\n",
+      "3492 SWT1\n",
+      "3493 CAPN8\n",
+      "3494 ENSG00000285053\n",
+      "3495 PLD5\n",
+      "3496 CATSPERE\n",
+      "3497 SCCPDH\n",
+      "3498 ENSG00000242282\n",
+      "3499 ENSG00000269973\n",
+      "3500 CYRIA\n",
+      "3501 TMEM214\n",
+      "3502 CYP1B1\n",
+      "3503 ENSG00000233230\n",
+      "3504 COMMD1\n",
+      "3505 TMEM17\n",
+      "3506 ALMS1\n",
+      "3507 IGKV2D-24\n",
+      "3508 STARD7-AS1\n",
+      "3509 MAP4K4\n",
+      "3510 MAP3K2-DT\n",
+      "3511 DARS1-AS1\n",
+      "3512 ENSG00000288066\n",
+      "3513 SLC4A10\n",
+      "3514 SLC38A11\n",
+      "3515 GPR155\n",
+      "3516 ENSG00000280374\n",
+      "3517 FKBP7\n",
+      "3518 NCKAP1\n",
+      "3519 OSGEPL1-AS1\n",
+      "3520 NABP1\n",
+      "3521 GTF3C3\n",
+      "3522 MOB4\n",
+      "3523 IKZF2\n",
+      "3524 ENSG00000197585\n",
+      "3525 STK36\n",
+      "3526 DNPEP\n",
+      "3527 ENSG00000042304\n",
+      "3528 TTLL3\n",
+      "3529 OXNAD1\n",
+      "3530 NKIRAS1\n",
+      "3531 GOLGA4\n",
+      "3532 CX3CR1\n",
+      "3533 SLC25A20\n",
+      "3534 CACNA1D\n",
+      "3535 VGLL3\n",
+      "3536 BTLA\n",
+      "3537 ZNF148\n",
+      "3538 LINC02614\n",
+      "3539 SLC41A3\n",
+      "3540 ESYT3\n",
+      "3541 HES1\n",
+      "3542 TMEM175\n",
+      "3543 FAM53A\n",
+      "3544 HAUS3\n",
+      "3545 RNF4\n",
+      "3546 FAM184B\n",
+      "3547 ANAPC4\n",
+      "3548 NIPAL1\n",
+      "3549 DANCR\n",
+      "3550 SCFD2\n",
+      "3551 CXCL8\n",
+      "3552 FGF5\n",
+      "3553 LINC02267\n",
+      "3554 LINC02503\n",
+      "3555 ENSG00000251555\n",
+      "3556 AGA\n",
+      "3557 ING2-DT\n",
+      "3558 MIR4458HG\n",
+      "3559 ANKRD33B\n",
+      "3560 DNAJC21\n",
+      "3561 CPLANE1\n",
+      "3562 MOCS2-DT\n",
+      "3563 MCCC2\n",
+      "3564 ANKRA2\n",
+      "3565 GCNT4\n",
+      "3566 SERINC5\n",
+      "3567 EDIL3\n",
+      "3568 ARRDC3-AS1\n",
+      "3569 PJA2\n",
+      "3570 IRF1\n",
+      "3571 CNOT8\n",
+      "3572 SGCD\n",
+      "3573 RARS1\n",
+      "3574 RGS14\n",
+      "3575 RACK1\n",
+      "3576 ENSG00000227803\n",
+      "3577 ACOT13\n",
+      "3578 H2BC3\n",
+      "3579 H4C3\n",
+      "3580 ENSG00000204713\n",
+      "3581 TRIM26\n",
+      "3582 FKBPL\n",
+      "3583 ARMC12\n",
+      "3584 C6orf89\n",
+      "3585 RNF8\n",
+      "3586 OOEP\n",
+      "3587 NT5E\n",
+      "3588 RTN4IP1\n",
+      "3589 AFG1L\n",
+      "3590 DSE\n",
+      "3591 C6orf118\n",
+      "3592 PDCD2\n",
+      "3593 GPER1\n",
+      "3594 PSMG3-AS1\n",
+      "3595 OCM\n",
+      "3596 ZDHHC4\n",
+      "3597 ENSG00000226816\n",
+      "3598 CPVL-AS2\n",
+      "3599 GLI3\n",
+      "3600 ZP3\n",
+      "3601 PEG10\n",
+      "3602 PMPCB\n",
+      "3603 POT1\n",
+      "3604 SND1\n",
+      "3605 RBM28\n",
+      "3606 MKLN1\n",
+      "3607 CYREN\n",
+      "3608 ZC3HAV1L\n",
+      "3609 ENSG00000253300\n",
+      "3610 IKBKB\n",
+      "3611 CHCHD7\n",
+      "3612 RUNX1T1\n",
+      "3613 DPY19L4\n",
+      "3614 ANKRD46\n",
+      "3615 FZD6\n",
+      "3616 OXR1\n",
+      "3617 EXT1\n",
+      "3618 ENSG00000271959\n",
+      "3619 ENSG00000254741\n",
+      "3620 EXOSC4\n",
+      "3621 GPAA1\n",
+      "3622 DOCK8-AS2\n",
+      "3623 UHRF2\n",
+      "3624 LINC01235\n",
+      "3625 PRSS3\n",
+      "3626 KIF24\n",
+      "3627 PAX5\n",
+      "3628 PTGER4P2-CDK2AP2P2\n",
+      "3629 ANKRD20A4P\n",
+      "3630 ENSG00000286079\n",
+      "3631 ELP1\n",
+      "3632 DPM2\n",
+      "3633 IL2RA\n",
+      "3634 CELF2\n",
+      "3635 MSANTD7\n",
+      "3636 STAM\n",
+      "3637 ZEB1-AS1\n",
+      "3638 BMS1\n",
+      "3639 RASSF4\n",
+      "3640 CABCOCO1\n",
+      "3641 CCSER2\n",
+      "3642 CYP2C9\n",
+      "3643 PDLIM1\n",
+      "3644 ACTR1A\n",
+      "3645 NT5C2\n",
+      "3646 ACSL5\n",
+      "3647 TCF7L2\n",
+      "3648 CACUL1\n",
+      "3649 LMNTD2\n",
+      "3650 LMNTD2-AS1\n",
+      "3651 SMPD1\n",
+      "3652 RRP8\n",
+      "3653 OLFML1\n",
+      "3654 PSMA1\n",
+      "3655 CSTF3\n",
+      "3656 PDHX\n",
+      "3657 TSPAN18\n",
+      "3658 C1QTNF4\n",
+      "3659 PRG2\n",
+      "3660 SLC43A3\n",
+      "3661 KLHL35\n",
+      "3662 SERPINH1\n",
+      "3663 LINC02728\n",
+      "3664 SLC36A4\n",
+      "3665 KDM4D\n",
+      "3666 ENSG00000255384\n",
+      "3667 ENSG00000285909\n",
+      "3668 JHY\n",
+      "3669 MSANTD2-AS1\n",
+      "3670 B4GALNT3\n",
+      "3671 WNK1\n",
+      "3672 PARP11\n",
+      "3673 GABARAPL1\n",
+      "3674 TAS2R14\n",
+      "3675 ENSG00000070018\n",
+      "3676 PTHLH\n",
+      "3677 ERGIC2\n",
+      "3678 TMTC1\n",
+      "3679 SPATS2\n",
+      "3680 KRT6C\n",
+      "3681 AAAS\n",
+      "3682 USP15\n",
+      "3683 SLC35E3\n",
+      "3684 ENSG00000257764\n",
+      "3685 RAB21\n",
+      "3686 TRHDE\n",
+      "3687 NAP1L1\n",
+      "3688 METTL25\n",
+      "3689 POC1B\n",
+      "3690 TMPO-AS1\n",
+      "3691 GAS2L3\n",
+      "3692 UNG\n",
+      "3693 SDS\n",
+      "3694 HCAR1\n",
+      "3695 ENSG00000287837\n",
+      "3696 WBP4\n",
+      "3697 FNDC3A\n",
+      "3698 SPRY2\n",
+      "3699 GPR18\n",
+      "3700 ING1\n",
+      "3701 METTL3\n",
+      "3702 TRAV25\n",
+      "3703 PRMT5-AS1\n",
+      "3704 COCH\n",
+      "3705 PPP2R3C\n",
+      "3706 MIA2-AS1\n",
+      "3707 DNAAF2\n",
+      "3708 STYX\n",
+      "3709 TBPL2\n",
+      "3710 ENSG00000214770\n",
+      "3711 SLC10A1\n",
+      "3712 SYNJ2BP\n",
+      "3713 ASB2\n",
+      "3714 ENSG00000257275\n",
+      "3715 PLD4\n",
+      "3716 IGHA2\n",
+      "3717 IGHGP\n",
+      "3718 ARHGAP11B-DT\n",
+      "3719 SLC12A6\n",
+      "3720 PLCB2\n",
+      "3721 SHF\n",
+      "3722 CEP152\n",
+      "3723 TMOD2\n",
+      "3724 RFX7\n",
+      "3725 PCLAF\n",
+      "3726 INTS14\n",
+      "3727 THSD4\n",
+      "3728 CCDC33\n",
+      "3729 CIB2\n",
+      "3730 ENSG00000286817\n",
+      "3731 ZSCAN2\n",
+      "3732 ENSG00000276278\n",
+      "3733 MAN2A2\n",
+      "3734 ENSG00000287855\n",
+      "3735 NUBP2\n",
+      "3736 PRSS27\n",
+      "3737 FLYWCH1\n",
+      "3738 PKMYT1\n",
+      "3739 ENSG00000188897\n",
+      "3740 ACSM3\n",
+      "3741 EEF2K\n",
+      "3742 NDUFAB1\n",
+      "3743 ADCY7\n",
+      "3744 B3GNT9\n",
+      "3745 NQO1\n",
+      "3746 ATXN1L\n",
+      "3747 MBTPS1-DT\n",
+      "3748 IRF8\n",
+      "3749 ZCCHC14\n",
+      "3750 ACSF3\n",
+      "3751 OVCA2\n",
+      "3752 CLUH\n",
+      "3753 VMO1\n",
+      "3754 RABEP1\n",
+      "3755 ENSG00000230971\n",
+      "3756 PEMT\n",
+      "3757 USP22\n",
+      "3758 SDF2\n",
+      "3759 FLOT2\n",
+      "3760 TAOK1\n",
+      "3761 SRCIN1\n",
+      "3762 CNTNAP1\n",
+      "3763 AARSD1\n",
+      "3764 MAPT\n",
+      "3765 NOG\n",
+      "3766 ENSG00000263089\n",
+      "3767 PTRH2\n",
+      "3768 ENSG00000264853\n",
+      "3769 ENSG00000284526\n",
+      "3770 TNRC6C\n",
+      "3771 C1QTNF1\n",
+      "3772 CBX2\n",
+      "3773 ENSG00000275902\n",
+      "3774 RAB40B\n",
+      "3775 NDC80\n",
+      "3776 ENSG00000267702\n",
+      "3777 CDH20\n",
+      "3778 PRSS57\n",
+      "3779 CELF5\n",
+      "3780 ICAM3\n",
+      "3781 TECR\n",
+      "3782 BRD4\n",
+      "3783 BABAM1\n",
+      "3784 CCDC194\n",
+      "3785 ENSG00000268030\n",
+      "3786 LIN37\n",
+      "3787 PROSER3\n",
+      "3788 ZNF529\n",
+      "3789 PSMC4\n",
+      "3790 ZNF574\n",
+      "3791 CXCL17\n",
+      "3792 ZNF225\n",
+      "3793 DHX34\n",
+      "3794 FUT2\n",
+      "3795 RASIP1\n",
+      "3796 ENSG00000273837\n",
+      "3797 ZNF836\n",
+      "3798 ZNF320\n",
+      "3799 ZNF888\n",
+      "3800 ZNF17\n",
+      "3801 ZNF211\n",
+      "3802 RPS5\n",
+      "3803 OXT\n",
+      "3804 ADISSP\n",
+      "3805 PCNA\n",
+      "3806 FAM182A\n",
+      "3807 BCL2L1-AS1\n",
+      "3808 CHMP4B\n",
+      "3809 ITCH-AS1\n",
+      "3810 RAB5IF\n",
+      "3811 DBNDD2\n",
+      "3812 CD40\n",
+      "3813 PARD6B\n",
+      "3814 CASS4\n",
+      "3815 NKILA\n",
+      "3816 SH3BGR\n",
+      "3817 RRP1B\n",
+      "3818 PWP2\n",
+      "3819 YBEY\n",
+      "3820 C21orf58\n",
+      "3821 IGLJ1\n",
+      "3822 IGLC5\n",
+      "3823 ENSG00000240972\n",
+      "3824 LRRC75B\n",
+      "3825 FBXO7\n",
+      "3826 CSF2RB\n",
+      "3827 SUN2\n",
+      "3828 ST13\n",
+      "3829 KDM5C\n",
+      "3830 IL2RG\n",
+      "3831 RPS4X\n",
+      "3832 TSPAN6\n",
+      "3833 TRMT2B\n",
+      "3834 ZBTB33\n",
+      "3835 PABIR2\n",
+      "3836 SMIM10\n",
+      "3837 ZNF449\n",
+      "3838 LDOC1\n",
+      "3839 TMLHE-AS1\n",
+      "3840 ENSG00000273748\n",
+      "3841 RER1\n",
+      "3842 CA6\n",
+      "3843 CTNNBIP1\n",
+      "3844 UBIAD1\n",
+      "3845 CASP9\n",
+      "3846 LINC00339\n",
+      "3847 C1QB\n",
+      "3848 XKR8\n",
+      "3849 COL16A1\n",
+      "3850 ZSCAN20\n",
+      "3851 LSM10\n",
+      "3852 BEST4\n",
+      "3853 MOB3C\n",
+      "3854 OSBPL9\n",
+      "3855 CYB5RL\n",
+      "3856 PATJ-DT\n",
+      "3857 PGM1\n",
+      "3858 HS2ST1\n",
+      "3859 CDC14A\n",
+      "3860 DPH5\n",
+      "3861 RNPC3\n",
+      "3862 CSDE1\n",
+      "3863 ENSG00000287979\n",
+      "3864 NBPF20\n",
+      "3865 HJV\n",
+      "3866 SF3B4\n",
+      "3867 MRPS21\n",
+      "3868 GABPB2\n",
+      "3869 CRTC2\n",
+      "3870 MTX1\n",
+      "3871 CCDC181\n",
+      "3872 KIFAP3\n",
+      "3873 LINC02803\n",
+      "3874 RALGPS2\n",
+      "3875 CHIT1\n",
+      "3876 SLC45A3\n",
+      "3877 RD3\n",
+      "3878 CCSAP\n",
+      "3879 COX20\n",
+      "3880 CMPK2\n",
+      "3881 UBXN2A\n",
+      "3882 GTF3C2-AS2\n",
+      "3883 PPM1G\n",
+      "3884 PLB1\n",
+      "3885 LCLAT1\n",
+      "3886 CYP1B1-AS1\n",
+      "3887 HAAO\n",
+      "3888 PUS10\n",
+      "3889 ENSG00000273445\n",
+      "3890 IGKV2-24\n",
+      "3891 ZNF892\n",
+      "3892 FAHD2B\n",
+      "3893 ENSG00000277701\n",
+      "3894 MCM6\n",
+      "3895 TANK-AS1\n",
+      "3896 TTN\n",
+      "3897 ITPRID2\n",
+      "3898 ITGAV\n",
+      "3899 SLC40A1\n",
+      "3900 HECW2\n",
+      "3901 LINC01891\n",
+      "3902 D2HGDH\n",
+      "3903 ENSG00000215023\n",
+      "3904 MKRN2\n",
+      "3905 WNT7A\n",
+      "3906 RARB\n",
+      "3907 LINC02084\n",
+      "3908 UQCRC1\n",
+      "3909 IFRD2\n",
+      "3910 ABHD14A\n",
+      "3911 IL17RD\n",
+      "3912 KBTBD8\n",
+      "3913 PPP4R2\n",
+      "3914 ADPRH\n",
+      "3915 SNX4\n",
+      "3916 TMCC1\n",
+      "3917 RBP1\n",
+      "3918 P2RY1\n",
+      "3919 KPNA4\n",
+      "3920 ALG3\n",
+      "3921 DNAJB11\n",
+      "3922 ENSG00000286909\n",
+      "3923 ZNF595\n",
+      "3924 FGFRL1\n",
+      "3925 JAKMIP1\n",
+      "3926 CCDC96\n",
+      "3927 ENSG00000251379\n",
+      "3928 ZCCHC4\n",
+      "3929 RELL1\n",
+      "3930 PDS5A\n",
+      "3931 KIT\n",
+      "3932 HTN1\n",
+      "3933 MRPS18C\n",
+      "3934 PYURF\n",
+      "3935 PPA2\n",
+      "3936 CASP6\n",
+      "3937 LARP1B\n",
+      "3938 RAB33B-AS1\n",
+      "3939 SMARCA5-AS1\n",
+      "3940 NPY1R\n",
+      "3941 AADAT\n",
+      "3942 CTNND2\n",
+      "3943 CDH6\n",
+      "3944 AMACR\n",
+      "3945 TNFAIP8\n",
+      "3946 SLC22A4\n",
+      "3947 ENSG00000249593\n",
+      "3948 HARS1\n",
+      "3949 SCGB3A2\n",
+      "3950 FABP6-AS1\n",
+      "3951 SLU7\n",
+      "3952 UBTD2\n",
+      "3953 ADAMTS2\n",
+      "3954 ENSG00000285761\n",
+      "3955 LY6G5C\n",
+      "3956 CLIC1\n",
+      "3957 HLA-DPB1\n",
+      "3958 COL11A2\n",
+      "3959 RPS10-NUDT3\n",
+      "3960 SPDEF\n",
+      "3961 GSTA4\n",
+      "3962 PTP4A1\n",
+      "3963 B3GAT2\n",
+      "3964 PHIP\n",
+      "3965 OSTM1\n",
+      "3966 SESN1\n",
+      "3967 AMD1\n",
+      "3968 FYN\n",
+      "3969 HDAC2-AS2\n",
+      "3970 PTPRK\n",
+      "3971 OPRM1\n",
+      "3972 NUDT1\n",
+      "3973 GNA12\n",
+      "3974 IGF2BP3\n",
+      "3975 PSMA2\n",
+      "3976 TPST1\n",
+      "3977 KCTD7\n",
+      "3978 BCL7B\n",
+      "3979 YWHAG\n",
+      "3980 SEMA3C\n",
+      "3981 BAIAP2L1\n",
+      "3982 ZNHIT1\n",
+      "3983 ENSG00000272219\n",
+      "3984 ENSG00000273055\n",
+      "3985 METTL2B\n",
+      "3986 CEP41\n",
+      "3987 MEST\n",
+      "3988 NDUFB2-AS1\n",
+      "3989 MRPS33\n",
+      "3990 AGK\n",
+      "3991 ENSG00000283063\n",
+      "3992 TRBV5-6\n",
+      "3993 TRBV19\n",
+      "3994 ZYX\n",
+      "3995 OR2A25\n",
+      "3996 PRKAG2\n",
+      "3997 AGPAT5\n",
+      "3998 TNKS\n",
+      "3999 TUSC3\n",
+      "4000 VPS37A\n",
+      "4001 ATP6V1H\n",
+      "4002 LINC02986\n",
+      "4003 FABP5\n",
+      "4004 NUDCD1\n",
+      "4005 UTP23\n",
+      "4006 ZHX1\n",
+      "4007 NDUFB9\n",
+      "4008 CYRIB\n",
+      "4009 GSDMD\n",
+      "4010 OPLAH\n",
+      "4011 LRRC14\n",
+      "4012 RLN2\n",
+      "4013 PDCD1LG2\n",
+      "4014 PTPRD-AS1\n",
+      "4015 NFIB\n",
+      "4016 MOB3B\n",
+      "4017 LINGO2\n",
+      "4018 CCDC107\n",
+      "4019 FAM201A\n",
+      "4020 ANKS6\n",
+      "4021 LINC01492\n",
+      "4022 SMC2-DT\n",
+      "4023 ZNF462\n",
+      "4024 CDK9\n",
+      "4025 BBLN\n",
+      "4026 PPP1R26\n",
+      "4027 NOTCH1\n",
+      "4028 DPH7\n",
+      "4029 ENSG00000236990\n",
+      "4030 ENSG00000232807\n",
+      "4031 TAF3\n",
+      "4032 CUBN\n",
+      "4033 STAM-DT\n",
+      "4034 NEBL\n",
+      "4035 ZNF248\n",
+      "4036 LINC02632\n",
+      "4037 MSMB\n",
+      "4038 FUT11\n",
+      "4039 VDAC2\n",
+      "4040 COMTD1\n",
+      "4041 ENSG00000287475\n",
+      "4042 GLUD1\n",
+      "4043 RNLS\n",
+      "4044 PANK1\n",
+      "4045 CEP55\n",
+      "4046 ARHGAP19\n",
+      "4047 DCLRE1A\n",
+      "4048 FGFR2\n",
+      "4049 HTRA1\n",
+      "4050 PSTK\n",
+      "4051 MKI67\n",
+      "4052 MGMT\n",
+      "4053 LRRC56\n",
+      "4054 IGF2\n",
+      "4055 NAP1L4\n",
+      "4056 ZNF215\n",
+      "4057 ZBED5-AS1\n",
+      "4058 SPON1\n",
+      "4059 NCR3LG1\n",
+      "4060 LDHA\n",
+      "4061 ANO5\n",
+      "4062 CD82\n",
+      "4063 CREB3L1\n",
+      "4064 MDK\n",
+      "4065 MYBPC3\n",
+      "4066 MYRF\n",
+      "4067 COX8A\n",
+      "4068 CDC42EP2\n",
+      "4069 B4GAT1\n",
+      "4070 FAM86C1P\n",
+      "4071 RELT\n",
+      "4072 MMP3\n",
+      "4073 SCN2B\n",
+      "4074 TMEM218\n",
+      "4075 ZBTB44\n",
+      "4076 ITFG2-AS1\n",
+      "4077 FGF23\n",
+      "4078 C1RL\n",
+      "4079 PZP\n",
+      "4080 KLRD1\n",
+      "4081 SLCO1A2\n",
+      "4082 SOX5\n",
+      "4083 DIP2B\n",
+      "4084 CBX5\n",
+      "4085 GPR84\n",
+      "4086 BAZ2A\n",
+      "4087 LINC02397\n",
+      "4088 CDK17\n",
+      "4089 ARL1\n",
+      "4090 ENSG00000257543\n",
+      "4091 C12orf76\n",
+      "4092 HVCN1\n",
+      "4093 PLA2G1B\n",
+      "4094 ENSG00000287639\n",
+      "4095 LINC00426\n",
+      "4096 CCNA1\n",
+      "4097 GPALPP1\n",
+      "4098 TBC1D4\n",
+      "4099 ENSG00000278727\n",
+      "4100 GPC6\n",
+      "4101 DOCK9\n",
+      "4102 RASA3\n",
+      "4103 CDC16\n",
+      "4104 TRAV8-6\n",
+      "4105 PRMT5-DT\n",
+      "4106 C14orf119\n",
+      "4107 GNPNAT1\n",
+      "4108 DDHD1\n",
+      "4109 KTN1-AS1\n",
+      "4110 JDP2\n",
+      "4111 SLIRP\n",
+      "4112 CRIP1\n",
+      "4113 APBA2\n",
+      "4114 NOP10\n",
+      "4115 ENSG00000274281\n",
+      "4116 GPR176-DT\n",
+      "4117 SRP14\n",
+      "4118 IVD\n",
+      "4119 ENSG00000273972\n",
+      "4120 GABPB1\n",
+      "4121 MYO5C\n",
+      "4122 TPM1\n",
+      "4123 CIAO2A\n",
+      "4124 TIPIN\n",
+      "4125 PPCDC\n",
+      "4126 NRG4\n",
+      "4127 IDH3A\n",
+      "4128 NGRN\n",
+      "4129 HDDC3\n",
+      "4130 CHASERR\n",
+      "4131 MAPK8IP3\n",
+      "4132 TBL3\n",
+      "4133 MIR3677HG\n",
+      "4134 ENSG00000261140\n",
+      "4135 PRSS21\n",
+      "4136 MEFV\n",
+      "4137 ZSCAN32\n",
+      "4138 EMP2\n",
+      "4139 TNFRSF17\n",
+      "4140 ENSG00000276564\n",
+      "4141 MYH11\n",
+      "4142 ABCC1\n",
+      "4143 LYRM1\n",
+      "4144 ENSG00000287809\n",
+      "4145 TAOK2\n",
+      "4146 KRBOX5\n",
+      "4147 TMEM208\n",
+      "4148 FHOD1\n",
+      "4149 VAC14-AS1\n",
+      "4150 CFDP1\n",
+      "4151 ENSG00000261783\n",
+      "4152 ENSG00000261218\n",
+      "4153 ZC3H18\n",
+      "4154 LINC00304\n",
+      "4155 ENSG00000280136\n",
+      "4156 TLCD3A\n",
+      "4157 ITGAE\n",
+      "4158 UBE2G1\n",
+      "4159 SPNS3\n",
+      "4160 ENSG00000275413\n",
+      "4161 LRRC75A\n",
+      "4162 RSKR\n",
+      "4163 ENSG00000264304\n",
+      "4164 HEATR9\n",
+      "4165 TOP2A\n",
+      "4166 G6PC1\n",
+      "4167 LINC02071\n",
+      "4168 RNFT1-DT\n",
+      "4169 TACO1\n",
+      "4170 ENSG00000278730\n",
+      "4171 WIPI1\n",
+      "4172 ITGB4\n",
+      "4173 MXRA7\n",
+      "4174 CBX8\n",
+      "4175 EIF4A3\n",
+      "4176 RNF213\n",
+      "4177 SLC38A10\n",
+      "4178 LINC00482\n",
+      "4179 ACTG1\n",
+      "4180 SLC25A10\n",
+      "4181 PCYT2\n",
+      "4182 L3MBTL4\n",
+      "4183 FAM210A\n",
+      "4184 ENSG00000265737\n",
+      "4185 SNRPD1\n",
+      "4186 HRH4\n",
+      "4187 NFATC1\n",
+      "4188 OAZ1\n",
+      "4189 TLE5\n",
+      "4190 SIRT6\n",
+      "4191 RPS28\n",
+      "4192 ZNF561\n",
+      "4193 P2RY11\n",
+      "4194 KRI1\n",
+      "4195 GIPC1\n",
+      "4196 OR7A17\n",
+      "4197 CRTC1\n",
+      "4198 ENSG00000261770\n",
+      "4199 ZNF567-DT\n",
+      "4200 ZNF790\n",
+      "4201 ENSG00000268756\n",
+      "4202 MED29\n",
+      "4203 IRF2BP1\n",
+      "4204 DBP\n",
+      "4205 C19orf73\n",
+      "4206 GARIN5A\n",
+      "4207 IGLON5\n",
+      "4208 ZNF83\n",
+      "4209 LILRB4\n",
+      "4210 RDH13\n",
+      "4211 TNNT1\n",
+      "4212 ZNF134\n",
+      "4213 INSM1\n",
+      "4214 TP53INP2\n",
+      "4215 YWHAB\n",
+      "4216 ENSG00000277022\n",
+      "4217 RBM11\n",
+      "4218 CYYR1\n",
+      "4219 TIAM1\n",
+      "4220 SOD1-DT\n",
+      "4221 RCAN1\n",
+      "4222 HMGN1\n",
+      "4223 FAM3B\n",
+      "4224 IL17RA\n",
+      "4225 TMEM121B\n",
+      "4226 ADA2\n",
+      "4227 MICAL3\n",
+      "4228 TRMT2A\n",
+      "4229 ENSG00000287864\n",
+      "4230 SUSD2\n",
+      "4231 GUCD1\n",
+      "4232 CCDC117\n",
+      "4233 EWSR1\n",
+      "4234 RRP7A\n",
+      "4235 TBC1D22A-DT\n",
+      "4236 CHKB\n",
+      "4237 YY2\n",
+      "4238 DDX3X\n",
+      "4239 MIR222HG\n",
+      "4240 ABCB7\n",
+      "4241 CSTF2\n",
+      "4242 CENPI\n",
+      "4243 MT-ATP8\n",
+      "4244 LINC01786\n",
+      "4245 MMP23B\n",
+      "4246 TTC34\n",
+      "4247 LINC02780\n",
+      "4248 PRDM2\n",
+      "4249 ENSG00000261135\n",
+      "4250 PITHD1\n",
+      "4251 MAN1C1\n",
+      "4252 STMN1\n",
+      "4253 GPN2\n",
+      "4254 SDC3\n",
+      "4255 ENSG00000236065\n",
+      "4256 ENSG00000225313\n",
+      "4257 DLGAP3\n",
+      "4258 SMAP2\n",
+      "4259 CDKN2C\n",
+      "4260 ZRANB2\n",
+      "4261 NEGR1\n",
+      "4262 KYAT3\n",
+      "4263 RPAP2\n",
+      "4264 DDX20\n",
+      "4265 MOV10\n",
+      "4266 ENSG00000236140\n",
+      "4267 FMO5\n",
+      "4268 NBPF19\n",
+      "4269 CERS2\n",
+      "4270 TDRKH-AS1\n",
+      "4271 ADAM15\n",
+      "4272 CLK2\n",
+      "4273 FCGR2B\n",
+      "4274 ENSG00000273004\n",
+      "4275 LINC00862\n",
+      "4276 CAPN2\n",
+      "4277 ENSG00000237101\n",
+      "4278 ZNF496\n",
+      "4279 ENSG00000271947\n",
+      "4280 ITGB1BP1\n",
+      "4281 IFT172\n",
+      "4282 ZNF512\n",
+      "4283 PPP1CB-DT\n",
+      "4284 LTBP1\n",
+      "4285 SLC8A1-AS1\n",
+      "4286 COX7A2L\n",
+      "4287 LINC01800\n",
+      "4288 LINC02245\n",
+      "4289 SPRED2\n",
+      "4290 ENSG00000273275\n",
+      "4291 IGKV1-27\n",
+      "4292 FAM178B\n",
+      "4293 ACTR1B\n",
+      "4294 RGPD4-AS1\n",
+      "4295 TLK1\n",
+      "4296 PDK1-AS1\n",
+      "4297 CHRNA1\n",
+      "4298 ENSG00000270277\n",
+      "4299 LINC01792\n",
+      "4300 RAPH1\n",
+      "4301 SPAG16\n",
+      "4302 ZNF142\n",
+      "4303 BCS1L\n",
+      "4304 CNPPD1\n",
+      "4305 OBSL1\n",
+      "4306 TRIP12\n",
+      "4307 ENSG00000237126\n",
+      "4308 CNTN4\n",
+      "4309 LSM3\n",
+      "4310 METTL6\n",
+      "4311 EPM2AIP1\n",
+      "4312 APRG1\n",
+      "4313 LIMD1\n",
+      "4314 SLC6A20\n",
+      "4315 SETD2\n",
+      "4316 C3orf18\n",
+      "4317 RBM15B\n",
+      "4318 DNASE1L3\n",
+      "4319 CEP97\n",
+      "4320 ACP3\n",
+      "4321 STRIT1\n",
+      "4322 SLC33A1\n",
+      "4323 TNK2-AS1\n",
+      "4324 PCGF3-AS1\n",
+      "4325 CPLX1\n",
+      "4326 TMEM129\n",
+      "4327 C4orf19\n",
+      "4328 TMEM165\n",
+      "4329 ENSG00000269559\n",
+      "4330 MAPK10\n",
+      "4331 AP1AR\n",
+      "4332 CLCN3\n",
+      "4333 NEIL3\n",
+      "4334 IRF2-DT\n",
+      "4335 CENPU\n",
+      "4336 MIR3945HG\n",
+      "4337 NPR3\n",
+      "4338 RIMOC1\n",
+      "4339 PDE4D\n",
+      "4340 ENSG00000272354\n",
+      "4341 GTF2H2\n",
+      "4342 ENSG00000272040\n",
+      "4343 CHD1-DT\n",
+      "4344 PGGT1B\n",
+      "4345 SNX24\n",
+      "4346 PPP2CA-DT\n",
+      "4347 NQO2\n",
+      "4348 E2F3\n",
+      "4349 MRS2\n",
+      "4350 H4C1\n",
+      "4351 H1-3\n",
+      "4352 NRM\n",
+      "4353 ENSG00000272221\n",
+      "4354 HSPA1B\n",
+      "4355 SLC39A7\n",
+      "4356 TEAD3\n",
+      "4357 LHFPL5\n",
+      "4358 BRPF3-AS1\n",
+      "4359 FGD2\n",
+      "4360 ENSG00000261068\n",
+      "4361 MRPL14\n",
+      "4362 ENSG00000266680\n",
+      "4363 OGFRL1\n",
+      "4364 COQ3\n",
+      "4365 TRAF3IP2\n",
+      "4366 MROCKI\n",
+      "4367 RSPH4A\n",
+      "4368 CCN2\n",
+      "4369 TNFAIP3\n",
+      "4370 UST\n",
+      "4371 EZR\n",
+      "4372 ENSG00000177706\n",
+      "4373 ADAP1\n",
+      "4374 SLC29A4\n",
+      "4375 TNRC18\n",
+      "4376 ENSG00000230733\n",
+      "4377 ENSG00000243004\n",
+      "4378 TYW1B\n",
+      "4379 SEMA3A\n",
+      "4380 DMTF1-AS1\n",
+      "4381 RUNDC3B\n",
+      "4382 ENSG00000228113\n",
+      "4383 ASB4\n",
+      "4384 DYNC1I1\n",
+      "4385 SPDYE3\n",
+      "4386 ATXN7L1\n",
+      "4387 LINC03008\n",
+      "4388 ENSG00000273319\n",
+      "4389 CHCHD3\n",
+      "4390 SLC35B4\n",
+      "4391 UBN2\n",
+      "4392 PARP12\n",
+      "4393 DENND11\n",
+      "4394 TRBV7-2\n",
+      "4395 TRBV15\n",
+      "4396 ENSG00000286912\n",
+      "4397 ENSG00000255495\n",
+      "4398 ENTPD4\n",
+      "4399 POLB\n",
+      "4400 LINC02947\n",
+      "4401 ENSG00000272343\n",
+      "4402 PENK\n",
+      "4403 TMEM70\n",
+      "4404 E2F5-DT\n",
+      "4405 C8orf88\n",
+      "4406 GASAL1\n",
+      "4407 DNAAF11\n",
+      "4408 SLA\n",
+      "4409 JRK\n",
+      "4410 ENSG00000272842\n",
+      "4411 NDUFB6\n",
+      "4412 GALT\n",
+      "4413 PIP5K1B\n",
+      "4414 TGFBR1\n",
+      "4415 FKTN\n",
+      "4416 PHF19\n",
+      "4417 TRAF1\n",
+      "4418 ADGRD2\n",
+      "4419 DOLPP1\n",
+      "4420 NCS1\n",
+      "4421 WDR5\n",
+      "4422 SNAPC4\n",
+      "4423 NMT2\n",
+      "4424 KIF5B\n",
+      "4425 ZNF22-AS1\n",
+      "4426 ALOX5\n",
+      "4427 ZFAND4\n",
+      "4428 SGMS1\n",
+      "4429 CDK1\n",
+      "4430 NRBF2\n",
+      "4431 ENSG00000271409\n",
+      "4432 ENSG00000272599\n",
+      "4433 PLAC9\n",
+      "4434 WAPL\n",
+      "4435 RAB11FIP2\n",
+      "4436 TIAL1\n",
+      "4437 FUOM\n",
+      "4438 IFITM3\n",
+      "4439 PKP3\n",
+      "4440 ACP2\n",
+      "4441 MADD\n",
+      "4442 VPS37C\n",
+      "4443 NUDT22\n",
+      "4444 SIPA1\n",
+      "4445 EFEMP2\n",
+      "4446 NADSYN1\n",
+      "4447 ENSG00000248671\n",
+      "4448 RAB38\n",
+      "4449 DISC1FP1\n",
+      "4450 HOATZ\n",
+      "4451 HINFP\n",
+      "4452 SC5D\n",
+      "4453 NRGN\n",
+      "4454 WNT5B\n",
+      "4455 GALNT8\n",
+      "4456 C1RL-AS1\n",
+      "4457 M6PR\n",
+      "4458 A2M-AS1\n",
+      "4459 TAS2R20\n",
+      "4460 LMO3\n",
+      "4461 RASSF8\n",
+      "4462 TMEM117\n",
+      "4463 TUBA1B-AS1\n",
+      "4464 DNAJC22\n",
+      "4465 TAMALIN\n",
+      "4466 KRT5\n",
+      "4467 SARNP\n",
+      "4468 ORMDL2\n",
+      "4469 MBD6\n",
+      "4470 NAV3\n",
+      "4471 PAWR\n",
+      "4472 ATP2B1-AS1\n",
+      "4473 ACACB\n",
+      "4474 MAPKAPK5\n",
+      "4475 TPCN1\n",
+      "4476 WSB2\n",
+      "4477 EIF2B1\n",
+      "4478 BRI3BP\n",
+      "4479 ULK1\n",
+      "4480 PABPC3\n",
+      "4481 N4BP2L1\n",
+      "4482 MED4\n",
+      "4483 DZIP1\n",
+      "4484 ENSG00000224356\n",
+      "4485 EFNB2\n",
+      "4486 PCK2\n",
+      "4487 NEDD8\n",
+      "4488 ACTR10\n",
+      "4489 PCNX4\n",
+      "4490 ENSG00000258561\n",
+      "4491 PLEK2\n",
+      "4492 FLVCR2\n",
+      "4493 CYP46A1\n",
+      "4494 MEG3\n",
+      "4495 ENSG00000258861\n",
+      "4496 IGHV3-7\n",
+      "4497 DPH6\n",
+      "4498 BUB1B\n",
+      "4499 DUOX1\n",
+      "4500 GALK2\n",
+      "4501 AQP9\n",
+      "4502 RNF111\n",
+      "4503 ENSG00000276651\n",
+      "4504 ANKDD1A\n",
+      "4505 CD276\n",
+      "4506 SNUPN\n",
+      "4507 PSMA4\n",
+      "4508 GOLGA6L4\n",
+      "4509 CERS3\n",
+      "4510 FAM234A\n",
+      "4511 AXIN1\n",
+      "4512 SNHG9\n",
+      "4513 TBC1D24\n",
+      "4514 ENSG00000089486\n",
+      "4515 ENSG00000259939\n",
+      "4516 CPPED1\n",
+      "4517 MOSMO\n",
+      "4518 ENSG00000275441\n",
+      "4519 AKTIP\n",
+      "4520 CMTM2\n",
+      "4521 PDF\n",
+      "4522 ZFHX3-AS1\n",
+      "4523 CTRB1\n",
+      "4524 ENSG00000260495\n",
+      "4525 ENSG00000272923\n",
+      "4526 GALNS\n",
+      "4527 ENSG00000256982\n",
+      "4528 CRK\n",
+      "4529 NTN1\n",
+      "4530 ENSG00000266538\n",
+      "4531 ZNF286A\n",
+      "4532 ZNF287\n",
+      "4533 GID4\n",
+      "4534 DRG2\n",
+      "4535 FLII\n",
+      "4536 GIT1\n",
+      "4537 ENSG00000265334\n",
+      "4538 TMEM98\n",
+      "4539 ENSG00000277501\n",
+      "4540 EPOP\n",
+      "4541 CISD3\n",
+      "4542 P3H4\n",
+      "4543 MAP3K14-AS1\n",
+      "4544 ARL17B\n",
+      "4545 CCDC47\n",
+      "4546 ABCA6\n",
+      "4547 MGAT5B\n",
+      "4548 LINC02078\n",
+      "4549 DCXR\n",
+      "4550 ENSG00000264635\n",
+      "4551 EPB41L3\n",
+      "4552 ENSG00000287509\n",
+      "4553 ENSG00000267199\n",
+      "4554 KATNAL2\n",
+      "4555 C18orf32\n",
+      "4556 ENSG00000286283\n",
+      "4557 ATP8B1\n",
+      "4558 GAMT\n",
+      "4559 ENSG00000267769\n",
+      "4560 CLPP\n",
+      "4561 PRAM1\n",
+      "4562 ZNF562\n",
+      "4563 ENSG00000267062\n",
+      "4564 ADGRE5\n",
+      "4565 NFKBID\n",
+      "4566 HCST\n",
+      "4567 EGLN2\n",
+      "4568 ZNF575\n",
+      "4569 ZNF226\n",
+      "4570 PPP1R37\n",
+      "4571 EML2-AS1\n",
+      "4572 PNMA8A\n",
+      "4573 ZNF845\n",
+      "4574 ENSG00000125505\n",
+      "4575 LILRA5\n",
+      "4576 LENG9\n",
+      "4577 ENSG00000267523\n",
+      "4578 ENSG00000268912\n",
+      "4579 TBC1D20\n",
+      "4580 NDUFAF5\n",
+      "4581 BFSP1\n",
+      "4582 ZNF133\n",
+      "4583 CD93\n",
+      "4584 ABHD12\n",
+      "4585 ERGIC3\n",
+      "4586 TGIF2\n",
+      "4587 SNAI1\n",
+      "4588 ENSG00000235415\n",
+      "4589 FAM209A\n",
+      "4590 ENSG00000179242\n",
+      "4591 FNDC11\n",
+      "4592 HSF2BP\n",
+      "4593 DUXAP8\n",
+      "4594 HIRA\n",
+      "4595 RTN4R\n",
+      "4596 ENSG00000197210\n",
+      "4597 HIC2\n",
+      "4598 CHCHD10\n",
+      "4599 ENSG00000279467\n",
+      "4600 ENSG00000272787\n",
+      "4601 XBP1\n",
+      "4602 CBX6\n",
+      "4603 RIBC2\n",
+      "4604 TAFA5\n",
+      "4605 PIR\n",
+      "4606 PRRG1\n",
+      "4607 CDK16\n",
+      "4608 USP27X\n",
+      "4609 STARD8\n",
+      "4610 RPL36A\n",
+      "4611 TCEAL4\n",
+      "4612 TCEAL3\n",
+      "4613 ZCCHC18\n",
+      "4614 KLHL13\n",
+      "4615 XIAP\n",
+      "4616 STAG2\n",
+      "4617 BCORL1\n",
+      "4618 MECP2\n",
+      "4619 ENSG00000280195\n",
+      "4620 MT-CO2\n",
+      "4621 MAFIP\n",
+      "4622 C1orf174\n",
+      "4623 MUL1\n",
+      "4624 ENSG00000231953\n",
+      "4625 ENSG00000236528\n",
+      "4626 AHDC1\n",
+      "4627 DNAJC8\n",
+      "4628 TEKT2\n",
+      "4629 EDN2\n",
+      "4630 PPIH\n",
+      "4631 CDC20-DT\n",
+      "4632 TTC22\n",
+      "4633 DLEU2L\n",
+      "4634 SPATA1\n",
+      "4635 DDAH1\n",
+      "4636 OVGP1\n",
+      "4637 OLFML3\n",
+      "4638 NRAS\n",
+      "4639 CD58\n",
+      "4640 TTF2\n",
+      "4641 NBPF26\n",
+      "4642 APH1A\n",
+      "4643 PSMD4\n",
+      "4644 S100A4\n",
+      "4645 UBAP2L\n",
+      "4646 MNDA\n",
+      "4647 TSTD1\n",
+      "4648 DNM3\n",
+      "4649 KIAA0040\n",
+      "4650 TOR3A\n",
+      "4651 ENSG00000236069\n",
+      "4652 ENSG00000287989\n",
+      "4653 ENSG00000261573\n",
+      "4654 KIF14\n",
+      "4655 NAV1\n",
+      "4656 RNPEP\n",
+      "4657 TMEM183A\n",
+      "4658 SOX13\n",
+      "4659 SNAP47\n",
+      "4660 H2BC26\n",
+      "4661 PXDN\n",
+      "4662 MYT1L\n",
+      "4663 CPSF3\n",
+      "4664 ENSG00000243491\n",
+      "4665 SF3B6\n",
+      "4666 FOSL2\n",
+      "4667 HEATR5B\n",
+      "4668 MAP4K3-DT\n",
+      "4669 CCDC88A\n",
+      "4670 SERTAD2\n",
+      "4671 NFU1\n",
+      "4672 SNRNP27\n",
+      "4673 TMEM87B\n",
+      "4674 HS6ST1\n",
+      "4675 BAZ2B\n",
+      "4676 GALNT3\n",
+      "4677 RBM45\n",
+      "4678 SCHLAP1\n",
+      "4679 TRAK2\n",
+      "4680 ENSG00000231955\n",
+      "4681 SNHG31\n",
+      "4682 EPHA4\n",
+      "4683 SETD5\n",
+      "4684 ENSG00000272529\n",
+      "4685 ARPP21\n",
+      "4686 CCRL2\n",
+      "4687 SHISA5\n",
+      "4688 IP6K2\n",
+      "4689 SEMA3F-AS1\n",
+      "4690 RPL29\n",
+      "4691 ENSG00000286699\n",
+      "4692 LINC01205\n",
+      "4693 ZNF80\n",
+      "4694 ZBTB20\n",
+      "4695 PLA1A\n",
+      "4696 RAP2B\n",
+      "4697 LINC02029\n",
+      "4698 GNB4\n",
+      "4699 CLCN2\n",
+      "4700 LINC00887\n",
+      "4701 MUC4\n",
+      "4702 PCYT1A\n",
+      "4703 PTPN13\n",
+      "4704 SPARCL1\n",
+      "4705 TIFA\n",
+      "4706 GYPA\n",
+      "4707 GALNT7-DT\n",
+      "4708 DCTD\n",
+      "4709 OTULIN\n",
+      "4710 ENSG00000272370\n",
+      "4711 GTF2H2C\n",
+      "4712 FAM81B\n",
+      "4713 RHOBTB3\n",
+      "4714 LNPEP\n",
+      "4715 RGMB\n",
+      "4716 CHD1\n",
+      "4717 CAMLG\n",
+      "4718 SMIM33\n",
+      "4719 ARSI\n",
+      "4720 TCOF1\n",
+      "4721 CCDC69\n",
+      "4722 LINC02533\n",
+      "4723 ENSG00000288046\n",
+      "4724 H4C9\n",
+      "4725 H4C11\n",
+      "4726 MRPS18B\n",
+      "4727 SAYSD1\n",
+      "4728 ZNF451\n",
+      "4729 SNX14\n",
+      "4730 ENSG00000287616\n",
+      "4731 SOBP\n",
+      "4732 BCLAF1\n",
+      "4733 NHSL1-AS1\n",
+      "4734 ADAT2\n",
+      "4735 TAB2\n",
+      "4736 ZC3H12D\n",
+      "4737 AFDN-DT\n",
+      "4738 PHF10\n",
+      "4739 SUN1\n",
+      "4740 CCDC126\n",
+      "4741 SNX10\n",
+      "4742 PURB\n",
+      "4743 ENSG00000260997\n",
+      "4744 ZNF117\n",
+      "4745 LIMK1\n",
+      "4746 RCC1L\n",
+      "4747 RHBDD2\n",
+      "4748 FAM200A\n",
+      "4749 LAMTOR4\n",
+      "4750 BCAP29\n",
+      "4751 LSMEM1\n",
+      "4752 EZH2\n",
+      "4753 KRBA1\n",
+      "4754 ACTR3B\n",
+      "4755 ENSG00000255394\n",
+      "4756 ENSG00000253557\n",
+      "4757 DCTN6-DT\n",
+      "4758 ENSG00000253939\n",
+      "4759 GOLGA7\n",
+      "4760 BPNT2\n",
+      "4761 TMEM64\n",
+      "4762 TNFRSF11B\n",
+      "4763 LYNX1\n",
+      "4764 MROH1\n",
+      "4765 SCX\n",
+      "4766 RPL8\n",
+      "4767 PUM3\n",
+      "4768 CCDC171\n",
+      "4769 BNC2\n",
+      "4770 PLIN2\n",
+      "4771 SLC24A2\n",
+      "4772 LINC01242\n",
+      "4773 RGP1\n",
+      "4774 ENSG00000235659\n",
+      "4775 SMC5\n",
+      "4776 LINC01504\n",
+      "4777 PCA3\n",
+      "4778 UBQLN1-AS1\n",
+      "4779 TUT7\n",
+      "4780 ERCC6L2-AS1\n",
+      "4781 GNG10\n",
+      "4782 ATP6V1G1\n",
+      "4783 ENSG00000230289\n",
+      "4784 GTF3C4\n",
+      "4785 LINC02846\n",
+      "4786 IDI2-AS1\n",
+      "4787 ABI1\n",
+      "4788 ENSG00000285803\n",
+      "4789 JMJD1C-AS1\n",
+      "4790 VPS26A\n",
+      "4791 TSPAN15\n",
+      "4792 AGAP5\n",
+      "4793 ADIRF\n",
+      "4794 TWNK\n",
+      "4795 SUFU\n",
+      "4796 SORCS3\n",
+      "4797 ZNF511\n",
+      "4798 SIGIRR\n",
+      "4799 SWAP70\n",
+      "4800 ENSG00000203258\n",
+      "4801 RPS13\n",
+      "4802 PIK3C2A\n",
+      "4803 UEVLD\n",
+      "4804 PAX6\n",
+      "4805 NAT10\n",
+      "4806 ENSG00000251194\n",
+      "4807 AMBRA1\n",
+      "4808 ATG13\n",
+      "4809 AGBL2\n",
+      "4810 STX3\n",
+      "4811 TCN1\n",
+      "4812 PRPF19\n",
+      "4813 STIP1\n",
+      "4814 GPR137\n",
+      "4815 TIGD3\n",
+      "4816 PDE2A-AS2\n",
+      "4817 NDUFC2\n",
+      "4818 SESN3\n",
+      "4819 CBL\n",
+      "4820 THYN1\n",
+      "4821 CCND2\n",
+      "4822 NANOG\n",
+      "4823 ENSG00000288043\n",
+      "4824 CLEC4A\n",
+      "4825 KLRK1-AS1\n",
+      "4826 ENSG00000275560\n",
+      "4827 PPFIBP1\n",
+      "4828 LINC02422\n",
+      "4829 ENSG00000276115\n",
+      "4830 DDX23\n",
+      "4831 CERS5\n",
+      "4832 ATF7\n",
+      "4833 COQ10A\n",
+      "4834 NDUFA4L2\n",
+      "4835 R3HDM2\n",
+      "4836 MON2\n",
+      "4837 MON2-AS1\n",
+      "4838 ENSG00000286563\n",
+      "4839 TESC\n",
+      "4840 ENSG00000274292\n",
+      "4841 HPD\n",
+      "4842 HCAR3\n",
+      "4843 NCOR2\n",
+      "4844 ANKLE2\n",
+      "4845 TNFRSF19\n",
+      "4846 LINC00384\n",
+      "4847 HSPH1\n",
+      "4848 HNRNPC\n",
+      "4849 TRAV3\n",
+      "4850 TRAV22\n",
+      "4851 AJUBA\n",
+      "4852 ENSG00000215271\n",
+      "4853 LTB4R2\n",
+      "4854 EGLN3\n",
+      "4855 ENSG00000258938\n",
+      "4856 ENSG00000254718\n",
+      "4857 VASH1-AS1\n",
+      "4858 GALC\n",
+      "4859 PACS2\n",
+      "4860 MTA1\n",
+      "4861 IGHV3-43\n",
+      "4862 IGHV5-51\n",
+      "4863 IGHV3-72\n",
+      "4864 NSMCE3\n",
+      "4865 TRPM1\n",
+      "4866 CDIN1\n",
+      "4867 BMF\n",
+      "4868 HAUS2\n",
+      "4869 TMEM62\n",
+      "4870 PDCD7\n",
+      "4871 C15orf61\n",
+      "4872 GLCE\n",
+      "4873 PAQR5\n",
+      "4874 LRRC49\n",
+      "4875 CELF6\n",
+      "4876 NPTN\n",
+      "4877 HMG20A\n",
+      "4878 FAH\n",
+      "4879 ENSG00000261407\n",
+      "4880 CCDC78\n",
+      "4881 ZNF213-AS1\n",
+      "4882 DNAJA3\n",
+      "4883 ENSG00000261789\n",
+      "4884 ENSG00000263080\n",
+      "4885 MRTFB\n",
+      "4886 BFAR\n",
+      "4887 ENSG00000259925\n",
+      "4888 GP2\n",
+      "4889 DCUN1D3\n",
+      "4890 ERN2\n",
+      "4891 NSMCE1-DT\n",
+      "4892 TBX6\n",
+      "4893 NPIPB13\n",
+      "4894 ENSG00000261512\n",
+      "4895 HEATR3\n",
+      "4896 MT1G\n",
+      "4897 MMP15\n",
+      "4898 GINS3\n",
+      "4899 ENSG00000237718\n",
+      "4900 DPEP2\n",
+      "4901 NUDT7\n",
+      "4902 SDR42E1\n",
+      "4903 CDT1\n",
+      "4904 ZNF778\n",
+      "4905 ANKRD11\n",
+      "4906 CHMP1A\n",
+      "4907 CDK10\n",
+      "4908 SMYD4\n",
+      "4909 MED31\n",
+      "4910 ALOX12\n",
+      "4911 EIF5A\n",
+      "4912 SENP3\n",
+      "4913 TRAPPC1\n",
+      "4914 VTN\n",
+      "4915 ENSG00000264125\n",
+      "4916 SLFN12\n",
+      "4917 RAMP2-AS1\n",
+      "4918 FAM215B\n",
+      "4919 LINC01482\n",
+      "4920 TTYH2\n",
+      "4921 JPT1\n",
+      "4922 BAIAP2-DT\n",
+      "4923 HGS\n",
+      "4924 FN3K\n",
+      "4925 ENSG00000264254\n",
+      "4926 PTPRM\n",
+      "4927 ENSG00000273284\n",
+      "4928 TUBB6\n",
+      "4929 CDH2\n",
+      "4930 ZNF24\n",
+      "4931 INO80C\n",
+      "4932 SETBP1-DT\n",
+      "4933 DCC\n",
+      "4934 RPL36\n",
+      "4935 ARHGEF18\n",
+      "4936 XAB2\n",
+      "4937 ANGPTL4\n",
+      "4938 ZNF564\n",
+      "4939 DAND5\n",
+      "4940 ENSG00000267598\n",
+      "4941 ILVBL\n",
+      "4942 GTPBP3\n",
+      "4943 ZFP14\n",
+      "4944 KCNK6\n",
+      "4945 IFNL1\n",
+      "4946 POU2F2\n",
+      "4947 NECTIN2\n",
+      "4948 IGFL2\n",
+      "4949 CCDC9\n",
+      "4950 MAMSTR\n",
+      "4951 IL4I1\n",
+      "4952 ENSG00000269392\n",
+      "4953 ZNF350-AS1\n",
+      "4954 TTYH1\n",
+      "4955 EPS8L1\n",
+      "4956 ZNF419\n",
+      "4957 ZNF329\n",
+      "4958 ZNF446\n",
+      "4959 TRIM28\n",
+      "4960 SRXN1\n",
+      "4961 ANKEF1\n",
+      "4962 THBD\n",
+      "4963 ACSS1\n",
+      "4964 ZNF341\n",
+      "4965 MMP24OS\n",
+      "4966 UQCC1\n",
+      "4967 DHX35\n",
+      "4968 CCN5\n",
+      "4969 SNX21\n",
+      "4970 ENSG00000286159\n",
+      "4971 HELZ2\n",
+      "4972 LINC02573\n",
+      "4973 NDUFV3\n",
+      "4974 MCM3AP-AS1\n",
+      "4975 ZNF74\n",
+      "4976 MYH9-DT\n",
+      "4977 ENSG00000273096\n",
+      "4978 APOBEC3C\n",
+      "4979 ENSG00000279175\n",
+      "4980 TUBGCP6\n",
+      "4981 TYMP\n",
+      "4982 HCCS-DT\n",
+      "4983 AP1S2\n",
+      "4984 USP9X\n",
+      "4985 WAS\n",
+      "4986 APEX2\n",
+      "4987 ENSG00000286977\n",
+      "4988 IGBP1\n",
+      "4989 LINC00891\n",
+      "4990 CHM\n",
+      "4991 FAM199X\n",
+      "4992 PRPS1\n",
+      "4993 ENSG00000261409\n",
+      "4994 RAP2C\n",
+      "4995 MOSPD1\n",
+      "4996 MTMR1\n",
+      "4997 SLC10A3\n",
+      "4998 ENSG00000278198\n",
+      "4999 ENSG00000278817\n",
+      "5000 PLEKHN1\n",
+      "5001 MRPL20-AS1\n",
+      "5002 ANKRD65\n",
+      "5003 RBP7\n",
+      "5004 KIF17\n",
+      "5005 HNRNPR\n",
+      "5006 ATP5IF1\n",
+      "5007 AGO4\n",
+      "5008 INPP5B\n",
+      "5009 ST3GAL3\n",
+      "5010 ENSG00000225721\n",
+      "5011 KTI12\n",
+      "5012 MIR4422HG\n",
+      "5013 ERICH3\n",
+      "5014 ACADM\n",
+      "5015 LINC02801\n",
+      "5016 TRMT13\n",
+      "5017 VCAM1\n",
+      "5018 DPH5-DT\n",
+      "5019 MAB21L3\n",
+      "5020 GPR89A\n",
+      "5021 POLR3GL\n",
+      "5022 LINC01138\n",
+      "5023 ENSG00000264207\n",
+      "5024 ADAMTSL4\n",
+      "5025 ENSG00000282386\n",
+      "5026 JTB-DT\n",
+      "5027 TPM3\n",
+      "5028 EFNA3\n",
+      "5029 RIT1\n",
+      "5030 ATF6-DT\n",
+      "5031 ENSG00000232995\n",
+      "5032 NUF2\n",
+      "5033 MPZL1\n",
+      "5034 ENSG00000213062\n",
+      "5035 CENPL\n",
+      "5036 CDC73\n",
+      "5037 CNTN2\n",
+      "5038 NUAK2\n",
+      "5039 DEGS1\n",
+      "5040 ENSG00000237481\n",
+      "5041 ENSG00000273367\n",
+      "5042 ENSG00000228830\n",
+      "5043 ZNF695\n",
+      "5044 ODC1-DT\n",
+      "5045 SLC35F6\n",
+      "5046 CAD\n",
+      "5047 GCKR\n",
+      "5048 SPAST\n",
+      "5049 ENSG00000272054\n",
+      "5050 RPS27A\n",
+      "5051 LBX2\n",
+      "5052 ENSG00000271452\n",
+      "5053 LRRTM4\n",
+      "5054 ENSG00000287625\n",
+      "5055 PTCD3\n",
+      "5056 IGKV2D-30\n",
+      "5057 TTL\n",
+      "5058 CFAP221\n",
+      "5059 GYPC\n",
+      "5060 TMEM163\n",
+      "5061 LINC02993\n",
+      "5062 PSMD14-DT\n",
+      "5063 FASTKD1\n",
+      "5064 METTL5\n",
+      "5065 STK17B\n",
+      "5066 MAIP1\n",
+      "5067 HYCC2\n",
+      "5068 ENSG00000273118\n",
+      "5069 SLC11A1\n",
+      "5070 TMEM198\n",
+      "5071 AGFG1\n",
+      "5072 EIF4E2\n",
+      "5073 ENSG00000259793\n",
+      "5074 SCLY\n",
+      "5075 MOBP\n",
+      "5076 ITIH4\n",
+      "5077 PXK\n",
+      "5078 CCDC80\n",
+      "5079 ZBTB20-AS2\n",
+      "5080 GSK3B\n",
+      "5081 FAM162A\n",
+      "5082 CHST13\n",
+      "5083 SEC61A1\n",
+      "5084 BFSP2\n",
+      "5085 STAG1\n",
+      "5086 STAG1-DT\n",
+      "5087 DBR1\n",
+      "5088 HPS3\n",
+      "5089 SOX2\n",
+      "5090 EEF1AKMT4\n",
+      "5091 LRCH3\n",
+      "5092 CYTL1\n",
+      "5093 AFAP1\n",
+      "5094 ACOX3\n",
+      "5095 TLR6\n",
+      "5096 TEC\n",
+      "5097 PDGFRA\n",
+      "5098 DAPP1\n",
+      "5099 ENSG00000270394\n",
+      "5100 TRIM2\n",
+      "5101 ZDHHC11\n",
+      "5102 CCL28\n",
+      "5103 GZMA\n",
+      "5104 BTF3\n",
+      "5105 F2RL2\n",
+      "5106 ATP6AP1L\n",
+      "5107 XRCC4\n",
+      "5108 WDR36\n",
+      "5109 MCC\n",
+      "5110 IL3\n",
+      "5111 SMAD5-AS1\n",
+      "5112 FBXO38-DT\n",
+      "5113 ARHGEF37\n",
+      "5114 CCNJL\n",
+      "5115 GPRIN1\n",
+      "5116 TBC1D9B\n",
+      "5117 RIPK1\n",
+      "5118 ENSG00000225092\n",
+      "5119 LINC00240\n",
+      "5120 PRR3\n",
+      "5121 IER3-AS1\n",
+      "5122 PHF1\n",
+      "5123 ENSG00000204092\n",
+      "5124 FRS3\n",
+      "5125 RCAN2\n",
+      "5126 PHF3\n",
+      "5127 SMAP1\n",
+      "5128 IBTK\n",
+      "5129 LAMA4\n",
+      "5130 CALHM6\n",
+      "5131 PEX3\n",
+      "5132 EPM2A-DT\n",
+      "5133 PPIL4\n",
+      "5134 ENSG00000227627\n",
+      "5135 GTF2H5\n",
+      "5136 WTAP\n",
+      "5137 THBS2-AS1\n",
+      "5138 FAM120B\n",
+      "5139 CPVL\n",
+      "5140 DPY19L1\n",
+      "5141 AOAH\n",
+      "5142 MPLKIP\n",
+      "5143 COA1\n",
+      "5144 ENSG00000229628\n",
+      "5145 LANCL2\n",
+      "5146 ZNF736\n",
+      "5147 ENSG00000226824\n",
+      "5148 SPDYE16\n",
+      "5149 POMZP3\n",
+      "5150 APTR\n",
+      "5151 CROT\n",
+      "5152 TMEM130\n",
+      "5153 TRIM4\n",
+      "5154 GIGYF1\n",
+      "5155 PLOD3\n",
+      "5156 RASA4\n",
+      "5157 IFRD1\n",
+      "5158 SND1-DT\n",
+      "5159 SND1-IT1\n",
+      "5160 EPHA1\n",
+      "5161 TPK1\n",
+      "5162 ZNF777\n",
+      "5163 KMT2C\n",
+      "5164 CTSB\n",
+      "5165 NKX3-1\n",
+      "5166 ENSG00000272256\n",
+      "5167 ENSG00000272375\n",
+      "5168 GINS4\n",
+      "5169 PIP4P2\n",
+      "5170 BAALC\n",
+      "5171 MTBP\n",
+      "5172 FAM91A1\n",
+      "5173 WASHC1\n",
+      "5174 ENSG00000284418\n",
+      "5175 RASEF\n",
+      "5176 KIF27\n",
+      "5177 GOLM1\n",
+      "5178 GADD45G\n",
+      "5179 NANS\n",
+      "5180 SLC44A1\n",
+      "5181 PRPF4\n",
+      "5182 MEGF9\n",
+      "5183 STXBP1\n",
+      "5184 SH2D3C\n",
+      "5185 DOLK\n",
+      "5186 TPRN\n",
+      "5187 ITGB1\n",
+      "5188 LINC01553\n",
+      "5189 MSS51\n",
+      "5190 AP3M1\n",
+      "5191 CCNJ\n",
+      "5192 SLF2\n",
+      "5193 PPRC1\n",
+      "5194 ELOVL3\n",
+      "5195 SORCS1\n",
+      "5196 TACC2\n",
+      "5197 ENSG00000226899\n",
+      "5198 PRAP1\n",
+      "5199 DEAF1\n",
+      "5200 WEE1\n",
+      "5201 AMPD3\n",
+      "5202 LYVE1\n",
+      "5203 PLEKHA7\n",
+      "5204 SAA1\n",
+      "5205 ENSG00000246225\n",
+      "5206 CD44\n",
+      "5207 ALKBH3\n",
+      "5208 MPEG1\n",
+      "5209 SLC3A2\n",
+      "5210 FAU\n",
+      "5211 TMEM134\n",
+      "5212 CARD17P\n",
+      "5213 ZBTB16\n",
+      "5214 CD3G\n",
+      "5215 ENSG00000254740\n",
+      "5216 ENSG00000279342\n",
+      "5217 KDM5A\n",
+      "5218 FOXM1\n",
+      "5219 LINC02470\n",
+      "5220 PYROXD1\n",
+      "5221 ENSG00000258101\n",
+      "5222 SMUG1\n",
+      "5223 ENSG00000257894\n",
+      "5224 OAS1\n",
+      "5225 BICDL1\n",
+      "5226 RILPL1\n",
+      "5227 SLC15A4\n",
+      "5228 LATS2\n",
+      "5229 CDK8\n",
+      "5230 RPL21\n",
+      "5231 PAN3-AS1\n",
+      "5232 PAN3\n",
+      "5233 HMGB1\n",
+      "5234 NBEA\n",
+      "5235 ENSG00000231856\n",
+      "5236 CKAP2\n",
+      "5237 LINC00393\n",
+      "5238 ANKRD10-IT1\n",
+      "5239 C13orf46\n",
+      "5240 TTC5\n",
+      "5241 TRAV12-1\n",
+      "5242 ENSG00000288100\n",
+      "5243 SNX6\n",
+      "5244 FAM177A1\n",
+      "5245 ENSG00000258526\n",
+      "5246 KLHL28\n",
+      "5247 SOCS4\n",
+      "5248 ZFP36L1\n",
+      "5249 ENSG00000273565\n",
+      "5250 SPTLC2\n",
+      "5251 IGHV1-69-2\n",
+      "5252 ARHGAP11B\n",
+      "5253 ENSG00000259408\n",
+      "5254 TMEM87A\n",
+      "5255 ATOSA\n",
+      "5256 RAB27A\n",
+      "5257 LACTB\n",
+      "5258 USP3-AS1\n",
+      "5259 RPP25\n",
+      "5260 ENSG00000269951\n",
+      "5261 ST20\n",
+      "5262 HOMER2\n",
+      "5263 IDH2\n",
+      "5264 ENSG00000278022\n",
+      "5265 MRPL28\n",
+      "5266 GNG13\n",
+      "5267 UBALD1\n",
+      "5268 ABAT\n",
+      "5269 ENSG00000261560\n",
+      "5270 NPIPB2\n",
+      "5271 RBBP6\n",
+      "5272 ZG16\n",
+      "5273 ZNF764\n",
+      "5274 ENSG00000260616\n",
+      "5275 MT1M\n",
+      "5276 CCL22\n",
+      "5277 TMEM170A\n",
+      "5278 CHST6\n",
+      "5279 RNF166\n",
+      "5280 DEF8\n",
+      "5281 TIMM22\n",
+      "5282 ENSG00000262810\n",
+      "5283 PIMREG\n",
+      "5284 SLC16A11\n",
+      "5285 RANGRF\n",
+      "5286 FAM106A\n",
+      "5287 LYRM9\n",
+      "5288 CRLF3\n",
+      "5289 CCL23\n",
+      "5290 PLXDC1\n",
+      "5291 RAPGEFL1\n",
+      "5292 KRT14\n",
+      "5293 GHDC\n",
+      "5294 LRRC46\n",
+      "5295 DLX4\n",
+      "5296 STRADA\n",
+      "5297 C17orf58\n",
+      "5298 FAM20A\n",
+      "5299 NHERF1\n",
+      "5300 PRCD\n",
+      "5301 ENSG00000267568\n",
+      "5302 CCDC40\n",
+      "5303 CSNK1D\n",
+      "5304 VAPA\n",
+      "5305 SLC14A1\n",
+      "5306 SNHG22\n",
+      "5307 MBD2\n",
+      "5308 FECH\n",
+      "5309 ATP8B3\n",
+      "5310 PIAS4\n",
+      "5311 HNRNPM\n",
+      "5312 EIF3G\n",
+      "5313 SLC44A2\n",
+      "5314 BRME1\n",
+      "5315 BISPR\n",
+      "5316 MVB12A\n",
+      "5317 SLC5A5\n",
+      "5318 ENSG00000267234\n",
+      "5319 ENSG00000118620\n",
+      "5320 POLR2I\n",
+      "5321 ENSG00000276846\n",
+      "5322 C19orf33\n",
+      "5323 NCCRP1\n",
+      "5324 AKT2\n",
+      "5325 PINLYP\n",
+      "5326 LIG1\n",
+      "5327 RRAS\n",
+      "5328 LIM2\n",
+      "5329 NLRP2\n",
+      "5330 GP6\n",
+      "5331 TMEM86B\n",
+      "5332 CPXM1\n",
+      "5333 CDS2\n",
+      "5334 CFAP61\n",
+      "5335 FAM182B\n",
+      "5336 DLGAP4-AS1\n",
+      "5337 ENSG00000204117\n",
+      "5338 SYS1\n",
+      "5339 CSTF1\n",
+      "5340 MTG2\n",
+      "5341 LAMA5\n",
+      "5342 LKAAEAR1\n",
+      "5343 ENSG00000280800\n",
+      "5344 ENSG00000286018\n",
+      "5345 ENSG00000142188\n",
+      "5346 ATP5PO\n",
+      "5347 ADARB1\n",
+      "5348 UFD1-AS1\n",
+      "5349 ENSG00000273139\n",
+      "5350 IGLV3-27\n",
+      "5351 ENSG00000273295\n",
+      "5352 UPB1\n",
+      "5353 ENSG00000279110\n",
+      "5354 ZMAT5\n",
+      "5355 SEC14L2\n",
+      "5356 PATZ1\n",
+      "5357 RTCB\n",
+      "5358 CDPF1\n",
+      "5359 ADM2\n",
+      "5360 MIOX\n",
+      "5361 ENSG00000237531\n",
+      "5362 FTSJ1\n",
+      "5363 USP27X-DT\n",
+      "5364 SPIN2A\n",
+      "5365 ITGB1BP2\n",
+      "5366 PHKA1\n",
+      "5367 ENSG00000228139\n",
+      "5368 RAB33A\n",
+      "5369 CTAG2\n",
+      "5370 VWA1\n",
+      "5371 CENPS\n",
+      "5372 ENSG00000272078\n",
+      "5373 EXOSC10-AS1\n",
+      "5374 SPATA21\n",
+      "5375 PLA2G2C\n",
+      "5376 S100PBP\n",
+      "5377 ZMYM1\n",
+      "5378 RIMKLA\n",
+      "5379 LINC00853\n",
+      "5380 CC2D1B\n",
+      "5381 CTH\n",
+      "5382 FUBP1\n",
+      "5383 GBP2\n",
+      "5384 ENSG00000284734\n",
+      "5385 LRRC8D\n",
+      "5386 FNBP1L\n",
+      "5387 ENSG00000273221\n",
+      "5388 PPM1J\n",
+      "5389 LINC01357\n",
+      "5390 LRIG2-DT\n",
+      "5391 PDE4DIP\n",
+      "5392 MTMR11\n",
+      "5393 ARNT\n",
+      "5394 ENSG00000285651\n",
+      "5395 S100A9\n",
+      "5396 RAB25\n",
+      "5397 PRCC\n",
+      "5398 ENSG00000176320\n",
+      "5399 CD1B\n",
+      "5400 PFDN2\n",
+      "5401 MPZ\n",
+      "5402 PRDX6\n",
+      "5403 RC3H1-IT1\n",
+      "5404 LINC01036\n",
+      "5405 RPS6KC1\n",
+      "5406 RAB3GAP2\n",
+      "5407 NCOA1\n",
+      "5408 ATP6V1E2\n",
+      "5409 SANBR\n",
+      "5410 WDPCP\n",
+      "5411 IGKV6D-21\n",
+      "5412 NCAPH\n",
+      "5413 C2orf49\n",
+      "5414 ENSG00000271590\n",
+      "5415 CKAP2L\n",
+      "5416 ACTR3-AS1\n",
+      "5417 ANKRD30BL\n",
+      "5418 MMADHC-DT\n",
+      "5419 RBM43\n",
+      "5420 TANC1\n",
+      "5421 SP3\n",
+      "5422 ZNF804A\n",
+      "5423 SLC39A10\n",
+      "5424 ANKRD44-AS1\n",
+      "5425 ENSG00000183308\n",
+      "5426 STRADB\n",
+      "5427 ZFAND2B\n",
+      "5428 ENSG00000233538\n",
+      "5429 PDE6D\n",
+      "5430 UGT1A7\n",
+      "5431 LINC01237\n",
+      "5432 TSEN2\n",
+      "5433 VILL\n",
+      "5434 ZNF619\n",
+      "5435 LIMD1-AS1\n",
+      "5436 KIF9\n",
+      "5437 TMA7\n",
+      "5438 APEH\n",
+      "5439 SEMA3B\n",
+      "5440 HTD2\n",
+      "5441 ATXN7\n",
+      "5442 LINC00506\n",
+      "5443 ZBTB20-AS4\n",
+      "5444 SLC15A2\n",
+      "5445 CCDC14\n",
+      "5446 COPB2-DT\n",
+      "5447 ATP1B3\n",
+      "5448 CCNL1\n",
+      "5449 TRIM59\n",
+      "5450 ST6GAL1\n",
+      "5451 TNK2\n",
+      "5452 ENSG00000236833\n",
+      "5453 ENSG00000273179\n",
+      "5454 ENSG00000260296\n",
+      "5455 RHOH\n",
+      "5456 NSUN7\n",
+      "5457 ENSG00000272986\n",
+      "5458 ENSG00000249001\n",
+      "5459 TSPAN5\n",
+      "5460 GAR1-DT\n",
+      "5461 ENSG00000248432\n",
+      "5462 SMIM43\n",
+      "5463 OTUD4\n",
+      "5464 IL6ST-DT\n",
+      "5465 SMIM15\n",
+      "5466 ENSG00000249713\n",
+      "5467 TICAM2\n",
+      "5468 GRAMD2B\n",
+      "5469 SHROOM1\n",
+      "5470 PPP2CA\n",
+      "5471 PCDHA4\n",
+      "5472 PCDH1\n",
+      "5473 HAVCR2\n",
+      "5474 ENSG00000182230\n",
+      "5475 MAML1\n",
+      "5476 LINC00847\n",
+      "5477 BTNL9\n",
+      "5478 SCGN\n",
+      "5479 TRIM38\n",
+      "5480 ENSG00000261839\n",
+      "5481 TCF19\n",
+      "5482 SKIC2\n",
+      "5483 HLA-DRB1\n",
+      "5484 MTCH1\n",
+      "5485 GUCA1B\n",
+      "5486 UBR2\n",
+      "5487 ENSG00000271754\n",
+      "5488 CILK1\n",
+      "5489 COL21A1\n",
+      "5490 EEF1A1\n",
+      "5491 TTK\n",
+      "5492 FIG4\n",
+      "5493 DDO\n",
+      "5494 SERINC1\n",
+      "5495 VNN2\n",
+      "5496 ENSG00000273151\n",
+      "5497 MAFK\n",
+      "5498 ELFN1-AS1\n",
+      "5499 OSBPL3\n",
+      "5500 NFE2L3\n",
+      "5501 CHN2\n",
+      "5502 FKBP14\n",
+      "5503 LINC00265\n",
+      "5504 ADCY1\n",
+      "5505 ENSG00000284523\n",
+      "5506 CYP3A43\n",
+      "5507 MBLAC1\n",
+      "5508 LRCH4\n",
+      "5509 GNB2\n",
+      "5510 EMSLR\n",
+      "5511 ORAI2\n",
+      "5512 PIK3CG\n",
+      "5513 ENSG00000272854\n",
+      "5514 CAV1\n",
+      "5515 ST7-OT4\n",
+      "5516 NOS3\n",
+      "5517 LINC01003\n",
+      "5518 PAXIP1-AS2\n",
+      "5519 BLK\n",
+      "5520 FHIP2B\n",
+      "5521 LOXL2\n",
+      "5522 ANK1\n",
+      "5523 TGS1\n",
+      "5524 ENSG00000274956\n",
+      "5525 YTHDF3-DT\n",
+      "5526 COPS5\n",
+      "5527 LINC03020\n",
+      "5528 GEM\n",
+      "5529 PLEC\n",
+      "5530 DOCK8\n",
+      "5531 HACD4\n",
+      "5532 VCP\n",
+      "5533 TESK1\n",
+      "5534 CD72\n",
+      "5535 MELK\n",
+      "5536 SMC5-DT\n",
+      "5537 PSAT1\n",
+      "5538 ENSG00000286842\n",
+      "5539 CENPP\n",
+      "5540 OGN\n",
+      "5541 ENSG00000287070\n",
+      "5542 NIPSNAP3B\n",
+      "5543 LINC01505\n",
+      "5544 SLC25A25-AS1\n",
+      "5545 TSC1\n",
+      "5546 RXRA\n",
+      "5547 MAMDC4\n",
+      "5548 ARRDC1\n",
+      "5549 TASOR2\n",
+      "5550 KIN\n",
+      "5551 SUV39H2\n",
+      "5552 RSU1\n",
+      "5553 CACNB2\n",
+      "5554 ARHGAP12\n",
+      "5555 ZNF485\n",
+      "5556 DNAJC9-AS1\n",
+      "5557 ADIRF-AS1\n",
+      "5558 LIPF\n",
+      "5559 PGAM1\n",
+      "5560 ENSG00000287419\n",
+      "5561 ENSG00000274897\n",
+      "5562 HBE1\n",
+      "5563 ZNF143\n",
+      "5564 GTF2H1\n",
+      "5565 SPTY2D1\n",
+      "5566 LMO2\n",
+      "5567 LBHD1\n",
+      "5568 RTN3\n",
+      "5569 MAP4K2\n",
+      "5570 TM7SF2\n",
+      "5571 EHBP1L1\n",
+      "5572 MRPL11\n",
+      "5573 C11orf24\n",
+      "5574 P2RY2\n",
+      "5575 SLCO2B1\n",
+      "5576 MIR4300HG\n",
+      "5577 CEP295\n",
+      "5578 PANX1\n",
+      "5579 DYNC2H1\n",
+      "5580 CARD16\n",
+      "5581 IL18\n",
+      "5582 TTC36-AS1\n",
+      "5583 MCAM\n",
+      "5584 CD4\n",
+      "5585 PHB2\n",
+      "5586 CLEC4E\n",
+      "5587 ENSG00000111261\n",
+      "5588 STK38L\n",
+      "5589 SLC38A4-AS1\n",
+      "5590 PFKM\n",
+      "5591 ENSG00000257475\n",
+      "5592 ITGB7\n",
+      "5593 GRIP1\n",
+      "5594 PPP1R12A\n",
+      "5595 NDUFA12\n",
+      "5596 NTN4\n",
+      "5597 ASCL1\n",
+      "5598 GLT8D2\n",
+      "5599 NAA25\n",
+      "5600 SLC8B1\n",
+      "5601 P2RX4\n",
+      "5602 RSRC2\n",
+      "5603 VPS37B\n",
+      "5604 LINC02361\n",
+      "5605 ZNF268\n",
+      "5606 TPTE2\n",
+      "5607 SUCLA2\n",
+      "5608 PIBF1\n",
+      "5609 OBI1\n",
+      "5610 MIR17HG\n",
+      "5611 TEX29\n",
+      "5612 ENSG00000259001\n",
+      "5613 ANG\n",
+      "5614 TRAV29DV5\n",
+      "5615 RBM23\n",
+      "5616 DHRS4-AS1\n",
+      "5617 CFL2\n",
+      "5618 RPL10L\n",
+      "5619 LRR1\n",
+      "5620 BMP4\n",
+      "5621 CDKN3\n",
+      "5622 OTX2-AS1\n",
+      "5623 JKAMP\n",
+      "5624 SYT16\n",
+      "5625 ACOT2\n",
+      "5626 PTGR2\n",
+      "5627 CPSF2\n",
+      "5628 SNHG10\n",
+      "5629 GSKIP\n",
+      "5630 ENSG00000247970\n",
+      "5631 WDR20\n",
+      "5632 ATP5MJ\n",
+      "5633 TEX22\n",
+      "5634 ENSG00000247765\n",
+      "5635 EIF3J\n",
+      "5636 ATP8B4\n",
+      "5637 DMXL2\n",
+      "5638 MAPK6\n",
+      "5639 CERNA1\n",
+      "5640 THAP10\n",
+      "5641 TBC1D2B\n",
+      "5642 CHRNA3\n",
+      "5643 PRC1\n",
+      "5644 CCNF\n",
+      "5645 AMDHD2\n",
+      "5646 PAQR4\n",
+      "5647 LITAF\n",
+      "5648 MIR193BHG\n",
+      "5649 GPRC5B\n",
+      "5650 PALB2\n",
+      "5651 ENSG00000246465\n",
+      "5652 CYLD-AS1\n",
+      "5653 RPGRIP1L\n",
+      "5654 RSPRY1\n",
+      "5655 ENSG00000276131\n",
+      "5656 NOL3\n",
+      "5657 KCTD19\n",
+      "5658 NUTF2\n",
+      "5659 HSD17B2\n",
+      "5660 ZNF778-DT\n",
+      "5661 ENSG00000268218\n",
+      "5662 ABR\n",
+      "5663 RILP\n",
+      "5664 ENSG00000262823\n",
+      "5665 DERL2\n",
+      "5666 PIK3R6\n",
+      "5667 TMEM220-AS1\n",
+      "5668 ENSG00000230709\n",
+      "5669 PROCA1\n",
+      "5670 LASP1NB\n",
+      "5671 RPL19\n",
+      "5672 CCDC200\n",
+      "5673 MEIOC\n",
+      "5674 MYL4\n",
+      "5675 TRIM25\n",
+      "5676 ICAM2\n",
+      "5677 MILR1\n",
+      "5678 KPNA2\n",
+      "5679 LINC00511\n",
+      "5680 FOXJ1\n",
+      "5681 TK1\n",
+      "5682 CANT1\n",
+      "5683 CHMP6\n",
+      "5684 LRRC45\n",
+      "5685 METTL4\n",
+      "5686 SPIRE1\n",
+      "5687 SS18\n",
+      "5688 HSBP1L1\n",
+      "5689 ZNF556\n",
+      "5690 ZNF57\n",
+      "5691 ATCAY\n",
+      "5692 SAFB2\n",
+      "5693 EPS15L1\n",
+      "5694 CRLF1\n",
+      "5695 ANKRD27\n",
+      "5696 GPATCH1\n",
+      "5697 UBA2\n",
+      "5698 WTIP\n",
+      "5699 ZNF30\n",
+      "5700 SDHAF1\n",
+      "5701 PPP1R14A\n",
+      "5702 B3GNT8\n",
+      "5703 ZNF222\n",
+      "5704 MARK4\n",
+      "5705 C19orf81\n",
+      "5706 C19orf84\n",
+      "5707 ZNF606\n",
+      "5708 ZBTB45\n",
+      "5709 DDRGK1\n",
+      "5710 MACROD2\n",
+      "5711 ENSG00000286483\n",
+      "5712 MYL9\n",
+      "5713 MAFB\n",
+      "5714 IFT52\n",
+      "5715 PI3\n",
+      "5716 STAU1\n",
+      "5717 LINC01524\n",
+      "5718 RBM38\n",
+      "5719 DNAJC5\n",
+      "5720 VPS26C\n",
+      "5721 ENSG00000272948\n",
+      "5722 ERVH48-1\n",
+      "5723 LINC01678\n",
+      "5724 PCBP3\n",
+      "5725 PCNT\n",
+      "5726 ASPHD2\n",
+      "5727 RNF215\n",
+      "5728 OSBP2\n",
+      "5729 H1-0\n",
+      "5730 KIAA0930\n",
+      "5731 RABL2B\n",
+      "5732 ARSL\n",
+      "5733 TRAPPC2\n",
+      "5734 PRAF2\n",
+      "5735 PHF8\n",
+      "5736 NAP1L2\n",
+      "5737 DIAPH2-AS1\n",
+      "5738 BEX5\n",
+      "5739 LINC00630\n",
+      "5740 MT-ND6\n",
+      "5741 SLC35E2B\n",
+      "5742 DNAJC11\n",
+      "5743 ENO1-AS1\n",
+      "5744 KDM1A\n",
+      "5745 SRRM1\n",
+      "5746 ENSG00000228172\n",
+      "5747 SLC30A2\n",
+      "5748 SPOCD1\n",
+      "5749 CCDC28B\n",
+      "5750 BEND5\n",
+      "5751 GNG12-AS1\n",
+      "5752 SLC44A5\n",
+      "5753 GIPC2\n",
+      "5754 ODF2L\n",
+      "5755 BTBD8\n",
+      "5756 ATXN7L2\n",
+      "5757 SLC16A1-AS1\n",
+      "5758 BCAS2\n",
+      "5759 LINC01765\n",
+      "5760 ITGA10\n",
+      "5761 SNAPIN\n",
+      "5762 CREB3L4\n",
+      "5763 HAX1\n",
+      "5764 ENSG00000237390\n",
+      "5765 LINC01133\n",
+      "5766 ENSG00000227741\n",
+      "5767 NCSTN\n",
+      "5768 PBX1\n",
+      "5769 RCSD1\n",
+      "5770 ABL2\n",
+      "5771 CSRP1\n",
+      "5772 BTG2\n",
+      "5773 RAB29\n",
+      "5774 FLVCR1-DT\n",
+      "5775 ENSG00000277007\n",
+      "5776 ENSG00000287259\n",
+      "5777 CDC42BPA\n",
+      "5778 LINC01641\n",
+      "5779 ACTN2\n",
+      "5780 HNRNPU\n",
+      "5781 NLRP3\n",
+      "5782 ADCY3\n",
+      "5783 EIF2AK2\n",
+      "5784 STON1\n",
+      "5785 ERLEC1\n",
+      "5786 RTN4\n",
+      "5787 MEIS1-AS2\n",
+      "5788 WDR54\n",
+      "5789 ZAP70\n",
+      "5790 LYG1\n",
+      "5791 POLR1B\n",
+      "5792 MZT2B\n",
+      "5793 R3HDM1\n",
+      "5794 ATF2\n",
+      "5795 HNRNPA3\n",
+      "5796 SPATS2L\n",
+      "5797 CRYBA2\n",
+      "5798 WDFY1\n",
+      "5799 AGAP1\n",
+      "5800 GPR35\n",
+      "5801 ITPR1\n",
+      "5802 RAD18\n",
+      "5803 IL17RC\n",
+      "5804 RBMS3\n",
+      "5805 GPD1L\n",
+      "5806 EXOG\n",
+      "5807 TRAK1\n",
+      "5808 ACKR2\n",
+      "5809 CCDC71\n",
+      "5810 GMPPB\n",
+      "5811 CACNA2D2\n",
+      "5812 CEP15\n",
+      "5813 EOGT-DT\n",
+      "5814 C3orf52\n",
+      "5815 ITGB5\n",
+      "5816 EEFSEC\n",
+      "5817 PCOLCE2\n",
+      "5818 SERPINI1\n",
+      "5819 MAGEF1\n",
+      "5820 AHSG\n",
+      "5821 TFRC\n",
+      "5822 DLG1\n",
+      "5823 PARM1\n",
+      "5824 BMP2K-DT\n",
+      "5825 HADH\n",
+      "5826 OSTC\n",
+      "5827 BBS12\n",
+      "5828 ENSG00000248991\n",
+      "5829 PDGFC\n",
+      "5830 EXOC3-AS1\n",
+      "5831 NDUFS6\n",
+      "5832 SRD5A1\n",
+      "5833 LINC02211\n",
+      "5834 ENSG00000259786\n",
+      "5835 UGT3A2\n",
+      "5836 LMBRD2\n",
+      "5837 SLC1A3-AS1\n",
+      "5838 RICTOR\n",
+      "5839 FYB1\n",
+      "5840 ENSG00000271788\n",
+      "5841 SHLD3\n",
+      "5842 WDR41\n",
+      "5843 LYRM7\n",
+      "5844 ARAP3\n",
+      "5845 DELE1\n",
+      "5846 LARS1\n",
+      "5847 CAMK2A\n",
+      "5848 B4GALT7\n",
+      "5849 LTC4S\n",
+      "5850 BTNL8\n",
+      "5851 PRPF4B\n",
+      "5852 SSR1\n",
+      "5853 PAK1IP1\n",
+      "5854 TMEM14C\n",
+      "5855 CD83\n",
+      "5856 BTN2A2\n",
+      "5857 H3C10\n",
+      "5858 HLA-A\n",
+      "5859 ENSG00000284954\n",
+      "5860 NOTCH4\n",
+      "5861 HLA-DMA\n",
+      "5862 ITPR3\n",
+      "5863 NUDT3\n",
+      "5864 RAB44\n",
+      "5865 LRFN2\n",
+      "5866 CLIC5\n",
+      "5867 PAQR8\n",
+      "5868 ENSG00000236740\n",
+      "5869 LMBRD1\n",
+      "5870 SLC35F1\n",
+      "5871 VNN3P\n",
+      "5872 ENSG00000270638\n",
+      "5873 SAMD5\n",
+      "5874 ENSG00000213121\n",
+      "5875 ENSG00000227598\n",
+      "5876 THBS2\n",
+      "5877 BZW2\n",
+      "5878 HOXA1\n",
+      "5879 GPR141\n",
+      "5880 TMEM60\n",
+      "5881 KRIT1\n",
+      "5882 GNG11\n",
+      "5883 ZSCAN21\n",
+      "5884 ACHE\n",
+      "5885 DOCK4\n",
+      "5886 WASL-DT\n",
+      "5887 CPA5\n",
+      "5888 TRBV7-3\n",
+      "5889 DEFA1B\n",
+      "5890 PRAG1\n",
+      "5891 ENSG00000253356\n",
+      "5892 SLC20A2\n",
+      "5893 THAP1\n",
+      "5894 SDCBP\n",
+      "5895 ENSG00000240915\n",
+      "5896 LACTB2-AS1\n",
+      "5897 MSC\n",
+      "5898 OSGIN2\n",
+      "5899 RAD21\n",
+      "5900 PRNCR1\n",
+      "5901 NAPRT\n",
+      "5902 ZNF7\n",
+      "5903 RFX3\n",
+      "5904 PTPRD\n",
+      "5905 SMIM27\n",
+      "5906 SIT1\n",
+      "5907 LINC01410\n",
+      "5908 ABHD17B\n",
+      "5909 TLE1-DT\n",
+      "5910 ENSG00000231616\n",
+      "5911 CKS2\n",
+      "5912 ENSG00000271155\n",
+      "5913 TRIM14\n",
+      "5914 ERP44\n",
+      "5915 ENSG00000235119\n",
+      "5916 WHRN\n",
+      "5917 TLR4\n",
+      "5918 TOR2A\n",
+      "5919 SET\n",
+      "5920 TBC1D13\n",
+      "5921 PTGDS\n",
+      "5922 TMEM203\n",
+      "5923 ZMYND11\n",
+      "5924 DIP2C\n",
+      "5925 LINC02649\n",
+      "5926 CELF2-AS1\n",
+      "5927 ITGA8\n",
+      "5928 VIM\n",
+      "5929 LINC02680\n",
+      "5930 CXCL12\n",
+      "5931 EIF4EBP2\n",
+      "5932 ENSG00000272140\n",
+      "5933 PLAU\n",
+      "5934 ANKRD22\n",
+      "5935 FBXL15\n",
+      "5936 SFXN2\n",
+      "5937 ADRA2A\n",
+      "5938 MCMBP\n",
+      "5939 EDRF1-AS1\n",
+      "5940 CALY\n",
+      "5941 PSMD13\n",
+      "5942 LGR4\n",
+      "5943 ENSG00000283341\n",
+      "5944 CSTPP1\n",
+      "5945 LTBP3\n",
+      "5946 ZNRD2-DT\n",
+      "5947 UCP2\n",
+      "5948 C2CD3\n",
+      "5949 UVRAG\n",
+      "5950 SORL1-AS1\n",
+      "5951 GLB1L3\n",
+      "5952 DCP1B\n",
+      "5953 VWF\n",
+      "5954 KLRC1\n",
+      "5955 MGST1\n",
+      "5956 ENSG00000273989\n",
+      "5957 ENSG00000275097\n",
+      "5958 ENSG00000287388\n",
+      "5959 H3-5\n",
+      "5960 WNT1\n",
+      "5961 SMAGP\n",
+      "5962 NR4A1\n",
+      "5963 TAFA2\n",
+      "5964 MDM1\n",
+      "5965 IFT81\n",
+      "5966 RITA1\n",
+      "5967 CFAP251\n",
+      "5968 ZNF664\n",
+      "5969 MMP17\n",
+      "5970 NUP58\n",
+      "5971 TNFSF11\n",
+      "5972 GGACT\n",
+      "5973 TPP2\n",
+      "5974 SLC10A2\n",
+      "5975 CUL4A\n",
+      "5976 ARHGEF40\n",
+      "5977 TRAV5\n",
+      "5978 TRAV40\n",
+      "5979 LRP10\n",
+      "5980 DHRS4L2\n",
+      "5981 ENSG00000286825\n",
+      "5982 STXBP6\n",
+      "5983 HEATR5A-DT\n",
+      "5984 BEGAIN\n",
+      "5985 CINP\n",
+      "5986 IGHV3-11\n",
+      "5987 ZNF770\n",
+      "5988 ENSG00000259659\n",
+      "5989 ENSG00000276533\n",
+      "5990 FBXL22\n",
+      "5991 LCTL\n",
+      "5992 STOML1\n",
+      "5993 DNM1P35\n",
+      "5994 POLG\n",
+      "5995 CHSY1\n",
+      "5996 RPUSD1\n",
+      "5997 NDUFB10\n",
+      "5998 ALG1\n",
+      "5999 NDE1\n",
+      "6000 CCP110\n",
+      "6001 ERI2\n",
+      "6002 OTOA\n",
+      "6003 ENSG00000260306\n",
+      "6004 KATNIP\n",
+      "6005 SGF29\n",
+      "6006 CD19\n",
+      "6007 HIRIP3\n",
+      "6008 SEPHS2\n",
+      "6009 ORAI3\n",
+      "6010 TRIM72\n",
+      "6011 ADGRG1\n",
+      "6012 ZNF319\n",
+      "6013 RRAD\n",
+      "6014 LINC01228\n",
+      "6015 PRPF8\n",
+      "6016 INCA1\n",
+      "6017 ASGR1\n",
+      "6018 ACAP1\n",
+      "6019 PLSCR3\n",
+      "6020 FGF11\n",
+      "6021 MPDU1-AS1\n",
+      "6022 SCO1\n",
+      "6023 ARHGAP44-AS1\n",
+      "6024 GRAP\n",
+      "6025 RNF135\n",
+      "6026 ENSG00000266385\n",
+      "6027 TAF15\n",
+      "6028 ENSG00000273576\n",
+      "6029 THRA\n",
+      "6030 ENSG00000278834\n",
+      "6031 SPOP\n",
+      "6032 DDX5\n",
+      "6033 NAT9\n",
+      "6034 GGA3\n",
+      "6035 MRPL38\n",
+      "6036 RNF157\n",
+      "6037 TIMP2\n",
+      "6038 MCRIP1\n",
+      "6039 NPB\n",
+      "6040 GPS1\n",
+      "6041 ENSG00000261888\n",
+      "6042 TWSG1\n",
+      "6043 ENSG00000267705\n",
+      "6044 ZNF532\n",
+      "6045 RTTN\n",
+      "6046 SOCS6\n",
+      "6047 ZNF516\n",
+      "6048 RPS15\n",
+      "6049 EEF2\n",
+      "6050 ENSG00000268565\n",
+      "6051 LDLR\n",
+      "6052 ZNF136\n",
+      "6053 ENSG00000269243\n",
+      "6054 PGLS-DT\n",
+      "6055 CCDC124\n",
+      "6056 PDE4C\n",
+      "6057 ZNF626\n",
+      "6058 ZNF492\n",
+      "6059 PEPD\n",
+      "6060 FXYD3\n",
+      "6061 FXYD5\n",
+      "6062 ZBTB32\n",
+      "6063 WDR62\n",
+      "6064 RYR1\n",
+      "6065 ZNF223\n",
+      "6066 PPM1N\n",
+      "6067 SPHK2\n",
+      "6068 ENSG00000269194\n",
+      "6069 SIGLEC10\n",
+      "6070 ZNF649\n",
+      "6071 ZNF525\n",
+      "6072 VSTM1\n",
+      "6073 ZNF667\n",
+      "6074 RBCK1\n",
+      "6075 SNRPB\n",
+      "6076 TMX4\n",
+      "6077 PLCB4\n",
+      "6078 RRBP1\n",
+      "6079 PLAGL2\n",
+      "6080 BPIFB1\n",
+      "6081 ENSG00000224635\n",
+      "6082 MYBL2\n",
+      "6083 AURKA\n",
+      "6084 SS18L1\n",
+      "6085 PPDPF\n",
+      "6086 ARFRP1\n",
+      "6087 ENSG00000273590\n",
+      "6088 GET1\n",
+      "6089 TFF1\n",
+      "6090 WDR4\n",
+      "6091 YDJC\n",
+      "6092 IGLV6-57\n",
+      "6093 SMTN\n",
+      "6094 SYN3\n",
+      "6095 TTLL12\n",
+      "6096 PRR34\n",
+      "6097 ENSG00000212939\n",
+      "6098 ALG12\n",
+      "6099 ZBED1\n",
+      "6100 BCLAF3\n",
+      "6101 INE1\n",
+      "6102 CCDC22\n",
+      "6103 ZXDA\n",
+      "6104 BEX3\n",
+      "6105 VSIG1\n",
+      "6106 PSMD10\n",
+      "6107 SASH3\n",
+      "6108 TMLHE\n",
+      "6109 ENSG00000260197\n",
+      "6110 RNF207\n",
+      "6111 EPHA2\n",
+      "6112 CDC42-IT1\n",
+      "6113 LINC01355\n",
+      "6114 RCAN3\n",
+      "6115 AGO3\n",
+      "6116 ZFP69\n",
+      "6117 ZNF691-DT\n",
+      "6118 PODN\n",
+      "6119 SLC1A7\n",
+      "6120 LEPR\n",
+      "6121 ZRANB2-DT\n",
+      "6122 NEXN\n",
+      "6123 ENSG00000267734\n",
+      "6124 GBP5\n",
+      "6125 PRMT6\n",
+      "6126 EEIG2\n",
+      "6127 PSMA5\n",
+      "6128 DENND2D\n",
+      "6129 ENSG00000243960\n",
+      "6130 LINC01750\n",
+      "6131 NBPF14\n",
+      "6132 NBPF9\n",
+      "6133 RFX5-AS1\n",
+      "6134 ENTREP3\n",
+      "6135 ENSG00000287839\n",
+      "6136 TSACC\n",
+      "6137 ACKR1\n",
+      "6138 NECTIN4\n",
+      "6139 RABGAP1L-DT\n",
+      "6140 RPS27AP5\n",
+      "6141 CDK18\n",
+      "6142 INTS7\n",
+      "6143 ZNF678\n",
+      "6144 FAM110C\n",
+      "6145 ENSG00000287126\n",
+      "6146 LINC01376\n",
+      "6147 CENPA\n",
+      "6148 FOSL2-AS1\n",
+      "6149 GEMIN6\n",
+      "6150 CLHC1\n",
+      "6151 ENSG00000225889\n",
+      "6152 ENSG00000203395\n",
+      "6153 GFPT1\n",
+      "6154 EXOC6B\n",
+      "6155 BOLA3\n",
+      "6156 SEMA4F\n",
+      "6157 MAT2A\n",
+      "6158 IGKV3-20\n",
+      "6159 MIR4435-2HG\n",
+      "6160 ZC3H6\n",
+      "6161 ENSG00000273073\n",
+      "6162 DARS1\n",
+      "6163 NEB\n",
+      "6164 STAM2\n",
+      "6165 DAPL1\n",
+      "6166 TANK\n",
+      "6167 CCDC141\n",
+      "6168 NUP35\n",
+      "6169 MYO1B\n",
+      "6170 PNKD\n",
+      "6171 CATIP-AS1\n",
+      "6172 TUBA4B\n",
+      "6173 DES\n",
+      "6174 ENSG00000240224\n",
+      "6175 AGXT\n",
+      "6176 SEC13\n",
+      "6177 ANKRD28\n",
+      "6178 TMPPE\n",
+      "6179 ENTPD3-AS1\n",
+      "6180 ALS2CL\n",
+      "6181 TAGLN3\n",
+      "6182 UMPS\n",
+      "6183 ENSG00000248787\n",
+      "6184 SCHIP1\n",
+      "6185 NAALADL2\n",
+      "6186 MUC20\n",
+      "6187 SPON2\n",
+      "6188 MRFAP1\n",
+      "6189 KLHL5\n",
+      "6190 ATP8A1-DT\n",
+      "6191 CORIN\n",
+      "6192 LINC02562\n",
+      "6193 BDH2\n",
+      "6194 ENSG00000272567\n",
+      "6195 ZNF827\n",
+      "6196 KLHL2\n",
+      "6197 CEP44\n",
+      "6198 KLKB1\n",
+      "6199 MRPL36\n",
+      "6200 TRIM36\n",
+      "6201 SKP1\n",
+      "6202 REEP2\n",
+      "6203 PTTG1\n",
+      "6204 SH3PXD2B\n",
+      "6205 CAGE1\n",
+      "6206 KDM1B\n",
+      "6207 ENSG00000272345\n",
+      "6208 H1-4\n",
+      "6209 H2BC10\n",
+      "6210 H4C8\n",
+      "6211 H2AC15\n",
+      "6212 ENSG00000232040\n",
+      "6213 GABBR1\n",
+      "6214 POLR1H\n",
+      "6215 B3GALT4\n",
+      "6216 GLO1\n",
+      "6217 GJB7\n",
+      "6218 SEC63\n",
+      "6219 CD164\n",
+      "6220 CENPW\n",
+      "6221 CCDC28A-AS1\n",
+      "6222 ENSG00000273132\n",
+      "6223 PLEKHG1\n",
+      "6224 ARMT1\n",
+      "6225 ENSG00000286482\n",
+      "6226 RNASET2\n",
+      "6227 ENSG00000237773\n",
+      "6228 GIRGL\n",
+      "6229 SPMIP4\n",
+      "6230 HOXA10-AS\n",
+      "6231 MRPS17\n",
+      "6232 PHKG1\n",
+      "6233 POM121C\n",
+      "6234 STEAP1\n",
+      "6235 AKAP9\n",
+      "6236 MEPCE\n",
+      "6237 PCOLCE\n",
+      "6238 VGF\n",
+      "6239 COG5\n",
+      "6240 DLD\n",
+      "6241 BMT2\n",
+      "6242 AASS\n",
+      "6243 KLHDC10\n",
+      "6244 SLC13A4\n",
+      "6245 SLC37A3\n",
+      "6246 TRBV28\n",
+      "6247 TRBV29-1\n",
+      "6248 ZNF425\n",
+      "6249 LRRC61\n",
+      "6250 RBM33-DT\n",
+      "6251 ANGPT2\n",
+      "6252 SLC35G5\n",
+      "6253 SLC39A14\n",
+      "6254 GNRH1\n",
+      "6255 MAK16\n",
+      "6256 AP3M2\n",
+      "6257 HGSNAT\n",
+      "6258 RAB2A\n",
+      "6259 SNX16\n",
+      "6260 CNGB3\n",
+      "6261 UQCRB\n",
+      "6262 MATN2\n",
+      "6263 NCALD\n",
+      "6264 EPPK1\n",
+      "6265 DNAJA1\n",
+      "6266 FAM219A\n",
+      "6267 ENHO\n",
+      "6268 CCL21\n",
+      "6269 CA9\n",
+      "6270 IDNK\n",
+      "6271 ZNF510\n",
+      "6272 C5\n",
+      "6273 MVB12B\n",
+      "6274 EGFL7\n",
+      "6275 AGPAT2\n",
+      "6276 LCN12\n",
+      "6277 AKR1C3\n",
+      "6278 SEC61A2\n",
+      "6279 ZNF25\n",
+      "6280 RASGEF1A\n",
+      "6281 PTPN20\n",
+      "6282 ADO\n",
+      "6283 ENSG00000272892\n",
+      "6284 PBLD\n",
+      "6285 UNC5B\n",
+      "6286 ENSG00000227540\n",
+      "6287 PPP3CB\n",
+      "6288 FRAT1\n",
+      "6289 HPSE2\n",
+      "6290 COX15\n",
+      "6291 SHTN1\n",
+      "6292 FAM204A\n",
+      "6293 FAM53B\n",
+      "6294 LINC01001\n",
+      "6295 ENSG00000255026\n",
+      "6296 ENSG00000270972\n",
+      "6297 SLC25A22\n",
+      "6298 BTBD10\n",
+      "6299 ELF5\n",
+      "6300 OR5AS1\n",
+      "6301 ZNHIT2\n",
+      "6302 SLC25A45\n",
+      "6303 KCNK7\n",
+      "6304 LRP5\n",
+      "6305 DHCR7-DT\n",
+      "6306 DGAT2\n",
+      "6307 JRKL\n",
+      "6308 MSANTD4\n",
+      "6309 ST3GAL4\n",
+      "6310 ACAD8\n",
+      "6311 IQSEC3\n",
+      "6312 ENSG00000286866\n",
+      "6313 MRPL51\n",
+      "6314 PEX5\n",
+      "6315 OLR1\n",
+      "6316 ARHGDIB\n",
+      "6317 DNAI7\n",
+      "6318 BMAL2\n",
+      "6319 ENSG00000273680\n",
+      "6320 FGD4\n",
+      "6321 ZCRB1\n",
+      "6322 ENSG00000276390\n",
+      "6323 HDAC7\n",
+      "6324 SLC4A8\n",
+      "6325 KRT7\n",
+      "6326 KRT73-AS1\n",
+      "6327 SPRYD3\n",
+      "6328 ENSG00000249753\n",
+      "6329 ENSG00000215159\n",
+      "6330 PPP1R12A-AS1\n",
+      "6331 MRPL42\n",
+      "6332 SNRPF\n",
+      "6333 SART3\n",
+      "6334 RPLP0\n",
+      "6335 PIWIL1\n",
+      "6336 DDX51\n",
+      "6337 CHFR\n",
+      "6338 ENSG00000277020\n",
+      "6339 POLR1D\n",
+      "6340 FAM216B\n",
+      "6341 LRRC63\n",
+      "6342 KPNA3\n",
+      "6343 DLEU2\n",
+      "6344 NEK3\n",
+      "6345 SUGT1\n",
+      "6346 MZT1\n",
+      "6347 FBXL3\n",
+      "6348 MYCBP2\n",
+      "6349 ENSG00000287923\n",
+      "6350 ENSG00000283384\n",
+      "6351 UPF3A\n",
+      "6352 ZNF219\n",
+      "6353 RAB2B\n",
+      "6354 TRAC\n",
+      "6355 ARHGAP5\n",
+      "6356 SAMD4A\n",
+      "6357 VTI1B\n",
+      "6358 RBM25-AS1\n",
+      "6359 PAPLN-AS1\n",
+      "6360 GTF2A1-AS1\n",
+      "6361 LINC02320\n",
+      "6362 AQR\n",
+      "6363 ENSG00000259326\n",
+      "6364 RPUSD2\n",
+      "6365 AP4E1\n",
+      "6366 ENSG00000259697\n",
+      "6367 ENSG00000265967\n",
+      "6368 HAPLN3\n",
+      "6369 ENSG00000259212\n",
+      "6370 NHLRC4\n",
+      "6371 JPT2\n",
+      "6372 PAM16\n",
+      "6373 INO80E\n",
+      "6374 TLCD3B\n",
+      "6375 PPP4C\n",
+      "6376 ENSG00000261346\n",
+      "6377 ENSG00000232748\n",
+      "6378 FUS\n",
+      "6379 ENSG00000260267\n",
+      "6380 ARL2BP\n",
+      "6381 TPPP3\n",
+      "6382 CTRL\n",
+      "6383 SLC7A6OS\n",
+      "6384 DHODH\n",
+      "6385 ENSG00000261592\n",
+      "6386 SLC43A2\n",
+      "6387 ENSG00000263220\n",
+      "6388 ENSG00000285822\n",
+      "6389 PIPOX\n",
+      "6390 CCT6B\n",
+      "6391 ZNF830\n",
+      "6392 ENSG00000273687\n",
+      "6393 NT5C3B\n",
+      "6394 RAB5C\n",
+      "6395 AXIN2\n",
+      "6396 ENSG00000278740\n",
+      "6397 TSEN54\n",
+      "6398 CDK3\n",
+      "6399 PYCR1\n",
+      "6400 SECTM1\n",
+      "6401 ENSG00000260011\n",
+      "6402 YES1\n",
+      "6403 MTCL1\n",
+      "6404 NAPG\n",
+      "6405 MPPE1\n",
+      "6406 CABLES1\n",
+      "6407 MOCOS\n",
+      "6408 SYT4\n",
+      "6409 ENSG00000267476\n",
+      "6410 ADNP2\n",
+      "6411 PLPP2\n",
+      "6412 TPGS1\n",
+      "6413 CNN2\n",
+      "6414 CIRBP\n",
+      "6415 TIMM13\n",
+      "6416 GADD45B\n",
+      "6417 SHD\n",
+      "6418 NDUFA11\n",
+      "6419 CD70\n",
+      "6420 MARCHF2\n",
+      "6421 ATG4D\n",
+      "6422 SWSAP1\n",
+      "6423 ZNF44\n",
+      "6424 ZNF799\n",
+      "6425 MRI1\n",
+      "6426 NOTCH3\n",
+      "6427 IL12RB1\n",
+      "6428 LSM4\n",
+      "6429 ELL\n",
+      "6430 TSSK6\n",
+      "6431 CYP2F1\n",
+      "6432 CEACAM3\n",
+      "6433 GIPR\n",
+      "6434 ZNF616\n",
+      "6435 TMEM238\n",
+      "6436 ZFP28\n",
+      "6437 SMOX\n",
+      "6438 SHLD1\n",
+      "6439 CHGB\n",
+      "6440 ABALON\n",
+      "6441 SRC\n",
+      "6442 UBE2C\n",
+      "6443 SPATA2\n",
+      "6444 OGFR\n",
+      "6445 EVA1C\n",
+      "6446 CBR3-AS1\n",
+      "6447 CBR3\n",
+      "6448 MYH9\n",
+      "6449 APOBEC3G\n",
+      "6450 PNPLA3\n",
+      "6451 NUP50\n",
+      "6452 WNT7B\n",
+      "6453 GRAMD4\n",
+      "6454 PPP1R2C\n",
+      "6455 ZNF81\n",
+      "6456 TFE3\n",
+      "6457 ENSG00000229151\n",
+      "6458 WNK3\n",
+      "6459 COX7B\n",
+      "6460 ARMCX6\n",
+      "6461 ARHGEF6\n",
+      "6462 F8\n",
+      "6463 MT-ND1\n",
+      "6464 ENSG00000238009\n",
+      "6465 ENSG00000272512\n",
+      "6466 PUSL1\n",
+      "6467 SLC35E2A\n",
+      "6468 PRXL2B\n",
+      "6469 ICMT\n",
+      "6470 ERRFI1\n",
+      "6471 ASAP3\n",
+      "6472 SRSF10\n",
+      "6473 SLC9A1\n",
+      "6474 TRNAU1AP\n",
+      "6475 MECR\n",
+      "6476 MATN1\n",
+      "6477 PUM1\n",
+      "6478 MARCKSL1\n",
+      "6479 SMIM12\n",
+      "6480 CFAP57\n",
+      "6481 COA7\n",
+      "6482 DNAI4\n",
+      "6483 MIER1\n",
+      "6484 IFI44\n",
+      "6485 COL24A1\n",
+      "6486 FRRS1\n",
+      "6487 LINC02785\n",
+      "6488 ENSG00000260879\n",
+      "6489 GPSM2\n",
+      "6490 C1orf162\n",
+      "6491 HMGCS2\n",
+      "6492 BNIPL\n",
+      "6493 CDC42SE1\n",
+      "6494 S100A8\n",
+      "6495 PEA15\n",
+      "6496 XCL2\n",
+      "6497 DNM3OS\n",
+      "6498 FASLG\n",
+      "6499 IER5\n",
+      "6500 LAMC2\n",
+      "6501 SMG7-AS1\n",
+      "6502 SMG7\n",
+      "6503 ENSG00000262180\n",
+      "6504 LRRN2\n",
+      "6505 DYRK3-AS1\n",
+      "6506 PIGR\n",
+      "6507 MIR29B2CHG\n",
+      "6508 LINC02817\n",
+      "6509 SDE2\n",
+      "6510 ENSG00000286859\n",
+      "6511 C1orf198\n",
+      "6512 GNPAT\n",
+      "6513 ENSG00000258082\n",
+      "6514 ENSG00000171853\n",
+      "6515 MAPRE3\n",
+      "6516 TRIM54\n",
+      "6517 PPP4R3B-DT\n",
+      "6518 USP34-DT\n",
+      "6519 ALMS1-IT1\n",
+      "6520 TPRKB\n",
+      "6521 EVA1A\n",
+      "6522 ENSG00000273080\n",
+      "6523 CNNM3\n",
+      "6524 ANKRD23\n",
+      "6525 PAX8-AS1\n",
+      "6526 DDX18\n",
+      "6527 NIFK\n",
+      "6528 ENSG00000270557\n",
+      "6529 GCA\n",
+      "6530 METAP1D\n",
+      "6531 DLX1\n",
+      "6532 COL5A2\n",
+      "6533 ASNSD1\n",
+      "6534 INPP1\n",
+      "6535 CCDC150\n",
+      "6536 RFTN2\n",
+      "6537 CLK1\n",
+      "6538 IGFBP5\n",
+      "6539 B3GNT7\n",
+      "6540 KCNJ13\n",
+      "6541 HDLBP\n",
+      "6542 JAGN1\n",
+      "6543 FANCD2\n",
+      "6544 CCDC174\n",
+      "6545 THRB\n",
+      "6546 TOP2B\n",
+      "6547 NGLY1\n",
+      "6548 ACVR2B-AS1\n",
+      "6549 EIF1B-AS1\n",
+      "6550 ENSG00000273211\n",
+      "6551 IMPDH2\n",
+      "6552 GNL3\n",
+      "6553 IL17RB\n",
+      "6554 CPOX\n",
+      "6555 CMSS1\n",
+      "6556 TBC1D23\n",
+      "6557 ZBTB11\n",
+      "6558 KALRN\n",
+      "6559 CFAP92\n",
+      "6560 NUDT16-DT\n",
+      "6561 HLTF\n",
+      "6562 LINC02006\n",
+      "6563 C3orf33\n",
+      "6564 EIF5A2\n",
+      "6565 ENSG00000237473\n",
+      "6566 NCBP2AS2\n",
+      "6567 TACC3\n",
+      "6568 NELFA\n",
+      "6569 QDPR\n",
+      "6570 ENSG00000250541\n",
+      "6571 UCHL1\n",
+      "6572 SRD5A3\n",
+      "6573 DCK\n",
+      "6574 SCARB2\n",
+      "6575 SCD5\n",
+      "6576 WDFY3\n",
+      "6577 METAP1\n",
+      "6578 ANKRD50\n",
+      "6579 RAB33B\n",
+      "6580 GALNT7\n",
+      "6581 SCRG1\n",
+      "6582 ACSL1\n",
+      "6583 FASTKD3\n",
+      "6584 DAB2\n",
+      "6585 MRPS36\n",
+      "6586 SLF1\n",
+      "6587 FER\n",
+      "6588 VDAC1\n",
+      "6589 ENSG00000250069\n",
+      "6590 ATOX1\n",
+      "6591 CCNG1\n",
+      "6592 KCNMB1\n",
+      "6593 ZNF346\n",
+      "6594 MGAT1\n",
+      "6595 TUBB2B\n",
+      "6596 GFOD1\n",
+      "6597 H2AC6\n",
+      "6598 ZNF391\n",
+      "6599 VARS2\n",
+      "6600 HLA-DQB2\n",
+      "6601 TAP1\n",
+      "6602 RPS18\n",
+      "6603 TAPBP\n",
+      "6604 SMIM29\n",
+      "6605 C6orf132\n",
+      "6606 GUCA1A\n",
+      "6607 DLK2\n",
+      "6608 TMEM14A\n",
+      "6609 CYB5R4\n",
+      "6610 MANEA\n",
+      "6611 TSTD3\n",
+      "6612 HINT3\n",
+      "6613 PDE7B-AS1\n",
+      "6614 MAD1L1\n",
+      "6615 ENSG00000234710\n",
+      "6616 ANKMY2\n",
+      "6617 KLHL7\n",
+      "6618 CYCS\n",
+      "6619 PLEKHA8\n",
+      "6620 TRGV8\n",
+      "6621 SUGCT\n",
+      "6622 DTX2\n",
+      "6623 PVRIG\n",
+      "6624 TRIM56\n",
+      "6625 POLR2J\n",
+      "6626 ENSG00000287733\n",
+      "6627 TMEM213\n",
+      "6628 MGAM\n",
+      "6629 TRBV6-1\n",
+      "6630 GSTK1\n",
+      "6631 ZNF398\n",
+      "6632 ARHGEF10\n",
+      "6633 PIWIL2\n",
+      "6634 TNFRSF10A-DT\n",
+      "6635 TTI2\n",
+      "6636 PKIA\n",
+      "6637 ENSG00000272509\n",
+      "6638 NDUFAF6\n",
+      "6639 CCN3\n",
+      "6640 ENSG00000272502\n",
+      "6641 ENSG00000286162\n",
+      "6642 RNF38\n",
+      "6643 LERFS\n",
+      "6644 PGM5\n",
+      "6645 LINC01506\n",
+      "6646 TLE4\n",
+      "6647 TMOD1\n",
+      "6648 STX17-DT\n",
+      "6649 ENSG00000237548\n",
+      "6650 LRSAM1\n",
+      "6651 SETX\n",
+      "6652 SURF1\n",
+      "6653 SURF2\n",
+      "6654 AJM1\n",
+      "6655 ENSG00000226647\n",
+      "6656 UPF2\n",
+      "6657 PTER\n",
+      "6658 PDSS1\n",
+      "6659 SYT15-AS1\n",
+      "6660 RHOBTB1\n",
+      "6661 ZNF365\n",
+      "6662 EIF5AL1\n",
+      "6663 TSPAN14-AS1\n",
+      "6664 WAPL-DT\n",
+      "6665 ENSG00000272817\n",
+      "6666 AVPI1\n",
+      "6667 R3HCC1L\n",
+      "6668 PSD\n",
+      "6669 ENSG00000286609\n",
+      "6670 LINC01435\n",
+      "6671 STK32C\n",
+      "6672 EPS8L2\n",
+      "6673 RHOG\n",
+      "6674 HBD\n",
+      "6675 ZDHHC13\n",
+      "6676 PRMT3\n",
+      "6677 TRIM44\n",
+      "6678 TTC17\n",
+      "6679 LRP4-AS1\n",
+      "6680 ENSG00000255139\n",
+      "6681 FEN1\n",
+      "6682 LRRN4CL\n",
+      "6683 ENSG00000236935\n",
+      "6684 SYVN1\n",
+      "6685 CCS\n",
+      "6686 GRK2\n",
+      "6687 DHCR7\n",
+      "6688 COA4\n",
+      "6689 ANKRD42-DT\n",
+      "6690 ENSG00000287028\n",
+      "6691 CADM1\n",
+      "6692 LNC-RHL1\n",
+      "6693 VSIG2\n",
+      "6694 FBXL14\n",
+      "6695 TIGAR\n",
+      "6696 KLRC3\n",
+      "6697 LMNTD1\n",
+      "6698 ENSG00000257239\n",
+      "6699 ENSG00000275481\n",
+      "6700 ADCY6\n",
+      "6701 LMBR1L\n",
+      "6702 MCRS1\n",
+      "6703 ACVR1B\n",
+      "6704 TNS2\n",
+      "6705 LINC01465\n",
+      "6706 DPY19L2\n",
+      "6707 TBC1D30\n",
+      "6708 DUSP6\n",
+      "6709 PMCH\n",
+      "6710 ENSG00000287982\n",
+      "6711 ANAPC5\n",
+      "6712 LINC02359\n",
+      "6713 ZNF26\n",
+      "6714 XPO4\n",
+      "6715 CCDC169\n",
+      "6716 EXOSC8\n",
+      "6717 SLC25A30-AS1\n",
+      "6718 DACH1\n",
+      "6719 AJUBA-DT\n",
+      "6720 BCL2L2-PABPN1\n",
+      "6721 EMC9\n",
+      "6722 ENSG00000287765\n",
+      "6723 EAPP\n",
+      "6724 PSMA6\n",
+      "6725 TRAPPC6B\n",
+      "6726 SOS2\n",
+      "6727 DDHD1-DT\n",
+      "6728 PELI2\n",
+      "6729 RTN1\n",
+      "6730 DHRS7\n",
+      "6731 RAB15\n",
+      "6732 DNAL1\n",
+      "6733 NPC2\n",
+      "6734 FOS\n",
+      "6735 GTF2A1\n",
+      "6736 FLRT2\n",
+      "6737 IFI27\n",
+      "6738 TCL6\n",
+      "6739 TRMT61A\n",
+      "6740 TDRD9\n",
+      "6741 OTUD7A\n",
+      "6742 SPINT1-AS1\n",
+      "6743 SPINT1\n",
+      "6744 SLC28A2\n",
+      "6745 SQOR\n",
+      "6746 HACD3\n",
+      "6747 ENSG00000260288\n",
+      "6748 CHRNA5\n",
+      "6749 ENSG00000259805\n",
+      "6750 ENSG00000259654\n",
+      "6751 TICRR\n",
+      "6752 TRAF7\n",
+      "6753 ERVK13-1\n",
+      "6754 PARN\n",
+      "6755 LCMT1-AS2\n",
+      "6756 ENSG00000260796\n",
+      "6757 ZNF48\n",
+      "6758 ZNF771\n",
+      "6759 PRSS53\n",
+      "6760 DDX19B\n",
+      "6761 HSDL1\n",
+      "6762 GLOD4\n",
+      "6763 NUP88\n",
+      "6764 ALOX12-AS1\n",
+      "6765 ENSG00000263427\n",
+      "6766 ENSG00000264666\n",
+      "6767 SPECC1\n",
+      "6768 POLDIP2\n",
+      "6769 LIG3\n",
+      "6770 RAD51D\n",
+      "6771 SLFN13\n",
+      "6772 DUSP14\n",
+      "6773 TCAP\n",
+      "6774 SLC4A1\n",
+      "6775 TBX21\n",
+      "6776 ATP5MC1\n",
+      "6777 IGF2BP1\n",
+      "6778 SMURF2\n",
+      "6779 SSTR2\n",
+      "6780 SMIM6\n",
+      "6781 PRPSAP1\n",
+      "6782 SEPTIN9\n",
+      "6783 ENSG00000273355\n",
+      "6784 DSG2\n",
+      "6785 ENSG00000274744\n",
+      "6786 CFAP53\n",
+      "6787 RAB27B\n",
+      "6788 MIR122HG\n",
+      "6789 PMAIP1\n",
+      "6790 DSEL\n",
+      "6791 ENSG00000273669\n",
+      "6792 STK11\n",
+      "6793 REEP6\n",
+      "6794 SF3A2\n",
+      "6795 GNA15-DT\n",
+      "6796 ANKRD24\n",
+      "6797 CHAF1A\n",
+      "6798 CD320\n",
+      "6799 EPOR\n",
+      "6800 ENSG00000197332\n",
+      "6801 MAP1S\n",
+      "6802 JUND\n",
+      "6803 ZNF493\n",
+      "6804 POP4\n",
+      "6805 LGI4\n",
+      "6806 LINC01531\n",
+      "6807 ZNF850\n",
+      "6808 RPS16\n",
+      "6809 ENSG00000269296\n",
+      "6810 CYP2S1\n",
+      "6811 MEGF8\n",
+      "6812 EXOC3L2\n",
+      "6813 CD37\n",
+      "6814 MED25\n",
+      "6815 ENSG00000105501\n",
+      "6816 ZNF613\n",
+      "6817 TMEM190\n",
+      "6818 NAT14\n",
+      "6819 ZNF581\n",
+      "6820 ERVK3-1\n",
+      "6821 SNX5\n",
+      "6822 TTLL9\n",
+      "6823 TM9SF4\n",
+      "6824 SULF2\n",
+      "6825 OSBPL2\n",
+      "6826 SLCO4A1-AS2\n",
+      "6827 ZNF512B\n",
+      "6828 ENSG00000280145\n",
+      "6829 HUNK\n",
+      "6830 BACE2\n",
+      "6831 GATD3\n",
+      "6832 PTTG1IP\n",
+      "6833 ATP6V1E1\n",
+      "6834 PEX26\n",
+      "6835 CDC45\n",
+      "6836 GP1BB\n",
+      "6837 IGLV2-18\n",
+      "6838 MORC2\n",
+      "6839 ENSG00000279217\n",
+      "6840 KCTD17\n",
+      "6841 PLA2G6\n",
+      "6842 PMM1\n",
+      "6843 NCAPH2\n",
+      "6844 ENSG00000285756\n",
+      "6845 WWC3-AS1\n",
+      "6846 HCCS\n",
+      "6847 TMSB4X\n",
+      "6848 CDKL5\n",
+      "6849 RPGR\n",
+      "6850 TSPAN7\n",
+      "6851 LAS1L\n",
+      "6852 RAB9B\n",
+      "6853 INTS6L\n",
+      "6854 TTTY10\n",
+      "6855 MT-CYB\n",
+      "6856 PRKCZ\n",
+      "6857 TNFRSF14\n",
+      "6858 VAMP3\n",
+      "6859 LINC02606\n",
+      "6860 ENSG00000284735\n",
+      "6861 ENSG00000271895\n",
+      "6862 C1QA\n",
+      "6863 IFNLR1\n",
+      "6864 SYF2\n",
+      "6865 FAM167B\n",
+      "6866 ZNF362\n",
+      "6867 CSMD2\n",
+      "6868 CLSPN\n",
+      "6869 COL8A2\n",
+      "6870 SNIP1\n",
+      "6871 GNL2\n",
+      "6872 ATP6V0B\n",
+      "6873 FOXD2\n",
+      "6874 TXNDC12\n",
+      "6875 GPX7\n",
+      "6876 CZIB\n",
+      "6877 TACSTD2\n",
+      "6878 ALG6\n",
+      "6879 DR1\n",
+      "6880 TRIM33\n",
+      "6881 SYCP1\n",
+      "6882 SLC22A15\n",
+      "6883 LINC02802\n",
+      "6884 NBPF10\n",
+      "6885 NUDT4B\n",
+      "6886 H4C15\n",
+      "6887 SCNM1\n",
+      "6888 RPS27\n",
+      "6889 SLC50A1\n",
+      "6890 CD84\n",
+      "6891 CREG1\n",
+      "6892 ANKRD45\n",
+      "6893 CACYBP\n",
+      "6894 BTG2-DT\n",
+      "6895 YOD1\n",
+      "6896 C4BPA\n",
+      "6897 TRAF5\n",
+      "6898 ENSG00000232436\n",
+      "6899 ENSG00000272750\n",
+      "6900 TRIM11\n",
+      "6901 GNG4\n",
+      "6902 LYST\n",
+      "6903 ZNF669\n",
+      "6904 ENSG00000286707\n",
+      "6905 XDH\n",
+      "6906 GALM\n",
+      "6907 LINC02898\n",
+      "6908 ENSG00000152291\n",
+      "6909 IMMT\n",
+      "6910 RGPD2\n",
+      "6911 TBC1D8-AS1\n",
+      "6912 KIF5C\n",
+      "6913 BMPR2\n",
+      "6914 SMARCAL1\n",
+      "6915 ENSG00000267034\n",
+      "6916 RTP5\n",
+      "6917 LHFPL4\n",
+      "6918 SH3BP5-AS1\n",
+      "6919 ENSG00000287069\n",
+      "6920 UBE2E2\n",
+      "6921 CMC1\n",
+      "6922 SLC25A38\n",
+      "6923 SS18L2\n",
+      "6924 DAG1\n",
+      "6925 IP6K1\n",
+      "6926 TWF2\n",
+      "6927 ENSG00000287502\n",
+      "6928 GUCA1C\n",
+      "6929 MYLK\n",
+      "6930 TF\n",
+      "6931 SLC25A36\n",
+      "6932 B3GNT5\n",
+      "6933 IL1RAP\n",
+      "6934 DYNLT2B\n",
+      "6935 BST1\n",
+      "6936 UGDH-AS1\n",
+      "6937 LIMCH1\n",
+      "6938 OCIAD1\n",
+      "6939 ENSG00000287534\n",
+      "6940 ART3\n",
+      "6941 ABCG2\n",
+      "6942 PPM1K\n",
+      "6943 ENSG00000246090\n",
+      "6944 PPP3CA\n",
+      "6945 TRPC3\n",
+      "6946 ENSG00000249806\n",
+      "6947 CEP72\n",
+      "6948 SPEF2\n",
+      "6949 PTGER4\n",
+      "6950 C7\n",
+      "6951 FBXO4\n",
+      "6952 CERT1\n",
+      "6953 ENSG00000285190\n",
+      "6954 TMEM161B\n",
+      "6955 GPR150\n",
+      "6956 MACIR\n",
+      "6957 ISOC1\n",
+      "6958 PKD2L2-DT\n",
+      "6959 FAM114A2\n",
+      "6960 C1QTNF2\n",
+      "6961 PRR7-AS1\n",
+      "6962 TXNDC5\n",
+      "6963 HIVEP1\n",
+      "6964 GMPR\n",
+      "6965 DEK\n",
+      "6966 H3C1\n",
+      "6967 ZNF322\n",
+      "6968 TRIM39\n",
+      "6969 MSH5\n",
+      "6970 TAP2\n",
+      "6971 RRP36\n",
+      "6972 ZNF318\n",
+      "6973 MYO6\n",
+      "6974 UBE3D\n",
+      "6975 PNISR-AS1\n",
+      "6976 QRSL1\n",
+      "6977 NT5DC1\n",
+      "6978 CEP85L\n",
+      "6979 LAMA2\n",
+      "6980 FBXO30\n",
+      "6981 LINC02983\n",
+      "6982 MIOS-DT\n",
+      "6983 ETV1\n",
+      "6984 CRPPA\n",
+      "6985 IL6\n",
+      "6986 NPY\n",
+      "6987 LINC02981\n",
+      "6988 RALA\n",
+      "6989 H2AZ2-DT\n",
+      "6990 TBL2\n",
+      "6991 HGF\n",
+      "6992 CDK6-AS1\n",
+      "6993 PON2\n",
+      "6994 PDAP1\n",
+      "6995 CPSF4\n",
+      "6996 ZSCAN25\n",
+      "6997 CLDN15\n",
+      "6998 PSMC2\n",
+      "6999 CPA4\n",
+      "7000 ARHGEF35-AS1\n",
+      "7001 C8orf58\n",
+      "7002 SLC25A37\n",
+      "7003 PTK2B\n",
+      "7004 ENSG00000254165\n",
+      "7005 MTFR1\n",
+      "7006 ENSG00000286675\n",
+      "7007 ENSG00000253214\n",
+      "7008 ATP6V0D2\n",
+      "7009 RIPK2\n",
+      "7010 ANGPT1\n",
+      "7011 PKHD1L1\n",
+      "7012 ENSG00000253372\n",
+      "7013 FBXL6\n",
+      "7014 MLANA\n",
+      "7015 TUSC1\n",
+      "7016 CAAP1\n",
+      "7017 ACO1\n",
+      "7018 FBXO10\n",
+      "7019 ZNG1F\n",
+      "7020 ENSG00000287168\n",
+      "7021 ZNF658\n",
+      "7022 BICD2\n",
+      "7023 RNF20\n",
+      "7024 SMC2\n",
+      "7025 STOM\n",
+      "7026 ZBTB6\n",
+      "7027 MAPKAP1\n",
+      "7028 PAEP\n",
+      "7029 ADARB2\n",
+      "7030 ENSG00000287235\n",
+      "7031 ITIH5\n",
+      "7032 SRGN\n",
+      "7033 NPFFR1\n",
+      "7034 DNAJB12\n",
+      "7035 SFTPD-AS1\n",
+      "7036 HELLS\n",
+      "7037 CALHM1\n",
+      "7038 BCCIP\n",
+      "7039 C10orf143\n",
+      "7040 ADAM8\n",
+      "7041 SYCE1\n",
+      "7042 MIR210HG\n",
+      "7043 DRD4\n",
+      "7044 MOB2\n",
+      "7045 RIC3\n",
+      "7046 LINC02709\n",
+      "7047 ENSG00000246308\n",
+      "7048 SERPING1\n",
+      "7049 PATL1\n",
+      "7050 ZFTA\n",
+      "7051 TRPT1\n",
+      "7052 RPS6KA4\n",
+      "7053 PYGM\n",
+      "7054 PITPNM1\n",
+      "7055 TCIRG1\n",
+      "7056 RNF121\n",
+      "7057 ENSG00000251143\n",
+      "7058 ENSG00000254698\n",
+      "7059 MTMR2\n",
+      "7060 ENSG00000288012\n",
+      "7061 NPAT\n",
+      "7062 H2AX\n",
+      "7063 PRB3\n",
+      "7064 STRAP\n",
+      "7065 BICD1\n",
+      "7066 GPR84-AS1\n",
+      "7067 CD63-AS1\n",
+      "7068 RAB5B\n",
+      "7069 PHLDA1\n",
+      "7070 HCFC2\n",
+      "7071 FAM216A\n",
+      "7072 ACAD10\n",
+      "7073 HRK\n",
+      "7074 VSIG10\n",
+      "7075 LINC01089\n",
+      "7076 ABCB9\n",
+      "7077 TMEM132B\n",
+      "7078 ZNF10\n",
+      "7079 LHFPL6\n",
+      "7080 ENSG00000273149\n",
+      "7081 LPAR6\n",
+      "7082 FAM124A\n",
+      "7083 DHRS12\n",
+      "7084 FARP1\n",
+      "7085 TMCO3\n",
+      "7086 TOX4\n",
+      "7087 TRAV8-3\n",
+      "7088 TRAV26-1\n",
+      "7089 JPH4\n",
+      "7090 NOP9\n",
+      "7091 PRPF39\n",
+      "7092 RPL36AL\n",
+      "7093 PPP1R36\n",
+      "7094 PALS1\n",
+      "7095 SLC39A9\n",
+      "7096 MAP3K9\n",
+      "7097 ENTPD5\n",
+      "7098 IRF2BPL\n",
+      "7099 TECPR2\n",
+      "7100 EIF5-DT\n",
+      "7101 LINC02280\n",
+      "7102 IGHV1-14\n",
+      "7103 IGHV4-28\n",
+      "7104 ENSG00000274253\n",
+      "7105 UBE3A\n",
+      "7106 ENSG00000261064\n",
+      "7107 LINC01852\n",
+      "7108 PHGR1\n",
+      "7109 C2CD4A\n",
+      "7110 TMEM266\n",
+      "7111 RAMAC\n",
+      "7112 C15orf40\n",
+      "7113 UBE2Q2P16\n",
+      "7114 ZNF774\n",
+      "7115 VPS33B-DT\n",
+      "7116 NOXO1\n",
+      "7117 LINC01569\n",
+      "7118 ZNF500\n",
+      "7119 ENSG00000262020\n",
+      "7120 MPV17L\n",
+      "7121 ENSG00000280206\n",
+      "7122 ENSG00000263331\n",
+      "7123 ENSG00000275807\n",
+      "7124 SH2B1\n",
+      "7125 PRRT2\n",
+      "7126 ENSG00000278713\n",
+      "7127 PHKG2\n",
+      "7128 ENSG00000261630\n",
+      "7129 ENSG00000286845\n",
+      "7130 CDH11\n",
+      "7131 NPIPB15\n",
+      "7132 ENSG00000260177\n",
+      "7133 TLCD2\n",
+      "7134 SRR\n",
+      "7135 GGT6\n",
+      "7136 C17orf100\n",
+      "7137 C17orf49\n",
+      "7138 TMEM256\n",
+      "7139 LLGL1\n",
+      "7140 NLK\n",
+      "7141 BLMH\n",
+      "7142 ENSG00000277511\n",
+      "7143 ENSG00000267364\n",
+      "7144 GAST\n",
+      "7145 TUBG1\n",
+      "7146 EFTUD2\n",
+      "7147 ENSG00000262879\n",
+      "7148 CBX1\n",
+      "7149 COL1A1\n",
+      "7150 ANKRD40\n",
+      "7151 LINC02073\n",
+      "7152 MSI2\n",
+      "7153 HELZ\n",
+      "7154 HID1-AS1\n",
+      "7155 CLUL1\n",
+      "7156 SEH1L\n",
+      "7157 PCAT18\n",
+      "7158 SLC14A2\n",
+      "7159 ZNF516-AS1\n",
+      "7160 FSTL3\n",
+      "7161 SCAMP4\n",
+      "7162 LRG1\n",
+      "7163 PSPN\n",
+      "7164 SLC25A23\n",
+      "7165 SAXO5\n",
+      "7166 STXBP2\n",
+      "7167 ZNF414\n",
+      "7168 C19orf38\n",
+      "7169 ZNF709\n",
+      "7170 JAK3\n",
+      "7171 ZNF708\n",
+      "7172 UQCRFS1-DT\n",
+      "7173 MAP4K1\n",
+      "7174 SARS2\n",
+      "7175 ACP7\n",
+      "7176 ATP1A3\n",
+      "7177 LIPE-AS1\n",
+      "7178 FOSB\n",
+      "7179 AP2S1\n",
+      "7180 ZNF160\n",
+      "7181 ZNF787\n",
+      "7182 ZNF547\n",
+      "7183 NSFL1C\n",
+      "7184 VPS16\n",
+      "7185 DNAAF9\n",
+      "7186 CRNKL1\n",
+      "7187 BCL2L1\n",
+      "7188 CTNNBL1\n",
+      "7189 ACTR5\n",
+      "7190 STK4-DT\n",
+      "7191 TP53RK\n",
+      "7192 ENSG00000267882\n",
+      "7193 BCAS4\n",
+      "7194 DOK5\n",
+      "7195 CTCFL\n",
+      "7196 IFNAR2\n",
+      "7197 CLIC6\n",
+      "7198 ENSG00000272825\n",
+      "7199 ENSG00000273203\n",
+      "7200 AIFM3\n",
+      "7201 IGLV9-49\n",
+      "7202 IGLV3-6\n",
+      "7203 CASTOR1\n",
+      "7204 MORC2-AS1\n",
+      "7205 RBFOX2\n",
+      "7206 APOBEC3F\n",
+      "7207 L3MBTL2\n",
+      "7208 RTL6\n",
+      "7209 ENSG00000235111\n",
+      "7210 CRELD2\n",
+      "7211 ZFX\n",
+      "7212 ARAF\n",
+      "7213 ENSG00000286031\n",
+      "7214 RIBC1\n",
+      "7215 PAGE2\n",
+      "7216 ZMAT1\n",
+      "7217 RBM41\n",
+      "7218 NXT2\n",
+      "7219 SMARCA1\n",
+      "7220 GPC4\n",
+      "7221 FMR1-IT1\n",
+      "7222 ENSG00000278782\n",
+      "7223 ENSG00000272004\n",
+      "7224 LINC01134\n",
+      "7225 PHF13\n",
+      "7226 DFFA\n",
+      "7227 SPEN\n",
+      "7228 ZNF436-AS1\n",
+      "7229 KPNA6\n",
+      "7230 AKIRIN1\n",
+      "7231 SCMH1\n",
+      "7232 PDZK1IP1\n",
+      "7233 ELAVL4\n",
+      "7234 ECHDC2\n",
+      "7235 CZIB-DT\n",
+      "7236 LRP8\n",
+      "7237 LRRC8D-DT\n",
+      "7238 EPHX4\n",
+      "7239 GSTM1\n",
+      "7240 GSTM3\n",
+      "7241 ZNF697\n",
+      "7242 NOTCH2\n",
+      "7243 SV2A\n",
+      "7244 PLEKHO1\n",
+      "7245 LINC02988\n",
+      "7246 C1orf54\n",
+      "7247 PSMB4\n",
+      "7248 SNX27\n",
+      "7249 S100A10\n",
+      "7250 TCHH\n",
+      "7251 SLC27A3\n",
+      "7252 KCNN3\n",
+      "7253 SCAMP3\n",
+      "7254 SSR2\n",
+      "7255 CD244\n",
+      "7256 NR1I3\n",
+      "7257 DDR2\n",
+      "7258 DARS2\n",
+      "7259 KDM5B\n",
+      "7260 CHI3L1\n",
+      "7261 PRELP\n",
+      "7262 LPGAT1\n",
+      "7263 ACBD3-AS1\n",
+      "7264 ITPKB\n",
+      "7265 LNCATV\n",
+      "7266 ENSG00000240963\n",
+      "7267 ADSS2\n",
+      "7268 GEN1\n",
+      "7269 LINC00954\n",
+      "7270 GAREM2\n",
+      "7271 BIRC6\n",
+      "7272 SIX3\n",
+      "7273 PPP4R3B\n",
+      "7274 C2orf81\n",
+      "7275 INO80B\n",
+      "7276 WBP1\n",
+      "7277 IGKV1-39\n",
+      "7278 MRPS9\n",
+      "7279 TMEM37\n",
+      "7280 FMNL2\n",
+      "7281 MYO3B-AS1\n",
+      "7282 FAM171B\n",
+      "7283 CALCRL\n",
+      "7284 MARS2\n",
+      "7285 FASTKD2\n",
+      "7286 KANSL1L\n",
+      "7287 MREG\n",
+      "7288 IGFBP2\n",
+      "7289 CXCR2\n",
+      "7290 ATG9A\n",
+      "7291 BRK1\n",
+      "7292 ENSG00000272077\n",
+      "7293 ZNF502\n",
+      "7294 NDUFAF3\n",
+      "7295 TCTA\n",
+      "7296 RBM5-AS1\n",
+      "7297 ALAS1\n",
+      "7298 DNAH12\n",
+      "7299 KCTD6\n",
+      "7300 SUCLG2-DT\n",
+      "7301 TMF1\n",
+      "7302 FRMD4B\n",
+      "7303 GPR27\n",
+      "7304 PROS1\n",
+      "7305 LINC00882\n",
+      "7306 ENSG00000272967\n",
+      "7307 COX17\n",
+      "7308 POLQ\n",
+      "7309 SRPRB\n",
+      "7310 CEP63\n",
+      "7311 PLSCR2\n",
+      "7312 TSC22D2\n",
+      "7313 GMPS\n",
+      "7314 EIF2B5\n",
+      "7315 TBCCD1\n",
+      "7316 RTP4\n",
+      "7317 P3H2\n",
+      "7318 TMEM44-AS2\n",
+      "7319 ENSG00000286004\n",
+      "7320 IQCG\n",
+      "7321 MSX1\n",
+      "7322 BMP2K\n",
+      "7323 MMRN1\n",
+      "7324 EXOSC9\n",
+      "7325 ABCE1\n",
+      "7326 ENSG00000286753\n",
+      "7327 CAPSL\n",
+      "7328 ENSG00000272382\n",
+      "7329 PAIP1\n",
+      "7330 CDC20B\n",
+      "7331 TRAPPC13\n",
+      "7332 HMGCR\n",
+      "7333 PDE8B\n",
+      "7334 IL4\n",
+      "7335 IGIP\n",
+      "7336 TMCO6\n",
+      "7337 SAP30L-AS1\n",
+      "7338 PWWP2A\n",
+      "7339 BNIP1\n",
+      "7340 DOK3\n",
+      "7341 HNRNPH1\n",
+      "7342 SNRNP48\n",
+      "7343 SMIM13\n",
+      "7344 ZNF165\n",
+      "7345 ZSCAN12\n",
+      "7346 HCG27\n",
+      "7347 NFKBIL1\n",
+      "7348 DDAH2\n",
+      "7349 ANKS1A\n",
+      "7350 OARD1\n",
+      "7351 PTCRA\n",
+      "7352 PPP2R5D\n",
+      "7353 SCIRT\n",
+      "7354 BAG2\n",
+      "7355 ENSG00000223821\n",
+      "7356 FBXL4\n",
+      "7357 PKIB\n",
+      "7358 MAP3K5\n",
+      "7359 HECA\n",
+      "7360 PACRG\n",
+      "7361 TBP\n",
+      "7362 PSMG3\n",
+      "7363 ZNF853\n",
+      "7364 MINDY4\n",
+      "7365 CDK13-DT\n",
+      "7366 SPDYE1\n",
+      "7367 POLD2\n",
+      "7368 ZNF716\n",
+      "7369 NCF1\n",
+      "7370 UPK3B\n",
+      "7371 PTCD1\n",
+      "7372 AZGP1\n",
+      "7373 EFCAB10\n",
+      "7374 NRCAM\n",
+      "7375 LINC01393\n",
+      "7376 TRBV12-5\n",
+      "7377 TRBV14\n",
+      "7378 ZNF862\n",
+      "7379 ZNF596\n",
+      "7380 PPP1R3B\n",
+      "7381 ATP6V1B2\n",
+      "7382 ENSG00000254092\n",
+      "7383 XPO7\n",
+      "7384 ADAMDEC1\n",
+      "7385 STMN4\n",
+      "7386 BRF2\n",
+      "7387 FNTA\n",
+      "7388 RPS20\n",
+      "7389 ENSG00000251867\n",
+      "7390 KLF10\n",
+      "7391 KCNV1\n",
+      "7392 LINC00861\n",
+      "7393 GLIS3\n",
+      "7394 PSIP1\n",
+      "7395 SMU1\n",
+      "7396 ATOSB\n",
+      "7397 SPAG8\n",
+      "7398 ENSG00000277350\n",
+      "7399 APBA1\n",
+      "7400 ENSG00000274421\n",
+      "7401 GDA\n",
+      "7402 SPTLC1\n",
+      "7403 ENSG00000232063\n",
+      "7404 GALNT12\n",
+      "7405 BSPRY\n",
+      "7406 HSPA5\n",
+      "7407 SWI5\n",
+      "7408 MED22\n",
+      "7409 KCNT1\n",
+      "7410 USP6NL\n",
+      "7411 LINC02664\n",
+      "7412 ENSG00000226578\n",
+      "7413 CCDC6\n",
+      "7414 DNAJC12\n",
+      "7415 SGPL1\n",
+      "7416 PAPSS2\n",
+      "7417 STAMBPL1\n",
+      "7418 CPEB3\n",
+      "7419 TCTN3\n",
+      "7420 ENSG00000225850\n",
+      "7421 WBP1L\n",
+      "7422 CFAP58\n",
+      "7423 ADD3\n",
+      "7424 ENSG00000233547\n",
+      "7425 ATE1\n",
+      "7426 ABRAXAS2\n",
+      "7427 UTF1\n",
+      "7428 ILK\n",
+      "7429 ENSG00000255823\n",
+      "7430 CYP2R1\n",
+      "7431 SAA2\n",
+      "7432 PEX16\n",
+      "7433 PSMC3\n",
+      "7434 CYB561A3\n",
+      "7435 FADS3\n",
+      "7436 ZFPL1\n",
+      "7437 CTSF\n",
+      "7438 CARNS1\n",
+      "7439 NDUFV1\n",
+      "7440 NUMA1\n",
+      "7441 FCHSD2\n",
+      "7442 PPME1\n",
+      "7443 HIKESHI\n",
+      "7444 CWC15\n",
+      "7445 ATM\n",
+      "7446 SIK2\n",
+      "7447 SDHD\n",
+      "7448 ENSG00000224077\n",
+      "7449 KLRK1\n",
+      "7450 PDE6H\n",
+      "7451 ENSG00000272369\n",
+      "7452 DAZAP2\n",
+      "7453 ENSG00000257550\n",
+      "7454 ARHGEF25\n",
+      "7455 HELB\n",
+      "7456 THAP2\n",
+      "7457 CEP290\n",
+      "7458 ENSG00000286907\n",
+      "7459 UTP20\n",
+      "7460 UQCC6\n",
+      "7461 POLR3B\n",
+      "7462 RIC8B\n",
+      "7463 FBXO21\n",
+      "7464 RAB35-AS1\n",
+      "7465 PXN\n",
+      "7466 COX6A1\n",
+      "7467 COQ5\n",
+      "7468 P2RX7\n",
+      "7469 SETD1B\n",
+      "7470 MTRFR\n",
+      "7471 TMED2-DT\n",
+      "7472 ENSG00000214650\n",
+      "7473 ENSG00000278266\n",
+      "7474 STOML3\n",
+      "7475 CPB2-AS1\n",
+      "7476 RB1\n",
+      "7477 OLFM4\n",
+      "7478 TM9SF2\n",
+      "7479 GAS6\n",
+      "7480 RNASE3\n",
+      "7481 TRAV4\n",
+      "7482 LINC02332\n",
+      "7483 SLC7A7\n",
+      "7484 PRMT5\n",
+      "7485 CMTM5\n",
+      "7486 ADCY4\n",
+      "7487 GEMIN2\n",
+      "7488 MAP4K5\n",
+      "7489 ENSG00000284930\n",
+      "7490 ENSG00000258891\n",
+      "7491 FBLN5\n",
+      "7492 ENSG00000288302\n",
+      "7493 LINC00638\n",
+      "7494 IGHV1OR15-1\n",
+      "7495 ITPKA\n",
+      "7496 MYEF2\n",
+      "7497 TCF12-DT\n",
+      "7498 TMC3\n",
+      "7499 MEX3B\n",
+      "7500 AP3B2\n",
+      "7501 ENSG00000259767\n",
+      "7502 PDE8A\n",
+      "7503 GDPGP1\n",
+      "7504 MPG\n",
+      "7505 TPSAB1\n",
+      "7506 ENSG00000277602\n",
+      "7507 ECI1-AS1\n",
+      "7508 PDPK1\n",
+      "7509 TIGD7\n",
+      "7510 THUMPD1\n",
+      "7511 NPIPB3\n",
+      "7512 PLK1\n",
+      "7513 LCMT1-AS1\n",
+      "7514 SEPTIN1\n",
+      "7515 ITGAL\n",
+      "7516 PYCARD-AS1\n",
+      "7517 KIFC3\n",
+      "7518 LINC02141\n",
+      "7519 LCAT\n",
+      "7520 CALB2\n",
+      "7521 CCDC92B\n",
+      "7522 TEKT1\n",
+      "7523 TMEM88\n",
+      "7524 CYB5D1\n",
+      "7525 COX10-DT\n",
+      "7526 TMEM199\n",
+      "7527 PSMD3\n",
+      "7528 KRT10-AS1\n",
+      "7529 FZD2\n",
+      "7530 ENSG00000131484\n",
+      "7531 CRHR1\n",
+      "7532 LRRC37A2\n",
+      "7533 SNF8\n",
+      "7534 ENSG00000247011\n",
+      "7535 SNHG16\n",
+      "7536 C17orf99\n",
+      "7537 ENGASE\n",
+      "7538 ENSG00000262831\n",
+      "7539 METRNL\n",
+      "7540 EMILIN2\n",
+      "7541 DLGAP1-AS2\n",
+      "7542 LINC02958\n",
+      "7543 ENSG00000274275\n",
+      "7544 MBD1\n",
+      "7545 MALT1\n",
+      "7546 CTDP1\n",
+      "7547 GIPC3\n",
+      "7548 ZFR2\n",
+      "7549 UHRF1\n",
+      "7550 S1PR2\n",
+      "7551 DNM2\n",
+      "7552 YIPF2\n",
+      "7553 CALR\n",
+      "7554 ZSWIM4\n",
+      "7555 PRKACA\n",
+      "7556 SIN3B\n",
+      "7557 KLHL26\n",
+      "7558 DDX49\n",
+      "7559 SLC7A9\n",
+      "7560 U2AF1L4\n",
+      "7561 ZNF234\n",
+      "7562 KLC3\n",
+      "7563 ENSG00000269148\n",
+      "7564 NAPA\n",
+      "7565 EMP3\n",
+      "7566 ENSG00000197813\n",
+      "7567 AKT1S1\n",
+      "7568 TBC1D17\n",
+      "7569 PPP2R1A\n",
+      "7570 NDUFA3\n",
+      "7571 KIR2DL4\n",
+      "7572 SSC5D\n",
+      "7573 SLC27A5\n",
+      "7574 IDH3B\n",
+      "7575 MAP1LC3A\n",
+      "7576 SPAG4\n",
+      "7577 PHF20\n",
+      "7578 ENSG00000287107\n",
+      "7579 ZFP64\n",
+      "7580 ZNF217\n",
+      "7581 KCNQ2\n",
+      "7582 TPD52L2\n",
+      "7583 ENSG00000205930\n",
+      "7584 RUNX1\n",
+      "7585 ENSG00000228107\n",
+      "7586 ENSG00000160285\n",
+      "7587 KLHL22\n",
+      "7588 ENSG00000279278\n",
+      "7589 SMARCB1\n",
+      "7590 RHBDD3\n",
+      "7591 UQCR10\n",
+      "7592 LIMK2\n",
+      "7593 ENSG00000279652\n",
+      "7594 TMEM184B\n",
+      "7595 TOMM22-DT\n",
+      "7596 ENSG00000272669\n",
+      "7597 TCF20\n",
+      "7598 GTSE1\n",
+      "7599 AKAP17A\n",
+      "7600 CLCN4\n",
+      "7601 ASB9\n",
+      "7602 TXLNG\n",
+      "7603 REPS2\n",
+      "7604 TAB3\n",
+      "7605 GPKOW\n",
+      "7606 NUDT11\n",
+      "7607 NONO\n",
+      "7608 ENSG00000271533\n",
+      "7609 TSC22D3\n",
+      "7610 FIRRE\n",
+      "7611 SLITRK4\n",
+      "7612 TREX2\n",
+      "7613 TMEM187\n",
+      "7614 MXRA8\n",
+      "7615 PIK3CD-AS1\n",
+      "7616 OTUD3\n",
+      "7617 TCEA3\n",
+      "7618 ZDHHC18\n",
+      "7619 EYA3\n",
+      "7620 SYNC\n",
+      "7621 MACF1\n",
+      "7622 CYP4B1\n",
+      "7623 LINC01562\n",
+      "7624 TTC39A\n",
+      "7625 PIGK\n",
+      "7626 ADGRL2\n",
+      "7627 SLC30A7\n",
+      "7628 CD160\n",
+      "7629 ENSG00000276509\n",
+      "7630 GPR89B\n",
+      "7631 ENSG00000227733\n",
+      "7632 CIART\n",
+      "7633 PRUNE1\n",
+      "7634 TNFAIP8L2\n",
+      "7635 SELENBP1\n",
+      "7636 JTB\n",
+      "7637 HCN3\n",
+      "7638 PAQR6\n",
+      "7639 ETV3\n",
+      "7640 FCER1A\n",
+      "7641 ENSG00000283696\n",
+      "7642 CD247\n",
+      "7643 BLZF1\n",
+      "7644 FIRRM\n",
+      "7645 GLUL\n",
+      "7646 PDC-AS1\n",
+      "7647 PTPRC\n",
+      "7648 SHISA4\n",
+      "7649 SNRPE\n",
+      "7650 RASSF5\n",
+      "7651 TAF1A\n",
+      "7652 TAF5L\n",
+      "7653 TTC13\n",
+      "7654 TSNAX\n",
+      "7655 TARBP1\n",
+      "7656 IRF2BP2\n",
+      "7657 TFB2M\n",
+      "7658 ROCK2\n",
+      "7659 LAPTM4A\n",
+      "7660 ENSG00000233005\n",
+      "7661 OST4\n",
+      "7662 KRTCAP3\n",
+      "7663 ENSG00000270210\n",
+      "7664 TRMT61B\n",
+      "7665 PRKD3\n",
+      "7666 ENSG00000272156\n",
+      "7667 PLEK\n",
+      "7668 IGKV3-15\n",
+      "7669 IGKV2D-29\n",
+      "7670 DUSP2\n",
+      "7671 SH3RF3\n",
+      "7672 ENSG00000270019\n",
+      "7673 CYP27C1\n",
+      "7674 ARL6IP6\n",
+      "7675 ACVR1\n",
+      "7676 GAD1\n",
+      "7677 ITGA4\n",
+      "7678 PMS1\n",
+      "7679 C2orf69\n",
+      "7680 HJURP\n",
+      "7681 RAB17\n",
+      "7682 STT3B\n",
+      "7683 XYLB\n",
+      "7684 EIF1B\n",
+      "7685 ZNF660\n",
+      "7686 NT5DC2\n",
+      "7687 NEK4\n",
+      "7688 ARL6\n",
+      "7689 BBX\n",
+      "7690 RETNLB\n",
+      "7691 ABHD10\n",
+      "7692 MRPL3\n",
+      "7693 GPR171\n",
+      "7694 MFSD1\n",
+      "7695 TNFSF10\n",
+      "7696 KCNMB2-AS1\n",
+      "7697 LINC01840\n",
+      "7698 CEP19\n",
+      "7699 ENSG00000230732\n",
+      "7700 ZNF721\n",
+      "7701 MFSD10\n",
+      "7702 PROM1\n",
+      "7703 STIM2\n",
+      "7704 CCNG2\n",
+      "7705 ENOPH1\n",
+      "7706 HPGDS\n",
+      "7707 NFKB1\n",
+      "7708 IL2\n",
+      "7709 ENSG00000273449\n",
+      "7710 HPF1\n",
+      "7711 IRF2\n",
+      "7712 CCDC127\n",
+      "7713 SLC9A3\n",
+      "7714 MED10\n",
+      "7715 ENSG00000271771\n",
+      "7716 MARVELD2\n",
+      "7717 ENC1\n",
+      "7718 ANKRD31\n",
+      "7719 FAM151B\n",
+      "7720 MEF2C-AS1\n",
+      "7721 SRP19\n",
+      "7722 ENSG00000271797\n",
+      "7723 FCHSD1\n",
+      "7724 NDFIP1\n",
+      "7725 SH3RF2\n",
+      "7726 PDE6A\n",
+      "7727 TIGD6\n",
+      "7728 RAB24\n",
+      "7729 ENSG00000287920\n",
+      "7730 TBC1D7\n",
+      "7731 CASC15\n",
+      "7732 ALDH5A1\n",
+      "7733 GMNN\n",
+      "7734 H3C7\n",
+      "7735 H3C8\n",
+      "7736 ENSG00000284656\n",
+      "7737 MICA\n",
+      "7738 HCG25\n",
+      "7739 PRIM2\n",
+      "7740 HMGN3-AS1\n",
+      "7741 WASF1\n",
+      "7742 TRMT11\n",
+      "7743 RNF146\n",
+      "7744 HIVEP2\n",
+      "7745 NUP43\n",
+      "7746 ENSG00000235381\n",
+      "7747 FBXL18\n",
+      "7748 ARL4A\n",
+      "7749 AHR\n",
+      "7750 STK17A\n",
+      "7751 SBDS\n",
+      "7752 NSUN5\n",
+      "7753 CLDN3\n",
+      "7754 GTF2I\n",
+      "7755 STEAP2\n",
+      "7756 CYP3A7\n",
+      "7757 ZKSCAN1\n",
+      "7758 SERPINE1\n",
+      "7759 DNAJB9\n",
+      "7760 CAPZA2\n",
+      "7761 SPMIP1\n",
+      "7762 TMEM140\n",
+      "7763 CNOT4\n",
+      "7764 ENSG00000104714\n",
+      "7765 DEFA1\n",
+      "7766 XKR6\n",
+      "7767 CNOT7\n",
+      "7768 PNMA2\n",
+      "7769 HTRA4\n",
+      "7770 ENSG00000260588\n",
+      "7771 RNF170\n",
+      "7772 MSC-AS1\n",
+      "7773 TERF1\n",
+      "7774 OTUD6B\n",
+      "7775 ENSG00000253282\n",
+      "7776 DEPTOR-AS1\n",
+      "7777 SNTB1\n",
+      "7778 MYC\n",
+      "7779 PTP4A3\n",
+      "7780 SHARPIN\n",
+      "7781 SMARCA2\n",
+      "7782 GLDC\n",
+      "7783 ENSG00000187186\n",
+      "7784 ENSG00000286782\n",
+      "7785 FAM221B\n",
+      "7786 RECK\n",
+      "7787 GNAQ\n",
+      "7788 NAA35\n",
+      "7789 LINC00092\n",
+      "7790 XPA\n",
+      "7791 OLFML2A\n",
+      "7792 RABEPK\n",
+      "7793 ZDHHC12-DT\n",
+      "7794 COMMD3\n",
+      "7795 CSGALNACT2-DT\n",
+      "7796 LINC02675\n",
+      "7797 DDX50\n",
+      "7798 ENSG00000244733\n",
+      "7799 TOLLIP\n",
+      "7800 LINC02696\n",
+      "7801 MS4A10\n",
+      "7802 TMEM109\n",
+      "7803 VWCE\n",
+      "7804 TAF6L\n",
+      "7805 ATL3\n",
+      "7806 DNAJC4\n",
+      "7807 PPP1R14B\n",
+      "7808 FRMD8\n",
+      "7809 RBM4B\n",
+      "7810 GDPD5\n",
+      "7811 RAB39A\n",
+      "7812 CRYAB\n",
+      "7813 LINC00900\n",
+      "7814 MPZL2\n",
+      "7815 UBE4A\n",
+      "7816 TMEM25\n",
+      "7817 SIAE\n",
+      "7818 FAM118B\n",
+      "7819 NINJ2\n",
+      "7820 PIANP\n",
+      "7821 TPI1\n",
+      "7822 PRB2\n",
+      "7823 GSG1\n",
+      "7824 CACNB3\n",
+      "7825 LARP4\n",
+      "7826 MYG1\n",
+      "7827 PA2G4\n",
+      "7828 ENSG00000287200\n",
+      "7829 SLC16A7\n",
+      "7830 TSPAN19\n",
+      "7831 NFYB\n",
+      "7832 ENSG00000277715\n",
+      "7833 CAMKK2\n",
+      "7834 PSPC1\n",
+      "7835 ZDHHC20\n",
+      "7836 UBL3\n",
+      "7837 BORA\n",
+      "7838 DNAJC3-DT\n",
+      "7839 CLYBL\n",
+      "7840 ENSG00000277159\n",
+      "7841 GAS6-AS1\n",
+      "7842 TRAV6\n",
+      "7843 REC8\n",
+      "7844 TGM1\n",
+      "7845 ENSG00000278002\n",
+      "7846 ATL1\n",
+      "7847 PSMC6\n",
+      "7848 LINC01303\n",
+      "7849 PSEN1\n",
+      "7850 ENSG00000258944\n",
+      "7851 CALM1\n",
+      "7852 ENSG00000258572\n",
+      "7853 AMN\n",
+      "7854 NUDT14\n",
+      "7855 IGHV4-31\n",
+      "7856 GOLGA8O\n",
+      "7857 CCDC32\n",
+      "7858 CHP1\n",
+      "7859 ENSG00000275601\n",
+      "7860 EIF3J-DT\n",
+      "7861 ENSG00000259539\n",
+      "7862 ENSG00000259274\n",
+      "7863 RPL4\n",
+      "7864 IQCH\n",
+      "7865 BTBD1\n",
+      "7866 SYNM\n",
+      "7867 ASB7\n",
+      "7868 SNRPA1\n",
+      "7869 SNRNP25\n",
+      "7870 HBZ\n",
+      "7871 METTL26\n",
+      "7872 ENSG00000228201\n",
+      "7873 ENSG00000274751\n",
+      "7874 ZNF597\n",
+      "7875 XYLT1\n",
+      "7876 ITPRIPL2\n",
+      "7877 ENSG00000260072\n",
+      "7878 NPIPB6\n",
+      "7879 ENSG00000275857\n",
+      "7880 KCTD13\n",
+      "7881 MIR762HG\n",
+      "7882 PRSS36\n",
+      "7883 C16orf87\n",
+      "7884 SIAH1\n",
+      "7885 CMTM1\n",
+      "7886 NOB1\n",
+      "7887 ENSG00000260816\n",
+      "7888 MBTPS1\n",
+      "7889 ENSG00000261327\n",
+      "7890 SPATA2L\n",
+      "7891 TUBB3\n",
+      "7892 AURKB\n",
+      "7893 RCVRN\n",
+      "7894 ELAC2\n",
+      "7895 ENSG00000264739\n",
+      "7896 RAI1\n",
+      "7897 SREBF1\n",
+      "7898 KCNJ12\n",
+      "7899 ENSG00000263531\n",
+      "7900 STAT5A\n",
+      "7901 PSMC3IP\n",
+      "7902 RUNDC1\n",
+      "7903 ARL4D\n",
+      "7904 MPP2\n",
+      "7905 DBF4B\n",
+      "7906 TBKBP1\n",
+      "7907 AKAP1\n",
+      "7908 ENSG00000265912\n",
+      "7909 SOCS3\n",
+      "7910 OXLD1\n",
+      "7911 ARHGAP28\n",
+      "7912 DSG3\n",
+      "7913 ELP2\n",
+      "7914 LINC01630\n",
+      "7915 GRP\n",
+      "7916 RBFADN\n",
+      "7917 HDGFL2\n",
+      "7918 TNFAIP8L1\n",
+      "7919 KDM4B\n",
+      "7920 MAP2K7\n",
+      "7921 ENSG00000287275\n",
+      "7922 LIMASI\n",
+      "7923 DOCK6\n",
+      "7924 ZNF20\n",
+      "7925 FARSA\n",
+      "7926 ENSG00000273218\n",
+      "7927 UNC13A\n",
+      "7928 RAB3A\n",
+      "7929 MAU2\n",
+      "7930 ZNF257\n",
+      "7931 PDCD2L\n",
+      "7932 ZNF567\n",
+      "7933 ZNF420\n",
+      "7934 ZFP30\n",
+      "7935 COQ8B\n",
+      "7936 RPS19\n",
+      "7937 DEDD2\n",
+      "7938 BLOC1S3\n",
+      "7939 PPP1R13L\n",
+      "7940 BICRA-AS2\n",
+      "7941 POLD1\n",
+      "7942 ENSG00000167634\n",
+      "7943 ZNF579\n",
+      "7944 ZNF583\n",
+      "7945 ZNF772\n",
+      "7946 ZNF324B\n",
+      "7947 ENSG00000272874\n",
+      "7948 SDCBP2-AS1\n",
+      "7949 TGM3\n",
+      "7950 EBF4\n",
+      "7951 SLC23A2\n",
+      "7952 PARAL1\n",
+      "7953 LINC01597\n",
+      "7954 CPNE1\n",
+      "7955 UBE2V1\n",
+      "7956 BMP7\n",
+      "7957 SLCO4A1\n",
+      "7958 COL9A3\n",
+      "7959 IL10RB\n",
+      "7960 KCNE1\n",
+      "7961 AGPAT3\n",
+      "7962 ENSG00000275799\n",
+      "7963 ITGB2-AS1\n",
+      "7964 DGCR11\n",
+      "7965 TBX1\n",
+      "7966 LZTR1\n",
+      "7967 RFPL1S\n",
+      "7968 THOC5\n",
+      "7969 MFNG\n",
+      "7970 EIF3L\n",
+      "7971 GRAP2\n",
+      "7972 CERK\n",
+      "7973 CHKB-DT\n",
+      "7974 ARSA\n",
+      "7975 RBM10\n",
+      "7976 PORCN\n",
+      "7977 TSR2\n",
+      "7978 GNL3L\n",
+      "7979 NLGN3\n",
+      "7980 ATP7A\n",
+      "7981 SH3BGRL\n",
+      "7982 APOOL\n",
+      "7983 POF1B\n",
+      "7984 TCEAL2\n",
+      "7985 TMEM255A\n",
+      "7986 HCFC1\n",
+      "7987 UTY\n",
+      "7988 DFFB\n",
+      "7989 FBXO44\n",
+      "7990 SDHB\n",
+      "7991 ECE1-AS1\n",
+      "7992 C1QC\n",
+      "7993 HMGCL\n",
+      "7994 SFN\n",
+      "7995 TAF12\n",
+      "7996 EPB41\n",
+      "7997 RNF19B\n",
+      "7998 CAP1\n",
+      "7999 FOXJ3\n",
+      "8000 TMEM269\n",
+      "8001 DMAP1\n",
+      "8002 PIK3R3\n",
+      "8003 RAD54L\n",
+      "8004 ATPAF1\n",
+      "8005 CYP4A22-AS1\n",
+      "8006 SHISAL2A\n",
+      "8007 RPF1\n",
+      "8008 CD53\n",
+      "8009 ENSG00000273010\n",
+      "8010 CTTNBP2NL\n",
+      "8011 ATP1A1-AS1\n",
+      "8012 GDAP2\n",
+      "8013 GATAD2B\n",
+      "8014 RAB13\n",
+      "8015 SHE\n",
+      "8016 CRABP2\n",
+      "8017 HDGF\n",
+      "8018 ITLN1\n",
+      "8019 OLFML2B\n",
+      "8020 COLGALT2\n",
+      "8021 PTGS2\n",
+      "8022 PPP1R15B\n",
+      "8023 RHEX\n",
+      "8024 FCMR\n",
+      "8025 ENSG00000228106\n",
+      "8026 LIN9\n",
+      "8027 RHOU\n",
+      "8028 ZNF670\n",
+      "8029 PIGF\n",
+      "8030 TTC7A\n",
+      "8031 LINC03050\n",
+      "8032 KRCC1\n",
+      "8033 IGKV2-29\n",
+      "8034 TMEM127\n",
+      "8035 CIAO1\n",
+      "8036 COA5\n",
+      "8037 UGGT1\n",
+      "8038 LRP1B\n",
+      "8039 ENSG00000223523\n",
+      "8040 SGO2\n",
+      "8041 FZD7\n",
+      "8042 INO80D-AS1\n",
+      "8043 ERBB4\n",
+      "8044 LINC01953\n",
+      "8045 RNF25\n",
+      "8046 ENSG00000273301\n",
+      "8047 MFF\n",
+      "8048 NGEF\n",
+      "8049 SAG\n",
+      "8050 COPS9\n",
+      "8051 SETMAR\n",
+      "8052 IQSEC1\n",
+      "8053 ENSG00000261468\n",
+      "8054 OSBPL10-AS1\n",
+      "8055 CMTM6\n",
+      "8056 CSRNP1\n",
+      "8057 EXOSC7\n",
+      "8058 QRICH1\n",
+      "8059 ENSG00000213600\n",
+      "8060 ACTR8\n",
+      "8061 ENSG00000271843\n",
+      "8062 ENSG00000279277\n",
+      "8063 ZBED2\n",
+      "8064 KLF15\n",
+      "8065 NME9\n",
+      "8066 LINC02917\n",
+      "8067 SAMD7\n",
+      "8068 FHL1P1\n",
+      "8069 ZMAT3\n",
+      "8070 ENSG00000286624\n",
+      "8071 ACAP2\n",
+      "8072 PAK2\n",
+      "8073 ENSG00000288380\n",
+      "8074 RGS12\n",
+      "8075 FAM200B\n",
+      "8076 LAP3\n",
+      "8077 AASDH\n",
+      "8078 HTN3\n",
+      "8079 HNRNPDL\n",
+      "8080 ENSG00000248161\n",
+      "8081 CYP2U1-AS1\n",
+      "8082 RPL34-DT\n",
+      "8083 UGT8\n",
+      "8084 NDNF\n",
+      "8085 AFG2A\n",
+      "8086 PLK4\n",
+      "8087 CLGN\n",
+      "8088 SPOCK3\n",
+      "8089 PALLD\n",
+      "8090 LPCAT1\n",
+      "8091 LINC02899\n",
+      "8092 GOLPH3\n",
+      "8093 RASGRF2\n",
+      "8094 SNX2\n",
+      "8095 PRRC1\n",
+      "8096 LEAP2\n",
+      "8097 DPYSL3\n",
+      "8098 RPS14\n",
+      "8099 ENSG00000248544\n",
+      "8100 NHP2\n",
+      "8101 ZNF879\n",
+      "8102 TRIM7-AS1\n",
+      "8103 DSP\n",
+      "8104 HCG11\n",
+      "8105 HLA-B\n",
+      "8106 APOM\n",
+      "8107 PKHD1\n",
+      "8108 ZNF292\n",
+      "8109 SCML4\n",
+      "8110 FAM229B\n",
+      "8111 DCBLD1\n",
+      "8112 CITED2\n",
+      "8113 VTA1\n",
+      "8114 ENSG00000278899\n",
+      "8115 ESR1\n",
+      "8116 IGF2R\n",
+      "8117 PLG\n",
+      "8118 ITGB8-AS1\n",
+      "8119 KLHL7-DT\n",
+      "8120 MALSU1\n",
+      "8121 HNRNPA2B1\n",
+      "8122 NEUROD6\n",
+      "8123 CLDN4\n",
+      "8124 SRRM3\n",
+      "8125 CCDC146\n",
+      "8126 CASD1\n",
+      "8127 SMURF1\n",
+      "8128 FAM185A\n",
+      "8129 FSCN3\n",
+      "8130 LINC-PINT\n",
+      "8131 CALD1\n",
+      "8132 ENSG00000170379\n",
+      "8133 ARHGEF5\n",
+      "8134 ENSG00000261451\n",
+      "8135 ASAH1-AS1\n",
+      "8136 DPYSL2\n",
+      "8137 ZNF395\n",
+      "8138 EXTL3\n",
+      "8139 ENSG00000285669\n",
+      "8140 ADGRA2\n",
+      "8141 STAU2\n",
+      "8142 FABP4\n",
+      "8143 CA1\n",
+      "8144 INTS8\n",
+      "8145 RAD21-AS1\n",
+      "8146 GLI4\n",
+      "8147 APTX\n",
+      "8148 ANKRD18B\n",
+      "8149 S1PR3\n",
+      "8150 NOL8\n",
+      "8151 TBC1D2\n",
+      "8152 ABCA1\n",
+      "8153 RGS3\n",
+      "8154 CNTRL\n",
+      "8155 DAB2IP\n",
+      "8156 SLC2A8\n",
+      "8157 PIP5KL1\n",
+      "8158 ENSG00000272696\n",
+      "8159 LINC00963\n",
+      "8160 LINC02908\n",
+      "8161 CCNY-AS1\n",
+      "8162 HKDC1\n",
+      "8163 ENSG00000272630\n",
+      "8164 ZSWIM8\n",
+      "8165 PTEN\n",
+      "8166 FAS\n",
+      "8167 ENSG00000231575\n",
+      "8168 SORBS1\n",
+      "8169 ENTPD1\n",
+      "8170 DNMBP\n",
+      "8171 GSTO1\n",
+      "8172 DHX32\n",
+      "8173 INPP5A\n",
+      "8174 ENSG00000254910\n",
+      "8175 ANO9\n",
+      "8176 TNNT3\n",
+      "8177 CD81-AS1\n",
+      "8178 SBF2-AS1\n",
+      "8179 DKK3\n",
+      "8180 NAV2\n",
+      "8181 IMMP1L\n",
+      "8182 COMMD9\n",
+      "8183 PACSIN3\n",
+      "8184 RAPSN\n",
+      "8185 CCDC86\n",
+      "8186 B3GAT3\n",
+      "8187 C11orf98\n",
+      "8188 NRXN2\n",
+      "8189 ZDHHC24\n",
+      "8190 CABP4\n",
+      "8191 ENSG00000285693\n",
+      "8192 CLNS1A\n",
+      "8193 ME3\n",
+      "8194 TIMM8B\n",
+      "8195 TTC12-DT\n",
+      "8196 DDX6\n",
+      "8197 ENSG00000250493\n",
+      "8198 SPA17\n",
+      "8199 DDX25\n",
+      "8200 PRDM10-DT\n",
+      "8201 IGSF9B\n",
+      "8202 ERC1\n",
+      "8203 SPSB2\n",
+      "8204 ENSG00000257027\n",
+      "8205 PRR4\n",
+      "8206 BHLHE41\n",
+      "8207 CPNE8\n",
+      "8208 SCAF11\n",
+      "8209 ZNF641\n",
+      "8210 RND1\n",
+      "8211 CCDC65\n",
+      "8212 ZNF740\n",
+      "8213 PDE1B\n",
+      "8214 ENSG00000274979\n",
+      "8215 GLIPR1\n",
+      "8216 ENSG00000258044\n",
+      "8217 LUM\n",
+      "8218 CFAP54\n",
+      "8219 BLTP3B\n",
+      "8220 SLC41A2\n",
+      "8221 TRAFD1\n",
+      "8222 RPH3A\n",
+      "8223 MLXIP\n",
+      "8224 RAN\n",
+      "8225 ENSG00000277368\n",
+      "8226 KBTBD6\n",
+      "8227 SETDB2\n",
+      "8228 LINC00402\n",
+      "8229 TRDC\n",
+      "8230 RALGAPA1\n",
+      "8231 DLGAP5\n",
+      "8232 KCNH5\n",
+      "8233 ZBTB25\n",
+      "8234 GARIN2\n",
+      "8235 ACTN1\n",
+      "8236 EXD2\n",
+      "8237 SIPA1L1\n",
+      "8238 ACOT4\n",
+      "8239 LIN52\n",
+      "8240 ENSG00000273729\n",
+      "8241 HISLA\n",
+      "8242 CATSPERB\n",
+      "8243 GON7\n",
+      "8244 IFI27L1\n",
+      "8245 LINC00239\n",
+      "8246 CLBA1\n",
+      "8247 FAM30A\n",
+      "8248 TJP1\n",
+      "8249 MTMR10\n",
+      "8250 CHAC1\n",
+      "8251 INO80\n",
+      "8252 SLC30A4\n",
+      "8253 RORA\n",
+      "8254 PPIB\n",
+      "8255 ARNT2\n",
+      "8256 SAXO2\n",
+      "8257 ZNF710\n",
+      "8258 SEMA4B\n",
+      "8259 NR2F2\n",
+      "8260 ENSG00000261505\n",
+      "8261 SYNGR3\n",
+      "8262 SRRM2\n",
+      "8263 ANKS3\n",
+      "8264 PPL\n",
+      "8265 C16orf89\n",
+      "8266 COQ7\n",
+      "8267 LINC02890\n",
+      "8268 NSMCE1\n",
+      "8269 ENSG00000274092\n",
+      "8270 ENSG00000283662\n",
+      "8271 SULT1A1\n",
+      "8272 TMEM219\n",
+      "8273 CTF1\n",
+      "8274 VKORC1\n",
+      "8275 DNAJA2-DT\n",
+      "8276 MTHFSD\n",
+      "8277 FANCA\n",
+      "8278 ENSG00000177946\n",
+      "8279 LIAT1\n",
+      "8280 CLDN7\n",
+      "8281 TTC19\n",
+      "8282 TVP23B\n",
+      "8283 BLTP2\n",
+      "8284 ENSG00000276250\n",
+      "8285 DDX52\n",
+      "8286 STAT3\n",
+      "8287 COX11\n",
+      "8288 MKS1\n",
+      "8289 TSPOAP1\n",
+      "8290 ENSG00000273702\n",
+      "8291 SMARCD2\n",
+      "8292 MIF4GD-DT\n",
+      "8293 TMEM94\n",
+      "8294 QRICH2\n",
+      "8295 ENSG00000267546\n",
+      "8296 ST6GALNAC2\n",
+      "8297 ASPSCR1\n",
+      "8298 DUS1L\n",
+      "8299 NARF-AS2\n",
+      "8300 TYMS\n",
+      "8301 EPG5\n",
+      "8302 ME2\n",
+      "8303 SERPINB2\n",
+      "8304 ZNF236\n",
+      "8305 ENSG00000274828\n",
+      "8306 AP3D1\n",
+      "8307 FZR1\n",
+      "8308 KHSRP\n",
+      "8309 SH2D3A\n",
+      "8310 KANK3\n",
+      "8311 TIMM29\n",
+      "8312 RAB3D\n",
+      "8313 ELOF1\n",
+      "8314 TNPO2\n",
+      "8315 OR7C1\n",
+      "8316 ANO8\n",
+      "8317 ARMC6\n",
+      "8318 ZNF730\n",
+      "8319 KCTD15\n",
+      "8320 ZNF792\n",
+      "8321 KRTDAP\n",
+      "8322 ENSG00000267260\n",
+      "8323 ZNF540\n",
+      "8324 CAPN12\n",
+      "8325 ENSG00000269688\n",
+      "8326 CNFN\n",
+      "8327 SNRNP70\n",
+      "8328 ENSG00000268686\n",
+      "8329 FCGRT\n",
+      "8330 ZNF480\n",
+      "8331 ENSG00000288253\n",
+      "8332 ZNF256\n",
+      "8333 LAMP5\n",
+      "8334 TASP1\n",
+      "8335 ENSG00000182584\n",
+      "8336 EDEM2\n",
+      "8337 CEP250-AS1\n",
+      "8338 RBM12\n",
+      "8339 RBM39\n",
+      "8340 NDRG3\n",
+      "8341 MMP9\n",
+      "8342 TCEA2\n",
+      "8343 BACH1\n",
+      "8344 LINC01679\n",
+      "8345 TUBA8\n",
+      "8346 CLDN5\n",
+      "8347 ARVCF\n",
+      "8348 RAB36\n",
+      "8349 CRYBB2\n",
+      "8350 MN1\n",
+      "8351 TTC28-AS1\n",
+      "8352 GAS2L1\n",
+      "8353 APOL3\n",
+      "8354 GCAT\n",
+      "8355 SEPTIN3\n",
+      "8356 NAGA\n",
+      "8357 ENSG00000260613\n",
+      "8358 PPP6R2\n",
+      "8359 ZNF41\n",
+      "8360 CCNB3\n",
+      "8361 SNX12\n",
+      "8362 GCNA\n",
+      "8363 PLS3\n",
+      "8364 STK26\n",
+      "8365 ATP6AP1-DT\n",
+      "8366 LAGE3\n",
+      "8367 ENSG00000142606\n",
+      "8368 TNFRSF25\n",
+      "8369 ZBTB48\n",
+      "8370 ENSG00000284716\n",
+      "8371 CLSTN1\n",
+      "8372 ENSG00000284708\n",
+      "8373 KIAA2013\n",
+      "8374 ENSG00000225986\n",
+      "8375 TMEM50A\n",
+      "8376 ENSG00000237429\n",
+      "8377 LINC02574\n",
+      "8378 MED18\n",
+      "8379 YTHDF2\n",
+      "8380 LAPTM5\n",
+      "8381 HPCAL4\n",
+      "8382 MYCL\n",
+      "8383 ZMYND12\n",
+      "8384 TCEANC2\n",
+      "8385 CYP2J2\n",
+      "8386 ZNF644\n",
+      "8387 ENSG00000259834\n",
+      "8388 LRIG2\n",
+      "8389 AP4B1\n",
+      "8390 PDZK1\n",
+      "8391 POLR3C\n",
+      "8392 ANKRD35\n",
+      "8393 ENSG00000272755\n",
+      "8394 H2BC21\n",
+      "8395 PRPF3\n",
+      "8396 FALEC\n",
+      "8397 GOLPH3L\n",
+      "8398 ENSG00000229021\n",
+      "8399 S100A11\n",
+      "8400 ENSG00000284738\n",
+      "8401 B4GALT3\n",
+      "8402 NDUFS2\n",
+      "8403 ILDR2\n",
+      "8404 METTL18\n",
+      "8405 COP1\n",
+      "8406 RGL1\n",
+      "8407 ATP2B4\n",
+      "8408 DTL\n",
+      "8409 COA6\n",
+      "8410 GREM2\n",
+      "8411 RPS7\n",
+      "8412 KLF11\n",
+      "8413 PREB\n",
+      "8414 MPV17\n",
+      "8415 LINC02580\n",
+      "8416 FAM136A\n",
+      "8417 ADD2\n",
+      "8418 DUSP11\n",
+      "8419 RTKN\n",
+      "8420 REG1B\n",
+      "8421 VAMP5\n",
+      "8422 IGKV1-17\n",
+      "8423 PROM2\n",
+      "8424 ANKRD36B\n",
+      "8425 INPP4A\n",
+      "8426 SLC20A1\n",
+      "8427 POLR2D\n",
+      "8428 CYTIP\n",
+      "8429 PJVK\n",
+      "8430 ANKRD44-IT1\n",
+      "8431 METTL21A\n",
+      "8432 ILKAP\n",
+      "8433 SUMF1\n",
+      "8434 CAMK1\n",
+      "8435 ENSG00000269886\n",
+      "8436 EMC3\n",
+      "8437 ENSG00000261734\n",
+      "8438 DLEC1\n",
+      "8439 ABHD5\n",
+      "8440 KIF15\n",
+      "8441 GNAI2\n",
+      "8442 HYAL2\n",
+      "8443 ACY1\n",
+      "8444 THOC7\n",
+      "8445 LINC02018\n",
+      "8446 DCBLD2\n",
+      "8447 RPL24\n",
+      "8448 ENSG00000272844\n",
+      "8449 GAP43\n",
+      "8450 RAB43\n",
+      "8451 PIK3R4\n",
+      "8452 PPM1L-DT\n",
+      "8453 PDCD10\n",
+      "8454 MRPL47\n",
+      "8455 TPRG1\n",
+      "8456 SLC2A9\n",
+      "8457 PGM2\n",
+      "8458 UBE2K\n",
+      "8459 DCAF4L1\n",
+      "8460 UBA6-DT\n",
+      "8461 CXCL11\n",
+      "8462 PRDM8\n",
+      "8463 SEC24B\n",
+      "8464 ZGRF1\n",
+      "8465 MIR302CHG\n",
+      "8466 GASK1B\n",
+      "8467 CDKN2AIP\n",
+      "8468 LRP2BP\n",
+      "8469 PARP8\n",
+      "8470 FST\n",
+      "8471 SLC38A9\n",
+      "8472 THBS4\n",
+      "8473 RASA1\n",
+      "8474 LYSMD3\n",
+      "8475 NR2F1\n",
+      "8476 STARD4-AS1\n",
+      "8477 TGFBI\n",
+      "8478 SNHG4\n",
+      "8479 SLC35A4\n",
+      "8480 RNF14\n",
+      "8481 HMHB1\n",
+      "8482 YIPF5\n",
+      "8483 SLC26A2\n",
+      "8484 FBXW11\n",
+      "8485 UIMC1\n",
+      "8486 TUBB2A\n",
+      "8487 ENSG00000286281\n",
+      "8488 ENSG00000234261\n",
+      "8489 JARID2\n",
+      "8490 BTN2A1\n",
+      "8491 ENSG00000225595\n",
+      "8492 LTA\n",
+      "8493 LTB\n",
+      "8494 VWA7\n",
+      "8495 AGPAT1\n",
+      "8496 HLA-DPA1\n",
+      "8497 PIM1\n",
+      "8498 SRF\n",
+      "8499 MAD2L1BP\n",
+      "8500 PLA2G7\n",
+      "8501 GSTA2\n",
+      "8502 LRRC1\n",
+      "8503 KCNQ5\n",
+      "8504 TMEM30A\n",
+      "8505 ZBTB24\n",
+      "8506 ENSG00000287097\n",
+      "8507 FABP7\n",
+      "8508 ARFGEF3\n",
+      "8509 ARID1B\n",
+      "8510 BRAT1\n",
+      "8511 FOXK1\n",
+      "8512 ICA1\n",
+      "8513 STK31\n",
+      "8514 HERPUD2-AS1\n",
+      "8515 SFRP4\n",
+      "8516 SUN3\n",
+      "8517 ASL\n",
+      "8518 ENSG00000232019\n",
+      "8519 DMTF1\n",
+      "8520 ADAM22\n",
+      "8521 ENSG00000287672\n",
+      "8522 ASNS\n",
+      "8523 ENSG00000239521\n",
+      "8524 AP1S1\n",
+      "8525 HBP1\n",
+      "8526 DUS4L\n",
+      "8527 ENSG00000243220\n",
+      "8528 ENSG00000271553\n",
+      "8529 ENSG00000273391\n",
+      "8530 ENSG00000225932\n",
+      "8531 PRKAG2-AS1\n",
+      "8532 ENSG00000246477\n",
+      "8533 TRMT9B\n",
+      "8534 ASAH1\n",
+      "8535 POLR3D\n",
+      "8536 WRN\n",
+      "8537 SPIDR\n",
+      "8538 ENSG00000253608\n",
+      "8539 CLXN\n",
+      "8540 ARFGEF1-DT\n",
+      "8541 C8orf34-AS1\n",
+      "8542 ENSG00000253636\n",
+      "8543 CFAP418\n",
+      "8544 VPS13B-DT\n",
+      "8545 LINC01609\n",
+      "8546 PHF20L1\n",
+      "8547 ENSG00000259891\n",
+      "8548 LYPD2\n",
+      "8549 FAM88E\n",
+      "8550 ENSG00000106819\n",
+      "8551 TNFSF15\n",
+      "8552 WDR38\n",
+      "8553 RALGPS1\n",
+      "8554 CERCAM\n",
+      "8555 ENSG00000227619\n",
+      "8556 SURF6\n",
+      "8557 CCDC183\n",
+      "8558 LINC02656\n",
+      "8559 MCM10\n",
+      "8560 PRTFDC1\n",
+      "8561 ENSG00000256892\n",
+      "8562 TIMM23B-AGAP6\n",
+      "8563 IPMK\n",
+      "8564 SLC25A16\n",
+      "8565 ANXA7\n",
+      "8566 DYDC2\n",
+      "8567 ENSG00000287077\n",
+      "8568 KIF11\n",
+      "8569 NDUFB8\n",
+      "8570 NHLRC2\n",
+      "8571 SCART1\n",
+      "8572 SCGB1C1\n",
+      "8573 IFITM1\n",
+      "8574 IRF7\n",
+      "8575 MUC2\n",
+      "8576 ENSG00000287312\n",
+      "8577 HBB\n",
+      "8578 ZBED5\n",
+      "8579 HPS5\n",
+      "8580 ENSG00000287753\n",
+      "8581 YPEL4\n",
+      "8582 EEF1G\n",
+      "8583 RELA\n",
+      "8584 RBM14\n",
+      "8585 KDM2A\n",
+      "8586 CLCF1\n",
+      "8587 CCND1\n",
+      "8588 DDIAS\n",
+      "8589 PRSS23\n",
+      "8590 DCUN1D5\n",
+      "8591 AASDHPPT\n",
+      "8592 ENSG00000255467\n",
+      "8593 FDXACB1\n",
+      "8594 APOA4\n",
+      "8595 HMBS\n",
+      "8596 ING4\n",
+      "8597 RBP5\n",
+      "8598 LDHB\n",
+      "8599 ENSG00000274987\n",
+      "8600 ENSG00000275764\n",
+      "8601 KMT2D\n",
+      "8602 NCKAP5L\n",
+      "8603 MYG1-AS1\n",
+      "8604 SP1\n",
+      "8605 CNPY2\n",
+      "8606 ENSG00000258302\n",
+      "8607 CEP83-DT\n",
+      "8608 C12orf42\n",
+      "8609 WASHC4\n",
+      "8610 PWP1\n",
+      "8611 OAS3\n",
+      "8612 FBXW8\n",
+      "8613 TAOK3\n",
+      "8614 BCL7A\n",
+      "8615 HCAR2\n",
+      "8616 SNRNP35\n",
+      "8617 GTF2H3\n",
+      "8618 CHFR-DT\n",
+      "8619 MTMR6\n",
+      "8620 EPSTI1\n",
+      "8621 INTS6\n",
+      "8622 NEK5\n",
+      "8623 CFAP97D2\n",
+      "8624 RPGRIP1\n",
+      "8625 CHD8\n",
+      "8626 TRAV38-2DV8\n",
+      "8627 CEBPE\n",
+      "8628 PNN\n",
+      "8629 MAX\n",
+      "8630 ENSG00000258760\n",
+      "8631 ENSG00000269927\n",
+      "8632 ENSG00000260810\n",
+      "8633 MOAP1\n",
+      "8634 WARS1\n",
+      "8635 HSP90AA1\n",
+      "8636 LINC02323\n",
+      "8637 CDCA4\n",
+      "8638 IGHV4-34\n",
+      "8639 NIPA1\n",
+      "8640 ENSG00000244952\n",
+      "8641 ENSG00000259345\n",
+      "8642 ENSG00000273855\n",
+      "8643 PLA2G4D\n",
+      "8644 C15orf48\n",
+      "8645 GABPB1-AS1\n",
+      "8646 DNAAF4\n",
+      "8647 ENSG00000277144\n",
+      "8648 MTFMT\n",
+      "8649 MESD\n",
+      "8650 WHAMM\n",
+      "8651 ADAMTSL3\n",
+      "8652 ENSG00000259219\n",
+      "8653 JMJD8\n",
+      "8654 KCTD5\n",
+      "8655 ENSG00000274367\n",
+      "8656 CEP20\n",
+      "8657 RPS15A\n",
+      "8658 ENSG00000274460\n",
+      "8659 ENSG00000239791\n",
+      "8660 ENSG00000261997\n",
+      "8661 PDP2\n",
+      "8662 FCSK\n",
+      "8663 MTSS2\n",
+      "8664 GINS2\n",
+      "8665 CYBA\n",
+      "8666 ENSG00000278341\n",
+      "8667 DOC2B\n",
+      "8668 METTL16\n",
+      "8669 PITPNM3\n",
+      "8670 SLC5A10\n",
+      "8671 IFT20\n",
+      "8672 ENSG00000265254\n",
+      "8673 ENSG00000270894\n",
+      "8674 SP2-DT\n",
+      "8675 TOB1-AS1\n",
+      "8676 SPAG9\n",
+      "8677 LINC01483\n",
+      "8678 ENSG00000261222\n",
+      "8679 GALR2\n",
+      "8680 ENSG00000285535\n",
+      "8681 TEPSIN\n",
+      "8682 USP14\n",
+      "8683 GNAL\n",
+      "8684 AFG3L2\n",
+      "8685 PTPN2\n",
+      "8686 KCTD1\n",
+      "8687 ENSG00000267397\n",
+      "8688 ENSG00000267707\n",
+      "8689 CD226\n",
+      "8690 GZMM\n",
+      "8691 HCN2\n",
+      "8692 C19orf25\n",
+      "8693 ENSG00000288156\n",
+      "8694 DAPK3\n",
+      "8695 SNAPC2\n",
+      "8696 RAB11B-AS1\n",
+      "8697 UBL5\n",
+      "8698 FBXW9\n",
+      "8699 KLF1\n",
+      "8700 GCDH\n",
+      "8701 PGLS\n",
+      "8702 NIBAN3\n",
+      "8703 PGPEP1\n",
+      "8704 KXD1\n",
+      "8705 SLC25A42\n",
+      "8706 LINC03049\n",
+      "8707 LRFN3\n",
+      "8708 ZNF790-AS1\n",
+      "8709 MAP3K10\n",
+      "8710 SERTAD1\n",
+      "8711 CEACAM21\n",
+      "8712 ERFL\n",
+      "8713 LYPD5\n",
+      "8714 ZNF45\n",
+      "8715 ZNF296\n",
+      "8716 SIX5\n",
+      "8717 PRR12\n",
+      "8718 CPT1C\n",
+      "8719 ZNF473\n",
+      "8720 NR1H2\n",
+      "8721 LIM2-AS1\n",
+      "8722 ZNF761\n",
+      "8723 PPP1R12C\n",
+      "8724 TNNI3\n",
+      "8725 ITCH\n",
+      "8726 ENSG00000101353\n",
+      "8727 RPN2\n",
+      "8728 PREX1\n",
+      "8729 PELATON\n",
+      "8730 ENSG00000149656\n",
+      "8731 ENSG00000278955\n",
+      "8732 ENSG00000244676\n",
+      "8733 ATP5PF\n",
+      "8734 KCNJ6\n",
+      "8735 ENSG00000272991\n",
+      "8736 PRDM15\n",
+      "8737 PDXK\n",
+      "8738 CSTB\n",
+      "8739 COL6A2\n",
+      "8740 MED15\n",
+      "8741 PPM1F\n",
+      "8742 IGLV1-51\n",
+      "8743 IGLV1-44\n",
+      "8744 IGLV2-8\n",
+      "8745 NIPSNAP1\n",
+      "8746 PIK3IP1-DT\n",
+      "8747 ENSG00000287817\n",
+      "8748 ENSG00000287269\n",
+      "8749 EIF3D\n",
+      "8750 MPST\n",
+      "8751 FAM83F\n",
+      "8752 SNU13\n",
+      "8753 EFCAB6\n",
+      "8754 ENSG00000273145\n",
+      "8755 ENSG00000273272\n",
+      "8756 ENSG00000167393\n",
+      "8757 ASMTL-AS1\n",
+      "8758 PIGA\n",
+      "8759 SCML2\n",
+      "8760 MAP7D2\n",
+      "8761 SMS\n",
+      "8762 PTCHD1\n",
+      "8763 UXT\n",
+      "8764 ENSG00000228427\n",
+      "8765 BEX4\n",
+      "8766 NUP62CL\n",
+      "8767 OCRL\n",
+      "8768 EOLA2\n",
+      "8769 IDH3G\n",
+      "8770 TKTL1\n",
+      "8771 NOC2L\n",
+      "8772 INTS11\n",
+      "8773 MRPL20\n",
+      "8774 ENSG00000215014\n",
+      "8775 ESPN\n",
+      "8776 FAM110D\n",
+      "8777 RPS6KA1\n",
+      "8778 PHACTR4\n",
+      "8779 ENSG00000284702\n",
+      "8780 PABPC4\n",
+      "8781 EIF2B3\n",
+      "8782 ZSWIM5\n",
+      "8783 ENSG00000234810\n",
+      "8784 CACHD1\n",
+      "8785 FPGT\n",
+      "8786 MIGA1\n",
+      "8787 TGFBR3\n",
+      "8788 CNN3-DT\n",
+      "8789 DBT\n",
+      "8790 KCNC4\n",
+      "8791 LINC02805\n",
+      "8792 OTUD7B\n",
+      "8793 PI4KB\n",
+      "8794 DCST1-AS1\n",
+      "8795 ISG20L2\n",
+      "8796 ENSG00000273160\n",
+      "8797 ENSG00000228697\n",
+      "8798 F5\n",
+      "8799 KLHL20\n",
+      "8800 MR1\n",
+      "8801 LINC02770\n",
+      "8802 RGS13\n",
+      "8803 B3GALT2\n",
+      "8804 IPO9-AS1\n",
+      "8805 ZBED6\n",
+      "8806 MIXL1\n",
+      "8807 PARP1\n",
+      "8808 WNT9A\n",
+      "8809 SPRTN\n",
+      "8810 SNTG2-AS1\n",
+      "8811 RN7SL832P\n",
+      "8812 SLC66A3\n",
+      "8813 MYCNOS\n",
+      "8814 GPN1\n",
+      "8815 HNRNPLL\n",
+      "8816 LRPPRC\n",
+      "8817 EFEMP1\n",
+      "8818 PAPOLG\n",
+      "8819 MCEE\n",
+      "8820 NOTO\n",
+      "8821 SMYD5\n",
+      "8822 DGUOK-AS1\n",
+      "8823 DOK1\n",
+      "8824 KCMF1\n",
+      "8825 LINC01943\n",
+      "8826 IGKV5-2\n",
+      "8827 APPAT\n",
+      "8828 LIPT1\n",
+      "8829 BCL2L11\n",
+      "8830 MAP3K2\n",
+      "8831 ENSG00000241772\n",
+      "8832 RBMS1\n",
+      "8833 ENSG00000259915\n",
+      "8834 MFSD6\n",
+      "8835 NRP2\n",
+      "8836 ZDBF2\n",
+      "8837 AAMP\n",
+      "8838 RHBDD1\n",
+      "8839 UGT1A1\n",
+      "8840 ARL4C\n",
+      "8841 ENSG00000286864\n",
+      "8842 GPC1\n",
+      "8843 CAPN10\n",
+      "8844 SLC6A1\n",
+      "8845 AZI2\n",
+      "8846 KLHDC8B\n",
+      "8847 ENSG00000271976\n",
+      "8848 ASB14\n",
+      "8849 FAM107A\n",
+      "8850 PRICKLE2\n",
+      "8851 ROBO2\n",
+      "8852 ENSG00000273374\n",
+      "8853 TMEM45A\n",
+      "8854 LINC01215\n",
+      "8855 TIMMDC1-DT\n",
+      "8856 ABTB1\n",
+      "8857 TRH\n",
+      "8858 NCK1\n",
+      "8859 IL20RB\n",
+      "8860 RNF13\n",
+      "8861 ERICH6-AS1\n",
+      "8862 TIPARP\n",
+      "8863 ENSG00000263826\n",
+      "8864 EIF4A2\n",
+      "8865 RPL39L\n",
+      "8866 ENSG00000227189\n",
+      "8867 FRYL\n",
+      "8868 CXCL3\n",
+      "8869 ENSG00000229717\n",
+      "8870 LINC01088\n",
+      "8871 SNCA\n",
+      "8872 CISD2\n",
+      "8873 IL21\n",
+      "8874 WDR17\n",
+      "8875 PDCD6-DT\n",
+      "8876 TRIO\n",
+      "8877 C5orf22\n",
+      "8878 GAPT\n",
+      "8879 PTCD2\n",
+      "8880 SLCO4C1\n",
+      "8881 ZNF608\n",
+      "8882 PDLIM4\n",
+      "8883 ENSG00000248559\n",
+      "8884 CDKN2AIPNL\n",
+      "8885 C5orf24\n",
+      "8886 DCTN4\n",
+      "8887 NUP153\n",
+      "8888 TDP2\n",
+      "8889 H2AC8\n",
+      "8890 BTN3A2\n",
+      "8891 SFTA2\n",
+      "8892 CCHCR1\n",
+      "8893 AIF1\n",
+      "8894 GPANK1\n",
+      "8895 PFDN6\n",
+      "8896 RPL10A\n",
+      "8897 C6orf226\n",
+      "8898 KHDRBS2\n",
+      "8899 SH3BGRL2\n",
+      "8900 MANEA-DT\n",
+      "8901 SNX3\n",
+      "8902 TSPYL1\n",
+      "8903 ABRACL\n",
+      "8904 SERAC1\n",
+      "8905 PHF14\n",
+      "8906 AGR2\n",
+      "8907 NUP42\n",
+      "8908 FAM221A\n",
+      "8909 ENSG00000287574\n",
+      "8910 ELN-AS1\n",
+      "8911 PHTF2\n",
+      "8912 ARPC1B\n",
+      "8913 THAP5\n",
+      "8914 CALU\n",
+      "8915 CREB3L2\n",
+      "8916 TRBV7-6\n",
+      "8917 TCAF2C\n",
+      "8918 ARHGEF35\n",
+      "8919 RNF32-DT\n",
+      "8920 NOM1\n",
+      "8921 ENSG00000272267\n",
+      "8922 DOK2\n",
+      "8923 ENSG00000250714\n",
+      "8924 ENSG00000253645\n",
+      "8925 ENSG00000271938\n",
+      "8926 PRKDC\n",
+      "8927 ENSG00000261542\n",
+      "8928 EYA1\n",
+      "8929 MIR2052HG\n",
+      "8930 IMPA1\n",
+      "8931 UBR5\n",
+      "8932 ENSG00000261670\n",
+      "8933 ENSG00000254275\n",
+      "8934 TIGD5\n",
+      "8935 C8orf82\n",
+      "8936 ZNF252P-AS1\n",
+      "8937 ENSG00000227914\n",
+      "8938 ENSG00000237359\n",
+      "8939 SPMIP6\n",
+      "8940 GLIPR2\n",
+      "8941 TRMT10B\n",
+      "8942 IARS1\n",
+      "8943 IPPK\n",
+      "8944 TDRD7\n",
+      "8945 PTGR1\n",
+      "8946 HSDL2-AS1\n",
+      "8947 MIR600HG\n",
+      "8948 ARPC5L\n",
+      "8949 LINC01503\n",
+      "8950 ASS1\n",
+      "8951 FAM107B\n",
+      "8952 ENSG00000273153\n",
+      "8953 ENSG00000230534\n",
+      "8954 CREM\n",
+      "8955 ARHGAP22\n",
+      "8956 HK1\n",
+      "8957 MICU1\n",
+      "8958 POLR3A\n",
+      "8959 PPIF\n",
+      "8960 SHLD2\n",
+      "8961 HPS1\n",
+      "8962 MRPL43\n",
+      "8963 ARMH3\n",
+      "8964 GSTO2\n",
+      "8965 DUSP5\n",
+      "8966 LINC01163\n",
+      "8967 ENSG00000277959\n",
+      "8968 SIRT3\n",
+      "8969 LSP1\n",
+      "8970 TSPAN32\n",
+      "8971 CHRNA10\n",
+      "8972 STIM1-AS1\n",
+      "8973 HBG2\n",
+      "8974 FBXO3\n",
+      "8975 FJX1\n",
+      "8976 RAG2\n",
+      "8977 PTPMT1\n",
+      "8978 SLC43A1\n",
+      "8979 UBE2L6\n",
+      "8980 MS4A6E\n",
+      "8981 CD5\n",
+      "8982 PGA3\n",
+      "8983 SDHAF2\n",
+      "8984 NXF1\n",
+      "8985 PLAAT3\n",
+      "8986 MEN1\n",
+      "8987 ENSG00000270117\n",
+      "8988 PCF11-AS1\n",
+      "8989 EED\n",
+      "8990 TMEM135\n",
+      "8991 TYR\n",
+      "8992 POGLUT3\n",
+      "8993 ZPR1\n",
+      "8994 FXYD6\n",
+      "8995 ATP5MG\n",
+      "8996 MIR100HG\n",
+      "8997 LINC02972\n",
+      "8998 CLEC2B\n",
+      "8999 CLEC7A\n",
+      "9000 ATF7IP\n",
+      "9001 ENSG00000258344\n",
+      "9002 ENSG00000258199\n",
+      "9003 RBMS2\n",
+      "9004 ENSG00000277895\n",
+      "9005 IL26\n",
+      "9006 ENSG00000256325\n",
+      "9007 ZFC3H1\n",
+      "9008 CCDC59\n",
+      "9009 TMTC3\n",
+      "9010 TMEM119\n",
+      "9011 USP30-AS1\n",
+      "9012 CLIP1\n",
+      "9013 DNAH10\n",
+      "9014 RFLNA\n",
+      "9015 ENSG00000275389\n",
+      "9016 SCARB1\n",
+      "9017 GALNT9\n",
+      "9018 SACS\n",
+      "9019 PCOTH\n",
+      "9020 RNF6\n",
+      "9021 LINC00544\n",
+      "9022 MTRF1\n",
+      "9023 CCDC122\n",
+      "9024 RCBTB1\n",
+      "9025 TEX30\n",
+      "9026 TNFSF13B\n",
+      "9027 MCF2L\n",
+      "9028 CHAMP1\n",
+      "9029 TRAV39\n",
+      "9030 C14orf93\n",
+      "9031 ENSG00000258414\n",
+      "9032 GPX2\n",
+      "9033 GPR65\n",
+      "9034 NRDE2\n",
+      "9035 SNRPN\n",
+      "9036 ENSG00000274307\n",
+      "9037 ENSG00000276724\n",
+      "9038 DPH6-DT\n",
+      "9039 PAK6\n",
+      "9040 ENSG00000174171\n",
+      "9041 EHD4\n",
+      "9042 CCNDBP1\n",
+      "9043 TUBGCP4\n",
+      "9044 DUOXA2\n",
+      "9045 FGF7\n",
+      "9046 CLN6\n",
+      "9047 HEXA-AS1\n",
+      "9048 UBE2Q2\n",
+      "9049 ENSG00000260988\n",
+      "9050 TM6SF1\n",
+      "9051 ENSG00000259704\n",
+      "9052 CHD2\n",
+      "9053 ENSG00000268836\n",
+      "9054 TSR3\n",
+      "9055 ATP6V0C\n",
+      "9056 CHP2\n",
+      "9057 CD2BP2\n",
+      "9058 BCL7C\n",
+      "9059 ENSG00000260757\n",
+      "9060 CRNDE\n",
+      "9061 MT1E\n",
+      "9062 CMTM3\n",
+      "9063 TERB1\n",
+      "9064 ENSG00000261248\n",
+      "9065 ENSG00000276007\n",
+      "9066 CMIP\n",
+      "9067 CBFA2T3\n",
+      "9068 SMG6\n",
+      "9069 P2RX1\n",
+      "9070 SLC16A13\n",
+      "9071 CTC1\n",
+      "9072 ENSG00000265401\n",
+      "9073 ENSG00000266651\n",
+      "9074 ZNF624\n",
+      "9075 TRIM16L\n",
+      "9076 PRPSAP2\n",
+      "9077 ZNF207\n",
+      "9078 TADA2A\n",
+      "9079 ENSG00000277969\n",
+      "9080 HSPB9\n",
+      "9081 HROB\n",
+      "9082 RUNDC3A\n",
+      "9083 ENSG00000276728\n",
+      "9084 LINC03057\n",
+      "9085 ENSG00000262967\n",
+      "9086 VEZF1\n",
+      "9087 DHX40\n",
+      "9088 DDX42\n",
+      "9089 BPTF\n",
+      "9090 EVPL\n",
+      "9091 UBALD2\n",
+      "9092 RPTOR\n",
+      "9093 B3GNTL1\n",
+      "9094 TGIF1\n",
+      "9095 DSC3\n",
+      "9096 CELF4\n",
+      "9097 CNDP2\n",
+      "9098 TXNL4A\n",
+      "9099 ENSG00000267666\n",
+      "9100 ARHGAP45\n",
+      "9101 ENSG00000267317\n",
+      "9102 UQCR11\n",
+      "9103 SGTA\n",
+      "9104 S1PR4\n",
+      "9105 NCLN\n",
+      "9106 RAX2\n",
+      "9107 PTPRS\n",
+      "9108 ZNF266\n",
+      "9109 S1PR5\n",
+      "9110 MIR23AHG\n",
+      "9111 REX1BD\n",
+      "9112 COPE\n",
+      "9113 GATAD2A\n",
+      "9114 ZNF486\n",
+      "9115 ZNF431\n",
+      "9116 LINC00664\n",
+      "9117 LINC02987\n",
+      "9118 ZNF780B\n",
+      "9119 ENSG00000286177\n",
+      "9120 DMAC2\n",
+      "9121 PCAT19\n",
+      "9122 ZNF225-AS1\n",
+      "9123 DACT3\n",
+      "9124 FLT3LG\n",
+      "9125 SPIB\n",
+      "9126 SIGLEC7\n",
+      "9127 ZNF813\n",
+      "9128 CACNG8\n",
+      "9129 ZNF749\n",
+      "9130 ZNF606-AS1\n",
+      "9131 ZNF497\n",
+      "9132 UBE2M\n",
+      "9133 MCM8\n",
+      "9134 POLR3F\n",
+      "9135 CST7\n",
+      "9136 NOL4L\n",
+      "9137 ENSG00000277301\n",
+      "9138 ASIP\n",
+      "9139 DHX35-DT\n",
+      "9140 DDX27\n",
+      "9141 PTGIS\n",
+      "9142 FAM209B\n",
+      "9143 GNAS-AS1\n",
+      "9144 MIS18A-AS1\n",
+      "9145 ITSN1\n",
+      "9146 ENSG00000279442\n",
+      "9147 COMT\n",
+      "9148 ENSG00000236540\n",
+      "9149 ENSG00000279159\n",
+      "9150 YWHAH\n",
+      "9151 APOL6\n",
+      "9152 C1QTNF6\n",
+      "9153 LGALS2\n",
+      "9154 ENSG00000279080\n",
+      "9155 ENSG00000237037\n",
+      "9156 OGFRP1\n",
+      "9157 POLDIP3\n",
+      "9158 MCAT\n",
+      "9159 STS\n",
+      "9160 SCML1\n",
+      "9161 POLA1\n",
+      "9162 UBQLN2\n",
+      "9163 PCDH11X\n",
+      "9164 RBMX2\n",
+      "9165 AFF2\n",
+      "9166 CD99L2\n",
+      "9167 HMGB3\n",
+      "9168 PLXNA3\n",
+      "9169 RPS4Y1\n",
+      "9170 PRKY\n",
+      "9171 ENO1\n",
+      "9172 ENSG00000272426\n",
+      "9173 LINC01635\n",
+      "9174 ELOA\n",
+      "9175 PNRC2\n",
+      "9176 RAB42\n",
+      "9177 SNRNP40\n",
+      "9178 ENSG00000284721\n",
+      "9179 TMEM35B\n",
+      "9180 ZMYM6\n",
+      "9181 ZMPSTE24-DT\n",
+      "9182 MUTYH\n",
+      "9183 SYDE2\n",
+      "9184 BCL10\n",
+      "9185 GLMN\n",
+      "9186 RPL5\n",
+      "9187 DIPK1A\n",
+      "9188 TMEM167B\n",
+      "9189 KCNA2\n",
+      "9190 WDR3\n",
+      "9191 PIAS3\n",
+      "9192 PPIAL4G\n",
+      "9193 S100A5\n",
+      "9194 CHTOP\n",
+      "9195 ATP8B2\n",
+      "9196 CKS1B\n",
+      "9197 SPTA1\n",
+      "9198 DUSP23\n",
+      "9199 ADAMTS4\n",
+      "9200 GPR161\n",
+      "9201 TOR1AIP2\n",
+      "9202 NEK2\n",
+      "9203 ENSG00000260505\n",
+      "9204 MIA3\n",
+      "9205 BROX\n",
+      "9206 RNF187\n",
+      "9207 COG2\n",
+      "9208 FMN2\n",
+      "9209 LINC00276\n",
+      "9210 ATAD2B\n",
+      "9211 CENPO\n",
+      "9212 SUPT7L\n",
+      "9213 RBKS\n",
+      "9214 CD207\n",
+      "9215 NAGK\n",
+      "9216 REG3A\n",
+      "9217 CAPG\n",
+      "9218 PLGLB1\n",
+      "9219 LMAN2L\n",
+      "9220 PDCL3\n",
+      "9221 IWS1\n",
+      "9222 ENSG00000287463\n",
+      "9223 CCNT2-AS1\n",
+      "9224 KIAA2012\n",
+      "9225 DNAJB2\n",
+      "9226 SP140\n",
+      "9227 SP140L\n",
+      "9228 GPR55\n",
+      "9229 RAMP1\n",
+      "9230 ANKMY1\n",
+      "9231 CRELD1\n",
+      "9232 GHRL\n",
+      "9233 VGLL4\n",
+      "9234 OXSR1\n",
+      "9235 LZTFL1\n",
+      "9236 FYCO1\n",
+      "9237 QARS1\n",
+      "9238 LAMB2\n",
+      "9239 SFMBT1\n",
+      "9240 EIF4E3\n",
+      "9241 GBE1\n",
+      "9242 MYH15\n",
+      "9243 GSK3B-DT\n",
+      "9244 PARP9\n",
+      "9245 ACAD11\n",
+      "9246 CEP70\n",
+      "9247 ENSG00000241168\n",
+      "9248 MYNN\n",
+      "9249 LINC00501\n",
+      "9250 MELTF-AS1\n",
+      "9251 MSANTD1\n",
+      "9252 NSG1\n",
+      "9253 STX18-AS1\n",
+      "9254 WFS1\n",
+      "9255 TRMT44\n",
+      "9256 ATP10D\n",
+      "9257 MOB1B\n",
+      "9258 SDAD1\n",
+      "9259 THAP9-AS1\n",
+      "9260 SLC39A8\n",
+      "9261 SGMS2\n",
+      "9262 CYP2U1\n",
+      "9263 MAD2L1-DT\n",
+      "9264 ENSG00000251600\n",
+      "9265 GYPE\n",
+      "9266 ENSG00000287216\n",
+      "9267 LINC02275\n",
+      "9268 SLC9A3-AS1\n",
+      "9269 LINC02982\n",
+      "9270 ENSG00000272335\n",
+      "9271 SGTB\n",
+      "9272 IL13\n",
+      "9273 EPIST\n",
+      "9274 SLC25A48\n",
+      "9275 ENSG00000254363\n",
+      "9276 DOCK2\n",
+      "9277 ERGIC1\n",
+      "9278 GRM6\n",
+      "9279 F13A1\n",
+      "9280 PHACTR1\n",
+      "9281 DTNBP1\n",
+      "9282 STMND1\n",
+      "9283 H1-1\n",
+      "9284 H2BC12\n",
+      "9285 ZFP57\n",
+      "9286 HCP5\n",
+      "9287 HLA-DRB5\n",
+      "9288 SCUBE3\n",
+      "9289 CMTR1\n",
+      "9290 RHAG\n",
+      "9291 BACH2\n",
+      "9292 UFL1\n",
+      "9293 FAXC\n",
+      "9294 ENSG00000260000\n",
+      "9295 METTL24\n",
+      "9296 ENSG00000260188\n",
+      "9297 ULBP2\n",
+      "9298 MTHFD1L\n",
+      "9299 ENSG00000271551\n",
+      "9300 TMEM242\n",
+      "9301 AP5Z1\n",
+      "9302 LINC03073\n",
+      "9303 HYCC1\n",
+      "9304 TRA2A\n",
+      "9305 SCRN1\n",
+      "9306 ELMO1\n",
+      "9307 TRGC1\n",
+      "9308 ZNF107\n",
+      "9309 GTF2IRD1\n",
+      "9310 TMEM225B\n",
+      "9311 SPDYE6\n",
+      "9312 EFCAB10-AS1\n",
+      "9313 NAMPT\n",
+      "9314 ASB15\n",
+      "9315 TRIM24\n",
+      "9316 TRBV5-5\n",
+      "9317 SLC4A2\n",
+      "9318 WDR86\n",
+      "9319 PTPRN2-AS1\n",
+      "9320 NCAPG2\n",
+      "9321 MCPH1-AS1\n",
+      "9322 PDLIM2\n",
+      "9323 FBXO16\n",
+      "9324 NSD3\n",
+      "9325 ADHFE1\n",
+      "9326 SLCO5A1\n",
+      "9327 LRRCC1\n",
+      "9328 VIRMA\n",
+      "9329 ATP6V1C1\n",
+      "9330 BAALC-AS1\n",
+      "9331 DERL1\n",
+      "9332 MTSS1\n",
+      "9333 LINC02964\n",
+      "9334 PTCSC1\n",
+      "9335 IQANK1\n",
+      "9336 SLC39A4\n",
+      "9337 RIC1\n",
+      "9338 KLHL9\n",
+      "9339 DCAF12\n",
+      "9340 PCSK5\n",
+      "9341 ZNF484\n",
+      "9342 FBP1\n",
+      "9343 TNFSF8\n",
+      "9344 FPGS\n",
+      "9345 DNM1\n",
+      "9346 PTGES\n",
+      "9347 CAMSAP1\n",
+      "9348 CAMSAP1-DT\n",
+      "9349 LCN15\n",
+      "9350 LCNL1\n",
+      "9351 SFMBT2\n",
+      "9352 OPTN\n",
+      "9353 MASTL\n",
+      "9354 WAC\n",
+      "9355 EPC1\n",
+      "9356 ZNF239\n",
+      "9357 SYT15\n",
+      "9358 SGMS1-AS1\n",
+      "9359 BICC1\n",
+      "9360 LINC01515\n",
+      "9361 ENSG00000282915\n",
+      "9362 MYOF\n",
+      "9363 DNTT\n",
+      "9364 LDB1\n",
+      "9365 SFXN4\n",
+      "9366 ACADSB\n",
+      "9367 CDKN1C\n",
+      "9368 LDLRAD3\n",
+      "9369 SLC15A3\n",
+      "9370 SNHG1\n",
+      "9371 SLC29A2\n",
+      "9372 C11orf80\n",
+      "9373 PPP1CA\n",
+      "9374 GAL\n",
+      "9375 MRPL48\n",
+      "9376 SPCS2\n",
+      "9377 EMSY-DT\n",
+      "9378 MPZL3\n",
+      "9379 CRTAM\n",
+      "9380 HSPA8\n",
+      "9381 PRDM10\n",
+      "9382 FKBP4\n",
+      "9383 GNB3\n",
+      "9384 CREBL2\n",
+      "9385 ENSG00000255968\n",
+      "9386 CELA1\n",
+      "9387 GALNT6\n",
+      "9388 KRT1\n",
+      "9389 EIF4B\n",
+      "9390 HOXC4\n",
+      "9391 SUOX\n",
+      "9392 PIP4K2C\n",
+      "9393 YEATS4\n",
+      "9394 CRY1\n",
+      "9395 SUDS3\n",
+      "9396 GCN1\n",
+      "9397 GJB2\n",
+      "9398 ENSG00000286330\n",
+      "9399 EDNRB\n",
+      "9400 ARHGEF7\n",
+      "9401 CCNB1IP1\n",
+      "9402 OR10G3\n",
+      "9403 DCAF11\n",
+      "9404 G2E3\n",
+      "9405 ENSG00000285830\n",
+      "9406 ENSG00000258377\n",
+      "9407 ENSG00000259071\n",
+      "9408 ENSG00000270062\n",
+      "9409 FRMD6\n",
+      "9410 GNG2\n",
+      "9411 ENSG00000258928\n",
+      "9412 MAPK1IP1L\n",
+      "9413 TRMT5\n",
+      "9414 FAM161B\n",
+      "9415 BBOF1\n",
+      "9416 YLPM1\n",
+      "9417 LINC01220\n",
+      "9418 ENSG00000274492\n",
+      "9419 PPP4R3A\n",
+      "9420 NDUFB1\n",
+      "9421 IGHG1\n",
+      "9422 IGHD\n",
+      "9423 LINC00221\n",
+      "9424 IGHV3-53\n",
+      "9425 GOLGA8N\n",
+      "9426 LINC02895\n",
+      "9427 TEX9\n",
+      "9428 TRIP4\n",
+      "9429 ENSG00000259635\n",
+      "9430 ENSG00000261351\n",
+      "9431 MAP2K5-DT\n",
+      "9432 ANP32A\n",
+      "9433 INSYN1\n",
+      "9434 COX5A\n",
+      "9435 ABHD17C\n",
+      "9436 SNHG21\n",
+      "9437 SH3GL3\n",
+      "9438 TTC23-AS1\n",
+      "9439 TPSB2\n",
+      "9440 NMRAL1\n",
+      "9441 MGRN1\n",
+      "9442 IQCK\n",
+      "9443 GGA2\n",
+      "9444 ATP2A1-AS1\n",
+      "9445 FBXL19-AS1\n",
+      "9446 IGHV3OR16-9\n",
+      "9447 SHCBP1\n",
+      "9448 ESRP2\n",
+      "9449 TERF2\n",
+      "9450 ENSG00000262890\n",
+      "9451 GLG1\n",
+      "9452 ZNF469\n",
+      "9453 SPG7\n",
+      "9454 MRM3\n",
+      "9455 YWHAE\n",
+      "9456 HASPIN\n",
+      "9457 C17orf107\n",
+      "9458 SLC52A1\n",
+      "9459 CCDC42\n",
+      "9460 HS3ST3B1\n",
+      "9461 SHMT1\n",
+      "9462 ENSG00000265478\n",
+      "9463 ERAL1\n",
+      "9464 CCL1\n",
+      "9465 MYO19\n",
+      "9466 TNS4\n",
+      "9467 CAVIN1\n",
+      "9468 MLX\n",
+      "9469 FMNL1-AS1\n",
+      "9470 MRPL10\n",
+      "9471 ACSF2\n",
+      "9472 GDPD1\n",
+      "9473 HEATR6-DT\n",
+      "9474 SOX9-AS1\n",
+      "9475 ARMC7\n",
+      "9476 SRP68\n",
+      "9477 MFSD11\n",
+      "9478 RAC3\n",
+      "9479 NDUFV2-AS1\n",
+      "9480 PSMG2\n",
+      "9481 RMC1\n",
+      "9482 LINC00907\n",
+      "9483 SERPINB4\n",
+      "9484 BSG\n",
+      "9485 MYDGF\n",
+      "9486 ARRDC5\n",
+      "9487 MCOLN1\n",
+      "9488 MYO1F\n",
+      "9489 ZNF846\n",
+      "9490 ZNF442\n",
+      "9491 ENSG00000267633\n",
+      "9492 ADGRE2\n",
+      "9493 LINC01764\n",
+      "9494 ZNF682\n",
+      "9495 FFAR1\n",
+      "9496 ALKBH6\n",
+      "9497 ZFP36\n",
+      "9498 SUPT5H\n",
+      "9499 CKM\n",
+      "9500 POLR1G\n",
+      "9501 PRRG2\n",
+      "9502 PTOV1-AS2\n",
+      "9503 ZNF841\n",
+      "9504 ZNF347\n",
+      "9505 ZNF677\n",
+      "9506 ENSG00000287608\n",
+      "9507 KIR3DL2\n",
+      "9508 HSPBP1\n",
+      "9509 EDDM13\n",
+      "9510 KIZ\n",
+      "9511 TPX2\n",
+      "9512 ZHX3\n",
+      "9513 LINC01260\n",
+      "9514 ZNFX1\n",
+      "9515 LINC02910\n",
+      "9516 ENSG00000283078\n",
+      "9517 ENSG00000275895\n",
+      "9518 GABPA\n",
+      "9519 N6AMT1\n",
+      "9520 ENSG00000235023\n",
+      "9521 SNAP29\n",
+      "9522 CRKL\n",
+      "9523 IGLV3-10\n",
+      "9524 ADORA2A-AS1\n",
+      "9525 SGSM1\n",
+      "9526 MIATNB\n",
+      "9527 PICK1\n",
+      "9528 DMC1\n",
+      "9529 BRD1\n",
+      "9530 EGFL6\n",
+      "9531 CTPS2\n",
+      "9532 GK\n",
+      "9533 CYBB\n",
+      "9534 TIMP1\n",
+      "9535 SYP\n",
+      "9536 KDM5C-IT1\n",
+      "9537 ARHGEF9\n",
+      "9538 AMMECR1\n",
+      "9539 C1GALT1C1\n",
+      "9540 ZNF75D\n",
+      "9541 MMGT1\n",
+      "9542 ENSG00000184258\n",
+      "9543 ENSG00000276345\n",
+      "9544 C1QTNF12\n",
+      "9545 DVL1\n",
+      "9546 ENSG00000284747\n",
+      "9547 GPR157\n",
+      "9548 SPSB1\n",
+      "9549 C1orf127\n",
+      "9550 KAZN\n",
+      "9551 CELA3B\n",
+      "9552 TMEM222\n",
+      "9553 SMPDL3B\n",
+      "9554 PHC2\n",
+      "9555 SFPQ\n",
+      "9556 MANEAL\n",
+      "9557 TESK2\n",
+      "9558 ENSG00000261737\n",
+      "9559 IGKV1OR1-1\n",
+      "9560 ENSG00000276216\n",
+      "9561 NOTCH2NLA\n",
+      "9562 ACP6\n",
+      "9563 H2BC18\n",
+      "9564 THEM4\n",
+      "9565 SPRR2A\n",
+      "9566 SPRR2F\n",
+      "9567 ARHGAP30\n",
+      "9568 PPOX\n",
+      "9569 FCRLB\n",
+      "9570 FMO4\n",
+      "9571 SLC41A1\n",
+      "9572 LYPLAL1\n",
+      "9573 MRPL55\n",
+      "9574 RNF144A\n",
+      "9575 LINC01954\n",
+      "9576 TRIB2\n",
+      "9577 ENSG00000236989\n",
+      "9578 RHOB\n",
+      "9579 HADHB\n",
+      "9580 C2orf74-DT\n",
+      "9581 CEP68\n",
+      "9582 APLF\n",
+      "9583 TGFA\n",
+      "9584 ENSG00000287250\n",
+      "9585 SFTPB\n",
+      "9586 FER1L5\n",
+      "9587 RGPD4\n",
+      "9588 MAP3K19\n",
+      "9589 SPOPL-DT\n",
+      "9590 ACVR2A\n",
+      "9591 RIF1\n",
+      "9592 CERS6\n",
+      "9593 BBS5\n",
+      "9594 ENSG00000213963\n",
+      "9595 CYP20A1\n",
+      "9596 ABI2\n",
+      "9597 ENSG00000270659\n",
+      "9598 FN1\n",
+      "9599 DIRC3\n",
+      "9600 TNS1\n",
+      "9601 GPBAR1\n",
+      "9602 ENSG00000268896\n",
+      "9603 FAM124B\n",
+      "9604 CUL3\n",
+      "9605 TIGD1\n",
+      "9606 PPARG\n",
+      "9607 PRKAR2A-AS1\n",
+      "9608 SPCS1\n",
+      "9609 FILIP1L\n",
+      "9610 SLC12A8\n",
+      "9611 OSBPL11\n",
+      "9612 MBD4\n",
+      "9613 PLS1\n",
+      "9614 LINC01206\n",
+      "9615 UTS2B\n",
+      "9616 PDE6B\n",
+      "9617 FAM193A\n",
+      "9618 NOP14\n",
+      "9619 GRK4\n",
+      "9620 MRFAP1L1\n",
+      "9621 BOD1L1\n",
+      "9622 SGCB\n",
+      "9623 STAP1\n",
+      "9624 CSN1S1\n",
+      "9625 RUFY3\n",
+      "9626 HERC5\n",
+      "9627 MGARP\n",
+      "9628 FGA\n",
+      "9629 GUCY1A1\n",
+      "9630 GASK1B-AS1\n",
+      "9631 ANXA10\n",
+      "9632 FBXO8\n",
+      "9633 TRIP13\n",
+      "9634 ENSG00000286986\n",
+      "9635 ENSG00000272049\n",
+      "9636 IPO11\n",
+      "9637 ENSG00000269983\n",
+      "9638 BHMT2\n",
+      "9639 ENSG00000251314\n",
+      "9640 TICAM2-AS1\n",
+      "9641 PHAX\n",
+      "9642 HNRNPA0\n",
+      "9643 WDR55\n",
+      "9644 PCDHGA8\n",
+      "9645 ARHGAP26\n",
+      "9646 RBM27\n",
+      "9647 NIPAL4-DT\n",
+      "9648 ENSG00000253456\n",
+      "9649 FABP6\n",
+      "9650 NQO2-AS1\n",
+      "9651 TMEM170B\n",
+      "9652 ZKSCAN3\n",
+      "9653 HLA-C\n",
+      "9654 UNC5CL\n",
+      "9655 PTK7\n",
+      "9656 ENPP5\n",
+      "9657 CCNC\n",
+      "9658 MARCKS\n",
+      "9659 ECHDC1\n",
+      "9660 ENSG00000236166\n",
+      "9661 SGK1\n",
+      "9662 MAP7\n",
+      "9663 REPS1\n",
+      "9664 ENSG00000130023\n",
+      "9665 CCZ1\n",
+      "9666 AGR3\n",
+      "9667 ZNRF2\n",
+      "9668 ENSG00000273014\n",
+      "9669 BBS9\n",
+      "9670 SEPTIN7-DT\n",
+      "9671 MRPL32\n",
+      "9672 SEMA3D\n",
+      "9673 PPP1R9A-AS1\n",
+      "9674 ZKSCAN5\n",
+      "9675 CCDC136\n",
+      "9676 ENSG00000273329\n",
+      "9677 AGBL3\n",
+      "9678 ENSG00000230649\n",
+      "9679 HIPK2\n",
+      "9680 RAB19\n",
+      "9681 TRBV27\n",
+      "9682 PRSS2\n",
+      "9683 ENSG00000268955\n",
+      "9684 LINC02950\n",
+      "9685 ENSG00000269899\n",
+      "9686 CCAR2\n",
+      "9687 FZD3\n",
+      "9688 RBPMS\n",
+      "9689 ENSG00000272010\n",
+      "9690 PEX2\n",
+      "9691 CPQ\n",
+      "9692 NIPAL2\n",
+      "9693 TATDN1\n",
+      "9694 TMEM71\n",
+      "9695 TG\n",
+      "9696 TOPORS\n",
+      "9697 NFX1\n",
+      "9698 CCL19\n",
+      "9699 SHB\n",
+      "9700 TRPM3\n",
+      "9701 DAPK1\n",
+      "9702 ROR2\n",
+      "9703 ZNF169\n",
+      "9704 ANP32B\n",
+      "9705 ALG2\n",
+      "9706 INVS\n",
+      "9707 COL27A1\n",
+      "9708 CDK5RAP2\n",
+      "9709 RAB14\n",
+      "9710 LRRC8A\n",
+      "9711 LCN8\n",
+      "9712 C8G\n",
+      "9713 MAN1B1\n",
+      "9714 IDI1\n",
+      "9715 AKR1C2\n",
+      "9716 CDNF\n",
+      "9717 FAM171A1\n",
+      "9718 ARMC3\n",
+      "9719 P4HA1\n",
+      "9720 USP54\n",
+      "9721 EXOSC1\n",
+      "9722 ZDHHC16\n",
+      "9723 DNMBP-AS1\n",
+      "9724 CWF19L1\n",
+      "9725 CNNM2\n",
+      "9726 ATP5MK\n",
+      "9727 MXI1\n",
+      "9728 ENSG00000228484\n",
+      "9729 TUBGCP2\n",
+      "9730 CEND1\n",
+      "9731 CHID1\n",
+      "9732 DENND5A\n",
+      "9733 LRP4\n",
+      "9734 NR1H3\n",
+      "9735 TMEM138\n",
+      "9736 SAXO4\n",
+      "9737 NEAT1\n",
+      "9738 CCDC85B\n",
+      "9739 ENSG00000255320\n",
+      "9740 AIP\n",
+      "9741 KMT5B\n",
+      "9742 ENSG00000286369\n",
+      "9743 ENSG00000254484\n",
+      "9744 LAMTOR1\n",
+      "9745 FAM168A\n",
+      "9746 PGM2L1\n",
+      "9747 RPS3\n",
+      "9748 AQP11\n",
+      "9749 ANKRD49\n",
+      "9750 USP28\n",
+      "9751 TAGLN\n",
+      "9752 PCSK7\n",
+      "9753 C12orf60\n",
+      "9754 ABCC9\n",
+      "9755 NELL2\n",
+      "9756 KANSL2\n",
+      "9757 GPD1\n",
+      "9758 ITGA7\n",
+      "9759 BLOC1S1\n",
+      "9760 E2F7\n",
+      "9761 PRDM4\n",
+      "9762 GLTP\n",
+      "9763 VPS29\n",
+      "9764 RAD9B\n",
+      "9765 TCTN1\n",
+      "9766 RBM19\n",
+      "9767 LINC00173\n",
+      "9768 MAP1LC3B2\n",
+      "9769 MPHOSPH8\n",
+      "9770 LINC02340\n",
+      "9771 FOXO1\n",
+      "9772 RB1-DT\n",
+      "9773 BIVM\n",
+      "9774 ANKRD10\n",
+      "9775 ENSG00000258515\n",
+      "9776 TRAV1-1\n",
+      "9777 ZFHX2-AS1\n",
+      "9778 ARHGAP5-AS1\n",
+      "9779 TIMM9\n",
+      "9780 ENSG00000287913\n",
+      "9781 PCNX1\n",
+      "9782 NUMB\n",
+      "9783 EML5\n",
+      "9784 TCL1B\n",
+      "9785 AHNAK2\n",
+      "9786 CRIP2\n",
+      "9787 IGHV4-4\n",
+      "9788 IGHV2-5\n",
+      "9789 IGHV1-24\n",
+      "9790 ENSG00000286786\n",
+      "9791 ENSG00000259298\n",
+      "9792 GLDN\n",
+      "9793 ENSG00000274667\n",
+      "9794 MAP2K1\n",
+      "9795 AKAP13\n",
+      "9796 TTC23\n",
+      "9797 MSRB1\n",
+      "9798 RPS2\n",
+      "9799 NPIPA1\n",
+      "9800 APOBR\n",
+      "9801 ATXN2L\n",
+      "9802 YPEL3-DT\n",
+      "9803 CORO1A\n",
+      "9804 TBC1D10B\n",
+      "9805 TMEM265\n",
+      "9806 NUDT21\n",
+      "9807 NAE1\n",
+      "9808 SF3B3\n",
+      "9809 DHX38\n",
+      "9810 HSD17B2-AS1\n",
+      "9811 APRT\n",
+      "9812 ENSG00000261118\n",
+      "9813 FAM157C\n",
+      "9814 MIS12\n",
+      "9815 DLG4\n",
+      "9816 ELP5\n",
+      "9817 SOX15\n",
+      "9818 FAM83G\n",
+      "9819 TNFAIP1\n",
+      "9820 SPAG5\n",
+      "9821 C17orf75\n",
+      "9822 CCL3-AS1\n",
+      "9823 HNF1B\n",
+      "9824 PSMB3\n",
+      "9825 CACNB1\n",
+      "9826 KRT13\n",
+      "9827 NAGLU\n",
+      "9828 TUBG2\n",
+      "9829 PTGES3L\n",
+      "9830 KPNB1-DT\n",
+      "9831 KAT7\n",
+      "9832 DGKE\n",
+      "9833 TSPOAP1-AS1\n",
+      "9834 FTSJ3\n",
+      "9835 GNA13\n",
+      "9836 CD300E\n",
+      "9837 SAP30BP\n",
+      "9838 SYNGR2\n",
+      "9839 ENSG00000262580\n",
+      "9840 ANAPC11\n",
+      "9841 ENSG00000264339\n",
+      "9842 NDUFV2\n",
+      "9843 RALBP1\n",
+      "9844 LINC01882\n",
+      "9845 ESCO1\n",
+      "9846 PSMA8\n",
+      "9847 DSC1\n",
+      "9848 C18orf21\n",
+      "9849 HAUS1\n",
+      "9850 CCDC68\n",
+      "9851 PTBP1\n",
+      "9852 ZNF555\n",
+      "9853 SMIM24\n",
+      "9854 SEMA6B\n",
+      "9855 DENND1C\n",
+      "9856 PCP2\n",
+      "9857 ENSG00000268149\n",
+      "9858 PIN1\n",
+      "9859 ZNF441\n",
+      "9860 GDF15\n",
+      "9861 ENSG00000160229\n",
+      "9862 PDCD5\n",
+      "9863 NUDT19\n",
+      "9864 RHPN2\n",
+      "9865 GARRE1\n",
+      "9866 HPN\n",
+      "9867 TYROBP\n",
+      "9868 TBCB\n",
+      "9869 ZNF383\n",
+      "9870 ZNF573\n",
+      "9871 EID2B\n",
+      "9872 C19orf47\n",
+      "9873 SMG9\n",
+      "9874 SAE1\n",
+      "9875 EHD2\n",
+      "9876 ZSWIM9\n",
+      "9877 SULT2B1\n",
+      "9878 FUZ\n",
+      "9879 SPACA6\n",
+      "9880 ZNF808\n",
+      "9881 FCAR\n",
+      "9882 AURKC\n",
+      "9883 ZNF587\n",
+      "9884 ENSG00000268201\n",
+      "9885 SIRPB1\n",
+      "9886 ENSG00000235820\n",
+      "9887 KIZ-AS1\n",
+      "9888 ENTPD6\n",
+      "9889 PLCG1\n",
+      "9890 PKIG\n",
+      "9891 PCK1\n",
+      "9892 CBR1\n",
+      "9893 HLCS-AS1\n",
+      "9894 SIK1\n",
+      "9895 LINC01665\n",
+      "9896 YPEL1\n",
+      "9897 IGLV1-47\n",
+      "9898 IGLV7-46\n",
+      "9899 ENSG00000279085\n",
+      "9900 TOM1\n",
+      "9901 ENSG00000279833\n",
+      "9902 ATF4\n",
+      "9903 SREBF2-AS1\n",
+      "9904 SERHL2\n",
+      "9905 ARHGAP8\n",
+      "9906 NUP50-DT\n",
+      "9907 NHIP\n",
+      "9908 MIR3667HG\n",
+      "9909 ENSG00000273188\n",
+      "9910 TLR8\n",
+      "9911 FAM9C\n",
+      "9912 ADGRG2\n",
+      "9913 SLC38A5\n",
+      "9914 RBM3\n",
+      "9915 PCSK1N\n",
+      "9916 AMER1\n",
+      "9917 CXorf65\n",
+      "9918 CXCR3\n",
+      "9919 FAM226B\n",
+      "9920 FRMPD3\n",
+      "9921 ALG13\n",
+      "9922 SLC25A5\n",
+      "9923 PABIR3\n",
+      "9924 FHL1\n",
+      "9925 ENSG00000286265\n",
+      "9926 SKI\n",
+      "9927 CHD5\n",
+      "9928 FHAD1\n",
+      "9929 ENSG00000272510\n",
+      "9930 ZBTB17\n",
+      "9931 RPA2\n",
+      "9932 SERINC2\n",
+      "9933 TRIM62\n",
+      "9934 TFAP2E\n",
+      "9935 ENSG00000287587\n",
+      "9936 TMEM53\n",
+      "9937 IPP\n",
+      "9938 UQCRH\n",
+      "9939 LRRC8C\n",
+      "9940 CD101\n",
+      "9941 ENSG00000271427\n",
+      "9942 ENSG00000237343\n",
+      "9943 SRGAP2B\n",
+      "9944 TXNIP\n",
+      "9945 VPS72\n",
+      "9946 C2CD4D\n",
+      "9947 SPRR2E\n",
+      "9948 IL6R-AS1\n",
+      "9949 IL6R\n",
+      "9950 SHC1\n",
+      "9951 RUSC1-AS1\n",
+      "9952 ASH1L-AS1\n",
+      "9953 SMG5\n",
+      "9954 TMEM79\n",
+      "9955 CD1A\n",
+      "9956 TIPRL\n",
+      "9957 SCYL3\n",
+      "9958 GORAB-AS1\n",
+      "9959 GORAB\n",
+      "9960 ZBTB37\n",
+      "9961 TOR1AIP1\n",
+      "9962 RGS16\n",
+      "9963 NCF2\n",
+      "9964 ZBTB41\n",
+      "9965 ENSG00000287684\n",
+      "9966 TP53BP2\n",
+      "9967 H2AC25\n",
+      "9968 SH3BP5L\n",
+      "9969 ENSG00000224400\n",
+      "9970 KLHL29\n",
+      "9971 GTF3C2\n",
+      "9972 PPP1CB\n",
+      "9973 ENSG00000276517\n",
+      "9974 PNPT1\n",
+      "9975 DNAAF10\n",
+      "9976 MOB1A\n",
+      "9977 MTHFD2\n",
+      "9978 TMEM150A\n",
+      "9979 USP39\n",
+      "9980 LINC03052\n",
+      "9981 LINC00342\n",
+      "9982 SMPD4\n",
+      "9983 ENSG00000237844\n",
+      "9984 LRP2\n",
+      "9985 PLEKHA3\n",
+      "9986 GULP1\n",
+      "9987 ANKRD44\n",
+      "9988 CTDSP1\n",
+      "9989 COL6A3\n",
+      "9990 UBE2F\n",
+      "9991 TWIST2\n",
+      "9992 HDAC4-AS1\n",
+      "9993 DTYMK\n",
+      "9994 MLH1\n",
+      "9995 LRRFIP2\n",
+      "9996 RPSA\n",
+      "9997 CCR2\n",
+      "9998 CDC25A\n",
+      "9999 ATRIP\n",
+      "10000 CELSR3\n",
+      "10001 ENSG00000271858\n",
+      "10002 DNAH1\n",
+      "10003 ARF4\n",
+      "10004 GXYLT2\n",
+      "10005 IMPG2\n",
+      "10006 PCNP\n",
+      "10007 CD96\n",
+      "10008 CFAP91\n",
+      "10009 PARP14\n",
+      "10010 ZNF639\n",
+      "10011 EPHB3\n",
+      "10012 ATP13A3\n",
+      "10013 MELTF\n",
+      "10014 BDH1\n",
+      "10015 IDUA\n",
+      "10016 CTBP1-AS\n",
+      "10017 C4orf48\n",
+      "10018 ENSG00000251408\n",
+      "10019 MRFAP1L2\n",
+      "10020 ARAP2\n",
+      "10021 SLAIN2\n",
+      "10022 CLOCK\n",
+      "10023 NMU\n",
+      "10024 ENSG00000205682\n",
+      "10025 LAMTOR3\n",
+      "10026 UBE2D3-AS1\n",
+      "10027 ANK2\n",
+      "10028 SNHG8\n",
+      "10029 SEC24D\n",
+      "10030 MAP9\n",
+      "10031 ENSG00000286454\n",
+      "10032 GHR\n",
+      "10033 PELO-AS1\n",
+      "10034 NSA2\n",
+      "10035 PPIP5K2\n",
+      "10036 EFNA5\n",
+      "10037 REEP5\n",
+      "10038 GDF9\n",
+      "10039 CXXC5\n",
+      "10040 JAKMIP2\n",
+      "10041 ENSG00000271795\n",
+      "10042 ENSG00000272112\n",
+      "10043 HAVCR1\n",
+      "10044 LINC03000\n",
+      "10045 ARL10\n",
+      "10046 FAM50B\n",
+      "10047 RREB1\n",
+      "10048 RIPOR2\n",
+      "10049 H2BC4\n",
+      "10050 HMGN4\n",
+      "10051 NKAPL\n",
+      "10052 DDR1-DT\n",
+      "10053 HSPA1L\n",
+      "10054 HLA-DRA\n",
+      "10055 BYSL\n",
+      "10056 CENPQ\n",
+      "10057 EFHC1\n",
+      "10058 KHDC1\n",
+      "10059 BCKDHB\n",
+      "10060 CEP57L1\n",
+      "10061 ENSG00000237851\n",
+      "10062 GINM1\n",
+      "10063 ENSG00000285642\n",
+      "10064 TULP4\n",
+      "10065 TAGAP-AS1\n",
+      "10066 MAP3K4-AS1\n",
+      "10067 QKI\n",
+      "10068 ENSG00000240859\n",
+      "10069 ZFAND2A\n",
+      "10070 MRM2\n",
+      "10071 CARD11\n",
+      "10072 RAPGEF5\n",
+      "10073 PALS2\n",
+      "10074 STARD3NL\n",
+      "10075 ZNF713\n",
+      "10076 CDK6\n",
+      "10077 BUD31\n",
+      "10078 LINC01004\n",
+      "10079 KMT2E\n",
+      "10080 CCDC71L\n",
+      "10081 TRBV9\n",
+      "10082 TRBV23-1\n",
+      "10083 TRBC2\n",
+      "10084 REPIN1\n",
+      "10085 GIMAP5\n",
+      "10086 ASIC3\n",
+      "10087 ENSG00000280273\n",
+      "10088 BIN3\n",
+      "10089 TNFRSF10A\n",
+      "10090 ENSG00000253708\n",
+      "10091 ENSG00000247134\n",
+      "10092 ENSG00000253475\n",
+      "10093 GGH\n",
+      "10094 CSPP1\n",
+      "10095 CPA6\n",
+      "10096 ZFAND1\n",
+      "10097 CNBD1\n",
+      "10098 MIR378D2HG\n",
+      "10099 PLEKHF2\n",
+      "10100 DEPTOR\n",
+      "10101 CD274\n",
+      "10102 ARHGEF39\n",
+      "10103 C9orf85\n",
+      "10104 GKAP1\n",
+      "10105 CDK20\n",
+      "10106 ENSG00000287769\n",
+      "10107 SPIN1\n",
+      "10108 NXNL2\n",
+      "10109 FAM120A\n",
+      "10110 MSANTD3\n",
+      "10111 PTPN3\n",
+      "10112 PALM2AKAP2\n",
+      "10113 ZFP37\n",
+      "10114 SPTAN1\n",
+      "10115 PMPCA\n",
+      "10116 AKR1E2\n",
+      "10117 CUL2\n",
+      "10118 ZNF248-AS1\n",
+      "10119 ZNF32\n",
+      "10120 WDFY4\n",
+      "10121 RTKN2\n",
+      "10122 FAM241B\n",
+      "10123 PALD1\n",
+      "10124 ENSG00000271933\n",
+      "10125 ENSG00000261438\n",
+      "10126 KAZALD1\n",
+      "10127 ENSG00000278601\n",
+      "10128 ATRNL1\n",
+      "10129 SEC23IP\n",
+      "10130 ENSG00000231705\n",
+      "10131 MRPL17\n",
+      "10132 MPPED2\n",
+      "10133 PRR5L\n",
+      "10134 LINC02716\n",
+      "10135 ZNF408\n",
+      "10136 ZBTB3\n",
+      "10137 YIF1A\n",
+      "10138 IL18BP\n",
+      "10139 CLPB\n",
+      "10140 SLC35F2\n",
+      "10141 PTS\n",
+      "10142 ENSG00000247416\n",
+      "10143 ZBTB44-DT\n",
+      "10144 IQSEC3-AS2\n",
+      "10145 NINJ2-AS1\n",
+      "10146 VAMP1-AS1\n",
+      "10147 IFFO1\n",
+      "10148 NOP2\n",
+      "10149 KLRC2\n",
+      "10150 APOLD1\n",
+      "10151 GPRC5D-AS1\n",
+      "10152 AEBP2\n",
+      "10153 YAF2\n",
+      "10154 TWF1\n",
+      "10155 ENSG00000269514\n",
+      "10156 C12orf54\n",
+      "10157 LALBA\n",
+      "10158 SCN8A\n",
+      "10159 PYM1\n",
+      "10160 ESYT1\n",
+      "10161 MYL6\n",
+      "10162 GLIPR1L2\n",
+      "10163 SYT1\n",
+      "10164 FGD6\n",
+      "10165 LINC02453\n",
+      "10166 LINC01234\n",
+      "10167 ENSG00000275759\n",
+      "10168 MRPS31\n",
+      "10169 KBTBD6-DT\n",
+      "10170 RUBCNL\n",
+      "10171 CAB39L\n",
+      "10172 DLEU7\n",
+      "10173 ATP7B\n",
+      "10174 ENSG00000184497\n",
+      "10175 RNASE2\n",
+      "10176 TRAV13-1\n",
+      "10177 TRAV24\n",
+      "10178 TM9SF1\n",
+      "10179 GMPR2\n",
+      "10180 NOVA1\n",
+      "10181 ENSG00000258711\n",
+      "10182 TMX1\n",
+      "10183 SNAPC1\n",
+      "10184 TGFB3\n",
+      "10185 VIPAS39\n",
+      "10186 RCOR1\n",
+      "10187 IGHV3-33\n",
+      "10188 IGHV2-70D\n",
+      "10189 NUSAP1\n",
+      "10190 TP53BP1\n",
+      "10191 TRIM69\n",
+      "10192 ENSG00000259342\n",
+      "10193 BLOC1S6\n",
+      "10194 UNC13C\n",
+      "10195 RSL24D1\n",
+      "10196 PIGBOS1\n",
+      "10197 FAM81A\n",
+      "10198 RORA-AS1\n",
+      "10199 C2CD4B\n",
+      "10200 ENSG00000259370\n",
+      "10201 SNAPC5\n",
+      "10202 PEAK1\n",
+      "10203 CRTC3-AS1\n",
+      "10204 VPS33B\n",
+      "10205 ENSG00000270127\n",
+      "10206 LINS1\n",
+      "10207 SLX4\n",
+      "10208 ENSG00000261216\n",
+      "10209 ENSG00000175604\n",
+      "10210 ARHGAP17\n",
+      "10211 ENSG00000259807\n",
+      "10212 ZNF785\n",
+      "10213 ZNF668\n",
+      "10214 FTO\n",
+      "10215 MMP2\n",
+      "10216 GNAO1\n",
+      "10217 ENSG00000260621\n",
+      "10218 MT3\n",
+      "10219 CX3CL1\n",
+      "10220 SPMIP8\n",
+      "10221 ENSG00000260927\n",
+      "10222 GOT2\n",
+      "10223 HSF4\n",
+      "10224 CENPT\n",
+      "10225 SLC12A4\n",
+      "10226 VPS4A\n",
+      "10227 ENSG00000261602\n",
+      "10228 LINC01568\n",
+      "10229 ENSG00000287125\n",
+      "10230 PFN1\n",
+      "10231 TXNDC17\n",
+      "10232 MPRIP-AS1\n",
+      "10233 ENSG00000266311\n",
+      "10234 ENSG00000205212\n",
+      "10235 FAM222B\n",
+      "10236 EFCAB5\n",
+      "10237 ENSG00000266340\n",
+      "10238 CCL2\n",
+      "10239 CCL13\n",
+      "10240 UNC45B\n",
+      "10241 AP2B1\n",
+      "10242 PPY\n",
+      "10243 ATXN7L3-AS1\n",
+      "10244 ACBD4\n",
+      "10245 HOXB3\n",
+      "10246 MMD\n",
+      "10247 C17orf67\n",
+      "10248 MIR142HG\n",
+      "10249 SKA2\n",
+      "10250 APOH\n",
+      "10251 CD300LF\n",
+      "10252 FBF1\n",
+      "10253 AFMID\n",
+      "10254 CARD14\n",
+      "10255 FN3KRP\n",
+      "10256 ENSG00000265943\n",
+      "10257 MAPRE2\n",
+      "10258 ZBTB7C\n",
+      "10259 STARD6\n",
+      "10260 HMSD\n",
+      "10261 PARD6G-AS1\n",
+      "10262 TMEM259\n",
+      "10263 ONECUT3\n",
+      "10264 ENSG00000269318\n",
+      "10265 DUS3L\n",
+      "10266 RETN\n",
+      "10267 ENSG00000267939\n",
+      "10268 PPAN\n",
+      "10269 PIK3R2\n",
+      "10270 ZNF90\n",
+      "10271 ZNF208\n",
+      "10272 TDRD12\n",
+      "10273 ARHGAP33\n",
+      "10274 APLP1\n",
+      "10275 CCER2\n",
+      "10276 ZNF780A\n",
+      "10277 TMEM91\n",
+      "10278 ZNF283\n",
+      "10279 ZNF233\n",
+      "10280 ERCC1\n",
+      "10281 SYMPK\n",
+      "10282 PGLYRP1\n",
+      "10283 PPP5D1P\n",
+      "10284 GRWD1\n",
+      "10285 VRK3\n",
+      "10286 LINC01872\n",
+      "10287 ZNF415\n",
+      "10288 LENG8-AS1\n",
+      "10289 PPP6R1\n",
+      "10290 FIZ1\n",
+      "10291 ZNF805\n",
+      "10292 ZNF671\n",
+      "10293 ZNF274\n",
+      "10294 ENSG00000283103\n",
+      "10295 ZNF584-DT\n",
+      "10296 CDC25B\n",
+      "10297 PET117\n",
+      "10298 ENSG00000226465\n",
+      "10299 GSS\n",
+      "10300 EPB41L1\n",
+      "10301 HNF4A\n",
+      "10302 PTPN1\n",
+      "10303 NELFCD\n",
+      "10304 ADRM1\n",
+      "10305 TCFL5\n",
+      "10306 ERG\n",
+      "10307 AATBC\n",
+      "10308 MCM3AP\n",
+      "10309 ENSG00000280341\n",
+      "10310 MRPL40\n",
+      "10311 RTL10\n",
+      "10312 IGLV2-14\n",
+      "10313 ADORA2A\n",
+      "10314 ENSG00000286326\n",
+      "10315 DUSP18\n",
+      "10316 IFT27\n",
+      "10317 XPNPEP3\n",
+      "10318 RANGAP1\n",
+      "10319 LINC01315\n",
+      "10320 ENSG00000270022\n",
+      "10321 CYB5R3\n",
+      "10322 MID1\n",
+      "10323 PIM2\n",
+      "10324 FAM156B\n",
+      "10325 OPHN1\n",
+      "10326 ZDHHC15\n",
+      "10327 LPAR4\n",
+      "10328 THOC2\n",
+      "10329 LINC00892\n",
+      "10330 HAUS7\n",
+      "10331 SLC6A8\n",
+      "10332 LRRC47\n",
+      "10333 HES2\n",
+      "10334 SPEN-AS1\n",
+      "10335 ID3\n",
+      "10336 WDTC1\n",
+      "10337 SNHG12\n",
+      "10338 PTPRF\n",
+      "10339 TMEM69\n",
+      "10340 MKNK1\n",
+      "10341 NRDC\n",
+      "10342 PLPP3\n",
+      "10343 ITGB3BP\n",
+      "10344 IL23R\n",
+      "10345 DNAI3\n",
+      "10346 SH3GLB1\n",
+      "10347 CDC7\n",
+      "10348 ALG14\n",
+      "10349 TLCD4\n",
+      "10350 PALMD\n",
+      "10351 RBM15-AS1\n",
+      "10352 SLC16A4\n",
+      "10353 LRIF1\n",
+      "10354 BCL2L15\n",
+      "10355 PEX11B\n",
+      "10356 ENSG00000263464\n",
+      "10357 PBXIP1\n",
+      "10358 FCRL3\n",
+      "10359 NME7\n",
+      "10360 TEX35\n",
+      "10361 SOAT1\n",
+      "10362 ACBD6\n",
+      "10363 RGS8\n",
+      "10364 ARL8A\n",
+      "10365 ETNK2\n",
+      "10366 PPP2R5A\n",
+      "10367 FBXO28\n",
+      "10368 COQ8A\n",
+      "10369 EXOC8\n",
+      "10370 TOMM20\n",
+      "10371 EDARADD\n",
+      "10372 ENSG00000225300\n",
+      "10373 AHCTF1\n",
+      "10374 ZNF672\n",
+      "10375 SMC6\n",
+      "10376 CDC42EP3\n",
+      "10377 MORN2\n",
+      "10378 NRXN1\n",
+      "10379 CHAC2\n",
+      "10380 GKN2\n",
+      "10381 TTC31\n",
+      "10382 ENSG00000224959\n",
+      "10383 ENSG00000287937\n",
+      "10384 CCDC93\n",
+      "10385 HNMT\n",
+      "10386 ACVR1C\n",
+      "10387 DPP4\n",
+      "10388 OLA1\n",
+      "10389 ITPRID2-DT\n",
+      "10390 CAVIN2\n",
+      "10391 CFLAR-AS1\n",
+      "10392 ACSL3\n",
+      "10393 DNER\n",
+      "10394 PASK\n",
+      "10395 ENSG00000189229\n",
+      "10396 HRH1\n",
+      "10397 RAB5A\n",
+      "10398 CMTM7\n",
+      "10399 STAC\n",
+      "10400 KLHL18\n",
+      "10401 ARIH2OS\n",
+      "10402 CHDH\n",
+      "10403 PDE12\n",
+      "10404 EOGT\n",
+      "10405 MITF\n",
+      "10406 TFG\n",
+      "10407 NECTIN3\n",
+      "10408 SIDT1-AS1\n",
+      "10409 SEC22A\n",
+      "10410 TXNRD3\n",
+      "10411 ATP2C1\n",
+      "10412 RASA2\n",
+      "10413 GYG1\n",
+      "10414 KCNAB1\n",
+      "10415 KLHL24\n",
+      "10416 FAM131A\n",
+      "10417 TADA2B\n",
+      "10418 ENSG00000261490\n",
+      "10419 WDR19\n",
+      "10420 APBB2\n",
+      "10421 SLC30A9\n",
+      "10422 TXK\n",
+      "10423 CSN3\n",
+      "10424 TSPAN5-DT\n",
+      "10425 ENSG00000273447\n",
+      "10426 C4orf33\n",
+      "10427 LINC02363\n",
+      "10428 FRG1-DT\n",
+      "10429 EXOC3\n",
+      "10430 ENSG00000272416\n",
+      "10431 ELOVL7\n",
+      "10432 LINC02197\n",
+      "10433 FAM151B-DT\n",
+      "10434 LIX1-AS1\n",
+      "10435 EGR1\n",
+      "10436 DNAJC18\n",
+      "10437 PCDHGA5\n",
+      "10438 MYOZ3\n",
+      "10439 NUDCD2\n",
+      "10440 INSYN2B\n",
+      "10441 NOP16\n",
+      "10442 GCNT2\n",
+      "10443 TMEM14B\n",
+      "10444 SYCP2L\n",
+      "10445 ENSG00000286885\n",
+      "10446 MBOAT1\n",
+      "10447 CARMIL1\n",
+      "10448 H2BC9\n",
+      "10449 LEMD2\n",
+      "10450 TAF11\n",
+      "10451 ZNF76\n",
+      "10452 MAPK14\n",
+      "10453 KIF6\n",
+      "10454 TFEB\n",
+      "10455 CNPY3\n",
+      "10456 KLC4\n",
+      "10457 ENSG00000231769\n",
+      "10458 EYS\n",
+      "10459 ADGRB3\n",
+      "10460 ELOVL4\n",
+      "10461 SNHG5\n",
+      "10462 SRSF12\n",
+      "10463 FRK\n",
+      "10464 EPB41L2\n",
+      "10465 PEX7\n",
+      "10466 RAB32\n",
+      "10467 CAHM\n",
+      "10468 CEP43\n",
+      "10469 AFDN\n",
+      "10470 HDAC9\n",
+      "10471 GCK\n",
+      "10472 IGFBP3\n",
+      "10473 DDC\n",
+      "10474 BUD23\n",
+      "10475 ELN\n",
+      "10476 RSBN1L\n",
+      "10477 CDK14\n",
+      "10478 DLX6-AS1\n",
+      "10479 TRAPPC14\n",
+      "10480 CPED1\n",
+      "10481 FAM3C\n",
+      "10482 ZNF800\n",
+      "10483 STMP1\n",
+      "10484 BRAF\n",
+      "10485 FAM131B\n",
+      "10486 MCPH1\n",
+      "10487 ENSG00000255046\n",
+      "10488 NUDT18\n",
+      "10489 EXTL3-AS1\n",
+      "10490 ZNF703\n",
+      "10491 LINC01301\n",
+      "10492 ENSG00000254777\n",
+      "10493 CPNE3\n",
+      "10494 LINC00534\n",
+      "10495 OTUD6B-AS1\n",
+      "10496 LRRC69\n",
+      "10497 RIDA\n",
+      "10498 ST3GAL1\n",
+      "10499 ADGRB1\n",
+      "10500 RUSC2\n",
+      "10501 GNE\n",
+      "10502 ALDH1B1\n",
+      "10503 GLIDR\n",
+      "10504 GAS1\n",
+      "10505 TSTD2\n",
+      "10506 AKNA\n",
+      "10507 ZBTB43\n",
+      "10508 FUBP3\n",
+      "10509 RPL7A\n",
+      "10510 MRPS2\n",
+      "10511 CAMK1D\n",
+      "10512 PCBD1\n",
+      "10513 NUTM2A-AS1\n",
+      "10514 CH25H\n",
+      "10515 MARVELD1\n",
+      "10516 BTRC\n",
+      "10517 C10orf95\n",
+      "10518 MFSD13A\n",
+      "10519 ARL3\n",
+      "10520 TRUB1\n",
+      "10521 CUZD1\n",
+      "10522 ENSG00000250397\n",
+      "10523 OSBPL5\n",
+      "10524 CYB5R2\n",
+      "10525 RPL27A\n",
+      "10526 SBF2\n",
+      "10527 BMAL1\n",
+      "10528 RRAS2\n",
+      "10529 TMEM86A\n",
+      "10530 LRRC4C\n",
+      "10531 MAPK8IP1\n",
+      "10532 CKAP5\n",
+      "10533 FNBP4\n",
+      "10534 PRPF19-DT\n",
+      "10535 ATG2A\n",
+      "10536 BATF2\n",
+      "10537 ARL2\n",
+      "10538 RCE1\n",
+      "10539 RAD9A\n",
+      "10540 TBC1D10C\n",
+      "10541 ENSG00000256448\n",
+      "10542 SIK3\n",
+      "10543 UBASH3B\n",
+      "10544 DCPS\n",
+      "10545 APLP2\n",
+      "10546 RAD52\n",
+      "10547 ITFG2\n",
+      "10548 CD69\n",
+      "10549 CLEC9A\n",
+      "10550 ENSG00000261324\n",
+      "10551 DERA\n",
+      "10552 CPNE8-AS1\n",
+      "10553 C12orf40\n",
+      "10554 RAPGEF3\n",
+      "10555 TMT1A\n",
+      "10556 ITGA5\n",
+      "10557 LINC01619\n",
+      "10558 USP44\n",
+      "10559 ENSG00000287957\n",
+      "10560 ATP2A2\n",
+      "10561 MORN3\n",
+      "10562 LATS2-AS1\n",
+      "10563 SPATA13\n",
+      "10564 STARD13\n",
+      "10565 ENOX1\n",
+      "10566 INTS6-AS1\n",
+      "10567 MBNL2\n",
+      "10568 TRD-AS1\n",
+      "10569 TRDV3\n",
+      "10570 NUBPL\n",
+      "10571 CLEC14A\n",
+      "10572 LINC02315\n",
+      "10573 TRIM9\n",
+      "10574 KIAA0586\n",
+      "10575 RDH12\n",
+      "10576 FLVCR2-AS1\n",
+      "10577 VASH1\n",
+      "10578 SEL1L\n",
+      "10579 TTC8\n",
+      "10580 IFI27L2\n",
+      "10581 VRK1\n",
+      "10582 NIPA2\n",
+      "10583 GOLGA8A\n",
+      "10584 KNL1\n",
+      "10585 JMJD7\n",
+      "10586 PPIP5K1\n",
+      "10587 AFG2B\n",
+      "10588 EID1\n",
+      "10589 FAM227B\n",
+      "10590 TRPM7\n",
+      "10591 LIPC\n",
+      "10592 CALML4\n",
+      "10593 UACA\n",
+      "10594 GRAMD2A\n",
+      "10595 HEXA\n",
+      "10596 RASGRF1\n",
+      "10597 STARD5\n",
+      "10598 TMC3-AS1\n",
+      "10599 EFL1\n",
+      "10600 CPEB1\n",
+      "10601 HDGFL3\n",
+      "10602 ENSG00000259755\n",
+      "10603 ENSG00000260646\n",
+      "10604 RNPS1\n",
+      "10605 GLYR1\n",
+      "10606 RSL1D1\n",
+      "10607 GSPT1\n",
+      "10608 PDXDC1\n",
+      "10609 CLEC19A\n",
+      "10610 KDM8\n",
+      "10611 SLX1B\n",
+      "10612 SRCAP\n",
+      "10613 PRSS8\n",
+      "10614 KATNB1\n",
+      "10615 RIPOR1\n",
+      "10616 PRMT7\n",
+      "10617 TAT-AS1\n",
+      "10618 ATP2C2\n",
+      "10619 LINC02132\n",
+      "10620 ENSG00000241525\n",
+      "10621 OR1A1\n",
+      "10622 OR3A3\n",
+      "10623 SPNS2\n",
+      "10624 SLC13A5\n",
+      "10625 PER1\n",
+      "10626 MYO15A\n",
+      "10627 PHF12\n",
+      "10628 ENSG00000221995\n",
+      "10629 ENSG00000214719\n",
+      "10630 ENSG00000274341\n",
+      "10631 CCL8\n",
+      "10632 CCL3\n",
+      "10633 FKBP10\n",
+      "10634 HOXB-AS1\n",
+      "10635 MRPL27\n",
+      "10636 INTS2\n",
+      "10637 KCNH6\n",
+      "10638 PITPNC1\n",
+      "10639 LINC01978\n",
+      "10640 BAIAP2\n",
+      "10641 THOC1\n",
+      "10642 ENSG00000272746\n",
+      "10643 ENSG00000264365\n",
+      "10644 SMAD4\n",
+      "10645 MALT1-AS1\n",
+      "10646 CCBE1\n",
+      "10647 R3HDM4\n",
+      "10648 ENSG00000064932\n",
+      "10649 THOP1\n",
+      "10650 GNA11\n",
+      "10651 UBXN6\n",
+      "10652 ZNF358\n",
+      "10653 PDE4A\n",
+      "10654 ZNF653\n",
+      "10655 ZNF433-AS1\n",
+      "10656 ZNF625\n",
+      "10657 NANOS3\n",
+      "10658 SMIM7\n",
+      "10659 NWD1\n",
+      "10660 IFI30\n",
+      "10661 ZNF681\n",
+      "10662 FXYD7\n",
+      "10663 LSR\n",
+      "10664 HAUS5\n",
+      "10665 CAPNS1\n",
+      "10666 LGALS7B\n",
+      "10667 PRR19\n",
+      "10668 IRGQ\n",
+      "10669 BCL3\n",
+      "10670 PTGIR\n",
+      "10671 C5AR2\n",
+      "10672 ASPDH\n",
+      "10673 ZNF528-AS1\n",
+      "10674 ZNF331\n",
+      "10675 MYADM\n",
+      "10676 KIR3DL3\n",
+      "10677 ZNF530\n",
+      "10678 ZNF417\n",
+      "10679 MZF1-AS1\n",
+      "10680 CSNK2A1\n",
+      "10681 ITPA\n",
+      "10682 TRMT6\n",
+      "10683 FERMT1\n",
+      "10684 LINC01427\n",
+      "10685 ZNF337\n",
+      "10686 SLPI\n",
+      "10687 LSM14B\n",
+      "10688 GMEB2\n",
+      "10689 LINC00158\n",
+      "10690 LINC01695\n",
+      "10691 GNB1L\n",
+      "10692 IGLV10-54\n",
+      "10693 KIAA1671-AS1\n",
+      "10694 HPS4\n",
+      "10695 CHEK2\n",
+      "10696 SLC35E4\n",
+      "10697 APOL1\n",
+      "10698 ENSG00000272582\n",
+      "10699 TAB1\n",
+      "10700 SHISA8\n",
+      "10701 TTLL1\n",
+      "10702 SMC1B\n",
+      "10703 MIRLET7BHG\n",
+      "10704 LINC02939\n",
+      "10705 OFD1\n",
+      "10706 ATP6AP2\n",
+      "10707 ZNF182\n",
+      "10708 TIMM8A\n",
+      "10709 PNMA5\n",
+      "10710 VBP1\n",
+      "10711 TNFRSF18\n",
+      "10712 MIB2\n",
+      "10713 CDK11A\n",
+      "10714 CFAP107\n",
+      "10715 STPG1\n",
+      "10716 ZMYM4\n",
+      "10717 AIRIM\n",
+      "10718 ELOVL1\n",
+      "10719 CCDC17\n",
+      "10720 LINC01389\n",
+      "10721 AGBL4\n",
+      "10722 ZYG11B\n",
+      "10723 LRP8-DT\n",
+      "10724 GBP6\n",
+      "10725 GFI1\n",
+      "10726 ENSG00000285923\n",
+      "10727 CD101-AS1\n",
+      "10728 REG4\n",
+      "10729 ANXA9\n",
+      "10730 MLLT11\n",
+      "10731 POGZ\n",
+      "10732 RORC\n",
+      "10733 SEMA4A\n",
+      "10734 TBX19\n",
+      "10735 LHX4\n",
+      "10736 LINC01344\n",
+      "10737 RGS1\n",
+      "10738 ENSG00000229191\n",
+      "10739 PTPN7\n",
+      "10740 NENF\n",
+      "10741 EPRS1\n",
+      "10742 DNAH14\n",
+      "10743 RYR2\n",
+      "10744 KIF3C\n",
+      "10745 CGREF1\n",
+      "10746 ABHD1\n",
+      "10747 YPEL5\n",
+      "10748 FEZ2\n",
+      "10749 CEBPZ\n",
+      "10750 SRBD1\n",
+      "10751 ENSG00000228925\n",
+      "10752 CNRIP1\n",
+      "10753 HTRA2\n",
+      "10754 KDM3A\n",
+      "10755 RMND5A\n",
+      "10756 ZNF2\n",
+      "10757 UNC50\n",
+      "10758 LINC02611\n",
+      "10759 TXNDC9\n",
+      "10760 EIF5B\n",
+      "10761 IL1R2\n",
+      "10762 ECRG4\n",
+      "10763 THORLNC\n",
+      "10764 ENSG00000260163\n",
+      "10765 LIMS2\n",
+      "10766 LCT\n",
+      "10767 RND3\n",
+      "10768 ERMN\n",
+      "10769 PLA2R1\n",
+      "10770 SCN9A\n",
+      "10771 ITGA6-AS1\n",
+      "10772 ENSG00000271011\n",
+      "10773 HECW2-AS1\n",
+      "10774 PPIL3\n",
+      "10775 TMEM237\n",
+      "10776 INO80D\n",
+      "10777 STK11IP\n",
+      "10778 COPS8-DT\n",
+      "10779 TRNT1\n",
+      "10780 ENSG00000271870\n",
+      "10781 LRRN1\n",
+      "10782 ENSG00000233912\n",
+      "10783 TBC1D5\n",
+      "10784 SLC4A7\n",
+      "10785 SCN11A\n",
+      "10786 NKTR\n",
+      "10787 ZNF35\n",
+      "10788 TMIE\n",
+      "10789 ENSG00000272434\n",
+      "10790 MON1A\n",
+      "10791 PCBP4\n",
+      "10792 FOXP1\n",
+      "10793 ENSG00000277855\n",
+      "10794 ZNF654\n",
+      "10795 OR5H14\n",
+      "10796 SENP7\n",
+      "10797 ZDHHC23\n",
+      "10798 CCDC191\n",
+      "10799 FSTL1\n",
+      "10800 GOLGB1\n",
+      "10801 CD86\n",
+      "10802 CSTA\n",
+      "10803 MRAS\n",
+      "10804 ENSG00000261826\n",
+      "10805 ARHGEF26\n",
+      "10806 PLCH1\n",
+      "10807 GFM1\n",
+      "10808 PLD1\n",
+      "10809 ACTL6A\n",
+      "10810 DCUN1D1\n",
+      "10811 CRYGS\n",
+      "10812 ATP13A3-DT\n",
+      "10813 RPL35A\n",
+      "10814 ATP5ME\n",
+      "10815 PCGF3\n",
+      "10816 SH3TC1\n",
+      "10817 ARL9\n",
+      "10818 UBA6\n",
+      "10819 JCHAIN\n",
+      "10820 GC\n",
+      "10821 ANTXR2\n",
+      "10822 HNRNPD\n",
+      "10823 CDS1\n",
+      "10824 ADH5\n",
+      "10825 IL15\n",
+      "10826 GAB1\n",
+      "10827 PPID\n",
+      "10828 AHRR\n",
+      "10829 SLC9A3-OT1\n",
+      "10830 TERT\n",
+      "10831 MTREX\n",
+      "10832 MIER3\n",
+      "10833 CCDC125\n",
+      "10834 AP3B1\n",
+      "10835 JMY\n",
+      "10836 MCTP1\n",
+      "10837 GLRX\n",
+      "10838 JADE2\n",
+      "10839 SAR1B\n",
+      "10840 MACROH2A1\n",
+      "10841 STING1\n",
+      "10842 PCDHB9\n",
+      "10843 CSF1R\n",
+      "10844 ENSG00000275765\n",
+      "10845 CYFIP2\n",
+      "10846 UBLCP1\n",
+      "10847 ENSG00000254186\n",
+      "10848 FAM193B\n",
+      "10849 FARS2\n",
+      "10850 H2AC16\n",
+      "10851 ENSG00000272009\n",
+      "10852 HLA-DOB\n",
+      "10853 ENSG00000272223\n",
+      "10854 RSPH9\n",
+      "10855 GCLC\n",
+      "10856 ZNF451-AS1\n",
+      "10857 PRDM1\n",
+      "10858 ENSG00000269919\n",
+      "10859 BEND3\n",
+      "10860 MFSD4B\n",
+      "10861 KPNA5\n",
+      "10862 KIAA0408\n",
+      "10863 FUCA2\n",
+      "10864 STX11\n",
+      "10865 STXBP5\n",
+      "10866 AKAP12\n",
+      "10867 TAGAP\n",
+      "10868 ENSG00000273230\n",
+      "10869 FAM220A\n",
+      "10870 CDCA7L\n",
+      "10871 HIBADH\n",
+      "10872 AQP1\n",
+      "10873 TRG-AS1\n",
+      "10874 INHBA-AS1\n",
+      "10875 AEBP1\n",
+      "10876 DNAJC30\n",
+      "10877 CASTOR2\n",
+      "10878 SLC25A40\n",
+      "10879 TFPI2\n",
+      "10880 SEM1\n",
+      "10881 AGFG2\n",
+      "10882 PRKRIP1\n",
+      "10883 SMKR1\n",
+      "10884 DENND2A\n",
+      "10885 SMARCD3\n",
+      "10886 DPP6\n",
+      "10887 UBE3C\n",
+      "10888 PTPRN2\n",
+      "10889 ENSG00000287970\n",
+      "10890 GFRA2\n",
+      "10891 BMP1\n",
+      "10892 ENSG00000251034\n",
+      "10893 CLU\n",
+      "10894 BAG4\n",
+      "10895 TACC1\n",
+      "10896 ENSG00000254802\n",
+      "10897 PDE7A-DT\n",
+      "10898 YWHAZ\n",
+      "10899 ENSG00000283959\n",
+      "10900 LRP12\n",
+      "10901 DSCC1\n",
+      "10902 SQLE\n",
+      "10903 TRIB1\n",
+      "10904 ZFAT\n",
+      "10905 ZC3H3\n",
+      "10906 MROH6\n",
+      "10907 ZNF250\n",
+      "10908 RLN1\n",
+      "10909 SPINK4\n",
+      "10910 TMC1\n",
+      "10911 ALDH1A1\n",
+      "10912 PRUNE2\n",
+      "10913 LINC03062\n",
+      "10914 ENSG00000273381\n",
+      "10915 ERCC6L2\n",
+      "10916 CTSV\n",
+      "10917 NR4A3\n",
+      "10918 TMEM38B\n",
+      "10919 LHX2\n",
+      "10920 NIBAN2\n",
+      "10921 ST6GALNAC4\n",
+      "10922 NUP214\n",
+      "10923 BRD3OS\n",
+      "10924 CDC123\n",
+      "10925 AGAP9\n",
+      "10926 REEP3\n",
+      "10927 GHITM\n",
+      "10928 ACTA2\n",
+      "10929 IFIT2\n",
+      "10930 RPP30\n",
+      "10931 SLC35G1\n",
+      "10932 SFRP5\n",
+      "10933 HPS1-AS1\n",
+      "10934 ADD3-AS1\n",
+      "10935 AFAP1L2\n",
+      "10936 DOCK1\n",
+      "10937 ENSG00000273521\n",
+      "10938 TAF10\n",
+      "10939 USP47\n",
+      "10940 MICAL2\n",
+      "10941 API5\n",
+      "10942 HARBI1\n",
+      "10943 FAM111A-DT\n",
+      "10944 MIR194-2HG\n",
+      "10945 SAC3D1\n",
+      "10946 SNX32\n",
+      "10947 CD248\n",
+      "10948 PTPRCAP\n",
+      "10949 TESMIN\n",
+      "10950 GVQW3\n",
+      "10951 TSKU\n",
+      "10952 PCF11\n",
+      "10953 C11orf54\n",
+      "10954 DIXDC1\n",
+      "10955 C2CD2L\n",
+      "10956 C1S\n",
+      "10957 SPX\n",
+      "10958 CCDC91\n",
+      "10959 TUBA1C\n",
+      "10960 NCKAP1L\n",
+      "10961 RPS26\n",
+      "10962 MSRB3\n",
+      "10963 IRAK3\n",
+      "10964 CAPS2\n",
+      "10965 TMTC2\n",
+      "10966 NTS\n",
+      "10967 GALNT4\n",
+      "10968 ENSG00000286903\n",
+      "10969 SSH1\n",
+      "10970 ENSG00000258337\n",
+      "10971 UBC\n",
+      "10972 ENSG00000232243\n",
+      "10973 IFT88\n",
+      "10974 ESD\n",
+      "10975 LMO7\n",
+      "10976 IRS2\n",
+      "10977 ENSG00000254846\n",
+      "10978 DHRS4\n",
+      "10979 DTD2\n",
+      "10980 BAZ1A\n",
+      "10981 LINC00609\n",
+      "10982 KLHDC2\n",
+      "10983 SPTB\n",
+      "10984 FUT8\n",
+      "10985 GALNT16\n",
+      "10986 ZNF410\n",
+      "10987 CIPC\n",
+      "10988 ZC3H14\n",
+      "10989 OTUB2\n",
+      "10990 DICER1\n",
+      "10991 EVL\n",
+      "10992 DEGS2\n",
+      "10993 SLC25A29\n",
+      "10994 MEG8\n",
+      "10995 PGBD4\n",
+      "10996 EIF2AK4\n",
+      "10997 DNAJC17\n",
+      "10998 EPB42\n",
+      "10999 PIERCE2\n",
+      "11000 ZNF280D\n",
+      "11001 USP3\n",
+      "11002 ENSG00000264937\n",
+      "11003 IQCH-AS1\n",
+      "11004 MAP2K5\n",
+      "11005 SEMA7A\n",
+      "11006 SCAMP5\n",
+      "11007 CPEB1-AS1\n",
+      "11008 MRPS11\n",
+      "11009 RHCG\n",
+      "11010 ENSG00000274215\n",
+      "11011 TM2D3\n",
+      "11012 ENSG00000228779\n",
+      "11013 FBXL16\n",
+      "11014 ENSG00000261294\n",
+      "11015 NPW\n",
+      "11016 BRICD5\n",
+      "11017 ZG16B\n",
+      "11018 ZNF205\n",
+      "11019 ENSG00000262312\n",
+      "11020 DNAH3\n",
+      "11021 UQCRC2\n",
+      "11022 ZKSCAN2-DT\n",
+      "11023 EIF3CL\n",
+      "11024 ENSG00000261367\n",
+      "11025 ITGAD\n",
+      "11026 DNAJA2\n",
+      "11027 ENSG00000261114\n",
+      "11028 PLLP\n",
+      "11029 SETD6\n",
+      "11030 CNOT1\n",
+      "11031 SLC38A7\n",
+      "11032 CMTM4\n",
+      "11033 DYNC1LI2\n",
+      "11034 CBFB\n",
+      "11035 CYB5B\n",
+      "11036 ENSG00000259972\n",
+      "11037 ENSG00000182376\n",
+      "11038 SERPINF1\n",
+      "11039 CXCL16\n",
+      "11040 ZNF594\n",
+      "11041 KDM6B\n",
+      "11042 TMEM107\n",
+      "11043 ENSG00000266709\n",
+      "11044 COPS3\n",
+      "11045 RAB34\n",
+      "11046 CCL15\n",
+      "11047 MRPL45\n",
+      "11048 STAC2\n",
+      "11049 PHB1\n",
+      "11050 SLC35B1\n",
+      "11051 PCTP\n",
+      "11052 DYNLL2-DT\n",
+      "11053 PRR11\n",
+      "11054 NT5C\n",
+      "11055 ENSG00000267737\n",
+      "11056 CEP131\n",
+      "11057 ENSG00000263731\n",
+      "11058 CTIF\n",
+      "11059 TCF4\n",
+      "11060 LINC03069\n",
+      "11061 NEDD4L\n",
+      "11062 CYB5A\n",
+      "11063 ATP9B\n",
+      "11064 LINC01002\n",
+      "11065 MADCAM1-AS1\n",
+      "11066 RNF126\n",
+      "11067 ELANE\n",
+      "11068 REXO1\n",
+      "11069 ODAD3\n",
+      "11070 ZNF844\n",
+      "11071 ZNF14\n",
+      "11072 ZNF99\n",
+      "11073 LRP3\n",
+      "11074 TMEM147\n",
+      "11075 SERTAD3\n",
+      "11076 POU2F2-AS2\n",
+      "11077 ZNF526\n",
+      "11078 TRAPPC6A\n",
+      "11079 ENSG00000268423\n",
+      "11080 PRKD2\n",
+      "11081 CRX\n",
+      "11082 ODAD1\n",
+      "11083 PTOV1\n",
+      "11084 NLRP11\n",
+      "11085 ZNF470-DT\n",
+      "11086 TRAPPC2B\n",
+      "11087 ENSG00000276449\n",
+      "11088 SIRPB2\n",
+      "11089 SIRPD\n",
+      "11090 UBOX5\n",
+      "11091 SLX4IP\n",
+      "11092 TOMM34\n",
+      "11093 ENSG00000285796\n",
+      "11094 RNF114\n",
+      "11095 RPS21\n",
+      "11096 GID8\n",
+      "11097 PCMTD2\n",
+      "11098 USP16\n",
+      "11099 SETD4\n",
+      "11100 HLCS\n",
+      "11101 DIP2A-IT1\n",
+      "11102 ENSG00000225335\n",
+      "11103 TMEM191B\n",
+      "11104 ENSG00000273164\n",
+      "11105 SDF2L1\n",
+      "11106 IGLVI-70\n",
+      "11107 ZNF280B\n",
+      "11108 IGLC2\n",
+      "11109 ENSG00000272973\n",
+      "11110 TTC28\n",
+      "11111 APOL4\n",
+      "11112 FOXRED2\n",
+      "11113 APOBEC3H\n",
+      "11114 SGSM3\n",
+      "11115 RBX1\n",
+      "11116 TOB2\n",
+      "11117 SREBF2\n",
+      "11118 WBP2NL\n",
+      "11119 ARFGAP3\n",
+      "11120 TTLL1-AS1\n",
+      "11121 ENSG00000280434\n",
+      "11122 PRR34-AS1\n",
+      "11123 ENSG00000279345\n",
+      "11124 TBL1X\n",
+      "11125 MBTPS2\n",
+      "11126 HDAC6\n",
+      "11127 HUWE1\n",
+      "11128 ALAS2\n",
+      "11129 KLF8\n",
+      "11130 YIPF6\n",
+      "11131 XIST\n",
+      "11132 RPS6KA6\n",
+      "11133 SEPTIN6\n",
+      "11134 ZNF280C\n",
+      "11135 ENOX2\n",
+      "11136 MCF2\n",
+      "11137 LINC00632\n",
+      "11138 BGN\n",
+      "11139 IKBKG\n",
+      "11140 TPRG1L\n",
+      "11141 NPHP4\n",
+      "11142 H6PD\n",
+      "11143 SRM\n",
+      "11144 ENSG00000284602\n",
+      "11145 RHD\n",
+      "11146 LDLRAP1\n",
+      "11147 NUDC\n",
+      "11148 LCK\n",
+      "11149 CDCA8\n",
+      "11150 UTP11\n",
+      "11151 ZNF684\n",
+      "11152 GUCA2A\n",
+      "11153 MED8\n",
+      "11154 PATJ\n",
+      "11155 LEPROT\n",
+      "11156 DEPDC1\n",
+      "11157 ENSG00000273487\n",
+      "11158 AMY2B\n",
+      "11159 NTNG1\n",
+      "11160 WDR77\n",
+      "11161 TRIM45\n",
+      "11162 LIX1L\n",
+      "11163 H3C13\n",
+      "11164 DENND4B\n",
+      "11165 MUC1\n",
+      "11166 MSTO1\n",
+      "11167 FCRL1\n",
+      "11168 IGSF8\n",
+      "11169 NIT1\n",
+      "11170 CFAP126\n",
+      "11171 ENSG00000288093\n",
+      "11172 UCK2\n",
+      "11173 TNNT2\n",
+      "11174 OBSCN\n",
+      "11175 SLC35F3\n",
+      "11176 SH3YL1\n",
+      "11177 E2F6\n",
+      "11178 RDH14\n",
+      "11179 ADGRF3\n",
+      "11180 SLC4A1AP\n",
+      "11181 LINC01126\n",
+      "11182 PPM1B\n",
+      "11183 GMCL1\n",
+      "11184 PCBP1\n",
+      "11185 GGCX\n",
+      "11186 RNF181\n",
+      "11187 VWA3B\n",
+      "11188 IL18RAP\n",
+      "11189 LINC01965\n",
+      "11190 ENSG00000235319\n",
+      "11191 ENSG00000280878\n",
+      "11192 MERTK\n",
+      "11193 SLC20A1-DT\n",
+      "11194 ENSG00000272667\n",
+      "11195 SPOPL\n",
+      "11196 KYNU\n",
+      "11197 SCN3A\n",
+      "11198 ENSG00000229337\n",
+      "11199 ENSG00000283839\n",
+      "11200 C2orf88\n",
+      "11201 STAT1\n",
+      "11202 COQ10B\n",
+      "11203 ALS2\n",
+      "11204 NBEAL1\n",
+      "11205 XRCC5\n",
+      "11206 PID1\n",
+      "11207 ATG16L1\n",
+      "11208 SH3BP4\n",
+      "11209 ACKR3\n",
+      "11210 RAB17-DT\n",
+      "11211 HES6\n",
+      "11212 RAF1\n",
+      "11213 ZBTB47-AS1\n",
+      "11214 ZNF662\n",
+      "11215 XCR1\n",
+      "11216 LTF\n",
+      "11217 CCDC12\n",
+      "11218 LINC02019\n",
+      "11219 MANF\n",
+      "11220 STAB1\n",
+      "11221 PSMD6-AS2\n",
+      "11222 ZNF717\n",
+      "11223 ENSG00000286447\n",
+      "11224 CRYBG3\n",
+      "11225 SLC35A5\n",
+      "11226 ARMC8\n",
+      "11227 IL12A\n",
+      "11228 LINC02067\n",
+      "11229 MECOM\n",
+      "11230 POLR2H\n",
+      "11231 HRG-AS1\n",
+      "11232 CRMP1\n",
+      "11233 ENSG00000205959\n",
+      "11234 FGFBP1\n",
+      "11235 ENSG00000272650\n",
+      "11236 ADGRL3-AS1\n",
+      "11237 ENSG00000286848\n",
+      "11238 NAP1L5\n",
+      "11239 ENSG00000287552\n",
+      "11240 EMCN\n",
+      "11241 SLC9B2\n",
+      "11242 ENSG00000249604\n",
+      "11243 MAD2L1\n",
+      "11244 ABHD18\n",
+      "11245 ANAPC10\n",
+      "11246 TMEM184C\n",
+      "11247 LINC03074\n",
+      "11248 MND1\n",
+      "11249 CTSO\n",
+      "11250 TMA16\n",
+      "11251 MARCHF1\n",
+      "11252 GLRA3\n",
+      "11253 SLC25A4\n",
+      "11254 SORBS2\n",
+      "11255 SLC12A7\n",
+      "11256 TRIM23\n",
+      "11257 CCNB1\n",
+      "11258 SCAMP1-AS1\n",
+      "11259 LMNB1\n",
+      "11260 C5orf63\n",
+      "11261 ACSL6\n",
+      "11262 SIL1\n",
+      "11263 CD74\n",
+      "11264 NSG2\n",
+      "11265 DDX41\n",
+      "11266 ENSG00000247679\n",
+      "11267 SERPINB9\n",
+      "11268 SLC22A23\n",
+      "11269 LY86-AS1\n",
+      "11270 HCG18\n",
+      "11271 RPP21\n",
+      "11272 LSM2\n",
+      "11273 TNXB\n",
+      "11274 ENSG00000286340\n",
+      "11275 HMGN3\n",
+      "11276 MDN1-AS1\n",
+      "11277 MTRES1\n",
+      "11278 TBC1D32\n",
+      "11279 SMPDL3A\n",
+      "11280 HBS1L\n",
+      "11281 AHI1\n",
+      "11282 LINC03004\n",
+      "11283 ZDHHC14\n",
+      "11284 AGPAT4\n",
+      "11285 INTS1\n",
+      "11286 STEAP1B\n",
+      "11287 ENSG00000287093\n",
+      "11288 JAZF1\n",
+      "11289 FKBP9\n",
+      "11290 POLM\n",
+      "11291 GRB10\n",
+      "11292 TRIM73\n",
+      "11293 PPP1R9A\n",
+      "11294 ZNF394\n",
+      "11295 CYP3A4\n",
+      "11296 GPC2\n",
+      "11297 MOSPD3\n",
+      "11298 DNAJC2\n",
+      "11299 SMIM30\n",
+      "11300 SVOPL\n",
+      "11301 ZNF212\n",
+      "11302 ABCB8\n",
+      "11303 DNAJB6\n",
+      "11304 DEFA4\n",
+      "11305 ENSG00000269918\n",
+      "11306 FAM66D\n",
+      "11307 CHMP7\n",
+      "11308 ENSG00000253632\n",
+      "11309 ASH2L\n",
+      "11310 ZMAT4\n",
+      "11311 UBE2W\n",
+      "11312 ENSG00000254202\n",
+      "11313 PDP1\n",
+      "11314 RPL30-AS1\n",
+      "11315 ZNF707\n",
+      "11316 NRBP2\n",
+      "11317 LINC02851\n",
+      "11318 FRMPD1\n",
+      "11319 CNTNAP3B\n",
+      "11320 NMRK1\n",
+      "11321 ZNF189\n",
+      "11322 HDHD3\n",
+      "11323 ALAD\n",
+      "11324 MED27\n",
+      "11325 UBAC1\n",
+      "11326 PAXX\n",
+      "11327 CCNY\n",
+      "11328 FZD8\n",
+      "11329 ZNF487\n",
+      "11330 DEPP1\n",
+      "11331 TMEM273\n",
+      "11332 ANK3\n",
+      "11333 TYSND1\n",
+      "11334 SFTPA2\n",
+      "11335 ANXA11\n",
+      "11336 IFIT3\n",
+      "11337 NKX2-3\n",
+      "11338 SLC25A28-DT\n",
+      "11339 ENSG00000273162\n",
+      "11340 POLL\n",
+      "11341 SMC3\n",
+      "11342 NANOS1\n",
+      "11343 IKZF5\n",
+      "11344 CHST15\n",
+      "11345 VENTX\n",
+      "11346 ENSG00000255328\n",
+      "11347 MRPL23\n",
+      "11348 TRIM34\n",
+      "11349 CSTF3-DT\n",
+      "11350 TIMM10\n",
+      "11351 MS4A2\n",
+      "11352 ENSG00000267811\n",
+      "11353 WDR74\n",
+      "11354 SF1\n",
+      "11355 MRPL49\n",
+      "11356 POLA2\n",
+      "11357 ENSG00000268635\n",
+      "11358 LNCRNA-IUR\n",
+      "11359 CFAP300\n",
+      "11360 BIRC2\n",
+      "11361 CWF19L2\n",
+      "11362 ENSG00000286044\n",
+      "11363 CHEK1\n",
+      "11364 SNX19\n",
+      "11365 B3GAT1\n",
+      "11366 ADIPOR2\n",
+      "11367 ENSG00000245667\n",
+      "11368 ENSG00000256427\n",
+      "11369 YBX3\n",
+      "11370 GUCY2C-AS1\n",
+      "11371 ERP27\n",
+      "11372 PLEKHA5\n",
+      "11373 INTS13\n",
+      "11374 ENSG00000247903\n",
+      "11375 SENP1\n",
+      "11376 CCDC184\n",
+      "11377 AQP5\n",
+      "11378 GTSF1\n",
+      "11379 ERBB3\n",
+      "11380 TBK1\n",
+      "11381 LRRIQ1\n",
+      "11382 DYNLL1\n",
+      "11383 NRAV\n",
+      "11384 RNF10\n",
+      "11385 SFSWAP\n",
+      "11386 ENSG00000273552\n",
+      "11387 FLT3\n",
+      "11388 RFXAP\n",
+      "11389 GPC5\n",
+      "11390 ATP11A\n",
+      "11391 NRL\n",
+      "11392 CBLN3\n",
+      "11393 ENSG00000287156\n",
+      "11394 PIGH\n",
+      "11395 TMEM63C\n",
+      "11396 UBR7\n",
+      "11397 DDX24\n",
+      "11398 IGHV3-64D\n",
+      "11399 IGHV3-23\n",
+      "11400 THBS1\n",
+      "11401 SRP14-DT\n",
+      "11402 OIP5\n",
+      "11403 LTK\n",
+      "11404 MFAP1\n",
+      "11405 ARPP19\n",
+      "11406 MNS1\n",
+      "11407 ALDH1A2\n",
+      "11408 ANXA2\n",
+      "11409 ENSG00000275454\n",
+      "11410 SCAPER\n",
+      "11411 HYKK\n",
+      "11412 ENSG00000271983\n",
+      "11413 RAB40C\n",
+      "11414 TELO2\n",
+      "11415 MRPS34\n",
+      "11416 IL32\n",
+      "11417 SNX29\n",
+      "11418 IGSF6\n",
+      "11419 ENSG00000286808\n",
+      "11420 MYLK3\n",
+      "11421 ENSG00000262714\n",
+      "11422 BBS2\n",
+      "11423 PLEKHG4\n",
+      "11424 HYDIN\n",
+      "11425 ZFP1\n",
+      "11426 MON1B\n",
+      "11427 MLYCD\n",
+      "11428 SPAG7\n",
+      "11429 LINC00324\n",
+      "11430 RPL26\n",
+      "11431 USP43\n",
+      "11432 GAS7\n",
+      "11433 ENSG00000287196\n",
+      "11434 SLC6A4\n",
+      "11435 RHOT1\n",
+      "11436 ARHGAP23\n",
+      "11437 MLLT6\n",
+      "11438 RPL23\n",
+      "11439 ENSG00000236194\n",
+      "11440 CCR10\n",
+      "11441 CDC27\n",
+      "11442 SP2-AS1\n",
+      "11443 UTP18\n",
+      "11444 OR4D1\n",
+      "11445 RNF43\n",
+      "11446 RAD51C\n",
+      "11447 RPS6KB1\n",
+      "11448 APPBP2\n",
+      "11449 TLK2\n",
+      "11450 DNAI2\n",
+      "11451 MYO15B\n",
+      "11452 CYTH1\n",
+      "11453 MYOM1\n",
+      "11454 RAB12\n",
+      "11455 ABHD3\n",
+      "11456 PTGR3\n",
+      "11457 ENSG00000267655\n",
+      "11458 ENSG00000286245\n",
+      "11459 MIDN\n",
+      "11460 HSD11B1L\n",
+      "11461 RANBP3\n",
+      "11462 ZNF700\n",
+      "11463 WDR83OS\n",
+      "11464 SYCE2\n",
+      "11465 CC2D1A\n",
+      "11466 AKAP8L\n",
+      "11467 ZNF738\n",
+      "11468 ENSG00000196081\n",
+      "11469 ZNF91\n",
+      "11470 URI1\n",
+      "11471 GRAMD1A-AS1\n",
+      "11472 ENSG00000283907\n",
+      "11473 COX6B1\n",
+      "11474 ZNF146\n",
+      "11475 ACTN4\n",
+      "11476 SNRPA\n",
+      "11477 B9D2\n",
+      "11478 CADM4\n",
+      "11479 ZNF224\n",
+      "11480 STRN4\n",
+      "11481 BBC3\n",
+      "11482 CTU1\n",
+      "11483 ZNF28\n",
+      "11484 ZNF784\n",
+      "11485 ENSG00000267096\n",
+      "11486 ZNF471\n",
+      "11487 ZNF154\n",
+      "11488 IDH3B-DT\n",
+      "11489 PCED1A\n",
+      "11490 SNAP25\n",
+      "11491 ASXL1\n",
+      "11492 TOP1\n",
+      "11493 SDC4\n",
+      "11494 CEBPB-AS1\n",
+      "11495 ADNP\n",
+      "11496 CDH26\n",
+      "11497 HSPA13\n",
+      "11498 APP\n",
+      "11499 IFNAR1\n",
+      "11500 C2CD2\n",
+      "11501 ENSG00000277352\n",
+      "11502 LINC01637\n",
+      "11503 AP1B1\n",
+      "11504 ENSG00000225450\n",
+      "11505 ENSG00000279494\n",
+      "11506 MAPK8IP2\n",
+      "11507 SHOX\n",
+      "11508 KLHL34\n",
+      "11509 PHEX\n",
+      "11510 LINC03099\n",
+      "11511 LINC01204\n",
+      "11512 KRBOX4\n",
+      "11513 P2RY10\n",
+      "11514 DIAPH2\n",
+      "11515 PLP1\n",
+      "11516 AMOT\n",
+      "11517 SLC6A14\n",
+      "11518 SLC25A43\n",
+      "11519 RHOXF1\n",
+      "11520 IDS\n",
+      "11521 ENSG00000261773\n",
+      "11522 ZFY\n",
+      "11523 MT-CO1\n",
+      "11524 MT-ATP6\n",
+      "11525 ENSG00000271254\n",
+      "11526 ENSG00000238260\n",
+      "11527 TP73\n",
+      "11528 GPR153\n",
+      "11529 ENSG00000284642\n",
+      "11530 MTOR\n",
+      "11531 EFHD2-AS1\n",
+      "11532 CELA2A\n",
+      "11533 ENSG00000238142\n",
+      "11534 UBR4\n",
+      "11535 CEP85\n",
+      "11536 THEMIS2\n",
+      "11537 TAF12-DT\n",
+      "11538 OPRD1\n",
+      "11539 LINC01226\n",
+      "11540 RBBP4\n",
+      "11541 TFAP2E-AS1\n",
+      "11542 SF3A3\n",
+      "11543 ENSG00000284719\n",
+      "11544 CTPS1\n",
+      "11545 SLFNL1-AS1\n",
+      "11546 SLC6A9\n",
+      "11547 CPT2\n",
+      "11548 LDLRAD1\n",
+      "11549 DOCK7\n",
+      "11550 SRSF11\n",
+      "11551 NEXN-AS1\n",
+      "11552 PKN2\n",
+      "11553 LRRC8B\n",
+      "11554 ENSG00000235795\n",
+      "11555 CEPT1\n",
+      "11556 ENSG00000272982\n",
+      "11557 ST7L\n",
+      "11558 HIPK1-AS1\n",
+      "11559 LINC01762\n",
+      "11560 SEC22B\n",
+      "11561 LINC00623\n",
+      "11562 NUDT17\n",
+      "11563 C1orf56\n",
+      "11564 RUSC1\n",
+      "11565 GON4L\n",
+      "11566 UBQLN4\n",
+      "11567 TAGLN2\n",
+      "11568 KLHDC9\n",
+      "11569 POU2F1-DT\n",
+      "11570 GAS5\n",
+      "11571 UCHL5\n",
+      "11572 IPO9\n",
+      "11573 PCAT6\n",
+      "11574 MDM4\n",
+      "11575 NUCKS1\n",
+      "11576 AIDA\n",
+      "11577 LINC01703\n",
+      "11578 MAP3K21\n",
+      "11579 LGALS8\n",
+      "11580 ZBTB18\n",
+      "11581 LINC00299\n",
+      "11582 ENSG00000271855\n",
+      "11583 TAF1B\n",
+      "11584 UCN\n",
+      "11585 TOGARAM2\n",
+      "11586 ENSG00000276334\n",
+      "11587 DHX57\n",
+      "11588 ENSG00000234936\n",
+      "11589 ENSG00000230773\n",
+      "11590 VRK2\n",
+      "11591 FANCL\n",
+      "11592 PEX13\n",
+      "11593 ASPRV1\n",
+      "11594 PCYOX1\n",
+      "11595 CHMP3-AS1\n",
+      "11596 IGKV1-5\n",
+      "11597 SH3RF3-AS1\n",
+      "11598 SOCAR\n",
+      "11599 C2orf76\n",
+      "11600 ENSG00000286873\n",
+      "11601 SAP130\n",
+      "11602 EPC2\n",
+      "11603 PPIG\n",
+      "11604 SSB\n",
+      "11605 DCAF17\n",
+      "11606 RAPGEF4\n",
+      "11607 CALCRL-AS1\n",
+      "11608 CD28\n",
+      "11609 FZD5\n",
+      "11610 ENSG00000227021\n",
+      "11611 DIS3L2\n",
+      "11612 MTERF4\n",
+      "11613 SEPTIN2\n",
+      "11614 NEU4\n",
+      "11615 OGG1\n",
+      "11616 RPL15\n",
+      "11617 OXSM\n",
+      "11618 SNRK-AS1\n",
+      "11619 NBEAL2\n",
+      "11620 MAPKAPK3\n",
+      "11621 GRM2\n",
+      "11622 UBA3\n",
+      "11623 NFKBIZ\n",
+      "11624 ATG3\n",
+      "11625 SPICE1\n",
+      "11626 QTRT2\n",
+      "11627 CFAP100\n",
+      "11628 MGLL\n",
+      "11629 ACKR4\n",
+      "11630 NPHP3\n",
+      "11631 BFSP2-AS1\n",
+      "11632 ENSG00000287688\n",
+      "11633 LINC00880\n",
+      "11634 SENP2\n",
+      "11635 ATP13A4\n",
+      "11636 FAM43A\n",
+      "11637 NOP14-AS1\n",
+      "11638 LINC02481\n",
+      "11639 KLF3-AS1\n",
+      "11640 SMIM14\n",
+      "11641 FDCSP\n",
+      "11642 CXCL5\n",
+      "11643 SEPTIN11\n",
+      "11644 CFAP299\n",
+      "11645 GPAT3\n",
+      "11646 HERC6\n",
+      "11647 TIGD2\n",
+      "11648 PDLIM5\n",
+      "11649 TRMT10A\n",
+      "11650 ENSG00000286734\n",
+      "11651 LINC02615\n",
+      "11652 SLC7A11\n",
+      "11653 NOCT\n",
+      "11654 MAML3\n",
+      "11655 RNF150\n",
+      "11656 TLR2\n",
+      "11657 CPE\n",
+      "11658 SH3RF1\n",
+      "11659 CFAP96\n",
+      "11660 SEMA5A\n",
+      "11661 TTC23L-AS1\n",
+      "11662 KIF2A\n",
+      "11663 MAST4-AS1\n",
+      "11664 F2R\n",
+      "11665 AGGF1\n",
+      "11666 ERAP1\n",
+      "11667 DMXL1-DT\n",
+      "11668 CDC23\n",
+      "11669 SRA1\n",
+      "11670 ENSG00000286634\n",
+      "11671 F12\n",
+      "11672 RUFY1\n",
+      "11673 MAPK9\n",
+      "11674 ENSG00000272463\n",
+      "11675 C6orf52\n",
+      "11676 GPLD1\n",
+      "11677 H2BC13\n",
+      "11678 H2BC14\n",
+      "11679 HLA-G\n",
+      "11680 TRIM31\n",
+      "11681 ATAT1\n",
+      "11682 ENSG00000286974\n",
+      "11683 SRPK1\n",
+      "11684 PXT1\n",
+      "11685 SLC29A1\n",
+      "11686 ENSG00000225096\n",
+      "11687 CGAS\n",
+      "11688 TENT5A\n",
+      "11689 PRSS35\n",
+      "11690 MICAL1\n",
+      "11691 NUS1\n",
+      "11692 HDDC2\n",
+      "11693 MAP3K5-AS2\n",
+      "11694 MIOS\n",
+      "11695 UMAD1\n",
+      "11696 ENSG00000228005\n",
+      "11697 ZNF273\n",
+      "11698 LINC03011\n",
+      "11699 AUTS2\n",
+      "11700 CLIP2\n",
+      "11701 GTF2IRD2\n",
+      "11702 ERVW-1\n",
+      "11703 BET1\n",
+      "11704 SLC12A9\n",
+      "11705 PNPLA8\n",
+      "11706 TRBV5-4\n",
+      "11707 PRSS1\n",
+      "11708 ZBED10P\n",
+      "11709 ENSG00000283674\n",
+      "11710 CSGALNACT1\n",
+      "11711 KCTD9\n",
+      "11712 PLPP5\n",
+      "11713 ENSG00000272092\n",
+      "11714 IL7\n",
+      "11715 MTDH\n",
+      "11716 ZNF623\n",
+      "11717 MFSD3\n",
+      "11718 B4GALT1\n",
+      "11719 STOML2\n",
+      "11720 PABIR1\n",
+      "11721 TRPM6\n",
+      "11722 ENSG00000267559\n",
+      "11723 ENSG00000286375\n",
+      "11724 SLC46A2\n",
+      "11725 TMEM268\n",
+      "11726 PSMD5\n",
+      "11727 MRRF\n",
+      "11728 ENSG00000261094\n",
+      "11729 DENND1A\n",
+      "11730 PSMB7\n",
+      "11731 URM1\n",
+      "11732 TOR1A\n",
+      "11733 LINC01451\n",
+      "11734 TMEM141\n",
+      "11735 RBM17\n",
+      "11736 PFKFB3\n",
+      "11737 SEPHS1\n",
+      "11738 ST8SIA6-AS1\n",
+      "11739 ZNF37A\n",
+      "11740 ENSG00000285884\n",
+      "11741 TFAM\n",
+      "11742 PPA1\n",
+      "11743 NDST2\n",
+      "11744 IFIT1\n",
+      "11745 LINC00865\n",
+      "11746 RBP4\n",
+      "11747 BLNK\n",
+      "11748 PDCD11\n",
+      "11749 ENSG00000273108\n",
+      "11750 HSPA12A\n",
+      "11751 TNNI2\n",
+      "11752 SLC22A18\n",
+      "11753 PRDM11\n",
+      "11754 ARHGAP1\n",
+      "11755 ZFP91\n",
+      "11756 BEST1\n",
+      "11757 GANAB\n",
+      "11758 TMEM223\n",
+      "11759 STX5\n",
+      "11760 SCYL1\n",
+      "11761 DPP3\n",
+      "11762 LRRC51\n",
+      "11763 PLEKHB1\n",
+      "11764 TAF1D\n",
+      "11765 FAM76B\n",
+      "11766 TMEM123\n",
+      "11767 ZC3H12C\n",
+      "11768 LINC02762\n",
+      "11769 FXYD2\n",
+      "11770 TRAPPC4\n",
+      "11771 ENSG00000288061\n",
+      "11772 TBRG1\n",
+      "11773 NRIP2\n",
+      "11774 DUSP16\n",
+      "11775 GPRC5A\n",
+      "11776 ENSG00000276148\n",
+      "11777 DDN-AS1\n",
+      "11778 ANKRD52\n",
+      "11779 ENSG00000257740\n",
+      "11780 DDIT3\n",
+      "11781 CRADD\n",
+      "11782 APAF1\n",
+      "11783 GNPTAB\n",
+      "11784 ANKRD13A\n",
+      "11785 KNTC1\n",
+      "11786 ATP6V0A2\n",
+      "11787 PGAM5\n",
+      "11788 MTIF3\n",
+      "11789 MEDAG\n",
+      "11790 SLC15A1\n",
+      "11791 METTL17\n",
+      "11792 TSSK4\n",
+      "11793 CTSG\n",
+      "11794 AKAP6\n",
+      "11795 FKBP3\n",
+      "11796 MGAT2\n",
+      "11797 POLE2\n",
+      "11798 PYGL\n",
+      "11799 WDHD1\n",
+      "11800 SLC38A6\n",
+      "11801 PRKCH\n",
+      "11802 JDP2-AS1\n",
+      "11803 STON2\n",
+      "11804 LYSET\n",
+      "11805 PPP4R4\n",
+      "11806 SNURF\n",
+      "11807 ZFYVE19\n",
+      "11808 WDR76\n",
+      "11809 SEMA6D\n",
+      "11810 DTWD1\n",
+      "11811 TCF12\n",
+      "11812 SNX1\n",
+      "11813 CLK3\n",
+      "11814 C15orf39\n",
+      "11815 ISL2\n",
+      "11816 SKIC8\n",
+      "11817 CEMIP\n",
+      "11818 ALDH1A3\n",
+      "11819 RHBDL1\n",
+      "11820 SOX8\n",
+      "11821 ENSG00000261613\n",
+      "11822 ROGDI\n",
+      "11823 USP7-AS1\n",
+      "11824 SNN\n",
+      "11825 SMG1-DT\n",
+      "11826 DCTPP1\n",
+      "11827 RNF40\n",
+      "11828 ITFG1\n",
+      "11829 C16orf95-DT\n",
+      "11830 PIEZO1\n",
+      "11831 MNT\n",
+      "11832 RPAIN\n",
+      "11833 CHD3\n",
+      "11834 SLC25A35\n",
+      "11835 ADORA2B\n",
+      "11836 MPRIP\n",
+      "11837 TOM1L2\n",
+      "11838 ALKBH5\n",
+      "11839 EPN2\n",
+      "11840 CCDC144NL-AS1\n",
+      "11841 KSR1\n",
+      "11842 ENSG00000264608\n",
+      "11843 RPL23A\n",
+      "11844 CCL5\n",
+      "11845 NR1D1\n",
+      "11846 KRT15\n",
+      "11847 COASY\n",
+      "11848 ENSG00000267042\n",
+      "11849 ARHGAP27\n",
+      "11850 SKAP1-AS2\n",
+      "11851 MBTD1\n",
+      "11852 STXBP4\n",
+      "11853 HSF5\n",
+      "11854 ENSG00000265100\n",
+      "11855 ENSG00000267009\n",
+      "11856 CD300C\n",
+      "11857 LINC01973\n",
+      "11858 ENSG00000264548\n",
+      "11859 ENSG00000266401\n",
+      "11860 GATA6\n",
+      "11861 TTC39C\n",
+      "11862 TRAPPC8\n",
+      "11863 RPL17\n",
+      "11864 AZU1\n",
+      "11865 ENSG00000267283\n",
+      "11866 GNA15\n",
+      "11867 MICOS13\n",
+      "11868 SPC24\n",
+      "11869 ZNF763\n",
+      "11870 IER2\n",
+      "11871 PODNL1\n",
+      "11872 NDUFA13\n",
+      "11873 ENSG00000274104\n",
+      "11874 DPF1\n",
+      "11875 ZNF235\n",
+      "11876 NOVA2\n",
+      "11877 ARHGAP35\n",
+      "11878 C5AR1\n",
+      "11879 TMEM143\n",
+      "11880 CA11\n",
+      "11881 PPP1R15A\n",
+      "11882 RPL13A\n",
+      "11883 CNOT3\n",
+      "11884 LILRA6\n",
+      "11885 ZNF865\n",
+      "11886 ZNF543\n",
+      "11887 ZNF551\n",
+      "11888 ZNF324\n",
+      "11889 ENSG00000286787\n",
+      "11890 ENSG00000229728\n",
+      "11891 FKBP1A\n",
+      "11892 SEC23B\n",
+      "11893 RIN2\n",
+      "11894 NAPB\n",
+      "11895 ENSG00000274414\n",
+      "11896 DNMT3B\n",
+      "11897 TRPC4AP\n",
+      "11898 NORAD\n",
+      "11899 SERINC3\n",
+      "11900 STK4\n",
+      "11901 B4GALT5\n",
+      "11902 ATP5F1E\n",
+      "11903 ENSG00000278927\n",
+      "11904 ENSG00000154640\n",
+      "11905 SCAF4\n",
+      "11906 SLC5A3\n",
+      "11907 ENSG00000286549\n",
+      "11908 ENSG00000160284\n",
+      "11909 CLTCL1\n",
+      "11910 ENSG00000268292\n",
+      "11911 PPIL2\n",
+      "11912 SPECC1L\n",
+      "11913 ENSG00000279298\n",
+      "11914 TFIP11\n",
+      "11915 TFIP11-DT\n",
+      "11916 ENSG00000273428\n",
+      "11917 LINC01521\n",
+      "11918 ENSG00000273176\n",
+      "11919 SSTR3\n",
+      "11920 PACSIN2\n",
+      "11921 GPM6B\n",
+      "11922 EIF2S3\n",
+      "11923 FUNDC1\n",
+      "11924 LINC02595\n",
+      "11925 SLC9A7\n",
+      "11926 PRICKLE3\n",
+      "11927 PAGE4\n",
+      "11928 IQSEC2\n",
+      "11929 PDZD11\n",
+      "11930 HDAC8\n",
+      "11931 ARMCX5\n",
+      "11932 MORF4L2\n",
+      "11933 ENSG00000287024\n",
+      "11934 ENSG00000287918\n",
+      "11935 MAGEA10\n",
+      "11936 FLNA\n",
+      "11937 EMD\n",
+      "11938 DNASE1L1\n",
+      "11939 ZFY-AS1\n",
+      "11940 ENSG00000277666\n",
+      "11941 LINC01409\n",
+      "11942 PLA2G2A\n",
+      "11943 PINK1\n",
+      "11944 DDOST\n",
+      "11945 RUNX3\n",
+      "11946 MTFR1L\n",
+      "11947 FGR\n",
+      "11948 ENSG00000269967\n",
+      "11949 ZBTB8A\n",
+      "11950 ENSG00000286379\n",
+      "11951 FHL3\n",
+      "11952 EBNA1BP2\n",
+      "11953 UROD\n",
+      "11954 RAVER2\n",
+      "11955 IL12RB2\n",
+      "11956 CRYZ\n",
+      "11957 RABGGTB\n",
+      "11958 CNN3\n",
+      "11959 RWDD3\n",
+      "11960 AGL\n",
+      "11961 RBM8A\n",
+      "11962 S100A12\n",
+      "11963 FLAD1\n",
+      "11964 ENSG00000228863\n",
+      "11965 ENSG00000224985\n",
+      "11966 TOMM40L\n",
+      "11967 FCGR3A\n",
+      "11968 FCRLA\n",
+      "11969 TADA1\n",
+      "11970 DCAF6\n",
+      "11971 TNR\n",
+      "11972 RALGPS2-AS1\n",
+      "11973 PPFIA4\n",
+      "11974 IKBKE\n",
+      "11975 CD55\n",
+      "11976 FH\n",
+      "11977 ENSG00000287513\n",
+      "11978 TMEM18\n",
+      "11979 TMEM18-DT\n",
+      "11980 NOL10\n",
+      "11981 GREB1\n",
+      "11982 SPDYA\n",
+      "11983 DPY30\n",
+      "11984 ZFP36L2\n",
+      "11985 RHOQ-AS1\n",
+      "11986 PPP1R21-DT\n",
+      "11987 ASB3\n",
+      "11988 CCT7\n",
+      "11989 TLX2\n",
+      "11990 IGKV1-9\n",
+      "11991 RNF149\n",
+      "11992 POU3F3\n",
+      "11993 CD8B2\n",
+      "11994 CLASP1\n",
+      "11995 SCN7A\n",
+      "11996 PHOSPHO2\n",
+      "11997 PPP1R1C\n",
+      "11998 LINC01825\n",
+      "11999 NDUFS1\n",
+      "12000 PLEKHM3\n",
+      "12001 LINC01963\n",
+      "12002 ENSG00000273361\n",
+      "12003 DOCK10\n",
+      "12004 DGKD\n",
+      "12005 HDAC4\n",
+      "12006 ENSG00000269982\n",
+      "12007 HDAC11\n",
+      "12008 CCR4\n",
+      "12009 GLB1\n",
+      "12010 WDR48\n",
+      "12011 ZNF501\n",
+      "12012 PLXNB1\n",
+      "12013 WDR6\n",
+      "12014 MST1\n",
+      "12015 INKA1\n",
+      "12016 NPRL2\n",
+      "12017 BAP1\n",
+      "12018 PTPRG\n",
+      "12019 SNTN\n",
+      "12020 TAFA1\n",
+      "12021 USF3\n",
+      "12022 PDIA5\n",
+      "12023 KBTBD12\n",
+      "12024 TMCC1-DT\n",
+      "12025 PFN2\n",
+      "12026 ENSG00000272440\n",
+      "12027 ACTRT3\n",
+      "12028 C3orf70\n",
+      "12029 LRPAP1\n",
+      "12030 PTTG2\n",
+      "12031 SLC4A4\n",
+      "12032 ANKRD17-DT\n",
+      "12033 PF4V1\n",
+      "12034 GPRIN3\n",
+      "12035 ARHGEF38\n",
+      "12036 PRMT9\n",
+      "12037 DCLK2\n",
+      "12038 TRIM61\n",
+      "12039 DDX60\n",
+      "12040 CBR4\n",
+      "12041 SPCS3-AS1\n",
+      "12042 BASP1-AS1\n",
+      "12043 GOLPH3-DT\n",
+      "12044 BRIX1\n",
+      "12045 ENSG00000251131\n",
+      "12046 ENSG00000286314\n",
+      "12047 OCLN\n",
+      "12048 MEF2C\n",
+      "12049 RFESD\n",
+      "12050 LINC01023\n",
+      "12051 EPB41L4A-DT\n",
+      "12052 ENSG00000234290\n",
+      "12053 ENSG00000230612\n",
+      "12054 PCBD2\n",
+      "12055 PAIP2\n",
+      "12056 ENSG00000272742\n",
+      "12057 PURA\n",
+      "12058 PCYOX1L\n",
+      "12059 CPEB4\n",
+      "12060 SERPINB9P1\n",
+      "12061 NRN1\n",
+      "12062 NHLRC1\n",
+      "12063 PRL\n",
+      "12064 H1-5\n",
+      "12065 VARS1\n",
+      "12066 PRRT1\n",
+      "12067 HLA-DQA1\n",
+      "12068 DAXX\n",
+      "12069 PNPLA1\n",
+      "12070 LRRC73\n",
+      "12071 POLR1C\n",
+      "12072 NFKBIE\n",
+      "12073 RUNX2\n",
+      "12074 ADGRF1\n",
+      "12075 COL19A1\n",
+      "12076 RARS2\n",
+      "12077 TSPYL4\n",
+      "12078 ROS1\n",
+      "12079 FAM184A\n",
+      "12080 TPD52L1\n",
+      "12081 PHACTR2\n",
+      "12082 LTV1\n",
+      "12083 ENSG00000233330\n",
+      "12084 MYCT1\n",
+      "12085 CNKSR3\n",
+      "12086 MRPL18\n",
+      "12087 MICALL2\n",
+      "12088 ZNF12\n",
+      "12089 THSD7A\n",
+      "12090 ENSG00000226690\n",
+      "12091 ANLN\n",
+      "12092 BLVRA\n",
+      "12093 ZNF92\n",
+      "12094 GNAI1\n",
+      "12095 PCLO\n",
+      "12096 LMTK2\n",
+      "12097 ZCWPW1\n",
+      "12098 MOGAT3\n",
+      "12099 CAV2\n",
+      "12100 LUC7L2\n",
+      "12101 TBXAS1\n",
+      "12102 ENSG00000244701\n",
+      "12103 TRBV25-1\n",
+      "12104 TRBC1\n",
+      "12105 AOC1\n",
+      "12106 PAXIP1-DT\n",
+      "12107 GTF2E2\n",
+      "12108 RAB11FIP1\n",
+      "12109 ENSG00000183729\n",
+      "12110 ENSG00000255321\n",
+      "12111 C8orf34\n",
+      "12112 FSBP\n",
+      "12113 CSMD3\n",
+      "12114 ZHX2\n",
+      "12115 ENSG00000288067\n",
+      "12116 ENSG00000259820\n",
+      "12117 SLC52A2\n",
+      "12118 VPS28\n",
+      "12119 ENSG00000288023\n",
+      "12120 BRINP1\n",
+      "12121 ZNF79\n",
+      "12122 CIZ1\n",
+      "12123 ENSG00000230684\n",
+      "12124 SARDH\n",
+      "12125 SNHG7\n",
+      "12126 TRAF2\n",
+      "12127 ENSG00000287277\n",
+      "12128 PRPF18\n",
+      "12129 SAR1A\n",
+      "12130 CDH23\n",
+      "12131 PSAP\n",
+      "12132 VCL\n",
+      "12133 LRMDA\n",
+      "12134 BMPR1A\n",
+      "12135 MARCHF5\n",
+      "12136 FRAT2\n",
+      "12137 NPM3\n",
+      "12138 TAF5\n",
+      "12139 RNH1\n",
+      "12140 GATD1\n",
+      "12141 RPLP2\n",
+      "12142 SYT9-AS1\n",
+      "12143 EIF3F\n",
+      "12144 EIF4G2\n",
+      "12145 CALCB\n",
+      "12146 BDNF-AS\n",
+      "12147 SLC1A2\n",
+      "12148 CRY2\n",
+      "12149 ENSG00000270072\n",
+      "12150 SELENOH\n",
+      "12151 DAGLA\n",
+      "12152 LRRC32\n",
+      "12153 CNTN5\n",
+      "12154 ENSG00000268472\n",
+      "12155 ZW10\n",
+      "12156 JAML\n",
+      "12157 RPUSD4\n",
+      "12158 FLI1\n",
+      "12159 RHNO1\n",
+      "12160 ENSG00000278356\n",
+      "12161 SCNN1A\n",
+      "12162 CHD4\n",
+      "12163 GPR162\n",
+      "12164 ENSG00000275963\n",
+      "12165 FAM234B\n",
+      "12166 SINHCAF\n",
+      "12167 ENSG00000276136\n",
+      "12168 IRAK4\n",
+      "12169 VDR\n",
+      "12170 TNS2-AS1\n",
+      "12171 SPRYD4\n",
+      "12172 B4GALNT1\n",
+      "12173 LEMD3\n",
+      "12174 HMGA2\n",
+      "12175 LLPH\n",
+      "12176 ENSG00000288102\n",
+      "12177 BTG1-DT\n",
+      "12178 ACTR6\n",
+      "12179 SYCP3\n",
+      "12180 HELLPAR\n",
+      "12181 APPL2\n",
+      "12182 TMEM263\n",
+      "12183 LINC02356\n",
+      "12184 DDX54\n",
+      "12185 NOS1\n",
+      "12186 PXN-AS1\n",
+      "12187 KDM2B\n",
+      "12188 CCDC62\n",
+      "12189 ZNF605\n",
+      "12190 GPR12\n",
+      "12191 N4BP2L2-IT2\n",
+      "12192 RAP2A\n",
+      "12193 ENSG00000274922\n",
+      "12194 ENSG00000276248\n",
+      "12195 ENSG00000287735\n",
+      "12196 TRAV21\n",
+      "12197 PRKD1\n",
+      "12198 FBXO34\n",
+      "12199 ENSG00000275569\n",
+      "12200 MNAT1\n",
+      "12201 ALKBH1\n",
+      "12202 NRXN3\n",
+      "12203 SPATA7\n",
+      "12204 SERPINA10\n",
+      "12205 ENSG00000258702\n",
+      "12206 ENSG00000259444\n",
+      "12207 IGHV6-1\n",
+      "12208 NPAP1\n",
+      "12209 AVEN\n",
+      "12210 ENSG00000261136\n",
+      "12211 BAHD1\n",
+      "12212 RMDN3\n",
+      "12213 PATL2\n",
+      "12214 ENSG00000275709\n",
+      "12215 ENSG00000259712\n",
+      "12216 ZNF609\n",
+      "12217 ENSG00000260274\n",
+      "12218 ENSG00000260206\n",
+      "12219 LETR1\n",
+      "12220 SELENOS\n",
+      "12221 GFER\n",
+      "12222 CREBBP\n",
+      "12223 VASN\n",
+      "12224 SYT17\n",
+      "12225 CRYM\n",
+      "12226 NPIPB4\n",
+      "12227 LCMT1\n",
+      "12228 IL4R\n",
+      "12229 RABEP2\n",
+      "12230 SEZ6L2\n",
+      "12231 CCL17\n",
+      "12232 CSNK2A2\n",
+      "12233 MARVELD3\n",
+      "12234 OSGIN1\n",
+      "12235 MAP1LC3B\n",
+      "12236 DPEP1\n",
+      "12237 LINC02193\n",
+      "12238 ENSG00000287553\n",
+      "12239 POLR2A\n",
+      "12240 PFAS\n",
+      "12241 SNHG29\n",
+      "12242 NEK8\n",
+      "12243 TMEM132E\n",
+      "12244 ENSG00000275532\n",
+      "12245 MED1\n",
+      "12246 GSDMA\n",
+      "12247 KRT16\n",
+      "12248 VPS25\n",
+      "12249 BECN1\n",
+      "12250 KIF18B\n",
+      "12251 SPATA20\n",
+      "12252 PPM1D\n",
+      "12253 ERN1\n",
+      "12254 ABCA10\n",
+      "12255 FDXR\n",
+      "12256 HID1\n",
+      "12257 ZACN\n",
+      "12258 JMJD6\n",
+      "12259 ENSG00000266998\n",
+      "12260 BIRC5\n",
+      "12261 ENSG00000262413\n",
+      "12262 SMCHD1\n",
+      "12263 ENSG00000263847\n",
+      "12264 OSBPL1A\n",
+      "12265 ENSG00000274918\n",
+      "12266 SEC11C\n",
+      "12267 PIGN\n",
+      "12268 PEAK3\n",
+      "12269 MRPL54\n",
+      "12270 STAP2\n",
+      "12271 PTPRS-AS1\n",
+      "12272 TINCR\n",
+      "12273 C3\n",
+      "12274 VAV1\n",
+      "12275 ZNF490\n",
+      "12276 DNASE2\n",
+      "12277 DDX39A\n",
+      "12278 OR1I1\n",
+      "12279 FAM32A\n",
+      "12280 CPAMD8\n",
+      "12281 BST2\n",
+      "12282 FCHO1\n",
+      "12283 ENSG00000267383\n",
+      "12284 UQCRFS1\n",
+      "12285 GRAMD1A\n",
+      "12286 PSENEN\n",
+      "12287 ZNF527\n",
+      "12288 TGFB1\n",
+      "12289 XRCC1\n",
+      "12290 ZNF284\n",
+      "12291 ENSG00000267282\n",
+      "12292 GEMIN7\n",
+      "12293 FTL\n",
+      "12294 TRPM4\n",
+      "12295 ZNF614\n",
+      "12296 BRSK1\n",
+      "12297 AVP\n",
+      "12298 AP5S1\n",
+      "12299 ENSG00000261411\n",
+      "12300 COMMD7\n",
+      "12301 PROCR\n",
+      "12302 PPP1R16B\n",
+      "12303 L3MBTL1\n",
+      "12304 TSHZ2\n",
+      "12305 ENSG00000179253\n",
+      "12306 TAF4\n",
+      "12307 PRPF6\n",
+      "12308 ADAMTS1\n",
+      "12309 CCT8\n",
+      "12310 URB1-AS1\n",
+      "12311 PAXBP1-AS1\n",
+      "12312 SON\n",
+      "12313 ABCG1\n",
+      "12314 RANBP1\n",
+      "12315 DRG1\n",
+      "12316 EP300\n",
+      "12317 ENSG00000273192\n",
+      "12318 FANCB\n",
+      "12319 ENSG00000231846\n",
+      "12320 LANCL3\n",
+      "12321 BCOR\n",
+      "12322 FOXP3\n",
+      "12323 USP51\n",
+      "12324 DLG3\n",
+      "12325 GPR174\n",
+      "12326 HNRNPH2\n",
+      "12327 ENSG00000239407\n",
+      "12328 TCEAL9\n",
+      "12329 MID2\n",
+      "12330 DOCK11\n",
+      "12331 FGF13\n",
+      "12332 C1orf159\n",
+      "12333 PEX10\n",
+      "12334 ENSG00000272449\n",
+      "12335 ENSG00000287384\n",
+      "12336 TMEM51\n",
+      "12337 ENSG00000237301\n",
+      "12338 CPLANE2\n",
+      "12339 RCC2\n",
+      "12340 MDS2\n",
+      "12341 UBXN11\n",
+      "12342 FCN3\n",
+      "12343 WASF2\n",
+      "12344 ENSG00000287244\n",
+      "12345 KDM4A-AS1\n",
+      "12346 SSBP3\n",
+      "12347 CIMAP2\n",
+      "12348 EFCAB7\n",
+      "12349 ANKRD13C-DT\n",
+      "12350 ZZZ3\n",
+      "12351 CCDC18\n",
+      "12352 ENSG00000260464\n",
+      "12353 ENSG00000232811\n",
+      "12354 CIMAP3\n",
+      "12355 WNT2B\n",
+      "12356 RSBN1\n",
+      "12357 VANGL1\n",
+      "12358 ENSG00000224950\n",
+      "12359 ANKRD34A\n",
+      "12360 RPTN\n",
+      "12361 MEF2D\n",
+      "12362 METTL25B\n",
+      "12363 CD48\n",
+      "12364 USF1\n",
+      "12365 MPC2\n",
+      "12366 TSEN15\n",
+      "12367 TRMT1L\n",
+      "12368 CFH\n",
+      "12369 C1orf74\n",
+      "12370 IARS2\n",
+      "12371 MTARC2\n",
+      "12372 LINC01132\n",
+      "12373 RGS7\n",
+      "12374 EXO1\n",
+      "12375 RAB10\n",
+      "12376 WDR43\n",
+      "12377 CDC42EP3-AS1\n",
+      "12378 ENSG00000240401\n",
+      "12379 XPO1\n",
+      "12380 B3GNT2\n",
+      "12381 AFTPH-DT\n",
+      "12382 LINC01888\n",
+      "12383 ACTG2\n",
+      "12384 MRPL53\n",
+      "12385 IGKV1D-43\n",
+      "12386 ITPRIPL1\n",
+      "12387 AFF3\n",
+      "12388 ENSG00000286737\n",
+      "12389 GCC2-AS1\n",
+      "12390 SEPTIN10\n",
+      "12391 SOWAHC\n",
+      "12392 MALL\n",
+      "12393 MYO7B\n",
+      "12394 ZEB2-AS1\n",
+      "12395 DPP4-DT\n",
+      "12396 CDCA7\n",
+      "12397 ZC3H15\n",
+      "12398 TFPI\n",
+      "12399 PIKFYVE\n",
+      "12400 CPS1\n",
+      "12401 ENSG00000273466\n",
+      "12402 ATG4B\n",
+      "12403 KAT2B\n",
+      "12404 TTC21A\n",
+      "12405 PTPN23-DT\n",
+      "12406 DHX30\n",
+      "12407 GNAT1\n",
+      "12408 ENSG00000287707\n",
+      "12409 TNNC1\n",
+      "12410 HESX1\n",
+      "12411 DENND6A\n",
+      "12412 ROBO1\n",
+      "12413 PHLDB2\n",
+      "12414 PODXL2\n",
+      "12415 ALG1L2\n",
+      "12416 SELENOT\n",
+      "12417 ENSG00000243176\n",
+      "12418 SMC4\n",
+      "12419 TNIK\n",
+      "12420 NCEH1\n",
+      "12421 TM4SF19\n",
+      "12422 ZBTB49\n",
+      "12423 ENSG00000287104\n",
+      "12424 LINC02447\n",
+      "12425 N4BP2\n",
+      "12426 RBM47\n",
+      "12427 CHIC2\n",
+      "12428 STBD1\n",
+      "12429 HNRNPD-DT\n",
+      "12430 PRDM5\n",
+      "12431 MGST2\n",
+      "12432 INPP4B\n",
+      "12433 USP38-DT\n",
+      "12434 SFRP2\n",
+      "12435 LRAT\n",
+      "12436 ASIC5\n",
+      "12437 LINC03067\n",
+      "12438 MARCHF6\n",
+      "12439 BASP1\n",
+      "12440 WDR70\n",
+      "12441 SELENOP\n",
+      "12442 ERCC8\n",
+      "12443 CD180\n",
+      "12444 LINC02997\n",
+      "12445 FCHO2\n",
+      "12446 HOMER1\n",
+      "12447 ARRDC3\n",
+      "12448 TMEM232\n",
+      "12449 SEMA6A-AS1\n",
+      "12450 HINT1\n",
+      "12451 SMIM32\n",
+      "12452 ETF1\n",
+      "12453 ENSG00000251330\n",
+      "12454 FAM153A\n",
+      "12455 CNOT6\n",
+      "12456 H2AC11\n",
+      "12457 OR14J1\n",
+      "12458 HLA-F-AS1\n",
+      "12459 DHX16\n",
+      "12460 LINC00243\n",
+      "12461 LINC01149\n",
+      "12462 HLA-DOA\n",
+      "12463 ZBTB22\n",
+      "12464 CUTA\n",
+      "12465 SYNGAP1\n",
+      "12466 PPARD\n",
+      "12467 TREML1\n",
+      "12468 NCR2\n",
+      "12469 CCND3\n",
+      "12470 ENSG00000214732\n",
+      "12471 CUL7\n",
+      "12472 SLC35B2\n",
+      "12473 CDC5L\n",
+      "12474 TMEM30A-DT\n",
+      "12475 HACE1\n",
+      "12476 MED23\n",
+      "12477 MOXD1\n",
+      "12478 LRP11\n",
+      "12479 TIAM2\n",
+      "12480 TSPAN13\n",
+      "12481 GARS1-DT\n",
+      "12482 HERPUD2\n",
+      "12483 C7orf25\n",
+      "12484 VPS50\n",
+      "12485 GAL3ST4\n",
+      "12486 CTTNBP2\n",
+      "12487 NDUFB2\n",
+      "12488 GIMAP8\n",
+      "12489 NUB1\n",
+      "12490 ENSG00000272812\n",
+      "12491 ADAM28\n",
+      "12492 LEPROTL1\n",
+      "12493 GSR\n",
+      "12494 HOOK3\n",
+      "12495 NSMAF\n",
+      "12496 ENSG00000287975\n",
+      "12497 TRAM1\n",
+      "12498 OSR2\n",
+      "12499 ENSG00000260368\n",
+      "12500 MRPL13\n",
+      "12501 PTK2\n",
+      "12502 RHPN1\n",
+      "12503 HSF1\n",
+      "12504 ENSG00000272115\n",
+      "12505 ZNF34\n",
+      "12506 CNTLN\n",
+      "12507 MYORG\n",
+      "12508 IL11RA\n",
+      "12509 EXOSC3\n",
+      "12510 ZNG1E\n",
+      "12511 VPS13A\n",
+      "12512 ZNF483\n",
+      "12513 SNX30\n",
+      "12514 DELEC1\n",
+      "12515 WDR5-DT\n",
+      "12516 TUBB4B\n",
+      "12517 AKR1C1\n",
+      "12518 CALML3\n",
+      "12519 OLAH\n",
+      "12520 MALRD1\n",
+      "12521 MIR1915HG\n",
+      "12522 HNRNPF\n",
+      "12523 SPOCK2\n",
+      "12524 ENSG00000232934\n",
+      "12525 RGS10\n",
+      "12526 NSMCE4A\n",
+      "12527 PHRF1\n",
+      "12528 ADM\n",
+      "12529 RNF141\n",
+      "12530 CAPRIN1\n",
+      "12531 PHF21A\n",
+      "12532 GLYAT\n",
+      "12533 DTX4\n",
+      "12534 EML3\n",
+      "12535 STX5-DT\n",
+      "12536 FKBP2\n",
+      "12537 DPP3-DT\n",
+      "12538 RBM4\n",
+      "12539 CASP4\n",
+      "12540 APOA1\n",
+      "12541 ARCN1\n",
+      "12542 CCDC15-DT\n",
+      "12543 FEZ1\n",
+      "12544 TMEM45B\n",
+      "12545 C12orf4\n",
+      "12546 ENSG00000234428\n",
+      "12547 CAPRIN2\n",
+      "12548 SLC2A13\n",
+      "12549 PRICKLE1\n",
+      "12550 KRT86\n",
+      "12551 ENSG00000257181\n",
+      "12552 RAB3IP\n",
+      "12553 KRR1\n",
+      "12554 ATP2B1\n",
+      "12555 LINC02413\n",
+      "12556 ENSG00000258365\n",
+      "12557 NUAK1\n",
+      "12558 ENSG00000276308\n",
+      "12559 ENSG00000256152\n",
+      "12560 RILPL2\n",
+      "12561 DHX37\n",
+      "12562 LINC00621\n",
+      "12563 USP12\n",
+      "12564 USPL1\n",
+      "12565 ELF1\n",
+      "12566 TSC22D1\n",
+      "12567 LCP1\n",
+      "12568 SPRYD7\n",
+      "12569 DIS3\n",
+      "12570 LINC00449\n",
+      "12571 TRAV36DV7\n",
+      "12572 NPAS3\n",
+      "12573 MIS18BP1\n",
+      "12574 LINC01588\n",
+      "12575 ENSG00000273888\n",
+      "12576 ARMH4\n",
+      "12577 TMEM30B\n",
+      "12578 SYNE2\n",
+      "12579 ENSG00000285612\n",
+      "12580 DPF3\n",
+      "12581 ZFYVE1\n",
+      "12582 KCNK13\n",
+      "12583 DGLUCY\n",
+      "12584 DLK1\n",
+      "12585 TRAF3\n",
+      "12586 ZFYVE21\n",
+      "12587 PPP1R13B-DT\n",
+      "12588 IGHE\n",
+      "12589 IGHV3-49\n",
+      "12590 EMC4\n",
+      "12591 RHOV\n",
+      "12592 FOXB1\n",
+      "12593 ENSG00000274383\n",
+      "12594 RAB11A\n",
+      "12595 LINC02896\n",
+      "12596 CHRNB4\n",
+      "12597 BCL2A1\n",
+      "12598 RCCD1\n",
+      "12599 ENSG00000275092\n",
+      "12600 RPL3L\n",
+      "12601 ENSG00000263280\n",
+      "12602 RRN3\n",
+      "12603 ENSG00000261596\n",
+      "12604 LINC01567\n",
+      "12605 MAPK3\n",
+      "12606 BCKDK\n",
+      "12607 CES1\n",
+      "12608 ENSG00000180917\n",
+      "12609 SLC7A5\n",
+      "12610 VPS9D1\n",
+      "12611 ENSG00000277597\n",
+      "12612 MIR22HG\n",
+      "12613 ASPA\n",
+      "12614 TAX1BP3\n",
+      "12615 NCBP3\n",
+      "12616 TNK1\n",
+      "12617 TP53\n",
+      "12618 RASD1\n",
+      "12619 ENSG00000270091\n",
+      "12620 ABHD15-AS1\n",
+      "12621 C17orf50\n",
+      "12622 KRT10\n",
+      "12623 FAM117A\n",
+      "12624 AKAP1-DT\n",
+      "12625 EFCAB3\n",
+      "12626 ARSG\n",
+      "12627 PRKAR1A\n",
+      "12628 SLC25A19\n",
+      "12629 ENSG00000287193\n",
+      "12630 TBCD\n",
+      "12631 GAPLINC\n",
+      "12632 APCDD1\n",
+      "12633 PIEZO2\n",
+      "12634 GREB1L\n",
+      "12635 ENSG00000263823\n",
+      "12636 SERPINB3\n",
+      "12637 TIMM21\n",
+      "12638 LINC00683\n",
+      "12639 GALR1\n",
+      "12640 MFSD12\n",
+      "12641 DPP9\n",
+      "12642 TICAM1\n",
+      "12643 FDX2\n",
+      "12644 CCDC159\n",
+      "12645 ENSG00000275091\n",
+      "12646 TPM4\n",
+      "12647 KLF2\n",
+      "12648 LPAR2\n",
+      "12649 GPI\n",
+      "12650 ENSG00000267605\n",
+      "12651 SIPA1L3\n",
+      "12652 ZNF576\n",
+      "12653 KCNN4\n",
+      "12654 EMC10\n",
+      "12655 ZNF766\n",
+      "12656 ZNF600\n",
+      "12657 TSEN34\n",
+      "12658 LILRA1\n",
+      "12659 ZNF71\n",
+      "12660 C20orf96\n",
+      "12661 ENSG00000277287\n",
+      "12662 LINC01730\n",
+      "12663 WFDC2\n",
+      "12664 TNNC2\n",
+      "12665 MIR646HG\n",
+      "12666 NTSR1\n",
+      "12667 PTK6\n",
+      "12668 STMN3\n",
+      "12669 RTEL1\n",
+      "12670 C21orf91\n",
+      "12671 MRPL39\n",
+      "12672 LTN1\n",
+      "12673 DOP1B\n",
+      "12674 ENSG00000273210\n",
+      "12675 U2AF1\n",
+      "12676 ENSG00000276529\n",
+      "12677 ENSG00000280007\n",
+      "12678 SCARF2\n",
+      "12679 TPST2\n",
+      "12680 NEFH\n",
+      "12681 TUG1\n",
+      "12682 PHETA2\n",
+      "12683 PRR5\n",
+      "12684 CRLF2\n",
+      "12685 LINC02968\n",
+      "12686 TBC1D25\n",
+      "12687 MTMR8\n",
+      "12688 TAF1\n",
+      "12689 MORC4\n",
+      "12690 SLC25A14\n",
+      "12691 ENSG00000275063\n",
+      "12692 RPL22\n",
+      "12693 ENSG00000271746\n",
+      "12694 UQCRHL\n",
+      "12695 ALDH4A1\n",
+      "12696 ENSG00000272084\n",
+      "12697 CDC42\n",
+      "12698 OSCP1\n",
+      "12699 CCDC24\n",
+      "12700 RNF220\n",
+      "12701 TSPAN1\n",
+      "12702 BTF3L4\n",
+      "12703 JAK1\n",
+      "12704 LRRC40\n",
+      "12705 USP33\n",
+      "12706 BCL10-AS1\n",
+      "12707 LRRC8C-DT\n",
+      "12708 CLCC1\n",
+      "12709 GSTM2\n",
+      "12710 SIKE1\n",
+      "12711 FAM72D\n",
+      "12712 ENSG00000285184\n",
+      "12713 C1orf43\n",
+      "12714 SYT11\n",
+      "12715 KHDC4\n",
+      "12716 SLC25A44\n",
+      "12717 NTRK1\n",
+      "12718 CD5L\n",
+      "12719 PYHIN1\n",
+      "12720 SDHC\n",
+      "12721 DHX9-AS1\n",
+      "12722 ENSG00000223881\n",
+      "12723 CSRP1-AS1\n",
+      "12724 ADIPOR1\n",
+      "12725 REN\n",
+      "12726 DUSP10\n",
+      "12727 IAH1\n",
+      "12728 ENSG00000270100\n",
+      "12729 LDAH\n",
+      "12730 HADHA\n",
+      "12731 AGBL5\n",
+      "12732 NRBP1\n",
+      "12733 YIPF4\n",
+      "12734 GPATCH11\n",
+      "12735 SLC3A1\n",
+      "12736 MCFD2\n",
+      "12737 ANXA4\n",
+      "12738 RNF103\n",
+      "12739 IGKV1D-16\n",
+      "12740 TSGA10\n",
+      "12741 NCK2\n",
+      "12742 TMEM185B\n",
+      "12743 TSN\n",
+      "12744 SFT2D3\n",
+      "12745 CSRNP3\n",
+      "12746 IFT70A\n",
+      "12747 FRZB\n",
+      "12748 OSGEPL1\n",
+      "12749 HSPE1\n",
+      "12750 WNT10A\n",
+      "12751 GLB1L\n",
+      "12752 COL4A3\n",
+      "12753 THUMPD3\n",
+      "12754 HACL1\n",
+      "12755 ENSG00000271964\n",
+      "12756 EOMES\n",
+      "12757 ENSG00000286996\n",
+      "12758 CCR5\n",
+      "12759 PTPN23\n",
+      "12760 NICN1\n",
+      "12761 PARP3\n",
+      "12762 SELENOK\n",
+      "12763 TASOR\n",
+      "12764 B4GALT4\n",
+      "12765 CD80\n",
+      "12766 ENSG00000284624\n",
+      "12767 MLF1-DT\n",
+      "12768 ENSG00000260743\n",
+      "12769 SST\n",
+      "12770 TMEM44\n",
+      "12771 LMLN\n",
+      "12772 GAK\n",
+      "12773 UVSSA\n",
+      "12774 LCORL\n",
+      "12775 CNGA1\n",
+      "12776 RASSF6\n",
+      "12777 EIF4E\n",
+      "12778 ALPK1\n",
+      "12779 ENSG00000286467\n",
+      "12780 SCOC-AS1\n",
+      "12781 NR3C2\n",
+      "12782 ROPN1L\n",
+      "12783 ESM1\n",
+      "12784 CCNO\n",
+      "12785 MAP3K1\n",
+      "12786 PART1\n",
+      "12787 CENPH\n",
+      "12788 SMN2\n",
+      "12789 BDP1\n",
+      "12790 ENSG00000271815\n",
+      "12791 VCAN\n",
+      "12792 ATG12\n",
+      "12793 C5orf15\n",
+      "12794 TXNDC15\n",
+      "12795 PITX1\n",
+      "12796 NME5\n",
+      "12797 PCDHB1\n",
+      "12798 ENSG00000253792\n",
+      "12799 NEURL1B\n",
+      "12800 PDLIM7\n",
+      "12801 ENSG00000250999\n",
+      "12802 HUS1B\n",
+      "12803 SOX4\n",
+      "12804 H2BC8\n",
+      "12805 ABHD16A\n",
+      "12806 SLC44A4\n",
+      "12807 WDR46\n",
+      "12808 SYNGAP1-AS1\n",
+      "12809 BRPF3\n",
+      "12810 ENSG00000272316\n",
+      "12811 FKBP1C\n",
+      "12812 SDHAF4\n",
+      "12813 RIMS1\n",
+      "12814 SENP6\n",
+      "12815 HEY2\n",
+      "12816 ECT2L\n",
+      "12817 ENSG00000198719\n",
+      "12818 ABCA13\n",
+      "12819 ENSG00000285886\n",
+      "12820 TMEM120A\n",
+      "12821 STYXL1\n",
+      "12822 TRRAP\n",
+      "12823 ZNF3\n",
+      "12824 TFR2\n",
+      "12825 NAMPT-AS1\n",
+      "12826 LINC02577\n",
+      "12827 WASL\n",
+      "12828 LRRC4\n",
+      "12829 SSBP1\n",
+      "12830 CASP2\n",
+      "12831 FAM131B-AS2\n",
+      "12832 ATP6V0E2-AS1\n",
+      "12833 RARRES2\n",
+      "12834 PNOC\n",
+      "12835 IDO1\n",
+      "12836 ENSG00000286837\n",
+      "12837 LINC01289\n",
+      "12838 RDH10\n",
+      "12839 ZC2HC1A\n",
+      "12840 ENSG00000225885\n",
+      "12841 MAL2\n",
+      "12842 NSMCE2\n",
+      "12843 DENND3-AS1\n",
+      "12844 PARP10\n",
+      "12845 MTAP\n",
+      "12846 RPP25L\n",
+      "12847 POLR1E\n",
+      "12848 ENSG00000284612\n",
+      "12849 HNRNPK-AS1\n",
+      "12850 ENSG00000286835\n",
+      "12851 KIAA1958\n",
+      "12852 KYAT1\n",
+      "12853 FAM78A\n",
+      "12854 REXO4\n",
+      "12855 DPP7\n",
+      "12856 GTPBP4\n",
+      "12857 MACROH2A2\n",
+      "12858 LIPA\n",
+      "12859 KIF20B\n",
+      "12860 GOT1-DT\n",
+      "12861 COL17A1\n",
+      "12862 CTBP2\n",
+      "12863 EDRF1\n",
+      "12864 LINC02870\n",
+      "12865 RIC8A\n",
+      "12866 ENSG00000239920\n",
+      "12867 AKIP1\n",
+      "12868 ELP4\n",
+      "12869 TNKS1BP1\n",
+      "12870 SSRP1\n",
+      "12871 OSBP\n",
+      "12872 MS4A3\n",
+      "12873 UQCC3\n",
+      "12874 FERMT3\n",
+      "12875 CDC42BPG\n",
+      "12876 CTSW\n",
+      "12877 CORO1B\n",
+      "12878 CDK2AP2\n",
+      "12879 UCP3\n",
+      "12880 SMCO4\n",
+      "12881 ARHGAP42\n",
+      "12882 CASP1\n",
+      "12883 COLCA1\n",
+      "12884 POU2AF1\n",
+      "12885 SIDT2\n",
+      "12886 CENATAC\n",
+      "12887 RNF26\n",
+      "12888 GSEC\n",
+      "12889 CD9\n",
+      "12890 NCAPD2\n",
+      "12891 MGP\n",
+      "12892 ENSG00000274315\n",
+      "12893 PKP2\n",
+      "12894 SLC38A2\n",
+      "12895 FMNL3\n",
+      "12896 FAIM2\n",
+      "12897 BIN2\n",
+      "12898 KRT72\n",
+      "12899 ATP5MC2\n",
+      "12900 CALCOCO1\n",
+      "12901 NACA\n",
+      "12902 MARS1\n",
+      "12903 LTA4H\n",
+      "12904 HSP90B1\n",
+      "12905 TDG\n",
+      "12906 TMEM263-DT\n",
+      "12907 ACADS\n",
+      "12908 CDK2AP1\n",
+      "12909 GOLGA3\n",
+      "12910 ZMYM2\n",
+      "12911 SPATA13-AS1\n",
+      "12912 BRCA2\n",
+      "12913 LINC02334\n",
+      "12914 TPT1-AS1\n",
+      "12915 CLN5\n",
+      "12916 HAUS4\n",
+      "12917 AP1G2\n",
+      "12918 ENSG00000259321\n",
+      "12919 AP4S1\n",
+      "12920 CDKL1\n",
+      "12921 ENSG00000274015\n",
+      "12922 RGS6\n",
+      "12923 C14orf178\n",
+      "12924 EIF5\n",
+      "12925 KLC1\n",
+      "12926 IGHV3-20\n",
+      "12927 IGHV3-47\n",
+      "12928 CYFIP1\n",
+      "12929 TUBGCP5\n",
+      "12930 HERC2\n",
+      "12931 CDAN1\n",
+      "12932 MYO1E\n",
+      "12933 ENSG00000259238\n",
+      "12934 CA12\n",
+      "12935 UBAP1L\n",
+      "12936 ENSG00000261187\n",
+      "12937 IREB2\n",
+      "12938 ENSG00000273691\n",
+      "12939 CRTC3\n",
+      "12940 LINC02207\n",
+      "12941 HBM\n",
+      "12942 HBA2\n",
+      "12943 STUB1\n",
+      "12944 HMOX2\n",
+      "12945 CARHSP1\n",
+      "12946 NTAN1\n",
+      "12947 CDR2-DT\n",
+      "12948 ENSG00000251417\n",
+      "12949 MVP\n",
+      "12950 DOC2A\n",
+      "12951 CHD9NB\n",
+      "12952 MT1X\n",
+      "12953 WWOX\n",
+      "12954 PLD2\n",
+      "12955 SLC25A11\n",
+      "12956 ZNF232\n",
+      "12957 AIPL1\n",
+      "12958 ENSG00000265749\n",
+      "12959 NDEL1\n",
+      "12960 PIK3R5-DT\n",
+      "12961 FBXW10\n",
+      "12962 TRAF4\n",
+      "12963 NSRP1\n",
+      "12964 EVI2A\n",
+      "12965 ENSG00000278668\n",
+      "12966 MRM1\n",
+      "12967 KRT19\n",
+      "12968 RND2\n",
+      "12969 ASB16\n",
+      "12970 GFAP\n",
+      "12971 SCRN2\n",
+      "12972 LUC7L3\n",
+      "12973 TEX2\n",
+      "12974 RGS9\n",
+      "12975 SPHK1\n",
+      "12976 LINC02080\n",
+      "12977 ENSG00000261924\n",
+      "12978 NARF\n",
+      "12979 ENSG00000282965\n",
+      "12980 RNMT\n",
+      "12981 RNF138\n",
+      "12982 PSTPIP2\n",
+      "12983 ATP8B1-AS1\n",
+      "12984 CPLX4\n",
+      "12985 SERPINB13\n",
+      "12986 FGF22\n",
+      "12987 MEX3D\n",
+      "12988 BTBD2\n",
+      "12989 HMG20B\n",
+      "12990 TBXA2R\n",
+      "12991 TNFSF9\n",
+      "12992 RAVER1\n",
+      "12993 CDKN2D\n",
+      "12994 KANK2\n",
+      "12995 MAST1\n",
+      "12996 HAUS8\n",
+      "12997 ENSG00000269481\n",
+      "12998 LINC00662\n",
+      "12999 ZNF155\n",
+      "13000 ZNF230\n",
+      "13001 SLC6A16\n",
+      "13002 IRF3\n",
+      "13003 CD33\n",
+      "13004 FPR1\n",
+      "13005 ZNF611\n",
+      "13006 OSCAR\n",
+      "13007 LILRA4\n",
+      "13008 LAIR2\n",
+      "13009 TMEM150B\n",
+      "13010 CHMP2A\n",
+      "13011 RNF24\n",
+      "13012 ESF1\n",
+      "13013 SNRPB2\n",
+      "13014 LCDR\n",
+      "13015 ID1\n",
+      "13016 RALY\n",
+      "13017 EPB41L1-AS1\n",
+      "13018 DPM1\n",
+      "13019 RPS21-DT\n",
+      "13020 ENSG00000273759\n",
+      "13021 ENSG00000280433\n",
+      "13022 PSMG1\n",
+      "13023 MX1\n",
+      "13024 LRRC3\n",
+      "13025 LINC00205\n",
+      "13026 IGLV3-21\n",
+      "13027 IGLV2-11\n",
+      "13028 IGLC1\n",
+      "13029 ZNF70\n",
+      "13030 TBC1D10A\n",
+      "13031 LL22NC01-81G9.3\n",
+      "13032 IL2RB\n",
+      "13033 KDELR3\n",
+      "13034 APOBEC3D\n",
+      "13035 PDGFB\n",
+      "13036 TNFRSF13C\n",
+      "13037 ENSG00000213279\n",
+      "13038 ZBED4\n",
+      "13039 SELENOO\n",
+      "13040 CSF2RA\n",
+      "13041 TASL\n",
+      "13042 EBP\n",
+      "13043 GATA1\n",
+      "13044 ENSG00000228906\n",
+      "13045 NSDHL\n",
+      "13046 ARHGAP4\n",
+      "13047 LINC00278\n",
+      "13048 SAMD11\n",
+      "13049 PRDM16\n",
+      "13050 CAMTA1\n",
+      "13051 TARDBP\n",
+      "13052 MICOS10\n",
+      "13053 CNR2\n",
+      "13054 HMGN2\n",
+      "13055 ZCCHC17\n",
+      "13056 AK2\n",
+      "13057 PSMB2\n",
+      "13058 MFSD2A\n",
+      "13059 PPCS\n",
+      "13060 ENSG00000285728\n",
+      "13061 LRRC42\n",
+      "13062 FYB2\n",
+      "13063 ATG4C\n",
+      "13064 CTBS\n",
+      "13065 SELENOF\n",
+      "13066 ENSG00000258634\n",
+      "13067 HIPK1\n",
+      "13068 TSPAN2\n",
+      "13069 TENT5C\n",
+      "13070 SETDB1\n",
+      "13071 S100A13\n",
+      "13072 FCRL4\n",
+      "13073 CD1D\n",
+      "13074 ENSG00000287040\n",
+      "13075 COPA\n",
+      "13076 MGST3\n",
+      "13077 IVNS1ABP\n",
+      "13078 TMEM9\n",
+      "13079 KLHL12\n",
+      "13080 EIF2D\n",
+      "13081 IL10\n",
+      "13082 TLR5\n",
+      "13083 LINC01736\n",
+      "13084 EGLN1\n",
+      "13085 ALKAL2\n",
+      "13086 TRAPPC12-AS1\n",
+      "13087 PTRHD1\n",
+      "13088 EFR3B\n",
+      "13089 SLC30A6\n",
+      "13090 KCNK12\n",
+      "13091 LINC01122\n",
+      "13092 RAB1A\n",
+      "13093 DYSF\n",
+      "13094 ELMOD3\n",
+      "13095 CD8B\n",
+      "13096 CYTOR\n",
+      "13097 EIF2AK3\n",
+      "13098 C2orf15\n",
+      "13099 MITD1\n",
+      "13100 RPL31\n",
+      "13101 FHL2\n",
+      "13102 ENSG00000235881\n",
+      "13103 CHCHD5\n",
+      "13104 IL1B\n",
+      "13105 STEAP3\n",
+      "13106 WDR33\n",
+      "13107 IMP4\n",
+      "13108 TEX41\n",
+      "13109 NMI\n",
+      "13110 ENSG00000226266\n",
+      "13111 ATP5MC3\n",
+      "13112 UBE2E3\n",
+      "13113 ENSG00000238171\n",
+      "13114 ATIC\n",
+      "13115 ARPC2\n",
+      "13116 KCNE4\n",
+      "13117 COL4A4\n",
+      "13118 TRPM8\n",
+      "13119 LINC02610\n",
+      "13120 ENSG00000220256\n",
+      "13121 DUSP28\n",
+      "13122 LINC03100\n",
+      "13123 IL17RE\n",
+      "13124 TATDN2\n",
+      "13125 NR1D2\n",
+      "13126 GORASP1\n",
+      "13127 ZNF621\n",
+      "13128 NCKIPSD\n",
+      "13129 PRKAR2A\n",
+      "13130 APPL1\n",
+      "13131 PSMD6\n",
+      "13132 POPDC2\n",
+      "13133 HCLS1\n",
+      "13134 RARRES1\n",
+      "13135 LRRC34\n",
+      "13136 KLHL6-AS1\n",
+      "13137 AP2M1\n",
+      "13138 IGF2BP2\n",
+      "13139 RPL9\n",
+      "13140 THAP9\n",
+      "13141 ENSG00000214559\n",
+      "13142 WWC2\n",
+      "13143 CMBL\n",
+      "13144 CDH12\n",
+      "13145 ITGA2\n",
+      "13146 ZSWIM6\n",
+      "13147 SREK1IP1\n",
+      "13148 TNPO1\n",
+      "13149 SCAMP1\n",
+      "13150 ENSG00000249476\n",
+      "13151 STARD4\n",
+      "13152 CDO1\n",
+      "13153 MARCHF3\n",
+      "13154 BRD8\n",
+      "13155 CYSTM1\n",
+      "13156 FBXO38\n",
+      "13157 GM2A\n",
+      "13158 LARP1\n",
+      "13159 MAT2B\n",
+      "13160 RPL26L1\n",
+      "13161 ENSG00000253172\n",
+      "13162 DCDC2\n",
+      "13163 H3C2\n",
+      "13164 H3C3\n",
+      "13165 H4C4\n",
+      "13166 H3C12\n",
+      "13167 PPP1R11\n",
+      "13168 FLOT1\n",
+      "13169 RGL2\n",
+      "13170 KCTD20\n",
+      "13171 KLHDC3\n",
+      "13172 CD2AP-DT\n",
+      "13173 KHDC1-AS1\n",
+      "13174 PGM3\n",
+      "13175 PNISR\n",
+      "13176 ENSG00000272476\n",
+      "13177 ARHGAP18\n",
+      "13178 TMEM181\n",
+      "13179 ENSG00000286760\n",
+      "13180 TMEM184A\n",
+      "13181 ITGB8\n",
+      "13182 HOXA4\n",
+      "13183 HOXA9\n",
+      "13184 TAX1BP1\n",
+      "13185 NOD1\n",
+      "13186 INMT\n",
+      "13187 EPDR1\n",
+      "13188 TRGV7\n",
+      "13189 TRGV4\n",
+      "13190 MAGI2-AS3\n",
+      "13191 ABCB1\n",
+      "13192 MCM7\n",
+      "13193 TAF6\n",
+      "13194 SPACDR\n",
+      "13195 ENSG00000279168\n",
+      "13196 UPK3BL1\n",
+      "13197 ANKRD7\n",
+      "13198 TSPAN33\n",
+      "13199 STRIP2\n",
+      "13200 COPG2\n",
+      "13201 ENSG00000271522\n",
+      "13202 ZC3HAV1\n",
+      "13203 MKRN1\n",
+      "13204 TRBV30\n",
+      "13205 GIMAP4\n",
+      "13206 RHEB\n",
+      "13207 INSIG1\n",
+      "13208 ADAM7-AS1\n",
+      "13209 NEFL\n",
+      "13210 IDO2\n",
+      "13211 VDAC3\n",
+      "13212 CEBPD\n",
+      "13213 ZBTB10\n",
+      "13214 SAMD12\n",
+      "13215 ENSG00000214803\n",
+      "13216 RNF139\n",
+      "13217 ZNF572\n",
+      "13218 PCAT1\n",
+      "13219 ENSG00000253988\n",
+      "13220 ZFP41\n",
+      "13221 ADCK5\n",
+      "13222 RECQL4\n",
+      "13223 C8orf33\n",
+      "13224 UBE2R2\n",
+      "13225 UBAP2\n",
+      "13226 NPR2\n",
+      "13227 DCAF10\n",
+      "13228 AUH\n",
+      "13229 NINJ1\n",
+      "13230 FANCC\n",
+      "13231 ZNF782\n",
+      "13232 SLC31A1\n",
+      "13233 NEK6\n",
+      "13234 ENSG00000228395\n",
+      "13235 ZDHHC12\n",
+      "13236 GPR107\n",
+      "13237 FBXW5\n",
+      "13238 ENSG00000287175\n",
+      "13239 SUV39H2-DT\n",
+      "13240 CSGALNACT2\n",
+      "13241 WASHC2A\n",
+      "13242 ARID5B\n",
+      "13243 FRA10AC1\n",
+      "13244 INPP5F\n",
+      "13245 PAOX\n",
+      "13246 ENSG00000251661\n",
+      "13247 ENSG00000270105\n",
+      "13248 CRACR2B\n",
+      "13249 INS\n",
+      "13250 ASCL2\n",
+      "13251 ENSG00000183562\n",
+      "13252 LINC00958\n",
+      "13253 ENSG00000246250\n",
+      "13254 DGKZ\n",
+      "13255 F2\n",
+      "13256 ARFGAP2\n",
+      "13257 CELF1\n",
+      "13258 MTCH2\n",
+      "13259 PTPRJ\n",
+      "13260 RTN4RL2\n",
+      "13261 PLCB3\n",
+      "13262 ENSG00000255449\n",
+      "13263 PICALM\n",
+      "13264 TRPC6\n",
+      "13265 BCO2\n",
+      "13266 BUD13\n",
+      "13267 DSCAML1\n",
+      "13268 GRIK4\n",
+      "13269 ZNF202\n",
+      "13270 HEPACAM\n",
+      "13271 CCDC15\n",
+      "13272 NCAPD3\n",
+      "13273 B3GAT1-DT\n",
+      "13274 ZNF384\n",
+      "13275 C2CD5\n",
+      "13276 SSPN\n",
+      "13277 ENSG00000276900\n",
+      "13278 ATG101\n",
+      "13279 KRT8\n",
+      "13280 ZC3H10\n",
+      "13281 TSPAN31\n",
+      "13282 KICS2\n",
+      "13283 LINC02384\n",
+      "13284 CPM\n",
+      "13285 TMCC3\n",
+      "13286 FICD\n",
+      "13287 GIT2\n",
+      "13288 CUX2\n",
+      "13289 KSR2\n",
+      "13290 RFC5\n",
+      "13291 SRSF9\n",
+      "13292 ENSG00000275964\n",
+      "13293 SKA3\n",
+      "13294 RFC3\n",
+      "13295 CYSLTR2\n",
+      "13296 TRIM13\n",
+      "13297 WDFY2\n",
+      "13298 DOCK9-DT\n",
+      "13299 UBAC2-AS1\n",
+      "13300 NDRG2\n",
+      "13301 TRAV8-1\n",
+      "13302 TRAV12-3\n",
+      "13303 CHMP4A\n",
+      "13304 GPR135\n",
+      "13305 TTC9\n",
+      "13306 GSTZ1\n",
+      "13307 LINC02960\n",
+      "13308 RPS6KA5\n",
+      "13309 ITPK1\n",
+      "13310 ENSG00000269940\n",
+      "13311 KATNBL1\n",
+      "13312 RAB8B\n",
+      "13313 RBPMS2\n",
+      "13314 SMAD3\n",
+      "13315 ISLR2\n",
+      "13316 ENSG00000248540\n",
+      "13317 ENSG00000286488\n",
+      "13318 AEN\n",
+      "13319 FANCI\n",
+      "13320 RGMA\n",
+      "13321 ARRDC4\n",
+      "13322 TEDC2-AS1\n",
+      "13323 DNASE1\n",
+      "13324 PLA2G10\n",
+      "13325 ENSG00000260136\n",
+      "13326 ZNF747\n",
+      "13327 ZNF629\n",
+      "13328 SLC5A2\n",
+      "13329 LONP2\n",
+      "13330 MT2A\n",
+      "13331 CPNE2\n",
+      "13332 CTCF-DT\n",
+      "13333 ST3GAL2\n",
+      "13334 TAT\n",
+      "13335 GCSH\n",
+      "13336 MEAK7\n",
+      "13337 C16orf95\n",
+      "13338 ENSG00000276337\n",
+      "13339 PITPNA-AS1\n",
+      "13340 PITPNA\n",
+      "13341 ARRB2\n",
+      "13342 DNAH9\n",
+      "13343 SLC47A2\n",
+      "13344 ENSG00000277450\n",
+      "13345 ENSG00000266490\n",
+      "13346 NF1\n",
+      "13347 PSMD11\n",
+      "13348 MYO1D\n",
+      "13349 RFFL\n",
+      "13350 NLE1\n",
+      "13351 ENSG00000270871\n",
+      "13352 RDM1\n",
+      "13353 PIGW\n",
+      "13354 ORMDL3\n",
+      "13355 RETREG3\n",
+      "13356 PSME3\n",
+      "13357 GRN\n",
+      "13358 ENSG00000267288\n",
+      "13359 KANSL1\n",
+      "13360 KANSL1-AS1\n",
+      "13361 ITGB3\n",
+      "13362 PSMD12\n",
+      "13363 COG1\n",
+      "13364 MGC16275\n",
+      "13365 TRIM47\n",
+      "13366 CYGB\n",
+      "13367 NARF-AS1\n",
+      "13368 CHMP1B-AS1\n",
+      "13369 CABYR\n",
+      "13370 ENSG00000264151\n",
+      "13371 PIK3C3\n",
+      "13372 ENSG00000154839\n",
+      "13373 POLI\n",
+      "13374 ENSG00000266844\n",
+      "13375 POLRMT\n",
+      "13376 ATP5F1D\n",
+      "13377 CACTIN\n",
+      "13378 FSD1\n",
+      "13379 PRR22\n",
+      "13380 CAPS\n",
+      "13381 GTF2F1\n",
+      "13382 TRIP10\n",
+      "13383 PNPLA6\n",
+      "13384 CTXN1\n",
+      "13385 CCL25\n",
+      "13386 PIN1-DT\n",
+      "13387 SMARCA4\n",
+      "13388 DHPS\n",
+      "13389 BEST2\n",
+      "13390 ENSG00000267458\n",
+      "13391 ENSG00000267670\n",
+      "13392 OR10H1\n",
+      "13393 ANKLE1\n",
+      "13394 NR2C2AP\n",
+      "13395 VSTM2B\n",
+      "13396 DPY19L3-DT\n",
+      "13397 NUDT19-DT\n",
+      "13398 CEP89\n",
+      "13399 ZNF181\n",
+      "13400 FXYD1\n",
+      "13401 CD22\n",
+      "13402 ENSG00000196381\n",
+      "13403 ENSG00000267152\n",
+      "13404 ENSG00000268262\n",
+      "13405 FKRP\n",
+      "13406 ENSG00000268746\n",
+      "13407 TULP2\n",
+      "13408 LILRB2\n",
+      "13409 ZNF470\n",
+      "13410 ZNF773\n",
+      "13411 ZNF135\n",
+      "13412 PSMF1\n",
+      "13413 STK35\n",
+      "13414 NAA20\n",
+      "13415 ENSG00000260257\n",
+      "13416 PIGU\n",
+      "13417 MMP24\n",
+      "13418 PLCG1-AS1\n",
+      "13419 LPIN3\n",
+      "13420 OSER1\n",
+      "13421 VAPB\n",
+      "13422 MRGBP\n",
+      "13423 UCKL1\n",
+      "13424 RWDD2B\n",
+      "13425 CECR2\n",
+      "13426 DGCR6L\n",
+      "13427 MMP11\n",
+      "13428 ENSG00000279548\n",
+      "13429 SMDT1\n",
+      "13430 PARVB\n",
+      "13431 UPK3A\n",
+      "13432 SLC25A6\n",
+      "13433 ARSD\n",
+      "13434 PRDX4\n",
+      "13435 NDUFB11\n",
+      "13436 UBA1\n",
+      "13437 PLP2\n",
+      "13438 CYSLTR1\n",
+      "13439 ATG4A\n",
+      "13440 TMEM164\n",
+      "13441 ATP11C\n",
+      "13442 MT-ND5\n",
+      "13443 ENSG00000260179\n",
+      "13444 TMEM201\n",
+      "13445 LINC01772\n",
+      "13446 MRTO4\n",
+      "13447 EPHB2\n",
+      "13448 GPATCH3\n",
+      "13449 RCC1\n",
+      "13450 ENSG00000270103\n",
+      "13451 ZMPSTE24\n",
+      "13452 RNF11\n",
+      "13453 SSBP3-AS1\n",
+      "13454 ENSG00000203605\n",
+      "13455 AK4\n",
+      "13456 SASS6\n",
+      "13457 TAF13\n",
+      "13458 STRIP1\n",
+      "13459 PHGDH\n",
+      "13460 ECM1\n",
+      "13461 S100A14\n",
+      "13462 ENSG00000271380\n",
+      "13463 ZBTB7B\n",
+      "13464 TRIM46\n",
+      "13465 ENSG00000260460\n",
+      "13466 SLAMF6\n",
+      "13467 ENSG00000234425\n",
+      "13468 DUSP12\n",
+      "13469 NOS1AP\n",
+      "13470 SFT2D2\n",
+      "13471 C1orf53\n",
+      "13472 MIR181A1HG\n",
+      "13473 FMOD\n",
+      "13474 PLEKHA6\n",
+      "13475 PLXNA2\n",
+      "13476 HSD11B1-AS1\n",
+      "13477 WDR26\n",
+      "13478 ENAH\n",
+      "13479 IBA57\n",
+      "13480 GGPS1\n",
+      "13481 COLEC11\n",
+      "13482 FAM228B\n",
+      "13483 LINC01381\n",
+      "13484 SLC30A6-DT\n",
+      "13485 C1D\n",
+      "13486 CD8A\n",
+      "13487 ENSG00000230747\n",
+      "13488 ARID5A\n",
+      "13489 MZT2A\n",
+      "13490 ARHGAP15-AS1\n",
+      "13491 MYO3B\n",
+      "13492 NFE2L2\n",
+      "13493 ENSG00000222043\n",
+      "13494 CERKL\n",
+      "13495 FSIP2\n",
+      "13496 NEMP2\n",
+      "13497 IRS1\n",
+      "13498 RBM44\n",
+      "13499 SRGAP3-AS2\n",
+      "13500 ATG7\n",
+      "13501 NUP210\n",
+      "13502 FBLN2\n",
+      "13503 MRPS25\n",
+      "13504 DPH3\n",
+      "13505 FBXL2\n",
+      "13506 PDCD6IP-DT\n",
+      "13507 VIPR1\n",
+      "13508 TMEM115\n",
+      "13509 ENSG00000272181\n",
+      "13510 ENSG00000287595\n",
+      "13511 LINC00960\n",
+      "13512 TBILA\n",
+      "13513 POGLUT1\n",
+      "13514 PARP15\n",
+      "13515 MYLK-AS1\n",
+      "13516 ENSG00000273174\n",
+      "13517 RHO\n",
+      "13518 ENSG00000273486\n",
+      "13519 FAIM\n",
+      "13520 PLOD2\n",
+      "13521 ENSG00000286714\n",
+      "13522 NMD3\n",
+      "13523 GOLIM4\n",
+      "13524 CCDC39\n",
+      "13525 KIAA0232\n",
+      "13526 ZNF518B\n",
+      "13527 CLNK\n",
+      "13528 PACRGL\n",
+      "13529 REST\n",
+      "13530 CENPC\n",
+      "13531 CXCL9\n",
+      "13532 LINC02994\n",
+      "13533 ADH4\n",
+      "13534 ENPEP\n",
+      "13535 ANXA5\n",
+      "13536 ARFIP1\n",
+      "13537 DCHS2\n",
+      "13538 FGB\n",
+      "13539 TRIM60\n",
+      "13540 ENSG00000249807\n",
+      "13541 ANKH\n",
+      "13542 LINC02100\n",
+      "13543 PURPL\n",
+      "13544 MTMR12\n",
+      "13545 TMEM267\n",
+      "13546 MRPS30\n",
+      "13547 EMB\n",
+      "13548 GPBP1\n",
+      "13549 TMEM171\n",
+      "13550 ST8SIA4\n",
+      "13551 FAM53C\n",
+      "13552 MATR3\n",
+      "13553 MFAP3\n",
+      "13554 GEMIN5\n",
+      "13555 C5orf58\n",
+      "13556 RANBP17\n",
+      "13557 DUSP1\n",
+      "13558 TRIM41\n",
+      "13559 PXDC1\n",
+      "13560 TFAP2A-AS2\n",
+      "13561 TMEM14B-DT\n",
+      "13562 ADTRP\n",
+      "13563 JARID2-DT\n",
+      "13564 ENSG00000233358\n",
+      "13565 H4C12\n",
+      "13566 H3C11\n",
+      "13567 PGBD1\n",
+      "13568 ABCF1\n",
+      "13569 CFB\n",
+      "13570 EGFL8\n",
+      "13571 PSMB9\n",
+      "13572 TBC1D22B\n",
+      "13573 DNPH1\n",
+      "13574 MLIP\n",
+      "13575 MTO1\n",
+      "13576 ENSG00000271860\n",
+      "13577 PDSS2\n",
+      "13578 L3MBTL3\n",
+      "13579 ENSG00000227678\n",
+      "13580 PHACTR2-AS1\n",
+      "13581 KATNA1\n",
+      "13582 TCP1\n",
+      "13583 PRKN\n",
+      "13584 ENSG00000227508\n",
+      "13585 GET4\n",
+      "13586 ENSG00000287665\n",
+      "13587 WIPI2\n",
+      "13588 ABCB5\n",
+      "13589 MTURN\n",
+      "13590 LSM5\n",
+      "13591 LINC00997\n",
+      "13592 FIGNL1\n",
+      "13593 PSPH\n",
+      "13594 POM121\n",
+      "13595 TMEM243\n",
+      "13596 PEX1\n",
+      "13597 COPS6\n",
+      "13598 ENSG00000170632\n",
+      "13599 IMMP2L\n",
+      "13600 ENSG00000238202\n",
+      "13601 ENSG00000224746\n",
+      "13602 TRBV10-1\n",
+      "13603 CSMD1\n",
+      "13604 EGR3\n",
+      "13605 TNFRSF10D\n",
+      "13606 ENSG00000272338\n",
+      "13607 ERLIN2\n",
+      "13608 PLPBP\n",
+      "13609 STAR\n",
+      "13610 ENSG00000253829\n",
+      "13611 TCEA1\n",
+      "13612 TMEM68\n",
+      "13613 CLVS1\n",
+      "13614 ARMC1\n",
+      "13615 PDE7A\n",
+      "13616 MYBL1\n",
+      "13617 CHMP4C\n",
+      "13618 RALYL\n",
+      "13619 WWP1\n",
+      "13620 RBM12B-DT\n",
+      "13621 VIRMA-DT\n",
+      "13622 ENY2\n",
+      "13623 ARC\n",
+      "13624 B4GALT1-AS1\n",
+      "13625 BAG1\n",
+      "13626 CCL27\n",
+      "13627 CLTA\n",
+      "13628 PTAR1\n",
+      "13629 FAM120AOS\n",
+      "13630 AOPEP\n",
+      "13631 PTBP3\n",
+      "13632 PTPA\n",
+      "13633 ENSG00000176868\n",
+      "13634 NACC2\n",
+      "13635 ZMYND19\n",
+      "13636 WDR37\n",
+      "13637 IL15RA\n",
+      "13638 APBB1IP\n",
+      "13639 ENSG00000229227\n",
+      "13640 DKK1\n",
+      "13641 ENSG00000260400\n",
+      "13642 KLLN\n",
+      "13643 HHEX\n",
+      "13644 OLMALINC\n",
+      "13645 CUEDC2\n",
+      "13646 SLK\n",
+      "13647 SHOC2\n",
+      "13648 CCDC186\n",
+      "13649 CD151\n",
+      "13650 CTSD\n",
+      "13651 RRM1\n",
+      "13652 ENSG00000254397\n",
+      "13653 SOX6\n",
+      "13654 PRRG4\n",
+      "13655 DEPDC7\n",
+      "13656 ENSG00000286626\n",
+      "13657 KBTBD4\n",
+      "13658 NUP160\n",
+      "13659 CPT1A\n",
+      "13660 PPFIA1\n",
+      "13661 C11orf97\n",
+      "13662 CCDC82\n",
+      "13663 SMIM35\n",
+      "13664 CD3E\n",
+      "13665 RPS25\n",
+      "13666 EI24\n",
+      "13667 TSPAN9\n",
+      "13668 ENSG00000121380\n",
+      "13669 WBP11\n",
+      "13670 ENSG00000247934\n",
+      "13671 OVCH1-AS1\n",
+      "13672 PCED1B\n",
+      "13673 ATF1\n",
+      "13674 NFE2\n",
+      "13675 ZNF385A\n",
+      "13676 CD63\n",
+      "13677 PAN2\n",
+      "13678 PTGES3\n",
+      "13679 PRIM1\n",
+      "13680 CTDSP2\n",
+      "13681 NUP107\n",
+      "13682 HAL\n",
+      "13683 EID3\n",
+      "13684 ENSG00000257221\n",
+      "13685 SAP18\n",
+      "13686 KBTBD7\n",
+      "13687 MYCBP2-AS1\n",
+      "13688 LINC01232\n",
+      "13689 F7\n",
+      "13690 TRAV20\n",
+      "13691 DAD1\n",
+      "13692 OXA1L\n",
+      "13693 REM2\n",
+      "13694 ACIN1\n",
+      "13695 BCL2L2\n",
+      "13696 DHRS2\n",
+      "13697 BRMS1L\n",
+      "13698 CGRRF1\n",
+      "13699 LINC00520\n",
+      "13700 DAAM1\n",
+      "13701 SGPP1\n",
+      "13702 SRSF5\n",
+      "13703 GOLGA5\n",
+      "13704 ENSG00000258560\n",
+      "13705 PPP2R5C\n",
+      "13706 IGHA1\n",
+      "13707 IGHV1-3\n",
+      "13708 IGHV2-70\n",
+      "13709 CCDC9B\n",
+      "13710 MAPKBP1\n",
+      "13711 LRRC57\n",
+      "13712 STARD9\n",
+      "13713 TTBK2\n",
+      "13714 DUOX2\n",
+      "13715 SECISBP2L\n",
+      "13716 LYSMD2\n",
+      "13717 NEDD4\n",
+      "13718 DENND4A\n",
+      "13719 AAGAB\n",
+      "13720 TSPAN3\n",
+      "13721 AP3S2\n",
+      "13722 FURIN\n",
+      "13723 LINC01579\n",
+      "13724 NME4\n",
+      "13725 DECR2\n",
+      "13726 TMEM204\n",
+      "13727 ACSM2B\n",
+      "13728 CDR2\n",
+      "13729 AQP8\n",
+      "13730 BOLA2\n",
+      "13731 SPN\n",
+      "13732 C16orf54\n",
+      "13733 CD2BP2-DT\n",
+      "13734 ENSG00000260167\n",
+      "13735 RBL2\n",
+      "13736 ENSG00000260823\n",
+      "13737 NLRC5\n",
+      "13738 CCDC102A\n",
+      "13739 ENSG00000276259\n",
+      "13740 PHAF1\n",
+      "13741 ZFP90\n",
+      "13742 IST1\n",
+      "13743 PMFBP1\n",
+      "13744 ENSG00000261079\n",
+      "13745 DYNLRB2\n",
+      "13746 MPHOSPH6\n",
+      "13747 WFDC1\n",
+      "13748 COX4I1\n",
+      "13749 TRPV1\n",
+      "13750 GPS2\n",
+      "13751 NLGN2\n",
+      "13752 LINC00670\n",
+      "13753 ENSG00000265494\n",
+      "13754 ZSWIM7\n",
+      "13755 ENSG00000258472\n",
+      "13756 TLCD1\n",
+      "13757 ENSG00000265791\n",
+      "13758 ENSG00000287644\n",
+      "13759 SMARCE1\n",
+      "13760 KLHL11\n",
+      "13761 HSD17B1\n",
+      "13762 RAMP2\n",
+      "13763 IFI35\n",
+      "13764 ENSG00000282199\n",
+      "13765 NMT1\n",
+      "13766 ENSG00000267198\n",
+      "13767 NFE2L1\n",
+      "13768 SEPTIN4\n",
+      "13769 UNK\n",
+      "13770 UNC13D\n",
+      "13771 PGS1\n",
+      "13772 AATK\n",
+      "13773 DLGAP1-AS1\n",
+      "13774 RIOK3\n",
+      "13775 ZSCAN30\n",
+      "13776 PARD6G\n",
+      "13777 ADAMTSL5\n",
+      "13778 ABHD17A\n",
+      "13779 CSNK1G2-AS1\n",
+      "13780 LSM7\n",
+      "13781 MAP2K2\n",
+      "13782 ELAVL1\n",
+      "13783 HOOK2\n",
+      "13784 GADD45GIP1\n",
+      "13785 ADGRL1\n",
+      "13786 SLC35E1\n",
+      "13787 ENSG00000259242\n",
+      "13788 ENSG00000268362\n",
+      "13789 CCNE1\n",
+      "13790 FAAP24\n",
+      "13791 ZFP82\n",
+      "13792 ZNF566\n",
+      "13793 ZNF529-AS1\n",
+      "13794 ENSG00000267672\n",
+      "13795 PSMD8\n",
+      "13796 DLL3\n",
+      "13797 GSK3A\n",
+      "13798 PHLDB3\n",
+      "13799 VASP\n",
+      "13800 OPA3\n",
+      "13801 EML2\n",
+      "13802 AP2A1\n",
+      "13803 VSIG10L\n",
+      "13804 SPACA6-AS1\n",
+      "13805 ZNF468\n",
+      "13806 NLRP12\n",
+      "13807 RPL28\n",
+      "13808 C19orf18\n",
+      "13809 SIGLEC1\n",
+      "13810 MAVS\n",
+      "13811 CST3\n",
+      "13812 ENSG00000277938\n",
+      "13813 PABPC1L\n",
+      "13814 ZSWIM3\n",
+      "13815 ENSG00000278231\n",
+      "13816 PEDS1\n",
+      "13817 PFDN4\n",
+      "13818 FAM210B\n",
+      "13819 ENSG00000275993\n",
+      "13820 MIR548XHG\n",
+      "13821 ENSG00000232855\n",
+      "13822 ENSG00000273271\n",
+      "13823 SYNJ1\n",
+      "13824 ENSG00000177692\n",
+      "13825 ETS2\n",
+      "13826 ICOSLG\n",
+      "13827 AIRE\n",
+      "13828 HDHD5\n",
+      "13829 C22orf39\n",
+      "13830 SLC2A11\n",
+      "13831 GGT1\n",
+      "13832 LINC01422\n",
+      "13833 PRR14L\n",
+      "13834 MCM5\n",
+      "13835 DNAL4\n",
+      "13836 SAMM50\n",
+      "13837 DENND6B\n",
+      "13838 CD99\n",
+      "13839 DYNLT3\n",
+      "13840 ELK1\n",
+      "13841 PQBP1\n",
+      "13842 PPP1R3F\n",
+      "13843 ERCC6L\n",
+      "13844 HMGN5\n",
+      "13845 SYTL4\n",
+      "13846 ENSG00000286163\n",
+      "13847 MAP7D3\n",
+      "13848 HES4\n",
+      "13849 ATAD3B\n",
+      "13850 TNFRSF14-AS1\n",
+      "13851 UBE4B\n",
+      "13852 FBXO6\n",
+      "13853 EMC1-AS1\n",
+      "13854 FAM76A\n",
+      "13855 PEF1\n",
+      "13856 TMEM39B\n",
+      "13857 BMP8B\n",
+      "13858 EXO5\n",
+      "13859 YBX1\n",
+      "13860 HYI\n",
+      "13861 KIF2C\n",
+      "13862 DYNLT4\n",
+      "13863 ZYG11A\n",
+      "13864 NDC1\n",
+      "13865 USP24\n",
+      "13866 LINC01135\n",
+      "13867 ENSG00000269933\n",
+      "13868 EXTL2\n",
+      "13869 S1PR1-DT\n",
+      "13870 ELAPOR1\n",
+      "13871 LAMTOR5\n",
+      "13872 DRAM2\n",
+      "13873 PTPN22\n",
+      "13874 ENSG00000256374\n",
+      "13875 ENSG00000287978\n",
+      "13876 LINC00624\n",
+      "13877 H2AC21\n",
+      "13878 ARHGEF2-AS2\n",
+      "13879 CCT3\n",
+      "13880 VSIG8\n",
+      "13881 TMCO1\n",
+      "13882 XCL1\n",
+      "13883 FAM20B\n",
+      "13884 NIBAN1\n",
+      "13885 RO60\n",
+      "13886 COA6-AS1\n",
+      "13887 WDR64\n",
+      "13888 CNST\n",
+      "13889 ENSG00000287116\n",
+      "13890 ENSG00000203635\n",
+      "13891 TTC32\n",
+      "13892 WDR35\n",
+      "13893 DNMT3A\n",
+      "13894 GTF3C2-AS1\n",
+      "13895 SNX17\n",
+      "13896 CCDC121\n",
+      "13897 ENSG00000273165\n",
+      "13898 SOS1\n",
+      "13899 EPAS1\n",
+      "13900 FBXO11\n",
+      "13901 EGR4\n",
+      "13902 IGKV3-11\n",
+      "13903 IGKV1D-8\n",
+      "13904 DHRS9\n",
+      "13905 SLC25A12\n",
+      "13906 ENSG00000226963\n",
+      "13907 PRKRA\n",
+      "13908 BZW1\n",
+      "13909 NDUFB3\n",
+      "13910 RETREG2\n",
+      "13911 AP1S3\n",
+      "13912 ENSG00000225963\n",
+      "13913 ARMC9\n",
+      "13914 STK25\n",
+      "13915 XPC\n",
+      "13916 SUSD5\n",
+      "13917 ZBTB47\n",
+      "13918 ZNF852\n",
+      "13919 ELP6\n",
+      "13920 USP4\n",
+      "13921 CDHR4\n",
+      "13922 NAA80\n",
+      "13923 NISCH\n",
+      "13924 ENSG00000287487\n",
+      "13925 HGD\n",
+      "13926 GTF2E1\n",
+      "13927 ANAPC13\n",
+      "13928 CPB1\n",
+      "13929 SERP1\n",
+      "13930 MBNL1-AS1\n",
+      "13931 ENSG00000286585\n",
+      "13932 IL12A-AS1\n",
+      "13933 TM4SF19-AS1\n",
+      "13934 LINC02012\n",
+      "13935 TBC1D1\n",
+      "13936 ENSG00000272936\n",
+      "13937 MTHFD2L\n",
+      "13938 CDKL2\n",
+      "13939 TET2\n",
+      "13940 LEF1\n",
+      "13941 HHIP-AS1\n",
+      "13942 LRBA\n",
+      "13943 ETFDH\n",
+      "13944 SAP30-DT\n",
+      "13945 ENPP6\n",
+      "13946 CFAP97\n",
+      "13947 PLEKHG4B\n",
+      "13948 ZFR\n",
+      "13949 ENSG00000250697\n",
+      "13950 IL7R\n",
+      "13951 SLC1A3\n",
+      "13952 ENSG00000286656\n",
+      "13953 ENSG00000272308\n",
+      "13954 CARTPT\n",
+      "13955 MSH3\n",
+      "13956 ENSG00000247121\n",
+      "13957 FAM174A\n",
+      "13958 SLC12A2-DT\n",
+      "13959 MINAR2\n",
+      "13960 CATSPER3\n",
+      "13961 ENSG00000270021\n",
+      "13962 UBE2D2\n",
+      "13963 TTC1\n",
+      "13964 CLTB\n",
+      "13965 RNF44\n",
+      "13966 FAM193B-DT\n",
+      "13967 TRIM7-AS2\n",
+      "13968 TRIM7\n",
+      "13969 ENSG00000284607\n",
+      "13970 GTF2H4\n",
+      "13971 ENSG00000225339\n",
+      "13972 TREM2\n",
+      "13973 DST\n",
+      "13974 ENSG00000271761\n",
+      "13975 ADGRB3-DT\n",
+      "13976 RNGTT\n",
+      "13977 PNRC1\n",
+      "13978 GPR63\n",
+      "13979 SOGA3\n",
+      "13980 ENSG00000224478\n",
+      "13981 EIF3B\n",
+      "13982 ENSG00000272361\n",
+      "13983 TOMM7\n",
+      "13984 ENSG00000230751\n",
+      "13985 ENSG00000287584\n",
+      "13986 LINC00174\n",
+      "13987 MTERF1\n",
+      "13988 STAG3\n",
+      "13989 SRRT\n",
+      "13990 RASA4B\n",
+      "13991 UBE2H\n",
+      "13992 WDR91\n",
+      "13993 TRBV5-1\n",
+      "13994 TRBV11-2\n",
+      "13995 TMEM176A\n",
+      "13996 FASTK\n",
+      "13997 ENSG00000254556\n",
+      "13998 DMTN\n",
+      "13999 TRIM35\n",
+      "14000 CCDC25\n",
+      "14001 PBK\n",
+      "14002 DDHD2\n",
+      "14003 PLEKHA2\n",
+      "14004 RB1CC1\n",
+      "14005 MCMDC2\n",
+      "14006 PPP1R42\n",
+      "14007 ENSG00000254162\n",
+      "14008 MTERF3\n",
+      "14009 PTDSS1\n",
+      "14010 GRHL2\n",
+      "14011 UBR5-DT\n",
+      "14012 ENSG00000254263\n",
+      "14013 ENSG00000254028\n",
+      "14014 SCRIB\n",
+      "14015 BOP1\n",
+      "14016 ZNG1A\n",
+      "14017 ENSG00000272871\n",
+      "14018 NOL6\n",
+      "14019 EBLN3P\n",
+      "14020 ANKRD18A\n",
+      "14021 ENSG00000165072\n",
+      "14022 TLE1\n",
+      "14023 TRMO\n",
+      "14024 ENSG00000236896\n",
+      "14025 MRPL50\n",
+      "14026 HSDL2\n",
+      "14027 ZNF618\n",
+      "14028 HMGA1P4\n",
+      "14029 PKN3\n",
+      "14030 LINC02913\n",
+      "14031 ENSG00000270755\n",
+      "14032 DBH-AS1\n",
+      "14033 INPP5E\n",
+      "14034 ECHDC3\n",
+      "14035 VSTM4\n",
+      "14036 CCAR1\n",
+      "14037 HECTD2\n",
+      "14038 PLCE1\n",
+      "14039 NOC3L\n",
+      "14040 PRDX3\n",
+      "14041 ENSG00000273891\n",
+      "14042 DENND2B\n",
+      "14043 NRIP3\n",
+      "14044 CAT\n",
+      "14045 RAG1\n",
+      "14046 TMEM179B\n",
+      "14047 PLAAT4\n",
+      "14048 P2RY6\n",
+      "14049 LIPT2\n",
+      "14050 NEU3\n",
+      "14051 ENSG00000236304\n",
+      "14052 CREBZF\n",
+      "14053 BACE1-AS\n",
+      "14054 USP2\n",
+      "14055 ENSG00000254561\n",
+      "14056 OAF\n",
+      "14057 ENSG00000255537\n",
+      "14058 RAD51AP1\n",
+      "14059 FAM66C\n",
+      "14060 KLRG1\n",
+      "14061 LOH12CR2\n",
+      "14062 ETNK1-DT\n",
+      "14063 REP15\n",
+      "14064 RPAP3-DT\n",
+      "14065 CCNT1\n",
+      "14066 ENSG00000270175\n",
+      "14067 TARBP2\n",
+      "14068 GDF11\n",
+      "14069 CDK2\n",
+      "14070 STAT2\n",
+      "14071 RAP1B\n",
+      "14072 PRANCR\n",
+      "14073 TBC1D15\n",
+      "14074 ATXN7L3B\n",
+      "14075 ENSG00000274021\n",
+      "14076 LINC02391\n",
+      "14077 PLEKHG7\n",
+      "14078 UBE2N\n",
+      "14079 VEZT\n",
+      "14080 ISCU\n",
+      "14081 SELPLG\n",
+      "14082 PPTC7\n",
+      "14083 ATXN2\n",
+      "14084 PRKAB1\n",
+      "14085 ABHD13\n",
+      "14086 TRAV2\n",
+      "14087 PPP1R3E\n",
+      "14088 NFKBIA\n",
+      "14089 MDGA2\n",
+      "14090 ENSG00000269906\n",
+      "14091 NIN\n",
+      "14092 CCDC198\n",
+      "14093 ENSG00000258875\n",
+      "14094 CCDC88C\n",
+      "14095 DICER1-AS1\n",
+      "14096 BCL11B\n",
+      "14097 ENSG00000258593\n",
+      "14098 IGHG3\n",
+      "14099 ATP10A\n",
+      "14100 FMN1\n",
+      "14101 LINC02345\n",
+      "14102 CHST14\n",
+      "14103 TYRO3\n",
+      "14104 CAPN3\n",
+      "14105 HYPK\n",
+      "14106 CCPG1\n",
+      "14107 PIAS1\n",
+      "14108 NPTN-IT1\n",
+      "14109 ARID3B\n",
+      "14110 TMED3\n",
+      "14111 ENSG00000259175\n",
+      "14112 ENSG00000277782\n",
+      "14113 ZSCAN2-AS1\n",
+      "14114 AGBL1\n",
+      "14115 DET1\n",
+      "14116 MFGE8\n",
+      "14117 ARPIN\n",
+      "14118 CHTF18\n",
+      "14119 IFT140\n",
+      "14120 ENSG00000167962\n",
+      "14121 HAPSTR1\n",
+      "14122 METTL9\n",
+      "14123 IL21R-AS1\n",
+      "14124 ENSG00000260744\n",
+      "14125 CNEP1R1\n",
+      "14126 CNGB1\n",
+      "14127 ZNF821\n",
+      "14128 HP\n",
+      "14129 HSBP1\n",
+      "14130 GLTPD2\n",
+      "14131 GP1BA\n",
+      "14132 ATP1B2\n",
+      "14133 WRAP53\n",
+      "14134 FBXW10B\n",
+      "14135 TRIM16\n",
+      "14136 ENSG00000197815\n",
+      "14137 SSH2\n",
+      "14138 UTP6\n",
+      "14139 MED24\n",
+      "14140 ENSG00000285486\n",
+      "14141 OSBPL7\n",
+      "14142 MED13\n",
+      "14143 PRR29\n",
+      "14144 HELZ-AS1\n",
+      "14145 RHBDF2\n",
+      "14146 GAA\n",
+      "14147 TSPAN10\n",
+      "14148 ENSG00000272688\n",
+      "14149 TTR\n",
+      "14150 GALNT1\n",
+      "14151 ENSG00000278674\n",
+      "14152 ENSG00000267764\n",
+      "14153 SERPINB5\n",
+      "14154 ENSG00000268798\n",
+      "14155 TCF3\n",
+      "14156 SH3GL1\n",
+      "14157 SHFL\n",
+      "14158 GET3\n",
+      "14159 ASF1B\n",
+      "14160 CYP4F8\n",
+      "14161 UCA1\n",
+      "14162 HSH2D\n",
+      "14163 ENSG00000269399\n",
+      "14164 COLGALT1\n",
+      "14165 CLIP3\n",
+      "14166 ZNF585B\n",
+      "14167 ZNF569\n",
+      "14168 ENSG00000180458\n",
+      "14169 HNRNPL\n",
+      "14170 LGALS14\n",
+      "14171 AXL\n",
+      "14172 MYPOP\n",
+      "14173 MEIS3\n",
+      "14174 KDELR1\n",
+      "14175 HSD17B14\n",
+      "14176 NAPSA\n",
+      "14177 NKG7\n",
+      "14178 ENSG00000286125\n",
+      "14179 ZNF776\n",
+      "14180 FAM110A\n",
+      "14181 POFUT1\n",
+      "14182 ACSS2\n",
+      "14183 DLGAP4\n",
+      "14184 TTI1\n",
+      "14185 ENSG00000225806\n",
+      "14186 EDN3\n",
+      "14187 LINC00659\n",
+      "14188 HAR1A\n",
+      "14189 ENSG00000280071\n",
+      "14190 TTC3\n",
+      "14191 LINC01547\n",
+      "14192 LINC01694\n",
+      "14193 DIP2A\n",
+      "14194 IGLV4-69\n",
+      "14195 CRYBB1\n",
+      "14196 SEC14L4\n",
+      "14197 CBY1\n",
+      "14198 HDAC10\n",
+      "14199 DHRSX\n",
+      "14200 EFHC2\n",
+      "14201 CFP\n",
+      "14202 FOXO4\n",
+      "14203 RTL5\n",
+      "14204 JPX\n",
+      "14205 MAGEE1\n",
+      "14206 MAGT1\n",
+      "14207 TCEAL1\n",
+      "14208 TMSB15C\n",
+      "14209 RNF113A\n",
+      "14210 HPRT1\n",
+      "14211 PRRG3\n",
+      "14212 CETN2\n",
+      "14213 UTS2\n",
+      "14214 NMNAT1\n",
+      "14215 ENSG00000285646\n",
+      "14216 FBLIM1\n",
+      "14217 PADI6\n",
+      "14218 AUNIP\n",
+      "14219 DHDDS\n",
+      "14220 ENSG00000228634\n",
+      "14221 ADPRS\n",
+      "14222 POU3F1\n",
+      "14223 TRIT1\n",
+      "14224 KDM4A\n",
+      "14225 IPO13\n",
+      "14226 RPS8\n",
+      "14227 FAF1\n",
+      "14228 ORC1\n",
+      "14229 MRPL37\n",
+      "14230 IFI44L\n",
+      "14231 MCOLN2\n",
+      "14232 SETSIP\n",
+      "14233 ENSG00000237480\n",
+      "14234 SLC25A24\n",
+      "14235 CAPZA1\n",
+      "14236 PMVK\n",
+      "14237 DPM3\n",
+      "14238 CRP\n",
+      "14239 ODR4\n",
+      "14240 ELF3\n",
+      "14241 CR1\n",
+      "14242 SLC30A1\n",
+      "14243 HLX\n",
+      "14244 GUK1\n",
+      "14245 URB2\n",
+      "14246 LINC02897\n",
+      "14247 NBAS\n",
+      "14248 SDC1\n",
+      "14249 MFSD2B\n",
+      "14250 ASXL2\n",
+      "14251 KHK\n",
+      "14252 MTA3\n",
+      "14253 MTIF2\n",
+      "14254 ENSG00000236498\n",
+      "14255 ARHGAP25\n",
+      "14256 FBXO41\n",
+      "14257 SLC4A5\n",
+      "14258 KANSL3\n",
+      "14259 ENSG00000270190\n",
+      "14260 RGPD8\n",
+      "14261 SLC35F5\n",
+      "14262 ENSG00000260059\n",
+      "14263 KCNJ3\n",
+      "14264 PKP4\n",
+      "14265 CFAP210\n",
+      "14266 ADAM23\n",
+      "14267 CREB1\n",
+      "14268 NHEJ1\n",
+      "14269 SERPINE2\n",
+      "14270 SLC16A14\n",
+      "14271 PPP1R7\n",
+      "14272 EGOT\n",
+      "14273 BRPF1\n",
+      "14274 VHL\n",
+      "14275 EAF1\n",
+      "14276 UBP1\n",
+      "14277 CCDC13\n",
+      "14278 HEMK1\n",
+      "14279 RRP9\n",
+      "14280 DENND6A-DT\n",
+      "14281 LRIG1\n",
+      "14282 CGGBP1\n",
+      "14283 NIT2\n",
+      "14284 LINC02042\n",
+      "14285 NEPRO-AS1\n",
+      "14286 LSAMP\n",
+      "14287 WDR5B-DT\n",
+      "14288 CHCHD6\n",
+      "14289 PLSCR1\n",
+      "14290 PTX3\n",
+      "14291 KCNMB3\n",
+      "14292 MFN1\n",
+      "14293 ENSG00000270178\n",
+      "14294 KLHL6\n",
+      "14295 ABCC5\n",
+      "14296 ECE2\n",
+      "14297 LSG1\n",
+      "14298 NCBP2\n",
+      "14299 SLBP\n",
+      "14300 POLN\n",
+      "14301 EVC2\n",
+      "14302 MAN2B2\n",
+      "14303 MED28\n",
+      "14304 KCNIP4\n",
+      "14305 ADGRA3\n",
+      "14306 KLF3\n",
+      "14307 TLR10\n",
+      "14308 ENSG00000287382\n",
+      "14309 PPAT\n",
+      "14310 NOA1\n",
+      "14311 POLR2B\n",
+      "14312 COX18\n",
+      "14313 KLHL8\n",
+      "14314 PGRMC2\n",
+      "14315 NAA15\n",
+      "14316 FGG\n",
+      "14317 PDLIM3\n",
+      "14318 FRG1\n",
+      "14319 TTC33\n",
+      "14320 RPL37\n",
+      "14321 ENSG00000249492\n",
+      "14322 ENSG00000251044\n",
+      "14323 S100Z\n",
+      "14324 MBLAC2\n",
+      "14325 CCDC112\n",
+      "14326 PPIC\n",
+      "14327 KIF20A\n",
+      "14328 TCERG1\n",
+      "14329 JAKMIP2-AS1\n",
+      "14330 STK10\n",
+      "14331 ENSG00000249684\n",
+      "14332 FAM153CP\n",
+      "14333 CTC-338M12.4\n",
+      "14334 H2AC4\n",
+      "14335 ENSG00000272468\n",
+      "14336 H2AC12\n",
+      "14337 ENSG00000286652\n",
+      "14338 ZSCAN16-AS1\n",
+      "14339 MPIG6B\n",
+      "14340 BAK1\n",
+      "14341 TREM1\n",
+      "14342 BICRAL\n",
+      "14343 ENSG00000271857\n",
+      "14344 ENPP4\n",
+      "14345 FILIP1\n",
+      "14346 ORC3\n",
+      "14347 AKIRIN2\n",
+      "14348 NDUFAF4\n",
+      "14349 AK9\n",
+      "14350 ARG1\n",
+      "14351 PLAGL1\n",
+      "14352 EPM2A\n",
+      "14353 ULBP1\n",
+      "14354 ENSG00000285492\n",
+      "14355 ACAT2\n",
+      "14356 MAS1\n",
+      "14357 ENSG00000242474\n",
+      "14358 ZNF316\n",
+      "14359 HOXA10\n",
+      "14360 LINC01176\n",
+      "14361 PPP1R17\n",
+      "14362 MATCAP2\n",
+      "14363 TRGV2\n",
+      "14364 CAMK2B\n",
+      "14365 C7orf57\n",
+      "14366 IKZF1\n",
+      "14367 TMEM248\n",
+      "14368 BAZ1B\n",
+      "14369 MDH2\n",
+      "14370 SAMD9\n",
+      "14371 CYP3A5\n",
+      "14372 ENSG00000272918\n",
+      "14373 SRPK2\n",
+      "14374 RINT1\n",
+      "14375 CDHR3\n",
+      "14376 ARF5\n",
+      "14377 MTPN\n",
+      "14378 LINC00996\n",
+      "14379 AGAP3\n",
+      "14380 ENSG00000254319\n",
+      "14381 PPP3CC\n",
+      "14382 R3HCC1\n",
+      "14383 NUGGC\n",
+      "14384 KIF13B\n",
+      "14385 SARAF\n",
+      "14386 MCM4\n",
+      "14387 MRPL15\n",
+      "14388 SNHG6\n",
+      "14389 RPL7\n",
+      "14390 ENSG00000253706\n",
+      "14391 SPAG1\n",
+      "14392 SYBU\n",
+      "14393 ENSG00000286535\n",
+      "14394 PEG13\n",
+      "14395 PYCR3\n",
+      "14396 OMD\n",
+      "14397 SLC25A25\n",
+      "14398 GOLGA2\n",
+      "14399 EXOSC2\n",
+      "14400 POMT1\n",
+      "14401 PIERCE1\n",
+      "14402 DNLZ\n",
+      "14403 MRPL41\n",
+      "14404 PITRM1\n",
+      "14405 GDI2\n",
+      "14406 ENSG00000230322\n",
+      "14407 SKIDA1\n",
+      "14408 ENSG00000273038\n",
+      "14409 WASHC2C\n",
+      "14410 TIMM23B\n",
+      "14411 A1CF\n",
+      "14412 CISD1\n",
+      "14413 NEUROG3\n",
+      "14414 PCGF5\n",
+      "14415 LCOR\n",
+      "14416 PYROXD2\n",
+      "14417 SCD\n",
+      "14418 BORCS7\n",
+      "14419 PDCD4-AS1\n",
+      "14420 GPAM\n",
+      "14421 VTI1A\n",
+      "14422 PTDSS2\n",
+      "14423 PHLDA2\n",
+      "14424 ARFIP2\n",
+      "14425 TUB\n",
+      "14426 PARVA\n",
+      "14427 ACCS\n",
+      "14428 NDUFS3\n",
+      "14429 MS4A6A\n",
+      "14430 PGA5\n",
+      "14431 SNX15\n",
+      "14432 CAPN1\n",
+      "14433 DRAP1\n",
+      "14434 POLD4\n",
+      "14435 FOLR3\n",
+      "14436 RAB6A\n",
+      "14437 ENSG00000287425\n",
+      "14438 NARS2\n",
+      "14439 TENM4\n",
+      "14440 CCDC90B\n",
+      "14441 MMP8\n",
+      "14442 C11orf71\n",
+      "14443 SLC37A4\n",
+      "14444 PUS3\n",
+      "14445 NTM\n",
+      "14446 COPS7A\n",
+      "14447 USP5\n",
+      "14448 NECAP1\n",
+      "14449 A2ML1\n",
+      "14450 MAGOHB\n",
+      "14451 KRAS\n",
+      "14452 ENSG00000275476\n",
+      "14453 DDX11\n",
+      "14454 KIF21A\n",
+      "14455 SLC38A1\n",
+      "14456 ENSG00000258181\n",
+      "14457 TUBA1B\n",
+      "14458 TMT1B\n",
+      "14459 RDH16\n",
+      "14460 DTX3\n",
+      "14461 AGAP2-AS1\n",
+      "14462 PPM1H\n",
+      "14463 IFNG-AS1\n",
+      "14464 PTPRB\n",
+      "14465 AMDHD1\n",
+      "14466 NEDD1\n",
+      "14467 PRDM4-AS1\n",
+      "14468 CMKLR1\n",
+      "14469 FAM222A\n",
+      "14470 BRAP\n",
+      "14471 TRIAP1\n",
+      "14472 PSPC1-AS2\n",
+      "14473 MRPL57\n",
+      "14474 RCBTB2\n",
+      "14475 ENSG00000276436\n",
+      "14476 ENSG00000286416\n",
+      "14477 NALF1\n",
+      "14478 MYO16\n",
+      "14479 ENSG00000258768\n",
+      "14480 PSME2\n",
+      "14481 IPO4\n",
+      "14482 GMFB\n",
+      "14483 PPM1A\n",
+      "14484 ENSG00000272909\n",
+      "14485 MED6\n",
+      "14486 EIF2B2\n",
+      "14487 ENSG00000259138\n",
+      "14488 ENSG00000258752\n",
+      "14489 GSC\n",
+      "14490 TNFAIP2\n",
+      "14491 AKT1\n",
+      "14492 IGHG2\n",
+      "14493 KNSTRN\n",
+      "14494 RPAP1\n",
+      "14495 MGA\n",
+      "14496 SLC27A2\n",
+      "14497 SPPL2A\n",
+      "14498 DPP8\n",
+      "14499 ENSG00000259442\n",
+      "14500 FES\n",
+      "14501 SPATA41\n",
+      "14502 ENSG00000260316\n",
+      "14503 EME2\n",
+      "14504 E4F1\n",
+      "14505 SRRM2-AS1\n",
+      "14506 ZNF213\n",
+      "14507 ZNF263\n",
+      "14508 CLUAP1\n",
+      "14509 USP7\n",
+      "14510 LINC02177\n",
+      "14511 NUBP1\n",
+      "14512 TXNDC11\n",
+      "14513 FBRS\n",
+      "14514 ENSG00000261840\n",
+      "14515 ENSG00000260060\n",
+      "14516 ITGAM\n",
+      "14517 TENT4B\n",
+      "14518 BRD7\n",
+      "14519 MATCAP1\n",
+      "14520 GFOD2\n",
+      "14521 PSMD7-DT\n",
+      "14522 RFLNB\n",
+      "14523 ENSG00000262533\n",
+      "14524 TRPV3\n",
+      "14525 SHPK\n",
+      "14526 GABARAP\n",
+      "14527 CDRT4\n",
+      "14528 MIEF2\n",
+      "14529 ENSG00000264577\n",
+      "14530 MYO18A\n",
+      "14531 CORO6\n",
+      "14532 OMG\n",
+      "14533 CDK5R1\n",
+      "14534 IGFBP4\n",
+      "14535 HCRT\n",
+      "14536 RPL27\n",
+      "14537 NBR2\n",
+      "14538 ASB16-AS1\n",
+      "14539 HOXB2\n",
+      "14540 HOXB4\n",
+      "14541 B4GALNT2\n",
+      "14542 YPEL2\n",
+      "14543 CEP112\n",
+      "14544 ENSG00000274561\n",
+      "14545 SLC16A5\n",
+      "14546 ENSG00000262833\n",
+      "14547 FAAP100\n",
+      "14548 CCDC57\n",
+      "14549 THOC1-DT\n",
+      "14550 PPP4R1\n",
+      "14551 CEP76\n",
+      "14552 NPC1\n",
+      "14553 AQP4-AS1\n",
+      "14554 ARK2N\n",
+      "14555 ARK2C\n",
+      "14556 ALPK2\n",
+      "14557 GTSCR1\n",
+      "14558 ECSIT\n",
+      "14559 LINC01841\n",
+      "14560 LINC01842\n",
+      "14561 RASAL3\n",
+      "14562 ENSG00000269044\n",
+      "14563 ENSG00000268056\n",
+      "14564 NPHS1\n",
+      "14565 ZNF345\n",
+      "14566 FBL\n",
+      "14567 FCGBP\n",
+      "14568 MIA\n",
+      "14569 PLAUR\n",
+      "14570 APOE\n",
+      "14571 CLASRP\n",
+      "14572 NPAS1\n",
+      "14573 NOSIP\n",
+      "14574 SIGLEC12\n",
+      "14575 TMC4\n",
+      "14576 LILRB1\n",
+      "14577 ENSG00000286546\n",
+      "14578 NINL\n",
+      "14579 COX4I2\n",
+      "14580 KIF3B\n",
+      "14581 PXMP4\n",
+      "14582 RBL1\n",
+      "14583 ENSG00000232043\n",
+      "14584 RIPOR3\n",
+      "14585 ENSG00000280164\n",
+      "14586 ENSG00000280018\n",
+      "14587 ENSG00000278932\n",
+      "14588 TCP10L\n",
+      "14589 MX2\n",
+      "14590 IGLC7\n",
+      "14591 ENSG00000181123\n",
+      "14592 RNF185\n",
+      "14593 DEPDC5\n",
+      "14594 APOBEC3B\n",
+      "14595 FAM118A\n",
+      "14596 CPT1B\n",
+      "14597 ENSG00000269902\n",
+      "14598 MED12\n",
+      "14599 ZMYM3\n",
+      "14600 ARMCX3\n",
+      "14601 SH2D1A\n",
+      "14602 MAMLD1\n",
+      "14603 CCNQ\n",
+      "14604 CMC4\n",
+      "14605 ENSG00000274791\n",
+      "14606 GNB1-DT\n",
+      "14607 PIK3CD\n",
+      "14608 MAD2L2\n",
+      "14609 ENSG00000288398\n",
+      "14610 TMCO4\n",
+      "14611 EIF4G3\n",
+      "14612 ZC3H12A-DT\n",
+      "14613 PPIE\n",
+      "14614 LINC01144\n",
+      "14615 FGGY\n",
+      "14616 LRRC7\n",
+      "14617 GNG5\n",
+      "14618 AHCYL1\n",
+      "14619 ATP5PB\n",
+      "14620 LINC01356\n",
+      "14621 ATP1A1\n",
+      "14622 FAM72C\n",
+      "14623 ENSG00000287190\n",
+      "14624 AQP10\n",
+      "14625 ADAR\n",
+      "14626 EFNA1\n",
+      "14627 GLMP\n",
+      "14628 XPR1\n",
+      "14629 ARPC5\n",
+      "14630 PHLDA3\n",
+      "14631 CYB5R1\n",
+      "14632 NFASC\n",
+      "14633 CR2\n",
+      "14634 CR1L\n",
+      "14635 LAMB3\n",
+      "14636 OBSCN-AS1\n",
+      "14637 ID2\n",
+      "14638 YWHAQ\n",
+      "14639 GDF7\n",
+      "14640 ENSG00000223522\n",
+      "14641 NLRC4\n",
+      "14642 ENSG00000225187\n",
+      "14643 EPCAM-DT\n",
+      "14644 LINC02576\n",
+      "14645 CLEC4F\n",
+      "14646 SFXN5\n",
+      "14647 PCGF1\n",
+      "14648 IGKV2D-28\n",
+      "14649 MRPL30\n",
+      "14650 MFSD9\n",
+      "14651 LINC01102\n",
+      "14652 SULT1C2\n",
+      "14653 PTPN18\n",
+      "14654 C2orf27A\n",
+      "14655 ZRANB3\n",
+      "14656 GTDC1\n",
+      "14657 ENSG00000271320\n",
+      "14658 BAZ2B-AS1\n",
+      "14659 IFT70B\n",
+      "14660 UNC80\n",
+      "14661 RPL37A-DT\n",
+      "14662 ENSG00000259855\n",
+      "14663 BHLHE40-AS1\n",
+      "14664 SRGAP3\n",
+      "14665 NR2C2\n",
+      "14666 OSBPL10\n",
+      "14667 POMGNT2\n",
+      "14668 CCR9\n",
+      "14669 TRAIP\n",
+      "14670 CADPS\n",
+      "14671 SHQ1\n",
+      "14672 EBLN2\n",
+      "14673 NSUN3\n",
+      "14674 IFT57\n",
+      "14675 HHLA2\n",
+      "14676 DPPA4\n",
+      "14677 NEPRO\n",
+      "14678 NDUFB4\n",
+      "14679 EFCC1\n",
+      "14680 FNDC3B\n",
+      "14681 ENSG00000242539\n",
+      "14682 ENSG00000244459\n",
+      "14683 CC2D2A\n",
+      "14684 SEPSECS-AS1\n",
+      "14685 CEP135\n",
+      "14686 PLA2G12A\n",
+      "14687 GAR1\n",
+      "14688 C4orf3\n",
+      "14689 GATB\n",
+      "14690 FHDC1\n",
+      "14691 TMEM192\n",
+      "14692 SNX25\n",
+      "14693 DROSHA\n",
+      "14694 PDZD2\n",
+      "14695 NADK2\n",
+      "14696 ANXA2R-AS1\n",
+      "14697 NNT\n",
+      "14698 ARL15\n",
+      "14699 RAB3C\n",
+      "14700 ZBED3-AS1\n",
+      "14701 TMEM167A\n",
+      "14702 LINC01554\n",
+      "14703 FAM174A-DT\n",
+      "14704 FBXL17\n",
+      "14705 SLC25A46\n",
+      "14706 FEM1C\n",
+      "14707 ALDH7A1\n",
+      "14708 GRPEL2\n",
+      "14709 IRGM\n",
+      "14710 ZNF300\n",
+      "14711 G3BP1\n",
+      "14712 MED7\n",
+      "14713 ZNF354B\n",
+      "14714 MRNIP-DT\n",
+      "14715 ATXN1-AS1\n",
+      "14716 H4C2\n",
+      "14717 H2AC13\n",
+      "14718 MICB-DT\n",
+      "14719 EHMT2\n",
+      "14720 ENSG00000285976\n",
+      "14721 UBE2J1\n",
+      "14722 ENSG00000287789\n",
+      "14723 MAP3K7\n",
+      "14724 ATG5\n",
+      "14725 TRAF3IP2-AS1\n",
+      "14726 ASF1A\n",
+      "14727 PDE7B\n",
+      "14728 ENSG00000285875\n",
+      "14729 SF3B5\n",
+      "14730 SYNJ2\n",
+      "14731 ENSG00000261003\n",
+      "14732 PDGFA\n",
+      "14733 GPR146\n",
+      "14734 VWDE\n",
+      "14735 AMPH\n",
+      "14736 CDK13\n",
+      "14737 LINC00957\n",
+      "14738 TBRG4\n",
+      "14739 EGFR\n",
+      "14740 VKORC1L1\n",
+      "14741 POR\n",
+      "14742 CLDN12\n",
+      "14743 CYP51A1\n",
+      "14744 SLC12A9-AS1\n",
+      "14745 POLR2J2\n",
+      "14746 PUS7\n",
+      "14747 GPR22\n",
+      "14748 CFTR\n",
+      "14749 AHCYL2\n",
+      "14750 CPA1\n",
+      "14751 WEE2-AS1\n",
+      "14752 TRBV2\n",
+      "14753 MFHAS1\n",
+      "14754 ZDHHC2\n",
+      "14755 PCM1\n",
+      "14756 SORBS3\n",
+      "14757 ENSG00000253200\n",
+      "14758 INTS9\n",
+      "14759 ADAM32\n",
+      "14760 CHD7\n",
+      "14761 ASPH\n",
+      "14762 ENSG00000253190\n",
+      "14763 RRS1\n",
+      "14764 HNF4G\n",
+      "14765 RIPK2-DT\n",
+      "14766 TSPYL5\n",
+      "14767 EIF3E\n",
+      "14768 PVT1\n",
+      "14769 AGO2\n",
+      "14770 RHPN1-AS1\n",
+      "14771 MAF1\n",
+      "14772 PLPP6\n",
+      "14773 JAK2\n",
+      "14774 ENSG00000170152\n",
+      "14775 SEMA4D\n",
+      "14776 PHF2\n",
+      "14777 PRXL2C\n",
+      "14778 NIPSNAP3A\n",
+      "14779 TXN\n",
+      "14780 ENSG00000285447\n",
+      "14781 ORM2\n",
+      "14782 FBXW2\n",
+      "14783 ENTR1\n",
+      "14784 ENSG00000228302\n",
+      "14785 BAMBI\n",
+      "14786 MTPAP\n",
+      "14787 ZNF33A\n",
+      "14788 UBE2D1\n",
+      "14789 MCU\n",
+      "14790 CAMK2G\n",
+      "14791 SFTPD\n",
+      "14792 TMEM254\n",
+      "14793 ENSG00000272631\n",
+      "14794 FFAR4\n",
+      "14795 HIF1AN\n",
+      "14796 ZDHHC6\n",
+      "14797 BUB3\n",
+      "14798 OAT\n",
+      "14799 NLRP6\n",
+      "14800 CARS1\n",
+      "14801 FHIP1B\n",
+      "14802 ARL14EP\n",
+      "14803 C11orf96\n",
+      "14804 MS4A4A\n",
+      "14805 CSKMT\n",
+      "14806 MACROD1\n",
+      "14807 KAT5\n",
+      "14808 RAB1B\n",
+      "14809 FOLR2\n",
+      "14810 USP35\n",
+      "14811 RDX\n",
+      "14812 ENSG00000255045\n",
+      "14813 TIRAP\n",
+      "14814 LTBR\n",
+      "14815 ENSG00000272173\n",
+      "14816 ENSG00000287713\n",
+      "14817 SLC2A3\n",
+      "14818 MFAP5\n",
+      "14819 ENSG00000255882\n",
+      "14820 KLRC4\n",
+      "14821 CMAS\n",
+      "14822 TM7SF3\n",
+      "14823 YARS2\n",
+      "14824 CNTN1\n",
+      "14825 KCNH3\n",
+      "14826 SLC11A2\n",
+      "14827 ENSG00000258317\n",
+      "14828 CYP27B1\n",
+      "14829 RASSF3\n",
+      "14830 ALKBH2\n",
+      "14831 OAS2\n",
+      "14832 ENSG00000248636\n",
+      "14833 RHOF\n",
+      "14834 SBNO1\n",
+      "14835 ZNF84\n",
+      "14836 TEX26-AS1\n",
+      "14837 SPART\n",
+      "14838 SUPT20H\n",
+      "14839 LACC1\n",
+      "14840 SMIM2-AS1\n",
+      "14841 LRCH1\n",
+      "14842 NUDT15\n",
+      "14843 ENSG00000275202\n",
+      "14844 ATXN8OS\n",
+      "14845 ENSG00000280169\n",
+      "14846 LINC01297\n",
+      "14847 SALL2\n",
+      "14848 TRAV26-2\n",
+      "14849 TRAV41\n",
+      "14850 ENSG00000278784\n",
+      "14851 GZMB\n",
+      "14852 HECTD1\n",
+      "14853 RPS29\n",
+      "14854 L3HYPDH\n",
+      "14855 AKAP5\n",
+      "14856 ZFYVE26\n",
+      "14857 PGF\n",
+      "14858 LINC02328\n",
+      "14859 CHGA\n",
+      "14860 CEP170B\n",
+      "14861 JAG2\n",
+      "14862 IGHV7-4-1\n",
+      "14863 IGHV4-39\n",
+      "14864 GOLGA8M\n",
+      "14865 FAM98B\n",
+      "14866 ADAL\n",
+      "14867 GTF2A2\n",
+      "14868 OAZ2\n",
+      "14869 ADPGK-AS1\n",
+      "14870 DNAJA4\n",
+      "14871 CTSH\n",
+      "14872 MTHFS\n",
+      "14873 IL16\n",
+      "14874 BLM\n",
+      "14875 MCTP2\n",
+      "14876 LUNAR1\n",
+      "14877 PKD1\n",
+      "14878 TFAP4\n",
+      "14879 ENSG00000260349\n",
+      "14880 LDAF1\n",
+      "14881 XPO6\n",
+      "14882 MVP-DT\n",
+      "14883 SULT1A3\n",
+      "14884 IRX3\n",
+      "14885 PSME3IP1\n",
+      "14886 CIAPIN1\n",
+      "14887 AGRP\n",
+      "14888 ZDHHC7\n",
+      "14889 GEMIN4\n",
+      "14890 NXN\n",
+      "14891 HIC1\n",
+      "14892 RAP1GAP2\n",
+      "14893 PELP1-DT\n",
+      "14894 MINK1\n",
+      "14895 ENSG00000266498\n",
+      "14896 LGALS9C\n",
+      "14897 NATD1\n",
+      "14898 SARM1\n",
+      "14899 ABHD15\n",
+      "14900 ASIC2\n",
+      "14901 DHRS11\n",
+      "14902 MIEN1\n",
+      "14903 CDC6\n",
+      "14904 RARA-AS1\n",
+      "14905 KAT2A\n",
+      "14906 STAT5B\n",
+      "14907 GPATCH8\n",
+      "14908 ARL17A\n",
+      "14909 UBE2Z\n",
+      "14910 NGFR\n",
+      "14911 EME1\n",
+      "14912 ABCC3\n",
+      "14913 ENSG00000264112\n",
+      "14914 SMG8\n",
+      "14915 CLTC\n",
+      "14916 RNFT1\n",
+      "14917 ENSG00000277476\n",
+      "14918 LINC00470\n",
+      "14919 HDHD2\n",
+      "14920 CDH7\n",
+      "14921 CSNK1G2\n",
+      "14922 IZUMO4\n",
+      "14923 CREB3L3\n",
+      "14924 ILF3\n",
+      "14925 PRKCSH\n",
+      "14926 ZNF791\n",
+      "14927 MAN2B1\n",
+      "14928 AKAP8\n",
+      "14929 AP1M1\n",
+      "14930 MYO9B\n",
+      "14931 UPF1\n",
+      "14932 BORCS8\n",
+      "14933 YIF1B\n",
+      "14934 SIRT2\n",
+      "14935 EID2\n",
+      "14936 SHKBP1\n",
+      "14937 ZNF404\n",
+      "14938 ZNF221\n",
+      "14939 SELENOW\n",
+      "14940 ENSG00000269825\n",
+      "14941 ENSG00000267776\n",
+      "14942 SNPH\n",
+      "14943 JAG1\n",
+      "14944 PLTP\n",
+      "14945 KCNB1\n",
+      "14946 SAMD10\n",
+      "14947 ENSG00000273017\n",
+      "14948 ENSG00000261610\n",
+      "14949 MRPS6\n",
+      "14950 ENSG00000283633\n",
+      "14951 LINC01311\n",
+      "14952 HMGXB4\n",
+      "14953 RPS19BP1\n",
+      "14954 TMA7B\n",
+      "14955 ADSL\n",
+      "14956 CHADL\n",
+      "14957 TEF\n",
+      "14958 CENPM\n",
+      "14959 NFAM1\n",
+      "14960 PARVG\n",
+      "14961 ENSG00000272821\n",
+      "14962 GRIPAP1\n",
+      "14963 MAGED2\n",
+      "14964 ENSG00000227486\n",
+      "14965 PJA1\n",
+      "14966 EDA\n",
+      "14967 GLA\n",
+      "14968 GPRASP1\n",
+      "14969 STEEP1\n",
+      "14970 RAP2C-AS1\n",
+      "14971 RTL8C\n",
+      "14972 HTATSF1\n",
+      "14973 PNMA3\n",
+      "14974 RAB39B\n",
+      "14975 ENSG00000278847\n",
+      "14976 ATAD3C\n",
+      "14977 PER3\n",
+      "14978 SLC45A1\n",
+      "14979 ENSG00000037637\n",
+      "14980 ENSG00000055070\n",
+      "14981 PINK1-AS\n",
+      "14982 KHDRBS1\n",
+      "14983 EVA1B\n",
+      "14984 ZC3H12A\n",
+      "14985 MEAF6\n",
+      "14986 ZFP69B\n",
+      "14987 RIMS3\n",
+      "14988 LINC02918\n",
+      "14989 CMPK1\n",
+      "14990 C1orf52\n",
+      "14991 GBP3\n",
+      "14992 CCDC18-AS1\n",
+      "14993 VAV3\n",
+      "14994 PSRC1\n",
+      "14995 CD2\n",
+      "14996 CHD1L\n",
+      "14997 CTSS\n",
+      "14998 ZNF687\n",
+      "14999 MRPL9\n",
+      "15000 S100A16\n",
+      "15001 KRTCAP2\n",
+      "15002 FDPS\n",
+      "15003 MEX3A\n",
+      "15004 MIR9-1HG\n",
+      "15005 FCRL2\n",
+      "15006 SLAMF1\n",
+      "15007 FCGR3B\n",
+      "15008 ALDH9A1\n",
+      "15009 MRPS14\n",
+      "15010 ENSG00000272906\n",
+      "15011 ASCL5\n",
+      "15012 PM20D1\n",
+      "15013 UTP25\n",
+      "15014 NSL1\n",
+      "15015 AKT3-IT1\n",
+      "15016 ENSG00000272195\n",
+      "15017 SMYD3\n",
+      "15018 OR2B11\n",
+      "15019 TRIM58\n",
+      "15020 OR2M3\n",
+      "15021 ID2-AS1\n",
+      "15022 DNAJC27-AS1\n",
+      "15023 ATL2\n",
+      "15024 RHOQ\n",
+      "15025 EPCAM\n",
+      "15026 ENSG00000231918\n",
+      "15027 FBXO48\n",
+      "15028 LINC01890\n",
+      "15029 AUP1\n",
+      "15030 HK2-DT\n",
+      "15031 POLE4\n",
+      "15032 IGKV3D-11\n",
+      "15033 FAHD2A\n",
+      "15034 NEURL3\n",
+      "15035 ARHGEF4\n",
+      "15036 ARHGAP15\n",
+      "15037 NR4A2\n",
+      "15038 GALNT5\n",
+      "15039 IFIH1\n",
+      "15040 ENSG00000239467\n",
+      "15041 NIF3L1\n",
+      "15042 ENSG00000286223\n",
+      "15043 RPE\n",
+      "15044 SGPP2\n",
+      "15045 ITM2C\n",
+      "15046 THAP4\n",
+      "15047 LMCD1-AS1\n",
+      "15048 PRRT3-AS1\n",
+      "15049 CAPN7\n",
+      "15050 COLQ\n",
+      "15051 CCR5AS\n",
+      "15052 ABHD14B\n",
+      "15053 MUSTN1\n",
+      "15054 TKT\n",
+      "15055 CCDC66\n",
+      "15056 CLDND1\n",
+      "15057 CIP2A\n",
+      "15058 MCM2\n",
+      "15059 EPHB1\n",
+      "15060 USP13\n",
+      "15061 YEATS2\n",
+      "15062 ABLIM2\n",
+      "15063 SMIM20\n",
+      "15064 ENSG00000250075\n",
+      "15065 AREG\n",
+      "15066 TMEM150C\n",
+      "15067 HPSE\n",
+      "15068 ARHGAP24\n",
+      "15069 SMARCAD1\n",
+      "15070 MANBA\n",
+      "15071 LEF1-AS1\n",
+      "15072 METTL14\n",
+      "15073 MFSD8\n",
+      "15074 FAM218A\n",
+      "15075 PDCD6\n",
+      "15076 ENSG00000286094\n",
+      "15077 MYO10\n",
+      "15078 CDH18\n",
+      "15079 LINC02241\n",
+      "15080 MRPS27\n",
+      "15081 ZNF366\n",
+      "15082 ENSG00000255647\n",
+      "15083 TSLP\n",
+      "15084 SEMA6A\n",
+      "15085 LRRTM2\n",
+      "15086 IK\n",
+      "15087 SMIM3\n",
+      "15088 ENSG00000253980\n",
+      "15089 IL12B\n",
+      "15090 RNF144B\n",
+      "15091 H1-2\n",
+      "15092 ENSG00000287252\n",
+      "15093 H2AC14\n",
+      "15094 ZSCAN31\n",
+      "15095 ENSG00000272540\n",
+      "15096 C2\n",
+      "15097 HLA-DQB1\n",
+      "15098 ZBTB9\n",
+      "15099 ETV7\n",
+      "15100 MDGA1\n",
+      "15101 MOCS1\n",
+      "15102 MED20\n",
+      "15103 GCM1\n",
+      "15104 ELOVL5\n",
+      "15105 ENSG00000233844\n",
+      "15106 MMS22L\n",
+      "15107 USP45\n",
+      "15108 SMPD2\n",
+      "15109 PERP\n",
+      "15110 HEBP2\n",
+      "15111 CCDC28A\n",
+      "15112 SASH1\n",
+      "15113 SUMO4\n",
+      "15114 SCAF8\n",
+      "15115 ENSG00000286874\n",
+      "15116 RAMP3\n",
+      "15117 ERV3-1\n",
+      "15118 RABGEF1\n",
+      "15119 LINC02604\n",
+      "15120 DDX3ILA1\n",
+      "15121 STEAP4\n",
+      "15122 PPP1R35-AS1\n",
+      "15123 RELN\n",
+      "15124 FOXP2\n",
+      "15125 NDUFA5\n",
+      "15126 HILPDA\n",
+      "15127 DGKI\n",
+      "15128 TRBV4-1\n",
+      "15129 ENSG00000211745\n",
+      "15130 XRCC2\n",
+      "15131 SOX7\n",
+      "15132 ELP3\n",
+      "15133 UBE2V2\n",
+      "15134 TTPA\n",
+      "15135 RBM12B\n",
+      "15136 CDH17\n",
+      "15137 RRM2B\n",
+      "15138 CASC11\n",
+      "15139 TRAPPC9\n",
+      "15140 THEM6\n",
+      "15141 GML\n",
+      "15142 AK3\n",
+      "15143 LRRC19\n",
+      "15144 UBE2R2-AS1\n",
+      "15145 ENSG00000260100\n",
+      "15146 FAM88B\n",
+      "15147 ZFAND5\n",
+      "15148 AGTPBP1\n",
+      "15149 CT70\n",
+      "15150 DNAJC25\n",
+      "15151 NR6A1\n",
+      "15152 TTC16\n",
+      "15153 OLFM1\n",
+      "15154 QSOX2\n",
+      "15155 NPDC1\n",
+      "15156 DCLRE1C\n",
+      "15157 ST8SIA6\n",
+      "15158 OTUD1\n",
+      "15159 ENSG00000262412\n",
+      "15160 ZNF438\n",
+      "15161 ITGB1-DT\n",
+      "15162 MARCHF8\n",
+      "15163 NCOA4\n",
+      "15164 ERCC6\n",
+      "15165 PARG\n",
+      "15166 EGR2\n",
+      "15167 TET1\n",
+      "15168 DDX21\n",
+      "15169 ZMIZ1-AS1\n",
+      "15170 PRXL2A\n",
+      "15171 TBC1D12\n",
+      "15172 NEURL1\n",
+      "15173 SFR1\n",
+      "15174 ABLIM1\n",
+      "15175 BAG3\n",
+      "15176 GLRX3\n",
+      "15177 PGAP2\n",
+      "15178 TRIM6\n",
+      "15179 CTR9\n",
+      "15180 KCNC1\n",
+      "15181 ENSG00000285751\n",
+      "15182 HNRNPUL2\n",
+      "15183 VPS51\n",
+      "15184 CFL1\n",
+      "15185 EIF1AD\n",
+      "15186 BRMS1\n",
+      "15187 ACY3\n",
+      "15188 ALDH3B1\n",
+      "15189 CCDC90B-AS1\n",
+      "15190 TMEM126B\n",
+      "15191 ALKBH8\n",
+      "15192 KMT2A\n",
+      "15193 IFT46\n",
+      "15194 GRAMD1B\n",
+      "15195 KCNJ5\n",
+      "15196 NDUFA9\n",
+      "15197 ENSG00000219410\n",
+      "15198 C12orf57\n",
+      "15199 C3AR1\n",
+      "15200 LINC01252\n",
+      "15201 ENSG00000256084\n",
+      "15202 MRPS35-DT\n",
+      "15203 ABCD2\n",
+      "15204 SOAT2\n",
+      "15205 SHMT2\n",
+      "15206 IFNG\n",
+      "15207 MDM2\n",
+      "15208 TPH2\n",
+      "15209 OSBPL8\n",
+      "15210 C12orf50\n",
+      "15211 DCN\n",
+      "15212 NUDT4\n",
+      "15213 MMAB\n",
+      "15214 IQCD\n",
+      "15215 COG6\n",
+      "15216 LINC00598\n",
+      "15217 FGF14\n",
+      "15218 CCDC168\n",
+      "15219 RNASE1\n",
+      "15220 TRAV16\n",
+      "15221 LINC02307\n",
+      "15222 ENSG00000282885\n",
+      "15223 CHURC1\n",
+      "15224 SMOC1\n",
+      "15225 ISCA2\n",
+      "15226 MLH3\n",
+      "15227 AHSA1\n",
+      "15228 TTC7B\n",
+      "15229 LINC02295\n",
+      "15230 COA8\n",
+      "15231 ZBTB42\n",
+      "15232 TEDC1\n",
+      "15233 TMEM121\n",
+      "15234 GABRB3\n",
+      "15235 GOLGA8R\n",
+      "15236 ENSG00000269930\n",
+      "15237 FAN1\n",
+      "15238 CHRNA7\n",
+      "15239 PDIA3\n",
+      "15240 ENSG00000227161\n",
+      "15241 SENP8\n",
+      "15242 ENSG00000285560\n",
+      "15243 ENSG00000273923\n",
+      "15244 LYSMD4\n",
+      "15245 HBQ1\n",
+      "15246 ENSG00000269937\n",
+      "15247 MMP25-AS1\n",
+      "15248 ENSG00000262668\n",
+      "15249 GDE1\n",
+      "15250 ACSM2A\n",
+      "15251 EIF3C\n",
+      "15252 STX1B\n",
+      "15253 ZNF646\n",
+      "15254 ZNF843\n",
+      "15255 VPS35\n",
+      "15256 NETO2\n",
+      "15257 N4BP1\n",
+      "15258 OGFOD1\n",
+      "15259 MT1H\n",
+      "15260 NUP93\n",
+      "15261 LINC00920\n",
+      "15262 TK2\n",
+      "15263 ENKD1\n",
+      "15264 C16orf86\n",
+      "15265 ENSG00000274698\n",
+      "15266 EXOSC6\n",
+      "15267 CDH13\n",
+      "15268 DPH1\n",
+      "15269 ZBTB4\n",
+      "15270 SAT2\n",
+      "15271 ENSG00000235979\n",
+      "15272 DHRS13\n",
+      "15273 ENSG00000226377\n",
+      "15274 SYNRG\n",
+      "15275 TBC1D3L\n",
+      "15276 KRT23\n",
+      "15277 ENSG00000229732\n",
+      "15278 ENSG00000266821\n",
+      "15279 SAMD14\n",
+      "15280 CACNA1G\n",
+      "15281 ENSG00000267637\n",
+      "15282 TANC2\n",
+      "15283 CEP95\n",
+      "15284 NOL11\n",
+      "15285 C17orf80\n",
+      "15286 RNF157-AS1\n",
+      "15287 NPTX1\n",
+      "15288 ENSG00000262049\n",
+      "15289 MRPL12\n",
+      "15290 HEXD\n",
+      "15291 DLGAP1\n",
+      "15292 LDLRAD4\n",
+      "15293 ENSG00000266850\n",
+      "15294 B4GALT6\n",
+      "15295 SIGLEC15\n",
+      "15296 IER3IP1\n",
+      "15297 MYO5B\n",
+      "15298 WDR7\n",
+      "15299 ENSG00000266900\n",
+      "15300 PHLPP1\n",
+      "15301 MBP\n",
+      "15302 BSG-AS1\n",
+      "15303 ENSG00000267412\n",
+      "15304 ZBTB7A\n",
+      "15305 YJU2\n",
+      "15306 FUT6\n",
+      "15307 ZNF426\n",
+      "15308 ZNF121\n",
+      "15309 ZNF561-AS1\n",
+      "15310 ZGLP1\n",
+      "15311 CDC37\n",
+      "15312 ENSG00000267174\n",
+      "15313 ENSG00000267277\n",
+      "15314 PRDX2\n",
+      "15315 RNASEH2A\n",
+      "15316 DNAJB1\n",
+      "15317 SLC27A1\n",
+      "15318 ISYNA1\n",
+      "15319 COMP\n",
+      "15320 LINC00663\n",
+      "15321 ZNF506\n",
+      "15322 ENSG00000267481\n",
+      "15323 CEBPG\n",
+      "15324 SPRED3\n",
+      "15325 RINL\n",
+      "15326 NFKBIB\n",
+      "15327 ETHE1\n",
+      "15328 APOC1\n",
+      "15329 RELB\n",
+      "15330 SNRPD2\n",
+      "15331 GNG8\n",
+      "15332 ZNF665\n",
+      "15333 ENSG00000267838\n",
+      "15334 LENG8\n",
+      "15335 KIR2DL1\n",
+      "15336 GP6-AS1\n",
+      "15337 ENSG00000286230\n",
+      "15338 ZNF835\n",
+      "15339 ENSG00000268713\n",
+      "15340 ZNF552\n",
+      "15341 FASTKD5\n",
+      "15342 RASSF2\n",
+      "15343 PLCB1\n",
+      "15344 SPTLC3\n",
+      "15345 LINC00652\n",
+      "15346 BLCAP\n",
+      "15347 SLC2A10\n",
+      "15348 ENSG00000197670\n",
+      "15349 SYCP2\n",
+      "15350 ZGPAT\n",
+      "15351 ENSG00000277117\n",
+      "15352 USP25\n",
+      "15353 LINC01684\n",
+      "15354 DONSON\n",
+      "15355 DSCAM\n",
+      "15356 RSPH1\n",
+      "15357 RRP1\n",
+      "15358 COL6A1\n",
+      "15359 S100B\n",
+      "15360 IGLV8-61\n",
+      "15361 IGLV1-36\n",
+      "15362 IGLV4-3\n",
+      "15363 HSCB\n",
+      "15364 ENSG00000279714\n",
+      "15365 CYTH4\n",
+      "15366 ANKRD54\n",
+      "15367 ENSG00000273076\n",
+      "15368 PLXNB2\n",
+      "15369 KLHDC7B-DT\n",
+      "15370 RRAGB\n",
+      "15371 ZXDB\n",
+      "15372 ZC3H12B\n",
+      "15373 EFNB1\n",
+      "15374 NEXMIF\n",
+      "15375 UPRT\n",
+      "15376 GPC3\n",
+      "15377 CD40LG\n",
+      "15378 RBMX\n",
+      "15379 F8A3\n",
+      "15380 IL9R\n",
+      "15381 MT-ND4L\n",
+      "15382 ERRFI1-DT\n",
+      "15383 NPPA\n",
+      "15384 SLC66A1\n",
+      "15385 PDIK1L\n",
+      "15386 CRYBG2\n",
+      "15387 TRAPPC3\n",
+      "15388 DNALI1\n",
+      "15389 RRAGC-DT\n",
+      "15390 C1orf50\n",
+      "15391 TIE1\n",
+      "15392 PTCH2\n",
+      "15393 LINC02777\n",
+      "15394 LPAR3\n",
+      "15395 PKN2-AS1\n",
+      "15396 GCLM\n",
+      "15397 F3\n",
+      "15398 LAMTOR5-AS1\n",
+      "15399 KCNA3\n",
+      "15400 RHOC\n",
+      "15401 PHTF1\n",
+      "15402 SRGAP2C\n",
+      "15403 ILF2\n",
+      "15404 THBS3-AS1\n",
+      "15405 DCAF8\n",
+      "15406 F11R\n",
+      "15407 FCER1G\n",
+      "15408 POGK\n",
+      "15409 TNFSF4\n",
+      "15410 DDX59-AS1\n",
+      "15411 CAMSAP2\n",
+      "15412 ELF3-AS1\n",
+      "15413 MAPKAPK2\n",
+      "15414 CD34\n",
+      "15415 HHAT\n",
+      "15416 FLVCR1\n",
+      "15417 NVL\n",
+      "15418 RSAD2\n",
+      "15419 ENSG00000227047\n",
+      "15420 TP53I3\n",
+      "15421 MEMO1\n",
+      "15422 PPP1R21\n",
+      "15423 PSME4\n",
+      "15424 ETAA1\n",
+      "15425 MXD1\n",
+      "15426 CCDC142\n",
+      "15427 CHMP3\n",
+      "15428 ENSG00000287670\n",
+      "15429 CRACDL\n",
+      "15430 TNFAIP6\n",
+      "15431 PRPF40A\n",
+      "15432 GPD2\n",
+      "15433 WDSUB1\n",
+      "15434 GCG\n",
+      "15435 SCN2A\n",
+      "15436 ENSG00000235192\n",
+      "15437 GORASP2\n",
+      "15438 CYBRD1\n",
+      "15439 HIBCH\n",
+      "15440 ENSG00000272211\n",
+      "15441 ANKRD44-DT\n",
+      "15442 ICA1L\n",
+      "15443 ENSG00000261338\n",
+      "15444 TTLL4\n",
+      "15445 USP40\n",
+      "15446 RFTN1\n",
+      "15447 SATB1\n",
+      "15448 TRMT10C\n",
+      "15449 GCSAM\n",
+      "15450 ARHGAP31\n",
+      "15451 HACD2\n",
+      "15452 SLC35G2\n",
+      "15453 CHST2\n",
+      "15454 SEC62\n",
+      "15455 SLC7A14\n",
+      "15456 ETV5\n",
+      "15457 MB21D2\n",
+      "15458 GP5\n",
+      "15459 CTBP1-DT\n",
+      "15460 LETM1\n",
+      "15461 ENSG00000251615\n",
+      "15462 RAB28\n",
+      "15463 CXCL13\n",
+      "15464 PRKG2\n",
+      "15465 PKD2\n",
+      "15466 ADH1C\n",
+      "15467 AP1AR-DT\n",
+      "15468 TIGD4\n",
+      "15469 TMEM144\n",
+      "15470 MARCHF6-DT\n",
+      "15471 ZNF622\n",
+      "15472 NDUFS4\n",
+      "15473 ANKRD55\n",
+      "15474 SREK1\n",
+      "15475 MAST4\n",
+      "15476 ZBED3\n",
+      "15477 BHMT\n",
+      "15478 DMXL1\n",
+      "15479 PITX1-AS1\n",
+      "15480 ENSG00000280987\n",
+      "15481 ANKHD1-DT\n",
+      "15482 NDUFA2\n",
+      "15483 FAF2\n",
+      "15484 ZNF354A\n",
+      "15485 H2BC6-AS1\n",
+      "15486 ZNF184\n",
+      "15487 H2BC15\n",
+      "15488 H4C13\n",
+      "15489 NCR3\n",
+      "15490 LY6G5B\n",
+      "15491 LY6G6E\n",
+      "15492 FANCE\n",
+      "15493 ABCC10\n",
+      "15494 GTPBP2\n",
+      "15495 TNFRSF21\n",
+      "15496 KIAA1586\n",
+      "15497 MDN1\n",
+      "15498 ASCC3\n",
+      "15499 ZUP1\n",
+      "15500 MAN1A1\n",
+      "15501 RNF217\n",
+      "15502 RPS12\n",
+      "15503 TBPL1\n",
+      "15504 MTFR2\n",
+      "15505 TFB1M\n",
+      "15506 CHST12\n",
+      "15507 RADIL\n",
+      "15508 GGCT\n",
+      "15509 TMED4\n",
+      "15510 MYO1G\n",
+      "15511 CRCP\n",
+      "15512 TP53TG1\n",
+      "15513 AP4M1\n",
+      "15514 AKR1B1\n",
+      "15515 CLEC5A\n",
+      "15516 TRBV10-2\n",
+      "15517 ZNF783\n",
+      "15518 GIMAP6\n",
+      "15519 MYOM2\n",
+      "15520 MSRA\n",
+      "15521 DLC1\n",
+      "15522 BNIP3L\n",
+      "15523 CHRNA2\n",
+      "15524 TCIM\n",
+      "15525 ST18\n",
+      "15526 LYPLA1\n",
+      "15527 RP1\n",
+      "15528 TOX-DT\n",
+      "15529 ENSG00000249328\n",
+      "15530 RAD54B\n",
+      "15531 FBXO43\n",
+      "15532 LINC03047\n",
+      "15533 AARD\n",
+      "15534 LINC00964\n",
+      "15535 CCDC26\n",
+      "15536 LY6E\n",
+      "15537 TOP1MT\n",
+      "15538 LINC01239\n",
+      "15539 EMICERI\n",
+      "15540 SIGMAR1\n",
+      "15541 RC3H2\n",
+      "15542 PTGES2-AS1\n",
+      "15543 DYNC2I2\n",
+      "15544 ABO\n",
+      "15545 TMEM250\n",
+      "15546 SEC16A\n",
+      "15547 NRARP\n",
+      "15548 EHMT1\n",
+      "15549 PITRM1-AS1\n",
+      "15550 ENSG00000240291\n",
+      "15551 KIAA1217\n",
+      "15552 ZNF33B\n",
+      "15553 PRKG1\n",
+      "15554 LINC02671\n",
+      "15555 PRF1\n",
+      "15556 PPP3CB-AS1\n",
+      "15557 ZNF503\n",
+      "15558 LINC00863\n",
+      "15559 ANKRD1\n",
+      "15560 SEC31B\n",
+      "15561 LZTS2\n",
+      "15562 EMX2\n",
+      "15563 ENSG00000277687\n",
+      "15564 EEF1AKMT2\n",
+      "15565 FANK1\n",
+      "15566 CFAP46\n",
+      "15567 SCT\n",
+      "15568 KRTAP5-AS1\n",
+      "15569 IRAG1\n",
+      "15570 OR4P4\n",
+      "15571 MS4A7\n",
+      "15572 ENSG00000255118\n",
+      "15573 LINC02723\n",
+      "15574 RELA-DT\n",
+      "15575 MUS81\n",
+      "15576 PC\n",
+      "15577 RPS6KB2-AS1\n",
+      "15578 AAMDC\n",
+      "15579 CTSC\n",
+      "15580 MMP7\n",
+      "15581 MMP1\n",
+      "15582 EXPH5\n",
+      "15583 PAFAH1B2\n",
+      "15584 ENSG00000271751\n",
+      "15585 VPS11-DT\n",
+      "15586 CCDC153\n",
+      "15587 TBCEL\n",
+      "15588 CACNA1C-AS1\n",
+      "15589 VAMP1\n",
+      "15590 ENSG00000256101\n",
+      "15591 A2M\n",
+      "15592 TMEM52B\n",
+      "15593 GPR19\n",
+      "15594 CDKN1B\n",
+      "15595 PLBD1\n",
+      "15596 RASSF8-AS1\n",
+      "15597 RESF1\n",
+      "15598 DNM1L\n",
+      "15599 PUS7L\n",
+      "15600 ARID2\n",
+      "15601 ENSG00000274591\n",
+      "15602 SLC48A1\n",
+      "15603 PRKAG1\n",
+      "15604 IGFBP6\n",
+      "15605 MUCL1\n",
+      "15606 SMARCC2\n",
+      "15607 STAC3\n",
+      "15608 FRS2\n",
+      "15609 CNOT2\n",
+      "15610 RLIG1\n",
+      "15611 POC1B-AS1\n",
+      "15612 UBE3B\n",
+      "15613 ANAPC7\n",
+      "15614 PPP1CC\n",
+      "15615 B3GNT4\n",
+      "15616 MPHOSPH9\n",
+      "15617 AACS\n",
+      "15618 CRYL1\n",
+      "15619 PARP4\n",
+      "15620 GTF3A\n",
+      "15621 ALOX5AP\n",
+      "15622 KCNRG\n",
+      "15623 ENSG00000274270\n",
+      "15624 SUGT1-DT\n",
+      "15625 DNAJC3\n",
+      "15626 OR11G2\n",
+      "15627 TRDV1\n",
+      "15628 LTB4R\n",
+      "15629 SRP54\n",
+      "15630 PRORP\n",
+      "15631 FBXO33\n",
+      "15632 ENSG00000258808\n",
+      "15633 ENSG00000261242\n",
+      "15634 MTHFD1\n",
+      "15635 ERH\n",
+      "15636 PAPLN\n",
+      "15637 PNMA1\n",
+      "15638 ZC2HC1C\n",
+      "15639 BTBD7\n",
+      "15640 PAPOLA\n",
+      "15641 ENSG00000256705\n",
+      "15642 IGHEP1\n",
+      "15643 IGHV1-2\n",
+      "15644 IGHV1-46\n",
+      "15645 IGHV4-61\n",
+      "15646 RTF1\n",
+      "15647 GANC\n",
+      "15648 ENSG00000261684\n",
+      "15649 CATSPER2\n",
+      "15650 TMOD3\n",
+      "15651 SLC24A1\n",
+      "15652 MEGF11\n",
+      "15653 SMAD3-DT\n",
+      "15654 UBL7-DT\n",
+      "15655 COMMD4\n",
+      "15656 PTPN9\n",
+      "15657 ISG20\n",
+      "15658 POLG-DT\n",
+      "15659 CRAT37\n",
+      "15660 ENSG00000275016\n",
+      "15661 SNRPA1-DT\n",
+      "15662 NHERF2\n",
+      "15663 EEF2KMT\n",
+      "15664 ATF7IP2\n",
+      "15665 BMERB1\n",
+      "15666 ENSG00000261448\n",
+      "15667 CLN3\n",
+      "15668 ENSG00000260367\n",
+      "15669 ENSG00000260517\n",
+      "15670 QPRT\n",
+      "15671 ZNF267\n",
+      "15672 ENSG00000261541\n",
+      "15673 E2F4\n",
+      "15674 DDX28\n",
+      "15675 TANGO6\n",
+      "15676 PDPR\n",
+      "15677 RFWD3\n",
+      "15678 ZNRF1\n",
+      "15679 CDYL2\n",
+      "15680 CMC2\n",
+      "15681 TAF1C\n",
+      "15682 CRISPLD2\n",
+      "15683 VPS53\n",
+      "15684 MYBBP1A\n",
+      "15685 SCIMP\n",
+      "15686 NLRP1\n",
+      "15687 ASGR2\n",
+      "15688 CNTROB\n",
+      "15689 PIK3R5\n",
+      "15690 TOP3A\n",
+      "15691 ENSG00000263986\n",
+      "15692 MIR4733HG\n",
+      "15693 MAP3K14\n",
+      "15694 GOSR2\n",
+      "15695 PNPO\n",
+      "15696 SRSF1\n",
+      "15697 ENSG00000274712\n",
+      "15698 CD300A\n",
+      "15699 FADS6\n",
+      "15700 KCTD2\n",
+      "15701 RECQL5\n",
+      "15702 CD7\n",
+      "15703 MYL12-AS1\n",
+      "15704 ANKRD12\n",
+      "15705 MIB1\n",
+      "15706 ENSG00000268573\n",
+      "15707 C18orf54\n",
+      "15708 ENSG00000267462\n",
+      "15709 RNF152\n",
+      "15710 RBFA\n",
+      "15711 PLEKHJ1\n",
+      "15712 SPPL2B\n",
+      "15713 ENSG00000267571\n",
+      "15714 CRB3\n",
+      "15715 ZNF557\n",
+      "15716 ICAM4\n",
+      "15717 ZNF440\n",
+      "15718 SLC1A6\n",
+      "15719 CYP4F3\n",
+      "15720 PLVAP\n",
+      "15721 SUGP1\n",
+      "15722 ZNF253\n",
+      "15723 ZNF585A\n",
+      "15724 LINC01480\n",
+      "15725 ENSG00000268027\n",
+      "15726 PPP5C\n",
+      "15727 PPFIA3\n",
+      "15728 KCNC3\n",
+      "15729 ENSG00000268734\n",
+      "15730 ZNF582\n",
+      "15731 ZNF460\n",
+      "15732 LINC01722\n",
+      "15733 EIF6\n",
+      "15734 SCAND1\n",
+      "15735 BCAS1\n",
+      "15736 ENSG00000285091\n",
+      "15737 LIME1\n",
+      "15738 JAM2\n",
+      "15739 MORC3\n",
+      "15740 LCA5L\n",
+      "15741 ZBTB21\n",
+      "15742 IGLV3-1\n",
+      "15743 SEZ6L\n",
+      "15744 ENSG00000279914\n",
+      "15745 ENSG00000226471\n",
+      "15746 ENSG00000278920\n",
+      "15747 APOL2\n",
+      "15748 ENSG00000284633\n",
+      "15749 TSPO\n",
+      "15750 ENSG00000280011\n",
+      "15751 ENSG00000273289\n",
+      "15752 MAPK11\n",
+      "15753 SCO2\n",
+      "15754 IL3RA\n",
+      "15755 BEND2\n",
+      "15756 RP2\n",
+      "15757 USP11\n",
+      "15758 SYN1\n",
+      "15759 ENSG00000204620\n",
+      "15760 SUV39H1\n",
+      "15761 CLCN5\n",
+      "15762 TMSB15A\n",
+      "15763 SOWAHD\n",
+      "15764 GDI1\n",
+      "15765 TTTY14\n",
+      "15766 ENSG00000277400\n",
+      "15767 LINC01128\n",
+      "15768 WRAP73\n",
+      "15769 RERE-AS1\n",
+      "15770 ENSG00000228549\n",
+      "15771 ARHGEF10L\n",
+      "15772 ENSG00000271420\n",
+      "15773 ZNF593\n",
+      "15774 ADGRB2\n",
+      "15775 NDUFS5\n",
+      "15776 COL9A2\n",
+      "15777 HPDL\n",
+      "15778 PRDX1\n",
+      "15779 PRPF38A\n",
+      "15780 FGGY-DT\n",
+      "15781 DOCK7-DT\n",
+      "15782 LINC01725\n",
+      "15783 ZNF326\n",
+      "15784 GSTM4\n",
+      "15785 WARS2\n",
+      "15786 ENSG00000224363\n",
+      "15787 RFX5\n",
+      "15788 S100A6\n",
+      "15789 ATF6\n",
+      "15790 QSOX1\n",
+      "15791 NR5A2\n",
+      "15792 TNNI1\n",
+      "15793 ENSG00000270188\n",
+      "15794 KMO\n",
+      "15795 CHML\n",
+      "15796 ENSG00000272148\n",
+      "15797 EML4\n",
+      "15798 FOXN2\n",
+      "15799 ENSG00000228033\n",
+      "15800 AFTPH\n",
+      "15801 PNO1\n",
+      "15802 AAK1\n",
+      "15803 BOLA3-DT\n",
+      "15804 SH2D6\n",
+      "15805 TGFBRAP1\n",
+      "15806 ENSG00000286545\n",
+      "15807 FAM168B\n",
+      "15808 KLHL23\n",
+      "15809 ERICH2\n",
+      "15810 DLX2\n",
+      "15811 OSBPL6\n",
+      "15812 ENSG00000270956\n",
+      "15813 ENSG00000273456\n",
+      "15814 WDR12\n",
+      "15815 ENSG00000272807\n",
+      "15816 LANCL1\n",
+      "15817 IQCA1\n",
+      "15818 LINC01238\n",
+      "15819 UBE2E1\n",
+      "15820 MYD88\n",
+      "15821 KIAA1143\n",
+      "15822 PFKFB4\n",
+      "15823 RBM6\n",
+      "15824 CYB561D2\n",
+      "15825 RFT1\n",
+      "15826 ABI3BP\n",
+      "15827 RABL3\n",
+      "15828 CASR\n",
+      "15829 ENSG00000286919\n",
+      "15830 TPRA1\n",
+      "15831 CPA3\n",
+      "15832 COMMD2\n",
+      "15833 ANKUB1\n",
+      "15834 P2RY14\n",
+      "15835 SI\n",
+      "15836 RPL22L1\n",
+      "15837 SOD3\n",
+      "15838 STIM2-AS1\n",
+      "15839 USP46-DT\n",
+      "15840 ERVMER34-1\n",
+      "15841 EXOC1\n",
+      "15842 PPBP\n",
+      "15843 HSD17B11\n",
+      "15844 ARHGAP10\n",
+      "15845 RAPGEF2\n",
+      "15846 NSUN2\n",
+      "15847 ATPSCKMT\n",
+      "15848 NUP155\n",
+      "15849 PRKAA1\n",
+      "15850 ERBIN\n",
+      "15851 IQGAP2\n",
+      "15852 LHFPL2\n",
+      "15853 ENSG00000251574\n",
+      "15854 APC\n",
+      "15855 YTHDC2\n",
+      "15856 MEIKIN\n",
+      "15857 KIF3A\n",
+      "15858 UBE2B\n",
+      "15859 HBEGF\n",
+      "15860 PCDH12\n",
+      "15861 ANXA6\n",
+      "15862 EBF1\n",
+      "15863 NSD1\n",
+      "15864 PRELID1\n",
+      "15865 TMED9\n",
+      "15866 GFPT2\n",
+      "15867 MYLK4\n",
+      "15868 SERPINB6\n",
+      "15869 ENSG00000285424\n",
+      "15870 LYRM4\n",
+      "15871 BLOC1S5\n",
+      "15872 JARID2-AS1\n",
+      "15873 H2AC7\n",
+      "15874 H4C6\n",
+      "15875 HLA-F\n",
+      "15876 PRRC2A\n",
+      "15877 SNRPC\n",
+      "15878 SRSF3\n",
+      "15879 CPNE5\n",
+      "15880 TOMM6\n",
+      "15881 PEX6\n",
+      "15882 AARS2\n",
+      "15883 TRAM2-AS1\n",
+      "15884 CD109\n",
+      "15885 ANKRD6\n",
+      "15886 TMEM244\n",
+      "15887 RSPH3\n",
+      "15888 PNLDC1\n",
+      "15889 CCZ1B\n",
+      "15890 MGC4859\n",
+      "15891 ENSG00000237070\n",
+      "15892 HOXA6\n",
+      "15893 DDX56\n",
+      "15894 LINC02848\n",
+      "15895 CALN1\n",
+      "15896 DBF4\n",
+      "15897 PILRB\n",
+      "15898 ORC5\n",
+      "15899 IQUB\n",
+      "15900 ENSG00000226380\n",
+      "15901 PLXNA4\n",
+      "15902 PTN\n",
+      "15903 KIAA1549\n",
+      "15904 ENSG00000284691\n",
+      "15905 TMEM176B\n",
+      "15906 ATG9B\n",
+      "15907 GALNT11\n",
+      "15908 GS1-24F4.2\n",
+      "15909 MSRA-DT\n",
+      "15910 NEIL2\n",
+      "15911 PDGFRL\n",
+      "15912 ENSG00000245025\n",
+      "15913 PPP2CB\n",
+      "15914 SFRP1\n",
+      "15915 GPAT4-AS1\n",
+      "15916 ENSG00000261449\n",
+      "15917 PLAT\n",
+      "15918 UBXN2B\n",
+      "15919 RRS1-DT\n",
+      "15920 PAG1\n",
+      "15921 CA13\n",
+      "15922 AZIN1\n",
+      "15923 ENSG00000272384\n",
+      "15924 FBXO32\n",
+      "15925 ANXA13\n",
+      "15926 LRATD2\n",
+      "15927 ST3GAL1-DT\n",
+      "15928 LINC02904\n",
+      "15929 WDR97\n",
+      "15930 ZFTRAF1\n",
+      "15931 ZNF251\n",
+      "15932 RFX3-DT\n",
+      "15933 SLC1A1\n",
+      "15934 CDC37L1-DT\n",
+      "15935 RANBP6\n",
+      "15936 ENSG00000232035\n",
+      "15937 CIMIP2B\n",
+      "15938 TPM2\n",
+      "15939 TLN1\n",
+      "15940 CARNMT1\n",
+      "15941 ASTN2\n",
+      "15942 GSN\n",
+      "15943 AK1\n",
+      "15944 PLPP7\n",
+      "15945 FCN1\n",
+      "15946 PPP1R26-AS1\n",
+      "15947 CLIC3\n",
+      "15948 SAPCD2\n",
+      "15949 MAN1B1-DT\n",
+      "15950 PFKP\n",
+      "15951 ENSG00000228951\n",
+      "15952 ASB13\n",
+      "15953 ENSG00000286439\n",
+      "15954 CCDC3\n",
+      "15955 PHYH\n",
+      "15956 BEND7\n",
+      "15957 ENSG00000239665\n",
+      "15958 ANKRD26\n",
+      "15959 MPP7\n",
+      "15960 ASCC1\n",
+      "15961 UBTD1\n",
+      "15962 GBF1\n",
+      "15963 DUSP5-DT\n",
+      "15964 PDCD4\n",
+      "15965 PLEKHA1\n",
+      "15966 C10orf88\n",
+      "15967 UROS\n",
+      "15968 GATD1-DT\n",
+      "15969 ZNF195\n",
+      "15970 FAR1\n",
+      "15971 COPB1\n",
+      "15972 ENSG00000260196\n",
+      "15973 SLC39A13-AS1\n",
+      "15974 MS4A1\n",
+      "15975 FADS2\n",
+      "15976 TUT1\n",
+      "15977 TRMT112\n",
+      "15978 CDCA5\n",
+      "15979 ENSG00000254452\n",
+      "15980 LRFN4\n",
+      "15981 GAB2\n",
+      "15982 DLG2\n",
+      "15983 AMOTL1\n",
+      "15984 BIRC3\n",
+      "15985 NKAPD1\n",
+      "15986 TTC12\n",
+      "15987 ST14\n",
+      "15988 GAPDH\n",
+      "15989 MLF2\n",
+      "15990 ENO2\n",
+      "15991 PDE3A\n",
+      "15992 RNF41\n",
+      "15993 IL23A\n",
+      "15994 GIHCG\n",
+      "15995 GNS\n",
+      "15996 CHPT1\n",
+      "15997 NT5DC3\n",
+      "15998 TCP11L2\n",
+      "15999 MTERF2\n",
+      "16000 CIT\n",
+      "16001 SIRT4\n",
+      "16002 MSI1\n",
+      "16003 ENSG00000278291\n",
+      "16004 FGF9\n",
+      "16005 PROSER1\n",
+      "16006 CDADC1\n",
+      "16007 RNASEH2B\n",
+      "16008 DCT\n",
+      "16009 SWINGN\n",
+      "16010 ENSG00000288044\n",
+      "16011 FOXA1\n",
+      "16012 ZBTB1\n",
+      "16013 HSPA2\n",
+      "16014 FUT8-AS1\n",
+      "16015 ENSG00000286423\n",
+      "16016 BATF\n",
+      "16017 ERG28\n",
+      "16018 ENSG00000235785\n",
+      "16019 GPR132\n",
+      "16020 IGHV2-26\n",
+      "16021 RAD51-AS1\n",
+      "16022 DUT\n",
+      "16023 LEO1\n",
+      "16024 MINDY2-DT\n",
+      "16025 DRAIC\n",
+      "16026 ENSG00000261460\n",
+      "16027 BBS4\n",
+      "16028 ULK3\n",
+      "16029 SIN3A\n",
+      "16030 ENSG00000177699\n",
+      "16031 GOLGA6L10\n",
+      "16032 RPS17\n",
+      "16033 ENSG00000285667\n",
+      "16034 SLCO3A1\n",
+      "16035 WDR24\n",
+      "16036 UQCC4\n",
+      "16037 MLST8\n",
+      "16038 PGP\n",
+      "16039 ECI1\n",
+      "16040 ZNF174\n",
+      "16041 SEC14L5\n",
+      "16042 SHISA9\n",
+      "16043 ENSG00000276571\n",
+      "16044 KNOP1\n",
+      "16045 USP31\n",
+      "16046 SBK1\n",
+      "16047 NUPR1\n",
+      "16048 HERPUD1\n",
+      "16049 POLR2C\n",
+      "16050 DDX19A-DT\n",
+      "16051 DDX19A\n",
+      "16052 EMC6\n",
+      "16053 ANKFY1\n",
+      "16054 C17orf114\n",
+      "16055 USP6\n",
+      "16056 STX8\n",
+      "16057 ATPAF2\n",
+      "16058 GRAPL\n",
+      "16059 MFAP4\n",
+      "16060 AKAP10\n",
+      "16061 ENSG00000256618\n",
+      "16062 ENSG00000263657\n",
+      "16063 ADAP2\n",
+      "16064 CCL7\n",
+      "16065 CCL18\n",
+      "16066 CWC25\n",
+      "16067 NEUROD2\n",
+      "16068 PGAP3\n",
+      "16069 FMNL1\n",
+      "16070 FLJ40194\n",
+      "16071 ZNF652\n",
+      "16072 ZNF652-AS1\n",
+      "16073 MTMR4\n",
+      "16074 ENSG00000224738\n",
+      "16075 NPLOC4\n",
+      "16076 ARHGDIA\n",
+      "16077 MYL12B\n",
+      "16078 LINC01900\n",
+      "16079 ZNF396\n",
+      "16080 GNG7\n",
+      "16081 SLC39A3\n",
+      "16082 ENSG00000269604\n",
+      "16083 PET100\n",
+      "16084 RAB11B\n",
+      "16085 KEAP1\n",
+      "16086 NACC1\n",
+      "16087 ABHD8\n",
+      "16088 IQCN\n",
+      "16089 ENSG00000105750\n",
+      "16090 ZNF100\n",
+      "16091 DPY19L3\n",
+      "16092 CHST8\n",
+      "16093 ENSG00000267053\n",
+      "16094 LINC00665\n",
+      "16095 RASGRP4\n",
+      "16096 MRPS12\n",
+      "16097 EXOSC5\n",
+      "16098 PAFAH1B3\n",
+      "16099 IGFL4\n",
+      "16100 RPS9\n",
+      "16101 COX6B2\n",
+      "16102 ZSCAN5A-AS1\n",
+      "16103 ZNF814\n",
+      "16104 ZSCAN18\n",
+      "16105 A1BG\n",
+      "16106 SDCBP2\n",
+      "16107 SIRPA\n",
+      "16108 DZANK1\n",
+      "16109 RALGAPA2\n",
+      "16110 NXT1\n",
+      "16111 NANP\n",
+      "16112 E2F1\n",
+      "16113 AAR2\n",
+      "16114 ELMO2\n",
+      "16115 NFATC2\n",
+      "16116 RAE1\n",
+      "16117 RAB22A\n",
+      "16118 GNAS\n",
+      "16119 YTHDF1\n",
+      "16120 NRIP1\n",
+      "16121 IL10RB-DT\n",
+      "16122 DSCR9\n",
+      "16123 KCNJ15\n",
+      "16124 PKNOX1\n",
+      "16125 H2BC12L\n",
+      "16126 TRPM2\n",
+      "16127 ESS2\n",
+      "16128 DGCR8\n",
+      "16129 IGLV7-43\n",
+      "16130 SNRPD3\n",
+      "16131 LRP5L\n",
+      "16132 PES1\n",
+      "16133 ENSG00000272720\n",
+      "16134 CCDC134\n",
+      "16135 LINC00106\n",
+      "16136 CNKSR2\n",
+      "16137 WDR45\n",
+      "16138 KANTR\n",
+      "16139 PAGE5\n",
+      "16140 LINC01285\n",
+      "16141 PGRMC1\n",
+      "16142 TMEM185A\n",
+      "16143 BRCC3\n",
+      "16144 LINC00115\n",
+      "16145 B3GALT6\n",
+      "16146 SCNN1D\n",
+      "16147 TMEM52\n",
+      "16148 LNCTAM34A\n",
+      "16149 CTRC\n",
+      "16150 AGMAT\n",
+      "16151 ARHGEF19\n",
+      "16152 ZBTB40\n",
+      "16153 NCMAP\n",
+      "16154 STX12\n",
+      "16155 PTP4A2\n",
+      "16156 KIAA1522\n",
+      "16157 MTF1\n",
+      "16158 CCDC30\n",
+      "16159 SLC2A1\n",
+      "16160 OMA1\n",
+      "16161 DNAJB4\n",
+      "16162 BRDT\n",
+      "16163 TMED5\n",
+      "16164 ABCD3\n",
+      "16165 AMIGO1\n",
+      "16166 CHI3L2\n",
+      "16167 RPRD2\n",
+      "16168 ENSG00000237588\n",
+      "16169 PIGC\n",
+      "16170 RASAL2\n",
+      "16171 VASH2\n",
+      "16172 LBR\n",
+      "16173 TMEM63A\n",
+      "16174 ARID4B\n",
+      "16175 ENSG00000273058\n",
+      "16176 ENSG00000260855\n",
+      "16177 PGBD2\n",
+      "16178 POMC\n",
+      "16179 ENSG00000270640\n",
+      "16180 EHD3\n",
+      "16181 MSH6\n",
+      "16182 EML6\n",
+      "16183 IGKV6-21\n",
+      "16184 ANKRD36\n",
+      "16185 UXS1\n",
+      "16186 ENSG00000231731\n",
+      "16187 MMADHC\n",
+      "16188 METTL8\n",
+      "16189 MAP3K20\n",
+      "16190 ENSG00000271996\n",
+      "16191 TTN-AS1\n",
+      "16192 RPL37A\n",
+      "16193 CCL20\n",
+      "16194 ARL8B\n",
+      "16195 EAF1-AS1\n",
+      "16196 GOLGA4-AS1\n",
+      "16197 LARS2\n",
+      "16198 BSN-DT\n",
+      "16199 RASSF1\n",
+      "16200 DCP1A\n",
+      "16201 ARL6IP5\n",
+      "16202 NAA50\n",
+      "16203 IQCB1\n",
+      "16204 KPNA1\n",
+      "16205 HEG1\n",
+      "16206 GATA2-AS1\n",
+      "16207 ENSG00000203644\n",
+      "16208 TMEM108\n",
+      "16209 ENSG00000268129\n",
+      "16210 CP\n",
+      "16211 TM4SF18\n",
+      "16212 EIF2A\n",
+      "16213 ENSG00000243305\n",
+      "16214 PPM1L\n",
+      "16215 SKIL\n",
+      "16216 ABCF3\n",
+      "16217 DGKG\n",
+      "16218 XXYLT1-AS2\n",
+      "16219 ENSG00000229178\n",
+      "16220 TAPT1\n",
+      "16221 TBC1D19\n",
+      "16222 LINC02472\n",
+      "16223 PCDH7\n",
+      "16224 TMEM156\n",
+      "16225 LNX1\n",
+      "16226 PF4\n",
+      "16227 ENSG00000163633\n",
+      "16228 CCSER1\n",
+      "16229 BMPR1B\n",
+      "16230 RPL34\n",
+      "16231 FABP2\n",
+      "16232 ENSG00000273472\n",
+      "16233 RPS3A\n",
+      "16234 RNF175\n",
+      "16235 C4orf46\n",
+      "16236 GALNTL6\n",
+      "16237 ADCY2\n",
+      "16238 LINC01513\n",
+      "16239 ENSG00000260786\n",
+      "16240 ANXA2R-OT1\n",
+      "16241 MRPS30-DT\n",
+      "16242 ENSG00000271752\n",
+      "16243 ENSG00000240535\n",
+      "16244 ENSG00000271828\n",
+      "16245 F2RL1\n",
+      "16246 CMYA5\n",
+      "16247 ZCCHC9\n",
+      "16248 SKIC3\n",
+      "16249 MAN2A1-DT\n",
+      "16250 LOX\n",
+      "16251 HSPA4\n",
+      "16252 PKD2L2\n",
+      "16253 PROB1\n",
+      "16254 ECSCR\n",
+      "16255 NRG2\n",
+      "16256 PCDHGA10\n",
+      "16257 GALNT10\n",
+      "16258 ITK\n",
+      "16259 MIR3142HG\n",
+      "16260 FLT4\n",
+      "16261 ENSG00000286310\n",
+      "16262 TPMT\n",
+      "16263 ENSG00000271755\n",
+      "16264 HLA-DQA2\n",
+      "16265 VPS52\n",
+      "16266 UQCC2\n",
+      "16267 SCUBE3-AS1\n",
+      "16268 FKBP5\n",
+      "16269 PPIL1\n",
+      "16270 BTBD9\n",
+      "16271 KCNK17\n",
+      "16272 TREML2\n",
+      "16273 YIPF3\n",
+      "16274 XPO5\n",
+      "16275 ENSG00000287562\n",
+      "16276 ENSG00000227706\n",
+      "16277 KCNQ5-DT\n",
+      "16278 ENSG00000223786\n",
+      "16279 SLC16A10\n",
+      "16280 TRAPPC3L\n",
+      "16281 ENSG00000286438\n",
+      "16282 ALDH8A1\n",
+      "16283 ENSG00000232876\n",
+      "16284 MYB\n",
+      "16285 PSMB1\n",
+      "16286 PRKAR1B\n",
+      "16287 PRKAR1B-AS1\n",
+      "16288 COX19\n",
+      "16289 PMS2\n",
+      "16290 INTS15\n",
+      "16291 GSDME\n",
+      "16292 WIPF3\n",
+      "16293 EEPD1\n",
+      "16294 NUDCD3\n",
+      "16295 H2AZ2\n",
+      "16296 SEC61G\n",
+      "16297 EIF4H\n",
+      "16298 GTPBP10\n",
+      "16299 GATAD1\n",
+      "16300 FAM133B\n",
+      "16301 SLC25A13\n",
+      "16302 BRI3\n",
+      "16303 POLR2J3\n",
+      "16304 SLC26A4\n",
+      "16305 LSM8\n",
+      "16306 PODXL\n",
+      "16307 LINC03060\n",
+      "16308 KDM7A-DT\n",
+      "16309 SHH\n",
+      "16310 RNF32\n",
+      "16311 DEFA5\n",
+      "16312 ENSG00000254936\n",
+      "16313 FUT10\n",
+      "16314 ENSG00000269924\n",
+      "16315 PREX2\n",
+      "16316 GDAP1\n",
+      "16317 MRPS28\n",
+      "16318 MAILR\n",
+      "16319 ASTILCS\n",
+      "16320 GRINA\n",
+      "16321 HGH1\n",
+      "16322 TONSL\n",
+      "16323 ENSG00000260912\n",
+      "16324 CDKN2B-AS1\n",
+      "16325 TJP2\n",
+      "16326 C9orf40\n",
+      "16327 ENSG00000287930\n",
+      "16328 ENSG00000237422\n",
+      "16329 ALDOB\n",
+      "16330 RAD23B\n",
+      "16331 POLE3\n",
+      "16332 CFAP157\n",
+      "16333 SLC27A4\n",
+      "16334 IER5L\n",
+      "16335 FNBP1\n",
+      "16336 ABCA2\n",
+      "16337 SSNA1\n",
+      "16338 ENSG00000287016\n",
+      "16339 HNRNPH3\n",
+      "16340 TACR2\n",
+      "16341 RPS24\n",
+      "16342 PI4K2A\n",
+      "16343 BBIP1\n",
+      "16344 PNLIPRP1\n",
+      "16345 SLC18A2\n",
+      "16346 WDR11\n",
+      "16347 ZRANB1\n",
+      "16348 MTG1\n",
+      "16349 TMEM9B-AS1\n",
+      "16350 DNAJC24\n",
+      "16351 MARK2\n",
+      "16352 RASGRP2\n",
+      "16353 PPP6R3\n",
+      "16354 P4HA3\n",
+      "16355 POLD3\n",
+      "16356 FDX1\n",
+      "16357 ENSG00000278376\n",
+      "16358 P3H3\n",
+      "16359 SLC2A14\n",
+      "16360 BCAT1\n",
+      "16361 KRT6A\n",
+      "16362 ESPL1\n",
+      "16363 IKZF4\n",
+      "16364 NXPH4\n",
+      "16365 GLI1\n",
+      "16366 KIF5A\n",
+      "16367 AGAP2\n",
+      "16368 ENSG00000275180\n",
+      "16369 ENSG00000273824\n",
+      "16370 NUP107-DT\n",
+      "16371 SLC25A3\n",
+      "16372 PEBP1\n",
+      "16373 ZNF891\n",
+      "16374 GJB6\n",
+      "16375 POMP\n",
+      "16376 FRY\n",
+      "16377 RGCC\n",
+      "16378 TDRD3\n",
+      "16379 RBM26\n",
+      "16380 NDFIP2\n",
+      "16381 PCCA-DT\n",
+      "16382 TRAV9-2\n",
+      "16383 TRAV17\n",
+      "16384 OXA1L-DT\n",
+      "16385 SLC7A8\n",
+      "16386 SCFD1\n",
+      "16387 ENSG00000257831\n",
+      "16388 RTRAF\n",
+      "16389 ATG14\n",
+      "16390 ARID4A\n",
+      "16391 PLEKHH1\n",
+      "16392 DLST\n",
+      "16393 RPS6KL1\n",
+      "16394 TMED10\n",
+      "16395 CEP128\n",
+      "16396 TDP1\n",
+      "16397 GLRX5\n",
+      "16398 CCNK\n",
+      "16399 ENSG00000259088\n",
+      "16400 PPP1R13B\n",
+      "16401 ADSS1\n",
+      "16402 IGHM\n",
+      "16403 SCG5\n",
+      "16404 ENSG00000287704\n",
+      "16405 APH1B\n",
+      "16406 PIF1\n",
+      "16407 ENSG00000261544\n",
+      "16408 ZWILCH\n",
+      "16409 NEO1\n",
+      "16410 MAN2C1\n",
+      "16411 IQGAP1\n",
+      "16412 IGF1R\n",
+      "16413 ALDH1A3-AS1\n",
+      "16414 UBE2I\n",
+      "16415 CLCN7\n",
+      "16416 ENSG00000261889\n",
+      "16417 CORO7\n",
+      "16418 METTL22\n",
+      "16419 NOMO2\n",
+      "16420 LINC02175\n",
+      "16421 IL21R\n",
+      "16422 ATP2A1\n",
+      "16423 KCTD13-DT\n",
+      "16424 RUSF1-DT\n",
+      "16425 ORC6\n",
+      "16426 ENSG00000288026\n",
+      "16427 CYLD\n",
+      "16428 DYNC1LI2-DT\n",
+      "16429 CIAO2B\n",
+      "16430 CES4A\n",
+      "16431 PSKH1\n",
+      "16432 ENSG00000263276\n",
+      "16433 PLA2G15\n",
+      "16434 ENSG00000275383\n",
+      "16435 ENSG00000275236\n",
+      "16436 PSMD7\n",
+      "16437 LDHD\n",
+      "16438 ATMIN\n",
+      "16439 COTL1\n",
+      "16440 CAMKK1\n",
+      "16441 LINC01996\n",
+      "16442 HES7\n",
+      "16443 ADPRM\n",
+      "16444 TMEM220\n",
+      "16445 ENSG00000227782\n",
+      "16446 TMEM11\n",
+      "16447 LGALS9\n",
+      "16448 ALDOC\n",
+      "16449 TP53I13\n",
+      "16450 SLFN11\n",
+      "16451 CSF3\n",
+      "16452 WIPF2\n",
+      "16453 LINC00910\n",
+      "16454 TOM1L1\n",
+      "16455 COIL\n",
+      "16456 ENSG00000287337\n",
+      "16457 TRIM37\n",
+      "16458 CD79B\n",
+      "16459 ENSG00000265218\n",
+      "16460 PRKCA\n",
+      "16461 ABCA5\n",
+      "16462 NUP85\n",
+      "16463 GALK1\n",
+      "16464 TEN1\n",
+      "16465 UBE2O\n",
+      "16466 ALYREF\n",
+      "16467 CENPX\n",
+      "16468 DCXR-DT\n",
+      "16469 RPRD1A\n",
+      "16470 RELCH\n",
+      "16471 JSRP1\n",
+      "16472 FEM1A\n",
+      "16473 MRPL4\n",
+      "16474 ENSG00000274425\n",
+      "16475 ZNF439\n",
+      "16476 MRPL34\n",
+      "16477 ENSG00000267934\n",
+      "16478 ENSG00000267423\n",
+      "16479 CEBPA\n",
+      "16480 SPINT2\n",
+      "16481 DYRK1B\n",
+      "16482 NUMBL\n",
+      "16483 BCKDHA\n",
+      "16484 ZNF428\n",
+      "16485 PLA2G4C\n",
+      "16486 RPL18\n",
+      "16487 PIH1D1\n",
+      "16488 C19orf48P\n",
+      "16489 KIR2DL3\n",
+      "16490 ENSG00000167633\n",
+      "16491 ZNF544\n",
+      "16492 XRN2\n",
+      "16493 ADA\n",
+      "16494 CSE1L\n",
+      "16495 TMPRSS2\n",
+      "16496 ENSG00000239415\n",
+      "16497 USP18\n",
+      "16498 LRRC74B\n",
+      "16499 ENSG00000099991\n",
+      "16500 GRK3\n",
+      "16501 ENSG00000206028\n",
+      "16502 PIK3IP1\n",
+      "16503 RAC2\n",
+      "16504 MAFF\n",
+      "16505 GTPBP1\n",
+      "16506 ACO2\n",
+      "16507 ENSG00000270083\n",
+      "16508 GTSE1-DT\n",
+      "16509 MAP3K15\n",
+      "16510 CFAP47\n",
+      "16511 ENSG00000287767\n",
+      "16512 ENSG00000226530\n",
+      "16513 FTX\n",
+      "16514 ARMCX1\n",
+      "16515 COL4A5\n",
+      "16516 TENM1\n",
+      "16517 ENSG00000288098\n",
+      "16518 FAM50A\n",
+      "16519 SMIM1\n",
+      "16520 CEP104\n",
+      "16521 ENSG00000284703\n",
+      "16522 MASP2\n",
+      "16523 EXOSC10\n",
+      "16524 CAMK2N1\n",
+      "16525 RAP1GAP\n",
+      "16526 PIGV\n",
+      "16527 TMEM234\n",
+      "16528 CITED4\n",
+      "16529 TOE1\n",
+      "16530 STXBP3\n",
+      "16531 SARS1\n",
+      "16532 LINC01160\n",
+      "16533 ADAMTSL4-AS1\n",
+      "16534 MINDY1\n",
+      "16535 LYSMD1\n",
+      "16536 ENSG00000270361\n",
+      "16537 LY9\n",
+      "16538 DYRK3\n",
+      "16539 IL24\n",
+      "16540 RCOR3\n",
+      "16541 ENSG00000287445\n",
+      "16542 SMYD2\n",
+      "16543 C1orf115\n",
+      "16544 PSEN2\n",
+      "16545 ENSG00000213029\n",
+      "16546 ENSG00000233461\n",
+      "16547 ENSG00000230628\n",
+      "16548 B3GALNT2\n",
+      "16549 RNASEH1-DT\n",
+      "16550 GRASLND\n",
+      "16551 ADAM17\n",
+      "16552 DPYSL5\n",
+      "16553 EIF2B4\n",
+      "16554 CEBPZOS\n",
+      "16555 ENSG00000287387\n",
+      "16556 MSH2\n",
+      "16557 ENSG00000287875\n",
+      "16558 VAMP8\n",
+      "16559 IGKC\n",
+      "16560 REV1\n",
+      "16561 RGPD3\n",
+      "16562 NPHP1\n",
+      "16563 LINC01106\n",
+      "16564 ERICH2-DT\n",
+      "16565 ITGA6\n",
+      "16566 CCNYL1\n",
+      "16567 SPEG\n",
+      "16568 NYAP2\n",
+      "16569 BOK\n",
+      "16570 EDEM1\n",
+      "16571 THUMPD3-AS1\n",
+      "16572 CHCHD4\n",
+      "16573 ACVR2B\n",
+      "16574 ENSG00000271937\n",
+      "16575 ZNF445\n",
+      "16576 ZDHHC3\n",
+      "16577 TMEM158\n",
+      "16578 SACM1L\n",
+      "16579 USP19\n",
+      "16580 ADAMTS9\n",
+      "16581 SLC25A26\n",
+      "16582 FOXP1-DT\n",
+      "16583 LINC00877\n",
+      "16584 ASTE1\n",
+      "16585 NCK1-DT\n",
+      "16586 COPB2\n",
+      "16587 XRN1\n",
+      "16588 P2RY13\n",
+      "16589 ENSG00000287916\n",
+      "16590 RSRC1\n",
+      "16591 MLF1\n",
+      "16592 MAP3K13\n",
+      "16593 CLDN1\n",
+      "16594 SLC51A\n",
+      "16595 UBXN7\n",
+      "16596 UBXN7-AS1\n",
+      "16597 FBXO45\n",
+      "16598 CPEB2\n",
+      "16599 FBXL5\n",
+      "16600 LDB2\n",
+      "16601 PPARGC1A\n",
+      "16602 DHX15\n",
+      "16603 ENSG00000287262\n",
+      "16604 GNPDA2\n",
+      "16605 COMMD8\n",
+      "16606 SPATA18\n",
+      "16607 RASL11B\n",
+      "16608 IGFBP7\n",
+      "16609 CXCL1\n",
+      "16610 THAP6\n",
+      "16611 PLAC8\n",
+      "16612 FAM13A\n",
+      "16613 GRID2\n",
+      "16614 MTTP\n",
+      "16615 BBS7\n",
+      "16616 HHIP\n",
+      "16617 IQCM\n",
+      "16618 GUCY1B1\n",
+      "16619 SAP30\n",
+      "16620 CCT5\n",
+      "16621 NIM1K\n",
+      "16622 TAF9\n",
+      "16623 SERF1B\n",
+      "16624 MAP1B\n",
+      "16625 ANKDD1B\n",
+      "16626 MTX3\n",
+      "16627 ZFYVE16\n",
+      "16628 POU5F2\n",
+      "16629 ENSG00000251023\n",
+      "16630 KIAA0825\n",
+      "16631 CAST\n",
+      "16632 DCP2\n",
+      "16633 SLC22A5\n",
+      "16634 AFF4-DT\n",
+      "16635 CD14\n",
+      "16636 ENSG00000288095\n",
+      "16637 KCTD16\n",
+      "16638 ENSG00000253736\n",
+      "16639 HRH2\n",
+      "16640 RMND5B\n",
+      "16641 CLK4\n",
+      "16642 HEIH\n",
+      "16643 SIRT5\n",
+      "16644 MCUR1\n",
+      "16645 ENSG00000261584\n",
+      "16646 GNL1\n",
+      "16647 ATF6B\n",
+      "16648 HMGA1\n",
+      "16649 CDKN1A\n",
+      "16650 MRPL2\n",
+      "16651 POLH\n",
+      "16652 HSP90AB1\n",
+      "16653 TRAM2\n",
+      "16654 LCA5\n",
+      "16655 CASP8AP2\n",
+      "16656 FUT9\n",
+      "16657 KLHL32\n",
+      "16658 STX7\n",
+      "16659 WAKMAR2\n",
+      "16660 UTRN\n",
+      "16661 LUADT1\n",
+      "16662 MPC1-DT\n",
+      "16663 ENSG00000184786\n",
+      "16664 ENSG00000286228\n",
+      "16665 FGL2\n",
+      "16666 SAMD9L\n",
+      "16667 CNPY4\n",
+      "16668 TSC22D4\n",
+      "16669 ENSG00000259294\n",
+      "16670 NAPEPLD\n",
+      "16671 SYPL1\n",
+      "16672 MDFIC\n",
+      "16673 EXOC4\n",
+      "16674 AKR1B10\n",
+      "16675 ESYT2\n",
+      "16676 VIPR2\n",
+      "16677 RNF122\n",
+      "16678 HEY1\n",
+      "16679 E2F5\n",
+      "16680 RBIS\n",
+      "16681 NECAB1\n",
+      "16682 TP53INP1\n",
+      "16683 VPS13B\n",
+      "16684 DCAF13\n",
+      "16685 TMEM65\n",
+      "16686 CHRAC1\n",
+      "16687 DGAT1\n",
+      "16688 LRRC24\n",
+      "16689 DOCK8-AS1\n",
+      "16690 SNAPC3\n",
+      "16691 NUDT2\n",
+      "16692 ENSG00000269900\n",
+      "16693 CNTNAP3\n",
+      "16694 RMI1\n",
+      "16695 ENSG00000229694\n",
+      "16696 ENSG00000234229\n",
+      "16697 CDC26\n",
+      "16698 ORM1\n",
+      "16699 ENSG00000272593\n",
+      "16700 ENSG00000267834\n",
+      "16701 RAPGEF1\n",
+      "16702 SURF4\n",
+      "16703 PNPLA7\n",
+      "16704 ARRDC1-AS1\n",
+      "16705 ENSG00000287945\n",
+      "16706 DDIT4\n",
+      "16707 ZMIZ1\n",
+      "16708 MORN4\n",
+      "16709 ABCC2\n",
+      "16710 SFXN3\n",
+      "16711 CFAP58-DT\n",
+      "16712 ADRB1\n",
+      "16713 CYP2E1\n",
+      "16714 BET1L\n",
+      "16715 PGGHG\n",
+      "16716 KCNQ1\n",
+      "16717 TRIM5\n",
+      "16718 TMEM41B\n",
+      "16719 GALNT18\n",
+      "16720 LDHAL6A\n",
+      "16721 SVIP\n",
+      "16722 HIPK3\n",
+      "16723 HSD17B12\n",
+      "16724 DDB2\n",
+      "16725 SLC39A13\n",
+      "16726 CD6\n",
+      "16727 FIBP\n",
+      "16728 LINC02754\n",
+      "16729 MRPL21\n",
+      "16730 MYEOV\n",
+      "16731 ENSG00000204971\n",
+      "16732 PAK1\n",
+      "16733 KCTD21\n",
+      "16734 TMEM123-DT\n",
+      "16735 PPP2R1B\n",
+      "16736 ALG9\n",
+      "16737 CEP164\n",
+      "16738 CENATAC-DT\n",
+      "16739 NLRX1\n",
+      "16740 CDON\n",
+      "16741 FOXRED1\n",
+      "16742 TIRAP-AS1\n",
+      "16743 LINC02551\n",
+      "16744 ENSG00000250132\n",
+      "16745 KLRB1\n",
+      "16746 ST8SIA1\n",
+      "16747 ITPR2\n",
+      "16748 ENSG00000276814\n",
+      "16749 BCDIN3D\n",
+      "16750 ASIC1\n",
+      "16751 SMARCD1\n",
+      "16752 KRT4\n",
+      "16753 LINC02381\n",
+      "16754 RDH5\n",
+      "16755 TMBIM4\n",
+      "16756 RNFT2\n",
+      "16757 RNF34\n",
+      "16758 LINC00944\n",
+      "16759 POLE\n",
+      "16760 ENSG00000287530\n",
+      "16761 SLC46A3\n",
+      "16762 NUFIP1\n",
+      "16763 PHF11\n",
+      "16764 EBPL\n",
+      "16765 FGF14-AS2\n",
+      "16766 ENSG00000285789\n",
+      "16767 RAB20\n",
+      "16768 CARS2\n",
+      "16769 PNP\n",
+      "16770 RNASE6\n",
+      "16771 LINC00641\n",
+      "16772 CDH24\n",
+      "16773 HEATR5A\n",
+      "16774 MIA2\n",
+      "16775 ENSG00000258757\n",
+      "16776 PSMA3\n",
+      "16777 ENSG00000258682\n",
+      "16778 LINC03033\n",
+      "16779 HIF1A\n",
+      "16780 PPP2R5E\n",
+      "16781 MAP3K9-DT\n",
+      "16782 SAMD15\n",
+      "16783 SERPINA1\n",
+      "16784 TRMT61A-DT\n",
+      "16785 IGHV4-59\n",
+      "16786 GABPB1-IT1\n",
+      "16787 SLTM\n",
+      "16788 RPS27L\n",
+      "16789 ARIH1\n",
+      "16790 ENSG00000275645\n",
+      "16791 RCN2\n",
+      "16792 ST20-AS1\n",
+      "16793 FSD2\n",
+      "16794 MRPL46\n",
+      "16795 LMF1\n",
+      "16796 THOC6\n",
+      "16797 ENSG00000262370\n",
+      "16798 ENSG00000267072\n",
+      "16799 RMI2\n",
+      "16800 NOMO1\n",
+      "16801 DCTN5\n",
+      "16802 ENSG00000278133\n",
+      "16803 RUSF1\n",
+      "16804 TOX3\n",
+      "16805 CETP\n",
+      "16806 ADGRG5\n",
+      "16807 DPEP3\n",
+      "16808 CDH1\n",
+      "16809 WWP2\n",
+      "16810 TMEM231\n",
+      "16811 GABARAPL2\n",
+      "16812 GSE1\n",
+      "16813 SLC22A31\n",
+      "16814 RPH3AL\n",
+      "16815 PSMB6\n",
+      "16816 KIF1C\n",
+      "16817 DVL2\n",
+      "16818 CD68\n",
+      "16819 ALOX15B\n",
+      "16820 MYH3\n",
+      "16821 B9D1\n",
+      "16822 ANKRD13B\n",
+      "16823 G6PC3\n",
+      "16824 EFCAB13\n",
+      "16825 ENSG00000264895\n",
+      "16826 NACA2\n",
+      "16827 ENSG00000274565\n",
+      "16828 MAP3K3\n",
+      "16829 ENSG00000176809\n",
+      "16830 SMIM5\n",
+      "16831 CBX4\n",
+      "16832 SIRT7\n",
+      "16833 MILIP\n",
+      "16834 ENSG00000272799\n",
+      "16835 ACAA2\n",
+      "16836 BCL2\n",
+      "16837 ENSG00000266990\n",
+      "16838 APC2\n",
+      "16839 DOT1L\n",
+      "16840 AMH\n",
+      "16841 ENSG00000267011\n",
+      "16842 ENSG00000214248\n",
+      "16843 ZNF558\n",
+      "16844 ILF3-DT\n",
+      "16845 CNN1\n",
+      "16846 ZNF726\n",
+      "16847 ZNF599\n",
+      "16848 TMEM147-AS1\n",
+      "16849 ENSG00000267892\n",
+      "16850 CEACAM4\n",
+      "16851 SLC1A5\n",
+      "16852 JOSD2\n",
+      "16853 FPR2\n",
+      "16854 FPR3\n",
+      "16855 ZNF880\n",
+      "16856 CCDC106\n",
+      "16857 EPN1\n",
+      "16858 ZSCAN5A\n",
+      "16859 ZNF264\n",
+      "16860 NRSN2-AS1\n",
+      "16861 MRPS26\n",
+      "16862 CENPB\n",
+      "16863 RBBP9\n",
+      "16864 LINC01431\n",
+      "16865 APMAP\n",
+      "16866 NECAB3\n",
+      "16867 MANBAL\n",
+      "16868 FAM217B\n",
+      "16869 SLC17A9\n",
+      "16870 ENSG00000281383\n",
+      "16871 SMIM11\n",
+      "16872 SLC37A1\n",
+      "16873 TMEM191C\n",
+      "16874 ENSG00000274422\n",
+      "16875 IGLV3-25\n",
+      "16876 PCAT14\n",
+      "16877 OSM\n",
+      "16878 HMOX1\n",
+      "16879 SH3BP1\n",
+      "16880 CYP2D6\n",
+      "16881 ENSG00000281849\n",
+      "16882 MSL3\n",
+      "16883 NHS\n",
+      "16884 GPR34\n",
+      "16885 ZNF630\n",
+      "16886 TMSB15B-AS1\n",
+      "16887 CUL4B\n",
+      "16888 MT-ND2\n",
+      "16889 KLHL17\n",
+      "16890 ISG15\n",
+      "16891 TNFRSF4\n",
+      "16892 CPTP\n",
+      "16893 SLC25A34-AS1\n",
+      "16894 PADI4\n",
+      "16895 CAPZB\n",
+      "16896 PLA2G2D\n",
+      "16897 MACO1\n",
+      "16898 ENSG00000225643\n",
+      "16899 ENSG00000233478\n",
+      "16900 SNHG3\n",
+      "16901 TXLNA\n",
+      "16902 ZBTB8B\n",
+      "16903 KIAA0319L\n",
+      "16904 C1orf122\n",
+      "16905 MYCBP\n",
+      "16906 AKR1A1\n",
+      "16907 FAAH\n",
+      "16908 TAL1\n",
+      "16909 LINC02812\n",
+      "16910 HOOK1\n",
+      "16911 ENSG00000248458\n",
+      "16912 ENSG00000287647\n",
+      "16913 AP4B1-AS1\n",
+      "16914 ENSG00000276110\n",
+      "16915 UBE2Q1\n",
+      "16916 ASH1L\n",
+      "16917 LAMTOR2\n",
+      "16918 CD1E\n",
+      "16919 ENSG00000198358\n",
+      "16920 RNF2\n",
+      "16921 PLA2G4A\n",
+      "16922 LINC01353\n",
+      "16923 DSTYK\n",
+      "16924 FAM72A\n",
+      "16925 ENSG00000261000\n",
+      "16926 NEK2-DT\n",
+      "16927 TATDN3\n",
+      "16928 C1orf131\n",
+      "16929 ODC1\n",
+      "16930 HS1BP3\n",
+      "16931 ATRAID\n",
+      "16932 LINC00486\n",
+      "16933 PPM1B-DT\n",
+      "16934 PRKCE\n",
+      "16935 UGP2\n",
+      "16936 PCBP1-AS1\n",
+      "16937 ENSG00000286623\n",
+      "16938 MOGS\n",
+      "16939 TRABD2A\n",
+      "16940 IGKV3D-20\n",
+      "16941 TMEM131\n",
+      "16942 IL1R1\n",
+      "16943 RGPD5\n",
+      "16944 TMEM177\n",
+      "16945 ENSG00000227902\n",
+      "16946 CIR1\n",
+      "16947 ENSG00000272551\n",
+      "16948 SESTD1\n",
+      "16949 CASP8\n",
+      "16950 ENSG00000229352\n",
+      "16951 CXCR1\n",
+      "16952 USP37\n",
+      "16953 STK16\n",
+      "16954 CHPF\n",
+      "16955 ERFE\n",
+      "16956 CRBN\n",
+      "16957 BHLHE40\n",
+      "16958 RPUSD3\n",
+      "16959 LINC00852\n",
+      "16960 TMEM40\n",
+      "16961 DYNC1LI1\n",
+      "16962 ARIH2\n",
+      "16963 ENSG00000235236\n",
+      "16964 RAD54L2\n",
+      "16965 PHF7\n",
+      "16966 SLMAP\n",
+      "16967 LMOD3\n",
+      "16968 GPR15\n",
+      "16969 EAF2\n",
+      "16970 LINC02035\n",
+      "16971 ENSG00000273437\n",
+      "16972 NEK11\n",
+      "16973 B3GALNT1\n",
+      "16974 SPTSSB\n",
+      "16975 TTC14\n",
+      "16976 LINC02043\n",
+      "16977 RUBCN\n",
+      "16978 HTT\n",
+      "16979 HS3ST1\n",
+      "16980 LIAS\n",
+      "16981 ENSG00000269921\n",
+      "16982 CSN2\n",
+      "16983 PPEF2\n",
+      "16984 ELOVL6\n",
+      "16985 TNIP3\n",
+      "16986 ELMOD2\n",
+      "16987 ENSG00000287292\n",
+      "16988 ENSG00000270681\n",
+      "16989 MSMO1\n",
+      "16990 NEK1\n",
+      "16991 DNAH5\n",
+      "16992 C5orf34\n",
+      "16993 FAM169A-AS1\n",
+      "16994 RIOK2\n",
+      "16995 CEP120\n",
+      "16996 CDC42SE2\n",
+      "16997 SPATA24\n",
+      "16998 PCDHGA6\n",
+      "16999 GNPDA1\n",
+      "17000 SPINK5\n",
+      "17001 SH3TC2\n",
+      "17002 PPARGC1B\n",
+      "17003 SLC36A1\n",
+      "17004 ENSG00000286749\n",
+      "17005 TIMD4\n",
+      "17006 LSM11\n",
+      "17007 RPL26L1-AS1\n",
+      "17008 ATP6V0E1\n",
+      "17009 SFXN1\n",
+      "17010 EXOC2\n",
+      "17011 LYRM4-AS1\n",
+      "17012 RIOK1\n",
+      "17013 RANBP9\n",
+      "17014 BTN3A1\n",
+      "17015 LINC01012\n",
+      "17016 ZKSCAN8\n",
+      "17017 IER3\n",
+      "17018 LST1\n",
+      "17019 AGER\n",
+      "17020 ILRUN-AS1\n",
+      "17021 CLPS\n",
+      "17022 TMEM63B\n",
+      "17023 ENSG00000236996\n",
+      "17024 FAM135A\n",
+      "17025 RWDD2A\n",
+      "17026 ENSG00000272008\n",
+      "17027 PM20D2\n",
+      "17028 SYTL3\n",
+      "17029 MPC1\n",
+      "17030 IQCE\n",
+      "17031 RBAK\n",
+      "17032 RPA3\n",
+      "17033 GARS1\n",
+      "17034 ENSG00000272908\n",
+      "17035 UPP1\n",
+      "17036 SPDYE21\n",
+      "17037 PTPN12\n",
+      "17038 PDK4\n",
+      "17039 UFSP1\n",
+      "17040 ENSG00000237513\n",
+      "17041 CBLL1-AS1\n",
+      "17042 TFEC\n",
+      "17043 CADPS2\n",
+      "17044 LINC03072\n",
+      "17045 LINC00513\n",
+      "17046 BPGM\n",
+      "17047 NUP205\n",
+      "17048 EPHA1-AS1\n",
+      "17049 ENSG00000287636\n",
+      "17050 REEP4\n",
+      "17051 GPAT4\n",
+      "17052 SMIM19\n",
+      "17053 ENSG00000272076\n",
+      "17054 TOX\n",
+      "17055 DNAJC5B\n",
+      "17056 ENSG00000287127\n",
+      "17057 DECR1\n",
+      "17058 CCNE2\n",
+      "17059 SLC25A32\n",
+      "17060 EMC2\n",
+      "17061 ATAD2\n",
+      "17062 LINC02055\n",
+      "17063 LY6E-DT\n",
+      "17064 FOCAD\n",
+      "17065 AQP3\n",
+      "17066 DCTN3\n",
+      "17067 PIGO\n",
+      "17068 FXN\n",
+      "17069 NTRK2\n",
+      "17070 CTNNAL1\n",
+      "17071 SCAI\n",
+      "17072 ZBTB34\n",
+      "17073 ST6GALNAC6\n",
+      "17074 ENSG00000268050\n",
+      "17075 PRRX2\n",
+      "17076 USP20\n",
+      "17077 VAV2\n",
+      "17078 ENSG00000204683\n",
+      "17079 BMI1\n",
+      "17080 PIP4K2A\n",
+      "17081 YME1L1\n",
+      "17082 ODAD2\n",
+      "17083 MAP3K8\n",
+      "17084 ENSG00000287420\n",
+      "17085 NRP1\n",
+      "17086 CSTF2T\n",
+      "17087 LDB3\n",
+      "17088 TLL2\n",
+      "17089 PIK3AP1\n",
+      "17090 GOLGA7B\n",
+      "17091 CUTC\n",
+      "17092 ERLIN1\n",
+      "17093 ENSG00000272572\n",
+      "17094 C10orf95-AS1\n",
+      "17095 DMBT1\n",
+      "17096 ADAM12\n",
+      "17097 PNPLA2\n",
+      "17098 SLC22A18AS\n",
+      "17099 CSNK2A3\n",
+      "17100 IGSF22\n",
+      "17101 LIN7C\n",
+      "17102 WT1-AS\n",
+      "17103 LARGE2\n",
+      "17104 CLP1\n",
+      "17105 TMX2\n",
+      "17106 TKFC\n",
+      "17107 TMEM216\n",
+      "17108 FADS1\n",
+      "17109 FTH1\n",
+      "17110 EHD1\n",
+      "17111 CAPN1-AS1\n",
+      "17112 KLC2\n",
+      "17113 SSH3\n",
+      "17114 RPS6KB2\n",
+      "17115 ATG16L2\n",
+      "17116 PRCP\n",
+      "17117 RAB30-DT\n",
+      "17118 CEP126\n",
+      "17119 NCAM1\n",
+      "17120 TMPRSS4\n",
+      "17121 CD3D\n",
+      "17122 TLCD5\n",
+      "17123 TNFRSF1A\n",
+      "17124 ENSG00000247853\n",
+      "17125 ATN1\n",
+      "17126 LINC00937\n",
+      "17127 CLEC4D\n",
+      "17128 CLEC1A\n",
+      "17129 ENSG00000165714\n",
+      "17130 ETFRF1\n",
+      "17131 LRRK2-DT\n",
+      "17132 GXYLT1\n",
+      "17133 RPAP3\n",
+      "17134 FKBP11\n",
+      "17135 BCDIN3D-AS1\n",
+      "17136 MFSD5\n",
+      "17137 ENSG00000257553\n",
+      "17138 NEMP1\n",
+      "17139 TMEM19\n",
+      "17140 SOCS2\n",
+      "17141 PARPBP\n",
+      "17142 ENSG00000286067\n",
+      "17143 PSMD9\n",
+      "17144 GLT1D1\n",
+      "17145 ENSG00000250790\n",
+      "17146 LINC00539\n",
+      "17147 UFM1\n",
+      "17148 LINC00562\n",
+      "17149 THSD1\n",
+      "17150 UBAC2\n",
+      "17151 NAXD-AS1\n",
+      "17152 PIP4P1\n",
+      "17153 ABHD4\n",
+      "17154 PSME1\n",
+      "17155 ENSG00000276698\n",
+      "17156 ENSG00000258081\n",
+      "17157 ABHD12B\n",
+      "17158 ENSG00000277050\n",
+      "17159 KTN1\n",
+      "17160 ABCD4\n",
+      "17161 PSMC1\n",
+      "17162 IGHV1-18\n",
+      "17163 ENSG00000258410\n",
+      "17164 ENSG00000259523\n",
+      "17165 GOLGA8Q\n",
+      "17166 LINC03034\n",
+      "17167 LPCAT4\n",
+      "17168 INAFM2\n",
+      "17169 DISP2\n",
+      "17170 NDUFAF1\n",
+      "17171 SLC28A2-AS1\n",
+      "17172 GATM\n",
+      "17173 DUT-AS1\n",
+      "17174 USP8\n",
+      "17175 BNIP2\n",
+      "17176 ICE2\n",
+      "17177 PML\n",
+      "17178 ENSG00000286813\n",
+      "17179 ARNT2-DT\n",
+      "17180 KLHL25\n",
+      "17181 FAM174B\n",
+      "17182 LINC00923\n",
+      "17183 RAB11FIP3\n",
+      "17184 PIGQ\n",
+      "17185 SNHG19\n",
+      "17186 PRSS22\n",
+      "17187 NAA60\n",
+      "17188 ENSG00000275056\n",
+      "17189 ENSG00000275445\n",
+      "17190 ENSG00000275494\n",
+      "17191 SLX1A\n",
+      "17192 KAT8\n",
+      "17193 NKD1\n",
+      "17194 ENSG00000261653\n",
+      "17195 ENSG00000276075\n",
+      "17196 CARMIL2\n",
+      "17197 THAP11\n",
+      "17198 SMPD3\n",
+      "17199 CDH3\n",
+      "17200 AP1G1\n",
+      "17201 CTRB2\n",
+      "17202 KIAA0513\n",
+      "17203 C16orf74\n",
+      "17204 SNAI3\n",
+      "17205 ALOX15\n",
+      "17206 TNFSF12\n",
+      "17207 EIF4A1\n",
+      "17208 MPDU1\n",
+      "17209 PMP22\n",
+      "17210 MED9\n",
+      "17211 LINC02076\n",
+      "17212 ULK2\n",
+      "17213 ENSG00000265287\n",
+      "17214 ENSG00000276241\n",
+      "17215 GSDMB\n",
+      "17216 MSL1\n",
+      "17217 KRT17\n",
+      "17218 PLEKHH3\n",
+      "17219 TMEM106A\n",
+      "17220 MEOX1\n",
+      "17221 SLC25A39\n",
+      "17222 DCAKD\n",
+      "17223 ENSG00000262265\n",
+      "17224 ENSG00000204584\n",
+      "17225 XYLT2\n",
+      "17226 TOB1\n",
+      "17227 SCPEP1\n",
+      "17228 MARCHF10\n",
+      "17229 LIMD2\n",
+      "17230 PRR29-AS1\n",
+      "17231 KCNJ2\n",
+      "17232 CDC42EP4\n",
+      "17233 GRIN2C\n",
+      "17234 MIF4GD\n",
+      "17235 AANAT\n",
+      "17236 TMC8\n",
+      "17237 ENSG00000262979\n",
+      "17238 ENSG00000267136\n",
+      "17239 ZNF397\n",
+      "17240 DYM\n",
+      "17241 KDSR\n",
+      "17242 ZNF407\n",
+      "17243 ZNF699\n",
+      "17244 ZNF559\n",
+      "17245 ANGPTL6\n",
+      "17246 ZNF563\n",
+      "17247 TRIR\n",
+      "17248 TRMT1\n",
+      "17249 ENSG00000267474\n",
+      "17250 NDUFB7\n",
+      "17251 ENSG00000267033\n",
+      "17252 KCNN1\n",
+      "17253 MAST3\n",
+      "17254 SSBP4\n",
+      "17255 FKBP8\n",
+      "17256 ZNF254\n",
+      "17257 LINC01535\n",
+      "17258 CEACAM6\n",
+      "17259 LYPD3\n",
+      "17260 CEACAM19\n",
+      "17261 ZNF175\n",
+      "17262 TFPT\n",
+      "17263 ZNF580\n",
+      "17264 ZIK1\n",
+      "17265 ZNF586\n",
+      "17266 ZNF343\n",
+      "17267 BTBD3\n",
+      "17268 GZF1\n",
+      "17269 SNTA1\n",
+      "17270 RALY-AS1\n",
+      "17271 AHCY\n",
+      "17272 DYNLRB1\n",
+      "17273 ENSG00000286803\n",
+      "17274 ENSG00000277235\n",
+      "17275 SLC12A5\n",
+      "17276 TUBB1\n",
+      "17277 ENSG00000268858\n",
+      "17278 ENSG00000275139\n",
+      "17279 MIF-AS1\n",
+      "17280 ENSG00000239446\n",
+      "17281 TRIOBP\n",
+      "17282 FBLN1\n",
+      "17283 TRABD\n",
+      "17284 CIMAP1B\n",
+      "17285 GTPBP6\n",
+      "17286 LINC02154\n",
+      "17287 DIPK2B\n",
+      "17288 ZNF674-AS1\n",
+      "17289 WDR13\n",
+      "17290 TIMM17B\n",
+      "17291 MAGIX\n",
+      "17292 SPIN4\n",
+      "17293 RENBP\n",
+      "17294 RPL10\n",
+      "17295 TAFAZZIN\n",
+      "17296 ENSG00000286009\n",
+      "17297 ENSG00000278384\n",
+      "17298 MRPL20-DT\n",
+      "17299 ENSG00000149527\n",
+      "17300 KIF1B\n",
+      "17301 PLEKHM2\n",
+      "17302 PADI2\n",
+      "17303 ECE1\n",
+      "17304 ZNF683\n",
+      "17305 SLC2A1-DT\n",
+      "17306 CFAP144\n",
+      "17307 ST3GAL3-AS1\n",
+      "17308 TUT4\n",
+      "17309 LINC01748\n",
+      "17310 SSX2IP\n",
+      "17311 ZNHIT6\n",
+      "17312 SNX7\n",
+      "17313 MFSD14A\n",
+      "17314 LRRC39\n",
+      "17315 GNAI3\n",
+      "17316 RNF115\n",
+      "17317 NBPF11\n",
+      "17318 ENSG00000272196\n",
+      "17319 SPRR1B\n",
+      "17320 NAXE\n",
+      "17321 FCRL6\n",
+      "17322 SH2D1B\n",
+      "17323 ENSG00000285280\n",
+      "17324 CRB1\n",
+      "17325 LEMD1-DT\n",
+      "17326 IL19\n",
+      "17327 BATF3\n",
+      "17328 KCNK1\n",
+      "17329 SDCCAG8\n",
+      "17330 ENSG00000232347\n",
+      "17331 ENSG00000284824\n",
+      "17332 LINC01871\n",
+      "17333 KIDINS220\n",
+      "17334 ENSG00000260077\n",
+      "17335 ENSG00000272275\n",
+      "17336 C2orf50\n",
+      "17337 TTC32-DT\n",
+      "17338 FKBP1B\n",
+      "17339 RASGRP3\n",
+      "17340 FAM98A\n",
+      "17341 RMDN2\n",
+      "17342 CALM2\n",
+      "17343 C2orf42\n",
+      "17344 ZNF638\n",
+      "17345 GCFC2\n",
+      "17346 TCF7L1\n",
+      "17347 GNLY\n",
+      "17348 MAL\n",
+      "17349 ANKRD36C\n",
+      "17350 GPAT2\n",
+      "17351 MGAT4A\n",
+      "17352 IL18R1\n",
+      "17353 IL1RN\n",
+      "17354 DPP10\n",
+      "17355 ARL5A\n",
+      "17356 UBR3\n",
+      "17357 WIPF1\n",
+      "17358 AGPS\n",
+      "17359 ENSG00000284052\n",
+      "17360 FLACC1\n",
+      "17361 ICOS\n",
+      "17362 ENSG00000223725\n",
+      "17363 IDH1-AS1\n",
+      "17364 BARD1\n",
+      "17365 CYP27A1\n",
+      "17366 PSMD1\n",
+      "17367 ENSG00000235978\n",
+      "17368 PRRT3\n",
+      "17369 IRAK2\n",
+      "17370 GHRLOS\n",
+      "17371 TAMM41\n",
+      "17372 SATB1-AS1\n",
+      "17373 PDCD6IP\n",
+      "17374 TRANK1\n",
+      "17375 ENSG00000283849\n",
+      "17376 TCAIM\n",
+      "17377 TLR9\n",
+      "17378 PDZRN3\n",
+      "17379 SLC49A4\n",
+      "17380 CNBP\n",
+      "17381 TOPBP1\n",
+      "17382 MRPS22\n",
+      "17383 IFT80\n",
+      "17384 ENSG00000270096\n",
+      "17385 RFC4\n",
+      "17386 LPP\n",
+      "17387 FGF12\n",
+      "17388 LINC01063\n",
+      "17389 RNF212\n",
+      "17390 LINC00504\n",
+      "17391 ATP8A1\n",
+      "17392 PAICS\n",
+      "17393 FAM47E\n",
+      "17394 RASGEF1B\n",
+      "17395 GSTCD\n",
+      "17396 SMIM31\n",
+      "17397 ENSG00000248551\n",
+      "17398 RWDD4\n",
+      "17399 STOX2\n",
+      "17400 DAP\n",
+      "17401 OSMR\n",
+      "17402 DHX29\n",
+      "17403 SMN1\n",
+      "17404 CCNH\n",
+      "17405 LINC01170\n",
+      "17406 ENSG00000250602\n",
+      "17407 ENSG00000283897\n",
+      "17408 RAPGEF6\n",
+      "17409 ENSG00000271737\n",
+      "17410 EIF4EBP3\n",
+      "17411 PCDHGA1\n",
+      "17412 ABLIM3\n",
+      "17413 FAXDC2\n",
+      "17414 THG1L\n",
+      "17415 MRNIP\n",
+      "17416 GMDS\n",
+      "17417 SERPINB1\n",
+      "17418 H2BC17\n",
+      "17419 ZSCAN26\n",
+      "17420 TNF\n",
+      "17421 CSNK2B\n",
+      "17422 ENSG00000273333\n",
+      "17423 PBX2\n",
+      "17424 DEF6\n",
+      "17425 TMEM217\n",
+      "17426 TAF8\n",
+      "17427 MEA1\n",
+      "17428 EEF1A1-AS1\n",
+      "17429 SYNCRIP\n",
+      "17430 SLC35A1\n",
+      "17431 MAP7-AS1\n",
+      "17432 RAC1\n",
+      "17433 CREB5\n",
+      "17434 TRGV5\n",
+      "17435 ZMIZ2\n",
+      "17436 TYW1\n",
+      "17437 FZD1\n",
+      "17438 PPP1R35\n",
+      "17439 SAP25\n",
+      "17440 CUX1\n",
+      "17441 UPK3BL2\n",
+      "17442 PRKAR2B\n",
+      "17443 ENSG00000285106\n",
+      "17444 ENSG00000271204\n",
+      "17445 TRBV6-5\n",
+      "17446 TRBV13\n",
+      "17447 EPHB6\n",
+      "17448 CUL1\n",
+      "17449 GIMAP1\n",
+      "17450 CDK5\n",
+      "17451 CLN8-AS1\n",
+      "17452 ENSG00000284957\n",
+      "17453 MTMR7\n",
+      "17454 SH2D4A\n",
+      "17455 LPL\n",
+      "17456 DCTN6\n",
+      "17457 ENSG00000272155\n",
+      "17458 ENSG00000271971\n",
+      "17459 ZNNT1\n",
+      "17460 ENSG00000287208\n",
+      "17461 TAF2\n",
+      "17462 C8orf76\n",
+      "17463 PSCA\n",
+      "17464 LINC02878\n",
+      "17465 ENSG00000255224\n",
+      "17466 HAUS6\n",
+      "17467 ENSG00000230074\n",
+      "17468 FANCG\n",
+      "17469 CREB3\n",
+      "17470 ENSG00000250850\n",
+      "17471 CARD19\n",
+      "17472 AMBP\n",
+      "17473 ENSG00000279571\n",
+      "17474 ENSG00000241084\n",
+      "17475 ENSG00000236986\n",
+      "17476 RALGDS\n",
+      "17477 RABL6\n",
+      "17478 EDF1\n",
+      "17479 ENSG00000231483\n",
+      "17480 ARHGAP21\n",
+      "17481 LINC02916\n",
+      "17482 STOX1\n",
+      "17483 KCNMA1\n",
+      "17484 GPR15LG\n",
+      "17485 MINPP1\n",
+      "17486 ZFYVE27\n",
+      "17487 BLOC1S2\n",
+      "17488 NFKB2\n",
+      "17489 ENSG00000260461\n",
+      "17490 ENSG00000270589\n",
+      "17491 LRRC27\n",
+      "17492 TMEM80\n",
+      "17493 AP2A2\n",
+      "17494 DCHS1\n",
+      "17495 PTGDR2\n",
+      "17496 DDB1\n",
+      "17497 BSCL2\n",
+      "17498 NAA40\n",
+      "17499 CCDC88B\n",
+      "17500 PPP2R5B\n",
+      "17501 AP5B1\n",
+      "17502 SART1\n",
+      "17503 ANKRD13D\n",
+      "17504 CHKA\n",
+      "17505 RSF1\n",
+      "17506 ALG8\n",
+      "17507 RAB30\n",
+      "17508 IL10RA\n",
+      "17509 VPS11\n",
+      "17510 ESAM-AS1\n",
+      "17511 LINC02725\n",
+      "17512 CACNA2D4\n",
+      "17513 ACRBP\n",
+      "17514 MED21\n",
+      "17515 IPO8\n",
+      "17516 ENSG00000274964\n",
+      "17517 ANO6\n",
+      "17518 ARF3\n",
+      "17519 WNT10B\n",
+      "17520 TROAP\n",
+      "17521 COPZ1\n",
+      "17522 R3HDM2-DT\n",
+      "17523 CCT2\n",
+      "17524 KCNMB4\n",
+      "17525 PHLDA1-DT\n",
+      "17526 CSRP2\n",
+      "17527 LIN7A\n",
+      "17528 USP30\n",
+      "17529 ARPC3\n",
+      "17530 ALDH2\n",
+      "17531 MAPKAPK5-AS1\n",
+      "17532 ENSG00000274227\n",
+      "17533 PTPN11\n",
+      "17534 CFAP73\n",
+      "17535 UNC119B\n",
+      "17536 MICU2\n",
+      "17537 MTUS2\n",
+      "17538 FRY-AS1\n",
+      "17539 SMAD9\n",
+      "17540 VWA8\n",
+      "17541 ENSG00000280710\n",
+      "17542 ERCC5\n",
+      "17543 TRAV8-2\n",
+      "17544 CARMIL3\n",
+      "17545 TINF2\n",
+      "17546 DACT1\n",
+      "17547 HIF1A-AS3\n",
+      "17548 ESR2\n",
+      "17549 PLEKHG3\n",
+      "17550 SUSD6\n",
+      "17551 MIDEAS\n",
+      "17552 AREL1\n",
+      "17553 FCF1\n",
+      "17554 TTLL5\n",
+      "17555 TMED8\n",
+      "17556 SYNE3\n",
+      "17557 MOK\n",
+      "17558 CDC42BPB\n",
+      "17559 ENSG00000256050\n",
+      "17560 LINC02298\n",
+      "17561 VPS18\n",
+      "17562 OIP5-AS1\n",
+      "17563 GOLM2\n",
+      "17564 ENSG00000273674\n",
+      "17565 ZFAND6\n",
+      "17566 NTRK3\n",
+      "17567 ABHD2\n",
+      "17568 ANPEP\n",
+      "17569 NUDT16L1\n",
+      "17570 SEPTIN12\n",
+      "17571 RBFOX1\n",
+      "17572 PRKCB\n",
+      "17573 GDPD3\n",
+      "17574 ENSG00000285367\n",
+      "17575 ENSG00000261804\n",
+      "17576 ENSG00000125122\n",
+      "17577 ATP6V0D1\n",
+      "17578 ENSG00000270165\n",
+      "17579 PHLPP2\n",
+      "17580 TXNL4B\n",
+      "17581 CTU2\n",
+      "17582 WDR81\n",
+      "17583 ZMYND15\n",
+      "17584 DHX33\n",
+      "17585 MAPK7\n",
+      "17586 CPD\n",
+      "17587 TEFM\n",
+      "17588 ERBB2\n",
+      "17589 C17orf113\n",
+      "17590 ENSG00000267758\n",
+      "17591 PLCD3\n",
+      "17592 HEXIM2-AS1\n",
+      "17593 COPZ2\n",
+      "17594 NFE2L1-DT\n",
+      "17595 SNX11\n",
+      "17596 ABI3\n",
+      "17597 ITGA3\n",
+      "17598 NME2\n",
+      "17599 ACE\n",
+      "17600 SLC16A6\n",
+      "17601 KIF19\n",
+      "17602 RAB37\n",
+      "17603 H3-3B\n",
+      "17604 SNHG20\n",
+      "17605 SLC26A11\n",
+      "17606 ENDOV\n",
+      "17607 NDUFAF8\n",
+      "17608 P4HB\n",
+      "17609 LINC01970\n",
+      "17610 ENSG00000261845\n",
+      "17611 ZNF521\n",
+      "17612 SLC39A6\n",
+      "17613 SMAD7\n",
+      "17614 ENSG00000267787\n",
+      "17615 DOK6\n",
+      "17616 POLR2E\n",
+      "17617 ENSG00000261526\n",
+      "17618 LINGO3\n",
+      "17619 ZNF77\n",
+      "17620 INSR\n",
+      "17621 ARHGEF18-AS1\n",
+      "17622 FBXL12\n",
+      "17623 TMED1\n",
+      "17624 ENSG00000267379\n",
+      "17625 ENSG00000277825\n",
+      "17626 ENSG00000269578\n",
+      "17627 OCEL1\n",
+      "17628 B3GNT3\n",
+      "17629 ENSG00000268081\n",
+      "17630 LINC01801\n",
+      "17631 ZNF461\n",
+      "17632 LRFN1\n",
+      "17633 PRX\n",
+      "17634 LTBP4\n",
+      "17635 CD79A\n",
+      "17636 FBXO46\n",
+      "17637 INAFM1\n",
+      "17638 CYTH2\n",
+      "17639 ZNF350\n",
+      "17640 ZNF615\n",
+      "17641 ZNF578\n",
+      "17642 LILRB3\n",
+      "17643 ZNF550\n",
+      "17644 ZNF584\n",
+      "17645 ZNF132-DT\n",
+      "17646 MGME1\n",
+      "17647 CBFA2T2\n",
+      "17648 SLC32A1\n",
+      "17649 ENSG00000280441\n",
+      "17650 ENSG00000280594\n",
+      "17651 TIAM1-AS1\n",
+      "17652 CFAP298\n",
+      "17653 ENSG00000159131\n",
+      "17654 CHAF1B\n",
+      "17655 ENSG00000184441\n",
+      "17656 SLC19A1\n",
+      "17657 ENSG00000286990\n",
+      "17658 IGLV3-16\n",
+      "17659 KIAA1671\n",
+      "17660 SFI1\n",
+      "17661 GGA1\n",
+      "17662 NPTXR\n",
+      "17663 APOBEC3A\n",
+      "17664 SGSM3-AS1\n",
+      "17665 L3MBTL2-AS1\n",
+      "17666 RPS6KA3\n",
+      "17667 MSN\n",
+      "17668 CHIC1\n",
+      "17669 TAF9B\n",
+      "17670 FMR1-AS1\n",
+      "17671 TNFRSF9\n",
+      "17672 SLC25A33\n",
+      "17673 LZIC\n",
+      "17674 PEX14\n",
+      "17675 CASZ1\n",
+      "17676 ENSG00000226849\n",
+      "17677 EMC1\n",
+      "17678 USP48\n",
+      "17679 HDAC1\n",
+      "17680 FAM229A\n",
+      "17681 YRDC\n",
+      "17682 DYNLT5\n",
+      "17683 ANKRD13C\n",
+      "17684 SLC44A3\n",
+      "17685 RAP1A\n",
+      "17686 PTGFRN\n",
+      "17687 SPAG17\n",
+      "17688 BOLA1\n",
+      "17689 CGN\n",
+      "17690 ENSG00000232536\n",
+      "17691 TDRKH\n",
+      "17692 RABGAP1L\n",
+      "17693 C1orf220\n",
+      "17694 RGSL1\n",
+      "17695 C1orf21\n",
+      "17696 RGS18\n",
+      "17697 ASPM\n",
+      "17698 KIF21B\n",
+      "17699 TMEM81\n",
+      "17700 ELK4\n",
+      "17701 KCNH1\n",
+      "17702 CENPF\n",
+      "17703 ESRRG\n",
+      "17704 FAM177B\n",
+      "17705 H3-3A\n",
+      "17706 ARF1\n",
+      "17707 CHRM3-AS2\n",
+      "17708 GCSAML\n",
+      "17709 ZNF692\n",
+      "17710 ASAP2\n",
+      "17711 CRIM1\n",
+      "17712 DYNC2LI1\n",
+      "17713 CAMKMT\n",
+      "17714 LGALSL\n",
+      "17715 ENSG00000226756\n",
+      "17716 PPP3R1\n",
+      "17717 NAT8\n",
+      "17718 DCTN1\n",
+      "17719 IGKV1-6\n",
+      "17720 SNRNP200\n",
+      "17721 ENSG00000241962\n",
+      "17722 ANAPC1\n",
+      "17723 DBI\n",
+      "17724 CACNB4\n",
+      "17725 MARCHF7\n",
+      "17726 CD302\n",
+      "17727 SCN1A-AS1\n",
+      "17728 EEF1B2\n",
+      "17729 LINC02832\n",
+      "17730 DAW1\n",
+      "17731 PTMA\n",
+      "17732 LRRFIP1\n",
+      "17733 ENSG00000283635\n",
+      "17734 ASB1\n",
+      "17735 RNPEPL1\n",
+      "17736 FARP2\n",
+      "17737 EFHB\n",
+      "17738 SGO1\n",
+      "17739 LINC02033\n",
+      "17740 CTDSPL\n",
+      "17741 HIGD1A\n",
+      "17742 DCAF1\n",
+      "17743 DENND6A-AS1\n",
+      "17744 PDHB\n",
+      "17745 PSMD6-AS1\n",
+      "17746 SIDT1\n",
+      "17747 GRAMD1C\n",
+      "17748 UPK1B\n",
+      "17749 ENSG00000288436\n",
+      "17750 RYK\n",
+      "17751 RBP2\n",
+      "17752 SSR3\n",
+      "17753 TIPARP-AS1\n",
+      "17754 CLDN11\n",
+      "17755 NDUFB5\n",
+      "17756 PSMD2\n",
+      "17757 OPA1\n",
+      "17758 ENSG00000287005\n",
+      "17759 DGKQ\n",
+      "17760 SLC26A1\n",
+      "17761 CD38\n",
+      "17762 SEL1L3\n",
+      "17763 RBPJ\n",
+      "17764 SMIM14-DT\n",
+      "17765 NFXL1\n",
+      "17766 ENSG00000286599\n",
+      "17767 SULT1B1\n",
+      "17768 GRSF1\n",
+      "17769 CNOT6L\n",
+      "17770 CXXC4\n",
+      "17771 SPRY1\n",
+      "17772 NDUFC1\n",
+      "17773 SCOC\n",
+      "17774 GYPB\n",
+      "17775 LSM6\n",
+      "17776 TMEM154\n",
+      "17777 RETREG1\n",
+      "17778 TARS1-DT\n",
+      "17779 CKMT2-AS1\n",
+      "17780 SSBP2\n",
+      "17781 CETN3\n",
+      "17782 LUCAT1\n",
+      "17783 RAD50\n",
+      "17784 SMAD5\n",
+      "17785 DIAPH1\n",
+      "17786 SPINK9\n",
+      "17787 ADRB2\n",
+      "17788 GABRG2\n",
+      "17789 SIMC1\n",
+      "17790 HNRNPAB\n",
+      "17791 ENSG00000248367\n",
+      "17792 ZFP62\n",
+      "17793 ENSG00000287265\n",
+      "17794 IRF4\n",
+      "17795 ECI2\n",
+      "17796 HULC\n",
+      "17797 NUP153-AS1\n",
+      "17798 KIF13A\n",
+      "17799 HFE\n",
+      "17800 H2AC17\n",
+      "17801 C6orf47\n",
+      "17802 GPSM3\n",
+      "17803 HLA-DMB\n",
+      "17804 ENSG00000272217\n",
+      "17805 NFYA\n",
+      "17806 SLC17A5\n",
+      "17807 TPBG\n",
+      "17808 PREP\n",
+      "17809 ENSG00000232311\n",
+      "17810 C6orf58\n",
+      "17811 ZBTB2\n",
+      "17812 AIRN\n",
+      "17813 ENSG00000285730\n",
+      "17814 PDGFA-DT\n",
+      "17815 LFNG\n",
+      "17816 SDK1\n",
+      "17817 CYTH3\n",
+      "17818 DNAH11\n",
+      "17819 SNHG26\n",
+      "17820 HOXA-AS2\n",
+      "17821 KBTBD2\n",
+      "17822 SEPTIN7\n",
+      "17823 MRPS24\n",
+      "17824 ENSG00000228434\n",
+      "17825 OGDH\n",
+      "17826 STX1A\n",
+      "17827 METTL27\n",
+      "17828 ENSG00000267697\n",
+      "17829 TRBV18\n",
+      "17830 ENSG00000273314\n",
+      "17831 PPP1R3B-DT\n",
+      "17832 MTMR9\n",
+      "17833 CDCA2\n",
+      "17834 KAT6A\n",
+      "17835 LYN\n",
+      "17836 SDR16C5\n",
+      "17837 VXN\n",
+      "17838 ARFGEF1\n",
+      "17839 ENSG00000253238\n",
+      "17840 CFAP418-AS1\n",
+      "17841 MED30\n",
+      "17842 ENPP2\n",
+      "17843 TRMT12\n",
+      "17844 ERMP1\n",
+      "17845 TTC39B\n",
+      "17846 SLC25A51\n",
+      "17847 ANXA1\n",
+      "17848 UGCG\n",
+      "17849 MORN5\n",
+      "17850 GAPVD1\n",
+      "17851 EEIG1\n",
+      "17852 NAIF1\n",
+      "17853 SPACA9\n",
+      "17854 ADAMTS13\n",
+      "17855 NDOR1\n",
+      "17856 RNF208\n",
+      "17857 SFTA1P\n",
+      "17858 VIM-AS1\n",
+      "17859 SIRT1\n",
+      "17860 ENSG00000236154\n",
+      "17861 LRRC20\n",
+      "17862 ENSG00000269926\n",
+      "17863 SAMD8\n",
+      "17864 FGFBP3\n",
+      "17865 CHUK\n",
+      "17866 SLC18A2-AS1\n",
+      "17867 IFITM5\n",
+      "17868 ENSG00000270030\n",
+      "17869 ENSG00000255237\n",
+      "17870 IFITM10\n",
+      "17871 LINC00294\n",
+      "17872 LPXN\n",
+      "17873 MRPL16\n",
+      "17874 GNG3\n",
+      "17875 TTC9C\n",
+      "17876 LINC02724\n",
+      "17877 FOSL1\n",
+      "17878 ENSG00000245156\n",
+      "17879 BBS1\n",
+      "17880 ARAP1\n",
+      "17881 PAAF1\n",
+      "17882 MYO7A\n",
+      "17883 INTS4\n",
+      "17884 TMEM126A\n",
+      "17885 C11orf65\n",
+      "17886 ENSG00000245869\n",
+      "17887 USP2-AS1\n",
+      "17888 MSANTD2\n",
+      "17889 ETS1\n",
+      "17890 LPCAT3\n",
+      "17891 ENSG00000284634\n",
+      "17892 LINC02446\n",
+      "17893 HEBP1\n",
+      "17894 FGFR1OP2\n",
+      "17895 DDX11-AS1\n",
+      "17896 DENND5B\n",
+      "17897 LINC02416\n",
+      "17898 CNPY2-AS1\n",
+      "17899 ATP23\n",
+      "17900 IL22\n",
+      "17901 TXNRD1\n",
+      "17902 ALDH1L2\n",
+      "17903 C12orf43\n",
+      "17904 TCTN2\n",
+      "17905 EP400\n",
+      "17906 VPS36\n",
+      "17907 CKAP2-DT\n",
+      "17908 GPR180\n",
+      "17909 PCID2\n",
+      "17910 TEP1\n",
+      "17911 TRAV10\n",
+      "17912 TRAV34\n",
+      "17913 ENSG00000275552\n",
+      "17914 THTPA\n",
+      "17915 LINC01551\n",
+      "17916 PAX9\n",
+      "17917 FERMT2\n",
+      "17918 TMEM229B\n",
+      "17919 LTBP2\n",
+      "17920 LINC02288\n",
+      "17921 DYNC1H1\n",
+      "17922 ANKRD9\n",
+      "17923 ENSG00000269910\n",
+      "17924 CHRFAM7A\n",
+      "17925 SNAP23\n",
+      "17926 ENSG00000259618\n",
+      "17927 ENSG00000279145\n",
+      "17928 PARP16\n",
+      "17929 UBL7\n",
+      "17930 IMP3\n",
+      "17931 ENSG00000259986\n",
+      "17932 ENSG00000260496\n",
+      "17933 TRAP1\n",
+      "17934 ZC3H7A\n",
+      "17935 ENSG00000277369\n",
+      "17936 ENSG00000262732\n",
+      "17937 TMC7\n",
+      "17938 LINC02195\n",
+      "17939 TUFM\n",
+      "17940 LAT\n",
+      "17941 KIF22\n",
+      "17942 ZNF689\n",
+      "17943 COQ9\n",
+      "17944 ENSG00000274220\n",
+      "17945 MAFTRR\n",
+      "17946 RPA1\n",
+      "17947 RNF227\n",
+      "17948 BORCS6\n",
+      "17949 MAP2K4\n",
+      "17950 ARHGAP44\n",
+      "17951 FLCN\n",
+      "17952 ENSG00000266111\n",
+      "17953 EVI2B\n",
+      "17954 RAB11FIP4\n",
+      "17955 PPP1R1B\n",
+      "17956 BRCA1\n",
+      "17957 MPP3\n",
+      "17958 LINC01976\n",
+      "17959 UBTF\n",
+      "17960 ENSG00000267160\n",
+      "17961 LINC02086\n",
+      "17962 CALCOCO2\n",
+      "17963 PDK2\n",
+      "17964 HEATR6\n",
+      "17965 PECAM1\n",
+      "17966 SDK2\n",
+      "17967 RPL38\n",
+      "17968 BAHCC1\n",
+      "17969 ENSG00000260563\n",
+      "17970 FOXK2\n",
+      "17971 IMPA2\n",
+      "17972 PRELID3A\n",
+      "17973 LAMA3\n",
+      "17974 CHST9\n",
+      "17975 ZCCHC2\n",
+      "17976 ZNF407-AS1\n",
+      "17977 LMNB2\n",
+      "17978 ENSG00000267469\n",
+      "17979 RANBP3-DT\n",
+      "17980 ENSG00000267563\n",
+      "17981 ADAMTS10\n",
+      "17982 ACP5\n",
+      "17983 RAD23A\n",
+      "17984 ADGRL1-AS1\n",
+      "17985 ADGRE3\n",
+      "17986 ZNF333\n",
+      "17987 WIZ\n",
+      "17988 C19orf44\n",
+      "17989 ENSG00000279529\n",
+      "17990 MAST3-AS1\n",
+      "17991 ENSG00000284428\n",
+      "17992 PLEKHF1\n",
+      "17993 USF2\n",
+      "17994 ZNF260\n",
+      "17995 ZNF875\n",
+      "17996 PLD3\n",
+      "17997 CYP2B6\n",
+      "17998 BICRA\n",
+      "17999 LIN7B\n",
+      "18000 ADM5\n",
+      "18001 PNKP\n",
+      "18002 ENSG00000269873\n",
+      "18003 ZNF460-AS1\n",
+      "18004 ZNF304\n",
+      "18005 ZNF549\n",
+      "18006 ATRN\n",
+      "18007 PYGB\n",
+      "18008 CEP250\n",
+      "18009 RPRD1B\n",
+      "18010 TTPAL\n",
+      "18011 DNTTIP1\n",
+      "18012 ZNF335\n",
+      "18013 ENSG00000273451\n",
+      "18014 SLC9A8\n",
+      "18015 STX16\n",
+      "18016 ENSG00000280095\n",
+      "18017 SLX9\n",
+      "18018 BID\n",
+      "18019 RGL4\n",
+      "18020 NF2\n",
+      "18021 ENSG00000100362\n",
+      "18022 SLC16A8\n",
+      "18023 MIEF1\n",
+      "18024 TNRC6B-DT\n",
+      "18025 LINC01644\n",
+      "18026 PLCXD1\n",
+      "18027 ASMTL\n",
+      "18028 WWC3\n",
+      "18029 SAT1\n",
+      "18030 PDK3\n",
+      "18031 OTUD5\n",
+      "18032 CACNA1F\n",
+      "18033 FAM120C\n",
+      "18034 KIF4A\n",
+      "18035 GPRASP2\n",
+      "18036 GPRASP3\n",
+      "18037 TBC1D8B\n",
+      "18038 LONRF3\n",
+      "18039 LAMP2\n",
+      "18040 IRAK1\n",
+      "18041 ENSG00000277761\n",
+      "18042 AGRN\n",
+      "18043 PGD\n",
+      "18044 MFN2\n",
+      "18045 SLC25A34\n",
+      "18046 IFI6\n",
+      "18047 MATN1-AS1\n",
+      "18048 AGO1\n",
+      "18049 SH3D21\n",
+      "18050 CSF3R\n",
+      "18051 B4GALT2\n",
+      "18052 HECTD3\n",
+      "18053 NASP\n",
+      "18054 TTC39A-AS1\n",
+      "18055 RAB3B\n",
+      "18056 DENND2C\n",
+      "18057 LINC02804\n",
+      "18058 H2AC20\n",
+      "18059 SLC39A1\n",
+      "18060 GBA1\n",
+      "18061 PMF1\n",
+      "18062 ENSG00000160818\n",
+      "18063 PEX19\n",
+      "18064 HSD17B7\n",
+      "18065 CCDC190\n",
+      "18066 TMCO1-AS1\n",
+      "18067 SUCO\n",
+      "18068 RC3H1\n",
+      "18069 RC3H1-DT\n",
+      "18070 COP1-DT\n",
+      "18071 ENSG00000224810\n",
+      "18072 TPR\n",
+      "18073 ENSG00000240710\n",
+      "18074 PROX1\n",
+      "18075 SUSD4\n",
+      "18076 NTPCR\n",
+      "18077 RBM34\n",
+      "18078 TBCE\n",
+      "18079 ENSG00000259776\n",
+      "18080 CEP170\n",
+      "18081 ACP1\n",
+      "18082 ENSG00000172554\n",
+      "18083 ENSG00000233251\n",
+      "18084 C2orf74\n",
+      "18085 FAM161A\n",
+      "18086 EHBP1\n",
+      "18087 SNRPG\n",
+      "18088 PAIP2B\n",
+      "18089 ENSG00000270696\n",
+      "18090 SUCLG1\n",
+      "18091 DNAH6\n",
+      "18092 LINC01918\n",
+      "18093 RANBP2\n",
+      "18094 EPB41L5\n",
+      "18095 LINC01806\n",
+      "18096 SPC25\n",
+      "18097 ENSG00000232555\n",
+      "18098 ENSG00000287149\n",
+      "18099 COL3A1\n",
+      "18100 SF3B1\n",
+      "18101 ENSG00000222017\n",
+      "18102 SATB2\n",
+      "18103 CASP10\n",
+      "18104 CARF\n",
+      "18105 TMEM169\n",
+      "18106 CATIP\n",
+      "18107 LINC00471\n",
+      "18108 NPPC\n",
+      "18109 ENSG00000269894\n",
+      "18110 RBSN\n",
+      "18111 SH3BP5\n",
+      "18112 COL7A1\n",
+      "18113 HYAL1\n",
+      "18114 STIMATE\n",
+      "18115 PRKCD\n",
+      "18116 RPP14\n",
+      "18117 FAM3D\n",
+      "18118 CFAP44\n",
+      "18119 MIX23\n",
+      "18120 RAB6B\n",
+      "18121 ATR\n",
+      "18122 U2SURP\n",
+      "18123 ENSG00000241679\n",
+      "18124 MBNL1\n",
+      "18125 DHX36\n",
+      "18126 ENSG00000272990\n",
+      "18127 SOX2-OT\n",
+      "18128 EHHADH\n",
+      "18129 TRA2B\n",
+      "18130 LPP-AS2\n",
+      "18131 WDR53\n",
+      "18132 ZNF141\n",
+      "18133 SLC49A3\n",
+      "18134 ENSG00000287778\n",
+      "18135 ENSG00000251152\n",
+      "18136 SEPSECS\n",
+      "18137 SRP72\n",
+      "18138 SPINK2\n",
+      "18139 AFP\n",
+      "18140 EREG\n",
+      "18141 PPM1K-DT\n",
+      "18142 DNAJB14\n",
+      "18143 ENSG00000260651\n",
+      "18144 SLC9B1\n",
+      "18145 INTS12\n",
+      "18146 FAM241A\n",
+      "18147 USP38\n",
+      "18148 ING2\n",
+      "18149 ENSG00000286388\n",
+      "18150 SUB1\n",
+      "18151 TARS1\n",
+      "18152 SKP2\n",
+      "18153 ANXA2R\n",
+      "18154 GPX8\n",
+      "18155 DIMT1\n",
+      "18156 POLR3G\n",
+      "18157 ADGRV1\n",
+      "18158 ENSG00000250049\n",
+      "18159 ENSG00000249746\n",
+      "18160 SLC12A2\n",
+      "18161 CCNI2\n",
+      "18162 PCDHGB6\n",
+      "18163 SPARC\n",
+      "18164 HIGD2A\n",
+      "18165 TSPAN17\n",
+      "18166 MXD3\n",
+      "18167 PRR7\n",
+      "18168 DBN1\n",
+      "18169 TRIM52-AS1\n",
+      "18170 EDN1\n",
+      "18171 H4C5\n",
+      "18172 H3C6\n",
+      "18173 ENSG00000285571\n",
+      "18174 H2BC11\n",
+      "18175 ATP6V1G2\n",
+      "18176 RNF5\n",
+      "18177 TSBP1-AS1\n",
+      "18178 PACSIN1\n",
+      "18179 DAAM2\n",
+      "18180 MDFI\n",
+      "18181 TJAP1\n",
+      "18182 CD109-AS1\n",
+      "18183 LYRM2\n",
+      "18184 ARMC2\n",
+      "18185 SLC18B1\n",
+      "18186 ADGRG6\n",
+      "18187 WDR27\n",
+      "18188 LINC02889\n",
+      "18189 POLR1F\n",
+      "18190 NIPSNAP2\n",
+      "18191 CHCHD2\n",
+      "18192 NUPR2\n",
+      "18193 ENSG00000273448\n",
+      "18194 LAT2\n",
+      "18195 SRI\n",
+      "18196 CFAP69\n",
+      "18197 GNGT1\n",
+      "18198 NPTX2\n",
+      "18199 ENSG00000242798\n",
+      "18200 LHFPL3\n",
+      "18201 GPR85\n",
+      "18202 ENSG00000287547\n",
+      "18203 MKLN1-AS\n",
+      "18204 TRBV6-6\n",
+      "18205 TRBV20-1\n",
+      "18206 OR2A1-AS1\n",
+      "18207 PAXIP1\n",
+      "18208 ENSG00000283128\n",
+      "18209 FBXO25\n",
+      "18210 PLAG1\n",
+      "18211 PENK-AS1\n",
+      "18212 NCOA2\n",
+      "18213 TMEM67\n",
+      "18214 RGS22\n",
+      "18215 RIMS2\n",
+      "18216 ENSG00000286010\n",
+      "18217 CYC1\n",
+      "18218 VLDLR-AS1\n",
+      "18219 RCL1\n",
+      "18220 KDM4C\n",
+      "18221 ENSG00000273226\n",
+      "18222 KLF9\n",
+      "18223 RORB\n",
+      "18224 SEC61B\n",
+      "18225 STX17\n",
+      "18226 GOLGA1\n",
+      "18227 ENSG00000227218\n",
+      "18228 DDX31\n",
+      "18229 CACFD1\n",
+      "18230 PHPT1\n",
+      "18231 ANKRD16\n",
+      "18232 MEIG1\n",
+      "18233 ARL5B\n",
+      "18234 ZEB1\n",
+      "18235 JMJD1C\n",
+      "18236 UNC5B-AS1\n",
+      "18237 NUDT13\n",
+      "18238 LINC02626\n",
+      "18239 PIDD1\n",
+      "18240 TIMM10B\n",
+      "18241 LINC02547\n",
+      "18242 NUCB2\n",
+      "18243 GAS2\n",
+      "18244 CCDC34\n",
+      "18245 ABTB2\n",
+      "18246 MS4A12\n",
+      "18247 MS4A8\n",
+      "18248 MTA2\n",
+      "18249 UBXN1\n",
+      "18250 ESRRA\n",
+      "18251 CATSPER1\n",
+      "18252 PACS1\n",
+      "18253 SHANK2\n",
+      "18254 XNDC1N\n",
+      "18255 DNAJB13\n",
+      "18256 XRRA1\n",
+      "18257 ARRB1\n",
+      "18258 CFAP68\n",
+      "18259 PIH1D2\n",
+      "18260 ARHGAP32\n",
+      "18261 CACNA1C\n",
+      "18262 CD27\n",
+      "18263 LPAR5\n",
+      "18264 EMG1\n",
+      "18265 CLSTN3\n",
+      "18266 FOXJ2\n",
+      "18267 PHC1\n",
+      "18268 KLRF1\n",
+      "18269 CLEC1B\n",
+      "18270 EIF2S3B\n",
+      "18271 EMP1\n",
+      "18272 ETNK1\n",
+      "18273 PCED1B-AS1\n",
+      "18274 TMEM106C\n",
+      "18275 HOXC5\n",
+      "18276 HNRNPA1\n",
+      "18277 DGKA\n",
+      "18278 NABP2\n",
+      "18279 ENSG00000257342\n",
+      "18280 CDK4\n",
+      "18281 METTL1\n",
+      "18282 AVIL\n",
+      "18283 MGAT4C\n",
+      "18284 NR2C1\n",
+      "18285 ELK3\n",
+      "18286 PLBD2\n",
+      "18287 CCDC60\n",
+      "18288 ENSG00000274191\n",
+      "18289 MIPEP\n",
+      "18290 UBE2L5\n",
+      "18291 TPT1\n",
+      "18292 HNRNPA1L2\n",
+      "18293 TGDS\n",
+      "18294 UGGT2\n",
+      "18295 STK24\n",
+      "18296 SUPT16H\n",
+      "18297 NGDN\n",
+      "18298 IRF9\n",
+      "18299 RABGGTA\n",
+      "18300 ENSG00000258844\n",
+      "18301 SLC25A21\n",
+      "18302 LINC02302\n",
+      "18303 NEMF\n",
+      "18304 ENSG00000237356\n",
+      "18305 GCH1\n",
+      "18306 LGMN\n",
+      "18307 YY1\n",
+      "18308 KLC1-AS1\n",
+      "18309 IGHG4\n",
+      "18310 KLF13\n",
+      "18311 ARHGAP11A-DT\n",
+      "18312 UBR1\n",
+      "18313 COPS2\n",
+      "18314 DIS3L\n",
+      "18315 EDC3\n",
+      "18316 CSK\n",
+      "18317 PEX11A\n",
+      "18318 LRRC28\n",
+      "18319 ENSG00000232386\n",
+      "18320 LRRK1\n",
+      "18321 BICDL2\n",
+      "18322 NLRC3\n",
+      "18323 UBN1\n",
+      "18324 COQ7-DT\n",
+      "18325 ENSG00000260751\n",
+      "18326 ZKSCAN2\n",
+      "18327 NFATC2IP\n",
+      "18328 SPNS1\n",
+      "18329 BOLA2B\n",
+      "18330 ZNF688\n",
+      "18331 PHKB\n",
+      "18332 ADGRG3\n",
+      "18333 PARD6A\n",
+      "18334 NFAT5\n",
+      "18335 MLKL\n",
+      "18336 FA2H\n",
+      "18337 CNTNAP4\n",
+      "18338 ENSG00000278058\n",
+      "18339 KLHL36\n",
+      "18340 USP10\n",
+      "18341 KLHDC4\n",
+      "18342 SPATA33\n",
+      "18343 VPS9D1-AS1\n",
+      "18344 ENSG00000260507\n",
+      "18345 ENSG00000262050\n",
+      "18346 CTNS\n",
+      "18347 MED11\n",
+      "18348 ZNF232-AS1\n",
+      "18349 KIAA0753\n",
+      "18350 CTDNEP1\n",
+      "18351 ENSG00000266378\n",
+      "18352 TRPV2\n",
+      "18353 PIGS\n",
+      "18354 ENSG00000265625\n",
+      "18355 ENSG00000274767\n",
+      "18356 FBXL20\n",
+      "18357 NBR1\n",
+      "18358 NSF\n",
+      "18359 LRRC59\n",
+      "18360 MRPS23\n",
+      "18361 CUEDC1\n",
+      "18362 TUBD1\n",
+      "18363 ENSG00000264985\n",
+      "18364 SLC9A3R1-AS1\n",
+      "18365 ACOX1\n",
+      "18366 SRSF2\n",
+      "18367 LINC01993\n",
+      "18368 ZNF519\n",
+      "18369 ANKRD29\n",
+      "18370 ENSG00000278607\n",
+      "18371 SLC66A2\n",
+      "18372 MADCAM1\n",
+      "18373 PCSK4\n",
+      "18374 MOB3A\n",
+      "18375 CARM1\n",
+      "18376 ENSG00000268945\n",
+      "18377 PGLYRP2\n",
+      "18378 USE1\n",
+      "18379 ARRDC2\n",
+      "18380 PBX4\n",
+      "18381 ENSG00000268119\n",
+      "18382 TSHZ3\n",
+      "18383 HAMP\n",
+      "18384 LNROP\n",
+      "18385 LIPE\n",
+      "18386 CLEC11A\n",
+      "18387 ZNF432\n",
+      "18388 MYADM-AS1\n",
+      "18389 KMT5C\n",
+      "18390 IL11\n",
+      "18391 PRNP\n",
+      "18392 LINC02967\n",
+      "18393 ENSG00000286444\n",
+      "18394 CDK5RAP1\n",
+      "18395 VSTM2L\n",
+      "18396 CYP24A1\n",
+      "18397 TFAP2C\n",
+      "18398 RBM38-AS1\n",
+      "18399 CIMIP1\n",
+      "18400 BIRC7\n",
+      "18401 UBE2G2\n",
+      "18402 BCL2L13\n",
+      "18403 SRRD\n",
+      "18404 PITPNB\n",
+      "18405 SEC14L6\n",
+      "18406 SELENOM\n",
+      "18407 LARGE1\n",
+      "18408 ENSG00000100365\n",
+      "18409 PDXP\n",
+      "18410 LGALS1\n",
+      "18411 TBC1D22A\n",
+      "18412 NLGN4X\n",
+      "18413 GPR143\n",
+      "18414 ACOT9\n",
+      "18415 CHST7\n",
+      "18416 SLC35A2\n",
+      "18417 TSPYL2\n",
+      "18418 ENSG00000256045\n",
+      "18419 ARMCX4\n",
+      "18420 ENSG00000232611\n",
+      "18421 ABCD1\n",
+      "18422 MPP1\n",
+      "18423 EIF1AY\n",
+      "18424 UBE2J2\n",
+      "18425 ENSG00000228037\n",
+      "18426 PLEKHG5\n",
+      "18427 NOL9\n",
+      "18428 KLHL21\n",
+      "18429 MIR34AHG\n",
+      "18430 FBXO2\n",
+      "18431 PLOD1\n",
+      "18432 VPS13D\n",
+      "18433 CROCC\n",
+      "18434 SELENON\n",
+      "18435 CNKSR1\n",
+      "18436 ARID1A\n",
+      "18437 AZIN2\n",
+      "18438 THRAP3\n",
+      "18439 RRAGC\n",
+      "18440 RHBDL2\n",
+      "18441 ERMAP\n",
+      "18442 PDE4B\n",
+      "18443 CCN1\n",
+      "18444 GBP4\n",
+      "18445 SLC35A3\n",
+      "18446 RTCA-AS1\n",
+      "18447 GSTM5\n",
+      "18448 INKA2\n",
+      "18449 FAM72B\n",
+      "18450 MCL1\n",
+      "18451 TUFT1\n",
+      "18452 YY1AP1\n",
+      "18453 BCAN\n",
+      "18454 APOA2\n",
+      "18455 RGS5\n",
+      "18456 ATP1B1\n",
+      "18457 PRDX6-AS1\n",
+      "18458 SYT2\n",
+      "18459 LINC00467\n",
+      "18460 ZC3H11B\n",
+      "18461 DISP1\n",
+      "18462 PUM2\n",
+      "18463 OTOF\n",
+      "18464 CLIP4\n",
+      "18465 QPCT\n",
+      "18466 SLC8A1\n",
+      "18467 EML4-AS1\n",
+      "18468 CCDC85A\n",
+      "18469 REL\n",
+      "18470 LGALSL-DT\n",
+      "18471 RAB11FIP5\n",
+      "18472 HK2\n",
+      "18473 ENSG00000286045\n",
+      "18474 RPIA\n",
+      "18475 IGKV1-8\n",
+      "18476 IGKV1-12\n",
+      "18477 STARD7\n",
+      "18478 LONRF2\n",
+      "18479 ZNG1B\n",
+      "18480 ACTR3\n",
+      "18481 INSIG2\n",
+      "18482 ERCC3\n",
+      "18483 LINC01856\n",
+      "18484 RPRM\n",
+      "18485 CHROMR\n",
+      "18486 CWC22\n",
+      "18487 STAT4\n",
+      "18488 ORC2\n",
+      "18489 FBXO36\n",
+      "18490 SP110\n",
+      "18491 NCL\n",
+      "18492 NDUFA10\n",
+      "18493 CAND2\n",
+      "18494 XPC-AS1\n",
+      "18495 SNRK\n",
+      "18496 LRRC2\n",
+      "18497 KIF9-AS1\n",
+      "18498 TREX1\n",
+      "18499 RBM5\n",
+      "18500 PPM1M\n",
+      "18501 ENSG00000287873\n",
+      "18502 MUC13\n",
+      "18503 GATA2\n",
+      "18504 RAB7A\n",
+      "18505 GP9\n",
+      "18506 ENSG00000286729\n",
+      "18507 ZBTB38\n",
+      "18508 SLC9A9\n",
+      "18509 SIAH2\n",
+      "18510 LEKR1\n",
+      "18511 LINC00881\n",
+      "18512 PARL\n",
+      "18513 ENSG00000273370\n",
+      "18514 RNF168\n",
+      "18515 CTBP1\n",
+      "18516 MXD4\n",
+      "18517 ZFYVE28\n",
+      "18518 SH3BP2\n",
+      "18519 STX18\n",
+      "18520 ENSG00000245748\n",
+      "18521 GRPEL1\n",
+      "18522 ENAM\n",
+      "18523 RCHY1\n",
+      "18524 USO1\n",
+      "18525 ANXA3\n",
+      "18526 COPS4\n",
+      "18527 FLJ20021\n",
+      "18528 PDE5A\n",
+      "18529 JADE1\n",
+      "18530 HMGB2\n",
+      "18531 ZFP42\n",
+      "18532 CFAP90\n",
+      "18533 SNHG18\n",
+      "18534 OTULIN-DT\n",
+      "18535 PIK3R1\n",
+      "18536 SLC30A5\n",
+      "18537 BTF3-DT\n",
+      "18538 ENSG00000249856\n",
+      "18539 TBCA\n",
+      "18540 TENT2\n",
+      "18541 ATG10\n",
+      "18542 MIR4280HG\n",
+      "18543 FAM172A\n",
+      "18544 ANKHD1\n",
+      "18545 APBB3\n",
+      "18546 CSNK1A1\n",
+      "18547 PDGFRB\n",
+      "18548 ENSG00000249738\n",
+      "18549 SPDL1\n",
+      "18550 ENSG00000250274\n",
+      "18551 SCGB3A1\n",
+      "18552 CDYL\n",
+      "18553 LY86\n",
+      "18554 ATXN1\n",
+      "18555 PRSS16\n",
+      "18556 C6orf136\n",
+      "18557 HSPA1A\n",
+      "18558 BRD2\n",
+      "18559 HSD17B8\n",
+      "18560 KIFC1\n",
+      "18561 PGC\n",
+      "18562 CRIP3\n",
+      "18563 MMUT\n",
+      "18564 SAMD3\n",
+      "18565 RMND1\n",
+      "18566 ENSG00000285159\n",
+      "18567 TTYH3\n",
+      "18568 ACTB\n",
+      "18569 TRGV9\n",
+      "18570 ENSG00000272831\n",
+      "18571 HSPB1\n",
+      "18572 CD36\n",
+      "18573 CYP51A1-AS1\n",
+      "18574 RBM48\n",
+      "18575 ZNF655\n",
+      "18576 TES\n",
+      "18577 KCP\n",
+      "18578 TRBV11-3\n",
+      "18579 PDIA4\n",
+      "18580 ATP6V0E2\n",
+      "18581 ACTR3C\n",
+      "18582 LMBR1\n",
+      "18583 DEFB1\n",
+      "18584 FAM66B\n",
+      "18585 ENSG00000254093\n",
+      "18586 LZTS1\n",
+      "18587 PEBP4\n",
+      "18588 EIF4EBP1\n",
+      "18589 FGFR1\n",
+      "18590 TM2D2\n",
+      "18591 IKBKB-DT\n",
+      "18592 ENSG00000272518\n",
+      "18593 ENSG00000253500\n",
+      "18594 STK3\n",
+      "18595 FAM83A\n",
+      "18596 CPSF1\n",
+      "18597 COMMD5\n",
+      "18598 TMEM8B\n",
+      "18599 ZBTB5\n",
+      "18600 LINC00484\n",
+      "18601 MIRLET7A1HG\n",
+      "18602 KLF4\n",
+      "18603 TMEM245\n",
+      "18604 SUSD1\n",
+      "18605 RPL35\n",
+      "18606 LAMC3\n",
+      "18607 ENSG00000130723\n",
+      "18608 KLF6\n",
+      "18609 PROSER2\n",
+      "18610 PARD3\n",
+      "18611 TIMM23\n",
+      "18612 ENSG00000249860\n",
+      "18613 ZWINT\n",
+      "18614 COL13A1\n",
+      "18615 SLC29A3\n",
+      "18616 FAM25A\n",
+      "18617 CFAP43\n",
+      "18618 EIF3A\n",
+      "18619 WDR11-DT\n",
+      "18620 LHPP\n",
+      "18621 POLR2L\n",
+      "18622 STIM1\n",
+      "18623 APBB1\n",
+      "18624 TMEM9B\n",
+      "18625 IRAG1-AS1\n",
+      "18626 TEAD1\n",
+      "18627 ENSG00000287548\n",
+      "18628 C11orf58\n",
+      "18629 LDHC\n",
+      "18630 KIF18A\n",
+      "18631 METTL15\n",
+      "18632 TCP11L1\n",
+      "18633 SLC35C1\n",
+      "18634 OR5A2\n",
+      "18635 CPSF7\n",
+      "18636 FADD\n",
+      "18637 MMP13\n",
+      "18638 PDGFD\n",
+      "18639 ENSG00000256257\n",
+      "18640 CACNA1C-AS2\n",
+      "18641 ENSG00000256967\n",
+      "18642 LINC02367\n",
+      "18643 DDX47\n",
+      "18644 H2AJ\n",
+      "18645 FAR2\n",
+      "18646 ASB8\n",
+      "18647 TFCP2\n",
+      "18648 ACVRL1\n",
+      "18649 RPL41\n",
+      "18650 MYL6B\n",
+      "18651 EEF1AKMT3\n",
+      "18652 TSFM\n",
+      "18653 ENSG00000257322\n",
+      "18654 CCDC38\n",
+      "18655 WASHC3\n",
+      "18656 LINC00939\n",
+      "18657 LINC00943\n",
+      "18658 COG3\n",
+      "18659 ZC3H13\n",
+      "18660 ENSG00000274929\n",
+      "18661 TMTC4\n",
+      "18662 ENSG00000286931\n",
+      "18663 KHNYN\n",
+      "18664 SDR39U1\n",
+      "18665 POMT2\n",
+      "18666 ENSG00000260711\n",
+      "18667 CCDC85C\n",
+      "18668 BTBD6\n",
+      "18669 ENSG00000270055\n",
+      "18670 ENSG00000284906\n",
+      "18671 SPTBN5\n",
+      "18672 CTDSPL2\n",
+      "18673 HDC\n",
+      "18674 LINC00926\n",
+      "18675 CCNB2\n",
+      "18676 LOXL1-AS1\n",
+      "18677 LINC01418\n",
+      "18678 MESP1\n",
+      "18679 TARS3\n",
+      "18680 FAHD1\n",
+      "18681 TSC2\n",
+      "18682 ZNF75A\n",
+      "18683 PMM2\n",
+      "18684 ENSG00000260276\n",
+      "18685 RSL1D1-DT\n",
+      "18686 ENSG00000183889\n",
+      "18687 UBFD1\n",
+      "18688 SULT1A2\n",
+      "18689 ENSG00000260570\n",
+      "18690 NPIPB12\n",
+      "18691 YPEL3\n",
+      "18692 CORO1A-AS1\n",
+      "18693 SETD1A\n",
+      "18694 ENSG00000261474\n",
+      "18695 ENSG00000276867\n",
+      "18696 ENSG00000260086\n",
+      "18697 MT1F\n",
+      "18698 ENSG00000258122\n",
+      "18699 ZDHHC1\n",
+      "18700 ENSG00000261386\n",
+      "18701 UTP4\n",
+      "18702 ENSG00000250685\n",
+      "18703 MVD\n",
+      "18704 PAFAH1B1\n",
+      "18705 RNF167\n",
+      "18706 ENSG00000263272\n",
+      "18707 ACADVL\n",
+      "18708 RNF222\n",
+      "18709 TMEM11-DT\n",
+      "18710 SEZ6\n",
+      "18711 ENSG00000266947\n",
+      "18712 GGNBP2\n",
+      "18713 LASP1\n",
+      "18714 VAT1\n",
+      "18715 DHX8\n",
+      "18716 NPEPPS\n",
+      "18717 RSAD1\n",
+      "18718 LPO\n",
+      "18719 BRIP1\n",
+      "18720 METTL2A\n",
+      "18721 DCAF7\n",
+      "18722 USP36\n",
+      "18723 TBC1D16\n",
+      "18724 MAFG\n",
+      "18725 HEXD-IT1\n",
+      "18726 TYMSOS\n",
+      "18727 MYL12A\n",
+      "18728 TTC39C-AS1\n",
+      "18729 IMPACT\n",
+      "18730 LINC01915\n",
+      "18731 ENSG00000277324\n",
+      "18732 FAM174C\n",
+      "18733 DAZAP1\n",
+      "18734 ZNF554\n",
+      "18735 ZNF627\n",
+      "18736 YJU2B\n",
+      "18737 CHERP\n",
+      "18738 ZNF714\n",
+      "18739 C19orf12\n",
+      "18740 RBM42\n",
+      "18741 ITPKC\n",
+      "18742 DMWD\n",
+      "18743 BCAT2\n",
+      "18744 ZNF816\n",
+      "18745 PRPF31\n",
+      "18746 ZNF582-DT\n",
+      "18747 ENSG00000268205\n",
+      "18748 ENSG00000286949\n",
+      "18749 A1BG-AS1\n",
+      "18750 ZNF837\n",
+      "18751 TMEM230\n",
+      "18752 TLDC2\n",
+      "18753 CHD6\n",
+      "18754 SRSF6\n",
+      "18755 CEBPB\n",
+      "18756 CTSZ\n",
+      "18757 PRELID3B\n",
+      "18758 PHACTR3\n",
+      "18759 SLC2A4RG\n",
+      "18760 C20orf204\n",
+      "18761 SAMSN1\n",
+      "18762 ENSG00000226751\n",
+      "18763 MAP3K7CL\n",
+      "18764 ENSG00000159086\n",
+      "18765 PI4KA\n",
+      "18766 MAPK1\n",
+      "18767 ENSG00000279738\n",
+      "18768 CACNA1I\n",
+      "18769 TNRC6B\n",
+      "18770 DESI1\n",
+      "18771 MEI1\n",
+      "18772 ATP5MGL\n",
+      "18773 BIK\n",
+      "18774 ATXN10\n",
+      "18775 FAM9B\n",
+      "18776 CLTRN\n",
+      "18777 KLHL15\n",
+      "18778 MID1IP1\n",
+      "18779 LINC01560\n",
+      "18780 GSPT2\n",
+      "18781 SPIN3\n",
+      "18782 AR\n",
+      "18783 NAP1L3\n",
+      "18784 BEX1\n",
+      "18785 TMSB15B\n",
+      "18786 DANT2\n",
+      "18787 UBE2A\n",
+      "18788 G6PD\n",
+      "18789 PCDH11Y\n",
+      "18790 DDX3Y\n",
+      "18791 FAM41C\n",
+      "18792 ACAP3\n",
+      "18793 CDK11B\n",
+      "18794 GNB1\n",
+      "18795 MEGF6\n",
+      "18796 ACOT7\n",
+      "18797 PARK7\n",
+      "18798 DHRS3\n",
+      "18799 DDI2\n",
+      "18800 NBL1\n",
+      "18801 ENSG00000233755\n",
+      "18802 RHCE\n",
+      "18803 PAQR7\n",
+      "18804 PAFAH2\n",
+      "18805 TRNP1\n",
+      "18806 MAP3K6\n",
+      "18807 GPR3\n",
+      "18808 ENSG00000225886\n",
+      "18809 ENSG00000287510\n",
+      "18810 MRPS15\n",
+      "18811 BTBD19\n",
+      "18812 TM2D1\n",
+      "18813 TYW3\n",
+      "18814 CELSR2\n",
+      "18815 ENSG00000225075\n",
+      "18816 BCL9\n",
+      "18817 ENSA\n",
+      "18818 C2CD4D-AS1\n",
+      "18819 SPRR1A\n",
+      "18820 POU2F1\n",
+      "18821 ATP6V1G3\n",
+      "18822 LGR6\n",
+      "18823 ENSG00000240219\n",
+      "18824 TMCC2\n",
+      "18825 SPATA17\n",
+      "18826 ENSG00000276997\n",
+      "18827 ARV1\n",
+      "18828 ZNF124\n",
+      "18829 LINC01814\n",
+      "18830 ITSN2\n",
+      "18831 DTNB\n",
+      "18832 ACYP2\n",
+      "18833 USP34\n",
+      "18834 PELI1\n",
+      "18835 ACTR2\n",
+      "18836 MEIS1\n",
+      "18837 GKN1\n",
+      "18838 TEX261\n",
+      "18839 MPHOSPH10\n",
+      "18840 SPR\n",
+      "18841 LBX2-AS1\n",
+      "18842 PARTICL\n",
+      "18843 TEKT4\n",
+      "18844 MRPS5\n",
+      "18845 ZNF514\n",
+      "18846 ASTL\n",
+      "18847 PTPN4\n",
+      "18848 BIN1\n",
+      "18849 CCDC115\n",
+      "18850 CCDC74A\n",
+      "18851 PSMD14\n",
+      "18852 COBLL1\n",
+      "18853 SCRN3\n",
+      "18854 PLCD4\n",
+      "18855 GMPPA\n",
+      "18856 FARSB\n",
+      "18857 MFF-DT\n",
+      "18858 ENSG00000235419\n",
+      "18859 SNORC\n",
+      "18860 COPS8\n",
+      "18861 TMEM43\n",
+      "18862 ZNF385D\n",
+      "18863 ZNF620\n",
+      "18864 NME6\n",
+      "18865 P4HTM\n",
+      "18866 RHOA\n",
+      "18867 ZMYND10\n",
+      "18868 CISH\n",
+      "18869 TEX264\n",
+      "18870 POC1A\n",
+      "18871 GLT8D1\n",
+      "18872 ENSG00000272182\n",
+      "18873 ENSG00000285399\n",
+      "18874 ST3GAL6\n",
+      "18875 HMCES\n",
+      "18876 DZIP1L\n",
+      "18877 GPR160\n",
+      "18878 PRKCI\n",
+      "18879 MCF2L2\n",
+      "18880 TMEM41A\n",
+      "18881 FYTTD1\n",
+      "18882 ADD1\n",
+      "18883 DOK7\n",
+      "18884 STK32B\n",
+      "18885 BLOC1S4\n",
+      "18886 ENSG00000288075\n",
+      "18887 C1QTNF7-AS1\n",
+      "18888 UGDH\n",
+      "18889 DCUN1D4\n",
+      "18890 ENSG00000273156\n",
+      "18891 CAMK2D\n",
+      "18892 LINC01091\n",
+      "18893 INTU\n",
+      "18894 SMAD1\n",
+      "18895 MMAA\n",
+      "18896 FHIP1A\n",
+      "18897 FBXW7\n",
+      "18898 PLRG1\n",
+      "18899 HPGD\n",
+      "18900 NNT-AS1\n",
+      "18901 SNX18\n",
+      "18902 ENSG00000249236\n",
+      "18903 PLK2\n",
+      "18904 DEPDC1B\n",
+      "18905 ADAMTS6\n",
+      "18906 NLN\n",
+      "18907 ARHGEF28\n",
+      "18908 DMGDH\n",
+      "18909 DHFR\n",
+      "18910 NUDT12\n",
+      "18911 HSD17B4\n",
+      "18912 P4HA2\n",
+      "18913 MIR3936HG\n",
+      "18914 HSPA9\n",
+      "18915 RELL2\n",
+      "18916 SPINK7\n",
+      "18917 NDST1\n",
+      "18918 RBM22\n",
+      "18919 TNIP1\n",
+      "18920 CPLX2\n",
+      "18921 LINC01962\n",
+      "18922 PPP1R10\n",
+      "18923 MDC1\n",
+      "18924 MICB\n",
+      "18925 BAG6\n",
+      "18926 RPS10\n",
+      "18927 PI16\n",
+      "18928 CCDC167\n",
+      "18929 USP49\n",
+      "18930 GNMT\n",
+      "18931 CD2AP\n",
+      "18932 IL17A\n",
+      "18933 ME1\n",
+      "18934 SMIM8\n",
+      "18935 C6orf163\n",
+      "18936 LIN28B\n",
+      "18937 GJA1\n",
+      "18938 HSF2\n",
+      "18939 AKAP7\n",
+      "18940 ENSG00000227220\n",
+      "18941 ENSG00000228408\n",
+      "18942 SYNE1\n",
+      "18943 MAP3K4\n",
+      "18944 FSCN1\n",
+      "18945 NDUFA4\n",
+      "18946 GPNMB\n",
+      "18947 JAZF1-AS1\n",
+      "18948 DBNL\n",
+      "18949 PPIA\n",
+      "18950 SNHG15\n",
+      "18951 SEPTIN14\n",
+      "18952 RFC2\n",
+      "18953 ELAPOR2\n",
+      "18954 DLX5\n",
+      "18955 MUC12\n",
+      "18956 MET\n",
+      "18957 IMPDH1\n",
+      "18958 COPG2IT1\n",
+      "18959 IFT56\n",
+      "18960 ZNF467\n",
+      "18961 FAM85B\n",
+      "18962 ERI1\n",
+      "18963 PSD3\n",
+      "18964 DOCK5\n",
+      "18965 DUSP4\n",
+      "18966 XKR9\n",
+      "18967 TPD52\n",
+      "18968 SDC2\n",
+      "18969 COX6C\n",
+      "18970 EBAG9\n",
+      "18971 TRPS1\n",
+      "18972 TBC1D31\n",
+      "18973 ASAP1-IT2\n",
+      "18974 KIFC2\n",
+      "18975 PPP1R16A\n",
+      "18976 KANK1\n",
+      "18977 DENND4C\n",
+      "18978 RPS6\n",
+      "18979 CDKN2A\n",
+      "18980 GRHPR\n",
+      "18981 BMS1P14\n",
+      "18982 CEMIP2\n",
+      "18983 OSTF1\n",
+      "18984 UBQLN1\n",
+      "18985 ENSG00000285987\n",
+      "18986 NFIL3\n",
+      "18987 MFSD14B\n",
+      "18988 CCDC180\n",
+      "18989 HEMGN\n",
+      "18990 TEX10\n",
+      "18991 TTLL11\n",
+      "18992 RBM18\n",
+      "18993 PPP6C\n",
+      "18994 LCN2\n",
+      "18995 SPOUT1\n",
+      "18996 PHYHD1\n",
+      "18997 AK8\n",
+      "18998 GPSM1\n",
+      "18999 CARD9\n",
+      "19000 FUT7\n",
+      "19001 LARP4B\n",
+      "19002 RPP38\n",
+      "19003 ENSG00000273107\n",
+      "19004 ENKUR\n",
+      "19005 THNSL1\n",
+      "19006 RAB18\n",
+      "19007 CTNNA3\n",
+      "19008 MRPS16\n",
+      "19009 ZCCHC24\n",
+      "19010 HTR7\n",
+      "19011 TM9SF3\n",
+      "19012 CALHM2\n",
+      "19013 XPNPEP1\n",
+      "19014 RBM20\n",
+      "19015 CASP7\n",
+      "19016 ENSG00000273599\n",
+      "19017 TCERG1L\n",
+      "19018 HRAS\n",
+      "19019 TRIM66\n",
+      "19020 IPO7\n",
+      "19021 EXT2\n",
+      "19022 MED19\n",
+      "19023 SPINDOC\n",
+      "19024 BANF1\n",
+      "19025 NDUFS8\n",
+      "19026 TPCN2\n",
+      "19027 CTTN\n",
+      "19028 CAPN5\n",
+      "19029 RBM7\n",
+      "19030 TMPRSS13\n",
+      "19031 ENSG00000255422\n",
+      "19032 NECTIN1\n",
+      "19033 ESAM\n",
+      "19034 ROBO4\n",
+      "19035 SLC37A2\n",
+      "19036 STT3A\n",
+      "19037 SMIM10L1\n",
+      "19038 FLJ13224\n",
+      "19039 KRT2\n",
+      "19040 MIRLET7IHG\n",
+      "19041 RXYLT1\n",
+      "19042 TSPAN8\n",
+      "19043 ERP29\n",
+      "19044 GATC\n",
+      "19045 PUS1\n",
+      "19046 PXMP2\n",
+      "19047 SLC7A1\n",
+      "19048 PCDH9\n",
+      "19049 KCTD12\n",
+      "19050 NAXD\n",
+      "19051 LAMP1\n",
+      "19052 DCUN1D2\n",
+      "19053 TRAV13-2\n",
+      "19054 C14orf28\n",
+      "19055 ENSG00000259118\n",
+      "19056 COX16\n",
+      "19057 ENSG00000258807\n",
+      "19058 ENSG00000258982\n",
+      "19059 MTA1-DT\n",
+      "19060 IGHV1-58\n",
+      "19061 ARHGAP11A\n",
+      "19062 LINC02694\n",
+      "19063 ENSG00000259536\n",
+      "19064 ENSG00000261822\n",
+      "19065 SORD\n",
+      "19066 SLC30A4-AS1\n",
+      "19067 SPG21\n",
+      "19068 ITGA11\n",
+      "19069 PKM\n",
+      "19070 PARP6\n",
+      "19071 CYP1A2\n",
+      "19072 ENSG00000259407\n",
+      "19073 WDR93\n",
+      "19074 LINC00924\n",
+      "19075 PGAP6\n",
+      "19076 HAGH\n",
+      "19077 ELOB\n",
+      "19078 ENSG00000232196\n",
+      "19079 NAGPA\n",
+      "19080 CIITA\n",
+      "19081 ENSG00000262380\n",
+      "19082 VPS35L\n",
+      "19083 TNRC6A\n",
+      "19084 ENSG00000274904\n",
+      "19085 ENSG00000260740\n",
+      "19086 CFAP20\n",
+      "19087 ACD\n",
+      "19088 SLC7A6\n",
+      "19089 HAS3\n",
+      "19090 TERF2IP\n",
+      "19091 SYCE1L\n",
+      "19092 CA5A\n",
+      "19093 IL17C\n",
+      "19094 TCF25\n",
+      "19095 P2RX5\n",
+      "19096 PELP1\n",
+      "19097 ENSG00000262089\n",
+      "19098 PHF23\n",
+      "19099 MAP2K3\n",
+      "19100 SUZ12\n",
+      "19101 ACACA\n",
+      "19102 IKZF3\n",
+      "19103 CNP\n",
+      "19104 ZNF385C\n",
+      "19105 EZH1\n",
+      "19106 COA3\n",
+      "19107 HDAC5\n",
+      "19108 RUNDC3A-AS1\n",
+      "19109 PLEKHM1\n",
+      "19110 ANKRD40CL\n",
+      "19111 ENSG00000267248\n",
+      "19112 APPBP2-DT\n",
+      "19113 GPRC5C\n",
+      "19114 WBP2\n",
+      "19115 COLEC12\n",
+      "19116 LINC00667\n",
+      "19117 ZBTB14\n",
+      "19118 RAB31\n",
+      "19119 LINC01254\n",
+      "19120 ENSG00000273141\n",
+      "19121 ENSG00000273348\n",
+      "19122 TPGS2\n",
+      "19123 KIAA1328\n",
+      "19124 LIPG\n",
+      "19125 CNDP1\n",
+      "19126 CDC34\n",
+      "19127 ENSG00000268536\n",
+      "19128 NRTN\n",
+      "19129 ADGRE1\n",
+      "19130 ENSG00000267510\n",
+      "19131 AP1M2\n",
+      "19132 ZNF443\n",
+      "19133 IL27RA\n",
+      "19134 UBA52\n",
+      "19135 LINC01224\n",
+      "19136 FFAR2\n",
+      "19137 ENSG00000267698\n",
+      "19138 THAP8\n",
+      "19139 ZNF607\n",
+      "19140 FAM98C\n",
+      "19141 ECH1\n",
+      "19142 ENSG00000269246\n",
+      "19143 ZNF546\n",
+      "19144 HNRNPUL1\n",
+      "19145 GRIN2D\n",
+      "19146 FUT1\n",
+      "19147 PRMT1\n",
+      "19148 ZNF524\n",
+      "19149 ZNF132\n",
+      "19150 MZF1\n",
+      "19151 CRLS1\n",
+      "19152 WFDC3\n",
+      "19153 PCIF1\n",
+      "19154 NPEPL1\n",
+      "19155 ZBTB46\n",
+      "19156 TMPRSS3\n",
+      "19157 ENSG00000278158\n",
+      "19158 SUMO3\n",
+      "19159 ENSG00000182871\n",
+      "19160 LINC00528\n",
+      "19161 SLC25A1\n",
+      "19162 PPM1F-AS1\n",
+      "19163 LINC01659\n",
+      "19164 GGT5\n",
+      "19165 PISD\n",
+      "19166 RFPL2\n",
+      "19167 ENSG00000235237\n",
+      "19168 CBX7\n",
+      "19169 SYNGR1\n",
+      "19170 SCUBE1\n",
+      "19171 PPARA\n",
+      "19172 MLC1\n",
+      "19173 LMF2\n",
+      "19174 GLRA2\n",
+      "19175 PHKA2\n",
+      "19176 PCYT1B\n",
+      "19177 GPR82\n",
+      "19178 MAOA\n",
+      "19179 SMC1A\n",
+      "19180 FAAH2\n",
+      "19181 MIR223HG\n",
+      "19182 OTUD6A\n",
+      "19183 ENSG00000271147\n",
+      "19184 BEX2\n",
+      "19185 SLC25A5-AS1\n",
+      "19186 XPNPEP2\n"
      ]
     }
    ],
@@ -6783,7 +19655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -6792,7 +19664,7 @@
        "Text(0, 0.5, 'Response of GZMA')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -6825,7 +19697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6960,7 +19832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6978,12 +19850,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
@@ -7003,12 +19875,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
@@ -7028,12 +19900,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
@@ -7058,7 +19930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7102,155 +19974,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-02-13 18:28:47,825 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Maximum value of z_qp: 87.419\n",
-      "2025-02-13 18:28:48,076 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Minimum value of z_qp: -192.053\n",
-      "2025-02-13 18:28:48,077 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of query genes after TPM filtering: 19187 / 19187\n",
-      "2025-02-13 18:28:48,078 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of prompt genes after TPM filtering: 19187 / 19187\n"
+      "2025-02-13 19:53:04,524 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Maximum value of z_qp: 87.419\n",
+      "2025-02-13 19:53:04,784 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Minimum value of z_qp: -192.053\n",
+      "2025-02-13 19:53:04,785 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of query genes after TPM filtering: 19187 / 19187\n",
+      "2025-02-13 19:53:04,786 - cellarium.ml.utilities.inference.cellarium_gpt_inference - INFO - Number of prompt genes after TPM filtering: 19187 / 19187\n"
      ]
     }
    ],
    "source": [
     "network_ctx.process(\n",
     "    response_normalization_strategy=\"mean\",\n",
-    "    feature_normalization_strategy=\"none\",\n",
+    "    feature_normalization_strategy=\"z_score\",\n",
     "    feature_max_value=None,\n",
     "    query_response_amp_min_pct=None,\n",
     "    min_prompt_gene_tpm=0,\n",
     "    min_query_gene_tpm=0,\n",
     "    norm_pseudo_count=1e-3,\n",
-    "    query_hv_top_k=4000\n",
+    "    query_hv_top_k=None,\n",
     "    # z_trans_func=lambda x: 10. * np.tanh(0.1 * x)\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_qp = network_ctx.z_qp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.random.randn(5, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "std_p = np.std(z_qp, axis=0)\n",
-    "std_q = np.std(z_qp, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 320x320 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 281,
-       "width": 296
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(np.log(query_marginal_mean_q), np.log(std_q), s=1);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inds, y = quantile_normalize_select(\n",
-    "    x=np.log(1 + query_marginal_mean_q),\n",
-    "    y=np.log(std_q),\n",
-    "    n_bins=50,\n",
-    "    top_k=4000,\n",
-    "    min_x=1e-2,\n",
-    "    max_x=np.inf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f59fc5a29e0>"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 320x320 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 281,
-       "width": 298
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(x, y, s=1)\n",
-    "plt.scatter(x[inds], y[inds], s=1, color='black')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7263,118 +20017,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RPS29 0.25002084343528413\n",
-      "RPS27 0.21294385750728573\n",
-      "RPL27A 0.1590639182449272\n",
-      "RPL41 0.14990689821601214\n",
-      "RPLP2 0.13662548202267263\n",
-      "RPL13A 0.13641175988069157\n",
-      "RPL21 0.11553732740678681\n",
-      "RPS17 0.08892434738396542\n",
-      "RPL37A 0.061956671192742965\n",
-      "RPS20 0.04917304533748178\n",
-      "RPL23A 0.04873089597486011\n",
-      "RPL37 0.046089742866304975\n",
-      "EEF1A1 0.044034336056322405\n",
-      "PCDH11Y 0.04343167968857392\n",
-      "KCNB1 0.04127985377065494\n",
-      "CFAP47 0.03748759320394966\n",
-      "RPL34 0.03587074850772351\n",
-      "RPS2 0.03556800513846539\n",
-      "OTX2-AS1 0.03553745893655207\n",
-      "MT-ND2 0.035414843137776486\n",
-      "PPP1R9A-AS1 0.0338932791690593\n",
-      "LINC00609 0.030390189847234045\n",
-      "B2M 0.029091092346177443\n",
-      "ENSG00000231918 0.028745888666242086\n",
-      "ENSG00000253693 0.027283196696631175\n",
-      "TPT1 0.025285768878777592\n",
-      "KCNH1 0.024198590635440633\n",
-      "MIR4300HG 0.02390461364024318\n",
-      "NLGN4Y 0.02355975181251663\n",
-      "EIF1 0.02352267579187997\n",
-      "NALF1 0.022670562217922328\n",
-      "RFX3 0.022317085872722263\n",
-      "DCC 0.021412266776110075\n",
-      "DSCAM 0.0213945160004499\n",
-      "ENSG00000253190 0.020578815190038117\n",
-      "MIR2052HG 0.02031291760862122\n",
-      "ENSG00000237844 0.019711817968797497\n",
-      "PLEKHG1 0.019701591012197473\n",
-      "RPL3 0.019571959140483017\n",
-      "ADAM7-AS1 0.019454167348354614\n",
-      "CCDC30 0.019453652266162004\n",
-      "RPL31 0.019252236317242846\n",
-      "RPS28 0.01859884091902055\n",
-      "PCDH11X 0.018570039785022138\n",
-      "RPL7 0.01812956335632531\n",
-      "PRB2 0.01795112164992992\n",
-      "KIAA2012 0.017616835479990517\n",
-      "RPL26 0.017189171695448483\n",
-      "LINC01567 0.017044294258476863\n",
-      "NDUFA11 0.0\n",
-      "LSM4 0.0\n",
+      "CD52 0.1565533716449199\n",
+      "CD8A 0.1087066918919002\n",
+      "CD8B 0.09596716435108299\n",
+      "CORO1A 0.085173706945404\n",
+      "CD5 0.08301620381954343\n",
+      "CFL1 0.0726518930900382\n",
+      "IL32 0.07257330127816455\n",
+      "LPCAT4 0.07223186160228495\n",
+      "RPTOR 0.06968672203996511\n",
+      "HLA-A 0.0696851968356667\n",
+      "LINC00649 0.06890599868411967\n",
+      "BICDL1 0.06805181122968419\n",
+      "PDCD1 0.0672461223781432\n",
+      "TMSB10 0.06645898340367819\n",
+      "SIT1 0.06544472687329668\n",
+      "MCF2L2 0.06525994229800716\n",
+      "NSD2 0.06479313932293165\n",
+      "TBC1D19 0.06458169681690892\n",
+      "MT-ND4 0.06352731072653243\n",
+      "ARHGDIB 0.06309593824861949\n",
+      "ARHGAP35 0.06304704219006997\n",
+      "MT-ATP6 0.061960330263534265\n",
+      "CDCA8 0.061554303510713095\n",
+      "RPLP0 0.058425760957946056\n",
+      "TRANK1 0.057791098828607515\n",
+      "CDCA7 0.05756253213025655\n",
+      "CD70 0.05739751919045351\n",
+      "OGDH 0.057197951938705874\n",
+      "AUNIP 0.05630002280641244\n",
+      "MFHAS1 0.05567510097052668\n",
+      "SELENOI 0.05440760443460392\n",
+      "CEP192 0.05413910669041138\n",
+      "PPIA 0.05325518815138104\n",
+      "MT-CO2 0.05266676553479995\n",
+      "CCDC65 0.05199403065933024\n",
+      "LIN54 0.051130373537667695\n",
+      "SIRPG 0.05050659856859182\n",
+      "HNRNPLL 0.05047679746345229\n",
+      "PBX4 0.05011483231573842\n",
+      "L2HGDH 0.049930734889259615\n",
+      "GSDMB 0.049878527356148686\n",
+      "PCED1B 0.04961844660342912\n",
+      "STAMBPL1 0.04953134503550424\n",
+      "RAG1 0.049371951769269486\n",
+      "LINC00342 0.049325682524741885\n",
+      "GBF1 0.04909852559629893\n",
+      "CBLB 0.04909128016854497\n",
+      "SFI1 0.04904501094976081\n",
+      "WDHD1 0.048611590413553514\n",
+      "TMSB4X 0.04567373284762452\n",
+      "B2M 0.03526637370557435\n",
+      "AXIN2 0.0\n",
+      "ENSG00000278740 0.0\n",
+      "TSSK6 0.0\n",
+      "PYCR1 0.0\n",
+      "CDK3 0.0\n",
+      "TSEN54 0.0\n",
+      "RAB5C 0.0\n",
+      "NT5C3B 0.0\n",
+      "CYP2F1 0.0\n",
+      "ENSG00000273687 0.0\n",
+      "ZNF830 0.0\n",
+      "CCT6B 0.0\n",
       "PIPOX 0.0\n",
+      "CEACAM3 0.0\n",
+      "ZNF616 0.0\n",
       "ENSG00000285822 0.0\n",
-      "IL12RB1 0.0\n",
       "ENSG00000263220 0.0\n",
-      "SLC43A2 0.0\n",
-      "ENSG00000261592 0.0\n",
-      "NOTCH3 0.0\n",
-      "DHODH 0.0\n",
-      "SLC7A6OS 0.0\n",
-      "CTRL 0.0\n",
-      "TPPP3 0.0\n",
-      "ARL2BP 0.0\n",
-      "ENSG00000260267 0.0\n",
-      "FUS 0.0\n",
-      "MARCHF2 0.0\n",
-      "ATG4D 0.0\n",
-      "SWSAP1 0.0\n",
+      "SECTM1 0.0\n",
       "ZNF44 0.0\n",
-      "ENSG00000261346 0.0\n",
+      "SWSAP1 0.0\n",
+      "ATG4D 0.0\n",
+      "MARCHF2 0.0\n",
       "ZNF799 0.0\n",
-      "ENSG00000232748 0.0\n",
       "MRI1 0.0\n",
-      "CD70 0.0\n",
+      "NDUFA11 0.0\n",
+      "SHD 0.0\n",
+      "GADD45B 0.0\n",
+      "TIMM13 0.0\n",
+      "CIRBP 0.0\n",
+      "CNN2 0.0\n",
+      "NOTCH3 0.0\n",
+      "MPPE1 0.0\n",
+      "PLPP2 0.0\n",
+      "ADNP2 0.0\n",
+      "ENSG00000267476 0.0\n",
+      "IL12RB1 0.0\n",
+      "SYT4 0.0\n",
+      "LSM4 0.0\n",
       "MOCOS 0.0\n",
       "CABLES1 0.0\n",
-      "SYT4 0.0\n",
-      "MPPE1 0.0\n",
       "NAPG 0.0\n",
       "MTCL1 0.0\n",
-      "ENSG00000267476 0.0\n",
       "YES1 0.0\n",
+      "ELL 0.0\n",
       "ENSG00000260011 0.0\n",
-      "ADNP2 0.0\n",
-      "PLPP2 0.0\n",
       "TPGS1 0.0\n",
-      "SECTM1 0.0\n",
-      "ELL 0.0\n",
-      "PYCR1 0.0\n",
-      "CDK3 0.0\n",
-      "TSEN54 0.0\n",
-      "CIRBP 0.0\n",
-      "TIMM13 0.0\n",
-      "ENSG00000278740 0.0\n",
-      "AXIN2 0.0\n",
-      "RAB5C 0.0\n",
-      "NT5C3B 0.0\n",
-      "ENSG00000273687 0.0\n",
-      "GADD45B 0.0\n",
-      "ZNF830 0.0\n"
+      "SNRPF 0.0\n",
+      "SART3 0.0\n",
+      "PIWIL1 0.0\n"
      ]
     }
    ],
    "source": [
-    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['MALAT1']]\n",
+    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['CD3D']]\n",
     "inds = np.argsort(network_ctx.a_pp[i, :])[::-1]\n",
     "for j in inds[:100]:\n",
     "    print(filtered_perturb_gene_symbols[j], network_ctx.a_pp[i, j])"
@@ -7382,7 +20136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7392,16 +20146,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "50"
+       "53"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7419,33 +20173,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Feb 13 06:20:11 PM: Computing 20-nearest neighbors, with max_distance=None\n",
-      "Thu Feb 13 18:20:12 2025 Building RP forest with 17 trees\n",
-      "Thu Feb 13 18:20:17 2025 NN descent for 14 iterations\n",
+      "Feb 13 07:54:26 PM: Computing 20-nearest neighbors, with max_distance=None\n",
+      "Thu Feb 13 19:54:27 2025 Building RP forest with 17 trees\n",
+      "Thu Feb 13 19:54:33 2025 NN descent for 14 iterations\n",
       "\t 1  /  14\n",
       "\t 2  /  14\n",
       "\t 3  /  14\n",
       "\t 4  /  14\n",
       "\tStopping threshold met -- exiting after 4 iterations\n",
-      "Feb 13 06:20:32 PM: Computing quadratic initialization.\n",
-      "Feb 13 06:20:35 PM: Fitting a centered embedding into R^2, for a graph with 19187 items and 1879506 edges.\n",
-      "Feb 13 06:20:35 PM: `embed` method parameters: eps=1.0e-05, max_iter=1000, memory_size=10\n",
-      "Feb 13 06:20:35 PM: iteration 0000 | distortion 3.861732 | residual norm 0.936287 | step length 0.00684439 | percent change 0.00327134\n",
-      "Feb 13 06:20:36 PM: iteration 0100 | distortion 0.161198 | residual norm 0.000158078 | step length 1 | percent change 0.647835\n",
-      "Feb 13 06:20:37 PM: iteration 0200 | distortion 0.155643 | residual norm 6.65539e-05 | step length 1 | percent change 0.266546\n",
-      "Feb 13 06:20:37 PM: iteration 0300 | distortion 0.152120 | residual norm 3.81087e-05 | step length 1 | percent change 0.311538\n",
-      "Feb 13 06:20:38 PM: iteration 0400 | distortion 0.150891 | residual norm 3.87037e-05 | step length 1 | percent change 0.269843\n",
-      "Feb 13 06:20:39 PM: iteration 0500 | distortion 0.149535 | residual norm 1.96447e-05 | step length 1 | percent change 0.0752643\n",
-      "Feb 13 06:20:39 PM: Converged in 523 iterations, with residual norm 9.49637e-06\n",
-      "Feb 13 06:20:39 PM: Finished fitting in 3.605 seconds and 523 iterations.\n",
-      "Feb 13 06:20:39 PM: average distortion 0.15 | residual norm 9.5e-06\n"
+      "Feb 13 07:54:49 PM: Computing quadratic initialization.\n",
+      "Feb 13 07:54:53 PM: Fitting a centered embedding into R^2, for a graph with 19187 items and 1879564 edges.\n",
+      "Feb 13 07:54:53 PM: `embed` method parameters: eps=1.0e-05, max_iter=1000, memory_size=10\n",
+      "Feb 13 07:54:53 PM: iteration 0000 | distortion 4.361144 | residual norm 1.88672 | step length 0.00343867 | percent change 0.00331192\n",
+      "Feb 13 07:54:53 PM: iteration 0100 | distortion 0.158075 | residual norm 0.000127348 | step length 1 | percent change 0.7543\n",
+      "Feb 13 07:54:54 PM: iteration 0200 | distortion 0.154176 | residual norm 4.83483e-05 | step length 1 | percent change 0.204841\n",
+      "Feb 13 07:54:55 PM: iteration 0300 | distortion 0.153371 | residual norm 3.4722e-05 | step length 0.651394 | percent change 0.193755\n",
+      "Feb 13 07:54:55 PM: Converged in 356 iterations, with residual norm 8.29392e-06\n",
+      "Feb 13 07:54:55 PM: Finished fitting in 2.778 seconds and 356 iterations.\n",
+      "Feb 13 07:54:55 PM: average distortion 0.153 | residual norm 8.3e-06\n"
      ]
     }
    ],
@@ -7462,12 +20214,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
     "def get_gene_familities(network_ctx: GeneNetworkAnalysisBase, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
-    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in network_ctx.query_gene_symbols if gene_symbol.startswith(prefix)]\n",
+    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in network_ctx.prompt_gene_symbols if gene_symbol.startswith(prefix)]\n",
     "    gene_ids = [network_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
     "    gene_symbols = [network_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
     "    return gene_ids, gene_symbols\n",
@@ -7494,7 +20246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -7505,77 +20257,56 @@
        },
        "data": [
         {
-         "hovertemplate": "<b>%{hovertext}</b><br><br>membership=2<br>x=%{x}<br>y=%{y}<extra></extra>",
+         "hovertemplate": "<b>%{hovertext}</b><br><br>membership=6<br>x=%{x}<br>y=%{y}<extra></extra>",
          "hovertext": [
           "SDF4",
+          "MTF2",
           "MRPL35",
-          "LYAR",
-          "GTF3C6",
+          "ORMDL1",
+          "NUP54",
+          "C7orf50",
           "NDUFA8",
           "ZNRD2",
-          "PTMS",
           "GPN3",
+          "NIP7",
           "OGFOD3",
           "DSTN",
-          "TCEAL8",
           "GBP1",
           "MRPL24",
-          "WDR1",
+          "RNASEH1",
+          "UQCC5",
           "PFDN1",
           "MRPL22",
           "HDAC2",
-          "PRDX5",
-          "RNASEH2C",
-          "EMC7",
-          "SERF2",
-          "SUPT4H1",
           "LMAN1",
-          "GMFG",
-          "BLVRB",
           "ETFB",
-          "AURKAIP1",
-          "LYPLA2",
           "DNAJC19",
-          "UQCRQ",
-          "ZMAT2",
-          "LMAN2",
-          "SFT2D1",
-          "ATP5MF",
-          "OS9",
-          "MRPL52",
-          "SEC11A",
-          "NAA38",
-          "SUMO2",
-          "STX10",
-          "NDUFA6",
           "FUNDC2",
           "AKR7A2",
-          "PDIA6",
-          "HINT2",
+          "DENR",
+          "WDR18",
+          "BCL2L12",
           "PIN4",
-          "SSR4",
           "MAGOH",
           "UBXN4",
+          "RAD1",
           "POP7",
           "LSM1",
           "ELOC",
-          "TALDO1",
-          "TMEM258",
-          "GSTP1",
-          "ALG5",
-          "ETFA",
+          "IPO5",
           "DNAJC7",
-          "RTF2",
-          "HSD17B10",
+          "ARL16",
           "MRPL33",
-          "COX5B",
-          "TIMMDC1",
+          "HAT1",
+          "RUVBL1",
           "COPG1",
+          "EIF4G1",
           "LARP7",
           "ZNF330",
           "CLPTM1L",
+          "BOD1",
           "MRPS18A",
-          "COX7A2",
+          "INTS10",
           "GFUS",
           "PLGRKT",
           "CHMP5",
@@ -7584,301 +20315,245 @@
           "MRPL58",
           "CNIH4",
           "MTLN",
-          "RNF7",
           "POLR2K",
           "CHCHD1",
           "ECHS1",
           "REXO2",
-          "TMED2",
           "CDIPT",
           "TRAPPC2L",
-          "PSMA7",
+          "METTL23",
           "PHF5A",
+          "FAM104B",
           "PBDC1",
-          "NDUFA1",
           "DKC1",
           "MDH1",
-          "TRGC2",
+          "SDHA",
+          "FMC1",
           "PUF60",
+          "HSPA14",
+          "BNIP3",
           "TSG101",
           "MRPS35",
           "COX14",
           "MLEC",
           "CNIH1",
-          "ALDOA",
-          "CKLF",
-          "UBB",
           "TXNL1",
           "RAB8A",
           "RFXANK",
-          "EIF3K",
           "TIMM50",
-          "BAX",
           "RUVBL2",
           "ISOC2",
           "POLR2F",
-          "BCAP31",
+          "POLR3H",
           "SPCS3",
-          "AIMP2",
           "ARPC1A",
           "RRAGA",
           "TRUB2",
-          "DNAJC15",
-          "NDUFS7",
+          "NELFB",
+          "EIF2S1",
+          "NME3",
           "DDA1",
-          "ROMO1",
           "PFKL",
           "PDHA1",
-          "DPM2",
-          "NDUFAB1",
+          "MOB4",
+          "ACOT13",
+          "CHCHD7",
           "PSMC4",
-          "ADISSP",
-          "CHMP4B",
           "FBXO7",
-          "RER1",
           "SF3B4",
           "MTX1",
           "COX20",
-          "CMPK2",
           "IFRD2",
           "ALG3",
           "MRPS18C",
-          "ZNHIT1",
-          "FABP5",
-          "COX8A",
-          "ARL1",
-          "NOP10",
-          "SRP14",
+          "CASP6",
+          "HARS1",
+          "SLU7",
+          "MRPS33",
+          "NUDCD1",
           "TBL3",
-          "OAZ1",
+          "CFDP1",
+          "PITHD1",
           "CERS2",
-          "COX7A2L",
           "LSM3",
+          "RBM15B",
+          "CHCHD3",
           "TMEM70",
           "NDUFB6",
-          "M6PR",
           "ERGIC3",
           "C1orf174",
-          "DNAJC8",
+          "PPIH",
           "PSMD4",
-          "LAMTOR4",
-          "PLIN2",
-          "ATP6V1G1",
+          "TMEM183A",
+          "SNRNP27",
+          "MRPS18B",
           "ZNF511",
-          "TESC",
-          "HNRNPC",
-          "TRAPPC1",
-          "JPT1",
+          "THYN1",
+          "C15orf61",
           "NDUFV3",
-          "TPM3",
-          "ENSG00000232995",
           "METTL5",
           "EIF4E2",
-          "GTF2H5",
           "MPLKIP",
-          "ITGB1",
-          "SLC3A2",
-          "SMUG1",
+          "PRPF4",
+          "TMEM134",
           "MRPL28",
           "MVB12A",
           "POLR2I",
           "RTCB",
           "PFDN2",
-          "TMA7",
           "APEH",
-          "ATP1B3",
           "SMIM15",
           "COPS5",
-          "PGAM1",
-          "ATP5MJ",
+          "VCP",
+          "RSPRY1",
           "DERL2",
+          "CHMP6",
+          "DDRGK1",
+          "HAX1",
           "CSRP1",
           "ERLEC1",
-          "OSTC",
-          "NDUFS6",
           "PAK1IP1",
           "TMEM14C",
+          "BZW2",
           "TMEM203",
-          "UCP2",
           "SMAGP",
           "DHRS4L2",
-          "CINP",
           "RPUSD1",
-          "NDUFB10",
-          "GPS1",
+          "SPOP",
           "CCDC124",
-          "PEPD",
-          "RBCK1",
-          "PSMA5",
-          "PNKD",
+          "YDJC",
+          "GEMIN6",
           "MRPL36",
           "H1-4",
           "POLR1H",
           "GLO1",
-          "CENPW",
-          "MRPL51",
-          "ARHGDIB",
-          "ZCRB1",
+          "ARMT1",
+          "MRPS17",
           "SUGT1",
           "VTI1B",
           "JPT2",
-          "PPP4C",
-          "COX7B",
-          "IFI44",
+          "ARL2BP",
+          "TPGS1",
           "NCBP2AS2",
-          "VDAC1",
+          "QDPR",
           "COA4",
+          "ANAPC5",
+          "EXOSC8",
           "PSMA6",
-          "IFI27",
-          "SQOR",
           "TRAF7",
           "POLDIP2",
           "ATP5MC1",
-          "SNX5",
           "ATP6V1E1",
+          "GNL2",
           "TXNDC12",
           "CZIB",
-          "TWF2",
+          "OCIAD1",
           "PDAP1",
           "STRAP",
-          "RPL36AL",
+          "TOX4",
           "C17orf49",
-          "AP2S1",
-          "SSR2",
+          "TP53RK",
+          "IFNAR2",
+          "DFFA",
           "MRPS9",
-          "BRK1",
           "NDUFAF3",
-          "COX17",
           "SRPRB",
           "PAIP1",
+          "POLD2",
           "SWI5",
           "ILK",
+          "PEX16",
           "HIKESHI",
-          "COX6A1",
+          "SDHD",
           "PRMT5",
+          "FBLN5",
           "PSMD3",
-          "SNF8",
-          "YIPF2",
           "NAPA",
-          "EMP3",
-          "NDUFA3",
+          "PPP2R1A",
           "IDH3B",
-          "UQCR10",
-          "JTB",
-          "OST4",
+          "TSNAX",
           "ITGA4",
           "MRPL3",
           "HPF1",
-          "CAPZA2",
           "SHARPIN",
-          "ATL3",
           "PPP1R14B",
-          "TPI1",
+          "MYG1",
           "PSMC6",
+          "PDCD2L",
           "SDHB",
-          "CAP1",
-          "COPS9",
           "EXOSC7",
-          "LAP3",
           "MALSU1",
-          "GSTO1",
-          "COMMD9",
-          "TIMM8B",
-          "PPIB",
           "NSMCE1",
           "VKORC1",
-          "ASPSCR1",
           "ELOF1",
+          "SNRNP70",
           "LAGE3",
-          "TMEM50A",
           "B4GALT3",
           "NDUFS2",
           "COA6",
           "PREB",
           "FAM136A",
-          "VAMP5",
           "MRPL47",
+          "SLC35A4",
+          "YIPF5",
+          "AP1S1",
           "DCUN1D5",
           "HMBS",
-          "EPSTI1",
-          "UBL5",
-          "PGLS",
-          "ATP5PF",
-          "CSTB",
-          "IDH3G",
+          "USP14",
           "NOC2L",
           "GPN1",
           "AAMP",
+          "CDKN2AIPNL",
           "PFDN6",
-          "ARPC5L",
           "MRPL43",
           "PTPMT1",
-          "UBE2L6",
-          "ATP5MG",
-          "CLEC2B",
           "CLN6",
           "TSR3",
           "TXNL4A",
-          "UQCR11",
-          "COPE",
+          "DMAC2",
           "COMT",
           "YWHAH",
+          "ELOA",
           "SNRNP40",
-          "DUSP23",
-          "PDLIM2",
-          "PPP1CA",
+          "PDCL3",
           "MRPL48",
-          "SPCS2",
-          "NDUFB1",
           "ANP32A",
-          "COX5A",
           "PSMG2",
           "BSG",
-          "MYDGF",
           "ENSG00000276345",
           "MRPL55",
           "SPCS1",
+          "CCZ1",
           "MRPL32",
-          "ATP5MK",
-          "CHID1",
           "CCDC85B",
           "VPS29",
-          "CORO1A",
           "NUDT21",
-          "PSMB3",
+          "NAE1",
           "SAP30BP",
-          "ANAPC11",
           "NDUFV2",
           "RALBP1",
-          "TBCB",
-          "SLC25A5",
-          "UQCRH",
           "VPS72",
           "SHC1",
+          "USP39",
           "UBE2F",
+          "ZFAND2A",
           "BUD31",
           "MRPL17",
-          "YIF1A",
-          "MYL6",
-          "PFN1",
-          "TXNDC17",
-          "ADRM1",
+          "GMPR2",
+          "PET117",
           "MRPL40",
           "CYB5R3",
           "OLA1",
-          "STMP1",
-          "PCBD1",
+          "NUDCD2",
           "HEXA",
-          "MRPL27",
           "SMIM7",
           "ITPA",
+          "VBP1",
           "NENF",
           "TXNDC9",
-          "ATP5ME",
-          "MACROH2A1",
+          "PPID",
           "SEM1",
           "CDC123",
-          "GHITM",
           "RPP30",
           "TUBA1C",
           "ESD",
@@ -7886,36 +20561,29 @@
           "UQCRC2",
           "COPS3",
           "CYB5A",
-          "TMEM147",
+          "GID8",
           "SRM",
           "UTP11",
-          "MED8",
-          "STAT1",
+          "WDR77",
           "CCDC12",
-          "MANF",
           "POLR2H",
           "LSM2",
-          "SMIM30",
-          "IFIT3",
           "MRPL23",
           "TIMM10",
           "MRPS34",
           "SPAG7",
-          "WDR83OS",
-          "COX6B1",
-          "TOP1",
-          "ATG3",
+          "BBC3",
+          "SRSF11",
+          "PEX13",
+          "PPIG",
           "SRPK1",
           "HDDC2",
           "KCTD9",
-          "MTDH",
           "PSMB7",
-          "TMEM141",
-          "IFIT1",
           "SCYL1",
+          "TRAPPC4",
+          "COASY",
           "MICOS13",
-          "FKBP1A",
-          "ATP5F1E",
           "DDOST",
           "EBNA1BP2",
           "UROD",
@@ -7923,3048 +20591,2703 @@
           "DPY30",
           "LRPAP1",
           "MRPL18",
-          "BLVRA",
-          "VPS28",
-          "RNH1",
+          "NPM3",
           "EIF4G2",
           "LLPH",
-          "SELENOS",
-          "VPS25",
           "MRPL54",
+          "DDX39A",
           "FAM32A",
           "UQCRFS1",
-          "NDUFB2",
+          "DRG1",
+          "MPC2",
+          "CDC5L",
           "MRPL13",
           "EXOSC3",
-          "CASP4",
+          "NSMCE4A",
           "ZFYVE21",
           "EMC4",
           "RAB11A",
           "TAX1BP3",
-          "TPM4",
+          "TP53",
           "EMC10",
-          "C1orf43",
+          "MRPL39",
+          "PRR5",
           "SDHC",
           "ADIPOR1",
           "TSN",
-          "UQCC3",
-          "ATP5MC2",
-          "HSP90B1",
+          "THUMPD3",
           "STUB1",
           "CARHSP1",
           "NTAN1",
-          "SLC25A11",
-          "CHMP2A",
+          "NARF",
           "DPM1",
           "PSMG1",
           "HMGN2",
           "AK2",
           "PSMB2",
+          "LRRC42",
           "SELENOF",
           "PTRHD1",
-          "CHCHD5",
+          "MITD1",
           "IMP4",
           "NMI",
-          "ATP5MC3",
-          "ARPC2",
+          "ATIC",
           "AP2M1",
           "SREK1IP1",
           "RPL26L1",
+          "TAF6",
           "RHEB",
           "VDAC3",
-          "ZDHHC12",
+          "C8orf33",
           "ATG101",
           "GCSH",
           "PSMD11",
           "PSME3",
+          "PSMD12",
           "PSMF1",
           "NAA20",
-          "PRDX4",
           "PLP2",
-          "C1D",
+          "MRPS25",
+          "DNPH1",
           "COPS6",
           "PLPBP",
-          "ENY2",
+          "TCEA1",
           "BAG1",
           "CLTA",
           "CUEDC2",
           "EI24",
+          "PRIM1",
           "GADD45GIP1",
           "PSMD8",
           "SAMM50",
-          "YBX1",
-          "LAMTOR5",
           "TMCO1",
-          "BZW1",
-          "NDUFB3",
           "ANAPC13",
           "CLTB",
           "CCDC25",
           "MRPL50",
-          "PRDX3",
-          "CAT",
-          "TMEM179B",
+          "TARBP2",
+          "PRKAB1",
           "TMED3",
-          "HSBP1",
-          "GET3",
-          "KDELR1",
+          "ADPRS",
           "MRPL37",
-          "IFI44L",
           "PMVK",
           "DPM3",
           "PPP1R7",
+          "NCBP2",
           "SLBP",
+          "PPAT",
           "NDUFAF4",
-          "ARF5",
           "R3HCC1",
+          "MCM4",
           "MRPL15",
-          "MRPL41",
-          "GDI2",
+          "EXOSC2",
           "NDUFS3",
           "CAPN1",
+          "MAGOHB",
           "TRIAP1",
+          "IPO4",
           "ECSIT",
-          "MAD2L2",
-          "GNG5",
-          "ARPC5",
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           "THUMPD1",
           "SNHG16",
           "CALR",
@@ -11265,15 +23566,13 @@
           "CNOT7",
           "MYC",
           "TMEM109",
-          "MYG1",
           "PA2G4",
+          "NFYB",
           "REC8",
           "SNRPA1",
           "METTL26",
           "NOB1",
-          "DEDD2",
           "EIF3L",
-          "EPB41",
           "RPF1",
           "HDGF",
           "ENSG00000279277",
@@ -11289,7 +23588,6 @@
           "THOC7",
           "PIM1",
           "ZBTB24",
-          "FOXK1",
           "NDUFB8",
           "EEF1G",
           "RELA",
@@ -11297,7 +23595,6 @@
           "LDHB",
           "CNPY2",
           "PNN",
-          "MOAP1",
           "HSP90AA1",
           "MESD",
           "PTPN2",
@@ -11315,38 +23612,33 @@
           "EIF4A2",
           "TSPYL1",
           "ABRACL",
-          "HK1",
           "ZPR1",
           "ZFC3H1",
           "CCDC59",
           "UBALD2",
           "FLT3LG",
           "DDX27",
-          "UBQLN2",
           "RPS4Y1",
           "ENO1",
           "PNRC2",
           "CHTOP",
           "CKS1B",
-          "RNF187",
           "QARS1",
-          "TRA2A",
           "NSD3",
           "SNHG1",
           "HSPA8",
           "FKBP4",
           "EIF4B",
+          "YEATS4",
           "MAPK1IP1L",
           "SFPQ",
           "NOP14",
           "ENSG00000286986",
           "HNRNPA0",
-          "CCAR2",
           "TATDN1",
           "ANP32B",
           "AIP",
           "TIMM9",
-          "ATXN2L",
           "APRT",
           "PTBP1",
           "PDCD5",
@@ -11354,9 +23646,7 @@
           "RBM3",
           "RPA2",
           "PPP1CB",
-          "USP39",
           "PCNP",
-          "ZNF639",
           "SNHG8",
           "NSA2",
           "TRBC2",
@@ -11365,10 +23655,10 @@
           "VPS4A",
           "PPAN",
           "ZNF90",
+          "ADRM1",
           "RTL10",
           "SNHG12",
           "TOMM20",
-          "NUDCD2",
           "TMEM14B",
           "SNHG5",
           "BUD23",
@@ -11378,30 +23668,26 @@
           "RNPS1",
           "RSL1D1",
           "GSPT1",
-          "SRCAP",
           "R3HDM4",
           "MYADM",
           "EIF5B",
-          "NKTR",
           "HNRNPD",
           "TAF10",
           "RPS26",
           "UBC",
           "BAZ1A",
-          "DNAJA2",
           "PHB1",
           "RNF126",
           "TRAPPC6A",
           "RNF114",
           "RBX1",
-          "TOB2",
           "NUDC",
           "PCBP1",
           "NBEAL1",
           "XRCC5",
           "RPP21",
-          "ZNF394",
           "DNAJC2",
+          "DNAJB6",
           "SMC3",
           "SF1",
           "SNRPA",
@@ -11415,12 +23701,9 @@
           "RBM17",
           "TFAM",
           "PPA1",
-          "GANAB",
           "TAF1D",
           "TMEM123",
           "FKBP3",
-          "CLK3",
-          "CHD3",
           "RPL17",
           "NDUFA13",
           "EIF2S3",
@@ -11433,30 +23716,24 @@
           "LUC7L2",
           "SNHG7",
           "SAR1A",
-          "NPM3",
           "EIF3F",
           "SELENOH",
           "SINHCAF",
           "DDX54",
-          "POLR2A",
           "SNHG29",
           "GRAMD1A",
           "COMMD7",
           "CCT8",
           "SON",
           "RANBP1",
-          "MPC2",
           "WDR43",
           "ZC3H15",
-          "CHIC2",
           "CUTA",
           "CCND3",
           "TRAM1",
-          "HSF1",
           "TUBB4B",
           "HNRNPF",
           "RGS10",
-          "RBM4",
           "KRR1",
           "KRT10",
           "CDC42",
@@ -11465,15 +23742,10 @@
           "HSPE1",
           "SELENOK",
           "SSBP1",
-          "DPP7",
           "GTPBP4",
           "SSRP1",
-          "AP1G2",
-          "RNF138",
-          "RAVER1",
           "SNRPB2",
           "RALY",
-          "TARDBP",
           "MICOS10",
           "EIF2D",
           "PSMD6",
@@ -11488,16 +23760,13 @@
           "SMDT1",
           "NDUFB11",
           "MRTO4",
-          "RCC1",
           "MZT2A",
           "REST",
           "MRPS30",
-          "ABCF1",
           "TCP1",
           "LSM5",
           "TMEM243",
           "ENSG00000170632",
-          "WBP11",
           "PTGES3",
           "CTDSP2",
           "SAP18",
@@ -11512,13 +23781,13 @@
           "ELAVL1",
           "PFDN4",
           "HDHD5",
-          "ELK1",
           "PQBP1",
           "PEF1",
           "CCT3",
           "RO60",
           "STK25",
           "SERP1",
+          "LEF1",
           "CFAP97",
           "UBE2D2",
           "SRRT",
@@ -11548,13 +23817,13 @@
           "YWHAQ",
           "C4orf3",
           "G3BP1",
+          "ASF1A",
           "TBRG4",
           "EIF3E",
           "MAF1",
           "BUB3",
           "ILF3",
           "PRKCSH",
-          "AP1M1",
           "ADSL",
           "HTATSF1",
           "KHDRBS1",
@@ -11564,7 +23833,6 @@
           "DDX21",
           "GLRX3",
           "VPS51",
-          "NDUFA9",
           "C12orf57",
           "CHURC1",
           "AHSA1",
@@ -11582,20 +23850,17 @@
           "PPP1CC",
           "GTF3A",
           "CLN3",
-          "E2F4",
           "SRSF1",
           "NDUFS5",
           "RPL22L1",
           "HSD17B11",
           "UBE2B",
-          "BLOC1S5",
           "SNRPC",
           "SRSF3",
-          "DDX56",
           "DUT",
+          "POLR2C",
           "NXT1",
           "PTP4A2",
-          "SLC2A1",
           "ARL6IP5",
           "EIF2A",
           "PSMB1",
@@ -11615,27 +23880,22 @@
           "IGKC",
           "CCT5",
           "TAF9",
-          "DCP2",
           "HMGA1",
           "HSP90AB1",
           "RBIS",
           "CDC26",
-          "DDIT4",
           "EBPL",
           "PSMA3",
           "THOC6",
-          "SH3BP1",
           "CAPZB",
           "SNHG3",
           "C10orf95-AS1",
           "FKBP11",
-          "PML",
           "EIF4A1",
-          "SLC25A39",
+          "LIMD2",
           "TRIR",
           "TRMT1",
           "ZNF580",
-          "AHCY",
           "TRABD",
           "GTPBP6",
           "CNBP",
@@ -11645,9 +23905,10 @@
           "CCT2",
           "NME2",
           "NDUFAF8",
+          "HDAC1",
           "ARF1",
+          "GRSF1",
           "LSM6",
-          "HNRNPAB",
           "MED30",
           "IMP3",
           "TUFM",
@@ -11659,14 +23920,11 @@
           "SNRPG",
           "SF3B1",
           "U2SURP",
-          "DHX36",
           "TRA2B",
           "SRP72",
           "DIMT1",
           "HIGD2A",
-          "RNF5",
           "SRI",
-          "ENSG00000287547",
           "UBXN1",
           "CFAP68",
           "EMG1",
@@ -11676,49323 +23934,45289 @@
           "NCL",
           "GRPEL1",
           "HMGB2",
+          "TBCA",
           "ANKHD1",
           "HSPB1",
           "EIF3A",
           "C11orf58",
-          "CTDSPL2",
           "UTP4",
-          "FAM174C",
           "DAZAP1",
           "PRPF31",
-          "TMEM230",
           "PRELID3B",
           "MRPS15",
           "TMEM43",
-          "BLOC1S4",
           "HSPA9",
-          "RBM22",
-          "BAG6",
           "SNHG15",
           "SPOUT1",
           "MED19",
-          "RBM7",
           "ERP29",
           "PKM",
           "ECH1",
           "PRMT1",
           "SUMO3"
          ],
-         "legendgroup": "0",
+         "legendgroup": "1",
          "marker": {
-          "color": "#d60000",
+          "color": "#8c3bff",
           "size": 2,
           "symbol": "circle"
          },
          "mode": "markers",
-         "name": "0",
+         "name": "1",
          "showlegend": true,
          "type": "scattergl",
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+          "AGO4",
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+          "RIPK1",
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+          "NCOA1",
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-          "CCDC14",
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-          "CENPP",
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@@ -61003,9 +69227,8 @@
           "ZNF827",
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-          "DHCR7-DT",
           "CHFR",
-          "UPF3A",
+          "AP4E1",
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@@ -61013,71 +69236,93 @@
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-          "NDUFAF6",
-          "PTER",
-          "EPS8L2",
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+          "ATP2B4",
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+          "SFMBT1",
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-          "H1-1",
+          "C11orf80",
+          "CREBL2",
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+          "GGA2",
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-          "TRIB2",
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+          "CWF19L1",
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+          "PCSK7",
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+          "PSMA8",
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@@ -61085,156 +69330,206 @@
           "BCKDHB",
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+          "SPTAN1",
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           "KLRC2",
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+          "CDC25B",
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+          "WDTC1",
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+          "BMAL1",
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+          "CD69",
+          "PDXDC1",
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+          "ZNF337",
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+          "JADE2",
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+          "TAGAP",
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           "FAM111A-DT",
+          "NCKAP1L",
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+          "BRICD5",
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           "APOBEC3H",
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+          "AMY2B",
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+          "NMRK1",
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+          "SCAPER",
+          "IL32",
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+          "ZNF224",
+          "ASXL1",
+          "MT-CO1",
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-          "H2BC13",
+          "LINC00623",
+          "NBEAL2",
+          "SPICE1",
+          "ANKRD52",
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+          "TTC39C",
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           "TMEM143",
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+          "DOCK10",
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+          "NCK2",
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+          "TMEM181",
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+          "NLRC5",
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+          "PPP1R3F",
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+          "SOS1",
+          "NISCH",
+          "LRBA",
+          "ENSG00000247121",
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+          "DIP2A",
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+          "BICRAL",
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+          "PPP3CC",
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-          "USP5",
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+          "PPM1A",
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+          "SPPL2A",
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+          "TENT4B",
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+          "NPC1",
+          "ZNF345",
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+          "NR2C2",
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+          "INTS9",
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+          "LINC02328",
           "BLM",
-          "EME1",
+          "XPO6",
+          "RAP1GAP2",
+          "SARM1",
+          "STAT5B",
           "CENPM",
           "RAB39B",
           "ARHGAP15",
+          "CAPN7",
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+          "ABCD2",
           "IFNG",
           "SYNRG",
-          "RNASEH2A",
+          "LDLRAD4",
+          "WDR7",
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-          "HIBCH",
           "ENSG00000272211",
-          "H2BC15",
-          "MTFR2",
-          "TFB1M",
           "TOX-DT",
           "FBXO43",
+          "SEC16A",
           "PRF1",
+          "CDKN1B",
           "MPHOSPH9",
-          "UBL7-DT",
+          "SRP54",
+          "PTPN9",
           "ISG20",
-          "CMC2",
           "CD7",
           "ANKRD12",
           "ENSG00000268027",
           "LIME1",
-          "SUV39H1",
+          "EML4",
+          "AAK1",
           "ANXA6",
           "ORC5",
           "PAG1",
           "ANKRD26",
+          "GBF1",
+          "FAR1",
           "BATF",
           "STX8",
+          "ANKRD36",
           "TBC1D19",
           "TMEM156",
+          "SKIC3",
+          "ITK",
           "FUT10",
           "SLC27A4",
           "AGAP2",
@@ -61243,16 +69538,24 @@
           "IL21R",
           "ENSG00000099991",
           "LY9",
+          "SACM1L",
           "WAKMAR2",
+          "RAPGEF1",
+          "CD6",
           "RNF34",
           "PPP2R5E",
+          "LMF1",
+          "KIAA0319L",
           "CASP8",
-          "TIMD4",
+          "HTT",
+          "CEP120",
           "EXOC2",
-          "CCNE2",
+          "BTN3A1",
+          "SYTL3",
+          "NUP205",
           "CD3D",
           "LPCAT4",
-          "DCAKD",
+          "TMC8",
           "FCRL6",
           "ANKRD36C",
           "AGPS",
@@ -61260,1683 +69563,5289 @@
           "BARD1",
           "TAMM41",
           "TRANK1",
+          "ESR2",
+          "TMED8",
           "RAB37",
+          "CBFA2T2",
+          "SFI1",
           "CHIC1",
-          "C1orf21",
+          "KIF21B",
           "ANAPC1",
+          "DCAF1",
           "CD38",
+          "RBPJ",
+          "CNOT6L",
           "H2AC17",
           "ENSG00000285730",
           "OGDH",
-          "CDCA2",
+          "CHUK",
           "LPXN",
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        "                            \n",
-       "var gd = document.getElementById('33775c9f-46dc-4dd6-8224-9da104f88035');\n",
+       "var gd = document.getElementById('6a3c7602-f345-4efc-95e4-3c9d84a0195f');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -67705,13 +80520,6 @@
    "source": [
     "network_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb b/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
new file mode 100644
index 00000000..9dfec37a
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
@@ -0,0 +1,5561 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Determine cell types for metadata prompting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Process validation datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_meta_df = pd.read_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"cellxgene_validation_donors__third_draft.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stephen's validatdion table\n",
+    "# what we have called intestine is actually \"small intestine\"\n",
+    "\n",
+    "tissue_ontology_term_id_to_coarse_name_map = dict()\n",
+    "for row in validation_meta_df['tissue_name_ont_coarsename_coarseont'].values:\n",
+    "    split_row = row.strip(\"()\").split(', ')\n",
+    "    coarse_name = split_row[2].strip(\"'\")\n",
+    "    if coarse_name == \"intestine\":\n",
+    "        coarse_name = \"small intestine\"\n",
+    "    tissue_ontology_term_id_to_coarse_name_map[split_row[1].strip(\"'\")] = coarse_name\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9869031982544a6ae24473a0fb42074",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "obs_list = []\n",
+    "\n",
+    "for val_idx in tqdm(range(1, 111)):\n",
+    "    \n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    obs_df = val_adata.obs.copy()\n",
+    "    obs_df['tissue_coarse'] = obs_df['tissue_ontology_term_id'].map(tissue_ontology_term_id_to_coarse_name_map)\n",
+    "\n",
+    "    columns_to_keep = [\n",
+    "        \"cell_type\", \"assay\", \"suspension_type\", \"disease\", \"sex\", \"tissue_coarse\", \"total_mrna_umis\",\n",
+    "        \"cell_type_distance_from_root\", \"dataset_id\", \"donor_id\"]\n",
+    "\n",
+    "    group_cols = [\"cell_type\", \"donor_id\"]\n",
+    "\n",
+    "    obs_df = obs_df[columns_to_keep]\n",
+    "\n",
+    "    agg_df_1 = obs_df.groupby(group_cols, as_index=False)[\"total_mrna_umis\"].mean().rename(\n",
+    "        columns={\"total_mrna_umis\": \"total_mrna_umis_avg\"})\n",
+    "    agg_df_2 = obs_df.groupby(group_cols, as_index=False)[\"total_mrna_umis\"].count().rename(\n",
+    "        columns={\"total_mrna_umis\": \"n_cells\"})\n",
+    "\n",
+    "    obs_df = obs_df.drop_duplicates(subset=group_cols)\n",
+    "    obs_df = obs_df.drop(columns=[\"total_mrna_umis\"])\n",
+    "    obs_df = obs_df.merge(agg_df_1, on=group_cols, how=\"left\")\n",
+    "    obs_df = obs_df.merge(agg_df_2, on=group_cols, how=\"left\")\n",
+    "\n",
+    "    obs_list.append(obs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat(obs_list, ignore_index=True).drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "included_assays = [\n",
+    "    \"10x 3' v3\", \"10x 3' v2\",\n",
+    "    \"10x 5' v1\", \"10x 5' v2\"]\n",
+    "\n",
+    "df = df[df[\"assay\"].isin(included_assays)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>tissue_coarse</th>\n",
+       "      <th>cell_type_distance_from_root</th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>donor_id</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>bab7432a-5cfe-45ea-928c-422d03c45cdd</td>\n",
+       "      <td>#138</td>\n",
+       "      <td>5040.933212</td>\n",
+       "      <td>4956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>bab7432a-5cfe-45ea-928c-422d03c45cdd</td>\n",
+       "      <td>#138</td>\n",
+       "      <td>4264.749176</td>\n",
+       "      <td>2731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>bab7432a-5cfe-45ea-928c-422d03c45cdd</td>\n",
+       "      <td>#138</td>\n",
+       "      <td>9288.528169</td>\n",
+       "      <td>142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>bab7432a-5cfe-45ea-928c-422d03c45cdd</td>\n",
+       "      <td>#138</td>\n",
+       "      <td>3819.324324</td>\n",
+       "      <td>666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>differentiation-committed oligodendrocyte prec...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>bab7432a-5cfe-45ea-928c-422d03c45cdd</td>\n",
+       "      <td>#138</td>\n",
+       "      <td>4756.629870</td>\n",
+       "      <td>308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1896</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>56c4912d-2bae-4b64-98f2-af8a84389208</td>\n",
+       "      <td>ndGBM-06</td>\n",
+       "      <td>4770.666667</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1897</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>56c4912d-2bae-4b64-98f2-af8a84389208</td>\n",
+       "      <td>ndGBM-06</td>\n",
+       "      <td>10234.568627</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1898</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>56c4912d-2bae-4b64-98f2-af8a84389208</td>\n",
+       "      <td>ndGBM-06</td>\n",
+       "      <td>7086.076923</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1899</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56c4912d-2bae-4b64-98f2-af8a84389208</td>\n",
+       "      <td>ndGBM-06</td>\n",
+       "      <td>5751.000000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1900</th>\n",
+       "      <td>radial glial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>male</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>56c4912d-2bae-4b64-98f2-af8a84389208</td>\n",
+       "      <td>ndGBM-06</td>\n",
+       "      <td>1226.000000</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1789 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              cell_type      assay  \\\n",
+       "0                                       oligodendrocyte  10x 3' v3   \n",
+       "1                               cerebellar granule cell  10x 3' v3   \n",
+       "2                                           interneuron  10x 3' v3   \n",
+       "3                                               unknown  10x 3' v3   \n",
+       "4     differentiation-committed oligodendrocyte prec...  10x 3' v3   \n",
+       "...                                                 ...        ...   \n",
+       "1896                                    microglial cell  10x 3' v3   \n",
+       "1897                                         macrophage  10x 3' v3   \n",
+       "1898                                           monocyte  10x 3' v3   \n",
+       "1899                                natural killer cell  10x 3' v3   \n",
+       "1900                                  radial glial cell  10x 3' v3   \n",
+       "\n",
+       "     suspension_type       disease   sex tissue_coarse  \\\n",
+       "0            nucleus        normal  male         brain   \n",
+       "1            nucleus        normal  male         brain   \n",
+       "2            nucleus        normal  male         brain   \n",
+       "3            nucleus        normal  male         brain   \n",
+       "4            nucleus        normal  male         brain   \n",
+       "...              ...           ...   ...           ...   \n",
+       "1896            cell  glioblastoma  male         brain   \n",
+       "1897            cell  glioblastoma  male         brain   \n",
+       "1898            cell  glioblastoma  male         brain   \n",
+       "1899            cell  glioblastoma  male         brain   \n",
+       "1900            cell  glioblastoma  male         brain   \n",
+       "\n",
+       "      cell_type_distance_from_root                            dataset_id  \\\n",
+       "0                              5.0  bab7432a-5cfe-45ea-928c-422d03c45cdd   \n",
+       "1                              7.0  bab7432a-5cfe-45ea-928c-422d03c45cdd   \n",
+       "2                              4.0  bab7432a-5cfe-45ea-928c-422d03c45cdd   \n",
+       "3                              NaN  bab7432a-5cfe-45ea-928c-422d03c45cdd   \n",
+       "4                              4.0  bab7432a-5cfe-45ea-928c-422d03c45cdd   \n",
+       "...                            ...                                   ...   \n",
+       "1896                           8.0  56c4912d-2bae-4b64-98f2-af8a84389208   \n",
+       "1897                           5.0  56c4912d-2bae-4b64-98f2-af8a84389208   \n",
+       "1898                           5.0  56c4912d-2bae-4b64-98f2-af8a84389208   \n",
+       "1899                           7.0  56c4912d-2bae-4b64-98f2-af8a84389208   \n",
+       "1900                           4.0  56c4912d-2bae-4b64-98f2-af8a84389208   \n",
+       "\n",
+       "      donor_id  total_mrna_umis_avg  n_cells  \n",
+       "0         #138          5040.933212     4956  \n",
+       "1         #138          4264.749176     2731  \n",
+       "2         #138          9288.528169      142  \n",
+       "3         #138          3819.324324      666  \n",
+       "4         #138          4756.629870      308  \n",
+       "...        ...                  ...      ...  \n",
+       "1896  ndGBM-06          4770.666667       18  \n",
+       "1897  ndGBM-06         10234.568627       51  \n",
+       "1898  ndGBM-06          7086.076923       13  \n",
+       "1899  ndGBM-06          5751.000000        1  \n",
+       "1900  ndGBM-06          1226.000000        3  \n",
+       "\n",
+       "[1789 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keep a reference to the original dataframe\n",
+    "validation_stats_df = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>32283.096081</td>\n",
+       "      <td>40379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>19390.023813</td>\n",
+       "      <td>38825.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>3466.119566</td>\n",
+       "      <td>34701.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>4337.817282</td>\n",
+       "      <td>8561.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>17218.924481</td>\n",
+       "      <td>7344.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>13455.119639</td>\n",
+       "      <td>7249.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>6141.094851</td>\n",
+       "      <td>7105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>6325.213182</td>\n",
+       "      <td>6695.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sst GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>14236.769992</td>\n",
+       "      <td>6387.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>12343.866509</td>\n",
+       "      <td>5841.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>3531.395319</td>\n",
+       "      <td>5683.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>16460.512190</td>\n",
+       "      <td>5123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>astrocyte of the cerebral cortex</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>7024.244142</td>\n",
+       "      <td>4539.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>neural cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>3671.033365</td>\n",
+       "      <td>3740.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>3133.254010</td>\n",
+       "      <td>3494.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>mature astrocyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>1619.839781</td>\n",
+       "      <td>3439.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>8287.577188</td>\n",
+       "      <td>3309.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>4264.749176</td>\n",
+       "      <td>2731.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>mature microglial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>754.149411</td>\n",
+       "      <td>2453.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>corticothalamic-projecting glutamatergic corti...</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>34544.952352</td>\n",
+       "      <td>2273.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>L6b glutamatergic cortical neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>42263.586519</td>\n",
+       "      <td>2037.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>sncg GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>13442.716510</td>\n",
+       "      <td>1984.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>2991.238648</td>\n",
+       "      <td>1739.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>choroid plexus epithelial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1962.060115</td>\n",
+       "      <td>1688.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>near-projecting glutamatergic cortical neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>25631.228839</td>\n",
+       "      <td>1350.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>chandelier pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>15983.292474</td>\n",
+       "      <td>1183.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>L5 extratelencephalic projecting glutamatergic...</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>56934.420491</td>\n",
+       "      <td>1064.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>caudal ganglionic eminence derived GABAergic c...</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>18562.883256</td>\n",
+       "      <td>998.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>L2/3 intratelencephalic projecting glutamaterg...</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>9379.776991</td>\n",
+       "      <td>565.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>mesenchymal cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1004.686337</td>\n",
+       "      <td>448.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type suspension_type  \\\n",
+       "0   L2/3-6 intratelencephalic projecting glutamate...         nucleus   \n",
+       "1                                              neuron         nucleus   \n",
+       "2                                     oligodendrocyte         nucleus   \n",
+       "3                                           astrocyte         nucleus   \n",
+       "4                pvalb GABAergic cortical interneuron         nucleus   \n",
+       "5                  vip GABAergic cortical interneuron         nucleus   \n",
+       "6                                glutamatergic neuron         nucleus   \n",
+       "7                      oligodendrocyte precursor cell         nucleus   \n",
+       "8                  sst GABAergic cortical interneuron         nucleus   \n",
+       "9                                      malignant cell            cell   \n",
+       "10                                    microglial cell         nucleus   \n",
+       "11               lamp5 GABAergic cortical interneuron         nucleus   \n",
+       "12                   astrocyte of the cerebral cortex         nucleus   \n",
+       "13                                        neural cell         nucleus   \n",
+       "14                                   GABAergic neuron         nucleus   \n",
+       "15                                   mature astrocyte         nucleus   \n",
+       "16                                         macrophage            cell   \n",
+       "17                            cerebellar granule cell         nucleus   \n",
+       "18                             mature microglial cell         nucleus   \n",
+       "19  corticothalamic-projecting glutamatergic corti...         nucleus   \n",
+       "20                  L6b glutamatergic cortical neuron         nucleus   \n",
+       "21                sncg GABAergic cortical interneuron         nucleus   \n",
+       "22                                   endothelial cell         nucleus   \n",
+       "23                     choroid plexus epithelial cell         nucleus   \n",
+       "24      near-projecting glutamatergic cortical neuron         nucleus   \n",
+       "25    chandelier pvalb GABAergic cortical interneuron         nucleus   \n",
+       "26  L5 extratelencephalic projecting glutamatergic...         nucleus   \n",
+       "27  caudal ganglionic eminence derived GABAergic c...         nucleus   \n",
+       "28  L2/3 intratelencephalic projecting glutamaterg...         nucleus   \n",
+       "29                                   mesenchymal cell         nucleus   \n",
+       "\n",
+       "                assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0              [10x 3' v3]         32283.096081  40379.0  \n",
+       "1              [10x 3' v3]         19390.023813  38825.0  \n",
+       "2   [10x 3' v2, 10x 3' v3]          3466.119566  34701.0  \n",
+       "3              [10x 3' v3]          4337.817282   8561.0  \n",
+       "4              [10x 3' v3]         17218.924481   7344.0  \n",
+       "5              [10x 3' v3]         13455.119639   7249.0  \n",
+       "6              [10x 3' v2]          6141.094851   7105.0  \n",
+       "7   [10x 3' v2, 10x 3' v3]          6325.213182   6695.0  \n",
+       "8              [10x 3' v3]         14236.769992   6387.0  \n",
+       "9   [10x 3' v2, 10x 3' v3]         12343.866509   5841.0  \n",
+       "10             [10x 3' v3]          3531.395319   5683.0  \n",
+       "11             [10x 3' v3]         16460.512190   5123.0  \n",
+       "12             [10x 3' v3]          7024.244142   4539.0  \n",
+       "13             [10x 3' v3]          3671.033365   3740.0  \n",
+       "14             [10x 3' v2]          3133.254010   3494.0  \n",
+       "15             [10x 3' v2]          1619.839781   3439.0  \n",
+       "16  [10x 3' v2, 10x 3' v3]          8287.577188   3309.0  \n",
+       "17             [10x 3' v3]          4264.749176   2731.0  \n",
+       "18             [10x 3' v2]           754.149411   2453.0  \n",
+       "19             [10x 3' v3]         34544.952352   2273.0  \n",
+       "20             [10x 3' v3]         42263.586519   2037.0  \n",
+       "21             [10x 3' v3]         13442.716510   1984.0  \n",
+       "22  [10x 3' v2, 10x 3' v3]          2991.238648   1739.0  \n",
+       "23             [10x 3' v3]          1962.060115   1688.0  \n",
+       "24             [10x 3' v3]         25631.228839   1350.0  \n",
+       "25             [10x 3' v3]         15983.292474   1183.0  \n",
+       "26             [10x 3' v3]         56934.420491   1064.0  \n",
+       "27             [10x 3' v3]         18562.883256    998.0  \n",
+       "28             [10x 3' v3]          9379.776991    565.0  \n",
+       "29             [10x 3' v3]          1004.686337    448.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "blood\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1]</td>\n",
+       "      <td>3750.452919</td>\n",
+       "      <td>15635.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>5918.412073</td>\n",
+       "      <td>15511.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3041.429188</td>\n",
+       "      <td>9283.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>4589.421460</td>\n",
+       "      <td>9105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>4060.653027</td>\n",
+       "      <td>7317.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3876.332703</td>\n",
+       "      <td>5547.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CD8-positive, alpha-beta cytotoxic T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3894.209560</td>\n",
+       "      <td>4782.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CD14-positive, CD16-negative classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5123.484024</td>\n",
+       "      <td>4632.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3, 10x 5' v2]</td>\n",
+       "      <td>5692.380368</td>\n",
+       "      <td>4489.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>mature B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3, 10x 5' v2]</td>\n",
+       "      <td>6323.356494</td>\n",
+       "      <td>4084.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>type I NK T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1712.290701</td>\n",
+       "      <td>3963.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>central memory CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>5184.589340</td>\n",
+       "      <td>3963.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>4618.718611</td>\n",
+       "      <td>3604.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD14-positive monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>4891.869582</td>\n",
+       "      <td>3356.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1]</td>\n",
+       "      <td>3933.570238</td>\n",
+       "      <td>3004.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>blood cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5918.926772</td>\n",
+       "      <td>2720.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>early pro-B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>8281.433811</td>\n",
+       "      <td>2572.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>effector memory CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1]</td>\n",
+       "      <td>2399.100003</td>\n",
+       "      <td>2528.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CD16-positive, CD56-dim natural killer cell, h...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3584.113028</td>\n",
+       "      <td>2512.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3576.484208</td>\n",
+       "      <td>2237.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3835.183022</td>\n",
+       "      <td>2007.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>5660.777605</td>\n",
+       "      <td>1950.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>3677.270055</td>\n",
+       "      <td>1690.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>4430.008905</td>\n",
+       "      <td>1672.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>activated CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3952.758107</td>\n",
+       "      <td>1652.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>CD4-positive helper T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3085.676707</td>\n",
+       "      <td>1640.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1, 10x 5' v2]</td>\n",
+       "      <td>9413.930613</td>\n",
+       "      <td>1483.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2184.748499</td>\n",
+       "      <td>1333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>activated CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3813.840945</td>\n",
+       "      <td>1135.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>mature NK T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>3387.291002</td>\n",
+       "      <td>1110.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type suspension_type  \\\n",
+       "0                                  classical monocyte            cell   \n",
+       "1                     CD4-positive, alpha-beta T cell            cell   \n",
+       "2                                 natural killer cell            cell   \n",
+       "3                     CD8-positive, alpha-beta T cell            cell   \n",
+       "4   naive thymus-derived CD4-positive, alpha-beta ...            cell   \n",
+       "5                                              B cell            cell   \n",
+       "6           CD8-positive, alpha-beta cytotoxic T cell            cell   \n",
+       "7     CD14-positive, CD16-negative classical monocyte            cell   \n",
+       "8                                              T cell            cell   \n",
+       "9                                       mature B cell            cell   \n",
+       "10                                   type I NK T cell            cell   \n",
+       "11     central memory CD4-positive, alpha-beta T cell            cell   \n",
+       "12  naive thymus-derived CD8-positive, alpha-beta ...            cell   \n",
+       "13                             CD14-positive monocyte            cell   \n",
+       "14                             non-classical monocyte            cell   \n",
+       "15                                         blood cell            cell   \n",
+       "16                                   early pro-B cell            cell   \n",
+       "17    effector memory CD8-positive, alpha-beta T cell            cell   \n",
+       "18  CD16-positive, CD56-dim natural killer cell, h...            cell   \n",
+       "19                                       naive B cell            cell   \n",
+       "20                                  regulatory T cell            cell   \n",
+       "21                                      memory B cell            cell   \n",
+       "22                                 gamma-delta T cell            cell   \n",
+       "23             CD8-positive, alpha-beta memory T cell            cell   \n",
+       "24          activated CD4-positive, alpha-beta T cell            cell   \n",
+       "25                         CD4-positive helper T cell            cell   \n",
+       "26                        conventional dendritic cell            cell   \n",
+       "27             CD4-positive, alpha-beta memory T cell            cell   \n",
+       "28          activated CD8-positive, alpha-beta T cell            cell   \n",
+       "29                                   mature NK T cell            cell   \n",
+       "\n",
+       "                                      assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0                         [10x 3' v2, 10x 5' v1]          3750.452919  15635.0  \n",
+       "1              [10x 3' v2, 10x 5' v1, 10x 5' v2]          5918.412073  15511.0  \n",
+       "2              [10x 3' v2, 10x 5' v1, 10x 5' v2]          3041.429188   9283.0  \n",
+       "3              [10x 3' v2, 10x 5' v1, 10x 5' v2]          4589.421460   9105.0  \n",
+       "4              [10x 3' v2, 10x 5' v1, 10x 5' v2]          4060.653027   7317.0  \n",
+       "5              [10x 3' v2, 10x 5' v1, 10x 5' v2]          3876.332703   5547.0  \n",
+       "6                         [10x 5' v1, 10x 5' v2]          3894.209560   4782.0  \n",
+       "7                                    [10x 5' v2]          5123.484024   4632.0  \n",
+       "8                         [10x 3' v3, 10x 5' v2]          5692.380368   4489.0  \n",
+       "9                         [10x 3' v3, 10x 5' v2]          6323.356494   4084.0  \n",
+       "10                                   [10x 5' v1]          1712.290701   3963.0  \n",
+       "11                        [10x 3' v2, 10x 5' v2]          5184.589340   3963.0  \n",
+       "12             [10x 3' v2, 10x 5' v1, 10x 5' v2]          4618.718611   3604.0  \n",
+       "13                        [10x 3' v2, 10x 5' v2]          4891.869582   3356.0  \n",
+       "14                        [10x 3' v2, 10x 5' v1]          3933.570238   3004.0  \n",
+       "15                                   [10x 5' v1]          5918.926772   2720.0  \n",
+       "16                                   [10x 3' v3]          8281.433811   2572.0  \n",
+       "17                        [10x 3' v2, 10x 5' v1]          2399.100003   2528.0  \n",
+       "18                        [10x 5' v1, 10x 5' v2]          3584.113028   2512.0  \n",
+       "19             [10x 3' v2, 10x 5' v1, 10x 5' v2]          3576.484208   2237.0  \n",
+       "20             [10x 3' v2, 10x 5' v1, 10x 5' v2]          3835.183022   2007.0  \n",
+       "21             [10x 3' v2, 10x 5' v1, 10x 5' v2]          5660.777605   1950.0  \n",
+       "22             [10x 3' v2, 10x 5' v1, 10x 5' v2]          3677.270055   1690.0  \n",
+       "23                        [10x 5' v1, 10x 5' v2]          4430.008905   1672.0  \n",
+       "24                                   [10x 5' v1]          3952.758107   1652.0  \n",
+       "25                                   [10x 5' v1]          3085.676707   1640.0  \n",
+       "26  [10x 3' v2, 10x 3' v3, 10x 5' v1, 10x 5' v2]          9413.930613   1483.0  \n",
+       "27                                   [10x 5' v1]          2184.748499   1333.0  \n",
+       "28                                   [10x 5' v1]          3813.840945   1135.0  \n",
+       "29                        [10x 3' v3, 10x 5' v1]          3387.291002   1110.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "small intestine\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>5553.572236</td>\n",
+       "      <td>15499.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>mesenchymal cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>4525.519896</td>\n",
+       "      <td>5780.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>2111.129280</td>\n",
+       "      <td>4706.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>3862.732963</td>\n",
+       "      <td>4629.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>2028.931384</td>\n",
+       "      <td>4371.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>1724.007334</td>\n",
+       "      <td>3844.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>4313.221224</td>\n",
+       "      <td>2755.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>9551.070341</td>\n",
+       "      <td>1974.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>plasmacytoid dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>1077.892595</td>\n",
+       "      <td>1788.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>intestinal epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>14221.150038</td>\n",
+       "      <td>1196.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>myeloid leukocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>9155.548561</td>\n",
+       "      <td>1112.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>1820.231397</td>\n",
+       "      <td>1016.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>2619.770902</td>\n",
+       "      <td>876.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>19046.254248</td>\n",
+       "      <td>785.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>mature NK T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>6381.814649</td>\n",
+       "      <td>669.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>539.665183</td>\n",
+       "      <td>397.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>4183.078142</td>\n",
+       "      <td>392.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>4333.699789</td>\n",
+       "      <td>375.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>401.460049</td>\n",
+       "      <td>351.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>4785.271331</td>\n",
+       "      <td>347.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>8962.634515</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>endothelial cell of lymphatic vessel</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>19092.907642</td>\n",
+       "      <td>255.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>blood vessel endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>14693.201031</td>\n",
+       "      <td>194.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>IgG plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>14230.556150</td>\n",
+       "      <td>187.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>23113.061898</td>\n",
+       "      <td>176.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>intraepithelial lymphocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>663.515398</td>\n",
+       "      <td>151.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>7797.837072</td>\n",
+       "      <td>147.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>glial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>6974.967147</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>myofibroblast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>8356.375296</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>peripheral nervous system neuron</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>6706.427083</td>\n",
+       "      <td>96.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               cell_type suspension_type  \\\n",
+       "0                            plasma cell            cell   \n",
+       "1                       mesenchymal cell            cell   \n",
+       "2                                 T cell            cell   \n",
+       "3                             macrophage            cell   \n",
+       "4        CD4-positive, alpha-beta T cell            cell   \n",
+       "5        CD8-positive, alpha-beta T cell            cell   \n",
+       "6                                 B cell            cell   \n",
+       "7                             fibroblast            cell   \n",
+       "8            plasmacytoid dendritic cell            cell   \n",
+       "9             intestinal epithelial cell            cell   \n",
+       "10                     myeloid leukocyte            cell   \n",
+       "11                             mast cell            cell   \n",
+       "12                     regulatory T cell            cell   \n",
+       "13                            enterocyte            cell   \n",
+       "14                      mature NK T cell            cell   \n",
+       "15                          naive T cell            cell   \n",
+       "16                              monocyte            cell   \n",
+       "17           conventional dendritic cell            cell   \n",
+       "18                   natural killer cell            cell   \n",
+       "19                        dendritic cell            cell   \n",
+       "20                      endothelial cell            cell   \n",
+       "21  endothelial cell of lymphatic vessel            cell   \n",
+       "22         blood vessel endothelial cell            cell   \n",
+       "23                       IgG plasma cell            cell   \n",
+       "24                  enteroendocrine cell            cell   \n",
+       "25            intraepithelial lymphocyte            cell   \n",
+       "26                              pericyte            cell   \n",
+       "27                            glial cell            cell   \n",
+       "28                    myofibroblast cell            cell   \n",
+       "29      peripheral nervous system neuron            cell   \n",
+       "\n",
+       "                assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0              [10x 3' v2]          5553.572236  15499.0  \n",
+       "1              [10x 3' v2]          4525.519896   5780.0  \n",
+       "2   [10x 3' v2, 10x 3' v3]          2111.129280   4706.0  \n",
+       "3   [10x 3' v2, 10x 3' v3]          3862.732963   4629.0  \n",
+       "4              [10x 3' v2]          2028.931384   4371.0  \n",
+       "5              [10x 3' v2]          1724.007334   3844.0  \n",
+       "6   [10x 3' v2, 10x 3' v3]          4313.221224   2755.0  \n",
+       "7   [10x 3' v2, 10x 3' v3]          9551.070341   1974.0  \n",
+       "8              [10x 3' v2]          1077.892595   1788.0  \n",
+       "9   [10x 3' v2, 10x 3' v3]         14221.150038   1196.0  \n",
+       "10             [10x 3' v2]          9155.548561   1112.0  \n",
+       "11             [10x 3' v2]          1820.231397   1016.0  \n",
+       "12             [10x 3' v2]          2619.770902    876.0  \n",
+       "13  [10x 3' v2, 10x 3' v3]         19046.254248    785.0  \n",
+       "14             [10x 3' v2]          6381.814649    669.0  \n",
+       "15             [10x 3' v2]           539.665183    397.0  \n",
+       "16             [10x 3' v2]          4183.078142    392.0  \n",
+       "17             [10x 3' v2]          4333.699789    375.0  \n",
+       "18             [10x 3' v2]           401.460049    351.0  \n",
+       "19  [10x 3' v2, 10x 3' v3]          4785.271331    347.0  \n",
+       "20  [10x 3' v2, 10x 3' v3]          8962.634515    275.0  \n",
+       "21  [10x 3' v2, 10x 3' v3]         19092.907642    255.0  \n",
+       "22             [10x 3' v3]         14693.201031    194.0  \n",
+       "23             [10x 3' v2]         14230.556150    187.0  \n",
+       "24  [10x 3' v2, 10x 3' v3]         23113.061898    176.0  \n",
+       "25             [10x 3' v2]           663.515398    151.0  \n",
+       "26  [10x 3' v2, 10x 3' v3]          7797.837072    147.0  \n",
+       "27  [10x 3' v2, 10x 3' v3]          6974.967147    141.0  \n",
+       "28  [10x 3' v2, 10x 3' v3]          8356.375296    121.0  \n",
+       "29             [10x 3' v2]          6706.427083     96.0  "
+      ]
+     },
+     "metadata": {},
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+    },
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+      "kidney\n"
+     ]
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>abnormal cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>12538.982838</td>\n",
+       "      <td>2911.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>8449.707044</td>\n",
+       "      <td>2236.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>8449.237204</td>\n",
+       "      <td>1569.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>3534.306313</td>\n",
+       "      <td>1283.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>4329.951364</td>\n",
+       "      <td>1114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>5883.656285</td>\n",
+       "      <td>875.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>5464.683603</td>\n",
+       "      <td>866.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>7850.607488</td>\n",
+       "      <td>851.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>epithelial cell of proximal tubule</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>4531.826804</td>\n",
+       "      <td>486.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>9716.801338</td>\n",
+       "      <td>226.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>4798.933799</td>\n",
+       "      <td>221.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>7220.564493</td>\n",
+       "      <td>130.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>kidney connecting tubule epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>12307.425000</td>\n",
+       "      <td>114.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>2962.136200</td>\n",
+       "      <td>95.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>kidney distal convoluted tubule epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>8528.461538</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>6221.957729</td>\n",
+       "      <td>55.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>6540.887446</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>mature NK T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2961.834656</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2960.441532</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>alternatively activated macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>5491.722222</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>cytotoxic T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2207.071429</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>14543.025385</td>\n",
+       "      <td>34.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>4800.825000</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>kidney interstitial fibroblast</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>7532.136364</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>podocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1976.125000</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>parietal epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2754.111111</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>leukocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>10889.750000</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>8954.500000</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2831.875000</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>8027.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type suspension_type  \\\n",
+       "0                                       abnormal cell            cell   \n",
+       "1                                          macrophage            cell   \n",
+       "2               kidney collecting duct principal cell            cell   \n",
+       "3   kidney loop of Henle thin descending limb epit...            cell   \n",
+       "4                                              T cell            cell   \n",
+       "5                                    endothelial cell            cell   \n",
+       "6   kidney loop of Henle thin ascending limb epith...            cell   \n",
+       "7   kidney loop of Henle thick ascending limb epit...            cell   \n",
+       "8                  epithelial cell of proximal tubule            cell   \n",
+       "9            kidney collecting duct intercalated cell            cell   \n",
+       "10                        renal interstitial pericyte            cell   \n",
+       "11             vascular associated smooth muscle cell            cell   \n",
+       "12           kidney connecting tubule epithelial cell            cell   \n",
+       "13                                             B cell            cell   \n",
+       "14    kidney distal convoluted tubule epithelial cell            cell   \n",
+       "15                        conventional dendritic cell            cell   \n",
+       "16                                           monocyte            cell   \n",
+       "17                                   mature NK T cell            cell   \n",
+       "18                                natural killer cell            cell   \n",
+       "19                 alternatively activated macrophage            cell   \n",
+       "20                                   cytotoxic T cell            cell   \n",
+       "21                                        plasma cell            cell   \n",
+       "22                                          mast cell            cell   \n",
+       "23                     kidney interstitial fibroblast            cell   \n",
+       "24                                           podocyte            cell   \n",
+       "25                           parietal epithelial cell            cell   \n",
+       "26                                          leukocyte            cell   \n",
+       "27                                           pericyte            cell   \n",
+       "28                             non-classical monocyte            cell   \n",
+       "29                                    epithelial cell            cell   \n",
+       "\n",
+       "                assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0              [10x 3' v2]         12538.982838   2911.0  \n",
+       "1              [10x 3' v2]          8449.707044   2236.0  \n",
+       "2              [10x 3' v3]          8449.237204   1569.0  \n",
+       "3              [10x 3' v3]          3534.306313   1283.0  \n",
+       "4   [10x 3' v2, 10x 3' v3]          4329.951364   1114.0  \n",
+       "5   [10x 3' v2, 10x 3' v3]          5883.656285    875.0  \n",
+       "6              [10x 3' v3]          5464.683603    866.0  \n",
+       "7              [10x 3' v3]          7850.607488    851.0  \n",
+       "8              [10x 3' v3]          4531.826804    486.0  \n",
+       "9              [10x 3' v3]          9716.801338    226.0  \n",
+       "10             [10x 3' v3]          4798.933799    221.0  \n",
+       "11             [10x 3' v2]          7220.564493    130.0  \n",
+       "12             [10x 3' v3]         12307.425000    114.0  \n",
+       "13  [10x 3' v2, 10x 3' v3]          2962.136200     95.0  \n",
+       "14             [10x 3' v3]          8528.461538     78.0  \n",
+       "15             [10x 3' v3]          6221.957729     55.0  \n",
+       "16             [10x 3' v3]          6540.887446     43.0  \n",
+       "17             [10x 3' v3]          2961.834656     41.0  \n",
+       "18             [10x 3' v3]          2960.441532     39.0  \n",
+       "19             [10x 3' v3]          5491.722222     38.0  \n",
+       "20             [10x 3' v3]          2207.071429     35.0  \n",
+       "21  [10x 3' v2, 10x 3' v3]         14543.025385     34.0  \n",
+       "22  [10x 3' v2, 10x 3' v3]          4800.825000     29.0  \n",
+       "23             [10x 3' v3]          7532.136364     23.0  \n",
+       "24             [10x 3' v3]          1976.125000     16.0  \n",
+       "25             [10x 3' v3]          2754.111111      9.0  \n",
+       "26             [10x 3' v3]         10889.750000      8.0  \n",
+       "27             [10x 3' v2]          8954.500000      6.0  \n",
+       "28             [10x 3' v3]          2831.875000      5.0  \n",
+       "29             [10x 3' v3]          8027.000000      2.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "liver\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>hepatic pit cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>4727.118963</td>\n",
+       "      <td>2410.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5183.405436</td>\n",
+       "      <td>2290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Kupffer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>9744.089530</td>\n",
+       "      <td>2091.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5103.622728</td>\n",
+       "      <td>2062.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>9768.034407</td>\n",
+       "      <td>2021.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5362.041940</td>\n",
+       "      <td>1677.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>9390.691564</td>\n",
+       "      <td>1274.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>12846.595798</td>\n",
+       "      <td>989.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>periportal region hepatocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>14351.769046</td>\n",
+       "      <td>967.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>mature B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>6559.016918</td>\n",
+       "      <td>693.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>4720.298866</td>\n",
+       "      <td>634.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>centrilobular region hepatocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>7285.722313</td>\n",
+       "      <td>561.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>endothelial cell of pericentral hepatic sinusoid</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5713.951689</td>\n",
+       "      <td>387.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>8799.194535</td>\n",
+       "      <td>375.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>21972.115532</td>\n",
+       "      <td>360.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>2459.422123</td>\n",
+       "      <td>316.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>8728.158871</td>\n",
+       "      <td>294.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>intrahepatic cholangiocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>16676.755074</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>endothelial cell of hepatic sinusoid</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>3054.446741</td>\n",
+       "      <td>259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>inflammatory macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>3583.329430</td>\n",
+       "      <td>246.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>endothelial cell of periportal hepatic sinusoid</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>7138.774523</td>\n",
+       "      <td>244.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>3694.571458</td>\n",
+       "      <td>241.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>vein endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>12179.436024</td>\n",
+       "      <td>237.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>hepatic stellate cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>8652.199863</td>\n",
+       "      <td>218.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>13984.999155</td>\n",
+       "      <td>198.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5804.000684</td>\n",
+       "      <td>189.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>innate lymphoid cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>2463.088542</td>\n",
+       "      <td>98.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>erythrocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>5520.033088</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>midzonal region hepatocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v2]</td>\n",
+       "      <td>14318.738373</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v2]</td>\n",
+       "      <td>5916.731100</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           cell_type suspension_type  \\\n",
+       "0                                   hepatic pit cell            cell   \n",
+       "1                    CD8-positive, alpha-beta T cell            cell   \n",
+       "2                                       Kupffer cell            cell   \n",
+       "3                                natural killer cell            cell   \n",
+       "4                                         macrophage            cell   \n",
+       "5                    CD4-positive, alpha-beta T cell            cell   \n",
+       "6                                           monocyte            cell   \n",
+       "7                                         hepatocyte            cell   \n",
+       "8                       periportal region hepatocyte            cell   \n",
+       "9                                      mature B cell            cell   \n",
+       "10                                            T cell            cell   \n",
+       "11                   centrilobular region hepatocyte            cell   \n",
+       "12  endothelial cell of pericentral hepatic sinusoid            cell   \n",
+       "13                        endothelial cell of artery            cell   \n",
+       "14                                       plasma cell            cell   \n",
+       "15                                 alpha-beta T cell            cell   \n",
+       "16                                        fibroblast            cell   \n",
+       "17                        intrahepatic cholangiocyte            cell   \n",
+       "18              endothelial cell of hepatic sinusoid            cell   \n",
+       "19                           inflammatory macrophage            cell   \n",
+       "20   endothelial cell of periportal hepatic sinusoid            cell   \n",
+       "21                                gamma-delta T cell            cell   \n",
+       "22                             vein endothelial cell            cell   \n",
+       "23                             hepatic stellate cell            cell   \n",
+       "24                       conventional dendritic cell            cell   \n",
+       "25                                        neutrophil            cell   \n",
+       "26                              innate lymphoid cell            cell   \n",
+       "27                                       erythrocyte            cell   \n",
+       "28                        midzonal region hepatocyte            cell   \n",
+       "29                                         mast cell            cell   \n",
+       "\n",
+       "                assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0              [10x 5' v2]          4727.118963   2410.0  \n",
+       "1              [10x 5' v2]          5183.405436   2290.0  \n",
+       "2              [10x 5' v2]          9744.089530   2091.0  \n",
+       "3              [10x 5' v2]          5103.622728   2062.0  \n",
+       "4   [10x 3' v2, 10x 5' v2]          9768.034407   2021.0  \n",
+       "5              [10x 5' v2]          5362.041940   1677.0  \n",
+       "6              [10x 5' v2]          9390.691564   1274.0  \n",
+       "7   [10x 3' v2, 10x 5' v2]         12846.595798    989.0  \n",
+       "8   [10x 3' v2, 10x 5' v2]         14351.769046    967.0  \n",
+       "9              [10x 5' v2]          6559.016918    693.0  \n",
+       "10             [10x 5' v2]          4720.298866    634.0  \n",
+       "11  [10x 3' v2, 10x 5' v2]          7285.722313    561.0  \n",
+       "12             [10x 5' v2]          5713.951689    387.0  \n",
+       "13             [10x 5' v2]          8799.194535    375.0  \n",
+       "14  [10x 3' v2, 10x 5' v2]         21972.115532    360.0  \n",
+       "15             [10x 3' v2]          2459.422123    316.0  \n",
+       "16             [10x 5' v2]          8728.158871    294.0  \n",
+       "17             [10x 5' v2]         16676.755074    275.0  \n",
+       "18             [10x 3' v2]          3054.446741    259.0  \n",
+       "19             [10x 3' v2]          3583.329430    246.0  \n",
+       "20  [10x 3' v2, 10x 5' v2]          7138.774523    244.0  \n",
+       "21             [10x 3' v2]          3694.571458    241.0  \n",
+       "22             [10x 5' v2]         12179.436024    237.0  \n",
+       "23  [10x 3' v2, 10x 5' v2]          8652.199863    218.0  \n",
+       "24             [10x 5' v2]         13984.999155    198.0  \n",
+       "25             [10x 5' v2]          5804.000684    189.0  \n",
+       "26             [10x 3' v2]          2463.088542     98.0  \n",
+       "27  [10x 3' v2, 10x 5' v2]          5520.033088     81.0  \n",
+       "28  [10x 3' v2, 10x 5' v2]         14318.738373     81.0  \n",
+       "29             [10x 5' v2]          5916.731100     35.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lung\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>10122.769629</td>\n",
+       "      <td>8713.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>7534.843272</td>\n",
+       "      <td>7659.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2724.637339</td>\n",
+       "      <td>7484.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>5798.839615</td>\n",
+       "      <td>5653.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2750.362418</td>\n",
+       "      <td>4960.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>5089.378173</td>\n",
+       "      <td>4748.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2181.541895</td>\n",
+       "      <td>3835.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2724.590943</td>\n",
+       "      <td>3471.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>3093.368587</td>\n",
+       "      <td>2773.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 5' v1]</td>\n",
+       "      <td>7370.905991</td>\n",
+       "      <td>2739.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>ciliated columnar cell of tracheobronchial tree</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4193.345603</td>\n",
+       "      <td>2600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>10019.185491</td>\n",
+       "      <td>2245.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2403.237844</td>\n",
+       "      <td>2240.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>3348.854801</td>\n",
+       "      <td>1990.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>vein endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2807.620026</td>\n",
+       "      <td>1312.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>CD1c-positive myeloid dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>6668.270959</td>\n",
+       "      <td>1282.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>elicited macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3078.491411</td>\n",
+       "      <td>1262.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>1579.006277</td>\n",
+       "      <td>1089.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>endothelial cell of lymphatic vessel</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>4473.695015</td>\n",
+       "      <td>992.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2]</td>\n",
+       "      <td>1756.472251</td>\n",
+       "      <td>955.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>5149.716216</td>\n",
+       "      <td>854.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>myeloid cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3]</td>\n",
+       "      <td>13164.515265</td>\n",
+       "      <td>850.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4569.399684</td>\n",
+       "      <td>633.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>plasmacytoid dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>4046.405750</td>\n",
+       "      <td>599.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>pulmonary interstitial fibroblast</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6221.755952</td>\n",
+       "      <td>504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>8201.865800</td>\n",
+       "      <td>473.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2583.776376</td>\n",
+       "      <td>443.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>2690.963294</td>\n",
+       "      <td>423.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>4280.959934</td>\n",
+       "      <td>376.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>fibroblast of lung</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 3' v2, 10x 3' v3, 10x 5' v1]</td>\n",
+       "      <td>4712.523028</td>\n",
+       "      <td>372.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          cell_type suspension_type  \\\n",
+       "0                               alveolar macrophage            cell   \n",
+       "1                                    malignant cell            cell   \n",
+       "2                   CD8-positive, alpha-beta T cell            cell   \n",
+       "3                                type II pneumocyte            cell   \n",
+       "4                   CD4-positive, alpha-beta T cell            cell   \n",
+       "5                                        macrophage            cell   \n",
+       "6                        capillary endothelial cell            cell   \n",
+       "7                                            B cell            cell   \n",
+       "8                                classical monocyte            cell   \n",
+       "9                                            T cell            cell   \n",
+       "10  ciliated columnar cell of tracheobronchial tree            cell   \n",
+       "11                                      plasma cell            cell   \n",
+       "12                              natural killer cell            cell   \n",
+       "13                                regulatory T cell            cell   \n",
+       "14                            vein endothelial cell            cell   \n",
+       "15             CD1c-positive myeloid dendritic cell            cell   \n",
+       "16                              elicited macrophage            cell   \n",
+       "17                                type I pneumocyte            cell   \n",
+       "18             endothelial cell of lymphatic vessel            cell   \n",
+       "19                                         monocyte            cell   \n",
+       "20                           non-classical monocyte            cell   \n",
+       "21                                     myeloid cell            cell   \n",
+       "22       epithelial cell of lower respiratory tract            cell   \n",
+       "23                      plasmacytoid dendritic cell            cell   \n",
+       "24                pulmonary interstitial fibroblast            cell   \n",
+       "25                                        club cell            cell   \n",
+       "26                pulmonary artery endothelial cell            cell   \n",
+       "27                                        mast cell            cell   \n",
+       "28                          epithelial cell of lung            cell   \n",
+       "29                               fibroblast of lung            cell   \n",
+       "\n",
+       "                           assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0   [10x 3' v2, 10x 3' v3, 10x 5' v1]         10122.769629   8713.0  \n",
+       "1              [10x 3' v2, 10x 3' v3]          7534.843272   7659.0  \n",
+       "2   [10x 3' v2, 10x 3' v3, 10x 5' v1]          2724.637339   7484.0  \n",
+       "3   [10x 3' v2, 10x 3' v3, 10x 5' v1]          5798.839615   5653.0  \n",
+       "4   [10x 3' v2, 10x 3' v3, 10x 5' v1]          2750.362418   4960.0  \n",
+       "5   [10x 3' v2, 10x 3' v3, 10x 5' v1]          5089.378173   4748.0  \n",
+       "6   [10x 3' v2, 10x 3' v3, 10x 5' v1]          2181.541895   3835.0  \n",
+       "7   [10x 3' v2, 10x 3' v3, 10x 5' v1]          2724.590943   3471.0  \n",
+       "8   [10x 3' v2, 10x 3' v3, 10x 5' v1]          3093.368587   2773.0  \n",
+       "9              [10x 3' v2, 10x 5' v1]          7370.905991   2739.0  \n",
+       "10                        [10x 5' v1]          4193.345603   2600.0  \n",
+       "11  [10x 3' v2, 10x 3' v3, 10x 5' v1]         10019.185491   2245.0  \n",
+       "12  [10x 3' v2, 10x 3' v3, 10x 5' v1]          2403.237844   2240.0  \n",
+       "13  [10x 3' v2, 10x 3' v3, 10x 5' v1]          3348.854801   1990.0  \n",
+       "14  [10x 3' v2, 10x 3' v3, 10x 5' v1]          2807.620026   1312.0  \n",
+       "15  [10x 3' v2, 10x 3' v3, 10x 5' v1]          6668.270959   1282.0  \n",
+       "16                        [10x 5' v1]          3078.491411   1262.0  \n",
+       "17  [10x 3' v2, 10x 3' v3, 10x 5' v1]          1579.006277   1089.0  \n",
+       "18  [10x 3' v2, 10x 3' v3, 10x 5' v1]          4473.695015    992.0  \n",
+       "19                        [10x 3' v2]          1756.472251    955.0  \n",
+       "20  [10x 3' v2, 10x 3' v3, 10x 5' v1]          5149.716216    854.0  \n",
+       "21             [10x 3' v2, 10x 3' v3]         13164.515265    850.0  \n",
+       "22                        [10x 5' v1]          4569.399684    633.0  \n",
+       "23  [10x 3' v2, 10x 3' v3, 10x 5' v1]          4046.405750    599.0  \n",
+       "24                        [10x 5' v1]          6221.755952    504.0  \n",
+       "25  [10x 3' v2, 10x 3' v3, 10x 5' v1]          8201.865800    473.0  \n",
+       "26  [10x 3' v2, 10x 3' v3, 10x 5' v1]          2583.776376    443.0  \n",
+       "27  [10x 3' v2, 10x 3' v3, 10x 5' v1]          2690.963294    423.0  \n",
+       "28  [10x 3' v2, 10x 3' v3, 10x 5' v1]          4280.959934    376.0  \n",
+       "29  [10x 3' v2, 10x 3' v3, 10x 5' v1]          4712.523028    372.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "thymus\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CD8-alpha-alpha-positive, alpha-beta intraepit...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5111.774100</td>\n",
+       "      <td>2670.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>double-positive, alpha-beta thymocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5686.260267</td>\n",
+       "      <td>2480.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4200.292530</td>\n",
+       "      <td>2317.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5042.266733</td>\n",
+       "      <td>655.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3255.285000</td>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>double negative thymocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6386.249482</td>\n",
+       "      <td>566.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3775.479545</td>\n",
+       "      <td>440.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6310.054936</td>\n",
+       "      <td>204.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>alpha-beta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2652.188811</td>\n",
+       "      <td>143.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>myofibroblast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5130.400000</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5052.190315</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4916.870370</td>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4370.133333</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3600.025000</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>13199.358974</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5061.655172</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>10559.619048</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4732.894737</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>vascular associated smooth muscle cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5045.285714</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6294.125000</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>plasmacytoid dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5786.166667</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>B-2 B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5530.625000</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3141.285714</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>B-1 B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5224.714286</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>13512.166667</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3012.400000</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>cortical thymic epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>10784.600000</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3142.200000</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>erythrocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4468.600000</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>keratinocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6928.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type suspension_type  \\\n",
+       "0   CD8-alpha-alpha-positive, alpha-beta intraepit...            cell   \n",
+       "1               double-positive, alpha-beta thymocyte            cell   \n",
+       "2                                          fibroblast            cell   \n",
+       "3                                   regulatory T cell            cell   \n",
+       "4                     CD4-positive, alpha-beta T cell            cell   \n",
+       "5                           double negative thymocyte            cell   \n",
+       "6                     CD8-positive, alpha-beta T cell            cell   \n",
+       "7                                              T cell            cell   \n",
+       "8                                   alpha-beta T cell            cell   \n",
+       "9                                  myofibroblast cell            cell   \n",
+       "10                                natural killer cell            cell   \n",
+       "11                                       naive B cell            cell   \n",
+       "12  naive thymus-derived CD4-positive, alpha-beta ...            cell   \n",
+       "13                                   endothelial cell            cell   \n",
+       "14                                     dendritic cell            cell   \n",
+       "15  naive thymus-derived CD8-positive, alpha-beta ...            cell   \n",
+       "16                                 gamma-delta T cell            cell   \n",
+       "17             CD4-positive, alpha-beta memory T cell            cell   \n",
+       "18             vascular associated smooth muscle cell            cell   \n",
+       "19                                         macrophage            cell   \n",
+       "20                        plasmacytoid dendritic cell            cell   \n",
+       "21                                         B-2 B cell            cell   \n",
+       "22                                      memory B cell            cell   \n",
+       "23                                         B-1 B cell            cell   \n",
+       "24                                        plasma cell            cell   \n",
+       "25                                           monocyte            cell   \n",
+       "26                    cortical thymic epithelial cell            cell   \n",
+       "27             CD8-positive, alpha-beta memory T cell            cell   \n",
+       "28                                        erythrocyte            cell   \n",
+       "29                                       keratinocyte            cell   \n",
+       "\n",
+       "     assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0   [10x 5' v1]          5111.774100   2670.0  \n",
+       "1   [10x 5' v1]          5686.260267   2480.0  \n",
+       "2   [10x 5' v1]          4200.292530   2317.0  \n",
+       "3   [10x 5' v1]          5042.266733    655.0  \n",
+       "4   [10x 5' v1]          3255.285000    600.0  \n",
+       "5   [10x 5' v1]          6386.249482    566.0  \n",
+       "6   [10x 5' v1]          3775.479545    440.0  \n",
+       "7   [10x 5' v1]          6310.054936    204.0  \n",
+       "8   [10x 5' v1]          2652.188811    143.0  \n",
+       "9   [10x 5' v1]          5130.400000     65.0  \n",
+       "10  [10x 5' v1]          5052.190315     61.0  \n",
+       "11  [10x 5' v1]          4916.870370     54.0  \n",
+       "12  [10x 5' v1]          4370.133333     45.0  \n",
+       "13  [10x 5' v1]          3600.025000     40.0  \n",
+       "14  [10x 5' v1]         13199.358974     39.0  \n",
+       "15  [10x 5' v1]          5061.655172     29.0  \n",
+       "16  [10x 5' v1]         10559.619048     21.0  \n",
+       "17  [10x 5' v1]          4732.894737     19.0  \n",
+       "18  [10x 5' v1]          5045.285714     14.0  \n",
+       "19  [10x 5' v1]          6294.125000     12.0  \n",
+       "20  [10x 5' v1]          5786.166667     12.0  \n",
+       "21  [10x 5' v1]          5530.625000      8.0  \n",
+       "22  [10x 5' v1]          3141.285714      7.0  \n",
+       "23  [10x 5' v1]          5224.714286      7.0  \n",
+       "24  [10x 5' v1]         13512.166667      6.0  \n",
+       "25  [10x 5' v1]          3012.400000      5.0  \n",
+       "26  [10x 5' v1]         10784.600000      5.0  \n",
+       "27  [10x 5' v1]          3142.200000      5.0  \n",
+       "28  [10x 5' v1]          4468.600000      5.0  \n",
+       "29  [10x 5' v1]          6928.333333      3.0  "
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
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+      "heart\n"
+     ]
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+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cardiac muscle myoblast</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>10005.930778</td>\n",
+       "      <td>21050.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>fibroblast of cardiac tissue</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2971.909108</td>\n",
+       "      <td>13244.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cardiac endothelial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2555.895108</td>\n",
+       "      <td>8661.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>immature innate lymphoid cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2228.929211</td>\n",
+       "      <td>4504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2306.912812</td>\n",
+       "      <td>3264.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>mural cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1798.207321</td>\n",
+       "      <td>2303.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1662.228321</td>\n",
+       "      <td>1742.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>myeloid cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2147.811363</td>\n",
+       "      <td>1658.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>cardiac muscle cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>6741.016132</td>\n",
+       "      <td>1397.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>lymphoid lineage restricted progenitor cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1742.586052</td>\n",
+       "      <td>1287.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>smooth muscle myoblast</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>3087.490577</td>\n",
+       "      <td>846.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>neuronal receptor cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>2132.948445</td>\n",
+       "      <td>665.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>lymphocyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1056.483810</td>\n",
+       "      <td>252.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1940.973208</td>\n",
+       "      <td>224.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>adipocyte of epicardial fat of left ventricle</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>6209.791238</td>\n",
+       "      <td>201.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>1477.633553</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>adipocyte</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>[10x 3' v3]</td>\n",
+       "      <td>4806.500000</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        cell_type suspension_type  \\\n",
+       "0                         cardiac muscle myoblast         nucleus   \n",
+       "1                    fibroblast of cardiac tissue         nucleus   \n",
+       "2                        cardiac endothelial cell         nucleus   \n",
+       "3                   immature innate lymphoid cell         nucleus   \n",
+       "4                                        pericyte         nucleus   \n",
+       "5                                      mural cell         nucleus   \n",
+       "6                                endothelial cell         nucleus   \n",
+       "7                                    myeloid cell         nucleus   \n",
+       "8                             cardiac muscle cell         nucleus   \n",
+       "9     lymphoid lineage restricted progenitor cell         nucleus   \n",
+       "10                         smooth muscle myoblast         nucleus   \n",
+       "11                         neuronal receptor cell         nucleus   \n",
+       "12                                     lymphocyte         nucleus   \n",
+       "13                                      mast cell         nucleus   \n",
+       "14  adipocyte of epicardial fat of left ventricle         nucleus   \n",
+       "15                                 cardiac neuron         nucleus   \n",
+       "16                                      adipocyte         nucleus   \n",
+       "\n",
+       "     assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0   [10x 3' v3]         10005.930778  21050.0  \n",
+       "1   [10x 3' v3]          2971.909108  13244.0  \n",
+       "2   [10x 3' v3]          2555.895108   8661.0  \n",
+       "3   [10x 3' v3]          2228.929211   4504.0  \n",
+       "4   [10x 3' v3]          2306.912812   3264.0  \n",
+       "5   [10x 3' v3]          1798.207321   2303.0  \n",
+       "6   [10x 3' v3]          1662.228321   1742.0  \n",
+       "7   [10x 3' v3]          2147.811363   1658.0  \n",
+       "8   [10x 3' v3]          6741.016132   1397.0  \n",
+       "9   [10x 3' v3]          1742.586052   1287.0  \n",
+       "10  [10x 3' v3]          3087.490577    846.0  \n",
+       "11  [10x 3' v3]          2132.948445    665.0  \n",
+       "12  [10x 3' v3]          1056.483810    252.0  \n",
+       "13  [10x 3' v3]          1940.973208    224.0  \n",
+       "14  [10x 3' v3]          6209.791238    201.0  \n",
+       "15  [10x 3' v3]          1477.633553    115.0  \n",
+       "16  [10x 3' v3]          4806.500000      2.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nose\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>assay_list</th>\n",
+       "      <th>total_mrna_umis_avg</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>tracheal goblet cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>11535.702018</td>\n",
+       "      <td>2020.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>basal cell of epithelium of trachea</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>8211.102237</td>\n",
+       "      <td>1159.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>secretory cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>8674.336407</td>\n",
+       "      <td>1065.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ciliated cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6560.115789</td>\n",
+       "      <td>646.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1933.067823</td>\n",
+       "      <td>338.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>3113.299020</td>\n",
+       "      <td>204.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>dendritic cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>9231.552545</td>\n",
+       "      <td>163.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>multi-ciliated epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>10402.805195</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Langerhans cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>4117.785714</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2883.625000</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>pulmonary ionocyte</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5945.078947</td>\n",
+       "      <td>34.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>naive T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1828.140625</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1356.375000</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD16-positive, CD56-dim natural killer cell, h...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1954.695652</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>duct epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>11939.777778</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>8848.211538</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2373.533333</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1517.611111</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>alternatively activated macrophage</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5956.777778</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>mature NK T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2132.125000</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>plasmacytoid dendritic cell, human</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>6327.700000</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>mast cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2447.333333</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>innate lymphoid cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2774.571429</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>squamous epithelial cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>9096.050000</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CD16-negative, CD56-bright natural killer cell...</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>2479.500000</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1779.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>9270.333333</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5333.666667</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>1655.500000</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>[10x 5' v1]</td>\n",
+       "      <td>5343.000000</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type suspension_type  \\\n",
+       "0                                tracheal goblet cell            cell   \n",
+       "1                 basal cell of epithelium of trachea            cell   \n",
+       "2                                      secretory cell            cell   \n",
+       "3                                       ciliated cell            cell   \n",
+       "4              CD8-positive, alpha-beta memory T cell            cell   \n",
+       "5                                       memory B cell            cell   \n",
+       "6                                      dendritic cell            cell   \n",
+       "7                      multi-ciliated epithelial cell            cell   \n",
+       "8                                     Langerhans cell            cell   \n",
+       "9                                            monocyte            cell   \n",
+       "10                                 pulmonary ionocyte            cell   \n",
+       "11                                       naive T cell            cell   \n",
+       "12                                         neutrophil            cell   \n",
+       "13  CD16-positive, CD56-dim natural killer cell, h...            cell   \n",
+       "14                               duct epithelial cell            cell   \n",
+       "15                                          club cell            cell   \n",
+       "16                                 gamma-delta T cell            cell   \n",
+       "17                                         macrophage            cell   \n",
+       "18                 alternatively activated macrophage            cell   \n",
+       "19                                   mature NK T cell            cell   \n",
+       "20                 plasmacytoid dendritic cell, human            cell   \n",
+       "21                                          mast cell            cell   \n",
+       "22                               innate lymphoid cell            cell   \n",
+       "23                           squamous epithelial cell            cell   \n",
+       "24  CD16-negative, CD56-bright natural killer cell...            cell   \n",
+       "25                                       naive B cell            cell   \n",
+       "26                                        plasma cell            cell   \n",
+       "27                                             B cell            cell   \n",
+       "28                                  regulatory T cell            cell   \n",
+       "29                                             T cell            cell   \n",
+       "\n",
+       "     assay_list  total_mrna_umis_avg  n_cells  \n",
+       "0   [10x 5' v1]         11535.702018   2020.0  \n",
+       "1   [10x 5' v1]          8211.102237   1159.0  \n",
+       "2   [10x 5' v1]          8674.336407   1065.0  \n",
+       "3   [10x 5' v1]          6560.115789    646.0  \n",
+       "4   [10x 5' v1]          1933.067823    338.0  \n",
+       "5   [10x 5' v1]          3113.299020    204.0  \n",
+       "6   [10x 5' v1]          9231.552545    163.0  \n",
+       "7   [10x 5' v1]         10402.805195     78.0  \n",
+       "8   [10x 5' v1]          4117.785714     50.0  \n",
+       "9   [10x 5' v1]          2883.625000     49.0  \n",
+       "10  [10x 5' v1]          5945.078947     34.0  \n",
+       "11  [10x 5' v1]          1828.140625     33.0  \n",
+       "12  [10x 5' v1]          1356.375000     32.0  \n",
+       "13  [10x 5' v1]          1954.695652     24.0  \n",
+       "14  [10x 5' v1]         11939.777778     24.0  \n",
+       "15  [10x 5' v1]          8848.211538     17.0  \n",
+       "16  [10x 5' v1]          2373.533333     15.0  \n",
+       "17  [10x 5' v1]          1517.611111     12.0  \n",
+       "18  [10x 5' v1]          5956.777778     10.0  \n",
+       "19  [10x 5' v1]          2132.125000      8.0  \n",
+       "20  [10x 5' v1]          6327.700000      7.0  \n",
+       "21  [10x 5' v1]          2447.333333      7.0  \n",
+       "22  [10x 5' v1]          2774.571429      7.0  \n",
+       "23  [10x 5' v1]          9096.050000      7.0  \n",
+       "24  [10x 5' v1]          2479.500000      7.0  \n",
+       "25  [10x 5' v1]          1779.333333      3.0  \n",
+       "26  [10x 5' v1]          9270.333333      3.0  \n",
+       "27  [10x 5' v1]          5333.666667      3.0  \n",
+       "28  [10x 5' v1]          1655.500000      2.0  \n",
+       "29  [10x 5' v1]          5343.000000      2.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "coarse_tissues = df[\"tissue_coarse\"].unique()\n",
+    "cell_type_strings = dict()\n",
+    "\n",
+    "for coarse_tissue in coarse_tissues:\n",
+    "    tissue_df = df[df[\"tissue_coarse\"] == coarse_tissue]\n",
+    "\n",
+    "    agg_df = tissue_df.groupby([\"cell_type\", \"suspension_type\", \"cell_type_distance_from_root\"]).apply(\n",
+    "        lambda group: pd.Series({\n",
+    "            \"assay_list\": sorted(group[\"assay\"].unique().tolist()),\n",
+    "            \"total_mrna_umis_avg\": group[\"total_mrna_umis_avg\"].mean(),\n",
+    "            \"donor_dataset_count\": group[[\"donor_id\", \"dataset_id\"]].drop_duplicates().shape[0],\n",
+    "            \"disease_list\": sorted(group[\"disease\"].unique().tolist()),\n",
+    "            \"sex_list\": sorted(group[\"sex\"].unique().tolist()),\n",
+    "            \"tissue_list\": sorted(group[\"tissue_coarse\"].unique().tolist()),\n",
+    "            \"n_cells\": group[\"n_cells\"].sum(),\n",
+    "        })\n",
+    "    ).dropna().reset_index()\n",
+    "\n",
+    "    # Group by 'cell_type' and keep only the row with the highest 'n_cells'\n",
+    "    agg_df = agg_df.sort_values(\"n_cells\", ascending=False)\n",
+    "    agg_df = agg_df.drop_duplicates(subset=\"cell_type\").reset_index(drop=True)\n",
+    "    agg_df = agg_df[['cell_type', 'suspension_type', 'assay_list', 'total_mrna_umis_avg', 'n_cells']]\n",
+    "\n",
+    "    print(coarse_tissue)\n",
+    "    display(agg_df.head(30))\n",
+    "    s = '[\\n'\n",
+    "    for cell_type, n_cells in zip(agg_df[\"cell_type\"].tolist(), agg_df[\"n_cells\"].tolist()):\n",
+    "        s += \"\\t\" + f'\"{cell_type}\", # {n_cells}' + \"\\n\"\n",
+    "    s += ']'\n",
+    "\n",
+    "    cell_type_strings[coarse_tissue] = s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['brain', 'blood', 'small intestine', 'kidney', 'liver', 'lung', 'thymus', 'heart', 'nose'])"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_type_strings.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Brain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"L2/3-6 intratelencephalic projecting glutamatergic neuron\", # 40379.0\n",
+      "\t\"neuron\", # 38825.0\n",
+      "\t\"oligodendrocyte\", # 34701.0\n",
+      "\t\"astrocyte\", # 8561.0\n",
+      "\t\"pvalb GABAergic cortical interneuron\", # 7344.0\n",
+      "\t\"vip GABAergic cortical interneuron\", # 7249.0\n",
+      "\t\"glutamatergic neuron\", # 7105.0\n",
+      "\t\"oligodendrocyte precursor cell\", # 6695.0\n",
+      "\t\"sst GABAergic cortical interneuron\", # 6387.0\n",
+      "\t\"malignant cell\", # 5841.0\n",
+      "\t\"microglial cell\", # 5683.0\n",
+      "\t\"lamp5 GABAergic cortical interneuron\", # 5123.0\n",
+      "\t\"astrocyte of the cerebral cortex\", # 4539.0\n",
+      "\t\"neural cell\", # 3740.0\n",
+      "\t\"GABAergic neuron\", # 3494.0\n",
+      "\t\"mature astrocyte\", # 3439.0\n",
+      "\t\"macrophage\", # 3309.0\n",
+      "\t\"cerebellar granule cell\", # 2731.0\n",
+      "\t\"mature microglial cell\", # 2453.0\n",
+      "\t\"corticothalamic-projecting glutamatergic cortical neuron\", # 2273.0\n",
+      "\t\"L6b glutamatergic cortical neuron\", # 2037.0\n",
+      "\t\"sncg GABAergic cortical interneuron\", # 1984.0\n",
+      "\t\"endothelial cell\", # 1739.0\n",
+      "\t\"choroid plexus epithelial cell\", # 1688.0\n",
+      "\t\"near-projecting glutamatergic cortical neuron\", # 1350.0\n",
+      "\t\"chandelier pvalb GABAergic cortical interneuron\", # 1183.0\n",
+      "\t\"L5 extratelencephalic projecting glutamatergic cortical neuron\", # 1064.0\n",
+      "\t\"caudal ganglionic eminence derived GABAergic cortical interneuron\", # 998.0\n",
+      "\t\"L2/3 intratelencephalic projecting glutamatergic neuron\", # 565.0\n",
+      "\t\"mesenchymal cell\", # 448.0\n",
+      "\t\"L4 intratelencephalic projecting glutamatergic neuron\", # 447.0\n",
+      "\t\"vascular leptomeningeal cell\", # 432.0\n",
+      "\t\"differentiation-committed oligodendrocyte precursor\", # 308.0\n",
+      "\t\"central nervous system macrophage\", # 274.0\n",
+      "\t\"forebrain neuroblast\", # 257.0\n",
+      "\t\"mature T cell\", # 251.0\n",
+      "\t\"macroglial cell\", # 226.0\n",
+      "\t\"cerebral cortex endothelial cell\", # 200.0\n",
+      "\t\"mural cell\", # 176.0\n",
+      "\t\"monocyte\", # 151.0\n",
+      "\t\"interneuron\", # 142.0\n",
+      "\t\"L5/6 near-projecting glutamatergic neuron\", # 116.0\n",
+      "\t\"ependymal cell\", # 98.0\n",
+      "\t\"chandelier cell\", # 61.0\n",
+      "\t\"pericyte\", # 56.0\n",
+      "\t\"cerebral cortex neuron\", # 37.0\n",
+      "\t\"Bergmann glial cell\", # 35.0\n",
+      "\t\"fibroblast\", # 31.0\n",
+      "\t\"dendritic cell\", # 30.0\n",
+      "\t\"progenitor cell\", # 29.0\n",
+      "\t\"leukocyte\", # 24.0\n",
+      "\t\"glial cell\", # 21.0\n",
+      "\t\"sst chodl GABAergic cortical interneuron\", # 16.0\n",
+      "\t\"cerebral cortex GABAergic interneuron\", # 15.0\n",
+      "\t\"natural killer cell\", # 11.0\n",
+      "\t\"vascular associated smooth muscle cell\", # 6.0\n",
+      "\t\"radial glial cell\", # 3.0\n",
+      "\t\"B cell\", # 2.0\n",
+      "\t\"brain vascular cell\", # 1.0\n",
+      "\t\"plasma cell\", # 1.0\n",
+      "\t\"meningeal macrophage\", # 1.0\n",
+      "\t\"erythroid lineage cell\", # 1.0\n",
+      "\t\"mast cell\", # 1.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['brain'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brain_cell_types = [\n",
+    "\t\"GABAergic neuron\", \n",
+    "\t\"glutamatergic neuron\", \n",
+    "\t\"astrocyte\", \n",
+    "\t\"oligodendrocyte\", \n",
+    "\t\"oligodendrocyte precursor cell\", \n",
+    "\t\"microglial cell\", \n",
+    "\t\"cerebellar granule cell\", \n",
+    "\t\"endothelial cell\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Blood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"classical monocyte\", # 15635.0\n",
+      "\t\"CD4-positive, alpha-beta T cell\", # 15511.0\n",
+      "\t\"natural killer cell\", # 9283.0\n",
+      "\t\"CD8-positive, alpha-beta T cell\", # 9105.0\n",
+      "\t\"naive thymus-derived CD4-positive, alpha-beta T cell\", # 7317.0\n",
+      "\t\"B cell\", # 5547.0\n",
+      "\t\"CD8-positive, alpha-beta cytotoxic T cell\", # 4782.0\n",
+      "\t\"CD14-positive, CD16-negative classical monocyte\", # 4632.0\n",
+      "\t\"T cell\", # 4489.0\n",
+      "\t\"mature B cell\", # 4084.0\n",
+      "\t\"type I NK T cell\", # 3963.0\n",
+      "\t\"central memory CD4-positive, alpha-beta T cell\", # 3963.0\n",
+      "\t\"naive thymus-derived CD8-positive, alpha-beta T cell\", # 3604.0\n",
+      "\t\"CD14-positive monocyte\", # 3356.0\n",
+      "\t\"non-classical monocyte\", # 3004.0\n",
+      "\t\"blood cell\", # 2720.0\n",
+      "\t\"early pro-B cell\", # 2572.0\n",
+      "\t\"effector memory CD8-positive, alpha-beta T cell\", # 2528.0\n",
+      "\t\"CD16-positive, CD56-dim natural killer cell, human\", # 2512.0\n",
+      "\t\"naive B cell\", # 2237.0\n",
+      "\t\"regulatory T cell\", # 2007.0\n",
+      "\t\"memory B cell\", # 1950.0\n",
+      "\t\"gamma-delta T cell\", # 1690.0\n",
+      "\t\"CD8-positive, alpha-beta memory T cell\", # 1672.0\n",
+      "\t\"activated CD4-positive, alpha-beta T cell\", # 1652.0\n",
+      "\t\"CD4-positive helper T cell\", # 1640.0\n",
+      "\t\"conventional dendritic cell\", # 1483.0\n",
+      "\t\"CD4-positive, alpha-beta memory T cell\", # 1333.0\n",
+      "\t\"activated CD8-positive, alpha-beta T cell\", # 1135.0\n",
+      "\t\"mature NK T cell\", # 1110.0\n",
+      "\t\"mucosal invariant T cell\", # 1068.0\n",
+      "\t\"macrophage\", # 1053.0\n",
+      "\t\"central memory CD8-positive, alpha-beta T cell\", # 1020.0\n",
+      "\t\"effector memory CD8-positive, alpha-beta T cell, terminally differentiated\", # 970.0\n",
+      "\t\"mature alpha-beta T cell\", # 912.0\n",
+      "\t\"monocyte\", # 907.0\n",
+      "\t\"intermediate monocyte\", # 887.0\n",
+      "\t\"pro-B cell\", # 745.0\n",
+      "\t\"effector memory CD4-positive, alpha-beta T cell\", # 727.0\n",
+      "\t\"precursor B cell\", # 725.0\n",
+      "\t\"CD16-negative, CD56-bright natural killer cell, human\", # 708.0\n",
+      "\t\"CD14-low, CD16-positive monocyte\", # 708.0\n",
+      "\t\"CD4-positive, alpha-beta cytotoxic T cell\", # 705.0\n",
+      "\t\"common lymphoid progenitor\", # 466.0\n",
+      "\t\"plasmacytoid dendritic cell\", # 441.0\n",
+      "\t\"dendritic cell\", # 391.0\n",
+      "\t\"CD14-positive, CD16-positive monocyte\", # 293.0\n",
+      "\t\"plasma cell\", # 278.0\n",
+      "\t\"platelet\", # 255.0\n",
+      "\t\"plasmablast\", # 245.0\n",
+      "\t\"double-positive, alpha-beta thymocyte\", # 240.0\n",
+      "\t\"CD1c-positive myeloid dendritic cell\", # 200.0\n",
+      "\t\"transitional stage B cell\", # 136.0\n",
+      "\t\"lymphocyte\", # 118.0\n",
+      "\t\"hematopoietic precursor cell\", # 96.0\n",
+      "\t\"immature B cell\", # 96.0\n",
+      "\t\"double negative T regulatory cell\", # 94.0\n",
+      "\t\"hematopoietic stem cell\", # 67.0\n",
+      "\t\"class switched memory B cell\", # 27.0\n",
+      "\t\"innate lymphoid cell\", # 25.0\n",
+      "\t\"common dendritic progenitor\", # 21.0\n",
+      "\t\"megakaryocyte-erythroid progenitor cell\", # 21.0\n",
+      "\t\"progenitor cell\", # 20.0\n",
+      "\t\"hematopoietic multipotent progenitor cell\", # 15.0\n",
+      "\t\"double negative thymocyte\", # 13.0\n",
+      "\t\"mature gamma-delta T cell\", # 12.0\n",
+      "\t\"CD141-positive myeloid dendritic cell\", # 8.0\n",
+      "\t\"erythrocyte\", # 8.0\n",
+      "\t\"megakaryocyte\", # 7.0\n",
+      "\t\"granulocyte monocyte progenitor cell\", # 6.0\n",
+      "\t\"enucleated reticulocyte\", # 5.0\n",
+      "\t\"mast cell\", # 4.0\n",
+      "\t\"neutrophil\", # 3.0\n",
+      "\t\"peripheral blood mononuclear cell\", # 1.0\n",
+      "\t\"granulocyte\", # 1.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['blood'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "blood_cell_types = [\n",
+    "\t\"classical monocyte\", # 15635.0\n",
+    "\t\"non-classical monocyte\", # 3004.0\n",
+    "\t\"natural killer cell\", # 9283.0\n",
+    "\t\"CD4-positive, alpha-beta T cell\", # 15511.0\n",
+    "\t\"CD8-positive, alpha-beta T cell\", # 9105.0\n",
+    "\t\"CD8-positive, alpha-beta cytotoxic T cell\", # 4782.0\n",
+    "\t\"CD8-positive, alpha-beta memory T cell\",\n",
+    "\t\"naive thymus-derived CD4-positive, alpha-beta T cell\", # 7317.0\n",
+    "\t\"naive thymus-derived CD8-positive, alpha-beta T cell\", # 3604.0\n",
+    "\t\"naive B cell\", # 2237.0\n",
+    "    \"memory B cell\", # 1950.0\n",
+    "\t\"regulatory T cell\", # 2007.0\n",
+    "\t\"gamma-delta T cell\", # 1690.0\n",
+    "\t\"conventional dendritic cell\", # 1483.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Intestine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"plasma cell\", # 15499.0\n",
+      "\t\"mesenchymal cell\", # 5780.0\n",
+      "\t\"T cell\", # 4706.0\n",
+      "\t\"macrophage\", # 4629.0\n",
+      "\t\"CD4-positive, alpha-beta T cell\", # 4371.0\n",
+      "\t\"CD8-positive, alpha-beta T cell\", # 3844.0\n",
+      "\t\"B cell\", # 2755.0\n",
+      "\t\"fibroblast\", # 1974.0\n",
+      "\t\"plasmacytoid dendritic cell\", # 1788.0\n",
+      "\t\"intestinal epithelial cell\", # 1196.0\n",
+      "\t\"myeloid leukocyte\", # 1112.0\n",
+      "\t\"mast cell\", # 1016.0\n",
+      "\t\"regulatory T cell\", # 876.0\n",
+      "\t\"enterocyte\", # 785.0\n",
+      "\t\"mature NK T cell\", # 669.0\n",
+      "\t\"naive T cell\", # 397.0\n",
+      "\t\"monocyte\", # 392.0\n",
+      "\t\"conventional dendritic cell\", # 375.0\n",
+      "\t\"natural killer cell\", # 351.0\n",
+      "\t\"dendritic cell\", # 347.0\n",
+      "\t\"endothelial cell\", # 275.0\n",
+      "\t\"endothelial cell of lymphatic vessel\", # 255.0\n",
+      "\t\"blood vessel endothelial cell\", # 194.0\n",
+      "\t\"IgG plasma cell\", # 187.0\n",
+      "\t\"enteroendocrine cell\", # 176.0\n",
+      "\t\"intraepithelial lymphocyte\", # 151.0\n",
+      "\t\"pericyte\", # 147.0\n",
+      "\t\"glial cell\", # 141.0\n",
+      "\t\"myofibroblast cell\", # 121.0\n",
+      "\t\"peripheral nervous system neuron\", # 96.0\n",
+      "\t\"innate lymphoid cell\", # 95.0\n",
+      "\t\"memory B cell\", # 79.0\n",
+      "\t\"intestine goblet cell\", # 69.0\n",
+      "\t\"gut endothelial cell\", # 60.0\n",
+      "\t\"neural cell\", # 48.0\n",
+      "\t\"intestinal crypt stem cell\", # 46.0\n",
+      "\t\"epithelial cell\", # 46.0\n",
+      "\t\"activated CD4-positive, alpha-beta T cell, human\", # 46.0\n",
+      "\t\"IgA plasma cell\", # 44.0\n",
+      "\t\"erythroblast\", # 32.0\n",
+      "\t\"vein endothelial cell\", # 28.0\n",
+      "\t\"neutrophil\", # 28.0\n",
+      "\t\"T follicular helper cell\", # 26.0\n",
+      "\t\"gamma-delta T cell\", # 25.0\n",
+      "\t\"neuron\", # 19.0\n",
+      "\t\"transit amplifying cell\", # 18.0\n",
+      "\t\"lymphocyte\", # 14.0\n",
+      "\t\"leukocyte\", # 13.0\n",
+      "\t\"enteric smooth muscle cell\", # 10.0\n",
+      "\t\"M cell of gut\", # 10.0\n",
+      "\t\"dendritic cell, human\", # 9.0\n",
+      "\t\"endothelial cell of artery\", # 7.0\n",
+      "\t\"epithelial cell of lung\", # 3.0\n",
+      "\t\"myeloid cell\", # 1.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['small intestine'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intestine_cell_types = [\n",
+    "\t\"enterocyte\", # 785.0\n",
+    "\t\"endothelial cell\", # 275.0\n",
+    "\t\"enteroendocrine cell\", # 176.0\n",
+    "\t\"pericyte\", # 147.0\n",
+    "\t\"intestine goblet cell\", # 69.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Kidney"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"abnormal cell\", # 2911.0\n",
+      "\t\"macrophage\", # 2236.0\n",
+      "\t\"kidney collecting duct principal cell\", # 1569.0\n",
+      "\t\"kidney loop of Henle thin descending limb epithelial cell\", # 1283.0\n",
+      "\t\"T cell\", # 1114.0\n",
+      "\t\"endothelial cell\", # 875.0\n",
+      "\t\"kidney loop of Henle thin ascending limb epithelial cell\", # 866.0\n",
+      "\t\"kidney loop of Henle thick ascending limb epithelial cell\", # 851.0\n",
+      "\t\"epithelial cell of proximal tubule\", # 486.0\n",
+      "\t\"kidney collecting duct intercalated cell\", # 226.0\n",
+      "\t\"renal interstitial pericyte\", # 221.0\n",
+      "\t\"vascular associated smooth muscle cell\", # 130.0\n",
+      "\t\"kidney connecting tubule epithelial cell\", # 114.0\n",
+      "\t\"B cell\", # 95.0\n",
+      "\t\"kidney distal convoluted tubule epithelial cell\", # 78.0\n",
+      "\t\"conventional dendritic cell\", # 55.0\n",
+      "\t\"monocyte\", # 43.0\n",
+      "\t\"mature NK T cell\", # 41.0\n",
+      "\t\"natural killer cell\", # 39.0\n",
+      "\t\"alternatively activated macrophage\", # 38.0\n",
+      "\t\"cytotoxic T cell\", # 35.0\n",
+      "\t\"plasma cell\", # 34.0\n",
+      "\t\"mast cell\", # 29.0\n",
+      "\t\"kidney interstitial fibroblast\", # 23.0\n",
+      "\t\"podocyte\", # 16.0\n",
+      "\t\"parietal epithelial cell\", # 9.0\n",
+      "\t\"leukocyte\", # 8.0\n",
+      "\t\"pericyte\", # 6.0\n",
+      "\t\"non-classical monocyte\", # 5.0\n",
+      "\t\"epithelial cell\", # 2.0\n",
+      "\t\"plasmacytoid dendritic cell\", # 2.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['kidney'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kidney_cell_types = [\n",
+    "\t\"kidney collecting duct principal cell\", # 1569.0\n",
+    "\t\"kidney loop of Henle thin descending limb epithelial cell\", # 1283.0\n",
+    "\t\"kidney loop of Henle thin ascending limb epithelial cell\", # 866.0\n",
+    "\t\"kidney loop of Henle thick ascending limb epithelial cell\", # 851.0\n",
+    "\t\"epithelial cell of proximal tubule\", # 486.0\n",
+    "\t\"kidney collecting duct intercalated cell\", # 226.0\n",
+    "\t\"renal interstitial pericyte\", # 221.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Liver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"hepatic pit cell\", # 2410.0\n",
+      "\t\"CD8-positive, alpha-beta T cell\", # 2290.0\n",
+      "\t\"Kupffer cell\", # 2091.0\n",
+      "\t\"natural killer cell\", # 2062.0\n",
+      "\t\"macrophage\", # 2021.0\n",
+      "\t\"CD4-positive, alpha-beta T cell\", # 1677.0\n",
+      "\t\"monocyte\", # 1274.0\n",
+      "\t\"hepatocyte\", # 989.0\n",
+      "\t\"periportal region hepatocyte\", # 967.0\n",
+      "\t\"mature B cell\", # 693.0\n",
+      "\t\"T cell\", # 634.0\n",
+      "\t\"centrilobular region hepatocyte\", # 561.0\n",
+      "\t\"endothelial cell of pericentral hepatic sinusoid\", # 387.0\n",
+      "\t\"endothelial cell of artery\", # 375.0\n",
+      "\t\"plasma cell\", # 360.0\n",
+      "\t\"alpha-beta T cell\", # 316.0\n",
+      "\t\"fibroblast\", # 294.0\n",
+      "\t\"intrahepatic cholangiocyte\", # 275.0\n",
+      "\t\"endothelial cell of hepatic sinusoid\", # 259.0\n",
+      "\t\"inflammatory macrophage\", # 246.0\n",
+      "\t\"endothelial cell of periportal hepatic sinusoid\", # 244.0\n",
+      "\t\"gamma-delta T cell\", # 241.0\n",
+      "\t\"vein endothelial cell\", # 237.0\n",
+      "\t\"hepatic stellate cell\", # 218.0\n",
+      "\t\"conventional dendritic cell\", # 198.0\n",
+      "\t\"neutrophil\", # 189.0\n",
+      "\t\"innate lymphoid cell\", # 98.0\n",
+      "\t\"erythrocyte\", # 81.0\n",
+      "\t\"midzonal region hepatocyte\", # 81.0\n",
+      "\t\"mast cell\", # 35.0\n",
+      "\t\"plasmacytoid dendritic cell\", # 33.0\n",
+      "\t\"cholangiocyte\", # 33.0\n",
+      "\t\"B cell\", # 21.0\n",
+      "\t\"erythroid lineage cell\", # 5.0\n",
+      "\t\"erythroblast\", # 3.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['liver'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "liver_cell_types = [\n",
+    "\t\"hepatic pit cell\", # 2410.0\n",
+    "\t\"Kupffer cell\", # 2091.0\n",
+    "\t\"hepatocyte\", # 989.0\n",
+    "\t\"periportal region hepatocyte\", # 967.0\n",
+    "\t\"centrilobular region hepatocyte\", # 561.0\n",
+    "\t\"endothelial cell of pericentral hepatic sinusoid\", # 387.0\n",
+    "\t\"endothelial cell of artery\", # 375.0\n",
+    "\t\"fibroblast\", # 294.0\n",
+    "\t\"intrahepatic cholangiocyte\", # 275.0\n",
+    "\t\"endothelial cell of hepatic sinusoid\", # 259.0\n",
+    "\t\"hepatic stellate cell\", # 218.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"alveolar macrophage\", # 8713.0\n",
+      "\t\"malignant cell\", # 7659.0\n",
+      "\t\"CD8-positive, alpha-beta T cell\", # 7484.0\n",
+      "\t\"type II pneumocyte\", # 5653.0\n",
+      "\t\"CD4-positive, alpha-beta T cell\", # 4960.0\n",
+      "\t\"macrophage\", # 4748.0\n",
+      "\t\"capillary endothelial cell\", # 3835.0\n",
+      "\t\"B cell\", # 3471.0\n",
+      "\t\"classical monocyte\", # 2773.0\n",
+      "\t\"T cell\", # 2739.0\n",
+      "\t\"ciliated columnar cell of tracheobronchial tree\", # 2600.0\n",
+      "\t\"plasma cell\", # 2245.0\n",
+      "\t\"natural killer cell\", # 2240.0\n",
+      "\t\"regulatory T cell\", # 1990.0\n",
+      "\t\"vein endothelial cell\", # 1312.0\n",
+      "\t\"CD1c-positive myeloid dendritic cell\", # 1282.0\n",
+      "\t\"elicited macrophage\", # 1262.0\n",
+      "\t\"type I pneumocyte\", # 1089.0\n",
+      "\t\"endothelial cell of lymphatic vessel\", # 992.0\n",
+      "\t\"monocyte\", # 955.0\n",
+      "\t\"non-classical monocyte\", # 854.0\n",
+      "\t\"myeloid cell\", # 850.0\n",
+      "\t\"epithelial cell of lower respiratory tract\", # 633.0\n",
+      "\t\"plasmacytoid dendritic cell\", # 599.0\n",
+      "\t\"pulmonary interstitial fibroblast\", # 504.0\n",
+      "\t\"club cell\", # 473.0\n",
+      "\t\"pulmonary artery endothelial cell\", # 443.0\n",
+      "\t\"mast cell\", # 423.0\n",
+      "\t\"epithelial cell of lung\", # 376.0\n",
+      "\t\"fibroblast of lung\", # 372.0\n",
+      "\t\"endothelial cell\", # 324.0\n",
+      "\t\"dendritic cell\", # 211.0\n",
+      "\t\"fibroblast\", # 208.0\n",
+      "\t\"alveolar type 2 fibroblast cell\", # 202.0\n",
+      "\t\"conventional dendritic cell\", # 145.0\n",
+      "\t\"multi-ciliated epithelial cell\", # 132.0\n",
+      "\t\"epithelial cell\", # 119.0\n",
+      "\t\"CD16-positive, CD56-dim natural killer cell, human\", # 104.0\n",
+      "\t\"erythrocyte\", # 96.0\n",
+      "\t\"lung ciliated cell\", # 92.0\n",
+      "\t\"pericyte\", # 85.0\n",
+      "\t\"CD16-negative, CD56-bright natural killer cell, human\", # 56.0\n",
+      "\t\"respiratory basal cell\", # 54.0\n",
+      "\t\"bronchus fibroblast of lung\", # 49.0\n",
+      "\t\"blood vessel endothelial cell\", # 49.0\n",
+      "\t\"large pre-B-II cell\", # 31.0\n",
+      "\t\"epithelial cell of alveolus of lung\", # 28.0\n",
+      "\t\"late pro-B cell\", # 27.0\n",
+      "\t\"ionocyte\", # 27.0\n",
+      "\t\"precursor B cell\", # 23.0\n",
+      "\t\"neutrophil\", # 20.0\n",
+      "\t\"endothelial cell of artery\", # 19.0\n",
+      "\t\"small pre-B-II cell\", # 17.0\n",
+      "\t\"B-1b B cell\", # 16.0\n",
+      "\t\"pre-B-I cell\", # 16.0\n",
+      "\t\"dendritic cell, human\", # 15.0\n",
+      "\t\"mature NK T cell\", # 15.0\n",
+      "\t\"immature B cell\", # 15.0\n",
+      "\t\"neuron\", # 15.0\n",
+      "\t\"smooth muscle cell\", # 13.0\n",
+      "\t\"reticulocyte\", # 10.0\n",
+      "\t\"tracheobronchial smooth muscle cell\", # 10.0\n",
+      "\t\"lung macrophage\", # 10.0\n",
+      "\t\"alveolar type 1 fibroblast cell\", # 9.0\n",
+      "\t\"mesothelial fibroblast\", # 9.0\n",
+      "\t\"group 3 innate lymphoid cell\", # 8.0\n",
+      "\t\"T-helper 17 cell\", # 8.0\n",
+      "\t\"vascular associated smooth muscle cell\", # 8.0\n",
+      "\t\"promonocyte\", # 7.0\n",
+      "\t\"nasal mucosa goblet cell\", # 6.0\n",
+      "\t\"myofibroblast cell\", # 6.0\n",
+      "\t\"lung goblet cell\", # 6.0\n",
+      "\t\"mesothelial cell\", # 6.0\n",
+      "\t\"mucus secreting cell\", # 6.0\n",
+      "\t\"group 2 innate lymphoid cell\", # 5.0\n",
+      "\t\"hematopoietic stem cell\", # 5.0\n",
+      "\t\"erythroblast\", # 5.0\n",
+      "\t\"pro-B cell\", # 4.0\n",
+      "\t\"megakaryocyte\", # 3.0\n",
+      "\t\"platelet\", # 2.0\n",
+      "\t\"stromal cell\", # 2.0\n",
+      "\t\"respiratory epithelial cell\", # 2.0\n",
+      "\t\"megakaryocyte-erythroid progenitor cell\", # 2.0\n",
+      "\t\"lung pericyte\", # 1.0\n",
+      "\t\"acinar cell\", # 1.0\n",
+      "\t\"basophil\", # 1.0\n",
+      "\t\"Schwann cell\", # 1.0\n",
+      "\t\"B-1a B cell\", # 1.0\n",
+      "\t\"eosinophil\", # 1.0\n",
+      "\t\"promyelocyte\", # 1.0\n",
+      "\t\"lung neuroendocrine cell\", # 1.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['lung'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lung_cell_types = [\n",
+    "\t\"alveolar macrophage\", # 8713.0\n",
+    "\t\"type II pneumocyte\", # 5653.0\n",
+    "\t\"capillary endothelial cell\", # 3835.0\n",
+    "\t\"ciliated columnar cell of tracheobronchial tree\", # 2600.0\n",
+    "\t\"type I pneumocyte\", # 1089.0\n",
+    "\t\"epithelial cell of lower respiratory tract\", # 633.0\n",
+    "\t\"pulmonary interstitial fibroblast\", # 504.0\n",
+    "\t\"club cell\", # 473.0\n",
+    "\t\"pulmonary artery endothelial cell\", # 443.0\n",
+    "\t\"epithelial cell of lung\", # 376.0\n",
+    "\t\"fibroblast of lung\", # 372.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Heart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\n",
+      "\t\"cardiac muscle myoblast\", # 21050.0\n",
+      "\t\"fibroblast of cardiac tissue\", # 13244.0\n",
+      "\t\"cardiac endothelial cell\", # 8661.0\n",
+      "\t\"immature innate lymphoid cell\", # 4504.0\n",
+      "\t\"pericyte\", # 3264.0\n",
+      "\t\"mural cell\", # 2303.0\n",
+      "\t\"endothelial cell\", # 1742.0\n",
+      "\t\"myeloid cell\", # 1658.0\n",
+      "\t\"cardiac muscle cell\", # 1397.0\n",
+      "\t\"lymphoid lineage restricted progenitor cell\", # 1287.0\n",
+      "\t\"smooth muscle myoblast\", # 846.0\n",
+      "\t\"neuronal receptor cell\", # 665.0\n",
+      "\t\"lymphocyte\", # 252.0\n",
+      "\t\"mast cell\", # 224.0\n",
+      "\t\"adipocyte of epicardial fat of left ventricle\", # 201.0\n",
+      "\t\"cardiac neuron\", # 115.0\n",
+      "\t\"adipocyte\", # 2.0\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_strings['heart'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heart_cell_types = [\n",
+    "\t\"cardiac muscle myoblast\", # 21050.0\n",
+    "\t\"fibroblast of cardiac tissue\", # 13244.0\n",
+    "\t\"cardiac endothelial cell\", # 8661.0\n",
+    "\t\"cardiac muscle cell\", # 1397.0\n",
+    "\t\"cardiac neuron\", # 115.0\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_cell_types = brain_cell_types + blood_cell_types + intestine_cell_types + kidney_cell_types + liver_cell_types + lung_cell_types + heart_cell_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_cell_types)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finalize the cell type list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_list = []\n",
+    "\n",
+    "for tissue, cell_types in zip(\n",
+    "    ['brain', 'blood', 'small intestine', 'kidney', 'liver', 'lung', 'heart'],\n",
+    "    [brain_cell_types, blood_cell_types, intestine_cell_types, kidney_cell_types, liver_cell_types, lung_cell_types, heart_cell_types]):\n",
+    "    df = pd.DataFrame(\n",
+    "        {'cell_type': cell_types,\n",
+    "         'tissue': tissue,\n",
+    "         'assay': 'TBD',\n",
+    "         'suspension_type': 'TBD',\n",
+    "         'sex': 'male',\n",
+    "         'disease': 'normal',\n",
+    "         'total_mrna_umis': 0.,\n",
+    "        })\n",
+    "    df_list.append(df)\n",
+    "\n",
+    "cell_types_df = pd.concat(df_list, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# (deprecated)\n",
+    "# updated_rows = []\n",
+    "\n",
+    "# for i_row, row in enumerate(cell_types_df.itertuples()):\n",
+    "#     _df = validation_stats_df[(validation_stats_df[\"cell_type\"] == row.cell_type) & (validation_stats_df[\"tissue_coarse\"] == row.tissue)]\n",
+    "#     _df = _df.groupby([\"assay\", \"suspension_type\", \"sex\"], as_index=False).apply(\n",
+    "#         lambda group: pd.Series(\n",
+    "#             {\n",
+    "#                 'n_cells': group[\"n_cells\"].sum(),\n",
+    "#                 \"total_mrna_umis_avg\": sum([a * b for a, b in zip(group[\"total_mrna_umis_avg\"].tolist(), group[\"n_cells\"].tolist())]) / group[\"n_cells\"].sum(),\n",
+    "#             }\n",
+    "#         )\n",
+    "#     ).dropna().sort_values(\"n_cells\", ascending=False)\n",
+    "    \n",
+    "#     if not _df.empty:\n",
+    "#         updated_row = row._asdict()\n",
+    "#         updated_row['assay'] = _df.iloc[0][\"assay\"]\n",
+    "#         updated_row['suspension_type'] = _df.iloc[0][\"suspension_type\"]\n",
+    "#         updated_row['sex'] = _df.iloc[0][\"sex\"]\n",
+    "#         updated_row['total_mrna_umis'] = _df.iloc[0][\"total_mrna_umis_avg\"]\n",
+    "#         updated_row.pop('Index')\n",
+    "#         updated_rows.append(updated_row)\n",
+    "#     else:\n",
+    "#         updated_rows.append(row._asdict())\n",
+    "\n",
+    "# cell_types_df = pd.DataFrame(updated_rows)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let us use to census to identify the most abundant assay and suspension type for each tissue\n",
+    "\n",
+    "import cellxgene_census\n",
+    "\n",
+    "census = cellxgene_census.open_soma(census_version=\"2024-05-20\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain\n",
+      "blood\n",
+      "small intestine\n",
+      "kidney\n",
+      "liver\n",
+      "lung\n",
+      "heart\n"
+     ]
+    }
+   ],
+   "source": [
+    "default_disease = \"normal\"\n",
+    "default_sex = \"male\"\n",
+    "\n",
+    "updated_rows = []\n",
+    "\n",
+    "for tissue in ['brain', 'blood', 'small intestine', 'kidney', 'liver', 'lung', 'heart']:\n",
+    "    print(tissue)\n",
+    "    \n",
+    "    obs = cellxgene_census.get_obs(\n",
+    "        census, \"homo_sapiens\",\n",
+    "        value_filter=(\n",
+    "            f\"\"\"is_primary_data == True and tissue_general == \"{tissue}\" and disease == \"{default_disease}\" \"\"\"\n",
+    "            f\"\"\"and sex == \"{default_sex}\" \"\"\"\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    obs = obs[obs[\"assay\"].isin(included_assays)]\n",
+    "    top = obs.groupby([\"assay\", \"suspension_type\"]).size().sort_values(ascending=False)\n",
+    "    top_assay, top_suspension_type = top.index[0]\n",
+    "    \n",
+    "    tissue_df = cell_types_df[cell_types_df[\"tissue\"] == tissue]\n",
+    "    top_obs = obs[(obs[\"assay\"] == top_assay) & (obs[\"suspension_type\"] == top_suspension_type)]\n",
+    "\n",
+    "    for i_row, row in enumerate(tissue_df.itertuples()):\n",
+    "        cell_type = row.cell_type\n",
+    "        total_mrna_umis = top_obs[top_obs[\"cell_type\"] == cell_type][\"raw_sum\"].mean()\n",
+    "\n",
+    "        updated_row = dict()\n",
+    "        updated_row['cell_type'] = cell_type\n",
+    "        updated_row['assay'] = top_assay\n",
+    "        updated_row['suspension_type'] = top_suspension_type\n",
+    "        updated_row['tissue'] = tissue\n",
+    "        updated_row['sex'] = default_sex\n",
+    "        updated_row['disease'] = default_disease\n",
+    "        updated_row['total_mrna_umis'] = total_mrna_umis\n",
+    "        updated_rows.append(updated_row)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_cell_types_df = pd.DataFrame(updated_rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5527.708887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10683.399862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5380.963698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5083.603161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6609.696174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3645.962305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3935.779702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3115.828938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3922.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8436.318182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2923.925418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3285.603705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3720.503504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD8-positive, alpha-beta cytotoxic T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5061.393152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5221.671616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5649.398766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6333.568573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4984.057779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6689.483383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5216.285765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4950.552077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>14936.870968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8772.175057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10069.068281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10636.632432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4604.858974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>intestine goblet cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7435.644048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6444.458460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4345.735849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5964.088479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4911.535804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>epithelial cell of proximal tubule</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4491.198843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4906.310584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3130.350171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>hepatic pit cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2761.142458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Kupffer cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6850.818612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11532.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>periportal region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4790.702890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>centrilobular region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9280.724561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>endothelial cell of pericentral hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2734.614608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3987.627119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7274.262295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>intrahepatic cholangiocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6998.971698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>endothelial cell of hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3836.788282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>hepatic stellate cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4090.862637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8422.037378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6109.480329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3094.649239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>ciliated columnar cell of tracheobronchial tree</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5754.694686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2744.064915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5956.795231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>pulmonary interstitial fibroblast</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5294.602205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9484.199912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2321.547941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>15015.278925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>fibroblast of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6561.739316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>cardiac muscle myoblast</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11938.346771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>fibroblast of cardiac tissue</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3099.779698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>cardiac endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2268.612916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>cardiac muscle cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8017.024743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2238.708571</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type      assay  \\\n",
+       "0                                    GABAergic neuron  10x 3' v3   \n",
+       "1                                glutamatergic neuron  10x 3' v3   \n",
+       "2                                           astrocyte  10x 3' v3   \n",
+       "3                                     oligodendrocyte  10x 3' v3   \n",
+       "4                      oligodendrocyte precursor cell  10x 3' v3   \n",
+       "5                                     microglial cell  10x 3' v3   \n",
+       "6                             cerebellar granule cell  10x 3' v3   \n",
+       "7                                    endothelial cell  10x 3' v3   \n",
+       "8                                  classical monocyte  10x 5' v2   \n",
+       "9                              non-classical monocyte  10x 5' v2   \n",
+       "10                                natural killer cell  10x 5' v2   \n",
+       "11                    CD4-positive, alpha-beta T cell  10x 5' v2   \n",
+       "12                    CD8-positive, alpha-beta T cell  10x 5' v2   \n",
+       "13          CD8-positive, alpha-beta cytotoxic T cell  10x 5' v2   \n",
+       "14             CD8-positive, alpha-beta memory T cell  10x 5' v2   \n",
+       "15  naive thymus-derived CD4-positive, alpha-beta ...  10x 5' v2   \n",
+       "16  naive thymus-derived CD8-positive, alpha-beta ...  10x 5' v2   \n",
+       "17                                       naive B cell  10x 5' v2   \n",
+       "18                                      memory B cell  10x 5' v2   \n",
+       "19                                  regulatory T cell  10x 5' v2   \n",
+       "20                                 gamma-delta T cell  10x 5' v2   \n",
+       "21                        conventional dendritic cell  10x 5' v2   \n",
+       "22                                         enterocyte  10x 3' v2   \n",
+       "23                                   endothelial cell  10x 3' v2   \n",
+       "24                               enteroendocrine cell  10x 3' v2   \n",
+       "25                                           pericyte  10x 3' v2   \n",
+       "26                              intestine goblet cell  10x 3' v2   \n",
+       "27              kidney collecting duct principal cell  10x 3' v3   \n",
+       "28  kidney loop of Henle thin descending limb epit...  10x 3' v3   \n",
+       "29  kidney loop of Henle thin ascending limb epith...  10x 3' v3   \n",
+       "30  kidney loop of Henle thick ascending limb epit...  10x 3' v3   \n",
+       "31                 epithelial cell of proximal tubule  10x 3' v3   \n",
+       "32           kidney collecting duct intercalated cell  10x 3' v3   \n",
+       "33                        renal interstitial pericyte  10x 3' v3   \n",
+       "34                                   hepatic pit cell  10x 3' v2   \n",
+       "35                                       Kupffer cell  10x 3' v2   \n",
+       "36                                         hepatocyte  10x 3' v2   \n",
+       "37                       periportal region hepatocyte  10x 3' v2   \n",
+       "38                    centrilobular region hepatocyte  10x 3' v2   \n",
+       "39   endothelial cell of pericentral hepatic sinusoid  10x 3' v2   \n",
+       "40                         endothelial cell of artery  10x 3' v2   \n",
+       "41                                         fibroblast  10x 3' v2   \n",
+       "42                         intrahepatic cholangiocyte  10x 3' v2   \n",
+       "43               endothelial cell of hepatic sinusoid  10x 3' v2   \n",
+       "44                              hepatic stellate cell  10x 3' v2   \n",
+       "45                                alveolar macrophage  10x 5' v1   \n",
+       "46                                 type II pneumocyte  10x 5' v1   \n",
+       "47                         capillary endothelial cell  10x 5' v1   \n",
+       "48    ciliated columnar cell of tracheobronchial tree  10x 5' v1   \n",
+       "49                                  type I pneumocyte  10x 5' v1   \n",
+       "50         epithelial cell of lower respiratory tract  10x 5' v1   \n",
+       "51                  pulmonary interstitial fibroblast  10x 5' v1   \n",
+       "52                                          club cell  10x 5' v1   \n",
+       "53                  pulmonary artery endothelial cell  10x 5' v1   \n",
+       "54                            epithelial cell of lung  10x 5' v1   \n",
+       "55                                 fibroblast of lung  10x 5' v1   \n",
+       "56                            cardiac muscle myoblast  10x 3' v3   \n",
+       "57                       fibroblast of cardiac tissue  10x 3' v3   \n",
+       "58                           cardiac endothelial cell  10x 3' v3   \n",
+       "59                                cardiac muscle cell  10x 3' v3   \n",
+       "60                                     cardiac neuron  10x 3' v3   \n",
+       "\n",
+       "   suspension_type           tissue   sex disease  total_mrna_umis  \n",
+       "0          nucleus            brain  male  normal      5527.708887  \n",
+       "1          nucleus            brain  male  normal     10683.399862  \n",
+       "2          nucleus            brain  male  normal      5380.963698  \n",
+       "3          nucleus            brain  male  normal      5083.603161  \n",
+       "4          nucleus            brain  male  normal      6609.696174  \n",
+       "5          nucleus            brain  male  normal      3645.962305  \n",
+       "6          nucleus            brain  male  normal      3935.779702  \n",
+       "7          nucleus            brain  male  normal      3115.828938  \n",
+       "8             cell            blood  male  normal      3922.025000  \n",
+       "9             cell            blood  male  normal      8436.318182  \n",
+       "10            cell            blood  male  normal      2923.925418  \n",
+       "11            cell            blood  male  normal      3285.603705  \n",
+       "12            cell            blood  male  normal      3720.503504  \n",
+       "13            cell            blood  male  normal      5061.393152  \n",
+       "14            cell            blood  male  normal      5221.671616  \n",
+       "15            cell            blood  male  normal      5649.398766  \n",
+       "16            cell            blood  male  normal      6333.568573  \n",
+       "17            cell            blood  male  normal      4984.057779  \n",
+       "18            cell            blood  male  normal      6689.483383  \n",
+       "19            cell            blood  male  normal      5216.285765  \n",
+       "20            cell            blood  male  normal      4950.552077  \n",
+       "21            cell            blood  male  normal     14936.870968  \n",
+       "22            cell  small intestine  male  normal      8772.175057  \n",
+       "23            cell  small intestine  male  normal     10069.068281  \n",
+       "24            cell  small intestine  male  normal     10636.632432  \n",
+       "25            cell  small intestine  male  normal      4604.858974  \n",
+       "26            cell  small intestine  male  normal      7435.644048  \n",
+       "27         nucleus           kidney  male  normal      6444.458460  \n",
+       "28         nucleus           kidney  male  normal      4345.735849  \n",
+       "29         nucleus           kidney  male  normal      5964.088479  \n",
+       "30         nucleus           kidney  male  normal      4911.535804  \n",
+       "31         nucleus           kidney  male  normal      4491.198843  \n",
+       "32         nucleus           kidney  male  normal      4906.310584  \n",
+       "33         nucleus           kidney  male  normal      3130.350171  \n",
+       "34            cell            liver  male  normal      2761.142458  \n",
+       "35            cell            liver  male  normal      6850.818612  \n",
+       "36            cell            liver  male  normal     11532.500000  \n",
+       "37            cell            liver  male  normal      4790.702890  \n",
+       "38            cell            liver  male  normal      9280.724561  \n",
+       "39            cell            liver  male  normal      2734.614608  \n",
+       "40            cell            liver  male  normal      3987.627119  \n",
+       "41            cell            liver  male  normal      7274.262295  \n",
+       "42            cell            liver  male  normal      6998.971698  \n",
+       "43            cell            liver  male  normal      3836.788282  \n",
+       "44            cell            liver  male  normal      4090.862637  \n",
+       "45            cell             lung  male  normal      8422.037378  \n",
+       "46            cell             lung  male  normal      6109.480329  \n",
+       "47            cell             lung  male  normal      3094.649239  \n",
+       "48            cell             lung  male  normal      5754.694686  \n",
+       "49            cell             lung  male  normal      2744.064915  \n",
+       "50            cell             lung  male  normal      5956.795231  \n",
+       "51            cell             lung  male  normal      5294.602205  \n",
+       "52            cell             lung  male  normal      9484.199912  \n",
+       "53            cell             lung  male  normal      2321.547941  \n",
+       "54            cell             lung  male  normal     15015.278925  \n",
+       "55            cell             lung  male  normal      6561.739316  \n",
+       "56         nucleus            heart  male  normal     11938.346771  \n",
+       "57         nucleus            heart  male  normal      3099.779698  \n",
+       "58         nucleus            heart  male  normal      2268.612916  \n",
+       "59         nucleus            heart  male  normal      8017.024743  \n",
+       "60         nucleus            heart  male  normal      2238.708571  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the full table\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(final_cell_types_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_cell_types_df.to_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"cell_types_draft_1.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: add \"prior\" cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine highly expressed genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eda7b89848b948f2980e474f72d54d40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "# randomly sample 5_000 cells from each row of the final_cell_types_df and use that to identify highly expressed genes\n",
+    "max_cells = 10_000\n",
+    "\n",
+    "tpm_ng = np.zeros((len(final_cell_types_df), 60530))\n",
+    "feature_id = None\n",
+    "feature_name = None\n",
+    "\n",
+    "for n, row in tqdm(enumerate(final_cell_types_df.itertuples())):\n",
+    "\n",
+    "    obs = cellxgene_census.get_obs(\n",
+    "        census, \"homo_sapiens\",\n",
+    "        value_filter=(\n",
+    "            f\"\"\"is_primary_data == True and tissue_general == \"{row.tissue}\" and disease == \"{row.disease}\" \"\"\"\n",
+    "            f\"\"\"and sex == \"{row.sex}\" and cell_type == \"{row.cell_type}\" and assay == \"{row.assay}\" \"\"\"\n",
+    "            f\"\"\"and suspension_type == \"{row.suspension_type}\" \"\"\"\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    if len(obs) > max_cells:\n",
+    "        soma_joinids = obs[\"soma_joinid\"].sample(max_cells, random_state=1).tolist()\n",
+    "    else:\n",
+    "        soma_joinids = obs[\"soma_joinid\"].tolist()\n",
+    "    \n",
+    "    adata = cellxgene_census.get_anndata(\n",
+    "        census,\n",
+    "        organism=\"Homo sapiens\",\n",
+    "        obs_coords=soma_joinids,\n",
+    "    )\n",
+    "\n",
+    "    if feature_id is None:\n",
+    "        feature_id = adata.var['feature_id'].values\n",
+    "    else:\n",
+    "        assert np.all(feature_id == adata.var['feature_id'].values)\n",
+    "\n",
+    "    if feature_name is None:\n",
+    "        feature_name = adata.var['feature_name'].values\n",
+    "    else:\n",
+    "        assert np.all(feature_name == adata.var['feature_name'].values)\n",
+    "\n",
+    "    tpm_g = np.asarray(adata.X.sum(0)).flatten()\n",
+    "    tpm_g = 1e6 * tpm_g / tpm_g.sum()\n",
+    "\n",
+    "    tpm_ng[n, :] = tpm_g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# generate an anndata object\n",
+    "\n",
+    "final_adata = sc.AnnData(\n",
+    "    X=tpm_ng,\n",
+    "    obs=final_cell_types_df,\n",
+    "    var=pd.DataFrame(\n",
+    "        {\n",
+    "            'feature_id': feature_id,\n",
+    "            'feature_name': feature_name,\n",
+    "        }\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# finally, filter\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "model_var_names = sc.read_h5ad(REF_ADATA_PATH).var.index.values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tpm_threshold = 1.\n",
+    "\n",
+    "_final_adata = final_adata[:, final_adata.var['feature_id'].isin(model_var_names)]\n",
+    "X_ng = _final_adata.X\n",
+    "\n",
+    "# renormalize to TPM\n",
+    "X_ng = 1e6 * X_ng / X_ng.sum(1, keepdims=True)\n",
+    "\n",
+    "# expression mask\n",
+    "mask_g = (X_ng > tpm_threshold).sum(0) > 0\n",
+    "\n",
+    "# filter\n",
+    "_final_adata = _final_adata[:, mask_g].copy()\n",
+    "_final_adata.X = 1e6 * _final_adata.X / _final_adata.X.sum(1, keepdims=True)\n",
+    "_final_adata.var = _final_adata.var.reset_index(drop=True)\n",
+    "\n",
+    "_final_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
new file mode 100644
index 00000000..846237ec
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
@@ -0,0 +1,4295 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Does CellariumGPT recapitulate empirical mean for a given cell type?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase\n",
+    "\n",
+    "# reset matplotlib params\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load validation cell type table\n",
+    "val_adata = sc.read_h5ad(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation_filtered.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 61 × 28680\n",
+       "    obs: 'cell_type', 'assay', 'suspension_type', 'tissue', 'sex', 'disease', 'total_mrna_umis'\n",
+       "    var: 'feature_id', 'feature_name'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5527.708887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10683.399862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5380.963698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5083.603161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6609.696174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3645.962305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3935.779702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3115.828938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3922.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8436.318182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2923.925418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3285.603705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3720.503504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD8-positive, alpha-beta cytotoxic T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5061.393152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5221.671616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5649.398766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6333.568573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4984.057779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6689.483383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5216.285765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4950.552077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>14936.870968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8772.175057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10069.068281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10636.632432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4604.858974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>intestine goblet cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7435.644048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6444.458460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4345.735849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5964.088479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4911.535804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>epithelial cell of proximal tubule</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4491.198843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4906.310584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3130.350171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>hepatic pit cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2761.142458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Kupffer cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6850.818612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11532.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>periportal region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4790.702890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>centrilobular region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9280.724561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>endothelial cell of pericentral hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2734.614608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3987.627119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7274.262295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>intrahepatic cholangiocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6998.971698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>endothelial cell of hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3836.788282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>hepatic stellate cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4090.862637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8422.037378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6109.480329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3094.649239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>ciliated columnar cell of tracheobronchial tree</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5754.694686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2744.064915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5956.795231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>pulmonary interstitial fibroblast</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5294.602205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9484.199912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2321.547941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>15015.278925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>fibroblast of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6561.739316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>cardiac muscle myoblast</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11938.346771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>fibroblast of cardiac tissue</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3099.779698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>cardiac endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2268.612916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>cardiac muscle cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8017.024743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2238.708571</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type      assay  \\\n",
+       "0                                    GABAergic neuron  10x 3' v3   \n",
+       "1                                glutamatergic neuron  10x 3' v3   \n",
+       "2                                           astrocyte  10x 3' v3   \n",
+       "3                                     oligodendrocyte  10x 3' v3   \n",
+       "4                      oligodendrocyte precursor cell  10x 3' v3   \n",
+       "5                                     microglial cell  10x 3' v3   \n",
+       "6                             cerebellar granule cell  10x 3' v3   \n",
+       "7                                    endothelial cell  10x 3' v3   \n",
+       "8                                  classical monocyte  10x 5' v2   \n",
+       "9                              non-classical monocyte  10x 5' v2   \n",
+       "10                                natural killer cell  10x 5' v2   \n",
+       "11                    CD4-positive, alpha-beta T cell  10x 5' v2   \n",
+       "12                    CD8-positive, alpha-beta T cell  10x 5' v2   \n",
+       "13          CD8-positive, alpha-beta cytotoxic T cell  10x 5' v2   \n",
+       "14             CD8-positive, alpha-beta memory T cell  10x 5' v2   \n",
+       "15  naive thymus-derived CD4-positive, alpha-beta ...  10x 5' v2   \n",
+       "16  naive thymus-derived CD8-positive, alpha-beta ...  10x 5' v2   \n",
+       "17                                       naive B cell  10x 5' v2   \n",
+       "18                                      memory B cell  10x 5' v2   \n",
+       "19                                  regulatory T cell  10x 5' v2   \n",
+       "20                                 gamma-delta T cell  10x 5' v2   \n",
+       "21                        conventional dendritic cell  10x 5' v2   \n",
+       "22                                         enterocyte  10x 3' v2   \n",
+       "23                                   endothelial cell  10x 3' v2   \n",
+       "24                               enteroendocrine cell  10x 3' v2   \n",
+       "25                                           pericyte  10x 3' v2   \n",
+       "26                              intestine goblet cell  10x 3' v2   \n",
+       "27              kidney collecting duct principal cell  10x 3' v3   \n",
+       "28  kidney loop of Henle thin descending limb epit...  10x 3' v3   \n",
+       "29  kidney loop of Henle thin ascending limb epith...  10x 3' v3   \n",
+       "30  kidney loop of Henle thick ascending limb epit...  10x 3' v3   \n",
+       "31                 epithelial cell of proximal tubule  10x 3' v3   \n",
+       "32           kidney collecting duct intercalated cell  10x 3' v3   \n",
+       "33                        renal interstitial pericyte  10x 3' v3   \n",
+       "34                                   hepatic pit cell  10x 3' v2   \n",
+       "35                                       Kupffer cell  10x 3' v2   \n",
+       "36                                         hepatocyte  10x 3' v2   \n",
+       "37                       periportal region hepatocyte  10x 3' v2   \n",
+       "38                    centrilobular region hepatocyte  10x 3' v2   \n",
+       "39   endothelial cell of pericentral hepatic sinusoid  10x 3' v2   \n",
+       "40                         endothelial cell of artery  10x 3' v2   \n",
+       "41                                         fibroblast  10x 3' v2   \n",
+       "42                         intrahepatic cholangiocyte  10x 3' v2   \n",
+       "43               endothelial cell of hepatic sinusoid  10x 3' v2   \n",
+       "44                              hepatic stellate cell  10x 3' v2   \n",
+       "45                                alveolar macrophage  10x 5' v1   \n",
+       "46                                 type II pneumocyte  10x 5' v1   \n",
+       "47                         capillary endothelial cell  10x 5' v1   \n",
+       "48    ciliated columnar cell of tracheobronchial tree  10x 5' v1   \n",
+       "49                                  type I pneumocyte  10x 5' v1   \n",
+       "50         epithelial cell of lower respiratory tract  10x 5' v1   \n",
+       "51                  pulmonary interstitial fibroblast  10x 5' v1   \n",
+       "52                                          club cell  10x 5' v1   \n",
+       "53                  pulmonary artery endothelial cell  10x 5' v1   \n",
+       "54                            epithelial cell of lung  10x 5' v1   \n",
+       "55                                 fibroblast of lung  10x 5' v1   \n",
+       "56                            cardiac muscle myoblast  10x 3' v3   \n",
+       "57                       fibroblast of cardiac tissue  10x 3' v3   \n",
+       "58                           cardiac endothelial cell  10x 3' v3   \n",
+       "59                                cardiac muscle cell  10x 3' v3   \n",
+       "60                                     cardiac neuron  10x 3' v3   \n",
+       "\n",
+       "   suspension_type           tissue   sex disease  total_mrna_umis  \n",
+       "0          nucleus            brain  male  normal      5527.708887  \n",
+       "1          nucleus            brain  male  normal     10683.399862  \n",
+       "2          nucleus            brain  male  normal      5380.963698  \n",
+       "3          nucleus            brain  male  normal      5083.603161  \n",
+       "4          nucleus            brain  male  normal      6609.696174  \n",
+       "5          nucleus            brain  male  normal      3645.962305  \n",
+       "6          nucleus            brain  male  normal      3935.779702  \n",
+       "7          nucleus            brain  male  normal      3115.828938  \n",
+       "8             cell            blood  male  normal      3922.025000  \n",
+       "9             cell            blood  male  normal      8436.318182  \n",
+       "10            cell            blood  male  normal      2923.925418  \n",
+       "11            cell            blood  male  normal      3285.603705  \n",
+       "12            cell            blood  male  normal      3720.503504  \n",
+       "13            cell            blood  male  normal      5061.393152  \n",
+       "14            cell            blood  male  normal      5221.671616  \n",
+       "15            cell            blood  male  normal      5649.398766  \n",
+       "16            cell            blood  male  normal      6333.568573  \n",
+       "17            cell            blood  male  normal      4984.057779  \n",
+       "18            cell            blood  male  normal      6689.483383  \n",
+       "19            cell            blood  male  normal      5216.285765  \n",
+       "20            cell            blood  male  normal      4950.552077  \n",
+       "21            cell            blood  male  normal     14936.870968  \n",
+       "22            cell  small intestine  male  normal      8772.175057  \n",
+       "23            cell  small intestine  male  normal     10069.068281  \n",
+       "24            cell  small intestine  male  normal     10636.632432  \n",
+       "25            cell  small intestine  male  normal      4604.858974  \n",
+       "26            cell  small intestine  male  normal      7435.644048  \n",
+       "27         nucleus           kidney  male  normal      6444.458460  \n",
+       "28         nucleus           kidney  male  normal      4345.735849  \n",
+       "29         nucleus           kidney  male  normal      5964.088479  \n",
+       "30         nucleus           kidney  male  normal      4911.535804  \n",
+       "31         nucleus           kidney  male  normal      4491.198843  \n",
+       "32         nucleus           kidney  male  normal      4906.310584  \n",
+       "33         nucleus           kidney  male  normal      3130.350171  \n",
+       "34            cell            liver  male  normal      2761.142458  \n",
+       "35            cell            liver  male  normal      6850.818612  \n",
+       "36            cell            liver  male  normal     11532.500000  \n",
+       "37            cell            liver  male  normal      4790.702890  \n",
+       "38            cell            liver  male  normal      9280.724561  \n",
+       "39            cell            liver  male  normal      2734.614608  \n",
+       "40            cell            liver  male  normal      3987.627119  \n",
+       "41            cell            liver  male  normal      7274.262295  \n",
+       "42            cell            liver  male  normal      6998.971698  \n",
+       "43            cell            liver  male  normal      3836.788282  \n",
+       "44            cell            liver  male  normal      4090.862637  \n",
+       "45            cell             lung  male  normal      8422.037378  \n",
+       "46            cell             lung  male  normal      6109.480329  \n",
+       "47            cell             lung  male  normal      3094.649239  \n",
+       "48            cell             lung  male  normal      5754.694686  \n",
+       "49            cell             lung  male  normal      2744.064915  \n",
+       "50            cell             lung  male  normal      5956.795231  \n",
+       "51            cell             lung  male  normal      5294.602205  \n",
+       "52            cell             lung  male  normal      9484.199912  \n",
+       "53            cell             lung  male  normal      2321.547941  \n",
+       "54            cell             lung  male  normal     15015.278925  \n",
+       "55            cell             lung  male  normal      6561.739316  \n",
+       "56         nucleus            heart  male  normal     11938.346771  \n",
+       "57         nucleus            heart  male  normal      3099.779698  \n",
+       "58         nucleus            heart  male  normal      2268.612916  \n",
+       "59         nucleus            heart  male  normal      8017.024743  \n",
+       "60         nucleus            heart  male  normal      2238.708571  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# shoow the full dataframe\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(val_adata.obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_cell_type_indices = [\n",
+    "    2,  # astro\n",
+    "    3,  # oligo\n",
+    "    5,  # microglia\n",
+    "    12, # CD8\n",
+    "    10, # NK\n",
+    "    22, # entero\n",
+    "    59  # cardiomyocyte\n",
+    "]\n",
+    "\n",
+    "val_cell_type_names = [\n",
+    "    'astrocyte',\n",
+    "    'oligodendrocyte',\n",
+    "    'microglia',\n",
+    "    'CD8+ T cell',\n",
+    "    'NK',\n",
+    "    'enterocyte',\n",
+    "    'cardiomyocyte'\n",
+    "]\n",
+    "\n",
+    "query_gene_ids = val_adata.var['feature_id'].values\n",
+    "query_gene_symbols = val_adata.var['feature_name'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6820b9c673864c38984ac02c18886c3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "733b56d9776e4136a95f8cb8603055f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2ce657efef9498f83a313a04f9bb694",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28928b6240604612a683e5d87b03ff9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ca2ec837abc45df91861799744d735b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2248f33d9c844138c1307c85b62d4d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a07df6f9d1a4445bfabebae0272152f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_X_ng = np.zeros((len(val_cell_type_indices), len(query_gene_ids)))\n",
+    "\n",
+    "for n, val_cell_idx in enumerate(val_cell_type_indices):\n",
+    "\n",
+    "    # arguments\n",
+    "    row = val_adata.obs.iloc[val_cell_idx]\n",
+    "\n",
+    "    assay = row.assay\n",
+    "    suspension_type = row.suspension_type\n",
+    "    prompt_metadata_dict = {\n",
+    "        \"cell_type\": row.cell_type,\n",
+    "        \"tissue\": row.tissue,\n",
+    "        \"disease\": row.disease,\n",
+    "        \"sex\": row.sex,\n",
+    "    }\n",
+    "    total_mrna_umis = float(row.total_mrna_umis)\n",
+    "\n",
+    "    query_chunk_size = 1_000\n",
+    "    max_counts = 500\n",
+    "    upper_percentile = 0.5  # doesn't matter, we only need the logits\n",
+    "    upper_pad = 1  # doesn't matter, we only need the logits\n",
+    "\n",
+    "    gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
+    "        assay=assay,\n",
+    "        suspension_type=suspension_type,\n",
+    "        prompt_metadata_dict=prompt_metadata_dict,\n",
+    "        total_mrna_umis=total_mrna_umis,\n",
+    "        query_gene_ids=query_gene_ids,\n",
+    "        query_chunk_size=query_chunk_size,\n",
+    "        total_prob_mass=upper_percentile,\n",
+    "        symmetric_range_pad=upper_pad,\n",
+    "        max_counts=max_counts,\n",
+    "    )\n",
+    "\n",
+    "    model_X_ng[n, :] = gex_range_dict['gene_marginal_mean_q'].cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "empirical_X_ng = val_adata.X[val_cell_type_indices, :].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renormalize\n",
+    "model_X_ng = 10_000 * model_X_ng / model_X_ng.sum(axis=1, keepdims=True)\n",
+    "empirical_X_ng = 10_000 * empirical_X_ng / empirical_X_ng.sum(axis=1, keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hhRcmu5kAANgWWQsAQGKRtQAAJBZZCwBA4pG3AAAkFlkLAEBikbUAAIkVfuKuoaFB3/3ud1VRUaFf//rXuuuuu5LdJAAA0gpZCwBAYpG1AAAkFlkLAEDikbcAACQWWQsAQGKRtQAAD1b4ibMFCxaooqJCw4YN02233Zbs5gAAkHbIWgAAEousBQAgschaAAASj7wFACCxyFoAABKLrAUAeFDwE2dPPvmkJOncc89VdnZ2klsDAED6IWsBAEgsshYAgMQiawEASDzyFgCAxCJrAQBILLIWAOBBwU8ctbS0aP369ZKk8ePHa9OmTbr44os1dOhQ5eTkaPjw4Zo7d64+//zzJLcUAAB7ImsBAEgsshYAgMQiawEASDzyFgCAxCJrAQBILLIWAOCLgp842rp1q9ra2iRJX3zxhcaPH6+nnnpKu3btUm5urrZt26aFCxdq/PjxWrRoUZJbCwCA/ZC1AAAkFlkLAEBikbUAACQeeQsAQGKRtQAAJBZZCwDwZRljTLIbkS5WrVqlo446SpLkcDg0ePBgPfbYYzrllFPkcDj08ccfa968eVq7dq3y8vL03//+V2PGjIl4zJKSkrDP7dixQ5mZmRo8eHBcXwcA9BaVlZXKyspSY2NjspuCGJG1AGAvZK39kLUAYC9krf0kImsl8hYAEom8tR/6tgBgL2St/ZC1AGAvZK39kLUAYC+JzlpW+Ikjl8vl9/3ChQt16qmnyuFw/5i/973v6eWXX1ZBQYGam5t17733dut8xhg5nc5uHQMAeqP6+nrt3LlTTqfTOxsC7IGsBQB7aGpq0tdff03W2hBZCwD20Nraqh07dqi9vZ2stZmezlqJvAWArnA6ndq5c6e+/fZb+rY2RN8WAFKfMUbV1dWqqakha22IrAUAe2B8lH2RtQBgDz01PiozYUfuhfr27ev9/rvf/a5OOumkoG2+853v6MILL9QjjzyiJUuWRD3m9u3bwz7nqbiNtA0AoIPL5dJPfvITPfLII5Kkfv36+b13I/WRtQCQ+v7617/qpz/9qSQpLy9PRUVFSW4ROoOsBYDU9/bbb+v000+XMYYZ92woEVkrkbcAEE+lpaU6/vjj1d7eLpfLpcGDBysjIyPZzUIn0LcFgNS2e/dunXbaadqxY4ckqbi4WLm5uUluFTqDrAWA1Mb4KPsjawEg9fXk+ChW+ImjffbZx/v9uHHjwm7nea6ioiLhbQIAuLW3t2vu3LnezuzPf/5zOrM2RNYCQGq75557vJ3ZE088UYWFhUluETqLrAWA1Pbqq6/qtNNOU1NTk/bff38NHDgw2U1CJ5G1AJDaNm7cqClTpmjr1q3q16+f3njjDYp9bIi8BYDUVVdXpxNPPFErV66Uw+HQ/PnzKfaxIbIWAFIX46PSA1kLAKmtp8dHUfATR8XFxRo6dGjM21uWlcDWAAA8Wltb9YMf/EBPPPGEJOm6667r9lKmSA6yFgBSkzFGt912m6655hpJ0umnn66XX36Z92EbImsBIHW98MILOuOMM9TS0qKDDjpIK1asYACyDZG1AJC6PvnkE02dOlU7duxQUVGR3n77bR199NHJbha6gLwFgNRUVVWl448/Xh988IEyMjL01FNPae7cucluFrqArAWA1MT4qPRB1gJAakrW+CgKfuLsxBNPlOSeASwcz3MjRozoiSYBQK/W0tKic889V88//7wk6Xe/+53uuOMOOjo2RtYCQGoxxuj666/XTTfdJEk677zz9PzzzzMro42RtQCQep555hmdd955amtr02GHHaZly5Z16mYfUgtZCwCpZ926dZo2bZoqKys1ePBgLVu2TBMmTEh2s9AN5C0ApJavv/5a06ZN00cffaTs7Gw9//zzuuCCC5LdLHQDWQsAqYXxUemHrAWA1JLM8VEU/MTZnDlzJEmff/65Xn/99aDnv/76az399NOSpNNOO61H2wYAvdG9996rf/3rX5Lcy+hdf/31SW4RuousBYDU8uabb+rOO++UJM2ePVtPP/20srOzk9wqdAdZCwCp5auvvtLcuXPldDo1efJkvf322xo4cGCym4VuIGsBILW0t7frggsuUG1trYYNG6YVK1bokEMOSXaz0E3kLQCklquuukqff/65cnNz9dJLL+nMM89MdpPQTWQtAKQWxkelH7IWAFJLMsdHUfATZzNmzNDMmTMlSXPnztWrr74ql8slSfr44491xhlnqLGxUUVFRfrFL36RzKYCQK/wv//7vzr99NP14IMP6pe//GWym4M4IGsBILWceOKJuu6663TZZZdpwYIFyszMTHaT0E1kLQCklmHDhunvf/+7pk+frjfffFOFhYXJbhK6iawFgNSSmZmp5557TocffrhWrFihsWPHJrtJiAPyFgBSy4MPPqiJEyfqlVde0SmnnJLs5iAOyFoASC2Mj0o/ZC0ApJZkjo+yjDGmx87WS9TV1WnGjBn68MMPJUl5eXnKysrSt99+K0kqLCzUiy++qOOOO65b5ykpKZEkbd++vXsNBoA043K55HB01LQaY0IuUcv7qH2RtQCQHE6X0aMrS7W6tFpHjBqoeVNGKcNhydOtDMxb3kfti6wFgOSLpW/L+6h99VTWSvydAEA4XEdOf/RtASC5yNr0R9YCQHKRtemPrAWA5AqVtVLPj49ihZ8EKCws1KpVq3TPPfdo4sSJyszMVGtrqw444ABdffXV+uSTT+JyoxYAEKy6ulpHH320/vOf/3gfC9WZhb2RtQCQHI+uLNUd//5Uz9x1jX5z02/16MpSSe6sJW/TC1kLAMn1u9/9Tj/4wQ/kdDq9j5G16YWsBYDkeumll3T00Uerrq7O+xhZm37IWwBInk8++USHHXaYNmzY4H2MrE0/ZC0AJA/jo3oHshYAkqetrU0XXHCB7rzzTu9jyRofxQo/NkZVLQD4++abb3TCCSfos88+U9++fVVeXq6ioqKw2/M+imj4GwEAf3MfeUfP3f2/at68WpJ0+o2P6+XfXhJ2e95HEQ1/IwDgzxijG264QXfccYck6cEHH9QVV1wRdnveRxEL/k4AwN+zzz6riy++WO3t7br00kv16KOPRtye91FEw98IAPhbv369TjzxRNXW1uqwww7T+vXrIw6I4n0U0fA3AgD+GB+FeONvBAD8tbS0aNasWfrXv/4lSVq1apUmT54cdvtEv49mJuSoAAD0sIqKCs2YMUNffvmlcnNz9eyzz0bszAIAgM5pamrSmkeu8xb7DJj2I502g9mC/n/27jw+puv/H/jrTmSRTShRBBG7Vmsp1SJBQsS+U1QsUdVq1dKW0lKtrfqxfWmLFLHVUnsrEQkRFFVLa1cSS2wRkX2fub8/8ss0IctMMjN37p3X8/HwaCa5c+eduuY15973uYeIiMhQRFHE5MmTsXTpUgDA8OHDMXbsWGmLIiIiUpigoCCMHj0aGo0GrVu3xqJFi6QuiYiISFFOnjwJPz8/JCYmonr16vjll1+42gAREZEB5e+PsrK2hf/MpajgUlHqsoiIiBQjLS0Nffv2RWhoKABg0aJFxU72MQWVpK9ORERkANHR0fD09MS///4Le3t7/P777/Dz85O6LCIiIsVITk5Gt27dcOXMcQDAW8M/xYLZXyCgvYfElRERESmDRqPB+PHjtZN9xo4di6CgIJQrx/s1ERERGcpPP/2EkSNHQqPRoH379jh06BAqVmRTFBERkaFERESgc+fOSExMRO3atREZGYlGjRpJXRYREZFi5O+PEqxt8VL/r7A/vioCj0VJXRoREZEi5PVH5U32WbFiBaZOnSpxVVzhh/4/URShVqshiqLUpRCVikqlgkql4t2BLNCNGzfQqVMn3L9/H05OTggODkbbtm2lLovoBaIoQqPRQKPRSF0KUakway1XQkIC/Pz8cOrUKQiCgMDAQIwePVrqsogKxbEtyZkgCLCysmLWWiC1Wo0xY8YgKCgIAPDxxx9j6dKlPBbILDFrSc6YtZZt6dKlmDRpEgDAx8cHe/bsgYODg8RVEb2IWUtyxqy1bAcPHkSfPn2QkZGBevXqITw8HLVq1ZK6LKIXMGtJ7njN1nLl74+ytnNApf6zYOfWBABw5nY8xnnVlbhColzsjyK5Y9ZaLnPuj+KEHwumVquRnJyMpKQkpKamSl0OUZmpVCpUqFABFStWhK2trdTlkAmkpKSgY8eOePDgASpWrIiDBw+iVatWUpdFVEBmZiaePXuGxMREDmZJ9pi1lkcURQwYMACnTp2ClZUVNm7ciHfeeUfqsogK4NiWlMbBwQHOzs5wcnKClZWV1OWQCXzxxRfayT6ff/455s+fz4sIZFaYtaQ0zFrLs23bNu1kn+7du+PXX3+FnZ2dxFUR/YdZS0rDrLU8ly9fRq9evZCVlYXGjRsjPDwc1apVk7osIi1mLSkNr9lanuf7o8bN/xm/RNtof97KvZKE1RHlYn8UKQmz1vKYe38UJ/xYqJycHNy9exeZmZlSl0JkMBqNBs+ePUNCQgJq1aoFe3t7qUsiI3N0dMTcuXMxbdo0HDx4EK+//rrUJREVkJbDdmlDAAEAAElEQVSWhrt37/IOUaQYzFrLIwgCvv32W5w/fx5r1qxBv379pC6JqACObUmJUlNTkZqaivj4eNSqVQvlyvH0ndJ9/PHH+PXXX+Hv748vv/ySk33IrDBrSYmYtZanV69e6NSpEypWrIgtW7bAxsam5CcRmQizlpSIWWt5mjRpgvfffx9Hjx5FaGgoXF1dpS6JSItZS0rEa7aW5/n+qFebvgb3Y1E4czserdwrIaC9h9QlkoVjfxQpDbPW8ph7f5Qg8h1Wttzc3AAAMTExej1PrVbjzp07yMzMhCAI2rvr2NrasqGAZEuj0WhPHGdnZ8PKygp16tSBtbW11KWRCSQlJcHZ2Vnv55X2fZQsR1mOkezsbERHR0OtVsPa2hqVKlWCg4MDVCqVocskMglmrWVj1pKxlOUY4diWlEYURWRmZmrvNiqKImxtbVG7dm3eEdkCMGvJmHgemSgXs9aypaWlwcbGplRN58xbKgmzligXs9ayiaKIlJQUODk56f1cZi2VhFlL9B9es7VsPI9MxsL+KKL/MGstm7lmLW+lYoGSk5O1g1k3Nzc4OjpKXRKRQdja2sLJyUn7ATIhIQFVqlSRuiwysKNHj2LHjh1Yvny5dmBQmoAlMraEhASo1WpYWVmhdu3a/NBPisCstQzR0dH44osvsHr1au3FWWYtmSOObUmJbGxs4OTkBGdnZ8TExGgbpVxcXKQujQwoOTkZ77//Pr755ht4eOTeeZFZS+aIWUtKxKy1DBqNBpMnT0bPnj3h7e0NALwLJ5klZi0pEbPWcqxatQpZWVn46KOPAOTeDbk0k32IjIlZS0rFa7aWgf1RJBfsjyIlYtZaBjn1R3HCjwVKSkoCkHtQcjBLSmNtbQ0XFxc8ffoUqampDFmFOXjwIPr06YOMjAy4urriq6++krokoiKlpqYCAFxcXDiYJUVh1irbjRs34O3tjZiYGKSlpWHv3r1Sl0RUJI5tSckcHR3h7OyMxMREJCUlsTFKQRISEtCtWzecPHkSp06dwuXLl2FnZyd1WUSFYtaSkjFrlUutVmPMmDEICgrCmjVr8Pfff6NevXpSl0VUKGYtKRmzVtmWLl2KSZMmAQDc3d3Rs2dPiSsiKhyzlpSM12yVjf1RJCfsjyKlYtYqm9z6o7hmmoURRVEbsLy7CimVg4MDACA9PR0ajUbiashQ9u3bh169eiEjIwONGzdGQECA1CURFUmj0SA9PR3Af+9JRErCrFWmS5cuwdPTEzExMahYsSJmzpwpdUlEReLYlixB3rGdmpoKURQlroYM4enTp/D29sbJkydhZWWFb7/9lpN9yGwxa8kSMGuVJzs7G8OGDUNQUBAAYMKECahbt67EVREVjllLloBZq0zz5s3TTvbp3r07OnfuLHFFRIVj1upHFEU8Sc7A7bhUPEnO4Pu2TPCarTKxP4rkhP1RpHTMWmWSY38UJ/xYGLVarf3a1tZWwkqIjCf/THGGrDJs374d/fv3R1ZWFl5//XVERESgevXqUpdFVKT87z28ewUpEbNWec6fP48OHTrg8ePHqFKlCo4cOYJWrVpJXRZRkTi2JUuQ/9jOf8yTPD1+/BgdOnTAuXPnYG1tje3bt+Odd96RuiyiIjFryRIwa5UlMzMTAwcOxLZt2wAAs2fPxoIFCyAIgsSVERWOWUuWgFmrLKIoYubMmZgxYwYAoH///ti1axdvZEFmi1mrn7iUTDxMzEBSRjYeJmYgLiVT6pJIB7xmqzzsjyK5YX8UKR2zVnnk2h/FCT8WJv8dGHiRg5Qq/7HNu47I34YNG/DOO+8gJycHrVq1wuHDh+Hq6ip1WUTFYt6S0jFrleXUqVPo2LEjnj59imrVquHo0aN4/fXXpS6LqFjMWrIEzFvliImJgaenJy5dugRbW1vs2bMH/fr1k7osomIxa8kSMGuVIy0tDb1798bevXsBAAsXLsSsWbP4/kVmjVlLloBZqxyiKGLq1KmYO3cuAGDo0KHYunUrbGxsJK6MqGjMWv2kZqqLfUzmiVmrLOyPIjli3pLSMWuVRc79UeWkLoCIiKgomzdvhr+/PwCgXbt2+P333+Hs7CxxVURERMpx9uxZdO7cGSkpKahVqxbCw8NRr149qcsiIiJSjCdPnsDT0xPR0dGwt7fHvn374O3tLXVZREREiqFWq9GjRw8cOXIEAPB///d/mDBhgsRVERERKcunn36KxYsXAwDGjBmDVatWwcrKSuKqiMiQHGytkJSRXeAxEZkO+6OIiIiMS+79UVzhh4iIzNbbb7+NGjVqoFOnTggJCeFgloiIyMAaNmyIpk2bom7duoiMjJTVYJaIiEgOXnrpJXTq1AlOTk44ePAgJ/sQEREZmJWVFfr37w+VSoU1a9Zwsg8REZERdO/eHXZ2dpgwYQJWr17NyT5EClTZ0RbVKtjB2c4a1SrYobKjrdQlEVkU9kcREREZl9z7o7jCDxERma06derg+PHjqFq1KsqXLy91OURERIrj6OiI4OBgpKamonr16lKXQ0REpDgqlQqrVq3CZ599hgYNGkhdDhERkSJ9+OGH6NSpExo3bix1KURERIrUsWNHnD9/Hg0bNoQgCFKXQ0RGIAgCqjjZoYqT1JUQWSb2RxERERmX3PujuMIPERGZDVEUsWTJEsTExGi/5+7uzsEsERGRAe3cuRN//PGH9nGFChVkOZglIiIyV+fPn8emTZu0j62srDjZh4iIyIAeP36M7777DqIoar/HyT5ERESGk5mZiTlz5iA9PV37vUaNGnGyDxERkYGwP4qIiMj4lNQfxQk/RERkFkRRxGeffYbJkyfD29sbT58+lbokIiIixdm0aRMGDRoEPz8//P3331KXQ0REpDinT59Gp06dMGLECOzYsUPqcoiIiBTn/v378PLywueff46ZM2dKXQ4REZHipKeno2/fvpg1axb69esHjUYjdUlERESKwv4oIiIi41NafxQn/JDihYSEYOTIkWjSpAkqVKgAFxcXtGjRAkuWLEFGRobU5RERAI1GgwkTJuD7778HALRv3x4uLi7SFkVEOmPWEsnDmjVrMGLECGg0GjRt2hTu7u5Sl0REOmLWEslDZGQkfHx8kJCQgJo1a6J58+ZSl0REOmLWEsnD7du34enpievXr8Pe3h6dOnWSuiQi0hGzlkgeUlJS0L17dwQHBwMAunfvDpWKbUVEcsG8JTJ/7I8ikjdmLZE8KLE/iiNzMksREREQBKHQPzY2NnB1dUW7du0wY8YMREdHF7uvn3/+GVevXsXo0aOxZcsWLFmyBFZWVpg8eTJ8fHyQk5Njot/KuEJDQzFo0CDUqlULdnZ2ePnll9GpUycEBgZCrVYb9bX37t1b4O9o9uzZOj0vLS0Nixcvxttvv40qVaqgfPny8PDwwKhRo3D69Gmj1kzmQ61WIyAgAD/88AMAYMKECVi9ejWsrKwkroxI2Zi1+jN21j579gxbtmzB5MmT0aFDB9SvXx8VK1aEtbU1XnrpJbz11lv44osvEBUVVex+Ro4cWeTfbVF/OnToUOb6ybwtW7YM7733HkRRRKdOnRASEoIKFSpIXRaRojFr9WeqcW1aWhr+7//+D97e3nB1dYWNjY32tVatWoXs7Oxin8+spcKEhYWha9euSElJgYeHByIjI1GvXj2pyyJSNGat/kyRtbGxsThw4ADmzJmDnj17olq1aqXKw4SEBISHh2PBggUYMGAA3N3dtftRwsU50t+///4LT09PREVFwcnJCSEhIfD29pa6LCJFY9bqT05ZW5SzZ8+iXLly2n2OHDmyzPskeUhMTISvry+OHDkCQRCwZs0aTJgwQeqyiBSPeas/U/ZH3bt3D59//jleffVVODs7w9nZGa+++io+//xz3Lt3r0z7dXZ25vljC8P+KCJpMGv1J5f+KCD3nOHy5csxYMAANGjQAA4ODrC1tUW1atXg5+eHn376CampqWWumeRDsf1RIslWjRo1xBo1auj1nKysLPHKlSvilStXxKysLCNVVnZHjhwRAej0x9bWVly5cmWR+7p///4L30tNTRVr164tAhB37NhhzF/F6HJycsRRo0YV+/+odevW4qNHj4zy+vHx8WK1atUKvN6sWbNKfN61a9fE+vXrF1mzSqUSv/zyy1LVJJfjnHL/roYMGaL9e//0009FjUZjstcvzfsoWZbSHiNyeB9i1urOVFm7e/dunf8+/ve//xW5H39/f53/bvP+jBs3Tq9a5XCM03/mz5+v/bv28/MT09LSTPbazFoqCbOWWSuKph3Xnj17VnR3dy/2tZo1aybevXu3yH2YImtFUR7HOeXav3+/aGtrKwIQGzVqJMbExJjstZm1pAulnkdm1urOlFlb3Gt4eXnpvJ/i8rp27dplrlMU5XGcU67Lly+LL7/8sghAdHFxEU+fPm3S12feUkmYtcxaOWZtYbKyssSmTZsW2Ke/v3+Z9mfuxznliouLE1u2bCkCEK2srMRNmzaZ9PWZtVQSpWatKDJv9WHq/qjdu3eLzs7ORb5WhQoVxD179pRq3127djVIhsvlOCf2R5H54zVbZq0oKrc/yt3dXTx16lSpapXDMU7/UXJ/FFf4IbPXu3dvXLx4Ufvn5MmT2LRpEzp27AgAyMzMxIcffoh9+/YV+vzq1au/8D17e3u0bNkSAHDz5k3jFW8Cn3zyCdatWwcAaNy4MdavX48///wTe/fuRdeuXQEAf/75J3r06IHMzEyDv/6kSZPw8OFDVK1aVefnPH36FF27dsW///4LABg2bBhCQkJw+vRprFy5Em5ubtBoNPjmm2+wePFig9dM5iErKwuDBw/G1q1bAQBfffUVFi5cCEEQJK6MyPIwa4tnyqytXbs2RowYgaVLl+LXX3/F8ePHcerUKezevRsffPABnJyckJmZiSlTpiAwMLDQfcydO7fA32dRf1555RXtc0aNGlWmusl8zZo1C9OnTwcA9O3bF7t370b58uUlrorI8jBri2eqrI2KikKXLl1w+/ZtAEDPnj2xe/dunDt3DocOHcKHH36IcuXK4cKFC/D19UVSUlKh+2HWUn47d+5E3759kZmZiddeew1Hjx5FjRo1pC6LyOIwa4sn1TnkatWqoWfPnqV6riiK2q9feukldOnSBY6OjoYqjWTk77//hpeXFx49eoTKlSvjyJEjaN26dan3p9aIWHX0FgKCzmDV0VtQa8SSn0REzNoSyDFrC5M33tXnui/JX2xsLDp27Khd3Wnbtm0YNmxYqffHrCUqPeZt8UyZt3/88QeGDBmCpKQk2NnZYebMmYiMjERkZCRmzpwJW1tbJCYmYvDgwTh58qRe+16/fj1CQkKYtxaE/VFE5oNZWzy59UfFxMQAAJydnTFixAisX78ex44dw9mzZ/HLL7/Ay8sLAHD79m106dIFV65cKVPNZN4U3x9ltKlEZHSWcgeL4u4aNG3aNO12zZo103n/cXFxYsWKFUUAYkREhAEqlsaff/4pCoIgAhBfffVVMSkpqcDPNRpNgVmsxc12LY3g4GARgGhvby+uXbtW+zolrfDz4YcfFrvt/fv3tXfrK1++vN53xpXLcW7pcnJytHevmD9/viQ18A4WVBJLuYMFs7Zopsza7OzsEre5ceOG6OLiIgIQq1atKubk5JTqtW7cuKGtuUmTJno/Xw7HOOVasGCBCEB85513JPm7YtZSSZi1zFpTZm2fPn20+5k2bVqh2+zdu1dbz2effVbq1ypr1oqiPI5zEsXff/9dtLa2Ft944w3x6dOnJn99Zi3pQqnnkZm1ujH1OeQvv/xS3Lt3b4G7XebtW587Fi9atEjcvn27GBUVpf1e3p0yucKPZbl69aro6uoqvvzyy+Lly5fLvL+fIm6KtT//Tfvnp4ibJT6HeUslYdYya+WYtc/7559/RGtra1GlUolbtmzR6e++JHI4zkkUnz17JjZv3ly0tbUVf/vttzLvj1lLxqDUrBVF5q2uTJm3arVafO2110QAokqlEsPCwl7Y5tChQ6JKpRIBiK+//rqoVqt12veDBw+0fx9bt27lCj8Wgv1RJAe8ZsuslWN/lL+/v7hy5UoxNTW10H1oNBrxo48+0tbs6+urd61yOMYpl9L7o7jCD8naV199BXt7ewDAhQsXEBsbW+JzRFFEQEAAnj17hm7dumlnccrRwoULtXc6XLFiBZycnAr8XBAELF++XPv97777DhqNxiCvnZycjHHjxgEA5syZgzp16uj0vPj4eKxevRoAULduXXz55ZcvbFO9enXMnz8fAJCeno6lS5capGYyL1ZWVtiwYQP27duHadOmSV0OERWBWWu6rC1XrlyJ29SvXx+DBw8GADx+/BjXrl0r1WutX79e+zVXHFC2zz//HHv27MHGjRthbW0tdTlEVAhmrWmy9unTp9q7cdWsWRPffPNNodv16tUL/fv3BwAsX768yFV+SsKstRzdunVDcHAwwsLCUKlSJanLIaJCMGtNew55zpw56NWrV6F3u9TH1KlTMXDgQJ3PO5NyNWrUCGFhYYiMjESTJk3KvL8zt+OLfUxE+mPWyjNr81Or1Rg9ejSys7PxwQcf4K233jLYvsn8ubi4IDQ0FKGhoejevXuZ98esJTIO5q3p8vbAgQP4559/AAAjRoyAt7f3C9v4+Pjg3XffBZC7KmlwcLBO+/7ggw/w7Nkz9OjRQ3vNl5SP/VFE8sCslV9/1Pr16/HBBx9o/96eJwgCvv/+e7z88ssAgEOHDiE+nuMTpVJ6fxQn/JCslS9fvsAFnrt375b4nJkzZ2LPnj2oWbOmdvk5OUpLS9MOGOvVq1fkhwVnZ2cMHDgQQG7wHTt2zCCv/9lnn+Hu3bto0aIFPvnkE52ft2/fPmRnZwPIbXqysrIqdLshQ4bAwcEBALBz584y10vmITExEQcPHtQ+tra2Rs+ePSWsiIhKwqyVLmuLkn9QnZGRoffzNRoNNm7cCCB3EJ13MpqUQa1WY+fOndoTMUDustRFfeYiIukxa02TtWfOnNGedPb19S32RHK3bt0A5Obs/v379X4tZq3y7d27F5mZmdrH3t7eqFChgoQVEVFxmLXmN64lKklkZCQeP36sfdy0aVPUr1/fIPtu5V6p2MdEpD9mrfyz9vvvv8dff/0FNzc3zJs3T+pyyARu3ryJ8+fPax9XrlwZnp6eBtk3s5bIOJi3psvbX3/9Vft1QEBAkduNGTOm0OcUZdu2bdizZw8cHR3xww8/lKo2kg/2RxHJD7PW/Ma2Ze2PAgAbGxu0bdsWQO413KioKIPURtKztP4oTvgh2cv/j7Okf6hLly7FvHnz8NJLLyEkJASurq7GLs9o/vrrL6SlpQEAOnToUOy2HTt21H599OjRMr92REQEVq1ahXLlyiEwMFCvN8jIyEjt18XVbWdnhzZt2gAAoqOjdfoAReYtPj4ePj4+6N69O/bs2SN1OUSkB2at6bO2KGlpadr3UJVKhQYNGui9j7CwMNy7dw8A4Ofnh6pVqxqyRJJQdnY23n33XQwYMKDIlSuIyDwxa42ftfnv1lRS9uXd5QnIHf/qi1mrbN999x369OmDd955Bzk5OVKXQ0Q6Ytaaz7iWqCS///47unTpgs6dO+Pp06cG339Aew9M92sEn8aumO7XCAHtPQz+GkSWiFkr36y9fv06Zs+eDQD44YcfXriLMynPlStX4Onpic6dO+PSpUsG3z+zlsh4mLemydu8vqby5cujdevWRW7Xpk0blC9fXqfXiouLw0cffQQAmDdvHmrWrFmq2kge2B9FJF/MWvMZ2xqiPypPVlaW9mulTgaxNJbYH8UJPyRrarUaN27c0D6uU6dOkduuWLECkyZNQpUqVXD48OECs3Hl6PLly9qvS/pd8v/8ypUrZXrdtLQ0BAQEQBRFTJ48Gc2bN9fr+VLVTdKKjY1Fx44d8ddff0EQBO0qT0Rk/pi1uaTMrMzMTNy+fRsbN25EmzZtcPPmTQDAe++9V6qLr+vXr9d+PWrUKEOVSRLLysrC4MGD8csvvwAAG5CJZIRZm8vYWevo6Kj9OiEhodht8/88f426YtYqkyiKmD17Nj7//HPtY7VaLXFVRKQLZm0unoslOdi5cyf69u2rXUnPGGNbK5WAcV51EejfCuO86sJKJRj8NYgsDbM2lxyzVqPRYMyYMcjIyMCgQYN413kLcOHCBXh5eeHhw4cAYJRrtsxaIuNg3uYydt6mpqbi9u3bAID69evD2tq6yG2tra1Rr149AMCdO3eQnp5e5LYfffQRnjx5gjZt2uDDDz/Uuy6SD/ZHEckXszaXkvqjgNwxz8mTJwEUzG6SL0vtj+KEH5K1n3/+Gc+ePQMAdOnSBS4uLoVut2TJEnz00UeoXbs2jh8/jtdee63Mrz179mwIglDmP/kbgfSRf8Wbku78kP/nZV0pZ+bMmbh16xbq1q2rvduTPvJe38HBARUrVix2W0PWTdJ58OABvLy88M8//8DGxga7du3SLu1IROaPWZvLlFkL5C77nle/nZ0d6tSpgxEjRuDixYsAgB49euC7777Te7+JiYnYvXs3AKBy5cro0aNHmWsl6aWnp6Nv377av9t58+Zhzpw5EldFRLpi1uYydtbmP/lc0t2m8v/8zp07er0Os1aZRFHEtGnT8PXXXwMAhgwZgu3bt8PW1lbiyohIF8zaXKYe1xLpa/PmzRg8eDCys7PRsmVLHDlyhCslEskEszaXHLN2xYoVOHHiBCpWrIjly5dLXQ4Z2Z9//omOHTsiLi4OVatWxdGjR/W+uScRSYd5m8vYeRsTEwNRFHV6rfzbaDQa7arvz9u3bx+2bt0Ka2trrFmzBioV2zWViv1RRPLGrM2llP6oPKtXr0ZcXBwAoGfPnlzVVuYsuT+qnNQFEOkrJSUFt27dwtq1a/HDDz8AAJydnbFgwYJCt1+6dCkmT54MR0dHfP3117h7926BoKlevbosZ9gmJydrv85/p+LC5P95/ufp69SpU1i2bBkAYNWqVdqlafWR9/ol1fz8NmWpm6Rz584deHt749atWyhfvjz27t2Lzp07S10WEZWAWZtLiqwtSa1atbBixQp07969VCeDt27dioyMDADA8OHDi70rFclDamoqevXqhcOHDwPI/fc4ceJEiasiopIwa3OZMmvr1auHV199FZcuXcKlS5cQFBQEf3//F7a7dOkSNmzYUOrXYtYqj0ajwcSJE7FixQoAwMiRIxEYGAgrKyuJKyOi4jBrc5njuJaoMD///DPGjh0LURTx9ttv48CBA6hQoYLUZRFRMZi1ueSctdHR0fjiiy8AAN9//z0nWSrc8ePH0a1bNyQnJ8PNzQ3h4eFo0KCB1GURUQmYt7lMmbf6vJYur5eQkIDx48cDAKZNm4ZXX31V75pIHtgfRSRPzNpc5ji2LWt/FABcv35dO+61sbHBt99+a8gSycQsvT+KE37I7AUFBSEoKKjIn7dp0wbLly8v8u4ze/bsAZAbziNHjnzh5/7+/qWa2frBBx9gwIABej/veW5ubqV6Xv6lYG1sbIrdNv8dZ4tbQrY4mZmZGD16NDQaDUaOHAlvb+9S7Sfv9UuqGTBM3SSdW7duoVOnTrh79y4cHR3x22+/wcvLS+qyiKgQzNrCmTpr8+vSpYv2bhVZWVm4e/cugoODsX79eowbNw7Tp0/HRx99pPd+161bp/161KhRZa6TpJWUlITu3bvj+PHjEAQBP/30E9577z2pyyKiQjBrC2fqrF2wYAF69uwJURQREBCAf//9F6NGjUKtWrUQHx+Pffv2Yfr06cjIyIC1tTWys7ORlpam12swa5VFrVbj/fffR2BgIABg/PjxWLFiBe/CSWSGmLWFk3JcS6SrFStWaM9xdOjQAfv379epsY6ITItZWzg5Z+3YsWORmpqKjh07YvTo0VKXQ0YUHh6OXr16IS0tDXXq1EF4eDjq1KkjdVlEVAjmbeFMmbf6vJYurzd58mQ8ePAADRs2xIwZM/Suh+SB/VFE8sGsLZwS+6MSEhLQu3dvJCUlAQAWLlyIxo0bl7lekgb7ozjhh8pArREReCwKZ27Ho5V7JQS094CVSjBpDU5OTvjwww/RqlWrIreJiIgwymu7urrC1dXVKPvWhZ2dnfbrrKysYrfNzMzUfl2aVXkA4Ouvv8bVq1fh6uqK//3vf6XaB5Bbd1paWok1A4apm6Rz6dIlxMTEoEKFCggJCUGbNm2kLolIdpi1lpW1+Tk7Oxe4w1OLFi3Qp08fvPfee/Dx8cHHH3+Mq1evau8wootr167h9OnT2v0ZYllhklZMTAwuX74MlUqFdevWYcSIEVKXRCQ7zFrLytru3btjyZIlmDJlCnJycjB37lzMnTv3he0WLVqE7777Dk+ePNFrWXdmrfIkJyfjjz/+AJB7cf7777+HIJj2PYJICaTOW2ZtLlOPa4l0odFocOjQIQCAr68vdu/ezeOPqBSYtcxafa1evRrh4eGws7PD6tWrJa2FjO/48eNIS0tDgwYNEB4eXuomQCJLJnXWAszbPMbOW31eq6TXCw0Nxbp16yAIAtasWVOgQZqUhf1RRGXHrLWcrH2eMfqj0tPT0atXL1y/fh1A7kSsTz75pMy1knTYH8UJP1QGgceiMD/4GgAg7GosAGCcV12Dv07v3r21S6mJoojHjx/jjz/+wP/93/8hLi4O7777LhISEjBhwgSDv7Y5y990lJKSUuy2+X+uT7NSnvPnz2PRokUAgGXLlqFSpUp67yP/66elpZVYM1D2uklavXv3xubNm9GgQQO0aNFC6nKIZIlZKy1TZq2uWrZsiW+//RYTJkzAjz/+iAEDBqBTp046PTf/XUS44oAyNGnSBAcPHsSdO3cMcrcXIkvErJWWFFk7ceJEvPXWW1iwYAEOHTqk3a8gCGjbti2++uordO7cGV9++SUA6DX+ZdYqj4uLC8LCwrBt2zZMnDiRk32ISskUecusLZw5jmuJ8lOpVNi+fTu+//57TJ06lQ1wRKUkx6wVRRFxKZlIzVTDwdYKlR1tZfl5W45ZGxMTg08//RQAMGvWLNSrV0+yWsg0vvrqKzg5OWHYsGGoWrWq1OUQyRLPI0vLlHmrz2sV93opKSnau86/9957aN++vd61kHywP4qo7Ji10jLHsW1p+6OysrLQt29fHDt2DAAwYMAA/Pzzz0ark0yD/VEKnvCTnJyMJ0+e4MmTJxAEAVWqVEGVKlXg6OgodWmKceZ2/AuPjRGyLi4uBWZwNm3aFD4+PvD390ebNm3w6NEjTJkyBe3atUOzZs0M/vpFiY2NRWxsbJn34+bmBhcXF72fV6tWLe3X9+7dK3bb/D/P/zxdLViwADk5Odrnbt269YVtrly5ov360qVL2m0aNmxYYInDWrVq4fHjx0hNTcWzZ89QsWJFo9VNpnf9+nXUrVsX5crlxsuQIUMkroiMiVlrfMxay8laffTt21d7cmHbtm06DWjVajU2btwIIHd53aFDhxq1RjKehw8fwtbWVtuA3qpVq2LvMEPyxqw1PmatZWZt69atsWvXLuTk5ODRo0dIT09HtWrVtP+27t27h4yMDADAK6+8otM+mbXKkZGRgfv376Nu3dz3gmrVqvGuXwrHvDU+U+Qts7Zw5jquJcsmiiKuXLmi/Zxla2uLGTNmSFwVGROz1vjkmLVxKZl4mJg77krKyAYAVHGyK+4phWLW6u///u//kJSUBGdnZ7i5uRV63ffJkyfar6Ojo7XbuLm5oV27diarlUrv8uXL2qwVBAGTJ0+WuCIyJmat8fE8suXkbc2aNSEIAkRRLPG18r+eIAgFVlDbuHEj7ty5AysrKzRv3rzQvM0vNjZWu03FihXh6+urd+1kWuyPsizMWuNj1lpO1upD3/6o7OxsDBo0CAcPHgQA9OzZE1u2bIGVlZVR6yTjYH9UQYqZ8BMXF4e9e/fi6NGjOHr0KGJiYgrdrmbNmvD09ISXlxd69+6NypUrm7hS5WjlXkk7mzbvsSnVrl0bq1atQu/evZGVlYVJkybhyJEjJnv9H374AV9//XWZ97Nu3TqMHDlS7+flbzrKP9mmMPl/3qRJE71fK28Zvrt37+Kdd94pcfudO3di586dAHLvnpx/ws8rr7yCM2fOaOtq27at0eom0zp+/Di6deuG3r17IygoCCqVSuqSyMCYtabHrLWcrNVH/n9Tt2/f1uk5oaGhePDgAQCgV69eZVqtj6Rz584deHt7o1KlSggLC4Ozs7PUJZGBMWtNj1lr2Vlbrly5Ahdh8+SNWQGgTZs2Ou2LWasMqamp6N27Ny5fvozIyEjUr19f6pLICJi3pidl3jJrzXNcS5ZLo9Fg4sSJWL16Nfbv348uXbpIXRIZAbPW9OSYtamZ6hceVynFjYGZtfrLu+6blJSEd999t8TtIyMjERkZCSD3btic8GP+fv75Z4wdOxbff/89J/ooFLPW9Hge2XLy1sHBAbVr18bt27fx77//Ijs7G9bW1oVum52djZs3bwIA3N3dYW9vr/1ZXt6q1Wq8//77Jb7u1atXtf1Yr7/+Oif8mDn2Rykfs9b0mLWWk7X60Kc/Sq1WY+jQodi7dy8AwNfXFzt27Cgyx8m8sT/qRbL/tBEeHo7BgwfDzc0N7733HjZv3ox79+5BFMVC/9y9exebN2/Ge++9Bzc3NwwZMgTh4eFS/xqyFNDeA9P9GsGnsSum+zVCQHsPk9fQq1cveHl5AQAiIiIQHBxs8hqk0qpVK5QvXx5A7u9enPwfPjw9PY1ZVony/r6A4uvOyMjAqVOnAOQOjHlXSfN2+PBh+Pr6Ijk5GSdOnChw5y+SP2atdJi10jLXrL1//772a13vGLNu3Trt16NGjTJ4TWR8t27dgqenJ27duoWrV6/i+vXrUpdEBsSslQ6zVlrmmrXbtm0DkHtXxsGDB+v0HGat/CUlJaFr164IDw/Ho0ePcPLkSalLIgNj3kpH6rxl1ppf1pJlUqvVGDduHFasWIGsrCyL+rdoKZi10pFj1jrYWhX7WC6YtWRuVqxYgYCAAIiiiP379yMnJ0fqksiAmLXSkTprAdOPbUVRxJPkDNyOS0VqprTvJabO27z/z+np6fjzzz+L3O7UqVNIT08v02uR/LA/StmYtdKxxKw1J+Y6ttW1P0qj0eDdd9/Fr7/+CgDw9vbGnj17YGtra9T6yDjYH1UEUab++OMP8e233xZVKpUoCIIoCIJoa2srtmnTRpw4caK4fPlycfPmzWJISIh44MABcdOmTeLy5cvFjz/+WHzzzTdFW1tb7fNUKpXYtm1b8Y8//pD619JLjRo1xBo1auj1nKysLPHKlSvilStXxKysLCNVVnZHjhwRAYgARH9//2K3jYiI0G775ptvmqZAM9GvXz/t7x4REVHoNomJiaKzs7MIQHR1dRVzcnKMUkv+v7NZs2YVuV1cXJxobW0tAhDr1q1bZD3r1q3T7m/KlCl61SKX41wpfv/9d9HW1lYEIDZo0EC8d++e1CXprDTvo5aEWVv6Y0QO70PMWt2YU9bmmT9/vram2bNnl7h9fHy89n26evXqBqlPDse4kly5ckWsVq2aCECsUKGCePLkSalL0hmztnjMWmZtHmat+WTtX3/9JapUKhGA2L17d52eY4ysFUV5HOdK8fTpU7FVq1YiAFGlUonr16+XuiSdMWtLxrxV7nlkZq1uzCFr817fy8urTPupXbu2CECsXbu2QeqSw3GuFNnZ2eKwYcO0x8KkSZNEjUYjdVk6Y94Wj1nLrBVF/bNWo9GIsUnpYvSTFDE2KV1W7wnPU1LW5omOjtb57744cjjOleS7777T/r35+vqKqampUpekM2Zt8Zi1ys1aUTTfsW1sUrr4971n2j+xSelGfb2SmDJv9+3bp32tkSNHFrmdv7+/drv9+/eX6rXKmuFyOc6Vgv1RysWs5TXbPDyPbD7XbEVRt/4ojUZTIJO9vLwMOhaSwzGuJOyPKposJ/z0799fG66Ojo7i8OHDxZCQEDEzM1PnfWRmZooHDhwQhw4dKjo6OmrDdsCAAUas3LA4oP1Phw4dtNsfOHDA+AWaidOnT2t/76ZNm4pJSUkFfv58mC1atKjQ/eT//13aQaSuE35EURQ/+OCDYre9f/+++PLLL4sARDs7O/Hu3bt61SKX41wJdu3apZ3A9eqrr4qPHj2SuiS9cEBbNGZtLg5o/8OsNX7Wrl69WszIyCi2nrCwMNHe3l4EIFpbW4s3btwo8XdYsWKF9rWnTZtW4va6kMMxrhQXLlwQq1SpIgIQX3rpJfHs2bNSl6QXZm3RmLW5mLX/YdYaP2s1Go14//79Imu5fv266ObmJgIQHRwcxFu3bun0Oxgja0VRHse5EsTGxoqvv/66CEAsV66cuG3bNqlL0guztnjM21xKPY/MrNWNOZxD5oQfy5aZmSn2799fexzMmDFDdo39zNuiMWtzMWtzMWvln7V5OOFHXjQajTh79mzt31nv3r1LvNZgbpi1RWPW5lJq1oqi+eZt9JOUAhN+op+kGO21dGHKvFWr1WLTpk1FIPfmQOHh4S9sExYWpr1xVNOmTUW1Wl2q34sTfuSD/VHKxazNxWu2/+HYVl79UePGjdO+Xrt27cSUFMN+ZpHDMa4U7I8qXjnI0K5du1CtWjVMmzYNY8aMgb29vd77sLGxgZ+fH/z8/JCWloY1a9Zg4cKF2LVrlxEqJmObPXs2OnToAAD4+uuv4efnJ21BJtK6dWt88MEH+OGHH3Dx4kW8+eabmDZtGpo0aYKHDx/ixx9/1C4t2KJFC0yYMEHiinPNnj0bv/32G+7evYuvv/4aN2/exIgRI+Di4oKzZ89i3rx5ePToEQBgzpw5qFmzpsQVU2G2bNmCESNGQK1Wo0WLFjh48CAqV64sdVlkIMxaeh6z1vhZ++mnn2L69Ono378/3nrrLdStWxfOzs5ITU3FjRs3sG/fPuzZsweiKAIAvvnmG9SvX7/E/a5fv1779ciRI0tdH5nemTNn4Ovri2fPnqFq1aoICwvDq6++KnVZZCDMWnoes9b4WatWq+Hu7o7u3bvDz88Pr7zyCsqXL4+HDx8iJCQEgYGByMjIgLW1NTZs2AAPDw+d9susla8HDx7Ax8cHV69ehY2NDbZv347evXtLXRYZEPOW8mPWmuYc8vHjx3Hz5s1Cf/bo0aMCuQkA7dq1Q7169V7Y9sKFC7hw4UKB76WkpGj/+/x+mjVrhmbNmpW2bDKSjIwMDBgwAL///jsA4Ntvv8WMGTMkrooMiVlL+TFr5ZW1pAyiKGL69OlYuHAhAGDQoEHYtGkTrK2tJa6MDIVZS88zVd462FohKSO7wGMpmTJvVSoVfvjhB3h7eyMrKwvdu3fHp59+ii5dugAAQkNDsWjRImg0GtjY2OCHH36ASqUyyO9J5on9UcrGrKXncWwrn/6oqVOnYtWqVQAAd3d3LFq0CNHR0cW+tpubG1xcXEpdOxkH+6N0YLSpREa0dOlSMT3d8EuFZmRkiEuXLjX4fo2Fd7AoyMvLS/uc4OBg4xZoRnJycsQRI0Zof/fC/rzxxhviw4cPi9yHqVf4EcXcpdfq1atXZM2CIIjTp08vVS1yOc7lLDMzU2zQoIEIQGzTpo347NkzqUsqFd7BomjM2ly8g0VBzFrjZm2FChWKfY28P05OTuIPP/ygU+0XL17UPu/tt98uza9fKDkc40qQdwdkNzc38fr161KXUyrM2qIxa3Mxawti1ho3a7Ozs0vM2Ro1auh1ty5jZa0oyuM4l7t58+aJQO7qwiEhIVKXUyrM2uIxb3Mp9Twys1Z3pjyHnP8uj7r8WbduXaH7mTVrll770eWc9PPkcJzLXUhIiCgIgghAXLx4sdTllBrztmjM2lzM2v8wa+WTtcXhCj/yER0dLTo5OWn/rnJycqQuqVSYtUVj1uZSataKovnmrUajEWOT0sXoJylibFK6WazSaer+qF9//VX7HlvUNdudO3eW6XfStZaiyOU4lzP2RykfszYXr9kWxLGtPPqj8laEN+YYWQ7HuBKwP6pkspxePnHiRNjZ2Rl8v7a2tpg4caLB90umMXv2bO3XX3/9tXSFmJiVlRWCgoIQHByM/v37w83NDTY2NqhSpQo6dOiAVatW4eTJk3j55ZelLrWAxo0b4++//8b333+PN998E5UqVYKtrS3c3d0xYsQI/PHHH5g3b57UZVIRbGxsEBoaiuHDhyM0NJSznhWIWUuFYdYaN2uPHz+ORYsWoV+/fnjttdfw8ssvo1y5cnBwcEDt2rXRo0cPLFu2DFFRURg/frxO+8x/N8dRo0aVqT4yvQ0bNuDdd99FZGQkGjRoIHU5ZGDMWioMs9a4WVuuXDkEBQXB398fTZo0QaVKlWBtbY1q1aqhU6dOWL58Oa5du6bXnbqYtfI2bdo0TJ48GcHBwfD19ZW6HDIC5i09L3/WzvxqFm7HpeJJcob2ToFKJddzyCR/vr6++Omnn/Djjz9i0qRJUpdDRsCspedxXMusJdNyd3fH77//jk8++QRr166FlZW0K3CQ4TFrqTCmyFtBEFDFyQ7ulR1QxckOgiAY5XX0Yeq87d+/Py5duoSpU6eicePGcHR0hKOjI5o0aYKpU6fi0qVL6Nevn0Fei8wX+6OUj1lLheHYVn79USRv7I8qmSAq/UqWgrm5uQEAYmJidH5Odna2donvevXqcSlnUiQe58aTkJCgqMFrad5HybKU9hjh+xApHY9x42HWkqVh1hIVjce5cTBryRLxPPKLniRn4GFihvZxtQp2qOJk+Av7ZN6UfpxLJSkpCY6OjlCpZHnPwUIxb6kkzFqiwvE4N46cnBxkZGTA0dFR6lIMhllLJWHWEhWOx7nx8DwyWRpesyUqHI9x42HW6kc5Z9uJiMhoRFHEnDlz8Prrr+POnTtSl0NERKRIu3fvRp06dRAZGSl1KURERIr0999/o2HDhli1apXUpRCRxFIz1cU+JqLSefLkCby8vDB+/HjFr5xFREQkhaysLAwZMgTdunVDamqq1OUQEREpDvujiIiIjI/9UfqT7YSfI0eOYPTo0Rg9ejSuXr2q8/OuXr2qfd7x48eNWCERkTKIoogvvvgCs2bNwt27d7Fs2TKpSyITYdYSEZnOL7/8goEDByIhIQFfffUVG6MsBLOWiMh0/vrrL3Ts2BGxsbGYPXs2kpKSpC6JTIR5S4VxsLUq9jER6e/hw4fo0KEDLly4gPXr1+PixYtSl0QmwqwlIjKNjIwM9O/fHzt37sSxY8ewd+9eqUsiE2HWkiGJoognyRm4HZeKJ8kZvB5FlA/7oywXs5aIyHTYH1U6spzwo9FoMH78eAQFBUGtVqNx48Y6P7dx48ZQq9VYv349JkyYYMQqiYjkTxRFfPLJJ1iwYAEAYMSIEfjuu+8kropMgVlLRGQ669atw7Bhw6BWq9GmTRvs2bMHgiBIXRYZGbOWiMh0Tpw4AW9vbzx79gw1atRAREQEnJ2dpS6LTIB5S0Wp7GiLahXs4GxnjWoV7FDZ0Vb7MzY/Eenv7t278PT0xJUrV2BnZ4d9+/bhtddek7osMgFmLRGRaaSmpqJnz5747bffAACLFy/G0KFDJa6KTIFZS4YWl5KJh4kZSMrIxsPEDMSlZEpdEpFZYH+U5WLWlp5aI2LDydu4/TQVOWoNz6MSUYnYH1V6spzwc+jQIdy4cQMODg743//+p/fzFy9eDCcnJ1y8eBGHDx82QoVERPKn0Wgwbtw4LF++HAAwbtw4rFu3DuXKlZO4MjIFZi0RkWn88MMPGD16NERRhKenJ0JDQ+Hi4iJ1WWQCzFoiItM4cuQIfH19kZSUBHd3d0RGRqJhw4ZSl0UmwrylogiCgCpOdnCv7IAqTnYFLiix+YlIP1FRUfD09MTNmzfh4OCA4OBg+Pr6Sl0WmQizlojI+JKTk+Hn54ewsDAAueeUJ02aJHFVZCrMWjK01Ex1sY+JLBH7oywbs7b0Ao9FYVn4TSSl5yBbLSI+NUvqkojIjLE/qmxkOeFn165dAHJnUleuXFnv57/00kvw9/eHKIrYsWOHocsjIpK9nJwcjBw5EmvWrAEATJw4ET/++CNUKlnGBpUCs5aIyPgWL16MDz/8EADQuXNnBAcHw8nJSeKqyFSYtURExhcSEoJu3bohNTUV9evXR2RkJDw8PKQui0yIeUulweYnIt1du3YN7du3x507d+Ds7IxDhw6hQ4cOUpdFJsSsJSIyrmfPnqFz5844duwYVCoV1q1bh/Hjx0tdFpkQs5YMzcHWqtjHRJaG/VHErC29M7fjCzxOy+J5VCIqHPujyk6Wn0z+/PNPCIKAnj17lnofPXr0AACcPn3aUGURESnGunXrsHHjRgDA9OnTsWTJEi6dZ2GYtURExnXu3DlMmTIFANCzZ0/s27cP9vb2EldFpsSsJSIyrsTERLzzzjvIyMjAK6+8gsjISNSsWVPqssjEmLdUGmx+ItKNKIoYMWIEHjx4gEqVKuHw4cN46623pC6LTIxZS0RkXNOnT8fp06dhZWWFzZs3Y+TIkVKXRCbGrCVDq+xoi2oV7OBsZ41qFexQ2dFW6pKIJMX+KGLWll4r90oFHtvb8DwqEb2I/VGGIct1B+/evQsAaNiwYan30aBBAwDAnTt3DFITEZGSjB49GpGRkWjYsCFmzpwpdTkkAWYtEZFxtWjRAosXL8bJkyexadMm2NjYSF0SmRizlojIuCpUqIBt27Zh1qxZ2L9/f6nuzEfyx7yl0shrdkrNVMPB1orNT0RFEAQBmzdvxqBBg7BhwwY0bdpU6pJIAsxaIiLj+u6773D58mVMmTIFffr0kbockgCzlgxNEARUcbJDFd5QnQgA+6OIWVsWAe09UE7QwFmVCGsrAZUc2PNARC9if5RhyHLCT3JyMgCgYsWKpd6Hi4sLACAlJcUQJRERyZ5Go9EuSWtlZYUNGzbwrhUWjFlLRGR4oigCgDZfJ02ahE8++YR5a6GYtURExpF/bNulSxd07tyZWWvBmLdUGmx+Iipe/qytX78+zp07x6y1YMxaIiLDy5+1zs7OiIyMZNZaMGYtEZHhsT+K8mPWlp6VSsCIt9xx8+ZNAOC/IyLSYn+U4amkLqA08gIyPj6+1Pt49uwZgNwTJEREli45ORne3t7YtGmT9nsMV8vGrCVzIYoiniRn4HZcKp4kZ2gHBERyo9FoMH78eEyZMqXAccy8tVzMWiIiw/vxxx/RrVs3ZGRkaL/HrLVszFsiIsOKiIhAy5Yt8fDhQ+33mLWWjVlLRGRYUVFRaN68Oc6cOaP9HrPWsjFriYgMi/1R9DxmLRGRYbE/yjhkOeHn5ZdfBgCcP3++1PvIe27evoiILFVCQgI6d+6MiIgIjB49WrtUKVk2Zi2Zi7iUTDxMzEBSRjYeJmYgLiVT6pKI9JaTk4NRo0Zh1apVWLJkCYKDg6UuicwAs5aIyLCWLFmCDz74AAcPHsSiRYukLofMBPOWiMhwDh48CD8/P1y4cAHvv/++1OWQmWDWEhEZzvXr1+Hp6Yl//vkHgwYNQlZWltQlkRlg1hIRGQ77o6gwzFoiIsNhf5TxyHLCT7t27SCKInbs2FHqfWzfvh2CIKBdu3YGrMz85Z8hxzvkk1JxVqju4uLi0KlTJ5w+fRpWVlYICgpCrVq1pC6LSuH8+fOYOnUqvLy80KRJE9StW7fAzx89eoRdu3bht99+02l/zNqyYd4aTmqmutjHJA1mre6ys7MxbNgwbNiwAQAwbdo0+Pn5SVwVmQNmbdkwa8kSMG91N3fuXEyePBkA0KNHD3z66acSV0TmgnlbesxasgTMWt3t3bsXvXr1QkZGBpo0aYKffvpJ6pLITDBrS49ZS5aAWau7ixcvwtPTE/fv30fFihWxY8cO2NjYSF0WmQFmbekxa8kSMGt1x/4oKgqztmyYt6R0zFrdsT/KuGQ54ad3794AgB07duD48eN6P//YsWPagM7bl6WwsrLSfp2ZyTvkkzJlZ2drv1apZPk2ZxKPHj1Chw4dcP78eVhbW2PHjh145513pC6L9JSRkYFRo0bhjTfewJIlS3Ds2DFcu3YNt2/fLrCdg4MDRo0ahd69e+PSpUsl7pdZWzb533vyvyeR/hxsrYp9TNJg1uomMzMTAwYMwPbt2wEA33zzDebNm8eTAASAWVtWHNuSJch/bOc/5uk/oihixowZmDlzJgBgwIAB2LlzJ+zs7CSujMwF87b0mLVkCZi1utm2bRsGDBiArKwsNGvWDBEREahWrZrUZZGZYNaWHrOWLAGzVjfnzp1Dhw4dEBsbC1dXV0REROCNN96QuiwyE8za0mPWkiXgNVvdsD+KisOsLRtT9keJoognyRm4HZeKJ8kZnGBEJsGs1Q37o4xPlkefr68v3njjDWg0GvTp00evoD127Bj69u0LAGjZsiW6du1qrDLNkiAIcHBwAAAkJydLXA2RcaSmpgIAypcvz5AtQkxMDLy8vHD58mXY2dlh37592vdGkpfBgwdjw4YNEEURXl5e+OSTTwrdzsnJCX379oUoiti1a1eJ+2XWlo1KpUL58uUB/PeeRKVT2dEW1SrYwdnOGtUq2KGyo63UJRGYtbpIS0tDr169sG/fPgDA999/j5kzZ3IwS1rM2rLh2JYsQd6x7eDgwPwohCiKmDJlCubNmwcAGD58OH755RfeAZkKYN6WHrOWLAGztmRBQUEYOnQocnJy0Lp1axw+fBhVqlSRuiwyI8za0mPWkiVg1pbs5MmT6NSpE+Lj41G9enUcPXoUr732mtRlkRlh1pYes5YsAa/Zloz9UVQSZm3ZmLI/Ki4lEw8TM5CUkY2HiRmIS+GEXjI+Zm3J2B9lGrI9+tasWQNHR0c8e/YMHTp0gL+/P44fP46cnJwXts3JycHx48fh7++vPVni4OCANWvWSFC59JydnQEASUlJSElJkbgaIsPKzs5GQkICAGhP3lBBOTk56Ny5M27cuAEHBwf8/vvvFjngUIIdO3Zg//79sLW1xYEDB3D48GF88803RW7frVs3ALkDTl0wa8sm7z0oISGBq/yUgSAIqOJkB/fKDqjiZMfBgBlg1upm9OjRCA0NBQCsXLkSU6ZMkbgiMkfM2rLh2JaULCUlBUlJSQD+O9apoO+//x5LliwBAIwdOxZBQUEoV66cxFWROWLelh6zlpSMWVuysLAwjBw5EhqNBu3bt8ehQ4dQsWJFqcsiM8SsLT1mLSkZs7ZkMTEx6NKlCxITE1G7dm1ERkaiUaNGUpdFZohZW3rMWlIyXrMtGfujSFfM2rIxVX9Uaqa62MdEhsas1Q37o0xDEGW8rlloaCgGDRqEpKQkbfOnjY0NPDw84OLiAiA3RKKiopCVlQUg9+6fTk5O2Lp1K/z8/KQq3SDc3NwA5J4I0odarcadO3eQmZkJQRDg7OwMJycn2NrasomWZEuj0SA1NRXx8fHIzs6GlZUV6tSpA2tra6lLM0t79+7FmDFjsHfvXrRt21bqciRT2vdRc9GtWzccPHgQX3/9NWbOnAkgd1a5k5MTBEGAWl1wYHP9+nU0btwYNWrUwL1793R6DWZt6Y+R7OxsREdHQ61Ww9raGpUqVYKDgwNn+5NsMWv1c+nSJXh7e2PBggUYNWqU1OVIRu5ZawrM2tIfIxzbktKIoojMzEwkJycjKSkJoijC1tYWtWvXhpWVldTlmZ2nT5+iY8eO6NixI5YuXWqx/+6Ztbph3vI8MhHArNVXTk4OBg8ejKSkJOzZs8eiL2ozb0vGrGXWEgHM2tL45ptvsGHDBoSHh6NWrVpSlyMZZm3JmLXMWqI8vGarH/ZH5WLWloxZa/79UU9TMvE4OUP7uKqTHV5ytDXoaxABzFp9sT8ql7GzVtYTfgDg5s2beP/993H48GHt954flOX/FTt27IiffvoJ9evXN1mNxlKWgyMnJwd3795FZiaXtSPlEQQBtWrVgr29vdSlmLWkpCSLv6OW3Ae0VatWRVxcHK5evYoGDRoAKH7CT3x8PCpXrgxbW1ukp6fr/DrM2tIfI2lpabh79y5k/nGL6AXMWt0wa+WftabCrC39McKxLSmZra0tatWqxVVripGcnAxHR0eLbtBg1uqOecvzyETPY9aWLCsrCxqNBnZ2dlKXIinmrW6Ytcxaoucxa0smiiKSk5N5HplZqxNmLbOW6Hm8ZqsbXrNl1uqKWWv+/VE5ag3UImAlAOWseMNlMj5mrW6YtZzwo7Nz587h119/xbFjxxAdHY34+HgAQKVKlVCnTh20b98e/fr1wxtvvFHgebdu3ULdunWlKLnMynpwqNVq7d11UlNTDVkakSRUKhUqVKiAihUrwtaWs7fzO3fuHFasWIFVq1ZxpnE+ch/Q2traIicnB/Hx8ahQoQKA4if8PH36FFWqVIGdnR3S0tL0fj1mbelkZmbi2bNnSExMhEajMVRpRJJg1hbt0aNHmDhxIlauXInKlStLXY7ZkHvWmhqztnQ4tiWlcXBw0N5tlHdA/k9mZibGjRuHyZMn47XXXpO6HLPBrNUf81Z/zFpSGmZt4URRxKxZs9CsWTP069dP6nLMCvNWP8xa/TFrSWmYtUXbvn07rl27hq+++krqUswKs1Y/zFr9MWtJaXjNtmjsjyocs1Y/zNrSYX8UKQmztmjsjyqcsbNWlrdS2b59OwYNGlTgey1atECLFi302s+1a9fg4+NjsR9krKys4OLiAhcXF4iiCLVazRUISLZUKhVUKpVF39W3KKdOnULXrl2RmJgIR0dHLF++XOqSyEBcXFwQFxeHuLg47YSf4kRFRQEAqlSpUuK2zFrDsbW1xcsvv4yqVatCo9FwUEuyxawtWkxMDLy9vXHjxg3cu3cPJ06c4P8nKhGz1nA4tiWlEAQBVlZWzJBCpKWloW/fvggNDUVwcDCuXbuGihUrSl0WyQDz1jDMKWvVGhGbT9/BhbsJaFbLBcPerA0rFd83STfM2qKJoogpU6ZgyZIlsLa2xsmTJ9GyZUupyyIZYNYahjllLVFZMGuLt2HDBowaNQoajQa1a9eGv7+/1CWRDDBrDYNZS0rCa7ZFY38UlQaz1nDYH0VKwawtGvujpCPLCT/vvvsuypcvj549e5Z6H5cuXYK3tzfi4uIMWJl8CYLApbSJFOjo0aPo0aMHUlJSUKtWLUycOFHqksiAmjZtiiNHjiAiIkKnO0Ts2bMHANCqVasSt2XWGl7ehS7ezY5IWaKjo+Ht7Y3o6GjY29vjm2++4WCWdMKsNQ6ObYmUJzk5Gb169UJERAQA4Msvv+RkH9IZ89bw9M1atUZE4LEonLkdj1bulRDQ3qNME3TWHr2F+cE3AAD7L8UiR1RhnJc875pJZC40Gg0+/PBD/PTTTwAAf39/NGvWTNqiSDaYtYbHcS2RMq1atQrvv/8+AKBdu3bo27evxBWRXDBrDY9ZS6RM7I+i0mLWGh77o4iUif1R0lJJXUBpZGdnY9CgQTh48GCpnn/u3Dl07NgRT548MXBlRETmIzQ0FH5+fkhJSUHdunVx7Ngx2S4bSoXr06cPRFHE3LlzkZiYWOy2V65cwfLlyyEIAgYMGFDivpm1REQlu3HjBjw9PREdHQ0nJyccPHgQ3t7eUpdFMsGsJSIqWUJCAnx9fREREQFBEBAYGIgJEyZIXRbJCPNWeoHHojA/+BrCrsZifvA1BB6LKtP+ztyOL/YxEelHrVZj9OjR2sk+H330EVatWsWGDNIZs5aIqGRLly7VTvbx9vZGSEgInJ2dJa6K5IJZS0RUMvZHWZbdu3djwIABqF27Nuzt7V+YxHn37l0sXrwYK1as0Gl/zFoiopKxP0p6spzwU716dWRmZqJfv344cuSIXs89deoUfHx88PTpUwiCgJUrVxqpSiIi6ezfvx89e/ZEeno6GjdujMjISNSqVUvqssjAxo4dC3d3d9y5cweenp44cuTIC0uPJyQkYPXq1fDy8kJaWhqaNm2KwYMHl7hvZi0RUfEuXboET09PxMTEoGLFiggPD0e7du2kLotkhFlLRFS8p0+fwtvbGydPnoSVlRU2bdqEMWPGSF0WyQzzVnqGnqDTyr1SsY+JSHfZ2dkYNmwYgoKCAACfffYZli1bBpVKlpcOSSLMWiKi4s2fPx+TJk0CAHTv3h2//fYbHBwcJK6K5IRZS0RUPPZHWY74+Hj4+PhgwIAB2LVrF+7du4eMjIwX+qSqVq2KBQsWYOLEiTh9+nSJ+2XWEhEVj/1R5kGWZ+3Dw8NRtWpVpKeno1evXjhx4oROzzt27Bh8fX2RkJAAlUqFwMBA7Z1UiIiU4sCBA+jXrx+ysrLw+uuvIyIiAtWrV5e6LDICW1tb7N+/H5UqVcLFixfh4+ODqlWrapdKrFKlCipXrozx48fj6dOncHV1xc6dO3VaSpFZS0RUtBs3bqBDhw54/PgxKleujCNHjqBVq1ZSl0Uyw6wlIipaSkoKOnTogHPnzsHa2hrbtm3D0KFDpS6LZIh5Kz1DT9AJaO+B6X6N4NPYFdP9GiGgvUeZ9kdkqURRxNChQ7Ft2zYAwOzZs7FgwQKdzhsS5cesJSIq2oIFC/DFF18AAPr3749du3bBzs5O4qpIbpi1RERFY3+U5VCr1ejevTsOHz4MGxsbjBw5EosXLy50W1tbWwwcOBCiKGLv3r0l7ptZS0RUNPZHmQ9ZTvhp2LAhDh06hMqVKyM1NRXdunXDmTNnin1OeHg4unXrhuTkZJQrVw4bNmzAqFGjTFQxEZHptGzZEh4eHmjVqhUOHz4MV1dXqUsiI3rllVdw4cIF9OrVCwCQnp4OURQhiiKePn0KjUYDURTRo0cPnDlzRudli5m1RERFq127Nlq3bo1q1arh6NGjeP3116UuiWSIWUtEVDQHBwd069YNtra22L17N/r37y91SSRTzFvpGXqCjpVKwDivulj17hsAgHEb/8Kqo7eg1oglPJOI8hMEAf369YNKpcLChQsxa9YsTvahUmHWEhEVrXPnznB2dsbQoUOxdetW2NjYSF0SyRCz9j/P0rI5/iOiAtgfZTnWrl2L06dPw8XFBadOncLatWsxduzYIrfv0qULAOg0eYdZS0RUNPZHmQ9BfH5NOxk5f/48fHx88OzZM7i4uODIkSOFHkwHDhzAwIEDkZ6eDhsbG2zevFkRjQJubm4AgJiYGIkrISJz8+DBAzg6OsLZ2VnqUsya0t5Hb9++jfDwcFy7dg2JiYlwdHSEh4cHunTpggYNGpRqn8xaZR0jRGQ46enpePz4Mdzd3aUuxazxfbRkzFoeI0RUOFEUcf36dTRq1EjqUswa30d1w7xV3nGy6ugtzA++pn083a8RxnnpdpMTIvrP1atX0bhxY6nLMHtKfB81NGZtyceIWiMi8FgUztyORyv3Sgho7wErFSfaESndjRs3ULduXVhZWUldillj1paMWeuGR4kZcPswyOLHf/xMQVQQ+6N0I/es7dixIyIjI7Fs2TJMmDABAJCamgonJycIggC1Wl1g+1u3bqF+/fqoWrUqHj58qNNrMGvlfYwQkfGwP0o3xn4fleUKP3maN2+OkJAQODs7IyEhAZ07d8aVK1cKbLNnzx70798f6enpsLW1xY4dOxQRsERE+a1atQrXr1/XPq5evToHsxbI3d0dY8aMwaJFi7B69WosXrwYEyZMKPVkH4BZS0SUJzQ0FMHBwdrH5cuX52CWDIJZS0SU699//8WPP/6ofSwIAif7kMEwb5XnzO34Yh8T0YsSExPx7bffFmiC4WQfMhRmbckCj0VhfvA1hF2Nxfzgawg8FiV1SbKi1ohYdfQWAoLOcHUHMltqtRrffvstEhIStN9r0KABJ/uQQTBr/2Pp4z9+piBLx/4oy3Tx4kUAQI8ePXTa/qWXXgIAPHv2TOfXYNYSEeVif5R5kvWEHwBo1aoVfv/9dzg4OCAuLg7e3t64ceMGAGDbtm0YPHgwMjMzYW9vjz179qBnz54SV0xEZFjz58/H+++/D29vb9y7d0/qcsiEIiMjERkZCX0W68t7jj6YtURk6fbv34+ePXuiX79+OHbsmNTlkAIxa4nI0l2+fBmenp744IMPsGrVKqnLIYVi3spLSU29LWtXKvYxERX09OlTeHt748svv8T48eOlLocUillbPE5WLRs2N5O5y87OxrBhw/Dll1/Cz88PGRkZUpdECsSszdXK3bLHf/xMQZaM/VGWKzk5GQBQsWJFnbbPzs4GAJQrV06v12HWEpGlY3+U+ZL9hB8AaNu2Lfbv34/y5csjNjYW3t7eWLhwIYYPH47s7Gw4Ojpi//798PX1lbpUIiKDEUURX331Fb744gsAwJtvvomqVatKXBWZUocOHdCpUyekp6frtL1ardY+R1/MWiKyVDt27EC/fv2QlZWFBg0aoGHDhlKXRArFrCUiS3X+/Hl06NABjx49QuXKldGmTRupSyIFY97KR8lNvc/f/IR3+ScqyuPHj9GxY0ecPXsW1tbWfI8jo2LWFu355mRLb1bWF5ubyZxlZmZi0KBB2LZtGwDA19cXtra2EldFSmXJWWtrbYXpfo0Q0N5D6lIkxc8UZInYH0V5K/Y8fPhQp+3zVoEqzXFiyVlLRJaN/VHmTRETfoDcpufdu3fDxsYGDx48wBdffAG1Wg0nJycEBwejY8eOUpdIRGQwoijis88+wzfffAMAGDp0KLZt2wYbGxuJKyNT02d1n7I8B2DWEpHl2bRpE4YMGYKcnBy88cYbOHLkCFxdXaUuixSMWUtElub06dPo1KkT4uLiUK1aNRw9ehSvv/661GWRwjFv5aGkpt6zd54V+5iIct2/fx9eXl64ePEibG1tsXv3bvTv31/qskjhmLWFC2jvgel+jeDT2JXNyqXA5mYyV+np6ejTpw/27NkDAFiwYAFmz54NQRCkLYwUzVKztqK9NcZ51YWVyrL/ffEzBVka9kcRALRo0QIAcPDgQZ2237p1KwDgrbfeKtXrWWrWEpHlYn+U+VPMhB8A6NKlC3799VeUK1cOoiiiYsWKCAsLQ9u2baUujYjIYDQaDSZMmIDvv/8eADB69Ghs2LBB72VIyfKo1WoAgJWVVan3wawlIkuxZs0ajBgxAhqNBm3btkVYWBgqVWIjARkfs5aILEVkZCR8fHyQkJCAWrVqITIyEk2aNJG6LLIQzFvzV1JTL5t+iUp2+/ZteHp64vr16yhfvjx+++03dO/eXeqyyEIwa19kpRIwzqsuAv1bsVm5FNjcTOYoJSUF3bt3R0hICABg+fLl+PzzzyWuiiwFs9Zy8TMFWRL2R1GeQYMGQRRFzJs3DzExMcVuGxERgdWrV0MQBAwbNqzUr8msJSJLwf4oeZDlp59OnToV+3MXFxfExcWhQoUKJZ5QEQQB4eHhhiyPiMho1Go13nvvPaxduxYA8OGHH2L58uVQqRQ1f5OM5NatWwByc7IkzFoismT/93//h48//hhA7vvh3r174ejoKHFVpDTMWiKyZGFhYejVqxfS09Ph4eGBw4cPo3bt2lKXRQrEvJWvvCbeM7fj0cq90gtNvSX9nMjS3bx5E506dcK9e/fg6OiIAwcOoH379lKXRQrErCVTyWtuHudVV+pSiAAAiYmJ6NatG/744w8IgoBVq1Zh7NixUpdFCsSsJSJLxf4oym/48OFYtmwZzp8/jzZt2uCbb76Bl5eX9udpaWm4efMmtm7diqVLl0KtVsPLywt+fn4l7ptZS0SWjP1R8iHLCT8RERE6LYF8584d3Llzp8ifi6LIpZSJSFZUKhXs7OwAAFOnTsV3333H9zELEhkZWej3jx8/rj0uCqNWq/HgwQOsXLkSgiCgWbNmJb4Ws5aILJm9vT0AwM/PDzt37kT58uUlroiUiFlLRJasfPnyEAQBjRo1QlhYGGrUqCF1SaRQzFv5Kqmpl02/RMWztbWFlZUVXFxcEBISgjfffFPqkkihmLVEZKmsra1hY2MDlUqFoKAgDB8+XOqSSKGYtURkqdgfRfmpVCrs27cPHTt2xM2bNxEQEAAA2mPCyclJu60oimjcuDG2bdum076ZtURkydgfJR+ynPBTq1YthiMRWSRBEPB///d/6Ny5M3r37s33QgvToUOHF/7ORVHU6Y4UedsKgoD333+/xG2ZtURkycaMGYOXX34ZPj4+sLW1lbocUihmLRFZsrZt2yI0NBT169eHq6ur1OWQgjFvyRTUGhGBx6IKrDZkpeJxR9KqWbMmDh8+jMTERJ1u/kNUWsxaIrJU9vb22L9/P06ePInOnTtLXQ4pGLOWiCwV+6PoeTVq1MDZs2fx5Zdf4ueff0ZqauoL29ja2mLs2LGYO3dugUlAxWHWEpElY3+UfAiiKIpSF0Gl4+bmBgCIiYmRuBIiMqb09HSEhYWhZ8+eUpeiOHJ7H31+aeK8AacuUS4IAl555RVMmTIF/v7+RqlPieR2jBBR6YiiiJ07d6Jfv35cBt7A+D5KJeExQmQ5Dhw4AE9PTy4Db2B8HyVd8DgxrlVHb2F+8DXt4+l+jbjyEEnizJkzqFKlCtzd3aUuRXH4Pkol4TFCZBnu37+P27dvo23btlKXojh8H6WSuLm54VlaNhbv/oM3WSBSMPZHGY/SsjYtLQ0nT57EtWvXkJiYCEdHR3h4eMDLy0vniT5UkNKOESIqHPujjMfY76OyXOGHiMhSpKSkoHfv3jh8+DDWrl2LUaNGSV0SSSg6Olr7tSiK8PDwgCAIuHz5snZ5xcJYW1ujUqVK2uWOiYjoPxqNBh9//DFWrlyJjz76CMuWLeMdfIiIiAxszZo1GDduHDp06IDff/+dy8ETkSSMtRLPmdvxLzzmhB8ytWPHjqF79+6oXLkyIiMjtRcXiYikpEv2cqU8kos7d+6gU6dOePz4MQ4dOoS33npL6pKILE5mtlp7swWOuYiUh/1RpA97e3t4e3vD29tb6lKIiGSD/VHyxgk/RERmKjExEd27d8eJEycgCAJycnKkLokkVrt27QKPa9WqBZVKhTp16nAyDxFRKajVarz33ntYu3at9rEoihzQEhERGdDy5csxceJEALk3LlCr1RJXRESWKvBYlLY5LOxqLADDNIm1cq+k3V/eYyJTCgsLQ+/evZGWlobKlSvzPDIRmQ1dstdY+UxkSDdv3kSnTp1w7949ODo6Ijs7W+qSiCwab7JApDzsj6KSbNiwAQAwfPhwnVekyHvOiBEjjFYXEZFcsD9K/mQ/4efcuXM4dOgQYmJiIIoiatSogU6dOuHNN9+UujQiolKLj4+Hr68v/vrrL6hUKgQFBWH48OFSl0Vm5vbt2yZ5HWYtESlRdnY2/P398csvvwAApkyZgkWLFnEwS5Jg1hKRUi1cuBDTpk0DAHTt2hW7du3i6j4kGeYtGWslnoD2Htr95a1MQGQqv//+O/r374/MzEw0bNgQ4eHhqFGjhtRlkYWy9KxNzcxBQNAZrlKTjy7Zy5XyyNxduXIFPj4+ePjwIVxcXBASEmIx72tkfiw9a/OoNSLUGpFZS6QQ7I8iXYwcORIqlQoDBgyAvb19idur1Wrtc/SZ8MOsJSIlYn+UMsh2wk9CQgKGDx+O4ODgQn/eoUMH/PLLL3B1dTVxZUREZRMbG4vOnTvjn3/+Qbly5fDLL79gwIABUpdFZuinn37CsGHD4OTkZJT9M2uJSKmysrIwZMgQ7N69GwDw5Zdf4uuvv+ZglkyOWUtESiWKImbPno05c+YAAPr06YOtW7fC1tZW4srIEjFvKY+xVuKxUgkY51WXzclkcjt37sQ777yD7OxsNG3aFIcOHULVqlWlLossELM2V3JGDsKuxnKVmnx0yV6ulEfm7MKFC+jcuTPi4uJQuXJlHDp0CM2aNZO6LLJAzNqCjlx/gsBjURabtWqNiMBjUQVuOsHJTyRX7I8ifYiiaLTnMGuJSKnYH6Ucuq1vZ2bUajW6d++O4OBgiKJY6J+IiAj4+voiKytL6nKJiHT24MEDeHl54Z9//oGNjQ12797NwSwV6YMPPsDLL7+MESNG4MiRIwbdN7OWiJQqPT0dffv21Q5m582bhzlz5nAwSybHrCUipRJFEZ9//rl2ss+QIUOwfft2TvYhSTBvKb+A9h6Y7tcIPo1dMd2vEVfiIVnbvHkzBg8ejOzsbLRs2RJHjhzhZB+SBLO2cM+vWmOpdMne57cZ1bYOVh29hYCgM1h19BbUGv2b+ogM4c8//0THjh0RFxeHl19+GREREZzsQ5Jg1hbOkrM28FgU5gdfQ9jVWMwPvobAY1FSl0RUKuyPImPKzs4GAJQrV/J6CMxaIlIq9kcpiywn/Gzbtg0nT56EKIpo0KAB1q9fjzNnzuDs2bPYuHEjXn31VYiiiH/++Qdr166VulwiIp39+++/iI6ORvny5fHbb7+hR48eUpdEZkwQBKSnp2Pz5s3w8fFBvXr1MHfuXMTExJR538xaIlKq2NhYXLhwAQCwdOlSTJ8+XdqCyGIxa4lIqTIyMhAZGQkAGDlyJDZt2gRra2uJqyKpWVlZGeyPLhdp8zBvpaXWiEZr2C3NvvNW4gn0b4VxXnV5B2SStSNHjkCtVuPtt99GeHg4XnrpJalLIgvFrC0cV6nJpUv2Pr/NuhPRBm9iNuZnElKuv/76CwkJCahZsyYiIyPxyiuvSF0SWShmbeEsOWufn+xkyZOfSN7YH0XGdPnyZQBA5cqVS9yWWUtESsX+KGXR/eqoGdm6dSsAoG3btjh8+HCBpoHmzZtjyJAh8PPzQ1hYGLZv3473339fqlKJiPTi5eWFXbt2wdHREZ6enlKXQ2bu9u3bWLduHYKCghAdHY2oqCh89dVXmD17Njp37ozRo0ejd+/epWquY9YSl4MnpapduzbCw8Nx6tQpjBw5UupyyIIxa4lIqcqXL4+QkBCsXr0aU6dOhUoly/sNkYGJojRNlcxbaeXddRgAwq7GAgDGedU1+30XheNkMierVq1C3bp18dFHH8HR0VHqcsiCMWv/42RXDj6NXbUZQaVTWBNzWTNeis8NJH8ffPABVCoVunbtCnd3d6nLIQvGrP2PrbUKlR1t0LRGBYxqW0fqciTTyr2SNs/yHhPJEfujqDgbNmwo9PubN2+Gra1tkc9Tq9V48OABgoKCIAgC3njjjRJfi1lLRErF/ihlEUSprraWQc2aNfHgwQOEhISgc+fOhW7zxx9/oF27dqhQoQKePXtm4gpNw83NDQAMspKD0vFiLJmzW7duoUaNGrCzs5O6FIujpPfRiIgIrF27Frt27UJaWpp26cVKlSph+PDhGDVqFF577TWd98eszaWkY0Rfq47e0l4EBYDpfo14EZRkKz4+HhkZGahevbrUpVgcS34fLQmzNhePESJlyMnJwa1bt9CwYUOpS7E4cnkfDQoKMuj+/P39ddqOeZtLquMkIOhMgSYkn8auCPRvZfb7LgrHySS1y5cvc3UBicglb6XArM3FY8RwjJG3UnxuIHm6fPkymjRpor3GRqbD99GiMWtzubm54VFiBtw+zD2/YMnjMfY/kZyxP0o6cstalUpV4DNZXouzrp/TRFGESqVCWFgYOnToUOy2zNpccjtGiKhw7I+SjrHfR2W5wk9cXBwAoEWLFkVu07JlSwBAUlIS1Go1rKysTFIbmSdj3bmJA2kqq7///hudO3dG69atsWvXLtjY2EhdEslUhw4d0KFDB6xcuRJbt27FunXrcOrUKTx9+hTLly/H8uXL0aJFC4wZMwZDhw6Fs7Nzsftj1pIx7qRIJIXY2Fh07twZWVlZOHr0KFxdXaUuiQgAs5aIlCMrKwvvvPMODh8+jMOHD6N58+ZSl0RmSNcJOobGvJVWae86rMs5VynuaMxxMklFFEXMmTMHc+bMwZYtWzB48GCpSyLSYtaSoeWtjpT/c0BZcSUE0sWuXbswZMgQTJkyBfPmzeOkHzIbzNrCWfJ4zEolYJxXXZ1+f7n1NMmtXtIP+6NIX/nXMcj7bFbS2gblypVD5cqV8cYbb2DSpEklTvYBmLVEpBzsj1I2WU74yczMhCAIKF++fJHb5F+6LzMzE/b29qYojcyUsS7Gcgl4KoszZ87A19cXz549w19//YWYmBh4eJT9wgVZNicnJ4wdOxZjx47F9evXsXbtWmzatAkPHz7EuXPncO7cOUyZMgX9+vXD6NGj0bFjx0L3w6wlXgQlJXjw4AF8fHxw9epV2NjY4J9//oGPj4/UZREBYNYSkTJkZGSgf//+OHDgAADg6NGjnPBDZoV5K63SNuzqcs7VGM3AJeE4maQgiiKmT5+OhQsXAgB+++03Tvghs8KsJUPTp4lZV1J8biB52bJlC0aMGAG1Wo3Q0FB8+eWXfK8is8GsLRzHY7qRW0+T3Ool3bE/ivSl0WgKPM5b8SclJcXgOcesJSIlYH+U8qmkLoDIFJ4f7Btq8F/YRCIiXRw/fhze3t549uwZ3NzcEBkZycEsGVzDhg2xcOFCXL9+HUOGDNHe6SI9PR1btmyBj48PGjVqhLVr15Z4FwyyPAHtPTDdrxF8Grtiul8jXgQl2blz5w48PT1x9epVlC9fHr/99hsHs0RERAaUmpqKHj16aCf7LF26FJ988om0RRGRWclr2A30b4VxXnV1viuvLudcS7vvsuA4mUxNo9Fg4sSJ2sk+I0eOxPr166UtiohIhqT43EDysXbtWgwfPhxqtRpvvfUWwsPD2cBJZKac7MpxPKYnufU0ya1e0g37o8gQPD094enpyZV1iIgKwf4oyyDLFX7y6LqMMpdbJmPduYl3daTSOHz4MHr27Im0tDTUqVMH4eHhqFOnjtRlkQL99ddfWLt2LbZu3YrExEQAuXcFrV27NsqXL49r167hxo0bGDt2LNauXYvff/8dFSpUKLAPZq3lMsadFIlM5datW+jUqRPu3r0LR0dH/Pbbb/Dy8pK6LKJCMWuJSI6SkpLQvXt3HD9+HIIg4KeffsJ7770ndVlERWLeyktR51zVGhGBx6IKnOM1ZcMux8lkSmq1Gu+//z4CAwMBAOPHj8eKFSugUvE+fmSemLXKIXXeEpnSypUrMWHCBABAhw4dsH//fjg6OkpcFVHhmLVUGnLraZJbvVQy9keRoURERBj9NZi1RCRH7I+yHLKe8NO1a1edZu0Wt50gCAgPDzd0aWRmjHUxlkvAk74OHDiAfv36ITMzEw0aNEB4eDjc3NykLosUJC4uDhs3bsS6detw+fJlALmTfKytrdGrVy+MHTsWnTt3hiAIOH36NBYvXowdO3bg5MmTmDVrFpYuXVpgf8xaIpKbq1evwtvbGw8fPkSFChUQEhKCNm3aSF0WUZGYtUQkN/Hx8ejatSvOnDkDlUqFdevWYcSIEVKXRWZu9OjRBtuXIAj4+eef9XoO89Z8FdZUXNQ518BjUZgffA0AtA1AnHxDSpSTk4ORI0di8+bNAIDJkyfj+++/Z0MJmTVmrXIwb8lSLFq0CJ999hkAwNfXF7t27eLKPmQQWVlZCA0NxZkzZ/DkyRNkZmYWGMNmZ2cjOTkZVlZWL9yIsTjMWiA5IwdhV2OZT3qQW0+T3Oql4rE/igxp2rRpGDNmDOrXr2+012DWEpHcsD/KsgiiKIpSF6EvlUplkAsboihCEASo1WoDVGV6eR+CY2JiJK6EiHSh0Wjw1ltv4c8//8Srr76KsLAwVK1aVeqyLJpS3kc1Gg2Cg4O1q/RkZ2cjL94bNWqEMWPGwN/fH5UrVy70+StWrMDHH3+MWrVq4fbt2wCYtXmUcowQWZLx48fjp59+wksvvYTQ0FC0aNFC6pIsGt9Hi8aszcVjhEh+AgMDMXbsWJQrVw5btmzBwIEDpS7JosnlfVSq3GPe5jL2cVKWlQBWHb2lbSoGgI4Nq8BKJRS6n4CgMwXu9OvT2BWB/q0M94s8hysckFT+/PNPtG3bFjk5OZg5cybmzJnDyT4Sk0veSoFZm0tJx4ip85ZICnFxcWjSpAmePHmC3r17Y9u2bbC1tZW6LIumlPfRoKAgTJs2DbGxue+jheXb7du3Ub9+fahUKty9e7fEfgFmbS43Nzc8SsyA24dBAJhPROaO/VHmR+5Zm5eHb7/9NsaMGYNBgwYZbLI2szaX3I8RIkvE/ijzYuz3UVmu8FOrVi1e3DAyXswkMjyVSoXffvsNn3zyCZYtW1bk5AsiXd24cQNr167Fxo0b8ejRIwC5A0h7e3sMGDAAAQEBaNeuXYn7GTVqFD7++GPcv39f+z1mLRHJ1fLly5GdnY1PPvkEr776qtTlEBWJWUtEchUQEICYmBi0aNECvXr1krockglPT09Jco95axrPrwSgEUWoBEGnc8tnbscXeHzk+hPtfoDcOzbnnau+/TS1wLat3CsZ+lcpgCsckFRat26NLVu24ObNm5g+fbrU5RAVi1krL7pc/23lXqnAhB9j5y2RFCpXroxDhw5h5cqVWLlyJaytraUuiRRg7ty5+OqrryCKIhwcHNCoUSOcPXv2he3c3d3RpUsXhISE4Ndff8WHH35Y7H6ZtYVjPhGZN/ZHkaE5OjoiJSUFJ06cwB9//IGJEydi0KBBGDVqFN5+++0y7ZtZS0Ryxf4oyyLLFX4olzFngz1/Z8Xpfo14MZOolBISEuDi4iJ1GVQIud+dIO8uE3lR3qJFCwQEBGDo0KFwdnYu1b7keqcJY5H7MUJkKZi15ovvo1QSHiNE8sCsNV98HyVdGPs4eX4lgHquDrgZ+9/knOLOLT9/Hjq/eq6OcH/JHmqNqJ0IlPf9gS3djH6TKlOtcMCbbxEApKWloVy5crCxsZG6FCoE85ZKIpdjRJfrv8wlUipRFJGUlIQKFSpIXQoVQi7vo0U5c+YM3nzzTQiCgE8//RSzZs2CWq2Gs7Nzoddf16xZg3HjxqFPnz7YtWuXRFXLi5ubG1Izc9D/+/3MJyIzxvPI5kvuWZueno5ff/0Va9euRWRkpHY1HQBo2LAhRo8ejREjRsDV1VXiSuVL7scIkaVg1povY7+PqoyyV5K95++s+PxjItLNypUr0bBhQ1y5ckXqUkihnJ2dMX78eJw9exZ//fUX3n//fb0n+wDAkSNHcPjwYSNUSERkXIcPH0adOnWwf/9+qUshIiJSpFu3bqFZs2aYO3eu1KUQkZl68c7KBZuuiju3HNDeA9P9GsGnsSs6NqxS4Gc3Y1MQdjW2wGQfAHB/yR7jvOoavbnr+d/LWHeQzltJKOxqLOYHX0PgsSijvA6Zr6SkJHTt2hXDhg1DTk6O1OUQkYLpcv3XSiVgnFddBPq3MkneEpmCWq3Ge++9By8vL8THs++BDG/58uUAgOHDh2PBggUoX758sSsFvPHGGwCAS5cumaQ+pXCwLaeofFJrRKw6egsBQWew6ugtqDW8XzfJG/ujyJjKly+Pd999F0eOHMHNmzcxc+ZM1KxZE6Io4tq1a/j8889Rs2ZN9O3bF/v374dGo5G6ZCIig2N/lGWT5YSfOXPmYM6cObzwYUSmuphJpGTff/89JkyYgNjYWCxYsEDqckiBgoKC8PDhQ6xcuRLNmzcv0768vLzg5eWlfcysJSI5CA4ORvfu3ZGQkIAZM2ZwlTKSFWYtEcnBtWvX4OnpiTt37mDRokV49OiR1CUR6YV5axr5J+1M92uE/i3cCvy8uHPL+ZuKA/1bafdTz9WhyOeY6lz1879XQHsPo7wOb75l2Z49e4bOnTvj2LFj2LVrF06cOCF1SUR6YdbKC6//kiXKycnByJEjERgYiL///hsbN26UuiRSoMjISAiCgI8//lin7fPu/Pzw4cMSt2XWKhdv/kBKwv4oMqU6depgzpw5iI6ORmhoKAYPHgxbW1tkZ2dj37596NOnD9zc3DBt2jTcuHGjxP0xa4lIDtgfRYIoirK7RYBKpYIgCEhOToa9vb3U5ZSoe/fuOHDgAADA398f69evN8h+jbn8E5drJyo9URTx7bff4quvvgIA9OrVC9u3b4etra3EldHzuBxp0Zi1uXiMEJmv3bt3Y/DgwcjOzsYrr7yCsLAwvPzyy1KXRc/h+2jRmLW5eIwQma9//vkHPj4+ePLkCV566SWEhoaiRYsWUpdFz1HS+2hSUhLOnj2LJ0+eICMjAyNGjCjzPpm3uUx9nDx/bnlU2zpYdyJar3PNq47ewvzga9rHHRtWgZVKUOS56ud/1+l+jTDOq66EFZGpPHnyBF26dMGFCxdgZWWFzZs3Y/DgwVKXRYVQUt4aGrM2l1yOEX2u//JaMSlBVlYWhg0bhl9//RUA8MUXX+Dbb78tduUVkoZc3keLYmdnh+zsbDx9+hQuLi4AgNTUVDg5OUEQhBea8RISElCpUiVYW1sjMzOz2H0za3PJ/RgpTEDQGYRdjdU+9mnsikD/VhJWRKQ/9kfJhxLfR/NLTEzEli1bsHbtWpw9exYAtJ/53nrrLQQEBGDw4MEoX778C89l1uaS4zHCcStZCvZHyYOx30fLGWWvpPXLL79oA1ZO8u6syAuLRPoRRREzZszA/PnzAQADBw7E5s2bYW1tLXFlRMol16wlotLbunUrhg8fDrVajebNmyM0NBSVK1eWuiwixWLWElmev/76C76+voiPj4erqyvCw8Px6quvSl0WKdTVq1cxbdo0HDhwABqNRvv9/BN+rl+/joEDB8LW1haRkZGFXpiVOyXl7fPnlvNPaAm7GguNKEIlCMVeiM1bTae4bYq7oCuni72F/a6kfA8fPoSPjw+uXLkCGxsbbN++Hb1795a6LCJFU1LWlpY+13/zVh0AoG1G5nVjkpOMjAwMGjQI+/fvBwB8++23mDFjhsRVkVLZ29sjMTERaWlp2gk/xclb2adixYpGrsy0mLXFe36c2rJ2pQITfvRdeU9O415SJvZHkTmpUKECxo8fj169euHTTz/F1q1bAeQep3/88QdOnjyJyZMnY8KECZg2bZosJvYUhllbEMetZAnYH0V5VFIXoGTx8fH45JNPUKFCBTRu3FjqcojIyERRxKRJk7SD2REjRmDLli0czJLRHDt2DFZWVvDw8CjQFFUYtVoNDw8PlCtXDidPnjRRhcbHrCWyPOvWrcPQoUOhVqvRpk0bHD58mINZIiNi1hJZnj/++APe3t6Ij49HjRo1EBkZyck+ZDQhISFo3bo1fvvtN6jVaoiiiMIWpG/YsCGsra1x7tw57N27V4JKjUvpeXvmdnyBxzvPxWB+8DWEXY3F/OBrCDwW9cJz8hqSA/1bYZxX3UKblvIu6Ba2n+J+Zm50+V1JWe7evQtPT09cuXIFdnZ22Lt3Lyf7EBmZ0rPWGP6Mji/2MZE5S0tLQ69evbSTff73v/9xsg8ZlYdH7qT9v/76S6ftw8PDAUBR51tMkbXP0rKx6ugtqDUvnjeQg+fHqYCI6X6N4NPYFdP9Gul98wc5jXtJedgfReYkOzsbv/76K7p16wZ3d3ds27YNQO5x2q5dO3Tt2hUqlQoJCQmYO3cumjVrhgcPHkhctf44rn3R8+edn39MyqLWiFh19BYCgs7I+jOhPtgfRflxwo8RTZ48GbGxsZg/fz5cXV2lLoeIjGzv3r1YtmwZAGDcuHFYt24dypXjQmpkPNu3b4coihg5ciRUquIj3crKCqNHj4ZGo9EObpWAWUtkWaKiojB27FiIoghPT0+EhobqdLc8Iio9Zi2RZcnMzMTgwYORlJQEd3d3REZGomHDhlKXRQr14MEDDBo0CKmpqWjbti0iIiLw+PHjIrcfPHgwRFHEwYMHTVilacg5b3W5yPbiXYoLTmgp7YXY4i7o8mIvmbNx48bh5s2bcHBwwIEDB9C1a1epSyJSPDlnrVQ0z03Cfv4xkTmbP38+Dh06BAD44YcfMHnyZIkrIqXz9fWFKIpYvHhxidsmJSXhf//7HwRBQLdu3UxQnWmYImszs9Wyntjy/Lj07J1nZbr5A8e9JCX2R5E5+PvvvzFx4kRUr14dgwcPRkhICNRqNSpXrowpU6bg6tWriIyMxIEDB3Dnzh18+umnsLGxwa1bt2Q5GZzj2hc9f95Z39XySF4sbbIz+6PoeSad8BMdHY0///wTkZGRpnxZSYSFhSEoKAhvvvkmxo0bJ3U5FsUSZ3KSeejduzcmTpyIiRMn4scffyxxAgZRWR0/fhyCIKBz5846bZ+33bFjx4xZlskwa4ksj4eHB37++Wf4+voiODgYTk5OUpdEpGjMWiLLkP88yvpTMdi6bTtatGiByMhI7R1qC9uW51yorP73v/8hJSVFe1cyT09P2NvbF7n922+/DQA4d+6cqUo0CbnnrS4X2QLaexS4a3H/Fm4Ffl7aC7HFXdDlxV4yZ4GBgWjRogVCQ0PRsWNHqcshUjy5Z61Unu855gJ0JCczZsyAr68v1q1bh/Hjx0tdDlmAjz/+GA4ODjh27Bj8/f2RkpJS6HbXrl1Dly5dcOfOHVSqVAljx441caXGYeqsNcXEFmOcAzP0OJXjXpIS+6NIKgkJCVi5ciVatmyJFi1aYMWKFXj69CkEQYCvry927NiB+/fvY9GiRQVuZla9enUsXLgQQUFBEEVROzlcLjiuLdzz5531XS2P5MXSJjuzP4qeZ/Sp1bGxsZg7dy62bNmC+Pjcf2CCICAnJ0e7zY0bNzB16lTY2tril19+kf2M7/T0dIwbNw7lypXDqlWr+KHWxPIuMgNA2NVYAMA4r7pSlkQKptFotP/GBUHAkiVLtF8TGVtMTAwAoH79+jptX7du7nvh/fv3jVaTqTBriSxL/rz19/fHiBEjmLVERsasJbIcgceiMO/AFQiCCmFXYzHdrxH++uuvQrOW51zIkEJCQiAIAmbNmqXT+eC8CWh37941dmkmo4S8Lewi2/PvC1YqAeO86mq/r9aIUAm527Zyr1TshVi1RkTgsagC2+bd9Tjvefl/lrf9n9Hx6NiwClQC0LrOS7zYS5LLP66tUaNGkVlLRIalhKyVSus6LyH82pMCj4nMWf6stbOzQ3BwMLOWTKZq1apYu3YthgwZgk2bNmHXrl146623tD8fOHAgbt26hX/++QcajQZWVlbYsGGDIpr2pMhaU0xsMcY5sOfHsKPa1sGqo7cKHe/quj+NCOw8FwNAhEYUodaIeq8UZGrFjfPJvLE/iqQUGhqKtWvXYt++fcjMzIT4/1cgrVmzJkaNGoXRo0ejVq1aJe6nV69eAICHDx8atV5D4ri2aM+fdyZla+VeSfu5LO+xErE/iopi1Jk1Z8+eRc+ePfH48WNtyBamQYMG+Pfff3Hjxg0EBwejZ8+eOu3//fffL/PkIEEQ8PPPP5dpH8/76quvEBUVhalTp+L111836L6pZLpcZCYyhIyMDAwePBidOnXCxIkTAXAgS6aVd3coXY+7vO0SEhJ0fg1mrfnhSVCyJKIoYubMmbh//z7Wrl1b4CQykVIwa4lIatu2bcPj/ZvhOmA2VLb2xZ5H4TkXMqQ7d+4AAN544w2dtndwcAAApKam6v1azFvjKc1FNn0uxBbXZFXYflYdvaXdHgCm+zXi+xRJ7uzZsxg1ahR2796tvSEPx7WkNMxa+SnpPHNhE2uJzNWjR4/Qo0cPLFiwAD4+PgCYtWR6AwcOhIODA0aPHo3Y2FiEhYVpf7Zr1y5tz1TVqlWxfv16+Pr66rV/Zi1ga21lsrv3G+Mc2PNj2Pzj19JMKrJSCVAJwM3Y3J6BhSHXoRIEsx8D84ZC8sT+KJJa165dIQgCRFGEtbU1evbsiYCAAPj6+up1LNrZ2RX5M2YtkXlT+nkK9kdRSYw24SchIQE9evTA48ePUb9+fcyYMQNNmjRB69atC91+0KBB+Oabb3DgwAGdJ/xs3rzZILUaMmTPnTuHJUuWoFatWpg9e3aZ9+fm5lbkzx4+fIhq1aqV+TWUxlJmcpK00tLS0KdPHxw6dAj79u1Dp06d0LRpU6nLIgtTuXJlPHjwAP/++y9eeqnku+v9+++/AIBKlXR/X2TWml/W8iQoWQpRFDFp0iQsW7YMANCxY0f4+/tLXBVR7kWNW7duITExscDKtYXx9PQscX/MWvPLWiJLsn79ekSs+hKiRoOEYxtRyWdcsedReM6FDEnfixR5N68ozR2Qmbdlz9uimoKNfZFN3yYrTkwkc/PHH3/Az88PSUlJGDFiBI4fP86LtKRIzFr5jW1LOs/MOyWTXNy7dw/e3t74999/MWTIEERHRyti1RSSp27duuHOnTvYunUrwsLCcO3aNSQmJsLR0REeHh7w9fXF8OHDUb58eb33zazNzVpT5ZIpzoEZYvwqxzGwHGu2dOyPInNRv359jBkzBv7+/nB1dS31fqKjowv9PrNWfuNasixKPk/B/ijShdEm/CxZsgSPHz9G48aNcfLkSTg7Oxd790UvLy988803OHPmjM6vUdyqQVJQq9UYO3Ys1Go1VqxYob3rJJmW0mdykvSSk5PRo0cPREZGAgBWrlzJwSxJolWrVti7dy82btyINm3alLj9xo0bAQAtWrTQ+TWYtebH0k+CcoUjy6DRaDB+/HisXr0aADB27Fi8++67EldFli40NBTz58/HH3/8UeJEHyC3iVmX7Zi1RCSVH3/8ER988AEAoN5rrfBWwCS0bVyr2PMoPOdChlS9enXcvHkTV69eRbt27Urc/vTp0wAADw/9jzvmbdkV1RScd5EtoL0HAo9FYdzGv0o9VitsvKdvkxUnJpI5iYiIQI8ePZCamoratWtjw4YNnOxDisWslR9LP89MyhAVFQVvb2/cvn0bDg4O2L59Oyf7kORsbW3h7+9v8AY9Zq1pmeIcmCHGr3IcA8uxZkvG/igyF0ePHkX79u0Nsq/atWsX+n1mLRFJgf1RpCujTfjZt28fBEHAnDlz4OzsXOL2DRo0AFD0DNrCpKSkwN7evtQ1GtrixYtx7tw59O3bV+dVikoSExNT5M+Km3FryZQ8k5Okl5CQgK5du+L06dPaZThHjRoldVlkoQYOHIg9e/Zg9erV6NSpE/r371/ktrt378bq1ashCAIGDx6s82swa80vay39JChXOFK+nJwcjB49WjtJ8eOPP8bSpUvZFEWSWrBgAWbMmKHXiV5dt2XWml/WEilZXjP9ulUrcPqXpQCAzp07Y/fu3TpdLOI5FzIkLy8v3Lx5E2vXri1xwo8oili2bBkEQYC3t7fer8W8LXveltQUbIixWmH70LfJihMTyVwcPHgQffr0QUZGBurVq4fw8HDUqlVL6rKIjIZZK7+xrdzPM/PGUHT9+nV4e3vj/v37cHZ2RnBwMN5++22pyyIyGmatabPW2OfA1BoRGlFEPVcHAAL6t3Ar1fhVjmNgOdZsqdgfRebEUJN9isOslde4lmNCUgL2R5E+jDbhJyoqCgB0ujsjAO2koOTkZGOVZFRRUVGYPXs2nJycsHz5cqnLISIjiIuLQ5cuXXD+/HlYWVlh48aNeOedd6QuiyzYkCFD8L///Q/nzp3DoEGDMGzYMPj7+6NZs2ZwcnJCcnIyLly4gKCgIGzZsgUajQbNmzfH8OHDpS69VJi1uSz9JCjvPKls2dnZGDZsGHbs2AEAmDZtGubNm8fBLEnqzz//xIwZMwAAn3/+OUaMGIFXXnkFgiDgn3/+QXZ2Ni5evIiNGzciLCwMr7zyCtatW4cqVapIXLn+mLVEyhd4LArTvvwaCcdyTxw3fasj9u3bBzs7O4krI0v04Ycf4ueff8aGDRvQrl07jB49utDtcnJyMHHiRJw8eRLlypXD+PHjTVypYck1b0tqCjbEWK2ofejTZMWJiWQO9u7di0GDBiErKwtNmjRBWFgYqlWrJnVZRBZDrlmrC0M2NMn9PDNvDGXZLl68CB8fH8TGxqJSpUoIDQ1Fy5YtpS6LLJhKpYJKpUJSUpJOjcJqtRrW1tZQqVQ6rRJvbpSctaYSeCwKC0Ouax+rBJQq0+U4BpZjzZaI/VFkbkRRxL179wAANWvWLLZ/QKPRaCe2yPXmK8zaknFMSHLH/ijSl9Em/GRnZwMAbGxsdNo+b6KPXJeemzx5MtLS0jB37ly4uLggJSWlwM/VajWA3AvUeT+zt7eHSqUyea1EpD9RFNGzZ0+cP38e1tbW2LZtG/r27St1WWThBEHAnj170KFDB0RFRWHz5s3YvHlzoduKooi6deti7969sv1gyKzNZeknQeV+50kq3qRJk7SD2Tlz5mDmzJmyfc8i5Vi5ciVEUcSAAQMwf/78Aj+rU6cO7O3t0axZM7z77rtYv349xo4dizFjxuD06dMSVVx6zFoi5du8MUg72ce+YVu0HPMtJ/uQZF5//XVMnToVixYtwtixYxEUFAQfHx/tz1euXIlbt25h165duHfvHgRBwNdff43atWtLWHXZyTVvS2oKNsRYjeM9UoJz585hwIAByMnJQbNmzRAaGirLmwEQyZlcs1YXRa2GV5pJQHI/z8wbQ1muhIQEdOrUCXFxcXB1dcWhQ4fw2muvSV0WkV6rw5flOeZAyVlrKswxMmfsjyJzdPDgQXTv3h1NmjTBxYsXi91WpVKhe/fuuHLlCg4ePFjgnLNcMGtLxiwluWN/FOlLEI00gvTw8MCdO3dw+vRpvPHGGwCA1NRUODk5QRAEbejk2b9/P3r37o2mTZvi77//LnbfKpUKgiAgOTnZbJbRa9asWYl1P+/8+fNo1qxZqV8zbxm94pbaIyLDOXr0KPr164dNmzbBz89P6nLIAJTyPpqYmIjp06dj3bp1yMzMfOHndnZ2GDNmDObOnatdUa8kzNpcSjlGlITL8irb7du34enpiY8//hhTp06VuhwyACW8j9atWxe3b98ucEI4LyeTkpJeuGnF5MmTsWzZMsybNw+ff/55kftl1uZSwjFCJCfLQy7is7FDUK5CVbzU7RN80f0VXgCROSW8j06bNg2LFi2CKIqFXszIO309Y8YMfPPNN3rtm3mbyxTHSVFjNX3GcEoa7ynpdyH9iKKIcePG4e+//0ZISAgqVqwodUlkAErIW2Nh1uYy1TESEHSmwORYn8auaOVeSTsJCACm+zWyiM/4q47essjfm3L9+OOPmDt3LsLCwtCoUSOpyyEDkHvW6puHOTk5sLGx0WmFH2ZtLrkfI89jjpG5Y3+U8sj9fXT06NFYv349vvvuO536CBYvXoypU6ciICAAq1evLnZbZm0uuR0jzFKSO/ZHKY+x30eNtsJP27ZtcefOHfzyyy/aCT/FWbVqFQRBgJeXl7FKIgvCC6pkDF5eXoiOjtZ5wgSRqVSoUAE//PADFi5ciOPHj+PWrVtISkqCs7Mz6tWrh3bt2sHR0VHqMokMQu53nqTiubu749KlS8xaMiuPHj0CADRo0ED7PZVKBVEUkZmZ+cKEn1GjRmHp0qXYsWNHsRN+iIik8GGXVyGu/gX/PEpHa4/KL6zQQSSFBQsWYPDgwVi2bBnCw8Nx//597c9eeukldOnSBZMnT0bLli0lrJJKUtRYrbCVCIoaz+k73jPnc8D6/N6kLIIg4Mcff0R6ejrPxxGRwRW2Gp6x72psrnlb0uqDpGzjx4/HsGHDeB6ZZCs2Nve9nJ8Xlauk/MzLrT+j46ERRfwZ/VT7fUPlrLlmOMkD+6PI3Jw5cwaCIKBjx446bZ+33enTp41ZFkmIY0KSO/ZHkb6MtoZbQEAARFHEihUrcOjQoWK3XbhwIQ4cOAAAeO+994xVklFduHABoigW+SdvIpO/v7/2e2WZUUvFy7ugGnY1FvODryHwWJTUJZEM3bhxA8OGDUNaWpr2ewxYMmdOTk7w8/PDhAkT8MUXX2DChAno2rWrYk4WM2vlT60RseroLQQEncGqo7eg1hhloUmLoYT/nwkJCRgyZAju3bun/R6zlsyNRqMBUPDia97XeRdm86tRowYAICpKfmMQZi2R8uTk5GD8+PE4fvw4gNxm+ol+r+HnUW9inFddXuQns9G8eXOsX78e9+7dQ3JyMmJiYpCQkIAnT55g8+bNiprso4S81WcsUlgTcnH70Wff5nwOuLjfm5RnyZIlCAwM1D62srJSzPk4IjlSQtYWJaC9B6b7NYJPY1dM92uEgPYeaOVeqcA2zz8uK3PN27yJwoH+rTi2sQAHDx7E5MmTtat/AjyPTOapsFVr8xNFEffv38fXX38NAGjYsKEpyjI4JWetoZSUn3k51rpOJRy5/gTh154YPGfNNcPJPLE/isxd3moJderU0Wn72rVrAwAePHhgtJqMiVlbMo4JSW7YH0VlZbQVfry8vDB8+HBs2rQJ3bp1w9ChQ9GpUyftzw8cOICbN29i69atOH36NARBwIQJE/Dqq6+WuO9atWpBpVJBpTLafCWSOWPfzYqU79KlS/Dx8cHjx4+hVquxdetWqUsiMilmLRWmrHeCKuoOx7zDVOnI/Y7RcXFx6NKlC86fP49//vkHf//9N6ytraUui+gFVatWxb179/D48WNUqpTbNFO7dm1cunQJFy5cQKNGjQpsf/v2bQBARkZGsftl1hKRsWVnZ2P48OHYvn07Nm/ejMuXL6NmzZpSl0VUIgcHhxdW0Cst5q1x6DMWKWwlguL2A0DnfZvzOeDifm9Slrlz52LmzJkQBAF16tSBt7e31CURmRSz1rQKWw1vVNs6OBX1FBfvJ6JpjQoY1Va3BjhdmXPekmXYu3cvBg0ahKysLFSrVg2ffvqp1CURwcrK6oXviaKo16RvQRAwcODAErdj1sqTrvlZ1HZFXTvV55oqM5x0xf4okoOsrCwAgFqt1mn7vO1SUlJK3JZZS0TGxv4oMgSjTfgBgMDAQGRlZWH79u3YtGkTNm3apL2jRc+ePQFAexeW4cOHY/HixTrtN6+BigzHnBptDVFLaS6omtP/A5LWuXPn0KVLFzx9+hRVqlTBF198IXVJRDp5+vQpTp48idu3byM5ORlOTk5wd3fH22+/rW1Q1hWzVt6MlWllnWBS1IlluU9ckYqcT9Q/evQInTt3xqVLl2BtbY25c+dyMEtmq1mzZrh37x7u37+Pxo0bAwDefvttXLx4EatWrcKQIUMKbP/9998DKPkOU8xaIjKEoj73ZWZmYtCgQdi3bx8A4NNPP4Wbm5vE1RKZHvPWOPQZiwS099Buk/c+VdR+dpyNQe1K5V/4XlFjWn3PAZvy/G9xvzcpgyiKmDlzJubNmwcA6NevH9q3by9xVUSmx6yV3roT0Thy/QkA4Mj1J1h3Itqg5wg5iZWktG3bNgwfPhw5OTl4/fXXMXLkSKlLIgKAAqtN6fL951lZWWH48OGYNGlSidsya+VJ1/wsaruirp0a6gYcRHnYH0VyUbVqVdy5cweXLl1Cx44dS9z+8uXLAIAqVaqUuC2z1rjYk0uWjv1RZChGnfBjY2ODrVu3YsiQIVi6dClOnjyJ7Oxs7c9VKhXatGmDKVOmoF+/fsYshUpgTo22hqilNBdUzen/AUnn1KlT6Nq1KxITE1G9enWEh4e/cNd2InNz584dfPrpp9izZ0+hd7OwsrJCv3798N1336FWrVoSVEimZqxMK+sEk6JOLMt54oqU5HqiPiYmBt7e3rhx4wbs7Oywa9cu+Pn5SV0WUZH8/Pywf/9+nDp1Cj4+PgCAgIAArF69GpGRkfD09MTAgQOhVquxb98+HD16FIIgYPDgwRJXTkSWoLDPfe+2qoa+ffsiNDQUALBo0SJMnTpVshqJinLjxg28//77cHZ2xq5du4q9g6JarUa/fv2QkpKCNWvWwMODkyekpM9YpLCVCIraz83YFNSsWHDCz83YFAQeiyr0+fqeAzbl+d/ifm+SP1EUMWXKFCxZsgQAMGzYMKxfvx7lyhn1khsRUaGMfW6Vk1hJKkFBQRg9ejQ0Gg1at26NkJAQVKxYUeqyiAAA69atK/B41KhREAQBP/74I2xtbYt8nrW1NSpXrozmzZvr1IBM8qVrfha1XVH5bqgbcBAB7I8ieWnTpg3u3LmDNWvW6DThZ/Xq1QCAN99809ilUQnYk0uWjP1RZEgmWYeuT58+iIiIQEJCAi5evIjjx4/jwoULiI+Px4kTJ/Se7NO8eXPtHUoNaffu3WjevLnB9wsAEREREEUR69evN8r+y6qwQaFUDFFL3gXVQP9WGOdVV7u07aqjtxAQdAarjt6CWlPw7irm9P+ApHH06FF07twZiYmJqFWrFiIjIzmYJbN38uRJNGvWDDt37kROTg5EUXzhT05ODnbs2IHXX38dp06d0mm/zFr5UmtE7Dh7r8D3DJVpzzdx6TvBJKC9B6b7NYJPY1dM92ukPbFc1v1aqqL+f5qz6OhoeHp64saNG7C3t8fvv//OwSyZvV69esHZ2RkHDx7Ufq9ly5aYNm0aRFHEiRMn8Mknn2DKlCk4evQoRFFEq1at8NlnnxW7X2YtERnC85/zTly9h+7du2sn+6xYsYKTfchsbd26FREREXBzcyt2sg+QeyOLWrVqISIiAtu2bdP5NZi3xqHPWKS4c7IB7T1Qz9WhwPYqQXjhe0WNaQs7B1wcnv8lQ9BoNPjggw+0k30CAgIQFBTEyT5ksZi10jPkudXCclufvC3pWiyRrn766SeMHDkSGo0G7dq1w6FDhzjZh8yKv79/gT95hg8f/sLP8v8ZOnQounTpotdkH2btf56lZcsmX3TNz6K2Kyrf9cl9fcfMZcXPAfLC/iiSm+HDh0MURWzbtg2LFy8udtulS5di69atEAQBw4cPL3HfzFrj4jlZslTsjyJDM+kViPLly+OVV14p837+/vtv9O3bF82bN8fHH3+Mfv36wdHRsVT7SklJwa+//orly5fj77//LnNtcmVOd4g3Vi0lzRY2p/8HZHonTpyAn58f0tPTUbduXRw+fJgroZDZe/bsGXr16oXExERYW1tjzJgxGDx4MF555RU4OTkhJSUFly5dwvbt2xEYGIjExET07t0b169fh4uLS7H7ZtbKV+CxKNyMTS3wPUNlWlnvBFXUHY55h6nSkdsdo+/fvw9PT0/ExMTAyckJwcHBaNu2rdRlEZWoWrVqePbs2Qvfnzt3Ll577TX88MMPuHDhAjIzM+Hh4YHBgwfjs88+g52dXbH7ZdYSkSHkP5chqnNwYsVkRF0+D0EQEBgYiNGjR0tcIVHRDh48CEEQ0LNnT52279WrF1auXIkDBw5g+vTpOj2HeWsc+oxFijsna6USMLBlTe3PAaB1nUpoXadSge8ZakzL879kCB9++CF++uknAMBHH32EpUuXljhpkUjJmLXSUmtEaETx/0+WFdC/hVuZzq0+n9unop7CSiVoz9mW1CjMOzeTIaxZswbjx48HAHh7e2Pv3r1wcHAo4VlE0oqOjgYA2NvbG3zfzNr/ZGartTmj9Hwp6tqpOV9T5ecA+WB/FMlRt27d0KVLF4SGhuLTTz/Fnj174O/vj2bNmsHJyQnJycm4cOECNmzYgOPHjwPI/SzZu3fvEvfNrDUunpMlS8T+KDIGo034GT16NARBwE8//QRra+sStxdFEWPGjIEgCPj555+L3TY4OBhTp07FuXPnMGrUKHzwwQfo1asX/Pz80Lp1azRs2LDY51+7dg1//vknDhw4gP379yMjIwOiKKJp06ZYtGiRXr+nUpjToNBYtZS0tK05/T8g03v11VfRpEkTpKWlISwsDNWrV5e6JKISLVu2DE+fPkWFChVw8OBBtG7dusDPbW1t4eXlBS8vL4wcORJdunRBXFwcli1bhlmzZhW7b2atfD2fd/VcHQ2WacaaYFLSftUaEYHHogpktLHvQkWG9/LLL6N9+/YICQnBwYMH0apVK6lLIiqzwYMHY/DgwaV6LrOWiAzh+XMZCVWGYMYX/2Djxo145513JK6OqHh37twBADRt2lSn7fNuJHX37l2dX4N5Kz1dzslqRGDnuRgAuU3LY9oZ5zwtz/+SIfTu3Rs///wzJk2ahAULFkAQeH6CLBuzVlqBx6KwMOS69rFKQJnOmz6f20euPwGge9NuSblPpAsvLy9UrVoVb7zxBn799dcSb6pDZA7S09ONtjIGs/ZFlpAvRV07NeebAfJzgHywP4rkauvWrfDz88Pp06dx4sQJnDhxotDtRFFEmzZtsGPHDp32y6w1rrxzsH9Gx0Mjivgz+qn2++z7IaVifxQZgyCKolHW0FSpVBAEAcnJyTrdxUKtVsPa2hqCIECtVpe4vUajwc8//4x58+bhzp07BS6qODs7o06dOqhUqRIqVaoEURQRHx+PZ8+eISoqCsnJyQBywx0APDw8MGPGDIz8f+ydd3gUVRfG39k0SIMkBJGahGCoKiQEEAOEHroUaUKIRJAiIGBBkS5VlPphMBC6CFIEpSa0iJQQQOmSiqGYCmmQsjvfH3GX3cn2ndl6fs/zPZ+7e2fu3ZDcd865p4wZY1GHM3Xr1gUAZGRkmHgl5o9YwiJya4LMMQ0As8Iak3FJKJCTkwOxWIyaNWuaeimEkbD0fTQwMBDXr1/HqlWr8NFHH2kcv3btWkydOhWtWrXClStXNI4nrbXM35Gos8kKlZDNSe/0Tdwx5+9E6EZZWRnS09Ph7+9v6qUQRsIS91FjQlpLvyMEIQR3794VLMiEMD8seR+tUqUKysrKkJOTo7ELLQA8ffoUnp6ecHJywvPnz7Weh/RWuN8TVTae9P3LqTlIyylGctbLLrTK7Dmy+QhL4+7duwgICLCofYAwDEvWW2NAWmu635HIrQkKlZK7NqmJ6HD9g0i4miyPNvcmTSf4Ijk5GfXq1YOjo6Opl0IYCUvXWjs7O7Rt2xZjx47F0KFDee9KRVpb8Tvy5NkL1J201WT6QgUK1UPPAZYFxUfZHpautVLKysrw3XffYc2aNXj06FGlz+vUqYNp06Zh6tSpsLfXvhcCaa32vyMU90MQ2kHxUbaH0FprsQk/UliWxfHjx/H999/j6NGjKCsrU/hcKprcr+no6Ig+ffpg/Pjx6Natm9bzmRPW8iBmDLgPDKEB3ogOb03Gt42zc+dONG7cGIGBgaZeCmEiLH0f9fDwQH5+PlJTU7VqsfzgwQP4+PigWrVqyMvL03oe0lrL+h0xZ2ezvgY83wfXhPG4ePEi0tPT9e6AQlg+lriPmgLSWvodIQh9ycjIwE8//YTp06db1MERwR+WvI++8soryM7Oxo0bN9C0aVON42/fvo3mzZvD09MT2dnZOs9Hesv/7wnXxvusZwBEDIO9iRlIyiysNF7eJytvu6blFCEp82VSENl8hDlRXFyMFStW4PPPP4eTk5Opl0OYCEvWW2NCWmv83xFd/a2afMfyn4slrM6FFM3ZN02YLyzLYsWKFRg9ejRq1apl6uUQJsLStVYaHwUAzs7OePfddxEREYG3336b13lsXWszC0owctVviBoVBEd7kdHXQIHK6qHnAPOG4qMIS9daZdy5cwfJycnIz8+Hu7s7/P39DS6GZutaC2j+HTGnuB/SHsLYqPudo/goQmit1T6NVWCKi4sBQOcDE4Zh0LNnT/Ts2RPFxcW4cOECzp07h9u3byMrKwtZWVlgGAbe3t7w9vZG8+bN0aFDB7Rt25baP5sxfIsxt3WsnYghcbdxNm7ciA8//BAeHh44f/48VT8mLJIXL14AgNZVoqTjSkpKdJqHtFZ/TGFcWmMr99Y+ngqGf2sfT97XRvDPuXPn0Lt3bzx//hwuLi7o06ePqZdEEGYLaS1BEPqQlpaGzp07IzU1Fc+fP8fs2bNNvSSC0ImmTZvi3LlzOHTokFYJP4cOHQIABAQE6DUf6S3/cG28fVczFBJ3uDCAyiBiecjmI8yFwsJC9O3bF2fOnMGff/6Jffv2UYItQaiBtNb4RIb4AYCC/1kd0fEpssAsqb9V3j8r71tW5tuWosrvbc6+acI8kUgkmDx5MjZs2IBt27bh/PnzqFatmqmXRRA6s3XrVmzZsgVnzpxBUVERtmzZgi1btsDf3x9jx47lLaHN1rVW8p8dGXM+1SRao+85p61AzwHmC8VHEdZKkyZN0KRJE17vaetaqw18x/0YElelycYlCL5R9TtH8VGEMTCbhJ9Tp04BAGrXrq33PZydndGlSxd06dKFr2URAqCNSPMtxhQoTMizevVqTJs2DQDw5ptvol69eqZdEEHoSa1atfDgwQNcv35dK+27du0agIoqyvpCWqsbZFwqoq8e63pwTZiekydPon///nj+/DkaNmyIFi1amHpJBKEVfn4V+wvDMEhOTlZ4T1fk76ELpLUEQWjD/fv30blzZ2RkZMDNzQ0dO3Y09ZIIQmd69eqFs2fPYvny5Rg0aBAaNWqkcmxSUhJWrFgBhmHQt29fg+cmveUHro1XkdKjmvTcYpxSkeTjX9MVPl7Oetl8VMmREIJnz54hLCwMFy5cAMMw6NWrFyX7EIQOkNbqjy66pmtgrS6BWeruzfV7X0zJUdrFj3SZUIdYLEZkZCS2bNkCAAgNDYWbm5tpF0UQejJq1CiMGjUKaWlpiImJwbZt25Ceno779+9j1qxZmD17NsLCwvD++++jT58+sLOzM3hOW9ZaXRJt+NQlIQKVCUJoKD6KIPTHlrVWHXzH/RgSV0XJuISxUfY751eaQvFRhFHgLeHn/fffV/r+hx9+CHt71dOIxWI8evQIv//+OxiGQadOnfhaEmGmaCPSfIsxBQoTUpYuXYpZs2YBqAgs+fnnn1G1alUTr4og9CMkJAQ7duzAnDlzEBISAkdHR5VjS0tLMXfuXDAMg5CQECOu0rYh41IRffWYKkJZFocPH8bgwYNRWlqKxo0bIy4uzqCkfoIwJmlpaQCgEMgnfU9XKBiQIAihuHXrFrp27YonT56gevXqOH78OIKDg029LILQmQkTJuCbb75BdnY23nrrLSxbtgwjRoxQqIT44sUL7Nq1C59//jny8vJQo0YNTJw40YSrJuTh2ngSFlh27K7sc2cHEYrLJLLXz56XqbzXkMC6ett8VGyD4JucnBz06NEDiYmJsLOzw9atWzFy5EhTL4sgCBtBma5FhvjpHUgsH4QslrAKn6XlFCHqbLLOgclcv/fpe1mIjk/B+I4NSZcJrSgrK8Po0aOxe/duAMAnn3yCZcuWkT+NsHh8fHwwf/58zJ8/H3Fxcdi8eTMOHjyI58+f49dff8Wvv/4Kb29vjB49GhEREbx3JbAVAht4aD2WT10SIlCZIISE4qMIa+bPP/9EfHw80tLSUFBQADc3N/j4+CAkJARvvPGGqZdn1fAd92NIXBU1ASCMDfd3zunRNfQZ9xHFRxFGgbeEny1btlRywLAsi507d2q8lmUrnIve3t748ssv+VoSYaZoI9J8izEFChMsy2Lu3LlYuHAhAGDgwIH48ccf1SZIEIS5M3HiROzYsQMXL15Ely5dsGbNGrRs2bLSuOvXr2Pq1KmyiqSTJk0ywWptEzIuFSE9tn727t2LESNGoLy8HK+//jpOnjyJmjVrmnpZBKE1c+fO1eo9giAIU3Ht2jV0794d2dnZqFGjBk6ePIk333zT1MsiCL1wdXXFTz/9hLCwMOTk5OCDDz7AxIkT0ahRI7i5uaGgoAD3799HWVkZWJaFk5MT9u7dC3d3d1MvnfgPro0nlrAQMfIJQCyWHbsnG1+tqgOyC0tlr0MDvGEnYgwu0ETFNgg+yczMRNeuXXHjxg3Y29tj9+7dGDRokKmXRRCEDaFM1wDoHUgsH4QMVOjvP3nPkZRZiKTMIiw5ehcSFgoarikBqHKXv5f6S7pMaKKkpATDhw/HgQMHAFT43qQF6wjCmpB2BMjPz8euXbsQExODhIQEZGZmYuXKlVi5ciXatGmDsWPHYujQoXB1dTX1ki0I/ZNU9dElbgefqFFBCjpJ2keYGxQfRVgzCQkJmDRpEhITE1WOCQoKwvr16xEUFGTEldkOfMf9GBJXRU0ACGMj/zsnSruIjXOmU3wUYTR4S/jp0KGDghPm7NmzYBgG7du3V9uO1sHBATVq1EBQUBBGjx6NGjVq8LIeiUSCv/76q1IWb4sWLXhpj0vojzYiTWJM8AnLsvj000/xzTffAABGjBiBrVu3qu0+RhCWQNu2bTFjxgysXLkSf/zxB4KCguDr64umTZvCzc0NhYWFuHXrFlJTU2XXzJw5E23atOFlftJazZCeEbbEjh07EB4eDolEgqCgIBw/fhyenrad5EZYHuaW8ENaSxCEPJcvX0aPHj3w9OlTvPrqq4iNjUXTpk1NvSyCMIhOnTohPj4eY8aMwe3bt1FaWopbt25VGte8eXNs2bIFrVq14n0NpLfCMfZtP4gYRtZR4PS9LNlnoQHeiA5vrVM3AVVQsQ2CLx49eoQuXbrg7t27cHJywr59+9C7d29TL4sgLB7SWt1QpmuGBBJzr7UTMfDxckZSZqHsvX1XM2SvtUkoigzxw8WUHAVtl+qvLrrMDaDWtdMQYXk8f/4cgwYNwtGjRwEAy5Ytw6effmriVRGEsLi7u+PDDz/Ehx9+iNu3b2PBggXYs2cPAODSpUu4dOkSpk2bhpEjR2L69Ol47bXXdJ7D1rQ2MT0XgPEq/2vq4EM2KWFOUHwUYc0cPHgQQ4cORXl5uazBgIuLC1xdXVFYWIiioiIAFUlB7du3x969e9GvXz9e5rYVrc0rLkNEzGWIGCDY18soNpohcVVUdJgwNtLfOZd/LiB8wTSKjyKMCm9Pc2fOnFF4LRKJAADHjh2Ds7MzX9NoJDs7G/Pnz8f27dtRUFBQ6XM3NzeMHj0ac+bM4S25iNANbUSaxJjgE4ZhZPvQ2LFjERUVZVUP24Rts2LFCtSoUQPz5s1DSUkJUlJSFBJ8pEauk5MT5s+fz8vBCWmt9pCeEbaEs7OzLOH/t99+Q7Vq1Uy9JIKwWEhrCYJQRpUqVSASiVC/fn3ExcXB39/f1EsiCF4ICgrCzZs3cfLkScTGxiI5ORn5+flwd3eHv78/unXrhi5duvA+L+kt/6gKghrfsSHGbrmsMFbEgLfDYiq2QfCFo6Mj7O3t4ezsjEOHDgmy9xCELUFaqx+qdE3fQGJVQciKHXpYhWs0JRTZiRhEh7eulKyjbv3K0BRATVgf9vb2cHJyAgCsXbsWkydPNvGKCMI4lJWV4cCBA4iJiUFsbCwYhpGd4TIMg6KiIvzwww/YtGkTZs6ciSVLlmh1X1vVWmNX/teUeEs2KWFOUHwUYa08fPgQI0eORFlZGapXr47PP/8cQ4cORYMGDWRj0tPTsWfPHixduhR5eXkYMWIE/v77b9SuXVvveW1Na0vKxLLCDnF3K/5faBuN4qoIS4TiowhTwLBSK5Jn5s2bB4Zh8OWXXxotS/zChQvo168fcnNzoe5rMQwDLy8vHD58mLcuB6agbt26AICMjAwTr4QgzB+WZXHo0CH07dtXlpBIENa0j2ZlZWH79u2Ij49Henq6QkWJkJAQvPfee/D29jZ4HtJagiDUcfLkSbRr1w6urq6mXgphJtA+qjuktQRBqOPq1avw8vJSOMQibBvaR/WD9FYYIrcmKAQPd2nsjWBfLySk5SI1uwjJWUWyz0IDvBETEaxwPVX5J8yBf//9FykpKWjXrp2pl0KYEaS3umOLWptXXIZvD/whiH7pqpHy4wMbeAJgkZiepxCELH8/Ccti2bF7sutnhTU2SrAV99mha5OaiA5vLfi8hGkpKSlBXFwcevXqZeqlEGaEtWrttWvXEBMTg127diEvL0+mibVq1cKYMWMwduxYODs7Y+vWrfj+++/x4MEDMAyDVatW4aOPPlJ7b9Ja49mKUWeTZQmqgHKdNMSeJVuY4BuKjyKUYelaO3PmTHz77beoXbs24uPj4evrq3JsamoqQkJC8PjxY0yfPh0rVqzQa05b1Nonz16g7qStsvfU2WikX4StQ/FRBBehtVawhB9j8/DhQzRr1gz5+flgGAbvvPMOBg0ahKZNm8ra9t2+fRv79u3DgQMHwLIsqlevjps3bxqUxWtKLP1BjCCEpKysDL/++iveeecdUy+FMGMsfR998OABAMDDwwNubm6Cz0daSxAElwMHDqBv377UBp5QCe2jukFaSxDWgyEBefLj4+Li8Pobb2L/rad0aEIohfZR3SG9FQ5uEFRogLesIiSXLo1rYtMYxcNibYKoCIJvbt++DYlEgubNm5t6KYQZQ3qrG7aqtdLAKGPolyZ7Q1dNNVWgFmm/bZCbm4tr165R5zxCLdaktXl5edixYwdiYmLw559/AqgIvrezs0PPnj0RGRmJPn36VOq4UVpaivHjx2Pr1q1o0qQJbt26pXIOW9XaopJyDPrmsFG1SixhsfFcCvZdzQDAYlCruhjXoWGluQ3RNNJDwlAoPorQBkvX2ubNm+POnTvYvHkzwsPDNY7funUrIiIi0KxZM9y4cUPn+WxVa7kJP+o0ifSLsDUoPorQhNBaK9hvXtu2bTF27FgMGzbMKEHIixcvRn5+PlxdXXHgwAGlDqM33ngDw4cPR2xsLAYOHIhnz55h8eLFWLduneDrIwjCeJSUlGD48OE4cOAAVqxYgZkzZ5p6SQQhCD4+PhCJRDhy5Ai6d+8u+HyktQRBSGFZFvPmzcOCBQswYsQIbNu2jdrBE1bBggULeL3fnDlzdBpPWksQ1kN0fIrsoENasVrdQYey8V5Z1zB8+HDU9msMttdXEDm5aHUvgiDUQ3prGOoCgqUdA6SfXU7NVXkfCctCLGEVgqQS0hTHJ6Tl0n5HCMr169fRrVs32NnZ4dy5c3jttddMvSSCsApsXWuNoV+a7A1dNdVOxGB8x4ZG113us4P0NWE9ZGZmolu3brh79y5++eUX9OzZ09RLIghBYFkWx48fx+bNm3H48GGUlpbKOgH4+Pjg/fffx/vvv682ANjR0RFLly7F1q1bkZKSonY+W9XaghfliL2TaVT/WHR8CpYdexnMLGIYpYlGyrQ3MsRPq4RasoUJQ6D4KMJWkBZE1jY2SjouPT1dr/lsVWudHOwQGuANEQME+3qptdFIvwhbgeKjCHNBsISfy5cvIyEhAR9//DEGDx6M999/Hx06dBBqOhw5cgQMw2D+/Pkaq8N07doV8+bNw8yZM3HkyBHB1kToBrX5I/jg+fPnGDRoEI4ePQqgopIFQVgrzs7OeP78OVq1amWU+UhrjQPpIWHusCyLzz77TNb6WiKRQCKRkEFLWAXz5s0Dw/C35+qa8ENaSxDWg64HHdzxP+7aifjo+ZBIJCguLUcViVjrexEEoR7SW8NQFmDMDWKKGhUks2Pj7mbKrm3o7YLkrCIAwOl7WYiOT1HYz1r7eMruKX1NEEJx+fJl9OjRA0+fPkWtWrXIj0wQPGLrWiuEfnF9xtykWq6NoI2mauOHFtpXbapEI8I4PHz4EF27dsXdu3fh6OiI8vJyUy+JIASjfv36ePToEYCKMxRHR0cMGDAAkZGR6Nq1q9b3eeWVVwBUdPtRh61rLWA8/5i2Pj5l2qttQSCyhQl9ofgowpYQiyvOSLTtrCGNXZBIJHrNZ6ta6+HsgJiIYK3Gkn4RtgDFRxHmhGAJP6GhoThz5gyKi4uxfft2bN++HQ0bNkRERATCw8N5b1335MkTAMDAgQO1Gj948GDMnDkTjx8/5nUdhP7oWv3WGFDQtWVRWFiIfv364fTp0wCAVatWYerUqSZeFUEIR926dXH//n2UlJQYZT7SWn5RpTHmqIcEIUUikWDKlClYv349ACAiIgI//PADGbOE1VC/fn1eE350hbSWIKwHXQ865McX/Hkc546vA8uyeOuttzDki/VYFf9Q63sRBKEeW9XbopJyRG5NMNjHqSzYCYBSO7Zyx58cWcKP9P3xHRvK7OPLqblaV5AkCEOIj49H7969UVBQgHr16iEuLg6NGjUy9bIIwmqwVa11crDDrLDGgugX12ccGuCt8DnXRtCmc442fmjyVRP6kp6ejs6dOyMlJQVVq1bFL7/8gm7dupl6WQQhGA8fVvhtmjZtirFjx2L06NHw8vLS615z5szR6KO2Va2Vx1j+Ma6PL7CBB6LOJss0NqK9L2LOpyq1Z8dvv6JwL1XJQqp0m+KVCHVQfBRha9SpUwfJycn4448/0L9/f43jL1y4AAB6xyiT1mqGOrYS1g7FRxHmhmAJP3FxcUhPT0dMTAy2bduGtLQ0JCUlYfbs2ZgzZw569OiBsWPHom/fvlpn3qrD09MTT548gaurq1bjXVxcZNcR5oE5tvkjR7bl8OzZM/Tu3Rvnz58HwzD4/vvvMW7cOFMviyAEJSwsDPfv30dcXBxGjx4t+HyktfyiSmPMUQ8JAqiomjN+/Hhs2rQJADBx4kSsXbsWIpHIxCsjCP5IS0sz6fyktQRhPeh60CH9fMsPG5B+bC2AimI6hw4dQlVnF1R1daNDE4LgCVvV24IX5Yi9k2mwj1NZQqMqO5ZbtV/Csoi7myUbF9jAA4CifQwAs8Iakx1MCEZsbCz69++P4uJi+Pn5IS4uDj4+PqZeFkFYFbaqtY52jEwT+Q7I5WqtiKnQS3kbQVlQsC5dRpX5oclXTehDUlISOnfujH/++Qeurq747bff0KFDB1MviyAEJSIiApGRkWjXrp3B95o3b57GMbaqtVJCA7yN5h/j+vgkrGLBi4spOTh976WdK2/PaioIxNVu+W65AMUrEaqh+CjCFgkNDUVSUhK++OILdOrUCdWqVVM5Nj8/H1988QUYhkHnzp31ms/WtVYbqGMrYc1QfBRhjgj629egQQPMmzcPKSkpiIuLw4gRI1ClShWIxWIcPXoUgwcPRp06dTBjxgzcunXLoLnatm0LALh69apW469cuaJwHWF6uMatOVSsVVWxkjAvcnNz0bVrV5w/fx4ikQhbt24lY5awCWbMmIHq1avjq6++krWJFxLSWn5RpTHmqIcEUV5ejtGjR8uM2RkzZmDdunVkzBIEz5DWEoT1ID3oiA5vLQt61zT+2aV9uLhzJQCgZ8+e+O233+Dq6qrzvQiCUA/prWE+zsgQP8wKa4yuTWrKuhhob8dy9y9G6XrIB0sIxW+//YY+ffqguLgYAQEBOHfuHCX7EIQA2KrWSpNrlxy9i+j4FF7vzdXWYF+vSjaCNChY2zVoo9/kqyZ05fbt2+jQoQP++ecfVK9eHbGxsZTsQ9gEmzZt4iXZR1tsVWul3fSiw1sbzT/G9cslpivaqzcePlN4LW/PRrT3RWiAN2q4OiI0wBsR7X0VxmrSbrKVCWVQfBRhq0ybNg12dna4e/cuWrdujQMHDqC8vFxhTHl5OQ4ePIjg4GDcuXMHdnZ2ene+slWtJQiC4qMI88Vov4GhoaHYsWMHnjx5gg0bNiA4OBgsyyIrKwurVq3C66+/jjZt2uCHH35AQUGBzvefPn06RCIRvvjiCxQXF6sd+/z5c3z55Zews7PD9OnT9f1KBM8oOyw2NeTItgzS09Nx79492NvbY/fu3Rg1apSpl0QQRqFu3bo4duwYWJbFm2++iW+++Qa3b9/GixcvBJmPtJZfVGmMOeohQeTl5SEhIQEA8NVXX2HFihVgGAo2Jgi+Ia0lCNtFLBbj1KlTAIABAwbg4MGDqFq1qolXRRDWCemtYT5OZUmI2tqx3MAo6WvywRLGIj4+HiUlJWjRogXOnj2LOnXqmHpJBGGVkNbyH5CrjdbqGhSszT3JV03oyl9//YUnT57Ay8sLp06dQps2bUy9JIKwSmxVaz2cHRAZ4ofo+BREbk1A1NlkiCWsUdfAtVdb1Kmm8vOY86k4fS8L2YWlOH0vCzHnUxXGcrV6b+I/Ct+HbGVCGRQfRdgqTZo0wTfffAOWZZGcnIzBgwfD3d0dr7/+Otq3b4833ngD7u7uGDRoEP7++28AwMqVK9GkSRO95rNVrVWHWMIi6myyyTSYIIwFxUcR5grDsqzJdt67d+9i8+bNskQg6R9F1apVMXjwYIwfP16nKhibNm3CpEmT0LhxYyxbtgzdu3dX+ENjWRbHjx/H559/jnv37mHdunUYO3Ys79/LWNStWxcAkJGRYeKVWC9iCYuN55Kx72oGAAaDWtXFuA5+VE3XDDl//jxyc3PRt29fUy+FsCAsfR+1s7OT/TfLslo/XDIMU6nShbaQ1vIHt017ZAjpC2He/PPPPzh06BAmTZpk6qUQFoSla60pIK0lCNuluLgYq1atwieffAIHBwdTL4ewEGgf1Q9b1NuiknIM+uawSe3PqLPJWHL0rux1aIA3osNbA4BZ2sdkt1sfLMti5cqViIiIgJeXl6mXQ1gQpLe6Y4ta++TZC9SdtBUAMCusMcZ3bGjUNXB1Vt0a5DUusIEHAAaJ6aR3BD9s374drVq1QrNmzUy9FMKCsHSt/fvvv/Hhhx/C3d0d+/fvV1v9WywWY+DAgSgsLMQPP/wAPz/9kiltUWsB4KudZ7XWO13Q1v7jjoto74uY86lKr4vcmoDYO5mya7s2qYmoUUGy68USFqfvZSncX/77kE1KqILiowh9sHStlbJnzx5MnToV//77r8oxr7zyClavXo13333XoLlsWWuVaQ/X5vSv6YohgXV50yfSPcKcoPgoQh+E1lqTJvyUlpZi//792LhxI86ePQugQggByMSxffv2WLt2Ld544w219+rcuTMA4P79+3j48CEYhoGLiwsaNWoEV1dXFBYW4v79+ygqKgIA1KlTB/7+/irvxzAM4uLiDP6OQmItD2Lmji4OcsJ4PHjwANWqVUO1atU0DyYIFVj6Pqpvq0iGYSAWi3W+jrTW8iCDmDCEoqIiZGZmwtfX19RLISwYS99HAeDcuXM6X2Nvbw93d3fUqVMHHh4eWl9HWksQtoVEIsHdu3fRtGlTUy+FsGAsZR+V6mlISIjJK6GR3poOsYRF5NYEhYAmc/a1kl/YOrh58yaaN29u6mUQFo657KOWgq1qramTa3UJVObqsTykd4Su3L59GwEBAQpF6ghCVyxdaxcsWIB58+Zh4sSJWLduncbxH330Ef73v/9h0aJFmDVrls7z2bLWNpy6HdmFpbL3uzapKSskYQjKClTYiRiDdF1ZYHQ9j6oKGuzl4oicIv6/D2FdUHwUwQeWrrXylJWV4fDhw4iPj0d6ejoKCgrg5uYGHx8fhISEoE+fPgYXVbNlra02dpPsPXn7kJvIqmwMoH+cEvliCVNC8VEEHwittfaC3FUDV69exebNm/Hjjz/i6dOnACoSferUqYOIiAi4urpiy5YtuHv3Ln7//Xe0a9cOZ8+eRevWqo2aM2fOgGEYWcIQy7IoLCzEtWvXlI7PyMhQ+0M19cGzJWNtwcXcNroJabn0MGFikpKS0LlzZ9SvXx/Hjx+Hi4uLqZdEECYhJibGqPOR1loe0fEpMoNYaniThhHakJ+fj169euHBgwc4d+4cfHx8TL0kgjAZnTp1MkizfHx8MGzYMEybNg3e3t5qx5LWEoTtIBaLMX78eOzatQtHjx5Fx44dTb0kghCUTp06QSQSIT8/H87OziZdC+mt6bATMZX8xObsayW/sOWzdu1aTJkyBevXr8fEiRNNvRyCsBlsVWtdnOx1DtDV5UxV1Vju+1GjgtSey0bHp6hM9gFI7wjdiIuLQ79+/TBixAhs3LjRYv5eCYJvjh8/DoZhtO620a9fP6xfvx5HjhzRK+HHVrW24EW5QrIPALT28eTl3lz7T6qVhpyvRrT3xcWUHFxKzUVxqRhJmYVIyixUGOPBSfjh6/sQ1gPFRxFEZRwcHDBw4EAMHDhQsDlsVWtLxYq9I+Ttw9Y+nkoTfrg2pL5xSuSLJUwFxUcRloLREn5yc3Oxfft2xMTE4MaNGwAqhNDe3h69e/dGZGQkwsLCZN0KPv30Uxw9ehQfffQRUlJSMHv2bBw/flzl/UePHm0xwmgKjJmEY23BxdyHFTKw9YeP38M7d+6gS5cuePz4MQoKCpCcnIzXX39doBUThHkTHh5u1PlIay0PbQxia0vUJQwnNzcXPXv2REJCAkQiEa5cuUIGLWHzGNIYNzU1FUuXLkV0dDQOHjyIdu3aqRxLWksQtkF5eTnCw8Oxa9cuAMDJkycp4YewCUzYaF4B0lthUWdjiiUsxBLF3wNz9rWSX9iyWb58OT777DMAwK+//ooPP/xQ727ZBEHoBmmt9uhypqpqrLp7KNNlrs+YizK9Ix8yoYwjR45g4MCBKCkpwblz55CXlwdPT3peImyT9PR0AECLFi20Gt+sWTMAFV079IG0Fqjh6ogPQvwQGeLHy/0CG3goDWIGdA84lurm3sR/kJRZpHbsoFZ1IWKgoLF8QzpuuVB8FEGYDlvVWkc7xe8sbx9KNYqrb1wbUt/EHfLFEqaA4qMIS0LQhB+WZXH06FHExMTg8OHDKCsrkx3s+vv7Y+zYsRgzZgxeeeUVpdeHhYVh165daNu2La5cuaJ2ri1btvC9fKvCmEk41pZtK31YEdLA1gVLNsYN/T38888/0a1bN2RlZcHLywsnT57UaMxa8s+LIMwN0lrLQxuD2NoSdQnDyMzMRLdu3fDXX3/B3t4eu3btwuDBg029LIIwKampqUhPT8fYsWORmpqKoUOHon///mjcuLGsZfu9e/fwyy+/4KeffoKvry+io6Ph6emJpKQkHDx4EDt37kRWVhb69euHe/fuqQx+IK0lCOuntLQUw4cPx/79+wEAs2fPxoIFC0y8KoKwLUhvheFlQFOGrGJx7J1MXEzJgZ2IQWsfT0hYKHQUCA3wNrmvVR3m5hcmtINlWcyfPx/z588HAPTv3x8//fQTJfsQhBEhrdUeXc5UVY29nJqj8P7l1BzZPZT5frk+406v1UAbvxpITM9FYANPSFgWkVsTFM7UyIdMcNm/fz+GDRuGsrIytGjRAidPnqRkH8Kmyc7OBgCtO9pKx2VmKk8w0QRpLfBBiB/PWqQ6hkR6vqpt7Im8biojNMBbZidL7yGkrpKOWyb6xEcRhDXTtm1bjB07FsOGDYObm5vg89mq1ro42WNWWGOl/lCpXkW098X47Vdw4+EztKhTDRHtfRXuoW/iDvliCWND8VGEpSFYws8XX3yBbdu24fHjxwAqDlmqVKmCgQMHIjIyEp06ddLqPkFBQQCAp0+fCrRS28CYSTiUbSsslmyMG/J7mJCQgB49eiAvLw+vvPIK4uLiZJVv1GHJPy+CIAhD0cYgFuoZgRIuLY9Hjx6ha9euuHPnDhwdHfHzzz+jb9++pl4WQZic6tWrIzQ0FE+fPsW5c+fw1ltvVRrz+uuvY8iQIZg8eTJ69eqFiIgIJCYmonnz5hgwYADef/99hIWFITc3F2vXrsXcuXNN8E0IgjA1L168wKBBg3DkyBEAwNdff40vvvjCxKsiCILQDVW2nqqAJmmCT+ydTPjXdFX4zE7EmLWdKD3EJl+i5cCyLD7//HMsX74cADB06FBs374dDg4OJl4ZQRCEcnQ5U1U1Vsxpoij/WpnvN2pUkOy/Ff22DRF1NhlLjt4DoHimZm3FHgnD2LVrF0aPHg2xWIxWrVrhxIkT8PLyMvWyCMKkVKtWDdnZ2Xj06BGqV6+ucfyjR48AAC4uLgKvzDrxcnHkPQg4MV1R6/xrusDHy0XhfFXb2BNV3fT8a7piSGBdo5+Zko5bHvrGRxGENXP58mUkJCTg448/xuDBg/H++++jQ4cOpl6WVaLJHxpzPlXm8z19Lwsx51MVxuubuEO+WMKYUHwUYYkIVtJs6dKlePToEViWRfPmzbFmzRo8evQIO3bs0DrZBwBEIhHq16+PBg0aCLVUm4DrIBYyCScyxA+zwhqja5OamBXWWNBsW7GERdTZZERuTUDU2WSIJazmi3REarTH3snEkqN3ER2fwvscuqDMGLcU9P09/P3339GlSxfk5eWhbt26OHfunNbGrCX/vAiCIAxFahBHh7fG+I4NlTqPhXpGMDf9JNSTnp6ODh064M6dO6hatSp+/fVXMmYJ4j+WL1+O9PR0LFiwQGmyjzxt27bFggULkJqaKgsyBIAOHTpgxowZYFlWFuhPEIRtUVRUhD59+sj2gO+++46SfQiCsEhU2Xra+dwUfbdUKIrgE4lEgilTpsiew8eMGYOdO3dSsg9BEGaNLmeq8mM/6xkACQtEbk3AjYxnCuMup+bKzkyV+X7V+YxVnakZ85yZMG82b96M9957D2KxGO3atUNcXBwl+xAEgKZNmwIADh06pNV46biAgADB1mTNeLg48J4w07K+h8Lrd1rWraSV2saecHXSv6YrZoU1xvFpHVSe1woJ6bhlYUh8FEFYM6GhoQCA4uJibN++HaGhoXjttdewZMkSWSItYRw06aE2cUoEYUooPoqwVATr8OPq6orhw4cjMjISrVu3NuheaWlp/CzKhjFmyztjZtsao3uLuVW7sOQOSvr+Hi5evBgFBQXw9fVFXFwcfH19NV/0H5b88yIIefz8+Nu3GYZBcnIyb/cjLBuhnhHMTT8J9URHRyM5ORmurq749ddf0bFjR1MviSDMhv379wMA+vfvr9X4/v37Y8qUKThw4AAWL14se3/QoEFYtGgRkpKSBFknQRDCokv3QmVjT5w4gbi4OADA999/j/Hjxxtz+QRBELyhytbj+uDsmModBwa1qgsRwxjFR03YHn///Tc2bdoEAJgwYQLWrVsHkUiwmncEQRC8wD1TlRY6VGZ3yI+t6MRTubMeABSXimWf6er7VXWmZsxzZsJ8KS4uxoIFC8CyLDp16oTDhw/D1dVV84UEYQP06tULZ8+exfLlyzFo0CA0atRI5dikpCSsWLECDMNQYKGe1KlWBWIJy2sAMdfWvZyaA3uRov2qTeyJWMJCwgL+3q7IKy5FdWd7DGpVx+hdfeQhHbcsDImPIghrJi4uDunp6YiJicG2bduQlpaGpKQkzJ49G3PmzEGPHj0wduxY9O3bF/b2goVE2wSqbFIp6vRQ27M0Xc7cCIJvKD6KsFQYlmX5b4mCCoePs7OzELfmhYcPH8oOf+bMmWPi1ehH3bp1AQAZGRkmXonpiNyaoPAA0bVJTUSHG5ZgxoXrNJ8V1tikAcu2+MCTn5+PiRMnYunSpbLfe22xxZ8XoT2WtI9qE6DAMAyUyTr3fYZhIBaLeV2fMkhrbRs+9JP2cOMhrYT83nvvoW3btqZeDmFFWMM+6uLighcvXiAzM1OriqU5OTnw9vZG1apVUVRUJHs/Ly8PXl5ecHBwQElJCS9rI60lCGFQ9gwiX3AEePlso8vY1atXo3r16ggPDzfF1yKsFEvZR0UiERiGwffffw8nJyeD7zd69GgeVqUdtq633H1OwrJYduye7HPufrg3MQNJmYWV7hMa4I3o8NZk0xGCEhsbi5MnT2Lp0qVgGPpdI/jDUvTWUrF1rZVHW58q93xUGfqcmZI/ltBEUlISFi1ahP/9739mHQtCWB6WrrWFhYVo2LAhsrOz4enpiWXLlmHEiBGoUqWKbMyLFy+wa9cufP7558jOzkaNGjWQlJQEd3d3wddnLVr75NkL1J20FYD+cTuqtC5o0UlkF5bKxjk72qG49OV5urQTn/y1Ee19EXM+Va1fUEpogDfsRAzpK6ERQ+KjCEIdlq61XE6fPo1NmzbhwIEDeP78ucwPVKNGDbz33nt4//33jdody1q0tqikHNXGbpK991nPAEzo5K8wTp3dqK1Na27xuIRtQfFRhFAIrbWCJfyYO5cuXUK7du2MFvQsBNb2IKYPxhB/cm6bhqdPn6J69eqmXgZh5VjSPjp//nyVn23btg2pqalwdHREx44d0bRpU7i6uqKwsBC3b9/G2bNnUVpaCj8/P4waNQoAMHfuXMHXTFpr2/Chn2TkCwtpLWEMrGEfrVGjBvLy8nD8+HF07dpV4/iTJ0+iR48e8PT0RHZ2tuz9f//9F6+++iq8vb3x77//8rI20lrC1hHKXlf2DJKQlqu04Ii6sZKSIjCOzujW9BXei5MQhBRL2UelCT98wDAMysvLebmXNti63nL3uc96NoaIQaW9V7on/xCfohAk5WjHoL1/DUSNCoKjvWHdVshPS3ApLS1FeXk5BRwTgmMpemup2LrWyqNtoUOuPocGeOOfvOcKSbfkSyX4gGVZPHv2jPzIhOBYg9aeOXMGYWFhKCkpAcMwcHBwQKNGjeDm5oaCggLcv38fZWVlYFkWTk5OOHbsmNGqiVuL1son/PjXdMHxaR15O3uMiLmM0/eyZO87O9ihuOzlz0qZJmvjQ1SGf00XDAmsRzYtIYPObAljYA1aq4yCggLs2rULW7ZswaVLlwBA5ocOCgpCZGQkhg0bBjc3N0HXYS1am1lQgtoTtsje86/pgtjpnbS+hyabVpUPWYgi/wQhD2ktYQyE1lrB+tcVFhZi//79YBgGI0eOVNuZQCwWY9euXWBZFoMHD6bDGSuHz4NZY7SetRMxGN+xITnFdcSQf+cff/wRkyZNwtGjR9GmTRuBV0oQloGqBJ0xY8YgNTUVo0aNwrfffqu080BOTg4+/vhj7NixA6mpqdiyZYvAqyX4QH4fDWzgCYBFYnqeTnuqKYOh+NDPhLTcSq9Jj/nhypUr6NmzJ7799lujVkcnCEskKCgIJ06cwOzZs/H2228rVGXkUlJSgtmzZ4NhGAQFBSl8dvv2bQBAzZo1BV0vQdgS8hUzpQcIfDwrKHsGae3jqXBI0drHU+3YYwl3kbn7S1TxbYWgsG8MXhNBWAs2WnvKIpE/fJUnMT0X0eGtK+23qqoYl4pZnL6XhZjzqSr3aG1tV6H2fcIyefHiBQYPHoySkhIcPnxY7XM6QdgS5eXlsLfn5/j3f//7HyZOnMjLvQjtUGV3cFF1PsrVU4AfPzNhm7Asiy+++AJ79+7FuXPnULt2bVMviSDMmk6dOiE+Ph5jxozB7du3UVpailu3blUa17x5c2zZsgWtWrUywSqth6TMImw8lwwRw+h0Dqrq7DFqVBDCVp9DclYRACgk+wDKNVkbH6KqtUttW7JpCYqPIgjDcHNzw/jx4zF+/HjcvXsXmzdvxo4dO/DkyRNcuXIFV65cwccff4zBgwdj/PjxaNeunamXbNZIJFz/vW72oiabVpUPWZXtSxB8QPFRhLUgWMLP/v37ERERga5du8q6CajCzs4OO3fuxMmTJ+Hg4IDhw4cLtSzCDODzYJaSccwXff+dN2/ejMjISLAsi/nz5+PIkSOCrpMgLJmdO3di27ZtePfdd7F161aV47y8vLBt2zaUlJRg+/bt6NKli0ZtJkyPsn1U/r+12VPNLRhK1wQkbQ+4Cd04f/48evXqhfz8fHz11VcYMmQIqlatauplEYTZ8tFHH+HEiRNISEhA+/btsWTJEnTt2lWhqAXLsjh58iRmzZqFa9eugWEYfPTRRwr3OXjwIBiGobbQBMEjQiUHc59Brj14irTsYnQK8IYdAwT7esmC55Q9r3RvYI+vDn6Fspx/gIJMdKy1zOA1EYS1cOTIEXr2NBFFJeWI3JqgdRCUroev3D3Z0Y5BqZhV+FzVHq2t7UpFIQgpRUVFGDBgAGJjYwEAx44dw4ABA0y7KIIwE4YPH46ffvpJbSFGbfj222/xySefUMKPkZHaGZdTcyFhWVxOzZG9L6/dqs5Hlb3Hh5+ZsD1YlsW0adOwZs0aAMCGDRuwcOFCE6+KIMyfoKAg3Lx5EydPnkRsbCySk5ORn58Pd3d3+Pv7o1u3bujSpYupl2m5cMzYfVczkJRZkaCjra5xfXliCSuzlX28nGUJP0BFVwMfLxeVxYeV+QW5Wi5iAAkLhe5BUsimJSg+iiD4xc/PD61atcKVK1fw77//Aqh4ri0uLsb27duxfft2tG/fHmvXrsUbb7xh4tVaBoNa1dVpvKbi/Vz/bg1XR3wQ4idIkX+CACg+irAuBE34AaB18s6IESNw4sQJ7N27V+M1586dM3h90urKhPGxtINZU3ZHsGT0+Xdev349Jk+eDADo2LEjfvrpJ8HWRxDWwA8//ACGYTBz5kytxn/66afYu3cvfvjhB40JP6S1poe7j3I/00Y7zU1zdU1AMkYnP1vj1KlT6Nu3L4qLi+Hj44O4uDgyZglCA71798bHH3+M7777DtevX0dYWBiqVq2Khg0bwtXVFUVFRUhKSsLz589l10ydOhW9e/eWvc7Ly8OePXvg7Owse5+0liAMR6jkYOkzx97Ef5CUWYScolLkFJUiKasQ/jVdEOzrVWms9HkltDaL0E4dkfUwHS4uLvj111/RpHEAL+siCGugQ4cORu3uTnr7koIX5Yi9k6l1EJSuh6/cPbm9fw2FgKa0nCJEnU1W6lvV1nalohAEAOTn56NPnz6Ij48HUBGATMk+BPGSffv2ITw8HNu3b9f7HosXL5Z1r9UEae1L8orLVGqdtkgTeQDI/Khxdyv0lKuN2p5f8uFnJmwLiUSCDz/8ED/88AMA4MMPP8T8+fNNvCqCsCy6deuGbt268XIv0lo5KjUNrmxbatI1eV+eWMLK7NbYO5kIDfBWGDsksJ7O55jKknKlmr03MQNJmYWy98mmtW0oPoog+OPq1avYvHkzfvzxRzx9+hRARaJPnTp1EBERAVdXV2zZsgV3797F77//jnbt2uHs2bNo3bo1ANJaVYQGeGNcB91idDQV7+f6d6X+ZorNJYSA4qMIa0OwhB+piGnbBk9a5fjOnTsax3bq1EkrJzNhnljaway5dUewFHT9d/7mm2/wySefAAC6d++OAwcOGDUAhCAskZs3bwIAGjbUbk/y86swxLQxNElrhUGXJFJ1Ld+11U6hNFffZFhdE5Cokx+/HD16FAMHDsSLFy/QqFEjxMXFoV69eqZeFkFYBCtXrkSTJk0wa9Ys5OTkoLi4GDdu3Kg0ztPTE4sXL8a4ceMU3vfw8MDjx48V3iOtJQjDESo5WPoMkpCWK6sSKiUpswhLjt79r0InI5s7alQQ7v99D6GduuDRo0dwd3fHsWPHtPaLEQQhDKS3ytEmCErZ4auqa8QSFhIW8K/pCoDFoFZ1MfZtP8ScT5UFNEn3T6Cyb1Vb25WKQhB5eXno2bMnLl++DJFIhM2bNyM8PNzUyyIIs2PXrl1wcnJCdHS0ztfOmTMHX3/9NQCgdu3aGseT1r6kpEysUut0RRs/qqrzy9JyCcZvv4IbD5+hRZ1qCPLxMNjPTNgO5eXliIiIwI4dOwAA06ZNw7fffkt/5wRhQkhrKyMtSCFhgWXHXnam1UbX5M8eI7cmKHwmYhjMCmtcyeaUPxcNbOABgEFi+ssxmnRfOqeygGZ5qBix7UDxUQRhOLm5udi+fTtiYmJkZ7Ysy8Le3h69e/dGZGQkwsLCZN1vP/30Uxw9ehQfffQRUlJSMHv2bBw/fhwAaa08blXs0bVJTQWdijqbzJs2KfPvUmwuIQQUH0VYI4Il/Dx8+BAAUKtWLa3Gv/LKKwCAR48eaT0Hy1Yq30BYAJZ2MGtu3REsBW3/nVmWxcKFCzF37lwAQL9+/bBnzx44OTkZba0EYalIOwk8fPgQHh4eGsdLtfnFixdaz0Fayy+6GKry+2hgA08ALBLT83TSTqE0V1+D29KSfq2JAwcOYOjQoSgrK0OzZs0QGxur9XM6QRAVREZGYtSoUTh8+DDOnz+PtLQ0FBYWwtXVFQ0aNED79u3Rr18/nZ9jSWsJQn+ETg5Wl4C972qGLBko9k4mMpLvIurzCGRlZcHT0xMnTpxAYGCgIOsiCEJ3SG8V0cYWU2ZPqgo+io5PUQiyEjEMHO1FcsmTLysYK/OtcueKaO+r9CCZikLYNllZWejevTuuX78Oe3t77NixA0OHDjX1sgjC7Bg6dCh++uknxMTEoEqVKli3bp3W13766adYuXIlWJZFgwYNEBcXp/W1pLUv2Zv4j8FBUKr8qPJanJZTrHCNVGPHb78i61Zw+l4Wfk/KhqeLA3KLymRj/Wu6YlCrOpCwQOTWBAoqJgAApaWlGDFiBPbt2wcA+PLLL7Fw4UIKfiQIPcnPz8eDBw9QUFAANzc31K9fH+7u7nrfj7T2Jc3rVMP4jg0hlrAQMfqfg3L1NtjXU6nNqexcVPrfF1NyEB3eWisN1WTTUsCz9UPxUQRhGCzL4ujRo4iJicHhw4dRVlYm00d/f3+MHTsWY8aMkcUhcwkLC8OuXbvQtm1bXLlyRen9bR0XJ3tEh7eWvY46m2yQNinzJ3O1kGJzCb6h+CjCWhEs4cfBwQEvXrxAYWEhqlWrpnF8YWHFoZ9EItE4tmrVqnjx4gV69+6NwYMH67W+5ORkLFq0SK9rCcOwtINZCk7WD23/nc+fPy8zZocMGYKdO3fCwcHBGEskCIunYcOGuHXrFqKiorB27VqN4zds2CC7ThOktcKgi6HKh14KpbmqvoemylOWlvSrDZZQbSsrKwujRo1CWVkZWrZsiRMnTqBGjRqmXhZBWCROTk4YPHiw3tooD2ktQZg/kSF+kLAs9l3NQF5RGXKKSuU+fan3LCvB+rkfIy8rC6+88gpiY2PRvHlz4y+YIIhKkN6+RFl1RnUosydVHfCqs3W18a1y5zL0IJmwTmbMmIHr16/D0dERe/bsQf/+/U29JIIwS3bs2IGSkhIcPHgQGzZsgJOTE1auXKnxuilTpmD9+vVgWRYNGzZEXFwc6tevr/E60trKJGUWITo+xSDtkmr15dQcSFjgcmouJCyLS6m5OPNfMg8XqcbeePhM4f0yMYvcojI09HaBbw0XqqJMqGTDhg2yZJ9Fixbhyy+/NPGKCMLyEIvFiIqKwg8//IAbN24oBA4zDIMWLVpg/Pjx+OCDD2BnZ6fVPUlrK/NPbkXSq6HnoKrOLblnf5dTc1Xe4/S9LIN1XwoFPFs/FB9FEPrzxRdfYNu2bXj8+DGAiuScKlWqYODAgYiMjESnTp20uk9QUBAA4OnTp7L3SGtVY6g2aWN3UmwuwScUH0VYM4Il/NStWxd37tzBxYsXMWjQII3jL168CEC79vCtWrXCH3/8AQAIDw/Xa32XLl2ySpG1VYQMtrXG4GRz4u2338aiRYvw999/Y9OmTbC3F2xbIgirY9iwYZg9ezb+97//wcvLC7Nnz1b6N1ReXo5FixZhw4YNYBgGw4cP13hv0lph4NtQNVWyiarvoclYt5SkX11+rpZwMO7t7Y0ff/wRK1aswKFDh1C9enVTL4kgCJDWEoQlYCdiMKGTPyZ08q/0fCBhIetmwTAivDb8Kzw6uBzHftmHgIAApZ0pCIIwPqS3L+FWZ9QHVQe86mxdfXyr3Hkup+ZUugftq7bHqlWrkJSUhLlz56JHjx6mXg5BmC12dnb46aef8M477+DIkSNYtWoVqlSpgq+//lrlNePHj0d0dDRYlkVAQADi4uK0Oq8FSGtVYWiArtSPCkDme4y7W7n7qH9NF/h4uShobIs61WQdfuR59rxM4VmAgooJLpMmTcL58+fRrl07fPzxx6ZeDkFYHE+ePEHfvn1x9epVAJW7BLAsiz///BOTJk3C5s2bcfjwYZUdCOQhra0MX9agsnNLsYRF5NYEmZbG3slEaIC32vvwpaEU8Gz9UHwUQejP0qVLZf/dokULfPDBB3jvvfd0jn0QiUSoX78+RCKR7D3SWtVwtUksYSGWsFr7ZrWxOyk2l+ATio8irBnBnhw7duyI27dvY/ny5RgwYIDa6hRisRjLly8HwzBaZdsGBwfj/PnzSEhI4HHFhCUjZLCtpQQnWxISiQQMw8hawH/55ZdgWZZawhOEjsyYMQO7d+/GzZs3sXDhQmzcuBF9+vRBkyZN4OrqisLCQty5cwe//fYbnjx5AgBo3rw5pk+frvHepLXCwLehaqpkE1Xfw1oOiXX5uZrzd5ZIJDJHVd++fdGnTx/SWoLgifz8fDx48AAFBQVwc3ND/fr14e7urtM9SGsJwrKQ+gaklbCvpOWiUyMvZDx9gaSsIjwR1QDzzjKsuJiP4H9TZMlA5poQTBC2Auktv6gKPlJn6+rjW+XOI2Fh9oUWCGGQt2s9PT1x/vx5smsJQgscHBywf/9+9OnTB7GxsVi6dCmqVKmCr776SmEcy7KIiIjA9u3bwbIsmjdvjpMnT2oVfCyFtFY5fAXocn2PXIYE1qukiVGjgjB++xX8npSNMvHLYPPmdaopFCYIbEBBxYSi1trb2+Onn34irSUIPRCLxejduzeuX78OlmXRtGlTDBkyBM2aNYObmxsKCwtx8+ZN/Pzzz7h16xYSExPRp08fXLp0SSHgWBmktZWRsLoFG+tCdHxKpcRZEQPMCmuMhLRcBDbwqNRxjy8NpYBn64TiowiCH1xdXTF8+HBERkaidWvDihqlpaUpvCatVU1kiB8upuTItFHXznb6dH8nCH2g+CjCFhAs4WfChAn4/vvvceXKFbz77rvYvHkzqlWrVmlcfn4+xo4di4SEBIhEIkycOFHjvYODgwFUtN968OCBVi3lCfPG0A4F5hxsSyhSXl6O999/Hw0aNMDChQtl75PAEoTuODk54dSpUxgyZAjOnj2LJ0+eYNOmTZXGSStIdezYEXv37oWjo6PGe5PWCgPfhqqp9E/V97CWylO6/FzN9Tt/++23OHfuHPbu3StrBU9aSxCGIRaLERUVhR9++AE3btxQqNDIMAxatGiB8ePH44MPPlBb8EIKaS1BWCYbz1Uk8zxPScTT+O1o8+E3kLrXGIbB6XtZ+CfvucI15KMgCCA1NRUA4OzsbNR5SW/5RVXwkT62rjp/MHeey6nk+7VF7t27hyFDhmD79u144403AJBdSxC64OjoiEOHDqFnz544d+4c5s2bh6pVq2LmzJkAKgIhRo4ciT179oBlWbRs2RInTpyAl5eXTvOQ1r7EyUGEGq6OaFGnGiLa+/JyT67vUZ7QAG+FQGCxhMX3Z5Ox+fdUPC8To52fJxhGhFuPnqFFnWoI8vFQSKD9rGeALHiZgoptk6dPn6JPnz6YNm0aBg8eDIC0liD0ZfPmzbh27RpEIhFWrlyJadOmVRozaNAgzJ07F6tXr8aMGTNw9epVbN68GZGRkWrvTVpbmZTsYvRYdQ5DAuvy3gFWWbJtsK+Xgs07rkNle1YT2sREUcCz9UHxUQTBH0+ePBHMt0xaqxo7EVNJr3TxzVIyK2EMKD6KsBUES/hp3rw5Zs6ciRUrVuDgwYOIjY1Fv3798Oabb8LNzQ0FBQW4fv06Dh06hMLCQgDA9OnTZQc36pCKLMuySEhI0Etkvb29MXr0aPrDNhMM7VBgrsG2hCJlZWUYOXIk9u7dCwDo0qWLVl29CIJQTY0aNXD69Gns378fMTExOH/+PJ4+fSr7vHr16mjfvj3ef/99vPPOO1rfl7TWOBia8KpO/wy9tz5Yi7Guy3OFOX7nRYsWySq2rlmzBjNmzDDxigjC8nny5An69u2Lq1evAoBCso/09Z9//olJkyZh8+bNOHz4sMaKyKS1BGGZ7LuageL7F5H1y1JAXI6b+1bDvRdXaxX3CPJREATQoEEDk8xLessvugQfabJJ1fmDlc0Td5c/368p7GVCN27cuIGuXbsiMzMTw4YNw82bN7VKqicIQpEqVargt99+Q/fu3XHhwgV89tlnqFKlCj788EMMGTIEhw4dAsuyCA4OxrFjx1C9enWd5yCtfUlJmQTZhaU4fS8LMedTeQnWlfc9BjbwAMAgMV21tq44fk/2+tz9HIQGeOPK7G4V99qqWK06MT0P0eGtKajYRsnOzkb37t1x7do1XL16FW+//TZq1apl6mURhMUi7Y41ZcoUpck+8kydOhVpaWlYvXo1du/erXXCD2mtIkmZhTKbkk8t454RchNsAf0ScwyNiSIsD4qPIgh+EbKQFGmtegyJy6VkVkJoKD6KsCUYlhupxCMsy2LmzJn47rvvKiZTImjS6WfOnInly5cLtRSrpG7dugCAjIwME6/EcCK3Jig8GHRtUhPR4dq3X6RDWvPnxYsXePfdd3H48GEAwMKFCzF79mwTr4qwdaxpH5Xn2bNnKCwshKurq9LueoT2CP07EnU2WebcBSrawUeG+KnUNK7eRbT3Rcz5VKVjld1b3oi2RO001pot8WcDVDxXz549G4sXLwYAtOoUhtffm422/q9YzHcgrA9r0FqxWIzg4GBcv34dLMuiadOmGDJkCJo1awY3NzcUFhbi5s2b+Pnnn3Hr1i0wDINWrVrh0qVLsrbRhGqs4XeEsC40PQe0GDUHN3d+DbASONT0Q5tJK+FfrzZO38uSjfmsZwBEDGNxzxKEZUL7KKENpvg90WST6uIP5ttG07Q2wrQkJiaie/fuyM3NRc2aNREbG4sWLVqYelmEjWPpeltQUIDOnTsjMTERDMOgefPmuHHjBgCgffv2OHLkCNzc3Ey8Ssumbt26ePLsBepO2gpA93NOQ5Dq5A/xKcguLFX4zEEEzOzx0udM+kcAwOPHj9G1a1fcvn0bDg4O2LNnDwYMGGDqZRE2jqVrbc2aNZGTk4O//voLzZo10zj+1q1baNGiBWrUqIHMTOWd3AhFuForhW/NFeqM0NCYKMKyoPgowhyxdK0lhEfV74ih2qjt9ZYap0OYBm581JAhQ7Bjxw44OjqaeGWELSO01grW4QeoSPBZuXIlhg0bhlWrViEuLk7BWK1Zsya6deuGqVOnIigoSMilEBzMTSAN7dBD2cDmTXFxMd555x2cOHECAPDNN99QNi1BCEi1atUo0cdC4LaFl75WVeFJVfUnZfqn7N7y4yyxkpSx1myJzxUsy2L69OlYtWoVAKBN9wF4/EYETt/Pw+n7eQDM/9+XIMyVzZs349q1axCJRFi5cqXSCo2DBg3C3LlzsXr1asyYMQNXr17F5s2bNVZnJAjCdKjyi6h73tiyZQtu7apI9nF8NQA1352PER2aY1yHhkrvRdpLEIQto8km1cUfzLeNpmlthOn4448/EBYWhvz8fNSpUwdxcXEICAgw9bIIwuJxc3PDyZMnERoaij///FOW7NOpUyccPnwYLi4uJl6h9SFEl09tbBguZZKXvmZjdyo3t7NoooJ//vkHXbp0wf3791GlShUcOHAAPXv2NPWyCMLiefbsGQCgTp06Wo2vXbs2ACA/P1+wNdkKfGqukNplaEwUYTlQfBRBCMvTp0/x888/49KlS3j8+DGeP38Odf0OGIZBXFycEVdofRjqm9U2zscSY5gI08CNjxo1ahQ2b94Me3tB0yEIwuQY5Te8devW2LlzJwCgsLAQ+fn5cHd3h6urqzGmJ5RgbgJpbCczYTwKCgrQt29fnD17FgCwfv16TJw40cSrIgiCMA+UOXfVBR7pEpSkyXFsiQFOlrhmYyCRSDBx4kRERUUBAMaNG4fytu/jyb1s2Rj6WRGE/vz0009gGAZTpkxRmuwjz9SpU5GWlobVq1dj9+7dlPBDEGYM1y+yNzEDQwLr4nJqjsI4qYZu2LBBZss2eqM12n64HO2b1FNI7iGtJQjCnMkrLkPU2WSjBdtybdLABh6IOpus0LEWMI0/mAKtzJMzZ86gT58+KCoqgo+PD+Li4uDnR+cEBKErDx48UPlZdHQ0+vfvj8ePH+PNN9/Ehg0bkJOTg5ycHJXX1K9fX4hlWiVuVezRtUlNnXVN2+BiVWe7XJ+pMqR2jTHtFnM7iyaAlJQUdOnSBWlpaXBxccGhQ4fQuXNnUy+LIKwCT09PZGZmIiUlBa1atdI4PjU1FQDg4eEh9NKsktAAb9iJGINsSWX6q4126ZsURDFRtgHFRxGEsOzYsQMfffSRLGFWXaKPFIahogOmRts4H4oHIrRBWXzUhg0bIBKJTLwyghAeo6e0ubq6UqKPGUACSRiLkSNH4uzZs2AYBps2bUJERISpl0QQVotEIkFeXh6Ki4s1GrZ0UGseRLT3xcWUHNx4+Awt6lSTBTypCjziBiWJJSwityYodSgrcxzLO6HFEsXfEW6AkzlWYKSgLOUsXLhQZsxOmTIFq1atwsZzKYiTS/ihnxVB6M9ff/0FABg7dqxW4yMjI7F69WrZdQRBmCdcv0hSZiGWHL2L0ABvhfdb+3ji8OHDsoPZbt264eDBg3B2djbaWgmCIPigpEwsC1wSyg8sb0e2rO+BTgHeuPmfvSthgWXHtOtYq+tcutqslhxoZY62Oh8kJycjLCwML168gL+/P06dOoV69eqZelkEYZH4+PhoFdB0/fp1NG3aVO0YhmFQXl7O19KsHhcne0SHt1Y7Rt/gYkD12S7XZ6oMU/gG6SzavHjx4oUs2cfd3R1HjhxB+/btTb0sgrAaAgMDcfToUaxduxYxMTEax69bt052HaE9IhGD0ABvRI0KgqO9YUGdyvRXlXZxz1dP38tSuE6q5+rsNCoYZBtQfBRBCMexY8cQHh4OlmXBMAxatmwJf39/VK1a1dRLIzSgbZwPn/FA1upDJZTHR1FiH2ErUA8rG8XcAmbNucoTPQAYxvz583HhwgWsWbMGw4cPN/VyCMIqiY6OxubNm3Ht2jWUlpZqHE8HteZDzPlUmVP49L0sxJxPVRt4JP+ZMoeyvHYqcxxHnU2W6S2gWAEror2vQrVlCcti2bF7Ku+vLXzqqDGDsixJ/ydMmIDdu3djwIABWLx4MRiGsegANoIwN549ewYAqFOnjlbja9euDQCy6lIEQZgPYgmLjedSsO9qBvKKlD83ixgGs8IaKyZNl9dDr169IBKJsHfvXlSpUsXIKycIguCPy6nCBdsq8/ECFfbuP3nFCmMNDfo1xJ9syYFW5uxHNwQ/Pz9MmzYNhw4dQmxsLF599VVTL4kgLBptqhwT/KNNNz1dgou5qDrblfcDBjbwAMDgSlouJCwLEQME+3pVKgZlDH+nuZ1F2zpVqlTB0qVL8dFHH+HIkSMICgoy9ZIIwqoYOXIkjhw5gm3btqFGjRr4+uuv4ejoWGlcWVkZZs+ejS1btoBhGLz33nsmWK3lIvnvbDTmfKrBdpAy/VWlXfL6reo+1minEbpD8VEEIRxLly4Fy7IICAjAwYMHERAQYOol2RxCd7njM8bFWn2ohPL4KIKwFYyS8JObm4vt27cjPj4eaWlpKCgogJubG3x8fBASEoJRo0bB09MwJ58+7abt7e3h7u6OunXrIjAwEP3794e7u7tB67AUVHUUMBXmXOWJHgAMo2XLlkhOTraZvy2CMCZlZWXo168fTpw4AUD4g1zSWv5RpX/S/ykzmKWfRW5NUHqtLvPZiRhZ1Un5ZKDYO5nwr+mi8/2VwaeOGjMoy5L0v2bNmrh06RLc3NxkxqwlB7ARhLnh6emJzMxMpKSkoFWrVhrHp6amAgA8PDx0nou0liCEJTo+RdZdQoqXiwNyispkr4N9PStpqJ2jI/bt2weRSKQ0WIMgCMvC1vVWIqDvgGtzKqJ48GZo0K85+5OFxFq/N8MwWLx4Mb744gu4ubmZejkEYdHMnTvX1EuwWa3VppueLsHFXFQFPyn3A1aen+v/VbdOPqCCRObH0KFDERYWZnF/WwRhCQwfPhzR0dE4ffo0vv32W2zbtg19+/ZF06ZN4ebmhsLCQty6dQu//vorsrIqivl17twZw4YN02s+W9VaKXwUsuDqr1jC4nJqLkIDvMGABQsGl1NzZfOpu4+12mmE7lB8FEEIx9WrV8EwDNavX2+UZB9b11pl6BtDo23sCp8xLqTN1ouy+CiCsBUET/j57rvvMHv2bLx48QKAYjDytWvXcODAAXzxxRdYvHgxpk6dqvc8Z86ckf23/B+y/Hya3q9SpQo++eQTfPXVV7Czs9N7LZaAso4CpqzaL1SVJz7WRw8AupGRkYEZM2YgKioK1atXBwCrenglCHNi1apVOH78OADgzTffxOjRoxEQEABnZ2dB5iOt5R9N+qfOYNZHO9VdUzkwi59gLEvVUXNed3FxMcaNG4fZs2ejcePGAEhrCUJIAgMDcfToUaxduxYxMTEax69bt052na6Q1hKEsCgLRK9W1QEeLo4AGAxqVReRIX5gWRaffvopOnXqhN69ewOAQlcfS+oESBBEZWxdb3XdrnTZ87g2pzyDWtWFiNEu6FebOW21a4A1fe+tW7fi33//xaeffgqg4u+Lkn0IwnDMIeHH1rVWnR9R2T7OTYzhdmKX6qAhwU9iCYu9if9ovU7pNYbYPVSQyPRcuHABMTEx2LBhg+zviPzIBCEcBw8exNChQ3Hs2DFkZWUp9SVLNS8sLAy7d+/Wey5b11o+ClnI66/4v85BUkIDvGWv4+5motNrNRSu9XS2h6drFZkvEUCl5CGxhCV/oQ1A8VEEYTykWqVNcUY+sHWtVYY5x9BwsSYfqq1D8VEE8RJBE36+/PJLWTs9AGjQoAGaNWsmq2Bx8+ZNpKen4/nz55g+fTqysrKwaNEiveYaPXo0GIZBfHw8UlJSAACNGzdG48aN4erqisLCQty7dw937twBAPj5+eHtt99GYWEhkpKScOPGDTx//hwLFy5ESkoKtm3bxs8PwUzhS4D5qn4vVJUnPtZHDwDak5qais6dOyMtLQ05OTk4efIkZdIShIDs3LkTDMPgnXfewd69ewX/eyOt5Z/IED9IWGDf1QwALCSsogNYnV7ro53qruHqnS7BWOqwVB0113UXFBSgb9++OHv2LM6ePYs7d+7A1dXV1MsiCKtm5MiROHLkCLZt24YaNWrg66+/Vtrho6ysDLNnz8aWLVvAMAzee+89necirSUIw9AUkKYsED0lu1j23yIGkEgkCB04CvGHf8Sq1WuQmJiI11s0V7jGkjoBEgRRGVvX22BfL53G67LnyducgQ08ADBITFfck7XZL7WZ01a7BljL9/7+++8xYcIEAICPjw/effddE6+IIAg+sXWtTcspRtTZZKUJMsr2cW5ijBCdeKLjU5CUWaTwniZ/p752j6UXSLD09Us5c+YM+vTpg6KiInh4eGDZsmWmXhJBWD1ubm44cuQIfvnlF2zatAnnz59HXl6e7HMPDw+8/fbbiIyMRN++fQ2ay9a1lgGrNDlWGar2dXn9jdyaoHDN+aRshdf/5BUrvM4tLkducSFETEWCa2SIHy6m5CgUXY6OTyF/oZVD8VEEYVwaNmyIa9euIS8vT5ZgJyS2rrXKMNcYGmXo60O1FnvQWqD4KIJQRLCEn4SEBCxZsgQAEBwcjDVr1iA4OFjpuClTpuDSpUtYsmQJ+vfvj9atW+s835YtW7Bw4UJs27YNvXr1wnfffYdGjRpVGpeUlITp06fjt99+w+jRozFnzhwAQEpKCj766CMcPXoUO3fuxHvvvYfu3bvrvA5zRl6QxBLFihemrtovVJUnPtZnLYeoQvP333+jc+fOePjwIdzd3TF//nwyZglCYO7fvw+gomqjMf7eSGv5x07EQMQASZmFAIBlx+5BxDBadfHRRzvVXaPusNkQDNFRXY1pPo1vc9T/p0+fIiwsDBcvXgTDMJg/fz4ZswRhBIYPH47o6GicPn0a3377LbZt24a+ffuiadOmsmIWt27dwq+//oqsrIoDvc6dO2PYsGE6z0VaSxCGoSkgLTLED3uvZCApq1Dp9ZeSM7FlySe4ePwAAKDq6z3wR04VvM4ZZ0lVzAiCqIyt6q2Tgx1mhTVWatuos6V02fOU25y674/azGmrXQPUfW9LOZBetWoVPv74YwBA165dZd30CIKwHmxVa+3tKvbcpMxCmV2ij34JYW9w7+lf01Wjv/Nyam6l13wl7pozlr5+ADh+/DgGDBiAFy9ewN/fH5MmTTL1kgjCpujfvz/69+8PAMjPz0dBQQHc3Nx4rURuq1orhQWj9V6tbF+PDPFTsJ1a1vdQOI8tFSvGU6VkKSb8SJFqtDSBSNlnhHVC8VEEYXxGjx6Nq1ev4uDBgzK/kpDYutYqwxxjaFShr+/YGuxBa4HiowiiMoIl/Kxbtw5ARbLPmTNnUKVKFaXjWrdujTNnzqBjx45ISEjA+vXrsWXLFp3nO3HiBObOnYuwsDAcPnxY5YO0v78/fvnlF/Tp0wfz589HcHAwevbsCT8/Pxw6dAghISG4dOkSNm3aZPEiy0VekICKNrR2IkZBgMUSFhvPpcg6DQxqVRfjOjRUeTBo7pm7fKxP3wcASzlg5YObN2+ia9eu+Pfff+Hh4YETJ04gKCjI1MsiCKvHyckJL168QP369Y0yH2mtMPDdxUcVmnRJF73TReMMCcLS1Zjm0/jmO3jM0OeC7OxsdO/eHdeuXYOdnR22b9+O4cOH87I2giA0c/DgQQwdOhTHjh1DVlYWYmJiKo2RdrYNCwvD7t279ZqHtJYgFNFVPzUFxtmJGAwJqqvgG5HCisvx145FSDx9BADg3mYwqncMx5X0vEpjzd0XQhCWTGlpKT788EMwDINNmzYJMoet6q2Hs4NOAVDaFKKQh09fKO2z+mEJB9KLFy/Gl19+CQDo3bs3fv75Z5VnRwRB6MepU6d4vV/nzp11vsZWtdZOJFJ4rW+grxA6yL3nkMC6CjqtTMclrGKwM/e1Kiy9QIKlr//QoUMYMmQISktL0bRpU8TGxuLVV1819bIIwurx8/MDwzBYs2aNQkK7u7s7r4k+UmxVa6WFLLhJqer2amX7OgAF26lTgLfSa0UMIGEBVQoor9Fkx9oOFB9FEKbhww8/xJ49ezBv3jy0a9cObdu2FXQ+W9VaeZTZidZegMnS7UFrgeKjCEI5giX8nDt3DgzDYOHChRoPbJycnLBgwQL07NkT586d02u+1atXg2EYfPnllxqz5qXjjh49irVr16Jnz54AADs7O3z66acYOHAgLl26pNc6zBmuINmJGESHK3ZTio5PwbJjLwNfuJ0GuAiRuWst1fkt4YCVD65evYru3bsjJycH3t7eiI2Nxeuvc+sfEwQhBE2bNsWFCxfw8OFDVKtWTfD5SGt1Q1s947uLjyr41CVjaZyuxrQ5G9+G/MyePHmCbt264ebNm3BwcMDu3bsxcOBAwdZKEERl3NzccOTIEfzyyy/YtGkTzp8/j7y8l0kAHh4eePvttxEZGYm+ffvqPQ9pLUEooqt+anOwHhnih4spOTh9L0v2np+nA3IOfYfE83EAgGpvj0S1t4aBYRiV9wAso4oZQVgaZWVl2LJli6AJP6S3leHaUj/EpwCo2O+ke9zl1BxI2JfV/rk2Lp92Iu2z+mHONjHLsvjqq6/w9ddfAwAGDRqEXbt2wdHR0cQrIwjro2vXrrxVF2cYBuXl5TpfZ6taK5ZIFF7rG+irSge5/uaI9r6IOZ+q1XmqJm1VpuPcW2l7VGvpAc/q1m/uxR737NmDkSNHory8HG+88QZOnjwJb2/lQewEQfDLw4cPUV5ejjfffNMo89mq1soXsoi7q53WKNvXubYTN4FIikRFpk9Db2e8G1RfQU/JjrUNKD6KIEyHo6MjDh8+jPfffx8dO3bEsGHD0L17d9SuXRt2dnZqr+3QoYPO89mq1uYVlyHqbLKsG54txL7KY072bEXjhuT/Gjcw/zVuMC8bVAhUxUeZuz1OEMZAsISfJ0+eAABatWql1fjAwECF63QlMTERANCkSROtxkvHSa+TEhwcDADIzMysdI25o2lT00aQuIat9D1VYs139XvAvKvzy6Pp5y3kAau5CNiNGzfQuXNnPHv2DLVr10ZcXBwaN25s9HUQhK3ywQcf4I8//sDOnTtlARNCQlqrG9rqmbEcwHzqkrGCiHQ1ps3J+Oai788sLy8PHTt2xN9//w0nJyfs378fvXr1EmqZBEFooH///ujfvz8AID8/HwUFBXBzc+OtSiNpLUEooqt+avNcZSdiFOxnlmVxf+cCZPx1HgCwdNkyVG8zSOM9rL2KGUFYM6S3leHaUtmFpTJ7Vn6/k74nDayS3we5AVKXU/W3E2mf1Q9ztonnzp0r812NHDkSW7Zsgb29YMdTBGHzsFp2YhEKW9XacnHFz92/piuGBNbV28+rSge5/mb5QgaazlM1aasy2yvY1wtxd18WSgj29dJq/ZYe8Kxu/eYc8LZv3z4MHz4cEokEwcHBOHbsGDw8PEy9LIKwGWrVqoWMjAw4OTkZZT5b1VopumhNRdc6/Bcsy0LCsghs4KFgOxWXirWa18vFEeM6+CmNz9HWjjWXWB9Cdyg+iiBMj6OjI15//XX89ttv2LFjB3bs2KHxGn0LWdiq1paUiWU2j6mKC5lSKw21Z/lce0Xjhnuy18uO3YWIMR8bVAjUxUeZsz1OEMZCsBOVKlWqoLS0FEVFRfDy0uwALCoqAgC9DeD8/HwAQG5urlbOq9zcXIXrpLi4uACAxsxfc0TTpqaNIHEPBaXv8YU2ombOlQjl0fTzFvKA1VwEzN/fH61atUJycjJOnTqFhg3N79+JIKyZ8PBw/Prrr1ixYgVatGiBYcOGCTofaa1uaKNnyqoyCmW48qlL2t7LEGNWLGEhYSsOyAEWg1ppPiQ358NkfX/+1atXR/fu3ZGRkYFDhw6hS5cuQi2RIAgV+Pn5gWEYrFmzBr1795a97+7uzluijxTSWoJQRFf91PZgXf6+DMMgtGcf7LjxB9asWYPJkycDICcxQVgzpLeVkdpOP8SnILuwVPa+vB2rycaVcILLua8J4TFnm7hnz5749ttvMXz4cHz//fdW8XdDEObK6dOn9b42JSUFixcvRkpKikFJQ7autT5ezkbphn4+KbvS5/rOq8z20ldXLD1xV936zfkMu02bNmjQoAHq1KmD3377jXefFUEQ6nn77bexe/duXLt2Dd26dRN8PlvXWl20xk7EQMQASZmFAIBlx+7hs56N4V/TBUmZRbJx/jVd4OPlgmsPniKn6KVdLGKAKg52CPb1xMZRQXC0Fxm0dnOJ9SF0h+KjCMK05Ofno2vXrkhMTDRKkQtb11qpHWiK4kKm1EpD7Vk+165r4wZ9MadkZHXxUeZsjxOEsRAs4cfPzw/Xr1/H4cOHMWnSJI3jDx8+LLtOH+rVq4ekpCTs2rULX331lcbxu3btAgDUrVtX4f3Hjx8DgEW2t9a0qWkjSNzqFtoE13KRisDl1FxIWBYipqLqk7at/sy5EqE8mn7eQh6wmouAVa1aFYcOHUJeXh7q1atn9PkJwtbZtm0bevXqhWvXrmHkyJFYt24dwsLCtGpZO3r0aJ3nI63VDW30TFNVxr2JGbJqkIYaVHzqkrb3MsSYrahWcVf2WsQwGn8G5naYLG8YBzbwxGc9A5CYnqfTz59hGKxevRqTJ09GQECAwCsmCEIZDx8+RHl5Od58803B5yKtJQhFhKqkVfm+vfBFxACqyEgQNoIt623U2WSlB3dSWwp42cUHULRjNdm4XHONChQbH3OzieV56623kJiYiEaNGkEkMixAjiAI9XTs2FHnax49eoSFCxciJiYGZWVlYFkW1apVw/Tp0/Vagy1rLSDcuSZXi0vFikFuYgkLsYTVy4+szPYyZ10xFeZ8hl23bl2cOXMGXl5esqBCgiCMx5QpU7Bnzx4sWLAAoaGhgneztHWt1RVufE1iei6GBNZTsH+HBNbD+I4NseFMssL5pISt6ADEAJi4M9HgQFhzifUhdIfiowjCtCxbtgxXrlwBUNE9etiwYWjYsCGqVq0qyHy2rrWGFIEwFKG00hiJLXyuXejGDVLMKRlZXXyUOdvjBGEsBLMypQHIX331Fd566y20bNlS5dg///wTc+bMAcMw6NOnj17z9e/fH9988w0WL16MRo0aqe1ysGfPHnz99ddgGAYDBgxQ+Oz3338HAPj6+uq1DlPCx6ZmJ2IwoVNDTOj0ctMWS1iVh8HKkBcBKdKW79qImjlXIpRH089bSEe4KQXs8OHDcHFxQefOnQEArq6ucHV1Ndr8BEG8ZMyYMWCYl/vxhQsXcOHCBY3XMQyjV8IPae1LlHXmiTmfqqBd2ugZVxdvPHym8Dops1CmqYbqCZ+6pO29LqfmVnqt7fzW4PDmGsazwhojOry1xutu3bqFixcvYuzYsQAAkUhEyT4EYUJq1aqFjIwMvbvR6gJpLUEoIlQlrad5ucg5/xM2fv65LOiYkn0IwnawVb0tKinXeHCnzo7VZOMG+3rJfMDS1+aIOVUstGbKysqwZMkSTJ8+XeY7JruWIMyPzMxMLF68GBs3bkRJSQlYloWLiwumTJmCTz75BNWrV9frvraqtSIRg9AAb0S0F2a9Uu1dHXcfxaXiSp+fvpeFyK0JiA5vrbO2UXKPdpjbGfbatWvRp08f2d9I/fr1TboegrBl2rRpg/Xr12Py5Mno2rUrvvnmGwQFBQk2n61qbWZBCbqsPIMGnlXRxq+G1vacLp3sxnXwg4ip3AFXvmCjhIVCTJUuULCqZUHxUQRhPuzduxcMw2DWrFlYtGiR4PPZqtY6OdhhVljjSkUgjOlTFUorjZHYwufaKxo3sP81bmD0atygDaaOzdI2Psrc7HGCMAWCJfx8/PHH+N///oenT5/irbfewrhx4zBkyBA0bdoUbm5uKCwsxK1bt/Dzzz9j48aNePHiBTw9PTF16lS95ps1axZ27tyJx48fY+TIkVi9ejX69++PgIAAuLq6oqioCPfu3cMvv/yCS5cugWVZvPrqq5g1a5bCfTZt2gQA6N69u8E/A2PD56YmL9JiCatgvALqxU5ZOzn5dWkSNUtxKptSREw19969ezFixAg4Ojri9OnTCA4ONsq8BEGoRp9Wtfq2tyWtfYmmzjwAZFqmTs+4utiiTjXZfeSRJs5YUkCSWMIiLadI4T2JDr971uDw1scwvnbtGrp164acnBw4ODjolZxHEAS/vP3229i9e7fs71NISGsJS0JX574pAqyVafGAxq7o2rUrbt68iczMTKxatUrQNRAEYX7Yqt4WlYpRTe61MvtEnV9Wk8/WUg7czKliobVSUlKCYcOG4eDBgzh9+jROnjwpeIVzgiB0Izc3F8uXL8f69etRXFwMlmVRpUoVTJgwAbNmzUKNGjUMur+taq3kv/PUmPOpBgVEGWI7nb6Xhej4FI3aRgmw+mEuZ9gsy2LOnDlYtGgRvv32W1y4cAG1atUy6ZoIwtaRJgN4eXkhPj4ebdq0gZeXF/z8/ODs7KzyOoZhEBcXp/N8tqy1yVlFSM4qwql72QC0s+d06WSnqgOuPPuuZuid8GMptjNB8VEEYW5kZGQAACIjI40yn61qrYezAwBg/PYrCpppTJ+qUFppjMQWPtde0bjBHxM6+fO1PKWYMjZLl/goc7HHCcKUCHbK4unpiQMHDqBPnz4oLCzEunXrsG7dOqVjWZaFq6srDh48CE9P/TYMDw8PnDp1Cn369EFycjIuX76My5cvq5yvYcOGOHz4MDw8PGTv5+XlYfDgwRg8eDDeffddvdZhSribmq6deeRR1qVHiiaxU9ZOTvq+MlGzVKeyKUXEFHNv374dY8aMgUQiwZtvvgl/f2EfJgiC0IxEIjHqfKS1L9HUmUeVVirrDCQdL98paG/iP0jKfJksI2FZiwtIio5PQXKWYsKPLvJuDQ5vXQ3jixcvomfPnnj27BleffVVQau/8YmlPssRhLZMmTIFe/bswYIFCxAaGiposCJpLWFJ6OrcN0WANVeL/Z1L0KFDB/z9999wcnISPImPIAjlbNu2TeVnJSUlsv/evn27yoIVhiTG26reSiSKP0u+D+6E9FfyaXOYumKhtfP8+XMMHDgQx44dAwCEhYVRsg9BmBEFBQX45ptvsHr1ahQUFIBlWTg6OmLs2LGYPXs2Xn31VV7msVWtlSLVFn1tIFXXqTu7VTa/PnMQ5g/Lsvjkk0+wcuVKAMBbb71lcJIeQRCGc+bMGTAMo2DDZmdnIzs7W+11DKOfXWPrWitFW3tOH3tVei7J7fQDACwr0TsWi4JVLQOKjyII88PLywuPHj2Cu7u7UeazVa1V1SVeG58qXz5cIbRSLGEhFtg/bqmYKjbLUuOjCMKUCHrS0qFDB1y7dg0zZ87E4cOHlQYm29nZoV+/flixYgX8/AzbLAICAnDjxg2sW7cOW7Zswe3btyuNadKkCcaMGYPJkyejatWqCp95eHhgxowZBq3BnDDEWauqSw+gWeykm/7l1FxIWBYiBgj29VKokhEZ4ldRYWr7FZ07CBHGZ+PGjfjwww/Bsizat2+P3377DdWqVdN8IUEQVgdpbQWaOvOo0kpV2iyve/I6KTWoLqfmKNzHmAFJ2hrl3HHcNQMVzwPaosqIt6TkEl0M43PnzqF3794oLCxE/fr1ERcXZzHOYwoQIKydNm3aYP369Zg8eTK6du2Kb775RlCHE2ktYSnoGjDN92GANmPltdjXqRgbPxuN1NRUODs749ChQ+jSpYvW31cVlvRsQhDmwpgxYzQGNbEsizFjxqj83NBOmLastzVcHfFBiB9v3eGNsffxaXNYQzdZc6WwsBD9+vXD6dOnAQBr167F5MmTTbwqgiAAoLi4GKtXr8bKlSuRl5cHlmVhZ2eH0aNHY86cOWjQoAHvc9qy1kq1Rd8kU1XXcf2tVR1EeF5W+exdG23TdW1k95gHEokEkydPxoYNGwAAY8eORVRUFOzs7Ey8MoIgRo8erXfyjr7YstZKMcSe06RtdiIGkSF+uJiSo3AGDAD1PF3oXMyKofgogjBPQkJC8NNPP+HmzZvo0KGDUea0Ra0tLClX2iVeG5+qOceNRMenKOh5aIC3IIkt5vwzUIV8bJb881FgAw8ADBLT+fcDWHJ8FEGYEsFLqzVs2BAHDhxAVlYW/vjjD6Snp6OgoABubm7w8fHhvepMlSpVMHPmTMycORNPnz5Feno6CgsL4erqivr16ytk0Vo7hlQr5Ip0aIA37ERMpWBVVUawpixbQzoI8Qk5qDWzatUqfPzxxwAqWlH/8ssvcHV1NfGqCIIwJaS1lRM5pJ15NCV2qNNmZZokr4dxdzUnFAmBtgYpd1xogLfC54YYzPI/G0tKFNa28sjJkyfRv39/PH/+HH5+fjh16pQggRZCQRWyCWunc+fOACoqR8XHx6NNmzbw8vKCn58fnJ2dVV7HMAzi4uL0mpO0lrAEdA2Y5vswQJuxUi3uXFuCzp0HIiMjA25ubvjtt98QEhKi6StqhSU67wnC1HTo0EFlIJRYLMbvv/8OhmEEP7i1Vb39gGNr6oOx9z4+bQ5r6CZrjjx79gxhYWG4cOECGIbBxo0bERkZaeplEYTNU1JSgvXr12PZsmXIzs4Gy7IQiUQYNmwY5s+fL3gwha1prZODHWaFNZZpizIbSJtzSVW2E6cYMZwd7dDWzwsMWLBgIGIYBPtqp2262nNk95gesViMyMhIbNmyBQAwefJkrF69GiKRyLQLIwgCAGR/m8bG1rRWJGLQ0NsFDTyroo1fDYPsOW20jRsc7OXigMgQP1xJy1MYR+di1gPFRxGE+TJz5kzs27cPCxcuxIkTJ4yWaGtrWsty7E6prchtACAtSCFv0/IdNyJk13c7ESNIjLClx84oez6S/28+voulx0cRhCkRLOFnwYIFAIDevXsjMDAQ3t7e6N+/v1DTKaV69eqoXr26Uec0JwypVqjs4FOZyOnr4DWkgxCfkINaPUuXLsWsWbMAAL169cLPP/9cKRudIAjzIjU1FVlZWXjx4oVRqlrYqtYqS+TQJrFDnTar0yRTBiRpa5Byx4kYBrPCGvNifKtLFL6calkGMpdff/0VgwYNQmlpKRo3bozY2FjUqVPH1MvSCaqQTVg7Z86cAcMwYOU8nNnZ2cjOzlZ7HV+OZlvVWsL80fX5RJvxujjCuWOlhwvcZ4/bt2+jS5cuePLkCRyd3TBx+Ra81f5t7b6kFli6854gTMGZM2dUflZYWAh3d3cAkHUJMQa2oLfcIGRD4O59q+PuQ8IC4zoIU0yJT5tD28IMhPbk5uaie/fuSExMhJ2dHbZu3YqRI0eaelkEYdOUl5cjKioKS5YswePHj2X27DvvvIMFCxagWbNmRl+TLWith7ODgr4os4G0OZdUZTuJOH6GnKIynL6XBf+aLhgSWE8n/6v8HIENPCFhWURuTVDpx+Vq/97EDCqiaETKy8sxatQo7N69GwDwySefYNmyZUbvJkIQhHljC1rrYCfCu0G6aZ4qtPHpcce0rO+BCZ38EXU2GXF3lduoVHTYcqH4KIIwb1q1aoXNmzcjMjIS/fr1w6pVq9CwoXH9e7agtfI09HbB5dQKLZQvWCy1aaVFi6Xv8x03wldcr1jCQsypoCGUdpsydoaP76EuppuP809riI8iCFMiWMLP/PnzAQADBw4UagpCA4YEB2t78KlvYIu2HYSEhgJz1COtVDFw4ED8+OOPcHR0NPGKCIJQRmZmJr7++mvs2rULubkV+xrDMCgvL5eN+fvvvzFz5kw4OTnhxx9/hL294E3+CCWo02Z1mmTsgCRuRx15VBmkXG0P9vXkbc3qjEoJt8SIkeDL6Hd2doZIJMLrr7+OkydPombNmgKsVlioQjZh7YwePZoCKAhCCbo+n2gzXhdHOHeshIWC4/9iSg6iw1ujSpUqeFHOQlTVHV5DF2F3mhN841N4e66ixFeC4BfSXOHgBiEbAnfvKy4VY9mxuxAxwhRTIpvDvHFwcICjoyMcHBzw448/YtCgQaZeEkHYNJs2bcKiRYvw4MEDWaJPWFgYFi1ahJYtW5p4dbaFMhtIm3NJVbZTsK+nQnCxlKTMIiw5ehcSloWIYbTyV8rPEXU2GUuO3gOgaEvJX8vV/qTMQkTzaFcR6hGJRLIu03PnzsXcuXPpuZkgCJukpEws8/8ZqkHa+PRUdeuTsCz8a7oAYDCoVV0FG5WKDlsuFB9FEOZN586dAQBeXl44cuQIjhw5Aj8/P9SuXRt2dnYqr2MYBnFxccZaplWRnFWE5KwimR06vmNDtTYt3z5cvuJ6uR37QgO81Wq3MptYW0zpx+bjGYT77MP9zFCsIT6KIEyJYNG+Xl5eyMnJMUkGnlgsxq+//or4+HikpaWhoKAAbm5u8PHxQUhICPr06aNW6K0FYwQH6xvYom0HIaGhwBz1TJ48GT4+PujZsyclBxCEmZKYmIi+ffvi33//Veg8wOW1117D/fv38ffff+Po0aPo27evQfOS1uqHOm02hSapSlrhdtTRJjHXUMNVXQKNOqPSVEWx+HLYd+7cGceOHUOLFi3g6WmZzyFUIZuwdrZs2WKSeUlrCVtEl+cJ7lhplTEpp+9lyQLQusxYhz+SsuBYo77sGr50i4LQCcKyIb3Vj8gQP+xN/AdJmUUK72u7v+paQIFsDvPGzc0NR44cwbVr1xAaGmrq5RCEzfPBBx/IutT6+/tjzpw5eOuttwAAKSkpOt/Pz8+w51vS2gqk2peWo6id2vqAxRIW5RIWXi6OyH9RhjJx5bOAfVczZNrM9VdK57+cmvtfYhAQ7OuFyBC/SkFU8raUFGXaT0UUjYdIJMLGjRvRv39/9OvXz9TLIQhCA6WlpThx4gQSEhKQlZWFkpISbNq0SfZ5WVkZCgoKYGdnh2rVqhk8ny1qLVeD9CnSp41PT1W3vmXH7snGiBgozEVFhy0Xio8iCPPmzJkzMltXSnJyMpKTk9Vex0eivC1qLRepnqmLa+Lbh8tXDBVXm+1EjFrtVmYTa4umn8FL2zwHEraik2+wLz+x03w8gyh2A/YAwCAxnb/zT2uImqDa/gABAABJREFUjyIIUyLYE2rjxo1x/vx5ZGRkwMPDQ6hpKnHw4EFMnjwZjx8/Vvr5d999h9q1a2PdunXo37+/0dZlLvDdPlbfwBapuEkN4vHbr5gk8YcCcxSRSCTYv38/Bg0aJHvg7dOnj4lXRRCEKp4+fYo+ffrg33//RaNGjfDll1+iadOmCA4OVjr+3XffxcKFC3HkyBGDEn5Ia4XBFJqkKmlFmcEbHd5a7b0MNd7VJdDI/2zEElah+kawr5de8xmKIcby4cOH0bVrV1kb+I4dO/K+PoIgLBvSWsJW4T5PiCUsos4mV3o+kvdrRI0KkvkRpFXGXmTchr17TfwQXxHQ2KVtS1x5+jKZWXoowIePhILQCcJyIb3VHzsRgyGB9RQKRQAV+2rk1gSNeypVPLZ80tLS8O+//6JNmzYAgOrVq1OyD0GYGQzDIDk5GeHh4QbdQ76LvK6Q1r6EW2DJv6YrhgTW1doHHB2fghXH7ym85+xgh+Iysdw7iror76/kzg8AcXcrfKzKii1xfZ3KtJ+KKApLYWEhzp49i969ewMA7OzsKNmHICyArVu34vPPP0dmZsW+yrIsGIZRSPh5+PAhGjVqBJFIhAcPHuCVV17Rez5b1dq0nGJEnU1WWshQWxtTG5+ePt36qOiw5UDxUQRhWYwePdokXS5tTWvt7Rj413RB3epVcebvbNn7Uj0zZlwTX3Np0mZtbGK+UG6b8+Mf5+MZRPnzkWHrovgoguAPwRJ+RowYgd9//x3btm3DihUrhJpGgf/973/46KOPAFQYzo6OjvD394ebmxsKCwtx//59lJaW4uHDhxg4cCDWrVuHCRMmGGVt5gLfh6lCBvcaAwrMeYlYLMYHH3yAmJgYfPrpp1i6dCm1gycIM+e7777Dv//+iyZNmuDChQtwd3dHUVGRyvEdO3bEwoULkZCQoPecpLX6oymg1BSapMoprapFPZ9Jw9quBVD82ShbhynQ11hes2YNpk6dip49e+LgwYNwcnISaokEQVgopLUE8RJlPgMASv0IkSF+uJiSgyPHTyJr30LYudcAM2IplhwtxWc9AzArrLHSxCH5e11MyVHoamiKTsQEQRgH0lvDiQzxg4Rlse9qBgAGdT2qyoozaPLz6lJAQShbVGgb15q5f/8+unTpgmfPnuHUqVMIDAw09ZIIguCgrhO8sbBFrc0rLlMIPpaHq30+Xs46+YG5HU0BKCT7hAZ4I9jXC8uOKU/I4c4v/37UqCBcTMlRKLLE9XWKJSwkLAv/mi4AGAxqpX2yEqE7z549Q69evXDhwgXs3LkTw4cPN/WSCILQgq+//hpz5swBy7JwcXFB48aNkZiYWGmcj48PunfvjmPHjuHnn3/GpEmT9JrPFrVWGr6SlFko8+kpK2TIR5CuKpuRez7ILXxBRYctA4qPIgjLY8uWLUaf0xa1toarE2Knd1IZl2MnYhS0DoBgflW+Yqg0abP0fFGdTcwX6mxzob+nKaD4KILgF8ESfsaPH4+9e/di1apVaNSoEcaNGyfUVACAe/fuYerUqWBZFr6+vliyZAkGDBgAR0dH2ZiysjIcPHgQX3zxBZKTkzF16lR07twZAQEBgq7NnDB1+1juwwDXQS3keugQVzVlZWUYPXo0du/eDaCikgVBEObPoUOHwDAMFixYAHd3d43jX3vtNQBAamqqXvOR1mpGqjWXUrKRnvscz56XoUWdaogaFYSY86lmV8FYVdKKqhb1Qq5f2wQac0nW1cdYXrZsGT7//HMAFRVSSW8JwjIoLCzE/v37wTAMRo4cCZFIpHKsWCzGrl27wLIsBg8eDGdnZ53mIq0lCEWU+TC47E3MkOlxP88n2LF/AdjyUgAM2P+0NjE9D9HhrSs9P3Dvp22gOkEQlg3pLT/YiRhM6OSPCZ38AQCRWxWLi6jz8+pSQEEoW9TUhagsldu3b6NLly548uQJqlevblDnD4IghCEmJsbUS7BZrS0pEysEH8tjaKVdiYYkLjsRg3Ed/CBilPsrlVUslr4v7e6urshSdHwKlh172WFIxMBszlit7fw3JycHPXr0QGJiIkQiEcRiseaLCIIwOQkJCfjqq6/AMAw+/fRTzJ07F2KxWOX57YABA3D06FHExcXplfBjq1rLlUN1hQx1hasnEpaVaR+36JB0brGEVepPNIdzTEI1FB9FEIQ22KrWSlEXl2NpflVNMUZ2IgZRo4IwfvsV3Hj4DC3qVENEe19B1qLONjcUY8ZSaWOHU3wUQfCPYAk/27dvx9ChQ3H//n1MmDABa9asQa9eveDn56cx6Gn06NE6z7dq1SqIxWIEBATg/Pnz8PSsvAk6ODhgyJAh6NKlC9q3b4+///4bq1evxv/+9z+d57NUdDV0+XaScgU/NMC70vqEwtIeNoxFSUkJhg8fjgMHDgAA5syZg3nz5lH1CoKwAFJSUgAAb7/9tlbjpU7lgoICveYjrdWMsvarp+9lYfz2K5X083JqDgDopLFiCYuN51L+q6DMYlCruhjXoaHe2qwqaUWfFvWGYo7VJtShi7HMsizmzZuHBQsWAADeeecd/Pjjj1S9giAshP379yMiIgJdu3bFqFGj1I61s7PDzp07cfLkSTg4OOhcgZW0liAUUebDkLCK3X6SMguRlFmIXw7sR/bhFYBEDI+6/nAZMA92LtVl12lzf3mMXSCFIAjjQXrLD1y/cWADxT01sIEnos4mK7V5dbH/hLJFTV0YyxK5fv06unXrhuzsbNSoUQMnT57Em2++aeplEQTBITw83NRLsHmtVaYphvo+ue5fTxcH5BaVyV5LE3dU+Sul811OzYWEZSFigGBfL7X+YO530vQdTYWh57/mlDCUmZmJrl274saNG7C3t8ePP/6IwYMHm2QtBEHoxpo1awAA7733HpYuXQoAKCoqUjk+KCgIAHDz5k295rN1rZWirJBhYAMPSFgodN3RZl/n6klFV7uXSLVPXjN1KXxBmAfWHh9lTs81BGHp2LLWlpZLFJJfokYFwdH+ZUFMXe1DS9ibYs6nypJ4T9/LQsz5VEE0/aVtngMJC4gYBsG+5h8fxUWdHU7xUQQhHIIl/IwZM0bhgfjOnTu4c+eOxusYhtEr4ScuLg4Mw2Dx4sVKBVYeT09PfP311xg8eDDi4uJ0nsuS0dWhzHeSDFfwRQwwK6yxUYJ7zdkZbSqeP3+OQYMG4ejRowCAJUuWyDJrCYIwf8rKKg705CtIqEOa6OPi4qJhpHJIazWjqv2q1BCWR8JCZ42tqKT4MqFo2bF7EDGM3nqmS9KKsuAtPjGHzj3yjobABh5gWWD/tYcwJLmKZVl89tlnWLFiBQBg+PDh2Lp1KxwcHAT4BgRBCMH+/fsBQOvknREjRuDEiRPYu3evzgk/pLWELaGNg1+ZD+P7s0mV7lV46zRyfvsOYCVwrNUILgMX4LX6teDj5aLWUa6qIicgbEESgiBU4+LiInilN9JbfuD6jT/rGaDg55WwLJYcrVwNGdDN/lOW/MnHITEfFaBticuXL6NHjx54+vQpatWqhdjYWDRr1szUyyIIwkyxda1VpimG+j6Dfb0Qd/elvfJBSEOV3XyUYej85qybhp7/mkvByIcPH6Jr1664e/cuHB0dsW/fPvTp08fo6yAIQj/OnTsHhmEwZcoUrcbXrVsXAPD48WO95rN1rW3o7YJ3g+op1b/Lqbl6dfGufMaraGMq0z5z1keiMrYQH2UuzzUEYQxycnJw4cIFpKWloaCgAG5ubvDx8cFbb72lURu1wZa1dvz2KwrJL+O3X0FMRLDsc131T9PepKrwsfRaYyQKGSuu2BziovhA1c+L4qMIQlgES/gBKgIcjXENUOEEA7TvciAdJ73OVtBVNC6n5lZ6HRnih+j4FKVVoDSJKlfwg329jCZiQhjblpCBrIqioiL069cPp06dAgCsXr1aaweUMiz5Z6EvtvidCfOiVq1aSE9PR3JysqwSlDquXr0KAKhfv75e85HWKqJsD1BVJb5aVQeFANKG3i648fCZwhhtDEZlCUW6GJqG7VvcZzT9ntnMGWWOBinaJlcpJA3Vr46bP6+SVXCJiIjADz/8ADs7O2G+AEEQgnD79m0AQLt27bQa37ZtWwDQquAFF9JawpZQprtSf4P8swrXZ3DgmuLve8Gfx5F7bB0AFk51mqDmkHkQObkgOasI7wbVU6vd8j4SZc9JBEFYJ6S3/MC1TxPT8xAd3lq27/JV6VhZ8icfASyW1mXWlMTHx6N3794oKChAvXr1EBcXh0aNGpl6WQRBmDG2qrVODnaYFdZYrabo659VpltSe4aP++szv7lg6PmvORSMTE9PR5cuXZCcnIyqVavil19+Qbdu3Yy6BoIgDOPff/8FADRsqN3+IQ16LC0t1Ws+W9VaKfU8nRX2ankbkYuyfV2bM966HlUxqFUdJKbnqdS+iPa+uJiSIyv8GNHel6dvSPAN3/FR5oo5PNcQhNCkp6fjk08+wcGDByEWiyt9bmdnh4EDB2L58uV6x0YBtqu1ecVluMSJF+bGNulqH2ram1QVPgZ0L6KsL+acxGuO8anKfl4SiQRTpkzB+vXrAVB8FEEIgWAJP0JXYuQi3RjKy8u1Gi8VfJFIpGGk5cDH5s6tbJ+ao9hmWMKySo1laVUpTaJqSoewEHNbcnWEx48f48aNG2AYBt9//z3GjRtn0P0s+WehL7b4nQnzon379khPT8ePP/6oVcJPVFQUGIZBx44d9ZqPtFYRVUGqAHApJRtpOcX4J+85ACC3qETh2uQsRX0FFA1GVZquLKFIF0NTl32Lu4YrSoK5dMUcDVF5VHVokv9cmy5M0p/xiT/TgZNnAAATJ07E2rVrrfbvgSCsGaljtlatWlqNf+WVVwAAjx490nku0lrCllDm4AcUnfcXU3IQHd4adiIGpeUSjN9+Balyz1Esy+JF+p8AWDjVfx01B30FkWNVhXtqa6NZS1UtgiA0Q3rLD1z7NLCBB6LOJsvsvcAGHrwckirbn/kIYBFy3zd321dXLl26hIKCAvj6+uLUqVPw8fEx9ZIIgjBzbFVrPZwdlBYxkNeAjeeSsezYyw54EpbFhE7+Gu+trW4JdW5lzvaSoee/5hDYde/ePTx48ACurq747bff0KFDB6OvgSAIw3B2dsazZ89QXFyM6tWraxwv7ezj4eGh13y2qrVSbiopqqiKwAaV93VVZ7wXU3JkBRzP3MtCOz8vRIe3VnnvmPOpCh0QNv2eWqkDnyXbgtYE3/FR5oo5PNcQhJBcuHABvXr1Qn5+vsqmAuXl5di7dy+OHz+Oo0ePygo16oqtam1JmRjFpYqJVC3qVFN4rat9qGlvUlX4WNl7Qtmk5lzkwhzjU5X9vIqLi/DHH38AoPgoghAKQTv8GJP69evjzp07iI2NxahRozSOj42NlV1nLaja3LU5YJSO2ZuYgaTMQoV7yCM1TpWhjaia0iEsxNyWXB3B398fsbGxuHnzJkaMGGHw/Sz5Z6EvtvidCfMiMjISO3fuxLp169CzZ0+1Fd+WLVuGI0eOgGEYvR1YpLWKqNoDpIbNnxkpKBNXOBnyilU7AWq4OuKDED8Fg1GVpkeG+EHCQqGVrS6Gpi77FncNDb1dFD7nGuHSZwluB8CI9r6IOZ+KhLRciCWszPGtrSFqzEApVR2a5D/XhPzPWORYFW9PWYWWJX9h+vTpYBhy6hOEJeLg4IAXL16gsLAQ1apV0zi+sLDCntKnCAZpLWFLcAPBW9b3wN7EDIUxp+9lIXJrAuxEDFKziyolTTMMg6DRXyIp1g9VWvWDyN4JZZKXhzx0oEkQhDJsVW/zissQdTZZkEr/gQ08cCk1F2fk7D1/b1eEBngrdIfnC3MPYNH1ENbcE4RmzpwJJycnDBw4EHXq1DH1cgiCsABsVWsBYOO5l5WJKxJ6gAmdXmpAhV8XCq+1SfjRlsupOZVeW/u5lTbnv+q01hwCu7p3746ffvoJtWvXRps2bYw+P0EQhuPn54dr167hypUr6Nevn8bxcXFxAIDmzZvrNZ8tay0AVKvqIPtvsYSFWKI86LuCyp+pOi/l2mGa4j+499l3tXK8lbXrsKXAd3yUuWIOzzUEIRR5eXno168fnj17BgcHB4wdOxZDhw5Fs2bN4ObmhsLCQty8eRN79uxBdHQ0nj17hv79++PevXtaJeNysXWt9XJxBMNUJPtEjdJcgFodmvYmdYWPjeUDNlZMsyY/sLLPzTE+VdnPy9XVFSdOnMDWrVspPoogBMJqEn66deuG27dv44svvkCnTp1Qr149lWMzMjLw5ZdfgmEY9OjRw4irFBZVm7s2B4zqWtzKE+zrBQmrPBlIk6ia+8GlPpj74TKXzMyKtdasWRMA8Prrr+P111/n5d6W9rPgA1v8zoR50bFjR7z33nvYsWMHevXqhREjRqBz586yz48cOYKkpCTs3r0bly5dAsMwmDx5st7OY9JaRS3jOo+le4AqTXV2tMNbDb0Ukl4A4IMQv0q6rM7RPKFTQ4VDYl3QZd/iriE5qwihAd6yTkNcI1xVB0D5aljK5jBVVUplcAPWWBbYf+0hdEmualnHFUfPX4ODZ0UQVEgLP4zvqDoZjyAI86du3bq4c+cOLl68iEGDBmkcf/HiRQBA7dq1dZ6LtJawLSofoEsPxOXhPkewLIuy7HQ4evsAABwcHOEQNBhiVDyrqXteIQjCcigoKMC9e/fAMAzq168Pb29v3u5tq3pbUiaW2Va62FSqfLryh3obziTLkn2kJGUVIimrEP41XRDs68Xrd+EeEke091XoLmRqv7Ouh7DmWKXx5s2baNasmexg9qOPPjLpegiCsCxsVWsBVQk98ns6V5/01ytlGs2Nd1Yb/2xDqNNaUxWr/Pvvv+Hj4wNHR0cAwDvvvGPU+QmC4JcePXrg6tWr+PbbbzUm/OTn52PlypVgGAa9evXSaz5b1loAAFuR5GMnYhAdn6LgP/RycUROUansdWJ6XqXLW9ZXLESUmlWIqLPJOneqrRygrCi8exMzTG6f2jJCxkeZK+bclZEgDGX16tXIyclBtWrVcPz4cQQHByt87uTkhI4dO6Jjx44YM2YMunfvjuzsbKxevRpz587VeT5b19oWdathS0Sw5oFaoGlv0lT4mM8kRlPHL2vyAyv73JzjU0tLS5GWlobXXnsNAFCjRg3MmDHDxKsiCOtFsJ5ZIpEI9vb2KC4u1mq8WCyWXaMP06dPR5UqVfDo0SO0bNkS3333HTIyFJ2qGRkZWLVqFVq1aoWMjAxUqVIFH3/8sV7zmSPczVz6WtkBIxd1LW6lhAZ4/yecikZqQ29nzAprrFFUpYIUeycTS47eRXR8isY5zZ3IED/MCmuMrk1qavUz4AuxhK2oyLk1AVFnkzVULKng0aNH6NixI7p164bcXM3/3rquIaK9r0l+FqbEVP/+BCFPdHQ03n33XYjFYuzYsQPvv/++LBijb9+++Pjjj3Hx4kWwLIuRI0fi22+/1Xsu0lpFLTt9LwuhAd6V9gBuFUUpbXw9ER3eGtHhrTXuHao0XR/k92sJC3zWM0CrfUvZnHYiBtHhrVVWuVLGjYfPdJqDizbPMXwhdTREh7fGhE7+mBjqj9jpHRE7vRMmdPLXaOg/f/4cv307E89++hyB7gWkDQRhJXTs2BEsy2L58uWyduyqEIvFWL58ORiGQadOnXSei7SWsCUS0xU1/aaaZwYpLMvi6ZkYPN4yFcX3K5LrwKkQpe55hSAI03P37l3MmDEDvXv3RkREBH7++WeFz7OzszFy5Eh4eXmhTZs2CA4ORq1atdC6dWscO3aMlzXYut5eTtXNptLGp8sNbpYnKbOId1+wvO02vmNDbPpdcY0bzyXzNpc+6GrTq7N79fEDG8qOHTvwxhtv4OuvvxZ8LoIgrBPb1lruPq34elCrumpf64IyjRZx7CPua1tFHx+zkBp8+fJltGnTBiNGjEB5eTlv9yUIwnRMmTIFLi4uiI+PR3h4uKwLPJe7d++ie/fuSE9Ph6enJz744AO95rNtrQWSs4tlNiZ3T/dwcVR4rcweq1T0MLv4v+BaRqf4D268CFfXkzILrSIuyhLhOz6KIAjTc/jwYTAMgwULFlRK9uHSunVrLFiwACzL4vDhw3rNZ+ta+09ukdHmkhY+jp3eEcendYSIYTB++xVEx6cgMsSP1zM/U8cva7JNlb021/jU58+f45133sFbb72FW7dumXo5BGETCNrhh2V1d3zpcw0A1KtXD9u2bcPw4cORm5uLmTNnYubMmXBxcZG17ZMa1SzLws7ODtu2bUPduvo7Us0NVe3vtMny5I7xr+nynzHKIDFdMaOVWwHDt4arVtUBzLG9nKFwM5Clzl+hs4B1rfqYnp6OLl26IDk5GVWrVsXt27fx9ttvC7IGS/831QWqjkGYA46Ojti9ezeGDRuGVatW4cKFCygrK5N9LhKJ0LZtW8yYMQMDBw40aC7S2sqBUSKmIqBUHu65n4Mdg7f9a8ja3Gqzd/DZbpu7X88Ka1xpzarWwO3Ooy5ISVmbXaCixa/8PXStum/O1SrkKSoqQr9+/XDq1CkAQI8a+RhH+kAQVsGECRPw/fff48qVK3j33XexefNmVKtWrdK4/Px8jB07FgkJCRCJRJg4caLOc5HWErYEV+OrVXVAduHL6psOdgzKxC8frFhWgmexG5F/9VcAgEfOTbzVpy/+yVUsNGOuzwoEQQB79+7FqFGjFGzWbdu2YciQIdi9ezeKiorQvXt3/Pnnn5V8xImJiejTpw/Wr1+P8ePHG7QOW9dbiY7+d00+XbGERV5RiVb3iQzxk1VQDGzgAWW+Z31Q3k3BX6978YGuNr06u9fY3X+io6Mxbtw4sCyLI0eO4NNPP5V1HiAIgtAWW9baQa3qYtmxewqv5RnXwQ8iRrNGSKsOX07NgYSt8EMH+1Z0tYs5n4qEtFyk5SjaQglpuQj29UTc3ZeaEuxL9hGgn49ZKA3+/fff0atXLxQUFODSpUt48uSJVfzuE4St88orr2Dz5s0YNmwYduzYgf3796Ndu3ayz4cMGYLk5GT89ddfkEgkMu1zc3PTaz5b1lopUtuUu8fX9aj6339V7hAgRVXhocT0XFlQsTYoixfadzUDSZkvg6StIS7K0hAiPoogCNOTklKRmNG/f3+txvfv3x9Tp05FcrJ+hYFsXWsZxvA+Evp00xHaF6rK122szj+abFNln5tjfCo3Pur8+fNo1qyZiVdFENaPoAk/uiA9xGUMqDQ0ePBg1K5dG5MnT8b169cBQEFcpbRs2RLr1q1TMLCtAVWbuzYHjMrGvBQtxfvpG3hrKQG7hrDxXAqWHXv50CFhgQmd+BdbXZKnkpKS0KVLFzx48ACurq747bffDDJmpQ84P3AynMlRQRCmZcCAARgwYACeP3+OlJQUPHv2DK6urvDx8YG7uztv89i61nIDo+RfS/dHbkebDo28sWmM6gQbsYTFxnPJ2Hc1AywL1PN0ht1/B7hRo4IMNiL1TbiVVsfnGrWqeNnhKBcSloWIAYJ9vRQOofUxjPlMfhKK/Px89OrVC+fPnwfDMPj+++8xbtw4Uy+LIAieaN68OWbOnIkVK1bg4MGDiI2NRb9+/fDmm2/Czc0NBQUFuH79Og4dOiTTw+nTp+ONN97Qaz5b11rCOtDGMc7V+EspOUjOenkY3sDTGUn/vWYlYuQeX4/Cv04AADr0H4FhU+dh2fG/ZeP9a7piSODLQ3xjOecJgtCOBw8eICIiAqWlFYl9VatWBcMwKC4uxt69exEaGoq0tDRcv34dTk5OGDZsGFq3bg2GYZCQkIAff/wRJSUlmDp1Kjp37oxGjRoZtB5b1ltdt0JNPt3o+BTkFJUpvNcpwBsZec+RlFmocJ2yA1v5/9bft8j9Uqbd73U9hFVn9xqziNbatWsxZcoUAECnTp1w+PBhSvYhCEJvbFVrx3VoCBHDqPRlatIIqR2z98o/MntIStzdTOy58o+C3SSP/Hzm7Es1Bfr8XITQ4Li4OPTr1w/FxcXw9fXFqVOnrCYgkCCIiqQeFxcXvP/++8jMzERsbKzss/3798tiol555RVs2bIFPXr0MGg+W9VaKRVFJBT3eLGEVShCuO9qBkQMo+AXFEvYSoWHpHDtXV39i3YiBkMC68nsXmX3JISF7/gogiDMhxcvXgAAXFxctBovHVdSorlQkSpsWmtZVHQ8NeBsTZ/kHX3tMG01m+vrDmzgiaizydib+I8sYVfIokuabFNLsOkpPoogTIfZJPxkZlZslK6urgbd56233sLVq1dx9epVxMfHIz09HQUFBXBzc4OPjw9CQkLQsmVLPpZsMWhzwKjLIaS+wmIJgmQoyqtJ8i/+2iZP3blzB126dMHjx4/h6OyGSctj0P7tEIPmln8Y466JIAjTU7VqVcGz5m1Za7n2oPxrVftjkIb9MTo+RaHqo/TAVlqJkVs5uaKyo2JSjToj25CEW12eD9SNNaTahDlWq5AnNzcXPXv2lHX0iImJwejRo029LKuCArYJc2Dp0qUoLy/Hd999h4KCAuzatQu7du1SGCM9sJ05cyaWL19u0Hy2rLWEdaCNE1+Zxp+SO5AfFFgXIobBxjP3cXfPChTfPgsAcGs9APYhkbjC6T7s4+WscC9jd0QgCEI969evR3FxMVxdXREdHY3BgweDYRgcPnwYY8aMwYYNG/Dvv/+ievXqOHXqFN58803ZtRMmTMC0adMQGhqKp0+fIioqCt98843Ba7JVvQ329VJ4rel5W5NPl3sI61/TFZv+6yrLve/47VdUrsuQINqKbgp3FV5bEursXmMV0Vq+fDk+++wzAEDPnj2xf/9+VK1aVcNVBEEQ6rFFrTXUl6nKxyyFm+zjX9MFPl4uaO1T0f3HWD40S/PX6fPvwrcGHzlyBAMHDkRJSQkCAgIQFxeHOnXqGHRPgiDMj169eiE9PR27d+9GbGws7t69KyvS6Ofnhx49euC9997j7VnbFrX2JRW6I7/HR25NUBiRlFmEJUfv/nemysiSguT11NPFAW/WrY42fhXnrfIaJ59ApK1/0RbioswV+fio6tWr49ixY2jTpo2pl0UQBE/UqlULDx48wPXr19GlSxeN469duwagItHWEGxNa+3tKvQ1KatQZpvqa99y/cZ7E//RaDvqa4dpeyb4spBxRTfdfYmVi21I1y7EmaI629QS7GxufNSmTZtR4vs2IrcmmO2aCcKaEDzhR1PHHpZl8ejRIyxYsAAAEBAQwMu8rVq1QqtWrXi5ly2iTkD0dVabe8AuP7AaXvODNk6CP//8E926dUNWVhZEVd3h9e5C7E6vAt/4FIP+DbgPYzVcHfFBiB85KgjCBrFFrQ329ULc3SyF10CFbu5NzFBxlXot4O6r3M9UBa5Kka5H1d5OjmXhyMzMRLdu3fDXX3/B3t4eu3btwpAhQ0y9LKuDArYJc4BhGKxcuRLDhg3DqlWrEBcXJytaAQA1a9ZEt27dMHXqVAQFBfE2ry1qLWEd6FOBS9kzi7i8DFFzJsmSfaq1G4pqIe8hOasY3O4NYgkLsYSV+S6M2RGBIAjNxMbGgmEYfP7553j33Xdl7/fr1w/z5s3DtGnTwDAMvvvuO4VkHymvv/465s6di2nTpuHUqVO8rs1W9NbJwQ6zwhor2IRiCYvIrQlqA5g0+XQDGygewg5qVVe2F3Ov4x7YymNIEO24Dn4QMdZp9wpt07Msi/nz52P+/PkAgP79++Onn36Ck5MTr/MQBGHb2IrWFpWUqwxy0TZwR52vWBlDAuvJtDbqbLKCD+1iSg7sRIwgQTe24K/jU4P37duH4cOHo6ysDC1atMDJkycNDjwkCMJ8cXJyQnh4OMLDw402p61orTyJ6bkAFLVHlc2572qGrGo/l1b1PRD9X9EKro3MRRv/oq5xUZYQ3GsJyMdHeXl54eTJk1YVgE8QBBASEoIdO3Zgzpw5CAkJUduVurS0FHPnzgXDMAgJMawwuhRb1FpAt7M1rqYFNvBQ0OWkzCL0WHUWQwLrqdQ7fe0wVWeCynRW+n3UFdswReF7c7ezlcVH5dZsZdZrJghrg7eEHzs7u0rvsSyrU8cehmEoSNIEKBM2roBcTMlBdHhrMiw1UFFN8p7CayHQ5CQQS1h8+Nl8ZGVlwdHNA15DFsHRuwEAw4OcuE6SD+QehAiCIKwdVcZldHwKkjILlV6TyKk+z0WXgCdVB77q9napZkj1ffz2KzbrMObbab5792789ddfcHR0xN69e9GvXz8eV0tIoYBtwpxo3bo1du7cCaCiZXt+fj7c3d0N7lRLENaGNhW4VDnZpXu8WMLii+/34Xr8SQCAd2g4nINf+oy4la1P38uqeNb573pjdUQgCEI7kpOTAQADBgyo9NmAAQMwbdo0AECfPn1U3qNPnz6YNm0aUlJShFii1ePh7FDpOTo6PqVSIJPuz9uVCyCpsr3kberABh4AGCSmGx5Ea82FpoT+bo8fP8aaNWsAAEOHDsX27dvh4OAgyFwEQRDWTsGLcsTeyUTsnUyFTgKtfTwhYSHrRscNguF2EtBEaIC3QiKPFK4PTdeOBLpgC/46vjS4vLwcCxYsQFlZGVq1aoUTJ07Ay8tL84UEQRCEWpT5+iJD/LA38R8lyT3qOwlIUWYja5pTir5nkOYe3GspfPvtt8jKysIrr7yCuLg4NGvWzNRLIgiCZyZOnIgdO3bg4sWL6NKlC9asWaM0se/69euYOnUqLly4AIZhMGnSJBOs1nIpFyvapIENFLVPnd5xNe2zno3hX9NVIZZK2n0PUK53+tphqs4EVemsqtgr/5quGBJYV6mvWugkXXO3s5XFR3G7K5rbmgnC2uAt4YdllTsgVb3Pxc7ODu+99x4+/vhjvpZEaIkyYVPmFI42sDOMLlhqFYtxHRrKHPjSQ2tTtKyLjk/Bo+aj4PKwANXaDYGD58uW8IYGOfFdVdJS/60Jwtj4+fFXwZVhGFmwFaEbqoxLdZUXNe27kSF+kLAs9l3NAMsC9TydYccwCPatvMeqSg7SZm83N4exKfZ/vn8GH330EZ48eYJOnTqhe/fuvKyRqAwFbBPmiqurKyX6EIQc8toe2MADn/VsjMT0XAQ28ISEZSvZxqp0WSxhsfFcCqLjU5BT5A7P3h9DUpyPep0GI6eoTO0a5B3J1OWQIMyL4uJiAECtWrUqfSZf3Vxd8KP0M+m9CMNRZsuqet5WZcNxi1wkpuep3OOV29R0AGhKateujePHj2Pr1q1YvXq10sJuBEEQhO7IdxKIvZMJ/5qK/gN524Xb1T00wBsihvkvaQgI8vEEwOBKWq7sPWX+VHWFpbQJutHFX2tqf50lnS3a29vj2LFj+OSTT7Bu3TpUr17d1EsiCEIARCIRRCIR8vPz4ezsrHG8WCyGg4MDRCIRysvLjbBCK4EBHOwYvNWwBiLa+1b62E7EYEhgvUq6GuzrJUu8lb6nTfIsAHQK8EZGXkW3cQkLhQ7j8mw8l6KQ3CthgQmdNNu75h7caylERUVBJBJh1qxZeO2110y9HIIgBKBt27aYMWMGVq5ciT/++ANBQUHw9fVF06ZN4ebmhsLCQty6dQupqamya2bOnIk2bdqYcNXWgGLst7qYG66mrT11H1UdRErvujcxwyB7jmsTSp8LLqVkIz33OX6IT8HFlBwwnPVLdZZr0/rXdFHbeUjTd+cDU9vZmlAWH2XuayYIa4O3hJ+YmBiF1xEREWAYBhs2bICTk5PK6xwcHFCjRg20bNkS3t7eWs21bds2g9bKZfTo0bzez9To6uRUZkAqcwob07A0ZVCyPk5i7jVRo4JM8h2ePn2K6tWrIyEtF4y9I2r0ngYA8HJxxJv1qisNHtcVvqtKmlsAOkGYK2lpaRrHMAyjNNGW+z7DaDaUSGt1g6ubqhzFyrATMZjQyR/jOjSsZJBy9Uh6r8upLw93g329tNrbzc1hbIr9n4+fgVRrgYq/pcWLFysdZ0mHzuYOBWwT1gppLWFtcLV9VlhjRIe3RtTZZCw5ek/2PlCh+ZdTcxSuv5yag/EdG2Ld8Rv49nQaGFFFwLFrs1AAkCX7+Nd0RT2Pqkqrbco7kq252wNBWCLu7u7Iy8tDRkYGPD0VD30ePnwo++/Hjx+jWrVqSu/x5MkTAICbm5vW85LeqkeZLavqeVuVDRfYwEPhHoENPMzO/iQUEYvFKCoqgru7O4CKLpatW7c28aoIgrBUSGuVU8lNz3lD3nbh6qadiEF0eOV9ecMZFsuOVdhWcXezIGFZTOjkL/tc3ocmlrAKNhPfBaNM7a+zhLNFeT/yq6++ih07dph2QQRBCI62xZB1vYa0Vg4WKBOzOPt3FmLOpyrd+1VplIiBxjM7ZTZysK8nzvynqcuO3YWIUa45+65mVHqtTcIPBcrqj7zWVqlSpVLcIkEQ1seKFStQo0YNzJs3DyUlJUhJSVFI8JHqqpOTE+bPn49PP/1Uq/uS1qqGW+xJnd+Xq2nFpWIUl4oBVMSu5hSVyj5LyixEUmah3vYc1ya8mJIDOxGD9NznSM6qKL5x+l4WGnq7KFwn1Vllzwua4nmE9nmb2s5Whqb4KHNcM0FYM7wl/ISHhyu8joiIAAC89957WlWw0IUxY8ZoFaysDQzDWJXIiiUVVXO5bdojQ/xUBp0qMyAjQ/xwMSVHK2dwabkE47dfwY2Hz9CiTjX8b2Qgtl1IMyjA1ZSHwvo4ibXpkiT0dzhy5AhGjBiBvXv3orWPn8K/aU5RKYJ9Pc3O2Q2YXwA6QZgrc+fOVfnZtm3bkJqaCgcHB3Ts2BFNmzaFq6srCgsLcfv2bZw9exalpaXw8/PDqFGjtJqPtFY5qpI45I0YaYe3K2m5uJiSg8up/2fv3OOiqNc//pldLrKLIruApaDcEvVkpYBZhqJoaseuhl3NTNLscsrq5M/T9WQ369Tpbihm3k4WerqYtwIFyVQu6slSMGBBMBXYRZRdFNid3x/rDDOzM7uzyy4L7Pf9ev1eL9mdnfnudn7zmef5Ps/n0bNNOfb0UCwgFeo5U7Tqyn1SKmHsrcYUe/d/T62ps0nzY8eOYfLkyfj73/+Op556yu6xPWHTuadACrYJvRWitYTehlDbs0tqkJESK6n5FkE9g67BhHs++gnfvPU4/EIHQfvXRaAoW+evaK2KNdlwpQmaQCB4h+HDh+OXX37Bhg0bcNVVV/He+/LLL9l/b968Gc8//7zoOTZt2gQAiI+PF31fDKK39nFmY1M6huMfv7mkFlEa/n6AvdiLmCV0Le3t7Zg7dy6OHz+On376iW36IRAIBFchWttB3z5+mDw8AsnRGuyr1LNFRgAQGRqE9KQo0SIYuTlL8ULijucibg5NTF8d4cx+nbfzdd19b/Ff//oX3nvvPezZs8epZ1cCgeA7MAXJcjSUaK04cu79FhpYsacCJdWNrHGvvXgzIyUWZguNVT/r0NJmBk3TKNLJ1Rybbl9Z34MUyroGtz5qypQp3l4OgUDoQhYvXoyHHnoI69atQ0FBAaqrq3H+/Hn07dsX0dHRSElJwf333y978ABAtNYewvjUXvzKaNjKgko0NLfyPndNVAjGxGhRVGVAld7ITsQFXIvnhDGhmEkgAJxquoD4CDUACjNHR7JrdCWmFX73xCEaZOZXuC2vLVyT2UK79fzOIqc+ytu5AQLB13Bbw48QpnvW3c0+DK64Y3jyPN2FrIJKGwFjBE6q6HTuuBjsr9SzDTtzx8Ww7lFyksEL1hWz19xdVo8ZHxWwSWxXC1y96WLhSpJYzpQkT36Hb775BnfddRfa2trw/PPPY+8v+5BdUtPph6OugDiWEAjykGr4efDBB6HT6TB79my899570Gq1Nsfo9XosWrQI69evh06nwxdffCHrmkRrO2A2SLn3Vq7GcYMYq4t9Ke/zuaX1yC6psTsCVqglR0422bzfmfu4VMLYW40p9u7/nlpTZ5Lm//vf/zBlyhTU19fjtddew+zZs3n//ybcRBdOLeiKhiYCgdB5YmOt9wWKolBRUcF7zVm455CCaC2hO+OMXpktNMyCDp7yOiNW7KmQ1HyFYPPkjxN/4uevXkRbXSUunq5A39EzEDgwQfRaAEgCmUDoYcyYMQN79+7FBx98gMsuuwz33XcfKIpCdnY2Xn/9dVAUhXvuuQevv/46rr32WkyePJn3+V27duGNN94ARVE27zmC6K0V48V2ZKwp4t3TndlElLqfl1TzY9nyeiPK6402k2+ldIWYJXQdra2tuPfee7F582YAwNdff42MjAwvr4pAIPQGiNZaUQf6sZN5CnXCqT0KyRhGbJ9WHGE8Jp1PdEfxUlfv1zkTg3p7rVLQNI2lS5ey+zn/+te/8Nlnn3l5VQQCoTtSV2e9hwUHB8s6nmitLVL3frEYk/n318UncGdiFAAKJdW2eqNUUCiqMrATCPKON0hOBhAyc3QkO4mP+VsOpFDWeYT1UWlpaVAobI2jCARC7yU8PBxPP/00nn76abedk2gtH1WAEk+mXWFTU2Ov5obRNAA2NVNjYrSSNVWuxHPCmFAKU6uZrfHafLAGCgou1+cIv7uFpvHmdqv2eyKv7c28uaP6KAKB4B081vAzZMgQT50aFovFY+fu6QiLhQGrwNlrYlm9V8dr2GFG38oNLIUFyVUNRt7frhQoe9PFwpUksfAzVXojEoeEYvG0YbxEgSf48ssvMXv2bJjNZowaNQrbtm2Dv58S6YlRnX446gqIYwmB4DobNmzA2rVrMWvWLKxZs0byOK1Wi7Vr1+LixYtYt24d0tLSHE76IVrLhxtIcVlZUAmAHxCKaTFgLXplziGmi0ItCQny57ledPY+LqXr3nJDtHf/99SaXE2aFxcX48Ybb0RjYyMGDBiAnJwcm2BWGGxPTOA7xnRFQxOBQOg8VVVVAPjOisxrzuLICYpoLaGrcbbh1Bm9EjMfAYBNJTX4cVEqgA7NnzsuBpn5Fag2dOQO2psNqNv4Atr0JwClH8Jv/T/RZh/AmrfIKqgk2kkg9DAWLlyIDz/8EKdPn8azzz6LZ599ln2Ppmmkpqbi3XffxaZNmzB16lSkpqYiOTkZFEWhuLgYu3btAk3TUKlUeOSRR2Rfl+htB+cvtCPnWJ3de7q9e79UDCe1ucoYSjFwN3K55+7uDv3dFWd1/cKFC0hPT8cPP/wAAHjttddIsw+BQHALRGvFGROjQW5pHe9vKaT2abmYLTQiQ4NQXtfMvia3kFgu3t6vcyYG9fZaxaBpGv/4xz/w1ltvAQDS09Px0UcfeXlVBAKhq3GUE6ZpGn/++SdeffVVAEBCgnj+iwvRWlsoQNJYQmqfFgAq6k28phwxvRHWPzW1tGHJ9GEONWf++DgoKKpbaVNvRKw+ijT7EAiEzkK01paB/YOQkRLL6mziEA0AWtbUvIyUWFhoZkotzU7WYXS7UGfAxIRwKChrI5ArmsmNCc0WmrdHGReuRlNLG2iaht7Yxr7uqGbLEcJ6o4w1Rbz33Z3X9lbeXE59FIFA8A4ea/ixWCw4e/YsAECjsU1iFhYW4s0330RpaSm0Wi3uvfdeLFy40G3j8XwV4QbrxIRwVuCkmliE4iBWvGyPkYNCeKJpFjQqu1Kg7E0XC1eSxMwx2SW1KK9rRnmdEct2lGHJ9GG8jW138/nnnyMjIwM0TWPs2LHYvn07+vfv7/L38AbEsYRAcJ2VK1eCoiheoZQ9nnvuOWRnZ2PlypUOG34IfKSSww3NrTYBoSMnieySGlGNzUiJxf5KPaupFSKOyI4QS2wDuBQ062GhrW76Y2I6ioG85YZo7/7fnRwa9+7di5tuugnnzp3DoEGDkJubK7oBI/zfiIKiJDcASEEbgdB9EZuoJzVlj0DoaTjbcOpIrzoS83ocrjkreo6zpnZW8zNSYrFiTwWuezOHl2BvP1eHMxufR3vjKVB+AQi//XkExSbCX0mhTZhgkFgLgUDo/vTr1w8//PADZsyYgVOnTvHeGzFiBL766iuEh4cjMzMTc+fORV5eHvLy8thjaJqGUqnEihUrEBUV1cWr731I3Uft3fu5MRw39kwcosHiaQnYfPAkrwhZGMdJnbs7xX89CWd03Wg04rbbbkNOTg4A4L333sOiRYu6ZqEEAoHgozizRycnV7hiTyXyOHuxqQnhmHdDjORkPlfw9n6dMzlTb69ViMViwaJFi/Dhhx8CAB544AGsWrUKfn4eK8UgEAheRqlU2rxG07TsiT2AtTkoPT3dncvyGfyU1om1YsYSch3/GYR6I6x/GjkoRJbmdDdt6o3Yq48iEAgEgnu57ZqBuPHf+aiot5r3CafmAdK5SKWCwsLUOCxM5b8vnOyzZPowl3VTKlfNjY2F12Nw1x6jp/Pa3siby62PIhAI3sFjWaYvv/wSDzzwABITE1FYWMh7b//+/Zg4cSJaW1vZMXb79u1DYWEhvvjiC08tqVfjqAPWQgPxEcHgds0yCMVBrHjZ3jUpACp/BUxtHd3O8RFqRGvV3brRRApXAnHmM0VVBt7GtieLkD755BM8/vjjAIAJEyZgy5Yt6Nu3r82aSEKBQOi9/PbbbwCAuDh5/38eG2u9Hx89etRja+qtOEoOc+/3wsYdIeV1RlFXeqWCstmQFToiO0JqTL0wiGXcJZniW+Y7dBfd7i5r+iknFzNuvhmtF1qgvSwSefn5iI8TX4vwfyNjYjQ9oqGJQCDwIQ0/hN6Msw2nUnrF5AKyS2pQXmeU+jgAIFQVwP47q6CS56AJAG2Np1D/1fNob6oD5d8HEXe+hD6DrwIA9A1UwmBql1wbgUDoeYwaNQplZWX46quvcPjwYfTp0wejR4/GrFmz2GKpOXPmQKVS4dVXX8Xvv/8OAPD390dqaipefPFF3HDDDd78Cr2G5GiN6Eao3FhFGHsunjYMM0cPuuTaSNnkn5lziZ3bUfzn7CQbX0Gurp87dw4zZsxAQUEBAGD58uVOTckiEAgEgvM4q12OYq+iKgMOnTjL+0xtYwtW/dwRY+Ucq4OFprEwNd7ldXibnpozNZvNeOSRR5CVlQUAWLBgAT799FMybYBA6OUwdU5yXxeiVCpx//33k0Z8FwkOtJa6icVFmbOT2H+3W2hew6wYQr3JnJ2EBeuKceRkE0YOCmHP19X0NB33NI7qowgEQu/ixIkT7L8HDx5s85qzMOcgOCbQX4kl04dhX6WebfYRw5maWEbTmCEErpzDHlI1skIDf4bOxppcQ0RrnXaH6bI76eq6qV27duHmm2+GyWRCdHQ0du3ahZiYGI9ek0AgOIfHGn527twJmqZx77332rz3zDPP4OLFi+jTpw8mTZqEqqoqHD16FOvWrcN9992HKVOmeGpZvRbuJivA74DNzK/Ash0d7ykofkExIwYrCyrR0NzKvu5IVIXX5DJzdCQvqewK3SmAlbuWrkpGHzt2DE888QQA4MYbb8Q333wDlUrlkWsRCITuS0tLCwDg5MmTCA0NdXj8yZMnAQAXLlzw6Lp6IxkpsTZBIBfu/Z5p0ulwOw7F5pKTKK933BDaWR0RS2xLwZ3o190aRLtD06rRaMTM9FlovdACP80g9Ln9NeTW0oiXWJIzwXZ3aWgiEAgEgm/h7HMGo0+FOgMsNI1CnR4AYKFpm8YdKWYmRrL/Zj7PRb/9A7Q11YEKVGNA+isIHDQcgNWtusZg4jX8qPyVuC5OwzM3IRAIPY/g4GDMmzfP7jHp6elIT09HU1MTWlpaEBYWRtzR3Uh8RDAyUmJFDSPEYhWxvGihjh9rbi6pQTlnA1hBQXSqrfDcgOP4z9kJdb6CXF1/5ZVXUFBQAIVCgc8//xxz5szpqiUSCASCz+Ksdklp5Io9FXZiL/pSo20Hmw/W8vZmva2hzu7z9tSc6caNG9lmn6eeegrvvfceKMp3C7IJBF9h9erVvL/nzp0LiqKwfPlyBAYGSn7O398fYWFhGDVqFMLDwz29zF7LhdZ2ZKwpgtnCb7BKjtbwYszWdgvbvBMS5I/BoX1Ag0JtYwtAiZtVBPgpsHrumK78OqJ4W8e7E6Q+ikDwPZgmA4qi0N7eznvNWbjnIDgmVOWPBRPibJpzhIjlIqViQKkaY0+bPDDPBEwu3F2xpr06bXfSlXVTRqMRd999N0wmE4YOHYrc3FxERkY6/iCBQOhSPLZTeujQIVAUZeO6WF5ejn379oGiKGzbtg2pqamwWCy45ZZbsG3bNqxevZo0/LiAPUc/R25/SgUlOo1AzEGKK8b2iogBx4lMR4nerghguWtIHBIKgEJJte165K6lq5LRw4cPx/Lly7F9+3Z89dVXdpNGYnSnZioCgeA6cXFx+P3335GZmYmPPvrI4fHLly9nP0dwDqWCQnpipGgQGh+hxtxxMcjMr+DdV7mBl4KieJ+VClw7qyNSBT9i04nkTvRzhd6gM2q1GikL30DOF+8h4o4XoQwOtdsM7Uywbe/Y3vDbEQgEAqF74uxzBqNXQMe0wNzSesRHqO1+LjUhHLWGFjSaWrGp5AQAGvPHx8EiYnB62S3P4NSm16CZ9gQCL+soTLs2RotrY7Q885In0q7AwlTyHEsg+BIhISEICQnx9jJ6HemJkaK5XSbeEcYqmfkVNnlRi8C1utHUyvs7u6TWJqZxdYPS2Ql1voJcXV+6dCn+97//Yf78+bjrrru6cokEAoHgszirXWIaac0R6iQ/c/uoQfjm0J+CV/k5RG9rqLP7vN3BBMoV7r33XuzZswdhYWF47bXXSLMPgeAjCBvp586dCwC4//77SSNCF9DSTrPaMjEhHEoFJRoXrd6rY2ugGppbMVij4k38ETOr8CbcPcIqPX+qgi/Hwp2tjyIQCD0PsYl5cqfoETpPZn6F5O+tVfsjIyVWNBcpFQMKDQG1an/MHx8nmc90d82Mu2NNd8Ta3a0uSK1WY9OmTfj73/+O7777DpdddpnX1kIgEKTxWMNPfb01SIqOjua9/tNPPwEAEhMTkZqaCgBQKBR45plnsG3bNhw4cMBTS+rV2HP0k+P2l1VQyWv2mZgQzoqqlBgLz8ulpNoAwL6QOUr0dkUiWmwNYuuRuxZPJqNpmgZN0+wI+AULFmD+/PlOJY6ZhwXulApfdwMhEHoyd999N1544QV8+umn0Gq1eOGFF0Rdj9vb2/Haa69h+fLloCgK99xzjxdW2/PJSInFB7l/wNRq5r0eFarCqp8rWbfFnGN1sNA0FBTFOuJToEVHuYoFca7oiNjI2KRoDSw0jeKqRkxMCAcFGjQoHDnZ5NREP7nXtdBgv5+FBlug29N0xmKxsFp7y9TJ+M08iNVaTzuMAMSxi0Dozpw9exabNm3CgQMHcOrUKbS0tNhNLlMUhdzc3C5cIYFgH3cVWguLyOLC1RisUXGeA2jklVmfi/TGVizbUYZCnQFHTjYBAGjaAoqyai2Cw3HZnPdt4tqSagMyZydBQfU8d2cCgUDorgT6K7Fk+jD2fip3QoxYXlS499hfFQC9sY39u7yuGeV1zW6JabpqonpPw56uc+NatVqNnJwcUnxMIBAIXYiYdtkr5hF7L6ugEnpjq9QloKAUmDk6kmeSAJpGZn4Fe25va6i3G448CVdrKYrCZ599RrSWQPBxdDprkyZp9ul6lAoKWXOSea8x2iqcTsDkJxk+yP0DFhqYP168yLYri3HNFhoZa4p4dVtcfC0Wdkd9FIFA6Lns3r1b1msE92O82M4zM/ZXUujbxw+GS7lfvbENCooS1UOpGFBoCHhVZH+7sWFX1cy4qvPuiLW7S10QN7YdP3489u/fT7QW3a8hi0Bg8FjDj8FgvYELC48LCgpAURSmT5/Oez0hIQEAcOrUKU8tqVdjz9FP6j177hCM6yLzOS7ZJTWXJuJosHhaAkqqG2G20KLTgexhL9FrttCi43eFdPbmam9KEXc93k6K0zSNRYsW4cKFC2zBPgCnBVZqRGJvSrITCL7EM888g40bN+K3337D0qVLsWLFCsyYMQPDhw9HcHAwmpubcezYMWzduhWnT58GAFx55ZV4+umnvbzynolSQeHaGI1NonV3WT1qGk281zYfrEV5HV9bAdtRru4K4sRGxgLAm9vLbK7NdWcGHGuaPa0V05Xc0jrERwTzXuspOrN69WqsXbsWW7duhUql8vjkPrHftjdvhBMIPZn169fjiSeewLlz5wDIc5EiyTBCb0EYD88cHWnTiMONwzPWFNmcg3l+ulB7DIYfP0HEnS/Br18EAPH/X0mO1vRYd2cCgeAcBoMB+/btw6lTp9DY2AiKoqDRaDBw4ECMHTsW/fv39/YSew2hKn/ePVUs3hGLUcTyohbaOvWNYebojqlBVXojLx7ubEzTVRPVewsnTpzALbfcgo8++ggpKSkAyHMpgUAgdDVi2mUvDyz2nr39SwAortJjxQPJUFBgTf7K6428ie7e1lBv7616CqPRiNtvvx133nkn5s+fD4BoLYFAAIYMGeLtJfgsQn2x1zgTEuTPM0U0tZrZ5lmxyeJCjd5fqUfWnGSPFHwKTZoBID4iGNFalc/Fwu6qjyIQCD2XCRMmyHqN4H5azfw98DYzDY06kG34AaTzvVIxoEJw/xb+LcSVmhlXaohdrddyNtburnVBwvoogGgtQ3dpyCIQhHis4UelUuH8+fM4c+YMb2M2Pz8fAHD99dfzjg8KCgIAtmOQIJ/M/Aq2ASdxSCgrCIxwKRUUT2iY96SaPwD7E4LK66ybtjnH6rBk+jBkzUkWFSZH2Ev02ps4BLg+qUa4zsQhoZJTirjrEQr13HEx7O/u6CGhs01JFosFCxcuxIoVKwAAkydPxp133in781ykNgh6S5KdQPA1AgMDsWvXLqSnpyM/Px+nT5/GqlWrbI5jipInTJiA7OxsBAQEdPVSew2Zs5OwYF0x9pY3CAJd4X1d/D7PjKplNKFQ554gTiwYFDvGlY1ee4GM9Maz46bdrsAZDf7000/x2GOPAQCWLl2KN9980+OFxmK/bW/dCCcQejI7duzAnDlzQNM0KIrCqFGjEB8fz8awBEJvx96zQ6HOgP2VenaSoIKiYJZoiLtQ/SvqNr8Kuu0C9Ns+xIC7X7M5Jj4iGOmJkT61gU0g+CI0TWPjxo149913cejQIcnjKIpCUlISnn32WZdzYYQOjBfbkbGmiBcbCeMdrkEEE5eI6cCKPRW8c1MU2HM5azLhCNIAKp/KykpMmjQJ1dXVuOuuu1BRUUGeWQkEAqELaTS1sRN2hNplr5hH7D1hjlCIhe7QyKIqA7tXyj23Iw31tGOttxuOPMH58+fx17/+FQUFBcjNzcXEiRNxxRVXeHtZBAKB4LMI64gA8cYZFpqGKkAJU6uZ9/LmkhpRgyPhPu7usnpkFVRiwYQ4t+uo2J5remKkz8XC7qyPIhAIBILzBCjFtIy/78cY+Qt1TyoGTIoORW5pR3ybFB1qdw2u1My40qDB1G9x/5aju87mq7tjXZBYfRShg+7QkEUgiOGxhp/4+HgcOnQI27dvZ6f3/Pzzzzh16hSUSiWuu+463vF1ddYb2IABAzy1pF4Jd4weVwRccYeScofginGV3uRS0lgMe4le4dq4E4eE34eLo5ur8HdYPG0YlkwfdqlhKhQAhZJq6UlIiUNCYaGBmz4skN1o1JmOz/b2djz00ENYt24dAODJJ5/EzJkzZX1WDOHDQnyEGumJUZIOnmQUHYHQ/QkLC8Pu3bvx3//+F6tXr8bevXtx9uxZ9v3+/ftj3LhxeOihh3D77bd7b6G9hAA/BVbPHWNTwCR0urfQYB2huFho8DRhYkI4731XgzipYFDsNWc1214gI7XxbP09KK9v5srV4Pfeew/PPPMMAGDKlCl48cUXu2R9Yr9t5uwk9t+9ZSOcQOjpvPXWW6BpGgkJCfj222/Z+JZA8AWk4kThs5AjWipLUP/N66DbW+EXejm0Nz1pc8zEhHCPOWQSCITuQ2NjI2bOnMmaQtmbmkfTNIqKinDXXXdh8uTJ+PrrrxESEtJVS+11nL/QjpxjdXZjI6n4TxhDllQ38o7j/t0bi3t7AqWlpUhLS8Off/6Jfv36YdOmTaTZh0AgELqYi21m3oQdLvaKecTe4+qp2ULbFC4fOdnENhcJP1+lN7Hv2YuvPO1Y29uadhsbGzF9+nQcOHAACoUCq1atIs0+BALBhrKyMqxZswZFRUU4ffo0TCaT3biXoihUVFRIvk8QR0kBz00bJqp1wiYdLhUNJtHXG02toppoEflvx8TJ7tZRoZ6LNTP1dtxdH0UgEAgE51EH+mFiQjgvBqVpGnHhalTUW6e6cxtguUjHgPIMlBlcyS+7OhWIi67BKNrI1Fm6W12Qt+qjvImzddHebsgiEKTwWMPP9OnTcfDgQbz44ovw9/fH5Zdfjueffx4URWHixIno168f73jGzTEqKspTS+qVCMfocXHWHUrKHYIrxu50aLSX6HV003R1Uo3wc6t+rsTDKbHInJ3EuYnz1yMWqIudV+ohwdWOz7a2Ntx3333Izs4GACxZsgSvv/56p0bniT0QMd9bzMGztyThCQRf4I477sAdd9wBAGhqakJzczOCg4NJMZQHMFtoWGga8RFqABRmjo7E/PEd7sjMMQrKmlRut9A4UnsWF9ot+LW2iXcuBQVe46mFho3jshwcNdF2JkC0p8nMOQt1elho6+jdMTF8t2hP4igok6PBr732GhvA3nzzzfj666/Rp08fj66bQey37W0b4QRCb+DgwYOgKAqffPIJafYh+Byf5ZfjnZ3HAVjjxHaLBY9NvMLOlD9bTMf3oeH7ZaDN7eg/MAbq2/8Jv2Db2F1o9EEgEHofbW1tSEtLw//+9z/QNA2lUokpU6Zg3LhxuOKKK9C/f3/QNI2mpiaUlZXhl19+QU5ODsxmM3JycjBlyhT88ssv8PPzWErbZ5DKT8rdyLJ3HIlpup4jR45g8uTJqKurg0ajwY8//ojExERvL4tAIBB8FjGdtZe/7cixGmC2WJBdXIPsklrMHB3JFgFlrCniFVw1NHcUJjOfzy6pQXmdEeV1zZKNR0BHTnVlQaXDdROs1NfX48Ybb8Thw4ehVCqxfv163H333d5eFoFA6Ga88cYbeOWVV2A2m+02+XDpTO2HLxMdppLULGGTTqjKDwqKgt7YJnm+/qoA3vvZJbXISImFWKqSiX/d7fxur5bHF/BEfRSBQCAQnMd4sR0KisLEhHCcMJhQUW9ERb1tw6wzuldSbRD5272xpysNGrWNLby/KxtMoo1Mnlibt3Lo3qyP8ibONmoTUzNCd8Vju6NPPfUUVqxYgfr6evztb38DYO32VCgUeP75522O37JlCyiKwg033OCpJXUJNTU1+Oabb7B7924cPnyYnWgUGRmJ1NRUPPHEE7jyyivddj3xMXpWzBYare0WrN6rg66hmfdeu4XGA9dFY3+lHr/WnkV/VQD2V+qxv1IPBQWMidGKBpDcpLOFptnRdl095t3epBp7CD/HTYjLbdiROq/ca8p5oLhw4QJmzZqFLVu2ALCOznvhhRccfs4R9h4WyCg6AqH3EBIS0qsbfbpaa4VkFVRi2Y4y9m8FBRsNVCooVpNW7KmEwWRNEgtHxFcbWjAmBsicndQpJyjh/d2dU9vsabK3C7kc/Wb2NJimabzwwgt44403AADp6elYv349AgICumLpAEiQSCD0FJRKJQBg9OjRXl5J1+FtrSW4D1efCZjPvZ/zB+/1z3/WwU+hQJXeKOv6xmN70LDlXwBtQWT8cBT9nIfvSs+LulQLY2UyhZZA6H288sorOHz4MABgxowZ+OSTTxyaP504cQKPPfYYtm7dipKSEixduhT//Oc/u2C1nsXbWiuVn7QXo/CnoGuweFoCSqobSSzjZUpKSnDjjTfCYDAgIiICP/30E6666ipvL4tAIBC8jje1NnFIKDLzK2xiGak8Ktc4iWu6uGxHKRSUNd+ZNSeZbdJpaG5lj+FO4yvUGVBe1xGrFerE9/m4OVUuZgtt183YV2O006dPIy0tDUePHoW/vz++/vpr3Hbbbd5eFoFA6GZs3ryZrecICgrClClTkJCQAJVK5eWVeRZv6e0doyOxPK8cmw/WQmjOKJQmrToQ5fW2uUyt2h+h6kDMHB0JwKq7DOV1zcgqqMSYGC1ySzvyl9ypO+52fvf2nqs38VR9FIFA6LnExrov19jTp+l1tdaev9CO3FKrvllNkMVxRveEmmm20DwjZAC8WNNCd+iy3Nopl2pvRJpKPVEv2x3qgrpDfZQ3cbYu2pefywjdG481/Gi1WuzevRsLFizA3r17AQCDBw/Gu+++iwkTJvCONRgM+PbbbwEAaWlpnlqSx6mpqcGQIUN4bh3BwcFoa2vD8ePHcfz4cXz++ed477338MQTT7jlmupAPyyZPszq+kTT+LWmEQZTOwDr+LwF64ptxrwDQF5ZPWZ8VMCO2tMb29h/A2CDVqnRe0BH0lnq2M7g6KYpNlFADsznpBLiYggfOrjIaTRyRbQfffRRNpj917/+xY7R8yRkFB2BQOgJeENrhcgNBKQ2ThUUEBumRnk9323RnY2X7hwj350DGeFvVqgzsK8nR2swd1wM72+uBn/66adsMDt79mx8/vnnXe4U3p1/WwKB0EFcXBwOHTqExsZG9O/f39vL8TjdQWsJ7sOVZwIm0S6WSzh3oZ33fKNR+bE5CC4ThoYjNy+fbfYJuHwoYue8jcsGRGDBgAgsmBAnWizW2bUTCITui8lkwieffAKKonDPPfdg/fr1sj43ePBgbNmyBffddx++/PJLfPjhh1iyZEmPdp3zttbGhasl85P2YhThfXnJ9GHImpPs9vUR5FNfX4+0tDQ0NTVh4MCByM3NxbBhw7y9LAKBQPA63tLaQH8llkwfBgsNl2IZMQNCJkcs1RTE3csTTjQQ/m3vOoB1X9mem7EvxmgWiwXTpk3D0aNH0adPH3zzzTeYNm2at5dFIBC6IR9//DEAYOTIkdi+fTsGDhzo5RV5Hm/oLUVZY1qaprBsZ4c5I7dJVtik02gSn+yjN7ZBb2yDgrLW9Gw+WMNrnC2qMrCT9rh1SVkFlchIiUVGSiwsNC41HdGw0PYbZ3sj7moG9kZ9FIFA6N5UVVU5PIaiKNGJesLXe/KkMG/nkQH+b6cKsJpkjokOZWtx5MCtneWaAXLrVLmxZnxEMO/zYrVTYhoklte2p1UzR0fyGn4BoEpvQmZ+hVsNLpypC/KU0UZX10d1N8MQUhdN6C14tKpxxIgRKCgowPnz52E2myWLo2iaxrZt2wAA48aN8+SSPAozmvfGG2/EnDlzkJaWhgEDBsBsNuPQoUN4+umnUVBQgL/97W8YOnQopk6d6pbriiV4GX4ub5D8XIWIiwUXe4XGnSlKdscNXbzxyHFymfkctxMYABKHSN/EuQ8diUNCAVAoqZa/dleKeZcsWYIdO3bghRdewKOPPip5nJzfUu7vPXdcDPZX6nHkZBNGDgpx6sGMQCB4H4PBgHXr1qGgoABVVVU4f/48+vbti+joaKSkpGD27NnQaHr+A6u3tJaLo0CAue+uLKgU/byFho1TBHOP5p63Sm9EZn4F5o6Lweq9Oqd0U0qnndHg7haAiSH8zSw0LbrpLKbB999/P7744guMGjUKn332GRQKRdcsmkAg9DgeeOABHDx4EN9++y0WLVrk7eV4nO6gtQT34UrsvmJPpWizDwD06+MHvbFjk/z8Rf70QlWAEk+mXYGMlFjcaDiPs8PHo/1cPSLufBnNdAA7hViYgHfX2gkEQvdl8+bNOHfuHDQaDVasWOH051esWIGdO3eisbERmzdvxn333eeBVXYN3tbappY2l2I74X05u6SmW8aJvkR4eDheeuklfPjhh8jNzUVcHNFJAoFAALyntaEqfyyYEIeMNUW81+XGMmIGhMLcsz2TQaEkS0m0PaNDT+0N91QUCgWWLVuGe++9F9nZ2Zg0aZK3l0QgELophw4dAkVReP/9932i2Qfwjt7StLXOafVenc17jC5JFRUzaNX+vPwmE9tai347mojaLTQWrCtGcrQGSdEa9j1uXZKCsk4DAoBlO8qgoKher41c3NUMLLc+ikAg+A4vv/yy5Htr166FTqeDv78/JkyYgBEjRiA4OBjNzc04evQo8vPz0draitjYWMyePbsLV+1+vJ1HjgwNwszRg7D5YC3K64wwtVr3BPOON2D1Xp3sGiRu7axYvGwLv5FLrDlDrgbZO27+eOszw+aDtWg0XoTe2MYzbe7KWmhnv5ezdHV9VHczDOkOU5YIBHfQJTbmffv2tfu+Vqu1mfpjj1dffbWzS+Lx0ksvueU8oaGhOHjwIEaNGsV7XalUIikpCTk5OUhOTsavv/6Kt99+260iK+XG1GYWd2+Sg71Oxs50Pbrzhu56cln4u4j/TtKC7FkBuuKKK1BaWop+/frZPU7Obyn39169V8cmO3aX1bMPZgQCofvz73//Gy+88AIuXLgAADx3h0OHDuGbb77BP/7xD7zxxht48sknZZ2TaK00jhokpSb78OHrTuIQDSw0jfgINRqNbdAbW1FeZ8Sb20uxv1LPc7jYX6mHUkHZDRSldFqOJjDal11cw464zzlWh+ziGqQnRXWrgi5hUFao0/Pet/dcEBISgl27diE4OLjbuLr0hCYrAsEXeeSRR/D111/jlVdewXXXXYexY8e65bxEawldgSuxu9WVko8qQIlrYzRIig7FOzuPs68Lcw4DQ4JY7b0zaQjK6xaBNrdB4d8HemMbrnn1R3ZDwNFzDXFbIhB6F7/88gsoisLcuXOhUqmc/rxarcacOXPw73//Gz///LPshp/uqLfe1torB4U4PEYsNhHel8vrjOwUAHuxDIlzPMvTTz+NjIwMh3lkAoFA8BREaztoNLVheV452i2Oi5PEsE4KoLGppAZnTe0IVQXAQoM3LcCeyaBwosGYGK3kdQDxQmhP7Q07S3d6fpg6dSp0Oh3RWgKBYJf2dusE7Guuucbt5+6OWgt4N7Y9d8F2ag+jS0oFxWqd0JxRq/bHQzfE8PKbTGwrnGKQx9mbjY9Q895j9h99sRmWi7u+v9z6KAKB4DtINfw8+OCD0Ol0mD17Nt577z1otbYxj16vx6JFi7B+/XrodDp88cUXDq9HtFacvLJ6XBerRbRWzZuCB3Tc81fsqWCbYnOO1cFC01iYGi95TmFcmTgkFIU6vp7MHB0JgMLmklo0mlrxdfEJWGga88fHsXGh8DOFOnENEh7HPBswMebC1DgsTLU2InHX5YymdbYWmhv/VulNvPe46+hMnNzV9VHd7RnJlYENBEJ3pEsaftzNK6+84tYbj7tENiQkxEZguQQEBOD+++/Hc889h+LiYrdcEwAy8ytsbvZC4sPViAwNQm1jC0BRiAwNYgNUANCo/aEAhRCVP4ZoVLg2Vmu3k7EzXY+ObujOiJOryeWS6ka7fzN0VbdpQ0MDHn30UXzwwQe4/PLLAUBWMCtHHOUKaHcTWgKBII/nn38eb731FtvkM2TIEPzlL39B37590dzcjN9++w3V1dVoaWnB008/jfr6erz22msOz0u0Vhphg+SCdcXImpPMapVUEy6XmaMjoaAoVussNM1zjeJyQBCACsfbit2r+dPprOfPWFNkNzhkkGpYKq83Ou1k4QyuBKdiQRl3U5v7XNDW1oaFCxdiwYIFSE5OBuC4Kb2r6W4uFwQCwUpAQAC2bNmChx56CBMmTMDdd9+NG2+8EQMHDoRSqbT72fHjx0u+R7SW0BXIjd25OtxobOW9p1X7o/D5KVAqKJgtNBQUhc0Ha3GysQUtbRbesUe3rcZDjSX45KUncUBngJ9SCbOi4/9PmGYfBnvPNcRtiUDoXRw8eBAAkJqa6vI5Jk2ahH//+9/sueTQHfXW21qbHK1xGH+JxSYZKbHILqll3YsB6z06IyUWGWuKJO/p3S3OcXVj1N2Fx66e77vvvsP+/fvxxhtvsP/bJkVRBALBmxCt7eBim9kmx6tV2zbtSGEtNoqHgqLw5vZS6I2tWLbDqqEKCg41w5UYakyMFknRofjm0EkAlN21dmWM5s3nh99++w1vv/02Vq5cicDAQABEawkEgmNiYmJw9OhRnDt3DqGhoW49d3fUWsC7sa0fBTw9LeGScRGFmaMjWV0yW2hejMpFb2zDppJaBPkreHlNx3u7/N+f2X/syYZF7ohxXf3+rtZHEQgE32bDhg1Yu3YtZs2ahTVr1kgep9VqsXbtWly8eBHr1q1DWlqaw0k/RGulYXRCahqt0ERw88Fauw0/wrjSQoOn2XHhasy7IRar9+pQXm/NQ1tjY/4kPQvNN9oQ/i31ekNzK97cXorskhqkJ3YYHndG0ztbc2vPVJpZh/D5xlGc3NX1UcLnisQhnXtG6k4mIARCd8LjDT+tra348ssv8dNPP6GsrAxNTU2su4UYFEWhoqLC7jkHDx7cbZzYnaVPnz4ArCP33IHxYjvvhi8cP8sQHaZG1pxk9m/uTZHr3tRgbMWspCiHouNM16PtDT3U7g3dmc5fV5PLckW6K5pgTp8+jcmTJ+P3339HeXk5ioqKHBYOMsj5HnK/a09ORhAIvkpRURHefPNNAMCYMWPw4YcfYsyYMaLH/e1vf8OBAwfw5ptv4tZbb2Uf6KUgWmtLa7sFC9YVY295A+/13WX1PFdjs8X+dL3UhHDWeYLRFOHYWi7CwlguUrrE1enM/Aq8uV28mShxSEdwKOUYIXbNjJTYTgVXYsGZM9OHpK4r9Vxw4cIFzJo1C1u2bMF///tflJaWIiIiQvZ6uwrSfEsgdF8CAgJw1VVXYevWrVi/fj3Wr1/v8DMURdmNfYnWEroCR7E7O9mvpMbGnYth5KAQLFhXzGqrgqJsjqVpGmcL1uHcvq+xercChxv9YejrXPGXUPeI2xKB0Ls4deoUAGDYsGEun2P48OEAgD///FP2Z3qq3npSaw+daHQYf4nFJhkpsYgKDeI1/JgtNFbsqbQppOLe06XO5a0NO1cLiN1deOzK+b766ivcf//9aG9vR1RUFB599FGXr08gEAjugmitfZimnayCCjx0QwwUlAIl1fb1T6idH+36w2ZSKteAikFuDCXUoIkJ4WyMt2xHKRSUuCZ1Zm/YWa13R57UlTUcPHgQU6ZMgcFggL+/P1atWuXUNQkEgu+Snp6Of/7zn9i+fTseeeQRt567p2ot4Dm9vaxfIOaPj2PNFbm396wC2xiVi67Bdh+UqY0RFjMzWI0cbY0WE4dosHhaAkqqG3ucYZE7YlxX6rU6Ux9FIBB8m5UrV4KiKDz77LOyjn/uueeQnZ2NlStXOmz4IVorjdlCo1CnR2pCOGoMJpw1taI/ZxqtsCm20dgmaSIhFqMtWMdvVKqoN2L1Xp1oMy43LhSeXkGJn18qBCyv4xsed8bgorM1t8LvqlX7g6IojBwUgrnjYgCIP99Ixcmu1kd1Jo4XPlcsnpaAJdOHOfV7StWzdwcTMQKhu+DRhp9ff/0Vd9xxB3Q6HTt5QAhFUbz35IhnVVWVu5bY5eTl5QEARo4c6ZbztZr5v6vU7yfl3LhgQpxNkbG7i0uFN/T4cDUmJoRDQVEYE2N7Q3em89fVAiChSM8dF4PM/AobwXJ3Ewz3v0HikFAY6k7h7b/dh3NnatCnTx+8/vrrTgWzch425D6QEPdkAqHn8fHHHwOwNvvk5eWxgZyQ5ORk5OXlYcKECSgqKsInn3zicGwt0VpbFqwrlkwQM9rpKIkMANfGaGyCIqHeaNUBCFX7g6ZpVNSbOK/zG3vbLTQ+2V1+yYGRxszRkbwxtszapLE+R9hzjBCSHK1xmAR2xS1aziauo+uKPReYTCbcfvvt+PHHHwEA//jHP5xu9ukq9wix8cVizycEAqFrOXfuHCZPnoySkhLJuNYViNYSvElHow9/UgNDfLgaoCg0Gi8i77i12TnnWB0y91Tg6sj+vGNpmkbjriycL/4OAKAeMQGNwUMcriEuXI2K+o7GIWI6QSD0bpqamgCgU07H/fv3B2DVZrn0VL31pNYmR2scxl9iOVGxeHd3WT1qGltEryF1riq90Sk3QlewF8O5WkDsboMGZ8+3Zs0aPPTQQ7BYLBgzZgzuuecel69NIBAI7oRorTz0xja8s/M4+7dQ/4SFLVzEJqUyBlRC5OQxhRp05GSTzfud1eXOFhG7Y3/W2TXs27cP06dPR1NTEwYOHIi///3vTl+TQCD4Ls8++yw2btyIl19+GWlpabjiiivcdu6eqrWA5/SWUiol7/OOp/V0EBYcgIdTYnm1MUwtD0DZNOkKjRZzjtVhyfRhPPPlnoI7Ylxn67VqamqQlpaGP/74w6X6KAKB4Nv89ttvAIC4OHn3nNhY67396NGjDo8lWiuOVu3PywdPTAhHRb0RemMbaxYxc3QkO5kWsJpeSMWrYkMAxKYHOZoqBFin1eaW1vP+Fns2EB4nhNG/zpgQOlNzKxazC78rUxO2u6weq/fqJJ9vxOLkztRHdSaOF66vpLoRWXOS3TbpiJglEwhWPNbwYzAYMG3aNJw+fRpxcXG444478M4774CiKCxevBgmkwlHjhzBnj17YLFYMGLECNx5552eWk634MCBA/j2228BAPPmzZP1mcjISMn3Tp06hX6acN5rIUH+aGhuZf+OjwhGemKkqHM+4wAlFA1dQzPmri7kNeR0prhUeEMvrzeivN6IJdOHSdyIhddyf2GrUkHxxHZ/pV50k9mVJhipZLpwtN72fb/izMbnYW46A8o/EI+8sRLTp093+ns4etiQ+0BC3JMJhJ7Hnj17QFEUli5dKtnswxAYGIhXX30V06ZNw549e7pohV2PJ7SWGSUu3PjkUqU3YXleBbKLq3mva1T+MJj4k/dKqhttPs/oC+Ourze2Qm9svRQwm3jHFeoMrJbkldUjjxNgC8fYArabo2JrEWp1fIQaQzQqMHvLJy45dVwV2R9zx8Vg4Xq+y0ahTs+7pitu0XI2cZ1NPp8/fx4333wz8vPzAVib5B577DHJ46Vwt5OzFGLji7viugQCwT7Lli1jx6Dfd999uPvuuxEXF4egoCAvr8w7eFJrCe5HKj511OxLA6gQaQQyGNuwv1LfcRxtgeHHT9F8eAcAIPjqqdBMfQygFJLnVgUo8cSkeMy7IZZ16SKmEwRC76e52XpPcRS72iMwMJB3rt6KK1oLONbbPiFapCaEI7u4BgZjK+99azFTh24cqGxAXLgaTS1trJPgoxtKJM7OL0qemBDOu6d3xLvWJtPyOqPNpDhnN+xcMZlgzu9qAbG7jaGcOd9nn32GhQsXAgBSUlLwww8/oF+/fp26PoFAIPgyntJaStXR2ExBqJAi67gUWxVVGdButrBmCwAwYWgYiqoaJae/S2mnnDymUIOE5ibuMGPobBGxO0wKnVlDXl4eZsyYAaPRiCFDhiA3N1d2ISGBQCAAQENDA1avXo377rsPSUlJ+Nvf/oZp06Zh4MCBDhsaBg8e3EWr7Fo8kUdmtPZcS5vNfX5lQSUAIHGI9L6oEKbZp8O0V4NRg/tfMimmMHN0pE286W4zCG/hSfNjsThdp9Nh0qRJqKqqgkqlwpYtWzBp0qROXZNAIPgWLS1W06GTJ0/KMpU6efIkAOu0k96KJ7UWAFoE8aiYWUTm7CRsPljDy/dKaaPYEICdT03g1e0CYHXEQjOfoVlNZhCLGYXTgpj1AdaaJgsN1BhMKHezGaGjmltHk2u436VKb+IZNTK/pVC3hTl4QLo+Sq7BcWeecdzxXGGvaZuYRhIIVjzW8PPhhx/i9OnTSEhIQElJCVQqFd555x0AwAsvvACVSgUAOHHiBDIyMpCbm4uWlha89dZbnlqSVzEYDLj33nthsVhw7bXXYu7cuW45rzrQD6kJ4Wyxb0W9EalDw+CnVDh0L2QcoISbrhX1Jra4OLdUPBnsjNO9VKGxlCgIO39nju540LDXTOOs876crlBXmmCkkulcF8w2w0mc+fIfMDfrQQUEISL9nzivSZB9DWfoqqkEBAKh6zl9+jQAYPTo0bKOT0xM5H2ut+EprWXoJ2iq5VJe18zTLgaxwXtigQijN0VVBl4grKAoLJ6WgE0lNThrasfmkpMO1ynUV25wKNw4Zoq6hFodFaoCRVEYG2MdS8/oF+MgITCZZMf1MnpTpTfx3pfjFi1nE1dOkMis4+ffq7H3k2egO3oYFEUhKysLDz30kMSvxv+sUDO7KnkvfO7w9BRGAoEgj+zsbFAUhSVLluC1117z9nK8iqe1luB+nHW6jI8IRlRokN2JhS1tFgAAbTGjddcnaD5sdYnqm3gzQtPmWyc521nTk2lXsHpGTCcIBN+BpmlZk919HU9qbYBSwTOM4GP9byOWL2XiQKkc78zRkVBQlMN87RBNkOhUOUDehp2jjVFHJhNsnOViAbG7p6PLPd/777+PRYsWAQAmT56Mb7/9Fmq1ulPXJhAIBF+mq+JaigIcDQmuNpiwS0Kbj5xskmz2AaS1U04eU7gvzLgHc00kO0tni33cYVIodw07d+7EbbfdhgsXLiA+Ph65ubm9tvieQCB4jpiYGPbfNE3jjTfewBtvvOHwcxRFob293ZNL8wqe1tuRg0Js7vMNza14c3spNCo/qPyVMLXZ6qhG5Y+rIkOgVFAYE6NFRkosVuypZPd4hTEvM73A0R5nT8TdMa69puPjx49j0qRJOHnyJPr27Yvt27dj3LhxAEg9E4FAkE9cXBx+//13ZGZm4qOPPnJ4/PLly9nP9Ua6IrY1XdoPZBg5KMSmMUepoJCeGMXLKUtro+0QAKWCQtacZFEtWJgah4Wp4v/9xGJGMY0WHiemO55GTo0y83/WSYK2v6WYbnP18uzZs5g+fTr2799vUx8l1+C4M8847niuEGtqUiooYhpJIHDwWMPPtm3bQFEUnnnmGba5R4zBgwdj27ZtGD9+PN555x2kpqZi2rRpblmDTqdDfX09Lly4gPHjx7vlnK7Q0tKC22+/HZWVlQgLC8PGjRtljyWtra2VfI/puK1t5BfV1p5tQc7TqTbHS43AYwTDWmRsu+kqlgwWG7G3MDVedJ3CxDF3PWLMHx8LBSUuAMJg20IDC1PjXHLet9cVarbQMFtopwNJs4VGdgn/vxnz+zHXazt7Gqf/sxgW41ko+gQjYtarCLx8qMcSAV01lYBAIHQ9ffr0QWtrK4xGI7RarcPjjUZrIwnjiuwOfEVrAYA2S2+wSsPXEa06gG2OEdMYoVaPibFqA9OIqzeKNxwJz8GFG8Auz6vgNfww6+MGX8LCKa3an3e+oioDFIJCPQrgTbFztCZhsDd3XIysZK6cIDGroBJvbPkVp9f/Ha1nKqBQKLF+/Trcc889omsTfparmRYaUFBAlZ7vPu3p5D2TZBA2TvXUTQMCoafD6ERGRoZXru9LWktwDjmboXKdLuPD1YjUqFBraMEBnXSszEW/7X0Yf98NAJh6z3wci7rZpphfFaDE4xPjQVHWyYIkKUsgELpr00930NvOaC3gWG8bmi8iROL9kmoDAOmGUMYN8eviGlRwHBDjwtWYP77DNImLMI87YSh/Un1qQjiUFAULTaNQZ51wYK+wR87GKIO9zUlXC4jdPR1dzvk++OADttnnr3/9KzZt2tSpKVkEAoHgTXxBa083dThHR2tVqGzoyO0F+SswMKQPKjivNbXwJ8NzMRjF3wsLDmCnEQC2caEw1nNsPtWxbxutVblN59xdROypNeTm5uKWW25Ba2srRowYgZycHDKFmEAguIRwWprw766gO2gt4Nk88ummC9Co/fHRPaMRFGA958qCSp5ho8Fk20ClVQdg/vgO/cwqqEShTo/9lXqHudDCS+9z9zS5f3P1pSc1r7g7xpVqOtbpdBg/fjzOnDmD0NBQ7Ny5E8nJyexxpJ6JQCDI5e6778YLL7yATz/9FFqtFi+88AL8/GxLn9vb2/Haa69h+fLloChKVp2IXHxFa8XQqgPw0T2j8cSXB3HkZBM7FR6QH/8JhwDcMWoQMvMr2M9lzk7qlG7KWYeY/nH122qaTKGkmv/vzui6MzXKrsTSFy5cQFpaGg4ePAilUol16/j1UfaMQfjfXYPF0xJc2s+191wh9/nIUVMTgUDwYMNPeXk5ALBd+VxaW1t5TUB+fn546aWXcNNNN+Gzzz7rVMNPXV0dXn/9dfznP/+BwWC9WQldMY4fP45nn30WgYGB+PLLL0XF311cvHgRd9xxB/bs2YOQkBDs3LkT0dHRbr2GMFcglTvISIkVHYHH/beYS6MwGdzabsFHuX/wXttUUiPq5Ah03NC5o3DtiYI9ARAb7bcw1XYzWo4DvvD7xoWr2Q1rZvqRs4FkVkGlTdMU8/sx1/PrF44+g0bAcuoYnv73OpzxixD9PdyVDOgtI4UJBIItsbGxOHz4MLZs2YLHHnvM4fFbtmxhP9cZfFFrAeD0ecfNNkKEDTp6YyuW7ShFoU6PrDnJNvd1OWNnASA+Qo2zpjZeAlsVoMQTk+LtBl3WIi7h3/yJdsKpMnrB5jKja8wUQACgAZtmn/gINaK1alGNE2o916XCXjJXTvK5qMoAyi8AQVeMRWt9NSY+9jobzDrSVqFmbj7Ib1Z2p8ulPYSFbPERaqQnRpECaQLBS2i1Wvz555/o169fl13TV7WWYEVuLChnM1TK6XLxtAQsmT6MTeAeqGywM/VBHNXQ62E8lo+/znkC0+9/DO2H/0Sj8SLv2cHUagZFQdIghEAg+B4jRoxwuenH3QVT3Ulvu0Jr283Sv58wfyn2vlJBIVqr4jX8nDW1shPkhVolzOP+drKJf1KaRlKMhjWVyi2tx/5KPevaN3dcDFbv1bF6yDQF2Vs/Q3coMnYHaWlp0Gq1mDhxIjZs2ICAgABvL4lAIBCcwte0lmFiQjg+vS8Rj24oYQuhmL+5DT8hdibKS/FwSiwv7hPGhdxYz5EGempCQXcpdpaTS77mmmuQkJAAhUKBn376CeHh4ZLHEggEgj12797tlet2J60FukZvDcY2PPHlQayeO4a9x0uZQzBQnCk9Qud8R1hoWjQHyy2UZYqVHU2j7c1IPVdERUVh3LhxKCgoQE5ODq666ire50g9E4FAkMszzzyDjRs34rfffsPSpUuxYsUKzJgxA8OHD0dwcDCam5tx7NgxbN26FadPnwYAXHnllXj66ac7dV1f01qFROymN7biiS8PsjrHTIVfMCFOdhOpcAiAlMa6ipx1iMWrYvutUv+WOre9OFgq5w7Y1ihLfQd7e8J9+vTBrbfeiiNHjuCrr77C7bffzvusvdhfeN4l04cha04y3Inc5l53NyMTCL0Rj6lLc7O1OPKyyy5jXwsKCsKFCxdw7tw59O/fn3d8UlISAKCoiF9k6gwlJSW4+eabcebMGbsbwEOHDsUff/yB48ePY/v27bj55ptdvqY9Wltbceedd2LHjh0IDg7G9u3bMXr0aLdfJ0rD32iNDFWxAaWw0zRzdhJvo3TuuBhk5legUKeHmQbiw4MB0IgMDYJSocCYGNtk8IJ1xTYj+86a2nk35v2VtoXM7rkpC/+7Wv8WCpPZQiNjTZHo5jDzd6HOgIkJ4VBQwJgYLQp1Bt7v6EogKQxG4yPU7O/H3WgedWMWbowOwBXx0ud3l5NFbxkpTCAQbLnppptw6NAhvPjii7j++usxatQoyWP/97//4aWXXgJFUZgxY4bL1/RVrQWAdotzxWWqACVMreJTgaQaS+WMnQWA9MQoAPwE9pNpV4g213K1WI4miF0vPiIY0VoVe06zhcb+Sj27YS1WsJeeGCVbt8SSuY6+Cxdu8Gy+9N8p5Pq7oR52A9JnTmGPc6Sttt+d/9/cnS6X9hD+HtFaNQlqCQQvkpKSgq+++gq//fZbl7g1+bLWEqzIjQXlbIYycajQ6XLzwVq2MddC04IJgPJQDb0OAx/6BL9po/Dbj8c7XvdX8HIGVqMO0vBDIBCsVFdXe3sJALqX3npDaylY48+QIH/MuyHGJn9ZqNNfmnhKIemSVmSsKYIwLNYb21jNstUq/sHnLvDNJPKON6D2bAvvNW5hFNe4KudYHSYm8AtwJyaE85qDuG6QGZeKoT0VR3VVIfOVV16J/fv3Izo62qOFAgQCgeAJfFFrA/2VWDJ9GKsLq+eOYd/LzK+wMUyqqDeyetZuttiNy4STfRiEcWFJdSOy5iTL0sDONMja08Ke5NSv1WqRk5MDPz8/aDRk/5RAILjOhAkTuvya3Ulrga6NbY9wDCUY/couqbUx52UYOahj3m2hg4k+E67Q4mTTBQAUZo6ORHEV33xCmIN1Zhqtu+guzbVcpJ4r/Pz88OWXX6K2tlbUlJTUMxEIBLkEBgZi165dSE9PR35+Pk6fPo1Vq1bZHMdo4oQJE5Cdnd0pAx1f1Fp/pULyvSMCQycxnbOnUcJ6KKEZsSd0U7geC02zBlSM/tibwCO2PrlNQ+z3dPCsIud7O9oTfvHFFzFr1iwMGzbM5rP2Yv+uaLwlzb0Egvvw2C5Rv3790NjYiKamJoSGhgIAwsLCUFtbi/LycgwePJh3/Llz5wAAer20U589zp49ixkzZuDMmTO44oor8Pzzz2PEiBEYM2aM6PGzZs3C0qVLsW3bNo+IbFtbG9LT0/HDDz9ApVJh69atuO6669x+HQBQCopsTzaa8OZ2vmsE999Sjvpc0pM6inQZRwpmo/fnctuEc3+VP2+KgasTcuzR2m6BRbCrPHN0JAC+MAldM4Sbw8IpR0umD2PXyZ1WkBytkXwIkXpdGIymJ0ZBqaCQn58Pg8GABbff3qniZ1d+z97iaEkgEGxZtGgRPv30U5w9exbXX3895s+fj/T0dIwYMQJ9+/ZFc3Mzfv/9d2zatAkrVqzAhQsXoNFo8OSTT7p0PV/WWgDoG+gHg6lN8n2t2p/naH9tjMZmE5eLvWCQm5TNSImFhaYvuSNbk8vCAMxREMlco1Cnv9TsSok29TLXE2plemKkTfKa69whVnhlT2+E3zlxiG0yV+y7SDUBZRVU4tUv89BSfgD9km7lFH4NdypYFWomN9hn1tUVkOS2a3THDQ5C7+DZZ5/F5s2bsXTpUvz4448uTyWQg69rLcGK3FhQjl4wyXOA3yhcXmdEeZ0ROcfqEB+hlrUuy0UTzhV9i5DrZoFSWtNJ/toom+NstzzIvZhAIADjx4/3qIY6Q3fSW29pLQ2rqYXe2IpCnR6PTrQ2Zgo3XRlTJW58ODEhHEdONvEaScXi28j+QSiv6zBXahOdMCT9vwnhBvKRk0088ybu8/7yvHLeRq2Fpj3abOqpQmaLxYK33noL8+fPR1hYGAAgPp40zRIIhJ6Hr2ptqMqfp6HcPJVUcbFSQSFrTrKo5nIRTvZh6EweUUz3hQ20Urk1e7nblQWVvGO7WzHP559/jqSkJHbCQEREhJdXRCAQCM7TnbQW6PrYNiTIn/23UkFd2ku16hO3binIX4GxsVpkzrYaUJstNKr0RpvzAVYz3/TEKBv9y8y3TqRlEGqtvSJhT+3vdcfmWu5zRX5+Pr7/7ld2wkBAQIBosw9A6pkIBIJzhIWFYffu3fjvf/+L1atXY+/evTh79iz7fv/+/TFu3Dg89NBDNlNOnMVXtTZAKZ2vHTkohBeziumcMxrVFXUxwvUI90QZ/ZGawMOFWZ/Yd7S3t8toJBMzZ5fU8PLmcr63cI0xgUZ88MEHbP0fRVG4YmiCaExvb0hDV/w3IPVPBIL78FjDT0JCAvbv349Tp06xY+OuueYa1NbW4ocffsCkSZN4x2/duhUA2OYgZ/n3v/+NM2fOYPjw4di3bx/69esHo1E8UAOsXbxLly7t1EQhKdra2jBr1ix8//33CAoKwpYtWzzqBD0mRsNrVGk0SY9/FyZVpYLPQp2ePW7Fnkos22F/pK1wypDYtbi4UpC5YF0xKjmj7jVqfxRXNSIzv4Ln2ijs/hXbHBZbp7Cg2kLzvzv3IUTq4UQsGN25cyduu+02mM1mbN26FVOmTIEc3CV2ZNwdgdB70Wg0+OabbzBjxgw0Nzfj448/xscffyx6LE3TCA4OxrfffuuyU50vay0AjIzsj/zj/A3X+HA1osPUNhPlEoeEwkIDv9Y28RLLXKr0RmTmV8BCw0ZrhI0t826IhYKiUFRlgIKy6uiqn3WXNIu+NNFPelLOvDVFyOME3qmXGnLEtJfZZBbqtPC8XBSUtYFWrq5/ll+Bd3Z2FGONvyIMi6cloKS6scONeV2x6DW5+ptdUoP0xCj8tO8Qzvzn/2A+3wBYLFAOXyA6ZtaRtjKbAsz1EodobNbVFZDktmt0xw0OQu9g9OjR+Pzzz5GRkYFbbrkF77//PuLiPPO/LV/XWoIVubGgULOY6QtSDcTMsVV6k8BBynEBvuVCM+qyX8HFP0vR1ngSYTOelSzcD/JXooUz4ScyNAhmC02aMAkEHycvL8/bS2DpLnrbXbR2b4VeUj+4Zg8MNY0mzLshRtQcQehsLDTG0KoDeDHyzNGRUFC2Jk6AtYCL21TU0NyK3WX1PPMmBmtszP/bkw0/cptzncmBm81mzJs3D2vWrMGmTZtQUFAAtVpeUy6BQCB0N4jW2uaphIZJDIyGMkU4QqQm+zC4M4/oTG5NTAsBiJpMdqdinvfffx+LFi1CREQEfvnlF4/ldwgEgm/zzTffYMOGDSgqKkJ9fT1aW1vR3t7Ovn/ixAls2rQJAQEBePzxx126RnfRWsA7ejtEE8T7O6tAvKZpXHwYb78wq6DSpq4JsBpbZM5Owuq9OixYV8yL3/g5WOv+LzeGFuZyudNoPbW/152d8p2tjyL1TAQCwRXuuOMO3HHHHQCApqYmNDc3Izg4GCEhIQ4+KR9f1tr4iGAANG4fNQgKSoGSaoNNTZTU1HVnNKor6mJsa6P5cbd1f5X/nSlQ+O+hk6BpC6I0aigvGSoz31fM5EJqb5ebH7bWdVEYolEhKlRlY9RsbzABd43Xh7XiX3+7B2frT2PDPh1GTrsXY2K0orVnjvS1K/4bkPonAsF9eKzhZ+LEidi/fz9+//13tpv0jjvuwJYtW/DJJ59g+PDhuPvuu2E2m/Hdd9/hhRdeAEVRNo1Acvn+++9BURReffVV9OvXz+HxQ4cOBQDodDqXridFe3s77rnnHnz77bcIDAzEt99+6/J3kktGSiz2VerZQl7uJqoQYVJVqkOVO0hHuGEqxpHas4gLV/OCY3sJXGHSmCnYFT4YcDdBhY06BmMbckvrkFtqndrDBM2JQ0J530nYXSzVbaxUUFBQFNtBu2xH6SWh7CC7pNbuw4kwGP3++++Rnp6O1tZWDB8+HH/5y1/Yz8iZ6MCcm4gdgUCQYvz48Th8+DCeeeYZbNmyBRaLxeYYpVKJW265Be+8846ke44cfFlrAcBPZMN1ZmIUW5wEAHPHxQAAvi4+gYp6k83xVrcICuV1zSivM+LN7bZaI9bY8nVxDauxYtPqlu0og4KynTRnttCY+v4em7GweWX1WLGnQrIAiqtnYnolvM6YGK3d44Wb1Z//zP/fxJ4/Gmw2tcUCYqH+ltcZ8c+1O9C46SWYzxug6BOMwKi/OCyMLtQZYKFpFOr07OvMtYXPKEumDxNtHvIkJLntGt15g4PQs2E0RqvVYtu2bdi2bRtiY2MxcOBAKJVKyc9RFIXc3FynruXrWkuwIjcW5OqFdXovfwS9mLN04hAN2i0079lg5uhIbCqpEd3sBgBzyznUffUiWs9UAJQCqrgxdqd0GExtvPxAngcmABMIBEJn6A562520ts1MI+dYnagBhZjzsTV3StmYPgC2z+Sh6kBerjojJYY1s+DGi8JYUtj8w0X8OV+oS55tMpXbnCu3cLqtrQ2zZ8/GV199BQCYMmUKVCqVu5dNIBAIXYYvay1T7CTUUMYw6YDOgGq9EWdNrbgqsj+bTwbE92z/MjDEJr8qln91R7zlTG5NuNYqvcnmO4s1K8lthvXEJO833ngDzz//vHX9yckYNGhQp85HIBAIQgwGA2bNmoXdu3eDpjsKboR5tAEDBuCtt96CXq9HcnIyrr32Wqev1R20FvCe3l4bG8b7W8roWBirMfuCQpQKCqt+rhSdHGubg7U1cWTW4C7NckR3dcq3Vx9FIBAInYXRlyVLlvCaCUNCQtza6MPgq1p7/kI7u4dYXNWIrDnJUCo64kJuHYuYLgo1KnFIKK8pSKw22JP7h8L1cA2okqOtZopcYys/hQIA2N+got7EGlBxp8wLryG1tyuWH2YQGltJ5ZK5jc2t9VXYu+klXDhnrY+qCRyCutJ65JbWi9aeOfptpWqT3BmTk/onAsF9eKzh59Zbb8Ubb7yBrVu3IiMjAwBw//3348MPP8ShQ4fwyCOP4JFHHmGPp2kaKpWKTbQ5S2WltXPyhhtukHU8I8Tnz5936XpimM1m3H///di8eTMCAwPxzTff4MYbb3Tb+aVQKijUNrbwXtOqAxCq9ueNf5t4yc2fC/P3yoJKnmOigpd0oOEIvbENemObbLcKsYLdN7eX8gqYhZugwkYdLtzPLJ42jLfhLNZdLHxwkFqX8LuX1zUjq6BSVgCdnZ2Ne++9F+3t7bj66qvx448/8sbCC0Wa27TE3ewmYkcgEBwRGxuLb775BvX19fjll19QXV2N8+fPo2/fvoiOjsb111+PsLAwxydygC9rLQAkRfMn6mnU/sguPsFOnxM25oiRnhiFoiqDoAGHrzVijS3CcwqbYAGrhjEj6R0VSAHyHY/Fgkp7hchyCppa2sw21+FqeXZJDWaOjhKdrsPV39YzFTjz1YuwtJxDn76hmPrsh5g+4TqHhdFAR0NVbmk9b42ebBrxxEY1oYPuusFB6Pnk5eWBoijeJm1FRQUqKirsfs5eQ4QUvq61BCvOxIKMtoi5STHF0xlrimzibIYJQ8Mxf3ws5t0QgwXrirHneD3MnEcTs7ERZza+gLaGakDhh/BbnoMq4XqH62pq4RuRkCZMAoHQnfC23nZnrS2qMvCcAKUoqTYga04y2xzEOCALjZiEG6jcnKcQrv4JJ7hzEXvOnzk6krfmmaMj5Xxdl5HbnCsnvrx48SLuuusufPfddwCAV155BS+99JJLz5IEAoHQXfBVrTVebBedcAN0GCYBwK5LOebdZfVYsK74UgEVhbnjYvBZfjkaTR1TIPKP2xooODvlWiwnyZzHnsmTvdwac47skhqU1xltDKcA4GGRwi25a3fnJG+apvHSSy/htddeAwDMnDkT//nPfxAQEODS+QgEAkEMs9mMv/71rzhw4AACAwNxzz334KqrrsLTTz9tc2xgYCDS09OxfPlyfPfddy41/HhbawHv6G2gvxJLpg+zicGEGqZV+yND0HTa2m7BoZqzouc9dKIR+cf5e6pi+6hSMV5X1/V0R/NgR/VRBAKB0FkKCgpgsViwevXqLrmer2otl91l9chYU8TGrEKEuvh1cQ1mjo7kTcs5oDOwQwzEzI4B+bGeK/U2Uo25zDWFeejsklqcNbXyXmP0Xjg0QRWgxJNpV9it85VqSuaeV+pY5n3mdW59lEIVggF3LUVABPcZwLb2zFXcGZMTCAT34bGGn+TkZHz77bfw8+u4hFKpxI4dO/DAAw9g586dvONjY2OxevVqjBgxwqXrtbVZi0nkJucYcVWr1S5dT4y9e/eyLnw0TWPu3Ll2jy8qKkJUVFSnr5uZX4E/z7YIXqWx5fEUPLqhBL/WnkV/VQAoikJWQaVoInfeDTG8DtQxMR03fOuGacd78eFqzEyMwoHKBuQdb+Bd9cjJJtatSUpQzRYa7RbxJiJhATNX2DJnJ2HBumIcOdmEkCB/yYJqZsOZKzJCQZUKuMW6ejcfrOU1TgkLqpmHAZ4L5fF8rH1rMSwWC5KTk7Fjxw5oNHwRFYq0VKOTpyHFxwRC7yE8PBy33nqrx87vy1prha9dBmMbDIKpevaafbiNt7YFUJRNUlZsAh+DWBNscrRGdoGUFXn3emeT13IKmsbEaNigXozyOiOW7Si1ma6TkRLLbiJfPFmKM9kvg75ohDJYA82s1zF9wnWy9NPeGj3ZNEKCYs/SHTc4CL2DBx54oMsKLonWEpyFqy1czBaajfXsNQD//Ec9pr6fj5mjI5E1Jxmf5VfgnZ3W+L/9XAPOfPU82g0nAaU/Im7/B4Li+FPv/BUU2kTie6nJugQCwbe5ePEizpw5AwDQaDQIDg528Akrzc3NMBisz/CXXXZZp4szva233tZaVYASF9rMEEvPJkdrkF1iu3FpajXbHAfYxjhCIyZ7DT72kJpKL2ZoBQDzx8faNBZ5ErnNuY7iS5PJhDvuuIPdK1m2bBmee+459y+YQCAQuhhf1dqWNjO4vtJadQBGDe5vdxoeU0ClVFAwW2hesw9Doa7D0MGR4QN3v48xQMwuqWUbcri65IzJkxBGC63mVh058fiIYERrVZKfl2u25C5TJpqm8fe//x3vvvsuAODee+/FmjVrePULBAKB4A4+//xzHDhwAP3798fu3btx9dVXw2g0ijb8AMCNN96I5cuXY+/evS5dz9taC3hHbwOUFKsR3LqWjJRYnrmw3tiGTSW1KK4yIClaA4BCVkGFzb4ug170deu5hdNouXgr59ndzIPXrVuHBx980G59FIFAIHSWAQMG4NSpU+jbt2+XXM9XtVbI7rJ6rNhTiYWpthNghLpYUW/E2zs7aoyLqxpt6oKENVVFVQbexHl7taueqLcR5m/FzCwSh4Re+hd/TQP7B7HXZ54XCnUGWGgaCspq/JE4RDzPDQBVDUbMXV3IOTZUNJecHK3B1pw9bH1UUEgYQu9cCv8w/n9rqdozV/CkUTKBQHAdj2azbrnlFpvXwsPDsX37duh0Ohw+fBgXL15EbGwskpKSoLg0Es0VLrvsMlRXV6OiogJJSUkOjz948CAAYPDgwS5fU4jFYmH/3draym5gS2E227rbO4uUW5Te2IaFG0pY0dQb21BRb2RdowB+InfC0HCkJoTjt5NNGDkohDdCfv74OBsxUCoolFTbdqA2NLey55W6yWcVVEoW+dorCgrwU2D13DEAbINqVwqJxBpdxJLZCori/cbCgmoGZkzh+cM7YNj5CQAaN9xwA7Zu3Yp+/frZXM+eoGeX1HZZAw4pPiYQCHLxVa0FrJqx+eDJTp1DqaCgVFDISImFhaYvuT9QACibMbVSE/gAa4FT5uwkrPpZd+kcNGaOjnToZqXyV8DU1vH7OXI8ZnSrSm+yOW9nj18xOwnz1xWjUGdAkL8SVw4KsXHOAmyDRqWCQnpiFF5esQl1m/4JurUFyn7hGHD36/APHSgZZNpqsHigDHi2aaRQZ7D5m2iu++huGxyE3sMXX3zRZdfyZa0liOPIoKFQpxf93O6y+kuJZfH32fPTTKNtGT7aVY4+/ta8THvTGZz58h9obzoDyj8Ql9/5EvrFjUIL51kCgE2zjypAiScmxWPeDbGSk3UJBILvsmjRImRmZiIiIgIlJSWyG37OnTuHMWPGoL6+Hk899RRbsOkq3tZbb2vtE5PikVVQySto8ldSePbGBNZkgUubmX/vVwUoYaGtGiXc+Nt8sBY7nxrfqWdyxixKqw7AWVMrb/IcE1cLkYoFvG10ZC++bG5uxs0334y8vDwAwEcffYTHH3+8y9ZGIBAInsRXtVbYTBuq9ueZGQHiTa32TBqs57WeWMrwQaoRl1v4zEXMYfjr4hqs2FOJljYzxkSHYu64GFmaKfw+6YmRdp8D5JgtiRWOuVJQTdM0Hn/8cXz66acAgHnz5iEzMxNKpdLpcxEIBIIj/vOf/4CiKLz66qu4+uqrHR5/5ZVXAgCOHz/u0vW8rbWAd/T2/IV25ByrQ86xuku6e2m/lQYaTbYmjRX1RuSW2tdZSWgamfkVNlNwJyaEQ6mguk3O09tx78qVK7FgwQLQNL8+ikAgENxNUlIStmzZgmPHjmHcuHEev56vaq0Y1ql3ttNbHcWyQuN/MZKjNbJrV11pQnF0bm7+tkpv5BladGDVVXtT5sXi9dzSeiyelsAaZFkbhyhsPnhpUm699f86jrU10zJbaJQdLkR99ougL7ZAM2AgCvJ2o+CM0qa5iDH9cAeeNEp2J95+DiIQuhqv2dfExMQgJibG8YEyGTduHKqrq/Hll1/KEtnMzExQFIUJEya4bQ2pqamgafHJNZ6i1Sx9vb3lDaKviyVyuUW2u8vqsXqvjhU3qQ1TKadF5hpSgip2/bDgADycEssKj6OiIO6apEbRS8Ecz0wIAPiCLvyucgt/me+lCOgDAAiNH42t27ajX19r8YKt42WHoAublsrrmlFe19wlDTjCArBCnZ4UyhIIBFF8VWsB6z1czMnBGZgASKmgoKAoVoOW7SiFgoJNYwvzNzconJgQzo7LXZgaZxNUcxHql1yNZRAGpPERaqQnRkl+Tni8Vh2AULU/LLR1g5YbVAX4KfDFpSZegKvNtbzfWSxonDsuBj/kXoZtFIU+2oHQpL8Gv5AIyePNFhoZa4p4k/TEAmUGdzeNcJ9TqvT85IDFC/9bJhAI3Rtf1lqCOI4S0RLDcwFYG4dDgvxlX8vUamYnOFDKAEDpByogCAPufBmLH7od3x/+k008S3FtjAYl1Y1QUDqbhmYCgeDb/Pnnn8jKygIAfPbZZxg4cKDszw4cOBBZWVm45ZZb8Omnn2Lx4sWIiIhweS3e1ltvaW2gvxJLpg9jTSje2dlRWBYVGsTmCG8fNYj3Xpsg/2xqNbNxrJgDYlZBZafu/1kFley0OSHObix62+jIXnzp7++PPn36gKIorFixAhkZGV22LgKBQPA0vqq1FkGAFtk/yCYvKpxAIAfm42L7q9zpd8L3pYqrGD3lajjXZTnveAMWrCtmTRjt4ax5kpzjhYVjUhP+5MC4az/++OP44IMPOmU+SiAQCPY4cuQIAGDGjBmyjtdqtQCAxsZGl67nba0FvJ9HthpZtDo+0Em06gDoja0orzfize2liI/gm5UoFZRNQ687cLVg1dtxL6O1kyZNwvfff+/WyRYEAoHAZeHChfj+++/x9ttv47vvvvP49YjWcrFdg1h8KkRo/C+EifUWrCu2ObdYTbA9Y18pnGkSigpViTb8WIcixNmdMi/1e5RUNyJrTjLvmiXVBsnrCI/NzK/AlwfPwEJT8Ot/OZ754EuMGDYUI4bZ6j0zrADo/DOBJ42S3Ym3n4MIhK6m18yrzsjIwIYNG/Dxxx9j2rRpmDJliuSxy5Ytw7Zt20BRFObPn9+Fq3Q/AUrpAE+4GctgttAYE6OVbNYB5HXAcm/s7RaaN7UncYhGMiAVaxR6mFMI5GyRrbOFuVIOWFIPC0yRkqPzM99LPSIVij59ERh1Jb48eAYLJgSz5+fCFfTWdgsWrCvGkZNNoGma57Apd1qBqx2qwgIxewVj3RXSrUvwVR566CGnP+Pn54d+/fohMjISiYmJuOGGG0BR8v7/xVe1FpAOzoL8KLS0d9w4Vf5KmNpsXTMmJoRj7rgYZOZXXGr84E/BkbrXizXtyL3fiemjM3op/M7RWrXdzwqP1xtboTe2YtmOMiiojgYmqXv2gglxoqN6hazeq8OR1jBEzFoKZd8wTEkaZtdJS8xZRCxQ9hRSzx1Ax2Y9gUAgMPiy1hKsCHVSOB1O+MygsPMc19DcajMpUC7K4FAMuOt1mI0GBF4+FO/99Af7np+CQrtI4KhVB/AabAGSVCUQCB385z//QXt7O6699lrceuutTn9+xowZuP7667Fv3z785z//wVNPPeXyWnxVbwOUFBu3PZwSh+KqRva+XdlgQmWDCbml9UhNCMfiaQkoqW5Eld4kaX5RqDNgxQNJPFMlAMguqXE5N2e20MguqRVd+7j4MN5Uejm44vrYVQQGBuK///0v8vPzMW3aNG8vh0AgENyKr2qtkLzjDcgqqLTJeWbOTmKNmYSGgBMTwlHTaOJp65gYa1G42P4qd/qd8P0rB/ZD3nG+OWR8hNqmKEnMxViOEzNzfWdyznKOF+q31IQ/R1AUhWXLlmHcuHG45ZZbZO+DEAgEgiucP38eABAaGirr+LY2az2In59rJVtEa+GWZp/4cDVuHz0I3xw6CYDCzNGRKK7SC6YCdX7qnBxcLVj1dtx77733QqPRYMKECQgKCuqy6xIIBN9j6tSpeP755/H666/jvvvuw7vvvovLLrvMY9cjWtsBd5INQ+IQ8UEBseFqnGtpw8hBIfj0vkTcsCyXVwfLwDU7lpomY2uuL23sK4Xw3FV6IzLzK9j8tbCeJzUhHEdqm3jPGVxzZ6l4Vmpwgthzg9SxwrUBVl0PvPwKDLjLWh9VeUFaa935TOCqUXJX1/B6+zmIQOhqek3Dz4QJE3D//fdj/fr1uOmmm3Dvvfdi0qRJ7Pvbtm1DeXk5Nm7ciAMHDoCiKDz++OPsqNyeijrQD/5KitfcQwEIClCyzrxCdpfVg6ZppCaE85p0uMgJUrk39uV55YJz0aKj45UKColDNPj71IRLQTONmaMjMXdcDJbnlVvH7V4KpOePt97w3S0EUkXbUg8LgP1AmqZpbN68GXNvvY3d4A6KTWSvxXxW6uHEbKGxYF2xZEcz978F97fgNlkxI4vtTXqQQlggduRkk83DQ3eHdOsSfJUvvvii05tUgwYNwquvvooHH3zQ4bG+qrWAdMA1MFSFWUlRrEZ9XVSDioaOzVFVgBJPpl3BbupKNX6YLbZTcADbIGp5XjmW7bA6HVvv/TQWpsbL/h72NFX43qjB/Xnfud1sQcaaIkktdjT5j/kNuFN8hPdse0Hjzp07MXbsWFbHAwcmsJ+x56QlpvtdOW7WnrMJs1lPIBAIDL6stQQrwthmYkI47/3EIRq2gTg5WoOkaA1yS6XNPJyh9UwlKP9A+GsGAQD8+oXBr1+Y7M+Hqv15CfDOFHwTCITex+7du0FRFO677z6Xz3Hvvffil19+QU5OTqcafnxVb89faEfOsTrkHKtDdkkNrFlkW/LK6nFdrBZZc5J5roBCDtU0wmyhkZ4YxTumvM7o8pQfqem6rWbaZiq9HKTysd7izJkzOHbsGFJTUwEAQUFBXmv2IQZKBALBk/iq1orB5AaFe1hMYVKhTo+JCeFQUBTGxHQULImZIolNB+Jqm9A8ymyhbRp+Zo6OZO/3TB5WTO9HDgpxzw/gAp3R74sXL2L79u247bbbAFibflxpNicQCARn0Wq1OHPmDE6dOoWQEMf30LIy617fgAEDXLoe0Vp5xIer0Whqk2wOSk+KwoIJcXhs4hXsa5n54DX8zBwdCQVF2eiyp+uY5BasdnXcy9RH3X777VAqlQBATCwIBEKXwBgiDxo0CBs3bkR2djauueYaxMbGQqVSSX6OoiisWrXK6ev5utaGqvwwenAoxsRokZESy+reAZ0B1Xoj/jzbIvq5ykvTY3eX1WPtviqEqgNFG364xg5S02RszfWdN/ZlzsXUKZXXGdn4d8GEOJtr1Da28J4bhBNnpfS/I8Y3wELTUFBgfzupNTHHnjCYUFFv5K0t+kI5xo4dy+o8Ux9lT+e7Qy68q2t4u8N3JhC6kl7T8AMAWVlZaG1txddff43169dj/fr1bCH0zTffDADsmLv7778f7733ntfW6k78FQq0mTuae2hAstmHIe94AxZPS0BtYwtvEzUsOAAPp8Q6PYatpLrR7t8AeE6/S6YPQ87THSMMM/Mr2AJmAFi2oxQKynrDd0YIpERV2CjDJT48GOlJkZIPC/YCaZqm8dxzz+Ff//oX5s2bh5n3LcayncfZ96US7YlDQmGhgXlfFKJKbxVt3poighGtVdkkDDLWFEk2BmUVVKKk2vlkwpgYfoFYQ3Mr78GmJ0C6dQm+yvjx40FRFCoqKlBba3XAVavVuOKKKxAcHIzm5maUl5ejudl6n4+KikJMTAyam5tRUVGBpqYm1NbWYt68eSgrK8Obb77p8Jq+qrVim6kAQFH8qTn7KvW8hp8+/grWmb9Qp+d9Vqv2ZwPb3WX1sgqirI2xHVjv/Y2y7/32NFX4XqqgwJjZHJbSYq7OCZ0pk6M1Difs2WP9+vWYM2cOxo4diztf+BQ5xzreSxwi7pbGaH+Vnq+xwoDc0wgDzIkJ4XYnEhEIBIKvaq0vw5/4yn9PQYHnVmWhaby5vaP5d8IVWmjV/jhraoPEkF9oVH5oNLXD3jDXi3+Woe7rl0AFBGHAvW/Bv7+0K5rUdJ+ZoyN5cX1nCr4JBELv48iRIwCsG6WukpKSwjtXZ/B1vRW6+AvJLqlBoc4AM00jLlwNigIsFgsqGzo2cg3GNixYV4ysOck8YwdAXpwnlse1cfSnwNM3Z3N+UpvF3uDkyZNIS0tDdXU1tm3bhokTJ3ptLQAxUCIQCJ7H17WWIXGIBl8Xn+C9VqjTw0LTvPhp8bQEh5PaGeMjqQnpQjOljDVFIiuyzR8zxVurftahpc2MMdGhyJyd5LXmUFf1u6WlBTNnzsT27dvx8ccf47HHHvPkMgkEAoHH6NGjsX37duzcuRPDhg1zePzGjRsBANddd53L1yRaKw237mnq+/k2DT8alT/mpcTCQsPG7FBMhxiNBayxbGZ+BW/arTtiKlcLVrsy7hXWR61YsQIKhcJj1yMQCAQuXENkmqbR3t6O4uJilJSUSH6GpmmXG34A39baRlM727AiNPaVS1GV4dLeoW2dEFfnpIyB3dnMcdbEfxZg8sy2xsb8PVDhxFmpnKrciTjcOJsx/ViwrphXO/zlfzagIOufiB5xDcY99q6oSYgY3SEX3tU1vN3hOxMIXUmvavgJCAjAxo0bcffdd+P999/Hvn372FG4AKBQKDB27Fg888wzuOOOO7y4UvdhvNgOvzbp5p4AJYVWiaqfzQdrbQpyHk6JdekmKxQ/s4VGTaNJ8njhzVzM/X5lQSUA2wJpMSFgxFAYVDNThYSFx1xmJg7inU/uw4LFYsETTzyBTz/9lP3bYqERHxEMZnIRt8OZEZbM2Ul2pzwAQHpipM13zCqolPwOgHVkMePSCchPJjBCt7KgEg3NHQ83cgW3O7hBkm5dgq+Sl5eH1atX49FHH8WoUaPw1ltvYfLkybypPzRNIycnB0uWLMHvv/+OV155BXPnzmU//8wzz+DQoUN4++23cdttt+Haa6+1e01f1FoGMV2zWGjM+6IQY2K0mDsuBrUG/jEGYxtyS+uQW1oHrTqA957QyULefZd/f9Ub25y69wv1ltFascKq30422T3Pgglxohog+fq6YtFzObpnr1y5EgsWLGATJbTNc4245gi1Nj4iGOmJkV2uU1IbA67QHTSXQCB4Hl/WWl/F3sTXMTFauwVb+X/oxT4GrToALa1mmNrMMJja7V7/Qs1vqNv0T9CtLVAGqACLfQMRMTJSYjF/fCw2HzzpdME3gUDwDfR66/1q4MCBLp/j8ssvBwA0NDQ4ONIxRG+txEeoMUSjRpXeyNtULK8z2jQFxYarbT7/a+1ZKBUU0hMjefEXM1XAXvwitjkqzPGNHxouOcVADnI3Wj1NVVUV0tLSUFlZiaCgIJjNzmutuyEGSgQCwdP4utaGBQdg5KAQbCqpQUU9P2dsoW2NnTaX1LLTA6wGR5SoyR9X2xxprdhE9pJqAwDbRqJHJ8bj0Yn8SfLcyT9d0Rwqtp8qN/fZ3NyMW265Bbt37wZg3bMlEAiErmTWrFnYtm0b3njjDcycORORkZGSx+bl5WHFihWdnoLr61prD27dk7AmCgBGDwmFn4Li6ZyFtpovOdKhzhgc2sPVgtWuinuF9VE0TbN7twQCgdAVMIbIXYmva61wYq094sPViNKoeLlcxrw3PiIYNG1BlEYNpYzGFSY2LNQZLjW7SE/Lkfpsh5Ei7DYcCfW33WLBOxyzf64BsdlCI7ukhnee7JJazB0Xg9V7dbLqeBzlxM8f3oH8Hz8BaBonG1uQV1YHRaAaExPC2f8enqgT4v5uiUM0AGie+TSzdkffsatreLtL/p9A6Cp6VcMPw2233YbbbrsNLS0tqKysRFNTE4KDgxEdHY1+/fp5e3lupaXNDHsDgaM0KpvpMQzldUZsLjkpKYxCAbQnTlzxaxc01/grKUSFBqGyoSOhLbyZiyWdmUkzEwUTBsSEQCqottcgwyCcRsSfxGN1UBa6epjNZsyfPx+ff/45AOCxxx7DX2Y+yZvuo6CsHb5iCXGxBicGqVGATFG2HJxJJnDdSIQb83LoDm6QpFuX4KsUFhbikUcewejRo7F792706dPH5hiKojBlyhSMHz8eqampWLBgAUaMGIFrr70Wqamp2LNnD8aMGYPS0lJ89tlnDht+GHxJawHrvU7MAbmywYTKBhNyS+uxv1KPcgnNBcC6R8VHqJGeGGUTWMq570q5XwBAdnHNpeCSwszRkZg/3jbAEuotd6qb8L2Rg0IkdZRZqzPuFcLzM7+DWHMso7cffvghnnzySQDAxIkT8f333+OpzZzxPhDfpAZstTZaq/JKkOfOALM7aC6BQOg6fE1ruzOebrg8ImiyVQUoMTZGg2qDCSsLKrG/Us9uMJtFputwYSYhCF0rpWipOoz6zUtBt1+EX//LMODu1+EXMsCp9cdHqNnnDrGCbwKBQODS3m6/CdEeTJOEOzd2id5aN1iX35/I5n2rGkwor7d1a6wUiXf7q6zGFmK5OUfxi1jDSebsJN55xPLR3sbZ54I//vgDaWlpqKmpQXBwMLZt28ZOq/ImxECJQCB0Fb6qtfZyq2LTXRtNbTa6yf23WA7QkdaKTa2Xe78XK2QqqjKwGu9MfCxXO13NfTY1NeGvf/0r9u7dC4qikJmZiYcffljW9yQQCAR3cf/99+ODDz7AoUOHMHbsWCxdupQ34dZkMqG8vBwbN27E+++/D7PZjAkTJmD69Omdvravai2X/kF+UCgotLSacXn/IBzQWePNueNiAFjNkbj5UrOFRqGOH5NuPtgxucCeDknV+nQ2purOBati9VEffvghme5DIBC6lLy8PK9d21e1dtTg/vi6uMbxgQBmJkZh/vhYu802s5IGy9I5YR3wkunDZOujMK60Gvd3wJ0CKBarrtgjrM/lG1gJ68bK65p5xo6OYlmpnPj+Sj2+27AKjbkrAACh8aMQfPM/oAgIAgCH5zdbrPXN3OOyS2rYmixuDC72vcXiceG/5cTr3qjhJcbJBF+iVzb8MAQFBeEvf/mLt5fhVSrqjWxDj5kGCnUGmFo7HATL65tRXt8sKozCGzk3KWwVhVqeUz4TfE5+L593njYzjcoGEyYmhEOpoERv5hkpsbDQNDYfrMWfZy/w1qigKCyZPsyuENhroHGEMPDmfhdrs04Z+50B4KHrB2POnDn48ssvAQDPPvss3n77bTy8lj+5gGm6ERNqsQYnwNrskzUnWdLtkktqQjiujdGgpLrRZoKRK8kEuYIrFEk5E5g8TVckP8jDAaE78q9//Qvt7e147bXXRJt9uAQGBmLp0qW48cYb8e677+Lrr78GAKjVaixZsgQPPPAACgoKnF6Dr2httowgVlisK0W0Vs06MHKdouQEOvPHx7KfEd77uc1Gy3aUQkHZBlgPXBeNr4trUFlv5A2idVRYxTTAfnPoJAAaFppGa7sF2SV8J0p7GmBv0o1Yc+zZ/Zvwf//3fwCAadOm4b///S+CgoJkFyX1xuIl4sBMIPgmvqK13RlPN1wKC8GujdGABljzjt1l9Zj+wR5QFMWbnhMXrrYx+JAY8CuKqbwQ9d++CZjb4KeJxIC7X4Nf3zC7n1EAiBFcNz0xStQMpLsUZhMIhO5BeHg4ampqUFtbiwEDnGssZKittcYfWq3WnUsD4Ft666+03rPbzDTK65pZjWNya3NXF4o2/IgxRGud+iOWm3MUv4jFbGLnYfLG2SU1yC6plTS46CqceS44evQo0tLScPr0afTv3x87duyQbbTiaYhmEwiErsaXtDY2XI1fa89Kvt/QbGvQEKrylzRu+CD3D1ho2OifI61VKihkzUm22dsCHLv3ihUyJUdrXIqP5X7GldynwWDA1KlTUVxcDEqhwPiMl2EZOglmC0328AgEQpeiUCjw/fffY+LEiSgvL0dGRgaADsOKvn37ssfSNI3hw4fjq6++cusafElrAeu0gGityqaguLLeiMp6I3aV1tk0vjLsLqtH6lB+HrRRoMNSOmRrcBjM1k31Rtra2kTro7p6ygaBQCB0B3xNazcfPGljAqVR+yNUFYATeiPaOINVS6oNUCrieLndjDVFvM86ivHMFhor9lTio11/8F5nTPLl1Gra1hDzN07n3WBtBl6wrphXc8Vou9VwmP+9GANiqfpkYa2Yve8plRP/bftattmnT0wirs54HbpGcfM0sfNnFVTaPPOU1xl5uX/usc4MLxB7T+o7Mvl9Jq+wYF2xx+tsiXEywZfo1Q0/vkCQv9LhMUdqm9AvyI83YUfIB7l/ILukBjNHR2LeDbFYvVdnM1FGKE7CDeEOxCuMmMSyFAqKQrRWjahQ/ni/aoMRY2I0PEfj5Xnll8bdW6cYJA6xbaDRqv2hN7ZBCm7HrhRCwdpffgY7P/g7vvnmGwDASy+9hFdeeQUURUkWFou9zlyzUKe/NBq4Y1yhUNyEa+CumzlWrBnFWeQ2zQhFUs4Ept4AeTggdEf27t0LALjmmmtkHT9q1Cje5xgYZ9nTp0+7b3G9COPFdruTexhCgvxFN2uFMPdJV5oVuZ/h3vur9EabDVixAOvRDSWik/+kCqu4fy/Pq2CLjJftKMMBnYFXdMz9bs7C1TqaprHs9aXQ/bQGgNUtZePGjQgMDATAFHvh0nNAR/PR6r06nq4mRYfi71MT8M0h6/OChYZXN3rd0TjaG5uYCAQCoSfg6YbLzNlJWLCuGEdONmHkoBBkzk7C9W/l8o4R0++mljakDg1DbWML/mziG2cIoQDEhKnQ1NIGvbENxrK9aPj+HcDSDv/waAy4aymU6lDJzzNYLq0lNkyFcxfaMXJQCOuUCXRvJ0oCgeBdhg0bhpqaGuzatQuJiYkunSMnJ4c9F8EFLoUfbSLdodnFNWysQglyu1q1P66K7C9aJHVCb8Tc1YWiuU1H8YvchpOsgkos21HG/i1lcNFVyH0uOHz4MKZMmYKGhgaEhYXhp59+kp2/6QqIZhMIBIL7CfRXihozAIDKX4GL7RaeSYMqQImBIX0QqVGhxiC9h2tqNYvqn5xcodT9fsWeSrYwWsy9V6h38RHByEiJxYJ14uaH9pCrnc7mPuvq6jBlyhT8+uuvUCj9oJnxLKpCEyX2rgkEAsHzDBo0CCUlJXjxxRexatUqGI22ehAYGIiHH34Yr7/+Oq8JiOA8UaFB7L+LJYpUfy5vkPx8bWMLJiaEs7GusPFWSofsGRz2NlpbW3H33XeL1kcRCAQCofcjNvH96sj+GBurRXZJLa9eiNFNbl2M2cLPMzuK8ax5YFtT/IbmVry5vRT7K/WskT5zHWHtbeKQUF5cOXN0JBQUxeq22ULjTZFrANY63jExWsm4VMrgX2jsKPZbMM8MwueIeTfE4OWXX0Zx9icAgKArxiL8lsWYdW0cu245gwAcNew4MuqS+m7c6zkTr3dlnS0xTib4EqThp4ejCvSDyl8JU5t0YU+DsRUNEq5QDKZWM8rrjFi2owybSmpFk9FS4+eFN8mZoyN5m7AMUjd64Ug5wDrppqaxBeV1zTbdpmKbvIunJfCCccAaaDPCJ1YI/XBKLG/dTJcwU0As1khUfuJPHP2lEADw5ptvspMHmOsxvwd3k1oq4JebCBAKqnDdQNdu0ApFUs4Ept4AeTggdEf0euuELaPRKMvh2GSybhoaDPz/Pffv39/ta+tNtJotdt/3U1iTymLayUWrDsD88fYbTe0hFgzyJ9Lxg1KxIPKAYDx9gJLCMzcm2F0T83mhi4Zw1H18uBoW2qrpYromDOisQTdsA31LO/4sLQEA3H333Vi7di38/f3Zt5UKCgoKvOajQp3B5hklt9TalMroP3dT3BtT29wR0BIHZgKBQPAOnm64DPBTYPXcMbzXpOJvLg3Nrcg73oAl062F72KTYRlogGcCcrHmN8DSjoDL4hEx61Uog/rZvZa/kuIViDPn2l1Wj9V7dSQ2IhAIDpkyZQp+/PFHfPzxx/jb3/7GNvTL5eLFi/j0009BURRuvPFGD62yl2NnClx5vRHl9UZRc5/5462OfCv2VGJT8Qn82XQBF9stsNAdnwOsMRjQEedw45fEIRq0WyyY/F4eGAOn+eNjZeUznXEQ7ArkPhccPnwYDQ0NuOyyy5CTk+NT7p8EAoHgqwQoKZsccVhwgGR8Z2o187TUEUL9E8sVys17WvdCpa8j1Lv0xEgoFbbmh1V6EzLzK+zmV+Vqp7O5z8rKSpSXlyMgIAATHn0TxwMTeN+BxKkEAsEb9O3bF++//z7eeOMN7Nu3D6WlpWhqakJwcDBiY2MxYcIE0ujjJriO/MJpPQxihhcMfzZdYI0xuDia2ONL5gkNDQ04ePAgANv6KAKBQCD4Jv+rPcuLb+Mj1EhPjGJ1k1sXA1hrgJlY0lGMZ69pBbBq/9T39yA9MRIWmrapT84trcPiacOweFoCO0gAoNjaKgCY/F6+5PktdEdcyjQSMXVR3GadQp0BFpoGBRo0KFAUhYkJ4VBQwJgYrehvwa0R4j5HtLa2Ii8vDwCQNGkGRt77D1wbH8HG2FL1TULkNOww56nS207zFebyhVOAzRYa+yv1rHkl1wxSiNlCI7uEn3OQE6O7WsdFjJMJvkSPbPiJjXVfcSNFUaioqHDb+bqas6ZW+Ntp9nEFsWT0wymxeOC6aCzcUIK95Q28wFgoCsVVjUgdGoaaRhPOmtoRqvLH7aMjJYuAxUbK1TS2YIhGxesEZm78YuJeXNWIMTEa1DSawN00ZoRPWAg9MSHcRvyEXcLLdpRh8bQELJk+jO1KrrkYhD63voL7+p6xCWalAnup1+UW/na34l6hSI6J0fhEQoM8HBC6I5dddhlqamrw3Xff4YknnnB4/LfffgsAGDBgAO/1hgaruxG3aYhorXzaLYBO3+LwuM6aHdnTDevUG5o3/Y7RC65bo5Bx8WEOR+YKm3IZgvwVvEkCURoVG1DnHKvD18U1GKJRwULT7OQBLpsP8h0/tOoA6I2toJT+iJj5EgbU5mP9+g8ASoHM/Ao2aFZQQLWB/3sLpxBKvc48S7jSfNPZJiF3NI760iYCgeALEK3tOXgjJsucnYT5a4tQWNUI0OCZfKgClDwNLtQZsOKBJGTuqYDBzpRbLqFpD0MZrEXfUdOhCFQ7PN5fqUCbWTz3UFRlYEez+4KzJYFAcI3Zs2fj5ZdfRm1tLR5++GGsXbvWqc8//PDDqKmpgUqlwuzZs2V/juit89QYWhAfEQzGEIm5py9MjYOCst9gyo1zuPFLZn4Flu04zh7nzJQesc1Kd+blnI315D4XPPjggzCbzRg/fjyuuOIKt62XQCAQuhtEaztoaTMjRPDavBti7DbXOANX/4RuxkwRErfoyX7eU7oAWljsI2ZymF1Sg/I6I8rrmh1O1JGrnc7mPseOHYvvv/8eFosFlQGxvGcUsodHIBC8jUqlQlpaGtLS0jp9LqK1jhGaFMqBMUUWEq1VOb0P5w2jwa5g4MCByM3NRW5uLubPn+/t5RAIBAKL0WjEvn37UFZWhqamJrS3t9s9/qWXXrL7PtFa+Qj3IaO1arvTY5QKCllzknmvCXVz7rgYrN6rs2lEEYOJQeMjxPc2S6qt5xQzBrYiHQsrKIqNS4GOPDjX6IobswrrkZdMH2b3t8guqbV5RggICMAPP/yATz75BH//+9+hVCrZ98SGF0g9YwhjdQZujbSwGYvb5OwoHl+xp4KtG9tdVo9VP1diYWq86LFZBZW8WjBAXozuqolyd6utJhA8SY9s+KmqqnJ4DEVRoGnbG7Tw9Z4+arS13f7UAXdgutiO7OIa7KvUI09Q8GtPFLgitjyvnJdgttA0e9MXa+Apr2tGJGcEL9Bx4xfb5BV27SoosOJmttCw0OBtVM8fH2cjfmLrKKluxLu3DcXukqMohwIA4B96OfoMv1rsp3IKuYW/9gTVG4kDXxVJX/3ehO7N9OnTkZmZiRdffBGjR4/GuHHjJI/dt28fXnzxRVAUhenTp/PeO3DgAAAgKiqKfY1oLRfn169VByBUHcALYpgxs9klNazDhdQ9W+z+LqUb1iCvgtfswzS9ArZujSp/JVSBSowcFILM2Ul2v4dYUy7DQzfEQEEp2OCyxmDivV9Rb3Qw9ajjfyO0xYyW+mpAdTkAQBGowpU3PYAF6w/ajKcVQ8olU2p0rlhg7UhLOzuhhzSOEggEIURrew6eari0F88F+ClwXVwY8o43sMczidd9FQ281y00DaWCwjWRIdhV1mBzHYbW+ioEhEcDAChKgZCxd8pe65gYjU0+gPs9pr6fzyaQPT2anUAg9EwiIiLw7LPP4tVXX8WGDRtw5swZLF++3OFmamVlJRYuXIicnBxQFIWnn34aERERsq9L9FYcexPjy+s74thCnYHn5OfIZVEqzunMlB57BhfuwNlYz95zwe+//44RI0aw/1uZN2+e29ZJIBAI3RWitR20C6YIWKfmUaLFxHJRBSgxsH+Qjf4J92QBaxGSVh3Ae03KoGHm6Ejevmrq0DD4KRW82NSeyWFRlYH3vezpujtjap1Oh4iICPQJUlm/U20/tkCMWQfZwyMQCL0NorWOMbW5XjfFGBIyuLJ/19k9xO5EU1MTmpqaMHjwYABAXFwc4uJ65nchEAi9j/b2drz88sv4+OOP0dzc7PgDl3DU8EO01nWEuilWFyPcD7XQYE2Lc47VYX+lXrImKDZMhcoGk8g70hNm7dXkCmNhLmNiNLzPiJ2DW5/151m+6fGBSj17bHK0BolD+L9FeV0zsgoqkXFDNI4fP47hw4cDsE6IFJuiJzR3ZtYt1mgjFasrFRS79yz8Ts40OQvrzjYfrJVs+BFeJz5CLStGd9VEuTsZJ/fWJnBC96FHNvy8/PLLku+tXbsWOp0O/v7+mDBhAkaMGIHg4GA0Nzfj6NGjyM/PR2trK2JjY51yg+yuOPuQoKCs4+ek0Kj8YTDxO3FNbRbJkfL2RIF70xXe9N/78ThW/azDyEEhSJIYKVdjMGLJ9GGi7lHCTd7iKj3vsysLKtljhZN7mG5cgH+TNYv8MCM0CkyePBlVf55B4G1L4dfXOgpY+LDiys1aKOrWcXjO4Y3EgadFsrsKX3d6OCAQGJ5//nl8+eWXOH/+PFJTU5Geno5bbrkFCQkJCA4OhtFoRFlZGb777jtkZ2fDbDajb9++eP7553nnWbNmDQBg8uTJ7GtEa7nYEU4JMlJiMX+8VYNWFlSiobkjUVxeZ2Tv3WKbrQB4U3WY+7tUw4hV5zoCUkcOFQND+yDn6VRR14xVP+uwuaQWjaZW9FeJPyZyR/KKOTM4gpkcyDTr0uZ2NGz9N2oqCjFg1lIEDhqGuHC1wyYfhokJ4fj0vkQ8uqEER042ISTIH0M0Klwbq+34Tpeakiw0DbOFtvkty+uaUV7XbFdLOzuhhzSOEggEIURrCcJ4zjrJjmK14oDQmfLSKPl95XyN/KW8Hg9+fgC1jdITB5sObMLZvDXQ3vQUgkfKdxb1UwA3XBGO5fclYu2+KptR7lKNua5MsiMQCL2fl19+GSUlJdi6dStycnIwdOhQpKamYvz48Rg+fDj69+8PADh79ixKS0uRn5+PvLw80DQNmqZx00034Z///KfT15TCV/VWqw7Az4snce7roQAomymsAHhx6f5KPWoktCY+XI30pCjJOKczU3qs04XiJTcQO4ujWE9urnLr1q2YOXMmFixYgPfff9/nNvcJBILvQrRWnPhwNbLmJGP+2iJZx1MQz0KPidHgulgtCnUGFOr0UFDAmBgtCnV6kaPBK1gGrHorjD2zS2oxc3QkFk9L4DX22tuLE+qhcI9Trq53Zg/w6NGjmDx5MoYPH47bFn+Id3dVsd8JANnDIxAIvRaitfbxV1JoM9vfy9Wo/XFNZAhONF6wiXszUmKhoGz375zRrM7uIXYXDAYDpk6dCoPBgD179mDQoEHeXhKBQCDwuPPOO7FlyxbQNI3w8HDU19eDoigMGjQI586dw7lz5wBY62lDQ0PRt29fWeclWusaceFqNjZldFKsLkYYk1pN+zs4crKJ93d8hBrRWjWvjimroJIX784cHWmTz46PCGavz41XzRZrnZBSQWH++DhYaBqf/6yDqdWMy/sHIVqrxrUx/BoeqfqsFXsqJBuGqg0m7OLk0xdPS0B8hJrXgHOgvA75mS9iy5Yt+OmnnzB27FjJ31dsWrC9Rht763b0nmOEz0DScbzwOumJUbLi/t5gotybmsAJ3ZNe1fDz4IMPQqfTYfbs2Xjvvfeg1WptjtHr9Vi0aBHWr18PnU6HL774wsOr9SzqAKXjgzgwPS3+Sgr+CoWNk6PB1IaJCeE4crKJV5wshXxR4N+02yw0GppbsbusHjSsxbq2RULS7lHCTd7MfCC3tOPzzBQFwH5wLXTAig1X43TTBQT5K5D+l35Y88JD+PXXX+Hn54f5ka24ODBCtEjXtZu1MOngfEF5T0scyEmK9Fbh666NTISeTWRkJLZu3Ypbb70VBoMBX331Fb766ivRY2maRmhoKL799ltERkayrzc2NiIhIQFDhw7F3Xffzb5OtNZ5lBTQXxWAeTfEsBN2hKNeuWSX1MBsofH2Tu4EPGtzrlATi6oM7DQeYcLZkVOy0KFi5mjrf3/h/VbomiHcHAaA1IRwrLo0bpdpZuIidMES4+GUWNb5wtLehrcXL4TpWD4A4MKJXxE4aBhOGMQcOsRRUMDafVXs2huaW3FnonVa1aMbSmC20GyQv2xHGRQUP8lQpTfKcqPsbHBJGkcJBIIQorUEoYZvKqlBRb1VA3OO1SE2TMV7v7y+GfPWFOFAVSPv9Ytm8Cb+cKFpGk17/4OmvV8CAFrKD0B95STZRcjtFiCvrB5r91WJ6ljGGvHitZ6YhCUQCJ6Hoihs3rwZTz31FD777DPQNI3du3dj9+7dkp9h3BHnz5+PDz74wOkmCqK3tuiNraL39ZJqg11DB268KCyoig5T2411PD2lpzMIYz2zhUbGmiLJDWnANle5efNm3HPPPWhra8Pu3btx/vx59OvXr+u+BIFAIHgRorXiRGqsE2gO15yVdbzUDmGNwWgzbTW3tP7S9CD7MC66C9YV814vr2vGsh2lWDJ9GLIu5XodIdTDxdMSRE0buYjti7m6B3j48GFMmTIFDQ0NaG1tRWjJUQAdMXN33x+1h5gx1+q9OrKfSCAQWIjWikMBeG7aMGQXn5Bw/u/AYGzDtbFhuBb8Pdu4cDWKqwwYE6NB5uwkKBUUzBYamfkVyC6pkTXRnCki5tLZ3Kg3akvq6uowZcoUtj7q0KFDpOGHQCB0K7Kzs/H9999DpVJh8+bNmDp1KhQKBQCgtLQUKpUKNTU1WLt2LZYtW4bAwEBs2LAB119/vcNzE611jYp6IyrqjcgtrUd2SQ1rHCzMO9vWNPF1Uzg4KT0xivd5BcWvX5qYEI7542MB0IJaqEFQKijMHReDr4trUHFpuMHusnpkFVRiwYQ4KBUUiqsaoTdaByJU1hsxRKOyMYCy0DTiI9Tg5rJb2y34KPcPyd/jVBN/4k9JdSPSE6PYZw/a3IYfPliM+iMFAIBdu3aJNvwwzwF/nhUz37L/PGDPiLgzJsXWurNS3t+urMHVtfcUelotN6Hn0SMbfsTYsGED1q5di1mzZrGTCsTQarVYu3YtLl68iHXr1iEtLa3Hd9fGhwfjz7MtNs079mgz02gzix//a+1ZXBXZX9SlNzUh/JJrMG2zMSt202UEyEaZOfx2sgnXRPW3eT1Ko7J5TSqwnTsuBvsr9dhb3oBWzmZzoc4Ai+DaVXojMvMrkJESa3OTrbwk9Of0dfgg61GcPlGBgIAAbNq0CTfffLPNGhgHzFU/8wue5dysS6ob7f4th57W2Sonkd9bha+3NjIRvM+4ceNw9OhRvPrqq9iwYQOamppsjunXrx/uu+8+vPTSSxgwYADvvdDQUHz44YeyruWrWhugVNi8FhumwhAtfwqNmbYGmcL7FqOP2SV8d4nyOiNW/azjnXfzwVpEa231LzlaI9kw4sgpef74ON60gLnjYpCZX2HTrLO3XLxQmMu1MVooFRQy8ytEm5gyUmJRqOM3DsWGqUBRwFlTO0JV/uyUndaLF7Dt38/ixEFrs0//8Q8g5LpZAGDjxBUXrsYQrRo0TePX2rNs8A1Ym5mF2iHmTM1QeGlaAteNkhuYct09hN+N+7meGFwSCITuj69qrS9hb8rsqbMX+X8LEsMAbAq97EHTNM7mf4FzBzYDAFTDxyPsr0+7NHFAbkNsfEQw0hO7TxE3gUDofgQEBODTTz/FPffcg7fffhs7d+5Ee3u76LF+fn648cYbsXjxYqSkpLhtDURvrfGpsGBILLaUYohWzYu5kqM1dguSHE3p8aZRDjfW406uY34LR7nKDRs2YM6cOTCbzUhMTMTOnTtJsw+BQPB5iNYCNQaTU/GbFJREMc+Rk02IC1ezBUwAbP4GKGQVVCJxSKioxhfq5O+/CfWwpLoRWXOS7X7edrJQDYTFSXL2AAsLCzF16lScPXsWAwYMQG5uLn5u6INiTn7aXfuj3ngmsWfM1dv2E4k5IoHgPojWAqEqf2wqcdzsw7CyoBLzbojBc1MT8N9DJ9FovMgpVLaaMi5MjbMxDWaQ0qysgkrevujEhHCXcqPCvHFXasGff/6JtLQ0lJaWsvVRM2bMcGrN5L5OIBA8zdq1a0FRFB555BFMnTpV9JioqCg8//zzuPXWWzFhwgTceuutOHToEM8UWS5Ea52jvM7I6qejmiaatupljcGE8noj28wTH6Fmm4a4COPRIyebkFVQKVKObG3cXbCuWBAb83VcOFGI+zdjBsXVdgVlzW/PW1MEU5tF8jcwtfJrsbl1RRv3l+PAilfQUmk15Ljt4Wfwj3/8Q/Q8Us8iAHD7KPvNuPaMiDtjUjx/vPhERGfX4Oraewo9rZab0PPoNQ0/K1euBEVRePbZZ2Ud/9xzzyE7OxsrV67s0SJ7/kI7yuulnRddQW9sw+6yeqReEtazplb0VwUgSqOCkgKveEfoOCTEngAxjBwUIrqhXKgzYHleOeaPj2ODQqmmidV7daINSodrGnlFwQD/AUPsuu1NdTiz8Xm0nz2FoKAgfPfdd5gyZYrod5LaBJdzs3bHDb6nFR/LaebprcLXWxuZCN2DiIgIfPzxx/jggw/w22+/oaqqCs3NzQgODsaQIUMwcuRIKJXOTYQTw1e1VowojQoKisLEhHCbBhSuOwTQEZTMHReDa/75I69B1yCYhkPTFpv7oKPEsCOnZOb6GSmxWLGnAmPfyIHB1GZznlYH4+4BYPPBGpRUW6ficAkLDsDDKbHISIlFSTX/fnfuQjtGDgpBRX099MZWLNtRhr2lJ/Fn9qvYvWsXACA07WH0S7pV8roV9UYM1qigVFAIVQXwfm8FRYnoufR3sdC0jRsld9Kg8L8fgyvBJUl0EwgEZyFa2/sRjnqPDVOxm9JCEw9VgB9a2hxP3hWDpi1ozFmB8wd/AACor5wM7fQnQClceyaUisvEYlKidQQCQQ4pKSlISUmByWTCvn37oNPpYDBYYwmNRoOYmBiMHTsWarXa7dcmemt19s8qqGSd9gt1BphpGhq1PwyceGtiQjgUFIUqvZG3SRoZGoSZoyNRUt15x37Au0Y53FhPOLmO0TepXOWqVavw8MMPg6ZpXH/99di2bRtCQkK6ZN3egsS5BAJBDkRrARd8FkRpFMnjAtYp5w3N/HgxWqvGrKQodiJBeV0z3txeisXThmHJ9GFYsaeS54p8uOYsa5DIvZeL3etd2bsT7otxp6zLPc/PP/+Mm266CefPn0dkZCRyc3MxdOhQDLtkoOHu/VFvPJOIFa8J3+8t+4nEHJFAcB9EawGDqU10v1OKhmbrHqVtg6yVj3b9AQUFFOr0op+X0izhfVypoFyKkezVVomZdriL6upqpKWloaKiQrQ+yh7kvk4gELqSkpISAMBdd91l857Fwm/CuPLKK/HSSy9h0aJFePfdd/Hvf//b6esRrbVCgV99I/xbSKFOb6MFHSbJ1liVabiNjwjmHRet7Zgiz41L2838/74Nza14c3spVAH8Pc+SagOyCiBaQ8zV8ZGDQnjHjBzUkc8VNvICHTEZYy4sh/iIYFa770+6DP9ceDfb7BOa9jC042z/d8y9nhQKdyUbnKQ3NON0BT2tlpvQ8+g1DT+//fYbACAuTt5NJTbW+v9MR48e9diauhNSYivcxOWipIDcZ1IBgOfkn1vaIWrc4I07Ck/KAdFfSfFc+/0VFChYnfS5hU6AtevVWghFYWFqnOj5mMBW6jrCZh8uRVUGZM5OYv+tazCi9PgfOLPxeZjP1cMvUIXt27dhwoQJNp+zhxzHDqnRf87S08RUzoZAbxW+3trIROheKJVKXH311bj66qs9cn5f1dpWs61DQ/7xjmk4GpW/zftCd0TGRUJYyCvU5iiNmp1ad+RkE0YOCmFHyEvBOCXPHx+HFXsqsPlgDTYfrMXM0ZGYd0MMVu/V2bhBcRE+IygoIFQVAFNrO1oE7hTWjWLbJHh/VQD7PYXTChqaW3nXtVw0YdNrz+HiyaOgKApDZy7ChbhJkt+PQWztADAmRmOjHRaaP7oX6Jg4IEzUl1Q32vy+7tpEJYluAoHgLL6qtb7EppIa3t9iU3wYrhwUgqIqg40jlCNoixmGnZ+g+dcfAQDBo26CZsojoCjbqYVipCaEo9ZgQuMl8487Rdy0GHpaTEogELofKpUKaWlpXXpNordWmBynWEGRVh2AjJRYzB9vbeTJLeWbHuWV1eO6WC2y5iTbnI8hu6TGYUESs3ErnEDbVYWtwoLmxCG2+TthvMlMzf1i5XLs3/AuACA1NRVbtmxBcHCw6HV6EyTOJRAIcvBVraUoQKv2x0M3xEBBUbz8pCpA6XRsB4DXoAMAfgqgXcJQ2ELTlyawG3g53OIqA1Y9mIxCnZ63x6s3too6MIvd6xk9LNTpYaE7pqjb03qp6YHxEWpEa9UO9wBzcnJw6623wmQyISYmBrm5uYiJsZpPOhuLym1Y9YZ5n/B3Ehaf9ab9RGKOSCC4D1/VWmdRUIBg21K02Qew1ie9ub0UceF84xFrfBwjqVmO6kBc1SAujGmHu++Z5eXlSEtLw4kTJxAcHIwffvjBpj7KHuS+TiAQuhK93lrnERUVxb7m5+cHs9kMk8lkk5e79dZbsWjRImzbts2lhh+itVaENU2ObISFugt0xG9FVQZBvRH/YGa6TnK0Bu0WGu/s5Nf8CBHG2IlDQkX1dMLQMFhoIGNNERKHaJA4JBS/1jahpc2MMdGhbP0wIK7HVQ1GzF1diDaR2jFprPo/6+ow3Dzjrzh1rBgABc3Ux9D3mmlIjtZIPiNIxdIALhkw2//fJDFs8h5k35zgaXpNw09LSwsA4OTJkwgNDXV4/MmTJwEAFy5IF9f0JoRa6q+k0K+Pn92GGK4AiwVqQsRG4QkFqF8ff15yus1CY1dZA3aVNUCKd3aW4t8/lSFKo8Jtowbxzlde14yp7+9BVGgQ7zNtMqYUVOlNrJPlgglxmPdFEYoObYP5XD2oQDWm/f1D0WDWnqgC1k5aRyKZVVDJS/Qzo/96O3KaeXqr8PXWRiaCb0G0Vhwx9yjLpdmxTCDFOFU4otbQgps+3MMeu7usHqv36mTdE4XasmxHKQp1eslGGQY/QTOuhbaOYwX4hV/Cpl1VgBJB/grojW2sW+T+SsfXa6koxMWTRwFKAc1NT2LM1Dvx26nzvAZkJpkulXRXBSgxsH8Q2zAr1A6zxTrxiPubR2tV7PvcjW0m8e6JpkyS6CYQCM5CtLb3c9bUzvubAj8OjA1T4fS5iwCA/OP2NVWKtoZqNP++GwDQN/k2hE6cB8qB45O/gkKIyp9tNg7wk9ccRCAQCD0RordWkqM1kgVFemMrmFRldkmt6DHC+EaYMy2vMzosSJJyMLa34ekIZz4nLGhePC0BS6YPs/ksN97MzK/A69+W4M/vVgMA/jJmPLZt24agoCDRa/Q2SJxLIBDk4KtaS9OA3tgGP4UCGSmxUFAU2yBzwmCSzHU6g7DZhzulgJlczuSmGZi/x8RoeXlRBuG9XOpev2BCHCy0Ne8MALmlddhXqYffpYIkoeZ2uDjXoryumX09PTHKoXbQNI3XXnsNJpMJQ4cORW5uLiIjI+1+xh5yG1a9Yd4n1lzMmHj1tv1EYo5IILgPX9VaZxErOnaEUK/1xlZsPlgLBUWJ3qMd1YG4qkFCPBF7LV++HCdOnEBISAh27NiBsWPHOvV5cl8nEAhdib+/P9rb26FQdOxf9evXD42NjTh58iQiIiJ4xzOT4xkNdBaitXzCggPQXxXAi+3E4E6gsTVbCuXpxszRkVBQlI2Bcc6xOrhSRXtAZ0BtY4vN6xQ64lih1l4XF8bbExUaQgFAeb0R5U7G8+V1Rry5vRSFOcexd+9eKBQKPLB4GZQJE9jnhRV7Knnr+qroBGLC1EiK1iA1IRx5DiYVSUEMmwiE3kuvafiJi4vD77//jszMTHz00UcOj1++fDn7uZ6Mn9K1JhFH028AvgBLBWr2Ak4xB0SzhcbbDrpvhVhooNVMo6LeiG8OnUR8hJpXwFte14zyumZMTAiHUkFB19CMinoT7xxijldMcfK+Sj1WzE6ChaYRmjoXdNtFBF89FWGxV7Jdw8z3yCqoRKHOIDnmFwCq9Ebe57qLQ1R3oLc288jBl787offgq1oLF0JJ5tbPDdDkUF5vGxwzrsgA7BYtiRVqHTnZ5PCa18eF2RQUZxfXYNuT49nzJkdr8EHuH2gzdzhkXGgz2zhmyLmeekQq2s81wK//ZVAPuwF7yg2I0QZBowoAKOvUO6bhyDqxqBaNxjZew7Cp1YzyumbJhlmlgkJ6YhSvcIx5drGXeHf3JipJdBMIBGfxXa31HUJVATxNCwpQYmBoHwAUIkODRJO3AKDyV8DUJs85KiAiFuG3LUHr6T8QMu5eh80+gNWMg5nKJ7fZmEAgEHoqRG/5E8ql8rsHKhuwv1IvuYkrjG8yUmJtCnoZ9/+iKgMSh4QCoFBS3RF3CePYsOAAPJwSi7njYpCxpoi3yQuAZ/IgFR87s6EpvH5JdSOy5iTb1cGiKgMUgWoMuOt1NBX+F8kPv+wzzT4AiXMJBII8fF1rmcl1jNaKNbeKQQF4dmoCFBSFzQcdG0jFRwRjiEbF268sqjJAmC5l/u5owOGfW3gvt3ev33yQ3wicx9Hq/ZV61o2Y2zSbkRJ7aX9V/nQgiqLwzTff4IknnsC7776LAQMG2P0tHCF3X9Yb5n1i+4e9dT+RmCMSCO7D17VWDCUFyPAFBmCNiZnpP7/WnnVYO8UU7XKND4Xalzk7SVZtUHZJjWgsO3dcDL4urpGsQ/JE7LVs2TKYTCY8/PDDGD16tM37jgw1yH2dQCB0JYMHD0ZZWRlOnTrFNvckJCRg//792LdvH0aNGsU7/n//+x8A8BqEnIFoLZ+Rg0JQ02iyeV04zbbaYERmfgUbB/LNlobZmC0xCCfAu9CzK7nH+tuf5yQ/YxsbOr6yKkCJxyfGg6KAd3aW2W0w/l/glbj14Wdx1k+DI6qrMdJCY+64GCgVlE18XdlgQmWDCbml9fj71ARcF6tl42gFRWFMjDyt9dW6ZALBF+g1DT933303XnjhBXz66afQarV44YUX4Odn+/Xa29vx2muvYfny5aAoCvfcc48XVus+PDkVptpgYgWYG6glDgmFhbb+W6P25znypyaE2zg5MUnJllYz/vrhHigpINBfiaQhodjzh/RkHzFqDSY8c2OCaHL8yMkmPJwSi3azxabhR2q8PQDs+p8O19U2QW9sBaVQQjv1Maj8lTYbyoC8pHxFvREV9UbkHKtDdkkt0hMjbQJfe8ny1nYLFqwrxpGTTcRd2cOQEYaEnsRDDz0EwLrRtWrVKt5rzsI9hzP4qtYGuNBcmxStQWZ+BT7a9Yfd41KHhqH2bAsACqBpUVcIxhUZgN2iJTHnp5GDQmwm7vgpgJCgAISqAjAzMRLzbojB9A/28BLI5fVGrPpZx9scTh7SH/l/6Nm/xYLWKweFiAbRlgvNoALVbMFxyNg7ee/r9B0uG9wmHgVF8TafhckCe4GpVJJZqgHTE5uoJNFNIBCcxVe1tqfjKK7gvh+pCeI1+OqNrZwGIOmMcKuD3Wq63XoOyi8AAKCKHwNV/BiXvg9J/BIIhN4O0VtrrMUU3k5MCAdFUThc08jL8x6ubeL9zSU+PBgWmrYxS4oKDeI1/Fho2iaO5f5bGMc+fGkSe2Z+hU0sy9Une009UhuaYnrtTPMKTdNoampiP+MfFoWwm57E2Csuk/xMb4TEuQQCQQ6+rrUNza14c3spvi6uQY3BtiBKChrAN4dqkZ4YhW1/G4/Ve3Uo1BlgoWm2KJmrj1GhQUiK1iC31FbLuJN8xsRoAcCmAUfsXm620LDQ1mYigGYnrPNXKY5Uoy5DtaGFfU5g1iw85uzZs+jfvz8AIDQ0FOvXr5e8njPI1Xxi3udZyO9LILgPX9daMUJU/pIxLAAE+SswNlaLsbFantGiHDNDBuGxjrQPEJ+GW15ntPnM6r063l5takI4ro3RoKS60a2xF1dr/fz82AJ1MRwZapD7OoFA6EquvfZalJWVoaqqCldffTUAIC0tDfv27cO7776LWbNmISwsDADQ2NiIJUuWgKIojBw50qXrEa3tQEnBJlfL0MdPgcv7BeJsSzv0xla2SRYQM1sy2JgtZeZXyDbJcBWxuikGYWxYUt3o8HymVjOrgdnFNahs4Mf93PqohuZWNGhSrW9cMl685p87MTBUhUbjRclrfHPoJHKenuCUxjL57yo9fz3EsIlA6D1QNE270hDZ7bh48SKSk5Px22+/gaIoDBgwADNmzMDw4cMRHByM5uZmHDt2DFu3bsXp06dB0zRGjhyJoqIiBAQEeHv5LhEZGYkz5y5g0KNrZH/GX0mhb6AfDKaOQDfIXwFVgB/PZZhLfIQa6YlRbNGSPaFNHRqGVQ+OES1u+iDnD5jaOop0Y8NUuCt5MIqqDGi30LwC4dgwlY0YAtbR9D8umoCsgkobByp2veFq2WP0LtT+jvrNS6G58TGoh6dIHjd5uLUz3N5EI3ssmT6MJ8D2CsLmri7kPWRMTAjH6rnShVrublrpqiaY7tBsI/zfsvC/E6H3ExkZCQCora11cKT3USgUbLOE+dKUFe5rcqFpGhRFsedwBl/VWgAImrMCbXYKbYVJVwsNyck+/koKQzQqzEyMxPzxcey9z56+iunQpGERoGmabRD99L5EfPGL7pILhHVSjlgzDwAsnjYMC1M77nfzvijibQwDgFbNn0AQGuSH5otmmGkaCopCu0jHzzM3DsW3h07yrtfeVIczG5+HduQE9Ln+ftHvJ/yuWXOSAQAZa4p43zk+IphXPObJ+7Y7dao7aB6B4C16ktZ2B3xZa3vy/0YcxRXC95nptFV6Iy+mFeqcXCytF1D/36Wg/AIQfvs/QCn9Xfwm4usnEAjdm95wH+1qfFVvTzddQORj1jyycHr5kunDUKgz2MSFUqQmhPNyufERwYgKDbLJaSoofsExl8nDI5A5O0k0VhLGgswaGX0Svs+NI8V0OSMlljcxiPu6nFjNYrHgySefRE5ODnbtzsP3Zc1eie9IbEkgeA+it87hq1rr7J6tPRjd4977Rw0OxeYSfjHR4mnWiUDC5h05eiGmK9zCXu46GJbnlWPZjjKH608bFo5VD1r3NqXy3lz9BoBVq1bhueeew/YdP2L5b2a3GiN6Q0OJbhMIzkG01jl8VWu5cS2DKkCJC21mnlEhBfEWVSYva93LpWVpmvDzUgXDQl1j4OqBrsHIi8W5emkvznUX+XsKcNOMm3Ht7Odw16y7HGpTV6yJQCB0HT1dazdv3oz09HT87W9/w/vvvw8AOHnyJIYPHw6j0Yh+/fph0qRJMJvN2Lt3LwwGa7PJF198gdmzZzt9PaK1HQgHAsghLlwNigJvH1Rs/1GoNf5KSrI2Ky5cjSiNCoU6A88oWAwFBcSGB7N1U6v36i4NOtAAoNnarrnjOt4Tq/VictyHa5p49VNpw8KRFK3BR7nlvHpopj5KNTwFoeMfkPVbiREfoUbO06lOfUYYewtrvt0JiXcJBHE8rbW9ZsJPYGAgdu3ahfT0dOTn5+P06dOi0wuY/qYJEyYgOzu7xwosg7PtWm1mmtfsAwAtbRZc3t9fsuGH6bzdV9EAP6XCpguUS97xBmQVVPLEWZgcZtBdSkhnzk5ij2NE4IHronHDsl28NfkrKAwODUJWQSU7dSiroBIrCyrR0MxZu8zi95bq/6F+86ug2y7i7M/roRo6VrIw6tCJswgJEv9/F/9Lkx/sFYI745AsdAVx5CjiyFXDWdx9PiGM4GeX1LLFbJ64jhzICENCT2L8+PE2zT1ir3kSX9VawHrvsketoQWr5nQ078z7olDy2DazdZLP5oO1UFBUR0FTSixvFDwXxnGBG+hW6zuSwrvL6jHjowL8uGgCFqbGs/faRzeUiK5h88FazB8fixV7KrD5YC0aRYJz4XNBY0s7+2+LxAPId4f+5CWq2xpP4czGf8B8rh76wu8x4C83wi8kQuqn4X1X5t+8Ii+aZgPqpEvBNtdR2tkA0l4Q6k49dNe5SNBMIPR+fFlrexLC+3Ghzn5cIYw7lAoKWXOSbRKvNE3DT2F/Qq0Qy0UT6ja9gou1RwEAF6p/RVBsoqzPCpPm8RHB7IRaAoFA6M34ut6q/BU2phAHKvW4NlYru+GnxsD/fHlds03TKlNEJdXw0262Ch530sCCdcVIjtZg1OD+vFjw/9l797i46mvv/7P3MBOYIQFmGKIGAgwosWrVEPASSYjamrS2VtOovcQYxaQeH2svp7Xxdlqrpqm9WltPIjaNOfaoMUZrq7FNTIBEI5fEejkJEQYIRA3DDJAwMzDD7P38MdnDvs6NAQZY79fr93vCzL58oZ792Wt91/qsyhKrRJ/CufSrTaCprrMrYu2nT0/SXb2wMHS8cL7EzCoQwHe+8x1UV1cDAL56989R9f++j40rF4x7PDbWeVuCIIhEMV21NpEWm9uaOhUNOGqmhM/sa8MdFTaFLkVy2w9wvKQZVri2PH4V9FLQxzWLgtfcfrALPA/kmY3QMUFDC/H7RbvTgwDHQ8cyimsKiPX7ySefxN133w0AuPE/fgxc9QMAwbz32q2NYY0Ro2EiJhCQbhMEMZZMV62Vo2OgWvCbptdJim8FxLpXZDWFvbYwXU/AYtJj48oFoaLgAMdL4sxopset3lwv0cv3OvuxsaY15gm08fDWW29h2ZevhW/Qi7rn/4RW4+cAhNemsV4TQRBELFxzzTW47rrrMDQ0MhVlzpw52Lp1K77xjW+gv78fO3bsAMMwIf2766674mr2AUhrBYqsJtwwfw4ef/NoTOeJ9c5o0OGSQnMoDytGrjXfu/ocNLa7UHvUAXkpbp/Hp8hra8HxwZw1ywCGFFYzHhTv1e463I17l5Zg3bJ5ClMNuZlUsDFI2jjs7/0U3c/fh+GTDrgPvoaZFy6NWB+lxfL5uVHXBwnHCfG7QIHFNGZ1SRMd71LtFDFdmTINPwCQnZ2NPXv24OWXX8bmzZuxf/9+9PX1hb7PzMzEwoULcdttt+H666+fuIUmIfYoxHDv0Z6oriUkoYWHqFYilwew/o0jOGB3onpVmWQz9oDdqSg09nM89hztwZ7T6xALsbhIavn8XNS3SQumU1gGcy1G5JuN6HB58NGBveje8RgQ8CPFPAezb3o0rAuy0+3TbIgK1+gj0O70hAJ1HcsoRE/4G+hYRjFG8II5GWGvneimlbFugtFqAJuIZhtKUBCTib1790b12VgzXbWWZRjNJhcAaHEMoGpLQ6igKQppCDXUcqcn5jS0u1BeaEZ5oQWN7U5wfPC+pflZeKe1Bx8c70eR1YR8iwmXFJqxqbZVcr1WhxsX/PRNXFJoxoICMx5/U9uViuc5/HdNiyIot5iCyQctzYvEJ33e0L/9PZ048cL9CAy4oEs14aI7fgmHcSSYLbaa0OvxS+61RFbEJfxbaBJtcbjR4nBj3bJ5ADDqAFKux9uaukKFzonUw0Rda6KDZoIgxofpqrWTCfnzeEmJVfK9PK5QizsCHA+O51FsNeGT/kF4fIGok9QCAe8pdG97CL5PPwYYFpZl90Td7AMAeVlpYFkGwmTANYsoGUoQxPRhOuutx6/sLH3H7sS/u/pQZDWh3+uXmiupwDCRXfbFG6Jqk9rFxlFyba2UaeslhRaJRqk19QioFfSq5ad7Bnyh3LS82Fk4d3h4GLfeeiuee+45AMDMBdeh27Y0tFYyLyIIgtBmOmstoCwSjpWWbjfKH92FLGP4YjGxngn7jGK0imDUmmGFY8Txq3B9IKh7OpYByzAhXW8V5WrFMW2rwx3S+dJ86TWLrSasWJAX0u/HH38cP/7xjwEEi/i6y++CS/QqEskYMVkh3SYIYqyZ7loLQHM/NtWg3vAj5tP+wbDfZ6bpJWbKVRU2ScFwgONDxooAA45HqNlVsc7TeizXNKd7RGfDxbmj5fXXX8cNN9wA39CQpD4qkjaN5ZoIgiBiJT09HTt27FB8ft111+Hw4cP485//jPfeew9DQ0Ow2Wy46aabsHjx4lHdc7prbZqexd/vroCOZdDY3qs55S4SHl8Ae5odeGafHXdWFku+E7Slvs0FjudxsMOFS20WHHO50eqQDiVwyoyMs9MNyEjTh91f/f3uj7GtqfP0PuiIibNWk8wf3mrB3VcWS0w1Nta0Sn73IqsJcrUX10cxM0y48I4N6EvPiap2DAjGycEhB3xorfKcuVBbJvytWAYoL7QophIJxFsHG01d0kTHu1Q7RUxXplTDj8ANN9yAG264AQDQ39+PgYEBpKenIyMjfOMEkRhaukeSuICKO7+MPc2O4CQEhsFejRcDHSMN1sUiUVVhA8fjdCDNg+M5dLq8kvOHOR52hxv5ZiM+3P8vOF79JcANQ5+dj9k3PwKdKUtxT6OG64cWRoNO4h5SnBN0BGnpdqOle0CSEJeL3p5mR+hvtnHlAqzd2igZVR+ORDetjHUTTDROXuMFJSgIIn6mm9bOTNWh1zMc9hhxgVCaPnLxk8D2g12hDVKhaJhlhGHzPLYf7AoFqE63H3PNRqxdXKQ6DUgIlN/vCr8JmptlxB/falF8nmXSI99s1HRgjoSgm75uO0688CA4Tz/YtFmw3vgwHMZ8ybHCpm51nR31bSMNTsIkPx3LhCYfbWvqlJyrpiXyhuNokF9HrNeJ1MNEXWuig2aCIMaX6aa1yUC0bkDy5zHLQNXtSUBr0oDc+SmmtXr6ceKFB+DvbgNYHbKv/U+Yzq2IeB4DIDWFwZmZabD3jCTKWQbU7EMQxLRkOuntDL0OxTnpikk8QHACvNfPwen2I8uobYok8LWLzkJTR6/CadFiMuDiuZkSHRUm+Fzzu1rFvevbnACg2Fj9UFYE1dThAjAS+8Tq0h8uP72/RWpyJcRZPp8P3/zmN7F9+3YAwIVfWY3ec28ITVom8yKCIIjomE5aK+aKYgtqP3ZGPM5i0iuKlgTCmRHK2dPswKZaO+6sDGqTEN8KRkqAtAhGLb8qjmefrrNLGoDFuqeWoywvNCsmBY6cI61wWl6ai7WLi8DzPH76s5/hZz/9KQDgwoVX4+Udr+Cu59+X5L15HhJTxckC6TZBEOPFdNXacPB85BHqXl8AZmMKXBr7vy6PH0tKrCGzR3nOV94Eu2HnEdS3qTfgapniCgiaORbT6Hbs2IGbbroJfr8fZxWeA/bLD4bqoyJp00RMyCMIgoiH/Px8/OxnPxuz609XrfX6OVz7hzp8vTQ3bLNPWgqLS4osmnW/An94qwUsw4Q0Vbwnu6DAHGpa2X3EgcoSq6LhR87tV9jwkqyWKE3PwisyvPL4AmjpdmPDzmZsP9iFFaV5WL2wEGu3Nqr+Th5fABt2NoNlmFCD77ZG6T1aHW7kmY2hn+X1UTk3PgynqUAeCoewmPQAGEm8f/38ObhrydmS4+Sxt7i2TGD3EQeKc9Iln2WnG3BHhS3uOtho6pImOt6l2iliujIlG37EZGRkTHlxTQb0OkYy6WZbY1dIkFcvLJQ05ORmGRUCH2l6kLzbVSwSwUAaoYR1uBGCNW+8AscrGwCeg2F2Ec686WEgTf2/j1iafQDgkkKz5EVgRWkeGtpdEqEVxEVtk7mh3YWqChs272+DjmVCwhspeZ3oppWxboKR/+7FOSasKM2bkGYbSlAQRGKY6lrrHhqGwRu+2UeOV8UxWROZxkVyxRDcnzauXIBL1++CS2VD2BVhI7jT5VF1dW7pdiMvy6hyRvQMffoxul98ENzgAFhTJmbf9AgM1gIAwcCVYYLT7FYvLAw9h4GRaT3CxrDweXWdXRG0luabQ4Vh4rWLG46jQavoq6HdFWq41dLDWEbEJkpbJzpoJghi4pjqWpssROsGJH8elxdawsYV0U4aiJbhARe6n78ffmcnoEuB9bqfwHj2pVGdywPwDvNgZJJFiVCCIIipr7dZRj1WlOaGLTICgF6PetGxmN/t+hjDKmMLbruiEHctCbo0BjgeT+1tleSE5Q0/HA/V9cgnoEcb+4jjtNL8LAAMmjpcKM03496lJWjq6EWA4yXX9skSz2UFZgwODmLFihX4+9//DgAoveE7uODa1XGtKZGQeRFBEJOdqa61KToGxTkmLJ+fC56HZsMPywSnBmSZDMgzK/dMI6HlYlxdZ8eaRUFtqNrSoJpn1tqnFCavq+VrAaCtxw3fMAdDCquao6yqsCkMqgStbOrolayhqaMXPM/jvvvuwy9+8QsAgHFeBXov+3/YWn88ZIz4bpsLHl9AMv1gMsWtpNsEQUwEU11ro8Go16EvgokjEMyTajX7CLAMg+pVZaGf5XuD8r1KsdGvGHkuWG4mPFbx5f/+7/9i5cqVCAQCmD9/Pl5/YydeOXyStIkgCGIUTDetDU5vbQt7jHeYwyWFFlxmsyjMI8R4fAGsf+MI3mntwQfHT4YaXnYd7lY0regYBuuWzVOdHC80tHA8r4iL777ybNS3OVH7cY9i6m5Ltxvr3ziCFxs7w04FAkZi5021rWhROfaD0ybM4eqjtLh4bhaGOV6SC2ho71Ucp6xn0qgh5qW/6O1X2EYVO0dTlxRPvBtLjVUi1kgQU5Ep3/Az1dHp4nvoySfmCBRbTVhemoumjl4MBzhJI44t2yhxABbjl12sxTGAFsdA6MEqbshp6XajyGqKKJxLSqz44Hi/5CUg22TALKMev9t1FJtqW3HbFYX4zuLiqIqlhk/14NMdvwo2+5xVgtkrfgakpkc8Dwi6U2akpeCYy4NhUX202NFj9cJCbN7fphAyNXHRSnzHM24uEU0raoI6VklzNcGfTI5cBEFMP04NDiMjwphVeeNrJIqsJhRmm1BWYMY7dqdqgKjFBXOCyQNDCouL8zJVp/FEWkm7U9sJg0FwSsHvdh2VNC4ZDTqclZmm6gYdui8XQM/ffglucAC6dAtm3/wo9Jbc0PeCW+WeZgeW/b4Wb9yzCIYUNqz7gvw7i8mA7U2dqn+zWIuVBU2SJwnKCswR9TUWzU5UgyltEhMEQYwt0boBhXseSwuNzQB4NHX0KmLGgEqRdLT0/uu/4Xd2gkkxwHr9/UizlcZ+DVnDMCVCCYIgpj69Hj+GOR5mk17VOCIW1Jp9gGAOWCA4zW6kULil243F51hxvM8LgMfy+blolG1kCpu1WnnWSKjFacK/1y2bh+pVZSGtlm8+i50Pf7nhF6Fmn6wrq9Bz9rXY0+wI6+48HpB5EUEQRHIzHODR0u0GxwM7DnVpHsfxwakBLo8frY7gnml7j1t131aNOypsqG9zKvZZnW4fqk9PzdMylRLvUwIjce3qhYWSfcJbLivAxtrW0DtDq8ONtVsbsXl1ueY+X/WqMsVeY4DjFfFvWYEZ//rXv0LNPqbzr4Jl2XfBsLpQHL55dTmqtjRI9HyyGVWE0+1EFjoRBEEQUmI19mUZKAqCBdp6BhDg+NAzelPtSJy763A3bFaT4pxoXPDvvrIYLMOM6X7f8ePHsXr1agQCAVx66aV44403kJmZibU51kmlpwRBEAJ1dXWorKxEfn4+WlpawLKs5rGBQABnn302jh07hrq6Olx22WXjuNKpRzSTZ5s6XPjTt0rxYmOnZsOPgPpgAKkYlxcG9ZHjgzlf8RruOF3XWrWlQXJOkdWI+nZXxMEDkWqWgZHYOWhmpcTp9inqo+auXA9u1lkRr12an4U/vNUi+Uw+8R5Qxu0cD0m+XYDj5S8y8e9BC/cVD3fgeF7yPgTEl6eOpy463BoBqp0iph9TsuHH5XJh69atqKurQ3t7O06dOoWZM2eioKAAFRUVWLlyJczmqVHMwsVZJKSVNHa5fXi3zQUdw+ASWzYusVnQ2N4LjudDEwXkWEyGsMKu1oyTb04DoC2g5tMTAM4/a5ZEhIe5AOyO4L28fg6Pv3kUKazSTUoOywDn2PIx9KUfYODfO2G9/n6wM0YmGFhMBpx31izsa1F29wJAlskgKXAuzknHitJcSQJWnLDmeB6bau1obHdiSYkVLMOEXkQAhBLfm2oFl0sGHA80tksdQEabvA6XMBZ/J3a2HK2gRoI2pgkiPmpraxN6vUWLFo3q/OmktZHISGXRPxjDRB8ABRZjyBFK7v6khtGgg9GgwwVzMkKTZ4DgRAFxw49WQ6+ccK8PPILB0dutPagRafBdS4LJZ7UAUhiLy7A6WL+2Dn1vPoGsr/4E+swzNO/T6nBjzdZGXGazKBqQ2p0ebKxpRVWFTaHxTrdP870j1mJlQZOqKmwKvYyEWlG42nUSuVEbjYbSZjFBTB1Ia8efaN2Awj2PwxUay00fLCYDvL5h1al74TB/8T8wfMqBrMrVSJ37+ZjOFWBOO2NRIpQgiOnOdNLbIX8Aj7/ZPKb3ECboCA01co73ebHrB4sR4IK50/c6+yTfC5u1AOLKH4YzhRLyrFqTC8T3/uEPf4gDBw6gJ/NcHD+zInSMkNMlCIIgomc6aa3Ak7s/hnc4+jgvmmIjgcoSKzieR4eGoVM4LTQadJJCHbHWbqxplcSyB+xORYPwu20u1XMF1D5/am+rJA62WU2ob3MBhUX4ybp1ePujDrTPuxkMEyzWExqEhAbb0Tj2JnOeNJGFTgRBENNRa+UYDTqk6XVRFSTLCbdnau/xYFOtHSwT1NhDx/qk36toeLQu+OLYdCyYM2cOnn32WWzatAk7duzAzJkzx+xeBEEQ48GLL74Inudx6623hm32AQCdTofbbrsNDz30EF544YVRN/yQ1iqn08l5u9WJS9fvittoKjfLiKA9cdAoSqi9Edcniet1AeW+7qd9Q2h1aJsfh0NuVLV6YSE21rTik75BzXOE+ijnG7/HV77/ONr96RGbnYqtptA0WzGC8bMYcYwdzKe3ojjHBPAMcs1p0J1uWpYbfghTduONiXUsIxnusGFnM1hm9O8t0RpvRgPVHxPTlSnX8PPb3/4WDzzwAAYHgw9bXtTBeOjQIezYsQP33XcfHnvsMdxzzz0TtcyEEammd/E5VtQcjX4UvMvjD42L232kG/cunYfyQrNk81OgOMeEFaV5mt2jAhfPzcLLsm7Xf3edDBtsu9x+vHVE2cDT51W+ODS0u0LFz2pj/HieAwcWdocbpnMrYJx3BRhGKl5Otw+1H4fr7pX+pQssRolgBDgeVVsaFE0zAuuWzVNNfLMME1rvhp1HsKTEKjlmtC7L4RLG4u/kTDaXLIKYDlRWViqeXfHCMAyGhyOPMtdiumltJGJt9gGCjTrif6tN6RFTVpCFP99aDh3LIMDx2FjTGpoccO/SktDkAI7nsWHn6Aq4WIZBdZ1d0uwjfK795jHyuWG2DfO+8yRcnsj/je1tdkjG1FpMejjdfrR0D4Q0KjSFp7ELLQ716ULywF5OpEA2nmBQbcM5GTZqk2ENBEGMHtLaiSERbkDhiqvkJhqxbEDzPBcqgNKZMnHGyt+M6t0w06inRChBENMe0tvEI4+L5PB8MH6Wb9YCwUnqo2lAVZsgIF+bGLnu37awIPSdwWDAK6+8gk210t+FJuIRBEHExnTV2liafaLBYjLg4rmZYR19BQIcj/JCdZNEjy+gKNTRatRVM4H0+AKorrPHFEeK3ZB5noPd4Ybd4cbuI934ydLb8POf23DHs42h/dU9zQ588bc1KLAYsaDAjHuXzkNTR3wxerx50vFoFEpkoRNBENOb6aq1cjy+QNgiZDX0Ogb+KFwUtx/skpgDayGeGisg15SNKxeMefMpx3GhQvgbb7wRK1asSFiNAUEQxESyb98+MAyDL3zhC1Ed/4UvfAEPPfQQ6urqRnVf0tog5YVmSW2PnHi0WCBNz0quvf1gF1iGUZgnF1iMEhPe0vws/OiaEjxT1wqXZzjmKX9iPjzeh8/nZoJlgvW8G2tb8et/HlU9VtiztWUbcTJ9Hr742HN46tsLcO0f6iI2/OSZjYoGHYvJIDF+FiO8S2xr7ESLqNF4eWku7qxUTjkCRnLYo6kdGouYdbSmHpFIZtMPgkgUU6rh5/7778cvfvGLkLDm5+fjvPPOw8yZMzEwMIAPP/wQHR0d8Hq9+MEPfgCHw4FHHnlkglc9OsI9ktL04buZo6G6zg557Gc06EIu/w3tLsyfm4XF52Sjob0XaXoWt1yej0PH+vHh8X5cMCdDc6y8mKJsI/oHhyOKnhptPW5U19lRVWFDQ7sr1EDD8zz6334e/p5jyP7Kf4JhdQAQdTBrMelxUV4WyguVBdRywamusyvEWEx9m0v2wmEGwOOZfW2S49gEuyyHE99whWi0eU0QyQmvGMM5/kxHrWUTHACYjXqsXlgY+ll41j9dZ9fUwZqjPaHNVHlQtm7ZvJDDcIDj8W6bSzPQjjSVDwAWFGSpasQf3voYZ2akKj73HH0bnx7Yjpwbf4aU1HTwQFTNPmrINVrQrbWLi7CtSTkq12LSY82iooiBWqKaYMQBYml+lmLDee3WRtX1jye0WUwQk5/pqLXJQiLcgMJNn71gTkbYuFELX3cbel77FazX/QT67DwA0ce1WuSZjZEPIgiCmMKQ3kYHy4R3OwYAszEF8/PNmnGRmDyzCYB6XvKD4/2hHG+kjTi1zTut/KyWQYRY9x0OB65YeDkefPBBXHvttQCCWltVYQPHC8XKvGQqAkEQBBEe0lolWcYU9MaVN+WxceUCVNfZ8cw+5QQ9MXuaHSgvtGBJiVUz/hRPKt/WpF7ErBW/xp5rDP7vzwf86HntVzDMLkLGZTcCABo7evGdSlahq60ON1odbuw+4sC9S0vinq4Xb550PAyVxrrQiSCI6QFpbfwU55iQm5mGvUfDmQIH+aTPG9U1xVNjBSJpSiILU3mexyOPPIIPP/wQzz33HFJSguWA1OxDEMRUoasrWC9y9tlnR3V8UVHweXv8+PG470laO0Kny4MlJdZQvjie/U4tjIYUeP0jdUwt3W6sf+MILCa95LjSfLPCkN9i0sddnyTG6faHrrv7SDeMGnXXnqNvo/90fZT99GvE3qM9uGLDW5JarEJLGsAwaOsZmThkNurxflef4pprFtlgSBm5n/j9IMDxqn/r7Qe7cGdlkSK2FJtqjaZ2aCxi1kQYb4aDzJGJ6cCUafhpaGjA+vXrAQDl5eV44oknUF5ernrcd7/7Xbz77rtYv349rrvuOpSVxZcoTAbCBWdePxfTdB811IqCPb4AGttdqtNsPL4AtuzvgMsTHM+3p9kBo0EX+UYMgzsqbJruj+FodQRFnuNHxIbnefTVbsHJAy8BAAbmXoCZF38ppuuuWVQkcbgSGpzUBCdc8wwA+AMBfPG3NaHGJ60CsPJCc0JdluXiKzhd6lhG8V2R1YQCiwnlhYkXVIIgRk9bW1vkg8aY6aq1XKTqphhxefy4bP1uZJn0WD4/V6I34XRwW1MXGtpdaHdKR9CKgzIdyyAlTCL4orxMlBeawwaGAQ6qrsgeX0DRwOv+vxr0/P3XAM+hb8+fYVn2Xc17C4F/uDlBnKypLdggK6A8K/N085RWMtw3zGHt1kbsb5Em7ONtggnXbAUkx0ZtOO0nCCL5ma5aOxnR2owNFgbzqK5rk8TTRVYTGIbBkhIr/t3ZG3XyeejTj9H94kPgBk+h5++/whmrfpeQTVodyQJBENMY0tvo4fjgJmFnr0cxVV3AnJ6K6lVlIW2Ux6xidKc1TK1BtmfAh/VvHME7rT3QsQw4PmiOJOQqxTGN2uadVn5W7vwojxs//fRTXH311fi///s/rLj5G/j6hh244vzC0DEsg1AhtHwqAkEQBKEOaa068TX7AJ6hAP67phWPvxndZPemDhc2rlyAtVsb8cHxfmSk6SV53QDHY1OtctoeABh0DBYWZ+NP3yrFs++0KxqCtPKdWjHy8vm5+MXfP4DjlfXwtjbA07wfqfkXYsZZJSjNzwpdU2vftLrOjjWLiuLKbcabqx0PQ6WxLnQiCGLqQ1o7OvKyjKrT7MQIE4Dk0wrkk4GMBh3uueps1Wd5JE1JVGEqz/O477778Itf/AIAUFlZiTvvvDPm6xAEQSQzAwPBuCTaPTLhuL6+vrjuR1orRTBmsJgMyDLqYcs2wt6jnQeOhSyjXrVG2ekO1h8Lhk4cr6xxEo6JF6OehcevnNLrVfksXH2UfP06HavIpwv11GKEBp1omnykBN9F1GJLIX4eTf1SuJg13oblRBhvhoPMkYnpwJRp+HnyyScBBAV27969SE1VOtADQFlZGfbu3YvFixejoaEBf/zjH/GXv/xlHFeaWCZq2kO44FcuToNRjMs75vLg3TYXKs/JRqfLg2O93qjG54rZfrALr3+3As/Xd6Dphd/hVNNrAADTeUuQfuE1qudojenVMcDbrU4McxwOHes7nXRWFyahkFZMWgoL7/CI8De096q+CAiojfeNFTUxraqw4YDdGXoJ2NPsCE2IkH/X6nDjxgV5JHQEkaTk5+dP9BKmrdaOBU63D063Dxt2NuNduxOX2LLR2O4K64jR0j2g6rbY7nTjqb2t4HgOOw4dR2+YgJbj+ZBOrF5YiLVbGxX32by/LeIUIAAYeP9fcL7xBAAehjNLkLnktrDHC1IZTt1dsrW/1NQJlgkGk8vn50qm7QFAq8Mj+R3kyXC13w+IvwkmUoCYiI3a0bp5hdN+giCSH9LayYPWZmywMJhRaKmQCAcAoz4KUwwAg12H0b3tv8D7PNClW5D9lR/F1OyTZdSjVyV5DADlhZaor0MQBDHVIL2NDR3L4M3vLZa4J4pZPj8XALCptlUSs5mNKYoG1/LC4KaieHJOh9Mtyc/KHZZ3H+kGx/O4s7I4FC89XSedbrCtqQvL5+eqFguXFZg1dbutvQOXXLEYjuMdYFIMmPWlH6Gucwh1nUdwwO5E9aoy2igkCIKIA9La0SE3TPIOc/jdrqNRn19WYMbm/W0h3e4Z8MFs0odyr3uaHejsVZ9W4AsEC4uefac9ZGgRnHQXbN7Ryndqae23Ss/AE/95K7ytDQCArCW3Y8ZZJaLfVJnPFON0++PObcabq02EqVOkHO9YFzoRBDH1Ia2ND7NRj8/nZcY1lUCvYzBzRoqiHuruK89WfZ6r1RHJNSUR8SbP8/je976HJ554AgCwcuVK3HHHHTFdgyAIYjKQnZ2NTz75BB9//DEslsh7XB9//DEAwGyOz6SVtFYdodZpNIjjUwDIzUrDDfNz8cw+rVolHu/anXjH7oz6HkaDDmfMmhGxKSlnpgHtrsGI14u1Piqaku4lJdbQNN9tTZ2ahltqCDn5cLGlEAPXt7nA8Tzq25yhzyPVIIW7bqIn6SRq4mEyGDQTxFgzZRp+amtrwTAMfv7zn2sKrMCMGTPw8MMPY+nSpaitrR2nFU4sDIDCbCNumJ+HP+1tUbhQiDGb9Oj3+BGu30ZrlLsaQgxrNOhQXpAFHoxi8pA/wOOtI+ruTdHD45m6VjQ+90sM/PtNAIBlwZcw99q7kZWeqphKINxXjQAP1Bx1hNYpFoNdh7tPO0xCtavWYjIAALzDIy8gvmHtZh9AfbxvaC1Rilq4gjMxQpJAxzKa3xEEQahBWjs27D3aIyloWrdsHqoqbNhUaz+9mcqD53m0OkYCUQZAqp6F18+hpdut6sYoJjvdINFuQSeqV5Xhgv96Ex5Rc240wfmpg/+A619PAQBm5J2PnOUPgZ1hjPZXjhphih8QnLwHAH94S/oeI29CFmuZVoNyvE0wkQLERGzUjjY4Jn0niMkNae3kQb4ZK0ziKyswo74t/ARYTxSmGIMd76N7+8Pg/YPQZczG7JsfhT7zjKjXZzHp8c66q7F5fxsa2l0hE4umDnIPJgiCIL2NjbYeN9Y824jOXunmqNzAKBi/jjA4LM27FueYQsfqWAZ3VhbhzsoiXP2bGlVzCzHbD3bhzspiSbwkpqV7ABzPYUmJVZKnFRwS125tlBzf0O7CF/IYlF2+CK4Tx8HoU5Hz9YeQOvfzoWOEuFFtimrVloZRbT4SBEFMdaa71qbpWYDn4R2Oz7RR7axoTRK1tE9utCS/i9Ggk+RchZhX3MwrSN7GmlbFnqVQOCSwqbYVg54BvLT+bhxp2g8AMH/xPzDz4i+FjmnqcAEI7ldWryoLFjk1dqJFtp+rldscq6YacaNQab4ZHB+79ie6AIogCELOdNfaaJEbIlVV2NDU0as4zmIy4PYrCsEwwPaDx9HSPaDQXn+AVzT7WEx6rFmknmfdVGuXxKeVpzVazGgLUzmOw3e+8x08/fTTAIC1a9fiT3/6E1iWjek6Y02iCnkJgpjelJWV4dVXX8XWrVtx6aWXRjx+69atAID58+fHdT/S2tiQN/FoYbOacP3FZ+Ev+ztCtUl7j/ags9erWavU0u2OqRkGADy+gGqzj9xgQ6vZR3yMVn1UZpoOvmFeMSEohQGGA5H3glkmaMislu8WI0xVys1Kg45lUF5oCb1ThNNYISYGELrH7iPBd5PRxKeJNshKVPxMk3SJ6cCUafj57LPPAEQv0qWlpZLzJita3aByEeUB2Hs8+PN+u/oJIrTEt7LECh0TFIpjLg8sJgO8vkBUxUoAkKbXIUXHot2ZmHF+cgL+Ydz//Tsx8O/dAICZpV+F6co74PIG4PJqi76a22Qktjd1ocWhvhGt9vKRbzHB3jOyBlu2CSsW5KKx3QWOR6ggbPXCwlBBltr0hXCipiWm4ZIEiexspSCdICYWl8uFrVu3oq6uDu3t7Th16hRmzpyJgoICVFRUYOXKlXE7VwhMV61N0SXmWSYPHLUQnt9C8RMQ3EQVB3k81EfIapGRptdsjDkzU70pVouT9TvQu+cZAEBqwcWw3nA/WH34BMdoGfmbFINlGMnfQt6ELNaycA3K8QSd4xEgJiI4jkbfSbcJIjmZrlqbTET7fJQ/a4VJfLsOd6OyxDqqNXjtTXDseBT8sA8pWWdh9s2PImVWbNd0uv1Yu7UR1avKZDpCBUYEQRCkt7EhnlIn5oI5GTKdlOplml5aOAweqNrSAJaBZFMyNystYsOPcG15vCRmx6HjKLCYJJ8Jhghy3c5jelFR8TW4TnwCxmDE7Bt/hhlzzlVcs6HdhY0rF4T+LTZ+CpenpXiLIIjpznTX2iE/hxiGs44avY6BXseivCALG1cuUNU+OddfnAuWYUKGU7lZRuyV5VjV8pQAVAtxZAMM0O3sxY9v/y58nzaDYVmYl34X6RdcLTlGnLMUCpGqKmyKqYJae5dj1VQjbhQK5uWbY77HeE8IpHcPgph+THetjRb59POXDx5HnjlNcRzDBJ//QkNQ5Bg1SJZphuR5K34eHzrWJzm2vs2F6jq75Bkdad8x3PN9eHgYt912W6ig/Z577sFvf/vbmCbEjxfUCEsQRCJYsWIFXnnlFWzatAlXXnklli9frnnsjh07sGnTJjAMg5tuuimu+5HWRsao1+GyIgvaner5YzkMAPA8fv3PjxXfxVKzNBpiteUIVx/lG4ai2QcAhnmgI4qpQe1OD/7dpW5ivKTEis5eD1q63aGpSisW5Cn0U01jqypskvcHuUGHEJ/GG0cmepJOouJnmqRLTAemTMNPamoqfD4f3G53VGP73O6gSMyYMWOslzbuMNBu2ommk1aNyhIrnjntrhSpq1QLp9sXNrkcDq3xuGIOvvwUTp5u9pl16QpkLrolymA29oC31xN5+kGR1Xj6/gzOypghafhZXjoHd1YWSwq4dx/ploys33W4W3WEvZaoaYlpuCRBIguXKUgniInjt7/9LR544AEMDgYDBl7UDXro0CHs2LED9913Hx577DHcc889cd9numptml6XkOtEGziquQVXVdiwrakr6gSzHLXguDQ/qBPL5+fil282K75Xw324NhTMphWXw3rdT8CkGOJaUyyIA0T52FkGPJaUWMEyDMoLpVq2ceUCrN3aiA+O9yMjTS/5O0QbdKoFudHo20QGx9HoO+k2QSQn01Vrk4lon4/iZ227U+os9W4MI+Xl+Bwd6N7+c4Abhj57Lmbf9Ch06VlxXSveiXYEQRBTHdLbxLCn2YHb/1KPS2zZaOpwKRp3zp+TgUttZmw/2BV0YXS4Q479gpMgAElxsYDczGr5/FwAynhJTK/bD0BqNCXOj3J8cFLQ8JAHv/3BSvQ7uzHDNAtZX38YM84oVr1mWYFZslFYtaVB8v22pk7VWC+a9wkqzCUIYioz3bWWA2KvIhoF/gAPfyCAvUd78Mw+O+6sLJbErOKGVYGXD3Yiz2wKaXdLt/u08SODAM/jxcZO9Mn2QtUm2ta3BfcsWdF+LM/zcOx4LNTsU7D8XnC2haHvjQYd7r7ybNWcpXjaT6S9y0Q31ahpc7z3SHQBVCQo10sQ04/prrXx0uvxYa5Kw0/PgA/r3ziCTbWtuGBORtTXE2JVgXD1VB5fIPSd8IyOVJga7vm+bt26ULPPvT/5CQquuR13PNuYlPHleDfCEgQxNbn55pvx61//GgcPHsSNN96Ib33rW1i1ahUuuugizJw5E6dOncJ7772HLVu24K9//Ss4jsPFF1+Mb3/723Hdj7Q2Mh5/AG+39MA7rG5WnMIEm18EhIEFiSLaqULxEqk+ih9l4K9Wx1VsNWHFgrzQ5F7x/vO2pi5FLjkao44lMqNKIT6NN45MtFHyeMfPBDGZmTINPzabDe+99x5ee+013HXXXRGPf+2110LnTTVGIyUpLINhmQ1TcY4Jz6wqUxWJeNHrmKjHzwPS8bha585c8FV4jr4D0wVXIfPymzWvxTKQOE0NxjAhQcDjk04EEnfVCsw1m0IJdHmBtuDmIf97yqcvyH8GtEVNS0zDJQnCfRdp01n+vVY3MEEQY8v999+PX/ziF6Emn/z8fJx33nmYOXMmBgYG8OGHH6KjowNerxc/+MEP4HA48Mgjj8R1L9La+DAagg1DEmdjDVgGisZPHcugNN+M3MzUuBt+1OFP//+j12Pj2Zci8+z5yMjIgu7K/4cAo0/geqRYTAZcPDdTM0DscHkkf48lJdaQrgqaZUhhsXl1OYAR3RIahQTdipT0jjfIncjgOBrnCkquE0RyQlo7fmjFO5Gej/LzNq5coNjIjWUKnxx99lzMvPhLGOz8ELNv+jl0xug3mNWI5vlOBccEQUw3SG8Tx96jPdh7tCf0c5HVFNqorDnqwOVFFhRYTJKcqcDvdyudHLPTDbijwqaYgi7ERasXFuKA3YkPjvdjVpoedtGmqOB0CATz2StK87B6YSE21rSGip2FGDLlkm9h1oH/wZXf+z0OuWep/m5LSqyKeEw54c+t2lwbTbxFhbkEQUxlSGsnju0Hu7BmURE21bbipaYufNo/CPA80lIYeEWVVq0OD1od0kKrrl4vVpTmKgqVi3PSsaI0F1UVNhyQGVxwp/clygvN2H0kqGcMwyBr0S3o3v4wLEvvBme7THLOPVedHVbzonXlletyu9ONjTWtcce0atocqfBIK54ej0nxYijXSxDTD9La+MhI06HDpV1o7HT7sfdojyS2VUNcjCsmmnqq+jZn1M/ocM/3733ve9ixYwduvfVWWCu+EXV8GU0uONH5YirkJQgiETAMg1deeQWVlZWw2+147rnn8Nxzz6key/M8ioqK8Oqrr8Y9+Yy0dgSjXgePX73eSa3Zx2Iy4PO5GQrjCfVrs6pTcsRoNfZ4fYGozo8X49mXIjX/IrCp6cj+yg/B6KT1UWdmpEkGACSCXLMxpLvKXPQAWroHJFqvprHy9weWYbBu2TxFfBpvHJnoSTrjHT8TxGRmyjT8fOlLX8KhQ4fw4IMP4vLLL8fFF1+seey///1vPPTQQ2AYBtdee+04rjL5kTf7AEF3xE21dqxZZIs4Al5MYbYRbRpdufKGHXmiORxajUIpM7Nx5q2/BzvDGPZ8+a+o9UISDq+fQ5HVhMJsE8oKzFi9sBBP17Xiz/va4PVzKCsw45hTO1EgBLDyv+cFc6QvO/Kf1TaaBRItppE2neXfa3UDEwQxdjQ0NGD9+vUAgPLycjzxxBMoLy9XPe673/0u3n33Xaxfvx7XXXcdysrKYr7fdNVaty+AWEtt9ToGOiaYdIim0UdArlHi5p9YidRc29DmBMsw+ONbLVFfk0kxYOZX7gOv0yPAJGbykRZef0A1iazljiX/W8n1UNBJAKLpeg7VY8XEG+QmS3CsBSXXCSI5ma5aC4x/04lWvBPp+ag1Gl1tOms8MAyDrKvuAO8fBGtQuk1GQq7/0TzfqeCYIIjpxnTW27Gm3yvdeBV0XS2mVYuVzz9rVugdQC0u2ry/LaS3PQM+LCmxQscyaHdKDSEKLCasXVwkma4uJv2Cq7DoK1/FwnPzcEj0vXA9rXcRtem7arFeNPEWFeYSBDGVIa2dSBhU19mxYWd0E93FfNLnxbamLsXnBRZjSKPkYbrws3wqOzvPitZzn0f7yZHCK6GxN1HFO8J1BG1u6XYrJifEgpo2b1y5IPRvtcIjrXh6vHK8AqX5WZJ3j9L8+CYFEwQxeSCtjY/P+oeiKgousBjx9dI8vNTUiWMujyTfajEZkG8xqZ4njwUtJkPImEJApSRLk3Cx5Zw5c3Do0CHMnDlTMY02XHwZTS440fliKuQlCCJR5ObmoqmpCevWrcPmzZsxNDSkOCY1NRW33347Hn30UcyapW70Ew2ktUHS9DosKMhE7cfOyAef5sK8TEXsqIbRoAtbT2U06HBWRirOykxFQ3uvwnAxFgPGND2Ly2xm1H3cg2+SeowAAQAASURBVGhPY1IMsC5/AIxOD4ZV1kcxTOTfIVb2NjtCBlNi/Wx3uiWmWoLWa2ms+P2hvNCsGp8mS83QeMfPBDGZYSd6AYni+9//PrKystDf34/LL78c99xzD/bt2weXywW/34/e3l7s27cP3/ve93DZZZeht7cXWVlZuOeeeyZ66UmJ2TjSC+Z0+7Bh5xFU19mxemEhKs/JDvsfjsWkx7pl83DSOxzmKPG99FhQEHn0oRxuyAPH334Jf++noc8iNfskkn6vH9WryrB2cRE272/D428ehdPth8cXQM1RB1plHbxLSqy4+twcrFs2LySuVRU2rFs2L/T5xpULwv5cfXrS0nigNfJP62ehG1j+OxIEMXY8+eSTAILNPnv37lVt9gGAsrIyyfd//OMf47rfdNVaYXpSLPgDPAaHeUWAqdeN3TNcr2NQWWJFsdUUWkM4OlxerH/jSNjENs9zcO3aCG/7e6HPWH2qajAbK5H+FMJo++o6u+TzaNyxwh0TSc/kyIPaaIPceM8bL+TvIKTbBJEcTFetBUY2EXcd7lZ9/icaLT2I9HzUOq+zV2n4kKZnYTZGnoZ36tDrONn0WuhnhmFibvYR4vCPfrY05ud7rNpIEAQx2ZnOeiumyGqCLTuxudTz50jtMoRNxnXL5uGqeTlYUmJFWop2MLj3aA+q6+wIcHzQoX9LAzbWtMI3zGFjTSuelr0fdPZ6AQB5WVLdFOIvQdMGj70P5z+fAs+PxL8Lzw26Md+7dB6Kc9JRnGNCeWFwep9QqCtHxzJYUZqrei8x0cRbiYgZ5X+nQCyVY4QE+lsSRGIhrR0/bFZZwTHPqzbtAMEYtTgnXfNaHl9AdcK8WKPKC6X7ucLPHe1t2PPU/fj19SW41GYBwzAoPFN67B0VNk2NFRPtM1koCiqwSN9n4o1p1bRZuIewHyxfe/LE0/K/KU3tJYipDmltfEQ7AWBBgQUsAxRmm3BFcbbkO6fbh91H1HPY0cSCbAyTJsTX+/7iXOx56gG0traGvp85cyaA2OLLaLQr0foWSU8JgiBiISMjA3/605/Q3d2Nf/zjH3jiiSfwyCOP4IknnsDrr7+O7u5u/OEPfxhVsw9AWiswzHExNfsAwAG7U3XogJxIjTIeXwAtDjdqP3ZG1dwTTl28fg51LU4Ewlwm1vqoT/uCE3UTjZru5mVJ497SfDM21rRi7dZGAMCfvlUKAFi7tREcz+PepZH3iqlmKDmhPDURjikz4cdsNmPHjh249tprMTAwgCeffDJUCC2H53mkp6fjlVdegdmcXMWXycKgX/mgOGB34oWGY7BrTO0RuDA3EwDQ5/GFPU6gz+tHXUtPTOvjBgdw4sX/gu/TZvg+PYqzbv8TmBRD1OezTDCoTGGZmLp9xXiGhvHU3hasWVQUMcAtzjGpNuvIx7oDQTEVd6xOVAdrpC5e+fda3cAEQYwdtbW1YBgGP//5z5Gamhr22BkzZuDhhx/G0qVLUVtbG9f9pqvWGlIS1x8912yUjIC3mAzgwUvGzxZZTZiblYp9LS74Y3hxT2EZdPV68En/oOr3OgYQ9wAdCzOyHgB4LgDnG7+H+8O3MPD+P3Hm6iehzzpT83hbthF9Hh9cnugafiP0I4UQCroEd+XSfKn+FFtNyDMbJVMVwiWyY3WpiNd1KtndqsglgyCSk+mqtcD4u9xr6YHa81E8fUieoC7Nz0J1nV3iqiTg9XMR482TDa+g961qAEBKxmwYi9UbuCNxUV5maM2xPt+TxcGJIAhivJjOeiumz+NXOA0LZKWloDdKMycxHU43fvjFc/Dqe8cBMAhwPDbVtqKx3YUAx6Or1wtfhGBQeCcQuwlrTdILuvkHC5Pl03mAoKa99o834NjxKPhhH3TGDGRe8U0UWU1YvbAQOpYByyB0jQ07m8EyTFgdXb2wEAfsTnxwvB8XzMnA6oWFimOiibfEMWNpfhY4Hqja0qA5XUhtGiJN6Usc9LckiMRCWjs+2LKNuP7iOXj1vePodQd1vcWhjE0F0gw6vP7dCnzpiTrVxh6BYqspaFUMHsvn54Y0K8Dx4Hicbhoa+a65uRlXXXUVjh8/jsOdDvRX/CB0LTV9joT8mczxPFiG0ZwIHCmmjXaicDz53GSJp5s6XCo/k44RxFSGtDbxWEwGZJn0WD4/FwCP9W+MTMsrsprQ7/WD5yGJo+U5bHksGOB41LdJ49nywuj/NxCud9OFFixduhTvvvsuGhrq8dFHH0lqA2LRsGi0K1n0jSAIIhwzZ87EsmXLxuz6pLVBIhkNq+HxBbC32QGb1QR7mBhVjlGvQ1lhFlJYBh0uj+reazgirTTc7xJrfRQAePyJm+wjRtBdcWwMSONrjh95V5Hnz3cd7g4NFggH1QwlJ5SnJsIxZRp+AGDRokV477338MMf/hCvvfYaOE5ZWKPT6fDVr34Vjz/+OGy25Cq8TBa0Rs0dsEfXLRsUlCMRjxs5PqblIeDpR/eLD8F3ohVgWGRWrIyp2Ue4JxfgY34pYZmR9Xr8HDbsbEZ9mwudEYqmV5TmhZLH8sQyxwMbdibfQzpSUiDZC5kJYjrw2WefAQDmz58f1fGlpaWS8+KBtDZ+jHod8i0mScMPZM0+ANDqcKPX44up2QcIFhWHC3hP79GGCBvMBobR8/dfw3OkDgAws/QrSMk8Q3Fcmp4NvRtEaggWozbCXoueAV/ovSKoj9J152alYePKBdi8vy0qTYpVv+INcik4JggiXqar1o73JmIseiBPqIrheWBbU6fkM7E+hqP/7RfQV7c1eE5RGdIKLopy9UrkLsuxQLEdQRDTkemqt2LUYjKh9DWeZh8AaOvx4JVDx0Nx7y/fbI5whpKyArOiEfiD4/0Rz9OxDKpXlUlyrymdTXC98gj4YT/0lrlIv2gpgGDcvXl/G9YuVho5hWs6DnA81m5tDG2e7ml2hK4TK+KYcWNNq6ywOZiHDtfcI3//iLR2Ijzj3XxOENMB0tqxx97jwa//eTTq411uPy5bvwuRpr+sWJCHtYuLQpq6dmsjygrMCHC8RNvfbXPhX/sasOs3d+NUrxNZWVko+MIt+LfIj6qz14M3v7dY08lfrRlH/kzefrArlPcWCpnEJouRYtpoi2XiyecmSzxNhdkEMT0hrR07Gtt7JT+3ahQrv93qxOrN9di4ckEwJlVpMK1eVYZNtXa81NSJPo8PLzZ24h27EzommNMVN6Kq6WKvy4kvfvGLOHToEHQ6HR555BGFEWgsGhaNdiWLvhEEQUw0pLWj47P+wZhqgzz+AGqO9uDepfNQXmiJqf54NERbHyVGr2MkNVd6XVDLteqwzCY9zCZD2JquIqsJNy7IC+lufZs0NmYZJtTEU7WlQfKdPH8eT24zWrOMycZk+70oT02EY0o1/ACAzWbDjh074HA48Pbbb6OjowOnTp3CzJkzUVBQgMsvvxzZ2dmRLzSN0RqXF+0knHpZ8JtIAgO9OPHC/fD3HAPYFFiv+zGM51w+ZveTo1Z7reYuKWZJiVUSAMsTy0EnrBGS5SEdKSlAhcwEMfGkpqbC5/PB7XbDYolc5Ol2BwOHGTNmjOq+001rh+KcBCdnbaUNr773ieSzPo9f9Vh5E1AiGI7y1+CH/XC8+gt4W94FAGRc8S1kXH4zGNmIeZYJOpXEQ5ZJH3VQL/B0nR0cD7zU1CX5fO/RnlCBVTSaNF76NdmCRoIgkovpprXA+G8ixqIH4Sa6vnzoeFgHZTV4nkdf3VacfOdFAICxZCGyv/KfYHT6mK4jII85Y9Ugiu0IgpiuTEe9jUR8EZ6USMZIctL0LIyGFGQaU5CXZcS7dic6ZNe4YE5GxBxsab4ZG2tasa2pEy3dbrgP16Ln778GuAByi84FvvQAdMaM0PHbGjuxrakLvbLYNFyBbHWdXbEOrVxutHoc4HhF806wsDk4eUEo3pW/j6htDlNxb/xQoTRBjA2ktcmHM0zeOTjZwACOH9Ex8X6mxSQ1X3yz5h10v/AguMFTmJlpxu633sITh4YAkVa2dLtR/uguVFXYsGaRUgvVmnHkz2R5g9KeZkewEem0/kaKaceyWCZZ4mkqzCaI6QtpbeJwun1wun3YsLMZi88Ov+8umCh7fAHsaXZg7dZGXGqzKCbVCi78AB9qGnK6/aF/7z4S1ExBR+S62O904NmHqvDRRx9Br9fjhRdewPXXXx/T76UWm0bSLi19o71PgiCmI6S10SE20RcQtDJWth/swpvfW4RtTV1hp9Mmgmjro8ToWQbDssYef4CH0aCDP6D++15w1izoU3RhG34KLEaJ9gZk9VjCzwGOR0D2x5bnzyPlNqfTNPnJ9ntRnpoIx5Rr+BGwWq247rrrJnoZUx61aUDxCHU0DJ904MTz92O49xMwKQZYv3Yf0ooWjMm9RgsDwGwy4LYrCvGdxUWSIFdZLCYVYHpIEwQRLTabDe+99x5ee+013HXXXRGPf+2110LnJQLS2uixmAx49b1PFONq45h+O6Zw/kE4Xn4Ug+2HAACZlbch45Ib1I/lgcHh+H6B5fNzUV1nD7vBLKdnwBeaiCdHvkmbDAnn0QSNybB+giCSg+mktclSJKOGsthIjFILw5ll8DyP3reqcarxVQCA6bwlsHzpe2BYXczrMhp0uPvKYqxZJI05J1vikiAIYqKZTno7HnAxGkN4/Ry8fh+CBVA9iu+XlFhDU121NnmXlFgB8Fj/RnDqwMAHu+F84/cAz8FqOw+H3q3Fj/7WItn0lDfsFueYsKI0L2yBrFoTsFYuV20ij3B9uW4rN3qlf0MhNlR7HynOSUeBxUjFvaOECqUJYmwhrY0NBkCWUQ+XhlnUWDFS6HwkNGlO/r3A0PHDOLHtp+CH3NClmzH3ll/iiUNDqg264msKRUT1bS5wPK/qQrxx5YLQv8sKzOB4KPLCsTTtqBXLTLX8azLnVAiCGB9IaxNLQwST4+GANP/7wfF+hY4ImrjrcDeKraYw9xrRNLH2Dp904NH/uBMnT3QiNTUVL7/8MpYtWxbT7wEkNldMeWeCIKYzpLXhUTPRF7BlG9Hp8sIf7iARPM9BxzJYPj9Xs0YoXhiMZF5jqY8So/V7hKuZbmjvwyW28DXBbT1uVG1pCMWonS5pzlj4WW5KJeTPn9lnx/aDXQCYkJGHVpyrpulTdbLMZPu9KE9NhGPKNvwQY0uansVdS86GjgU27GzWPC6FjX6iQDgC7j589tefINB/Aox+BqzLH0Ja/oWjv3CUyMfwCdiyjbD3KF0rzSY9qioK0dTRi+o6uyRRLE8sL5+fC5ZhIj6kY0k+T7VENUEQ6nzpS1/CoUOH8OCDD+Lyyy/HxRdfrHnsv//9bzz00ENgGAbXXnvtOK5y+mLLNqHf64PT7Q9tlo4WPctEHQTHCs8F0P3Swxg69j4AwPyF72Dm/MT/t2IxGfBumyvqyYHRIDhYCFoXKeE8Hjo5mqCREuYEQRDji1wXVi8sxOb9bWhod6E0PwscHywC/qRvUJKsZRngeK83pnv17nkm1OyTfuE1MF9zFxiGjWvdHl8ALMNINCw4JUA6DS/ZE5cEQRDE1EIrF2wx6ZFlNKDX41eNj7UMIViGCeny8vlz8G6bC3tlG5rVq8qwdmsjAGDgoz1wvv5bAMCM3M/hvj/8D7ItZlSvKgvpfbvTo2gcKrCYIuqlPK8rn7InRm0ijxDnie8jP644Jx3L58+R5NzFOWN509OK0lzS+QRAhdIEQSQTPJDwZp80PRtTPnZbUxeWz89VbTYd+vRjnHjhQfD+QehmWTH75scwkHZGxGl8guYJeqhGWYFZ8UwOcDzq25wxuRWLUSuWUTbmdmFFaS7tpxIEQRAAAE8EzUxhWfhEDv4XzMlAaX6WpmmUfIqtGLGmCdcQ10cZjUa89tpruPLKK2P8LYIkssh1shXMEgRBEMnBXIsJDIBWlRpbNXgwqNrSAH9AW4/VJgpFd+3T/+8Y1keJm4oEPP7gVECWAfLNaZhrSUfNUWkMbe/xwN7jCb1PyPePhZ/letzp8uBLT9Si1z2SdxdMN2KZhDtVJ8tMtt+L8tREOKjhZxqRlaZDlmmGaoNKrHj9HPQ6BlUVNrxrd2LvUaX7IpCYZh8AYI2zkJZ/IdxH9iFnxU+Rmvu5xFw4ShYWZ0s2kgUKLEbkW0x4t80lKfxyuv2hTVl5oa5aYll4UIcjluLf8SgUpqYigph4vv/97+NPf/oT+vr6cPnll2PNmjVYsWIFPve5z2HmzJkYGBjARx99hJdeegmbNm3C4OAgzGYz7rnnnole+rRAaPZJJPkWE1ocYzOylmF1MJVcjqHOD2FZ+v+Q/vkvjsl9nG6fqqaaTXqcf1YGPjzeF5wcxAeD3mjY0+xAdZ1d1QFL+Fmsg+Ohk6MJGilhThAEMTZoxTCbau0hh6hdh7vxjt0Z0irtyT7BRHKsDazG4nIMHHod6Rdeg6yr1oQdCS9HxyinA25r6pTEYsEpAdJ3hWRPXBIEQRDjwww9iyKrCa0O+TSZ8eGC3Ez8ZXU5AhyPqi0NEYuCBdqdbuw+EtTjXYe7ce/SElxms6C+zQmODzYEVdfZQ4VRM+acC93MbOjNuVj+k9/hP774eWysaZUcn5eVFpdeauV11dCayCPEd8J7SbtT+r+HUGwsN4gS8sdCkfJkcvijPDJBEMTEMyczDXMyU/HhJyfh9XPgOR7eMJu4QZ3ksW7ZPPz6n83wiYJRvSUXhpxCBDx9mH3zo0iZlRPVGsoKzKrT8gBhgu3ZmrpWXmhBZ68XAI/l83Nj0j+1YhllY+6AamMuQRAEQagh7F/qGCA/24T5+VnY3nRc83g1c2EAKLKasHphoeiTYJwk1EcNt76Nf765EwsXLoxrnYJhoph2pwcba1ojxmVqcVwiC2YpTiQIgpj8aBnoyznmdMdUr2x3uGGPkMNWa/aJdj3A2NZHhVsBxwNtTi9uLJuLD4/3axpHB82vpBOOls/PBaDMO8sn2YuvoRXfqml6rJNlxFpemm8GwKOpozcmXR+P9wGamENMJSZlw8+zzz6b0OvdcsstCb1estLrDaDXG3uzT4E5FR2uQYUYbWvqRH2bCx8cP5mYBYaBYViYr7kLsy5ZDr15zpjfTwzLAE99qxRffqJW8fJRc7RHUXClhlhA4+3CjKX4dzwKhWn6AEFMPGazGTt27MC1116LgYEBPPnkk3jyySdVj+V5Hunp6XjllVdgNkdOvpHWjp5+73DCr7m8NBcsAzxdZ0fPwOgnBsmZOf9apOZfCL0lL+HX1kJwt3C5/aj9WL2BWIwt24iTg8PgeUiC3/o2J4Cg5smT1/KE83joZCxBozyIlbuAUaE2QUxNSGvHH60YJjjefIT6NvUCpESQOvfzOPPWJ5BinhNTsw8AFFrTFcXJLd3usE2vxTkmSlwSBDGtIb0dIctowD+/vxibau2orrMnZAptLHS6ghuPOpYJu2lXlG1Cr9cH12kDDXmDUlNHL6pXlQEYmRCw+0g37l06L9jQhDNwxrc2QGfKwmHHENZubVRtLlp8jhXH+2IrHI4lr6s1kUeI78TvJUBwso94soDWfRLh8DfehVWURyaIqQ1p7cSh1zGYmZoS0sxwtDjcmkVAWjyzrw13VNhwaWEWaltGYk1zRjqw4qfg/ENISZfmLc0mPViGwfmnJx28+t5xgGeQa04LNd+qEZxgC1U9qq6zS4qc6ttcWLOoKKyeRdK6SI25UwUqpiaIqQFp7cQQzeSAAB8sSv71P4/GdY9Whxub97eFTCGEPLVQH1VuWRN3sw8Q1FB5PBxtk6taHJfIglmKEwmCSCZIa+Mj2uaaYy6v4jOzMQWDw7zEYH+0yOuEIjER9VEC1XVt4Hnt9Y7orPB+MBLrivW43elGS7d6rB+uzqiqwgaOx+lr8+BOryWWvLOalov/Hc11xuN9gCbmEFOJSdnwc+utt8ZcGKMFwzDTRmTjpd01qPp5S7e2YCSCoc9a4O/pQPr5VwEIdtaOVbNPCstgWEP0Z+gYPPtOu2qncZTvLQkp1I3FLWM8RtGNZbE0JaAJInoWLVqE9957Dz/84Q/x2muvgeOUrnw6nQ5f/epX8fjjj8Nmiy7xRlqbfBRZTbj9ikJs3t+GTKM+IQ0/gYFeDHy4C7Mu+Xrof+/xDmYjSanRoJME+VrOHxwPSbGU2aQHeB5Zphl493ThtqAn46GTsQSN8iD23qXzsG7ZPHKYIIgpDmlt4okUR8hjGKFZ9JM+aaI5Ta9LWIKZH/aj/8CLmHXJcrD6VABBN+R44DQSv+JYTK5xK0rzKJYiCGJaQ3qrpL7NOS7NPoriKH7kf4fSfKlemU16XJSbgUts2RjmODz+pnaxVGl+FjbWtOLpOnvwsjyPUw078FTgSljPCOaOUzJmAwhOYdeaJHS8z4tdP1gc768XEa2JPKsXFkrWL1BgMY7ppqP4PSnA8aG/y3gUVtEUW4KY2pDWThz+AA+X2480PRvz9Nlo6BnwYf0bR2DLNsF9ZB906Wak5n4OQ8M82BkmsDNMinOE5qO9zQ4wAAosJonuAIDFZFB9F2lod6lOspPriDDtHYBmYVCkoqGRxtxOyV73VDNeomJqgpgakNZODBlpKej1xG/uaDbpo2rKFeKjB6r/hvfeqpHUR1196Xmh4+KpodGarCe+b7TnCscnqmCW4kSCIJIJ0tqxRa0m98K8LBxzeRI6jT5io26Y+qhoGn3DEWtcrhYTV56TjRQdK9F5lmFCMeuGnUfAMpDo8caaVkmdFCA1ltIieG2EjKo27GwGyzAxafFo3jO0rkHvAwQRnknZ8CMQrsuRiMySEitYhkGA59HRM4DPTg4FBZYH/BEUTHDjHw3hxugNHT+CE9v+C/yQB0zKDJjmXTHKu0Vei1bDzyU2S1iBErOkxAody6iOqRstsbhljMcourEslqYENEHEhs1mw44dO+BwOPD222+jo6MDp06dwsyZM1FQUIDLL78c2dnZcV2btDZ+tHQlHvQsA57jcM1v96LNqXS/iIfhkz048cL9GHYdBzfkQdbiVQm5bqI5KzNNMc1AoDjHhAKLCWUFZsU0BiGJ7vIMo9XhxltHRvQk2Ua2yt8zmjpcqF5VRtpHENME0trEESmOkMcw8mZRgdULC7B5fxucsg3ZWJO9nH8QjpcfxWD7IQx90oycFT8Fw7Cx/EoS7A43jHod0gw6SSJYHIslm8YRBEEkC6S3QdTcfccKuWZy4LGxphX1bU7Ye6QbuS63H//u6scltmzsOHhc9Xo6Bsg0GnDA7kTN0eB0WJ7n0ftWNU41vopT7+1E78pfQZc2K7oFqvw3IRRQ1be5wPE8WAYoL7SELaSKVHQlN4NQ24QFxr7AWD5RSMxYb6SOh+kGQRATD2ntxOH1c7BlGzWNkqJBzzKae8Mf7P0bel7/PRh9Ks741i+AnOjiTK13jiyTXrW4qazArBrXq03jUdu3FetZpKIhrcbcqRZDU/EUQUwtSGvHlqw0HbLSU3HS68cFczLA8Xwo9oyHi3Iz0RFFIXNZgRkHDhzAr+75FoYHR+qj9DoGHM8jwPHQsUxcNTRaE+2E72I5N9FxHMWJBEEkI6S14wfLJKL6OHoi1UeNtrwrESYcKTo2NNleQB7TCSZSQg46OKlnZArQ9RfPAcswofPC5bVHGy+O5j1D6xr0PkAQ4ZnUDT8ZGRlYsWIFvv3tb2POnLGZ/JL0xNFcnMIAs9JSUPexAzwPZKTpkWXUxyQ8DKO6JxoTWs0+g8c+QPf2h8H7vNDNssIwe+wTj1q/e5HVhKe+vQB3/k9j2PMtJj0+n5sJlkFYN43RTK6JZVLAeIyiG8tCMkpAE0R8WK1WXHfddQm9JmktMDM1BRaTHr0e/6iDvFhgANiyjWjt8cDP8bAnqNEHAPx9n6H7+fsx3H8CjH4GUvMvTNi1tTAb9cgyGWJ26Fg+PxcsA4ULMRCcWiDoA8fz2H1EPZgUEPQk2Ua2UhBLENMb0trEESmOkMcw8mbR7HQDbltYiJeaOhXNPkCMzT4+L7q3P4yhYx8AANKKykbV7CPg8Qfg8QdCZhPyWCzZNI4gCCJZIL0FPjs5qNnwMR6c6B8Ke3+n24/1bxyBxWRQ/T7AB50PR5p9OLj++RQG3nsDAJCad77qpAEgmOOVx6Icz6NqS4NES2/f0oC9suLk3UeCP69dXIQAx2NTbWtoA3X5/FxwPI/H32wGECy64njgzsroXZKz0w24o8I25gXG4QytxjoGpYZkgpgekNZOLJ+dHIr73HBOxKfeewOuN/8IADDkFCAl4wwAwbx1v9ePk4PDSGEA73D0AfP1F89BCsuivs0Jjg8WepUXBqfgfemJOsmx25q68Pp3K3DA7pTkhQXt0sqpyvOtAW6kYFrMVI+hKe9MEFML0tqx5aK5Zlxqs6C+zYkADzS090q+1+sYzEpNQabRgNysNEkzUGWJFV29XomB4SU2CxgGqvuiZqMeF8/NwoKCLBw59C7uXncHhgc9kvoof4CXuO2Hy33L65BWLyzE5v1tqG9zwmLSS3Ld0cagYx3HUZxIEEQyQlo7fpQXmjHM8Wh1jBhXpLDAcOKH145ZfVSiW5bk8ZoQx4oRJvECCNU+3VlZjDsriwFIzaYiNQiPNl4Ua3m8wxHofYAgYmNSNvxcd911eP3119Hf349nnnkGmzdvxpVXXolbbrkFN9xwA9LS0iZ6ieNHHKoxzAfd7gVcHj9cnsijZAVm6IChQOz3jYTZmILjH9XD8fKj4IeHkJJ5JmZ/41GkzMpJ/M2iwGIy4PqLc3Hn/zRir4pzh17HQK9jUV5oRlmBObSxK94ElpPIyTWxNg+NptlIjWiS4PHekxLQBDHxkNZKUSv6jYV4xr/yAD7tHxzVfdXwu47jxPP3I3CqB4whDTkrforU3PMinzhKXB4/sjQKt+RYTHpkmWZg+fxcrFlkC2mOmq6MEJ2+JIpE6ioFsSMk+n2FIJIZ0trEEymOUIthxM2id1TYcMDuVLgih5tOqwY35Eb3i/+FoU+OAGBgXno3Zl74xRh/myBaLs06llG4PBEEQRBKSG9FTLRBZdSv9ZEXynMBON/4A9wf7gIAzCz9CrKuugMMw6LynGzoWFYyoWf1wkIs+32tpNDK3uOBvceDXYe7ccDuRGevBy3d6gYVDe0uVFXYULWlQVJsvGGnskFp+8GusA0/8veVOyps41JkLL+vVvPwWDDVi6kJYrpDWpsc+APxV0XJC4gETja8it63ngYApOZfCOsND4I1pAa/ZJhQzlyeOS+2mpBnNmpO+GEZVqELAS7YiCuf9N7SPYDN+9tQvapMMy8s/mxkWp9T0vC7p9mB6jr7uGlRsuQ4Ke9MEFMD0trxQd5cKscf4OF0+0P6Z8s24tP+QRgNKSgvMGPTShs2729TPHOF+iExF+Zl4plby3DPr/6CP6z7Ttj6KKGxJ1zuW16HFO53iTYGHes4juJEgiCSCdLa8YVlgNULC7GtsVPy+Zg0+4xhfVQ86XazUY/P52WCAdDh8uDTPi+MhhTcdkWhJF4TYmQtPdcyz5c3CG9r6tSMS0cbLyZCy8fifSBZ4nGCGAsYfpLOonO5XPjrX/+KZ599Fo2NwekrDMPAZDJh+fLlWLlyJa688soJXuXYkpubi8/6B5F715aJXkpCGGp9F5/tWA8EhpFizsXsmx9FykzLRC9Lk3uXlgBgsP1gFz7p88LjG+mCuvrcnFDxldj58ZO+Qc3jBKIVHXFHLgCsWzZPsxhaxzKax48l8d6ThJcYL3JzcwEAXV1dE7yS5IS0NvjfSM/AEM74zl8meikJwedox4kXHgDn7gObmo6cGx/GjDPPGbf7m40pkqZjOXIHjFi0qmpLgyTRbTEZcGHuLPBgQi6RidSTidDVRJHMOjuZ/66EOqS14SGtTex/I4kwRbjksV3oGfBJjrOYDHC6fRpXkV3TexLdLz4E32ctAMMi+9ofwPS5yrh/pytLstHh8iqcILWej8n8jCcIYmwgrY0M6e3E5pEZAAXZRvRHaTplNurDHscHhjH4r9+j+997AACzLlmOzMW3gmGCenfv0nmhhhuxLsonxsbCumXzAEB1QpHRoJPke4tzTNj1g0rNa02UVifbO0KyrYcgIkF6Gx7S2sm9Z6tmctH/zovoq30WQHBirfVr68CkRGfmdNW8HGy6ZUHoOd/u9EgaeYpz0vHm9xZJnvvynKAYtb1UOcG9WDuq6+ya8Xs010kUlOMkiNghrQ0PaW3ya+26ZfOwemEh1m5txAfH+3HBnAz86VulePaddjxdZ5fknK+al4OvWT7D125YDm7YH7Y+KlIdEKDcI81ON0juV5yTjgKLUVLMS/EYQUw/SGvDQ1o7vlprNurR7/UjBr/FmBmP+ih5bjgclSVWPLOqTFNzY8lla8WY4WLrsY5LkyXfK/8b3Lt0HlgGE74uYnow1lo7aRt+xDQ3N2PLli3461//imPHjgEICu6cOXPw7W9/GytXrsS55547watMPMke0MaC+8g+9Lz2OMAFoLcWYPZNj0BnypzoZWmypMSK8kILNuyMLJDhhLQ4Jx0rSnNDQqLWnasltvKgXUhUayWRtY6PhViFORH3JIixhALa6JnOWnvi5CDm/Mfk11rfiVaceOFBcN6TYNNmYfbNj8CQM76OfrEEu8DIWHlh9LzcWUKsSRwPiS6PdbA6mTUumTecJ/PflVCHtDZ6prPWAsnz38jqzfWKBGpqCoPB4cipk4C7DydeeAB+RzvApiD7qz+CqWRhTPcvzDaiTTTR50fXlODQsV5FU+s7666CIYVVnJ/Mz3iCIMaGZHuOJjvTWW+nQh6ZD/jheHUDvB8fAABkLPwmMhZ+I9TsA0hjCLkuClNtYmn+WVJiRfWqMqzd2ijRYwGb1QS7qDH33qUluLOyOK7fbzpB7yzEZIP0NnpIa5NHa9P0LMADg8NcTM7DfXX/g/63nwcAGM+5HNlf/REYnT7q85eUWLF5dXnoZ7V90nuXloBlmFBut77NqToBAYhc6BzJ+Vh8TzWNHouiJMpxEkTskNZGD2lt8mitmKvPzVHEmoImyrXwi2lt2PzI9zE8PKxZH2UxGZBl0mP5/FysWVQUVpvUYt9wNUdq8VhVhS0pinQJghg7SGujh7Q2ObU2FsaqPkrHIKomJTVTqx9dcw5SWFZTa8PVGAOAUc8Cp82ON61coLpPLI5v5eYbYx2XJku+Vx6PF+ekS/4OlIcmxpKx1lrl/9VPQkpKSvDYY4+hvb0du3fvxi233AKTyYSuri5s2LAB559/PsrKyvDkk0/C6XRO9HIJFVh9KgAGhjPOxuxvrE/aZh+LyYB7l85D9aoyNHW4FN9npxtCwbCAfFQeECx4BoKj6Ne/cQTVdXYAwKZauyIhrXY+IB3RC4yM8tvWJB13uK2pEwGOVxwv/zkahFHAuw53S9atRSLuSRBEcjCdtXbyt0YHYVIMAMNCl27G7G/+YtybfQAomn3MRj1+dE0JstPV3SF7BnxY/8YRrN3aqNAfuSYBPNYtm4erz81RaPFYMJk1Tv5uofWuMRFM5r8rQYyW6ay1E0WA47GxphVVWxqwsaY1+PPKBbCYpLoUTbMPADC6FDCsDtDpYb3h/pibfSrPyUZellHymZD0FeN0+7B5f5vqNZL5GU8QBJEMkN4mJwZdlEVEDBuaLpBZeSsyr/impNkHCOq78P9ta5JuqnS6PCjNN4NlGCwpseKqeVYsKbFKjjEb9bCYDCi2pofywDqWUY1N5M0+S0qsWLMouTcL1d5/JgJ6ZyGIqQtpbfLg9XPwxtjsAwCMIRUAYPzcYmRfd2/EZp80vbTcQKhXEjSnvs0Fi0l6je0HuyS5XbkcmU16XFmSLcnzau1RVtcp91Y1fjPVT2Pd+4yGWHKcyaLNBEFMHkhrk5PSfDM+ON4v+Uz4uarChnuXzkNxTjqKc0wwpKaBYRgsWLAAv3jmJVyz4Bzcu3Qe7l1agqvPzcGSEiucbh9aut3YsLM5ojbJr7+gIAv3LtXeM1WLx8ZCDwmCICYrpLWTn7Gqj8pIi84Mw6tihLzj4PGwWhspP+rxc/D4Atjb7NDcJ9axDNYuLkL1qjKsKM2VfDfWtTfJku9V/p7SGJvy0MRkZkpM+FHD6/Vi+/btePbZZ/HWW2+B4zgwDAO9Xo8jR46goKBgopc4asayq5aB/FE39ng7/o0ZZxSDnWFK+LUtJj0y0gyw97gjHywihWWg1zEwGlJw2xWF+M7iEecMta5atQ5QtePknaNCB+3Vv6mRfK51TSD2MX6JcOSI1ZFKWGN9mwscz4NlgPJCC7mBEEkDOViMDtLayYfP0Q4mxQB91lkTvZQQ65bNA4CwThXy0fNXn5sDABPqkpgs42jjIVmcNdSYzH9XQh3S2tExXbQWGL//RkZiFCfanR60iop0hbjp9r/UY+/Rnviu7z0Jf88xpOadH9N5i8/JxpbbLsGCR/4l0bzsdAPeve9qXPO72qhcmJL5GU8QxNhAWjt6poveJnNsK3cfDgcfGIa37SCMxeWaxyw+JxsNbb3w+MNPmI0lZ+ob5nDZ+t1wuqU6LY9VteLSZIl1kuVdIVnWQRDRQno7OkhrkweWAWaksADPY5gD/BrNJZ6P30Va0YKgqUUEKkus2CvSccG1eFtTl2LPU0C+T3rVvBxwvHSv896l88AyEE0BcmH3EWU++Pa/NEg+18Ji0uPzuZlgGAa8aM9SPl3o6nNzsHHlglHpdiy6T5pIEEFIa0cHae34I3fut2Ub0dnrhV9k+5+WwsI4Q4csowG5ZqNEL6+f3Yuf3vYVZGRkKK4dz6S4WPRE7diGdhdNpyOIKQ5p7eggrZ18jEV9lF7HSLQ+FuTvDlfNs+KZW7Wn4y4psYJlgEPHeuHyDEuuFY1Oj0U+Otw1kyW2la+R43ls2Nk84esipgdjrbUpY3LVJCAtLQ3f/va38e1vfxuNjY34xje+gdbWVvj9fvh8vsgXmOaMR7OP5+g7SLMtAJMS7HxNy79wzO7ldPvhdPsjHyhjmOMxzPHw+n34454WNLS7oDudAF69sBAcH3ShAngsn5+rOk2gqsIGjudPH8dg+fzg/1Fv2DkicKHOUln/ndmk15xQIHTkrl1chKotDZLvjAadZIpCQ7srdOxoBKuswCwJ8iN1/gprBEYKuYWkOY0EJojJD2lt8jN47H3oLXnQmbIAAAZrwZjfkwGQmaZDrzd8YZXAtqZOvP7dRRKtzM1KkyS9L5iTIdnsFfQnFk1KNGIdnmwI7xZiDU4WJvPflSDGAtLa0SM3ITjmkjb5iKlvCzr6xNLs43cdB+fzYsYZxQAAXdos6GJs9gGA8sKgjsk1j+eDbsPL5+dKYsi2Hjd8w5xiXHsyP+MJgiCSFdLbiSUrLQVlBWa839UvaaYBAKNeBwPnxacff4A0WymA4ES9cM0+AFATpZY3tLui1srN+9sU6zv/rFmS94Z2pyfo0K+S5xTckoGRWDZS3DMWm7JqTosTEX/ROwtBTC9Ia5MHjg9O/hHDcwF4jr4DY8nC0OQ849mXRLxWdroBd1TY4A9wklzu9qbjqgaMRVYTgpcP5n/FDT/lhWaFRr3U1BmK33cd7kalbCqfkA/movQ2dbr9igbj3Ucciml/ZQVmVd2OZV8zlhznaLU5WZqKCYKYWEhrxw+jQYd7rjobLzYekxTt2ns8imO9w8Fpe063H+/v3yWpj/qAz8UPXzmq+uyW1+W0O92hWBOA6nM/Fj3Riscmct+VIAgi2SGtTX7Goz5K3uyj1zEIcLxiaq0askH1inPU9Lm6zi4xqBCIRqfHovYmXI47WfK98t87wPFgGWbC10UQiWDKNvwMDg7i5ZdfxrPPPovdu3eD44LJy5SUFBgMhgle3eQkkVN/+g9sQ1/NFqSdcxmsX70XjC75/1MURuIBwQQwx/O4s7IYd1aGn75TVmDGmkVFuLOyWPK92JVKEJLcrDS0iIrPPj8n6OaxsaY1bKJWHvBfUmhWLYweLfEKs9bIvlg3uQmCSC6mjdZO0r0xT8u7cLyyHnpzLmZ/4zHo0maNy315IOpmHwBo6XbjmX1tYBkGBRYTSvPN4HgOXS4Pek8nyo853ag8Jxs6lkV5oVR/KCiLHWqqIYjJw7TR2gQhjsNK87MAMNh+UNtRWM57nb1o64nuWADwOTrQ/cID4APDmP3N9aNKHB861gcA2LhyAdZubcS7bS54fAE43T6sf+MI7l1agiKrKVTs1OpwY+3WRmxeLS14pmc8QRBE7JDeTiy93mH88s1m6FUKU60GHz565j44jx2F9Ws/gfGcyxN6b62C3rWLixT5XaExWEx5oQWXFWVjW1MnWrrdaOkeCF1LrsXy/Oi2ps6IBbnxNAlFIhpDJ+k7lRkAj6aO3oQWEdM7C0FML0hrkxc+MIyef/wGnsO1yFj4TWRe8c2oz739ikJUVdhQ/uguyedqzT4AMNdsDO1dtnQPwGIyIMuklxgqijWqzyMtmut0uUMTCMT5YLksFVtNABPMO0cDyzCK667d2ig5Ziz3NWM1W5STiPcFahoiiMkPae34MeQP4IDdGVPxlFp9VDCGdGPX4e5Q7ZGAoHHCtLyWbrfEMV/tuR+LngjxmFBIvHZrI0rzzbh3aYkk9hsLSHMIgpiskNYmNxNVHxXLtJ8so0EysICVdQCp5Uvr25yK6xRb06PW6UTprnCdp+vsks/FDcbJmu9N1nUB9F5ExE7yd1nEyJ49e7B161Zs374dAwMD4E+7+lx88cW45ZZb8M1vfhNWqzXCVQg1EtHsw/M8+vc9h/63nxd/mIArjz/bD3ZJgm5g5CEsHlOvllzVEhIdyyp+jiZRK2/EWb2wEJv3tyW8CDpeAVRLLiSLmyRBELEz7bR2EsqU+8g+9Lz2OMCdbrzhuPAnjAGpKQwGh6P744mLscV6IeB0+9Da41GMVpUno8cyAIo20ApwPDbVtkom+61ZREEZQRCxMe20NkGoxU6xEMtkWN+JVpx44UFw3pNg02aNWmsPHevDU3tbsGZRETavLkfVlgbJ79DU0Yt+r3RtHxzvH9U9CYIgpjvTTm+TPCTxyywNA+5eHPjzg/A52gFWF/rfJx7MJj0uzsvEggIzAAZNHeELetcuLlK8V8jd/wHg5UPHUWAxQv7HVctzyvOjLd1uXPO7WqwozdWML2PNn0YTt0Zj6KT1TkWmTQRBxMq009pJBj/sh+NvG+D9+MDpD2LVWgbVdXbFBDzlUUCh1aT43On2wen24YldR/Gbfx3FrFQ9Fp9jRQobbKrd1tgpidH7PMOqGregwCxxPF5emoumjt6oG37KC82Kvc949jXjLdQZrQtyIvZbx6LJmCCI8YG0dvwJ8MCeZgdsKtomR14fZU2fgStLsvH+px6Jfsprj4S6nIZ2l8TMqr7NiQ6XdJKQ8NyPR0/kz/91y+ahelVZxPNGw2g0h4piCYKYCEhrk59kqI+KROU52bjElo0NO0caeMsLwxsxlRWYodZPtGJBblj9E18jwPEh443RxHpi/RZDEwFHB8XiRKxMiYaf5uZmPPvss3juuefQ2dkZEtY5c+bgm9/8Jm655Racd955E7xKgud59O3djJP1LwMAjOcuRvaXvz8ppvuo0ev2I8DxEgHVErdok6vlhWbsPtIt+TmaRK1aI04ydabSSGCCmPyQ1k4eBj7aA+c/fgvwHAxnnI2cGx+GLm3muK+DYWKZDRjdcWoaOF4BULT3qa6zY8PO5tDPG3YeActQUEYQRGRIa5XEuoEnj53CUZRtxCf9g/D6Y0/6Dn3SjO4XHwI35IYu3Yycmx6BIXtuzNcR43T7sGFnM6rr2lBVYcPFczMl8VJpfpYkKQsAF5yeCEsQBEFEz7TW20lkZjF8qgcnnn8Aw64uQKeH9fr7YCyKr+jIlm3Ezu8thiElaLTkG+ZQ3+bEptpWvNjYqTg+wPGo2tKAdqe0iOr9rj7JtD0Ap52WldMBAxyvyBtXVdgkJlHC+evfOIJtTZ1YUZqneNfRcmjWekeKJm6NxtAp3DsVmTYRBBGJaa21kwjOPwTHjscw2NYEAMhcfCsyLv16TNdobHeezgErMZv0cJ1u1uEB2B1uzd5j72nTKKfbh5qjjpDpE8dDUgzldPuw63C3yiQE6ZXfbXPhkkKLqhGIxaRHptGAPo8PmUYDvn5af+XEs68Zb556tG7Do50QBCSmaYggiPGDtDY5+Kx/EBaTQbPxVV4fteDKr6Du9ZeQOsOAq39TIztPXSXlz/g9zQ7IvDLQ7nRjY01r0NAigp5EmmQ7Hs//0WgOFcUSBDFekNZOHsa7PkqvY2Ka6iPQ2esF2pwotqYD4JGblRaa3iPOCcu1rkjWYFxkNYXi02hyxHLi1Xq5fmenG3BHhS1sgzE16kaGYnEiViZnpwUAp9OJ//3f/8Wzzz6LpqZgMpLneZhMJlx//fW45ZZbcNVVV2kmGonxhec5uP61EQOH/gEAMF3wBViW/j8wrG7c1pCmZ+Mq5tLC6fYFJwqIHrJam6GRkquCwNW3ObGkxAqWYVBeOHUaY9SS1aN1rCIIYuwhrZ18nHpvJ1xv/hEAjxlzPoecFf8FdkZkh6mxIJLmGvUsLi/ORlmBGRzPS5pktGh3ekJJayEQHK8AKNr7qL0LUFBGEIQWpLXhiXUDT74BKqbYakKe2QiWAebnm7G9qTOu+HCw80N0v/Qz8D4vdLOsmH3zo9BnnRXzdbQINv4cQaViigCDjSsXYO3WRnxwvB8XzMnAxpULEnZfgiCIqQzp7eRiuP8ETjx/P4b7PgOTMgPW5Q8ireCimK9TYE5FoXUmWIbB5v1toThy7dbGUAOteGqA8K4gbq4VI0wCXFJihY5lcOhYn6RAy6jXweMPukjuaXYo8sY6lsGK0lzVzdaWbnfo82jyp1rvSImKj8O9U03G3DRBEGMPae3kgvN50b395xg69j4AIOvqtZhV+pXQ97ZsIwDA3uNRPT90HR64tFBdM1wq03P7PD6sWzZP0QArR9CvNYtsYBmoHi+ehNDUIdW/vc0OXFJoDt6rsQstjpFzz5+TiZqj0vcAtYKjePY1J6pQJxH7rYloGiIIYmwhrZ140lJYeIdH8skeXwAeXzAGLLaaMCcrDftbnBjmeEV91OrVq1H6rZ/g2j++A4DHnEyjRNuWz89VvafwTN9UG5yoJ2/2AbTjSTUiTbIN9/xPVNHuaDSHimIJghhLSGsnHxNRHxVPsw8AtDrcUiOp0/8WptVWVdhQXWfH03V2yXmf9nkV11q7tfF0ndWISUa4HLEYLd2NpPNy/b7jdKNxOKhRNzIUixOxMikbfr72ta9h586d8Pv94HkeLMuisrISt9xyC5YvXw6TaWIKWwl1eC4A584n4f7gXwCAmfO/jKyr14Jh2HFdh7yYK5a5A0CwQ7bP45NsBMsDyNL8LEViu8hqCjljiDdlxQIp76wV3KvE5ySyMSYZOmhH61hFEMTYQlo7+TjZ+Df07t4EAEjN/zysNzwI1pA2wavS5q4ri/GdxcWorrOjsb0XS0qs+OB4P3oGRgqmLCYDsowG9HqG4HT7Q+7HQDAQFFyTxYxFABTLfdQKoygoIwhCDdLayMS6gSeOnUrzs8DzwMuHjgPgsXx+Lm6/woZn9rXhD299HNqMjQVv+3twbP85+OEhpGSegdk3P4aUjJyYrxMNHx7vl/zc1OHCnZVF2Ly6fEzuRxAEMVUhvZ1c+F3HceL5BxA45QBjSEPO1/8LqXnnK44zG/XITNOj3eVRLXgCgO5TPrS7ghumu490Y1tTF1aU5uL9rj71ExhGNT8qN5FiGaB6VRmu/s1eScPP0LD03ULtvUV4V9nW1ImWbjfkyM/Ryp9qvSMlaoNQ/E518dwsNLS78OHphuPVCwvjuiZBEFMX0trJBTfkRve2n2Lo+GEADMzX3IWZFy2VHHNycBgX5mZGbPhhAHA8j+IcE3ge6HX74PIoG30EPp+bibWLi1BVYUPVlgbNJtvS/KB+CTrY0O5SaRBiQnud7U6lpjZ19KJ6VZnCaEoeawcbh4I6G2nvNNK+5kQV6iRiv5VMGgkiuSGtTQ7OykxFr9ev2tTae1r/hjleUR+16LpvoWzlOmx482jo+JZud8hMQv7cVdMjefGvGptqW1Hf5kJ5oRmrFxZi8/6203lyMwAeTR29Cs1kmWBtUjTPf3nRLsfzYBkm5pqj0WgOFcUSBDFWkNZOPiZbfVQ4hFyvmlGUR1bvLDQO7TrcjeKcdMV11HLEQOSJPJGac+LRb2rUjQzF4kSsTMqGn7/97W8AgIyMDHz961/Ht7/9beTl5QEATpw4EfP1bDb6P5SxhPd54fs0mEydVX4DMitXJ0W3M8MAfBQdP8U56VhRmqvamCMUAI8ErsrfSxDa3UdGhFQskAfsTnwgSzCLBW4sGmPkIn3A7kT1qjIam0cQRAjS2skFz3MYPO3ImGorhfVr94HVzxjz+6awwLBsOIKOASKZWhTnpIeafcS6uqTEKtnoXbMo6ApRtaVBEpBua+pEfZsL7U6pC8aSEqtqABRPo6v4nADHS9aldR8gGJBxPI/tB7sAMFg+P5eCMoIgVCGtjUysG3jy2GljTWuoKGjDzmZU19klBg6xMnT8cLDZx5yL2Tc/gpSZ2XFfS4yaGcUFczIk2kOblwRBEPFBeju58Ds7ERhwgp1hQs6ND2PGWSWqx7k8/rAFxQAwJAtWBQMJvWYsyKtuhg7LOoqEH5fPz5UUEMvj4LICMwIcj021rXipqRO9Hj8YMMgy6nH9/DlgGRbbD0onFihzzepovSMlaoNQ/E61saYVe0+/k+xpdmDz/jbamCUIQgJp7eRi+JQTfmcnwLCwfPn7SD9vieKY88+ahfJCs2RfUw0eCDu1PcuYApZh4fUHUF5oxsaVC0I5V/m+qPLKI6jp8/L5uYrcspi3W51YvbkejOxaXr/c/GPk+9G6D0/mQh0yaSSI5Ia0NjnIzUpDv3dY9Tun2xcyhJDXRxV89W48s79dcQ7LMKheVab4XK2W5/w5GaG4TAun24/dR7qx+0jwHCG3rDW9FQDKCy1RP//lRbvBeNYtuUc01xmN5kxmrSUIIrkhrZ1cTFR9VDjMxhQM+vnQBHgAMJv0yDIaJDVNalw8NzPsVB5t1E2LqypskncBIDiZ8IDdidULC1Vzz5Gac+LRb2rUjQzF4kSsMDwfTctDcsGybMIaRhiGwfCwelCW7OTm5uKz/kHk3rVlopcSkcBAL9xH6jCz9CtJ0ewTDbZsIxiGQb/XjwvmZGDjygXQsYzCdWpJiTUUiF/zu9qwY+ivPjfo/hwuqAakE37GAnnhNDDye1DTDzGdyM0Njsfu6uqa4JUkH6S1QSaT1vLDfpxs2IFZZdeDSdGPyz2NBp1iQoJex0QcY7ukxIqNKxfgS0/USlyNLSY9Pp+bCZZhUF44kqgN5/go5upzc1ST4xtrWjUn6WkhPyea+xAEoYS0VhvS2iDi/0bkDZpiJ8J4JpOqxT2jged5nGr8G0yfWwydKTOua5iNes0CZYvJgCyT4fQ0otH97gRBTB9Ia8NDehtkMsW27sN10JvPgmH26HKjavGqGHnDbZHViK+XzsW7bc6whVRXzcvBM7eWhd5bnq6zSybVit0SwxUir1s2T3XCQTTx6nhOb5e/T1E8TExXSG+1Ia0NMpm0dujTjzF8shumkoWq32cZU3BRXhY6XB7YVQqTjHod7lxShPeO9UVsChJMFYX4XmvKneT6Bh3OykzF8vm5WLMoqImbau2nDZb40OdrtzZKNKo4x4Ret18yga/IapIUV1Wek429R3tCP//omnOQwrJoaHeh3emR7PFGo3njqcmJYjKumZgekNZqQ1obJBm0Vs8y8GuNmRURTX3UkhIrNq8ulxhF9HmG4fUHFLHs4nOy8UFXvyKvrLUnm51ukMSpYopz0lFgMcasAfJ90+Kc9GmhmwQxlSCt1Ya0NkgyaG20TER9lBZGvU7S6CPGZjUBPB92gu7ic6y4vMiimUcWpgLKjYrvXVqiOW1P0NxNtXZJjCy8f8iJVFM1WpNl0n1iujDWWjspJ/wAwUIbInnh/EMIDDihzzoLAKBLz8KsBV+d4FVpo9cxyEjTIyNNj3yzEZfYLHi71YmaoyPuhWu2NuIvq8sVwrOn2YGqLQ3o7PVETFILnapaRWdGgw53X3n2mDtRqLlh7Wl2oLrOTh2jBEGEIK1Nbnieh7+nAwZrAQCASdEj47Ibx3UNHl8AaSksvCLnZK1mn8JsE9p6gjq5p9mBO55tVOim0+3HnmaHJHjcWNMqCVotJoMkIBWj5QgRz6jYcA4a5DxBEESiIK2VouWmG2+Mohb3xIrP0T6itQyDWWXXjep64fYLLp6bKdkUJUcfgiCIxEB6m9z4ezqRYj4LDKsDAyD7gsWSGDNewjX7AMFmnyUlVnT2etHSPYBWhwcbdh7BvUtLcJnNgl/9s1k1vi0vHIkHOV45Qf6OCltIv8PFlUJcKs81RxOvjqfzXyKcGGlzlyCmPqS1yc3wqR4wKTOgS5sJAJhx5tmYcebZmsf3eobDmi95/AH8/l9HYUhhI95bmLAndxcOh8cXQEu3Gxt2NoPjeTS29+KD4/0hc0bhvnKNWlGah6fr7JJr9Xv9WLdsnqaxCMdDs6hKrnlqejbaqUATwWRcM0EQpLUTgdpUdq1mn3jqozqcblRtaVAU76pRc7QHFpNB8bmeZeEPKGPfcBOBVpTmJmS6DscDG3aOaGg0sSJpEEEQyQxpbXKTDPVRWpyZOQOtDvWGHrvDjSKrKez5De0uPH3LAhywO/HB8X6cPycDZQVZOHSsD6X5ZgA8mjp6UV5oRnmhBU0d0hyrmpYKn8tjZK1pu5Gm6IXTcHGsLF6vcB3SeoJIHJOy4Wfz5s0TvQQiDJzPi+7tP4ffeQxnfHMD9OY5E72kiPgDPHoGfOgZ8OHGBXmoqrDhV/+UjqGvbwtu0Go1y4RD7Owo0NDuUgTvHl8ALIMx3/BUG90nrIlEliAIgLQ22eF5Dq5/bcTA+28i54YHkWYrnbC1hCvEMhp0uLzIgrICMzbVSgPJfR9ra6dYj+QFUlkaDT9LSqyaDbPxFCjJzxFcM8ZzRDwVRRHE1Ia0Vkk8DZrhqKqw4fE3mzEcheuiGqfe2wnXm39EZuVqZFxyQ9zrEON0q0/3AaihlCAIYiwgvU1uBjs/RPdLP0NacTmyv/wD6PUpGI5yc91o0OHMjFSJaz8AsEywEScaPjjej0yj1AGyqaMX1avK8PvdH0sKp3QM8OOl80LxYHWdXVLcBEjj0gDHIxBmIYLuy2PP0vwsbKxpTZo4MNJmbzRQURdBTG1Ia5Ob4f5unHj+PrCpMzH75kfAzghfaBT1dXlg2B99g65WUZEAywCpeuV0vmfq7HB5gu7Ye5odWPNsA565tRzVdXbUtzmxpMQqmRYv3/u8YE6GoklW/HPVlgbJ/YpzTCiwmKIqcNrW1IlgOfgIDe2uUCNQsmi5nETnXgiCGHtIaycGHRPUu0jEWx9l7/GEdfuXo7Y3embGDLSqXKOsIAuX2SyahbdqhNuTVPtOQJjAx/HBGDic5pEGEQSRrJDWJjfJVB8lZ0mJFaX5WfjVP49qHtPv1d4bBgDwPDbvbwvFsnubHbjMZkH1qrLTk3eCNcy7Dndj3bJ5MU1fv2BOhiJGjodwGq6W+xX/m7SeIBLHpGz4WbVq1UQvgdCAG3Kje9vPMHT8/wAwGPr06KRo+BEjCJTcwTFNH3SM0mqWCccdsm5VIZkc4Hhc87sayYSD8QhqdSyD6lVlqNrSIPk9qMCMIAgB0trkhecCcO58Eu4P/gUA8LYfSqqAVswlhWZUrypDgOMVjbQag4AA4HTyOYi8+Gn5/FywDPCu3YkOlwf9Xn/I3VEtiSwUWVlMBnj9AZQXZGH1wsKIa1crahrvjVkqiiKIqQ1prZJYGzSFjcb6Nhc4ngfLAAtOuwvuONiFXo8/bKFtOE42/g29uzcBAAbbGjGr7DowrC6ua8kRCoii3WwlCIIg4of0Nnnxtr8Hx8s/B+8fgu+TZnDek/CzWRHPMxv1cHn88PgCimYfIPpmHwAhAygxQjx6RkYq7KLr52ebJPGY2vQeHcuE4sbqOrtm/njxOVZwPI+qLQ0ozc/CvUvnhZwZxZMGdh3uBscHi6AnKjZNxDShWIq6yPiCICYfpLXJi7/3E5z43/sROOUAY+iH33UcM848Z0LWMis1RaK5eh0j2YflePXpfKeGpJ/Vt/dKcqZAsMBK0Jo/fasU//Fck2QiUDjUpgRpaZRcz+TT64XrJXtONxHT+wiCGF9IayeGmakp6PUOhz1mLOujiqwm1ZhXTK7ZiOvn5+H3u49KdHXz/nbcUWHT3DtVI5x+aX3HMsFpfgCwYWczWEZ9yoAAaRBBEMkKaW3ykuz1UR8c70d7z4DksxQWEPsmy5tu5MyeNUMRb25r6kJVhU3l886Y8qYbVy7A2q2NEWPkSHFsOA2PZsq9GMr/EkT8TMqGHyI5CXhPofvFh+D77GOAYWH58veRft6SiV5WzJTmZ512ZZJy2xXB4mChWUYQnkPH+iRuGnodg+9ffQ4YBorCLTXBWlGaJ0lMj1dQK/89qMCMIAgi+eEDw+j5x2/hOVwDAJhVdj0yl9w2watSwjLAorOzg9pyehy9vJE2PCPHhmu8eet0ULyn2YHN+9tUR8bKp+ntPdojOVaLRBQ1jZbROl1RoEwQxGQjFgf5AMcrDAwAYPeR6I0ZtOg/8BL6av4CAEgtLIX1+vsS1uwDhC8gIgiCIIjpgKe1AY4djwEBP1LMuZh98yPQmSI3+wDAYJgps2Ky0w3geV4xXc9s0uPU4LBmjMrxHDbWtKLfI3NP5nlc/Zu9ABgsn5+L0nzlFHjBcELHMhE3OmuOBt9ZxM6MQXOoWsmx2w92hQqooi0aDnA8NtW2nnZaDq53zaKJiwdjKepK9iJpgiCIyYKv5xi6X3gAgQEX2Bkm5Nz48IQ1+wBAn0xX02ekoNejdDk2GnRI0+uQZdRjeWkuquvaJHuwaXqdQmOFvIAwdWf5/FyUF1rQ2O7E2q2NkglAci2MNg8RaXJfcY4JK0rzUFVhw9qtjZLvkm16QSKm9xEEQUwH+gfDN/tEUx9l1Ovg57iY9kgtJgNuv6IQqxcW4to/1IVt+qk52oOuXq/i+j0DvlBcpTV5Tr6HWN/mlFxDrF9a+5Wx7mOSBhEEQRCxMBnqo4KGUtLPGDC4d+k5obrh1QsLsXl/G95tc6HD6cYxpwd+UXyZn52uyJ+2dA/gmt/VIi8rTXLtlm43WrrdUedNDSksNq8uj/h7RNL0cBouX7sYtTww5X8JIn6o4YdICAF3H0688AD8jnaA1SH7Kz+Cad4VE70sTdJSWHz36nPQ1BEcYcvxHHYcOg6AwQG7S+HKtKTEiu8sLg79LC4CfmpvKzbsHGnY8Qd4vHzoOFaU5iocMzbV2kPHCg6NaxZNXFCbDMXMBEEQRHTwAT8cf/slvEffAQBkXH4zMq74Fhhm/At25A6MaXoWXv9I0VW+xQgwDH75ZrPa6RFp6ugN/VtLq7QCTq0CcLVjk53ROl1RoEwQxGQjlvgknGt+vPA8j/59f0X/2/8LAEg7+1JYv3ovmBR9Qq6fnW7AHRU22sgkCIIgpjXu5v3o+dvjADcMvbUAs2/6edTNPgDE/hBhuaPChm1NXYqGn0yjAYN+Dv6AcooAAPxxT6vqhAF7jyf07w07j+BH15yDe5fOQ3WdPVSIvKfZgaotDdCxDIbDFAXLr7+tqTNUhCU094wgvU40rogcH3RXFq+XZSYmHgxwPDgeKM5JB8Bj+fzcsO9CozW+IAiCIADfCTtOvPAAOO9JsGmzMPumR2CYPfZxqNmkxynvsKRwScDlkRZNqzX7AEGN9PgCOH/OLKxZVIR3Wp2o/bgn9P15Z81CaX6WZjFRS7dbooECu4+o50ajzUNEykEUWEYmAcpzuqX5WdhY05o0pky0N0wQBBEd4abHRlsf5fEHY79opvUION0+1Lc58fKh44pzWEa5rvYe7evWtwXjK7W9Qvke4pISq+Rc8Z6k1n5lrPuYpEEEQRBEtCRTfVSs+DkeLMNg48oF2FTbii89UQvBlOnpWxbgjmcb8daREf1kGQZVFTZsa+qU1Cy3dA+gpXsAledkI0XHot3pkeSOxXnT0ZoBR9L0cBoubgYqzTcD4BVDEsRQ/pcg4ocafohRM3zKiRPP349hVxegS4H1a/fBWBy5M3QiGeZ5PLPPjgvmZOD2K4JdtIJgyjdVLSYDNq5coCmKaxbZsP2gUnCF4FgsSEFXRUh+vrOyiIJagiAIIiycfwg9r6yH1x50B8xcdAsyLrtxwtYjd4qSB9VtPR60iYqhtMhON6iOr41mE1Qr4IymAHyyjIgfrdMVBcoEQUwV1JKU4Vzz44HnefTt3YyT9S8DAIznLkL2l38ARpe4tMl5Z86c8MIegiAIgphIBj7aA+c/fgvwHAxnFCPnxoehS5sV0zX4KDp+9DoGHA/ccPEchRGFPUKRlVqzjxp/3NOKe646GxflZUgmDGrFozarCSc9fvS4fYrvWrrdoXcdMcU5JiyfnyspXI7GFTHYXCNlW1PXhLyHVNfZJWZZLMOEXYNarE/TawmCIKJn6JNmdL/4ELghN3SmLOTc9AgM1vxxubfLrd7EEw81R3twze9q4BqQ6mZDuwudvZHzzmqMJjcq12iLySCZPCSYUAVNJnlJoyvAkCkTQRDEFCJSfZQwrU6sE5/2D2LR2RZ89MlJ9Hn8iDTwZ+/RHtXP1ZqQwl2L43mFhm1r6kRDuwvtTmls3OnyaBo1aO1XVlXYwPF8aLosxyM09ZYgCIIg4iXZ6qPiQdBfNVMmnpeKN88HtXNFaV4odhTT1efFrh9UYmNNq+T70nxzqK4qwPGS6bdAbHHnaGqTYm3oHa3xMUFMZ6jhhxg17g93Y9jVBSZlBqw3PIC0wosnekkR8Qd49Az4sKfZgUse+xdOesON4+XxpSfqQo1AclEMJ7jK5LE82o5+dC9BEAQxfRnq/BBeexMAIOuqOzBrwXUTvCIpvmEu8kEqXDAnAxtXLsAz+9pOJ4Oj3wTVCjjVCsArS6zo6vUiGjfhZGK0TlfyQDnA8ZRoJwhiUqI2sUz+jDPqdThjlgGfnhySTJ2LluH+Ezj13hsAANP5V8Oy7G4wrG5U6zab9JKCp5qPnaius1NhD0EQBDEt4QN+9L/9AsBzmDHnXOSs+CnYGaaYryPovFGvwww9iz6PX5Fh9Qf40BSeJSXWhE8FBIKNQevfOAKzKbpJgHaHGzarSbXhBxhxQBS/3yyfn4c1i2xgGSbsZqsyDlbmnFu6B1C1pQHVq8rGNSaM1YhCLdan6bUEQRDRc/Ld7cFmn5lWzL75EejNcyZ6SQps2UbJ5DwtxEaLAl4/pzCaspj0ABhJYbUaWkVE0TSWynMQVRU2sAxQ3+ZEu9OjKKwSEDRcDJkyEQRBTG4i1UcJ0+rkn9V+7ByzNRkNOlXzCpZRalhLt1tVY1tE5hhyowat/Uody4BlmND15NNlybyBIAiCiIdkr4+Sk8ICGWl6yaT5sgKzau1SQ7sLcilkwGNjTSvq21xYUmLFAbtTttcdPEGeN+V4HuvfUE64FY7Riju19FleczVWuj1a42OCmM5Qww8xamZdugIB70kYz74UqXnnT/RyYqbXE67ZB3C6/RJBBpSiKAiPfLSePHksd2Tsdfvw1N4WrFlURIEtQRAEoUmarRTma+4CwGPmRcsmejkK4p2au6fZgc3723BnZRHurBxJ/l7zuxrJcfVtwSS4POCUJ5eFhhYxS0qs417QlCxUVdhwwO4MbTjvaXZQoTlBEJMSeUL06To7Vi8sQGWJFfVtruAmqj8Au9Mb9z30mWcgZ8VP4Wl+G1lX3g6GYUe15iUlVrzf1af4PJHj1QmCIAhiMsHo9Jh908Poq90K8xf/A6whbVTX8/iD+h+OHYeO483vLUbVloYxafoBpNMM5M2+cjpd2sXNwiatFD4qIwh5AVfQSAP4w1stkqIvISYUmmjG4x0kVsdGtd+XCqUJgiCix3LtD8D8MxWZV3wLKRmzJ3o5EowGHc7KSMWcrLSoGn6iR9rss/hsCy4tsqKx3QmODxYtlxdqFxHJG0sP2J2KfLJaQZLwvXjSnxzheHIvJgiCmDokqj6KAVAYZRNsJO6+8mywjLJeqbzQItGwdqcnZHQsRq9j4BeNCool5qpvcyl+Fs4l8waCIAgiHpK9PkrOMIdQbXFxTjpWlI6YEMtNIdqdbuRlGSWfdbi8eKt5ZNBAZYkVe0W5bCHXK8+b3v6Xes01hYs7tfQ5Ft0ezR73aI2PCWI6Qw0/RFxwgwNgU9MBAAzDwHxl1QSvSB0dIx1hq9cx0LNsxM3gSMhFURAitc1SMWsWFaG+zRXaYHa6/diwsxksw4yZiFERGUEQxOSEG/KAMaSGCo5nXrR0glekjV82L17YVG3qkI6OVUOeNK6usyucpdqdntDGqbDpqmMZha5V19kl9xrLZp/JoK86llGsiQqjCIKYjMiLY3oGfHj8zaOjvi7PBcAP+0IFx6m55yE197xRXxcIPoMzjQaFeYQ4lqQNT4IgCGI6IM4jp8zKQfa1Pxy3ewv9M2UFZrzf1Q+vP4AzZs0AwMPeE3ujMMsAXJiB7acGpcZSNqsJdpFLsjx2Nup1uLTIAp7nUd/mRIdLuqZn9rWBZZiI8aZWETLLMIqp9ELzzHi9gyTCsZEKpQmCIMIj1lpWn4rsL/9gglekjscXQIvDLZkgEA06BpiRoguztyvV1+P9g6fNpaRmG2u3NqrmceWNpWqmSVoFSWqOzWLkk+nJvZggCGJyMhb1UTyAzl5pDChvvBGQT/BJ07MoLzQjhWVDTa2CM7/a3qWgYRtrWhUxIqCMVWOJueTGFeKfybyBIAiCiJbJVB8lhoE0Iu3zjJhRVFXYwPE8th/sQq/bD6fbF5q0t6TECh3LqNZT6RgG65bNixhDyvPURVYTCrNNEeNOLX2ORbdpj5sgJobRWdYS0xJftx3Hn/4OTh16faKXEhGDTvqfuD/AhxLCeh2DLGNsPW/FOSasWzZPUxSFYLl6VRnWLpZO7RESyh8c71ecFykhPBoEgd11uBvr3ziC6jr7mN0rGgJccAxh1ZYGbKxpVUxiIAiCIICAuw+fPfdjuN78E3iFw2/yYDToVD+/tMiKNYtsKCswg2UYLCmxwqhXf+0UT+UJcDy2NXUpjmmVbQLvaXaEdK1qS0PofLmeqjW8JIpk01ct5El5KowiCGIyUlVhw7pl8zR1Jx74gB89r25A97b/AucbTNh1BcoKzMi3mCSfFVlNklhSLXFKEARBEFMFnufRt++v+OTPd2O4vzvyCWOxBgCbau345ZvNcLp98PgCsPd4sGJBPpaUWGO+XqT4Ul4kxYAP+/5y91Vn45JCM/Y0O7D7iEPhtNwz4Isq3tTKSVdV2BS/Z1mBeVzfQcLly6NFeBe8+tycsLl5giCI6Yin+W10/fftGOz8cKKXMmYEeEiafQotaSi2mlCck44fX1MCefr8k75Byf5jpDyuWr5U0MZIe5rycytLrLh3aYlEsxKhhQRBEMTEMdr6qHCPfXkM6Q/wqCyxwmIyhL2m18+h5mgP2p1u1Le5UF1nR4DjI2pOVYUNxTnpmtc1GnQozkkHxyPqOh757yf+mfYoCYIgiGiYqPooBkBRtimsVkciTS/N/YrzuTqWwZ2Vxdj1g0pcmJcpvTfDaJoXC828pflZ2NbUiWt+V4un9irjUZaRnssgul9ES59j0W3a4yaIiYEm/BAxMfTpUXS/+BC4wQH0v/08TJ+rBDvDGPnECUP7JcAf4HH7FUV4+WAnOpwecLz20UVWE25ckDcq9/5NtXZs2Kl0ywCiC2zjnSQwWteMRE8woA5fgiCI8AyfcqL7hQfgd3bC7+rCzPlfhiGncKKXpYrYTUpMU4cLm2p5bNjZHPps8dkW1HzsVBy7p9mBqi0NqF5Vdnq6j3KUfDjEjovj6fo7WVypyEGSIIjJjjgeSdPrNLUnFvhhHxw7HoPX3ggA8LYcgOlzlaO+rkBxzkhjz1tHRnRprlkaO5NbPUEQBDFV4XkefTVbcPLdlwAAp5peQ9aVt4/7OuwON7YfVJpKbD/YiXxz7DntgIrTcjhaHR7FZ4J7oxCfrd3aKPm+OMeEPo8fPQMjbpDxxps6lgnF2vKYcDK9g2hNVSAIgpjuuP9vL3r+/huA59C37znMvvkxMMzUbybRsSx2/bASALCxphUuj3SyrscXCO1DRuNSXFVhwwG7U+KqLGhjpD1Nce61NN8MgEdjuwscD9S3Be+7emEhNu9vS+pJ8QRBEIQ6iaiPmpWqQ583+py2jgHq778a1XV2bGvqREu3WzMn3upwo9Xhxu4j3djW2Ik8sxEsA5QXWlT1RscyWFGaqzrlBzg9ja97ABt2HgHLqNfxyOuHFhSYsfvIiIaWF1pC/6Y9SoIgCCISE1kfxQPocMY2gVbOfywpgl7H4uk6e9h8rvw+ws/yveIlJVasXliIqi0NkhhVTZvLC83YLdqHbnEMoMUxELEeV0ufY9Ft+brbnZ6gWQbFuwQxplDDDxE1g10foXvbT8H7vNDNzMbsmx9N8mYfYDjCHuwr7x2HvUe58SqHkQmmPIiNJlkr31zW6xjkW4y4/uI54HigaktD2ERvvI0yoy0iS3SDzmQpkCYIgpgIhk9248T/3o/hvk/BpMyA9Yb7k7bZR8Co10kcFgGgrceN/S09ks+O9w1i3bJ5qG9z4b3OPjjdI8HunmYHbt/SAF2YDelwI20FLRnPxPFkKdKmwiiCICY74ngkEXC+QThe/jkGO/4NAMi68o6ENvsAwIrSPOhYRlE0JG5SBWjDkyAIgpia8DyH3t1P41TTawAA0/lXI7Py1glbT697SPFZS7cbnIpbcZqehdfPaV5L7RuLySCJb4XYtd3pURhaLCmxSpwbxRNvBVaU5gGA5P1HHm/GYtCkFhPSOwhBEMTkZ+D9f8L5xh8A8Jhx1jzkXH//tGj2AYBeUYNPOBfhbU2daGh3hZ3KI2gqywR1mmWYkJuy2vXle5pinX1qb6vEAAsAdh/pluQFks0IMdGmjwRBEFOJWOqj1PZKBWJp9gGAd1pduP0v9WAZBi5R4XAkWhxutDiCxcO7jziwrakLK0pzFc/21QsL8Y7difo2F1JTGFyQmwkdy+Lfsr1brToeef3QvUvnYd2yearxJe1REgRBEOFIhvqoSLXFasgNnXQsA46XGiKX5mdJzunzSDX90z4vqrY0oDQ/Cz++pgQvHzoOgEd5oRnP7LMraqIA4OnT02qFe4rj1nanGy3dI01F4epxtfQ5Ft0W7i00J7d0D0iMNwiCGBuo4YeICm/Hv+HY/jB4/xB0GbMx++ZHoc88Y6KXFRa1DVqzSQ+XeyQRrLbhq440uSkPYjfV2kPBr3ayVvqGkG8xYtcPKrGxpjWqhpp4G2UEga1vc4YcpYK5bR5NHb0RE7iJbtCZLAXSBEEQ442/91OceP5+BE52g9GnIufr/4XUuRdM9LIiopbAbnWouGAwTEg/xC4TAnubHbCY9JLP5IEyAGyqbcX7Xf2SpHOA4yWNs+MRQMZTIEUbqARBEJGRPysFR1w5DMLNc1WHG/Kg+6WfYajrIwCA+Zq7MPOiZaNbsIhiqwkrTk+GBYKJUflzXhxP0YYnQRAEMdXguQBcb/4RA+//EwCQfvGXYP7Cd8Aw7IStyen2q36uZgJlNOjCNvzI0THA+XMyUF6YhUPH+iRxnjjnK9DZK71ndZ1083ZJiVUSWwanBWQpzKLkuWmOB1gGEWPNAMdjU639tDEVj+XzlcVfBEEQRPJz6uDf4frXfwMAZuSdj5zlDyW9QWMiyTKO5JDle45igkVHwTy1OM+8emEhNta0hpqBxFq8btk8SYwey56m2lRBAPjgeL/k52QyQky06SNBEMRUIdb6qNkz9WhzjX4yPRDcd917tCfygREQCm8DHA8dy4TiRY4P7skCgMcH1BztQWWJVbLvCgDtTrfEqV/I2wvFxgJNHS5Uryoj/SAIgiBiYrLUR8n3o4usJmxcuQAAsHZrI56us+OCORm4eG6m5DxfgMPqzfX44Hg/LpiTgfPOmoXaj52h7z1+DrsOd2PX4W5UllhDxlEbdjbDaNCprqVnwIf1bxzBtqZOrCjNC9VGrV1cpMhFj3U9rrDH3dDuirrRiCCI0UMNP0REvK0N6N7xGBDwI8U8B7NvehQps7InelkRSZNt0JqNKbhtoQ2b97eFglWtDV85y+fnSn6WN8HIg1818Vo+P1fSybt8fi4CHI9tTZ0RzwXib5QRBBYYcYYUF1pHSuAmukGHHCQJgiCU+J2dwWB2wAVmhgmzV/wUM+acO9HLiohex8AfiK7cWtDScK6Lgi4X56Sruk5trJG6JBbnmJCXZVR1SBzr5pp4irRpA5UgCCIy8mflkhKr6nGxNvsEBgfQ/eJD8H16FGBYWJbdg/QLrhrlaqUsL81VPNfJ8IAgCIKYLvBcAM7Xfwf3R3sAADMXXIesK6sm1bSBaHPFAgEeqDnqAMMAlxSasa2pE9uaurB8fi5uv6JQ4ugPBAuPhWl/wbywtDBY3CystlkrvFPI4+rtB7tCm8LhYs3qOjs27BzZ+N2wsxmsyJyDIAiCSH76330ZfXv/DABILbgY1hvuB6tPneBVxQbLACqD9qJmeWkwzxzgeHC8ctpecY4JACOZtKdjGVSvKgMA1aZcAfkebWx7muq/1AVzMiTvA2UF5qQxhkq06SNBEMRUIJ76qDbX4DitLnaefKslZN6463A3iq0mxTFqplst3W6JU784by+G8t0EQRBErEym+igegM1qgv206XGrw43N+9sked89zY5QM63Axr2t8JyuW97T7IDZKDU/FiPXYY9P2kQsbzqSazQwfvW48li2NH/s9sGTJW4miGSCGn6IsHCDA3C89isg4Ic+ey5m3/QodOlZkU9MAlyyDdoL87Lwq382axytxKjXIc3A4oLcTHA8J3FRDOcYBSgnDehYBmsWFYFlGIkIVdfZJV2ugLbwiYVZzdkxkqCFK7AOl8BN9AsBuVgTBEFI4XkePf/4DQIDLrBps5Bz48OYcUbxRC8rKqJp9hE37wDhXRcFCixGhU4EOB4vNkqbZPPNRkXx2LbGLmxr6kKveyhUrJUszTVjsYEaTZBLgTBBEJMJ+bOSZYIOu8JI8Hjpq/lLsNmH1SH72v+E6dyKuK+lZxn4VaqTmjp6FZ+R4QFBEAQxXRh4/1+hZp9Zl92EzIpvT6pmHwGjQafYVI1EjWxTd8POI2AZoHpVGa75Xa2k4FiIA4N54QHJddTywmpxpDKu5hXHRDNBPtyxyQDFsgRBEFKGPmsJNfukFV8C63U/AZOiXTSUrERq9tGKuQGg8pxsrFkU1C15I6s4D72pVvpdaf6IxobbL1Xb35XvaWrpk9z4UZgCvHphITbvb1PsD6sZQ2ldO1pNjEU7AxyPgOzvTEXbBEFMdyZrfZTFpEeWcQZyzWl41+6UGCMLzT4CvR6f/HSk6bXj4Po2Z8jBX4zRoMNZmangeD40RUhgtLFcuPMpTiQIgpjcTMb6qE6XdGp7Q7tLMclVHsEODUunyLs82kZTaXpWVYcFA2atEFqc1x2velx5LHvv0hKsWzZvTPbByVCZIJRQww8RFjY1Hdbr7kX/vr/CuvxB6IwZE72kmLGY9FizqEjVlSIcHn8gOC5XtGEriIcgTtuauiQbs+EmDagJqzwoLs5J1xQ+4fyqChuqtjSo3iMc4QqswyVwqUGHIAhibGEYBtlf+RF6Xt0Ay5e/D4O1YKKXFDPZ6QbcfkUhAAaN7S4EOA5dvV6ACW50ipOtgs7VtzkR4IEulxe9Hp/EhVFNl6rr7Gh1SAu9OR64tFCqby2OAfmpAJKjiGkspjxEE+RSIEwQxGRC/qwsL7SE4qAv/GYv7D2eMGdrk1V5G/w9nZhVfj2MZ18a9/qMehZ3XVmMx988qrp2tQ1HiqcIgiCI6UD6hV/EUNdH0JtzkXH5TRO9nKhQKyjm+dhHDqid8XSdHYAw9X2k4Ljd6cFTe1vxUtMxyfHFOSbVvLBaHDkSV7vA8TyOuaIzlFLLDydzYS/FsgRBEFJmnFGMrCurMPRJM7Kv/SEY3dTb5i/MNqG9R9vso6vXi7VbG1FWYEZ9m1P2LR/adx3mpMVSHD9SbCXXwyUl1lBTjXjv9YDdCR3LKIqJtfRJzfhRPrlPQL4/vK2pEw3tLsUahHOj1cRYGomq6+ySyUNLSqxkUkIQxLQnWeujdExwwqwWTrcfTrcfKxbk4pJCs6QBVY5aQfFtVxRix6HjClMKYKRRV66fHl8ALd1u1cmxcj06YHeielVZ1IaF4XSP4kSCIIjJzWSsj5IbIV+Yl4GDx5QmkGLys0emAkXilssL8N6xPuxr6ZHcS35fuVFVtHndRDbLymPZpo5eVK8qGxMtjsZQmRqBienG1MsEEgmB5zkwDAsASCucj9SCiyelI6OA8LDffST8RIFoEBqHGtpdWD5/DgAGTR0jU3f+uKdFcryQpC0rMCtcnORj7VaU5kYUHXkCVlhLJOGsqrBJxgkCwY3kFaV5E5bAVRNdACTEBEFMC8Raq886C2es+t2k1dqMND1uv8IGQwoLoAgba1pDyVbB2Tics4SWHog/b3cqC7xZhpFMTmh3ujWnPwjB7kQGfGMx5SGaIHcsJgsRBEGMFeGelWdlpsbU8CPWWnaGEbO/+YtRa21ZoRm3LbShsb0X73f1IdNoQL7FhEsKw7v0EgRBEMRURKy1DMPC8uUfTKq4Vm16gNgJeTT0DPiw/o0jqCyxojgnPTSFtqV7QNIAJLCiNE81NlV7NxLiagCh9w5AOWFX7VocD2w/2AWADxl0JCsUyxIEQYw0ogr6Oqvsa+B5flLprRbyxlu9jkFbmGYfAGhxuNHicGPX4W4sKbFKv+sO5oZ3He6GxSSdfPTnfe04dKwvtFcLQLF3KzTrCmgZL2rpUywGivKiaWHtcoRrR6uJWsep5Srkx+pYhibHEwQxbUn2+ihDCiuJVY16HcAAqSmsZGLA03V23H6FDZXnZGPv0Z7Q50tKrOjs9aCl262IeY0GXbCJVsP8gmWCTbEcH4w5AR48D4lJo1yX5Bqzp9mB6jp71IaF8vMFja6qsE3aOJE0lSCI6c5Uqo/avK9dc2KPUIt7y2UFWPiL3ZLj0vQsjAYdeF468efV9z6Jqjno7ivPBsvEXm+UyL3rsTA5Hs29aF+emG5Qww+h4NTBv8PTUo+cGx4Ak2IAgEkrsEDQYbFqSwNK87NQbDWhRSaQlSVWlBVkYceh43C5fXC5tUfoAQDH8xKhWLdsHqpXlWFjTavqZq04wSxuuIl3rJ3aqPnS/MhjhNUStQUW04SKnJroAiAhJghiyjPY8T5cb1Uj5+v/hZSZFgCTW2tbHW6sebYBlxVlo77Nhfc6+yTfb2vqUjR1BrUr2DQrnoAgRqwTapQXmiUbqeJGIwF5wdNEBnxjMTUvmiB3PINugiCI0aL2rPQNc1j6+9qonZAAwN/3GRwvPwLL0rsx46wSAInR2pqjPbjzuabQFFin248bF+SF1iufLFvfNjk2HAmCIAgiVrghD7pf/jnSL/gC0s+/EsDkjmsTBQtAXD61V2bcpEY0U9+jKeotsBgjmmvcWVmEOysn9t0k2kInimUJgpju8DwH15t/BKNPRdaVVSGdnSp6K2+8lbsXR2JfSw/0OgYzU1PAgJFMkD85OCw51un2Ydfhbuw63A2OB+6sHNFWtZyyHHExcSL0SWpi5VGdqCC+drT31DpOrTiaiqcIgiCCTIb6KKFJR8cAGUY9LszNxKU2C95p7ZE09vQM+LBh5xH8+JqS03u2TnB88Pfp1aiD8vgCqBFdw2IySDS13enGf9e04vE3R6YGLSmxShp+ygqkk+8DKuYa0RgWiifeiREMNYR7jVaHJ6L5hjSVIIjpzFSrj9Jq9imyGvHm9xaHNIXnpU22Xj8Hr5+DxWSQfB5u/9ti0uPiuVmqRlDRkshm2bEwOR7NvSZrI3AioGbi6Qk1/BASTta/jN49fwYA9L+7HZkLvzHBKxo9Lrc/lMSVCyYA6BgGdy05G3ctORtP7W3RHG+bnW7AHRU2xYh4QSiUo+ODblTiBPUHx/sl38cz1k4ewAaJ7mGdbJukaqKrdsx0EWKCIKYHXnsTHDseBT/sg+tfTyHnhgcmekkxwzIAeGkRU317rySpLKalewDVp92X1Bo9dx3uxramrlBjjhCEyHWhyGo8Hfgzqk7Eam7Ft19hw+b9bVi7tRFlBWZFIXYidCbZpwaNZ9BNEASRaAIcj2WxNvs4O3Hi+fsRGHDB8bdfYs4dG8HoEpf+eNeuHhMCQYMIMfKfCYIgCGIqEBgcQPeLD8H36VEMdR1G6tzzkTIrZ6KXlRTEMx9o+fw5qjFkpFgzUq5XXlB0wO6EjmUmfAMw2kInimUJgpjO8FwAztd/B/dHewAAaQUXIa2obIJXlVwI+68utz846UBEuFB8+8EuSfOr2t6kHLHGyvVp9cJCbKxpjTs3nJeVJmn4WVJilei12j3DTfNTO07tnSGa32M6F08RBDE9SIb6KJYBVPpjVAnwQd3b0+zAnmYHiq0m1eOe2WfH+XMy8HarM+aGWoBHmn5kolCrw40/72uTrZlRGBvLTRyLrCZFU5CccBPvlpRY8cHxfvQMjDQfNbS7sHHlgtC/440TJ6L5hjSVIIjpylSoj4oWhmFRXWcPxYQDPvVMsbixNhKzUlNw8dyskI7Ek9Mtzc+S6G00gwW0GAuT49HcK9lqoccTaiaenlDDDwEgOBK+/+3n0b/vOQBAWnE5Mi5ZPsGrSjxqgikuwGrq6NU8t9/rx6ZaO86fM0vyeYDjUbWlAe1Oj+IcefB8wZyM0IQfIH7Xp21NXZLkb1OHC0BRaD1aG8HJtkmqJbrTVYgJgpj6eI6+DcervwS4Yeiz58LyxbsmeklxEW3iWUykjdOW7oFQMKLllnjjgrlhAxQdy2DNIltojC3LMHhmnz3UzLvrcDeWlFgl50SjM5GKrJJ9atB4Bt0EQRCJprrOLtkYjISvuw0nXngAnKcfbOpMWL+2LqHNPoDSbkGsJfIcKxnpEARBEFONgKcfJ154EP5uO8CwyP7y96nZZ9SovzBEijWF3K7g2iwYXAgxqzwOF0+e53ged1YWx7TKRJldRFvoFC6WJQdDgiCmMnzAj57XfgVP834AwKzLbkSqbcEEryq54SFNWA+HTWDzIR2pb3Oq7u8CQHGOCflmEzieD5k+ih2V1SYERZsblhdFy5t8BE0LcLykCWfjygVh9U5LO9X2h6P5PaZz8RRBEFObZKqP0jFM/KZRGtMRnG6/ZGqPnNQUBoPD6vd0qkwC8voDkp/LC81Yu7go1Oizdmsj2p3SHH6BxYQbF+Shvs2lqqXCvwH1iXc6lsEdFTaJXpYVmBOy56k2WWj1wkJs3t82ZjEmaSpBENORqVIfFS3ymqdZqXrN5h75EIElJVawTDAGrG9zwXO68bbN6Q1N2RMbOpXmmwHwaOrolfxbXcPkejZ1cqjJVgs9nlAz8fSEGn4I8DyPvtpncfLANgCAseQKZH/lPxNeFJWsMKIk8Py5WSrTc4L4Azycbh9qjvagyGpCv9ePjDS9pIFHjNGgg8c3EvgWW03YuHKBIkgEYtug1LEMVpTmKgJbgXAbwclW8BtOdKejEBMEMbVx/18Nev7+a4DnYJhdhJwbH4bOmDHRy4oauROTnDMzZqDVob45Cqg3daqxrakrlFStb3OFAtvyQktUbolyHSzOkbpbsQwUrlORUNNWIYktJKHFJCqQogImgiCmKwGOx6baVrzU2Ik2jcIbNYY+/RjdLz4EbvAUWGMmZt/8CAzWgoSv78zMNNy4IE9VS8oLLdh9xCH5mSAIgiCmCsMDLnQ//wD8zmOALgXW634C49mXTvSykpoiqwmf9g9K8sRyGtqckkkDQiz49OlJuaHjZLGmkOsFRqbp7j4ykg9WnxQfJDjdILaGn0SZXSSi0IkcDAmCmKrwwz44Xv0FvC31AIDMipXIuPymCV5V8nNmZhpYhpEUCmsxJyMV1/yuJjRBQIsVpXkAxBobjPXleiMvshEacBvaXZqFV/JzdCyD6lXKCU6J0rto9ofVioUSMUWBIAgi2Ui2+ih/PC6Lp5mTmQYAcA0MweVRNupowTIsAO0YVU55oRmX2SwKPZA3sMrPUcarDmxr6sKK0lxF86m48RRQn0ZXVWGTNO1yfHDaUHlhbHuoapOF1m5tlJhkAImNMadzQTJBENOTyV4fFQ16HQO9jkWqnoVL1DC7rakLDe0unD8nAzVH1euKhWaf4pz0kC6G01UBuVZp/VusYcFBApD9nPg86kTUNyVbLfR4Qs3E05Pp0dFBaMLzPHp3P41TTX8DAJjOvxKWZfeAYXURzpw6dLi8CHA8dCyDd0+7SkRCKHgWj4+Vc2bG/2fvvuOjrg8/jr+/lz0IJGEpIyEg4sIqKFX2UnHWgZuCFaU/rVZt1VoHtlat2tbVIYoFitatuEAKlKVVQXCACgpJmCKQBRlk3ff3R7zL7cu4y9337vV8PHw05NYnhOaV7/c+n+8n1W1idJ+cdCUn2nxGprUnbF0PBofk5chuNu0ydFJ+jvNEskM0r970F914DTGA2FX5xRKVLHpCkqnkw49Uj8m/ky01M9LDCiopwdDogd10Un6OPi4sCbjg54ITeyvRZnNeqUmmqe1lNaqoqddxvTrrquH9lGAzvHap87Rlb6XbSVWpaYHOjNH99Y8VW9x26/F1RWLPNyZ3lx9y+/PJ/XJb3Rlfb3ZK8nuw3Z4tcF15/n7guFqHlRb/sGgJQEtU1TY4j2emjyzQUyu3Oq9W1FKHdn6tva/MlFlXrYTMXPW49H4l5fZu17g8L+LgcNGQPn5bwht3AIBY1XBgr75/8U41lH0nIzFZ3c6/U2kFQyI9rKhlM6Ts9CT95IRemvtBccAFP5/tLHeen5b8T5ry96adv6v5TR9ZILtp6rX1O7WtpNrtqpG7yw9p1sqtrTpG857QXOL8fGuO90Lx+xJXMAQQi+z1h7Tv9ft1qPhTSVL22KuVdfL5ER6VNeTnpGtYQW7QyVGStPJb/+8FD+ieqfzcdGefZsz/xO12X73xnGRjN02v93wdHzvO7zZ6TO52NN7zXGpHvufra7JQPE+eAhCbYm1+lL9JxMHU2+0tvm96coJOdnQxyKJXz476uo/n7gcOLdmNTpLXwiDJ/cIXLTF9ZIHX+9UbdlV4fW2uz9fe9ztpKoB4YtX5UcHkZCS5LeypbzRV39jodd53y95KZ2M8d/IxJLf9afNz051t8GxmW60tLnW7gLG/489Q4wJNHYs5CfGJBT9x7sCa15wHs5k/OkM5p10nw7BFeFTtZzOkll4IY+u+qqZtZkf319riMrfbPCMbjOuW6x8X7nebGB3oWK+1b1B6X+miefLz2CO7ud03VJFmwi4AtE1N0acqWfS4JCmlz7HqfuE9sqWkR3hULeN64BdsO3mbYfthMlHTlYLLquqc29Mu37xPcz4o0ozR/b12qfPlYz9vZL62fqfb531dkdjzjUnHwbXrlTFay9ebnYEOtj8uKtW1o8x2d9LzNcJ5Zalw4aAeQEscPNSgpV/vdU5+eX/L/lY9vuHAfu19+W6Z9YeU0LlH02KfLj3bPS5fE3PHHtlN147y3xLeuAMAxCKzsUHfv3RP02KfpFR1v/AepeYNjvSwolaizVCD3VRJVb3+/J9vgt6/tKpeT6/aKpthaE1RqT7b4X6OOj05QYd3TtWHW/drTVGp19WL/V3Nr+l2w+cOBtV1jT4nWQXiPaFZbTrea8vvS57npofkZXMFQwAxp2Th487FPjmnXadOJ5wZ4RG1n6OJ4TasINd53veVT3ZoS4ALVwUyeUhvZ58a7WaLJkZ5TrJZE+Dikq4XuXJ9T9nXTgnhfM/XFyYLAYgHsTo/qrVcJx77UtA1Q99V1Kim3q7qukY9vHiz2w6zklTXYFfRfvfe9slO83oufzvP+tvBNthxor/3Zz0nNwea05RgM7zerz6uV2e3Tns2l/c7AaBlrDw/qiX6d0vXdxW1AS/u5CrJZlN9Y/N9sz0WDbn2xlczC7qm68IhffTp9jI12k23VvlzUn6O1wWlfB1/ugrFvGAu0NSxmJMQn1jwE+cyB5+uqi9XKLXvYGWPv0aGYd0FHEk2Q31z01VeXaeSqpZvVys1H/x5ru5J9Fhl64tnEF1j99/NzRPV7KbcrtToqj1brHnG0mY07YQQ6hOyHMACQNuk5g1W+sBTZa+rUbcL7pQtKTXSQ2oxuynnBOwB3TIC3nfdtlLNXi099J7vxTyONzrXFJUoNyPZuRjIF8+D4+YuejbUu6nTRxboo8ISrwPdvJy0gN0KdADr781OXyeoJWnF5n3OxcTt4e8kuGMsVugwB/UAWqslJyo9JWZ1VdaPL1LVxmXqcekDSszqFvxBreDryoSzVm7lYggAgLhhJCQqe8xVKln0uLpdcLdSex8V6SFFhdZeLCqQRxZv9nsBq+q6Rm3ZV+WcvLxs017ZTen/xjQdWwWaoOt54QxPrTlG857Q3Lrjvfa8cex5bvr2MwaF5Rw4AERS5+GX6dCODcoePU2Zx02I9HBColOKTWU1LZsI5Ytna321t6BrutsuANNHFujpVVs1e3Vhi94vTrIZykxNlM1oWixkN6VrRzVNGPZcoNOS3gzJy9GyTcHPbSTYDM2eepLb5zrqPV9/4/E1WYgLQgKIJbE0PyqcKmrqVVPvvguQY07V06sK9dr6ndpdVq1ql/sk2gy3CxfaTdN5UYuxR3bTjtJqtwW5bV3E6u+9U8/JzcHmNHke3141vJ/mfFDkt7m83wkALWPl+VGu0pMSVF3vfixbWlXvtlinJQ7rnKKt+6udfz6+V5Z+3L+bz9407UC3w+3iTQXdMnX92KaLINc12HXKg8vc5lolJxiqc5nbnJ6cILspfVLsfiEKX8efrkIxL7g9858BtAwLfuJcQlon9bziYRnJaZY/mD11QK5WfuP7StCeEe7fLcNt9x3HwZ9nqE/t7/85JWlAtwyd3C9H67aVed3mOeF4eYDJv+25apJnLE/ulxuW1ZscwAJA2xi2BHU991bJNGUkJkd6OG0X5PeEYLveFJdUt+iNTocB3TKU3zVDQ/KyZTel6fPWqnd2mtv26hee2NvrcQk2w+cbjsEuJOl5APtRYYnbgl7Ptrq229eVNELRyUCvYZWDYw7qAXSULqdeqqwh54blKlF9stM0a8pQZ1+adlnlYggAgPiSfsQwpfadHVNXZGwvf4eZbdnJwN9D/O1k37TjbdPvH4Gv5hd4LA12UxP+skKSoQtP7K1rR/mfxOvrdZZtaj7eKy6p0qyVW/1OBG7NG8eek4s9d0tYt61Us6eexO9gAGJKcte+6nXN0zHV2rYs9hk9sKt2lR9y20HewVfVymvqnd1YU1QiuyntKK0JuNgnLcnmnMhcbzdVVt1035Kqej303ibZDO/3Rf2dd/bs25iBXd1uHzOwqxITbC06v9tR7/m2BheEBBBLYml+VDjV1Hv3u7ikStPnrfV7wS7P4+BX1+3Q1n3NE5xvPX2gEm22di9i9XXhR8ei3BnzP3G77yvrdvpdsOrr+DZQc3m/EwBaxurzo2yGlJ2epKzUBBWV+D+eTbRJDXbftyUYUnZGso7r1VlD87P1yOLmHeB3lNfqZNPUkLym850fFZbIZhjOHd0nD+njtjOPa2/mfFDkdYw8fEBXtyZW1zXqofc2tXq32PbOC260m7KbpgZ0z5DjPDMXaAJCjwU/ccZsqFPJe08q6+QLlNy9nyTFzInjnWXVfm87vEuaJg/tHfDqDJ4Hf+nJCRrWL1c/LsjV2qISFZfWaEdptduOP31y0vXQe5sleZ/k9HXi118M27PFWkdtsc4BLAC0jGmaKl/9nJK791PGoBGSJCMhKcKjar/e2WlqekvVUGlVrduVK9KTE3TV8H7OHYE8eS60laSkILvo9c5J1+ypJ7lNqpaCbzUr+b66ky3IiXvPA1jXq1BJ3m9iura70W56neQORSc9X8PzSorBhPLqi219ro76PQVA/Kn6aqXqS3eqy4grnJ8L5bGt6+Qfz4s3cDEEAEA8qN2zRQfXvaPcM34hI6HpbYRYOY/c0XIyklp99UcHfwuByqpqNeEvKyWZPyzU6e/zGO3CE3s7z1976t8tQytcjmMdE5ynjyzwefzneVx41fCm9xdeWbdTW/ZWasveKufxu6/fjVrzO5Tn5GJ/b1Kz6wAAK2uoLFXZ0lnKOe06JaR3lhQfrU20SUkJNq+dAxx2ldW47QAQTGlVva6d/4lb04I55Oe1HZ5ZXahje3V2+9yQvGyf9/Xs28bdB9z+nJhg06wpQ/X0qq3aXlql8uoGZacny242dcy1W6E6lxrKPnIOBICVxfL8qI62ZW+V244DwWwvrXH789+Wb9Uvxx/hdmGtlvLsmufDbYah2asLVVziPr6m49TKkCxY5f1OAPAt1uZH2c2mC0EE2y3W32IfSWo0pf2VdVq+eZ9MSbefcaReWbtdhSU12rK30ue5WsdFlXztQDdr5VatLS716tyA7hmaNWWo5nxQpGdWF2p/ZfNiIJthtGq3WM85Vo45UC09npy9utDt67IZ4hwtEAYs+Ikj9vpD2vf6/TpU/Klqij5Vr2ueki01M9LDCiH/kbhwSO+gV2fwDFd1XaMeXrxZd0wapGEFXfXfzc2TjQd0z9CFJ/bRq+t2uL3OmqJSt0m5nqENxyIZX4uFwvFGJwewABCcaZoqW/aMDq57S7IlKLFLT6X0HBDpYbVbks1we8O0oGu622Sl6rpGPft+kT4u9L0rXkWN98FwoMU+krSjtGkhr68rKQbaalbyfXWnk/v5brC/ZrsK9iamY0ytXZDTGm1ZHBzKqy+29bnas6gZAPyp3LBUJYuekEy7Ejv3UOZxE0LyvK6LUT0nHrm2gIshAABi3aGdX2vvKzNl1lXLlpyqnIk/j/SQLK2mrlG3nzFI67aV6tPt5V5XYmwL1zeeH3pvs2yG4fNiEVePKJDNMLS2uPSHicqG1m1r3jXH8+Icjp10fB3/+TsuXFtc6rYbr79j6Nb8DuV5LsDfm9TsOgDAqhoO7NP3L96phrLdaqwsVY8rHo6jnQYMt4tseNpdcajVz9iaxT5S02b2ZoDT0/sr63w8Z/P3x7W3jR6rc4/r1dnrwlCek59KquqcC20d3Qrle7uh7CPnQABYVezPjwqf6rqmHQ2SEpo6FOw9XV88H1Nd1xjwAhGBPL2qUA+919y1gm4ZbrfbTdPt4pG5GcmqqW90fh2S93Fqa7vr+X5no910TsDm4hMA4lWszI8y5Hs32WAXMfbFc+efFZv36ZSCXO05EPx88OPLvpXdNHXtqObeeF4g2dXkIX2UnGhz3tf1fttKq3Ryvxz9/YohmvNBkWbM/yRgr1znBbvuTtvS40kuFAF0DBb8xAl7bbX2vvZ71e7YKEnqMuLyqDuYLeiaLpthyG7aVV7doIqaerWmmf7umpuRLLtp6h8rtjrfzPR1VcIhedm6/YxBevZ99xWvr6zbqbycNLfnzM9tOoD0fEO0qKTK+ZyuER3QPVOTh3TcVnW80QkAHc807Spd/HdVfv6eJCnzuIlK7mGdxZH9ctNUVOJ+taUEo+nqE/Ueb1p+5+NN19fW7dSWfZVen5e83+T0JclmuL3OdxWHNGvlVh3fp7PbG4p1DY0+m+7p5H452lFWrWDbxfpqdp/stFbv1hONC1tCeVDNATqAaHHw04Uq/c/fJUkpfY5V+sBTQ/bcgU7auraAiyEAAGLZoe1faO+rv5dZf0gJnXuo00k/ifSQLK+m3q41RSWaPfUkt4lKbZGbkawu6Ule56Udx2j+zgu7H781f7xsk/uxenFJtT7fWeHzuf0dF7Z0IrC/36F8TbTyfM6T++X4PObmWBWAFdWX79H3L96pxorvZSSlqsvIKXG02EdqsJsqrW7we7vr5NxwGd4/R6u3lAa/o4t120rlaKjnOWXXXemvGt5Pcz4ocuvajPmf+HxO126F8r3dUPTR0ec1RaUae2Q32Qzp5H65nAMBYAlWmB9lBW1Z6BPMK+t2tnpxzGvrd7r9uXBflVt7HReucPB1kQ3P49T2dpc5WQDindXnR7nyV7u2dNDXzj9ri0tV2xD8OLe6rtHrwk6vrHNvYP9uGSqvrlNNvV3/21qiBrupT7eXOec9v7befSf2Wau2Oi/mHKhXrvOdps9b6zX+YI3jQhFAx2DBTxywH6rU9y/PVN13myUZyp10ozIHT4z0sLwU7q/2+fn0pARV1wePXkW175WwJVV1emRx81WTHHGZPrJA0+etdVuResekQbpmZIHbSdoteytV5nFAWFxSrU+3l3t/DfuqnCc/XeXlpIflCk3+eJ7IfWXdjna/DgesAOCfaW9UycLHVPXlcklSpyHnKHv8tZZ5ozY9KUE7yrwX8fg7fk1LSvC6AmNZda3P+xZ0y9CJednaUVat3eWH/L5hm5WW5HYC1nGlp5wM9+1+V31bolXfNp24Xfr1Xn1UWOI8oetonecVE9cUlejaUb5PLng2Mz83XbOmDA3rbj3h0JIJUu05qOYAHUA4dUpNVHpyQtBJPQfWLlDZf2dLklLzT1C3C+6ULSk1bOPq3y1D/bpmeLUgGhd5AgAQCjWF67TvjftlNtQpMfsw9bj0ASVmdYv0sKKWv6s/+rJ88z6d/uhK9cpOV056kt+LXQX7naikqk6De3f2WvDjOEbzPC/t2JHel+kjC/TKJzu0xeW5PJ9XapqYPeEvK73OkTtes6WLof39DuXrvPNVw/vpo8ISbdhVoeN6ddZVw/v5fE6OVQFYTX3JzqbFPpUlMpLT1X3y75Ta+6hIDyumORbLmqapA4cadFyvzhqS1/oFP66NCbYrvWfvPHvlMCTP/3MGm1QV6P3mUPTRc1HTHZMGcR4EgCVYZX5UvNqyt1JPryrU/41pTVO8D553lNUoPzddkjQ0P9frYhYOyQmGhg/o6nVM2d7uei4y4uITAOKJ1edHtdaAbpmSYWrL3ubzpoF2rfU0JC9b67eVqbS63vm5nIwkGZJzB3dXrhd2ct1V3cHxmJXf7NPKb5rnPd9+xiDl56a7PabU4/nDtXjH1/nhjpgjDcQbFvzEuMbqCu19+R7Vfb9VMmzqevavlHH06EgPq3Va/HO+5UFwHLx57jawtrhUf79iiJ5eVeg26djxcW5Gkkqq6n3G1PU57B77wLv+uTULZ9oaPs/wbtnbtBCpPYuOuFoiAPhmNjZo/9t/UvXm9yVJWT++SF1GTbXUwWxLFta68jWhydeBqNS0GPbP//km6HOWVNVp7JHdtGFXhdtOe54HoJ58bSXr2azlm/f57WCjx+5FJ+XnKMFmuB2QSk2TjZ59v/CHq0g17Rp07ajoOSD19ftFKHegYDcLAOGUkZKoQ0FaVPG/l1S+er4kKa3/Ser2kztkJCa36fX6d0tXv66ZP0ysMbVuW5nb9uQO+bkZbhN2AACIZdXffqR9b/5RamxQUm5fdb/0D0rMjL/FEzZDsrdwFU9rr/G4ZV+V2+IaXw7vnCpTvhfeONgMw3nFRsl029XW87x0o92uWSu3+jwPnGAzNHloH7fJvK66Zib73LF3QPcMTR7Sx/ma7V0M7eu8s9R8vL988z7N+aDI5/NzrArASur2Fev7F++SvbpcttRO6n7JfUrpOSDSw4p5JVV1bu/5Lt+8Tx8XurfH8f6vw4BuGbpwSB85zhl4Nqa1E6Acj335k+3aus/1ApjN3W7tcwZ6vzkUfeR9YQBWFBPzo2JEenKC6hvtPndH+MuSpos2Oi7WGGzu0oUn9na70KOkH3YvqNTSr/dqzMCuGtA9Q5Kh3tlpWuFyDFvXaLodUzreIy4u8X0RDX88u5ub4f7eBBefcMckbyB2xcL8KH/SkmxKT0702ilud3mNrh/XX48sbp73NKwgV6ZpauU3+30+15gju2lnWbUkQx8XlXot9vng9vH614fFPs/LnpSf88PuPjvcPj+ge4bXBZlcvbZ+pyYP6e3zYhMOjotOBPo53ZbjSV/nh2et3OrWTl8XcwbQOiz4iWH2uhp9/8Idqt+/XbIlqtt5tyl94KmRHlarBZv45eBrW1Z/huTleEVRagrmnA+K/D5XS345abSb+mJnudvnXPsU6ASpZ0ztpuk8cG3NrjrTRxbolXU73RYmtXdbeK6WCADeTNPU/rceVvU3/5MkdR5xhTqfemlMHMwGUlYdeBFOWyXYDF01PN/tQLk1HK3zdcVEfx2U5Lbtu+Ng1bOVHxWWuE1yeui9TbIZoV+021aev188s7pQUtPvBKF4I5bdLACEU1l1vboFmDFb8eHLzsU+6UcOV9dzfi0jIcn/A4K4eGhfr59njXbTbQdYSTq5H8c8AID4UL11rfYteFCyNyqpe4F6XHKfEtI7R3pYEdHSxT7h0miaOljTEPA+J/fL0YzR/b2uiNxoN7WjtNrtczvLagKeB75qeD+9/MkOnwuMrhlZ4HWs6eD4fCiOdX2ddw42ydjzmHvWlKG8SQwgqtWX7NT3/75D9kMHZUvvoh6X3Kfk7r53L0P4eV4Ay3N3vd456QEv9uQ6AWpIXrbspjR93lqf54Fdm2UYNrfnWbetzOdztmRSVaBWtuRcbrDz17wvDMBqYmV+VKwItHNtfaOph95rOk61GfJ5zOro1JqiEjWaUv9uGSqvrleX9KZpjq4LaFe4TLY+/4TDdUpBrp5ZXeh2gclX1u3Q9JEFenrVVrfFQ/27ZejioX1a3V3HnDLPC2KgSVvmpAGIfrE+P6qm3q6aeu85w9X1jXp2dZHbzuwrNu/zWvyZZDM08ohcDSvoKrtpOhegem4scEKfLkpLTtD0kQVe52X7d8vQ9JEFP+zu436+dvKQPl7zltyZPp/T1avrtjsveOVosefP6VDNDfJ1oWZfr+cPi0cBbyz4iWFGUqrS+p+shvI96vaT3yqt/9BID6lNHG+yJhhScmLw7fBy0hNVWt38pmhaok0ypLTkBNlk6LjenWU3Ta8ojj2yW9Ok2Pmf+H3uzmlJbgeFBV3TZTMMlVXXKzs9Sb26pPqM6sn9cp0fD8lzP0Hqul2750FP01UomrlO3g0UsASboclDertNZg601XygRUeO1+JqiQDgzTAMpR85XNXffqQuo6ep87ALIj0kS0lKMNyu7HRSfo4+LCwJ8Ah3/btluB2oDsnL1qyVW7WmqNTrtkAdTLAZXjs4eN5nw64Kr9d/ZnXhD1dPNrRum3s3O/pEpucbsPsr65yvzwlUANGuNshFHlL7nSjj49eUPuBk5Z55kwxbQptfq1/XdF013HtSlaMFnsdCAADEg5SeA5TYpadsKRnqfvHvlZCaGekhxa2i/dVKS7L5vT03I1l2s+kcruf54dmrC712EPJcv7SmqMTtGHHOB0U+3wDu3y1Da4pKfC6A2rK3Slv2VgU81m3NG7L+zjsHmmTM5CEAVpPYuYeSDx+o+r1F6nHp/UrK7RPpIcWkgm4Z2ra/Sj42EwjI873nFR47xntynQDledViyb1JnhefcuW4cnNbFrG2d0GOZ0vtpimbYTjH4Th3wjkSAFYRK/OjYkF6UoLX4lpfXlu3Q/ld3edEOeYuPb2q0DkR2ZVjVyB/bf3n+0Vac+dEr/ts2Vul2asLf9gpt5kR4OKOrnxdbFKS8nMz2n0sGosTmtkpEIhN8Tw/qtTHRZHLq90XB9XbTQ0r6KoZo/tr+ry1fp+raH+V6hrsmvNBkddiqYuH9lGCzfD6OTqge6amjyzQVcP7adLjq3yez73wxN5KsBla9MtRmjH/E23YVSHTNN12s926r1oPLtqkAd3dz/87Fsb66o9np64a3k9zPigK2i1/7ZSauxCoga05/xuLLQV8YcFPmJSUlOjhhx/Wm2++qe3btys1NVXHHXecpk+frilTpnTIGAzDUJfRU5V53AQl5fbukNcMp0bT+4SrLwcOuR841jQ0Pcbx2OWb92lHWY3bfQZ0z9TsqSc5r+7vKzZjj+wmQ6ZbMPt1zdCz0052/nnCX1b4fNz0kQXOsLzqsbPQx0UlzknCa4o8Jzm7hyfQ5F1fcZV8n4gNdBLYXyy5sj+AaBMNrZWkjKNHK7lHAW/SBlDQLUP9ctNlN+W2MNZ1sY+jl47FrQ4Jhvy+QZuXm6G+OenasKtCx/Xq7HYVCkkaM7CrdpbXSDKcE6Kk5v918PVmqGcrj+vV2WtR7/7KOrerQLl2s7VXI27vAaej855XrOIEKoD2iJbWpvQcoMN++hcldunZrsU+UtMk2jkfFPn82cgxDwCgo0VLaxMystXj0gdkS06TLSW9w14XvtXU25WenKD6RrtM01SDyynxkqo653HvtaMK3I4r1xR5vxHcOzvN7Xy25wIefzv4bN1X5XzcmCO7aWdZjSRTMuW2qMjfMWdr3pD19TtYsItPMXkIQEtFS2uNxCR1+8lvZa+uUGLn7h32uvEgwWh6v/bCIb117aj+unreWueVlNvj4x+66tkiz3O6wZrkPVErQ/m5Gc7HB1t44++8cXsv1Og5rtfW73ReLNO13fQVQEtEQ29jbX6UFSXaDI04oqvXsak/u8sPqVd2mtvnhuRlS5LXwhyHNUWlevqnTYu51haX6tPt5c7ddiSppKpe0+etlc1oumCG623PrC6U6fWec8vem3V09pV1O9wuLh2KHfBi8YIW7BQIhF40tFZifpQrX/OYXvlkh15Zt1NlVbV+H1e4v1o/fnCpSl0W4vTvlq6+ORnOhnpuKNAnO00z5n+ik/JztOiXozTngyKtKSr94fhRGpqfI8nQ1XPXyG5KNsPQNSMLZDdNtzlNDrvL3edOOxbGtuQ8r+suQ4G6ddXwfvqosEQbdlWoc1qSzws2B2pga87/ej7PK+t2avKQ3iz8QcxhwU8YbNq0SWPHjtWePXskSZmZmTp48KBWrVqlVatW6Z133tELL7wgm83/lfraqr50lw4Vf6ZOJ54lqemgNt4OZht8XXbQk8dR3OQhvZ0/3Ju2cS10O/DLzUjS7Kkn6elVW/Xfzc3bwQ71OijxDkSCzVCCzXC7wpOrFS4BzM1IcrvtwhN7y2YEn7zbaDc1fd5anzH1Nbl4TVGJxh7ZTTbD0Mn93E8C82YpACuIZGvthyp1YN3b6nzKxc5JxxzMBla4r0rl1XU6rldnr5OrDttKqnT1vLWqqHG/MkajqR+aJRWXVLsdBH6+o/kk7vLN+7S91P0qFjvLa5wnXR96b5PWFJVoR2m12+SkMT8sNPLk+YbpVcP76dn3m678tLv8kN+t6B0LljwPwMN9NWLHBClJfnf4A4DWiGRrTXujKj58WVlDzpHthx0GknJ6hez5OcYBAESDSLZWkg5+ulCpfY9zHs8mdsoN8gh0JMcxZ3pSghrs3sefr63bqTVF7m+ujj2ym9t9Jg/p7TXRyuZx1chAV1p02Flao8U3j/J5jtvfMWd7zzEHW4jN5CEALRHp1tYUrpNMu9L6N+0sbktKkY3FPiHXr1uGFt80Wk+vKtTpj63SrrLqkDxv8f5K/XdTU2tcF+J4ntP1PA88JM+9SZ7N6pPdtLjabpqatXKr/rp8i9v9/S288dTei5Z4/w7g/jsC504AtBTzo+CQlZrQqkW31fWNWvnNfo/POnrke95XcUmVc8LzrClDfe4E5HkBRwfXOVcOF57Ysn8vju46Fuv6WnDb1os9dtQcrY7c/aC9C5MBuGN+lHV47r7uj+tiH0kyZDj7tWzTXvXvmq7+3TJUUVOvzmlJbueAX/5kh/JzM36Yt2xq3bYyrSkq9erfsk17dfsZg3THpEF6Zd1Obdlb6bzN13wn1/64NqO4xP0Ye8OuCr+PczXngyLnmPZX1mnskd2cmzG4dsLVK+t2OvsU6PyvZ9M8z4Fv2Vvpd1MFwMpY8BNidXV1Ouecc7Rnzx4NGjRI8+fP19ChQ1VXV6dnnnlGN998s15++WUde+yxuvvuu0P72vuK9f1Ld8leVS4ZhjqdcGZInz+W9MpO0+ShfXweXCTYDGV7TEbOzkiR1Hw1p2buBz8Xntjb62DSsRjHM36+OLbQG9A9022VqeeOBZ4ni2evLvSKtq+t9jy3jb9j0iCvqIXjzVK2zQMQSpFsbWN1hfa+fI/qvt+qxoP7lXvGDSF9fqsLtEV7aVW9j5O2zQr3V6twv+9WJtgMzZ56krMnjqsneS4cKvM4KPY8F+zrBO/OshqfTfJ8w9R1V6C0pAS/C34cu/HdfsaRumPSIOdVNRy7+DkaGK6Tty05gUqXAQQTydaajQ3a/86fVb1ptWoKP1HPy/4oIzEp+ANbgQmhAIBIi2RrJaniw5dVvupfSsjMVc8pf1JiVrfgD0JYpCf7P76U5PcYu6y6Vlv2Vbp9zmbI6zjU89pYJ/dz/z3Iccy4pqjE6yIbDlv2VWr6vLWaPfWkFk/aCeU5Zl/HsEweAhBMpFtb/e3H2vfmg5IM9bjkPqX2OTbkr4Emu8pqdNqjK302TJIKuqa7nXd2TDIakpcju2nXG5/ukmTItNu11eV+31Uccnue19bvVH5uhtvnnlldqGMPz/J4Rff4el7V2PPijd46ZuGNZ0s934vm3AmAlmB+VPzJzUhWaVWdz+U4pdUN7X7+19bt0Gvrd3i/5/sDx260jo5eO6rA7WIYLTWge4YmD+njdSwZ7D3UQAtuW3KxR8/nv2p4P7f3oKXwNbgjdxJq78JkAM2YHxV5hvwtQ3WXlGCo3te2Py3guVDI9djUc8Gqo4XLNgW+iJMkrS0q0bCCXOXlpKtPdpq+2FnunJ/sybU/nnOMXR3Xq7Nbd1t6QSjHvC/P13Q9Nt6yt1KnP7ZSk4f00VXD+zmfx/P8r2fTPC+E5ToGWoRYwoKfEHvmmWe0ZcsWpaWlaeHCherXr+kHT3Jysq6//nodOHBAv/3tb/XQQw/puuuuU25uaK6cWLtni/a+fI/sNQdkS++slF6DQvK80SwnI0md05JUvL+6RVF1taO0WtOnua8UdT1QO/+EXnpkcfN2dr26pOnpVYVeV6NYt61UUnMUrh1VIMls2l7WNGQ37X4PLNOTE3RY51S/J6Bdx2X32JHoo8L9WretOWYfF5Z4Pd7XVnstmVwcjjdLY3ELWgCRE6nWNlaW6fuX7lL9/m2SLVFp/YaE5HljyZC8LkqwGfpga0mbD2R9OSk/x+0EqL/t1cs9dgfqnZPegitoNI8z0Enc2asLvba6HdA9Uxee2EuSoWffd9+N79n3i3TNyAINzc9xvlG6bFPT7wQzRvcP+QJbz7HPmjLU7yIeugwgmEi11myo1763HlLNtx9JktIKhkoJ7T9tMeqIrtpdUSPJ0IUn9mZCKAAg4iLWWtNU+erndODDlyRJKYcfqYSMLiF5bvgW7A3h+ka7258TjKadboMxTe87ndwv1+fOr76unuh8PZdJONPnrfV7rnr55n16elWh/m9M/xZN2gnlOWZ/x7BMHgIQSKRaK0lVX6/W/nf+JNkbldS9n5Jy2Gkg1MYe2c05Uamm3u63X5L0XUWtstMSdKjBlGEYMiX9/YohTZONVhcqPzfjhwUvptv53/TkRNXUu06s8r7C8P7KOq3wuMjVum1lbn/2vKpxIE3nm3uHdeGNv/PIjXZTNoPFtABah/lR8cfzYoyh5vm+bm5Gkt+JyY75TrOmDNW18z/RmqJS1dY3tuiYOj83QzNG91ddg13T563Vhl0VOq5XZ52Un6OHf5gr5nr86WuhzpwPity62ZL5WJ7Htx8Vui9WGntkt7A1uKN2EgIQWsyPiryWznzKSk3026xIWf3tfv3XpTNjjuzmNv95zJHdnOejX/lkp15Zt0MXnthba4u85yLnZiTr2lEFbg1supBG02YInnOsWjInavrIAucFnx227K1y253HV6s8m2YzmhrqOU+bC1kg1rDgJ8Tmz58vSbr00kudgXV1ww036IEHHlBlZaXeeOMNTZ8+vd2vWbtrk75/ZabM2iolZOaoxyX3K6lr7G+dV1pV77W9ndSyN0Urauq9DqRe/mSH3rlhpP71YbHeWL/T7f4rv9mnXeU1Xs+z7Ou9OvruRfq/sf113ZgjJEk2w1B+boYa7GbA7Wqr6xq1dV+Vxh7ZTTvKaty2zduyt1Jb9lY6o9d0dSnX8ex3jluStpX63hHh8WXfOkN87aiWTS5u75UWfE2W5sARQChForUNB/br+5fuVEPpLikhSd3P/63S+p8U/IFxZvUW74O+tsrJSHJOjHpmVaFe/mRHwDduJXlduTjhh6sbf1xUqs92lPn8vcFut2vWyq366Sn5OvvJ1c7X8FwI49kySTLtdn1cVKqNP1yd0fUNW8dOPwO6Z7o9xtHAUC+wbc0iHroMIJhItNZef0j73nhAh4rWS5K6jLlKnYdd2K7nTE9K0A3jj9C1o9jJDAAQXSLRWkkqW/6sDq5dIEnKOHqMcs+6WYYtISTPHe/87dQT7A1hz4tlOP4Y6IqQ6Uk2dUlLdrt6ck56kttxpqsdpdWSYai4pEp209S1o/o7fzeqa7BrxvxPtGFXhbLSAu+q+Nr6nbp2VIHz3O+QvGxJhvPCVL4mPLX2WJNzywBCJVKtrdy4TCULH5dMu5IPO0LdJ/9eCWmdQvLcaPb+lv1eV8P3p6a+Ua7XiVqxeZ8mPb5KFw3p7Vzgs/TrvRpzZLcfzuWauvDE3jJNOSf8SnK7gMgzqwv9Lt5paLSr0W46W+vrvLI/k4c0v8Zr63fKNO36sLBEa4pKdXI/361t7fkOf+eR4+lK/MF2bwDQcsyPQiglGlKDR94Nw1BuRrLPhUaO+U5zPigKODfLl0a7qUa7qRnzP3FODl6+eZ++2Fnhdr9nVhdKktvCYM+FOku/3iu7Ka/fTYpLqvSPFVvkOG4ekpet19a5z0XbsMv99RJsRtia1JqLUdJKIHowP8oacjKS9LMR/fTI4m8iPRQ39R5t2lnqPv95WL9c2Qz3i0c99N5m9e/mvrutJB04VK+nVxXq8WXf6uT8bD3905M054MiPbiouY+vrNvpPK4NNifK0Rp/HOeAfTXJs2kn98v1OvYe0D2DC1kg5rDgJ4QqKyu1Zs0aSdKkSZN83iczM1MjR47UokWLtHTp0nZH1mys1/cv3y2zrkYJWd3U49L7lZR9eLue0+pacrWGkqp6/dFj27mt+6p05hOrVLTf9+KZ0qpar8+Zkqrr7frzf77V3A+26bheWV5Xcgpmw64KXT2iQB8X7vf52GdWFcoM8Nbw2uJSVdT4Xh1cXdeoLXurfjjwNH7YgSi8V2fydaI41LsYAIhfEWmtadf3/75dDRXfy0hKUbcL7lZa/o/a9ZzwLdHWvGjHc3HO/jZcMWroD1dm/GjrflXX233ep3B/jR5ctEmzVm31ek3XSUSeLZOattF1bKW7v7JOBd0ydKCm3uPNXt/bsLfnDdT2ToCiywACiURrJVN7X/2dardvkCRlT5ihrCHntPM5pVP65+r/xsT+RBUAgLVEprWSvbbKudgnc/Bpyjn9ehb7hJCvxT7t4brYx2a4X+Ciut6uYo8LQHXJSHZ+7HnM53qF5Ife26zZq4uUnZGk80/opdfW7VLh/qbbPScue76uZPo89+v42HPC08uf7FB+boZO7uc+OSjQlZEb7abbc/j6eiJxDNuayU7tnRjFxCqg/SLW2vpDKnn3UUlSSu+j1f2ie2VLSW/388Jbe3eX37qvSo8sdt/N3XWi8Mtrt+vAoQb1y01TxaEGHaq36+OiEl09op/P3fTcnueb/Tr5/qXO1nru5ufP6IFdZTelGfM/UYPddF4ocuu+puYv2+Td2g8LSzSsX47WbStza0ag1haXuF9UK1oW0nZka1tz8SwA/jE/CqHmudhH8r87Xk5Gkj7auk8vf7JD2/1cIFmSctITZTNsajTtKq9ucL5ru3zzPs1eXei14Kam3v3Y3nGBx6QE9854Pu619TvdLvIsyWWuVhPP95kl6bhend12I2jJ8W5bO9iai1H6auX0kQUcqwIdjPlR1lFaVa+//Ce6Fvv4sttjw4NHl2xWp1Tvi0D5motc32g6F+Cu+Ga/rv3XWiUm2Nzus2VvpdfuPP6OtVxbI8lrga+jif6aJHk3zbW1k4f0oVOIOSz4CaFNmzbJNJsOD4499li/9zv22GO1aNEiffnll+1+TXvNAUlSYpfD1OPS+5XYuXu7nzNe+DoVXOxnsY/kPfHYU0mV97btLbG/sk4PvbdJA7p7r4yVgk9wPik/x+2NUH9eW79T/zcmcEhDwdeE41lThjo/Zht4AO0RkdZWV8hu2mUkp6n75HuV2vuYdj8nfGto2XufQTm2krWb0kPv+X7j1ZOvzrueVJ0+skB209Rflnzj9w3lPRWH9MvxR7gdlF54Ym/ZDCOkDWzv4tpQ7y4EILZEorWN1QfUWFkqyVDOGTeo0/Gnterx6ckJum7MAH1SXOJ2THZyPxY0AgCiTyRaK0lm/SFJUqch5yh7/DUyDFuQRyBa+NrAwPNzhfuqdPqjq9QrO00bdpYpKcFQp9REZacne+2WW1JVp5KquqBXnCzomuG2WMhuSk+v2ur3/p4Tnrbuq9LWfVXOCcqzp56kBJvhdUzrOnnZ0yvrdurtX4zQR4Ul2rCrQsf16qyrhntfzbQl2jM5uDUTg9s7MYpJyED7Ray1tU0/M1PzBqvbBffIlpwakudFeATaIKiopGkSVInLOeMVm/fplAeX6dhenUPW2jFHdlOCYchumtpeWh30XLZna1ds3udcqOTajNa0ttFuqq7B3u6dgxzPZYXWsnsgEBrMj0IklVbVa/k3JcHv57IzrqeXP9nu/DfscHJ+tk7p39VrNz/P94Y9F+p4TqBuiQHdM/X3K4bouufXtep4t63HjK25GKWvVkriWBXoYMyPspZ2XpeiQ1R7LGytc1nE48qzc76sKS7TL8cf4XNBa0uOsTxb86M+nZ079bjOY/J3/ObZNOZBIR4Ypudvr2izt956S+edd54kqaKiQllZWT7v9/jjj+umm25Sbm6u9u8PvECkd+/efm/btWtX0wdGgmzpWbxJGwqGfK8ECiGbzVBSgqH6RlN2l7PJiQmGGgKUPzHBUILNpuQfrhxR12gqOcFQRkrTur2y6jrVNdjl7//RiQmGumamhO4L8aOqtkEHDzUfNHdKTXSOEYg23333nRISElRX1/qdQxAZEWutjKbW2vh5ZgUpSQnKTk9SWXW9auvbdpVlw5B6ZHm/Kb+/stZvr202Q907paiqtsGr06Hk+XU5vt5wvy7QFrTWeiLXWsmWmikjseXHLDaboYzkBLefefwsBBBvaK31hKO1Ust6aySlsdNAnAl2zjmQTqmJqmu0q9bPjrmeUpJsAe/rOE/seUxrsxlu58mDPW9bzze357y1v+Pwlt43OcFo8Wu35rXQceittUSytUpIUkJaJzW94Yh40N7WSnJrRCDBWuvvvLhnaz3/HG+t5b3s6ERrrYf5UYglhiElJ9q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377zzTlOSOWTIkIDnWrFihefY0tLSgMedTqd58803h3wthYWF5ttvv235WqZPn+5p86JFi8y0tDTLa4wZM8Y8cOCAWVVVZY4bN87yGMMwzIULFwY8x7XXXmtKMo844gizpaUl6N+ytLTUNAzDlGQ++uijAY/PnTvXTElJCfo6s7KyzBdeeCHo9aurq83zzjsv7N/e+/d89NFHm5LMSy+9NOh1TdM0Fy5c6Dl/y5Ytpmm2/TsJ9TV//nyf6+zatcscPXp0yHNOPfVUs7KyMmR7ACCayFqylqwFgNgia8lashYAYousJWvJWgCIPfKWvCVvASC2yFqylqwFgNgia8lashbxQMEPkl5NTY05bNgwU5LpcDjM2bNnm2+//bZZUVFh7t2713zrrbfMO++80zzyyCN9zrvuuuvMG2+80XzhhRfMzZs3m19++aW5e/duc926deZPfvITMysry5Rk/vKXv7R8Xveb58CBA01J5vnnn2+uWrXKrKysNEtKSszXXnvNNE1XAE6cONHz5nj99debH3zwgblv3z7z3XffNS+99FJTkllcXNzhkP3Nb37j8+a7bNkys6KiwiwtLTXnzp1r5uXlmZLMvLw8s6SkJOB8d8gOGDDAzMjIMCdNmmT+97//NSsrK80dO3aYt956q+f6c+bMMS+88EIzNzfXfOihh8zt27ebVVVV5htvvOEJo7y8PLOqqsrnOd59913PNV555ZWgf0/3h42srCxz//79Po8999xznmuMGDHCXLJkiblnzx6zvLzcfPLJJ80jjjjClGSmpqaa69atC7j24cOHzdNOO81zjalTp5rLly839+zZY1ZWVprr1683H3jgAfPYY4/1+T0/8MADpiQzIyMj4HV5c/+bOOOMMzw/a2hoMA8ePOh5zr/+9a/mwYMHfb6am5s9x9fW1ppHHXWUKcns3bu3+bvf/c78+OOPzerqanPHjh3mH//4R7Nnz56mJHPSpElma2tr0PYAQLSQtWQtWQsAsUXWkrVkLQDEFllL1pK1ABB75C15S94CQGyRtWQtWQsAsUXWkrVkLeKFgh8kveuvv97zBvbcc88FPc77jSwSr776qinJzM3NNQ8cOBDwuHe15GWXXWY6nU7L6yxZssRz3O233255zLRp0zzHtDdk9+7da2ZkZJiSzNNPP91sbGwMOH/dunWeStmLL7444HF3yEoyJ0+ebFn1euWVV3oCLDU11XzrrbcCjtmyZYunInbevHkBj7sriy+66CLL34PT6TSLiopMSeZVV13l89jhw4fNfv36mZLMYcOGWYbdjh07zF69epmSqwLYnzssJZkPPvigZRvc7fD+HezZs8fz+3v44Yctz9mxY4fntT/xxBMBj7uf17+C1t+Pf/xjU3JVH5eVlVke895773n+5v/85z9DXg8AooGsJWvdyFoAiA2ylqx1I2sBIDbIWrLWjawFgNghb8lbN/IWAGKDrCVr3chaAIgNspasdSNr0dUo+EFSq62t9bzZXHHFFVG/fp8+fUxJ5rJlywIec4dsSkqKuXv37qDXOP/8801JZr9+/YJuyVdZWWmmp6d3KGR/+9vfeh7bsGFD0Hb84Ac/8LS3oqLC5zHvkN22bZvl+a+88ornmCuvvDLo87i3f7v66qsDHnv44YdNSWZaWprlFnBvvvmm5zlWrFjh89g///lPz2OLFy8O+vzeFcabN2/2eWzAgAGmJHPcuHFBzw/moosuMiWZJ554ouXjc+bM8XwoO3jwYMDjkYRsXV2dmZOTY0oyH3/88ZDtmTFjhinJnDJlSrteBwC0F1lL1vojawEgushastYfWQsA0UXWkrX+yFoAiD7ylrz1R94CQHSRtWStP7IWAKKLrCVr/ZG16EoOAUnsrbfe0uHDhyVJM2bMaPf55eXlmjNnjs444wwVFhYqLS1NhmF4viorKyVJ27ZtC3qNE088UQMGDLB8zDRNvf3225KkCy64QOnp6ZbH9e7dWxMmTGh3+yVpzZo1kqTi4mKNHTs26HGXXXaZJKm1tVVr1661PGbo0KE6+uijLR8bNmyY5/vzzjsv6PMMHz5ckvTll18GPHbVVVcpMzNTzc3NWrRoUcDjTz75pOe5/H8f7teZkZGh73znO0Gf3/06vc+RpE8++URffPGFJGn69OlBzw9m1qxZkqT3339f7733ns9jTqdTCxculCRdeumlys3Nbff1JWnt2rWqr6+XJE2cOFF1dXVBv0aNGiVJ2rBhQ4eeCwAiRdaStf7IWgCILrKWrPVH1gJAdJG1ZK0/shYAoo+8JW/9kbcAEF1kLVnrj6wFgOgia8laf2QtuhIFP0hqO3bs8Hx/4okntuvcl19+Wcccc4zuuecerV27VtXV1WppabE8tra2Nuh1hg4dGvSx2tpa7d+/X5J0zDHHhGzPscceG77RFnbu3BnR+ccdd5zn+7KyMstjgn1YkKSsrKx2HdfY2BjwWH5+vi666CJJ0vz5830eO3DggF544QVJrg9MhmH4PO5+ncOHDw/6YUWSioqKlJOTI8n3dXbm34rk+mAxePBgSW0fBtzefPNNff7555Kkq6++ut3Xdtu6davn+6OOOko9evQI+vXTn/5UkjwfBAEgVshastYfWQsA0UXWkrX+yFoAiC6ylqz1R9YCQPSRt+StP/IWAKKLrCVr/ZG1ABBdZC1Z64+sRVei4AdJ7cCBA57ve/ToEfF5ZWVluvzyy1VXV6eioiI9/PDD2rhxo7788kvV1tbq4MGDOnjwoAYNGiRJQcNXkrKzs4M+VldX5/k+XKVlRysxDx48KCn86/d+3H2Ov5SUlIieM5LjTNO0/Lm7OvXDDz/Uxo0bPT9/5pln1NjYKIfDYVkhHenrlNp+l96vs6P/VtwcDodmzpwpSXr66ac91dxSW+iOGDFCZ555Zruv7Rbqw1ww3u0AgFgga8laK2QtAEQPWUvWWiFrASB6yFqy1gpZCwDRRd6St1bIWwCIHrKWrLVC1gJA9JC1ZK0VshZdhYIfJLW8vDzP98GCw8qTTz6pQ4cOKS8vT+vWrdONN96oMWPGqH///srLy1Nubq5yc3N93pg7wjs4vQPXSrjHg3EHRnuu35GQiZaJEyd6ttrzrk51V9l+4xvf8Hy48Rbp6/Q+xvt1dvTfirerr75aDodD1dXVevnllyVJ+/fv10svvSRJnhDuKO9/LwcOHJBpmhF9AUAskbVkrRWyFgCih6wla62QtQAQPWQtWWuFrAWA6CJvyVsr5C0ARA9ZS9ZaIWsBIHrIWrLWClmLrkLBD5Ka+81akt5///2Iz3Mfe/bZZ6tfv36Wx3z++eedDtmePXuqV69ekqQtW7aEPPaTTz7p0HMUFRVJkj7++OOQx3300UcB58SDYRi65pprJLkqaQ8dOqQtW7bo3XfflSTPY/7cbf7ss8/U1NQU9PplZWWqr6/3OUfq+L8Vb0OGDNG5554rqe0Dgvs1pKamavr06R26rpv3loze2/4BQDyRtWStP7IWAKKLrCVr/ZG1ABBdZC1Z64+sBYDoI2/JW3/kLQBEF1lL1vojawEgushastYfWYuuRMEPktqZZ56pzMxMSdLChQsjPs+99Vhra2vQYxYtWtS5xskVKOPGjZMk/ec//wkaDvv27dOqVas69Bzjx4+XJJWWlmrz5s1Bj1u8eLEk1xZ4Z5xxRoeeK1pmzJih1NRU7d+/Xy+++KInsHr37q3vfOc7lue4X+fhw4f173//O+i13a/T+xxJOuaYYzRw4EBJ0t///vcOt929DeAbb7yh8vJyT9snT56s/v37Bz0vNTVVUuh/cxMmTFBGRoYk6dlnn+1wGwEgmshastYfWQsA0UXWkrX+yFoAiC6ylqz1R9YCQPSRt+StP/IWAKKLrCVr/ZG1ABBdZC1Z64+sRVei4AdJrUePHp4qzKefflpLliwJemxLS4vne3f14ttvv62qqqqAYz/66CP95je/iUobr776aknSnj17dM8991ge89Of/jRkpWgoV111leeN+cc//rHnA4S3DRs26PHHH5ckTZkyRb179+7Qc0VL//79df7550uSHnvsMT311FOSpCuvvFLp6emW51xwwQWeCuhf/vKX2r9/f8AxpaWluv/++yVJY8aM0UknneTz+E033SRJeuutt/T73/8+aPtM0wwaht/97nfVp08fOZ1O/fznP9fGjRsltf2dg3H/zr/44ougx+Tl5enaa6+VJM2dO1crVqwIec1Dhw5p586dIY8BgM4ia8lab2QtAEQfWUvWeiNrASD6yFqy1htZCwCxQd6St97IWwCIPrKWrPVG1gJA9JG1ZK03shZdzgSS3P79+81hw4aZksyUlBTz+uuvN9955x2zsrLSrKioMNeuXWvefffdZlFRkeecFStWmJJMSeaYMWPM119/3dy7d69ZUlJizp0718zPzzf79+9vFhQUmJLMO++8M+B5J0yYYEoyp0+fHrJ9TqfTc6wk8/rrrzf/97//mVVVVeaGDRvMqVOnmpLM4uJiU5I5ZMiQgGt4t7e0tDTg8d/85jeex08//XTz9ddfNysrK82ysjLz4YcfNnv27GlKMvPy8izPnz59uinJnDBhQtDXUVpa6nmOFStWBD0ukmuZpmm+8sornuu5v/73v/+FPOfZZ5/1HHvMMceYL774orlnzx5z9+7d5oIFC8wBAwaYkszU1FRz3bp1Aec3NTWZp512mucal19+ufnf//7X3Lt3r7lv3z5z06ZN5u9+9zvzuOOOs/w9uf3sZz/zaXe/fv3M5ubmkG2/4IILTEnmsGHDzA0bNpgNDQ1mc3Oz2dzcbDqdTs9xtbW15jHHHON5HT/84Q/N1atXm3v37jWrq6vNzz77zHzxxRfNH/7wh2bv3r3Nhx56KOTzAkA0kLVkLVkLALFF1pK1ZC0AxBZZS9aStQAQe+QteUveAkBskbVkLVkLALFF1pK1ZC3ihYIfdAs7d+40TzzxxIA3bf8vbz/84Q+DHpefn2+uWbPGHDJkSKdD1jRNs6Kiwjz++OODPt9VV11l3nnnnR0OWafTad58880hX3thYaH51ltvWbYvHiHb0tJiDhw40HPNsWPHhjzebe7cuWZKSkrQ15mVlWW+8MILQc+vrq42zznnnLD/VkKF7JYtW3yOvfnmm8O2+8033zQNw7B8rvnz5/scu2fPHp8PZqG+/vjHP0b0ewOAziJryVqyFgBii6wla8laAIgtspasJWsBIPbIW/KWvAWA2CJryVqyFgBii6wla8laxINDQDdw5JFHauPGjVqwYIEmT56sfv36KS0tTX369NGJJ56oG2+8UW+99ZbPOY888ogWLFig0047TTk5OcrKytLw4cN144036r333tOZZ54Ztfb16dNHGzZs0L333qvjjz9eWVlZys/P17hx4zR//nwtWrSoU9c3DEMPPfSQ3nnnHV155ZU68sgjlZGRoby8PJ100kmaM2eOPv30U40bNy5Kr6jzUlJSNHPmTM9/u7dDDOfHP/6xPvjgA1177bUaNmyYsrKylJOTo2OPPVY/+clP9Omnn2rKlClBz8/Pz9fy5cv1wgsv6KKLLtLAgQOVnp6ugoICHX/88brmmmu0bNkyHXnkkUGvMXLkSJ9/H5G0/eyzz9by5ct1wQUXqH///kpNTQ16bL9+/bRy5Ur9+9//1ve+9z0VFRUpKytLaWlp6tu3r84880zdcssteuedd/SjH/0o7HMDQDSQtWQtWQsAsUXWkrVkLQDEFllL1pK1ABB75C15S94CQGyRtWQtWQsAsUXWkrVkLeLBME3TjHcjAMDKAw88oF/+8pfKysrSl19+qZ49e8a7SRGbPHmyXnvtNZ1++ul655134t0cAAAskbUAAMQWWQsAQGyRtQAAxB55CwBAbJG1AADEFlkLu2OHHwAJa8GCBZKkiy++2FYBu3v3br3xxhuSIq8GBgAgHshaAABii6wFACC2yFoAAGKPvAUAILbIWgAAYoushd1R8AMgIS1dulRbt26VJP3gBz+Ic2va5+GHH1Zra6t69uypyy+/PN7NAQDAElkLAEBskbUAAMQWWQsAQOyRtwAAxBZZCwBAbJG1SAap8W4AAHhrbW3Vpk2b9H//93+SpDPOOEPjxo2Lc6si09jYqFdeeUV//OMfJUk33HCDcnJy4twqAAB8kbUAAMQWWQsAQGyRtQAAxB55CwBAbJG1AADEFlmLZGKYpmnGuxEAIElFRUXauXOn578zMjL0zjvvaPTo0XFsVXhlZWUqLi72+dnQoUP1wQcfKDc3N06tAgAgEFkLAEBskbUAAMQWWQsAQOyRtwAAxBZZCwBAbJG1SDaOeDcAAPz16tVL55xzjlauXJnwAevviCOO0LRp07Rq1SoCFgCQsMhaAABii6wFACC2yFoAAGKPvAUAILbIWgAAYousRbJghx8AAAAAAAAAAAAAAAAAAAAAAAAggbDDDwAAAAAAAAAAAAAAAAAAAAAAAJBAKPgBAAAAAAAAAAAAAAAAAAAAAAAAEggFPwAAAAAAAAAAAAAAAAAAAAAAAEACoeAHAAAAAAAAAAAAAAAAAAAAAAAASCAU/AAAAAAAAAAAAAAAAAAAAAAAAAAJhIIfAAAAAAAAAAAAAAAAAAAAAAAAIIFQ8AMAAAAAAAAAAAAAAAAAAAAAAAAkEAp+AAAAAAAAAAAAAAAAAAAAAAAAgARCwQ8AAAAAAAAAAAAAAAAAAAAAAACQQCj4AQAAAAAAAAAAAAAAAAAAAAAAABIIBT8AAAAAAAAAAAAAAAAAAAAAAABAAqHgBwAAAAAAAAAAAAAAAAAAAAAAAEggFPwAAAAAAAAAAAAAAAAAAAAAAAAACYSCHwAAAAAAAAAAAAAAAAAAAAAAACCBUPADAAAAAAAAAAAAAAAAAAAAAAAAJJD/Dx0CC8/QxzekAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1680x1680 with 49 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 1661,
+       "width": 1662
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "panel_size = 3\n",
+    "n_cells = len(val_cell_type_indices)\n",
+    "\n",
+    "fig, axs = plt.subplots(ncols=n_cells, nrows=n_cells, figsize=(panel_size * n_cells, panel_size * n_cells))\n",
+    "\n",
+    "for i in range(n_cells):\n",
+    "    for j in range(n_cells):\n",
+    "\n",
+    "        ax = axs[i, j]\n",
+    "\n",
+    "        x = np.log1p(empirical_X_ng[i, :])\n",
+    "        y = np.log1p(model_X_ng[j, :])\n",
+    "\n",
+    "        # linear regression r-squared\n",
+    "        r_squared = np.corrcoef(x, y)[0, 1] ** 2\n",
+    "\n",
+    "        ax.scatter(x, y, s=1, label=f\"R² = {r_squared:.2f}\")\n",
+    "        ax.legend(loc='upper left')\n",
+    "\n",
+    "        ax.grid(False)\n",
+    "        ax.set_xlim((0, 6))\n",
+    "        ax.set_ylim((0, 6))\n",
+    "        ax.plot([0, 6], [0, 6], color='black', linestyle='--', linewidth=1)\n",
+    "        ax.set_xticks([0, 2, 4, 6])\n",
+    "        ax.set_yticks([0, 2, 4, 6])\n",
+    "\n",
+    "        # reduce tick font size\n",
+    "        ax.tick_params(axis='both', which='major', labelsize=11)\n",
+    "        ax.set_xlabel(f\"Empirical log1p(TPKC)\\n{val_cell_type_names[i]}\", fontsize=12)\n",
+    "        ax.set_ylabel(f\"{val_cell_type_names[j]}\\nModel log1p(TPKC)\", fontsize=12)\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### How sensitive the model is to the total number of mRNA UMIs?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_cell_type_indices = [\n",
+    "    2,  # astro\n",
+    "    3,  # oligo\n",
+    "    5,  # microglia\n",
+    "    12, # CD8\n",
+    "    10, # NK\n",
+    "    22, # entero\n",
+    "    59  # cardiomyocyte\n",
+    "]\n",
+    "\n",
+    "val_cell_type_names = [\n",
+    "    'astrocyte',\n",
+    "    'oligodendrocyte',\n",
+    "    'microglia',\n",
+    "    'CD8+ T cell',\n",
+    "    'NK',\n",
+    "    'enterocyte',\n",
+    "    'cardiomyocyte'\n",
+    "]\n",
+    "\n",
+    "total_mrna_umis_list = np.linspace(500, 40_000, 30)\n",
+    "\n",
+    "query_gene_ids = val_adata.var['feature_id'].values\n",
+    "query_gene_symbols = val_adata.var['feature_name'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c37bb3152dfc4398af5ccb488aba3975",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8e8ff95b2744b559e019f7e554df6c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfa7fbf14ed54a47b9723013072442d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46400764c1a14644b499a4729e8116ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71eadfcb586b41b483e00faaf08d9541",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbcb503473654b3dae0f1964b85bf4fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c63c90abb3a34825a6f9bf7acf1a699d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e05617d8f2734187a9e6d2478d7dccf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d667116a9d04ff8946e2c8bdfbdf24a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "770d39d8ffc64623b82d9b2028d44309",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9518d42371a04bc8b9637d088a519777",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a1af62615a31416391790951abca7794",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82aa8abd6bfc4313a7d6072e31d29e78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "model_id": "9656c9b74c454fccb812f8d59f801063",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# empirical gene expression\n",
+    "empirical_X_ng = val_adata.X[val_cell_type_indices, :].copy()\n",
+    "\n",
+    "# model gene expression\n",
+    "model_X_nmg = np.zeros((\n",
+    "    len(val_cell_type_indices),\n",
+    "    len(total_mrna_umis_list),\n",
+    "    len(query_gene_ids)))\n",
+    "\n",
+    "for n in range(len(val_cell_type_indices)):\n",
+    "\n",
+    "    # arguments\n",
+    "    row_index = val_cell_type_indices[n]\n",
+    "    row = val_adata.obs.iloc[row_index]\n",
+    "\n",
+    "    assay = row.assay\n",
+    "    suspension_type = row.suspension_type\n",
+    "    prompt_metadata_dict = {\n",
+    "        \"cell_type\": row.cell_type,\n",
+    "        \"tissue\": row.tissue,\n",
+    "        \"disease\": row.disease,\n",
+    "        \"sex\": row.sex,\n",
+    "    }\n",
+    "\n",
+    "    for m, total_mrna_umis in enumerate(total_mrna_umis_list):\n",
+    "\n",
+    "        query_chunk_size = 1_000\n",
+    "        max_counts = 500\n",
+    "        upper_percentile = 0.5  # doesn't matter, we only need the logits\n",
+    "        upper_pad = 1  # doesn't matter, we only need the logits\n",
+    "\n",
+    "        gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
+    "            assay=assay,\n",
+    "            suspension_type=suspension_type,\n",
+    "            prompt_metadata_dict=prompt_metadata_dict,\n",
+    "            total_mrna_umis=total_mrna_umis,\n",
+    "            query_gene_ids=query_gene_ids,\n",
+    "            query_chunk_size=query_chunk_size,\n",
+    "            total_prob_mass=upper_percentile,\n",
+    "            symmetric_range_pad=upper_pad,\n",
+    "            max_counts=max_counts,\n",
+    "        )\n",
+    "\n",
+    "        model_X_nmg[n, m, :] = gex_range_dict['gene_marginal_mean_q'].cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renormalize to TPKC\n",
+    "empirical_X_ng = 10_000 * empirical_X_ng / empirical_X_ng.sum(axis=1, keepdims=True)\n",
+    "model_X_nmg = 10_000 * model_X_nmg / model_X_nmg.sum(axis=2, keepdims=True)\n",
+    "\n",
+    "# bin lograithmmic gene expression into linear spaced quantiles\n",
+    "n_bins = 10\n",
+    "n_cells = len(val_cell_type_indices)\n",
+    "n_umis = len(total_mrna_umis_list)\n",
+    "r_squared_bnm = np.zeros((n_bins, n_cells, n_umis))\n",
+    "\n",
+    "for n in range(n_cells):\n",
+    "    # get the empirical mean expression for the cell type\n",
+    "    empirical_X_g = empirical_X_ng[n]\n",
+    "\n",
+    "    x = np.log1p(empirical_X_g)\n",
+    "    x_min = x.min()\n",
+    "    x_max = x.max()\n",
+    "    x_bins = np.linspace(x_min, x_max, n_bins + 1)\n",
+    "\n",
+    "    for b in range(n_bins):\n",
+    "        x_bin = x_bins[b]\n",
+    "        x_bin_next = x_bins[b + 1]\n",
+    "        x_bin_mask = x <= (x_bin + 1)\n",
+    "        x_bin = x[x_bin_mask]\n",
+    "\n",
+    "        for m in range(n_umis):\n",
+    "            y = np.log1p(model_X_nmg[n, m, :])\n",
+    "            y_bin = y[x_bin_mask]\n",
+    "            r_squared = np.corrcoef(x_bin, y_bin)[0, 1] ** 2\n",
+    "            r_squared_bnm[b, n, m] = r_squared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eva6/n+OOO65R20RHR7uGdvr888/11VdfeVR3Tk6OPvjgA0nVhzCqfLLWNE3XEEeByDAMXX/99ZJUazsrP6ss440//vGP2rJli6xWq+6//3798MMPyszMVGZmptatW6d///vfGjZsWI0n4wGA+Ep8Jb7WjfgKwBPEVmIrsbVuxFYAABDw/Jz0BXymrqeFS0tLzZSUFFOS2aNHD/Pw4cM1tt22bZuZkJBgSjJPPvnkGutnzZrVqCdjG+Ojjz4yJZmxsbFmXl5ejfW+elpYkvm3v/2txvrS0lLXU6g2m83s0qWLefDgwRrl/vOf/7j2s2nTpjrbWd/fya233up6Mnft2rW1ljn6aWHTNM2SkhKzS5cupiRz6NChNbapLF/f08LTp093lfvqq6+qrRs0aJDrePBGcz4tbJqmuXXrVtff3/bt213ltm/fbhqGYUoyt23bZpqmd08Lt23b1pRk3nXXXY36HgBCC/HVifjqRHytfd/EVwBNQWx1IrY6EVtr3zexFQAAtEb0dEXQW7x4sQ4cOCBJevzxx9W2bdsaZbp37657771XkvT9999r3bp1zdaeCy+8UO3atVNBQYFWrVrVbPV06dJFf/rTn2p8Hh4erjFjxkiSKioq9MADDyg5OblGuauvvtr1dKj7E9S1KSgocL0OHjyolStXavz48fr3v/8twzD04osv6qSTTmp02yMiIvSXv/xFkrR8+XJ98sknjd62UuW8N926ddMZZ5xRbV3lE8Pbtm3T119/3eR9t5QePXro9NNPl2mamjt3ruvzuXPnyjRNDRkyRN27d/e6noqKCknOeYwAoLGIr9URX4mvRyO+AmgqYmt1xFZi69GIrQAAINCRdEXQW758uSTnzdDo0aPrLHfVVVfV2MZTGRkZuv/++3X66acrKSlJYWFhMgzD9Tp48KAkadOmTV7VU59zzz1XVqu11nU9evRwLZ9//vm1lmnTpo3atWsnSdq3b1+9dcXFxble7du315AhQzR37lx17txZ69at06233trk9k+aNMl1U/bAAw80advNmze7fhS47rrragwtdPXVVys8PFxS1Q1uoKq8yT76xtV9nbdOPPFESdJTTz2lDz/8UHa73Sf7BRDciK81EV+Jr+6IrwCaithaE7GV2OqO2AoAAAIdSVcEvV27dkmSevbs6bpZqU16erpiYmIkSTt37vS4vvfff1/HHHOMHn30Ua1atUpZWVmupzGPlpub63E9Danvyc+oqKgmlSsuLvaoDXv37tWdd96poqKiJm9rs9lccwetXr3aNcdNY7jPI1PbvDFJSUkaOXKkJGn+/PkqKSlpcvtaypVXXqmIiAht3bpVK1eu1MqVK7V161ZFREToyiuv9Ekdjz/+uMLDw5WZmalRo0YpJSVFY8aM0TPPPKMff/zRJ3UACD7E15qIr8RXd8RXAE1FbK2J2EpsdUdsBQAAgY6kK4Jefn6+JOcTrQ2JjY2ttk1T7dy5U1dffbUKCgqUnp6uf/7zn1qzZo327dun3Nxc5efnKz8/X6mpqZJU5w2tL9T1pLAn5UzTbHB95evQoUP63//+p0GDBkmSli5dqqlTpzaqLUcbN26c+vbtK8n5xHBD7ahsS+XTtL169VJubq7WrFlT43XKKadIcv54sGjRIo/aFxYWJkkqKytrVHn3cpXbNiQxMVGjRo2S5HxKuPKm/OKLL1ZCQkITWlu3wYMH65tvvtGll16qsLAwHT58WO+9957uvvtunXjiiTruuOP04Ycf+qQuAMGD+Op9OeJr7YivAEIVsdX7csTW2hFbAQAAWobN3w0AmlvlDWtBQUGDZSvLNOYmtzYzZ85USUmJ2rRpo2+++UYpKSm1lsvLy/No/61BUlKSLrjgAp155pkaPHiwfvrpJ82cOVM33HBDjflpGmK1WvXwww/rqquu0o8//qgFCxZo7Nix9W7z5Zdfavfu3ZKkLVu2aODAgQ3WM3v2bF199dVNapsk141jY5/6zs7Odi0nJiY2up7x48dr4cKFevvtt6t95ksnnXSS3nvvPRUWFurbb7/VqlWr9Omnn2r58uXasGGDRo0apQULFujyyy/3ab0AWi/ia8sivtaN+AogWBBbWxaxtW7EVgAAAM/Q0xVBLz09XZLzJqa+pzp37typwsLCats01Q8//CBJGj58eJ03rbt37w7qG9dK0dHRmjlzpmtOmnvvvdej/YwdO1YnnHCCJOnBBx+Uw+Got7wn89x89tlnDc79U5vK+YWKiopcQ4HV55dffpEkdejQQdHR0Y2u58ILL1S7du2UnZ2t7OxstWvXThdeeGGT29sYMTExGj58uP785z9r2bJl+uGHH5SUlCRJeuSRR5qlTgCtE/HVP4ivNRFfAQQLYqt/EFtrIrYCAAB4hqQrgt7QoUMlSaWlpfrvf/9bZ7n58+fX2KaS+3A6dru9zn2UlpY2WKZy+KBQcPLJJ+uqq66SJK1YsUJLlixp8j4Mw3DdMP36669644036ixbUFCghQsXSnIOYeQ+dFRtrzVr1khy/nu9/vrrTW6b+3Hy/vvv11v24MGDWrlypSTpzDPPbFI9YWFh1Z5mvuaaa2SztcxABccff7zr33Djxo0tUieA1oH46j/E1yrEVwDBhNjqP8TWKsRWAAAAz5F0RdAbNWqU68nde++9Vzk5OTXK7NixQ3//+98lOW+2TjrppGrrk5OTXcu//fZbnXV1795dkvMm7fDhwzXWr1+/Xo8//niTv0Nr9uCDD7rm3vnrX//q0T5Gjx7tmmfn4YcfrrPcwoULXU98X3vttQ3u9+STT1afPn0kyTXfTFNccMEF6tq1qyTp73//e53Hhmmauuuuu1zzIN18881NruvRRx/Vr7/+ql9//dXjv8faFBYWas+ePfWW2bZtmyS5nhoGAIn46m/EV+IrgOBDbPUvYiuxFQAAwFskXRH0wsPD9fzzz0tyDtN0+umna9GiRTpw4IB+++03zZ49W2eccYays7Nls9n0r3/9q8Y+TjrppGo3X3v37lV5ebkqKipcNyOSXE9VZmVl6fzzz9dnn32mzMxM7dixQ88//7zOPPNMxcbGqm3bti3wzQND3759XTeRy5Yt0/Llyz3aT+XNWuVNVG0qh2eKjY3V6NGjG7XfyratX79ea9eubVKbrFarXnrpJRmGof3792vQoEF68cUXtWnTJuXk5Gjv3r3673//q/POO8/1lPO1116r4cOHN6keSWrTpo369u2rvn37qk2bNk3evi4HDx5U9+7ddckll2jWrFn6+eefdejQIe3fv18rV67U+PHj9cknn0hyPqUMAJWIr/5FfCW+Agg+xFb/IrYSWwEAALxmAkHiyy+/NCWZksxZs2bVWP/cc8+ZVqvVVeboV1RUlPnuu+/Wuf8JEybUua27W2+9tc5yiYmJ5vLly820tDRTkvnggw/WWc9ZZ53l0d9DffuuNGvWrFrb3pR9uf99NGTLli2mzWYzJZkjRoyotm7Hjh2u/cyYMaPe/QwdOrTa3+ef//xn17qdO3eahmGYkszrrruuwTa5t61yf7fffnujt3P3xhtvmNHR0XX+u1e+rrvuOrO4uLjO/VT+fY8bN67JbRg3bpwpyUxLS6t330cfV+5///W9RowYYRYUFDS5XQBaP+KrE/GV+FrfvomvAJqC2OpEbCW21rdvYisAAGiN6OmKkHHHHXfoxx9/1JQpU9SjRw9FRUUpJiZGxx57rO666y5t3rxZl112WZ3bz5gxQ0888YROOeUUxcXFyTCMWsv961//0muvvabTTjtNMTExioqKUs+ePTV16lStW7dOZ5xxRnN9xYDVs2dPjR8/XpL0+eef65tvvvFoP48++mid6+bOnSvTNCU1bngm97ZVDv80b948lZeXN7ld1157rbZv366HHnpIQ4YMUXJysmw2m+Li4tSnTx/deOONWrFihebOnavIyMgm7785de3aVStWrNDDDz+sc845Rz169FBMTIzCw8OVmpqq0aNH6+2339ann36qmJgYfzcXQAAivvoP8ZX4CiA4EVv9h9hKbAUAAPCGYVZe6QEAAAAAAAAAAAAAmoyergAAAAAAAAAAAADgBZKuAAAAAAAAAAAAAOAFkq4AAAAAAAAAAAAA4AWSrgAAAAAAAAAAAADgBZKuAAAAAAAAAAAAAOAFkq4AAAAAAAAAAAAA4AWSrgAAAAAAAAAAAADgBZKuAAAAAAAAAAAAAOAFkq4AAAAAgP9vz44FAAAAAAb5Ww9ib2kEAAAADNIVAAAAAAAAYJCuAAAAAAAAAIN0BQAAAAAAABikKwAAAAAAAMAgXQEAAAAAAAAG6QoAAAAAAAAwSFcAAAAAAACAQboCAAAAAAAADNIVAAAAAAAAYJCuAAAAAAAAAIN0BQAAAAAAABgCj7h5i+YyLkEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 960x480 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 459,
+       "width": 942
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "panel_size = 3\n",
+    "ncols = 4\n",
+    "nrows = n_cells // ncols + 1\n",
+    "\n",
+    "fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(panel_size * ncols, panel_size * nrows))\n",
+    "\n",
+    "axs_flat = axs.flatten()\n",
+    "i_ax = 0\n",
+    "\n",
+    "for n in range(n_cells):\n",
+    "    ax = axs_flat[i_ax]\n",
+    "\n",
+    "    r_squared_bm = r_squared_bnm[:, n, :]\n",
+    "\n",
+    "    for b in range(n_bins):\n",
+    "        color = plt.cm.viridis_r(b / (n_bins - 1))\n",
+    "        ax.plot(\n",
+    "            total_mrna_umis_list,\n",
+    "            r_squared_bm[b],\n",
+    "            color=color,\n",
+    "            # label=f\"log1p(TPKC) bin {b + 1}\",\n",
+    "            marker='.',\n",
+    "            markersize=2,\n",
+    "            lw=1)\n",
+    "    \n",
+    "    ax.axvline(\n",
+    "        x=val_adata.obs.iloc[val_cell_type_indices[n]].total_mrna_umis,\n",
+    "        color='red',\n",
+    "        linestyle='--',\n",
+    "        linewidth=1,\n",
+    "        label=f'{int(val_adata.obs.iloc[val_cell_type_indices[n]].total_mrna_umis)} UMIs'\n",
+    "    )\n",
+    "    ax.legend(loc='lower left', fontsize=10)\n",
+    "    ax.grid(False)\n",
+    "    ax.set_title(val_cell_type_names[n])\n",
+    "    ax.set_xlabel(\"Total mRNA UMIs\", fontsize=12)\n",
+    "    ax.set_ylabel(\"R²\", fontsize=12)\n",
+    "    ax.set_ylim((0.5, 1)) \n",
+    "    ax.set_xlim((0.8 * min(total_mrna_umis_list), 1.2 * max(total_mrna_umis_list)))\n",
+    "    ax.set_xscale('log')\n",
+    "\n",
+    "    i_ax += 1\n",
+    "\n",
+    "for i_ax in range(i_ax, len(axs_flat)):\n",
+    "    axs_flat[i_ax].remove()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/12_linear_response_by_cell_type_prompting_script.ipynb b/notebooks/cellariumgpt_inference/12_linear_response_by_cell_type_prompting_script.ipynb
new file mode 100644
index 00000000..f0e0e760
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/12_linear_response_by_cell_type_prompting_script.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import pickle\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.linreg import batch_linear_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# global parameters\n",
+    "cuda_device_index = 0\n",
+    "checkpoint_path = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ref_adata_path = \"/home/mehrtash/data/data/extract_0.h5ad\"\n",
+    "gene_info_tsv_path = \"/home/mehrtash/data/gene_info/gene_info.tsv\"\n",
+    "validation_adata_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad\"\n",
+    "output_path = \"/home/mehrtash/data/data/linear_response/100M_long_run_last_checkpoint\"\n",
+    "cell_index = 0\n",
+    "\n",
+    "# gene expression range determination parameters\n",
+    "query_chunk_size = 1_000  # int\n",
+    "total_prob_mass = 0.5  # float\n",
+    "max_counts = 1000 # int\n",
+    "symmetric_range_pad = 1 # int\n",
+    "max_query_genes = None # int or None\n",
+    "total_mrna_umis = None # int or None\n",
+    "\n",
+    "# linear response analysis parameters\n",
+    "n_points = 5 # int\n",
+    "query_chunk_size_linear_response = 64 # int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.makedirs(output_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load validation anndata\n",
+    "val_adata = sc.read_h5ad(validation_adata_path)\n",
+    "print(f\"Total number of cells in validation anndata: {val_adata.shape[0]}\")\n",
+    "\n",
+    "assert cell_index < val_adata.shape[0], \"cell_index is out of range\"\n",
+    "print(f\"Selected cell index: {cell_index}\")\n",
+    "val_adata_row = val_adata.obs.iloc[cell_index]\n",
+    "print(val_adata_row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Loading CellariumGPT checkpoint ...\")\n",
+    "device = torch.device(f'cuda:{cuda_device_index}')\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "    ref_adata_path=ref_adata_path,\n",
+    "    gene_info_tsv_path=gene_info_tsv_path,\n",
+    "    device=device,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_query_gene_ids = val_adata.var['feature_id'].values\n",
+    "print(f\"Total number of query genes from validation AnnData: {len(all_query_gene_ids)}\")\n",
+    "\n",
+    "if max_query_genes is not None:\n",
+    "    query_gene_ids = all_query_gene_ids[:max_query_genes]\n",
+    "    print(f\"Limiting to {max_query_genes} first query genes for linear response analysis.\")\n",
+    "else:\n",
+    "    query_gene_ids = all_query_gene_ids\n",
+    "    print(f\"Using all {len(all_query_gene_ids)} query genes for linear response analysis.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assay = val_adata_row.assay\n",
+    "suspension_type = val_adata_row.suspension_type\n",
+    "prompt_metadata_dict = {\n",
+    "    \"cell_type\": val_adata_row.cell_type,\n",
+    "    \"tissue\": val_adata_row.tissue,\n",
+    "    \"disease\": val_adata_row.disease,\n",
+    "    \"sex\": val_adata_row.sex,\n",
+    "}\n",
+    "\n",
+    "if total_mrna_umis is None:\n",
+    "    total_mrna_umis = float(val_adata_row.total_mrna_umis)\n",
+    "    print(f\"Using total_mrna_umis from validation AnnData: {total_mrna_umis:.3f}\")\n",
+    "else:\n",
+    "    print(f\"Overriding total_mrna_umis from validation AnnData: {val_adata_row.total_mrna_umis:3f} with {total_mrna_umis:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Determining gene expression range ...\")\n",
+    "gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
+    "    assay=assay,\n",
+    "    suspension_type=suspension_type,\n",
+    "    prompt_metadata_dict=prompt_metadata_dict,\n",
+    "    total_mrna_umis=total_mrna_umis,\n",
+    "    query_gene_ids=query_gene_ids,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    total_prob_mass=total_prob_mass,\n",
+    "    symmetric_range_pad=symmetric_range_pad,\n",
+    "    max_counts=max_counts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dose_response_dict = ctx.generate_gene_dose_response_for_metadata(\n",
+    "    assay=assay,\n",
+    "    suspension_type=suspension_type,\n",
+    "    prompt_metadata_dict=prompt_metadata_dict,\n",
+    "    total_mrna_umis=total_mrna_umis,\n",
+    "    query_gene_ids=query_gene_ids,\n",
+    "    perturb_gene_ids=query_gene_ids,\n",
+    "    x_lo_p=gex_range_dict['x_lo_q'].cpu().numpy(),\n",
+    "    x_hi_p=gex_range_dict['x_hi_q'].cpu().numpy(),\n",
+    "    n_points=n_points,\n",
+    "    query_chunk_size=query_chunk_size_linear_response,\n",
+    "    max_counts=max_counts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Performing linear regression ...\")\n",
+    "n_query_vars = len(query_gene_ids)\n",
+    "n_perturb_vars = len(query_gene_ids)\n",
+    "\n",
+    "doses_pi = dose_response_dict['doses_pi']\n",
+    "responses_mean_pqi = dose_response_dict['responses_mean_pqi']\n",
+    "\n",
+    "slope_qp, intercept_qp, r_squared_qp = batch_linear_regression(\n",
+    "    x_bn=np.repeat(doses_pi[None, :, :], n_query_vars, axis=-3),\n",
+    "    y_bn=responses_mean_pqi.transpose(1, 0, 2)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Generating output ...\")\n",
+    "\n",
+    "output_dict = {\n",
+    "\n",
+    "    # global parameters\n",
+    "    \"checkpoint_path\": checkpoint_path,\n",
+    "    \"validation_adata_path\": validation_adata_path,\n",
+    "    \"cell_index\": cell_index,\n",
+    "\n",
+    "    # gene expression range determination parameters\n",
+    "    \"total_prob_mass\": total_prob_mass,\n",
+    "    \"max_counts\": max_counts,\n",
+    "    \"symmetric_range_pad\": symmetric_range_pad,\n",
+    "    \"max_query_genes\": max_query_genes,\n",
+    "\n",
+    "    # linear response analysis parameters\n",
+    "    \"n_points\": n_points,\n",
+    "\n",
+    "    # prompt metadata\n",
+    "    \"assay\": assay,\n",
+    "    \"suspension_type\": suspension_type,\n",
+    "    \"total_mrna_umis\": total_mrna_umis,\n",
+    "    \"prompt_metadata_dict\": prompt_metadata_dict,\n",
+    "    \"cell_index\": cell_index,\n",
+    "\n",
+    "    # expression range\n",
+    "    \"x_lo_q\": gex_range_dict['x_lo_q'].cpu().numpy(),\n",
+    "    \"x_hi_q\": gex_range_dict['x_hi_q'].cpu().numpy(),\n",
+    "    \"range_q\": gex_range_dict['range_q'].cpu().numpy(),\n",
+    "    \"gene_logits_qk\": gex_range_dict['gene_logits_qk'].cpu().numpy(),\n",
+    "    \"gene_logits_mode_q\": gex_range_dict['gene_logits_mode_q'].cpu().numpy(),\n",
+    "    \"gene_marginal_mean_q\": gex_range_dict['gene_marginal_mean_q'].cpu().numpy(),\n",
+    "    \"gene_marginal_std_q\": gex_range_dict['gene_marginal_std_q'].cpu().numpy(),\n",
+    "\n",
+    "    # dose response\n",
+    "    \"query_gene_ids\": query_gene_ids,\n",
+    "    \"perturb_gene_ids\": query_gene_ids,\n",
+    "    \"doses_pi\": dose_response_dict['doses_pi'],\n",
+    "    \"responses_mean_pqi\": dose_response_dict['responses_mean_pqi'],\n",
+    "\n",
+    "    # linear regression\n",
+    "    \"slope_qp\": slope_qp,\n",
+    "    \"intercept_qp\": intercept_qp,\n",
+    "    \"r_squared_qp\": r_squared_qp,\n",
+    "}\n",
+    "\n",
+    "print(\"Saving output ...\")\n",
+    "output_file_name = os.path.join(output_path, f\"linear_response_cell_index_{cell_index}.pkl\")\n",
+    "\n",
+    "with open(output_file_name, 'wb') as f:\n",
+    "    pickle.dump(output_dict, f)\n",
+    "\n",
+    "print(\"Script finished successfully.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/linear_response_analysis.py b/scripts/linear_response_analysis.py
new file mode 100644
index 00000000..94a4d576
--- /dev/null
+++ b/scripts/linear_response_analysis.py
@@ -0,0 +1,216 @@
+#!/usr/bin/env python
+import os
+import torch
+import pickle
+import warnings
+import numpy as np
+import scanpy as sc
+import pandas as pd
+import matplotlib.pyplot as plt
+import argparse
+
+from cellarium.ml.utilities.inference.cellarium_gpt_inference import CellariumGPTInferenceContext
+from cellarium.ml.utilities.linreg import batch_linear_regression
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Command line tool for gene expression range and linear response analysis using CellariumGPT."
+    )
+    
+    # Global parameters
+    parser.add_argument("--cuda_device_index", type=int, default=0,
+                        help="CUDA device index (default: 0)")
+    parser.add_argument("--checkpoint_path", type=str,
+                        default="/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt",
+                        help="Path to CellariumGPT checkpoint")
+    parser.add_argument("--ref_adata_path", type=str,
+                        default="/home/mehrtash/data/data/extract_0.h5ad",
+                        help="Path to reference AnnData file")
+    parser.add_argument("--gene_info_tsv_path", type=str,
+                        default="/home/mehrtash/data/gene_info/gene_info.tsv",
+                        help="Path to gene info TSV")
+    parser.add_argument("--validation_adata_path", type=str,
+                        default="/home/mehrtash/data/data/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad",
+                        help="Path to validation AnnData file")
+    parser.add_argument("--output_path", type=str,
+                        default="/home/mehrtash/data/data/linear_response/100M_long_run_last_checkpoint",
+                        help="Directory for output files")
+    parser.add_argument("--cell_index", type=int, default=0,
+                        help="Index of the cell in the validation AnnData to analyze")
+    
+    # Gene expression range determination parameters
+    parser.add_argument("--query_chunk_size", type=int, default=1000,
+                        help="Chunk size for gene expression query (default: 1000)")
+    parser.add_argument("--total_prob_mass", type=float, default=0.5,
+                        help="Total probability mass (default: 0.5)")
+    parser.add_argument("--max_counts", type=int, default=1000,
+                        help="Maximum counts (default: 1000)")
+    parser.add_argument("--symmetric_range_pad", type=int, default=1,
+                        help="Symmetric range pad (default: 1)")
+    parser.add_argument("--max_query_genes", type=int, default=None,
+                        help="Maximum number of query genes (default: None, meaning use all)")
+    parser.add_argument("--total_mrna_umis", type=float, default=None,
+                        help="Total mRNA UMIs (default: None, will be taken from the AnnData)")
+    
+    # Linear response analysis parameters
+    parser.add_argument("--n_points", type=int, default=5,
+                        help="Number of points for the linear response analysis (default: 5)")
+    parser.add_argument("--query_chunk_size_linear_response", type=int, default=64,
+                        help="Query chunk size for linear response analysis (default: 64)")
+    
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+    
+    # Create output directory if it doesn't exist
+    os.makedirs(args.output_path, exist_ok=True)
+    
+    # Load the validation AnnData
+    print(f"Loading validation AnnData from {args.validation_adata_path} ...")
+    val_adata = sc.read_h5ad(args.validation_adata_path)
+    print(f"Total number of cells in validation AnnData: {val_adata.shape[0]}")
+    
+    if args.cell_index >= val_adata.shape[0]:
+        raise ValueError("cell_index is out of range")
+    
+    print(f"Selected cell index: {args.cell_index}")
+    val_adata_row = val_adata.obs.iloc[args.cell_index]
+    print(val_adata_row)
+    
+    # Set device and load the checkpoint
+    print("Loading CellariumGPT checkpoint ...")
+    device = torch.device(f'cuda:{args.cuda_device_index}')
+    
+    ctx = CellariumGPTInferenceContext(
+        cellarium_gpt_ckpt_path=args.checkpoint_path,
+        ref_adata_path=args.ref_adata_path,
+        gene_info_tsv_path=args.gene_info_tsv_path,
+        device=device,
+        attention_backend="mem_efficient",
+        verbose=False
+    )
+    
+    all_query_gene_ids = val_adata.var['feature_id'].values
+    print(f"Total number of query genes from validation AnnData: {len(all_query_gene_ids)}")
+    
+    if args.max_query_genes is not None:
+        query_gene_ids = all_query_gene_ids[:args.max_query_genes]
+        print(f"Limiting to {args.max_query_genes} first query genes for linear response analysis.")
+    else:
+        query_gene_ids = all_query_gene_ids
+        print(f"Using all {len(all_query_gene_ids)} query genes for linear response analysis.")
+    
+    assay = val_adata_row.assay
+    suspension_type = val_adata_row.suspension_type
+    prompt_metadata_dict = {
+        "cell_type": val_adata_row.cell_type,
+        "tissue": val_adata_row.tissue,
+        "disease": val_adata_row.disease,
+        "sex": val_adata_row.sex,
+    }
+    
+    if args.total_mrna_umis is None:
+        total_mrna_umis = float(val_adata_row.total_mrna_umis)
+        print(f"Using total_mrna_umis from validation AnnData: {total_mrna_umis:.3f}")
+    else:
+        total_mrna_umis = args.total_mrna_umis
+        print(f"Overriding total_mrna_umis from validation AnnData with {total_mrna_umis:.3f}")
+    
+    print("Determining gene expression range ...")
+    gex_range_dict = ctx.predict_gene_expression_range_for_metadata(
+        assay=assay,
+        suspension_type=suspension_type,
+        prompt_metadata_dict=prompt_metadata_dict,
+        total_mrna_umis=total_mrna_umis,
+        query_gene_ids=query_gene_ids,
+        query_chunk_size=args.query_chunk_size,
+        total_prob_mass=args.total_prob_mass,
+        symmetric_range_pad=args.symmetric_range_pad,
+        max_counts=args.max_counts,
+    )
+    
+    print("Generating gene dose response ...")
+    dose_response_dict = ctx.generate_gene_dose_response_for_metadata(
+        assay=assay,
+        suspension_type=suspension_type,
+        prompt_metadata_dict=prompt_metadata_dict,
+        total_mrna_umis=total_mrna_umis,
+        query_gene_ids=query_gene_ids,
+        perturb_gene_ids=query_gene_ids,
+        x_lo_p=gex_range_dict['x_lo_q'].cpu().numpy(),
+        x_hi_p=gex_range_dict['x_hi_q'].cpu().numpy(),
+        n_points=args.n_points,
+        query_chunk_size=args.query_chunk_size_linear_response,
+        max_counts=args.max_counts,
+    )
+    
+    print("Performing linear regression ...")
+    n_query_vars = len(query_gene_ids)
+    doses_pi = dose_response_dict['doses_pi']
+    responses_mean_pqi = dose_response_dict['responses_mean_pqi']
+    
+    # Repeat doses_pi along a new axis so that its shape matches the required dimensions
+    x_bn = np.repeat(doses_pi[None, :, :], n_query_vars, axis=-3)
+    # Transpose responses_mean_pqi to match the expected dimensions
+    y_bn = responses_mean_pqi.transpose(1, 0, 2)
+    
+    slope_qp, intercept_qp, r_squared_qp = batch_linear_regression(
+        x_bn=x_bn,
+        y_bn=y_bn
+    )
+    
+    print("Generating output ...")
+    output_dict = {
+        # Global parameters
+        "checkpoint_path": args.checkpoint_path,
+        "validation_adata_path": args.validation_adata_path,
+        "cell_index": args.cell_index,
+        
+        # Gene expression range determination parameters
+        "total_prob_mass": args.total_prob_mass,
+        "max_counts": args.max_counts,
+        "symmetric_range_pad": args.symmetric_range_pad,
+        "max_query_genes": args.max_query_genes,
+        
+        # Linear response analysis parameters
+        "n_points": args.n_points,
+        
+        # Prompt metadata
+        "assay": assay,
+        "suspension_type": suspension_type,
+        "total_mrna_umis": total_mrna_umis,
+        "prompt_metadata_dict": prompt_metadata_dict,
+        "cell_index": args.cell_index,
+        
+        # Expression range outputs
+        "x_lo_q": gex_range_dict['x_lo_q'].cpu().numpy(),
+        "x_hi_q": gex_range_dict['x_hi_q'].cpu().numpy(),
+        "range_q": gex_range_dict['range_q'].cpu().numpy(),
+        "gene_logits_qk": gex_range_dict['gene_logits_qk'].cpu().numpy(),
+        "gene_logits_mode_q": gex_range_dict['gene_logits_mode_q'].cpu().numpy(),
+        "gene_marginal_mean_q": gex_range_dict['gene_marginal_mean_q'].cpu().numpy(),
+        "gene_marginal_std_q": gex_range_dict['gene_marginal_std_q'].cpu().numpy(),
+        
+        # Dose response outputs
+        "query_gene_ids": query_gene_ids,
+        "perturb_gene_ids": query_gene_ids,
+        "doses_pi": dose_response_dict['doses_pi'],
+        "responses_mean_pqi": dose_response_dict['responses_mean_pqi'],
+        
+        # Linear regression outputs
+        "slope_qp": slope_qp,
+        "intercept_qp": intercept_qp,
+        "r_squared_qp": r_squared_qp,
+    }
+    
+    print("Saving output ...")
+    output_file_name = os.path.join(args.output_path, f"linear_response_cell_index_{args.cell_index}.pkl")
+    
+    with open(output_file_name, 'wb') as f:
+        pickle.dump(output_dict, f)
+    
+    print("Script finished successfully.")
+
+if __name__ == "__main__":
+    main()

From 75f98fea7368bcd30eceb861013eedbd53a3e9a4 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 15 Feb 2025 00:10:15 +0000
Subject: [PATCH 41/64] tqdm auto

---
 cellarium/ml/utilities/inference/cellarium_gpt_inference.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 36b536da..73731451 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -10,7 +10,7 @@
 import scanpy as sc
 import pandas as pd
 from scanpy import AnnData
-from tqdm.notebook import tqdm
+from tqdm.auto import tqdm
 from functools import cached_property
 from more_itertools import chunked
 

From ebb29aaee19ecc86c10467b35dc443430abfdedd Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 15 Feb 2025 00:12:10 +0000
Subject: [PATCH 42/64] remove unused imports

---
 scripts/linear_response_analysis.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/scripts/linear_response_analysis.py b/scripts/linear_response_analysis.py
index 94a4d576..3d137090 100644
--- a/scripts/linear_response_analysis.py
+++ b/scripts/linear_response_analysis.py
@@ -2,11 +2,8 @@
 import os
 import torch
 import pickle
-import warnings
 import numpy as np
 import scanpy as sc
-import pandas as pd
-import matplotlib.pyplot as plt
 import argparse
 
 from cellarium.ml.utilities.inference.cellarium_gpt_inference import CellariumGPTInferenceContext

From d606c6a95dfc43bebf3beaad5cd7bab5af3c9da6 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sat, 15 Feb 2025 00:41:33 +0000
Subject: [PATCH 43/64] bash script for linear response analysis

---
 scripts/run_linear_response_analysis.sh | 74 +++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 scripts/run_linear_response_analysis.sh

diff --git a/scripts/run_linear_response_analysis.sh b/scripts/run_linear_response_analysis.sh
new file mode 100644
index 00000000..fc525b42
--- /dev/null
+++ b/scripts/run_linear_response_analysis.sh
@@ -0,0 +1,74 @@
+#!/bin/bash
+# run_linear_response_analysis.sh
+# Usage: ./run_linear_response_analysis.sh <cuda_device_index>
+# Example: ./run_linear_response_analysis.sh 1
+
+# Check if a CUDA device index was provided; exit if not.
+if [ -z "$1" ]; then
+    echo "Usage: $0 <cuda_device_index>"
+    exit 1
+fi
+
+CUDA_DEVICE_INDEX="$1"
+
+# Default parameters (feel free to modify these defaults)
+CHECKPOINT_PATH="/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt"
+REF_ADATA_PATH="/home/mehrtash/data/data/extract_0.h5ad"
+GENE_INFO_TSV_PATH="/home/mehrtash/data/gene_info/gene_info.tsv"
+VALIDATION_ADATA_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad"
+OUTPUT_PATH="/home/mehrtash/data/data/linear_response/100M_long_run_last_checkpoint"
+
+QUERY_CHUNK_SIZE=1000
+TOTAL_PROB_MASS=0.5
+MAX_COUNTS=1000
+SYMMETRIC_RANGE_PAD=1
+
+# Leave these empty to use the default (None) behavior in the python script.
+MAX_QUERY_GENES=""
+TOTAL_MRNA_UMIS=""
+
+N_POINTS=5
+QUERY_CHUNK_SIZE_LINEAR_RESPONSE=64
+
+# Settings for cell index splitting
+CELLS_PER_GPU=8
+
+# Compute the start and end cell indices for this device.
+START_CELL=$((CUDA_DEVICE_INDEX * CELLS_PER_GPU))
+END_CELL=$((START_CELL + CELLS_PER_GPU - 1))
+echo "CUDA device index: $CUDA_DEVICE_INDEX"
+echo "Processing cell indices from $START_CELL to $END_CELL"
+
+# Loop through the designated cell indices using seq for portability
+for cell_index in $(seq $START_CELL $END_CELL); do
+    echo "Processing cell index: $cell_index"
+    
+    # Build the command; conditionally add parameters if they are not empty.
+    CMD="python linear_response_analysis.py \
+        --cuda_device_index $CUDA_DEVICE_INDEX \
+        --checkpoint_path \"$CHECKPOINT_PATH\" \
+        --ref_adata_path \"$REF_ADATA_PATH\" \
+        --gene_info_tsv_path \"$GENE_INFO_TSV_PATH\" \
+        --validation_adata_path \"$VALIDATION_ADATA_PATH\" \
+        --output_path \"$OUTPUT_PATH\" \
+        --cell_index $cell_index \
+        --query_chunk_size $QUERY_CHUNK_SIZE \
+        --total_prob_mass $TOTAL_PROB_MASS \
+        --max_counts $MAX_COUNTS \
+        --symmetric_range_pad $SYMMETRIC_RANGE_PAD"
+    
+    # Append --max_query_genes if set
+    if [ -n "$MAX_QUERY_GENES" ]; then
+        CMD="$CMD --max_query_genes $MAX_QUERY_GENES"
+    fi
+    # Append --total_mrna_umis if set
+    if [ -n "$TOTAL_MRNA_UMIS" ]; then
+        CMD="$CMD --total_mrna_umis $TOTAL_MRNA_UMIS"
+    fi
+
+    CMD="$CMD --n_points $N_POINTS \
+        --query_chunk_size_linear_response $QUERY_CHUNK_SIZE_LINEAR_RESPONSE"
+
+    echo "Running command: $CMD"
+    eval $CMD
+done

From b369dc7f6e7e253adc13910625832630fa3a88d9 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Thu, 20 Feb 2025 21:18:07 +0000
Subject: [PATCH 44/64] metadata benchmarking intro

---
 .../inference/cellarium_gpt_inference.py      |    78 +-
 .../metadata_benchmarking/__init__.py         |     0
 .../metadata_benchmarking/build_resources.py  |   111 +
 .../calculate_metrics.py                      |   246 +
 .../inference/metadata_benchmarking/utils.py  |   213 +
 ...near_response_by_cell_type_prompting.ipynb |    27 +-
 ...cell_types_for_gene_network_analysis.ipynb |   219 +-
 ...on_by_cell_type_prompting_comparison.ipynb |    41 +-
 .../13_marker_gene_experiments.ipynb          |   644 +
 .../14_linear_response_network_analysis.ipynb |   357 +
 .../01_generate_resources.ipynb               | 24498 ++++++++++++++++
 .../xx_cell_type_ontology.ipynb               |  1246 +
 .../xx_development_stage_ontology.ipynb       |   854 +
 .../xx_disease_ontology.ipynb                 |   868 +
 .../xx_sex_ontology.ipynb                     |   318 +
 .../xx_tissue_ontology.ipynb                  |   720 +
 16 files changed, 30372 insertions(+), 68 deletions(-)
 create mode 100644 cellarium/ml/utilities/inference/metadata_benchmarking/__init__.py
 create mode 100644 cellarium/ml/utilities/inference/metadata_benchmarking/build_resources.py
 create mode 100644 cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
 create mode 100644 cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
 create mode 100644 notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 73731451..db3034ae 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -306,8 +306,8 @@ def get_gene_value_logits_by_metadata(
         prompt_metadata_dict: dict,
         total_mrna_umis: int,
         query_gene_ids: list[list],
-        max_counts: int | None = None
-    ) -> t.Tuple[dict, dict]:
+        max_counts: int | None = None,
+    ) -> torch.Tensor:
         
         metadata_prompt_masks_dict, metadata_dict = self.process_user_metadata(
             assay, suspension_type, prompt_metadata_dict, total_mrna_umis)
@@ -518,10 +518,10 @@ def get_marginal_mean_std(
         log_counts_2_k = torch.arange(0, MAX_COUNTS, device=gene_logits_qk.device).pow(2).log()
         gene_mom_1_q = torch.logsumexp(gene_logits_qk + log_counts_1_k[None, :], dim=-1).exp()
         gene_mom_2_q = torch.logsumexp(gene_logits_qk + log_counts_2_k[None, :], dim=-1).exp()
-        gene_marginal_means_q = gene_mom_1_q
+        gene_marginal_mean_q = gene_mom_1_q
         gene_marginal_std_q = torch.clamp(gene_mom_2_q - gene_mom_1_q.pow(2), 0.).sqrt()
 
-        return gene_marginal_means_q, gene_marginal_std_q
+        return gene_marginal_mean_q, gene_marginal_std_q
 
 
     def get_gene_value_logits_from_tokens(
@@ -583,7 +583,7 @@ def calculate_gene_mean_std_from_logits(
             log_counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2).log()
             gene_mom_1_nq = torch.logsumexp(gene_logits_nqk + log_counts_1_k[None, None, :], dim=-1).exp()
             gene_mom_2_nq = torch.logsumexp(gene_logits_nqk + log_counts_2_k[None, None, :], dim=-1).exp()
-            gene_marginal_means_nq = gene_mom_1_nq
+            gene_marginal_mean_nq = gene_mom_1_nq
             gene_marginal_std_nq = torch.clamp(gene_mom_2_nq - gene_mom_1_nq.pow(2), 0.).sqrt()
         else:
             gene_probs_nqk = torch.exp(gene_logits_nqk)
@@ -591,10 +591,10 @@ def calculate_gene_mean_std_from_logits(
             counts_2_k = torch.arange(0, max_counts, device=gene_logits_nqk.device).pow(2)
             gene_mom_1_nq = (gene_probs_nqk * counts_1_k[None, None, :]).sum(dim=-1)
             gene_mom_2_nq = (gene_probs_nqk * counts_2_k[None, None, :]).sum(dim=-1)
-            gene_marginal_means_nq = gene_mom_1_nq
+            gene_marginal_mean_nq = gene_mom_1_nq
             gene_marginal_std_nq = torch.clamp(gene_mom_2_nq - gene_mom_1_nq.pow(2), 0.).sqrt()
 
-        return gene_marginal_means_nq, gene_marginal_std_nq
+        return gene_marginal_mean_nq, gene_marginal_std_nq
 
 
     def get_marginal_mean_std_multi_cell(
@@ -677,25 +677,14 @@ def predict_gene_expression_range_for_metadata(
 
         assert 0.0 < total_prob_mass < 1.0, "The upper percentile must be between 0 and 1."
 
-        if query_chunk_size is None:
-            query_chunk_size = len(query_gene_ids)
-        
-        partial_gene_logits_list = []
-        with torch.inference_mode():
-            # Process gene IDs in chunks to limit memory usage
-            for partial_query_gene_ids in tqdm(list(chunked(query_gene_ids, query_chunk_size)),
-                                            desc="Processing gene chunks"):
-                partial_gene_logits_nqk = self.get_gene_value_logits_by_metadata(
-                    assay=assay,
-                    suspension_type=suspension_type,
-                    prompt_metadata_dict=prompt_metadata_dict,
-                    total_mrna_umis=total_mrna_umis,
-                    query_gene_ids=partial_query_gene_ids,
-                    max_counts=max_counts,
-                )
-                partial_gene_logits_list.append(partial_gene_logits_nqk)
-
-            gene_logits_qk = torch.cat(partial_gene_logits_list, dim=1)[0]
+        gene_logits_qk = self.get_gene_value_logits_by_metadata_chunked(
+            assay=assay,
+            suspension_type=suspension_type,
+            prompt_metadata_dict=prompt_metadata_dict,
+            total_mrna_umis=total_mrna_umis,
+            query_gene_ids=query_gene_ids,
+            query_chunk_size=query_chunk_size,
+            max_counts=max_counts)[0]
 
         # first, find the mode of the counts distribution for each gene
         gene_logits_mode_q = torch.argmax(gene_logits_qk, dim=1)
@@ -736,6 +725,39 @@ def predict_gene_expression_range_for_metadata(
             'gene_marginal_std_q': gene_marginal_std_nq[0],
         }
 
+    def get_gene_value_logits_by_metadata_chunked(
+        self,
+        assay: str,
+        suspension_type: str,
+        prompt_metadata_dict: dict,
+        total_mrna_umis: int,
+        query_gene_ids: list[list],
+        max_counts: int | None = None,
+        query_chunk_size: int | None = None,
+    ) -> torch.Tensor:
+        
+        if query_chunk_size is None:
+            query_chunk_size = len(query_gene_ids)
+        
+        partial_gene_logits_list = []
+        with torch.inference_mode():
+            # Process gene IDs in chunks to limit memory usage
+            for partial_query_gene_ids in tqdm(list(chunked(query_gene_ids, query_chunk_size)),
+                                            desc="Processing gene chunks"):
+                partial_gene_logits_nqk = self.get_gene_value_logits_by_metadata(
+                    assay=assay,
+                    suspension_type=suspension_type,
+                    prompt_metadata_dict=prompt_metadata_dict,
+                    total_mrna_umis=total_mrna_umis,
+                    query_gene_ids=partial_query_gene_ids,
+                    max_counts=max_counts,
+                )
+                partial_gene_logits_list.append(partial_gene_logits_nqk)
+
+            gene_logits_nqk = torch.cat(partial_gene_logits_list, dim=1)
+
+        return gene_logits_nqk
+
     def generate_gene_dose_response_for_metadata(
         self,
         assay: str,
@@ -1539,9 +1561,9 @@ def from_old_jacobian_pt_dump(
             jacobian_qp=old_jac_dict['jacobian_qg'].cpu().numpy(),
             prompt_empirical_mean_p=prompt_empirical_mean_p,
             query_empirical_mean_q=query_empirical_mean_q,
-            prompt_marginal_mean_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            prompt_marginal_mean_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_mean_q'].cpu().numpy(),
             prompt_marginal_std_p=old_jac_dict['prompt_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
-            query_marginal_mean_q=old_jac_dict['query_marginal_dict']['gene_marginal_means_q'].cpu().numpy(),
+            query_marginal_mean_q=old_jac_dict['query_marginal_dict']['gene_marginal_mean_q'].cpu().numpy(),
             query_marginal_std_q=old_jac_dict['query_marginal_dict']['gene_marginal_std_q'].cpu().numpy(),
         )
 
diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/__init__.py b/cellarium/ml/utilities/inference/metadata_benchmarking/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/build_resources.py b/cellarium/ml/utilities/inference/metadata_benchmarking/build_resources.py
new file mode 100644
index 00000000..47bc5adc
--- /dev/null
+++ b/cellarium/ml/utilities/inference/metadata_benchmarking/build_resources.py
@@ -0,0 +1,111 @@
+import json
+import logging
+import typing as t
+
+import networkx as nx
+import owlready2
+from smart_open import open
+
+logging.basicConfig(level=logging.INFO)
+
+from cellarium.ml.utilities.inference.metadata_benchmarking import utils
+
+
+def build_ontology_ancestor_dictionary(
+    owl_graph: nx.DiGraph,
+    owl_names: t.List[str],
+    owl_names_to_idx_map: t.Dict[str, int]
+) -> t.Dict[str, t.List[str]]:
+    """
+    Build cell ontology ancestor dictionary resource. This resource allows a fast indexing of each CL node. It
+    contains all ancestors for each of the CL node. It allows to fetch all ancestors for any node with a runtime O(1).
+
+    :param owl_graph: CL ontology graph
+    :param owl_names: list of CL names
+    :param owl_names_to_idx_map: map from CL name to index
+    """
+    logging.info("Building cell ontology ancestor dictionary...")
+    owl_ancestors_dictionary = {}
+
+    for owl_name in owl_names:
+        current_ancestors = []
+        orders = []
+
+        for owl_ancestor_name in sorted(nx.ancestors(owl_graph, owl_name)):
+            cell_order_idx = owl_names_to_idx_map[owl_ancestor_name]
+            current_ancestors.append(owl_ancestor_name)
+            orders.append(cell_order_idx)
+
+        sorted_ancestors = [ancestor for _, ancestor in sorted(zip(orders, current_ancestors))]
+
+        owl_ancestors_dictionary[owl_name] = sorted_ancestors
+
+    return owl_ancestors_dictionary
+
+
+def build_benchmarking_ontology_dictionary_resource(
+    owl_graph: nx.DiGraph,
+    owl_names: t.List[str],
+    n_hops: int
+) -> t.Dict[str, t.Any]:
+    """
+    Build an ontology ancestor and descendant dictionary resource.
+
+    This resource provides fast indexing of each node up to a specified number of hops (`n_hops`).
+    It includes ancestors and descendants for all nodes, along with hop level information.
+    The hop level information contains:
+
+        * Ancestor nodes for each hop, up to `n_hops` levels deep:
+            - If `hop_1`, it includes immediate ancestors.
+            - If `hop_2`, it includes ancestors of ancestors, and so on.
+
+        * Descendant nodes for each hop, up to `n_hops` levels deep:
+            - If `hop_1`, it includes immediate descendants.
+            - If `hop_2`, it includes descendants of descendants, and so on.
+
+        * Union of all ancestor and descendant nodes for each hop level.
+
+    The resource allows retrieval of all ancestors and descendants for any node at any hop with O(1) runtime.
+
+    Output Example
+    --------------
+
+    .. code-block:: python
+
+        {
+            "key1": "value1",
+            "key2": "value2",
+            "nested_key": {
+                "subkey1": "subvalue1",
+                "subkey2": "subvalue2"
+            }
+        }
+
+    :param owl_graph: The ontology graph.
+    :param owl_names: A list of node names.
+    :param n_hops: The number of hops to consider.
+    """
+    ontology_resource_dict = {}
+
+    for owl_name in owl_names:
+        ontology_resource_dict[owl_name] = {}
+        ontology_resource_dict[owl_name]["all_ancestors"] = utils.get_all_ancestors(owl_graph, node=owl_name)
+        ontology_resource_dict[owl_name]["all_descendants"] = utils.get_all_descendants(owl_graph, node=owl_name)
+
+        for top_n in range(n_hops):
+            nodes = utils.get_n_level_ancestors(owl_graph, node=owl_name, n=top_n)
+            hop_all_ancestors = set.union(
+                *[utils.get_all_ancestors(owl_graph, node=node_cl_name) for node_cl_name in list(nodes)]
+            )
+            hop_all_descendants = set.union(
+                *[utils.get_all_descendants(owl_graph, node=node_cl_name) for node_cl_name in list(nodes)]
+            )
+
+            ontology_resource_dict[owl_name][f"hop_{top_n}"] = {
+                "nodes": nodes,
+                "all_ancestors": hop_all_ancestors,
+                "all_descendants": hop_all_descendants,
+            }
+
+    return ontology_resource_dict
+
diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py b/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
new file mode 100644
index 00000000..2f8d5d81
--- /dev/null
+++ b/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
@@ -0,0 +1,246 @@
+import concurrent.futures as concurrency
+import logging
+import multiprocessing
+import traceback
+import typing as t
+
+import numpy as np
+import anndata
+import pandas as pd
+from smart_open import open
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_precision(tp: float, fp: float) -> float:
+    """
+    Calculate precision.
+
+    :param tp: True positives.
+    :param fp: False positives.
+    :return: Precision value.
+    """
+    try:
+        return tp / (tp + fp)
+    except ZeroDivisionError:
+        return 0.0
+
+
+def calculate_f1(precision: float, recall: float) -> float:
+    """
+    Calculate F1 score.
+
+    :param precision: Precision value.
+    :param recall: Recall value.
+
+    :return: F1 score.
+    """
+    try:
+        return (2 * precision * recall) / (precision + recall)
+    except ZeroDivisionError:
+        return 0.0
+
+
+def calculate_tps_and_fps(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    num_hops: int,
+    ontology_resource: t.Dict[str, t.Any]
+) -> t.Tuple[t.List[float], t.List[float]]:
+    """
+    Calculate true positives and false positives for each hop level.
+
+    :param query_cell_obj: The query object containing ontology-aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param num_hops: Number of hops to consider.
+    :param co_resource: Ontology helper resource which has already precalculated top_n_level descendants and
+        ancestors.
+
+    :return: A tuple of lists containing true positive and false positive scores for each hop level.
+    """
+    hops = [ontology_resource[ground_truth_ontology_term_id][f"hop_{i}"] for i in range(num_hops + 1)]
+    true_positives = [0.0] * len(hops)
+    false_positives = [0.0] * len(hops)
+
+    for match in query_obj["matches"]:
+        match_ontology_term_id = match["ontology_term_id"]
+        match_score = match["score"]
+        match_ontology_data = ontology_resource[match_ontology_term_id]
+        match_ancestors = match_ontology_data["all_ancestors"]
+
+        for i, hop in enumerate(hops):
+            hop_match_intersect = hop["nodes"].intersection(match_ancestors)
+            hop_all_descendants = hop["all_descendants"]
+            hop_all_ancestors = hop["all_ancestors"]
+
+            if match_ontology_term_id in hop_match_intersect:
+                true_positives[i] = max(match_score, true_positives[i])
+            elif match_ontology_term_id not in hop_all_descendants.union(hop_all_ancestors):
+                false_positives[i] = max(match_score, false_positives[i])
+
+    return true_positives, false_positives
+
+
+def calculate_metrics_for_query(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    ontology_resource: t.Dict[str, t.Any],
+    num_hops=4,
+):
+    """
+    Calculate performance metrics for a query cell against the ground truth.
+
+    :param query_obj: The query object containing ontology aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param ontology_resource: Ontology precalculated resource used in CELLxGENE.
+    :param num_hops: Number of hops to consider.
+
+    :return: A dictionary containing sensitivity, specificity, and F1 score for each hop level.
+    """
+    if ground_truth_ontology_term_id not in ontology_resource:
+        metrics_na = {
+            "query_cell_id": query_obj["query_cell_id"],
+            "detail": f"Couldn't find {ground_truth_ontology_term_id} in the ontology resource",
+        }
+        for hop in range(num_hops + 1):
+            for metric in ["sensitivity", "specificity", "f1_score"]:
+                metrics_na[f"hop_{hop}_{metric}"] = np.nan
+
+        return metrics_na
+
+    true_positives, false_positives = calculate_tps_and_fps(
+        query_obj=query_obj,
+        ground_truth_ontology_term_id=ground_truth_ontology_term_id,
+        num_hops=num_hops,
+        ontology_resource=ontology_resource,
+    )
+
+    sensitivities = [tp for tp in true_positives]
+    specificities = [1 - fp for fp in false_positives]
+    precisions = [calculate_precision(tp=tp, fp=fp) for tp, fp, in zip(true_positives, false_positives)]
+    f1_scores = [
+        calculate_f1(precision=precision, recall=sensitivity)
+        for precision, sensitivity in zip(precisions, sensitivities)
+    ]
+
+    query_cell_metrics = {"query_cell_id": query_obj["query_cell_id"], "detail": ""}
+
+    for i, (sensitivity, specificity, f1_score) in enumerate(zip(sensitivities, specificities, f1_scores)):
+        query_cell_metrics[f"hop_{i}_sensitivity"] = sensitivity
+        query_cell_metrics[f"hop_{i}_specificity"] = specificity
+        query_cell_metrics[f"hop_{i}_f1_score"] = f1_score
+
+    return query_cell_metrics
+
+def calculate_metrics_for_prediction_output(
+    ground_truth_ontology_term_ids: t.Iterable[str],
+    model_predictions: t.List[t.Dict[str, t.Any]],
+    ontology_resource: t.Dict[str, t.Any],
+    num_hops: int,
+) -> pd.DataFrame:
+    """
+    Calculate performance metrics for metadata predictions.
+
+    This function processes the predictions and ground truth terms from the input AnnData object,
+    computes performance metrics (sensitivity, specificity, and F1 score) for each hop level, and returns
+    the results as a pandas DataFrame.
+
+    :param ground_truth_ontology_term_ids: Iterable containing ground truths to benchmark against
+    :param model_predictions: The result from the metadata predictions, containing query results and matches for each cell.
+    :param co_resource: Cell ontology precalculated resource used in CZI.
+    :param num_hops: Number of hops to consider.
+
+    :return: A pandas DataFrame with query cell IDs as the index and sensitivity, specificity, and F1 score
+         for each hop level as columns.
+    """
+    df_result = pd.DataFrame()
+
+    for query_res_obj, ground_truth in zip(model_predictions, ground_truth_ontology_term_ids):
+        query_cell_metrics = calculate_metrics_for_query(
+            query_obj=query_res_obj,
+            ground_truth_ontology_term_id=ground_truth,
+            ontology_resource=ontology_resource,
+            num_hops=num_hops,
+        )
+
+        df_result = pd.concat([df_result, pd.DataFrame([query_cell_metrics])])
+
+    df_result = df_result.set_index("query_cell_id")
+    return df_result
+
+
+def split_into_batches(data_list, batch_size):
+    """
+    Split a list into batches of a given size.
+
+    :param data_list: List of data to be split into batches.
+    :param batch_size: The size of each batch.
+
+    :return: List of batches, where each batch is a list.
+    """
+    return [data_list[i : i + batch_size] for i in range(0, len(data_list), batch_size)]
+
+
+def calculate_metrics_for_cas_output_in_batches(
+    ground_truth_ontology_term_ids: t.List[str],
+    model_predictions: t.Dict[str, t.Any],
+    ontology_resource: t.Dict[str, t.Any],
+    num_hops: int,
+    batch_size: int = 20000,
+) -> pd.DataFrame:
+    """
+    Calculate performance metrics in batches using multiprocessing.
+
+    :param ground_truth_ontology_term_ids: List of ground truth labels.
+    :param model_predictions: List of prediction result dictionaries.
+    :param ontology_resource: Ontology precalculated resource used in CELLxGENE.
+    :param num_hops: Number of hops for evaluation.
+    :param batch_size: Size of each batch. Default is 5000.
+
+    :raises ValueError: If the length of ground_truth_ontology_term_ids and cas_result do model_predictions match.
+
+    :return: DataFrame containing the calculated metrics.
+    """
+    result_dfs = []
+    num_workers = multiprocessing.cpu_count()
+    with concurrency.ProcessPoolExecutor(max_workers=num_workers) as executor:
+        futures = []
+
+        model_predictions_batches = split_into_batches(
+            data_list=model_predictions["data"], batch_size=batch_size)
+        ground_truth_ontology_term_ids_batches = split_into_batches(
+            data_list=ground_truth_ontology_term_ids, batch_size=batch_size)
+
+        for i, (model_predictions_batch, ground_truth_ontology_term_ids_batch) in enumerate(
+            zip(model_predictions_batches, ground_truth_ontology_term_ids_batches)
+        ):
+            calculate_metrics_kwargs = {
+                "model_predictions": model_predictions_batch,
+                "ground_truth_ontology_term_ids": ground_truth_ontology_term_ids_batch,
+                "ontology_resource": ontology_resource,
+                "num_hops": num_hops,
+            }
+            logger.info(f"Submitting batch {i}. Length: {len(model_predictions_batch)}...")
+            future = executor.submit(calculate_metrics_for_prediction_output, **calculate_metrics_kwargs)
+            futures.append(future)
+
+        logger.info("Waiting for metrics to be calculated...")
+        done, not_done = concurrency.wait(futures, return_when=concurrency.ALL_COMPLETED)
+        logger.info("Aggregating results")
+        for future in done:
+            try:
+                # Attempt to get the result of the future
+                batch_metrics_df = future.result()
+            except Exception as e:
+                # If an exception is raised, print the exception details
+                logging.error(f"Exception occurred: {e}")
+                # Format and print the full traceback
+                traceback.print_exception(type(e), e, e.__traceback__)
+            else:
+                result_dfs.append(batch_metrics_df)
+
+    df_result = pd.concat(result_dfs)
+    logger.info("Done.")
+    return df_result
+
+
diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py b/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
new file mode 100644
index 00000000..8973d182
--- /dev/null
+++ b/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
@@ -0,0 +1,213 @@
+import logging
+import typing as t
+
+import networkx as nx
+import owlready2
+
+
+def build_nx_graph_from_owl_ontology(
+    owl_ontology: owlready2.Ontology, owl_classes: t.List[owlready2.EntityClass]
+) -> nx.DiGraph:
+    """
+    Build a directed graph from an OWL ontology. Ontology object only considers immediate parents, while traversing the
+    ontology ancestors, while in this algorithm we need to consider all ancestors. Network X library provides this
+    functionality as we build the whole graph based on immediate parents and children of each node.
+
+    :param owl_ontology: Ontology object
+    :param owl_classes: List of ontology classes
+
+    :return: Directed graph
+    """
+    logging.info("Building nx graph from OWL ontology...")
+    owl_graph = nx.DiGraph(name="OWL graph")
+    owl_classes_set = set(owl_classes)
+
+    for owl_class in owl_classes:
+        owl_graph.add_node(owl_class.name.replace("_", ":"))
+
+    for owl_class in owl_classes:
+        for parent_owl_class in owl_ontology.get_parents_of(entity=owl_class):
+            if parent_owl_class in owl_classes_set:
+                owl_graph.add_edge(
+                    u_of_edge=parent_owl_class.name.replace("_", ":"), v_of_edge=owl_class.name.replace("_", ":"))
+
+        for child_owl_class in owl_ontology.get_children_of(Class=owl_class):
+            if child_owl_class in owl_classes_set:
+                owl_graph.add_edge(
+                    u_of_edge=owl_class.name.replace("_", ":"), v_of_edge=child_owl_class.name.replace("_", ":"))
+
+    return owl_graph
+
+
+def get_n_level_ancestors(graph: nx.DiGraph, node: str, n: int) -> t.Set[str]:
+    """
+    Return the set of all ancestors of a given node in a directed graph up to `n` levels deep.
+
+    This function traverses `n` levels of the graph in reverse (from child to parent), starting from the specified
+    `node`. It collects all nodes that are reachable from `node` within `n` levels of predecessors (ancestors).
+    The original node is also included in the resulting set.
+
+    :param graph: A directed graph represented by a NetworkX DiGraph object, where nodes represent entities and
+        directed edges represent relationships between them.
+    :param node: The node for which to find ancestors. This is the starting point of the traversal.
+    :param n: The number of levels to traverse upwards in the graph, i.e., how far back to go in the graph to find
+        ancestors. If n = 1, it returns immediate predecessors; if n = 2, it includes predecessors of predecessors,
+        and so on.
+
+    :return: A set containing the node itself and all ancestors reachable within `n` levels from the node. If the node
+        has no predecessors or if `n` is 0, the set will contain only the node.
+
+    Example
+    _______
+
+    .. code-block:: python
+
+        >>> import networkx as nx
+        >>> G = nx.DiGraph()
+        >>> G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D')])
+        >>> get_n_level_ancestors(G, 'D', 2)
+        >>> # Output: {'B', 'C', 'D'}
+
+    :notes:
+        - The function includes the node itself in the result.
+        - Predecessors (ancestors) are determined by traversing the graph from the node upward.
+        - If `n` is greater than the number of levels in the graph, the function will return all possible ancestors
+          of the node.
+    """
+    ancestors = set()
+    current_level = {node}
+
+    for _ in range(n):
+        current_level = {pred for ancestor in current_level for pred in graph.predecessors(ancestor)}
+        ancestors.update(current_level)
+
+    ancestors.add(node)
+    return ancestors
+
+
+def get_n_level_descendants(graph: nx.DiGraph, node: str, n: int) -> t.Set[str]:
+    """
+    Return the set of all descendants of a given node in a directed graph up to `n` levels deep.
+
+    This function traverses `n` levels of the graph (from parent to child), starting from the specified `node`.
+    It collects all nodes that are reachable from `node` within `n` levels of successors (descendants).
+    The original node is also included in the resulting set.
+
+    :param graph: A directed graph represented by a NetworkX DiGraph object, where nodes represent entities and
+        directed edges represent relationships between them.
+
+    :param node: The node for which to find descendants. This is the starting point of the traversal.
+
+    :param n: The number of levels to traverse downwards in the graph, i.e., how far to go in the graph to find
+        descendants. If n = 1, it returns immediate successors; if n = 2, it includes successors of successors,
+        and so on.
+
+    :return:
+        A set containing the node itself and all descendants reachable within `n` levels from the node.
+        If the node has no successors or if `n` is 0, the set will contain only the node.
+
+    Example
+    _______
+
+    .. code-block:: python
+
+        >>> import networkx as nx
+        >>> G = nx.DiGraph()
+        >>> G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D')])
+        >>> get_n_level_descendants(G, 'A', 2)
+        >>> # Output: {'A', 'B', 'C'}
+
+    :notes:
+        - The function includes the node itself in the result.
+        - Successors (descendants) are determined by traversing the graph from the node downward.
+        - If `n` is greater than the number of levels in the graph, the function will return all possible descendants
+          of the node.
+    """
+    descendants = set()
+    current_level = {node}
+
+    for _ in range(n):
+        current_level = {succ for descendant in current_level for succ in graph.successors(descendant)}
+        descendants.update(current_level)
+
+    descendants.add(node)
+    return descendants
+
+
+def get_all_ancestors(graph: nx.DiGraph, node: str) -> t.Set[str]:
+    """
+    Return the set of all ancestors of a given node in a directed graph.
+
+    This function finds all ancestors of the specified `node` by traversing the directed graph upwards.
+    It collects all nodes that have a path leading to the `node`. The original node is also included in the resulting set.
+
+    :param graph: A directed graph represented by a NetworkX DiGraph object, where nodes represent entities and
+        directed edges represent relationships between them.
+
+    :param node: The node for which to find ancestors. This is the starting point of the traversal.
+
+    :return: A set containing the node itself and all ancestors reachable from the node. If the node has no ancestors,
+        the set will contain only the node.
+
+    Example
+    _______
+
+    .. code-block:: python
+
+        >>> import networkx as nx
+        >>> G = nx.DiGraph()
+        >>> G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D')])
+        >>> get_all_ancestors(G, 'D')
+        >>> # Output: {'B', 'C', 'D'}
+
+    :notes:
+        - The function includes the node itself in the result.
+        - Ancestors are determined by traversing the graph from the node upward.
+        - The function returns all ancestors, regardless of how many levels deep they are.
+    """
+    ancestors = {node}
+
+    for anc in nx.ancestors(graph, node):
+        ancestors.add(anc)
+
+    return ancestors
+
+
+def get_all_descendants(graph: nx.DiGraph, node: str) -> t.Set[str]:
+    """
+    Return the set of all descendants of a given node in a directed graph.
+
+    This function finds all descendants of the specified `node` by traversing the directed graph downwards.
+    It collects all nodes that have a path originating from the `node`. The original node is also included in the
+    resulting set.
+
+    :param graph: A directed graph represented by a NetworkX DiGraph object, where nodes represent entities and
+        directed edges represent relationships between them.
+
+    :param node: The node for which to find descendants. This is the starting point of the traversal.
+
+    :return: A set containing the node itself and all descendants reachable from the node. If the node has no
+        descendants, the set will contain only the node.
+
+    Example
+    _______
+
+    .. code-block:: python
+
+        >>> import networkx as nx
+        >>> G = nx.DiGraph()
+        >>> G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D')])
+        >>> get_all_descendants(G, 'A')
+        >>> # Output: {'A', 'B', 'C', 'D'}
+
+    :notes:
+        - The function includes the node itself in the result.
+        - Descendants are determined by traversing the graph from the node downward.
+        - The function returns all descendants, regardless of how many levels deep they are.
+    """
+    descendants = {node}
+
+    for desc in nx.descendants(graph, node):
+        descendants.add(desc)
+
+    return descendants
diff --git a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
index b1152be6..f583a449 100644
--- a/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
+++ b/notebooks/cellariumgpt_inference/09_linear_response_by_cell_type_prompting.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,22 +81,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ba5d175e5bb44f990ec454e69fa19f0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Processing gene chunks:   0%|          | 0/37 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'ctx' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m max_counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2000\u001b[39m\n\u001b[1;32m      4\u001b[0m upper_pad \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m----> 6\u001b[0m gex_range_dict \u001b[38;5;241m=\u001b[39m \u001b[43mctx\u001b[49m\u001b[38;5;241m.\u001b[39mpredict_gene_expression_range_for_metadata(\n\u001b[1;32m      7\u001b[0m     assay\u001b[38;5;241m=\u001b[39massay,\n\u001b[1;32m      8\u001b[0m     suspension_type\u001b[38;5;241m=\u001b[39msuspension_type,\n\u001b[1;32m      9\u001b[0m     prompt_metadata_dict\u001b[38;5;241m=\u001b[39mprompt_metadata_dict,\n\u001b[1;32m     10\u001b[0m     total_mrna_umis\u001b[38;5;241m=\u001b[39mtotal_mrna_umis,\n\u001b[1;32m     11\u001b[0m     query_gene_ids\u001b[38;5;241m=\u001b[39mquery_gene_ids,\n\u001b[1;32m     12\u001b[0m     query_chunk_size\u001b[38;5;241m=\u001b[39mquery_chunk_size,\n\u001b[1;32m     13\u001b[0m     total_prob_mass\u001b[38;5;241m=\u001b[39mupper_percentile,\n\u001b[1;32m     14\u001b[0m     symmetric_range_pad\u001b[38;5;241m=\u001b[39mupper_pad,\n\u001b[1;32m     15\u001b[0m     max_counts\u001b[38;5;241m=\u001b[39mmax_counts,\n\u001b[1;32m     16\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'ctx' is not defined"
+     ]
     }
    ],
    "source": [
diff --git a/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb b/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
index 9dfec37a..02922828 100644
--- a/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
+++ b/notebooks/cellariumgpt_inference/10_determine_cell_types_for_gene_network_analysis.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,16 +44,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "validation_meta_df = pd.read_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"cellxgene_validation_donors__third_draft.csv\"))"
+    "validation_meta_df = pd.read_csv(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,13 +71,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b9869031982544a6ae24473a0fb42074",
+       "model_id": "c84c4ef520494717b1269bef0b9a8a81",
        "version_major": 2,
        "version_minor": 0
       },
@@ -326,11 +326,12 @@
     "    val_adata = sc.read_h5ad(val_adata_path)\n",
     "\n",
     "    obs_df = val_adata.obs.copy()\n",
+    "    obs_df['adata_path'] = val_adata_path\n",
     "    obs_df['tissue_coarse'] = obs_df['tissue_ontology_term_id'].map(tissue_ontology_term_id_to_coarse_name_map)\n",
     "\n",
     "    columns_to_keep = [\n",
     "        \"cell_type\", \"assay\", \"suspension_type\", \"disease\", \"sex\", \"tissue_coarse\", \"total_mrna_umis\",\n",
-    "        \"cell_type_distance_from_root\", \"dataset_id\", \"donor_id\"]\n",
+    "        \"cell_type_distance_from_root\", \"dataset_id\", \"donor_id\", \"adata_path\"]\n",
     "\n",
     "    group_cols = [\"cell_type\", \"donor_id\"]\n",
     "\n",
@@ -351,13 +352,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "df = pd.concat(obs_list, ignore_index=True).drop_duplicates()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 190,
@@ -5506,7 +5514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 338,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5526,15 +5534,200 @@
     "_final_adata.X = 1e6 * _final_adata.X / _final_adata.X.sum(1, keepdims=True)\n",
     "_final_adata.var = _final_adata.var.reset_index(drop=True)\n",
     "\n",
-    "_final_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation.h5ad\"))"
+    "_final_adata.write_h5ad(os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation_filtered.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scratch space"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata = sc.read_h5ad(df[df['cell_type'] == 'enterocyte'].iloc[0].adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import typing as t\n",
+    "\n",
+    "\n",
+    "def load_gene_info_table(gene_info_tsv_path: str, included_gene_ids: list[str]) -> t.Tuple[pd.DataFrame, dict, dict]:\n",
+    "    gene_info_df = pd.read_csv(gene_info_tsv_path, sep=\"\\t\")\n",
+    "\n",
+    "    gene_symbol_to_gene_id_map = dict()\n",
+    "    for gene_symbol, gene_id in zip(gene_info_df['Gene Symbol'], gene_info_df['ENSEMBL Gene ID']):\n",
+    "        if gene_symbol != float('nan'):\n",
+    "            gene_symbol_to_gene_id_map[gene_symbol] = gene_id\n",
+    "\n",
+    "    gene_id_to_gene_symbol_map = {\n",
+    "        gene_id: gene_symbol for gene_symbol, gene_id in gene_symbol_to_gene_id_map.items()}\n",
+    "    for gene_id in included_gene_ids:\n",
+    "        if gene_id not in gene_id_to_gene_symbol_map:\n",
+    "            gene_id_to_gene_symbol_map[gene_id] = gene_id\n",
+    "\n",
+    "    return gene_info_df, gene_symbol_to_gene_id_map, gene_id_to_gene_symbol_map\n",
+    "\n",
+    "gene_info_df, gene_symbol_to_gene_id_map, gene_id_to_gene_symbol_map = load_gene_info_table(GENE_INFO_PATH, adata.var.index.values.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.var['feature_name'] = list(map(gene_id_to_gene_symbol_map.get, adata.var.index.values.tolist()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000187642</th>\n",
+       "      <td>PERM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000078808</th>\n",
+       "      <td>SDF4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000272106</th>\n",
+       "      <td>ENSG00000272106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000162585</th>\n",
+       "      <td>FAAP20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000272088</th>\n",
+       "      <td>ENSG00000272088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000224281</th>\n",
+       "      <td>SLC25A5-AS1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000122121</th>\n",
+       "      <td>XPNPEP2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000228836</th>\n",
+       "      <td>CT45A5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000231937</th>\n",
+       "      <td>ENSG00000231937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000268916</th>\n",
+       "      <td>CSAG3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>36601 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    feature_name\n",
+       "ENSG00000187642            PERM1\n",
+       "ENSG00000078808             SDF4\n",
+       "ENSG00000272106  ENSG00000272106\n",
+       "ENSG00000162585           FAAP20\n",
+       "ENSG00000272088  ENSG00000272088\n",
+       "...                          ...\n",
+       "ENSG00000224281      SLC25A5-AS1\n",
+       "ENSG00000122121          XPNPEP2\n",
+       "ENSG00000228836           CT45A5\n",
+       "ENSG00000231937  ENSG00000231937\n",
+       "ENSG00000268916            CSAG3\n",
+       "\n",
+       "[36601 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x440 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 391,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.highest_expr_genes(adata, n_top=20, gene_symbols='feature_name')"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
index 846237ec..455d2e5f 100644
--- a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
+++ b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
@@ -34,12 +34,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
     "ROOT_PATH = \"/home/mehrtash/data\"\n",
     "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\"\n",
     "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
     "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
     "\n",
@@ -55,7 +58,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129.ckpt\"\n",
+    "state = torch.load(CHECKPOINT_PATH, map_location=DEVICE, weights_only=False)\n",
+    "state['state_dict'].pop('pipeline.1.token_embedding.embedding_dict.gene_query_mask.weight');\n",
+    "state['state_dict'].pop('pipeline.1.token_embedding.embedding_dict.total_mrna_umis.weight');\n",
+    "state['state_dict']['pipeline.1.gene_embedding.E.gene_value.weight'] = state['state_dict']['pipeline.1.gene_embedding.E.gene_value.weight'].squeeze()\n",
+    "torch.save(state, \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -77,7 +94,7 @@
        "    var: 'feature_id', 'feature_name'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -88,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -912,7 +929,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6820b9c673864c38984ac02c18886c3f",
+       "model_id": "05fe5d496a5e4e8cb4de3abb788f85df",
        "version_major": 2,
        "version_minor": 0
       },
@@ -926,7 +943,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "733b56d9776e4136a95f8cb8603055f3",
+       "model_id": "0467e8fa80c54bd1bd8d348a7c045659",
        "version_major": 2,
        "version_minor": 0
       },
@@ -940,7 +957,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2ce657efef9498f83a313a04f9bb694",
+       "model_id": "440a33e515ab4a95a64dd87b9f3196bf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -954,7 +971,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28928b6240604612a683e5d87b03ff9d",
+       "model_id": "bfbd03775a6541f6b49af116a37c3f54",
        "version_major": 2,
        "version_minor": 0
       },
@@ -968,7 +985,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0ca2ec837abc45df91861799744d735b",
+       "model_id": "1d9a57afba654b0c9f49537021141101",
        "version_major": 2,
        "version_minor": 0
       },
@@ -982,7 +999,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2248f33d9c844138c1307c85b62d4d9",
+       "model_id": "3d4224c8a093401f9b2e82aff6d79b2b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -996,7 +1013,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a07df6f9d1a4445bfabebae0272152f",
+       "model_id": "0982dc17172b48409e6cfd994fbb7520",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb b/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
new file mode 100644
index 00000000..1fed2f1d
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Marker genes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "query_gene_ids = ctx.model_var_names\n",
+    "query_gene_symbols = list(map(ctx.gene_id_to_gene_symbol_map.get, query_gene_ids))\n",
+    "\n",
+    "cell_1_dict = {\n",
+    "    'assay': \"10x 3' v2\",\n",
+    "    'suspension_type': \"cell\",\n",
+    "    'prompt_metadata_dict': {\n",
+    "        \"disease\": \"normal\",\n",
+    "        \"sex\": \"male\",\n",
+    "    },\n",
+    "    'total_mrna_umis': 10_000,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "cell_2_dict = {\n",
+    "    'assay': \"10x 3' v2\",\n",
+    "    'suspension_type': \"cell\",\n",
+    "    'prompt_metadata_dict': {\n",
+    "        \"cell_type\": \"CD8-positive, alpha-beta T cell\",\n",
+    "        \"tissue\": \"blood\",\n",
+    "        \"disease\": \"normal\",\n",
+    "        \"sex\": \"male\",\n",
+    "    },\n",
+    "    'total_mrna_umis': 10_000,\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd6e046bd15e4c988e14c9ff3266ce98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/37 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee8f3ca8cc53468aaf2b43d6663a7ec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/37 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "query_chunk_size = 1_000\n",
+    "upper_percentile = 0.5\n",
+    "max_counts = 500\n",
+    "upper_pad = 1\n",
+    "\n",
+    "gene_logits_1_nqk = ctx.get_gene_value_logits_by_metadata_chunked(\n",
+    "    assay=cell_1_dict['assay'],\n",
+    "    suspension_type=cell_1_dict['suspension_type'],\n",
+    "    prompt_metadata_dict=cell_1_dict['prompt_metadata_dict'],\n",
+    "    total_mrna_umis=cell_1_dict['total_mrna_umis'],\n",
+    "    query_gene_ids=query_gene_ids,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    max_counts=max_counts,\n",
+    ")\n",
+    "\n",
+    "gene_logits_2_nqk = ctx.get_gene_value_logits_by_metadata_chunked(\n",
+    "    assay=cell_2_dict['assay'],\n",
+    "    suspension_type=cell_2_dict['suspension_type'],\n",
+    "    prompt_metadata_dict=cell_2_dict['prompt_metadata_dict'],\n",
+    "    total_mrna_umis=cell_2_dict['total_mrna_umis'],\n",
+    "    query_gene_ids=query_gene_ids,\n",
+    "    query_chunk_size=query_chunk_size,\n",
+    "    max_counts=max_counts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_entropy(logits_qk: torch.Tensor) -> torch.Tensor:\n",
+    "    probs_qk = logits_qk.exp()\n",
+    "    assert torch.allclose(probs_qk.sum(dim=-1), torch.ones_like(probs_qk.sum(dim=-1)))\n",
+    "    entropy_q = -torch.sum(probs_qk * logits_qk, dim=-1)\n",
+    "    return entropy_q\n",
+    "\n",
+    "entropy_1_q = get_entropy(gene_logits_1_nqk[0, :, :])\n",
+    "entropy_2_q = get_entropy(gene_logits_2_nqk[0, :, :])\n",
+    "delta_entropy_q = entropy_2_q - entropy_1_q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps = 1e-8\n",
+    "mean_1_nq, std_1_nq = ctx.calculate_gene_mean_std_from_logits(gene_logits_1_nqk, max_counts)\n",
+    "mean_2_nq, std_2_nq = ctx.calculate_gene_mean_std_from_logits(gene_logits_2_nqk, max_counts)\n",
+    "lfc_q = torch.log2(mean_2_nq[0, ] + eps) - torch.log2(mean_1_nq[0, :] + eps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_equal_q = torch.logsumexp(gene_logits_1_nqk[0, :, :] + gene_logits_2_nqk[0, :, :], dim=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log_pdf_1_qk = gene_logits_1_nqk[0, :, :]\n",
+    "log_pdf_2_qk = gene_logits_2_nqk[0, :, :]\n",
+    "log_cdf_1_qk = torch.logcumsumexp(log_pdf_1_qk, dim=-1)\n",
+    "log_cdf_2_qk = torch.logcumsumexp(log_pdf_2_qk, dim=-1)\n",
+    "log_prob_2_lt_1_q = torch.logsumexp(log_pdf_1_qk + log_cdf_2_qk, dim=-1)\n",
+    "log_prob_1_lt_2_q = torch.logsumexp(log_pdf_2_qk + log_cdf_1_qk, dim=-1)\n",
+    "log_pval_q = torch.where(lfc_q > 0, log_prob_2_lt_1_q, log_prob_1_lt_2_q)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kl_12_q = (log_pdf_1_qk.exp() * (log_pdf_1_qk - log_pdf_2_qk)).sum(-1)\n",
+    "kl_21_q = (log_pdf_2_qk.exp() * (log_pdf_2_qk - log_pdf_1_qk)).sum(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'CD8-positive, alpha-beta T cell vs. prior (male, normal)')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 960x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 804,
+       "width": 803
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "\n",
+    "fig, ax  = plt.subplots(figsize=(12, 12))\n",
+    "\n",
+    "x = lfc_q.cpu().numpy()\n",
+    "# y = -log_pval_q.cpu().numpy()\n",
+    "y = kl_21_q.cpu().numpy()\n",
+    "\n",
+    "ax.scatter(\n",
+    "    x,\n",
+    "    y,\n",
+    "    s=1,\n",
+    ")\n",
+    "\n",
+    "# increase xlim and ylim by 20% on both sides\n",
+    "xlim = ax.get_xlim()\n",
+    "ylim = ax.get_ylim()\n",
+    "ax.set_xlim(xlim[0] - 0.2 * (xlim[1] - xlim[0]), xlim[1] + 0.2 * (xlim[1] - xlim[0]))\n",
+    "ax.set_ylim(ylim[0] - 0.2 * (ylim[1] - ylim[0]), ylim[1] + 0.2 * (ylim[1] - ylim[0]))\n",
+    "\n",
+    "mask = (y > 2)\n",
+    "mask = mask | (x > 5)\n",
+    "indices = np.where(mask)[0]\n",
+    "\n",
+    "texts = [\n",
+    "    ax.text(\n",
+    "        x[i], y[i],\n",
+    "        query_gene_symbols[i],\n",
+    "        ha='center', va='center', fontsize=10)\n",
+    "    for i in indices\n",
+    "]\n",
+    "\n",
+    "adjust_text(\n",
+    "    texts, expand=(1.2, 2),\n",
+    "    arrowprops=dict(arrowstyle='->', color='red') \n",
+    ");\n",
+    "\n",
+    "ax.set_xlabel(\"log2 fold change\")\n",
+    "ax.set_ylabel(\"$KL(case || prior)$\")\n",
+    "ax.set_title(\"CD8-positive, alpha-beta T cell vs. prior (male, normal)\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Entropy experiments\n",
+    "(deprecated)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fcac0bc68c0>"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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V5YMPPiAlJSXozJtGo2HOnDml9lEUQoiaduTIEUaOHMm+ffvQ6XQsXLiQSZMm1fSwRDnJkqKoEgaDgaVLl/LOO+8wcOBAGjVqhNFopEOHDtx33338+eef9OrVq1rGotPpeP/991m/fj2TJ0+mTZs2hIWFYTKZ6NixI7fffjt//PEHjz76aLWMRwghKmrXrl0MGDCAffv2ERERwdKlSyXYqqNkhktUGY1Gw7Rp05g2bVqZz2ndunW52g2V5/iBAweWq/F1QQsXLqw33QaEEHXThg0buOyyy8jMzCQuLo4ffvihxJZl1cLjhs3zIHkztOoH/WaARmoVloUEXEIIIUQts3TpUq655hry8/Np2bIlK1as4JxzzqnpYXmDrVVnVgf2LfN+HTCz+OOFnywpCiGEELXIggULGDduHPn5+Zxzzjls3LixdgRb4J3ZKumxKJYEXEIIIUQtoKoqzz//PDfddBNut5u+ffuyfv16WrZsWdNDO6tVv5Ifi2LJkqIQQghRwzweD7Nnz+bll18G4NJLL+WLL77AZDLV8MgK6TfD+7VgDpcoEwm4hBBCiBrkdDq56aab+PjjjwGYPHky77//fu1sOabRenO2JG+r3GRJUVTYlClTUFUVVVVD1q5HCCHqM5vNxhVXXOEPtu677z4WLlxYO4MtUSkywyWEEELUgNOnTzNmzBh++eUXAJ5//nlmz55dw6MSoSIBlxBCCFHNjh49ysiRI/nzzz/RarW89957TJkypaaHJUJIAi4hhBCiGv3555+MGDGCY8eOERYWxueff85ll11W08MSISYBlxBCCFFNfvnlF0aPHk16ejoxMTF8//33DBgwoKaHJaqBJM0LIYQQ1WDZsmUMHTqU9PR0mjdvzvr16yXYakAk4BJCCCFCbNGiRVx++eXk5ubSsWNHNm3aRJcuXWp6WKIaScAlhBBChNArr7zC9ddfj8vlolevXmzYsIFWrVrV9LBENZOASwghhAgBVVV58MEHuffeewEYPnw4a9asISEhoYZHJmqCJM0LIYQQVczlcnHbbbfxwQcfADBhwgQ+/PBDDAZDDY9M1BSZ4RJCCCGqUF5eHldeeaU/2LrzzjtZtGiRBFsNnARcQgghRBXJyMhgxIgRfPfddwA8+eSTzJ07F41G/rlt6GRJUQghhKgCx48fZ+TIkezevRuNRsNbb73FtGnTanpYopaQgEsIIYSopH379jFixAiSk5MxGo188sknjBs3rqaHJWoRCbiEEEKISti6dSuXXnopFouFqKgovvvuOwYNGlTTwxK1jCwqCyGEEBW0evVqBg8ejMVioUmTJvz0008SbImgJOASQgghKuCzzz5j1KhRWK1W2rVrx6ZNm+jRo0dND0vUUhJwCSGEEOU0b948rrvuOpxOJz179mTjxo20bdu2poclajEJuIQQQogyUlWVRx99lDvvvBNVVRk8eDDr1q2jSZMmNT00UctJ0rwQQghRBm63mzvuuIN33nkHgCuvvJKPP/6YsLCwGh6ZqAtkhktUiSNHjvDggw/Su3dv4uPj0ev1xMTE0KtXL+6++242b95c5Jxt27YxZ84chg4dSosWLTAajURERNC6dWuuueYavv/++1Lv6/F4WLx4MZdffjktW7YkLCyMiIgIkpKSuOCCC7jrrrtKvU52djYvvPACF198MU2aNMFgMJCQkMCgQYN45ZVXyM3NrfCfixCifsjPz+eaa67xB1u33347n332We0Ntjxu2DgXFk/wfvW4a3pEDZ6iqqpa04OozxITEwE4duxYuc5zOp0cOHAAgPbt26PX64sc4wsEIiIiKjnKilNVlSeffJInnngCp9NZ6rE+33zzTZlq1IwdO5bFixcTHh5e5DWbzcZll13G2rVrS72O0+lEpys6obtixQomTZrE6dOniz23devWfPfdd3Tt2rXU+5R0/9L+f9Y1O3fuBKBbt241PBLREFXn+y8rK4uxY8eybt06AObMmcOcOXNQFCXk966wjXNh1aNnHw9/HAbMrLnx1AMV/ffcR5YURaXce++9vPrqqwBERkZy2223MWzYMBo3bozNZmP37t0sW7aMH374IeA8l8tFZGQko0eP5uKLL6ZTp05ER0djsVjYu3cvb775Jn/++SfffPMNd999N2+//XaRe//73//2B1t9+/bllltuoX379kRHR5OZmcmePXtYu3Yt//3vf4OOfcWKFYwZMwaXy0VCQgLTp0+nZ8+eJCYmcvr0aZYtW8b8+fM5cuQIw4cPZ9u2bTRv3rxq/wCFELVaamoql156KTt27EBRFObNm8cdd9xR08MqXfLmoo8l4KpZqgipFi1aqC1atCj3eQ6HQ92zZ4+6Z88e1eFwBD3GZrOpNputskOssOXLl6uACqgdO3ZUk5OTiz228GsnTpxQs7Oziz3e7XarN954owqoGo1GPXjwYJFjWrZsqQJq7969VafTWey1Tp8+XeS5zMxMNT4+XgXUSy65RLVarUHP3bJlixoREaEC6tSpU4u9R2nK8v+zrvnjjz/UP/74o6aHIRqo6nj/HThwQG3btq0KqHq9Xv3ss89Cer8qteFVVZ0Tdfa/Da/W9IjqvIr+e+4jOVyiwp566ikAFEXh888/JykpqdhjC7/WtGlTIiMjiz1eo9Hw8ssvo9Vq8Xg8fPvtt0WOSU1NBWDAgAFBlwt94uLiijw3f/58LBYLJpOJjz/+GJPJFPTc3r17+3+bXbRoEXl5ecXeRwhRf+zYsYMBAwZw6NAhzGYzy5Yt4+qrryYtx84Ri420HHtAmkSt02+Gdxmx46Xer/1m1PSIGjwJuESFpKens2HDBgCGDx9O9+7dK3W9/Px8jh49yp9//snu3bvZvXs3x48fp1GjRgBs3769yDktWrQA4LvvvuPUqVPlut/XX38NwLBhw/z3KI6varTD4WDr1q3luo8Qou5Zt24dgwYN4uTJkyQkJLBu3TqGDh2KxeogJd3GIYuVlHQbFqujpodaPI3Wu4Q48VPvV422pkfU4EkOV33nccPmed71+1b9vL/lVMFfvB07dvh/u6toG4vMzEzmzp3Ll19+yZ9//onbXfwuGovFUuS5m2++mUceeYSDBw/Svn17xo8fz4gRI+jTpw/t2rUr9lput5tt27YB8O2335Yr8dU3qyaEqJ+++uorJk6ciN1up3Xr1qxcuZIOHToAYLO7yMpzkpXr8j9OiDTW5HBFHSIBV323ed7ZnSr7lnm/VkHiZFpamv/7iiSS79y5k5EjR5Y5gAm2lPd///d/pKWl8cYbb5CTk8OHH37Ihx9+CHiXLEeNGsWtt95Knz59As7LyMjA5XKVe8yAlIgQoh579913uf322/F4PHTt2pXly5f7P99UVSXf6SbX7gYFosJ0mIzyT6goO3m31He1cKeK0+nk6quv9gdbN954I9dddx3nnHMOjRs3xmg0+medkpKSOHr0aNBcCa1Wy9y5c7n77rv55JNPWLNmDVu2bMFqtZKamsoHH3zABx98wG233cabb76JRuNdQS8YbI0dO5YnnniizGP3bQsWQtQfqqry1FNP8cgjjwBw4YUX8t133xETE+M/xmJ1YLU7cXo86LUKZqOeeLOhhkYs6iIJuOq7Vv3Ozmz5HleBhIQE//fHjx8v17lr165l3759gHeW6umnny722IyMjFKv16ZNG/71r3/xr3/9y79c+O233zJ//nzS09N5++236dKlCzNmeJNGGzVqhKIoqKqKw+GgS5cu5Rq/EKL+8Hg8zJw5k3nz5gFwxRVX8MknnxSp/Wezu8jOd4GqEGHQEabX1u46XKLWkaT5+i5EO1V69uzpnzH66aefynXurl27/N9PmDCh2OP27t2L1Wot17W1Wi29evXiySefZP369f4xfvrpp/5j9Hq9v4jpli1bKry8KISo2xwOB5MmTfIHWzfddBNffPEFVpemyE5Ek1FHdLie6AjvV1lOFOUlAVd9F6KdKrGxsVx44YUArF69mj/++KPM5xYMcErKiXrzzTcrPkDg3HPP9e9kLJx076tyf/r0aRYuXFip+wgh6p6cnBzGjBnj/2Xs//7v/3jvvffIzPcE3YkYbzaQFGeibbyZpDiTLCeKcpOAS1TYQw89BHin5K+55hpSUlKKPfbo0aP+7zt27Oj/vrhg5+uvv/b/1hlMeno633zzDR6Pp9hjdu/e7W/B0LZt24DXZs6c6a/Pdc8995TaHujYsWO8//77JR4jhKgb0tLSGDJkCKtWrQLg1Vdf5amnnsJidbA3NZuU9FwybU6y8pzY7N5fEBVFISHSSOt4EwmRRllOFOUmc6KiwoYPH84999zDK6+8wr59++jatWuR1j579uzhhx9+4L///S8Oh/c3xZEjR9K0aVNSU1N5++23ycjIYPLkyTRv3pwTJ07wxRdf8NFHH9G+fXsyMjICdkT6ZGdnM27cOBITExk7dix9+vShbdu2hIeHY7FY2LBhA2+++aZ/OWD69OkB58fGxvLZZ58xatQorFYrw4YN46qrrmLs2LG0a9cOrVbL6dOn2blzJytXrmTNmjX07duXm2++OfR/sEKIkDly5AgjR45k37596HQ6PvzwQyZOnEhajp2UdBtpVgd5DjfhBm3tWToMUXkfUb2keXWINYTm1XPmzOHpp58usY6W71ifNWvWcPnll2Oz2YIe27ZtW/773/9yySWXkJyczKBBg/yNY8H7odmmTZtSx6fX63nuuee45557gr6+fv16Jk6cWKb/P6NGjSq2L2NppHm1EFWrIu+/Xbt2cckll3D8+HEiIiJYsmQJl1xyCQBHLDYOplk5mp6L0+2heUw4vVrH1Y7ZrMKNqDuMAEUrwVc1k+bVokYpisLjjz/O5MmTmT9/Pj/++CPJyclYrVYiIyPp0KEDF110Edddd13AeUOGDGHbtm0899xzrF69mhMnThAZGUmbNm0YN24cd955J1FRUcXet1WrVvz222+sWLGCTZs2ceTIEU6ePElmZiZms5l27doxePBgbr311oAlzMIuvPBCDhw4wMcff8zSpUvZtm0baWlpuN1uYmNjad++PX369GH06NEMHjy4yv7chBDVa8OGDVx22WVkZmbSqFEj/vvf/wbU6PPNZDnc3jSFML0GRVFqPtiCouV99q/0fq3C2ooi9GSGK8Tq+wyXKBuZ4RKiapXn/bd06VKuueYa8vPzadmyJStXrqRz584Bx6iqyvaUTPaezMbpUmkRG0a7hEhaxwfvs1qtCs9wFdTxUu+mKBFylZ3hahBJ81OmTPH/plKW/0pK1hZCCFF3LFiwgHHjxpGfn8+5557Lpk2bigRb4J2tbxkXQccmkSTGhRMTYagd+VsQWN6nw4jA16qotqIIvVrybhJCCCGqjqqqvPDCCzzwwAMA9OvXj++//96/OzkYX6kHm92FyairPaUffOV9BswMnkAv6oQGF3C9/fbbNG7cuMRjunfvXk2jEUIIUdU8Hg+zZ8/m5ZdfBrwbXr744otS0y98pR9qdUPqgsGXqFMaXMA1YsQIWrduXdPDEEIIEQJOp5Obb76Zjz76CIAbbriB9957r17kTYq6rUHkcAkhhKj/cnNzGTt2rD/YmjVrFgsWLJBgS9QKDW6GSwghRP2Tnp7OZZddxqZNmwB47rnnuP/++0s8R1VVLFZHQM5WjZSBkMKmDYIEXEIIIeq0v//+m0suuYTdu3ej1Wp59913mTp1aqnnWawOUtJtZOU5iQ73zoLVSP7W5nlnyz7sWwaqBxSNNwBLOlMrLGWLBGN1XIMLuG699Vb27dtHamoqer2ehIQEzj//fEaPHs3EiRMxGGrJrhQhhBCl2rdvHyNGjCA5OZmwsDDeWfgxFw69hLQce6kzVja7i6w8J1m5Lv/jGgm4Chc23fEJWPZ6v/cVNy34vSTM10kNLodr1apVJCcnY7fbsVqtHD58mC+//JKpU6fSrl071qxZU9NDFEIIUQZ79uxh4MCBJCcnExUVxWdfL+XcvoM5ZLGSkm7DYnWUeL7JqCM6XE90hK5m+yaWp5ZW4eBM1BkNotL8lClT+PLLLxkyZAi9e/emdevWGI1G0tLS2Lx5M0uWLCEvLw8AjUbDl19+ybhx48p8fV/12WBOnDhBfHy8vyt9Wamqitvtxmg00rJlS3S6oh8Evv91taL1hCiRy+Xi6NGj2O12tFptvfh/5nQ6ASQhWdSITZs2cd9995Gbm0ujRo2YP38+7Tt0xOVR8agqGkVBp1HQarx/1zyqiqqCooCmwN+/4p6vdg4ruBygO7PKkp8d/LiwKDCYq29cwm/48OHo9foKV5pvEAHX1q1b6dy5M2Zz8DfpiRMnuO666/jpp58Ab6ucffv20aJFizJdP9QBV2JiYtB/1CTgqjucTifHjh2TgEuIKrB69WoefPBBnE4niYmJvP322yQmJuJRVVweFbdHRQF0Gg06rYJH9T7nUUGjgFaj1GxwVRaFAzDf9xJs1RgJuKqIzWbj/PPPZ+9e77r5nXfeyWuvvVbp61a095LH4/GPJSkpCZOpaD8v6aVYd+Tk5PjfA507d64XAZf0UhQ14Z133uH2229HVVU6derEunXraNq0KeD9JXRvag4HTmbRet8CEnN2oGszAEuXaRxKzyMr10V0hI628eba0SOxMNmtWKtJL8UqYjKZePjhh/2Pv/vuuxocjXdpMzw8HPAGg6Juy8nJAbzvs/oQbAlR3VRV5amnnuK2225DVVXOP/983n//fX+wBd7Z/jC9ls6H/0OXPS8Rc/RHzD8/Tvzud2tHrlZpfLsV9y3zft0sfX3rEwm4ChgyZIj/++TkZP8MUk3xzWplZmb6l29E3WO1WsnO9uZjREVF1fBohKh7PB4Pd999t/+X4ssvv5w333yTyMjIIseajDri038PfC71V5LiTLSNN5MUZ6o9PRILK5wQLwny9UotDfNrRkJCQsDjzMzMGl2ui4mJISMjA7fbTXJyMnFxcZhMJjQab5zscnm3MkswVvuoqordbicnJ4fs7GxUVcVoNAb9B0IIUTyHw8HUqVNZvHgxAFOnTuWdd95hz549QY+PNxuwtRkAR3/0P6e06l/7eySCdxmxYBmI8uxeFLWeBFwFWCyWgMexsbE1NBIvvV5PYmIiKSkpOJ1OTp48GfC6x+MB8AdgovYyGo0kJSWh1Uo+hhBlZbPZuOqqq1i+fDkA999/P88++2yJy/KKomC++G4w6gJzoWpSWXOzfOOsLeMWVUoCrgLWrl3r/75ly5b+HKqaFBERQZs2bcjIyCArK8sfZAHY7XaAWjFOEZzJZCIqKorIyEgJtoQoh/T0dMaMGcPmzd5ltRdeeIFZs2ahqiqnsvNxur2fhaey80mINAYGYRqttzhobSkQWriSPAQfW20bt6hSEnCdkZuby5NPPul/fNlll9XgaAIZjUaaNm1KkyZN8Hg8/qDLN6Xevn37mhyeCEJRlHpT/kGI6nbs2DFGjhzJnj170Gq1vP/++9xwww2k5dg5mp7LkdM2mp0JuDbst9A63kTLuIia64VYEo8bti8KfG77ItmJ2ADV+4Drww8/pHHjxowcObLYpbeTJ08yceJE/vzzTwDCwsJ44IEHqnOYZeL7R9w3U+L7YJE6SEKI+mLv3r2MGDGClJQUwsLC+Pzzz7nssstIy7GTkm5j38kckk/n0jjOW9FonyUbu9uNivdxrcvT2jzvbJseH8te73/7lnmDr56TJPBqAOp9wLV9+3bmzp1L06ZNGTFiBN26daNp06YYjUYsFgubN2/myy+/9O9I1Gg0fPjhhyQlJdXwyIUQomHZunUrl156KRaLhejoaL7//nsGDhwInO176HSpGHQavL9vKhh1Wpwulaw8Z831QixJ4Z2GBhM4CpT6sew9u9woS4n1Wr0PuHxSU1P5z3/+U+IxLVu25P3332f48OHVNCohhBDgrR4/btw4rFYrTZs2ZcWKFQFFdX19D9VYleYxRgz53nZsXVtEoShK7a2vVWTn4QDYv7LoccmbJeCq52rhu7Nq3X///fTu3ZstW7awbds2UlNTsVgsWK1WzGYzTZs25YILLmDMmDGMHz9elueEEKKaffHFF1x//fU4HA7atWvHypUradu2bcAxvtpZjUxGTEYdJw6nAdC9ZRw2u8tbf6s21tcqvPOwzz9hy3zvUmLBpUYpAVHvSWufEKtsK4CSSGsVUZPk/SeqwltvvcUdd9yBqqr06NGD5cuX06RJk1LPq/PvP2njU+dU9t/zej/DJYQQovZRVZUnn3ySRx/15i8NGjSIb7/9lujo6BoeWTWREhANjgRcQgghqpXH42HmzJnMm+ftFTh27Fg++eQTwsLCanhkQoSOlCgXQghRbRwOB9dff70/2Lr55pv54osvJNgS9Z4EXEIIIaqFzWbj8ssv55NPPgHgwQcf5N1330Wnq8HFFo8bNs6FxRO8Xz3umhuLqNdkSVEIIUTIWSwWLrl0NL9v/RWAF198kXvvvReL1RGwy7DaK8WXte2OEJUkM1xCCCFC6ujRowwYeCG/b/0VjVbLYy+9wQ23zsBidZB82sr2oxlsOWxhb2oO1b5xvnBh0sKPhagiMsMlhBAiZP766y9GjBjB0aNHMRjDmPXsW1x4ySXY7C4AjmbkcSTNhqIBVIVGZmP1VosvUpi0jPWw/GUdNoHqATTQur+UdxDFkoBLCCFESPz666+MGjWK06dPEx0Tw4vvfEyjdt05mZUPKjSLDkOnUVA04FHB6VGrvz1P4cKkvselKbgU6bN/ufRGFMWSgEsIIUSVW7VqFePGjcNms9GsWTOWL1+OIaE121LSSc22k53vxKOqxJuMKCg4PSotY8Oqvz1PSfWwSipOWtzSo/RGFMWQHC4hhBBV6vPPP2f06NHYbDbat2/Pxo0b6datG2F6LYqiYMt3kXw6l79OZGPQaejdJo6eLWNo1chcu9rz+Gax9i3zft087+xrpS09Si6YKERmuIQQQlSZN998kxkzZqCqKj179mT58uU0btwY8Dagbhxp5ERWPk63B4dbJfl0Loqi0DIuomZ2KZYkWEK9b9bKvxR5Jocr/Qic3nf2WOmNKAqRgEsIIUSlqarK448/zmOPPQbAxRdfzLfffktUVJT/mHizga4tYsh3ejiemYfd5SHNmo/d7UbFuzuxWvO3SlNSQn3hpchgy49CFCABlxBCiErxeDzcddddvPHGGwCMHH0Z7y74iMjIyIDjFEWhcVQYvds0IiXdxv7UHLLtLpwulaw8Z/UnzJemPAn10htRlEICLiGEEBXmcDi44YYb+OyzzwAYO2Eydzz8LKdy3YRZHUEDKF+eloJCRq4dgOhwfeUS5ktKcK8oCaJEFZKASwghRIVYrVauvPJKVq5cCcAdd89i/K33kZ3nRqsNPmOlqqq/unxibDiJseHkOtz+SvMVJhXjRS0nAZcQQohys1gsjB49ml9/9bbqeeWVV5h08z9JSbehKE6iw/VEGLSk5dgDWvdYrA5S0m1k5XmPSYoz0TreVPkBlZTgLkQtIAGXEEKIcklJSWHkyJH89ddf6HQ6FixYwPXXX+9vy2Ozu4gwaDlttXPQYsXpVmkZG46qmjiWkcf+1BzsbhU1FhqZqihvq6IV4ysqFEuYol6TgEsIIUSZ/fnnn4wYMYJjx44RHh7Ol19+yahRowBvUnxCpLc1T1qOnYNpNg6kWb2dbwCNoiEj1072mbY+QNUVOq1oxfiKkiVMUU4ScAkhhCiTLVu2MGrUKNLT04mJieH7779nwIABQY+12V04PSoeFTQa0GvP1tcK02nR6xRiIwwl5m0VzPcy6RXid72LknImoIocHHhwVSW4F5656vNP2DK/6ONN8wLPkyVMUQoJuIQQQpRq5cqVjB8/HpvNRvPmzVmxYgVdunQp9niTUUfL2DAA9BqF9glm4kwGVFQURSE6XE/LuIgSC50WzPfqeOADlK3Pel/YtwzGLAODufw/SGlLgYVnro5sgP0rgz8uSAqdilJIwCWEEKJEn376KTfccANOp5MOHTqwcuVKWrduXeI53pkrM/HmsIAdiIqiBCTRl8Rmd5GV5yQr14Xx+K+BL7ocUJFNjaUtBRZOvj++o+THpsbQf4YUOhWlkl6KQgghivXGG28wceJEnE4n5513Hhs2bCg12IKz+VytGkUAkHw6F4vVQbzZQOt4EwmRxlLb+JiMOqLD9URH6LA37x34oq6CJSSC7Wb08bhBdQe+3rxHyY/7z/AGbJIwL0ohM1xCCCGKUFWVf//73/z73/8GYMiQIXz99dcBrXqCnePPuSqmDASUvX2PbwbMZndhbDYTNSbibA5XRZYTIfhuRt8y4/ZFYNl79rUOI+DaRfDLG7DjE+9zSf2gVX9I2SItfES5SMAlhBAigMfjYebMmcyb500MHz9+PB9//DFWl4YjFlvAcmBajp1jGXkAhOs12BwusvNd/uCq4LKg73FZA66Cux4BGDgTOLP8t3NnxX64fjO8zaZ9AZTqgU2vwerHih6bkQyf3+Cd9fIFYj/+G4Y/DhM/rdj9RYMlAZcQQgg/p9PJjTfeyCefeAOSadOmMX/+fNJzXUVmqlRVZcMBC/tO5mDQaWgSaSDCaOBMH2r/TJfv+Eq376ks30zWjk/OBlCrH4P4TsGPt+wNnPHykR2JogIk4BJCCAFAbm4uV111FcuWeZfcHnzwQZ5++ukzie75ZOY6OZaRxyltPgoKHo+HP1IyOGixEq7XkdsoghYxKjEmI1FhuoCZsLImyodUwYT5kvgCsGDBFsiORFEhEnAJIYQgIyODMWPGsGnTJgBeeOEFZs2a5X/dNzNld7mxuyAj105WrpN0m4OsPCfWfDfheg1xJgPxGjAb9cSbDUWXBWtS4YR5nx7XgaIJLBVRODjrMAIUreRtiQqTgEsIIRq4EydOMHLkSHbt2oVGo+G9995j6tSpAcfEmw3ERhiIDNPhdHnXDPOdbowGLTqNFo/qISvfyYmsfMxhOow6Tam7EKtd4YT5+E7Qc9LZWlwFlwmDVa6XnYiiEiTgEkKIBuzgwYOMGDGCQ4cOYTQa+fTTTxk7dmyR4xRFoWVcBCrq2TwuFZrHhGPLd5PncqGoLi74+z90TfkTU+ZFMPK+8gcpoexRWFwQ5XHDxrlFn6+KyvVCnCEBlxBCNFA7d+5k5MiRpKamEhkZybfffsvgwYOLPb5wPlZiTDgoYDbqsOTk0/PoR1ya9o734C3rIcpY/oAllD0KiwuipC+iqAYScAkhRAO0ceNGRo8eTVZWFvHx8Sxfvpzzzz8fCF5PS1EUFEUJCLoiDFq6toihbbyZ1Kx82hz9K/AmFdnNF6wwacFr+GbAtN29xU897rMzYAVnx5L6eJ8rWC/LN5tVeAattHsKUQUk4BJCiAbmhx9+4KqrriIvL4+WLVuyatUqOnU6WxqhYLHSqDAdp616wvRaTEYdqqpyNCPX/5rZ6H2tXWMzER0uhNS1Z29Ukd18wQqTFuSbjRr2GbjyvY99wVGwmSrf99sXefO1VM/Zmlu+Y0q7pxBVQAIuIYRoQBYtWsSUKVNwuVx07tyZlStXkpiYSFqO3T+jZc13+ouVZuW50GvziTB462kpKMW+lnTBPzGH6QJnj8orWJ5VQcFmo3y7CjfNK/66lr3eYKxwza1N86DfHTDsMakeL0JKAi4hhGgg5s2bx5133glAr169+OGHH4iPjyctxx5Q1NRkOFusNNfuxun2+CvFx4Qbin3NZjKSUNlE89KS1YPNRpW1vlYwtlPeGS+pHi9CTAIuIYSo5zweDw889CgvPvsUAEOHDuXrr78mMjISKNp+Jy7CQCOzCZvdRb7TjdXu9LfrSYwNP1MItehr1VJF3jf7pA3z5nCdNwM+nRR4jKmxd9YKAqvKw9maW5vmeYMtH8nbEiEmAZcQQtRjHo+Haf+czgfvvAXAhSNGs+DjRf5gC/C331FVFVVVych1Yg7T06pRBEDQBPqESGPQ5PqiA6jiMg++GbCCvRRVd+Ax/WecDZ763+W9/5FNgAdSfvE2n+53R2D/RMnbEiEmAZcQQtRThfsinj/iSiY++DRpuR5aFjjOFygpKGTkOsjIdeBRPQHJ8q0aRRQpZFqmKvKhLrmweR7sX3n2cYcRgTlYBQua+sexHNoPl7wtUa0k4BJCiHqocF/ESybdRv9rZ6DRFp1d8gVONruLzDzHmYR4B/kOD4oGGkca6dosksb/e6/8M1WhLrlQ+PqKNniZiPRDgccdWAVtLpS8LVFtJOASQoh6JiMjg8suu4yNGzcCMOeJpxk4/iYOW6yYjTrC9RpUVS0yY+VbWgRIzcrjZI4dm91NTr6LTgcXwtZnvAeWZ6Yq1CUXSrp+acn0krclqpEEXEIIUY8E64s4ZcoU9qbmkO9y4XSr2BwuLFZHkaXAeLMBVVVBzeXASSsZNgcuj4o1X4P+2C+BNyprsFJamYfSlJYDVtL1C89+RSRAbtrZx5K3JaqRBFxCCFFPHDp0iOHDhwftixim1xJh0JGV6yI734XN7ioScPmqyWfmObDaneQ53YBCrsPFYVM3mlCBoqaV7UlYWg5YSdcvPPvVf7p3h2Jl6oQJUUEScAkhRD1QWl/EgsuFBUs4FN5paM13cirHTq7DjUGnwaOCw62yOvoqaAedHf8jpvNFoQtWCs9oJW8KfL08y4DFNauWZURRAyTgEkKIOq6kvog+vp2I1nwndpcHa74TIKBVj6/oaeNIIzn53ppcqGB1uMi1u9jZ6kYMreI4r1Vs6H6YwjNaHUYEvp7UBzbODd5LsbDKzq4JUYUk4BJCiDqstL6IPr6diADp6TaOZzmLtOoBb9HTri1iiI3wHhum13A8Mw9Ljp2EqDASY8ND+wMVzrtC460C75ulUj3F91IUohaTgEsIIeqoxYsXc+ONNwb0RWzZsmWJ5xSuKl+wVU90uB5zmJ6ESCONo8IA7wxYQmRYycVNq1LhvKvW/QNnqRZPCDxedhqKOkICLiGEqIOK64sIRfOyfNXhoWguV8FWPcECqjIVN61Kpe1qDHWZCSFCRAIuIYSoQ1RV5fHHH+exxx4DivZFBG8rnoLNqAF/wOQLqIK16vFdPy3HHjRYC6nCyfITFnmfL1wSIlgvRSHqAAm4hBCijvB4PMycOZN58+YBMH78eBYvXozRGDj7VHjZsGAJiNJmrEoK1oIPqop6JQYr/wDBS0IU7KVYmb6MQlQjCbiEEKIOcDqdTJkyhcWLFwNwyy238NZbb6EN0qqnuBIQZREQrHncaPe+D5bfiw+mqqpXYrAWQMGOkXwtUUdpanoAQgghSpabm8sVV1zhD7YeeOAB3nnnnaDBFniXDVvGRhATbkBBQVVVbwX5MvAFa9EROjoe+pC4TU96A6lVj3qDq8LKEiiVReFcLNVdtP+hryTE4gngsFbsPkLUEAm4gHfeecdfYVlRFC6++OKaHpIQQgDevoiDhw7zN6F+/vnnefbZZ0vMq/K9lpHrYF9qNrv+ziItx16m+8WbDSTFmWgbbyYhfXvgi8GCqcKBUkWT2PvN8JZ/6Hipt/bW/pVg2et9zWDyPucrCbFvGeRne4Muj/tsELZxrvexELVQg19STElJYdasWTU9DCGEKOLEiRMMGzGSPbu9fREffvZVptx+a5nOPZaRx8E0K6ey88m2u4iNMPhLPZQkIMer3QA4tOLsi8GCqcr2SvQpWKS0cOkHh80bgGUkBz7vclTdkqYQIdbgA66bb76ZnJwcTCYTNputpocjhBBAYF9Evd7APU/Po/uQS/n18GmaRYeTGBtOQqQxcKarQAJ746geKJHjgUrsMCxLMBWKau6FSz8UR2eAQ0GWNCXgErVQgw643n77bVavXo1Op+OJJ57g3nvvrekhCSFEQF9Ec2QkL779EY07X8Cp7HxyHS7+zswjI9dMt8SYwB2EBWZ7ElnGxd1dbG0xmcaRxopViA91axyPGza9Bjs+8T7ucR30v+tsYLd90dllRd/rvubTYVFgMEtdLlFnNNiAKzk5mdmzZwMwe/ZsevbsWcMjEkIIb1/EMWPGkJmZSXx8PMuWLaNVp67sTc0mLScfm92Fze4i0qDjaERuYL2sQjlWSTk70beNL1LQtKTCqNVq8zxY/djZx6sf8wZUviCv34zgJScKloWoqiVNIUKsQQZcqqpy0003kZOTQ+fOnZkzZw6bN1dwZ40QQlSR5cuXM378+GL6IkZhd7lRVRWbw012vpNdf2eg12poEhVG1xYxJCT1Qykw22Nr0guTUUcjkz4gwApoWG3UEP7bfzCn/la5OloVESwJf/uis2MoywybNKgWdUSDDLjeeust1qxZg0aj4b333itSNFAIIarbZ599xvXXX19sX8R4s7eptILCyex8UrPyOZaZi9ujYLW7iQk3kN7mBjz/yCby1G+kxZ1HcsK1JKXbOG3VYXO4/MVMCzasTvpzIeY/nvfepLqTzoPlaln2SkNqUS81uLIQR44c4f777wdg+vTpDBgwoIZHJIRo6N555x2uu+46XC4X559/PuvXr/cHW75WO8mnc1EUhTbxJppEh6ECWXlOMmzemasMm53tR9M5lpFLdr6TdJsDS3Y+WXlOLFa7P8DKynMC3oKo0WEKLY58FTiYitbRKi+P21vmIb4jaPQ1MwYhqlGDmuHyLSVarVZat27NM888U9NDEkI0cM899xwPPvggAIMGDeK7774jKirK/3rBVjtRYTqcLpVcuxujTiEqXIfDqRJh0JKd76LFnwu46PibAPwjZxO7o8Kwt5mByeCd4YLAhtXave8TnnUgcEBVlXResOVPUh/vcylbzi5bFs7fCsUYhKhFFLWs5YfrgTfeeIMZM7wJlStXrmT48OH+19atW8fgwYMB74feunXrynzdxMTEYl87ceIE8fHxrFq1qmKDLoHT6f1NVa/Xl3KkEFVP3n+Vo6oqc+fOZcGCBYD3c+f5558nLCywVpbbo+LyqHjOfFQrgAp4PCqeM5/eWg1oFAW9PROt52yBU1VnRA2PQ6MoeFQVVQVF8R4LQG46uPLP3kyjA3PjqvkBHVZvcdJgwqK8NbQK31uj85Z6MJhLvby8/0R1Gz58OHq9nmPHjlXo/AYzw3X48GEeeOABAG666aaAYEsIIaqT2+3mqaeeYsmSJQCMHj2af//730GDB2+ABPha9PifV1DOPkKjKKhaAxQIuBTd2TpdGkUpWpJLZwgMegwRVfLzAd6AqsTXCv2ub4goU6AlRF3VIAIu31KizWajWbNmvPTSS1V6/ZKiXd/sV7du3ar0nuCt1ROqawtRGnn/VYzD4WDy5Mn+YGv69Om89tpraDTBU2oLlnDId7qx2p1k57vO5G05yXe6SYgKo3uLKCKNWrRb3sCU+huGtgNQzitlx2HBZb9W/aC048tj41xY/Wjw13ytewo+vu7Tct1b3n+iulV2NrVBBFzz5s3zLxHOnz+fmJiYGh2PEKJhys3N5corr2T58uUAPPLII/z73/8utS+ir9WOx+Nh30kr+U4P4SYjTaOM5NjdRIfriQw3eIugjphd9gGFsqRCwfpYhXO4jmwKPFbRVl8pCiFqSL0PuA4ePOhPSL3mmmu44ooranhEQoiGKDMzkzFjxrBx40YAXn75Ze655x7vi4VnmoqphXXa5sSWb6fp7neIT/8dR/O+5J5/O6ZwY0Bh01qhtGBu//Kz30uSvGgA6n3A9fHHH5ObmwtAkyZNePLJJ4Med/jwYf/3ycnJAcfNnj1banUJISrs5MmTjBw5kj/++MNf/2/q1KlnDyimAXPhivA5eQ6Mv82n/Z4zaRFHf4QoY92rWSXV4UUDVO8DroKbMF9//fUynXPkyBEeeeQR/+MZM2ZIwCWEqJDk5GSGDx/O/v37MRgMfPLJJ4wfP77QQcEbMBcsCREdrsfucNP85G9Bj61VSpuxk+rwogGq9wGXEELUlL/++ovhw4dz7NgxTCYT33zzDcOGDSt6YDENmG12l79gKUCe3Y3L3J3Wp38ucmyFlHEps9wKz9gd2eDN06ru1kFC1CL1PuB67LHHeOyxx0o9rjJ1uIQQorDff/+dSy65BIvFQmxsLD/88AN9+/YNPMgX8BzZ5N2pp2igVX//EpvJqCM63LszKjpcDyrsbXsDDreHDvm70LYeQGJlluOKWcqsMI8bNr0GP78Q+LxvR2J1tw4Sohap9wGXEEJUt59++onLLruMnJwcmjZtysqVK+natav3xYKzSqo7oDyC9aJHMfW/y79r0ZcI78vh8sR4UIG0sNtQosLo2iK6crNFxSxllpvvZ9q+yNsLsbR7SsAlGiAJuIQQogp9//33XH311eTn59OmTRtWr15N27Ztzx5QcFapkLwDG9jX7iZaxkUQbzYElIQAb05qN43GH4BVemdiMUuZQPDlRt/4Cy9BlvAzBb2nEA2QBFxCCFFFFi1axI033ojb7eYf//gHK1eupHnz5oEHldCY+WB4Vw6fzMajejht1ROm1/oDK0VRigRglVbSbsFgy40QfAmytGbT8Z0grq3sSBQNmgRcQghRBQr2au3duzc//PADjRo1KnpgoVkle5th5LlUjkR0Y1fziRhdKkcz8tBr7EQYtf4croKzXAVLRfiCsVIVlyBf3G7BwkHU9kUQ26boMQNmegubFgzKCus5SZYRRYMnAdcZF198MQ2oj7cQooqoqspTTz3lLyUzdOhQvvnmG8xmc5HjLFYHtg5Tibe7MKX+itKqP4a+08nOdeNJz6Vtrh08bpr9+T6xlm2cjjuPU11uwWY6O6tVuFQEULYZr/ImyBdebrTshdhWRY8pjikBWlwgs1pCnBHSgOuKK65g+vTpjBgxIpS3EUKIGqGqKrNmzeLll18GYNy4cSxevJgcp4LFYguYgSoYKJ1uN5WkXjNIiDSiAAmR3uMsVgfaza8Rt9tb2LRZ6lqORegxtrzXf8/CpSJsdlfZAq7yJsj3m1E0CV7RwPDHiy5BpmwJcv50GHhP6eMSooEI3i21iixdupRLL72U9u3b8+KLL3L69OlQ3k4IIaqNy+Xi5ptv9gdbU6ZM4fPPPyfHqZCSbuOQxUpKug2L1QEEBkpZeU5sdlfA9Xz5WXFpWwOeTzi9LSA53lcqIjrC+9VkLOPvzYVno0pLXtdovUuBAef09wZpEz/1fvXtkCx8rQ4joP9dZRuXEA1EyJcUVVXl8OHDPPDAAzzyyCNcffXV3H777fTv3z/UtxZCiJCw2+1MnDiRr776CoC7776bl156CY1Gg81uD5yByrOTsPMtmh3ciC6uJ/va3ugPlILmY+EJuJdRBxTI0SpcKqLMOxUr0k6nrOcEO06KmwoRIKQB16pVq3jzzTdZunQpLpcLu93OokWLWLRoEV27duWOO+5g0qRJmEymUA5DCCGqjNVqZdy4caxevRqAJ554goceesifuF64WGn87nfh58cxAomHVhBh0OHud5d/CbFIPpZSaOGh0OMK71SsSDudsp4jrXqEKFVIlxSHDh3KkiVLSE5O5rHHHqNly5aoqoqqquzatYt//vOfNG/enBkzZrB79+5QDkUIISotPT2dYcOG+YOt119/nYcffjhgl2C82UBSnIm28WaS4kyYUn8NuEbY8bP5TkGXGVsVmv0v/FgIUSeFNODyadasGY8++iiHDx/mm2++4ZJLLkFRFFRVJScnh/nz59O9e3cuvPBCPv30U5xOZ3UMSwghyuzEiRMMGjSILVu2oNVq+eijj/xlIAryzUC1jjd5k+ILBUzHo3v6c7uC5mP1m+FNTO94qfdrKHb4edywcS4snuD96nFX/T2EEAEUtYZqIRw5coS3336bBQsWcOrUKe9gfO0s4uO5+eabufXWW2ndunVNDK/KJCYmAnDs2LEqv/bOnTsB6NatW5VfW4jSNKT336FDhxg+fDiHDh3CaDTyxRdfcNlll5Xt5DP1r3L3r+d4dE92JU0m2mSkbbyZVo0iKlZTq7I2zg2sDD/88Tq3HNiQ3n+idqjsv+fVMsMVTOvWrXnmmWc4evQoixcv5sILL/QvN6alpfHcc8/Rvn17xowZw7JlJRTUE0KIENq9ezcDBw7k0KFDmM1mli9fXvZgC/z5TbYrF5F13j+JNhn9s1mK6iFh51u0XnkzCTvfQlE9pV+vKgQrEVGYzIIJUaVqLODy0ev1TJgwgR9++IG77vJuI/b9hufxeFi2bBljxozhnHPO4csvv6zJoQohGpgtW7Zw0UUXceLECRqZDax99VYuvujCUs9TVZW0HDtHLDbScuyoqloktyvebDhbjHTfMu/XN/tVT3BTlhIRhce2eV5oxyREPVfjleZ3797NW2+9xccff0xOTo4/twugZcuWHD16FIB9+/Zx7bXXcu211/LRRx+h1cqWYyFE6Kxdu5bLL78cq9VKi0iFVZP1nHP0PdicVOryW3HV4IvsLiw8s2TZ6w1uVI93d2JVl1nwtfc5sslbK0vReJPyg+WJlbdQqhCiRDUyw+V0Ov3LiN27d2f+/PlkZ2ejqipms5k77riD//3vfyQnJ7N582YmTZqEVqtFVVU+++wz3n333ZoYthCigfAVbbZarbRvYmbDTSbOSTgT8JTWqJnSi5z6FVd8dMcnoZld8s1a7V8O+1eeLWQaLJgrb6FUIUSJqjXgOnToEA888AAtWrRg8uTJbNq0yZ+3de655zJv3jz+/vtv5s2bxznnnANAnz59+Oijj9i2bRvNmjVDVVXee++96hy2EKIB+eSTTxg/fjx2u50uXbqw/p37aR1z9qPS7nSSu/AqrGteQnUHD6TKXA3etyMxvlPJgypDkFcmZcnd8qmO3ZJCNCDVUmn+u+++Y/78+axevdofYAFotVp/v8XBgweXeJ0uXbowa9Ys7rvvPg4cOBDqYQshGqB33nmH22+/HVVV6d27N8uWLSMuJhoaRUHyZuxOJ8bD3hpcHFmFFTAPuS/gGh6PB0tOPqlZ+YTrtSTGhBdfDd5XMLTfDO/sk28JUfXA6sfOHldVs0uFG1KXdF0pZipElQppwPX444/z3nvv8ffffwP4A63GjRszbdo0br/9dlq0aFHm63Xq5P0tMCcnp+oHK4Ro0F588UVmz54NwMXnNOa7J8cTGRMdEHi4F14VcI4mpegM0b6TVrYfzeRUTj6NI8NoHhNOk+jwkm9eOLjxuIvmcBXHl5dVlnyvirT3EUJUiZAGXI899lhAEnz//v2ZPn06V111FXq9vtzXk0R5IURVU1WVOXPm8MQTTwAwuoOOL8blEb7xaYgIC5jh8ST1gyOrAh8XYrHaOZWTT1q2w/+43Mozu+TLy4Kzs1fFnSezVkLUmJAvKYaFhTFx4kSmT59Ojx49KnWtQYMGcfjw4aoZmBCiwfN4PNxzzz289tprAEzol8R/hmag154pPlpoZ55p0EyseGe2PEn9MA0qGrjEm400jgwDoHFkGPHmcvY8LC/ZTShEnRDSgOull15i6tSpxMTEVMn1wsLCaNWqVZVcSwjRsLndbqZNm8aCBQsAuOWWW3hr8rlo1zzmPyY9/nzcOXZ/BXhFqyuSs1VYxyZmwDuzFW82+h+HTHnysoQQNSakAdc999wTyssLIUSFOBwOJk2a5C+mfN999/HCCy+gqB5UjQbr/p85au7B3vhraXnaCpiJNxvK1IZHo9HQuVlU9f0wkpclRJ1Q44VPhRCiOuXm5nLllVeyfPlywLu55+GHH/YGT4oWS/fb2RJ1FQdOWfGczgNFS7zZu0QYrJhpjStLXlZ5EuuFECFRrQGXx+Nh8+bNbNmyhePHj5OTk0NkZCTNmzenb9++9O3bF42mxrsNCSHqqezsbMaMGcP69esBePXVV7lrxnRsa1/25mW17EdKmxs4npmPNd+FyahFr1EwGXUBxUzBW9y0VgRcZVGexHohREhUS8Clqipz587l5Zdf9peICKZFixbMmjWLO++8M+hUvRBCVJTFYuGSSy7h999/R6PR8O6773LTTTdhXfMS5p8f9x50ZBWG0zbUJhMxGXU0iTLSvrHZX0crKkxHVp6LXLubfKcbVVWLflZVxWxSsGtAxa8rifVC1LiQB1w5OTlcdtll/t8ofSUigjl27Bj33HMPX3/9NUuXLsVsDnGyqRCiQTh+/DjDhw9nz5496PV6Fi1axNVXXw0UraUVa9lKWOINRIfp6dg0ik5NIwHvZ1d2nos8hxNVr8Vqd2KxOorOclXFbFKwa0DFryuJ9ULUuJAHXGPHjuXnn3/2P+7ZsyfDhw+nY8eOmEwmbDYb+/fvZ9WqVWzbtg1VVfn5558ZO3Ysq1evDvXwhBD13OHDhxk2bBiHDh0iLCyMr776iksvvdT/euHaWhnxF2DUKiREhZEYG46iKKTl2Dmakcup7Hxy8l1k2lzYXR40iqZo8nxVzCaVpQVPea5bUmK95HcJUS1CGnAtWrSItWvXoigKTZs25cMPP2TYsGFBj33mmWf48ccfmTJlCn///Tdr165l8eLFTJw4MZRDFELUY3v27GH48OEcP36cyMhIvv/+ey666KKAYwrW1spp0ouTLSeh5rqBs7Pxvvwtu1sl1+HG7vKg0ylk5DqKznJVxWxScdeo6HVLSqyX/C4hqkVIA67//Oc/AISHh7N27Vo6duxY4vFDhw5lzZo19OzZk7y8PBYuXCgBlxCiQrZt28bIkSOxWCzExcWxYsUKLrjggiLHFaytdcpiQ2OxEqZTUBSFXIcbONuMWo0Fg1bBmu/CoPPOAhVJnq+KMg0lXaOqyz9IfpcQ1SKkAdeOHTtQFIWpU6eWGmz5dOjQgalTp/LGG2/wxx9/hHJ4Qoh6asOGDYwePZrs7GyaNWvGqlWr+Mc//lHqeb7ACiA6XI/J6P2I9CXNNzK5aBETjtXuJDvfFXCMX1W0zynuGqFoyyP5XUJUi5AGXNnZ2QD06dOnXOf17duXN954Q5pUCyHKbcWKFYwbN468vDwSk1qx5LsfOPfcc8p0ri+wsuY7sbs8WPOd/o0+uQ43JqOOpLhwTtucAQVQ6zQpnCpEtQhpwNW0aVNSUlLKXVvLl4DapEmTUAxLCFFPLVmyhOuuuw6n00nrdh146I1FnCSaXw6epml0GOYwfbEV4sH72eNbHkxPt3E8y4k33vKWf/DOfplIiDT6K88nn84tsfJ8rScNrYWoFiGtMuqb2dq2bVu5zvv9998B6NdPpraFEGWzcOFCrrnmGpxOJ53+0Y1H3/6CHCWKP45l8VvyaXYcyyQl3YbF6ij2GqqqkpZjZ29qNinpeWTmOknLzudUjp2sXG/ivM3uLXxqsTpISbdxyGIt9bqAdzfgxrmweIL3q8ddlT9+zd1LCFEmIQ24brvtNlRV5f333+f48eNlOuf48eN88MEHKIrCbbfdFsrhCSHqiXnz5jF16lQ8Hg+duvfioTcWYzDFoGjA4fSQne8iLdsREDAF4wui0qx28hwu74xXVBiNI41ER+gCcrYKVp4v7brA2d2A+5Z5v26eV5V/BDV3LyFEmYQ04Bo8eDD33XcfWVlZDB06lF27dpV4/O7duxk2bBhZWVnMnj2bQYMGhXJ4Qog6TlVVnn76ae68804Azu01kGsenk8uRiIMWtonRNK+sZk2jUwkRBmICtOR73RzxGIjLceOqqr+Wa0jFhtH03PJzHWgeiDcoCUhQkuf4x8z8Lc76ZHyH5Jiwvw5W74E+8KBWLHKUlurqlTnvYQQZRLSHK6ff/6Zyy67jCNHjrBkyRLOO+88RowYwbBhw+jQoYO/8OmBAwdYtWoVq1atwu12c9VVVzFq1KiAgqmFFa6lI4RoWFRV5cEHH+T5558HYMglYxh/33Nk5HvzrZLiIkhqZPInwBt1Gm8ivN3J8aw8/25EONuU2tcII8akJzpcT6cDCzCv97b9MR5aiRphQBnozXXyBV5lTp4v727AshQkLe4Y2XkoRK0T0oDr4osv9ieRKoqC2+1m+fLlLF++POjxvr5kS5YsYcmSJcVeV1EUXK5Spu+FEPWWx+Nh+vTpvPXWWwCMufI6nnrpNU7Z3Fhy8kmICqNlXAQJkcaAGllHLDaOZ+UFNKAG/EuDUeE6YiOMxJkMmIw69H//EnBfx6GNGM8EXL4E+zI3sC7vbsCyFCQt7hjZeShErRPy1j6FeyeW1EuxLK8LIRo2l8vFzTff7C+sfPG46xk941FO2Zwkxppo39gcfMbJ4yZ+51vEHN6IJe58snrc6l8GLFh7yxeoAaQ37Y3x0Er/JWxNe1HG8Kqo8u4GLEtB0uKOkZ2HQtQ6IQ24brzxxlBeXgjRwDgcDq6//nq++OILAIZdO41e18zAYnUQHeGgfeMoWsebgp+8eR7mn73LgzFHf+SYXot60d1nAjNT0KVBd5/pHHO4MB7/FXvz3hj7TA/1j3hWWZYFZelQiDojpAHXggULQnl5IUQDkp+fz9VXX833338PwJS77qfr6Klk5joxGXU0jvQmyqfl2AOCJ39trEKzQcbjW0jJyC1xaTA+KhzLoHtrpshpvxmgemDHJ97Hqsebs1Uwj0uWDoWoM0K+pCiEEJVls9m44oor+PHHHwF46PFnOG/0JI5Ycok1GejQ2EzXFjHA2QR43zKhP5AqNBt0Mqanv5xDcXlYAcGYxw2bXis5ib0qabSgaMCy1/t49WPexwWXCWXpUIg6QwIuIUStlpWVxejRo9m4cSOKojB//nwGXX4d21Iy0GigcWQY7RtH0jgqjCMWmz8BHgo1lj4z+2M/uJG0uJ6cantj2co5+JQlib2qSWNpIeqNkNbhEkKIyjh9+jRDhw5l48aNaDQaXn/rPUZeeT02u4twvZYIvY5wgw6jzvtRVmJtrDOzQYbJn2EcdC9tG0eTFGcq+zJhTdS2KpyTJTlaQtRZ1TrDlZmZybJly/jll184fvw42dnZREVF0bx5c/r27cuoUaOIjo6uziEJIWqp1NRUhg8fzu7du9Hr9bz45vv0GTaKQxYrqqoSFa4nOsJbL8sc5l0+LEttrHKXc/CpiQR1ydESot6oloDL6XTyyCOP8Oabb2Kz2YIeM2/ePEwmE9OnT+fxxx9Hr9cHPU4IUf+lpKQweMhQDh08gN5g5LG579O212COZuSBqhAVriU2wuCvl+ULrCocTJVFTQQ/kqMlRL0R8oArJyeHoUOH8vvvv5daY8tqtfL888+zZs0afvzxR8xmc6iHJ4SoZQ4ePMjgIUM4mpKCMTyCO55+mxbdBuD0qOi1ChEGHVFhOiIMZxPWVVXFYnUE351YVST4EUJUQsgDrkmTJrF161YANBoNl156KZdeeimdOnXCbDZjtVrZu3cvy5cvZ9myZbjdbrZu3cqkSZP49ttvQz08IUQt8ueffzJs2DCOHz+OyRzJPS8uoOU5PTBqFVrGhmE26gnTa8l3ugNa9Jy26rA5XMF3JwohRC0Q0oDrxx9/5Pvvv0dRFFq1asUXX3zB+eefX+S4oUOHcscdd7B9+3auvvpqDh06xPfff8+PP/7I0KFDQzlEIUQtsWPHDkaMGEFaWhrRsXE8/PpHNGt3DuEGHa3iTCQ1Mvlnrgq36MlzuHG4PcF3JwohRC0Q0l2KH3/8MQAmk4k1a9YEDbYK6tmzJ6tXr/YvJfrOF0LUb1u2bGHw4MGkpaXRKKEJj83/DE1CO46l5wMKibHhJEQa/cuEhXcjxpuNxe9ODMbjho1zYfEE71ePO/Q/pBCiQQvpDJevbs6UKVNo3bp1mc5p3bo1U6ZMYd68eWzcuDGUwxNC1AI//fQTY8aMwWq10rxFIs+89zkWTRyp2fmgQqzVzt+Z+TSJDvefU3g3YiOTntM2Z9DdiUHzu2qippYQokELacCVmpoKQO/evct1nu943/lCiPpp2bJljB8/nvz8fFq3acuX3/0ApjicG14n8uRv7A/vwuHoqUU23CiKEhB0gTcIC7aMaLE6ilafL6mgqMftLXJaXRXlhRANQkgDLrfbO02v1Zbvw8p3vO98IUT98/XXX3PttdfidDpJateB+Yu/pmVSKyK2voH54FwA/mHdxC9R4UR0ua/I+ZZMK/olk2mWtou8hK5YrvyIhNjIIsfZ7K6i1edLqqkls19CiBAIaQ5XkyZNAPjjjz/KdZ7v+MaNG1f5mIQQNW/RokVcffXVOJ1O2nT6B4+9/QWGqHhyHW7Mqb8FHNvKtpMwfdFf2kzf3EjMsbUY7RZijq3F9M2NQe8VtPp8vxkw/HHoeKn3a8GaWjVRUV4IUe+FdIarT58+HDlyhAULFvDggw8SExNT6jkZGRksWLAARVHo06dPlYxj69at/Pbbb2zdupXdu3eTlpaGxWIhPz+fqKgo2rdvT//+/bnhhhvo0aNHldxTCBHc+++/z7Rp01BVlY5dz2PWywuIiWt0NhgqNPvkTuyDOUxfJBcrLm1nwHWNaTtJy7EXqcUVtPq8ohRfU6smKsoLIeq9kAZcV199NZ999hkWi4XRo0fz9ddflzhrdfLkScaPH09aWhqKonDNNddUyTiGDRtGVlZW0NdOnz7N6dOn2bJlC6+88go33HADb731FuHh4UGPF0JU3GuvvcbMmd4gp3vv/sx+4X0iIs0kmA1n+xr2m4GqguPQRmxNe2HsM514s6FILlZUk+5oD6/2X9vVpHvRXK0zOxvLVX1e2ukIIUJAUUsr/15Jffv25ddff0VRFKKiorjpppsYMWIEnTp1wmQyYbPZ2Lt3LytWrGDhwoX+wKh3795s3lw1U/kxMTHodDp69+5N9+7dadOmDbGxsXg8Ho4fP866dev4/vvv8Xg8AIwcOZJly5ZVSaXqxMREAI4dO1bpaxW2c6f3N/xu3bpV+bWFKE1533/PPPMM//rXvwAYMmwEj7/+Pna8vRCT4kwBAZGqqliy89BueQNT6m8Y2g4gueNUDqV7a29FR+hoG2Og9Y+3wvEd0LwHR4a+w6FMx9nX4820jjdV+c8tagf5/BPVrbL/noe80vzXX39Nv379SElJITs7m1dffZVXX3212ONVVaVVq1Z89dVXVTaG9evX06VLl2IDqHvuuYetW7cycuRI0tPTWbFiBV999RVXXnlllY1BiIZKVVUeffRRnnzySQBGXXYFX372CTlOhWMZef5jVFX1/x21WB3Y188lceuz3oscWkG8w8XpdlPB46bjgQ9ISN8O7QbCdZ+CRospx0603fv7Y5lqcQkhRDUKadI8QLNmzdi2bRvjx48Hzn6wBvsP4KqrrmLr1q00a9asysbQtWvXUmerLrjgAv9v3wDfffddld1fiIZKVVUeeOABf7B14aVjeeTlt7G6NCiKgopKZp6Doxm5WKwO/3k2uwvj8V8DrmVK/ZWkOBM9jn5E4tZnMR5a4d1NuOk1wFsWIinORNt489nlSSGEqCWq5VfAuLg4vvzyS/bu3cuXX37JL7/8wt9//01OTg6RkZG0aNGCvn37ctVVV9GpU6fqGFJQXbp08X8vNcCEqBxVVbn77rt57TVvQDRqYDdeGe7Ase1tbBffjapoSEnPJS3bQUKUgbiIs3W0TEYdmQkXkHB8jf96tqa9va/vXxJ4ox2fwMB7yp+rJYQQ1aha59w7derEQw89VJ23LJf9+/f7v2/atGkNjkSIus3j8XDHP//J2++8A8ANA1uxYMhhNFlHIGsD1ngTx86ZRp7DTa7TSZ5Di93l8Z8fbzZwtMdtbLc7iT+9jfzmvdF3mYa36VfhtNMzj8tSsLScRU2DVqmvgtxOIUTDE9KAKy4uDoArr7ySd999N5S3qrS//vqLp556yv/46quvrsHRCFF3ud1upk2bxoIFCwCY2cfAK0PSAwIV/bFfsCTegMutEq7XEW7QYdSdzXBQFIWW8ZGk9LuL/Wd2HSaFn5m5imkFln1nbxjTyvu1LAVLy3BMwSAr3+nGaneSne8K2PkohBDlFdKAKycnB4/HU+7WPqH0yy+/+JcLnU4np06dYuPGjSxZsgSHw5tDcscddzBmzJiaHKYQdZLL5eLGG29k8eLFANzf38Czw4xFZoXSYnuQZrOj0yo0Dg8jKS4cc5g+4Jig9bMAlEIzUr7HJbXrKfhcKccULD+R63DhdOOfRLPZXRJwCSEqJKRlIVq0aEFqaiqffvpprZkxGjNmDP/973+Dvta9e3fuu+8+Jk+eXK5r+raKBnPixAni4+NZtWpVua5ZFk6nEwC9Xl/KkUJUvcLvP6fTyf/93//53+u33DSFGVOuCboE59YYAAWPVo+qN6HTatCUdanOYYX87LOPw6LAYC7++bKcW3BsHhWXR8WjquhcNjQeJ6rGgKo3odUqZR+nCCn5/BPVbfjw4ej1+tpZFqJz586kpqaSkpISyttUiejoaEaMGMF5551X00MRos5xOBzMnj2bdevWAXD7HdOZev21KG5b0OO1HseZr3ZUjYLiUcDlAJ2haJBUmO/1wscX93xZzi1A47Shd9m937u9X3GfGaeulLEJIUQxQjrDNX/+fKZPn06fPn2qrIhpVcrLy+Po0aOsXr2aF198kcOHD6PX63nxxRe56667quQeUvhU1Fe+91+HDh0YP348y5cvB+DFF1+k/xU3EvvdDXTO3ljqddT4TiiWvWefGP544DJfORPdK2Xj3LM5XoV1vBQmfhqa+4pyk88/Ud0q++95SOtw3XzzzXTr1o1ff/2VZ599NpS3qpDw8HA6duzIHXfcwa5du7joootwOp3MnDmT9957r6aHJ0Stl5uby5gxY/zB1rx587jvvvtoEm3E2qRXma5RZIGucJ6VL9F93zLv183zKj/w4pTUqFp6KgohKiGkAZfBYGDp0qX06NGDhx56iLFjx7J27Vrsdnsob1shJpOJhQsX+vNN5syZQ4i7HglRp9lsNqZPn86aNWtQFIV33nmH6dOnAxAZZkDpP4MD3e8nM3EIrtj2Qa+hxneCHtcFPlk4sAmW6B4qhe/dYYR3Zmv449JTUQhRKSHN4Wrbti0AdrsdVVVZunQpS5cuRavV0qhRo1IbRCuKwsGDB0M5xABt2rThnHPOYc+ePRw/fpy9e/fSuXPnaru/EHVFZmYmt99+Ozt37kSj0fDo869zxbWT/e15vDsKo7BdeDe6P95Ct/7xoNdRek7yBjKKpthm0WpSPxRfCQff41D9YMEaV4dq+VII0aCENOA6cuSIf8ao4E4ll8vFqVOnSjy3YF+16hQZGen/PiMjo9rvL0Rtl56ezogRI9i5cydarZapD71Es14j2fV3Ft0SY0iINAZUfbf//UvRi8R3gp6TUPtOx2JzYet0C6Yu04jf9S7Kp5MCgh1L12nYM3MxHv8Ve/PeGLtOIyFUP5xG680fK1xOQgghKinkleaLW5arjct1Ho8nYEYtISFkH+tC1ElpaWkMGzaMnTt3otPpeOKpZ3CdO5xT2flEGXVB61SlxfYkkZX+x3lRbQm/fQPoDFhy7P6aVx33v4/y+3Peg/YtA9UDA+/B5lQ51P4msprfQHSEjrZONXQBlxBChEhIc7g8Hk+l/nO73aEcXhFffPEFFosF8Dbd9i2JCiG8NeUuvvhidu7cicFg4KWXX2bosKEkRBowGXWoQL7TXeovU+HZh1B/mU9ajp29qdmkpOeSaXMSu//LwAN3fAJ4+ypGh+uJjvB+NRmrtSOZEEJUiXr/yTV//nw6dOjA0KFDS1yi/Oabb7jlllv8j6dPn45GE9J4VIg649ixYwwZMoT9+/cTFhbG2x8upnubJuDIZOjp78mxu2h6YDvhuzQ49HqM7Qf6lwQTMrYXuZ7j0EZSWk4mzeogz+EmXA8GR2bQexdbcV4IIeqQeh9wbdmyhTvuuIPmzZszbNgwunXrRpMmTQgPDycnJ4d9+/axbNkyduzY4T9n6NCh3H///TU3aCFqkSNHjjBkyBAOHz5MhMnEy+8t4jz+wGP3bnppv/PFoicdXgnbF0HPSRja9IdDKwNetjXtRVaeE1VVCTfo6JHyIfr804HX6H4tbJyLkryZhFb9SJAEdiFEHRbSgOs///kPAEOGDCmx/U1hx48fZ/Xq1QDccMMNVTKW48eP+8dTHL1ez1133cVTTz0l7SKEAA4ePMiQIUNISUkhMjKSVxd8hiuhE4bf3yG/eSk9Ui17YdWjKMMeg2GP+ZcI6T4Bg8PNeRv/iSXufLJ63ErzXTsCz43v5N25WFozao8bddM8HIc2YmvaC3ef6cRHhdfIhhshhChJSAOuKVOmoCgKX3/9dbkCrl27djFlyhQ0Gk2lA67XXnuNsWPHsn79erZv387BgwdJS0vDbrdjMpmIj4+nS5cuDBo0iAkTJtCiRYtK3U+I+mL//v0MHjyYv//+m5iYGD79aim6ph347XA6+8O60rKsF0rZ4q3QPvAeb9X4TyZg3u+d8Yo5+iPW6DAMbQfAoRVnz+k5qWzNqDfPQ1n9KEbAeGgFxxwuLIPulQbTQohap1YvKVbFTsaoqCjGjh3L2LFjKz8gIRqIvXv3MnjwYE6cOEFcXByrVq0iLqkTB9OsRIbp2N1qEk10WehUJ9YLH8Zs1GE/tIk8hxNdVjLmnENnL1awmOjmebA/cHnRnPobTFjkLTlfuBZXgfpbQSu9FwrKjMd/DbpTUgghalqtDLh8gZYsCwhR/f7880+GDBlCamoqjRo14scff6R79+6k5diJidDTuVkUqhqJmu/GrUDuP+7EFGnkROdbOWSxkmWz0zXlI5pnbSeiw4WBhUyDVYlv1S94/atgRUiDnVsgKNNrVEx6+dwQQtQ+tXIbXnp6OgARERE1PBIhGpY9e/YwePBgUlNTiY+PZ82aNXTv3h3w7hZsGRtBbIQBjaKACh4VjmbkYrE6iNBByz1vM2TtFTQ9vARPYp+ildqDtM5R+04nLcfOEYuNtBx7+Wa2+81AbT/C/zDm2Frid71bmT8CIYQIiVo5w7VkyRIAWrYsc5aIEKKSdu/8gyGDBpKWaSU+xsTHXy6lWZtOqG4Xyi9voCRvJqLJBTTOtdP46BZSz7sfly6CnDwnNruL+D/eovHOl85ecMOTpHvA3e8u4s0G74x1kFkri83lL34aHe7drJIQaTzbtBqKT5rXaFEK7VxUUjYDUileCFG7VFnA9e233/Ltt98Gfe21117jm2++KfF8VVWxWq1s376dw4cPoygKAwYMqKrhCSFKsHPnToZeNABLlo0mJoU1E8FwehUp6R0J/3UB5jO9EM37lmE+c0666w4U8Bcj1RwtulzoPrKJlE43A2eCqCBLhzZ7Pll5TrJsdpL+fA9T1nbocCHqkU0BPRPV5E0ovvM8bm9AlrwZ1EIFktMPwca50gdRCFGrVFnAtWPHDhYuXFgk70pVVdauXVuua6mqisFg4K677qqq4QkhirFjxw6GDRvG6SwbTc0Ka2+MoHO8lrw/PyC50024Dm8s9lyt6qRFnIl4swFbUj84sirg9ZMxPck6MwNWXCK7r5J80p/v0f6P571PHlmFo80wCp5ha9rbH+wFzH4BdBgBGcneUhRnylEA0hNRCFFrVPmSYrD8i/LuNuzVqxfPPvssXbp0qaphCSGC2LZtG8OHDyc9PZ2mUXrWTTbQKd47KxTuTCdp3wLAU+z5Gr3RH0iZBs3EqqoYdn+KR1XJNyXSJGMbMQc+wNis+MAn3mwAj5volK8Dnnd74ED3+zGl/oataS90XaZh9s1sbZoXeBFFC3FtvcGWT7AyEkIIUUOqLOCaMmUKF198sf+xqqoMGTIERVF44oknSl0e1Gg0mM1m2rZtS3R0dFUNSwhRjN9//51hw4aRmZlJXOOmPHfX5XRyfhpwTOKRr4u03LFGtkWf0AHCosDgn3NC0eowD50FQ2ehbphL2Oozs0wn1qLGRMDA4MGPoigk7H4XMvYHPO9p1Z+sdlNJybvFOwMWbiw6s+XjS8YvrYyEEELUkCoLuFq1akWrVq2CvuYrLCqEqB1+++03hg8fTlZWFk0bRfHBTR3IjjDxh70X3fN/8x8XnnWg6Mk9rscw5F7YtavY63sT1ws/LmG26cimwMeNOmIaNJOkXHdgD8XCZSVMjaH/jKKlJ4orIyGEEDUkpLsUfblbsjQoRO2xZcsWRowYQXZ2NokxetZN9NDO+Adk/cE3jW4hr0V/Ojv+h9mWgi59n/88V3g8+b3uwDRoZuk18grVxyp9tqnQsmVcaxStjoRIXWDuV+Hr9p8RuGxYuJaXEELUEiENuGRWS4jaZfPmzYwcOZKcnBxaRSusvcFIm9iz5fi6qXuxD11EdLMolE2vBSzfHel4E1ntppKU6yYhspSPjrIULfXxuL0J7wUpxZQILM91hRCiFqmVdbiEEFVv48aNXHLJJVitVpLiI1g3SUPrmMDARtuqP2F6LRarg/i+00GFrL0/c9jUld2NJ9A810Ej09lEeRxWWDzhbPDjK8MQrHJ8cTbPC0x2B2jVP/ix5bmuEELUItUacHk8Hg4ePEhGRgb5+fllOueiiy4K8aiEqP82/PwTl14yEmuenTbNGrFgzg20PvG+//W0sNacbn811q63kH3adqYAqQm6387ORhM4mGaFM+WuTMYzHxsOK+Rne5f49i2D7Yu8TafLW/+qcF5WfCeZuRJC1DvVEnD98ssvPPfcc6xatYq8vLwyn6coCi6XK4QjE6L++/nnnxl1yQhseQ7axSqsvdbOKbOJbxNuo7VtJydiepB3/j9JiIzA4fCQlev9O2ezn/27Z9RpMWoVYiOM3uR1AJcj8EYVrX9VOC+r5yQpWCqEqHdCHnDNnTuXWbNm4fF4yl2PSwhROevWrWP06NHk5jnoEKdh7Y0RtIjSkJ+2jR8azSG80QQ6NDbTKTyMtJxcWu9bwDmZ23G17EtYs5mg0RIdriM7X4deqxBhKBAI6QzgCjJTXd76V5KXJYRoAEIacG3evJl77rkHRVFQVZUWLVowePBgEhMTMRqDV50WQlSNdevWMWrUKPLy8ujUSMOaGyNoHunN2Tpi6oZeoyXcoMOjwsG0HDofXEDPlDMFRU+u89bOGnAXp6169Bo7TreK1e7EYnV4c7h8NbjiOwXmYJW3/pXkZQkhGoCQBlyvvvqq//snnniC//u//0OjKWb3kRCiyvz888+MHj3aG2w1i2TtdSrNzgRblvDW7E6aTHutlnCdllyHm4OWbPpYfg+4hpKyGQbOJEyvJcKoJSvXRXa+K7BNj8EMd2w+29dQZqiEECKokAZcGzduRFEULr/8ch566KFQ3koIccaGDRsYNWoUubm5dEgwsmpKLM0Mmf7Xd8SNwhRmJM5k4GRWPqdz7WTanOw1/IMeuQUS2M/MVPl6HcLZRtUBZIZKCCFKFdKAy2KxAHDZZZeF8jZCiDM2bNjAJZdeis1mo0OchnWT9TQ/E2xlaWKwaaPQoBJv1qJoNOTYnWTlOtHrNGxrMYmOTSI51/k/DG0HopyZqfIlyQdUfK/DVFXFYnUE/DylFnIVQohKCmnAFRsby6lTp4iMjAzlbYQQeHMmL7n0UmxWK63iI1g7WePP2QKI9mQS7cmk+d/z2RUVzqam1+PyeAjT6Whk1tCxcRQtzn0QY1RYwHUVRSEh0hhY8b0Os1gdpKTbyMpz+mfu6svPJoSovUKaUOVr6ZOcnFzKkUKIyvjll18YOXIkNquVhOZJPHnfFFpEFf/XO/b074TrtbSJN9Mt0cQ1jq+57M97Sdj5trfyu4/HDRvneoubbpwb+FodZbO7yMpzkpXr/Vqw/IUQQoRKSGe4brzxRn788Ue+/PJLZs2aFcpbCdFg/frrr/52Pc0TWzH5iff4zRBJQo6ZPlkr8aByUmnMObYt/nMy4y+gbbyJ+EgjjXe+Tdzul7wvJK8GhbP5WJvnna2t5auVVcdztUrNSRNCiBAI6SfNpEmTWLBgAevWrePll1/m3nvvDeXthGhwfv31V4afaUTdMqkVj83/jL9sBjKzHfwYdx2OPnfSLsHMH0fTSfvzA5KsO8hp0gtHr3/SKT7Ku5S2NnB3YkAdrcJV4De9Dkc2ABroNPNsaYg6pL7lpAkh6oaQBlyKorBkyRKuvPJKZs+ezc6dO7n33nvp1q1bKG8rRL2nqio/rv+F8ZddSk52Fo2bteCZ97/AFR4POemMtn7BPzL/h8fTj/jW9zHsHy3IafcAJ7LsuDweEsKMNDJ5Z3mKVHovWEer8Gu2NNi/8sxrU0P/g4ZAfctJE0LUDSENuNq2bQuAy+VCVVU++ugjPvroI0wmE3FxcaXW5FIUhYMHD4ZyiELUSWs2bOHKy0d5g624KJbfEEmb7bfyV+PRxFvzGJ3xnvfAPzeRmf07Br0etUlv0jtNIc+lYnO4OG1zEm82YOkyDa3NgSn1N/Rt+3O6yzRsFpt39qfvdBSATfPAdqroQAq39xFCCBFUSAOuI0eO+Ldb+76qqorVasVms5V4rqqqslVbiCC2b9/OlZdfSnZWJs2j9ay/3kPbsONgg76HXydHGxtwfMzf6wCIOLIKx8GfSOn7JnC2V2JKZj5ZSTcQ3elmTAYdtsz8Ajv4TCT4lhd9uVwF6WQ5TgghyiLk2aLF9U+UvopClN8ff/zBsGHDyMrMpEWkwk83GGkbGzhTbFCD9Dc8I9GygcxDH2LvMwOTURewYw8gz+HG4Q5sYJ0QaSzQ73ATqB5AA2FRdTKHSwghakJIAy6PxxPKywvRoOzcuZOhQ4eSnp5Os5gw1k3W0S6u6LJ8tj6BBHtKsddpc+wbdPm7MbQdAF2nBe7Y03houuI2wtN2kZfQFeeVH3lPClZNfufOKv35hBCiPpP90ELUAbt372bo0KGcPn2aRglNmDtrPO1zF/lfd6JDj3dWKsGewqGYATTL/Ytwx+ki1zJlH4Tsg3BoBfHbPyYqKgm3Cp6W/Yj43ydo0g8AYDy2FvW/N8H1X1TPDymEEPWYBFxC1HJ7du9iyIX9sWRaaRwbyYyXFhKRswZyzx7jC7Z8Yh3HOdjuRrr8+bL/uSxTW8L0WoyZ+/3PKaf3YTy9z/vgyOoi91ZO7KjSn0UIIRqqag24nE4nW7ZsYc+ePaSnp+NwOHj00SCJuEIIAPbv38/QQQNJy7TSxKSwbqKHtkemYqfkZPXY3MMYjn3L3wkXku9UOWzuzuEOU7g05ysStz5T9gE071G5H0AIIQRQTQGX0+nk6aef5vXXXycjIyPgtcIB1+zZs/n2229p2bIlP/74Y3UMT4ha6fDhwwwZMoTU9GziIxR+vCGCzvFacOdiKDi9VQxTzkFMOQf5pd1MfomdQGNFy6l/3EJibDhsXwSWvSWerzbqgGXUB2dLREiTZyGEqLCQB1yZmZmMHDmSrVu3FtmZGOzD+/LLL+ell17i4MGDbNu2jfPOOy/UQxSi1jmafIQhAy7g2Il0Yk16Vl9v4B+NtQHHuBQ9HmM0WdHnostJITb3SNBrtcvbyeFON9M40khiIzO0menddbh5HhzZBHhA0UBSX+8JKVugVT8sXaZ5S0YEafKsqioeVUVVIS3HLsGYEEKUIuQB14QJE/jtt98A6NevHzfeeCPHjx/n8ccfD3r8hRdeSIsWLTh+/DjLli2TgEs0OMePHWVIn64cOWklyggrJxro3Coejz0joNu8TnVCvoWE/J9JbXwRFBNwxeb/zcjMz3F3mk68r7q6Rova/y4s3W4v0uLG0t2Bze4iPdNOZq6D7Dxvw2p/iQjAYnXg9qh4VEhJ99bUk8rtQghRvJAGXD/88AMrV65EURRmzpzJyy97E3i//fbbEs8bNmwYH374IZs3by7xOCHqm9TUVC7ufz4HTlox6WH5pAguaK4Fe0aJ58Vk7ibH3IZI6+GzT+ojwJmLLn0fcZuehAgdDLwHVVWxWB0cTc8lI9cOHjedDv8HR/p2nIl9SGlzA1l2D74J6egIXZEmzza7C48KHlUlJ88ZEIwJIYQoKqQB18cffwzAueeey0svvVTm87p27QrAX3/9FZJxCVEbnT59mkFDhrL/aBrhOvjvxAj6tSzbX9EwRzphjnTyotoSnn3I+6SzUJ7Xjk9g4D1YrA5S0m3sT80h2+7i/KMfkbj/FQCMh1YQnZVPSqspRIXriI0wEmcyFGnybDLqOK0AKEWCMSGEEEWV3Mywkn755RcUReG6664rV35H06ZNATh1KkjvNiHqoczMTEaMGMG+P/dg1Cl8d10Eg1p7gxg3wf/uZEa0wqMptFsxP6vUe/mqy9vd3imsJlnbAl6PT/+d6AgdMRF6WsZF0DreREKkMeDvcLzZgFajoNMoJMWZAoIxIYQQRYU04Dp58iQAHTp0KNd5RqN3aSI/v/gWJULUFzk5OVwyciTbtm1Dr4UlV4cxrO3ZGSMtwdtgxeQmo/EENo9ONXUu/kY9rgO8s1PR4XoSY8Npl2CGpP4Bh+naDKBtvLnEQEpRFDSKglajFAnGhBBCFBXSdQCt1ruryuVylXJkoPT0dABiYmKqekhC1Co2m41Ro0ax5ddf0Srw2ZXhjO6oL/d17BjJMjblRFR3osN0xJ346eyL8Z2g5yR/P0RfENXIdCZZvuMsiA2H5M3Qqh/mfjMwa7TBbiOEEKKCQhpwNW7cmMOHD3Pw4MFynff7778DkJiYGIphCVEr5NmsXH7ReWzYth+NAovGhzPunLIFWyoKSoGZLyN2GtuTaZz8JtnmNuRFt8PgyEKbeB5cuwh0Z2eqFMU7KxWQ5F64T6IQQogqFdIlxb59+6Kqaqm7EgvKzc3lyy+/RFEUBg4cGMLRCVFz7HY744f2Ys22/SjAwivCuLZLeWa2gi8zAkRZDxOedRBtngX2r4Qt8ys9XiGEEJUT0oBr/PjxAGzbto2PPvqoTOfcdddd/iXFa6+9NmRjE6KmOJ1OrrnmGpZv8e7CfXtMGJO7B+ZKWbUxHIkbSFpY66DXKFfGVHLVlVdRVZW0HDtHLDY8avFBnxBCiEAhD7i6deuGqqpMmzaN1157DYfDEfTYgwcPctVVV7FgwQIUReHiiy9mwIABoRyeENXO5XIxadIkvvvuOwBevzSMaecXTUw3uzPRaUsOq1zacFRFi0cp5a9xq34VHm9hvpIShyzWM4VPJegSQoiyCHnxnM8//5y+ffuSmZnJPffcw5w5c2jRooX/9eHDh5OSksKBAwcA72/QCQkJ/Oc//wn10ISoVh6Ph1tuuYUvvvgCgL7XTmdg979w2LdjoOjGksS09SVez9KoF01P/Rx8tisiAVqcD637+5Plq4KvpERWrotIQCPxlhBClEnIA66OHTvy008/ceWVV3LgwAGysrLIzs72byNfs2YNgL/PYocOHVi6dGlAUCZEXae6Xdw9YQgffukNov4xeipTu2noYf/Nf4xbheImtRzGOHQeO4rbgerx4A6LwZx3rPgbDrgzJEnwvpISAJo8kGoQQghRNiFdUvTp2rUru3bt4vXXX+eCCy5Ao9GgqmrAf127duXll19m586ddOzYsTqGJUS1UFWVB268hNfPBFuz+hn46fxVDHWsCjiupBVEgz0djdOG4nGiwY0+/zTmnEPBD47vVKWzWgGXNhtIijPRNt6MVuOtxSWEEKJ01daPw2g0Mn36dKZPn47VauXo0aNkZmZiNptp0aIFcXFx1TUUIarVo088zQuLfgTg1vP0PD/ciKJk04jsMp3vUTRoVE/wF+M7oQKKZa//KbXHJJQQ1dEqWFJi53EJtoQQoqxqpAGa2WzmnHPOqYlbC1Gt3nzzTZ6c8zAAE7vqeHN0WLmrshcbbAHEtsLhhryw5jjdYG/RB2PXaSSU8dq+RtY2u8vfL1GqxgshRNWTjrNChICqqrzx9nvcOX06AGM66Vl4RRhaTRUEMxHxENEIYpJg/0qMgBHYfe59nGp/E22dapkDLt+uw6w8pz83K6AgaqGfqWBwJoQQouyqJYdLiIbmw8Wfc/f02wAY2kbLF1eFoS+lzEOZ9bsDZvwKmsCgp0nmdqLD9eUKhgruOszKc2KzF9+Gq2BJiJT04utwFazVlZZj92+IEUKIhkwCLiGq2MqVK7n1phtwe1T6t9Ty7YQIwnTlD7ZcmjBSG1+ISxsR+MIfn3m/FqqvpW3dv8SG08H4dh1GR+iKDdZ8AdTe1GxS0vPIzHWSleekuDiqcGBmsQavvSeEEA1Jg1gXsFqtrF69mnXr1vH777+zb98+MjIyMBgMNG7cmPPOO49x48Zx1VVXYTQGX04RojSqqvL9qrVcM3YsToeDHk01/HdiBCZDxWa2rM37sWfg2/RbNgpd1oGiB/h2Ip5pOh3XbwaUM1neF5wVzOEqzBdApVnt5DnchBu8wZliD37NgrNmvsfFLVMKIURDUe8DrpdffpmHHnqI/Pz8Iq85nU4OHz7M4cOHWbJkCXPmzOE///kP/fv3r4GRirpu1c+bmDB+LPl5eXSM17Hy+jBiwoIHWx5Kn14ON+hpG2/G3W0CrH/y7As9rvN+1Wgr3XQ6aCPrQnwBlOqBcIOWhDOlIU5kBf/ZCtbqKu8SpxBC1Ff1/pNw3759/mCrWbNmDB06lF69etGkSRMcDge///47H330Eenp6Rw8eJDhw4ezevVq+vWrunYoov773//+x1VXXEauLYfWMQo/Tg4jwVR8SOVGhyZIdfmCnIl9Aci7YDomowElxTuTFaoaW8UpHEAlxZlIiDRyopjjyzJrJoQQDU29D7gURWHYsGHcd999DB8+HK02cMll8uTJPPTQQ1x++eX88ssv5ObmMnXqVPbs2YNGIyluonSHDh1i6LBh5GRl0MyssHqyicSokt87+jPBllsbjtadV+R1jz6Cva0mkWWxeoOd7reTMLDqK8eXRXkDqLLMmgkhRENT7wOup556qtSiqgkJCSxZsoT27duTl5fH3r17Wb9+PYMGDaqmUYq66sSJEwwfPpyTqak0CldYfUME7eLKHqhrcAd/3plL9M73SWk1BTiTB2XSweZ5/pwtKpCzVRESQAkhROXV+ymcslawb968ORdddJH/8c6dO0M1JFFPnE47xdD+PTl06BBmAyy/PoJzE8oXACnu4nfwxaf/Hrh7cPM8WPUo7Fvm/bp5XmV/BCGEENWk3gdc5REVFeX/Pjc3twZHImo7m83GJYN68+eRkxi08N2ECC5oXp5gq1DCucGEqjcFPGVt0ouYcAMtYyO8y3jJmwPPKfxYCCFErVXvlxTLY/fu3f7vW7duXXMDEbWa3W5nxJhxbP0zGY0Cn14ZzuA25fyrFBEPuWn+h9a+97G39Q0YfptP1KnfOBHTk+PNJtIKFUVRvO12WvXzzm75tKo/GzukxZAQor5TVCkDDcC6desYPHgwAAaDgdTUVGJjY8t0bmJiYrGvnThxgvj4eFatWlUl4yzI6XQCoNfrq/zaIjiPx8NDDz3MDz/8F4DHHrybcaNHlv9CxijvJJfLgao14NJG4HB7cHtUPCooCmgVBb1Og06jnG0J5LCCywE6AxjMVfeDVUBVvv88qur/2TUKaDUKGgm4RAnk809Ut+HDh6PX6zl27FiFzpcZLrzLh7fffrv/8Z133lnmYEs0HKqq8vzzz/uDrXv+eXPFgi1FAeOZYMkAHo+Kx6OiquBRvffxzmp5g4+AuMNghnpYZcH3s3vbBSlovF+EEKLeaPAzXKqqctVVV/HVV18B0KFDB37//XciIyOr5Pq+2a+KRsQl8SX2d+vWrcqvLQJ5PB5uv+8h3n31WQDu72/gueFhFbtYfEeY8Zv/YVqOnZR0G7v/zuTAKRv5Tg/Noo10T4yhW8vYWru8VpXvP9+fga+Jtq/WlxDFkc8/Ud0q++95g5/huu+++/zBVmRkJF9++WWVBVui/vj3c6/6g62be+p5dlglgoEeEwMe+upa5dndKGjIc7ppHBVG2wRzgwk6pFiqEKK+a9AB17/+9S9eeeUVAMxmMz/88IP8tiSK+Oyzz3jioVkADOjUiLfGOCo246TVw5BHUPtOx5JjDwguvIFVFOFGrX+WxxzWcHJTqqvWlyTnCyFqSoMNuB5++GGeeeYZ4GywNXDgwBoelahVPG5WvDKdyQ+8g6qqdGjdnLfHR6DTnKrY9S7+PxgwE0uh5TOAhEijzPJUA18j7sJ/9kIIEWoNMuD617/+5Q+2IiMjWbZsGQMGDKjhUYnaZvO7sxj/f2/jdMN5zTSsnZBDlMFasYvFd4ABdwNnm0Fn5br8jxMijZWa5ZGZm7Ip7s9eCCFCrcEFXA888ADPP/884C10umzZMvr371/DoxK1zV97/seYe18n1wkdG2lYNimCKGMlApi4dqDRoqoq+U43uXY3KBAVpvNWka8kmbkpm8KNuKviz14IIcqiQX3azJo1i5deegmA6OhoVqxYQZ8+fWp4VKK2+fvvvxkxeCDpuW6aRyqsvD6CxqbKNWVQk/qj4A2MrHYnTo8HvVbBbNRXydKhzNyUjSzbCiFqSoMJuO655x5effVVAGJiYli5ciW9evWq2UGJWicz/TQjLryAo6cyiTbC8kkRtIqpeLBlN8aT1vUWjF2nkYD3H/rsfBeoChEGHWF6bZUs/VXlzE19Xp6URtxCiJrSIAKuu+++m7lz5wLeZtarVq3ivPPOq+FRidomLy+PUYN6sedwKgYtfDMhgq5NyteMurD97W7kVPubaOtUSSB0S1pVOXMjy5NCCFH16n3A9cgjj/iDLfBWkU9JSSElJaXE85KSkiQoa0DcTgfXDO/F5t2HUYCPx4VzcevK/fXITBzMqS63BARWoVrSqsqZG1meFEKIqlfvA67169cHPP73v/9dpvNuvPFGFi5cGIIRidpo5nXD+H7j/wB49RIjV/+jEjWw4juh9piEs+s02jrVgMCqrIFRSct6lVnyK8u5klguhBBVTz5JRYPmdru5++GneGOJNzC/v7+Bu/qUfzbHrYvAlXQhxnYDoN8MFI2WBCChguMqaVmvMkt+ZTlXEsuFEKLq1fuAa926dTU9BFGL/fvVt5n37BwAru+m55kKtuzJSOiFddQCWsebKnR+4Zkna76z2GW9ii75qarK0fRc9p3MwelSUWNVGpmKzrZJYrkQQlS9eh9wCVGcVStX8PQDdwIwrK2O9y8PQ1OB3XjWyLYkD3uHpEosvRWeeTIZil/Wq+iSn8XqICPXQU6+K+BaQgghQk8+bUWDtH37dsaPvQK328N5zTR8dU04Bm35g61sUxsOjF9FUnxkpZbeCs9axUUYaGQ2BV3Wq+iSn83uvbZRp8WoVYiNMMpyoRBCVBMJuESDoqoqW3b8j+FDRmDNs9MmRuG/EyOIrEAV+VPGJNYN/ILekeGVXn4rPGtlDtOTYNKRsPMtSN4Mrfp5m17nurHmO7G7PBh15asPZjLqiInQoyjee7SMi6g39bWEEKK2k4BLNCgHjp5kzGWXY820EB+hsOL6CJqaK1bYNNfcmkYxUVWyLBd01mrTa7DqUe8B+5Zhs7tIaTeVlPRc8hxuwg06kuLCgbIlzUsyvBBC1BwJuESDkZuby7hxV3D678OE6+D768Lp0KjshU1VoOB8kK7NALq2iK6SwCVoonry5oBjNCmbyWp+PWnZDnKdTiL0OqLDdWVOmpdkeCGEqDkScIkGweVyMWTsdfxv268owKLx4fRJLN/b33bhIzjcKqbU3zC0HUBi/xmgqVwl+hK16gf7lvkfepL6ER2uJyHKQJ5DS/iZxPq6lvhen1sHCSFEcerWJ7UQFaC6Xdw1YShbVv0MwMsjjYw7p2yFTZ3GOBRTPPn/uJa9bW8ky+4hutPNJMWZSAhlsAXQb4b365kcLlPf6STluomLMPhzuMxhpTe/rm0BjrQOEkI0RBJwiXrN7Xbz3F1XM3+JN9i6s7eBmX1KXwL0oGHPOTM51fVW2jaOBiDLYg1Zu5ugQZFGCwNmev/Du5yZEKkr930LBziqqqIoSpkCsFBUtZfWQUKIhkgCLlF/edx8+tQtzHnrGwAu66jjlZHGMgUMB7vezal/3B6wZBfKdjehnPUpHOAo5KGiluleoahqL62DhBANkXzSiXpJVVV+fX82dzy9EJcHzm+m4ZMrw9FqSg+27MY4oofOQu+iyG4+32xNI5OetBx7lS3ThXLWp3CAA5T5XpUZV3Hnym5JIURDJAGXqJd2/XWQq2e/RrYdkqIVvp8YgclQtoAorcs0EmOKtugpWBfL2rQXKW1u8OZ0lXOZzqfgklu+001U2NmZtKqc9Skc4KiqiopapntVZjaquHNlt6QQoiGSgEvULy4HOQuvZuqj/+VolpsoI/wwsWy1tjwoHOx6N9EXzgx+wOZ5/rpY5n3LMFpsHGgyiYSoMH/NiPIsvRVccosK02E26ok3h4V81qdgLlVp96rMbJTMZAkhxFkScIl6xbnoWia98APbTrjRaeCrayL4R+PSdxN6NHqSp+4mJia2+MCgUF2sGMtWcuOuJc/hXTpDoVxLb4WX3OLNYRVufl2SorlUpjLPMFVmNkpmsoQQ4qyKldgWohZy5tmYPf8Hlu7zBjDvXhbG0LZl+51CM/QRWic2ASD5dC5pOXZUVQ08qFW/gIeZ8RcQodcTbtASHa73/hdR9tpYviW38pxTEQUDu6w8p7+nYiioqkpajp0jFlvwP0MhhGigZIZL1AuqqvLijb2Zu8UBwMMXGpjSo/QlLIcxDut5txPXb0bpO/IK1MWyNjmfmFwn4/beh715bwxt7kLR6opdPgtWIqG6ltyqc1eg1NgSQojgJOASdZ7qdrHkmWk8umQPAFefq+Pfg0v/Rz41YSB/j1pIUnwUaLTY7PkBS3zWfCcQGBApZ+pimTbMxbz+Se+Fjq/BExPO6e7/LPZexQUi5V5y87i9uWRniqHSr/Rq99WZS1WVuy1rW8FWIYSoDAm4RJ23/aN/cetT3vIPFzTXsHBsOJpS/mF2GGLJu2oxLcONqKrKEYutyE5Bu8tDejGzNUpKYD6Xbf96khMnk53vCjqzY7O7yMx1ciwjj1PafBSUigUQBRL3/W1/BhST5H9GdeZSVWQ2rbjASmbLhBD1iQRcok6zWCxcOfsNMvKheaTCtxMiiNCXEmxhZMvlP9E5IgxVVTmakevfKWgy6NAo3tRGm91FZp6T7LwgszWF+hwejezO0Yx8zlRbKDKz4ws87C43dhdk5NqxWB3lDyAKJe6TvLnUgKuifPlYxzLyAEiMDSfebOC0zZsH5lHVIoFtRWbTiguspCK9EKI+kYBL1Emq20Xmqhe56s6nOGLJJVwH302IoHlk8H0gKuDQhLPX0IWFSU/SPs1JRKQNBSXgH3WNovFXYfcmfCvBk9r7zSDd6sB+eAPJph7sbz6RGAVM4cET4OPNBmIjDESG6XC6vFFZhQKIQoFe4UT+qmSxOtj1dyYH02wAZOSaaRETju3Mrsxoj1pk201FZtOKC6ykIr0Qoj6RTzBRJ+WseZmHH3mUnw5486w+GhfO+c2Lz2XK08XydJfv+Ds9HzVfJeW0jWYxYSTFRhRbhT0qXEtshIE4k6HobI1Gy6lut7Et5hpO5eSToNERbzbSLCY86MyOoigkxoaTkesgLTsfUIgwVKD5daGG1v7HIWCzuziVY+dUth1QiTLqMOo0ONwesnJdRAKaKtiEWFxgJXW8hBD1iQRcos5x2vN5d+6zvLnVG2w9OdjIlefqSzxnR4uJGLQa4kx63B4PGq2GPIebcL0Gjcb7vcmgIzZCj4rqL2egqir5Trf/ccG8K6NOQ7hBR4ReR4RRT9PostTRUlEV79cK0WhR+9+Fpdvt3kDE5iLerEFRlCpPMjcZdTSONJKT7515SogKI95sxOY4MxuYB1WRw15cYCV1vIQQ9YkEXKJOUe25LLutNQ/8kAHApK56/nVhyTMf+cY4Dne6iXg7ROi9rW0iw/WEG3TkOT2geHC4PdgcLuJMBpLiTCgoZOQ6SE7PJc/hwu2BOJOB9o3NdGoaiaIomMP0JMWFE31mGdEcVnLQl+twoygKYTodiqKQ63CX74c/s0PRcXAj9rieHGp7I9GmMMCb81TVSebxZgNdW8QQG+G9RuEcrsy/lVI3J5SFBFZCiIZAAi5RZ3g8Hv549Dxu+DQNtwp9E7W8d3lYqbM4J865mYjwMMKNoGhAr9EQYfQWK1VQyMxzkGlzkpXnJM/hplPTKGIj9GTmObDkODhlzcPhVIkz60FRaWQ2VqgJc6Vzks7sUDQCiYdWkJnr5FS32/w5T1WdZK4oCo2jwmgcFRbwvC84+v/27jtOrqr+//jr1umzfdM2PSGhhZqEhI406VIFpIWoWPiCgorYsCsq4k++XxCVgFLEAKJCKBK6ggESSEJJIaQnZDdbp8+99/z+mN3JzPaUzSa7n+fjkQczO2funNkss++c8jmLN0qJBiGE6C0JXGKv8ebSlVz2xxU0tR5I/fhFAfxmN7/0KyfBIZcSPuCzjGpMFZ1Z6LeMooOcm5JZEmmHVSmHxmSGiqCPqN+kKmqTcrI046I8yLqqQ32u0RXBXk3d7fSapHY7FIc0LiJdENx6G+ikvpUQQux+ErjEHs9zsmx++pfc8tVbWL7VI2DC3z8dZEgXB1IrgOO+BUd/lbqESyyVJWSblAdtwn6rKGAopdA0jWTGZVXKobY5xdr6BCNK/EweGuWgESUMjfipi6dpSGQxNY3NTSmseLrLmltd2emps3Y7FI0xMxlVHsoHt94GOqlvJYQQu58ELrHH2/LEj/nfX/2Up1bkju2Zc3aAg4d2vcNvy2FfZchxX6e2JV0ULCrCoQ7Boi0EQZTGZIa19QmaEllAURX1MWFIhLFVYZZtbmFlbQtZV1EXT+N4dFlza3v1ZsRJKUXdAZ/FiGcIbX4De9yRlM8srjLf20An9a2EEGL3k8Al9mheJsXLD9zKT17Nha1vHGlz0QFdL05/PzSdVaNmcTrbFywqwzYTqiLUtpZACJomWVfln+O3DIK2SVPCwfFU0Tqw7tZi9SZM9WbEqS6WYW1jiqZRl1My6WpGlYeo6uFIn65IfSshhNj95JNW7LGUUrz6/RO56u+5SuenTjD48Qldj8Qs8R3GIxNu5Vg7N5W2PcFC0zQmDY0A5EeyRpYFOl0f1X4dWHdrsXoTpnoTDHflqJTUtxJCiN1PApfYM3kuq+Z+jyv/7z8ksjChXOfBc4MYeueLu1eXzODFA2/joPII+w+PAtsfLNpCV0XY1+E5nV2rNwvNOw1KIbPoAOrQAZ+lJGChFCgF9fFMh9fYlaNSUoZBCCF2PwlcYs/juaTv/RSf+9E8PmpUhG34+6cDlAU6DzhxvYSVJ93DkSGbfT66j9CTC2D0TLQZX97uYNFpGPFc+M8dRFf9G2PoVNzpX+r19ToNSu0OoK5UwEHXoJGgIZGmIZFGtS4Qa+vHrhqVkh2KQgjRPyRwiT2L5+I9cCHf/P0zPP9RrjDo/Z8KsF9V1+uVaqP7g65T/e4fCL/509wXlz9NfTyDO+N/ul6E3tvg8dodaM/l6l/5Vj3D+oxD3bFf7VWQ6zQotSvvoK19jaqjrms9LDtDU8JB07JF04a7alRKdigKIUT/6HxfvRD9xH35Vzz4t3n8+vXcIvlbjvVx9uTuK7hXxz9gyJK7KF32cPG1Vv+HtfVx6mKZDs9pCx6r6mJdtslrF5B8GxcQTzu9ej9tQWlMZW6HpKZpHQ+cbr3fNhrW6WHZu0jhFGdTMtvr9yGEEGLnyAiX2KO89civ+dw/UwCcM9nkO8f2PHUWzNZz4Pu/7vD1zSWH5EPFTi1Cb1f/Kj182s6FoRlfRinIrPo38aFTcQ/4LJVK7ZbF7LJDUQgh+od82oo9Qjad4tU/3MBnH/yYpAOTKnTuOyeww2f11QfHsnLiFYxqFyraphLr4xmUUkQDPZd2aB+QfNO/tHNhSDeoO+ga1o68LDe115gC3chPGVaGbepiGdZsTezydVayQ1EIIfqHBC6xR1jy4Le4/bd382GDImjBoxcGiPp2PGRsGHMuVZFgUSV22DaV2JjIAhplQZuR5cHug4duoB11Hb6jrqOr1U7buxi9uxG2vlxnJTsUhRCif0jgEv0ulYjzzP3/yz+W5cLHH84MsH91x0XyHsWLDmORcWy1hoOmkRkxnRK/hb1pAXXlh5E5+HNMqox2OZXYnHQoCZqUh+xdEj62NyR1N7W3N1SCV0rhKYVSUNuSlt2OQgjRAwlcol9l0ynuveYIvv1CGoBrp9lcfGDHRfIZNO7hfDzDZqb9IcHxR+Ee8SWSbu6XfE1ZgMqwzdZ4FjPtMKqL6bJerWHy3KI6Wcz4MkrTux3B2t6Q1N3U3t6wzqoulsH1FJ6CtfVxQHY7CiFEd/a8T3IxaHiZFO99c1++++gaPAUzagx+eXLnv7RtFNcwl9+qz/DTku9x2rBhHOv3sW9lqKhd4XSZUoralnRRqOnVGqZ2dbIA6qZcw5qtMdY1pLB0jQnVYSYNjexwYdLupvba+hRLZUk7HrFUNv/1nRlF2pU1uOJpB0+BpxQtXWxMEEIIsY0ELtEvXNflo9tO5Jq566hNKKpDGnMvCGAb3QeAQ/mAN20DT6miUON5Hss/jlEXS1MZ9rHPkDBb49lOp/l6XMPUrgwEa14jPmk26xpSfFQXQ9cATVER9u3ywqRQeKA21NfH2di0a9Zy7cq1YSGfyVYNQNtjR+GEEGJPIp+Sol88v3AJjz+2gNfXu+ga/OW8ACOiPZeFWx0+iEnVISYPjRaFmuUfx1i4toEtLSmqI34A/JaxY2uh2pWBYPQMQj4TS9fQNVAeRQdbQ98sRt/Va7l29XmMG3QNXUFNu40JQgghOpLAJXa7VGMdW346k/97IzdV9pMTfBw/tuOP4iZ9OMO8jfn7iwPTWDH2Mo4aW8H0ceVF02F1sTRbWlLUNmfy9ycNjfY4zdfZNBtHfIl42kFf+xreqBmEjvgSlbrBhOowaKrDwdZ9ZWfWchW+r6Cd24CQK4UB0cD2F1bt7PukaxposnZLCCF6QwKX2K3STVtZ/Z0xXPNkEoCzJ5l8/ciOoyNZDG4Z+XvOSP6TccnFfGAdwLzwpxiuGQR9JrpePBpWEbIJWCaWniFgmVSEerdeq7NpNoC146+iafhnKAlYjEq4VEXMLg+27is7M01Z+L6UAlrPZgQoC/p6LoXRzfUKv09CCCF6RwKX2H08l9QvxnHxI0liGRhTqjHn7ECnC7dr9aHYvgDvVl/BgqzDx00pWpIOTUmHrbF0fjF80DbAcwm/9X+ctvY11oQPYsvIqykP2b2a5utsmg3odOptd9ew0jStKHRB7xfOF76vdNZBaeA3zV6Xwmg/ohVLZTv9PgkhhOgdCVxit0k99T2+9WyStzd7mHpu3VZZoPPwoJsmEZ+JzzawDI1k2gVNozRg4XgUjd6M+uD3THznFwCMrH2JlVE/ydHX96pPXU3bFX4taBsddjv2Rc2pzqbtdmShu1KKVNYlkXFAU1RGfGhaLsD1diqx/euG7I7fp+adf8tCCDFoSOASu0W6dj1P3Hcb/9u6butnn/AxvabrH78F0ZPJeLmF6SNK/Gi6Tkk6y6jyEDVl/qLRm0mbFhQ9t7L+LbK9XJ9UGbbBczGW/ZHQ5jewxx0JM74EhPLBRylVFD6UUmiatssDWO44n+LSEz5T3+6F7nWxDLF0lqwLlq4zojRARdhHIuP2emqy/chfedCmIhwqes+bdvodCyHE4CGBS/S5RKyFjT/cl9n/yK3bOn2iyVdmFP/Sdzz4o3kxh9urWB85mFcqLiKddWnWNKrCMKYySNrxqI74CPus/BoupUySw6bBlpfy1zLHHkk0ZPVqVErTNKqW/h7+86PcF1Y9k1sIfuR1+WCzui5eFD40kijULj96J552OpSemFAV2e6F8/G0Q3PKAQVBn0HANqmO+rerL+1H/sJ+S44EEkKInSCBS/QpN5th6c9P5NrHEjSloSaqcd85/qJDqT3gOP1P+ENRFlWFGVLip8LUaUk7mIZOxnUB8BkGmqbhtwwqI/5ta7iGfYX6kC8/QhWe+WVqu6jB1alO6m6pmf+Tn95LZV2i/uKpxl199E7bNGBjPE0i4xK0DLKuwmfqRSNLFb0IkruiUr0cci2EELuWBC7Rp5Y/8BUefu5tFmzwMDR46LwAFcHiHYbvsD+VlWVURHyMLPPxiYa5DG9exOrQQbxWfRG6BsmMS7A1SIT9VodAUHbSje2O2kkVhaJYKotSivUNuVG2mrJAfiF8Z3W3CtcwRf0mYZ9FZdhPyGfieR4NiQypjINSiq3+9E5PLbZNAxqGRonfpCRoM7Is0GFkqbYl3WOQ3BVhSQ65FkKIXUsCl+gzqc2rWfnMHG57PVcb6wfH+zhqVCc/cv4wB4woYXhJkCM+foBDV/0GgFF1L5NxPd4eeTkRD6rCPka1FtnsaTF5+1GetOOxqq6JD2tjADQkQkypKcs9Z8aXc08qODsxXl8c2CrDfsa0HiO0pTkFKFoyDs1pBzRQKLbGLPyWsUPhq20aMGxbRPwWVWEfoyvCHcLSnnKwtRxeLYQQ22dQBC6lFCtXrmThwoUsXLiQt956i0WLFlFfXw/A6NGjWb16df92coBJJpM0/PJArv5HCoCTxhncdFTnIy3LfQeyz5AoR46vILrynaLHxsYXs9TQKA/ZTBoazYeLWCrL2voktc0pqqJ+yoPFpQ7aj/LEUllqm1OtYQlAw2caQK5ivXbkdXDkdfnnB20DpciVVFBmvngoQCLj5sKF0khms9S15EaELCNFsGA33/YEobaAqHJFswjanf+v2Zvpwt7ubNyZsxXl8GohhNg+gyJw3Xjjjdx222393Y1BQ7kO//z557n37ylqE4rKoMZ95wQ6rNt6hUNYah7Igsi5nGJoZD1ID58GG5/Pt0sOm8aE6jATqopHe9KORzLjkMg6JDMOaccr6kNnU2JVUT/NaYd4OjcVWBtLE+g2LCiUlvtvobbQUxW1SWZyi9ItXSPrejs88tT23jQ0GhIZGhIZVOvrFh7GrZRCQ6M0YFNTFuh0urC3o2CdBbO20cOeQpgcXi2EENtnUAQut3XRdZtgMMjEiRN55513uniG2BkN/7iZLQvm8tTK3C/8P57lZ1ikeN3W/caF/M68ENPQGW+Y+C0Dn6njO/o66m2T0OYFZEccQfjAzzI94Mv/4m8blaltSeF4HgHLJGDnntudyrDNgSNKKAvabGpKknZclJdb/N5ZWGgbxfKbJpqmkci4RdeCXKmEtOPhM3XSjkcsnaU55ezQQvW2gBhPOzQmMzQlHDStOCzVxTKsa0jkA5KmaZ2God4umu+q6Ova+jiNidw0cGFV+sLXksOrhRBi+wyKT8n99tuP66+/nkMPPZRDDz2UyZMns27dOsaOHdvfXRtwkps+YsOz/8eNz+am7j5/mMVZkzoeBTM1tJ6nImFMTWNcVYRR5cH8AnFO/hoAPiDc7nltozJ18SymrlMdtfLP7Y6maVRH/VRH/dS2BIuLenYSFroLLZ2NnnV6JmM3uprO6+51ezty1dtF8529VttrbGhIkXJcor50h5G2tteQw6uFEKL3BkXg+tznPtffXRgUstks3v8exGf+liTtwqQKnV+d3Hn9p9qyw5hSFSUSMCkL2qyrT4LKBZHKsM3WeLZDYKiLZVi2uZnaWBrPUwRsk6qwnV9IX6i79UldBRLP81j+cYy6WJqKkE1NaYCKkK9XAWp7d/V1tc6qu7DU25Gr3valq9cqCVh8bKZIOZB2VaejgJqmyeHVQgixHQZF4BK7x8MP/Jm3nkuz+GMPS4cHzwsQsounvFqMUtJ2KZlslvp4io1N4KEImhpnxB8jnVjCOxWH8tE+VxEN+CgLbQsca+vj1MYyJDMuAdtgVHmAkWVBANbUtlC59PeENi9AGz2T2v1ns2RTLL+o/sARJVRFfEUhbHRFMD9NWduSZsn6RlZsaSGRcRkS9XPoqDImD4v2yfeqq9Gq7sLSrq6N1dlrtV9LBjtey0sIIcQ28ikqdonGpf+h8l/Xcvt/c7+kf3SCj0OHbdvZpxS8oR/ENPcdIslGTkzexZqGFH/Wz0IpuEL9k2OTcwAYWfcSW1rSLJtwNZOHRdBIkMg41MYyeJ5XNLLVduxOycI7Cb9za+7Flj9NpiHJh9EL2NKcojntUBbMjXB1NqrUNtr03sYm1jYkcV2FpkFdLN1n368dKU66O2pjtb1GZ4vnhRBC7LjuVxoL0QvJhlqy957CrNaje44bY3BDu6N7XtMmo4zidVb7Zt7FcXKlBQ5w3y16bFjjImLp3OHUDYk0tbE0yYyDruuMKg/kS0QkMi5NySyhzW8UPT/8cdv9wmKo20aV2qbJCr+uaTp+UyfsN6iO+KkM912wqWwNjOMqw51OiRZqG4FbXRentiWdLx3Rl9qC15jK0LYCsUIIIXaYjHCJnaKUIvObCVz3TIpNMUWJD/50TgBDL/4FPZMPWGhMBWfb1zZED2JkIIDP1Gn2psL6bYdQr40cTGnQQtdyRRmUBwHb6LBmq22kKD50Knz8Qv755piZjK8KEfYZ+C0jf3RO4RE9baNKbdeYNDTMsFI/tqFREfJhG1qfFfXcntGq3tbVatPVAv4drbnVF3amBpgQQuyNNLU7/rm8B1q9enV+l+LOFj6tqanp8rFNmzZRWVnJv/71rx2+fley2SwAltX9Dr2+lE0neOmZJ7jhOz8G4EffupEzT/1E5201G0+3MJWDq1tkjAAaGqauoesaRjaOcjM4mGSMIKahY7YGN0+BroGha0X1vHKP5Sqe69k4mpsB0wY7jKdUvjgngKaBjoamtd4urAvWeo22L7U9r6vX3J1cT+F4ucruupb7frUPtIUK33db/9uusyvf0878/HXWx/78Hou9z57w+ScGl5NOOgnLsli/fv0OPV9GuMQO87Jpmjev5ke//C0Axx81gzNOOaHL9o5m4ZkhPF1DKTA0MHUNDQ2FImuG8IwgnqfQyK370gC9tfxA+5DkuAUhxNDAV1xEQtc0lJb75e4plQtbOp2GlbYdd7AtmHhKAbnXZhdmgcJw15uQkWtHri/atlDYFaXo2H86+Vo/5ptO+yh5SwgxgEng2gW6S7tto19TpkzZ5a+7ePHiPrt2Tzwni/p+BRc+mqShyaEioPHQ9CUMmf/pfJuYMliopoCus8x3AAtHXMKJ+1XhtwwyrofyoDRgYRs6mdYq7amsQ0vGBaWoivo5uKaEsVWtQcpz4bU7YM1rbCk7lPll5/NxLEt1pOsdhW2HPbe0TsfVlId6nMbbnue0nxqrCFkdSloUTpW1P3x6RC/6s73Tb531H+jyPe3o9N7O/PztyN+LEIX68/NPDE47O5oqgUvskJX3zuaNdx0eez+3KOvO0/0MCRfvwUgR4uv2t/DbOiNKg0ypilIasikL2ijUtsKjtkk8k7tONGBSjULTchXMiwqavnYH/Ou7AFTzFDVjmlkavQjoekdhZ6UUegoY21N+of36qq2x3Hvpar3Vjhw+vb27EytCuX4kMy4hOxcC295fZ+9pe9eI7Qq7usSFEELs6SRwie3WuHwRofce48tP5XYlXrS/yQX7d0z+LVqESNDCb2hYpo6paTTGkox6//eU1r1Fevg07KP+h8poID8q1HZIdCLjdvxFvOa1ouuPTSymquayDjsKCwNV4aHTbXoKGNsTcNoHqGTGzY/WtT1eeJ3eloPYmUXlW+NZ4hmHjOsRzzjUxTJomtbltXYkBO6s3VHiQggh9iQSuMR28ZwskT8fy6VPpGhMwZCQxv+e1nk1+XnGceh6bk1UczLDyi1xDlr3JyZu+L9cg43PEwvaaMd/tfXiLoE37oQ1rxGvOIyth3+haHSG0TNg+VP567sjjqCmLMiIkgATq0P5r9e2pFm8vomP6mIoBRVhH6VBi9JgLujEUlnW1ifzRVHLg3avpww7O1OwKEAVjNZ1Fqh6O7KzM6NO7QOURrJoRLH9tXakJpgQQojtI5+sYrt8eMfFvLY0y7wVuV/md5/ppyK4bSpxlVdFRMuwxBvLXOsMLFeRcj2ynsLSE+xf/0TR9fS1r+XDRcnCO4m0Fi+NrH2OVxsTNBx5PUeMr8iFnBlfzj1pzWvEhkxl5dCLcWJZElmXrfEs1dHcaNb6hiRLNjSyqi6O4yqqIzbjqiL5w6DTjkcy45DIOiQzuftd6Sn4tA9Q5UGTFVviRdN5hXo7stPbUafOAmH7AAV0ey2Z3hNCiL4ngUv0WuOSV4iuf4avPJNbL/WZKcUHU2cUjNNrATjBeJv3vHn8nfPxte5KPC32N4Zl1xZd0xs1Ix8uhrcrXlpS+yZvbm5hwpAIlWGb2liW9TWXQ83lNMTTrKyNUduSzleSr45uG2lLOy7prJcrEqrAZ2j50RulFAHbIGhZBGwDn9l1/d942qExkWV9Q5ItRgoNrWiUq32Aqm1JF03nbY1nd2jarLejTp0FwvYBSimVP4C6s2vJ9J4QQvQ9CVyi16Jzz+Azz6SoTyoqgxq/PqX4F3S7YxM5WL3PqyELNI1ExmX/luJq8onoeGr3n026tSBp++Kla0IHYxhaviL8kg2NfFgbB3IHTa+tT9IQz+bvt6kpC7DPkAigYRsaoytCjK0MM7I82LqgPI1laFRFbUaWBYoX5reTCyxQH0+TdlxsS6emLEB11N/p6FJvRqZ6sz6rt6NOXb1eYYBSSnVYwyWEEGL3ksAleuW9J25l1YosDy3N/WL/zal+KoPdnwz1vrEvZSEL2zRoTmZZkT2AwzP/zT++euSn2FSfRClFS9Ll43GXE/YZ2Bv+y4eBKawbdgk1fpNU1sVTHh/WJlj5cQzL1PGbGomMQ8ZzyDgemYJpwcqwzb5Do/hMg4BlsP/wKNVRP5qWqxwfS2fJOIpUxqU56eRGgFpDSXuVYRtdg7Tj0Zx0qW1Js74hSXXU32F0qa2afSLtggZRv9npyFRv1mf1dtSpNyNhMoIlhBD9b1AErsbGRn75y18Wfa2pqano8W9/+9sdnvejH/2oz/u2N1i36DlGvPIjTp2XAuCTE0wuPqDnH52Az2BcRYAj6/5KSf2bfGDvx98rZ3OAu4ym6sN5d/gl1K5rIpFxMHWN8rAP/9grOfCo64nEM4ysbSHrKmLpLNl4LhSh5QqmWrpG0GfiemBber6aPOR26SWyLgHboCRgoet6UVmE5pRDLOWQcly2NKdYF7TyoaQ9TdMoDdqUh2wc1+t2d59GEk95ZD0Py9AI+6xOR5N25a5AWX8lhBB7h0ETuH784x93+XhTU1Onj0vgyhnxt/O4/sU065oVIStXc6steLQdzdKZQ7XlZJoe4+BV/w+A/fkPTw39AvMP/E1u5Cjr0pxK0JjIYugajgcRv0ki4+K3DIK2SVMiF5AsXaMqYhNPBwj7DSpDNsmsS20snSsL0UWtK6UUGtum04KtIexjM0XKgbSr8gdZdxV6asoCNCTCRH0mVVE/NWUBoOPoEkBzygGlEbRN/JbR6ajZrtwVKKNXQgixdxgUgUvsuPee/n80rne4Y0EGgJ9+ws/o0m1TiS96+1GjNVKuxdA1KKc5/1hL1VRGNy0sut4+6aU0+HOBQ9M1on4fpq6TdlxMA3ymTsDSaUhki6fmbJOM6zGs1KMqbDM06mfFljglAcWYimBRWYjCQKOUoiGRoTGZoSRgMbIsyKjyEBoaDYnce+op9FRFfEypKe0witR+dMnzPBoSaVKOQ1QZ+RpgnVWjh1CH6/XHgc5yiLQQQuwegyJwjRkzhkF6RvdOydRvZtwr3+bwJ1Io4Igagy9O3bbAPO7BCcZ7+ft/iV5FSdCfC1WVh7Gg+iLqVmY4nRfzbdZHD6IxkSWVdSgN+DhwRITmlEtdLI0ORHwWH9bG2BrLUJ/IYOrQnDQJ2iZKaWgKmpMuTYkW1jQkyTgeUb/B8o9jBGyzQ6Cpj2doSGTy03cVIZcxlaHcrsfW9VhAt+u4uhpF0jStKHQlM07ujEQFhQcD1sUyrNkaY11DCkvXmFAdZtLQSIfr9UfF9/54TSGEGIwGReASO0b/f5O4fUGGd2s9TB1+f6a/6OBnUzMBJ3//IPUBDYGp+D0dx1M0pbI85j+b5pIM+2bfZX34EJ6xziK7rgHL1BlbEeKgkaWMrQxRGjRpTrrEMi7rGxM0JbPEUrlK8fGUx7sbm3A9RWnQxlOKZZtbWNuQIOV4bG5MsjWWZXx1uLW4aSgfkIK20emok6ZpaJqWLwjaVjahu4rsnSkMLIm0S9bz8Fkmmparlg+5MLauIcVHdblRQDRFRbhjgOuPiu/98ZpCCDEYSeASnVr10n2YjR4/eClXc+uGGTYHVBcfk7NBq2YcG/P3UxmHmR/l1muN2PIS6VEuDaELeMm8lH9ripTjsb4uQSrr4NMN4hmHYaUBDqwpRaHQtFxoMXQN5YHjeTQmPGpbUmyNZ/GUYkxFiJrSAB6QSDsksh6GBhsaE/gsI1/ctDg0aB1GnaDriuyNrVONZUEfI8uD3QavomtoYBm59VuF05Qhn4mla+gaKA+yruo02PRHxXepMi+EELuHfLqKjjyXMfP/h4v+lSKehZqoxreP2RYOMgr+ow7m+/bX+Yz+FCc6udpZFdlNRZcZn1zCPuNn0RBPUxdLszWeIZFySGRdsoYikXZoTmapi6VpSmRJZl3KQxbVEYsN/jTJjI/mRJaFaxrZ2NDC+dl/MK1uGenh09FHXkpdLIOVylIWsCgJWPnipkHbyBUgTTtsjaVpSmZpTrn47Gy+phd0vui9KZllQ0OKlOMS9aXzI19dVXkvLAMxssxP2Gfht4wOa70mVIdBU2RdxciyQKfBpj92HMouRyGE2D0kcIkO1v7kRD74yOGR93Lh5PZT/IQLqpquVsP5vPt1SjDB0BmdWd/pddwR0zm69gEiH7/JCt8B/Mk8A7+lk3EUStMxdQOfqfH2ukZiaac1wNiUBX2MLg8xotTPwjUNvLmmgbNTj3Nl+r7chZf9J7crcfzFNCUyRAMmw0v8GLqBhsbWWK7ae3PKoTGRobYlQzLrkMwYRcf4dFWRvbc7GOtimVzJioIyEJOGRjqMhmmaxqShESrCvl4tlN+dU3qyy1EIIXYPCVyiSMNH71OdfIsTW2tunTze4Nx92/+YaKRdaEo47OMvrh6/zhhJo78Gp+YIqgzY7+1fATCSl2iqyPJw+FNoWgZd1wj7dOrjWbJumqaUAwoaEhnGlCtKgrn6WRG/xZASP4ds+qDodarrF3H09GtJOx62ofFxc5q6eJqmVBZT03CUAqWRyrroGgQtk4BlEE87rK6LF4Wb9hXZe7uDsa2mV09lIKDrYCOL1oUQYnCQwCWKROccwc9ey7Ci3sM24I5P+juEiEfdowFwPHjLm8zRvJl/7JXQybw1/FKqo0EuX3NT0fPGJ5cQCp+P55oYho5lGmRcD9vUMXUNNPCZRtHIUmXEx37DojRuPRzWb6tSHx86lbDfYkzYpi6W4b1NLXxYG0N5UBHxUR6yCNomUWUAGpoGngdr6uMs39JCdcTHgSNKi85fbAtFla3X7GmabVesf5JF60IIMThI4BJ5i//xcyJNHj9+JbdQ/uszbSZWbFsoX6tK+L1zGn9wT8cATBPuVafhKsVh+gcsNffjOfNMqtMuvmSGNeGDGPbxi/nnv63vi6MgGrLQNZ2wbRLxmwRbR4ZMQ2d41E9J0MoHmFHlAZRSrAp8gZUVIYY0LKSx+nBW1FxKSX3uXMVYKkt9PEN9LItt6UwIWkyoirQWT831P5FxWdV62HVtS4aWlENZ0FcUuNq0H41SSuXXhBVeL2gbjCwLUhFy8+Uo2tr1dpejLFoXQojBQT7dBZALFQe8+RM+/VyKpAOjSzS+eXTxSEuD2lZc1DQgbBu4HtztnI6XPZ0wGl9KPM4RmeVsyhzM66MuoSWVpbphIYvYjycDn2J40KTMb9OYyuJ6CkMHw9AZEvXjtwxKAmaH3YGVET+JrMvafWfzQcqhPpklVZukKq0oD9qkHQ9DB9vSiPoNKkO+TtdS1cczrV/rfU021VqCYmXrMUMRX67kQ5vCvhZOD0b9JltjxQvouzqrEWTRuhBCDHQSuAQA//j+WZSvc5jbulD+Vyf7CVrFAWEffSM36w8B8KBxFj7TwFPguC5Z4HKeYHbqAQAOiP2HZNblT/an0MrPImTrjPbbDCsNoqFwlcJVkMgqfBZEbDO/hqo8VLxwPJbKsrY+QW1zBsf1cDwXD0hmHNKOh8/UCfpMqsMBqqI2Q0s6ToNC10f0dKculmHllhgrW6crwz6TcMAk66gOOxkLpwebkg6WkcpNa3YTvmTRuhBCDA4SuARNS17idPclZjybWyh/zOjOFspvM11fxl81DcvQCVoaYV+QRNZhWnYZbNsEyLDGRay3Pkk4YDIkGuGIcRVoGmxoTOKzclNzYZ/J6PIgJUErv3C8/bRa2vFIZlwS2Sw6YBq5HY4B28Bn6oT9FqPKg5S0locI+y0609URPd2Jpx2ynsL1FMmsi2loVJg2SrnQbidj4fRgIu2Sdb0O4UsWxgshxOAkgUsQmXsWDyzN8uZGDw247eTOR4jaGJrH7fyC97L783z4fMIBP4YGm5oPgYZtC+jf8CbRFE9yUfZpjk2vwLKOYsmoy6gpC1IRdtGBqqif/YdH0XW9y5IJdS1pHFcRaN1pWBay8uEm7Ld2aFqubV1WIuN2O+UX8pmMLPPTkszSmMidxxj125QGtk1MtoXEwn6ksi6xdJbmlFMUvtoel8AlhBCDiwSuQW7lS39iaFbxzfm5hfKXH2Rx2PBtC+Xjysdr3n5oKDw0TDyON94G4DjnLSozJm8Pu4qo32Td8Kt4fUuQ0bF3WKgm85emE/iM83euyTwAGeCd/xBLZak76AtMHhrJn33YFna6KplQG09jGhrVAX+nxUV7Oy1XuMYqd7Zmbvdid6NOuRAVJpX1qI2ZKA90HcZWhAj7rQ4L5Nv6UVhfqzB8ycJ4IYQYnOSTf5AbN/9afvhahg0tiqAFPz6hOHRsUBXMzn4tf/9Z+2tFjx+bms/Wsi9QHQ1QFrSxJ3+FjUqxYvkWwu99zOGZZUVr1P2bFvDh0M8wvMTHqPIAW+NZ1mxNdDrK1LYmSnkQsA2qwjajK8K92v3XmcI1VinHQVPgs8z8Y50Frm1BMEqgoF5W2G91G/K6Cl+yMF4IIQYnCVyD2MIHv0F1i8et/2krA+FjRFQvarNOVRXdbx9zlFK8t7GFRMZj9D5BDh5ZwvKPYwRsg7KQzbLk/hyT3jbNuJDJvLu5mUjQzE0jZpwui362L5kwqjy0U1NxhdcrrM/Vm1GnndlNKAvjhRBCSOAaxA7+4C4+/1KaRBZGRDRunNkxRIzSatHx8NCxNHhcHcXXeDj/+LPmCaxpiBPPOFSEbbKuYmssTcbxsEyNx3xno4Ap7nssMffjn8aZVKayxNIudbE0mW7WNnUXcjobNdI0rcuvt79eYT2tno7a0TSt29DU3WsKIYQQIIFr0PrXLWczqt5lzttZAH5wvI+Q3TEkTNQ3Mtt4kgf0M/FbJvc7Z6N5BtP0Zbxv78/DnAEpF7/psrkphaLtAOgktq4T8vt5wj6f5wyDgM+gWjcYUxlkXGWQyrCPeCYXtjobZeou5HR1JE7b1xtbj+ZpX9Or/TRfb65bGbapbUmzviEJ5MpLVEV8+VC1s8fzdBfYJMwJIcTAIIFrkPqE9yIXv5jGVTC5UufygzovpQBwofEyR+nLWKT24/fqVP6ozuDP2lmUGDa2rmMYGrahYRs6ukZuYblyOKXpr4xLLmFVYAofjLqU0VVRQj6bUeXBfAjaGs/u0DRdV0fitNXsWl0bR2kwLOLL18kqDEFdhaTOrguweH0DSzY0k3Zc9qmOctTEynyV+p09nqe7kChnLQohxMAggWsQmnvL4Yzb7PLXd3MB4YfH+3JnGXZhgr6BCWzgGLWQtHK5lzMxDYOgraNpGrquYRkaZUGL4SV+ogGbIzbfz4z63wNwSPJ1VtSUUHrA1zqM0BRO87Xd780ITldH4rTV7GpMZlAKfIaRr5NVGFS6CkmdXTeedlhVl+Cj2hhp1wOlMaYylA9cO3s8T1tf1tcnqU9ksHSNfYeXcOCIEhIZV85aFEKIAUAC1yB0nreC01/IFTk9bJjOed0UOW3vcGM5f9E0rrGe4NDUB7xvH8AT9lmc0jSXw976AG30TIyZX6Zs4btFzxve/Dah7ZgabNPVlFpX67t8pk7ANikN5u6XhaxOQ1BXIamr64Z9Bn7bRGUdfKZOYyLD6rp4/gxFCO3wLsS2vqx0PRriGVwFltmCBpQGbZRSRAOGlJQQQoi9mHx6DzJzbxlH9VqHp1e6APzkE90XOW3vXWNfvmA9w2fT9wFwWPq/TEotZmr2jVyDxldpifpIDj8C1s3PP0+NmtFpeGo/0hRL5daUtbVRSrGuIdEhkLVfj9W2xqoxkSHiMzlwRAmaplEWtPPTc4W6CladrRurDNscMrIMlEYs41IRsvGUx6q6WGufQju1C7HttTc3pYinndyfjMvHzanWydCu34cQQoi9gwSuQcTNZjjXrePY53NlII4dbXDSOKOHZ8FWojQR4VnzBF4sPY9vtfyk6PH9s0uK7jd+8DKbT/09W1pSVNYvRB89k6pjr+t0NCvky5012JTMksg4bG5KYcXT+SKhKFjbkDtHsSpqUx60Oy0sur4hyaq6GEopKsM2ZUEfJQGLgKXnQ9yOnmGoaRqTh0WpjPiJpx3q4xkaEuldNs3X1pepY8rxWzpbWtIopfBbBs1Jh5Kg2eF8SSGEEHsXCVyDyIIfVhFb7fLvdW2jW75ejW5V0EwFzUwYEmFFpAStRRU9HiRVdH+Jvh91W5JEJlzNluDnGVcZBt1gXX0LKza3kHYVqgwqQg6jK4JsjVlYejpfUiKrFKjWfiny5ygmMwZpx0MpxbLNLazcEiPrKUw9Nzq0pTlFrraWhs80aUplSWZcApZBNGAVjRL1VEKivcKAFvKZKBSa1vnZjzsqd9ZjmVSnF0KIAUg+xQcLz+UIDY59OTe6dcp4g5kjt++v/+D6efzVPJOUo7pss0YfyT+C57B/IsvwEj9KQX08QyrrUh9P0ZTKksi42IbGiNIAAH7LIOgzaEo4ZAFT13C83AHQAUsnYBkELZOAbeIzdepiGVbWtrCyNobjenguNCQzuJ5iSNRPyNbJeora5gyJbBalIGgbaJrGxqYEE6oiTBoa2eEdgDtTBLW7kCfV6YUQYuCSwDVIvPODcprWOryyNje69e1jOg8Wy73hPOoew+H6cgw8Tmg9NxGgMrma6VseZqG2LzN4s9Pnvxg4CZ/PIuI3aEk5JLMOTckMlq6T9TxcN7eTMJZyiKWzbGlOsakxyZq6BIahse/QMI4LWxNpsq6HH41owKIkaBH1m6Qdjw2bm6mPZ/BcRX3CIZ7KkMg4OF4uvO03LEplxEYpjy3NDqtqk7SksvhMnS3NSWqbc+UXfKa+QzsAd6ZyfG9DnlSnF0KIgUUC1yAxRcHJraNbx40xOGpU7q/eU1BYEeIR91juds/kdy7YeDylf4Px2ob84/tm3uU7/m/gM3VO915kWGZN/rGlwem8OexiakqDoGBVbYyk4xG0DCojPspDFmVhC9PUsE2D5pTDuxub2dSUZENTgqjfxHHDDC3xk3RcmhJOfuF7ecjOT7PVxjIYmkZl1IdCI5bKEEt7uJ4ilsriKEXINlGe4t2NLWxoSJJIZ7F0nfqEH9BYucXHhOrwTpVz2BE7W7NLCCHE3kkC1yAw95YSatY7PLcqN7r1nYLRLV2D592DcDF4w5vEH9zTc18HNF3nH9pxfIUH8u0/sPZH103+4TufaanlRYGrNOznuMnDiKUd1rSO4miaBkGbsqDFhKoIiYxLQ2uBz5KAxeamFLWxNMm0R9gHrlKE/blSDkptm7oM2gZbY2lWfhwj5XiUBHKHR0+s0gj5dN5xm0g7Hn7bxPEUm5vTrNgSY1VtnPp4GgUELJOQ46KArKfwmToV4R0v57AjdrZmlxBCiL2TfNoPAud5cNYruZAzo8bg+DHFOxOn6R+QxI+Oxz3uJ1Ho+G2wNTA1WE0Npq7xWuQknrbOwYm7JJTD29pkpvBa/jolk45hv+FRXl9VT9rxQIHf1hlR4mdCVZjykI1tZklnXRzPI2SbjK0M0ZTM7SKsjvipDPu2lWhAoyGRyf9pSmZobi2QGg1YTBoapSJkURq0KAnYNKccKsM2I8sCfNycpiGexfEUSoFtalSGbGoqQgyN+hlZ5ifst7pdM9UXR+hUhnN1tTRyxwQppXL35bgeIYQY0CRwDXBzbylnwscuT67IBZXvHNMxSIS1NGHSfMJ4m7v5FV/mJiqDNhdmHufLXuvolgtZT6M27hDPutiGzl98Z6JHdA7hfbyRMxhy2BdINmVwPA+/mVvsPqIswIxxlZSHbNY1JFhbnySZcQjYBkGfSU1pgENHlVEXS1MZ9rHPkHB+/VI87dCYzNCUcEhnHTwUftPAMjVKW0fA1tYnqQj7OGFyNRsac7sla8oChGyTdfUJ4hmHhkSGsN/kwOElTBoaoSLs71DTancdodN2ELZC0ZTMtu521GRaUQghBjgJXAPceZ7Lla/nRrcOGapz6oTu/8qP1N+l1NRA05ii3it6bFxiMS3qFFw3F6jCfj//8l3Ef/0mB1aXcFhjmsZkBtfz0DSNqM8kaJrUNqfY2JjEU4ra5hSJrEPQyk0bVoR8TB4WBTru4AvaRj78KGVCazgpCVi5QNVaEDU39aihablpOk3TmDQ0AsDI8iB+U2do1EfKyT2/pizQYQSrL9dWtX9fsVRW1nEJIcQgI4FrAJt7y1RmtHg8tDQ3ZXfDjJ7rbvk0h4u8J7g3dQZvepM4Rnsr/9hScz9MV8cyDIaXBtl/eISmlEs87RLLuLnDlzXwPA3T0GhJO8Rqm3lnYyMRn8nw0gCVER+prEnANoj6TVJZN3dGoeO1jmjl+loatBhZFmRUeW6NVdDOTYMmMm4+tGxsyoWWlOOgKfBZuR/ntgCz7/AS9h1eAkBtSzp3QHQy0+moUl+urWo/ehayZR2XEEIMNvJJP4Cd5y3nW29kcDwYEdG4cP/iv+46L0Kl3tLheYeo97lPP4O/6GfhMwwOUR+wKnAg80OfotqBqD93dI6nIJPNYmi5dVJNySy2paPrCsf1WLM1SSzjgIKqiE3YZ3LA8BJCvlw9rbTjEUtneX9zimTGIda6PstnGmgaVIRcxlSGuhz9aQstUWVQOMLVWYDpaQSru9paO7u+q/1rlwft3b5YXwghRP+SwDVAvXP3FYzLKu56Kzed+D/TbSyjOCT80T0FzzX5rDmPSq05//WFTMY2coVCHzY+xd9MnYqQTXXEz4whYUw9F6pWbUmQcT1CPhNNy51nqOm5wqSNCYeM45F1PGxTx2eaRP25gJTIuCQyLkrl1jG1TTN6Xm7HYcZxSaRdUlm3ywXlhQGp/ehXZwGmpxGs7upe7ez6rvavXbhYXwghxOAggWuAOnD949z5TpbGFIQs+OyhHUPIofoqPpu9kQe1M7hCf4rDeJ+F7MuD+pmYSgOlSLgOnqejgjYjyoKcvP8w6lrSvLm2nqynsEyNkqBJyGcSTzskMi61sRRbWzJ4QGnAZGjUzz5DSxhe4mdNfYLalhTxjEvINqmK2FSEbVQMQOG3dExdJ+N6rG9IkMi4DC/xsaouzsamFCNKAkwbW4ZhGB12GHZnZ6rD7+z6rp15bSGEEAODBK4BSqG4/b+5EDLrEJuyQMdRoje8SQAkXZ052unco07HMk0MHTKei+cpFDq2AYauk0g7LFrbwKjyAFVhX+uROSYTq8MopXhnfRNbWtI0J1Kc2vwI+2XfY0vZITQP/xyhoE1tLE1zMsua+gQtKYey1urx1REfGhrprAsa+Kxclfq6WJraWJolG1xWbonTnMowLBpAoZg4JEoslS1Y+5V7r6VBOz8q1n4KcEdHlXZ2fZdUjRdCCCGBawCae1MJ4XUuK+s9NOC66R1HVJ53D8oXOTUNiAZsPA8MXeF5OqlsFoVGwMoFjozj8uHWOAnHwzJgXGWY0eUhdB1sQ2dtfQLPc8k4Dqc0PcrFLffkXmjT6yyM+Hiz5vLcGi0PLEMjYBvYpo7fNvFZBqW6RlNCAw1MHTJZl/pEBsdR1MaTbGpIEM/mCqEu+ziGzzJYW58gmXGJpx0U4Ddz06AayXzZhV1R4kFGqIQQQuwsCVwD0Hk2nNu6duuUCQbjy/UObY7U3+Vzxj9RwFRtBW+nJvF3+2yiQT/JrIurDDwg4jcJ+Q1SWZd41sUANjUHOHxUGZVLfk/L8pd53z6AhSXnsTXp0hh3mZheWvRaJbVvscI6F0uH0eUhhpXmykD4LSNXgNRnEc+0FjT1m2QdlTtrMeOg64qAaRAO2Bimw9CIn9KA1br2a9vh1CGfmZvebA1Y2zMF2NOieBmhEkIIsbMkcA0wT9xyAAe1eDyxPBc2rjms89EYn+Zwk/Vw/v6JLMRJwt+187BMA01X2ChMQ8dnmCjlYihFyGcyojRA5dLfE37lB4SBYbzIpsYkDxhnk3Y8Fuv7cTj/zV97TWgKKcfBH7CoKvFz1ITK/JRf0DZQSpHIuJT4LYK2QV0mjc8yKAvaZB2P4aUBQj6LVNZlv2ElHDKqhA1NKaqiNsmMQcAyiAYsyoI2I8uDuert5EbD2kpPrK6Ld7nDcHcVPRVCCDF4SeAaYE7z1vHDRVlclSsFcfo+vf8rPkxfxp9SWaK+XFwxTR3T0NA1ha1rRAI2o8oDVIdtsgv/XfTcCaklxMzTSDse95unUVFlc7S9gs2lh/Ca/xwCKUVFyMfo8iBVEV9+1Ki2JV1UwLQhobGxMUljIkNpyGZoqZ+GeBbL0BleFmTSsDC6rqOhMaosSNA28FsGYb+VD1OFa7jaDrze2JTsMkzJgdJCCCH6mgSuAcZVij8syk0nXn2Ihan3vl7UG94kPBSuAsMADYinsqQyLn7ToDxio2saSze2kGIyJzI//9ylxn5omoaOjqvpPBk+n63jKzANg0BLmlAAJlRFGFUR6rLCe8pxiKUcmlNZklmXSMDEQCOZcdjUnEQBmxttfHY2PxpVGfF3CEeFU4Cr6+JsbEp2G6ZCPpOo36QpmSWRcbotRyGEEELsCAlcA8jcm0rwrXZY36zQNZjdSSmIWi8CaFTp2+puLfdG8Ih7DPd6p+OzwNA1LF0j7bikHAXKw2eZuApiKY+IT+ff6nQ2hJJMzixlqbEffw+cTcDT8ZlgGxq6BhsbU1SEbDQtdzD1+KpQhwXnhTsAo8oABfGMQ8gyKQ3aeCgSWZdE2iOezrJ6axxN18g6ClWWGzXrbjSqNzsMK8M2W2MWlp4m6ypi6Sx1sYyMcgkhhNhlJHANIOfZcMHi3NE4p000GVnScbH8H9zT+L17JrONJ5mqL+NNbxJ/cE/H0HQCPoOwT8cyDbKuRyrrkXI8PMBVuRGijOsS95lohsY//OcAigOz72KkNf7uPwsMGw3YGsuSdVtIZIIMK/EzJOqjItzxaKH2BUy3lqT5sC5G1lWMLAvQnMwtZI8GTDRNoyXtUniJnko0VIQstsZMkq11vypCVoc2mqbhtwyCPoOmhENzypFpRSGEELuUBK4B4p0HvsLIlMovlr/ioI7BYr57ML93z8RD5273TO52z0SD3HotDQKWwZCon4Cl83Fzmljr8/Tc+nM8pci6irSj8Gnwqczf+Ux8DgDTMgvIuh6PBs4DwHU9Uo6BbVmMKA2iaRqJjNuhT5qmFYWuinAumLVVjfc8DwVEfSYKsE2dWNrFZ2iUBX09lmjYGs8SzzhkXI94xmFrPNtpkOrLsxSFEEII+a0yQBy47B7++H6WjAtRH5zRbrF8rRdFoTHbeJI/uKfjkRv9as1SeAp0PbegvDzkI5lxaUxk8ZTCMnQMHUzDQCO3A9DQTQ503yt6jcnZd0noZ2Nq4ABKgeu4lPp1SgK5HYi1Lel8wVKfqRP2Wyil8gvnSwIWo8pDjKkM5fqnFFN0vWgBfHPKoSRgMbI82OM6q94uiJdaW0IIIfqSBK4BQtPg/iW56cQL9rPwm8VBpEpv5kQWcaKxqDU0aUzVl/FG65QiSkfXdHTdIOrPnfcXDWbJOiZoHhoaoBHwGUR9FgHbZJ12EAclXsu/xmJtX3QdLNPAp2v4DZ1o0CbotxlVHkIpxdr6eL5gacA2GVUeQEPrMhQVLoDvrF5WT3o7ciW1toQQQvQlCVwDwL9u2Y8JjR4vr8lN2V02peN0YqHzjFfYR98AwEnGQjTgPu9MHMfFcRwc18UydcqDftKOQ8pRKE+h6RoRn0l11EdZ0OYd8xKClkFVwyIWMZknjTOoCgQo8Rvoei6glQYtqsLbdgwWFiwNWiYlAZPSgN1noUhGroQQQuwJJHANAJ/wNnDru7nRrZFRjaNHG9v1/Kn6Mu7xzsTxFLVNKUK2kVu3pYHfNCkJaLlzFTWdkJ2bHrQNnVjGZX75p/FKL6QxmWU0GiNK/ZSFbFwPPBTVET+VrQGpbbQpX7DUzt2vKQt0OPtwV5GRKyGEEHsCCVwDgKbBY+/nAteF+1vo3axrmu8ezAJvEt/Ut1WZX+BNwlWQcVzWN6VoSGexdANdg7DfYmRpiKDfJJ7OLT7PuJBxHXIrwHSiAYsxVREi/lyIGlHiw/U0XKWoDPvYZ0gY2LZjsCrkw4hoDIn6iATsosOlhRBCiIFIAtdeLhvbyvpmjzc2egB8anLXf6W1XpQF3iT+6J6OQi9ewwUkHYXjuWQ8ham72KaBbRqkXI99ykMYWu4YnOZEBo/codXoMKIkwPRxFblyEPEM0UAuZOl6cVmK/I5Bz6PEZxEJ2BKyhBBCDAoSuPZylpfmH8tyi82HhDSOqOl6OrFKb+ab+sOogrIQAHrrH00DXdPQlcsVah6HOR/wYWYK7+mfoSRgEvVbRAIWyYwfn6mhaRq2qTOhKkJ5yGZdQ6Lb8gtyhI4QQojBSgLXAPC3D3LTiWdNMjF6cZTPVH1ZPmzZOhgaGDr4fRamprjEeYL/UfeDgqPib/JqMkTK/yXKQz4mVEcI2rlQ11YrqzJss2ZroscwVVRVvheHSne2K1GO2xFCCLE3GnSB64UXXuC+++7j1VdfZdOmTdi2TU1NDaeeeiqf+9znmDhxYn93cbs0t8R4cXVud2J304mF3vAm5W/7TJ3SoIXPNKgImSQcOLzxAyioUTo+sYSNIR8jWw+e7kxvj9ABen2odF0sw9r6eL4+V2dthBBCiL3BoAlc6XSa2bNnc//99xd9PZFI0NjYyNKlS7njjjv42c9+xnXXXddPvdw+yaaNvLHoHVwFAROOH9vzX+cKb3h+zVbAgOElPoaXh/BbBiUBi3jK4cP0FGbG3sw/JzV8OqPKO56DWKg35Re6O1Q6lsp2eL5MQQohhBgoBkXgUkpx6aWX8uijjwIQDoeZNWsWU6dOJZ1O88wzz/DII4+QSqW4/vrrsSyLL37xi/3c654FNPjvm28DcMxoo0Ox086sUUPw0AkYML46xAHDSrB9JqUBi9HlIWKZLEvsy/jrBp393ffwRh7B0OOu7zHotD+iB+h2CrD9iFja8ahvN5olx+0IIYQYKAbFb7D7778/H7aqqqp46aWX2HffffOPX3311cydO5eLLroIpRRf/epXOe200xgzZkw/9bj3Xn9zEQAnjuvdX+UCbzIBA2rKgxw9oYoRFUGSGY+wz2Tf4VHKgxY1pUG2jPofNkd8HDCihMqov8N1OltftT1TgO1HxGKpLBubikezRlcEi9pI0VIhhBB7qwEfuJRSfOc738nfv+OOO4rCVpsLLriAF154gTvvvJN0Os33v/995syZszu7ut02fbyFNetyFeN7ClzLvRE84h7Dg/rpDI362GdIhKpogJDPJmRDSdAkaJsMLQ0ytDTY42t3Fq62Zwqws4Kk7UezpGipEEKIgULvucne7dVXX2XNmjUAjB49mvPPP7/LtjfccEP+9qOPPko6ne7z/u2ouTeVsGjxuwCUBzSmDOn+r3KdGsITgbP5emQ+t7m/4LzUYwyLGBy+4U8c9eaX2WflPYSs3u8ALAxXTclsfhSqJGBREjS3ewqwMpw7b3FcZbjH9WJCCCHE3mbAj3DNmzcvf/vUU0/tUIyz0Pjx49lnn31Yvnw5LS0tvPzyy5x00km7o5vb7Twbrlq+EoCpw/Vuq8sDLLX258vBf3FJc+uo3frXievvE1r7fO7+xudRpUE4qncbBjpbX7Uz5xbKaJYQQoiBbMCPcC1evDh/e9q0aT22L2xT+Nw9jabB+8s/BODQYd2fnfgRNSwdeQkzzOVFX/fXLSm+5trXev36nY1ItYWmMZUhqiI+qZklhBBCtBrwI1zLli3L3x47dmyP7QvbfPDBB716jZqami4f27RpE5WVlbs8vKkT/8L7t10IQMXJN7L4+KO7bJs2w3zeDtLkfJPFmS9ve8D0gVMwbeqPwg70sxnYtN3PEnu7bDZXymNP/oeJGLjk50/sbtlsFsuydvj5Az5wNTQ05G9XVlb22L6wTWNjY190aZfYsGkzsVgMgH0ndV2sVekGlj+CrmtgRkDXwMmAaYMdgky84H54d3VfCCGEGFQGfOBqaWnJ3w4EAj22L2zT3Nzcq9dYv359l4+1jX5NmTKlV9fqrcp7j+Taz17Bh6vX8sl3/wftvW3Tdy4aygyQHj6dxKf+TGVpWKb3xC7XNrKwq3+2hegN+fkTu9vOjG7BIAhcA9W/bZ3Zl386d+dfF6EUJDXwjryZ0Ak3oBkmJhDq114KIYQQAgZB4IpEItTX1wOQTCZ7bF/YJhqN9lm/dtYFP2vK/wtP+34TAD1XzxJCCCFEfxjwuxRLS0vzt+vq6npsX9im8LlCCCGEEDtqwAeuyZMn529/9NFHPbYvbFP4XCGEEEKIHTXgA1fhgsoFCxb02L6wjSzGFEIIIcSuMOAD12mnnZa//fTTT+N5XpdtP/zwQ5YvzxUHjUQiHH1017WthBBCCCF6a8AHriOPPJJRo0YBsGbNGh555JEu2/7qV7/K3z733HPx+/193j8hhBBCDHwDPnDpus4PfvCD/P1rr7220wryjzzyCHfddRcAPp+P7373u7utj0IIIYQY2AZ8WQiAyy+/nMcff5zHH3+cLVu2MG3aNGbNmsXUqVNJp9M888wzzJ07F6UUAL/4xS8YN25cP/daCCGEEAPFoAhcmqbx0EMPMWvWLB566CFaWlr4zW9+06Gdz+fjJz/5Cddee20/9FIIIYQQA9WgCFwAfr+fBx98kNmzZ3Pvvffy73//m02bNmHbNjU1NZxyyil8/vOfZ5999unvrgohhBBigBk0gavNCSecwAknnNDf3RBCCCHEIDLgF80LIYQQQvQ3CVxCCCGEEH1MApcQQgghRB+TwCWEEEII0cckcAkhhBBC9DEJXEIIIYQQfUwClxBCCCFEH5PAJYQQQgjRxzTVdoCg6BO2beO6LsOGDdvl185mswBYlrXLry1ET+TnT/Qn+fkTu9umTZswDINMJrNDz5cRrj5mWRaGYfTJtevq6qirq+uTawvRE/n5E/1Jfv7E7mYYxk4FfBnh2ovV1NQAsH79+n7uiRiM5OdP9Cf5+RN7GxnhEkIIIYToYxK4hBBCCCH6mAQuIYQQQog+JoFLCCGEEKKPSeASQgghhOhjEriEEEIIIfqYlIUQQgghhOhjMsIlhBBCCNHHJHAJIYQQQvQxCVxCCCGEEH1MApcQQgghRB+TwCWEEEII0cckcAkhhBBC9DEJXEIIIYQQfUwClxBCCCFEH5PAtZd54YUXuPLKK5kwYQKhUIiysjIOPPBAvva1r7FixYr+7p4YgGKxGI8//jjXX389Rx99NEOGDMG2bcLhMOPGjeP888/ngQceIJ1O93dXxSB09913o2la/s9xxx3X310SolNSaX4vkU6nmT17Nvfff3+Xbfx+Pz/72c+47rrrdmPPxEB222238a1vfYtUKtVj2/Hjx/OnP/2JmTNn7oaeCQFr167lgAMOoKWlJf+1Y489lhdffLH/OiVEF8z+7oDomVKKSy+9lEcffRSAcDjMrFmzmDp1Kul0mmeeeYZHHnmEVCrF9ddfj2VZfPGLX+znXouBYPny5fmwNWzYMD7xiU8wdepUhgwZQiaT4a233uLPf/4z9fX1fPjhh5x00kk899xzzJgxo597LgaDq6++mpaWFkKhEPF4vL+7I0S3ZIRrL/DnP/+Zyy+/HICqqipeeukl9t1336I2c+fO5aKLLkIphc/n44MPPmDMmDH90FsxkHzhC19g5cqV3HDDDZx00kkYhtGhTW1tLWeddRavv/46AJMmTeK9995D12XFgug7v/vd77jmmmswTZNbb72Vr371q4CMcIk9lwSuPZxSirFjx7JmzRoAHn74YS688MJO237xi1/kzjvvBODKK69kzpw5u62fYmCqr6+nvLy8x3YbN25kwoQJJJNJAF588UWOPfbYvu6eGKTWrFnDgQceSEtLC9/85jc5+eSTOf744wEJXGLPJf8E3cO9+uqr+bA1evRozj///C7b3nDDDfnbjz76qCxiFjutN2ELYPjw4RxzzDH5+4sXL+6rLolBTinFrFmzaGlpYfLkyXzve9/r7y4J0SsSuPZw8+bNy98+9dRTu52mGT9+PPvssw8ALS0tvPzyy33ePyHaRKPR/O1EItGPPRED2V133cXzzz+Pruv84Q9/wOfz9XeXhOgVCVx7uMKRgmnTpvXYvrCNjDKI3Wnp0qX527J+UPSF1atX8/Wvfx2AL33pSxx55JH93CMhek8C1x5u2bJl+dtjx47tsX1hmw8++KBP+iREey+++CLvv/8+ALZtc/LJJ/dzj8RA0zaVGIvFGDNmDD/96U/7u0tCbBcJXHu4hoaG/O3Kysoe2xe2aWxs7IsuCVEkkUhwzTXX5O9fe+21lJWV9WOPxED0f//3f7zwwgtArthpKBTq5x4JsX0kcO3hCgv6BQKBHtsXtmlubu6TPgnRRinFZZddlh+JnThxoixiFrvcRx99xDe+8Q0AZs2axUknndTPPRJi+0ngEkLssBtuuIHHHnsMgEgkwiOPPEIkEunnXomBpG0qMR6PM2zYMH71q1/1d5eE2CESuPZwhb+82mocdaewTeGuMSF2tZtvvplf//rXQO70g3nz5jFlypR+7pUYaO644458Xa0777yT0tLSfu2PEDtKAtcervDDpa6ursf2hW3kg0n0lW9/+9v5RcttYeuoo47q516JgebDDz/kpptuAuDCCy/k7LPP7uceCbHj5CzFPdzkyZNZtWoVkFvH0FZNuSsfffRR0XOF2NVuvvnmfNiKRCI89dRTsj1f9In7778/X9NtyJAh/OhHP+q0XeHn3po1a4rafe1rX5NaXWKPIIFrDzdlypR88dMFCxYwa9asbtsvWLCg6LlC7Erf+MY3uPXWW4HclPVTTz3FzJkz+7lXYqAqPHnut7/9ba+es3r1ar7zne/k73/5y1+WwCX2CDKluIc77bTT8reffvppPM/rsu2HH37I8uXLgdzIw9FHH93n/RODx4033pgPWyUlJTz77LMStoQQopckcO3hjjzySEaNGgXkhsofeeSRLtsW7t4599xz8fv9fd4/MTh85Stfyf98lZaW8q9//Yvp06f3c6/EQHfLLbeglOrxT1t9LsgdXl34mKxlFXsKCVx7OF3X+cEPfpC/f+2113ZaQf6RRx7hrrvuAsDn8/Hd7353t/VRDGzXX389t99+O5A7zHr+/PlMnTq1fzslhBB7GVnDtRe4/PLLefzxx3n88cfZsmUL06ZNY9asWUydOpV0Os0zzzzD3Llz8+sdfvGLXzBu3Lh+7rUYCL7zne/wm9/8Jn//2muvZe3ataxdu7bb540aNYpDDz20r7snhBB7DU0VrkoUe6xUKsWsWbN46KGHumzj8/n4yU9+wle/+tXd2DMxkB133HG89NJL2/28K664gnvvvXfXd0iITrz44ov5HdzHHntsvm6XEHsSGeHaS/j9fh588EFmz57Nvffey7///W82bdqEbdvU1NRwyimn8PnPf5599tmnv7sqhBBCiHZkhEsIIYQQoo/JonkhhBBCiD4mgUsIIYQQoo9J4BJCCCGE6GMSuIQQQggh+pgELiGEEEKIPiaBSwghhBCij0ngEkIIIYToYxK4hBBCCCH6mAQuIYQQQog+JoFLCCGEEKKPSeASQgghhOhjEriEEEIIIfqYBC4hhBBCiD4mgUsIIYQQoo9J4BJiD7J69Wo0TUPTNK688sr+7s5Ou/jii9E0jcMOOwzP8/q7O2KQmj17NpqmccABB+A4Tn93RwxSEriEEH1i/vz5/OUvfwHgV7/6FbouHzei97LZLIsWLeIPf/gDX/ziFzniiCMIBAI79A+SH/7whwSDQd59911uv/32PuuzEN0x+7sDQoiBRynFDTfcAMDxxx/Pcccd178dEnudI444goULF+6Saw0bNozPf/7z/PrXv+ZHP/oRs2bNory8fJdcW4jekn9yCiF2uYcffph33nkHgG9+85v93BuxN3Jdt+h+RUUFEydO3OHr3XjjjViWRVNTE7feeuvOdk+I7SaBSwixy/385z8HYJ999uHEE0/s596IXeHee+/NT+etXr26z1/v+OOP59vf/jaPPfYYq1evpq6ujptvvnmHrzd8+HDOOeccAO666y5aWlp2UU+F6B2ZUhRC7FIvv/wyb7/9NgBXXnklmqb1b4fEXunXv/71Lr/mVVddxdy5c2lqauLee+/l2muv3eWvIURXZIRLiL3Yc889x5VXXsnEiROJRCIEg0HGjh3Lpz/9aR599FGUUr26jlKK+++/n1NOOYUhQ4bg9/sZPXo05557LvPmzQN6v4Py3nvvzd++4IILun3dW265JX/NtuctXbqUL3zhC0ycOJFQKMSQIUP4xCc+wd///vcOz1+2bBnXXnst++67L+FwmNLSUo455hgeeOCBXr3vtvf+6KOPcumllzJ+/Pii7+PFF1/Mk08+2eM1HMdh/vz53HTTTRx//PGMGDECv99PIBBgxIgRnH766dx1110kk8kerzVmzJj896TNP//5T8466yxqamrw+XwMGTKEM888s1d9E9uceOKJlJaWAjBnzpz+7YwYfJQQYo/x0UcfKUAB6oorruiyXVNTkzr99NPzbbv6c8QRR6gNGzZ0+5oNDQ3qmGOO6fY6V111lVq5cmWPfXMcR5WVlSlAjRgxosf3+73vfS9/zTlz5qjf/e53yrbtLvtx/fXX5587Z86cbtvOmjWrx9dftmyZOuigg3r8Pp588smqvr6+y+scf/zxPV4DUKNHj1YLFy7stk+jR4/Ot0+lUuqiiy7q9prXXXddj+9zV5gzZ07+NT/66KPd8prd9aG7/z+6U/j/zapVq3ZtB4XohkwpCrGXSafTnHDCCbz11lsAhEIhrrjiCqZPn45hGLzzzjvMmTOHuro6Xn/9dWbOnMlbb71FRUVFh2s5jsOpp57Kf//7XwCCwSBXXHEFRxxxBKZpsmTJEu69917mzJlDKpXqsW///e9/aWhoAHK7zLbHvHnzePTRRykpKeHaa6/lkEMOQSnF888/z5/+9Cdc1+X2229n2rRp+P1+rrrqKoYOHcpVV13FAQccgOu6PPfcc9x///14nsc999zDcccdx2WXXdbp6y1ZsoRjjjmGxsZGAA499FDOPvtsxo8fj2marFixgoceeoj33nuPZ599lpNPPplXX30Vn8/X4VqJRIJAIMAxxxzD4Ycfzrhx44hGo2QyGVatWsU//vEP3njjDdasWcMpp5zC22+/zfDhw3v8nsyePZuHH36YyZMnc8kllzBx4kTS6TTPPfccDz74IJ7n8Zvf/IYjjjiCT3/609v1/R6sZsyYkR8ZnDdvHl/60pf6uUdi0OjvxCeE2KY3I1w33HBDvs2ECRPU6tWrO7Spra1Vhx12WL7dBRdc0Om1fvazn+XbjBo1Sq1cubJDm4aGBjVz5syiUZWu+vaLX/wi3+aXv/xlj++3cIQLUAcffLD6+OOPO7T74x//mG8zbtw4VV5erk444QTV3Nzcoe2dd96ZbztlypROXzeZTKqJEycqQFmWpebMmdNpO8dx1HXXXZe/3ne/+91O2z377LOqpaWl2/d6zz33KE3TFKA+97nPddmucIQLUNdee61yHKdDuz//+c89vs9daaCMcM2fPz9/jUsuuWTXdlCIbkjgEmIP0lPg2rp1qwoGgwpQpmmqd955p8trrV27VoXDYQUoTdPU+++/X/R4JpNRQ4cOzb/eyy+/3OW1NmzYoCKRSI+/7C677LJ8myeeeKLH91sYuCzL6jTwtZkwYUK+bUVFhdq6dWun7TzPU2PHjs23XbduXYc2d9xxR/7x2267rds+ep6nZsyYoQBVVlamUqlUj++rK5dccokCVCgUUtlsttM2hYHrmGOOUZ7ndXm96dOn59uuX79+h/vVGwMlcG3atCl/jf3222/XdlCIbsiieSH2IvPmzSORSABw+umnM2XKlC7bjhw5kiuuuAIgvzC80H/+8x82b94MwLRp0zj66KO7vNbw4cO59NJLe+zfRx99lL/d2RRmd8444wzGjx/f5eNHHXVU/vbll1/eZeFKTdM45phj8vfffffdDm3aFuhHo1G+8IUvdNsvTdPy38eGhob89OuOaPsex+NxFi9e3GP7r3zlK93u8jzppJPyt5cuXbrD/RpMCn8ud0d5CyHayBouIfYir7/+ev72Kaec0mP7T37yk/zv//5vh+cCRcGhN7WyTjzxRO66665u29TX1+dvb2/gmjFjRrePDx06tNdthw0blr/dtqasTUtLC4sWLQJgxIgRPP300z32bePGjfnb7733XlGga5NOp3nkkUf45z//yTvvvMOmTZuIxWIdCni2Wb9+PYceemi3rztz5sxuH6+pqcnfbv8+t9eYMWNYs2ZNr9qOHTu228evuOKKot2qexLLsgiHw8RiMRKJBKlUCr/f39/dEoOABC4h9iIbNmzI3540aVKP7QvbFIaG9ve7G1lqM2HChB7bFC6sj0QiPbYv1FNAK1ysvj1t2y/2X7duXT4Evf/++3zqU5/arn4Whso2CxYs4OKLL2bVqlW9vk5TU1OPbSorK7t9vLv3KboWjUaJxWIAJJNJCVxit5DAJcRepLA6digU6rF9OBzO325ubi56rO0XTm+v1Zs2hb+4mpubi0alerI9h1vvzEHYbbsSd1Qmkym6v2bNGk488cT8382YMWM49dRTmTRpEtXV1fj9/nx/n3/+eX77298CHY+u6czuPPD77rvvzk9Xd6aw77/73e+orq7usu2oUaN2ef92pcL/FwKBQD/2RAwmEriE2IsUjhrF4/Ee2xeGqmg0WvRYYRjrzbV606Zw5Gnr1q09tu8Phe/7hBNOYP78+Tt1vR//+Mf5sHXDDTdw6623dhmUCkco9zQnn3xyt48XBtWTTz6ZMWPG9G2H+kgmk8n/fxEKhWR0S+w2smheiL3IiBEj8reXLVvWY/vCNu3rPhXe//DDD3u81sqVK3tsU7i2p7Optz1B4fdw7dq1O329Z555BoCqqip+/vOfdzsqVbipQPSPwn8I7K2hUeydJHAJsRcpLCba9ou+O4ULwtsXIp0+fXr+9nPPPdfjtXrTpnDX5Hvvvddj+/5QUVHBgQceCORC5IoVK3bqem07PceNG4dhGF22U0rlj0kS/afw5/Lggw/uv46IQUcClxB7kdNPP51gMAjAk08+2W1pgQ0bNnDfffcBudIG5513XtHjM2fOzK+xWrBgAa+88kqX19q0aVOvzics3D3YflfknuSqq67K3/7GN76xU9dqW9u2cuXKbs+ufOCBB3j//fd36rXEznvttdfyt3vaBSrEriSBS4i9SFlZGV/84heB3LE8559/fqfTYlu3buXcc8/Nry06//zzmTx5clEby7K4/vrr8/c/85nPdDq12NjYyAUXXFC0YL8r06dPp6ysDGCn6lX1tbbDsQH+9re/cfXVV3e7Rk0pxSuvvMKNN97Y4bG2kcKtW7fy05/+tNPnz5s3j2uuuWYX9FzsrMJ/CHzyk5/sx56IwUYWzQuxl/nhD3/ICy+8wFtvvcWKFSvYf//9ufLKK5k2bVr+LMV77rmHuro6AEaPHs2dd97Z6bVuuOEG/va3v/Hf//6XtWvXMmXKlKKzFJcuXcqcOXPYvHkzF198MQ899BDQ9e45wzA455xzmDNnDhs2bGDFihX5YLMn8fv9/POf/+Soo46irq6Oe+65h8cff5zzzz+fww47jPLyctLpNJs3b2bJkiU899xzbNiwgfHjx/PLX/6y6Fpf+cpX8lO33/rWt3j++ec544wzGD58OB9//DHz5s3j6aefxjRNLrvsMv785z/3x1ve6yxatKhDsd7CEd1Fixbx7W9/u+jxsWPHcvXVV3d5zWw2y7///W8ADjnkkB7riQmxK0ngEmIv4/f7mT9/Ppdccgnz5s0jFotxxx13dNp2+vTpPPbYY13WrTJNk6eeeoqzzz6bV155hUQiwZ133tkhoF155ZXcdNNN+cDVXY2tq666ijlz5gAwd+5cbr755h15m31u0qRJLFy4kMsvv5wXX3yR+vp67r777m6fU1hotM3JJ5/MT3/6U26++WaUUsyfP7/DzsdwOMzdd99NOp2WwNVL77zzDj/+8Y+7fHzx4sUdptSPPfbYbgPXc889l99tWTitLMTuIFOKQuyFSkpKePLJJ3n22We5/PLLGT9+fH6L+6hRo7jggguYO3cur732Wofdie2VlZXx0ksv8ac//YmTTjqJqqoqbNtm5MiRnHPOOTzxxBPMmTOnqJJ5d4VHjz766Pxi5Pvuu6/bdU39beTIkbzwwgu8/PLLfOlLX+Kggw6ioqICwzAIhUKMHTuWT37yk/zgBz/gjTfe4MUXX+z0OjfddBOvvvoqF154ISNGjMCyLMrKypgyZQrf+MY3WLx4MRdffPHufXOig7Z/CESj0fxxTULsLprakz8NhRB7jN/85jf5NV+PP/44Z599dpdtH374YT796U8Dud2UPdV4EqKvbdq0idGjR5PNZvn617/Oz3/+8/7ukhhkJHAJIXqUTqc56KCDWLZsGT6fjw0bNnQ7yqWU4tBDD+Xtt9/muOOO44UXXtiNvRWioxtuuIHbbruNkpISVq1a1eXh50L0FZlSFGKQe/vtt7stUhqLxbj00kvzRVQvuuiiHs8y1DQtv7j8xRdf7HIqTojdYfPmzfzud78DchsbJGyJ/iAjXEIMcjfddBO33347J5xwAjNmzGDs2LH4/X4aGxtZuHAhf/3rX/PVuYcOHcrixYupqqrq1bUvueQSHnroIQ499FDefPNNNE3ry7ciRKdmz57NH//4R/bff38WLVqEZVn93SUxCEngEmKQu+mmm3q1nmW//fbj8ccf3yPLPAghxJ5OApcQg9zGjRt5+OGHeemll1ixYgVbt26lvr4e27aprq7m8MMP55xzzuGiiy7q9ugaIYQQXZPAJYQQQgjRx2TRvBBCCCFEH5PAJYQQQgjRxyRwCSGEEEL0MQlcQgghhBB9TAKXEEIIIUQfk8AlhBBCCNHHJHAJIYQQQvQxCVxCCCGEEH1MApcQQgghRB+TwCWEEEII0cckcAkhhBBC9DEJXEIIIYQQfUwClxBCCCFEH5PAJYQQQgjRx/4/yYh6U6T+tHkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 302
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "x1 = np.log1p(mean_1_nq[0, :].cpu().numpy())\n",
+    "y1 = entropy_1_q.cpu().numpy() \n",
+    "x2 = np.log1p(mean_2_nq[0, :].cpu().numpy())\n",
+    "y2 = entropy_2_q.cpu().numpy() \n",
+    "\n",
+    "ax.scatter(\n",
+    "    x1, y1,\n",
+    "    s=1,\n",
+    "    alpha=0.2,\n",
+    "    label=\"prior\",\n",
+    ")\n",
+    "\n",
+    "ax.scatter(\n",
+    "    x2, y2,\n",
+    "    s=1,\n",
+    "    alpha=1,\n",
+    "    label=\"case\",\n",
+    ")\n",
+    "\n",
+    "# max entropy\n",
+    "def max_ent(mu):\n",
+    "    return (1 + mu) * np.log(1 + mu) - mu * np.log(mu)\n",
+    "\n",
+    "xx = np.linspace(1e-6, 5, 1000)\n",
+    "yy = max_ent(np.exp(xx) - 1)\n",
+    "\n",
+    "ax.plot(xx, yy, color='black', linestyle='-', linewidth=1)\n",
+    "\n",
+    "# ax.set_xlim((0, 0.05))\n",
+    "# ax.set_ylim((0, 0.1))\n",
+    "\n",
+    "ax.set_xlabel(\"log(mean + 1)\")\n",
+    "ax.set_ylabel(\"entropy\")\n",
+    "\n",
+    "ax.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fcac0eadcf0>"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 330
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.special import xlogy\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "# max entropy\n",
+    "def max_ent(mu):\n",
+    "    return xlogy(mu + 1, mu + 1) - xlogy(mu, mu)\n",
+    "\n",
+    "x1 = np.log(mean_1_nq[0, :].cpu().numpy())\n",
+    "y1 = entropy_1_q.cpu().numpy() \n",
+    "x2 = np.log(mean_2_nq[0, :].cpu().numpy())\n",
+    "y2 = entropy_2_q.cpu().numpy() \n",
+    "\n",
+    "y1_max = max_ent(np.exp(x1))\n",
+    "y2_max = max_ent(np.exp(x2))\n",
+    "\n",
+    "def logit(x):\n",
+    "    return np.log(x / (1 - x))\n",
+    "\n",
+    "z1 = logit(y1 / y1_max)\n",
+    "z2 = logit(y2 / y2_max)\n",
+    "\n",
+    "ax.scatter(\n",
+    "    x1, z1,\n",
+    "    s=1,\n",
+    "    alpha=0.2,\n",
+    "    label=\"prior\",\n",
+    "    color='blue'\n",
+    ")\n",
+    "\n",
+    "ax.scatter(\n",
+    "    x2, z2,\n",
+    "    s=1,\n",
+    "    alpha=0.2,\n",
+    "    label=\"case\",\n",
+    "    color='orange'\n",
+    ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# ax.set_xlim((0, 0.05))\n",
+    "# ax.set_ylim((0, 0.1))\n",
+    "\n",
+    "ax.set_xlabel(\"log(mean)\")\n",
+    "ax.set_ylabel(\"logit(entropy / max entropy)\")\n",
+    "\n",
+    "ax.legend()\n",
+    "\n",
+    "# ax.set_xlim((0, 5))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.stats import norm\n",
+    "\n",
+    "def bin_and_zscore_with_neglogp(x1, z1, x2, z2, n_bins):\n",
+    "    \"\"\"\n",
+    "    Bins the range of x1 into n_bins, computes the mean and standard deviation of z1 for points in each bin,\n",
+    "    and then z-scores z2 based on the bin that each x2 value falls into.\n",
+    "    Additionally, computes the two-sided p-value in negative log space (i.e., -log(p))\n",
+    "    for each z-scored value, working entirely in log space for improved precision.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        x1 (np.ndarray): Covariate array for the first response.\n",
+    "        z1 (np.ndarray): Response array corresponding to x1.\n",
+    "        x2 (np.ndarray): Covariate array for the second response.\n",
+    "        z2 (np.ndarray): Response array corresponding to x2 (to be z-scored).\n",
+    "        n_bins (int): Number of bins to divide the range of x1.\n",
+    "        \n",
+    "    Returns:\n",
+    "        z2_zscored (np.ndarray): z-scored values of z2 based on the statistics computed from z1.\n",
+    "        neg_log_p (np.ndarray): The negative log of the two-sided p-values (i.e., -log(p)).\n",
+    "        bin_means (np.ndarray): Array of the means of z1 computed in each bin.\n",
+    "        bin_stds (np.ndarray): Array of the standard deviations of z1 computed in each bin.\n",
+    "        bin_edges (np.ndarray): The edges used for the bins.\n",
+    "    \"\"\"\n",
+    "    # Define bin edges (equal-width bins from min(x1) to max(x1))\n",
+    "    bin_edges = np.linspace(np.min(x1), np.max(x1), n_bins + 1)\n",
+    "    \n",
+    "    # Initialize arrays to hold the mean and std for each bin\n",
+    "    bin_means = np.empty(n_bins)\n",
+    "    bin_stds  = np.empty(n_bins)\n",
+    "    \n",
+    "    # Compute bin statistics for z1 based on x1\n",
+    "    for i in range(n_bins):\n",
+    "        # For all but the last bin, include the left edge and exclude the right.\n",
+    "        # For the last bin, include both edges.\n",
+    "        if i < n_bins - 1:\n",
+    "            mask = (x1 >= bin_edges[i]) & (x1 < bin_edges[i+1])\n",
+    "        else:\n",
+    "            mask = (x1 >= bin_edges[i]) & (x1 <= bin_edges[i+1])\n",
+    "        \n",
+    "        if np.sum(mask) > 0:\n",
+    "            bin_means[i] = np.mean(z1[mask])\n",
+    "            bin_stds[i]  = np.std(z1[mask])\n",
+    "        else:\n",
+    "            # If no points fall in this bin, assign NaN.\n",
+    "            bin_means[i] = np.nan\n",
+    "            bin_stds[i]  = np.nan\n",
+    "\n",
+    "    # Determine the bin index for each x2 using the same bin edges.\n",
+    "    # np.digitize returns indices in 1..n_bins, so subtract 1 for 0-indexed bins.\n",
+    "    bin_indices = np.digitize(x2, bin_edges, right=False) - 1\n",
+    "\n",
+    "    # Prepare output arrays, defaulting to NaN.\n",
+    "    z2_zscored = np.full(z2.shape, np.nan)\n",
+    "    neg_log_p  = np.full(z2.shape, np.nan)\n",
+    "    \n",
+    "    # Identify valid x2 entries that fall within our bins.\n",
+    "    valid = (bin_indices >= 0) & (bin_indices < n_bins)\n",
+    "    valid_idx = np.where(valid)[0]  # indices of valid entries\n",
+    "\n",
+    "    # For valid entries, check that the corresponding bin standard deviation is nonzero and not NaN.\n",
+    "    valid_std = (bin_stds[bin_indices[valid]] != 0) & (~np.isnan(bin_stds[bin_indices[valid]]))\n",
+    "    good_idx = valid_idx[valid_std]\n",
+    "\n",
+    "    # Compute the z-scored values for valid entries.\n",
+    "    z2_zscored[good_idx] = (z2[good_idx] - bin_means[bin_indices[good_idx]]) / bin_stds[bin_indices[good_idx]]\n",
+    "    \n",
+    "    # Compute the two-sided p-values in log space.\n",
+    "    # Two-sided p-value: p = 2 * norm.sf(|z|)\n",
+    "    # Thus: log(p) = log(2) + norm.logsf(|z|)\n",
+    "    # And: -log(p) = -log(2) - norm.logsf(|z|)\n",
+    "    log_p = np.log(2) + norm.logsf(np.abs(z2_zscored[good_idx]))\n",
+    "    neg_log_p[good_idx] = -log_p\n",
+    "    \n",
+    "    return z2_zscored, neg_log_p, bin_means, bin_stds, bin_edges\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z2_zscored, neg_log_p, bin_means, bin_stds, bin_edges = bin_and_zscore_with_neglogp(x1, z1, x2, z2, 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-1.0, 20.0)"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AqDQEOj7Ys2ePnnrqKUnSpEmTdMstt5StLNu3b0/9e8GCBWUrBwAAAAB/BTGtVbWHRMlRKpU2/VfN5Am689qFemD3IUmmVi6akzWMamtt1JOxodS0a1JpApX09ihJyxfUpUZFeQ3Oqr39AQAA+I1Axwfpo3Pe//7364ILLihLOXp7e/Xv//7vqe3rrruuLOUAAAAA4D+naa2KDSryDYnogC+dO65u0h1X576+0YihjtUtGdclaPb2mCxHPlh7BwAAID+soVOkV199Vffdd19q+/bbb8/r/fX19TIMQ4Zh6Lvf/a7ja774xS/qmWeecd1PT0+Prr32Wp0+fVqSdPnll+v666/PqywAAAAAwiuI9WDyXfsk2QG/de+g1m3uVUdXrOgy5COeMNXe2ae2Dd1q7+zLmOprvApyvZxsde5He2TtHQAAgPwwQqdI999/v44dOyZJmj9/vpYvX+77MR544AH97d/+rS699FItX75cl156qWpra2UYhg4fPqytW7dqy5YtMs3RP6wvuOAC/ehHP1I0GvW9LAAAAEA1qoSRJ0GsB2NffyVXp3w+o4SCqNNyjehwOpdkecrdZoJuu9nq3I/2mG/7AwAAGO8IdIqUPt1aW1ubDCO4P+CfffZZPfvss66vWbp0qb773e9q/vz5gZUDAAAAqDaVMPVTEOvB5Nspn08HfBB16ve0c17DEKdzkRSKNhN0281W5360xyBCSgAAgGpGoFOE5557To899pgkaeLEifrIRz4SyHHuu+8+Pf7443rqqae0Z88eHT16VENDQzp9+rSmT5+u+vp6veUtb9EHPvABtba2BlIGAAAAoJoFsT5NJci3Uz6fDvggRvP4PaLDaxjiZWqwcrWZoNtukKNogggpAQAAqhmBThEuvvji1DRnhRoYGMj5mssuu0yXXXaZPvaxjxV1LAAAAADOmPrJm3w64IMYzeP3iA6vYUi2cwlDmwm67eZT50FM/1YJ0yECAACUCoEOAAAAgHGPqZ/8F8RoHi+BUj4BgNcwxO1cyt1m8m27+a4HlE+I5/f0b/GEqbYN3dq+74hv+wQAAKhkBDoAAAAAxj2mfvJfUKN5csknVPAahjidSzxR3GwNfsm37Qa5HpDf0791dMVSYY5f+wQAAKhkBDoAAAAAAItST3PlxyiTZPnyCRWKCfL8Ho1SKkGuB+T39G9OZWM6RAAAMJ4R6AAAAAAALEodVvgxyiT53lKth+T3aJRSCXI9IL+nLrSXdfmCOqZDBAAA4xqBDgAAQBmwyDOAMPM7rPD7medWvlKth1Sq4MhvQa4H5PfUhU5l5bMSAACMZwQ6AAAAZVCpU/WgfAgBUUp+hxV+P/Pcyleq9ZBKFRz5LVv9hHENKda2AgAAsCLQAQAAKINKnaoH5UMIiEIVEgb6HVb4/cwLQ5hSSNhAMJtbPnVEfQIAgPGGQAcAAKAMKnWqHpQPISAKVUgY6PfICL+feZU6ciOswWyYgpF86iis9QkAABAUAh0AAIAyCMO3y1FZCAFRqDCEgTzzRtmvxT1dMUkq+8iScgYj9jBpZ7/39hqGtg0AAFBKBDoAAABlUKnfLkf50CGOQpUqDHQb5cEzb5T9Whw9cSYVpJSzbsoZjNjDpOUL6iy/d2uvbm07TKOOAAAA/EKgAwAAAFQAOsRRqFKFgUx/lVuy7u/piunoiTOpn5d7ZEk5RwDaw6SIIa1dsdBTe3Vr27RHAABQjQh0AAAAAKCKlSoMrKbprwod3ZHrfclrISkVNkjln0KxnCMA7WHSkoaZnturW9uupvYIAACQRKADAAAAAChaNa3zVOjoDq/vC9sUikGHfm5BV1B1UU3tEQAAIIlABwAASGKueQBAccIWUhTDPrpjZ/9Q6udun5E7++3vcx4VMt6mUHQLuoKqi2pqjwAAAEkEOgAAQBJzzQOwIuRFvortmC9Vm0s/TvO8Wkmmeg4csxzTProjYcrTZ2TCNF23x6tyTH823kIzAAAwPhDoAAAASeNnrnk6qQFvCHlRaqVqc07HsR/TPrrDPvIm22ek/eMkTB8v5fz8Y/ozAAAAfxDoAAAASeOns4VOasCb8RLy+onAuDilanP24zgd02l0x7be3J+RSxpmalvvEct2WJTz84/pz6x4VgAAgEIR6AAAAEnjp7OFTmrAm/ES8vqJwLg4pWpz9uPYf+fE62dkmD9Ly/n558f0Z9UUgvCsAAAAhSLQAQAAksbPXPN0UgPehLljOqwIjItTqjaXfhynNXSceP2MDPNnaaV//lVTCMKzAgAAFIpABwAAjCt0UgPehLljOqyK6TCvptEHhSpVmxuvbbvSP/+qKQSp9HANAACUD4EOAAAYV8ZrRx6A4BXTYV5Now8QTpX++VdNIUilh2sAAKB8CHQAAAAAwAfFdJhX0+gDIAjVFIJUergGAADKh0AHAAAAAMosyNEHlTadW6WV1y/J8144+YwmRSOKJ8xxcd5eEYIAAAAQ6AAAAAC+GK+d0PBHkKMPKm06t0orr1/ufiym9Vt6dff76nT6bEJ3PxbTHVcXd948lwAAAKoLgQ4AAADgg/HaCQ1/5Bp9UEzHfKVN51Zp5fXLA7sPZWwXG+jwXAIAAKgukXIXAAAAAKgGTp3QgF+SHfNb9w5q3eZedXTFPL/XPn1b2BeTL6a88YSp9s4+tW3oVntnn+IJ0+/iFX2s7O+zv9972bPtk+cSAABAdWGEDgAAAOCDINdAAYoZtVJpi8kXU95Sjkgp9FjZ3rdy0Ryt37Iv9bo5r5+itg3dap43Q5KhngPZR2dl26f9uRRPmIGuzcMUbwAAAMEi0AEAAAB8UGmd5qgsxQSGlbaYfDHlLeV0bYUeK9v7br+qSRHD0OSJxyRT2vHc/1+SLNc9+e+21kZLcLKz33mfba2NejI2pO37jkiStu87oo6uWOhCLgAAAHhDoAMAAADP+PZ1dpXWaY7KQmDoTalGyiVHutiP7UW2MiafIc8884yOnTyT9f07+4csIc3WvYO6ekGd5TWLLpyh9s4+dQ8M6+CxU5bfhTHkAgAAgDcEOgAAAPCMb1+7I/CCV/m2FQJDb0oVfHV0xVKBiiQtX1Dn+Vheyjgpmn2524Qpy7El6eDwScv2zv4h7XjuqOP7g5wOkqknAQAAgkWgAwAAAM/49rU7Ai94RVvxjz0cu3Vpg6SxkSwRw9CSBn8DVvuzMBoxPO87WziXPI+Fk89oUjSiO69doJ4DxzLW0LFPryZJhu3QOweOWbbnz6pR/cyawEd3MZIMAAAgWAQ6AAAA8IxvX7sj8IJXtBX/2MOx9OnIkrb1+huaBfEsTJ7H3e+r0+mzCUUMQx2rW9JeMVb25PlIo6ODljTUav2WfamfnTwTt+x7VfPckrQvRpIBAAAEi0AHAAAAnvHta3cEXvCKtuIfezi25/DxrK/zK2gI4lloP49NPYccRxVlO/YDuw9p/+BI6nXzZ01T/cypPKsBAACqCIEOAAAAPOPb1+4IvOAVbcU/9nDs8tnTM0boJF/nl0KfhW5rJ9nPY//gCXV0xTKOke3Yq5rnpkYqjW7PKehZHea1wJJl29k/pISpQKbTAwAACDMCHQAAAMAnBF7wirbin7bWRiXM0REqkqnF9bVa0lCrXQPDGZ3+5ea2dlJba6M29Ry0vH5Tz0HPYUU+IaFbaBPU+k5+BEXpZUvyezo9AACAMCPQAQAAAABUrGjEUMQYHdEiSXf9bJ/Wrlio73xkied9lGpUitvaSdGIoVXNcyUNpX6/f3DEcZSOk1whYfo5xhNmahSTPbQJan0nP4Iie9nSf06gAwAAxoNIuQsAAAAApIsnTLV39qltQ7faO/sUT5jlLhIccJ2QdOZcQrfeu1OL/+FR3XrvTp05lyh5GZxCiHwkw4atewe1bnOvOrpint6X731gn/bNvt3W2qgJUWuQlO+5ZJN+jvYp6dKPYS9T87wZvtzrxV4jp7I5/ZxnEwAAqGaM0AEAAECoBDXdD/zFdQq3Uq6DsmbjrlRAsH3fEa3ZuEv33up9dIwf7OvP5LteTqGjUtzug7H1XoaVME1FDGlxfa3uvHaheg44T4sWjRiaMjGqV+LnCj6XbNwClPRj2KduS5jy5V4v9hqll81pDZ0knk0AAKCaEegAAAAgVIKa7gf+4joFw68gppSd2nsOH3fdLgW39WO81GmhYYPbfeC83ssRrV2xUB2rW7Lus2by6H+mX3PJLLXU1+rWpQ1q7+wruk3Yz3H5gjpFI0ZGfdmnbmvb0J31HJ1kq+981vjJxsvaUzybAABANSPQAQAAQKj48S1uP5VypEMlCdt1qhZ+BTGl7NS+fPZ0yxRel8+eXvC+Cr3f3Dr6vdRpoWGD231QzHovNZMnqGP1IklSe2efL23C6Ry91G2+93q2+vYSxviBZxMAAKhmBDoAAAAIFT++xe0npu9xFrbrVC38CmJK2andfvNirdm4S3sOH9fls6er/ebFBe8riPvNS50WGja43QfN86zXIKlU08HZFXuOyanjdvYPpX7uFAjZy5t8vdcgqdgQnWcTAACoZgQ6AAAACJVSfYvbK6bvcRa261Qt/ApiStmpPWlCxLc1c+z326aeg75PNdY8r9aXKcykXPeBadlqqqvRDYvn5n0t3NpEKUYQJs9RGltLZ1vv6Igsp/O2lzffNXiKDfV4NgEAgGpGoAMAAAC4GO/T9zDlXPHyqUO/gphK7dS232/7B0e0f3DE16nGEqapdZv3SQp21F3PgWOW7YYLatTW2pj3/eTWJko5gtBruG0v787+/EJxQnQAAIDsCHQAAAAAF+N9+h6mnMstV2CTTx3ag5h4wvRtNEmYZKuz9PttYOik9g+eSL3H3rHvNSiz12nbhm7L74MKDJzCYHtbeDI2pGjEyKv89rLbt4O6P72G207l3dbrPRQf7yE6AACAGwIdAAAA+KJaR3JU6kgHL7xcM74tn1uuwKaYOnTadyGjPMImW52l32/tnX2p10iZHfuFho3FBgZen3VOYfCajbssr9m+70jW8nsJ8koZfhQabuf7vvEeogMAALgh0AEAAIAvGMlRebxcM74tn1uuwKaYOnTat5TfmiRh5CXkytWxX2hQVmxg4PVZ5xQG29tCtvIfGzmjdZsP5jxGoecST5i6+7E+PdBzSMdOntWMqRO1snmObr+qKWs4WGi4ne/7qjlEBwAAKBaBDgAAAHzBSI7K40enOnIHNvnWYfoIkHjCzDhWOe81v0bieQm5cnXsFxqUFRsYFFP/6W0hnjBTI3SksfKPnD6n0+cSno6RPJfkqK01G3eped4MSYZ6DmS/Rh1dMa3fsi+1PTRyRuu37FPEMHhuAwAAhBiBDgAAAHzBSI7K40enOqRblzboydiQ9hw+rstnT9etSxssv/dah8mwZFPPIcvaMcsX1FnWWZFUtnvNr5F4fgSF5Qobi3nWpbcFp3BMks7EExnvy3UMp+uS/m/7NbKHUuk/r7RwEAAAYDwh0AEAAIAvGMlRebhm/rj3if7USIvt+47o3if6C+oUT++UTxeNGOpY3ZLaLud1yzU6xWsnvR9BYanCxtT0ZLsPSTJ0/ZWzdee1C9Rz4FhR9Z8t3Ln5Ymt9LV9Ql/MYO/uHsv7OKaTJNvVboeGg1+tuH33mtoYQAAAAMhHoAAAAwBeM5HCX3pH50Uujqplc/j/FuWb+8GsKtGyjJuyd7OW8brlGp1TCWlrZRsZkCyTs05N9+Wf7tHbFQkvIVuxok/R6u6GxTpMnRHTNJbM878s2M5+FU0jT1tqohGlmrKFTSDgVT5hq29DtKZzJFlpKTNMJAADgRfn/KxIAAAAYB+wdtqgsbh32fk03aN/P/Fk1WrlojhKm1LahOxTTUuUaHVRsuFWKabiyTU+WLYhyCtrs51VskJVxDEOWwCiXiGGto/l101R/wdSsI4iiEUN3XD1fd1w93/MxkuzXKGHKshaQlP26ZwstpfzvG6ZsAwAA4xGBDgAAAFAC9o5Mp3UyEF5uHfZ+TYHmtJ+wjXjJNTqo2HDL6XyT9bCzf1gJ01TEkJY0zCy4A98pdHJ6TfIcnaYna55Xq/bOvtS12tlfXJBlP8akaMTzeyVpSUOttvWOvX/V4jmBtRP7NZo/a1rGa7Jdd/t5Oq0PVWg5pPCNBgMAAPAbgQ4AAABQAsV22KK83Eae+DUFmtN+/JrOrVSKDbeyhS32abq29R7Rpp6DWtU8N+9gJ1volC2ISk1P9toaOisXzZFkat3mfan3LV9gHXWXb5CVXm+vOy//KRlLua5SZgBmne/Nbc0fp3IWOqqm0u4NAAAAPxDoAAAAACVQbIctysuvadUq5biFKjbccjrfbNN07R8cSQU9+RzPLfxw+pnT9GRtG7ot+4wY0toVCwsOVNLr7ZlnnsnrvaVmv0YrF81RxDA8hTR+rv+U7d5gKjYAAFDN+K9IAAAAoAQqqcMWmUo5AsLpuMnpxnb2D6V+HtZO6mI61LPVs33Ks3T5jszIFirkChrSzyuesI5KWdIws+Cgwl5fb5uR9y7ynn7M6zVyel22UTalHh2Tra0wFRsAAKhmBDoAAKCq8U1doHqU8372c2RBIceVxqYd29Y7ugB9WDupi+lQd6pne6j12+GT6jsykvp987wZlvVsgmoX6eclWdd/uXVpQ8FlsNfXD1a+Ie8RfPlOP2Y/5i9jQzp07KSSU8rdftVo+bNdy3LcC3bZ7kmmYgMAANWMQAcAgDIhaCgNvqkLVI/xfD9XUid1UGU1DOmtDTPVfvNi3ftEf+rzM2EqkNEpdvbzikYMdaxukSS1d/ZZyvBkbCgV9uTav32/Z+IJ1eQsjVW+U/PZj7lj35HUv9dv6VXEGK3DSmp3Sfa6iCdMxRMmf2MBAICqQKADAECZjOeOyVKqxM6oIBAgohqE4X4u171UirV0/Do3v8vq9HmZPt3WwNCI5fX5jk6RvH3+2s+reV5talSOvQzbXwtIvOzfvt9J0UjOstiv1S1vq9cvY0Pa2T+sKRMjOpdIuIYY9mPaJevQj2tZ6numrbVRT8aGUtdg+74j6uiKjcvPfgAAUH0IdAAAKJMwdEyOB5W2oHhQCBBRDcJwP5frXgp6DZ94wlTbhu68gohs7GVNTke2s39ICVOKGIaWNBQ+Mia5nT79Wbp8R6c4ff56WTsmYZpat3mfp/K71aN9vzWTR7K+NsneDp+MDaVG2Zw8E9ddP3tOEyKRrMdNP2Y8Yaaue1KyDv1od6W+Z6IRI6Nd8TcWAACoFgQ6AACUSRg6JseDci1kHjYEiKgk2b7RH4b7uVz3UtBr+HR0xTI69Qs9N3tZ/2XHfq3fYg0+tvWOhhAdq1tyhjpOn5f26zB/1jTVz5zqqV14+fz1snZM24ZuxzLYA5Jcn+/2tn3F1GjONXTs5//E/qMZr9nZP5x6rX1kTPo1iidM3f1Ynx7YfUjJNXTaWht9G1lTjnuGv7EAAEC1ItABAKBMwtAxOR6UayHzsKFzC5UkW2d6GO5nv+4lvzrLg+p0l/x7TowGBZm8ToWV7fMy/TrMnTFF7TcvVjRiKJ4wU1OhOdWJl89fLyGEvS2sap6TCkjs1ySX9DZ/Q2Ndztfbj30mbma8ZnQEUe6RMdGIoTuunq87rp5v+bl9XSD7+722vXJ8/pRiRBvTmAIAgHIg0AEAoEzC0DGJ8YMAEZUkzCPK/LqX/JqGymnqrWjEyOhkztUBbe90X76gzsfnRPaObi/X1unz0m2dFLe69TOEyNYWCvl8t7f5M/FE6t/Zpn9LmKa+9fP9Onkmnnrt1ElRvfH152nlojnaVeR9lOs+9NqGy/H5U4oRbUxjCgAAyoFABwAAYBwgQES55fON9jCPKLNPVVXot/T9Cq3s+8m2Bk6uDminTne/RhysXDRH67cUtt5NOnt924uXrEO3uvUzhHB7rubbNprnzbC0+UnRSOrf2cocMQxLmCNJn3znRanytHf2aVuv96nf7HLdh17bsL2eco2gKkSpR8yEOXQGAADVjUAHAAAAGMcK7QjN9325OtLT99c8r1Z3XrtAPQeOuX6jv9zTHhXzLX2/Qiv7ftKldzLbO6Cd1lfxM/TNdj2b582QZKjnQP6jNez1vXyBdWqyZB261W2hIYTTebm1ufzbhvsoJqcyZ64jVGOpTy+hlNv5uL0/njAVT1inefPahoMY3VLqETNhDp0BAEB1I9ABAAAAxrFCO0LzfV+ujvS7H+vT+i37Uvu789oF6ljdEkjZ/VLMt/T9moYqfT/xhJkaoSNZO5ntHdBe11cplP3arF2x0HY9ix+NFDEMrV2xMKMO3eq20I74ZPCxqeeg9g+OpM5Lcq63zABtyFKmW5c26N4n+lPb9unR0qdcy1bmzDV85lrCJS8jM93uIbf3d3TFLG0tnyn6ghjdUuoRM0xjCgAAyoVABwAAABjHCu0Izfd9uTrSH9h9KGPbvkh7sWXwWzHf0vdrGsRcU8Al2TugkwFDkt9159e1ST8n+4iQJQ21eddhoR3x6cFHumznlRmgKWOto/Tp8eyjjdKnXMtW5tF1dJL3jqmEOTpqJp9Ran7d/9GI4fm4QYxuse8zOYIoqBF7TGMKAADKhUAHAAAAGMcK7VzN9325O9LtHa+5O2LLPe1RoeFAUFPFOa1V8i87+lId/isXzVH7zYtTxypmfZVc/Lo29iBl+YI6RSOGa30XOurEjT3ASMp2XpkBmvX9ew4ft2xHDKVGG73uvKhqJo/9p3q2MkcjhiKGtH/whCRp/ZZ9ihhGXueW73VKtt2BoZMZ+/EqiNEtba2NlpBs+74j6uiKBRK4lHuqRwAAML4R6AAAgKLRuQFUpnhi9Fv982fVSDK0ctEcz52r+XbK5upIX7lojtZv6bVs+10GvxUaDpRqqriOrpilTtM7/IOuO7/27zQSJDl1Wzxhqr2zL+OzJ4iRW/bgY/6saVrVnP1+cWob23rH3n/57OmWKcuWNMxMvf6ZZ57xXK5izzXf62QP2ObPqtGq5rl5Xd8gRrc4jRAKasReuad6BAAA4xuBDgAAKBqdG0BlGu3w35fajhjyHMb63Sl7+1WNihj5BQCVOu1RqaaKcxpVkjxW0HXn1/7dRpD8a2ef7vrZ2LpL5xKm/nz5/EBGbt26tEFPxoa05/BxXT57utpvXqxJEyK53/gae3BiX0On0MCred4My7k2z5uR1/vzvU72NlU/syY091+pRuwFff/yJRkAAOCGQAcAABSt3OtYYHypls6ukdPn1Lahu6znEKZ712vHcjVc/1J1PNuPE8SxCrke+bzHbQTJvz3eb3ntvz3erz9fPj+Q0Uf3PtFvmc7r3if6Xduq0zna27c/gVr+UxVmK5+X+6iQtluqe7ZUI/aCvn/5kgwAAHBDoAMAAIpW7nUsUHmK6eCrhs6ukdPn9Mqr57R175GynkMl3rtBXv9q63hua21UwpRlDR2/j1XI9cjnPW5B36mzccft9Pf4dU3zDT/t5/hkbMiy9o9f7arnwLDDdu77odD7qJC2W6pndlCjzuxt6NalDZKCu3/DFLQDAIDwIdABAABFK/c6Fqg8xXTwVUNn15l4wrLtdg5BhgyVeO8Gef0rvePZ6Th3XN2kO64Orm0Vcj38uoZLGmq1w7IOTWYgWcw1Ta+feMK0/K6lvta1/uznmBzd43e7KjSUtZdvU8+hwILMSn9mZ2tDQZ1DJQbtAACgdAh0AABA0Sp1HQuUTzEdfNXQ2TUpGtHps2Ohjts5BBkyVOK9G+T1r/SO53z50bYKuR5+XcO7b16sNRt3Wda1sSvmmqbXjyQtX1BnGWXjVn9O090VUoZcCg1l7eXbP3hC+wdP5GwHhbQZ+zo/V174erV39lXMtImlfi5UYtAOAABKh0AHAAAAJVdMh241dHbVTB79M/yaS2blPIfxFjLkEuT1r9SwMJ4wdfdjfa9NrWZo5aI5uv2q0U7yfEaRZGtbbvso5HrY33Pr0oaCOvgnTYjo3luXuL6meZ71mjbP835N7fUTjRjqWN0iabRONvUcynh9sv7SzzGeMFMjdCR/21W+oWzyWu7sH9byBXWKGNKB4ZPaPzjieB52hT2PrNeye+BYamRVJUybWernQiUG7QAAoHQIdAAAAFByxXTKV0tnV83kCepYvSjn6yo1ZAhKkNe/kHZZqnV33HR0xbR+y77U9votvYoYo53k+Ywiyda23PZRyPWwv6e9sy/Aqe6sU6U91T+UCrtycaufjq6Y9g+eyHh9Uq51fApVbHuzjzpau2KhljTMtPzM7RljH21zLmEqnjBdy2Bf5+fXh49btsMeUlfDlwgAAED1INABAABAyVVLKFMKpe5MDENAUS6FtMtSrbvjxj5qIvmzNcuaXEdUeG1bQY8SC3L/PQeOWbZ37Duijq6Yp/271Y+9zPNn1Vh+73Qf+XFO9vb2ZGzIMg1crnvVqa6TU9Xt7B9SwpR29o++xnl/1m0v9WkPxi6fPT2wEUtB4PMKAACECYEOAAAAEGKl7kx06jDuWN0SSKhTDeFRGKbEc1qvJdlJ7jbKJNcokuS1CHqUmNP+/WobTnXj9Rq53Xv2/a5qnmspX1BBn729bc9z6jKnuk6ep6RUmbf1Ou/PPtomWSa34zpNsXfvE/05g8RqeD4AAAD4jUAHAACgAHQ0oVo5dRh7HdGQrzCMbilWGKbEa2ttVMI0LWvoJDvJvY7CcbsWfo0Sy/bcdNq/X22jrbVRT8aGfB0REk+YSpim5s+qkb2+k/wM+tLrLZ4ws77OyzHyGXXktD+38DAbp2DMS0gdpucDn/kAACAsCHQAAAAKEKaOpnKhg6s6FTOiIV9BjW7xu2267S8M62tEI4buuHq+7rh6fqqsazbuskz1late3a6FX6PEsj03nfbvV9uIRgx1rG7xbQ0bKXPNooihjPbl56gj+7o3yxfUac/h4zp64kzGMXNxqutkuQaGRnLuzy089FsYRr8l8ZkPAADCgkAHAACgAGHqaCoXOriqU74jGooJT4Ia3eJ323Tbn59T4vkRRBV67qUYaWR/bt7TFZPkvFaLH+Wx12f7zYtTx8mnru2v3dk/lHFe9jr2MurIvv6NNHr9Fk4+o0nRiM6cS+jeJ/pT9ZQUjRi6rbUxI+QpNFixB0bzZ03TqmbnoCY9PAya322ymPuLz3wAABAWBDoAAAAFCMM0S+VGB1d1yndEQzHhSVCjW4ppm06dvqVq634EUYWW1cu1KDZwsj83j544kzpfL4FIvtzqM1dd26c5S1+rZvmCuozzsnMK+uxBkH39G2l0DZu731en02cTWrNxlyVYTT9eev00z5shydCajbvUPK9WkqmeA8c8XyN7m6mfObWg9u33yDi/nw/F3F985gMAgLAg0AEAAChAGKZZKjc6uKpXPqNOigk7/Bzdkq6YtunU6etlf7k6s710dvsRHBV67l6uRbGBU/I5eU9XzDJdmNN5+tE23OozV13bR62kixiG1q5YmPfz32X5m4zySNKew8ct2xdMm6TbWhtTbSdZP+2dfRnXJf3fuerQr2e53yPj/H4+FHN/8ZkPAADCgkAHAACgAEF1RFcSOrhKI+xrFYUx2CumbTp1+rbfvDjn/nJ1Znvp7PYjOAryviw2cEo+NyVZwpKg2oxbfeaqa6eAJWlJQ21Bz/+Ikf2+TR4/vUyXz55uGaFz22vrIdm5ldXLNfJrdFbYR20W86ziMx8AAIQFgQ4AAAUIewcrUArpHVzcE8EJ+1pFYQz2iul8der09bK/XJ3ZXjq7vdRlrvbg9dwLuWcL6RB3Ok6p2ozbcey/u3Vpg9o7+1LbzfOs53r1gjodOnZKkqmEaSqeMPN+xi1pqNW23rF9Ll9Ql7GGjiRNnnhMk6IRtd/8Zt37RH/OerJfF/vvcvFrdFYYw910YXxWAQAA5ItABwCAAoS9gxUoNe6J4IT9W++V+M11tzAjV0d/tuAjV2e2l85uP4Ijrwq5ZwvpEM92nFK0mXza5nce79f6LWPlvPPaBVq7YqF29g8rYZr67fBJ9R0ZkSSt37JPEcPIu/xO9WdvS2uWNemZZ56RJE2aEMla/vQ23Dxvhu68dqF6Dgw7rqHjB78CSbfzCPrLAJX4rAIAALAj0AEAoABh72BFcBiJ4ox7Ijhh/9Z7kIK639zCDHunr9P6JE5tO1dntl+jA/xqD17uWXv937q0wdfjlPN5am8D82dNs/y+58AxdaxuUcJUKuhJV8gzzs9AwV7+tSsWqmN1S177cKt/+++a583wJZDMdR5S5v0VT5i6+7GYHth9SJKplYvm6ParmvjsBQAA4xKBDgAABRjPHaxBC3tgwkgUZ9wTwamGaYIKva+Dut/yCSC9vjZXZ7ZfnfmlDIbs9X/3YzENjZxJbUu5r4fbcdyubzGfBV4CgMx1Z8yMckt6bR/Ox/jod3cqYY6ujbOkYayMfn+OOe3PjxDdrf7tv7vz2oVau2Kh788hL+fR0RWzhGqFjpACAACoBgQ6AAAUoBo6WMMq7IEJI1GccU8EpxqmCSr0vg7qfssngAxbWFnKYMhe/8kwJ/33xUzT5nZ9820z6aFHPGFq+74jqd85BQD267py0RxFDCM1xdrO/iFJkmlag56JUUPnnzfBsn9J2tY7qE09h7SqeY4Spqn1W/Y5lr2QsMepLvxol271b/9dz4FhdaxuKajduZ2zl/PIDN/47AUAAOMXgQ4AAAWohg7WsAp7YJJPJ1rYRxv5iXsCbgq9r4MKU/IJIKsxrPT6bLLXv9Pvc3F7Nrhd33zbTHro4cT+frf1bJL72dZ7RI0XTLXs52zc1NDIWcdj7B88oXWbezV/Vo3l5zv7h1PHSg+bvIabTnXRfvPijPLny63+/bz33MI5L/eXUzssd7AKAABQLgQ6AAAgVML2bXi7fDp3wz7aKGzGUwA23hR6XwcVpuQTQFZ6WOl0X3l9NiXre1PPQe0fHEn9fP6sGq1qnlv09XC7vvm2GadRHOns7892Xe37efnVc677dWZ9biVMM2vY5CXcdKoLP9qlW/0n/72zf0gJcyyUKuS57BbOZTuP9HbbPK9Wf/meBfrx04eVnEKvGoJVAACAQhDoAACAUAn7t+Hz6UQL+2ijsCEAq16F3tflDFP8CBjDEFI63Vf5rgvU1tqYsSaNH+fidn3zbTP20OPqBXU6dOyU8g0A7Pu5fPb0jOnVkprqanRh7VQdHD6p/UfGAq/R6dvGyp6cvi3b8XLx63PRqT1mq//ktZHSRywV9ly212k8YaptQ7frPWFvt2tXLNTWTy/Leh6E/wAAYLwg0AEAAKFS6d+GTxf20UZhQwBWvSrxvvYjYAxDSOl0X+X7bIpGDEWM0SnFpNIsSp/eZrx04LtNoZYP+35uXdqgNRt3WUKd9BFK0YiRtXzp9bOtd+z9yxfUKRoxXMOZkdPnLKFHrvvHSx0V0h6TI3PSt/O97ul16nXKOT/XVwIAAKgmBDoAAAABCftoo7AhAEOYOHUoJ6cr8xoaBB1SxhOm7n6s77WRM4ZWLpqj26+ylsnpvirk2VTOwNVLB75foaHTfuzXuH5mTcbv3Y6db9g0cvqcXnn1nLbuPaKtewf1o12/1Q2LL3R9n5c6KuQaJkzTdduL9Ppp29DtqQz5rK90T1dMkvfp4Aod4cPIIAAAEAYEOgAAIJSqoeOkEkcllBMBGMLE3qH89G9f0pIvbdXQyBlJ3kYGBB1SdnTFtH7LvtT2+i29ihjWMmULE/J9NpUjcE1+DiQ77JNKPXqv2HPPt75PnY1btvuOnEyFNckRS/Yp8HYNHLO8x6mOCjkP+8dusR/DXsuQz/pKR0+csdRPLoWO8GFkEAAACAMCHQAAEEqV3nFSDYFUqRGAIUySHcibeg5p/+CJVJCTLtf0U0GHlPaRCsmf5TN6xCu/zyXfKcLSlXr0Xq5zL9XzPnltR4O8sXpZv2Wfli+os7w2WUfpZWueN0N3XrtQPQe8X8MlDTMt08UtaZhZ1Dl4bUe51ld6MjaUsbaR16Cv0NFmTAsKAADCgEAHAACEUqV3nFR6IAWMZ+md4G5yTT8VdEhpH6mQ/FkQ/D6XQqYImzopqr94x/ySj97Lde5+P++nTIzqlfi5jJ8nr61Tu4wY0toVCzOCEnvZ1q5YqI7VLZ7LktzPzv4hJcyxNXUKDa38aEfRiOF4bK9tv9ARV0wLCgAAwoBABwAAhFKld5xUeiAFjGfZRobYlXvQXVtroxKmaVlDp1KmKvTyjLR/Dpw8E1fEcO7MLyf7uezsH0r9vJAROzWTR/8z/Z0LZylhmooYoyNjktfWKchb0jDTMSgptmzJAEZS6p7Y1jsWWnkZnRTECCZ7HSxfUOep7ccTphKmqfmzapTvPcO0oAAAIAwIdAAAQChVesdJpQdS1YKp71AIeyf4/LoayTB0bOSMZeq1YqefKlY0YuiOq+frjqvnl7UchfDyjGxrbUxNeZcUlnA8/dkST1hHaiVMFT1ip2byBH3nI4scfzca5Mmyhk62z0h7PRdatmwBnJfRSUGMWM22NlQu9nWnIoY8fyYwLSgAAAgDAh0AABBKld5xUumBVLXwqyORYGh8sXeCr1o8N+toBBTGyzMyGjG0qnmOZbRUWMJx+yiu5QvqFI0YaqmvTU1LlmQPoYp9nowGeU264+qmnPuz13OusmWTLYDzMtLKfszk2lPF1EOhfyMwehYAAFQ6Ah0AAIAAVHogVS386rxjTaTxJVvYwH3tn/S6zCeQCEuIZn+2RIzRkKN7YDhjbSV7COXn8ySeMNW2oVvb9x1J7W9Tz0Gtap6bqkd7m01OmeZUtmyyXQcvI63s9ZHc9vu56iUgYvQsAACodAQ6AAAAqFp+dd5Vwre6GUXkH4IbfyXb5s7+4Yw1YaIRw7Vjv9TXIv0+ap5XK8lUz4FjGfeU/dnyPwePa1vvkdT2sosv0K8Pv6xTZ+P6ZWxIty5t0KQJEUn+Pk86umKpMCdp/+BIqj7t+y00IMt2Hbzsz/4YSm77/Vz1EhCFNSAEAADwikAHAAAAvvM7XCh0f3513lXCt7rDNIqIcKl0Sl3XhRzPPj2ZJG3rPaJNPYe0qnmOdvYPWX5XzsDU6T5K/3eyXMlnyaaeg9o/OGJZW0mSfn345dTPduw7ots37tLbGmc6rrlTzPPEHorYf2evR78DMi/7W9Iw0xJ2Jdee8vu56iUgIqwFAACVjkAHAAAAvvM7XCh0f3513lXCt7rDNIooTOFSkMIQXBVa14WWvZDjZQsd9g+e0LrNvZpZM9Hy83IFpvGEqU09h7L+Pv2eSj5bugeGtX9wJOO1p87GLds7+4e1I20kzdUXX6BDL52SZChhjh7bqf5zXSd7KJLOaz0G1Y7HRmYNafmCOkUMQ0saxp6ffj9XKyF4BwAAKBaBDgAAAHznd7hQ7rCiEr7VHabOzHJfr1IJQ3BVaF0XWvZCjucWOkjS0MhZSdL8WdO0qnlO1o79oAO0jq6Y9g+eyPp7p3vKfm7zZ9VoVfNc/TI2ZAlwpkyM6OSZsZDn0EunUkHQ+i29ihjO9Z/rOqWHIk5TxGWTXpfxhGlZg8d+jELZR2atXbHQst9oxLCUP3k+hV7TbAFRGIJXAAAAvxDoAAAAwHd+hwthCCvC3ikYplFEYbhepRCG4KrQui607IUcL9kWn+of1oGhEb3w0imdPJvIeF39zKmOZUjee8npzaTR4OHJ2JA6Vrdk3If2dXASZkI/fvqwJEMrF83R7VdZ793k6+/piln2M7+uRiub57gGJE73XTRi6NalDVqzcZf2HD6uy2dP1+L6GbrrZ8+lvdNa5p39w6n9fPTSqGomT0htp9vUc9Dy7Ck0bHaaBm/sGId8eb55aWN+hqLZ6iIMwSsAAIBfCHQAAADgO7/DhTCEFYV0CpYyBArTKKIwXK9SCENwVWhd28seT5hZp/1yO96tSxvU3tnn2saTbVOSft47dsyZNZMsa89kq79s4cP2fUfU0RWztPl4wlTbhu6MESdJTqNhsu1/1eK5Oe+nbPfdpAkRdaxuSd3/EcPQndcuVM+B0XpKmKNlSUqYZqoMNzTWpX5uv077B0cyzrkQbmvv7B884csxvNwfpQhFwxC8AgAA+IVABwAAAL4rx8LbQSukU7AavxnuJaQKw/XKxs+QLQzBVaF13dbaqCdjQ6ngwykc8XK89s4+z23cfg+9ee7rtaSh1rH+0q/TwFDmGjXp+7SHM9vTpjpze0+2kTkXTJuk21obPV1Pt/Zkv//XrliojtUtqfdFjLG2s7N/yLLfM/HREUxtrY3a1HPIMhVctmdPPm3bHrbYwzU/Qg8v90fzPGs5RqeN81cYglcAAAC/EOgAAAAAHhTSKViN3wyv9JCqFFM8VYJoxMjo7Hdrn9nCgnzauP0eWtJQm7X+3KYEs+/Tfnyv78l2jNtaGz1fU7f25FY3Tm1nW+9YEDUpGknVuWQ6lj+fstglw5Wd/UNKmNLB4VOeRkvlExp5uz/MHNveZStbGIJXAAAAvxDoAAAAAB4U0ino1zfDw7R+T6WHVG7l97uew3Td7JJTrKVza5/2sGBTz0Gtap6bMcLCbR/53EP26zR/1jTNq52quGnq4PCIDCOilYvmZOzDfs9dvaBOLfUzLGvopJcjXa6ROU7XM3ONm0Op3zfPm1FQ3bzuvNE1dOyB0/xZ07SqOfOck/K5N9OnwUs/xtRJUb2lYXQqPSd3P9an9Vv2SRptBwnT1B1Xz896Xrn0HDjmup2PbIFWJQevAAAAdgQ6AAAAgAeFdAr69c3wMI2KqfTpi9zK73c9l+O6eQ2R7FOTLV9Ql1fAsn9wROs29+rOaxdo7YqFntp4PveQ/Tqtap4jyRo+RAxlnJvTPReNGPrz5RflPMZH3z763jUbdznWndP1zFzj5oT2D57Q1r2D+uy7L1ZTXY0ODp/U3NqpuuVt9VnPN71unnnmGUmj4VC6+plTM9YLSl7r5nm1OvfaNG3p52d/nf287Nf15Jm4tu87onuf6He8Tg/sPpSxXUyg4+V54rVNV3rYDAAA4AWBDgAAABAQv74ZHqaOykqfvshe/luXNqi9s++1tVpOWl5bbD2X47p5DZHsZXOagi2dveM9qefAMXWsbvH9vNpaG5UwkwGCqYRpapetzDv7R7ftHf1e77lblzboydiQ9hw+rstnT1fCNC2jTyRr3Tldz/abF6f+PTA0ov2DY+v93PvEQGoas74jI/r493p0761LPJ3/yOlzlnVzpMyww+laJ6UHdG5tItt1TR9pZA1Q7G0ke5vxEsR4eZ54bdOVHjYDAAB4QaADAAAAhFyYOiorffoie/nbO/uyrtVSbD0Hed2KXdMm37IlO9o39RyyBA329/k1zVw0YihiKHWs9Vv2afmCOstrEqZZ1Aioe5/oT41S2r7viA4eO2X5vb3unOosvT3Z29Kps3HL/vYcPu5anmTdLZx8RmfPWUfbzKyZmBF2uK0XlB7QubWJbNc1faSRNFavKxfN0fotY+e4ctGcrGXwEsR4eZ7Yy39PVyxV9vS2VelhMwAAgBcEOgAAAEDIOY1WiCfM0KzH4odyrTeTuVZLjepn1vjSIRxkB3O2znKvQU2+ZUt2vLe1NmZcp1zlcnqPl2trvzYRw7BM77azfyjj9U7BgNfwS3JfUyhXndl//8vYkHakTWtnmqMB4q1LG3TvE/0Z5UnW3d3vswZXkjSjZnJGnWUbXWMvu1ubSL+udz8W0wO7D+l3L53SyTNjYVR6vd5+VaMiRmHrIBU6Qs1e/qMnzqTaWPr+7OFQPGGmRt+FbQ0rAACAQhHoAAAAACHnNFohYhgVO0rGSbnWCcpcq2Wub8cNcjRTts5yr0FNoWXL9T6nckkq6Nrar82ShtqMY2/rHQtM4gnnoNNr+CVzdKqyiCEtaZiZUXe5zt3++1uXNmjNxl16qn9YJ8/ENTQyGkQ8GRtKjQxKL4/biJvrr5ydEU6kX+vmebWSTPUcOJZx3b20CfszJp1TAFTI9St0hFqyvPd0xXT0xJnUz3MFRGFaewwAAMAvBDoAAABABQjTOjpBKNf5Veo0Tdk6y8s9JZ5TuQq9tl5GxKSHI9v3HVFHVyxj37nCr009B7V/cET7j4z+b+2Khb7U36QJEd176xK1bei21Il96rVkeex1t3xBnaIRQy31tVmnl/Nyrb22CXs9TZ0U1V+846KC7wm/7q1k+SVZprTLFRBV+zMTAACMTwQ6AAAAGJfKNcVXocK0jk4QynV+5Q5AChXWICpbuQq5tl6uTa51b5LHSz/+wNBJtXf2qa21MTUyZv/giOs+imE//uWzp6dCqOTvpbG6mzzxmCZFI+pYfWXqmdS2oduyz3zL6OV5Zy/nyTNxRQwV/Fz0+97Kt81X+zMTAACMTwQ6AAAAIVRpYUMlqrTpeMLage+Xaj8/v9nXtFmzcVdgzwovzyP7a9pvXjwWRgS0BlRHVyxjijCnTvuMkTiDJ7Ruc6829RzUqua5ap6XX8e/W33EE6bufqzvtXM1tHLRHH3kD+v1ZGxIew4f1+Wzp+tbNy7SX/xgd2r71qUNkkavaVtro3b2PK0z8YQ6umKpfRcbTnh53rW1NqbqKCkZHIXhMynfgIhnCgAAqEYEOgAAACFUaWFDJaq06XgqdSSJV9V+fkEpxbPCyzHcXpPPGlD5BAf2e3j+rJqs68M4jcTZPziidZt7dee1C7R2xULLMd3K4XauHV0xrd+yL3WM9Vt6tbPfOi3cX/xgt2X73if6Le9viJyTNDa9WK61kbzUmZfnXTRiaFXzXMdpzSrxM4lnCgAAqEaRchcAAAAAmbItLA7/2L/hznQ8qESleFZ4OUau19i37+mKqb2zT/GEKWk0lGjv7NN7vv6Y1m3u1da9g1q3uVd3Pzb6urYN3ZbXS5n37Krmua6jRrLd47sczicZYCTL0dEV83RuTnVjXzPHaQ2dXPtOhhMdq1u0ZlmT5Tzdyprk9XnX1tqotSsW6ppLZmntioWp4Ghnv7Vc9u0gJNuE07UHAAAYrxihAwAAEELM/R88puMJvzBM8+RUjluXNujeJ/rLXi6pNM8KL8donjfD8prmeTNc93H0xBnLCJT0ESDpOrpiGho5IylzZEi+9/DY1GuHLFO1xROmZfRJwjTVc+CY5b3pI1rs59I8b4baO/vUPTDsGDrY18zJtobO2L+HHH+XjZfRN17qyu1+S5jW87JvB6ESRwUBAAAEjUAHAAA4CktH6nhF2BA8puMJv3J16NqffwlzdOqsZDmejI1NoVXujuZSPCu8HcP6+fBU/7B6DnSnXp98zz1dMR09cSb1up39w6np0Jwkw5yk9LDC7R7O9hmWvu5Q8nebdh20vPeB3Ye0qnlu1hDLXh8JU5Yw6uoFdTp07KSSa+h89O3WAPDWpQ36zuOx1Do7CVOpNYVG19A5rjPxRGqEzJlzCa3ZuEt7Dh/XZbOnq6W+Vk//9ljqvLwEbk51ZV/vZ86MKdqRpV3bP/5L8eeAX9Ni8vcMAACoJgQ6AADAEd+MLS/CBoRVKTtHy7XOkf35N3/WNMvvnabMylauoOurFM8KL8foOWC9Vk7BwJplTZYwTBob6WEPJbKJJ0y1beh2rMv0uo4nzKyhm/18NvUcsh3FcA2x0t8fT5h6z9cfs7x7QsTQ1k9fbflZ+us7ukbDnOR6Puu39CpiKDWVWs3kCaqRtOatr41E2tCdOpcd+45k1G2hoZ59vZ/0UUvJ/SXraEnDTG3rHbtuSxpmejpGMfwafcbfMwAAoJoQ6AAAAEeVtmA8gNIoZedouaYezBwtYp1eym3KLLug6sstKHL6XbIsuYKlfAIoe4CSzaaeQ6n9Gba6TO46PZRID2MkafmCOkUjhmtIkzw/p2nbpLE1X5zOa+WiOakRWMntbCNa7HXT0RXLCEK8tod0bp+x9gDRfl7JcuYqq/065lpvyW1UUilGjfp1TP6eAQAA1YRABwAAOGINF4wHTMWTv1J2jpZr6kH782/lojmKGIbrGjrZBFVfbkGR0+8keQqW8gmg7OHEzJpJGVOkSaMjP/YPntDWvYNavqDO8rvkSA/7qBen+7JtQ7flvfa6fCp21LGc0uhIoGzndftVjYoYslzf5Jo46cd3qhv79Z0/qyav9pDUPK82dcyPXhpVzeSx/1S3B4j283Li5To6jYpKhmduo5JKJdcxR6eMS05dZ2rlojm6/aqmjGc4f88AAIBqQqADAAAcsYYLxgOm4snfeOgcdXr+JTuXk7x2bgdVX25BkdPvnN7vVP7kSJb07Wznad/vDFugMzpVnZmaWkySIoahtSsWun62ZOvIz1WXB4ZPWbZrayZq0YUz1FJfq539Qxllt0/Blhxx895vPpYqc3LNpI7VLY71ai/Tqua5rqGw/fXzZ03TquY5rwVOo9Of3dBoDb3ab16cWkMnYZoaHjmb+l3EcA6mvQSJba2NOpcw9W+P9+vU2biWNNSq/ebFmjQhkrX8YTI6ZdxYoLh+yz5FDMPxPCX+ngEAANWBQAcAADhiDReMB0zFk79blzboydiQ9hw+rstnT9etSxsCO1ahgVuxI6/8fP4F1ZnsFm5k+12uYCmeMDUwNGL5WbYRIE7HGR3JZD1X+yieJQ21BU0PJuWuy+Onzlq2I4ahjtUtqfNIXwOmed6MjP1nmw5t+74j6uiKOdar1+s7OpqkTw/0HNLMmkl6/ZQJmjuzRlFj9Dx32Z5FZ+KJ1L8nTYjo3luXSJLaO/ssZTwwfMqyxk6yfF6CxGjE0ISIkQrhduw7onuf6E9dt7CPXPQaVPL3DAAAqCYEOgAAAPCs2qYoGw+jTfx27xP9qc7j7a91AAfVUVpo4ObHyCu/2nqxnclZpx9zCRLcfucWPHR0xdR3xBro2Ne8SedlJJOXwMPr9cpVl/apyS6fPV3SaB0+1W/v/M+8lm5rynQPDOufP9ScEWZ6vb6jo0n2pbaHRs6o7+hJSdK23kE11dVYXj8pGnGc9i1Zf5t6Dmr/4EhqOjt7WdtvXpz6t1vQlG00VyWMXHSaMo5nOAAAqHYEOgAAAPCs2qYoYyoeZ25hhr0DeFPPocCCvUIDNz9GXoWlrWcrhz1IiCfMjADAXt5cwYNToGE6BB9JXsIML6/xa6Rc+tRkl8+engo1Orpi2mFbg6bnwLCk3GvKpP+umDDTLSySpL4jI2qqq9HxU2c1+bUpz9yue/fAsGUqO3tZvQZNTveY0/UI46idttZGJUxZ1tDhGQ4AAKodgQ4AAAA889LxWkmjeMI2FU9Y6s4tzLB3AO8fPKGOrlggdVho4GYvYzxhKp4w86rLsEzH57UcfgRQToHGweGTedddvuzHffq3x/QvO/Y7LnDvJn1qsnROYYpTOJje3kanZDPUcyAtINu4y/L6TT2HPN+rbmFRUnJ01OlzCZ2NW0dG2a+7fX/LF9QpGjHyDqaz3WP2kMfevhKmMqbXK/WzKhoxdMfVTbrj6nA8vwEAAEqBQAcAAACeeRkxEZaRDZUoLHVn7wC/pysmabTzt621MTXdU/rrncpZrrVs2lob9WRsyDKaIt/QKSzT8Xkthx8BVFtro34ZG7KMZtl/ZCSwwC79uOnXa2jkbNYF7gvhFH7YQ4/sbTV7iJKc7szLvdrW2qhf9h3VjueOeiqzfe0i+3XPNt1dvtLvsWQd7Owf1vIFdYoY0pKGmY5h1gO7D6WmeuM5DwAAUDoEOgAAAPDMy4iJsIxsqERhqTt7x/XRE2dSQdOaZU1a1TzXsjB7tpChXAFVNGJkdG7nW5f20RoJU2rb0F3y0QheRyn5EUBFI4be0lCbMT1ZrrrzI7hzer1fIwC9hB9e2mr6fgaGRjyFmunn+J2PLFFHVywjEJ0/q0ZzZ0y1rP+T/Hn9zJpUmc+cS+j2jbu0s39YUyZG9L/e3qD2mxc7nv+Zc4mM6ecmvTaVWzbpdSBJa1cszDoyT3IfQeRFWEYkAgAAVBICHQAAMG7RmZQ/LyMmwjKyoRKFpe6SHdf3dMV09MSZ1M+TnbZeQ4ZyBlTF1mV6W2/v7CvbyCmvo5T8Wg+q58CxjJ/lqjt7GPJkbEgdq1ssz9Ncz1uvC9xnC17c9u/XOj7Z2oQkNc+bkbGGkf3zJPl+p/VopNHAMD3UWdU811KGtg3dqbDt5Jm47vrZc5oQiTjub83GXZYRams27nKcji7dzv7hjO3k8e3tK2GaWr9lX+q1hTyrwjIiEQAAoJIQ6AAAgHGLzqRg+NWxPB6Fpe6SHc+SHEfiOE3T5NSRHURA5TWI9bMuwzJyyo1f60F5mZ7Mzl4/TlPc5Xreel3gPtu1KPZ5nm9bzQw45Pn42a5Vx+oWdXTFNHniMU2KRtTWbD3/PYePZ+wrWR/2Y9tfm9y23z+3Lm3QvU/0p0YdpUuf9s1e5njCVMQwirq/ynlf8YUOAABQqQh0AADAuFUJnbSVyK+O5XLx0tEXVGdg2OrOSyji1pEeREDlteM+n7rMd/RItYw6czrvQtZmcRpdY3+euj1vk+XoOTCsVc1zUseMJ8yMUS/ZrkWxz3MvbdWpvlJtfUN3Ucd3cvdjfeo5cEzN82olmTJt6+pIo+fvdO6Xz55uGe1z+ezpkpxHU9mnektyu+x+PKuCvq/c7mu+0AEAACoVgQ4AAAi9oDrPq7WTFsXx0tE3XjoDi52qKtv7i7mngwhivYweSR6rmkadZTvvfDvq21obM4IB+/PU7XmbrRxO4UPEGB01FDEMLWkYuxaFPs/tbTHbmjRO5UyfWs6Pz5Pk/u9+X51On02kpjSzh2URQ3r9lAn6aFoAZz/2rUsbMtbQkTLvH6dRP0lLGmbmfQ75CPq+cruv+UIHAACoVAQ6AAAg9ILqPK/WTtowqcRpbbx09NEZOKaQjuxi7ukggthc19O+9smajbtc23OltPti2rFTEJKcusvpeer2vN3ZP2R57c7+Ia1Z1uQ4lVvS2hULfQnd8mmLblPLFXJ8ex3a17DJJmFKa5bNdx0JF40Yjmvm2O8f+0ie5QvqUgFV+jkE0aaDHpHo1r75QgcAAKhUBDoAACD0guo8D9v0VtWoEkeyeOnoozNwTCEd2cXc00EEsV6vp9f2XCntvph27Da6x4nb8zZhOm87TeWWlC10y7ee82mLblPLFXJ8ex0uX1CXV7lzjYRzYr9/0tfQcQtqnEYnpQc/YQws3do3X+gAAACVikAHAACEHp3nlasSR7J46eijM3BMIR3Z+d7TbmuX+MHr9fTaniul3Tt17tvXrEnvqE+/DgNDJy37KuYcI4bhuJ0s36aeQ9o/eMLymnjCVNuGbsv1KmQEST5tsa21Ub+MDWlH2oiW0fVtRstz92MxPbD7kCRTKxfN0e1XNbmWwd5OktPJeVHo56DT/erl/s02WirMgaXbfc0XOgAAQKUi0AEAAKFH53nlqsQwzktHH52Bxcn3ng56xIvX6+m1PVdKu7efd3tnn2s9p18Hu2LOcUlDrbb1jtVXwjQVT5ip8nUPDFsCnakToxmBgqSC2kg+bTEaMfSWhlpLoCONDifq6Ipp/Zaxulm/ZZ8ihuFaBns7WdIw87XgxMz6Hkm6ekFdyT8H3UZLbeo5FMrpBXlOAwCAakSgAwAAQo9OmcpFGJcpnjA1cvqczsQTau/sC1UHaKk43dNua3SEZcSL1/bc1tqohGm+NlrDUMJUKqDIJgzr7uSqZ/vv58+qUf3MmqLv7bbWRj0ZG0qFNOlr00iZYcLJs3HXcjuVPZt8P196Dhxz3C6kDE7tKWGaksb21VQ3VXNnTNWO546mfnZw+KQ6umIFtZF4wlR7Z5++83i/Tp2Na0n9DN19S4uiEcO1/bW1NjqOlJKk/YMntH/wRMVMxQYAAFDJCHQAAAAQGMK4TB1dMTVEzkkaG1GQb/2EofPfb/ZROJt6DmpV81y1tTaWdMSLW916bc/RiKGIYWj/4Igkaf2WXkUM9+tcyCgkv9tBrnq2/35V81zf1jOzlzs9DEkPPgaGTmaECslyppfNPiWbl3rxUp/Z6shpBEtLfW0B7cl6PEOGDr30quVnfUdGtG5zr56MDaljdUte17yjK6Yv/2xfanvHc0e1ZuMuvbVxpmv7i0YMrWqeYxmhNX/WNElmqp1LlTEVGwAAQCUj0AEAAABKqHtgWA2N1u18Oz2DnoKsHOwjHPYPjqTOsZQjvfyq23xHFRUyCsnvhepz1XO23/sRLLmFSenBR/q0cJI0s2aiEqapj759rGzxhFlQsODl2merg9HRNUqtofP+K2crYUrv+fpjqQDKS1l6DgyrKa3a9x8Zyfpa+0gmO6fr4jSSaM/h4xnXy2kaNadzd5uGr9iRdNUYXAMAABSLQAcAAABwEcQoCGnItp2fsExB5qdsa3Qkz61UI73sdbup52DqmufTFvIdVVTIKCS/F6rPNQIp2++zBSH51Fc+09lJSk3/NTRy1rJezZplTWrb0G15T/fAcCp8sJclWcad/UP6n4PHM95nP9dsdRCNGLrj6ibdcXXmekS59pkUT4yuHZSPTT2Hstar03Vxus8unz094+du06illz/9uqUHaennU+jz0l7+hGkqYhipa3jr0gbd+0Q/gQ8AABhXCHQAAAAAF7m+tZ/eIZwwpYhhaElD9s7FttZG7ew5rjPxhNauWFjQaJNSTkFWKvaO+qRs5xbUt/czO7ZHUqMg8hm9k8+oonjCVMI0NX9WjSRDKxfN8dQu3Baqdwsx/GYPlnb2jwaWm3oOpqbjylVf+Uxnt2ZZk7oHhi3tJD0ocbo/sl07txEmxdxXTiNhcu2zoyum7fuO6Mb5dZ6Ps3/wRNZROvYy3NMV00ff3qDPvnuB7n1ibA2d9psXW9arGhga8TyNWvp1S05zl20tpHzZy9/RFdPQyNlUWdLXXfJjpCIjggAAQCUg0AEAAABc5BoN49QhvK03e+diNGKoZvIE1Uha89bCOh/9moLMrw5MP/aT7Bh2CiGcBDXtnNPi78lrns/IqHzWj+roimn9lrF1TSKGPI0Ichsd4RZi+M0eoCRMFTQ65e7HYqkpy1YumqPbr2pybEdOI1mSQUm2cGzNxl2OZckWvMyfNU1trY0Z1+DWpQ3q6IrpO4+PBSJ339KiSRMirnUyf1ZNak2o9PNI3/fOfmtZpk6K6uSZeEbZZtZMTAUb6ediZy/D0RNntH7LPq1dsVA9n39XxuuzTWuXLlebd1sLKV/28qefszQ6VZxfx5KqcypLAABQfQh0AAAAqhTfNvZHrtEw2TqEg5wGLZ+wwI1fHZi5pkbKp+15Pbegpp1zWvw928L3fo2MynYudz8W0/ot6fWq1JRedksaZmpJQ612DQwrYUo7+4d1YPik436T/HpG2APGp2JDjq9zm4JrNNQaq/PRgMtQxFBG+ZIjWZKWL6jTrUsb1N7ZlxHGJcOxbNcu2yinVc1zFI0Y+pcdfZZr8MvYkHakHXvHc0e14huP6ZFPLbOc161LG/RkbEh7Dh/X9CkTdeGMqY7nnH7fLF9gHZnzloZay3nOnzVNq5rnKGGalgAwWztMXpd7umI6euJM6ufp7cCpDeQKCt34eY+kl2Ng6KTlukqjU8XlU7ZcqnEqSwAAUH0IdAAAAKoU3zb2R67RMNk6hCthGjS/OjDt+3lg9yHPU20VKshp59wWvnf6ebHBSLZzGR2tMuaB3YcsgY79Hl+7YqGWNMz0PIWY/f2beg5pVfOcvMtvD+GezBLouE3BlZymLd1oOzqRKt/ooBxT3/r5/ozj3/tEv+N5J/e7s39YyxfUKWKMhl/J0TcJ01RT3VQdGzkrwzA0Y+okrWyekwqIvvXz5y3763ruSMYx+o6M6D1ff8xSd/c+0Z8KG46eOKO+IyP6+WvbyfO33zcRQ1q7YqEmTzymSdGI2m9+s2WNmOSaMbsGjmWci5PkdZGsI6bSg7VsnxPp06h5GTE3NvXkaD0bMmXKSI06KmbUntOooeUL6vTPH2rWx7/Xoz2Hj+vy2dN169KGvPZvV41TWQIAgOpDoAMAAJBFpY9w4dvG/sg1YiTZwem0hk6hStX2/OrAzAy1/Jt2KcleJ7e8rT41AsKPztxsx0mv+2xtodjwNHtoaNpead12usft5s+qUf3MGsfOePvr9w+eSJ1HMdcrYmRvq9naQsJ+qpLs55se8KRrqa/NOlJuYOiktvWOhTBrVyxMHb+9s88y0kWShkbOKGIoa0AUdyxnZt15Gblnv2+WNMzUmmVNeuaZZyRJkyZELO3NHmqkn4ubttZGy3oz6cFars+JaMSwtM/k/uzPI/vUk8sX1KWO5zYFpVdO90j6SK3t+47o3if6fT8GAABA2BDoAAAAZFHpI1z4tnFp+DX9WbpStT2/OjDt+0mYskyf5Ufbs9eJvYO62M7cbMeRctd9seFptja0ctEcS+CwctEcy++z3ePpP1u5aE5q+jvJ2hmfbXRZsQHckobaVCe+Xba2YA+BZtZMkmm6B1rS2Fo3khzPpe/IiGU7/dzcQpdcooZzuLOp52BqqjIn6eef7/1nL9emnkOewl63tW28fE54uSfsZfNjfRuncDV9H35/aSGIZzkAAIDfCHQAAMC4ks/Ih0of4cK3jStXqdqeXx2Y9v3EE2bG2ifFCqLD2MtxvOy30PDU7Xk0GggYmj9rmiRTKxfN0e1XWcvhdo+nh2vZOuOTr9/UczA1RV4+5c92HsnRUt0Dw2qeVyvJVM+BY65twR4CDY2c0dDI6Lov2daOkcbWukkfKfc/B1/S0MhZx+Okn1uu6RLTfzcxauhsWoLz2fcsVMTIrLv9gyOp7ca6GtXXTpEpw3Hknpf7L71u7SHR/sETes/XH9P7r5yt7oFh/fq10WrtNy/WpAmRjHNyaqPpbah53gwlTKltQ7elPdrviXu6Ypb3dnTFNDBkDc7s69vEE2bGfnPJFSTxpQUAADAeEegAAIBxJZ9v31d6ZxHfNq5ctL1M9jrxe0H0bMfxst9Cw1O351FHV8wyykkyHMMfp3pO/1nbhm7L79IDquT7k9NXeS2/PcBJD1rs67B4lV6HA0MjlpCkfubU1HouUnJtIUMrF81JvS99vZj0KdbSzayZqIQ5tn7MrUsbUtP2TZ8yUfNqp+otjdY1aZKBTTLMmV9Xo7m1U9Xdf1SmDF1YW6O5M6bq4PBJ7beNBoodGdEHFs8t6j6wT2U2s2ZSKuiSRkOdu342FnJt33dEazbuUsfqlqwhW/o1Tm9D/7KjL9Xmtu4d1C9jQ/rO6paMe+LoiTOWMqX/Oxm+Jdf7SQZRyXs1nxGHucJVvrQAAADGIwKdAnzkIx/Rhg0bPL/+W9/6lj7xiU/4dvyf/OQn+t73vqddu3bpxRdfVE1NjebNm6frrrtOt99+u974xjf6diwAAKpNPt++p7MI5ULby2Svk/QOYz/rqJC6LzTAcnse2X+Xvn5MPp3iXgKqfMtvD6Lmz6rJeh5epZfBvlZMsszRiKHbr2pKTSHnNMjDbbq0oZGzWr9lnyLG6LHufaI/FTQcPXFGN9jCl7bWRm3qOWTdiWFYgsSk0ZFUzuVxCsy8rollP58ZNRMtgY6TPYePZw0L3a7LaFA2Zsdra+0k74F7umI6emLs2E51nQzf0o/nFiq6ydV2+dICAAAYjwh0KsixY8d044036mc/+5nl56+++qqGhoa0e/duff3rX9fdd9+tG264oUylBAAg3PL59j2dRchXPlP6uaHtZXKqkyDqqJR17/Y8ypwOzDrdltdO8SDCwcyOfGsbL2S0VPq90zyvVndeu0C7BoaVMKWd/WPr/+Q7DVe28q9Z1uQYqKWHL/GEmQrRxjivjaOM9X7GypOtzPGEqbsf67OMOLr9qsaM96efTyJh6v+8Z4H+8+lDllFM6S6fPb3AaRszzyH5vuR7nYK2XJ+phY44JNgGAADIRKBTpPb2ds2aNcv1NW9605uKPs6rr76qP/qjP9IvfvELSVJdXZ3a2tp02WWX6eWXX9aPf/xjPfLIIzp+/LhuuukmTZkyRdddd13RxwUAoNrQQYQg5TOlnxd+BUQojXyvl9vzyP47+/ox9k7xbMfOFlAV07bsHfRzZkzRykWzc66T48Z+76xdsVCL62empgDb1juohCn1HPA2DdfO/mHFEwkdOnZKx06etYxqSdadU9Bgn+Is3fxZNVq5aE7GOj6pOmieqwd6DurYyTN6/dRJ+rPmuWprbdSajbsyytzW2qi2Dd2W0T7Jc12admnbWhv1o10H1ffadG6xoyfVPTCsn/3vZanrd+WFMzLW0Ln3if68QxSnc0t/n5c1m5yufaGfuwTbAAAAmQh0ivTud79b9fX1gR/nrrvuSoU5F198sbZv326ZWu1jH/uYvvrVr+qzn/2s4vG42tra9Pzzz+v8888PvGwAAFQSOogQpMK+FZ+d3wFREkFRMPK9Xm7PI/vv4gkzNdWYU6d4vscupm21tTbqydhQKozYse+I3tY4Ux2rWzy934nTvTMwdNLyswd2H9Kq5jl5T8Pl1N6T55E8VvLn9vAl3arXAhpJ+tbP9+vkmXjacSO64+om3XF1Zh02z7MGR83zRoMjp6nbHth9SEuvmWE5n+Onzlpes+fw8ZyfZYWEKLdf1STJeG3EkGlZoyjbMyO5rpEbPncBAAD8Q6BTAV5++WWtX78+tb1x40bHdXI+85nPaNu2bdq8ebMGBwf1T//0T/rCF75QyqICAACMa4VOLZSN3wFRUlBBUbmEJaAK6npJuTvF8z12MWVNjvwp9P1O16t53gxb6DFDA0P2KcVMX9c4cvq5/R5uqqtR/cwaLWkYa1cRw7CEOZK0pMHtXreHHqa6B455em08YWr6lImWtWsunz0965Hsddt+82JJ8nR/RCOGYyh15lxCK77xWGqUUPozo9qeJQAAAGFHoFMBfvrTn2pkZPSP57e//e1asmRJ1td+5jOf0ebNmyVJ3//+9wl0AAAASsjvKf3sncvxhKl4wiw6rAgyeCiHsHQq+x3oFXNse1uxr9cyZ8aUjPcXczwv70+GDZt6DqXWphnbh71NGxlTgK1cNCfw0R720Ud9R0Z0w+K5qTVv2jv7dE9XzPKe+bOmud7rPQeOZWxnW+tn5aI5ksZGJnV0xVJBiiTV1kzUP3+oWZJzMOZ0L0gq6v5Ys3GXpQyS+zpEbtP7Nc+rlWRapudLf56FJZwFAAAIKwKdCvDwww+n/v2+973P9bXLli1TTU2NRkZG9Nxzz+n555/XRRddFHQRAQCoSHQcwW9+dzbbO5e37zuijq5Y0fsvZ/Dgp+Q9bO9gL1dAVco1uuzPr1uXNri2lY6umCUc2T94QssX1CkaMXKW1elZme+5xhNmxpoxSfZQQBpdK6f95sWu08wFwWn00c7+0fJt6jmo/YP2UUPSquY5GaFErvCsrbUxY3/z62okScdOntGkaETxhJlRN8MjZ/XvvxxwHB2zqeeg7MGYU90mz8frZ9+ew8czfua2DpG9vSTMsfWB0l/rFC6FJZwFAAAIKwKdIt1+++167rnn9OKLL2rixImqq6tTc3Oz3ve+9+mmm27SpEmTij7GM888k/q32+gcSZowYYKuvPJKPf7446n3EugAQGmld2R89NKoaibzcRtWdByVBsFZ4Yqd2iqbbJ3xlXatsi1gX66AqpRrhTg9v9zailPHfjRieFr3JtuzMp9zvfuxPscwRxqdXi1iGBnBQLnWXrGHFAnTdGxnF0ybpMtnT9fO/iFJSt0vXsKzaMTQqua5lv3OrZ2q9Vv26e731en02YTaNnTr4LGTGcdNHs8eZDqFTcl7Idv5ePnsu3z2dMu1a6qrcV2HyN5e5s+alnXf9udZtY0eBAAA8Bs9TEV69NFHU/8+ffq0Tpw4of7+ft1///36/Oc/rw0bNugd73hHwfs3TVPPP/98aruhoSHnexoaGlKBTm9v5n94AACCld6RcUNjXZlLAzd0HJUGwVlxghhNk62jvNKulf0evmDaJN2WNnqkmjk9v9zaitMUX17bktOxkh33XsO/B3oOZf3dpl0H9dD/7yrLeRR7DdPDySsvnKHugWH9+vBxXT57utpvXqxJEyKOr02OdkovSzJAsUsPOrb1jv6/0zRk0ugol2TbTNaTPQxJjpxJyhaAJUw5BkxJ82eNrvtjr8ds55Prs6/95sVas3GX9qTVX/IcnJ4lmedvX0NojL0NVsvoQQAAgKAYpmlm/+sKjj7ykY/o/vvv1zve8Q4tWbJE9fX1mjx5so4cOaJf/vKXeuCBB3Tq1ClJUiQS0f3336/3v//9BR3rlVde0fnnn5/afvnll/W6173O9T2f/vSn9U//9E+SpM9+9rO66667PB1rzpw5WX/3wgsv6IILLrAEWBhz9uxZSdLEiRPLXBIAYXDs5BmdPpuQJL3+vIgMQ5rsw4hNJyOnz+lMPKFJ0QgjgQowcvqcXnn1XGr7dedNoB4DkH5PSNLkiRHNmBrMPVEp8v3boVT3uh/XqpTPpWq5hwups2zn7ravkdPndOpsXJI0ZWJUUydP0EkPx3Y6lqS86n7w5dNKuPyn5+QJEc2o8e+5YC+z27FytSP77ydEDU2ZGNWZeMJyv0yIGrpg2mTXY7vVU/J9rz9vNGx66VXrvqMRQ5OikYzjGoaUXrW5rkXQ90229pJsZ+n/djouf9sAzuh3AOCEZ0Plede73qWJEyfq0KHsX3hyw19HBfjEJz6hb3/725o2LXPo+Mc//nF9+ctf1o033qjOzk4lEgl9+MMf1nPPPafZs2fnfaxXXnnFsj1lypQsr3R+zcsvv5z3MQEAxZkUjVg7WjIWenaWbwdGeodJ8nh0fOQnWV90HAXLfk8kO/TgXc3kCaopwXGKvValfi7Z72FpbP2RSrmfC62zbM8vt7ZSMzl7UOF2bKdjHTt5xvKaM/FEjjbq/j3Cswl/v2d4Jp7I+jv7seyvTZ5L+ufy686bkPlZcfqc5X45Fzd19MRpTZkY1bTzJujVs3HFE6YlbHGrp5rJE1KBm5NJ0YimTp6gMyPWujckRaKjf21MmZg51atTkJcsS/r5+BWkJN+bfi72dunWVkr1vAMAAKhElfFfOSGzePFi19+/4Q1v0EMPPaTm5mbt27dPJ0+e1Pr16/XNb36zRCUsjFsqmBy9c8UVV5SqOBUluc4R9QNAsq+hE9F5kybkfD60d/ZZpk9Zu2JhzmmO2jZ0a+veselYrrlkljpWLyqu8EAAMtZlaQ73uiylENa/HYq9VuV8LhXyHA2DctaZ27FzrafkWN9vda7v0dcetPxsxtQJOnZybBTH8gV1uvdW/+4He/nS2Y/ldC6JV6X1W8Z+due1C3XH1dbziydMvefrnY5r1yTbXz71lCxLQ3x0SrTbHzqimTWTNJQW4CxfUJd1KrbU/lvG9u9UD2tXLLRNmTddCdN6vun3T7It7OwfUsKUIoahJQ3u0+zlez9W2vpdQDmE9W8HAOXFs6HyFDuaikAnIDU1Nfqbv/kb3XzzzZKkBx98sKBAxz692qlTp3JOuZac7k2SZbo2AEBppM8nn/zjKpdC1nJhnnn4oRSdaOVa2Bz5K/ZalfO5lOs5GtYO43LUWbIuBoZOZpQlKdd6Svb1X9zWvLFfm/mzavRfn2jVx7/XY1mXxamMhV6v9PKlr6Fz2ezpWlxfq7YN3an9Jl+7s39YCdPUU7GjerL/mGV/D+w+lBHoSNLcGVMdA51k+/NaT2OhybAWLhgdbbZ8QZ32HD5ueZ19225Tz0FLXTmt55Ncqyf9+s6vm5bxmuT1Tm8LSdt63dfYsh83uW6P/Xomz3vT/8fe/0e3cZ533vB3BgQlAqopAAI3a5EiQTKmnp44jQWSritTptLsRurT3a2rKLubHsVRTEur7Hbb3XNarf94n+553931Ub1Jk02bLGWqiqIn59kTRsl53vNuLTeWJZFWLFGE3MZ51qJMAqRIJSlBgJJLgBZJzLx/DGcw9z33DAYgSIHk9TnH5wjAYOb+OaCv73yvKzaF0ek5oz1O5yYIgiAIgtjMkKCzinz60582/j0xMYFsNgufz1fUObZt24aqqiosLWlPr83MzBQUdGZmZox/b9++vajrEQRBEI+GUgJ6xQTTCMKOQkFbOyo1OE48Wh7lfanQfbTUtb7alDpmK9mDfIC+tc6Pw9EG5tqFBDI34p+dcHQ42oCaag/OHu101cZS5suufWbniPm8+nF2rp7ZzAIjAnlkCX2DcVu3TEdT0OJsuZFI43o8BVkCOiMhZs7M/f3nLWF4PJLw3E/urHV06IxOZ9A3GDf6w+8LAFBU1TK/fAo9c70jkSikv28nmua4tHaKCuG4i8Qi0bkJgiAIgiAIDRJ0VpFwOMy8vn//ftGCjiRJeOKJJ/C//tf/AgAkEglEIhHH7yQSCePfu3fvLup6BEEQxKOhlIDeRnc9kGCwNpTiDgMqNzhOPFoe5X2p0H201LVeLMXeu0ods2L2IN8m3aGh0xTyW75bSCBz0083wpHdOXkRqBzzlVNU9MfY1G/m89oJFwCQyizgzfenmbG2O761zo+ermacHogzaczMXLqdNM4junYuxwoi1R4Jv9ESQrQxgMnZLFQVUFUV97ML+GhJRXYhX6/mtcE4ABjuo+8P38VYMj+eQu95mwABAABJREFUsmSd3+2+Kia1myzZC3I6TqIpoDmMPLKEjqYgbsRTzLFDiZTjGEYbA8L3zdDfCQRBEARBbEZI0FlFzE4ZAAgECv9RKuKTn/ykIegMDQ0xzh+epaUlvPvuu8x3CYIgiMpno4szpUCCwdpQarqntQqOE4RbCt1H1yq12Vrdu4rZg3yb9rexD56Zx8LsKtnfFmbqpTidE7D20xqslzCUSC27VKx1WE4PjOHUxRFhH0TzVWxAv28wbkmPZj6vyM0CACG/F6nMItOv48+12B5/aE89PLKEC7fsa5Saz+N0bZ2FnIord2Zw5U7+/zHt6vTMzC0Yr48/14LPt+9iPtfdQXobOpqCUFSVGXtFhW19IEATawqJph5ZQt8LHQCA65ygoxt47PtdWJihvxMIgiAIgtiMkKCzily+fNn4d0NDA2pqako6z2/91m/hf/yP/wEA+Ku/+iv8h//wH2yPvXr1KjIZ7Y/uj3/84/j4xz9e0jUJgiAI4lFDgkH5EQU/S033RDWciPWGea1HGwNQVFhSaJWDldy7ihEo7Pag6Bx8m+6mszh5YDdiE9Z9z7ss7IrZ8+f8xqUPoKjAsX35NvNtHJ2eM+qkANY6LLwAEvJ78dSugO29qdiAPt/mkL/acCvp90NFVXEhNoXZ7CIAFdt91WgI+nDFlOZsPJXFty6P4kYiBV+1B6qiYH4p76jRxQpVVWzbArD3TfP63FIl4eGS83eBvMtF/+5rg3HMzOVdNk51fHQBtKerGX2DcQyPzy4LeFr7RWndWuu2oSnks12bTr8LssQeq7+2a3tsIg3Aed+s5d8J5AYiCIIgCKJSIEFnlchms/hP/+k/Ga//yT/5JyWf65/+038Kv9+PTCaDwcFBDA0NobNTnG/6q1/9qvHvL3zhCyVfkyAIgiAeNSQYlB+74Gcp7jCq4bSxEQUvARQV0FztAOhKUpvZ1VEpx/VWcu8qRqDQ52QokYaiqst1WlTcSKQN8UE/B9+msWQGsgTDPWHGbZCcP2d2IYdTF29DlvJtNt8nxlMZoduDPT87ntt91ehoChpt4se82IA+3+ZUZgGXbk8zwpIsSRhNZkzHLGIsmcH+tjAmZ7MYnc5gdHoOf/qG2EkEAD96dwr/en8rGoJ+JtVZ0O/Fr9VvZ2ro6JjX509/+lNkHi6htW4bI4Dx6MKR/l2ArQGkrz0n95ooLZ4dh6P1juPr9LvQGQka46y/dtN2J9by7wRyAxEEQRAEUSmQoFMk586dQ11dHT772c9ClmXhMX/3d3+HL3zhC3j//fcBAFu3bsXJkyeFx3Z3d+Pq1asAgD/5kz/Bf/yP/9FyTG1tLf7oj/7I+OyLX/wi3nrrLTz++OPMcV/96lfx+uuvAwB27NiBf/fv/l0pXSQIgiCIisAuMERPyZZOOZ9mXo9pAtdq7WyENSoKXgLioubFnKPY9eI0lis5fyl7we31ViJ28u0y10Lh15AoEK7XZeHP2XukHf2xSUZQcSvU2AXJe7qaLecEgP7YlNFeOxGNv57OoT31TM2ZhqDPcczdtNW8hqKNQZw80IbYxKxFYNLHw66mi0eW0BTy26YgM/Pz+x+h9+qYxZUS9G0xasrwc2pu54u/6oF/SxWaQj5HQYc/P+tC09KoFXKh8f0V9a+1bhsOR+sLrmWn34VC+8Ltb/7RvRGcvZbAUCKNnKKgNewHJAmH9hRu30og1zBBEARBEJUCCTpF8u677+Ib3/gGPvaxj+Ef/+N/jE9+8pP42Mc+hi1btmBmZgbvvPMOfvCDHyCb1Z7EkmUZ586dw65du1Z03T/+4z/GxYsXcf36dYyMjOBTn/oUXnrpJXziE5/Ahx9+iB/96Ed44403AAAejwevvfYaamtrV9xfgiAIgnhU2AWG6CnZ0tksric7EWCt1s5GWKOi4KXomGLSW5USAHUay5Wcv5S9sBap1KKNAaZdfC0UN+3i6WgKwiNLOBxtcOWAcCtIic4JaGnV+gbjlvb2dDXjejzFpPLi67Ac29cMWcpfW0+HZu6r+bxu2sqvoZcP7kbfCx0WgSnaGETv1TGMp8SCTbQxAFmSHOvc6GQXcnjl9dsI+b3M+6PJOYwm55i1rK8Nszj2+eYwFpYUjKeylnOb0V0uOlYBbcToNwAjvZp5vOxq2BRKr8ZTaI0XegjA7W8+v4Z0ZAmrKpxvlt9PgiAIgiAqHxJ0SuSXv/wlvvvd7zoe09DQgDNnzuAf/aN/tOLr1dTU4H/+z/+Jf/Ev/gV+/OMfI5lM4r/8l/9iOe6xxx7Df//v/x2/8zu/s+JrEgRBEEQlQk/Jls5mSZNmJwKs1dpZL2vUKQBrF7wsJqBZjgCo01iu5Pyl7AW31zs9EDdcJm++Pw1F1YLN7kQ+cUDaaQ3ZF5XXatAoqoqcohYl1Jhrqxw/P4xoYwCAxNTc8ciSUXPmm2+NIruQc2yvR5bQ90JH0UF/c5oufszdOAV5UWgoIa4ro6iqIYBoY1eNVCZf00VPb9Zatw2Ait/51E4Mj6cxND7L9N1MKrPIfEfkCOJTnuk8XFKE7pyWsB9NIT86I87r1k6U5dehfo7+2BRzvULp1XhWS8jm+/HevQe2x63mfXaz/H4SBEEQBFH5kKBTJH/8x3+Mzs5O3LhxA7du3cIvf/lLzMzMYG5uDtu2bcPHPvYxtLe347d/+7fxu7/7u/B6vYVP6pJgMIi//uu/xo9+9CN873vfw82bN/F3f/d38Pv9aGxsxG//9m/j+PHj2LlzZ9muSRAEYcdGSClErE/oKdnSWY9p0krBTgRYq7WzXtaoUwDWKXjpNqBZjgCo01iu5Pyl7AW317twa8ryujHoY97TRQUerRC8FX4NidKIXbh1zyIApDKLOHVxBLKU76/bPtul3dNdEn0vdMAjSzjR3QpZklzXP1FUFeMzGbx79z5OD8SxvaYKDUEfPLJk1JXR/5744jNN+P7wJCbTWTQEffjiM02u2s5fj38t+hvm+Plh5jgumxl+9O4UI8h4PTK+8+Wn0XPuZkHXju50EY1RIYcVT2SHX1j7iIffO+OprMV9dHM8bazjxqAPDYEapr5PTlFxeiC+vKZVHNpTjxefbcbZawncHE/jqV0B3BxP42f3HoAb5rIJLHw/ntxZK3TorPZ9drP8fhIEQRAEUfmQoFMkjz/+OL7whS/gC1/4QlnOd+XKlaK/8/zzz+P5558vy/UJgiBKZSOkFCLWJ/SULFEIOxFgrdbOelmjTu4Xu+Cl6D07gd8pAOr2oQCnsVzrAKv766mW1yJRQQS/du1ql4jSiDnVWxEF13lRCFARm5g1xtlJaLg8kmTSqrld832DcZy6OMK8l8osYGxGSy2m1wDS3UGnB+KGS2YsmcFXvhfD2aOdtu0SwS8rWRL/DVNYOGBPZCcUizCPCT9G/PdD/mpsqZLxcEkRnivaGEDv1THXe0dP5SZaGx1NQYtDqLXOj85ICIA+X/nPTl0cwVAibYyLU78L1TNy+zAQP27mGjqKqjICFEEQBEEQxGaABB2CIAiiJNZLSiFi40FPybpnszrp7AKna7V21ssaLZeTqBSB3+131sNY8vvs+ad24tU37hif1wd8kDiRR9+GooLvAAruWdFvsJOwIJpbJwdOf2wKDYEax367EQALtdvpGFEaMrt0WyL0sZ1IzzPvd0ZCwvHrPdJu/NssHORTsoERN3ih+LXBOGbmFsDD1wni4WsL6QLWr2ytwmf+tzqL2Kao7tL36XNyczzNOIt0utvCUFTgzNtx5v3R6YxxftF8Oc2BRwIi4W04tMcqQgKl3StEa8v8Wp/n4+eHN9XvHEEQBEEQmxcSdAiCIIiSWC8phQhiM1OJTrqNIjJthH6Uy0lUisC/kR4K4PfZyQO7sb8tbATor4wksb8tzHzH7IAQ7VHzWIjWmug3WKtno6V4U1UFDUE/PJJkW2vFSVwZnZ7D6PQc9reF4ZElRBsDuJFI44rJsVLK775bN4td257cWVvwGvp48TVhWuv8OBxtwNG9EVyPpyzXLCQcLCwpGEqk8N69B3hyZy2O7o0wc2OXCmxyVnMf8XPdH5sy3Ff8vePhkoLqKtmSWi2nqPjs168y790cT+Po3giOnx822tZ7pB3VVbLRN9GYT83O48qIVTQzn1f0Xbt+AkBO1daOLMHok3mMxlNZyzVWuu8r8XeOIAiCIAhiNSFBhyAIgiiJ9ZJSiCA2Km4EhUoMmq9F8G2jXGO1KZf7pRSBfyM9FMDvs9hE2rIXZUnCywd3W34z3exR0VoT/QZ7ZAmyBEPEGEtm0Vq3DZ0RbWz5e0a0sbC4IkuSIbA8HQnh6UiQSctWLJropOJCbAqz2UUAENbQMfcVAHzVHjwdCRouGh5z33KKKhQcmkJ+HH+uBb1Xx5jPCzlodM5eSxjfuzySxNlrCQCsk6gl7McvHnyE7ELOeG90OmO0zczo9Bxeef02FFUsuizk8inXDJFqeBKjSdZt09EUxPHzw0zbjp8fNlLT8anX8rCuMa9HwmIu/x4vEvI1dKzny2Nex3xKN77tK6USf+cIgiAIgiBWExJ0CIIgiJJYD2lwCGIteFRODTeCQiUGzdci+LZa11jtJ83XK6UI/JX2UMBK9rHdPjO/1xkJCn8z3exRfj0PJVLG+3xb7UQDHdZJ1GaITOMzWYwmrTVWxlMZXLo9bXzn5YO7La6RYvDIEk50t+JEd6vtMTlFhaJq9YN0EeHYvrw4IOq3k2hgPu/CkoL+2KSlTW7mWnRf4RlL2gscdk6ZH8Qm8df/7jkm7RoAVHtkk9tILJ6E/NXo6WrGa4Ns2jQ+LZqiAqoKhPxeBHzVOBRtAKAy9YwWc6rhyDKP74nuFpzottbS6ulqxme/PmBbm8fcdzOtdX40hfxl2/eV+DtHEARBEASxmpCgQxAEQRAEsQIelVPDjWhRaUFzYG2Cb6t1jdV+0ny9UorAX2kPBdjtYzdCj57Cy5yKyyywOO09N3uUX89ONVTsRAOR+HDh1j00hXyGW8dcH6a1zo+GgM/idOmPTRpjIKr/Y645U6q43TcYZ9oCSDg9MIa+wThSmUWm3z1dzTg9MIZvvjVa8Ly6c4UXRvS9W2iu3Qh3drx7dxZP7dqOkwd242s/HmGcML+4P4/Pfn0AUFV0P7EDHlnGr2z1wL+lqqBQFfB74ZElSxo0c2o6fjxTmUXIEtDT1YILt+4xgszk7Dze+MN9xvz2Xh2zHQ+PLOFwtJ5pn57WzryO+XFrCPjQe6TdsjZKFVUr8XeOIAiCIAhiNSFBhyAIgiAIYgU8qnQvbkSLSguaA2sTfFuta6z2k+ablXK73Eo5n90+diPYilJx6fuuUC0cN3uUX89DCft7jl16LZH4oNfJefP9afzRZ5+wpIQ7fn7Y0hY9fZhobMwOk1LEbX18eLfJhVtTQheIPmdml4mOV5awqKiW93nnSkvYD0UFes7dZFK1aTVuJg1xwiNLxtgOJdJQVBVDiRTam4I4eWA3YhPWVG8tYb/h2EllFvHqG3fw8sHdaAz6mLRp2UXF6N9oMoOXD+6Gf0uG6aMdh/bUI6eoaG8K4KdT9zG/qKCTS00nOoe+ZnhBZnR6znZ+vz88iaaQz0iLZx4Tp73W09XMrI3LI0njGmZKfTiiEn/nCIIgCIIgVhMSdAiCIIhNw0YoYk5UHo8q3ct6fSp5LYJvdtdY6T2An+vD0QYKIpaBYtwx+vFOcygSGvpe6HCca7t97EawtTuGb7+iwnBKmB0mhfojWs96GjRzW83H8uc9ujeCM2/H0VrnByBhNvPQcLsAwI/evYc3/303cw0nt8/x51qM1G86P526X3CsnLB3o1iFGb19vLilIxJzAFicLA2BGs4NlGd0OrNc40bFie5WY2zN83jpdhInD2hp6Pj5vhFPWVKw3RxP41C0wfaa+jHPBLYYfTTPQUvYj11BH2RJQmckuOxQiuPVN+4YxzwdCaG6SmbGiZ9Hfc30dDVbxD993vh1PZbMYCyZwaXb2vgdf67FIuro5+SdPKL6bm730Uqhv/0IgiAIgthokKBDEARBbBo2QhFzovJ4VMJKOYWRjRTwcurLSu8B61VEq3TcumOux1PojAQNR4bdHPLns3MEmBGlTQPcCbZ2x/Dt12rCWNtZ7Jp0sw75+0Pv1THGyRLyV3PfsO53O7dPTlHRc+6mpY7Udl81IxK5TWWmw8/bjm3VePHZZtyIz1jSpIX8XiiqipwqFm54Wnb48PmOXUZauKFECooK3LARhMxcuDXF1Py5cGuK+fwHyzV5tPe1mj+6K4VHF9eGEtpaU1QVadOY6ccAGWQeLmEokcb+tjBkCYwzhm+ftb35NdTT1QxFtbZPnxd+7vV5sxP0AFZsEe1Tvg7PSvbRSqG//QiCIAiC2GiQoEMQBEFsGh5VaixiY7MR0r1spICXU19Weg/YCHNdibh1x1weSWJylhURRHMoCkTzx4ncM6K0aW7EE7tjrKmuWPGhoylY0posZR3y1wn4vEhlFozXh/bU217H7Pbh04rx6OnHeEeVnWPKPA85zlXz0vL3r9yZMd7zeT3ILuaQyizi1MURtIT9rvr/iw8fQlHzfQLgWJuGhRef2Hb+4v5HjOPm1MURyJIEXrNqCfuMsbQbQ/2Yodi7+PuPlgw3zMsHd9uu39nMQ2H7+DWu18bR6b065lj/hk+VZsYstoj2KcDef1eyj1YK/e1HEARBEMRGgwQdgiAIYtPwqFJjVQIbyYFBlJ+1CnitxTp06sta3QNovxWHXSBX7BAQuwnMYx5tDKK7LYwrpkA0P9du3DN6SqlC4ondMXz7D+2phyxJln66WZPlThf4/J6dGB6fNRxJLz4bcdW/nnM3bY8bS2YgS0DfCx3M+06OKT7Nmtcj4bGtXnz52Yiwjo9viybo6EjcEHS3hfF0JIg/+/EdLOTywkt2IYdTF29DliBMJ+YEL3Y9/1Q9Xn0j73Yyt8fc585IyBBkAODz7bvgkSXHa0uShL7BOHZvUZj3+2NThrvo5ngaS4rKrG9RewsJ9dZ2aG1TVBWAhNiE1ofOSBDD47NQVJVxCuk4OXn6Y5NsXaYS9lG51/5m+tuPIAiCIIiNCQk6BEEQxKZhM6dL2kgODKL8rFXAay3WoV1fcooKRcVy4D6fdkj/rJwCDO234rAL5IocApooYr2P82N+8kAbnmkO2d7v3bhnRPBrxRxk59eOXcF4cz/d/i7x/dOC65LrNctfx86RVAh+f4X8XibNWrGOKX4eFnMqUpkF/PDWFF5aTgtmhq+BIxLJPLKEoURa6Czpj00J3UAhfzXjWNLZ3xbGsX3snLi5NehtUVRVS3WmAu/EU0aqNx5ftQfZhZxRt+fcP/sHzOej03M4fn7Y1tnjq/bAV+1ZFufEDjF+bvh5GZ2ew+j0HPPem+9P4+WDu3HmS6xIZ8a8tngH1+h0BqPTGdtUbG6gVJkEQRAEQRAsJOgQBEEQm4bNnC6JUo4QTqw04FVqjYzVWId2fekbjDNpkWQpX6i73AKMXT/N4/Tir3rg37L+/hRfS/eRR5bQ90KH8Hp8+qn+5TomOrGJWfS90GE7j27dMzyi9GGiFFN6+80py46fH7aMmdvfJX5NXbg1ZdSVMV/Xbn746/BOG7d70SoMqUxtHpEQJhLmCtVpGUtmLALG/rYweo+0CwU0c9+HEprLpMYrY36RdbroooV+Pl1gOLo3gt/6b4PGZzqTs1kc++4w404ZFjhsmnf48MsPH6LG6zHcRR5ZgixJxjyNJvN1gFrCfoyZXj++fStbJ0gCqjzsvnrv3gPLdXWyCzlkF3KMOBdtZMc22sjOjXkux1MZS50inUJrwyNLxrnMNX8m0vPMeNrtk0JQqkyCIAiCIAiW9fd/kQRBEARBFA2lHHHPZkyXtdKAl9U9AIuLwm1h7JVi1xenoGC5hSa7fprH6fPN4ZLPXwzr3X3kZm32DcYtwehCa8uNe0YEv1b4ILto7ZRjzKzCBzuH+nXdXqvUvciLVMPjs+huC2MynYUkAYqqIqeozBrThbnTA2OaWwUSFFVbm/o8nB6IWxwy/Nh6ZAnVVbLteuDTt7npizk93OFoveX7usNE59LtJPa3sXvXLM5kF3KokvNisV16taaQD5+L1hvjUR+oYa6TU1R4uHxyvDvJjvwaZK1Aiqqg9+qYJQXa8edaLPV0zCwpKnrO3XS8f/Bj//LB3eiM2NcpKuYeS3+/EARBEARBsJCgQxAEQRCbAEo54h5Kl1U8YveA9mR2sYWxVwunoGC5A4Z2/eTHaSGnWL5bbtbKfVQM5RaZ+DaF/NU4ute+JgxQuojJrxU+yC5aO3z7+mOTRfdZlDLN7DjLKZqQ4nZ+7Nao27mxE09OXRyBLFmFMd6tYq5nc/y5FhzdG8Gvv/Im0qb0bdmHbG2ajqagY/uKqYujn0/HSMkY3obZ7AK2+6ogQWJcNTqyJOHlg7uN9Gm88GSuGxNtDAgdSJ2READk3TvTc9jfFsbkbBaj0xks5VQsQUVL2I8H84t4cmctvvV7UXz3nXGjTpSiKvjRu/cwm1lkxDC9X7GJWeaaP3r3ntDVBYhdVDpXXDhrROuu90i78W8+FVsx91j6+4UgCIIgCIKFBB2CIAiC2ARQyhH3UHq64rG6B9gnw4spML9aOAUFyx0wtOsnP07VHrnocxcrhqyV+6iY9pVbZOLblMosMDVhyikg8WtFVEOnUPtGpzNaCrYVpI3KKSpuJFJGsP3ySBKnB8Zci5N2a9Tt3DiJJ/wa08f/tcG47XEeWULA52UFnUVN0An5vejpajZcQXbts0vfJmJ/WxhH90YMxwovOOh1c0SilaKqRn0cc7o5HXPdmOawH75qD7Z6ZdRurcKHHy3hyZ21yCkq/vJagvne5Ow87mcXmfd058/lkST+yZ+/jcPRevQeaYdHltB7dYxx9YT8XgR8Wxxq9YhdXQCb3vC1wThm5qz1hPjvmBGtO/MaE+1Bt9DfL6vPZnQmEwRBEMR6hgQdgiAIgiAIE5Wa3qWSAy7F1NV4VP1wCgqWK2BYqG/mcfqVraXV0ClWDFkr91Ex7Su3yNTT1Yz+2CQT3Dafs5wCkrleiN4PPXWVDr8Oju6NoD82xdQT6Y9NrWgPeGQJU7NZ5r0Lt6bwxh8+Z7StFHHSaW7M/cpZ1QIDfo3ZuXmijUFGUBlLZi3HAEAqs2jUvHJqn7mOi17zpr0piOvxNK7eyYs1Qb8XP526j4PfGGDq2Jh5bTCOF59tRvcTO3Dlzgzz2eWRJPoG48vp0pyJm1Kx6WLVlTszlnMCsNTvEX2uj+Px51osY5HKLCKVWcRoMn8es8OnoymIP33Dud4RAGyvqbYVdOy+U+i+sF5FmUr+3S0n5EwmCIIgiPUFCToEQRAEQRAmKjW9i5uAizn4FG0MAJAQm1ib4vXmcYs2BnHyQBtiE7OWMazEwFG5gnZOfeOv4d8iDiQXolgx5OjeCK7HU3jv3gM8ubO2YCqyQhQKzLppX7lFJo8s4XC0gRENzOccSnApz4aLT3lmptAaFn3O12cZnZ7D6PTcCvcA3377/titcf79aKP93PDCTMhfjYDfi+efqocsQbjfAeua2LGtGi8tO1xeed3qcBHx2mAc1+MpTKbnmffN7RO5mPS+mdGFlVSGdcOYmZlbwKmLt3HywG4807LD4lrRzlme++mObdXY7vMygqQsS5BsTq+v5/GZwvcQs8OnvSmA/W1h23tBoRpEulOKZyOLHpX4e7UakDOZIAiCINYXJOgQBEEQBEGYqNQnid0EXETBJ/O/y9Enu+Adf+2XD+5mCo4X04+1plxBO6e+8df4y39ahxqvx1JAvhDFiiFnryWMVFKXR5JMKrLVwE37yiGailwwdudUVNZNMposPuWZGWtNnCnHWi7meiJDiTT+ZnKWERPs9kChQPmhPfVMHZ1De+ot6+x6PGWk0hKtcf79kwfa8PLB3cJxtDpCFpDKLGB4PI2+Fzps1zG/Jl5adjT1nLspPB7QnCVm98zM3AKTEq21zo/D0QbHtVNIoBDhq/Ygu5Cv3TM8nsbpL7Zb6suMp7KoD9Q4umpqvDLmFwvXyXqpy5rezStLeLgk/q4mhBXXLwD4i7dGkV1uz+WRJM68ncCJ7vy6K1SDKODfUrCe0pvvTy+7o6QNIfBU4u/ValCpzmSCIAiCIMSQoEMQBEEQBLEOcBNwKaauRanYBYb5a+v1MvhgXiUGjsoVtHPqG3+NpZyKv88tFS0sFCuGrEVAkneGnTywm3GG8axENNWvZU5hZl6HonOKYskrGQdrTZw5Zh6d6okAwKXb05bziSgkNB7b1wxZYtfC8fPDzDn02jqxiVnmfb3//PqITcyi74UOV3VSzNfQ+y8SoezWLH++/W1heGTJEOhe+u4wkyrNTFPIX1AEG0+JU7iZ8Xk9Rq0eAKjxsoJOTlXRc+4mI+YAeYdV0OdF2lT3xuuRsJjTBMT5RcVIefaJnbXoaArg3bv3he5JHX2MAHa+WsN+NO3wo6MpiKFEStiXoM+LgL8a97OLCPi8qA/UMKndspy4dCE2xawf3p3Fc2hPvfB9fg1duDVluI3Wu6ulEn+vVoNKdSYTBEEQBCGGBB2CIAiCIIh1gJuAi1NR8HIFouwEAv7aM3MLTL0HnUoMHJUraOfUN7u5KVZYKFYMWYuA5OmBMaNmku7yELmz7HCbsimniIPrgPM4dkZCuHSb/c5KxqFQzR6ndcDvn9a6bcbn/Djwgfv+2KRljPi1IFpnF25N4XC0QbgO7NaHkzDD993cfzsRSrRmReNknvef3XsAO9yIYDytdX48/1Q9bo6n8bPltGN8XZntviqkMvnUapPprG2dHQCMmAMAtTVeJjVbZIdfuBcWlhQMJVJGKrlv/V4UAKCqKq7HU2j5OLv+G4I+4zyKqlrWs94WvT2pzAIORRuE6eJ0ZrMLQneWXoNIggoVEmRJQmfE/l5tXXNs24u9x1VSCrdK/L1aDSrVmUwQBEEQhBgSdAiCIAiCINYBbgIubB0b+6fAV4JdAFg/v6jWhLnNbvqx1gG9cgXtnPqWD4ZPMWma+MB0ufu+FgFJvjj8hVtTONHd6vr7blPe9Q3GhWIO4CzQ9HQ1Q1H1dqo4tKd+ReNQqGaP0zrg98/haL0xv/w47G8LM98dnc5gdDrjOEY9Xc04PRBnRAlAsl0Hovd54YwXZnq6mi3Cmt5/keDb09UsXNOF7gXzJucMoDmt9rfVCcUFfd/ozkAdX7UHj2+vwaE99Ti2j91LOUXF6YExtNb5AUg4tKceiqrg1TfuGMek5z4Sts2OJ3fWMuOSU1RhWsXj54eZVIi//c1BRjj6l63s3MtMQR1394PYhJYKT1FVQ3A1w4tXTu4svS+9V8cs88ivIUUFkwqwWPG0kurWkNBBEARBEEQlQoIOQRAEQRDEBkEcfCpvIMouMGxOKWUX6HbLWgf01iJop19DD25v8c6i2iOjJ8oGpsvd97UJSPIB5uIEKL3Iu/m1qL2ilIJuaql4ZAknuluYeiGAWDwD4Cio6d8ZSqSxvy0MWdIcQG4FomLcO7IkLTsmUvibyQdM8N3O9aAH2PnaOnbrgA/IK6qKG4k0rnDCmfl6Hlky6vIUSqPW0RTE6YG40R6txgoscyGioymAq6aUYY0hH05/sV04H/3DkxgVOGmyCzmMTs9BlmARVfoG44zQMZRIYTLNpmmbnWdFJSe6n9iB3iPtFrFGlFbxPc59xF+XpzOSv4/GJpxr3ejk771sv1vDfhxub7AIPYXu1Xb3Jn5t6e6j95ZdUHp9K7e4Td9JEARBEASxWSFBhyAIgiA2OZWU3oSofAoJBOVwhNilddsIa1Ufv5/+9KfGazOVWoTbaewP7am3CAjFoKiq42sdUc0VPQ2VXduc2i0KUJuD3G++P413xmZw5kudwu8AwMsHd68oZV5OUfHtK2O4cGsKs5mHzLGdkaCp7g4rsEQbgxa3BKClv/tBbBIhfzUCvmocimrOFCdE48DDB/vt7gO8G0pRVVyIiRxchcesMxJkBJ3ETBZ9g3FDFB1KpDGeyjimRNPR09eZx4vfa3buLxFeGfiVGi/SmXzKtaebd6C6ShbuafN89w3GwS/xhqDPth8tYT8UFeg5d3O51k2AmaeQ34tUhk39tr8tbKwJXgBq2uE37qeyJJW9HtfZawlG0Dp7LVHUHnGbvnM12Ai/MQRBEARBbHxI0CEIgiCITU4lpTch1j/lcITYpXXbDGu1UotwO439sX3NTHH1YkU8Pl5qFz+1q7nSe3XMtm1O7eZr1AwlUpjgnBJX7swwIgKf0mulgpvmEmFrvrTWbcPhaD3TX/5zQMUrr+eFJ0VVMZRIM6JEKrOAC7cmIUvO7gaR88lMS9hvcVnYBb49sgRZgpFW8NTFEYT8Xua7P78/j29fGQOgIjYxaxs4f/fufdu22tXJsUNRYVkHTjXHCrEr5LcIMGfejkOWYBFczOJbTlGZOfJVe/B0JIhv/V4UX/leTCgq7Qr6GIfTyQO78fLB3aYUZ9aUavpciPqp31NWWo/LLp0cv576Y1NFCSRu03euBpvhN4YgCIIgiPUPCToEQRAEsclZDUcAPeW6vqi0+bJz+ZSyViutb4Wo1CLcTmNfiohnnheFcyt0RkKOx/Pz6NQ2p8/462qvrWvDSUQoJLgVWn8iMaUp5GPGUlR3h//ehVtTGJ22OjxGpzMF3Q2FhI2xZMbisnAKfPNt2+6rZhwk2YUcI2LpghRfd0nUro6mYEEBiqe1bhtXg0ZrY++RduPfS4rKpJnzeiQ8trUKX9rbhCrZg+HxNBIzc4jPaIKfyE2jO0lEgosuvvH8w9othstsT2MAb38wg0XTwtTS+rFt12vjmF0/vJhnXpf6PWQokYKi5lMc2qUTtFurPV3NuB5PFUwnx8/b6PQcRqfnXAsk5UzfWSyV6pAkCIIgCIIwQ4IOQRAEQWxyVsMRQE+5roxyiBDFnOP0wBiTZkoUXF0L+Db3HmHrZTitVbv+itaiXZH2SqBSi3CX+z7Bpy7b3xaGR5ZsRSyne0q0kW3bkqKa0lPZt5sPlMuSZEkfp3+HD/Tu2FaNl7qa0dPVXHRaN/Pc8u3j2wjYi3zs95zXr1Ngmg/UA5pzJLuQrx9jrmMCAP3Dk8w5bsTzKc1ynFL2uWgDZMnquDDzg9ik5Z6jpW9Tl9O3aXMj7rtGc9gPCcD97AIjIB3aU29xY43PZNBz7iZkSUJnJIjFnMIIOos5FanMIqo9nuVxa8FnvnZV2HYeXnDpOXfT9tixpJZGDgC++td3LJ97ZAnRxiAu3c73dzGnpemLTeTXg11NI/0cvEBy6fY0rsdT6Huhw/VaNbt+dETryrxex2cyTG2joUTK9b3tUYjbleqQJAiCIAiCMEOCDkEQBEFsclYjaEJPua6McghixZxDC5iyrx+FoFOozXZrNbccwNcD0ubvitYiYE2/ROvTmXLfJ/h58ciS4VRwc3x/bNIknLACwhXTOjh5oI1xS5jb3RlhA+Wdkfzneg0YOxHhpa5mY804pXwrfC9k2979xA7L2IpEPn4+FBWMEFXjlTG/qBivo40B2KGP/emBuNHv+u01uGKqX2OuYwKACdIDwOAHM3jLJIjwAp0uBNilSrufXRK260R3q1DoATSniaKqkCXN1aVfJ6eoTF9uxGeYvujt1/tw6fY0fF6PsF3m+l2zGVaM8nokLOas9Z54EaCQA+rmeBqqQ90ovqbU1TtJXL2TX+PX4yljrHkRnL+OGd5h4+Z3243gYV6vR88OMWvlbyYfoPfqmCsR/VGI25XqkCQIgiAIgjBDgg5BEARBbHJWI2hCT7mujHIIYsWdgw+sPRq3SqE2263VvsG4pfaE/l3RWiTBsXjKfZ9wc48wO19418fotOaw8MgSxlNZy3d1YhOzjFvCjF1NnhPdLTjRbe864AO9TuupUD9jE7PM6yqP7Motxs+HVuA+376couJP3zCn+SocPDfXvhmdzqD7iR0YGp9lnDp26c4WufkRCXSGuyc2ZVyH+XzZVVUo2G+3FnOKytSqMfelENnFnPB9vU5M32AcKU7QaQjUGCnYAMDn9eD3f/PjFtdWtDGIkwfaEJuYxU/GUsx4AtqauB5nHUQA4PFI6NnbjOPnhx3bbhayTw9oYsmxfS2GuGW3hwBWsOI/T8xk8OJ3bhpCp0eWcHRvBNfjKbx37wE+sbMWS4oinDf9uu/de8CcM5VZKJgC8FFSqQ5JgiAIgiAIMyToEARBEARRdugp15VRDkGsmHPwaaYO7akv+nrloNR+i4LM+nfdpKsiwXHl8AHsQgXv3dwj+LRsIX81E1QXFZDncZrbYoK3omP1Po+nWMFAVL/Erp/lEr/N7cspKj779QHm89hEGoBzP/l9NHV/3iI+5BQVnRFnx4l+XE5RmXnX29jT1YwXz91kUpylMgt48/3pkhxz+jz0xyZdiTfFoLtY+JRtLWEfGoN+RtDJLuZw4dYkZAlQVJVJY/nywd3oe6EDe/7ff82MaU2VBEWFRfiQJQk7tm1ZTrkWKDjeOqnMIk5dHIEsaWPN76GWsJ+p/6Ovt9MDVlF8LJnBWDJjuNiOP9eCs9cSxnFXRpKMG04/BrDuXZ61FtHXWy01giAIgiAIJ0jQIQiCIAii7NBTriujHIJYMec4tq+Zebr/UQlwpfabD4rvbwsb33WTrqrc/S138HA9BCNF6fLM/+bvBW7uEXwQPeDzWlwSOq11fjSF/EIxabXgg9atddtwOFrPXLNQP92uRX4NHN0bwdlrCdu6PbwDxo1QZE0NZl1jl0eS6IyEcPJA27JrZdFyjH6cOZ0Xz9ORIKZms9o1VJVJy+U22K+PyfeH72Isae/SEhHyeaFKwIPsIvisaT6vjKwpXd3N8TR4c8uuoB8dkRCTZg7Q3ECvvH4brWE/87597RjJUrMJAPxbPMg+XMJnvnYFaZu6Q61hP+qDPkYYM7dZlHKyKeTH59sbloXXABRVc0a9e/e+8BqFzic6Rv+3Gb4m01qL6FTXjyAIgiCIjQQJOgRBEARBEBVGOQSxlboPHgWltsMudVa5r+MGp3o+peImGLnWog9/PV58McPWu3EPH0SvD9TgcHuDMGXX4WhDwTEu9xjxQevZzENcj6cwlEgzaaoKtcHNWuTXwPV4ynaN8e1qrfO7ErYK1eXRGR5P48yXOnBsXwtTqwYqXAkzfYNxw70CaAKs+XtLOQWf+doVABIO7anHsX3WVF56CjE3Li0Rtb5qxGfEbh6zmKNfU5LYeZxMz+NuetL2/LNZVugaT2WRU1QE/dVImz5b4urj7NhWjZe6mgE8wN9/tOTsOJI0YUwk6OiCCS/SKapqrDlz7adC2J1PdIzouN//dCtkSXpkDw1Qmk2CIAiCIDYSJOgQBEEQBEEQFU2hQHw5BJpyBfud6vmUiptg5Fo/gc5fb39b2PbY0emMo1vDDpkLontk2XAJmAWdkL8aR/dGCp7v9MAYkwbrnbEZnPlSp1AscLMG+KB1KrNozL05TZVTG5YUBVWyXPCa/BrgU3SdHojj6N4IqqtkS7sORxuE5ywkLul1eU4PsPVjlGURgq83xAsE5gB/TlEN8efn9+eZdkyms2it8wOQUB+oYQSKUxdvYyiRQt8LHVpdngKpvOzg0/XxafJ4PBIM587lkSRCfi/z+WjSWgPIzDxXk2csmUHHf/4xsg+XmPcXOXuQqmpC5kKOFZXESJYaTAAQ9HkN581TuwJoDvsRXxbMzM4pJ7dNjVfGrzeHIEuSIU4CrOjn5IazE9kflYhCdf0IgiAIgthIkKBDEARBEASxwXnU7o2VXm8txIpyXcOpnk+puAlGrvUT6Pz1ZElaTsOVEKZFK6U9nZGgIYzorwGRkLKAs9cSBc+vOUnyXLkzwwhNojXQ09Vsu5b1oPVrg3HMCNJiifrMt+Ev304Yacuc1h3fZ87YgVRmAcfPD+Ps0U7Xadz4/upCjdbGvDtmKJHCpdt5kcVuKztdV3PliIUYszsHUC2f6yJET1cz+mP2rhhn2PPy7i+t7k3+NZ+GTZ+j1rptAFShcybkr14+dsFSewgA0qb0dD6vB5BgOS6VWcCpi7dx5p/WFeoQnn9qJ6pkyeKY2e6rMsZa5KbR16WT2+bf/uYThlBnxq14XimuTx2q60cQBEEQxEaCBB2CIAiCIIgNzqN2b5RyPbMoNJ5i62P0x6bKLkqVSxBxqudTKm6CkWv9BDp/PV1ssatxI2pPIeFP1O+cokJRrTU53M2Xdb28Nhg3rsWvgf7YpKW4PZBfy2bHgcg1Ip4Dtg3zXHqvm+NpoYikj0V/bBKj0xnhOOuuHbfBdL6/F25NMUKFLgp0RkKMoNMZCQEAFpYUHD8/jPfuPcCTO2vRe6Td9roioXPHtmps93k5cUS8p/tjU0bfzTTv8GE28xCz81YBxYxdrR+d3//NFvz//vaXiCczAkkpT1PIh46mIDPfIb8Xqcyi7doXkV10bm8up0KSNLdNwFeN+qAPP7v3gLnGj25N4VC0Ad1P7MCVOzPG+w8KjIW+Llm3TQCqCvzw3XvQxC8VOUUt6R5biTW/Kk1gIgiCIAiCWAkk6BAEQRAEsampxOBTuXnU7o1SrueUWml0eg6nB+KQpXwwUks/VPoclksQKbaejxvcBCPX+gl00fWOnx+2HNda58fhaIOwPYWEP3O/9X0qCuoD7ubr0J56i0tkZm7BaAO/BkanMxZHjXkt620aSqSwvy0MCVoonE9T5dSGTq4GSkdT0HZc8unmxOnCntxZW3AMzFgcGgIl48KtKbzxh/sAWNfW8fPDRoq5yyNJwyHk6lrAcq0YVgw7tKceP4jdxViSFXH5mkk68Zms8H0RfNq1/W1heGQJHU1BvBNPYSzpnIZN7we/9ocSacZJZibo9zLOnGJQVSCdXTTmvufcTXZ9JjM4dfE2Th7YDUmSjLlwEpbMAjN/X+m9OmaM86mLI5AldynS+N/QJUXBq2/cAZB3fp3obi1pDAiCIAiCIAgrJOgQBEEQBLGpWWv3yqPASaxYDUGrHOIILwrxjgzNTaAFH83XKnUO9SDnUCINRVUxlEgZ7xczHo/qSfC1vq7oeiJ3kl77REQxwp+dwKcXkXcjYB3bpx2j13HhHT69R9rRH5vixAO27ea1zLfp5YO7LWIPv69efDaCoUTKcLV86/ei+O47447CmHlc+DFuCfvxYH7RcMi4IS9EpbG/LQxZ0lw378RTXPozAFBt1xZfx+fqnSS+dXkUkgSmropHltDT1QxF1dO5qTi0p96YM0VVjTRvAPC5aIPhigK09GSFHC085vRnOj1dzYYIzN/rdKeWE7oYoo+H7qSaSNsLQR8tKmje4Ud8Jn9M0OdFOmsVeapkYElQOqdQirTYRNpSb8rrkeCVJfyD2q2QJQmShOUUei1l2Y9m+N9Qvt7QhVtTFSfobIYHOQiCIAiC2LiQoEMQBEEQxKZmrd0rj4JCtS3KLWiVwy3CBy+fjgSNJ9A17BMjlTKHovRZepqpjbYeVoti3UnFCH92Bdxf6mp2PT8eWcKJ7hac6G5B79UxRozpaArCI0s4HK23OEaAvOCgqDBSUTndO+z21dlrCcbV8t13xplxA4Boo/24lOIA44PX5jRyQF6IuhGfsXy3fnsNes7dFF6rtsbL1A5SVOBP38if19xv89jzyJJkuI50x8nLB3fj5ngaOUXl9r3VaSNC/9zswjGLMTxP7qxlruOVJSyaiuoE/V50RoI4fn7YOBcv6Hkka+2d7EKOEXMAYLvPi19r2I6fTrEp1J79eBjvce8BwFO7tqP36pghwN1NZxk3UUdTENfjKeY7izkVizkVCZODSZYk27WSU7QUa2bGUxn0Xh0ruMb4fcCnEbRLo2fHWogtm+FBDoIgCIIgNi4k6BAEQRAEsamxCypvpCd4ndwbpQpaTuNTDrcIH7g+ujeCs9cStkFpM3bCgJs55cfDXGOlmPkv9/oxp/dSVDatV6Wsy2LnvRjhj9+nrXXbcDhaX5JY6HRt0ft9g3FGcJAlCB0T5nVnt69E7wNggsvdT+zAyQO7mRSCOqXsLT547av2CNumcoH3oN9r1GYRBb0bg76Cacrc3E/4MYlNpNH3QoeRZsxMa902/NW/7TLuBXt2BdA3GBc6XgBtvPpe6HC8PgB86/ei2HvqkpEebZETNySAqaXUOzCGWS6VGi/m2BGfySI+k0VreBsj3rw39QDZhSXL8TfHZ5m0fCcP7IYswbgXDCXSuJsunHpuKGFNF5i/n8IinI1OZ4x14zSHonpa5vZqoqh71kJs2QwPchAEQRAEsXEhQYcgCIIgiE2NXWC3XEGlSheGSk2PttpBN1Hg2vw6p6iQJcm2hk6pbebHw1xjxa5/ojku9/iIUo7pdTuKaVclrT0+dZXZ/cC3s9y1ieyEEdH7dsFfJ0HKbl+J3ufPf+XODJ5p2eFKiHADf35zqjlz2/i0XfxrPuj9dHMIb3EiAE9HU7DgOnS6B/GfHY7Wo7pKNuao9+qYrZjDn8uJ774zbql101q3DYCK0ekMUtxnpdbFMTObfci8tnMdDSXY+bsQm0LTDh9yKhjhpBCKmlec+PuT1lcxevpJu/kTie9n3k4Y6fUA1XC1uaFYsaWU+1y5aqYRBEEQBEE8CkjQIQiCIAhi3bAaAWq7wG65nuCt9NQupaZHW6snnO3mXDxvxTkBRG3W+//aYJxJJ+W2vos+x+UeH7uUY8W2q5LWno6bdq5FjSC7tWYX/HVqUzEOIICtAwUA/bGpku5zoj7Y1V4J+b04ti8vTHVGgoZICACf2FnLCAaacMr2UVGBC7FJzGYXsN1XjUN76i01dArN79G9EbwTT2EokUaN14MlU/ovRVXRWucHVKA+6LPUtuL3RY1XxtPNIciSBLVALSzzWI2nrE6jhkCNpU5QKXg9EhYF9p1UZtFICfeTsZRFaNPh3x9NzmE0OSc8VsdX7QFUFVlT+rO76YxtusCf35+3PZeiwjJ/+rwytZ9McypLMGpRnbo4AlkSp7oTUazYUsp9rhxpQQmCIAiCIB4VJOgQBEEQBLFuWMsAdbme4OUDZ4Wedl5rSg2U8+Pjtt5CsZRzzt3MqaiWjt2xOiLxptxPgNsF5YttVyUKOpXQzpyioufcTSPtlHmtmYO/0UYt3V/PuZtCZ1ihtIOi93u6mnE9nmJSXo1Oz2F0eg5vvj8NRc270QrdM0T75ejeCK7HU3h7dIYRFj5Zv93SDr2fHU1BLCkq5wDJX1MXQ2ITaRxubzDapL+vqsD1eApDiRQm0qxYwM/v2WsJ4zrZhRxefWMEf/l2HIDEuFZGl9O7mWtb8fvi15fFnPFUxkgHZz5eb9+NeAp/M3Xf1mnTEvZbUpABmmsnnXlYlEPnsa1VFoePjgQVHU0hvHv3vq2gUwpPR4KYnJ03RBUAGEtmNSecYNz0a/P1ifa3hS1OraFEilmv5r2ij6+eqlKnmD1drNhSyv1jLQRigiAIgiCI1YIEHYIgCIIg1g1rGfgt1xO8fOBM9LTzegwq6ePRH5taDj67q7dQLOWc82LmdCX1XczHl+sJcP37oho6xbSrEqmEdp4eiFsC+LxLRk/x9crr+VoqOivZy3qdF7OgZObCrSmjhg9/Hd6Rw6fn6o9NQlFV4Xn5QD0f5Obr18Qm0tBdcHZCqyg1IA8v/orcZ3YCiI5eD8a8z3KKuJ+ANg49Xc04PTBmW3vLV+1BjdeD7b4q8HLZjm3VeKmrGT1dzZhfyGHfn76FdHYRErSkYnZ0t4XR0RTAq2/cEX7+TjyNt0ZmHPtaDDu2VaO2xms7Dny6QN6JuN1XhYDfC0DC7z61E5IEXLh1j23zWBrZRVZ80s9rN//F7OlixZZKuH8QBEEQBEGsJSToEARBEASxbljLwE25nuDlA/t8wLVSXROF0Mfn5niaeQq83P0p55wXM6fFHGtX36WcT4CXcr6ViEprWX9HVIOj9+rYmrrYtHofLGaXDABjvdvhtPYLjaeeRlCIyjsk8tfhhZWWsJ/rQ0bYN0BLseaE096zcx7yzgyd1jo/AEko/tq5z5wYT+XTh+n7ghegzIxOZ9A3GLcdC0BzqWQXcsJaNrqY0zcYR39syqjb4yTmAMBUOoszL3SgSpZxemDMIlTNm1KiAZpD5lMNtdi2tQqLS4olXdtzT+wAAFy9IxaBntxZayvmAHknmr7PeGfYWDJr/PvmeFp4Ll7MAYDETAYLS4plXZiFsGJxew+i9GkEQRAEQWw2SNAhCIIgiA1GpRdCXwnrMXAjCsSb61Ss96eJyyW4mNetOZVVtDGIkwfamJoclYBonz0qYa64OkPuzqU7r4DVd5Lx7dRcMGIX2+rd39jQPB9I18UaJ/FBX/uiNvLCS39sEoejDUz7+XO31vlxONqAd+IppmaKubg9H0AfS2YsabPA+U1a67bhcLS+4F7Ku8LSyKkqvj98F/2xKRzaU49oI59yMWukNhNxONpgK/72dDXjnbEZXLERKXzVHks6srFkxkgfplNIGBpKpDBbRKq01rAfkDQPjqKqOD0Qx6mLzu4jnom0luasp6vZVuwyk8osoDMSgoQHeLikWGrvSJJW08ks6HS3hVG1XOtJF9bM6HV6zA6mN9+ftog5PMXUDxpLZnDs/DCeaQ4xc/DS8n0xp6hFi7T8nrkeTxk1rdykNyQIgiAIgtiokKBDEARBEBuM9VIIvRQ2QuBmPYpSTpSrP6J1q//75YO70fdCR0nnLYcA4CZADzy6fbbStpj7Z5e2Sg9Gm8dvtcQVpzR7qzXuh/bUM6m49raEGIFBF2v4VIM6IX81FDU/Jnwgmh8W3aVyPZ5C3wsd8MgSerqaoai6W0jFoT2a6MIH6c3nEokYAb+XEXQO7amHLNnX7SokCAJsPalTF2/j5IE2vHxwt+2a2bGtGk/urIUsAZ2RkDFuIvHXI0s486VO9A3GNfFIUTA1Ow9IEg7tqQegCtOk8WtSVIvITE5Rhe4bOxQA8eU5PnVxBK3hba6/q7OYU/HK67fRH5tEbY2XSW9mx1AijY8/If7sykgSU7NZ5r2q5ZR9OmZh7bknwlAB/M3kfaicnaiQYFPI7SNq95nldvC/B6XsW/4+IKrZQxAEQRAEsRkhQYcgCIIgNhiVUGCcsGcjiFJmytWfUlNZFaIcAoDoHJW0z0Rt0UUnN2KLm7onM3MLlhpJqyWuFJPqq1zjfmxfC2RJYtK+nb2WsASm9fWeT781idHpDFKZBZy6eNsQTsxcHklif1tYeN3LI0nDaeKRJcgSDKHo1MWR5VpJISZI3xkJGf8WiRj1AR8O7alnXG1mcUYn78aatK3RA4j3ZmxiVqv709WMz379quVz3Zlhxkn8dbqP5BQVF2JTGE1mmPf1NWkWxXqPtOPA168iPpO1nEckYPiqPfhoMQdFkDstzl1vIpWxHuQSfXwDvirczy45pmp7d3IW/6J1u+3nvMsop6hG+jl+jN8Zm8EVmxo9ToLN/rYweo+0G3sg2hiAogI/encKUIH6oA/XRmcYB1GN12M7j6XsWyfHFf1dQxAEQRDEZoYEHYIgCILYYGz2AsEbOeXcWrOWY+kmlVUplEMAEJ2Db+94KssUel9LRHveSWzh55Wv62SGT3dlHr+Vjq3d+nIK/K/W/U0UiHYSKtkaUvlAv2htAIAsSXj54G6Ls0f/jtOY9h5ph6Kqy84dyXAC6S6a3iPtOPiNAYwtCxBXRpJ4pjlU0NVmJ+Tp60HvC59eDciPe99gnOk/oIkBdnWQ3Iq//Nr43T31+NM3rC4dgBXFzl5LCMUcAJb6NQDwGy0hLCkqrrhwoiyKVB9Y0/M5MZtdYl63hv1IZxaMmjwAkM4sWtw0ZlKZBeaavChoHmM+zZuv2oPHt9cAUNHeFEBnJGSktgRUDI+noajaej17LWFJJfmv97ca//6Ly6N41TQnX342YtvmUvat+T7Au8A22981BEEQBEEQZkjQIQiCIIgyYw5EvfirHvi3rO3P7UZL6VUslZQKa7WwC4SXW4BZy7EUOQ30GiLmNVxsH8shAIjOkU+9pbkbRqfnLA6W1UYfi6FEGvvbwkx6q+Pnh5ljndKW8e4RveZGR1MQigqmboh5/IodW37uFDWfSsu8vpwC/6t5fytl//BjkFNUDCVSaAn7DYEFADojQcPZ03Pupm1wWjSmmnNHMoQT3Qmkj8/ZawnmWoBVXOP7dnRvBP2xSWGfFFVl1sfJA204eaDNEJT0VHD6dcyE/NWGs6OYe0dO0WrU6OnmdtZuxdUPUsb3n/t4yPa7AHB6II6hRAoT6XnH4/j6QvpYHD8/jPfuPUBtjdcyloXwemQs5nKFDxTQtMOPhqCvqNRmACwCkp2Yyrtw/mHtVkNQfPWNO0w9JY8sMfWr9FpvdvP2UlczhsfTeO/eAzy5sxYvOexF83qJNmp7v+fcTcd9Zr4PiPYmQRAEQRDEZoUEHYIgCIIoM+Zg6eebxWl2VpONltKrWCopFdZqYSe0lFuAWYuxNAfqJrnaEE0hv+V6xfaxHAKA6Bx2Do2hRMpSB+XFZ5uF6btWCu+wOHlgNwDg+Plh5Dg3wXgqYziI+HmVJRj1UPgAa05RLfVXnMbFbXvffH8arXV+5nM362ul9zezCJZTVUyms5AkLNdqgUVgKpS6zslFYBbGzGPTGQlhcnYe5jo5ovOZv+e0F0Up0XhxjR/7d+Ipi7MG0ERUPrZ+4dY9NIV8hrhq7j8vQKUyCzh+fthyjm9c+gCKChzbJw7e9w3GGeGQb9tP4vYuMv265rR0djxZX6vV54GK55/aCUUFvvK9GH7d5Gg6PRDHN9/6gHGmObGYU2w/q5KBhqAPD7ILSHPuHEBbj4Vq2bjBTkztPdJuiFVP7qyFBDCCFS9EF5O+8czbCWO9Xx5J4szbCZzodna0HX+uZVk0sgq5Tmz2v2sIgiAIgiDMkKBDEARBEGWGD4gsOAR7iPIjesJ9o6VhswvulluAEbkPCj1VXSxO9VuijQFL2qZi+1iOQKDTOfgx4h0tpy6OYCiRthT0fibAnqeUNcqPxYVbbDqv/W1hTM7OY3R6DqPTGWOc+TZ3RkK2/XPqO/9ZTlGFabbs2guw/VtpGiU3Y2i33k5dHBEKTAAcBUTzGPScu8l838MVq9evb14fsiQxbbQbbyc3FP/Z/rawRVzjx94uzd7haAMAMOKItn7mmP6bhTHe9XJ5JImQv5o5b3YhZziLzAJBtDEAVQX+/PKosD06blOaATCubW5TyF+NT9azbpXh8VlmX2qpxrR95EbMkQCoprbJEiy1eJYUIDGThZdbh16PhIaA2JkTqBH/Lzrv+gLELkYz1VUy+l7oMOZq3KYOkH4fLSZ9oyZa57lwa8pW0OGvJbq2mY32m00QBEEQBFFOSNAhCIIgiDLDB0SqPfIjbM3mQ/SE+0ZLw2YX3C13fRE790E5x5AP7rXWbUNTyGek++LnbaV9LHegkF9vokA5/wT+zfE0nglsYd4rZY1aa7Ww0WSPLKEp5GNEHr0mi7nNq+EYEvWBb++hPfW27p/VuD4gdrPksQpMTsFnfi1FGwMF12YxgqT5/NHGIE4eaENsYtaVU4pf0/zY13hlRrTwVXvwB7/5cRzdG8GZtxNordsGQIWqqhhL5p1zek0Wc7o8EWYxRdR/fp6KwSMBW7wefOyxLcJ6OQG/F41BHyNKfaqh1nLc26MzzOtX37htEWQA+xo5/Dv6dz0SwB/O199ZzKmIz7DiigQgEvbjPjd2Xo+EZ1t34Fu/F8V3fpJgUt8d26fN/emBMcv7+hpwEs119LUqWkv26Rv5EXAnurm5h2+032yCIAiCIIhyQoIOQRAEQZQZc0DkV7aufQ2dzY7oCfdKSMNWTiHBLi1TueuLOLkPyjWGfHDvcLTeOK/omisVI8odKBStN732hA5fx0ILYFprnvCvC7WLn28+yK4HSkU1WVYjfRHfBz34r691p9R1q3F90RhaRbA8dgKTXfCZX0snD+y2pK4zk1NUSyo8J0GSP//LB3dbHD+AOxcaP/ZLioJX37hjfP77n2410mGZHUT728KMoDMzt4BXXr9tcTOF/F6kMou219cRiWQ8sqTVltkV9MEjAeOpLONMyama40ck5gD6PEqMoNMZCeGdeIo5jhdpRGKO+biWsA+AhMl0FgsOjqEizEQMKoC4oH7PYk4T07/zkwQ02Uf3BmkX0lxf+X1/6uJtfPPSB/jXn27FvxI4N3Va6/xoCvmZtSqqW8O7evQ1e2hPPXNdPW1hIdz8ThUjpJazhhw5gwiCIAiCWA9QhIkgCIIgyow5IPLTn/70UTeHQPmdK6XAB2evx1Poe6GjpGCRXQB3NesMrNYYOgX37ArEr6SPpQgnxQT5erqaXdXQ+X9+9h7zvVLGV5TyTJYk4ViW041jNx58H/TgP6CJZm7nrtSgarSRvX600TqGPV3NuB5PMQKb1yNhb0sILz4bQXWVzLSPn09F1UQZjyxZ1lJsIo2+Fzps+9c3GLfU2HGaj0ICmVvsCspXyXLBWj2T6Sxa67bh5/fnuTRk7PVVVZzqTKfGK+Pf/uYTQpGMR1E1YSOezODlg7uhqrCkGhOxY1s1ntxZi+HxWbQ3BXDywG7EJvL96x+etHxHl0bccDc9L3TqiFKtrQZ9gwlmbDUxRUJswirYZBdzePWNEQyPp9EZCQnH+3C0QbhWF5YUHD8/jBuJNDPnrXXbcDiar/l0bJ/2Xd0ZBEiGYOm0f93cB5zuh6tZQ46cQQRBEARBrAdI0CEIgiAIYsNTbudKKfCB0ssjSfQNxtdNsGi1xtApuLca1yxFOCkmyOeRJZzobrHUkijWQVFKX+3Gstwin9146G1+bTCOmbl84HkoUZybq/SgqrsUUJOz88zrxZyKK3dmcPZaQiiSyhKMtHV6TSTdEWCm0Fri7wEeWXIUZgoJZHbwAo65rpN+Pn2uVBW4Hk9hKJFCZyRkSRs3aiOk6G6m/tgkRqczSGfz7pz9bWGLGLBzu8/YEyJRzY6hRAoTabETh+exrVXGOS/dnsbJA23oPdKu3WfPD2M2a3UQFaPD2NXxEYk5LWHN/ZJTVdxNzeF+dhEfLarILhauzWOHSCi7cGsKh6P1tgLZ5ZEkVAAnD7RheHwWiqpCggoVkpEekhdcjp8fFs7N/Wz++jlFxemBMfQNxg1nll4jCXCuO+UGp/vhataQqwQ3L0EQBEEQRCFI0CEIgiAIYsOzms4Vt4hSPa2nYNGjGMPVuGYpwslaBPkqYY26xW489D7wwXpFLc6+UOp4xyZmHV8Dmlhkrilkdx2zKMKnnOJdNro7qdBaciMmiurmnHk7wQhkhYrI8/WutHo4bD8BWOqqXLqdZNLGjc9kMZrMj1VL2A9JAnR3ztG9EfTHpix9eO/eA9R4PayjR8qvgUJCFtMvl+4cAJYUbK8NxC2uFhEtYR9mMwtIZ5csn1XJEpZKsN80hXzojAQL1q4phM/rKSACqcsuMhXfvPQBsouK5YgrI0k80xzCmS9p6fp6r44Z7dLTQ5rXE1/zS0cXFL8/PAkJYrFPlN7t5njaqGWn33eP7o1YXItuXTyrWUOuEty8BEEQBEEQhSBBhyAIgiAIYg0QPZW+GsGiSqwBUEltKkU4oSAfS6Hx4Ke22Km2O3+hdeRmnpzqt5iv03PupisHiUeWhHVtRO09ujditMEsADmLMX6bGkwszoXvrU4iu3Ewp407enaIEXRUAGPTWhD/1MXbuB6fEYpjZvHJ+K6q4ttXRqGowI/encKsi3o7ADDl0p0jIi1w5IiQJBlB/1aks9a+1NZ4CwpCIibS85hIW1O8uSHkr4Ykae6pQo6eQ3vq4ZElHNvXgpwCfO3HI0LHkFkE1J05OryDjl9vPE4Cm13dLlHKUfM6B+xdPG73UTlcjpXg5iUIgiAIgigECToEQRAEsQGppAA6oaEHfkW1LMpJJdYAqMQ2FQMF+Vj48Ti6N4Leq2PG6/amoKUg/UrOr78utI7czBMv+ogcNnytG0CrH9IU8jGCi34+O+zay699JzFmdDqD0elMQSeQk1ClpUdjayspqriOjbk//E/GL+6zqep+MpZiXvP1aFrr/ICqOTnGktnlmi/2+Ko5V49+0lXGzrEFAAGfF0/W1+IKV3PJLv2am3MWIpVZgJ2pLeSvxifrayFL2r4yr9n/+tf24xttDBp7lHec8Q663iPtOH5+GAN3kijQTQa+JpR5vR0/P8wcy7uAnFx4bvdROVyO68kpSRAEQRDE5oUEHYIgCILYgKz3APpGZS2CRZVYA8BtmypViFwvQT59/IYSaSiqygR9SxlHu/ngx8OcwunN96eZtF28AOFmju3Gu9A6En3PzdP9+vVzioreq2M4PTBmGYvD0Xocf65F2H473K57JzHG3Dc7JxAgTuloLmKvj00eNlLfEvbj8+0NTH86IyFGmCuUeizor2bcLIejDeiPuXepLOas6cKef2onhsdnC7qlQn4vPrGzFlfvzLi6ltcj4bGtVUb9FzsORetxbF8L+gbj6B+exGgy4yjmhLgxMNLUqUB90AcZKnIq8LN7H+LDjxYLCkM8n2qoxZkvdRrr8Pj5YXQ0BS2uGwDwyhIkCWgI+rCQW8Kf/XhUeM67qQw+87UrACQc2lOPY/uacfZoJz7ztasFhamQ34uAf4vxPX0v8fuQX59uXGc6lfibRhAEQRAE8SghQYcgCIIgNiAUANm8VGJ6MLdtqlQhslKFJh29fXqBejOXbieXxR2p6Pa7nQ/+fmNO21XqOUWUsradnu7n51VRVaGLJOj3GkJQMeKe2/aKxBheGCjU156uZsv8N4V8tsLphVts7ZvIDr9FCFNUFa11fsxmFpHKLFjEh99oCTECyid21uLXm4OITcwa60xUY8cOkbghQRKm7BO5ZCTJ/Z5sDPoASXIUdPa3hXFsX35Mfv7go4LnDXDztivoM4SL0WQGLx/cbYxzz7mbQpeUE7rbjV/X+9vClmMXlwW4sWQGvVfituccM9UeOnXxNm4kUng6EoSdTSjk9+JTDQF0RtzfS0SuPr6Gjh2V+JtGEARBEATxKCFBhyAIgiA2IBQA2VyICqmbg6qPGrcpyypViCxGhFgr8ceu7oqIC7emjED/m+9Poz82xTg37HA7H8Xcb0qdY01g0FwngIpDe+pdrW2n6/Hz2lrnF54jnVnE2WuJotei23Xf09WM7w/fxVjSXC9Gxf62sCW1lh0eWcLhaAOTus1JOOWFv/FUFr1Xx4w10TcYt02RtmNbNV7qasbRvREcPz9srL2rd5L4jZaQ4STKKSrqAzWMyyMSqkEixaZuE6ZaW+bP3xpFRyRgeV/l3ELbfdVMWrRC7AzUoKMp5JimTHejmR1oZkL+agT8XmYstfR2+Tm/YVOvJqeoyDk5nvj8ddDGSVG1ceXXtSwBJw/sXhbqVPz8/kfMmD5csrqf7LgykmTGkhcXe7qacaK71fX5ALEQ6lYYpZSXBEEQBEEQLCToEARBEMQGhAIgmws+MP3ywd2O6ZnWGreuhkoVIosRIdbKZeRUd8UKK9qMTs8Z33Vqm9v5KOZ+U+ocawJDvr+yJLkSyvjrRRsDtrVEnAq2iOa8kHjndt17ZAm7gn5G0EllFnF5JMk4OgrhVNso2hiAqgI/fPcefs7VwgGsa8IpDdxLXc1Gm/g56I9NMaKQWRhoCfst1/Z6JHQ2BXDFJlVadjGHn3H1VgBgySR2tIT9aAz5MZZk59PrkfArW6pQ6/NianaecfRcvTOD96bu2/YRAD7VEMDRs0O4Nmqfxk1VVXS3hSFLElRVxfB4CnsaA1hSVLw2GLfUp1FUTcjpOXeTEWG9smQ4arQTW6+VXcjh1MXb6BscQ22Nl/msMxLC8edacKJbE4s6/vOPGUGntsaLdNY5vZwd84u82MbO+WqL2Osl5SVBEARBEMRaQYIOQRAEQWxANkMApNLTYK0llehsKWV+zAHpaKOWAqvn3M1HPr/RRl4U0EQIUX2W/mG2Zoj+RH65cQq4t4a3oSFYY7g7FBWMGGI+h1PbnAQCp5o6TvR0NUNR9ZRfkuE4KDS3pa5xvg+KClshTHdXDCVSGE9lGYFAJDw5iXfFrn/ZJl1YMXs5p6i4Hk/hvXsPkFNULCkqXn1jhGlfIfTrRRsDlu/ozpyermajf+MzrIgyOj2H0wNjONHdapkzXnABtDRrV+7MaLVmoKUl45lfdHaXNIV86IwE8dZttr2LORXp7KKtkJHOLjme94e3JhGfyVre93llZBcVpDILSGUWMJbMoiWcF5TMdYd4ZAk4PRC3OOp0MSfo8+LvHzq3K5VZZFLFdT+xA4oK416pqJqrzMz2mirbcfB6JFTJku048+6pC7emEJvIr+tKTZVJEARBEASxUSFBhyAIgiCIdUm5gkgbQRiqRGdLKfNjFga0NEdsMPrRBQn5x+W113wfr8dTloA0/4S+WwqtS37O97eF4ZEl4bE5RYUswVJjpdA64YUac+op0Zy42UseWYIsSUY7Tl28DVkqPLelrnG+Dz3nbjKft9ZtQ1PIx7RXVF9H5DoqJp0b4NzHzkgQl25bRZdi9rI5/dnlkST+toADZce2aktx+sRMBi9+5yZygnX75M5aYxx4h4mZvsEEju1rEdYGsmMsmVlOp2elMxLE05EQfhCbxN1UlnWyAFBU4OjeiCFmpTMLcMpm5hZezKn2SPiN1h3C1G4isUpEe1MIfYP29WxEoovFvcMxdX8eVy7m11rIX205RvbItt9fzKnC+kV2jE7PYXR6zphbfh8MLaeZc6qXs95+YwmCIAiCICoJEnQIgiAIgliXlMuVshGeLq7EFHsrnZ9H4TqyEyRiE7PMcfprvo3vCVJDlRq3LLQuRXPukSXbPhx/rsV4mr7UdVJoTtzupVLm1s0azykqTg+MGe6fQ3vqcWyfsxB2OFrPuGp4B5JTmjW+BkpHU9D4/DUuaD+USFnab26X3p+hRBqKqrqunWOGX39//5Gz00N327z4nSEj5dlYMmMrTlweSRpihFPNplRmAU/+yUX8w9qt6G4LwyNJ+JvJ+0wdFq9HEogIVlGhJezH6SPtqK6SIUtid5UsSTh7LeHYpnJQH6gpmKbNDlkCmsPbcCORYsbBDU5ijgZ7k+HP390WxtORoKUmUrVHwoKNkCMS+/QaU2ZRWF/P5j2lqCpzHzg9EDfa9Kh/YzfCAxwEQRAEQRAk6BAEQRAEsS4plyulEtOVFUspKfbKEdhyOsdK5+dRuI54QaI/NonD0QZLyjW9LXwba2u8mJljg6mdkZCra/NjqQsAOvy6NM85LzLoQVg+eOp2ndjNa6E5cbuXnOraODl7CrVdq7OTD1qL3D9OwpAbQYqvXdTdFsbU7DygqnhnbAbfH54UCiI5U6o30bnLkSaTD8A/trWKSc3VEvZDkiQAKg7tqTfGeUpQU8cOp1R/ZrKLCsZmshibyeLlg7vR3hRk0v79u888gaHxNON2+Wef2onvXBtnBAnJVCvJ7tqdkaDrdtlR45ULpnYTpV+rkoFnW3fY1gDSUdS8s6WceD0S6gM1jud9OhLEsX0tGEqkmfWxt3WHrQhWW+OFBBX7l+sDdUas6dUA7V5xdG8EAGzvXbzAdHpgDEOJtHHOtRRUNsIDHARBEARBECToEARBEASxLimXK6US05WtBeUIbDmdY6Xz8yhcR3xQeHQ6g1dev42TB9rw8sHdlrb0dDXjejxlBEXHkpl8gN8UNHcDP5b728LM507rkg+y8n0q17ya01o9ubPWCOSa22jeS0uKis987Sr0sTi2rwUeWXKsa6M90T+GgH+L0GHjhCioz6d/0l03pTqH+GOmZrOGY0FU/0VnMs2KAeZz686gM28nML+YQ2dTAKe/2IHqKtn43I342nukHcfPDxvz094UNGroAMDn2xts1oJ4fPe3hTFp6h+QX4fmeZYlOKY4uzmeRu+RdgDAhdgUZrMLuHBrEvXba5jjfnRryhL8H52eQ99g3Kjbw9Ma9hvryW16NxHzi4orUYfn2Y+HceaFDvzjP7vqOu2aE5roprmO3KRrXMypuDKSREvYjwfzi6it8VraEZuYhUeW0PdCB7OOvvhME77yvRjeu/cAn9hZC1VVcVXg1Hr54G7mvm6+510eSeLstYRlTznVEUplFnHp9rSRYnAtBZVKeoCD3EIEQRAEQZQKCToEQRAEQaxLyvFEO1CZ6crWgnIEtpzOUa75WUvsan7EJmbR90KHpS96PRjmPUnCm//+uaKvzY+lLElCEcnNd82YhSC3AUS7eTWntTIHcnXMe8nsFAKAUxdHIEvamihU10Yv+s47bIqtKwRY0z8B9ueLNgYKirvWa7gLwN7PskKF+dx9g3H8qUl4uXJnBsfPD+Ps0U7jczfia3WVbHxH71+VLBVcQ4f21DPuGUATc/pe6DCuLzqHaJ5F6EKMLAGjSc1JksosYCzJilwiB4x+HUUVX+dwewMjEg4lUnhnLIWsSZjxeWXUVHuQXcg5CjbFijlanzQHUWPQxwgpVRKwZKPHOAlHu4I+yJJkk5LOHv3aM3MLCPq9SJucWdFGba2JamLpY3plJGlbw+jmeJpJ1zg5O2/5XHQf4Gt22Z3brmbVaogblfQAB7mFCIIgCIIoFRJ0CIIgCILY1KxH4aEclCOwtZrBsUcR7MoHIqeYFEZO/eKfonfzVL0Ifiw7I0HX65L/7v62sJEirdiUYqLz6f0vJAKa9xIv0ujHi+r42Alp/DWc2p9TVCiqipawD/ezSwj4qnEoWo/hcfvUdfz5Th7YXVBEE7mLeDEEsNaI2e6rxrF9LbbCCI+5Hg7/eX9sylXA2+297dg+rS0Xbk0Bqor6QA0kSULPuZtGLR/dYWOeO90RJEJL7Kah194pNS1aR1MQ/bFJ5j1ftQd/8JsfN8bR3NdvXxllUu9lFxVG4CknfzM5i/b/9GPLvm8I1kCSZUyls1AUlRF3FpbEbQn6qgyB5Z+3hIXHuMEs5miI70nW+RAf19EUdHQB6oKRjqhml53wp99b1up+X0kPcFSSW4ggCIIgiPUFCToEQRAEQRCbkHIEtko9h5unsd0Gu8r1ZLf5PIf21ANQEZuYLdgv/lKlPlTOj+XRvZGCdWXsvlus80ZHH4OhRHq5doYWzNfPX4yAJxJpoo0B9Jy7aanxYyek8dfg2//94bvLgX6tjoi5HktPVwSyBEykWTeB0/liE2mhE8sML5LkFBWyBNxIpDGRyuB+dgGfrN+OaGMQ//Wv86LC56INQnElp6jCVGKfePwxY/75z/U0ZG6Cv272h0eWcKK7BSe6W9B7dcwSuDenz+KD7qJ5Dvq8SGdZUUGvmcIfq4uPSznFUoemJezH59sb0NPVjP7YFPPZYk6xpHnT+zo8Pmus34n0fNnr1pixiicaiZR9XSI7483fP8yVo0kWzrydgCxJBR1th/bUQ5YkDCU0R5R5/9sJdxriDvF1vsx7H9DqTymq9v54inVn9cemVsWtU0kPcFSSW4ggCIIgiPUFCToEQRAEQRAmNkte+3IEtko9h5unsUXBLtHclOvJbv48Lx/cbaSccqIzEmIC3p2RkPHvYtaSKB2S2365nYdCAUT+KXxz7QygOAGvp6sZirrs+liuoQNIlqf0dVFJf6L/9MDY8nckSw0ivv3mlF180P7CrSkm3VNrnR+How2O5ysloKqPPQC8tVwT5PJIEp2RoKuUeX2DcWZMvB4Je1tC6IyEmLkI+b1ImcQDt0/zF7s/7Fw0rw3Gsd3ntRzbe6Sdmefnn9qJH936uUXQ+ZvJ+2hvCuCPP9uGH757D/qaePHZZpy9lkD/MOvA6W7TatPo+4VPC7eYU5mUfELB4IkdmM08dB4gACF/taV2z6OgmBRrANBatw0NgZqCKe9m5hbwyuu30R+bxOFoA47ujeDstYRQuDWvZzNOLrrYxGzBtopq+Ni52wBtP49Oz23oVGSV5BYiCIIgCGJ9QYIOQRAEQRCEibVK/bJZhCMRbtw3omCXaG6KSVvjNOalpr8RtTOnqDg9EEffYNwIFBe7llYjHU+hAKI1tdckM0bFCHhm1wegjf1nvz5gOc4somjfacWJ7lbLcVpKNSzX+VABFRh1LELP7qWmkN/VGisVq9tHXHep0Peee0KrXfPid9iUdQFfNSPoiMQn0foudh1FG8WB+5m5BczMsaJHTlFx7LvDUFQVjcEatDcFcSORNurkmEllFnDq4ghePribqTElcgQBgAwVHlnK9ymRgs8rW1Kn6f3hhTEAFsfPc0+EIUHFjUSaqWGz3VdVEYIOjywBkR0+JLjaQl6PhL2tO3D6SLsmlAzGLfVqfNUePFzMMW6g0ekMXnn9Nk4PxJn+njywu+A6daqPZSe2i5xgTrWzgPz+Nvdlo6YiqyS3EEEQBEEQ6wsSdAiCIAiCIEyUEkgvRZzZzAWR3Tgj+HQ9fYNxvDYYZ47Rx9uty8JpzEt1a4iCcr1Xx4RPnvfHJo02/3oAyD5cQs+5m8I1U6g9btec6Di7dcYH80enM7apvfjz6k/927WnbzBucdHsbwu7FlH6BuPMmO5vC1sEHXPtIP7p/0JrbKWUun7svsfXZKkP+nC4vcFRfBKtb/780cZggVR+7HVlAGYJpbVuG5pCPizmFIuAYnaq2cHXURpPiUU5PVWeU+0WvX/6eQsxNZuFJAG+6irML+YFjfqAj3F76bSE/RhzFA3tqZKBva07IEsS7qaz+MWDj5BdsE+pxtdcAgBFhXBPL+ZUXBlJ4szbCaP+UWPQj4aAD7Kkfc/JtcOLVxduTRnCqwh+r3/xmSZ85XsxvHfvAZ7cWYsvPtNkSaWopWyTHO9PItdPU8iHjqYgM+dOe2kzPxhBEARBEMTmhQQdgiAIgiAIE6UEZksRZzZzQeRinRF2QV3zd92cy2nMV9OtoTM6ncHodAZvvj+Nc//sH+DhkoI3308K10yh9vBr7no8ZYgZ5qBmcWvTmvLJbl2Krm8O6PLt4cektc6PPlNKrUIMJVLMawkqTh5oY9KzHduX77de28bNfBYrTokodf0c3RvB9XjKCI4f3RtZ7h+LDDiKTzlFXa4nlEdPiWZul6KqeOV1rbaPaD3w6bNYPwxwOFqP48+1IPr/+bGr/vGp4jqaggVFGgB4MK99Zygh3ku+ag9+/9Mft63vJMJOnJmancf+tjDeu/cAtTVe7ApsBSQZkiShIejDZDqD+9mlolw8Swpw9c4MmsN+xF2IQrqY46v2MMLPz+9/ZPudr/14hHEBAssOF04M5M9pxTnVm9NevzySxFe+F7MISOaUh3b3p56uZuZcQPH39M38YARBEARBEJsXEnQIgiAIYhNAT7G6p5TAbCnizGYuiOzkjHCTNmrHtmq81NXM1HxwE8RzGvNi22S3f+yK3PN1OhZy4tRRhdpj51YyiylAPqhZzNoU1cKwW5f8ed+798C2PYoKy5gcjjYAgKNbxDzufNF0Ffbp2YDi3DeFxCmgcJC4VLfP2WsJJjj+W/9tEIej9RaHjvmVXS0pc5oqQJu7Qmmu+PVgJ4yY9xwAzC86CQQazz0RBqDi5vgsarwefPnZCHq6mnHsu8PMca1hP2azC4zw8+TOWgBWp5LOH/zmxy0CaE5RcebtBD78aLGoejT3swuG2DMzt4BdQR8jMpw8sBsXbk2VlJbNjZhjZqtXZsQX/d+tdX7MZtgxWsypljbxLjgA6IwE8UxzyJKWTUerb2VPob3Ov9Zg74/8/eBEd4uwrk6x9/TN/GAEQRAEQRCbFxJ0CIIgCGITQE+xuqeUwGwp4ky5HCEbTaxzkzbqJYe0YU6UOubF7B++lkfIX42erggAiUkBxsep3Qp6btwN5qBmMWuTP9YpJRp/7JM7a23TPPEuAv28hcbVqa/v3XuA3qtjrta7VtNozNbNUyhgvVpBYs1VM8W8Nzo9h1dev43WsJ9539xF0bjxDqaWsN8QOcz3Bz6tHr8e9Pnuj00x4gC/5zqbApYaNWaaw34MxVOYX9KEy+xCDsPjaXj2tyInSCf3V3+wD8fPDxtOJd1ZJAncI6J16ZElDI2nSxJdtnP1ifj519wmrFAiAZCW05vp8KJtKaRN7TBzP7uI2hrW7eQWj6TNq6Kq2h5QJdQHa+CRgM5IyBhLfa0MJdLLKdO0z/k1w+91/vX+tjA6IyFh2kmATfG20rSHlfhgxEb7TSYIgiAIovIgQYcgCIIgNgH0FOvqUoxQwAd7epcLW5dKoaB4pQWXCrVHtFb5tFGliF8rGYdi9g9/7FO7tuNEdyuTAoyvG9Jat811n0RuJT6gag5qFrM2+fRfTmuTP685TRlfNJ0PcuvnFKUIM4+rXbotQHNS6OveXJPFrn7PqYsjxutTF2/jwq0pHI7Wo6eruaA4tVo1PER1hXQm0qwjqTMSMv4tWo+8KWxX0AcAltom3U/sQGudH4CE55/aCUWFpY7T8edahGNq5vQXO3DwGwOWNGai9aijCyV3U2yfJ2b+HmevJeCRJcZ5BwCKJfmctn5EY8yvF1kCgv5qzMyx6y/kr8aT9bWGoMHXW1J5tVXgElK5t1vCfjSG/BhPZYp25bhhZm7B0g+3TKTn8eJ3hhgBrj6wFZIsM8eJBNRLt5M4eaANLx/cbZuSUJSiEIBxv3v37iwnRLl3TxWinKkyywX/m6yosKR/JIGHIAiCIIiVQIIOQRAEQWwCKvEp1lKpNIECWFl6J2BlbqlCYoMonVQxtUvKTaH+i9ZqOYrXr2TcV+Jy0Y8196H36hiAvKPicLTe9XyI3EpOwfdixo5P/3X2WkKY8o13u/BpknKKis9+/aowvRMARBsDtinCzPDptpp3+PDLDx8yKan09c/PrXlMeAENyDthrsdTFsHQHKCONmp1Z3rO3US0MQBAQmyCTXVW6rqyq7UE5GuqtNb5cTjawMypaI1ZxQzJ4hYDwAT1h8fTltRyep+GEqnlQLR4XVZXyYjs8FsEnZe6mm2FOD2N2oN5Nl3b3324aDuGU5ywpfdXRI2XrRWjqFo6NTP728LoPdJuzC8AfOk3mjCUSOFGIo3sQs7igknNPRRez8xYMmNbo0eGtRaRWyTYyx8eCTBnltOFOl4kHJ2es7ynrwPzb4LdeoxNzKLvhQ5jbXzlezF0NAXxrd+L4uy1hPGaF4D1+8G3r4wygmqhFG/FUI7fhnLDj6PZ4UUOaYIgCIIgygEJOgRBEASxCajEp1hLZb2njyu3W6qQ2MBf7/JIEn2D8Uc2Znx79Fow5kLZ+nHFup2cxL2VjHsxbXJzbE9XM4ZiD7CQU7C/LWyky+KFBEBFbGLWUkjcfP6jeyNlEzjdjJHI7SJL7B70yBIORxscUsNJlmuJXEp8N2RJshR372gKCtsNoGBqOkCvWzOAw9EGJiBtFt9eeV3rr3mf6f9eybri964oZVdTyG85n90au3Q7fy5FVS11lnhEqeUA67jp5+XbYZei73qcTf8GaA4WXTgL+Lh+cvPMjCH3WcjvxdG9Eab2kr5vttd4LeO3nPENPq8H//rTrZAlCb/1jQGMLosvb74/ja+/eQfzi/aSy+z8ku1nbihVzAG0PcCXA/JVe7DVK1vSs+nCn34/sDpjxOi/CXb1k3KKioUlBcfPDzMCoNtaU8f2ae/pIjAgIaeoj/xBjNXCOo7sBJJDmiAIgiCIlUKCDkEQBEFsAsr9FOujdMms9/Rx5XZLFRIQREG6tR4zc20G3i1hTp11/LmWVXM7rWTci2mTm2M9sgT/lirg4RIuj/wdAC210TvxFK5wAVLzv0VOnFIFTtEe5sco2hhkAuc9Xc3Cp/hF68lcs+Pn9z9ihBgtsMsGOUUupc5ICJdumxwmnFsk5K82nCRmRCKPE6PTGWYNAvnxcRJF9HERrSs390h+7/KpvwDNzcTPgWiNmc/Fp7zTaQmzjpraGi+Txstp3Ozm2Nx+vW38T0HzDh8+F20wnBzP79mJV9/Ii4KdkaCx7rU+59dd/fYaxsn15M5anHk7Lyq++f403hmbcaznAwDZxRzjSDLjJOasJi1hPz4XbcA33/rAIlTqNIZ8iM+wLqXsQs5yfNDvhaKqOH5+2HDLdP7nN1235bXBOF58NoI//mwbfnhrCj+/P4/s8rhcHkniwDcGLKnknGpN8etfcw5p3xeJwBsJ675WGRF8PTukCYIgCIKoDEjQIQiCIAiiaB6lS6aS0seVImyV2y3lJCDkFBWKan3yf63HzKm4vU4pIlMx4t5qudRWIm7OL7JBWaeaMaLUYnwNGv24UsUwUSCSd6eIBEJ+PeljcuHWPWHaNXP6p9a6bUYtG34sj+6NcO1hBY9UZsEQfPa3heGRJWMOFBVCtwGguTxEzgXz2LlZs+Z1xK8rN/dI3nEVbQxoAfV37wFQcWhPPdNnrR6HihPdrZa2mO8DPeduMp/t2FaNl7qaHesc6e4ac3v5vurY1QHLKSp6r45Z6v80hvxMH04e2O1Yk8W87gBNrNDdKFfuzGBofJY5v8gRJIIXIB41n4vW40R3CwA24K+zpUrG//y3+/Cv/s9hvP3BjMWpY0aCxIhcABDwWR1LdszMLeDUxRHsbwsb7iUzorpATrWm+PXfWreN+a7dfaoSU6oWC/+brNVPkzaEQ5ogCIIgiMqABB2CIAiCIIrmUbpkKil9XCnC1lq6pbT0WPmgtKgex1rgxi1RSGRy4ygZT2XQe3VMGARcrVoL5RQ3+RogZkTuCZFY4lass9vD5jHihYGb42n0Hmk3nDfmGjpmTg/ELU4TQBMWtvu8TLubQj7jelp6M3uR6ald29HdFsbP7j2AqqqMKOORJfS90GG6mn30u6erGbIkoT82xYhLHU1BW2fOjm3VePHZCMw1dJzS3RVKLajDr5+XD+7Gm//+OePzz3ztCnOeC7emhIKOGX5fbPdVA8jvgZ6uZnz261eZ73hkCR5ZwhefacL3hydxN5XBr9R48Wv129EZCRl1hHhhzVyD5fTAGCNMtIb9ONzeYBEqYxNpYU0WPSUb3zY+tRi/R3KcwcYrS+h6Ioy/mZxlvssLEE7wot/WKgkfLTkoKiWQU7Q1PzyeRndbGEPLNXyMz1UtLZkkSY5iDmBNY3dzPI1D0XqhUOSEW9Er5K9mahHxv8XWez7bATsnm9nNsh5TqoqoxDo/BEEQBEGsb0jQIQiCIAiiaB6lS4Z/qh2wBknXikpI/+YkKPDtE9XjWAvsajPwrgonnBwlemBeT53VH5s0hKvVXhcrWQM1Xg/z+svPRlAla09y79kVwFAihZ/9/EM8ubPWcKuIxjHkr8anGmrRGQm5FutEe5gPrkYbA5ZjPLKEE92tjqKCJvZY2e7zoiHgYwQd872jUC0cO8cNAIynsoyYF5tgXRytdX40hfyWekROKezYtldDlqTl7zoLUH2DcYynWJcKn1oQ0ILZvMuqPzbJOFZmLU6iwuv56N4IrsdTuLEsEIxOzzH7QlGtYmBOUZFTVHzlezEjLZsuhsgSGKcW77bQa7Dw864uB/EnuDSL8ZkMFpYUnL2WYMbvejyFzkhIKFQ6scTl3PNWyfjLL3WYUj1qafkkAM1hv9BtwhPwbWEEnXKLOQBw9lqCEWH4lHhLORUHvj6A+Ixze1vrtuHQnnpGRNVqcGF5rlTUb68pmJYOsIpeIX81ANXiaPvys02orpINgbBvMG6kexOJ7fUBH55/qh4/WnafKaq4Lk9rnZ+5znpLqUoQBEEQBLEWkKBDEARBEETRPGqXzKNM+WamEtK/OQkKldA+IL9ehhJpKKoKWYIhPrgVXJwcDzfH04zTQlQTZbVwU3fGro++LVXY3xbGe/ce4MmdtXipq9kIkvZeHTMCsJdHkjh7LYGermb0xyYtAe9UZgGdkVBRfRXtYbOz5s33p/HHn21jUmPp3ymcFokLsHskLOZUjE5nMDqdsRXyROvVyd2lB6u1884xc86f63C0QZjyjH9ynr+ezysju6gY51dULKfJKixAAVoBe7Prgk/rxs/l6HQGB78xwAT2zezcXoOeczeXA/YqYhOzljk4ey0hdKLo+4IXZIC8KMM7NN6798C6flWruDGUsIpPd9PzQnEsnszg+Plhy3kvjyQxOTsv7LcTfGuqPRL+4vIHePfufXQ0BdHeFBI6xpxQVOe6OiK3GQDUVMn42PatSHB1b0TwKRd/8eAjyzGFxBwARspCWbKv21If8FkEIwns2O1vCzOuG7taTABwc3wWOUVzENmJ7dfjKeP7V0aSmExnjeufujiCoYSophG7JqjeDEEQBEEQhBUSdAiCIAiCKJpHnUKkEpwxQGnCVrlrBDiJNpVSN6Yc64Xvp9nxYOcAWot14abujF0bsg+XjICmLtrYuav0vhyONgiD5MX2VTQnvMPih+/ew5v//jnLeQsJqppbIB9M3hWswVgyH+C2pkfTYOvJaGM5nrIPaB+O1i+Leflj9HEode3za6mmugrZxbyLQkt5JhZMc4pqSS/2+PYaS1o3c1tF2Ik5AHD1Tt7NoMPX1imc4lDsNrk5nrY4ND6xsxY5zgGjCASdpVzOUq9l0SFP2I1EGo9vr7G2THBuM807fIgXEEvuzy/h1TfuANDGJujzOh4votA1XlpeT/xenF9SXIk5ANDZFGBcM3bpFu3wVXvwdERL/cfvZz5d4pWRJPa3hZm1pY+0ORWnUy0m/nx9g3Ecf67F9l7F/ybw61qU3u3QnnpGmKJ6MwRBEARBEFZI0CEIgiAIYt1RKc6TUoSKcruLnALXj6puzGoUttb79dpgHDNzbK0IvfaGqCbKalMokHp6YAxDCS2VlDmV1ou/6kGWe0Lfjbsqn2Zu0jZ1WenwwXT7wD//2jz/x/a1MEXAebeA2cUUbQxAr0uj/xsAhhIpRljQ0krthLmGjT4WonEqde3z+6l/eJITKlTmWLML4fJy0NyMU4DaTogshb7BOI7ty6dyc+L5p3aiSpaF++Xo3giOnx/Ge/ce4LEaL346eR/pbN55w7s8AG1ufvbzD4tqr54KjqeQoBPZsQ2H2xtw4dYUZjOLFhFJhLn9K8UjAfueCBsiinn+i8FX7cHpL3bgt/7bQNEp5nSyCzlcHknizNsJyxoTrS1Jkiy1gQD7VJyF1qe+7+1qmUUbnb/Pi4f728I4ti8vKplZjd8UgiAIgiCI9QoJOgRBEARBWKj04MmjTvm2EuyC4aWO+aNwSxUK6K9GSjxzkM/8VLxe18Vcy2G114XTXPHBzVRmEZduJ3HpdpIJ/n6+OWw5rxt31Wr29fmndhrOBv21iEKCKr8mc4pqEXh4FxP/byv59GK9R9qN8S7XvYCfU/0avBh1aE89009+j95NZ5frgEg4tKfeNkBtbrs5FaGioiSBIJVZxO7/1+vwyrJFKOSRJdmyhnRH1Fe+F8OvN4fQGRGnKROlBTu0Zye++dZo0W3W8XokNIb8eP6pnfiLy87nyS0LPo1BP+q3K3jv3gOLQLGa5Jbn58zbcZzobnX9u1jjlTG/mE/j9rHHtuAr34tZakrxREI1eDC/hL9/uARA7Hq6EJvE6LLIptci6j3SbhGbVNVaCwew7l9z7aH9bWHIkoR34imLg0j/nrVmk5ba7+SBfMpGPn0bn96t0G9epaRZJQiCIAiCqARI0CEIgiAIwkKlB08eZcq3lYpddsHwSh9zM4UC+quZEq+cjqRS59Jprnq6mtE/nA+wmhGlGNJprdtWVF9WYw/Ikuz4WqdYEaWQi8kNev2dN9+fxk/GZuD1yMa1yzEO/Jz2x6ZwOFqPF59tZsQovq/8XjC7V2QJRaciNK/JxMwck6ou6PM6uk0WcyoWc4XTdt1MpBhHx7d+L4pj54dxxVycPmytswPYpQWT0BkJGt8vFq2+0hz+/K0PGNFDxJWRpPA6W6skPFxSbTxl5UdLvddqmf/uJ3agMxLCn715hxFfPuL6FZ/JGmnd9reF8dOp+0KxJbLDj788+jQA4MXv3MSl21bRcyLNpnjT0zf2vdDB3N+GEinmOF+1B7//6Y9b1rR5LwDAyQNtGE9lmLUd9HvxxWea0Ht1TFjXCwBiE7Poe6HD8YEFt3u30G9KpT6EUqntIgiCIAhifUOCDkEQBEEQFvg6EEOJR1OjphJZqfBiFwyvlLpAbhD1wRy44lM+2aUDKyXYVU4ho9S5dJorjyyhIegTCjpO2aQO7dn5yAN/sYm04LV1PAoJEYXa7zbV2P62MDyyhJ+MphjXydXluiPlFD75YPfo9JyxNpzWm3kvjKcyTGC7PzZV9Dyax/bo2SFG0JEk9jwhf7WrlGM8E+ks3jKJN+/EUxaRZDZrPa/PKyMrEFyGx9M4faQdv/5f3nSV3sxOmCok5jghSzJU5NdIyO9FwL8Fv/vUTkiSJi4kZjKO9YmKQmUdYjcSaUykMnjv3gNIkoTHtlYxAo2T0HRtdMZ2jSiQ8O0ro0aKOREi105/bFIodl66nZ/nP/jNjzMuuryQyI7RD4YnMcbVBUpnFrH31CWkHdxR5vt+Mfdt0b2k0EMElfpARKW2iyAIgiCI9Q0JOgRBEARBWOCLXouKYG9WViq82AW2KqUukBtEfei9OsY81a0H450cHI862FXqXBaaKz42qwfC9eB7a902/MpW7c/wz/xvdctpyPDIA3+F+uUk2hQzl2YRRFRDx1wjxyNL+NX/46Jtm/tjU2URwezKzhRaE+a9wO+B0ek59A3GhenxPLIkHE8AOD0Qx4VbU/j5/XnmWgGflxFweroiUFVY3CAA0BrehoZgDWQJGE9lGSHjASem8AI+ANTWePBkfRjXRmeMc4vEHED7faiukvFrDdtdpYtLZxdLFqPs4NPMPbUrgL4XOgAAC0sKhhLDuJsqk5gDoD5YAyA//9fjKWOML48kUVXEOlzIqVouNwHvTT2wiG1VMrBUQPsanc6gbzDOCDaKqjLpAI/ujRi1rPiUaGbuzs4L37cTc1rr/DgcbTDWc7HCPX8v+f7wJBpD/uX0b0BnJGT5TanUByIqtV0EQRAEQaxvSNAhCIIgCMICH2uhDCF5Vkt4WY26QGuZ7oUPXL137wFe6mq2XNPcpvFU1nKOtQx2lTqXheaqMxJinoR/POBjCsA3hXzwb9H+DO97YY92Ti4NmWgsyjGfTuco1C8n0aaYwKVY1LT7NxzTeY1Oz2F0em7FIpgs2buJ3NLT1WxJP6WPi2jcROMJQFi7BgB+56md+NG79zCZzqIh6MOXfiOCr3wvxog5sgT84WeegNcjGTWHciqbCq7W58WMWUwRaAm7QtuEY94a3obZ7EPGfaIvQbsxFBHwewsKOoVSzDlhnrfj54dd1yXyyhIW7dQ9E5OpDHrO3TT2CZ9OccnFOdwgGiNdzPF6JKE7R8e8B/sG40wtKEB1PS5O1zDTWrcNh6P1zD0lp6joOXfTuI6bfcrfS8aSeWfVywd3C79bqQ9EVGq7CIIgCIJY35CgQxAEQRCEBT4g3RkJPcLWVBarIbwAq1MThQ8YX4+nGNdMOcUdPnA1M7fApKwStUl0DjM5RcXpgTFcuDUFvsh8OSh1LgvNFX9eRWWD9Fo/WbeAm8BfORxNTuco1C8+0Ko7O4pJs1cIkeB0+kg7jp8fxnv3HuCxrVWQAEiyDKgqk9quVEFQdy+YMQen3eKRJRyONjDrO6eoFgfMa4NxANY0b/z46viqPXh8ew1+eGvKqLsylszgK9+LWYQERQXevTvLBNBbwj7mmF2BrcY5AKu7JeSvxlRa7MqoD9bgcHs900f996EzEhTWeBFxaE89AAk/iE3ibjprdRjVbcOhPfW24pYdIX81An4vlhQF374yhthEGje48a/2SPiVrWJB6d9+phW3Ju7jRiJtUy9IY2wmi7GZrFFvycnF6lYkAooTsR6z6YOOeQ/ya+vCrSlh3ZtS2d8WRt8LHZZ7c99g3CIaFdqnTikZ7b67Wr/LK6VS20UQBEEQxPqGBB2CIAiCICysNAhRipNgvRQPXg3hxUw5x4EP4hXzlHSx6GvktcE4ZubyQcab42km5dQ4l/aotc6PppDf1hFifqr81MXbkKXytXu15pI/b05RmSL0PV3N+H9+9h7zHTd7rhzpe9wWFx9KpKComuuiMyKuY6GoatFp9grBC079sUkcjjYYBd7565kFnVJFpNMDbNC5uy2MM8vpugrtRX6/fvGZJnx/eJJJv9US9jPf0cXO/W1h5n29/XwwO7uQYxxeOu/de4And9ZaAua8yCNJMvtFSXasJZPKLNgKBZOpDJ6OhNBatw2AikN78qKXyKEkIuT34sVnm1FdJeNEtzVVHQBDvJUl4BuXPrAVVzwSm61Mb/urb9yxvf7e1h3ojISEYtHfTj7A2aOd+MzXrrgWPERzY6Yx5IMKuKrf81GhXGomnMSc/W1hZg/ye/fn9z9yfR0zsqQ5DOOmmjotYT96j7QLf6dE6fz27AoYqd5EaRZ7uppxPZ4Suofs9vhq/y6XSqW2iyAIgiCI9Q0JOgRBEARBWFhpEKIUJ8GjrqdSKZRzHEp50rlU9DUDgAnOdjQFHV05h6MNrh0h+nvrbV242U8iEUgPenY0BXF0b6QkFwwvOEQb7Z1AfHoknUu3p/FOPIWOpgBTg+NmYoY5TpZg1C0pFX7OR6czxtrhP5MlCS8f3L3ip981B1ieoUQafYNxxln15vvTywIXK7qJXHB84N4ukG/XfkXV26QCKhjRysyTO2vRe6QdB78xwFyDF3kO7aln2i0KsosI+b1MajUAuD+/xAghP4hN4czbCaMtvENJRCqziLPXEpr7amAMPxieFNSFUY2x2OqVbQUdl9nAEPJXQ5KAT+ysRXtTEMPjKbSE/Za5ye+H8j1MYDd/Ivh+itroRMjvxbF9LRbxUV9buuDm5D5yQlGB+EyWaddYMoOz1xLC+5vIuTSUSOHKHe3eYb4XMekIlwVckbBMEARBEASx2SFBhyAIgiCIslOKk4CKB2u4HQc3Th6z64Mver1aufz1a+qBuKFEGhNp3pWzDU0hX8EgvEiQ2iw1CERCgXn++CfwzZjXhnne33x/GicPtNmKIKL0SDpXRpJMTRVZAlQu6C3KKlWs48xOhNS/b/6sMxIs09PvbMOzCzm88vrtZRdKHi1NlebG0Iu13+fSY/HuGCfaBWvZI0s40d2CE91an3j3SkvYjwfzi4aA4pElfC7agAu3pqCqChqCfkjAcgH5fBDcLLgCcJUaLeDfYhF0Aj42zZfZifTrr7yJTzUEXIkQrw3GbV0YABCbmMXpgbjQRcOLPy1hHyRJwkTKmrpNZ7vPiwfzi5hMZ5l13N0WxtRsFrpIqe8Hp3Rv3W1hTKWzFqEm6PcibRovCcLyREJCfi/mFxVGaJElQFVV+LwyFhXVVS2bDz9awveHJ3EjPoOnm3fg6N4Izl5LGPunMegrS6q1B/PsuvjGpQ/QH5uypMUUbfWf/fxD2/Pyjs7OSKhi3boEQRAEQRCPChJ0CIIgCIIoO6UUAqbiwRpux8HJycMH0XuPtBvfEQXyy5nmzc6pY+ZwtN5VEL6nqxmKqjI1dDbCE9o5RUXm4RIWcgp6r44Jx5sX9nihwCNLtnNkFwgHtEB53wsdwvG3q+Ei4uZ4GrLEXn8ynUVOUZl2Fes409J2TVnSWOUUFUf3Roxru3XkFFrbOUVFfcAmyM25C2YzD5nXItGCd8fwQX6dkL8aF2KThihgrnFlTkMVbQzg5IHdTEoqc/t7r44xcz2WzKfCOnlAXEDeLPTemphlarYEfFWINgaFtZ+ad/iwM1Bj6zhJZxbxlssaOjNzC7ZiDqDd9/pjU8LP+Kxku4J+4bl81R48XrsVCvJzZU4HCWiC8+PbawwhAtDGVOTgaQn7sSuoiUdCaYVbL27FHAAW4QzIu2HsEDmoFnMqxpIZjCUzeGtkhhHN3nx/2pLmTxcIP7GzFtHGAL5zLcGcs/uJHXi6eQcjZgLWda6nBeTTYvL1+ADgE48/Zjh0eKKNAXLrEgRBEARBFIAEHYIgCIIgyk4pNXg2W/Fgu0Cz23FwcvLYBcTs3AyrEUDj21fIlWM3Hie6W3Giu3VFbVkrcoqK0wNjjABlflpdp28wjoi8BCAvevHjzQt7fADVSfDkU4jxbew5d1MoDvDXtBMjzNc3Oz1GkxmcHhjDsX0tpppJbEC6kPPOI0s4HK23iIGXR5JGWqdi1qZdTR69732DccaxYaaeEy9EQXczrXXb0HukHcfPDxtzlc4sYn9bGJOz80xAXFSnhq9xpf/75YO7hanscoqK/tikbXsuxCYNp48Z833m3buzzGdPNQTQe6QdfYNxi6gRn8k6Cgxu8FV7Cqb70t1n3x+27xsA7NhWjZe6mm1TyGUXchhNZhyTp5mFiBuJFN6bemBbm0aS4ChCbfdVI51dcmyzGzySdi1BtjKDmioJT9Zvt127OpZ6StDG9717D/BYjdeYW/085jW+vy2Mvhc64JElHNvXzNyfdecPXzMNYPe4JsrnUwge2lMPVYWtoANI5NZdZ6yX+ocEQRAEsZEgQYcgCIIgiLJTSg2ezVY82E5EEY2DKGDi5OQpNiC2GgE0vn2FXDkb4ansvsE4Tl0cMV7zT6vr3BxPI2LStPpjUwWFPT510tG9EabGDhtEYyPBIb8XT+0KWNKvAWzb+Gt+8ZkmPHvqEhPk1YPo+rGnB8aYzy/cmoIsSbbuLH2dOgUB9XPzweJi1qV+/tcG48z75po8x59rcRQe3daa0TkcrYdHljA5y4oeHlnCG3+4z+jvu3dnC4pDZvh+633TnEz26bN+fn8en/naVeiB9GP7Wowxtqtr9U48hSf/5A1kF0ursaIT8lcj4PMyglj3sjvELELUeGV87LFqfPiRgoCvGoeieRFUdVI0oNXDAWBJ6cjDn6Ul7Mcv7s8ju8hafQqJI3Z1dfQ9UUiAcktOdRZzAKBmS5UlBaIo5WFtjZfZQxPprKNbyYzZBSj6XbJzYpp/i/gUggDQc+6mbb90Jxq5ddcPG+G3myAIgiDWGyToEARBEASxYSn2ydG1fNK0GBFFFDBxcvIUGxBbjQBasY6rSn0q2+2asHNLiPqhjW/KeD06PYfR6bmCwp759bevjBrikVbP5S4+374LPV3Ny/U/8sJST1czTnS3WgKpfNv4a/ZeHbMIDy91NTPfsdZZsT5h76v24PHtNfidTz2Od+IpvDYYR63JHcAHAe3S9nU0BS3zYRa6oo1BACpiE7OWmlE8et/t6jTp69VNrRkAaK3zG7U/eJFlPJVF32AcPctj95mvXmXGzOuR8NhWr60zJKeoyC1H6/NCzpzwWDPZRcU47tTFEQwl0kbg367WzzwncpRKT1fEuK7OTyfvI+DzWq7XtONXcPZoJwB2v/3iwUfMsR5JEzt0JtMZRtTwSECtz95VBgBBnxcN27fibtqd06h5hw+yJGE2u4A0l3JPZ3uNF9fjKUt7y0mVBCyZ+h7wVTP91GobWfs9lsxgf1sYHlkquCdqvKx7ysnNp2OXFtPpvmlXJ0v/jHf1KKpqSeVIVA6V+ttNEARBEBsZEnQIgiCIioPSNxDlotgnR9fySdNiRBS7gImdo6lYMWU10t0V67gqt6jkFPgv5r5iSdk1PImGoM9ScF4UyAeA8VTGUienp6sZQ7EHWMgpaK3zM99zGwzj06qNJbNGO4/ta4EsSZb5LHaMRe4Vfm3wxeMP7amHLLFpw/S0Vj+8NWWk7LK6A1KWfovWJT8ffI0QO/hUX3rf9Wv0xyYxOp3B6PSccX7z9fW6NsPjKSiqVi/I7D45tKde6AgCwJzz+HMtqA/WYDSZF2QWc6oh5tR4ZXQ2BTA5+xHiM9r5L48k0bd8XjvnkxucgvnlIuT3oqerGcf2teD4+WHms3R2kanXo3NlJImjZ4fwzX+5B//sL94W1iYCgOoqmRGc7nPpzXKqluIuEqrBhx8tCQWOdHYRVz5IWd6345cfPrSkiGsN+9EQ9OHusttldPm/1WSJc9/wZpztvmpb15ckaWLOtVG7NGcaX342gipZu28UcvPp6GkxzWkW+wYBRVUZwdn8fXZf5UVY831ZlsCIkbIkubovrvbfj/T3qRVyVBEEQRDE2kOCDkEQBFFxUPoGolxUQuoxO4oRUYoNmBQrplRCurtyi0puAv+6QOAUnOPXhDl4e+n2NPpjUzgcrceNOBskrpK14u18ii9AG2//lir4ARyONhR0oYiDhuIgokjsyykqeq+OYSiRwv62MCNGOSFKm8e349i+ZsiSeN6+cekDJhg+kbJ3RYjSRYnWJT8fN1ymRfv9T7cKRS79GjfH00Jhzbov8mPaNxjHUCKtORRiUwWD+vo5PQ7x3/lFBVcFggPfbzu6n9iBKo9c0I2hs2NbtaU+00pIZRYhS1qqLjsnRk2VjPmlvDCjQhOb9r36lqO7xizmNIf9iNuM90R6Hlu9ntI7YUJY70fS1o30COP4v7g/z7xuDPrwuWg9vvbjO1jMsZtpIpWxFcl09reF8a9MzkDezSdKC6mjO3nM99eQn3ViDSW0NW0+x2r8Fq/234/096mVzVb/kCAIgiAqARJ0CIIgiIqD0jcQ5aISUo/ZUYyIshkCJuUWlfj7CJ9iSv+8UHDOKT0QkHdftIT9zPu1NWwKJLv7mBsXiqhdvDPG3F4evl7Kywd3F6zRJKrjI1p3dvN2/LkWS2qwLVUe29osssvoOD8fwoD7MnqqKXOf7NZXtJE9r+YcsEc/l9mJ4KbtOUUVilduvguwLqTWOj/qAz4m7djTzTtworsFOUXF6YE4LsQmMZtdQHYhJ0yn9uTOWvQeaceZtxP4QWwSd9NZiyAgosYr26Zn6x+eQn9sClBV4XGdzUFcvWN1iziJOTy/dEhvpqjO62KlaE6ulTlyvB7J1TjbwY/p080hALCcM+T34n5WnMrPjC7QfPvKGC7cmsIsl15OlBZSp28wbhEEebeQoha+15op9bd4tf9+pL9PrVTCAyEEQRAEsdkgQYcgCIKoOCh9A1EuKiH1WDnYKAGTtUxXw99HeBdCR1PQVXAun5LLuW7Jg3k2gBngUiCJ7mOZh0s4fn4YHU1B9B5pN8bCTbt0Z4zuEJEloDMSEq7ZQuezE5DcrDunOeVFp6/sb0FsYhbv3XvA1NABgM6Iu/u8eY+Op9jAemvYj0PRekv6JnfwwXXnYLve72++Nerq7PvbwoZYV6wbRv9uTlFxPZ7Ce/ce4MmdtfjW70XxT/78bebY2EQagDZ3x/Y1YyiRcnQOXR5J4uy1hFG4vvfqGCP+6UJlPJlhRqTGK+MPP/MEXhuMW9LnmdPJ8XS3heEpwtrCp8rT2VolwYVOYaA75nRqvDKeadmBu2n34owsAVuq7IWsYliJmMOj12/iU9wBWhq1H966Z5uOTSenqPjsn121rBWvR7KIkPz9o5B7rLVum0WwdRJC9Ou11m0DoBp1edyw2n8/0t+nBEEQBEFUAiToEARBEBVHpQbVifXHekw9tpEpZ7qaQuIQfx8R1dAxtwPIB+dE59aD8UMJcQ0VXjA6FG2wTUUGaGLO33+0hDffT1rGwk3QsJi1Wuh8bp86F42LaE7194fHrSne9DkSncsN5n7z4sPh9oaS11NsYpZ5PTyeXk5TZxXM9JpJono2vmoPno4EmbWwvy2Mvhc64FmuT2Im6KvChx/lsMTZdlrDfjTt8Btrt28wzoiKl0eS+Mr3YhaR0Ty3bsWj/tiUxZWl91uCChUSfvHgI0ZYCfq3GGNdqK5Pa50fTSE/s2Yu3WZdb3aOFTunzSd2bsfAB841YQAg5K9GT1cE/bEpJkXb/KKCv528jyfra10LOopqdca4RUIhibB0VBU49t1hKCp7BW3vybbp1kJ+LwL+LagP1NiuE9Gc/GR0Br/51Sv4XLQBLz4bQY5buy1hP3PNw9F6AGDm3EkI6RuMM0KwnsLPDav99yP9fUoQBEEQRCVAgg5BEARRcVBQfX1DRYMJO8qZrub0QD7o9+b701BU4ER3/lyi+wj/2i44Zyc88XVpzOtcJBg5pfhayLGBYfNYlDtoWOh8bp86F40LP6f9sSn0xyaZILnuMDHfB8pxn//iM034/vAkJtNZNAR9+OIzTcznxdyL+DEYT2Vx6TYb5L50O4nr8RT6XuiwdSX8m/2tOP5ci+11+ev8WkNAHEyXJKFoZoavH6Q7NfS+f394UthGntHpOfQNxg1XVk9XM94Zm8EVQVo0nUPRBgDA0b0RfOPND2xT6QGaU+tEd6vx+ujeCHqvjiGdzbtGGoO+gjWIdPa3hTGZtq/HpFNTJeN+dgGvvjEiTHOXyizgykgSQZ+XactqUKqYUyVLqK3xAlBtXTZjyXyNnO62MKpMaQZFrh2dp3YF0PdCh6VeTiGyiwrGkhmcungbQ4kUs35bwn783//6Wfz+/3XLcJId3Rth3IeF7ml6vR3z60p5KGM9/H1Kf4MRBEEQxMaHBB2CIAiCIMrKRiwavJkDJOXseznT1Vy4NWV5bRZ03GAXnHMjPLkRjAB74efjXjbEax6LcgcNC53PrYAkGhd+TkVp6S6PJA3BoBzoY3p6YMwIco8lM/jK92I4e7TT+NzsaHnz/Wn0x6ZwaM9OABJiE/b1gnKKautY0PtiV1tJhXi/6G0aSqTR3RbG3VQGD+YXLaKMRwJyar42k94mEbx7pSHgM/7dNxi3dWaIeG0wDkBbC6cH4o5izv62MF58NoJvXxlF32DCUczRyN8vcoqK4+eHLQLKoWg9LsSmHEWdkN+LLz8bASDh7dHC7pz5JXdummLFnCpZsjiqVoslRUUq4z633HtT9/HUrgAAbayd2qnfcwrVCXO8HlebbCyZwe//X7eM/XN5JIlnXnkTX35WSxHpBr7Joi5s5t/kQmzEv8EIgiAIgmAhQYcgCIIg1ojNEoDYiEWDyxUgWY9roJzBofI6T9go32zmIXKKWpbxLKfwxI/f9bj2RPvp/z0MQKsTcTjqvkYET05RcXogvixwafUmju1rKWocRIKPaK2KxsWppo2ZQjUz9GtFG4MAVMc6OHaOlffuPUDv1TGLQ0hndHoOpy6OGK91Z5c5NV7vkXYc+669q0HvS++RdgCw1JD50a17hihhdo7ZtdkyFlzwuj82CbMgwmOuMXN5JInTA2M40d1qKwJ5PRIaQ37UB2pwxSRazcwt4JXXb+N6PGURmXhkScLZawlmLJ3Q6/oAmrOOF8ta6/w4tq8FQ4m0UNAJ+asR8Ffj0J56AKrwujXe8tS2ccNaiTkiaqokzC/ZXz+VWcSb708b95orNsKkuS6ToqqWlHduRavswyXLe7zIk8os4tU32H0H2P+O8PV2+NcAiRZObMS/wQiCIAiCYCFBhyAIgiDWiM0SgIg2Bpigb7Qx8AhbUx7KFSBZ7TWwGoJROYND5XSeHNpTzwR2U5nFsrlA3ApPbsabHz8+2AmoxjGlzBdfb+LUxRHIkn2qt2LOK6qNA2gpkHIq0D+spVc7tKcevUfaHUWLnKIaghs/boqaD9Kb7x2aIKIy6boAe8dK5uGSK9HEzIVbrIvnejxVMD1WtDFotJ+vnTTLuT1059hQAZGk2iNhQVCvpFB9F96l8823RiFLkuU+rLOYUzE6PYfnn3ocEoBrozPMdd3U3MkpCvpjUwWP04k2BvHtK2O4cGsKEylrfw5HG+CRJVsHRyqzgFRmAacu3oZHcIwsATVeDxZzzo4UAAj4vJY5skOWxO4QOyKhGiRS88LP+Bo6ZiGuGJzEHB6Ri6naI2Fv6w70HmmHR5bQe3VMKJDtCtagMeTHjUTasZ1ZgYjG7wkRZkcYf8/rjASZejudEaugvpqihZv7eiU/nBFtDHJ/g5X+QAJBEARBEJUJCToEQRAEsUZsnqcm+aCG+yBHpQZJyuXYWO01sBqCUTndKiuBXxsvPtuMC7fuMSm+yjWehYSnfEqvvBPEbrz58eODnaPTmqtF9P1SBCP9vWLGQXQdUW2cnq5m47xm4UQXkXq6mg0HEo857Rq/TkN+r23bfhCbhCxJjk4hHTuHRsjvta0/wju9Lo8k0RL2Me8FfVX4tYYAZElCZ0QToF55PR8E398Whme5bkn/8CSXIks7P1+wnqch6CsqRZod2YUcXnn9Nk4e2I0/+uwT+LMf34Eo89hfvp1wGBMWvsbMjUTa0Q1T45XxdHMIHtN42bl5vB5N4Pv2lVFMuKiLI9C8oKju06Y5iTktYT8zB3/02TYoqoo/f2vUlfvnw4/shQ9zs/e3hdHRFMSfmlwrzTt8iM8U7r8TPq+HSX+3KBishZyWTvDM2wlHofFw+y6c6G5Bz7mbzF6ToM0vL+S01vnRGPRDUVVIUNEc9iPusJ51RxhgvWe6EdRX83fJze9ouX9r7e71pf1NxM/7o3OUEQRBEASxOpCgQxAEQRBrRKUExlcbLb0O/9pdoKNSXUwrSRVmDsjkFPu6KcVgF+RZDcGolL4XG4RykzZMtDYOR+sZYWGt9pSdE6U/NmXpMz9+eg2dLd5Zi5vAXPw7p6joOXfTEEfsBB9+TQHO4yCaG9HYimrj6IKMk4jkNM/6Mfz3nYSF2cyiJWWdLEnY3xbG307OIp21pnwy0xL24dCeBvzw3XsAVDz/lFZD50fvTgGQUB+osbhgJElmXmtijibKDCXSmEizx3tkCX0vdACARbzQ0oTB4j4J1FRBliWmBhAvKKwEvUaQXRkZfsx3bKvGJ3bWMim6Qn6vsX7NfSokbswvKrgyksT+tvByKj57oWIxpzLCBmAVJtaC1rAff/UH+3Dmbf0+JAHQXENuU7m5rXXz9ugMbiTSCPq8kCTgk/Xb8a3fi+K3/tsAEisQdezGTDSefYNjAFT8zeR95v2gz4tfa9iO4fE0eq9aHbcqxK6choAP7U0BW+HuuSd24NebQzjzdoJJUei2PpkZLU2clq5S/71YWfpOFje/o+X+rbX726eUv4liE7OOrwmCIAiCWP+QoEMQBEEQa0R564dULisRrirVxbSSVGF88N/8NH+pa8AuyLMaomEpfS82COUmbZhobeh1TNZ6T9ml/BqdnsPo9BzTZ9H4HX+uBT/96U8xywWAzdpM36C11oj+NH0+VRmbIksPwDuNg2hu7MaWr0Wj70eRQ8ZNgfWOpqCtCGWHxNXPMPe3JewvKOjUB3yMYHBzfBZT6XmjVsvo9JzFTXBoT71RVyenqAXTR0UbA+i9OmbUADp5oA2xidnldJMSXvzOkEXUmJ1fQmvdNouw0lrnx0QqK3RXFENHU9B2nYp4cmctOiNBRtBJZRYN5xXvhnOD07h5JLHbBgCeaQmhM6K1fyw5tyKRwy0qgBP/ZwzjqYwhqp26eButdX7H7/Gp1NywmFOxmMsxtY++85OEbco5NziJYL4t1s9SmUWh+BLwe415u3R7Gt1tYZw80IY/+/EdYUpAncsjSfx06r7t516PvJw6UWLu9eMzGfReHSvKjcv/XsiSVFYnr5vf0XL/1tr97VPK30Sb5eEhgiAIgtjMkKBDEARBEGtEOeuHVDIrEa42YiCCD8iYn+Yv1zn1IM9aiIalpAErFIRykzZMtDac9tRqpu/j26I/KW4WP74/PAlFzTslhNfnXpqLf4vGZDyVMWpLvPn+9PJ18zy1K4Bj+1qYfuuOIP31UCLFfEd/XzS2h6MNQgdUT1czFBWMo0pfa+Y1GG0MQlEV/OjdewAkKCpwemDMVZ0WnYCv2tb58GC+cJqtmwn26XRRkXgZwMsHd2MokYaiqhgeT6EzEkLvkXYcPz9se25ftQdPR4JQVeCVi3mRTBdthxJpx77y9WTs3DktO3yAJDGfh/zVeLK+Fj+794BxPPiqPajxevD94btoCDqLEWZkCRgetz7J3x+bxFAiBdUhZZyv2gOo9g4RQEutZhapZFlCzkYg6IwEjX39q//HRdd9KHRNJ8aSGeH4zxZIS1eMmGNXJwkA/usbI5BWcHtyGns3NW107nMC6ZWRJJ5pDmFv646C53By2uV/y9n+jyYzTOq11fh9KRY3v6N2x5T6u2P3t08pfxNtloeHCIIgCGIzQ4IOQRAEQRBlZSXC1UYMRKyGSGV3zrUQDd24b4rts6iAu/4dPUA2lEhhf1vYqGNSaG2sZvo+0TrlnVhjyYzxFLnd9as9bGovc/FvfgzF6bisKfz4fptr2rz5/jRawn7Ld+z2ndN+lCWgKeQT1nswCyOKCkPocnI87NhWLQw8H4rau2XcBKpdpe6S8nOjj92l20ljfOwcR9mF3LIz4QHzvtvguS42tIb9mM0uMsJVyF8NSdL6qBewN9dtSmUWcGUkiZC/2tKm7EIOqcwCxpJZ12ncOiMhvDM2Y3lfr/EkosYrY35RMZwmTnhlGYs5+/ouz308BG+VxxAhdcfTgl3OOKdreSTsbQnhyh1rf4rFbRo1NzjVScqpcKUO6ffAnKriRjzFpINrDfsBSQJUFfWBGnhkCZ2REI7ujeDM2wl8860PXM0Vj+7YO35+GO/de4DaGq9tP1rrtqEp5DPcaWZBG7BP/6WLMqX8voynskW7fJxw8ztqd0ypvzul3INX0n6CIAiCINY3JOgQBEEQBLGqboZi2EiBiFKFCDeUS/ji5513c4jWAV9EeyhhfTq6UPv46/IZuLrbwsZ3eKHk5YO7Xa2P1XyKW7ROe7qaLSnKCl3fv6UKLx/cLRwnfgyHEilLEFVLDSYx3+cdJe/dY8UG8zn2L4+z3b7T39cFq+Pnh5fnK18nRheNdEGnsJghvq+8tJwq7vTAmFHDRKullG+f3Xrtj00VnQ7MjF7nxi713JKi4MxgHPfnlyxrFShD0F+ynkN/fXkkibPXEsb83BxPM2tMP04Xyvj1dz+7gJDfK3RPhPxefKphOzojIW39Dk8ynxdyuNRUe1zXl8ku5hDy27utJFk2hKs/f2sU//WvxbVY3PDYVi/g4CgqFac0cW4YS2ZQJcO2rpETrXXbcDhab+zX3qtjFreZWRQcTWbg9Wh78ujeCE50t2B4PGUIlYAmyPmqPczaEM2P7tj79eYQPLJkiDXD42kmRZ2Gyv12OIsx5vcBd/dt/d6o329Hp+cYl8+jpNTfnUL34EfdL4IgCIIgKgsSdAiCIAhik+Om+DpRHPyYAu6FCDeUK8hTyM0BWNeBwgVK+delXJd3bVTJ+ZoIbgJkIkFyrdL3ma/dEPDZCjp217ebR9Ecm4Ox+9vCOLYvX6PHfB1zvz/x+GO2TgWP7K72RKH5KiaFml6fZiihuXfMQqdHlnCiu3W51oa4vaJ6RJoQpBWzT889RDprFS92bKtGbU0VxpL5eiwhvxfH9uVTFYqe/O8bjONmIs3U6vFVeyxOh9Y6P5pCfouoxTtkRI6ZQlvIvObtAuJNIT86moKM+Ak4p8E6to9be1zOL6+HddXwpAXndkorFvB7bQWdKyNJnB4YAyDhqysQcwBNlLghSB9XiKCvCtmFHD5aErd/hWWNAAC1Nfailh3NYT/e+MN9zF4VpWTkz7uYU3HlzgyOffcmvvPlp9EZCTH3kPlFBfOLipEicDzFurF2bKs2hFb+HvDywd0486UO4/6ni6qj0xmhuGJ27+1vC0OCChWS5UEHN/dt/T7Ai5v9salH9jCKzkZMG0sQBEEQROVBgg5BEARBbHJExdfLnZN+s/Eox7QYtxUfFOTdHKI286cSnbpQ2hlrMJI9iTkIJgqQiRw+fHqzYl1MpbrUeAdRyF+NgN+L55/aCVmSLSmHSr22qD/6MebvRxsDOHlgt3HdnKLaCjqiYKOoLYXmyy2tdX7DcaMHifVzr8QlqAlBLTjR3YLPfO2qUNB5aXn8zHPFCxp2T/77qj3MufTaOeY9fjjaIHQR3YhbnVXPPRHG1Tv57xbSCcxrXnf83U1nmfMu5lQs5BQEfVW2TiJAc2X4q6vwZH0tju6NMJ8d2lPPFJvPLuRcp2zTcaq1Ur+9BvUBn7COEQC8NjDGCGcrwa1zyEw6u4Sg34uPlgrXZioVRVEQ9Hnx0ZKCLVUyZgVrlUdSVctecEoFyHNtLKU9ZLC8vl8bjDN1lzyyhN4j7fjyd24yAsmX90aM/cE7M/uHJxl3nyau5F1y/G/H6YE4s7ZOHtiNE93W38Ni7tv8GIxOz2lOwkf4t8tGTBtLEARBEETlQYIOQRAEQWxyRE/60lOlK+NRjmkxOfz5gBhfk0TUZv4p785IyHJMIVcNf13dteEm9ZjoafHWum3C65ndHDlFNepyiAQD0biZRQe3BbpTmQWkMguokuXlazsHF3uvjjEpw+zmzMmVJXp6vu+FDq0P524yx+o1LuxS4Yncek7zlZjJWBwoErSUTzyHow3G+JkDvG++P4134ikj0K+7xfpe6HAUdUQp2GYzDy3HtdZtY/pqF2y1e/K/xss6clRVRWckhM5IELGJWUQbA1BUbayN1HfL83Q9nmKuIRJH4skMmsN+xE2fdbeFUSVLxvlOD4wZae4AreaM+VxX7yQZkcgOzZWxgMsjSRw7P4wzy+ukbzCO4fGUJT3bLx58VPCcgCZy/f6nW6GqwN9O3heKalfuzFj2qplyiTkrQeQ6Winbazy4P6+tn9n5fB8f317jStC5P2895ujeCE4PxF25fRZzKk4PxI09K7rP9w3GLetnKJHCV/Zrbrmcwgpko8kMI54UcqZoaRTzfPOtDyBLsNxTi3GfitJcPuqHUShFGkEQBEEQawEJOgRBEASxyeEDMftN9UuI0niUY1pMDn9eLBHV0DGTU1QoKpaDsioO7dFqOvCB9Wijc3DPzm3iNvWYVTBj7QgiIapY15D+upDIw/fV/P1CQb3MwyVLiiy33zUzlEhZXtsFWg9H623Pbecs6z3Sbvybn68vn73BCAuNwRrIsswIOr5qDx7fXgNF1daQR5YsAV7eAXB5JCl82t681szpzXQRSJRi7HC03ggauwm28mP24rMR3BxP40YijexCDqnMIk5dvG3UNdFqClnXyemBOG4krOLu/aw1CM/LVk9HgkzqOX68bk7cd+yDG66MJPHid4bwdPMOxj1hhk8tZ0eNVwYg4U/fEJ9HZyLl3u2znpElIBLy4XPtDfjmW6Pig7hcewFfFWYFolbAV828zikqjp0fLip1W9/gGLM3msN+fDi/iCd3ak6tr3wvZvnO4Acz+MzXruLQnnpMzmYtn+t7VnMGBnHyQBtiE7M2zhS2r9mF3Irr3nhkCYejDcw9lB5GIQiCIAhiM0CCDkEQBEFscpxSOa1HRKmbAJSczqkUHuWYFpPD364miVPA3xz4lSXJKNBtFj5OHmjDywd3YyiRhqKqhuBgNw7FptuypBtSNdFMlmAUeOcp1jXU0RR0JfIEfV6E/FrA1RxgdRNYXMiJ00IVG5Tk02uZX4tEO5FTaWFJWa5hYm2LniJNP49+Xo8sQeWkiL+deoBP1m9n3ssu5DA6PYdTF29DlvQALtvoGq9sEQ9Ewhaf4s4MnzLQIwFdT4QtqcUKcXRvBNfjKbx37wGe3FlrOG4++/UBJq2UnpKtNczWFPr+8KTFOWDmfnYRssTO0/gMe+yZtxPQZB4VsYlZzHJCFe8aKpUrd2YwxNWb0V005r4CgNcjYdGmkEwqs2gRnUTYfd8OO5FDhM/rQXaxtDGRJU1fKdS6rVWSbY0dM4oKSJKMoUQaC0viNtUHahjh066fKoCjZ4cgS0B7UxA3EmlL2joJQCTsR2NgK1RJxntTD5j7ES906m6wyyNJHPzGAJ5/qt4iTOdUGPtWv8exfVQt933zfdN8H9fS+VlrI63UUbORUpytJO0kQRAEQRCbCxJ0CIIgCGKTs9FShIicGIDVabGa/X2UY+o2gF8KdqII/35sYtZI+aWPu56m7fhzLZY5uh5PWdJ8OY2dpdZJUvvv5YO7XaeXc+MaMrdH/w7fV3NqKb24uNvAYrVHZl631vlxONpQdFBS4orZ669FAUJz6q4335+Goqo40d2K4+eHLUFfs7PMzuEkc9dOZRZxeSSJoN+LoH8LoKpM0Pq1wTgA4PmnduLVN+4Y73/52QiGx2ctqaD4PvBOHjOc4QE5VXOhnL2WYFLv8WnaeFfa2WsJox2XR5J45pVLeLK+1iJw6PBpswrVnDFrGrpIwuscM3MLtq4ZQHMNXbg1ibGk1Tkhojnsxy/uzwtry/DC0KE9OxGbmLX0tyHoY9LC8ahq8XVrnPBVe/Br9dtta0DxbKmS4CKDGQBN7DOPuV3NITPdbWFMpjOux3w0OYfRpHXNeD0SHtvqxd3ZeVfnGUvm0xqa012aaQ77sSvow414ClnTHLeG/YAk2a5d/fw/vDXp2IaAz8sIRN3LArqZC7emDBGTv48f26fdK3ih0614bSd2bKS/X4pJl0oQBEEQxOaGBB2CIAiCIDYUdq4K/r2NGijhA1y8ewYoPUjEiyLRxiB6r45hnEujpAfp3ApAvLPCPD92gTxRrRPRvOaLyaeFLp6couL0wNiyu0Ay0sjxrhQ7kceMR5YMIcsN/i1VePng7qLFNn5MFK6+hbqsbPABQkUF+gYTzLEXbk3hRHerZQ581R70Hmk3rsPPsT7WnZEgLt22jkc6s4h0ZhEtnHtlZm4Br7x+GycP7Lb0Hc/lnXR6XRqzK+bN96exvy3MnG9/WxiTs1mMTmeMgDPvJOmPTRljW0hMvB5PCUSqBYsjgqU414mZJU7JkRzO1lrnR1PIz6wVO7cSj5MQY0Wy7PWWsL+gUMW7iFbKP6zdip/9/EPXx5vr0wCwuKCCfi8kSEhlFiwCmhs8EiBBLnygDb5qzVW1mFONWlvlIp15KJ4fSVp2xzivk6kC4tLze3aiSpYttcxYgYndN+Z9p9+zRXXJ3LBWYkchl8xqumiKSZdKEARBEMTmhgQdgiAIgiA2FHZODLdpyDYa5QwS8QKHlnInn0ZHrymiH2c3F/z7oiLdOk6BPDfp5fgUXbyLR0sjl++DOS2YWRzjhaG7gif1C62r/DlS+GKbvBz/9KP3SHtRQUF+TPiUX/qp+Lm/cGtKEETWDubn4OlIEGevJWwFA72v+lx/49IHwhRgY8mMUAwYHk+jM8KOl1lEs0tZJkuwCEHHzw8zx/JpvUan54x6PIXExMsjSYtoJKK7LYyp2XnMZh4K6/a4JeD3Im36Pv/azOFoA7N2e7qaGUGqXFy4NYXGoA/728KGwMSPk4i0W3uMC2q8ckEByYzPKzPOFAAI+qsxM5df73bjKkICUMUJg4oK1AdrhK4bM7yQpFOuNHki7NPSqXAjODYEfY7jLUuyxQlj/T0AIxyZ951OqY6atRI7CglHqyksFZMulSAIgiCIzQ0JOgRBEASxjqAc64Vxyqm/EfLsF8tKg0SiNacHsHrO3WSObQr5HAN++mvz+0/tCmAokYKv2oMarwcvPhuxzJmZ/tgkky5LdH4zhQKBIgdXf2zSsrd4Yejkgd0AVIuzxwnzOf55iyYavPL6bfTHJo1Ua272s7XN7Hfam0IABLWGBIHdQ3vqAQC9R9px/PywUTem90i7pVB6a902NIV8zFjrAVo+mGvmwbw1kM7X3wCs6fhEtDcFLQFhaz+t6PNeSEwEtID8/raw5X1zSj1FVXFlxFoTxExr2I9D0YblVFRWEaB5hw//33/ThX/2F28bwfR0ZtG4TrQxCEDF8PgsFFXFjfiM4SBqbwoAkFwJLbozRETI78V2XzUTzB+dnjPa290WLuBOKj8hv9e1SOar9uDpSBCf2rUdf/bjD5jPFD4HXxGosAqDk+ksGgJbC37XLn1buRw5VbKEJTc54qDt79gEWyMptOxUUpYrYH2yfju++S/34LlXL9u2MTaRBsAKF7w4k1NUXLg1WdA1WQprJXYU+3vhpn9u/27bSPWACIIgCIJYXUjQIQiCIIh1hNunQzez8GP3BPCjyrP/qOdipUGilThk7ObC/P6Xzg7h6nKNjOxCDkPjaXxlfytzTvM1RqczGJ3OMG1xmle7NurzMp6y1sMYnc5Yniy31glKo++FDpzobuW/botIPNKv98rrt3E9nmJq8NitE75PVteAFuwVOarMbqTutjAAFT3nbqKjKYi+FzqYa/LXORyttx3rY/ua8YPYpPApf1402S+ov2GXjs+KcyB0KacIa67wjiKzKHj8/DDTPkXVBK4zb8cZwe7YPm1OcoqKz379aoF2aq6HY/uacWxfM578k4uMg0SWgMPtu1BT7UFkB+tg4lP3mdMm6ojS3Nnxb/a3wiNLuDmeRk5Rmb4e28emwXr37n0mqO9Us2glBB2cSAH/FougI0pF1xL2oynkQ2ckhJ+MpSznKcaR44bRZGYFyfU0nFLqucWNmBPyefHJhu2GGGjGPLYnD7QBkBzFHADL4qIzHlnC4WgDs1b534RSfw/XSuwo9JtW6HNR/9z+3baR6gERBEEQBLG6kKBDEARBEOsIt0+HriQtyKMWIDYaj7rQ8UqDRE5rThRkK3b98AFj/rX5GuOpLON0cPN09NG9EVyPpwznie7q4Z0gfN0V/tyFhCGzQHD2WsLS/5yiIlcgEGuu5QLYrxN+3IcSbDBbfyJf9AS9LEnCFEmiaxYTRPXIEppC1rRNIX812psC6IyEEJuwr7+xpKj49pVRS60enkJOgW9fGWMEndbwNhyK1kNR88KV2dkF5N1J+vhfHknizNtxyJKExqAfiqpieDwFLRQvWVwIgOZeOrRnJ24k0oaj5fJIEqcHxiBLEh4usenA9LGXpcJB4sIil1UokCVgq9eDzgi7B4cSqWVBTUJnRBsLcw0pXm5Y4NqtI0pxJmqHCFkCAr5qzC/kMG86h8/rwdPNQUymrSIrT2SHttbGkhlcup2E17M2v1Fu2qZjru+ks1Ixxy2fbNhuEVE9smS5h16ITWFUIMIGfV4uhZ67losE096rY0JRuZjfw7USOwr9pkUbgzh5oA2xiVnhPVH0e0+1cQiCIAiCKDck6BAEQRDEOsJt2pGVBBAetQCx0VjvwRynNScKspndBPz6EYk9NV6ZSQdV42WLjpuvwTsVnNLu6Nf6/nDeNaIH6k90t1rmpTHkYwKvvGDDB8L1QB6/X8w1TfhUYuYAq8cjCbwmeZzWiWjczeKI3bjw3+NT5vHXLDaI2hkJcUXStTRTr75xBy8f3M24Tvj6L1dGkkxqL7s0YXZ90+fpzNtx5v2mHT7IEoxaT3ZzxIuOfYMJi2OB75tOa902vPGH+wBgWRjJo6Vbsxepbo6n0Xuk3fi3KEgcbSycUi7IpSlTVM3xdmUkibPXEgDA7B1daDvzNltDisfODfJMyw50RoL46l+PYMEkhLoJ+ysqxPVaJNjWAzKftyXsxwOuXg+fHq0UvB4J9bVb8eCjRaRtatIsuLxO8w4f+l7owMKSgv/9m4NIlMHdU4iQ3wsAqPVV4wYnjOuuL/4eOmtT9yi4rZoRdPi0bYWEbL0uGP970FrH1vsq5+9hOR5GcfObxt/LzPC/K68NxvHkzlrmvbWsjUMP6BAEQRDExoQEHYIgCIJYR7h9Yn4l+ebXuwBRaaz3QsdOa04ULHJaPyKx8MvPRvDqG3eM47/8bKSktvDY1WL55lujkCUJe3YFmHl5/ql6VMmS5dz8eV4+uNsxFRtf00R3HL02yAoNVbKEgK8aLx/cLUyFVcw6KWZczHPGO4bcpkeye9+tm0r/vlP9l8e3b2WEkJC/GgG/F4qqYmFJsbig7Oa7oylomaOf/v/Z+/voNq4zzRd9qkBAIsCYBEBoYos0CZI2NX2tdCx+OI4smUqc2O4zM92JovTMylXcatNSOz2edHd6rpe7Z3pmzkxfLycnX+2ZZCTTIyvq3F7HitIzc89ty4lkiaSUWPyQ05YnFmUCIEXKySEIkHIToAQQVfeP4i7U3rWrUAABkqL3b62sWEShatfeG/XxPvt93ul56t8nRqawt6OBmg/F1DvZ19GgB69Z8eb9+ZvUv10SYNQEcoqKQ8dH0NUcwPe+1IGjF2L6v/PBV7MU4PdWIeDzQJJk7N3RgCUlh2/+5D3TdoCWlSNJdBA3kcrghVNjpgA7r408fh5NQFFVfKIliIH3zBZ3JeFQ8eCKQWUgm1MRSy6iNeTDHdVuTCQWbbffXCWh2uPCHEf8aQr64JIlfOWHo4hWqL0sRNDj1R9aUlQ88q1+KEoOLSEfPljMYvvWWlxLpkxzvTXkw29//C5qPnU0+altnArZhep9ObnOORUlKrUYpZhnIvZ+P7uQwdmxOFV/azVr44gFOgKBQCAQbEyEoCMQCAQCwW2E0xXzK/Gbv90FiPXGeil0XOpKXbs5xwsWsfPnZ5EEDhwdwve+1IETo3T2AslOqJJlx7ZeTjNGrGyq0pkcnn/tCnruraf+Lkv8ejzsfk6MTlNtZc+XrRejqCpXaPC4tEwkckze+DilmH5hhQ+7QKNVMNDq706zqazEFyN7dzTo9nBE7CIixFAsaQoes+NUX+PBU7ta9HMyjhGrG2iWUxL2tIcsM0R4tG2pwb6OBuo3zsJmGf3po9sgSzCJeKffncHhgYhe94UEyQ/v7zRl/QDAXHpJFxL6BqOcs8qjqMAnwlZZPuZrQG01a7eVR5by2T9nx+JoqfdaHtcJxlo66aw5I2stiMRTCAerC253c0nFrSV+Jk9sdgHfPxfBhXFnYpdbliBJzjOAAMDvdUMCLMeKEPS5qew3wtmxOHrurUckTtvIReIpjE7OM1vn50lOUXFidIr6lM0IIqIHe33UftfF3Q/Z682J0Wn9d2e8h/HsO8shXhTzTETO56XBKGYX8kIZWxdrtRALdAQCgUAg2JgIQUcgEAgEgg3ISvzm14sAsVFwMharYYtSiZW6vGARsY86MhBBIpXVA7//5MVB08r6ruZASXPVSX+xQTiWd97/gPo3aylktZ/xmQWMzyzof+PVjDBmj7D1bYI+Nz7WUIdMTgFuLSGnqHDJ0qrViGDHzC7QaBazprTsm1k6AMwLnPbuaoGiqnptFkWFfq5s4NVIW8iHhoAXJ0enAUkLAI9M0GPDZvZoQiEdCH9qV4up1tNQLIGJRJqb4TE6mbT8zRlFB0Czg+tergs0FEvgzWgCsiSZis8bcUnA7ntDePKhMDxVMnp3teDR7/RT2ySZzIqzY3F8/D/8pKDQUSibSJa07Kn/c/gaoszY7d3RgBMj9N+T6Sy8bhmQJHz0jk3UZ5vdtBUeu79icMsS/F636bzXAzEmO6el3gtIkinbxmrEJxKLen0qJ9wd9OKLnY0FhU4jc+msw9pB1ttciCRQ7ZapWkYA8A7zGzPWruobjJoy0Vjhsqs5gJyiQlHV5SwwCXt3NODg7hb9WucU9jo0PrOg95NxP+zvz+73WAzF1hIjbXJqD1pJVrJAZy3s2oRFnEAgEAgEzhCCjkAgEAgEAorVCiwL8vDsa4xZE+UIaDhZqVtsMIUXLCLzh7UYY4uJt23xFS0WkvadGJ3W7bysxCljEF9RteMbi3+zmTRWgS621osR0ofs78Wuvg057r9oC+FWVsGTrwyhyiVzxaBKBLPYeiwdTdYBPrOYleLWg+EFTl2yBFmS9O1fOHVFz4KyDbRKEpVN8MKpMexpD1GbsGNntHZjs2ZIW8h4WNXBySkqusNB6nzbtviwr6MRB3aG8fL5qF5XJ53J4dzVWZy7as6+CPrc8Ps2YS6VoYSWnKoJNEcvxPQsJ7vaOoRyZK10h7XfJeO6hoC3CoCK6TmztVh6OcDfGPBiX+fdGJ3U5uTPowlutkch3C4JVbJECQdZRTVlh5SbzVXaSd9cWllwPzabRl21qxxN4hKJp/DCqSuo3SyDUzrKErvaQS4JeOieevRz5qnx+7x92F0f2XsJW++KXNv7Bun6TLJkrlXlBCtxnojl5HopMfJauS6dpTwTrZeFMStpx1rYtQmLOIFAIBAInCEEHYFAIBAIBII1hg2QGW2YFFXVraecBPmtRBknK3WLDabYBYvYgGBjwEtlRuzraCw6uGdl1cUTp9ggnF0R746mABRVRe+xYW4fT83xg84dTX58/1wEJy9NQ1UVNAZ8cEkSusP8ujJdzQGcGKGtiogowKtDcWJ0Cvs6GosSdgqLcnTQ82Isoa+aZ7GriWPE+FXj8ScSdL+dGJ1G764W20DrXOqW6W9TyTSefawdo5NzprGbSNAiU3PQq1vYHe6P2NZ3MnJ2LI7ucECvacTWDBqKJR3V1UmkskiksuhpD3GFj6FYwqK2SGVoDeWFU0mSqc9uLqlUwJ1H/9VZfLK1Xs/iOrAzjP/HX5xCVilOIMnmVNyxucqUCVIq/MpCZrb6vWjwV9uKGk5QAcwtli6uuV2SrfgCaFZ2N26a+2f3PfWQJAk/i8xa7oNY4RnJqcB1poYTwa5OUmvIh8P7O03iMoG9l2x2y5SgQ67t5bL7Isc+MTpF/dZzKqj7VQ8j/HaHg0Ufq1ysh4UxK812WQu7NmERJxAIBAKBM4SgIxAIBAKBQLBCcoqKIwMR3V7KaC3jBDt7sJOXpvUglhORxUqUMQbnrQQMq2CKVX0Xu2DR4f2dOHR8BJev38D2rbX43pc68IOfT6xoxbJVEJwVp6wCWVaZNFq9lzFTn5FzZDMpvB4XHggHoKjAN17PC0wk2+DMFX5dGQCmOkJG2Hob4zMprrWQHYVEuRGmD8+NxdE3GOXu364mjpHO5qAunhjrwrCMzyygbzCK7nCQypRpDfnQHPRCUcH97ng8BVmireEK1epxUt+JZXRyDn1PdFF9kVO030kxtXUALWDetsVnmjskiM62xV9dhblFfj0WgC8IsFZwPG4sZvXf5d4dDZQNGGuTRdy72ED/qyPXKBHNxsFr1XAqJ91IZzFfoMZMKVTJgKpaiyIAKCuzQmKOHdfn01Qmk9ct6xlUBGt9jf/BpirzPgAg6PPgta/uhqdK1u8bfYNRHPzBCBRVhQQViqploiWW555xDraEfHh15BpOjE6jwU/XISrVdoxch0hbrMTxy9M3KHu3D7td7EqzXdainqKo4SgQCAQCgTOEoCMQCAQCgUCwQlhrGaO9lBOMYos5IE5HTwutWGVFD1KvhATBDu/vXA70mAUMXjCFDWiTzJFGv9dUmN7YLk+VjKMHuqm2rHTFMts+YonFBu6KDWTZrQq2KnR/dixumbnD7sMIG1Q3YlUQnSesdTT5AUi6FRYJuLNWd2w7eIHfE6PTBVdxazVxNIERqooGfzVcsozusCYOkvnEwtoxvTQYxZMPtVAZN3o207Fh7j7IebAB3d5dLSZrPTLf2dpFpL7TqyNT3Bo6ADAxm8Lh/gjVB32D0aLFHEDLDugOB00i2PSy9SBbY2iOyf5h65r8q0/fgypZokTjkYmkLh5aoarQz+ng7hacvDRlafX2p4+242IsacosisTTePy7A5b9ZgUrPCSKqJXzUGsAl67dWLHlnALVUvRymuXDY8lBolGhbCQnWTsA8D5jifdgaxCTCdpC0oq9OxowFEua5jBPzAGAg7tb4KnKZ3JZZUVaYawxND6zgD3tIco+1EixGSRmcZwWdBIGm8NS7d3WC+WoJbPSbJe1sI1bL1Z1AoFAIBCsd4SgIxAIBAKBwBHFBBhul8K25WonL+hfTPDEGKhi26SooASAQtkobI0ULdhOixtWgR5eMIUX0ObVUlkNaxRe+3jjVWwgy25VsG1Wh00s1mpl8cHdmuXYJvccPC4Zzz7WjhffGKdEDzbQ29EUwOH+CGU5ZGwTa9dm1w6ZLaQCLfA6PrOA0+/O4NWRKX2FvhGtJk6+Xs14PIXnHt+mzRsbIebO2s2UEDC7kMELp67guce3URk3pK1Wfb2kqOj+y5/qooBRqGOLkJ+5MoOgz2Pat0uW0Bz0WgoT43FzRhQrDDlhT3tIn6tHBqKUVdv7N27qAouxxhCL11OFxWz+e//jrevY29GoCzkXYwlcnr7B/a6RRCqD51+7gjejCciSJvCYjuWWcVddNVfMIbB95kSMqPa4LIWDQqxUzPF6XKa5x+J3kOFUSZxm7Swy9X+ciElejwt31VUDkBzXkjHOW8JKrQGnkmm8/scP69aFRhtE473Nae044/2uoa7a8vezmlZdlXjeKUctmZVmu6yFbdx6sKoTCAQCgeB2QAg6AoFAIBAIHFFMgOF2KWxbrnbyAtErtZcx1n6RJesVq+w5PPtYO1UHhJepYBXo4QlLbMaHFathjeI02GOVaWQVdLNbFWyXPTWXprMqWkI+hIM+vYaO3Tm8/fbbAICnu9ooWz0AaAp4sa+zUc/EuRgrXIj+8nU6uF9f48FThgwWQnc4YJvVEYmn8InnT0OWJGzfWovD+zt1ccdKKGP727gq/2I0wQ2q8wKuVllAisq3cbPLpCIiSlvIh8aAV8/c6WwOUHZvPIwZUWwdICcYs3vqvG5K0Elncvrv1S5Y7me+Nx5PWWZ2OcEuyyidVTAeTznK+CDc7a9GZNa+b0o3GcOKxBy3LOEP97Tiv5yN2G6nKOWp57Pa9F8tnDGWzuQwPrOAF05dwR6mtowVRIMwii4dTX5bm8KWei+agj7IksT9nY7HU7qlI3uvattSQ21rl/FJYDOGyLWGvTYXuh+VU4SpxPNOOWrJiGwXgUAgEAg2LkLQEQgEAoFA4IhiAgzrubCtXdF2XjudBH5Y+ySef3+pAaRCIgbb17w6IMYA9kQihY4mP559bJtu18UL9Dix2rGz01lL2EDWgZ1hk23cm9EE+p7ogkuWbPvY+FlmScGh4yO4GEsincmZLKRa6n2mrBMWMg+2bcoAKvDkK8No8HspQWdvRwNVI6aQmAMA27fWUgHNp3a1cM/nwM4w3owmcPn6DagqKNGAQLIWzo7Fcej4iG6dZyUEGvfJikAA8Aan/UuKiu+fG9dt14hlnCZu0VlAVsIHyVwaiiUxkbASIyS9X85cmcGzj23Ds49tw8lL05hL3eLagHU05evwOLEZYwvMdzUHcGQgSgkwrPUcT1htDfnQFPRBVVVcSxZnb1YpAl430pkl3GSyRN6/cdO0LZu1U6nsl572EB4IBzAcS+AX0zdMx8mpKr7x+tWC+5lfzI8HOz63M6yVnCxJ2NMeKmgdOJFIU/P29Lsz6GkPaXVpVAkNgWpMJVNUPR9ZkvCJlqB+zf3st89RnwP5+6r5d2wt+ZGFCOz9kt2HS9Zqa1nVebOinCJMJZ53ylFLRmS7CAQCgUCwcRGCjkAgEAgEAkcUE2BYz4Vt7YQKXjvZwA/PFsYlS3i6pw1P97TpgaVDx0eobZwGkIoVfgr1NQlsnRidXrbWSuGFU2NcyysjbJCKDXjuaQ/pgkgxVMKeht3ngZ1h6vOXz5tt486OxdF7bLioczh6IWYbFCX1hnjnRdpIbNOO/G/aqnkitvW0hzA9twgS5MwpKjeACQBtW2qwd8dWGGvofPnBZnzlh6O6qML2gdNzYDk/PosnXxlGdzjfr2zQ1LjPs2NxHL0Q0zNcFFVF2xYfVFU7M1Jj49xYXBeq7CzjToxOc9ulZR1Y1+4hvD9P1x8h/UVEI0ArcE/bWKn6eTqBaBjGmk6PfmeA2qbaTf9+SP8ZzzsST+FaMk2JIk6FhqDPg8VsjrutW5aQ5RVPckAynYXbZf598OrDfGRzlWMRhxXBiuHy9DxckgQFEvd4pZzqRhFzALNM0h0OOJrLkXhqeVFCHqOY3BioRnOwhhJsWJvCL3bebbq/knuSKZNV1X7HsqSNmfH3r6gw3Xf7nuhylF3qhHKKMJV43hHZNQKBQCAQCOwQgo5AIBAIBAJHFBNgWM/BCKsA+b4Oc1YNb/tCtjDsCmdFBZ7uMa9OtgogFbtyuFBfk0DX8ESSCmITKzUrQYUNUt1Vt5nKIiFCVrFUwp6GJ7oZx6lti4/7vbNjcd0OyInQZBUU9XpceCAcsJ0bhTKeLk/f0LNlXjg1BlmS0LurBTkmOm0W0rRjHO6PUKLKoeMjXLGKPYe2LTW4278Zk8lF3FjMIqeomEvng+TZnIozV2Z0mzbjfCP/ZvdJ7M2MNX+0Y/HHATBbxhGMc3ZzlQRZltEdDuDw/k48/dcjlvsjsNZdXc3m4DZbk6RvMIqRiWTRwkBz0GcYc/rLdd4q9O5qWQ6Ya0IXYK5pxNZVqXbLlNhQ7ZZNYkrA68bQnz9iOceyioo97SFcvn4DswvmjKxCOK31YhRXAgXq0zTX+xxlP/FIpLK2toHlZCXCE2DOliknhfqYtV1krdP4dZCsW3t2LI4eC/s2cj85sDNsma2aX1ygXReIzd+zj20DAEwtC9panag507H7BqNle7ZgreQ6mvwl7QeozPOOyK4RCAQCgUBghxB0BAKBQCAQOKKYAMN6Dkbw6t00B72WbbUr1M4TZdgVzn2DURzc3eJ4FW+xK4dLrSszu5AxFYA3wgapFFXFC6fy2RB22Sh22J1fqdk77D7N4oD1PsjxnQhNVnPhmU/dg9FJ+3ErtEKetT4j2xtXrbeGfDi8vxM5RcWTrwxhaGIO1W4XnnwobDq+UayyO4d9y/ZuhCf+20X0X53ltpG0ydhPiqqaRCdFVS3EK+txYC3j2rb48P7cItIG8UKz/crh3HIWULGCS2vIZxncNqIJBlpbiK2gVssoaWt/Z/xNf+7+BnzjdUP2kCphKJbQBSoi2hFhx4qPNdRR/bKYVUyizscb6+CSJd36sW8warKSm5pbxJMPtXDr8FRCdPiHRWuhoS3kw96OxhXVBCqE03Mi2SE8WkPORSer41VKzAGAjzf68UA4gO+cvsrPmNrkAgAcOj6CjiY/etpD1PzN5lTTOe7d0WA7z6eS/LpJ5H5CMmme7mkzbUMvLsgf88U33qNES1mSuLW+jIsQVv5swV6LSs8SXc/POwKBQCAQCDYmQtARCAQCgaAEKmEbJVgdWJsjwN4ixShsOCu8TIfwEqlMUSuLnQg/pcw/UufkwvgsMoZV2SdGp7j7YYNUOUWFLGkWYEuGfjj97gyODETh97mxd0cDDu5uLdkirtTsHXaf9911B84ZRInP3b8VVbKk11oxBjDJ8Z0IaWTMhmJJKKoKWQK6w1r9iL5BcM+LjBVb40WSALtYPi+TJBJP4eiFGN6MJvTzS2dy+PrrY2gNmbNfhieSy20z29ENTyTR0aQJdUZh7n+9/4Flm3KKqgdVCVrNm/y5EQslHnt3NOBidJYam9aQF1/svBsHdobx8vmYnsGyd0cDXnxj3LItwxNJU3YLAOy+px6yBOoYhBuLWV34API2hHZMzS2iOeiFLEn4/pc6sPOFM1RWRGvIh3C9z/SbZvsgMptCZJaeA0OxpGVfAUBrvSbg/dZfDVLtZIP3CoBHvtUP0m8/f+4RHDo+Ql2rxmcWcDE6awrqA5URHTj6gs6+zkYc2BnGkf5xzC0uVeDozs+ptroKc2l+G4rJIKoudw0eCdjkktEW8uH9Gze5+/77qTk8EA7gmU/dg6+/brYevJZcpK6nPffWmyz8moNefLGzkboHnLx03bJZv2JqJ3lcEnU/IVaWrC2pETY7hj23oVgSnc1+eN0uKsOu0CIEgtX9ka6fR4/tyUtTVE058TwnEAgEAoFgPSMEHYFAIBAISqAStlGrSTkFqVL2Verxy9FuUkTZaQFlo7DhpPDy3h0NVCYLkBcHnKzidSL8lDL/rGqnjM+kMD6TKrgfYz9oweM8iVQGiVRGzzoo1SKOFTCODERwYGcYnirZ9tzYfeYUlQrokzbZjaGd0MR+58iXO/V5Rz4biiXQc2+9VgdHkqCo+c+M2SpBnwd+nxsel4xbjNcXyQYxtovNJBmeSHLtyXjB567mgOVcOfRwKw73R/QaNOQzNlPGLUtoCvrQEKi2qL1D//5I+0mGC0BbGp68NEV/2zBfZAlUBktLyKfX3OGdGwDTKv6fRxLYdU899rSH8PfT85T4cke1mxI+/r//8iF0/Kef2gbitbpTCzj97gx+FkmYLK6agz5uLarRyTnT31hyqooHW4JUX1FIgKdKxt4dW03XFEJ1lUwJNOQ32PdEFx79Tj8ltp27Oouee+u17Kd5s0jgJKul5956XL7+gSmjjGCX8RL0eXBwdwsO7Azj0PGRiok5xfAPN8vThrLX4FGBW0sKxm1EpUQqi+dfu4Jqt8y14mPHgSdydoeDnPsS/cWAzw1p+XjseX5ks9s0F3jWk8Zr6FKB1Do2G5SlUNbqkYGI/n2SRXhwdyt6jw1b1g9zeh8UCAQCgUAgWA8IQUcgEAgEghIoZ0HdtaCcglQp+yr1+OVqd6kWKU6+d3B3K4ZiyYKZPFbCghPBqpT5x36nvsaDOq+HWvnvfB5bB+RWYhHHiiqJVBaHjo/g6IHuosQ8Npj+8vkoZClfL4h3fDuhyW7eWdUt0eykVJMFHxG/bi15salKRtuWGhCBgc1ussomYzPFWII+txbA3NWCQ8fpOjOkvo22Sp22TxqeSOLw/k4cOj6Ci7Ek0pkcsoqK8fiCyZHI45Kws60eXc0BKjvA2He8uf3+PL3C37hjdo42B6rRFPDi8vUbuG9rLbqa/Xjr2jw6mvxQVGBkImGqcZJVVLyx3Ddf++y9+O9vXcdUMo2azVWUOPTCqTEMLZ8jj2q3jCVFpWqM/CzCC4jzs/vsrBoJb0/NwSVpIslUMo3ILD0eN3TbMmvRepEtAIT8b3BfR6NpbvKC+gS7MDupx6Ko9vtoDnoRnTXbcrkk4PcfCusZY3bzt9z4q6twM6tw+4rzp9sOnt1aIapk4GufbXe0KGH7XXeg/70EtQ0RkKyEPQLP0tIKIvwOxRK2200k0jjcH7G8B7DX3JOXpiFLkmnOtW2pQXPQi4lEusT74PpEZJALBAKBQLDxEYKOQCAQCAQl4LQeynqlnIJUOcQFp8dfabtXI9DhNAOIJxIAcCRYsfOPrWdD9m88Pvudp5a3MwbZnM5jXhYSYaXFpb97hq6ncDGWRO+xYUrEsBNVTr87gz1M4W47q56couLIQJSy+mLnhd28s6uNw9qRmZCA03/ysOXHVnPpwM4wDv5gWK+hU1vtRtRg57W9oU471+MjRdS30cbfUyVzszvYcH8mp43HVDKt26wR+zmeaKZlA5mP2+Cv1tvItvWBlnru/LfaF8v3z0X0ucQrHn9h3FqY4AXJl5gC8kGfm/vbzikqFFULGENVoagqV+RIppf07Byvx2X6fPvWWgAw1UcqREeTH4f7IxiKJW3rwAR9HgDm+k08nnxIO8+Xz0dNn7ldEu7Y7MbvPxTG6OQc91xzKvCN1zURjSeMWVFMHRsrCmUCVaKG0HrA7pyWlLzQwV7vDu7WfnPaNVHCO+//g+n7Xk8VFrP5eRP0eXD/3XWWtqR218m2LT7s62ik2kFl+IV8aAx4MZVMYzyewvjMQgHrNXN9HN7xSf0w9npyuz3PsdzuGeQsxT63CUFLIBAIBB8GhKAjEAgEAkEJOK2Hsl4ppyBVyr5KrROz0navVqDDSSYPTyTgbWOsYUP6o6MpgGcfa8fo5JxJ6HgzmsDUXFoPxpPzLGR1Vsw8Pri7FbKkFXmPJdJU5sOPRqfxo9FpzKcz2N5Qh67mAN66NucosOKSJTwQDlABwXQmx810sBNVZAl47vFteGkwitmFDPc7hL7BKFWcnQhVxqLe7LwjAXNSV8ka+lzdLonK+PC47K3kAGvLvwdb6/Hy73XDJUt48pVhStCZTqbxvKEPjVZu7Or3ti0+NAd9JjGQFaIa6qqxd0cjXj5P9+l4PIXxeArPPb6tqPlOOLdcc6ObGfegzw1FVZFZUnD0Qmx53vsBSCZRwcrmq5ANViZXXBif3brO69HrYxlrdLC1eZ59bBtkCTgxMmVpocW21e2ScHh/JwCgo6lwtg+hJeTDydFp6jitIR+ag178YuoGJd74fW40B322+yZZE5oQyBdxszkViVQGP740jS90NOKNK9b7679aXGZOPkupcqxnMaeSYtP4TArPv3YFiqri6Z426vqSU1RLMdrrcWExS89Xv8+Dvie6kFlScOj4CN6enked14OLyxmB999N181pCfkQDvrQHTbfF6zuVY9+e4A65lCMv6BDW3Bwhfq3LNELJ/a0h/T9Fvs8txaCQTHHvN0zyFmKfW5zsv1aiz5rfXyBQCAQ3P4IQUcgEAgEG4rVekkq1bJrvVBOQYq3r0LjUGqdmJW2e7UDHXb9YCVOWQlWbH889/g29D3Rhd5jw9QxeVZGdjV87OaxVfuN87/32DAl6BhX058bi+v1PZwKaMTy6/L1G1BVFQlOZgWQtx7rG4yaClyTuhBA4QwkntBw8tI0Jeiw805R6f1qGSoSsktLGJmcx60lBU31PvzOx7fi//hJPghOxJy2LT58ZHMVfJv4j+JW/c7OgTejCRze3wlFpUO+7zOFy0mmD8G4+n1fR6NpTHh9cu7qLCRJgmoRXT4xOm173bWzIDs7FsfUHJ3VkUhldVs0NjOLhYg5MoBC5lNBnweL2RwloNTXeHBgZxjDE0m8c/0GaqvdjrJCInEtIP7zaALdzQG8fD7GzXYZnUxqv9VdLZzMJz4PtdUbakc5C+n7vVXcmkOReAp3B7zwe+l6J4qi4uONfltB51oihVdHpjCXulXw+JF4CoqqYE97qGyWalbzjYfX4yp/LZs1xsnpSwA2V8m4lVMs6xfZQa53TmzRAL5Q+vn7t+JwfwQnRqf0+Z1IZRGJp/DGlRn0MFmT0XgKv9tpvvYA/Oerw/0RzfrRAHvdIxzc3QJZKrx4gVyjin2eW4sMmGKOWeqCmfUqKBT73OZk+7XOYlrr4wsEAoHg9kcIOgKBQCDYUIiXJGeUU5AqZK/EGwerrANjYMHqpdwYXAdQVCBita3y7OZjsRkzVv3hpFYHa8nmtL+c/J6cHJ9tsx2eKhlHD3QD4Ftr1dd48ORDYSgq8Oh3BqhMiLZQDRoD1XoWyoGdYf24VgIgv/10/7BznBXRSPaLsa3ReAojEwluplBz0Gcp5gD8ot5P97SZ5sDZsTgOHR8xBc/ZgKtxnlsJsEcGojg5OoW5dMYyiGwXpB+fWcD4zMJye4Gne6xrFPFrAPHn5OXrNyyP6XFJVJaNk0oiH2+sQ3eYHqundrVQ85L8XpyKEkbhksdEIqXX/ODVtTHi9bjwQDiA732pQ88CYwVLKxZuWYsZvHOJzqbx40tTtvvM5NSiLM/+8xvjyJShMI1blpBVVF2ACvrcluIu4ZlP3YO+wagjC7lCWGV9rSdI9o4Kcy2l1pAPTUEfVFWFLGlz2rr2kfbbs7NF83urMJc2W9d5PS7cVbcZQ7GEbW0lUrvLSDELKnhtk6DqvxHegoPeXS04MhDBo9/pByDh8/dvRUeT39Gzg53gsRYZME6PSdk9GixEWW6nZ+Vin9ucbL/WWUxrfXyBQCAQ3P4IQUcgEAgEGwrxkrQ+KGYcrAILvDoxJMhSaiBiJRk+paxotesHK1GN/ZtVFgoJUtgFytu21KDRX21Ze6bQebLH5I0jOb5xZbbdvnOK6lhQ6t3VgjejCeqceLV/CMn0LX0V95krcV0IsctAUlTVFCzeu6OB2saJ9R8vYPnO+x/gld9/wNRebeys+4pX1Pvpnjau+GQneJBC9gd2hk2BT1KcntTZKTWjom1LDeZSGSqIfvLSNA7ubjH1m1HEZQWTrXWb0eCvxlAsSQlS27fWWrbtI5vdBYP3rM2doqoFrwMkKGwk6HOjzuspStwI+jxIpDK6tRWgzWm738ozn2rDwd2tRQlKhGyRFnIAMD23WNT29TUeqKp13R1e7SEjTrKoACDLqCkfb6yzFSX2tIdwcLd2vSjW1o3HSsScStfk8Xpc+GjtZm42FiFc78Ph/Z3oG4zqAndbyAdIErbWVdN9pGrCSEeTdbYWT8wBNPF4fCZV8NrPy+opZkEF79qnQrJ9FtDsNPMZkl9/Pf/fhe6Fds8ZdoJBpTJfnIoarIWoLJmvZcDt9axc7HObk+3Xug7mWh9fIBAIBLc/QtARCAQCwYZCvCStD4oZB7tMHGMw/+xYXA+U8LYvxEoDLaUISeWYj6wNTtuWGuzdodW0MGbdWGU7HTo+Qu2vUH/Z2e7w2k+vho7iR6NT+NX8IlRo1meAiv7lICwZQ6eBI2IVVuicCGzhe9Y6zUhmScHj3x2gAvSkXogxAOTU+u/NKF2bBsgXtWe3P7AzjNG3foFMTtEzN+i5aC7qTfbDClx2ggfJPOFlzAF8UcwpbpeEh9rqcXh/J37rrwaYAL+98ErG1Sha9DOBeq9bxp111QCIpR3Q2RwAIOHkJU0QcZKJ4ZZpQUeWtONbZfrlBVTaAs7v24RiwvQ97SG4JNrebngiid5dLWj0ey0D4KOTc+gbjJrGtC3kgwoUJSg5ocFfjehsuvCGyzy1qwWKCipoXAyl5u50h4Po3dWC7r88TY272yXhTz7TjoO7tfGbnitv/5RCpRN70pkcJmbtzzM2m8L2f/86V0hJLtxCT3sI08lFjMcXML5sHVjtluF2SXDLMtLZ8lnXBXxu6trs9bjwzKfu0X+DTu7NvcvzThO7tcyTkYk5ahv23sYT2Y28NBjV980ez+45w04wqFTmi1NRw+nz0e30rFxsRrmT7de6DuZaH18gEAgEtz9C0BEIBALBhkK8JK0PihkHq8ACb5U82V8pgYiVBlpKEZLKMR/ZoFRTwAtZgl6g3HguvEBGsf3Fnmfblho0B70F2++SJcgSHXD+ZGuwKKuYQvV6rM6pbYsPgETZrwHA+/M3LQQT4NDxEVNwvDno1TNXSDvI6naCMQhobBOrDQZ9br2oPXsOh/sjCMvaincyJ8lnOUVFg7+aOheSMcQTuA7sDOPohZjJ1q1tSw2+/GAzDhwdwvlxWiwZiiUgSStbNZ7NaVk9L5+PoaGumhIoGuqq9X7KH5Med97v20g6qyAST+lj9Oxj23Qbt9HJpKM6NADQ1exH/3v5MbTL9Ovd1WLKjPG6ZaSzimluEdwuCTWbXKYMhgfC2u/MKOhkcwq6/tNPkUzng9st9V5KUOHNOUDLLPr1B4Xr2JRCz731uHz9hsnSrEqWUOd1o7bajaZANR5oqdevAS++8d6q1avxelw4MhDBqyPXoDJ1U5oCXgDAo9/ux1w6gxuL9rZsGwU2g8jvrcKtJRWLmVxB4S+ZzuLcWBwBr5v6O8muyuZyJhHGKfU1Hty3tZayIKzzeqh9ffXT95gyaQrdm12yhKd7Wikrx8P9EZy5Yn1vs6qxQ5hdyJiuv8Z9Wd037QSDSmW+OLGpLdRuIx/2Z+W1roO51scXCAQCwe2PEHQEAoFAsKEQL0nrg2LGwS6wwAtOlBqIWGmgpRQhya4fnGYMsUEpRVWLOpcDO8N4M5rA5es3sH1rrV5Xxgr2PPd1NJRc56AYAa4YwY03B3iZRelMDs+/dkW3XgPy/X5h3Gzd1NUcMLWjNeSjtiFBwDejCfQ90aWPWXc4SAXvD+7WBDZejYfhiSTChmlrHL++wSgVDCVWUux8Oby/Uz82+a7x/Pd1NOArPxzlZu8oKvCJsH3to4fvDWFqLm1r6wRoK+aNYkdryMe1xWLnMRFWnKJlW/Etj3gEfW707tKCn0ZB59zVWWz/96+j2u2itidz11SPiLEQ83pclJCRzalcO6rRyTl0NNFznc1CAoD5xSyCPjcWswq6wwF8+cFmvDpirmtTKIumUADeqiZMdDZtue8/+cy9+MoefobbA+FAyTZ9TpAloK7ajWQ6i3Qmh3Qmx62hs9VfXXK20EbCyhLNDqOwyCJBE1GLrUn01K4W033AeA3Z0x4y3bNLvTcXehZgb6etIS++0NGIl8/HKPHbzkq02OeM1ch8KbU2nxHxrCwQCAQCwe2NEHQEAoFAIBCUhVItzewCC7zgRKmBiJUGWsq9otWpgMF2oSwVdy5HL8Qo27qjF2KOrEiGYkkoqqpnCzgZz5UIcMUE9XhzwGjJMzGbwpIhev3imfcgS5Kl8ANoQgTPzi0ST2FPewiXr9+ggoCsfRxrCaSoKo4M5OsZGMdYC/TnRQZj4J/th6m5tKnODW++8PqZzZIhkL5g7duMuF0SPmCyHdwuCU0BL8aNIg8j1Myn+cFftoC5otJ1e3rurUd3OIj/fHbcIvMjf5wDO8M4MhDhBvgJiVQWsgSc/MV102dEIDCylFPw3TPvWe6P4FTIWFJUvPhG4f0ZRZhzY3F85YejjmzV2kI+7O1oxMhEAksKcGHcvk3F1oQJ+jyQJEmveZVZUnDo+IguDL/4L3ag+y9/ahK8ClHtlgvW2SHttRMcCMXWAFoLnJ7zWuyvbYsPyYWMqa8/1lCHp3taMTqZLCieavuh7SqtvsPLzOPdN5w8TxR6FmBF9i923o1DD7dCliROTTNzO4t9ztBqsml9QWzhKpH5wt4jWOu49SzUFPOcWKl6RAKBQCAQbASEoCMQCAQCgaAsVMI7vpzBiZUKMuUOlDgVMNigFKklQb5T6FwKHYdbd4fJ+CDHL8Vizmm/rURwI+cwOpnEvo4GHBmgV5Wns4p+LheZWjduGXjonpCe8cLL/nDJEp7a1WISgox9SSznSLbKC6fGlgN7vO3ZrCtFFzvYrBWrguNsEI/Xz1b1dTqb/Vr20P5OHDo+gouxpEngIEFV4/fdLhkq1OX6MBI6mwM4MXKN+p6VDdcvpm/gjTEtQ+X0uzPLNnl5qlwyXLJk+f2tddV6zShFha2YQ9DEyMJKhlVWkfHz5qAP3WHN4u63/mrQ0oJNBlDrraKyrIqBlznGY29HIwAVv5ia5/aF1+1Cd0sAU8k0fnXjZtH2aIlUBi+cuoIfjU6hKVCNX0zf0MWns2Nx/Mu/uYTucMC233iUU9gAgLlUZWzoykk3Y/u3ElpCPvzPP3wIz/zNJQxcjSNXYHpLsP8F7OtoxOGBiOnviqois6Q4yoYDtHG4GE3g59EEppJpy4ywpZxC1X5j61mRvx/uj+Drr+dtRXOKapktZoXVfbJSdmN9g1EqW0yW7G0lS4UdEzvruPUG+5x4YnQK+zoauWJNpeoRCQQCgUCwERCCTgksLCzg9OnTOHfuHEZHR3H16lXMzc3B4/Fgy5Yt2LFjBz73uc/hC1/4AjZt2lSWYzY3N2NyctLx9pcvX8Z9991XlmMLBAKBQOCESnnH21HMCs71tnK1o4kOyrD2TISVZil1NPmp40zMpqi6MlZBk1LGcyV97DTIxhtz9hyCPjf3u8MTSUwmaXupxqAPn2gJ4is/HNVr0rCZKx1NfiiqlrVgFIpY0YntMzaUSrYfnZxDq+H0/vat65Ro09MewvRcGu/PWwfinQTxrASbi7EkDu5WqewtQLMp8/s26SvLD+wMU99PZ3KIxNOIxNPY0x4CoJrsutiAfdDnRp3XY846Uc2r9M39l4fYlWliUI3ldkYUVas/9MKpMdvtrLKKCJF4Cl/o0OoYfeWHo2hkahxRxwTf/qo15EWD38u1XTOSKRChD/o8y78N1fa8sooCCfxaKl63y3HRe2MdIyNDHBFwLbixuAS3S8JSTnUg3a0+QZ+nrEF9WQKqPS70PdGF7r88XdAOzapPvG4X7qzbBEVV8Q83zfO1/+osPv6//wTVbhf83ir8w80lLNlocYlUFm84EDHPGX7HgHXtt5fPx6jvvXw+VrSgY3U/qtSzwEqfgZw+z5B7I1s3bTWeuVYK20fjMynL+9haPFMKBAKBQHC7IASdIvnWt76FP//zP8fNmzdNn2WzWcRiMcRiMZw8eRL/7t/9O/zgBz/AJz/5yTVoqUAgEAgEq8tqeMezrMUKTjboQorSF28Lwoba+KG3lQef6LaMx+kAilXQZLXH0+l5sjZmigqMTtLnUFft5mYtdDUH8Iupeepvv75x0zSH+p7oosZYUUGtvCaC0asjU1BU4ODuFm52z94dDZAlySRSaX1pWLHPDP10Mk3bmi3Ds347MTpVcM4t5ehI7Llluzh27O+/24++J7r0f7tkCUcPdKP32LBplf7ZsTjejDrLOpiYNZ9LMn0Le9pDkCUJ3eF83zjJBnh/nm+zxWYjTCUXIUtzy8fRRNOhWAIXIglkDcJJnddTMONHqyWS32ZPewguWcJEIm0p7hi5O+DTBbbL129AVWEbjK+v8XAzrCRJC+oPxazFL0Cr7WNlDedUzLGFsdorlAlSKXIqkCuUplIh3C4Jn2yth6IoGBzn/xYSqUzRdncALDt0fCaF+/7dKdxZW11UbRsA8LplbQKpKtJZTZx94dQYqt0y9Xsg8KwJjbgkFMwQsuPE6LR+7WLvq4vMHGX/vR5Z6T3T6fMMuVcCsLWOW4+WZVYZXzyxZi2eKQUCgUAguF0Qgk6RXL16VRdz7rzzTnz6059GV1cX/tE/+kfIZDIYHR3F8ePHkUwmEYlE8JnPfAanT5/Ggw8+WJbjh0IhHDlypOB2TU1NZTmeQCAQCG4f1vrlvVI2JgTe+dmt4KxUf7BBF2M2RzGi0ujknOW/y9l2VuwgWAk3OUVF77FhdDQF8Oxj7RidnOOO51p54Ws1avK8+MZ7eCBMB3rYGGNryIcvdjZy68ZUu+ki96RfjOJS77Fhan8ksJ9IZfHCqSuQJW3MSXYPqTPy5EMt8FTJ+n4yS5rd0OXrN/DCp+pQJUl47vFt+Hk0QQk4c0zGSH2NB0/tauHWABqfSVG1fIwcOj5iGdQnY+EkYGYVhGOzcXi1PayEkmQqi7NjcTz7WLt+XkOxJHraQzj/Xtw2G8AqyMyO+3h8AeNxTWx59rFtkCXN2q0p4NP/DgBNQR++0NGAvsGYZZCcPQ+XLKHviS58/1yEEvusuJZI4+DxEcdWbNu31uLw/k7Td0hmVtDncbSfSvGP7tiEWCIvrDmN61fJElXfqhhW8t1KkM2p6L8aR7jea7vd3zMisiNsTnMxqyDKEUkJEoAql4RGv5fazqre0Z21m02Zdk5wIuawWY1GxmcW9GsXe19tqfdSbepu9lu3w+L+slrPROQ4Q7GkLh4brVGdUmxGSqFnrvVoWUbaeGJ0mhLCefeeSj9TCgQCgUBwOyMEnSKRJAmPPPIIvva1r+Ezn/kMXC4X9fn+/fvx53/+5/hn/+yf4c0330Q6ncaBAwfwy1/+ErIsr/j4Xq8Xv/M7v7Pi/QgEAoFg4+H05b1SQY5KW5rxzs8uIF2uYAbbX1pdjjyXr9+g/u3UFmQ12s47jvHvVBFnVaUK1Z9+dwbPPb6NytgwUkwbeSIYyWgpfv7RUcR0JoezY3G0hny6NRQbnAzX+/S2GbMktm+tRWdzAN94PW9dxQssFaojQcbcaGF2diyOQ8dH0PdEl35+RoEll1NRVaX9Ztg5xWaMPGWoa9S7q8UUDLOac+zcNDKRSKOjyY9nH9uG0Un7gFnvrhacGJniZg0ZySmqHtD8xdQNRxkEJy9Nm4qUs3g9Lvzhnjb8l7PjlmJO0MfPyjIexyqTpqs5AFkCPt5Yi4lEmrIY87pdUKGahKqJhGZdqKhmUeujtZsRY+ZgZDaFCBOE97plKshutEI7OxbH0QsxPBAOcEUg0reFMmPcLombfbFSPrhZWsbESgSZUr/LExrLCTvWLHOLZkuzSqJCE5uisyk9k+yta/OWv8cbi9llC0Xgmk3NJd5cY+cwK7r9/kPNqJJlkz0Y4chARBfBjTQHfWgK+vTr9OH9nZbna3UvWi1BgxXZn3t8W0nHKTYjpdAz13q0LCNtJiK+nViz3mxyBQKBQCBYTwhBp0j+8i//EoGA/cNVKBTCyZMn0dbWhsXFRYyNjWFwcBAPP/zwKrVSIBAIBB9GnL68r8dVm07gnR8J8vCCAuUKZrD9RQJfBNYWyaktiN3q03IGYsh+h2IJKCooiyu2iDOL3XHZNr40GNWPxwo07LalZDQRrGqi3Fi0DuZPJNJ63SBPlYyjB7r1z3KKiirZbItmpHdXC7575j1LMYGMOe88jdkzbNAys1wgnI1Tf6GjEbLEnxsuWVrug/y4WdVfYuem1y2j2lOFRCqD8ZkFvHBqDM8+Zi3aGY+5r7PRVnTRzkcTBPe0h7gWSXvaQ6Z6PsmFDE6MTJu2NfJAOIAqWbK1f1ILxvrpDdq2+NAc9C1b6ql4/jV+PRorezJS+6Fti4/6+ydbg5hM8i3hzPtWmH/TxzoxOo2mQLXtPgqddq7MGS3EZqtYu6+1pJJiznqHZJI98q1zttlnZ8fiaNtSgy92NuLAzjA+8fxpJBmBdDNPGJPoaz0rur11bV6/vvCuH+TYLA+0BB3fF6zul6slaJTrOGyG54Gd4RW1az1blgmxRiAQCASClSEEnSIpJOYQ7rrrLuzevRuvv/46AODtt98Wgo5AIBAIKorTl/f1uGrTCbzzswsKlCuYwfbXVHKRsiLj1dBxQrnbzsu8AqD/jVjAGMUWuyL0hY7LtpFYQWmiES1G2GW4FJp/7Hk9+VALZEnCkYEIlZFRW+2mVoD3tIdwefqGLl5YFV42joNV9ppLlvBAOGASR+7ye7F3RwNVG4c9T3J+OUU1tVFV86JW0OeB3+fB3h0Nek0eo33g4f6IoaYPHVS9GJ3Vv2OEZCMRESWdVZDO0kHdF994D7LEF+KM8OzqrOBt09MeQt8TXVQNJABIprNIpq3FuIDXjc7mgC4YWsHbR097CC5JQk5V8ffX6Ln+ufsbUCVLGIol8Isp60ymQiSZrIOfR5O2wlMxjM8sQFFWJkaUoufYZfWUkuyzVvV1SmW9WbuthJyiIqeolkK4EeN1MuDbZBJ07qqtNmWZbXbLtvM9NptC53/6Ke7bWouH7w2h/6r99YPYSx7YGaaueXbXJ/a629EUwOH+CCYSadN2laBczxpshufRC7EVPZsJyzKBQCAQCDYuQtCpIHfccYf+3+l08b7EAoFAIBAUg9OX9/W0arMY+7digxPlCmaw/TUeX4AsSVRWQ7lWmpL+uBhNoDXkw43FLLVS166/2EwiRVUxFEvaZsPYCS172kO2fUY+Y610Xnwjn8lC9m0ci5yiFpXRZJVRNhRL4syVfNubAl58sbPRIHqoJqsqYxFup8fq3dWCIwNRTCZSelHx7nAAR/Z3wlNF2+nyRA9yfn2DUcrKiyWRyiCRymAolsDB3fa1ENiskHNXZ/HZb5/DFzvvps7PUyWj74kuPPqdAUu7sXQmp++btcFhxcrD+ztx9ELMZPlmtLvjsWdZzHHJEg7ubsHJS1MYn7G3byMEfB7KEq8YLk/fwPaGWq5l2VAsWTC47IRkWrOrmppLY3wmZQpuB30e/GbDHZhIpEuqUxKdTSPgdUOSAL/XAxWw7euV4vdWYS5dXouw200aqaSYU+3WrhmrlTV0diyO3mPDejbrydFpvD9/0zLzDNB+Gw3+auo33tMeQndzAF9nfous6GPEJeXn6rmxOHbfE8Se9hAuX7+B2mo3dx4Te8nD/RHHmcTsvZ7NuGvb4sO+jsaKCRrletYo92IbkQUjEAgEAsHGRQg6FeSdd97R/7u5ubks+0wkEvjMZz6Dy5cvI5lMwufz4a677sKDDz6If/7P/zkeeeSRshxHIBAIBLcfTl/e18OqTSJMGAPDhYI2xQYnyhXM0GqW0AHocmU1ZZYUpp6LH994/Sq1jXGlrlm0yWfDsKuRtZohdMCMbbdxLmi2XaqeeVQoY8OYQWK00mED2kREscuCscMqyNUdDlCCDrHo0WvNHBs27Wt8ZgFHBiKQJYkrWrB9+NJglJuVIi2fP69P+p7o4p5foWwowtmxOJ48NoyXDbV3hmL0d+c4QdRIPL2cIaXi6Z42ANrvrPfYsKWYw57rqyNTepCV1DriCYIHdob1eXvf1lpcm7Xf/9Rc3oLMJUvY11HYvk1Hsp6DhUikMlwxBwDOv1dYzClUl4dwMWY9th9rqMW1uUVLMcftkuCWJcuC9UA+++hjDXWmmk/lJujbVHZBhyXgc0OCtGLbtoDPDaj87Ky1ptotY1OVjHmmfs5a2L+dHYvjse8OYCqZdlRPiRXDW0M+HNnfiZfP22fJBbxVSBrmDnuon0eT+vFnFzJ6vS3WChQwXy9PjE5bLvxg7/Xstb856KuoqFGuZ431tNhGIBAIBALB+kZS1cKO04LiOXfuHPbs2QMA8Hg8+PWvfw2/31/y/pqbmzE5OVlwu09+8pP467/+a4TDxXvuNjQ0WH72q1/9CvX19fjpT39a9H4/DGSz2ouk2+1e45YIBIL1hrg+8EndWsI/3DQHDTe5Zfi9njVokT1sez+yuQq+TStfFzOXyuDWUj7AR4JbLKRf5tIZ3DIWoHZJWLII0PE+c9Lu1K0lZHIKPC7Z0TmqAOZTGdzKKZZL8VfSX3Z9n7q1pNdqqXa7qGNYzTG2XzZVydQYOKXYc2LbU7dZW6k/f5N/bOP+2XkCaDoH7ylekoCa5e8tZnOW88NqrlHbyBIUw0ZkHlr1LcHlkpCzmXupW0tYuLVEtV+WJEgy6O9JWrA0x4wXJE2sMp6bJGn7WGnNGLJ/j0tGJqdQv7di4fUDwWr8CuFk3FbC7bT/f3THZsymbln28VohS/nfTqnnWuj6UEl4c/Mjm6sK/h42VcnwVMlIZXLUdSO/Y1D3CLv7vd01ptC1t1L360pD7qVZRYVbllDn86B0OVuwURHvFQKBgIe4Ntx+fOYzn4Hb7cb0tH0tUSvW/5PNbUg6ncYf/MEf6P9+5plnViTmED760Y/iM5/5DO6//37ceeedAIDp6Wn89Kc/xU9/+lOoqoqf/exneOCBB/Czn/0MbW1tKz6mQCAQfNgoNpi91vu9Xcnk+EEhj0vm/n2tIWNW7jHMMkEvy9ifCsylMwW9i6pcWr0X0o//kMsHtTZVyfBuqrKdi8ZAGAncFTrX9K2lgoJIJqfAZ7sFH6NgYwUJ6pNzLXZs2DFwSrHnxM4hWVKhQtUEJY4YRu2fE9VzyXwxT1VhK7YQZFmCz+0yCStG3LKEW4b+8bhkpG4tIWVTM6PKJSFYswmJhVtU+xazOe13r4I7X1SoCNVsRvrWElK3lrRAuKoJPC5ZQpVLouZs6tYSNb9VFcip2rYmUccQSC4khBn3zxOHCgoxknY48hvMcX60WnKBBLUEM7JKl3ZZi/1LEiAtD1Ixx59PryzDp1IoKqCUSWQyzTdGFCGJKuUcN978TmVycNtkbAIApPx1jrtgw0WL53b3e+P1khVvC117K3W/rjTGe+ktRUX61tJt03aBQCAQCASri8jQKTOqquILX/gCfvzjHwMA7rnnHoyOjuIjH/nIivY7ODiInTt3Qpb5D74XL17Evn37MDU1BQC4//77MTIyYrl9sZDsnVKVw43O22+/DQD42Mc+tsYtEQgEK8Ho2Q4Azz2+bcUWGm+//TZSt5bwL07+qqz7vd1h+9rocW9n87WeYK3D2HojTs7lwNEhys6rpz2EB1uCGIoloaiqvprduM2e9hBcsqTXCjAWujbOLZ61mdGyjd0eAJ58ZQhnruSP9eltIbz8e92W59y7qwWHjo9QNjFejwvdzX6cuzpreRwn/crWaSE88o+34PD+TvQNRk31ex75x1v02ka9x4apdpFi24oKvHAq3wd72kMmSzWWoM9jsodyck6spd5hQ92dt99+G3OpDJ74H/8397vG/bO/F0CbKw+EA3jxjXHbouR25/TkQ2FTTQzCnvYQDu/vxMvnYzh5aRqAioa6ampcrdrdu6sFvceGC/Yr77uHHm7FPX/+dyZrqLYtNdi7owEHd2u/K9K3F8ZnkTFs2xbyYdymxkxLvZdrf/bsY9twcHfL8txzXuPHDidzi1CoDtFqUOnsHDuCPg9+b2czRibmMHA1ftvV3Sk3R/63EADg4P/Pfv7svqceA+/Z/ybLSUvIh1g8xR0f8vsl13Djfaw7HCzpHglU5tmMRzE1/SoBe88y3s9uZ9a6XzcaIu4gEAh4iGvD7cdK4+xiyUeZ+drXvqaLOR/5yEfwox/9aMViDgDs2rXL9vMHHngAp06dwv33349MJoO33noL//2//3d8/vOfX/GxBQKB4MNCuQvSEthslHLtdzWo1Is4r47P7faCz9azsao3Ysfh/Z3cgD9V54apB0DqtACaYDAUS+rfP7AzTG3H+voXmuNsMJf995GBqC6GkBo+rO9/OpPD9NyiXh+hOxwsqk4TKzqxdDUHLLcx1hxg27V9a+1yrSA/nn1sG0YnzUJcTlGpAHzblhrs62gwCWctIR+WluvT2M3fQ8dH9P2dHYvj0PERHD2gCWQpTmaT1+PCXXXV+Pz9W6Go+f0f2BmGoqroG4zpwtK5sTgkAHfWbkIkzq/PYqTaLVP1OxKpDF4+HzNt1xbyYV9nXlyVJejCGity1Nd48ORDLQBUjEzMQVFVDMUSprpDPEGMh921cXxmAS+cugJZAg7sDOPx7w7wBRCm5g6T0IBff3ALzz2+DUOxhF67o7NZy6R/9Dv9ZRFy8sdW0dMesqzjQ6h2y/gff/gQnvmbS0WLYOsNt0tyVKeFJZHK4Js/uVp4QwHFaoo5ABDl/OZaQz7cHfBiKJYAAKpmGotdrRmr543VqvvH3tNPjE6t6kKTjVpDh+1XoPCzkUAgEAgEAnuEoFNG/uzP/gzf/va3AQA1NTX4u7/7u1VVR3/jN34D+/fvx8svvwwA+J//838KQUcgEAiKoFIv06ytyFq9pJcizlTqRbxcRYSLpZwCFSuOXBinA2svDWoFpO2O4amS9QC/FXbz8uiFGCUYHL0Qs+3TQnNcZoLh7L+1TA3636//0W5TNs14PIXxeKqkldRsvxKCPg8O7m7Rs4KMkOwbY6DPGAQ0CjWn353Bc49vo1Y+k7loNT9yioqhWFLfRzSe0gvTW/0ucoqKizH6XC5fv6H/N8928KufvgeHHm6lVqST/T/d04bRyTlq/IoJ/vOKsfPs7JqYAuJW4wFA7/O+wSgmk2luRhUA+H1uR4JOblkkq9nkwlyabxs3PJHEz6MJk5hDxLAGf7VlOwAAqjmozMuAKgcqJEzPLRbcbjGr4Jm/ubSmonYx2TmsOGjkjs1VSKSyZWyZoBwEvG4k0/xxefjeEC5Pz2MunS0qM6q+xoP7ttbi7al5/fd45kocRwai+P2HmiFLsi6cl/q8Qa4vq7H4g73Wjc+k9PasxrPKaglXTsgpKo4MRPXsTC07srWkvq/UYimBQCAQCD7MCEGnTPybf/Nv8PzzzwPIizkPPfTQqrfjU5/6lC7o/PKXv1z14wsEAsHtTKVepn2bqvDc49vW/CW9FHFmo72Il1OgYsWRDLMqfXYhU5ZgkHFedjQFqMwNsiKaUGh8Cs3x7nAAZ67MUP+mYcN9Ws2SfR0N3IA4aU8x9nRsvxJ+s7FOPzd2m6eWV4RbMTVHZ7BY9RMrNOYUFYf7IxieSJr2we7vwM4wlW3V2ew3WaHVVruRU1S9zpGxwPie9hA1Prz2WvUNoNkWNgd93CwjQOVmnnz0jk0m+7GJRIrKDGLryBgt/3g2fjz27miALElcG722kA/N9eZ2W9HVHMB3z7xn+ns6k8P4zALGZxbQGvLhVzduIp0xV7DZ7Jap8zt6IaaLr5XgfUbQcbskqKoKtozQubE4WkL8yiAt9V5IkoRfzd9EmiPCsVlIpeBUzAn63KZ5HfBWQZIkJFJZIeasUySLOHxLyIfusB/X59NYzOYshToACPjcSBrGV1XBzT5LpDL4xuv5bKvT785gSVHxh3vs67uy172XBqO2ma/lziC2ur6u1nPPWi104dE3GKWsSV84NQZZkkpq20bNPBIIBAKBYC0Rgk4Z+LM/+zNdzPnIRz6C1157DTt37lyTtoRCIf2/5+fn16QNAoFAcLtSyZfp9fCSXoo4s9FexMspUJHgO1vHxeOSKHFnpcEgMi/ZuiSn353BnvYQtW2h8Sk0x8k5kdoHRgsdlyxh744Gynps744G6ntswL6jKYDD/RHq7zx7Os36iohWmiVa32CUyuowln08sDOMN6MJrtUcwU5ocDqPjwxEqPO1oqs5YLJXe3t63rRdJJ5C32AUhx5u1YtdP/KPtxQUtUg/DsUSy1Z2EhSVFkAa/V4AmsVdZ7Mff/vWdQASPn//VgzFEiZB5+F7gpieN2ePROIpROIp0zgBmpjT90SXnrVE6hjxCHjd+HhjLVRIGI4loQK4O+BFMnWLCgo3Brzoe6LLZC3Io2dZ9DoyELGtG2RXiyaZzuL0uzM4/e4MjgxEHWUOlcq1ZNokwGRzKjdbQoXW7paQD79eFqMINxbthZLVrDfDa4cKUGPqhJZ6L24sZpHO2IsIAufYCXuqyrfDkwBKfLGDHeNifjv/7XysoKDDXvdmFzImkdd4Py13BrHVfex2f+4pBV5mZqnPMusp80ggEAgEgo2CEHRWyLPPPouvf/3rAIA77rgDr732Gj75yU+uWXtmZ/N2L36/f83aIRAIBIL1RyniTLEv4uu9+G05BSoijgCghIOdbfVUEKrUYxj7sqPJj4uxpGk1tCxJZc3+4p3TmSvaMQ893IqDu1shS5Ll8ZoCXjT6q/XaOYqq4vnXzIKI0XoM0KzbjILPc49vw8cba/Vja+ea3561mnv5fNTULjYg1balBs1Bb1H9xFrMed0uKkDfGvLi7oAPQ7GkyV7NKkhNBJAH/Vr2Xt8TO0zbsL87th+ffawdgISpuUUAKrbWbqYEsp72kC7gfP11viAlyXLB2jvsOMkS9DlZKKMmmc5ChVQw64aMK/vb5AWfq2QJLlnC7z8UdhyEtqOSYk5PewjvMP1HIGIOLwD/6xs3Ue12UYKOnZjjliVknabXVAgrezwrqt2yKTNMsHLsZoGV3ZpV5k654dk7slgtkjBivJ+WO4PYuHiCfY65nSjHcxgvW6nUZ5n1lHkkEAgEAsFGQQg6K+BP//RP8c1vfhMAUFtbi9dffx0PPPDAmrbp7Nmz+n+3t7evYUsEAoFAsN4oZZVksS/i6734bTF94DQowu6TZyfmZL/s3xVV1bNDrGy2usOBigRKrAJlVvOBzYYhtXOssi62b61lAv10KPLEKC2kAJpAZNU+TRDSBAzSV2xAau+OrbroA9jXNspDf55VaJFGkqwFi+5wAA+2BHFidIrKjiFWfH+z9049S4eF7ecnXxmiPv/R6BQlxswxwsRQzLruDYEVG9wuCZ9sDaL/an5x0B2bq6jAak6FZdaTS9I+N8IKQjwmk4v4/rkIFFVF25YazKVuIZHKmsQcIB9Q/IOH2/C3b71vXytnmaDPUxbhplhbs+m5Rc48p+HtL53JUWIOr15N0OeG37cJW+uq0X/VeR2lYvB6XFBVtSLZMyIjZ2WUw2LP63HhmU/dA0C1zUJsDfkwn87YiorVbhl31W1Gg9+Ld65/wP29dYcDjqw3eYskWkM+3FjMmrIxK5VBvBYCRDkXw5TjOax3VwsUFVQNndtN2BIIBAKBYCMjBJ0S+eM//mN85zvfAQDU1dXhJz/5Cbq6uuy/VGGuXLmCH/zgB/q//+k//adr2BqBQCAQrDdWI0hRzIrZcgQwit2HXR+YBRXoHvK8oAi7/eH9nfqx7frZKtjC/r1tC7+eBsFYc6XcFBMoyymqSYCxqvnStsWHfR2NpkCeUbwCQAXqjd/5/rkITl6aNgkYrPAyPJHE4f2d+n+T8SwmyJVTVDT4q6m2mEUG+rhBnweSpAlWh/d3wlMl67V1LozPUlZ8qeWgPampwzs+mV8TCTqbYZ7JiGAD5GyGBw9WbMjmVEzPLerB09pqt8m6bChqLRTVet0mSyY7QYNkO43PLFC1GkzbeVyodrtQ563CkqLi++fGMTo5h0ZmbIwYA8Av/osdeOZvLuHy9Ru4b2stphIpRJjsELY2CA+rALrX49I+V1QsGgrjJBdu4a7azdxMo2Jgx7Y15MNrX90NT5WMR77Vb9q+SoapPk8pfPSOTWgM+ComGJULIiYUyjbbSJQjH+urn75Hty/0V7swt8i/XkTiKQR8bupvXg99fdlaV43TX+sBAPQeGzYtQGgN+XBkfyeODESpe+rPowk969R4TTYukjBmAp4di+PohZh+3V6Jldd6yyYu52KYcmQuuWQJT/e04ume9bMgRyAQCAQCQR4h6JTAH/3RH+G73/0uACAQCOCnP/0pduww22U4obm5GZOTkwCAo0eP4vd+7/dM2/zH//gf8du//dv42Mc+Zrmf0dFR7N27F7du3QIAbN++HZ///OdLapNAIBAIKsN6CyBUgmKEgHIEMFa6D+OYGANHmqBSQ23LBkVKPbZVsMXsWW89N4y1TJycm9V8s9qmmEBZ32DUFFgn487bD0/0yimqnj0zkUhRGS3NQR8OPdyKw/0RU+C/NeTFFzvvpsQ3cnzWPufl83Stl0JBrr7BKLfgt9fjwl111Xr9IONxD+5uMYl+xto6RhRFxT/cXNJr6vCOb5UN4/fSWSfd4QDV1rrqKtR5zYIMIehz43tf6sA//c/nqbEzbs+zPGLrwexpD2FqbhHjMwu6IOKWJbirZHSHA/jelzrwg59PYCiWwMB7s5SwsaQ4Ux3IuWmF1ulMgofvrcf1+ZuAqmJr3WZcn7+JuXRWP4+zY3E88zeX9P4/NxbHnvaQSdCBWnyIPOhzI5HKWgpnyXQWA+/Ncj9bCaQOk0uW8D5TAynoc6O22oPorHUNIaf86sZN3FgszkptLcjm1FKGb9VxuyQ0Bb1QFLUidnNOsnbcLgl3bHbjyYfC6N3Vgv/aHzH9pngkU1m0hnwI1/vQ1RzAiZEpjBuuFXPpLDJLCo5eiJnEZ+O9irWwHLzKr49jXHjBZnmSbVb6PGV3/16LZ7Vy2sdttNqHa82H4dldIBAIBLcfQtApkn/7b/+tLuYAwDPPPINr167h2rVrtt+7++67SxZ9Tp48ib/4i7/Ab/zGb2DPnj34jd/4DQQCAUiShOvXr+P06dM4deqUXiy4vr4er776KlwuV0nHEwgEAkFlWO92ZOWgGCGgHAEMq304fQG3C5qz4TE2KGLXfrvj84ItOUVFjqmDsXdHA1VbBgDqazx4alcLene1IKeo6D02jMvXb1AZIbxzs5pv7DZvRhN68M1pNpe5Vo1PH/dSssIa/V5K0CH9zivSLEmSPt6yhOV6Q1rGT++xYVOmlZGOJvtag7zjAZol1vjMAi5GZzE1l0bQ54Hf68Hnd2yljkuEJFbM8bgkKlPnpcEoFBVQVBV/+9Z1EHubkYk5y7bt7WjQz7erOYAvP9iMr/xwFBdjSaQzObNgwZBIZfGDn09gX0eDzfy3p21LDfqe6MKh4yN0FpOiIpvJ4dxYHD/4+YQ+/geODlF90RjwWgpORq4lrG3V3rl+Q7eCGrfYl7kOkIS2LT5qjt1cKk4RCPo8+M3GOrxxhc5EsAuqez0ueN0uzJbB/u2bPxkz2dsBWkbUuavlEZFuZhUsZitXY6hcLFVIICk32ZyK8ZmUntFVbpzM4GxORSKVwY8vXceb0VkMvJdwvP/5dAZdzY0YnkhqmYuG31silcHB4yOUqNwW8qHBX42pZBqPfmdAE8AZ5Y2dwxOJFA73RwreL4GVP0/Z3b/X4lmtnCLMSjKXBGbKuXBICJbDNRMAAQAASURBVEICgUAgKBdC0CmSwcFB6t//4T/8B0ffe+KJJ/DKK6+s6Ni//OUv8ctf/tJ2m507d+KVV15BW1vbio4lEAgEgvJT7gK+65FiAvjlCGCsNNhjFbQHNEGFZI3wgiJ27bcTSnjBFjbwv6c9hIO7WyBLdC0BIub0DUZxZCCqZ2mcHYvj0PERHD3QbXluvPnGbnN2LG6ZMWIF2w/7OhqLDlawwhqpEWL07ecVaSZZTMZ5d7g/gudfy9ceYjOt2O86PS8WY+A8kcrg5KVpXaAg3+PNr51t9dRYzy5kTILTC6fGsKc9RP1tT3sILlmiAkJknA73R2xrtQD5jBLCUCyJI1/WbOmMc4nF65aR5tQ82dfRoLfHqp+GJ5L6fJVAW6F970sdeOiFM1SbZAlgdE3csLCCApzVYmHrAHU2+9EdDlDzjc1wKkQilcFkwiwg2QXVu8MBSEDBcXICGwivdsvY6q/G1FxpwgZPiOKdi99bhbn0+s/aWc8UskJcDcbjCxiPF64/ZaTO66F+M+x1wVS3S5Koa+QLp66gpd7L3TexcBufSenHYG3VhmIJKGr+OEMxWowq9nnK7v69Fs9q5RRh1qIG0EZmpfPhw7CYSyAQCASrjxB0bgP++q//GufPn8fFixdx+fJlzM7OIpFI4NatW6itrUVzczMeeOAB/O7v/i527dq11s0VCAQCgQXCBoOmHAEMq304fQG3Cka3banBwd2tVNC8mPZbCSUkuM2u1GS3d8mSrfjDy6pgMxGczDfe+RcbrCjHOLLnn0hlkUhlIUvQxaHeXS1UzQUAgKqaVnSz++IF3gFgdDIJwPo8e3e14MTotGWdFhY224T0h7F/e9pD6Gz2Y2ouDUmyd/qSJeDZx7bpRak7m/2QJVk/P7tzJhAB5b6ttZicXaDEE0VV9fk9FEvgzBW+0HBnXTV1bi31Xvxu1936OPfuasGb0QRXqOhqDpjm67OPtUOWJPzL/88oFjO0IMOKOQBQV+3Gwd0tGJ5IYklRqfH/aO1mRAtk+bDZGydHp7G3oxH/+tF7lzOiJHQ3B9DV7MffvnUd15OLVC0cK5xkFwGaWLL73np0NfvxjdevOvpOsSxmFSrjyIiWQebG9FzaMhPJaX7Swi1rMcKJ5RcABLxuJNP29YoE6wMJQJUs4SObOSEDiRbETXW7OBc3q0yqu+roelgnRqeo+wm5H5HryJkrM/C6ZWofxT5P2d231uJZjbUIPXR8RGRzrBNWOh8+DIu5BAKBQLD6CEGnSM6dO1fW/U1MTBTc5r777sN9992HP/iDPyjrsQUCgUCwuggbDL71xEpebK1Wojp9ASdjcGJ0igqIkuyDUo7NOz6Qf6nnrdS0ai/vGFbB++1ba7nnZuxrtv8P7AybgvHFBCuKsRIxbqvZnUkYndS+19HEF9aGYgn93F2yhJef6ELfYFQXWsbj2oruN6MJPVuE3ZdVQXpidWfVfpcsmSzJSJYMKyxY7Z8dA0VV8cIpLXtIVfmr1Qnd4SAA6IFOoxjArvJl50/bFh8a/V6qdgyLLOXHZDJpndkxn6Yzd2RG5HTJEvqWx2UoloSiavZ33eEgDuwM47f+is5u7xuMWWYD8WgMeg2r9JPoubceU3OL+NX8Ta6YU1/jQZ3XYynEjcdTeOHUFexpD+m/+a+/Poa2LTXY19GILz/YjH/y4qBjwYaFzTJSoVkDauJR4e1XSmvIB0kC5lJZJFIZ/X/lwOq3BDgXheYXhZhzu6BCs1BMprMmEY7Nartv6x3oN2TksLZsLG1batAc9HJtMcdntDpqxuscm5FDsoNaQz7cHfDqmTtOBRC7+/daPqsVyuYQ9l2rz0rng9XznRhLgUAgEKwEIegIBAKBQFAkpb6ECRuM1bOecPoCzq6KLVcA58DOMP7PkSkq4NzRFLBcqVlMwKCjyU8FB7weFx4IB3B4fyf33Iz9q9mR0f1PgvGlnHsx48nblvz3s4+147nHt5msv9hANzmn4YkkFbAnwsXpd2fQ0x6C2yXZBp9bQj4MxZKUmMVrP29cXLKE75+LcEUSY0bMkqLoq6wP7+/UMq6YAt9VLgmP/OMt6GgKYElR8MqFCSxmc+gOa2LbV344ankOLw1G9Tby2nno+IjldwFNcGGzZ9q21OBz92/F8EQS7yzXZrqWSFGZPe/P39SzogBQc+fIlzupa+Hh/ohJWCkkLgR8biSNNmwoVOuK5qnldhXans1oG59ZwPOvXcGrI1Ns8kFR8MSZoeXaRk63B7TsiHC9F3cHvLgwnkDWoepzd8CLowe60Xts2NYycK0op3glWF2MIsyBnWEcvRDTf/us4OKSJTz7WDtOXpqGqmrikPF+uK+jQb/WZpYUDMUSuHz9BlSVvkaQe6Tl70TKX//PXLG/B2WWtGuyVd05Y9vX6lmtUDaHsO9afVY6H6ye78RYCgQCgWAlCEFHIBAIBIIiES9hhbESvVbLeqKYF/BKrJI8eiHGyR5QTWKMlqlSbMCAbtszn7oHT/dYf894fhMJsy0YKygBzlc5s+NJgnq8vrSrV3RydBqQJCxm6aC3bBFZt6vb4qQWSjSe4mZ38NrPGxfNro3G65b1rI5zY3G9HXaZWNVuF/qe2AFAEz9IIPPcWBxHL8Rsz3N2IUPVm2DH0CrrCdAyjXiiT3PQiypZ0tt+diyOoM9DbZPO5HThAyoQmc3XDVJUUHORHfNqt2xZ96Ztiw/7Ohrx88gsVXtD5eyHR9Dngd/nhqKqePKhfF9YZVNt31rLtYkrNTPHjs1VctG1U1RoFlVNQZ9jMQcAriVS6D02jJxQTj7UuCRzrSUjpG4NoZAIDgCqqkJdtlIz3rNyioo3o7Sg0x0O4tDDrTi4uxW9x4ZNNeKMCwdePh+1rC01kUjh++fGcc0yi5C+R9g9Uxw6PqIfh1d3bj3Ay+awu4fb3XMF6wOr5zthxSYQCASClSAEHYFAIBAIikS8hBXGSvRaj9YTlRDoeAHo0ck5dDSxdmbFnyMrJhSqBWOX3UD6v9Q+YMdTUWlLuROjU9jX0YjeXS224oSVNU932NoqT1FVnLw0rVtLlQO79hvnI/dcbNI6jIW897SHIEsSPrLZBd+mKn3uk4wbwvBEUs+6sqvlQ64//7V/XLdlO/3uDL722Xuwpz1EBUqJaHJgZxh9g1FTcLCr2ZxFRvqWDQDzhI//4/UrGIol9IwkVlSo9ri4gk7Q58Hf/avd8FTJpuLqU8lF7O1osM02CfjcurXYC6fGMBRL6lZm7zCZOPU1Hjy1q0XPMHjhtSsoXDFnZaiODcnMsJlEhYjMphFZrlXCBunrazw4sLMZ3/7pVTgoEyS4jfFUWYunXrdZYOSJOUGfG7Vejy5+R+IpROIpvd4WuUf81/5x6joTDlbrv2NFhUmskUBn9p0cnTYdF5CQSGUwPpPSbSrZbTRRSKLs2uyeKdjfkpPfFm8/YNpfzueVYurmAeZ7FmC+dwtrr/WJqKspEAgEgpUgBB2BQCAQCIpEvIQVplhrMSeCQqWCEpUQ6HgBf16wvJAY42TfheafVXaDcZV0qX1AavAQCxuJETXGZ1L6uFrVK+JRX+PB9q21+upjXhANkKj9sHVjeOdLRAZ2GxLkv8iICcb2G/uDZ6nH1pQwoqgqFZB77vFt8G1K6efEC9Z1NQe4loBs+3OKit5jw/jZOL1C/pULE7j/bj/1t+agD4cebsX3z41TQdK2kA/7Ohv1fi7Vqiu3HLx9/LsDkCSYxtlopWYkkcrg0PERuGQJClNMfTy+AEBF25YaS1GL3a/VHACA+7bW4sToNE6MTmHvjgY013sti7WzsKKWU+bSS0V/h8D2RzGwQfqnloVQIeZsfIiYw6vRlLYQelgSqSzqvB7uZ+TaPBRLYMCQUQcAscQiYolFnLkyg7YtNabvTibTeMOQwchmARIxx4qgz4OhP39Ev57LkrNnCjYrj607x8PKKrRSWdpO6uYZre9YAZx3714vWeVCWKIRdTUFAoFAsBKEoCMQCAQCQZGIl7DCWIkOK7GeqFRQgm1rR5Mfh/sjKwo6aBkkwMlL0wBU7N3RoGeV8CzXeFgFP4qdf1aZMVNzi3qNF9YKjogEvFoJxv44eiFGWdjsaQ9x20DG89DDrTiwM4wHnz9jG7AzBt7IanCADqKxgcLmoA+H93fq2S6zC/n9t4Vq0PdElx4AZC2AnnwoDAD4+6l5bntOjE7r551TVBw6PkKJOXvaQzi8v1PvJy0TS13OyvKbVqCfGJ3Ctk/WwOOScWJkivrM45Kws60eB3aGTXPg8P5O/fiXr99AbbXbUrxYzCqWv0NtXhqQ8r8lK+GtGCGjFNsy43kEfW6qbs/o5Bz2dTQ4rqNjhdftokS3F06NoTXks/1Oa8iHcL0PXc0BnBiZsi30DgD+ahduLqmW2RHF0FKE2GSHSwLqvG4sKQp+zMxFwcZmpc5782n+ddqYGWKPuQE3FmkBlrXa5H3HiN/n1u9BxTxTHN7faaqhUwjefnjbVFIgYa/jxvpDQL52ENmW1z7234Xam1NUHBmIaPcKVUJDoBouSbPSK1WIWS/C0npB1NUUCAQCwUoQgo5AIBAIBEWyXl/C1tPqx2JFB6vaMkYqZXXHtpW1MHkzmtDFAKe4ZAlP97Ryatuw+7Dep1Xwo9j5lw/Q07Zd4zMLGJ9ZwOl3Z/DsY9vw3OPbTBkgp9+dwasjU3qAng3CsGMiS1r2CXssY5Dp6IWYScx5+N4Q3rl+A4vZHLrDAVOmDz/LiA76GTNa3owmKIGgMVBNjV93OICpuTQACXt3NACwD06OzyygbzCKQw+3om/QXO/BJUvwVMmmccksKXj8uwMmgWN8JoVbWS9uZRWTQJDJaf1/9EKMapdWo0bFUCypH98oWrF0h/O/u6FYcvm72qp6NuljLpXVBTxSN8iYFfSzSKKkzJRS8fs2UYJOR5MfiqqibYsPUAEF4NZAAgC3LFnWnEmbAsewqc2RZyKRxkQiha11mwsKOoGazUgu3MQiPxmpKH79wS3bz10AnIxKTtWyLb7x+lW4XR/eFfGCUpD0311DwKsH9dnMECvmUlm0hHzU77W22k1du8i1pW1LDfZ1NODVkSnq989Crtl28MRsT5VcdM0cK1F8NbO07Z6nnDxrlWJ12zcYpbI4tUxJmOz2ikHYFQsEAoFAUD6EoCMQCAQCwQbBbvXjaos9xYtehYWOSlndsW3tPTZMfX52LK4H81dKMfVvCgU/nI4pz7ZrIpGisi9GJ5Poe6JL2445f1aMMLaDHRNSCNt4LDbIZLaP8eETLQH0X9UCRefG4ss1FPLkFBVTc4vU3/buaIAsSfoxDuwM65lV15J0m43dwgaqiF1PIch587a1mouHjo+Y+s+pbRfvOCcvTdva1bWGfLixmNVXn5OxB/LC0JkrcfS0h6h2JVIZnH53xiRg6llVR4dsbcwIEoBNVRJuLuUFFSfn2xryUe3RxjYfpMwpKr7+urmOBmDOYjGKOQGf29LmTd9+2ZbMqp3Gdo3PpBDwunEzm7O0rSolO8kSm0QFts+cUqjwvUBghNSmAoDP7WjAH+5pA6Bl6BgzQwgtIR8aajdhYDxJfd/rdumCaiSewp72EC5fv0EJO81BLw493ApFBVUXB6BtxpxkRZcrk9pYrw2QoKj5jM7VytK2e55y8qxl7AtNHNeec9jFG4D1Yg0jpQoxwq5YIBAIBILyIQQdgUAgEAg2CHYCwHq3unAidLD1Wg7sDHP3tVLximdRVq6VpMUENAptW+yYGgM/h/sjVEaKcd9sthSvXQSroJldkMlsH9NomrtkdXbQ58H2BrrugdfjwgPhAJ58qEXPigGA75+LmIKAhM7mgC72TCTMAhXbJl49HnLe7LbGWkQAPf/YmjwAcGftZkeBeN5K8Pfnb5q2I7WB7OY6278uSdIzsiYSaSqb6uxYHJ/9dj/m0hn8w2IWdwd9+PHTO/Hg82e4GS5GVIAScwDg6Z5WXJqcw8DVOFgtoaXei9/tutvS1o+M7SPf6rc8Zri+Bk1BH1dwCng9kArU48g3nm5c2xYftDpNdN2eZNosEJVaW6cQdv095SCzSCAoJ985fRVVsrR8L6br5rSGfPjich2uR79j/r2yc1mWtJpOvPvQwd0tGIrRWZaszVghypVJ7ZIlyFK+XtsLp65AXraoXE/PUHbY3fuN2C3WMFKqECPsigUCgUAgKB9C0BEIBAKBYINgJwCsd6sLJ0LHy+fpei0vn49xLM1WLl717moxWXaVEsDgCUtORSnSDsA6+OF0THntsN+3tfjFihelBM2sjs0LHiVSGUzP0YHrdCanW5IZj8vWhXFJQLjeh70dDQAkyyCWsQ28wtrs33jbkto6fYNRU+0ZI60hH5oC1ZSgE/C5cX9jHTqbAwAkjE6ax4RY2LGiwZ72EGUHmFNUU/0n8ncjOVWFsixgNPqrTaKFsX2ReAp7vnmWOy3aQjVoDFTj76fmkEwvcc/576fmcfRANzr+409NwsoHN7Xv2M2jnKJiLmVtPdYdDliuJi9kj2bEmHFD+tV4LbGjq9mPfqYwPI/NTPaSHXbWcYBmzeeUoM8Dv8+NBr+XqiHEPa5LElk8Ai7ZnIrnX7tC2XASJMl4ny28gCKnalk+baEazKUzqKuuws8jsxiKJdEdDug1yYZiCeRU4MTIFE6MTmHvjgYc3N3qaJGGVgcmStWyc/pdI07utVTNmWUrz4O718721gq7zBt2sYaemcSpoVMKq2FXvJ6shwUCgUAgqCRC0BEIBAKBYJnb/UXQLki/3q0unKzcZAP2Jy9NcwWdlYpXLlnSg7krWUnKE5YAUKIUK0qw7bALfjgdUyuBy2iNduj4iH6ebLYUkK9tcGBneMW/Ed55WdX5AbQaDDyGJ5KUtRsb9M+pWkB/KJbE5es3TOdjtO9xyRI1B0mbeP3Pa39OUfHksWFusDzo8wBQUef1oCnog8pkgiRTWfxiah6dzYHlAGCr6VrUFKBFF49Lws62et1WjbSh99gwZaFzYnQajf5qU/bKubE41daA183NPDG2keXhe+vxiZZ6jE4m4fdtshR0dtztx+H+CBYz5s9nFzL63LSa50cGIqZ6GkSg2LujQQ882mWVFQvpU0VV0Rry4Vc3biKzpGCJI7C0hnymmkRWOBVzyLHLQc+99Xj597p10ZE3T2VJC64DwpJNUBhehqFxuu7d0WCZLUmYTi7i3Fh+m0Qqg8iydSKxcmPtIgHghVNjkCXJ0T1ds9e0/q7TZz4n91rWypNk8vDsR1f6XLmSZ1Vehqkxw5Og1QFsw9M9bStqayHK/dy93rPRBQKBQCAoF0LQEQgEAoFgmdv9RdBOAFjvVhfOVm6ygUZ+4LEc4lU5VpLyhCXeNqUew+mYFmvFxwv4kCwQo13L6Xdn8OrIFFWzxVMll3Quxjo/RlEC0AJ9e9pDmJpLU5kvS4qK7r88XdBOi2fF9fn7t+Ire9qoYJJVPQEnAae+wahl5kPvrrx9EAmEaiJPnkQqqwcbe3e14MlXhnBuOePj9Lsz6Lm3nto+k1MpQZAVcwjjMwsmgYzHYgErNSP1NR48tauFW+eCx49Gp6gaNzzsfgeskAvk63LIkrQ8FuUV3u+/228K0BKq3TKqPS5d5IrEU/jVDbMV3kopRVdhs2sCPjfeef8DPHlsGF3Nfrx1bR5dzX5MJlKIGcbEJhFIIHBEY8Cr//fB3S16DayOpgAA1Vz/S7KfdCdGp9C7q2VF903ed18duaZfy43XMLtnPif3Wqt2ArB9rixF0FjJs6pVhulaUe7nbnYcXhqMAsCan6dAIBAIBOVGCDoCgUAgECyz3m3JioUXKLidz0db9TtG/ZvHWolXbH93NNHCSE5RMcXYh60kU8pKdCrUjkJWfIf3dwIAhmIJKCogSxL6BqPc4BoRKM6OxXHo+AiOHugu+XzIOfU90YVHv9NPBf9csoS/+1e7cej4CC5fv4E7qt0FraPsGIol8JU9bbaWWkPL9W+MFmpWASdeMM/rceGrn74HiqqahBYrAYXs5xxj33X5+g20banB+/OLlO3aUCyhtXFkGuPxwsKNFdVuFxYNlmNul4SPbK7iZubct7VWr73jBJ6YYyyQDuTnJDt3D+wMW2ZoAflrNC+rTD8WU9/GiaXY8EQSLokf/FvMKlRfAVr/VaKGTrHc7ffi7qAXl6/fQOrWkj5+xoyscmYyWRHwublzR7A+MWZoWUEyOYzCtxHj74W9NxHLR6MdGaByBVPC+EwKTx4bNlluAkBHU8BkLekkswYAIvE0IvE0Tr87g7YtNdRnVsF/Jws8eMfqajbbQbLPlUcGIno/nH53BoqqFsyKWcmz6mrYnhVDuZ+72XFwkgUqEAgEAsHtiBB0BAKBQCBYZrVsyVbL2u12zzhiObi7FbIkFRRqiglY8ALIpDj7jrv9GIol8M77HzjKQGH7+18/2o497SFcvn4DtdVuKghGLMxKFZvsagOw7Xj2sXY89/g26hxJMIytrdLVHND7T1HzAbczV7RAk12hZGJrttL57ZIl7OtoNBXLPnohX0NpdsE6K6ct5FtO2JDQ4K/mCj/vvP8BAPt6AoqqcsUeXsCpo8lv6pc779gMABjhHMMq+M8LAAJaBg9rO6a1EY5qvBQK2G5vqEP/1Xw/ZXMqntrVaipMDsBSSGsN+XBjMYvaajfXkskIEXOMVn6H+yMm8ezNaMI2A4tkJhVjE5bNqWgJ+RC1aeNQLIlqt8vxPn9vZzP++1vXuecdDlYjlljU/+2SgNpqNxQlh/mbimn7ldAY9HKD7avNxxrqShJcXVJpmUm3IxL4OaZOxJVyY3c8r9uFZz59D558KLxc0yaJPe0hTCYWEJ3Nz+vOZr/lPthstxffeA+dTYGCVo/GOWS0WQRUPP9aXgQBQIlH5P7T0eRHa8hncz2iT5wE/9+MJvRxkCUJ3eHC9zGq5syyaMWrD8c+V/KtZO0FnfVuoVsM5T4X0ucvDUap54TbfYGWQCAQCAQsQtARCAQCwYZhpYHk1crsWC2hZSNlHFVKBGPH4s1owmS7BWgZKAePj+AVmwwUtr//9q3rut0VK0A0B70lCU7kvHm1AQAt+ERWGRNGJ+fQ90SXfjyjbRrA99DXjhuj9nPy0jRe/6OH9XONzaaoQNl9W2uXg/L5OjhW87vQePJ+i4eOjxTsLwDY19lIBfd4VmTbt9YC4K+qJpZiJPuFhR9wMs/FyGwKz792BUGf27KtsoTlVeIqPnd/AxRVRWzWXgxp21KDpoAXiqqaagNZUShAXCVLaNvio7KiXnzjPUdZJ21baqg6PbMLmkWeLEmYSKRsxR3yO2DnJMHu/AI+tyPxgncOzYFq7OtowMlL03h//qZpm3QmZ/qb1y0jneULMC5Jwmtf3Y2P/+8/MX3vg5tLePaxdvQNRpFIZZFTYRvELoUqGbg74MXPxmcLb1xhtLEvvB3bn6TWFE+43IhY/SRdsgRlHalaWUXByETSJO4GvPR1za7kE3tvTGdyGHivOMHv/rvr0PdEFwCg99iwaf9WNqJ72kOW16C9Oxr0e6bxHs1eV9iaPjysas4Ueq5k+81J6axyP6uuZf3Icp8LWZACwLQoRCAQCASCjYQQdAQCgUCwYVipULJaVhSrJbTcbqs47YIKKx1bq32zY2EXQCYWXFb7M4sD1pEZp2Nhdd68LA5TjQKLY7HfJTZn5LwO90fw6sg1U2bEXCqLQ8dH0NUcwOH9ncgpqm6Btn1rLTqbA44zWgqNJ++3aJcdFA5WwyXLgCRBUbXzIHPnWpLuk4C3Ct/7UgcO90cwFEuipd5L2YLVVXv0fRgJ+jxajR9OwMnO8ssuQC3LEk7/iSaSWYkaLPs6GqjsqXIwmUyh0e+l5o9TC7H5dMYUhXwzmoDX4yoYnCdz0ypTqrbabZmNtRJLLxX54KvTfr+zbjPuDvgwmUiZbOR+/NZ1uGSJ22eJVBayJEGysHArB3dsdhesU1Qpgj43PtZQB1kCOpsDUFXg5fOxgt+7tUSLY4vZHLK58mYs3Y4Uk222GmRzqi5oGGFFyR+/dR1f2cPPLLG7djuloymfAWT3bMNeS2QJeoYqqeczOjlnesYodA0w3seKEUAKPVc2BryU4NQY8Bbcf7mfVdcym7tSz93lEorWUuwSCAQCgcAOIegIBAKBYMNwu2SkrJbQsla1ZErFLqiw0rG12jc7Ftu31lqu+jdaMPH2x/Y3G3TnZcIUwuq8+QEyOshAMk3YY9nNP7uaMolUBqffnaHO9xMtQf2cisloKTSevCAKOQ9jBhCg9Wt3OKhnLL1w6gpkSRvfIwNRROJ0oPs3G/34wc8nLM9zPL5AZT8Zz38olsDB3S16+4ZiCeRU4PL0PHdfhdhsmFN29m+E1pAPQ7EEfjFV2vEAoOfeenSHg/jxpetaTZ5sDuMzKYzPpHSLQDtLO5bZhYxpe16dGSNtW3zY19Goj6lVwDcST6GnPYTL0/NlzdyYSi6i99gwOpr8BVfEk3o/pPaGl2vFptqO30uDUShOlt6XyPzi2mW11Hk9VOaEU8s3VrdYDzWINgpVsoSlVfZtm0yk8Mi3zlH2n4TeXS14dWTKMlOmNeSDBKlAHTD7DE4Cey3pNNx/ZAno3dVqCsgb92dVI8jqPnn63RmcGJ3Sr2fFBvvZWl0uyWybClRWYLldnp2tsBJdrISiYkSa1RoLIRwJBAKBoFiEoCMQCASCDcPtkpFSCaEls6RQ2RKk3st6Kn5bCLugAju2xErL6Yuv1b7ZsTDW0MnmVKquyH1b79CPaczWMe6PLQTN1vwp9gXdak5rfv2gaugAEiVEPLWrhTv2dvPPKijNFpI/MTptslbb0x6ivsMG7Z2cF8EqiELGjA18sHZsxHbu5ChdnwCAPialcHYsjr7lfTvJ6jAS8LlxM6tQgevskqJnExVaxU5qQRSqT0Nga3TU13jw5EMtICvUGwPVpgCqS5ZMoqZmhQXbWjZOMc4JK4u9iUSaEuym5xbLbsM1Hl/AeHzBUdYAqfdDWMyahYff+fhWuF2y5f6KEch231MPSZJw/r2443oyLkmqqGBkRySeQu+xYXQ2+9dF/R4BLMWcgM+9osw2u3o02ZyK8ZkUXjg1BlmSqHuPS9YsCT/x/z7NtRuMxFNoDfmov7H3nNHJJHIKfe0/vL/TdE89sDOMN6MJ/VlIUaHfF60C8sbgf05R8fuvDFP3/ofvDdneJ8dnUvr9oNjnre5wgMqA6g6b66hVWmC5XZ6drShWdClm+9Uai41W81IgEAgElUcIOgKBQCDYMNwuGSmVsJg4dHxED6adHYvj0PERHLWp98KyHlYH2gUVrFbQOn3xtdo3byyMgR3SJ+wxWfGCFwApdpztslJ4c1qWtBok5O85RcVQLB/IOrAzzN0v+buTfiI0+r2IGmq7GAPuhKnkIp59rJ1rZ8NS6LfKZvsMxRJ6PzqxYyPFrYkYYaQ7rI1VqRZApYpByVTWVKfm1pKCvsEoJS4OxRJQVJgyZW4UmYXBhnS3b61dtuWzXgXf0eTHj0anqL99vLEWk8l0WQSda4k03owmcGBnmGsh1LurBU8eG6ba+P4cnWEV9HkctcXrceGu2s24Pr9IZQtZFaN3Cu+7/+XsOL6ypxWbqyTcXCp+70GfB5KkjdH3vtSBV342gem5NH41fxOZXA5LBZzIcopqCoA7ZaX9AWj3nMEia6IIVp+ViDk97SFMJc1ijiyZa3QNTyRNwvuBnWH8ZmOdpeg3n6Z/03czVmRdzQFHQe+jF2LUs9AUc/0oFJB3yRLeYaxX37l+g7peWd0nSwn2W90LV1NguV2ena0oVnQpZvvVErtu9yypUlgP7x0CgUBwOyMEHYFAIBBsGFarBs56hK394rRYOmE9rA60CyoYx9auIHIp+7bC7phGX/5yBUDsslIK1aB5M5pAdzhIBbKOXojh0MOt3G2tBDFWVJCXV/47WXk/Hl+ALOXr8djBrohmX+rZAKHx33bCF1vcus5bRQX/e9rNK607mvw4OTqNcWbleUu9F+H6GkwkUqbAIlCaIKRyAvNk/rLXL7a2C88O0EkQv77GY2slSGip9+Lk6HWTRV1ncwCdzYGSavZ4PS4qIymTU7mCMxlT1k4PANKMddv2hlpMzy0CqooGfzXeef8DbgZMOpMzjSmwcvGCx2JWwTd/8l7J3ydz9OxYHB3/6adF248pAJQSa6+oADa5gFsrdDwrJDoJbm+m5xZN14bWkA+f39GAb7xOXxtyiopHvzNAZXDaWa5p0IHcpoAXX+xstM3E5N37zYI7vd9CAfmcouKDm7TwxWblWdl/lhLs5z23On1eySkqjgxEqUxd1u6uEBshqF6s6FLM9sU8O66kL2/3LKlSWA/vHQKBQHA7IwQdgUAgEAg2AGzAdvvW2qK+vx5WBzoV5Ep58V2p2MceU1FR9gBIMWPAbnt2LI7z47Pc77Pbvs3UeymU/cKKWYXOoVAtHLaveC/1MlNXwPhvO+ELoK3QGgM+fKGjkZs1ZPyOLEkmC7XYbBpNQR/+r2d24Qc/n+AGdEgNnelkGnPpbMHMEZUzTazmLyuuSdAK0Bvtx5xkZDy1qwUXY4WziqKzae7fT45OY29HA9pCPkogCXjdXPskI+lMDj3tIfxsfBYZQ1sHrsbxe//tIlyyjO5wgLJFsiPoc+Oc4Tq3r7MRD7bWF21/txaQWjws1W6ZyiBai1oyKxVzBBsHa5HY/DdNoFHx7GPbcHJ0CnPpDFSAKx7biTlul2S6dj7QEiyYicm7dnY00dt8/v6tcMkS9/rNuz/1DUZN59/d7Kf+bcwoZL9fDpw+r/QNRqnrJs/urhAbIahe7IKdYrYv5tlxJX15u2dJlcJ6eO8QCASC2xkh6AgEAoFAsAE4vL/TVEOnGG6n1YGsR77RQqxSq02LtXxz0g6yDQnYTyXpgLrdGPAsX9ggFPk+u22d10OJAiT7xarNdrVd2JoKTmvhGGFf6k+MToOtVG8sbM1uT+rlkEwdYwbSubE4HmwJWmYNGWtPsYXESWDyn7w4iJ/88cOmdrNBHm1Vej9lqcbyAWObJkuSbi/HzhESSALs6/VY2XwZ69W8OjLF+aYzxuNaXYyee+spQefJXWGoKvC9sxEsZnOWmS/TyUXsbKungrw5FTh3VRMgz1yZ4Vrj8aF/Q9898x7+cE8rvvbZe/Htn141ZXatJ6o9fEHno3dsQiyxuAYtEgjMsPcRt0vCHz9yLyQJ3Cy9v33rOk7/SQ9kqbi6YsbsPfaYbVt83IC2XdCb3L9+NHqN+o4kma/VBF72KruYIOjz4MiXuyzvjyvNCrfar5NnCJ79Z7FB8Y0QVC92HCqVzb+SvvwwOgzcTu8dAoFAsB4Rgo5AIBAINiQbwUaiGDxVclE1c1gqtTqwEuPAeuQTazGgcqtNi7V8c9IO4zZG2rbUYF9Hg+0YsMIFbx/k++zYXowlKRHm8vUbONwfsSwerVmgqegbjJlWUTcHffhiZyOGYkkoqmoSJ5wEONiXen6NF9Vye1Ivh7SXnV9HBiJ4aTCqC52eKlmfl0cGogWzaiLxlF7nxg6XLGFfR6NtUJPNpFNUFWeuxHHmShxHBqLo3dWCg7vp38gQk13D1pDxbqrCzaW8UOT1uPDVT99D/dbY+hSlMD1Hiw6yJEOWwRUpjMylM/i7r+7Cx//D6yb7NAJvDNyyZvdnjPXWed3UtulMDt94/Sr2tIdKEnOc1uMBzLVmeLVDXBJglTRldZzJ5MYXc8pRp0ewNnyyNYgfvzUNqJpl5VAsyWSRadcYq9pibSEfGgNe073KLhNtX0cj9znBzqpzSVHwjdevmr5z8tK0pQ0ZL9OVrY93cHcLPFUyZYFJxB+y4GElzzVWzwpOniF4iy2KDYqLoHr5EH1ZHB/GrCSBQCAoJ0LQEQgEAsGGZCPYSKwmlVodyBsHYlNCRABZArrDQcdBETuRYDVWmzp5aS/Ujpyi4sQoP2uiOejVA1aH+yOml10SxOoOB9EdDiwXuqezQvZ1NOh9yRvbN66YBZG2LTXcNrtkCU/3tOHgbk3MYgWJ4YkkVWfnzBXt/w893MrtKzYQRzKshieSmEikuBkuo5Nzer8pqiZYvT+/SAUFSXvZY5JspLNjcRz8wTAebK3n1mqxw+k8ssrkAoA97SE9k44nxCVSGbxw6gpkKX+tyiwpeGtqjtpu+9Y78GBrvZ7ZdYGx2svmFLwZTWAolkR3WJs3H2swFyPnCRJ2zDH2aiMTCUwm+TZtRuq8y68bUnEBzyyncY3+akiSZBq7YmuGEch4nbw0DVVV0OD34fpcGpPJtClrgG0NaZ5RrCiljM16zioqF05O0Uk9KIEz7ITFYum/mr++jMdTePjeEPqv5q8ln79/KwC+uAAAezu0ui7kmm91jQe0OfAnn2nXf5d2C0LYZ4ugz83d5/iMtSDPa7NVfTye+EOODZT+fGn1rODkWYbUmzPW0Ck2KC6C6uVD9GVxrEZW0odtcZ9AIPhwIQQdgUAgEGxINoKNxEaANw6A2ZrFKAIUwk5QKXaFZCkve05e2gu1o28wahnUItvyxDAA1N+ee3wbXv+jh3FkILIc1JEKBnXIZy8NRqli8nOpW9x2GOkOBzA1lwYgocFfbZkhRH5vvL6yq4FjXAVt1SdWtVbINnbZS0MTc7rVF4/WkA9NAS9+MT2PpMGWzulKW2OAwmjnZswOKjS/jNeqQ8dHqHYAwMVYEj+LJFCzucr0GaDZF+XFtRkoqoqu5gDOj89SAXOnQkJ9jceUWQQAE4l0gSLnGl/oaMSh4yOmFflOavCwTM2l8YWOu01zgNc+O9wuCTvb6vHkQ2F4qmQ83UOLrU8eG6Zq9djBdmMxWT/lwO2S0Oivxq/mF7HIsd67XRBiTvmoZFdOz9Ei7lAsgUvX5tDR5Mezj23Dy+fp+8rL52OQJQm9u1psr/GANgeGYgkc3J1fvGC1MIfNXJy3uZZYPf/x7hXdYXPtHsBasLLbvxOsnhXYv08k0jjcH6GeUbTFFq3U9atYPoxWX4UoVQQQfbn+EIv7BALBRkYIOgKBQCDYkAjrg/UBbxysrFmGYomisyCMgoqWvaGibYsPToQNoLSXPScv7YVEH7YPgj4PPt5Yp2dU8Lbh9RuxOBudnMPeHY0AVIxOJtE3aK7JwrYfoIU1Y12dPe0h9O5qoQIbbMaJHeT3xusrO7GVnDvJPpElybZP2rbUoDnopfrYJUuWwZdqt4tr9eP1uPBAOGCyZCPtIMFDNkuKF+wh3zVmARmtAe0Cg+T7OUWFS5a4mSeLy7ZlPDGHBy+Dy4qWei9uLC5RgsRTu1q4c6+QmBP0eXQLuZfPx6jPvB4X/D4PJegQ4UiWgF9MzVPzMX/MNC5GZ/H/erQdP37rOsiq9CcfarHMfOKRzak4NxbHoeMjcMkSOpr8ACSMTmrz3KmYw+O+rXfgZ5HEqgkU2ZyK6GwazcFqTNjU4mFtz6rdMj7ZWr/c16snQAnWH8VY4rGJdkQgP/3uDFpDPhzYGcY3Xs/X2mEtMQvZhZ4di+PIQARP97TZ3isUps6a3c/N6vnPJUvoe6LLdD3nYZd96eT50koksHpWIP9/YnQK4zMpjM8sUP3odP+C4hEiwMZBLO4TCAQbGSHoCAQCgWBDIqwP1h6jPRZrB8ILaDvNGLASVLTsjXwgSZbgKBvCyEuDUQDWYohT7Lz+e3e1mIL6B3e3FFwR3NEU0AUcwkQirWc3Gbc1BiF4xwdAiV9QgfF43saKiCLfPxexzIhhQ4B72kNUTQEr7MRWq7El9nMTCXp1+L6OBkdWOl6PC93NfiiqhIH38oG4tpAP+zobTePNE73OXNHqJnSHA/o84wV7rGojGbOWrOz2AC2gSSyCis084UPPY49LQsZUhFyr26SoKvUbCvo8yCkqlkrwBUukMrgYS+DkpWmkb9Ei2p21m02C0O/vDOu1IyYTaa6gA2gB5On5RezraMSXH2zGV344ipfPx3Df1lr860fvxehEEpPJRVzj2KaxsLZJK6Ul5KMsqpzgdkn4yKaqorOVWOzEHMAcsF/MKugOBzCZTK1Y0An43I4FRsHqU8he0cmvW5aAcNCLhrpqS4E4Ek9heCKJ5x7fZsoANQZSp+bsrRpPjk5DliRMJOjjGO8VVrfn+hoPnnyoBdrihrmC9yOnWRWF7umFBBUrkcDq+OTvwxNJqr9PjE45sqAj+xcUjxABNg5icZ9AINjICEFHIBAIBBsSYX1gz2qs5mTtsWRJolaksgEfucgaGyxOX8KN584Gqslq4jejCfQ90bXiPskpKlV3xlhHiLTRKuDEbmOsUwNo9mB2WRLk/K2s24yB+z3tIUrQIS+9mo0bH1VVsac9RGXRsP1l7OuOpgAAFcOxBFpDPtxYzGL71lq9ho4dRwbouWQUYlioTC0VaAh44VoOaA5cpcWRBn+17dzn1U1gg5HDE0n07mrBkYEoTl6axvvz/MC6MWtpX0cjgAR3OwA4MTqN3l0tes0dYts2mUgjOuss2wbQxrU7HKT6bmdbvWke/d2/2gVPlYzeY8PU9xOpDL5uWG3P4nW7kM5aFzdnM11IJpQEc4YPmSfGtrVt8aHRby6oPj6TwvOvXcGrI1P6fsixJEiOrODKRdDngd/nxufub8DLg5Giv5/NqbqYE/S5oapYsbjjlOGJJH7743fhmz95r+C2dlkcQsxZ35SjTpOiApHZNCKzadt7zzvXb+CVA90A6AzQjiY/vn8ugr7BaEEBcS6dpb5LBGfj9b47HNQXMxh5apd5cQSLlR2mU3jPl0YrOZ6gUqpIwAaktWydlOkYlRQhPmzZP0IE2DiIxX0CgWAjIwQdgUAgEAg+hKzGak6rAIOV5Vd3eGUvzYVewnlWWFYYsyRWwpGBiCkYTfqhkODIBo3YYPuNRfsgKjl/NqtnKJaAxIhnVoWg7dZuR+JpROJpPPf4NsvzsBKTCEYrMjtMwpIk2R7TKFaN2wT3z12dtR1nvj0a3XcdTQFKtGMh4tOBnWEc7o/o4lbN5irczObQtqUGDf5qSvwYn1nQ23V0OTgKAIuZHP7Ji4OYSqZRJUtIL9uv8Qj6PLpI+uxj7fpq9S8/2Ix/8uKgHpCNxFOO7eBYPlrrQXTWPjPEyF111bpIxZJMZ019OJ/OYu+OACaTaUQ54zjBiFtDsSTXUq9SeN0ufLyxFt3hIBQVSKaXivo+K5L4fZvQHPSWLWOoELHZFEYn5xxtKyrcbDwKZe5YYXfvqa12awsZGPvMk6PXqUUDdvi9bkr0aQp4AWg1xcj9iWfdRqxCC2G0Zzy7bL1ovM6WQiFBpVSRwBiQnkikqWcX4zEqKUJ82LJ/1osIkFNUU31EUl9K4AyxuE8gEGxkhKAjEAgEAsGHkNWwlCgUYCjlpdlupWih/VlZYVlRSp+w7Ts5as5wKTXQwvZnbbWbynAitG3xYV9HPnuFDdgpKvCJML0vq0LQe3c0UOIID9JPvLGxqpfE+749bNTRHIUkxye2eU6xO37vrhb8PJqgxJbP378VLlmyzJxiafBXY3giSQUfT787g7/Zeyfqazbh9J90Iaeo+Oy3zyESz2f/GGtKZZYUHDw+gqFYEtVuGc98ug2jk/NUu9jgbCKVwZkrMzhzZQbPPb4Nh/d3om8win/6n8+bVte/OnINJ0anoKqaEOTUguvuYI1J0An6PFBVhStujM8s4OAPhvW6G4WYXcjghVNjaA15uZ+bHNVWWXVIZ3M4cyWOM1fiy9aSxcE2dy51i/PXyrGamUyC9Uc46MXcYrboDCs7QScST6H32DD6nujiLtywg9TdAmjrR0VVLe3KjNjVTzPC1ibj1Sorlko87wB0QNqYBQTkM4C7mgN6pmslRIgPmwXZehEB2MUpL5y6AlkCHvSvYaMEAoFAsG4Qgo5AIBAIBB9CVsNSolAAo5SXZruVooX250RcMGLXJ1bCEts+t4sOLuUDVsXDrkiOxFMI+jyo81ahMeCDy8L6jLWykyXrQswsB3e3QpYkXIwlMZlIYT6dQZ3XQwWCc4qq9wc7Nk4yPpzMPVZY2rujQT82GQe2UDWPlpAPU0xtFbvju2QJD4QDlHAyFEvgyJe7AJBV0/ZBcSvxIpNT4DMcpzHgowQdo1hx6PiI3oZ0Jse1yDKKOawoMxRL2hYiNx63GGRoq+KN+02kMqZ5b2Rowj4jhNjxGcVKSaLtkEhdLmNtCa9btrV/Kwd2tmNWVnvFkEhl9dpBhezsBIKV8v6Nm7jzjk1FCzpOalP1DUaX64VZ23ay3H93HZ7u0RYHyFJeNGezTImgUOpzDFubbPvWWv2/S7UXc/K8Y9yGfKcY6zLj9433O+OzUCVECGFBtjbwnlmHJ5J40L9pDVojEAgEgvWGEHQEAoFAINggFBOIqJSlBK8N5QwwrGSlKBuUaNviw94djSDFkzua/AAkjE4W7hMrYYltHxv4KjaAw/YnK84kUhkkUhl8sfNuy37oDgdw5soM9W+e+GU1f3jbGS3Gzo7FDbYgeYYnkji8vxOAVsiZV0i7bYvP0dwjwpKxFk/vsWFbEUcrjh0GGVN2WzaTyYoRRoA4d3WWsuxh0YQFays0gseVFylyiorL0/Qq8elkXiAodgW5ysgOOVU11bMhuCRzpktbyIdJRvjiMZlM47Wv7sZv/dVgQRtDwq0CIoWqqkjforfZu6MBskRfr9iMu7v8XsdtKBVjb7AZUcTqLehzY3tDnWV/W+F2SVR/p7M5tIR8XKu5tcRO1PowHH8jsZhVEE2sXIjkQe6F7G8y6PMAUHXh0ghZHMC77xjr5RBBodTnGLY2GblPAc7txXj3y0KCzUqty4z9wlqwVjJrxkk/b7Q6O+vhfHgLYrS5v77uCQKBQCBYG4SgIxAIBALBBqGYYEGlLCUq7bXOvuBOJNI43B9x9LLNC0rwv1NY5LASlqwyUuprPHjKEPBxCtufe9pD3O0K2YaRbYzBGPbcFDVvc0POgQTO2e9OzdFBwJOXpk2CTVdzXjgankhyBZ19HY2mMbDyjadtZ+xt4AC2OLY5ANYc9DmamznVHD6+ME5n3XjdLnyixY8HWupxYGcYRy/EuFlDe9pDcMkSupoD8G3K9we/UHj+uOyK8kKwK+7Pv2f+btDnRiKVNduWQcs2kR2EzUn9nb07tlIZVA+2BDHwHj8zqYBGhOgsnS3k9bgAwCQOszU6ppKlZRmVilXtkUQqi7en5ovOsOGJZ+GgFxLWlzXaWospa338DyOypM3F9+dvYnGpsFgNaNd/9j4Z9Hnw8cZadDYHoKjA316axvs3bupi6NmxOB79Tr8utBeyUy1mYYIRT5WMowe69W2/8sNRS5tQ473VuO8lJS+Sn353Boqq9ZPd84+TBSlOhYTVzJpx8rxYzme/9SCmrIe6Qb27WqCoKvUs1LurBf/rncur2g6BQCAQrE+EoCMQCAQCwQZhPficF9OGUl7aSSCHZHyMzyzoL91WdVzIPtmgRE5R9SL1Vsc/MhDFC6fyL/WKCjzdY231wraP8NSu0jKV2P6UJeC5x7fhxOg0tfK5kG0Yb+UwG7Bo2+Kjvke2ZYMagHnVtbZuPk/blhpKvOJlR1llx1j5xpP+s7POaw350Bz0ojscNO271AAYTyjIMMH3dDYHFZLeRjLPWHGqOxzEwd3aPHv77bf17/POqcFfDUCbpzua/Oi/GrctYF4lS1iy2ICNwRJbMyucZBgRhmJJk+j1QEsQO9vqqYyql8/HuDWfCpHO5PTfoCzlBRx52WKwszmofw5oAhAJEAN0XY6RiSTeuBKvuCiQTBdnYWXFRCJtytAptZC9QOAUdo411/uKEhWDPjdeHZmCylwXtLpeWs2pPe0hjHP2OT6TwvOvXcGrI1P4Ymde2GHvYUbh3M72FLAOxLPbvhlNYGqOvt4b7xN2dfhOXppGc5Cu9cU+/zi5BxVqP3nGGYolsac9BFkC935nZDUEknI+fzoZw0qf03p4nnbJEp7uacPTPW2relyBQCAQ3B4IQUcgEAgEghWwHlYSEtaDz7mTNpA+M4oSxpd2J6IMm/FBXrZXEszhbcvaiJ28NI2ne1oLrhi2ymwpBmI/Y6SzOQgAaApUo9FfrQe1C+2fd65mEYGet7zaBUOxBCSJFW98yzVu8oGuvTu2UufPFmwmwbhDx0dMY8wTN06MTOnf7WjyU3OsNZQPNkbiKXyxs3FFATB2/jnNCbh8/YYuEBrt+3JKvtYLK04ReJldLlmzZOsbjOKbP7la8PiSw8tO0OdxHJytkvOBXSsRIacopjpBb12bo4qiA5rwU0yWEctLAxGTUHLmyoyewUPI5mgxSoWKg7u1sZYlCQPvzdpayVkJJqtt9eV1u7jj1BSoRszCJotnn1ftlvHROzZZfqdUJGhzTohLGw8ypl6PC3fWbi66PpSxFpQVhWwkI/EUtViDJ77wasgUE4hnt6UtOWuwr6OBuk+w90MateDzz4GdYbwZTehWb+S+aNcmtv2sqPTc49sKCg2rkW1SzudPXh+wz1S8jGK7cyr2WX09PE8LBAKBQGCHEHQEAoFAIFgB68GWgVCpujhOySkqFDVfsJzYQ7BYrXItRpTpaKJftrUsgOJWVTrblo1Wav8uZEFSDku7vsGoya4LUCm7MSfBHIB/rmzAglen5M0oHcBSVOATYfp7JNNGy55IahYho9P66mtewWbNNs2Y+ZQvgs2KWAAwHk9hPJ7C6Xdn8Oxj2/Dc49sMBbOTVOD7pcEoAC149vL56LKdWT646HZJyKna57yADjv/etpDiMQL23nVVru52UwsvHlG+to43r+YmsMj3+oHOJZvK2GRsQFzuyQ0BbxQVNVkd8Zm9hCbNiPTc+ZgLy/4tVKd2yrrxZiNA5ity5KpLPqW54TV6nojVgIF+2evx4W7ajfj+vwiFovIaHIKz67N63aZ6mgZ4elU2ZyKiRWKOTyRS0XZp+aKEJlL5eeuuuqK1aW6b2utozpT5HrJ3sNYQYiILRMJ6wwbFiuLVECznVSYCW43v4zPO1bPYEcvxKjac0cvxAqK+2z7S8kcqWS2SX7BRGJ5wYSzRSZ28PrASUZxOS3hSn2eXk+LvAQCgUCwsRGCjkAgEAgEK2A92DIQKlUXxymaVVY+YCpLEvdF1soyiwQuViK0FLOq0lyPJ2Wqx6NlnuQFlL07Giz3V27YfnDJEkYn50zbOBlvXr9Y1RQyZkldnqaDZpIk2X7PuGrWrp3sub34xjgVmA/43KY6MITRyaRexHp4ImkKus0uZPD8a1dMAgkhm9NqHzz4/Bkc3N2it5+cMxGECLIk4bnHt2EolsRSLod33v8Ai1kFnU0BSFDwv371D9i+tdZxrRNe5pVLltD3RBeVueZklTt7XjyCPg/qvFWYTy8hkcpwBZDJRBpbaz22tm2ANv6sqPP+jZvUNnvaQ9zgV3c4SBU2X03sbPpKJZ3JYTyewp985h587+w4bi5VXk1IZ3OIzBZXK8huPJ1yOwglt0MbbzcmE5Wr3aSqwLOPtePkpeu2otHPIgl87+y4KTOTFYRyKi3YBn1u9Brq1vEC7cZ7GVvvTLN6HIMs5a00eWKqsT5eoWcwJ882hYSEQs84vPOsZLZJKRlDheD1waHjI8xW5oxiO4p9Vi/1eXo9LfISCAQCwcZGCDoCgUAgEKyAD5stg93qQ6cvzIXqqbCf5xQVvceGqeOxwgb5txNLE0K+3o0WQCfe/UD+Bfzg7lY9c8TpKs1yrdBk+yE2m8KvmOA56RujxZfR0szK8qxQ8Mkqi0pVVdvvsRZ1xnOxOzdWZAj4NlkKOjlFxaPf6acs9/a0h3D5+g2qRkshS59EShN+SDFrtu4RYTKRwktf1gQkY58MvBfHc49vw7EnPwFAyzp6w2LFudEW7uxYHEcGovi4bwmZnEKJiHk7QTrA2RLy4dfLmSDGuHXQ58FiNmfqP/Y8D+7Win1brUbPKiom5m5Z7oPAq4FDjh30eeD3edAdDlKfG1dw97SHMBRL2ra3Ely6NofFCh3zf/7ifciyDMB6/1WyOdtppcgAyp8XJPiwIgHwe93Y3lCH63NpjMdTttaEVmyukuDdVAW/14MGfzWm59KYS2dN1/P+q3F8sjWI5qDXVtBJZ3L4+utjpszMJUWhBJ1pptZZIpWlFpVYBdqJReqRgQgucq5NxueY7nAAZ67Q19Bi6uM5eV4sJCQUEnzY8zwxOoW9Oxrw7GPbMDqp1TRTVPMzValUYlETrw+cZBTbsVrP6utpkZdAIBAINjZC0BEIBAKBYAWstc3ZamO3+tDpC7NVhgfvc+OqWePxzNk1aRzuj0BRUdDShGAVQDe+gPOKMRcKgJRrhWbvrhYoqiaSvD+XprI/gj4PPtZQa+ob8t9W9QWctsMqo8Eu7pNTVCRTdMDf63Hhq5++x/S7IP9+aTDKFQmMwRq2Hg0v6+ZiLIlqt0z9bfvWWkc1W05emrYNKEbiKV2gYzkxOkUV7VZUVRO1VKAh4IVruV4PawvXNxjF8z21AGASEXk2QBKAtMHWyy1LyCoqEilz3/EYiiXRHba2F7KD1KgxBjq9Hpcp8JlIZZBIZUx1go4MRLhZW6uJlThYDniF3VnKLeYAqyPm8GryCDYmKjRbw+vzi2zyQ1HcXFJxcykLv9eDT7bWo3dXCw7+YMQkhADa9X/71lrT390uySQmsZmZrLUar83kXp5TVJwYpRcbGK/dWnYx/xplfI4x3pPtbGWtYJ99DuwM4/vnxpf3J2HvjgYc3J1/vrBaHMK7l1tlmI7PpPDCqTF94YyioqjaM4XgPfdVwnbMLjO41O9Xgg/bIi+BQCAQrB1C0BEIBAKBYAWstc1ZpTAWktfqm+QD00aMxWqdFp0vpv5M77Fh0/HIilogn1UxPrOA51+7gtYQ7av+6siUKZjABhtYKxf2BbxYgYYN/BsDR8XgkiXIErhigyTBdn9sdgq7SrRQwMWqtgCbfWGkbzBqCpx3hwPcvjIGYthMoD3tITz5kJZhBGg2N1r7zPOBkM7ks1RIMWtSQ0cTWCTcVbcJP48mOavOC0eseTWHAC1Y9uSxYbz8RNfyeEl6ls94PIU97SGuLRwrxBjHhxKGIOHz92/Fi2+8R22ftfGX8rplSvwBgJyioHdXi2UWkh0PhAP4REuQGqdCGTbGQGofE2DcCPTcW48ql4yJRLqsNUaqZAl3bK7CBzezUNS1txErZ40cGct1d8q3S0GRVMkoOK+czmevx4WP3rHJVHeLEInnM17Z6x9hdiGDs2Nx9Nxbj+n5RRBR42J0FueuzlLbsnVUWPbuaMBQLEmJ+ORe3jcYNZ3X+Iwm1PPq83g9LtxVt9kk2LhkCU/3tOLpntKe99hnn8P9tNhNxHDyTGW8Xhd69rDrG3K+z792ZbnGYZ6VZpDwhJJK2I6t9Fl7tZ7VP2yLvAQCgUCwdghBRyAQCAQCgQlecODMlTj2tIeov/GCLOXwUDfu305smWcKpc+n6UA5ya4wtocNNrBWLuwLeCELjUICkTFwVCxWmTLbt9baFnRms1OKFal6d7WYatDsaQ/hwM4wDvdHHFnuAZoNjp21C09kmJpL47f+alAPwNllZvFoDnr1c3m6pw1P97Tpnx3uj1BzdU97CN3hQMEMkpyiYiiWRM+99bgQSVCi0LmxOPoGo8vnQq8CZ/vPJUtcEYBd1dzR5MfeHY0YmUjiR6NTWMw6z8dgxRwAmJ5bhEuWsK+j0Tbox+NaIoWu5gCCPo9lRpDRUg7QVtN/+ptnl+v22GfHBLxuJNOVy6ApBQnWwsOe9hD6lgW875+LUHXDVsqSoq6rvtjMycQqFWEPt/aUM1MsnclZijlGhieSpsxOtwwYL1NVLhmn/6RH//eTD4Vx8AfDGJrQrFTvrN0MRVUxwrnHEAG/d1cLDu5uNS1UIG2wahvvvvLVT9+zKot0eO0if+Ndp+3EF54oxf/t0le2lWaQ8ISSjWY7VkzG0UZd5CUQCASC9YcQdAQCgUAgEJiws9xixQ+2WG05X96tVjtarUb1ez2mADLbHvbcTl6aQnPQZ/miXkhU4glEbVtqLG3crMgpKo4MRCj7FVYc8npceCAcwOH9nXDJkl7/h0AKNBtr6PA88wsFXFyyZOoHlyzh6IWYY8s9QMtSGY+nLFfpumQJe3c0UkFxXgYJsZE5sDNcMMukoylgKToZ5xOxchuZyGeWTSYXqf4M+tyo83ooYcbtMgdySH/arW6XJQl9T3SZRKWgz4OfRWZxuD+iB/NLsUazYzKZRsd//CnqqquKFlAis2l8/XVrwSvo8+D/emYXdr5wRs/QmksvYS695Gj/fp9bXyUfiy8g4iBIXApVMuBxuZDOFhYoqhjLp9aQD81Bn56teGQgAlUFXhqIlNgWCUtrnX7jgGxO4dpfCQROmZhNmcTRh+4J2S448FTJeOX3H9CvlZG4ZhvGLigB6EUcVsF0q4UA5LirlVVhXvxhbldXc8Dy+ctOfGHP8ZlP3QNZ0rKUjWL75+7fiipZrti55hQVOaW8otFaU4mMI4FAIBAIVooQdAQCgUAgEJiws9wqVKy2nC/vVgEaNuBBhAzNH54Wetj2sO3VLNtSywWEp/XVvjwRgARAjIEZ1sv/5KUp03kU6pOcogkuxiDXC6eucLOHjELLvo4GShwwFmgm1nTG/ZLzduJ7z4pJOUU1+fOba8jk6wuoKqhAkrWoVThYPLuQ0c/TKsvE7ZLwR4/cA0DF86/xawQY5xMrrDz3+DZ0h+mV0R9rqDPV4eEFtzua/DgxSo+7102LB7FECo98qx+qqqCnPYQql4SlnFYHp5+xFyqFti01UBSFu2o+u3wcpzV3isHvdeMrPxx1VKempz2Et6fmKUFJkmR97vxsPFFSG8LBasQSi7bbeKpc+MVffBa/9VcDBW3n2DGemE1BVVW9b89cKVybyY7bQcwB+HNdIHBCwOdGMpU11ZjqaQ/h8P5OywUHX36wGV/54SguX79hsvybSi6ipz2Ec4ZrMrk3vBlN6JlzLAd2hvFmNIHL12+gttqNpoAXD7TkbWHLlVVRKJPDvPijHc8+1k4t4iBtMt57jVlIVljVmGGfiWRJrmgGSd9g1JSZervbjm2EjKNK1DUSCAQCwdoiBB2BQCAQCAQmyAs4W0OH92K+Fp7hrCBBhAyyMtSuaLGxvaz9FanFwwo7bACEFQOMGIPFTgIxgDkIQhidTKLviS7L4IExULV9ay0O7AwX3O93z7yHO2s3oac9BJckoTvM9703ikk5ReW2z2gnx9YXYPuIiFpsYIFno0NgbWOGJ/KFsdnspGxOhQQJo5Nz1D54wRetSDYtwAzFkuhs9qNtiw8kuGbXNrdLQlPQh707GvS+MHJn3SZE4nlxJWoIbEbiaXy5fYvlvu3g1cgBgKaAF5OJ4urjENgsDFmyrrMhL9dvqpIlLGYVPQvLDpcENNX78PHGWlyM0KLN5+7fulyUvHTrspZQDf55dxOODEQsLd7SmRxePh9Do99bdB2hnApH9lICgUDL2vN73VyRd3ouDU+VzAjreQGezSgxMh5fwN6OrXiwJYiXBqOYXcgL1GeXrS9598qjF2L6/Wt2IYMvdjZWJCBfKJODFQZGJ+fQ90QXZQsKWIszdliJUqOT7DGTAConRrDnyMv2vd2o5KKl1aKYLCMh/ggEAsHtgRB0BAKBQCAQmChmxWo5VrfyXiABWL5U2mXNjEwk0OivhixpRertjtPR5OfWTyHCDsB/6WWDFm1bNEsmViAy1nOxoxSLFYAOVJ0di+PohZhtAAnQAtuReBqReBrPPb5NF3OOMPZRPxqdwk/++GEcergVT74yZNt2nmCypCgI+txYzCroag5gSVHQe2yYEodOvztjstEhtWa6mgOm1cVdzQF9vvXuasH2f/86Jfh8+/RVuF0ytT+2D0k2FBvUV1SVmQsqcjZV4T+yqQqqqqBvMIpFxsarbUsN9u7YWrA2TzFI0OzJrDJhcqoKSM6CLqzlF5uJYZdAsvueerzy+w+g99iwY2u4nKoJWt85PW767L+dj5n6r1hI5iCbQcZy8tK046LvAsGHlWq3jGq3q6A1o1WtqdpqF+bSVtmA9DWKvUdNJWnhlBWXiQgCmOvMWGVOVCLDgvfMUug4ToWBctZhqaQYweuDjSB+sKzFoqVyU8xvQFjMCQQCwe2BEHQEAoFAIBCsOmwgwBhMNwYDrF4qnWbNnLlCf88qC8WqLsuJ0WlHdXX2dTRybbycBjN4FndtIV/BwEGxASSr7/OyjSLxFI4MRPF0T6ttgN9KMDFm9PRfjaP/Kt+mSpYkS1u5nKJlh/ECKbwVo9mcimxOEwcCXjd+s7EOF2NJvBlNQIIKFRKuJdOmFeBtIR8kJjT5o9EpKsOGJZnOWgY8SVaWLEkYnkhiSVEpmyAAqHa7kJGsq5S7JKAp6MV8OoNkegkqYGtr9vbUPAI+j+XnhIfvrV+Rxdvl6zdwuD9iqv/QGvJZrqy3w8oGzi5LyEhryKdbNik2AhwAmDycBAKBicWsgkVOFiBrd/a1z7ZjdDKJ8+OzlCg8n16yvDY2+Kupem7sPapmcxV1natjMn2MdW/ejCZsa/EY/15ukeHIQIR6ZlFUteBx1kIY0OwsVd3WTVG1+2o5Mi54gf+NIH6wlFNgWyuK+Q1sBIs5gUAg+DAgBB2BQCAQCDYgq22ZUOzx2ECAZnOVh5dZUuil0irLxfg9s+WJZmlGslRYYWd8ZgG9x4ZN3vxWQQu2joyiqqbgCa+veMEpAOg9NkzZ3RmFjr7BqKl+Dy+AZCVWAUBsNoUBC6EF0M7j6Z5WU6ZTW8iH5nofN2BjZR9nRXc4YAqWsH10eH8ndz51NdWh/z1+3ZWbS4rjdjQGvGDDl9eS1jVZWCs4Qn2NB9u31mIoprWpd9kK8PvnxqlAaEu9F5mcYpsBRGy+vB6Xo3OwE5gAzVbtobZ6dDUHViToJFJZPP/aFTx8bz2CPg8Wszl0N/vx/f9nJx564Yyl5VmxOC0z0xjwOsqEag15Cws+ZcDKEk8guJ3Z0x7C977Uode3qa1249JkEp9oCUKSJLxxJR8sZq9DXrcLD7YGoaiqqZ4bey99MzKLs4br02821OETLUHTvdYlS+h7ooub2ctSCZFBu8fT/379jx62Pc5aCAMuWctUJs8AL5y6AlkqT8aFVeCfZND2DUZx6PiIsO1aBxTzG9iIWVYCgUCwERGCjkAgEAgEa4wTMaTYbVhbK6CylgnFWjSYxRdzBoxxX8a/WWGVjWL8ntWLqtHG69Hv9FMCyNmxOB79Tj/2dTQWDEpowRPotk4vnBqDLElUX7B9RYo5k+AUEWCMtUlIEXaynyMDdM2RtpAP+zobcWBnGIf7I9Q82dfRSGXgBH0e+H1uKIpaOKNCVXG4P4JJxgZnX2ejZcDGSlgz0tMeQtWyrZrRLs8uY4vUSCLbdTT5ocJ6LLI550H1a8k0fnXjJvN96+D/nbWbuX1339Za/XdnHDO2rk90No1bWZ/p+zzY83CauWLej3ZNuBgrPD6AJgDd7a/GnIVQZBSFzl2dxQ9+PoHeXS0FxRXy8ynlHFhaQz5MJZ1mBUmIzpZWY8gp4WA1JhLWQqAT3LKEbIHOqXbLWFJU2zkqEJSTy9dv4Cs/HKVq0UTiKbwxFkfPvfX2X5aAzmY/To7SIohRADDeI88ygrPVc49RILF7RnIqpBS3MIX9u7RuMznKlXFhtqy1DvwL2671hUuWKFEHgOX83ohZVgKBQLAREYKOQCAQCARrjJMX32K3Yam0ZUKxAQNWWNm7o8HSWsvpS6WWjULXyGjbUkN9r9CLqkuWTAIIoBW8N9bUsRsPkp1BGIolbOvaaILRgG7TNTyR5GbUGPuUXR08l87qAgvbrgM7w3gzmsDl6zewfWstDu/vhKdKxm/8xSlqHzKA5novVfy9wV9N9UXbFp8ubFn1ATu2VnUWjFk3Rqs6XsbW0LIIYRxfnnjndbuQXq7HUkyw26lVWFvIhwZ/Nc5ZZLiw9R9OjE7rAqvdPieTacv2FlPfxohVv/Myi6yOG5m1tpxjGZ5I4vD+TgCaXd18egl1Xjd+deMmdUyXLOEjm6oK1ucgBH1uy6yfX924iWq3zP2MhR2bSvDrDzLcPi+GQmIOAK4dlkBQSWYXMpYZj4VE4nQmxxV6eYs0jBZhc6ms44Up5RAQnO4jp6ho8FdTzxp7dzSYtrETh1Yzi7pcGRdmy9p2k10qQdh2rT+czu/1KkwKBAKBgEYIOgKBQCAQrDFOXnxL2cZIpS0TigkY5BTNikwL2kvYu6MBB3e36C+RRop5qdTEmAZKgNjX0WC5otcKK/szIN/vduPBxmPZf/MyicZnFvR2O8k0YkP1iVRGDw6x7QKgn8vZsThePh+DLJkzP+p8buzrvFu3i9u7owEjE3RmSXPQZxCu6GOdGJnS7eOAvPjCC0+fG4tr2T0WVnjvz9PZMr+YmtPrIdlxFxNk41Hv8+C+hlpMJVOQJBmqqtjWygn63PD7NoH0yY9Gpyy3ZcNx4zMLensCPje3Bo4KvvjkcUnIWIg8LkmrLWEUOtwuqaSMjZ576yFLEhRVxfTcIiBJUFUH2VsMRLR6uqcNsiTh+deucOvjZHOqIzHH7ZLwx4/ci95dLfitvxrgipzpTM6xQGXVl+VkMWtuy662AAbHnWVFCQSrgZXYy8PJdaUUgbEt5IOigqqn45Ilk0WYkaGYtShQDgGBtw+yeMEoWvQNRikLzT3tIRzcbbYetQues5+fGJ1ylAVsxKkoxC5k4WXyOjmm2bJ2Dn1PdHH7Wdh20ay2DTIPIbIJBALBxkIIOgKBQCAQrDFOXnxL2WZPewgug61VJSnGoqFvMEqt1pUlfoH7SrfDCqM3P5vxQ/qd7euJRAqH+yPo3dViqjfD/ruQYESyHIZiSSiqStXQIezd0WBa8UzOmZ0n7Ev8yUvTXNFj+9Zaysbt5KXraPRXU9sY5x1b/2U8nsKRgQhkSbNdU1X7IN+J0Wk9qMG2mwTpScaNk7ose9pD6A4HqXPg8dTuFiqIceDokK2gs31rrZ6R88KpMQR9HsttG/zV2NfZiOGJJCYSKSooyRNzAOvsIJfNTyKnarVsgj43AAmJVMYUdGVDsG6XhKWcavr7uauzaAn5EDW0o6Xea31wC86OxXFkIIKne9oc2e4VIptTcTE6C0mSUGAqUZlZ643RyRvcv3s9LtxVtxlQJShQqf4XCCpJwOdB764wRibmoKgq/n5qnhJZq90yfJuqsH1rLTqbA/jG64VrVBWLVvuKL3hYXT94NbByioojAxG8dW3O9HdSv85pMJ13/+QJM2z7pubSBa1H2eA5+zmbBeyEUjMu2IxYp8csRqRZS9uuSognK93nerCgEyKbQCAQbCyEoCMQCAQCwRrj5MW31G1WawVgMR71J5gMh5cGowCs/bwr0Q6n++GtziVtBfJZKMZgTHc4QGWTdIcDpn2z9XIIXc0BR+dwcHcrhmJJShQyto9tL53xQwfF6ms8eGpXiynjhmSXWAmDvNolmljkLDA9PrOgZ+mQ/b40GMXsQj6ro1CQnq3FA2gC4VBME1RYsSTo8+DAzjD1N1ZwM7KnPWT63O/1cDNPAGAysYDucBATiTTmHIhQdiwu5cfp4XtDgKrg/HgCRt3GidBFsFtlz4oJv/7glvOGGjh5aRpP97RZZpkVS/97CfS/lyi4XTqbQwtjF1hqraFyYzWHb2Zzjn8rAkE5Idevl3+vC4AmahvvJYtZBYtZzWKts9mP5x7fhm/+ZMw2y42t/eR1y0gbMncevrcekiThnWXrT/aqaxQ8rK4fEjQxwrjYQVHBXRxx1pAF6jSYzrt/Hjo+Ymon277xGU28t7MeZYPnVud4YnTK8TNcqRkXpX6vGJGmMnWLnFEJ8WSl+1wP2TGiNo5AIBBsLISgIxAIBALBGuPkxbdc29ixGpYQfYNRUxBzdiFT9MrU1aJQ0WWt3k0+28WYYWP30mwlGDm1QjGKQuy27BxgX+IVVaWye57alc9Y4dmakWOx5y+BV7ukuPlyYnSKajcAyzpQgJbVYLTYqlpumxFy/r3Hhk2CTiKVwdELMap/WAGuNeRDc9CH7nDeXsf4+d4OzXaNl1kTS9zE121WsxNxzK6mDg+3S0JXc8iRuFEWShRC5lJZ5BTVUqCrJPOMjdt6EHPsWG/tWy8CWDnYSOdSKb71k6sYiiagwr4Gzt9euo59nY2o2VxlmWUIAM98ug2bqlz6Igci5rRtqcG+jgYoKvSMnLNjcexpD1HfNwoe+QUT9IIHFfb3BxYnFqlGePdPnjBjvKdOJNKmZwDjIgWr5wB2UQiBJw5ZUWrGBfu9nKKarO94VKK2SiXEl0qIJyvd53rIjhG1cQQCgWBjIQQdgUAgEAgEAFbHEsLOimm9+3nz+of3kl7MS/NKrFBcskQFjQDgwM4wjl6IcUUeIk6MTMwtZ57QVm5WQTQSeGDPv6c9hPF4PhDV0x7CAw4sz4yMz6SoWjp2dnQ8SzVeUIQITxMJfvYDmxFWKLON9/nL56OOz7FtSw2ag150NPkBSBidTKI7HMBHNn/ArbnC461rc/hZxF7MYetiFFMngyWdzSFc70Ns1tyH7Mp7I4lUBp/5dj8+WMxi+9ZaHNgZrohdEw8ndXmMbK6SkFNAZRUA5vPzul3I5HJYYk6ZFRfLxUrGbSVsJAFkI52LkVLrZPHIKirOLltJ2jGZTDsSUf7ruSjuqtsM1tSxOejVBXYjsgQ89/g2ruBB7lkHdoZx6PgILutZPcWdu5VFajHBdKv7A7lvG+/Zxn0Xeg7gLepgxaGhWIJ7bLu2FXtOOUXV77en353Bm9EElZFb6SzvSogvlRBPVrpPkR0jEAgEgnIjBB2BQCAQCAQAVscSws6Kab35ebMZKSS4QnCajVMMVmNglT3FiixGMcQoCJEVuEah5LnHt1HjW8hmjm2byyIgNxSjBZmAtwrJ9JLtOdMZM0FMzS3i/flFKmDukiUc3N0CWbLvb2OfAJrNmtEijc0Iswq8sX1+eH+nnl1TW+12nHmyr6PBFPg7/e4M/mbvnaiv2YQ//ey9+O6Z96hArd9bhZtZBZIkIZ3h1xAK+NzUink21LnSsC9PzAGAzmY/BmwyhYh929mxON6enkdryGdZJ2gtubnE7yFVVREOViOWWARgbZnW1RxA/1Wz8LhSNqgWIVghVZK9bWKlsDqmSwJl/5jO5jDO+Z1biSrd4SB13c0pqik79eiFmH4v4WX1EAJeNyXoBn0e9O5qMS1WKOU+XUiYKbTvQpnPduKQosJ2gQdvUYcTEcZ4TFZoW21xpxLiSyXEk5XuU2THCAQCgaDcCEFHIBAIBAKHrIYl2VqyGpYQvAwMYsni5AW5HGPgdB+sWMKziCn3S7rVGFhlT7Eiy+XrdAF2Ipb0DUZNWS/F2M7w2sYG5AimmjO+Tbi5pFpmMxjnWd9g1DLDx2l/s33i97q5NW9452+cG+zKZUCbvzwrN8CcWeF2SfijR+5B764Wbu2oTE6BD8Do5JwpaDpnI4CRmkcXo7N4Y8y8wp4NtJab//X+ByYxyYpEKssVo1bLEquU4ywuqbqYY0W1W8bfX+NnG1a7ZSxyMpiEDZhgJVjoj2tGoWtM2xYf9nU0mkQVUv9mKJaAoqogWYu8TBH2fiZLEp57fJupRloynaWE40QqA1mCfl+vZDC90L6LyXxmRQO2rh3vnrXSzGq7RTa8xSHlphLiS7Hj7eSZ8MMqyGz09x6BQCC4nRGCjkAgEAgEDlkNS7K1ZDUsIVyyZHoZJJYsTih1DAoF6nn7YIUBEkwqdSWuE6zGwCpzhw3GbN9aSwk3RCzhWd0tKSpyiuq4jQd2hvUg2/attfjyg83cej9akC6PVXYGESaM/ci2k9iV2c1Htt87mpgAlcQ/P55gyWb3GDkxOmVZgBswZ1ZkcyqqZBkuWcLh/oipdpTHpdUgYoOWhbixmMWb0QQ6m4NcQafSi/h5Ak2xlCJssIJZuN6L//uDW7a2Z5USULSi8fzPfJ4qLGbzAqIEwO9zo3ZzVUGhSCC4nbATKe8O+AAAh46P6Ndvtk7amSvWGW6866yiqvp+WGH9BvODPDE6Td2D2fuElT1pMTi55xeT+cwTDYz123j3rGL2z2uvlf0a7zh2z1qlPv+sB6FkPT/b8/p1NVnPfSMQCAQfdoSgIxAIBAKBQ1bDkmwtqcSLNe9llBUhJhIpHO6POAoAlDoGdoF6q32YM1ICJa/EtXop5wVAeDY0bD0YEthhBSBekIp3LgBwbiyOIwMRPN3TZtVtFKz9zVd+OMoVxpzGxJ5aDswZrXZyTHSQ2JWx2Al0Pe0htG2pAaBi744GAJIp62dPe0jvG+O+rOruAFq9nxffeM/ZyS1zMcYvyt22pQa+TdpjeDH2bYAmFJ0di2Pwahyu5b6urXYXXUOmXHg9LlS7XVjM5ipSU4ZQzdS2kSUJD4QDlgFII6tZl4btAxVAMpV1lNG0FqxVzR7B7YHd/LATTH8xNYc3lsWI0+/O4NWRKbz21d22dfQKcXYsrtdcK7SYYXxmgbq/FWNPyt6Xc4pK1fI5vL8TnirZ0T1/IpGmzmGl9XtYismstmov+Z/VfdVJu2/nwH8lnu3LldnC69cH/StqWlFs9PcegUAguJ0Rgo5AIBAIBA6xe3Fez7YEa9k23ssoCUqcGJ3G+MwCxmdSVE0TO0q1hbMLIlntg81IObAzXNQxjC++bD8oKl1rxioAwqsH4/e5oaj57BpWZGL/nVNUKCq/iPvJS9N6wKvQPHFq79YdDlquvN7THoIsSbrlDqAFBY2Cy572EOXbz8NOoDtnCEKdvDSNvTsa8KefbcfRCzEsZnPobvbje1/qsAxc2VGsYPGLqTn0HhvGUo624Nq7YyuANFK3lkquL2O0YForMQfQ+qSSQg6h2lOFtCHzZT6dRSTubNwqLVi4ZQk1m1yYW1yyrLmzXvn/s/f/0W2c5503/J0BAQmAYhIAoW1M0iRIWtQ+a2VjUaTjyJKl/ljbabrPpoqyu80rO4ppaZ19summbfz67Lt99mezSprU+bFJJNPrqKrbs6EVt+d5t5YTyxJFO7YpUm7t9LUokyBpUnKXJABSFkAKIGbeP4YznPueewYDECQh6fqckxMTGMzcMwNh7vv6Xtf3IjGHcCLM9SBzCy9gjkyn8dB3zuGz7fW2Fl8t0SA+mFtw/D3RnzOiZIZ7v/4yU0Fofr4VY0/Kz1nM4s+ZoWkcPjGAZw92un7mA1YLOje4SbIpprK6UHDefLxiK0P4fevP90qcE/Osht1wuQQu0T27N7RhxeNzy1pYMRMEQRClQYIOQRAEQbjEaeFcydmJ6zk2uwCCXrUwPHXN8p4TpdrC8YtSN6IBX5Hy7GujjuNzWvjy1+HkhUnm3PVt+P3zn0uks0ikszhyagiypDVEFtnIaP0JYIgn9oLFcoCl0PfErb2b+R61N4YBqBgcTxnX2nyc0xenEQn6mBFNpDJ46Xfvdwz+uM3yHp5K48ipIextixpBybOXZpjqIp5IkQHMSNCHUMArbAieTOdsgpfauWU5oad1cxBNkSDGEmmLRdtq01wbRHymuGNGgt6iLNj8XhkBXxWuLuRcNXgPB734cGERH9lYhWp/FXNf5rNsnyE3lSatm4NoCAVcC3huySkqUvP2fY8I4kZAZKH2yCebsKFKxpvxBMaTGczN51Dt95YkRGufkbC3Lcr10bP/d8n/HuvPGZHYEQpuYH6PUukcuo6fX7LiDJVkT3p+LGkRf/S/eXtP7Xm3/DkzTZHguvTw4atuzDgF5+32a5f4wc8PFBUVOyfmWQ274XJVtojnlWs3N1gLK2aCIAiiNEjQIQiCIAiXOC2cK9mWYD3H5iRylJL5V6otnGhRWm57N1HTZ/11q+WZNfQsOn+nhsX6+OxsZNygWZKx+zP/bRaM2hvDeOLBNkOcEdm7ldJTgBdPhqfShq2OHfx10SuX6kMBpkJH581R5+xsM9vqqzGZykBVgfpQAL+8POco8Bza3Wxcp2Pn4q7EoMHxJHaGNxh9dHT2tzfg8P0t+OHZYRw5NVRwP2b8XhnzOaXwhjbMzbPjFlV0hQNehIJeSJK89N1Rixqn1nsmi3DAnUWcnuVvtiyLBH2o9lchPsNaGW10cf5NkSB+8Pl2PPidc4iXWBlFEDcrNQLrxgvjSfz4i/dwz4IQFBX472eGhdU0TkLv4HgS3Y90WJ4Th08MCLffVl+Ne2JhJinAjn3b65lqz0Q6i5ffncLL707haw+0MT3wzM+v9saw1p/n+Hmh6MFXcVb7vUvb8c9xlflcJVQ28JVCbpJZ3O7PLNTwc6z+UXfzp0qobl8Nu+Fy3X/R3PXvfvlOWcbohkrocUQQBEGIIUGHIAiCIMpApSzeRazn2Jyy+0rN/MsrKo6di+PkhUnofVIO7W5xDAKIFqWFAgn8dWtvDBu9XkTb68cArE2f+XNVVDYQrvd0MY9JD5q1bg5CVbVQkTkInVdUS9DESajQaYkG0RQJoDMWYa45f755RcUDT/UalSIvvzuFJx/aiqMHdqC7L44vPTeIjqYwjh7YYVyHo70jFoHJHDzKKyri02xlkgi34lnP4ASGp9JG5dK+7fW4JxZBdx8rrPBBRz47e09bFBPJDD6YnWcEIXMWukjgaN28yfge6Of3zKuatVt2MY9FG33h7jtCADIIbqhigozL96P4gFbA53El6NhVsvABWNH5LuQUSJKEfdvrcGj3soBZbMXL1YXSq1kS6SxSGatoxp/7xioJC4vsmebyKj713b5VE3PCAS8+vL7oqvqoVLyyhJxT85IikSXtf3bf1fXGqfLK65FW9VrfaiQzOcv1/uWVqwCsgfy9bVHcXrNRWEnYtasZsiThzXgCfzM5y9ivdTSFhc9ju+SFs0PTuLc5gu5HOgqO/9DuZsiS9vx46/1Z5hnw07cu4+Wv3i+0J9WeW+zz2PzcOrgzhoe+c854HoxMa0kHg+Mp5vjmvyulsoFPoPDIkqtr6XZ/+rNadE9PXyw871xpBXklCEIiynX/SVAhCIIg7CBBhyAIgiDKQCmLt7VaiK51YEF0XqLFaKkL1e6+OJOFq9uPlbIfp0CCSITRgz5OgQcnmzlzw2RZkiz3nhdEnDgzNI29bVHmNV6o0GmOBo0g9sh0Gp/b0WAZu/l87frK9I8mLc2k34gnDGu3tydnLWM0b/f2pHO1i46eFW3370P/7mi2fcsBxZMXLmPf9jrhMSJBL+6+IySsLlJUVVjZY0YUvNzfXm+Mqbsvjm+85K5a5U9+fgl3/dZm1AR8wvcHSmgcfnVhESF/VUHrLz1YWyVLyCuqbbD8rttvw9lLM8xrmVzesLEDgEO7W9AZC2MilQFUCQpUV2LJokmQcGOVxuNGzxDF+XsvlddqjWe1exkFfB48vqcZ3/rZe2Xbp27NWKnddJxGRWJO+eGv6D+6vRqA9blmJ+Lef2cEgPZsu6c5gv/++XZ86blBoxfdw/c2CRMjzM+fsZk0Y2HZP5q09HUR2Xma5xS//u2z3HOAPTPzvsYS7G8WL3p4ZAmx2iAj8Ovjt0uWKdayTISbbYtPTAk5JqYUwm0vSb6i127eyX+vegYnixpTpdodkxBDEARBrDYk6BAEQRBEGShl8bZaC1E7QaXcC0u7QMJqL7B1KzMzpdjIFdMgGAC6jp93dUw3FVF23xe3vWF0JKh48qGths2bBLHdzftcwEo0dvOY+HPVEfXjcVud4WY7jwQ01Qbx8L1Nrr5H/LUenrqG750WB7tDwQ221UV252vmM3fX4fxYCv2jSfi9HnzxvhgTpCrm3i0qKq4vKphJX8fXX5wwzlFRtaDj6yMzTh8XkssX18dlUVHRXBuwWJdJAO7fUltQNPneK8OW6pw9bVHMpq8jmXE/jtUKyVfJN1/1Ri6v4K/euuK4jVs7OzOLZaz4IW4u3k9cE1qR2fGLeBK972nPaN4G9MzQNNO/zPy7bn7+fOF/vMkIOnlFKx8TPRP4/eho9mvLAnt9jR95RWUEeLMVmRk39qdmkcJcUav37LETJYqZH7nZtvjElNJ62+jzvf7RBPa2RSFLEjpjzr0kn3xoa8FqIL4P0fDUtYKWq2Yq2e640lm+p9r8UZZgVG5XQpUTQRAE4QwJOgRBEASxTqzWQnStMhbtjiPKuFxpFRKbTZsRvl8okMJTrBWd2+3ZIM+yN7+bsTn1zBGhwmrzJoK3Uyr2XFs3b8L+9nqhmCYi4PPgnljYtdijN+POq1oF0ZeeG7Rcp/7RBBRVXbLak7Bvez0evS9m2K7pZGxsx/Ztr7f9ztpd9/u3ROH1SEYgTK/iyWTzqDLZzIkaTrshzwkOJy9MCi2MRIT9VZBk2VKN5PfKWFRURsyQAYiuCi/mAJrAwlfmiMhk85b7e3ZoGs21gaIEndViPqeUVP1TLFXy2tmV5fIqE+gWsbCooLnWj/jM/NoMiripGU3MYzShfZd0K7JFxb6ikRdReRtQ/m/RvGcyxX533748h7yiOorm+n70ucLAWAot0eWqmrOXZvDAU73Y396Arl3Nln21bt6EpkjAtprErg8fa9tWeN5VzLyP3/bpvrgxFv35uFqJKTy8APbkQ1sLJqS427f1F7qYuXAl2x1XOiJRU7cJJlGMIAii8iFBhyAIgiDWidVaiK5VxiIf3O8fTQiD48NT1zA8dW1F4pJTNm0k6GMyfrVMQ6udGU+xVnRut7cGeQrbtImO0d4YAiBhcHz5v595NY6Za8sBfM0qyXrPI0Gf0HasdpMPj+1qdn2umoiyfJwdTWFjwV+IzlgYnbGIZcwiNnrZfi26PY8ZRQWTcX3k1EWcHJxAQzhQUAQJ+Dw4OTgB3mBM/7fRtasZr8cTliDl5VQG+3c0CJt2P90XZ7LPAa1CZTI1D6gqkuls0ZUSV2YXXG/7j+8I4RPNEcu/C1EfnWoXVmzlIj6TYQKpK0UX+0phLepOvB4Zi8rqKzoBrweZnLUBPU8mmycxh1gVdCuyH54dKWhRqcPbgPJ/C+c9Evu8TqZz6O6LOyY8jCXSONo7svScEM8VhqfSxu8lv6/97fWOz+ZCffj4RJOVVPDabTtzLWuMX9/3aiWm8LiZV/L71u+JUyIL34fIzZhKsXYjrNgJpFTlRBAEcWNAgg5BEARBrBOr1dumlAV7Kf18+CCr/jfjhZ9IM8H2UheKTpm5oaCXES/MVQ5OIkqxNnml2Orx4/7JwPvoGZyAXmFyaLd2nfnrr1uBaSwfT5bYSpzOmHZv+Xu+rb4a7wj61Txm08/I7lyB5eOdvjiFPW1RBHys+ML/DWhB5SOnhrC3LYovfLIJf/yzS8Lj1G7yLVXZTDK9V6r9Xibop1u88AxPa70WIkEfQkEv8nnFyCbnxyOqbND/bXhkCc880oEHnuplvq/D0/YBwJlrWUuFymRqHsNT14Tn6gb+OjohSRIWFQUBr2xblaQzt7C2FTONkSDqQwH8YmQGi3n7Pj1OSABitQFIklQ2cWg1EAloq4EbMWe1qFqqyiBubfTfy8Fxd/aSAZ8HRw/sYPqV8f3LDu6MWXq6/PbddZZ+ZD2Dk/h//q/78JOBCeHvgS7WtG7eVHBc58eSOHpgh/Hfpc6/nBJN2hvDBXsFFTqu/t7TfWxShHketVqJKTxu5pX6vnoGJ5cSebR78kY8AY8suerxs7ctWnBMpVi7EVbsBFKqciIIgrgxIEGHIAiCINaJ1WqaWsqCvRSbNj7APpHMMNZivA0JUPpCUbTo1wMEiqoylRsprn/MSrMNSxG77MY9Mr2cxXvk1EXIknadC13/vKLi2LkRnByc1MSLgBf72uuNe2u+53mF7XOjix2fubvO4vEPwPHceEFKlJXtZK12Zmga4wn7YPyj98UgSxIj5rREg7gjHGCCdrpf/+mL4uzsRDqLRDqLcMBreywzkaAPXVylkkeWsL+9QRige/pcHHfV3YbWaBBX5hbshRd17YLeqqrimy+JhTLrtsXvf09bFBPJTEliyt9OpCx9nEo5vlvLvhuVYquPvLKE3DoIK7qY4/fKayZgEZVFJOjTbDEV1fJcE4n6gPZs8FXJzPMJgDE/AIAfnh0xKmpefncKr8cTmEiKBJtr+PT3+lz8HrH/Pva2RTHBCe0dTWHH+ZfbZ77Vti2IpkjQmJeYq3PfiCfQ/UhHUfM+UWKFPn5+m9VMTAHczSv1fZ8fSzLXW9QzyWm/heZXa903ZyVzwEpmuRLb2kOHIAiCqHxI0CEIgiCIm4xyVJK4WSDzAXa9WsK8aC9XFZLdol8XOlo3BwFIqA/5LaKDk4jkZqHuJLZkFxUcPjGAdy7P4a66anQ0hfHW+8vWH2y1UsZSvaFfZ7vrr49Pz3jVSaSz6B9N4tDuFs5+JISTg5PMvu6+owbdj3RYPP5/MjCBD0zihCjY4mRxo9u3HdwZwzOvxnHywiRS6ZylKojvh8AiWc49Vhu09KNRVFWzRRuZcezvwluc2QUaE+ksZAnGd+ho7wieeXUUmes5hANefHh9kekFMZPOFuwrs7ctClUt3ONkJbREtYBhZyzsup8RADRGAsLeUzyRoBeh4AajeuzYuRFGLHXLSsSckN+DUHADetdIzAkHvK6t8ey+T6UgoXgrufUQc8yoqopw0IvkCsU6Yv2RAIQCXsxn85h30QQqkc4aSQi8Neibo0nmuav3UNOrYPhn6LFzcTx6XwySBHzvlWHmOE5Wbm7E5X3b6xnLVe35NLrUe03Fvu1aIoQ2d4gzrx/a3aLZyrlMcLHatjUYz+0HnjrHbHtmaBrdffGShIdi5lGrJT4UM690mjcU6vHjhrXum1NKwlO574N5f4/+Hx4EN6w8jLdaSWUEQRDE2kCCDkEQBEEQJS2Q3VirlWvBaLef7r44E3BOXrvOvN8SDToGP9ws1J3ErsMnBozs07ND00YwyrwvUdNkHd1jvr0xZLn+eUVF1/HzjtUv3UtNkvlzMKPfS/48RMExPtjiVPljtm+TJcm2j00DV21j5plX47iL65XT3hjGwBgrVkwkNTHimS90orsvbvT26b007RgUVx3e1O3veBFqfrG0YLVHloSVMOGAFx+rr14StiQoqoL4zLK4UkzVQ6w2yFjLuO1ndHW+8DlFgl58vCGEzpgWBO3ui+OZV0dd7b+cLCyqzPVZbT687t6OLuci8O2WG9HAbGFRxUKJ/z6IykKFVQB3Q8/ABBRVxcBYCoqq4vnBSeb3Xa9+fPS+ZWs1XkxOpLMWS7VyEAn6MDCWQmds2bb0aO8I01NHliTh60dODUGWJMcECx47oaW7Ly603uwZnCgpsF/MPKoU8aFYCokVTvOGcogv5UoUKrUSy+77YN6f+bzLcR/M9/VzzdGS90MQBEHcPJCgQxAEQRAVwnraOpSyQDYHGcplrVYs/EKbD1A1hAOO15D/PB9wySuqpVrEfG7vXJ5z3DcvjiiqylSy6B7zX3ugDU8+tJW5/t198YKWU069hQAtwKVXcmy/I2SbNSs6N57OWBidsQgGx63fEX4cHgmoCXjxxfti+OLOZlurnJlrWUFGtorOWIQRK4an00Z2s1751d0Xx99OzAqDknolhVPmudn+rhxoNjtWkWVhUWGqe7RqsmX475cT5r4M7Y1h/MEDW/Dtn11CvsAu3ARuE+kcTl+cwumLU7Z9KkqtUClGtFprS69coYtn3pb6yBC3OMPTaceqPb2Sp380URbLRKceYZGgl6kGTKSzxm8YAEdxRvTs1N9zm+BiJ7TYPZeHp9JFVemUMidcCzuyQqKR+bqIzsENTuderkShUiux7L4PTj2VVnof+PuazZP1pZmb1RaPIAjCCRJ0CIIgCKJC4BeXiqr1WFiLBcpKF8jlypgsFidrDwCYTDoH7fnP8wEXXlQxN+zNKyoUh+YkeoWN+d49vqcVh3a3oPO/vsxs+9O3LuPlr97PXP9CYg0AvPX+LJxy/bUA1zROX5zGni21ttuFg14c3t1iaVBt7k/k1HyYv455VRMIzo8m8a/33omf/dv78eiP+wvalgHA4HgKP/h8O46dizOVM+aAiFPgBAByDsGO1s2bcCU1X9Ym8+GgFwd3xvD0UsWUGb+XE0G42xXc4EE2U7hKJBL04s34suWcfj92b2F7zRTbl0WEXUVVqXZjAV8VOmPV6L1U2T1xZAnYUEV9Yohbk3JaCr4zySY7RII+ix2nGzpjYbxz+arwszUBL7p2tWBw3Fol/HRfHG/EE0Z1p44ejBfNHRYVFb/+7V5AVbFnSy08sozOWPHzmWIsx5wopdpmLezIihGNSp1brkWl0UorsQrtz8xK7wN/X30eeUX7u9lYi+8LQRBEpUGCDkEQBLGuUFbVMvxi8OSF5b4plb5A8ciSsPGxm3u5ku9A165mvBF3yAQW7MZ8vO13hCw9NMxVOpbKE1kyxtbdF7f0kmipDSIWDTrabXT3xQXBKWsE3k3jafN+Wjdvwr7tdQAkYYDrl1euWi/GEo/e12yptHr53SlLNUmhgMc3Tl1kqkX6x1IAtOs2OevUS2eZsUQah04MWK6ROSBSSOxyqrpoigRwxeVY3JJM53DoT8/jtRHWKi7g9eDgzib88c8uGa/lFVYsSLkQcwBNIOMFsZ7BSdwRDjB9TVa7iMTrkYqqagEAFSomU2tno1YqsiRhscjMZwlArDawpjZxBFFu/FUS/sFHfBhNlOe3kU92+OJ9McgSbHut8bRuDqIhFHCs8hmZzkCWYOkRB2jVn+bPas/Heq0f2/HzSxWObXjhrcsAVNSHAky16PB0Gk8+tHVFPW/6RxMYS2QYgbyYwH4p1TZrkVzjRjQqZl4n2nYtKo1WWolVaH9726LwyFJZ7oP5O7WhSkY2r+Bo78gtvWYysxbfF4IgiEqDBB2CIAhiXaGsqmWsWZ1sQKTSFyil3suVfAc8soTuRzqMYMBinrW32re93tXxzAxPpfHAU73Y394g7G2jIxIVPtehNUXuOn6eed1870SfE42TF6sKZU43RQJ4fE/r0l9WG7xtddW2wbG33k8hr6joGZhkXh9PiLObefSAB19VA2jBGi3g4Bx00AWr4am0pR+PJozEmHEUso8DNPEBYAWejqYwFhXVsfl2KYiqj24PbYRHZjNp3QRMWzdvQn3IX3CMw1PXhL0aAO2albMKaSUk0zmL+LkSJKxOD5rFEtQwFSAxh6h4Ctkezi+qrn6b3P7bm+d+e2RJwuN7WvD4nlZ0HT/v+PvdunkTXvrd3Th8YqDgcfRna8HkDgAnL0wYzxa9wvHlr94PAJZntnnfOk4ihZ21mNmC01zh64b2RvY5195YWAwqlx2Z07m6EY2KmdeJtl2LSqNyi1+i/ZVLbNHvKwBcX9QSR/RrVsnrgrViLb4vBEEQlQYJOgRBEMS6UmpW1c1Y2cMvBs12V0DlL1Dc3Mu8ouLYuThOXpgEoGLf9noMLFVxOH3OLfc01+Ke5ggGx1O2C3Q3VmZ6b5snHtxq6W2jI8rG1N93WlyKPndot/V8zdVAOgGfB7dXb8Tl2XlLcI7/fpgzOhUVkCQJe9uimEhmMMxZanU0hbVGztOsOKALIa2bN2F/ez0evrcJB5/txzuX57CtrhpHD+yAr2pZsHj0vhjT7DqTzePYuThkCYCDPR0gsCYzkcnl8XRfHLIk4eTgBFKZLMIBL8JBH+pCfvTaWLmZhZzWzUHsb2/AwZ0xLCoK3pmcxWwmV7D/jBlZApprN6EutBFvT6SQmncWTOpq/PjeK++5PwCAlmgQL/3ubhw7FxcKOs0uq0HKKeZEgl4AEhLpbNHVOYUs4AI+Dz5avdHW5k0EdbIhiOIol42g2397fp+HOebgeBKA9pwrJMjvb9cSHPj+YnvbophIzTMitv7cEz0vzYiEb/NcQzQmvYeeuSrXTqQQvedU4esGRVUc/y4Ft3Nnp3N1IxoVM7cXbXv0wA7jv1er0qhc4tdq7U/E+bEkYs3s3yTorJ/tM0EQxHpCgg5BEASxrpSaVXUzVvbwi8G8okKWpBtmgeLmXnb3xXHk1HLVyJFTQ2iJsrZexQpX/HfhyYe24uiBHTh2Lo4HnjoHXTg6tLvFsL9wU90BaEGo7kc6hFm6/aMJ7G2LQpYkw2ffTQZrMVmcfIZuJpu3iDEAsGdLreX7Yc7oNFfqtEY3Mdu1Rjeha1ezYzZ0UySAw/e34OCz/UYG9JmhaRw+MYBnD3Ya2x2+vwU/fWuSqbD53ivvOVYXRYI+bKuvLliN8tTPL1ma0iczOdSHA0vWcBLqavy2fVqaIkHDVu6bL10SblMIRQWGp69ZhC8RLdGgrdDkhAQJh/50AH8zwQqdAZ8HX/7VO3FycKLofRaDLhqav2eSJKEm4C2pB0ahopdMNo9Upvj9Eu4pR1+lKhlYpNZCBIddJeA/+mg1+oaXf/+23xEy/rtrVzMUVV1K7JDw23fXQZLAJGLw/esiQR86YxEcPRDDs6+Non80CUVV8WZ8Bm/EE5AlyWLz1hINoikSwHgyY6n6BLTnq94zrr0xhK890IZnXh01fufODE0zPfWcRArReyutGtDs4Ni///XeO4vaB4/buXOxyVa8UMTPXZzOXXSd1kIcuRHRrmOC+5ug7wtBELciJOgQBEEQ60qpWVW3gl9yoQVKJVUp5RUViqpVcugCitvqGHNmfrGWJKJ96n/zwpEsSYY1C6BVriwqKn55eQ7zOQW/Ur0RkgqMzDj73ZsDIgAMn/28ohrBIf1+iO6d033l76mbjNzWzZvwzBc6be99/yjb1yWZuc78rS7lXDsJXfp1eOcy2+ia/9sjS9jf3sBcH17MaY0Gkcos91BIpLOW/iottQFcmVtgMrx5MUfHrXVae2MIR3tH8HRfnB3P5iAawwHkFRWTqXlcmVtYcWPwcNCLuXmrxVhLbaCgRZedYJTJ5peqrYqLzBdrT/aVX9MChnw/iplrqye6lNOObTVpjQaRTGeZnls3AuXoq0Rizq2Hm9+Oj1ZvwIjg9+zvrrDPhucHJ3D4/hajSuXxPa2GRahWuTuCsUQaY4kMFBUY4J7riXQWR05dhCxBmKigE/B5AACdTSEce7gDvirZYj8KaHMNQMXXX9QqSvVkkLvvqGGeg05VPGOJtNHHhLdnbW8MlaFqgH+mFz+/4+cU/aPu5s7FilG8UPTEg2221c08VF3hnq5dzegfnEM2r+DJh7bStSIIgriFIUGHIAiCWFdKzaoiv+TKqlLiK29kSWwtUqg6xs6SxEm8En0XRMKRHriwq1yJT6fxxINbIUvOgQU7MbEc94Pfh1Z54sy+7XXMtXn43iZ86blBvHN5DnfdfhveT3J9EThBYGRa6xn0mbvrcP+WqHZ+KrDRKyEU9OGzSzZlR3tHoHKf3VZXbRmPfs2e7osLRYD9OxpwfizJfQ/Ye/7B1eulWQTZiB1a8E4SBgA/c3cdqmQZPYMTwuqnUrATKGbnF22rXNz0vDkzNG0J6YUCVUhlFm0/U0ws3yMBB3fG8MyrcbRuDuLK7MrFrXKyWr1z3JLK3HhiDo8MgLQZwg2F/q21bt6E+pqNQkFnlhO04zMZw36Tf45r84dlq84jpy4u/WZb0Z+3dtap+u/V2UszePa1USOJo2dwkrFdm0hlLAkJhapq9Gebvi/dmlVj5eILz77t9cy8StRrrxD8nIK/rnZz52JFFv5+DI6nLNXNdtitAyopaalS8MgSghuqEARw+BM3VxIbQRAEURwk6BAEQRA3JJTRV1lVSm7HolmtwOihUx8KMBUWdsEFJ7HE7rvAC0f8vkUBIZHFGo9dwMfuGvBBiYM7NcsYUZDCOiY2eNG6eRP2ba8DIGFwXK/iAXNtfjIwYVQ9nRVYfoWDG5DkBIDhqbTFhuye5ig8sgRZkvB0H2tT5vVI2Nlaa/jcm7G3eguiIRzAGyMz+FsukLZvez36R5cbWrsVEfh+MvUhPyPKhANVeGx3Cw7tbsGhPxVbyp0cvIz4jFXI8Xokpl9MSzQISZKEvRicGo4HfB5AVZHJKY6WZXwTcTv4IOu16+UTXBQV+NR3+4TnWAmsd++cxA1SSeSE/i31eiSoqkqVN0TJ7Nteb2sBKaoK6+6LG7+BL787hTfiCXhkCWMJ6++vLGnVr7wQoz9v+YoYEeYkjv3t9czzSGTBZp4/iOaW+rPt/FiSGZPtXKIPrpI87OYIA2PJJVtXoDMWKWmey49Nv66F5s5ukq2W7WeTlntYjiQrft6nqFYb4ltd4CEIgiBuXUjQIQiCIG5IyC957auUiq2Ssdv+8T0teHxPi+0+RTgJRqLvQteuZuQVFc+8Oor5XB6dTSE8fG8TY4kmCgi1N4bxw7MjeH5wArOZLEIBH/a1N+DQ7sL9cezuBx+UeD2eMEQsLUgB43rw+6gP+bFvex3TW8AjS0vXTRvDWILNjp5I2tt5AcC+9gZGPLHjjGmMAa/MvJfLq4bUxNvM6eNTVNXobaNlFqtMFrZOSzSIQ7ubl5pmuyPkr8I/viMEWZLQGAkaAS+rvdyiUS2Wt6neEYk5APC7v74FA2NJvHN5DtvqqnH0wA54ZAldx89brt18TkFLNIi5+Ryq/V7GRvCj1RuZv+0oVawwi046Aa8Hfp+n6J43KsTNw91Szgoat31feOFtNQgHvetiC7eaFUmrfc2Imxetn1crFFV1rGoM+DyMOM//Hjk9gzpjEa1v284YDp8YMH6HD+6MLW3BBvJF/0bzioq8omq/26bn9lgiw/zO1W7y4bFdzcbzq9Dc0u5ZX6hS2C7RhZ8jvBFnn8+6rWsp8GPVr2s55s68/axOKda5gMgejn2en7yw3J9vvavSCYIgCGK9IUGHIAiCIMqEW3GiXKx1lVKxVTJuLMjcCnPFile6dZseQDp7aQZfem6QESmeeHArnniwzWjMLBIdEumcxbffbsx294MP6vAe9icvTBqCTteuZiaYc3ZoGvc2R9D9SAfz/corqm0wrCEcsBUQmqNBnBycgArVUt3iREZQfXJmaBqHTwww1xSAYT9nvo4nBydsG99/MLcAwF3GtU5qfpGp7NIDXouKgtMX2evSMziJ82NJvD0x62rfXo+ExkgAsgR0P9JhCHn69ZclLWD1zuU5xlIuVhss6j6tJreH/LgjHMArF91dz3JRTonAbd+XlQgTvM2dnYiUWqfKHJJciErko9UbIEsSTg5OOm7XGQvj3uYIzo8l8db7s64E5oDXgy//2p3G8/PZ10aN39AzQ9OGjRqfABAO+CyCzpmhaXT3xRmr1cP3t1h66jxm0+/ODqe5l1OlsN28hZ8jiKzgShUuVnOeaGd7Z2edW4hC9nC8iGdXBb3Syh2yeiMIgiBuBEjQIQiCIG4a1nsRJhIw7g2t3vHWukrJTpgwX2/zWMphCbds6ZFYsh6R0BlzDkron3m6L868zgdJdHs1vTEzADz64/PCfboZO58FDGjBFF6M8ns9nKWYyuyD/86KevTwtG4OoikSFPbQ6YxFcOH91KqIC3aBJ/7eO2VxZ7J5dB0/jwm+108R6MftH01Z3tN6HbivOsnlVQxPpXHk1BD6R5PwyNKStR0r9u1tizLXMz59DT84M4wL72vVVEcP7MDhE2Kbt5XilSXc2xLGufcSwvd1C7sVHcMjASqQc6usVDAt0SA+c3c9/vuZ9xhrPF8VYG6JY3eqN/4VIAhn3FbEAcDIdAZff/EiIkGf43avvjeDjqYQjh7Ygc7/+nNX+w5s8ECWgEN/eh6Kav+M4Z+rKZveVufHkkaCidnSTFFVI5lDUWFU8rjBbu4lqhTWx+AkpvDnsq2umnm2tDeGhZWwKxlrObDriVhqtbjVHk5i7OEUFUxPIbsqaGBllTvr3Z/S7Vpmvdc8BEEQxPpCgg5BEARx07DeizCRgHFvaMOaHX+14Rfviqo6Xu9yWMLxIsbetigjlujb2FUGmeGDJKLxKDa2XG4DKqLvIB/UWVRUfPOlZWGAb3TMX7e8oqLr+HmLtZqZ/e0NzLV/9mCn8dlj5+IYS2RwZbZ0wcQO/prqY80XKQKsVGiy62PkRHM0iFgkiB1NYQAqBsdTGEukmd4K5uojzT5umfeTGTRHg4gviVXxmQy+sXRfdduciVThCqhiAqnLqJicXWBe8VfJWFz6/r4Rn4HsQoYQWXqFAlVIZRZvKkuuhnAAf/k3k5Y+R7Pz5es/RBA3Mk21Qfz93ILr/mUAUO33QND+xkB71l1ClSxD9Gjd2xbF25NzTOVO5nreNnEBWH7GtDeG8MSDWzE47lwJ2dEUFj6XZUkyfuv5Klz+eG6D68fOjTAVv4d2N7sSU/g5At9nT5trLT9bAOtY1yO4r4+7fzS51N+m9F4/gMgeLsxcv7yiHaNQFfRK+0mud39Kt2uZ9V7zEARBEOsLCToEQRDETcN6L8LEAkbh3hlrTakLfz7owFcA6BUx+v7KYfXB39MzXO8ZWbI2HeY/o/vj80ES0Xj4y6D1CrgTABtQ0Zs589fP7jvIByWqZMl2HObr5hSo2tsWhUeW0N4YgqICXcfPW8ajWZ/ZB8f0qidFVTGeuIb4jLPo4/fKuKc5As9SpZT5mvJjbYkGXfWOsUPP/q72V+GDuQVLMN683evxhPZ9LEKDiE+n8c93LAth89k8dh45bbs9b7s1Mp1GwOex3d6tSOXzSFhYXB64m+uWU2AISTrzpu72vZdm0BIN8h+zEBPY7i3YXOf1xu+Vbb8DhTi7DrZ3BHEj8UFqnvkNccMdkU3Y196A//7KsNCWU+f8WJJ3y4LfKxsWlYdPDODN0SQy2TxjgWimdpMPd5kSCF5+dwp72qJ45pEO20pIvZcL/75I+DfPF+1sMwsF180VnE4iEY+oisb8d9dxtnJYNLe1C+6XMt9z+5lyV/8UmjOKjqf3STKz0n6Sa92fksftWqacax6q9iEIgrjxIEGHIAiCuGlY70WYaDH6d798Z03H4IZSs/pEi2lzv5KZa1ljv7xnfanYWXoAWu+ZpkiAeU2/9ubPmP3xC42nMxZhzunLv9oKWQKe7htltrML8Lj5Dha6Lub3+UCO2VpNX3Cb+wHw4xEFrgI+D/xeD2oCVeiMhXFot3avfv3bZ22vi858TsHZoWmmSbPdWN9PuuvPwze1b90cxP72BuP8Dj7b79jrJ5HOlhywNwdAPv29PsfG96L+D36vXFRGuwi/rwrBDRLmc3l0NoXww//XDvzW918tyiZOxPvJDP7ggS24MJ7CWxOzzLkFfB7cXuOHKkibL1Y00UM+q13Ps9ErQ5KkFV9vgiCsFCvmAIBHklAly4yYEw56Lb+ji4qKee7f7e01Gw0x553Lc9hY5fxb+tiuZvRwPXvOLvXIsZsn6Bamds9lu2e1k72p2+C607bF4mZeYRfcL2W+d+zciCFOvfzuFF6PJ1AlSGApN6XMGbv74kzyhC7irYS17k/J43YtU841z81S7UPCFEEQtxIk6BAEQRA3Deu9CFvrnjalUq6sPv36Pt0XZ5rDl7Myyu4YAJBKX8e+7XXMgra9UbMn0eyxJHzm7jrb6hWn45k9253sX/RtjUxa0+fbG0NYVNQloWTZgsUjS64XnfyCXRc6jp0bwQNP9QKQLJF083hEga57YmGcGZpGIp01gjayJOEKZ+GlE/B6oEJlAv18NZboWHaWXZGgjxFH9O3CAS+21VfjcmoePYOTUFTg0O5mSw8FHV4IKgVzAGTCpQBl5ov3xXB+LIXXhmeYsRRTnWTu/XD20gx+/Isx7G+vL/i9A5bt0UTk8ip+euEyJMASYM1k8ysWjHTWypjN7jyJ1aHGX4XZebrmhD2KquLNOFupW+P3osbvZUR4keD+2fYGHD4xYFvJGA568Y/ra5i+eT2DE5btzo8lcfTADiiqFpQ2P1v03/eDO2N4I57AO5fnsK2uGgd3xpiqWn6+6GTd6Ta47rRtMeQVFYqqoiUawGxmEaGAz+j5A4CpJBId226+5zQH0WzjljnrUKFU7gB6sfvjz0/Uh7DYY6z3XN7tWqbYNY/Tees9MXX6R9fW4aBc3CzCFEEQhBtI0CEIgiBuGtZ7EXajUK6sPv16A6zoUc7KKP0YfEN6AEikcwD4prnsdgNjSVd2Kfzx9G0e/TFbdVK7yYe7br8NZy/NGK+1N4aEnz/aO8LYnR05dREnL0xif3s9M06ncYkW7Ly1C4/5+nftaoai6gEaFfu21+P8WIrZ/uTgJIYdxAeRBQ5fjaUf6zun32OyrHVLMvNrH2+oQWcsbBHpkpkcek3XVbes4fv06LgRcyQAVbKEnCnYFQl6cfcdIeN66kGOKllGNr88TlkCZACLgsNEgl6EghsgSzLuiYUtAcvxRBrhoBcLOaXoipKTFybx0u/uxuvxRMHKo211NeiIhfDsq6NICgQPN6KSHigUVSDd6JRD9LtVydJ1IwpwZmjaYu0Yn8lY+o2Z8Xkk7GytxaP3NeOZV0dtt7u7IYRnvtDBvLZve73l2dfRFF4KSqvMb9geU6XGs6+NGs+QM0PTePa1UYsVKr9P8xxJtzctFFxXVJXpobOSpCL9udQzOMmI71oixkXDHpbvMciP026+5xz4dhZQzNes3AH0QvvjRYn2xuLns5Ue9He7lil2zeN03nz/SLt+kpXOeltvEwRBrCUk6BAEQRDELUa5K5nWpjJKHGAYHE+i+5EOW695vrpDVFniBL+o3VZXjc5YhBF0Tg5ehiwt9wzSgw1jgm7Rw1PX8PUXL1oCXnaLTtGC3Sl7WGQ3IktAUyRg3Jv+UfYajXOVKXZB8HDQi7lMDua3zo8lDZHp/FgSH63eyIgIH63eiM+2s0E4vdEx4K766eiBHbj366cdBQcvJ9roqIDl9a5dzXh8TyuA5b455goWaelzigrYmRAl0jkk0jkcOWW9lwCwqFirYtySSl/HA0+dQyp9veC2596bwS+vzAnFHLfcEQ6iKaL1UfrbidSK9lVJ+KtkbPTKSFGVSUmQtR3hhrl56+9c0uG3OptXDVHFTqwHtOcEz6Hd2nPj5OAkUpkcQgEvFFXrocJXlUymMgV729nBV/QcPbADvirZdntAe1Y/vqfVeLaI4MUIUV8/c/87p+dj/2gSEjeF8cgSuh9hRTC7+ZnTNdGEM/GxecGk3AF0fn89g5O21+Xld6fwxINtTFKPfn5O1SjFjrlcVUjrbQfmdN78MG5Ul7L1tt4mCIJYS0jQIQiCIIhbjEJZfcUuOteiMmpwXCxi8Is1fjHHB4z0ypI34gl0P9JRcDHNvz2RzFhEouHpa0zgxY1VFi9Q8eeh34P+0QQUFYztjFNfoXcuz6G7L27cM1FGJn9OvHjz1d9ogyzBkhksEig6msKWwFM44EVyyUZsZDoNRYUw4NK1qxlvxBO2AT39OgDAx+rtA3+AVbThad28iRG19H3zYg4A+H2eooLZ44nirdqc0MUit5QqHAGaeGe+riH/2i0NIkHNmmnEoT/SSphfVErqC7KWhANVCAc3oD4cgKSq+NvJOePfzloR8MqODe0JwgmRKJNM5xAJ+qBVzYi/z7pYf+hPz6N/LIWNVTLuqqvBldkMIMEQangrrMf3tEKWJHz9xYuMbWjKcpzlz9kFee3mOnYVPSuFfx7/ZGDCSIBw0//OjKKq+EQsUnLPPqfA96HdzZClZetYQMLguDhhp1wBdP1e8Ikww1PX0N0Xt70ug+MpJqlHx6kapdgxO+2rmPnyelcGOZ033z+yMxZZs3GVk/W23iYIglhLSNAhCIIgCIKhmEWn02K2nNmI/EK0dfMm7G+3Wprwizk9A5a39zqz1Ey50GJ6RxO7yHWyJhMFYFqiQUiSFmwyV5js214PWQL6RxPIKyp6BibQMzhp9NkRZeeevjhlnKNu7cLvVxes9P4zfBPp82NJy8Jdp3aTD4/tajbuk7nyZiyRsfRcCfg8UFQVA9x5L3BB9BfemsTLX91j8e1vb9Rsz96enLOtvjkzNO3YZ8Et9TUbMZZIYyyRMa5Nd19cKIYUW5lwI1h6eT0SFvOqpd/NbRu9zLVfy2qWRDqHj9XXrJqgcyOQzCwimVnE8LRm0bfWYo5Oc8SPseQ8CuiiBMEQCnjx+siM8D39d8Wu4nNxSay5t6UWZy/NIJPN49x7y7/zulCjV7yYnx1jnIh+8sKk9RmiqjjaO4KupWcaYA3y2s11nPqJlDqvySuq5XnMW2KaKybaG0PMnGdjlYQFk/+nLK0seO30WbEIJJ4rlSuA7lSR5NQX0E6McapGKXbMTvsqZr5cajUT/527N1TwI0KczrvQNVnv6iK3rKf19o1yjQiCuHkgQYcgCIK4ZaHJt5hiFp3HzsUNa46X352CogKP7yl+oVsI0WKTv1d299PO3svdYtp9hFMPLJiDDZ/b0WDbgBgA3ognGPs23RvfLju3Z3DCsAx7fE8rfnh2RGiNcvLCJGQJFhFmUVENOxleJHlsVzNzPfh+QPz1y2TzOHJqCHvboszrfi9f4bJ8n46dG7H0DioEXxFlR3NtgGnE3VwbQKx2E/KKUtQ1BoBI0IeP1VdjPJHG388tAJJUNguqSNAHq7RSGnZWczyigKpXlvDF+2L45kv2/ZhWm14Hoe5W63+zkkqrlZDJKYgn5ldl31XyknXhrXMbbwq8HgmqqqJQgVvKhQBp92/47FJShdPv8PdeGTbsTJ0tyKzztuHpNNPn7fD9LcZ+Dp8YQEdT2CLc6HMCp34ipc5ruvvilucxDytOsOf0iZZapqdaZyxiG7x2M7fVP8tfE37bQvsqVwDd6XvA9wUEYFQv6/eQH5eT8FPsmJ32Vcx8udRqJn6e/Rf7PorghuLDeE7nXeiarHd10Y0AXSOCINYaEnQIgiCIW5YbYfK9HqJTMYtO3rf+5IVJQ9App7e6mwW40/0U2XuNJdJGBq/dNR0cTxUcm6haSBRs4Md/tHdEWHmi32uR4DE8lWYqi+ys6ABVGCA5u2Qfw5+vXm3DW9zo37834wnGSs3MRHIerZs3AVCxb3s9VBX4hkkk+MzddTjaO4LzY0m89X7h68nzj26vRu8l+8C/RwJ2b4nibyfYfcdnMvjnHXegZ3DC8pkf9Q5jW12N7T4T6SzenpxD165mHNrdjLyi4oGnejG6wmqSlmgQL35lNz713XOO23k9EhojAXzm7jrIkozvvfKeUFCyE3NaokHMzeeYqjQeFcB3Xr4Ev1fG/DpZbjkd9VYSc25WKtzxjrBhrf7tOT3rAC1pQKs4tfbIad0cRFMkiI6mMBQVtj1fnKop+GQEfa7j1E+k1HmNk2DRujmI/e0NzByCf7Z7JEloXWomu6jg8IkBvDmaNJ4Xhea2hebBazVP5r8He9ui8MiSbfUQsJyko1cvm8dVTustp30VM18udUz8d38+ly9J0FkJ5e6VdDNC14ggiLWGBB2CIAjilmUtJ996QLtYUWY9RKfiFp184Gf573J6qx87N7K0qJXw23fXQZI0scV8PZ3up94suLsvbvSGGZ5iM3hFOAWbWqIBfG7HHYatmznDFWCDDVrcXTXOYd/2eotVmc5iXkFeUTWRRFWRyrC2ak72IzqapZskfE8Xm8zo1TayJNkGc+wYnl7OOn5+cBKNYT/2tkWNvj+K6q6vkK2woCpGIKu9MYSnz8UZYana77W1ZOsfTUCUvZ3KLOLce6xVUDhQhWRm2XJM689wEf2jCUyk5lcs5gCaxc7/8YensKioAAK229220QtA65306H0x9Ay8z1QfObG3LWp8152u+6L+JSDhhCCIdcD8vOwfTWJ05prwd+7khUkMT7H2ZPvbl6tfj52LG0kF9TV+piLTqZpCltj+bgd3xnC0dwTjSbZibTw5byR/8M/c9saQq/md01yiKRIs2OOmMxYumNRiZ0/qNLctNA9eq3mymypsfhxO4yqn9ZbTvoqZL7sZE2tLGwag4srs6lRQFkO55vM3M3SNCIJYa0jQIQiCIG5ZVmPyLfK6Tl9fLFmUWY+Mr2IWwvu21xsWWvrfOuX0Vjcfw1z9Yb6evOe81sx3Gf28zo8lGesTp2tqPoexRJoJKsVqN1nsyPTj8/dNC0gtH/PIqYuW7GCds5dmmICU1lx6mV+MJHDw2X4cPbDDsE975/Icqv1eNIb9uKe5lrF04wM8igpbAUTv5aNX29iJTgCMAJr5moxMp42+AHvbolpl0okB233saYuiaikLt380Iezt83cffIjjj36CuUfm70PIofeIourf0cKC0kJOLGwU078n4PUgk3O2Zlt04T+VSGeNht/9o0lXYk444EVNwIt3Ls+h6/h5/ODz7VBUFd88NeRYCVMKdtVaBEEQbtiz9HwwV1zolRZW2MB+6+ZNTB8c8+/7vu0NuLel1lU1RWcswtiOfeq755jnWSToRSKdw/DUNXz9xYt4I57A0QM7TBVDEt4cTRpWaE7zO/NcIq+ozHNFNPcsZf5kZ0/qNLflr8lYIsNULq9VkNpp3imqVK+U4Hm5e7aIkrh4/F5PWY5VDOWseLpZoWtEEMRaQ4IOQRAEccuyGpNvfjH2F/s+imyeDacWI8pUyqLVjkO7WyBLUhGNdYtDayRstcwyo19Pzvbe8rdOMdfUqY+M/jmR6GbNxrUOxpwdzItFZvTqHL2CJZPN48zQNB473g9Zlo3A0My1rKVnjyxpgbPJ5Dwg6SKNve3ZsKlJ85FTQ2iJBoXbtUSDeOl3dztWgZxZ6pEgykyWANzfFsWxAzvgq5KN10WCzra6ai5rNYQ/eKANL7ylBdTqQ36MTIsFj7cnZwFVgSwV7uORU0qXPSQANQEvPlxgRQ6PBPiqVmZp5tRDqEqWDIEomckZIsuZoWl86blBdD/Sgaf74mXpz7LRI6EuHEBDOGDpPVGJrKeVHEHcirRuDqIhFHAlglfJElOBIbIkq93kw6P3NeON+AyTEPHbd9cZVbtXZheYzwyOJ9H9SEdR1RR2zzH+9+PMkmWpLEnG85rvi2Oe34mECKeeemZKmT9tq6tmrn0k6MWh3S2Oc1v9vZ7BCQxPpQ3xCoAhdunntV5BapHIUQnjWg1LZCdrvtpNPjy2qxnBDeK54mpSbuHqZoSuEUEQaw0JOgRBEMQty2pMvvnFWDavwOeRmdeKEWUqYdHqxGovYLRGws6LV/16/vSty8zrP33rMr60t9WyfSnXNK9o/WUiQR/mc3l0NoVwcGfMOD4vEPHHUFSVqSoBlrODRWKRCD64dO69hEUm0oNJfIDqyYe2GvfoaO+IQyY0y8i0+NrPZrLoOn4ekiShJRq03a5ncBJ//W924di5ESRMooKK5X4+5j5Hiqri+YEJfDB3HZA0m5mjB3YIex+YA2p2wftEOoczlxKuznUlfSNUiBuE51XgnliYqbgqlsz1Rdv3qv1exo7PzGvDM+g6fr4sYg4ALORVpgJrLaiSJfiqZCzk8gUFOR4Scwhi9bj/zgjOj6WQMf07awwHcOzhDiPIzVeimMkrKrqOn7etuACAx5aeCb3c72f/aML2N9U8v+LtqxRVwQtvXQYgLdlZadgF0UV9y5wC7vox80s/Vl3Hzxvn//K7U+gZnDT67a3GvOnogR04fGIA71yew7a6ahzlEiZEsJXLy7/t+lyiEoLUdpXqqz2uQoKNG0vkYkUfJ2u+x5a+N2+//fZKT40gCIK4CSBBhyAIgiiZ1chOu9HhF2M+j4zghqqCzWztqITF9Hohqs4J+Dz4v/a2QpKAgbEkFBXL1QKWkhxxBLiUa9rdF2es3s5emjHECDvvd/Mx9ACPuYeO+Xtg3odmFSfh5OAEUzHDIzo7p6ohs3AismIrhkQ6x3w+HPQKhYPhqWv41Hf7sK2uWhiA+87p96CoKr7wyRi+9Nwg08wZAO5tjsBXJS/1wlnmzTh7fqUE73WLsqsLi8hcX2QCk+VEluyvjxucxhUK+GwFnWzePph6o1Dt96I64EXchYgkS9pPgBvdRwZKtqHb4AGuO7vqEcTNjyRbfpvGEhmjl9zRAzuQV1QcPjFg+V1vrg0wQgewLOjzz8gHnuq1HPqXV65aXtOrF8zPVSf7qiOnLkKWtAC8UxBdt13T0Z+x5u3NSQ16Zar+32b46pdy46uS8ezBzpI+WynV4JVkr1ZIsHFjiVxsH0x2Lqj10DH3iyQIgiAIHRJ0CIIgiJIpdqFyK8AH93VrhHKJMm5EtHIIbZUg1omqc+6JhTE4rgk5E8l5DE9rdienL06huZZtMm/u57NSRFm5xWSwemQJj+9pxeN7rBVDPLIkLX2PrFU9Tug9awDn4IxHltD9SIdhWTOeyDDVKXb2ZHvbonjn8hxmrlkFBCexYnjqGoanrgkreTLZPI6cGsLzg5PCyo+ewcmlQB/7eqE+NW4wW5Tx+KtkSLIkzNAuFhVS2apkePa11wNQ8a2fXXLVm6fceCStCsmJgM9T9HX0eiTk8qrRS8gNxZz+SqQ7EnMIQvxM1Cv4zM8ekah8dYGtOtSfpeJnpHXewVuLAcvVC4XGKDquU3+brl3NtrayetD9JJd4cn4saWv5CmjPtUpLhKqUavBKslcrJNi4EZqK7YN5KydxEQRBEMVBgg5BEARRMsUuVG5EihU2+MVYua0R3Iho5RDa3O6Dvz4Hd8bw7GujZQlW8N+vSNDnWHEQn8mgJRpEUySIzlh5F/2iDN5yZomKrveATTAq4PUwgkbA58E9sTB+8Pl2ps/MEw9uxeC49t+KCsbexiNLTB8AMzV+a8P7vW1RdD/S4dgzpxBNkSA+216P770ybAnwTyTFPXCGp66huy8OSSruO7T7zlp4PTIUVcXbk7NMhrUbOpvD+B9f6MSxc3E8PziB2UwWgIRQwIuxmTQWHYJ1Xo+Ej2ysQijgxWfbGxx7Fq2E1mgQh3ZrzbzXQ8xpiQYhQTJEVTty+eLkk5ZoEI2RIF5xaQsoQoK7Sp31JhL0IZXJFm0nRxArweuR8MnmMHrfc2dHKSK36Pzv+um+OGoCXuF7vCAzlsjgaO+IcL6wb3s9jpxafubsaYvi6IEdeObVOE5emISqgunrZe6LM5YQP1d0OprCrucw/PzHbJfKV9J2NIXxRtz+2upJDpWUCGUnJDjNgVcjeYjvz7ZW9moiCgk2boSmSql8IgiCIG4+SNAhCIIgSuZWWKhUWhWSnYhmXhTzQYxShDb+OPoim1+489fHbOVV7PXiF/btjez3KxS07xeiMzKdxud2NJT9HnXtakZeUfHMq6OWHjrlCGqI7quo8iEStFpsZbJ5nBmaxqe/12dUubz87hSefGgruh/pYPrzvPzuFH4yMIG5+ZxDBrH1jYlUBnlFhaICrZs3QVHygCTj6nwO1X6vsLqGD6p3xsI4fH8LZEmyiEIN4YBtb5an++Ko9osDgyL2bKnFPc21GBxP4hOxCDpjESYg6IbLqXkAWrWSJMEQhBLpLPxVEhYFio5XlrDzzlqcHZpGMp1DMq1dY8UpVXsF7N/RAI8sWezoykEhQSTg87jupVNsb6LZTA71oZVdM0kSODCuA4Wuo9vqI4IoJztba3FPLLIiQccjA04ulTPXspZqztbNm7C/vd4QTXoGJzA8lXa0Iju0uxmyZJ176NU85uebuTec+RnTujmIfdsboKgKTg5MYCK1AEVV8T8HJpDN5/Gtn70HgO1zc/TADgAo+Gznn92tm4Po2tVs+V1uiQYQq92EsURa2KumnJSzkpuf45n7AB07FzeerS+/OwVFBR7fs7Lkob1tUeb99VxXFBJs3FTTVErlE0EQBHHzQYIOQRAEUTK3wkKl0qqQ7EQ0p8qJUhbE/HEUVRUKW/z1eefyHPN3MdeLX9g/8WAb03tIUd1ZkPUMTrgSVYoRYjyyBI8sGQFYcw+dUkU/8/HzXIp+R1MYPQOTBfdhhg+w948mcHBnDMfOxR2340lmFi2vDU+lcfjEgLBCauZaFnvbopY+CfoZmYN4R3tH0D+awJ62KCYSGczOZxEK+PDP7q7HwFgCvxhJIMsJAKLAYJUMmBPEQ4EqbL8jhM5YBIoKJshk/h61N2qZ072XnHvLqLD/NzVvV54jAZNLQpDO989Yq5F4WqJBzM3nhFZ2ZiJBHwCgJlCFz7Y34ODOGH54dhivjzgHZT1LX+lidJVmgT2emXJY0dmRSGfxzuRsUZ/ZfWcEf3flKq4uLCKXVyum4qVChkEQDH/7fgrvJ9wJsjyRoA9fvC+Gk4MTiM+wySPNtUG8n0wzv82tmzehKRKwPGP1+UMhcaNQ0Fw0R+NpigTx+J4WZBcVfP+VYeSWfiDi02n88MwIs61ZXAJgSVjpfqSDmSfwc6X97ZrQ3hmL4PTF5efM53bcYVT0mPe/GoJFKZXcZqHGfH789TRfH63f0TInL0wWLejw+5cllNxz0kw5Em3KYX9GFmoEQRDEakGCDkEQBFEyN9tCpZKasdphJ6KJskSbIsGSF8T8cfhsUz3wwl8f3kqlmOvFn8PgeArdj3Sga5dmKzUwlsLetigkqFAhMT10zAxPpdHdFy/4vSxWiLET90oV/XjBQLOLC2DHkg3MeJINuAW8HmSyVrHFDkUFDp8YcKwC4HugOPU7edshyP7O5Tl0NoVw9tKM5b2mSABdu5rRdfw8893Y2xbFyFAaiXQOf/yzITz50Fbc21LrytKNd/tJZRbxejyJ0Zk0/n5ugXlP/x7p94QPQol4P5lBz8BEwe3M5BXVUhbiRvhoDPuhImBrJ+iVJeQU1biPh3Y3C6+n7bhKUBWupObRXBtEfKa0oO9K+fC6+++53ysbQtptGwtX8RHErU5qfhGpeff/xnRaN2/CX/+bXTh8YsAi5gAQ/l40hPw4emCHMJhuN79yskFrb9SSO1546zIAFfWhgGWfAJj9tjeGcbR3BMfOjWCeKyvK2JQZiYShM0PTlrmF3Zys2NfLiZs5iZ1Qo6gq0zOovTFksZtd/jz/cCn8sLFWYrP774xFyrKuWK/q+kroP0kQBEHcGpCgQxAEQRBLVFIzVjvsRDRRlmi5swrN2aZ60IS/PiL/ebe4rT568qGtjM1c/2gSfzMxywRyRVZ0/OKaD2h85/R7UFQtYC6yJxFV0YjGrW9baBHPH39kOo3PtjegfzQhDNSb++boNNcGhIE1AJAlCX9boNLhvjtrIUsS3rk8h2111ehoCuMbL4mroGoCPtteNDPXsjh7aQZ726KYSM1jeGpZaOtoCqO7L245J76aq2dwEo3hAFoKVIfYkcnmhddC75Nw7NwITg5OYtSFSJHLq5a+CIVQVKA+5Hf8XMDrgd/nYb6rKqSCYo6ZnwxM4BcjM+gViGfF4JGAmoAXm3wyxlPXmffmFxVXYk65etX4vTITaC3Gpm0+p9j+GyAIonzs216HZ18bdSUk64hEkOVndwJ726KQJQk7mkLIKyp+/du9SKWvG88aOytXneGpNPa2ReGRJUPsGRhLaskfkgRFVfH0uRFLXzge3s5UJAwBVnHEbk5W7OuFKEYocEpE0vdj11/o5IVJo2pKq3Ddiicf2mrY45n32d4YYqqm922vL3ge1krsrWWpyOFZr+r6SrNpJgiCIG5eSNAhCIIgiCXsFoA3QhVSMcJTKRmEdvsXBSdKvV5uq4/0+2I+Nm9joosqTotrPuiRyeZx5NRFyBIswSe+GmJPWxSKCnQdP4/2xjD2tEVxdun9M0PT6Dp+3mLNwsP3CAK0YICbCoPaTT48el8Mqgr8VM9UrvEzFTKKquKu228TVs0AWhD97y5fxbb6avzi//1r8FXJhhClZUKHoKjAC29NApCgumhK8s7lOTx6XzMAFYPjKeM+Hj4xYNm22u9lbMb0RtEAloJ8WrPsUsQdnUjQh/7RBBMQFFEuYWIiNY89W2ptr3kmlzeEuSqPBL/XAwkOmfKCr8/IdNr2moQD3oKBS528qvUGymRlV9uLKJelGJ81TxDE+sP/njw/OInZTPEVcHwwXZSkAQBft+l1xov/PB5ZMvWLWxYY9pqey4VRTeLSsjDEJxisZZW2kYhwYRKpdM6YGxQSCpzmg/y1jwS9XKIG+9AZHE8yldL8Ps3VPG7EGGsldpKpoC0X61VdXwk2zVQlRBAEcWtAgg5BEARBLFFp9mrFUEzWZykZhOW017NbbLqtPhLdl65dzUzQXs8K5hfXx87F8XRfHNvqqvGDz7ejZ3CSqSYBxMEnXgyYTM3j7NDyNWyNbmLeF2UlW7GGw93aRT22FDgxB8D2bW+AJEnMNfjaA22Qlipw7rr9NnTGIrjwfgqjM5ooMJ/L4szQNO75o58jHNyAufmccW3+9PUxJlvXDTPXsjhy6iKefGgruh/pMF7n72E46GWCZAGvh6lA0gN0XcfPOwo6Aa+8VPEiFjES6SxTWWZHU20AowWqPJqjQUwkMpaKGTN6ldU9zbVLQbis7T31yBKCG6qgOgg6xVSqAHAt5pghMYUgCBH870mp4jr/zOafyz0FhCLeypVHT+Dg9/vasFhY93ok3BEOMOeTSOdwZmh6WVzihCFzBVDX8fNrEijv7ovb9g10EgpEcyl93vV0H9tT7+MNIXTGwqZehcs96IDle2c3Pyt2XrhW8+z1qq6vhHUEVQlpkLBFEMTNDgk6BEEQBLFEpdmrObGShcp6ZhDy1S78YtN8Xu2NIQCSYZ8iS5q/uui+6IKQGV24MaMH188MTePx5wbREPJbBJ1CwScNNtDO97vRP+dk+zYgaAbMawWRoBeAxIgCkaBPWPUyOJ60XIOfXriMfe118MgS853e9h9eYrZLZRaRymjCwpmhafzm9/oQFwTvRBZgorH3jyaNa9DRFMbD9zbhjXgC71yeQ7XfawkM8nZydnZ2PNcXFXTEwugfTZYkTLREgwDEgcrmaBCpa9eNXhPx6TQ8Lv6JPfNqHHfdfhvzvWqJBtAQDjLZ4j6PVhkjS847tbvmNwLW7G9r3yaCIG5emqNB9AxMomdwAvu21+PQbmvvPf4ZrNO6eRP2t9dbeujkFQU/ODti9Cg7MzSNY+dGLLaoWZsfmt13RnHs4R2GwGGuFO0fTWI8yYr7ogog89yllPmYm8+I5x4adv2G7I7NV+bodMbCjCiTV1TI0urNg9dqnr1ePT4rYR1RCVVClQAJWwRB3OyQoEMQBEEQS6zXArAUVrJQWc8MQlG1i3mxKTovHb13jh38ec1c06pPdDHo3HszTMVD/2iSaVqvCyX8Apzfb0s0aLEfE1VSiHoAme8VH6Pn/97bFkX3Ix04di7OZMzq4+ODV+2NYfSPJpjXhqevGRm+vL2cE+M2/VPshAX+5bFEGqcvThnH/cnAhCGamINnIlqjQaaB9OvxhK1tTl5F0b1kZAmo9ldBliTMZnLCCpqA1yMUtNwIEXo/ITMSJNwTCzPnoQJIX1/EuE0vA51yiznFCCoBrweqqmJ+sbQqnqSgckqWJORd2PcRBHFj0hINoikSwGJeQe97y8+kI6eG8PzgJBrCAa2qVVKhqipGpq2/gVUyAFWFYvqtUFTg+cEJXJmdtwj4xVSTdsbCxnwPACN0KKpqm+RhFygvZT7m5jOihIbWaBD7dzQYz0i3x+bHXrvJh8cE853VmgezyTpa/x19TMUkJDkJWJVQkVEJ64hKqBKqBEjYIgjiZocEHYIgCIK4AVnJQuXgzphRLbGtrhoHd8aKPn6pC2dRxql5semUkVroHHnbNR29OsVivcXFlENBHx7fY92/OeMyr6iuGkLvbYs69gDq2tWMiaQ4kB/weXB7jR+dsQgA4NDuZsgS0D+aQF4FTg5OoLtvhKl82NMWxZvxGdveLeZju2FDlYxMiVZckaC1AqcYq55UJouu4+eNiixPgQqWYlFUGNVIdvAVQysllclhcDzFvLaQy2Mxr2J4WpydvloUUx2z0usgOtRaVhuFg16hqEQQRPG4FYNHptNoCAdwfiwlfM/8PNCrJHkWFWB4Oo0jp4bQP5os+Ny9klooOK7WzUHsb29gkiLyiopI0If5XB6dTSFL27LWzcsJBu2NISZQrlURlzYfc/OZrl3NeH2Efa7va69ntnN7bD7Iv62uuiRBpVTsknWKTUhyErCoIkOjEqqEKgEStgiCuNkhQYcgCIIgbkBWslB59rVRps/Ks6+NFr3o5RfOPYOT2N9eXzAwwI9bFz50gWjUpjJE/6wd+udFzZPHEhmMcVUQkaAPNf4qjDB9U8SRKnPGZdfx87ZjaIkGEasNWgQu0b3q7otj2EbkyGTzGJ66hiOnLkKWWI96kWUKoPX0sbOtMaNfQ/N49mypxd9OpJCaXw7e/4PbfGiq/QhkSYKiWkUsjwTIsiSsTAoFN9j2tBHBBwn1XgYAcPritDDg1xwNCitoqmQtEFgqAW/pQpYTmewi3np/lnnNLi4aCfpwdSFXdP+clVBOWzcJ9ue21sxlcrj/zgh+EU+u6fUkiJuRmoDVQtEOu6pKHrO4Ew56IUPCDFc1KXqu87gRn5siQUt/vG+8tNyj5uylGextizKf2d/eYJrT8HMb+2e8HfpcZSzBPr9En/HIEqqWrDl1BsdTTEKNqFL3aO+IJdnGLjFlrYQPN8k6pdjQmQUsqsjQqIQqoUqgUoStSqgcIwji5oQEHYIgCIKoQAotAFayUCnHopffx/DUNUNsKFRFo3/efF6aN71VrNjTFkUV1/9FhJ0/vD42nkO7m6GoKtNweN/2etv964gsUHweCQ3hABrDAbQ3hqCowOETA8aYzeesv//Mq2xjYq9HQmM4oI3XFOByClawWIPVkaAPj94XgwrghbcuA9Dsa77wSa1C6+3JWVQHfHjn8lVGzAGA0cQCRhMLhl1dSzTIBN6+9qDeOHr5mrdGg2gIB2wrj+woFGcfmU6juTaAufmcEUyMT6eXxqYJTu8nMxiZThtiTuvmTagP+V0HFXV4McfvlYW9eYoVLeZzCuZzy0HKgFdG3ubERRZwpVIlA1WyjIUCKtdH/FVlq2RZTdkk4PMUtAs0k1eB3vcS8LppfnQTUEliGnHz4PVIaAgFMJsp32+TiGQ6h71tUUsCAW9xWipu+uPJkmbvKppbDY6z22t/t7iej/E9BIHlPkF2n7FLCDE/e/e2RY1K5EVFwZFTlwBoYo2iqnh8T6tjYspKhA+3wWqnfnhO9rRdu5qZ/bc32otna1WRcasE6G/086wUYYsqxwiCWC1I0CEIgiCICqTQAmAlC5VyLHrtFueFAgN247YTK6qWmhEXQuQPXxPwMp76rZuDaIoELQGXkxcmoYVCJeQV1XHBKrJ129laizND0xiZTuMV0+svvzuFN+IJyJJm8yVLkq11TC6vYnhJpDALOmOJDI72jqBrV7Pwmns9Em7b6EVdjd/SPyCRzuL7rwyjIxYyRC3evqZQtrV5rOagkfn66Yt9XiBzwuthq3taN28CYN8cOz6TQevmIDPeV4dn0BgOYF97PWSJzfSezWRRV+Nn9qHfBx5etGndvAlNkYDtOe3ZUgsJwJkie/eYEVUBBXwebPTKKxZWIkEfABWJdA6LCrCoFK44CvlZa7IqWbtWa+iO5opcvvC5iO7zrVKd4/fKUFQVC4u3xvkSa0MuryLuUD0bi/jRHN2EsUTGtb1mwOdBJ9dXDAAmkhn8wQNteOGty0ils0iks8bvPv/cKAazDaqO6JnaGYvYzq3s5k5u52OiHoJNkYDt5/KKCkXVn48q9m3XhJ/DJwaY7TymedKvf/ss897JC5N4fE+rq/NwC18h5Kbah01sCQNQMTieYuYTomQjAMxc+IkH22wFN1HyTNfx82UXI26VAP2tcp6rDVWOEQSxWpCgQxAEQRAVyGouAMphQ6B/pmdwghESzIGBYrL77AQit4EG/vOPLY3PnMXaEArg6IEdzBhkSTLGb7Y4c6IzFsZEMoNUJodQwIv3HZraF/L993kkZE0BKlmS8ORDW43raq586trVDEXVAjSp9HUk0potVyKdRe+lGaENWSaXRy8nPLixrxHhEYhr5iCWkx2dHrybSGQwMpO2BOX2ba8DIOH5wQnMZrK4urBo2SbFCR26CHbk1JDFJmfmWha9l9hrbydO3F7jZ4KQ+009CrTv8ChTNTM5O4/97Q0rEnREZLJ5V4KFiIBX67u0r70eh3Y349CfDuD0RXE2tIiGcICxH1yJbd1q4iaYu6FKXFV1K7AadoEEUYi5ea0n2Wfb6/G9V4ZdVdFlsnlMJDOW6s/h6TSqZAkvf/V+dB0/zzzXnf7988L8/Vui+ERzmBEN+PmH+ZlqFkzsWOncqVAPQZ7uvjiOnFqew8iSZCRV2AsyYls4Mys9D6eKaPNcVTQHdJpfic6Lv2aD4yl0P9Jha9Gmz0nMVd/FiBErtX27mbhVznO1oV4+BEGsFiToEARBEEQFspoLgHLYEOj74O0wzM2GzdYihRbU+uf6RxNGNUtnzH2gwS5AYa6mOTM0ja7j55kqE7sFq92iXguwLFdsrNQeS6/u0emMhXH4/ha8GU8wQtmb8QQO39+Cx/do/+MDXQDw93OFm0IDQLXfi5lrxY+7vTGEo70j6B9NQlFVyJKWzXxwZwzPvjZq6VNkJpPNQwLwwVV2jAGfB1/5tTuRV1R84yVxgEjH6Vq/n8xgz5ZavDY8g2Jj2o2RID63o8HIHF5UlKUsZwn7ttcjFPAyx06lc+gZmETA67Ht2xAOshUve9qimEzNYzxhFbPMlJp9nsnlMTx9DW/GZ3DywgSuzLr7Luh4ZMkSWL1Rmc8p2H1nLQbGUpjP5cmCjCBWmWQmh9MXp3H64jT8Xtl2O756Tv+9iQR9zG+s/hy2S/QQ9Uq7tzmMjlitIc58ojmMQ7tb4JEl5nne3hgCIGFwXHu2H9rdjMf3uJsLFTt34ucR7Y0hYQ9BO+zmJ3y1i6KqRiXKZ+6uwzdfcraSXekc0Mn+1TxXLbbCw24eZzcXdtp/qWKEmzGvdoC+UqzOSIgoD5XSy4cgiJsPEnQIgiAIogJZzwVAMYtJu8CAyFrEaUG90gCD3ef5cfMCk92ClV/U/2RgAnPzObi18nfq9WG2LtPFEP4+j3N9aPi/RYEup+oOc7B+ZDqNSNB9c2v984Bkyco9fXEar8cTrnrViKqV/F4PHr63Ce3/+eeuxyJiZDpdshhxz5KI1rWr2dLf4Mipi2iOBpntE0s2QDqt0SBSmRzz2t0NNeiMRYz7+vC9TfhXfzaA+LTYUq5cnLWpGtpYJSGv2gtGioqiex+5IeCV16Vq5PV44paxWSOISkJUHVe7Sevn9uZoUvisCAVZ0byjKWzYjYn6mIkqCNWlDlJme1FZ0uYFoiC9/t89gxPYt70BZgsw0XO5lIA6f9wnHtxqsQvj98vbmZkRWbxplShDlmPoyTEDY0kc7UVZRQF+/uFkx2qmFEtep7mw0/6LFSPyiopj5+L43ivvFRzzas/PK8XqjISI8lApvXwIgrj5IEGHIAiCICqQci8AihFpyrGYLNZaZLVwasR7fiyJowd2GP+tX5e8oqJncJLZtlix4PYav7AfTEs0iO5HOphrL7rPc5mc499du5rxi5EZxk7NLoC9+84Iqjwe5hxqAr6iBJ1U+jpOXpgQvvfasL2IUKiXRyKdxae/12db6bLaRIJeI0ghEiEBMDZ2XllCjguyqQCquSoeRQVjL/PIM2+g973EKpyBO+pCAXwwt4Bc3nqdJYjFtipZgqKokCRAkoC8goLVLnzwtSMWRt97M2vei4fEHIKoHLbVVUOWJFvhX1U1UUCWJOxY6l32wFO9lr5wTpwZmsZEihWl9WC8U0XJ8FSasTXTe9+5rS7WEc2xrHZhScMuzA7ezsxOLDGfo+gYwLLlrG6/uZq2vaL5pJ2oUmg+6taqzUm0KVaM4O3tdNobwzjaO2IZ62oG6MtldSa6jgBWnLBFEARBVAYk6BAEQRDELUAxIk05FpOiDE6nBfVqWUyYF/Xm5r36GEV098WFYoxOwOfBvc0R/M3ELBPED/g8uL16I+pCfvzSpk9NUyQoPC/+/G/zV2HGtO/qgFdoG+OGc+8lLD1mig53m3oN8dgFz902Zl+NyhC3hIIbjPvhFPTT4cUcQCz2nRmaxj/5k17Mzeewra4arw6vjpgTDnjx4cKicFw6XllyFCRDgSokM4uW1xf1fapw/YX5aPVGSBIASFABS/8mgiBuPWQJ6B+1/33VKyyffGgrABgVJ8XDPhP1Z7xTYocIvs+cmzkQP8d6I55AZ6x4yyr+OSTqXWfGTtRYzf4nbgP9dqJKofmo2/mqk2hTrBghev63bg4CUJkKqDfiCUZgs5unrmROWy6rM7vKtEqo/iEIgiBWDgk6BEEQBHEDUeoisZjFfTkWk24zOHVWy2LCvKgXXbtj50aMnjgvvzsFRVUxUCCwf08sjGe+0ME03QWAr/zanQBg2ywY0KzTfnh2BGZ7F70Pkfn8W2oDzOdUVbMD0TNIiwlOaTtQ0BINYiKZQUM4gNkie//I0ur5tzeEA2Xr3dJSG8CVuQWh7Y8IRVWx47/8HNvqqrGjie1voBn4lI5+TqLqFwAoxyVNZgpXWTmJPQCQEog5pRKfWT9xrpxUydKyoEUQRFHwv52dsQjeiBcWtd2I6mZ469B92+shS9YAP9tzRkuGOHlh0jZxY1tdNfO7/db7Kfz6t3uxb3s9Du0Wz2X4sZ8ZmsaOprDFYq0Qxc6/7ESN1eh/Uuz8005UKTQfdTtfLWcFiUj029/eILyvQOF56krmtOWyOhNdR9E2JOgQBEHcmJCgQxAEQdxSVEqz0VIpZZGYV1RbL3bRtooKtG7eBEDFvu31JS0m9YW2LlYcPjHgeL35hWbP4GTZ741o8a81UAbzd0MowH/UoCUaNGzaRI2Jn3l11HEMw1PXLPYu+j7MSDLbWDo+k7GMVUQ44AUkIMnZqb2fWmB66HhsLqtd75+76qoZu5w9bVFUyRLeep+tUooEvbj7jhDiM2nGqkxE7SYfHtvVjIfvbcLjzw3iteEZ22ofcw8gJ1LzOVdijt8r4x98xGeM8czQNN4cTaK5NmCIEqsZzpclILihCvPrZDVnhmQLlo1VkrBHB0EQ7vjag22QJckQUBTVWvXi98oI+DyMINPeGEb/KCv8tESDUFVVKBbXBLzo2tWCwXGrHZYZ0bP/0O5mU8VrGKIeOj2DmuiTSOeQSOdw5NRFyNLynMup3w0AvPDWJF7+6p6iAua8+KSowKM/7oeiaokVnTF2HmUnaohEgZXOf8uVeFNIbCpVjFrJ+XXtaoai6nNCdu7rZNu7GpXu5RKq7K5juYU+giAIYn0gQYcgCIK4paiUZqNAaYvPUhaJfG8QJ/sz3kdclqQViSqi662LPObz5heew1PXcOxc3JJtW37xjd+fZKlGaY0G0RgJGAGVZ18btfiomxsTF8ubo0moKhsM2re9HicvTHA2Z4VD73zFRjhQhcd2t1iEJrsWI7qY0xoNoiEcgCxp2dWLisL2P1BVABK21bNCzxfvi6FKljE6zWY+ixpb3+b34jun38Oxc3HcVXcbI+Y01waWerfI2Le9Ho/eF8OhEwO2PRiM83fZF2g+p2AsuWA597WqMFFU4NrCom2Vjl11kAStgbjb8ywnAa9n3XodFcNKq2vcWgUSBLGMRwI2eD3wez3IKwBk7d9R/2hSWKU4n1OMZ0Lr5k3Y314PRWVtUb2yhFQ6ayt8j0xrv9cdTWH0jybxRjxhPLNKrR7R0Xvv8FU8+pwru6jgoe+cYxINrAkRxc9XrPMKtuJX74ejz6P6RxNCsUd0fub9lTL/5W3z+kdLq+4QJcN0HT9vzPNKrVApNL93mnN7ZAmP72nB43vshTG3tr36e+stnDhdx5VW/xAEQRDrDwk6BEEQxC3FavqKF0sp4lIpi0SRJ7tdA9qxhLipsBN2i+S8oqJncILZtmdw0sh6BViRp2eQFTDMliirJb7t217PCFi6ZYseNAGA/TsaADg3GOavccDnAQBhtQvPeCLNBIX2tkVxaHczZIm1b9PGtpz1DEjo7oszFTI8kiTj8T2ttkE1hw+i+5EO43vSdfw88/ZZU28Uc9NmRRVbzvFijtcjGdUxmWze0mvFLKycHJyALAHHDuzAM6/Gl86ZFTQiQS9CwQ2OvY9KoSUaxAdzC67uYymoNtrB7z+wBQNjKcs9UwFcdWGzVm4CXg82f6QKY8nKF3QWFRXhoBfXFhaRtVMuCYIoK3lV+y3PZPP445/ZJzeIno2zGe0Zxtud5hS1oK2k6Bl4+uL0ksjhnBDiNHfp7otb5kPA8pzr8IkBS9Xo7dUbMWx6bd/2+oLHcsLOgk5/3U7sWa3+iAr3wOL/dotdMox5nldKhUqh8ytlzl3ItteOctmmucXu+yW6jk7ncyM5FhAEQdzqkKBDEARB3FJUQtacTimLa36ReHBnDEd7RxwXZHbnrC/mzAILj5vrY7dI7u6LcxUmEB6nZ3AC58eSaAgFHCtSViq+iRavj94XQ/9oAu9cnsO2umo8el8MAPBGfPm1gztj+NJzg45j4a/xPbGwawFFD2bp6IKb6F4/+5pWaSNL2vsDYwmcvmh/HD2z+eiBHXjwqV7XFSjDU9c0q7ylc3RqKv3O5Tk8tpRVe/jEgKv921mrCccynTYFriSLmANoPYZs1ZEi0W3j+GzctUSWZNvAymoUkHgkrWrIbteZXL4ixZwqWYLPIyHDCYZzmRyqA+tTyUQQhD0icXzmWhZff/Ei9rZFi96fXULDycFJDE8vJ4T0DExi/456Zo7kNHcxiyV6woDZhou3kAOAfe3LSRd8EL8cCTw67Y1h9AyKLVjL1R9RNF/iH0nliP2XK8nKjbXwSo9VjBVaOfv7uKGU71clORYQBEEQxUOCDkEQBHFL4ZQ1t9bZaqWIS/wi0Y2Fht0580ELHidrNjN2i2S3DY6Hp9KGkMNWe6g4cmo507dY8Y2/n4oKoxrHfN31oP2ZoWkcPjGAiVTGGM+ZoWk8+9oo2hvZe6V57i+jXyfd/kQU7AGA1ugmNIT9jFDwsfoaoY0H34foU9/ts1QsdcYijoLOP/iIDz88O4zB8RTuiAQZQScc9EKChLn5nNCiqmdgghGTtPNLYoyrKNKDcT2DE479h1bK+bEkxmwEqWRmEcnMYkn79VfJmDc1Tena1YxDu1vwT/7kbFH7Cfg82FglWzLK7foSOfEnPx9CQ3j1riWPJEkW278bgUVFFX5386p7+z2CINYG3krS55GYKrp3Ls+57pdWiBSXKDE8fc2Y7+hzJLu5C28t9vGGEJ75Qgfz2ra6aua53RwNGvvQbcTMvQNXksDD26opqmqbhJNXWPsy8xz24M6YJVnFDlGwn59vdMYijuPnxyWaX5crycqNtXAlJXSVm1K+X8V8hqp5CIIgKg8SdAiCIIhbCqesubXOVlupJYPI0ky0ILM750KCC2/NZofdItmpqgMAWjcHAUhMYMIja1ZfgHZ+dtmubuDvZ+vmTcz7ovMXVWQsW5yZYYPI+jXmRSidSNCHL97XBFmSMTCWxN62qOH1//C9TfjSc4O2QRY74a1ncBKNYf/SviTsaArj9ZEZnHtv2b5sNDEvHA8AbL8jhI6msK2oNzydxvB0mrHFeyOesA226cLc/VtqcXl2HlAl1If9mEhmyhKg62gK4633Z1e8HzMt0SD+v1/eZVz/u+qqoajAA0/1Gv0Z7AgHvPjw+qJRbaTbDZnxSMBGr1y0oJPNqxiZTsPrkfCRDVUFbYdWykp6zhAEcXMTDnqxkFOQyytFVVfy8J/c2VrLPHNnrmUxcy2L5mgQfz+3AKja7+dCLm+pwrMj4PPgnlgY44lrSAgeO+Y5kt3chbcSyyuKpRL66IEdOHxiwHhu72gKGc9a8z71/y5HAo8Ob4EaCXrx8YYQ04Po5Xen8JOBCczN57CtrhpHD+zAs6+NMgksej9AUaBeFOw/emCH8d/Fzsns5tflsiYrZC0MrL0N2lpSyvermM9QNQ9BEETlQYIOQRAEQSyx1v11VmrJILI0KybjkF/MRYJexs7K7b7sFsm6AGAO2LREg5YAg1lQMB9TdH2KyRK0CDZckKa9MQxZgqPopI+J39fgeEo4npOcDUrA58FXfu1OI2hiPte9bVGcH0sy10ivEjL3r7ET3oanrhli2BMPtuHxPS0YHHdXFQUAY4kMxkQRLwHf+tkQ/ufAhNH3xolfXp4zvkfD09fwxINbl/ZxEYtcTC4S9OHjDTVQVBUSVCiQMJHMYDaTRbXfizsiQXhMzZ5/MvC+Y8+gYmmKBPCnr48Z1//s0DTOurRZcyOymCtFWjcHcTk1b+kn5EQuX7iHxHoSCfrKej/8VRLmV8NTjiCIkil3tVsk6MWh3S2GjejTfXHMXFv+HTE/Z778a3da+sntaYtiMjWPVDpr+f3JZPM4MzSNPW1RxGfmLcc2V7DoyRP83IWfUkym5oXB7GcPdhrb8CKLmZWKITz83O3Qbm2exI9BT6TQ5xX8XInvxWM+N1Gwv9g5a3ZRMUQvvgBUn1+Xy5rMjTix1jZoa0kpYlUxn6mk/qMEQRCEBgk6BEEQBLHESv3F19p+gF9gtW7eVFSQwK5Hi5vFnej8RZVB3Y90GNuZe5KYs0PNYyg0/mKyBPn7WR/yMw2L3xxNwCNJRrWMorIVOq2bN2F/+7Jnvui7wY8nEvQxY7i9xm9r72LXn+XM0DSOnRvB43tahecBAF6PxGRKn7wwicf3tBasitrbFjUs5ewsW0Rk86pQzNnbFsWbo0mmAoUXLAbHtWDWm/EZnL00w7wXCvoMKxuzfSCwHKTKKyp+1DuCzv/6Mq4ulDew2BmLuLYGXCmqCny0xu9KFCtE2F+F+UWlKHFIpyUaxB3hAN65PMcEUYulSpagKAoiQS+uLiyuKHNfpxQxxytLyFGFEUHcMHysvsZ4LmqVrct2qDzfe+U9fPlXW/HEg1sxOM7OE46dG8HJC5MAJECF0TMH0BIL9rZFIUGFCgmyJFkqWPTj83MIi5WpZBVC+M84PXt1McQ83wFgmTe6nVfazZvc9Lrj5zH8869ncAJdSz3xRMcohsMnBmznOWOJDI72jpRt7nwzV9/w34t7+YJxlCZWFfOZm9mujiAI4kaFBB2CIAiCWKKYBWEl2A/wC6z97fVFLYz5Hi1fem4QHU1hHD2wo+B+3J6/ecHIZ4/qQZFiFqHFZAnyfvESZ/hylvNb1yuGRMEUu+8GP56agJfJGK6r8SOvqEKveCe6+0YxOJ5CR1MYD9/bhJ8MTDC2Zbdt9HKZycvjVFTt/pjfb90cxP72Bk14OzFgqewK+DzojIWXbM1SaG8MY1HJ49s/e89ik6MTCfrQ/UgHjp2LM8G4zliYubYdTWF098UtYg4A7Nteb/y36N527WpG1/HztkEhMxKAKk7o8nokfLKlFr2Xlj8fCnhxfVGB3ytjUVEs/ZFWi5HpNFqW+iyslA+v50sWMWK1QRw9sMP1dbVjUVGRmi+tb1E5ITGHICoPp+q9iWTGeC5q2P8bzmTzOHJqCE882MYIELy96d62KCPozFzL4szQNJOYcfjEALPvp/viAKzCCv+85wUnUTDb/Bmtx55qPMNFfQtffncKb8QTTDWum3mVHtzX++ro/X54EWZ0hu11t62u2nYeY37+DU+l0d0XL3puJuLtyVnmb79XRl3IbySU8D2NVkIlVt8Um/hltz3/vfiLfR9FcMPahvGKFcwqIemNIAjiZocEHYIgCIJYopgFYSXYD5QrI1EURLDzVdexZnVOFly4lSPDz2kf/ALSXHGjW7DYoVcM6fc/r6g4di6+lP2r4jN310GW5ILjaQj5mSBK76VpHDsXhyxpjY31fjfmTGHAWnGTSGfx8rtTRtDHvM+9bVF0NIXxjZeWg1m6MOKRJcN6zTyu2QxrpccLGJlsHmeHpnFvc8ToYXTw2X6HMNtyEOzQ7mYAqpEpvaMxDKgqfnnlKu66/Tbk8ip+cHbY8nm9mslO8GpvDBclOuxpi0JVVUY4yuVVi5CXWroWmWwe33zpElqiwYKNuPlm3qVTnr2sRMTI5VV0/teXy2qVRhAEYeauumpMpsT904an0zh2Lo7H92hzJt3C1ImTFyaNRAStJx4rjssS8ORDWy32bWbhgH/GzFzLCkUFfi6o9fODUETR5zpu5o+iKt3uvrgx39IFJvP2/P5EPfVOX2SrjQ7f38LYnekWt6Ixdu1qRs/gJFOxW675bE3Ax9j43l7jR1MkyCSU3MzWXcUmftltz39vsnkF5UkNcU+xglklJL0RBEHc7JCgQxAEQRAlUE77gVIz2cqRkZhXVPRwfV/6RxNMXxfRYqy9McRldWr9XJwWbqUIUPy1EXne69v0DE6wAR+uGmIi6dzk3hxY6O5jq06++dIl47+dGvr2jyYs+9UCUcvBkicf2moRzH5y/n2MzIjHx/d0kSUJkgS0RoNIZXIIBbwAVCbj2S5wpekAqqVfko5ZmOOza820RoNLQo72PZQlybj2f/yzZaHp7CWrzZpOIp3FkVMXIUvia8mLXnZ4JGD3Fq3C6vE/G7C83z/mHCzUA44BrweZXF64DV/5UyoqJAR8Hsaibq0xVys54ffKJVm6EQRxcxIOel3303l7IonU/PLvHJ+08O2fD6G7bwQ1AR8awoGC++N7sIwn2OdlZyyyZN/GVu7omPvY8KJPoYQUfa4FLPebMYsobhElU/B9bPjtRechghdGfFUy0+PHDo8sYX97vW0vw5XQGGETJRojwYq37ipnZUmxiV922/PXzOexJhdVGpWQ9EYQBHGzQ4IOQRAEQZRAOf261zOTrbsvbumlwveSAUSLMfsFbv9oomzjt7s25v3zvVd0Uhm2AuGD2QXmb17UMAcWCvVV0e3A+IU/ANZ7HwBflaFfS90erWdwEle4sdl/2mo1owkjQ5AlyRhT/2gCzdGgpV8LLy7xtjhmYa4lGhSKPgDQEA44VmwVg11zZKcm02Z+/4GtRqa3pfcB4Fo8sRNzAGCxDGIOgLL0z1ltJAAbqiQScwiCMAgHvfh4fTX+ZnLOlahjFnNE5PIqEukcEumcYUc5N5+DoqrC/c9yz3NdHGqNBrF/R4NpDiaem+h9bHhhBnCXkCJKfik2SN21q5lJlgG0XjJjCfa5ULvJh8dMFmr8eYgsQp2qlQuJEqvVf+aeWBivXJxi/q70XjeF5uPFXNtixSu77flrFtxQ+fOIShfuCIIgbgZI0CEIgiCIEhBVx4gWem5Yz0w2/titm4OQJbFdmpnBcfsAvp0bVCnClRtrNzsxQeEGwgfsv3hfDANjKcOSRK/+yStatYsTHU1hHDs3YggrL787BUVVcWi3liGs249pVmisAKNfS74KyA0t0aBtpVHP4KRtdvIy/HmptnZjjRGtwkn03pujCXzh2X54JE1EKaYPDV8Jw3+3dLu7t96fLbifL//anUalEKD1TXo9nkDfpWmUQ4PRrdbMuwoHvEhm3GWp34ioABYWqS8NQRDLJNM5vDKkVVtWycCijd4bCfown8tbhHQPAKdfTfNzpjkaxPhMmvkNt0sugCQx8wh+bhIJevGx+hrDJu3gzhgUVV2ybJOgqipz7GPntISIHU1hABIGx5crRvnkF/3Z5TbI75EldD/SwVQU8/sEgMd2NRvWr0d7R5j9ikShvW1RZr5Z7FxrtfrPiMSbSux1Y6bQfLyYa1useMVvf3BnjLn/um3e22+/XdrJrSGVLtwRBEHcDJCgQxAEQRBlQrTQuzdU+HPrmcnGH3t/ewOAZTsRwBosEH3OjEgQAkoTrvjjiDJp7cYiOWSkasKVzPTY0XvodPfF2f42soSGsB93RILwSBI6l7JMH3iql9lnd18ch3a34PE9rXh8T6upeXFqqXeOJn6YF7p22IksV2bnbSsnhqeu4Y9fshdzIkEv9m2v56p7craBMlVVMTcvfi+TVQwruNMXp/EHD7Rhb1sU51wIKZ9oieCeWNh2oS8SuoQiigQMjCXQP5qAvHRfFFW1WNS5wc5uTXQqqZtYzCEIgiiEnZjj98q2vbkW8ir2tkUxkcowPVREyBKKEOTZDfn5wMfqa4zn+emLU3g9nmCeEc2cNWsincXpi9NMpaeoZ0/r5qDx7ComyK8LGufHksx1iAR9kCSt99CioqLr+HnkFVVofauLQm57HPJzrXLYirnZR6WKN05jLzQfL2YeW+z589ubq89vtD40lXrvCYIgbiZI0CEIgiCIMiFa6N0b2lDwc+uZyeZ0bKeFuvlz5qADAHTGxIJUKcKV+ThjibSwma6+zf88P474zHzBfQKacGW3MOdfzykq4jMZxGcyePKhraYFKntNEukcjp0bwaHdLZaePgC4z4pFMVnSAkyfubse/aNJS78TXsyRJbYiyikI1rWrGYd2t6B/NOnYm8bvlZFdVFz1r9F54cIkhl3aiamqiq6lDGQRIqFLVBGTyeaZoNvpi1OIBL3MNrWbfKj2e4XimBm/z75/Dk85alcCPk8Z9kIU09ODIIjVpZBN4zuX5/DofTE8PzjJ/CbzgrqTBSmPVgW7jD4f6B9NQFG1Y5rRK3WMY6Wce+stwz7v97c3GPMi/pn1dF8cb8QTTBIHP4fin/+6EHZ2aNo2KcHOnpSn0FyLF6AUVYUsScI5n534sRKr4JUKSiv9vNPYD+6M4Y14wlK5rbOWCVjUh4YgCIJwggQdgiAIgigT4oVe4SD3emay2R270HjMn9MtsjSbMRWKqlmWmRfYeUV7vSUawGxmEaGAD4oKy3ZOx+F75egLaX2b1+MJRtBZyLLBJa9HQmMkgH3b642gj/l+5U1ZsXb0DE4YwQOt2oWtJDl5YRKyJAl7+vCL8a5dzTh2Ls5kNCsqMDyVxjdfGkLr5k2249DZWFVYiAj4PLgnFsaj92njLhT4KKV3Ct+vyIkzQ9P4jW+fNSq56sMBw7qta1dzUfZtPHy1Ueb6IjLZPMIBL1KZnK0YY5dVXi74gOWCS/GIcCaZzhm2eARBVDYz17R+b3vaooygwz/DeLu2cNCLawuLqJIlZEzPp9bNQRzarT1Ts4sKDp8YMALxHU1hfENQser3ysz+tedQ4V+QfdvrIUvi5Bd+7jdzLWuqCtL+n59PsckqGaH1Gs9YIoOjvSNCAcMscrQ3hvDEg1sNu7iuXc3M+3zPHq23nvYaL3DYiR8rERtEdrWP72l19VmnMbnFaezPvjYqrNzWWcsErHKIR+WoxiIIgiAqExJ0CIIgCKJMiBZ6f/fLd9Z5VKuPR5YgSzACEkdODUHmfO01Gy2z1VcWR05dRP9oAkcP7MCzr40WXHAWypz8JZeJq3JBmlxexfBUGrKkiRpOVUZ2lmfDU2l098Vx+P4WPHpfDN87/R4TjEqlc+gfTQivE78Y18dg30encJDJTVVJJptnAhN8kKCUKodI0Idt9dWGEPOTgQn7HgcC4jPLWdF6Zc/pi9PoGZxEfchf1FhEBHweZLJ5I/jHBwjLRaw2gP3tDRgcTyGvqngznmAEMVkCPJJkuU8FWjTB65GgKGpZ+gCtF36vXJI4WCw38CUiiJuG5toA87sO2P8GTCTcVXMGfB7kFhXj+ZTlfhD3ba835gqHTwwwgfi3J9n5QO0mHx7b1YxFRcE3X7pkvP7RGn/BCk5NOFru/8IHyfW5yNN9ccxcsyYHiMQOp2QVM2abuuGpa8Z2/P6OnVu2Kn353Sl87YE2tDeG0TM4iZ7BCdSHAraVPyr3I2oer534sRKxQUv+Yf/WK5vdCA8rrVzhx673TRT1ZOT3vZYJWOUQj1YqflUKJEwRBEFYIUGHIAiCIMrErewZzS+CewYnmIWXXb+YM0PTTCCGX3CaF3Fm0UUXKLp2NRvvV/u9TDDlnuYI7m2OWIIsItuSruPnmXE1RQL43I4GvBmfwRujKUYQ0D//7GujlkB9Ip0VButFfYgA4NDuZiPrlxeVtIxgCf2jWkZtoaBTIXiLOv3+vD4yg7OXZmw/56+S0NkcwfmxFPxeD76wswkeScJP37oMQMX2xjDqQ4WDYm7QeySJ2NMWxaSL/gvA6gg4ojzu0ZkMJAl45gsdAIAfnh1hBDpFBRQ+WuaC3I2s5CyxFmLOjUZjOIArc/PI5VWqLCJuKj4Q2KRJNv30rsy5s1Qr/Du+vH/eWm2eezZvq6sWVq8oKhySKjTM9mqAc5BcJMwUEjv0Z3LP4CTz/NuzpRadsYjl3EQCBi+SPPPqKFN56vTcbAgHmOe3ebx2wk0hsYGvGAIk45pbf/iKs3BbaeVK165mvBFPMPNJPVFnpfsup/BQjjXFzWLbVknCFIlLBEFUCiToEARBEASxYvhFsJZNmjbsNJxszJyCFeZFHI++UDW/3xINYm4+h2111Th6YAd8VbJlG9ECnR+/bv/1RjxhCSrpn7cTqWRJQuvmIBNA4a3O9AWh7vMvSRLUpfFLkibmHNq9LDrNZ/P49Pf6MJHMYNPGKseKGq8sISe43rqlnL4A1TONewbYQBDflyewwYveJcEnk83jwniKEZ6+KbC1WQ08EvCZu+vx1MuXyi54xGoD8Miyo+2N3RFPXpg02cVQiJ6wZzy5XMFA3xTiZmJ+0SrgqjZidrnE3sHxJABtrrCtrpp5Lv3KbRsgyxIACfUhP5M08uRDW3H0wA5098UxMJa0rcj1SMDuLVFLNbBdkHy5f09yqS/N8lyCRxQU1hNU9HnBRDKDswKxSSwysNeaF7ScmExmLBZtOnbCTSGxQRSA1/97T1uU7bmnqugZZOchfFKQef5USuUKf71lTmy0S3gptiqmkoQHYG17/pQb1iaQrf5bT2Gq0u4xQRC3LiToEARBEASxYrp2NUNRVZy8MIkrswuMCGL2ZwesFl98IEZfcOYVFT2DE7bH7GgKWwIrsdoguh/psIwNcF6gi7bp7osz4wK0apVc3rnXTmcsjM5YmBGRzJYeAPCj3hFHIaR/NGn0BgCALz03aASckumco50VL+a0RoOoCwWYgFZeUY3KpOFpVsTYvSXKWLPUBLxMpu9rw/bVPKvJWCJj9CMoN3eEg6iSJaTS2aL76SSvZY17OzCWWpXxETcmVIVD3MqUu0qPnzu0Ny7PFXY0hfD25Czmcwp+pXoj4ibB4MrsPLMfu2SQK7PzzJjzKttHRQ8w88Flvp+fm+CuXVDYqdJHt40TzWG0nn7Lc4rOppCl8nZvWxQeWbJUAw9PpyFLsMydij0nM3YJL4AmlD350FajImnYxt5WTwoC2IB5KWPir/fetijzfin3UESlVcTYzX8rpcrEaRxOCV3rKUxV2j0mCOLWhQQdgiAIgiBWjNZHR7Kx9WAXiXc3hNAZCzP+83wPHUBbzPH70wMS5u0KZR+6XaArqiYajCXSUFRVGJyfX1Txxz9bDprsaYtiIpnBbCaLUMCHfe0NxrhElh66UPTUy5cs+zZzZmgaXcfPG+f69uQsO46cgljEj6sLi8LeNa2bg2iKBJcCXiq+/XP2eM+8OgpJgkWwCvg86GgKobMpbFiqKZxAxPcxcIPe02YliDKovR6pLNU6vZdKF4qSmRy2/YeXcE8sjLyytjZjAa8MFWRvVk6qJACShMVCjY5cQGIOcStgVxVaTpqjQXzm7tvxrZ+9Z3pVO2Z3X5zpi8PHpUVVtnxQ1vx84Z9XesCWDzC3bg5iv+mZXwxOQWE7MeSxpcpaEYd2t0CWJGZe9cyro0tWbCpT9ZtXVDzwVC8zvyp3UJqvDDHTGYvg8P0tOD+WZKpi9XnLWCLDvF6OsfHXVF4SlVbSn0ZEpVXE2M1/K6XKxGkc/D3Tvx/lvF+lUGn3mCCIWxcSdAiCIAiCKAv84ivg8+D2Gj/qQ35mca6oqmH5pWPOItT/ti7mNuHogR2G+AMAB3fGjMogQIKigqmE4bHzdeczVo+cGrJkcIroH00agZ9EOgdZgnFsfgyirGAnzBU14aDX8v5oYh5/8MAW/PTCZYvYsb+9AYfvb8EPzw4zWbs6VxdyFpEH0AJf33zpkrDBtQi3FQh8QM3rkfCRDVVIZuyt49zg9cjI5cvfL6dYMtk8zgxNw++Vi/7sSqo4PlrjR1MkiNMXxYEzongWVVi7hBMEYUu5xJyWaBAN4QAmkml8MHedeW7Ep9P4q7euMNufvDCJwfEUxhK82C9+/vMVLnaCw0erN2Bkevn5p80VrHOcpkiwYBBcVIGgv85v9+iP+w2rNTMBnwf3xMIW6zczosD943ta8Pge6/g8soT97Q0FrWhXgnlOx/fQ0d/jA+P6vOVo70jZxyay1V2NnpcrtWxbKyqlysRpHHbfj/XmRrnHBEHc/JCgQxAEQRCrRF5Rkb6+iGxewdHekZu+cSa/+Mpk80aTe7NHvbkBrY4oS8+6mKvHs6+NMtv1DE6gPhQwMk315saiIIbdceyYSGbQunkTrqTmkbHxo+eFCvNitL2RHf/YTNpi1eIW2SZA9dTL7zEVKpGg1/DiB6yNknUKVbW4EXMAqxDh80iuK3i6djXjG5ztXCFxI+SvwvVF1bgfK636KTeFKmVitQFMJDMwt5tYSSi0PuRHZyxMgg5BEBWP1yPB65Ftf7c/mFsQVmLqpLgEAN2Wy4KqYu9S9azZzuuxXc04uDOGruPn8fbkrMXCTac+FGAEHV3fLSUzn59zvBFPoDMWZhJIWqJBS7UsAAS8HmRyeSNhQLd+WwnL/fuS2NsWdezzs9JjWG202LHbBcbLFTBnE3jCeOLBNgyOp1Y1CL9Sy7a1olKqTJzGUanCyY1yjwmCuPkhQYcgCIIgVonuvjhi8iKA5aqMm3kBYF588ZYZc/Ns0KR/NGFs29EUNv7WOT+WxNEDO5htunY14/CJAWY7UUDn5OAEZAmCYIKzrzuPORDUEg1ibj6Hu26/DZ2xCP7Ha6OYuWbttaJn8mqwoXqRTzyPLGnWbzx31VczfW10eGFGkiSu2a9VCAp4ZWSKtOiyGxdPQzjgGJDTyeVVodgUDnqFFnI6KlShuBYJ+orufWNm41JvJPPl9EhAY22Q6cWwUkZdCmVumUzNQ1FVRApct3JRKT1hYrVBjM6U774QBLH65PKqY0VlIYG+xl+Fj9VX453Lc1BVML/5rZs3AYDRk2V4Oo0/eKANA2NJvHN5DtvqqvHwvU146DvnbJ9RPo+Ena21eJ+rkPnpW5fxpb2tRQeY84qKngG2D+CZoWlMpNj98/MjHf5Z1zM44brniZ2owtvGPfHgVgDAoT8dgKKqjMBTSgJSXtF6DJorjAHx3FcPjOtWtIdPDCzP9coQMOfFtCcf2irsE3QrUiliidM4SDghCIJwhgQdgiAIglglzo8lEWtm/zY31l3vZqRuMGdzFlrsmxdfvGXGtrpqJgPV3OD+5Xen0BINMvvqaAoLF3NOvuw6qUzW1pPb6fN72qLojyeFgkGsNmgEAvKKivNjSWFGLSAZ1+yZV0cdx9kSDSKVyTIZwqGAWJhobwzhnlgEJwcnMZ5M21bYzFzTzv31eAL3xMJC66jbQwFGbHODW0edkek0wkEvJADzWcW2sgmAMCBfSJSYnberlFqE3yuX3EtmYVG1fL7a73Ul5qynyPHB3ILQUm+1qAQxBwDuCG3ERDLNVDoRBHFzo0qSzXMXaAj58c7lOea1n16YMCptzgxN4/E/G3BMOMjmNetVr4efj2m/fMUGmLv74jaJHOz+q/1eYYIIj57A4qbniV1vkv5RNqnl5OAkhqfZ+YA+NyslkN7dF7fco0J2XqX0c3Ezj+YTePhEokqee682TmLaWl4TEm0IgiBKp3ijb4IgCIIgXMFbGOh/64vXl9+dwtdfvIjuvvh6DM8V+lhPX5zCmaFpnL447WrMXbua8eRDW/Hr/3AznnxoK44e2GH8vbctagmqjEyn0RINonaTD3vborZe8V27mvHEg1sRCfpsj10TYN/rGZww/Oq7djVbPhvwefDkQ1vxzCMdCGzwCPc5lkjjaO+IEUSwCyoNjieNa1YoQCNJwGtP/Br2tkULnvcPz45AURXUh/3wegpP384OTePIqSFhMOkzd9dhT1sUAZ8HAa8H4YC1P48ZL7e4d7r2AJBM55BI5yxiDt9fppw9tOdzSslijnkfXllCwOfBnrYo3MoX6yly5G5RReO1kUThjQiCuOExP394gb01GjTmFGeGpi3P3NnMIvN333szro7JJ0zs215fzJAN7CqC62o2IhL0wu+VEQ56mfkQ/5wMB72o3eSzPHcLVRuLepMAWg9DM6mMeJ5STDVzoc8VsvOyG6sTbubR/HEVFTfM3NsO3co5lckac9KVcCOtRwiCIAgWEnQIgiAIYpXo2tWMj2yswgavjCcf2spYC5gpdeG8FtiNrdCY9ay77kc6cPj+FviqZHTtakZHU9iSRaszMp3GzLWs4RVvt19ZYq1W9rRF8cSDbYZ49Nn2BuYzw1NpdPfFDTEmxAkYX/7VO40sRbte6MNTaXz9xYvoOn7eYg9nRrOPY69NwOcRiiDDU2n86etjePZgJwb+P7+BZw92wk6ryWTz+OZLl3B2aNqwpeGFFidaNweN6yNLkrGfTC6P5FJfAq8swSNZA0pVXLZyKOgsANnxK9UbS/rcWpJTVGSyeUgAPlZfw7wX8LFiXyUk9oqakfP370ZFluxamwOLCkqqzqmAW0YQNz1VJfwEWatiNES/cTr1Ib9wTlG7yYcnHmyzbO+yvRuznycf2opH72vW+iAeP19UEF0kZISDXvRemkEincN8TrH08OGfM8l0DjPXspbK3UIiiV1CEf/cqgmIDVtK7anCf25vW7SgnZfdWJ1wM4/mE4tYO9rKnnvb0d0Xx4cLi7ieU8oiwFTSeiSvqCX9OyMIgrhVIcs1giAIglglPLKE4IYqBAEc/oS9bdh6NSN1g51FWSlj5r3bnXh6aZHK2z/kFRU9g2zvlV9ensO9zREcPbADHlmzPDt5YZKxFdMXqebjB3we3BML49H7YpaxyZJ2/z6yocoQPADNtmVvW9Qy3kjQh1DQC0VVkVfYSHMmm8c9XBNk87h0MUnvPeSW6oAXj+1qRs/gZEELtf3tDYalRdfx88Jt9MAZX+mi/x0JehEKbkBdjV/ciLoA//vqdexti+LN0WTBXglu8XokW/u5lXBmaBpfe6ANqqriF3Htu7OjMYTJ1DziS1ZxaxVrkCVgo9cDqNaeCiLsKpUqpf+NW1bj+t5I508QlUiVDFTJEhYW7f81lSK23raxqug+YO8nMzh7yTqneGxJQHDTV83rkfC7v74FsiRZ5g2PLfVyMVvIFmMJpqgqWqJBfDC3AAD4lds2IF6gj9q2+hphvzxAS8xoigRd9Tyx603SGYsYlmoA8Nn2BsiSJLTVLQW74x7tHbG1Oiuln4ubebTIzuv0xRtj7s2jJyQ93RfHH91fbbxeyM6uEJW0HinFeo8gCOJWhgQdgiAIglgDzH7f7Y1hPPFgGwbHU+vWjNRtHx99bG4X+077FWX+NdcGIEsSUpkcE3zRe8EA7IKuuy9uES/4bT2yhP3t9YxA09EUthw/k80b1UD8e4oKKHmVEXN0ZElC6+YgI2ok0loG7ZFTQwgLKlhkCXjyoa3oGZxgPtfRFC5K6DJzm9+LnsFJXJmdt7zn93ogSQBUFZ2xsGHlllfUkgWQxJKVWrH9dwxUMKKWLK08aF8f8mO0QICsVAbHk5Bl2bhe596bWZcKD0UFOmNhdDSF8NPBSXwwd72gsOORrNnosdpAwWAiQRCEEw2hAOYWclhYLE58Ef0mmfnifTEMjKXwzuU5VPu9jr1udP5+7jrzd+0mHx7b1YyuXc04fGLA1bhyeRUvvHUZ+9vr8df/ZpcxHzDPzURVDIUCzd19cUt/Mze/vx1NIUiAMAFk3/Z6PL6nteA+APveJCLxRN+2HIiOW4ogVgg7EchpDlqKcFQp2M0TVyrAVNI1KeXfGUEQxK0MCToEQRAEsQbwmWdPPrQV3Y90VMx4APECu5QmwHb7FVX7zM3nmKzcgM/DVG/wCzonOwhztUv/aAJ726KQJQmdseVFqqjaSKv4sY80RYJeZoydsTA6Y2FbEYa3UNE+E8Hh+1twcGcMh08M4J3Lc9hWV42DO2P40nODzLatm4OYTecwY5NdHPB5sNErW/oJmJk3BfzPXprBp77bh/3t9VBUFb2XxNm/q43K+dkVI+YEvB6LiNFcG8D7STZAJgEIB32uMrN5/F6ZqW4ZS2QwN8/ey/Wq8Dg7NG2btS1CFDidFYiTdng9Ej7RHEF/fAbXTZf9RqvyIQiivIwWUUVqZkOVjIxN9WAk6IMsSfDIEh7b1YyDO2P41HfOCXvAmclx1bB6RU1eUW3tmmIRP0YTbBLE8NQ1JinEPOcQ7UsUROeFBN561Q7+ufPW+7O2Ten1YbhNyBGxkib0pR63UKC+lMoMu/Nw2tdKzn294a+hLEuMlbMb7O5fpVyTSqoWIgiCuBEgQYcgCIIg1oBKyzxbrfHw/WX6RxPLVl+7mvFGPMFknvIWK7fX+JkKEJG3ukiUAYD2xhC6jp9n9t+6eRM6Y2Hj+AAsVTLm47Vu3oSGkJ/ZR9euZsiSJMxgPD+WRF5Rhdm0OnuWPOTziorDJwaMbc8MTePwiQFMJNngUn0ogFR61nZ/G6tkoWjkhB6wao0Gi/pcqYis0OZL8eHR4WJGAa8szHZWod2vN0cTRQkgoiDfyHRaWG1VSRQjsIiqzezI5VX8/67MwePxAPllRYfEHIIgSsFOzAGAbfXVRjWL/nzfv6OhYOXqRzZW4fDuFsuzubsvbvtM3r/jDpwfS9paoIoEAvO2LdGgMc8xCxq8kCCyZhVRFwoI5zyiec4Lb03iX+9tXTdrKrvjFhJ6CgXqC81HixGSipnbrkQYW2v4axj0eXB4Z3H3fLW/Nyu9npVULUQQBHEjQIIOQRAEQawBlZZ5tlrj4ZNizX97ZAndj3TggafO2dp27dteD1myX9B1CXrGBHwe3F6zEW+OJi1BfFHmLduzJs2IO02RAI4e2IFj5+I4eWG5cke3LOsfTeKNeMKwnvvB59vxzKtxvD05a+v//8vLc+jui0NRrcKPKKhUSIgoJjDPkyris3xlkt4LwE3/m4aQf0X2XhahgvteOQUHT16YxGzGvkKHF2/u31KLX3JNtXWKFc7WmvvbongznrDtm7MSiu1nQRAEUSyRoBcTXKVlz+AE/vrf7LYkgPBU+73oH00atqLm57odF95PofuRDnT3xYUWqIUqbUam0xiZTuP0xWm8EU/AI0tL27HJLLrNav9oEn8zMWtbNbpvez0AdWm+IUFRgUfv0+Yb3zn9Hve8FdvXriQhxykIX+ha6MctJBQUCtQXmo+6ESL0sfJ9CPUKK5GwsF7CWCnCh/kafmSjB8ENxYfxVjuxbKXXs5KqhQiCIG4ESNAhCIIgiDXg4M4Y3ognGKuttYbt4xPC1x5ow0/fugxAa95rt+h12g+/GJUl9vP836L+NnvbokZQxMnPXT8uTyabx/AUK8zwmBeu5kWj2dsd0AIJHlmCLC1X7hw5NYTnByctnv56QMcccIoEfdhWX82IMnqPn9bNa1Md48TVhcJBer0PwcGdMaOfQKEqJDORoLegmLOxyrmptkdmG2vrdmvaNZQc+/gU6vHz4XVWkPq7K1ct1mo3As21gaKqkMwEvB78SrUP8Rlr/yWCIIjVRu+po/dmMzM8lcbhEwM4emAHHvzOOcZeNBzwGkkNozMZjM5kcPriFH4yMOGq747+jOeTO/T5x7FzI0y10J4ttbb70p+Joooc3WZVNM/g5zxab0Bt7EdOXYQsaYFwRdX+1tHEn/Im5DgF4QtVHenHLSQUFArUFxJ83AgRdj1mzgxNo+v4eaNnn9mGd70q5wsJH4Ws0d5+++2SjrvaiWWV5kRAEARxs0OCDkEQBEGsAbzV1rOvjZZloVMo08/8vjkory/OzaKFLNk3xnXaD7C8GO2MhXH64vKCkReK8oomHumB+X3b63FotztbBn7BrgskTkKOjnnhml1UjD42/+j223D/llr83ZWrqPZ78WZ8BgCEWbkiXhueYf5OpLNQFQWRoBezmRzXy4Q9x+YyN6kPB734x3XVUCUJ/aNJYSUNb4Mmoiag2YyZAwiP/vi863E4VYtEgl4c2m0NVPHYubPNZnLYVlddULRxgmvlg5lr1sxpWdKuQyVX6PCZyMWw0StDluQyjoYgCMKKBKBKYMHJP4r4/nn6PCkWCTCCjl2VKf+M5venJyqYxQKR0KBVyiwzmZrHkw9tLZjYIEuSsR0vSnTtaoaiLlfhdMYiePQ+LWHi8IkBS0WRHgg/tLtZWLFcTmsqpyC8pW/LUtURf9xihIJS+ri0N4aY/bc3hgqehxn+np2+OAVFhaveSKvBavQUcsNqW5qthRPBjWSTRxAEsdqQoEMQBEEQq0z6+qJlQVmuzDV+4dczOIH97Q3GIscuaxEA3uFsppzG5LQf8+f4PjlnhqbR3RdnMj717FdACxC4XYzxi+CmSBAdTWHhuPa0RdHRFMYLggoks7jWe2lZkJm5lsXIdBqvDM249r/PCgSS3vcSgi2BVDqLSNCHUMCLfe31UFXgGy8NCbd1i7mhcjKdAySp5KoNneGpNL7+4kUoqmr0Dhp1sLDhcbJk+3hDyMiC5i3d3DBzLYszQ9NF9Y7R0QN8dtY3ZhS1eLs1PQN4rVjJsZKZ3Iqs+wiCINwQMlXUOPErt22wJDg83RfHtrpq5jW3P3v3xMLMvEsXc/hgMADmNcsBJMnYrn80gb1tUciSZLFQ7YyFLaIEnwhjrsLpH7W3k9MD4XZCRynWVHaBcKcgPP+euerITDFCQWliBT9HtM4Znforijh5gbXu3bvU63AtWGlPoVJZbUuzteiBs142eQRBEJUICTor5MyZMzh+/DheffVVfPDBB/D5fKivr8eDDz6IQ4cO4c4771yV4/7lX/4lnnvuOQwMDODv//7vEQwG0djYiN/6rd/CoUOHcPvtt6/KcQmCIIjiyeat5QblylzjF356MB7QFjlOWYt33X4bzpoEDVHWo91xzJjPxSNLFoFGX4zmFRU9gxPC93Scsu9Ei2B9wfh0X5yptKiSJVTJkrACiReyROiZqPx+S4EXEhLpLFQVS3Z3YnRLlrvvqMH5sRR+MTwjFI8kztLuzVH7+1QsWsDDvZDjhh1NYXQdP+/avs2OUrQMN71/VkJz7SbUh/342/eTSM2v7rEIgiAqGQlawoFb4Tg+k0E4yFZF6gJ+seJ/SzQISZKWxBdNiNDFHD4YDIB5bU9bFMOmap992+stCS1PPrTVVhwyU0xCTevmTWiKBNY8EO4UhLezChbN05yEAvP2dtVITgyOJwV/24tK7Y1hACoGx1MOVVXsLEI0d10tnK653vPHjD7H1q/j1g1Z+Dyya5vktWIteuCQrRtBEMQyJOiUyPXr19HV1YU/+7M/Y17PZDKYnZ3FL3/5S3z/+9/Hf/tv/w1f+cpXynbcVCqFf/kv/yVeeukl5vWFhQUkEglcuHABTz31FI4dO4bPfe5zZTsuQRAEUTo+D2ttVM5MQLusRH2Rw79v9m7PKyoj6ACS6yxO3gPeaUz6YtTsE8+/px/X3KT45Xc1X/ymSNBoeqyfm6jfjjlwMpZIWyyp9Guyra66oKDwNxNzGE9mLKKXHV6BpYyO3ytbxIRnXh21VIqEg15cW1hEQziAH3y+HX6fx3iP9+DX4fdbjGgR8Hnw0eqNtnZyvDXZSokEvQDc9+K50Rievobh6SKt4CokFrPW1UUEQdzcqAAyDvabIpLpHPa2RfHO5TkmkSIU3OBK0AkHvKgJ+jAynTaea08+tNU2oaR/NInxJDtP8AhsxQ6fGGC20ecShYLXTokw1X4vc4772+sNkejwiQFbO6lSLadEgXCRKGXe17OvjQqtgnlx6I14wtIH0YyTsOUmucmNlZedmKBfr/7RBNNDR1FVplp8LezW+Ht39MAO4bUyz5HM6wX9Oh77zSiu5xSm+n01xleJdmZrYetGEARxo0CCTgmoqorPf/7zOHnyJABg06ZN+OIXv4iOjg5cv34dL730Ep5//nksLCzgd3/3d+H1evGlL31pxcddWFjApz/9afziF78AAESjUXR1deGuu+7C1atX8cILL+BnP/sZ5ubm8Du/8zvw+/34rd/6rRUflyAI4lamHAuc4IYqS4CgXIskfaHXM8jaR+iLHFEmoH7sruNsX5TB8SS6++A6i9PuHOyyD/mAQuvmTZaFKo8emNE9zx/fIw6g8NdBVFmiX5OjB3bg0IkB9F2atvr4e2VkcgoS6SwS6SyGp9KIBH1Cm669bVFMpOYxPHXNIubo+wEgDETNZxeZv/1e2chMHplO4/HnBvHjg53M+blt+twSDWBsJmM5Nx6/14NUJotwoArh4AbUhfyMBd37yfL19wGAqwuL6O6Lu97+VhAZKiVUspJxlLsXFEEQNy9+r4xPttRaLMt0PLKEx3Y1s/MBVXveSlAxnpxnnoPmXjkiK8mnl545imrtt5dXFEs/NpGtmFMQWTRHBLQ5DZ9UoifC8FUjetDejZ2UWzGFH1d7o/UcCh3PrhqCf92up6LdfoqtRlqJlZeT0KNbyq5WVRSPm/vLXytz5dBqV6fcCHZma2HrRhAEcaNAgk4J/Nmf/Zkh5kSjUfT29uIf/sN/aLz/6KOPoqenB//8n/9zqKqKr371q/jUpz6FpqamFR33m9/8piHmbNmyBWfOnGGs1f7Vv/pX+Na3voXf//3fRz6fR1dXF9577z3cdtttKzouQRDErUy5FjirZUOgL1bt7D+cLBBEQQp+wdgzOOmqaa1oTPy2/PF0KxPNhqNwQPjkhUk8vkd8fP2Y58eSTICmdXPQ6LXTtasZeUXFs6+NYjIlFjzmBRnFIYGgE/B68IPPt+O3vt9n2V732D990d7P/VeqNzJB8Owie9zXhmfQdfw8E6RpDAdcCTofzF53ZUlmPqdkZtES1berOCqVXF4tyjZntcWccKAKc/OLwu/BWolJ5a6CKoVQoAoSpJJ76nwwt8D8LQMoLi+fIIhbhfmcgs6Y9lw72juC758ZZipL84qKgztjUFRt/pVIZ43qx71tUbz4ld149rVRY67zxsgMzjhU0c5cy+LrL15EazTIvN4aDWIyNc+8FvDK6F+yLWWSXxg7rxAUFcbz2VzpIbJxA7R5iLm3IZ9MM5Gax+ETA7ZVxfxrZuzEFH7u+sSDbXjyoa3oH01CUVX0jyYs1Un88eyELKd+NaIx89vvb68vaj68GlZea2EPxuNGkCmmp1G5q1Os8/+JiqvSWY/7RhAEUamQoFMkqqri3//7f2/8/f3vf58Rc3T279+PM2fO4Ic//CGuX7+O//gf/yOeffbZko979epVHDlyxPj7xIkTwj45v/d7v4fTp0/jxRdfxNTUFP7kT/4E//f//X+XfFyCIIhbnRvFr7mURY5dppt5wTg8da1stg68H7uiKjhy6pL7HagqjvaOOFYJWQMHDczY7azLjEMIXtOFJ7MAksnl8aXnBoWVQHq2rJ2gEwn60BgJMoKOLEvIm5SFXF7Fy+9OMUEat7H/TK7U/i2Vs2i3w8narhgRRsKSiGXDSsQcCaX1+FkvUg7XwQ28CEpiDkEQTujzKEmSLDahZ4am8cyrcfSPJi2JFGbbL91G7ei5EVfHTGXYfTWEA5jgBJ1MTsHpi1PGs1ufO5jnV+Y5xMvvTqF1MysUiWzWmiJBR6FEqyq22nXyAfu8omLR4eFknp/y4xgcT6H7kQ4AcG1/ZjdHNL/OVxuNJTI42jtiK4itVVVFJdqH8fc9r6iWxB2na6X/9wZvCj6PjK728l5H6/cyXXZbN4IgCKJ8kKBTJK+++irGx8cBAI2NjfjsZz9ru+3v/d7v4Yc//CEA4OTJk/jRj36EDRs2lHTcv/qrv0I6rQWN7rvvPnR2dtpu+3u/93t48cUXAQB//ud/ToIOQRDEClhLv+ZyLkCd9mXn451XVCiqyliYAPZZetlFBYdPDBgCzdEDO+CrYvsFmeH92CdSbHao1yPB65HR0RRCZyyMn16YxMj08jb1IX/BaqlCgQMnT3sRe7bU4tBubR9HTrFBEL6psY5+3DfiCaGlTCKdtQT8P9kSYezOzBw7p/m/l2KBxt9LJ/Ztr4csAd95+ZKl90EoULXiwH85cKoaMse5vLKEnEPgazUFlxtJzCEIglhrxhJp/PDsMLr7RoXvn7wwKUyWAFjR4ti5EcOq1IxI+K/2e5kK0fcTaexrb8A3XhriP245Dv86izWpBIBj4N48TxlLpIXnKuq12N0Xx1mHHnRuqjn48bdEg5Ak7TwUVRurPtezSxQyv67PA98cTSKTzWN46hq+/uJFwyLXaT8rxWmee+zcCFM5pagqHt/TWnA/7Y1hACoGx1OrZpHMC2Hm+azTtdLfe/vtt42/y0nXrmb0DExg2FQJ3j9amUlsBEEQBAk6RfPXf/3Xxn8/+OCDkGX7wFVLSwu2bNmCS5cu4cMPP8S5c+fwG7/xGys+7m/+5m86bnv//fcjGAwinU7j0qVLeO+993DnnXeWdFyCIIhbnbXMLCynf7XTvuze6+6LM01ideyy9A6fGGAEmsMnBvDsQXHCgaghMR8IyeVV5PJ59F6agSxJaIpswh3hIGRJ87XXrVB0zAEXN81m84qKfJGlF5OzCzh8YgDtjSHcvyWK3kvLwZS76qqZ4Err5k1GY2OPLKH7kY6lZrxJvB5PMMLKZDLD9FU6uDOGT323T5ilm0hncfqifRBHRxTEur16I7M4t6O5NoBH74vBVyXj9ZEZnOXEJbOYsxp2ZC3RIObmc8grilA4KrbqxUnMIQiCIFaPxrAfjZEAXhtOCC0th6fSwrnGMvaBarNocfLCpKvxRII+LKkWBiMzGUgSjOcwX2lil7wjso6VJfEc0SlwL6r4MWPunWLenx28AGQ3d+XHf0c4YIzvyKmLkKXi5p3mRB0zIovcclfN8HPZnsFJYw7Gfze08YgFHdGc2Pzfov47pZyHWazhbfcqofrfI0toCAeYOaNSCb6wBEEQhBASdIpEz4gA4FglY97m0qVLxmdLFXSKOW5VVRXuvvtuvPrqq8ZnSdAhCIIojbX0ay6nvZvTvtw2urX7vL6YfW2YDfrbVawA2oKZz0Ldt70egIrvvTIstF3RefKhrcaxzTZm5oCL22bCosADAHgk2ASeNCuUl9+dwp4ttcx7HU0h3NscsV3Um787v/7ts8z5X1nqO2IWnva31zvawem0bt4EVVUt/XTMYk4k6EPXrma8GZ9xJejEZzKGlY3HIVkFKL+Y4/VIBXsD3YghhZZoAO8n58vei4ggCKKSGU/OYzw5X3hDjpZoAJ/bcQcUla2IDXg9uL1mI/YtBeuXEQfR+d/cRDprsW8Dlm3IdPs2UR9CHpFQoj/rzbgN3Ov76xmcYOYIIkFJ1LuGTyTRsZu78uPnE2X0v92KFXbzxiuz8xbrtXImLYmOrVcHabBjTqVzTPWRm3MAxBUq/HkoKiyiXiGBZy35b0zcAAA1D0lEQVSr/4uBH3YFtc8hCIIgOCRVJdm9GFpbWzEyonn1vvLKK9i7d6/j9n/4h3+I//yf/zMAoKurC08//XTRx1RVFRs2bEAup5WJx+NxxGIxx888/PDDOHHiBADgv/yX/4J/9+/+XcHj1NfX2773wQcfoLa2Fj//+c+LGPmtg35vvF7vOo+EIIhKo5jfh/T1RXy4sFyd8JGNVQhuKC33wmlfdu/xr5tx+rzOhioZoaBP+PlUJovrJhuvKo+E2k0bHI9p7NcrIxTwGcfO5hX4PDJzbfj9mz9jt40ZWZKYTERZliBLwGKefU0xqRmiY9hhd578PU5fX0Q6m2eOw7PBqwkududiHpvTOdt9xs09cYOeEM3PNCWJfW01Kn7WA0nSQkiyLMHv9QCA43Ws2ajdx9kF6jpDEATLrfb7wM8f0tcXMZ/LM89g0fPS/Bur/wa7fZ6sZI7llmLmdXbzG36b+aU+eX6vZ8Xj58e3oUrG9cXl71yha1RovmD+vJt52krGbt6vzyNb3rM7F6dzEM1rRfNZp++p0/gL3W8Rqxl3KOc6hCCItYVikjcev/EbvwGv14vJSXcVxzz061wkqVTK+O/a2lqHLa3bzM7OlnTMa9euGf841/K4BEEQxNqiL5pKWeAVsy+794IbqpBdVJjFfJVHsgQNsnkuwCQBGzwyamzEHADweWRmAawHvPl9SdLStqYx+DzLFSPBDVVgWxCL92/+jN02OhuqZHirZFwzLWKDvqWAfH75Na8s4bopUiQ6hh2GGMaJNdm8wpyPvp1TgEQ/rpNQo2/Dn/OGKhlYusb8vdY/o4/BHDTitxXC+aL5qsRBleCGKuQWFeQUFV5Zslx7fYxQUfiYRSBLgJf7bpULUdCDb8JdaFzZvGIRv9YTXnirJCp5bARBOKPPKwD7+U5wQxWyeYUJlNs9L837KBSczy/9cJRDDHFDMfM6u/mNZZsyjpsfHz8n46+50+ehanandnMcN/O0UsbOC3/6deZftzsX8znkFZX5jKgIzG4uWeg4ouO62W4tKec6hCAIglhd6Be6SD788EPjv/1+f8HtzdtcvXp1xcdczeM6qYJ69c7HPvYxV/u61dAt8ej6EATBc6P9PlhsR3ZarSN4z/cnH9qKw/c522ZY9tuu7Ve0r65PNgu3LWX//DZdx89b7dzuFdittGu2JObXDnbE8Oxro0WNi0d47T4h9mfvH01CUVVIUKFCgixJ6Iwtj40/F529bVF0P3I3PLJkPa97lsfs5prxY+JtYfjj8te2a1czjp2LL/nZq9i3vR6H2luY49iN0fw6398A0GzlPlZfzbzeEg3a2rc9+dBWIA+hrV046EWNvwqy7EFdzUb0cj2EeJoiflTJMiBJS+dU+N9JwOdBR1MYHkm7n1/4h1pgaO8n2+GRJfzw7HCBvhIaInvAcMALSZKEtkIAsGdLLao8stHw+em+ONNM3O+V4ffKSJr6Fz3xYBueH5wsaIfXXBvA7HwOHy4s4iMbtaWFqFG5iNZoEA2mHg7mYwMSTg5OIJXJotrvxR2RIDxL/wYevrcJX3puEL2Xpouq7trdGkb3F+7BY386gL73ivusmUjQi1BwA+pq/ExfrUIEvDIyLivmClElS1h0cQLhQBVqghvw93MLFmvLjVUSFhbF+wgHvFjI5ZFTVFTJEuYLjNvvldEZCxf8t7MeVEmAzWkiFKjCtYV8xfXdOvabUQDAof/l/vu1moQCVZAkydW/7fu31KIzFsELb10GoOIzd9dBlmQMjtvPK0S4eV7ysA3uQwCk5ePeU74G9zcrpVxzt58vZs5RDMXMLwudSzFzNP14Sl5lnt3FXrNiudHWFQRBrA3023DjsdJqKrJcKxKfz2dUy7z33ntobRU319N5+umncejQIQDAP/kn/wQvvfRS0ce8cuUK6urqjL9zuRyqqpy1uH/37/4d/uiP/ggAcOjQIRw9erTo45rRBZ1SS8FudujHkyAIO27G34dyNrYtd5PcSjveah5/WfhJLHm4Lwk+q3hOeUVdFmhUFfUhPzyyhM5YBAd3coJXGceRV1Qc7R3BM6+OYj6XR2dTCMce7oBHlljRzTQGXbwYHE8xPRHMYpksAZ2xCDNWUUBwYKz4a1zoXvO/Dfz9lCQJiqJgIpXBbGYRoYAX+9rr8YVPxvD4c4PoH03C7/Xgi/fF8K+WPP6PnRtZujcS6sN+eATnBwDZRQWHTwzgnctz2FZXbfRy4sebXVTw6e/1YSKZQX04gP/z47fj//mbK0hlcsZ4Du1usd33XXXVaG+swV/9zRUAEn777jpIEiz3xCL6cfu0Yz6bx29+9xzGZjLweCTcEQ5g3/Z64xjb7wihfzSBX165apynr0oW3OcwFFXVAtBL32tZgiGk7mjiAsO7rKJje2MIigq8cGESqUwOKlTUbKwCJAlXFxaxra4aP/h8O378i9Glc5WWAt3a92tRAX55eQ7zuTw6GmtwT3MtLryfMn0H2e/sw/c2mb4HMu66/TZUeTyOY+Wv84FPNOH//O+vYiKZwaaNVajeWAWPR7bcg7yi4ke9w3jm1VF8uLCI2zZ68cgnG3Hh/Vn8ssB3SPS9lKFCUYHJlNZnpS4UwGRKa1K/b/vSd/zPBtA/loLf68HBnTF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",
+      "text/plain": [
+       "<Figure size 960x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 804,
+       "width": 826
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from adjustText import adjust_text\n",
+    "\n",
+    "fig, ax  = plt.subplots(figsize=(12, 12))\n",
+    "\n",
+    "x = lfc_q.cpu().numpy()\n",
+    "y = neg_log_p\n",
+    "\n",
+    "ax.scatter(\n",
+    "    x,\n",
+    "    y,\n",
+    "    s=1,\n",
+    ")\n",
+    "\n",
+    "# increase xlim and ylim by 20% on both sides\n",
+    "xlim = ax.get_xlim()\n",
+    "ylim = ax.get_ylim()\n",
+    "ax.set_xlim(xlim[0] - 0.2 * (xlim[1] - xlim[0]), xlim[1] + 0.2 * (xlim[1] - xlim[0]))\n",
+    "ax.set_ylim(ylim[0] - 0.2 * (ylim[1] - ylim[0]), ylim[1] + 0.2 * (ylim[1] - ylim[0]))\n",
+    "\n",
+    "\n",
+    "# mask = (y > 5) | (x > 5)\n",
+    "# indices = np.where(mask)[0]\n",
+    "\n",
+    "# texts = [\n",
+    "#     ax.text(\n",
+    "#         x[i], y[i],\n",
+    "#         query_gene_symbols[i],\n",
+    "#         ha='center', va='center', fontsize=10)\n",
+    "#     for i in indices\n",
+    "# ]\n",
+    "\n",
+    "# adjust_text(\n",
+    "#     texts, expand=(1.2, 2),\n",
+    "#     arrowprops=dict(arrowstyle='->', color='red') \n",
+    "# );\n",
+    "\n",
+    "ax.set_xlabel(\"log2 fold change\")\n",
+    "ax.set_ylabel(\"$z-score$\")\n",
+    "ax.set_title(\"CD8-positive, alpha-beta T cell vs. prior (male, normal)\")\n",
+    "\n",
+    "ax.set_ylim((-1, 20))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb b/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
new file mode 100644
index 00000000..d2975ca2
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### _in silico_ perturbation by cell type prompting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase, \\\n",
+    "    load_gene_info_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_adata = sc.read_h5ad(\"/home/mehrtash/data/data/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# load linear response results\n",
+    "linear_response_file_path = os.path.join(ROOT_PATH, \"data\", \"linear_response\", \"10M\", \"linear_response_cell_index_59.pkl\")\n",
+    "\n",
+    "with open(linear_response_file_path, \"rb\") as f:\n",
+    "    linear_response_dict = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_info_df, gene_symbol_to_gene_id_map, gene_id_to_gene_symbol_map = load_gene_info_table(\n",
+    "    GENE_INFO_PATH, included_gene_ids=linear_response_dict[\"query_gene_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for k in linear_response_dict.keys():\n",
+    "    print(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(linear_response_dict['r_squared_qp'].flatten(), bins=50, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(linear_response_dict['slope_qp'].flatten(), bins=50, log=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate an AnnData containing just the metadata\n",
+    "adata_prop = sc.AnnData(\n",
+    "    X=np.zeros((1, 1)),\n",
+    "    obs=pd.DataFrame({\n",
+    "        \"cell_type\": [linear_response_dict[\"prompt_metadata_dict\"][\"cell_type\"]],\n",
+    "        \"tissue\": [linear_response_dict[\"prompt_metadata_dict\"][\"tissue\"]],\n",
+    "        \"assay\": [linear_response_dict[\"assay\"]],\n",
+    "        \"suspension_type\": [linear_response_dict[\"suspension_type\"]],\n",
+    "        \"total_mrna_umis\": [linear_response_dict[\"total_mrna_umis\"]],\n",
+    "        \"disease\": \"normal\",\n",
+    "        \"development_stage\": \"unknown\",\n",
+    "        \"sex\": \"male\",\n",
+    "    })\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata_prop.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_info_tsv_path = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "min_r_squared = 0.25\n",
+    "\n",
+    "response_qp = linear_response_dict[\"slope_qp\"].copy()\n",
+    "response_qp[linear_response_dict[\"r_squared_qp\"] < min_r_squared] = 0.\n",
+    "\n",
+    "query_marginal_mean_q = linear_response_dict[\"gene_marginal_mean_q\"]\n",
+    "query_marginal_std_q = linear_response_dict[\"gene_marginal_std_q\"]\n",
+    "\n",
+    "# note: prompt and query genes are the same in these experiments\n",
+    "prompt_marginal_mean_p = linear_response_dict[\"gene_marginal_mean_q\"]\n",
+    "prompt_marginal_std_p = linear_response_dict[\"gene_marginal_std_q\"]\n",
+    "\n",
+    "network_ctx = GeneNetworkAnalysisBase(\n",
+    "    adata_obs=adata_prop.obs,\n",
+    "    gene_info_tsv_path=gene_info_tsv_path,\n",
+    "    query_var_names=linear_response_dict[\"query_gene_ids\"].tolist(),\n",
+    "    prompt_var_names=linear_response_dict[\"query_gene_ids\"].tolist(),\n",
+    "    response_qp=response_qp,\n",
+    "    prompt_marginal_mean_p=prompt_marginal_mean_p,\n",
+    "    prompt_marginal_std_p=prompt_marginal_std_p,\n",
+    "    query_marginal_mean_q=query_marginal_mean_q,\n",
+    "    query_marginal_std_q=query_marginal_std_q,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.process(\n",
+    "    response_normalization_strategy=\"mean\",\n",
+    "    feature_normalization_strategy=\"z_score\",\n",
+    "    feature_max_value=None,\n",
+    "    query_response_amp_min_pct=None,\n",
+    "    min_prompt_gene_tpm=0.1,\n",
+    "    min_query_gene_tpm=0.1,\n",
+    "    norm_pseudo_count=1e-3,\n",
+    "    query_hv_top_k=None,\n",
+    "    z_trans_func=None,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_adjacency_matrix(\n",
+    "    adjacency_strategy=\"positive_correlation\",\n",
+    "    n_neighbors=50,\n",
+    "    beta=6.,\n",
+    "    self_loop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['TTN']]\n",
+    "inds = np.argsort(network_ctx.a_pp[i, :])[::-1]\n",
+    "for j in inds[:100]:\n",
+    "    print(network_ctx.prompt_gene_symbols[j], network_ctx.a_pp[i, j])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.compute_leiden_communites(\n",
+    "    resolution=5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(np.unique(network_ctx.leiden_membership))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymde\n",
+    "\n",
+    "network_ctx.make_mde_embedding(\n",
+    "    n_neighbors=20,\n",
+    "    repulsive_penalty=pymde.penalties.Log,\n",
+    "    max_iter=1000,\n",
+    "    init=\"quadratic\",\n",
+    "    device=\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_gene_familities(network_ctx: GeneNetworkAnalysisBase, prefix_list: list[str]) -> tuple[list[str], list[str]]:\n",
+    "    _gene_symbols = [gene_symbol for prefix in prefix_list for gene_symbol in network_ctx.prompt_gene_symbols if gene_symbol.startswith(prefix)]\n",
+    "    gene_ids = [network_ctx.gene_symbol_to_gene_id_map[gene_symbol] for gene_symbol in _gene_symbols]\n",
+    "    gene_symbols = [network_ctx.gene_id_to_gene_symbol_map[gene_id] for gene_id in gene_ids]\n",
+    "    return gene_ids, gene_symbols\n",
+    "\n",
+    "mito_gene_ids, mito_gene_symbols = get_gene_familities(network_ctx, [\"MT-\"])\n",
+    "ribo_gene_ids, ribo_gene_symbols = get_gene_familities(network_ctx, [\"RPS\", \"RPL\"])\n",
+    "hla_gene_ids, hla_gene_symbols = get_gene_familities(network_ctx, [\"HLA\"])\n",
+    "ifi_gene_ids, ifi_gene_symbols = get_gene_familities(network_ctx, [\"IFI\"])\n",
+    "trav_gene_ids, trav_gene_symbols = get_gene_familities(network_ctx, [\"TRAV\"])\n",
+    "hb_gene_ids, hb_gene_symbols = get_gene_familities(network_ctx, [\"HBA\", \"HBB\"])\n",
+    "\n",
+    "snap_n_gene_symbols = [\n",
+    "    'GAP43',\n",
+    "    'NRXN3',\n",
+    "    'HOMER1',\n",
+    "    'IL1RAPL2',\n",
+    "    'EPHA3',\n",
+    "    'RIMS1',\n",
+    "    'SV2B',\n",
+    "    'TRIM9',\n",
+    "    'SVOP',\n",
+    "    'RPH3A',\n",
+    "    'SYT12',\n",
+    "    'SYT1',\n",
+    "    'R3HDM2',\n",
+    "    'PDE4B',\n",
+    "    'DCC',\n",
+    "    'SLC4A10',\n",
+    "    'DNM3',\n",
+    "    'GRM1',\n",
+    "    'EGR4',\n",
+    "    'JUNB',\n",
+    "    'TFDP2'\n",
+    "]\n",
+    "\n",
+    "snap_n_gene_symbols = [x for x in snap_n_gene_symbols if x in network_ctx.prompt_gene_symbols]\n",
+    "snap_n_gene_ids = [network_ctx.gene_symbol_to_gene_id_map[x] for x in snap_n_gene_symbols]\n",
+    "\n",
+    "highlight_gene_sets = {\n",
+    "    \"Mito\": (mito_gene_ids, mito_gene_symbols, 'red'),\n",
+    "    \"Ribo\": (ribo_gene_ids, ribo_gene_symbols, 'blue'),\n",
+    "    \"HLA\": (hla_gene_ids, hla_gene_symbols, 'green'),\n",
+    "    \"IFI\": (ifi_gene_ids, ifi_gene_symbols, 'orange'),\n",
+    "    \"TRAV\": (trav_gene_ids, trav_gene_symbols, 'purple'),\n",
+    "    \"HB\": (hb_gene_ids, hb_gene_symbols, 'black'),\n",
+    "    \"TTN\": ([network_ctx.gene_symbol_to_gene_id_map['TTN']], ['TTN'], 'black'),\n",
+    "    # \"SNAP-N\": (snap_n_gene_ids, snap_n_gene_symbols, 'brown'),\n",
+    "}\n",
+    "\n",
+    "# disable\n",
+    "# highlight_gene_sets = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "network_ctx.plot_mde_embedding(highlight_gene_sets=highlight_gene_sets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
new file mode 100644
index 00000000..f3e4659f
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
@@ -0,0 +1,24498 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pickle\n",
+    "import logging\n",
+    "import typing as t\n",
+    "import pandas as pd\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from smart_open import open\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.utils import \\\n",
+    "    build_nx_graph_from_owl_ontology, \\\n",
+    "    get_all_ancestors\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.build_resources import \\\n",
+    "    build_ontology_ancestor_dictionary, \\\n",
+    "    build_benchmarking_ontology_dictionary_resource\n",
+    "\n",
+    "logging.basicConfig(level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "ONTOLOGY_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"ontology\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of\n",
+    "\n",
+    "def build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology: owlready2.Ontology,\n",
+    "    owl_classes: t.List[owlready2.ThingClass],\n",
+    "    prefix: str,\n",
+    "    extra_nodes: t.Optional[t.List[str]] = None,\n",
+    ") -> nx.DiGraph:\n",
+    "\n",
+    "    owl_graph = nx.DiGraph(name=\"OWL graph\")\n",
+    "\n",
+    "    if extra_nodes is not None:\n",
+    "        for node in extra_nodes:\n",
+    "            owl_graph.add_node(node)\n",
+    "\n",
+    "    names_set = set(_class.name.replace(\"_\", \":\") for _class in owl_classes)\n",
+    "    classes_set = set(owl_classes)\n",
+    "\n",
+    "    for _class in owl_classes:\n",
+    "        owl_graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    for self_class in owl_classes:\n",
+    "        # parents\n",
+    "        for parent_class in owl_ontology.get_parents_of(self_class):\n",
+    "            if parent_class not in classes_set:\n",
+    "                continue\n",
+    "            owl_graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "        # children\n",
+    "        for child_class in owl_ontology.get_children_of(self_class):\n",
+    "            if child_class not in classes_set:\n",
+    "                continue\n",
+    "            owl_graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "        # part of\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if PARTOF_RELATIONSHIP in prop.name:\n",
+    "                for related_term in prop[self_class]:\n",
+    "                    if related_term.name.startswith(prefix):\n",
+    "                        if related_term.name.replace(\"_\", \":\") not in names_set:\n",
+    "                            continue\n",
+    "                        owl_graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "        # deprecated terms (WHY???!!)\n",
+    "        if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "            for prop in self_class.get_class_properties():\n",
+    "                if \"consider\" in prop.name:\n",
+    "                    for substitute in prop[self_class]:\n",
+    "                        substitute = str(substitute)\n",
+    "                        if substitute.startswith(prefix):\n",
+    "                            if substitute.replace(\"_\", \":\") not in names_set:\n",
+    "                                continue\n",
+    "                            owl_graph.add_edge(substitute.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    return owl_graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cell ontology resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cl_ontology_owl_file_url = \"https://github.com/obophenotype/cell-ontology/releases/download/v2024-01-04/cl.owl\"\n",
+    "cl_datastore_terms_csv = os.path.join(ONTOLOGY_ROOT_PATH, \"datastore_cl_terms.csv\")\n",
+    "\n",
+    "cl_prefix = \"CL_\"\n",
+    "n_hops = 4\n",
+    "\n",
+    "cl_propagation_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"cl_propagation_resource.pkl\")\n",
+    "cl_benchmarking_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"cl_benchmarking_resource.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of CL terms in datastore: 804\n"
+     ]
+    }
+   ],
+   "source": [
+    "cl_datastore_terms_df = pd.read_csv(cl_datastore_terms_csv)\n",
+    "cl_datastore_terms_set = set(cl_datastore_terms_df['cell_type_ontology_term_id'].values)\n",
+    "print(f\"Number of CL terms in datastore: {len(cl_datastore_terms_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading ontology ...\n",
+      "INFO:root:All classes: 2914\n",
+      "INFO:root:Building nx graph from OWL ontology...\n",
+      "INFO:root:Filtered classes: 1026\n",
+      "INFO:root:Building nx graph from OWL ontology...\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Loading ontology ...\")\n",
+    "\n",
+    "owl_ontology = owlready2.get_ontology(cl_ontology_owl_file_url).load()\n",
+    "\n",
+    "# get all classes first\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_ontology.classes()\n",
+    "    if _class.name.startswith(cl_prefix)\n",
+    "    if len(_class.label) == 1\n",
+    "]\n",
+    "\n",
+    "logging.info(f\"All classes: {len(owl_classes)}\")\n",
+    "\n",
+    "# build the full graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology(owl_ontology=owl_ontology, owl_classes=owl_classes)\n",
+    "\n",
+    "# get all ancestors for datastore nodes\n",
+    "filtered_owl_names_set = set()\n",
+    "for owl_name in cl_datastore_terms_set:\n",
+    "    filtered_owl_names_set.update(get_all_ancestors(owl_graph, owl_name))\n",
+    "\n",
+    "logging.info(f\"Filtered classes: {len(filtered_owl_names_set)}\")\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_classes\n",
+    "    if _class.name.replace(\"_\", \":\") in filtered_owl_names_set\n",
+    "]\n",
+    "\n",
+    "# build the filtered graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology(owl_ontology=owl_ontology, owl_classes=owl_classes)\n",
+    "\n",
+    "owl_names = [_class.name.replace(\"_\", \":\") for _class in owl_classes]\n",
+    "owl_labels = [_class.label[0] for _class in owl_classes]\n",
+    "\n",
+    "if len(set(owl_labels)) != len(set(owl_names)):\n",
+    "    raise ValueError(\"Number of unique labels doesn't correspond to number of unique names\")\n",
+    "\n",
+    "owl_names_to_labels_map = {owl_name: owl_label for owl_name, owl_label in zip(owl_names, owl_labels)}\n",
+    "owl_names_to_idx_map = {owl_name: idx for idx, owl_name in enumerate(owl_names)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generating cell type ontology propagation resource from CL ontology...\n",
+      "INFO:root:Building cell ontology ancestor dictionary...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/cl_propagation_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Generating cell type ontology propagation resource from CL ontology...\")\n",
+    "owl_ancestors_dictionary = build_ontology_ancestor_dictionary(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    owl_names_to_idx_map=owl_names_to_idx_map\n",
+    ")\n",
+    "cell_ontology_resource = {\n",
+    "    \"ancestors_dictionary\": owl_ancestors_dictionary,\n",
+    "    \"ontology_term_id_to_label\": owl_names_to_labels_map,\n",
+    "}\n",
+    "\n",
+    "logging.info(f\"Writing output file to {cl_propagation_resource_file_path}\")\n",
+    "with open(cl_propagation_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(cell_ontology_resource, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generating cell type ontology benchmarking resource from CL ontology...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/cl_benchmarking_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Generating cell type ontology benchmarking resource from CL ontology...\")\n",
+    "ontology_resource_dict = build_benchmarking_ontology_dictionary_resource(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    n_hops=n_hops\n",
+    ")\n",
+    "\n",
+    "logging.info(f\"Writing output file to {cl_benchmarking_resource_file_path}\")\n",
+    "\n",
+    "with open(cl_benchmarking_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(ontology_resource_dict, output_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Development stage resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hsapdv_ontology_owl_file_url = \"http://purl.obolibrary.org/obo/hsapdv.owl\"\n",
+    "hsapdv_datastore_terms_csv = os.path.join(ONTOLOGY_ROOT_PATH, \"datastore_hsapdv_terms.csv\")\n",
+    "\n",
+    "hsapdv_prefix = \"HsapDv_\"\n",
+    "n_hops = 4\n",
+    "\n",
+    "hsapdv_propagation_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"hsapdv_propagation_resource.pkl\")\n",
+    "hsapdv_benchmarking_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"hsapdv_benchmarking_resource.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of HsapDv terms in datastore: 175\n"
+     ]
+    }
+   ],
+   "source": [
+    "hsapdv_datastore_terms_df = pd.read_csv(hsapdv_datastore_terms_csv)\n",
+    "hsapdv_datastore_terms_set = set(hsapdv_datastore_terms_df['development_stage_ontology_term_id'].values)\n",
+    "print(f\"Number of HsapDv terms in datastore: {len(hsapdv_datastore_terms_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading ontology ...\n",
+      "INFO:root:All classes: 260\n",
+      "INFO:root:Filtered classes: 194\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not find UBERON:0018241 in ontology, ignoring.\n",
+      "Could not find UBERON:0000113 in ontology, ignoring.\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Loading ontology ...\")\n",
+    "\n",
+    "owl_ontology = owlready2.get_ontology(hsapdv_ontology_owl_file_url).load()\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_ontology.classes()\n",
+    "    if _class.name.startswith(hsapdv_prefix)\n",
+    "    if len(_class.label) == 1\n",
+    "]\n",
+    "\n",
+    "logging.info(f\"All classes: {len(owl_classes)}\")\n",
+    "\n",
+    "# build the full graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=hsapdv_prefix)\n",
+    "\n",
+    "# get all ancestors for datastore nodes\n",
+    "filtered_owl_names_set = set()\n",
+    "for owl_name in hsapdv_datastore_terms_set:\n",
+    "    try:\n",
+    "        filtered_owl_names_set.update(get_all_ancestors(owl_graph, owl_name))\n",
+    "    except:\n",
+    "        print(f\"Could not find {owl_name} in ontology, ignoring.\")\n",
+    "\n",
+    "logging.info(f\"Filtered classes: {len(filtered_owl_names_set)}\")\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_classes\n",
+    "    if _class.name.replace(\"_\", \":\") in filtered_owl_names_set\n",
+    "]\n",
+    "\n",
+    "# build the filtered graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=hsapdv_prefix)\n",
+    "\n",
+    "owl_names = [_class.name.replace(\"_\", \":\") for _class in owl_classes]\n",
+    "owl_labels = [_class.label[0] for _class in owl_classes]\n",
+    "\n",
+    "# assert that names are unique, labels do not have to be unique\n",
+    "assert len(set(owl_names)) == len(owl_classes)\n",
+    "\n",
+    "owl_names_to_labels_map = {name: label for name, label in zip(owl_names, owl_labels)}\n",
+    "owl_names_to_idx_map = {name: idx for idx, name in enumerate(owl_names)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Building cell ontology ancestor dictionary...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/hsapdv_propagation_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "owl_ancestors_dictionary = build_ontology_ancestor_dictionary(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    owl_names_to_idx_map=owl_names_to_idx_map\n",
+    ")\n",
+    "hsapdv_ontology_resource = {\n",
+    "    \"ancestors_dictionary\": owl_ancestors_dictionary,\n",
+    "    \"ontology_term_id_to_label\": owl_names_to_labels_map,\n",
+    "}\n",
+    "\n",
+    "logging.info(f\"Writing output file to {hsapdv_propagation_resource_file_path}\")\n",
+    "with open(hsapdv_propagation_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(hsapdv_ontology_resource, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generating ontology benchmarking resource from HsapDv ontology...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/hsapdv_benchmarking_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Generating ontology benchmarking resource from HsapDv ontology...\")\n",
+    "ontology_resource_dict = build_benchmarking_ontology_dictionary_resource(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    n_hops=n_hops\n",
+    ")\n",
+    "\n",
+    "logging.info(f\"Writing output file to {hsapdv_benchmarking_resource_file_path}\")\n",
+    "\n",
+    "with open(hsapdv_benchmarking_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(ontology_resource_dict, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "life cycle stage\n",
+      "life cycle\n",
+      "\t life cycle stage\n",
+      "embryonic stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "prenatal stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "organogenesis stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "Carnegie stage 12\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 13\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 14\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 16\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 17\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 18\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 19\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 20\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 21\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 22\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "Carnegie stage 23\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t embryonic stage\n",
+      "\t prenatal stage\n",
+      "\t organogenesis stage\n",
+      "fetal stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "9th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t third LMP month stage\n",
+      "third LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "10th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t third LMP month stage\n",
+      "11th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t third LMP month stage\n",
+      "12th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fourth LMP month stage\n",
+      "fourth LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "13th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fourth LMP month stage\n",
+      "14th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fourth LMP month stage\n",
+      "15th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fourth LMP month stage\n",
+      "16th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fifth LMP month stage\n",
+      "fifth LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "17th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fifth LMP month stage\n",
+      "18th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fifth LMP month stage\n",
+      "19th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t fifth LMP month stage\n",
+      "20th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t sixth LMP month stage\n",
+      "sixth LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "21st week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t sixth LMP month stage\n",
+      "22nd week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t sixth LMP month stage\n",
+      "23rd week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t sixth LMP month stage\n",
+      "25th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t seventh LMP month stage\n",
+      "seventh LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "26th week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t seventh LMP month stage\n",
+      "eighth LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "31st week post-fertilization stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "\t eighth LMP month stage\n",
+      "ninth LMP month stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prenatal stage\n",
+      "\t fetal stage\n",
+      "immature stage\n",
+      "pediatric stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t postnatal stage\n",
+      "child stage\n",
+      "child stage (1-4 yo)\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t postnatal stage\n",
+      "newborn stage\n",
+      "infant stage\n",
+      "infant stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "2-5 year-old child stage\n",
+      "5-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "2-4 year-old child stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t postnatal stage\n",
+      "6-12 year-old child stage\n",
+      "juvenile stage (5-14 yo)\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t postnatal stage\n",
+      "adolescent stage\n",
+      "15-19 year-old\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "adult stage\n",
+      "prime adult stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t postnatal stage\n",
+      "adult stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t postnatal stage\n",
+      "early adulthood stage\n",
+      "young adult stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t postnatal stage\n",
+      "middle aged stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t postnatal stage\n",
+      "young adult stage\n",
+      "25-44 year-old stage\n",
+      "third decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "fourth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "fifth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t postnatal stage\n",
+      "late adulthood stage\n",
+      "late adult stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t postnatal stage\n",
+      "middle aged stage\n",
+      "aged stage\n",
+      "65-79 year-old stage\n",
+      "60-79 year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t postnatal stage\n",
+      "80 year-old and over stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t postnatal stage\n",
+      "79-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "2-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 2-4 year-old child stage\n",
+      "\t postnatal stage\n",
+      "3-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 2-4 year-old child stage\n",
+      "\t postnatal stage\n",
+      "4-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 2-4 year-old child stage\n",
+      "\t postnatal stage\n",
+      "6-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "7-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "8-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "9-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "10-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "11-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "12-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "13-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "14-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t juvenile stage (5-14 yo)\n",
+      "\t postnatal stage\n",
+      "15-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t 15-19 year-old\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "16-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t 15-19 year-old\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "17-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t 15-19 year-old\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "18-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t 15-19 year-old\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "19-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t 15-19 year-old\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t postnatal stage\n",
+      "20-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "21-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "22-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "23-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "24-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "25-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "26-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "27-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "28-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "29-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t third decade stage\n",
+      "\t postnatal stage\n",
+      "30-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "31-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "32-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "33-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "34-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "35-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "36-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "37-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "38-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "39-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t young adult stage\n",
+      "\t fourth decade stage\n",
+      "\t postnatal stage\n",
+      "40-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "41-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "42-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "43-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "44-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "45-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "46-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "47-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "48-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "49-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t fifth decade stage\n",
+      "\t postnatal stage\n",
+      "50-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "sixth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t postnatal stage\n",
+      "51-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "52-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "53-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "54-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "55-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "56-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "57-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "58-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "59-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t prime adult stage\n",
+      "\t adult stage\n",
+      "\t middle aged stage\n",
+      "\t sixth decade stage\n",
+      "\t postnatal stage\n",
+      "60-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "seventh decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t postnatal stage\n",
+      "61-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "62-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "63-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "64-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "65-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "66-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "67-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "68-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "69-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t seventh decade stage\n",
+      "\t postnatal stage\n",
+      "70-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "eighth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t postnatal stage\n",
+      "71-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "72-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "73-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "74-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "75-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "76-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "77-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "78-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 60-79 year-old stage\n",
+      "\t eighth decade stage\n",
+      "\t postnatal stage\n",
+      "1-month-old stage\n",
+      "2-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "3-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "4-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "5-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "6-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "7-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "8-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "9-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "10-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "11-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t infant stage\n",
+      "\t nursing stage (0-11 months)\n",
+      "\t postnatal stage\n",
+      "12-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 1-year-old stage\n",
+      "\t postnatal stage\n",
+      "1-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t postnatal stage\n",
+      "14-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 1-year-old stage\n",
+      "\t postnatal stage\n",
+      "19-month-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t child stage (1-4 yo)\n",
+      "\t 1-year-old stage\n",
+      "\t postnatal stage\n",
+      "mature stage\n",
+      "80-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "ninth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "81-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "82-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "83-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "84-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "85-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "86-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "87-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "88-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "89-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t ninth decade stage\n",
+      "\t postnatal stage\n",
+      "90-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "tenth decade stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "91-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "92-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "93-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "94-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "95-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "96-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "97-year-old stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t tenth decade stage\n",
+      "\t 90 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "nursing stage (0-11 months)\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t pediatric stage\n",
+      "\t postnatal stage\n",
+      "90 year-old and over stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n",
+      "\t adult stage\n",
+      "\t late adult stage\n",
+      "\t 80 year-old and over stage\n",
+      "\t postnatal stage\n",
+      "under-1-year-old stage\n",
+      "postnatal stage\n",
+      "\t life cycle stage\n",
+      "\t life cycle\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_print = 2000\n",
+    "\n",
+    "i_print = 0\n",
+    "for k, v in hsapdv_ontology_resource[\"ancestors_dictionary\"].items():\n",
+    "    print(hsapdv_ontology_resource[\"ontology_term_id_to_label\"][k])\n",
+    "    for v_ in v:\n",
+    "        print(\"\\t\", hsapdv_ontology_resource[\"ontology_term_id_to_label\"][v_])\n",
+    "    i_print += 1\n",
+    "    if i_print == n_print:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Disease resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mondo_ontology_owl_file_url = \"https://github.com/monarch-initiative/mondo/releases/download/v2024-01-03/mondo.owl\"\n",
+    "mondo_datastore_terms_csv = os.path.join(ONTOLOGY_ROOT_PATH, \"datastore_mondo_terms.csv\")\n",
+    "\n",
+    "mondo_prefix = \"MONDO_\"\n",
+    "n_hops = 4\n",
+    "\n",
+    "mondo_propagation_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"mondo_propagation_resource.pkl\")\n",
+    "mondo_benchmarking_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"mondo_benchmarking_resource.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of MONDO terms in datastore: 110\n"
+     ]
+    }
+   ],
+   "source": [
+    "mondo_datastore_terms_df = pd.read_csv(mondo_datastore_terms_csv)\n",
+    "mondo_datastore_terms_set = set(mondo_datastore_terms_df['disease_ontology_term_id'].values)\n",
+    "print(f\"Number of MONDO terms in datastore: {len(mondo_datastore_terms_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading ontology ...\n",
+      "INFO:root:All classes: 27656\n",
+      "INFO:root:Filtered classes: 397\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Loading ontology ...\")\n",
+    "\n",
+    "owl_ontology = owlready2.get_ontology(mondo_ontology_owl_file_url).load()\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_ontology.classes()\n",
+    "    if _class.name.startswith(mondo_prefix)\n",
+    "    if len(_class.label) == 1\n",
+    "]\n",
+    "\n",
+    "logging.info(f\"All classes: {len(owl_classes)}\")\n",
+    "\n",
+    "# build the full graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=mondo_prefix,\n",
+    "    extra_nodes=[\"PATO:0000461\"] # normal\n",
+    ")\n",
+    "\n",
+    "# get all ancestors for datastore nodes\n",
+    "filtered_owl_names_set = set()\n",
+    "for owl_name in mondo_datastore_terms_set:\n",
+    "    try:\n",
+    "        filtered_owl_names_set.update(get_all_ancestors(owl_graph, owl_name))\n",
+    "    except:\n",
+    "        print(f\"Could not find {owl_name} in ontology, ignoring.\")\n",
+    "\n",
+    "logging.info(f\"Filtered classes: {len(filtered_owl_names_set)}\")\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_classes\n",
+    "    if _class.name.replace(\"_\", \":\") in filtered_owl_names_set\n",
+    "]\n",
+    "\n",
+    "# build the filtered graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=mondo_prefix,\n",
+    "    extra_nodes=[\"PATO:0000461\"] # normal\n",
+    ")\n",
+    "\n",
+    "owl_names = [\"PATO:0000461\"] + [_class.name.replace(\"_\", \":\") for _class in owl_classes]\n",
+    "owl_labels = [\"normal\"] + [_class.label[0] for _class in owl_classes]\n",
+    "\n",
+    "owl_names_to_labels_map = {name: label for name, label in zip(owl_names, owl_labels)}\n",
+    "owl_names_to_idx_map = {name: idx for idx, name in enumerate(owl_names)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Building cell ontology ancestor dictionary...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/mondo_propagation_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "owl_ancestors_dictionary = build_ontology_ancestor_dictionary(\n",
+    "    owl_graph=owl_graph, owl_names=owl_names, owl_names_to_idx_map=owl_names_to_idx_map\n",
+    ")\n",
+    "mondo_ontology_resource = {\n",
+    "    \"ancestors_dictionary\": owl_ancestors_dictionary,\n",
+    "    \"ontology_term_id_to_label\": owl_names_to_labels_map,\n",
+    "}\n",
+    "\n",
+    "logging.info(f\"Writing output file to {mondo_propagation_resource_file_path}\")\n",
+    "with open(mondo_propagation_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(mondo_ontology_resource, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generating ontology benchmarking resource from MONDO ontology...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/mondo_benchmarking_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Generating ontology benchmarking resource from MONDO ontology...\")\n",
+    "ontology_resource_dict = build_benchmarking_ontology_dictionary_resource(\n",
+    "    owl_graph=owl_graph, owl_names=owl_names, n_hops=n_hops\n",
+    ")\n",
+    "\n",
+    "logging.info(f\"Writing output file to {mondo_benchmarking_resource_file_path}\")\n",
+    "\n",
+    "with open(mondo_benchmarking_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(ontology_resource_dict, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "normal\n",
+      "disease\n",
+      "injury\n",
+      "hereditary disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "familial partial epilepsy\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t brain disorder\n",
+      "\t monogenic epilepsy\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t focal epilepsy\n",
+      "\t epilepsy syndrome\n",
+      "\t childhood-onset epilepsy syndrome\n",
+      "\t adolescent-onset epilepsy syndrome\n",
+      "motor neuron disorder\n",
+      "\t disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t neuromuscular disease\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "reproductive system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "congenital nervous system disorder\n",
+      "\t disease\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "disorder of development or morphogenesis\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "otorhinolaryngologic disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "congenital heart disease\n",
+      "\t disease\n",
+      "\t disorder of development or morphogenesis\n",
+      "\t heart disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "\t congenital anomaly of cardiovascular system\n",
+      "metabolic disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "autosomal recessive disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t autosomal genetic disease\n",
+      "\t human disease\n",
+      "telomere syndrome\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t premature aging syndrome\n",
+      "brain disorder\n",
+      "\t disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "hereditary neoplastic syndrome\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t syndromic disease\n",
+      "\t human disease\n",
+      "\t neoplastic syndrome\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "chromosomal disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "pulmonary fibrosis\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "\t interstitial lung disease\n",
+      "skeletal system disorder\n",
+      "\t disease\n",
+      "\t musculoskeletal system disorder\n",
+      "\t human disease\n",
+      "inborn errors of metabolism\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t metabolic disease\n",
+      "\t human disease\n",
+      "bone marrow disorder\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "acute disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "post-infectious disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "inherited neurodegenerative disorder\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "cataract\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t lens disorder\n",
+      "\t disorder of visual system\n",
+      "\t human disease\n",
+      "integumentary system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "syndromic disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "monogenic epilepsy\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t brain disorder\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "hereditary motor neuron disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t motor neuron disorder\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t neuromuscular disease\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "autoimmune disease\n",
+      "\t disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "leukocyte disorder\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "infectious disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "cardiomyopathy\n",
+      "\t disease\n",
+      "\t heart disorder\n",
+      "\t muscle tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "gastroenteritis\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "aspiration pneumonia\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "lung disorder\n",
+      "\t disease\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "pneumonia\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t respiratory tract infectious disorder\n",
+      "\t human disease\n",
+      "\t pneumonitis\n",
+      "lower respiratory tract disorder\n",
+      "\t disease\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "respiratory system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "viral infectious disease\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t human disease\n",
+      "liver disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "skin disorder\n",
+      "\t disease\n",
+      "\t integumentary system disorder\n",
+      "\t human disease\n",
+      "lymph node disorder\n",
+      "\t disease\n",
+      "\t immune system disorder\n",
+      "\t lymphatic system disorder\n",
+      "\t lymphoid system disorder\n",
+      "\t human disease\n",
+      "respiratory system cancer\n",
+      "\t disease\n",
+      "\t respiratory system disorder\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "cancer\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "benign neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "benign urinary system neoplasm\n",
+      "\t disease\n",
+      "\t benign neoplasm\n",
+      "\t neoplasm\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "digestive system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "digestive system neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neuroendocrine neoplasm\n",
+      "\t disease\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "macular degeneration\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "small cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neuroendocrine neoplasm\n",
+      "\t neuroendocrine carcinoma\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t human disease\n",
+      "\t malignant endocrine neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neuroendocrine carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neuroendocrine neoplasm\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t human disease\n",
+      "\t malignant endocrine neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "intestinal cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t digestive system cancer\n",
+      "\t human disease\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "large intestine disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "inflammatory disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "connective tissue disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "epilepsy\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "autosomal genetic disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t human disease\n",
+      "B-cell neoplasm\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "glioblastoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t central nervous system disorder\n",
+      "\t neoplasm\n",
+      "\t central nervous system neoplasm\n",
+      "\t glioma\n",
+      "\t central nervous system cancer\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system cancer\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t malignant glioma\n",
+      "\t high grade malignant neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "\t high grade astrocytic tumor\n",
+      "disorder of orbital region\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "heart disorder\n",
+      "\t disease\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "vascular disorder\n",
+      "\t disease\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "adenoma\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "diabetes mellitus\n",
+      "\t disease\n",
+      "\t metabolic disease\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t glucose metabolism disease\n",
+      "\t pancreas disorder\n",
+      "\t endocrine pancreas disorder\n",
+      "\t human disease\n",
+      "kidney failure\n",
+      "\t disease\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "squamous cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t squamous cell neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "tongue cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t sensory system cancer\n",
+      "\t nervous system cancer\n",
+      "\t mouth disorder\n",
+      "\t digestive system cancer\n",
+      "\t head and neck cancer\n",
+      "\t tongue disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t head and neck neoplasm\n",
+      "\t oral cavity cancer\n",
+      "\t tongue neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t oral cavity neoplasm\n",
+      "glandular cell neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lung adenocarcinoma\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t lung carcinoma\n",
+      "\t respiratory tract neoplasm\n",
+      "\t non-small cell lung carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "frontotemporal dementia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t psychiatric disorder\n",
+      "\t cognitive disorder\n",
+      "\t dementia\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t hereditary dementia\n",
+      "synucleinopathy\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t metabolic disease\n",
+      "\t inborn errors of metabolism\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t neurodegenerative disease\n",
+      "\t proteostasis deficiencies\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "neurodegenerative disease\n",
+      "\t disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "proteostasis deficiencies\n",
+      "\t disease\n",
+      "\t metabolic disease\n",
+      "\t human disease\n",
+      "bone cancer\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t neoplasm\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t human disease\n",
+      "\t connective tissue neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "colon adenoma\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t adenoma\n",
+      "\t colorectal adenoma\n",
+      "\t epithelial tumor of colon\n",
+      "\t neoplasm\n",
+      "\t colonic disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t colonic neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t digestive system adenoma\n",
+      "colorectal adenoma\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t adenoma\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t digestive system adenoma\n",
+      "epithelial tumor of colon\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t neoplasm\n",
+      "\t colonic disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t colonic neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "adenocarcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "muscle tissue disorder\n",
+      "\t disease\n",
+      "\t musculoskeletal system disorder\n",
+      "\t human disease\n",
+      "autoimmune disorder of central nervous system\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t central nervous system disorder\n",
+      "\t autoimmune disorder of the nervous system\n",
+      "\t immune system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "central nervous system disorder\n",
+      "\t disease\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "autoimmune disorder of the nervous system\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t immune system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "autoimmune disorder of endocrine system\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t endocrine system disorder\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "endocrine system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "B cell deficiency\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t leukocyte disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t inborn error of immunity\n",
+      "\t human disease\n",
+      "\t immunodeficiency disease\n",
+      "\t immune deficiency disease\n",
+      "agammaglobulinemia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t leukocyte disorder\n",
+      "\t B cell deficiency\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t inborn error of immunity\n",
+      "\t human disease\n",
+      "\t immunodeficiency disease\n",
+      "\t immune deficiency disease\n",
+      "autoimmune disorder of gastrointestinal tract\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t digestive system disorder\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "musculoskeletal system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "intrinsic cardiomyopathy\n",
+      "\t disease\n",
+      "\t cardiomyopathy\n",
+      "\t heart disorder\n",
+      "\t muscle tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "psychiatric disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "hematologic disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "cardiovascular disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "hypersensitivity reaction disease\n",
+      "\t disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "immune system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "neoplasm\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "precancerous condition\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "immune system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lymphatic system disorder\n",
+      "\t disease\n",
+      "\t immune system disorder\n",
+      "\t lymphoid system disorder\n",
+      "\t human disease\n",
+      "breast cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "breast carcinoma by gene expression profile\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "progesterone-receptor negative breast cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "Her2-receptor negative breast cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "breast neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "male reproductive system disorder\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t human disease\n",
+      "endocrine gland neoplasm\n",
+      "\t disease\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "central nervous system neoplasm\n",
+      "\t disease\n",
+      "\t central nervous system disorder\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "bone neoplasm\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t connective tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t neoplasm\n",
+      "\t bone disorder\n",
+      "\t human disease\n",
+      "\t connective tissue neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neoplasm of thorax\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "musculoskeletal system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "glioma\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "central nervous system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t central nervous system disorder\n",
+      "\t neoplasm\n",
+      "\t central nervous system neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system cancer\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "nervous system neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "sensory system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system cancer\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "nervous system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "connective and soft tissue neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neuromuscular disease\n",
+      "\t disease\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "perceptual disorders\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "Crohn disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t digestive system disorder\n",
+      "\t immune system disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t inflammatory bowel disease\n",
+      "Crohn ileitis\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t gastroenteritis\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t immune system disorder\n",
+      "\t Crohn disease\n",
+      "\t small bowel Crohn disease\n",
+      "\t intestinal disorder\n",
+      "\t small intestine disorder\n",
+      "\t human disease\n",
+      "\t enteritis\n",
+      "\t inflammatory bowel disease\n",
+      "small bowel Crohn disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t gastroenteritis\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t immune system disorder\n",
+      "\t Crohn disease\n",
+      "\t intestinal disorder\n",
+      "\t small intestine disorder\n",
+      "\t human disease\n",
+      "\t enteritis\n",
+      "\t inflammatory bowel disease\n",
+      "gastritis\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t stomach disorder\n",
+      "\t human disease\n",
+      "idiopathic disease\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "eye disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t disorder of visual system\n",
+      "\t human disease\n",
+      "upper respiratory tract disorder\n",
+      "\t disease\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "mouth disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "post-viral disorder\n",
+      "\t disease\n",
+      "\t post-infectious disorder\n",
+      "\t human disease\n",
+      "respiratory tract infectious disorder\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "breast disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "tooth disorder\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t mouth disorder\n",
+      "\t human disease\n",
+      "polyp\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "female reproductive system disorder\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t human disease\n",
+      "allergic respiratory disease\n",
+      "\t disease\n",
+      "\t respiratory system disorder\n",
+      "\t hypersensitivity reaction disease\n",
+      "\t immune system disorder\n",
+      "\t allergic disease\n",
+      "\t human disease\n",
+      "allergic disease\n",
+      "\t disease\n",
+      "\t hypersensitivity reaction disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "anencephaly\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t congenital nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "Parkinson disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t brain disorder\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t parkinsonian disorder\n",
+      "\t human disease\n",
+      "\t basal ganglia disorder\n",
+      "bone disorder\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t human disease\n",
+      "colonic disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t large intestine disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "acute lymphoblastic leukemia\n",
+      "\t disease\n",
+      "\t acute disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t precursor lymphoblastic lymphoma/leukemia\n",
+      "\t non-Hodgkin lymphoma\n",
+      "\t lymphoma\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t acute leukemia\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t lymphoid leukemia\n",
+      "\t neoplasm of immature B and T cells\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "precursor lymphoblastic lymphoma/leukemia\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "non-Hodgkin lymphoma\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "mucositis\n",
+      "\t disease\n",
+      "\t inflammatory disease\n",
+      "\t human disease\n",
+      "pulmonary emphysema\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t upper respiratory tract disorder\n",
+      "\t tracheal disorder\n",
+      "\t human disease\n",
+      "\t obstructive lung disease\n",
+      "\t chronic obstructive pulmonary disease\n",
+      "lymphoma\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "type 2 diabetes mellitus\n",
+      "\t disease\n",
+      "\t metabolic disease\n",
+      "\t digestive system disorder\n",
+      "\t diabetes mellitus\n",
+      "\t endocrine system disorder\n",
+      "\t glucose metabolism disease\n",
+      "\t pancreas disorder\n",
+      "\t endocrine pancreas disorder\n",
+      "\t human disease\n",
+      "spindle cell neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "kidney benign neoplasm\n",
+      "\t disease\n",
+      "\t benign neoplasm\n",
+      "\t benign urinary system neoplasm\n",
+      "\t neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t kidney neoplasm\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "acute myocardial infarction\n",
+      "\t disease\n",
+      "\t acute disease\n",
+      "\t heart disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "\t myocardial infarction\n",
+      "\t myocardial disorder\n",
+      "malignant urinary system neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "leukemia\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "periodontitis\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t inflammatory disease\n",
+      "\t musculoskeletal system disorder\n",
+      "\t mouth disorder\n",
+      "\t tooth disorder\n",
+      "\t periodontal disorder\n",
+      "\t human disease\n",
+      "lymphoid system disorder\n",
+      "\t disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "esophageal disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "\t upper digestive tract disorder\n",
+      "intestinal disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "gastric cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t digestive system cancer\n",
+      "\t gastric neoplasm\n",
+      "\t stomach disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "digestive system cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "gastric neoplasm\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t stomach disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "glucose metabolism disease\n",
+      "\t disease\n",
+      "\t metabolic disease\n",
+      "\t human disease\n",
+      "pancreas disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t human disease\n",
+      "kidney disorder\n",
+      "\t disease\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "chronic kidney disease\n",
+      "\t disease\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "lymphadenitis\n",
+      "\t disease\n",
+      "\t lymph node disorder\n",
+      "\t inflammatory disease\n",
+      "\t immune system disorder\n",
+      "\t lymphatic system disorder\n",
+      "\t lymphoid system disorder\n",
+      "\t human disease\n",
+      "stomach disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "head and neck cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t head and neck neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "cognitive disorder\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t human disease\n",
+      "tongue disorder\n",
+      "\t disease\n",
+      "\t mouth disorder\n",
+      "\t human disease\n",
+      "ocular vascular disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t vascular disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t human disease\n",
+      "lens disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t human disease\n",
+      "prostate disorder\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t male reproductive system disorder\n",
+      "\t human disease\n",
+      "respiratory failure\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "inborn error of immunity\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "\t immune deficiency disease\n",
+      "small intestine disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "hypersensitivity pneumonitis\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t hypersensitivity reaction disease\n",
+      "\t immune system disorder\n",
+      "\t allergic respiratory disease\n",
+      "\t allergic disease\n",
+      "\t human disease\n",
+      "\t pneumonitis\n",
+      "\t interstitial lung disease\n",
+      "gingival disorder\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t mouth disorder\n",
+      "\t tooth disorder\n",
+      "\t periodontal disorder\n",
+      "\t mouth mucosa disorder\n",
+      "\t human disease\n",
+      "optic choroid disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t vascular disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t eye disorder\n",
+      "\t ocular vascular disorder\n",
+      "\t disorder of visual system\n",
+      "\t uveal disorder\n",
+      "\t human disease\n",
+      "fallopian tube disorder\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t female reproductive system disorder\n",
+      "\t human disease\n",
+      "disorder of visual system\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "rheumatic disorder\n",
+      "\t disease\n",
+      "\t connective tissue disorder\n",
+      "\t human disease\n",
+      "thoracic cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "acute kidney failure\n",
+      "\t disease\n",
+      "\t acute disease\n",
+      "\t kidney failure\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "retinal degeneration\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "anterior horn disorder\n",
+      "\t disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t spinal cord disorder\n",
+      "retinal disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "neurovascular disorder\n",
+      "\t disease\n",
+      "\t vascular disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "viral respiratory tract infection\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t respiratory system disorder\n",
+      "\t viral infectious disease\n",
+      "\t respiratory tract infectious disorder\n",
+      "\t human disease\n",
+      "dementia\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t cognitive disorder\n",
+      "\t human disease\n",
+      "degeneration of macula and posterior pole\n",
+      "\t disease\n",
+      "\t macular degeneration\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "retinal dystrophies primarily involving Bruch's membrane\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t disorder of orbital region\n",
+      "\t vascular disorder\n",
+      "\t psychiatric disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t perceptual disorders\n",
+      "\t eye disorder\n",
+      "\t ocular vascular disorder\n",
+      "\t optic choroid disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t neurovascular disorder\n",
+      "\t inherited retinal dystrophy\n",
+      "\t uveal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "inherited retinal dystrophy\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t disorder of orbital region\n",
+      "\t psychiatric disorder\n",
+      "\t perceptual disorders\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "sarcoidosis\n",
+      "\t disease\n",
+      "\t syndromic disease\n",
+      "\t connective tissue disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "\t autoinflammatory syndrome\n",
+      "pulmonary sarcoidosis\n",
+      "\t disease\n",
+      "\t syndromic disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t connective tissue disorder\n",
+      "\t rheumatic disorder\n",
+      "\t sarcoidosis\n",
+      "\t interstitial lung disease specific to adulthood\n",
+      "\t human disease\n",
+      "\t interstitial lung disease\n",
+      "\t autoinflammatory syndrome\n",
+      "interstitial lung disease specific to adulthood\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "\t interstitial lung disease\n",
+      "bile duct disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "nasal disorder\n",
+      "\t disease\n",
+      "\t otorhinolaryngologic disease\n",
+      "\t human disease\n",
+      "endocrine pancreas disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t pancreas disorder\n",
+      "\t human disease\n",
+      "primary viral infectious disease\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t viral infectious disease\n",
+      "\t human disease\n",
+      "squamous cell neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "urinary system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "uveal disorder\n",
+      "\t disease\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t human disease\n",
+      "tracheal disorder\n",
+      "\t disease\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t upper respiratory tract disorder\n",
+      "\t human disease\n",
+      "cholangitis\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t endocrine system disorder\n",
+      "\t bile duct disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "\t non-neoplastic bile duct disorder\n",
+      "nervous system disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "parkinsonian disorder\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t basal ganglia disorder\n",
+      "benign epithelial neoplasm\n",
+      "\t disease\n",
+      "\t benign neoplasm\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "periodontal disorder\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t mouth disorder\n",
+      "\t tooth disorder\n",
+      "\t human disease\n",
+      "mouth mucosa disorder\n",
+      "\t disease\n",
+      "\t mouth disorder\n",
+      "\t human disease\n",
+      "human disease\n",
+      "\t disease\n",
+      "neoplastic syndrome\n",
+      "\t disease\n",
+      "\t syndromic disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "malignant endocrine neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "malignant pancreatic neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t digestive system cancer\n",
+      "\t pancreas disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t pancreatic neoplasm\n",
+      "colorectal neoplasm\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "mixed neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "immunodeficiency disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "epithelial neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "Wilms tumor\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t mixed neoplasm\n",
+      "\t malignant mixed neoplasm\n",
+      "\t embryonal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "nasal cavity disorder\n",
+      "\t disease\n",
+      "\t otorhinolaryngologic disease\n",
+      "\t respiratory system disorder\n",
+      "\t upper respiratory tract disorder\n",
+      "\t nasal disorder\n",
+      "\t human disease\n",
+      "obstructive lung disease\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "colonic neoplasm\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t neoplasm\n",
+      "\t colonic disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "hematopoietic and lymphoid system neoplasm\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "drug dependence\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t human disease\n",
+      "\t substance-related disorder\n",
+      "\t substance dependence\n",
+      "connective tissue neoplasm\n",
+      "\t disease\n",
+      "\t connective tissue disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "kidney neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "kidney cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t kidney neoplasm\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lung cancer\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t lung neoplasm\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "idiopathic interstitial pneumonia\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t lung disorder\n",
+      "\t pneumonia\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t idiopathic disease\n",
+      "\t respiratory tract infectious disorder\n",
+      "\t human disease\n",
+      "\t pneumonitis\n",
+      "malignant mixed neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t mixed neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "substance-related disorder\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t human disease\n",
+      "astrocytic tumor\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "astrocytoma (excluding glioblastoma)\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "gingivitis\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t inflammatory disease\n",
+      "\t musculoskeletal system disorder\n",
+      "\t mouth disorder\n",
+      "\t tooth disorder\n",
+      "\t mucositis\n",
+      "\t gingival disorder\n",
+      "\t periodontal disorder\n",
+      "\t mouth mucosa disorder\n",
+      "\t human disease\n",
+      "\t stomatitis\n",
+      "stomatitis\n",
+      "\t disease\n",
+      "\t inflammatory disease\n",
+      "\t mouth disorder\n",
+      "\t mucositis\n",
+      "\t mouth mucosa disorder\n",
+      "\t human disease\n",
+      "hepatobiliary disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "soft tissue neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t connective and soft tissue neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "malignant glioma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t central nervous system disorder\n",
+      "\t neoplasm\n",
+      "\t central nervous system neoplasm\n",
+      "\t glioma\n",
+      "\t central nervous system cancer\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system cancer\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t high grade malignant neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "spinal cord disorder\n",
+      "\t disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "low grade glioma\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "demyelinating disease\n",
+      "\t disease\n",
+      "\t disorder of development or morphogenesis\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "neoplasm of mature B-cells\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "pneumonitis\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t human disease\n",
+      "acute leukemia\n",
+      "\t disease\n",
+      "\t acute disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "embryonal neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neoplasm with perivascular epithelioid cell differentiation\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t connective and soft tissue neoplasm\n",
+      "\t human disease\n",
+      "\t soft tissue neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "focal epilepsy\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "head and neck neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "myeloid neoplasm\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lung neoplasm\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t human disease\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "interstitial lung disease\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t human disease\n",
+      "lung carcinoma\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "respiratory tract neoplasm\n",
+      "\t disease\n",
+      "\t respiratory system disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "sarcomatoid carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t spindle cell neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t malignant spindle cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t anaplastic cancer\n",
+      "intestinal neoplasm\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "biliary tract disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "malignant spindle cell neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t spindle cell neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "renal cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t urinary system neoplasm\n",
+      "\t renal carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "renal cell adenocarcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t renal cell carcinoma\n",
+      "\t urinary system neoplasm\n",
+      "\t renal carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "clear cell renal carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t renal cell carcinoma\n",
+      "\t renal cell adenocarcinoma\n",
+      "\t clear cell adenocarcinoma\n",
+      "\t urinary system neoplasm\n",
+      "\t renal carcinoma\n",
+      "\t nonpapillary renal cell carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "rhinitis\n",
+      "\t disease\n",
+      "\t otorhinolaryngologic disease\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t upper respiratory tract disorder\n",
+      "\t mucositis\n",
+      "\t nasal disorder\n",
+      "\t human disease\n",
+      "\t nasal cavity disorder\n",
+      "lung large cell carcinoma\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t lung carcinoma\n",
+      "\t respiratory tract neoplasm\n",
+      "\t large cell carcinoma\n",
+      "\t non-small cell lung carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "large cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "non-small cell lung carcinoma\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t lung carcinoma\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "congenital anomaly of cardiovascular system\n",
+      "\t disease\n",
+      "\t disorder of development or morphogenesis\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "pilocytic astrocytoma\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t astrocytoma (excluding glioblastoma)\n",
+      "\t low grade glioma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "\t low-grade astrocytoma\n",
+      "\t low grade astrocytic tumor\n",
+      "clear cell adenocarcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "oncocytic neoplasm\n",
+      "\t disease\n",
+      "\t glandular cell neoplasm\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "small cell lung carcinoma\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neuroendocrine neoplasm\n",
+      "\t small cell carcinoma\n",
+      "\t neuroendocrine carcinoma\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t malignant endocrine neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t lung carcinoma\n",
+      "\t respiratory tract neoplasm\n",
+      "\t lung neuroendocrine neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lymphoid neoplasm\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "pleomorphic carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t spindle cell neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t sarcomatoid carcinoma\n",
+      "\t malignant spindle cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t anaplastic cancer\n",
+      "breast carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "hematopoietic and lymphoid cell neoplasm\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "myocardial infarction\n",
+      "\t disease\n",
+      "\t heart disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "\t myocardial disorder\n",
+      "upper digestive tract disorder\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "immune deficiency disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "kidney oncocytoma\n",
+      "\t disease\n",
+      "\t benign neoplasm\n",
+      "\t benign urinary system neoplasm\n",
+      "\t glandular cell neoplasm\n",
+      "\t neoplasm\n",
+      "\t kidney benign neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t benign epithelial neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t oncocytic neoplasm\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "syndromic agammaglobulinemia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t syndromic disease\n",
+      "\t leukocyte disorder\n",
+      "\t B cell deficiency\n",
+      "\t agammaglobulinemia\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t inborn error of immunity\n",
+      "\t human disease\n",
+      "\t immunodeficiency disease\n",
+      "\t immune deficiency disease\n",
+      "squamous cell lung carcinoma\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t respiratory system cancer\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t squamous cell carcinoma\n",
+      "\t neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t thoracic cancer\n",
+      "\t squamous cell neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t lung cancer\n",
+      "\t lung neoplasm\n",
+      "\t lung carcinoma\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "chromophobe renal cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t renal cell carcinoma\n",
+      "\t renal cell adenocarcinoma\n",
+      "\t urinary system neoplasm\n",
+      "\t renal carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lung neuroendocrine neoplasm\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t neuroendocrine neoplasm\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t endocrine gland neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t human disease\n",
+      "\t lung neoplasm\n",
+      "\t respiratory tract neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "basal ganglia disorder\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "reactive cutaneous fibrous lesion\n",
+      "\t disease\n",
+      "\t integumentary system disorder\n",
+      "\t skin disorder\n",
+      "\t human disease\n",
+      "urinary system neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "plasma cell neoplasm\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t neoplasm of mature B-cells\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "tubulovillous adenoma\n",
+      "\t disease\n",
+      "\t adenoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "myocardial disorder\n",
+      "\t disease\n",
+      "\t heart disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "chronic rhinitis\n",
+      "\t disease\n",
+      "\t otorhinolaryngologic disease\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t upper respiratory tract disorder\n",
+      "\t mucositis\n",
+      "\t nasal disorder\n",
+      "\t human disease\n",
+      "\t nasal cavity disorder\n",
+      "\t rhinitis\n",
+      "enteritis\n",
+      "\t disease\n",
+      "\t gastroenteritis\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t intestinal disorder\n",
+      "\t small intestine disorder\n",
+      "\t human disease\n",
+      "oral cavity cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t mouth disorder\n",
+      "\t digestive system cancer\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t oral cavity neoplasm\n",
+      "tongue neoplasm\n",
+      "\t disease\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t mouth disorder\n",
+      "\t tongue disorder\n",
+      "\t human disease\n",
+      "\t head and neck neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "myeloid leukemia\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t bone cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t connective tissue neoplasm\n",
+      "\t myeloid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t myeloproliferative neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t bone marrow neoplasm\n",
+      "\t myeloid hemopathy\n",
+      "\t bone marrow cancer\n",
+      "myeloproliferative neoplasm\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t bone cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t connective tissue neoplasm\n",
+      "\t myeloid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t bone marrow neoplasm\n",
+      "\t myeloid hemopathy\n",
+      "\t bone marrow cancer\n",
+      "high grade malignant neoplasm\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "lupus erythematosus\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t connective tissue disorder\n",
+      "\t immune system disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "renal carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t urinary system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "non-neoplastic bile duct disorder\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t bile duct disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "plasma cell myeloma\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t cancer\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t neoplasm of mature B-cells\n",
+      "\t myeloid neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t plasma cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "chronic obstructive pulmonary disease\n",
+      "\t disease\n",
+      "\t lung disorder\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t upper respiratory tract disorder\n",
+      "\t tracheal disorder\n",
+      "\t human disease\n",
+      "\t obstructive lung disease\n",
+      "substance dependence\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t human disease\n",
+      "\t substance-related disorder\n",
+      "B-cell acute lymphoblastic leukemia\n",
+      "\t disease\n",
+      "\t acute disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t acute lymphoblastic leukemia\n",
+      "\t precursor lymphoblastic lymphoma/leukemia\n",
+      "\t non-Hodgkin lymphoma\n",
+      "\t lymphoma\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t acute leukemia\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t B-cell non-Hodgkin lymphoma\n",
+      "\t lymphoid leukemia\n",
+      "\t neoplasm of immature B and T cells\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "B-cell non-Hodgkin lymphoma\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t non-Hodgkin lymphoma\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "indolent B-cell non-Hodgkin lymphoma\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t non-Hodgkin lymphoma\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t B-cell non-Hodgkin lymphoma\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "lymphoid leukemia\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neoplasm of immature B and T cells\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "Alzheimer disease\n",
+      "\t disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t psychiatric disorder\n",
+      "\t cognitive disorder\n",
+      "\t dementia\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t tauopathy\n",
+      "tauopathy\n",
+      "\t disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "amyotrophic lateral sclerosis\n",
+      "\t disease\n",
+      "\t motor neuron disorder\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t neuromuscular disease\n",
+      "\t anterior horn disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t spinal cord disorder\n",
+      "triple-negative breast carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t progesterone-receptor negative breast cancer\n",
+      "\t Her2-receptor negative breast cancer\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t estrogen-receptor negative breast cancer\n",
+      "breast tumor luminal A or B\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "acute myeloid leukemia\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t acute disease\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t bone cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t connective tissue neoplasm\n",
+      "\t acute leukemia\n",
+      "\t myeloid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t myeloid leukemia\n",
+      "\t myeloproliferative neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t bone marrow neoplasm\n",
+      "\t myeloid hemopathy\n",
+      "\t bone marrow cancer\n",
+      "nonpapillary renal cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t glandular cell neoplasm\n",
+      "\t adenocarcinoma\n",
+      "\t neoplasm\n",
+      "\t malignant urinary system neoplasm\n",
+      "\t kidney disorder\n",
+      "\t urinary system disorder\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t kidney neoplasm\n",
+      "\t kidney cancer\n",
+      "\t renal cell carcinoma\n",
+      "\t urinary system neoplasm\n",
+      "\t renal carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "heart failure\n",
+      "\t disease\n",
+      "\t heart disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "inflammatory bowel disease\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t digestive system disorder\n",
+      "\t immune system disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "dilated cardiomyopathy\n",
+      "\t disease\n",
+      "\t cardiomyopathy\n",
+      "\t heart disorder\n",
+      "\t muscle tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t intrinsic cardiomyopathy\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "hyperplasia\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "cancer or benign tumor\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "lymphoid hemopathy\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "neoplastic disease or syndrome\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "Orthocoronavirinae infectious disease\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t viral infectious disease\n",
+      "\t primary viral infectious disease\n",
+      "\t human disease\n",
+      "\t Nidovirales infectious disease\n",
+      "\t Coronaviridae infectious disease\n",
+      "scleroderma\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t connective tissue disorder\n",
+      "\t immune system disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "temporal lobe epilepsy\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t familial partial epilepsy\n",
+      "\t brain disorder\n",
+      "\t monogenic epilepsy\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t focal epilepsy\n",
+      "\t epilepsy syndrome\n",
+      "\t childhood-onset epilepsy syndrome\n",
+      "\t adolescent-onset epilepsy syndrome\n",
+      "CNS demyelinating autoimmune disease\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t immune system disorder\n",
+      "\t human disease\n",
+      "familial amyotrophic lateral sclerosis\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t motor neuron disorder\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t hereditary motor neuron disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t neuromuscular disease\n",
+      "\t anterior horn disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t spinal cord disorder\n",
+      "\t amyotrophic lateral sclerosis\n",
+      "type 1 diabetes mellitus\n",
+      "\t disease\n",
+      "\t metabolic disease\n",
+      "\t autoimmune disease\n",
+      "\t digestive system disorder\n",
+      "\t diabetes mellitus\n",
+      "\t autoimmune disorder of endocrine system\n",
+      "\t endocrine system disorder\n",
+      "\t autoimmune disorder of gastrointestinal tract\n",
+      "\t immune system disorder\n",
+      "\t glucose metabolism disease\n",
+      "\t pancreas disorder\n",
+      "\t endocrine pancreas disorder\n",
+      "\t human disease\n",
+      "age-related macular degeneration\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t macular degeneration\n",
+      "\t disorder of orbital region\n",
+      "\t psychiatric disorder\n",
+      "\t perceptual disorders\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t degeneration of macula and posterior pole\n",
+      "\t inherited retinal dystrophy\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "cirrhosis of liver\n",
+      "\t disease\n",
+      "\t liver disorder\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "vesiculobullous skin disease\n",
+      "\t disease\n",
+      "\t integumentary system disorder\n",
+      "\t skin disorder\n",
+      "\t human disease\n",
+      "multiple sclerosis\n",
+      "\t disease\n",
+      "\t disorder of development or morphogenesis\n",
+      "\t brain disorder\n",
+      "\t autoimmune disease\n",
+      "\t neurodegenerative disease\n",
+      "\t autoimmune disorder of central nervous system\n",
+      "\t central nervous system disorder\n",
+      "\t autoimmune disorder of the nervous system\n",
+      "\t immune system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t demyelinating disease\n",
+      "\t CNS demyelinating autoimmune disease\n",
+      "oral cavity neoplasm\n",
+      "\t disease\n",
+      "\t digestive system neoplasm\n",
+      "\t neoplasm\n",
+      "\t mouth disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "basal cell carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t basal cell neoplasm\n",
+      "keloid\n",
+      "\t disease\n",
+      "\t integumentary system disorder\n",
+      "\t skin disorder\n",
+      "\t human disease\n",
+      "\t reactive cutaneous fibrous lesion\n",
+      "opiate dependence\n",
+      "\t disease\n",
+      "\t psychiatric disorder\n",
+      "\t human disease\n",
+      "\t drug dependence\n",
+      "\t substance-related disorder\n",
+      "\t substance dependence\n",
+      "bone marrow neoplasm\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "primary biliary cholangitis\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t telomere syndrome\n",
+      "\t liver disorder\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "\t cirrhosis of liver\n",
+      "\t cirrhosis, familial\n",
+      "\t premature aging syndrome\n",
+      "cirrhosis, familial\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t telomere syndrome\n",
+      "\t liver disorder\n",
+      "\t digestive system disorder\n",
+      "\t endocrine system disorder\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t cirrhosis of liver\n",
+      "\t premature aging syndrome\n",
+      "cystic fibrosis\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t autosomal recessive disease\n",
+      "\t respiratory system disorder\n",
+      "\t autosomal genetic disease\n",
+      "\t human disease\n",
+      "non-compaction cardiomyopathy\n",
+      "\t disease\n",
+      "\t cardiomyopathy\n",
+      "\t heart disorder\n",
+      "\t muscle tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "influenza\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t respiratory system disorder\n",
+      "\t viral infectious disease\n",
+      "\t respiratory tract infectious disorder\n",
+      "\t viral respiratory tract infection\n",
+      "\t primary viral infectious disease\n",
+      "\t human disease\n",
+      "neuroepithelial neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "digestive system adenoma\n",
+      "\t disease\n",
+      "\t digestive system neoplasm\n",
+      "\t adenoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "estrogen-receptor negative breast cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "autoinflammatory syndrome\n",
+      "\t disease\n",
+      "\t syndromic disease\n",
+      "\t connective tissue disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "systemic lupus erythematosus\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t connective tissue disorder\n",
+      "\t immune system disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "\t lupus erythematosus\n",
+      "blastoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t embryonal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "colorectal cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t intestinal cancer\n",
+      "\t large intestine disorder\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t digestive system cancer\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "Nidovirales infectious disease\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t viral infectious disease\n",
+      "\t primary viral infectious disease\n",
+      "\t human disease\n",
+      "Coronaviridae infectious disease\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t viral infectious disease\n",
+      "\t primary viral infectious disease\n",
+      "\t human disease\n",
+      "\t Nidovirales infectious disease\n",
+      "pancreatic neoplasm\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t endocrine system disorder\n",
+      "\t neoplasm\n",
+      "\t pancreas disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "high grade astrocytic tumor\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t central nervous system disorder\n",
+      "\t neoplasm\n",
+      "\t central nervous system neoplasm\n",
+      "\t glioma\n",
+      "\t central nervous system cancer\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system cancer\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t malignant glioma\n",
+      "\t high grade malignant neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "hyperplastic polyp\n",
+      "\t disease\n",
+      "\t polyp\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "polyp of large intestine\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t large intestine disorder\n",
+      "\t polyp\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "anaplastic cancer\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "polyp of colon\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t large intestine disorder\n",
+      "\t polyp\n",
+      "\t colonic disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "\t polyp of large intestine\n",
+      "colon sessile serrated adenoma/polyp\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t adenoma\n",
+      "\t colon adenoma\n",
+      "\t colorectal adenoma\n",
+      "\t epithelial tumor of colon\n",
+      "\t neoplasm\n",
+      "\t polyp\n",
+      "\t colonic disorder\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t colonic neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t digestive system adenoma\n",
+      "\t polyp of large intestine\n",
+      "\t polyp of colon\n",
+      "\t colorectal sessile serrated adenoma/polyp\n",
+      "colorectal sessile serrated adenoma/polyp\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t digestive system neoplasm\n",
+      "\t large intestine disorder\n",
+      "\t adenoma\n",
+      "\t colorectal adenoma\n",
+      "\t neoplasm\n",
+      "\t intestinal disorder\n",
+      "\t human disease\n",
+      "\t colorectal neoplasm\n",
+      "\t epithelial neoplasm\n",
+      "\t intestinal neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t digestive system adenoma\n",
+      "lymphangioleiomyomatosis\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t connective and soft tissue neoplasm\n",
+      "\t human disease\n",
+      "\t soft tissue neoplasm\n",
+      "\t neoplasm with perivascular epithelioid cell differentiation\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "low-grade astrocytoma\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t astrocytoma (excluding glioblastoma)\n",
+      "\t low grade glioma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "\t low grade astrocytic tumor\n",
+      "tubular adenoma\n",
+      "\t disease\n",
+      "\t adenoma\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "epidermolysis bullosa\n",
+      "\t disease\n",
+      "\t integumentary system disorder\n",
+      "\t skin disorder\n",
+      "\t human disease\n",
+      "\t vesiculobullous skin disease\n",
+      "trisomy\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "\t aneuploidy\n",
+      "retinal drusen\n",
+      "\t disease\n",
+      "\t macular degeneration\n",
+      "\t disorder of orbital region\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t degeneration of macula and posterior pole\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "hereditary dementia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t psychiatric disorder\n",
+      "\t cognitive disorder\n",
+      "\t dementia\n",
+      "\t human disease\n",
+      "basal laminar drusen\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t macular degeneration\n",
+      "\t disorder of orbital region\n",
+      "\t vascular disorder\n",
+      "\t psychiatric disorder\n",
+      "\t cardiovascular disorder\n",
+      "\t perceptual disorders\n",
+      "\t eye disorder\n",
+      "\t ocular vascular disorder\n",
+      "\t optic choroid disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t neurovascular disorder\n",
+      "\t degeneration of macula and posterior pole\n",
+      "\t retinal dystrophies primarily involving Bruch's membrane\n",
+      "\t inherited retinal dystrophy\n",
+      "\t uveal disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t retinal drusen\n",
+      "Lewy body dementia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t metabolic disease\n",
+      "\t inborn errors of metabolism\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t synucleinopathy\n",
+      "\t neurodegenerative disease\n",
+      "\t proteostasis deficiencies\n",
+      "\t central nervous system disorder\n",
+      "\t psychiatric disorder\n",
+      "\t cognitive disorder\n",
+      "\t dementia\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t hereditary dementia\n",
+      "epilepsy syndrome\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "childhood-onset epilepsy syndrome\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t epilepsy syndrome\n",
+      "premature aging syndrome\n",
+      "\t disease\n",
+      "\t human disease\n",
+      "common variable immunodeficiency\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t hereditary neoplastic syndrome\n",
+      "\t syndromic disease\n",
+      "\t leukocyte disorder\n",
+      "\t B cell deficiency\n",
+      "\t agammaglobulinemia\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t inborn error of immunity\n",
+      "\t human disease\n",
+      "\t neoplastic syndrome\n",
+      "\t immunodeficiency disease\n",
+      "\t immune deficiency disease\n",
+      "\t syndromic agammaglobulinemia\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "Down syndrome\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "\t chromosome 21 disorder\n",
+      "\t autosomal anomaly\n",
+      "chromosome 21 disorder\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "\t autosomal anomaly\n",
+      "benign prostatic hyperplasia\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t male reproductive system disorder\n",
+      "\t prostate disorder\n",
+      "\t human disease\n",
+      "\t hyperplasia\n",
+      "\t cancer or benign tumor\n",
+      "arrhythmogenic right ventricular cardiomyopathy\n",
+      "\t disease\n",
+      "\t cardiomyopathy\n",
+      "\t heart disorder\n",
+      "\t muscle tissue disorder\n",
+      "\t musculoskeletal system disorder\n",
+      "\t intrinsic cardiomyopathy\n",
+      "\t cardiovascular disorder\n",
+      "\t human disease\n",
+      "primary sclerosing cholangitis\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t endocrine system disorder\n",
+      "\t bile duct disorder\n",
+      "\t cholangitis\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "\t non-neoplastic bile duct disorder\n",
+      "\t sclerosing cholangitis\n",
+      "sclerosing cholangitis\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t inflammatory disease\n",
+      "\t endocrine system disorder\n",
+      "\t bile duct disorder\n",
+      "\t cholangitis\n",
+      "\t human disease\n",
+      "\t hepatobiliary disorder\n",
+      "\t biliary tract disorder\n",
+      "\t non-neoplastic bile duct disorder\n",
+      "chromosome 18 disorder\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "\t autosomal anomaly\n",
+      "age related macular degeneration 7\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t macular degeneration\n",
+      "\t disorder of orbital region\n",
+      "\t psychiatric disorder\n",
+      "\t perceptual disorders\n",
+      "\t eye disorder\n",
+      "\t disorder of visual system\n",
+      "\t retinal degeneration\n",
+      "\t retinal disorder\n",
+      "\t degeneration of macula and posterior pole\n",
+      "\t inherited retinal dystrophy\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t age-related macular degeneration\n",
+      "acute promyelocytic leukemia\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t acute disease\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t bone cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t leukemia\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t connective tissue neoplasm\n",
+      "\t acute leukemia\n",
+      "\t myeloid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t myeloid leukemia\n",
+      "\t myeloproliferative neoplasm\n",
+      "\t acute myeloid leukemia\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t bone marrow neoplasm\n",
+      "\t myeloid hemopathy\n",
+      "\t bone marrow cancer\n",
+      "follicular lymphoma\n",
+      "\t disease\n",
+      "\t leukocyte disorder\n",
+      "\t B-cell neoplasm\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t non-Hodgkin lymphoma\n",
+      "\t lymphoma\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t neoplasm of mature B-cells\n",
+      "\t lymphoid neoplasm\n",
+      "\t hematopoietic and lymphoid cell neoplasm\n",
+      "\t B-cell non-Hodgkin lymphoma\n",
+      "\t indolent B-cell non-Hodgkin lymphoma\n",
+      "\t cancer or benign tumor\n",
+      "\t lymphoid hemopathy\n",
+      "\t neoplastic disease or syndrome\n",
+      "Barrett esophagus\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t esophageal disorder\n",
+      "\t human disease\n",
+      "\t upper digestive tract disorder\n",
+      "myeloid hemopathy\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "trisomy 18\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t syndromic disease\n",
+      "\t human disease\n",
+      "\t trisomy\n",
+      "\t chromosome 18 disorder\n",
+      "\t aneuploidy\n",
+      "\t autosomal anomaly\n",
+      "adolescent-onset epilepsy syndrome\n",
+      "\t disease\n",
+      "\t brain disorder\n",
+      "\t epilepsy\n",
+      "\t central nervous system disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t epilepsy syndrome\n",
+      "low grade astrocytic tumor\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t glioma\n",
+      "\t nervous system neoplasm\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t astrocytic tumor\n",
+      "\t low grade glioma\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t neuroepithelial neoplasm\n",
+      "aneuploidy\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "bone marrow cancer\n",
+      "\t disease\n",
+      "\t skeletal system disorder\n",
+      "\t bone marrow disorder\n",
+      "\t cancer\n",
+      "\t connective tissue disorder\n",
+      "\t bone cancer\n",
+      "\t musculoskeletal system disorder\n",
+      "\t hematologic disorder\n",
+      "\t immune system disorder\n",
+      "\t neoplasm\n",
+      "\t immune system cancer\n",
+      "\t bone neoplasm\n",
+      "\t musculoskeletal system cancer\n",
+      "\t bone disorder\n",
+      "\t human disease\n",
+      "\t hematopoietic and lymphoid system neoplasm\n",
+      "\t connective tissue neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "\t bone marrow neoplasm\n",
+      "localized scleroderma\n",
+      "\t disease\n",
+      "\t autoimmune disease\n",
+      "\t connective tissue disorder\n",
+      "\t immune system disorder\n",
+      "\t rheumatic disorder\n",
+      "\t human disease\n",
+      "\t scleroderma\n",
+      "non-specific interstitial pneumonia\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t lung disorder\n",
+      "\t pneumonia\n",
+      "\t lower respiratory tract disorder\n",
+      "\t respiratory system disorder\n",
+      "\t inflammatory disease\n",
+      "\t idiopathic disease\n",
+      "\t respiratory tract infectious disorder\n",
+      "\t human disease\n",
+      "\t idiopathic interstitial pneumonia\n",
+      "\t pneumonitis\n",
+      "autosomal anomaly\n",
+      "\t disease\n",
+      "\t chromosomal disorder\n",
+      "\t human disease\n",
+      "basal cell neoplasm\n",
+      "\t disease\n",
+      "\t neoplasm\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "luminal B breast carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t breast tumor luminal A or B\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "luminal A breast carcinoma\n",
+      "\t disease\n",
+      "\t cancer\n",
+      "\t carcinoma\n",
+      "\t neoplasm\n",
+      "\t breast cancer\n",
+      "\t breast carcinoma by gene expression profile\n",
+      "\t breast neoplasm\n",
+      "\t neoplasm of thorax\n",
+      "\t breast disorder\n",
+      "\t thoracic cancer\n",
+      "\t human disease\n",
+      "\t epithelial neoplasm\n",
+      "\t breast carcinoma\n",
+      "\t breast tumor luminal A or B\n",
+      "\t cancer or benign tumor\n",
+      "\t neoplastic disease or syndrome\n",
+      "amyotrophic lateral sclerosis 26 with or without frontotemporal dementia\n",
+      "\t disease\n",
+      "\t hereditary disease\n",
+      "\t motor neuron disorder\n",
+      "\t inherited neurodegenerative disorder\n",
+      "\t hereditary motor neuron disease\n",
+      "\t neurodegenerative disease\n",
+      "\t central nervous system disorder\n",
+      "\t neuromuscular disease\n",
+      "\t anterior horn disorder\n",
+      "\t nervous system disorder\n",
+      "\t human disease\n",
+      "\t spinal cord disorder\n",
+      "\t amyotrophic lateral sclerosis\n",
+      "\t familial amyotrophic lateral sclerosis\n",
+      "post-COVID-19 disorder\n",
+      "\t disease\n",
+      "\t post-infectious disorder\n",
+      "\t post-viral disorder\n",
+      "\t human disease\n",
+      "premalignant hematological system disease\n",
+      "\t disease\n",
+      "\t hematologic disorder\n",
+      "\t precancerous condition\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "COVID-19\n",
+      "\t disease\n",
+      "\t infectious disease\n",
+      "\t viral infectious disease\n",
+      "\t primary viral infectious disease\n",
+      "\t human disease\n",
+      "\t Orthocoronavirinae infectious disease\n",
+      "\t Nidovirales infectious disease\n",
+      "\t Coronaviridae infectious disease\n",
+      "gastric intestinal metaplasia\n",
+      "\t disease\n",
+      "\t digestive system disorder\n",
+      "\t precancerous condition\n",
+      "\t stomach disorder\n",
+      "\t human disease\n",
+      "\t cancer or benign tumor\n",
+      "long COVID-19\n",
+      "\t disease\n",
+      "\t post-infectious disorder\n",
+      "\t post-viral disorder\n",
+      "\t human disease\n",
+      "\t post-COVID-19 disorder\n",
+      "hydrosalpinx\n",
+      "\t disease\n",
+      "\t reproductive system disorder\n",
+      "\t female reproductive system disorder\n",
+      "\t fallopian tube disorder\n",
+      "\t human disease\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_print = 2000\n",
+    "\n",
+    "i_print = 0\n",
+    "for k, v in mondo_ontology_resource[\"ancestors_dictionary\"].items():\n",
+    "    print(mondo_ontology_resource[\"ontology_term_id_to_label\"][k])\n",
+    "    for v_ in v:\n",
+    "        print(\"\\t\", mondo_ontology_resource[\"ontology_term_id_to_label\"][v_])\n",
+    "    i_print += 1\n",
+    "    if i_print == n_print:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tissue resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uberon_ontology_owl_file_url = \"https://github.com/obophenotype/uberon/releases/download/v2024-01-18/uberon.owl\"\n",
+    "uberon_datastore_terms_csv = os.path.join(ONTOLOGY_ROOT_PATH, \"datastore_uberon_terms.csv\")\n",
+    "\n",
+    "uberon_prefix = \"UBERON_\"\n",
+    "n_hops = 4\n",
+    "\n",
+    "uberon_propagation_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"uberon_propagation_resource.pkl\")\n",
+    "uberon_benchmarking_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"uberon_benchmarking_resource.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of UBERON terms in datastore: 312\n"
+     ]
+    }
+   ],
+   "source": [
+    "uberon_datastore_terms_df = pd.read_csv(uberon_datastore_terms_csv)\n",
+    "uberon_datastore_terms_set = set(uberon_datastore_terms_df['tissue_ontology_term_id'].values)\n",
+    "print(f\"Number of UBERON terms in datastore: {len(uberon_datastore_terms_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading ontology ...\n",
+      "INFO:root:All classes: 15567\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Loading ontology ...\")\n",
+    "\n",
+    "owl_ontology = owlready2.get_ontology(uberon_ontology_owl_file_url).load()\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_ontology.classes()\n",
+    "    if _class.name.startswith(uberon_prefix)\n",
+    "    if len(_class.label) == 1\n",
+    "]\n",
+    "\n",
+    "logging.info(f\"All classes: {len(owl_classes)}\")\n",
+    "\n",
+    "# build the full graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=uberon_prefix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Could not find CL:0002322 in ontology, ignoring.\n",
+      "INFO:root:Filtered classes: 891\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get all ancestors for datastore nodes\n",
+    "filtered_owl_names_set = set()\n",
+    "for owl_name in uberon_datastore_terms_set:\n",
+    "    try:\n",
+    "        filtered_owl_names_set.update(get_all_ancestors(owl_graph, owl_name))\n",
+    "    except:\n",
+    "        logging.info(f\"Could not find {owl_name} in ontology, ignoring.\")\n",
+    "\n",
+    "logging.info(f\"Filtered classes: {len(filtered_owl_names_set)}\")\n",
+    "\n",
+    "owl_classes = [\n",
+    "    _class for _class in owl_classes\n",
+    "    if _class.name.replace(\"_\", \":\") in filtered_owl_names_set\n",
+    "]\n",
+    "\n",
+    "# build the filtered graph\n",
+    "owl_graph = build_nx_graph_from_owl_ontology_legacy(\n",
+    "    owl_ontology=owl_ontology,\n",
+    "    owl_classes=owl_classes,\n",
+    "    prefix=uberon_prefix)\n",
+    "\n",
+    "owl_names = [_class.name.replace(\"_\", \":\") for _class in owl_classes]\n",
+    "owl_labels = [_class.label[0] for _class in owl_classes]\n",
+    "\n",
+    "# assert that names are unique, labels do not have to be unique\n",
+    "assert len(set(owl_names)) == len(owl_classes)\n",
+    "\n",
+    "owl_names_to_labels_map = {name: label for name, label in zip(owl_names, owl_labels)}\n",
+    "owl_names_to_idx_map = {name: idx for idx, name in enumerate(owl_names)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Building cell ontology ancestor dictionary...\n",
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/uberon_propagation_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "owl_ancestors_dictionary = build_ontology_ancestor_dictionary(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    owl_names_to_idx_map=owl_names_to_idx_map\n",
+    ")\n",
+    "uberon_ontology_resource = {\n",
+    "    \"ancestors_dictionary\": owl_ancestors_dictionary,\n",
+    "    \"ontology_term_id_to_label\": owl_names_to_labels_map,\n",
+    "}\n",
+    "\n",
+    "logging.info(f\"Writing output file to {uberon_propagation_resource_file_path}\")\n",
+    "with open(uberon_propagation_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(uberon_ontology_resource, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Generating ontology benchmarking resource from UBERON ontology...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/uberon_benchmarking_resource.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "logging.info(\"Generating ontology benchmarking resource from UBERON ontology...\")\n",
+    "ontology_resource_dict = build_benchmarking_ontology_dictionary_resource(\n",
+    "    owl_graph=owl_graph,\n",
+    "    owl_names=owl_names,\n",
+    "    n_hops=n_hops\n",
+    ")\n",
+    "\n",
+    "logging.info(f\"Writing output file to {uberon_benchmarking_resource_file_path}\")\n",
+    "\n",
+    "with open(uberon_benchmarking_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(ontology_resource_dict, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "anatomical entity\n",
+      "rib\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t bone element\n",
+      "\t skeletal system\n",
+      "\t endochondral bone\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t skeletal element\n",
+      "\t postcranial axial skeletal system\n",
+      "\t endochondral element\n",
+      "\t rib skeletal system\n",
+      "\t rib endochondral element\n",
+      "appendage\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "subdivision of organism along appendicular axis\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "anatomical structure\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "material anatomical entity\n",
+      "\t anatomical entity\n",
+      "artery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t arterial blood vessel\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t vascular system\n",
+      "vein\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t venous blood vessel\n",
+      "\t vascular system\n",
+      "\t venous system\n",
+      "multicellular anatomical structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "organism subdivision\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "subdivision of skeleton\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t anatomical collection\n",
+      "\t skeleton\n",
+      "anatomical conduit\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "organism substance\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "neural crest-derived structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t ectoderm-derived structure\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "ectoderm-derived structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "mesoderm-derived structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "duct\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "haemolymphatic fluid\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t bodily fluid\n",
+      "\t hemolymphoid system\n",
+      "lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "endoderm-derived structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "pancreas\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "bone element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "central nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "cerebellar cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "exocrine gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "circulatory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "endocrine system\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "islet of Langerhans\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t pancreas\n",
+      "\t endocrine system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t endocrine pancreas\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t cell cluster\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "liver\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t endocrine system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t digestive system element\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t digestive system gland\n",
+      "\t main body axis\n",
+      "skeletal muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t muscle organ\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t skeletal musculature\n",
+      "sensory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "mesoderm\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t tissue\n",
+      "\t germ layer\n",
+      "\t multicellular organism\n",
+      "\t embryonic tissue\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t germ layer / neural crest\n",
+      "eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "trachea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t tracheobronchial tree\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t respiratory tube\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "skin epidermis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t epithelium\n",
+      "\t outer epithelium\n",
+      "\t tissue\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t external integument structure\n",
+      "\t ecto-epithelium\n",
+      "\t surface structure\n",
+      "\t integument\n",
+      "sclera\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t tunica fibrosa of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "extraembryonic structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multi-tissue structure\n",
+      "\t conceptus\n",
+      "\t entire extraembryonic component\n",
+      "\t developing anatomical structure\n",
+      "vasculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "outer epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t ecto-epithelium\n",
+      "\t surface structure\n",
+      "brown adipose tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t irregular connective tissue\n",
+      "ovary\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t gonad\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t female reproductive organ\n",
+      "cardiac muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t visceral muscle tissue\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t visceral striated muscle tissue\n",
+      "\t circulatory organ\n",
+      "bone marrow\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t bone element\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t hematopoietic system\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "retina\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "kidney\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t upper urinary tract\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "embryo\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "choroid plexus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t ventricular system of central nervous system\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular plexus\n",
+      "\t brain ventricle/choroid plexus\n",
+      "\t vasculature of central nervous system\n",
+      "\t brain ventricle\n",
+      "\t tela choroidea\n",
+      "\t vascular system\n",
+      "\t ventricle of nervous system\n",
+      "\t ventricular system of brain\n",
+      "\t vasculature of central nervous system plus retina\n",
+      "immune system\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "retinal neural layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t layer of retina\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t multi cell part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t nervous system cell part layer\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "primary circulatory organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t circulatory organ\n",
+      "tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "thymus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t immune system\n",
+      "\t mixed endoderm/mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t endocrine gland\n",
+      "\t immune organ\n",
+      "\t anterior region of body\n",
+      "\t hemopoietic organ\n",
+      "\t hematopoietic system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "\t hemolymphoid system gland\n",
+      "mixed endoderm/mesoderm-derived structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "cerebellum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "cerebral cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "hippocampal formation\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "tracheobronchial tree\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "uterus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t subdivision of oviduct\n",
+      "jaw region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t mouth\n",
+      "mouth mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t mouth\n",
+      "stomach\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "renal glomerulus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t epithelium\n",
+      "\t kidney\n",
+      "\t tissue\n",
+      "\t renal system\n",
+      "\t cortex of kidney\n",
+      "\t nephron\n",
+      "\t renal corpuscle\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t epithelial tube\n",
+      "\t kidney epithelium\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t excretory tube\n",
+      "\t upper urinary tract\n",
+      "\t organ subunit\n",
+      "\t parenchyma\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t cortex\n",
+      "\t trunk\n",
+      "\t renal parenchyma\n",
+      "\t uriniferous tubule\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "thyroid gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t endoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t endocrine gland\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "epithelium of trachea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t endoderm-derived structure\n",
+      "\t trachea\n",
+      "\t tissue\n",
+      "\t tracheobronchial tree\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t respiratory tract epithelium\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t respiratory tube\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t unilaminar epithelium\n",
+      "\t lower respiratory tract epithelium\n",
+      "\t endo-epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t tracheobronchial epithelium\n",
+      "\t pseudostratified ciliated columnar epithelium\n",
+      "\t respiratory system epithelium\n",
+      "\t ciliated epithelium\n",
+      "\t ciliated columnar epithelium\n",
+      "\t pseudostratified columnar epithelium\n",
+      "lens of camera-type eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t transparent eye structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "prostate gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t mixed endoderm/mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t reproductive gland\n",
+      "\t male accessory sex gland\n",
+      "\t male reproductive gland\n",
+      "respiratory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "blood\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t mesoderm-derived structure\n",
+      "\t haemolymphatic fluid\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hematopoietic system\n",
+      "\t bodily fluid\n",
+      "\t hemolymphoid system\n",
+      "digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "esophagus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "endometrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t uterus\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t uterine wall\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t subdivision of oviduct\n",
+      "\t reproductive system mucosa\n",
+      "colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "mammary gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t integumental system\n",
+      "\t exocrine gland of integumental system\n",
+      "\t gland of integumental system\n",
+      "bronchus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t tracheobronchial tree\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t respiratory tube\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "yolk sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t epithelial tube\n",
+      "\t extraembryonic membrane\n",
+      "\t organ component layer\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t epithelial sac\n",
+      "\t conceptus\n",
+      "\t membranous layer\n",
+      "\t entire extraembryonic component\n",
+      "\t extraembryonic tissue\n",
+      "\t sac\n",
+      "placenta\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t multi-tissue structure\n",
+      "\t female reproductive system\n",
+      "\t conceptus\n",
+      "\t entire extraembryonic component\n",
+      "\t female organism\n",
+      "myometrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t uterus\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t uterine wall\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t subdivision of oviduct\n",
+      "\t layer of smooth muscle tissue\n",
+      "\t layer of muscle tissue\n",
+      "eye trabecular meshwork\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t tissue\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t uvea\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t aqueous drainage system\n",
+      "respiratory tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "respiratory tract epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t endoderm-derived structure\n",
+      "\t tissue\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t unilaminar epithelium\n",
+      "\t endo-epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t pseudostratified ciliated columnar epithelium\n",
+      "\t respiratory system epithelium\n",
+      "\t ciliated epithelium\n",
+      "\t ciliated columnar epithelium\n",
+      "\t pseudostratified columnar epithelium\n",
+      "ear\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t sensory system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t vestibulo-auditory system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "dermis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t integumentary system layer\n",
+      "\t integument\n",
+      "intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "subcutaneous adipose tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t hypodermis\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t integumentary system layer\n",
+      "\t integument\n",
+      "\t irregular connective tissue\n",
+      "renal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t excretory system\n",
+      "mesonephros\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t upper urinary tract\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "uterine cervix\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t uterus\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t neck of organ\n",
+      "\t reproductive structure\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t subdivision of oviduct\n",
+      "chorion membrane\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t extraembryonic membrane\n",
+      "\t organ component layer\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t conceptus\n",
+      "\t membranous layer\n",
+      "\t entire extraembryonic component\n",
+      "heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "conjunctiva\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t multi organ part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t ocular surface region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t conjunctival sac\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "gingiva\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t jaw region\n",
+      "\t mouth mucosa\n",
+      "\t digestive tract\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t mouth\n",
+      "intestinal epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "adipose tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t irregular connective tissue\n",
+      "mesentery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "omentum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t visceral peritoneum\n",
+      "\t peritoneum\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t organ component layer\n",
+      "\t zone of organ\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t visceral serous membrane\n",
+      "\t peritoneal sac\n",
+      "\t viscus\n",
+      "\t abdominal wall\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "breast\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t external soft tissue zone\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "perirenal fat\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t irregular connective tissue\n",
+      "hypodermis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t integumentary system layer\n",
+      "\t integument\n",
+      "cortex of kidney\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t upper urinary tract\n",
+      "\t parenchyma\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t cortex\n",
+      "\t trunk\n",
+      "\t renal parenchyma\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "structure with developmental contribution from neural crest\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "coronary artery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t artery\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t primary circulatory organ\n",
+      "\t blood vessel\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t heart blood vessel\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t vessel\n",
+      "\t vasculature of trunk\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t arterial blood vessel\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t systemic artery\n",
+      "\t viscus\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t thoracic segment blood vessel\n",
+      "\t coronary vessel\n",
+      "\t heart vasculature\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "urinary bladder\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t renal system\n",
+      "\t lower urinary tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t cavitated compound organ\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t bladder organ\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t sac\n",
+      "spinal cord\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "striatum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t ventral part of telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t corpus striatum\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "substantia nigra\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t midbrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t midbrain nucleus\n",
+      "\t collection of basal ganglia\n",
+      "\t brain gray matter\n",
+      "skin of body\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t integument\n",
+      "saliva-secreting gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t gland of digestive tract\n",
+      "\t oral gland\n",
+      "upper respiratory tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "lower respiratory tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "cardiac atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "visceral muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "decidua\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t extraembryonic structure\n",
+      "\t placenta\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t multi-tissue structure\n",
+      "\t female reproductive system\n",
+      "\t conceptus\n",
+      "\t entire extraembryonic component\n",
+      "\t female organism\n",
+      "\t developing anatomical structure\n",
+      "photoreceptor array\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "lamina propria of large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t lamina propria\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t gastrointestinal system lamina propria\n",
+      "vermiform appendix\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "lamina propria of small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t lamina propria\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t gastrointestinal system lamina propria\n",
+      "lamina propria of mucosa of colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t lamina propria of large intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t lamina propria\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t colonic mucosa\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t gastrointestinal system lamina propria\n",
+      "lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "alveolus of lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t parenchyma\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t lobule\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t pulmonary acinus\n",
+      "\t alveolar system\n",
+      "\t bronchopulmonary segment\n",
+      "\t secondary pulmonary lobule\n",
+      "\t alveolus\n",
+      "\t lung parenchyma\n",
+      "\t main body axis\n",
+      "\t pulmonary lobule\n",
+      "camera-type eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "hypothalamus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t thalamic complex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t diencephalon\n",
+      "\t limbic system\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t gray matter of diencephalon\n",
+      "\t brain gray matter\n",
+      "\t gray matter of forebrain\n",
+      "skin of back\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t dorsum\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t dorsal trunk\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "colonic epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t epithelium of large intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "forebrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "duodenum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "jejunum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "ileum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "caecum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t digestive tract diverticulum\n",
+      "\t sac\n",
+      "ascending colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t proximal-distal subdivision of colon\n",
+      "transverse colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t proximal-distal subdivision of colon\n",
+      "descending colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t proximal-distal subdivision of colon\n",
+      "sigmoid colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t proximal-distal subdivision of colon\n",
+      "rectum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t anal region\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t internal anal region\n",
+      "\t terminal part of digestive tract\n",
+      "urethra\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t renal system\n",
+      "\t lower urinary tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t excretory system\n",
+      "dura mater\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t membrane organ\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "\t meninx\n",
+      "heart right ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac ventricle\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t right cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "heart left ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac ventricle\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t left cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "intestinal villus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "epithelium of intestinal villus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t intestinal villus\n",
+      "\t epithelium of small intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "epithelium of large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "interatrial septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac septum\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "interventricular septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac septum\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "sinoatrial node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t visceral muscle tissue\n",
+      "\t conducting system of heart\n",
+      "\t cardiac chamber\n",
+      "\t right cardiac atrium\n",
+      "\t cardiac muscle tissue of atrium\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t right cardiac chamber\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t myocardium of atrium\n",
+      "\t layer of muscle tissue\n",
+      "\t cardiac muscle tissue of myocardium\n",
+      "\t conducting tissue of heart\n",
+      "\t cardiac muscle of right atrium\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "atrioventricular node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t visceral muscle tissue\n",
+      "\t interatrial septum\n",
+      "\t cardiac septum\n",
+      "\t conducting system of heart\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t layer of muscle tissue\n",
+      "\t cardiac muscle tissue of myocardium\n",
+      "\t conducting tissue of heart\n",
+      "\t cardiac muscle tissue of interatrial septum\n",
+      "\t cardiac muscle tissue of cardiac septum\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "gallbladder\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t digestive system element\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t biliary system\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t bladder organ\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdominal viscera\n",
+      "\t main body axis\n",
+      "\t sac\n",
+      "adrenal gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t adrenal/interrenal gland\n",
+      "\t main body axis\n",
+      "\t chromaffin system\n",
+      "ciliary body\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t uvea\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t anterior uvea\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "epithelium of esophagus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t wall of esophagus\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gut wall\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t epithelium of mucosa\n",
+      "\t digestive tract epithelium\n",
+      "\t esophagus mucosa\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "nephron\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t kidney\n",
+      "\t tissue\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t epithelial tube\n",
+      "\t kidney epithelium\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t excretory tube\n",
+      "\t upper urinary tract\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t uriniferous tubule\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "visceral peritoneum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t peritoneum\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t organ component layer\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t visceral serous membrane\n",
+      "\t peritoneal sac\n",
+      "\t viscus\n",
+      "\t abdominal wall\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "peritoneum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t peritoneal sac\n",
+      "\t viscus\n",
+      "\t abdominal wall\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "pleura\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t mixed endoderm/mesoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t serous membrane\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pleural sac\n",
+      "\t serous sac\n",
+      "epithelium of small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "renal medulla\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t upper urinary tract\n",
+      "\t medulla of organ\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "renal pelvis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t renal pelvis/ureter\n",
+      "\t upper urinary tract\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "lower urinary tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t renal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t excretory system\n",
+      "ureter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t renal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t pelvic region element\n",
+      "\t excretory tube\n",
+      "\t renal pelvis/ureter\n",
+      "\t pelvic region of trunk\n",
+      "\t upper urinary tract\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "renal corpuscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t kidney\n",
+      "\t tissue\n",
+      "\t renal system\n",
+      "\t cortex of kidney\n",
+      "\t nephron\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t epithelial tube\n",
+      "\t kidney epithelium\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t excretory tube\n",
+      "\t upper urinary tract\n",
+      "\t organ subunit\n",
+      "\t parenchyma\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t cortex\n",
+      "\t trunk\n",
+      "\t renal parenchyma\n",
+      "\t uriniferous tubule\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "outer medulla of kidney\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t renal medulla\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t upper urinary tract\n",
+      "\t medulla of organ\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "inner medulla of kidney\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t renal medulla\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t upper urinary tract\n",
+      "\t medulla of organ\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "kidney blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t kidney\n",
+      "\t blood vessel\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t kidney vasculature\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t vessel\n",
+      "\t upper urinary tract\n",
+      "\t vasculature of trunk\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t cardiovascular system\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment blood vessel\n",
+      "\t abdominal segment element\n",
+      "\t blood vasculature\n",
+      "\t abdomen element\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t abdomen blood vessel\n",
+      "\t main body axis\n",
+      "exocrine pancreas\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t pancreas\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t serous gland\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "intestinal mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "nasopharynx\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t digestive tract\n",
+      "\t respiratory tract\n",
+      "\t upper respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t respiratory airway\n",
+      "\t pharynx\n",
+      "\t chordate pharynx\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "tonsil\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t respiratory system\n",
+      "\t digestive tract\n",
+      "\t gut-associated lymphoid tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t mucosa-associated lymphoid tissue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t pharynx\n",
+      "\t chordate pharynx\n",
+      "\t subdivision of tube\n",
+      "\t lymphoid tissue\n",
+      "\t lymphoid system\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t tonsillar ring\n",
+      "\t lymphomyeloid tissue\n",
+      "\t epithelium-associated lymphoid tissue\n",
+      "\t heterogeneous tissue\n",
+      "epididymis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t duct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "\t internal male genitalia\n",
+      "\t duct of male reproductive system\n",
+      "\t male genital duct\n",
+      "oviduct\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t multicellular organism\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "prepuce of penis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t external soft tissue zone\n",
+      "\t male reproductive organ\n",
+      "\t penis\n",
+      "\t intromittent organ\n",
+      "\t reproductive organ\n",
+      "\t prepuce\n",
+      "muscle organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "skin of abdomen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t abdominal segment skin\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "arm\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t forelimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t anterior region of body\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t multi-limb segment region\n",
+      "\t upper limb segment\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "cardiac septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "limb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "cardiac ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "brainstem\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "primary motor cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "spleen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t immune system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemopoietic organ\n",
+      "\t hematopoietic system\n",
+      "\t digestive system element\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t viscus\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdominal viscera\n",
+      "\t main body axis\n",
+      "skull\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t body proper\n",
+      "\t axial skeletal system\n",
+      "\t main body axis\n",
+      "abdomen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of face\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t skin of head\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of prepuce of penis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t prepuce of penis\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t male organism\n",
+      "\t external soft tissue zone\n",
+      "\t male reproductive organ\n",
+      "\t penis\n",
+      "\t intromittent organ\n",
+      "\t reproductive organ\n",
+      "\t skin of penis\n",
+      "\t prepuce\n",
+      "\t integument\n",
+      "scalp\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi organ part structure\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "neocortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "umbilical cord blood\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t mesoderm-derived structure\n",
+      "\t haemolymphatic fluid\n",
+      "\t embryo\n",
+      "\t blood\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hematopoietic system\n",
+      "\t bodily fluid\n",
+      "\t hemolymphoid system\n",
+      "\t late embryo\n",
+      "\t arterial blood\n",
+      "midbrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "skin of scalp\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t scalp\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t multi organ part structure\n",
+      "\t skin of head\n",
+      "\t head or neck skin\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "medial ganglionic eminence\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t ganglionic eminence\n",
+      "caudal ganglionic eminence\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t ganglionic eminence\n",
+      "thalamic complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t diencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t gray matter of diencephalon\n",
+      "\t brain gray matter\n",
+      "\t gray matter of forebrain\n",
+      "uvea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "orbital region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "germ layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t embryonic tissue\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t germ layer / neural crest\n",
+      "neural tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t ectoderm-derived structure\n",
+      "\t epithelium\n",
+      "\t embryo\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t epithelial tube\n",
+      "\t embryonic tissue\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t future nervous system\n",
+      "\t future central nervous system\n",
+      "\t ecto-epithelium\n",
+      "\t presumptive structure\n",
+      "chorionic villus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t extraembryonic structure\n",
+      "\t embryo\n",
+      "\t chorion membrane\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t extraembryonic membrane\n",
+      "\t organ component layer\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t conceptus\n",
+      "\t embryonic structure\n",
+      "\t membranous layer\n",
+      "\t entire extraembryonic component\n",
+      "\t developing anatomical structure\n",
+      "optic cup\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t multi-tissue structure\n",
+      "\t developing anatomical structure\n",
+      "\t immature eye\n",
+      "gut-associated lymphoid tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t mucosa-associated lymphoid tissue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t lymphoid tissue\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t lymphomyeloid tissue\n",
+      "\t epithelium-associated lymphoid tissue\n",
+      "\t heterogeneous tissue\n",
+      "conducting system of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t visceral muscle tissue\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t layer of muscle tissue\n",
+      "\t cardiac muscle tissue of myocardium\n",
+      "\t conducting tissue of heart\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "cardiac chamber\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "left cardiac atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t left cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "right cardiac atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t right cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "cardiac muscle tissue of atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t visceral muscle tissue\n",
+      "\t cardiac chamber\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t myocardium of atrium\n",
+      "\t layer of muscle tissue\n",
+      "\t cardiac muscle tissue of myocardium\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "endochondral bone\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t bone element\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "\t endochondral element\n",
+      "muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "smooth muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "striated muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "multicellular organism\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "sense organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "foregut\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "hindgut\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "gonad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t reproductive organ\n",
+      "testis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t gonad\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "layer of retina\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t multi cell part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t nervous system cell part layer\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "hindbrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "diencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "telencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "pallium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "ventral part of telencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "pons\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t brainstem\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "ventricular system of central nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "dentate nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t hindbrain nucleus\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t cerebellar nuclear complex\n",
+      "\t nucleus of cerebellar nuclear complex\n",
+      "\t segmental subdivision of nervous system\n",
+      "caudate nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t striatum\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t ventral part of telencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t corpus striatum\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t telencephalic nucleus\n",
+      "\t caudate-putamen\n",
+      "\t dorsal striatum\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "putamen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t striatum\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t ventral part of telencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t corpus striatum\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t telencephalic nucleus\n",
+      "\t caudate-putamen\n",
+      "\t dorsal striatum\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "limbic system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "cingulate gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "orbitofrontal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t frontal gyrus\n",
+      "\t gray matter of forebrain\n",
+      "parahippocampal gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "olfactory lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "olfactory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t olfactory lobe\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "metencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "respiratory tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "male reproductive system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t male organism\n",
+      "endocrine pancreas\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t pancreas\n",
+      "\t endocrine system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "lacrimal gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t multi organ part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t ocular surface region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t lacrimal apparatus\n",
+      "\t eye gland\n",
+      "\t lateral gland of orbital region\n",
+      "\t body proper\n",
+      "\t gland of ocular region\n",
+      "\t main body axis\n",
+      "reproductive system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "forelimb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t anterior region of body\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t appendage girdle complex\n",
+      "hindlimb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t appendage girdle complex\n",
+      "tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "internal genitalia\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t reproductive organ\n",
+      "exocrine system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "aorta\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t arterial blood vessel\n",
+      "\t thoracic cavity blood vessel\n",
+      "\t great vessel of heart\n",
+      "\t aortic system\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "external ear\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t sensory system\n",
+      "\t ear\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t vestibulo-auditory system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "uterine wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t uterus\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t subdivision of oviduct\n",
+      "nose\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t olfactory organ\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "tongue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t sensory system\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t disconnected anatomical group\n",
+      "\t gustatory system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "mucosa-associated lymphoid tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t lymphoid tissue\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t lymphomyeloid tissue\n",
+      "\t epithelium-associated lymphoid tissue\n",
+      "\t heterogeneous tissue\n",
+      "compound organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "anatomical system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "neural nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "anatomical junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "digestive system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "fallopian tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t pelvic region of trunk\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t subdivision of oviduct\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "paired limb/fin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "head\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "face\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "cranial suture\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t craniofacial suture\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of skeletal system\n",
+      "\t anatomical collection\n",
+      "\t skeletal joint\n",
+      "\t nonsynovial joint\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "\t fibrous joint\n",
+      "\t cranium\n",
+      "\t axial skeletal system\n",
+      "coronal suture\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t subdivision of skeleton\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skull\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t cranial suture\n",
+      "\t craniofacial suture\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of skeletal system\n",
+      "\t anatomical collection\n",
+      "\t neurocranium\n",
+      "\t vault of skull\n",
+      "\t skeletal joint\n",
+      "\t nonsynovial joint\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "\t fibrous joint\n",
+      "\t skeleton\n",
+      "\t primary subdivision of skull\n",
+      "\t cranium\n",
+      "\t body proper\n",
+      "\t axial skeletal system\n",
+      "\t main body axis\n",
+      "respiratory airway\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t respiratory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "right lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "left lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "lobe of lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "pharynx\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "skeletal muscle organ, vertebrate\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t skeletal musculature\n",
+      "diaphragm\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t respiratory system\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t thoracic segment muscle\n",
+      "\t respiratory system muscle\n",
+      "\t musculature of thorax\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "epithelial tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t irregular connective tissue\n",
+      "heart blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t primary circulatory organ\n",
+      "\t blood vessel\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t vessel\n",
+      "\t vasculature of trunk\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t thoracic segment blood vessel\n",
+      "\t coronary vessel\n",
+      "\t heart vasculature\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "hepatobiliary system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ system subdivision\n",
+      "muscle structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "iris\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t uvea\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t anterior uvea\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "cornea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t ocular surface region\n",
+      "\t tunica fibrosa of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "kidney vasculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t upper urinary tract\n",
+      "\t vasculature of trunk\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t cardiovascular system\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t vascular system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "kidney epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t epithelium\n",
+      "\t kidney\n",
+      "\t tissue\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t upper urinary tract\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "craniofacial suture\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of skeletal system\n",
+      "\t anatomical collection\n",
+      "\t skeletal joint\n",
+      "\t nonsynovial joint\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "\t fibrous joint\n",
+      "\t axial skeletal system\n",
+      "chordate pharynx\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t respiratory system\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t pharynx\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "extraembryonic membrane\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t conceptus\n",
+      "\t membranous layer\n",
+      "\t entire extraembryonic component\n",
+      "serous membrane\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "cranial skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t axial skeletal system\n",
+      "gastrointestinal system mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "neck of organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t zone of organ\n",
+      "\t organ part\n",
+      "reproductive structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "nasal cavity\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t ectoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t upper respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t nose\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t respiratory airway\n",
+      "\t olfactory organ\n",
+      "\t immaterial anatomical entity\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical space\n",
+      "\t anatomical cavity\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t olfactory apparatus chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t upper respiratory conduit\n",
+      "olfactory organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "cell cluster\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "endocrine gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t endocrine system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "submucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "organ component layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "organ system subdivision\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "zone of skin\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t integument\n",
+      "zone of organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "immaterial anatomical entity\n",
+      "\t anatomical entity\n",
+      "compound organ component\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t multi-tissue structure\n",
+      "serous gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "trunk region element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "simple eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "lateral structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "integumental system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "subdivision of tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "lymphoid tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t lymphomyeloid tissue\n",
+      "\t heterogeneous tissue\n",
+      "immune organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "lymphoid system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "lamina propria\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "subdivision of organism along main body axis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t main body axis\n",
+      "anterior region of body\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "craniocervical region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anterior region of body\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "secretion of exocrine gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "transudate\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t bodily fluid\n",
+      "anatomical space\n",
+      "\t anatomical entity\n",
+      "\t immaterial anatomical entity\n",
+      "hemopoietic organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t hematopoietic system\n",
+      "\t hemolymphoid system\n",
+      "thoracic cavity element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "hematopoietic system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hemolymphoid system\n",
+      "wall of esophagus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gut wall\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "multi-tissue structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "anatomical wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "musculoskeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "basal ganglion\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t organ part\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t collection of basal ganglia\n",
+      "\t brain gray matter\n",
+      "transparent eye structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "anatomical cavity\n",
+      "\t anatomical entity\n",
+      "\t immaterial anatomical entity\n",
+      "\t anatomical space\n",
+      "macula lutea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "organ part\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "bodily fluid\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "pelvic region element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t pelvic region of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "excretory tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t renal system\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t excretory system\n",
+      "renal pelvis/ureter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t renal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t upper urinary tract\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t excretory system\n",
+      "pelvic region of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "upper urinary tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t renal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t excretory system\n",
+      "organ subunit\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "subdivision of digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "digestive system element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "lower digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "multi organ part structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "regional part of nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t multi-tissue structure\n",
+      "subdivision of skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "skeletal element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "unilaminar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "embryonic tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "male organism\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "female reproductive system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t female organism\n",
+      "epithelial sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t epithelial tube\n",
+      "\t sac\n",
+      "conceptus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "embryonic structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t multicellular organism\n",
+      "\t developing anatomical structure\n",
+      "membrane organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "dense connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "anatomical lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "vasculature of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t vascular system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "vasculature of organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "lower respiratory tract epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t endoderm-derived structure\n",
+      "\t tissue\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t respiratory tract epithelium\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t unilaminar epithelium\n",
+      "\t endo-epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t pseudostratified ciliated columnar epithelium\n",
+      "\t respiratory system epithelium\n",
+      "\t ciliated epithelium\n",
+      "\t ciliated columnar epithelium\n",
+      "\t pseudostratified columnar epithelium\n",
+      "endo-epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t endoderm-derived structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "dense irregular connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "\t dense connective tissue\n",
+      "\t irregular connective tissue\n",
+      "multi cell part structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "central nervous system cell part cluster\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "gray matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "nucleus of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t brain gray matter\n",
+      "hindbrain nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "pectoral complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "pectoral appendage\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pectoral complex\n",
+      "\t appendage girdle complex\n",
+      "pelvic complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "pelvic appendage\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t appendage girdle complex\n",
+      "posterior region of body\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "membranous layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "anal region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "alimentary part of gastrointestinal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "internal anal region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t multi-tissue structure\n",
+      "\t anal region\n",
+      "mouth\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "proximal-distal subdivision of colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "pair of lungs\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical collection\n",
+      "anatomical collection\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "respiration organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t respiratory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "pleural effusion\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t transudate\n",
+      "\t bodily fluid\n",
+      "\t pleural fluid\n",
+      "\t secretion of serous membrane\n",
+      "pleural fluid\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t transudate\n",
+      "\t bodily fluid\n",
+      "\t secretion of serous membrane\n",
+      "hemolymphoid system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "morphological feature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "cerebral hemisphere\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "regional part of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "neurocranium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skull\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t primary subdivision of skull\n",
+      "\t cranium\n",
+      "\t body proper\n",
+      "\t axial skeletal system\n",
+      "\t main body axis\n",
+      "vault of skull\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t subdivision of skeleton\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skull\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t anatomical collection\n",
+      "\t neurocranium\n",
+      "\t skeleton\n",
+      "\t primary subdivision of skull\n",
+      "\t cranium\n",
+      "\t body proper\n",
+      "\t axial skeletal system\n",
+      "\t main body axis\n",
+      "postcranial axial skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "anatomical point\n",
+      "\t anatomical entity\n",
+      "\t immaterial anatomical entity\n",
+      "entire extraembryonic component\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multi-tissue structure\n",
+      "\t conceptus\n",
+      "external soft tissue zone\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "chest\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "colonic mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "meningeal cluster\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t anatomical collection\n",
+      "mucosa of large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "decidua basalis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t extraembryonic structure\n",
+      "\t placenta\n",
+      "\t decidua\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t multi-tissue structure\n",
+      "\t female reproductive system\n",
+      "\t conceptus\n",
+      "\t entire extraembryonic component\n",
+      "\t female organism\n",
+      "\t developing anatomical structure\n",
+      "mucosa of small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "lobe of cerebral hemisphere\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "late embryo\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t multicellular organism\n",
+      "gut wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "ileal mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t ileum\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "gland of digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t digestive system element\n",
+      "neck\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anterior region of body\n",
+      "dorsal region element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t dorsum\n",
+      "parenchyma\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "reproductive gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "medulla of organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t compound organ component\n",
+      "\t multi-tissue structure\n",
+      "corpus striatum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t collection of basal ganglia\n",
+      "\t brain gray matter\n",
+      "pes\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t autopod region\n",
+      "\t appendage girdle complex\n",
+      "lower limb segment\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "leg\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t multi-limb segment region\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "musculature of body\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "musculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "skeletal joint\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t anatomical collection\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "meninx\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t membrane organ\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "jejunal mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t jejunum\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "jejunal epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t jejunum\n",
+      "\t intestinal villus\n",
+      "\t epithelium of intestinal villus\n",
+      "\t epithelium of small intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t jejunal mucosa\n",
+      "\t intestinal villus of jejunum\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "intestinal villus of jejunum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t jejunum\n",
+      "\t intestinal villus\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t jejunal mucosa\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "skin of head\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t head or neck skin\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "visual cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t functional part of brain\n",
+      "\t cortical visual area\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "functional part of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "cortical visual area\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t functional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "occipital lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "mucous gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "subdural space\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t immaterial anatomical entity\n",
+      "\t anatomical space\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "simple columnar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t unilaminar epithelium\n",
+      "\t columnar epithelium\n",
+      "aggregate regional part of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t disconnected anatomical group\n",
+      "cerebral hemisphere gray matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "prefrontal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "frontal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "cerebral subcortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t organ part\n",
+      "exocrine gland of integumental system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t integumental system\n",
+      "\t gland of integumental system\n",
+      "male reproductive organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t reproductive organ\n",
+      "female organism\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "anatomical cluster\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t disconnected anatomical group\n",
+      "disconnected anatomical group\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "developing anatomical structure\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "columnar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "cavitated compound organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "upper digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "thoracic segment of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "subdivision of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "abdominal segment of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "germ layer / neural crest\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t embryonic tissue\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "visual system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "food storage organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "arterial blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t vascular system\n",
+      "thoracic cavity blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "great vessel of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "aortic system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t cardiovascular system\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "thoracic segment organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "cardiovascular system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "heart plus pericardium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "glandular system\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "gustatory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "biliary system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t organ system subdivision\n",
+      "entire sense organ system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "excretory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "dorsum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "central nervous system gray matter layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t multi cell part structure\n",
+      "\t nervous system cell part layer\n",
+      "cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "cervical spinal cord\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t spinal cord\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "\t regional part of spinal cord\n",
+      "\t spinal cord segment\n",
+      "anterior segment of eyeball\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "ocular surface region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "tunica fibrosa of eyeball\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "posterior segment of eyeball\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "chorioretinal region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "endochondral element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "pleural sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t serous sac\n",
+      "multi-limb segment region\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "hip\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t appendage girdle complex\n",
+      "nonsynovial joint\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t anatomical collection\n",
+      "\t skeletal joint\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "articulation\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t anatomical collection\n",
+      "\t articular system\n",
+      "articular system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t anatomical collection\n",
+      "axilla\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t pectoral complex\n",
+      "\t external soft tissue zone\n",
+      "\t appendage girdle region\n",
+      "\t pectoral girdle region\n",
+      "\t appendage girdle complex\n",
+      "shoulder\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t pectoral complex\n",
+      "\t appendage girdle complex\n",
+      "penis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t intromittent organ\n",
+      "\t reproductive organ\n",
+      "intromittent organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "reproductive organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "gonad primordium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t developing anatomical structure\n",
+      "\t primordium\n",
+      "female reproductive organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "subdivision of oviduct\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "internal male genitalia\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "duct of male reproductive system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t duct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t male organism\n",
+      "male genital duct\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t duct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "\t internal male genitalia\n",
+      "\t duct of male reproductive system\n",
+      "external integument structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t surface structure\n",
+      "\t integument\n",
+      "future nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t multicellular organism\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t presumptive structure\n",
+      "future central nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t multicellular organism\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t future nervous system\n",
+      "\t presumptive structure\n",
+      "non-connected functional system\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t disconnected anatomical group\n",
+      "fibrous joint\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t multi organ part structure\n",
+      "\t anatomical collection\n",
+      "\t skeletal joint\n",
+      "\t nonsynovial joint\n",
+      "\t articulation\n",
+      "\t articular system\n",
+      "extraembryonic tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t multi-tissue structure\n",
+      "\t conceptus\n",
+      "\t entire extraembryonic component\n",
+      "saliva\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t secretion of exocrine gland\n",
+      "oral gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t gland of digestive tract\n",
+      "primordium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t developing anatomical structure\n",
+      "ecto-epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "terminal part of digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "cerebellar hemisphere\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "parotid gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t saliva-secreting gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t serous gland\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t external soft tissue zone\n",
+      "\t gland of digestive tract\n",
+      "\t oral gland\n",
+      "\t subdivision of head\n",
+      "\t cheek\n",
+      "\t midface\n",
+      "\t temporofacial region\n",
+      "\t major salivary gland\n",
+      "\t buccal salivary gland\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t trunk\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "dorsal trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t dorsum\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "intestinal junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t anatomical junction\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t digestive tract junction\n",
+      "heart ventricle wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac ventricle\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t wall of heart\n",
+      "\t circulatory organ\n",
+      "myocardium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t layer of muscle tissue\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "head or neck skin\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "secretion of serous membrane\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t multicellular organism\n",
+      "\t transudate\n",
+      "\t bodily fluid\n",
+      "peripheral lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "rib skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t postcranial axial skeletal system\n",
+      "thoracic segment muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t musculature of thorax\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "respiratory system muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t respiratory system\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "lobe of liver\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t endocrine system\n",
+      "\t liver\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t digestive system element\n",
+      "\t anatomical lobe\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t digestive system gland\n",
+      "\t main body axis\n",
+      "right lobe of liver\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t endocrine system\n",
+      "\t liver\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t digestive system element\n",
+      "\t anatomical lobe\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t lobe of liver\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t digestive system gland\n",
+      "\t main body axis\n",
+      "left lobe of liver\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t endocrine system\n",
+      "\t liver\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t digestive system element\n",
+      "\t anatomical lobe\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t lobe of liver\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t digestive system gland\n",
+      "\t main body axis\n",
+      "caudate lobe of liver\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t endoderm-derived structure\n",
+      "\t exocrine gland\n",
+      "\t endocrine system\n",
+      "\t liver\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t hepatobiliary system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t digestive system element\n",
+      "\t anatomical lobe\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t lobe of liver\n",
+      "\t right lobe of liver\n",
+      "\t left lobe of liver\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t digestive system gland\n",
+      "\t main body axis\n",
+      "axial muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t main body axis\n",
+      "\t axial musculature\n",
+      "\t skeletal musculature\n",
+      "visceral striated muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t visceral muscle tissue\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "digestive tract diverticulum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t sac\n",
+      "zone of stomach\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t stomach\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "body of stomach\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t stomach\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "\t zone of stomach\n",
+      "cardia of stomach\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t stomach\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "\t zone of stomach\n",
+      "pyloric antrum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t stomach\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "\t zone of stomach\n",
+      "\t pylorus\n",
+      "pylorus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t stomach\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t foregut\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t zone of organ\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t food storage organ\n",
+      "\t zone of stomach\n",
+      "wall of small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "wall of intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "wall of large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "lobule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "visceral serous membrane\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t organ component layer\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "peritoneal sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "mesenteric artery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t artery\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t arterial blood vessel\n",
+      "\t cardiovascular system\n",
+      "\t abdominal aorta artery\n",
+      "\t systemic artery\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "abdominal aorta artery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t artery\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t arterial blood vessel\n",
+      "\t cardiovascular system\n",
+      "\t systemic artery\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "systemic artery\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t artery\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t arterial blood vessel\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t arterial system\n",
+      "\t systemic arterial system\n",
+      "\t vascular system\n",
+      "submucosa of digestive tract\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "muscularis mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t muscle tissue\n",
+      "\t smooth muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t organ system subdivision\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t gastrointestinal system smooth muscle\n",
+      "submucosa of small intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t submucosa of digestive tract\n",
+      "\t intestinal submucosa\n",
+      "intestinal submucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t submucosa of digestive tract\n",
+      "submucosa of large intestine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t submucosa of digestive tract\n",
+      "\t intestinal submucosa\n",
+      "submucosal gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t exocrine gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t mucous gland\n",
+      "muscle of abdomen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal segment muscle\n",
+      "\t abdominal wall\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdomen musculature\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "anterior abdominal wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal wall\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "anterior abdominal wall muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t muscle of abdomen\n",
+      "\t anterior abdominal wall\n",
+      "\t abdominal segment muscle\n",
+      "\t abdominal wall\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdomen musculature\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "renal parenchyma\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t upper urinary tract\n",
+      "\t parenchyma\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "renal papilla\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t kidney\n",
+      "\t renal system\n",
+      "\t renal medulla\n",
+      "\t renal pelvis\n",
+      "\t inner medulla of kidney\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t compound organ component\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t renal pelvis/ureter\n",
+      "\t upper urinary tract\n",
+      "\t medulla of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "uriniferous tubule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t kidney\n",
+      "\t tissue\n",
+      "\t renal system\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t epithelial tube\n",
+      "\t kidney epithelium\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t excretory tube\n",
+      "\t upper urinary tract\n",
+      "\t cavitated compound organ\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "gastrointestinal system lamina propria\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t lamina propria\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "viscus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "bladder organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t cavitated compound organ\n",
+      "\t sac\n",
+      "musculature of lower limb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t musculature of limb\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "appendage girdle region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "epithelium of mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "gastrointestinal system epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "digestive tract epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "reproductive system mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "layer of smooth muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t layer of muscle tissue\n",
+      "skin of leg\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t multi-limb segment region\n",
+      "\t skin of limb\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t hindlimb skin\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "muscle of pelvis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t pelvic region element\n",
+      "\t pelvic region of trunk\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal segment muscle\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "abdominal segment muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "skin of penis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t male organism\n",
+      "\t male reproductive organ\n",
+      "\t penis\n",
+      "\t intromittent organ\n",
+      "\t reproductive organ\n",
+      "\t integument\n",
+      "prepuce\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t external soft tissue zone\n",
+      "\t reproductive organ\n",
+      "abdominal segment blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "abdominal wall\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "hindlimb muscle\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t musculature of lower limb\n",
+      "\t limb muscle\n",
+      "\t pelvic complex muscle\n",
+      "\t musculature of limb\n",
+      "\t pelvic appendage muscle\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "muscle of leg\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t hindlimb muscle\n",
+      "\t musculature of leg\n",
+      "\t limb muscle\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t pelvic complex muscle\n",
+      "\t musculature of limb\n",
+      "\t pelvic appendage muscle\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "musculature of leg\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t musculature of limb\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "gastrocnemius\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t hindlimb muscle\n",
+      "\t muscle of leg\n",
+      "\t musculature of leg\n",
+      "\t hindlimb zeugopod muscle\n",
+      "\t hindlimb zeugopod\n",
+      "\t limb muscle\n",
+      "\t triceps surae\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t pelvic complex muscle\n",
+      "\t zeugopod\n",
+      "\t musculature of limb\n",
+      "\t pelvic appendage muscle\n",
+      "\t musculature of hindlimb zeugopod\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "hindlimb zeugopod muscle\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t hindlimb muscle\n",
+      "\t muscle of leg\n",
+      "\t musculature of leg\n",
+      "\t hindlimb zeugopod\n",
+      "\t limb muscle\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t pelvic complex muscle\n",
+      "\t zeugopod\n",
+      "\t musculature of limb\n",
+      "\t pelvic appendage muscle\n",
+      "\t musculature of hindlimb zeugopod\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "hindlimb zeugopod\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t multi-limb segment region\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t zeugopod\n",
+      "\t appendage girdle complex\n",
+      "limb muscle\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t musculature of limb\n",
+      "\t appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "triceps surae\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t hindlimb muscle\n",
+      "\t muscle of leg\n",
+      "\t musculature of leg\n",
+      "\t hindlimb zeugopod muscle\n",
+      "\t hindlimb zeugopod\n",
+      "\t limb muscle\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t pelvic complex muscle\n",
+      "\t zeugopod\n",
+      "\t musculature of limb\n",
+      "\t pelvic appendage muscle\n",
+      "\t musculature of hindlimb zeugopod\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "auditory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "temporal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "abdominal segment skin\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of thorax\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of limb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "pectoral girdle region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t pectoral complex\n",
+      "\t appendage girdle region\n",
+      "\t appendage girdle complex\n",
+      "skeleton\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t anatomical collection\n",
+      "subdivision of head\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of external ear\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t ear\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t external ear\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t skin of head\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t pinna\n",
+      "\t vestibulo-auditory system\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "pinna\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t sensory system\n",
+      "\t ear\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t external ear\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t vestibulo-auditory system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "upper limb segment\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t forelimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t anterior region of body\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "limb segment\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "paired limb/fin segment\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t appendage girdle complex\n",
+      "skin of shoulder\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pectoral complex\n",
+      "\t shoulder\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "skin of forearm\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t arm\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t forelimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t anterior region of body\n",
+      "\t organ part\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t multi-limb segment region\n",
+      "\t skin of limb\n",
+      "\t upper limb segment\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t arm skin\n",
+      "\t forelimb skin\n",
+      "\t forelimb zeugopod\n",
+      "\t integument\n",
+      "\t zeugopod\n",
+      "\t appendage girdle complex\n",
+      "musculature of thorax\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "pelvic complex muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "hindlimb skin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t skin of limb\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "skin of pes\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t pes\n",
+      "\t lower limb segment\n",
+      "\t skin of limb\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t hindlimb skin\n",
+      "\t autopod skin\n",
+      "\t integument\n",
+      "\t autopod region\n",
+      "\t appendage girdle complex\n",
+      "autopod skin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t skin of limb\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t integument\n",
+      "\t autopod region\n",
+      "\t appendage girdle complex\n",
+      "arm skin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t arm\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t forelimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t anterior region of body\n",
+      "\t organ part\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t multi-limb segment region\n",
+      "\t skin of limb\n",
+      "\t upper limb segment\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t forelimb skin\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "forelimb skin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t forelimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t anterior region of body\n",
+      "\t organ part\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t skin of limb\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "forelimb zeugopod\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t arm\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t forelimb\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t anterior region of body\n",
+      "\t pectoral complex\n",
+      "\t pectoral appendage\n",
+      "\t multi-limb segment region\n",
+      "\t upper limb segment\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t zeugopod\n",
+      "\t appendage girdle complex\n",
+      "inguinal lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t peripheral lymph node\n",
+      "\t inguinal part of abdomen\n",
+      "\t abdominal segment element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "inguinal part of abdomen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "abdominal segment element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of hip\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pelvic complex\n",
+      "\t hip\n",
+      "\t integument\n",
+      "\t appendage girdle complex\n",
+      "cheek\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t face\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t external soft tissue zone\n",
+      "\t subdivision of head\n",
+      "\t midface\n",
+      "\t temporofacial region\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "midface\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "temporofacial region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "dorsum of tongue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t sensory system\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t tongue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t disconnected anatomical group\n",
+      "\t gustatory system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "vascular plexus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "blood vasculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "ocular adnexa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "sublingual gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t saliva-secreting gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t gland of digestive tract\n",
+      "\t oral gland\n",
+      "\t major salivary gland\n",
+      "arterial system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "venous blood\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t mesoderm-derived structure\n",
+      "\t haemolymphatic fluid\n",
+      "\t blood\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hematopoietic system\n",
+      "\t bodily fluid\n",
+      "\t hemolymphoid system\n",
+      "venous blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t venous system\n",
+      "vestibulo-auditory system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "temporal part of head\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "primary subdivision of skull\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t skull\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t cranial skeletal system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t body proper\n",
+      "\t axial skeletal system\n",
+      "\t main body axis\n",
+      "cranium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "olfactory apparatus chamber\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t ectoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t upper respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t nose\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t respiratory airway\n",
+      "\t olfactory organ\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t upper respiratory conduit\n",
+      "nuclear complex of neuraxis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "midbrain nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t midbrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "tonsillar ring\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t respiratory system\n",
+      "\t digestive tract\n",
+      "\t gut-associated lymphoid tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t mucosa-associated lymphoid tissue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t pharynx\n",
+      "\t chordate pharynx\n",
+      "\t subdivision of tube\n",
+      "\t lymphoid tissue\n",
+      "\t lymphoid system\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t lymphomyeloid tissue\n",
+      "\t epithelium-associated lymphoid tissue\n",
+      "\t heterogeneous tissue\n",
+      "anatomical projection\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t morphological feature\n",
+      "major salivary gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t saliva-secreting gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t gland of digestive tract\n",
+      "\t oral gland\n",
+      "lymphomyeloid tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t heterogeneous tissue\n",
+      "lacrimal apparatus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t multi organ part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t ocular surface region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "anterior uvea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t uvea\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skeletal muscle of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "musculature of trunk\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "nervous system cell part layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t multi cell part structure\n",
+      "fovea centralis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t macula lutea\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "eyeball of camera-type eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "conjunctival sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi organ part structure\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t main body axis\n",
+      "eye gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "lateral gland of orbital region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t body proper\n",
+      "\t gland of ocular region\n",
+      "\t main body axis\n",
+      "buccal salivary gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t saliva-secreting gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t external soft tissue zone\n",
+      "\t gland of digestive tract\n",
+      "\t oral gland\n",
+      "\t subdivision of head\n",
+      "\t cheek\n",
+      "\t midface\n",
+      "\t temporofacial region\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "surface structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "skin of chest\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t skin of thorax\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "frontal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "cortex of cerebral lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "parietal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "telencephalic nucleus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "caudate-putamen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t striatum\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t ventral part of telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t corpus striatum\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t dorsal striatum\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "dorsal striatum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t striatum\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t ventral part of telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t corpus striatum\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "collection of basal ganglia\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t organ part\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "basal forebrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "brain ventricle/choroid plexus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "vasculature of central nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "\t vasculature of central nervous system plus retina\n",
+      "brain ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t ventricular system of central nervous system\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t brain ventricle/choroid plexus\n",
+      "\t ventricle of nervous system\n",
+      "\t ventricular system of brain\n",
+      "tela choroidea\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t ventricle of nervous system\n",
+      "segmental subdivision of hindbrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of nervous system\n",
+      "myelencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "tracheobronchial epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t endoderm-derived structure\n",
+      "\t tissue\n",
+      "\t tracheobronchial tree\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t respiratory tract epithelium\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t unilaminar epithelium\n",
+      "\t lower respiratory tract epithelium\n",
+      "\t endo-epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t pseudostratified ciliated columnar epithelium\n",
+      "\t respiratory system epithelium\n",
+      "\t ciliated epithelium\n",
+      "\t ciliated columnar epithelium\n",
+      "\t pseudostratified columnar epithelium\n",
+      "brain white matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t white matter\n",
+      "gray matter of diencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t diencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of forebrain\n",
+      "brain gray matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "regional part of spinal cord\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t spinal cord\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "entorhinal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t parahippocampal gyrus\n",
+      "\t olfactory lobe\n",
+      "\t olfactory cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t piriform cortex\n",
+      "\t gray matter of forebrain\n",
+      "limbic lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "abdomen element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdominal segment element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "epithelium-associated lymphoid tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t immune system\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t lymphoid tissue\n",
+      "\t lymphoid system\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "\t lymphomyeloid tissue\n",
+      "\t heterogeneous tissue\n",
+      "substantia nigra pars compacta\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t brain\n",
+      "\t substantia nigra\n",
+      "\t midbrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t midbrain nucleus\n",
+      "\t collection of basal ganglia\n",
+      "\t brain gray matter\n",
+      "esophagus mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t wall of esophagus\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gut wall\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "integumentary system layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t integumental system\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "pseudostratified ciliated columnar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t unilaminar epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t ciliated epithelium\n",
+      "\t ciliated columnar epithelium\n",
+      "\t pseudostratified columnar epithelium\n",
+      "abdomen connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "insula\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "claustrum of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t basal ganglion\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t aggregate regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t cerebral subcortex\n",
+      "\t disconnected anatomical group\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t collection of basal ganglia\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t basal nuclear complex\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "basal nuclear complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t basal forebrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of forebrain\n",
+      "systemic arterial system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t cardiovascular system\n",
+      "\t arterial system\n",
+      "\t vascular system\n",
+      "vascular system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t cardiovascular system\n",
+      "trunk blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "integument\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "right cardiac chamber\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "left cardiac chamber\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "brain meninx\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t membrane organ\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "\t meninx\n",
+      "apex of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t immaterial anatomical entity\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical point\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "body proper\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t main body axis\n",
+      "abdominal viscera\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "digestive system gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t endocrine system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t endocrine gland\n",
+      "\t digestive system element\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "smooth muscle of esophagus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t muscle organ\n",
+      "\t muscle tissue\n",
+      "\t smooth muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t wall of esophagus\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gut wall\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t esophagus muscle\n",
+      "\t main body axis\n",
+      "esophagus muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t wall of esophagus\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gut wall\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "gray matter of hindbrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "cerebellar nuclear complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "cerebellum lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "posterior lobe of cerebellum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t cerebellum lobe\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "nucleus of cerebellar nuclear complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t hindbrain nucleus\n",
+      "\t regional part of brain\n",
+      "\t nuclear complex of neuraxis\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t cerebellar nuclear complex\n",
+      "\t segmental subdivision of nervous system\n",
+      "heart layer\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "pulmonary acinus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t parenchyma\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t lobule\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t bronchopulmonary segment\n",
+      "\t secondary pulmonary lobule\n",
+      "\t lung parenchyma\n",
+      "\t main body axis\n",
+      "\t pulmonary lobule\n",
+      "alveolar system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "upper lobe of right lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t right lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t upper lobe of lung\n",
+      "\t right lung lobe\n",
+      "\t main body axis\n",
+      "upper lobe of lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "right lung lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t right lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "lower lobe of right lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t right lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t right lung lobe\n",
+      "\t lower lobe of lung\n",
+      "\t main body axis\n",
+      "lower lobe of lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "middle lobe of right lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t right lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t right lung lobe\n",
+      "\t middle lobe of lung\n",
+      "\t main body axis\n",
+      "middle lobe of lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "left lung lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t left lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "white matter of spinal cord\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t spinal cord\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "\t white matter\n",
+      "bronchopulmonary segment\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "secondary pulmonary lobule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t parenchyma\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t lobule\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t bronchopulmonary segment\n",
+      "\t lung parenchyma\n",
+      "\t main body axis\n",
+      "\t pulmonary lobule\n",
+      "ventricle of nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "skeletal element projection\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "\t morphological feature\n",
+      "\t anatomical projection\n",
+      "rib endochondral element\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "\t skeletal element\n",
+      "\t postcranial axial skeletal system\n",
+      "\t endochondral element\n",
+      "\t rib skeletal system\n",
+      "regional part of cerebellar cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t segmental subdivision of nervous system\n",
+      "presumptive structure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t multicellular organism\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "alveolus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "myocardium of atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t layer of muscle tissue\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "white matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "white matter of cerebellum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t white matter\n",
+      "axial skeletal system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t subdivision of skeletal system\n",
+      "abdomen musculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "layer of muscle tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "cardiac muscle tissue of myocardium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t visceral muscle tissue\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t layer of muscle tissue\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "conducting tissue of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t visceral muscle tissue\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t visceral striated muscle tissue\n",
+      "\t circulatory organ\n",
+      "cardiac muscle of right atrium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t cardiac atrium\n",
+      "\t visceral muscle tissue\n",
+      "\t cardiac chamber\n",
+      "\t right cardiac atrium\n",
+      "\t cardiac muscle tissue of atrium\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t myocardium\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t right cardiac chamber\n",
+      "\t body proper\n",
+      "\t heart layer\n",
+      "\t myocardium of atrium\n",
+      "\t layer of muscle tissue\n",
+      "\t cardiac muscle tissue of myocardium\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "cardiac muscle tissue of interatrial septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t visceral muscle tissue\n",
+      "\t interatrial septum\n",
+      "\t cardiac septum\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t cardiac muscle tissue of cardiac septum\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "male accessory sex gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t reproductive gland\n",
+      "\t male reproductive gland\n",
+      "adrenal/interrenal gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "\t chromaffin system\n",
+      "hemolymphoid system gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t hemolymphoid system\n",
+      "rectus abdominis muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t muscle organ\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t axial muscle\n",
+      "\t muscle of abdomen\n",
+      "\t anterior abdominal wall\n",
+      "\t anterior abdominal wall muscle\n",
+      "\t abdominal segment muscle\n",
+      "\t abdominal wall\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdomen musculature\n",
+      "\t hypaxial musculature\n",
+      "\t main body axis\n",
+      "\t axial musculature\n",
+      "\t skeletal musculature\n",
+      "hypaxial musculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t trunk\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "zeugopod\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "autopod region\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t paired limb/fin\n",
+      "\t lateral structure\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t appendage girdle complex\n",
+      "lung parenchyma\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t parenchyma\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "serous sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "gland of integumental system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t integumental system\n",
+      "cervical lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t anterior region of body\n",
+      "\t hemolymphoid system\n",
+      "\t neck\n",
+      "\t disconnected anatomical group\n",
+      "\t non-connected functional system\n",
+      "gray matter of telencephalon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of forebrain\n",
+      "primary visual cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t visual cortex\n",
+      "\t functional part of brain\n",
+      "\t cortical visual area\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "left cerebral hemisphere\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "right cerebral hemisphere\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "bony projection\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "\t morphological feature\n",
+      "\t anatomical projection\n",
+      "\t skeletal element projection\n",
+      "abdominal lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "visceral abdominal lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdominal lymph node\n",
+      "\t main body axis\n",
+      "mesenteric lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t immune system\n",
+      "\t mesentery\n",
+      "\t lymph node\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t serous membrane\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t abdominal lymph node\n",
+      "\t visceral abdominal lymph node\n",
+      "\t main body axis\n",
+      "thoracic lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "operculum of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t frontal lobe\n",
+      "postcentral gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "frontal gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "superior frontal gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t frontal gyrus\n",
+      "\t gray matter of forebrain\n",
+      "angular gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "spinal cord segment\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t spinal cord\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "\t regional part of spinal cord\n",
+      "piriform cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t olfactory lobe\n",
+      "\t olfactory cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "anterior cingulate gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t cingulate gyrus\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t anterior cingulate cortex\n",
+      "\t cingulate cortex\n",
+      "\t limbic cortex\n",
+      "\t gray matter of forebrain\n",
+      "anterior cingulate cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t cingulate cortex\n",
+      "\t limbic cortex\n",
+      "\t gray matter of forebrain\n",
+      "middle temporal gyrus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "left parietal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t parietal lobe\n",
+      "\t left cerebral hemisphere\n",
+      "right parietal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t parietal lobe\n",
+      "\t right cerebral hemisphere\n",
+      "right occipital lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t right cerebral hemisphere\n",
+      "\t gray matter of forebrain\n",
+      "left temporal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t temporal lobe\n",
+      "\t left cerebral hemisphere\n",
+      "right temporal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t temporal lobe\n",
+      "\t right cerebral hemisphere\n",
+      "right frontal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t right cerebral hemisphere\n",
+      "\t gray matter of forebrain\n",
+      "left frontal lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t left cerebral hemisphere\n",
+      "\t gray matter of forebrain\n",
+      "macula lutea proper\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t macula lutea\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "secondary olfactory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t olfactory lobe\n",
+      "\t olfactory cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "cerebellum vermis lobule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t cerebellar vermis\n",
+      "\t cerebellum lobule\n",
+      "\t segmental subdivision of nervous system\n",
+      "cingulate cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t limbic cortex\n",
+      "\t gray matter of forebrain\n",
+      "limbic cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "immature eye\n",
+      "\t anatomical entity\n",
+      "\t material anatomical entity\n",
+      "\t developing anatomical structure\n",
+      "submucosa of colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t submucosa of digestive tract\n",
+      "\t intestinal submucosa\n",
+      "\t submucosa of large intestine\n",
+      "respiratory system epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t respiratory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "sensory organ epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t sensory system\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "cardiac muscle tissue of cardiac septum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t cardiac muscle tissue\n",
+      "\t primary circulatory organ\n",
+      "\t tissue\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t visceral muscle tissue\n",
+      "\t cardiac septum\n",
+      "\t muscle tissue\n",
+      "\t striated muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t visceral striated muscle tissue\n",
+      "\t viscus\n",
+      "\t septum\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "abdominal fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t fat pad\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdomen connective tissue\n",
+      "\t body proper\n",
+      "\t adipose tissue of abdominal region\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "adipose tissue of abdominal region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdomen connective tissue\n",
+      "\t body proper\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "gonadal fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t gonad\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t reproductive structure\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t reproductive organ\n",
+      "\t irregular connective tissue\n",
+      "abdomen blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t abdominal segment blood vessel\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "thoracic segment blood vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t blood vessel\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t vessel\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t cardiovascular system\n",
+      "\t trunk\n",
+      "\t blood vasculature\n",
+      "\t vascular system\n",
+      "\t trunk blood vessel\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "coronary vessel\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t vessel\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "heart vasculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t vasculature of trunk\n",
+      "\t vasculature of organ\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t vascular system\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "abdominal segment connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "trunk connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "gland of ocular region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi organ part structure\n",
+      "\t subdivision of head\n",
+      "\t ocular adnexa\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "musculature of limb\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "pelvic appendage muscle\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t pelvic complex muscle\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "\t skeletal musculature\n",
+      "main body axis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "axial musculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t main body axis\n",
+      "venous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "cerebellar vermis\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "aqueous drainage system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t uvea\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t anterior segment of eyeball\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "cerebellum lobule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "ganglionic eminence\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "lateral ganglionic eminence\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t embryo\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t embryonic structure\n",
+      "\t developing anatomical structure\n",
+      "\t ganglionic eminence\n",
+      "ventricular system of brain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t ventricular system of central nervous system\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "forehead\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t face\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "gastrointestinal system smooth muscle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t muscle tissue\n",
+      "\t smooth muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t organ system subdivision\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "musculature of hindlimb zeugopod\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t multi-limb segment region\n",
+      "\t musculature of lower limb\n",
+      "\t musculature of leg\n",
+      "\t hindlimb zeugopod\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t zeugopod\n",
+      "\t musculature of limb\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t pelvic appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "lower leg skin\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t subdivision of organism along appendicular axis\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t limb\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindlimb\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t organ part\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t posterior region of body\n",
+      "\t lower limb segment\n",
+      "\t leg\n",
+      "\t multi-limb segment region\n",
+      "\t skin of leg\n",
+      "\t hindlimb zeugopod\n",
+      "\t skin of limb\n",
+      "\t limb segment\n",
+      "\t paired limb/fin segment\n",
+      "\t hindlimb skin\n",
+      "\t integument\n",
+      "\t zeugopod\n",
+      "\t appendage girdle complex\n",
+      "musculature of pelvic complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t appendage girdle complex\n",
+      "appendage musculature\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "pelvic appendage musculature\n",
+      "\t anatomical entity\n",
+      "\t appendage\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t paired limb/fin\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t pelvic complex\n",
+      "\t pelvic appendage\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t musculature of pelvic complex\n",
+      "\t appendage musculature\n",
+      "\t appendage girdle complex\n",
+      "esophagus muscularis mucosa\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t muscle organ\n",
+      "\t muscle tissue\n",
+      "\t smooth muscle tissue\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t muscle structure\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t wall of esophagus\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t muscularis mucosa\n",
+      "\t viscus\n",
+      "\t esophagus mucosa\n",
+      "\t body proper\n",
+      "\t smooth muscle of esophagus\n",
+      "\t esophagus muscle\n",
+      "\t main body axis\n",
+      "\t gastrointestinal system smooth muscle\n",
+      "ciliated epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "appendage girdle complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "Brodmann area\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "segmental subdivision of nervous system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t nervous system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "wall of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "vasculature of central nervous system plus retina\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t vasculature\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t anatomical cluster\n",
+      "\t disconnected anatomical group\n",
+      "\t cardiovascular system\n",
+      "\t vascular system\n",
+      "submucosa of ascending colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t ascending colon\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t proximal-distal subdivision of colon\n",
+      "\t gut wall\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t submucosa of digestive tract\n",
+      "\t intestinal submucosa\n",
+      "\t submucosa of large intestine\n",
+      "\t submucosa of colon\n",
+      "submucosa of ileum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t ileum\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t submucosa\n",
+      "\t organ component layer\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t gut wall\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t submucosa of digestive tract\n",
+      "\t submucosa of small intestine\n",
+      "\t intestinal submucosa\n",
+      "hemisphere part of cerebellar posterior lobe\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebellar cortex\n",
+      "\t cerebellum\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t hindbrain\n",
+      "\t metencephalon\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t cerebellar hemisphere\n",
+      "\t segmental subdivision of hindbrain\n",
+      "\t brain gray matter\n",
+      "\t gray matter of hindbrain\n",
+      "\t cerebellum lobe\n",
+      "\t posterior lobe of cerebellum\n",
+      "\t regional part of cerebellar cortex\n",
+      "\t segmental subdivision of nervous system\n",
+      "male reproductive gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t male organism\n",
+      "\t reproductive gland\n",
+      "sac\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "caecum epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t colonic epithelium\n",
+      "\t caecum\n",
+      "\t epithelium of large intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of large intestine\n",
+      "\t gut wall\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t digestive tract diverticulum\n",
+      "\t wall of intestine\n",
+      "\t wall of large intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "\t sac\n",
+      "gray matter of forebrain\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t regional part of brain\n",
+      "\t brain gray matter\n",
+      "digestive tract junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t anatomical junction\n",
+      "corneo-scleral junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t anatomical junction\n",
+      "\t head\n",
+      "\t face\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "chromaffin system\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t disconnected anatomical group\n",
+      "\t glandular system\n",
+      "\t non-connected functional system\n",
+      "external nose\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t nose\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t olfactory organ\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t external soft tissue zone\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "circulatory organ\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t circulatory system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "ciliated columnar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t unilaminar epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t ciliated epithelium\n",
+      "medial entorhinal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t parahippocampal gyrus\n",
+      "\t olfactory lobe\n",
+      "\t olfactory cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t entorhinal cortex\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t piriform cortex\n",
+      "\t secondary olfactory cortex\n",
+      "\t gray matter of forebrain\n",
+      "lateral entorhinal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t parahippocampal gyrus\n",
+      "\t olfactory lobe\n",
+      "\t olfactory cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t entorhinal cortex\n",
+      "\t limbic lobe\n",
+      "\t insula\n",
+      "\t gray matter of telencephalon\n",
+      "\t piriform cortex\n",
+      "\t secondary olfactory cortex\n",
+      "\t gray matter of forebrain\n",
+      "posterior part of tongue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t sensory system\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t tongue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t disconnected anatomical group\n",
+      "\t gustatory system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t dorsum of tongue\n",
+      "pigment epithelium of eye\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t sensory organ epithelium\n",
+      "esophagogastric junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t anatomical junction\n",
+      "\t digestive tract junction\n",
+      "irregular connective tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t multicellular organism\n",
+      "intestinal villus of ileum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t ileum\n",
+      "\t intestinal villus\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t ileal mucosa\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "ileal epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t intestinal epithelium\n",
+      "\t ileum\n",
+      "\t intestinal villus\n",
+      "\t epithelium of intestinal villus\n",
+      "\t epithelium of small intestine\n",
+      "\t intestinal mucosa\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t gastrointestinal system mucosa\n",
+      "\t organ component layer\n",
+      "\t mucosa\n",
+      "\t subdivision of tube\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t unilaminar epithelium\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t mucosa of small intestine\n",
+      "\t gut wall\n",
+      "\t ileal mucosa\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "\t wall of small intestine\n",
+      "\t wall of intestine\n",
+      "\t epithelium of mucosa\n",
+      "\t gastrointestinal system epithelium\n",
+      "\t digestive tract epithelium\n",
+      "\t intestinal villus of ileum\n",
+      "muscle of pelvic diaphragm\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t muscle organ\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t skeletal muscle organ, vertebrate\n",
+      "\t muscle structure\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t pelvic region element\n",
+      "\t pelvic region of trunk\n",
+      "\t musculature of body\n",
+      "\t musculature\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t muscle of pelvis\n",
+      "\t abdominal segment muscle\n",
+      "\t abdominal segment element\n",
+      "\t skeletal muscle of trunk\n",
+      "\t musculature of trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t skeletal musculature\n",
+      "skin of cheek\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t skin of face\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t lateral structure\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t external soft tissue zone\n",
+      "\t skin of head\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t cheek\n",
+      "\t midface\n",
+      "\t temporofacial region\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "somatosensory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t functional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t postcentral gyrus\n",
+      "\t gray matter of forebrain\n",
+      "primary somatosensory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t functional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t postcentral gyrus\n",
+      "\t gray matter of forebrain\n",
+      "\t somatosensory cortex\n",
+      "\t parietal cortex\n",
+      "parietal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "secondary somatosensory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t functional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t operculum of brain\n",
+      "\t postcentral gyrus\n",
+      "\t gray matter of forebrain\n",
+      "\t somatosensory cortex\n",
+      "upper lobe of left lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t left lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t upper lobe of lung\n",
+      "\t left lung lobe\n",
+      "\t main body axis\n",
+      "lower lobe of left lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t left lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t lower lobe of lung\n",
+      "\t left lung lobe\n",
+      "\t main body axis\n",
+      "lingula of left lung\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t left lung\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t upper lobe of lung\n",
+      "\t left lung lobe\n",
+      "\t main body axis\n",
+      "\t upper lobe of left lung\n",
+      "left colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "right colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "submucosal esophageal gland\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t exocrine gland\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t tube\n",
+      "\t exocrine system\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t gland of digestive tract\n",
+      "\t mucous gland\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t submucosal gland\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "bone spine\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t skeletal system\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculoskeletal system\n",
+      "\t skeletal element\n",
+      "\t morphological feature\n",
+      "\t anatomical projection\n",
+      "\t skeletal element projection\n",
+      "\t bony projection\n",
+      "dorsolateral prefrontal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "bladder lumen\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t renal system\n",
+      "\t urinary bladder\n",
+      "\t lower urinary tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t immaterial anatomical entity\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical space\n",
+      "\t cavitated compound organ\n",
+      "\t excretory system\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t bladder organ\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t sac\n",
+      "anterior part of tongue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t sensory system\n",
+      "\t digestive tract\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t tongue\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t mouth\n",
+      "\t disconnected anatomical group\n",
+      "\t gustatory system\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "pulmonary lobule\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t lung\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t lower respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t lobe of lung\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "\t anatomical lobe\n",
+      "\t pair of lungs\n",
+      "\t anatomical collection\n",
+      "\t respiration organ\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t lobule\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t bronchopulmonary segment\n",
+      "\t main body axis\n",
+      "inguinal fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t fat pad\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t inguinal part of abdomen\n",
+      "\t body proper\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "retroperitoneal fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t peritoneum\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t serous membrane\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t peritoneal sac\n",
+      "\t viscus\n",
+      "\t abdominal wall\n",
+      "\t abdomen connective tissue\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t abdominal fat pad\n",
+      "\t adipose tissue of abdominal region\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "epididymal fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t duct\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t epididymis\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t internal genitalia\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t male organism\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t male reproductive organ\n",
+      "\t reproductive organ\n",
+      "\t internal male genitalia\n",
+      "\t duct of male reproductive system\n",
+      "\t male genital duct\n",
+      "\t irregular connective tissue\n",
+      "mesenteric fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t mesentery\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t serous membrane\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t irregular connective tissue\n",
+      "omental fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t omentum\n",
+      "\t visceral peritoneum\n",
+      "\t peritoneum\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t serous membrane\n",
+      "\t organ component layer\n",
+      "\t zone of organ\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t visceral serous membrane\n",
+      "\t peritoneal sac\n",
+      "\t viscus\n",
+      "\t abdominal wall\n",
+      "\t abdomen connective tissue\n",
+      "\t body proper\n",
+      "\t serous sac\n",
+      "\t abdominal fat pad\n",
+      "\t adipose tissue of abdominal region\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "\t retroperitoneal fat pad\n",
+      "barrel cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t functional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t parietal lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t postcentral gyrus\n",
+      "\t gray matter of forebrain\n",
+      "\t somatosensory cortex\n",
+      "\t primary somatosensory cortex\n",
+      "\t parietal cortex\n",
+      "pseudostratified columnar epithelium\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t epithelium\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "\t unilaminar epithelium\n",
+      "\t simple columnar epithelium\n",
+      "\t columnar epithelium\n",
+      "layer of dura mater\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t dura mater\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t membrane organ\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "\t meninx\n",
+      "meningeal dura mater\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t dura mater\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t membrane organ\n",
+      "\t anatomical collection\n",
+      "\t meningeal cluster\n",
+      "\t meninx\n",
+      "\t layer of dura mater\n",
+      "arterial blood\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism substance\n",
+      "\t mesoderm-derived structure\n",
+      "\t haemolymphatic fluid\n",
+      "\t blood\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t hematopoietic system\n",
+      "\t bodily fluid\n",
+      "\t hemolymphoid system\n",
+      "hepatic cecum\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t digestive tract diverticulum\n",
+      "\t sac\n",
+      "ampulla of uterine tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t fallopian tube\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t pelvic region of trunk\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t subdivision of oviduct\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "lower esophagus\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t esophagus\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t trunk region element\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t upper digestive tract\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "retrosplenial region\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t cingulate cortex\n",
+      "\t limbic cortex\n",
+      "\t gray matter of forebrain\n",
+      "Brodmann (1909) area 4\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t primary motor cortex\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t Brodmann area\n",
+      "\t gray matter of forebrain\n",
+      "temporal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "peripheral region of retina\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t regional part of nervous system\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "visceral abdominal adipose tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t abdomen connective tissue\n",
+      "\t body proper\n",
+      "\t adipose tissue of abdominal region\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "subcutaneous abdominal adipose tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t subcutaneous adipose tissue\n",
+      "\t adipose tissue\n",
+      "\t hypodermis\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ component layer\n",
+      "\t organ system subdivision\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t integumentary system layer\n",
+      "\t abdomen connective tissue\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t adipose tissue of abdominal region\n",
+      "\t abdominal segment connective tissue\n",
+      "\t trunk connective tissue\n",
+      "\t main body axis\n",
+      "\t irregular connective tissue\n",
+      "cervical spinal cord white matter\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t spinal cord\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t dorsal region element\n",
+      "\t cavitated compound organ\n",
+      "\t dorsum\n",
+      "\t cervical spinal cord\n",
+      "\t regional part of spinal cord\n",
+      "\t white matter of spinal cord\n",
+      "\t white matter\n",
+      "\t spinal cord segment\n",
+      "skeletal musculature\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t multicellular organism\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t musculature of body\n",
+      "retrosplenial granular cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t limbic lobe\n",
+      "\t gray matter of telencephalon\n",
+      "\t cingulate cortex\n",
+      "\t limbic cortex\n",
+      "\t gray matter of forebrain\n",
+      "\t retrosplenial region\n",
+      "nose skin\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t skin of body\n",
+      "\t skin of face\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t nose\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t olfactory organ\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t external soft tissue zone\n",
+      "\t skin of head\n",
+      "\t disconnected anatomical group\n",
+      "\t entire sense organ system\n",
+      "\t non-connected functional system\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t external nose\n",
+      "heterogeneous tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t tissue\n",
+      "\t multicellular organism\n",
+      "upper respiratory conduit\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t endoderm-derived structure\n",
+      "\t respiratory system\n",
+      "\t respiratory tract\n",
+      "\t upper respiratory tract\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t respiratory airway\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t proximo-distal subdivision of respiratory tract\n",
+      "skin of forehead\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t skin of face\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t skin of head\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t forehead\n",
+      "occipital cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "isthmus of fallopian tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t fallopian tube\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t pelvic region of trunk\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t subdivision of oviduct\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "perifoveal part of retina\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t sensory system\n",
+      "\t eye\n",
+      "\t retina\n",
+      "\t nervous system\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t photoreceptor array\n",
+      "\t camera-type eye\n",
+      "\t orbital region\n",
+      "\t multicellular organism\n",
+      "\t sense organ\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t face\n",
+      "\t organ component layer\n",
+      "\t simple eye\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t multi-tissue structure\n",
+      "\t anatomical wall\n",
+      "\t macula lutea\n",
+      "\t organ part\n",
+      "\t organ subunit\n",
+      "\t disconnected anatomical group\n",
+      "\t visual system\n",
+      "\t entire sense organ system\n",
+      "\t posterior segment of eyeball\n",
+      "\t chorioretinal region\n",
+      "\t non-connected functional system\n",
+      "\t subdivision of head\n",
+      "\t eyeball of camera-type eye\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "periovarian fat pad\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ovary\n",
+      "\t tissue\n",
+      "\t connective tissue\n",
+      "\t adipose tissue\n",
+      "\t multicellular organism\n",
+      "\t gonad\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t fat pad\n",
+      "\t reproductive structure\n",
+      "\t female reproductive system\n",
+      "\t dense connective tissue\n",
+      "\t dense irregular connective tissue\n",
+      "\t female organism\n",
+      "\t reproductive organ\n",
+      "\t female reproductive organ\n",
+      "\t gonadal fat pad\n",
+      "\t irregular connective tissue\n",
+      "adrenal tissue\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t neural crest-derived structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endocrine system\n",
+      "\t tissue\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t adrenal gland\n",
+      "\t abdomen\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t anatomical system\n",
+      "\t endocrine gland\n",
+      "\t organ system subdivision\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t disconnected anatomical group\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t glandular system\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t abdominal segment element\n",
+      "\t abdomen element\n",
+      "\t body proper\n",
+      "\t adrenal/interrenal gland\n",
+      "\t main body axis\n",
+      "\t chromaffin system\n",
+      "colic flexure\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "hepatic flexure of colon\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t large intestine\n",
+      "\t digestive tract\n",
+      "\t colon\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t lower digestive tract\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t colic flexure\n",
+      "medial orbital frontal cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t limbic system\n",
+      "\t orbitofrontal cortex\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t gyrus\n",
+      "\t morphological feature\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t frontal cortex\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t frontal lobe\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t frontal gyrus\n",
+      "\t gray matter of forebrain\n",
+      "subicular complex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t hippocampal formation\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "temporoparietal junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "primary auditory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t auditory cortex\n",
+      "\t temporal lobe\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "insular cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t insula\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "agranular insular cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t cortex of cerebral lobe\n",
+      "\t brain gray matter\n",
+      "\t insula\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "\t insular cortex\n",
+      "basal zone of heart\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t zone of organ\n",
+      "\t trunk region element\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t viscus\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t circulatory organ\n",
+      "upper outer quadrant of breast\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t breast\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t external soft tissue zone\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t quadrant of breast\n",
+      "\t upper quadrant of breast\n",
+      "\t outer quadrant of breast\n",
+      "quadrant of breast\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t breast\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t external soft tissue zone\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "upper quadrant of breast\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t breast\n",
+      "\t multicellular organism\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t external soft tissue zone\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t quadrant of breast\n",
+      "outer quadrant of breast\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t breast\n",
+      "\t multicellular organism\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t external soft tissue zone\n",
+      "\t chest\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t trunk\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t quadrant of breast\n",
+      "posterior parietal association areas\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t neocortex\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "anterolateral visual area\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t visual cortex\n",
+      "\t functional part of brain\n",
+      "\t cortical visual area\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "lateral visual area\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t lobe of cerebral hemisphere\n",
+      "\t visual cortex\n",
+      "\t functional part of brain\n",
+      "\t cortical visual area\n",
+      "\t occipital lobe\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "wall of left ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t heart left ventricle\n",
+      "\t cardiac ventricle\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t heart ventricle wall\n",
+      "\t viscus\n",
+      "\t left cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t wall of heart\n",
+      "\t circulatory organ\n",
+      "anterior wall of left ventricle\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t mesoderm-derived structure\n",
+      "\t circulatory system\n",
+      "\t primary circulatory organ\n",
+      "\t heart\n",
+      "\t structure with developmental contribution from neural crest\n",
+      "\t heart left ventricle\n",
+      "\t cardiac ventricle\n",
+      "\t cardiac chamber\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t compound organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t lateral structure\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t thoracic cavity element\n",
+      "\t anatomical wall\n",
+      "\t organ part\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t cardiovascular system\n",
+      "\t heart plus pericardium\n",
+      "\t trunk\n",
+      "\t heart ventricle wall\n",
+      "\t viscus\n",
+      "\t left cardiac chamber\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t wall of heart\n",
+      "\t circulatory organ\n",
+      "\t wall of left ventricle\n",
+      "bronchopulmonary lymph node\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t immune system\n",
+      "\t lymph node\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t trunk region element\n",
+      "\t immune organ\n",
+      "\t lymphoid system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t hemolymphoid system\n",
+      "\t disconnected anatomical group\n",
+      "\t thoracic segment of trunk\n",
+      "\t subdivision of trunk\n",
+      "\t thoracic segment organ\n",
+      "\t trunk\n",
+      "\t non-connected functional system\n",
+      "\t body proper\n",
+      "\t thoracic lymph node\n",
+      "\t main body axis\n",
+      "duodeno-jejunal junction\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t anatomical conduit\n",
+      "\t digestive tract\n",
+      "\t small intestine\n",
+      "\t intestine\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t anatomical junction\n",
+      "\t digestive system\n",
+      "\t subdivision of tube\n",
+      "\t organ part\n",
+      "\t subdivision of digestive tract\n",
+      "\t digestive system element\n",
+      "\t alimentary part of gastrointestinal system\n",
+      "\t intestinal junction\n",
+      "\t digestive tract junction\n",
+      "fimbria of uterine tube\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t anatomical conduit\n",
+      "\t mesoderm-derived structure\n",
+      "\t oviduct\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t reproductive system\n",
+      "\t tube\n",
+      "\t anatomical system\n",
+      "\t fallopian tube\n",
+      "\t reproductive structure\n",
+      "\t lateral structure\n",
+      "\t subdivision of tube\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t pelvic region of trunk\n",
+      "\t female reproductive system\n",
+      "\t female organism\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t subdivision of oviduct\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "inguinal region skin\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t organ part\n",
+      "\t subdivision of trunk\n",
+      "\t abdominal segment of trunk\n",
+      "\t trunk\n",
+      "\t skin of trunk\n",
+      "\t abdominal segment skin\n",
+      "\t inguinal part of abdomen\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "transition zone of prostate\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t mixed endoderm/mesoderm-derived structure\n",
+      "\t prostate gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t zone of organ\n",
+      "\t organ part\n",
+      "\t male organism\n",
+      "\t reproductive gland\n",
+      "\t male accessory sex gland\n",
+      "\t male reproductive gland\n",
+      "peripheral zone of prostate\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t mesoderm-derived structure\n",
+      "\t endoderm-derived structure\n",
+      "\t mixed endoderm/mesoderm-derived structure\n",
+      "\t prostate gland\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t gland\n",
+      "\t male reproductive system\n",
+      "\t reproductive system\n",
+      "\t anatomical system\n",
+      "\t reproductive structure\n",
+      "\t zone of organ\n",
+      "\t organ part\n",
+      "\t male organism\n",
+      "\t reproductive gland\n",
+      "\t male accessory sex gland\n",
+      "\t male reproductive gland\n",
+      "cerebral nuclei\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t anatomical system\n",
+      "\t neural nucleus\n",
+      "\t organ system subdivision\n",
+      "\t multi-tissue structure\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t nucleus of brain\n",
+      "\t regional part of brain\n",
+      "\t telencephalic nucleus\n",
+      "\t brain gray matter\n",
+      "gustatory cortex\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t ectoderm-derived structure\n",
+      "\t central nervous system\n",
+      "\t cerebral cortex\n",
+      "\t nervous system\n",
+      "\t brain\n",
+      "\t forebrain\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t telencephalon\n",
+      "\t pallium\n",
+      "\t anatomical system\n",
+      "\t organ system subdivision\n",
+      "\t lateral structure\n",
+      "\t multi-tissue structure\n",
+      "\t organ part\n",
+      "\t regional part of nervous system\n",
+      "\t multi cell part structure\n",
+      "\t central nervous system cell part cluster\n",
+      "\t gray matter\n",
+      "\t cerebral hemisphere\n",
+      "\t regional part of brain\n",
+      "\t cerebral hemisphere gray matter\n",
+      "\t gustatory system\n",
+      "\t central nervous system gray matter layer\n",
+      "\t nervous system cell part layer\n",
+      "\t brain gray matter\n",
+      "\t gray matter of telencephalon\n",
+      "\t gray matter of forebrain\n",
+      "temple\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t multicellular organism\n",
+      "\t head\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t subdivision of head\n",
+      "\t temporofacial region\n",
+      "\t temporal part of head\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "skin of temple\n",
+      "\t anatomical entity\n",
+      "\t anatomical structure\n",
+      "\t material anatomical entity\n",
+      "\t multicellular anatomical structure\n",
+      "\t organism subdivision\n",
+      "\t ectoderm-derived structure\n",
+      "\t skin of body\n",
+      "\t multicellular organism\n",
+      "\t organ\n",
+      "\t anatomical system\n",
+      "\t head\n",
+      "\t organ system subdivision\n",
+      "\t zone of skin\n",
+      "\t zone of organ\n",
+      "\t integumental system\n",
+      "\t subdivision of organism along main body axis\n",
+      "\t anterior region of body\n",
+      "\t craniocervical region\n",
+      "\t organ part\n",
+      "\t skin of head\n",
+      "\t head or neck skin\n",
+      "\t subdivision of head\n",
+      "\t temporofacial region\n",
+      "\t temporal part of head\n",
+      "\t integument\n",
+      "\t body proper\n",
+      "\t main body axis\n",
+      "\t temple\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_print = 2000\n",
+    "\n",
+    "i_print = 0\n",
+    "for k, v in uberon_ontology_resource[\"ancestors_dictionary\"].items():\n",
+    "    print(uberon_ontology_resource[\"ontology_term_id_to_label\"][k])\n",
+    "    for v_ in v:\n",
+    "        print(\"\\t\", uberon_ontology_resource[\"ontology_term_id_to_label\"][v_])\n",
+    "    i_print += 1\n",
+    "    if i_print == n_print:\n",
+    "        break"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb
new file mode 100644
index 00000000..c0d00d26
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb
@@ -0,0 +1,1246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# cell_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_shortest_path_lengths_from_source(graph, source: str) -> t.Dict[str, float]:\n",
+    "#     # Perform a topological sort of the graph\n",
+    "#     topo_order = list(nx.topological_sort(graph))\n",
+    "\n",
+    "#     # Initialize distances with infinity for all nodes except the source\n",
+    "#     distances = {node: float(\"inf\") for node in graph.nodes()}\n",
+    "#     distances[source] = 0\n",
+    "\n",
+    "#     # Process nodes in reverse topological order\n",
+    "#     for node in reversed(topo_order):\n",
+    "#         if distances[node] != float(\"inf\"):  # Only process reachable nodes\n",
+    "#             for neighbor in graph.predecessors(node):\n",
+    "#                 if distances[neighbor] > distances[node] + 1:\n",
+    "#                     distances[neighbor] = distances[node] + 1\n",
+    "\n",
+    "#     return distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_longest_path_lengths_from_source(graph, source: str) -> t.Dict[str, float]:\n",
+    "#     # Perform a topological sort of the graph\n",
+    "#     topo_order = list(nx.topological_sort(graph))\n",
+    "\n",
+    "#     # Initialize distances with -infinity for all nodes except the source\n",
+    "#     distances = {node: float(\"-inf\") for node in graph.nodes()}\n",
+    "#     distances[source] = 0\n",
+    "\n",
+    "#     # Process nodes in reverse topological order\n",
+    "#     for node in reversed(topo_order):\n",
+    "#         if distances[node] != float(\"-inf\"):  # Only process reachable nodes\n",
+    "#             for neighbor in graph.predecessors(node):\n",
+    "#                 if distances[neighbor] < distances[node] + 1:\n",
+    "#                     distances[neighbor] = distances[node] + 1\n",
+    "\n",
+    "#     return distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "#     \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "#     .. note:\n",
+    "#         The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "#     \"\"\"\n",
+    "#     n_nodes = len(graph.nodes)\n",
+    "\n",
+    "#     distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "#     for name, self_idx in names_to_idx_map.items():\n",
+    "#         for ancestor_name, distance in get_shortest_path_lengths_from_source(graph, name).items():\n",
+    "#             if distance == float(\"inf\"):\n",
+    "#                 continue\n",
+    "#             ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "#             distance_matrix[self_idx, ancestor_idx] = distance\n",
+    "\n",
+    "#     return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_longest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "#     \"\"\"Returns a sparse matrix representation of longest distances.\n",
+    "\n",
+    "#     .. note:\n",
+    "#         The matrix element (i, j) = d iff d is the longest distance between i and j.\n",
+    "#     \"\"\"\n",
+    "#     n_nodes = len(graph.nodes)\n",
+    "\n",
+    "#     distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "#     for name, self_idx in names_to_idx_map.items():\n",
+    "#         for ancestor_name, distance in get_longest_path_lengths_from_source(graph, name).items():\n",
+    "#             if distance == float(\"-inf\"):\n",
+    "#                 continue\n",
+    "#             ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "#             distance_matrix[self_idx, ancestor_idx] = distance\n",
+    "\n",
+    "#     return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>2938728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CL:4023040</td>\n",
+       "      <td>1695623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>1294620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CL:0000624</td>\n",
+       "      <td>1238275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CL:0000625</td>\n",
+       "      <td>1179803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>CL:0011101</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>CL:0000103</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>CL:0000019</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>CL:0002280</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>CL:0000218</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>671 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    cell_type_ontology_term_id  num_cells\n",
+       "0                   CL:0000540    2938728\n",
+       "1                   CL:4023040    1695623\n",
+       "2                   CL:0000128    1294620\n",
+       "3                   CL:0000624    1238275\n",
+       "4                   CL:0000625    1179803\n",
+       "..                         ...        ...\n",
+       "666                 CL:0011101         14\n",
+       "667                 CL:0000103         12\n",
+       "668                 CL:0000019         11\n",
+       "669                 CL:0002280         10\n",
+       "670                 CL:0000218          8\n",
+       "\n",
+       "[671 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"cell_type_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(38307243)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_counts[\"num_cells\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>2938728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>1695623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>1294620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>1238275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>1179803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>bone marrow cell</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>bipolar neuron</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>sperm</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>type N enteroendocrine cell</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>myelinating Schwann cell</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>671 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type  num_cells\n",
+       "0                                               neuron    2938728\n",
+       "1    L2/3-6 intratelencephalic projecting glutamate...    1695623\n",
+       "2                                      oligodendrocyte    1294620\n",
+       "3                      CD4-positive, alpha-beta T cell    1238275\n",
+       "4                      CD8-positive, alpha-beta T cell    1179803\n",
+       "..                                                 ...        ...\n",
+       "666                                   bone marrow cell         14\n",
+       "667                                     bipolar neuron         12\n",
+       "668                                              sperm         11\n",
+       "669                        type N enteroendocrine cell         10\n",
+       "670                           myelinating Schwann cell          8\n",
+       "\n",
+       "[671 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"cell_type\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `cell_type`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL', 'unknown'}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/obophenotype/cell-ontology/releases/download/v2024-01-04/cl.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"CL_\"\n",
+    "\n",
+    "# the 'cell' node\n",
+    "CELL_ROOT_NODE = \"CL:0000000\"\n",
+    "\n",
+    "# the 'eukaryotic cell' node\n",
+    "EUKARYOTIC_CELL_ROOT_NODE = \"CL:0000255\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `cell_type`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(labels_counts[query_label]) - set(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    # for prop in self_class.get_class_properties():\n",
+    "    #     if PARTOF_RELATIONSHIP in prop.name:\n",
+    "    #         for related_term in prop[self_class]:\n",
+    "    #             if related_term.name.startswith(PREFIX):\n",
+    "    #                 graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [1, 0, 0, ..., 1, 0, 0],\n",
+       "       [1, 0, 0, ..., 0, 1, 0],\n",
+       "       [1, 0, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "670"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"unknown\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "890"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {CELL_ROOT_NODE, EUKARYOTIC_CELL_ROOT_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"cell_type_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/1128136463.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_counts_map = {label: count for label, count in zip(labels_counts[query_label], labels_counts[\"num_cells\"])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "    data={\n",
+    "        \"cell_type\": new_labels,\n",
+    "        \"num_cells\": [labels_counts_map.get(label, 0) for label in new_labels],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "220"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_ancestors_idx = [idx for idx, label in enumerate(new_labels) if labels_counts_map.get(label, 0) == 0]\n",
+    "len(empty_ancestors_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_cells = torch.tensor(df[\"num_cells\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.6097561 , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.03160556, 0.79013906, 0.        , ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.80128205, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       ...,\n",
+       "       [0.        , 0.        , 0.        , ..., 0.80128205, 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.        , ..., 0.        , 0.67204301,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+       "        0.57603687]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 0.8\n",
+    "probs = (1 - p) ** new_shortest_distances_matrix * p\n",
+    "probs = probs / probs.sum(axis=1, keepdims=True)\n",
+    "probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/3989491769.py:1: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n",
+      "  df[\"new_num_cells\"] = (num_cells[:, None] * probs).sum(axis=0).int().numpy()\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"new_num_cells\"] = (num_cells[:, None] * probs).sum(axis=0).int().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>num_cells</th>\n",
+       "      <th>new_num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>2938728</td>\n",
+       "      <td>1952112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensory neuron</td>\n",
+       "      <td>1605</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>primary cultured cell</td>\n",
+       "      <td>80</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cultured cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>fibroblast neural crest derived</td>\n",
+       "      <td>0</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>885</th>\n",
+       "      <td>metallothionein-positive alveolar macrophage</td>\n",
+       "      <td>357</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>lung interstitial macrophage</td>\n",
+       "      <td>302</td>\n",
+       "      <td>180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>deuterosomal cell</td>\n",
+       "      <td>67</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>respiratory suprabasal cell</td>\n",
+       "      <td>1792</td>\n",
+       "      <td>1204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>luminal hormone-sensing cell of mammary gland</td>\n",
+       "      <td>196476</td>\n",
+       "      <td>113177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>890 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         cell_type  num_cells  new_num_cells\n",
+       "0                                           neuron    2938728        1952112\n",
+       "1                                   sensory neuron       1605           1799\n",
+       "2                            primary cultured cell         80             64\n",
+       "3                                    cultured cell          0             12\n",
+       "4                  fibroblast neural crest derived          0             95\n",
+       "..                                             ...        ...            ...\n",
+       "885   metallothionein-positive alveolar macrophage        357            275\n",
+       "886                   lung interstitial macrophage        302            180\n",
+       "887                              deuterosomal cell         67             53\n",
+       "888                    respiratory suprabasal cell       1792           1204\n",
+       "889  luminal hormone-sensing cell of mammary gland     196476         113177\n",
+       "\n",
+       "[890 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([], [])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.sort_values(by=\"num_cells\", ascending=False).plot.bar(x=\"cell_type\", y=[\"num_cells\", \"new_num_cells\"], figsize=(20, 10))\n",
+    "# remove x-ticks label\n",
+    "plt.xticks([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 10000000.0)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_df = df.copy()\n",
+    "_df[\"num_cells\"] = 1 + df[\"num_cells\"]\n",
+    "_df[\"new_num_cells\"] = 1 + df[\"new_num_cells\"]\n",
+    "_df.plot.scatter(x=\"num_cells\", y=\"new_num_cells\", figsize=(5, 5), s=1)\n",
+    "plt.plot([0, 1e7], [0, 1e7], color=\"red\")\n",
+    "plt.loglog()\n",
+    "plt.xlim(0.1, 1e7)\n",
+    "plt.ylim(0.1, 1e7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## combine ontologies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/2248219605.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  tissue_onotology_data = torch.load(\"tissue_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  development_stage_onotology_data = torch.load(\"development_stage_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  disease_onotology_data = torch.load(\"disease_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  sex_onotology_data = torch.load(\"sex_ontology_data.pt\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n",
+    "tissue_onotology_data = torch.load(\"tissue_ontology_data.pt\")\n",
+    "development_stage_onotology_data = torch.load(\"development_stage_ontology_data.pt\")\n",
+    "disease_onotology_data = torch.load(\"disease_ontology_data.pt\")\n",
+    "sex_onotology_data = torch.load(\"sex_ontology_data.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology_infos = {\n",
+    "    \"cell_type\": cell_type_ontology_data,\n",
+    "    \"tissue\": tissue_onotology_data,\n",
+    "    \"development_stage\": development_stage_onotology_data,\n",
+    "    \"disease\": disease_onotology_data,\n",
+    "    \"sex\": sex_onotology_data,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['CL:0000540', 'CL:0000101', 'CL:0000001']"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data[\"names\"][:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(ontology_infos, \"ontology_infos.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb
new file mode 100644
index 00000000..a1de1457
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb
@@ -0,0 +1,854 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# development_stage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>development_stage_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2645739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>HsapDv:0000095</td>\n",
+       "      <td>1768880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HsapDv:0000144</td>\n",
+       "      <td>1768121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HsapDv:0000123</td>\n",
+       "      <td>1411475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HsapDv:0000136</td>\n",
+       "      <td>1396533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>HsapDv:0000219</td>\n",
+       "      <td>2240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>HsapDv:0000223</td>\n",
+       "      <td>976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>HsapDv:0000221</td>\n",
+       "      <td>930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>HsapDv:0000220</td>\n",
+       "      <td>867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>HsapDv:0000192</td>\n",
+       "      <td>509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>174 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    development_stage_ontology_term_id  num_cells\n",
+       "0                              unknown    2645739\n",
+       "1                       HsapDv:0000095    1768880\n",
+       "2                       HsapDv:0000144    1768121\n",
+       "3                       HsapDv:0000123    1411475\n",
+       "4                       HsapDv:0000136    1396533\n",
+       "..                                 ...        ...\n",
+       "169                     HsapDv:0000219       2240\n",
+       "170                     HsapDv:0000223        976\n",
+       "171                     HsapDv:0000221        930\n",
+       "172                     HsapDv:0000220        867\n",
+       "173                     HsapDv:0000192        509\n",
+       "\n",
+       "[174 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"development_stage_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>development_stage</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2645739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>80 year-old and over human stage</td>\n",
+       "      <td>1768880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50-year-old human stage</td>\n",
+       "      <td>1768121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29-year-old human stage</td>\n",
+       "      <td>1411475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42-year-old human stage</td>\n",
+       "      <td>1396533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>93-year-old human stage</td>\n",
+       "      <td>2240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>97-year-old human stage</td>\n",
+       "      <td>976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>95-year-old human stage</td>\n",
+       "      <td>930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>94-year-old human stage</td>\n",
+       "      <td>867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>19-month-old human stage</td>\n",
+       "      <td>509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>174 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    development_stage  num_cells\n",
+       "0                             unknown    2645739\n",
+       "1    80 year-old and over human stage    1768880\n",
+       "2             50-year-old human stage    1768121\n",
+       "3             29-year-old human stage    1411475\n",
+       "4             42-year-old human stage    1396533\n",
+       "..                                ...        ...\n",
+       "169           93-year-old human stage       2240\n",
+       "170           97-year-old human stage        976\n",
+       "171           95-year-old human stage        930\n",
+       "172           94-year-old human stage        867\n",
+       "173          19-month-old human stage        509\n",
+       "\n",
+       "[174 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"development_stage\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `development_stage`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'HsapDv', 'unknown'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"http://purl.obolibrary.org/obo/hsapdv.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"HsapDv_\"\n",
+    "\n",
+    "# the 'life cycle stage' node\n",
+    "LIFE_CYCLE_STAGE_NODE = \"HsapDv:0000000\"\n",
+    "\n",
+    "# the 'life cycle' node\n",
+    "LIFE_CYCLE_NODE = \"HsapDv:0000001\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['infant stage',\n",
+       " 'adult stage',\n",
+       " 'young adult stage',\n",
+       " 'middle aged stage',\n",
+       " '1-month-old stage']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these are the labels that are not unique\n",
+    "non_unique_labels = []\n",
+    "for i in range(len(labels) - 1):\n",
+    "    if labels[i] in labels[i + 1 :]:\n",
+    "        non_unique_labels.append(labels[i])\n",
+    "non_unique_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extract_names_to_labels_map = dict(zip(names_counts[query_name], labels_counts[query_label]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `development_stage`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [1, 1, 0, ..., 1, 0, 0],\n",
+       "       [1, 1, 0, ..., 1, 1, 0],\n",
+       "       [1, 1, 0, ..., 1, 1, 1]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [ 1.,  0., inf, ..., inf, inf, inf],\n",
+       "       [ 1.,  2.,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [ 1.,  2., inf, ...,  0., inf, inf],\n",
+       "       [ 1.,  3., inf, ...,  1.,  0., inf],\n",
+       "       [ 1.,  4., inf, ...,  2.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "110"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.index(\"15-year-old stage\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'15-year-old stage'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels[110]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['life cycle stage',\n",
+       " 'life cycle',\n",
+       " '15-year-old stage',\n",
+       " '15-19 year-old',\n",
+       " 'prime adult stage',\n",
+       " 'adult stage',\n",
+       " 'young adult stage',\n",
+       " 'postnatal stage']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[labels[idx] for idx in ancestors_matrix[110].nonzero()[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "173"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"unknown\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "191"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {LIFE_CYCLE_NODE, LIFE_CYCLE_STAGE_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"development_stage_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='development_stage'>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb
new file mode 100644
index 00000000..6761dbd4
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb
@@ -0,0 +1,868 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# disease"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>disease_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PATO:0000461</td>\n",
+       "      <td>25905102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MONDO:0100096</td>\n",
+       "      <td>4109684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MONDO:0001627</td>\n",
+       "      <td>1135641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MONDO:0018177</td>\n",
+       "      <td>1001237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MONDO:0007915</td>\n",
+       "      <td>708987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>MONDO:0012419</td>\n",
+       "      <td>1614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>MONDO:0005348</td>\n",
+       "      <td>1227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>MONDO:0004994</td>\n",
+       "      <td>1027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>MONDO:0019562</td>\n",
+       "      <td>1018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>MONDO:0002513</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   disease_ontology_term_id  num_cells\n",
+       "0              PATO:0000461   25905102\n",
+       "1             MONDO:0100096    4109684\n",
+       "2             MONDO:0001627    1135641\n",
+       "3             MONDO:0018177    1001237\n",
+       "4             MONDO:0007915     708987\n",
+       "..                      ...        ...\n",
+       "93            MONDO:0012419       1614\n",
+       "94            MONDO:0005348       1227\n",
+       "95            MONDO:0004994       1027\n",
+       "96            MONDO:0019562       1018\n",
+       "97            MONDO:0002513         57\n",
+       "\n",
+       "[98 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"disease_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>disease</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>normal</td>\n",
+       "      <td>25905102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>COVID-19</td>\n",
+       "      <td>4109684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>dementia</td>\n",
+       "      <td>1135641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>1001237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>systemic lupus erythematosus</td>\n",
+       "      <td>708987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>age related macular degeneration 7</td>\n",
+       "      <td>1614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>keloid</td>\n",
+       "      <td>1227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>cardiomyopathy</td>\n",
+       "      <td>1027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>localized scleroderma</td>\n",
+       "      <td>1018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>kidney benign neoplasm</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               disease  num_cells\n",
+       "0                               normal   25905102\n",
+       "1                             COVID-19    4109684\n",
+       "2                             dementia    1135641\n",
+       "3                         glioblastoma    1001237\n",
+       "4         systemic lupus erythematosus     708987\n",
+       "..                                 ...        ...\n",
+       "93  age related macular degeneration 7       1614\n",
+       "94                              keloid       1227\n",
+       "95                      cardiomyopathy       1027\n",
+       "96               localized scleroderma       1018\n",
+       "97              kidney benign neoplasm         57\n",
+       "\n",
+       "[98 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"disease\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `disease`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MONDO', 'PATO'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/monarch-initiative/mondo/releases/download/v2024-01-03/mondo.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"MONDO_\"\n",
+    "\n",
+    "# # the 'life cycle stage' node\n",
+    "# LIFE_CYCLE_STAGE_NODE = \"HsapDv_0000000\"\n",
+    "\n",
+    "# # the 'life cycle' node\n",
+    "# LIFE_CYCLE_NODE = \"HsapDv_0000001\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['obsolete coronary heart disease',\n",
+       " 'obsolete deafness, autosomal recessive',\n",
+       " 'obsolete Wilms tumor',\n",
+       " 'obsolete emphysema',\n",
+       " 'obsolete gastric cancer',\n",
+       " 'obsolete microcephalic primordial dwarfism',\n",
+       " 'obsolete familial adenomatous polyposis',\n",
+       " 'obsolete Heimler syndrome',\n",
+       " 'obsolete epilepsy, familial focal, with variable foci',\n",
+       " 'obsolete Frontometaphyseal dysplasia',\n",
+       " 'obsolete JMP syndrome',\n",
+       " 'obsolete Walker-Warburg syndrome',\n",
+       " 'obsolete marginal zone B-cell lymphoma',\n",
+       " 'obsolete splenic marginal zone lymphoma',\n",
+       " 'obsolete small cell neuroendocrine carcinoma',\n",
+       " 'obsolete primary bone dysplasia with disorganized development of skeletal components',\n",
+       " 'obsolete nut midline carcinoma',\n",
+       " 'obsolete craniodiaphyseal dysplasia',\n",
+       " 'obsolete herpes simplex virus keratitis',\n",
+       " 'obsolete meningococcal meningitis',\n",
+       " 'obsolete chronic neutrophilic leukemia',\n",
+       " 'obsolete Friedreich ataxia',\n",
+       " 'obsolete ovarian cancer',\n",
+       " 'obsolete thymoma type AB',\n",
+       " 'obsolete thymic carcinoma',\n",
+       " 'obsolete craniopharyngioma',\n",
+       " 'obsolete juvenile xanthogranuloma',\n",
+       " 'obsolete ampulla of vater carcinoma',\n",
+       " 'obsolete fibrolamellar carcinoma',\n",
+       " 'obsolete syringocystadenoma papilliferum',\n",
+       " 'obsolete cerebellar liponeurocytoma',\n",
+       " 'obsolete hepatoblastoma',\n",
+       " 'obsolete mitochondrial myopathy',\n",
+       " 'obsolete acute megakaryoblastic leukemia',\n",
+       " 'obsolete Wiskott-Aldrich syndrome',\n",
+       " 'obsolete adrenocortical carcinoma',\n",
+       " 'obsolete lactose intolerance',\n",
+       " 'obsolete carcinoid syndrome',\n",
+       " 'obsolete pigmented villonodular synovitis',\n",
+       " 'obsolete apnea, central sleep',\n",
+       " 'obsolete primary bone dysplasia with multiple joint dislocations',\n",
+       " 'obsolete primary bone dysplasia with increased bone density',\n",
+       " 'obsolete vibratory angioedema',\n",
+       " 'obsolete anus, imperforate',\n",
+       " 'obsolete SC phocomelia syndrome',\n",
+       " 'obsolete testicular regression syndrome',\n",
+       " 'obsolete Amish infantile epilepsy syndrome',\n",
+       " 'obsolete microcephaly, short stature, and impaired glucose metabolism',\n",
+       " 'obsolete Pierre Robin syndrome associated with collagen disease',\n",
+       " 'obsolete Pierre Robin syndrome associated with a chromosomal anomaly',\n",
+       " 'obsolete Pierre Robin syndrome associated with branchial archs anomalies',\n",
+       " 'obsolete Pierre Robin syndrome associated with bone disease',\n",
+       " 'obsolete autosomal ichthyosis syndrome with other associated signs',\n",
+       " 'obsolete rare bone disease related to a common gene or pathway defect',\n",
+       " 'obsolete rare disorder with female infertility due to a congenital hypogonadotropic hypogonadism',\n",
+       " 'obsolete type 11 collagen-related bone disorder',\n",
+       " 'obsolete dysostosis with predominant craniofacial involvement',\n",
+       " 'obsolete syndrome with synostosis or other joint formation defect',\n",
+       " 'obsolete deafness hyperuricemia neurologic ataxia',\n",
+       " 'obsolete overgrowth or tall stature syndrome with skeletal involvement',\n",
+       " 'obsolete dysostosis with brachydactyly without extraskeletal manifestations',\n",
+       " 'obsolete dysostosis with brachydactyly with extraskeletal manifestations',\n",
+       " 'obsolete ectrodactyly with and without other manifestations']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these are the labels that are not unique\n",
+    "non_unique_labels = []\n",
+    "for i in range(len(labels) - 1):\n",
+    "    if labels[i] in labels[i + 1 :]:\n",
+    "        non_unique_labels.append(labels[i])\n",
+    "non_unique_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extract_names_to_labels_map = dict(zip(names_counts[query_name], labels_counts[query_label]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx + 1 for idx, name in enumerate(names)}\n",
+    "names_to_idx_map[\"PATO:0000461\"] = 0  # Add the normal cell type\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx + 1: name for idx, name in enumerate(names)}\n",
+    "idx_to_names_map[0] = \"PATO:0000461\"  # Add the normal cell type\n",
+    "idx_to_labels_map = {idx + 1: label for idx, label in enumerate(labels)}\n",
+    "idx_to_labels_map[0] = \"normal\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `disease`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PATO:0000461'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "graph.add_node(\"PATO:0000461\")  # Add the normal cell type\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 1, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 1, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [inf,  0., inf, ..., inf, inf, inf],\n",
+       "       [inf, inf,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [inf, inf, inf, ...,  0., inf, inf],\n",
+       "       [inf, inf, inf, ..., inf,  0., inf],\n",
+       "       [inf, inf, inf, ..., inf, inf,  0.]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "98"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "350"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"disease_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "252"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(new_labels) - set(labels_counts[query_label].values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='disease'>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyVNbcDWiJ1j/pnlrfn3neHl9QSwAAAAAAgJq4EgQAAAAAAMiSEAQAAAAAAMiSEAQAAAAAAMiSZ4I0A88UAQAAAACAtc+VIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJbKmrsBNNz6J91T6/szz9tjrdYDAAAAAEBL4EoQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS2XN3QBanvVPuqfG92aet8cX2BIAAAAAAKiZK0EAAAAAAIAsCUEAAAAAAIAsuR0WX6jabqUV4XZaAAAAAAA0HVeCAAAAAAAAWXIlCC2KK0kAAAAAAKgvV4IAAAAAAABZEoIAAAAAAABZcjss/q0Uezut5qx3KzAAAAAAgIZpVAhy2WWXxfnnnx9z586NIUOGxG9+85vYaqutauz/lltuiVNPPTVmzpwZgwYNiv/+7/+O3XffvdGNBhquJQdAa7tegAQAAAAAeWpwCHLzzTfHscceGxMmTIitt946xo8fH8OHD4/XX389evbsuVr/TzzxROy///4xbty42HPPPWPixImx9957x/PPPx+bbLJJk0wEwNr0ZQ5wvuz1LbntX/Z64R0AAABA3Rocglx00UVx+OGHxyGHHBIRERMmTIh77rknrr766jjppJNW6//iiy+OESNGxM9//vOIiDjrrLPiwQcfjEsvvTQmTJhQZPMBgDX5Mgc4X/b6ltz2L3t9S257feoBAAD48mlQCLJ06dKYOnVqnHzyyYXXSktLY9iwYfHkk0+usebJJ5+MY489ttprw4cPjzvvvLPG8VRVVUVVVVXh/wULFkRExMKFCwuvraxaUmtbP9vv5xVTq772+pbc9i97fUtu+5e9viW3/cte35Lb/mWvb8lt/7LXt+S2f9nrW3Lb61O/yen31/r+y2cOX2v1zTnull7fktv+Za9vyW3/ste35LZ/2etbctu/7PUtue1f9vqW3Pb61APUZNUxWkqp1v5KUl19fMa7774bffv2jSeeeCK23XbbwusnnHBCPPLII/H000+vVtOmTZv4/e9/H/vvv3/htcsvvzzOPPPMmDdv3hrHc8YZZ8SZZ55Z32YBAAAAAAD/hmbPnh3rrrtuje836sHoa9vJJ59c7eqRlStXxvvvvx/dunWLkpKS1fpfuHBh9OvXL2bPnh0VFRUNGlcxtep99j67f7/6ltz25q5vyW1v7vqW3PaWXt+S297c9S257S29viW3vbnrW3LbW3p9S257c9e35La39PqW3Pbmrm/JbW/u+pbc9pZe35Lb3tz1LbntLb2+Jbe9PvUppfjoo49inXXWqXU4DQpBunfvHq1atVrtCo558+ZF796911jTu3fvBvUfEVFeXh7l5eXVXuvcuXOd7auoqGjUh1lsrXqfvc/u36++Jbe9uetbctubu74lt72l17fktjd3fUtue0uvb8ltb+76ltz2ll7fktve3PUtue0tvb4lt72561ty25u7viW3vaXXt+S2N3d9S257S69vyW2vq76ysrLO+tKGjKxNmzax5ZZbxqRJkwqvrVy5MiZNmlTt9lifte2221brPyLiwQcfrLF/AAAAAACAptDg22Ede+yxcdBBB8XXv/712GqrrWL8+PGxePHiOOSQQyIiYvTo0dG3b98YN25cREQcffTRseOOO8aFF14Ye+yxR9x0003x3HPPxRVXXNG0UwIAAAAAAPAZDQ5Bvv/978d7770Xp512WsydOzeGDh0a9913X/Tq1SsiImbNmhWlpf93gck3v/nNmDhxYpxyyinxi1/8IgYNGhR33nlnbLLJJk02EeXl5XH66aevdguttV2r3mfvs/v3q2/JbW/u+pbc9uaub8ltb+n1LbntzV3fktve0utbctubu74lt72l17fktjd3fUtue0uvb8ltb+76ltz25q5vyW1v6fUtue3NXd+S297S61ty25uifpWSlFIqaggAAAAAAABfQg16JggAAAAAAEBLIQQBAAAAAACyJAQBAAAAAACyJAQBgCa2fPnyGDt2bLzzzjvN3RQaIKUUs2bNik8++aS5mwIAAC3eihUrYsqUKfHhhx82d1NanH/nY5Ply5fHddddF/PmzWt0veNxPk8I0sItXLgw7rzzznj11VcbVLdkyZJ47bXX4sUXX6zW1WX69OmNbWpERHz44Ydx1VVXxcknnxzvv/9+REQ8//zz8Y9//KPO2o8//jiWLFlS+P/tt9+O8ePHxwMPPNCgNjR22os1e/bsaivgZ555Jo455pi44oor1vq4I4r77FuyZcuWxaGHHhozZswoeliffPJJLFy4sFpXl/vuuy8ee+yxwv+XXXZZDB06NA444ID44IMPim7Tl9nDDz9c9DDeeuutOOWUU2L//feP+fPnR0TEvffeG6+88krRw67NsmXLoqysLF5++eVG1ZeVlcX5558fy5cvb3QbWvoy++ijj8Z//ud/xrbbblto8/XXX19teVibGjPvpJRi4MCBMXv27C+kjWty0EEHxZQpU4oezptvvhn3339/fPzxxxHx6bQ1VGPWeTlZsWJFTJs2Lft1dUTz76PkpLH75l8Wxcz3Df2RxnzXeMXuYxX72Rd7XOa759/R888/Hy+99FLh/7vuuiv23nvv+MUvfhFLly5t0LCaY1vT2OOyVq1axW677fal2Z9qSYFCUxybrL/++jF27NiYNWtWE7Zs7SsrK4uf/OQnjf6+muJ4fJX58+fHyy+//IX9hjh16tS1NuyW5JNPPolnnnkm7r777vjTn/5UrWussiZsX4sya9asmDNnTpSWlsaAAQOiW7duzd2kevmP//iP2GGHHeLII4+Mjz/+OL7+9a/HzJkzI6UUN910U3z3u9+ttf69996LQw45JO699941vr9ixYpa6wcOHBg77rhjjBkzJr73ve9F27Zt6932F198MYYNGxaVlZUxc+bMOPzww6Nr165x++23x6xZs+K6666rtX7UqFGx7777xk9+8pP48MMPY+utt47WrVvHP//5z7joooviv/7rv2qtL3baV6xYEb/+9a/jj3/8Y8yaNWu1HZVVP1LW5IADDogf/ehHceCBB8bcuXNj1113ja997Wtx4403xty5c+O0006rtf6NN96Ihx9+OObPnx8rV66s9l5dtcV+9hERzz33XI3Tfvvtt9dZf/3118eECRNixowZ8eSTT0b//v1j/PjxscEGG8SoUaPqrL/11ltrHP/zzz9fY13r1q3jtttui1NPPbXOcazJkiVL4oQTTog//vGP8a9//Wu19+uab37+85/Hf//3f0dExEsvvRTHHXdcHHvssfHwww/HscceG9dcc02dbVi8eHE88sgja5z2o446qs76hx9+OHbeeec6+6tJY7/7ESNGxLrrrhuHHHJIHHTQQdGvX78GjfeRRx6JkSNHxnbbbRdTpkyJc845J3r27BkvvPBC/O53v4tbb721zmEUM9+st956dX6/tfn2t78djzzySKy//voNrm2KZbax077KO++8E3/605/WWH/RRRfVWnvbbbfFgQceGD/84Q/jr3/9a1RVVUVExIIFC+Lcc8+NP//5z3WOv5h1TmPnndLS0hg0aFD861//ikGDBtXZxtr87W9/W2Pbv/Od79Rat2DBghg2bFj079+/sOz07du33uP917/+Fd///vfjoYceipKSknjjjTdiwIABMWbMmOjSpUtceOGFtdYXs84rdjvZFIrZ1hxzzDGx6aabxpgxY2LFihWx4447xhNPPBHt27ePu+++O3baaac6x1/strK5FLuPElHcOiOi+G1dROOXu9mzZ0dJSUmsu+66EfHpD7ITJ06MjTfeOH70ox/VWlvsvnmxbS9WsfP9ypUr45xzzokJEybEvHnz4u9//3sMGDAgTj311Fh//fVjzJgxNdYWO981xTqn2G1lcyl2H6vYz77Y47KmWOc0hWKWu2Jqi91WFFO/ePHiOO+882LSpElrPK6s7aTHZcuWxVe/+tW4++67Y/DgwXW2sybNtb5bkxUrVsRLL70U/fv3jy5dutTZ/+mnnx6HHnpo9O/fv8Hj+vGPfxwnnXRSbLrppjF9+vT4wQ9+EPvss0/ccsstsWTJkhg/fnyNtU2xrSlmH6nY47JNNtkkpk+fHhtssEGd7VyTefPmxfHHH1+Ybz9/ck9dx2zFbKsiit9WNHYfpymOTY455pi49tprY+zYsbHzzjvHmDFjYp999ony8vJ61T/00EPx2GOPVfv99Dvf+U6d7XnhhRdi6tSpsdNOO8WAAQPilVdeicsuuyxWrlwZ++yzTwwfPrzOcW+11VYxbdq0Ri1vEcUdj0d8GkYcdNBB8eqrrxbmuZKSkkgpRUlJSZ3z3dixY+P444+P9u3bV3v9448/jvPPP7/Gbd03vvGNGDBgQBx66KFx8MEHxzrrrNOo9hfroIMOijFjxsQOO+zQ6GE0dn1/3333xejRo+Of//znau/V57OvUfo3c9lll6X11lsvlZaWVuu222679Nxzz9V7OLfcckvab7/90tZbb50233zzal19LFq0KN1zzz3pt7/9bbr44ourdbXp1atXmjZtWkoppRtvvDENHDgwLV68OF1++eVp6NChdY73gAMOSNttt1169tlnU4cOHdIDDzyQrr/++rTRRhulu+++u876v/71r+moo45KPXr0SJWVlelHP/pRevrpp+s1zbvsskv6+c9/nlJKqWPHjumtt95KKaX0+OOPp/79+9dZ361bt/Tyyy+nlFK68sor02abbZZWrFiR/vjHP6avfvWrddYXO+2nnnpq6tOnT7rgggtS27Zt01lnnZXGjBmTunXrVuf3llJKnTt3Tq+99lpKKaWLL744ffOb30wppXT//fenDTbYoNbaK664IrVq1Sr16tUrDRkyJA0dOrTQ1WeeK/az/8Mf/pBat26d9txzz9SmTZu05557pq985SupsrIyHXzwwXXWX3755al79+7p7LPPTu3atSuM/5prrkk77bRTnfUXX3xx6tixYzryyCNTmzZt0o9//OM0bNiwVFlZmX7xi1/UWT969Oh00UUX1dnfmvz0pz9NgwcPTrfeemtq165duvrqq9NZZ52V1l133XTDDTfUWd+hQ4c0Y8aMlFJKp59+evrud7+bUkpp6tSpqVevXnXWP//886l3796poqIitWrVKvXo0SOVlJSkDh061DnfrNKmTZs0YMCAdNZZZ6VZs2bVq2aVYr779957L1100UVpyJAhqaysLO22227p5ptvTlVVVfUa9zbbbJMuvPDClFL1+fbpp59Offv2rbO+2PnmqquuSrvvvnv617/+Va/2ft5vf/vb1Lt373TccceliRMnprvuuqtaV5til9lip/0vf/lLat++fdpkk01SWVlZGjp0aOrcuXOqrKxMO++8c531Q4cOTb///e9Xa//zzz9fr/m+2HVOMfPOn/70p7T99tunl156qc7xrMlbb72VNttss1RSUpJKS0tTSUlJ4e/S0tJ6DWP+/PnpwgsvTJtttlkqKytLI0aMSLfccktaunRpnbUHHnhgGj58eJo9e3a1ab/vvvvSxhtvXGd9Meu8YreTxSp2W9O3b9/07LPPppRSuuOOO9I666yTXn/99XTKKacUttm1KXa+/azFixenV199Nb3wwgvVurrce++96dFHHy38f+mll6YhQ4ak/fffP73//vs11hWzj5JS8euMYrd1xS5322+/fbruuutSSinNmTMnVVRUpG233TZ17949nXnmmbXWFrtv3hTrjIceeqjG9yZMmFBrbbHz/ZlnnpkGDBiQbrjhhmrL3U033ZS22WabWmuLne+KXecUu61MKaUlS5akxYsXF/6fOXNm+vWvf53uv//+etXPnj07XXbZZenEE09M/+///b9qXW2K3ccq9rMv9ris2PEXq5jlrthltthtRbH1P/jBD1KfPn3SCSeckH7961+n8ePHV+vqss4666S//e1vdfa3Jk2xvtthhx3S73//+7RkyZJGteHoo49OV111VUoppeXLl6ftttuusL15+OGH66wfMmRIatWqVfr2t7+dbrzxxvTJJ5/Ue9wVFRXpzTffTCmldN5556XddtstpZTSY489ltZdd91aa4vd1hS7j1Tscdm9996bhg4dmv73f/83vfvuu2nBggXVurqMGDEibbzxxunyyy9Pd9xxR7rzzjurdXUpZltV7Lai2H2cYo9NVpk6dWr62c9+lrp37566dOmSjjjiiDR16tQa+583b17aaqutUmlpaSorK0ulpaVpyy23TL17906tWrUqHKuuyW233ZZatWqVunXrljp27JgefPDB1Llz5zRs2LA0fPjw1KpVq3TjjTfW2eabb745DRgwIP3mN79JTzzxRIP3i4s5Hk8ppc022yzts88+6amnnkozZsxIM2fOrNbVpbS0NM2bN2+11//5z3/Wus4rKSlJhx9+eOrZs2cqKytLe+yxR7rjjjvS8uXL6xxnUxo1alRq3bp1GjhwYDrnnHPSO++8U+/aYtf3AwcOTD/96U/T3Llzi5mE1bSoEOTzYUFt3Zqcf/75aZ111km/+c1v0pVXXpkGDx6cxo4dm+6999504IEHpvbt2xcOAupqR3OtBNu2bVv4EfPAAw9MJ554Ykoppbfffjt16NChznH37t27EFp06tQpvf766ymllO6666603Xbb1Vm/yrJly9Jtt92W9tprr9S6dev0ta99LV144YVp/vz5NdZ8dqP/2Q3nzJkzU3l5eZ3jbNeuXXr77bdTSintt99+6YwzzkgppTRr1qzUrl27OuuLnfYBAwYUwpKOHTsWpuXiiy9O+++/f531n/0xfK+99krnnXdeSunT765t27a11q633nqF/huj2M9+0003TZdeemm1+pUrV6bDDz88nXbaaXXWDx48ON1xxx2rjf+ll15K3bp1q7N+o402ShMnTlyt/tRTT01HHHFEnfVnnXVW6ty5c/rud7+bzj333AYFj/369SvsEHfq1Cm98cYbKaWUrrvuujRy5Mg6x92lS5f0yiuvpJRS2m677dL//M//pJRSmjFjRr3m2x133DEdfvjhacWKFYVpnzVrVtphhx3SbbfdVmd9SsUdKBf73a8yderUdOSRR6Zu3bqlbt26pZ/97GeFHfmadOjQIU2fPr3auFP69LOrz3xb7HwzdOjQ1LFjx1ReXp6+8pWvNDjwXrWhX1NX14a/2GW22Gn/xje+Ufh+V9V/9NFH6Tvf+U66/PLL66xv165dYX332fG/9dZbX8g6p5h5p3PnzqlNmzaptLQ0tW3bNnXp0qVaV5c999wzjRo1Kr333nupY8eO6W9/+1t69NFH01ZbbZWmTJlSZ/3nrVp22rZtm7p3756OOeaY9Pe//73G/j97kPz5z74++wnFrPOK3U5+3iuvvJLuvffeeh+wFLutKS8vT7Nnz04ppXT44Yeno48+OqWU0vTp01OnTp3qrG+K9eX8+fPTHnvssdrJOvU9YNhkk03SPffck1JK6cUXX0zl5eXp5JNPTttss02tP44Vs4+SUvHrjGK3dcUud8X8IFvsvnlTrDPatGmTjj/++GpB6XvvvZf23HPP1Llz51pri53vN9xww/SXv/wlpVR9uXv11VfrHHex812x65xit5UppbTrrrum3/72tymllD744IPUq1evtO6666a2bdvWOe8XGx6u0th9rGI++2KPy4od/yqNPTGxmOWu2GW22G1FsfWVlZXpscceq7O/mpxzzjnpoIMOSsuWLWtwbVOs744++ujUo0ePVFFRkQ477LD05JNPNqgNxQa/KX36e86qH5M7d+6cfvKTn6RnnnmmzrpOnToV9uGGDRtWCJ3qM98Xu60pdh+p2OOyzx8Hrerqc1y0apx//etf6+yvJsVsq4rdVhS7j1PsscnnLV26NI0fPz6Vl5en0tLSNGTIkPS73/0urVy5slp/3//+99Pee++dFixYkD755JN05JFHptGjR6eUUpo0aVLq1q1bjcHpFltskc4+++yU0qfBbefOndPYsWML719wwQX1Cu9qOo6u73xTzPF4Sp9+36uOhRqjpKRkjb+RTpo0KXXv3r3Wunnz5qVly5alW2+9Ne2+++6Fk6JPOOGEwm+ZNencufNq80lNXV0ae3Jesev7Tp06FfbrmlKLCkHWX3/9enU1Haysv/766c9//nPh/9dffz1169atsAE/6qij0q677lpnO5pzJTho0KB08803p0WLFqUePXqkSZMmpZRSmjZtWr02Xp06dSrscK633nqFHaDp06fXa4f18z755JN00UUXpfLy8lRSUpLKy8vTgQcemN59993V+u3Ro0d6/vnnU0rVP7cHHnigzjMfUvp0h+/iiy9Os2bNShUVFemJJ55IKaX03HPP1evM4mKnvX379oWd/d69exdS87feeitVVFTUWb/VVlulE088MU2ZMiW1bdu2cHDy5JNP1nn2RKdOnQqfV2MU+9m3b9++8Nl17do1vfjiiymllP72t7+l3r1711nftm3bQlL+2fH//e9/r/eB1qr6Hj16FD67v//976lr16511jdmfbFKhw4dCt973759C0Ha9OnT67XDuddee6Xhw4ensWPHptatWxfS8/vvvz8NGjSozvrKysrCDzOVlZWFM6+eeuqptNFGG9VZ/3kNPVAu9rv/rH/84x/p9NNPT+Xl5alDhw6pVatWafvtty+cSfh5ffv2TY8//nhKqfp8c/vtt6cBAwbUOb5i55szzjij1m5tKnaZLXbaP/tjUufOnQvf0bRp0+p1JcoGG2yQHnzwwdXa//vf/z4NHjy4zvpi57ti5p1rr7221q4u3bp1K5yZVFFRUVh+J02aVK+d/c96991303nnnZc22mij1KFDhzR69Oi0yy67pLKyshqvbuvYsWPhAPuz0/7ss8/W67svZp1X7HZylcaeOVTstma99dZL999/f1q+fHnq169f4cfVl19+uc4D5JSaZn1Z7FWrjb36sJh9lJSKX2cUu60rdrkr5gfZYvfNm2Kd8fjjj6cNN9wwDRkyJL3yyivp7rvvTr169Uo77LBDnWcqFjvf17TcvfLKK3WuM4qd74pd5xS7rUypuCsiig0PP6uh+1jFfvbFHpcVO/6UijsxsZjlrthltthtRbH166+/fqOv5Egppb333jt16tQp9enTJ+22225pn332qdbVpqn2kVadkPmd73wntW7dOg0ePDidf/759TpjuNjg97OWLl2abrvttrTnnnum1q1bp0033TSNHz8+ffjhh2vsf+edd06jR49O1113XWrdunXhx9XJkyfXua0sdltT7D5SscdlkydPrrWry+DBgwvHRo1RzLaq2G1Fsfs4xR6brLJ06dJ08803pxEjRqRWrVql7bbbLl199dVp7NixqVevXqudPFBRUVFtG7Jo0aLUunXrwpU7q/ZP1+Sz+1UrV65MrVu3LqyrUvp0O92xY8c62/z5Ky8aeiVGsUaNGpVuvfXWBtetCiFKS0tXCyQqKipSaWlp+ulPf1pj/aoQ5LPeeeedNHbs2DRgwIBUWlqavvWtb9VY/9n548ILL0xdunRJP/jBDwonAf/gBz9IXbp0afDdUhpycl6x6/tDDjmkcNVeU2pRIUixPrvDkNKnC2NZWVnhB/tp06bVa0FszpXgZZddlsrKylLnzp3TkCFD0ooVK1JKKV1yySX1uozx61//errvvvtSSp8e5B144IHpnXfeSSeccEK9Nl6rPPvss+m//uu/UpcuXdK6666bfvnLX6bp06enKVOmpF122SV94xvfWK1mzJgxae+9905Lly5NHTt2TNOnT09vv/122nzzzQs7H7W55ZZbUuvWrVNpaWm1sOrcc89NI0aMWOvT/pWvfCU99dRTKaVPz+gfN25cSunTSyh79OhRZ/3DDz+cOnfunEpLS9MhhxxSeP3kk0+uc4fx0EMPLZxl1hjFfvZ9+/YtbLQ23XTTQgj4xBNP1Osgc/DgwYXLVD+703HJJZfU64z6DTbYoLDTs+WWWxZu7XD//fc36uyHhth0000LO2a77LJLOu6441JKnx541ecg7e2330577LFH2myzzaqtxI855pj0s5/9rM767t27FzYsgwYNKszDr776amrfvn2Dpyelhh0oF/vdL126NN1yyy1p5MiRqaysLG2zzTbpyiuvTIsWLUozZsxIP/zhD2v8Ufy4445L22+/fZozZ07hjPTHHnssDRgwoF4hRHPON5/38ccfN6j/YpfZYqe9V69ehW3T4MGDC2ffT5s2rV7h37nnnps23njj9NRTT6VOnTqlRx99NN1www2pR48e6ZJLLqmzvtj5rth5pxidO3cunCk3YMCAwm1q3nzzzXoF7kuXLk233npr2mOPPVLr1q3TlltumX77299Wu1XA7bffXuOPkyNHjkynnHJKSikV5p0VK1ak/fbbr/CDeG2KWecVu51cpbFnDhW7rTn99NNTZWVl+upXv5rWW2+9wi0ufve739V5q4SUip9vUyr+qtXGXn1YzD5KSsWvM4rd1hW73BXzg2yx++bFtn2Vjz76KP3whz9M5eXlqXXr1um8885b7azONSl2vt9iiy3S9ddfn1KqvtydeeaZafvtt6+1ttj5rth1TlPsJxRzRUSx4WEx+1jFfvbFHpcVO/6UijsxsZjlrthltthtRbH1119/ffre975X7TZuDXHwwQfX2tWmqdZ3nzVv3rx01llnpbZt26bWrVunUaNGFQKCNSk2+P2sqqqqdNNNN6XddtstlZWVpR122CENHDgwderUKd10002r9f/CCy+kTTbZJFVUVFTbHz3yyCPrvHqt2G1NsftIzblvndKn6+Xddtut2u95DVHMtqrYbcXaOJ5viM+eBNmjR4903HHHpVdffbVaPy+99NJqYViPHj0K+5QpfXr7x9LS0sKtomu7wr93796Fxw28//77qaSkpNrt5p555pkGn1BZrIYej6f06VW1u+++ezrjjDPSrbfeWu+r06+99tp0zTXXpJKSknTxxRdXCyUmTpxYOHGgJjXdRmuVv/zlL+mAAw6o1zTsu+++6Te/+c1qr//mN79Jo0aNqtcwUmr4yXnFru8XL16cdt9993TQQQelCy64oEF3c6nNv1UIMnTo0HTFFVcU/p80aVJq37594QDhtddeq1f639wrwWeffTbdfvvt6aOPPiq8dvfdd9frstbrr78+XXPNNSmlT8/U6d69e+GyujVtqD/vwgsvTJtssklhB+N///d/CxvgVWbPnp1atWq1Wu2HH36Yhg0bljp37pxatWqV+vXrl1q3bp122GGHtGjRojrHndKn92l+/vnnq43z6aefXm0lvibFTvuJJ56YzjnnnJTSpwdXZWVlaeDAgalNmzaFy1Hrsnz58tXuyT1jxoxaV3ApfXpA0b1790avAIr97Pfff//CPUDHjh2bevTokQ477LDUv3//eh2oXHnllalv377ppptuSh06dEh/+MMf0tlnn134uy5jxowp7FxdeumlqV27doXpOfTQQ+usX6Wqqiq99tprDbp8+6KLLip8xg8++GBq27Zt4dLR+tw3t1i77rpr4X6Zhx12WNpqq63SDTfckIYPH5622mqreg+nsQfKxXz3q3a2unbtmo4++ug13sd0zpw5qaSkZI31VVVV6bDDDktlZWWppKSkcLD9n//5n/W6H2ZTzDcffPBBuvLKK9NJJ51U2OGbOnVqve6HuXz58jR27Ni0zjrrpFatWhV2tk855ZQ6z2oodpktdtpHjRpV2F4ed9xxaeDAgenss89OW2yxRdpll13qrF+5cmVhGV91Fn/btm0LP87Xpdh1TrHzzptvvpl++ctfph/84AeF9fOf//znGs+o/aztt9++cLuB/fffP40YMSI99thjafTo0elrX/tanfXdunVLXbp0ST/96U9rvOz/gw8+SOuvv/4a33vppZdSz54904gRI1KbNm3S9773vTR48ODUq1evel1SXMw6rym2kyk1/syhYrc1KX36w95FF11UOEs0pU8PZOpzv+li59uUir9qtZirDxu7j5JS8euMYrd1xS53xf4gW8y+ebFtX2Xq1Klpo402ShtuuGFq165dOuSQQ+q9f13MfH/nnXemysrKdN5556X27dun888/Px122GGpTZs26YEHHqizvpj5rth1TlPsJxRzRUQx4WGx+1gpFffZrxp+Y4/LmmL8xZyYWMxyV+wyW+y2otj6oUOHpk6dOqWOHTumTTbZpFHPN22splrfrfL000+nn/zkJ6lz585pvfXWS6eddloaM2ZMateuXeEkjs8rNvhN6dPl+4gjjkhdu3ZNffr0SSeeeGK1W+ZccsklqWfPnvWejo8//rhez30rZltT7D5SsfvWKX26/3rBBRekMWPGpDFjxqSLLrqoxqtmPu+zt4Tq2LFjg2/pU8y2qthtRVMczxdzbFJaWpqGDx+e/vjHP9Y4ny1atGi1EHOfffZJ3/3ud9OiRYvS0qVL0zHHHJMGDhxYeP+pp56qMcj4z//8z7T11lunG264obBvus0226RXX301vfbaa2nHHXdM3/ve9+o17dddd1365je/mfr06VNY5//617+u1z5KMcfjKX36PJbKyspG305r8uTJjbp14JquBGmsDh06rPGWXm+88Uad+xrFnJxX7Pr+qquuSmVlZaljx46pf//+DbqbS21adAjS0AfJ3Xzzzal169bpP/7jP9Lo0aNTx44d00knnVR4f8KECWnbbbetc7xfhpVgU1m8eHGaOnVqeu+99+rV/8CBA9O55567xttdrVJVVVXrZXmPPfZYuuyyy9J///d/F26X0hwaOu2f98QTT6QLL7ww/elPf2rilq2umNs5fVZjP/t//etf6R//+EdKKaUVK1akcePGpb322isde+yxtT5o9bNuuOGGNHDgwMJGo2/fvvW+vG3FihXVNh5/+MMf0s9+9rN0ySWX1Ou5FosXL06HHnpoatWqVbWN35FHHlk4a7C+Zs6cmW677bZaH8T12Y3C5x/61tCHwD377LOF1HzevHlp+PDhqVOnTmmLLbao837PqxRzoFzMd//tb387TZw4sdYHBi5btqzOS6BnzZqV7rnnnnTzzTfX+iyEzyt2vnnhhRdSjx490sCBA1NZWVlhvvnlL3+ZDjzwwDrri3kA3yqNXWaLnfa33nqrMI8vWrQo/fjHP06bbrpp2nfffRt06XFVVVV65ZVX0tNPP13tgK0uTbHOSalx887kyZML2/U2bdoUvrdx48bV60qK++67r3BryzfeeCNttNFGqaSkJHXv3r3WMxNXue666xp1ptJnffjhh+nss89O++23Xxo5cmT65S9/Wet2uzb1WefVpLHbyWLOHCpmW1Ospphvi71qtdirDxur2HVGsdu6Ype7lIr/QbaxmqLt48aNS23atElHHnlk+vjjj9NLL72Uhg4dmgYMGFDn2YZNYcqUKWnYsGGpR48eqV27dmm77bar94PBm1JD1znFbitTKu6KiGLCw6bax2rJijkxsZjlrthltthtRbH1TXG712XLlqUHH3wwTZgwIS1cuDCl9OmV5nXt6zXF+m7evHnpggsuSF/72tdSmzZt0ne/+9107733Vrvy7dFHH631x71igt9Vz/DZfffda3xQ8XvvvVdrANlcmmIfqbHHZatuy9q3b9/CrdPWXXfd1K1bt1ofzr1KU9wSqrHbqmK3FcXu4xR7bNLY20a99dZbacMNN0xlZWWpdevWqXPnztWOR6+55ppqv6d+1ty5c9Ouu+6aOnbsmIYPH54+/PDDdOSRRxbCg0GDBtXr5KzLL788de/ePZ199tnVjqevueaael0BVezxeP/+/dMRRxzR6Idz33PPPYX9+s+67777qj2q4fMaG56syXrrrZcuuOCC1V6/4IIL0nrrrVdrbTEn5xW7vu/Vq1c655xzVjvpvlglKaUULdCkSZPiO9/5TgwYMCBee+212GSTTWLmzJmRUootttgiHnrooTXW3XvvvXHDDTdEVVVVDB8+PA4//PDCe//6178iIqJbt261jnvlypWxcuXKKCsri4iIm266KZ544okYNGhQ/PjHP442bdrUWv/cc8/FRx99FDvvvHPMnz8/Ro8eXai/+uqrY8iQITXWHnroobUO++qrr671/S+bDz/8MDp37lzj+/vuu29ce+21UVFREfvuu2+tw7r99tubuHXF22KLLWLSpEnRpUuX2HzzzaOkpKTGfp9//vkvsGV1f/Zry5IlS2LRokXRs2fPL2ycRx99dDz++OMxfvz4GDFiRLz44osxYMCAuOuuu+KMM86Iv/71r006vlatWsWcOXOiZ8+eUVpausbvPaUUJSUlsWLFiiYd95rssssucdhhh8W+++4b5eXla+xn+fLl8fjjj8eOO+641tvTWCtWrIiXXnop+vfvH126dFnr4xs2bFhsscUW8atf/So6deoUL7zwQgwYMCCeeOKJOOCAA2LmzJm11g8cODD+53/+J3bZZZdq9a+99lpsu+228cEHH6yxbtmyZdGuXbuYNm1abLLJJmthyr54CxcujIceeig22mijGDx48Bc+/obMO9tuu23st99+ceyxx1b73p555pnYd99945133mnw+N9///3o0qVLrduAVRYsWBArVqyIrl27rjaMsrKyqKioaPD4W5pvfetbcdxxx8Xee+8dBxxwQHzwwQdxyimnxBVXXBFTp06Nl19+uc5h1Hdbc8kll8SPfvSjaNu2bVxyySW19nvUUUc1aDoa44Ybbojly5fHwQcfHFOnTo0RI0bE+++/H23atIlrr702vv/97zfZuL7M+yhNoSHLXTHWxr55Q9vep0+fuPrqq2PkyJGF15YtWxa/+MUv4pJLLomqqqoaa8eOHVvrsE877bT6Nbqecpzv5s6dG3PmzIkhQ4ZEaWlpREQ888wzUVFREV/96ldrrJs+fXosWrQoNttss1i8eHEcd9xxhWPCiy66KPr379+k7Sz2s2/K47INNtig1vFPnz691vqIiMMOOyz69esXp59+elx22WXx85//PLbbbrt47rnnYt99943f/e53dQ7js4pZZ3xR65svg7fffjtGjBgRs2bNiqqqqvj73/8eAwYMiKOPPjqqqqpiwoQJDRpeQz+7Nm3axIYbbhiHHnpoHHzwwdGjR4/V+lm4cGGMGjUqHn744Qa1pT7OOuusOPTQQ6Nv37716r9r167x97//Pbp3717ndL7//vvV/j/22GPjrLPOig4dOsSxxx5b63guuuiiGt9bvnx5TJw4MYYPHx69evVqkuPxhh6Xfetb34qBAwfGlVdeWfgdbfny5XHYYYfF9OnTY8qUKY1uS12WL18e5557bhx66KGx7rrrrrXxrC3FHpvMnj07SkpKCtP+zDPPxMSJE2PjjTeOH/3oR7XWLlmyJB5//PGoqqqKbbbZJrp3717UtEyfPj2WLFkSX/3qVwvzQW023njjOPfcc2PvvfeuNu0vv/xy7LTTTvHPf/6z1vrGHo+v0qlTp5g2bVpsuOGGDZrOVTbbbLM477zzYvfdd6/2+n333RcnnnhivPDCC40abkNce+21cdhhh8XIkSNj6623joiIp59+Ou6777648sor4+CDD66x9vrrr4/99tsv2rZt2yRtacj6vmvXrvHss882+rOvSd1z3ZfUySefHMcff3yceeaZ0alTp7jtttuiZ8+e8cMf/jBGjBhRY93IkSOrHSB8Vl3hxyqlpaWFHdyIiB/84Afx/8n787ia1vd/HH82aJ4pGRtIRIhwzJR5KHFkyJAyHWTOcBDFwTGPxxQaHIQjHEMhyUwDZUqDUhwzoTI0XL8/+u713rs9rLX3vR2v8/k9H4/1eNTa+1rrXmvfwzXd13Po0KGC2+7q6sr9bWVlhZiYGMGylQdpSUkJ7t27h4KCAri5ufHKExGOHDmC+Ph4vHr1CuXl5RKfCwkkFBQU4NatWzLlR40aJVfu999/h62tLWfAe3t746+//oK1tTVOnz4tM/hjamrKDRBTU1PetimCOp79n3/+wZUrV2TKy3KOeHp6ck7nAQMGqN54MYjilsoo2aq8+48fP3LOto8fPyq8Pp9T7vPnzyAiGBgYwMDAAK9fv8aGDRvg5OSEHj16CHqGL1++IC0tTea79/DwUCh77NgxREVF4aeffpJ4b40bN0Z2drbU91mdYhcuXOAcmN9D+VYWcXFxvN/R1tZWGAB59eqVzHfftGlThdfNzMyUO+b4HCvTp0+Hs7Mz/P39UVZWhs6dO+PatWswMDDAyZMn0aVLF8UPBbZ+k5iYiB07dkidr1WrFl68eMF772fPnqF+/fpS58vLy1FSUiJXrkqVKqhbt67SAbK0tDQ0adIEmpqaSEtLU/hdvt9NHIWFhVLvjm/Me3t7o1OnTpgyZQo+f/6MVq1aIScnB0SEgwcPYtCgQVIy6pxzWPrO3bt3sX//fqnzVlZWvIq2PFQOaCjC0KFD0b9/f0yaNEni/KFDh3DixAmcPn1aofzevXthZGSEwYMHS5w/fPgwiouLMXr0aN42JCYmyh23ioxsQPl1UhYWLlyIoqIiABUO2n79+qFjx46oWrUqoqKi5MqpstasX78ePj4+0NPTw/r16+VeW0NDQ3D7VZ0vAWDEiBHc3y1btsSTJ0+Qnp6OunXryjU8VR074jqKp6en2px3qswZ3wN8405dznhW3VwWlJkzgIp5q3L/qFKlClavXo1+/foplI2Ojpb4v6SkBDk5OdDW1ka9evWUCoII+e3V3e9Y5xwWPUEEa2trWFtbS5xr3bo1r5y9vT33t6GhodLOY2V1LFa7RNwuMzExYfrtpk+fLvF/SUkJbt++jZiYGAQGBgq6xs6dO7nnnjx5MqpWrYpr167Bw8MDEyZMULpNyo47ZWRZdRx16kgiJCcn4+HDhwAqbCIXFxdBctOmTYOrqytSU1Ml/CdeXl4SCaZCocx7JyLExcXB1dUV+vr6cr9nYmIiYYOpM+Fh0aJFgtsLVOgZxsbG3N/KjJvbt29zNoOipD2+a2pra2PixInc7y3Sk5QBq12WlJQkEQARtWvOnDkS/jFFKCsrw7FjxyT6rYeHB7S0tBTKaWtrY9WqVQp9VXxQx1qhKlhtk+HDh2P8+PEYOXIkXrx4ge7du6Nx48b4888/8eLFC4XrvIGBAbp3787UfnGIr3tCkJOTI3Nu0tXV5WwFRVDVHhdh4MCBiI+PV9kRn5mZCScnJ6nzDRs2RFZWlkrXVBa+vr5o1KgRNm3axPk8GzVqhCtXrnBBEXkYOXKkWtuizHw/evRoREVF4ddff1VrG/6zO0HEI3Lm5ua4cuUKGjdujNTUVHh6evJm6MpCaWkp/vnnH9StW1fqs+/lXFIHysvL8csvv6BevXqYM2eOwu9OmzYNO3bsQNeuXVG9enWpBXPv3r0K5f/++2/4+PigsLBQSvnV0NCQyl4Qh52dHf7880+0a9cO586dg7e3N6KionDo0CHk5eXh7NmzAp5WdbA+e1hYGLfTp2rVqlLPLiRjiQURERFYvXo1MjMzAQANGjRAYGCgoIlJlXevzt0MPXr0wMCBAzFx4kQUFBTA0dEROjo6ePPmDdatW4dffvlFoXxMTAxGjRolc5EXcn8DAwPcu3cP9vb2EhkAqamp6NSpEz58+CDxfTs7OyQlJaFq1aqws7OTe10hv3teXh7q1Kkj9f6ICPn5+TLnG3G8ffsWQUFBco1cRWNOhPDwcFSrVg19+/YFAMyZMwc7d+6Ek5MTDhw4oDDTMDk5GaNHj8bDhw9Rebnge/e7du3CL7/8gmrVqsHa2lpqzPBleNauXRvHjh2Dq6srjh07hkmTJuHixYuIjIzEhQsXcPXqVYXyrP3GysoKsbGxcHFxkeg3586dg5+fH/Lz8xXKt2zZEjNmzMCIESMk5ENCQnDu3DlcvnxZruzu3btx9OhRREZGClYWNDU18eLFC4kxK2uJF/LsOTk5mDJlCi5evIgvX75w54WOeWtra8TGxqJZs2bYv38/Fi9ejNTUVISHh2Pnzp0yDTl1zjksfad27do4dOgQ2rVrJ/G7RUdHY/bs2TIDp+rMjrWwsMDVq1eldsykp6ejffv23K5VeWjQoAG31okjISEB48ePx6NHjxTKL1++HAsXLoSjo6PUWqmhoSF3py3wfddJIZlDrGsNK1jmSxEuXbqEhg0bSmVnlpSU4Pr16+jUqZOUzP/C7kPWOUOVtY513AUHByMwMBAGBgZYsmSJwr61ePFihdevDGV08y9fvmDz5s1yn/1H7Ib4+PEjfH194eXlxatnsv72LGCdc1TVE77HTnVlg4esOtb/KrZu3YqkpCReu4wVLONOFVnWeVqd8/yrV68wdOhQXLx4kasKUFBQgK5du+LgwYMyd1aIQxRscnR0lNCTcnNz4eTkhOLiYrmyrPNdeXk59PT0cP/+fTg4OCj8rjhYbTu+XRji4EsW+VHo0qULpk+frnJiJqtdVr16dURGRkolpcTGxmLUqFF4+fKlQvmsrCz06dMHz549g6OjIwDg0aNHqFOnDk6dOsXrpPb09MTAgQMFJQNVBqtNyWrPq2KbiMPc3Bw3btyAo6MjNm3ahKioKFy9ehVnz57FxIkTVdbPX758iR07dqi0Y1SorJOTE1asWAFPT0+JZ9+8eTP27t3LO2ew2OMA8Ntvv2HDhg3o27cvnJ2dUaVKFYnP+YKm1tbW2L9/v1RSzPnz5zF8+HC8evVKofyPRlFREVauXIm4uDiZfVdR32Gd76dOnYqIiAg0a9YMTZs2lXr3qs61/9mdIIaGhvj27RuAii3g2dnZaNy4MQConKl5//59tGjRQuYk1rx5c8651Lx5cybnkjqcmuLQ1NTEzJkz0aVLF15DKzIyEkePHpXajiUUs2bNgp+fH5YvX6509sCLFy9Qp04dAMDJkyfh7e2NHj16wNbWljcCCUhmeAIVW3Gjo6MF7yZgffZFixYhKCgI8+fPl9gJJBQs2xDXrVuHRYsWYcqUKWjfvj0A4MqVK5g4cSLevHmDGTNmKJRX5d2rczdDSkoKl2F75MgRWFtb4/bt2/jrr78QFBTE65gKCAjA4MGDERQUhOrVqyt9f1dXV5w6dQoBAQEA/i9bJjQ0FG3btpX6fk5Ojsy/VYGdnR1ntIjj3bt3sLOz450vRo4ciaysLPj7+8sM3gnB8uXLsW3bNgDA9evXsXXrVqxfvx4nT57EjBkzFBrofn5+aNCgAXbv3q30/ZctW4bffvsNc+fOVbrNQMVcLsqsPH36NLy9vdGgQQP4+flh48aNvPKs/cbDwwMhISE4dOgQgIp+k5eXh7lz58rcyVAZQUFBGD16NJ49e4by8nIcPXoUjx49QkREBE6ePKlQdsuWLcjKykLNmjVhY2MDQ0NDic9lKQ05OTmc4crab0eMGAEiwp49e1Tqdx8+fODmj5iYGAwaNAgGBgbo27ev3AxPdc45LH1n6NChmDt3Lg4fPgwNDQ2Ul5fj6tWrmD17ttwMMnXuWvz69StKS0ulzpeUlODz58+88nl5eTINfBsbG+Tl5fHKb9y4EXv27FG4NVoeWNdJRRASDGRda0JCQjB79mwp/ebz589YvXo1r6HGMl+K0KVLF1SvXh3R0dH46aefuPPv3r1D165dZa4Z6hg79vb2SExMlNoVXVBQgBYtWvAayKxzhiprHeu4Ew9sLFmyRGl5RVBGN/f398fZs2fx888/o3Xr1ir1GxYjVRZMTEwQHByM/v378wZBWH571n7HOueoqieoa85nCSCx6lgsdgkAuLm54ejRo1KldT9+/IgBAwYoDJgrQu/evTF//ny5QRB1JSayjDtVZFnnaXXqSAEBAfj06RPu37/PJVw8ePAAo0ePxtSpU3HgwAGF8uXl5TL75tOnT7kdD/LAOt9pamrCwcEBb9++VSoIwmrbCS2dzPc8KSkpqFKlCpydnQEAx48fx969e+Hk5IQlS5bwllUXh6jUbMOGDRWW3RNh0qRJmDVrFp4+fYqWLVtK2RZ8ibysdtmQIUPg7++PNWvWoF27dgCAq1evIjAwEMOGDeOVnzp1KurVq4cbN25wY+Ht27cYMWIEpk6dilOnTimU7927N+bNm4e7d+/KfH5FuzlYbUpWe14V20QcJSUl3C7A8+fPc8/asGFDPH/+XOnnEeHFixcIDg5WKQgiVHbmzJmYPHkyvnz5AiLCrVu3cODAAaxYsQKhoaG892Gxx4EKf5GRkRESEhKQkJAg8ZmQXeKenp6YPn06oqOjuUBdVlYWZs2a9V13EKlr9+DYsWORkJCAkSNHokaNGt99rRTH3bt3uV1AlUshM+0iVivDyL8IFiI5ebhz5w5pamrK/Cw3N5cj28rNzVV48KF3797k4OBAK1eupL1796pE6lQZp06domrVqvF+z9bWlh4+fKjSPYiIDAwMODIhZVGjRg26evUqERE1aNCADh06RERE6enpZGxszCvfvXt32rZtGxFVkO9YWVlR7dq1SU9Pj/744w9eedZnt7CwEETeJA8dOnSgiIgIIqogoTY2Nqa2bdtStWrVKDg4WKGsra0thYeHS50PCwuTSUJUGazvnhX6+vr05MkTIiIaPHgwR7qXl5fHS3JLRGRsbMz07i9fvkxGRkY0ceJE0tPTo2nTplH37t3J0NCQkpKSFMoGBwdTUVGR1Pni4mLe342ISENDg169eiV1Pjc3lwwMDHjljYyMBBOgy4P4+58zZw5H6n3v3j3eecPIyIgyMzNVuq+xsbHK8wVRBYlXbGwslZaWUp06dejkyZNEVNFuMzMzQfdn6TcFBQXUrVs3MjMzIy0tLapTpw5VqVKFOnXqRIWFhYKuoSoBHytpZUJCgkwytZKSEkpISOCVNzQ0pPT0dN7vyYODgwNFRUVRYWEhWVpacuRnd+7coapVq/LKP3nyRILgUoTy8nKuLysCS9/5+vUrjR07lrS1tUlDQ4MjvB0xYoRM8kt1o0uXLjRlyhSp85MmTaIOHTrwytepU4eOHz8udf7YsWNUq1YtXnlra2uliC7FwbpOivD582datWoV9e7dm1q2bEkuLi4ShzywrjWampoySbDfvHkjVz8UB8t8KYKGhgZNnz6dDAwMaO/evdz5Fy9eCCJZVXXsaGhoyHz2Fy9eUJUqVXjvyzpnqGOtY4GdnR29efNG6vz79+/Jzs5OpWsK1c1NTEzoypUrKt1DhKFDh1KNGjVozpw5tH79etqwYYPEoQouX74saK1l+e1Z+x3rnMOqJ7CiXbt21LZtWzp48CDFx8fTxYsXJQ5FYNWxWOwSIvm/3cuXL0lbW1vldv3+++9kY2Mj6L4igl0RybP4wTdns4w7dYzZHwkTExO6deuW1PmbN2+Sqakpr7y3tzeNGzeOiCrm7sePH9OnT5/Izc2NfH19ee/N+u5OnDhBHTp0oLt376okz2rbscDV1ZWOHDlCRBWk07q6ujRs2DCqX78+TZs2TaHs4MGDafPmzVxbHRwcqEqVKqStrc1dUxHkjRMh44WI3S77+vUrTZ06lXR0dEhTU5M0NTVJV1eXpk+fTl++fOGVNzAwoLS0NKnzd+7cIUNDQ155Wc8vdL5gXStYdRxW26R169Y0d+5cunTpEunp6XFtuX79ukLbIDU1VeERFRUl992xyFbGvn37qH79+tzvVatWLQoNDRUkS6S6Pa4OFBQU0E8//UTa2tpka2tLtra2pK2tTV27dqX3799/t/uK2zSiPl75ENL3TU1N/59bK/+zO0HWrVuHwsJCABXb2QsLCxEVFcURyclCixYtFF5TUYaleLmYJ0+eoF27dlJEPqWlpbh27Rovid3ly5dx5coVhQTo8lB5KyYR4fnz5zh16pSgrX1LlixBcHAw9uzZo7COpjz07NkTSUlJStfyAyq2jg8fPpzL3hBxs9y+fVtmnb7KYM3wZH12f39/HD58GPPmzVNaFqiIXorqAx86dAjOzs4S2xAVRcGfP3/OZUyIo127doKi96zvHmCrg1m/fn0cO3YMXl5eiI2N5XauvHr1SlDd2p9//hkXL15UuRZjhw4dcOfOHaxcuRLOzs44e/YsWrRogevXr3OZOPIQHByMiRMnSmUGFxcXK8xeEI1VDQ0NLFq0SEK+rKwMN2/eRPPmzXnb3rBhQ0HZ34pgZGSEt2/fom7dujh79izXNj09Pd5ru7u7IzU1VXA/EcfgwYO5/q0KxowZA29vby7roFu3bgAqiLyEZDyx9htTU1OcO3cOV65cQVpaGgoLC9GiRQuuHULQsWNHnDt3Tul7K1t6pTK6du0qcwfShw8f5GaTi6NVq1bIz8/ntpsri+nTp8PHxwdGRkawsbHh6gRfunSJd8wB7DuoWPqOjo4Odu3ahUWLFuHevXsoLCyEi4uL4IxD1l2Ly5YtQ7du3ZCamgp3d3cAFbw+iYmJgspGDhs2DFOnToWxsTFXOikhIQHTpk0TxF82Y8YMbN26FRs2bOD9bmWwrpPi11Elc4h1raH/L/u6MlJTUwXtRGGZL0XQ0NDA/Pnz0bFjR4waNQppaWlYu3Yt9xkflB07J06c4P6OjY2VyGovKytDXFycwtIhIrDOGaxrHeu4y83NlTmvfP36lZdwlFU3r1WrFm/2NB/OnDmDU6dOcbuFlUHl+vii9kdGRsrlURSHKr+9uvod65zDqicAbH0vNTUVycnJKo0bVh1LVbtEfPfFgwcPJDjSysrKEBMTI4gwujIPDxHhxYsXeP36Nf744w+5cura9coy7tQxZlXl2FSHfHl5uVRZEaCCR6jytWRh7dq16NmzJ5ycnPDlyxcMHz4cmZmZqFatGu8uEnW8u1GjRqG4uBjNmjWDjo6OlF3PV1VDVdtOHcjIyODsv8OHD6Nz587Yv38/rl69iqFDhyrUvS5duoQFCxYAqOByIiIUFBQgPDwcy5Yt492lzrpLnNUu09HRwcaNG7FixQqufFO9evUEVxfR1dXFp0+fpM4XFhYK2kEjpG/LA+tawarjiGyToKAg3L17V2nb5Pfff4eXlxdWr16N0aNHc37IEydOKOSv4quAI09vZpWtDB8fH/j4+KC4uBiFhYVSOi4fVLXHZaGsrAx3796FjY0NzM3Neb9vamqKa9eu4dy5c0hNTYW+vj6aNm0qs7ytInz79k3mfC+vtLq6dg+am5urzJeljvleHMrufpOH/ywniCrQ09PD0KFD5SrVz58/x65du3idK+I1OcXx9u1bWFlZCXIubd68WaLMgVBUrvGtqakJS0tLuLm5wc/PTyowUxmfP3+Gl5cXrl69CltbWykFSFaJFXFD5fXr1wgJCcGYMWNk1sRT5AwvKSnBxo0bkZ+fD19fX25rk4gsbOzYsQrbbmBgwJGDent7o3Hjxli8eDFneCmqP6rqs4ujrKwM/fr1w+fPn2U+O19NOiMjI9y7dw+2trbw8PBA+/btMXfuXOTl5cHR0VHhwtikSRMMHz5cihRo2bJliIqKwt27dxXem/Xds9bBPHLkCIYPH46ysjK4u7tzjrwVK1bg0qVLOHPmjEL54uJiDB48GJaWlirVYmSBpqYmXr58KVUf98KFCxgyZAhev34tU040VhMSEtC2bVsJ5UxHRwe2traYPXs2r/KSmJiIefPmISgoCE2aNJF6diGOPR8fH6Snp8PFxQUHDhxAXl4eqlatihMnTuDXX3+V2l4ojjdv3mD06NFo3bq1zPtXHvPizpSioiKsW7dO5RqaQEXfyc/Px+DBg7mSDeHh4TAzM4Onp6dC2R/Zb3405PXbjIwMuLq68m6Lzc7OxsSJEzFixAiZv7sQ7qvk5GTk5eWhe/fuMDIyAgCcOnUKZmZmvI46ee1/8uQJnJycBBHhsfQdFqiDl+LOnTtYvXo17ty5wynL8+fPF2TsfPv2DSNHjsThw4c5naC8vByjRo3C9u3beQ3F8vJy9O3bFxkZGXBycpL67RWVz2NdJ0UwNTXF6dOnlXboqrrWiLhGPnz4IMV3VlZWhsLCQkycOBFbt25VeH9l50tZEOf2uX37Njw9PeHk5ISNGzfCycmJd71VduyISgjJMlSrVKkCW1tbrF27lpdcm3XOYF3rVB13Ih13wIABCA8Pl+mMP3funEIuHVbd/MyZM9i0aRO2b9/Om0glD3Z2djh9+rQUl5BQWXGIt3/+/Pm8Bqwqv726+h3rnKMOPYFlzu/atSsWLFggOLlCnTqWqnaJOB+FLDeCvr4+Nm/eDD8/P4X3Dw4OlrqupaUlunTpwuTcEAqWccc6Zlk4NtUh7+npiYKCAhw4cAA1a9YEUEEe7OPjA3Nzc0RHR/M+Q2lpKaKiopCamsolCfn4+PAmGqpjvgsPD1f4OV/wWVXbThxJSUkct6aoRLsIivQkExMTJCcnw8HBAd27d0e/fv0wbdo0Qf4AfX19ZGRkoE6dOhg1ahRq1qyJlStXIi8vD05OTlyC8PfEj9KtgYrgV0pKCnbv3s057m/evIlx48ahZcuWCAsL+273Zl0r1GHPs6KsrAwfP36UcNzn5ubCwMBAblChWrVqWLVqFZeUVRn3799H//79ZeqmLLLiyMnJQWlpqZQNlJmZyekL3xPTp0+Hs7Mz/P39UVZWhk6dOuH69eswMDDAyZMnuUS/74XMzEz4+fnh2rVrEufpX+BdA4B9+/bh+PHjCA8PV5oOgXW+9/b2RqdOnTBlyhR8/vwZzZo1Q25uLogIBw8eFFSeXBb+nwiCCCWSc3V1hb+/v1xl9M6dO2jZsqXKBqZQ59KPnAS9vb0RHx+Pn3/+WWY9QlnZx0Jr7H7vQdi0aVOMHTsWXl5eaNKkCWJiYtC2bVskJyejb9++EplIsqDKs4tj2bJlCAoKUoksFgDatGmDrl27om/fvujRowdu3LiBZs2a4caNG/j5558VZhr+9ddfGDJkCLp168Y5ha5evYq4uDgcOnQIXl5eCu/NCgcHB/To0UPlOphARd3H58+fo1mzZlyfunXrFkxMTHiNnd27d2PixInQ09NTifjy9OnT0NLSQs+ePSXOx8bGory8XGamo7qcYmPGjMHGjRtVHteZmZkYPny4VJBOmYWvoKAACxcuRH5+Pn755Rf06tULQEWf19HR4bKKZOHvv//GyJEjZc5rsu4vJHNTJMtCkiwEqvSbyhmxiiBL4eUjbhaHIiNVHumlCIrIWoGKGsO9evXi6r+KZNLS0uDo6IiYmBiFbbtx4waGDx+O3Nxc7px41s73mutFGdUbN27EuHHjZO6g0tLS4iVfVPW+QsDnVKtWrRoSEhLQuHFjhIaGYvPmzRK7Fh8+fMjaXEHIyMjgMo6cnZ0FK59TpkxBaGgounbtKnOtVERWy7pOiuDk5ISDBw8KCrZVhiprTXh4OIgIfn5+2LBhg4QjXBS0lsUfVRnKzpeyIB4EET3PgAED8PTpUzx//lzuNVjHjp2dHRITE1GtWjXeNsoC65zButapOu7U5YxnwevXr+Ht7Y1Lly7BwMBAyi4QwhXIYqSyguW3Z+13rHMOq34JsM35ygaQ1KljqWqXPHnyBEQEe3t73Lp1S8Ie1tHRgZWVFbS0tAS1Ux3IzMyUy7OpKKOfZdyxjtkGDRqgT58+KnFsqkM+Pz8fHh4euH//PscZmZ+fjyZNmuDEiROcc/t7QB3znapQl2138OBBjBo1Cj179sTZs2fRo0cPZGRk4OXLl/Dy8lKoJ7m5uaFOnTro1q0b/P398eDBA9SvXx8JCQkYPXq0xDxaGQ0aNMCyZcvQt29f2NnZ4eDBg3Bzc+N2DvPx4kZERCj8XMgOJBawkiQXFBRg9OjR+Pvvv7l+U1paCg8PD4SFhfHyM4WEhCj8XNF8wbpWsOo4gwYNQuvWraU4oFatWoXExEQcPnxYobwIr1694pI6HB0deXdU9OzZEx07dsTChQtlfp6amgoXFxeZu2xYZMXRuXNn+Pn5SQU39+3bh9DQUFy8eFFKRl32OFBBSn/s2DG4urri2LFjmDx5MuLj4xEZGYkLFy4IskmLioqQkJAgM2jKF0Br3749tLW1MW/ePJmcHEKrCxUUFGD37t2cTtK4cWP4+fnJHDeVd2pmZWWBiJROJGed762trREbG4tmzZph//79WLx4MVJTUxEeHo6dO3cK5mqqjP9sEEQVIrlp06ZBQ0ND7jbD7OxsjB07Vu52IXU5l1gmQdbt/oaGhoiNjUWHDh14v/u98ODBA5kTAF+GpHiGp5ubG7elTehuAtZnNzc3x/r161UiiwWAixcvwsvLCx8/fsTo0aOxZ88eAMCvv/6K9PR0hVkjQEVW9fr167mJq1GjRpg1axa3q0MIVH33JiYmuH37NlO5ABZYW1tj6tSpmDdvnkrEl02bNsXKlSvRp08fifMxMTGYO3cuUlNTpWTU5RRjRevWraGtrY1p06bJdEh27tz5u97f1tYW/fr1w6JFi1QOgLGARWlQpd9UdjC8fv0axcXFHPFnQUEBlzEjS+EVz057+/Ytli1bhp49e3J95fr164iNjcWiRYu4Uj2ycPz4cYn/S0pKcPv2bYSHhyM4OBj+/v4y5caMGcO1w9vbWyIjT9Rvx40bx+twcnJyQqNGjTBnzhyZ/U6IQ/3p06c4ceKEzN9OXiBBXTuoAOX6TuVM7pSUFJSWlnIlSjIyMqClpYWWLVvyOtVYdy0CFbsxsrKyZBqJym6fVhbGxsY4ePAg+vbtq7Qs6zopgjoyRVVBQkIC2rVrJ7NMiBCoY74cM2YMNm3aJJF9//XrV4wfPx6XLl2SW85CnWNHFbDOGaxrHeu4Y3HGsxJEd+vWDXl5eXIJU4WU1HJxcUF2drZKRqqI1Lbyjo+ioiIEBARw+qo8qGO9UBWscw6rfgmw9b0flXAAsNslrJCXNKihoQFdXV1B5W127dqFX375BdWqVYO1tbWUY1JRv2cZd6xj1tDQEHfv3lWpvLQ65IEKv8P58+eRnp4OoMKuFLojacWKFahevbrUbp89e/bg9evXUo5acahjvsvLy1P4ubzyMOqy7Zo2bYoJEyZg8uTJMDY2RmpqKuzs7DBhwgTUqFFDapeTONLS0uDj44O8vDzMnDmTS8IMCAjA27dvsX//frmyf/zxB6ZNm8aVmk1JSYGmpiY2b96Mo0eP8pa8qVy6p6SkBMXFxdDR0YGBgYGgABSLXebj48OVOlUlIVWEzMxMiX4rtARpZZ9JSUkJcnJyoK2tjXr16imcL1jXClYdx9LSEhcuXJAqKXz37l1069YNL1++VCj/6dMnTJo0CQcPHuTWFS0tLQwZMgRbt26VG0CKjo5GUVERRowYIfPz9+/f48SJEzLHLYusOExMTJCSkiL1O2dlZcHV1RUFBQVSMuqyx4GKakJZWVmoXbs2xo8fDwMDA2zYsAE5OTlo1qwZbwL87du30adPHxQXF6OoqAgWFhZ48+aNQn+COAwNDZGcnMy0QzIpKQk9e/aEvr4+t4sqMTERnz9/5srEi0PRHFYZisYt63z/3Xa/fS+yke8NFiI5VeHr60u+vr6koaFBQ4YM4f739fWl8ePH0/Lly+n169e812nVqpXKbWclB3d0dKTU1FRhDywD4eHhMomrvn79KpO4WxzZ2dnUtGlTKRI7ETGPEDx//pxSUlKorKyMO3fz5k1BhOesz169enWVyWJFKC0tpXfv3kmcy8nJkUksqE6wvvsxY8YoRT4lC4mJiRQYGEhDhgwhLy8viYMP5ubmTGRkenp6lJOTI3U+JyeHl5z84sWL9O3bN6Xu5+XlRR8+fOD+VnTwQV9fn4lsVoR3797R6tWryc/Pj/z8/Gj16tX09u1bXjkjIyOV3z0r8WBKSgpZW1uTiYkJaWlpkaWlJWloaJChoaEgolrWfvPnn39S+/btJd5/eno6dezYkfbt28crP3DgQI7AUBybN28mT09Pldvk4eHB+70lS5YIJm+XBQMDAyaC5/Pnz5OBgQE1adKEtLW1qXnz5mRmZkampqbUtWtXXnlfX19uDKkClr6zdu1a6t+/v8Rc/e7dO/L09KQ1a9bw3tvZ2Zk2btxIeXl5ZGJiQteuXSMioqSkJKpevTqv/PXr18nOzk4m2auQ+bq0tJRCQ0Np2LBh5O7uTl27dpU4+FC3bl1Ba6osqGOdJCJ69eoVdenShTQ1NcnIyIjMzc0lDkVQdq0R72cfPnxQePCBZb5UF5QZOxs3bqTPnz9zfys6+MA6Z7CudazjjgWsBNH6+vrMpPBLlixReCiCOHmmOF6/fk1aWlq891b2t1dnv2Odc1j1BCK2vteoUSMaOHAg3bhxg3Jycig3N1fiUAR1kDsra5ccP36c04mPHz+u8OCDPKJW0VG3bl0KCgqSsPkqo27durRy5UpBz1oZLOOOdcx6eXlRVFTUD5NnhY2NDV29elXq/I0bN8jW1lahrDrmO76+wwdVbDtxGBgYcHalhYUFR9b94MEDsra2Vumanz9/FtSmxMREOnr0KH369Ik7d/LkSZXJhzMyMsjd3Z1iYmJ4v8tql/0vkiR/+PCBvLy8KCIiQuH3WNcKVh1HT09PpvzDhw9JT0+PV97b25scHBwoJiaG02ljYmLI0dGRhgwZonK7/g2YmJhQSkqK1PmkpCQyMjLilWe1x+vWrUuxsbFUWlpKderUoZMnTxIR0b1798jMzIxXvnPnzjRu3DgqKysjIyMjys7Opry8POrUqRP99ddfvPKurq50+fJl3u8pQocOHcjX15dKSkq4cyUlJTR69Gjq2LEj07UVgXW+d3BwoKioKCosLCRLS0uKi4sjIqI7d+5Q1apVVb7ufzYIYmhoqBbHoCpgdS6xTIJVq1ale/fuERHRrl27qGnTplRWVkaHDh2ihg0b8sqfPHmSevbsKdMhLATyDKU3b97wKh39+vUjT09Pev36NRkZGdGDBw/o8uXL1Lp1a7p06ZJS7cjPz6f8/HylZFifffny5RQQEKCSrDhevXpFly9fpsuXL9OrV6/kfk+djhnWd19UVER9+vSh0aNH05o1a5Q2Ug8cOEBVqlShfv36kY6ODvXr148aNGhApqam5Ovryys/ffp0+u2333i/Jw/Vq1fnJk1xnDt3jiwtLXnly8rK6NGjR3T58mVKSEiQOGTB19eXPn78yP2t6OBDx44d6dy5c7zfU4SEhAQyMTGhOnXqcM7AunXrkomJidxnEGHUqFG0a9cule7LMl8QsSsNrP3G3t5ertLFZ+QRVaxTshxDmZmZZGhoqFKbsrOzVZZVBv369aMjR46oLN+qVSsKCgoiIuJ+u0+fPpGHh4eggD0rWPpOzZo1uXVWHHfv3qUaNWrw3vvw4cNUpUoV0tTUpO7du3Pnly9fTr169eKVb9asGQ0ePJgePHhA79+/p4KCAomDD5MnTyZDQ0Py9vamadOm0fTp0yUOPuzZs4e8vb1lOtf4oK510t3dnRwcHGjlypW0d+9eCgsLkzjkQZW1RnyekudYERqAYpkvxfH+/XuKjY2lyMhICg8P5w4+I11Z2Nra0ps3b7i/5R1CnBuscwbrWqfKuGN1xqemplJqaippaGhQfHw8939qaiqlpKTQ8uXLycbGhrftLi4udP36deUfmhEfPnyggoIC0tDQoKysLAm98t27dxQeHi5ozlP2t1dnv2Odc1j1BCLJvtetWzeJtvHN+SzBQ1YdSwShdgmRZMCvcpBe2YB9eHg41a5dmxYuXEgnTpygEydO0MKFC6lOnTq0Y8cOWrZsGZmZmSn8fYyNjSk7O1vws4qDZdyxjtnQ0FCqW7cuLV68mI4cOaJ0AEkVeXUGH3V1denx48dS57Ozs0lXV1ehrDrmuzt37kgciYmJtHPnTmrYsKFcHU+ddnWtWrW4wIezszPt37+fiIiuXbtGJiYmgp4hKSmJIiMjKTIykpKTkwXJiKO8vJzKy8uVlpOFxMREcnR05P0eq13WqFEjpRNSZ8yYwfndZsyYofBQFWlpabxrNetawarjtGrVSmZwe/HixdSiRQteeQMDA5mO9EuXLvEmhFbGlStXZCZFfy/069ePBg8eTKWlpdy50tJSGjRokCC7itUeX7x4MZmamlLDhg2pbt263LPv3r2bfvrpJ155U1NTzvdrampKDx48IKKKoLGQcRcXF0dt27al+Ph4evPmjdLzFVFFEE1Wgtv9+/dJX19foaydnR2ns4nj/fv3vHoa63y/detW0tbWJjMzM2rWrBmXFLFp0ybq0qWLytf9zwZBunTpwuwYJCJq0qQJ5eXlqaFFwsEyCerr69OTJ0+IiGjw4MFcdldeXh5vByYiMjMzIx0dHZWyK4kqFF5ZCvKdO3d45atWrcotfCYmJtxkEBcXR82bN+e9d1lZGQUHB5OJiQnnmDA1NaWQkBCFWUIisD77gAEDyMTEhOzs7Khfv35KZ/QXFhbSmDFjSEtLizMStLW1yc/PT6azSZ2OGdZ3HxoaStra2mRkZEQ2NjZKG6nOzs60ZcsWIvo/h2h5eTmNGzeOc5QqQkBAAJmamlKnTp1oypQpSis948ePJ2dnZ4kMjszMTGratCn5+/srlGXNymbFoUOHyMnJifbu3UtJSUkSThahimSTJk1o3LhxUsrD+PHjqUmTJgplly1bRtWqVVMpACZvvoiLi6Nq1arxtptVaWDtN/r6+nTr1i2p8zdv3hQ039atW1fmzoE1a9ZQ3bp1eeUro7i4mKZNm0YNGjTg/e6LFy9oxIgRVKNGDdLS0lI6S27Hjh1Up04dlQ108Yx4MzMzLqhw584dQU7BwsJCWrhwIbVt25bq1atHdnZ2EgcfWPqOkZERxcfHS52/cOGCoIwjIrZdi6wZ9VWrVqVTp06pLN+8eXMyNjYmIyMjatKkCbm4uEgcisC6ToqgauaQKmvNxYsXucyoyjtzld1lzDJfinDixAkyNjYmDQ0NMjU1JTMzM+6Qp6uoc/ehqmCdM9Sx1ik77sSd8ZV1GyF6jrhuJssRbGBgQLt37+Ztd2xsLLVr147JwFUFfNnUWlpatGzZMt7rsP72LGCdc1j1BBFUnfNZgoesOpaydom64ebmJnM3Q1RUFLm5uRERUUREhMI128/Pj6uQoCxYxh3rmGUNIKkir87gY/369SkyMlLqfEREBK/895zvTp48SZ07d5b5mTrt6mHDhtHatWuJiCgkJIQsLS1p7NixZGNjwzvvvHz5krp06UIaGhqcD0JDQ4Pc3Nx4g5BEFcHDJk2akK6uLunq6pKzszNzgsTt27fJ2NiY93usdtnp06epV69evLvcxNGlSxd6//4997eiQ1VcvnyZN6Ofda1g1XFOnDhB2traNGrUKC4haOTIkaStrU3R0dG88nXq1OECd+JITU2lWrVq8cqLgyX4rAru379PVatWpXr16nFJpPXq1SNLS0u6e/cur7w67PHDhw/TunXrJJKww8LC6NixY7yy1apV43asinbjEFXs4hESgKpcwUXZ+YqIyMrKimJjY6XOx8TEkJWVFe/9ZSVcvHjxgqpUqaJQVh3zfVJSklp3vxERaatWROvHIzQ0FBMnTsSzZ88EEcnJQ25uLkpKSpS698uXLzF79mzExcXh1atXUkSKfPVbAwICMG3aNAQGBsLZ2VmpttevXx/Hjh2Dl5cXYmNjuRp2r169EkS8LI8PhQ8ichwNDQ24u7tDW/v/uk5ZWRlycnI4smV5KCsr4+oNV6tWDf/88w8cHR1hY2PDETQpwoIFC7B7926sXLmSIwe/cuUKlixZgi9fvuC3335TKK/qs4tgZmbG8cKogpkzZyIhIQF///23RPunTp2KWbNmYdu2bRLfv3DhAiwsLACAt8YnH9Tx7oODg1Wug5mdnc3Vl9fR0UFRURE0NDQwY8YMuLm58dYdvHv3LlfH8969exKfCSG9WrVqFXr16oWGDRtyZH9Pnz5Fx44dsWbNGoWyEydOhKurK06dOiWTjOp7Y8iQIQAgUXdX2XrRWVlZOHLkiARRpZaWFmbOnMlLkhcaGgojIyMkJCQgISFB4jMNDQ2F5OAaGhpo0KCBXOJBPlSpUoXrb1ZWVsjLy0OjRo1gamqK/Px8XnnWfuPu7o4JEyYgNDSUq5WZnJyMX375RVDd5ODgYIwdOxYXL15EmzZtAAA3b95ETEwMdu3apVC2MqEbEeHTp08wMDDAvn37eO/t6+uLvLw8LFq0SKV+K/p9ZJEICul3hoaGXK3gGjVqIDs7G40bNwYAXuJGABg7diwSEhIwcuRIldrP0ne8vLwwZswYrF27lqubevPmTQQGBgpeA6ytrWFtbS1xTnQtPrRp0wZZWVmCaxxXho6OjsqyADBgwACVZVnXSREaNmyIz58/Ky2nylojXoeZlWNJlfmyMmbNmgU/Pz+lCG9NTU25MVKZ7FVZfPv2DTk5OahXr56ErscH1jlDHWudsuNOnF9FERmtInlSA0G0SH92d3eXOM/37BYWFsjIyEC1atV4SUBl1XqPj48HEcHNzQ1//fUXp3OK2m9jY4OaNWvytp/1t2cB65zDqieIIOp7IjLx2rVrC5rz+/fvjxkzZuDu3bsybUJZnH3q0rGUtUvUjWvXrmH79u1S511cXHD9+nUAQIcOHRTyP9SvXx+LFi3CjRs3ZL4/RXOuquOOVRYALxEwH1SRF5/v5HFLCcW4ceMwffp0lJSUwM3NDQAQFxeHOXPmYNasWQplWd+dIjg6OiIxMVHmZ+J29YULF5jWyS1btnCctAsWLECVKlVw7do1DBo0SC4JtAgBAQEoLCzE/fv30ahRIwAVnJ2jR4/G1KlTceDAAbmy69atw6JFizBlyhSJMTtx4kS8efOGl9vgxIkTEv8TEZ4/f44tW7Zw11MEVrvM1dUVX758gb29vWCSZHEfCKs/ZNOmTRL/i54/MjISvXv3VijLulaw6jj9+/fHsWPHsHz5chw5cgT6+vpo2rQpzp8/L0h3XbhwIWbOnInIyEhOT3rx4gUCAwOxaNEiXnlxVPZ9fm84OTkhLS0NW7ZsQWpqKvT19TFq1ChMmTJFQm+RBxZ7XISff/5Z6pwQ/iKgYk1LTEyEg4MDOnfujKCgILx58waRkZFo0qQJrzxrvwcq+p+/vz/WrFmDdu3aAQCuXr2KwMBADBs2TKaM+HwRGxsrwRtTVlaGuLg4KS7VylDHfN+yZUu0bNlS4pwqvJXi+M8So6uLSE5EZqUMsVjv3r2Rl5eHKVOmyHTOeHp6KpSX5UQW2nZWcnBVIXIcBAcHY9asWTAyMuI+ExGJDRo0SCGJXceOHTFr1iwMGDAAw4cPx/v377Fw4ULs3LkTycnJUgtKZdSsWRPbt2+XMgiOHz+OSZMm4dmzZwxP+P1RrVo1HDlyBF26dJE4Hx8fD29vb7x+/VqubF5eHurUqSPV14gI+fn5cgngRGB99xYWFkhMTFSZGL127do4c+YMnJ2d0bRpU8yfPx/Dhg3D9evX0atXL3z48EGl6yoDIsK5c+e4xbNp06aCCIYNDQ2RmpqqslNRFECsDA0NDejp6aF+/frw9fWVImYW4cmTJwqvL4RwtH379ggMDJRybh47dgwrV67EjRs3eK+hDNRFPNijRw/4+vpi+PDhGDduHNLS0jB16lRERkbi/fv3uHnzplrbXRmvX7/G6NGjERMTwynqpaWl6NmzJ8LCwmBlZcV7jZs3b2LTpk14+PAhgAoCv6lTp3JKmDyEhYVJ9BtNTU1YWlqiTZs2UsSGsmBsbIzLly+jefPmvN/9HhgwYAD69u2LcePGYfbs2Th+/Dh8fX1x9OhRmJub4/z58wrlzczMcOrUKUFGmSyw9J3i4mLMnj0be/bs4ZIktLW14e/vj9WrV8PQ0JD3/klJSTh06JBM4kg+stno6GgsXLhQpUQJAFi7di0eP36MLVu2/OtBW3Xh7NmzCA4Oxm+//SbzHchL+mBda9LS0mSeF83XdevWha6urmoPJRDqILxVBcXFxQgICODIJDMyMmBvb4+AgADUqlUL8+bN+673V8dap+q4KykpQcOGDXHy5EnOKfVvonLArDLkOTjCw8MxdOhQ6OrqSpCAyoIiY/3JkyeoW7fuD5kvysrKEBYWxiWWVXbu8pHK/y+gvLwcy5Ytw9q1azmSTmNjY8yaNQsLFixQmDyk6DN5NqG6dCwWu0SEuLg4ub+diGhdHho0aICBAwdi5cqVEufnzZuH6OhoPHr0CElJSfD09JRr4ylywGhoaCgknFV13LHK/i8gJCQEs2fPlgq0f/78GatXr0ZQUJBCeSLCvHnzsGnTJm6u1dPTw9y5c3ll1fHuKhMRi5zZS5YsQXp6Ou7cucN7jR8FU1NTnD9/Hq1atZI4f+vWLfTo0UMmybMIdnZ2CA4OxqhRoyTOh4eHY8mSJbzBrcrzjYaGBiwtLeHm5oa1a9eiRo0aCuVZ7TJWkmQ/Pz9s3LiRS+wUoaioCAEBAbxzTuX5QmRbubm5Yf78+VLXVSfUoeOwwMXFBVlZWfj69SvnN8rLy4Ouri4cHBwkvquIIB5QzX/6o6GqPS5CUVEREhISZOqXfAlOSUlJ+PTpE7p27YpXr15h1KhRuHbtGhwcHLBnzx40a9ZMtYdSAt++fUNgYCC2b9+O0tJSABVBzV9++QUrV66UaduI5guRn1ocVapUga2tLdauXYt+/frJva865vunT5/ixIkTMt/9unXreOVl4T8bBHFyckKjRo0wZ84cmZOo0ImkT58+2L17N++kLw5W5xLrJPjixQs8f/4czZo14zrnrVu3YGJigoYNG/Lev6ysDMeOHeMmgcaNG8PDw0NQtlp4eDiGDBkCPT093u9WRmxsLIqKijBw4EBkZWWhX79+yMjIQNWqVREVFcVlksiDnp4e0tLS0KBBA4nzjx49QvPmzQVljbI8uwivX7/mdk84OjpKZP4pgoGBAZKTk6UM7Pv376N169YoKiqSK6ulpYXnz59LOV3fvn0LKysr3qAf67ufMWMGLC0t8euvv/I8pWwMHz4crq6umDlzJpYuXYrNmzfD09MT586dQ4sWLXidguIQz7L7N+Dm5oY5c+bw7nSSh/nz52Pbtm1wdnbmsgITExORlpYGX19fPHjwAHFxcTh69ChvAFVVREVFYc6cOQgICMBPP/0EoCKQvHXrVqxcuVKiTypysIqWC6GOkoSEBLRr107KgSkU6lQalO03ogCjpaUlnj59ys0ZDRs2lJqDZKGkpAQTJkzAokWLeLMkvgecnJzw559/cllL/zYeP36MwsJCNG3aFEVFRZg1axb3261bt453nbOzs8Pp06dVdkiq2nfKyspw9epVODs7Q0dHB9nZ2QCAevXqCQp+AMDBgwcxatQo9OzZE2fPnkWPHj2QkZGBly9fwsvLC3v37lUoz5IoAVTsZImPj4eFhQUaN24sNf6EzrfJyckSa6UyfUnVdVIEccVbHHzvgHWt0dTUVDi/ValSBUOGDMGOHTt49SBl50sRBg4ciKFDh8Lb21spORHc3Nxw9OhRmJmZSZz/+PEjBgwYINepPG3aNFy9ehUbNmxAr169kJaWBnt7exw/fhxLlizB7du3Bbfhy5cvKumJLGAdd7Vq1cL58+cFzzknTpxA7969UaVKFans2sqQlc3/o5GWloYmTZpAU1NTbvBPBKG761XBlClTEBYWhr59+8pMLFu/fr2g67DOOYDq+uX8+fOxe/duBAcHS+1UHzduHO9OdVXBqmOx2CVARWJcSEgIXF1dZf520dHRCuVPnDiBwYMHo2HDhpxDOCkpCenp6Thy5Aj69euHbdu2ITMzU2VHx/8yEhISsGbNGm6ddXJyQmBgIDp27Pjd5VntShEKCwvx8OFD6Ovrw8HB4bsnCYgga60mItSpUwcHDx7kDQI6ODjAx8cHPj4+Ug5goSgvL0dWVpbMAKCiJDt5fqTbt2+jc+fOUgEecejp6eHevXtSiXmZmZlwdnbmdqd8L7DaZQYGBrh+/brKTl95/fbNmzewtrbmnLvfG/+2L0Ic3759k9nn+BJi+apuiGPx4sUKP9+/fz88PT0F20XqQEFBAW7duiXz2SsHBcWhDnv89u3b6NOnD4qLi1FUVAQLCwu8efMGBgYGsLKyUhhsF/kTrKysmPTigoIC7N69W8Iu8/Pzk0iCEILi4mIJ21bIjnM7OzskJiaiWrVqSt2rpKQEvXr1wvbt21WeZ+Pi4uDh4QF7e3ukp6ejSZMmyM3NBRGhRYsWqifKqFxI6weDtWY2Cxo1aiSTLPffhirk4JmZmeTg4EAGBgZcfW8DAwNydHSU4Ev4t/D27VvBpF6tW7eWSX44ZcoUatOmDa8867Oz1s51c3OjwYMHc6R0RBU1/gcPHkzu7u4KZeXV/c3NzVWazEoEZd49ax3Mt2/f0rNnz4iogttlxYoV1L9/f5o5cya9e/eOV56VD4aI6Pz58zR//nzy9/enMWPGSByKcPToUaYanmPHjqWQkBCp80uXLqWxY8cSEVFQUBC1bNlS7jWysrJoypQp5O7uTu7u7hQQEKDUeFVUO1hUT1JRXUl11J/9/Pnzv1bnXASWflNWVkZVqlThaniqAhMTE5nEkfJQuW8pOvgQGxtLPXr0oJycHJXbf/HiRerXrx/Vq1eP6tWrR/3796dLly6pfD1lEBkZST///PO/Upe8MuQRfgoFKwdSbm6uwoMPonq58g4+vHz5krp27apSvWp11ZhXlZuDda05duwYOTo6UmhoKKWlpVFaWhqFhoZSo0aN6ODBg7Rv3z6qXbs2zZo1S+41WOdLVsJcebV7X758Sdra2nLl6taty5EXivotUYXuJKRWeGlpKYWEhFDNmjVJS0uLk1+4cCGFhobyyhOxrXWs4+63336j0aNHc/wwfBB/z6z1/YkqCCbXrFlD/v7+5O/vT+vWraOCggJBsiKUlpbS4cOHKSQkhEJCQujIkSNyn6dy++Xxmghtv6rrBSuHEeucow79skaNGjLH5rFjx6hmzZpKP5MqUEXHYrFLiIisra2ZuQhycnJo3rx5HIfLvHnzVNZbVCGKZhl3LLKRkZGkra1N3t7eHGeUt7c3ValShf7888/vLs/KJ8MK1vmusk5w6dIlevjwoeD5e926deTq6kqamprk6upKGzZsoOfPnwu+PwtfpIeHB3Xq1InTVYiInj59Sp07d6YBAwYolG3cuLFMcu6lS5fycjz+L0BVkuQPHz5QQUEBaWhoUFZWlsQ89+7dOwoPD6caNWp8hxb/H9SxVrDoOBkZGdShQwcmXghWlJeX0+vXr2WSZCuCjY0NBQcHc7zGykIVrjxxKGuPV0bnzp1p3LhxVFZWxumXeXl51KlTJ/rrr78UyqrDn5CYmEgWFhZUq1Ytbq2sXbs2Va1alZKTk1W+7r8BcT4UVdCqVStOhxe9+0+fPpGHhwf98ccfKl/3PxsEYSGSi4uLo8mTJ1Pfvn2pX79+FBAQQAkJCYLl1eFcUnUSZJ2Ae/fuTb169aK3b99y5968eUO9evWiPn368MqXlpbS6tWrqVWrVlS9enWlycWJKgzqmJgYKi4uJiISrLBevHiRDA0NqVGjRuTn50d+fn7UqFEjMjIyEmRosT77+PHjyd7enk6fPs0tvKdOnaJ69erRxIkTeeXT0tKoZs2aVLVqVXJzcyM3NzeqWrUq1apViyMNrgxRkEFTU5MmTJggEXiYOnUqtWnThtq1a8d7bxFUffeKSMi6du0q+P6qYt68eWRpaUl//PEH5wTeunUrWVpa0q+//sorv2TJEtLU1KTWrVuTp6cnDRgwQOJQBFWCBuIwMTGRGbDNzMwkExMTIqogxpJHuBwTE0M6OjrUunVr7rdv3bo16erq0tmzZ3nvT8TvVFXkYF27di0ZGBjQnDlzOCdcYGAgGRgY0Lp16xTet6ioiCZPnkyWlpYyCQiFoKSkhM6dO0fbt2+njx8/EhHRs2fPJMix5IG13zg5OamkrIswatQo3nckDkWOKGWdUmZmZqSjo0OamppkZGSk9FzNamATVRi5u3btonnz5nHzbnJyMj19+pRXloWcWwRV+07Lli3p/Pnzgu4hCwYGBpx+YGFhwRERPnjwgKytrVW+7r8Fb29vcnV15QgviSpICV1dXWno0KEKZVnXyR+NVq1acYSF4oiJiaFWrVoREVF0dDTZ29vLlGeZL0VQdeyL5jgNDQ2Kj4+XCJqmpKTQ8uXLycbGRq68vr4+F7gQD4LcuXOHW6sUITg4mOzt7Wnfvn0S1zp48CD99NNPvPKsax3ruBswYAAZGxtTjRo1qEePHv8aoTyRegzce/fukb29vUSij6GhIdna2sokDs3NzeV0QNbAK8t6UaNGDXr06JGgZ5QF1jmHVU8gqgicy3qG9PR00tPT45VXNYDEqmOpYpeIw8LC4ock0FWGqoFnlnHHOmYbNmwoc01Yu3YtNWzY8LvJi5yGmpqa3N+iQ+RbmDRpEu/9CwsLaeHChdS2bVuqV68e2dnZSRyK8L/k0Hv06BEFBQWRg4MDaWtrU/fu3Sk8PJxXrlmzZjR48GB68OABvX//ngoKCiQORcjLy6PmzZtTlSpVyN7enuzt7alKlSrk4uLCm9x65MgR0tLSop49e3LB7p49e5K2tjYdPXpUpkzlxEVFhxCw2GWqkiSL9B95h5aWFi1btkymbOW1XNGhCKxrBauO065dO+rUqROdPn2abt++TXfu3JE4hEBVu+z58+c0cuRIMjU15d65mZkZjRkzhl68eMF73/Xr11OzZs1IS0uLunXrRgcOHKAvX74IajNRBZn4tGnTVE6MU9YerwxTU1NKT0/n/hbZRzdu3CBHR0deeVZ/QocOHcjX11ciyFtSUkKjR4+mjh07ypVTV98nqpjzT506Rdu2beP0PNGhCNOnT6e5c+cKf9hKMDIy4vQMMzMzTje5c+eOQpuGD//Zclg7d+7EsmXL4OfnJ5hIDqgg79u5cyfMzc3RoEEDEBEyMzNRUFCASZMmYfPmzbz3Njc3R3FxMUpLSwWTOokjNjYWHh4eaN68Obdt+urVq0hNTcXff/+N7t27y5Vl3XJtaGjIkceJIzU1Fe3bt+dq2cpDUFAQQkNDMWvWLCxcuBALFixAbm4ujh07hqCgIIU18d6+fQtvb2/Ex8dDQ0MDmZmZsLe3h5+fH8zNzbF27VqF9waAf/75B1u3bkV6ejqAinp+kyZNEkTcyPrs6qidW1xcjD///FOi/T4+PtDX15f5fRFPREJCAtq2bSvBuSKq+zt79mzeLWbqePesyM7Oxt69e5GdnY2NGzfCysoKZ86cQd26dTnCZHlg5YOpUaMGVq1ahZEjRyrdbtbyddWrV8fq1aultmpGREQgMDAQL1++xIMHD9C5c2eZfcjFxQU9e/aUWTP57NmzvHU7WcFSf3by5MmIj4/H0qVLMXLkSGzduhXPnj3Djh07sHLlSvj4+Ci895MnT9CrVy/k5eXh69evXI36adOm4evXrzIJNcXB2m/+/vtvrFq1Ctu2bRNEXFYZohrh7u7uaNmypdS24crzJV9fEwdfv2OpDw9UzE3jx4+XIllct24ddu3axW3HlYe0tDR069YNpqamyM3NxaNHj2Bvb4+FCxciLy8PERERCuX5tm3zbdVm6TsxMTGYP38+li5dKvN3k8dHIYI6OJAiIyOxfft25OTk4Pr167CxscGGDRtgZ2cnqGxeaWkpLl68iOzsbAwfPhzGxsb4559/YGJiIsHpJQss9arVsU6KoOrWb5a1Rl9fH7dv35YqLZqeng4XFxd8/vwZubm5cHJyQnFxsZQ8a71uFoiXB5Gl2uvr62Pz5s0SpJzi6NSpEwYPHoyAgAAYGxsjLS0NdnZ2CAgIQGZmJmJiYhTev379+tixYwfc3d0l6kWnp6ejbdu2eP/+vUJ51rWOddyNGTNG4ed85bRY0LFjR9SvXx+7du3iyOhLS0sxduxYPH78GJcuXeK9Rtu2bWFpaYnw8HCON+r9+/fw9fXF69evce3ate/Wfpb1gpXDiHXOUQffYJs2bdCmTRsp0t2AgAAkJiYq5F3bt28fxowZg4EDB0rYhNHR0QgLC8Pw4cPlyrLqWIDydok45s6dCyMjI6VJdWW1QVatbyFl2OQRRW/duhXLli1TSBTNMu5Yx6yuri7u378vVdYoKysLTZo04S1rpKq8uvhkhg0bhoSEBIwcOVJmKbRp06bJlVX13SlTgtDIyAgNGzYU5B8Q4caNG/jll1+QlpbGWw6MlS+SiHD+/HmJcdetWzdBssnJyVi/fr0Et8GsWbPkliyVxzlZGRoaGrylZVjtMlVLnSYkJICI4Obmhr/++kuCDFtHRwc2NjZyf2u+tV0citZ51rWCVccxNDREcnKyoNL3sqCqXfbx40c0b94chYWF8PHxQcOGDUFEePDgAQ4cOABzc3OkpKTw2hZABddIWFgYDhw4gLKyMgwfPhx+fn5o0aIF77OzcOUpa49XhqWlJVf2rUGDBti8eTN69uyJ9PR0tGzZkrd0JKs/QZ5t8uDBA7i6usq0RwD19X2WcmABAQGIiIiAg4ODzHfPV+rS2toa8fHxaNSoEZycnLBy5Up4eHgI9t/Khcrhkx8MVTLljh49Sjo6OrR3716JDPiysjLavXs36ejoCCo1EBYWpvDgQ/PmzWVGxObOncub4cq65drc3JyuXr0qdf7KlSuCsoPt7e3p5MmTRCQZmdu4cSMNGzZMoezIkSOpZ8+elJ+fL5FlGBMTQ05OTgplv337Rm5ubkzbqVifXV9fXyIzVoR79+7xlqT69u0b2dvby5QXAl9fX6byQSzvXhwsu3j09fWpW7dupKOjw91/xYoVNGjQIF551iy7H5mttnTpUtLX16epU6dSZGQkRUZG0tSpU8nAwIDLWlm3bh1169ZNpryurq7Mfv/o0SPS1dUV3I6IiAhq164d1ahRg8vsXL9+PR07dkyhnK6ursydLBkZGbz3r1OnDsXHxxMRkbGxMXediIgI6t27N2+bPT09acSIEfT161eJfhsfH0/169fnlWftN+K7KfT09JTeTWFrayv34MuU+9HQ0dGRu4NJSL9zd3enwMBAIpLMKr969SpT5oZQsPSdyvqEslvOhw0bRmvXriUiopCQELK0tKSxY8eSjY2NoGybP/74g6pVq0bLli2TyKjfu3cvdenShVc+NzeXGjZsSAYGBhJliaZOnUoTJkzglTcyMqLbt29LnU9JSeEti8SyTopD1UxR1rWmefPmNHr0aPr69St37tu3bzR69Ghq3rw5EVXoDLa2tjLlWeZLWRAvU8OH3NxcysnJIQ0NDUpMTJTI5P/nn3+otLRUofzly5fJyMiIJk6cSHp6ejRt2jTq3r07GRoaUlJSEu/99fT0uLVFfMzdv3+fDA0NeeVZ1zrWcacsKmfCKTr4oKenRw8fPpQ6f//+fdLX1xfUHj09PZnZ+3fv3pW53lUus6bo4APLejFgwAAyNTUlOzs76tevn9LZiaxzDqueQMS2U51lRwCLjsVqlxBVrClmZmYql8p99eoV9e3bV252txDY2trKzNwPCwuTO0+LwDLuWMdsvXr1aPv27VLnt23bJki/ZZW/ePGi4NJRsmBqakpXrlxRSVbVdye0BKF4WTwh2d83b96kadOmkbW1NRkYGNCQIUN4Zbp27Upnzpzh/V5lfPv2jbS0tGTuzvsvgNUuU7XUqQi5ubmCS0+pG6xrBauO4+rqSpcvXxbWWBlQ1S4LCQmh+vXryyyf9/LlS6pfv77MEm2K8O3bN9qwYQPp6uqSpqYmNWvWjHbv3i3Xr+Tl5UVRUVFK3UMcrPZ49+7duV2tY8eOpdatW9O+ffuoZ8+e1Lp1a155Vn+ClZUVxcbGSp2PiYkhKysrXnlWsJQDY60m4+npSTt37iQiolmzZlH9+vVp2bJl1KJFC0FlO+XhPxsEUQX9+/enefPmyf18zpw55OHh8d3bwTIJsk7AI0eOpMaNG9ONGze42qnXr1+nJk2a0OjRo3nlDQwMuHp+1tbWnDMiOzubt1xC9erVue164pNvdna2IAOZtaYc67Oz1s6tWbOmysZGQUGBRBkvEd6+fSsoOML67t+8eUNubm6cE1AkP2bMGJo5cyav/E8//cQ5J8Tvf/PmTapVqxavPCsfzJw5c2TycgiFqgEEEfbt20c//fQTt9j99NNPEiUiiouL5Tq7ateuTYcOHZI6HxUVRXXq1BF0fxanKkv9WUNDQ26+qFWrFt28eZOIiB4/fiyo31lYWHDbT8X7TU5OjiAjk7XfsAa8lcXx48fp27dv3N8sTimiirKLCxYsoKFDh3KG4+nTpwWVuWA1sE1MTLjAo/hvl5ubK9gZzFJOi6XvsBpprLwUjRo1oujoaKm23717l6pWrcorz2qkstSrZl0nRVB16zfrWnP16lWqWrUqWVpacuVKraysqGrVqtxW9oiICFq1apVMeXXU61YHt4aqyMrKorFjx1KrVq2oUaNG5OPjw5WV4kOLFi0oMjKSiCTffXBwMHXo0IFXnnWtYx13Xbt2pffv30ud//Dhg0xDTZFRrayBrQ4Dt2nTphQXFyd1Pi4uTmbfE+JEFBr4ZVkvWDmMWOccVj1BhGfPntGvv/5KAwcOpIEDB9KCBQsk5lB5YAkgsepYLHYJEbtzY/jw4dS+fXtKTEwkQ0NDOnv2LEVGRpKjoyOXcMcHlsAzy7hjHbN//PEH6ejo0MSJEykiIoIiIiJowoQJpKurK3MsqVv+1KlTcks/nj59mlfe1tZW5b7zbzj0vn79Snv37pVbCrFyGawePXpQeHi4wpJO4iUmWfgi7ezsBJcvIiKpklGKju8NVrtMXSgqKqKHDx8qzZfIAta1glXHiYuLo7Zt2ypdSkwEVe2yNm3a0J49e+R+vnv3bkElT4kqgh9RUVHUq1cv0tLSovbt29OePXsoJCSEqlevLjehmpUrjxWJiYl04cIFIqoI/PTs2ZOMjY2pRYsWgsYyqz8hICCAateuTQcPHqS8vDzKy8ujAwcOUO3atWnatGmCn0PVUnas5cBYkJ2dzY3twsJCmjBhAjk7O9PAgQMFlWuVh/9kEETVKLq4gigLN27cEGQkE7E5l1gmQdYJ+P379+Th4UEaGhqko6PDRSUHDBggiJCsQYMGdOPGDSIiat++Pa1YsYKIKmo+W1paKpQ1MjLighjik68o45MPrDXlWJ/97t27TLVzlSXdFEevXr1o69atUue3bdsmKKOe9d2z7iQxNDTkCKkqK01CHKKsfDAs2WqsWdmsCA4OJjMzM1q5ciVdunSJLl26RCtWrCAzMzPBgR0Wp6oq9WdFcHZ25pzG7u7uHJnwxo0bBc21ZmZmdP/+fal2X758WZChxNpv1AkhhJ3qJNplzYhnNbAtLS0pJSWFiCR/u7Nnz1Lt2rV55VNTU8nS0pLq169P2tranPyCBQto5MiRvPKsfedHQl5GfUZGhuCdbyxGKku9atZ1UgRVM0VZ1xoioo8fP9K2bdu49UHcWOADy3wpAiu3BlFFX9mxYwctXbqUgoODJY7vhWPHjpGpqSmtXLmSDAwMaPXq1TR27FjS0dERVO9aHWsdC1QllFcH1GHgnjp1iho3bkyHDx+m/Px8ys/Pp8OHD5OzszOdOnXquzrKWNcLFrDOOerQE548eSJ3fecjgmUJILHqWCx2iTpgbW3N2eXGxsZckt/x48epffv2gq7BEnhmGXfqGLNHjx6l9u3bk4WFBVlYWFD79u0FJ1exyovmhco4c+YMNW3alFc+MjKSfv75Z5Vq9KvLoceHjx8/yrXxNDQ0qHXr1rRhwwZBnAYiGUW8fUJ3DIeGhlKfPn1kJjcquq+QQxa8vLy4eZ+VF4BVt05ISFB48EGV3WMuLi5cIkTz5s2lOAaF8g2yrhWsOo6sHerK7FJX1S4zNzfnbApZePjwIe9uhuTkZJoyZQqXZDRr1iwpHV/erlUidptYHELs8f81fP36laZOncr5LjU1NUlXV5emT58umFuFpUqAeCK6g4MDF0B/+PCh4F3+qlaT+V7QVq2I1o9FlSpVULduXd56jZXx5s0b1K5dW+7ntWvXxtu3b3mvk5CQgN69e6N9+/a4dOkSfvvtN1hZWSE1NRW7d+/GkSNHFMqPGzcO48ePx+PHj9GuXTsAFfVff//9d8ycOVOh7KpVq9C3b1+cP3+eq9l5/fp15Ofn4/Tp07xtNzMzw/Hjx5GZmSlRh1JoTUsvLy/ExcWhTZs2CAgIwIgRI7B7927k5eUprLsKVNQAjYiIwNKlSwFU1IMsLy/HqlWrBNWrLC0txZ49e3D+/HmVasqxPnuTJk2QmZkpUTt32LBhgmvnJiYmIi4uDmfPnoWzs7NU+48ePSpX9ubNmzKfr0uXLliwYAHvvVnf/dmzZxEbGys1fhwcHATxGJiZmeH58+ews7OTOH/79m3UqlWLV75z587IyMiQ4IMZOHCgYD6YtLQ0NG/eHABw7949ic/4alBv3rwZu3btwoABAyTqeLq6umL27Nm89xbh27dvePXqFcrLyyXO161bV6HcokWLYGxsjLVr12L+/PkAKuqSLlmyhLeGpQg5OTkya8Xq6ury1rEcNGgQbt68ifXr1+PYsWMAKsbNrVu35NafFWHMmDFITU1F586dMW/ePPTv3x9btmxBSUkJ73gFgB49emDDhg3YuXMngIrfqrCwEIsXL0afPn145Vn7DQCUlZXh2LFjErwEHh4e0NLSEiQfERGB1atXIzMzEwDQoEEDBAYGyuSnEe8blfuJspg3bx6WLVuGmTNnwtjYmDvv5uaGLVu28Mr/8ssvsLa2xtq1a3Ho0CEAFb97VFSUIE4KDw8PhISEcLIaGhrIy8vD3LlzMWjQIF75mTNnwtfXF6tWrZJof58+fRTWSBeBte8oy0fx8eNH3muKwMcpYmdnhzt37kjxvsTExKBRo0a81y8vL5epHz19+lTiXcpDnTp1kJKSolK9atZ1UgQTExPk5eVJ1b/Nz89X+Aysaw0AGBsbY+LEiYLbKg6W+VKEiIgI7Ny5E+7u7hLtaNasGfdOFWHXrl345ZdfUK1aNVhbW0uscRoaGggKCuL+V2e/9fT0xN9//42QkBAYGhoiKCgILVq04OW6E0GVtU4d7U9LS+P+fvDgAV68eMH9X1ZWhpiYGMF9R1WsWbMGGhoaGDVqFEpLSwFU2Dq//PKLVP1weejXrx8AwNvbW4obpn///tz/iuquqwrW9YIFrHOOOvQEOzs7PH/+HFZWVhLn3759Czs7O4Xve9asWZg6dSru3LkjYROGhYVh48aNCu/LqmOx2CWV8fTpUwBQaGdXRlFREffOzM3N8fr1azRo0ADOzs6C+e6Cg4MxZMgQXLp0SYJTJS4ujuuL8sAy7tQxZr28vODl5SXou+qWz8zMhJOTk9T5hg0bIisri1d+7dq1yM7ORvXq1WFrayvFj6ro91PHuxM9Q3x8vEzbKigoCMbGxnLHwaNHj3j5NCtDXZxeW7ZsQVZWFmrWrAkbGxupcVf53cXHx3N/5+bmYt68efD19ZXwA4WHh2PFihUy72dqasqtCXycanxg1a0rczeJriEC39o0ffp0FBQU4ObNm+jSpQuio6Px8uVLjvNBFjw9PaGrqwsAGDBgAG8b5YF1rWC158X7gSpQ1S77+PEjzMzM5H5uZmbGq4u1atUK3bt3x7Zt2zBgwACp+QKoWEeHDh0qU57VJgaUs8e/J758+SLFf8WnX+vo6GDjxo1YsWIFsrOzAQD16tWDgYGB4PtOmzYNrq6uSE1NRdWqVbnzXl5eGDdunEJZFxcXJCYmwsHBAZ07d0ZQUBDevHmDyMhIXo4TebzE/v7+/xovsSz8Z4nRd+/ejaNHjyIyMlKCHEkRNDU18fLlS1haWsr8/OXLl6hZsybvBNy2bVsMHjyYcy6JyB9v3bqFgQMHcoqgPBARNmzYgLVr1+Kff/4BUDEJBgYGYurUqbxOWRZycHXj+vXruH79OhwcHDgjSx7u3bsHd3d3tGjRAhcuXICHhwfu37+Pd+/e4erVq6hXr55CeUXOeiFkXj8aLKSb8kjd7969izZt2sglRBKB9d0bGxsjJSUFDg4OEn0+KSkJPXv25A0ezp49Gzdv3sThw4fRoEEDpKSk4OXLlxg1ahRGjRrFS3L8I6Gvr4/09HTY2NhIPHtmZiaaNm2Kz58/K5TPzMyEn5+fFCmpKs6IT58+AYAgR6Y4nJycsGLFCnh6eko8w+bNm7F3797vTq4uQm5uLlJSUlC/fn1BhJdPnz5Fz549QUTIzMyEq6srMjMzUa1aNVy6dEnK4aBuZGVloU+fPnj27BkcHR0BVBhOderUwalTp3jHDQthJyuMjIxw9+5d2NnZSfzmubm5aNiwIS/pJis+fPiAn3/+GUlJSfj06RNq1qyJFy9eoG3btjh9+rSU0VcZpqamSElJQb169STa/+TJEzg6OvK2n6XviOY1fX19tG7dGkCFs+jz5884e/asTAI/cVJqeRA65kNDQ7FkyRKsXbsW/v7+CA0NRXZ2NlasWIHQ0FC5RoIIQ4YMgampKXbu3MkRXFtaWsLT0xN169b9rgTP6sLUqVMRHR2NNWvWSDgGAwMDMWjQIGzYsEGmnDrWGj7HyveGvDXnwYMHaN26NS8JoI2NDSZNmoS5c+fy3kud/VadELrWqaP9qhLK8yUtiUOIQxqoIIhW1cBNSEgQ/N3OnTtLkXgrgiIHTWlpKZYvXw4/Pz/BDvAWLVogLi4O5ubmcHFxUfgbfm/9JC8vD3Xq1JHZhry8PN5EFUC+ffnkyRM4OTnxJptER0dj7dq1EkTHgYGBSgeQlNWxWOwSoMIxJXI+iuYlY2NjzJo1CwsWLOBIkOWhVatWWLZsGXr27AkPDw+YmZlhxYoV2LRpE44cOcKNBT4oSxRdGSzjjkVWhMLCQqm1hs8pxipvbW2N/fv3w83NTeL8+fPnMXz4cLx69UqhfHBwsMLPhay1LO+OL9j/b9k1qoDl3bm7u2Ps2LEYNmyYxPn9+/dj586duHjxojqaKBesdtmHDx8k/i8pKcHt27exaNEi/Pbbb3B3d1coX6NGDRw/fhytW7eGiYkJkpKS0KBBA5w4cQKrVq3ClStXmJ/x34Cq9jwLVLXLtLS08OLFCyb/6ZMnT6QSu/5NqGKP8+km4uCbb4qKijB37lwcOnRIps/s39Cvq1atimvXrsHR0VHKL+Dk5KTQlyjqM127dsWrV68watQojih+z549aNasmVzZUaNG4dWrVwgNDUWjRo24+8bGxmLmzJm4f/++lIy5ubngd//u3TtB36uM/2wQxMXFBVlZWSgpKREURQcqlNTx48fLXWSLi4uxa9cu3o6oTueSspMgq7JeVlaGsLAwxMXFyTTwv3cg4cOHD9iyZQtSU1NRWFiIFi1aYPLkyahRo8Z3vS+gnmf/Uc6Rrl27okmTJti8ebPE+cmTJyMtLQ2XL1/mvQbLu+/Tpw9atmyJpUuXck41GxsbDB06FOXl5by7n759+4bJkycjLCwMZWVl0NbWRllZGYYPH46wsDBBWfUFBQW4deuWzHc/atQoXnlVwRpAaN++PbS1tTFv3jzUqFFDauwqWjjUBVananl5ObKysmS++06dOn3PpqO0tBRRUVES/VaZrHKWftOnTx8QEf78808u2P727VuMGDECmpqaOHXqlEJ5Ozs7BAcHS90nPDwcS5Ys4c0qKyoqQkJCAvLy8qSyRviyhmrXro1Dhw6hXbt2Ev02Ojoas2fPFuxcUHUHkwhXr16V+O2E7CYAACsrK8TGxsLFxUWi/efOnYOfnx/y8/N5r6Fq3+nYsSPq16+PXbt2QVtbm7vW2LFj8fjxY1y6dElKRlkHJB/+/PNPLFmyhPudatasieDgYPj7+/PKqiN4GBcXJ3et3LNnj0JZdayT3759Q2BgILZv3y4zU1SU1SdLjmWtUcWxos7dFADQsmVLzJgxAyNGjJDo+yEhITh37hzvem9iYoI7d+7A3t6e917q7Lf5+fnQ0NDgHOG3bt3C/v374eTkhPHjxwu+jzJQR/ufPHkCIuKSmcQNfR0dHVhZWcnsN0J20QL/u0k6lXdLyYOGhgYeP36s8DtGRka4d+8ebG1tBV0zODgYgYGBMDAwUIszlWXO0dLSkruLw8rKSqFNKAqEbdy4EePGjZOwL8vKynDz5k1oaWnh6tWrvM/wX8T8+fOxe/duBAcHSziWlixZgnHjxuG3335TKL9v3z6UlpbC19cXycnJ6NWrF969ewcdHR2EhYVhyJAh/8Zj/BDk5ORgypQpuHjxooTfQGjQmVV+woQJuH79OqKjo7mEnqysLAwaNAitWrVCaGgow9Mphp+fHzZu3Cjl+ygqKkJAQACvjgEoF+wXwcLCAhkZGahWrRqvk02IY+3Ro0fYvHmzRPAtICCAS5r6HjAwMEBqaqrULpaMjAw0b96cNylSHWC1y2QhISEBM2fORHJyssLvmZiYIC0tDba2trCxscH+/fvRvn175OTkoHHjxt/9+X+UL0IcxcXFMu1CIYFvoGKOTktLE2yXaWpqSuwmqgwiwsePHwU78lUN+rLYxKrY43y6iTj49JTJkycjPj4eS5cuxciRI7F161Y8e/YMO3bswMqVK+Hj4yMlM3DgQISFhcHExAQDBw5UeH0huzbNzc1x9epVODk5SdgWV65cwaBBg/Dy5Uvea6gCa2trxMbGolmzZhL3ffz4MZo2bSozsSs8PFzw9UePHq1Su/6zQRBVlOYuXboIiirxbTdjdS65ubnh6NGjUlvLPn78iAEDBig0lliUdQCYMmUKwsLC0LdvX5kO2fXr1yuUB37Mol8ZIgdYnTp1BMuwPrs6sk5KS0tx8eJFZGdnY/jw4TA2NsY///wDExMTGBkZyZW7evUqunXrhlatWnFZEnFxcUhMTMTZs2fRsWNH3nuzgHUniQj5+fm4e/cuCgsL4eLiIngr8t9//w0fHx8UFhbCxMRE6t0LUVaTkpJw6NAhmYunosWDNYBgaGiI5ORkqbIuQvHy5UvMnj2bc0hWnrKFKh2qOlVv3LiB4cOHc44icQgxtOLi4qQy9KZPny7YGc4C1n4jbwdWamoq2rdvz5uRraenh3v37kmV3MvMzISzs7PCgPnt27fRp08fFBcXo6ioCBYWFnjz5g0MDAxgZWXF65RizYhX5w4mEQoKChRuqRbH2LFj8fbtWxw6dAgWFhZIS0uDlpYWBgwYgE6dOsndCaAO6Ovr4/bt21Jj9sGDB3B1df1XjEwRiouLUVhYqPSup9LSUhw8eFDC0BFqpAYHByMkJASurq4y18ro6Gi5surOzlQ1U1TVtUYVx4q6d1McP34co0ePxvz58xESEoLg4GA8evQIEREROHnyJG9pKX9/f7Rq1Urlkl6qomPHjhg/fjxGjhyJFy9eoEGDBlypooCAAF5ntLrWuv8iioqKsHLlSrmBR3nzvXgpLz4IdZCoAk9PTwwcOFBlY5QFrHMOyy4OUSAsISEBbdu2hY6ODveZjo4ObG1tMXv2bEHzj6oJB+rQsV69eoVHjx4BABwdHQWvNzVr1sT27dvh4eEhcf748eOYNGkSnj17JrgNQMV8n56ejrp166JatWpKyb569Urm+1PU71Udd6yyQEWCFBFh2rRpqF69utQawhd0ZpX/8OEDevXqhaSkJC5w/fTpU3Ts2FGmj0KdkOfLePPmDaytrbnEB0VQJtgvQnh4OIYOHQpdXV1eJxvfXPbXX39h6NChcHV15cpS3bhxA4mJiTh48CBv2deCggJut1NgYCAsLCyQkpKC6tWrKyy/6OjoCE9PT6xatUri/Jw5c3D8+HFuHCvCkSNH5NrDP2oHTXp6OlxdXXntKtbdY2VlZVi/fr3c51dkF7LalKw6zuvXrzFmzBicOXNG7rN9Dwh1SCsaM6xBW1abmMUeVwfq1q2LiIgIdOnSBSYmJtyOzcjISBw4cEAmpcGYMWOwadMmGBsbw9fXV6GdIWSHv7e3N8zMzJiqBIjrCg0bNpS7O0gcrNVkvhu+P+3I/3uYNWsWdejQgZ4/f07GxsaUmZlJV65cIXt7e1qyZAmvPAv5ooaGBr169UrqfG5uriBimqpVq8okQhOKI0eOkLa2Nv30008caWjbtm1JW1ubjhw5olD2zJkzdPnyZe7/LVu2ULNmzWjYsGEcYZUilJSU0MKFC8nExIQjBTIxMaEFCxbQt2/feOVZn71u3bq0cuVKleVZCImIiO7cuUPDhw8nJycnatmyJY0ZM4YjKeID67snIiooKKBly5bR4MGDqXfv3rRgwQL6559/BMlWRmlpKd2+fVvwvR0cHGjatGkqEfARER04cICqVKlC/fr1Ix0dHerXrx81aNCATE1NydfXl1d+3759VL9+fY6Eq1atWhQaGiro3q6urhLvXln06tWLnJyc6I8//qDo6Gg6duyYxKEsioqKZM4/8tCsWTMaPHgwPXjwgN6/f08FBQUShyJs3bqVtLW1aejQobRx40bauHEjDRs2jKpUqUJbtmzhvXdYWBidPHmS+z8wMJBMTU2pbdu2HHG0IrD2G3Nzc7p69arU+StXrvCSwBGxEXZ27tyZxo0bR2VlZRyBXV5eHnXq1In++usv3nt//fqVxo4dS9ra2qShoUFVqlQhTU1NGjFiBJWWlvLKt2vXjjp16kSnT5+m27dv0507dyQOPqxcuZIOHjzI/T948GDS1NSkmjVrCpIvKCigbt26kZmZGWlpaVGdOnWoSpUq1KlTJyosLOSVZ+k7VlZWFBsbK3U+JiZGMKn6u3fvaPXq1Rx54po1awSTYP5oWFtbU0REhEqyrOukCGPGjJFJRl5YWEhjxowRfB1l1xpjY2NubRaKixcvCj6E4tKlS9StWzeytLQkfX19at++vcw+KQvLly+natWq0ejRo2nNmjXc3Cs6xJGamkplZWXc34oOPpiZmXHkmRs3bqR27doREVFsbCzZ2dnxyqtjrWMZd6zrDZHqxI9Dhw6lGjVq0Jw5c2j9+vW0YcMGiUMe+Ih6lSUO/fr1K6WnpytNlL1t2zaytramWbNm0f79++n48eMSh9B75+fn05MnTyQOPqg654hsGE1NTZowYQL3/4wZM2jq1KnUpk0brg/zwdfXV2XC+YyMDOrQoYNKZLesOtaHDx9oxIgRnJ6goaFB2tra5OPjw6vfERHp6upyZObiSE9Pl0twq24kJSVR48aNZY4Dvven6rhjlSUiMjQ0VEg2/L3liSrmp9jYWFq1ahVt3rxZEDG1CKWlpbR69Wpq1aoVVa9enczNzSUOWfjw4QMVFBSQhoYGZWVl0YcPH7jj3bt3FB4eTjVq1BB0fz8/P9q2bZvg9qob9vb2tGjRIqnzQUFBZG9vr1A2NTWVLC0tqX79+qStrc3pHAsWLKCRI0cqlD116hTp6elRkyZNyN/fn/z9/cnZ2Zn09PQE+Tg2btxIRkZGNGXKFNLR0aEJEyZQt27dyNTUlH799VdeedZ1srJecefOHTpz5gx17tyZ2rdvzysfGRlJe/fuJaKKsV+tWjXS1NQkPT09CZtDHhYtWkQ1atSgNWvWkJ6eHi1dupT8/f2patWqUvpRZbDalKw6zvDhw6l9+/aUmJhIhoaGdPbsWYqMjCRHR0eJ30QclfU/Rcf3RLt27aht27Z08OBBio+PV1o3ZrWJWexxcSQmJlJERARFRERQUlKSYDlDQ0NOn6lVqxbdvHmTiIgeP35MhoaGgq/Dgvz8fHJycqJGjRpxvtyqVatSgwYNeP1CHz9+VFlX6N27Ny1cuJCIiIyMjOjx48dUVlZGgwcPpkGDBglqe2lpKR0+fJhCQkIoJCSEjhw5orSeWhn/+SBIUlISRUZGUmRkJKWkpPwr91TVuSSa8DU0NCg+Pl5iEUhJSaHly5eTjY2NTFl1Kes1atSQqbAKBcui36RJE26BTktLIx0dHZo/fz799NNPghzREydOJCsrK9q+fTv33rZv307W1tY0ceJEXnnWZ1fFOSIOT09PGjFiBH39+pWbwImI4uPjqX79+nLlvn37RmPGjKHHjx+rfG/Wd3/hwgW5nwkxtKZNm8YFDUpLS6l9+/akoaFBhoaGFB8fzytvYGDA9O6dnZ25dorefXl5OY0bN46CgoIEX0fZAAIRUVxcHLVt25bi4+PpzZs3Ekq/EKPZyMiIbt++rdQ91QkDAwPKzMxUSbZWrVq0efNmqfNbtmyhmjVr8so3aNCA4uLiiIjo2rVrpK+vTzt27KD+/fuTl5cXrzxrvxk5ciQ1btyYbty4QeXl5VReXk7Xr1+nJk2a0OjRo3nljxw5QlpaWtSzZ09u4e7Zsydpa2vT0aNHFcqamppyBq6pqSk9ePCAiIhu3LhBjo6Ogp/hyZMndOrUKYqKihIcNCWqeHcPHz4U/P3KsLW15QJIZ8+eJTMzM4qNjSV/f3/q3r274OtcvnyZtm7dSr///judO3dOsBxL3wkICKDatWvTwYMHKS8vj/Ly8ujAgQNUu3ZtmjZtGu+9ExISyMTEhOrUqUNeXl7k5eVFdevWJRMTE7lOBhcXF85R37x5c3JxcZF7yEJlp6Oigw8WFhaUlZXF+z1ZYF0nRdDU1JQ5175+/Zq0tLTkyrGuNT/asaIO2Nrayj0qByPEk3IUOdSFONENDQ0pJyeHiIj69+/POaafPHkiyCHKutapMu7EwTJnvHnzhtzc3Lh3JRoDY8aMoZkzZ/Le29TUlK5cuSLgKSWRm5sr+FCEoqIi8vPzIy0tLYkknSlTptCKFSt428ESgHn06JHKQQAi1eecLl26UJcuXUhDQ4PatWvH/d+lSxfq0aMHjR8/Xqk1U1WwJByw6lje3t7k4OBAMTExnE4aExNDjo6ONGTIEF751q1bU0BAgNT5KVOmUJs2bXjlS0tLKTQ0lIYNG0bu7u7UtWtXiUMImjZtSl5eXnTjxg3KyclRqt+rOu5YZYkq+p8yOo065b99+0ZaWlp09+5dle+vijNZNKblHVpaWrRs2TJB91cm2C8LlW0x0fHx40f6+vUrr7y+vr5M2ygjI4P09fUVyrq7u1NgYCARkYQ/4OrVq3L9QOLIy8uj+fPnc+vcr7/+Snl5ebxyRESOjo60f/9+qXsvWrSIJk+ezCvPapfJ0zPatm2rks1RVFREycnJ9Pr1a0Hft7e35wIGRkZGnK4rCiArAqtNyarjWFtbc85zY2Njzqd1/PhxuQEkRfqgIt1Q3WAN2rLaxCz2OFFFAKFDhw6koaHBBXo1NDSoffv2lJ+fzyvv7OzMBXvc3d1p1qxZRFTR72rVqsUr37VrV3r//r3U+Q8fPgheK/fv308lJSUUGRlJgYGB9Msvv9CuXbuouLiYZs+erVCWRVe4e/cuWVlZUa9evUhHR4d+/vlnatSoEVWvXl2QrXnv3j2yt7cnAwMDzg42NDQkW1tbpjXsPxsEefnyJXXt2lWqM7q5ucncKUFEEoEDRYdQKOtcEl/8ZRkKBgYGtHv3bpmy6lLW16xZQ5MmTRKcnVYZLIu+uIG8ePFiLvqXnJxM1atX5723iYkJnT59Wur8qVOnyMTEhFee9dlZnSMWFhbcBC6ueOTk5PC+OxMTE6YgCOu7NzMzkxnx3rBhAxkbG/PK16pVixITE4mIKDo6mgtILVy4UFDwzsvLi6Kioni/Jw8GBgbc81tYWFBaWhoRET148ICsra1Vvq4QiDsCVDHwGzVqpFKAl8+RyudUFaFr16505swZpe9PVNHv5M0XQjIf9PX1ucyJOXPmcBlS9+7do2rVqvHKs/ab9+/fk4eHB2loaJCOjg7p6OiQpqYmDRgwQFCWJFFFoN7Hx4datGhBLVq0IB8fH0G/Z7Vq1bg5XaR4EBE9fPhQ0K4/VrDuYNLT0+OMsqlTp9L48eOJqMLhZWZmppY2KgJL3/n69StNnTqV+701NTVJV1eXpk+fTl++fOG9d5MmTWjcuHESSRGlpaU0fvx4uRlHS5Ys4bLLlixZovCQBVmOR1nnhMw5c+bMoZCQEN7vyQLrOsmaKcq61rA6VogqdnH4+PhQ27Zt6enTp0REFBERwTSevhdyc3M5nYjFiU5U4RCdO3cuXbp0ifT09DgH7vXr1wUZeaqudSKoMu7EwTJnjBw5knr27En5+fkS+l1MTAw5OTnx3tvW1pYz6n8Epk6dSi1btqTLly+ToaEh1/5jx45R8+bNv+u9WXcdss45LLs4xJGYmEiBgYE0ZMgQzjkpOhSBJeGAVccyMDCQOS9dunRJkJ5x8eJFMjQ0pEaNGnG7rxo1akRGRkZ06dIlXvnJkyeToaEheXt707Rp02j69OkShxAYGRmpnKjDMu5Yx2xWVhZ169aNwsLCKCkpSemdd6zydnZ2gsaXPKjiTL548SLFx8eThoYGHT16VCIT/Nq1a/Ts2TPB92d16PIFZOrWrUtBQUHcTsnK6N27N+3Zs0fq/J49e6hHjx4K721iYsK9L/H1Ijc3l3R1dXnbzgJ9fX1uPbe0tOT6QEZGBllYWAiSZ7HLKusVeXl59PnzZ0Ft//btG9nb2zONOwMDA6791tbWlJycTERE2dnZvL4kVpuSVccxNjbmfBl169blgrCPHz/m9SOxwMzMTGqnl6xDEViDvuqwiVW1x4mIevbsSW3atJEI5KSnp1Pbtm2pZ8+evPLr1q3jbIhz586Rnp4e6erqkqampqCdgyxVhEQwNTWV6UedMWMGry+MVVdgqSbz008/Uf/+/SV29L979448PDyobdu2gq4hC9o/pggXOwICAvDp0yfcv38fjRo1AlBRr3v06NGYOnUqDhw4ICVz+/Zt3usKZaIHKuq7CSWHBSrq4ZEK5IvA//GUjBkzBhs3bhREICQLV65cQXx8PM6cOYPGjRujSpUqEp/zEet06dIFly9flqqpd+XKFV5eCh0dHa6W+vnz5zlyIgsLC0HEorq6ujJJF+3s7CTq8MoD67PXr18fixYt4jgCKsvzkTKVl5fLrHn49OlTKXK4yhgwYACOHTuGGTNmKPyePLC++9WrV6N37964dOkSVyd/7dq1CAkJ4SWHBv6vzisAnD59Gt7e3mjQoAFHjseHvn37IjAwEA8ePJD57ivXI64Mc3NzfPr0CQBQq1Yt3Lt3D87OzigoKOCt7//27VsEBQXJJd3kqwHKxzHEhw0bNmDevHnYsWOHYNJRoKLPqArxOuMBAQGYNWsWXrx4IfPdK6q37OHhgejoaAQGBkqcP378OPr168fbDiMjI7x9+xZ169bF2bNnORJSPT09fP78mVeetd+YmZnh+PHjyMrKkqi3XXn+U4SWLVti3759gr8vgouLCxITE+Hg4IDOnTsjKCgIb968QWRkJJo0acIrT0Q4cuSI3H7LN9/9/vvvmDNnDpYvXy7z3fGtQebm5sjPz0edOnUQExODZcuWce0SWrc2MTFRbvvXrVunUJal7+jo6GDjxo1YsWKFSnwUWVlZOHLkiMR6rqWlhZkzZyIiIkKmjDhHixAi4MoQfz/nz5/H3LlzsXz5cq5W9fXr17Fw4UIsX76c91pfvnzBzp07cf78eTRt2lTqt6/87jdt2sT9zbpOmpmZQUNDAxoaGmjQoIHU5xoaGgo54VjXmp07d8LIyAgJCQlSpNsaGhq87f/rr78wcuRI+Pj4ICUlBV+/fgVQUX99+fLlMuv+VgYfx4gydZ/p/6s5Le96NjY2Mv9WBb///ju8vLywevVqjB49Gs2aNQMAnDhxAq1bt+aVV3WtE0GVcScOljnj7NmziI2N5Wrri+Dg4IAnT57w3nvp0qUICgpCeHi44HlGHh48eCCz1rmi9e7YsWOIiorCTz/9JNFXGjduzFtjnRV37txh4k1jnXM6deokJaMsDh48iFGjRqFnz544e/YsevTogYyMDLx8+RJeXl4KZZ2cnPDmzRuV7suqY1WtWhWmpqZS501NTWFubs4r37lzZ2RkZGDr1q1IT08HUEHmOmnSJNSsWZNX/uDBgzh06BD69OnD+115cHd3R2pqqlJ6mQgs4451zL5+/RrZ2dkYM2YMd05DQ0NwjXxW+QULFuDXX39FZGQkLCwslG6/yCYAKubODx8+AAD69euHRYsWyZQR8ZRkZ2fD1tZWKZ9LZcgiMlYGYWFhWLBgAXx9fbn16datWwgPD8fChQvx+vVrrFmzBrq6uvj111+l5D08PDB37lwkJyfjp59+AlDBCXL48GEEBwfjxIkTEt8Vh66urkzbOyMjQ2aN/bS0NDRp0gSampq8PFB83E/W1tZ49+4dbGxsULduXdy4cQPNmjXjfFR8YLXLEhISMGTIEOjq6kqc//btGzePykOVKlWYuRtq166N58+fo27duqhXrx7Onj2LFi1aIDExUapNlcFqU7LqOI6Ojnj06BFsbW3RrFkz7jrbt29HjRo1lL6eUKxfv55prAIV/KoTJ07Es2fP0KRJE6V8CQC7TQyobo8DFf322rVrEvzHjo6O2Lx5syBeXnH/Xbdu3ZCeno7k5GTUr19f4bOLj/cHDx7gxYsX3P9lZWWIiYlRyCEkjj///BPDhg3DyZMn0aFDBwAV+pHIV6AIrLqCqakpFixYIKidlXHnzh0kJSVJ3Mfc3By//fYbWrVqpdI1gf8wMbqpqSnOnz8v9fC3bt1Cjx49UFBQ8N3uzepc+pEQV5ZkgY8UZ/v27QgKCoK3t7fMRV9c6a28GHh4eODbt29o3749li5dipycHNSqVQtnz57FlClTkJGRofDeISEhSE9Px969e7mF6uvXr/D394eDgwOv44j12e3s7OR+pqGhwUvKNGTIEJiamqpESLRs2TKsXbsW7u7uaNmyJQwNDSU+5zPyWN89AKxatQqbNm3ClStXEBUVxTl02rdvzytrY2ODXbt2wd3dHXZ2dti2bRv69u2L+/fvo0OHDnj//r1CeU1NTbmfCVH2hw8fDldXV8ycORNLly7F5s2b4enpiXPnzqFFixYKx2yfPn2QlZUFf39/meSD35sI1NzcHMXFxSgtLYWBgYGU4iCEFF5ZiJxw8pYHoYbWsmXLsGbNGrRv316COPDq1auYNWuWhCNdVh/28fFBeno6XFxccODAAeTl5aFq1ao4ceIEfv31V9y7d4/3OeRBFXLvsrIy3L17FzY2NoIW/ZSUFFSpUoUzFI8fP469e/fCyckJS5YsURi8TUpKwqdPn9C1a1e8evUKo0aNwrVr1+Dg4IA9e/ZwDkZ5mDZtGnbs2IGuXbvK7Ld8853o3VWWE2pgT5kyBSdPnoSDgwNu376N3NxcGBkZ4eDBg1i1ahUv+eLy5cuxcOFCODo6SrVfQ0MDFy5cUCjP2nfE8fHjR1y4cAGOjo5c0oUitG/fHoGBgVKByGPHjmHlypW4ceOGQvnExESUl5ejTZs2Eudv3rwJLS0tuLq6KpRv0qQJtm/fzim5Ily+fBnjx4/nAnryICL7lQVZ717R2lhZlm+dTEhIABHBzc0Nf/31l4RzRkdHBzY2Ngqda6xrDStcXFwwY8YMjBo1SoIA8Pbt2+jdu7eEASMPx48fl/i/pKQEt2/fRnh4OIKDg+Hv7897jYiICKxevRqZmZkAgAYNGiAwMBAjR45U7cEEoqysDB8/fpSYH3NzcznySkVgXetYxx3LnMFK/Oji4oLs7GwQEWxtbaWeXQhZ7ePHj+Hl5YW7d+9KrN+iuVPRnG1gYIB79+7B3t5eov2pqano1KkT5+AUh3jwkw+KdNRWrVph/fr1UvOVULDq5tWrV8fnz58xePBg+Pv7o127dkq3oWnTppgwYQImT57MvT87OztMmDABNWrUUBi4vXDhAhegVjbhgFXH2rlzJw4fPozIyEguePzixQuMHj0aAwcOxIQJE5R6D8qiZs2auHjxosyAt1C8efMGo0ePRuvWrWU61xQ5JlnGHeuYdXJyQqNGjTBnzhyZOhpfUJpV3sXFBVlZWSgpKYGNjY2UXcnXfkdHR0RERKBNmzbo0KED+vXrh3nz5iEqKgoBAQF49eqVXFk7OzuMGTMGvr6+SiWTimw4Q0NDzvkuCxoaGli7dq3Ca7m7u2PChAnw9vaWOH/o0CHs2LEDcXFxiIyMxG+//cYF+MShyL6o3JbKc+/YsWPx9u1bHDp0CBYWFkhLS4OWlhYGDBiATp06YcOGDVL3evHiBaysrBTaZ0J087Fjx6JOnTpYvHgxtm7disDAQLRv3x5JSUkYOHAgdu/erVCeVbfW0tLC8+fPpfSBt2/fwsrKirf9y5cvR0ZGBkJDQ6GtrXwu97x582BiYoJff/0VUVFRGDFiBGxtbZGXl4cZM2Zg5cqVcmVZbUpWHWffvn0oLS2Fr68vkpOT0atXL7x79w46OjoICwvDkCFDpGSEjhlAfnJZWVmZ3CRtobhx4waGDx+O3Nxc7pwyQVtFNvHu3bvRvHlzhfIs9jhQoUPv27dPKqHn1q1bGD58OLKyshTKR0REqBT8E0+KkjXm9fX1sXnzZvj5+Sm8vwj79+/HlClTcO7cOezevRvHjx9HfHw87xqsrK7AF6wVB18ArFmzZli/fj3c3Nwkzl+4cAHTpk3D3bt3Bd9LHP/ZIIixsTEuX74s1elv376Nzp07y4ywz549G2PHjlU520gEVudSeHg4qlWrhr59+wIA5syZg507d8LJyQkHDhzgVVySkpJw6NAhmZle3zsAw7Lo5+XlYdKkScjPz8fUqVM5Q37GjBkoKyuTaVANHDhQ4v/z589DV1eXcwCmpqbi27dvcHd3/58OPgEVOz569uwJIkJmZiZcXV2RmZmJatWq4dKlSwodBKxGnirvXhbmzp2L3bt3o6ysDGfOnOECYXxYsmQJNmzYgBo1aqC4uBgZGRnQ1dXFnj17sGvXLly/fl3QdVTFu3fv8OXLF9SsWRPl5eVYtWoVt3guXLhQoUPb2NgYV65c4XU686G4uFjmmOWb/MPDwxV+/j2CMEIyV0VQNF+xOkcLCgqwcOFC5Ofn45dffkGvXr0AVGTK6+joqJxVIBTTp0+Hs7Mz/P39UVZWhs6dO+PatWswMDDAyZMn0aVLF4XyrVq1wrx58zBo0CA8fvwYTk5OGDhwIBITE9G3b18pQ+fEiRPo3bs3c1YqULHLa9++fSpnWFbOgq8MUUafPJSUlGDjxo3Iz8+Hr68vXFxcAFRkFBkbG2Ps2LEK5atXr47ff/8dvr6+SrVbBJa+4+3tjU6dOmHKlCn4/PkzmjVrhtzcXBARDh48iEGDBim8d1RUFObMmYOAgACJZIGtW7di5cqVEoEUWeO/devWmDNnDn7++WeJ80ePHsXvv/+OmzdvKry/vr4+EhMTpbKj0tLS0KZNG0HZej8aT548Qd26dZXOPvvRa42BgQEePHgAW1tbCWeyaPyzZDHu378fUVFRUkGSyli3bh0WLVqEKVOmcEkKV65cwdatW7Fs2TKVd5TyYfHixfDz81N5RwnrWsc67ljmjD59+qBly5ZYunQpl+RiY2ODoUOHory8HEeOHFHYdkVOclEb+NC/f39oaWkhNDQUdnZ2uHXrFt6+fYtZs2ZhzZo1CrMVO3XqhMGDByMgIIBrv52dHQICApCZmYmYmBgpmcrr++vXr1FcXAwzMzMAFe9TFPyqvL6L22hJSUkqBwHUgdLSUvz9998ICwvDmTNnYG9vjzFjxmD06NGcsc8HQ0ND3L9/H7a2tqhatSouXrwIZ2dnPHz4EG5ubnj+/LlcWZaEA1V0LBcXF4l7ZWZm4uvXr5wzOi8vD7q6unBwcBAUfPvy5QvS0tJkJgXyZUavXbsWjx8/xpYtW1TONP77778xcuRImXY/3/tjGXesY9bQ0FDlHSzqkGdtP4szecOGDQgLC8O9e/fQtWtX+Pv7w8vLizcTv2vXroiOjoaZmZnSyRqVoa+vj7S0NDg4OEicz8zMRLNmzVBcXIycnBw0btyYt2KAsvjw4QN+/vlnzrFbs2ZNvHjxAm3btsXp06elAlLi+hCffca3/paXl6O8vJwLIBw8eJCzhydMmMDrDGa1yzQ1NfHy5UupHS+pqano2rUrbyDAy8sLcXFxMDIygrOzs9S7UtYXdOPGDe75+/fvr5SsslC3PV9cXIz09HTUrVsX1apVk/kddYyZGjVqYPTo0VzSsSpgDdqyQll7vDKOHz+O5cuXY+vWrVwiWlJSEgICAjB37lze6huqBv+ePHmichUhefjjjz8wc+ZMWFpaIj4+XtAaIgqay9MVxJGSkiIRrBX/rWXtTucLgJ0+fRpz5szBkiVLJHT7kJAQrFy5UiKBRhl98T8bBPH09ERBQQEOHDjAZQU+e/YMPj4+MDc3R3R0tJSMg4MDHj9+jDZt2mDs2LEYMmSI1OQpBKzOJUdHR2zbtg1ubm64fv063N3dsWHDBpw8eRLa2toKJ3C+Ldd8AZj/Gvh2b4jjv/DspaWlOHjwINLS0lBYWIgWLVrAx8cH+vr6P7ppUpAXGFmzZg06deokEQ3n24kCAEeOHEF+fj4GDx7MlYwIDw+HmZkZPD091dPo74BWrVph8+bNggM+lfH69WuMGTMGZ86ckfm5srsRhMLCwgIZGRmoVq0azM3NFRqY32M3yf8LqF27No4dOwZXV1ccO3YMkyZNwsWLFxEZGYkLFy7g6tWrCuVNTU2RkpKCevXq4ffff8eFCxcQGxuLq1evYujQocjPz5f4vpaWFl68eAFLS0u5CpNQ2NnZ4cyZM8xB/x+FGjVq4NKlSyor3CywtrZGbGwsmjVrhv3792Px4sVITU1FeHg4du7cyVtaky9ZgC/7ycjICGlpabC3t5c4n5OTg6ZNm3Kl/eShU6dO0NPTQ2RkJKpXrw4AePnyJUaNGoUvX77wBrjUBb5STIoQExMDIyMjTrndunUrdu3aBScnJ2zdulVh4Jp1rXn69ClOnDghM2jNV4bN3t4eO3fuRLdu3SSCIBEREVi5ciUePHjAe395ePz4MZo2bYrCwkKF37Ozs0NwcLBUZll4eDiWLFnCXEZEHpo3b4579+6hc+fO8Pf3x6BBg3idWuoE67hjwb179+Du7o4WLVrgwoUL8PDwwP379/Hu3TtcvXoV9erVU+v9ZKFatWq4cOECmjZtClNTU9y6dQuOjo64cOECZs2apXDeunLlCnr37o0RI0YgLCwMEyZMwIMHD3Dt2jUkJCSgZcuWCu+9f/9+/PHHH9i9ezdXLuLRo0cYN24cJkyYAB8fH4nvVy75VtlQFj/3vXQkWXj58iX27duH8PBwpKeno1evXvD390f//v0V9q/atWvjzJkzcHZ2RtOmTTF//nwMGzYM169fR69evWTupBGBNeFAWfA5v8XB5wiPiYnBqFGjZJbzkvfbVU5uu3DhAiwsLFQqUwwAtra2XAkm0Xr3X0D//v3h6+vLm1TxveTVDVWcySkpKQgLC8OBAwdQVlaG4cOHw8/PDy1atPjOra3I7B44cKBUsGbevHmIjo7Go0ePkJSUBE9PTzx79kzhtb58+QI9PT2l23DlyhUJf0C3bt2UvoYQDBw4EGFhYTAxMZGbkf69IQq+pqamonHjxhK7OMrKypCTk4NevXrh0KFDCq+jSlWPFi1aIC4uDubm5ggJCcHs2bOZy07+/xOWLl2K8PBw5OTkoF27dvD394e3t7dS75A1aJuTk4PS0lKZQcsqVarwlhdT1h6vDPFdPKK+K/q7si9Zlk+FNfinKuTt/jl8+DBatGghoZsqsm+U1RvEg7W3b9/G7NmzERgYKFGiee3atVi1ahVvAElc96q8K0b8f2X1xf9sECQ/P58zMurUqQOgIiLl7OyMEydOSNXlFeHSpUvYs2cP/vrrLwDA4MGDMXbsWKW2P7M6lwwMDLjI7dy5c/H8+XNERETg/v376NKlC16/fi1XVpUt1+KTf+UMoMoQkvWjDD5+/MhF5fi4J75Hthfrs6tjG+GPAuu7V2eJE1WwadMmjB8/Hnp6erw7VfiCMKdPn4aWlhZ69uwpcf7s2bMoKytD79695comJiZi3rx5CAoKkrnVnq/f+vj44MmTJ9iwYQO6dOmC6OhovHz5kitxJtoRJg51jJvw8HAMHToUurq6zNknjx49wubNmyV4MQICAiRqY/JBqEOUtfatOvuNnp4esrKyULt2bYwfPx4GBgbYsGEDcnJy0KxZM0G/TXJyMhwcHNC9e3f069cP06ZNQ15eHhwdHaUy8q2trbFr1y7O4SJLYRKK8PBwxMTEYM+ePYKDrKzvXnwni3g9ZFngyxBdtWoV/vnnH97sHHGoq26yvr4+MjIyUKdOHYwaNQo1a9bEypUrkZeXBycnJ14nNOtOqqpVq+LkyZOcsijCtWvX0LdvX96STllZWfDy8uKeAajQmRwcHHDs2DGZRoi4kVzZSVUZfI6p3bt3Y/369VwpJgcHB0yfPp139484nJ2d8fvvv6NPnz64e/cuXF1dMWvWLMTHx6Nhw4bfLekhLi4OHh4esLe3R3p6Opo0acLtAhI5uBVhxYoV2LdvH/bs2YPu3bvj9OnTePLkCWbMmIFFixYhICBApXZ9/vwZ8+fPx5kzZ/Do0SOF39XT08O9e/ekfufMzEw4Ozsz19RWhNu3b2Pv3r04cOAASktLMXToUPj5+cmt26tOHVFdOxhV3bX54cMHbNmyBampqZxTa/Lkyd+1Vrc4zM3NkZKSAjs7O9SrVw+hoaHo2rUrsrOz4ezszJvNnJ2djZUrV0q0f+7cuVz5CEWoV68ejhw5wu34EyE5ORk///yzVOBNmUCsrCDA99TNb968iT179iA8PBw1atTA+/fvYW5ujr1798rd/clSclVdYAk6qwoHBwf06NEDQUFBggMQ6k5uMzY2xp07d/6VQKM6sXPnTixbtgx+fn4q8QuwyrNixYoVqF69ulQZlj179uD169eYO3eu4GuVlJTgjz/+wNy5c1FSUgJnZ2dMnToVY8aM+W79+cSJExg8eDAaNmzIrU9JSUlIT0/HkSNH0K9fP2zbtg2ZmZky55CysjIsX74c27dvx8uXL5GRkQF7e3ssWrQItra2gspWqors7Gxs2LCBs8mcnJwwbdo0uWNAR0cHT548QY0aNVRKsFKHbi3yTwUHB2PWrFkwMjKSaJ+trS0GDRokiONVWejr6yMzMxO1a9dW+vlZbUp16jiDBg1C69atpcbWqlWrkJiYiMOHDyuUF4fI6S+yEYTg4sWL2Lt3L/766y9oaWnB29sbY8eOlSrdKwusQdvOnTvDz89Pyl+xb98+hIaG4uLFiwrllbXHK4PPjyIO8TaqK/gngrKcb4p2/4hDyO45VdG6dWssWbJEavPA6dOnsWjRIiQnJyuUZ9UX5eE/GwQBKhS+uLg4Ccec0Ch6UVERoqKisHfvXly9ehWOjo7w9/fHyJEjeRU5VZxL4rCyskJsbCxcXFzg4uKCmTNnYuTIkcjOzkazZs0UOlhU2XIdHByMwMBAGBgYqGXLfUJCAtasWSOx+AYGBsrcai++2Mgj+/ye2V6sz866jZDPESgOPoVV2ezUH/3uWRUHOzs7JCUloWrVqszlwJo2bYqVK1dKTcAxMTGYO3cuUlNT5cpmZmZi+PDhUkEyoe+uRo0aOH78OFq3bg0TExMkJSWhQYMGOHHiBFatWoUrV65Iyfzo304cf/31F4YOHQpXV1eJmtOJiYmCSgMpW5uer/YtXzavOvsNK7+Am5sb6tSpg27dusHf3x8PHjxA/fr1kZCQgNGjR0vURgUqSvmEhIQIMvr4fvfPnz/Dy8sLV69eFVyvmrXucGV5eRDSb8vLy9G3b19kZGTAyclJUIYoa98RoUGDBli2bBn69u0LOzs7HDx4EG5ubkhNTYW7u7tCEtuSkhJMmDABixYtEhxIroxhw4bh+fPnOH78OEdEV1BQgAEDBsDKykqQskxEOHfuHFfLWqQfyetbY8aMwaZNm2BsbMzEnxUUFIR169YhICBAIuNny5YtmDFjBkJCQnjbDlTshrl37x5sbW2xZMkS3Lt3D0eOHEFKSgr69Okjwa2hzsBn69at0bt3bwQHB3OJJlZWVvDx8UGvXr3wyy+/KJQnIixfvhwrVqzgnM66urqYPXs2li5dKujZK+/cIyJ8+vQJBgYG2LdvH6+u0KRJEwwfPlyKzHXZsmWIiorirZ/77ds3maVtlKndXlJSgr///ht79+5FbGwsGjZsCH9/f/j6+kqQK/4vrXWvX7+Gr6+vzNJPgPp3bap7t2bHjh0xa9YsDBgwAMOHD8f79++xcOFC7Ny5E8nJyUrxICkLAwMDJCQkyORp7NKli9wATElJCXr16oXt27crtetPnWVxgIodIJGRkdi7dy8eP36MAQMGwN/fH926dUNRURFCQkJw8OBBuYE2ZUuuqpPo+Efx/wAVjqXbt2//0ADE6NGj0bFjR8FBdpZxp84xy6ojscqXlZVh/fr1cstr87Xf1tYW+/fvl0oivXnzJoYOHSpox2FJSQmio6Oxd+9enDt3Dj/99BP8/f3x9OlTbN26FW5ubti/fz/vdVRFTk4OduzYwfFiOjo6YsKECYJIq0NCQhAeHo6QkBCMGzeO41SKiorChg0bpEpvqotDKTY2Fh4eHmjevDlX7vLq1atITU3F33//je7du0vJNG3aFC1atEDXrl05XU+ew10WN4G6dGugwoc2ZMgQlXbOiFBaWoqLFy8iOzsbw4cPh7GxMf755x+YmJhIBFdEaNu2LbezODg4GLNnz5b5PaBChxUHq02pTh3H0tISFy5ckEpMuHv3Lrp164aXL18qlC8tLUVwcDA2bdrE+RqNjIwQEBCAxYsXCy7FXFhYiIMHDyIsLAzXrl1Do0aN4O/vrzAhgTVoa2JigpSUFKnknqysLLi6uvJyQStrj6sL6gr+sXC+qRPJycmc/7dx48ZSiS+yoK+vj5SUFClOzYcPH6JFixY/rETzfzoIEhcXh7i4OJnG2p49ewRfJysrC3v37sX27dtRWFiIr1+/Kvy+Ks4lcbCQSrFsuVYH9u3bhzFjxmDgwIESi290dDTCwsIwfPhwie8nJCSgffv20NbWVmnL94/cxaIOsHCoiEOV7FTWd88KdTqjWaGvr4+HDx9KKba5ublo3LgxioqK5Mq2bt0a2tramDZtmsw6lnzvzsTEBGlpabC1tYWNjQ3279+P9u3bK6w1+z1+u7KyMkRHR0sELz09PXmJ5erVqwcfHx8p5+XixYuxb98+ZGdny5VVpTa9OmvfsoKVXyAtLQ0+Pj7Iy8vDzJkzuUBrQEAA3r59K9O4S09PR1ZWFjw8PLB3716uvnpl8JX18fb2Rnx8PH7++WeZ/VZW0Pd/6d1PmTKFy2QWyr2lrvb/8ccfmDZtGoyMjGBjY8PVN928eTOOHj2K+Ph4hdc2NTXFnTt3VA6CPHv2DJ06dcLbt285BfPOnTuoXr06zp07p1Tm1r8NS0tLbNq0CcOGDZM4f+DAAQQEBCgMIInDwsICV65cgZOTEzp06IBRo0Zh/PjxyM3NhZOTk8S8qc61Rjyr2NzcHFeuXEHjxo2RmpoKT09PwYbSt2/fkJWVhcLCQjg5Ock1uGUhLCxMor9ramrC0tISbdq0UVgGTIS//voLQ4YMQbdu3ST0tLi4OBw6dAheXl4y5TIzM+Hn54dr165JnFclCPHt2zdER0djz549uHDhAtq1a4d//vkHL1++xK5duzgCz++x1imbKSeCKrs2xaEsN4L4bs3Kv3llCKkVHhsbi6KiIgwcOBBZWVno168fMjIyULVqVURFRUmRSsrCq1evZLafzxHfv39/PHv2DKGhoVwpm+TkZIwfPx61atVSmBBkaWnJBQx+BPr374/Y2Fg0aNAAY8eOxahRo2BhYSHxnVevXsHa2lrqvagKdREdq6JjqdOR7+fnh/bt2zNnvb969Yrb4ebo6KhUlvpvv/2GDRs2oG/fvjKda5UdyizjTt1j9kciKCgIoaGhmDVrFhYuXIgFCxYgNzcXx44dQ1BQkKCd0g8fPpRac4XwX6WkpHA7BjU1NTFq1CgpztZ79+6hVatW/7McZvXr18eOHTvg7u4uUfoyPT0dbdu2lUqSUld1BRcXF/Ts2VNmGa+zZ8/K9IVcu3YNM2fORHZ2Nt69ewdjY2OZfVdDQ0PmmP9fsg2ePHmCXr16IS8vD1+/fuV24EybNg1fv37F9u3bpWQePXqExYsXIzs7GykpKXBycpJp+2poaKjdl6ROHUdfXx937tyRqsIg8inyjZVffvkFR48eRUhIiESS0pIlSzBgwABs27ZNwBNJ4tSpUxg1ahQKCgoUrlWsQVtTU1NcvHhR5m7TLl268JYJVsUer4yysjIcO3ZMIgjg4eEhiJODNfjHwvmmDrx69QpDhw7FxYsXJXjfunbtioMHDyqsWtGiRQs0adIEoaGhXLDn27dvGDt2LO7duydozBUUFGD37t0S797Pz08iqUpp0H8US5YsIU1NTWrdujV5enrSgAEDJA6hKCwspD179lD79u1JQ0ODGjZsyCszePBgqlatGk2cOJEWL15MS5YskTj48P79e5o8eTJ5eHjQmTNnuPNBQUG0bNkyhbLDhg2jtWvXEhFRSEgIWVpa0tixY8nGxoa8vLx4733r1i26ceOG1PkbN25QYmIir3zDhg1p3bp1UufXrl0r6N0piyVLllBRURH3t6KDD6zPXlBQQG/fvpU6//btW/rw4YOAp1EdrVq1oqCgICIiMjIyouzsbPr06RN5eHjQH3/88V3vTUQ0ZswYhcf/OqpXr05xcXFS58+dO0eWlpYKZfX19Sk9PV3le7u6ulJMTAwREfXv359GjhxJT58+pTlz5pC9vb3K11UG9+7dI3t7ezIwMCAXFxdycXEhQ0NDsrW1pbt37yqU1dfXp8zMTKnzGRkZpK+vr1DW1taWwsPDpc6HhYWRra2tcg/xg3D48GFat24d5efnc+fCwsLo2LFjKl/z8+fP9O3bN4XfEZ/7VIGBgQFdvnxZZfkfDSMjIzp58uQPu39iYiIdPXqUPn36xJ07efIkXblyhVd21KhRMtdJZVBYWEg7duygSZMm0axZsyg8PJy3z4jj/PnzNH/+fPL391d6vn78+DFlZGRInc/IyKCcnByFsqampjJlHz16RKampkKbT/3796eePXtSSEgIValShZ4+fUpERLGxseTg4CD4OsqievXq9ODBAyIiatSoER0/fpyIiO7cuUOGhoa88nv27KHi4uLv1j6hSEpKIh8fH2rRogW1aNGCfHx8KCUlRaFMu3btqFOnTnT69Gm6ffs23blzR+IQet/JkyeThYUF1ahRg+bOnSuxfmzatImsrKyYnk0esrOzqWnTpqShoUGampqkoaHB/a2pqckrb21tTTdv3iQiImNjY3r06BERER0/fpzat2+vUPbMmTNkaWnJ3VP8EHLv74W3b99SeXk57/eSkpKocePGEu9Nmfa/evWKevfuTRoaGqSjo0M6OjqkqalJvXv3ppcvXyqUnT59Os2dO1fwM1UGq27u5+dH165dU/id8vJyys3N5b3Wy5cv6e7du5SamipxVEZubi73u+Tm5io8FEEVHSssLIy+fPnC/a3o4ENRURH16dOHRo8eTWvWrKGNGzdKHHz48OEDjRgxgrS1tbn+pq2tTT4+PlRQUMArT1TxDuQddnZ2gq7x/4+wt7fndCwjIyPKysoiIqKNGzfSsGHDeOXr169PkZGRUucjIiJ437umpib17NmTDh06JFevKSwsJF9fX952sOD9+/cUGxtLkZGRFB4eLnHwQU9PjxufIruciOj+/fuCdAVVoaurK1fH0tXV5ZXX0NDgnZP/l+Hp6UkjRoygr1+/Srz3+Ph4ql+/Pq/8f/n5W7VqRcHBwVLnFy9eTC1atOCVNzExodOnT0udP3XqFJmYmAhuR1FREe3du5c6depEmpqa5ODgQCtWrBAsrwr69etHgwcPptLSUu5caWkpDRo0iHr16qXydYXY40REmZmZ5ODgIOFHMTAwIEdHR27u/J6oWrUqp0uYmJhwfqm4uDhq3rz5d7+/t7c3ubq6cvYRUcVc5+rqSkOHDlUoe/PmTbKysiJLS0tyd3cnd3d3srS0JCsrK07nVoTExESysLCgWrVqkZeXF3l5eVHt2rWpatWqlJycrPIz/Wd3gtSoUQOrVq1SebvvlStXsGfPHhw5cgREhMGDB8Pf35/LpFEEQ0NDxMbGSrDR/1tQdst1ZbRu3Rpz5szBzz//LHH+6NGj+P3333Hz5k2F8rq6urh//77M7WhNmjSRyvzg2+YtDr76/nl5eahdu7bg3RWVwfrsvXv3Rv/+/TFp0iSJ89u3b8eJEydw+vRpldolBKpkp7K+e3FUzhwtKSnBvXv3UFBQADc3t+9e75i1DuaECRNw/fp1REdHc9v2s7KyMGjQILRq1QqhoaFyZTt16oSgoCCVCev27duH0tJS+Pr6Ijk5Gb169cK7d++go6ODsLAwLiNWHvbu3QsjIyMMHjxY4vzhw4dRXFwsKNusbdu2sLS0RHh4ODdHvH//Hr6+vnj9+rVU5q84+vTpg8GDB0uVyNm7dy8OHjyI2NhYubKq1KZXZwk5ddZP/a+hYcOGOHToEO/YlgdV6j2ra7s/UJFNJiqjIxTq7DssEGWOu7u7o2XLllKkeXzPzorg4GCEhITA1dUVNWrUkMr4i46OVijPUns3ICAAVapUkSrROHv2bHz+/Blbt24V9Ax5eXmYNGkS8vPzMXXqVC7TeMaMGSgrK1OqrymDAQMGoG/fvhg3bhxmz56N48ePw9fXF0f/f+x9d1QUWfP2M+ScwUAQEUWSiqJrWANGRAXD6iomBLMoJpRdI6iIAXNWREFXVMy6CiYQMQIKuCZEBcyYF1BJ9f3BN/1jmNA906C77/s+5/Q5cGeq7+2eG+rWrarn8GEYGhri3LlzMuVr1aqFL1++MDolV7656lyvFYW2tjZSU1MV5rtzdnbG/fv30b17d4wZM4bxXKuMt2/fwszMTKJHPd+1jq+nnCJRm0Iowo1QGXx4y6Th8+fPuHDhAho3bsz6mzZt2hQNGjTA7NmzJUbecfXuffjwIZOCr3HjxmjUqBGrzOTJkxEVFYWGDRtKnC/ZOD1+pG4uRGpqKkaOHIl79+6JRXTUZCo3RXSsynwqly5dQtu2bVkjgqUhIiIC48ePh4aGBoyNjUX6DZfIu19//RW3bt3C+vXrRTyTAwIC0KxZM8TExCjULq7gM+74jlm21JBV0/JUt7y2tjbu3bsHKysr1KlTB6dOnULz5s3x+PFjuLi4sGaWWL58OZYvX44VK1YwUWbnz5/HrFmzMGPGDPz2229SZXNycmo8YoANJ06cwNChQ1FQUAA9PT2xvssWBdWiRQtMmzYNw4YNE4kECQkJwdmzZ5GUlFQj7ba0tMSqVavE1skDBw5g5syZyM3NlSlfOaqDK/4pujVQwZl35coV2NnZibx3SVHC1Q2+e0q+Os6JEyfQv39/eHt7i4y5ffv24eDBg6wE02ZmZkhMTJSYlqhDhw4yOYmBioiinTt34uDBgygtLcUvv/wCPz8/dOjQQaZcdeDu3bvo0KEDDAwMGF0uKSmJ0XOcnJxqtH4PDw8QEfbu3ctEir579w7Dhg2DkpISTp06JVOeb/pBvpxvfKGvr49z585JTHnavXt31nRkhYWF2Lt3r0iKZm9vbzF9TxLat28PW1tbbN++XYSUfvTo0Xj8+DEuXbqk2EMpfHzyg2FkZCT3yduLFy9o6dKlZGdnRwKBgNq0aUPbt28X8fTkAjs7O4mePfKisLCQ7t27x+oxVJ3Q1tZmTs0r4/Hjx6Sjo8Mq36BBA9qyZYtY+ebNmyWewFf2yBN640m7JEFZWZk5sVdSUuJ1es/32Q0NDUVOQIW4d+8eGRkZscpPnjxZomfU+vXrKSAgQKasIt6pfN89G8rKymjs2LG0bNkyiZ9PmzaN88UGExMTysjIECvPyMjg5FX68eNHat26NamoqDAeYioqKuTm5kYfPnyQKXvgwAFycHCgyMhISklJ4T1eCwsLKTU1lfLz8zl9v2HDhnThwgWx8oSEBGrUqBGne2hoaNCdO3fEyjMzM0lDQ0Om7ObNm8nU1JQmTZpE0dHRFB0dTZMmTSIzMzPavHkzHTt2jLmqwtHRkZYsWSJWvmjRInJycpJYnyQv1Kr/c+23ivSbtWvX0pcvX5i/ZV2SYGhoyPy2BgYGZGhoKPWqSZw8eZJ69OjB6rkvDfXq1aPk5GSx8mvXrkn1MK3qhamtrU0CgYB5XoFAQNra2py8M3fu3EmDBg2SKxqGT9+ZNm0aFRQUMH/zma8U8Uw9duwY441UeUxJuthQu3ZtioqKYv2eNOjq6kqM/srKymKN5vD39yc9PT1ydHQkPz8/8vPzIycnJ9LT0yN/f3+53iNXVOdak52dzczrBQUFNG7cOHJ2dqb+/ftz8gQvKSmhw4cPk6enJ6mqqpKdnR2FhYXRy5cvZcpJ6q/yRBV8+vSJ8yUNrq6uvKLHQkJCmIgdRcB3rePrKccnalNXV5eXN6CzszOdOnVKrPz06dPUpEkTTvcYOHAgrV+/noiIioqKqGHDhqSqqkoqKioUGxsrU1ZHR0fimP8e6NSpk9TLzc2NVf5H6uZCNGnShPr160fXrl2jJ0+eyBXNERoaShEREWLlERERFBYWJlNWER1LRUWFXr16RUT891W1atWiJUuWUFlZmULy0iJWL126RFpaWgq3iyv4jDu+Y7ZZs2Yil6OjI2lpaZGenh65uLjUuHyjRo2Y7Ajt2rVjPLljYmJYI+SJKqKjZs2aRRoaGoxepaWlJdFTvSr4ZmaoDjRs2JACAgIUjrg+evQo6evrU1hYGGlpadGKFSto9OjRpKamRvHx8TJl+/fvL3FsL1u2jH755ReZssHBwWRgYEBhYWF06dIlunTpEi1dupQMDAwoJCREoWdhQ3Xuy/jCwMCA/vrrLyISjcBJSkqqsShTIfjaIqpjP3/y5Elq27YtaWlpkbGxMbm5uVFCQgIn2eDgYBoyZAgTCUhE9PXrVxo6dKjMjCrLli2jxo0bMxl4tm7dSp8/f+ZUZ2UkJCRQ7969qUGDBtSgQQPq06cPXbp0ibP88+fP6bfffiMPDw8aMGAABQcHS4wCFaI69+NaWloSf3uuUeLz5s2jOnXq0MqVK0lDQ4MWLVpEfn5+ZGxszClq8ueff6YjR44QUUVWIHd3d7p8+TKNGDGCHB0dWeX5QkdHh27duiVWnpaWRrq6ujVat4aGBt27d0+s/K+//mLNSCIL/9pDkFmzZsk92SsrK5OZmRnNmDFDosLMFXyNS2/evCEPDw9eBmmuIddVYWRkJDHsOzk5mQwMDFjlN23aRGpqajR+/HiKioqiqKgoGjduHKmrq0s8HKm8CThy5AhziCJs75YtW6hhw4bMwK4KS0tL2rRpEz19+pQEAgGlpqZSTk6OxKumn13aBJiRkcFpENatW5dSUlLEylNTU8nc3FymrJeXF23bto2IiGbMmEG2tra0ePFiat68OXXp0kWiDN93zwX379+n2rVrS/ys6mZWT09PLB2Tnp4ep02uhoaGxJRU9+7dYzXiC1FeXk5xcXG0fPlyWr9+PSUmJnKSk2aI+l5pLtTV1SXONU+ePOH87E2aNJGYDuz8+fNSN8pCsBnlZBnnYmNjSVlZmUlrExISQj169CAVFRU6fPgw1w3EPwABAABJREFUa7vPnj1LzZs3pzNnzjAGvDNnzpCrqyvrJoNIsX5jbW1Nb9++Zf6W15hdnakm+MDAwIBJSaKjoyO3wqeurk6PHz8WK8/OzuYUcr93715q166dyPu/f/8+tW/fnvbs2cMq36xZM9LV1SUdHR1ycnJi5g3hxQZ5+06nTp2YA1G+RjlFUDlEXxEjeGUo4iRSGXp6ehJTJ6WkpLA6DMh6d4q+xy9fvsg05FfnWlOdePXqFa1cuZKcnZ1JVVWV+vTpQ0ePHpVoMGRLicNmUOXi7MCmY54/f57atGlDFy9epLdv33I+PKku8F3rDAwMmDnLxsaGMTY8evSIk44WHR1NkZGRRFTR101MTEggEJC6ujrFxMTIlB01ahTt2LGDtQ5p0NDQkPrsXI3BtWrVYtKW7d27l2xtbamwsJA2bdrEegjk5eXFelAiC6WlpbRjxw4aMmQIdenShdzc3ESumsSP1M2F4HOIpIjDgRCK6Fi2trb0+++/U0JCAgkEAjp69CglJiZKvNhgaGjIa62xtLSU+Nulp6dzfvd80vXyGXfVMWar4tOnT9SvXz+FnRjkkZ89ezZzgBYTE0MqKipka2tLampqcqWn+/vvv+nGjRuUmZkpYlyVhZYtW9LBgwfFyg8dOkStWrXiXDcfaGlpSXSMlAeXLl2irl27kqmpKWlqalK7du0oLi6OVY6PMb28vJxWrVpF5ubmjF5obm5Oa9as4ZT6kC/47suqorS0lG7dukXv37/n9P1BgwbRmDFjiKhi3n38+DH9/fff1Llz5xpPn8bXFlEd+3k+6Nu3L+nq6pKJiQmTlsjExIT09PSYNEPCqzJMTExo6tSprOmzZSE6OppUVFRo0KBBjCPhoEGDSFVVlfbu3cv30SSiOvfjhoaGEtfpy5cvc9pT800/eObMGTp06BARVTikCR36TUxMJNp3qhuenp7UoUMHev78OVP27Nkz6tixIycaikePHpG/vz/T76ZMmcJZdzAzM5M4r545c4bXwee/9hBkypQpZGBgQB06dBDzLpTm7Xfo0CEqKSnhXTdf45K3tze1a9eObt68Sdra2hQfH0/R0dFkZ2fHmgOdb97ewYMHU8eOHUVyrX748IE6duxIAwcOZH94Ijp8+DC1a9eOjIyMyMjIiNq1a8cpP37Lli0les2cOnVKai7DrVu3Mu9a2vW9nr1Tp07k7+8vVj5x4kT6+eefWeXV1dWleteyGRX5eqcq8u654NSpU2RiYsL6vfDwcOrTp4+IkvP+/Xvy8vKilStXssrzzYPJB4oYpKrTM9nS0lKi9/fRo0c5bxJPnTpFjo6OdPDgQcrLy6O8vDw6ePAg48lWk4YuRXLTC+Ho6CjVS5ALB9GP7Dc/GnwVPj75nokqFD5phnQufDB8OaD49p3qwLdv3+j+/fvVonfIA0WcRCqjpnLvyoOCggKaNGkSmZqaymXI57vWVDeuXbtGY8eOJXV1dbK2tiZ9fX2ytramixcvinzPxcWFaXNwcLDc3qkJCQnMtWvXLqpduzYFBQUx0UNBQUFUp04dmWNfklenPDoWX0M437Wuuj3l5Ina5MuNwIe3TAgNDQ3Kzc0lIqLhw4czhsycnBxWT8X8/Hzy8PCghQsXUmxsrNzRZ5MmTSJtbW0aNGgQBQQE0NSpU0WumsSP1M2F4HOIxNfhQF4d68iRI1SrVi3W6DMuY37q1KkSI1G4YuvWrdS1a1eRSLmXL19S9+7dJTrWSUJVTtBevXpRvXr1SF9fn5Urk8+4q44xKwkZGRlUr1697y5/9epVCg8Pp+PHjytcN1fwzcxQHejXrx/t37//u9RVFdXh2EdE9PnzZ4U88vmAr24dEBDAOAyUlpYynLza2tpiepEk5OXlkYODA9nb25OKigq1bt2ajI2Nyc7Orsa5PvjuKatjP88HPj4+nK/KqMyZ8eHDB7p58ybdvHmTNZNGZXxvTuHqxvDhw8nR0ZGuXbtG5eXlVF5eTlevXiUnJycaOXIkq7yWlhbjsF27dm2GyyI7O1suPpbK4Mr5Vh3Izc2lZs2akaqqKtnY2JCNjQ2pqqqSi4uLCGeqJJw5c4bU1NSoVatWjO2rVatWpK6uzungdPLkyWRhYUExMTGUm5tLubm5tG/fPrKwsOAcrSsJiiUB/QcgIyMDzZo1AwDcuXNH5DNpeQ779+8PoCL33r59+/Dw4UMAQKNGjeDt7S3GFSENa9asUazR/x8XLlzAsWPH4OrqCiUlJdSrVw/dunWDnp4eli5dil69ekmV9fX1RaNGjRARESExby8bVqxYgY4dO6JevXpwcXEBANy+fRu1atVCdHQ0p3v069dPjCOCCzIzM1G/fn2x8vr16+Pu3bsSZcaOHYshQ4YgJycHTZo0wblz52BsbCx33QD/Z1+8eDG6du2K9PR0dOnSBUBFLsabN28iPj6eVd7W1hZnzpyBv7+/SPnp06dhY2Mj9v3KfCgqKipwdnYGUJHHdcuWLaz1VYYi774ypk+fLvI/EeHly5c4deoUJ06K8PBwxMfHi3DWGBoaYvHixejevTtmzJghU37evHno378/srOzJebBrEkokrf21q1bnL7HZfwOGTIEU6ZMga6uLpN3MzExEQEBARg8eDCnenr37g0AGDRoEFMn/f+81X369GH+r4nc1S1atMCePXsUks3OzoaBgYFYub6+vkQenKr4Ef3m8+fPnL+rp6cn9bPHjx9LnBe4gsu4lIUxY8Zg6tSpKCkpkZjvmQ0vX75EaWmpWHlZWRlev37NKr9gwQL5G10JfPsOHxQVFWHy5MnYvXs3gIo8+TY2Npg8eTLMzc0RFBQkJmNkZISHDx/CxMQEvr6+WLt2LXR1dRWq/+vXr9i2bRvOnTuHJk2aQFVVVeRzthz7YWFh6NixI+zs7CTm3v0emDVrFi5evIjNmzdj+PDh2LhxI54/f46tW7ciLCxMqpwia42hoSFnXYotby8AvH79GtHR0YiMjMTjx4/Rt29fnDx5El27dkVhYSFCQkIwcuRI5OTkMDL37t1DYWEhDA0NERwcjPHjx0NLS4tTm4AKHhchQkJCsGrVKgwZMoQp8/T0hLOzM7Zt2yZ1brh48SLn+iQhICAAu3btQq9eveDk5CS3fsp3rZs7dy4KCwsBVLyD3r17o3379jA2NubELVBVzxFCIBBAQ0MDtra28PLyYvJBV8a+ffsQHx8PDQ0NJCQkiOWXZ+MB8vLywtSpU8V4y2bMmME5x7qlpSWuXr0KIyMjnDlzhnnmDx8+QENDQ6bs1atXkZycjNOnT4t9xkUviImJwYEDB+Dh4cGprdWJ762bS8KOHTswcuRI3LlzB05OTmJzrqzf0NLSEsnJyWI6enJyMurWrctat7w6Vt++fdG3b1+GC+HBgwcwMzPjLF8ZZWVlWL58OeLi4hRaazZv3oxHjx7BysoKVlZWACr4oNTV1ZGfn4+tW7cy301LS5N4D0kcV+Xl5ZgwYQIzlqSBz7irjjErCZ8+fWLl41BUvnnz5jh//jwMDQ0REhKCmTNnMutM69at0bp1a4XrlQfq6up4/fq12Ph6+fKlwvw08qJXr14IDAzE3bt34ezsLNeYBYCbN2+ivLwcP/30k0j59evXoaysDFdXV6myzs7O2L9/vxhvS0xMDBwcHGTW++TJE5SWlqJhw4YiOmJWVhZUVVVhbW0tU54v+OrWsbGxGDZsGIAKjosnT57g/v37iI6Oxpw5c5CcnCxT3sLCAunp6YiJiUFGRgYKCgrg5+eHoUOHQlNTU5FH4gy+e8rq2M/zQWRkpEJyqqqqePr0KSZNmoS4uDjGfiAQCODu7o4NGzaw9rvHjx8z9obK8PT0xO+//65Qu9hQXftxoMImN3LkSLRp04aZK0pLS+Hp6Ym1a9ey3t/CwgIvX76ElZUVGjRogPj4eDRv3hw3b96Eurq6TNmSkhJoamri9u3bItwnknTRmoKlpSXS0tJw7tw5EV4PLny5QUFBmDZtmtjeLSgoCLNnz0a3bt1kyq9cuRICgQAjRoxg7AqqqqqYMGGCzP0gG/61xOiKoLy8HIMHD0ZsbCwaNWrEEAXeu3cPjx49wsCBA7Fv3z65N27ygg/5oq6uLm7duiVGgicPhOQ06enp0NTURJMmTTBkyBAxBUASRo4cqTAJUvPmzeHk5IQdO3ZATU0NAFBcXIzRo0fjzp07UhVcIXbv3o3BgwezThaywOfZASA9PR3Lly/H7du3GfnffvsNDRs2ZJXduXMn/P39ERgYKLJ4hoeHY82aNRgzZozI91VUVPDixQuYmZlBWVkZL1++VHijwvfdu7m5ifyvpKQEU1NTdO7cGb6+vqxKq66uLk6cOIFOnTqJlF+8eBGenp74+++/WZ/h1KlTCA0NFXn3CxYsEDH+1BSio6OxZcsWPHnyBFevXkW9evWwZs0a1K9fH15eXjVad3FxMYYPH46DBw8y77m8vBwjRozAli1bmN9TFhITEznXJ+l93rx5ExcvXsSbN2/ECG1lbXI7duwIPz8/DBw4UCHltEOHDtDQ0EB0dDRDNvv69WuMGDECX79+5fRc8vYbaYYwSZD07EpKSpzXEFmGJSUlJeb9/fLLL6xGLEnIzs5GZGQksrOzsXbtWpiZmeH06dOwsrKCo6OjTFkiQlBQENatW8cQuGloaGD27NmshJtAxeHa8+fPsWPHDjRv3hxABXns2LFjYW5uzolo8ePHj4iNjUV2djYCAwNhZGSEtLQ01KpVC+bm5jJl5e07QicJLjh8+LDMzwMCApCcnIw1a9bA3d0dGRkZsLGxwbFjx7Bw4UKJh6Q6OjrM95SVlfHq1SuYmppyblNlVJ2vK0MgEHA6yHjx4gU2bNggslb6+/uzKt1fv37F+vXrpc4XbGuNEFZWVoiKikKnTp2gp6eHtLQ02NraIjo6Gvv27ZNKdqzIWiM8rOICLuTccXFxaNSoEUaPHo0RI0aIvbM3b96gdu3aIu+mTZs20NHRwc8//4zg4GDMnDkTOjo6EutgG39aWlpIT08X00sePnyIZs2a1RiBoomJCaKiohQ2hFfHWlcV79+/53zI5ebmhrS0NJSVlcHOzg5AxTtTVlZG48aN8eDBAwgEAly+fFnMUFW7dm1MmTIFQUFBUFJSkrudnz59gru7O1JSUmBhYQEAePbsGdq3b4/Dhw9LNDpVxaZNmxAQEAAdHR3Uq1cPaWlpUFJSwvr163H48GGZh1zW1tbo3bs35s2bpxCxe926dZGQkMCJCL0m8D11c0k4ceIEhg8fLtHownaIxIdgmq+OlZiYiHbt2ilseOa71gQHB3OuS17HiAcPHqBTp054+fKl1O/wGXd8x+y6detE/hc6l0VHR6Njx474448/ql1eU1MTWVlZsLCw4L2v5IMhQ4bg5cuXOHbsGPT19QFU6Ht9+/aFmZkZDhw4UONtkDVPczn4bdWqFWbNmiXmQHv48GEsW7YM169flyrLh+C6Y8eO8PX1FdNF9uzZgx07diAhIUFmuzt37iyxf37+/Bl9+/ZlHbN892UaGhp49OgRLCwsMHbsWGhpaWHNmjV48uQJmjZtKpfhWhHwsWMB/GwRNaHjyIvS0lIkJCQgOzsb3t7e0NXVxYsXL6CnpydV58zLy0PLli2hqqqKiRMnMsTqd+/exebNm1FaWoqbN28y86Ak2NraIjAwEOPGjRMp37JlC8LDw5GVlVV9D/n/UV378crIysoSOQTgao8NCgqCnp4efv/9d+zfvx/Dhg2DtbU1cnNzJR4QVIWNjQ2OHDmCpk2bcqqvusHHMVNDQwOZmZkS9yRNmjTB169fOd2nqKgI2dnZAIAGDRrI5SgmEQrHkPwLsWrVKjIyMqITJ06IfXbs2DEyMjKi1atXc7rXo0ePaM6cOTR48GAm/O7PP/+USD5cFXzIF/mEXBcXF5ONjQ0vPhQvLy9SVVUlW1tbWrJkiVwkmNevXyczMzMyNTVlcsKZmpqSmZkZXb9+XeE2cQHfZy8uLqZRo0ZJDFmXB5s2bRLJ41m/fn3avXu3xO9WJx/Kj3z3RBVhhNbW1nTo0CEmHVNsbCzVr1+fRowYIVO2pKSEgoODWcPtagqbNm0iExMTWrx4MWlqajIh3JGRkdSpUye57iV8dq4oLy+nnJwcKioqoocPH9KBAwfoxIkTnFKgVcWlS5do6NCh1Lp1a2bcRkVFsRLhLlmyhAQCATVu3Jg6duwoV17/gIAAMjU1JT09PRo9ejRdvXpVrjZnZWWRk5MTqampMURqampq5OjoyJp/W9F+w5dfoDrS0hAR3bp1i6ZMmUKmpqakr69PY8eOlWusJiQkkKamJnXt2pXU1NSYfrt06VIaMGCATNnS0lJKTEyk9+/fK5TvmaiC+6pnz54kEAhITU2NSWvYs2dPTiHr6enpZGpqSra2tqSiosK0f86cOTR8+HBWeXn7TuUw8JEjR5Kenh5ZWloy+XGtrKxIT0+PU85hKysrpq9XJm7MysqSSiDXtWtXcnZ2Jh8fHxIIBDR48GCFcpzzRXFxMXXu3JkePnyokLy3tzeZmJjQ+PHjacGCBXKnMRNCW1ubWdvMzc2Zvv/48WOZqX34rDXVAV9fX4ncY5VRXl4uNoffv3+ffv31V3J1dSUlJSVycnISI75t1qwZZ8LbwMBAsfLAwEBW8s0PHz5QXFwcRUdH0+7du0UuNtSpU4cePHjA+j1JqI61btSoURJTgxQUFHAaN6tXr6b+/fuLpIX8+PEj/fLLL7RmzRoqLCwkLy8v6t69u5gsX24EIsV5yyrj5s2bdPjwYfr777+ZspMnT9Lly5dlylXOUa0IVq5cSRMnTvxuqRmE+BG6uSTUq1ePJk2axBCOywM+BNN8daz/ZHBN18tn3PGRrcozZ2NjQz/99BP99ttvnFIcKSLfunVr6tq1Ky1cuJAEAgEFBgZScHCwxKsm8ezZM7KxsSF9fX1G1zYwMCA7Ozsmpd8/HXxTeilKcK2rqys1fZ++vj6rfGX+ucp4/fo1qaiosMrz2ZcRVejHcXFxVFpaSpaWlkwa+Dt37nDiZyUievjwIW3dupUWLVokd79V1I7F1xZRnft5RfH06VNq3LgxaWlpkbKyMtN/p0yZQuPGjZMq5+vrSx06dKAvX76IfVZUVEQdOnQgPz8/mXXLyylcHaiu/XhlVFeaY3nTD+7YsYM8PDxkEsHXJAQCAXXq1Imio6Ml9gNZsLCwoAMHDoiV79+/nywtLTnfJysri86cOUNFRUVERLz1zf+qQxBnZ2eKiIiQ+vmOHTvI2dmZ9T58jEtEkskXlZSUSENDg5V8kW/e3rp16/I6BCGqMG6Fh4dTkyZNSEVFhdzd3engwYMiOQOloaCggLZu3crkhNu2bRsVFBTwag9X8H12PT093hstId68eSOySZWE6uRDIeL/7ktKSujs2bO0ZcsWRsF+/vw563MQVeTLnjBhAqmrqzNtV1NTowkTJnBqg7a2tkQyse8Be3t7Js94ZYNmZmYmGRsbs8qXlZVRcHAw6enpMc+ur69PISEhEglyq8qqqqoqbJAUIjY2ljQ1NWn06NGkrq7OPMP69eupZ8+eMmXNzMyY+UoRlJSU0KFDh8jT05NUVVXJ3t6eVqxYwdlYINxkCnOrx8fHc174+PYbvvwCnTt3pj/++EOsfO/evdSxY0dObRC+vz59+pCqqio5OjpSeHg4vXnzRqZc69atKTw8nIhE++3169c55Z6VlqdcXjx8+JBZn+QxkHbp0oUx5FZuf3JyMud814r2nVmzZtHo0aPFODHGjh1LM2fOZJWvfFhaue23b9+Wmvv11atXNHv2bPrll1+Yw6Kquc6FF1coqjCamJgoPOfo6emxGly5wNnZmTEIdOnShWbMmEFEFSSCsvov37WGqOK3jo2NpUWLFtGiRYvo8OHDIn2hpiHNSMEVp06dIg0NDXJyciI/Pz/y8/MjZ2dn0tDQkMgNJsTx48dJV1eXBAIB6evrk4GBAXNx4bvjYwivjrVOSUlJ4nvLz88nZWVlVvm6devSX3/9JVZ+584dqlu3LhFVkGVLWvf5cCMUFxeTsrIyL9LRqhDmrOaKESNG0Pbt2xWur2/fvqSvr0/169en3r17yyRZrW58b91cEhQ9RKoOhwO+OtY/ASkpKRQdHU3R0dGcOeOEqMqzN3XqVPr1119JR0eHJk2aJFWOz7iriTH7PVCdh+18IdyTTpw4kWbMmEG7d+/mZEf4p8DIyEiiw0NycjJnY74i0NPTk8q3J+vwJT09ndLT00kgENDFixeZ/9PT0yktLY1CQ0NrXLcmquDP0NfXp8aNG5OVlRUz10VERFDr1q1Z5bdt20bKyspUq1Ytatq0qUL9VlE7Fp89ZXXt54kUN8R7eXnRsGHD6Nu3byJ7k4sXL5Ktra1Uubp168p0mExMTKQ6deqw1q8opzARkZubm0QOkk+fPnHinOO7Hy8sLCRfX19SVlYWOUDy9/enpUuXssqHhoZKtEFHRERQWFgYq3yzZs1IR0eH1NXVqVGjRoxjpvCqaUhyzLx27Ron2eDgYDIwMKCwsDC6dOkSXbp0iZYuXUoGBgac+Cvfvn1LnTt3Zmyewnc/atQomj59usLP9F91CKKhoSHTa/7p06ecCKn4GpeqQh7yxePHj5O+vr7CJHZLliyhkSNHVhtRa2pqKvn7+5OGhgaZmJjQ1KlTq2WCrwnwffYRI0ZIJHXiiqKiIhGy06dPn9Lq1aspLi5Oqsznz58pMzOTBAIBnT9/nm7fvi3xqmko6j1QFQUFBYzSJc8BjKenp1wn9VVx6tQpJvqqMs6cOUN//vmnTFkNDQ3GU6PyeH/48CGn+SIoKIhMTU1p06ZNzLNv3LiRTE1N6ffff2eVd3Bw4O3d16xZM8arsfIzpKWlUa1atWTK1q5du9rG9OvXr2nRokWkoaFBqqqq5OXlJZFYsrrAt9/UrVtXYnRfZmYmJ4VPU1NT4rt78OABaWpqytWWr1+/0qpVq0hdXZ0EAgGpq6vT8OHD6cWLFxK/r62tzRiGKv/mT5484US22qJFCzp37pxcbaxO6OnpMUalyu1/+vQpZ7JaRWFiYiKRtPL+/ftkZGTEKt++fXtat24dEVW0Xfg7+Pv7U48ePVjlra2t6e3bt3K2+v/AV2GcOnUqQ6osL+zt7Sk9PV0h2cpYtWoVQyh99uxZ0tDQYA421qxZwyqv6FqTlZVFDRs2FIn+0tLSIjs7O85Gzhs3btCyZctoxowZYga674W8vDz67bffGCP077//zuph27BhQwoICJCLlL2qsZuPIVzRte7Tp0/08eNHEggE9OjRI/r06RNzvX//nnbv3s1pvpZGzHrx4kXGuJSdnS0xmmvy5Mmkr69PHTp0IH9/f7l/9/r161eLLrd7925ycnIidXV1UldXJ2dnZ4qKimKVW7x4MZmYmChM7C4PyWpV8DUO8NXNhXjz5g0lJSVRUlISp/1Y1TYoeohUXQ4HRN9fxxJGBEu7uLTXzc2NBAIBGRoakqGhIQkEAurcuTOro0flNlS+OnfuTL/++itt3bqVdb/HZ9xV15jl692qqDzfw/Z/I9auXct4MVed4+Sd8wYPHkwdO3akjx8/MmUfPnygjh070sCBA2XK5ubmikQUXL9+nQICAmjr1q2s9fbu3ZsGDhwo5qQzYMAAcnd3lyon1AeFDpRVLy0tLZmOwtWJgwcP0qpVq0Tewa5duzgZxK2srDitC1whjx2L756S736eryHeyMiI2dtU3RfK2pOqqanJjIDJy8ur8X0Z3wgmvvvxKVOmUIsWLSgpKUkkCuzo0aPUrFkzVvl69epRcnKyWPm1a9fI2tqaVb5qVL2iUfZ8oYhjZnl5Oa1atUok2tbc3JzWrFnDab0aPnw49ejRg/Ly8kT67ZkzZ8jBwUHhZ/nXEqMrAk1NTXz8+JEhX6uKz58/c8q7npmZKTHXppmZGd6+fSt3u7S0tJic6WyYPHkyhg0bpnDe3ps3b+L8+fOIj4+Hs7MztLW1RT5ny3VeGS9fvsTZs2dx9uxZKCsrw8PDA5mZmXBwcMDy5csxbdo0udtXk+D77A0bNkRISAiSk5PRokULMXkuxJf9+/fH+PHj8fHjR7Rq1Qpqamp4+/YtVq1ahQkTJojJ6OrqwsnJCZGRkWjXrh0vPhQ+CAgIgKurK9LT00WI6fv168cpX7IQ2traaNKkidz19+zZE0FBQcjMzJT47tkI7IKCgiTmW6T/z3vQs2dPqbL169fH7du3xQjSz5w5w+TFlIXdu3djx44dIm1s0qQJzM3NMXHiRCxZskSmfFhYGAIDA7F582YRQix58ODBA4n5T/X19fHx40eZstOmTcPGjRuxZs0aheoW4saNG4iMjERMTAzMzMzg4+OD58+fo3fv3pg4cSJWrlzJ6/6SwLfffP78Gfn5+WLl+fn5nHhsLC0tsX37dixfvlykfMeOHbC0tOTwBEBKSgp27tyJmJgYaGtrY+bMmfDz88OzZ88QHBwMLy8v3LhxQ0zOwMAAL1++FCNbvXXrFiufBlBBNjtz5kwsWrRI4rtjI5HjC3V1dYm5gR8+fKgwVwZXlJaW4v79+wwvgBD3798X47iQhNDQUPTs2RN3795FaWkp1q5di7t37+LKlSuceGyePHnC/P3161e5+WCmTZsGVVVV5ObmisxRv/76K6ZPn47w8HCZ8qWlpdi5cyfOnTsn8beXxQMUHh6O2bNnY8uWLWJzprzPIETXrl1x//59pKamwtbWltMaouhaM2XKFDRo0ADXrl1juDzevXuHYcOGYcqUKTh16pRM+dDQUMydOxd2dnaoVauWGEE2V2RlZUnlVeHCyWNhYYHQ0FDO9QHA8+fPMWXKFLny7ArzuQvRr18/ueqsDEXXOgMDAwgEAggEAomcFAKBgBP3gJeXF3x9fREeHo6WLVsCqNAbZ86cyeRov3HjhsQ6MjMz4eLiAgC4c+eOWP1smDNnDn7//XdER0crTHa5atUqzJs3D/7+/mjXrh0A4PLlyxg/fjzevn0rUyffsWMHdHR0kJiYKDZHcSF2V5RwFQC2bt0qcU/l6OiIwYMHY/bs2TLl+ermhYWFmDx5MqKiopixpqysjBEjRmD9+vWcxkOjRo3w22+/4fLlyxJJlmW1wcnJCY8fPxZbq+XFj9CxmjVrJvJ/SUkJbt++jTt37rDyJwEVe9q///4bf/31l0ie+ZEjR2LKlCnYt28f6z1kcd2wgc+44ztm3717h0GDBuHixYsQCATIysqCjY0N/Pz8YGhoyLpO85XnosvUJLKzs7FmzRrcu3cPAODg4ICAgABWMns+WL16NYYOHQoNDQ2sXr1a6ve4zHkrV65Ehw4dUK9ePWbuv337NmrVqoXo6GiZst7e3hg7diyGDx+OV69eoWvXrnBycsLevXvx6tUrmWt8WFgYOnbsCDs7O7Rv3x4AkJSUhM+fP8vk83jy5AmICDY2Nrhx44aIHq2mpsbwj34PVOVRAdj51oT48OEDBg4cWC3tkNeOxXdPyXc//9tvvyE9PR0JCQlwd3dnyrt27YqFCxciKChIpnx5eblE7otnz55BV1dXqlydOnVw9+5dqZwfd+7cQe3atWXWnZeXB4FAwNzjxo0b+OOPP+Dg4ICxY8dKlcvIyGD+vnv3Ll69esX8X1ZWhjNnznDa0/Ldjx89ehT79+9H69atRXQ6R0dHhqdCFl69eoU6deqIlZuamsrkrRJCXk6smoKKigr69++PXr16YdOmTfjtt98wc+ZM/P777xg0aBCWLVsm8pylpaX4448/4O3tjWnTpjG2E1n9rSri4+MRFxcn1v8aNmyInJwcxR9G4eOTfyE8PDxo/PjxUj8fN24ca2oYoor81MLTvMonUocPH5bK6VHVK0zWJQt88/by8dYiqggBjo2NpV69epGqqiq1aNGCNm/eLJJD+fDhwzUSCnrhwgVe8nyfvWr+1cpX/fr1WeWNjY0Zr/Lt27dTkyZNqKysjA4cOECNGzfm9Azfvn2jvLw8uTlB+EJR74HqgiSvFXkioDQ0NCSGsD558oS0tLRkym7fvp3Mzc0pJiaGtLW1ad++fbR48WLmbzaoq6tLTAN0//59TpEkBgYGTFo0DQ0NxltOeHFB/fr16ezZs0Qk+vvt3r2b7O3tZcqWlZWRu7s72djYyO3d+/r1a1q5ciU5OjqSmpoaDRgwgE6fPi1y8i/0qqgJ8O03fPkFFE1LQ1SRisvJyYnx5jxx4oRY+rS8vDypaV5mzJhBP//8M718+ZLJIXz58mWysbHh5DVS9V0pkoKPD/z8/Khv375UXFzMRFPk5OSQi4sLBQQE1Gjd06ZNI2NjYwoPD2c8g1euXEkmJiacvfkfPXpEo0ePppYtW5K9vT0NHTqUMjIyOMmWlZVRSEgI1a1bV8Tba+7cubRjxw5W+Vq1ajEeqpXHe3Z2NqexVtWzVh4eoDdv3lCnTp1ISUmJdHR0FJqvfiS0tLQk/k63b9/m9O74pg8kqp50Dx8+fKCVK1cy886qVatEPFYloV+/frR//35ebecDRde6hIQEunjxIgkEAjp8+LBIHugrV67Q8+fPOdX/999/0+jRo0XSkKqpqdGYMWOYaKJbt27RrVu3quNxRVAdqQ6sra0l8ljs2rWLk6chH8yfP1/h3ObSIiGys7M5eZfy1c3Hjh1LNjY29OeffzIRRKdOnaIGDRrI3DNWVxtOnz5NzZo1oxMnTtCLFy9EIpkq760kga+O9fHjR4k5xt+9e8datywsWLCASWEoC3p6enTjxg2x8uvXr3PiNxBC0XS9fMYd3zHL17u1Orxj+XAr8MGZM2dITU2NWrVqxdg+WrVqRerq6hQfH1+jdVcnFE3pZWBgwOyp165dS23btiUiori4OE5z1vPnz+m3334jDw8PGjBgAAUHB3PiCiguLiYfH59qizxTFHyiZX19fWnz5s0K183HjsV3T8l3P68I32BlDBo0iMaMGcPIP378mP7++2/q3LmzTDtYQEAAOTs7S/T2f/36NTVp0oR1X/bzzz8zUanCfWmbNm3IxMRE5nxTXRFMfPbjRIqlOa4MW1tbio6OFiuPioriNOaJKvT67du3U1BQEDPeU1NT5eJn5oubN2/ShAkTyNDQkCwsLGjOnDn0+PFjunTpEnXp0oVatmwpJqOpqcmL+0ZHR4eJ4qn87m/evMkpO4M0/FdFgsyZMwedOnXCu3fvMHPmTDRu3BhEhHv37iE8PBzHjh3j5FEi9Ew6ePAgBAIBysvLkZycjJkzZ2LEiBESZW7dusWpjWweY/3798fFixcV9pTg460FVJwGl5eXY8iQIbhx44aYFxAAuLm5wcDAgFc9kuDu7g4LCwuMGjUKI0eO5OxJLQTfZ6/snasIioqKmJPP+Ph49O/fH0pKSmjdujXrSWZWVhZ8fX1x5coVkXIigkAgkHiyX51Q1HugOuvnA319fTx+/BjW1tYi5Y8ePRLz5KiK0aNHQ1NTE3PnzkVRURG8vb1Rt25drF27FoMHD2atu2nTptiwYQPWrVsnUr5hwwY0bdqUVZ5vBAYAjBkzBgEBAdi5cycEAgFevHiBq1evYubMmZg3b55M2SlTpuDixYtwc3ODsbGxXN7MFhYWaNCgAXx9feHj4yPRg79JkyaM1211g2+/2bJlC2bOnAlvb2+UlJQAqPCC8PPzw4oVK1jlPTw8kJWVhc2bNzPebn369MH48eNZ56/Nmzcz702S9whQEX0YEREh8bPQ0FBMmjQJlpaWKCsrg4ODA8rKyuDt7Y25c+eytp2Pd2V1IDw8HL/88gvMzMzw5csXdOzYEa9evUKbNm1Yo6f4YuXKlahduzbCw8MZD506deogMDAQM2bM4HSPBg0aYPv27QrVv3jxYuzevRvLly8XibRzcnLCmjVr4OfnJ1O+sLBQovfy+/fvOUUT8vnthwwZgufPnyM0NFQsEoINVedIWWDz0lQU6urqEqO8CgoKoKamxiqvpKTEeOErisWLF2PJkiWsHvDSkJKSgh49ekBTUxOtWrUCUBElsGTJEsTHx4tEHh8/fpz5u1evXggMDMTdu3clerOzeTkK8ebNGzx48AAAYGdnBzMzM05yiq51HTt2BFCho1lZWcnV5ypDR0cH27dvx+rVq/H48WMAgI2NDXR0dJjvSNJ5qwPCSBM+ePnyJdq2bStW3rZtW06ehnxw7NgxLFmyBB07doSfnx8GDBjAOXLZ0tISycnJYpEQycnJqFu3Lqs8X9380KFDiI2NRadOnZgyDw8PaGpqYtCgQdi8eXONtsHDwwNAxfiq3He56Pd8dazBgwejT58+mDhxokj5gQMHcPz4cfz555+KPBKGDRuGVq1asUaflJeXi80zAKCqqspZf8vJyYG7uztyc3Px7ds3dOvWDbq6uli2bBm+ffuGLVu2SJXlM+74jlm+3q185bdv344JEybAxMQEtWvXFota5BJxqCiCgoIwbdo0sQj9oKAgzJ49G926dauxuoWYPn26xHKBQAANDQ3Y2trCy8tLZpSPtra2TC92aSgpKWHmx3PnzjFra+PGjWXO1SUlJXB3d8eWLVvkjvQEKsbVkSNHavS3ZQPfaFlbW1vMmzcP165dkzvqDuBnx+K7p+S7n8/Pz5eoTxUWFnJ6d+Hh4ejRowccHBzw9etXeHt7IysrCyYmJjKj7hYsWIA///wTDRo0wLBhw0Tsp3/88Qdq167N2qfu3LnD6KMHDhyAs7MzkpOTER8fj/Hjx0uVr64IJj77cQBwdXXFqVOnMHnyZAD/11d37NiBNm3asMqPGTMGU6dORUlJCTp37gwAOH/+PGbNmsVpX5mRkYGuXbtCX18fT58+xZgxY2BkZITDhw8jNzcXUVFRrPfgg1WrViEyMhIPHjyAh4cHoqKi4OHhASUlJQAVWVN27dolZmcDgFatWuHWrVsKZwdo3749oqKisGjRIgBgbO/Lly+Hm5ubws/0XxUJQlRxuiskIq98GRsbU2xsLKd7fPv2jUaPHk0qKiokEAhIVVWVBAIBDRs2rMbJM/nm7eWLqKgoJp/m90Z+fj6tWrWKmjZtSioqKtS9e3fav38/ffv27Ye0R144OzvT2rVrKTc3l/T09BhCtZSUFFZehrZt21KHDh3ozz//pFu3bn13ThBFvQf+KRg7diw5OzuLRFFlZWVRkyZNyM/Pj/N9CgsL5c6hm5CQQNra2mRvb0++vr7k6+tL9vb2pKOjQ5cuXZLrXoqivLyciV4Rek9oaGjQ3LlzWWV1dHTo5MmTCtX7vZ6vpqEov8A/ATk5OXTq1Cnav3//P5avSRaSkpJo48aNtGzZMiaa6XuCizeuJJSWltLBgwcpJCSEQkJCKDY2ljMfVYMGDRg+lspeL/fu3eMUZdmzZ09mbAvn67KyMho4cCANGDBA7meRB5qamgqvSbK8qeX17lYUw4cPJ0dHR7p27RpDLn316lVycnKikSNHssovW7aMd6SSrq4u85srgp9//pl8fHxE+ltJSQmNHDmS2rdvL/JdWZ6N8no5fvr0iYYNG8boxgKBgFRUVGjo0KGsUSjVhUuXLtHQoUOpTZs2jHdcVFSUTFLP/xQ4OjpKJGdftGgROTk51Xj9aWlpNHnyZDIxMSEDAwMaP368RC//qli2bBkZGxvTzp076enTp/T06VOKiIggY2NjCg0NrfF2a2pq0t27d8XK79y5wxopLAnyktJXjlySdMkCXx3L0NBQ4rPfu3ePl4dlVFQUJx4eT09P6tChg0i01rNnz6hjx47Ut29fTnUpSvb7o8HXu5WvfHVzK8gDdXV1qfn5a5pbQIhOnTqRnp4eaWtrU/Pmzal58+ako6ND+vr69NNPP5GBgQEZGhrSX3/9JfUeikbStGrVimbPnk2XLl0iDQ0NRme6evUqK7esiYkJL12+ujiUFAXfaFm+uuGPtGPxBV++QaIKXXDPnj0UGBhIEyZMoO3btzN8QrLw/v17Gj9+PMPbJORxGjduHKcopMqk8n369GHmnpycHE5ZMX40kpKSSEdHh8aPH08aGhoUEBBA3bp1I21tbUpJSWGVLy8vp1mzZpGGhgZje9bS0uIcddelSxcKDAwkItH5Pjk5merVq6fwc3GFra0thYaGSuUhJaqwj0vizNm/fz/Z2NjQ+vXr6cqVK4w9RXixITMzk8zMzMjd3Z3U1NTol19+IXt7e6pVqxav7EgCIiLFj1D+nSgqKkJcXByysrIAVORy7d69u1x5kIGK/HaZmZkoKCiAi4sLGjZsKHdb8vLyAIBzVIOsnLECgYDxXpOF2NhYHDhwALm5uSguLhb5LC0tjVM7+KC4uFhinmtpXC2SkJaWhsjISObk2tvbG35+fqye9Xyf/dmzZzh+/LhEeVl50oV1e3t7o6ysDF26dEF8fDwAYOnSpbh06RJOnz4tVVZbWxupqalo3LgxaxtlQdF3/+zZM/To0QNEhKysLLi6ujLeA5cuXZLp6VlSUoJx48Zh3rx5vHIeFxYWIjExUeK7Z/P8+PTpE9zd3ZGSksJ4TT179gzt27fH4cOHayRyqTJevHiBjRs34v79+wAAe3t7TJw4kZOXY2V8/fpV7Nnl4WYoLi7Go0ePUFBQAAcHBxEPV2moV68e4uLiePc9RSD0Kh04cCA0NTUVugeffvOjkZSUhK1btyI7OxuxsbEwNzdHdHQ06tevj59//vm7tKGoqEjiu2PjW7C2tma8U+WZ26sLkvhEgIp1Ul1dnZNXv6L466+/4OnpiVevXjG8IkIukxMnTrDmAtbU1MT9+/dRr1496OrqIj09HTY2Nrh79y5atWqFgoICmfJ37txBly5d0Lx5c1y4cAGenp7466+/8P79eyQnJ3OKJE1JSZG6Vsriz2revDk2bdqE1q1bs9bBFUI1VVEPf3nw8eNHjBw5EidOnGA8DEtLS+Hp6Yldu3aJcWBURXl5OXr16oWHDx/CwcFBzEuRC++an58fWrZsifHjxyv0DJqamrh165bYnH337l24urqiqKhIofuy4ddff8WtW7ewfv16xjPu6tWrCAgIQLNmzRATE8P5XoqsdYcOHcLw4cMxdOhQREdH4+7du7CxscGGDRvw559/KuzV/m/BoUOH8Ouvv6Jr165MNFJycjLOnz+PAwcO8OJrkQclJSU4ceIEIiMjGd3Bz88PPj4+EscP/X9utnXr1jG/uYaGBmbPns3ZY5mPbt6lSxcYGxsjKiqK4V/68uULRo4ciffv3+PcuXOc2hAVFYUVK1aI7C0DAwMxfPhwTvI/Atra2oxHdWVkZmbip59+Yp0r+vfvL/I/EeHly5dISUnBvHnzWPOY5+XlMeuTcB+cl5cHJycnHD9+XGoO+sowNjbGlStXYGdnJ7JePn36FA4ODjU23/GFh4cHWrRogUWLFkFXVxcZGRmoV68eBg8ejPLycsTGxtaovJ6eHm7fvg0bG5vqfCxOsLS0xKpVq8S4HQ4cOICZM2ciNze3xtuwZs0aJCUlITIykllbPn36hNGjR+Pnn3/GmDFj4O3tjS9fviAuLk5Mni2SRpZNISEhAf369cPnz58xcuRI7Ny5EwDw+++/4/79+zL1hGnTpkFdXV0izyUXLF68GOHh4ejSpYtCHErKysp4+fKl2L7/3bt3MDMzY81MUadOHVy6dEkhmxlflJSUQFNTE7dv31aYY7O69pSK6DiXL19Gz549MWzYMOzatQvjxo0T4Rts0aIF9wdREETEcGWamppy1st/+uknuLm5oVevXujevTuuXbuGpk2b4tq1a/jll1/w7NkzmfK7d++GiYkJevXqBQCYNWsWtm3bBgcHB+zbt48XByFXZGdnIywsDOnp6SgoKEDz5s0xe/ZssfVTFgoKCnDv3j1oamqiYcOGnCNm9fX1kZaWhgYNGoisczk5ObCzs8PXr18VfawahzBapDIEAoFc2Ww+ffqEDRs2iLz7SZMmSc2UwQX/VYcgFy5cgL+/P65duyY20Xz69Alt27bFli1bGKKpypAWNikJbAp3aWkpgoODsW7dOsaYoaOjg8mTJ2PBggUSQ4OrC+vWrcOcOXPg4+ODbdu2YdSoUcjOzsbNmzcxadIk1jQjX79+xfr166USdspa9Ks7pdOLFy+wbds2hIWFQUVFBV+/fkWbNm2wZcsWODo6in2f77OfP38enp6esLGxwf379+Hk5ISnT5+CiBhjExtevXqFly9fomnTpsykcOPGDejp6ck0Mrds2RKrV69W2PBZHe++tLQUMTExyMjIYCagoUOHcjJO6+vr4/bt2wofgty6dQseHh4oKipCYWEhjIyM8PbtW2hpacHMzIzT4R8R4ezZs0hPT4empiaaNGkikSwcAFxcXDgv7DV9cFhYWIjZs2fjwIEDePfundjnNZ0KLTIyEmfOnEFkZKTcB8Xv3r3D/Pnzpc4X79+/lyk/depU/PHHH/j27RsGDRoEPz8/uYyr1dFvfhQUMehV5zqVn5+PUaNGST2cZet3a9aswa5du3Dnzh24ubnBz88P/fr1k6nwVWc6JCUlJZlj2MLCAj4+PliwYIFEBY0P2rRpA1NTU+zevRuGhoYAKsgcfXx8kJ+fLzYPV0WLFi0wbdo0DBs2TETZDQkJwdmzZ5GUlMTaBj4KY0xMDEaMGIEePXogPj4e3bt3x8OHD/H69Wv069dPZmrJ+Ph4BAcHY8mSJRJTFchzaBsREYHVq1czRsWGDRti6tSpGD16tEy5Fy9e4PLlyxLnHK6b1KysLJFDa1tbW05y/v7+2LFjB9zc3CSmA+OSlnPp0qVYtWoVevXqpVC6ByExa/fu3UXK4+LiMGLECLx+/ZrTs8gLbW1txMXFiekpSUlJcHd3R2FhoUx5vmudi4sLpk2bhhEjRoiMm1u3bqFnz54ihJr/NJSVlWH16tVSDx7Z1koh0tLSsGrVKibdg729PWbMmMEQ934PFBcX48iRI9i5cycuXLiAtm3b4sWLF3j9+jW2b9+OX3/9VaKcosYBvrr5nTt30KNHD3z79o1xpEpPT4eGhgbi4uIk7ieqQhop/caNG7F48WKZpPRCKOJwwFfHcnNzg5OTE9avXy9SPmnSJGRkZLCuNaNGjRL5X0lJCaampujcubPY/CMNRIRz586JzLddu3blJAsAhoaGSE5OhoODg8i4v3z5MgYMGCBzvuMz7viOWb7OCnzl+R6280FISAhWr16NoKAgJoVfcnIyli1bhunTp7Om6a0OmJub4+zZs3BwcBAp/+uvv9C9e3c8f/4caWlp6N69O96+fSsmX69ePUycOFHhtJVlZWX4/PkzoyMCwNOnT5n9iTRMnjwZUVFRaNiwocRDDDbdnq8zrZKSEl69eiXWxhcvXqBBgwb48uWLTPnly5fjxYsXvFNDFRcX48mTJ2jQoAFUVLhn97exscGRI0c4paOuCr57yurYz1eHIV5efPnyBWfPnoWbm5tYCvTPnz8jISEBPXr0kLlm8zn4AypSq27evBmdO3fG1atX0aVLF6xZswYnT56EiooKJwejfzPMzMwQFxcHFxcXkXXu7Nmz8PX1ZZzqaxJfv35FRkaGRF1DVrpctvSM3+MASyIUjiH5F6JPnz4yQwDXrl0rNfy2Kjmonp4eaWlpMQRo2trapKenx0oaSkQ0fvx4MjMzoy1btjChQFu2bKHatWtzJuH79u0b3b9/n3N6DSHs7Ozojz/+ICLRcKp58+bRpEmTWOW9vb3JxMSExo8fTwsWLKCFCxeKXLJQHSmdiouL6eDBg9SzZ09SUVGh1q1b0/bt26mgoICePHlCQ4cOlUr0zPfZW7ZsSfPnzxeR//vvv8nT05M2bdrEqf2K4vz589SmTRu6ePEivX37Vi7iRKIfn06Lb/htx44dacyYMVRWVsa8+9zcXOrQoQMdOnSoGltagcp9OigoiPT09Kh169YMcVubNm1IT0+PgoKCON3vw4cPFBcXR9HR0bR7926Riw0TJ04ke3t7io2NJU1NTdq5cyctWrSILCwsaM+ePXwflRXNmjUjXV1d0tHRIScnJ7nIH3v27EkNGzaksLAwioyMpF27dolcXFBSUkKHDh0iT09PUlVVJXt7e1qxYgW9evWKVfZ795vqRLNmzZj+UXm+SktLk5o+rzrXKW9vb2rXrh3dvHmTtLW1KT4+nqKjo8nOzk6u9GipqalMihRDQ0OaNGkSpaamSvxu1dB2Yfo2IWmgQCAgbW1tTiHvu3fvJgsLC5o7dy4dP36cjh8/TnPnziVLS0vaunUrLV68mAwMDCSmj+ELDQ0NunPnjlh5ZmYmp7Dvo0ePkr6+PoWFhZGWlhatWLGCIWz+HqShzs7OtGHDBiL6v75XXl5OY8aMYdZAaaicPqnyxTWlkhDz5s0jbW1tCgoKomPHjtGxY8coKCiIdHR0aN68eVLlIiMjSU1NjXR0dKhevXrfLY2WEHzSBwrBN93D5MmTycLCgmJiYig3N5dyc3Np3759ZGFhwZqqKyEhgXr37k0NGjSgBg0aUJ8+fTin3LG0tJRIKp+ens6a4oOI/1qnqanJpFuoPGdyJdj+kZg3bx7VqVOHVq5cSRoaGrRo0SLy8/MjY2NjTqlui4uLadSoUT+U8DYlJYUmTZpERkZGVKdOHZo9ezZlZWUxn69bt47MzMyqvd7q0M0LCwtp27ZtNH36dJo+fTrnFCFC8CGlf/PmDfXq1UtszhRessBXx7p8+TJpaGhQ+/btGZ23ffv2pKGh8cPSmX748EGu7/NJ18tn3PEds0QVxPSLFy+mgQMHUs+ePWnOnDky041Up3xoaOgPS69dXl5Oq1atInNzc0ZnMDc3pzVr1siVSo4PtLW16eLFi2LlFy9eJB0dHSKqWDukEU7zTVupKKrq+ZUvLrq9ohD2CyUlJVqyZIlIX1m1ahX17duXmjVrxnqfsrIycnd3JxsbG+rduzf169dP5GJDYWEh+fr6krKyMikrKzO/gb+/Py1dupRVfseOHeTh4cEphVNV8N1T/uj9vKJYs2YNde7cWernXbp0YfYMslBaWkrv378XKXvy5AmnNOOampqUk5NDRESzZs2i4cOHE1FF2koTExNW+epAWVkZPXjwgJKSkigxMVHkqmn4+flR3759qbi4mFnncnJyyMXFhXcKXi44ffo0mZiYKJwuly/42NGk4b/qEMTKykpi7lMh7t27R5aWlqz3CQ8Ppz59+ogM5Pfv35OXlxetXLmSVV5PT4/+/PNPsfJTp06Rnp6eTFm+k7+mpiY9ffqUiIhMTU0ZA/jDhw855RDV09Ojy5cvs35PErS0tOjevXsKyRJVPKOxsTEZGRlRQEAAZWZmin3n5cuXJBAIJMrzfXYdHR0m95yBgQFj5Lp9+3aN5+Pja1hS5N0LDU9cLjYsWrSIDAwMaMCAARQaGiq3sq2vr0/3799n/haO42vXrpGdnZ1EmbVr1zJ5P6vWJ0/9fn5+Erkz5s+fT6NGjWJt+/Hjx0lXV5cEAgHp6+uTgYEBcxkaGrLKW1paMoq6rq4uY1SIioqinj17ssrzRdWDTnkOPnV0dKr1kO3169e0aNEi0tDQIFVVVfLy8qLz589L/b4i/eafAr4GPb7rVO3aten69etEVNHvHjx4QEQV80K7du3kfRwqLi6mNWvWkLq6OikpKVHTpk0pIiJC6oZ379691K5dO+b3IyK6f/8+tW/fntNmoXPnzrR//36x8v379zPKfFRUVI30gyZNmkjsl+fPn+ecm//SpUvUtWtXMjU1JU1NTWrXrh3FxcVxbsP79+9pxYoVDA/RypUrOW/6tLS0mL5nZGTEGLbv3r1LtWvXlil78eJFhfPbV4aJiQnjtFAZf/zxBxkbG0uVs7CwoMWLF1NZWRnnuiqjtLSUduzYQUOGDKEuXbqQm5ubyMUGKysrXnpOdeDbt280ZcoUUlNTY3QFdXV1mjp1Kn39+lWqXHR0NKmoqNCgQYOYtXHQoEGkqqpKe/fuZa1369at1LVrV3r58iVT9vLlS+revTtt2bKFVZ7vWle/fn2GN6jynLl7926pzjHVhV27dokcfgUGBpK+vj61adOG0TtlwcbGhpGvrGuuXbuWhgwZwqkNenp6Ch+CnD59WoQ3ZcOGDdS0aVMaMmSImNFCEpycnEhFRYU8PDzoyJEjEvkR8/PzGf28X79+jANPVSOYvEaxH6mbC6Guri5y4CPEw4cPWddrPg4H1aFj3bp1i7y9vcnBwYFatGhBo0aNkotz4MOHD7R9+3YKCgpi1pjU1FSGk0cWwsLCKCYmhvl/4MCBpKSkRHXr1uX8XHl5eeTg4ED29vaMc5yxsTHZ2dmxGtf4jLvqGLM/Ej+Kd6ukpIR2797NODJ9/vyZPn/+XGP1SYO3tzfVr1+fDh8+THl5eZSXl0eHDx8mGxsbGjZsGBER7du3j1q0aCFR3tfXlzZv3vw9m/xDIewbAoGALC0tRfpLo0aNqHv37nTt2jXW+0yaNInU1dXJ3d2dRo4cST4+PiIXG6ZMmUItWrSgpKQk0tbWZtb5o0ePcjqEadasGeno6JC6ujo1atRILsc+vntKvjpOTk6OzKum0LJlSzp+/LjUz0+cOEEtW7assfqJKux2aWlpRFTxG0ZFRRER0aNHj0hbW7tG6yaq4OupX78+Y3v73ocAHz9+pK5du5KBgQEpKyuTpaUlqaqqUocOHb4LV6mtrS1NnDiRkwOqJERFRVHbtm2pTp06jE68evVqOnr0KKssXzuaNPxXHYJIU1KFyMrK4uSlWbduXalenlyI4ExNTSUexty9e5f1NJPv5F+/fn1mEmnRogWzMY2Li+PUkezt7TmR2EiCq6srL3LKzp070x9//CFzE19SUiLV0ML32WvVqsX8bvb29ozx//bt2zU+AfMhTiRS7N1XJ2EqX2W7MhFcw4YN6cyZM0RUcXApjbjS2tqa3r59y7t+PT09iRvChw8fsh5aCtsbEBBAhYWFrN+VBG1tbUa5MTc3ZwzTjx8//i4LPx+4urrS1atXq+Ve169fp/Hjx5OBgQFZWVnR/Pnzyc/PjzQ1NWnGjBkSZRTpN9WN0tJSevXqFb1580YuOb4GPb7rlK6uLmMIt7KyYg6/Hz9+TJqamlwfg4qLi2n//v3k7u5OysrK1K5dO9q5cyeFhIRQrVq1pBoLbGxsmPm6MlJSUlg9a4kqojGkjVth++V9Fq44deoUOTo60sGDB5nN9cGDB8nZ2ZlOnTolVxSfIkhMTCQ9PT2ytLRkDIlWVlakp6fHyWPJ3NycOfhwdnZmDiOuXLnCac6rDujr60slTtXX15cqZ2RkxIsob9KkSaStrU2DBg2igIAAmjp1qsjFhp07d9KgQYPknu+nTZvGbGSEEYeSrunTp3O+Z2FhIWVkZFBGRgan9jRu3FhixGZ4eDg1btyYVV5oXFBVVWUiSVRVVUlHR4eToYHvWhcaGkoODg507do10tXVpaSkJNqzZw+ZmpoyZKI1hUaNGjEHn1euXCEtLS3aunUr9enTh5MhX0tLi3n22rVrM9Fy2dnZnMccn4hbJycnOnXqFBERZWRkkLq6Ov3222/UunVrToapkJAQTkZvIXx8fBjDZ1UjmLxGMb66eWhoKEVERIiVR0REcCaO5kNKz8fhoDp1LEWQnp5OJiYmZGtrSyoqKoyeMmfOHMZTVxasra0pOTmZiIji4+PJwMCA4uLiyM/Pj7p168a5HSUlJRQdHS032S+fcVcdY5avd2tNeMd+D1R2SvxR+Pvvv5kIW6GzgJqaGo0ZM4ZZi2/dukW3bt2SKP8jI2n4Ii8vjzZu3EizZ88W0zHY0KlTJ7mjtSqDb7SslZUVM+dV3htlZWVJjdqpDD6OfXz3lHx1HEnOsFyjBvnAwMBA5iFLTk4OGRgYiJW7uLgwThTNmjUT0wO5Hj4RVRxaNm/enPz8/EhLS4ux7xw7dowcHR1Z5d3c3CT220+fPnFycGratCkNHDiQ7t69Sx8+fKCPHz+KXN8LSUlJtHHjRlq2bBljI/ge0NXVVXhvtWnTJjIxMaHFixeTpqYmM2YjIyOpU6dOrPJ87WjSwD2J3n8AzM3NcefOHam5nTMyMjjly/78+TNDClQZ+fn5+Pvvv1nl/f39sWjRIkRGRjL58759+4YlS5bA399fpuzRo0exf/9+tG7dWiTXtKOjI7Kzs1nr7ty5M44fPw4XFxeMGjUK06ZNQ2xsLFJSUsQI7iQhPDwcs2fPxpYtW+TO4bZs2TLMmjULoaGhcucKLykpQb169dC6dWuZOQdVVFTQsWNHiZ/xffbWrVvj8uXLsLe3h4eHB2bMmIHMzEwcPny4WklgJUHaM3GFIu++ar4/Pnjy5AkveRcXF9y8eRMNGzZEx44dMX/+fLx9+xbR0dFSyc0q18mnfk1NTSQnJ4uRuCUnJzMkmrLw/PlzTJkyRW4+DSFsbGzw5MkTWFlZoXHjxjhw4ABatWqFEydO1DihOwCMHDkSfn5+UvlTZGHTpk0ICgrC/Pnz4eTkJDc/wJs3bxAdHY3IyEhkZWWhT58+2LdvH3r06MHMfz4+PnB3d8fKlSvF5BXpN1WhKL/AqVOnsGzZMty4cQMlJSUAAF1dXfTp0wdLlixhJQsfM2YMAgICsHPnTggEArx48QJXr17FzJkzOeVL5rtO2dnZ4cGDB7C2tkbTpk2xdetWWFtbY8uWLZzWybS0NERGRmLfvn1QUlLCiBEjsHr1ahHuo379+qFly5YS5V++fInS0lKx8rKyMk6cBpaWloiIiBAjj4yIiGAIWN+9eyeSj7kybt68KTXPOlvO5d69ewMABg0axPRT+v/0a3369GH+V4QLiwsmTZqEX3/9FZs3b4aysjKAivc2ceJETJo0CZmZmTLlO3TogLNnz8LZ2RkDBw5EQEAALly4gLNnz6JLly4yZevXr49Ro0bBx8eHtY/LwvDhw7F582axd71t2zYMHTpUqpyfnx8OHjyIoKAgheqNiYnBgQMH4OHhoZD8unXrkJ2djVq1asHa2lpszpPGIXXr1i1mnrh165bU+8tDDq+lpSVXnujHjx8z/bMyPD098fvvv7PK9+3bl3NdksB3rQsKCkJ5eTm6dOmCoqIidOjQAerq6pg5cyYmT57Mq21syMvLY/YWR48exYABAzB27Fi0a9cOnTp1YpW3sLDAy5cvYWVlhQYNGiA+Ph7NmzfHzZs3OXNjNGzYECEhIUhOTpab8PbJkydMbvxDhw6hd+/eCA0NRVpaGqexUHVNKisrQ2ZmJurVqydxjq3MjcOFJ0cW+OrmW7duxR9//CFW7ujoiMGDB3PK+R8cHIxff/0Vly5dkkhKLwuFhYVMfn1DQ0Pk5+ejUaNGcHZ2ZuWcU0TH+vz5M1P++fNnmfdn09GmT5+OUaNGYfny5SK54j08PODt7S1TFqjgSRSuxydPnsSgQYPQvXt3WFtb46effmKVF0JFRQXDhg3j/H0h+Iw7vmP2xIkTGDp0KAoKCqCnpydGrD1ixIgalf+RaNWqFW7duvXjcsGjgot1+/btWL16NcPlYGNjAx0dHeY7zZo1kyq/bds26OjoIDExEYmJiSKfCQQCuUiy5UVKSopULho2bgQ2DiVZKCkpQW5uLl6+fKnw/tPIyIiVr0YW8vPzJXKmFBYWctKPFixYoHDdfPeUfHWcqrphSUkJbt26hVWrVrHy2grx8eNHxMbGIjs7G4GBgTAyMkJaWhpq1aoFc3NziTKlpaXIz8+XqtPn5+dL3K95eXkxcyFf/XDjxo2YO3cu8vLycOjQIRgbGwMAUlNTMWTIEFb5hIQEsbECVPBccOFZzMrKQmxsLGd+wJrCzz//rDA/MB/88ssvSEhIUGjsrl+/Htu3b0ffvn1F9uSurq6YOXMmqzxfO5o0/Fcdgnh4eGDevHlwd3cXM15++fIFCxYsYIwXstCvXz+MGjUK4eHhaNWqFQDg+vXrCAwMlGpMr1p+7tw5WFhYiJDwFRcXsxoY+E7+27ZtYww6kyZNgrGxMa5cuQJPT0+MGzeOVd7V1RVfv36FjY0NtLS0xBRuWURwQqK7qs/IxRikqqqKQ4cO8SJL4/vsq1atYojsg4ODUVBQgP3796Nhw4asRjFFkJGRAScnJygpKSEjI0Pmd2URJwL83n11QlEis9DQUMZwu2TJEowYMQITJkxAw4YNERERIVO2pKQEjRs3xsmTJ2Fvby93m6dOnYoJEyYgLS1NZLzv3LmTU3/s0aMHUlJSYGNjI3fdQAXxZHp6Ojp27IigoCD06dMHGzZsQElJSY30u6r49OkTunbtinr16mHUqFEYOXKkVEWpKgwMDPD582d07txZpJxrv7OwsECDBg3g6+sLHx8fmJqain2nSZMmUg3pfPoNAOzatQvjxo2DmpoajI2NxTaZ0jY50dHRmDRpEsaOHYsOHTogIiICPj4+qFevHmJiYtCiRQtcuXJF7GCtMvga9BRZpyojICAAL1++BFCxaXB3d8fevXuhpqaGXbt2scq3bNkS3bp1w+bNm9G3b1+xtQKoMJgPHjxYonyXLl0wbtw47Nixg9mYpaamYsKECZxIU1euXImBAwfi9OnTTP9ISUnB/fv3ERsbC6DioEMSSW9oaCjmzp0LOzs7MXJrLuvshQsX5DJWAxXGL64ybISrjx49QmxsLHMAAgDKysqYPn06oqKiWO+/YcMGfP36FQAwZ84cqKqq4sqVKxgwYADmzp0rUzYgIAC7du1CSEgI3Nzc4Ofnh379+nEyCk2fPp35WyAQYMeOHYiPj2cMmdevX0dubq5M487SpUvRu3dvnDlzRuKBP9ucqaamxmuTo+hG7+LFixL/5or+/ftj165d0NPTYx3f0owklpaWOH/+vNjznzt3jjFUygIf4wLAf60TCASYM2cOAgMD8ejRIxQUFMDBwUHEqFVT0NHRwbt372BlZYX4+HimL2toaLASxQIV8/X58+fx008/YfLkyRg2bBgiIiKQm5vLiVQbqDjgNTAwQGpqKlJTU0U+YzPKqampoaioCEDF7y0cY0ZGRqyGcqBCT3J2doafnx/KysrQsWNHXLlyBVpaWjh58iSngyBFwVc3f/XqlcSDfVNTU2YNZMOAAQNw/fp1rF69GkePHgVQQfB948YNVlJ6Pg4HiuhYhoaGePnyJczMzGBgYCBx3eGqo928eRNbt24VKzc3N8erV69kygrbkpeXB0tLS5w5cwaLFy9m6pdnX5KVlSXVaWH+/PlS5fiMO75jdsaMGfD19UVoaKhCxh1F5KdPn45FixZBW1tbZL2VhJrcX0ycOBEzZszAs2fPJB7Ysu1pqxM6OjoK1cfXuU+Ir1+/cnKoEyImJgYjRoxAjx49EB8fj+7du+Phw4d4/fo1+vXrxyr/22+/YebMmQgODoauri4OHToEMzMzDB06FO7u7jJlVVVVGd1QUSxcuBALFixAZGSkQv3e1dUVp06dYvZBwvlrx44daNOmDef7pKam4t69ewAqDrzZ5mmA/56Sr44jiczd1dUVdevWxYoVK1h1v4yMDHTt2hX6+vp4+vQpxowZAyMjIxw+fBi5ublS9weOjo44d+4cWrRoIfHz+Ph4ODo6ipULdcKysjK4ubmhSZMmCh+eGRgYYMOGDWLlwcHBMuUq28/u3r0rsi6VlZXhzJkznGwaP/30Ex49evRDD0HOnz+P8+fPS1znhETzNYUNGzZg4MCBSEpKkri3YnOykTS+1NXVUVhYyFo3XzuaNAhI6J74X4DXr1+jefPmUFZWhr+/P+zs7AAA9+/fx8aNG1FWVsachspCUVERZs6ciZ07dzKeeyoqKvDz88OKFSvEFnOgYuLjClleUR06dMDAgQMxefJk6OrqIiMjA/Xr18fkyZORlZWFM2fOcK5HEXTt2hW5ubnw8/MTMw4BFV7j0lDVU6Iq2KIdRo4ciWbNmnHeEP4TcPz4cc7f9fT0FPlfSUkJr169gpmZGZSUlCAQCCBpuHLZqPB993xRVFSEyZMnY/fu3QCAhw8fwsbGBpMnT4a5ubnCXrtcYW5ujnPnzil0CAIABw4cwNq1axmFyd7eHgEBARg0aJDE71f+3fPz8xESEoJRo0ZJXDiq/u5syMnJQWpqKmxtbb/bRiE/Px/R0dHYvXs37t69i65du8LPzw9eXl4SjdtCtGrVCioqKggICJA4X7D1u6SkJLRv375ankERWFpaYvz48fjtt9+gpKTEWc7e3h4LFy5kDOwpKSno168fcnNzIRAIMHjwYBQXF7N6bAEVB4eKGPQUWafY7nf//n1YWVnBxMRE5nfLysqwZ88eeHp6So20YEN+fj5GjhyJM2fOMH2stLQUPXr0wK5duyQ6A1TFkydPsHXrVjx8+BBAhbFp3LhxsLa2lilXq1YtLFu2DD4+Pgq1XREI50agIkJl8eLF6NGjB7Opu3r1KuLi4jBv3jzWNbBdu3YIDAwUM8gfPXoUYWFhuHbtWrW3vyrS0tKwa9cu7Nu3D2VlZfD29oavr69MT0M3NzdO9xYIBLhw4YLEzxYvXoz58+dLPcCSJidEeHg4Hj9+jA0bNsh9kPUjMWrUKKxbtw66urqs+qY0HXPz5s2YOnUqfH190bZtWwAV3uy7du3C2rVrOTmLCFFQUCC2SWPzKq+KH7HWKYqhQ4fi/v37cHFxwb59+5CbmwtjY2McP34cv//+O+7cuSPX/a5du8YclEuKzqlueHp6ori4GO3atcOiRYvw5MkTmJubIz4+Hv7+/swcKg0WFhY4evQoXF1dcfToUUyaNAkXL15EdHQ0Lly4gOTkZKmyr1+/xsyZM5nNfVU9t6addBo2bIgFCxaIRRJER0djwYIFjJd4TWHPnj0oLS2Fj48PUlNT4e7ujvfv3zMOB5IO6oVQRMdKTExEu3btoKKiwntvYGZmhri4OLi4uEBXVxfp6emwsbHB2bNn4evri7y8PJny/v7+OHnyJBo2bIhbt27h6dOn0NHRQUxMDJYvX84aCQMA27dvx4QJE2BiYoLatWuLzflc7iEEn3Enr6y2tjYyMzMVNuwoIu/m5oYjR47AwMBA5nrLZa3kA0n6tHCP+z0d86oDijj3lZeXY8mSJdiyZQtev37N7InnzZsHa2tr+Pn5SZVt0qQJxo0bh0mTJjFjrn79+hg3bhzq1KnDahTW1dXF7du30aBBAxgaGuLy5ctwdHREeno6vLy88PTpU5nyoaGhePjwIXbs2CGXM6MQLi4uyM7OBhHJFS0rxOXLl9GzZ08MGzaMcVS7e/curly5gsTERKmGeiHevHmDwYMHIyEhgTHIf/z4EW5uboiJiZHobFdTqC4d59GjR2jatCmrQblr165o3rw5E7knnK+vXLkCb29vqb/9tm3bMH36dMTExIg5i584cQJDhgzBqlWrMHbsWKl1a2ho4N69e6hfv77cz1cZRUVFEiOgpL0/of0MgEQbmqamJtavXw9fX1+Z9R45cgRz585FYGCgRFtOTeuowcHBCAkJgaurK+rUqSO21h85cqRG64+IiMD48eOhoaEh0SlUlp7k4OCApUuXwsvLS6TfrV+/HpGRkaxjPiIiolrtaAyqNbnWvwBPnz6lnj17ihDbKCkpUc+ePeUmFCwoKKD09HRKT0//LqQ0RBW54HR0dGj8+PGkoaFBAQEB1K1bN9LW1qaUlBRW+Z07d9KBAwfEyg8cOEC7du1ildfU1KxWomN5wJdcm++z37hxQyLp17Vr1+jmzZsSZfjwajx9+pQhDX769KnM658Ovlw2jx8/lprfX8hbIAtLliyhkSNHUklJidxtVwTVyafyT0Nqair5+/uThoYGmZiY0NSpU6WSaGpqaooQWyuCkpISOnv2LG3ZsoXJIf78+XP6+++/WWX59htF+QUqk5oLoaKiQs+fPyeiCn4TSflTawI/Yp0iquDgUpSktzIePnxIx44do2PHjjG50msatWvXlosYtiqsra0pODhYYaLC/v370/r168XK169fT15eXqzyMTExZGVlRStWrKCkpCRKSkqiFStWkLW1NcXExDD9QRq/16lTp5hcx5URFxdHf/75p1zPUlxcTGvWrCF1dXVSUlKipk2bUkREBLO2VTcMDAwoMjJSLpmqRMz6+vpUv3596t27t9wkzT8a5eXllJOTwykfviQcPnyY2rVrR0ZGRmRkZETt2rXjRFxIVDHfenh4kJaWlkie6u+11n358oWWL19OPXv2pBYtWsidc5oPPnz4QJMmTSJPT086ffo0Uz5//nxavHhxjdZdHcjJyaFevXpRkyZNaMeOHUz51KlTafLkyazy6urqlJeXR0REY8aMoYCAACKq6BNsedrd3d3JwcGBNm3aREeOHKGjR4+KXGxQRDevjGXLlpGxsTHt3LmT0akjIiLI2NiYQkNDWeWJiDp06EC7d+9WeNxVRmFhIaWmplJ+fj7rd/nqWDk5ORLnYuE8wgY/Pz/q27cvFRcXk46ODj1+/JhycnLIxcWF6QOyUFxcTCtWrKApU6aIcICtWrWKtm/fzukZrKysOHO3/JPQr18/2r9//w+T/5H4t+9piSrGqa+vLykrK5OysjKzr/X396elS5fKlA0ODiYbGxvas2ePSI78mJgYat26tUxZLS0tZn9hZGTE8LfdvXuXateuzdpuvhxKffv2JV1dXapTpw51795dbh2JDyeHEI8ePaLRo0dTy5Ytyd7enoYOHcq8BzYMGjSIXF1dRbh5//rrL3J1daXBgwfLlOW7p+SLynyCnz59oo8fP9K9e/fo119/paZNm7LK6+npMXvaynwqT58+JXV1dZmyQ4cOJYFAQPb29tS3b1/q27cvNW7cmJSUlFjfG1EFF++5c+fYH1IK3rx5Qx4eHnLzoTx9+pSePHlCAoGAbt68KTLPvHjxgkpLSznVL82G873029q1azNk8D8CtWrVoiVLllBZWZncstu3bydzc3OKiYkhbW1t2rdvHy1evJj5mw01ZUf7rzsEEeL9+/d048YNun79OkPa873x5s0bxkAhD2kun8m/YcOGdOHCBbHyhIQEatSoEau8i4sLLxI+PiRufMm1+T57y5Yt6eDBg2Llhw4dolatWrHK/2h8+PCBVq5cSX5+fuTn50erVq36bmROfInMOnToIPGgKjo6mjp27Mgqz1dp+5GYPHmyxEO+9evXc9pkVidevHhBYWFhZGdnR9ra2jRixAjq0qULqaioSCRkbd++PS/irqdPn1Ljxo1JS0tLZJMxZcoUGjduHKs8334TGBjIupmRBHt7e5G5IjU1ldTU1BhlKysri3WzUVBQQHPnzqU2bdpQgwYNqH79+iJXTaN///4SDQvLli2jX375hVWer8JbHVB0vVm2bBmvsbV69Wpq2rQpKSsrU9euXWnfvn309etXzvLa2tqUlZUlVs6l3xCxH8KyKe5CAveqOH36NDVp0oTTMxQXF9P+/fvJ3d2dlJWVqV27drRz504KCQmhWrVq0ZAhQzjdR17UqlVL7gMsNmJmeUiafzTKyspIVVVV7ndQUlJCwcHBjCFbEbRt25batGlDMTExdPHiRUpISBC52MB3rfP29iYTExMaP348LViwQCHjyo+CouTcVUltZV01CSsrK4qLi6PS0lKytLRkiG/v3LnDeuCvo6MjlXyYC/jq5uXl5TRr1izS0NBgDCpaWloUHBzMuQ0BAQFkampKenp6NHr06O9GVs5Xx1JSUqLXr1+Llb99+5aTceHjx4/UtWtXMjAwIGVlZbK0tCRVVVXq0KHDd3O60NXVZXRDeaHouOMrS0S0Y8cOsrKyogULFlBsbCzj7CG8alr+RyIxMVGiU1pJSQklJib+gBbJDz7OfQ0aNGD048p74nv37rHOl+bm5ozNx9nZmf744w8iIrpy5Qrp6emxttvLy4u2bdtGREQzZswgW1tbWrx4MTVv3py6dOnCKv9v15H09PToxo0bYuXXr18nfX19mbJ895R8dRxJxOgCgYCsrKzoypUrrPKmpqbMYXPlfhcfH08WFhas8vv37ycvLy9ycHAge3t78vLy4nwQe/r0aWrWrBmdOHGCXrx4IXagwwZvb29q164d3bx5k7S1tSk+Pp6io6PJzs6O0Tekobi4mHx8fHg55v3og1tFnTKrC4aGhrzq37NnD9na2jL7UHNzcxGHmx+B/9pDkB+JgoICGjVqFCkrKzOdQUVFhXx9famwsLBG61ZXV5d4Wv3kyRPS0NBglY+Li6O2bdvSxYsX6e3bt3JNYsePHyddXV0SCASkr69PBgYGzGVoaKjoI3EG32evrORUxuPHj0lHR6c6mlhjuHnzJhkZGZG5uTlj+LewsCBjY2NKTU2t8fore7pUXnhv377NSWnT1dWVahRkU1qI/t1KW926dSVGeaWmppK5uXmN119cXEyxsbHUq1cvUlVVpRYtWtDmzZtFxvvhw4clKu4HDhwgBwcHioyMpJSUFBEPdGle6JXh5eVFw4YNo2/fvon0m4sXL5KtrS2rPN9+U1paSu7u7tSxY0fy9/fnbFTasGED6evr06xZs2j+/PlUt25d8vPzYz7fs2cPq2fy4MGDqU6dOjRr1ixavXo1rVmzRuSqaZiYmEg8XM/IyCAzMzNWeb4KL1/wWW/KysrI3d2dbGxseEUDpKam0uTJk8nExIQMDQ1p0qRJnOZbKysrWrlypVj5ypUrycrKilWeTVlnU9w1NDSkrpVaWloy6xZGihkbG5OpqSnNmDGD7t27J/KdzMxMTmuuIggNDeXkuf6fDAcHB4WMsNra2ry8GbW1tXl5pfNd6/T09Ojy5csK188Xly5doqFDh1KbNm3o2bNnREQUFRVFSUlJrLL16tWj5ORksfJr166RtbW1VLlOnTqJXHp6eqSlpcVEv2hra5Oenh65ubmxtuHRo0c0Z84cGjx4MGMY//PPP+nOnTussgsWLCB9fX1q3LgxWVlZMYe+ERERrJ7N9vb2IlEA8qK6dPO///6bbty4QZmZmXIdWgtRUlJChw4dIk9PT1JVVSV7e3tasWIFvXr1SqYcH4cDvjqWQCCQ6IT39OlT1rm+MpKSkmjjxo20bNkyuQ9loqKiqF27dlSnTh1mTVq9ejXnCDRfX1/avHmzXHUKoei44ytLxN+79d8cZc738O2fAD7OfRoaGkxfryz7119/sTq6DBkyhMLDw4mIKCQkhExNTWn06NFUr149TvppdnY2MzcUFBTQuHHjyNnZmfr37/+viMKpupcQXp8/f6Zv376xyks7dE9LS2P93fjuKfnqOFUdSy5dukT37t3jnOWCb+QeH1Sdn+SNFK5duzZdv36diCp+B2FmgGPHjlG7du1Y5fX19aslO8GPwqxZsygkJOSH1T916lRasmQJ7/sUFhZKnPt/BP6riNH/KZg+fToSExNx4sQJtGvXDkBFjsMpU6ZgxowZ2Lx5s0z58vJyPHr0SCIxTocOHWTKmpmZISMjQywnenp6OoyNjVnbLiTNUoRgmy8JHF/wfXZ1dXW8fv1aLP/qy5cvOefFLCwsRGJiosR8hrJIhfhi2rRp8PT0xPbt25m2lpaWYvTo0Zg6dSouXbokU/7PP/+EsrIyevToIVIeFxeH8vJy9OzZU6Y8XyIzgUDAkJFVxqdPnzjljpXFs1PTmDJlCmxtbcV+3w0bNuDRo0dYs2aNTPl3795BX19frFxPTw9v376tzqZKRJ06dVBeXo4hQ4bgxo0baNasmdh33NzcJJKdCXNZV861KU/e36SkJFy5cgVqamoi5dbW1nj+/Dlr2/n2m6VLlyIuLo7hj+JKkD1p0iQoKSlhz549+PbtG3x8fDBv3jzm81atWuGPP/6QWffp06dx6tQpZo343igoKBB770AFMSIXolwPDw8AFbk6K78rrr89X/BZb6ZMmYKLFy/Czc1NLPepPGjevDmaN2+O8PBwbNq0CbNnz8bmzZvh7OyMKVOmYNSoURLvHRwcjNGjRyMhIQE//fQTgApS8DNnzmD79u2s9darV0+h9gqhr6+Px48fi62Vjx49YuWSadmyJbp164bNmzejb9++EjmD6tevj8GDB/NqozTcuHEDFy5cwMmTJ+Ho6ChWPxsPz5MnT1BaWoqGDRuKlGdlZUFVVZWVT+afgLCwMAQGBmLz5s1wcnLiLNelSxckJiYq/IwtW7ZEXl4eM1/KC75rnbm5OXR1dRWqmy8OHTqE4cOHY+jQoUhLS8O3b98AVKw1oaGh+PPPP2XKK0rOffHiRebvVatWQVdXF7t372a4mD58+IBRo0axcmslJiaiZ8+eaNeuHS5duoQlS5bAzMwM6enpiIiIQGxsrEz5hQsXwsnJCXl5eRg4cCDU1dUBAMrKyqycb2vWrEFQUBBDCC4vqkM3ByoIklu2bCl3/UKoqKigf//+6N+/P968eYNt27Zh3rx5+P333+Hh4YEpU6aIEZgDwKVLl7Bw4UKx8p49eyI8PFxmnYrqWEJSbIFAgHnz5omskWVlZbh+/bpEXU8afv75Z/z888+cvy/E5s2bMX/+fEydOhVLlixh2mtgYIA1a9bAy8uL9R62traYN28erl27Jjdhq6Ljjq8sALH9u7zgK/8jIeyfVfHu3Tu5+ep+FPLz8yVy0xUWFrLqjA4ODkhKShLT1WJjY1kJujds2MCQk8+ZMweqqqq4cuUKBgwYgLlz57K2u/I8qa2tjS1btrDK8IWhoSFnPfr9+/cyPzcwMJB5LwsLC/j4+GDBggUSuWc6d+6MgIAA7Nu3D3Xr1gUAPH/+HNOmTROzbVUF3z0lXx2HL39reHg4fvnlF5iZmeHLly/o2LEjXr16hTZt2mDJkiW87s2GyrqKIigsLGTGm6GhIfLz89GoUSM4Oztz4n3y8vLC0aNH/1W8wpXx9etXbNu2DefOnUOTJk3E1rlVq1bVaP1lZWVYvnw54uLieNWvpaX1Q2zAkvC/Q5AfgEOHDiE2NhadOnViyjw8PKCpqYlBgwbJPAS5du0avL29kZOTI0bww8WwNGTIEEyZMgW6urrMgUliYiICAgI4GSX4TGLPnz/HlClTeHX+Z8+e4fjx4xIPEdgGIN9n7969O3777TccO3aMWcQ+fvyI33//Hd26dWOVv3XrFjw8PFBUVITCwkIYGRnh7du30NLSgpmZWY0egqSkpIgcgAAVG7ZZs2bB1dWVVT4oKAhhYWFi5USEoKAg1kOQ0NBQ9OzZE3fv3kVpaSnWrl0rQmTGhg4dOmDp0qXYt28flJWVAVRMyEuXLuW08ercuTMOHz4sZqj//Pkz+vbtW6MEgIcOHRIhSheibdu2CAsLYz0EsbW1xZkzZ+Dv7y9Sfvr0aYUJFeXB6tWrMXDgQGhoaEj9joGBAZ48eSJWLqlMHpSXl0uc0549e8bJ2MW334SHh2Pnzp0KEWRPmDABEyZMkPhZVQOrJBgaGsLIyEjueqsLzs7O2L9/P+bPny9SHhMTAwcHB1Z5vgovX/BZb3bv3o1Dhw6hV69evNpQUlKCI0eOIDIyEmfPnkXr1q3h5+eHZ8+e4ffff8e5c+ckHob5+PjA3t4e69atY4z29vb2uHz5MnMowobo6Ghs2bIFT548wdWrV1GvXj2sWbMG9evXZzUseXl5YerUqThy5AgaNGgAoOIAZMaMGawEdI8fP2Y9hNHW1q6xg2kDAwP0799fYXkfHx/4+vqKjdHr169jx44dSEhI4NnCmseIESNQVFSEpk2bQk1NDZqamiKfSzMy9OzZE0FBQcjMzESLFi3EjFFsv/2OHTswfvx4PH/+HE5OTnITR/Jd68LDwzF79mxs2bKF90GgvFi8eDG2bNmCESNGICYmhilv164dFi9ezCpvaWmJ5ORkMdLQ5ORkxlDDhvDwcMTHxzMHIEDFOrJ48WJ0794dM2bMkCobFBSExYsXY/r06SJra+fOnbFhwwZO9f/yyy9iZSNHjmSV+/XXX1FUVIQGDRpAS0tLrN+wGcX46uaFhYUICwtjiNmrGpflJUa/ceMGIiMjERMTAzMzM/j4+OD58+fo3bs3Jk6ciJUrV4p8n4/DgaI61q1btwBU6PCZmZki9aupqaFp06aYOXMmp3udP39e6rvbuXOnTNn169dj+/bt6Nu3r8gew9XVlXP927Ztg46ODhITE8X2EwKBQObeis+4q44x+98G4dosEAjg4+PDHJYCFbp5RkYG2rZt+6OaJxf4OPfNnz8fI0eOxPPnz1FeXo7Dhw/jwYMHiIqKwsmTJ2XKVt4XKCkpsR4yS8LHjx8RGxuL7OxsBAYGwsjICGlpaahVqxbMzc1Z5WNjY3HgwAGJthhJBmm2fa482LVrF+bMmQMfHx+0atUKQMWcu3v3bsydOxf5+flYuXIl1NXV8fvvv4vJb9iwAZ6enrC2toalpSUAIC8vD05OTtizZ4/MuvnuKfnqOJJsCUBF39PQ0ICtra1M4nF9fX2cPXsWly9fRkZGBgoKCtC8eXN07dqVtW6+qF+/PiwtLcUOsIgIeXl5rPJ2dnZ48OABrK2t0bRpU8ZpYsuWLRIPo6uiYcOGCAkJQXJyskT9tiZtcNWBjIwMxjHhzp07Ip8p6qgnDzIzM5kDWi71u7i4cG4Xl0OsmsD/DkF+AIqKilCrVi2xcjMzMxQVFcmUHT9+PLPw1qlTR+6Ov2jRIjx9+hRdunRhDOLl5eUYMWIEQkNDWeX5nEL36NEDKSkpChtuz58/D09PT9jY2OD+/ftwcnLC06dPQURo3rw5qzzfZ1+5ciU6dOiAevXqMRPB7du3UatWLURHR7PKT5s2DX369MGWLVugr6+Pa9euQVVVFcOGDUNAQACrPB/o6ekhNzcXjRs3FinPy8vjZEzOysqSaPhs3LgxHj16xCr/888/4/bt2wgLC4OzszPi4+PRvHlzXL16Fc7Ozqzyy5YtQ4cOHWBnZ8d4NCYlJeHz58+cDjASEhLEFDWg4mQ9KSmJVZ4P+Hp+TJ8+Hf7+/sjPz2e8CM+fP4/w8PBqVSylYfjw4QrL8jVEde/eHWvWrMG2bdsAVCy0BQUFWLBgARNpIAt8+426uvoPi8RYtGgR5s+fj927d/8Qr4l58+ahf//+yM7OFul3+/btw8GDB1nl+Xos8QWf9cbIyIgx/iuCtLQ0REZGYt++fVBSUsKIESOwevVqkfm3X79+Mr2Of/rpJ+zdu1eh+vl61y5fvhzu7u5o3LgxLCwsAFQcPLZv317MgFcV39v4XBV8D1du3bolccy3bt1abOMqhNCrmgtq2lsLUNzgMHHiRACS28jFySY/Px/Z2dkYNWqUiBzX6C++a52rqyu+fv0KGxsbhYzpfPDgwQOJkdj6+vr4+PEjq/yYMWMwdepUlJSUiDz7rFmzZB5eVMbnz5+Rn58vVp6fny/Re7UyMjMzJR7ImpmZ1XjEKV89hq9uPnr0aCQmJmL48OEK7asA4M2bN4iOjkZkZCSysrLQp08f7Nu3Dz169GDu5+PjA3d3d7E5lI/DgaLzrdBJYdSoUVi7di309PQUuk9wcDBCQkLg6uqq0Lt78uSJRM93dXV1FBYWcr6HEELnQK7t4DPuqmPMnj9/HqtXr8a9e/cAVDg7TJ06lbNRkq/894ZwL0RE0NXVFTmgV1NTQ+vWrTFmzJgf1Ty5wMe5z8vLCydOnEBISAi0tbUxf/58NG/eHCdOnGA9uE1LS4Oqqiqzdz527BgiIyPh4OCAhQsXSjxQrYyMjAx07doV+vr6ePr0KcaMGQMjIyMcPnwYubm5iIqKkim/bt065hDi2LFjGDVqFLKzs3Hz5k1MmjRJogyXw3Cu2L17N8LDwzFo0CCmrE+fPnB2dsbWrVtx/vx5WFlZYcmSJRIPQSwtLZGWloZz587h/v37ACrGDZcxw3dPyVfH6du3L6NTVUZlPevnn3/G0aNHRZwhqkLRyD0+qF+/Pl6+fCkWPfX+/XvUr1+fVT8MCAhgIuwWLFgAd3d37N27F2pqati1axdr/RERETAwMEBqaipSU1NFPmM7LP8n4Ec7Fspbf9++fWumIdUIAVUdSf9DjaNLly4wNjZGVFQU41395csXjBw5Eu/fv8e5c+ekymprayM9PR22tra82vDw4UOkp6dDU1MTzs7OcinSHz9+REREBKN0OTo6wtfXV6Kht/KpdX5+PkJCQjBq1CiJIctsXoatWrVCz549ERwcDF1dXaSnp8PMzAxDhw6Fu7u7VK/rqsjKysLt27cVevbCwkLs3buXeXdNmjTBkCFDJKb8qAoDAwNcv34ddnZ2MDAwwNWrV2Fvb4/r169j5MiRzGJcE5gyZQqOHDmClStXMl42ycnJCAwMxIABA1gX39q1a+OPP/4QC+U/d+4cvL298ebNm5pqOoMXL15gw4YNIu/e399fprd8RkYGAKBZs2a4cOGCyHfLyspw5swZbN26FU+fPhWRq06jlpOTE8aPHy9mQFu/fj02b96Mu3fvstaxefNmLFmyBC9evABQkQ5q4cKFGDFiBOd2ygN5PKnZ0stkZ2djzZo1zHzh4OCAgIAATkbmZ8+eoUePHiAiZGVlwdXVFVlZWTAxMcGlS5ckhqJXhSL9RoilS5fi5cuXWLduHet35UF6ejqaN28uU+lzcXFBdnY2iAjW1tZic4wkzwlpXkKSwDbfAsCpU6cQGhrKzJdNmjTBggUL5DrgKCoqkugtxuYVbm1tDV9fX/j4+MDKyopzfUJEREQovN5ERkbizJkziIyMVOgASllZGd26dYOfn5/UlFCFhYXw9/eXarQvKyvD0aNHRdZZT09PxvtMFhwcHBAaGoq+ffsya6WNjQ3u3LmDTp06cTJqEhHOnj0rMm6kpduszlQH1YHS0lIkJCQgOzsb3t7e0NXVxYsXL6CnpwcdHR2Zsvr6+khISBAzzKWmpqJTp04Sjclubm6c2iUQCGo06vBHw8HBAfb29pg1axZq1aol1ie46Fp81rquXbsiNzcXfn5+EuuvTkNMVdjY2GDbtm3o2rWryJiLiopCWFgY6zovjKpdt24dM1dqaGhg9uzZYsZxaRgxYgSSkpIQHh7OeMhev34dgYGBaN++PXbv3i1V1sLCAgcOHEDbtm1F2n/kyBHMnDkT2dnZHN/EjwFf3Zxv6kk1NTU0aNCAWbNMTU3FvvP582d4eXmJGRNOnDiB/v37w9vbW6LDAZsxgY+OJamNFy5cQOPGjcWcpiShTp06WL58ucLOMg4ODli6dCm8vLxE+t369esRGRnJ2UM0IiICq1evRlZWFoAKr9+pU6di9OjRMuX4jDu+Y3bTpk0ICAjAL7/8wkQOXLt2DbGxsVi9erVUgzIf+erWERVFcHAwZs6c+a9JfSUN2dnZCAsLQ3p6OuNVP3v2bE7OfYqiZcuWCAoKwoABA/D48WM4ODigf//+uHnzJnr16sW6n+/atSuaN2+O5cuXi4y5K1euwNvbW2w/XBWNGzfGggULMGTIEBH5+fPn4/379xIjBz9//swctLJFt7EdyGpqaiIjI0NiytKmTZuiqKgIT548gaOjo0TH4qioKPz6668iUUgAUFxcjJiYGFZdg8+eEuCn45w/fx5z5szBkiVLRKJg5s2bh7lz50JfXx/jxo3DTz/9hIiICKn3+BEHp0pKSnj9+rXY2piTkwMHBwfOh95CFBUV4f79+7CysoKJiUl1NvUfjUePHiE7OxsdOnSApqam1NSCNYlnz54BAOMk92/F/w5BfgDu3LmDHj164Nu3b2jatCmACqOYhoYG4uLi4OjoKFW2c+fOmDVrFsPNwQfyeswAFWmVevToAU1NTWYCvnnzJr58+cJ491eGpHyMksDFS1BXVxe3b99GgwYNYGhoiMuXL8PR0RHp6enw8vJiXbh/NExNTXHlyhU0bNgQjRo1wvr169GjRw/cv38fLVq0EFsAqtOwVFxcjMDAQGzZsgWlpaUAKkLtJ0yYgLCwMDFloCrGjRuHq1eviqVHGTBgAFq2bIkdO3ZwauebN28khsyzGUQVhZKSEvMOJU11mpqaWL9+vUg+ZaB6jVo7d+6Ev78/AgMDJXp+yOP1lJ+fD01NTVZDHl9U9uRlgyzP67i4OHh6eqJZs2aMgSE5ORnp6emcPJ6ACoNmTEyMSOju0KFDxVK81AT69euHCxcuwNjYWCF+AWlIT0+Hi4uLzJzOwcHBMu+xYMECsbLqnG/5Ij8/H6NGjcLp06clfs5W/5o1a7Br1y7cuXMHbm5u8PPzQ79+/VjnKiFkvQu251fkAKoycnJyeEVEPHr0CL169cKzZ88YfoUHDx7A0tISp06dYjVuaWpq4v79+6hXr57IJjUrKwtNmjTBly9fFG6bJMgyrlZFTRqigYp37+7ujtzcXHz79g0PHz6EjY0NAgIC8O3bN9b813369IGmpqZYuoNff/0VhYWFUvvzPw3Z2dmIjIxEdnY21q5dCzMzM5w+fRpWVlYydUw+qC4nHUCxtU5LSwtXr15l9OrviaVLl2LPnj3YuXMnunXrhj///BM5OTmYNm0a5s2bx6RMYUNBQQHu3bsHTU1NNGzYkPN8B1QYBWbOnImdO3eipKQEQEXaUz8/P6xYsUKmwXHmzJm4fv06Dh48iEaNGiEtLQ2vX7/GiBEjMGLECInrTXWCz6EvX9SvXx9//vkn7O3tFb5HUlISK++KLCjqcMBXxxo0aBA6dOgAf39/fPnyBU2bNmUi7GNiYjBgwACZ8sbGxrhx44bCkZM7duzAwoULER4eDj8/P+zYsQPZ2dlYunQpduzYwSlV8fz587Fq1SpMnjyZOQy4evUqNmzYgGnTpiEkJIT1HnzGnaKyFhYWCAoKEnOQ2rhxI0JDQ1l57xSR/6foiF++fAERMU4mOTk5OHLkCBwcHNC9e/caq/c/Afr6+khLS0ODBg2wbNkyXLhwAXFxcUhOTsbgwYNZUwtVlq+sH+bk5MDOzo7hG5EGLS0t3Lt3D/Xq1YOZmRnOnj2Lpk2bIisrC61bt8a7d+/EZJSVlZkogMr78srgGjHaqFEj9O/fXyxFd1BQEI4cOYIHDx4gJSUFXl5eEsdA5bZUxrt372BmZlbjeyMhFNFxnJycsG3bNrGUccnJyRg7diz++usvnDt3Dr6+vsjNzRWT53vwqgiETqVr167FmDFjJPJPKSsrIzk5mdP9iouL8eTJEzRo0EAuzi++uHnzJsrLy8VSEgvbzyW1PB+8e/cOgwYNwsWLFyEQCJCVlQUbGxv4+vrC0NCQlT+ML8rLy7F48WKEh4ejoKAAQIVddsaMGZgzZ47MtaU63h0fPmxp+N8hyA9CUVER9u7dKxKKJ82wJ/RmByo2tnPnzkVgYKBE71YuxuSoqCisWLGC8Zhp1KgRAgMDOXnytG/fHra2thIJth8/fsxKsM0HtWvXxsWLF2Fvbw8HBweEhYXB09MT6enpaNeuHTMo/6no3r07fHx84O3tjTFjxiAjIwNTpkxBdHQ0Pnz4gOvXr4t8vyYMS0VFRYxHnzD/Mhd8+vQJ7u7uSElJEUuPIolroypSU1MxcuRI3Lt3TyEuG0Uh5M6xsbHBjRs3RDwQ1NTUYGZm9l022d87kuOfAhcXF/To0UOishofH//D8kByBdthkLQDILZImk+fPiEhIeG7Kds/AkOHDkVOTg7WrFmDTp064ciRI3j9+jWjRHHl20hLS8OuXbuwb98+lJWVwdvbG76+vpxSICoKRQ6gJKG4uFiiwsYW2eLh4QEiwt69exnvsnfv3mHYsGFQUlLCqVOnZMpXl3ftvxHC6JeIiAgYGxszz56QkIAxY8Yweo803L17Fx06dICBgYHEdAfyEI3/KFQlub537x5sbGwQFhaGlJQUMZLrCxcuwN/fH9euXRPzwvz06RPatm2LzZs3s240+vTpAx8fH1bDaU2hefPm2LRpE1q3bv3d6yYihIaGYunSpYz3qbq6OmbOnIlFixZ917YUFhaK6HlcvK2Li4sxadIk7Nq1C2VlZVBRUWHm2127dtWonvTo0SN4eHjg+fPnCh368sWePXtw7NixH5Z6kg/46li1a9dGXFwcmjZtij/++AMLFixAeno6du/ejW3btjHcIdIwe/Zs6OjoYN68eQo/w969e7Fw4UKmz9atWxfBwcHw8/PjJG9qaop169ZhyJAhIuX79u3D5MmTazydm6LQ0dHB7du3xQ6Ns7Ky4OLiwrqn5Sv/I9G9e3f0798f48ePx8ePH2FnZwc1NTW8ffsWq1at4pzZ4UdDHsNcdTk26unpITU1FQ0bNkS3bt3Qu3dvBAQEIDc3F3Z2dqxOLmZmZoiLi4OLi4uIfnj27Fn4+vqyHqLY2Njg0KFDcHFxgaurK8aMGYNx48YhPj4egwcPltj2xMREtGvXDioqKkhISJD5HtgOfo8fP46BAweicePGTErZlJQU3L9/H7Gxsejduzc2b96MrKwsiZkapEUkpKenw83N7btEKysKTU1N3Lx5U0wPzczMRKtWrfDlyxfk5OTA3t5eYhQM34PXfv36SfztKnOSeHt7M+s48H9OpYmJiWjTpo0Y/5S1tTVmzpzJypVZVFSEyZMnM7YxoYPT5MmTYW5uzokbhw+vcKtWrTBr1iwx7rPDhw9j2bJlYja86saIESPw5s0b7NixA/b29sy4jYuLw/Tp0/HXX3/VaP2//fYbIiIiEBwczDhcXL58GQsXLsSYMWOwZMkSqbJ83x1fPmxp+N8hyL8AwlNzaT+VPDmXV61ahXnz5sHf31+kE2/cuBGLFy/GtGnTZMpramri1q1bYmHSd+/ehaurKyunCR/07dsXvXr1wpgxYzBz5kwcO3YMPj4+OHz4MAwNDWWmEfsnICUlBX///Tfc3Nzw5s0bjBgxgokM2blz53f1XhQadOzs7Dh7v8mTHqUqmjZtigYNGmD27NkKp8n4T8D3iuT4p0BDQwOZmZliys3Dhw/RpEkTVo8joGJDd/HiRYmbDK5pQr43VFVV0a1bN4ncT0DFBufkyZP/0YcgderUwbFjx9CqVSvo6ekhJSUFjRo1wvHjx7F8+XJcvnxZrvuVlJRg06ZNmD17NkpKSuDs7IwpU6Zg1KhR3z0UmA0PHz6En58frly5IlLOdZ3W1tbGtWvXxFIqcD3wrw7v2n8rjI2NceXKFdjZ2Yls8J8+fQoHBwdOOoq86Q6qM31gdaBNmzYYOHAgQ3ItfAc3btxA//79mVB2ITw9PeHm5iZV/1u3bh0uXryII0eOyKx327ZtWLx4MXx9fRVKecoX8fHxCA4OxpIlSyTWryjvgTwoLi7Go0ePUFBQAAcHh++61kdGRmLw4MG8oiTz8vKQmZmJgoICuLi4sBomKkNRTz2+h758wTfyD6ho7/z586XqKlwMa6mpqSKRMJK4MqqCr46lqamJhw8fwtLSEiNGjEDdunURFhaG3NxcODg4sK41AQEBiIqKQpMmTdCkSROxdycPB1JRUREKCgo4pTmtDAMDA9y8eVPiO2jVqhUnTp4fAW9vb7i4uCAwMFCkfOXKlUhJSUFMTEyNyv9ImJiYIDExEY6OjtixYwfWr1+PW7du4dChQ5g/fz4zDv7JkNcwV12OjZ07d4alpSW6du0KPz8/3L17F7a2tkhMTMTIkSNZs2KMHj0a7969w4EDB2BkZISMjAwoKyujb9++6NChA2s6rdGjR8PS0hILFizAxo0bERgYiHbt2iElJQX9+/eXmobpyZMnMkm75cGTJ0+wdetWPHz4EEAFafa4ceNgbW0tVUZI1Jyeng5HR0eRKIKysjI8efIE7u7uOHDgQLW0sSbw888/Q1dXF1FRUcwhTn5+PkaMGIHCwkJcunQJ586dw6RJk/DgwQMxeb4Hpz4+Pjh69CgMDAzQokULABXr48ePH9G9e3ekp6fj6dOnOH/+vFh6Sb78UwEBAUhOTsaaNWvg7u6OjIwM2NjY4NixY1i4cCHrgT0brzBbVg8dHR2mzsp48uQJmjRpwsq7xheVHRYq6/WPHz9GkyZNavzQu27dutiyZYuYHn/s2DFMnDhR5gEa33fXrFkzNGrUCMHBwRK5xyTRMXDB/4jRvxOOHz+Onj17QlVVlTUnZ9UOVpn0jS+EPASVPdA9PT3h6OiIhQsXsh6C8CXYTkxMxMqVK0Vy1wrzFbNh1apVzCAPDg5GQUEB9u/fj4YNG34XslE+ICKYmZkxp/dmZmY4c+aMQvf6+vWr2Ak226JSNeTd1dVVrpB3oEKp6969u0Khyo8fP8ahQ4eqJU2GIti9ezdMTEwY7/NZs2Zh27ZtcHBwwL59+1gPYVJSUnDgwAGJ3gPyGLUk5Yn+T4apqSlu374ttjm9ffs2p43u9u3bMWHCBJiYmKB27doiC59AIPguhyCK8AvY29tjwIABUj0Zb9++jZMnT4qVVze3QmFhIRITEyX225omgSssLGR+Y0NDQ+Tn56NRo0ZwdnaWKxKhpKQER44cQWRkJM6ePYvWrVvDz88Pz549w++//45z585JJPRlS4HB1nc+fvyI2NhYZGdnIzAwEEZGRkhLS0OtWrVgbm4uU3bUqFFQUVHByZMnFSKLVVdXl6gUFhQUsJJeAhWbVE1NTcydOxdFRUXw9vZG3bp1sXbt2v/oAxCgwhAr6ZDp2bNnnHQUoELZDw0N5Vynogp4TUFekuv09HQsW7ZM6v26d+8uRuYsCePHjwcgeex9jxR8wjSxXbp0ESnnevjIB76+vli7di10dXVFyKwLCwsxefJk7Ny5s8bqFiIoKAgBAQEYOHAg/Pz8xFJmyEJISAhmzpwJS0tLWFpaMuVfvnzBihUrWOdLPp56iYmJuHbtmsgho7GxMcLCwnjxdHBFdRB4Dh8+HI8ePZLKRyMLb968weDBg5GQkMBEVX/8+BFubm6IiYmRqTfy1bEsLS1x9epVGBkZ4cyZM4zh/MOHDwxnpSxkZGSgWbNmACpSPVeGvOuelpaWQpE4w4cPx+bNm8X2gdu2bcPQoUPlvt/3goODA5YsWYKEhASR1DTJycmYMWOGCBedJH2Nrzzw43TEoqIiZj2Oj49H//79oaSkhNatWyMnJ6fG6q1OjB8/Hq6urjh16hQnPa+6UoGuWbMGQ4cOxdGjRzFnzhxmXx0bG8tpzg8PD8cvv/wCMzMzfPnyBR07dsSrV6/Qpk0bmd7cQmzbto055J00aRLjeOLp6Ylx48ZJlWvQoAHq1asHNzc3dO7cGZ06dVKYU6B+/fpi0W9sEM7zt2/fRo8ePUT2b8KIhB8VxcoVERER8PLygoWFBbNO5+XlMYcBQMU+Ye7cuRLlPT09ceTIEbGD02PHjqF3796s9deuXRve3t7YsGEDk/6ovLwcAQEB0NXVRUxMDMaPH4/Zs2eLObrJSp3NBUePHsX+/fvRunVrkbHm6OjIibPst99+w8yZMxle4UOHDonwCrNBXV0dr1+/FjPkv3z58ruk5SosLJS4Pr5//16u9I2K4v379xJ5who3bsxqj+D77rKyshAbG1v9NkT6H74LBAIBvX79mvlb2qWkpCTzPomJiVRSUiJWXlJSQomJiaztUFdXp6ysLLHyhw8fkrq6Oqv85MmTycLCgmJiYig3N5dyc3Np3759ZGFhQQEBATJlo6OjSUVFhQYNGkRr166ltWvX0qBBg0hVVZX27t3LWve/GWVlZaSqqkoPHz5USL6goIAmTZpEpqampKSkJHaxoVatWnT79m0iItq7dy/Z2tpSYWEhbdq0iZo1ayZRZu3atfTlyxfmb1kXG7y8vCg2NlaOJ65eNGrUiM6fP09ERFeuXCFNTU3aunUr9enTh/r16ydTdt++faSqqkq9e/cmNTU16t27NzVq1Ij09fXJx8fnezT/X4vg4GAyMDCgsLAwunTpEl26dImWLl1KBgYGFBISwipvZWVFYWFh36GlkvH06VNq3LgxaWlpkbKyMmVnZxMR0ZQpU2jcuHFS5Xx8fGjixIlSP7979y5ZW1uLle/atYu5wsPDydDQkAYPHsyMs8GDB5OhoSGtWrWKte1paWlUu3Zt0tPTI2VlZTI1NSWBQEDa2tpUv359Dk/PD66urnTmzBkiIurTpw8NHz6cnj17RrNmzSIbGxtW+dTUVPL39ydjY2MyNTWlGTNm0L1790S+k5mZSRoaGhLlmzVrJnI5OjqSlpYW6enpkYuLi8y609PTydTUlGxtbUlFRYX53efMmUPDhw9nbbuWlpZYW+XB8OHDydHRka5du0bl5eVUXl5OV69eJScnJxo5cqRc9yosLGR0j/8GDBo0iMaMGUNERDo6OvT48WP6+++/qXPnzv8187W5uTklJycTUcU7EPbfw4cPSxx70vRCIbKysqSOs38SEhISZF41CSUlJYnjLD8/n5SVlWu0biFKSkro8OHD5OnpSaqqqmRnZ0dhYWH08uVLVllp7X/79i0nHbNp06Y0cOBAunv3Ln348IE+fvwocsmCoaEh018r4/Lly2RoaMha9z8BOjo6jI4tLwYNGkSurq509+5dpuyvv/4iV1dXGjx4sExZvjrWxo0bSUVFhQwMDKhp06ZUVlZGRETr1q2jTp06KfQ83xv+/v6kp6dHjo6O5OfnR35+fuTk5ER6enrk7+9P06ZNY65/EqytrTld0vQ1vvI/Ukd0dnamtWvXUm5uLunp6dGVK1eIiCglJYVq1apVo3VXF7S0tGSum2woLS2lgwcPUkhICIWEhFBsbKxE+w5XfPnyhYqLizl/PykpiTZu3EjLli2js2fPKlwvV1y8eJEWLFhAHTt2JA0NDVJSUiJbW1saO3Ys7du3j169esX5XpcuXaKhQ4dSmzZt6NmzZ0REFBUVRUlJSayyu3btYmwb/0aUlZXR6dOnmX3hmTNnmHlbEirbahYtWkT6+vrk4eFBixYtokWLFlGvXr3IwMCAFi1axFq3iYkJPXjwQKz8wYMHZGxsTEREGRkZpK+vr/DzSYOmpiajy1bWa2/fvk16enqs8jo6OvTo0SMiIjIwMKA7d+4w8vXq1WOVHzx4MHXs2FFEn/nw4QN17NiRBg4cKO/jyI2ePXvS3Llziej/9jZlZWU0cOBAGjBgQI3X36pVK5o8ebJYub+/P/30008yZfm+Ozc3Nzp9+rT8jWbB/w5B/mXgu1FxdHSkJUuWiJUvWrSInJycWOW/fftGU6ZMITU1NcYAr66uTlOnTqWvX7/KlG3cuLFE4114eDg1btyYte7KbcjLy6OcnByRSxLS09M5XzUNBwcHunr1qkKyEydOJHt7e4qNjSVNTU3auXMnLVq0iCwsLGjPnj2s8hoaGpSbm0tEFQa22bNnExFRTk4OaWtrS5Sxtramt2/fMn/Lq2BXRn5+Pnl4eNDChQspNjaWjh07JnLVNDQ1NZk+MmvWLMaQeefOHTIxMZEp6+zsTBs2bCCi/1t4y8vLacyYMTR//vyabfi/HOXl5bRq1SoyNzdnDnrNzc1pzZo1VF5eziqvq6vLKDqKwM3NjT58+CBW/unTJ3Jzc2OV9/LyomHDhtG3b99ElK6LFy+Sra2tVLmvX79SYWGhwu0mIurfvz+tX79erHz9+vXk5eXFKt+xY0caM2YMlZWVMW3Pzc2lDh060KFDh3i1jQuio6MpMjKSiCo2tiYmJqSkpEQaGhoUExPDKq+kpEQ9evSgAwcOSN3YFRQUyGXY/vTpE/Xr14+ioqJkfq9Lly4UGBhIRKLKdnJyMidl2dXVldNmTBo+fPhAnp6eJBAISE1NjVlv+/bty2pQVBSfPn3ifP2TkZeXRw4ODmRvb08qKirUunVrMjY2Jjs7u+92GFRSUkJnz56lLVu20OfPn4mI6Pnz5/T3339/l/pnzJhBP//8M718+ZJ0dXUpKyuLLl++TDY2NrRw4UKx79vY2NCRI0ek3u/QoUNyG8UUMTLs3r1boh757ds32r17t9z3+x749OkTffz4kQQCAT169EhknLx//552795NderUYb0PXwenqnj16hWtXLmSnJ2dSVVVlfr06UNHjx6VaiwRCAT05s0bsfLz58+z6khE/AyC1XnoywcpKSkUHR1N0dHRlJaWJpesq6urwvq9np4e3bhxQ6z8+vXrrMYkvjoWEdHNmzfp8OHDIvPTyZMn6fLly3I9h9Ax7nujU6dOnC4uOp88qO4x+73xI3XEgwcPkqqqKikpKVG3bt2Y8tDQUHJ3d6/RuqsLfAxzd+7cIRsbG9LS0iIXFxdycXEhbW1tsra2pszMzGpuafVDeAjRunVruQ8hiCr0g/Pnz9O8efOoffv2pK6uTkpKSuTg4MAqK7SDjB49mtTV1Rn9fP369dSzZ0/FH4ojfrR+Jy/4HpZWhoGBgUSbzbFjx8jAwICIKpyqhX9XJ9q3b0/r1q0jov87BCCqMML36NGDVb5WrVqMo4G9vT3zHLdv35ZqB6uMZ8+ekY2NDenr6zNrioGBAdnZ2X2XdS8zM5PMzMzI3d2d1NTU6JdffiF7e3uqVasWc7hTk0hISCBtbW2yt7cnX19f8vX1JXt7e9LR0aFLly7JlOX77g4fPkwODg4UGRlJKSkp1Wa//R8nyL8M0gidHj58CFdXV3z+/Fmm/KFDh/Drr7+ia9euTJh5cnIyzp8/jwMHDqBfv36c2qEIwba6ujr++usvsXCmR48ewcnJiTV3rSJ51vnyqVRnepoTJ05g+fLl2Lx5s9zkqlZWVoiKikKnTp2gp6eHtLQ02NraIjo6Gvv27cOff/4pU75Ro0ZYvHgxevXqhfr16yMmJgadO3dGeno6unTpUuPEgSdOnMDw4cMl9k8uaSqEuTwlyQrJuHx8fBgCrqqoTATn4uKC6dOnY/jw4cjOzkbTpk1l5lLU1tbGX3/9BWtraxgbGyMhIQHOzs64d+8eOnfujJcvX7I8PT9UDmmvjMrP3qFDhxohLi0pKYG7uzu2bNkiV25wSRCm9+GakgYA/Pz80LJlSybNirxQUlLCq1evxNJCvHnzBubm5igpKZEpXx38AopCWu7WR48eoVmzZqz5Pw0MDHD9+nXY2dnBwMAAV69ehb29Pa5fv46RI0fi/v37MuWnT58usbxyv/Py8pLKk1AVRUVFuH//PqysrGBiYsL6/ZycnBrhCsrMzESfPn1k5k3W19dHWloaGjRoIPK75+TkwM7OTuJaVXlu+3/snXlcTdv//18nNI8alKGJpJG63XANIZKhZJYQlVmSZA6FMiWFK0STIbPMGcoUIUNUUhqUoZDrXoWrYf3+6Hf2t9MZ9u4M4nM9H4/zeLA76+y1z9nDWuv9fr9e6enpWL58OYKDg0XyJsjLy6N+JxMTE4GlwPzukbzgJUfGflYKQtCz8keiuroahw4dQkZGBioqKmBtbQ03NzeRvBKY8uLFCzg6OqK4uBj//vsvZd7o4+ODf//9F5GRkRLvAy+T6+rqari5ufE0ufb29sbVq1dx7949LgmcL1++wNbWFn379uX7LGJTU1OD4OBgREZGoqysjDr2gIAA6Ovr0xodN2vWDG/evOG6X5eXl0NLS+uHPO/orhsWi4XAwEAsW7ZM4OdI4tjv3LmDvXv3IjY2Fjo6Ovjrr7+gpqaG6Oho9OnTB8D/jXH//vtvKCsrcxxLTU0NKioqMGPGDGzfvl3gvvr164eFCxcykpVoyMePH+Hu7o7Tp09T98rq6mo4OzsjJiaGp9ycOMfmoshRsbl37x4WL16MFStWwNzcvFH3fCUlJdy4cYOSlWLz8OFD2NnZ0c7r2AgzxkpJSeE7bmZCdXU1AgMDERERQY1JFBUV4e3tjZUrV3J9Dz8aISEhaNWqFTw8PDi27927F+/evcOiRYv4tv0Z71f1EXWMKCqlpaV48+YNOnfuTEnr3L17F8rKyjxlV340Tpw4geXLl8Pf35/nOM/S0pJv2+7du0NTUxOxsbFQU1MDUCdBN3nyZLx7945rnUPcXLlyBVeuXOHpX0Qn3Xjs2DFMnDgRbm5uiI+PR3Z2NgwNDbFt2zacO3eOdj2iPt++fUNqairOnz+PnTt3oqKigtF6gK+vLyZNmsQxPn/48CEGDRqE0tJSge1ramoQFhbGV+Ja0PNCmPEd3bipPkwk6ET57URl7ty5OHjwIJYuXUqZ0t+7dw/BwcEYP348wsPDERUVhZiYmEb7PtJx8+ZNDBo0CBMmTEBMTAymT5+O7Oxs3Lp1C9euXaM8SvghDl/hyspK7N+/n8Mv0NXV9bs95/7++2/Kr5A9t5k9ezZ0dHS+y/5fv36N7du3c8xLZ82ahdatW9O2FeW7Yz8f6tMYP2x+/PIE+U6IehNkG2+yWCxMnjyZQ/+tpqYGjx8/ZqQFOXLkSNy5cwdhYWE4efIkgLqT+O7du4xM+NjIy8tzmbbS0a5dO1y5coVrIefy5cscGsT8EEZnXVQ/FTqDsMYwadIkfP78GZ07d4a0tDTXgoygB++HDx8oLT1lZWXqvT179sTMmTNp9z1v3jy4ublBUVERenp61AT4+vXrjH5Htl50w2AXU71ob29vTJgwAQEBAXzNogXh6OiIHTt2wMLCAra2tgDqHryPHz/G5MmTkZ2djf79++P48eMYNmwYV/sBAwbAy8sLVlZWyM3NxeDBgwGACm4IQk1NjZpctmnTBpmZmbCwsMDHjx8lugjOJiwsDO/evcPnz585Bsvy8vJQVFTE27dvYWhoiJSUFEbXUWNo0aIFHj9+LJbPaszEnE2HDh0QEBBAmUQ3fFjyGzDW73N2djbHoLimpgYXLlyg9XUAxOMvwGbIkCGIiopiPFhRV1dHYmIi/Pz8OLYnJiZCXV2dtn2LFi2ogYOWlhaKi4thYmICFRUVlJSU0LZ/+PAhHjx4gJqaGhgbGwOoC0Q3a9YMnTp1wp9//gk/Pz/cvHmTQwOfH/Ly8rC2tqZ9HxtJBECAukHk33//LfA9MjIyPBeecnNz+S6IqaqqcjyTCCEiexMYGRkxDj7W17X/+vUr/vzzT5iamnJohWdlZWHWrFk826ekpDDaDx2VlZVYt24d30laQUGBWPbDj+vXr+OPP/6Am5sbhx58dXU1rl+/LtCgWRz4+PjAxsYGGRkZHNfp8OHDMXXqVInum420tDR2796NFStW4MmTJ6isrISVlRXfINry5ctx/PhxdOzYEXPmzKGu95ycHGzfvh01NTW0i/gAsHbtWsTGxmLDhg0cx2pubo4tW7bQBkHY10dDXr58+cP5rrBJSUkBIQT9+vXDsWPHOILC0tLS0NPTYzRB5Hfs5eXlUFBQYNyfsrIyxMfHIzo6GgUFBXBxccGZM2fQv39/VFZWIigoCO7u7pTu/pYtW0AIgYeHBwIDAzm+Z7ZOOvseIghvb2/4+fmhtLS0UQuChBD8888/SEhIwKtXryi/QLqgrzjH5t7e3vj06ROysrJgYmICoG7c4O7uTi360KGqqop//vkH/fr149jO5J7fr18/+Pj44ODBg9S58urVK/j6+nI9QwQhzBjL0dERbdu2xZQpU+Du7t7oMaS3tzeOHz+ODRs2UOfJ7du3sWrVKpSXl2PHjh0C28fFxWHs2LFcmubfvn1DQkICh3+lJNi5cydP/yQzMzOMGzdOYBBEXNdsUyHqGFFUtLW1oa2tzbGNPb/7GWD7RzQMoAH0yX2PHj1Ceno6NacD6uaaa9eupRaXJUVgYCCCgoJgY2MjlGfdmjVrEBkZiUmTJlEeQgDQo0cPrFmzRmDbb9++IS0tDSkpKbh69Sru3LmDdu3aoXfv3ti2bRvs7Oxo9//s2TOe4zgVFRV8/PiRtn1gYCCioqLg5+eH5cuXY9myZSgqKsLJkydp1zKEGd+FhYXR9gmoO2fogiCi/nb1YScHN+YzwsLC0KpVK2zYsAFlZWUAgFatWsHX15e6Vzo4OAiVDEFHz549kZGRgZCQEFhYWODixYuwtrbG7du3Ga1jicNXWEFBAdOmTRPpOERBRUWF0VhcUrRu3ZqRbxAvRPnuxOmNXZ9flSDfCQMDA0bvY7FYPBcJpkyZAqDO4HnMmDEcC+jsicrUqVMZZdg2lhEjRiAmJgbKyspUMIYfgkyid+zYgXnz5sHDw4MK2KSmpiImJgbh4eECDbWAugvo/v37P0WGCC9iY2MF/l2QaZqlpSW2bt0KOzs79O/fH126dMGmTZsQERGBDRs24OXLl7T7T09PR0lJCQYMGEAZgp09exaqqqq05pOiZjwpKSnh0aNHaN++PW0/eTF16lTo6uoiICCAY/uaNWvw4sUL7N69GytXrsTZs2eRnp7O1f7jx49Yvnw5SkpKMHPmTOoBvXLlSkhLSwt8qIwfPx42NjaYP38+Vq9eja1bt2LYsGG4dOkSrK2taY3RRa3kOHjwIHbt2oWoqCjq+3v+/DmmT5+OadOmoUePHhg3bhy0tbVx9OhRgX0RBl9fX8jIyDA2obO2tsaVK1egpqZGm51OZ5At6L7J714JcGbn8nrEycnJYevWrTwnL/UZO3YsVFRUsGvXLigpKeHx48fQ1NTEsGHDoKur2yijt/oZS0yIiYmBl5cXBg0ahK5duwKoy+y9cOECdu/ejcmTJwts7+DggMmTJ2P8+PGYOnUqHj9+jLlz5yI+Ph5//fUX7ty5I7D9li1bcOPGDURHR1NZrH///Te8vLzQs2dPTJ06FePHj8eXL1+QlJTE1b6mpgYxMTF8F8OTk5O52ogzu7fhdUcIwZs3bxAfHw87OzueCx9svLy8UF5ejsOHD6Nly5Z4/PgxmjVrBhcXF/Tu3ZvnAty1a9cY9RsA7URPmO+uYf91dHSwevVqju0rV65ESUmJRLPFXF1dce3aNUycOJHnJM3Hx0di+wZEf1atXLkSHh4eQgfhmrJ6rD579uxBWFgY8vLyANQF1ObNmwcvLy+e73/x4gVmzpyJpKQkjsnxwIEDsX37dkZj2A4dOmDnzp2wt7fnOPacnBx0794df/31F8927OdERkYGzMzMOIwSa2pqUFhYCEdHRxw+fLixX8N348WLF9DV1W30ogR7TJ2YmAhHR0eeCU7Gxsa4cOEC7Wc5OTkhKSkJHTt2hJeXFyZNmsRVqff27Vtoa2tz3VOuXbuGHj16CG3wKWymXm1tLWRlZZGVlSVytamwqKio4PLly1yLj3fv3oWDgwOjhTVbW1s0b94cPj4+PI3RBd3zS0pK4OzsjKysLA6zW3Nzc5w6dYrLPFicY6z3798jPj4esbGxyMrKQr9+/eDp6QkXFxdIS0vTHTZUVFSQkJCAQYMGcWw/d+4cXF1daRMOmrqaQlZWFk+fPuW6vxUUFMDU1JRn1ac4r9mmRNQxoqikp6fzzcanm1f9CNAZuAsaQ3Tu3BlhYWFcQdPk5GT4+PjgyZMnYukjL3R0dLBhwwZMnDhRqPby8vLIzs6Gvr4+x3Ne0DUD1AV779y5AwMDA9jZ2aFXr16ws7NrdBa7oaEhdu3ahf79+3PsPy4uDuvWrUN2drbA9u3bt0dERASGDBnCsTYRERGBtLQ0gXODph7fifrbAXWB540bN1Jjw44dO8Lf37/Rn8lOFGNa2S4KVVVVmD59OgICAhivp4qDU6dOYdCgQWjRogVOnTol8L3Ozs4S7Qu/hFT2WpKurq7YDdIfP34Mc3NzSElJ0SbECqp8i42NhYaGBoYMGQIAWLhwIXbt2gVTU1McPHhQYkmPdPyqBPlOiBrFYi+26evrY8GCBSJlmdTW1uL58+c8F1f4RdfZg2xRsvFmzpwJbW1thIaGUpNZExMTHDp0iGf2fkNMTU0bLdtEd9OqT2NuYF+/fuUatNE9CAQFOeiYMmUKMjIyYGdnh8WLF8PJyQnbtm1DVVUV4wi2jY0NbGxsOLaxb0h08Mt4ysjIYCSHM2LECKSkpAgdBDl8+DDu37/PtX3cuHH47bffsHv3bri6uvL9LlRVVbFt2zau7YGBgbT73rZtGzWoW7ZsGVq0aIFbt25h5MiRWL58OW17USs5li9fjmPHjnF8dx06dMCmTZswcuRIFBQUYMOGDVRWkriprq7G3r17cfnyZfz2229c956G3/mwYcOoB3H97HRhEPa+WVhYCEIIDA0NcffuXY7sfWlpaWhpaTGSDwsNDcXAgQOpgf348eORl5cHDQ0NRtmhojB58mSYmJggIiKCmhCamJjg5s2bVFBEEMHBwVQF09q1azFp0iTMnDkTRkZGjBbBN27ciEuXLnHc11RUVLBq1So4ODjAx8cHK1asgIODA8/2Pj4+iImJwZAhQ2Bubs5ocbB+cKG8vBxr1qzBwIEDOTJMk5KSuIKhvGiYfSUlJQVNTU24u7tjyZIlAtuGhoZi1KhR0NLSwpcvX2BnZ4fS0lJ0796dbxYMkww2pgjz3dXnyJEjPIPBEyZMgI2NDc/fvzEVX4IGu+fPn8fZs2dpA+uSQtTs3MTERKxduxZ2dnbw9PTEyJEjGzWxEGf1mLCsWLECmzdvhre3N8e14+vri+LiYgQFBXG10dPTw7lz5/DXX3/h+fPnIITAyMiII1OVjlevXvHM3q+trRUoPch+Tjx69AgDBw6kkjSA/0vykdTzTVzo6enhxo0b2LlzJwoKCnDkyBG0adMG8fHxMDAwQM+ePXm2Y4+pCSFQUlLiSnDq1q0b4woiLS0tXLt2TWDlhqamJs/nqp2dHfLz8xEdHY38/HyEh4dDS0sL58+fh66uLszMzATuW9hntZSUFIyMjFBeXt6oIAhTiSiAfmxeW1vLU5KhRYsWXHMkfmRmZuLhw4dUFVVjaNeuHR48eIDLly9zyEz079+f5/vFOcbS0NCAr68vfH198eDBA0RHR2PWrFmYNWsWxo8fD09PT3Tu3JlvexkZGZ7V1AYGBoyCKE1d/dWuXTukpqZyLaylpqbyreAS5zXblIg6RhQFdpXPwIEDcfHiRTg4OCA3NxdlZWWMZbmbGvbCXXZ2Nlcgh8ViCVzYCwkJwdy5c7Fq1Sp069YNQF21blBQENavX89xf2t4/xJVwu7bt2+MlEP4oa2tjefPn3Nd9zdv3hSY5HXjxg3o6OigX79+6NOnD+zs7BhVtTdk6tSp8PHxwd69e8FisfD69Wvcvn0bCxYsYDQ3YFcrAnXSfexA7dChQ2nbN/X4TtTfbvPmzQgICMCcOXOoMfrNmzcxY8YMvH//Hr6+vow/63sEP9i0aNECx44dY/T7CuLjx484evQo8vPz4e/vj5YtW+LBgwdo1aoVT3UIFxcXSlJb0LP2e8gEd+nShSu5s/6zs0WLFhg7dix27tzJJWsryj7Zx8/eP6/EUrrjDw4OpqpCb9++jW3btmHLli04c+YMfH19aYPe/NZy6ycTCxUcE9pN5BdC4+vry/M1f/58snTpUrJ3715SXl7Os+2KFStIUVGR0Pu+ffs2MTAwIFJSUpSJHvvFxFhdWKqqqkhgYCApKSkR+jOuXLlCunfvTlJSUsj79+8ZmbU2PEZ+LybHXlFRQWbPnk00NTUpU/j6LyY8f/6cLFu2jIwbN44yaT137hzJzMxk/kUQQoqKisixY8cYGwJVV1eTqKgo4urqSuzt7Unfvn05XvxQVVUlampqREpKivo3+6WsrEykpKTIrFmzaPe/Zs0aoqGhQdzd3cmmTZtIeHg4x4sOLS0tnsaosbGxREtLixBCSFZWFq2BZ2VlJXn69CljU6WqqioSGxtLSktLafvIjwMHDpA+ffpwGFfl5eWRfv36kYSEBFJSUkJ69OhBRo4cybO9nJwcuXfvHtf2u3fvEjk5OUIIIYWFhYyMvYThexpN/ohUVVWRffv2EX9/fzJz5kyye/du8vnz50Z/jpmZ2XczDa2trSUvXrwQypyYjYKCAklJSeHanpKSQhQVFQkhhOTn5xMlJSWe7dXV1cnZs2eF3r+oxvDi4ObNm2T79u1k/fr15NKlS99ln4SI/t21atWKMqWvT3R0NHW/bAj7OSjqs1JfX58yH/yeDB8+nAwfPpxISUmRwYMHU/8fPnw4cXZ2Jvr6+ozMEwkh5MGDB8Tb25toaGgQVVVVMmPGDJ7GxbwYM2YMmTp1KiHk/8wbP336RPr160cmT54s9PE1Bg0NDXLgwAGu7QcOHCDq6uoS26+1tTWJj48nhBDKaJcQQgIDA0nPnj1p28fExPA0Rv8ZENWsddWqVaSiokLkfly+fJksWbKEeHp6kilTpnC8BHH16lUiJydH+vfvT6Slpan+h4SE8B2biItTp06Rnj17NsoQmH0vEvRiOrZ3dnYmvXv3Jq9evaK2vXz5ktjZ2REXFxdG/enVq9d3fUZIilevXpGVK1cSGRkZoqCgQJo1a0Z69uzJd44SGBhIXF1dOa7br1+/Ejc3N7Jq1Sq+++nSpQuxsrIiUlJSxMLCgjKHtrKyIpaWlkRJSYmMHj1a7MfXkPXr1xN1dXWyd+9eUlRURIqKisiePXuIuro6CQ4OFthWXNfsfxELCwuybds2Qsj/PStqa2vJ1KlTyYoVK5q4d8zIz88nlpaWXGMnJmsCDcdUvNrzu39JS0sTQ0NDsnr1aqHmFAsXLiRBQUGNbscmODiYmJqakrS0NKKkpERu3LhB9u3bRzQ1NSnjal5UVFSQ8+fPk0WLFhFbW1siLS1NzM3NyezZs8mRI0fI27dvGe2/traWrFmzhigoKFDfmaysLFm+fDmj9h07diRpaWmEEEJ69OhBQkJCCCGEJCQkEE1NTYFthRnf8Vvz4/WiQ9TfTl9fn+daSkxMDNHX16dtX1paSiZMmEB0dHRIs2bNhFoHE5ZJkyaRzZs3C90+IyODaGpqkg4dOpDmzZtTY5xly5aRiRMniqubEuPkyZPE2NiYREVFkcePH5PHjx+TqKgoYmJiQhISEsi+fftI27ZtiZ+fn9j2WVRURGpra6l/C3oJQk5Ojrx48YIQUncOs7/vzMxM2nU7QvjPT+vfJ3v37k0+fPjQqOP7JYfVBPTt21eg1vqzZ8/AYrF4aq1bWVnhyZMnQmcpdunSBR07dkRgYCBPqQpJZt4oKioiMzOT1oOBH+xy+4Z9Jt/JrHX27NlISUnB6tWrMXHiRGzfvh2vXr3Czp07sW7dOg79cV5cu3YNgwYNQo8ePXD9+nU8ffoUhoaGWLduHdLT0yUiZcRmzpw5VGYxr9+dn2ZlbGwspRe9ZcsWofWihZU1YrNmzRoEBwdj6tSpHGZcUVFRWLp0KZYtW4awsDCcO3cOly5d4mr/7t07TJ48mW95uqBzR15eHk+fPhW6XK99+/Y4duwYT9NLdiUHu7KEl8n6kCFDUFpaiqioKMq35+HDh5g6dSq0tbVx5swZnD59GkuXLpVoCfX3gp8hNy/oqqBELcFk+ws0lAiprq7GrVu3xO4v8M8//1DZNXSZroKycMQhMeLm5obbt28jNDSU45pbsGAB/vjjD8THxyMhIQGbNm3iWXXQunVrXL16FR07dhRq/6Iaw4tCU+uUi/rdrVu3DoGBgZg6dSqlsc02SQ4ICMDixYu52tBJO9RH0HWzb98+JCYmIjY2lstDSpJIQjK0qqoKp0+fRnR0NJKSktCpUyd4enpi8uTJfMdKL1++xMCBA0EIQV5eHmxsbKjqsevXr3PJvkgCVVVV3Lt3j+vaz83Nha2tLSN5H2FITEykKq2CgoIQGBiIZ8+eIS4uDmfOnMGAAQMEti8pKQGLxaLkf+7evYsDBw7A1NSUr5YwnRxQfeikgURBVLPWL1++gBBCXTMvXrzAiRMnYGpqyrfariHs75yfXviJEyf4tu3evTtGjx6N+fPnc/T/7t27GDFiBK3kqq6uLpXd26dPn0ZV/aqpqeHz58+orq5m7JcnTvnBxspR8eLIkSNYtWqVUCbJQNOa3VZVVSExMRF79+7FpUuXYGNjA09PT7i6uuLdu3dYvnw5Hjx4wFNmZvjw4bhy5QpkZGSoipGMjAx8+/aNy8+kfrYnuwo7MDAQfn5+fKu/mFSTiAIhBIsXL0ZERASVyS8rK4tFixbR+gMIc82Kq+KSzcePH3H37l2e542kxymioKCgQHkyqqur4+rVq7CwsMDTp0/Rr18/nnOhHw0nJyc0a9YMUVFRMDAwwJ07d/Dhwwf4+flh06ZN6NWrF9+2oty/hJGwqz+vqq2tRWxsLCwtLWFpacl1r6KbVxFCEBwcjJCQEEr+SUZGBgsWLOCSYBXEp0+fcPPmTcofJCMjA0ZGRsjMzGTU/tu3b3j+/DkqKipgamrKcQ8RxOLFi6GsrIylS5fi0KFDmDBhAvT19VFcXAxfX1+B0s/CjO+YVu2wWCxaqVsfHx/ExcUJ/dvJysoiMzOTa16Vl5cHCwsLvlJmbAYNGoTi4mLMmTOH5xiDiaqLsKxZswahoaGwt7fnqUpB56fSv39/WFtbY8OGDRxjnFu3bmH8+PEoKioS2L6p54W2trZYvXo1Bg4cyLGdrY5w9+5dnDx5En5+fsjPz5doXxqLlpYWkpKSYGVlBSsrK8yfPx8TJ05Efn4+OnfuTDufv3LlCpYtW4a1a9dSc9q7d+8iICAAy5cvh4qKCqZPn46uXbtiz549jPv1KwjSBIiqtf7w4UNER0fj4MGDqK6uxrhx4+Dh4cHITEtBQQEZGRkCDQcbIq5J5rBhwzBixAihZaHoBg2NkSP5+vVro8vFdHV1ERcXhz59+kBZWRkPHjxAhw4dEB8fj4MHD+LcuXMC2zd2khkREYFp06ZBVlaWr68EG7qbv4aGBuLi4ihD8MYiql60ONi/fz+2bduGZ8+eAQCMjY3h7e2N8ePHA6ibkLBL4xri5uaGFy9eYMuWLejTpw9OnDiBsrIy6qEqSBasT58+8PX1FfrhLi8vj+vXr3NJkd27dw92dnb4/PkzioqKYG5uzvNBUFpaiokTJ+LKlSvUgKe6uhr29vaIj49Hq1atkJKSgqqqKsaLJZJEVF+HhgPGBw8eoLq6mitg/Ntvv9EOGI2NjbFjxw7069cPt2/fhr29PVWC2bx5c9oSTFH1qvPy8pCYmIiioiKwWCwYGhpi2LBhfEvG6++vvq9JfZgGfc3MzLBnzx6q1L6xVFRUwNfXF3FxcaiurgYANG/eHO7u7ggLC4OCggIePXoEAFwBPqBOUqqgoADbtm0TyrxPT08Pc+fO5TKGDw0NRUREBM9FezrPqvoI+u2bWqdc1O8OqJMQDA8P5zAa9vHxwZgxY8TZVS6srKyQn58PQgj09fW5JmmSXIgG6hbVRJUMZfPt2zecOHECe/fuRXJyMv744w+8fv0aZWVl2L17N8aOHcuzXXV1NQ4dOoSMjAxUVFTA2toabm5uXIu7ksLb2xstWrTgmhAvWLAAX758wfbt2yW27xs3biAoKIjj2AXJ5tWnV69emDZtGiZOnIjS0lJ07NgR5ubmyMvLg7e3N89FSSaSlmxWrlzZqGNpDMLqpLNxcHDAiBEjMGPGDHz8+BHGxsaQlpbG+/fvsXnzZsycOZO2D6LohSsqKuLJkycwMDDg0jrv1KkTbf/37duH69ev4+rVq3j+/DnatGkDOzs7KigiKBgvil+euCCEMJaj4oWwnigAvdltw+CVOL2zvL29cfDgQRBCMHHiRHh5ecHc3JzjPaWlpWjdujVPaTB28JkJvDzUYmNjMW7cOLFrmTeWiooKPH36FHJycjAyMmLUH2GuWfa4jt/SC9NzBgBOnz4NNzc3VFRUQFlZmeOcYLFYPH97cfrJiELbtm1x/vx5WFhYwNLSEkuWLIGrqytu374NR0dHWi+ZHwENDQ0kJyfD0tISKioquHv3LoyNjZGcnAw/Pz88fPhQ4n1gS9ix5Xn5SdiJcyGejbBBCDa1tbW4d+8eUlJSkJKSgps3b+Lr168SH183JC0tDbdu3YKRkRGcnJxo319dXY2EhAQ8fvz4u4/vBP2OTH47c3NzjB8/HkuXLuXYvmbNGhw6dIg2kVJJSQk3btzgOeeTNKIm06qoqODBgwdo3749xxjnxYsXMDY2ph3jNPW8UE5ODg8fPuTyRc7JyYGVlRW+fPkidm8acVkKuLm5Uf08ePAgiouLoa6ujlOnTmHp0qW0gU9zc3Ps2rWLSwouNTUV06ZNQ1ZWFi5fvgwPDw8UFxcz7vMvT5AmQFStdXYkLTQ0lMpS7NGjB6Msxa5du+L58+eNCoLU18H7+vUr/vzzT5iamlIVAGlpacjKysKsWbMEfs6gQYOwePFiPHnyhGcUl86TQ1TN9ZqaGgQHByMyMhJlZWXIzc2FoaEhAgICoK+vD09PT4HtP3z4QC1eKisrUwPMnj17MpqgPnnyhKfhlpaWFk+vk7CwMLi5uUFWVpZvpQZQd/OnC4JIS0s36jdviJKSEp4+fUrpaCYmJiI6OhqmpqZYtWqVxLO1gLqbqKBqG0EDkOTkZCQmJsLGxgZSUlLQ09PDgAEDoKysjJCQEIFBkFmzZmH+/PkoKSnhed7SZWv17dsX06dP56rkmDlzJmWKx1584IW2tjYuXbqEnJwc5ObmAqhb3K+vPS2KPiwTGmNgKKqvQ0pKCvXvzZs3Q0lJCbGxsRx+KlOmTBGYZcWmpKSEOu9PnjyJUaNGUWbyffr0oW1PRPAXCAkJwYoVK1BbWwstLS0QQvDu3TssWrQIwcHBWLBgAVeb5ORkymMnOTlZ6AVwoK4awN/fHzt27OBa1GCCoqIidu/ejbCwMGpwaWhoyDHRETQQZmd4nT9/HmZmZlyL4XQBqMDAQHh5eeHq1as8jeF5Ia5KRn6/+/fSKRf1uwOAMWPGiBTwiI+PR2RkJAoLC3H79m3o6elhy5YtMDAwEBgQFlWjXlQWLlzIscAkTEb9/fv3qYUFGRkZTJo0Cdu3b6fuJVu3bsXcuXP5BkGaN29O+7ySNHv27MHFixepIOidO3dQXFyMSZMmcWSFMvUUY0qvXr14VmMyITMzk8ryOnz4MCwsLJCamoqLFy9ixowZPIMgkgxsNAZhddLZPHjwgBrnHT16FNra2nj48CGOHTuGFStWMBpjiqIXrqqqijdv3nCNQx4+fMhTK7shEyZMwIQJEwAAb968wbVr13DmzBnMmjWLr446G3EEOYTxY6kPi8XCgAEDaKuV+CGK72NkZCRiYmIYB6/E6Z2VnZ2NrVu3YsSIEXwX/jU0NDjGZfXhFdhoDIGBgRg6dCjXvj9+/Ahra2vahS1xoaioyCiRsD7CXLOi+oPWx8/PDx4eHggODmZcdSlOPxlR6N27Ny5dugQLCwuMHj0aPj4+SE5OxqVLl7gqiH5UampqKB8IDQ0NvH79GsbGxtDT06OS9eojLpPh+lhbW0NbWxvq6upYt24d9u7diz///BPdu3dHZGQk5eXE7/oVBWlpaS61EkHU1tYiPT0dV69eRUpKClJTU1FZWYk2bdqgb9++2L59O9+5rLgSnHgZbHfr1q1RyWLNmzennnXC8vz5c+Tn56N3796Qk5PjO+doiKi/Y2BgIMaOHYvr169TniCpqam4cuUK5dUriHbt2vEN4EoaUe+dMjIyPBUWcnNzOXxD+dHU88JOnTph3bp12LVrF7XmVlVVhXXr1lGBkVevXqFVq1Zi22fDZ0TDAH7970PQGG/79u1Yvnw5SkpKcOzYMcoL6P79+3B1daXtR35+Pk/1C2VlZWqMYGRk1Gjf6F+eIE2AqFrrbP7991+SkJBAHBwcSPPmzUnv3r1Jhw4diJKSEklISODZ5vjx48TU1JRER0eT9PR0xt4IbDw9PXnqLq5YsYJWc1hUTw5CCPnrr7/Ipk2biKenJ/H09CSbN28mHz9+ZNQ2MDCQGBoakn379hE5OTlKDzAhIYF069aNtr2FhQW5evUqIYQQe3t7SncvPDyctGnThrZ9mzZtSGpqKiGEUy/7+PHjxNDQkNExCMumTZvIrFmzKG2/xmJjY0OOHj1KCKk7N2VkZIirqyvp0KED8fHx4dnG19eX0ssVVQeTzb///ktKSkrIixcvOF50KCkpkcLCQkIIIbq6uuTmzZuEEEIKCgooXw1+8DtfmZ63b968If379ycsFotIS0sTaWlpIiUlRQYMGEB5jSQnJ5OkpCTaz2oKDh48SFq0aEGGDh1KpKWlydChQ0nHjh2JiooKrca9qL4OrVu35qlF/eTJE6Kjo0PbXlNTkzx48IAQUqdBHRcXRwip8+YR5KEiqr9AcnIykZKSIitXruTQqCwvLycBAQGkWbNm5Nq1awL7/u3bN75/e/funcC2hNT5+bDPNVlZWQ4/HzU1Ndr2ojJ58mSBLyakpaWR8ePHU1rh48ePp7R8JYG4dMoLCgpIbm4u1/bc3FzqPiQIcXx3ovDnn38SDQ0NsmbNGo5nZXR0NOnTp4/E9y8KAwYMIDt27CCE1I0XtLS0SNu2bYmsrCz5888/adubm5uT5s2bk8GDB5MTJ06Q6upqrve8e/eOsFgsnu2Dg4PJnj17uLbv2bOHrFu3rpFHIxyCfJx+ZE8nBQUF6vpwcnKivq8XL14QWVnZJuwZPcLqpLOpr5k8evRoyk+huLiYdozCRhS9cD8/P9KzZ0/y5s0boqSkRPLy8sjNmzeJoaGhQG+H+lRWVpKkpCSyZMkS0q1bNyIjI0O6dOlC5s2bR9u2pqaGPHv2jNy4cYNcu3aN40WHqH4sTU3Lli05POMaw4/gnSUKLBaL8kesT2lpKZGWlpbIPocPH075SNYf2/F6CUIc16woyMvLU+f6z0Z5eTnlwVNTU0NCQkKIk5MTmT9/fqN13ZuKnj17khMnThBCCHF1dSWOjo7k5s2bZNKkScTMzIzr/fXPdUEebEzmld++fSNHjhwhgwYNIs2bNyfdunUju3fvJhUVFaSwsJC4ubkRExMTsR4vm4qKCrJ8+XLSvXt30r59e2JgYMDx4oeSkhKRkpIirVu3Jm5ubiQqKorxfa/++Nfd3Z0oKyuTdu3aUdeprq4uUVZWZjQ+VlZWJgUFBYyPtyG5ublk586dZPXq1SQwMJDjRcf79+9Jv379qN+Zff1OmTKFzJ8/v1H9KCkpEcpnNz09nbi5uRFra2tibW1N3NzcqHkyHUlJScTBwYHRPEaS1NbWNno9y9PTk7i4uJBv375Rfi4vXrwgVlZWfNexCPlx/KtSU1OJuro60dTUJPb29sTe3p5oaWkRdXV1cvv2bUIIIXFxcWTDhg0S2f+lS5eItbU1uXDhAuXFfOHCBWJjY0MuXrwokX2y6dGjB3F0dOTwDXr79i1xdHQkvXr1ovrXsWPHRn3uLzmsJkBUrXVeWYpeXl4cWYpr1qxBWVkZV1tRSraBukzb9PR0rtJ2tjaiJEtY09PTMXDgQMjJyVHZgvfu3cOXL19w8eJFWFtbC2zfoUMH7Ny5E/b29hylcDk5OejevTv++usvge3DwsLQrFkzzJ07F5cvX4aTkxMIIaiqqsLmzZvh4+MjsP2CBQtw584dHDlyBB07dsSDBw9QVlaGSZMmYdKkSRLNaBw+fDhSUlLQsmVLoTKL65cRrl+/HsnJyUhKSkJqairGjRuHkpISrjZ9+/bFiRMnoKqqKnIJZ15eHjw8PHDr1i2O7UzP299//53KlnN2doaqqipCQkIQERGBo0ePCtRPpNPKZ+oVIqiSQxA1NTWIiYnhqxfNtHRZWCwtLTF9+nTMnj2bum4MDAwwffp06OjoCJQjEdXXQUlJCadPn+aq2khJSYGzszM+ffoksL2wJZii+guMHTsWqqqq2LlzJ8+/T5s2DZ8+faJK2HkxcuRIHD16lCvzpKysDPb29rTlo6JKjFRWVmLdunV8z7vvlaH5PRGXTrmdnR08PDy4vuN9+/YhKioKV69eFXvfxYmpqSmCg4Ph4uLC8azMzMxEnz59Gp9t8x3R0NDAtWvXYGZmhqioKGzdupUjO5ctD8aP1atXw8PDg1H2Oy/09fVx4MABroz8O3fuYNy4cWLNBP5fo2vXrujbty+GDBkCBwcHpKWloXPnzkhLS8OoUaNofSlqamoQFhbGt2qRThpIFIiIOumWlpbw8vLC8OHDYW5ujgsXLqB79+64f/8+5QvGC3FpvX/79g2zZ89GTEwMampq0Lx5c9TU1GD8+PGIiYlBs2bNBPb/jz/+wMOHD2FiYkJ5g/Tu3Zuq4BREWloaxo8fjxcvXnBlmTIZ34nqxyIu8vPzsWXLFuoeY2pqCh8fH1p/lEWLFkFRUZFR5UZDRB1jhYSEoFWrVvDw8ODYvnfvXqpyVRKwZTZcXFwQGxvLkUlbU1ODK1eu4NKlSzwz6kVlypQpiIiIgJKSEq2cl6BKF2Gv2YZkZ2fzvF/RqSOMGDEC48aNk7jE5S94k5SUhMrKSowYMQLPnz/H0KFDkZubC3V1dRw6dIiq9Gfz4sUL6OrqgsViiTSvFFXCTlRcXV1x7do1TJw4kad8H7+1kJ07d6Jv375Ce92xWbRoET58+IDIyEjquVRTU4NZs2ZBWVkZGzduFNje3d0dXbp0ga+vb6P3vXv3bsycORMaGhrQ1tbmkqCjk5CbNGkS3r59i6ioKJiYmFDPqqSkJMyfPx9ZWVkC29fW1lIy3ux7u5KSEvz8/LBs2TKea3zipL5/l7y8PNcYQ5JjLKCuwjksLAx5eXkA6rL/582bBy8vL9q2f//9N0aNGoX09HR8+vQJrVu3RmlpKbp3745z587xVXf4UfyrgDofnf3793OsJY0fP56qSJMk5ubmiIyM5KqsvXHjBqZNm0Y7txKFZ8+eYdiwYSgsLOTwbTM0NERiYiI6duyIkydP4tOnT42Sg/0lh9UE7Ny5E76+vhg3bhxPrXWgruwpKiqKq62FhQVycnLg4OCAPXv2UMZc9XF1deX7EBJ1Ai4nJ4fU1FSuIEhqamqjPTYai6+vL5ydnbF7927Km6K6uhpeXl6YN28erl+/LrD9q1eveEpC1dbWoqqqitH+2fTv3x85OTm4f/8+OnTowKh0NTg4GLNnz0a7du1QU1MDU1NTapK5fPlyrveL0yBaVVUVw4cPZ/x5DSGEUIOpy5cvY+jQoQDqSiP5LYjVL9sUtYRz8uTJaN68Oc6cOcNz0EWHj48PZbS3cuVKODo6Yv/+/ZCWlkZMTIzAtsIaojekU6dOXFqOTPDx8aFM7c3NzUWSSBKG/Px8Si5MWloalZWVYLFY8PX1Rb9+/QQGQdTV1ZGYmMjl65CYmEiVQwpi+PDhmDJlCkJDQzkMnv39/RmVRwtbgsme+Orr6wvlL3D37l3Ex8fz/fvEiRNpTdSKi4vh5eXFYfL15s0b9OvXjypxF4SoEiNeXl4CJzpMeffuHYePD5OyYzY1NTU4efIkNbgyMzODs7Mz7YKcsLAD0fr6+iLplD98+JAqNa9Pt27dMGfOHJH6+D0oLCykpPvqIyMjg8rKSq7tLVu2RG5uLjQ0NGj16iU9Sfr8+TM1Ibh48SJGjBgBKSkpdOvWjXbhoaqqCjExMRg1apTQQZDS0lLo6OhwbdfU1PwpzF6bkvXr12P48OHYuHEj3N3dKV3zU6dOUfd/QQQGBiIqKgp+fn5Yvnw5li1bhqKiIpw8eZLW5FhUWCwWli1bBn9/f6F00lesWIHx48fD19cX9vb2lLTRxYsXeV6LbBrqzrMlChsGyenu39LS0ti9ezcCAgKQmZmJiooKWFlZCfTyqE9OTg4UFBSocY6JiQmjAAgAzJgxAzY2Njh79qxQz5pnz56hd+/eXNtVVFTw8ePHRn2WsCQlJcHZ2RldunThkBkxMzPD6dOnBcpsff36Fbt27cLly5cbHbwSdYy1c+dOnjK9ZmZmGDdunMSCIGyZDRaLxTVWadGiBfT19REaGiqRfdcPbIgi5yXsNcumoKAAw4cPx5MnTzhkRtjnP13wb8iQIfD390d2djYsLCy4zhteQRRx+sn816lvUNyhQwfk5OTgw4cPfL/j+nPJFy9e4I8//uDy2ayursatW7cEzjtFlbATlfPnz+Ps2bM8x7iCmD59ulj2v3fvXty8eZNjHtCsWTPMnz8ff/zxB20QxMjICEFBQUhNTW20wfaaNWuwdu1aoe+LFy9eRFJSEtq2bcvVJ7rxKQAsW7YMe/bswbp166jv/+bNm1i1ahW+fv2KtWvXcrXhJQHFD16SQ/WpL8X4vVmxYgU2b94Mb29vDulHX19fFBcXIygoSGB7FRUVXLp0CTdv3uTwc6Hz/qo/Lxw7dqzE1zoFoaSkhBkzZjTJvvPz86Gqqsq1XUVFhdZUXlSMjY2RnZ2NixcvcgSABgwYQAX+hJJ3FEOVyi+E5NOnT5QM1adPnxi1CQoKIi9fvpRwz/gTEhJCZGVlibe3N4mPjyfx8fFkzpw5RF5enoSEhPBsc+XKFWJiYkKVH9fn48ePxNTUlFHJu6ysLHn69CnX9qysLEalx9bW1iQ+Pp4QwilHFRgYSHr27EnbXlwUFxeTs2fPkkOHDvGUTGHTULpCWVmZyMvLU2V4CgoKRFlZ+btIWvTt25dMmjSJxMXFkRYtWpC8vDxCCCFXr14lenp6jD8nLy+PXLhwgXz+/JkQQhiXM8rLy/P87YWlsrKS3L9/n5GsECF18klz5syhShC9vb0Zl/FWV1eTqKgo4urqSuzt7Unfvn05XnSoq6uTs2fPMtqXJGjTpg15/PgxIaROEu7AgQOEEEJu3bpFlJWVBbaNjo4mzZo1I0OHDiWrV68mq1evJkOHDiXNmzcn0dHRtPuurKwkM2fOJDIyMkRKSopISUkRaWlpMnPmTEpqTZJ8/vyZVFZWUv8vKioiYWFhtNJlcnJyAsuUS0pKaOVd3r59Szp16kTJxb169Yp07NiRjB49mtTU1DDqf3V1NTl69Cj13R8/fpynvA8vVFRUKNk4YaioqCBTpkwhzZo1o8r8mzdvTjw8PDi+U37k5eWRjh07ctzz5OXlibGxsdDSIUwpLi7m+P3u3LlDfHx8yM6dOxm1V1ZW5llenp6eTkleCkJfX59LYoCJ3IC4MDExISdPniSEcD4rIyIiiJWVFdf7Y2JiyNevXwkhddd8TEwM35eksbCwIOHh4aS4uJgoKyuTW7duEULqvvtWrVrRtm/dujXJzs4Wev8dOnSgxhn1iYuL+y6/3c9OdXU1lxxKYWEhT8mchhgaGpIzZ84QQurOW/Z9Ijw8nLi6uoq/s3woLi4mxcXFjW735s0b8uDBA477+507d8Q69mGCMFITtbW1JCMjg4SHh5MRI0YQDQ0N0rp1a+Lq6kp27dolsK28vDw1phQGAwMDcunSJUII5/0qNjZWYpIwDenSpQtZtGgR1/ZFixbxvGfWRxTJOlHHWDIyMjylYdiyt5JGX1+f8Tj8R0SUa3bo0KFk2LBh5N27d0RRUZFkZ2eTGzduEFtbW3L9+nXa9sJITNd/FoeGhhI1NTUybtw4Eh4eTsLDw8m4ceOImpoa2bx5M/Mv4ReNRkpKiucz7f379wLlsL59+0amTJkikpyTqOjr64s0RhIVVVVVanxan5MnTxJVVVXa9vr6+nxfdGM0JSUlkSToFBUVqXWf+s+qe/fukZYtW9K219HRIYmJiVzbT548SVq3bs2zDft+IOjVGFn6pkJDQ4Naf6jPgQMHiLq6ehP06PsTFxdHevToQXR0dEhRUREhhJDNmzfzvB7ETa9evThk3Ampk610cHAgvXv3lvj+JcGvIMhPxLdv34ihoaHID5+4uDjyxx9/cFxEYWFhjC+iQ4cOkT/++IPSlv/jjz/IoUOH+L7fyclJ4IAqPDycuLi40O5XS0uL5+LjhQsXiJaWFm37kydPEhUVFbJu3ToiLy9PNm7cSLy8vIi0tDRjPbvLly+TJUuWEE9PTzJlyhSOFx2BgYE8F/8+f/5MqyUZGhpKnJycOBYHPnz4QIYNG0Y2bdrEqO+ikJGRQczNzYmysjKHPvScOXMYLS6IqoNpY2NDbty4IfwBiMCFCxeItLQ0sbW1pTxMbG1tiYyMDKPzZvbs2URBQYGMGTOG+Pj4kHnz5nG86NDR0SHPnj0Tx6EIhaurKwkNDSWE1AVhNTU1iZeXF9HT06PVTCZEPL4OFRUVVMBYmOBHZWUlefr0aaM9kIT1F+Cnc82mtLSU0YCzuLiY6OrqEl9fX2JkZETGjh3LOIiRl5dHjIyMhA4iiDrRmTZtGjE0NCTnzp2j9EPPnj1L2rdvT2bMmEHbftCgQcTR0ZGUl5dT296/f08cHR3J4MGDhe4XE3r27En5x7A18rt37040NDQY6f4OHTqUjB49muO3qq6uJiNHjiSOjo607bds2cLx2rhxIxk/fjxp2bIl32QDXvz7778kJyeHVFVVMW5DCCG7d+8mbdq0IQkJCURBQYEcPHiQrFmzhvq3sLCD35LkyJEjpEWLFpTvEpvg4GBG3/3atWuJu7t7o78zNuvXryfq6upk7969pKioiBQVFZE9e/YQdXV1EhwcLNRn/iwkJyc36f7l5eUpjX5tbW1y//59Qkjdgi5dwF5UqqqqyPLly4mysjK1sKCsrEyWLVsm0N/pRyIqKoqYmZlR3mVmZmZk9+7djf6c2tpacu/ePeLu7k6aN29O+6zr27cvOX/+vLDdFtmPpXfv3iQ2Nlak+5OMjAzPpKZnz55JPJggyhhL1KCtOD0pvnz5IrbPYkppaSmZMGEC0dHRIc2aNeNaHJQk6urq1DhUWVmZ5OTkEELqEge7dOki0X0T8vP7yfzMsFgsDn17Ns+ePaP1gxXV00JU4uPjyahRoxglM0kCX19foq6uTkJDQ8mNGzfIjRs3yKZNm4iGhkajPEaFwcPDg5oTCsOgQYMoX122L0VNTQ0ZPXo0GTlyJG17GRkZnusBOTk5fBPrrl69yvjFBFGS60RBRUWF7zNWRUWF0WeIsoZXXV1NNm7cSH7//XfSqlWr7+6zWd+rUVZW9rt7Nebm5hJzc3MiLS1N2rdvT9q3b0+NE0VJYuFHeHg4NSZgB+n5vYTlVxDkJ0PULEVRDE+rqqpIYGBgo42YdHV1Bfb56dOnpF27drSf4+3tTdq2bUsSEhKoTLuDBw+Stm3bCjQ1qs/169dJ//79iaamJpGTkyM9evRgbEi9atUqIiUlRWxtbcmwYcOIi4sLx4sOYTM/CBHOINrKyooKmrCNnfi9hOXLly+MJvgTJ04kAwcOJCUlJRzZDxcuXCCmpqa07a9cuUK6d+9OUlJSyPv376lFVfaLDn6G7PPnzydLly4le/fu5VhsrY8oGX6EiF7JIaqpvagIa2D4I2QsvX37lgwePJhv9gsd6urq1HW3e/duYmlpSWpqasjhw4dJp06d+LZjsVhk7dq1fB/Ya9asYTzBfvbsGdHS0iJubm6NOgdEDSKIOtFRV1cnKSkpXNuTk5OJhoYGbXt5eXmqAqk+jx49EmhqLw5UVVWpBYnw8HDyxx9/EELqTAGZLAxlZWURdXV10r59e8rMsX379kRTU5M8efJE6H5t27aNkfFjZWUl8fDwIM2aNSPNmjWj7rdz5sxhHETZt28f6dChA5VZ2qZNGxIVFUXbztvbm+f2ioqK72aqLkp2rouLC1FSUiI6OjrEwcGhUUa5hNQtAC9cuJDIyspS9xl5eXlGwbOfHWlpaWJoaEhWr14tVCUEIXVBrNGjR5OuXbs2epzSsWNHavG3R48e1LmekJBANDU1heoPU2bMmEG0tLRIZGQkFWSPjIwk2trajIK+hNRlg/r7+5OxY8c2+rwTlYCAAKKgoEAWL15MEhMTSWJiIlm8eDFRVFQkAQEBtO3v379PJeuoqamR5s2bEysrK+Lr68szwap+MsLx48eJqakpiY6OJunp6Y1OVqitraWCtOz7laysLLXYRIePjw/R1NQkysrKxMvLizIZbQxt27Ylhw8f5tp+6NAhRvMbQhpfKS2OMZaoQVsWi0X69OlD4uPjhQpi1NTUkKCgINK6dWuOZ9Xy5csZPW9ExdHRkZiampI///yTnDhxgpw8eZLjRYco16yqqir12xkaGlJB5OfPn38XY3UFBQWei1d5eXkSH2P9V2GfG1JSUmTw4MEc54uzszPR19cnAwcOFPgZkyZN+u6VOg3XEJSUlIiioiIxNzcX23oCU2pqasj69etJ69atqft969atyfr16yW+GB8cHEw0NDSIu7s72bRpU6MXY588eUK0tLSIo6MjkZaWJqNGjSImJiakVatWjJLTbG1teY6x58yZQ7p27SrUMTUGUZPrRGHOnDk8g1x+fn5k1qxZtO1FXcMLCAggOjo6ZNOmTURWVpasXr2aeHp6EnV1dZEW4pliYmJCTpw4QQjhrCJ68uTJd6uEqa2tJUlJSdT5fvHiRYmtTenr65P3799T/xa2eksQv4zRfzKCg4ORm5uLqKgoLi1JJohqeKqoqIjMzEzo6+sz3qesrCwyMzN5+nEAdQZ+FhYW+PLli8DP+fbtG/z9/REZGUl5qbRo0QIzZ87EunXrhNZvZ4qOjg42bNjQKNOd+khJSaGsrIxLEz85ORljx47Fu3fv+LYVxiA6MDAQ/v7+kJeXx6pVqwTqwErSlB0AtLW1kZSUhM6dO3OcdwUFBbC0tKQ1b2Rr/jU8BsLQGL1v37548OABampqKEPy3NxcNGvWDJ06dcKzZ8/AYrFw8+ZNmJqacrSVlZXFkydPuLSxc3NzYWlpia9fvwrcd+vWrXH16lWhzeBENbVvSlRUVPDo0SMYGBgwbjNixAjExMRAWVmZ1veD7tjd3Nzw4sULbNmyBX369MGJEydQVlZGGcuxvU74IS8vj5ycHOjq6mLMmDEwMzPDypUrUVJSAmNjY8oAtyH6+vqMdJcbejTx0xL+/PkzZGRkODRw6fSaFRQUkJaWBgsLC47tGRkZ6NGjB+01Z2Vlhfz8fBBCoK+vz3Xe0RkAysvL4/79+zAxMeHYnpWVBVtbW57eEvVp2bIlzpw5w2UwnZqaCicnJ57HHxERIfAz6yNI97f+c87Z2Rk9evTAokWLUFxcDGNjY9pnFQC8fv0a27ZtQ0ZGBuTk5GBpaYk5c+agZcuWjPvYkIKCAnTp0oVW49fHxwepqanYsmULHB0d8fjxY8pAbtWqVVw+AoL4/PkzKioqoKWlxej97du3x4QJEzi8giorK+Ho6AigzkRPkkRHR2PcuHGQk5MTqr0oRrn1qaiowNOnTyEnJwcjIyOJj09+BN6/f4/4+HjExsYiKysL/fr1g6enJ1xcXBiZRkZERGDZsmWYPHkydu3ahSlTpiA/Px/37t3D7Nmzeepd12fx4sVQVlbG0qVLcejQIUyYMAH6+vooLi6Gr68v1q1bJ65D5UJFRQUJCQkYNGgQx/Zz587B1dUVf//9t8D2CQkJmDRpEgYOHIiLFy/CwcEBubm5KCsrw/Dhw0XyLmCCpqYmIiIiuLyyDh48CG9vb9q5QfPmzWFlZQU7OzvKFL2+2XVDpKSkOHwQGsL+G5PxHZtv374J5ccC1Gnxnzp1CrGxsTh//jw6dOgADw8PTJw4Ea1ataJtHxQUhLCwMCxevJh6ZqWmpmL9+vWYP3++QNPz8vJyjBkzBikpKWCxWMjLy4OhoSE8PDygpqYm0BtDmDFWfQghWLx4MSIiIihjbllZWSxatIiRj86jR48QHR2NgwcP4tu3bxg7diw8PT0ZefgAdd9bbGwsgoKCMHXqVGRmZsLQ0BCHDh3Cli1bcPv2baGOiylKSkq4ceMG5aXTGES9Znv16gU/Pz+4uLhg/Pjx+Ouvv7B8+XLs2rUL9+/f5/L14cW1a9ewadMmyjfN1NQU/v7+6NWrF21bPT09zJ07l8tPJjQ0FBEREYw8CoTl77//Rk1NDdd46MOHD2jevDmtN8HPCnt8ERsbizFjxnCMU9gmy1OnToWGhgbfz2DPX+zt7RvtaSEsgrwfGyLp9YT6sMfCdOfL/PnzsXr1aigoKND6rQryYBJ0n2WxWCgoKBD42UDduc+eG7B9KWbPns3TS64h165dw5AhQ6Crq8vhi1FSUoJz584xuu5FYfDgwSCEYP/+/dS1W15ejgkTJkBKSgpnz54V6/7q/1bV1dWIiYmBrq4uunXrBqDOI7S4uBiTJk3C1q1bBX6WqGt47du3R0REBIYMGQIlJSU8evSI2paWlsbTW0ucyMnJIScnB3p6ehzraHl5ebC0tGQ0LxWWqqoqyMnJ4dGjRzA3N2fUxsrKirH/FN16gqT4ZYz+k3Hv3j1cuXIFFy9ehIWFBdfDj25RsLGGpw2xt7fHtWvXGhUEadOmjcAgyOPHjxnd/KWlpREeHo6QkBDk5+cDqLspycvLM+rHvXv3UFtbi65du3Jsv3PnDpo1awYbGxuB7b99+8a1IMcE9sImi8VCx44dOW4KNTU1qKiooDU6EsYguv5AZNWqVXw/m0kctKamBmFhYTh8+DCKi4upyRIbugXZyspKnr/Thw8fGC0OiWrwNmzYMLRs2RLR0dHUYOnvv/+Gl5cXevbsialTp1IGh0lJSRxtNTU18ejRI64gyKNHjxgtDPr5+SE8PBzbtm0TylxaVFN7YRCXkZqLiwtOnjwJX19fxp+noqJCfU+CFlGYkJycjMTERNjY2EBKSgp6enoYMGAAlJWVERISQhsE6dChA06ePInhw4cjKSmJOo63b98KPG5hTcLEaTonIyPDMzhaUVHBaEFSKJOxenTv3h0rV65EXFwcZST35csXBAYGUoN3QQwdOhTTpk3Dnj17OO55M2bM4Gn4CQBhYWGM+sZisQROFM3MzBAZGYkhQ4bg0qVLWL16NYC6wAYTs1mgLvgZHBzM6L1MOXr0KKMgysmTJ3Ho0CF069aN455jZmZGPTsFUVhYiOrqahgZGUFeXp66d+fl5VGmtfy4ePEievXqBTU1NcybNw+fPn3CwIED0bx5c5w/f57+IEVk8eLF8PHxwejRo+Hp6dnoZ7a4FpsVFRXx+++/i+WzfhY0NDTg6+sLX19fPHjwANHR0Zg1axZmzZqF8ePHw9PTkzI758Wff/6JXbt2wdXVFTExMVi4cCEMDQ2xYsUKRia99YMcY8eOha6uLm7fvg0jIyM4OTmJ5Rj5ISMjw/O6MDAwYHS/DQ4ORlhYGGbPng0lJSWEh4fDwMAA06dPZzQ+FpWqqiqeY+DffvuNSjoSxIcPHxq1cNkwAUAU6i+o1k9iacyCavPmzTFixAiMGDECb9++xa5duxAQEIClS5di8ODBmDt3Lvr168e3fUBAAJSUlBAaGoolS5YAqHsGrFq1inZR0tfXFy1atEBxcTFH0sDYsWMxf/58gUEQYcZY9WGxWFi/fj0CAgKECtp26dIF4eHhCA0NxalTpxATE4OePXuiY8eOVBCpYeJXfeLi4rBr1y7Y29tzzIM6d+6MnJwcoY6pMbRr147RHIgXol6zy5cvp+bdQUFBGDp0KHr16gV1dXUcOnSItv2+ffswZcoUjBgxgjrHUlNTYW9vj5iYGIwfP15g+8DAQHh5eeHq1avUvPjOnTu4cOECdu/eTbt/URg3bhycnJwwa9Ysju2HDx/GqVOncO7cOYnuv6lgjy/09fWxYMECrjUcJuzZsweqqqq4f/8+7t+/z/E3urGtsHzPwEZjYPrMefjwIaqqqqh/84Nuji6O55aKigqWLVsmVFs7Ozvk5uZi+/bt1P1xxIgRmDVrFlq3bi1y3+i4du0a0tLSOOYh6urqHEbt4qThb/Xbb78BADWP0dDQgIaGBrKysmg/S9g1PDalpaVUUqGioiKV2DJ06FCBSQ7iwsDAAI8ePYKenh7H9gsXLnAlG4qbFi1aQFdXl3FCCsC5hvD161f8+eefMDU1peb/aWlpyMrK4noGfFdEK1b5xfeGLa3B70VHYw1PG7Jjxw6ira1N/Pz8yIEDB6iyefaLF3PmzCHm5uY8S6U/f/5MzM3N+UpoiJPff/+dHDlyhGv7sWPHiK2tLW37hQsXkqCgoEbvNyYmhkRHRxMWi0XCw8M5jOkOHDhAGbcKQlSD6A0bNvDcXl1dTcaNG0fbXtQyQFF1MEWldevWJCsri2t7ZmYmZSZ2//59niWFgYGBRFVVlaxbt45cv36dXL9+nYSEhBBVVVVG54OLiwtRUVEhBgYGZOjQod9d5kIYxGWktnr1aqKqqkpGjhxJgoODxabjyBQlJSVSWFhICKmT5WMbfRcUFDCSGxDVX6ApmThxIjEzMyNpaWmU0e3t27eJubk5cXd3l/j+nzx5Qlq3bk3U1dVJv379SL9+/Yi6ujpp06YNT2m/hvz111/E2dmZsFgsSqNeSkqKuLi4kI8fP0q07ykpKURVVZVISUlxaMUuWbKE7zWbkZFByS81lHNprLxLQ+mBLl26EG1tbdKsWTNG5uz1pS7rP+cfPXrEyBuhd+/ePE3M4+PjiZ2dHW37jIwM0rJlSxIeHk66detG7OzshPLyEYaqqipy/Phx4uzsTFq0aEGMjY3JunXryJs3b77L/n/xf7x69YqsXLmSyMjIEAUFBdKsWTPSs2dPvte/nJwc5VOnqalJHj16RAip0yJmYhralAQGBhJXV1fy9etXatvXr1+Jm5sbh48aP+Tl5alnVcuWLSkpwOzsbKKtrS2RPtdHVKkJNunp6SQ+Pp7Ex8dTniySxtHRkWzfvp1r+44dO8igQYMa9Vl37twhM2bMIKqqqkRXV5esWLGCeHp6Ejk5OeLn58foM/755x/yzz//MN5nq1atqHO9/v06Pz+fVpaoqcdYDfn69SvZvHkzkZGRISwWi8jIyJCJEyeS169f83y/rKwsdc3XP/asrKzvIsmUlJREHBwcqGuvMUjimi0vL2csL9KpUyeeskihoaEC5VrrIw7PPmFQU1PjKZP99OnTH/5e/4tfCMvevXt5yiYePnyY55i7IS9evOB7f2D7oUkSNTU1kpqayrX95s2b38UXQxSEXcNj05Ryq4RIzquRKVFRUWTw4MF8peMF4enpyVOedMWKFYz9WKKiooirqyuxt7cnffv25XgJyy85rP8YUVFRWLVqFUJDQ+Hp6YmoqCjk5+cjJCQEUVFRGDdunMD2bFkiXvArWy8rK4O1tTWaNWuGOXPmUHJEOTk52L59O2pqavDgwQPakvOvX79i69atSElJwdu3b1FbW8vxd7pyKkVFRUoWpD6FhYWwtLTkmTVdHx8fH8TFxcHS0hKWlpZc8jCCSiiBugj6H3/8wdWOjpqaGqSmpsLCwgLS0tIcVTBMs0i0tLQQEhICT09Pjs8dN24cMjMzqVJqfohaBpiZmQl7e3tYW1sjOTkZzs7OyMrKwocPH5Camor27dsLbH/9+nWBf+/du7fAvysqKuLMmTNccmJXr16Fk5MTPn36xFdqhhCCLVu2IDQ0FK9fvwZQl+Hn7++PuXPn0maOiEte5Xty7do1xu+1s7Pj+zdRS4cPHjzIJc/Bxt/fHxs3bhTY/vfff8eaNWswcOBAODs7Q1VVFSEhIYiIiMDRo0cZZcWXlpbizZs36Ny5M3X/u3v3LpSVldGpUyeebZjKMkkiY4vNx48f4e7ujtOnT1P3nOrqajg7OyMmJkbkKhsmfP78Gfv376cylkxMTODm5tYoqaK8vDw8ffoULBYLJiYmfCsKxU1NTQ3++ecfqKmpUduKioogLy/PswJMSkoKpaWl0NLSEij1wkTepaH0gJSUFDQ1NdGnTx++51x9evfujdGjR8Pb2xtKSkp4/PgxDAwM4O3tjby8PFy4cEFge2VlZTx48IDru37+/DlsbGzw8eNH2j7cvn0bAwYMQNeuXXHmzBmh5alEoaysDPv27UNsbCxycnLg6OgIT09PODk5CRzLHD16lG/VY1OVbf8sVFVVITExEXv37sWlS5dgY2MDT09PuLq64t27d1i+fDkePHiA7OxsrraGhoY4duwYrKysYGNjg6lTp2L69Om4ePEixo0bx6gaJC8vj+8YkYm8T2NoWIV7+fJlyMjIUNUuGRkZ+PbtG+zt7WmrtNu2bYvz58/DwsIClpaWWLJkCVxdXXH79m04OjrSymkJgzilJt6+fYuxY8fi2rVrUFVVBVD3DOrbty8SEhIEVgOEhISgVatW8PDw4Ni+d+9evHv3DosWLRK475YtWyI1NZUrGzInJwc9evRAeXk5bd/j4+MRHR2NvLw8ODk5wcvLCwMHDqTGdzdv3oSjoyOtjKQwKCkp4cGDBzAyMuKQuUhPT8fAgQMF9l8c8iziID09HXv37kVCQgIUFBTg7u4OT09PvHz5EoGBgfjnn39w9+5drna//fYbfH19MWHCBI5jDwoKwqVLlyQin9hQdrSyshLV1dWQl5fnmp8Juuc0xTVbHxkZGWRlZfF8TpubmwuU6q2qqsL06dMREBAgtJSaKPCTa33y5Am6du3KV2r2Z+ZnkIf5r1FSUgKgriKMH+KS0gKAjh07YufOnejbty/H9mvXrmHatGl49uyZwPbNmjXDmzdvuOYg5eXl0NLSalSmvjBMmjQJDx484KrQnzp1Kn777TfExMRIdP+Npf7vVVtbi9jYWKHX8JpSbpXN/v37sWrVKmrtonXr1ggMDORY25MUVlZWeP78OaqqqqCnp8e1/ijonqWiooL09HQuRZW8vDzY2NjQPivnzJmDmJgYDBkyBDo6Olz3UaYqEA35JYf1E1JdXY2rV68iPz8f48ePh5KSEl6/fg1lZWVaDVwvLy/Iyclh+fLl+Pz5M8aPH4/WrVsjPDycNgACgGtSyYRWrVrh1q1bmDlzJpYsWUItDLFYLAwcOBDbt29npLnr6emJixcvYtSoUbC1tW20tJCMjAzKysq4giBv3rxh5K/y+PFjSje2oVYrk77UXyz++vUr1+IKv7LOZs2awcHBAU+fPoWBgQEsLS1p99WQs2fPwsHBASoqKhg1ahSqq6sxZswY5OTkMJKaErUM0NzcHLm5udi2bRuUlJRQUVGBESNGMNbBbBi8AMAlKyaIYcOGwcPDA6GhoZREyb1797BgwQKqZO/u3bs8fTtYLBYl8cEOlCkpKdH2mY2oQQ4DAwOB55ckJrmCAhuNQdTS4ZkzZ0JVVZVLZ93X1xcJCQm0QRAfHx+8efMGQF05t6OjI/bv3w9paWlGgzW2v4C2tjbHdjq9ayYPZEmVrbNRVVVFYmKi0EEE9kI+P5gMtuXl5TF16lTGfeaFkZER1efG3vNfvnyJU6dO8VzMFjTgXblyJTw8PLjKjgXJQBUWFlILfaKe96JKDwQHB2PQoEHIzs5GdXU1wsPDkZ2djVu3bjEKcLJYLJ5JAWzZmYbwm9zLyMjg9evXHGXy33Ny36pVK/Ts2RO5ubnIzc3FkydP4O7uDjU1NURHR/N8rtT3pUhMTOTypfgFf7y9vXHw4EEQQjBx4kRs2LCBQz9YQUEBmzZt4ivb0K9fP5w6dQpWVlaYMmUKfH19cfToUaSnp9P6QwHA7t27MXPmTGhoaEBbW5vjnGSxWGIPgjQMJI8cOZLj/4IWVhrSu3dvXLp0CRYWFhg9ejR8fHyQnJyMS5cuwd7eXiz9bYg4pSa8vb1RUVGBrKwsKhiRnZ0Nd3d3zJ07FwcPHuTbdufOnTwTaczMzDBu3DjaIMi///7LU7KrqqqKkU5227Zt0b59e3h4eGDy5Mk8AzaWlpZc8nbW1ta4cuUK1NTUaBc4Bd33evXqhbi4OEp2kcVioba2Fhs2bOBaLGuIOGXFhGHz5s2Ijo7Gs2fPMHjwYMTFxWHw4MFUkNnAwAAxMTF8n50rVqyAu7s7Xr16hdraWhw/fhzPnj1DXFwczpw5I5E+i0t2VJhrVpyed+3atcOVK1e4xnSXL1+mvfe0aNECx44d+y4yLrywtbXFrl27uIKrkZGR1H3ofw1RJWbrI+zY9hd1a2iBgYGIiIiggtqKiorw9vbGypUruRbH60tpPXjwgO99nsn8pLi4mGfQUU9PD8XFxbTtyf/3yWpIRUUFJTssSSIiIuDu7o7u3btzJdeFh4dLfP+NpeEYR5Q1vKaUW2Xj5uYGNze3Rns1igNR7l9ycnJITU3lCoKkpqYyOm8TEhJw+PBhDB48WOg+8OJXJchPxosXL+Do6Iji4mL8+++/yM3NhaGhIXx8fPDvv/8iMjKS8Wc1xUX0119/4fnz5yCEwMjIiCPLlg4VFRWcO3dOaN1BV1dXvHnzBomJidTk9ePHj3BxcYGWlhYOHz4s1Ocy5fPnz1i4cCEOHz7MM7NL0KKijY0N1q9fL9JkODk5GS4uLti3bx/27NmD58+fIzk5mVEAytjYGHFxcejatSt69uyJoUOHYvHixTh06BC8vb3x9u1bofvFhIZR4qqqKjx8+BABAQFYu3Yt7fdSUVEBX19fxMXFUZPl5s2bw93dHWFhYVBQUMCjR48AgK9B4tu3b6ksjU6dOgnMbBQnDQcW7GO/cOEC/P39sXjxYonuX9QqHFE4e/Ys3NzccObMGfTs2RNA3WLL8ePHceXKFUZZ8fX5/PkzZXQuyHiQTatWrfDlyxeh/QV+FOoHnpmSmJjI8X/2eRcbG8s48+T169e4efMmz6xsJgGgPXv2ICwsDHl5eQDqAiLz5s2Dl5cXbdsrV67A2dkZhoaGyMnJgbm5OYqKikAIoSrS+NGlSxdkZmbCzs4Onp6eGDlyZKOMra9fv44//viDK7heXV2NW7duMb5m3r59y/O7YxIIz8/Px7p16zjMFxctWsSVeckLJycnyMnJ4eDBg2jWrBmAuufT2LFjUVlZyeXt8aOZZpaVlVGZ3QUFBXBxcYGnpyf69++PyspKBAUFISEhgafxa6dOnbBy5Uq4urpyZCazfSm2bdsm8f7/rNjb28PLywsjRozge71UV1cjNTWVZ6C9trYWtbW11HWTkJCAW7duwcjICNOnT6f11tDT08OsWbNoF81/RD58+ICvX7+idevW1AI4+9iXL1/eqLFyU6CiooLLly9zBQru3r0LBwcHgdVjsrKyVJJPfQoKCmBqaiowox0A+vbtC3Nzc64F1dmzZ+Px48e01QQ3btwQylA2MDAQ/v7+kJeXp70HCrrviVop3ZQYGRlRwSN+CU3fvn3DwYMH4e7uzvPvN27cQFBQEMezasWKFXBwcJBk10VGmGt2ypQpiIiIgJKSEiZPnixwTEaXQLVjxw7MmzcPHh4e1Ng0NTUVMTExCA8Px/Tp0wW2d3d3R5cuXYT2kxGF1NRU9O/fH7///js1f7ty5Qru3btH+Yr9gjeijG1/dqqqquDo6IjIyEiuBVWmzJw5E8ePH0dQUBCHufiqVavg4uKCHTt2iLPLHOjq6mLbtm1cvoaJiYmYPXs2Xr58ybMdu6IhPDwcU6dO5fBYrampobxtU1NTudpKogIpLy+Po8L/e1Xo/+LnZN26dQgMDMTUqVM5Koj27t2LgIAA2nWs1q1b4+rVqzwTlUXhVxDkJ8PFxQVKSkrYs2cP1NXVqQn61atXMXXqVGqxSJJUVlbi2rVrPDMQJJnZbGpqioSEBKEqIQDg1atX6N27N8rLyylz+EePHqFVq1a4dOkS46y958+fIz8/H71794acnBzfyHxDZs+ejZSUFKxevRoTJ07E9u3b8erVK+zcuRPr1q2Dm5sb37YXLlzAkiVLsHr1avz2229cZWhMzcFOnjyJ0aNHw8TEBMnJyYwWggHxlAF+/PgRd+/e5bmoN2nSJEb9aMi1a9cwf/58LnM4flRUVFCVE4aGhrSVUwDw6dMnzJo1CwcPHqT63axZM4wdOxbbt2/nKSskrgxBQWzfvh3p6ekSl9PiJRvTmCocUTOWDhw4gDlz5uDSpUvYs2cPEhMTkZKS0qiH4bdv31BYWIj27dszqvpiU11djdOnTyMmJgbnz5+HoaEhpkyZAnd3d67qkPps27YNEydO/C6SU4IQJYjAjwMHDuDQoUNcQZKGxMTEUAuX6urqXFnZdBVMK1aswObNm+Ht7c0xUdm2bRt8fX0RFBQksL2trS0GDRqEwMBAajFbS0sLbm5ucHR0xMyZMwW2f/jwIaKjo3Hw4EFUV1dj3Lhx8PDwYGR2LWrJ+v379+Hu7o6nT59ySWoxkdMSlezsbPTu3RuqqqrUYsSNGzfwzz//IDk5mSO7/0fDyckJSUlJ6NixI7y8vDBp0iQuM/m3b99CW1ubZ2WrvLw8nj59Cj09PWhpaeHSpUvo3Lkz8vLy0K1bN1ppHTbZ2dk873kNJ7//KzS1vApQNw569OgRV7XvLySPkpISbty4wZVE8vDhQ9jZ2XHJjNbHyMgIK1euxIQJEzi2x8fHY+XKlbTPCnEsqIpSYS8O/v77b2zbto0jEMC0Urops8KLioqgq6vLNU4khKCkpAS6urp821ZXVyM4OBgeHh5o27atRPspiJqaGpw4cYKSBTY1NcWwYcMaNVZsKk6cOIHQ0FCq7yYmJvD398ewYcNo265ZswahoaGwt7fnOa+U5HweqJt/b9y4EY8ePYKcnBwlKSbs4vZ/BVHHtj87mpqaVLBRGFRUVJCQkMClMHDu3Dm4urryleapqqqCnJwcHj16JPQYeNGiRTh06BCio6OpZKhr167Bw8MDo0aNwqZNm3i2Y1cEXrt2Dd27d+dICJGWloa+vj4WLFjA8zv50ZKUfmbi4+MRGRmJwsJC3L59G3p6etiyZQsMDAwY3XMby/+ShN7hw4cRHh7O8azy8fHBmDFjaNuGhoaioKAA27Zta7QihCB+BUF+MtTV1XHr1i0YGxtzZCkWFRXB1NSUp46mOC+ihw8fYvDgwfj8+TMqKyvRsmVLvH//ntJJl6T+7Pnz5xEREYHIyEgumRKmVFZWYv/+/cjIyKAGXa6urox8OsrLyzFmzBikpKSAxWIhLy8PhoaG8PDwgJqaGkJDQwW219XVRVxcHPr06cOhuR4fH4+DBw/i3LlzfNvWn2DU/y3ZARhei2L8Sq3T0tLQoUMHjgAIXdl1Q27fvt2oMsDTp0/Dzc0NFRUVUFZW5loQZaL1zYucnBzY2NhIRKeZzdixY/Hw4UNs3bqVYzHWx8cHXbp0QUJCAlcbcWUICoKfh4m4EaUKR1wZS3/++Sfmz58PTU1NpKSkMM46+fz5M7y9vREbGwsAVOWct7c32rRp06gqmsb4C6ioqKCqqgouLi7w8vJCv379GO+nIcIGXUUNIvCjoKAAlpaWtNdcu3btMGPGDCxZskSg/wI/NDU1ERERweUJc/DgQXh7e+P9+/cC29f3LlJTU8PNmzdhZmaGjIwMDBs2DEVFRYz6UVVVhdOnTyM6OhpJSUno1KkTPD09MXnyZL5BLikpKZSVlXFVi+Xm5sLGxob2mu3cuTPat2+PRYsWoVWrVly/N93zTxy6wa9fv6YW5djPyjlz5nAFFH40PD094eXlRZ3zvCCEoLi4mOf3KKovRUFBAYYPH44nT55w+MKwf0NJB7CaEhUVFTx69EikIMiNGzewc+dO5Ofn4+jRo2jTpg3i4+NhYGBAVQPyw9PTE7///jtmzJgh9P5FQVQvmfz8fERHRyM/Px/h4eHQ0tLC+fPnoaurCzMzM0l1myI9PZ1v/+nGiMOGDcPHjx9x8OBBSu7s1atXcHNzg5qaGk6cOMG37YYNG7BhwwZs3LiRelZeuXIFCxcuhJ+fH5YsWULbd1EWVMVVYf/x40fKa8zf3x8tW7ak/A7btGnD6DMaS1NnhYv6rFFUVERmZqZAqUlJkpWVBWdnZ5SWllJ+lbm5udDU1MTp06dpFztFuWb79euH48ePUx46bP755x+4uLhI/Lf7Ufxk/ouIIjcrrrFtY6DzwqiPpAOvvr6+kJGREdqDQUtLC9euXePykHr69Cl69+6Nd+/e8W1raGiIEydOUL5fjeXbt2+YOHEijhw5QgVZa2trMWnSJERGRtJWu06ZMgXh4eGME1/FwY/02zclO3bswIoVKzBv3jysXbsWmZmZMDQ0RExMDGJjYxlJyzeW+utHX79+xZ9//glTU1NqfpOWloasrCzMmjULISEhYt9/fWpqahAWFsZ3jCjsOh4Thg8fjpSUFLRs2RJmZmZca7aNXcNk8+OnOfyCg9raWp4Px5cvX/L1Kaiv40Z3EdHh6+sLJycnREZGQkVFBWlpaWjRogUmTJgAHx8f4Q6KITY2Nvj69SsMDQ0bbWDHRkFBAdOmTRNq/76+vmjRogWKi4s5Hp5jx47F/PnzaYMgHz58oDIUlZWVqf727NmTNnNDmJsrvwW6gQMHNvqzGtK9e3eBi0wN8fPzg4eHB4KDgznKOJny+PFjjv8TQvDmzRusW7eOr3yVuDhz5gySkpI4FmAGDhyI3bt3w9HRkWeb+oENSWVWHD169LssSPI6jwYMGABpaWnaKpwlS5ZgwYIFVMbSsWPHODKWeMFvwKWpqQlra2v8+eef1Da6AdeSJUuQkZGBq1evcuyvf//+WLVqVaOCII3xFygtLcWRI0cQHR2NAQMGQFdXl5KMYFpxVl5ejrFjxyI5OZkj6Orp6cko6Lpjxw7s3r2bI4jg7OwMS0tLeHt7CxUE+fLlCyIiIhgt6Hz+/Bnjxo0TKgAC1AUfbGxsuLb/9ttvPPXfG6KgoEAN0nR0dJCfn08tStAFUOpDCEFVVRW+ffsGQgjU1NSwbds2BAQEYPfu3Rg7diz1XnbgmcViYfLkyRySQDU1NXj8+DEjSbWCggIcO3ZM6BJzfrkt//77L+0ki03r1q0RHBzc6H03NJ1lw2KxICsriw4dOmDy5MmYMmVKoz+bCXv27OHa9vHjR46FJhaLxTeQJKovhY+PDwwMDHDlyhUYGBjg7t27KC8vh5+fH98sv/8VXFxccPLkSaHlVY4dO4aJEyfCzc0NDx8+xL///gugLhAfHBwsMFEEADp06ICAgADKcLfhGFGSmc2ieslcu3YNgwYNQo8ePXD9+nWsXbsWWlpayMjIwJ49e3D06FGJ9R2okx6bNGkSBg4ciIsXL8LBwQG5ubkoKyvD8OHDaduzJT709fWpZ1xJSQnMzc2xb98+gW39/f1RXl6OWbNmUfdsWVlZLFq0iFEABKiTMNy/fz+j9zbEx8cHNjY2yMjIgLq6OrV9+PDhjD2tHj9+jP79+0NFRQVFRUWYOnUqWrZsiePHj6O4uBhxcXFc72eKoOp3YcZY4oTfs4apRr29vT2uXbvWZEEQLy8vmJmZIT09nZKv+uuvvzB58mRMmzYNt27d4ttW1Gv26tWrXAtJQN08XRKG8A353n4y//zzD7V4S5cI8j0XeZuChkHhhnKzghDX2LYxNPRW4Ic4s7T5UV1djb179+Ly5cs8K5jo5oVz5szB6tWrER0dTY3R//33X6xduxZz5swR2HbZsmVYunQp4uPjhZp/S0tL49ChQ1izZg0VsLewsGCc2Ctp5Qde/Ei/fVOydetW7N69Gy4uLhwBOBsbGyxYsEAi+6y/fuTl5YW5c+dS3mH131NSUiKR/dcnMDAQUVFR8PPzw/Lly7Fs2TIUFRXh5MmTjPz22EkiBQUFWLBgQaOSRFRVVRmNQxvLr0qQn4yxY8dCRUUFu3btgpKSEh4/fgxNTU0MGzYMurq6tDdILy8v6Ojo8L2I9u7dK7C9qqoq7ty5A2NjY6iqquL27dswMTHBnTt34O7uTmkESoL+/fujuLgYnp6ePLNj+enNigttbW0kJSWhc+fOHFU4TDOjLS0tsXXrVtjZ2aF///7o0qULNm3ahIiICGzYsIGvFqSosMvSNTU1IScnx7jdqVOnGL+XTuJDQUEBT548EVqmgp010/B21a1bN+zdu7fR3hCNQVdXF2fPnuXS0n/8+DEGDx5M+7uVlJSAxWJR5f53797FgQMHYGpqyigg17CSixCC0tJSvHv3Dn/++afQQT1RYVKFI0zGEp0RKBsWi0WbKaenp4dDhw6hW7duHNfs8+fPYW1tzaiKRhR/AaBuQTsmJgZxcXF4+fIl+vfvD09PT7i4uAisQJs0aRLevn2LqKgomJiYUH1PSkrC/Pnzac1qVVVVce/ePa5M2NzcXNja2grUaAe4F7MJIfj06RPk5eWxb98+2mt+4cKFaNmypdCeNd7e3mjRogXXhGbBggX48uULtm/fLrC9i4sLhgwZgqlTp2LBggVITEzE5MmTcfz4caipqeHy5csC29+/f5+Sw5KRkcGkSZPg5eVFBSa2bt2KNWvWoKysjGrDXtiPjY3FmDFjOO637JL1qVOn0soQuri4YOLEiVxGy3REREQAqAvYr169mkPGpaamBtevX0dRURHjSY0whIWFYe3atRg0aBCl/Xr37l1cuHABvr6+KCwsRHx8PLZu3cp4gbExrF+/Hvr6+lRwasyYMTh27Bi0tbVx7tw52gw+UX0pNDQ0kJycDEtLS6ioqODu3bswNjZGcnIy/Pz8JPrdNzWiyqtYWVnB19cXkyZN4rhfP3z4EIMGDUJpaanA9k2Z2Syql0z37t0xevRozJ8/n6P93bt3MWLECImND9lYWlpi+vTpmD17NrV/AwMDTJ8+HTo6OowkNQghuHz5ModWeP/+/Rn3oaKiAk+fPoWcnByMjIwa5cMkCsJU2Dekf//+sLa2xoYNGzg+49atWxg/fjzXWKf+mLbhcxZgLjnaFFnhgGga9fWJjIxEYGAg3NzceN4zJC0fKCcnh/T0dK6qjczMTPz+++/48uUL37bCXrPsAFiXLl2QnJzMsZhaU1ODCxcuYOfOnTx/u5YtWyI3NxcaGhp8Ew7YSDIzVxjqVw3xq4QQpG7wX4CJ3KyoY9ufHUFzRCbzwuHDh+PKlSuQkZGhxoMZGRn49u0bl7JBwwxzKysrPH/+HFVVVdDT0+O6X/3okkRNmc3/syMnJ4ecnBzo6elx3O/z8vJgaWkp8FkhDlRUVJCens41p8/Ly4ONjQ1fGTdx0b59e0RERGDIkCEc446IiAikpaXhwIEDfNs2TBJ59uwZDA0NsXz5cp5JIt+LX5UgPxmhoaEYOHAgZRY4fvx45OXlQUNDAwcPHqRtf+TIEaSnp3NtnzBhAmxsbGiDIC1atKAye7W0tKiqCBUVFYlHIm/duoXbt28LXYYoKpWVlTyrGD58+MBosjZlyhRkZGTAzs4OixcvhpOTE7Zt24aqqirGJYSfP3/m+eASlClGCEGHDh2QlZXVKA3N+hVEgmAyYB04cCDS09OFDoI0zFiSkpKCpqYmo0wzUVm+fDnmz5+P+Ph4ygeitLQU/v7+CAgIoG0/fvx4TJs2DRMnTkRpaSn69+8Pc3Nz7N+/H6WlpbQR9Ia/A/vY+/TpI9HgDxtRqnCEyVgSZ0npu3fvuGQagLprmUnWSn1/galTp3L5CygoKMDPzw8bN27k+xmGhoYICgpCYGAgLl++jJiYGEyePBkKCgp4+/Yt33YXL15EUlISl1a2kZER34BLfSZOnIgdO3Zw3Vt27dol0H+ITVhYGMd3xD7vunbtysikNyQkBEOHDsWFCxd4ZmXzuufVrwJisViIiorCxYsX0a1bNwB1RmrFxcWMPIQ2b95MBegCAwNRUVGBQ4cOwcjIiPZ+a2FhgZycHDg4OGDPnj1wcnKiDMLZuLq6clU/spMQ2Pq8DSdITImKioK7uzsyMzNhbm7O9d3xWxgKCwsDUHeNRkZGcvSZHYRhKu0iLDdv3sSaNWu4JIl27tyJixcv4tixY7C0tERERIREgiCRkZFURvilS5dw6dIlnD9/HocPH4a/vz8uXrwosL2UlBRH9dK4ceMwbtw4xvuvqamhqnI1NDTw+vVrGBsbQ09PD8+ePRPiiH4e9uzZA1VVVdy/f5+rQpDFYtEGQZ49e0bpZNdHRUWFNmgLfP/M5voUFxdTVV5ycnL49OkTgLr7cLdu3WiDIE+ePOE5idTS0pJYdm998vPzMWTIEAB19wr2M9LX1xf9+vVjFARhsVgYMGAABgwYIFQfFBUVGXkuiRthKuwbcu/ePezcuZNre5s2bXgG7+qfqw8fPsSCBQvg7+/PIV0ZGhqKDRs2CNxvU2SFs/sM1D1rnjx5wqVR37lzZ0bZsWwFAl7P5O+xGN6xY0eUlZVxBUHevn1LW4kp7DXbpUsXsFgssFgsnlKpcnJy2Lp1K8+2YWFh1DnZcIwmDN/TT6Z+wEcS0jH/C3Tr1o02sU2Use3/AqKeO6qqqlwJRkwr9Jmui/yoiJrN/1/GwMAAjx494qrauXDhApe0miSQk5NDamoq1zpeamrqd1kLKy0tpZKBFRUVqaDL0KFDadfC5s+fj8mTJ1NJImwGDx6M8ePHM9q/JHzbfgVBfjLatm2LjIwMHDp0iDLQ8/T0hJubG6Msf1EvIisrKyq72M7ODitWrMD79+8RHx8vcbPUTp06STzSKohevXohLi6OqqJhsViora3Fhg0bGGWv15eI6N+/P3JycnD//n106NCB1uz93bt3mDJlCs6fP8/z74ImClJSUjAyMkJ5eXmjgiC8TGMbQ/1KkiFDhsDf3x/Z2dk8F0Tpsr2E9YARBzt27MDz58+hq6tLmTwWFxdDRkYG796945j48soCyczMpDKiDx8+DAsLC6SmpuLixYuYMWMG7cCjqY3K2BM2flU4gujWrRtu3rwJExMTDB48GH5+fnjy5AmOHz9OLWxLEhsbG5w9exbe3t4A/i+7MioqipGcG1s7VtB7NTU1GS2+sVgsNG/enPouq6qqBL5f1KArULcoyS+IUD/gwGviNHnyZEb74EdISAiSkpIone2GPkC8aJgl/9tvvwGoW6AD6haVNTQ0aKtgAHAEXBUUFBq1+D9mzBh4eHgILNHV0NDge48U9Zq9ffs2UlNTed7vBS0Msc/Dvn37UlmB35ukpCSsX7+ea7u9vT38/PwA1A18ha0QoqO0tJSa0J45cwZjxoyBg4MD9PX10bVrV55tHj9+DHNzc0hJSdHK1NA9q83Nzaks+q5du2LDhg2QlpbGrl27/ucNu0UNQmhra+P58+dc0jg3b9784b87bW1tfPjwAXp6etDV1UVaWho6d+6MwsJCvpJB9VFVVcWbN2+4qlkePnwoMT+J+qipqVGBmzZt2iAzMxMWFhb4+PEjbSVEbW0tYmJicPz4cRQVFYHFYsHAwACjRo3CxIkTf3iZDAcHB2zZsgW7du0CUHePraiowMqVKzF48GBGnyEjI8OzspTtL9GQ+mPa0aNHIyIigmNflpaWaNeuHQICAgQuvDXVGIu9ECmqRr2o8wxRCQkJwdy5c7Fq1Srq+0pLS0NQUBDWr1/P8Zs2PEZhr1n2PYFdNVL//JCWloaWlhZX0gWb+ooHoo7R6PxkxI2dnR31bwMDA7Rr147r3sBWLvgvwlRuVpSxrbgQxT+qqRFFUqqp5+Oisn//fuzevRtDhgzBqlWr4Orqivbt28PS0hJpaWkSlQz92Zk/fz5mz56Nr1+/ghCCu3fv4uDBgwgJCUFUVJTE9z9v3jzMnDkTDx48oNaU7ty5g7179zJKyBWVtm3b4s2bN9DV1UX79u1x8eJFWFtb4969e7RrEo1NEmlIQ9+2AQMGQElJCevXr2+Ub1tDfgVBfkKaN28ONzc3Rtm8DRH1IgoODqYmSmvXrsWkSZMwc+ZMGBkZ8dTiFifr1q2Dn58f1q5dy3MhXdIaohs2bIC9vT3S09Px7ds3LFy4EFlZWfjw4QNtyTcv9PT0GC/uz5s3Dx8/fsSdO3fQp08fnDhxAmVlZZT8BB3r1q2Dv78/duzYIVHKRbMAAQAASURBVPFgFRteEzdePgT8FvXY0i5MkOSDW9TMj6qqKuoBcfnyZSrg06lTJ7x584Znm8aYnUv6vBelCqepM5aCg4MxaNAgZGdno7q6GuHh4cjOzsatW7dw7do12vai+gsAdXJo0dHRiImJQXFxMXr37o3du3fTSh2JGnTNzMykJrINgwiZmZkc/WcjLp1yoK5qce/evY2aqP8o2YENn4U1NTV48uQJ9PT0GAcWRDFJ9vb2xoQJExAQEIBWrVo1rvNo2u+xZcuWOH36NJcvxOnTp6ks0MrKSsYZ1o1FTU0NJSUlaNeuHS5cuIA1a9YAqFtc4Rc86tKlC0pLS6GlpcU36Aswy0xevnw5KisrAdQ974YOHYpevXpBXV0dhw4dEvHo/reZOnUqfHx8sHfvXrBYLLx+/Rq3b9/GggUL+I5P58+fj9WrV0NBQYHWwFOSzxxRvWTGjRuHRYsW4ciRI9S9PjU1FQsWLGBU+SYqvXv3xqVLl2BhYYHRo0fDx8cHycnJuHTpEpdESH0IIXB2dqak5iwsLEAIwdOnTymJlpMnT0q8/6IgaoU9UJfIExQUhMOHDwOou1cUFxdj0aJFtM/6J0+e8JRyMzAwQHZ2tsC2TT3GEmVBsaqqCnJycnj06NF3m5c0ZOjQoQDqEh/YYyH2vd/JyYn6P697v7DXLHu8KGoASFRT+qb0kzEwMODZ9w8fPsDAwOB/Xg6LTm6WCd++fcPbt2+5ziN2sp6kENU/ShyIEoT58uULCCFUktmLFy9w4sQJmJqawsHBQWDbe/fuoba2liuhhi3/x8vH8EdClGz+/zpeXl6Qk5PD8uXL8fnzZ4wfPx6tW7dGeHh4o6rFhWXx4sUwNDREeHg4dY8wMTFBdHQ0xowZI/H9s2XkunbtSs1R9+zZg+LiYlofwMYmiTREHL5tvPjlCfIf5PDhwwgPD8fTp08B1F1EPj4+3+UiEgW2RAWvzJHvpSH6999/Y9u2bVQVjrW1NWbPng0dHR2e7xfXQr6Ojg4SExNha2sLZWVlpKeno2PHjjh16hQ2bNiAmzdvCvxsNTU1fP78GdXV1ZCWluaqGmKiA3nlyhWEhYVxnDfz5s1rlOZzYxCk710fSWt9i0rXrl3Rt29fDBkyBA4ODlSGaFpaGkaNGsVTN5ifXm59/uvauUwpKChASEgIxzW7aNEiLo8XXgjrL/Dt2zccP34ce/fuRXJyMnR0dODu7g4PDw/GGc2ZmZmwt7eHtbU1kpOT4ezszBF0bd++PfMvgSH8vHcawuS809bWxo0bNxpVfSYq4tLLnjdvHiwsLODp6YmamhrY2dnh1q1bkJeXx5kzZ9CnTx+B/ahvkrxr1y4uk+S1a9cKbF9fb1VYvqfMRX12796NmTNnYvDgwVSixb1793Du3DlERkbC09MToaGhuHv3rkSCAnPmzMGZM2dgZGSEhw8foqioCIqKikhISMCGDRt4BqBevHgBXV1dsFgsWqk5YaoSP3z4QHs+/q8gynlHCEFwcDBCQkKo6gMZGRksWLCAy8eOTd++fXHixAmoqqrSBoclGRwU1Uvm27dvmD17NmJiYlBTU4PmzZujpqYG48ePR0xMDN/McHHx4cMHfP36Fa1bt6aC7ez+L1++nG/wNzo6Gj4+PkhMTOT6/pOTk+Hi4oJt27Z9l0DO8+fPkZ+fj969e0NOTo7Lb0MQ1dXVSEhIwOPHj6lxAtMKe6BubjBq1Cikp6fj06dPaN26NUpLS9G9e3ecO3dOoDSitbU1zM3NERUVRZ0n3759g5eXFzIzM384nfkRI0YgJiYGysrKtAE+ugVJQ0NDnDhxoslkjpkkw7CpX8kACHfNnjp1CoMGDUKLFi1ofRfpKuSlpKSo4H19Xr9+jfbt29MqJzSVnwy772VlZVwLYC9evICpqSmVSPC/SmxsLMf/GyM3m5ubC09PT9y6dYtj+/eaE4rDP0oU6IIwdIFZBwcHjBgxAjNmzMDHjx9hbGwMaWlpvH//Hps3b8bMmTP5trW1tcXChQsxatQoju3Hjx/H+vXrcefOHbEco6QwNjZGXFwcunbtip49e2Lo0KFYvHgxDh06BG9vb4ESzf9lqqurceDAAQwcOBCtWrXC58+fUVFRwVNu+79CWloaNUZkJwzww8vLC+Xl5Th8+DBatmyJx48fo1mzZnBxcUHv3r2xZcsWge3F4dvGi19BkF80isLCQlRXV/M05mnRogWXjIE4oRusNhygAtzZFoIQtChWVVUFR0dHREZGNmpRT1wL+crKynj8+DH09fWhp6eHAwcOoEePHigsLISZmRntDaDhgKshdKbyf/75J3x8fDBq1ChKGigtLQ1Hjx5FWFgYZs+eLbB9cXExWrVqxVUyxy59lnTmirioqKjgyrqhq8S4evUqhg8fjn/++Qfu7u6UhNTSpUuRk5PDc5IoysRM3MydOxcdOnTgCtJt27YNz58/F/jwasqsmaqqKkyfPh0BAQGMr8OGGBgYYP/+/fjjjz9w6dIljBkzBocOHaIykPj5C7Rs2RKfP3/G0KFD4enpiYEDB3L4DDClsUFXUWHiNcKGbjE4JCQEb968aVQgWFRiY2Mxbtw4yMjIICYmRuC9X9A9r23btjh58iRsbGxw8uRJzJ49GykpKYiPj0dycjJt5Z+oJsnu7u7o1asXvLy8BB8wH+hkLngZR1pZWTF+VtItyqWmpmLbtm2UB4axsTG8vb0pzwRJUlVVhfDwcJSUlGDy5MmwsrIC8H9a6sJ+p7+gR5jzjk1NTQ1SU1NhaWkJeXl5PH/+HBUVFTA1NRVa7/dngT0O0tTUxPv37/HkyRNUVFTAysrquwaRhcHBwQH9+vXjK28XHByMa9euISkpSWJ9KC8vx9ixY5GcnAwWi4W8vDwYGhrCw8MDampqjKqlxcXNmzc5AilMkoTu3r0LJycnEEKoCkt2VeaZM2eoYDIvmmKMNWXKFEREREBJSQmTJ08W+NygW5Dcs2cPjh8/jvj4eA6/te9BdXU1goOD4eHhweW9Roew12z9wIWgMaGgxWz2mMrX1xerV6/muD/W1NTg+vXrKCoq4pIXbYi2tjZSUlJgYmICU1NTrFu3Ds7OzsjIyECPHj2oCiNxwq7WCw8Px9SpUzkkX2tqaqjzVhh1hf8KPXr0QPPmzbF48WLo6OhwXX+SDigqKCggKysL+vr6UFdXx9WrV2FhYYGnT5+iX79+fBUOxIWoQRgNDQ1cu3YNZmZmiIqKwtatW/Hw4UMcO3YMK1asoBI9eaGoqIjHjx9zJbMVFhbC0tKSUkqpjzgr7EVl8eLFUFZWxtKlS3Ho0CFMmDAB+vr6VDb/unXrJLr/nxl5eXk8ffq0SeXZm5KQkBC0atUKHh4eHNv37t2Ld+/eYdGiRXzbipIkAtSt5aampsLU1JRjTn3z5k2MHDkSZWVlQh3TryDILxqFnZ0dPDw8uBaQ9u3bh6ioKFy9erVpOsYHusX/+tAFAjQ1Namo5/fm999/x5o1azBw4EA4OztDVVUVISEhiIiIwNGjRym5G0nRtm1bLF68GHPmzOHYvn37dgQHB+PVq1cC20tJScHExASnTp3iyG4uKytD69atf+hqhsLCQsyZMwdXr17F169fqe2NybqpqanBP//8w5HhU1RUBHl5+R8+k6BNmzY4deoU5c/A5sGDB3B2duZZycKmqbNmVFRU8OjRI6GDIHJycsjNzUW7du3g4+ODr1+/YufOncjNzUXXrl3x119/8Wy3efNmTJw4kVGZJz+Ki4t5aiaz/0YXOPz69Su2bt2KlJQUniXzks4uHT58OJKTk6Gurg4zMzMu+cIfWTdYVlYWz58/R9u2bTFt2jTIy8tjy5YtKCwsROfOnWnl6uoPlrW0tHDp0iV07twZeXl56NatG8rLywW2X7t2LbZs2YIhQ4bwlH6kk/+ztbXFoEGDKJmLjIwMDpkLXplujcne+9l1kRtCl5FbH7rs3Ka+7poSYc67+sjKyuLp06dC3689PDwQHh7OJbVWWVkJb29vWg+rxiIuL5na2lrIysoiKyvru44v//nnHyqJg+6exi/ZQ1tbGxcuXECXLl14/v3hw4cYNGgQI91nYZk0aRLevn2LqKgomJiYUBPkpKQkzJ8/n5GHVF5eHt9r9nsYxlZWVmL//v3IyckBUFdpPX78eNrFgaYeY4mKlZUVnj9/jqqqKujp6XEdr6Tvl0pKSnjy5EmjE/ia6poF/i+57sWLF2jbti1HxYm0tDT09fURFBTE1wOLjYuLC4YMGYKpU6diwYIFSExMpCTs1NTUcPnyZbH3nV0txvbaq18hx+77ggULfvjgrzj4+vUrHj9+zPOeI2icoaCggPv376NTp06S7iJP2rZti/Pnz8PCwgKWlpZYsmQJXF1dcfv2bTg6OlISS5JC1CCMvLw8cnJyoKurizFjxsDMzAwrV65ESUkJjI2NBSaVqqur48yZM1w+kbdu3cKQIUN4zgnpKuzZf2sKZYfGZPP/1+nTpw/mzZsnskT6z4q+vj4OHDjAlcx2584djBs3jpEnoDBJIgAwduxYqKioYNeuXVBSUsLjx4+hqamJYcOGQVdXV2hZzl+eIP8xampqEBYWxldLkU4W6eHDh+jRowfX9m7dunEtkIsDUSPodIGNxsDWv2uKSLmPjw/1YF+5ciUcHR2xf/9+SEtLIyYmplGf9fXrV67fna6a4ePHjzw1Yh0cHARGf+tjYmICW1tbHD58mENj+kePw06YMAGEEOzduxetWrUSStKkWbNmqK6upmTLjI2NJVo1JU7Ky8uhoqLCtV1ZWRnv378X2DY7O5unwaKVlRWt1jUgWuYBUDfJO3nyJK1eJT+E8RcA/i/b7d69ezh48CByc3MBAB07dsT48eMZZWfy00wuLy9npJns6emJixcvYtSoUbC1tf3uUjyqqqqMtPAlhSh62a1atUJ2djZ0dHRw4cIF7NixAwDw+fNnRrI0opokR0VFQVFREdeuXeOqCmOxWLRBkKdPn1Ja9s2bN8eXL1+gqKiIoKAgDBs2jOditCiBjR/Jw0gYmE5qmExSm/q6a0qEOe/qY25ujoKCAqGDILGxsVi3bh1XEOTLly+Ii4sTexBEXF4yUlJSMDIyQnl5+Xdd/FNTU6PukaqqqjzPVbrFmQ8fPgj0LWrVqhXfZAFxcfHiRSQlJXFl8xsZGTGqbmRL+GloaEBbW5vje2CxWHyDIOL0rVNQUEDPnj2hq6tLjc+vXLkCQPCCqKhjLFFZs2YN3NzchL5mm3pBqV+/frh27Vqjx+PiuGbj4uIwduxYrgr5b9++UZI/vGAvNvXt25cKWAhDU/jJsCUJp0yZgvDw8B9yPPA9uHDhAiZOnMgzIYbueWFqako795IkwvpHiQs1NTWq4qJNmzbIzMyEhYUFPn78yEgWp0OHDjh58iSGDx+OpKQkan749u1b2vPRwcEBS5YsQWJiIjUv/vjxI5YuXYoBAwbwbMNkcfh7wEsdoVu3bujWrVsT9+znYNasWfDz88PLly/x22+/cQXsJV3F09SUlpbyVKHQ1NSkDTyy1WB69uyJnj17UtuZqsGIw7eNJ+QXPxXFxcWkpKSE+v+dO3eIj48P2blzJ6P2AQEBREdHh2zatInIysqS1atXE09PT6Kurk7Cw8Np2ysrK5MHDx5wbU9PTyeKiorMD4QhLBaLSElJERaLJfAlJSXVqM/98uUL+fvvvzledMyZM4coKyuT3377jUybNo34+vpyvL4nlZWV5P79++Tdu3eM3l9RUUFmz55NNDU1iZSUFNeLDldXV7Jhwwau7Rs3biRjx46lbS8lJUXKysrI5s2biYyMDHWulZaWNvq3+94oKCiQnJwcodtXVFSQKVOmkGbNmlHna/PmzYmHhweprKwUY08lg5mZGdm6dSvX9oiICGJiYiKwbcuWLcmtW7e4tqemphJVVVXafevp6ZHU1FSu7WlpaURfX5+2/erVq4mqqioZOXIkCQ4OJuHh4RwvOmbPnk309PRI//79ibq6Ovn06RMhhJCDBw8SKysrgW39/f0Ji8UiSkpKpHPnzqRz585EUVGRSElJkYULF9Lum8Vikbdv33JtLyoqIvLy8rTtlZWVyc2bN2nf96NSUVEhUnsWi0XKysq4tr969YrIysoKbLty5UqioqJCOnXqRHR1dcnXr18JIYTs2bOHdOvWjXbfnp6eZNWqVYQQQrZt20bk5ORI//79iaqqKvHw8BDiaBpHq1atSHZ2NiGEEBMTE5KYmEgIIeTRo0dEQUFB7PtjP4OZvP7X+dmvO1EQ9bw7f/486dKlCzl9+jR5/fo14zHa33//TT5+/EhYLBZ5/vw5R5sPHz6Q2NhYoqOjI56DrEdRURGpra2l/i3oRcepU6dIz549yZMnT8TeT35cvXqVVFVVUf8W9OKHlJQUz+cUm+8xxlNUVCS5ubnUv/Pz8wkhhNy7d4+0bNmStr2uri5Zt25do/err6/P6GVgYCDwc/Lz84mlpSXHfIfpPVPUMZaoWFpaEikpKdK9e3eyfft2xnOSH4UdO3YQbW1t4ufnRw4cOEASExM5XoIQ9Zplz4sa8v79+//5Z+XevXvJ58+fm7obTUaHDh3IrFmzSGlpaaPbXrlyhXTv3p2kpKSQ9+/fN3otQ1TKy8vJq1evCCGE1NTUkJCQEOLk5ETmz59PPnz4IPH9u7q6ktDQUEIIIUFBQURTU5N4eXkRPT09Mnz4cNr2R44cIS1atCBSUlKkf//+1Pbg4GDi6OgosO3Lly+JoaEhUVFRIX369CF9+vQhqqqqxNjYmBQXF4t2YN8BZWVlUlBQ0NTd+Cnht/YozBrkz0iHDh1IfHw81/a4uDjaMQ6LxSKmpqbk+fPnHNsbMz6sqqoi8fHxxN/fn8ycOZPs3r1b5GfILzmsn4xevXph2rRpmDhxIkpLS2FsbAwzMzPk5eXB29ubtmy7ffv2iIiIwJAhQzhM0SIiIpCWloYDBw4IbO/k5AQ5OTkcPHiQyoitqanB2LFjUVlZifPnz4vtWAHxatRXVlZi0aJFOHz4MM/sC7oMT0GmmywWS6DedVPD1rRfvXo1Jk6ciO3bt+PVq1fYuXMn1q1bBzc3N6429bPc/vnnH2zatAk9evTg8ARJTU2Fn58fli9fLnD/9XVwz58/D1dXV4wePRorVqyAvr7+Dy2H1bdvXyxbtkxoA/jp06fj8uXL2LZtG1VFdfPmTcydOxcDBgygssx/VPbu3Ys5c+bA398f/fr1A1CXoRgaGootW7Zg6tSpfNu6urrizZs3XFkzLi4u0NLSwuHDhwXum588SkFBAZURIAhB2Yl0PjyA8P4CsbGxmDFjBjZu3Ijp06dTckZVVVXYsWMHFi1ahJ07d/LM9BOXZrKpqSkSEhJ+2uwURUVFjBkzBh4eHhyZI3SISy/76NGjKCkpwejRo6kM49jYWKiqqmLYsGEC24pqkiwqospcNLZitH61SlFRERYvXozJkydTz4rbt28jNjYWISEhYq3O/BH52a87URD1vKuvkV8/G5/QVCOw5Sb4wWKxEBgYiGXLljXyiL4fampq+Pz5M6qrqyEtLc1lyE1Xpd1USElJYdCgQVzZ7Gz+/fdfXLhwQaJjvMGDB+O3337D6tWrKakEPT09jBs3DrW1tTh69KjA9srKynj06BGXzvv3wsnJCc2aNUNUVBQMDAxw584dfPjwAX5+fti0aRN69erFt62oYyxxkJWVhf379yMhIQEvX77EgAED4ObmBhcXF47xy4+IsL4cgOjXLD9z8IyMDPTt25fRNf/y5UucOnWK53OarpqjKT37WrVqhS9fvmD06NHw9PT8Ln5hPxLKysp4+PAhhzw0U9jnbMNnHt1z8n+FDx8+4OvXr2jdujVqa2uxYcMGany9fPlyRpVRpaWlePPmDTp37kx9n3fv3oWysjKtzBhbujAjIwNycnKwtLSEq6srl2ytILKzs3les3Ryq6Li7u6OLl26CK2O8F+Gbj1Skl4hVVVV6NSpE86cOQMTExOJ7UcQGzZswIYNG7Bx40aOtaCFCxfCz88PS5Ys4dtWSkoKI0aMQEpKCocaTFlZGXR0dLjkAL8Xv4IgPxlqampIS0uDsbExIiIicOjQIaSmpuLixYuYMWMG7aKegoICnj59Cl1dXejo6ODs2bOwtrZGQUEBrKysaLUcs7Oz0bt3b6iqqlID8xs3buCff/5BcnIyzM3NxXas4kaYQMCPwsiRI2Fra8sl/7Nhwwbcu3cPR44cEdheV1cXcXFx6NOnD5SVlfHgwQN06NAB8fHxOHjwIM6dO8fVRlym7gBnEASoO4+cnZ2hoKCAzMzMH3rQlp+fjxkzZmDChAkwNzfnGujQLXZpaGjg6NGj6NOnD8f2lJQUjBkzBu/evRN3l8XOjh07sHbtWrx+/RpAnTbkqlWr+Jbrs3n16hV69+6N8vJyKoDw6NEjtGrVCpcuXUK7du0EtjcyMsLKlSsxYcIEju3x8fFYuXIl7XnXVNja2sLV1ZXvQHPz5s1ISEjA3bt3uf4mLs3k8+fPIyIiApGRkd/NyE2c5tonT55ETEwMzp07B319fXh4eGDSpElo3bq1wHbi0ssWBX5+LoRh6S8hBEePHuWrUU/np1JQUICKigpYWlqisrISfn5+1CRx8+bNtOfDihUrEBUVRQW4ly1bhqKiIpw8eRIrVqwQKO1ib28PLy8vuLq6cmw/cOAAdu3a9cP5hgF1gbNp06ZBVlaWVuKGTtamKa67HwVRz7uG0m8NsbOz49uOEIJ+/frh2LFjHAbL0tLS0NPTo71vCIM4vWTo/OskETwUh2HrlClTGLUXVrOZCZmZmbC3t4e1tTWSk5Ph7OyMrKwsfPjwAampqbQLjZ6envj9998xY8YMsfSHPa1m+izU0NBAcnIyLC0toaKigrt378LY2BjJycnw8/MTGLAXdYwlblJTU3HgwAEcOXIEX79+pZVKFFWiuSkR9pplj5MyMjJgZmZGJUsAdd9HYWEhHB0daQNYV65cgbOzMwwNDZGTkwNzc3MUFRWBEEJdC4JoSj+Z6upqnD59GjExMTh//jwMDQ0xZcoUuLu7Q1tbW2L7/VHw8PBAjx494Onp2ei2wj4nxYUoUrM/Es+fP0d+fj569+4NOTk5KogkSQoKCjB8+HA8efKEQz6TvV9Jf3dr1qxBaGgo7O3teUo60Y1vf9F0tGnTBpcvX26yIAghBIsXL0ZERAT1nJaVlcWiRYtoE/DZ94z9+/djyZIl2LBhA+bOndsoX+Bnz55h69atePr0KYA6if05c+aI5I30Kwjyk6GoqIjMzEzo6+vD2dkZPXr0wKJFi1BcXAxjY2N8+fJFYHtjY2PExcWha9eu6NmzJ4YOHYrFixfj0KFD8Pb2xtu3b2n78Pr1a2zbto0jCj5nzhyOiackiIuLE/h3ugVZYQIBPwqamppITk6GhYUFx/YnT56gf//+KCsrE9heUVER2dnZ0NXVRdu2bXH8+HHY2tqisLAQFhYWlC6spOjbty9OnDgBVVVValt5eTlGjBiBGzdu8IwCi2OCLg7S0tIwfvx4FBUVUdsaY2QmLy+P+/fvcz24srKyYGtri8rKSkl0WyK8e/cOcnJyHNn1dIiSNSNK5kFDGrswIQoKCgp48uQJ38zSgoICWFhYCPztRdVMfvfuHcaMGYPr169DXl6e6/uWxOKCJMy13717h/j4eMTExODp06cYOHAgPDw84OzszLF40BBR9bJFQdRJoo+PD3bu3Im+ffvy9CGS5IIiIFrFqLy8PDIyMriCdLm5uejSpQsjzebvjYGBAdLT06Guri5y9VhTXHe/qOPFixfQ1dX9bj4sDbPIG3qC1O/Hj7gwVN+wle47+xH7X5+///6bmpewTTdnz57NU8O6ISEhIdi8eTOGDBkCCwsLrmuW6cJQXFwcNm7ciLy8PAB1HmD+/v6YOHGiwHZqamp48OABDAwM0L59e0RFRaFv377Iz8+HhYUF7T1THJnJ4uLRo0fYt28fEhISUF5eTjsnFSXg/rPCHicFBgbCz8+PYzzNTtQYOXIkbcWora0tBg0ahMDAQCgpKSEjIwNaWlpwc3ODo6MjrQeToqIiHj9+zDVOLSwshKWlJeW7IGnKysqwb98+xMbGIicnB46OjvD09ISTk5PASp2fmc+fP2P06NHQ1NQU6Z7TFDRMamTz+vVrtG/fnvaaFwZx+s6Vl5djzJgxSElJAYvFQl5eHgwNDeHh4QE1NTWEhoYKbJ+fn48tW7ZQi7Gmpqbw8fFhVNXTsOrv7t27KC8vZ1T1Jw5EHd/+lxHVo1RUgoODkZubi6ioKIFzX0lTUVGBp0+fQk5ODkZGRnyrgOsjqhrMsWPHMG7cONjY2HCo0dy7dw8JCQkYOXKkcAcjkpjWL747tra2ZNGiReT69etEVlaWPHr0iBBCyO3bt0mbNm1o2y9atIisXbuWEEJIQkICad68OenQoQORlpYmixYtkmjfRUVVVZXjpaCgQFgsFpGRkSFqamq07RUUFMiLFy8IIYS0adOG3LlzhxBCSEFBgUR00huyd+9ecvjwYa7thw8fJjExMQLbysrK8vSlePr0Ka2+PSGEWFhYULrO9vb2xM/PjxBCSHh4OKPzpj61tbWUDrYkofOD+V5ajCYmJmTEiBEkLS2NFBYWNlrru1+/fmT06NHky5cv1LbPnz+T0aNHE3t7e55tunTpQqysrBi9/pepra0lCxcuJLKyspQ+try8PAkMDGT8GVFRUcTMzIxIS0sTaWlpYmZmRnbv3i3BXhOipKREnj59yvfvOTk5RElJSaJ9sLe3J0ZGRmTdunUkOjqaxMTEcLx4oaqqStTU1Bi9moKIiAgiIyNDWCwW0dTUJAEBAT+kr46ofi5qamrk7NmzkugaI+Tl5alnpba2Nrl//z4hpE67XllZWWDbjh07En9/f67t/v7+pGPHjuLvLPmxzlthrrv/Nf79919SUlJCXrx4wfFiwocPH8jGjRuJh4cH8fDwIJs2bSLl5eWM2p4/f57cuHGD+v+2bdtI586diaurq8S1yi9dukSsra3JhQsXKG32CxcuEBsbG3Lx4kVGn1FdXU2OHDlCgoKCSFBQEDl69Cjl2SEJ6o9jTpw4Qdq3b08iIyNJRkYGycjIIJGRkcTIyIicOHFCYn34ERDFz4NNaGgokZeXJwsXLqT8JPz9/Ym8vDzZvHmzwLY9e/akvmNXV1fi6OhIbt68SSZNmkTMzMxEPTyJU1BQQNasWUNMTU1Js2bNSL9+/UhUVBT5+PEjbVtDQ0Ny5swZQkidnwtbMzw8PJy4urpKtN9srl69SoYOHUrat29P2rdvT5ycnMj169cZtRXlmo2JiaH8xoSh/velqqpKMjMzCSF1Hkx6enq07ZvaT6Y+aWlpZNq0aURGRobo6+sTFRUVoq+vT1JSUr5rP74XUVFRpHnz5kRRUZHo6ek1+p7z119/kU2bNhFPT0/i6elJNm/ezOh6EwW2l6KUlBRZu3Yth7/i5s2biYuLC+nSpYtE9s3Ed47pesDEiRPJwIEDSUlJCYeH1IULF4ipqanAthcuXCDS0tLE1taW8oO1tbUlMjIyjJ7z6urqJCMjgxBS58/BXte5cuWKxL67X4gHUT1KRcXFxYUoKSkRHR0d4uDgQIYPH87x+pFp6NGZlZVF2rdvT3mK0WFoaEgCAgK4tq9YsYIYGhoK3a+mCyX9QijWr1+P4cOHY+PGjXB3d0fnzp0B1JXl29ra0rZft24d9e+xY8dCT0+PkitwcnKSWL/FwV9//cW1LS8vDzNnzoS/vz9te0NDQxQWFkJXVxedOnXC4cOHYWtri9OnT3NUKEiKkJAQ7Ny5k2u7lpYWpk2bJlDuwMLCAocOHeIqOUtISICpqSntvqdMmYKMjAzY2dlh8eLFcHJywrZt21BVVUWrG8umsVluomZuFBYWMm4vSV68eIFTp06hQ4cOQrXfsmULHB0d0bZtW+p6zcjIgKysLJKSkni2cXFxEba7YsHa2hpXrlyBmpoarcQRnayRKLBYLKxfvx4BAQGNzjwA6rIMN2/eDG9vbw5/Al9fXxQXFyMoKEgi/ba2tsb+/fuxevVqnn+Pj4+HtbW1wM9gV77wg07q4NatW7h9+zZ1zjFhy5YtjN/7vSgrK0NsbCxiYmLw4sULjBo1Cp6ennj58iXWr1+PtLQ0XLx4EUCdn8rq1auhoKBAeavwg+l9rzGw98lisRAQEMDTz6VLly60n6OiotJoffqWLVsiNzcXGhoaUFNTE3jN0lUjtG3bFm/evIGuri7at2+PixcvwtraGvfu3aO99sLCwjBy5EicP3+ekhy7e/cu8vLycOzYsUYdE1Mkdd4SIarHhLnu/lfIzc2Fp6cnbt26xbGdMKyavH79OpycnKCiokLp0UdERCAoKAinT59G7969Bbb39/fH+vXrAdRVyc6fPx9+fn5ISUnB/PnzJVpBNW/ePERGRnL4Fw0cOBDy8vKYNm0alTXKj6ysLDg7O1Nef0DdeF9TUxOnT5+WiNRsfXmy0aNHIyIiAoMHD6a2WVpaol27dggICGjyMYkg+FUNs1gsyMrKQldXV+B9Sxxjza1bt2LHjh0cFenOzs4wMzPDqlWrBGqwL1++nKoKDQoKwtChQ9GrVy+oq6vj0KFDIvdNknTr1g337t2DpaUlpkyZAldXV7Rp04Zx+9LSUqrCXVFRkZJkHjp0KAICAiTS5/rs27cPU6ZMwYgRI6js+9TUVNjb2yMmJgbjx4/n21bUa7Zfv3549+4d5Tl29+5dHDhwAKamppg2bRpt3xUUFChZEh0dHeTn58PMzAwA8P79e9r2Dg4OWLJkCZefzNKlSzFgwADa9qJSVlaG+Ph4REdHo6CgAC4uLjhz5gz69++PyspKBAUFwd3dvVHeoD8Ly5YtQ2BgIBYvXtzoapf09HQMHDgQcnJy1LrP5s2bsXbtWmqsJgnCwsIA1D3PIyMjeUrNRkZGSmTfKSkpYvusixcvIikpibru2BgZGdGea4sXL4avry/HWhp7+6JFi2ivm5qaGigpKQGok0F8/fo1jI2Noaenh2fPnglxNMIjzPj2v0xpaSnPylJNTU28efNG4vtXVVUVvuKhibGzs+OobDQ1NcWdO3cwYsQIjuppfrx584an2s+ECROwceNG4TsmdPjkF01GdXU1V1ZbYWEhR5Ttv8S9e/eIsbEx7fs2b95MwsPDCSF1WXuysrJERkaGSElJkS1btki6m0RGRoYUFhZybS8sLKSt5jh16hRp3rw5mTRpEpVROnHiRNK8eXOhsvSKiorIsWPHqIwEOoTJchNn5kZTMnToUHL06FGRPqOyspLs2rWLzJ8/n8yfP5/s3r2bfP78WUw9FD+rVq2iMuxXrVol8PUjo6GhQQ4cOMC1/cCBA0RdXV1i+z19+jRp1qwZ8ff3J6WlpdT2N2/ekAULFpDmzZuT06dPC/yMefPmcbxmz55NevToQVRUVMjcuXNp+2BlZUVu374t8rEIC931T8exY8fI0KFDSYsWLUjnzp3J1q1byV9//cXxnufPn5MWLVpQ/+/Tpw/1Hjs7O9KnTx++L0nA/mwWi0X++OMPjv05ODiQadOmkdzcXNrPiYmJIePGjWvUPaJ+VmnD6oPGViOIWjFaUlJClixZQmUoLV26lBQXFzM+lqZGlOqxpr7umpI//viD9O7dm5w7d448fPiQPHr0iONFh7m5OZk6dSqprq6mtlVXV5Np06YRc3Nz2vYKCgrUGGvlypVk5MiRhBBC7t+/T1q1aiXcQTFEVlaWPHnyhGt7RkYGo2rdbt26EScnJ46x/YcPH4izszPp3r27WPvKC1lZWZKdnc21PTs7m1H/m5L6z5r6lcLs1/9j78zjofr+P/4aZN+XtNoiWYv2vbSntC8ksrRHSUqrj1b1SYv0SUqhz6dVqbSgUooSEtJmq2hf1ZeU7fz+6OH+jBkzY2auoTwfj3k8uHPPPedyl3Pey+stJSVFHBwcmLJxhY2UlBTJyclh2Z6dnU2kpKTqfbxPnz41SMa1oKxatYo8fPiQ7/YdO3YkSUlJhBBC+vbtS7Zs2UII+fXe0dDQEMoYOdGpUye2axh/f3/SqVMnjm0FvWf79etHwsPDCSG/5oYKCgqkd+/eRF1dnads53HjxpHg4GBCCCGenp5EX1+fbNy4kVhaWtaZZV6Tly9fEj09PaKkpETNU5SVlYmhoSHt7+vquZ2JiQnZuXMn22y/d+/eEQaDQes4RIWKigqVxVNf+vXrR2bNmsWUcVReXk4cHR1J//79hTXEOhk0aBDtmZV0Ii8vT83Da2aCpKSkEFVVVY5tpaSk2M7hnz59ytNzvjFk/YWFhRFTU1MiJSVFpKSkiJmZGfUcaqZu9PX1yZEjR1i2h4eH85wx2gx/jBo1ihw6dIhl+6FDh8jw4cP5Pm5zTZBmmjzp6ekYMGBAvTIPgF8R/vfu3YO+vj6tNSWq0dLSQmBgIEuBzHPnzmHhwoV4+fIlx/YXL17E5s2bkZ6eTun++vj4cCyCVlVVhb///hvnz59HWVkZhgwZAh8fH8jIyNRr7Lq6uvD19WXxxIaFheGvv/5iG0nHrXhbTdidgzCLjgpCcHAwNm7cCGdnZ7barZz6Li8vR6dOnXDhwgWRFbNqakycOBGhoaFQVFTExIkTOe7LrUC0srIyUlJS2NYn6NGjB4qKiji2d3R0hIuLC9cIZHbs2bMHy5YtQ0VFBRVl9/XrV0hISGDbtm1YvHhxvY8JAH/99ReKi4uxfft2jvvFxsbC19cXmzZtYnvdssu+Eqbu7rlz55h+Ly8vx/379xEWFgZfX1+uBSGVlJQwffp0uLq6onv37mz3KS0txbZt23iuL1If8vLycPjwYeTl5WH37t2UlqmWlhYVbVkXgtZzKS0txYQJE5CYmAgdHR2W/x2d2VfsuHPnDu7cudMkMkZr8uPHD5ZCu9z+J3VljwUGBsLDw4Nr9hg/993vgpycHO7du8d3oUIZGRmkp6dTUdXVPH36FF26dOGqNa6qqoqEhAQYGxujX79+cHBwwJw5c/D8+XMYGxvTWo9mwIABkJaWxpEjR6CpqQngV6Szg4MDfvz4wXU+JCMjg9TUVJZnS1ZWFrp3706LznpNLC0tYWpqioMHD1IRe2VlZXB1dUVWVlaDP3Pqw7lz57BixQp4eXlRkdHJycnw9/eHj48PKioq4O3tjWnTplHvTW6ZgjXhJWvQ1NQUdnZ2WLVqFdP2jRs34sSJE3jw4EE9zujPwdvbG4qKili1ahVOnDgBe3t76OjooKCggG3EtbCRkpLCw4cPWTK9c3NzYWpqih8/ftTZVtB7VkVFBUlJSTA0NERAQABOnDiBxMRExMbGYt68eVz1+fPz81FcXAxzc3OUlJTA09OTUnbYsWMHU6ZXXYiqnoyLiwtcXV2pdyw7CCEoKCjg6TyaGh4eHtDQ0GB5XvCCjIwM7t+/z/KeffToEbp169Yo664Jk5s3b3L8ntt6bfTo0ejatSs2bNgABQUFZGZmQltbG9OnT0dVVRUiIiLqbNu+fXvs2LEDU6ZMYdp+8uRJLFu2DAUFBRz7jomJQUlJCSZOnIjc3FyMGTMG2dnZVNYfNwUAQdmxYwfWrl2LRYsWoW/fvgCAhIQE7N27Fxs3buSYsfinI8wapX8C3759o9Y73GwL7NZFNW2Ar1+/xrp16zB16lT06tULwK+aIKdOnYKvry/mzZvH1xibnSBNDF1dXY6pa79zUaPaRnFCCN68eYPAwEC0b98ely9fpr1PTnAzxK9YsQInTpzA4cOHqZd0fHw8nJ2dMXnyZK5GTX7YsGED/vrrLwwdOhQyMjKIiYmBra0tDh06VK/jSEtLIysri2WhkJOTAzMzM44LBW5kZWWxTRvnNUWYF5kNQeA0Dl76btu2La5evVovJwg3OZuaNNZCu5WVlUhMTIS5uXm95OacnJwQEBAABQUFODk5cdyXm7yJm5sbWrRowWLEWLZsGUpLS7F3716O7cePH49Lly5BW1sbTk5OcHR0rJfUw8uXL3Hq1CkmCblJkyahffv2PB+jNrm5uejRowfX/3v1dVv7OiIcpGmqC+VyglN7Xjh69ChOnDjB4iSpzffv35nkpOqLs7Mzdu/eTaWeV1NSUgI3NzeOz8D4+HiMGjUKffv2xc2bN/H48WPo6enBz88PqampHBdJ7Pj27Rvi4uLQqVMnngzE1UUbJ0+ezLYwOi9On8rKSkRGRjIVbxw3blyDFNQrKipCSEgI1beJiQmcnZ0pZyCdlJSUYMWKFTh58iQ+ffrE8j2361ZDQwMBAQGwtbVl2n7s2DG4ublxlRnh5777XejevTt27tzJJAlVH/r27QsvLy8W6aWzZ8/Cz88PSUlJHNvb2NigrKwMffv2xYYNG/Ds2TO0bdsWsbGxWLRoEbKzs/kaFy/k5uZiwoQJyM7Opp7vhYWFMDAwwNmzZ7nKaXbu3Bk7d+5kMYLExcVh8eLFtBvRk5OTMXbsWBBCqKCgzMxMMBgMREVF8SS3Kyp69OiBDRs2YMSIEUzbY2JisHbtWiQnJ+Ps2bPw9PREXl4eAGDw4MFM+6alpaGiooJywGVnZ0NcXBxdu3blKj0J/CrcOW3aNAwdOpQyLiUmJuLatWs4efIkJkyYIIxTbRTQKTvZ0A53fX19eHl5Ye7cuUzbg4KC4O/vT83d2CHoPSsvL4+srCzo6OjAxsYGffv2xYoVK1BQUABDQ0OOThR+59fNNA7c3d0RHh6Ozp07w9zcnMXpxOm+0dTUxJEjRzB8+HCm7TExMXBwcMC7d++EPl5RS83WhN2avOZ8i9scKysrC0OGDIGlpSXi4uJgY2ODhw8f4vPnz0hMTORY4Hz9+vXYuXMnvL290adPHwC/nvNbt27F0qVL+ZLw+/z5c73W/ILAT0BrM78ghMDb2xsBAQFUcJW0tDRWrFjBIlVPFxERETh58iQKCgpYArwaW6CKuLg43rx5g5YtW9ZpW+Bmj+AFQdZVzU6QJsbu3buZfq+Oro2OjoaXlxe8vb1FNDL6qX1DMBgMaGhowMrKCv7+/my1+mpz7do1XLt2De/fv0dVVRXTd+yMYsK8CcvKyjBz5kycOnWKMkRVVVXBwcEBQUFBTHp5dXHv3j0mw5KFhQXH/Q0MDLBs2TJqgn/16lVYW1ujtLS0Xjqkwo5y+9///odjx47h4MGDuHfv3m9tGNq8eTOys7Nx8OBBng2QYWFhPB+fUy0ZfhGWE0ZaWhqPHz+Grq6usIbGlZoT9IqKCoSGhkJLS4uKHrh79y4KCgrg4OCAPXv2cD3ehw8fcOTIEYSFheHRo0cYOnQoXFxcMG7cONqj5dhx5MgRrFixAq9fv+a4H7fIY3bZV4Jmb/FCfn4+zM3NUVxczHE/BwcHDB48GAMGDOC4KKmLmhOwmnz8+BGtWrVCRUVFnW179+6NKVOmYOnSpVBQUEBGRgb09PSQnJyMiRMncs3amzp1KgYMGIBFixahtLQUnTt3xvPnz0EIwfHjx7nqusrJySEmJoZvYzI7rfLs7GyetcrDwsKgrq4Oa2trAMDy5csRHBwMY2NjHDt2jGNkJju96pSUFJSWltKqV13NwoULcf36dWzYsAEzZ87E3r178erVK+zfvx9+fn6YMWMGx/aCZo/xc9/9LsTFxWHNmjXYvHkzz1kwNes5PH78GMuXL4ebmxtTtNfevXvh5+eHadOmcey/oKAACxYsQGFhIdzd3alsMw8PD1RWViIgIEDQU+QIIQRXrlzBkydPAABGRkYYOnRone/SmtFxCQkJWL58Of766y+mc1+/fj38/PyYanXQRXVUeM3x29nZQU5Ojva+BaGuyOgnT57AwsICpaWlHLOBduzYgRs3biAsLAwqKioAftUgdHJyQv/+/eHp6cnTOO7du4edO3dSc3QjIyN4enpynacLg7KyMrbrGi0tLaH3NXjwYERGRkJZWZnFmVQbYWr508G+ffuwZMkSODs7Mxk1Q0NDsXv3bhbniDDv2Z49e2Lw4MGwtrbG8OHDkZSUhM6dOyMpKQmTJ0/mOs8Qxvw6Ly8Pu3btYgqWWLx4MV9zrvrg7u4OfX19qg5LNYGBgcjNzW2U9emECaf7hsFgcHS8uru7IzIyEtu3b2e6Zr28vDBp0iRa/na83vPcxi4MqusGVVNtB1u7di02bdqEIUOG8HSMwMBAZGRkoLi4GJaWlli4cCFXOxIhBLt27YK/vz+1BmvTpg28vLzg7u7e6Otr0BnQ+qdQXFzMV41SQQkICMDq1asxa9YsBAcHw8nJCXl5eUhJScHChQuxadOmBhkHr8THx6Nv376QkJBotOuiZifIb8LevXuRmppKS+FHboWRa9LYPJE18fX1xfr169GtWze0bt2a5ZwiIyMbZBzZ2dlU6rGZmRlPqb7v37/H9OnTcePGDSrqp6ioCIMHD8bx48ehoaHBtp2UlBRyc3OZIs+lpaWRm5vLUhSME8KKcrt58yZCQkJw+vRptGnTBhMnTsSkSZPqlLthx48fPyAtLc3z/sKEn74nTJiAa9euQV5eHmZmZiwGBW6STqJAWE6Ybt26YevWrTxNSoUFt0V5NfxM1tPS0nD48GEcPHgQ8vLysLe3x4IFC1gMpvfu3cOyZctw7tw5FsPf169fMX78eOzatYtj8eTaUmDVmW+pqalYu3YtLRJQdFNaWoqVK1fi8uXLXIsAurq64ubNm8jNzUXbtm0xcOBADBo0CAMHDmT5e9fk27dvIIRARUUFOTk5TM/GyspKREVFwdvbm6MTSV5eHg8ePICuri6TE+T58+fo1KkT14VCq1atEBMTg86dO+Po0aPw8fFBRkYGwsLCEBwcjPv373Ns36lTJ5w8eZJvmcbevXtDQ0ODxag3a9YsfPjwgaVwdW0MDQ2xb98+WFlZ4c6dOxgyZAh27dqFCxcuQEJCguMzq3///tDX18eBAwcop29FRQVcXV2Rn5/PVcpAULS0tBAeHo5BgwZBUVERaWlp0NfXx5EjR3Ds2DFcunSJY3tBs8f+ZATJPuO2FPkds2hqR8eRWoVKa/7+u527MLGwsEDnzp0RHBxMBROVl5dj9uzZyMjIwP3795GYmAh7e3u2ka7V2ULsZI2GDx/ONeBAlOTk5MDZ2Znlmd5UMs8+ffoENTU1AL8ypw4cOIDS0lLY2Nigf//+DTKGyMhI+Pv7MzmvvLy8MG7cOJZ9hXnP3rhxAxMmTMC3b9/g6OhIBeKtWrUKT5484bo2EHR+HRMTAxsbG3Tp0oVpXZeRkYGoqChai6O3bdsW58+fR9euXZm2p6WlwcbGhqsD6E+mrKwMXl5eCAoKooJ5WrRogfnz58PPz6/BjLKNjfj4eCxduhT37t2rc5/y8nKMHDkSQUFBHNcRvPC///0PAFiyzTlRUlICPz+/OoNx6VZzaZZtbLp06tQJPj4+sLW1ZVqXrlu3Dp8/f0ZgYKCoh9jkoF8XoZkGYdSoUVi5ciUtTpCa0gQ/fvzAP//8A2NjY0rLMykpCQ8fPsSCBQuE3ndd1J548kJQUBBCQ0Mxc+ZMuobFEx07dkTHjh3r1cbNzQ3/+9//8PDhQ0pW6dGjR3B0dIS7uzuOHTvGtl1FRQWL0b5FixYoLy+vV/+TJk3C3bt3sXPnTpw9exbAr4VCcnIy1yi3t2/fIjQ0FCEhIfj27RumTp2Knz9/4uzZszA2Nuap/8rKSmzevBlBQUF49+4dsrOzoaenh7Vr10JHR4drfQFBELRvZWVlrpHf3BCkPgE/CCu7ZOPGjVi2bBk2bNiArl27sjiAuOnjv3v3DsuWLaMmjLUNZewWmXRFHr558wZXrlzBlStXIC4ujtGjR+PBgwcwNjbGtm3bmLRU/f39YWVlxfb8lJSUMGzYMPz999/4999/6+yvtnSQmJgYDA0NsX79epY0+Lq4desW9u/fj/z8fJw6dQpt27bFkSNHoKury1OWgSDta2cTEULwv//9D7KyshzPu5qDBw8CAF69eoWbN28iPj4e/v7+mDt3Llq3bl3nIllZWRkMBgMMBoPtc5bBYMDX15dj38rKynjz5g1LhOX9+/d5kkP7+vUrVFVVAQDR0dGYNGkSZGVlYW1tDS8vL67t/f39sXz5cgQFBUFHR4fr/rVJT09Hamoq5QABfv0/Nm3axJPDubCwkIoUO3v2LCZPnow5c+agb9++GDRoEMe2qampTA4QAJCQkMDy5cvRrVu3ep9Lffn8+TP09PQA/Hq+VGeq9evXD/Pnz2fbpmb2GIPBwMGDBxEbG8s2e4wb0dHRkJeXp+6PvXv34sCBAzA2NsbevXuZ/ie/G/w8e4Utv9DQ70pBaOxR8k2FvXv3wsbGBu3ataMcxw8ePEBlZSUuXLgA4Jdxqa41yrdv3/DhwweW7R8+fKAMXXW1E0TzWhjMmjULEhISuHDhAtvgLrrhV3bywYMHGDt2LCUZd/z4cYwcORIlJSUQExPDzp07ERERwSKNRwcTJkzgOZBLWPcsIQR6enooKChARUUF03thzpw5PEmBCjq/9vb2Zlt3xdvbGytWrKDVCfLp0ye28piKiopcJSf/dCQlJbF7925s2bKFkvfr0KGDQPKxvwOamppcg6tatGjBlH0qCPVxflTj6uqK+Ph4zJw5UyTPa19fX0ybNg03b95kG9DaTN2I2oFVUFBAZX7JyMhQc5OZM2eiV69ejc4JUp/7rCHqMrOj2QnymxAREUEZXThRl8wNg8GAtLQ09PX1MWvWLCYt/poRx66urnB3d8eGDRuY2vv4+KCwsFCAM+CNkJAQ7Ny5k9JpNTAwwJIlS+Dq6sq1bVlZGfUA4ZX6yCfUTusFhKejGR0dzVJXotqowskgSgjBrFmzmCJDfvz4gXnz5jFNmHnJRujatStPxsuajB07Fjdv3oS1tTV27dqFkSNHQlxcHEFBQfU6zqZNmxAWFoZt27Zh9uzZ1HZTU1Ps2rWLVieIoH0L6pisXZ9g06ZNaNmyJTIyMhASElLv+gS8IKwC2dVyADY2NiwGcV4i5WbNmoWCggKsXbtWJBPG8vJynD9/HocPH0ZsbCzMzc2xZMkS2NnZUecdGRkJZ2dnJifI3bt3OUoTjh07ljLy14Wg183p06cxc+ZMzJgxA2lpafj58yeAXwb6zZs3c42IF7R97ZR8MTExaGhooGfPnvUyBKuoqEBNTQ0qKipQVlaGhIREnZlvwC8jBSEEVlZWOH36NNN7UVJSEtra2mjTpg3HPqdPn44VK1bg1KlTYDAYqKqqQmJiIpYtW8aTIbx9+/a4c+cOVFVVER0djePHjwP4lY3BSyaZvb09vn//Ti1sa8sKcasH07FjR7x7947F6Pv+/XuutQmAX5kwnz59gpaWFmJjY6l3l7S0NNdir4qKiigoKGCRpiksLORr0Vhf9PT08OzZM2hpaVEZNT169EBUVFSd2um1M3Oqo1OrDQzq6upQV1fHw4cPufbv5eWFrVu3Avhl6Fu6dCk8PT1x/fp1LF26lJZAlcYCPyntwix6K4p3pSD8ztJoDUmfPn3w7Nkz/Pfff1TdlylTpsDOzo565nAKfpowYQKcnJzg7+9PSfjdvXsXXl5eLBmZNVFRUaEkF6ud77WhOyMjPT0d9+7d46nWFB2EhYXBz8+P5dleWlqK8PDwOp0gy5cvh5mZGf777z8cOXIEY8aMgbW1NQ4cOADgV+CXn59fgzhBqikuLmYxbNWe3wrrniWEQF9fHw8fPmSJSOc18EHQ+fXjx4/ZGj6dnZ1pl6PS19dHdHQ0Fi1axLT98uXLVBBDM5yRlZWFmZlZg/cramNwbeNqdZa8n58funTpwrW9vb09QkJCWJx/dSFMNZTLly/j4sWLlAOioREkoPVPR9QOrFatWuHz58/Q1taGlpYWJZ/47NkzrpnUoqBLly6NPsu72QnSxKj9MCaE4O3bt/jw4QP++ecfru3XrVuHTZs2YdSoUdRkPzk5GdHR0Vi4cCGePXuG+fPno6KigsngW82pU6eQmprKst3e3h7dunWrd8Ht+rBu3Trs2LEDbm5uVBbKnTt34OHhgYKCAqxfv55je1dXVxw9erRehat27tzJ034MBoOtE+T+/ftU1gUnCRRuD9Oqqiq29QdatGjBMgGpCbuIfnt7e459cRpDbm4u20lPdaH32ly+fBnu7u6YP3++QKmn4eHhCA4OxpAhQzBv3jxqe+fOnSn9aroQRt8VFRW4ceMG8vLyqIX569evoaioCHl5eY5tvb29sXHjRqo+QTVWVla0ef7rWtDXhJeFlqBRcwkJCbh16xZPE1s6aN26NaqqqmBra4vk5GS24xg8eDCLcfXVq1ccDb7y8vJ48+YNx75TUlJQVVWFnj17Mm2/e/cuxMXFuUbVb9y4EUFBQXBwcKCM8MCv4sMbN27k2FYY7QXNJlq1ahVu3LiB+/fvw8jICAMHDoS3tzcGDBjA0YlSbaSoNoTzM1HdvHkzFi5ciPbt26OyshLGxsaorKyEnZ0d1qxZw7X9kiVLMGPGDMjLy0NbW5vKnrh58yZPi1ZBDRBbtmyBu7s7W63yrVu3Mjk52Tkxhw0bBldXV1hYWCA7O5sytjx8+JCrgWbatGlwcXFhq1ddu9g4HTg5OSEjI4O6XsaOHYvAwECUl5fXGWggzIj8Z8+eURmOp0+fxtixY7F582akpaU1SF0HUVNUVISQkBCm2mXOzs5so36FjSjelc2IlvLycnTq1AkXLlxgmp/Vh6CgICxbtgx2dnbUfF1CQgIuLi74+++/62wXFxdHOdlFldVjbGwsksj5atnJ6gzPms79yspKXLp0iaUeV01SUlIQFxcHc3NzSspswYIFlKRezbpAdPLs2TMsWrQIN27cYJK5pNt5JSYmBgMDA3z69InvtZGg15yGhgbS09NZ+k9PT+f4vxMGS5cuxaJFi/DhwweqsPy1a9fg7+//29cDEZQfP35gz549uH79Otv1ON2y5KI2BtdlXO3VqxdPNqiKigocOnQIV69eZZtBVXueKEw1FBUVFZ4ClumEn4DWZkTvwLKyssL58+dhYWEBJycneHh4ICIiAqmpqRyDNUSFsLO86aDZCdLEqB0VUx1dO2jQIJ4igRISErBx40aWxcL+/fsRGxuL06dPw9zcHAEBAWydIDIyMkhMTGSZNCUmJtJep2Hfvn04cOAAkyHFxsYG5ubmcHNz4+oE+fHjB4KDg3H16lWYm5uzOBXYGUgEvYlrTlIFmbBaWVlh8eLFOHbsGBXF/OrVK3h4eHDUgxVW1GlSUhLs7Ozw4sULlokHp4VCQkICQkJC0LVrVxgZGWHmzJmYPn16vft/9eoV2wjmqqqqekt7NXTfL168wMiRI1FQUICfP39i2LBhUFBQwNatW/Hz50+uWTEPHjzA0aNHWba3bNmStsWvsBb0gkbNtW/fXqQRDjt37sSUKVM4PtuUlZVZnhMaGhp4+vRpnQUrnzx5AnV1dY59L1y4EMuXL2dxgrx69Qpbt27F3bt3ObZ/+vQpW+ekkpIS1+LOwmgP/Mp8qGkQNTY2hpOTE0+LAD8/P2hoaMDHxwcTJ06st4Tg48ePUVhYyJcskaSkJA4cOIC1a9ciKysLxcXFsLCw4NlYsWDBAvTo0QOFhYUYNmwYZdjR09NrEAfSmDFjAPwq0F5bq3zs2LHU73U9u/fu3Ys1a9agsLAQp0+fpjTb7927x9WRsX37djAYDDg4OLDVq6abmhlZQ4cOxZMnT3Dv3j3o6+s3SMq1pKQkVXz56tWrVOaQqqpqvTLsmiKpqakYMWIEZGRkqCCbHTt2YNOmTYiNjYWlpSWt/YviXdmMaGnRooXAxVxlZWXxzz//4O+//2aSl+FWEL7m/EZUWT1bt27F8uXLsXnzZpiZmbGsa+iS4RJUdvLz589o1aoVgF9BIXJycizyjZykyISFvb09CCE4dOgQNDU1G9Sg6+fnBy8vL+zbtw+mpqb1bi/oNTd79mzMmTMH+fn5TAELW7du5apcICjOzs74+fMnNm3aRClL6OjoYN++fTxl2/7JuLi4IDY2FpMnT0aPHj0a3AkhamNw7fVWtR2MVxtUVlYWNRepzhysht3fUphqKBs2bMC6desQFhYmMvmyyspKREZGMq3Lxo0bxyRh2wwronZgBQcHUw7PhQsXQk1NDbdv34aNjQ3mzp0rsnHVhTCzvOmiuTD6H4a8vDzS09NZjLq5ubno0qULiouLkZeXB3Nzc5SUlLC09/Pzg6+vL2bPns2UNn7o0CGsXbuWowSMoCgrKyMlJYXFEJWdnY0ePXpwNcxxKphcnyLJZWVlePbsGTp06NBgL43CwkLY2Njg4cOHVJHzwsJCmJqa4vz58/Uqcs4PXbp0QceOHeHr68s28oNblGdJSQlOnDiBQ4cOITk5GZWVldixYwecnZ15kkjp2rUrPDw8YG9vz1QQav369bhy5Qpu3bol0PnR2ff48eOhoKCAkJAQqKmpUe1v3LiB2bNnU9JuddGuXTucPHkSffr0Yeo/MjISy5YtoxbtjZnv37+joKAAZWVlTNu5GSVjY2Ph7++P/fv381UbQRDKy8shIyOD9PT0ei9QnZyckJuby/baIISgf//+MDAw4OiklJeXR2ZmJos0wLNnz2Bubs7VQKCnp4fg4GAMHTqU6boJDw+Hn58fHj16RGv7mzdvYuzYsVBSUqKyVu7du4eioiJERUXVmT1WTUZGBuLj43Hjxg3cunULkpKSVHH0QYMGcXWKmJmZYevWrVTtlm7dulGyRJ06deL4t09ISOCpZgov8Fq/Spj68vHx8TyPjy7j3ffv3/9IvWobGxuUlZWhb9++2LBhA549e0YVXl60aBHLovt3on///tDX12eqCVNRUQFXV1fk5+fj5s2btPbfGN+VFRUVeP36NbS0tBq87z+FzZs3Izs7GwcPHhSZIacu/etqqWEtLS1aChZXO9hrv1/ozmSIj48XSHZSTEwM7969o6QtFRQUkJmZSQWOvHv3Dm3atKFdJkNeXh737t2DoaEhrf2wQ0VFBd+/f0dFRQUkJSUhIyPD9D072cvMzEyYmppCTEyMq+Y6t/k1IQS7du2Cv78/Xr9+DQBo06YNvLy84O7u3mDG9Q8fPkBGRoZrVnwzv1BSUsKlS5dE5oTQ1dXFpUuXmOS5/xSUlJSQmprKYofKyclBt27d8PXrV47tLSwskJeXB0IIdHR0WJzWdGfxPHz4EDY2Nnj79i31zMvOzoaGhgaioqL4csb+Kfz77784d+6cSB1YTZ1Hjx6xtQXZ2NhwbDdw4EC4uLhgypQpLO9JQWh2+zVBBPHiqqqqIioqiilaEgCioqKoSWxJSUmdhmlvb2/o6elh9+7dVDqdkZERDh8+jKlTpwpyWlyZOXMm9u3bx5KxERwcjBkzZnBtL2h0+/fv3+Hm5oawsDAAoApku7m5oW3btlwdQILoaLZv3x5paWm4evUqJcFkZGSEoUOHCnROvJKTk4OIiAie9OTZIScnB2dnZzg7O+Pp06eUHqe3tzeGDRuG8+fPc2y/bt06ODo64tWrV6iqqsKZM2fw9OlThIeHU4Uv6ULQvm/duoXbt29DUlKSabuOjg5evXrFtb2g9QmEAb8SJx8+fICTkxMuX77M9ntuC9xp06YJVBtBEFq0aAEtLS2+FuFr1qxB165d0bNnT3h6elKTzSdPnsDf3x/Z2dkIDQ3leAwpKSm8e/eOxQny5s0bnp71s2fPxuLFi3Ho0CEwGAy8fv0ad+7cwbJly3iSBBS0/cKFCzFt2jTs27cP4uLiAH79vxcsWICFCxfiwYMHHNt37twZnTt3pmQGMzIysHPnTixcuBBVVVVc/y+CyBJZWVmhbdu2sLW1hb29PXWc+hAeHo6///6bcnJ27NgRXl5edWrTC1NfXliODX6dl4Do9Krd3d2hr6/PIk8ZGBiI3Nxc2qU2AgMDsWDBAkRERGDfvn1o27YtgF/RkyNHjqS1b1GTmprK5AABfskKLV++nKt8nzBoDO/K2jx8+BCWlpYi0zzmhX/++QdnzpyBqqoq5s6dy5Rd/PHjR/To0YN2nXdBSElJwbVr1xAbGwszMzOWDA52Ne8mTpyI0NBQKCoqcpWS4KVmXrVES120aNEC06ZNw/79+4WaNS8qGS5hyE7WrFdYu1ZhdQ0yuunevTsKCwtF4gTh513UpUsXvH37Fi1btuSouc7LPIHBYMDDwwMeHh5UUE1D1O2qyYcPH6hi1p06deKaId0M0LZt2wb/P9VE1NkMopzjCaqG0pA1jtjh6uoKExMTpKamUpl3X758waxZszBnzhzcvn1bpONrzPj7+yMvLw+ampoN5sBqCsXFeSE/Px8TJkzAgwcPmN5Z1fMGbu8qCwsLLFu2DG5ubpg6dSpcXFyEIpfZnAnSxHj48CHGjh2Ld+/e8eXFPXDgAObPn4/Ro0dTmRwpKSm4dOkSgoKC4OLiAn9/fyQnJ+PEiRO0n099cHNzQ3h4ONq3b09d/Hfv3kVBQQEcHByYHkiciozn5uYiLy8PAwYMgIyMDGVY4sbixYuRmJhIFfiujtI+d+4c/vrrL441PwDA1taWo47m4sWL62ybn58v0mJxVlZWWL58uVCNOJWVlYiKisKhQ4e4OkGAX86E9evXIyMjA8XFxbC0tMS6des4FoYXFoL0raKigsTERBgbGzNFpyYkJGDSpEl49+4dx/ZlZWVYuHAhQkNDUVlZCQkJCao+QWhoKGVgpgt2EicpKSkoLS3lKnEyY8YMvHjxArt27cKgQYMQGRmJd+/eYePGjfD394e1tTXHvqsdjnUhqGwQN0JCQnDmzBkcOXKk3mmwqampmDVrFh49esQkSWRsbIzDhw+je/fuHNvb2trizZs3OHfuHOVsKioqwvjx49GyZUu2BS1rQgjB5s2bsWXLFkqeR0pKCsuWLWNJ5aajfXUWTW3jwtOnT9GlSxeuBbYJIbh//z5u3LiBGzduICEhAd++fYO5uTkGDhzItV6TqqoqEhISYGxsjH79+sHBwQFz5szB8+fPYWxsTJ0TOz5+/Ijjx4/j2LFjuHPnDszNzTFjxgzY2trylHW3Y8cOrF27FosWLaKi9RISErB3715s3LiRJQgB+BVZ27dvX0hISHDN5ODFySFIbYYPHz5g1qxZiI6OZvs9uwkrr5q0vBgUBaFt27Y4f/48Vdy8mrS0NNjY2ODly5e09v8no6mpiSNHjrC8F2NiYuDg4MD1XVeb/Px8lJaWwsjIiIp454So35XsyMjI4NkJIkjtMH4JCAjAypUr4eTkhK9fv+LkyZP466+/sHLlSgANF5EvCE5OThy/Z5f15+TkhICAACgoKPDVvjbnzp3DihUr4OXlxVRv0d/fHz4+PqioqIC3tzemTZuG7du3cz1eY0YY2Qjc/ubVCEvSty7y8vIwb9482Nvbw9TUlMWwVV/D0rdv3/Dff/8hJCSEbf1MQXnx4gXldHrx4gXHfbnJkfj4+MDZ2VkksiUlJSXUmr46IFBcXBwODg7Ys2dPc6Q1By5fvoyAgAAEBQWJ5H8n6mwGUc7xRKmGIgxkZGSQmpoKExMTpu1ZWVno3r0713XZnwwneUeAWTZNWIiJiTX64uK8MHbsWIiLi+PgwYPQ1dVFcnIyPn36BE9PT2zfvh39+/fneoyKigqcP38eYWFhuHz5MvT19eHs7IyZM2dCU1OTr3E1O0GaGL1794aGhgbCwsJYvLgfPnzgyYubmJiIwMBAKvrC0NAQbm5ulCYoN4qKihAREYH8/HwsW7YMqqqqSEtLg6amJhX1SAec5KxqUpe01adPnzB16lRcv34dDAYDOTk50NPTg7OzM1RUVODv78/xuNra2jhx4gR69erFZMzOzc2FpaUlVwkTZWVlvnU0xcTEqHSwyZMn015/BWD2QOfl5WHNmjXw8vJiqzvcmD3QombatGlQUlJCcHAwlfKvoaGBcePGQUtLi+dFXmFhIR48eFDv+gSCIojESevWrXHu3Dn06NEDioqKSE1NRceOHXH+/Hls27YNCQkJDXIO/GJhYYHc3FyUl5dDW1ubJcKUl8l+eno6cnJyQAhBx44deS7y/urVKwwYMACfPn2ChYUFdSxNTU1cuXKFksXjRllZGXJzc1FcXAxjY+N6G9P4bd+3b194eXmxRD6dPXsWfn5+SEpK4theRUUFxcXF6Ny5MyWD1b9/f5Yi9HUxduxYlJeXCyxL9OzZMxw9ehTHjh3DkydPMGDAAK7Sibq6uvD19WWJPg8LC8Nff/1Fe8E4QRyXAH/Oy9pGraNHj2Ls2LEsEYt0G7WkpaWRlZXFVvLT1NSUa/2AkpISrrUAeOXHjx8sWTR0afQ3Btzd3REZGYnt27czacx7eXlh0qRJdUZolpeXY+PGjUhLS0OvXr3g7e0Ne3t7ytFraGiIS5cu8SyJ2JDvSm73UmlpKbKzs7kuUGvXDqvONF68eDFPtcP4xcTEBKtXr4adnR0A4Pbt2xg/fjzmzZuH9evXNwknSGOgR48e2LBhA0aMGMG0PSYmBmvXrkVycjLOnj0LT09PocuyCeLw5gcxMTEqG4GTkaaxG2aA/693+Pz5c2pb9fnUZ/zXr1/HoUOHcObMGSgpKWHChAnYu3cv13Z5eXk4fPgw8vLysHv3brRs2RKXL1+GlpYWi6FS2HTp0gVZWVnU2nLSpEm0SLaxY+7cubh69SoCAwOZAkXc3d0xbNgw7Nu3r0HG0RT58OEDpk6dips3bzZ4hjwgGmNwTQSd4wnKyZMnsXv3bup5a2RkhMWLF/OshlJtQ8vLy4OXl1eD2dCAXxn2O3fuhJWVFdP2uLg4LF68mGuGfjMNCzdHd00acw0OdXV1xMXFwdzcHEpKSkhOToahoSHi4uLg6enJNYi8Nu/fv0dwcDA2bdqEyspKjB49Gu7u7izXNVdIM00KaWlpkpWVxbL9wYMHRFpamvb+MzIyiIaGBtHX1ycSEhIkLy+PEELI6tWrycyZM2nvXxBmzpxJRowYQQoLC4m8vDw19ujoaGJsbMy1vYyMDNWmZvv09HSiqKjItb2Ojg559OgRX2O/f/8+cXd3JxoaGkRJSYnMmTOHJCUl8XUsXmEwGERMTIwwGAy2n+rvxMTEaB0HIYQkJyezPd+kpCSSkpJCa9+6urrk48ePLNu/fPlCdHV1ubYvLCwkxsbGxMjIiEhISJBevXoRNTU1YmhoSN69e0fHkIWKtLQ0efz4Mcv2hw8fEhkZGY5tFRQUyLNnzwghhGhpaZGEhARCCCH5+flc21ZTUVFBIiIiyIYNG8iGDRvImTNnSEVFRf1Ogk/++usvjp/6kJCQQH78+FGvNsXFxWT//v1kwYIFxNPTk4SFhZGysjKe2jo5OZFv376xPaaTkxPX9kVFReTTp08s2z99+kS+fv3Ktf3x48eJlpYW+fvvv8mtW7fIrVu3yN9//010dHTI8ePHSUZGBvVhx4ULF3jqpy5evHhBxowZQ8zNzcnBgwep7UuWLCFubm71OlZFRQWJiooiXbp04el5JyUlRXJycli2Z2dnEykpqXr1zQ/9+vUjs2bNIuXl5dS28vJy4ujoSPr378+1fatWrcjdu3cJIb/u4adPnxJCCDl37hzp27cvT2Oo+Y5sSExMTMiePXtYtgcEBBAjIyOu7eXk5IiTkxO5desWX/0XFxeThQsXEg0NDSImJsby+Z35+fMncXd3J5KSktT5SklJkSVLlnB89i1dupRoaGgQV1dXoqenR2xsbIihoSE5fvw4OXnyJDEzMyN2dnZc+/f19SUlJSUs279//058fX0FOre6kJKSIo6OjnW+I+bOncvT/33cuHHE3t6e/Pz5k+neuX79OtHX16dl7IT8mtdWv6OrefDgAdHU1CTe3t7k7du3v/11e/To0Tq/W7ZsGU/HqGue9PjxY2pt9uzZM57nPbySkpJCVFVVSdu2bcmECRPIhAkTSLt27Yiamhq5d++eUPuq5vnz56Sqqor6mdOnsWNkZEQmTpxIkpKSyLNnz+o1/pcvX5KNGzeSDh06EDU1NSImJkaOHz9O/W24cePGDSIjI0OGDh1KJCUlqXt+y5YtZNKkSTwd48mTJ2ThwoXEysqKWFlZkYULF5InT57w1JYQQtLS0oibmxtRV1cnysrKZN68eSQ5OZnn9vyipqZGrl+/zrI9Li6OqKur095/U2bIkCHEwMCA+Pn5kcOHD5PQ0FCmz++OoHM8USJqG9rFixeJiYkJOXXqFCksLCSFhYXk1KlTxMzMjFy8eJF8/fqV+jTTjLBQVlYm+fn5hBBC9PT0SFxcHCGEkNzc3HrPie7evUvmzZtHlJWViZaWFlm3bh1xcXEhMjIyxNPTs17HanaCNDHMzc3JtWvXWLZfu3aNmJqa8nSMyspK8vTpU3Lr1i0SHx/P9OHGkCFDiJeXFyGE2ciRmJhItLW1eT8REaCpqUnS09MJIcxjz8vLI3Jyclzb9+/fnwQEBFDtq2/oRYsWkREjRnBtf+TIETJ58mS2i3ReKS8vJ6dPnyZjx44lLVq0ICYmJsTf35+8f/+e72PWBbfFTUMudLp3705OnTrFsv306dOkR48etPbNYDDYOivevn1LJCUleTpGeXk5OXLkCPHy8iLz588nBw4cIN+/f+ep7cSJE4mfnx/L9q1bt5LJkyfzdAxBaNmyJYmJiWHZHh0dTVq2bMmxbbdu3Uh0dDQhhJCxY8eSmTNnkpcvX5Lly5cTPT09rn3n5OQQAwMDIisrSywsLIiFhQWRlZUlhoaGJDc3l78TEhEKCgoNahQWExNje91++PCBiIuLc20/cuRIsnfvXpbt+/btI6NGjeLavi7naX2dqDk5OSQ6Opq6X3gxMJSXl5OwsDDy5s0brvtyIiEhgcyfP59oaGgQBQUFYm9vTy5fvsy1nYmJCdm0aRPL9g0bNvD8nhYEQRyXhAjHeSkqJ0hISAiRkZEh69atIzdu3CA3btwga9euJbKysiQ4OJhr+8jISDJu3DjSokULYmBgQLZs2UJevXrFc/8LFiwgRkZGJCIigsjIyJBDhw6RDRs2kHbt2pF///1XkFNrMpSUlJDMzEySmZnJ03xHS0uLXLx4kRBCyNOnTwmDwSCXLl2ivr9x4wZp27Yt1+PU9cz7+PEjbYb8rl27kn/++afO7+/fv89T36qqqpTxsua9Q4fhvCbt27cnN2/eZNn+8OFDoqmpSRwcHJqEE+TUqVNkypQppGfPntRcofrDDSUlJabrrZolS5aQVq1a8dR/ly5diKOjI/n58ye1raysjDg6OpIuXboQQn69T3R0dHg8I94Q1OH9pyMrK8s2YIETERERZNSoUUROTo5MnjyZnD17lvz8+ZNISEiQhw8f8nycXr16EX9/f0II8z1/9+5dnp53ERERVGCVh4cH8fDwIL179yYSEhIkIiKiXudUVlZGTp8+TcaMGUNatGhBzMzMyK5du0hRUVG9jsMrMjIybIMCs7KyiKysLC19/i7IyMhQtgxR8eXLF3LgwAHi7e1NBUvdu3ePvHz5kva+BZ3jCYOfP3+SwsJC8uLFC6YPN0RtQ6u9BqsZ6Frz96bwzm9oKioqyN9//026d+9ONDU1iYqKCtOHbsLCwjh+GjP9+vUjkZGRhBBCbG1tyciRI0lCQgJxcHAgJiYmXNu/e/eObN++nZiYmBBJSUkyadIkcvnyZSZ7wK1bt3iy5dak2QnSxBDUi3vnzh2iq6vLNsKfl4eeoqIiZXys+QB//vx5g0S4CoK8vDzJzs6mfq4ee3UkFTdu3bpF5OXlybx584i0tDRZvHgxGTZsGJGTkyOpqals23Tp0oVpMaagoEDk5eWJqalpvRdqNfnx4wfZsWMHkZKSIgwGg0hJSZGZM2eS169f1+s4TQU5OTm2BrX8/HwiLy9PS5/nzp0j586dIwwGg4SHh1O/nzt3jpw5c4YsXLiQdOzYkZa+a6Kurk4yMzNZtmdmZnJ1QggDNzc30q5dO3L8+HFSUFBACgoKyLFjx0i7du3I4sWLObY9cuQIOXz4MCGEkNTUVKKurk7ExMSItLQ0OX78ONe+R40aRUaOHMmUkfDx40cycuRIMnr0aEFOq16kpqaSI0eOkCNHjpC0tDS+jsGvUfjhw4fk8uXLTNffuXPn6tz/69evpKioiDAYDJKbm8v0Tvj8+TMJCwsjrVu35tqviooK20Xq48ePeXpeCupE/fjxI7GysqLeTdV/OycnJ7J06VKu/cvIyPDtoPX29iY6OjpEUlKSWFtbk6NHj9bLeR0REUHExcXJiBEjyPr168n69evJiBEjiISEBDlz5gxfY6oPgjguCRHceUmI6JwghBDyzz//kLZt21JzG11d3XovEt6/f0/8/f2JmZkZkZCQINbW1uT06dNMxkZ2tG/fnopwVVBQoAxs4eHhPDkP/0QkJCSYjCfS0tLUXI0QQl6/fs2T45bBYLANCLl27Rpt0cXu7u4c34O5ublk0KBBXI+jrKxMGVBr3ju3bt2i9T1va2tLlixZwva7rKwsKqOpMbN7924iLy9PFi1aRCQlJcncuXPJ0KFDiZKSElm1ahXX9hcuXCBKSkpM2V+LFi0ibdq0YetMZkdiYiJRU1MjGhoaZMiQIWTIkCGkZcuWRE1Njdy5c4cQ8usZsG3bNv5Osg4EdXgLSmhoKLlw4QL1u5eXF1FSUiK9e/duEpkgY8aMqbfDQFxcnKxatYol07a+ThA5OTkqmK6245OX9bSenh5Zu3Yty/Z169bx/J6u5ufPn+T48eNk+PDhREJCggwYMIDo6+sTBQUFnubq9cXKyopMmTKFlJaWUtu+f/9OpkyZQoYMGSL0/n4nLCwsqGeKKBB1NgMhwpnj8UN2djbp168fS4ZvU7GhVTuNePk0w8zatWtJ69atyfbt24m0tDTZsGEDcXFxIWpqamT37t2096+srMz0kZOTo+x/DeGEEYTo6Ghy+vRpQsivwEZDQ0PCYDCIuro628D+2rRo0YJ06tSJbNu2rc6g769fv/I0165JsxOkiSGoF7dz585kypQp5NGjR+TLly+kqKiI6cMNDQ0NyghY8wEeGxtL2rVrJ7wTpYFRo0aRNWvWEEL+P5OjsrKSTJkyhefU49zcXOLq6kq6d+9OjIyMyIwZM9gaqKvhJqdTX2mdlJQUMn/+fKKiokLatWtHVq9eTfLz88nNmzfJkCFDSPfu3Xk6Dj8ImnYtCKqqquT27dss2xMTE4mysjItfdaOWK/5kZSUJB07diRRUVFs29Y2WHP6cENaWprt37mmzAKd8Ctxwo6SkhJy79498uHDB572l5WVZXt/paen19vjzw/v3r0jgwcPJgwGg4r2YDAYxMrKqt7ZV/U1Cufl5RFzc3OWa5CbrE7Nfdh9xMXFycaNG7n2X9ffPjMzs0GMK4LKFw4cOJCKPKkvffr0IXv37uX5OmXHvXv3yIwZM4ilpSWxtLQkM2bM4NuBVl8EcVwSIrjzkhDROkGqef/+Pfnf//4n8HECAgKogAMNDQ2ydu3aOp1icnJyVERg27ZtKVmx/Pz8BnlmiZLS0lKybds2MmrUKNK1a1eeAz1qZ1vWvna4STIpKysTFRUVIiYmRv1c/VFUVCRiYmJkwYIFwjlJmpg6dSqZPXs2IeT/56f/+9//iJWVFZk1axZt/WZkZJBDhw7V+f2DBw/qLf3Y0BgaGlKSVjWvnbVr15KFCxfydIz//vuPqKiokNTUVDJ//nzSpk0bSgaQV759+0b27dtHReUHBQWxlaQUJoI6vAWlY8eOlBHj9u3bREZGhuzfv5+MHTuWTJgwgfb+BWX//v2kffv2xMfHh0RERPA0P58zZw5RUlIiffr0Ifv27SOfP38mhNTfCdK2bVuSmJhICGG+bs+cOcOTE0NGRqZO2U1e52ipqalk4cKFRFVVlbRu3ZqsWLGC6ZgBAQG0XEcPHjwgbdq0IWpqatSaUk1NjbRt25at5Hcz/09MTAzp06cPuX79Ovn48SNToFNDyBiJOpuhJsKa4/FKnz59yIABA8ilS5fI/fv3SXp6OtOHG03Zhvano6enRzn85eXlKWfW7t27ia2trUjGlJ2dTYYMGUIFrTUlPn36xLN0JLtsZWHQ7ARpYgjqxeUn9bcmLi4uZPz48aSsrIxaqL148YJYWFjwZFwRJQ8ePCAtW7YkI0eOJJKSkmTy5MnEyMiIaGpqNnppHX9/f2JqakpatGhBxo0bR6KiokhlZSXTPoWFhTxFS/KDMNOu+WH69Olk4MCBTI66L1++kIEDB5IpU6bQ2reOjk69jaHcpIDqk33VvXt3tnrmPj4+xNLSsl7j4pWMjAyW66u+EifCQEVFhVok1iQhIaFBIh+mTp1KunXrxpQR8fDhQ9KtWzcyffr0eh3rv//+I8XFxTzvP2bMGDJu3Djy4cMHIi8vTx49ekRu3bpFevTowXFCcOPGDXL9+nXCYDDImTNnmN4Jt2/f5lnaZ9CgQWTRokUs2xcsWED69evH83nwi6DyhSdOnCB6enpkz5495Pbt20w1SOqqQyIMysrKiJOTExXhyS/l5eXkypUrTIa0V69e8bTgE6bjkhDenJe1jUfV0gT1dfo2Ft6+fUu2bt1KjIyMiKysLJkxYwaJi4sj4eHhxMTEhAwbNoxtOzMzM2r+NWTIEEqjdvfu3TxJnDRl7OzsiLq6Opk3bx7x8fHhOdCjdrZl7WsnLCyM47syNDSUHD58mDAYDLJ7924mffSjR4+yDaBobDT12mGipGbWn4aGBvXeyM7O5ilrsZq9e/cSKSkp0q5dO4HWSQ2JoA5vQZGRkaGcvsuXL6ciwbOysppEbQd+5+ffv38noaGhZMCAAURKSorY2NgQcXFx8uDBA5779vT0JP369SNv3ryhsgYTEhKInp4eT47HUaNGsXVgHjp0iAwfPpxre1NTUyIhIUFGjx5NIiMj2dba+/DhA2EwGLydUD0pKSkhwcHBZOnSpWTp0qX1kgn+k6kdENXQMkaizmYQJbKysjxnB7KjMdjQSktLyd27d0lUVFSTnZ+LAllZWepd16pVK6rmVl5eHk91gekiJSWFGBoaiqx/XhC0xighgq2J64JBCCH1K6XeTFPGysoKy5cvx8iRI/lq//XrV0yePBmpqan43//+hzZt2uDt27fo3bs3Ll26BDk5OSGPWLh8/foVgYGByMjIQHFxMSwtLbFw4UK0bt2aa9tLly5BXFwcI0aMYNoeExODqqoqjBo1iqcxpKam4vHjxwAAY2NjdO3alWsbAwMDODs7Y9asWXWOtaysDMeOHYOjoyNP46gPHTp0wIwZM7B+/Xqm7T4+Pvj333+Rl5cn9D5r8urVKwwYMACfPn2ChYUFACA9PR2ampq4cuUK2rdvT2v/oiQqKgoTJ06EnZ0drKysAADXrl3DsWPHcOrUKYwfP17ofYqLi+PNmzdo2bIl9PT0kJKSAjU1NZ7aLl26lOd+duzYwfF7BwcHpKWlISQkBD169AAA3L17F7Nnz0bXrl0RGhrKc1/8oKSkhKtXr6J79+5M25OTkzF8+HAUFRVxbO/s7Izdu3dDQUGBaXtJSQnc3Nxw6NChOtuqq6sjLi4O5ubmUFJSQnJyMgwNDREXFwdPT0/cv3+fY98vXrxA+/btISYmxvkk6yAxMRFDhw5F9+7dMWTIEAC/rruUlBTExsaif//+fB2XVxQUFJCWlgYDAwMoKCggIyMDenp6SE1NxYgRI/Dp0yeO7dmdN4PBACEEDAYDlZWVTN+dP38eo0aNQosWLXD+/HmOx7axseH4vZKSEtLT06Grq8txv7p48eIFRo4ciYKCAvz8+RPZ2dnQ09PD4sWL8fPnTwQFBfF0nO/fv1PP5g4dOkBWVrbeY6meIjIYDI778XKdsfu7CwNLS0tcu3YNKioqsLCw4DjWtLQ0jsc6c+YMDh8+jJiYGBgbG8PV1RX29vZQVlam9snLy4ORkRHKyspY2u/cuRPi4uJwd3fH1atXMXbsWBBCUF5ejh07dmDx4sV8n2djR0lJCZcuXULfvn3r1U5Y1058fDz69OmDFi1a1Kv/xkJFRQWOHz+OzMxMan46Y8YMyMjIiHpojRo9PT2cPn0aFhYW6NatG2bPno25c+ciNjYW06dPx+fPn1na1DVPOXXqFCwtLdGhQwdqG7d5SjU5OTm4fv063r9/j6qqKqbv1q1bV48z4p2ysjJ4eXkhKCgIFRUVAIAWLVpg/vz58PPzg5SUFC39VtOyZUvExMTAwsICFhYWWLp0KWbOnIm8vDx07twZxcXFtPbfGMjJycHhw4cRFhaG4uJiWFtbY/LkyZg4cSLHdmVlZVi4cCFCQ0NRWVkJCQkJVFZWws7ODqGhoRAXF+fYPigoCOvWrcPUqVPRq1cvAEBSUhJOnToFX19ftGnThtqX3Zxlw4YNcHZ2Rtu2bfk462ZERXx8PMfvBw4cSGv/Ne/5mnPzK1euwNnZGYWFhbT2DwARERE4efIkCgoKWOZh3OZ4gtC9e3fs3LkT/fr146u9qG1o0dHRcHBwwMePH1m+o2t+/rtgaGiI8PBw9OzZE/369cOYMWPg7e2NEydOwM3NDe/fvxfJuNLT0zFgwAB8+/ZNJP3zwqhRozB27FgsWLCAaXtQUBDOnz+PS5cucWwvrDVxbZqdIE2Mmzdvcvx+wIABHL+PjIzEmjVr4OXlBTMzM5bForm5OU/jSExMZHIkDB06lKd2oqK8vBwjR45EUFAQDAwM+DqGubk5/Pz8MHr0aKbt0dHRWLFiBTIyMji2f/nyJWxtbZGYmEgZVIqKitCnTx8cP34c7dq142tcDYGsrCwyMzOhr6/PtD0nJwedO3fG9+/faR9DSUkJ/vvvP2RkZEBGRgbm5uawtbVtEINHfHw8tm/fzuS88vLyot0QXM3FixexefNmpKenU+fu4+ND22RXTU0Nly5dQs+ePSEmJoZ3795BQ0ODp7aDBw/maT8Gg4G4uDiO+xQVFcHR0RFRUVHU/7miogI2NjYIDQ2FkpIST33xi4KCAm7duoUuXbowbb9//z4GDhzIddJR05lUk48fP6JVq1aU0YIdKioqSEtLg66uLjp06ICDBw9i8ODByMvLg5mZGU/3XFFREUJCQqjr1sTEBM7Ozjz/3dLT0/H3338zXXcrV67k+xlaH0aPHo2uXbtiw4YNUFBQQGZmJrS1tTF9+nRUVVUhIiKCY/sXL15w/F5bW5vpdzExMbx9+xYtW7bkaJTlZaHg6OiILl26wMPDg+N+dTF+/HgoKCggJCQEampq1CLzxo0bmD17NnJycvg6bn0ICQnBzp07qb4MDAywZMkSuLq60t53ffH19YWXlxdkZWXh6+vLcV8fHx+O3yspKcHW1hYuLi4szs9qSktLsW3bNq7HAn5dh/fu3YO+vj7P86umirGxMY4fPy7S86yqqkJubi5bQzS3+bEo+fHjB6SlpUU9jCaJq6sr2rdvDx8fH+zduxdeXl7o27cvUlNTMXHiRISEhLC0EeY8BQAOHDiA+fPnQ11dHa1atWJyxDIYDFoNc4BwHN78MGPGDDx58gQWFhY4duwYCgoKoKamhvPnz2PVqlXIyspqkHE0BqqqqnDx4kWEhITg8uXL+PnzJ0/tCgsL8eDBAxQXF8PCwoLn+RWvAS6NxbjJLbikJtwCTZoRHa6urvj06RNOnjwJVVVVZGZmQlxcHOPHj8eAAQOwa9cuWvsPCAjA6tWrMWvWLAQHB8PJyQl5eXlISUnBwoULsWnTJtr6jouLw5o1a7B582a2NjRFRUWejiMqG5qBgQGGDx+OdevWQVNTs0H6/F3w9vaGoqIiVq1ahRMnTsDe3h46OjooKCiAh4cH/Pz8aO2/9vOTEII3b94gMDAQ7du3x+XLl2ntXxBUVVWRmJgIIyMjpu1PnjxB3759uQY10rUmbnaCNDHqim6thttEp77RsbUJDw/HtGnTWKKLysrKcPz4cTg4OHBsL0o0NDRw+/Ztvg14MjIyePz4MXR0dJi2P3/+HCYmJigpKeHYfuTIkSgqKkJYWBgMDQ0BAE+fPoWTkxMUFRURHR3Nsf2PHz+QmZnJdnFP94Rx9OjRmDJlCpycnJi2Hz58GMePH0dMTAyt/YuSf//9F05OTpg4cSIV4ZqYmIjIyEiEhobCzs6OY3t3d3fo6+vD3d2daXtgYCByc3NpnzDyw5w5cxAeHo7WrVujoKAA7dq1qzMqLT8/n5YxEEJQWFgIDQ0NvHr1ijLkGxkZsTjj6GLcuHEoKirCsWPHqKi6V69eYcaMGVBRUUFkZCTbdt++fQMhBCoqKsjJyWFyIFVWViIqKgre3t54/fp1nX33798fnp6eGD9+POzs7PDlyxesWbMGwcHBuHfvHlfjQnXGhIyMDJVFk5KSgtLSUsTGxsLS0rK+f44GJSsrC0OGDIGlpSXi4uJgY2ODhw8f4vPnz0hMTGSK1G1sbNy4Ef7+/hgyZAi6du3KEt1V+1lQGzU1Ndy+fRuGhoZMkXbPnz+HsbEx7U7ndevWYceOHXBzc0Pv3r0BAHfu3EFgYCA8PDxYMgJ/FyoqKhAcHIxJkyY1LxD54PLlywgICEBQUBCLk7EhSEpKgp2dHV68eIHaS5vGYgisC0VFRUyYMAH29vYYMmQI3xl8fyJVVVWoqqqChIQEAOD48ePUXH/u3LmQlJSkfQza2tpYsGABVqxYQXtfjYmioiKsWbMGhYWFmD9/PqUy4OPjA0lJSaxevVrEI+QOHUFO79+/Zwl+qc369euxbNkyFodVaWkp/v77b9qyh6ohhCAiIqLO7KUzZ84Itb+m5rRpTGRmZvK8L91BCKLOZujUqRN8fHxga2vLND9et24dPn/+jMDAQNr6rr6Ga2cb82JDKy8vh4yMDNLT02FqakrbGDmhqKiI+/fvN+r1U1Phzp07uHPnDgwMDDB27Fja+6v9/GQwGNDQ0ICVlRX8/f15UrQRFXJyckhKSoKZmRnT9gcPHqBnz55c17R0rYmbnSBNjK9fvzL9Xl5ejvv372Pt2rXYtGkTJVtSF/WNjq1NXZHNnz59QsuWLRv1xMXDwwNSUlJ8e2tbtWqFo0ePUpJE1Vy9ehV2dnZcU+FkZGRw+/ZtSs6pmnv37qF///4cb+Lo6GjMnDmTrbe0ISaMgqZdN2WMjIwwZ84clqjuHTt24MCBA9TCqS7atm2L8+fPs8iepaWlwcbGBi9fvhT6mIVBdHQ0cnNz4e7ujvXr17NIOlVDl7xLVVUVpKWl8fDhwwbJPGBHYWEhZXyvllwrLCyEqakpzp8/X2f2lpiYGEdJHgaDAV9fX44GgpiYGJSUlGDixInIzc3FmDFjkJ2dDTU1NZw4cYLlOVSb/v37Q19fHwcOHKCMQxUVFXB1dUV+fj7XrEJhUFRUhIiICOTl5cHLywuqqqpIS0uDpqYmTxIMgsgXAsCRI0cQFBSEZ8+e4c6dO9DW1sauXbugq6uLcePG1dlOUGc/JxksBoPB1XGooqKCxMREGBsbM034EhISMGnSJLx7945je0HR0NBAQEAAbG1tmbYfO3YMbm5ubFPp2aGoqIj09HTo6enRMUxakJWVxePHj+tlxA8ICOB5X24OsKbMhw8fMHXqVNy8eROysrIsUZLsZImESZcuXdCxY0f4+vqidevWLM9gujMHBSEyMhJHjx7FxYsXoaSkhGnTpsHe3h7dunUT9dCa4YGGfNZNnDgRoaGhUFRU5Cq5JGxD9u+GoEFOgkDHerqoqIhJupETixcvxv79+zF48GBoamqyPC8PHz5c7/6boYfqNQU3k11DOpASEhKYpBsbKpuh5hytZcuWuHLlCjp37oycnBz06tWLa1S5IAgqRaanp4fIyEh07txZmMPiGWdnZ/Tt2xcuLi4i6b+ZP5PBgwfD1NQUe/bsYdq+cOFCZGZm4tatWxzb07UmbnaC/CbEx8dj6dKluHfvHq391CWNk5GRgcGDB9O+yBUENzc3hIeHw8DAgG10Ljfd37lz5+LOnTuIjIykvOi5ubmYNGkSunfvjoMHD3Js37FjR/z7779UVHY1ycnJsLOzQ25ubp1tRZ3C+CdH8EhJSeHhw4cs2Qe5ubkwNTXFjx8/OLaXlpZGVlYW3+1FjZOTEwICAup0gnBi8ODBHJ0B3GQmTExMEBISQjneRAEhBFevXsWTJ08A/HKKcZvsx8fHgxACKysrnD59GqqqqtR3kpKS0NbWZnIc8srnz5+hoqLCtT4D8Mvpev/+fXTq1Ilp+6NHj9CtWzfaswkyMzMxdOhQKCkp4fnz53j69Cn09PSwZs0aFBQUIDw8nK/jvnz5EuvXr0dwcDDH/fbt24d169ZhyZIl2LRpE7KysqCnp4fQ0FCEhYXh+vXrdbYVtbN/2rRpUFJSQnBwMCUFpqGhgXHjxkFLS4t244SysjJSUlJYnI/Z2dno0aMH11o41dScrDYUdd0fDAYD0tLS0NfXx6xZs1iyGqsZNGgQlixZUq9aS7zWfuHFAdaUGTp0KAoKCuDi4sLWqEZHvbKayMnJISMjo8EyBWtTUVGBGzduIC8vD3Z2dlBQUMDr16+hqKgIeXl5no7xv//9DxERETh27Bji4uKgp6cHe3t72qPCAeGMv6FoTJHRACj5vHnz5tHeV8052axZszjOBxrCkF1UVITk5GSWbAIGg4GZM2fS3r8gCBrkJAh1rafj4uIwbdo0fPjwgWP7rVu3QkdHB9OmTQMATJkyBadPn0br1q1x6dIlroZWVVVV/PvvvywSz800PrgFsNZEFFmYDQk/NaAaCyEhIThz5gyOHDnCtC5sKL5//44pU6ZAQ0ODrZzX7xykIwyePn2KPXv2MClTuLm5UeouzbBH0BqjdK2Jm50gvwlPnjxBt27duBah42Z4qivCtbrYaEZGBkxMTKjIYuCXvMuzZ88wcuRInDx5sv6DbyA4aQDzovv79etXjBw5EqmpqVQE+MuXL9G/f3+cOXOGa/TNuXPnsHnzZuzdu5eK7ktNTYWbmxtWrFjB0ejSnMIoOvT19eHl5YW5c+cybQ8KCoK/vz9XLUJTU1PMmzcPixYtYtq+Z88e7Nu3D48ePRL6mBsLtReW5eXlSE9PR1ZWFhwdHbF7926O7aOiorBt2zbs27dPJOnDgmYEvHjxAlpaWjw5LYSNpqYmjhw5guHDhzNtj4mJgYODA+3ZBEOHDoWlpSW2bdvGZAy/ffs27Ozs8Pz5c76Om5GRAUtLS66OCGNjY2zevJnSEq3uPysrC4MGDeKYzSBMZz+vhcVr8vLlS4wYMQKEEOTk5KBbt27IycmBuro6bt68yVVmQ1Dc3NzQokULlsCAZcuWobS0FHv37uXpOKJwguzcuRObNm3CqFGjqICD5ORkREdHw8PDA8+ePcORI0ewZ88ezJ49m6X9yZMnsXLlSnh4eLANlvjd63oIgqysLO7cuSOyKEcrKyssX76ckuRpSOgo3Pjo0SPMmDEDmZmZtDte6So8SReNLTJ6y5Yt2LFjB6ytrf8o41JUVBRmzJiB4uJiKCoqstRCacwGSUDwICd+qHbUf/36leVvVllZieLiYsybN4/re1ZXVxf//fcf+vTpgytXrmDq1Kk4ceIEVTA6NjaWa/vLly+zBMrQRUBAAObMmQNpaWmu2ZO/6/3yO1DX/65moMmAAQPqlFAWFH5qQAmTW7duYf/+/cjPz8epU6fQtm1bHDlyBLq6ulwLpltYWCA3Nxfl5eXQ1tZmmV/SXTsqJCQE8+bNg7S0NNTU1Fie179zkI6gnD59GtOnT0e3bt0omeCkpCSkpKTg+PHjmDRpEq39T5o0CT169GCR3Ny2bRtSUlJw6tQpWvsXlPT0dGzbto2pri+vNUbpWhM3O0GaGLWjn6oL4/j5+aGiogIJCQkc26uoqDD9Xl5eju/fv0NSUhKysrJ1Tliri436+vrC09OTKSpMUlISOjo6mDRpUoNo74oSQgiuXLnCdBPzWmxTRUUF379/R0VFBZM8jYSEBMuLsPb/oTmFUXTs27cPS5YsgbOzM/r06QPgl1c7NDQUu3fvZnGO1ObQoUNYtGgRvLy8KAmja9euwd/fH7t27WJriPvd+euvv1BcXIzt27dz3K/mPSMpKQkZGRmm7+leYAuaERAdHQ15eXlqYrx3714cOHAAxsbG2Lt3L8vzWJi4u7sjMjIS27dvZ7puvby8MGnSJNpr0SgpKSEtLQ0dOnRgMoa/ePEChoaGfBsXeHWCyMjI4MmTJ9DW1mbqPycnB+bm5igtLWVpI0xnv6CFxSsqKnD8+HEmuYEZM2aw3APCYunSpUx9h4aGQktLi8rCunv3LgoKCuDg4MCS0lwX8+fPx4YNG6Curk7LmNkxadIkDBs2jCUie//+/YiNjcXp06exZ88eBAcH48GDByztBa2bVhN+HGBNGUtLS/zzzz8iy9yLjIzEmjVr4OXlxdYQTacDS1iFG3/8+IHz58/j6NGjiI6OhqamJmxtbWkvuklX4Um6aGyR0YJKIPKLlZUV2yCsb9++Yfz48TwVdReEjh07YvTo0di8eXODFWMXJoIGOfFDWFgYCCFwdnbGrl27mGT6qtfT1UY2TsjIyCA7Oxvt27fH4sWL8ePHD+zfvx/Z2dno2bMnvnz5wnUc0dHROHToEG3zipro6uoiNTUVampqIrtffgf4DWYVFrq6uvjw4QO+f/9OrWG+fPkCWVlZyMvL4/3799DT08P169cpGWFhIsoaUKdPn8bMmTMxY8YMHDlyBI8ePYKenh4CAwNx6dIlXLp0iWP7altaXfj4+AhzuCy0atUK7u7u8Pb2bq47Vk86dOiAGTNmsNRE9PHxwb///ou8vDxa+9fQ0EBcXBzbuhpDhw6lPbBR1NCxJm52gjQx6op+6tWrFw4dOsRXREdOTg7mz58PLy8vjBgxguO+YWFhmDZtGqSlpevdz59OWFgYz/vWlo1oDCmMKSkpdRbQ4yYlJiiFhYVgMBhUBk5ycjKOHj0KY2NjzJkzh9a+gV/GFX9/f6YUSC8vL451BWqyb98+bNq0iSqEraOjg7/++ov2yWpjJTc3Fz169ODqxOB2z9AtryJoRoCZmRm2bt2K0aNH48GDB+jWrRs8PT1x/fp1dOrUiVapirKyMnh5eSEoKAgVFRUAgBYtWmD+/Pnw8/NjyW4RNi1btkRMTAwsLCyYnBBXrlyBs7MzCgsL+TpufTJBtmzZgnHjxjH1v2fPHhw+fJhtxJWwnP1NsbA4p0zJmvCSNSlK5OXlkZ6ezjayt0uXLiguLkZeXh7Mzc1RUlLC0l7QummA4A6wpkpsbCx8fX2xadMmtvMURUVFWvsXpgOrvghauDEmJgZHjx7F2bNnISEhgcmTJ2PGjBk8B9mIevzNiAYxMTG8ffuWJVDj/fv3aNu2LcrLy2ntX05ODg8ePGhSdZ9qImiQkyDEx8ejT58+LM9JXmnTpg0iIiLQp08fGBoaYuPGjZgyZQqePn2K7t2749u3bxzbl5aWYsKECUhMTISOjg7LOOiOSm+GP/gNZhUWx44dQ3BwMA4ePMgkDT537lzMmTMHffv2xfTp09GqVStERETQOpaGxsLCAh4eHnBwcGB6T96/fx+jRo3C27dvRT1EjqiqqiIlJaVZVYQPZGVlkZmZybK2yMnJQefOnWmfI8nIyCA9PZ1FeuvJkyewsLBgG9jXmKisrMTZs2cpO5qJiQlsbGxoyxjjBQnuuzTTmHj27BnT72JiYtDQ0BDIKWFgYAA/Pz/Y29tTuvd1QbfR8XdGkL/dsWPHEBsbC2lpady4cYMlhZFuJ8jmzZuxZs0aGBoasmh9N0SUq52dHebMmYOZM2fi7du3GDZsGExMTPDff//h7du3tOtlT5gwARMmTOC7/fz58zF//nx8+PABMjIyXPW1a0Zlc4NuBxQd3Llzh6dnlqieN9UZAQwGA0OGDKkzI4Abz549g7GxMYBfEURjx47F5s2bkZaWRrsOs6SkJHbv3o0tW7ZQESodOnTgGK3JrchqTbgVXLWxscH69euprAkGg4GCggKsWLGC9rRh4Nc9tHDhQvz48QOEECQnJ+PYsWPYsmVLnfWbqqOwdHR0MH36dL4dRfv27cOBAweYCovb2NjA3Nwcbm5ubJ0g58+f5/n4NjY2fI2LE5xqpNSXa9eu4dq1a2wd5ocOHRJaP+xQVVVFVFQUixRfVFQUpcFcUlJSZ40jQaPG63KAeXh4oKCgoFE6wIRF9TOxWvO3Gk5OCF5rHAHcM/9qz48bkqqqKrbn9/LlS57qaU2YMAFjxoxBeHg4Ro8ezbdhlF8EHX8zDUtNVYBHjx4xGd8qKysRHR2Ntm3b0j6OESNGIDU1tck6QebPn49WrVrB39+fmqsYGRnhxIkTbIOchPm8GjhwIKqqqpCdnc32XcnNATpx4kTY2dnBwMAAnz59wqhRowAA9+/f56kukqOjI+7duwd7e3u2NZwaij8tY1JQ2GX41AxmpZs1a9bg9OnTTIZ0fX19bN++HZMmTUJ+fj62bdtG6zz/x48fyMzMZHvf0DE/rubp06ds70slJSWea+WJEkdHR5w4cQKrVq0S9VCaHIMGDcKtW7dYnq0JCQlca1oIAzMzM5w4cYLF3nX8+HHKztBYyc3NhbW1NV6+fEk5cbZs2YL27dvj4sWLPDnlcnJy6gzE5tcG2OwEaWLQldYtISFBRalzorKyEjt37qQ0R8vKypi+b+z6r6KGX0/o6tWr4evrK7IUxt27d+PQoUOYNWtWg/cNAFlZWZS++8mTJ2FqaorExETExsZi3rx5tDpB9PT0kJKSAjU1NabtRUVFsLS05Jq2XVpaCkIIZGVloaGhgRcvXuDgwYMwNjZmqddQzf3793kaW2NfNNQ2qlfL96WmpmLt2rU8HaOqqgq5ubl8LRL5pbo+T3p6OkaMGFFnRgA3JCUlqeiQq1evUpk/qqqqXKP0BMXZ2Rm7d++GgoICU/psSUkJ3Nzc2Bqja8oyCIq/vz8mT56Mli1borS0FAMHDsTbt2/Ru3dvbNq0qc523BwxvC40XF1dISMjgzVr1uD79++ws7NDmzZtsHv3bkyfPp1jWysrK3z48IHvzLPy8nKq7lNNunbtSmXl1IbXQtwNpXHPL76+vli/fj26deuG1q1bN/gzau3atZg/fz6uX79OvTNSUlJw6dIlqq7BlStXMHDgQI7HefToEds5DrcFNj8OsN8FfhxpwpTlE2VB2OHDh2PXrl0IDg4G8Os+LS4uho+PD08O73fv3onU2SDo+Jv55TA6f/482+eGsINVunTpQgVqVMus1kRGRoZn2UJBsLa2hpeXFx49esQ2+4tOg6SwqE+QkzCfV0lJSbCzs8OLFy9Y1B14ec/v3LkTOjo6KCwsxLZt26h56ps3b7BgwQKu/V+8eBExMTFc6xjQxZ+aMUkH9QlmFZQ3b96wncdWVFRQztg2bdrgf//7Hy39R0dHw8HBgW1dP7rnx61atUJubi50dHSYtickJDQJR3BlZSW2bduGmJgYmJubszyvm2JQZUNhY2ODFStW4N69e5Tka1JSEk6dOgVfX1+mQDY63ntr167FxIkTkZeXxyStfuzYsUZfD8Td3R16enq4c+cOFYz26dMn2Nvbw93dHRcvXuTY/sCBA5g/fz7U1dXRqlUrlkBsfm2AzXJYTQBuBcRqwi0joHa0abVRMjAwEO3bt8fly5c5tl+3bh0OHjwIT09PrFmzBqtXr8bz589x9uxZrFu3rrmYGQdyc3MxevRovHr1ivKEPn36lCdPqKhTGFu3bo2bN2/yVMCIDuTl5ZGVlQUdHR3Y2Nigb9++WLFiBQoKCmBoaEhrGmBdcgPv3r2DlpYWfv78ybH98OHDMXHiRMybNw9FRUUwNDSEpKQkPn78iB07dmD+/Pm0jV3UODk5Mf1enblmZWVVpwOoJoIuEgVFUPk/GxsblJWVoW/fvtiwYQOePXuGtm3bIjY2FosWLUJ2draQR/z/1FXP5OPHj2jVqlWdxnhhk5CQwKThOXToUI77175m6qI+UmLfv39HcXExz8XT+vfvz5R51rFjR5iamiInJwdubm5cJ1zCKizeFGndujW2bduGmTNnimwMiYmJCAwMxNOnTwEAhoaGcHNzo+ROOJGfn48JEybgwYMHTNKj1ZNubs8cZWVlpKSksLwrs7Oz0aNHjyYRLdiUOH/+PEaNGoUWLVpwzaai0yDLT+HGb9++URJh3JzidEuJ0VV48k/h2rVrsLGxgZ6eHp48eQJTU1M8f/4chBBYWloKXUKwek6kp6eH5ORkJslOSUlJtGzZskFkJjgFZTV2h301RUVFiIiIQH5+PpYtWwZVVVWkpaVBU1OT1myaLl26oGPHjvD19WUbMCDMoBR2dOrUCSdPnqS1VlJdNEXJ0MZOeno6BgwYQHuAlbW1Nd6+fYuDBw/CwsICwK/AvdmzZ6NVq1a4cOECoqKisGrVKrZ11wTFwMAAw4cPx7p166CpqSn043Niy5Yt+Pfff3Ho0CEMGzYMly5dwosXL+Dh4YG1a9fCzc2tQcdTXzjJ3jZ2qVtRw2sAMp3vvYsXL2Lz5s1IT0+n6hL7+PhwDeoSNXJyckhKSmKpZ5KRkYG+ffuiuLiYY3ttbW0sWLCApSi8oDQ7QZoAnAqI1YSXYmK1b2IGg0EZJf39/dG6dWuO7Tt06ICAgABYW1tDQUEB6enp1LakpCQcPXqUp7GKgpKSEpYC5LxSUVGBo0ePYsSIEXy/dEePHg1CCP777z8WT6iYmBhHT6iHhwc0NDRElsK4bds2vH79mvZiynXRs2dPDB48GNbW1hg+fDiSkpLQuXNnJCUlYfLkyXj58qXQ+6w2qIwfPx5hYWFMC5LKykpcu3YNV65coQxtdaGuro74+HiYmJjg4MGD2LNnD+7fv4/Tp09j3bp1VFZQM6yIepEoKAUFBViwYAEKCwvh7u4OFxcXAL/u58rKyno5uHnl27dvIIRARUUFOTk5TMaRyspKREVFwdvbm6fMP0EoLCykpSgirwhSMFZFRQVJSUkwNDREQEAATpw4wZR5xu096+bmhvDwcLRv355tYfGa0Ve/W+SVmpoakpOTm6zm8NixYyEuLo6DBw9CV1cXycnJ+PTpEzw9PbF9+3auae9/mgOspiwPN+pjbPvx4wdLND07R0DNIAVRG2TrW7ixpqO6ut5fbRqinkk1dBSe/FPo0aMHRo0aBV9fX0orvmXLlpgxYwZGjhz5Wwe7NGUyMzMxdOhQKCkp4fnz53j69Cn09PSwZs0aFBQUcC1CXQ2vz6uayMnJISMjgyfpKk7wm7V48eJF7NmzB0FBQSyR7XSjoaGBgIAApoxJ4Jf0s5ubG9so/2Z+IWgwq6C8ffsWM2fOxLVr16i5bEVFBYYMGYIjR45AU1MT169fR3l5OU/BbvVFUVER9+/fF8kckxCCzZs3Y8uWLVSWv5SUFJYtW4YNGzY0+HiaaaYpoKqqigsXLrAEoiUmJmLs2LFcVYQUFRWRnp4u9GyrZidIM/VCTk4Ojx8/hpaWFlq3bo2LFy9SkkAWFhb4+vWrqIdYJ/Ly8pg6dSqcnZ35Sv+VlZXF48eP+ZZcEMQT6u7ujvDwcHTu3FkkKYxVVVWwtrZGdnY2jI2NWfrnVh9AUG7cuIEJEybg27dvcHR0pKR8Vq1ahSdPntDSf7VBpWY0cDUtWrSAjo4O/P39MWbMGI7HkZWVxZMnT6ClpYWpU6fCxMQEPj4+KCwshKGhIdtiWsKszSBKUlJSUFVVhZ49ezJtv3v3LsTFxdlKBtVEWItEfmmK8n91GdOqYTAY8PX1xerVq1m+q66FwgvcimaKi4ujX79+sLe3x+TJk1mKOdKNIAVjBc08E0aR8WvXrmHnzp2Uk9TIyAhLlizhmkkjalasWAF5eXme5e7oQJACfOrq6oiLi4O5uTmUlJSQnJwMQ0NDxMXFwdPTk6tU4Z/mAKt+3nBbSvBiyC8pKcGKFStw8uRJfPr0ieX7phBVXh/i4+PRt29fSEhIID4+nuO+jT3S70+nZlCYiooKEhISYGJigoyMDIwbNw7Pnz+npd8tW7ZAU1MTzs7OTNsPHTqEDx8+CD1ykhM/fvwQqD6lKBg6dCgsLS2xbds2pkLHt2/fhp2dHcf/m6DPKysrKyxfvpyn+nLsEDRrUUVFBd+/f0dFRQVkZWVZ1nV0zm+bMyb5R9BgVmHx5MkTKpvd0NCQpWAzXTg7O6Nv375UUJkoKCsrQ25uLoqLi2FsbMy1zie79s+ePUOHDh2Yak42FLm5ucjLy8OAAQMgIyNDBVs00wwdODg4IC0tDSEhIZRM8d27dzF79mx07doVoaGhHNu7uLige/fumDdvnlDH1VwTpJl60a5dO7x58wZaWlro0KEDYmNjYWlpiZSUFL6LyDYU//77L0JDQ2FlZQUdHR04OzvDwcEBbdq04al9jx49kJ6ezrcTREpKiq1GZnFxMSQlJTm2ffDgAZV2mpWVxfRdQ7y43N3dcf36dQwePBhqamoN/rIcNGgQPn78iG/fvjEZU+fMmcOx0LMgVNef0NXVRUpKCtTV1fk6jr6+Ps6ePYsJEyYgJiaGKtj7/v37OiPFGnuGA68sXLgQy5cvZ3GCvHr1Clu3bsXdu3c5tu/Zsydyc3NF5gTx9fXlKP/HC4IYZPnh+vXrIITAysoKp0+fprLOgF8yGdra2nU+83itS8ELqampOHr0KNavXw83NzeMHDkS9vb2GDt2LK3vCmEUjDUxMUFQUBCsra1x5coVKsLr9evXLLWB2CFokfF//vkHixcvxuTJk7F48WIAv6ThRo8ejZ07d2LhwoUCHZ9Ofvz4geDgYFy9elUkDnt2spP1KcBXWVlJ1WZQV1fH69evYWhoCG1tba5Zf8Cv97OlpSUAIC8vjzqOuro607v7d1lwCrMY+fLly3H9+nXs27cPM2fOxN69e/Hq1Svs378ffn5+QuuHLp4+fYo9e/YwOS4XLVqETp06sd2/pmNDV1cX7du3Z7kuCCEoLCykb9A1qO/4Gwt1FatmMBiQlpaGvr4+Zs2axbPUIj/IyclRQRKtW7dGXl4eTExMAIDWqPb9+/ezzcA3MTHB9OnTaXeCVFZWYvPmzQgKCsK7d++QnZ0NPT09rF27Fjo6OiI1VPJCSkoK9u/fz7K9bdu2THMHdgj6vHJzc4Onpyfevn3Ltp4Kt8y5xYsXQ1dXF9euXWObtcgNUWX2A8DMmTOxb98+lvlAcHAwZsyYIaJRNQ1q10YUFZ06dRLJuyEwMBBTpkzBrVu32N43DSHLLikpCUVFRSgqKtbLAfL9+3e4ubkhLCwMAKjnpZubG9q2bQtvb2+6hgzgl/rI1KlTcf36dTAYDOTk5EBPTw8uLi5QUVGBv78/rf03wz9NMSizmoCAADg6OqJ3795M2WM2NjbYvXs31/b6+vpYu3YtFUgutHueNPPb4+HhwfOHGytWrCCbNm0ihBBy/PhxIiEhQfT19YmkpCRZsWIF3aciFN6/f0/8/f2JmZkZkZCQINbW1uT06dOkvLycY7sTJ04QPT09smfPHnL79m2SkZHB9OHGzJkziYmJCUlKSiJVVVWkqqqK3Llzh5iamhJHR0chnR09yMvLkwsXLoh6GE2SU6dOkRYtWhAxMTEybNgwavvmzZvJyJEjRTgy+pGTkyN5eXks2/Pz84m8vDzbNjXvqTNnzhBjY2Ny+PBhkpqaWu97TlD09PSo615eXp7k5uYSQgjZvXs3sbW15do+JyeHGBgYEFlZWWJhYUEsLCyIrKwsMTQ0pI5FF8+fPyeVlZW09sELVVVVJC4ujri6uhIVFRWipKREnJycaOuPwWAQMTExIiYmRhgMBstHVlaWhISEcDzG9evXibKyMhETE2Ma68qVK8mECRNoG3s1bdu2JXv27GHZHhgYSNq0aUN7/4IwaNCgOj+DBw+mvf9Ro0aRkSNHkk+fPlHbPn78SEaOHElGjx7NtX2/fv1IZGQkIYQQW1tbMnLkSJKQkEAcHByIiYkJXcNuhhDSvn17cv36dUIIIQoKCiQnJ4cQQkh4eDgZNWqUCEfGnYiICCIhIUF69epFzad79+5NJCQkSEREBNf2YmJi5N27dyzbP378SMTExOgYMhOCjl+U7Nixg6ipqRF7e3sSEBBAAgICiL29PVFXVyebNm0irq6uREpKigQHB9M2hnHjxlHH9/T0JPr6+mTjxo3E0tKSDBkyhLZ+paSkSH5+Psv2vLw8IiUlRVu/1fj6+hI9PT3y77//EhkZGWq+d/z4cdKrVy/a+xcUDQ0NkpaWRgj5NcerHn9sbCxp164dx7aCPq/YzU+q5y283PNqamrUPFhRUZE8efKEEELItWvXSJcuXbi2FyWLFi0iioqKxMTEhLi4uBAXFxdiampKFBUVyaJFi+pll/iTqbYlNCQVFRXk4MGDxNbWlgwZMoQMHjyY6UM3Bw8eJBISEkReXp5oa2sTHR0d6qOrq0tr35WVlcTX15coKipS6wwlJSWyfv16ntZb7u7upGvXruTWrVtM6+OzZ882yD07c+ZMMmLECFJYWMj0vIuOjibGxsa0998M/6xdu5a0bt2abN++nUhLS5MNGzYQFxcXoqamRnbv3i3q4fFEdnY2OX/+PDl//jz1vuSFmvd47Y8g93yzHNYfgDCkOerizp07uHPnDgwMDDB27Fh+hidS9uzZAy8vL5SVlUFdXR3z5s2Dt7c32+wCdnrT1SnIvEg9FBUVwdHREVFRUSye0NDQ0EYd/a+trY2YmBiRRQTq6upyjJzlptEvKNeuXcO1a9fw/v17liicamkuTrx9+xZv3rxB586dqesoOTkZioqKjT7KUhDU1NRw4cIFqvBhNbdv34a1tTW+fPnC0oabvEp97jlBEVT+T5A6QMKgqKgIycnJbK9bBwcHWvtmR1paGlxcXJCZmUnb/05YBWMrKytZMs+eP38OWVlZ2osEy8vLIz09nSUDKicnBxYWFlyLyP3JCFqALyYmBiUlJZg4cSJyc3MxZswYZGdnQ01NDSdOnICVlRWdw2/ScNPP5/bMkZeXx6NHj6ClpYV27drhzJkz6NGjB549ewYzM7NGfd136NABM2bMYCno6+Pjg3///ZfKCqoLMTExvHv3jul5Bfx6nhkbG6OkpEToY66JoOMXJZMmTcKwYcNYpBL279+P2NhYnD59Gnv27EFwcDAthXqBX3PQ4uJimJubo6SkBJ6enrh9+zYMDAywY8cOvjPIuWFgYAAfHx/Y29szbT9y5Ah8fHxonxvr6+tj//79GDJkCJOc1JMnT9C7d2+2c7zGhKurKz59+oSTJ09CVVUVmZmZEBcXx/jx4zFgwACO2RKCPq9evHjB8Xtu14yKigrS0tKgq6uLDh064ODBgxg8eDDy8vJgZmbGVmq3sUCnXeJPICQkBDt37kROTg6AX8+BJUuWwNXVlfa+Fy1ahNDQUFhbW7Ot1bhz505a+2/VqhXc3d3h7e3Nc7FqYbFy5UqEhITA19cXffv2BQAkJCTgr7/+wuzZs7Fp0yaO7bW1tXHixAn06tWL6XmZm5sLS0tL2ovat2rVCjExMejcuTNT//n5+TA3N2/Uc6w/naZck7mx0iyH9QcgqDQHJ3r37s1i4GzsvHv3DmFhYQgNDcWLFy8wefJkuLi44OXLl9i6dSuSkpIQGxvL0k4Q2QdCCL59+4bjx4/j1atXTHIDopL6qQ9//fUXfHx8cPjwYdrkpzixZMkSpt/Ly8tx//59REdHw8vLi9a+fX19sX79enTr1o3thI8XWrVqhVatWjFtq9ZF5IWIiIg6UyC51WYQJcOHD8fKlStx7tw5yslXVFSEVatWYdiwYWzbCFNeRVAElf+Lj49HUlISkySVmpoa/Pz8qAk0XURFRWHGjBkoLi6GoqIi03XLYDC4GiSFlXr78uVLHD16FEePHkVWVhZ69+5Na3HoasOBoJIBhBDcu3cPeXl5sLOzg4KCAiQlJRvk+WdjY4PIyEiWZ9u5c+e41iBqTLx8+RLAr/uooRBEdhIARowYQf2sr6+PJ0+e4PPnz3VK7jTz/1RLt1VTXl6O79+/U/cNt2eOnp4enj17Bi0tLXTq1AknT55Ejx49EBUVBWVlZRpHLjhv3rxhe3729vb4+++/62y3dOlSAL+eyWvXrmV6vlRWVuLu3bvo0qWL0MdbG37H3xiIiYnB1q1bWbYPGTIEnp6eAH4FJNApNVKzYKecnByCgoJo66sms2fPxpIlS1BeXk45aK9du4bly5dT504nr169YruGqaqq4lh3q7Hg7++PyZMno2XLligtLcXAgQPx9u1b9O7dm6tBU9DnlaCOMVNTU2RkZEBXVxc9e/bEtm3bICkpieDgYKEXkBU2dNolfnfWrVuHHTt2wM3NjbK/3LlzBx4eHigoKGBxZAub48eP4+TJkxg9ejSt/dRFWVkZpk2b1uAOEAAICwvDwYMHYWNjQ20zNzdH27ZtsWDBAq7PjA8fPrANoiopKWmQ+WVJSQnbNcznz58bvaT9n061bCLwywFfHYQ5ZswYkdZgrIvquS0viKo+YrMTpBmunD9/nud9a74YGhtnzpzB4cOHERMTA2NjYyxYsAD29vZMk9U+ffrAyMiIbXtBJqyEEOjr6+Phw4cwMDBoEo6PmgQEBCAvLw+amprQ0dFh0eOj2xBf27hSzd69e5Gamkpr30FBQQgNDcXMmTNp7acuAgICsHr1asyaNQvnzp2Dk5MT8vLykJKS0qhrAwDA9u3bMWDAAGhra1M1bdLT06GpqYkjR46wbUNXxCQ/TJgwAdeuXUPPnj3h5uYGe3t7hISEoKCggKrtwglBDbKC4OnpCWdnZ2zevJkvw72g9VCqtcoTExPRqVMnzJgxA+fOnWvQ/29OTg6uX7/ONhOG0zm8ePECI0eOREFBAX7+/Ilhw4ZBQUEBW7duxc+fP2kxcAUEBFA/GxsbY9OmTbhx4wa1yE1KSkJiYmKDGLYEoaqqChs3boS/vz8VVaagoABPT0+sXr2a9oXrmDFjMGfOHJYCfPPmzeN7flLTidlM3bCL+s7JycH8+fN5ClZwcnJCRkYGBg4cCG9vb4wdOxaBgYEoLy9v9EXkBw0ahFu3brHM7RISEtC/f/86292/fx/ArznigwcPmN4LkpKS6Ny5M5YtW0bPoGvA7/gbA6qqqoiKimJ5J0dFRVH3bklJCVXrh07KysrYvm+0tLRo6c/LywufPn3CggULqEAFaWlprFixAitXrqSlz5oYGxvj1q1bLO/1iIgIas7XmFFSUsKVK1eQkJCAzMxMFBcXw9LSEkOHDuXalp/n1fnz5zFq1Ci0aNGC69qa2/tqzZo1VIbY+vXrMWbMGPTv35/KWmzm92Tfvn04cOAAbG1tqW02NjYwNzeHm5sb7U4QSUlJkdowHB0dceLECaxatarB+/78+TNb9YZOnTrxFBjWrVs3XLx4EW5ubgD+vzbcwYMHGySguH///ggPD6fqHDIYDFRVVWHbtm08Z2f9qYiLi+PNmzcsTqxPnz6hZcuWtCtTNLWazNVzW27U5fxrCCdKsxxWE+Pw4cOQl5fHlClTmLafOnUK379/h6Ojo9D75NVo0RDyNIKgpKQEW1tbuLi4oHv37mz3KS0txbZt2+Dj41PncR49esQ2MprbhNXExAQhISHo1atX/QcvYnx9fTl+z+nvRSf5+fno0qULrSmkampqSE5O5lpQly46deoEHx8f2NraMqWvrlu3Dp8/f0ZgYKBIxsUrJSUl+O+//5CRkQEZGRmYm5vD1taWxZHGji1btkBTUxPOzs5M2w8dOoQPHz7QXvSzNklJSZTEBS/yfw4ODkhLS2MxyM6ePRtdu3ZFaGgobWOVk5PDgwcP+I4IFDT1tn379rC1tcWMGTPQuXNnvsYgCAcOHMD8+fOhrq6OVq1asWTCcHLcjh8/HgoKCggJCYGamhp1z924cQOzZ8+mJAiEia6uLk/7MRgM2iVOBEFQuQBBEVR28sePH9izZ0+dzrPGnHnXWElNTYW9vT2ePHlSr3YvXrzAvXv3oK+vz7VIMABcunQJ4uLiTNk8wK9MgaqqKowaNape/deHoKAgrFu3DlOnTqXmeElJSTh16hR8fX3Rpk0bal92c0UnJyfs3r0bioqKtI2RE4KOX5RUP+tHjx5NvWdTUlJw6dIlBAUFwcXFBf7+/khOTqbNOJydnQ0XFxfcvn2baXtDSXcWFxfj8ePHkJGRgYGBQYMZRc6dOwdHR0esXLkS69evh6+vL54+fYrw8HBcuHChzozf3xFenldiYmJ4+/YtWrZsyXFtze8105y1+PujrKyMlJQUGBgYMG3Pzs5Gjx49UFRURGv//v7+yM/PR2BgoEiuM3d3d4SHh6Nz584wNzdnWUvSGTDRs2dP9OzZkyloCQDc3NyQkpKCpKQkju0TEhIwatQo2NvbIzQ0FHPnzsWjR49w+/ZtxMfHo2vXrrSNHQCysrIwZMgQWFpaIi4uDjY2Nnj48CE+f/6MxMREkdk5mgI1n901ef36NTp06IDS0lJa+/f29oaioiJWrVqFEydOwN7eHjo6OlRQpp+fH639NzS1nXJpaWmoqKiAoaEhgF/PO3FxcXTt2pVvycRmJ0gTo2PHjti/fz/LxREfH485c+bg6dOnIhpZ46aiogLBwcGYNGkSNDU1+TpGfn4+JkyYgAcPHjDVLKieBHCbsEZFRWHbtm3Yt28fTE1N+RpDM8xs27YN//zzD54/f05bHytWrIC8vLzI0g1lZWXx+PFjaGtro2XLlrhy5Qo6d+6MnJwc9OrVC58+fRLJuBoCHR0dHD16FH369GHafvfuXUyfPp1W6azy8nLMnTsXa9eu5dlAXRtR1gGaOHEipk+fjqlTp/LVXtB6KNXGH1Ghra2NBQsW8OUoU1NTw+3bt2FoaMjkeHz+/DmMjY150trmNwulqdOmTRsEBQWxGErPnTuHBQsW4NWrVw0yjpycHMroXh/ZyRkzZiA2NhaTJ0+GpqYmyzUsKod/UyY9PR0DBgygXe/a3Nwcfn5+LDId0dHRWLFiBTIyMmjru6kHCzX18ScmJiIwMJBaAxkaGsLNzY1l7kAXffv2hYSEBLy9vdnKpooiEKChuHXrFtavX4+MjAwqk2LdunUYPny4qIfGltoGTE64u7vTOJLGQW5uLvLy8jBgwADIyMiIfO7WDGfc3NzQokULFmP/smXLUFpaSqvcLPArQ/769etQVVWFiYkJixPizJkztPbPKWOB7hoy8fHxsLa2hpaWFpMUWWFhIS5dusRT1mReXh78/PyYnpcrVqxgqWNHF1+/fkVgYCBT/wsXLkTr1q0bpP+mRvX7wsPDAxs2bIC8vDz1XWVlJW7evInnz5/znPkgLJp6Teb6sGPHDty4cQNhYWFUnc4vX77AyckJ/fv351shodkJ0sSQlpbGkydPoKOjw7T9+fPnMDIyot0T2ZSpaUzmh7Fjx0JcXBwHDx6Erq4ukpOT8enTJ3h6emL79u1cX34qKir4/v07KioqICkpCRkZGabvedXYFwWOjo5wcXHBgAEDRNK/hYUF06ScEIK3b9/iw4cP+OeffzBnzhza+l68eDHCw8Nhbm7OV9RJSUkJ5OTk+O5fT08Pp0+fhoWFBbp164bZs2dj7ty5iI2NxfTp0xvddSPMdH9paWk8fvyYxQmRn58PY2Nj/PjxQ+DxckJJSQnp6el8O0GqycnJwePHj8FgMBqsDlBISAjWr18PJycnmJmZsVy33P72hoaGCA8PR8+ePdGvXz+MGTMG3t7eOHHiBNzc3PD+/XuWNpmZmTA1NYWYmBgyMzM5Hp+XyG5BUFRURHp6Ol+ZMCoqKkhMTISxsTGTEyQhIQGTJk3Cu3fvOLYXJAulqSMtLY3MzEx07NiRafvTp0/RpUuXRj9HUVJSwqVLl2iv2fM7Uvt5TwjBmzdvEBgYiPbt2+Py5ctcj3Ht2jVcu3aNrfPw0KFDHNvKyMjg8ePHbOfHJiYmtBcXF5TU1NQ6azDRbVhqRjDk5ORw7949tlIpdNN83dQPYWZdCvK8EjWfPn3CtGnTEBcXBwaDgZycHOjp6cHZ2RkqKirw9/cX9RCbYYObmxvCw8PRvn17Kmvv7t27KCgogIODA9Ncn46sCCcnJ47fHz58WOh9NiZev36NvXv3MgXZLFiwgClbsrFSUFCA9u3bs3VyFhQU0Cbb2JSpfl+8ePEC7dq1g7i4OPWdpKQkdHR0sH79evTs2VNUQ/ztadu2LWJjY2FiYsK0PSsrC8OHD8fr16/5Om5zTZAmRsuWLZGZmcmyyMvIyICamhotfQYEBGDOnDmQlpbmGkHTmKNmevTogfv37/PtBLlz5w7i4uKgrq4OMTExiImJoV+/ftiyZQvc3d25eoF37tzZZKNrvn79iqFDh0JbWxtOTk5wdHRE27ZtG6z/8ePHM/0uJiYGDQ0NDBo0iPZFZ2ZmJlWYNCsri+k7Xv6fmpqamDp1KpydndGvX796929lZYXz58/DwsICTk5O8PDwQEREBFJTUzFx4sR6H49uxo8fT6WM1v6/1YSXiNL27dsjMTGRZdGamJjYIBPO8ePH4+zZszzV/+BEzTpADfUMmD17NgCw1Qfm5W/PTz2ULl26UP/7Ll26MGXMVffbUPIgU6ZMQWxsLObNm1fvtsOHD8euXbsQHBwM4Ne4i4uL4ePjw1MxyI0bN2LTpk0CybW9fPkS58+fZ2vYasz1ETp37ozAwECWuUJgYCBt0dDC1I5t27Ztg9QO+B2p/bxnMBjQ0NCAlZUVTwY1X19frF+/Ht26dWMbTc8NJSUl5Ofns8yPc3NzBQpE4IX8/HyBihEfP34cDg4OGDFiBGJjYzF8+HBkZ2fj3bt3mDBhghBHyh5Bxy9qqqqqkJuby9YY3RDBO8bGxvj48SPt/dRG1NdNYWEhGAwG2rVrBwBITk7G0aNHYWxsTGtwkiAIK4NY0OeVqPHw8ICEhAQKCgqYamFOmzYNS5cubXaCNFKysrJgaWkJ4FdWAQCoq6tDXV2daY1K1/X4uzs56qK8vBwjR45EUFCQQLKulZWViIyMxOPHjwH8eneMGzcOEhL0m2R1dXXrrGuhq6vbKLM8RU31+2Lw4ME4c+YMlYkgCl6/fo2EhAS285zGbH8VlG/fvuHDhw8s2z98+MC27irPkGaaFMuXLyfa2tokLi6OVFRUkIqKCnLt2jWira1NPD09aelTR0eHfPz4kfq5ro+uri4t/QuLEydOED09PbJnzx5y+/ZtkpGRwfThhrKyMsnPzyeEEKKnp0fi4uIIIYTk5uYSGRkZWsfeGHj//j3x9/cn5ubmREJCgowcOZKcOnWKlJWViXpojZrIyEgybtw40qJFC2JgYEC2bNlCXr16xXP7yspKUl5eTv1+7Ngx4ubmRgICAsjPnz/pGHKjYevWrURNTY0cOnSIPH/+nDx//pyEhIQQNTU1snnzZtr737BhA1FWViaTJk0imzdvJrt372b68MLBgweJiYkJkZSUJJKSksTExIQcOHCA5pELn9u3bxN/f39y/vz5Ovd5/vw5qaqqon7m9KGbzZs3E3V1deLo6Ei2b99er/9dYWEhMTY2JkZGRkRCQoL06tWLqKmpEUNDQ/Lu3TuufSsoKJC8vDy+x3716lUiKytLTE1NiYSEBOnSpQtRVlYmSkpKZPDgwXwftyG4ceMGkZOTI0ZGRsTZ2Zk4OzsTIyMjIi8vT27evElLn4MGDeLpw8vf7tKlS2TkyJENco02w0yrVq1IeHg43+3nzJlDzMzMSG5uLrUtJyeHmJubExcXF2EMsU4YDAYZNGgQOXLkCCktLa13ezMzMxIYGEgIIUReXp7k5eWRqqoqMnv2bLJu3TphD5cFQccvSu7cuUN0dXWJmJgYYTAYTB8xMTHa+v369Sv1uXbtGunduze5fv06+fjxI9N3X79+pW0Mor5u+vXrR92zb968IQoKCqR3795EXV2d+Pr60t6/MKmqqqLmL7wg6PNK1GhqapL09HRCyP9fO4QQkpeXR+Tk5EQ5tGaaAO/fvye3bt0it27dIu/fvxf1cBoEdXV1kp2dzXf7rKwsoqenR2RlZYmFhQWxsLAgcnJyREdHhzx48ECII2UPg8Fg+796/vw5kZWVpb3/Zvjn8OHDRFJSksjLyxNtbe0mZX8VlJkzZxIdHR1y+vRpUlhYSAoLC0lERATR1dUlDg4OfB+32QnSxPj58yeZOnUqYTAYpEWLFqRFixZEXFycODk5/fYGUUGpvTiqXiDxulDq168fiYyMJIQQYmtrS0aOHEkSEhKIg4MDMTEx4dpeTEyMrQHt48ePtC7U6ODevXtk0aJFRFpamqirq5MlS5YINDHghYqKChIREUE2bNhANmzYQM6cOUMqKipo7bM21Q9ffqh2IpmZmREJCQlibW1NTp8+zeTg+J0oKysjVlZWAl0XVVVVZPny5URaWpqIiYkRMTExIisr22CLa0GdvmvXriVycnLE29ubnDt3jpw7d454e3sTeXl5snbt2gY4A9ERHx/P9touLy8n8fHxtPcv6P+uvLycHDlyhHh5eZH58+eTAwcOkO/fv/PUt7OzM9m3bx/fY+/evTtlwKo2Tvzvf/8jNjY25J9//uH7uA3Fq1evyKpVq8jEiRPJxIkTyerVq+vl+BUl79+/J4MGDSJiYmJEXl6eqKioMH2a4Y36GhQJIURVVZXJgVFfioqKSK9evYiEhAR1r0tISJDBgweTL1++8H1cXrh//z5xd3cnGhoaRElJicyZM4fcvXuX5/aysrLk2bNnhJBff4fMzExCCCGPHj0irVq1omPITAg6flHSuXNnMmXKFPLo0SPy5csXUlRUxPShi+q1Q/Wn9u/1WV/wi6ivG2VlZfLkyRNCCCG7d+8mffr0IYQQEhMT02QMM/wGqgj6vBI18vLy1Py8phMkJSWFqKqqinJozTRiiouLiZOTExEXF6dsKRISEsTZ2ZmUlJSIeni0smTJErJixQq+2/fq1YuMHTuWfP78mdr2+fNnYmNjQ3r37i2MIbLFw8ODeHh4EDExMTJ37lzqdw8PD+Lu7k569uxJPbubYU9FRQU5ePAgsbW1JUOGDCGDBw9m+tBNu3btyMaNG0llZSXtfTU2SkpKyPz584mUlBQ1t5KUlCTz588nxcXFfB+3uSZIEyU7OxsZGRmQkZGBmZkZ3xJPfxIvXrzg+D23v2FMTAxKSkowceJE5ObmYsyYMcjOzoaamhpOnDgBKysrju3FxMQoqZiavH79Gh06dGj0WunVvHnzBuHh4Th8+DBevnyJSZMm4dWrV4iPj8e2bdsElg5iR25uLkaPHo1Xr17B0NAQwC+N+fbt2+PixYvo0KGD0PuspqqqChs3boS/vz+Ki4sBAAoKCvD09MTq1at5Lihakz179sDLywtlZWVQV1fHvHnz4O3tDVlZWbb7//jxA5mZmWxTILnVdhAlGhoauH37NgwMDAQ6TnFxMR4/fgwZGRkYGBhASkpKSCOkFw0NDQQEBMDW1pZp+7Fjx+Dm5ka7fEZ8fDy2b9/OlHbt5eXFU/E+QLDUW3Fx8TrTrlu2bPlbp11v2bIFO3bsgLW1Ndt6LNz+dgoKCkhPT0eHDh2goqKChIQEmJiYICMjA+PGjcPz589pHP3vw8uXLwGAkmrhhaFDh6KgoAAuLi5sC6M7OjoKdYy/GyEhIdi5cydycnIA/JICXLJkCVxdXbm2XbFiBeTl5bF27Vq++yeE4MqVK9T82NzcvEFrmVVUVOD8+fMIDQ1FdHQ0OnbsCGdnZ8ycORMaGhp1tmvXrh0uX74MMzMzmJubY+XKlbC1tcWdO3cwcuRIfP36tVGPX5TIyckhIyOjQWpt1SQ+Pp7nfQcOHEjLGER93cjLyyMrKws6OjqwsbFB3759sWLFChQUFMDQ0LDRr2vWrVuHHTt2wM3NjanQcWBgIDw8PNjKiVYjjOeVKBk9ejS6du2KDRs2QEFBAZmZmdDW1sb06dNRVVWFiIgIUQ+xmUbI3LlzcfXqVQQGBlK10xISEuDu7o5hw4Zh3759Ih4hfVTXYzEwMEDXrl1ZZDa5ya3KyMggNTWVbW2D7t270/a8rC4mHx8fj969e0NSUpL6rrquxbJlywReq//OLFq0CKGhobC2tmYrf7hz505a+1dTU0NycjKt9q7GTklJCSUB2KFDB4FlbpudIM1wRZh6278bnz9/hoqKCkftzWptdA8PD2zYsAHy8vLUd5WVlbh58yaeP3/OtaaIKCkvL8f58+dx+PBhxMbGwtzcHK6urrCzs4OioiIAIDIyEs7Ozvjy5YvQ+x89ejQIIfjvv/+gqqoK4Jcx1d7eHmJiYrh48aLQ+6xm5cqVCAkJga+vL9OE76+//sLs2bN51gZ99+4dwsLCEBoaihcvXmDChAlwcXHBy5cvsXXrVrRp0waxsbEs7aKjo+Hg4MDWYN4QtRUEwcPDA1JSUvDz8xP1UESCsrIyUlJSWCaW2dnZ6NGjB4qKimjr+99//4WTkxMmTpxIXbeJiYmIjIxEaGgo7OzsOLYPDQ3F3LlzISkpCTU1NZbi3twKhoqJieHdu3cshrPs7Gx069YN37594/PM6Kd2gedqGAwGpKWloa+vz7G4KqfvePnbtWrVCtevX4eRkRGMjY3h5+cHGxsbZGRkoG/fvpQztrFSVFSE5ORkts4zBwcHWvsW1GktKyuLO3fu0Fa/5HdGEIMiACxevBjh4eEwNzeHubk5i/OwKc0vf/78iX/++QcrV65EWVkZJCUlMXXqVGzduhWtW7dm2d/Ozg7dunXD0qVLsWHDBuzZswfjxo3DlStXYGlp2eAFrus7flFiZWWF5cuXY+TIkaIeSoMj6uumZ8+eGDx4MKytrTF8+HAkJSWhc+fOSEpKwuTJkylHdGNFkEAVQZ9Xly5dgri4OEaMGMG0PSYmBlVVVRg1alQ9z6Z+ZGVlYciQIbC0tERcXBxsbGzw8OFDfP78GYmJiX+0sa2ZulFXV0dERAQGDRrEtP369euYOnUqW+3+34VqZwI7GAwG4uLiOLbv3Lkzdu7cyRI0GxcXh8WLF+PBgwdCGWddODk5Yffu3ZTdphneUVdXR3h4OE91Ielg+fLlUFVVhbe3t0j6/x1pdoI0Aaont3JyclwdEnQsEms/9NPS0lBRUUFF5GdnZ0NcXBxdu3bl+gJoDDx69IhtsVleI+pzc3ORl5eHAQMGQEZGhir0WxfVBrEXL16gXbt2EBcXp76r9sCvX78ePXv25ONsGgZ1dXVUVVXB1tYWs2fPpgqF16SoqAgWFhZCKzpYEzk5OSQlJcHMzIxpe0MYBdu0aYOgoCCW6+PcuXNYsGABXr16xbH9mTNncPjwYcTExMDY2Biurq6wt7eHsrIytU9eXh6MjIxYrkngVyTt8OHDsW7dOmhqagrlnBoKQaNmRIEwn7dubm5o0aIFy37Lli1DaWkp9u7dK/B468LIyAhz5sxhyczasWMHDhw4QGWH1EX79u0xb948rFy5sl7ZThMnTgTw6/4YOXIkU9ZOZWUlMjMzYWhoiOjo6HqcDW8I638nJibGUtQdYC7s3q9fP5w9e5aWInnjx4+HtbU1Zs+ejWXLluHcuXOYNWsWVZTv6tWrQu9TWERFRWHGjBkoLi6GoqIii/Ps8+fPtPYvqNPa0tIS//zzD3r16kXrOH9HBM1848fAEBAQgDlz5kBaWpoKOKmLhigcmZqaikOHDuH48eOQk5ODo6MjFezg6+uLb9++ITk5maXd58+f8ePHD7Rp0wZVVVXYtm0blUW5Zs2aBivGye/4RUlkZCTWrFkDLy8vtpl35ubmDTKOL1++ICQkhCnz0snJiQrcoQNRXzc3btzAhAkT8O3bNzg6OuLQoUMAgFWrVuHJkycN7ryrL4IEqghqEDU3N4efnx+LUS06OhorVqxARkYG9xMQkK9fvyIwMBAZGRkoLi6GpaUlFi5c2Ogcnc00HmRlZXHv3j0YGRkxbX/48CF69OiBkpISEY2scVIz4CshIQHLly/HX3/9Rc0xk5KSsH79erbPgmYaD23atMGNGzfQsWNHkfRfWVmJMWPGoLS0lO08pzHaUwRh4sSJCA0NhaKiImVXqAt+5xnNTpAmwODBgxEZGQllZWWBJ12CsmPHDty4cQNhYWHU5PrLly9wcnJC//794enpSWv/gpCfn48JEybgwYMHTAauaiMNt4j6T58+YerUqbh+/ToYDAZycnKgp6cHZ2dnqKiowN/fn2P7wYMHU0aspsaRI0cwZcoUSEtLi6R/VVVVXLhwAX369GHanpiYiLFjx9JqWJOWlkZmZibLi+/p06fo0qUL1/RVJSUl2NrawsXFBd27d2e7T2lpKbZt2wYfHx+W7xQVFXH//v0mGZXF6XkF/IocamwI83lb7QRq3749NeG9e/cuCgoK4ODgwDSJEfYERkpKCg8fPmSRCMnNzYWpqSl+/PjBsT2/qbdOTk4AgLCwMEydOhUyMjLUd9VO39mzZ0NdXb1ex+UFYf3vrl27htWrV2PTpk3o0aMHACA5ORlr167FmjVroKSkhLlz56Jnz54ICQkR+nnk5+ejuLgY5ubmKCkpgaenJ2XY2rFjR6OWv+zYsSNGjx6NzZs31ynvRyeCOq1jY2Ph6+uLTZs2sV1oNEfQ1Y0oMt90dXWRmpoKNTU1gTOwBGHHjh04fPgwnj59itGjR8PV1RWjR49mciC/fPkSOjo6qKiooG0c/NKUx8/OSV/TYd0Q2bI3b97E2LFjoaSkhG7dugEA7t27h6KiIkRFRTWoJFtDQQhBYWEhVFRUUFFRwbS2ef78OWRlZVnkMBsbogxUkZGRwePHj6Gjo8O0/fnz5zAxMaHdmFxQUID27duzDeIrKCiAlpYWrf030zQZMmQI1NTUEB4eTtkESktL4ejoiM+fPzfqIB1RUB1UVU1t21PN3xuzssOfjr+/P/Lz8xEYGMgx8JkuNm7ciHXr1sHQ0JBFqrch7L8NjZOTEwICAqCgoEDZFeri8OHDfPXR7ARppl60bdsWsbGxbPUMhw8fjtevX4toZNwZO3YsxMXFcfDgQejq6iI5ORmfPn2Cp6cntm/fzlUn38HBAe/fv8fBgwdhZGSEjIwM6OnpISYmBkuXLsXDhw95Hkvtl2AznHFwcEBaWhpCQkIoo+Tdu3cxe/ZsdO3aFaGhobT13bNnT/Ts2ZMlytTNzQ0pKSlISkqqs21FRQWCg4MxadIkvrM4nJ2d0bdvX7i4uPDVvhnRwc0JVA0dExh9fX14eXlh7ty5TNuDgoLg7+9PafbXhaCpt76+vvDy8hKJIVxQTE1NERwczNbpOmfOHDx8+BBXr16Fs7MzCgoKWNrXlYVSU05r3LhxtEYIiwo5OTk8ePAAenp6IulfUKd1tUG19ru5IQ2qTRVRGhRFjYGBAZydnTFr1qw6o6jLyspw7NgxtnVl6pIHZDAYkJKSYtLwpgNBxy9KBK33JwzMzMzQu3dv7Nu3j8r2rqysxIIFC3D79m1aZU4qKysRGRnJlIEybtw4SEhI0NYn8Et6UFpaGg8fPmyyWvKiDFRp1aoVjh49yiKNc/XqVdjZ2eH9+/dC7a82f3Ldtmb4JysrCyNGjMDPnz8p2dCMjAxIS0sjJiaGxT70OzF48GCOdht267jGUDuqGcGZMGECrl+/DlVVVZiYmLAESNGd9aiiooKdO3di1qxZtPbzJ9HsBGmmXigoKCAqKoqtFqSNjQ3+97//iWZgPKCuro64uDiYm5tDSUkJycnJMDQ0RFxcHDw9PbnW5GjVqhViYmLQuXNnKCgoUE6Q/Px8mJub8yTJFB4ejr///psyQHbs2BFeXl6YOXOmUM5RmHBLP6sJ3Q//oqIiODo6IioqinrxVFRUwMbGBqGhoVBSUqKt7/j4eFhbW0NLS4tJ57ywsBCXLl3i6jyTlZXF48eP+V6If//+HVOmTIGGhgZfRZZFibOzM3bv3g0FBQWm7SUlJXBzc6OkE5oChYWFAH7JRDUF9u3bhyVLlsDZ2Zky5icmJiI0NBS7d+9mcY7URtDU22fPnqGiooLFOJKTk4MWLVqwRD82JmRkZJCSkgJTU1Om7Q8ePECPHj1QWlqKFy9ewMjICN+/f2dpP3jwYKSlpaGyspJFNrJTp054+vQpGAwGEhISYGxs3CDn1FBMnDgR06dPx9SpU0XSvyBOa4D7grV5kVo3/BgUhZXyXl5ejk6dOuHChQssMh0NwfPnz6GlpcWSlVAdLc8tqrp2tGht2rVrh1mzZsHHx6de8oS8Iuj4/3RkZGSQnp5OPe+r4dX5yi8PHz6EjY0N3r59y/Su0dDQQFRUFMs7TNiYmJggJCSkycoH1jdQRZgSHXPnzsWdO3cQGRlJZdzm5uZi0qRJ6N69Ow4ePMjbSfBJXXXbXrx4AWNj42ZZo2bq5Pv37/jvv//w5MkTAL/kd2fMmMGU+f07UlteuLy8HOnp6cjKyoKjoyN2794topE1Qzd0ZSPwSqtWrXDr1q0mG3AgCMeOHWOR2a3Gy8sLf//9N1/HpTdMpBmh8+PHD+zZswfXr19nW3Q0LS2N1v4nTJgAJycn+Pv7M0Xke3l51ctoLgoqKyspY6y6ujpev34NQ0NDaGtr4+nTp1zbl5SUsI1q/vz5M5PufV3s2LEDa9euxaJFi5i0yufNm4ePHz+yvFxFDZ2OhfqirKyMc+fOIScnh2nSVVvqhw4GDhyI7Oxs7N27l+p74sSJWLBgAdq0acO1fY8ePXD//n2+nSDHjh1DbGwspKWlcePGDZYUyMbsBAkLC4Ofnx+LE6S0tBTh4eGN3glSUVEBX19fBAQEUE5OeXl5uLm5wcfHh8Ux0JiYP38+WrVqBX9/f5w8eRLAr3vmxIkTGDduHNf2W7ZsQUxMDGVYqX3dcWPWrFlwdnZmmbDdvXsXBw8exI0bN+pxNg1L165d4eXlhfDwcMpA8OHDByxfvpyStMvJyanTIVad5XH48GFKPunr169wdXVFv379MHv2bNjZ2cHDwwMxMTEAfkn+ZWdnQ11dHSoqKhz/xnTX1RAEa2treHl54dGjR2ydZ7zW3uKXbdu2wdraGlevXmXrtOZGs5ODf7KysmBpaQngV50r4NdcS11dHVlZWdR+Na9tJSUl6ndB5hwtWrTgKvFHJx06dGAbVf3582fo6upyjaoODQ3F6tWrMWvWLCYJvrCwMKxZswYfPnzA9u3bISUlhVWrVjW68YuS8PBwjt87ODjQPgZLS0s8fvyYxQny+PFjKlqaDlxdXWFiYoLU1FQmieJZs2Zhzpw5uH37Nm19A4Cfnx+8vLywb98+2h0udFBfSdb/Y+/O42pM//+Bv05IKyWVLS2KaLE1ZkwkMbYhy4wZW0XWGVOGrJ8hy1gaM3YzlhFi7NvYl1GEGFnLUlrJkK2EytJy//7w63wdJ0q6z31OvZ6Ph8eD+z6n64U659z3dV3vd2m9XgGv36s6deoEe3t71KlTB8DrknOtW7fGb7/99lFf+30KdqrKZDJMmTJF4bo2Ly8PZ8+eLbTvI1EBPT09DB06VOoYKrdgwYJCj0+bNq3YvUkzMjIUekc5ODjA19dXre65kDKxJzmKMmrUKCxZsqTI3ndl0XfffQcjIyN07txZ4fjo0aOxefPmEk+CcCeIhunfvz+OHDmCr7/+WqkmHIBCewqUpuzsbIwdOxarV69GTk4OAKBixYoYPHgwfv31V6XGx+qkoGdJjx490K9fPzx+/BiTJ0/GypUrceHCBYWL9MJ06dIFzZs3x88//wxDQ0NER0fD0tISffr0QX5+PrZv3/7e51tbW2P69OlKF2QhISGYNm2aKA3F6ePk5OSgU6dOWL58eYln37du3YpJkyZh9OjRhTYHL6ppZ40aNeDv74+JEyeKsgJUDE+fPoUgCDA2NkZ8fLzCSrO8vDzs3bsXEydOVOvyecDrN96dO3dixowZCjdUp02bhh49emDZsmUSJyxcbm4uZs+eDV9fX/nF9Yf62K23VapUwcWLFwvtSeLi4iJKf4DScuPGDXTv3h3JycnyiY7bt2/DxsYGu3fvRv369fH333/j2bNnhe7iq127Nv755x+lXR7Xrl1Dhw4dcOfOHVy8eBEdOnSQN4sOCQlBnz59ULlyZYSEhLw3n7qVo3nT+16jVFVO6u7duwqT1g0bNnzvpHV0dDQcHR2hpaWF6Ojo935tVTVZpg83e/ZsxMXFYdWqVaKXAnqblpYW7t27pzSJUNxV1e3atcPw4cOVdlBt3boVK1asQGhoKNavX49Zs2bJv6/VKb+U3u6zl5OTg+zsbGhra0NPT08lk8ZbtmzB+PHj4efnp9Dw9vfff0dQUJDC7qTSfA3R1dXF+fPnCy1R/Mknn4i2A6WAsbExsrOzkZubC21tbaWV4Oo8Ya8OBEHAP//8g6ioKOjq6sLZ2Vn0/jEFu1/Cw8PRsmVLhVJ7BX3bxo4dWy5XHFPR5syZA3Nzc/j6+iocX716NR4+fIgJEyZIlEw6CQkJaNGiRZGvd+fPn0fHjh2hq6srX+xw7tw5PH/+HEeOHJEvIiF6W8+ePREWFgYTExNJynFJaf/+/ejfvz/27duHVq1aAXi983znzp0IDQ2Fvb19ib4uJ0E0TNWqVXHgwAH5TgKpZGVlyVf61atXT60nPwocPnwYWVlZ6NWrFxISEtC1a1fExcXBxMQEW7ZsUarL+rarV6+iXbt2aNasGcLCwuDp6Ylr164hPT0dERERRTYQ1tHRwdWrV5VuCsbHx8PJyUnSVYzqThAEbN++/Z07oMR88Tc1NZU3JS6Jj23aWa1aNZw7d06jGqMXVdpDJpNh+vTp+Omnn1SY6sNVrVoVmzdvVlp9cODAAfTt2xdPnjyRKFnRDAwMcPXq1RKXnfrYrbdVq1bF8ePH0bRpU4XjFy5cgLu7u1qXTgRe1zs/cuQI4uLiAAANGjTAF198UayJSAMDA+zbt0+pbOTx48fRrVs3PHv2DElJSWjSpIlSL4Dc3Fxs3LgRHTt2LHEfofKsJM1e37wBXPDaVdhHY/YEUW89e/ZEaGgoDAwM4OTkpPS5VIzPCQWrqhctWoShQ4cWuqq6QoUKiIiIeO/X0dXVRXR0dKHlAxs3bozs7GwkJyfDwcGh0BJ8UudXN/Hx8fjuu+8wbtw4dOzYUfTxinpfEKtRe+PGjbFgwQKl65ewsDCMGjVK1F4kADR6wr68GzRoEBYtWiTfrUpUHFZWVti4caNSz7yzZ8+iT58+5XJB5/r16zFhwoQiF/a1bt0atra2+PPPP+ULNXJzczFkyBAkJSXhxIkTqohLJbR9+3Zs3boVKSkpePXqlcI5sSvxSF2OS2obN27EDz/8gH/++QfBwcHYvXs3jh07ptT/8UOwHJaGqV27tlJpGSno6+tr3IrINy+EbG1tERsbi/T09CJLjxRwdHREXFwcli5dCkNDQ2RmZqJXr14YOXLkOxtJvsnW1hZbt25VKmWwZcsWrrgpwo8//ogVK1agbdu2he6AEtOAAQMQHByMoKCgEj3/Yz8Q+vj4YMuWLaKUwBDLsWPHIAgCPDw8sGPHDoUG0Nra2rC0tCxWKTGpVa5cudBJBGtra9Eb1X6sdu3aITw8vMSTIB+79dbNzQ1z5szBpk2bFBrFzpkzR76SQ51paWmhU6dO6NSp0wc/t3v37vD19cW8efPk5bPOnTuHsWPHokePHgBel7op7MNbxYoVMWLECPlWefow1tbW72z2+q6yPsnJyfLdauXxAl5dpKWlITAw8J2LHYpaZWlkZISvvvpKzIhKCnrJCYKAK1euKK2qbty4McaOHVvk17GwsCj0c0ZwcLB8N1paWprSroePVVr51Y2dnR2CgoIwYMAAUXbOvE2q1405c+bA398f06ZNU9iBMmPGDPzyyy8Kk+xi3Owuz5McJXm9Wrx4MYYNGwYdHZ0iP1uJXeq2rN80I3Hcu3ev0HsepqamSE1NlSCR6rxd9l0QBKSmpuL8+fOYMmVKkc8/f/68wgQI8Poz//jx4+Hi4lLqean0LF68WF6ydPfu3Rg0aBASExNx7tw5jBw5UvTxy/vrdb9+/ZCRkQFXV1eYmpoiPDz8o0vicyeIhjl48CAWL16M5cuXl7jHAEljx44d+Pbbb9G+fXv5Tp6IiAiEhoZi69at6Nmzp8QJ1Ve1atXw119/oUuXLiofu6DZq52dXaHlrIpqEP2x/P39sW7dOjRu3BjOzs4f3KBaSrdu3YKFhYXGlPF624wZMxAbG4s1a9bI+/68fPkSgwcPhp2dnejlBz/G8uXLMX36dPTv37/Q79uiejN87Nbb69evw83NDUZGRmjdujUA4OTJk3j69CnCwsLUrn54ad6cyMzMxOjRo7Fu3Trk5uYCeH2h4+PjgwULFkBfXx+XL18GgEJrb7u7u+PHH3+UT5hQ8X1Ms9ecnBwMHz4cU6ZMgbW1tdhR6S1dunRBQkICBg8eXOhiB3W+4fqxq6r37NmD3r17w97eXj5xev78ecTGxmL79u3o2rUrli1bhvj4eFHe88viqvDLly/Dzc1NabddWfLmZ6uCn5eCy/o3/yzmLrbExESsWbMGiYmJWLRoEczMzHDw4EHUrVtXqUxXWVKS1ytra2ucP38eJiYm732PkclkSEpKKvXMbyqq+kFYWJio45NmKrj2GTBggMLx9evXY+rUqaJ/30rp7dX4WlpaMDU1hYeHBzp06FDk883NzbF+/Xqlxx4+fBje3t64f/9+qeal0mNvb4+pU6eib9++MDQ0RFRUFGxsbBAYGIj09HQsXbpU6ohlSsEu5bdt27YNzZo1U6iOUtLPxJwE0TAPHz7EN998gxMnTkBPT0/pxhTrryr6kGbthd3UK6o++JuKszPmwoULWLBggXyVb8OGDREQEKBUMoYUWVtb4+DBgyWu+/cxCurnFkYmkxX7QuH69euFbqEs6mZ0aY0vlYyMDERGRha6Uk4VDUs/RkF5lcqVK8ubm0ZFReHVq1do166dwmPVrR7nx/ZmKI2tt3fv3sXSpUsV6l3/8MMPCjuD1IUYNycyMzPlj7WxsYGBgUGxnvexfYTKo9Iq61O1alVcvnyZkyASMDQ0xKlTp0rcSNrDwwM7d+6EkZGRwvGnT5+iR48eav9emZycjBUrViiU4Bs+fHiJd/OVF3v27FH4c8Hq3KVLl8LCwgIHDx5USY67d+/i1KlThX7WEWtVf3h4eLEf26ZNG1HG79y5M1xdXXHixAnExMTAxsYGQUFBOH/+fJG9EjVZSV6vnjx5ojYNkEePHq3w55ycHFy+fBlXr16Fj48PFi1aJFEyUmdz587F3Llz8euvv8on0kJDQzF+/HgEBARg0qRJEidUX/7+/ti1axd+++03eTmxiIgIjBs3Dl999RUWLlwobUB6Jz09PcTExMDS0hJmZmb4559/0LhxY8THx+Ozzz5DWlqa1BHLlPfd+3rTx9wH4ySIhmnfvj1SUlI0cqWcFIq6kfemwm7qva8++JvKU63wFy9eQEdHR6VjhoSE4NChQ1i9erVS40UxvNko92MlJSWhZ8+euHLlisL3UsHPbln+vtm7dy/69++PzMxMVKlSReH1SiaTqf2k7ce+fhCVxMf2ESqPSqvZq4+PD5o0aaJ0g4jE98knn2DJkiXysj4f6l3NvR88eIDatWsjJyenNGKSmnn79VImk8lX586bN69Y5Wo/1tq1azF8+HBoa2vDxMRE6bNOWV0d3bJlS/Tu3RtjxoxRWB0bGRmJXr164b///pM6omhK8npVoUIFebnGd03aSm3atGnIzMzEb7/9JnUUUkOCIGDixIlYvHixfFGfjo4OJkyYgMDAQInTiS8jIwPbt29HYmIixo0bh2rVquHixYswNzdH7dq13/vcV69eYdy4cVi+fLl8l3ilSpXw3XffISgoSF5xgNSPjY0NduzYgaZNm8LFxQVDhw7F8OHDceTIEfTp00ft72eQMk6CaBg9PT2cOXOmxCvl6MPcunWr2I8tqjxZmzZtMHjwYPTu3VslN/JLU35+PmbNmoXly5fj/v37iIuLg42NDaZMmQIrKysMHjxY1PGfP3+Onj17IiIiAlZWVko7oEq7IdWbFyo2NjY4d+4cTExMSvS1unXrhgoVKmDVqlWwtrZGZGQk0tLSEBAQgN9++01eKqi4CsoJ2dvbS7Iz5kPUr18fXbp0wezZsxVWZpM4qlWrhri4OFSvXh2+vr5YtGhRiXtIPX/+HIIgyP/fbt26hV27dqFRo0bF2vZdVIM/Nze3EuVSV7169cLatWtRpUqVIncgFrVrqKj3HZbCfLePLeszc+ZMzJs3D+3atSt0F47YddrLs3PnzmHixIkIDAyEo6Oj0vv8u/5PC3bsNmnSBGFhYQo7zfLy8nDo0CGsWLECN2/eFC17aTh58iRWrFiBpKQkbNu2DbVr18b69ethbW2tEX2UyjMLCwuMGDECkyZNUnn5zxcvXiA6OrrQHShF7TT+WAYGBrhy5Qqsra0VJkFu3rwJe3t7vHjxQtTxpVSS16uqVavi33//RcOGDd9ZulFqCQkJaNGiBW/q0XtlZmYiJiYGurq6sLOzKxc38KOjo9GuXTsYGRnh5s2buHHjBmxsbDB58mSkpKRg3bp1xfo62dnZSExMBADUq1eP18caYMiQIbCwsMDUqVPx+++/Y9y4cXB1dcX58+fRq1cvBAcHSx2RPhAbo2sYe3t7PH/+XOoY5UZp3mxq2rQpxo4dCz8/P3zzzTcYPHhwiVc8qtrMmTMREhKCuXPnYujQofLjjo6OWLhwoeiTID4+Prhw4QIGDBigksboRkZGSE5OhpmZGW7evKl0Yfkhzpw5g7CwMFSvXh1aWlrQ0tJCq1at5A0tCxqTvss333wDNzc3/PDDD3j+/DlcXFxw8+ZNCIKAzZs3q7wR7Ie4c+cO/P39NfYD3sdOBKjaq1ev8PTpU1SvXh0hISH45ZdfSjwJ0r17d/Tq1QsjRoxARkYGWrRoAW1tbTx69Ajz58/Hd999997nu7u7Kx178+dW3XYzvKv+aGEKqz9atWpV+d/vY8tdaNokx4fU3Be758DH7sgKDg6GkZERLly4gAsXLiick8lknAQRkZGREZ4+fapUq76oHVBNmjSBTCaDTCYrtM69rq4ulixZIkrm0rJjxw54eXmhf//+uHjxIl6+fAngdfmc2bNn48CBAxInpPfJzs5Gnz59VD4BcujQIXh7e+PRo0dK51Sxa9DIyAipqalK5QMvXbpU5KpoTVeS16v27dujbdu2aNiwIYDXJVff3LX4JqnK9505c0blu/1J8xgYGMj7V5UXY8aMwaBBgzB37lyFa6suXbqgX79+xf46enp6cHJyEiMiiWTlypXye0EjR46EiYkJTp8+DU9PTwwfPlzidFQSnATRMEFBQQgICMCsWbPg5ORU7JVy5VXTpk2LfcO8qN0Ec+bMgbm5OXx9fRWOr169Gg8fPsSECRPe+/yFCxfit99+w549exASEgI3NzfY2trC19cXXl5eMDc3L1ZOKaxbtw4rV65Eu3btMGLECPnxxo0bIzY2VvTx9+/fj8OHD6tsNeRXX32FNm3aoGbNmpDJZHBxcUGFChUKfWxRZQ7y8vLkH5aqV6+Ou3fvokGDBrC0tMSNGzeKzHLixAn89NNPAIBdu3ZBEARkZGQgJCQEM2fOVOtJkI4dO+L8+fOwsbGROkqJfOxEgKq1bNkSPXr0QPPmzSEIAvz9/d+562z16tXv/VoXL17EggULAADbt29HjRo1cOnSJezYsQOBgYFF/t0fP36s8OecnBxcunQJU6ZMwaxZsz7gb6Uab09GXrx4Ebm5uWjQoAEAIC4uDhUqVEDz5s0Lff6bN99LqzRaSfsIqZqRkVGR77OqLOV1/vx5bN26tdB/u6J24SQnJ4sZjd6jf//+qFSpEjZu3PhBix2Sk5MhCIK8DM+bK6u1tbVhZmb2zvdvdTFz5kwsX74c3t7e2Lx5s/y4q6srZs6cKWEy9fSxk9albfDgwdi2bRsmTpwo+lhv8vPzQ+/evREYGCjJNUSfPn0wYcIEbNu2DTKZDPn5+YiIiMDYsWPVvufbxyrJ69Vff/2FkJAQJCYmIjw8HA4ODpItEnp7x2pBL53z589jypQpkmQiUmfnzp3DihUrlI7Xrl0b9+7dK/L5WVlZCAoKQmhoaKE798pq2cSyoGARa4E+ffqgT58+oo65ePHiYj+WC7Q+HCdBNEynTp0AQKkpL2uFF65Hjx6l9rVWrFiBjRs3Kh13cHCQXwgUpWLFiujVqxd69eqFBw8eYOXKlZgyZQr+97//oUuXLvD39y90JaPU7ty5A1tbW6Xj+fn5KqmzbWFhodIJvpUrV6JXr15ISEiAv78/hg4dWuIV9Y6OjoiKioK1tTU+/fRTzJ07F9ra2li5cmWxJgeePHkiL+9x6NAhfPXVV9DT08OXX36JcePGlSiTqhRkvH79eqGTtup2M/dtHzsRoGp//fUXFixYgMTERMhkMjx58qTE5Siys7Pl3/NHjhxBr169oKWlhc8++6xYZQIL2w3xxRdfQFtbG2PGjFFaZS+1Y8eOyX8/f/58GBoaIiQkBMbGxgBeT+oMGjTog8vXlYSm9RF6899Oaps3b4a3tzc6duyII0eOoEOHDoiLi8P9+/fRs2fPD/pab/+7k7iuXr2KS5cuyScei6tg59TH7NiU2o0bNwotEVi1alVkZGSoPpCaK2oHbQFV/ezOmTMHXbt2xaFDhwr9rCPWRMz9+/cxZswYyRZRzZ49GyNHjoSFhQXy8vLQqFEj5OXloV+/fpg8ebIkmVSlJK9Xurq68oVk58+fxy+//CJZT5C3P6NpaWmhQYMGmDFjhlrudCaSWuXKlQvd+RwXF1essnZDhgxBeHg4vLy85IssSXNkZGQgMjKy0AksMSb9C+4/FHj48CGys7Pl7xkZGRnQ09ODmZkZJ0FKQiCNcvz48ff+IvFUrlxZSEpKUjqemJgoVK5c+YO+1tmzZ4URI0YIRkZGQt26dYXAwEBh8ODBgq6urhAQEFBakUtNs2bNhPXr1wuCIAgGBgZCYmKiIAiCMH36dKFVq1aij79v3z6hY8eOQnJysuhjvW3gwIHC06dPS/z8Q4cOCTt27BAEQRDi4+OFBg0aCDKZTKhevboQGhpa5PPt7OyELVu2CJmZmYKpqan8OZcvXxZMTExKnEsVZDLZO39paWlJHa9Iurq6wq1btwRBEITevXsL06ZNEwRBEFJSUgRdXV0poxXJyspKePToUYmf7+TkJCxatEhISUkRqlSpIpw+fVoQBEE4f/68YG5uXuKvGxMTI+jr65f4+apQq1Yt4erVq0rHr1y5ItSsWbPI59+7d08YMGCAULNmTaFChQqClpaWwq+idO3aVejevbvw8OFDwcDAQLh+/bpw8uRJoUWLFsKJEydK9HcqL5ycnISlS5cKgvB/71X5+fnC0KFDhcDAwGJ9jVWrVgkODg6Ctra2oK2tLTg4OAh//vmnmLFJEITWrVsL//zzT4mfv3btWmHfvn3yP48bN06oWrWq0LJlS+HmzZulEVE01tbW8r/7m5+xQkJChIYNG0oZjYrh559/FmQymWBvby+0adNGcHd3l/9q27ataOMOGjRIWLVqlWhfv7hu3bol7N+/X9iyZYsQFxcndRyV+NjXKyLSLIMHDxZ69OghvHr1SjAwMBCSkpKEW7duCU2bNhVGjRpV5POrVq0qnDp1SvygVOr27NkjGBoaCjKZTKhatapgZGQk/2VsbCz6+Bs2bBBcXV2F2NhY+bHY2FihdevWwl9//SX6+GURG6MTFZOdnR2mTp2KAQMGKBxfv349pk6dWuQ2xgcPHmD9+vVYs2YN4uPj0a1bNwwZMgQdO3aUrwY4deoUOnXqhMzMTNH+HiWxe/du+Pj4YNKkSZgxYwamT5+OGzduYN26ddi3bx+++OILUcc3NjZGdnY2cnNzoaenp7TKTtMa+KWnp8PY2LhYq0D++OMPjBo1CgYGBqhbty4uXboELS0tLFmyBDt37lSrVdhljbOzM4YMGYKePXvC0dERhw4dQsuWLXHhwgV8+eWXxdr+rKm2b9+Ofv36IS8vD+3atcORI0cAvF7xeuLECRw8ePC9zy9oVlxA+P+lFoKCgpCbm4tTp06Jlv1jGRoaYu/evUp9TY4dOwZPT088e/bsvc/v3LkzUlJS8MMPPxS62qt79+7vfX716tURFhYGZ2dnVK1aFZGRkWjQoAHCwsIQEBBQ7FXQqvL2//X7ODs7i5gE0NfXx7Vr12BlZQUTExMcP34cTk5OiImJgYeHB1JTU9/7/MDAQMyfPx9+fn5o2bIlgNc10pcuXYrRo0djxowZouYvz7Zt24Zp06Zh3Lhxha6mL+p7p0GDBli2bBk8PDxw5swZtGvXDgsXLsS+fftQsWLFIkuhSWnOnDn466+/sHr1anzxxRc4cOAAbt26hdGjR2PKlCnw8/OTOiK9h7GxMRYsWICBAweqdNzs7Gz07t0bpqamhf7MqHJ1qFDOds6V5PVqzJgx+Pnnn6Gvr19kSTexy7idO3cO+fn5+PTTTxWOnz17FhUqVICLi4uo4xNpmidPnuDrr7/G+fPn8ezZM9SqVQv37t1Dy5YtceDAAejr67/3+dbW1jhw4IC8JxBpjvr166NLly6YPXu2JCUM69Wrh+3bt6Np06YKxy9cuICvv/6apXxLQto5GCqJEydOCP379xdatmwp/Pfff4IgCMK6deuEkydPSpxM/RgbGwsPHz4UBEGQz9a+61dRfvnlF8HExERYvXq1cPPmTeHmzZtCcHCwYGJiIsyePbvI51eqVEmwt7cX5s6dKzx48KDQxzx58kRwd3f/sL+kipw4cUJo3769YGpqKujq6gqurq7C4cOHVTL22rVr3/urrDt//rywc+dO4dmzZ/Jj+/bt06gVJc+fP5c6wgfbtm2bUKlSJUFLS0to3769/Pjs2bOFTp06SZhMNVJTU4WLFy8KeXl58mNnz54VYmJiinxuwW6ft3cAtWzZsljPl5KXl5dgZWUl7NixQ7h9+7Zw+/ZtYfv27YK1tbXg7e1d5PMNDAyES5culXh8IyMj+a5DGxsbISwsTBAEQUhISFDLHUjv+r+WYvdX7dq1hejoaEEQXu8K2bhxoyAIgnD69GmhSpUqRT6/evXq8ue8aePGjWq/807Tvet7prjfO2/u3Bs/frzg5eUlCIIgXL16Vahevbqo2T9Wfn6+MHPmTEFfX1/+d9fR0REmT54sdTQqBnNzc0l2QKxatUqoWLGiYGBgIFhaWgpWVlbyX9bW1irLUB53zpXk9crd3V14/Pix/Pfv+iXm7qECn3zyibBt2zal4zt27BBatGgh+vhEmurkyZPC77//Lvzyyy8ftBts/fr1wtdffy1kZWWJmI7EoKenJ9+hKwVdXV0hMjJS6fjZs2fV8rpQE3AniIbZsWMHvLy80L9/f6xfvx7Xr1+HjY0Nli5digMHDuDAgQNSR1QrISEh6NOnDypXroyQkJD3PtbHx+e95wVBwMSJE7F48WJ5s1UdHR1MmDABgYGBRWY5efKkSurJk3p58eIFlixZgmPHjhVaR/LixYvF/loRERFwcXFB5cqVSzumKPLy8jB79mwsX74c9+/fR1xcHGxsbDBlyhRYWVlh8ODBUkcs0r1795CamorGjRvLm6JFRkaiSpUqsLe3lzid+nq7b4iWlhZMTU2ho6MjUaLiy87OxtixY7F69Wp5z6OKFSti8ODB+PXXX4tc7dWoUSNs2LBBacVOcbVu3RoBAQHo0aMH+vXrh8ePH2Py5MlYuXIlLly4gKtXr5bo64qlOD1iChT0bxBLv3794OLiIl9xu2TJEnTv3h3//PMPmjVrVuRuACMjI5w7dw52dnYKx+Pi4tCiRQv2ZxBRUd9HRX3vmJmZ4fDhw2jatCmaNm2KMWPGwMvLC4mJiWjcuLHa7bAtzKtXr5CQkIDMzEw0atQIBgYGUkeiYpgzZw5SU1M/qJFpaahRowb8/f0xceJEhaatqlKed8597OuV1AwMDBAdHa3UmzA5ORnOzs5F7ngloqI1bdpUYXdcQkICBEGAlZWV0u6xD7kfQKrVq1cv9OnTB998840k43fr1g137tzBqlWr0KxZMwCvd4EMGzYMtWvXxp49eyTJpck4CaJhmjZtitGjR8Pb2xuGhoaIioqCjY0NLl26hM6dO5fp8izqIjMzEzExMdDV1YWdnZ3G3JD+GDY2Njh37hxMTEwUjmdkZKBZs2ZFlgIrTS9evJBPQhVQZdP0D9W/f38cOXIEX3/9NczNzZVKBUydOrXYX6tKlSq4fPlysRqqq4MZM2YgJCQEM2bMwNChQ3H16lXY2Nhgy5YtWLhwIc6cOSN1xGJJSEhAYmIi3NzcoKurC0EQyk3Jh5LIyclBp06dsHz5cqWbyZokKysLiYmJAF5vRS5q8qPAkSNHMG/ePKxYsQJWVlYfPO7hw4eRlZWFXr16ISEhAV27dkVcXBxMTEywZcsWeHh4fPDXLC/S09Px4sUL1KpVC/n5+Zg7dy5Onz4NOzs7TJ48Wd7o/l38/PxQqVIlpVIkY8eOxfPnz/H777+LGZ8+Qv/+/REbG4umTZti06ZNSElJgYmJCfbs2YP//e9/ajd5SGVHz549ERYWBhMTEzg4OCjd3BKrFFu1atVw7tw51KtXT5SvXxRTU1MsXrwYffv2VTi+adMm+Pn54dGjR5Lk0gRPnjxBXl4eqlWrpnA8PT0dFStWFP26xsTEBPv27ZNPXhU4ffo0vvzySzx+/FjU8Yk0UWhoKEJDQwtd1Lh69Wqlx0+fPr3YX/tD7geQagUHB2PGjBkYNGhQoeUPPT09RR3/4cOH8PHxwaFDh+Rj5+bmomPHjli7di3MzMxEHb8s4iSIhtHT08P169dhZWWlMAmSlJSERo0a4cWLF1JHVGt5eXnYtWsXYmJiALxesdu9e3dUrFhR9LHT0tIQGBj4zh0B6tzXQktLC/fu3VN6kb1//z7q1q2Lly9fijp+VlYWJkyYgK1btyItLU3pfF5enqjjf4yqVaviwIEDcHV1/eiv9ebPvCawtbXFihUr0K5dO4XssbGxaNmypdpfZKWlpeGbb77BsWPHIJPJEB8fDxsbG/j6+sLY2Bjz5s2TOqLaMjU1ld98Lm/E6GH0IX2EpLZ+/XosX74cycnJOHPmDCwtLbFw4UJYW1sX2Q9Fan5+fli3bh0sLCzw2WefAXhdIz0lJQXe3t4K/5di12wvb9atW/fe897e3u89n5GRgcmTJ+P27dv47rvv0KlTJwCvbyxoa2vjp59+KrWspS0rKwtBQUHvvLmiyoUm9OEGDRr03vNr1qwRZdzRo0fD1NQU//vf/0T5+kUpzzvnPvb1qnPnzujWrRu+//57hePLly/Hnj17RK/s0LdvX6SmpmL37t2oWrUqgNevoT169ICZmRm2bt0q6vhEmmb69OmYMWMGXFxcCu33t2vXLomSkdjet9NSJpOp7D5UXFwcYmNjAQD29vaoX7++SsYti8S/80ulqkaNGkhISFBaXXrq1CmNuTEqlWvXrsHT0xP37t1DgwYNAAC//PILTE1NsXfvXjg6Ooo6vpeXFxISEjB48OBCdwSooze31x0+fFj+QRl4PfEQGhpaopXOH2r8+PE4duwYli1bBi8vL/z++++4c+cOVqxYgaCgINHH/xi1a9eGoaGh1DEkcefOHdja2iodz8/Pl5cZUmejR49GpUqVkJKSotDI7ttvv8WYMWPUehKkQoUKSE1NVZq4TEtLg5mZmegf2AYMGIDg4GC1//kUw8KFCz/q+X/99Rd69uypsPPk7dWi6mrZsmUIDAzEjz/+iFmzZsm/z4yMjLBw4UKVTYI8ePCg0JvJRTXXvnr1qnyrecEuoOrVq6N69eoKOwk04f1b04waNUrhzzk5OcjOzoa2tjb09PSKvKloZGSEpUuXKh3/kJWYUhkyZAjCw8Ph5eVV6M0VUm9iTXIUJS8vD3PnzsXhw4fh7OysNOEu9kStl5cXli1bpjTOypUr0b9/f1HHltrHvl6dPXu20P8fd3d3lUzY/vbbb3Bzc4OlpaW8dOfly5dhbm6O9evXiz4+kaZZvnw51q5dCy8vr4/6OufPn1dYjNu8efPSiEcievtaQipWVlYQBAH16tVTyQLusoz/ehpm6NChGDVqFFavXg2ZTIa7d+/izJkzGDt2LKZMmSJ1PLU2ZMgQODg44Pz58/KSGI8fP8bAgQMxbNgwnD59WtTxT548iVOnTqFx48aijlOaevToAeD1DZ+3e6ZUqlQJVlZWKrkRvHfvXqxbtw7u7u4YNGgQWrduDVtbW1haWmLDhg1qfbE1b948TJgwAcuXL//oGsErVqyAubl5KSUTX6NGjXDy5Emlv/f27dtL3C9BlY4cOYLDhw+jTp06Csft7Ow+qA+CFN61yfPly5fQ1tYWffzc3FysXr0aR48eRfPmzZVKSZXlVfRF9ZcqyujRozFixAh4enpiwIAB6NixIypUqFBK6cS1ZMkS/Pnnn+jRo4fCBJiLiwvGjh0r+vgXLlyAj48PYmJilH4GirNa69ixY2LGo/cobGdgfHw8vvvuO4wbN67YXyc7OxspKSlKZTOLmgCT0sGDB7F///5S2TFKqjd16lT4+vqqvA/ElStX5J+l3i73pqqJtODgYBw5cqTQnXNjxoyRP66sved/7OvVy5cvkZubq3Q8JycHz58/L5WM71O7dm1ER0djw4YNiIqKgq6uLgYNGoS+ffsqTaYR0eueXZ9//nmJn//ff/+hb9++iIiIgJGREYDXu68+//xzbN68Welak6hAdnY2/Pz85P2NC3qs+vn5oXbt2pg4caLECTUPJ0E0zMSJE5Gfn4927dohOzsbbm5uqFy5MsaOHQs/Pz+p46m1y5cvK0yAAK/LlsyaNQuffPKJ6OPb29ur5INtaSqY+ba2tsa5c+dQvXp1SXKkp6fLdzpVqVJFXk6mVatW+O677yTJVFwuLi548eIFbGxsPro0Tr9+/Uo7nqgCAwPh4+ODO3fuID8/Hzt37sSNGzewbt067Nu3T+p4RcrKyoKenp7S8fT0dLXtBVTQmFUmk2HVqlUKjXXz8vJw4sQJlTR0f3NFfVxcnOjjqZvExESsWbMGiYmJWLRoEczMzHDw4EHUrVsXDg4O731uamoqDh06hE2bNuGbb76Bnp4eevfujf79+3/UBZgqJCcnFzrBWblyZWRlZYk+vq+vL+rXr4/g4GCN2XFJ72ZnZ4egoCAMGDBAXgLgXR4+fIiBAwfi0KFDhZ5X57KZxsbGGrPbi5Tt3r0bs2bNQps2bTB48GB89dVXKvmMIPWkLXfOKfqQ16sWLVpg5cqVWLJkicLx5cuXq2xluL6+PoYNG6aSsYg03ZAhQ7Bx48YSLzoeMmQIcnJyEBMTI69IcuPGDQwaNAhDhgx552cXkl7BtfXbZDIZdHR0YGtrCzc3N9EWrE2aNAlRUVE4fvy4vNQrALRv3x7Tpk3jJEgJcBJEw8hkMvz0008YN24cEhISkJmZiUaNGinc6KLC1a9fH/fv31e6AfXgwYNCS/aUtj/++AMTJ05EYGAgHB0dlW6Gq3Nz7+TkZEnHt7GxQXJyMurWrQt7e3ts3boVLVq0wN69e+WrKdRV3759cefOHcyePbtEN+U0uVZ49+7dsXfvXsyYMQP6+voIDAxEs2bNsHfvXnzxxRdSxytS69atsW7dOvz8888AXr/+FjRbbtu2rcTpCrdgwQIAr3eCLF++XOEDmba2NqysrLB8+XLRc0h9c0ZK4eHh6Ny5M1xdXXHixAnMmjULZmZmiIqKQnBwMLZv3/7e51esWBFdu3ZF165dkZ2djV27dmHjxo1o27Yt6tSpI7/ZpI6sra1x+fJlpRXRhw4dUigpJ5akpCTs2LFDJe/ppBoVK1bE3bt3i3zcjz/+iCdPnuDs2bNwd3fHrl27cP/+fcycOVOtSxcCwM8//4zAwECEhIQUOvFO6u3y5cu4dOkS1qxZg1GjRmHkyJHo06cPfH19VbLICni9yhiASlcTl+f3+Xcp7uvVzJkz0b59e0RFRaFdu3YAXjddPnfuHI4cOSJ2TLnr168XunNO7Ea/RJrmxYsXWLlyJY4ePVqi8oPh4eE4ffq0fAIEABo0aIAlS5agdevWomSm0rFgwQI8fPgQ2dnZCtVk9PT0YGBggAcPHsDGxgbHjh2DhYVFqY//999/Y8uWLfjss88U7iM5ODio9TWhOuMkiIbx9fXFokWLYGhoiEaNGsmPZ2Vlwc/PD6tXr5YwnXqbM2cO/P39MW3aNPm27X///RczZszAL7/8gqdPn8ofK8aEhJGREZ4+fQoPDw+F44IgqLSpUknMmDHjvecDAwNFHX/QoEGIiopCmzZtMHHiRHTr1g1Lly5FTk6O2m+xP336NM6cOVPiMmiaXiu8devW+Oeff6SOUSJz585Fu3btcP78ebx69Qrjx4/HtWvXkJ6ejoiICKnjFapgwrJt27bYuXOnws43VXrzvepN5eG9auLEiZg5cybGjBmj8Pf38PAotGfB++jp6aFjx454/Pgxbt26Ja8jrK7GjBmDkSNH4sWLFxAEAZGRkdi0aRPmzJmDVatWiT5+u3btEBUVxUkQDfRmDzLg9Wej1NRULF26tFhlosLCwrB79264uLhAS0sLlpaW+OKLL1ClShXMmTMHX375pVjRP9q8efOQmJgIc3NzWFlZKd1cuXjxokTJqLiaNm2Kpk2bYt68edi7dy/WrFkDV1dX2NvbY/DgwRg4cKBCX73SkJ+fL5/ky8zMBAAYGhoiICAAP/3003ubuZaGNWvWoE+fPtDV1RV1HHX0sa9Xrq6uOHPmDObOnYutW7dCV1cXzs7OCA4OVmo0L4akpCT07NkTV65cgUwmk5ePLLjGUOdrUiIpREdHo0mTJgBKVn7QwsKi0H6YeXl5qFWrVqlkJHHMnj0bK1euxKpVq1CvXj0AQEJCAoYPH45hw4bB1dUVffr0wejRo4tc6FYSDx8+VOrxCby+pta0+0LqQia8q3A4qaV3Nbt99OgRatSoUWh9UXrtzYuBgheMtz/0iTkh0aJFC1SsWBGjRo0qdEdAmzZtSn3M0vJ2eZOcnBwkJyejYsWKqFevnsov0G/duoULFy7A1tZWret8A0CzZs3wxx9/yCfePpSRkZHG1gofMmQIBgwYAHd3d6mjlNiTJ0+wdOlSREVFITMzE82aNcPIkSNRs2ZNqaOptfL8XmVgYIArV67A2toahoaGiIqKgo2NDW7evAl7e3u8ePGiyK9RsANkw4YNCA0NhYWFBfr27Yv+/furpJzZx9iwYQOmTZsmX51Uq1YtTJ8+HYMHDxZ97EePHsHHxwctWrQodMclV7eqr7dv2MpkMpiamsLDwwPz5s0r8jW3SpUqiI6OhpWVFSwtLbFx40a4uroiOTkZDg4OyM7OFjP+RymqefvUqVNVlIQ+1qtXr7Br1y6sXr0aYWFh+Pzzz3H37l3cv38ff/75J7799ttSG2vSpEkIDg7G9OnT5Z8RT506hWnTpmHo0KGYNWtWqY1VGHNzczx//hy9e/fG4MGD1b5cY2n62NcrqXXr1g0VKlTAqlWrYG1tjcjISKSlpSEgIAC//fYbV6YTlbLdu3dj9uzZ+P333+Hi4gLgdZN0Pz8/TJgwQd6HldRPvXr1sGPHDvkkWIFLly7hq6++QlJSEk6fPo2vvvoKqamppT6+m5sbevfuDT8/PxgaGiI6OhrW1tbw8/NDfHw8S6mVAHeCaIinT59CEAQIgoBnz55BR0dHfi4vLw8HDhwodIaQ/o/U27avXr2KS5cuKWyD1BSXLl1SOvb06VMMHDgQPXv2VHkeS0tLlTegLKmgoCAEBARg1qxZcHJy+uAyaJpcK/zhw4fo1KkTTE1N0adPH/Tv31/pA4S6ysnJQadOnbB8+XL89NNPUsf5YL6+vu89L9ZODL5XvZ64TE1NhbW1tcLxS5cuoXbt2kU+v0+fPti3bx/09PTwzTffYMqUKWjZsqVYcUtd//790b9/f2RnZyMzM1Ol/99nzpxBREQEDh48qHRO3Xdclndvl3r8UA0aNMCNGzdgZWWFxo0bY8WKFfLyf+p+Q5KTHJrvwoULWLNmDTZt2oTKlSvD29sbv//+u3xX2pIlS+Dv71+qkyAhISFYtWqVwuSus7Mzateuje+//170SZA7d+5g7969WLt2Ldzd3WFjY4NBgwbBx8cHNWrUEHVsqX3s6xXwf73DkpKSsHDhwg/qHfaxzpw5g7CwMFSvXh1aWlrQ0tJCq1at5FUTCrvuI6KSGzhwILKzs/Hpp5+iYsXXt2Bzc3NRsWJF+Pr6Kly3fUi/UBJfampqoYv3cnNzce/ePQCvF3w9e/ZMlPFnz56Nzp074/r168jNzcWiRYtw/fp1nD59GuHh4aKMWdZxJ4iG0NLSeu92J5lMhunTp2vkzbryws3NDYGBgWjfvr3UUUrNlStX0K1bN9y8eVPUcaQux/UxClaLvf3zW9xdR3/99Rd2796tsbXCHz9+jG3btmHjxo04efIk7O3t0b9/f/Tr1w9WVlZSx3svU1NTnD59WiWlCUrb25OTOTk5uHr1KjIyMuDh4YGdO3eKMi7fq4CxY8fi7Nmz2LZtG+rXr4+LFy/i/v378Pb2hre3d5E3PAsmETp27Chakz2xJCcnIzc3V+lnJj4+HpUqVRL9Z97Kygpdu3bFlClTYG5uLupYpF7++usv5ObmYuDAgbhw4QI6deqE9PR0aGtrY+3ataV681lM33//PWbMmIHq1atLHYWKycnJCTExMejYsSOGDh0qX2X/pkePHsHMzKxUbp4X0NHRQXR0NOrXr69w/MaNG2jSpAmeP39eamMV5f79+/jrr78QEhKC2NhYdOrUCYMHD0a3bt1EL8ulid7uHRYTEwMbGxsEBQXh/PnzopRUeZOxsTEuXrwIa2tr1KtXD6tWrULbtm2RmJgIJycntd45R6SJQkJCiv1YHx8fEZPQh/ryyy9x7949rFq1Sl4d5dKlSxg6dChq1KiBffv2Ye/evfjf//6HK1euiJIhMTERQUFBCpUpJkyYACcnJ1HGK+s4CaIhwsPDIQgCPDw8sGPHDoWV4dra2rC0tGQ9wWLKzs4utAmc2GWVtm3bhmnTpmHcuHGF7ghQ97JOhTl16hS6deuGx48fizqOupXj+hBFzdAXVQatadOmSExMhCAIGl8r/L///sOmTZuwevVqxMfHq31JpNGjR6Ny5coICgqSOkqpyM/Px3fffYd69eph/PjxoozB96rX5VBGjhyJtWvXIi8vDxUrVkReXh769euHtWvXatzExodo06YNfH19lS7g/vrrL6xatQrHjx8XdXxDQ0NcvnxZXrOX1NuYMWOK/dgP7f+VnZ2N2NhY1K1bV6MmFKpUqYLLly/DxsZG6ihUTD///DN8fX2LtdOvNH366af49NNPsXjxYoXjfn5+OHfuHP7991+V5jl79ixWr16NkJAQ1KxZE48fP4axsTHWrFmj0WVRC5Tm61XLli3Ru3dvee+wgrKZkZGR6NWrl7zRvVhat26NgIAA9OjRA/369cPjx48xefJkrFy5EhcuXFDqeUBEVF7du3cPXl5eCA0Nld+Hyc3NRbt27bB+/XqYm5vj2LFjyMnJQYcOHSROS8XBSRANc+vWLdStW5dNcErg4cOHGDRoUKFlMgDxm8AVthKqoBmdupfpePsCq6AB4Pr169GmTRts3LhR5ZneLMfl5eWl8vFVpazUCs/JycH+/fvx119/Yf/+/ahWrRru3Lkjdaz38vPzw7p162BnZ4fmzZtDX19f4fyH3pRTBzdu3IC7u7soNUvfxPcqICUlBVevXkVmZiaaNm2qkTuKPlSVKlVw8eJFpcbkCQkJcHFxQUZGhqjj+/j4oHXr1hgyZIio41DpaNu2bbEeJ5PJEBYWJnIa9fDmDVFSX2JO4BVXeHg4vvzyS9StW1deMvHMmTO4ffs2Dhw4oJK+Dvfv38f69evlZZ169OiBwYMHo3379sjKysKMGTOwefNm3Lp1S/QsYivN16vS6B32MQ4fPoysrCz06tULCQkJ6Nq1K+Li4mBiYoItW7bAw8ND1PGJyqO8vDz8/fffiImJAQA4ODjA09OzTC+OKktiY2MRFxcH4HUJVlWVuH9Xn820tDSYmZmp9T1EdcWeIBomJiYGt2/fRqtWrQAAv//+O/788080atQIv//+O4yNjSVOqL5+/PFHZGRk4OzZs3B3d8euXbtw//59zJw5E/PmzRN9/OTkZNHHEMuCBQsU/qylpQVTU1P4+Phg0qRJkmSqUqUKpk+fjm7duqndJEh0dDQcHR2hpaWF6Ojo9z62qB1AmjLJ8S7Hjh3Dxo0bsWPHDuTn56NXr17Yt2+fRlxgXb16Fc2aNQMA+YeeApp6cz8xMVElO3D4XgXUrVsXFhYWADT3++VDyWSyQmviPnnyRCUf0uvXr49Jkybh1KlThe649Pf3Fz0DFV9p9mp7101pmUwGHR0d2Nraonv37hrbY4vUS3F7Joj52t+mTRvcuHEDf/zxB2JjYwEAvXr1wvfff6+SHZfdunXD4cOHUb9+fQwdOhTe3t4KP1/6+voICAjAr7/+KnoWVSjN16uP7R32sTp27Cj/va2tLWJjY5Geng5jY+Ny83mFSJUSEhLQpUsX3LlzR37zfM6cObCwsMD+/fu5g1kD2Nvbw97eXuXjvmvPwsuXL6Gtra3iNGUDd4JoGCcnJ/zyyy/o0qULrly5AhcXFwQEBODYsWOwt7fHmjVrpI6otmrWrIndu3ejRYsWqFKlCs6fP4/69etjz549mDt3Lk6dOiV1RPpAqirH9aG0tLRw7949mJmZyXskFPZS+yE7gC5cuKCwcuTtEmHqqHbt2khPT0enTp3Qv39/dOvWDZUrV5Y6Vrnw9g3Bgt1b+/fvh4+PD5YuXSrq+OX9vSo4OBgLFixAfHw8AMDOzg4//vhjmd+h0K1bN+jq6mLTpk3ylW15eXn49ttvkZWV9c6dmKXl7RtKb5LJZEhKShJ1fJJO27ZtcfHiReTl5clvMMTFxaFChQqwt7fHjRs3IJPJcOrUKTRq1EjitESab/DgwRgyZIh8F0phBEFASkoKLC0tVZhM/X1s7zAi0ixdunSBIAjYsGGDfLI4LS0NAwYMgJaWFvbv3y9xQnrTmDFj8PPPP0NfX7/InZ9i7fYsqMQyevRo/PzzzzAwMJCfy8vLw4kTJ3Dz5s1iL8qg/8NJEA1jYGCAq1evwsrKCtOmTcPVq1exfft2XLx4EV26dMG9e/ekjqi2qlSpgujoaFhZWcHS0hIbN26Eq6srkpOT4eDgoLImcNevXy+0J4mnp6dKxi+JglW8b6+gTE9PR8WKFVGlShVRx1fHclzv82YpoKJKABR1YfjgwQP06dMHx48fh5GREQAgIyMDbdu2xebNm2FqalpasUvdn3/+id69e8tzk+q8XbahYPeWh4cHfH19UbGiuBtBy/N7VWBgIObPnw8/Pz+FEiVLly7F6NGjMWPGDIkTiuf69etwc3ODkZGRvBTLyZMn8fTpU4SFhcHR0VHihFRWLVy4ECdPnsSaNWvkn0mePHmCIUOGoFWrVhg6dCj69euH58+f4/DhwxKnVZaYmCgvKbRw4UKYmZnh4MGDqFu3LhwcHKSOR2pozZo1MDAwQO/evRWOb9u2DdnZ2Wyuq8bKc+8wovJIX18f//77r1Ij66ioKLi6uiIzM1OiZFSYtm3bYteuXTAyMnpvKUQxy7UWLOy6desW6tSpo/C+oK2tDSsrK8yYMQOffvqpKOOXZZwE0TDVqlWTr2Jr1aoVvL29MWzYMNy8eRONGjVS2Y18TfTJJ59g5syZ6NixIzw9PWFkZIQ5c+Zg8eLF2L59OxITE0UdPykpCT179sSVK1cUdgYUbDtW53p+nTt3Rrdu3fD9998rHF++fDn27NmDAwcOiDr+26t737yhO2nSJBgaGoo6fknl5ORg+PDhmDJlyntXKL/Pt99+i6SkJKxbtw4NGzYE8PpGo4+PD2xtbbFp06bSjExUKsrze5WpqSkWL16Mvn37KhzftGkT/Pz88OjRoyK/Rn5+PhISEvDgwQPk5+crnHNzcyvVvKXt7t27WLp0KaKioqCrqwtnZ2f88MMPKi1D9OrVKyQnJ6NevXqiT/iReqhduzb++ecfpV0e165dQ4cOHXDnzh1cvHgRHTp0KNbPoCqFh4ejc+fOcHV1xYkTJxATEwMbGxsEBQXh/Pnz2L59u9QRSQ3Vr18fK1asULpBEx4ejmHDhuHGjRuiZwgNDUVoaGih71WrV68WfXxNVx57hxGVR9WqVcO+ffvw+eefKxyPiIhAt27dkJ6eLlEyUndt27bFzp07y0UpaVXhlaGGadWqFcaMGQNXV1dERkZiy5YtAF5v+a9Tp47E6dTbqFGj5M2Ap06dik6dOmHDhg3Q1tbG2rVrVTK+tbU1QkNDYW1tjcjISKSlpSEgIAC//fab6ON/jLNnzxa61c/d3R0//fST6ONraj+VSpUqYceOHZgyZUqJv8ahQ4dw9OhR+QQIAHlfhQ4dOpRGTCrDHj58KL8R0qBBA5XtHCrP71U5OTlwcXFROt68efNi9WP5999/0a9fP9y6dUupjN6HlNCTSq1atTB79mxJxs7Ozoafnx9CQkIAvP5+s7GxgZ+fH2rXro2JEydKkovE9+TJEzx48EBpEuThw4d4+vQpgNd1+N/ehasOJk6ciJkzZ2LMmDEKizo8PDxEL11ImislJaXQBTaWlpZISUkRffzp06djxowZcHFxQc2aNdlLogTKY+8wovKoa9euGDZsGIKDg9GiRQsAr++vjBgxQq2rgZD0SrMfFb3GSRANs3TpUnz//ffYvn07li1bJm+edvDgQXTq1EnidOptwIAB8t83b94ct27dQmxsLOrWrYvq1auLPv6ZM2cQFhaG6tWrQ0tLC1paWmjVqhXmzJkDf39/ta7n9/Lly0Jv3uXk5OD58+cSJNIcPXr0wN9//43Ro0eX6Pn5+flKzX2B1xMsb6+6IyqQlZUFPz8/rFu3Tv59UqFCBXh7e2PJkiXQ09MTdfzy/F7l5eWFZcuWKU0cr1y5Ev379y/y+SNGjICLiwv279+v0TeWnJyccODAAfkNHlWYNGkSoqKicPz4cYXvs/bt22PatGmcBCnDunfvDl9fX8ybNw+ffPIJAODcuXMYO3YsevToAQCIjIxE/fr1JUxZuCtXrhRa1tPMzEztdq2Q+jAzM5OX+X1TVFQUTExMRB9/+fLlWLt2Lby8vEQfqywqr73DiMqjxYsXw8fHBy1btpTFLLrOAABm1ElEQVRf1+fm5sLT0xOLFi2SOB29rVevXsV+7M6dO0VM8tp///2HPXv2FFpSX6yeJGUZJ0E0TN26dbFv3z6l4wsWLJAgjeYSBAG6urpo1qyZysbMy8uTr/CrXr067t69iwYNGsDS0lIlW9Y/RosWLbBy5UosWbJE4fjy5cvRvHlz0cfv2bNnsW8EquKN6EPY2dlhxowZiIiIQPPmzaGvr69w3t/f/73P9/DwwKhRo7Bp0ybUqlULAHDnzh2MHj0a7dq1Ey03abYxY8YgPDwce/fuhaurKwDg1KlT8Pf3R0BAAJYtWybq+OXtverNpnkymQyrVq3CkSNH8NlnnwF4vdorJSUF3t7eRX6t+Ph4bN++Hba2tqLlVYWbN28iJydHpWP+/fff2LJlCz777DOF9wwHBwfRS16StFasWIHRo0ejT58+8kUbFStWhI+Pj/x1x97eHqtWrZIyZqGMjIyQmpqqtKr/0qVL8glkorf17dsX/v7+MDQ0lJdJDA8Px6hRo9CnTx/Rx3/16pVSaRcqnnf1Dhs9ejRSUlLKdO8wovLIyMgIu3fvRnx8PGJjYwEADRs21PjP+mVV1apVpY4gFxoaCk9PT9jY2CA2NhaOjo64efMmBEFQ6b3MsoQ9QTRMmzZtMHjwYPTu3Ru6urpSx9E4Uq66ad26NQICAtCjRw/069cPjx8/xuTJk7Fy5UpcuHABV69eFT1DSUVERKB9+/b45JNP5DfeQ0NDce7cORw5ckTeAFcsAwcOxK5du1C1alV5mZkLFy7gyZMn6NGjh8LNrjVr1oia5UO9rxeITCZDUlLSe59/+/ZteHp64tq1a/IV1bdv34ajoyP27Nmj1qWFTpw4gc8//1ypJn9ubi5Onz6t9r0NNFn16tWxfft2uLu7Kxw/duwYvvnmGzx8+FD0DHl5edi1axdiYmIAvP6w36NHjzLZo+F9TfPeVJwGeh4eHhg/frzG75gxNDREVFQUbGxsVDamnp4erl69ChsbG4Xxo6Ki4ObmhidPnqgsC0kjMzNT/r5qY2MDAwMDiRMVbezYsTh79iy2bduG+vXr4+LFi7h//z68vb3h7e2NqVOnSh2R1NCrV6/g5eWFbdu2yd9X8/Pz4e3tjeXLl0NbW1vU8SdMmAADA4OPKvlaXpVG7zAiIir7WrRogc6dO2P69OnyaxszMzP0798fnTp1wnfffSd1RI3DSRAN8+OPP2Ljxo14+fIlvvnmGwwePFi+0pTe712rbpYuXYrRo0eLvurm8OHDyMrKQq9evZCQkICuXbsiLi4OJiYm2LJlCzw8PEQd/2NdvnwZv/76Ky5fvixvdjtp0iSVNPGbMGEC0tPTsXz5clSoUAHA6xus33//PapUqYJff/1V9AxSEgQBR48eVVg50r59e4lTFa1ChQpITU2FmZmZwvG0tDSYmZmpfW8DTaanp4cLFy4o9JIBXjcJbtGiBbKyskQd/9q1a+jWrRvu37+PBg0aAHjdn8HU1BR79+6Fo6OjqONrsl27dmHy5MkYN24cnJyclMrhOTs7S5Tsw3Tp0gXBwcGoWbOmysZ0c3ND79694efnB0NDQ0RHR8Pa2hp+fn6Ij4/HoUOHVJaFqLhevXqFkSNHYu3atcjLy0PFihWRl5eHfv36Ye3atfLPPUSFiY+Pl382d3JygqWlpWhjvbnrMT8/HyEhIXB2doazs7PSexVLdLybkZERzp07p3QNFRcXhxYtWiAjI0OaYEQkCkEQsH37dhw7dgwPHjxQKmmtbpUsSH0YGhri8uXLqFevHoyNjXHq1Ck4ODggKioK3bt3x82bN6WOqHE4CaKBcnNzsWfPHoSEhODgwYOwtbWFr68vvLy8YG5uLnU8taWOq27S09NhbGyssTXfVcXU1BSnTp2S30wtcOPGDXz++edIS0uTKNmHKXi5LS//31paWrh//75SM+64uDi4uLjIm9VS6WvXrh1MTEywbt066OjoAACeP38OHx8fpKen4+jRo6KO37JlS5iamiIkJATGxsYAgMePH2PgwIF4+PAhTp8+Ler4mkxLS0vpmEwmgyAIGtEYXUqnTp1C586dMWDAAKxduxbDhw/H9evXcfr0aYSHh6ukfCNRSaWkpODq1avIzMxE06ZNVbLIhMqGiIgIuLi4oHLlyqKOU5q7HsszPz8/VKpUSWmiaOzYsXj+/Dl+//13iZIRkRhGjRqFFStWoG3btjA3N1e6F6BulSxI0fbt27F169ZCe3JcvHhR1LFr1KiBY8eOoWHDhmjUqBGCgoLg6emJqKgouLq6IjMzU9Txy6KyV5OiHKhYsSJ69eqFXr164cGDB1i5ciWmTJmC//3vf+jSpQv8/f3VfleBFHJycuSllN7UvHnzQpt+q0K1atUkGfdjvHjxQunFv0qVKqKOmZubi9jYWKVJkNjYWI1oDv6xZdhCQ0MRGhpa6MqR1atXl3rej1XQTEwmk2HgwIEKF+V5eXmIjo5mHWmRLVq0CB07dkSdOnXQuHFjAK+bpero6ODw4cOij3/58mWcP39ePgECAMbGxpg1a5a8aTEVLjk5WeoIJZaWliZvyHv79m38+eefeP78OTw9PUUvmwgArVq1wuXLlxEUFAQnJyccOXIEzZo1w5kzZ+Dk5CT6+EQfo27duqhbt67UMUgDde7cGZcvXxa9/OCxY8dE/frlSXBw8Dt7h72544Y7aog03/r167Fz50506dJF6ij0gRYvXoyffvoJAwcOxO7duzFo0CAkJibi3LlzGDlypOjjf/bZZzh16hQaNmyILl26ICAgAFeuXMHOnTtZEaiEOAmiwSIjI7FmzRps3rwZZmZmGDhwIO7cuYOuXbvi+++/x2+//SZ1RLXi5eWFZcuWKX2YXLlyJfr37y9RKs2QnZ2N8ePHY+vWrYXuuhB7ZfKgQYMwePBgJCYmokWLFgBeXywEBQVh0KBBoo79sT62+eH06dMxY8YMuLi4oGbNmhqxi6SgmZggCDA0NFToX6StrY3PPvsMQ4cOlSpeueDo6Ij4+Hhs2LBBXkatb9++6N+/v0r6SdWvXx/379+Hg4ODwvEHDx6wCWARxCxlIpYrV66gW7duuH37Nuzs7LB582Z06tQJWVlZ0NLSwoIFC7B9+3b06NFD9Cz16tXDn3/+Kfo4RB/jzZucReFNUCoKCztolqtXr8ob2iYmJgJ43cutevXqCj0iNeEzPxEVrWrVqirtkUel548//sDKlSvRt29frF27FuPHj4eNjQ0CAwORnp4u+vjz58+X7/aYPn06MjMzsWXLFtjZ2fHzYQmxHJaGefDgAdavX481a9YgPj4e3bp1w5AhQ9CxY0f5B6VTp06hU6dO3Br1Fj8/P6xbtw4WFhaFrrp5s5YtX1AUjRw5EseOHcPPP/8MLy8v/P7777hz5w5WrFiBoKAg0SeR8vPz8dtvv2HRokVITU0FANSsWROjRo1CQECAWtfL/tgybDVr1sTcuXPh5eUlZkxRTJ8+HWPHjoW+vr7UUUgF3ixvdurUKYwfPx7Tpk2Tv97++++/mDFjBoKCgrgSqgiJiYlYuHChvKl8o0aNMGrUKNSrV0/iZIXr3LkzKlasiIkTJ2L9+vXYt28fOnbsKJ+M8PPzw4ULF/Dvv/+KniU/Px8JCQmF7pxzc3MTfXyi4mBZISpNBc1SeZONiEj9hISE4NChQ1i9erVKFqNR6dHT00NMTAwsLS1hZmaGf/75B40bN0Z8fDw+++wzjSnLTv+HkyAaRltbG/Xq1YOvry8GDhyoVGsfeH0jqnv37tyy/BZecJZc3bp1sW7dOri7u6NKlSq4ePEibG1tsX79emzatAkHDhxQWZaCG61il+AqLR/b/NDExASRkZFqe/OT1Fd8fPw7G/AFBgaW+nhaWloKqxbf7oHz5p/Z1+LdDh8+DE9PTzRp0gSurq4AXtd7j4qKwt69e/HFF19InFBZ9erVERYWBmdnZ2RmZqJKlSo4d+6cvAdHbGwsPvvsM9Gbvf7777/o168fbt26pbQymt93RFRWbdy4Ed27d+eiEw319OlThIWFwd7eHvb29lLHIaJS9vz5c/Ts2RMRERGwsrJSWHwLiN9XgkrOxsYGO3bsQNOmTeHi4oKhQ4di+PDhOHLkCPr06aOS3SBUujgJomFOnjypkrraRG8yMDDA9evXUbduXdSpUwc7d+5EixYtkJycDCcnJ9F3HT1//hyCIEBPTw8AcOvWLezatQuNGjVChw4dRB37Y31s88MJEybAwMAAU6ZMETOmKO7fv4+xY8fK+5m8/XbDG5Li+fPPP/Hdd9+hevXqqFGjhsLkhEwmE+XDdnh4eLEf26ZNm1Ifv6xo2rQpOnbsiKCgIIXjEydOxJEjR9TyQklLSwv37t2DmZkZAOVVyffv30etWrVE/5lv0qQJ6tevj+nTpxdaPrCgVB+Rurp9+zYAwMLCQuIkRCSWb775Bm5ubvjhhx/w/PlzNG7cGDdv3oQgCNi8eTO++uorqSMSUSn65ptvcOzYMXz99deFNkafOnWqRMmoKEOGDIGFhQWmTp2K33//HePGjYOrqyvOnz+PXr16ITg4uNTHNDY2LnY5RE7CfDj2BNEwLi4uyM7O1sibwaS5bGxskJycjLp168Le3h5bt25FixYtsHfvXhgZGYk+fvfu3dGrVy+MGDECGRkZaNGiBbS1tfHo0SPMnz8f3333negZPsbHND988eIFVq5ciaNHj8LZ2Vlp5Yg6l24bOHAgUlJSMGXKFI3pZ1JWzJw5E7NmzcKECRNUNiYnNkpHTEwMtm7dqnTc19cXCxcuVH2gYnr751uKn/f4+Hhs376dfWdIo+Tm5mL69OlYvHixfFGJgYEB/Pz8MHXqVKX3fSIAyMrKQlBQkHyhyds7PpOSkiRKRkU5ceIEfvrpJwDArl27IAgCMjIyEBISgpkzZ3IShKiM2b9/Pw4fPoxWrVpJHYU+0MqVK+XvryNHjoSJiQlOnz4NT09PDB8+XJQx1fl6ryzgJIiGeftm8KeffopKlSppzM1gKfFioeQGDRqEqKgotGnTBhMnTkS3bt2wdOlS5OTkqOQm/MWLF7FgwQIAwPbt21GjRg1cunQJO3bsQGBgoFp/339s88Po6Gg0adJE/rXepO6TCqdOncLJkyfl+Ul1Hj9+jN69e0uaISMjA8HBwfK+Fg4ODvD19eVq/CKYmpri8uXLSiX0Ll++LN9poY4GDhyIypUrA3g9eTtixAh5aZaXL1+qJMOnn36KhIQEToKQRvHz88POnTsxd+5ctGzZEgBw5swZTJs2DWlpaVi2bJnECUkdDRkyBOHh4fDy8uJCEw3z5MkTVKtWDQBw6NAhfPXVV9DT08OXX36JcePGSZyOiEqbhYWFxpTyJkVaWlrQ0tKS/7lPnz7o06ePqGP6+PiI+vXLO06CaJi3bwabm5trzM1gqfFioeRGjx4t/3379u0RGxuLCxcuwNbWFs7OzqKPn52dDUNDQwDAkSNH0KtXL2hpaeGzzz7DrVu3RB//Y3xsbx5N7u1jYWGhVAKLVKN37944cuQIRowYIcn458+fR8eOHaGrq4sWLVoAeL1radasWThy5Ih8YpCUDR06FMOGDUNSUhI+//xzAK97gvzyyy8KO8fUydsf1gcMGKD0GG9vb1HGjo6Olv/ez88PAQEBuHfvHpycnJRW0Kvi/YroQ23cuBGbN29G586d5cecnZ1hYWGBvn37chKECnXw4EHs379f3juKNIeFhQXOnDmDatWq4dChQ9i8eTOA1wtYdHR0JE5HRKVt3rx5GD9+PJYvXw4rKyup49AHevHiBaKjowtdSO3p6Sn6+ImJiVizZg0SExOxaNEimJmZ4eDBg6hbty4cHBxEH7+sYU8QDaOnp4fY2FjUrVsX33zzDRwcHDB16lTcvn0bDRo0QHZ2ttQR1ZaRkREvFjSUs7MzhgwZgp49e8LR0RGHDh1Cy5YtceHCBXz55Ze4d++e1BGpEEeOHMG8efOwYsUKfuBTgcWLF8t/n5WVhfnz5+PLL78s9Gawv7+/qFlat24NW1tb/Pnnn6hY8fV6i9zcXAwZMgRJSUk4ceKEqONrMkEQsHDhQsybNw93794FANSqVQvjxo2Dv78/J/DfoqWlBZlM9s4J14JzbIxO6srMzAzh4eFo2LChwvGYmBi4ubnh4cOHEiUjdWZtbY0DBw4ofd+Q+vvjjz8watQoGBgYoG7durh06RK0tLSwZMkS7Ny5U6MXQBGRMmNjY2RnZyM3Nxd6enpK12Xs66C+Dh06BG9vbzx69EjpnCquLcLDw9G5c2e4urrixIkTiImJgY2NDYKCgnD+/Hls375d1PHLIk6CaBjeDC45Xixoru3bt6Nfv37Iy8tDu3btcOTIEQDAnDlzcOLECRw8eFDihFTg7UZeWVlZ/MCnItbW1sV6nEwmE738n66uLi5dugR7e3uF49evX5f3tqKiPXv2DADkO+FI2YfsBrS0tBQxCVHJzJgxA7GxsVizZo28pNzLly8xePBg2NnZsWEqFeqvv/7C7t27ERISIu8VSZrjwoULSElJwRdffAEDAwMAr/sGGBkZccEeURkTEhLy3vMsf6S+7Ozs0KFDBwQGBsLc3Fzl47ds2RK9e/fGmDFjYGhoiKioKNjY2CAyMhK9evXCf//9p/JMmo6TIBqGN4NLjhcLmu3evXtITU1F48aN5XUZIyMjUaVKFaUbrSSdoj7kvYkf+Mouc3NzrF+/Hh06dFA4fvjwYXh7e+P+/fsSJSMiUi89e/ZEaGgoKleujMaNGwMAoqKi8OrVK7Rr107hsTt37pQiIqmhpk2bIjExEYIgwMrKSmmhycWLFyVKRh8iIiICLi4u8glQIiJSH1WqVMGlS5dQr149ScY3MDDAlStXYG1trTAJcvPmTdjb2+PFixeS5NJk7AmiYb7++mu0atVKfjO4QLt27dCzZ08Jk6m/efPmITExEebm5rxY0EA1atRAjRo1FI4V9Bog9cGJDfUwY8YMjB07VmnC9/nz5/j1118RGBgo6vjffvstBg8ejN9++02hr8W4cePQt29fUcfWdGlpaQgMDMSxY8cKrT3LHVREZYuRkRG++uorhWMWFhYSpSFN0aNHD6kjUCno3LkzLl++DBsbG6mjEJGI8vLy8PfffyMmJgYA4ODgAE9PT1SoUEHiZPQ+X3/9NY4fPy7ZJIiRkRFSU1OVKj5cunQJtWvXliSTpuNOECo3pk+f/t7zLDdAVLqePn1a6HGZTIbKlStDW1tbxYnKjwoVKiA1NRVmZmYKx9PS0mBmZiZ6/dJXr15h3LhxWL58OXJzcwEAlSpVwnfffYegoCCueHyPLl26ICEhAYMHD4a5ublSDxBONBIREZUNb67sJaKyKSEhAV26dMGdO3fQoEEDAMCNGzdgYWGB/fv3S3aDnYqWnZ2N3r17w9TUVJI+m2PHjsXZs2exbds21K9fHxcvXsT9+/fh7e0Nb29v3sMsAU6CEFGh3nUDuzBVqlQRMQlpqoKGxe9Sp04dDBw4EFOnTpWXOKPSoaWlhfv378PU1FTheFhYGL799luVNdrNzs5GYmIiAKBevXosRVgMhoaGOHXqlMJuTyIiosJcuHBBYWVx06ZNJU5EH4KTIERlX5cuXSAIAjZs2IBq1aoBeL0wbcCAAdDS0sL+/fslTkjvEhwcjBEjRkBHRwcmJiYK9zZU0Wfz1atXGDlyJNauXYu8vDxUrFgReXl56NevH9auXcudRCXASRAqd3ixUDxF3cAGAEEQIJPJRF9VTppp3bp1+OmnnzBw4EB56bLIyEiEhIRg8uTJePjwIX777TeMGzcO//vf/yROWzYUNKZ/8uQJqlSpovAznJeXh8zMTIwYMQK///67hCnpfT755BMsWbIEn332mdRRiEgkzZo1Q2hoKIyNjdG0adP3ft5iuVYqzIMHD9CnTx8cP34cRkZGAICMjAy0bdsWmzdvVloEQepp48aN6N69O/T19aWOQkQi0dfXx7///gsnJyeF41FRUXB1dUVmZqZEyagoNWrUgL+/PyZOnKjyRZuCIOD27dswNTXFo0ePcOXKFWRmZqJp06aws7NTaZayhD1BqNzgxcKHOXbsmNQRSMOFhIRg3rx5+Oabb+THunXrBicnJ6xYsQKhoaGoW7cuZs2axUmQUrJw4UIIggBfX19Mnz4dVatWlZ/T1taGlZUVWrZsKWFCKsoff/yBiRMnIjAwEI6Ojkrbrrnz7t1sbGxw7tw5mJiYKBzPyMhAs2bNRF+tRVRc3bt3l5cFZG8HKgk/Pz88e/YM165dQ8OGDQEA169fh4+PD/z9/bFp0yaJE1Jx9OvXT+oIRCSyypUr49mzZ0rHMzMzWR5azb169QrffvutJFUrBEGAra0trl27Bjs7O/aLKyXcCULlxrfffoukpCSsW7dO6WLB1taWFwtEpUxXVxfR0dFKKxXi4+PRuHFjZGdnIzk5GQ4ODsjOzpYoZdkUHh6Ozz//XOkGOqm/+Ph49OvXT2n1N3feFU1LSwv37t1T6oVz//591K1bFy9fvpQoGVHh8vLyEBERAWdnZ/kCHaLiqFq1Ko4ePYpPPvlE4XhkZCQ6dOiAjIwMaYJRkbKyshAUFITQ0FA8ePAA+fn5Cuc5YU9Utnh7e+PixYsIDg6WV0c4e/Yshg4diubNm2Pt2rXSBqR3Gj16NExNTSVbsOng4IDg4GBWCChF3AlC5cahQ4dw9OhR+QQIADRq1Ai///47OnToIGEy9RQdHV3sxzo7O4uYhDSVhYUFgoODERQUpHA8ODhYvpIhLS0NxsbGUsQrc54+fSrfJdC0aVM8f/4cz58/L/Sx3E2gvvr3749KlSph48aNhTZGJ2V79uyR//7w4cMKO6Dy8vIQGhoKKysrCZIRvV+FChXQoUMHxMTEcBKEPkh+fn6hCx0qVaqkdFOd1MuQIUMQHh4OLy8v1KxZk+/zRGXc4sWL4ePjg5YtW8pft3Nzc+Hp6YlFixZJnI7eJy8vD3PnzsXhw4fh7Oys9L47f/58UccPCgrCuHHjsGzZMjg6Ooo6VnnBnSBUbhgaGuLkyZNo0qSJwvFLly6hTZs2H9QIvDwo6AlS1EsEVybTu+zZswe9e/eGvb29fKXi+fPnERsbi+3bt6Nr165YtmwZ4uPjRf8AUR5UqFABqampMDMze2dPH+4mUH96enq4dOkSGjRoIHUUjVGwRb2w96xKlSrBysoK8+bNQ9euXaWIR/ReLi4u+OWXX9CuXTupo5AG6d69OzIyMrBp0ybUqlULAHDnzh30798fxsbG2LVrl8QJ6V2MjIywf/9+uLq6Sh2FiET2Zl+HO3fuyHvTNmzYELa2thKno6K0bdv2nedkMhnCwsJEHd/Y2BjZ2dnIzc2FtrY2dHV1Fc6np6eLOn5ZxEkQKjd4sfBhbt26VezHWlpaipiENFlycjJWrlyJGzduAAAaNGiA4cOHc1W2CMLDw+Hq6oqKFSvi+PHj711Z2KZNG1GzzJkzB+bm5vD19VU4vnr1ajx8+BATJkwQdXxN5ubmhsDAQLRv317qKBrH2toa586dQ/Xq1aWOQlRshw4dwqRJk/Dzzz+jefPmSg2SuXOPCnP79m14enri2rVr8t21t2/fhqOjI/bs2YM6depInJDexdraGgcOHFCoTkBEZVN+fj50dHTkfR2IPkRISMh7z/v4+KgoSdnBSRAqN3ixQESkGlZWVti4cSM+//xzheNnz55Fnz59kJycLFEy9bdt2zZMmzYN48aNg5OTk9K2a5YfJCpb3my2+ebkNXfuUVEEQcDRo0cRGxsL4PXKYk6gq7+//voLu3fvRkhICPT09KSOQ0QiY18HIvXBSRAqV3ixUHLr16/H8uXLkZycjDNnzsDS0hILFy6EtbU1unfvLnU8UhPR0dFwdHSElpZWkX1leDNXPG5ubnB3d0ebNm3g6uoKHR0dlY6vo6ODmJgYWFtbKxxPSkpCo0aN8OLFC5Xm0SRv3hAtUFDmiTdEixYaGvrOZrOrV6+WKBXRu4WHh7/3vNg794hItZo2bYrExEQIggArKyulxQ4XL16UKBkRiWHv3r2YO3cu+zoQqQE2RqdyRSaT4YsvvsAXX3whdRSNsmzZMgQGBuLHH3/ErFmz5DfhjIyMsHDhQk6CkFyTJk1w7949mJmZoUmTJu/sK8ObueLq0KEDTpw4gfnz5yM3NxcuLi4KkyJirzy0sLBARESE0iRIRESEvBwhFY67ZEpu+vTpmDFjBlxcXNhsljSGtbU1LCwslL5fC+qIE70LJ301U48ePaSOQEQq5O3tjezsbDRu3Jh9HYgkxp0gVK7wYqFkGjVqhNmzZ6NHjx4wNDREVFQUbGxscPXqVbi7u+PRo0dSRyQ1cevWLdStWxcymazIvjLsJSO+3NxcnDt3DuHh4Th+/DjCwsKgpaUl+k6MuXPnYu7cufj111/h4eEB4PXr7/jx4xEQEIBJkyaJOj6VTzVr1sTcuXPh5eUldRSiYqtQoQJSU1NhZmamcDwtLQ1mZmZcMECFKmrSl70OiYjUA/s6EKkP7gShcoMrREsuOTkZTZs2VTpeuXJlZGVlSZCI1FXBxEZOTg6mT5+OKVOmKO0GINVJSkrClStXEBUVhejoaBgaGsLNzU30cceNG4e0tDR8//33ePXqFYDXJbImTJjACZBiuHv3Lk6dOlXohL2/v79EqdTfq1evlPrQEKm7glJ3b8vMzFR5KUPSHMuXL8fatWs56avBLly4gJiYGACvewYUdq1FRJqPkxxE6oM7Qajc4ArRkmvUqBHmzJmD7t27K+wEWbJkCdasWcPatVSoqlWr4vLly5wEkUC/fv0QHh6Oly9fws3NDW3atIG7uzucnZ1VOgGcmZmJmJgY6Orqws7ODpUrV1bZ2Jpq7dq1GD58OLS1tWFiYqLw/yWTyZCUlCRhOvU2YcIEGBgYYMqUKVJHISrSmDFjAACLFi3C0KFDFcoU5uXl4ezZs6hQoQIiIiKkikhqzMTEBJGRkahXr57UUegDPXjwAH369MHx48dhZGQEAMjIyEDbtm2xefNmmJqaShuQiEpdfn4+EhISCl3gpIoFaqTZEhISkJiYCDc3N+jq6r5zAQ0VjTtBqNzgCtGSGzNmDEaOHIkXL15AEARERkZi06ZNmDNnDlatWiV1PFJTPXr0wN9//43Ro0dLHaXc2bx5M6pXr44hQ4bAw8MDrVq1Er0PSGEMDAzwySefqHxcTTZlyhQEBgZi0qRJhTZJp3d78eIFVq5ciaNHj8LZ2Vmp2ez8+fMlSkak7NKlSwBe7wS5cuUKtLW15ee0tbXRuHFjjB07Vqp4pOaGDBmCjRs3ctJXA/n5+eHZs2e4du0aGjZsCAC4fv06fHx84O/vj02bNkmckIhK07///ot+/frh1q1bSr0y2SeT3ictLQ3ffvstwsLCIJPJEB8fDxsbGwwePBjGxsaYN2+e1BE1DneCULnBFaIfZ8OGDZg2bRoSExMBALVq1cL06dMxePBgiZORupo5cybmzZuHdu3aoXnz5tDX11c4z7I+4nn8+DFOnjyJ48ePIzw8HDExMWjSpAnc3d3h7u6ODh06lPqYvXr1wtq1a1GlShX06tXrvY/duXNnqY9fVnB1b8m1bdv2nedkMhnCwsJUmIaoeAYNGoRFixahSpUqUkchDTJq1CisW7cOzs7OnPTVMFWrVsXRo0eVFolERkaiQ4cOyMjIkCYYEYmiSZMmqF+/PqZPn15oWfaqVatKlIzUnbe3Nx48eIBVq1ahYcOG8ooshw8fxpgxY3Dt2jWpI2ocToJQucGLhdKRnZ2NzMxMpQaeRG97XxkslvVRrYSEBMycORMbNmxAfn6+KCuOBg0ahMWLF8PQ0BCDBg1672PXrFlT6uOXFePHj0e1atUwceJEqaMQEZGa4qSv5jI0NMTJkyfRpEkTheOXLl1CmzZt8PTpU2mCEZEo9PX1ERUVBVtbW6mjkIapUaMGDh8+jMaNGyuUpU9KSoKzszMyMzOljqhxOAlC5QYvFkouOTkZubm5sLOzUzgeHx+PSpUqwcrKSppgRFSotLQ0hIeH4/jx4zh+/DiuX78OIyMjeX+QUaNGSR2R3iEvLw9du3bF8+fP4eTkxAl7IiKiMqR79+7IyMjApk2bUKtWLQDAnTt30L9/fxgbG2PXrl0SJySi0uTh4YHx48ejU6dOUkchDWNoaIiLFy/Czs5OYRLk/Pnz6NixI9LS0qSOqHHYE4TKjWPHjkkdQWMNHDgQvr6+SpMgZ8+exapVq3D8+HFpghFRoczMzFC9enW0bt0aQ4cOhbu7O5ycnKSORcUwZ84cHD58GA0aNAAApcbo9H7nz5/H1q1bkZKSglevXimcYxk2IiKS2tKlS+Hp6QkrKytYWFgAAG7fvg1HR0f89ddfEqcjotIQHR0t/72fnx8CAgJw7969Qhc4OTs7qzoeaYjWrVtj3bp1+PnnnwG8vhbMz8/H3Llz37vIm96NO0GIqEhVqlTBxYsXlbZwJiQkwMXFhbVridTMtWvX4ODgINn49+/fx9ixYxEaGooHDx4oNQFkA8B3MzY2xoIFCzBw4ECpo2iczZs3w9vbGx07dsSRI0fQoUMHxMXF4f79++jZsyfLsBERkVoQBAFHjx5FbGwsAKBhw4Zo3769xKmIqLRoaWlBJpMpXQMVKDjHxuj0PlevXkW7du3QrFkzhIWFwdPTE9euXUN6ejoiIiLYQ7IEuBOEiIokk8nw7NkzpeNPnjzhmzaRGpJyAgR4vXssJSUFU6ZMKbQBIL1b5cqV4erqKnUMjTR79mwsWLAAI0eOhKGhIRYtWgRra2sMHz4cNWvWlDoeERERgNfXVl988QW++OILqaMQkQiSk5OljkBlgKOjI+Li4rB06VIYGhoiMzMTvXr1wsiRI3ltU0LcCUJERerWrRt0dXWxadMmVKhQAcDrldzffvstsrKycPDgQYkTEpE6eVfTTyranDlzkJqaisWLF0sdRePo6+vj2rVrsLKygomJCY4fPw4nJyfExMTAw8MDqampUkckIiJCaGiofLdsfn6+wrnVq1dLlIqIiKhs404QIirSL7/8Ajc3NzRo0ACtW7cGAJw8eRJPnz5lQ3kiUmJhYfHO7d/0fpGRkQgLC8O+ffvg4OCgVDeYfS3ezdjYWL5rsXbt2rh69SqcnJyQkZGB7OxsidMREREB06dPx4wZM+Di4sLdskTlwJw5c2Bubg5fX1+F46tXr8bDhw8xYcIEiZKRunuzt8ybZDIZdHR0ULduXVSuXFnFqTQbd4IQUbHcvXsXS5cuRVRUFHR1deHs7IwffvgB1apVkzoaqak1a9bAwMAAvXv3Vji+bds2ZGdnw8fHR6JkJLYjR45g3rx5WLFiBaysrKSOo1EGDRr03vPsa/Fu/fr1g4uLC8aMGYOff/4ZS5YsQffu3fHPP/+gWbNmnEAiIiLJ1axZE3PnzoWXl5fUUYhIBaysrLBx40Z8/vnnCsfPnj2LPn36sHQWvVNBbxkA8gWGb06cV6pUCd9++y1WrFgBHR0dSTJqGk6CEBGRKOrXr48VK1agbdu2CsfDw8MxbNgw3LhxQ6JkZVtOTg46deqE5cuXw87OTpIMxsbGyM7ORm5uLvT09JR2M6Snp0uSi8q29PR0vHjxArVq1UJ+fj7mzp2L06dPw87ODpMnT4axsbHUEYmIqJwzMTFBZGQkG9oSlRM6OjqIiYmBtbW1wvGkpCQ0atQIL168kCgZqbvdu3djwoQJGDduHFq0aAHgddWAefPmYerUqcjNzcXEiRPx7bff4rfffpM4rWZgOSwi+iBOTk44cOAALCwspI5Cai4lJUXpwx4AWFpaIiUlRYJE5UOlSpXeuXVWVRYuXCjp+GXBw4cP5ROFDRo0gKmpqcSJ1N+bOxO1tLQwceJECdMQEREpGzJkCDZu3IgpU6ZIHYWIVMDCwgIRERFK18URERGoVauWRKlIE8yaNQuLFi1Cx44d5cecnJxQp04dTJkyBZGRkdDX10dAQAAnQYqJkyBE9EFu3ryJnJwcqWOQBjAzM0N0dLRSOaSoqCiYmJhIE6qcGDBgAIKDgxEUFCTJ+Cx1VnJZWVnw8/PDunXr5M1SK1SoAG9vbyxZsgR6enoSJ1Rv+fn5SEhIKLTZrJubm0SpiIiIXnvx4gVWrlyJo0ePwtnZWWm37Pz58yVKRkRiGDp0KH788Ufk5OTAw8MDABAaGorx48cjICBA4nSkzq5cuQJLS0ul45aWlrhy5QoAoEmTJkhNTVV1NI3FSRAiIhJF37594e/vD0NDQ/nNx/DwcIwaNQp9+vSROF3Zlpubi9WrV+Po0aNo3rw59PX1Fc6r4gI7Ly8Pf//9N2JiYgAADg4O8PT0RIUKFUQfW5ONGTMG4eHh2Lt3L1xdXQEAp06dgr+/PwICArBs2TKJE6qvf//9F/369cOtW7fwdrVXmUyGvLw8iZIRERG9Fh0djSZNmgAArl69qnCOTdKJyp5x48YhLS0N33//PV69egXgdYmsCRMmYNKkSRKnI3Vmb2+PoKAgrFy5Etra2gBel74OCgqCvb09AODOnTswNzeXMqZGYU8QIvogXbp0QXBwMGrWrCl1FFJzr169gpeXF7Zt24aKFV/Puefn58Pb2xvLly+Xv5FT6Xu7D8ubZDIZwsLCRB0/ISEBXbp0wZ07d9CgQQMAwI0bN2BhYYH9+/ezDvZ7VK9eHdu3b4e7u7vC8WPHjuGbb77Bw4cPpQmmAZo0aYL69etj+vTpqFmzptLNpKpVq0qUjIiIiIjKs8zMTMTExEBXVxd2dnaoXLmy1JFIzZ0+fRqenp7Q0tKCs7MzgNe7Q/Ly8rBv3z589tlnWL9+Pe7du4dx48ZJnFYzcBKEiIhEFRcXh6ioKOjq6sLJyanQLZ1UtnTp0gWCIGDDhg3yPg1paWkYMGAAtLS0sH//fokTqi89PT1cuHABDRs2VDh+7do1tGjRAllZWRIlU3/6+vqIioqCra2t1FGIiIiIiIg+yrNnz7BhwwbExcUBeN0rsl+/fjA0NJQ4mWbiJAgRFSktLU3ew+H27dv4888/8fz5c3h6eqJ169YSpyOi9/nvv/8AAHXq1FHZmPr6+vj333/h5OSkcDwqKgqurq7IzMxUWRZN065dO5iYmGDdunXQ0dEBADx//hw+Pj5IT0/H0aNHJU6ovjw8PDB+/Hh06tRJ6ihERERERESkRtgThIje6cqVK+jWrRtu374NOzs7bN68GZ06dUJWVha0tLSwYMECbN++HT169JA6KqmJMWPG4Oeff4a+vj7GjBnz3sey8aN48vPzMXPmTMybN08+4WBoaIiAgAD89NNP0NLSEnX8ypUr49mzZ0rHMzMzWQatCIsWLULHjh1Rp04dNG7cGMDrySMdHR0cPnxY4nTqzc/PDwEBAbh37x6cnJyUms0WbCMnIiIiIiLSBNevX0dKSoq8p0wBT09PiRJpLu4EIaJ36ty5MypWrIiJEydi/fr12LdvHzp27Ig///wTwOsbThcuXMC///4rcVJSF23btsWuXbtgZGQkeV+K8mzSpEkIDg7G9OnTFZprT5s2DUOHDsWsWbNEHd/b2xsXL15EcHAwWrRoAQA4e/Yshg4diubNm2Pt2rWijq/psrOzsWHDBsTGxgIAGjZsiP79+0NXV1fiZOqtsMk9mUwGQRDYGJ2IiIiIiDRGUlISevbsiStXrsivaQDI+x7y2ubDcRKEiN6pevXqCAsLg7OzMzIzM1GlShWcO3cOzZs3BwDExsbis88+Q0ZGhrRBiUhBrVq1sHz5cqXVIbt378b333+PO3fuiDp+RkYGfHx8sHfvXvlq/NzcXHh6emLt2rVsUE2iuHXr1nvPsx8RERERERFpgm7duqFChQpYtWoVrK2tERkZibS0NAQEBOC3335jafoSYDksInqn9PR01KhRAwBgYGAAfX19GBsby88bGxsXWvKGiKSVnp4Oe3t7peP29vZIT08XdWxBEPD06VNs3rwZd+7cQUxMDIDXuxnYsLpoc+bMgbm5OXx9fRWOr169Gg8fPsSECRMkSqb+OMlBRERERERlwZkzZxAWFobq1atDS0sLWlpaaNWqFebMmQN/f39cunRJ6ogah5MgRPReBVvt3vVnonfJyspCUFAQQkND8eDBA+Tn5yucT0pKkihZ2de4cWMsXboUixcvVji+dOlSeZ8JsQiCAFtbW1y7dg12dnac+PhAK1aswMaNG5WOOzg4oE+fPpwEeY89e/YUelwmk0FHRwe2trawtrZWcSoiIiIiIqIPk5eXB0NDQwCvq7TcvXsXDRo0gKWlJW7cuCFxOs3ESRAieq+BAweicuXKAIAXL15gxIgR0NfXBwC8fPlSymik5oYMGYLw8HB4eXmhZs2anEBToblz5+LLL7/E0aNH0bJlSwCvV5Lcvn0bBw4cEHVsLS0t2NnZIS0tDXZ2dqKOVRbdu3cPNWvWVDpuamqK1NRUCRJpjh49eijUyy3wZl+QVq1a4e+//1bY1UhERERERKROHB0dERUVBWtra3z66aeYO3cutLW1sXLlStjY2EgdTyOxJwgRvdOgQYOK9bg1a9aInIQ0kZGREfbv3y9vzE2qdffuXfz+++8KzbW///571KpVS/Sx9+7di7lz52LZsmVwdHQUfbyyxM7ODlOnTsWAAQMUjq9fvx5Tp07lDqr3CA0NxU8//YRZs2ahRYsWAIDIyEhMmTIFkydPRtWqVTF8+HB8+umnCA4OljgtERERERFR4Q4fPoysrCz06tULCQkJ6Nq1K+Li4mBiYoItW7bAw8ND6ogah5MgREQkCmtraxw4cAANGzaUOgqpmLGxMbKzs5GbmwttbW3o6uoqnBe7L4kmmzt3LubOnYtff/1V/sE2NDQU48ePR0BAACZNmiRxQvXl6OiIlStX4vPPP1c4HhERgWHDhuHatWs4evQofH19kZKSIlFKIiIiIiKiD5eeng5jY2NW2SghlsMiIiJR/PzzzwgMDERISAj09PSkjkMqtHDhQqkjaKxx48YhLS0N33//PV69egUA0NHRwYQJEzgBUoTExERUqVJF6XiVKlXkO2js7Ozw6NEjVUcjIiIiIiL6KNWqVZM6gkbjThAiIhJF06ZNkZiYCEEQYGVlhUqVKimcv3jxokTJiNRfZmYmYmJioKurCzs7O3lvJnq3Vq1awdDQEOvWrYOpqSkA4OHDh/D29kZWVhZOnDiBo0ePYuTIkWwmSEREREREVI5wJwgREYmiR48eUkcgCeXn5yMhIQEPHjxAfn6+wjk3NzeJUmkOAwMDfPLJJ1LH0CjBwcHo3r076tSpAwsLCwDA7du3YWNjg927dwN4Pbk0efJkKWMSERERERGRinEnCBEREZWqf//9F/369cOtW7fw9scMmUyGvLw8iZJRWZefn48jR44gLi4OANCgQQN88cUX0NLSkjgZERERERERSYWTIEREJJqMjAxs374diYmJGDduHKpVq4aLFy/C3NwctWvXljoeiaRJkyaoX78+pk+fjpo1ayo1bqtatapEyYiIiIiIiIiovOEkCBERiSI6Ohrt27dH1apVcfPmTdy4cQM2NjaYPHkyUlJSsG7dOqkjlln379/H2LFjERoaigcPHijtxhB7J4a+vj6ioqJga2sr6jhEixcvxrBhw6Cjo4PFixe/97H+/v4qSkVERERERETqhJMgREQkivbt26NZs2aYO3cuDA0NERUVBRsbG5w+fRr9+vXDzZs3pY5YZnXu3BkpKSn44YcfCt2J0b17d1HH9/DwwPjx49GpUydRxyGytrbG+fPnYWJiAmtr63c+TiaTISkpSYXJiIiIiIiISF1wEoSIiERRtWpVXLx4EfXq1VOYBLl16xYaNGiAFy9eSB2xzDI0NMTJkyfRpEkTlY0ZHR0t/31iYiImT56McePGwcnJCZUqVVJ4rLOzs8pyEREREREREVH5VlHqAEREVDZVrlwZT58+VToeFxcHU1NTCRKVHxYWFkolsMTWpEkTyGQyhXF9fX3lvy84x8boRERERERERKRK3AlCRESiGDJkCNLS0rB161ZUq1YN0dHRqFChAnr06AE3NzcsXLhQ6ohl1pEjRzBv3jysWLECVlZWKhnz1q1bxX6spaWliEmoPBkzZkyxHzt//nwRkxAREREREZG64iQIERGJ4smTJ/j6669x/vx5PHv2DLVq1cK9e/fQsmVLHDhwAPr6+lJHLFOMjY0Ven9kZWUhNzcXenp6SuWo0tPTVR2PSBRt27Yt1uNkMhnCwsJETkNERERERETqiJMgREQkqlOnTiE6OhqZmZlo1qwZ2rdvL3WkMikkJKTYj/Xx8RExCTBnzhyYm5srlMMCgNWrV+Phw4eYMGGCqOMTERERERERERXgJAgRERGVKisrK2zcuBGff/65wvGzZ8+iT58+SE5OligZEREREREREZU3bIxORESiWLx4caHHZTIZdHR0YGtrCzc3N1SoUEHFycq+ChUqIDU1FWZmZgrH09LSYGZmJnpj8nv37qFmzZpKx01NTZGamirq2FS+nT9/Hlu3bkVKSgpevXqlcG7nzp0SpSIiIiIiIiIpcRKEiIhEsWDBAjx8+BDZ2dkwNjYGADx+/Bh6enowMDDAgwcPYGNjg2PHjsHCwkLitGXLuzZ5vnz5Etra2qKPb2FhgYiICFhbWyscj4iIQK1atUQfn8qnzZs3w9vbGx07dsSRI0fQoUMHxMXF4f79++jZs6fU8YiIiIiIiEginAQhIiJRzJ49GytXrsSqVatQr149AEBCQgKGDx+OYcOGwdXVFX369MHo0aOxfft2idOWDQW7b2QyGVatWgUDAwP5uby8PJw4cQL29vai5xg6dCh+/PFH5OTkwMPDAwAQGhqK8ePHIyAgQPTxqXyaPXs2FixYgJEjR8LQ0BCLFi2CtbU1hg8fXujOJCIiIiIiIiof2BOEiIhEUa9ePezYsQNNmjRROH7p0iV89dVXSEpKwunTp/HVV1+xRFIpKdh5cevWLdSpU0eh1Ji2tjasrKwwY8YMfPrpp6LmEAQBEydOxOLFi+UliXR0dDBhwgQEBgaKOjaVX/r6+rh27RqsrKxgYmKC48ePw8nJCTExMfDw8ODrDBERERERUTnFnSBERCSK1NRU5ObmKh3Pzc3FvXv3AAC1atXCs2fPVB2tzCpoON62bVvs3LlTXoZM1WQyGX755RdMmTIFMTEx0NXVhZ2dHSpXrixJHiofjI2N5a8ntWvXxtWrV+Hk5ISMjAxkZ2dLnI6IiIiIiIikoiV1ACIiKpvatm2L4cOH49KlS/Jjly5dwnfffScvkXTlyhWlvhH08Y4dOybZBMibDAwM8Mknn8DR0ZETICQ6Nzc3/PPPPwCA3r17Y9SoURg6dCj69u2Ldu3aSZyOiIiIiIiIpMJyWEREJIp79+7By8sLoaGhqFSpEoDXu0DatWuH9evXw9zcHMeOHUNOTg46dOggcVrNN2bMGPz888/Q19fHmDFj3vvY+fPnqygVkeqkp6fjxYsXqFWrFvLz8zF37lycPn0adnZ2mDx5slpMDBIREREREZHqcRKEiIhEFRsbi7i4OABAgwYN0KBBA4kTlU1t27bFrl27YGRkhLZt277zcTKZDGFhYSpMRkREREREREQkHU6CEBGR6AreamQymcRJiKise/DgAR48eID8/HyF487OzhIlIiIiIiIiIimxJwgREYlm3bp1cHJygq6uLnR1deHs7Iz169dLHavMCwsLw8uXL6WOQaRSFy5cgKOjI2rWrAlnZ2c0adJE/qtp06ZSxyMiIiIiIiKJVJQ6ABERlU3z58/HlClT8MMPP8DV1RUAcOrUKYwYMQKPHj3C6NGjJU5Ydnl6eiI3NxeffPIJ3N3d0aZNG7i6ukJXV1fqaESi8fX1Rf369REcHAxzc3PuPCMiIiIiIiIALIdFREQisba2xvTp0+Ht7a1wPCQkBNOmTUNycrJEycq+nJwcREZGIjw8HOHh4Th9+jRevXoFFxcXtG3bFjNnzpQ6IlGpMzQ0xKVLl2Brayt1FCIiIiIiIlIjnAQhIiJR6Ojo4OrVq0o3JOPj4+Hk5IQXL15IlKz8uXbtGn799Vds2LAB+fn5yMvLkzoSUanr0aMHvLy88NVXX0kdhYiIiIiIiNQIy2EREZEobG1tsXXrVvzvf/9TOL5lyxbY2dlJlKp8iIuLw/Hjx3H8+HGEh4fj5cuXaN26NX777Te4u7tLHY9IFKtWrYKPjw+uXr0KR0dHVKpUSeG8p6enRMmIiIiIiIhIStwJQkREotixYwe+/fZbtG/fXt4TJCIiAqGhodi6dSt69uwpccKyS0tLC6amphg1ahS6du0KJycn9kegMm/v3r3w8vLC06dPlc7JZDLugCIiIiIiIiqnOAlCRESiuXDhAhYsWICYmBgAQMOGDREQEICmTZtKnKxs+/HHH3HixAlcv34dzZo1g7u7O9zd3dGqVSvo6elJHY9IFFZWVujatSumTJkCc3NzqeMQERERERGRmuAkCBERURmVkZGBkydPyhukX7t2DU2bNkVERITU0YhKnaGhIS5fvox69epJHYWIiIiIiIjUCHuCEBGRqB48eIAHDx4gPz9f4bizs7NEicqPvLw85OTk4OXLl3jx4gVevnyJGzduSB2LSBS9evXCsWPHOAlCRERERERECjgJQkREorhw4QJ8fHwQExODtzcdsj6/uPz9/XH8+HFcv34dxsbGcHNzw9ChQ+Hu7g4nJyep4xGJon79+pg0aRJOnToFJycnpcbo/v7+EiUjIiIiIiIiKbEcFhERiaJx48aoV68eJkyYAHNzc6XG3JaWlhIlK/t69+6NNm3awN3dHY6OjlLHIVIJa2vrd56TyWRISkpSYRoiIiIiIiJSF5wEISIiURgaGuLSpUuwtbWVOgoREREREREREZVTWlIHICKisqldu3aIioqSOgYRlTOvXr3CjRs3kJubK3UUIiIiIiIiUgPcCUJERKJ49OgRfHx80KJFCzg6OirV5/f09JQoGRGVRdnZ2fDz80NISAgAIC4uDjY2NvDz80Pt2rUxceJEiRMSERERERGRFNgYnYiIRHHmzBlERETg4MGDSufYGJ2IStukSZMQFRWF48ePo1OnTvLj7du3x7Rp0zgJQkREREREVE6xHBYREYnCz88PAwYMQGpqKvLz8xV+cQKEiErb33//jaVLl6JVq1aQyWTy4w4ODkhMTJQwGREREREREUmJO0GIiEgUaWlpGD16NMzNzaWOUq7Fx8cjJSUFlpaWbFJPZdrDhw9hZmamdDwrK0thUoSIiIiIiIjKF+4EISIiUfTq1QvHjh2TOka5MmfOHISGhgIAHj9+jPbt26NBgwb44osv0KBBA3Tu3BkZGRnShiQSiYuLC/bv3y//c8HEx6pVq9CyZUupYhEREREREZHEuBOEiIhEUb9+fUyaNAmnTp2Ck5OTUmN0f39/iZKVXX/88Ye8F8L48eORnp6OCxcuoGHDhrhx4wZGjBiBsWPHYtWqVRInJSp9s2fPRufOnXH9+nXk5uZi0aJFuH79Ok6fPo3w8HCp4xEREREREZFEZIIgCFKHICKissfa2vqd52QyGZKSklSYpnzQ0dHBjRs3YGlpCWtra4SEhMDNzU1+/sKFC+jWrRvu3r0rYUoi8SQlJWHOnDmIiopCZmYmmjVrhgkTJsDJyUnqaERERERERCQR7gQhIiJRJCcnSx2h3LG0tMTVq1dhaWkJmUyGihUV3+YrVKiArKwsidIRiScnJwfDhw/HlClT8Oeff0odh4iIiIiIiNQIe4IQERGVEUOHDsW4ceOQkJCAH374AWPHjkViYiKA15NSo0ePRocOHSROSVT6KlWqhB07dkgdg4iIiIiIiNQQy2EREZEo8vLysHbtWoSGhuLBgwfIz89XOB8WFiZRsrLN398fy5cvR7169XDz5k28evUKFStWRG5uLpo1a4a9e/eiRo0aUsckKnU+Pj5o0qQJRo8eLXUUIiIiIiIiUiMsh0VERKIYNWoU1q5diy+//BKOjo6QyWRSRyoXFi9ejO+++w779u1DUlIS8vPzUbNmTbi6uqJ9+/b8f6Ayy87ODjNmzEBERASaN28OfX19hfP+/v4SJSMiIiIiIiIpcScIERGJonr16li3bh26dOkidRQiKgesra3feU4mkyEpKUmFaYiIiIiIiEhdcCcIERGJQltbG7a2tlLHIKJyIjk5WeoIREREREREpIbYGJ2IiEQREBCARYsWgRsOVScnJwfjx4+Hra0tWrRogdWrVyucv3//PipUqCBROiLVEQSBrz1EREREREQEgDtBiIioFPXq1Uvhz2FhYTh48CAcHBxQqVIlhXM7d+5UZbRyYdasWVi3bh3Gjh2LjIwMjBkzBmfPnsWKFSvkj+GNYSrLgoODsWDBAsTHxwN43Sfkxx9/xJAhQyRORkRERERERFLhJAgREZWaqlWrKvy5Z8+eEiUpnzZs2IBVq1aha9euAICBAweic+fOGDRokHxXCBujU1kVGBiI+fPnw8/PDy1btgQAnDlzBqNHj0ZKSgpmzJghcUIiIiIiIiKSAhujExERlRF6enq4fv06rKys5Mfu3LkDDw8PfPLJJ5g7dy4sLCyQl5cnXUgikZiammLx4sXo27evwvFNmzbBz88Pjx49kigZERERERERSYk9QYiISBQeHh7IyMhQOv706VN4eHioPlA5UKNGDSQmJiocq127No4dO4Zz585h4MCB0gQjUoGcnBy4uLgoHW/evDlyc3MlSERERERERETqgJMgREQkiuPHj+PVq1dKx1+8eIGTJ09KkKjs8/DwwMaNG5WO16pVC2FhYUhOTpYgFZFqeHl5YdmyZUrHV65cif79+0uQiIiIiIiIiNQBe4IQEVGpio6Olv/++vXruHfvnvzPeXl5OHToEGrXri1FtDJvypQpiI2NLfRc7dq1ER4ejn/++UfFqYhUJzg4GEeOHMFnn30GADh79ixSUlLg7e2NMWPGyB83f/58qSISERERERGRirEnCBERlSotLS158+3C3mJ0dXWxZMkS+Pr6qjoaEZVhbdu2LdbjZDIZwsLCRE5DRERERERE6oKTIEREVKpu3boFQRBgY2ODyMhImJqays9pa2vDzMwMFSpUkDAhERERERERERGVF5wEISIiUWRlZUFfX1/qGEREREREREREVI6xMToREYnC3Nwcvr6+OHXqlNRRiIiIiIiIiIionOIkCBERieKvv/5Ceno6PDw8UL9+fQQFBeHu3btSxyIiIiIiIiIionKE5bCIiEhUDx8+xPr167F27VrExMSgY8eO8PX1haenJypWrCh1vDLp9u3bkMlkqFOnDgAgMjISGzduRKNGjTBs2DCJ0xERERERERERqQ4nQYiISGWWLFmCcePG4dWrV6hevTpGjBiBiRMnQk9PT+poZUrr1q0xbNgweHl54d69e2jQoAEcHBwQHx8PPz8/BAYGSh2RiIiIiIiIiEglWA6LiIhEdf/+fcydOxeNGjXCxIkT8fXXXyM0NBTz5s3Dzp070aNHD6kjljlXr15FixYtAABbt26Fo6MjTp8+jQ0bNmDt2rXShiMiIiIiIiIiUiHWISEiIlHs3LkTa9asweHDh9GoUSN8//33GDBgAIyMjOSP+fzzz9GwYUPpQpZROTk5qFy5MgDg6NGj8PT0BADY29sjNTVVymhERERERERERCrFnSBERCSKQYMGoVatWoiIiMDly5fxww8/KEyAAECtWrXw008/SROwDHNwcMDy5ctx8uRJ/PPPP+jUqRMA4O7duzAxMZE4HRERERERERGR6rAnCBERiSI7O5u9PiRy/Phx9OzZE0+fPoWPjw9Wr14NAPjf//6H2NhY7Ny5U+KERERERERERESqwUkQIiISTX5+PhISEvDgwQPk5+crnHNzc5MoVfmQl5eHp0+fwtjYWH7s5s2b0NPTg5mZmYTJiIiIiIiIiIhUh5MgREQkin///Rf9+vXDrVu38PZbjUwmQ15enkTJiIiIiIiIiIiovOAkCBERiaJJkyaoX78+pk+fjpo1a0Imkymcr1q1qkTJyqZmzZohNDQUxsbGaNq0qdK/95suXryowmRERERERERERNKpKHUAIiIqm+Lj47F9+3bY2tpKHaVc6N69OypXrgwA6NGjh7RhiIiIiIiIiIjUBHeCEBGRKDw8PDB+/Hh06tRJ6ihERERERERERFROcScIERGVmujoaPnv/fz8EBAQgHv37sHJyQmVKlVSeKyzs7Oq45Ubt2/fhkwmQ506dQAAkZGR2LhxIxo1aoRhw4ZJnI6IiIiIiIiISHW4E4SIiEqNlpYWZDKZUiP0AgXn2BhdXK1bt8awYcPg5eWFe/fuoX79+nB0dER8fDz8/PwQGBgodUQiIiIiIiIiIpXgThAiIio1ycnJUkcgAFevXkWLFi0AAFu3boWTkxMiIiJw5MgRjBgxgpMgRERERERERFRucBKEiIhKjaWlJQAgJycH9vb22LdvHxo2bChxqvInJydH3iT96NGj8PT0BADY29sjNTVVymhERERERERERCqlJXUAIiIqeypVqoQXL15IHaPccnBwwPLly3Hy5En8888/8ub0d+/ehYmJicTpiIiIiIiIiIhUh5MgREQkipEjR+KXX35Bbm6u1FHKnV9++QUrVqyAu7s7+vbti8aNGwMA9uzZIy+TRURERERERERUHrAxOhERiaJnz54IDQ2FgYEBnJycoK+vr3B+586dEiUrH/Ly8vD06VMYGxvLj928eRN6enowMzOTMBkRERERERERkeqwJwgREYnCyMgIX331ldQxyq0KFSogNzcXp06dAgA0aNAAVlZW0oYiIiIiIiIiIlIxToIQEVGpy83NRdu2bdGhQwfUqFFD6jjlTlZWFvz8/LBu3Trk5+cDeD0p4u3tjSVLlkBPT0/ihEREREREREREqsGeIEREVOoqVqyIESNG4OXLl1JHKZfGjBmD8PBw7N27FxkZGcjIyMDu3bsRHh6OgIAAqeMREREREREREakMe4IQEZEo3N3d8eOPP6JHjx5SRyl3qlevju3bt8Pd3V3h+LFjx/DNN9/g4cOH0gQjIiIiIiIiIlIxlsMiIiJRfP/99wgICMB///2H5s2bKzVGd3Z2lihZ2ZednQ1zc3Ol42ZmZsjOzpYgERERERERERGRNLgThIiIRKGlpVxxUSaTQRAEyGQy5OXlSZCqfGjXrh1MTEywbt066OjoAACeP38OHx8fpKen4+jRoxInJCIiIiIiIiJSDU6CEBGRKG7duvXe85aWlipKUv5cuXIFnTp1wsuXL9G4cWMAQFRUFHR0dHD48GE4ODhInJCIiIiIiIiISDU4CUJERFQGZWdnY8OGDYiNjQUANGzYEP3794eurq7EyYiIiIiIiIiIVIeTIEREJKrr168jJSUFr169Ujju6ekpUaKyLScnB/b29ti3bx8aNmwodRwiIiIiIiIiIkmxMToREYkiKSkJPXv2xJUrV+S9QIDXfUEAsCeISCpVqoQXL15IHYOIiIiIiIiISC0od60lIiIqBaNGjYK1tTUePHgAPT09XLt2DSdOnICLiwuOHz8udbwybeTIkfjll1+Qm5srdRQiIiIiIiIiIkmxHBYREYmievXqCAsLg7OzM6pWrYrIyEg0aNAAYWFh+H/t3XuQluV9N/Dvs8Dqcl7AbYIHDrIBjIvdaiWBWhOaUUoBo23aECN24mGcxgIBmaRNisdYR0W3NlNT6xTFpPEUk8YxqRqMoFJQhCFoqKhYQMSssNq4YEAenvePTJiXwfedzASe295+PjPMwHVfz+537n92mO9e12/evHlZs2ZN0RFL6+yzz86SJUvSt2/ftLW1pU+fPgc8f+CBBwpKBgAAAFBfrsMC4LCoVqvp169fkl8VIq+99lpGjx6dYcOG5YUXXig4XbkNHDgwf/qnf1p0DAAAAIDCKUEAOCxOPPHErF27NiNGjMj48eNz/fXXp7GxMbfddltGjhxZdLxSW7RoUdERAAAAAN4XXIcFwGHx8MMPZ+fOnTnnnHPy0ksvZerUqdmwYUMGDx6ce+65J5MmTSo6Yqnt3bs3jz/+eF5++eV87nOfS79+/fLaa6+lf//+6du3b9HxAAAAAOpCCQJA3XR1daW5uTmVSqXoKKW2adOmTJ48OZs3b87u3buzYcOGjBw5MrNnz87u3bvzzW9+s+iIAAAAAHXRUHQAAD44Bg0apACpg9mzZ+eUU07Jm2++maampv3rvx6YDgAAAPBBYSYIAJTME088keXLl6exsfGA9eHDh2fr1q0FpQIAAACoPydBAKBk9u3bl2q1etD6q6++mn79+hWQCAAAAKAYShAAKJkzzjgjHR0d+/9dqVTS3d2dyy+/PFOmTCkuGAAAAECdGYwOACXz6quv5swzz0ytVsuLL76YU045JS+++GKGDBmSZcuWpaWlpeiIAAAAAHWhBAGAEtq7d2/uvvvu/PSnP013d3d+7/d+L+eee+4Bg9IBAAAAyk4JAgAAAAAAlFLPogMAAL+9H/zgB7/x3unTpx/GJAAAAADvH06CAEAJNDQ0/Eb7KpVKqtXqYU4DAAAA8P6gBAEAAAAAAErpN/u1UQAAAAAAgP9llCAAUDKzZs3KLbfcctD6N77xjcyZM6f+gQAAAAAKogQBgJL57ne/m4kTJx60PmHChNx///0FJAIAAAAohhIEAEpmx44dGTBgwEHr/fv3z/bt2wtIBAAAAFAMJQgAlMyoUaPyH//xHwet/+hHP8rIkSMLSAQAAABQjJ5FBwAADq25c+fm0ksvzRtvvJFJkyYlSZYsWZKFCxemo6Oj2HAAAAAAdVSp1Wq1okMAAIfWrbfemq9//et57bXXkiTDhw/PFVdckZkzZxacDAAAAKB+lCAAUGJvvPFGmpqa0rdv36KjAAAAANSdmSAAUDLvvPNOdu3alSQ56qijsmPHjnR0dOSRRx4pOBkAAABAfSlBAKBkzjrrrCxevDhJ8tZbb+XUU0/NwoULc9ZZZ+XWW28tOB0AAABA/ShBAKBkVq9endNOOy1Jcv/99+dDH/pQNm3alMWLF+eWW24pOB0AAABA/ShBAKBkdu3alX79+iVJHnnkkZxzzjlpaGjIxz72sWzatKngdAAAAAD1owQBgJIZNWpUvv/972fLli15+OGHc8YZZyRJOjs7079//4LTAQAAANSPEgQASmbBggW57LLLMnz48IwfPz4f//jHk/zqVEh7e3vB6QAAAADqp1Kr1WpFhwAADq3XX38927Zty0knnZSGhl/9zsPTTz+d/v37Z8yYMQWnAwAAAKgPJQgAAAAAAFBKrsMCAAAAAABKSQkCAAAAAACUkhIEAAAAAAAoJSUIAAAAAABQSj2LDgAA/PZ+8IMf/MZ7p0+ffhiTAAAAALx/VGq1Wq3oEADAb6eh4Tc73FmpVFKtVg9zGgAAAID3ByUIAAAAAABQSmaCAAAAAAAApWQmCACU0M6dO7N06dJs3rw5e/bsOeDZrFmzCkoFAAAAUF+uwwKAklmzZk2mTJmSXbt2ZefOnRk0aFC2b9+e3r17p6WlJRs3biw6IgAAAEBduA4LAErmS1/6UqZNm5Y333wzTU1NWbFiRTZt2pSTTz45N954Y9HxAAAAAOrGSRAAKJmBAwdm5cqVGT16dAYOHJj//M//zNixY7Ny5cqcf/75+a//+q+iIwIAAADUhZMgAFAyvXr1SkPDr37Et7S0ZPPmzUmSAQMGZMuWLUVGAwAAAKgrg9EBoGTa29vzzDPPpLW1NaeffnoWLFiQ7du356677sqJJ55YdDwAAACAunEdFgCUzKpVq/L222/nk5/8ZDo7OzNz5swsX748ra2t+dd//decdNJJRUcEAAAAqAslCACUSK1Wy5YtW9LS0pIjjzyy6DgAAAAAhTITBABKpFarZdSoUWZ/AAAAAEQJAgCl0tDQkNbW1uzYsaPoKAAAAACFU4IAQMlcd911mT9/fp577rmiowAAAAAUykwQACiZ5ubm7Nq1K3v37k1jY2OampoOeN7V1VVQMgAAAID66ll0AADg0Oro6Cg6AgAAAMD7gpMgAAAAAABAKZkJAgAl9PLLL+drX/taZsyYkc7OziTJj370ozz//PMFJwMAAACoHyUIAJTM0qVL09bWlpUrV+aBBx5Id3d3kmTt2rW5/PLLC04HAAAAUD9KEAAoma985Su55ppr8uijj6axsXH/+qRJk7JixYoCkwEAAADUlxIEAEpm3bp1Ofvssw9ab2lpyfbt2wtIBAAAAFAMJQgAlMzAgQOzbdu2g9bXrFmTo48+uoBEAAAAAMVQggBAyXz2s5/Nl7/85bz++uupVCrZt29fnnrqqVx22WWZOXNm0fEAAAAA6qZSq9VqRYcAAA6dPXv25Itf/GLuuOOOVKvV9OzZM9VqNZ/73Odyxx13pEePHkVHBAAAAKgLJQgAlNSWLVuybt26dHd3p729Pa2trUVHAgAAAKgr12EBQMlcddVV2bVrV4499thMmTIlf/7nf57W1ta88847ueqqq4qOBwAAAFA3ToIAQMn06NEj27ZtS0tLywHrO3bsSEtLS6rVakHJAAAAAOrLSRAAKJlarZZKpXLQ+tq1azNo0KACEgEAAAAUo2fRAQCAQ6O5uTmVSiWVSiUf+chHDihCqtVquru7c8kllxSYEAAAAKC+XIcFACVx5513plar5Qtf+EI6OjoyYMCA/c8aGxszfPjwfPzjHy8wIQAAAEB9KUEAoGSWLl2aCRMmpFevXkVHAQAAACiUEgQASuyXv/xl9uzZc8Ba//79C0oDAAAAUF8GowNAyezatSuXXnppWlpa0qdPnzQ3Nx/wBwAAAOCDQgkCACUzf/78PPbYY7n11ltzxBFH5Pbbb8+VV16ZoUOHZvHixUXHAwAAAKgb12EBQMkcd9xxWbx4cT7xiU+kf//+Wb16dUaNGpW77ror3/nOd/LDH/6w6IgAAAAAdeEkCACUTFdXV0aOHJnkV/M/urq6kiR/8Ad/kGXLlhUZDQAAAKCulCAAUDIjR47MK6+8kiQZM2ZM7r333iTJgw8+mIEDBxaYDAAAAKC+XIcFACVz8803p0ePHpk1a1Z+/OMfZ9q0aanVann33Xdz0003Zfbs2UVHBAAAAKgLJQgAlNymTZvy7LPPZtSoURk3blzRcQAAAADqRgkCAAAAAACUUs+iAwAAv71bbrnlN947a9asw5gEAAAA4P3DSRAAKIERI0b8RvsqlUo2btx4mNMAAAAAvD8oQQAAAAAAgFJqKDoAAAAAAADA4aAEAQAAAAAASkkJAgAAAAAAlJISBAAAAAAAKCUlCAAAAAAAUEpKEAAomUWLFuW+++47aP2+++7LnXfeWUAiAAAAgGIoQQCgZP7+7/8+Q4YMOWi9paUl1157bQGJAAAAAIqhBAGAktm8eXNGjBhx0PqwYcOyefPmAhIBAAAAFEMJAgAl09LSkp/+9KcHra9duzaDBw8uIBEAAABAMZQgAFAyM2bMyKxZs/KTn/wk1Wo11Wo1jz32WGbPnp3PfvazRccDAAAAqJtKrVarFR0CADh09uzZk/POOy/33XdfevbsmSTZt29fZs6cmW9+85tpbGwsOCEAAABAfShBAKCkNmzYkLVr16apqSltbW0ZNmxY0ZEAAAAA6koJAgAAAAAAlFLPogMAAL+9uXPn5uqrr06fPn0yd+7c/+/em266qU6pAAAAAIqlBAGAElizZk3efffd/X//f6lUKvWKBAAAAFA412EBAAAAAACl1FB0AAAAAAAAgMPBdVgAUDI7d+7MddddlyVLlqSzszP79u074PnGjRsLSgYAAABQX0oQACiZCy+8MEuXLs15552XD3/4w+aAAAAAAB9YZoIAQMkMHDgwDz30UCZOnFh0FAAAAIBCmQkCACXT3NycQYMGFR0DAAAAoHBKEAAomauvvjoLFizIrl27io4CAAAAUCjXYQFACbS3tx8w++Oll15KrVbL8OHD06tXrwP2rl69ut7xAAAAAAphMDoAlMCnP/3poiMAAAAAvO84CQIAAAAAAJSSkyAAUFKrVq3K+vXrkyQnnHBCTj755IITAQAAANSXEgQASubVV1/NjBkz8tRTT2XgwIFJkrfeeisTJkzI3XffnWOOOabYgAAAAAB10lB0AADg0Lrwwgvz7rvvZv369enq6kpXV1fWr1+fffv25cILLyw6HgAAAEDdmAkCACXT1NSU5cuXp729/YD1Z599Nqeddlp27dpVUDIAAACA+nISBABK5thjj82777570Hq1Ws3QoUMLSAQAAABQDCUIAJTMDTfckL/+67/OqlWr9q+tWrUqs2fPzo033lhgMgAAAID6ch0WAJRMc3Nzdu3alb1796Znz55Jsv/vffr0OWBvV1dXEREBAAAA6qJn0QEAgEOro6Oj6AgAAAAA7wtOggAAAAAAAKXkJAgAlFC1Ws33v//9rF+/Pkny0Y9+NNOnT0+PHj0KTgYAAABQP06CAEDJvPTSS5kyZUq2bt2a0aNHJ0leeOGFHHvssXnooYdy/PHHF5wQAAAAoD6UIABQMlOmTEmtVsu3v/3tDBo0KEmyY8eOfP7zn09DQ0MeeuihghMCAAAA1IcSBABKpk+fPlmxYkXa2toOWF+7dm0mTpyY7u7ugpIBAAAA1FdD0QEAgEPriCOOyNtvv33Qend3dxobGwtIBAAAAFAMJQgAlMzUqVNz8cUXZ+XKlanVaqnValmxYkUuueSSTJ8+veh4AAAAAHXjOiwAKJm33nor559/fh588MH06tUrSbJ3795Mnz49d9xxRwYMGFBwQgAAAID6UIIAQInUarVs2bIlRx11VLZu3Zr169cnScaOHZtRo0YVnA4AAACgvpQgAFAi+/bty5FHHpnnn38+ra2tRccBAAAAKJSZIABQIg0NDWltbc2OHTuKjgIAAABQOCUIAJTMddddl/nz5+e5554rOgoAAABAoVyHBQAl09zcnF27dmXv3r1pbGxMU1PTAc+7uroKSgYAAABQXz2LDgAAHFo333xzKpVK0TEAAAAACuckCAAAAAAAUEpmggBAyfTo0SOdnZ0Hre/YsSM9evQoIBEAAABAMZQgAFAy/69Dnrt3705jY2Od0wAAAAAUx0wQACiJW265JUlSqVRy++23p2/fvvufVavVLFu2LGPGjCkqHgAAAEDdmQkCACUxYsSIJMmmTZtyzDHHHHD1VWNjY4YPH56rrroq48ePLyoiAAAAQF0pQQCgZD75yU/mgQceSHNzc9FRAAAAAAqlBAGAEvv1j/lKpVJwEgAAAID6MxgdAEpo8eLFaWtrS1NTU5qamjJu3LjcddddRccCAAAAqCuD0QGgZG666ab83d/9XS699NJMnDgxSfLkk0/mkksuyfbt2/OlL32p4IQAAAAA9eE6LAAomREjRuTKK6/MzJkzD1i/8847c8UVV+SVV14pKBkAAABAfbkOCwBKZtu2bZkwYcJB6xMmTMi2bdsKSAQAAABQDCUIAJTMqFGjcu+99x60fs8996S1tbWARAAAAADFMBMEAErmyiuvzF/8xV9k2bJl+2eCPPXUU1myZMl7liMAAAAAZWUmCACU0LPPPpubb74569evT5KMHTs28+bNS3t7e8HJAAAAAOpHCQIAAAAAAJSSmSAAUDKnn356Fi9enHfeeafoKAAAAACFUoIAQMm0t7fnsssuy4c+9KFcdNFFWbFiRdGRAAAAAAqhBAGAkuno6Mhrr72WRYsWpbOzM3/4h3+YE044ITfeeGN+/vOfFx0PAAAAoG7MBAGAkuvs7Mxtt92Wr3/966lWq5kyZUpmzZqVSZMmFR0NAAAA4LByEgQASuzpp5/O5ZdfnoULF6alpSV/8zd/kyFDhmTq1Km57LLLio4HAAAAcFg5CQIAJdPZ2Zm77rorixYtyosvvphp06blwgsvzJlnnplKpZIkefLJJzN58uR0d3cXnBYAAADg8OlZdAAA4NA65phjcvzxx+cLX/hC/vIv/zJHHXXUQXvGjRuX3//93y8gHQAAAED9OAkCACXzxBNP5LTTTis6BgAAAEDhlCAAAAAAAEApuQ4LAEpmx44dWbBgQX7yk5+ks7Mz+/btO+B5V1dXQckAAAAA6ksJAgAlc9555+Wll17KBRdckN/5nd/ZPwwdAAAA4IPGdVgAUDL9+vXLk08+mZNOOqnoKAAAAACFaig6AABwaI0ZMybvvPNO0TEAAAAACuckCACUzDPPPJOvfOUrWbBgQU488cT06tXrgOf9+/cvKBkAAABAfZkJAgAlM3DgwPziF7/IpEmTDliv1WqpVCqpVqsFJQMAAACoLyUIAJTMueeem169euXf/u3fDEYHAAAAPtBchwUAJdO7d++sWbMmo0ePLjoKAAAAQKEMRgeAkjnllFOyZcuWomMAAAAAFM5JEAAomfvuuy9XXHFF5s+fn7a2toMGo48bN66gZAAAAAD1pQQBgJJpaDj4oGelUjEYHQAAAPjAMRgdAErmlVdeKToCAAAAwPuCkyAAAAAAAEApOQkCACX1s5/9LJs3b86ePXsOWJ8+fXpBiQAAAADqSwkCACWzcePGnH322Vm3bt3+WSDJr+aCJDETBAAAAPjAOHhyKgDwv9rs2bMzYsSIdHZ2pnfv3nn++eezbNmynHLKKXn88ceLjgcAAABQN2aCAEDJDBkyJI899ljGjRuXAQMG5Omnn87o0aPz2GOPZd68eVmzZk3REQEAAADqwkkQACiZarWafv36JflVIfLaa68lSYYNG5YXXnihyGgAAAAAdWUmCACUzIknnpi1a9dmxIgRGT9+fK6//vo0Njbmtttuy8iRI4uOBwAAAFA3rsMCgJJ5+OGHs3Pnzpxzzjl56aWXMnXq1GzYsCGDBw/OPffck0mTJhUdEQAAAKAulCAA8AHQ1dWV5ubmVCqVoqMAAAAA1I0SBAAAAAAAKCWD0QEAAAAAgFJSggAAAAAAAKWkBAEAAAAAAEpJCQIAAAAAAJSSEgQAADisPvGJT2TOnDlJkuHDh6ejo6PQPAAAwAdHz6IDAAAAHxzPPPNM+vTpU3QMAADgA0IJAgAA1M1RRx1VdAQAAOADxHVYAADAIbNz587MnDkzffv2zYc//OEsXLjwgOf/93VYtVotV1xxRY477rgcccQRGTp0aGbNmrV/7+7du3PZZZfl6KOPTp8+fTJ+/Pg8/vjj+5/v2LEjM2bMyNFHH53evXunra0t3/nOdw74fvfff3/a2trS1NSUwYMH51Of+lR27ty5//ntt9+esWPH5sgjj8yYMWPyT//0T4f+pQAAAIVxEgQAADhk5s+fn6VLl+bf//3f09LSkr/927/N6tWr87u/+7sH7f3ud7+bm2++OXfffXc++tGP5vXXX8/atWv3P7/00kvzs5/9LHfffXeGDh2a733ve5k8eXLWrVuX1tbW/PKXv8zJJ5+cL3/5y+nfv38eeuihnHfeeTn++ONz6qmnZtu2bZkxY0auv/76nH322Xn77bfzxBNPpFarJUm+/e1vZ8GCBfnGN76R9vb2rFmzJhdddFH69OmT888/v16vDAAAOIwqtV//DwAAAOC30N3dncGDB+db3/pWPvOZzyRJurq6cswxx+Tiiy9OR0dHhg8fnjlz5mTOnDm56aab8s///M957rnn0qtXrwO+1ubNmzNy5Mhs3rw5Q4cO3b/+qU99Kqeeemquvfba98wwderUjBkzJjfeeGNWr16dk08+Of/93/+dYcOGHbR31KhRufrqqzNjxoz9a9dcc01++MMfZvny5YfilQAAAAVzEgQAADgkXn755ezZsyfjx4/fvzZo0KCMHj36Pfd/5jOfSUdHR0aOHJnJkydnypQpmTZtWnr27Jl169alWq3mIx/5yAGf2b17dwYPHpwkqVarufbaa3Pvvfdm69at2bNnT3bv3p3evXsnSU466aT80R/9Udra2nLmmWfmjDPOyJ/92Z+lubk5O3fuzMsvv5wLLrggF1100f6vv3fv3gwYMOBQvxoAAKAgShAAAKAQxx57bF544YX8+Mc/zqOPPpq/+qu/yg033JClS5emu7s7PXr0yLPPPpsePXoc8Lm+ffsmSW644Yb8wz/8Qzo6OtLW1pY+ffpkzpw52bNnT5KkR48eefTRR7N8+fI88sgj+cd//Md89atfzcqVK/cXJf/yL/9yQGnz688BAADloAQBAAAOieOPPz69evXKypUrc9xxxyVJ3nzzzWzYsCGnn376e36mqakp06ZNy7Rp0/LFL34xY8aMybp169Le3p5qtZrOzs6cdtpp7/nZp556KmeddVY+//nPJ0n27duXDRs25IQTTti/p1KpZOLEiZk4cWIWLFiQYcOG5Xvf+17mzp2boUOHZuPGjTn33HMP8ZsAAADeL5QgAADAIdG3b99ccMEFmT9/fgYPHpyWlpZ89atfTUNDw3vuv+OOO1KtVjN+/Pj07t073/rWt9LU1JRhw4Zl8ODBOffcczNz5swsXLgw7e3teeONN7JkyZKMGzcuf/Inf5LW1tbcf//9Wb58eZqbm3PTTTfl5z//+f4SZOXKlVmyZEnOOOOMtLS0ZOXKlXnjjTcyduzYJMmVV16ZWbNmZcCAAZk8eXJ2796dVatW5c0338zcuXPr9t4AAIDDRwkCAAAcMjfccEO6u7szbdq09OvXL/Pmzcv//M//vOfegQMH5rrrrsvcuXNTrVbT1taWBx98cP/Mj0WLFuWaa67JvHnzsnXr1gwZMiQf+9jHMnXq1CTJ1772tWzcuDFnnnlmevfunYsvvjif/vSn93+//v37Z9myZeno6MgvfvGLDBs2LAsXLswf//EfJ0kuvPDC9O7dOzfccEPmz5+fPn36pK2tLXPmzDn8LwoAAKiLSq1WqxUdAgAAAAAA4FB773PpAAAAAAAA/8spQQAAAAAAgFJSggAAAAAAAKWkBAEAAAAAAEpJCQIAAAAAAJSSEgQAAAAAACglJQgAAAAAAFBKShAAAAAAAKCUlCAAAAAAAEApKUEAAAAAAIBSUoIAAAAAAACl9H8AnpOPCM7tn8IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb
new file mode 100644
index 00000000..40545000
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# sex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>19181831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PATO:0000383</td>\n",
+       "      <td>16267559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2857853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sex_ontology_term_id  num_cells\n",
+       "0         PATO:0000384   19181831\n",
+       "1         PATO:0000383   16267559\n",
+       "2              unknown    2857853"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"sex_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>19181831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>16267559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2857853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sex  num_cells\n",
+       "0     male   19181831\n",
+       "1   female   16267559\n",
+       "2  unknown    2857853"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"sex\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='sex'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(4, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = names_counts[query_name].values.tolist()\n",
+    "new_labels = labels_counts[query_label].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['PATO:0000384', 'PATO:0000383']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove unknown\n",
+    "new_labels = new_labels[:2]\n",
+    "new_names = new_names[:2]\n",
+    "new_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "    },\n",
+    "    \"sex_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb
new file mode 100644
index 00000000..2c41934d
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb
@@ -0,0 +1,720 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# tissue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tissue_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>UBERON:0000178</td>\n",
+       "      <td>9446186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>UBERON:0002048</td>\n",
+       "      <td>2412727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UBERON:0000310</td>\n",
+       "      <td>2269902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UBERON:0009834</td>\n",
+       "      <td>1591509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UBERON:0002771</td>\n",
+       "      <td>1317262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  tissue_ontology_term_id  num_cells\n",
+       "0          UBERON:0000178    9446186\n",
+       "1          UBERON:0002048    2412727\n",
+       "2          UBERON:0000310    2269902\n",
+       "3          UBERON:0009834    1591509\n",
+       "4          UBERON:0002771    1317262"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"tissue_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>blood</td>\n",
+       "      <td>9446186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lung</td>\n",
+       "      <td>2412727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>breast</td>\n",
+       "      <td>2269902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>dorsolateral prefrontal cortex</td>\n",
+       "      <td>1591509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>middle temporal gyrus</td>\n",
+       "      <td>1317262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>left parietal lobe</td>\n",
+       "      <td>163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>vault of skull</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>kidney blood vessel</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>skin of face</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>skin of shoulder</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>264 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             tissue  num_cells\n",
+       "0                             blood    9446186\n",
+       "1                              lung    2412727\n",
+       "2                            breast    2269902\n",
+       "3    dorsolateral prefrontal cortex    1591509\n",
+       "4             middle temporal gyrus    1317262\n",
+       "..                              ...        ...\n",
+       "259              left parietal lobe        163\n",
+       "260                  vault of skull        123\n",
+       "261             kidney blood vessel         47\n",
+       "262                    skin of face         37\n",
+       "263                skin of shoulder         37\n",
+       "\n",
+       "[264 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"tissue\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `tissue`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL', 'UBERON'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/obophenotype/uberon/releases/download/v2024-01-18/uberon.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"UBERON_\"\n",
+    "\n",
+    "# the 'cell' node\n",
+    "LIFE_CYCLE_NODE = \"UBERON:0000104\"\n",
+    "\n",
+    "# the 'eukaryotic cell' node\n",
+    "ANATOMICAL_ENTITY_NODE = \"UBERON:0001062\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `tissue`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'embryonic stem cell'}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(labels_counts[query_label]) - set(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL:0002322'}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15567"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 1, 0, ..., 1, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 1, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [inf,  0., inf, ..., inf, inf, inf],\n",
+       "       [inf,  4.,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [inf,  6., inf, ...,  0., inf, inf],\n",
+       "       [inf,  6., inf, ..., inf,  0., inf],\n",
+       "       [inf,  5., inf, ..., inf, inf,  0.]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "263"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"CL:0002322\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "822"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {LIFE_CYCLE_NODE, ANATOMICAL_ENTITY_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"tissue_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 1701b637a86adbdf321de4bdb6f32d07b019a0c4 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 21 Feb 2025 00:33:58 +0000
Subject: [PATCH 45/64] metadata prediction CLI tool and script

---
 .../inference/metadata_benchmarking/utils.py  |   54 +
 ...on_by_cell_type_prompting_comparison.ipynb |   14 -
 .../01_generate_resources.ipynb               | 9804 ++++++++++++++++-
 .../02_generate_metadata_predictions.ipynb    |  322 +
 .../{ => yerdos}/xx_cell_type_ontology.ipynb  |    0
 .../xx_development_stage_ontology.ipynb       |    0
 .../{ => yerdos}/xx_disease_ontology.ipynb    |    0
 .../{ => yerdos}/xx_sex_ontology.ipynb        |    0
 .../{ => yerdos}/xx_tissue_ontology.ipynb     |    0
 scripts/metadata_prediction_analysis.py       |  257 +
 scripts/run_metadata_prediction_analysis.sh   |   55 +
 11 files changed, 10399 insertions(+), 107 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
 rename notebooks/cellariumgpt_inference/metadata_benchmarking/{ => yerdos}/xx_cell_type_ontology.ipynb (100%)
 rename notebooks/cellariumgpt_inference/metadata_benchmarking/{ => yerdos}/xx_development_stage_ontology.ipynb (100%)
 rename notebooks/cellariumgpt_inference/metadata_benchmarking/{ => yerdos}/xx_disease_ontology.ipynb (100%)
 rename notebooks/cellariumgpt_inference/metadata_benchmarking/{ => yerdos}/xx_sex_ontology.ipynb (100%)
 rename notebooks/cellariumgpt_inference/metadata_benchmarking/{ => yerdos}/xx_tissue_ontology.ipynb (100%)
 create mode 100644 scripts/metadata_prediction_analysis.py
 create mode 100644 scripts/run_metadata_prediction_analysis.sh

diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py b/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
index 8973d182..0d8833aa 100644
--- a/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
+++ b/cellarium/ml/utilities/inference/metadata_benchmarking/utils.py
@@ -39,6 +39,60 @@ def build_nx_graph_from_owl_ontology(
     return owl_graph
 
 
+PARTOF_RELATIONSHIP = "BFO_0000050"  # part_of
+
+def build_nx_graph_from_owl_ontology_legacy(
+    owl_ontology: owlready2.Ontology,
+    owl_classes: t.List[owlready2.ThingClass],
+    prefix: str,
+    extra_nodes: t.Optional[t.List[str]] = None,
+) -> nx.DiGraph:
+
+    owl_graph = nx.DiGraph(name="OWL graph")
+
+    if extra_nodes is not None:
+        for node in extra_nodes:
+            owl_graph.add_node(node)
+
+    names_set = set(_class.name.replace("_", ":") for _class in owl_classes)
+    classes_set = set(owl_classes)
+
+    for _class in owl_classes:
+        owl_graph.add_node(_class.name.replace("_", ":"))
+
+    for self_class in owl_classes:
+        # parents
+        for parent_class in owl_ontology.get_parents_of(self_class):
+            if parent_class not in classes_set:
+                continue
+            owl_graph.add_edge(parent_class.name.replace("_", ":"), self_class.name.replace("_", ":"))
+        # children
+        for child_class in owl_ontology.get_children_of(self_class):
+            if child_class not in classes_set:
+                continue
+            owl_graph.add_edge(self_class.name.replace("_", ":"), child_class.name.replace("_", ":"))
+        # part of
+        for prop in self_class.get_class_properties():
+            if PARTOF_RELATIONSHIP in prop.name:
+                for related_term in prop[self_class]:
+                    if related_term.name.startswith(prefix):
+                        if related_term.name.replace("_", ":") not in names_set:
+                            continue
+                        owl_graph.add_edge(related_term.name.replace("_", ":"), self_class.name.replace("_", ":"))
+        # deprecated terms (WHY???!!)
+        if "deprecated" in [prop.name for prop in self_class.get_class_properties()]:
+            for prop in self_class.get_class_properties():
+                if "consider" in prop.name:
+                    for substitute in prop[self_class]:
+                        substitute = str(substitute)
+                        if substitute.startswith(prefix):
+                            if substitute.replace("_", ":") not in names_set:
+                                continue
+                            owl_graph.add_edge(substitute.replace("_", ":"), self_class.name.replace("_", ":"))
+
+    return owl_graph
+
+
 def get_n_level_ancestors(graph: nx.DiGraph, node: str, n: int) -> t.Set[str]:
     """
     Return the set of all ancestors of a given node in a directed graph up to `n` levels deep.
diff --git a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
index 455d2e5f..a4f17be4 100644
--- a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
+++ b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
@@ -56,20 +56,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129.ckpt\"\n",
-    "state = torch.load(CHECKPOINT_PATH, map_location=DEVICE, weights_only=False)\n",
-    "state['state_dict'].pop('pipeline.1.token_embedding.embedding_dict.gene_query_mask.weight');\n",
-    "state['state_dict'].pop('pipeline.1.token_embedding.embedding_dict.total_mrna_umis.weight');\n",
-    "state['state_dict']['pipeline.1.gene_embedding.E.gene_value.weight'] = state['state_dict']['pipeline.1.gene_embedding.E.gene_value.weight'].squeeze()\n",
-    "torch.save(state, \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 68,
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
index f3e4659f..f2e28ebe 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
@@ -1,8 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate resources of metadata benchmarking"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,6 +25,7 @@
     "\n",
     "from cellarium.ml.utilities.inference.metadata_benchmarking.utils import \\\n",
     "    build_nx_graph_from_owl_ontology, \\\n",
+    "    build_nx_graph_from_owl_ontology_legacy, \\\n",
     "    get_all_ancestors\n",
     "\n",
     "from cellarium.ml.utilities.inference.metadata_benchmarking.build_resources import \\\n",
@@ -29,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,66 +45,6 @@
     "ONTOLOGY_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"ontology\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of\n",
-    "\n",
-    "def build_nx_graph_from_owl_ontology_legacy(\n",
-    "    owl_ontology: owlready2.Ontology,\n",
-    "    owl_classes: t.List[owlready2.ThingClass],\n",
-    "    prefix: str,\n",
-    "    extra_nodes: t.Optional[t.List[str]] = None,\n",
-    ") -> nx.DiGraph:\n",
-    "\n",
-    "    owl_graph = nx.DiGraph(name=\"OWL graph\")\n",
-    "\n",
-    "    if extra_nodes is not None:\n",
-    "        for node in extra_nodes:\n",
-    "            owl_graph.add_node(node)\n",
-    "\n",
-    "    names_set = set(_class.name.replace(\"_\", \":\") for _class in owl_classes)\n",
-    "    classes_set = set(owl_classes)\n",
-    "\n",
-    "    for _class in owl_classes:\n",
-    "        owl_graph.add_node(_class.name.replace(\"_\", \":\"))\n",
-    "\n",
-    "    for self_class in owl_classes:\n",
-    "        # parents\n",
-    "        for parent_class in owl_ontology.get_parents_of(self_class):\n",
-    "            if parent_class not in classes_set:\n",
-    "                continue\n",
-    "            owl_graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
-    "        # children\n",
-    "        for child_class in owl_ontology.get_children_of(self_class):\n",
-    "            if child_class not in classes_set:\n",
-    "                continue\n",
-    "            owl_graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
-    "        # part of\n",
-    "        for prop in self_class.get_class_properties():\n",
-    "            if PARTOF_RELATIONSHIP in prop.name:\n",
-    "                for related_term in prop[self_class]:\n",
-    "                    if related_term.name.startswith(prefix):\n",
-    "                        if related_term.name.replace(\"_\", \":\") not in names_set:\n",
-    "                            continue\n",
-    "                        owl_graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
-    "        # deprecated terms (WHY???!!)\n",
-    "        if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
-    "            for prop in self_class.get_class_properties():\n",
-    "                if \"consider\" in prop.name:\n",
-    "                    for substitute in prop[self_class]:\n",
-    "                        substitute = str(substitute)\n",
-    "                        if substitute.startswith(prefix):\n",
-    "                            if substitute.replace(\"_\", \":\") not in names_set:\n",
-    "                                continue\n",
-    "                            owl_graph.add_edge(substitute.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
-    "\n",
-    "    return owl_graph"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -106,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -149,6 +97,10 @@
      "output_type": "stream",
      "text": [
       "INFO:root:Loading ontology ...\n",
+      "* Owlready2 * Warning: ignoring cyclic subclass of/subproperty of, involving:\n",
+      "  http://purl.obolibrary.org/obo/CHEBI_36080\n",
+      "  http://purl.obolibrary.org/obo/PR_000000001\n",
+      "\n",
       "INFO:root:All classes: 2914\n",
       "INFO:root:Building nx graph from OWL ontology...\n",
       "INFO:root:Filtered classes: 1026\n",
@@ -200,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -258,6 +210,9596 @@
     "    pickle.dump(ontology_resource_dict, output_file)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cell\n",
+      "neuron\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "sensory neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "primary cultured cell\n",
+      "\t cell\n",
+      "\t cultured cell\n",
+      "\t experimentally modified cell in vitro\n",
+      "\t cell in vitro\n",
+      "cultured cell\n",
+      "\t cell\n",
+      "\t experimentally modified cell in vitro\n",
+      "\t cell in vitro\n",
+      "fibroblast neural crest derived\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "migratory neural crest cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t multi fate stem cell\n",
+      "\t motile cell\n",
+      "\t neural crest cell\n",
+      "fibroblast\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "neuronal receptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "sensory receptor cell\n",
+      "\t cell\n",
+      "embryonic cell (metazoa)\n",
+      "\t cell\n",
+      "experimentally modified cell in vitro\n",
+      "\t cell\n",
+      "\t cell in vitro\n",
+      "germ line cell\n",
+      "\t cell\n",
+      "stem cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "male germ cell\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t germ cell\n",
+      "germ cell\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "haploid cell\n",
+      "\t cell\n",
+      "sperm\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t male germ cell\n",
+      "\t germ cell\n",
+      "\t haploid cell\n",
+      "\t male gamete\n",
+      "\t gamete\n",
+      "male gamete\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t male germ cell\n",
+      "\t germ cell\n",
+      "\t haploid cell\n",
+      "\t gamete\n",
+      "female germ cell\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t germ cell\n",
+      "oocyte\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t germ cell\n",
+      "\t female germ cell\n",
+      "\t eukaryotic cell\n",
+      "eukaryotic cell\n",
+      "\t cell\n",
+      "oogonial cell\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t germ cell\n",
+      "\t female germ cell\n",
+      "primordial germ cell\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t motile cell\n",
+      "polyploid cell\n",
+      "\t cell\n",
+      "smooth muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "neural crest derived neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "glioblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "non-terminally differentiated cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "neuroblast (sensu Vertebrata)\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "surface ectodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t ectodermal cell\n",
+      "exocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "precursor cell\n",
+      "\t cell\n",
+      "single fate stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "somatic stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "epithelial fate stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "hematopoietic stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "hematopoietic cell\n",
+      "\t cell\n",
+      "hematopoietic precursor cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "erythroid progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "erythroid lineage cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "myeloid lineage restricted progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "megakaryocyte-erythroid progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "monopoietic cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "myeloid cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "macrophage dendritic cell progenitor\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "eosinophil\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t blood cell\n",
+      "\t myeloid leukocyte\n",
+      "\t granulocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "professional antigen presenting cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "neutrophil progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "basophil\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t blood cell\n",
+      "\t myeloid leukocyte\n",
+      "\t granulocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "histamine secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t biogenic amine secreting cell\n",
+      "common lymphoid progenitor\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "neural stem cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "neural cell\n",
+      "\t cell\n",
+      "multi fate stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "common myeloid progenitor\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "hematopoietic oligopotent progenitor cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "granulocyte monocyte progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "\t bone cell\n",
+      "\t CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor\n",
+      "\t bone marrow hematopoietic cell\n",
+      "\t hematopoietic oligopotent progenitor cell, lineage-negative\n",
+      "\t bone marrow cell\n",
+      "secretory cell\n",
+      "\t cell\n",
+      "myoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t muscle precursor cell\n",
+      "muscle precursor cell\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "connective tissue cell\n",
+      "\t cell\n",
+      "mesenchymal stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t connective tissue cell\n",
+      "ecto-epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "neurecto-epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "osteoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t connective tissue cell\n",
+      "preosteoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t connective tissue cell\n",
+      "\t bone cell\n",
+      "ciliated cell\n",
+      "\t cell\n",
+      "ependymal cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "ciliated epithelial cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t epithelial cell\n",
+      "epithelial cell\n",
+      "\t cell\n",
+      "leukocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "duct epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "branched duct epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "blood vessel endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "squamous epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "endothelial cell of vascular tree\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "embryonic blood vessel endothelial progenitor cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t mesodermal cell\n",
+      "non-branched duct epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "meso-epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "barrier cell\n",
+      "\t cell\n",
+      "columnar/cuboidal epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "mesothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "lining cell\n",
+      "\t cell\n",
+      "\t barrier cell\n",
+      "stratified epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "circulating cell\n",
+      "\t cell\n",
+      "blood cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "epithelial cell of lung\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "epithelial cell of pancreas\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "lymphocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "pro-T cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "lymphocyte of B lineage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "Kupffer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "tissue-resident macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "osteoclast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t bone cell\n",
+      "\t phagocyte\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t defensive cell\n",
+      "myeloid leukocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "phagocyte (sensu Vertebrata)\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t phagocyte\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t defensive cell\n",
+      "bone cell\n",
+      "\t cell\n",
+      "monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "granulocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t blood cell\n",
+      "\t myeloid leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "neuron associated cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "neutrophil\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t blood cell\n",
+      "\t myeloid leukocyte\n",
+      "\t granulocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "phagocyte\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t defensive cell\n",
+      "mast cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t histamine secreting cell\n",
+      "\t secretory cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t biogenic amine secreting cell\n",
+      "sensory epithelial cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "\t epithelial cell\n",
+      "interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "efferent neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "afferent neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "bipolar neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "multipolar neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "autonomic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "peripheral nervous system neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "cholinergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "catecholaminergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "mononuclear phagocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "mononuclear cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "ectodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "CNS neuron (sensu Vertebrata)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "GABAergic interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "cerebellar Golgi cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t cerebellar inhibitory GABAergic interneuron\n",
+      "\t cerebellar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "cerebellar inhibitory GABAergic interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t cerebellar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "granule cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "Purkinje cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t efferent neuron\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t cerebellar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "cerebellar neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "stellate neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t multipolar neuron\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "neuron associated cell (sensu Vertebrata)\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "macroglial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "astrocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "oligodendrocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t myelinating glial cell\n",
+      "myelinating glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "oligodendrocyte precursor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "microglial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t neural cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t neuron associated cell\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t glial cell\n",
+      "\t central nervous system macrophage\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "central nervous system macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t neural cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "mesodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "gut endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "corneal endothelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "fibrocyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "stromal cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "adipocyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stuff accumulating cell\n",
+      "stuff accumulating cell\n",
+      "\t cell\n",
+      "chondrocyte\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t glycosaminoglycan secreting cell\n",
+      "\t collagen secreting cell\n",
+      "\t extracellular matrix secreting cell\n",
+      "\t carbohydrate secreting cell\n",
+      "glycosaminoglycan secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t extracellular matrix secreting cell\n",
+      "\t carbohydrate secreting cell\n",
+      "collagen secreting cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t extracellular matrix secreting cell\n",
+      "pigment cell\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "melanocyte\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "visual pigment cell\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "glandular epithelial cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "extracellular matrix secreting cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "carbohydrate secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "protein secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "peptic cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t glandular cell of stomach\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "glandular cell of stomach\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "surfactant secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "club cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t surfactant secreting cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "epithelial cell of tracheobronchial tree\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "seromucus secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t mucus secreting cell\n",
+      "mucus secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "parietal cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t glandular cell of stomach\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "endocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "neuroendocrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t endocrine cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "chromaffin cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural crest derived neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t endocrine cell\n",
+      "\t neuroendocrine cell\n",
+      "\t amine precursor uptake and decarboxylation cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "amine precursor uptake and decarboxylation cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t endocrine cell\n",
+      "\t neuroendocrine cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "peptide hormone secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "insulin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t protein secreting cell\n",
+      "\t peptide hormone secreting cell\n",
+      "type B pancreatic cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t insulin secreting cell\n",
+      "\t pancreatic endocrine cell\n",
+      "pancreatic endocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "progenitor cell of endocrine pancreas\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "glucagon secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "pancreatic A cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t pancreatic endocrine cell\n",
+      "\t glucagon secreting cell\n",
+      "\t type A enteroendocrine cell\n",
+      "type A enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t glucagon secreting cell\n",
+      "somatostatin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "pancreatic D cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t pancreatic endocrine cell\n",
+      "\t somatostatin secreting cell\n",
+      "\t type D enteroendocrine cell\n",
+      "type D enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t somatostatin secreting cell\n",
+      "steroid hormone secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "stromal cell of ovary\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "testosterone secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "Leydig cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t testosterone secreting cell\n",
+      "\t interstitial cell\n",
+      "interstitial cell\n",
+      "\t cell\n",
+      "hepatocyte\n",
+      "\t cell\n",
+      "\t polyploid cell\n",
+      "\t epithelial cell\n",
+      "\t endopolyploid cell\n",
+      "endopolyploid cell\n",
+      "\t cell\n",
+      "\t polyploid cell\n",
+      "hepatoblast\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "contractile cell\n",
+      "\t cell\n",
+      "pericyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "myoepithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t contractile cell\n",
+      "myofibroblast cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "electrically responsive cell\n",
+      "\t cell\n",
+      "\t electrically active cell\n",
+      "cell of skeletal muscle\n",
+      "\t cell\n",
+      "slow muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t extrafusal muscle fiber\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t myotube\n",
+      "\t striated muscle cell\n",
+      "\t skeletal muscle fiber\n",
+      "extrafusal muscle fiber\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t myotube\n",
+      "\t striated muscle cell\n",
+      "\t skeletal muscle fiber\n",
+      "fast muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t extrafusal muscle fiber\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t myotube\n",
+      "\t striated muscle cell\n",
+      "\t skeletal muscle fiber\n",
+      "non-striated muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "visceral muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "smooth muscle myoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t myoblast\n",
+      "\t muscle precursor cell\n",
+      "cardiac muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "mechanoreceptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "somatosensory neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "auditory hair cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t mechanoreceptor cell\n",
+      "\t sensory hair cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "sensory hair cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t mechanoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "chemoreceptor cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "olfactory receptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t chemoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "taste receptor cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "\t epithelial cell\n",
+      "\t sensory epithelial cell\n",
+      "\t chemoreceptor cell\n",
+      "\t endo-epithelial cell\n",
+      "endo-epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "photoreceptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "electrically active cell\n",
+      "\t cell\n",
+      "absorptive cell\n",
+      "\t cell\n",
+      "Sertoli cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t mesothelial cell\n",
+      "\t lining cell\n",
+      "\t protein secreting cell\n",
+      "\t supporting cell\n",
+      "\t androgen binding protein secreting cell\n",
+      "\t seminiferous tubule epithelial cell\n",
+      "supporting cell\n",
+      "\t cell\n",
+      "androgen binding protein secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t protein secreting cell\n",
+      "seminiferous tubule epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t mesothelial cell\n",
+      "\t lining cell\n",
+      "insulating cell\n",
+      "\t cell\n",
+      "\t barrier cell\n",
+      "myelinating Schwann cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t barrier cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t myelinating glial cell\n",
+      "\t insulating cell\n",
+      "\t Schwann cell\n",
+      "Schwann cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "immature Schwann cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t Schwann cell\n",
+      "motile cell\n",
+      "\t cell\n",
+      "anucleate cell\n",
+      "\t cell\n",
+      "nucleate cell\n",
+      "\t cell\n",
+      "single nucleate cell\n",
+      "\t cell\n",
+      "\t nucleate cell\n",
+      "multinucleate cell\n",
+      "\t cell\n",
+      "\t nucleate cell\n",
+      "erythrocyte\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t blood cell\n",
+      "\t oxygen accumulating cell\n",
+      "oxygen accumulating cell\n",
+      "\t cell\n",
+      "reticulocyte\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "platelet\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t secretory cell\n",
+      "\t blood cell\n",
+      "\t anucleate cell\n",
+      "\t serotonin secreting cell\n",
+      "\t biogenic amine secreting cell\n",
+      "serotonin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t biogenic amine secreting cell\n",
+      "megakaryocyte\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "defensive cell\n",
+      "\t cell\n",
+      "macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "keratinizing barrier epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "stratified squamous epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "keratin accumulating cell\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "brush border epithelial cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "Merkel cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t neuron associated cell\n",
+      "\t neuron associated cell (sensu Vertebrata)\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "keratinocyte\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "transitional epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "primary sensory neuron (sensu Teleostei)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t primary neuron (sensu Teleostei)\n",
+      "eurydendroid cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "eye photoreceptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t photoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "gamete\n",
+      "\t cell\n",
+      "\t germ line cell\n",
+      "\t germ cell\n",
+      "\t haploid cell\n",
+      "crystallin accumulating cell\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "tracheal epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "epidermal cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t general ecto-epithelial cell\n",
+      "serous secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "milk secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "sebum secreting cell\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t sebaceous gland cell\n",
+      "sebaceous gland cell\n",
+      "\t cell\n",
+      "seminal vesicle glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "pneumocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t epithelial cell of alveolus of lung\n",
+      "epithelial cell of alveolus of lung\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "lysozyme secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t protein secreting cell\n",
+      "metanephric mesenchyme stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t connective tissue cell\n",
+      "\t mesenchymal stem cell\n",
+      "neural crest cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "mesenchymal cell\n",
+      "\t cell\n",
+      "\t motile cell\n",
+      "biogenic amine secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "pigment cell (sensu Vertebrata)\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "extraembryonic cell\n",
+      "\t cell\n",
+      "trophoblast cell\n",
+      "\t cell\n",
+      "\t extraembryonic cell\n",
+      "sphincter associated smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "vascular associated smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t perivascular cell\n",
+      "perivascular cell\n",
+      "\t cell\n",
+      "general ecto-epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "myotube\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t striated muscle cell\n",
+      "attachment cell\n",
+      "\t cell\n",
+      "\t supporting cell\n",
+      "hemocyte (sensu Arthropoda)\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t blood cell\n",
+      "\t blood cell (sensu Nematoda and Protostomia)\n",
+      "blood cell (sensu Nematoda and Protostomia)\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t blood cell\n",
+      "tendon cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t fibrocyte\n",
+      "\t stromal cell\n",
+      "\t supporting cell\n",
+      "\t attachment cell\n",
+      "plasmatocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t blood cell\n",
+      "\t hemocyte (sensu Arthropoda)\n",
+      "\t blood cell (sensu Nematoda and Protostomia)\n",
+      "ganglion interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "CNS interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "central nervous system neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "electrically signaling cell\n",
+      "\t cell\n",
+      "\t electrically active cell\n",
+      "excretory cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "reticular cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "follicular dendritic cell\n",
+      "\t cell\n",
+      "\t defensive cell\n",
+      "striated muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "epithelial cell of parathyroid gland\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t parathyroid glandular cell\n",
+      "preadipocyte\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "brown adipocyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t adipocyte\n",
+      "\t stuff accumulating cell\n",
+      "brown preadipocyte\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t preadipocyte\n",
+      "dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "Langerhans cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "conventional dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "mineralocorticoid secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t steroid hormone secreting cell\n",
+      "noradrenergic cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t biogenic amine secreting cell\n",
+      "cardiac myoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t myoblast\n",
+      "\t muscle precursor cell\n",
+      "neural progenitor cell\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "follicular epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "ovarian surface epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "CD4-positive helper T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "activated CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "inhibitory interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "granulosa cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t steroid hormone secreting cell\n",
+      "\t follicular epithelial cell\n",
+      "\t follicular cell of ovary\n",
+      "follicular cell of ovary\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t follicular epithelial cell\n",
+      "theca cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t steroid hormone secreting cell\n",
+      "\t stromal cell of ovary\n",
+      "\t androgen secreting cell\n",
+      "androgen secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t steroid hormone secreting cell\n",
+      "enkephalin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t endorphin secreting cell\n",
+      "endorphin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "type G enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t glandular cell of stomach\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t enkephalin secreting cell\n",
+      "\t endorphin secreting cell\n",
+      "\t gastrin secreting cell\n",
+      "\t mucous cell of stomach\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "gastrin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "mucous cell of stomach\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t glandular cell of stomach\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "paneth cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t lysozyme secreting cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "intestinal epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "paracrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "cardiac muscle myoblast\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t myoblast\n",
+      "\t muscle precursor cell\n",
+      "\t cardiac myoblast\n",
+      "\t cardiocyte\n",
+      "cardiocyte\n",
+      "\t cell\n",
+      "skeletal muscle fiber\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t myotube\n",
+      "\t striated muscle cell\n",
+      "perineuronal satellite cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t neuron associated cell (sensu Vertebrata)\n",
+      "syncytiotrophoblast cell\n",
+      "\t cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t extraembryonic cell\n",
+      "\t trophoblast cell\n",
+      "pigmented epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "primary neuron (sensu Teleostei)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "T-helper 1 cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive helper T cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "T-helper 2 cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive helper T cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "proerythroblast\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t nucleate cell\n",
+      "erythroblast\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "megakaryocyte progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "neuronal brush cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "\t hematopoietic oligopotent progenitor cell, lineage-negative\n",
+      "bone marrow hematopoietic cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t bone cell\n",
+      "\t bone marrow cell\n",
+      "promonocyte\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t monopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "retinal cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "retinal ganglion cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "promyelocyte\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "cardiac mesenchymal cell\n",
+      "\t cell\n",
+      "\t migratory neural crest cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t multi fate stem cell\n",
+      "\t motile cell\n",
+      "\t neural crest cell\n",
+      "\t mesenchymal cell\n",
+      "\t migratory cardiac neural crest cell\n",
+      "migratory cardiac neural crest cell\n",
+      "\t cell\n",
+      "\t migratory neural crest cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t multi fate stem cell\n",
+      "\t motile cell\n",
+      "\t neural crest cell\n",
+      "retinal cone cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t photoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t eye photoreceptor cell\n",
+      "\t electrically signaling cell\n",
+      "\t camera-type eye photoreceptor cell\n",
+      "camera-type eye photoreceptor cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t photoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t eye photoreceptor cell\n",
+      "\t electrically signaling cell\n",
+      "retinal progenitor cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "corneal epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t general ecto-epithelial cell\n",
+      "type EC enteroendocrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural crest derived neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t neuroendocrine cell\n",
+      "\t chromaffin cell\n",
+      "\t amine precursor uptake and decarboxylation cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t electrically responsive cell\n",
+      "\t endo-epithelial cell\n",
+      "\t electrically active cell\n",
+      "\t serotonin secreting cell\n",
+      "\t biogenic amine secreting cell\n",
+      "\t electrically signaling cell\n",
+      "\t enkephalin secreting cell\n",
+      "\t endorphin secreting cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "epithelial cell of alimentary canal\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "cell in vitro\n",
+      "\t cell\n",
+      "immature neutrophil\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t blood cell\n",
+      "\t myeloid leukocyte\n",
+      "\t granulocyte\n",
+      "\t neutrophil\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "myelocyte\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "alveolar macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t lung macrophage\n",
+      "lung macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "enterocyte\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t gut absorptive cell\n",
+      "gut absorptive cell\n",
+      "\t cell\n",
+      "cochlea auditory hair cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t mechanoreceptor cell\n",
+      "\t auditory hair cell\n",
+      "\t sensory hair cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t ear hair cell\n",
+      "skeletal muscle satellite cell\n",
+      "\t cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t muscle precursor cell\n",
+      "\t cell of skeletal muscle\n",
+      "enucleate erythrocyte\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t blood cell\n",
+      "\t anucleate cell\n",
+      "\t erythrocyte\n",
+      "\t oxygen accumulating cell\n",
+      "enucleated reticulocyte\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t anucleate cell\n",
+      "\t reticulocyte\n",
+      "pyramidal neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t multipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "retinal rod cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t photoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t eye photoreceptor cell\n",
+      "\t electrically signaling cell\n",
+      "\t camera-type eye photoreceptor cell\n",
+      "ear hair cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t mechanoreceptor cell\n",
+      "\t sensory hair cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "granulocytopoietic cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "basophil mast progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "GABAergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "acinar cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "group 1 innate lymphoid cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "mature alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "transporting cell\n",
+      "\t cell\n",
+      "hepatic stellate cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t extracellular matrix secreting cell\n",
+      "Mueller cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t retinal cell\n",
+      "\t radial glial cell\n",
+      "radial glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "pituitary gland cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "tanycyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "\t ependymoglial cell\n",
+      "Bergmann glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "astrocyte of the forebrain\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "\t brain macroglial cell\n",
+      "basal cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "juxtaglomerular complex cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "\t kidney cortical cell\n",
+      "prickle cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "mesangial cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "\t pericyte\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "mucous neck cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t mucus secreting cell\n",
+      "pinealocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endocrine cell\n",
+      "podocyte\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t excretory cell\n",
+      "\t renal filtration cell\n",
+      "\t epithelial cell of glomerular capsule\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney glomerular epithelial cell\n",
+      "\t kidney corpuscule cell\n",
+      "renal filtration cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t excretory cell\n",
+      "epithelial cell of glomerular capsule\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney glomerular epithelial cell\n",
+      "\t kidney corpuscule cell\n",
+      "cardiac valve cell\n",
+      "\t cell\n",
+      "\t cardiocyte\n",
+      "fenestrated cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "mural cell\n",
+      "\t cell\n",
+      "\t perivascular cell\n",
+      "glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "M cell of gut\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t defensive cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t transporting cell\n",
+      "ependymoglial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "non-myelinating Schwann cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t Schwann cell\n",
+      "Cajal-Retzius cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "PP cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "dopaminergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t catecholaminergic neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "endothelial tip cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "choroid plexus epithelial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t ciliated cell\n",
+      "\t ependymal cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t transporting cell\n",
+      "leptomeningeal cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t extracellular matrix secreting cell\n",
+      "urothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t transitional epithelial cell\n",
+      "retina horizontal cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "retinal bipolar neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t visual system neuron\n",
+      "visual system neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "ON-bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "OFF-bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "rod bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "cone retinal bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "type 1 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 2 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 3 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 4 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 5 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 6 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 7 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 8 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "type 9 cone bipolar cell (sensu Mus)\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "myeloid dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "plasmacytoid dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "mature B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "B cell, CD19-positive\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "transitional stage B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "immature B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "precursor B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "plasma cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t antibody secreting cell\n",
+      "antibody secreting cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "plasmablast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t antibody secreting cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "memory B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "naive B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "gamma-delta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "immature alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t immature T cell\n",
+      "immature T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "mature T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "CD4-positive, CD25-positive, alpha-beta regulatory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t regulatory T cell\n",
+      "regulatory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "alpha-beta intraepithelial T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t intraepithelial lymphocyte\n",
+      "CD4-positive, alpha-beta thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t immature alpha-beta T cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "CD8-positive, alpha-beta cytotoxic T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "activated CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "CD8-alpha-beta-positive, alpha-beta intraepithelial T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t alpha-beta intraepithelial T cell\n",
+      "\t intraepithelial lymphocyte\n",
+      "intraepithelial lymphocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "DN3 thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "\t double negative thymocyte\n",
+      "mature gamma-delta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t gamma-delta T cell\n",
+      "\t mature T cell\n",
+      "gamma-delta intraepithelial T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t gamma-delta T cell\n",
+      "\t mature T cell\n",
+      "\t intraepithelial lymphocyte\n",
+      "\t mature gamma-delta T cell\n",
+      "CD8-alpha alpha positive, gamma-delta intraepithelial T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t gamma-delta T cell\n",
+      "\t mature T cell\n",
+      "\t intraepithelial lymphocyte\n",
+      "\t mature gamma-delta T cell\n",
+      "\t gamma-delta intraepithelial T cell\n",
+      "thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature T cell\n",
+      "DN4 thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "\t double negative thymocyte\n",
+      "double negative thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "DN1 thymic pro-T cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t pro-T cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "double-positive, alpha-beta thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t immature alpha-beta T cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "CD8-positive, alpha-beta thymocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t immature alpha-beta T cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "memory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "naive T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "mature NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "small pre-B-II cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t precursor B cell\n",
+      "\t pre-B-II cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "pro-B cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "B-1 B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "B-1a B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t B-1 B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "B-1b B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t B-1 B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "B-2 B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "immature natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t immature innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "immature innate lymphoid cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "pre-natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "mature natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "lymphoid lineage restricted progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "hematopoietic multipotent progenitor cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "hematopoietic lineage restricted progenitor cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "common dendritic progenitor\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "\t CD115-positive monocyte OR common dendritic progenitor\n",
+      "mature conventional dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "follicular B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t B-2 B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "germinal center B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "classical monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "elicited macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "inflammatory macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t elicited macrophage\n",
+      "non-classical monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "intermediate monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "meningeal macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t neural cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t central nervous system macrophage\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "myeloid suppressor cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "alternatively activated macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t elicited macrophage\n",
+      "early T lineage precursor\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t pro-T cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "naive thymus-derived CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t naive T cell\n",
+      "CD4-positive, alpha-beta memory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "T-helper 17 cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive helper T cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "naive thymus-derived CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t naive T cell\n",
+      "natural T-regulatory cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t CD4-positive, CD25-positive, alpha-beta regulatory T cell\n",
+      "\t regulatory T cell\n",
+      "central memory CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "CD4-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell\n",
+      "effector memory CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "central memory CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD8-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "\t CD8-positive, alpha-beta memory T cell\n",
+      "CD8-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD8-positive, alpha-beta memory T cell\n",
+      "CD8-positive, alpha-beta cytokine secreting effector T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "CD8-positive, alpha-beta memory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "cytotoxic T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "\t effector T cell\n",
+      "effector T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "helper T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "\t effector T cell\n",
+      "effector memory CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD8-positive, alpha-beta memory T cell, CD45RO-positive\n",
+      "\t CD8-positive, alpha-beta memory T cell\n",
+      "immature NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t alpha-beta T cell\n",
+      "\t immature alpha-beta T cell\n",
+      "\t immature T cell\n",
+      "CD8-alpha-alpha-positive, alpha-beta intraepithelial T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t alpha-beta intraepithelial T cell\n",
+      "\t intraepithelial lymphocyte\n",
+      "Tc1 cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t CD8-positive, alpha-beta cytokine secreting effector T cell\n",
+      "type I NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t mature NK T cell\n",
+      "type II NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t mature NK T cell\n",
+      "CD4-negative, CD8-negative type I NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t mature NK T cell\n",
+      "\t type I NK T cell\n",
+      "activated CD4-negative, CD8-negative type I NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t mature NK T cell\n",
+      "\t type I NK T cell\n",
+      "\t CD4-negative, CD8-negative type I NK T cell\n",
+      "activated type II NK T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t mature NK T cell\n",
+      "\t type II NK T cell\n",
+      "CD4-positive, alpha-beta cytotoxic T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "early lymphoid progenitor\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t hematopoietic multipotent progenitor cell\n",
+      "CD16-negative, CD56-bright natural killer cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t mature natural killer cell\n",
+      "\t innate lymphoid cell\n",
+      "CD16-positive, CD56-dim natural killer cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t mature natural killer cell\n",
+      "\t innate lymphoid cell\n",
+      "mucosal invariant T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "long lived plasma cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t plasma cell\n",
+      "\t antibody secreting cell\n",
+      "class switched memory B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t memory B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "large pre-B-II cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t precursor B cell\n",
+      "\t pre-B-II cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "pre-B-II cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t precursor B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "pre-B-I cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t precursor B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "unswitched memory B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t memory B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "IgG memory B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t memory B cell\n",
+      "\t class switched memory B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "IgG plasmablast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t antibody secreting cell\n",
+      "\t plasmablast\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "IgA plasmablast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t antibody secreting cell\n",
+      "\t plasmablast\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "IgG plasma cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t plasma cell\n",
+      "\t antibody secreting cell\n",
+      "\t long lived plasma cell\n",
+      "IgM plasma cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t plasma cell\n",
+      "\t antibody secreting cell\n",
+      "\t long lived plasma cell\n",
+      "IgA plasma cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t plasma cell\n",
+      "\t antibody secreting cell\n",
+      "\t long lived plasma cell\n",
+      "hematopoietic oligopotent progenitor cell, lineage-negative\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t hematopoietic oligopotent progenitor cell\n",
+      "pre-conventional dendritic cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "CD11b-positive dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "CD34-positive, CD38-negative hematopoietic stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "\t hematopoietic stem cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "CD115-positive monocyte OR common dendritic progenitor\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "CD115-positive monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "\t CD115-positive monocyte OR common dendritic progenitor\n",
+      "cerebellar granule cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t granule cell\n",
+      "\t cerebellar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebellum glutamatergic neuron\n",
+      "cerebellum glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t cerebellar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "cerebellar granule cell precursor\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t neuroblast (sensu Vertebrata)\n",
+      "\t precursor cell\n",
+      "\t neural cell\n",
+      "cerebral cortex neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "hippocampal neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t cerebral cortex neuron\n",
+      "T-helper 22 cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive helper T cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "activated CD4-positive, alpha-beta T cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t activated CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "effector CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t effector T cell\n",
+      "naive regulatory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t CD4-positive, CD25-positive, alpha-beta regulatory T cell\n",
+      "\t regulatory T cell\n",
+      "\t naive T cell\n",
+      "memory regulatory T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t CD4-positive, CD25-positive, alpha-beta regulatory T cell\n",
+      "\t regulatory T cell\n",
+      "\t memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell\n",
+      "activated CD8-positive, alpha-beta T cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t activated CD8-positive, alpha-beta T cell\n",
+      "effector CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t effector T cell\n",
+      "CD14-positive monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "CD14-positive, CD16-positive monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t CD14-positive monocyte\n",
+      "dendritic cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "myeloid dendritic cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "\t myeloid dendritic cell\n",
+      "\t dendritic cell, human\n",
+      "plasmacytoid dendritic cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t plasmacytoid dendritic cell\n",
+      "\t dendritic cell, human\n",
+      "abnormal cell\n",
+      "\t cell\n",
+      "effector memory CD8-positive, alpha-beta T cell, terminally differentiated\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD8-positive, alpha-beta memory T cell\n",
+      "\t effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated\n",
+      "effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "neoplastic cell\n",
+      "\t cell\n",
+      "\t abnormal cell\n",
+      "malignant cell\n",
+      "\t cell\n",
+      "\t abnormal cell\n",
+      "\t neoplastic cell\n",
+      "innate lymphoid cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "erythroid progenitor cell, mammalian\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t erythroid progenitor cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "ILC1\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "group 2 innate lymphoid cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "group 3 innate lymphoid cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "NKp46-positive innate lymphoid cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "\t CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human\n",
+      "ILC1, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "\t ILC1\n",
+      "group 3 innate lymphoid cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "\t group 3 innate lymphoid cell\n",
+      "NKp44-positive group 3 innate lymphoid cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "\t group 3 innate lymphoid cell\n",
+      "\t group 3 innate lymphoid cell, human\n",
+      "NKp44-negative group 3 innate lymphoid cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "\t group 3 innate lymphoid cell\n",
+      "\t group 3 innate lymphoid cell, human\n",
+      "group 2 innate lymphoid cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t innate lymphoid cell\n",
+      "\t group 2 innate lymphoid cell\n",
+      "lymphocyte of B lineage, CD19-positive\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "T follicular helper cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive helper T cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "fraction A pre-pro B cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t pro-B cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "early pro-B cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t pro-B cell\n",
+      "\t lymphoid lineage restricted progenitor cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "late pro-B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t precursor B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "CD14-positive, CD16-negative classical monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t classical monocyte\n",
+      "\t CD14-positive monocyte\n",
+      "type I pneumocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t pneumocyte\n",
+      "\t epithelial cell of alveolus of lung\n",
+      "type II pneumocyte\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t surfactant secreting cell\n",
+      "\t pneumocyte\n",
+      "\t epithelial cell of alveolus of lung\n",
+      "\t lung secretory cell\n",
+      "lung secretory cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "pancreatic acinar cell\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t acinar cell\n",
+      "\t pancreas exocrine glandular cell\n",
+      "\t epithelial cell of exocrine pancreas\n",
+      "pancreas exocrine glandular cell\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t epithelial cell of exocrine pancreas\n",
+      "specialized cardiac myocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "enterocyte of epithelium of large intestine\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t enterocyte\n",
+      "\t gut absorptive cell\n",
+      "\t epithelial cell of large intestine\n",
+      "epithelial cell of large intestine\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "nodal myocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "\t specialized cardiac myocyte\n",
+      "brush cell of trachebronchial tree\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t brush cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "brush cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "pancreatic ductal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t epithelial cell of exocrine pancreas\n",
+      "epithelial cell of exocrine pancreas\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "interstitial cell of Cajal\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "bone marrow cell\n",
+      "\t cell\n",
+      "\t bone cell\n",
+      "interstitial cell of ovary\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t stromal cell of ovary\n",
+      "transversely striated visceral muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t striated visceral muscle cell\n",
+      "cortical cell of adrenal gland\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t steroid hormone secreting cell\n",
+      "\t adrenal gland glandular cell\n",
+      "adrenal gland glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "regular cardiac myocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "type I cell of adrenal cortex\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t steroid hormone secreting cell\n",
+      "\t mineralocorticoid secreting cell\n",
+      "\t cortical cell of adrenal gland\n",
+      "\t adrenal gland glandular cell\n",
+      "CD38-negative naive B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t naive B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "IgG-negative class switched memory B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t memory B cell\n",
+      "\t class switched memory B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "regular atrial cardiac myocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "\t regular cardiac myocyte\n",
+      "regular ventricular cardiac myocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "\t regular cardiac myocyte\n",
+      "endothelial cell of lymphatic vessel\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "vascular lymphangioblast\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t perivascular cell\n",
+      "\t lymphangioblast\n",
+      "epithelial cell of sweat gland\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "\t epithelial cell of skin gland\n",
+      "capillary endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t microvascular endothelial cell\n",
+      "microvascular endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "ciliated columnar cell of tracheobronchial tree\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t multi-ciliated epithelial cell\n",
+      "\t respiratory ciliated cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "multi-ciliated epithelial cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "respiratory ciliated cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "epithelial cell of uterus\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "late promyelocyte\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t promyelocyte\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "early promyelocyte\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t promyelocyte\n",
+      "\t granulocytopoietic cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "endosteal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t bone cell\n",
+      "olfactory epithelial cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t sensory epithelial cell\n",
+      "\t chemoreceptor cell\n",
+      "renal interstitial pericyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t interstitial cell\n",
+      "\t contractile cell\n",
+      "\t pericyte\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "\t kidney cell\n",
+      "\t kidney interstitial cell\n",
+      "epithelial cell of stomach\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "foveolar cell of stomach\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of stomach\n",
+      "basal cell of epidermis\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "\t progenitor cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "\t basal cell\n",
+      "\t stem cell of epidermis\n",
+      "stem cell of epidermis\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "glomerular endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t fenestrated cell\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney corpuscule cell\n",
+      "glomerular cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney corpuscule cell\n",
+      "granular cell of epidermis\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "epithelial cell of thyroid gland\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "renal beta-intercalated cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t renal intercalated cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "renal intercalated cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "epithelial cell of lower respiratory tract\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "brush cell of epithelium proper of large intestine\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t brush cell\n",
+      "\t intestinal tuft cell\n",
+      "intestinal tuft cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t brush cell\n",
+      "brush cell of trachea\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t tracheal epithelial cell\n",
+      "\t brush cell of trachebronchial tree\n",
+      "\t brush cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "bronchial epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "epithelial cell of esophagus\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "vertebrate lens cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t crystallin accumulating cell\n",
+      "anterior lens cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t crystallin accumulating cell\n",
+      "\t vertebrate lens cell\n",
+      "\t lens epithelial cell\n",
+      "lens epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t crystallin accumulating cell\n",
+      "\t vertebrate lens cell\n",
+      "secondary lens fiber\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t crystallin accumulating cell\n",
+      "\t vertebrate lens cell\n",
+      "\t lens fiber cell\n",
+      "lens fiber cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t crystallin accumulating cell\n",
+      "\t vertebrate lens cell\n",
+      "lactocyte\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t milk secreting cell\n",
+      "\t general ecto-epithelial cell\n",
+      "\t mammary gland epithelial cell\n",
+      "\t mammary gland glandular cell\n",
+      "epithelial cell of prostate\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "basal cell of prostate epithelium\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of prostate\n",
+      "luminal cell of prostate epithelium\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of prostate\n",
+      "basal epithelial cell of prostatic duct\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of prostate\n",
+      "\t basal cell of prostate epithelium\n",
+      "pulmonary interstitial fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t interstitial cell\n",
+      "smooth muscle cell of sphincter of pupil\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t sphincter associated smooth muscle cell\n",
+      "peripheral blood stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "\t hematopoietic stem cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t circulating cell\n",
+      "intestinal crypt stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "epithelial cell of small intestine\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "stromal cell of endometrium\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "decidual cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "\t extraembryonic cell\n",
+      "thyroid follicular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of thyroid gland\n",
+      "neuroepithelial stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "parathyroid glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "endothelial cell of sinusoid\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "enteroendocrine cell of colon\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t glandular cell of the large intestine\n",
+      "\t intestinal enteroendocrine cell\n",
+      "\t colon glandular cell\n",
+      "\t colon epithelial cell\n",
+      "enteroendocrine cell of small intestine\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t intestinal enteroendocrine cell\n",
+      "\t small intestine glandular cell\n",
+      "P/D1 enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t vasoactive intestinal peptide secreting cell\n",
+      "vasoactive intestinal peptide secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "pancreatic PP cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t pancreatic endocrine cell\n",
+      "\t PP cell\n",
+      "type I enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "GIP cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t enteroendocrine cell of small intestine\n",
+      "\t intestinal enteroendocrine cell\n",
+      "\t small intestine glandular cell\n",
+      "type L enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "type N enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "ghrelin secreting cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t peptide hormone secreting cell\n",
+      "epithelial cell of thymus\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "cortical thymic epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of thymus\n",
+      "medullary thymic epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of thymus\n",
+      "pigmented ciliary epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t pigmented epithelial cell\n",
+      "non-pigmented ciliary epithelial cell\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "epithelial cell of distal tubule\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "nephron tubule epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney cortex tubule cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "epithelial cell of proximal tubule\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "epithelial cell of skin gland\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t general ecto-epithelial cell\n",
+      "embryonic stem cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "myoepithelial cell of mammary gland\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t contractile cell\n",
+      "\t myoepithelial cell\n",
+      "\t mammary gland epithelial cell\n",
+      "mammary gland epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "mammary gland glandular cell\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t mammary gland epithelial cell\n",
+      "luminal adaptive secretory precursor cell of mammary gland\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "\t mammary gland epithelial cell\n",
+      "\t luminal epithelial cell of mammary gland\n",
+      "luminal epithelial cell of mammary gland\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "\t mammary gland epithelial cell\n",
+      "progenitor cell of mammary luminal epithelium\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "basal epithelial cell of tracheobronchial tree\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "ciliated cell of the bronchus\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t ciliated columnar cell of tracheobronchial tree\n",
+      "\t multi-ciliated epithelial cell\n",
+      "\t respiratory ciliated cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t bronchial epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "keratinocyte stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t multi fate stem cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "CD56-positive, CD161-positive immature natural killer cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t immature natural killer cell\n",
+      "\t immature innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "decidual natural killer cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t mature natural killer cell\n",
+      "\t innate lymphoid cell\n",
+      "endocardial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t cardiocyte\n",
+      "\t cardiac endothelial cell\n",
+      "cardiac endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t cardiocyte\n",
+      "gestational hematopoietic stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "\t hematopoietic stem cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "primitive red blood cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t primitive erythroid lineage cell\n",
+      "primitive erythroid lineage cell\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "primitive erythroid progenitor\n",
+      "\t cell\n",
+      "\t hematopoietic cell\n",
+      "\t erythroid lineage cell\n",
+      "\t myeloid cell\n",
+      "\t primitive erythroid lineage cell\n",
+      "keratocyte\n",
+      "\t cell\n",
+      "\t fibroblast neural crest derived\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "myometrial cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t uterine smooth muscle cell\n",
+      "uterine smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "respiratory epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "respiratory goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "Schwann cell precursor\n",
+      "\t cell\n",
+      "\t glioblast\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "CD141-positive myeloid dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "\t myeloid dendritic cell\n",
+      "\t plasmacytoid dendritic cell\n",
+      "Gr1-high classical monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "\t classical monocyte\n",
+      "\t CD115-positive monocyte OR common dendritic progenitor\n",
+      "\t CD115-positive monocyte\n",
+      "CD14-low, CD16-positive monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t non-classical monocyte\n",
+      "\t CD14-positive monocyte\n",
+      "CD1c-positive myeloid dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "\t myeloid dendritic cell\n",
+      "pancreatic stellate cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "epidermal Langerhans cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t Langerhans cell\n",
+      "\t conventional dendritic cell\n",
+      "\t CD11b-positive dendritic cell\n",
+      "MHC-II-positive classical monocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t myeloid lineage restricted progenitor cell\n",
+      "\t myeloid cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t monocyte\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t hematopoietic lineage restricted progenitor cell\n",
+      "\t classical monocyte\n",
+      "\t CD115-positive monocyte OR common dendritic progenitor\n",
+      "\t CD115-positive monocyte\n",
+      "\t Gr1-high classical monocyte\n",
+      "adipose macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "nasal mucosa goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t respiratory goblet cell\n",
+      "melanocyte of skin\n",
+      "\t cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "\t melanocyte\n",
+      "hair follicle cell\n",
+      "\t cell\n",
+      "auditory epithelial cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "\t epithelial cell\n",
+      "\t sensory epithelial cell\n",
+      "trophoblast giant cell\n",
+      "\t cell\n",
+      "\t extraembryonic cell\n",
+      "\t trophoblast cell\n",
+      "fetal cardiomyocyte\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "adventitial cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t supporting cell\n",
+      "enteric smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "kidney epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney cell\n",
+      "kidney cell\n",
+      "\t cell\n",
+      "subcutaneous adipocyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t adipocyte\n",
+      "\t stuff accumulating cell\n",
+      "epithelial cell of amnion\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "intrahepatic cholangiocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "\t cholangiocyte\n",
+      "cholangiocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "aortic smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t vascular associated smooth muscle cell\n",
+      "\t perivascular cell\n",
+      "\t blood vessel smooth muscle cell\n",
+      "blood vessel smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t vascular associated smooth muscle cell\n",
+      "\t perivascular cell\n",
+      "endothelial cell of artery\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "vein endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "aortic endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of artery\n",
+      "fibroblast of cardiac tissue\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t cardiocyte\n",
+      "skin fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "fibroblast of lung\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "fibroblast of lymphatic vessel\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "fibroblast of mammary gland\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "iris pigment epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "\t pigment cell (sensu Vertebrata)\n",
+      "mesenchymal stem cell of adipose tissue\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t connective tissue cell\n",
+      "\t mesenchymal stem cell\n",
+      "central nervous system pericyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "\t pericyte\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "placental epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t extraembryonic cell\n",
+      "fibroblast of breast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "renal cortical epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t kidney cortical cell\n",
+      "kidney cortical cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "retinal blood vessel endothelial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t retinal cell\n",
+      "retinal pigment epithelial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t pigment cell\n",
+      "\t visual pigment cell\n",
+      "\t retinal cell\n",
+      "smooth muscle cell of the pulmonary artery\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t vascular associated smooth muscle cell\n",
+      "\t perivascular cell\n",
+      "\t blood vessel smooth muscle cell\n",
+      "bladder cell\n",
+      "\t cell\n",
+      "\t lower urinary tract cell\n",
+      "bronchial smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t tracheobronchial smooth muscle cell\n",
+      "tracheobronchial smooth muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "smooth muscle cell of trachea\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t tracheobronchial smooth muscle cell\n",
+      "astrocyte of the cerebellum\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "\t brain macroglial cell\n",
+      "brain macroglial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "astrocyte of the cerebral cortex\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "\t astrocyte of the forebrain\n",
+      "\t brain macroglial cell\n",
+      "\t cerebral cortex glial cell\n",
+      "cerebral cortex glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "migratory enteric neural crest cell\n",
+      "\t cell\n",
+      "\t migratory neural crest cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t multi fate stem cell\n",
+      "\t motile cell\n",
+      "\t neural crest cell\n",
+      "adipocyte of breast\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t adipocyte\n",
+      "\t stuff accumulating cell\n",
+      "gingival epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "prostate stromal cell\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "acinar cell of salivary gland\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t acinar cell\n",
+      "\t salivary gland glandular cell\n",
+      "salivary gland glandular cell\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "immature astrocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "mature astrocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "mature microglial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t neural cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t neuron associated cell\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t glial cell\n",
+      "\t microglial cell\n",
+      "\t central nervous system macrophage\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "respiratory basal cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t basal cell\n",
+      "\t respiratory epithelial cell\n",
+      "glandular cell of esophagus\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of esophagus\n",
+      "endothelial cell of venule\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "glandular cell of the large intestine\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "endothelial stalk cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "tongue muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t electrically active cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
+      "\t myotube\n",
+      "\t striated muscle cell\n",
+      "\t skeletal muscle fiber\n",
+      "intestinal enteroendocrine cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "bistratified retinal ganglion cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "\t bistratified cell\n",
+      "bistratified cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "ON retinal ganglion cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "OFF retinal ganglion cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "S cone cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t photoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t eye photoreceptor cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cone cell\n",
+      "\t camera-type eye photoreceptor cell\n",
+      "type 3a cone bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "\t type 3 cone bipolar cell (sensu Mus)\n",
+      "type 3b cone bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "\t type 3 cone bipolar cell (sensu Mus)\n",
+      "type 5a cone bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "\t type 5 cone bipolar cell (sensu Mus)\n",
+      "type 5b cone bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t cone retinal bipolar cell\n",
+      "\t type 5 cone bipolar cell (sensu Mus)\n",
+      "H1 horizontal cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retina horizontal cell\n",
+      "H2 horizontal cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retina horizontal cell\n",
+      "A2 amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "\t bistratified cell\n",
+      "\t bistratified retinal amacrine cell\n",
+      "\t narrow field retinal amacrine cell\n",
+      "\t glycinergic amacrine cell\n",
+      "\t glycinergic neuron\n",
+      "bistratified retinal amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "\t bistratified cell\n",
+      "narrow field retinal amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "glycinergic amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "\t glycinergic neuron\n",
+      "starburst amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t cholinergic neuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "\t GABAergic neuron\n",
+      "\t GABAergic amacrine cell\n",
+      "GABAergic amacrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t amacrine cell\n",
+      "\t retinal cell\n",
+      "\t GABAergic neuron\n",
+      "ionocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "renal principal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "epithelial cell of nephron\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "renal alpha-intercalated cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t renal intercalated cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "pancreatic epsilon cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of pancreas\n",
+      "\t glandular epithelial cell\n",
+      "\t endocrine cell\n",
+      "\t enteroendocrine cell\n",
+      "\t peptide hormone secreting cell\n",
+      "\t pancreatic endocrine cell\n",
+      "\t ghrelin secreting cell\n",
+      "lymphangioblast\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t perivascular cell\n",
+      "mesenchymal lymphangioblast\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t connective tissue cell\n",
+      "\t mesenchymal stem cell\n",
+      "\t perivascular cell\n",
+      "\t lymphangioblast\n",
+      "cranial motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "visceromotor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "parasympathetic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t autonomic neuron\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "notochordal cell\n",
+      "\t cell\n",
+      "enteric neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural crest derived neuron\n",
+      "\t neural cell\n",
+      "\t autonomic neuron\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "striated visceral muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "skeletal muscle satellite stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t muscle precursor cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t skeletal muscle satellite cell\n",
+      "inhibitory motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t cerebral cortex neuron\n",
+      "cerebral cortex GABAergic interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "extravillous trophoblast\n",
+      "\t cell\n",
+      "\t extraembryonic cell\n",
+      "\t trophoblast cell\n",
+      "primary motor cortex pyramidal cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t multipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t pyramidal neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cerebral cortex pyramidal neuron\n",
+      "L5 extratelencephalic projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t multipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t pyramidal neuron\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cerebral cortex pyramidal neuron\n",
+      "\t extratelencephalic-projecting glutamatergic cortical neuron\n",
+      "inflammatory cell\n",
+      "\t cell\n",
+      "salivary gland cell\n",
+      "\t cell\n",
+      "small intestine glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "paneth cell of colon\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t lysozyme secreting cell\n",
+      "\t paneth cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t glandular cell of the large intestine\n",
+      "\t colon glandular cell\n",
+      "\t colon epithelial cell\n",
+      "colon glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t glandular cell of the large intestine\n",
+      "\t colon epithelial cell\n",
+      "transit amplifying cell\n",
+      "\t cell\n",
+      "transit amplifying cell of colon\n",
+      "\t cell\n",
+      "\t transit amplifying cell\n",
+      "transit amplifying cell of small intestine\n",
+      "\t cell\n",
+      "\t transit amplifying cell\n",
+      "intestinal crypt stem cell of large intestine\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t intestinal crypt stem cell\n",
+      "intestinal crypt stem cell of small intestine\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t intestinal crypt stem cell\n",
+      "\t epithelial cell of small intestine\n",
+      "stromal cell of lamina propria of small intestine\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell\n",
+      "colon epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "colon macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "colon goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t glandular cell of the large intestine\n",
+      "\t colon glandular cell\n",
+      "\t colon epithelial cell\n",
+      "\t large intestine goblet cell\n",
+      "\t intestine goblet cell\n",
+      "large intestine goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t glandular cell of the large intestine\n",
+      "\t intestine goblet cell\n",
+      "tuft cell of colon\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t brush cell\n",
+      "\t brush cell of epithelium proper of large intestine\n",
+      "\t intestinal tuft cell\n",
+      "\t colon epithelial cell\n",
+      "intestinal crypt stem cell of colon\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of large intestine\n",
+      "\t intestinal crypt stem cell\n",
+      "\t colon epithelial cell\n",
+      "smooth muscle cell of large intestine\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t enteric smooth muscle cell\n",
+      "lung pericyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "\t pericyte\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "endothelial cell of placenta\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t extraembryonic cell\n",
+      "\t placental epithelial cell\n",
+      "endothelial cell of uterus\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "fibro/adipogenic progenitor cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t motile cell\n",
+      "\t mesenchymal cell\n",
+      "centrocyte\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t germinal center B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "centroblast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t germinal center B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "T follicular regulatory cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "\t regulatory T cell\n",
+      "hair follicular keratinocyte\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "\t hair follicle cell\n",
+      "cardiac blood vessel endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t cardiocyte\n",
+      "\t cardiac endothelial cell\n",
+      "cardiac neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "spinal cord motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "lateral mesodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t mesodermal cell\n",
+      "intermediate mesodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t mesodermal cell\n",
+      "mesothelial cell of epicardium\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t mesothelial cell\n",
+      "\t lining cell\n",
+      "\t cardiocyte\n",
+      "double negative T regulatory cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t immature T cell\n",
+      "\t thymocyte\n",
+      "\t double negative thymocyte\n",
+      "exhausted T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature T cell\n",
+      "\t effector T cell\n",
+      "skeletal muscle fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t cell of skeletal muscle\n",
+      "\t muscle fibroblast\n",
+      "muscle fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "olfactory ensheathing cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "monocyte-derived dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "\t conventional dendritic cell\n",
+      "\t myeloid dendritic cell\n",
+      "chorionic trophoblast cell\n",
+      "\t cell\n",
+      "\t extraembryonic cell\n",
+      "\t trophoblast cell\n",
+      "sympathetic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t autonomic neuron\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "spiral ganglion neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "forebrain radial glial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t radial glial cell\n",
+      "pulmonary ionocyte\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t ionocyte\n",
+      "splanchnic mesodermal cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t mesodermal cell\n",
+      "\t lateral mesodermal cell\n",
+      "epithelial cell of urethra\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t urethra cell\n",
+      "\t lower urinary tract cell\n",
+      "hillock cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "tracheobronchial serous cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "\t serous secreting cell\n",
+      "tracheobronchial goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "\t respiratory goblet cell\n",
+      "endothelial cell of periportal hepatic sinusoid\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of sinusoid\n",
+      "\t endothelial cell of hepatic sinusoid\n",
+      "endothelial cell of hepatic sinusoid\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of sinusoid\n",
+      "endothelial cell of pericentral hepatic sinusoid\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of sinusoid\n",
+      "\t endothelial cell of hepatic sinusoid\n",
+      "periportal region hepatocyte\n",
+      "\t cell\n",
+      "\t polyploid cell\n",
+      "\t epithelial cell\n",
+      "\t hepatocyte\n",
+      "\t endopolyploid cell\n",
+      "midzonal region hepatocyte\n",
+      "\t cell\n",
+      "\t polyploid cell\n",
+      "\t epithelial cell\n",
+      "\t hepatocyte\n",
+      "\t endopolyploid cell\n",
+      "centrilobular region hepatocyte\n",
+      "\t cell\n",
+      "\t polyploid cell\n",
+      "\t epithelial cell\n",
+      "\t hepatocyte\n",
+      "\t endopolyploid cell\n",
+      "intestine goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "kidney tubule cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "forebrain neuroblast\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t non-terminally differentiated cell\n",
+      "\t neuroblast (sensu Vertebrata)\n",
+      "\t precursor cell\n",
+      "\t neural cell\n",
+      "lung goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t lung secretory cell\n",
+      "\t respiratory goblet cell\n",
+      "lung neuroendocrine cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t sensory epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t afferent neuron\n",
+      "\t endocrine cell\n",
+      "\t neuroendocrine cell\n",
+      "\t electrically responsive cell\n",
+      "\t chemoreceptor cell\n",
+      "\t electrically active cell\n",
+      "\t serotonin secreting cell\n",
+      "\t biogenic amine secreting cell\n",
+      "\t electrically signaling cell\n",
+      "\t lung secretory cell\n",
+      "lung ciliated cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t respiratory ciliated cell\n",
+      "smooth muscle cell of small intestine\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t enteric smooth muscle cell\n",
+      "smooth muscle fiber of ileum\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "\t enteric smooth muscle cell\n",
+      "\t smooth muscle cell of small intestine\n",
+      "urethra cell\n",
+      "\t cell\n",
+      "\t lower urinary tract cell\n",
+      "fibroblast of connective tissue of prostate\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "fibroblast of connective tissue of nonglandular part of prostate\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "fibroblast of connective tissue of glandular part of prostate\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "epicardial adipocyte\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t adipocyte\n",
+      "\t stuff accumulating cell\n",
+      "\t cardiocyte\n",
+      "adipocyte of epicardial fat of left ventricle\n",
+      "\t cell\n",
+      "\t connective tissue cell\n",
+      "\t adipocyte\n",
+      "\t stuff accumulating cell\n",
+      "\t cardiocyte\n",
+      "\t epicardial adipocyte\n",
+      "bronchial goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t bronchial epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "\t respiratory goblet cell\n",
+      "\t tracheobronchial goblet cell\n",
+      "small intestine goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t small intestine glandular cell\n",
+      "\t intestine goblet cell\n",
+      "intestinal villus goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t small intestine glandular cell\n",
+      "\t intestine goblet cell\n",
+      "\t small intestine goblet cell\n",
+      "duodenum glandular cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t small intestine glandular cell\n",
+      "ileal goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t small intestine glandular cell\n",
+      "\t intestine goblet cell\n",
+      "\t small intestine goblet cell\n",
+      "\t intestinal villus goblet cell\n",
+      "tracheal goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t tracheal epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "\t respiratory goblet cell\n",
+      "\t tracheobronchial goblet cell\n",
+      "serous cell of epithelium of trachea\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "\t serous secreting cell\n",
+      "\t tracheobronchial serous cell\n",
+      "serous cell of epithelium of bronchus\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "\t serous secreting cell\n",
+      "\t tracheobronchial serous cell\n",
+      "enterocyte of epithelium of small intestine\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t enterocyte\n",
+      "\t gut absorptive cell\n",
+      "\t epithelial cell of small intestine\n",
+      "enterocyte of epithelium proper of small intestine\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t enterocyte\n",
+      "\t gut absorptive cell\n",
+      "\t epithelial cell of small intestine\n",
+      "\t enterocyte of epithelium of small intestine\n",
+      "enterocyte of epithelium proper of ileum\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t enterocyte\n",
+      "\t gut absorptive cell\n",
+      "\t epithelial cell of small intestine\n",
+      "\t enterocyte of epithelium of small intestine\n",
+      "\t enterocyte of epithelium proper of small intestine\n",
+      "paneth cell of epithelium of small intestine\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t endo-epithelial cell\n",
+      "\t lysozyme secreting cell\n",
+      "\t paneth cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
+      "\t small intestine glandular cell\n",
+      "enterocyte of colon\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t enterocyte\n",
+      "\t gut absorptive cell\n",
+      "\t enterocyte of epithelium of large intestine\n",
+      "\t epithelial cell of large intestine\n",
+      "\t colon epithelial cell\n",
+      "basal cell of epithelium of trachea\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t endo-epithelial cell\n",
+      "\t tracheal epithelial cell\n",
+      "\t basal cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t respiratory epithelial cell\n",
+      "\t respiratory basal cell\n",
+      "microfold cell of epithelium of small intestine\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t defensive cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t transporting cell\n",
+      "\t M cell of gut\n",
+      "\t epithelial cell of small intestine\n",
+      "ventricular cardiac muscle cell\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "myocyte of sinoatrial node\n",
+      "\t cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t visceral muscle cell\n",
+      "\t cardiac muscle cell\n",
+      "\t electrically active cell\n",
+      "\t striated muscle cell\n",
+      "\t cardiocyte\n",
+      "\t specialized cardiac myocyte\n",
+      "\t nodal myocyte\n",
+      "\t transversely striated visceral muscle cell\n",
+      "\t striated visceral muscle cell\n",
+      "endothelial cell of arteriole\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "conjunctival epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "epithelial cell of lacrimal drainage system\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "epithelial cell of lacrimal sac\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of lacrimal drainage system\n",
+      "\t epithelial cell of nasolacrimal duct\n",
+      "epithelial cell of nasolacrimal duct\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of lacrimal drainage system\n",
+      "bladder urothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t transitional epithelial cell\n",
+      "\t urothelial cell\n",
+      "\t bladder cell\n",
+      "\t lower urinary tract cell\n",
+      "ciliary muscle cell\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "epithelial cell of stratum germinativum of esophagus\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "\t basal cell\n",
+      "kidney glomerular epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney corpuscule cell\n",
+      "parietal epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of glomerular capsule\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney glomerular epithelial cell\n",
+      "\t kidney corpuscule cell\n",
+      "epithelial cell of intermediate tubule\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "kidney loop of Henle epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney collecting duct epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney collecting duct cell\n",
+      "kidney collecting duct cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "smooth muscle cell of prostate\n",
+      "\t cell\n",
+      "\t smooth muscle cell\n",
+      "\t contractile cell\n",
+      "\t muscle cell\n",
+      "\t electrically responsive cell\n",
+      "\t non-striated muscle cell\n",
+      "\t visceral muscle cell\n",
+      "\t electrically active cell\n",
+      "mesothelial cell of pleura\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t mesothelial cell\n",
+      "\t lining cell\n",
+      "mesothelial cell of visceral pleura\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t mesothelial cell\n",
+      "\t lining cell\n",
+      "\t epithelial cell of lung\n",
+      "\t mesothelial cell of pleura\n",
+      "kidney interstitial cell\n",
+      "\t cell\n",
+      "\t interstitial cell\n",
+      "\t kidney cell\n",
+      "kidney medulla cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "kidney pelvis cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "kidney inner medulla cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "\t kidney medulla cell\n",
+      "kidney outer medulla cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "\t kidney medulla cell\n",
+      "papillary tips cell\n",
+      "\t cell\n",
+      "\t kidney cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney pelvis cell\n",
+      "\t kidney inner medulla cell\n",
+      "lower urinary tract cell\n",
+      "\t cell\n",
+      "kidney corpuscule cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "kidney interstitial fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t interstitial cell\n",
+      "\t kidney cell\n",
+      "\t kidney interstitial cell\n",
+      "kidney interstitial alternatively activated macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t interstitial cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t elicited macrophage\n",
+      "\t alternatively activated macrophage\n",
+      "\t kidney cell\n",
+      "\t kidney interstitial cell\n",
+      "kidney resident macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t interstitial cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t kidney cell\n",
+      "\t kidney interstitial cell\n",
+      "kidney pelvis urothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t transitional epithelial cell\n",
+      "\t urothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t kidney pelvis cell\n",
+      "kidney collecting duct principal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal principal cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney collecting duct epithelial cell\n",
+      "\t kidney collecting duct cell\n",
+      "kidney collecting duct intercalated cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t non-branched duct epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t renal intercalated cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney collecting duct epithelial cell\n",
+      "\t kidney collecting duct cell\n",
+      "kidney connecting tubule epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney proximal convoluted tubule epithelial cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t epithelial cell of proximal tubule\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney proximal straight tubule epithelial cell\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t epithelial cell of proximal tubule\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney outer medulla cell\n",
+      "\t kidney loop of Henle descending limb epithelial cell\n",
+      "kidney loop of Henle descending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "kidney distal convoluted tubule epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "macula densa epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t juxtaglomerular complex cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney blood vessel cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "kidney arterial blood vessel cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "kidney capillary endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "kidney venous blood vessel cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "glomerular capillary endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t fenestrated cell\n",
+      "\t capillary endothelial cell\n",
+      "\t microvascular endothelial cell\n",
+      "\t glomerular endothelial cell\n",
+      "\t glomerular cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney glomerular epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney corpuscule cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney capillary endothelial cell\n",
+      "kidney afferent arteriole cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney medulla cell\n",
+      "\t kidney corpuscule cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney arterial blood vessel cell\n",
+      "kidney efferent arteriole cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney medulla cell\n",
+      "\t kidney corpuscule cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney arterial blood vessel cell\n",
+      "kidney loop of Henle ascending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "peritubular capillary endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t capillary endothelial cell\n",
+      "\t microvascular endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney capillary endothelial cell\n",
+      "vasa recta cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t vein endothelial cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney capillary endothelial cell\n",
+      "\t kidney venous blood vessel cell\n",
+      "kidney cortex artery cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney arterial blood vessel cell\n",
+      "kidney afferent arteriole endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t endothelial cell of artery\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t endothelial cell of arteriole\n",
+      "\t kidney medulla cell\n",
+      "\t kidney corpuscule cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney arterial blood vessel cell\n",
+      "\t kidney afferent arteriole cell\n",
+      "kidney efferent arteriole endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t endothelial cell of arteriole\n",
+      "\t kidney medulla cell\n",
+      "\t kidney corpuscule cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney arterial blood vessel cell\n",
+      "\t kidney efferent arteriole cell\n",
+      "kidney loop of Henle thick ascending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney loop of Henle ascending limb epithelial cell\n",
+      "kidney loop of Henle thin ascending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney inner medulla cell\n",
+      "\t kidney loop of Henle ascending limb epithelial cell\n",
+      "kidney loop of Henle medullary thick ascending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney outer medulla cell\n",
+      "\t kidney loop of Henle ascending limb epithelial cell\n",
+      "\t kidney loop of Henle thick ascending limb epithelial cell\n",
+      "kidney loop of Henle cortical thick ascending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t epithelial cell of distal tubule\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney loop of Henle ascending limb epithelial cell\n",
+      "\t kidney loop of Henle thick ascending limb epithelial cell\n",
+      "kidney loop of Henle thin descending limb epithelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t epithelial cell of intermediate tubule\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney outer medulla cell\n",
+      "\t kidney loop of Henle descending limb epithelial cell\n",
+      "vasa recta ascending limb cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t vein endothelial cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney capillary endothelial cell\n",
+      "\t kidney venous blood vessel cell\n",
+      "\t vasa recta cell\n",
+      "vasa recta descending limb cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t vein endothelial cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney blood vessel cell\n",
+      "\t kidney capillary endothelial cell\n",
+      "\t kidney venous blood vessel cell\n",
+      "\t vasa recta cell\n",
+      "urethra urothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t transitional epithelial cell\n",
+      "\t urothelial cell\n",
+      "\t epithelial cell of urethra\n",
+      "\t urethra cell\n",
+      "\t lower urinary tract cell\n",
+      "sensory neuron of dorsal root ganglion\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t neuronal receptor cell\n",
+      "\t sensory receptor cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t somatosensory neuron\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "medium spiny neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t GABAergic neuron\n",
+      "hypothalamus cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "glycinergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "lung endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t epithelial cell of lung\n",
+      "\t endothelial cell\n",
+      "pulmonary artery endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of artery\n",
+      "cerebral cortex pyramidal neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t multipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t pyramidal neuron\n",
+      "\t cerebral cortex neuron\n",
+      "cerebral cortex endothelial cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "peripheral blood mononuclear cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t circulating cell\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "tonsil germinal center B cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t lymphocyte of B lineage\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t B cell\n",
+      "\t mature B cell\n",
+      "\t B cell, CD19-positive\n",
+      "\t germinal center B cell\n",
+      "\t lymphocyte of B lineage, CD19-positive\n",
+      "dermis lymphatic vessel endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of lymphatic vessel\n",
+      "lung microvascular endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t epithelial cell of lung\n",
+      "\t endothelial cell\n",
+      "\t microvascular endothelial cell\n",
+      "\t lung endothelial cell\n",
+      "endothelial cell of coronary artery\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t cardiocyte\n",
+      "\t cardiac endothelial cell\n",
+      "\t endothelial cell of artery\n",
+      "\t cardiac blood vessel endothelial cell\n",
+      "dermis microvascular lymphatic vessel endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of lymphatic vessel\n",
+      "\t dermis lymphatic vessel endothelial cell\n",
+      "embryonic fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "brain pericyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t contractile cell\n",
+      "\t pericyte\n",
+      "\t supporting cell\n",
+      "\t perivascular cell\n",
+      "\t mural cell\n",
+      "\t central nervous system pericyte\n",
+      "brainstem motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "hepatic pit cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
+      "liver dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t dendritic cell\n",
+      "prostate gland microvascular endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t microvascular endothelial cell\n",
+      "placental villous trophoblast\n",
+      "\t cell\n",
+      "\t extraembryonic cell\n",
+      "\t trophoblast cell\n",
+      "conjunctiva goblet cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t glandular epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t goblet cell\n",
+      "\t mucus secreting cell\n",
+      "\t conjunctival epithelial cell\n",
+      "bronchus fibroblast of lung\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t fibroblast of lung\n",
+      "cord blood hematopoietic stem cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t somatic stem cell\n",
+      "\t hematopoietic stem cell\n",
+      "\t hematopoietic cell\n",
+      "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
+      "\t circulating cell\n",
+      "\t peripheral blood stem cell\n",
+      "\t gestational hematopoietic stem cell\n",
+      "Hofbauer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "vip GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "extratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "lamp5 GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "near-projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "corticothalamic-projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "sncg GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "sst GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "pvalb GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "sst chodl GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "\t sst GABAergic cortical interneuron\n",
+      "direct pathway medium spiny neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t GABAergic neuron\n",
+      "\t medium spiny neuron\n",
+      "indirect pathway medium spiny neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t GABAergic neuron\n",
+      "\t medium spiny neuron\n",
+      "chandelier pvalb GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "\t pvalb GABAergic cortical interneuron\n",
+      "\t chandelier cell\n",
+      "chandelier cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "L6b glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "L6 corticothalamic-projecting glutamatergic cortical neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t corticothalamic-projecting glutamatergic cortical neuron\n",
+      "L5/6 near-projecting glutamatergic neuron of the primary motor cortex\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t near-projecting glutamatergic cortical neuron\n",
+      "\t L5/6 near-projecting glutamatergic neuron\n",
+      "L5/6 near-projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t near-projecting glutamatergic cortical neuron\n",
+      "L2/3 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t L2/3 intratelencephalic projecting glutamatergic neuron\n",
+      "L2/3 intratelencephalic projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "L4/5 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t L4/5 intratelencephalic projecting glutamatergic neuron\n",
+      "L4/5 intratelencephalic projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t multipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t pyramidal neuron\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t primary motor cortex pyramidal cell\n",
+      "\t cerebral cortex pyramidal neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t L6 intratelencephalic projecting glutamatergic neuron\n",
+      "L6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "vascular leptomeningeal cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t mesothelial fibroblast of the leptomeninx\n",
+      "\t mesothelial fibroblast\n",
+      "mesothelial fibroblast of the leptomeninx\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t mesothelial fibroblast\n",
+      "mesothelial fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "vascular leptomeningeal cell (Mmus)\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t neural cell\n",
+      "\t connective tissue cell\n",
+      "\t vascular leptomeningeal cell\n",
+      "\t mesothelial fibroblast of the leptomeninx\n",
+      "\t mesothelial fibroblast\n",
+      "differentiation-committed oligodendrocyte precursor\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t neuron associated cell (sensu Vertebrata)\n",
+      "caudal ganglionic eminence derived interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "meis2 expressing cortical GABAergic cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "thalamic excitatory neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "caudal ganglionic eminence derived GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "\t caudal ganglionic eminence derived interneuron\n",
+      "brain vascular cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "unipolar brush cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t CNS interneuron\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "hypendymal cell\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "midget ganglion cell of retina\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "parasol ganglion cell of retina\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "alveolar type 1 fibroblast cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t interstitial cell\n",
+      "\t pulmonary interstitial fibroblast\n",
+      "\t fibroblast of lung\n",
+      "alveolar type 2 fibroblast cell\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t interstitial cell\n",
+      "\t pulmonary interstitial fibroblast\n",
+      "\t fibroblast of lung\n",
+      "epithelial cell of proximal tubule segment 1\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t epithelial cell of proximal tubule\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "epithelial cell of proximal tubule segment 3\n",
+      "\t cell\n",
+      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t epithelial cell of proximal tubule\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "kidney connecting tubule principal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t renal principal cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
+      "\t kidney connecting tubule epithelial cell\n",
+      "respiratory hillock cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "\t hillock cell\n",
+      "BEST4+ intestinal epithelial cell, human\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t gut absorptive cell\n",
+      "valve interstitial cell\n",
+      "\t cell\n",
+      "\t secretory cell\n",
+      "\t interstitial cell\n",
+      "\t cardiocyte\n",
+      "\t cardiac valve cell\n",
+      "valve endothelial cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t endothelial cell\n",
+      "\t cardiocyte\n",
+      "\t cardiac valve cell\n",
+      "\t cardiac endothelial cell\n",
+      "L4 intratelencephalic projecting glutamatergic neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t central nervous system neuron\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
+      "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "ureteric bud cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "serous secreting cell of bronchus submucosal gland\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t seromucus secreting cell\n",
+      "\t serous secreting cell\n",
+      "\t tracheobronchial serous cell\n",
+      "suprabasal keratinocyte\n",
+      "\t cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t stratified epithelial cell\n",
+      "\t stuff accumulating cell\n",
+      "\t keratinizing barrier epithelial cell\n",
+      "\t stratified squamous epithelial cell\n",
+      "\t keratin accumulating cell\n",
+      "\t keratinocyte\n",
+      "\t epidermal cell\n",
+      "\t general ecto-epithelial cell\n",
+      "retinal astrocyte\n",
+      "\t cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t glial cell\n",
+      "\t macroglial cell\n",
+      "\t astrocyte\n",
+      "\t retinal cell\n",
+      "ON-blue cone bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "airway submucosal gland duct basal cell\n",
+      "\t cell\n",
+      "\t stem cell\n",
+      "\t precursor cell\n",
+      "\t single fate stem cell\n",
+      "\t somatic stem cell\n",
+      "\t epithelial fate stem cell\n",
+      "\t basal cell\n",
+      "perichondrial fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "lung perichondrial fibroblast\n",
+      "\t cell\n",
+      "\t fibroblast\n",
+      "\t connective tissue cell\n",
+      "\t fibroblast of lung\n",
+      "\t perichondrial fibroblast\n",
+      "diffuse bipolar 1 cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "diffuse bipolar 2 cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "diffuse bipolar 3a cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "diffuse bipolar 3b cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "diffuse bipolar 4 cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "diffuse bipolar 6 cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "flat midget bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "invaginating midget bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "giant bipolar cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t ON-bipolar cell\n",
+      "OFFx cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t bipolar neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal cell\n",
+      "\t glutamatergic neuron\n",
+      "\t retinal bipolar neuron\n",
+      "\t visual system neuron\n",
+      "\t OFF-bipolar cell\n",
+      "lung resident memory CD4-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t CD4-positive, alpha-beta T cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD4-positive, alpha-beta memory T cell\n",
+      "lung resident memory CD8-positive, alpha-beta T cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t T cell\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t mature alpha-beta T cell\n",
+      "\t CD8-positive, alpha-beta T cell\n",
+      "\t alpha-beta T cell\n",
+      "\t mature T cell\n",
+      "\t memory T cell\n",
+      "\t CD8-positive, alpha-beta memory T cell\n",
+      "metallothionein-positive alveolar macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t alveolar macrophage\n",
+      "\t lung macrophage\n",
+      "lung interstitial macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t connective tissue cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "\t lung macrophage\n",
+      "deuterosomal cell\n",
+      "\t cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "respiratory suprabasal cell\n",
+      "\t cell\n",
+      "\t precursor cell\n",
+      "\t progenitor cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory epithelial cell\n",
+      "small bistratified retinal ganglion cell\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t neural cell\n",
+      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t retinal ganglion cell\n",
+      "\t bistratified retinal ganglion cell\n",
+      "\t bistratified cell\n",
+      "luminal hormone-sensing cell of mammary gland\n",
+      "\t cell\n",
+      "\t exocrine cell\n",
+      "\t secretory cell\n",
+      "\t epithelial cell\n",
+      "\t duct epithelial cell\n",
+      "\t branched duct epithelial cell\n",
+      "\t paracrine cell\n",
+      "\t mammary gland epithelial cell\n",
+      "\t luminal epithelial cell of mammary gland\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_print = 2000\n",
+    "\n",
+    "i_print = 0\n",
+    "for k, v in cell_ontology_resource[\"ancestors_dictionary\"].items():\n",
+    "    print(cell_ontology_resource[\"ontology_term_id_to_label\"][k])\n",
+    "    for v_ in v:\n",
+    "        print(\"\\t\", cell_ontology_resource[\"ontology_term_id_to_label\"][v_])\n",
+    "    i_print += 1\n",
+    "    if i_print == n_print:\n",
+    "        break"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -267,7 +9809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +9825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -302,7 +9844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -375,7 +9917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +9947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -433,7 +9975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1704,7 +11246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1720,7 +11262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +11281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1805,7 +11347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1833,7 +11375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1859,7 +11401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -5354,7 +14896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5370,7 +14912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -5389,7 +14931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -5423,7 +14965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -5469,7 +15011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -5499,20 +15041,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Generating ontology benchmarking resource from UBERON ontology...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "INFO:root:Generating ontology benchmarking resource from UBERON ontology...\n",
       "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/uberon_benchmarking_resource.pkl\n"
      ]
     }
@@ -5533,7 +15069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -24472,6 +34008,88 @@
     "    if i_print == n_print:\n",
     "        break"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sex ontology\n",
+    "\n",
+    "- PATO:0000384 = male\n",
+    "- PATO:0000383 = female"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sex_propagation_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"sex_propagation_resource.pkl\")\n",
+    "sex_benchmarking_resource_file_path = os.path.join(ONTOLOGY_ROOT_PATH, \"sex_benchmarking_resource.pkl\")\n",
+    "\n",
+    "n_hops = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sex_ontology_resource = {\n",
+    "    \"ancestors_dictionary\": {\n",
+    "        \"PATO:0000384\": [],\n",
+    "        \"PATO:0000383\": [],\n",
+    "    },\n",
+    "    \"ontology_term_id_to_label\": {\n",
+    "        \"PATO:0000384\": \"male\",\n",
+    "        \"PATO:0000383\": \"female\",\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology_resource_dict = dict()\n",
+    "\n",
+    "for node_name in {\"PATO:0000384\", \"PATO:0000383\"}:\n",
+    " ontology_resource_dict |= {\n",
+    "    node_name: {\n",
+    "        'all_ancestors': {node_name},\n",
+    "        'all_descendants': {node_name},\n",
+    "        'hop_0': {\n",
+    "            'nodes': {node_name},\n",
+    "            'all_ancestors': {node_name},\n",
+    "            'all_descendants': {node_name},\n",
+    "        },\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(sex_propagation_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(sex_ontology_resource, output_file)\n",
+    "\n",
+    "with open(sex_benchmarking_resource_file_path, \"wb\") as output_file:\n",
+    "    pickle.dump(ontology_resource_dict, output_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
new file mode 100644
index 00000000..c3f037e0
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate metadata predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "cuda_device_index = 0\n",
+    "val_adata_index = 1\n",
+    "\n",
+    "checkpoint_path = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ref_adata_path = \"/home/mehrtash/data/data/extract_0.h5ad\"\n",
+    "gene_info_path = \"/home/mehrtash/data/gene_info/gene_info.tsv\"\n",
+    "adata_path = f\"/home/mehrtash/data/data/cellariumgpt_validation/extract_{val_adata_index}.h5ad\"\n",
+    "ontology_resource_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/ontology\"\n",
+    "output_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last\"\n",
+    "\n",
+    "rng_seed = 42\n",
+    "n_cells = 1000\n",
+    "n_genes = 1_000\n",
+    "chunk_size = 16\n",
+    "\n",
+    "os.makedirs(output_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load ontology resources\n",
+    "logger.info(\"Loading ontology resources ...\")\n",
+    "\n",
+    "ontology_benchmarking_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_benchmarking_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_benchmarking_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_benchmarking_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_benchmarking_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_benchmarking_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "ontology_propagation_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_propagation_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_propagation_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_propagation_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_propagation_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_propagation_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "n_hops_dict = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "ontology_benchmarking_resource_dicts = {}\n",
+    "for meta_key, path in ontology_benchmarking_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_benchmarking_resource_dicts[meta_key] = pickle.load(f)\n",
+    "\n",
+    "ontology_propagation_resource_dicts = {}\n",
+    "for meta_key, path in ontology_propagation_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_propagation_resource_dicts[meta_key] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.info(f\"Loading the model checkpoint from {checkpoint_path} ...\")\n",
+    "\n",
+    "device = torch.device(f\"cuda:{cuda_device_index}\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "    ref_adata_path=ref_adata_path,\n",
+    "    gene_info_tsv_path=gene_info_path,\n",
+    "    device=device,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.info(f\"Loading the validation AnnData object from {adata_path} ...\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if n_genes is not None:\n",
+    "    logger.info(f\"Using {n_genes} highly expressed genes.\")\n",
+    "    X_g = np.asarray(adata.X.sum(0)).flatten()\n",
+    "    highly_expressed_gene_indices = np.argsort(X_g)[-n_genes:]\n",
+    "    highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]\n",
+    "    adata = adata[:, highly_expressed_gene_ids]\n",
+    "else:\n",
+    "    logger.info(f\"Using all genes.\")\n",
+    "\n",
+    "if n_cells is None:\n",
+    "    logger.info(f\"Using all cells.\")\n",
+    "else:\n",
+    "    n_cells = min(n_cells, len(adata))\n",
+    "    logger.info(f\"Using {n_cells} random cells (seed = {rng_seed}).\")\n",
+    "    rng = np.random.RandomState(rng_seed)\n",
+    "    adata = adata[rng.choice(len(adata), n_cells, replace=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.info(f\"Predicting metadata for {len(adata)} cells ...\")\n",
+    "preds = ctx.predict_metadata_chunked(adata, chunk_size=chunk_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def propagate_probs_over_ontology(\n",
+    "        class_probs_nk: np.ndarray,\n",
+    "        class_names_k: t.List[str],\n",
+    "        ontology_resource_dict: dict[str, t.Any],\n",
+    ") -> t.Tuple[t.List[str], np.ndarray]:\n",
+    "    assert len(class_names_k) == class_probs_nk.shape[1]\n",
+    "    all_class_names_q = sorted(ontology_resource_dict.keys())\n",
+    "    propagated_class_probs_nq = np.zeros((class_probs_nk.shape[0], len(all_class_names_q)))\n",
+    "\n",
+    "    given_class_name_to_idx = {class_name: idx for idx, class_name in enumerate(class_names_k)}    \n",
+    "    for q, class_name in enumerate(all_class_names_q):\n",
+    "        for descendant_name in ontology_resource_dict[class_name][\"all_descendants\"]:\n",
+    "            if descendant_name in given_class_name_to_idx:\n",
+    "                propagated_class_probs_nq[:, q] += class_probs_nk[:, given_class_name_to_idx[descendant_name]]\n",
+    "    return all_class_names_q, propagated_class_probs_nq\n",
+    "\n",
+    "def convert_meta_adata_to_query_obj_for_scoring(\n",
+    "        meta_adata: sc.AnnData,\n",
+    "        metadata_key: str):\n",
+    "    assert metadata_key + \"_propagated_class_probs\" in meta_adata.obsm\n",
+    "    assert metadata_key + \"_propagated_ontology_term_ids\" in meta_adata.uns\n",
+    "    query_objs = []\n",
+    "    ground_truth_ontology_term_ids = []\n",
+    "    obs_index = meta_adata.obs.index.values\n",
+    "    for i_cell in range(len(meta_adata)):\n",
+    "        obs_row = meta_adata.obs.iloc[i_cell]\n",
+    "        ground_truth_ontology_term_id = obs_row[metadata_key + \"_ontology_term_id\"]\n",
+    "        query_obj = dict()\n",
+    "        query_obj[\"query_cell_id\"] = obs_index[i_cell]\n",
+    "        query_obj[\"matches\"] = []\n",
+    "        for ontology_term_id, score in zip(\n",
+    "                meta_adata.uns[metadata_key + \"_propagated_ontology_term_ids\"],\n",
+    "                meta_adata.obsm[metadata_key + \"_propagated_class_probs\"][i_cell]):\n",
+    "            query_obj[\"matches\"].append({\n",
+    "                \"ontology_term_id\": ontology_term_id,\n",
+    "                \"score\": score,\n",
+    "            })\n",
+    "        query_objs.append(query_obj)\n",
+    "        ground_truth_ontology_term_ids.append(ground_truth_ontology_term_id)\n",
+    "    return query_objs, ground_truth_ontology_term_ids\n",
+    "\n",
+    "logger.info(\"Inserting predictions into an AnnData object ...\")\n",
+    "# put the predictions back into an AnnData object\n",
+    "meta_adata = sc.AnnData(obs=adata.obs.copy())\n",
+    "\n",
+    "for meta_key, meta_preds in preds.items():\n",
+    "    meta_adata.obsm[meta_key + \"_class_probs\"] = meta_preds\n",
+    "    meta_adata.uns[meta_key + \"_ontology_term_ids\"] = ctx.metadata_ontology_infos[meta_key][\"names\"]\n",
+    "    meta_adata.uns[meta_key + \"_labels\"] = ctx.metadata_ontology_infos[meta_key][\"labels\"]\n",
+    "\n",
+    "# propagate predictions to make them ontologically consistent\n",
+    "for meta_key, ontology_resource_dict in ontology_benchmarking_resource_dicts.items():\n",
+    "    class_probs_nk = meta_adata.obsm[meta_key + \"_class_probs\"]\n",
+    "    all_class_names_q, propagated_class_probs_nq = propagate_probs_over_ontology(\n",
+    "        class_probs_nk=class_probs_nk,\n",
+    "        class_names_k=meta_adata.uns[meta_key + \"_ontology_term_ids\"],\n",
+    "        ontology_resource_dict=ontology_resource_dict,\n",
+    "    )\n",
+    "    meta_adata.obsm[meta_key + \"_propagated_class_probs\"] = propagated_class_probs_nq\n",
+    "    meta_adata.uns[meta_key + \"_propagated_ontology_term_ids\"] = all_class_names_q\n",
+    "    meta_adata.uns[meta_key + \"_propagated_labels\"] = list(\n",
+    "        map(ontology_propagation_resource_dicts[meta_key]['ontology_term_id_to_label'].get, all_class_names_q))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "meta_keys = ontology_benchmarking_resource_dicts.keys()\n",
+    "\n",
+    "results_dfs = []\n",
+    "for meta_key in meta_keys:\n",
+    "    logger.info(f\"Calculating performance metrics for {meta_key} ...\")\n",
+    "    query_objs, ground_truth_ontology_term_ids = convert_meta_adata_to_query_obj_for_scoring(\n",
+    "        meta_adata=meta_adata,\n",
+    "        metadata_key=meta_key)\n",
+    "    results_df = calculate_metrics_for_prediction_output(\n",
+    "        model_predictions=query_objs,\n",
+    "        ground_truth_ontology_term_ids=ground_truth_ontology_term_ids,\n",
+    "        ontology_resource=ontology_benchmarking_resource_dicts[meta_key],\n",
+    "        num_hops=n_hops_dict[meta_key])\n",
+    "    results_df.columns = [f\"{meta_key}_{col}\" if col != \"query_cell_id\" else col for col in results_df.columns]\n",
+    "    results_dfs.append(results_df)\n",
+    "\n",
+    "# merge results dataframes\n",
+    "final_results_df = pd.concat(results_dfs, axis=1)\n",
+    "meta_adata.obs.index.name = \"query_cell_id\"\n",
+    "meta_adata.obs = pd.concat([meta_adata.obs, final_results_df], axis=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.info(f\"Saving the results to {output_path} ...\")\n",
+    "meta_adata_output_file_path = os.path.join(output_path, f\"extract_{val_adata_index}_metadata_prediction_scores.h5ad\")\n",
+    "meta_adata.write_h5ad(meta_adata_output_file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger.info(\"Done!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_cell_type_ontology.ipynb
similarity index 100%
rename from notebooks/cellariumgpt_inference/metadata_benchmarking/xx_cell_type_ontology.ipynb
rename to notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_cell_type_ontology.ipynb
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_development_stage_ontology.ipynb
similarity index 100%
rename from notebooks/cellariumgpt_inference/metadata_benchmarking/xx_development_stage_ontology.ipynb
rename to notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_development_stage_ontology.ipynb
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_disease_ontology.ipynb
similarity index 100%
rename from notebooks/cellariumgpt_inference/metadata_benchmarking/xx_disease_ontology.ipynb
rename to notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_disease_ontology.ipynb
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_sex_ontology.ipynb
similarity index 100%
rename from notebooks/cellariumgpt_inference/metadata_benchmarking/xx_sex_ontology.ipynb
rename to notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_sex_ontology.ipynb
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_tissue_ontology.ipynb
similarity index 100%
rename from notebooks/cellariumgpt_inference/metadata_benchmarking/xx_tissue_ontology.ipynb
rename to notebooks/cellariumgpt_inference/metadata_benchmarking/yerdos/xx_tissue_ontology.ipynb
diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
new file mode 100644
index 00000000..33845bb4
--- /dev/null
+++ b/scripts/metadata_prediction_analysis.py
@@ -0,0 +1,257 @@
+#!/usr/bin/env python3
+import os
+import torch
+import warnings
+import numpy as np
+import scanpy as sc
+import pandas as pd
+import matplotlib.pyplot as plt
+import typing as t
+import pickle
+import logging
+import argparse
+
+# Set up logging
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+handler = logging.StreamHandler()
+formatter = logging.Formatter(
+    '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+# Suppress a specific warning from AnnData
+warnings.filterwarnings("ignore", category=UserWarning, message="Transforming to str index.")
+
+# Import required modules from the cellarium package
+from cellarium.ml.utilities.inference.cellarium_gpt_inference import (
+    CellariumGPTInferenceContext
+)
+from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import (
+    calculate_metrics_for_prediction_output
+)
+
+def propagate_probs_over_ontology(
+        class_probs_nk: np.ndarray,
+        class_names_k: t.List[str],
+        ontology_resource_dict: dict[str, t.Any],
+) -> t.Tuple[t.List[str], np.ndarray]:
+    assert len(class_names_k) == class_probs_nk.shape[1]
+    all_class_names_q = sorted(ontology_resource_dict.keys())
+    propagated_class_probs_nq = np.zeros((class_probs_nk.shape[0], len(all_class_names_q)))
+
+    given_class_name_to_idx = {class_name: idx for idx, class_name in enumerate(class_names_k)}    
+    for q, class_name in enumerate(all_class_names_q):
+        for descendant_name in ontology_resource_dict[class_name]["all_descendants"]:
+            if descendant_name in given_class_name_to_idx:
+                propagated_class_probs_nq[:, q] += class_probs_nk[:, given_class_name_to_idx[descendant_name]]
+    return all_class_names_q, propagated_class_probs_nq
+
+def convert_meta_adata_to_query_obj_for_scoring(
+        meta_adata: sc.AnnData,
+        metadata_key: str):
+    assert metadata_key + "_propagated_class_probs" in meta_adata.obsm
+    assert metadata_key + "_propagated_ontology_term_ids" in meta_adata.uns
+    query_objs = []
+    ground_truth_ontology_term_ids = []
+    obs_index = meta_adata.obs.index.values
+    for i_cell in range(len(meta_adata)):
+        obs_row = meta_adata.obs.iloc[i_cell]
+        ground_truth_ontology_term_id = obs_row[metadata_key + "_ontology_term_id"]
+        query_obj = dict()
+        query_obj["query_cell_id"] = obs_index[i_cell]
+        query_obj["matches"] = []
+        for ontology_term_id, score in zip(
+                meta_adata.uns[metadata_key + "_propagated_ontology_term_ids"],
+                meta_adata.obsm[metadata_key + "_propagated_class_probs"][i_cell]):
+            query_obj["matches"].append({
+                "ontology_term_id": ontology_term_id,
+                "score": score,
+            })
+        query_objs.append(query_obj)
+        ground_truth_ontology_term_ids.append(ground_truth_ontology_term_id)
+    return query_objs, ground_truth_ontology_term_ids
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Cellarium GPT Metadata Prediction CLI Tool"
+    )
+    parser.add_argument(
+        "--cuda_device_index", type=int, default=0,
+        help="CUDA device index to use (default: 0)"
+    )
+    parser.add_argument(
+        "--val_adata_index", type=int, default=1,
+        help="Index to choose the validation AnnData file (default: 1)"
+    )
+    parser.add_argument(
+        "--checkpoint_path", type=str,
+        default="/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt",
+        help="Path to the model checkpoint"
+    )
+    parser.add_argument(
+        "--ref_adata_path", type=str,
+        default="/home/mehrtash/data/data/extract_0.h5ad",
+        help="Path to the reference AnnData file"
+    )
+    parser.add_argument(
+        "--gene_info_path", type=str,
+        default="/home/mehrtash/data/gene_info/gene_info.tsv",
+        help="Path to the gene info TSV file"
+    )
+    parser.add_argument(
+        "--ontology_resource_path", type=str,
+        default="/home/mehrtash/data/data/cellariumgpt_artifacts/ontology",
+        help="Path to the ontology resources"
+    )
+    parser.add_argument(
+        "--output_path", type=str,
+        default="/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last",
+        help="Directory where output results will be saved"
+    )
+    parser.add_argument(
+        "--rng_seed", type=int, default=42,
+        help="Random number generator seed (default: 42)"
+    )
+    parser.add_argument(
+        "--n_cells", type=int, default=1000,
+        help="Number of cells to use (default: 1000)"
+    )
+    parser.add_argument(
+        "--n_genes", type=int, default=10000,
+        help="Number of highly expressed genes to use (default: 10000). Set to None to use all."
+    )
+    parser.add_argument(
+        "--chunk_size", type=int, default=16,
+        help="Chunk size for processing (default: 16)"
+    )
+
+    args = parser.parse_args()
+
+    # Compute the validation AnnData file path using the provided index
+    adata_path = f"/home/mehrtash/data/data/cellariumgpt_validation/extract_{args.val_adata_index}.h5ad"
+
+    os.makedirs(args.output_path, exist_ok=True)
+
+    logger.info("Loading ontology resources ...")
+    ontology_benchmarking_resource_path_dict = {
+        'cell_type': os.path.join(args.ontology_resource_path, 'cl_benchmarking_resource.pkl'),
+        'development_stage': os.path.join(args.ontology_resource_path, 'hsapdv_benchmarking_resource.pkl'),
+        'disease': os.path.join(args.ontology_resource_path, 'mondo_benchmarking_resource.pkl'),
+        'tissue': os.path.join(args.ontology_resource_path, 'uberon_benchmarking_resource.pkl'),
+        'sex': os.path.join(args.ontology_resource_path, 'sex_benchmarking_resource.pkl'),
+    }
+
+    ontology_propagation_resource_path_dict = {
+        'cell_type': os.path.join(args.ontology_resource_path, 'cl_propagation_resource.pkl'),
+        'development_stage': os.path.join(args.ontology_resource_path, 'hsapdv_propagation_resource.pkl'),
+        'disease': os.path.join(args.ontology_resource_path, 'mondo_propagation_resource.pkl'),
+        'tissue': os.path.join(args.ontology_resource_path, 'uberon_propagation_resource.pkl'),
+        'sex': os.path.join(args.ontology_resource_path, 'sex_propagation_resource.pkl'),
+    }
+
+    # Define number of hops for each metadata key
+    n_hops_dict = {
+        'cell_type': 3,
+        'development_stage': 3,
+        'disease': 3,
+        'tissue': 3,
+        'sex': 0,
+    }
+
+    # Load the benchmarking ontology resources
+    ontology_benchmarking_resource_dicts = {}
+    for meta_key, path in ontology_benchmarking_resource_path_dict.items():
+        with open(path, "rb") as f:
+            ontology_benchmarking_resource_dicts[meta_key] = pickle.load(f)
+
+    # Load the propagation ontology resources into a separate dictionary
+    ontology_propagation_resource_dicts = {}
+    for meta_key, path in ontology_propagation_resource_path_dict.items():
+        with open(path, "rb") as f:
+            ontology_propagation_resource_dicts[meta_key] = pickle.load(f)
+
+    logger.info(f"Loading the model checkpoint from {args.checkpoint_path} ...")
+    device = torch.device(f"cuda:{args.cuda_device_index}")
+
+    ctx = CellariumGPTInferenceContext(
+        cellarium_gpt_ckpt_path=args.checkpoint_path,
+        ref_adata_path=args.ref_adata_path,
+        gene_info_tsv_path=args.gene_info_path,
+        device=device,
+        attention_backend="mem_efficient",
+        verbose=False
+    )
+    logger.info(f"Loading the validation AnnData object from {adata_path} ...")
+    adata = sc.read_h5ad(adata_path)
+    if args.n_genes is not None:
+        logger.info(f"Using {args.n_genes} highly expressed genes.")
+        X_g = np.asarray(adata.X.sum(0)).flatten()
+        highly_expressed_gene_indices = np.argsort(X_g)[-args.n_genes:]
+        highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]
+        adata = adata[:, highly_expressed_gene_ids]
+    else:
+        logger.info("Using all genes.")
+
+    if args.n_cells is None:
+        logger.info("Using all cells.")
+    else:
+        n_cells = min(args.n_cells, len(adata))
+        logger.info(f"Using {n_cells} random cells (seed = {args.rng_seed}).")
+        rng = np.random.RandomState(args.rng_seed)
+        adata = adata[rng.choice(len(adata), n_cells, replace=False)]
+    logger.info(f"Predicting metadata for {len(adata)} cells ...")
+    preds = ctx.predict_metadata_chunked(adata, chunk_size=args.chunk_size)
+
+    logger.info("Inserting predictions into an AnnData object ...")
+    meta_adata = sc.AnnData(obs=adata.obs.copy())
+
+    for meta_key, meta_preds in preds.items():
+        meta_adata.obsm[meta_key + "_class_probs"] = meta_preds
+        meta_adata.uns[meta_key + "_ontology_term_ids"] = ctx.metadata_ontology_infos[meta_key]["names"]
+        meta_adata.uns[meta_key + "_labels"] = ctx.metadata_ontology_infos[meta_key]["labels"]
+
+    for meta_key, ontology_resource_dict in ontology_benchmarking_resource_dicts.items():
+        class_probs_nk = meta_adata.obsm[meta_key + "_class_probs"]
+        all_class_names_q, propagated_class_probs_nq = propagate_probs_over_ontology(
+            class_probs_nk=class_probs_nk,
+            class_names_k=meta_adata.uns[meta_key + "_ontology_term_ids"],
+            ontology_resource_dict=ontology_resource_dict,
+        )
+        meta_adata.obsm[meta_key + "_propagated_class_probs"] = propagated_class_probs_nq
+        meta_adata.uns[meta_key + "_propagated_ontology_term_ids"] = all_class_names_q
+        meta_adata.uns[meta_key + "_propagated_labels"] = list(
+            map(ontology_propagation_resource_dicts[meta_key]['ontology_term_id_to_label'].get, all_class_names_q)
+        )
+    meta_keys = ontology_benchmarking_resource_dicts.keys()
+
+    results_dfs = []
+    for meta_key in meta_keys:
+        logger.info(f"Calculating performance metrics for {meta_key} ...")
+        query_objs, ground_truth_ontology_term_ids = convert_meta_adata_to_query_obj_for_scoring(
+            meta_adata=meta_adata,
+            metadata_key=meta_key)
+        results_df = calculate_metrics_for_prediction_output(
+            model_predictions=query_objs,
+            ground_truth_ontology_term_ids=ground_truth_ontology_term_ids,
+            ontology_resource=ontology_benchmarking_resource_dicts[meta_key],
+            num_hops=n_hops_dict[meta_key])
+        results_df.columns = [
+            f"{meta_key}_{col}" if col != "query_cell_id" else col 
+            for col in results_df.columns
+        ]
+        results_dfs.append(results_df)
+
+    final_results_df = pd.concat(results_dfs, axis=1)
+    meta_adata.obs.index.name = "query_cell_id"
+    meta_adata.obs = pd.concat([meta_adata.obs, final_results_df], axis=1)
+    meta_adata_output_file_path = os.path.join(
+        args.output_path, f"extract_{args.val_adata_index}_metadata_prediction_scores.h5ad"
+    )
+    logger.info(f"Saving the results to {meta_adata_output_file_path} ...")
+    meta_adata.write_h5ad(meta_adata_output_file_path)
+    logger.info("Done!")
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/run_metadata_prediction_analysis.sh b/scripts/run_metadata_prediction_analysis.sh
new file mode 100644
index 00000000..30f065ab
--- /dev/null
+++ b/scripts/run_metadata_prediction_analysis.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+# run_metadata_prediction_analysis.sh
+
+# Usage check: GPU_ID is required as the first argument.
+if [ -z "$1" ]; then
+    echo "Usage: $0 <GPU_ID>"
+    exit 1
+fi
+
+GPU_ID=$1
+
+# ----------------------------
+# Default parameters (CLI args)
+# ----------------------------
+CHECKPOINT_PATH="/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt"
+REF_ADATA_PATH="/home/mehrtash/data/data/extract_0.h5ad"
+GENE_INFO_PATH="/home/mehrtash/data/gene_info/gene_info.tsv"
+ONTOLOGY_RESOURCE_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/ontology"
+OUTPUT_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last"
+RNG_SEED=42
+N_CELLS=1000
+N_GENES=10000
+CHUNK_SIZE=16
+
+# ----------------------------
+# Distribution of val_adata_index values
+# ----------------------------
+TOTAL_INDICES=110
+NUM_GPUS=8
+
+# Calculate the start and end indices for the given GPU.
+# Indices range from 1 to TOTAL_INDICES (inclusive).
+START_INDEX=$(python3 -c "import math; print(int(math.floor(${GPU_ID} * ${TOTAL_INDICES} / ${NUM_GPUS}))+1)")
+END_INDEX=$(python3 -c "import math; print(int(math.floor((${GPU_ID}+1) * ${TOTAL_INDICES} / ${NUM_GPUS})))")
+
+echo "GPU ${GPU_ID} processing val_adata_index from ${START_INDEX} to ${END_INDEX}"
+
+# ----------------------------
+# Loop over the assigned indices and run the Python CLI tool
+# ----------------------------
+for i in $(seq ${START_INDEX} ${END_INDEX}); do
+    echo "Running metadata prediction for val_adata_index = ${i} on GPU ${GPU_ID}"
+    python3 metadata_prediction_analysis.py \
+        --cuda_device_index ${GPU_ID} \
+        --val_adata_index ${i} \
+        --checkpoint_path "${CHECKPOINT_PATH}" \
+        --ref_adata_path "${REF_ADATA_PATH}" \
+        --gene_info_path "${GENE_INFO_PATH}" \
+        --ontology_resource_path "${ONTOLOGY_RESOURCE_PATH}" \
+        --output_path "${OUTPUT_PATH}" \
+        --rng_seed ${RNG_SEED} \
+        --n_cells ${N_CELLS} \
+        --n_genes ${N_GENES} \
+        --chunk_size ${CHUNK_SIZE}
+done

From bf74d04c9cd3ee2c4005a5f663b484eb12778a95 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Mon, 24 Feb 2025 22:28:49 +0000
Subject: [PATCH 46/64] updated metadata prediction to support random prompt
 genes

---
 .../inference/cellarium_gpt_inference.py      |  114 +-
 .../02_generate_metadata_predictions.ipynb    |   31 +-
 .../03_analyze_metadata_predictions.ipynb     | 1115 +++++++++++++++++
 scripts/metadata_prediction_analysis.py       |   43 +-
 scripts/run_metadata_prediction_analysis.sh   |    4 +-
 5 files changed, 1261 insertions(+), 46 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index db3034ae..1bcb7378 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -172,6 +172,8 @@ def generate_tokens_from_adata(
             adata: sc.AnnData,
             obs_index: int | list[int] | None,
             query_var_names: list[str],
+            n_rand_prompt_vars: int | None = None,
+            rng: torch.Generator | None = None,
             query_total_mrna_umis: float | None = None,
             metadata_prompt_masks_dict: dict[str, bool] | None = None,
         ) -> tuple[dict, dict]:
@@ -196,34 +198,75 @@ def generate_tokens_from_adata(
         adata_var_names = adata.var_names
         assert all([var_name in self.var_name_to_index_map for var_name in adata_var_names])
         assert all([var_name in self.var_name_to_index_map for var_name in query_var_names])
-        prompt_var_index = [self.var_name_to_index_map[var_name] for var_name in adata_var_names]
         query_var_index = [self.var_name_to_index_map[var_name] for var_name in query_var_names]
-        n_prompt_vars = len(prompt_var_index)
-        n_query_vars = len(query_var_index)
-        n_total_vars = n_prompt_vars + n_query_vars
 
         # cpu device for intermediate
         cpu_device = torch.device("cpu")
-        
-        # gene id
-        gene_ids_nc = torch.tensor(
-            prompt_var_index + query_var_index,
-            dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
-        
-        # gene prompt mask
-        gene_prompt_mask_nc = torch.tensor(
-            [1] * n_prompt_vars + [0] * n_query_vars,
-            dtype=torch.bool, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
-        
-        # gene value
-        try:
-            prompt_X_ng = np.asarray(adata.X.todense())
-        except AttributeError:
-            prompt_X_ng = adata.X
-        prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=cpu_device)
-        query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=cpu_device)
-        gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)
 
+        if n_rand_prompt_vars is None:  # using all genes in the AnnData as prompt
+
+            prompt_var_index = [self.var_name_to_index_map[var_name] for var_name in adata_var_names]
+            n_prompt_vars = len(prompt_var_index)
+            n_query_vars = len(query_var_index)
+            n_total_vars = n_prompt_vars + n_query_vars
+
+            # gene id
+            gene_ids_nc = torch.tensor(
+                prompt_var_index + query_var_index,
+                dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
+            
+            # gene prompt mask
+            gene_prompt_mask_nc = torch.tensor(
+                [1] * n_prompt_vars + [0] * n_query_vars,
+                dtype=torch.bool, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
+            
+            # gene value
+            try:
+                prompt_X_ng = np.asarray(adata.X.todense())
+            except AttributeError:
+                prompt_X_ng = adata.X
+            prompt_gene_value_nc = torch.tensor(prompt_X_ng, dtype=torch.float32, device=cpu_device)
+            query_gene_value_nc = torch.zeros(n_cells, n_query_vars, dtype=torch.float32, device=cpu_device)
+            gene_value_nc = torch.cat([prompt_gene_value_nc, query_gene_value_nc], dim=1)
+
+        else:
+
+            assert 0 <= n_rand_prompt_vars <= adata.shape[1]
+            assert rng is not None
+
+            n_prompt_vars = n_rand_prompt_vars
+            n_query_vars = len(query_var_index)
+            n_total_vars = n_prompt_vars + n_query_vars
+
+            # gene id
+            adata_var_index = torch.tensor(
+                [self.var_name_to_index_map[var_name] for var_name in adata_var_names],
+                dtype=torch.int64, device=cpu_device)
+            rand_prompt_adata_indices_np = torch.argsort(
+                torch.rand((n_cells, n_prompt_vars), generator=rng, device=cpu_device), dim=-1)
+            rand_prompt_vars_np = adata_var_index[rand_prompt_adata_indices_np]
+            query_vars_nq = torch.tensor(
+                query_var_index, dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_query_vars)
+            gene_ids_nc = torch.cat([rand_prompt_vars_np, query_vars_nq], dim=1)
+
+            # gene value
+            try:
+                _prompt_X_ng = np.asarray(adata.X.todense())
+            except AttributeError:
+                _prompt_X_ng = adata.X
+            prompt_X_ng = torch.tensor(_prompt_X_ng, dtype=torch.float32, device=cpu_device)
+            prompt_gene_value_np = prompt_X_ng[
+                torch.arange(n_cells, device=cpu_device)[:, None],
+                rand_prompt_adata_indices_np]
+            query_gene_value_nq = torch.zeros(
+                n_cells, n_query_vars, dtype=torch.float32, device=cpu_device)            
+            gene_value_nc = torch.cat([prompt_gene_value_np, query_gene_value_nq], dim=1)
+
+            # gene prompt mask
+            gene_prompt_mask_nc = torch.tensor(
+                [1] * n_prompt_vars + [0] * n_query_vars,
+                dtype=torch.bool, device=cpu_device)[None, :].expand(n_cells, n_total_vars)
+            
         # total mrna umis
         prompt_total_mrna_umis_nc = torch.tensor(
             adata.obs["total_mrna_umis"].values,
@@ -828,14 +871,23 @@ def generate_gene_dose_response_for_metadata(
         }
     
 
-    def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) -> dict[str, np.ndarray]:
+    def predict_metadata_chunked(
+            self,
+            adata: sc.AnnData,
+            chunk_size: int = 128,
+            n_rand_prompt_vars: int | None = None,
+            rng: torch.Generator | None = None
+        ) -> dict[str, np.ndarray]:
         metadata_prediction_chunks_dict = []
         for i in tqdm(range(0, len(adata), chunk_size)):
             first = i
             last = min(len(adata), i + chunk_size)
             if last == first:
                 continue
-            metadata_prediction_dict = self.predict_metadata(adata[first:last])
+            metadata_prediction_dict = self.predict_metadata(
+                adata=adata[first:last],
+                n_rand_prompt_vars=n_rand_prompt_vars,
+                rng=rng)
             metadata_prediction_chunks_dict.append(metadata_prediction_dict)
         metadata_prediction_dict = dict()
         for key in self.metadata_ontology_infos.keys():
@@ -844,12 +896,19 @@ def predict_metadata_chunked(self, adata: sc.AnnData, chunk_size: int = 128) ->
         return metadata_prediction_dict
 
 
-    def predict_metadata(self, adata: sc.AnnData) -> dict[str, np.ndarray]:
+    def predict_metadata(
+            self,
+            adata: sc.AnnData,
+            n_rand_prompt_vars: int | None = None,
+            rng: torch.Generator | None = None,
+        ) -> dict[str, np.ndarray]:
         # tokenize the given anndata: no query genes, just metadata (which will be included as query by default)
         tokens_dict, context_indices = self.generate_tokens_from_adata(
             adata=adata,
             obs_index=None,
             query_var_names=[],
+            n_rand_prompt_vars=n_rand_prompt_vars,
+            rng=rng,
         )
 
         # convert to cuda
@@ -865,8 +924,7 @@ def predict_metadata(self, adata: sc.AnnData) -> dict[str, np.ndarray]:
             for metadata_key in self.metadata_ontology_infos.keys():
                 metadata_logits_nk = logits_dict[metadata_key][:, context_indices[f'query_{metadata_key}'], :]
                 metadata_logits_nk = metadata_logits_nk - torch.logsumexp(metadata_logits_nk, dim=1, keepdim=True)
-                metadata_probs_nk = torch.exp(metadata_logits_nk)
-                metadata_prediction_dict[metadata_key] = metadata_probs_nk.cpu().numpy()
+                metadata_prediction_dict[metadata_key] = metadata_logits_nk.cpu().numpy()
 
         return metadata_prediction_dict
 
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
index c3f037e0..cf1141f7 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/02_generate_metadata_predictions.ipynb
@@ -65,7 +65,8 @@
     "\n",
     "rng_seed = 42\n",
     "n_cells = 1000\n",
-    "n_genes = 1_000\n",
+    "n_genes = 4_091\n",
+    "gene_selection_method = \"random\"  # \"random\" or \"highly_expressed\"\n",
     "chunk_size = 16\n",
     "\n",
     "os.makedirs(output_path, exist_ok=True)"
@@ -152,11 +153,20 @@
    "outputs": [],
    "source": [
     "if n_genes is not None:\n",
-    "    logger.info(f\"Using {n_genes} highly expressed genes.\")\n",
-    "    X_g = np.asarray(adata.X.sum(0)).flatten()\n",
-    "    highly_expressed_gene_indices = np.argsort(X_g)[-n_genes:]\n",
-    "    highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]\n",
-    "    adata = adata[:, highly_expressed_gene_ids]\n",
+    "    if gene_selection_method == \"highly_expressed\":\n",
+    "        logger.info(f\"Using {n_genes} highly expressed genes.\")\n",
+    "        X_g = np.asarray(adata.X.sum(0)).flatten()\n",
+    "        highly_expressed_gene_indices = np.argsort(X_g)[-n_genes:]\n",
+    "        highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]\n",
+    "        adata = adata[:, highly_expressed_gene_ids]\n",
+    "        n_rand_prompt_vars = None\n",
+    "        torch_rng = None\n",
+    "    elif gene_selection_method == \"random\":\n",
+    "        logger.info(f\"Using {n_genes} random genes (seed = {rng_seed}).\")\n",
+    "        torch_rng = torch.Generator().manual_seed(rng_seed)\n",
+    "        n_rand_prompt_vars = n_genes\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unknown gene selection method: {gene_selection_method}\")\n",
     "else:\n",
     "    logger.info(f\"Using all genes.\")\n",
     "\n",
@@ -176,7 +186,11 @@
    "outputs": [],
    "source": [
     "logger.info(f\"Predicting metadata for {len(adata)} cells ...\")\n",
-    "preds = ctx.predict_metadata_chunked(adata, chunk_size=chunk_size)"
+    "preds = ctx.predict_metadata_chunked(\n",
+    "    adata=adata,\n",
+    "    chunk_size=chunk_size,\n",
+    "    n_rand_prompt_vars=n_rand_prompt_vars,\n",
+    "    rng=torch_rng)"
    ]
   },
   {
@@ -231,7 +245,8 @@
     "meta_adata = sc.AnnData(obs=adata.obs.copy())\n",
     "\n",
     "for meta_key, meta_preds in preds.items():\n",
-    "    meta_adata.obsm[meta_key + \"_class_probs\"] = meta_preds\n",
+    "    meta_adata.obsm[meta_key + \"_class_logits\"] = meta_preds\n",
+    "    meta_adata.obsm[meta_key + \"_class_probs\"] = np.exp(meta_preds)\n",
     "    meta_adata.uns[meta_key + \"_ontology_term_ids\"] = ctx.metadata_ontology_infos[meta_key][\"names\"]\n",
     "    meta_adata.uns[meta_key + \"_labels\"] = ctx.metadata_ontology_infos[meta_key][\"labels\"]\n",
     "\n",
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
new file mode 100644
index 00000000..8f7f9175
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
@@ -0,0 +1,1115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "### Analyze metadata predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "METADATA_PREDICTIONS_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\")\n",
+    "PREFIX_LIST = [\n",
+    "    \"10M_001_bs1536\",\n",
+    "    \"19M_001_bs2048\",\n",
+    "    \"30M_001_bs2560\",\n",
+    "    \"100M_long_run_last\"\n",
+    "]\n",
+    "VAL_ADATA_IDX_RANGE = np.arange(1, 111)\n",
+    "N_HOPS_DICT = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "def load_predictions_anndata(val_adata_idx: int, prefix_idx: int) -> sc.AnnData:\n",
+    "    path = os.path.join(\n",
+    "        METADATA_PREDICTIONS_ROOT_PATH,\n",
+    "        PREFIX_LIST[prefix_idx],\n",
+    "        f\"extract_{VAL_ADATA_IDX_RANGE[val_adata_idx]}_metadata_prediction_scores.h5ad\")\n",
+    "    return sc.read_h5ad(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "465c2a5da48849358b18b84c369656f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8b4fad6da1f4933a0c19d8323aae0a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5389bc4351240d69eb06f947daf334e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66bb900c11914db994d6568817a8683a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d643b97294324c9b96df7bf5405f0804",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
+      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
+      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
+     ]
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(PREFIX_LIST))):\n",
+    "    prefix = PREFIX_LIST[prefix_idx]\n",
+    "    for val_adata_idx in tqdm(range(len(VAL_ADATA_IDX_RANGE))):\n",
+    "        val_adata_id = VAL_ADATA_IDX_RANGE[val_adata_idx]\n",
+    "        meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx)\n",
+    "\n",
+    "        results[\"prefix\"].append(prefix)\n",
+    "        results[\"val_adata_id\"].append(val_adata_id)\n",
+    "\n",
+    "        for key in [\"cell_type\", \"disease\", \"development_stage\", \"tissue\", \"sex\"]:\n",
+    "            for n_hops in range(N_HOPS_DICT[key] + 1):\n",
+    "                metric = f\"{key}_hop_{n_hops}\"\n",
+    "                sensitivity_col_name = f\"{metric}_sensitivity\"\n",
+    "                specificity_col_name = f\"{metric}_specificity\"\n",
+    "                try:\n",
+    "                    recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
+    "                    precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+    "                    f1 = 2 * (precision * recall) / (precision + recall)\n",
+    "                except:\n",
+    "                    recall = np.nan\n",
+    "                    precision = np.nan\n",
+    "                    f1 = np.nan\n",
+    "                \n",
+    "                results[f\"{metric}_recall\"].append(recall)\n",
+    "                results[f\"{metric}_precision\"].append(precision)\n",
+    "                results[f\"{metric}_f1\"].append(f1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.24239872085189104"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df[results_df[\"prefix\"] == PREFIX_LIST[3]][\"sex_hop_0_f1\"].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "prefix\n",
+      "val_adata_id\n",
+      "cell_type_hop_0_recall\n",
+      "cell_type_hop_0_precision\n",
+      "cell_type_hop_0_f1\n",
+      "cell_type_hop_1_recall\n",
+      "cell_type_hop_1_precision\n",
+      "cell_type_hop_1_f1\n",
+      "cell_type_hop_2_recall\n",
+      "cell_type_hop_2_precision\n",
+      "cell_type_hop_2_f1\n",
+      "cell_type_hop_3_recall\n",
+      "cell_type_hop_3_precision\n",
+      "cell_type_hop_3_f1\n",
+      "disease_hop_0_recall\n",
+      "disease_hop_0_precision\n",
+      "disease_hop_0_f1\n",
+      "disease_hop_1_recall\n",
+      "disease_hop_1_precision\n",
+      "disease_hop_1_f1\n",
+      "disease_hop_2_recall\n",
+      "disease_hop_2_precision\n",
+      "disease_hop_2_f1\n",
+      "disease_hop_3_recall\n",
+      "disease_hop_3_precision\n",
+      "disease_hop_3_f1\n",
+      "development_stage_hop_0_recall\n",
+      "development_stage_hop_0_precision\n",
+      "development_stage_hop_0_f1\n",
+      "development_stage_hop_1_recall\n",
+      "development_stage_hop_1_precision\n",
+      "development_stage_hop_1_f1\n",
+      "development_stage_hop_2_recall\n",
+      "development_stage_hop_2_precision\n",
+      "development_stage_hop_2_f1\n",
+      "development_stage_hop_3_recall\n",
+      "development_stage_hop_3_precision\n",
+      "development_stage_hop_3_f1\n",
+      "tissue_hop_0_recall\n",
+      "tissue_hop_0_precision\n",
+      "tissue_hop_0_f1\n",
+      "tissue_hop_1_recall\n",
+      "tissue_hop_1_precision\n",
+      "tissue_hop_1_f1\n",
+      "tissue_hop_2_recall\n",
+      "tissue_hop_2_precision\n",
+      "tissue_hop_2_f1\n",
+      "tissue_hop_3_recall\n",
+      "tissue_hop_3_precision\n",
+      "tissue_hop_3_f1\n",
+      "sex_hop_0_recall\n",
+      "sex_hop_0_precision\n",
+      "sex_hop_0_f1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k in results_df.keys():\n",
+    "    print(k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/2074687837.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  greens = cm.get_cmap('Greens', len(cell_metrics))\n",
+      "/var/tmp/ipykernel_2595801/2074687837.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  reds = cm.get_cmap('Reds', len(disease_metrics))\n",
+      "/var/tmp/ipykernel_2595801/2074687837.py:62: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  blues = cm.get_cmap('Blues', len(tissue_metrics))\n",
+      "/var/tmp/ipykernel_2595801/2074687837.py:68: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  purples = cm.get_cmap('Purples', len(dev_metrics))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# Suppose your dataframe is named `df` and PREFIX_LIST is defined, e.g.,\n",
+    "# PREFIX_LIST = ['prefixA', 'prefixB', 'prefixC', 'prefixD']\n",
+    "\n",
+    "# Define the list of metric columns you want to plot:\n",
+    "# metrics = [\n",
+    "#     'cell_type_hop_0_f1', 'cell_type_hop_1_f1', 'cell_type_hop_2_f1', 'cell_type_hop_3_f1',\n",
+    "#     'disease_hop_0_f1', 'disease_hop_1_f1', 'disease_hop_2_f1', 'disease_hop_3_f1',\n",
+    "#     'tissue_hop_0_f1', 'tissue_hop_1_f1', 'tissue_hop_2_f1', 'tissue_hop_3_f1',\n",
+    "#     'development_stage_hop_0_f1', 'development_stage_hop_1_f1', 'development_stage_hop_2_f1', 'development_stage_hop_3_f1',\n",
+    "#     'sex_hop_0_f1'\n",
+    "# ]\n",
+    "\n",
+    "metrics = [\n",
+    "    'cell_type_hop_1_f1',\n",
+    "    'disease_hop_3_f1',\n",
+    "    'tissue_hop_3_f1',\n",
+    "    'development_stage_hop_3_f1',\n",
+    "    'sex_hop_0_f1'\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Melt the dataframe so that each row corresponds to one (prefix, metric, f1 value) triple.\n",
+    "df = results_df\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'], \n",
+    "    value_vars=metrics, \n",
+    "    var_name='metric', \n",
+    "    value_name='f1'\n",
+    ")\n",
+    "\n",
+    "# Drop NaN values from the f1 column.\n",
+    "df_long = df_long.dropna(subset=['f1'])\n",
+    "\n",
+    "# (Optional) Create a new column for the metric group.\n",
+    "df_long['metric_group'] = df_long['metric'].apply(lambda x: x.split('_hop')[0])\n",
+    "\n",
+    "# Define the desired order for the metrics. This will also control the order in the legend.\n",
+    "metric_order = metrics  # assuming you want the order as defined above\n",
+    "\n",
+    "# Create a palette mapping so that each metric group gets its own colormap.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell type (using a Greens gradient):\n",
+    "cell_metrics = [m for m in metrics if m.startswith('cell_type')]\n",
+    "greens = cm.get_cmap('Greens', len(cell_metrics))\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = greens(i)\n",
+    "\n",
+    "# For disease (using a Reds gradient):\n",
+    "disease_metrics = [m for m in metrics if m.startswith('disease')]\n",
+    "reds = cm.get_cmap('Reds', len(disease_metrics))\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = reds(i)\n",
+    "\n",
+    "# For tissue (using a Blues gradient):\n",
+    "tissue_metrics = [m for m in metrics if m.startswith('tissue')]\n",
+    "blues = cm.get_cmap('Blues', len(tissue_metrics))\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = blues(i)\n",
+    "\n",
+    "# For development stage (using a Purples gradient):\n",
+    "dev_metrics = [m for m in metrics if m.startswith('development_stage')]\n",
+    "purples = cm.get_cmap('Purples', len(dev_metrics))\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = purples(i)\n",
+    "\n",
+    "# For sex (only one metric, assign a single color):\n",
+    "sex_metrics = [m for m in metrics if m.startswith('sex')]\n",
+    "if sex_metrics:\n",
+    "    palette[sex_metrics[0]] = 'orange'\n",
+    "\n",
+    "# Create the boxplot.\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(\n",
+    "    x='prefix',\n",
+    "    y='f1',\n",
+    "    hue='metric',\n",
+    "    data=df_long,\n",
+    "    order=PREFIX_LIST,       # enforce prefix order\n",
+    "    hue_order=metric_order,  # enforce the metric order\n",
+    "    palette=palette,\n",
+    "    showfliers=False         # do not show outliers\n",
+    ")\n",
+    "\n",
+    "plt.xlabel('Prefix')\n",
+    "plt.ylabel('F1 Score')\n",
+    "plt.title('Boxplot of F1 Scores by Prefix and Metric')\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title='Metric')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# Assume your dataframe is named `df` and PREFIX_LIST is defined, e.g.,\n",
+    "# PREFIX_LIST = ['prefixA', 'prefixB', 'prefixC', 'prefixD']\n",
+    "\n",
+    "# Define the list of metric columns:\n",
+    "metrics = [\n",
+    "    # 'cell_type_hop_0_f1', 'cell_type_hop_1_f1', 'cell_type_hop_2_f1', 'cell_type_hop_3_f1',\n",
+    "    # 'disease_hop_0_f1', 'disease_hop_1_f1', 'disease_hop_2_f1', 'disease_hop_3_f1',\n",
+    "    # 'tissue_hop_0_f1', 'tissue_hop_1_f1', 'tissue_hop_2_f1', 'tissue_hop_3_f1',\n",
+    "    # 'development_stage_hop_0_f1', 'development_stage_hop_1_f1', 'development_stage_hop_2_f1', 'development_stage_hop_3_f1',\n",
+    "    'sex_hop_0_f1'\n",
+    "]\n",
+    "\n",
+    "# Reshape the dataframe to long format.\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'], \n",
+    "    value_vars=metrics, \n",
+    "    var_name='metric', \n",
+    "    value_name='f1'\n",
+    ")\n",
+    "\n",
+    "# Drop NaN values.\n",
+    "df_long = df_long.dropna(subset=['f1'])\n",
+    "\n",
+    "# (Optional) Create a metric group column.\n",
+    "df_long['metric_group'] = df_long['metric'].apply(lambda x: x.split('_hop')[0])\n",
+    "\n",
+    "# Maintain the desired metric order.\n",
+    "metric_order = metrics\n",
+    "\n",
+    "# Create a custom palette, sampling from the middle of each colormap.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell type metrics using Greens.\n",
+    "cell_metrics = [m for m in metrics if m.startswith('cell_type')]\n",
+    "n = len(cell_metrics)\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For disease metrics using Reds.\n",
+    "disease_metrics = [m for m in metrics if m.startswith('disease')]\n",
+    "n = len(disease_metrics)\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For tissue metrics using Blues.\n",
+    "tissue_metrics = [m for m in metrics if m.startswith('tissue')]\n",
+    "n = len(tissue_metrics)\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For development stage metrics using Purples.\n",
+    "dev_metrics = [m for m in metrics if m.startswith('development_stage')]\n",
+    "n = len(dev_metrics)\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For sex metric, assign a single color.\n",
+    "sex_metrics = [m for m in metrics if m.startswith('sex')]\n",
+    "if sex_metrics:\n",
+    "    palette[sex_metrics[0]] = 'orange'\n",
+    "\n",
+    "# Create the violin plot with further reduced bandwidth.\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.violinplot(\n",
+    "    x='prefix',\n",
+    "    y='f1',\n",
+    "    hue='metric',\n",
+    "    data=df_long,\n",
+    "    order=PREFIX_LIST,       # enforce prefix order\n",
+    "    hue_order=metric_order,  # enforce metric order\n",
+    "    palette=palette,\n",
+    "    inner='quartile',        # display quartile markers\n",
+    "    linewidth=0.5,           # thinner outline lines\n",
+    "    cut=0,                   # do not extend beyond data range\n",
+    "    bw_adjust=0.5            # further reduce smoothing\n",
+    ")\n",
+    "\n",
+    "plt.xlabel('Prefix')\n",
+    "plt.ylabel('F1 Score')\n",
+    "plt.title('Violin Plot of F1 Scores by Prefix and Metric')\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title='Metric')\n",
+    "plt.ylim(0, 1)  # explicitly restrict y-axis to [0, 1]\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/137335904.py:42: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/137335904.py:48: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/137335904.py:54: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/137335904.py:60: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/137335904.py:84: FutureWarning: \n",
+      "\n",
+      "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:black'` for the same effect.\n",
+      "\n",
+      "  sns.stripplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# Assume your dataframe is named `df` and PREFIX_LIST is defined, e.g.,\n",
+    "# PREFIX_LIST = ['prefixA', 'prefixB', 'prefixC', 'prefixD']\n",
+    "\n",
+    "# Define the list of metric columns:\n",
+    "metrics = [\n",
+    "    'cell_type_hop_0_f1', 'cell_type_hop_1_f1', 'cell_type_hop_2_f1', 'cell_type_hop_3_f1',\n",
+    "    'disease_hop_0_f1', 'disease_hop_1_f1', 'disease_hop_2_f1', 'disease_hop_3_f1',\n",
+    "    'tissue_hop_0_f1', 'tissue_hop_1_f1', 'tissue_hop_2_f1', 'tissue_hop_3_f1',\n",
+    "    'development_stage_hop_0_f1', 'development_stage_hop_1_f1', 'development_stage_hop_2_f1', 'development_stage_hop_3_f1',\n",
+    "    'sex_hop_0_f1'\n",
+    "]\n",
+    "\n",
+    "# Reshape the dataframe to long format.\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'], \n",
+    "    value_vars=metrics, \n",
+    "    var_name='metric', \n",
+    "    value_name='f1'\n",
+    ")\n",
+    "\n",
+    "# Drop NaN values.\n",
+    "df_long = df_long.dropna(subset=['f1'])\n",
+    "\n",
+    "# (Optional) Create a metric group column.\n",
+    "df_long['metric_group'] = df_long['metric'].apply(lambda x: x.split('_hop')[0])\n",
+    "\n",
+    "# Maintain the desired metric order.\n",
+    "metric_order = metrics\n",
+    "\n",
+    "# Create a custom palette, sampling from the middle of each colormap.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell type metrics using Greens.\n",
+    "cell_metrics = [m for m in metrics if m.startswith('cell_type')]\n",
+    "n = len(cell_metrics)\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For disease metrics using Reds.\n",
+    "disease_metrics = [m for m in metrics if m.startswith('disease')]\n",
+    "n = len(disease_metrics)\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For tissue metrics using Blues.\n",
+    "tissue_metrics = [m for m in metrics if m.startswith('tissue')]\n",
+    "n = len(tissue_metrics)\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For development stage metrics using Purples.\n",
+    "dev_metrics = [m for m in metrics if m.startswith('development_stage')]\n",
+    "n = len(dev_metrics)\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For sex metric, assign a single color.\n",
+    "sex_metrics = [m for m in metrics if m.startswith('sex')]\n",
+    "if sex_metrics:\n",
+    "    palette[sex_metrics[0]] = 'orange'\n",
+    "\n",
+    "# Create the plot with boxplot and jittered points.\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot the boxplot without outliers.\n",
+    "ax = sns.boxplot(\n",
+    "    x='prefix',\n",
+    "    y='f1',\n",
+    "    hue='metric',\n",
+    "    data=df_long,\n",
+    "    order=PREFIX_LIST,       # enforce prefix order\n",
+    "    hue_order=metric_order,  # enforce metric order\n",
+    "    palette=palette,\n",
+    "    showfliers=False,        # do not show outliers\n",
+    "    linewidth=0.5            # thinner lines\n",
+    ")\n",
+    "\n",
+    "# Overlay the actual points with jitter.\n",
+    "sns.stripplot(\n",
+    "    x='prefix',\n",
+    "    y='f1',\n",
+    "    hue='metric',\n",
+    "    data=df_long,\n",
+    "    order=PREFIX_LIST,\n",
+    "    hue_order=metric_order,\n",
+    "    # palette=palette,\n",
+    "    dodge=True,    # position points within each box category\n",
+    "    jitter=True,   # add jitter for better visibility\n",
+    "    marker='.',\n",
+    "    color='black',\n",
+    "    alpha=0.7,\n",
+    "    size=3\n",
+    ")\n",
+    "\n",
+    "# Remove duplicate legend entries.\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "n_handles = len(handles) // 2\n",
+    "ax.legend(handles[:n_handles], labels[:n_handles],\n",
+    "          bbox_to_anchor=(1.05, 1), loc='upper left', title='Metric')\n",
+    "\n",
+    "plt.xlabel('Prefix')\n",
+    "plt.ylabel('F1 Score')\n",
+    "plt.title('Boxplot with Jittered Data Points for F1 Scores by Prefix and Metric')\n",
+    "plt.ylim(0, 1)  # restrict y-axis to [0, 1]\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_2595801/582546310.py:22: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/582546310.py:28: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/582546310.py:34: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_2595801/582546310.py:40: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x2000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# Example PREFIX_LIST (adjust as needed)\n",
+    "PREFIX_LIST = ['prefixA', 'prefixB', 'prefixC', 'prefixD']\n",
+    "\n",
+    "# -------------------------------\n",
+    "# User-specified options:\n",
+    "# Choose which metric to plot: 'f1', 'sensitivity', or 'specificity'\n",
+    "value_to_plot = 'f1'  \n",
+    "# Flag to select summary statistic: True uses mean/std, False uses median/IQR.\n",
+    "use_mean = True  \n",
+    "# -------------------------------\n",
+    "\n",
+    "# Define metric groups and available hops.\n",
+    "# For 'sex', only hop 0 is available.\n",
+    "metric_groups = {\n",
+    "    'cell_type': [0, 1, 2, 3],\n",
+    "    'disease': [0, 1, 2, 3],\n",
+    "    'tissue': [0, 1, 2, 3],\n",
+    "    'development_stage': [0, 1, 2, 3],\n",
+    "    'sex': [0]\n",
+    "}\n",
+    "\n",
+    "# Build the list of metric columns (e.g., \"cell_type_hop_0_f1\")\n",
+    "selected_metrics = []\n",
+    "for group, hops in metric_groups.items():\n",
+    "    for hop in hops:\n",
+    "        selected_metrics.append(f\"{group}_hop_{hop}_{value_to_plot}\")\n",
+    "\n",
+    "# Precompute a color palette for each metric column.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell_type: use Greens.\n",
+    "cell_metrics = [f\"cell_type_hop_{hop}_{value_to_plot}\" for hop in metric_groups['cell_type']]\n",
+    "n = len(cell_metrics)\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For disease: use Reds.\n",
+    "disease_metrics = [f\"disease_hop_{hop}_{value_to_plot}\" for hop in metric_groups['disease']]\n",
+    "n = len(disease_metrics)\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For tissue: use Blues.\n",
+    "tissue_metrics = [f\"tissue_hop_{hop}_{value_to_plot}\" for hop in metric_groups['tissue']]\n",
+    "n = len(tissue_metrics)\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For development_stage: use Purples.\n",
+    "dev_metrics = [f\"development_stage_hop_{hop}_{value_to_plot}\" for hop in metric_groups['development_stage']]\n",
+    "n = len(dev_metrics)\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For sex: assign a fixed color (e.g., orange).\n",
+    "sex_metrics = [f\"sex_hop_{hop}_{value_to_plot}\" for hop in metric_groups['sex']]\n",
+    "for m in sex_metrics:\n",
+    "    palette[m] = 'orange'\n",
+    "\n",
+    "# Melt the dataframe into long format for the selected metrics.\n",
+    "# Assume your original dataframe is named `df`.\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'],\n",
+    "    value_vars=selected_metrics,\n",
+    "    var_name='metric',\n",
+    "    value_name=value_to_plot\n",
+    ")\n",
+    "# Drop any rows with NaN values for the chosen metric.\n",
+    "df_long = df_long.dropna(subset=[value_to_plot])\n",
+    "\n",
+    "# Determine grid size: one row per metric group, columns = max hops among groups.\n",
+    "n_rows = len(metric_groups)\n",
+    "max_cols = max(len(hops) for hops in metric_groups.values())\n",
+    "\n",
+    "fig, axes = plt.subplots(n_rows, max_cols, figsize=(4 * max_cols, 4 * n_rows), sharey=True)\n",
+    "\n",
+    "# Loop over each metric group (row) and each hop (column).\n",
+    "for row_idx, (group, hops) in enumerate(metric_groups.items()):\n",
+    "    for col_idx in range(max_cols):\n",
+    "        # Turn off extra panels.\n",
+    "        if col_idx >= len(hops):\n",
+    "            axes[row_idx, col_idx].axis('off')\n",
+    "            continue\n",
+    "\n",
+    "        hop = hops[col_idx]\n",
+    "        metric_name = f\"{group}_hop_{hop}_{value_to_plot}\"\n",
+    "        ax = axes[row_idx, col_idx]\n",
+    "\n",
+    "        # Filter data for the current metric and drop any NaNs.\n",
+    "        df_metric = df_long[df_long['metric'] == metric_name].dropna(subset=[value_to_plot])\n",
+    "\n",
+    "        # Compute summary statistics per prefix.\n",
+    "        if use_mean:\n",
+    "            stats = df_metric.groupby('prefix')[value_to_plot].agg(['mean', 'std']).reindex(PREFIX_LIST)\n",
+    "            y_vals = stats['mean']\n",
+    "            y_err = stats['std']\n",
+    "        else:\n",
+    "            stats = df_metric.groupby('prefix').agg(\n",
+    "                median=(value_to_plot, np.median),\n",
+    "                q1=(value_to_plot, lambda x: x.quantile(0.25)),\n",
+    "                q3=(value_to_plot, lambda x: x.quantile(0.75))\n",
+    "            ).reindex(PREFIX_LIST)\n",
+    "            y_vals = stats['median']\n",
+    "            y_err_lower = y_vals - stats['q1']\n",
+    "            y_err_upper = stats['q3'] - y_vals\n",
+    "            y_err = [y_err_lower.values, y_err_upper.values]\n",
+    "\n",
+    "        x = np.arange(len(PREFIX_LIST))\n",
+    "        # Retrieve the precomputed color (default to gray if not found).\n",
+    "        line_color = palette.get(metric_name, 'gray')\n",
+    "\n",
+    "        # Plot black markers with error bars.\n",
+    "        ax.errorbar(\n",
+    "            x, y_vals, yerr=y_err,\n",
+    "            fmt='o', color='black', ecolor='black',\n",
+    "            capsize=4, markersize=6, linestyle='None'\n",
+    "        )\n",
+    "        # Connect the markers with a line in the precomputed color.\n",
+    "        ax.plot(x, y_vals, color=line_color, linestyle='-', linewidth=2)\n",
+    "\n",
+    "        ax.set_xticks(x)\n",
+    "        ax.set_xticklabels(PREFIX_LIST, rotation=90)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "        # Set the title to show the hop value.\n",
+    "        ax.set_title(f\"hop {hop}\")\n",
+    "\n",
+    "        # Label the y-axis with the metric group on the leftmost panel.\n",
+    "        if col_idx == 0:\n",
+    "            ax.set_ylabel(group, fontsize=12, fontweight='bold')\n",
+    "        # Label the x-axis on the bottom row.\n",
+    "        if row_idx == n_rows - 1:\n",
+    "            ax.set_xlabel('Prefix')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
index 33845bb4..dd4a53b3 100644
--- a/scripts/metadata_prediction_analysis.py
+++ b/scripts/metadata_prediction_analysis.py
@@ -118,9 +118,14 @@ def main():
         "--n_cells", type=int, default=1000,
         help="Number of cells to use (default: 1000)"
     )
+    # Updated default for n_genes to match the new notebook and added new argument for gene selection method
     parser.add_argument(
-        "--n_genes", type=int, default=10000,
-        help="Number of highly expressed genes to use (default: 10000). Set to None to use all."
+        "--n_genes", type=int, default=4091,
+        help="Number of genes to use. For 'highly_expressed', selects top genes; for 'random', selects random genes. Set to None to use all."
+    )
+    parser.add_argument(
+        "--gene_selection_method", type=str, default="random",
+        help="Gene selection method: 'random' or 'highly_expressed' (default: random)"
     )
     parser.add_argument(
         "--chunk_size", type=int, default=16,
@@ -185,14 +190,27 @@ def main():
     )
     logger.info(f"Loading the validation AnnData object from {adata_path} ...")
     adata = sc.read_h5ad(adata_path)
+    
+    # Updated gene selection logic to support both "highly_expressed" and "random"
     if args.n_genes is not None:
-        logger.info(f"Using {args.n_genes} highly expressed genes.")
-        X_g = np.asarray(adata.X.sum(0)).flatten()
-        highly_expressed_gene_indices = np.argsort(X_g)[-args.n_genes:]
-        highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]
-        adata = adata[:, highly_expressed_gene_ids]
+        if args.gene_selection_method == "highly_expressed":
+            logger.info(f"Using {args.n_genes} highly expressed genes.")
+            X_g = np.asarray(adata.X.sum(0)).flatten()
+            highly_expressed_gene_indices = np.argsort(X_g)[-args.n_genes:]
+            highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]
+            adata = adata[:, highly_expressed_gene_ids]
+            n_rand_prompt_vars = None
+            torch_rng = None
+        elif args.gene_selection_method == "random":
+            logger.info(f"Using {args.n_genes} random genes (seed = {args.rng_seed}).")
+            torch_rng = torch.Generator().manual_seed(args.rng_seed)
+            n_rand_prompt_vars = args.n_genes
+        else:
+            raise ValueError(f"Unknown gene selection method: {args.gene_selection_method}")
     else:
         logger.info("Using all genes.")
+        n_rand_prompt_vars = None
+        torch_rng = None
 
     if args.n_cells is None:
         logger.info("Using all cells.")
@@ -202,13 +220,20 @@ def main():
         rng = np.random.RandomState(args.rng_seed)
         adata = adata[rng.choice(len(adata), n_cells, replace=False)]
     logger.info(f"Predicting metadata for {len(adata)} cells ...")
-    preds = ctx.predict_metadata_chunked(adata, chunk_size=args.chunk_size)
+    preds = ctx.predict_metadata_chunked(
+        adata, 
+        chunk_size=args.chunk_size,
+        n_rand_prompt_vars=n_rand_prompt_vars,
+        rng=torch_rng
+    )
 
     logger.info("Inserting predictions into an AnnData object ...")
     meta_adata = sc.AnnData(obs=adata.obs.copy())
 
+    # Updated to include logits and compute class probabilities via np.exp()
     for meta_key, meta_preds in preds.items():
-        meta_adata.obsm[meta_key + "_class_probs"] = meta_preds
+        meta_adata.obsm[meta_key + "_class_logits"] = meta_preds
+        meta_adata.obsm[meta_key + "_class_probs"] = np.exp(meta_preds)
         meta_adata.uns[meta_key + "_ontology_term_ids"] = ctx.metadata_ontology_infos[meta_key]["names"]
         meta_adata.uns[meta_key + "_labels"] = ctx.metadata_ontology_infos[meta_key]["labels"]
 
diff --git a/scripts/run_metadata_prediction_analysis.sh b/scripts/run_metadata_prediction_analysis.sh
index 30f065ab..743a3d0d 100644
--- a/scripts/run_metadata_prediction_analysis.sh
+++ b/scripts/run_metadata_prediction_analysis.sh
@@ -19,7 +19,8 @@ ONTOLOGY_RESOURCE_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/ontology
 OUTPUT_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last"
 RNG_SEED=42
 N_CELLS=1000
-N_GENES=10000
+N_GENES=4091
+GENE_SELECTION_METHOD="random"
 CHUNK_SIZE=16
 
 # ----------------------------
@@ -51,5 +52,6 @@ for i in $(seq ${START_INDEX} ${END_INDEX}); do
         --rng_seed ${RNG_SEED} \
         --n_cells ${N_CELLS} \
         --n_genes ${N_GENES} \
+        --gene_selection_method ${GENE_SELECTION_METHOD} \
         --chunk_size ${CHUNK_SIZE}
 done

From a998bb118d82def57b9cb29809424c00e6926a9d Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Tue, 25 Feb 2025 19:08:17 -0500
Subject: [PATCH 47/64] Add `CellariumModule.test_step` method for any custom
 inference (#304)

* test_step

* mypy
---
 cellarium/ml/core/datamodule.py | 24 ++++++++++++++++++++++++
 cellarium/ml/core/module.py     | 21 +++++++++++++++++++++
 cellarium/ml/core/pipeline.py   | 11 ++++++++++-
 cellarium/ml/models/__init__.py |  3 ++-
 cellarium/ml/models/model.py    | 19 +++++++++++++++++++
 5 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/cellarium/ml/core/datamodule.py b/cellarium/ml/core/datamodule.py
index 09060ff0..1c3c3a7a 100644
--- a/cellarium/ml/core/datamodule.py
+++ b/cellarium/ml/core/datamodule.py
@@ -198,6 +198,20 @@ def setup(self, stage: str | None = None) -> None:
                 test_mode=self.test_mode,
             )
 
+        if stage == "test":
+            self.test_dataset = IterableDistributedAnnDataCollectionDataset(
+                dadc=self.dadc,
+                batch_keys=self.batch_keys,
+                batch_size=self.batch_size,
+                iteration_strategy=self.iteration_strategy,
+                shuffle=self.shuffle,
+                shuffle_seed=self.shuffle_seed,
+                drop_last_indices=self.drop_last_indices,
+                drop_incomplete_batch=self.drop_incomplete_batch,
+                worker_seed=self.worker_seed,
+                test_mode=self.test_mode,
+            )
+
     def train_dataloader(self) -> torch.utils.data.DataLoader:
         """Training dataloader."""
         return torch.utils.data.DataLoader(
@@ -228,6 +242,16 @@ def predict_dataloader(self) -> torch.utils.data.DataLoader:
             persistent_workers=self.persistent_workers,
         )
 
+    def test_dataloader(self) -> torch.utils.data.DataLoader:
+        """Test dataloader."""
+        return torch.utils.data.DataLoader(
+            self.test_dataset,
+            num_workers=self.num_workers,
+            collate_fn=self.collate_fn,
+            prefetch_factor=self.prefetch_factor,
+            persistent_workers=self.persistent_workers,
+        )
+
     def state_dict(self) -> dict[str, Any]:
         assert self.trainer is not None
         state = {
diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index 3da6363c..eb8230fe 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -291,6 +291,27 @@ def validation_step(self, batch: dict[str, Any], batch_idx: int) -> None:
         batch["batch_idx"] = batch_idx
         self.module_pipeline.validate(batch)
 
+    def test_step(self, batch: dict[str, Any], batch_idx: int) -> None:
+        """
+        Forward pass for test step.
+
+        Args:
+            batch:
+                A dictionary containing the batch data.
+            batch_idx:
+                The index of the batch.
+
+        Returns:
+            None
+        """
+        if self.module_pipeline is None:
+            raise RuntimeError("The model is not configured. Call `configure_model` before accessing the model.")
+
+        batch["pl_module"] = self
+        batch["trainer"] = self.trainer
+        batch["batch_idx"] = batch_idx
+        self.module_pipeline.test(batch)
+
     def configure_optimizers(self) -> OptimizerLRScheduler:
         """Configure optimizers for the model."""
         optim_fn = self.hparams["optim_fn"]
diff --git a/cellarium/ml/core/pipeline.py b/cellarium/ml/core/pipeline.py
index 61edf49e..5934a23e 100644
--- a/cellarium/ml/core/pipeline.py
+++ b/cellarium/ml/core/pipeline.py
@@ -8,7 +8,7 @@
 import torch
 from typing_extensions import Self
 
-from cellarium.ml.models import PredictMixin, ValidateMixin
+from cellarium.ml.models import PredictMixin, TestMixin, ValidateMixin
 from cellarium.ml.utilities.core import call_func_with_batch
 
 
@@ -70,3 +70,12 @@ def validate(self, batch: dict[str, dict[str, np.ndarray | torch.Tensor] | np.nd
         if not isinstance(model, ValidateMixin):
             raise TypeError(f"The last module in the pipeline must be an instance of {ValidateMixin}. Got {model}")
         call_func_with_batch(model.validate, batch)
+
+    def test(self, batch: dict[str, np.ndarray | torch.Tensor]) -> None:
+        for module in self[:-1]:
+            batch |= call_func_with_batch(module.forward, batch)
+
+        model = self[-1]
+        if not isinstance(model, TestMixin):
+            raise TypeError(f"The last module in the pipeline must be an instance of {TestMixin}. Got {model}")
+        call_func_with_batch(model.test, batch)
diff --git a/cellarium/ml/models/__init__.py b/cellarium/ml/models/__init__.py
index d97dd110..9f30da47 100644
--- a/cellarium/ml/models/__init__.py
+++ b/cellarium/ml/models/__init__.py
@@ -5,7 +5,7 @@
 from cellarium.ml.models.geneformer import Geneformer
 from cellarium.ml.models.incremental_pca import IncrementalPCA
 from cellarium.ml.models.logistic_regression import LogisticRegression
-from cellarium.ml.models.model import CellariumModel, PredictMixin, ValidateMixin
+from cellarium.ml.models.model import CellariumModel, PredictMixin, TestMixin, ValidateMixin
 from cellarium.ml.models.onepass_mean_var_std import OnePassMeanVarStd
 from cellarium.ml.models.probabilistic_pca import ProbabilisticPCA
 from cellarium.ml.models.tdigest import TDigest
@@ -20,5 +20,6 @@
     "PredictMixin",
     "ProbabilisticPCA",
     "TDigest",
+    "TestMixin",
     "ValidateMixin",
 ]
diff --git a/cellarium/ml/models/model.py b/cellarium/ml/models/model.py
index 25ba0290..b8275807 100644
--- a/cellarium/ml/models/model.py
+++ b/cellarium/ml/models/model.py
@@ -109,3 +109,22 @@ def validate(
         if loss is not None:
             # Logging to TensorBoard by default
             pl_module.log("val_loss", loss, sync_dist=True, on_epoch=True)
+
+
+class TestMixin(metaclass=ABCMeta):
+    """
+    Abstract mixin class for models that can perform testing.
+    """
+
+    @abstractmethod
+    def test(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        batch_idx: int,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Perform testing.
+        """

From 6bd85b7f4b3eac1fd9b86029f2d12aee60a2875d Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Thu, 27 Feb 2025 14:31:27 +0000
Subject: [PATCH 48/64] test step

---
 cellarium/ml/core/datamodule.py               |  16 +-
 cellarium/ml/core/module.py                   |  21 +
 cellarium/ml/core/pipeline.py                 |   9 +
 cellarium/ml/data/dadc_dataset.py             |   2 +-
 cellarium/ml/models/cellarium_gpt.py          | 200 ++++-
 .../ml/transforms/cellarium_gpt_tokenizer.py  | 684 ++++++++++++++++++
 6 files changed, 921 insertions(+), 11 deletions(-)

diff --git a/cellarium/ml/core/datamodule.py b/cellarium/ml/core/datamodule.py
index 09060ff0..a2e103e5 100644
--- a/cellarium/ml/core/datamodule.py
+++ b/cellarium/ml/core/datamodule.py
@@ -184,7 +184,7 @@ def setup(self, stage: str | None = None) -> None:
                 end_idx=self.n_train + self.n_val,
             )
 
-        if stage == "predict":
+        if stage in {"predict", "test"}:
             self.predict_dataset = IterableDistributedAnnDataCollectionDataset(
                 dadc=self.dadc,
                 batch_keys=self.batch_keys,
@@ -228,6 +228,16 @@ def predict_dataloader(self) -> torch.utils.data.DataLoader:
             persistent_workers=self.persistent_workers,
         )
 
+    def test_dataloader(self) -> torch.utils.data.DataLoader:
+        """Prediction dataloader."""
+        return torch.utils.data.DataLoader(
+            self.predict_dataset,
+            num_workers=self.num_workers,
+            collate_fn=self.collate_fn,
+            prefetch_factor=self.prefetch_factor,
+            persistent_workers=self.persistent_workers,
+        )
+
     def state_dict(self) -> dict[str, Any]:
         assert self.trainer is not None
         state = {
@@ -276,8 +286,8 @@ def load_state_dict(self, state_dict: dict[str, Any]) -> None:
                     "Cannot resume training with a different batch size. "
                     f"Expected {self.batch_size}, got {state_dict['batch_size']}."
                 )
-            if state_dict["accumulate_grad_batches"] != 1:
-                raise ValueError("Training with gradient accumulation is not supported when resuming training.")
+            # if state_dict["accumulate_grad_batches"] != 1:
+            #     raise ValueError("Training with gradient accumulation is not supported when resuming training.")
             if state_dict["shuffle"] != self.shuffle:
                 raise ValueError(
                     "Cannot resume training with a different shuffle value. "
diff --git a/cellarium/ml/core/module.py b/cellarium/ml/core/module.py
index 3da6363c..9cf78483 100644
--- a/cellarium/ml/core/module.py
+++ b/cellarium/ml/core/module.py
@@ -291,6 +291,27 @@ def validation_step(self, batch: dict[str, Any], batch_idx: int) -> None:
         batch["batch_idx"] = batch_idx
         self.module_pipeline.validate(batch)
 
+    def test_step(self, batch: dict[str, Any], batch_idx: int) -> None:
+        """
+        Forward pass for validation step.
+
+        Args:
+            batch:
+                A dictionary containing the batch data.
+            batch_idx:
+                The index of the batch.
+
+        Returns:
+            None
+        """
+        if self.module_pipeline is None:
+            raise RuntimeError("The model is not configured. Call `configure_model` before accessing the model.")
+
+        batch["pl_module"] = self
+        batch["trainer"] = self.trainer
+        batch["batch_idx"] = batch_idx
+        self.module_pipeline.test(batch)
+
     def configure_optimizers(self) -> OptimizerLRScheduler:
         """Configure optimizers for the model."""
         optim_fn = self.hparams["optim_fn"]
diff --git a/cellarium/ml/core/pipeline.py b/cellarium/ml/core/pipeline.py
index 61edf49e..509a2510 100644
--- a/cellarium/ml/core/pipeline.py
+++ b/cellarium/ml/core/pipeline.py
@@ -70,3 +70,12 @@ def validate(self, batch: dict[str, dict[str, np.ndarray | torch.Tensor] | np.nd
         if not isinstance(model, ValidateMixin):
             raise TypeError(f"The last module in the pipeline must be an instance of {ValidateMixin}. Got {model}")
         call_func_with_batch(model.validate, batch)
+
+    def test(self, batch: dict[str, dict[str, np.ndarray | torch.Tensor] | np.ndarray | torch.Tensor]) -> None:
+        for module in self[:-1]:
+            batch |= call_func_with_batch(module.forward, batch)
+
+        model = self[-1]
+        # if not isinstance(model, ValidateMixin):
+        #     raise TypeError(f"The last module in the pipeline must be an instance of {ValidateMixin}. Got {model}")
+        call_func_with_batch(model.test, batch)
diff --git a/cellarium/ml/data/dadc_dataset.py b/cellarium/ml/data/dadc_dataset.py
index e69c875e..32cecac2 100644
--- a/cellarium/ml/data/dadc_dataset.py
+++ b/cellarium/ml/data/dadc_dataset.py
@@ -563,4 +563,4 @@ def load_state_dict(self, state_dict: dict[str, Any]) -> None:
         # trainer.fit_loop.epoch_progress.current.completed
         self.epoch = state_dict["epoch"]
         # trainer.fit_loop.epoch_loop.automatic_optimization.optim_progress.optimizer_steps
-        self.resume_step = state_dict["resume_step"]
+        self.resume_step = state_dict["resume_step"] * state_dict["accumulate_grad_batches"]
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 3670828e..da506e40 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -5,11 +5,12 @@
 
 import lightning.pytorch as pl
 import numpy as np
+import pandas as pd
 import torch
 from torch import nn
 from torch.nn.attention.flex_attention import BlockMask, create_block_mask
 
-from cellarium.ml.layers import MultiHeadReadout, TokenEmbedding, Transformer, TransformerBlock
+from cellarium.ml.layers import MultiHeadReadout, TokenEmbedding, Transformer
 from cellarium.ml.models.model import CellariumModel, PredictMixin, ValidateMixin
 from cellarium.ml.utilities.layers import scale_initializers_by_dimension
 from cellarium.ml.utilities.mup import LRAdjustmentGroup
@@ -22,7 +23,7 @@ def use_cs() -> bool:
         return False
 
 
-torch.set_float32_matmul_precision("high")
+torch.set_float32_matmul_precision("highest")
 
 
 def prompt_diagonal_mask(prompt_mask_nc: torch.Tensor) -> torch.Tensor:
@@ -245,19 +246,19 @@ def _reset_parameters(module):
     @property
     def d_model(self) -> int:
         block = self.transformer.blocks[0]
-        assert isinstance(block, TransformerBlock)
+        # assert isinstance(block, TransformerBlock)
         return block.d_model
 
     @property
     def d_ffn(self) -> int:
         block = self.transformer.blocks[0]
-        assert isinstance(block, TransformerBlock)
+        # assert isinstance(block, TransformerBlock)
         return block.d_ffn
 
     @property
     def n_heads(self) -> int:
         block = self.transformer.blocks[0]
-        assert isinstance(block, TransformerBlock)
+        # assert isinstance(block, TransformerBlock)
         return block.attention.n_heads
 
     @property
@@ -267,13 +268,13 @@ def n_blocks(self) -> int:
     @property
     def attention_backend(self) -> Literal["flex", "math", "mem_efficient", "torch"]:
         block = self.transformer.blocks[0]
-        assert isinstance(block, TransformerBlock)
+        # assert isinstance(block, TransformerBlock)
         return block.attention.attention_backend
 
     @attention_backend.setter
     def attention_backend(self, value: Literal["flex", "math", "mem_efficient", "torch"]) -> None:
         for block in self.transformer.blocks:
-            assert isinstance(block, TransformerBlock)
+            # assert isinstance(block, TransformerBlock)
             block.attention.attention_backend = value
 
     def predict(
@@ -373,3 +374,188 @@ def validate(
         )
 
         pl_module.log_dict(loss_dict, sync_dist=True, on_epoch=True, batch_size=n)
+
+    # def test(
+    #     self,
+    #     trainer: pl.Trainer,
+    #     pl_module: pl.LightningModule,
+    #     batch_idx: int,
+    #     token_value_nc_dict: dict[str, torch.Tensor],
+    #     token_mask_nc_dict: dict[str, torch.Tensor],
+    #     prompt_mask_nc: torch.Tensor,
+    #     label_nc_dict: dict[str, torch.Tensor],
+    #     label_weight_nc_dict: dict[str, torch.Tensor],
+    #     obs_names_n: np.ndarray,
+    #     total_mrna_umis_n: torch.Tensor,
+    #     total_mrna_umis_downsampled_n: torch.Tensor,
+    #     cell_type_n: np.ndarray,
+    #     gene_prompt_size_n: torch.Tensor,
+    #     gene_query_size_n: torch.Tensor,
+    #     gene_query_nnz_size_n: torch.Tensor,
+    # ) -> None:
+    #     logits_nck_dict = self.predict(
+    #         token_value_nc_dict=token_value_nc_dict,
+    #         token_mask_nc_dict=token_mask_nc_dict,
+    #         prompt_mask_nc=prompt_mask_nc,
+    #     )
+
+    #     loss_fn = nn.CrossEntropyLoss(reduction="none")
+    #     key = "gene_value"
+    #     label_nc = label_nc_dict[key]
+    #     logits_nck = logits_nck_dict[key]
+    #     assert isinstance(logits_nck, torch.Tensor)
+    #     label_weight_nc = label_weight_nc_dict[key]
+    #     assert isinstance(label_weight_nc, torch.Tensor)
+    #     loss_nc = loss_fn(logits_nck.view(label_nc.numel(), -1), label_nc.view(-1).long()).reshape(label_nc.shape)
+    #     loss_n = torch.sum(loss_nc * label_weight_nc, dim=-1) / label_weight_nc.sum(dim=-1)
+    #     nnz_label_weight_nc = (label_nc > 0).float() * label_weight_nc
+    #     loss_nnz_n = torch.sum(loss_nc * nnz_label_weight_nc, dim=-1) / nnz_label_weight_nc.sum(dim=-1)
+
+    #     df = pd.DataFrame(
+    #         data={
+    #             # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
+    #             # "query_size": label_weight_nc.sum(-1).cpu().int(),
+    #             # "total_mrna_umis": total_mrna_umis_n.cpu(),
+    #             # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+    #             # "cell_type": cell_type_n.cpu(),
+    #             # "loss": loss_n.cpu(),
+    #             # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
+    #             # "loss_nnz": loss_nnz_n.cpu(),
+    #             "prompt_size": gene_prompt_size_n.cpu(),
+    #             "query_size": gene_query_size_n.cpu().int(),
+    #             "total_mrna_umis": total_mrna_umis_n.cpu(),
+    #             "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+    #             "cell_type": cell_type_n.cpu(),
+    #             "loss": loss_n.cpu(),
+    #             "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
+    #             "loss_nnz": loss_nnz_n.cpu(),
+    #         },
+    #         index=obs_names_n,
+    #     )
+    #     postfix = batch_idx * trainer.world_size + trainer.global_rank
+    #     output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_prefix_0_seed_3113024_all_measured/batch_{postfix}.csv"
+    #     # output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_all_prefixes_seed_3112024_all_measured/batch_{postfix}.csv"
+    #     df.to_csv(output_path)
+
+    # def test(
+    #     self,
+    #     trainer: pl.Trainer,
+    #     pl_module: pl.LightningModule,
+    #     batch_idx: int,
+    #     token_value_nc_dict: dict[str, torch.Tensor],
+    #     token_mask_nc_dict: dict[str, torch.Tensor],
+    #     prompt_mask_nc: torch.Tensor,
+    #     label_nc_dict: dict[str, torch.Tensor],
+    #     label_weight_nc_dict: dict[str, torch.Tensor],
+    #     obs_names_n: np.ndarray,
+    #     total_mrna_umis_n: torch.Tensor,
+    #     total_mrna_umis_downsampled_n: torch.Tensor,
+    #     cell_type_n: np.ndarray,
+    #     gene_prompt_size_n: torch.Tensor,
+    #     gene_query_size_n: torch.Tensor,
+    #     gene_query_nnz_size_n: torch.Tensor,
+    #     measured_query_mask_nc: torch.Tensor,
+    #     unmeasured_query_mask_nc: torch.Tensor,
+    # ) -> None:
+    #     logits_nck_dict = self.predict(
+    #         token_value_nc_dict=token_value_nc_dict,
+    #         token_mask_nc_dict=token_mask_nc_dict,
+    #         prompt_mask_nc=prompt_mask_nc,
+    #     )
+
+    #     key = "gene_value"
+    #     logits_nck = logits_nck_dict[key]
+    #     k = logits_nck.shape[2]
+    #     value_k = torch.arange(k, dtype=torch.float32, device=logits_nck.device)
+    #     assert isinstance(logits_nck, torch.Tensor)
+    #     probs_nck = logits_nck.softmax(-1)
+    #     mean_nc = probs_nck @ value_k
+    #     measured_mean_n = (mean_nc * measured_query_mask_nc).sum(-1) / measured_query_mask_nc.sum(-1)
+    #     unmeasured_mean_n = (mean_nc * unmeasured_query_mask_nc).sum(-1) / unmeasured_query_mask_nc.sum(-1)
+
+    #     df = pd.DataFrame(
+    #         data={
+    #             # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
+    #             # "query_size": label_weight_nc.sum(-1).cpu().int(),
+    #             # "total_mrna_umis": total_mrna_umis_n.cpu(),
+    #             # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+    #             # "cell_type": cell_type_n.cpu(),
+    #             # "loss": loss_n.cpu(),
+    #             # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
+    #             # "loss_nnz": loss_nnz_n.cpu(),
+    #             "prompt_size": gene_prompt_size_n.cpu(),
+    #             "query_size": gene_query_size_n.cpu().int(),
+    #             "total_mrna_umis": total_mrna_umis_n.cpu(),
+    #             "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+    #             "cell_type": cell_type_n.cpu(),
+    #             "measured_mean": measured_mean_n.cpu(),
+    #             "measured_size": measured_query_mask_nc.sum(-1).cpu(),
+    #             "unmeasured_mean": unmeasured_mean_n.cpu(),
+    #             "unmeasured_size": unmeasured_query_mask_nc.sum(-1).cpu(),
+    #             # "loss": loss_n.cpu(),
+    #             # "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
+    #             # "loss_nnz": loss_nnz_n.cpu(),
+    #         },
+    #         index=obs_names_n,
+    #     )
+    #     postfix = batch_idx * trainer.world_size + trainer.global_rank
+    #     output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/unmeasured/batch_{postfix}.csv"
+    #     df.to_csv(output_path)
+
+    # predict cell type
+    def test(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        batch_idx: int,
+        token_value_nc_dict: dict[str, torch.Tensor],
+        token_mask_nc_dict: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor,
+        label_nc_dict: dict[str, torch.Tensor],
+        label_weight_nc_dict: dict[str, torch.Tensor],
+        obs_names_n: np.ndarray,
+        total_mrna_umis_n: torch.Tensor,
+        total_mrna_umis_downsampled_n: torch.Tensor,
+        cell_type_n: np.ndarray,
+        gene_prompt_size_n: torch.Tensor,
+        gene_query_size_n: torch.Tensor,
+        gene_query_nnz_size_n: torch.Tensor,
+    ) -> None:
+        logits_nck_dict = self.predict(
+            token_value_nc_dict=token_value_nc_dict,
+            token_mask_nc_dict=token_mask_nc_dict,
+            prompt_mask_nc=prompt_mask_nc,
+        )
+        import pdb; pdb.set_trace()
+
+        key = "cell_type"
+        label_nc = label_nc_dict[key]
+        logits_nck = logits_nck_dict[key]
+        probs_nck = logits_nck.softmax(-1)
+        pred_label_nc = probs_nck.argmax(-1)
+
+        df = pd.DataFrame(
+            data={
+                # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
+                # "query_size": label_weight_nc.sum(-1).cpu().int(),
+                # "total_mrna_umis": total_mrna_umis_n.cpu(),
+                # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+                # "cell_type": cell_type_n.cpu(),
+                # "loss": loss_n.cpu(),
+                # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
+                # "loss_nnz": loss_nnz_n.cpu(),
+                "prompt_size": gene_prompt_size_n.cpu(),
+                "query_size": gene_query_size_n.cpu().int(),
+                "total_mrna_umis": total_mrna_umis_n.cpu(),
+                "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
+                "cell_type": cell_type_n.cpu(),
+                "loss": loss_n.cpu(),
+                "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
+                "loss_nnz": loss_nnz_n.cpu(),
+            },
+            index=obs_names_n,
+        )
+        postfix = batch_idx * trainer.world_size + trainer.global_rank
+        output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_prefix_0_seed_3113024_all_measured/batch_{postfix}.csv"
+        # output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_all_prefixes_seed_3112024_all_measured/batch_{postfix}.csv"
+        df.to_csv(output_path)
\ No newline at end of file
diff --git a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
index a06b0763..e0b5cd25 100644
--- a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
+++ b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
@@ -365,3 +365,687 @@ def forward(
             "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
             "prompt_mask_nc": prompt_mask_nc,
         }
+
+
+class CellariumGPTTestTokenizer(torch.nn.Module):
+    """
+    Tokenizer for the Cellarium GPT model.
+
+    Args:
+        context_len:
+            Context length.
+        gene_downsample_fraction:
+            Fraction of genes to downsample.
+        min_total_mrna_umis:
+            Minimum total mRNA UMIs.
+        max_total_mrna_umis:
+            Maximum total mRNA UMIs.
+        gene_vocab_sizes:
+            Gene token vocabulary sizes.
+        metadata_vocab_sizes:
+            Metadata token vocabulary sizes.
+        ontology_infos_path:
+            Path to ontology information.
+        prefix_len:
+            Prefix length. If ``None``, the prefix length is sampled.
+        metadata_prompt_token_list:
+            List of metadata tokens to prompt. If ``None``, the metadata prompt tokens are sampled.
+        obs_names_rng:
+            Cell IDs are used as random seeds for shuffling gene tokens.
+            If ``None``, gene tokens are shuffled without a random seed.
+    """
+
+    def __init__(
+        self,
+        context_len: int,
+        gene_downsample_fraction: float,
+        min_total_mrna_umis: int,
+        max_total_mrna_umis: int,
+        gene_vocab_sizes: dict[str, int],
+        metadata_vocab_sizes: dict[str, int],
+        ontology_downsample_p: float,
+        ontology_infos_path: str,
+        prefix_len: int | None = None,
+        metadata_prompt_token_list: list[str] | None = None,
+        obs_names_rng: bool = False,
+    ) -> None:
+        super().__init__()
+        self.context_len = context_len
+        self.gene_downsample_fraction = gene_downsample_fraction
+        self.min_total_mrna_umis = min_total_mrna_umis
+        self.max_total_mrna_umis = max_total_mrna_umis
+        self.gene_vocab_sizes = gene_vocab_sizes
+        self.metadata_vocab_sizes = metadata_vocab_sizes
+        self.ontology_infos = torch.load(ontology_infos_path, weights_only=True)
+        self.ontology_downsample_p = ontology_downsample_p
+        self.prefix_len = prefix_len
+        self.metadata_prompt_token_list = metadata_prompt_token_list
+        self.obs_names_rng = obs_names_rng
+
+    # def forward(
+    #     self,
+    #     metadata_token_n_dict: dict[str, torch.Tensor],
+    #     gene_token_n_dict: dict[str, torch.Tensor],
+    #     gene_token_ng_dict: dict[str, torch.Tensor],
+    #     obs_names_n: np.ndarray | None = None,
+    # ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
+    #     ### GENE TOKENS ###
+    #     n, g = gene_token_ng_dict["gene_value"].shape
+    #     m = len(metadata_token_n_dict)
+    #     c = self.context_len
+    #     # gene context length
+    #     j = c - m
+    #     device = gene_token_ng_dict["gene_value"].device
+
+    #     ## gene measurement tokens (assay, suspension type, etc.) ##
+    #     gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
+
+    #     ## gene id ##
+    #     gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
+
+    #     if self.obs_names_rng:
+    #         rng_n = [torch.Generator(device=device) for _ in range(n)]
+    #         [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
+    #         shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
+    #     else:
+    #         shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
+    #     shuffle_idx_nj = shuffle_idx_ng[:, :j]
+
+    #     for key, gene_token_ng in gene_token_ng_dict.items():
+    #         gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
+
+    #     ## gene value ##
+    #     gene_value_nj = gene_token_nj_dict.pop("gene_value")
+    #     total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+    #     # downsample gene values
+    #     max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+    #     downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+    #     if self.gene_downsample_fraction > 0:
+    #         gene_downsample_p_nj = torch.minimum(
+    #             torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
+    #             torch.tensor(1.0, device=device),
+    #         )
+    #         downsampled_total_mrna_umis_nj = torch.lerp(
+    #             torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
+    #             downsampled_total_mrna_umis_nj,
+    #             gene_downsample_p_nj,
+    #         )
+    #     gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+    #     gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
+    #     total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
+    #     if self.prefix_len is not None:
+    #         prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
+    #     else:
+    #         # sample prefix length
+    #         # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
+    #         max_prefix_len = j - 1 - 2048
+    #         prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
+    #         # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
+    #         # prefix_len_weights[0] = 1 / 10
+    #         prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
+    #     # create prompt and query masks
+    #     # query_len_n = j - prefix_len_n
+    #     # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
+    #     gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
+    #     gene_prompt_mask_nj = ~gene_query_mask_nj
+    #     if "measured_genes_mask" in gene_token_nj_dict:
+    #         measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
+    #         gene_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
+    #         gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
+
+    #     gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
+    #     gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
+    #     gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
+
+    #     gene_token_value_nc_dict = {
+    #         key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
+    #         for key, gene_token_nj in gene_token_nj_dict.items()
+    #     }
+    #     gene_token_mask_nc = torch.cat(
+    #         [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
+    #         dim=1,
+    #     )
+    #     gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
+
+    #     # gene label
+    #     gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
+    #     gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
+
+    #     ### METADATA TOKENS ###
+
+    #     ## metadata tokens ##
+    #     # assign token codes based on the ontology info
+    #     # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+    #     for key, ontology_info in self.ontology_infos.items():
+    #         if key in metadata_token_n_dict:
+    #             assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
+    #             metadata_token_n_dict[key] = torch.tensor(
+    #                 pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
+    #                 dtype=torch.int,
+    #             )
+    #     # create metadata query and prompt masks
+    #     if self.metadata_prompt_token_list is not None:
+    #         metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
+    #         for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
+    #             if metadata_token in self.metadata_prompt_token_list:
+    #                 metadata_prompt_mask_nm[:, metadata_token_idx] = True
+    #     else:
+    #         metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
+    #         metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
+    #         shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
+    #         metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
+    #     metadata_query_mask_nm = ~metadata_prompt_mask_nm
+    #     metadata_measured_mask_nm = torch.stack(
+    #         [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
+    #     ).bool()
+    #     metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
+    #     metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
+    #     # clamp unmeasured tokens to 0 in order to avoid error during embedding
+    #     # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
+    #     for key, metadata_token_n in metadata_token_n_dict.items():
+    #         metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
+    #     # metadata labels
+    #     metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
+    #     if self.ontology_downsample_p != 0:
+    #         # downsample metadata based on ontology
+    #         for key, ontology_info in self.ontology_infos.items():
+    #             if "shortest_distances_matrix" not in ontology_info:
+    #                 continue
+    #             if key not in metadata_token_n_dict:
+    #                 continue
+    #             metadata_token_n = metadata_token_n_dict[key]
+    #             shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
+    #             ontology_weights = (
+    #                 self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
+    #             )
+    #             metadata_token_n_dict[key] = (
+    #                 torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
+    #             )
+    #     # impute mask token for unmeasured metadata
+    #     # mask token is the last token in the vocabulary
+    #     for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
+    #         metadata_token_n_dict[key] = torch.where(
+    #             metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
+    #         ).int()
+
+    #     block_metadata_token_nm = torch.block_diag(
+    #         *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
+    #     )
+    #     metadata_token_value_nc_dict = {
+    #         key: torch.cat(
+    #             [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
+    #             dim=1,
+    #         )
+    #         for i, key in enumerate(metadata_token_n_dict)
+    #     }
+    #     block_metadata_token_mask_nm = torch.block_diag(
+    #         *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
+    #     )
+    #     metadata_token_mask_nc_dict = {
+    #         key: torch.cat(
+    #             [
+    #                 torch.zeros((n, j), dtype=torch.bool, device=device),
+    #                 block_metadata_token_mask_nm[n * i : n * (i + 1)],
+    #             ],
+    #             dim=1,
+    #         )
+    #         for i, key in enumerate(metadata_token_n_dict)
+    #     }
+
+    #     ### PROMPT MASK ###
+    #     prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
+
+    #     ### LABELS ###
+    #     block_label_nc = torch.block_diag(
+    #         gene_label_nj,
+    #         *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
+    #     )
+    #     label_nc_dict = {
+    #         key: block_label_nc[n * i : n * (i + 1)]
+    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+    #     }
+
+    #     ### LABEL WEIGHTS ###
+    #     block_label_weight_nc = torch.block_diag(
+    #         gene_query_mask_nj,
+    #         *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
+    #     )
+    #     label_weight_nc_dict = {
+    #         key: block_label_weight_nc[n * i : n * (i + 1)]
+    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+    #     }
+
+    #     return {
+    #         "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
+    #         "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
+    #         "prompt_mask_nc": prompt_mask_nc,
+    #         "label_nc_dict": label_nc_dict,
+    #         "label_weight_nc_dict": label_weight_nc_dict,
+    #         "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
+    #         "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
+    #         "cell_type_n": metadata_token_n_dict["cell_type"],
+    #         "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
+    #         "gene_query_size_n": gene_query_mask_nj.sum(-1),
+    #         "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
+    #     }
+
+    # def forward(
+    #     self,
+    #     metadata_token_n_dict: dict[str, torch.Tensor],
+    #     gene_token_n_dict: dict[str, torch.Tensor],
+    #     gene_token_ng_dict: dict[str, torch.Tensor],
+    #     obs_names_n: np.ndarray | None = None,
+    # ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
+    #     ### GENE TOKENS ###
+    #     n, g = gene_token_ng_dict["gene_value"].shape
+    #     m = len(metadata_token_n_dict)
+    #     c = self.context_len
+    #     # gene context length
+    #     j = c - m
+    #     device = gene_token_ng_dict["gene_value"].device
+
+    #     ## gene measurement tokens (assay, suspension type, etc.) ##
+    #     gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
+
+    #     ## gene id ##
+    #     gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
+
+    #     if self.obs_names_rng:
+    #         rng_n = [torch.Generator(device=device) for _ in range(n)]
+    #         [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
+    #         shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
+    #     else:
+    #         shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
+    #     shuffle_idx_nj = shuffle_idx_ng[:, :j]
+
+    #     for key, gene_token_ng in gene_token_ng_dict.items():
+    #         gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
+
+    #     ## gene value ##
+    #     gene_value_nj = gene_token_nj_dict.pop("gene_value")
+    #     total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+    #     # downsample gene values
+    #     max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+    #     downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+    #     if self.gene_downsample_fraction > 0:
+    #         gene_downsample_p_nj = torch.minimum(
+    #             torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
+    #             torch.tensor(1.0, device=device),
+    #         )
+    #         downsampled_total_mrna_umis_nj = torch.lerp(
+    #             torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
+    #             downsampled_total_mrna_umis_nj,
+    #             gene_downsample_p_nj,
+    #         )
+    #     gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+    #     gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
+    #     total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
+    #     if self.prefix_len is not None:
+    #         prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
+    #     else:
+    #         # sample prefix length
+    #         # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
+    #         max_prefix_len = j - 1 - 2048
+    #         prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
+    #         # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
+    #         # prefix_len_weights[0] = 1 / 10
+    #         prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
+    #     # create prompt and query masks
+    #     # query_len_n = j - prefix_len_n
+    #     # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
+    #     gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
+    #     gene_prompt_mask_nj = ~gene_query_mask_nj
+    #     if "measured_genes_mask" in gene_token_nj_dict:
+    #         measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
+    #         gene_query_mask_nj = gene_query_mask_nj
+    #         gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
+    #         measured_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
+    #         unmeasured_query_mask_nj = gene_query_mask_nj & (~measured_genes_mask_nj)
+
+    #     gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
+    #     gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
+    #     gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
+    #     measured_query_mask_nc = torch.cat([measured_query_mask_nj, torch.zeros((n, m), device=device, dtype=torch.float32)], dim=1)
+    #     unmeasured_query_mask_nc = torch.cat([unmeasured_query_mask_nj, torch.zeros((n, m), device=device, dtype=torch.float32)], dim=1)
+
+    #     gene_token_value_nc_dict = {
+    #         key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
+    #         for key, gene_token_nj in gene_token_nj_dict.items()
+    #     }
+    #     gene_token_mask_nc = torch.cat(
+    #         [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
+    #         dim=1,
+    #     )
+    #     gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
+
+    #     # gene label
+    #     gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
+    #     gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
+
+    #     ### METADATA TOKENS ###
+
+    #     ## metadata tokens ##
+    #     # assign token codes based on the ontology info
+    #     # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+    #     for key, ontology_info in self.ontology_infos.items():
+    #         if key in metadata_token_n_dict:
+    #             assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
+    #             metadata_token_n_dict[key] = torch.tensor(
+    #                 pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
+    #                 dtype=torch.int,
+    #             )
+    #     # create metadata query and prompt masks
+    #     if self.metadata_prompt_token_list is not None:
+    #         metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
+    #         for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
+    #             if metadata_token in self.metadata_prompt_token_list:
+    #                 metadata_prompt_mask_nm[:, metadata_token_idx] = True
+    #     else:
+    #         metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
+    #         metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
+    #         shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
+    #         metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
+    #     metadata_query_mask_nm = ~metadata_prompt_mask_nm
+    #     metadata_measured_mask_nm = torch.stack(
+    #         [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
+    #     ).bool()
+    #     metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
+    #     metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
+    #     # clamp unmeasured tokens to 0 in order to avoid error during embedding
+    #     # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
+    #     for key, metadata_token_n in metadata_token_n_dict.items():
+    #         metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
+    #     # metadata labels
+    #     metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
+    #     if self.ontology_downsample_p != 0:
+    #         # downsample metadata based on ontology
+    #         for key, ontology_info in self.ontology_infos.items():
+    #             if "shortest_distances_matrix" not in ontology_info:
+    #                 continue
+    #             if key not in metadata_token_n_dict:
+    #                 continue
+    #             metadata_token_n = metadata_token_n_dict[key]
+    #             shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
+    #             ontology_weights = (
+    #                 self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
+    #             )
+    #             metadata_token_n_dict[key] = (
+    #                 torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
+    #             )
+    #     # impute mask token for unmeasured metadata
+    #     # mask token is the last token in the vocabulary
+    #     for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
+    #         metadata_token_n_dict[key] = torch.where(
+    #             metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
+    #         ).int()
+
+    #     block_metadata_token_nm = torch.block_diag(
+    #         *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
+    #     )
+    #     metadata_token_value_nc_dict = {
+    #         key: torch.cat(
+    #             [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
+    #             dim=1,
+    #         )
+    #         for i, key in enumerate(metadata_token_n_dict)
+    #     }
+    #     block_metadata_token_mask_nm = torch.block_diag(
+    #         *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
+    #     )
+    #     metadata_token_mask_nc_dict = {
+    #         key: torch.cat(
+    #             [
+    #                 torch.zeros((n, j), dtype=torch.bool, device=device),
+    #                 block_metadata_token_mask_nm[n * i : n * (i + 1)],
+    #             ],
+    #             dim=1,
+    #         )
+    #         for i, key in enumerate(metadata_token_n_dict)
+    #     }
+
+    #     ### PROMPT MASK ###
+    #     prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
+
+    #     ### LABELS ###
+    #     block_label_nc = torch.block_diag(
+    #         gene_label_nj,
+    #         *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
+    #     )
+    #     label_nc_dict = {
+    #         key: block_label_nc[n * i : n * (i + 1)]
+    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+    #     }
+
+    #     ### LABEL WEIGHTS ###
+    #     block_label_weight_nc = torch.block_diag(
+    #         gene_query_mask_nj,
+    #         *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
+    #     )
+    #     label_weight_nc_dict = {
+    #         key: block_label_weight_nc[n * i : n * (i + 1)]
+    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+    #     }
+
+    #     return {
+    #         "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
+    #         "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
+    #         "prompt_mask_nc": prompt_mask_nc,
+    #         "label_nc_dict": label_nc_dict,
+    #         "label_weight_nc_dict": label_weight_nc_dict,
+    #         "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
+    #         "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
+    #         "cell_type_n": metadata_token_n_dict["cell_type"],
+    #         "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
+    #         "gene_query_size_n": gene_query_mask_nj.sum(-1),
+    #         "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
+    #         "measured_query_mask_nc": measured_query_mask_nc,
+    #         "unmeasured_query_mask_nc": unmeasured_query_mask_nc,
+    #     }
+
+    def forward(
+        self,
+        metadata_token_n_dict: dict[str, torch.Tensor],
+        gene_token_n_dict: dict[str, torch.Tensor],
+        gene_token_ng_dict: dict[str, torch.Tensor],
+        obs_names_n: np.ndarray | None = None,
+    ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
+        ### GENE TOKENS ###
+        n, g = gene_token_ng_dict["gene_value"].shape
+        m = len(metadata_token_n_dict)
+        c = self.context_len
+        # gene context length
+        j = c - m
+        device = gene_token_ng_dict["gene_value"].device
+
+        ## gene measurement tokens (assay, suspension type, etc.) ##
+        gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
+
+        ## gene id ##
+        gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
+
+        if self.obs_names_rng:
+            rng_n = [torch.Generator(device=device) for _ in range(n)]
+            [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
+            shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
+        else:
+            shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
+        shuffle_idx_nj = shuffle_idx_ng[:, :j]
+
+        for key, gene_token_ng in gene_token_ng_dict.items():
+            gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
+
+        ## gene value ##
+        gene_value_nj = gene_token_nj_dict.pop("gene_value")
+        total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+        # downsample gene values
+        max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+        downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+        if self.gene_downsample_fraction > 0:
+            gene_downsample_p_nj = torch.minimum(
+                torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
+                torch.tensor(1.0, device=device),
+            )
+            downsampled_total_mrna_umis_nj = torch.lerp(
+                torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
+                downsampled_total_mrna_umis_nj,
+                gene_downsample_p_nj,
+            )
+        gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+        gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
+        total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
+        if self.prefix_len is not None:
+            prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
+        else:
+            # sample prefix length
+            # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
+            max_prefix_len = j - 1 - 2048
+            prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
+            # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
+            # prefix_len_weights[0] = 1 / 10
+            prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
+        # create prompt and query masks
+        # query_len_n = j - prefix_len_n
+        # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
+        gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
+        gene_prompt_mask_nj = ~gene_query_mask_nj
+        if "measured_genes_mask" in gene_token_nj_dict:
+            measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
+            gene_query_mask_nj = gene_query_mask_nj
+            gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
+
+        gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
+        gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
+        gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
+
+        gene_token_value_nc_dict = {
+            key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
+            for key, gene_token_nj in gene_token_nj_dict.items()
+        }
+        gene_token_mask_nc = torch.cat(
+            [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
+            dim=1,
+        )
+        gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
+
+        # gene label
+        gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
+        gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
+
+        ### METADATA TOKENS ###
+
+        ## metadata tokens ##
+        # assign token codes based on the ontology info
+        # token values not in the ontology are treated as unmeasured and assigned a code value of -1
+        for key, ontology_info in self.ontology_infos.items():
+            if key in metadata_token_n_dict:
+                assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
+                metadata_token_n_dict[key] = torch.tensor(
+                    pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
+                    dtype=torch.int,
+                )
+        # create metadata query and prompt masks
+        if self.metadata_prompt_token_list is not None:
+            metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
+            for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
+                if metadata_token in self.metadata_prompt_token_list:
+                    metadata_prompt_mask_nm[:, metadata_token_idx] = True
+        else:
+            metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
+            # metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
+            # metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
+            # shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
+            # metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
+        metadata_query_mask_nm = ~metadata_prompt_mask_nm
+        metadata_measured_mask_nm = torch.stack(
+            [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
+        ).bool()
+        metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
+        metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
+        # clamp unmeasured tokens to 0 in order to avoid error during embedding
+        # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
+        for key, metadata_token_n in metadata_token_n_dict.items():
+            metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
+        # metadata labels
+        metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
+        if self.ontology_downsample_p != 0:
+            # downsample metadata based on ontology
+            for key, ontology_info in self.ontology_infos.items():
+                if "shortest_distances_matrix" not in ontology_info:
+                    continue
+                if key not in metadata_token_n_dict:
+                    continue
+                metadata_token_n = metadata_token_n_dict[key]
+                shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
+                ontology_weights = (
+                    self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
+                )
+                metadata_token_n_dict[key] = (
+                    torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
+                )
+        # impute mask token for unmeasured metadata
+        # mask token is the last token in the vocabulary
+        for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
+            metadata_token_n_dict[key] = torch.where(
+                metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
+            ).int()
+
+        block_metadata_token_nm = torch.block_diag(
+            *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
+        )
+        metadata_token_value_nc_dict = {
+            key: torch.cat(
+                [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+        block_metadata_token_mask_nm = torch.block_diag(
+            *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
+        )
+        metadata_token_mask_nc_dict = {
+            key: torch.cat(
+                [
+                    torch.zeros((n, j), dtype=torch.bool, device=device),
+                    block_metadata_token_mask_nm[n * i : n * (i + 1)],
+                ],
+                dim=1,
+            )
+            for i, key in enumerate(metadata_token_n_dict)
+        }
+
+        ### PROMPT MASK ###
+        prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
+
+        ### LABELS ###
+        block_label_nc = torch.block_diag(
+            gene_label_nj,
+            *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
+        )
+        label_nc_dict = {
+            key: block_label_nc[n * i : n * (i + 1)]
+            for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+        }
+
+        ### LABEL WEIGHTS ###
+        block_label_weight_nc = torch.block_diag(
+            gene_query_mask_nj,
+            *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
+        )
+        label_weight_nc_dict = {
+            key: block_label_weight_nc[n * i : n * (i + 1)]
+            for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
+        }
+
+        return {
+            "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
+            "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
+            "prompt_mask_nc": prompt_mask_nc,
+            "label_nc_dict": label_nc_dict,
+            "label_weight_nc_dict": label_weight_nc_dict,
+            "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
+            "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
+            "cell_type_n": metadata_token_n_dict["cell_type"],
+            "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
+            "gene_query_size_n": gene_query_mask_nj.sum(-1),
+            "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
+        }
\ No newline at end of file

From 7c725df9c11fc5672e66f216113ca53ea5271c9c Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Fri, 28 Feb 2025 00:32:52 +0000
Subject: [PATCH 49/64] fix stage

---
 cellarium/ml/core/datamodule.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cellarium/ml/core/datamodule.py b/cellarium/ml/core/datamodule.py
index 530ed9ba..02b9fe10 100644
--- a/cellarium/ml/core/datamodule.py
+++ b/cellarium/ml/core/datamodule.py
@@ -184,7 +184,7 @@ def setup(self, stage: str | None = None) -> None:
                 end_idx=self.n_train + self.n_val,
             )
 
-        if stage in {"predict", "test"}:
+        if stage == "predict":
             self.predict_dataset = IterableDistributedAnnDataCollectionDataset(
                 dadc=self.dadc,
                 batch_keys=self.batch_keys,

From f73736800b756b58abce4d09f5cd87899c3d10c0 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 28 Feb 2025 00:54:52 +0000
Subject: [PATCH 50/64] updates all around

---
 .../inference/cellarium_gpt_inference.py      |   51 +-
 .../calculate_metrics.py                      |  172 +-
 ...on_by_cell_type_prompting_comparison.ipynb |    4 +-
 .../13_marker_gene_experiments.ipynb          |   33 +-
 .../14_linear_response_network_analysis.ipynb |   99 +-
 ...ion_by_cell_type_prompting_benchmark.ipynb | 4759 +++++++++++++++++
 .../assay_conversion/01_load_pbmc_data.ipynb  |  617 +++
 .../01_generate_resources.ipynb               | 2852 ++++++----
 .../03_analyze_metadata_predictions.ipynb     |  668 +--
 ...alyze_metadata_predictions_new_style.ipynb | 1198 +++++
 .../05_loss_scales.ipynb                      |  333 ++
 11 files changed, 9325 insertions(+), 1461 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/15_mean_expression_by_cell_type_prompting_benchmark.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/assay_conversion/01_load_pbmc_data.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 1bcb7378..c250e991 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -1205,7 +1205,7 @@ def __str__(self) -> str:
 
     def process(
             self,
-            response_normalization_strategy: t.Literal["mean", "std", "none"] = "mean",
+            response_normalization_strategy: t.Literal["mean", "std", "none", "corr", "log1p"] = "mean",
             feature_normalization_strategy: t.Literal["l2", "z_score", "none"] = "z_score",
             min_prompt_gene_tpm: float = 0.,
             min_query_gene_tpm: float = 0.,
@@ -1217,6 +1217,8 @@ def process(
             query_hv_min_x: float | None = 1e-2,
             query_hv_max_x: float | None = np.inf,
             eps: float = 1e-8,
+            target_lib_size: float = 10_000,
+            included_gene_biotypes: set[str] | None = None,
             z_trans_func: t.Callable[[np.ndarray], np.ndarray] | None = None,
         ) -> None:
 
@@ -1230,6 +1232,18 @@ def process(
         elif response_normalization_strategy == "none":
             z_p = np.ones_like(self.prompt_marginal_mean_p)
             z_q = np.ones_like(self.query_marginal_mean_q)
+        elif response_normalization_strategy == "corr":
+            assert len(self.query_var_names) == len(self.prompt_var_names)
+            assert all(self.query_var_names[i] == self.prompt_var_names[i] for i in range(len(self.query_var_names)))
+            z_p = self.prompt_marginal_std_p
+            z_q = self.query_marginal_std_q
+            self.corr_pp = self.response_qp * (z_p[None, :] + norm_pseudo_count) / (z_q[:, None] + norm_pseudo_count)
+            self.corr_pp = np.clip(0.5 * (self.corr_pp + self.corr_pp.T), -1, 1)
+        elif response_normalization_strategy == "log1p":
+            prompt_lib_size = self.prompt_marginal_mean_p.sum()
+            query_lib_size = self.query_marginal_mean_q.sum()
+            z_p = 1 + self.prompt_marginal_mean_p * target_lib_size / prompt_lib_size
+            z_q = 1 + self.query_marginal_mean_q * target_lib_size / query_lib_size
         else:
             raise ValueError("Invalid Jacobian normalization strategy")
 
@@ -1268,6 +1282,31 @@ def process(
             self.mask_q = self.mask_q & hv_mask_q
             logger.info(f"Number of query genes after highly-variable filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
 
+        if included_gene_biotypes is not None:
+            assert isinstance(included_gene_biotypes, set)
+            gene_id_to_gene_biotype_map = {
+                gene_id: biotype
+                for gene_id, biotype in zip(
+                    self.gene_info_df['ENSEMBL Gene ID'].values,
+                    self.gene_info_df['Gene Biotype'].values
+                )
+            }
+            for gene_id in self.query_var_names:
+                if gene_id not in gene_id_to_gene_biotype_map:
+                    gene_id_to_gene_biotype_map[gene_id] = "Unkown"
+            biotype_mask_q = np.array([
+                gene_id_to_gene_biotype_map[gene_id] in included_gene_biotypes
+                for gene_id in self.query_var_names
+            ])
+            biotype_mask_p = np.array([
+                gene_id_to_gene_biotype_map[gene_id] in included_gene_biotypes
+                for gene_id in self.prompt_var_names
+            ])
+            self.mask_q = self.mask_q & biotype_mask_q
+            self.mask_p = self.mask_p & biotype_mask_p
+            logger.info(f"Number of query genes after biotype filtering: {np.sum(self.mask_q)} / {len(self.mask_q)}")
+            logger.info(f"Number of prompt genes after biotype filtering: {np.sum(self.mask_p)} / {len(self.mask_p)}")
+        
         # apply the mask to everything else
         self.prompt_var_names = [self.prompt_var_names[i] for i in range(len(self.prompt_var_names)) if self.mask_p[i]]
         self.prompt_marginal_mean_p = self.prompt_marginal_mean_p[self.mask_p]
@@ -1280,6 +1319,9 @@ def process(
         # apply the mask to z_qp
         self.z_qp = self.z_qp[self.mask_q, :][:, self.mask_p]
 
+        if hasattr(self, 'corr_pp'):
+            self.corr_pp = self.corr_pp[self.mask_q, :][:, self.mask_p]
+
         # clip and transform features
         self.z_qp[np.isnan(self.z_qp)] = 0.
         self.z_qp[np.isinf(self.z_qp)] = 0.
@@ -1325,7 +1367,12 @@ def compute_adjacency_matrix(
             **kwargs) -> None:
 
         n_query_genes = self.z_qp.shape[0]
-        rho_pp = self.z_qp.T @ self.z_qp / n_query_genes
+        if hasattr(self, 'corr_pp'):
+            logger.info("Using precomputed correlation matrix")
+            rho_pp = self.corr_pp
+        else:
+            logger.info("Computing correlation matrix from z_qp")
+            rho_pp = self.z_qp.T @ self.z_qp / n_query_genes
 
         if adjacency_strategy == "shifted_correlation":
             assert "beta" in kwargs, "Must provide beta for shifted correlation"
diff --git a/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py b/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
index 2f8d5d81..bf6a867d 100644
--- a/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
+++ b/cellarium/ml/utilities/inference/metadata_benchmarking/calculate_metrics.py
@@ -81,7 +81,75 @@ def calculate_tps_and_fps(
     return true_positives, false_positives
 
 
-def calculate_metrics_for_query(
+def calculate_hop_inclusion_scores(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    num_hops: int,
+    ontology_resource: t.Dict[str, t.Any]
+) -> t.List[float]:
+    """
+    Calculate inclusion score at each hop level.
+
+    :param query_cell_obj: The query object containing ontology-aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param num_hops: Number of hops to consider.
+    :param co_resource: Ontology helper resource which has already precalculated top_n_level descendants and
+        ancestors.
+
+    :return: A list containing inclusion score at each hop level.
+    """
+    hop_ground_truth_ontology_contexts = [
+        ontology_resource[ground_truth_ontology_term_id][f"hop_{i}"] for i in range(num_hops + 1)]
+    inclusion_scores = [0] * len(hop_ground_truth_ontology_contexts)
+
+    for i_hop, hop_ground_truth_ontology_context in enumerate(hop_ground_truth_ontology_contexts):
+        for match in query_obj["matches"]:
+            truth_set = hop_ground_truth_ontology_context["all_descendants"]
+            if match["ontology_term_id"] in truth_set:
+                inclusion_scores[i_hop] = inclusion_scores[i_hop] + match["score"]
+
+    return inclusion_scores
+
+def calculate_hop_calls(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    num_hops: int,
+    ontology_resource: t.Dict[str, t.Any]
+) -> t.List[float]:
+    """
+    Calculate correctness of argmax call at each hop level.
+
+    :param query_cell_obj: The query object containing ontology-aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param num_hops: Number of hops to consider.
+    :param co_resource: Ontology helper resource which has already precalculated top_n_level descendants and
+        ancestors.
+
+    :return: A list containing correctness of argmax call at each hop level.
+    """
+    hop_ground_truth_ontology_contexts = [
+        ontology_resource[ground_truth_ontology_term_id][f"hop_{i}"] for i in range(num_hops + 1)]
+    calls = [0] * len(hop_ground_truth_ontology_contexts)
+
+    best_score = -np.inf
+    best_ontology_term_id = None
+    for match in query_obj["matches"]:
+        if match["score"] > best_score:
+            best_score = match["score"]
+            best_ontology_term_id = match["ontology_term_id"]
+    
+    if best_ontology_term_id is None:
+        raise RuntimeError("No best ontology term ID found -- this should not happen!") 
+    
+    for i_hop, hop_ground_truth_ontology_context in enumerate(hop_ground_truth_ontology_contexts):
+        truth_set = hop_ground_truth_ontology_context["all_descendants"]
+        if best_ontology_term_id in truth_set:
+            calls[i_hop] = 1
+
+    return calls
+
+
+def calculate_metrics_for_query_v1(
     query_obj: t.Dict[str, t.Any],
     ground_truth_ontology_term_id: str,
     ontology_resource: t.Dict[str, t.Any],
@@ -132,11 +200,93 @@ def calculate_metrics_for_query(
 
     return query_cell_metrics
 
+def calculate_metrics_for_query_v2(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    ontology_resource: t.Dict[str, t.Any],
+    num_hops=4,
+):
+    """
+    Calculate performance metrics for a query cell against the ground truth.
+
+    :param query_obj: The query object containing ontology aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param ontology_resource: Ontology precalculated resource used in CELLxGENE.
+    :param num_hops: Number of hops to consider.
+
+    :return: A dictionary containing sensitivity, specificity, and F1 score for each hop level.
+    """
+    if ground_truth_ontology_term_id not in ontology_resource:
+        metrics_na = {
+            "query_cell_id": query_obj["query_cell_id"],
+            "detail": f"Couldn't find {ground_truth_ontology_term_id} in the ontology resource",
+        }
+        for hop in range(num_hops + 1):
+                metrics_na[f"hop_{hop}_inclusion_score"] = np.nan
+
+        return metrics_na
+
+    hop_inclusion_scores = calculate_hop_inclusion_scores(
+        query_obj=query_obj,
+        ground_truth_ontology_term_id=ground_truth_ontology_term_id,
+        num_hops=num_hops,
+        ontology_resource=ontology_resource,
+    )
+
+    query_cell_metrics = {"query_cell_id": query_obj["query_cell_id"], "detail": ""}
+
+    for i, hop_inclusion_score in enumerate(hop_inclusion_scores):
+        query_cell_metrics[f"hop_{i}_inclusion_score"] = hop_inclusion_score
+
+    return query_cell_metrics
+
+def calculate_metrics_for_query_v3(
+    query_obj: t.Dict[str, t.Any],
+    ground_truth_ontology_term_id: str,
+    ontology_resource: t.Dict[str, t.Any],
+    num_hops=4,
+):
+    """
+    Calculate performance metrics for a query cell against the ground truth.
+
+    :param query_obj: The query object containing ontology aware scores.
+    :param ground_truth_ontology_term_id: The ground truth ontology term ID.
+    :param ontology_resource: Ontology precalculated resource used in CELLxGENE.
+    :param num_hops: Number of hops to consider.
+
+    :return: A dictionary containing sensitivity, specificity, and F1 score for each hop level.
+    """
+    if ground_truth_ontology_term_id not in ontology_resource:
+        metrics_na = {
+            "query_cell_id": query_obj["query_cell_id"],
+            "detail": f"Couldn't find {ground_truth_ontology_term_id} in the ontology resource",
+        }
+        for hop in range(num_hops + 1):
+                metrics_na[f"hop_{hop}_call"] = np.nan
+
+        return metrics_na
+
+    hop_calls = calculate_hop_calls(
+        query_obj=query_obj,
+        ground_truth_ontology_term_id=ground_truth_ontology_term_id,
+        num_hops=num_hops,
+        ontology_resource=ontology_resource,
+    )
+
+    query_cell_metrics = {"query_cell_id": query_obj["query_cell_id"], "detail": ""}
+
+    for i, hop_call in enumerate(hop_calls):
+        query_cell_metrics[f"hop_{i}_call"] = hop_call
+
+    return query_cell_metrics
+
+
 def calculate_metrics_for_prediction_output(
     ground_truth_ontology_term_ids: t.Iterable[str],
     model_predictions: t.List[t.Dict[str, t.Any]],
     ontology_resource: t.Dict[str, t.Any],
     num_hops: int,
+    metric_style: t.Literal["hop_k_sensitivity_precision", "hop_k_inclusion", "hop_k_call"] = "hop_k_sensitivity_precision",
 ) -> pd.DataFrame:
     """
     Calculate performance metrics for metadata predictions.
@@ -153,19 +303,27 @@ def calculate_metrics_for_prediction_output(
     :return: A pandas DataFrame with query cell IDs as the index and sensitivity, specificity, and F1 score
          for each hop level as columns.
     """
-    df_result = pd.DataFrame()
-
+    metrics_list = []
+    
+    if metric_style == "hop_k_sensitivity_precision":
+        metrics_calculator = calculate_metrics_for_query_v1
+    elif metric_style == "hop_k_inclusion":
+        metrics_calculator = calculate_metrics_for_query_v2
+    elif metric_style == "hop_k_call":
+        metrics_calculator = calculate_metrics_for_query_v3
+    else:
+        raise ValueError(f"Unknown algorithm: {metric_style}")
+    
     for query_res_obj, ground_truth in zip(model_predictions, ground_truth_ontology_term_ids):
-        query_cell_metrics = calculate_metrics_for_query(
+        query_cell_metrics = metrics_calculator(
             query_obj=query_res_obj,
             ground_truth_ontology_term_id=ground_truth,
             ontology_resource=ontology_resource,
             num_hops=num_hops,
         )
+        metrics_list.append(query_cell_metrics)
 
-        df_result = pd.concat([df_result, pd.DataFrame([query_cell_metrics])])
-
-    df_result = df_result.set_index("query_cell_id")
+    df_result = pd.DataFrame(metrics_list).set_index("query_cell_id")
     return df_result
 
 
diff --git a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
index a4f17be4..0757b2ec 100644
--- a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
+++ b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Does CellariumGPT recapitulate empirical mean for a given cell type?"
+    "### Does CellariumGPT recapitulate empirical mean for a given cell type?\n",
+    "\n",
+    "Can we turn this into a quantitative benchmark?"
    ]
   },
   {
diff --git a/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb b/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
index 1fed2f1d..e6a9305e 100644
--- a/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
+++ b/notebooks/cellariumgpt_inference/13_marker_gene_experiments.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,12 +31,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
     "ROOT_PATH = \"/home/mehrtash/data\"\n",
     "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\"\n",
     "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
     "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
     "\n",
@@ -52,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,13 +89,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd6e046bd15e4c988e14c9ff3266ce98",
+       "model_id": "d7ea401c255741bea1787c9cc8070a40",
        "version_major": 2,
        "version_minor": 0
       },
@@ -106,7 +109,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee8f3ca8cc53468aaf2b43d6663a7ec8",
+       "model_id": "441c13d93cbc43299ed3b8a3efc2ab42",
        "version_major": 2,
        "version_minor": 0
       },
@@ -147,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -219,13 +222,13 @@
        "Text(0.5, 1.0, 'CD8-positive, alpha-beta T cell vs. prior (male, normal)')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 960x960 with 1 Axes>"
       ]
@@ -261,7 +264,7 @@
     "ax.set_ylim(ylim[0] - 0.2 * (ylim[1] - ylim[0]), ylim[1] + 0.2 * (ylim[1] - ylim[0]))\n",
     "\n",
     "mask = (y > 2)\n",
-    "mask = mask | (x > 5)\n",
+    "# mask = mask | (x > 5)\n",
     "indices = np.where(mask)[0]\n",
     "\n",
     "texts = [\n",
diff --git a/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb b/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
index d2975ca2..b57a9ad5 100644
--- a/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
+++ b/notebooks/cellariumgpt_inference/14_linear_response_network_analysis.ipynb
@@ -60,7 +60,7 @@
     "import pickle\n",
     "\n",
     "# load linear response results\n",
-    "linear_response_file_path = os.path.join(ROOT_PATH, \"data\", \"linear_response\", \"10M\", \"linear_response_cell_index_59.pkl\")\n",
+    "linear_response_file_path = os.path.join(ROOT_PATH, \"data\", \"linear_response\", \"100M\", \"linear_response_cell_index_13.pkl\")\n",
     "\n",
     "with open(linear_response_file_path, \"rb\") as f:\n",
     "    linear_response_dict = pickle.load(f)"
@@ -176,16 +176,41 @@
    "outputs": [],
    "source": [
     "network_ctx.process(\n",
-    "    response_normalization_strategy=\"mean\",\n",
+    "    response_normalization_strategy=\"log1p\",\n",
     "    feature_normalization_strategy=\"z_score\",\n",
     "    feature_max_value=None,\n",
     "    query_response_amp_min_pct=None,\n",
     "    min_prompt_gene_tpm=0.1,\n",
     "    min_query_gene_tpm=0.1,\n",
-    "    norm_pseudo_count=1e-3,\n",
+    "    norm_pseudo_count=0.,  # not needed for log1p normalization strategy\n",
     "    query_hv_top_k=None,\n",
     "    z_trans_func=None,\n",
-    ")"
+    "    included_gene_biotypes={'protein_coding'},\n",
+    ")\n",
+    "\n",
+    "# network_ctx.process(\n",
+    "#     response_normalization_strategy=\"corr\",\n",
+    "#     feature_normalization_strategy=\"z_score\",\n",
+    "#     feature_max_value=None,\n",
+    "#     query_response_amp_min_pct=None,\n",
+    "#     min_prompt_gene_tpm=1,\n",
+    "#     min_query_gene_tpm=1,\n",
+    "#     norm_pseudo_count=1e-3,\n",
+    "#     query_hv_top_k=None,\n",
+    "#     z_trans_func=None,\n",
+    "# )\n",
+    "\n",
+    "# network_ctx.process(\n",
+    "#     response_normalization_strategy=\"mean\",\n",
+    "#     feature_normalization_strategy=\"z_score\",\n",
+    "#     feature_max_value=None,\n",
+    "#     query_response_amp_min_pct=None,\n",
+    "#     min_prompt_gene_tpm=0.1,\n",
+    "#     min_query_gene_tpm=0.1,\n",
+    "#     norm_pseudo_count=1e-3,\n",
+    "#     query_hv_top_k=None,\n",
+    "#     z_trans_func=None,\n",
+    "# )"
    ]
   },
   {
@@ -197,7 +222,8 @@
     "network_ctx.compute_adjacency_matrix(\n",
     "    adjacency_strategy=\"positive_correlation\",\n",
     "    n_neighbors=50,\n",
-    "    beta=6.,\n",
+    "    beta=12.,\n",
+    "    # beta=1.,\n",
     "    self_loop=False)"
    ]
   },
@@ -207,7 +233,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['TTN']]\n",
+    "i = network_ctx.prompt_gene_id_to_idx_map[network_ctx.gene_symbol_to_gene_id_map['MT-CO1']]\n",
     "inds = np.argsort(network_ctx.a_pp[i, :])[::-1]\n",
     "for j in inds[:100]:\n",
     "    print(network_ctx.prompt_gene_symbols[j], network_ctx.a_pp[i, j])"
@@ -220,7 +246,7 @@
    "outputs": [],
    "source": [
     "network_ctx.compute_leiden_communites(\n",
-    "    resolution=5.0)"
+    "    resolution=10.0)"
    ]
   },
   {
@@ -249,7 +275,7 @@
     "\n",
     "network_ctx.make_mde_embedding(\n",
     "    n_neighbors=20,\n",
-    "    repulsive_penalty=pymde.penalties.Log,\n",
+    "    # repulsive_penalty=pymde.penalties.Log,\n",
     "    max_iter=1000,\n",
     "    init=\"quadratic\",\n",
     "    device=\"cuda\")"
@@ -307,8 +333,8 @@
     "    \"HLA\": (hla_gene_ids, hla_gene_symbols, 'green'),\n",
     "    \"IFI\": (ifi_gene_ids, ifi_gene_symbols, 'orange'),\n",
     "    \"TRAV\": (trav_gene_ids, trav_gene_symbols, 'purple'),\n",
-    "    \"HB\": (hb_gene_ids, hb_gene_symbols, 'black'),\n",
-    "    \"TTN\": ([network_ctx.gene_symbol_to_gene_id_map['TTN']], ['TTN'], 'black'),\n",
+    "    # \"HB\": (hb_gene_ids, hb_gene_symbols, 'black'),\n",
+    "    # \"TTN\": ([network_ctx.gene_symbol_to_gene_id_map['TTN']], ['TTN'], 'black'),\n",
     "    # \"SNAP-N\": (snap_n_gene_ids, snap_n_gene_symbols, 'brown'),\n",
     "}\n",
     "\n",
@@ -330,7 +356,58 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sorted(np.asarray(network_ctx.prompt_gene_symbols)[network_ctx.leiden_membership == 5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "\n",
+    "# Create a DataFrame for Plotly\n",
+    "df = pd.DataFrame({\n",
+    "    'x': network_ctx.embedding_p2[:, 0],\n",
+    "    'y': network_ctx.embedding_p2[:, 1],\n",
+    "    'label': network_ctx.prompt_gene_symbols,\n",
+    "    'log1p_mean': np.clip(\n",
+    "        np.log(1 + network_ctx.prompt_marginal_mean_p * 10_000 / network_ctx.prompt_marginal_mean_p.sum()),\n",
+    "        0, 0.5)\n",
+    "})\n",
+    "\n",
+    "# Create the scatter plot\n",
+    "fig = px.scatter(\n",
+    "    df,\n",
+    "    x='x',\n",
+    "    y='y',\n",
+    "    hover_name='label',\n",
+    "    color='log1p_mean',\n",
+    "    color_continuous_scale=[(0,'yellow'), (0.5, 'blue'), (1,'red')],\n",
+    ")\n",
+    "\n",
+    "# Update marker size\n",
+    "fig.update_traces(marker=dict(size=2))  # Adjust the size as needed\n",
+    "\n",
+    "# Update layout to decrease the width of the plot\n",
+    "fig.update_layout(\n",
+    "    width=800,  # Adjust the width as needed\n",
+    "    height=800,\n",
+    "    plot_bgcolor='white',\n",
+    "    xaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_1'\n",
+    "    ),\n",
+    "    yaxis=dict(\n",
+    "        showgrid=False,\n",
+    "        showticklabels=False,\n",
+    "        title='MDE_2'\n",
+    "    )\n",
+    ")"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/15_mean_expression_by_cell_type_prompting_benchmark.ipynb b/notebooks/cellariumgpt_inference/15_mean_expression_by_cell_type_prompting_benchmark.ipynb
new file mode 100644
index 00000000..32acd35f
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/15_mean_expression_by_cell_type_prompting_benchmark.ipynb
@@ -0,0 +1,4759 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Does CellariumGPT recapitulate empirical mean for a given cell type?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "\n",
+    "PREFIX_LIST = [\n",
+    "    \"10M_001_bs1536\",\n",
+    "    \"19M_001_bs2048\",\n",
+    "    \"30M_001_bs2560\",\n",
+    "    \"100M_long_run_last\"\n",
+    "]\n",
+    "\n",
+    "CHECKPOINT_PATH_LIST = [\n",
+    "    \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\",\n",
+    "    \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\",\n",
+    "    \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\",\n",
+    "    \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "]\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load validation cell type table\n",
+    "val_adata = sc.read_h5ad(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation_filtered.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 61 × 28680\n",
+       "    obs: 'cell_type', 'assay', 'suspension_type', 'tissue', 'sex', 'disease', 'total_mrna_umis'\n",
+       "    var: 'feature_id', 'feature_name'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5527.708887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10683.399862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5380.963698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5083.603161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6609.696174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3645.962305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3935.779702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3115.828938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3922.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8436.318182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2923.925418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3285.603705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3720.503504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD8-positive, alpha-beta cytotoxic T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5061.393152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5221.671616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5649.398766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6333.568573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4984.057779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6689.483383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5216.285765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4950.552077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>14936.870968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8772.175057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10069.068281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10636.632432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4604.858974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>intestine goblet cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7435.644048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6444.458460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4345.735849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5964.088479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4911.535804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>epithelial cell of proximal tubule</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4491.198843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4906.310584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3130.350171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>hepatic pit cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2761.142458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Kupffer cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6850.818612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11532.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>periportal region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4790.702890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>centrilobular region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9280.724561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>endothelial cell of pericentral hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2734.614608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3987.627119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7274.262295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>intrahepatic cholangiocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6998.971698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>endothelial cell of hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3836.788282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>hepatic stellate cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4090.862637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8422.037378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6109.480329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3094.649239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>ciliated columnar cell of tracheobronchial tree</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5754.694686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2744.064915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5956.795231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>pulmonary interstitial fibroblast</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5294.602205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9484.199912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2321.547941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>15015.278925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>fibroblast of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6561.739316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>cardiac muscle myoblast</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11938.346771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>fibroblast of cardiac tissue</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3099.779698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>cardiac endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2268.612916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>cardiac muscle cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8017.024743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2238.708571</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type      assay  \\\n",
+       "0                                    GABAergic neuron  10x 3' v3   \n",
+       "1                                glutamatergic neuron  10x 3' v3   \n",
+       "2                                           astrocyte  10x 3' v3   \n",
+       "3                                     oligodendrocyte  10x 3' v3   \n",
+       "4                      oligodendrocyte precursor cell  10x 3' v3   \n",
+       "5                                     microglial cell  10x 3' v3   \n",
+       "6                             cerebellar granule cell  10x 3' v3   \n",
+       "7                                    endothelial cell  10x 3' v3   \n",
+       "8                                  classical monocyte  10x 5' v2   \n",
+       "9                              non-classical monocyte  10x 5' v2   \n",
+       "10                                natural killer cell  10x 5' v2   \n",
+       "11                    CD4-positive, alpha-beta T cell  10x 5' v2   \n",
+       "12                    CD8-positive, alpha-beta T cell  10x 5' v2   \n",
+       "13          CD8-positive, alpha-beta cytotoxic T cell  10x 5' v2   \n",
+       "14             CD8-positive, alpha-beta memory T cell  10x 5' v2   \n",
+       "15  naive thymus-derived CD4-positive, alpha-beta ...  10x 5' v2   \n",
+       "16  naive thymus-derived CD8-positive, alpha-beta ...  10x 5' v2   \n",
+       "17                                       naive B cell  10x 5' v2   \n",
+       "18                                      memory B cell  10x 5' v2   \n",
+       "19                                  regulatory T cell  10x 5' v2   \n",
+       "20                                 gamma-delta T cell  10x 5' v2   \n",
+       "21                        conventional dendritic cell  10x 5' v2   \n",
+       "22                                         enterocyte  10x 3' v2   \n",
+       "23                                   endothelial cell  10x 3' v2   \n",
+       "24                               enteroendocrine cell  10x 3' v2   \n",
+       "25                                           pericyte  10x 3' v2   \n",
+       "26                              intestine goblet cell  10x 3' v2   \n",
+       "27              kidney collecting duct principal cell  10x 3' v3   \n",
+       "28  kidney loop of Henle thin descending limb epit...  10x 3' v3   \n",
+       "29  kidney loop of Henle thin ascending limb epith...  10x 3' v3   \n",
+       "30  kidney loop of Henle thick ascending limb epit...  10x 3' v3   \n",
+       "31                 epithelial cell of proximal tubule  10x 3' v3   \n",
+       "32           kidney collecting duct intercalated cell  10x 3' v3   \n",
+       "33                        renal interstitial pericyte  10x 3' v3   \n",
+       "34                                   hepatic pit cell  10x 3' v2   \n",
+       "35                                       Kupffer cell  10x 3' v2   \n",
+       "36                                         hepatocyte  10x 3' v2   \n",
+       "37                       periportal region hepatocyte  10x 3' v2   \n",
+       "38                    centrilobular region hepatocyte  10x 3' v2   \n",
+       "39   endothelial cell of pericentral hepatic sinusoid  10x 3' v2   \n",
+       "40                         endothelial cell of artery  10x 3' v2   \n",
+       "41                                         fibroblast  10x 3' v2   \n",
+       "42                         intrahepatic cholangiocyte  10x 3' v2   \n",
+       "43               endothelial cell of hepatic sinusoid  10x 3' v2   \n",
+       "44                              hepatic stellate cell  10x 3' v2   \n",
+       "45                                alveolar macrophage  10x 5' v1   \n",
+       "46                                 type II pneumocyte  10x 5' v1   \n",
+       "47                         capillary endothelial cell  10x 5' v1   \n",
+       "48    ciliated columnar cell of tracheobronchial tree  10x 5' v1   \n",
+       "49                                  type I pneumocyte  10x 5' v1   \n",
+       "50         epithelial cell of lower respiratory tract  10x 5' v1   \n",
+       "51                  pulmonary interstitial fibroblast  10x 5' v1   \n",
+       "52                                          club cell  10x 5' v1   \n",
+       "53                  pulmonary artery endothelial cell  10x 5' v1   \n",
+       "54                            epithelial cell of lung  10x 5' v1   \n",
+       "55                                 fibroblast of lung  10x 5' v1   \n",
+       "56                            cardiac muscle myoblast  10x 3' v3   \n",
+       "57                       fibroblast of cardiac tissue  10x 3' v3   \n",
+       "58                           cardiac endothelial cell  10x 3' v3   \n",
+       "59                                cardiac muscle cell  10x 3' v3   \n",
+       "60                                     cardiac neuron  10x 3' v3   \n",
+       "\n",
+       "   suspension_type           tissue   sex disease  total_mrna_umis  \n",
+       "0          nucleus            brain  male  normal      5527.708887  \n",
+       "1          nucleus            brain  male  normal     10683.399862  \n",
+       "2          nucleus            brain  male  normal      5380.963698  \n",
+       "3          nucleus            brain  male  normal      5083.603161  \n",
+       "4          nucleus            brain  male  normal      6609.696174  \n",
+       "5          nucleus            brain  male  normal      3645.962305  \n",
+       "6          nucleus            brain  male  normal      3935.779702  \n",
+       "7          nucleus            brain  male  normal      3115.828938  \n",
+       "8             cell            blood  male  normal      3922.025000  \n",
+       "9             cell            blood  male  normal      8436.318182  \n",
+       "10            cell            blood  male  normal      2923.925418  \n",
+       "11            cell            blood  male  normal      3285.603705  \n",
+       "12            cell            blood  male  normal      3720.503504  \n",
+       "13            cell            blood  male  normal      5061.393152  \n",
+       "14            cell            blood  male  normal      5221.671616  \n",
+       "15            cell            blood  male  normal      5649.398766  \n",
+       "16            cell            blood  male  normal      6333.568573  \n",
+       "17            cell            blood  male  normal      4984.057779  \n",
+       "18            cell            blood  male  normal      6689.483383  \n",
+       "19            cell            blood  male  normal      5216.285765  \n",
+       "20            cell            blood  male  normal      4950.552077  \n",
+       "21            cell            blood  male  normal     14936.870968  \n",
+       "22            cell  small intestine  male  normal      8772.175057  \n",
+       "23            cell  small intestine  male  normal     10069.068281  \n",
+       "24            cell  small intestine  male  normal     10636.632432  \n",
+       "25            cell  small intestine  male  normal      4604.858974  \n",
+       "26            cell  small intestine  male  normal      7435.644048  \n",
+       "27         nucleus           kidney  male  normal      6444.458460  \n",
+       "28         nucleus           kidney  male  normal      4345.735849  \n",
+       "29         nucleus           kidney  male  normal      5964.088479  \n",
+       "30         nucleus           kidney  male  normal      4911.535804  \n",
+       "31         nucleus           kidney  male  normal      4491.198843  \n",
+       "32         nucleus           kidney  male  normal      4906.310584  \n",
+       "33         nucleus           kidney  male  normal      3130.350171  \n",
+       "34            cell            liver  male  normal      2761.142458  \n",
+       "35            cell            liver  male  normal      6850.818612  \n",
+       "36            cell            liver  male  normal     11532.500000  \n",
+       "37            cell            liver  male  normal      4790.702890  \n",
+       "38            cell            liver  male  normal      9280.724561  \n",
+       "39            cell            liver  male  normal      2734.614608  \n",
+       "40            cell            liver  male  normal      3987.627119  \n",
+       "41            cell            liver  male  normal      7274.262295  \n",
+       "42            cell            liver  male  normal      6998.971698  \n",
+       "43            cell            liver  male  normal      3836.788282  \n",
+       "44            cell            liver  male  normal      4090.862637  \n",
+       "45            cell             lung  male  normal      8422.037378  \n",
+       "46            cell             lung  male  normal      6109.480329  \n",
+       "47            cell             lung  male  normal      3094.649239  \n",
+       "48            cell             lung  male  normal      5754.694686  \n",
+       "49            cell             lung  male  normal      2744.064915  \n",
+       "50            cell             lung  male  normal      5956.795231  \n",
+       "51            cell             lung  male  normal      5294.602205  \n",
+       "52            cell             lung  male  normal      9484.199912  \n",
+       "53            cell             lung  male  normal      2321.547941  \n",
+       "54            cell             lung  male  normal     15015.278925  \n",
+       "55            cell             lung  male  normal      6561.739316  \n",
+       "56         nucleus            heart  male  normal     11938.346771  \n",
+       "57         nucleus            heart  male  normal      3099.779698  \n",
+       "58         nucleus            heart  male  normal      2268.612916  \n",
+       "59         nucleus            heart  male  normal      8017.024743  \n",
+       "60         nucleus            heart  male  normal      2238.708571  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the full dataframe\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(val_adata.obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_gene_ids = val_adata.var['feature_id'].values\n",
+    "query_gene_symbols = val_adata.var['feature_name'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "deaf06d7b49d41caa79c76b03ff65ad7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "101e56e306934897bf2e251c84190922",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/61 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2812ed5ae6840dea09031e78e501987",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Processing gene chunks:   0%|          | 0/29 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "model_X_kng = np.zeros((\n",
+    "    len(PREFIX_LIST),\n",
+    "    len(val_adata),\n",
+    "    len(query_gene_ids)))\n",
+    "\n",
+    "for k in tqdm(range(len(PREFIX_LIST))):\n",
+    "\n",
+    "    prefix = PREFIX_LIST[k]\n",
+    "    checkpoint_path = CHECKPOINT_PATH_LIST[k]\n",
+    "\n",
+    "    ctx = CellariumGPTInferenceContext(\n",
+    "        cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "        ref_adata_path=REF_ADATA_PATH,\n",
+    "        gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "        device=DEVICE,\n",
+    "        attention_backend=\"mem_efficient\",\n",
+    "        verbose=False\n",
+    "    )\n",
+    "\n",
+    "    for n in tqdm(range(len(val_adata))):\n",
+    "\n",
+    "        # arguments\n",
+    "        row = val_adata.obs.iloc[n]\n",
+    "\n",
+    "        assay = row.assay\n",
+    "        suspension_type = row.suspension_type\n",
+    "        prompt_metadata_dict = {\n",
+    "            \"cell_type\": row.cell_type,\n",
+    "            \"tissue\": row.tissue,\n",
+    "            \"disease\": row.disease,\n",
+    "            \"sex\": row.sex,\n",
+    "        }\n",
+    "        total_mrna_umis = float(row.total_mrna_umis)\n",
+    "\n",
+    "        query_chunk_size = 1_000\n",
+    "        max_counts = 500\n",
+    "        upper_percentile = 0.5  # doesn't matter, we only need the logits\n",
+    "        upper_pad = 1  # doesn't matter, we only need the logits\n",
+    "\n",
+    "        gex_range_dict = ctx.predict_gene_expression_range_for_metadata(\n",
+    "            assay=assay,\n",
+    "            suspension_type=suspension_type,\n",
+    "            prompt_metadata_dict=prompt_metadata_dict,\n",
+    "            total_mrna_umis=total_mrna_umis,\n",
+    "            query_gene_ids=query_gene_ids,\n",
+    "            query_chunk_size=query_chunk_size,\n",
+    "            total_prob_mass=upper_percentile,\n",
+    "            symmetric_range_pad=upper_pad,\n",
+    "            max_counts=max_counts,\n",
+    "        )\n",
+    "\n",
+    "        model_X_kng[k, n, :] = gex_range_dict['gene_marginal_mean_q'].cpu().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renormalize\n",
+    "model_X_kng = 10_000 * model_X_kng / model_X_kng.sum(axis=-1, keepdims=True)\n",
+    "empirical_X_ng = val_adata.X.copy()\n",
+    "empirical_X_ng = 10_000 * empirical_X_ng / empirical_X_ng.sum(axis=1, keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "results_dict = defaultdict(list)\n",
+    "\n",
+    "for k in range(len(PREFIX_LIST)):\n",
+    "    for n in range(len(val_adata)):\n",
+    "        x = np.log1p(empirical_X_ng[n, :])\n",
+    "        y = np.log1p(model_X_kng[k, n, :])\n",
+    "        r_squared = np.corrcoef(x, y)[0, 1] ** 2\n",
+    "\n",
+    "        results_dict['val_cell_type_idx'].append(n)\n",
+    "        results_dict['prefix'].append(PREFIX_LIST[k])\n",
+    "        results_dict[f'r_squared'].append(r_squared)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'R²')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 561,
+       "width": 451
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "# Create the plot with boxplot and jittered points.\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "\n",
+    "# Plot the boxplot without outliers.\n",
+    "ax = sns.boxplot(\n",
+    "    x='prefix',\n",
+    "    y='r_squared',\n",
+    "    data=df,\n",
+    "    order=PREFIX_LIST,       # enforce prefix order\n",
+    "    showfliers=False,        # do not show outliers\n",
+    "    linewidth=1            # thinner lines\n",
+    ")\n",
+    "\n",
+    "# Overlay the actual points with jitter.\n",
+    "sns.stripplot(\n",
+    "    x='prefix',\n",
+    "    y='r_squared',\n",
+    "    data=df,\n",
+    "    order=PREFIX_LIST,\n",
+    "    dodge=True,    # position points within each box category\n",
+    "    jitter=True,   # add jitter for better visibility\n",
+    "    marker='o',\n",
+    "    color='black',\n",
+    "    alpha=0.7,\n",
+    "    size=3\n",
+    ")\n",
+    "\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "ax.set_xlabel('Model')\n",
+    "ax.set_ylabel('R²')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "      <th>r_squared</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3115.828938</td>\n",
+       "      <td>0.838061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2238.708571</td>\n",
+       "      <td>0.826051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5956.795231</td>\n",
+       "      <td>0.821417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3922.025000</td>\n",
+       "      <td>0.818895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4906.310584</td>\n",
+       "      <td>0.818298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6444.458460</td>\n",
+       "      <td>0.809279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>15015.278925</td>\n",
+       "      <td>0.808601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4345.735849</td>\n",
+       "      <td>0.806585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3987.627119</td>\n",
+       "      <td>0.797075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5964.088479</td>\n",
+       "      <td>0.694047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type      assay  \\\n",
+       "7                                    endothelial cell  10x 3' v3   \n",
+       "60                                     cardiac neuron  10x 3' v3   \n",
+       "50         epithelial cell of lower respiratory tract  10x 5' v1   \n",
+       "8                                  classical monocyte  10x 5' v2   \n",
+       "32           kidney collecting duct intercalated cell  10x 3' v3   \n",
+       "27              kidney collecting duct principal cell  10x 3' v3   \n",
+       "54                            epithelial cell of lung  10x 5' v1   \n",
+       "28  kidney loop of Henle thin descending limb epit...  10x 3' v3   \n",
+       "40                         endothelial cell of artery  10x 3' v2   \n",
+       "29  kidney loop of Henle thin ascending limb epith...  10x 3' v3   \n",
+       "\n",
+       "   suspension_type  tissue   sex disease  total_mrna_umis  r_squared  \n",
+       "7          nucleus   brain  male  normal      3115.828938   0.838061  \n",
+       "60         nucleus   heart  male  normal      2238.708571   0.826051  \n",
+       "50            cell    lung  male  normal      5956.795231   0.821417  \n",
+       "8             cell   blood  male  normal      3922.025000   0.818895  \n",
+       "32         nucleus  kidney  male  normal      4906.310584   0.818298  \n",
+       "27         nucleus  kidney  male  normal      6444.458460   0.809279  \n",
+       "54            cell    lung  male  normal     15015.278925   0.808601  \n",
+       "28         nucleus  kidney  male  normal      4345.735849   0.806585  \n",
+       "40            cell   liver  male  normal      3987.627119   0.797075  \n",
+       "29         nucleus  kidney  male  normal      5964.088479   0.694047  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_adata_ext = val_adata.copy()\n",
+    "val_adata_ext.obs['r_squared'] = df[df['prefix'] == '100M_long_run_last']['r_squared'].values\n",
+    "val_adata_ext.obs.sort_values('r_squared', ascending=False, inplace=True)\n",
+    "val_adata_ext.obs.tail(10)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/assay_conversion/01_load_pbmc_data.ipynb b/notebooks/cellariumgpt_inference/assay_conversion/01_load_pbmc_data.ipynb
new file mode 100644
index 00000000..ed860e00
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/assay_conversion/01_load_pbmc_data.ipynb
@@ -0,0 +1,617 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import scanpy as sc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_path = '/home/mehrtash/data/pbmc_benchmark/SCP424'\n",
+    "\n",
+    "counts_path = os.path.join(root_path, 'other', 'counts.umi.txt.gz')\n",
+    "genes_path = os.path.join(root_path, 'other', 'genes.umi.txt')\n",
+    "cells_path = os.path.join(root_path, 'other', 'cells.umi.new.txt')\n",
+    "meta_path = os.path.join(root_path, 'other', 'meta.counts.new.txt')\n",
+    "extra_meta_path = os.path.join(root_path, 'metadata', 'meta.txt')\n",
+    "harmony_path = os.path.join(root_path, 'cluster', 'tsne.harmony.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.io import mmread\n",
+    "\n",
+    "counts = mmread(counts_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load genes_path\n",
+    "with open(genes_path, 'r') as f:\n",
+    "    genes = f.read().splitlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33694"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(genes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load cells_path\n",
+    "with open(cells_path, 'r') as f:\n",
+    "    cells = f.read().splitlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "44433"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(cells)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "meta_df = pd.read_csv(meta_path, sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Method\n",
+       "inDrops              11350\n",
+       "Drop-seq             11052\n",
+       "Seq-Well              5676\n",
+       "10x Chromium V2 A     5184\n",
+       "10x Chromium V3       4027\n",
+       "10x Chromium V2       3362\n",
+       "10x Chromium V2 B     3222\n",
+       "Smart-seq2             584\n",
+       "CEL-Seq2               560\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_df[\"Method\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "44433"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(meta_df) - 584"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load extra metadata\n",
+    "extra_meta_df = pd.read_csv(extra_meta_path, sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>nGene</th>\n",
+       "      <th>nUMI</th>\n",
+       "      <th>percent.mito</th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>CellType</th>\n",
+       "      <th>Experiment</th>\n",
+       "      <th>Method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TYPE</td>\n",
+       "      <td>numeric</td>\n",
+       "      <td>numeric</td>\n",
+       "      <td>numeric</td>\n",
+       "      <td>group</td>\n",
+       "      <td>group</td>\n",
+       "      <td>group</td>\n",
+       "      <td>group</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pbmc1_SM2_Cell_108</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>437125</td>\n",
+       "      <td>0.0297434465355702</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cytotoxic T cell</td>\n",
+       "      <td>pbmc1</td>\n",
+       "      <td>Smart-seq2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pbmc1_SM2_Cell_115</td>\n",
+       "      <td>2438</td>\n",
+       "      <td>335596</td>\n",
+       "      <td>0.0311521658159055</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cytotoxic T cell</td>\n",
+       "      <td>pbmc1</td>\n",
+       "      <td>Smart-seq2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pbmc1_SM2_Cell_133</td>\n",
+       "      <td>1874</td>\n",
+       "      <td>302204</td>\n",
+       "      <td>0.0431128105727693</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cytotoxic T cell</td>\n",
+       "      <td>pbmc1</td>\n",
+       "      <td>Smart-seq2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pbmc1_SM2_Cell_142</td>\n",
+       "      <td>2480</td>\n",
+       "      <td>377420</td>\n",
+       "      <td>0.0260323569927476</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Cytotoxic T cell</td>\n",
+       "      <td>pbmc1</td>\n",
+       "      <td>Smart-seq2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31017</th>\n",
+       "      <td>pbmc2_inDrops_1_TAGTCTCT.GAGCCTTA.ATCCGCTA</td>\n",
+       "      <td>453</td>\n",
+       "      <td>717</td>\n",
+       "      <td>0.097629009762901</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Plasmacytoid dendritic cell</td>\n",
+       "      <td>pbmc2</td>\n",
+       "      <td>inDrops</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31018</th>\n",
+       "      <td>pbmc2_inDrops_1_TCCAGAAG.TTATGCGA.TAAGACGG</td>\n",
+       "      <td>592</td>\n",
+       "      <td>938</td>\n",
+       "      <td>0.035181236673774</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Plasmacytoid dendritic cell</td>\n",
+       "      <td>pbmc2</td>\n",
+       "      <td>inDrops</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31019</th>\n",
+       "      <td>pbmc2_inDrops_1_TGAATCCT.GAGCCTTA.CCCAAGCA</td>\n",
+       "      <td>406</td>\n",
+       "      <td>662</td>\n",
+       "      <td>0.138972809667674</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Plasmacytoid dendritic cell</td>\n",
+       "      <td>pbmc2</td>\n",
+       "      <td>inDrops</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31020</th>\n",
+       "      <td>pbmc2_inDrops_1_TGAATCCT.TTATGCGA.CATCTCCC</td>\n",
+       "      <td>1001</td>\n",
+       "      <td>2066</td>\n",
+       "      <td>0.0556631171345595</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Plasmacytoid dendritic cell</td>\n",
+       "      <td>pbmc2</td>\n",
+       "      <td>inDrops</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31021</th>\n",
+       "      <td>pbmc2_inDrops_1_TGAGCACA.GAGCCTTA.CGAGTCTG</td>\n",
+       "      <td>184</td>\n",
+       "      <td>253</td>\n",
+       "      <td>0.146245059288538</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Plasmacytoid dendritic cell</td>\n",
+       "      <td>pbmc2</td>\n",
+       "      <td>inDrops</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>31022 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             NAME    nGene     nUMI  \\\n",
+       "0                                            TYPE  numeric  numeric   \n",
+       "1                              pbmc1_SM2_Cell_108     2200   437125   \n",
+       "2                              pbmc1_SM2_Cell_115     2438   335596   \n",
+       "3                              pbmc1_SM2_Cell_133     1874   302204   \n",
+       "4                              pbmc1_SM2_Cell_142     2480   377420   \n",
+       "...                                           ...      ...      ...   \n",
+       "31017  pbmc2_inDrops_1_TAGTCTCT.GAGCCTTA.ATCCGCTA      453      717   \n",
+       "31018  pbmc2_inDrops_1_TCCAGAAG.TTATGCGA.TAAGACGG      592      938   \n",
+       "31019  pbmc2_inDrops_1_TGAATCCT.GAGCCTTA.CCCAAGCA      406      662   \n",
+       "31020  pbmc2_inDrops_1_TGAATCCT.TTATGCGA.CATCTCCC     1001     2066   \n",
+       "31021  pbmc2_inDrops_1_TGAGCACA.GAGCCTTA.CGAGTCTG      184      253   \n",
+       "\n",
+       "             percent.mito Cluster                     CellType Experiment  \\\n",
+       "0                 numeric   group                        group      group   \n",
+       "1      0.0297434465355702       0             Cytotoxic T cell      pbmc1   \n",
+       "2      0.0311521658159055       0             Cytotoxic T cell      pbmc1   \n",
+       "3      0.0431128105727693       0             Cytotoxic T cell      pbmc1   \n",
+       "4      0.0260323569927476       0             Cytotoxic T cell      pbmc1   \n",
+       "...                   ...     ...                          ...        ...   \n",
+       "31017   0.097629009762901      11  Plasmacytoid dendritic cell      pbmc2   \n",
+       "31018   0.035181236673774      11  Plasmacytoid dendritic cell      pbmc2   \n",
+       "31019   0.138972809667674      11  Plasmacytoid dendritic cell      pbmc2   \n",
+       "31020  0.0556631171345595      11  Plasmacytoid dendritic cell      pbmc2   \n",
+       "31021   0.146245059288538      11  Plasmacytoid dendritic cell      pbmc2   \n",
+       "\n",
+       "           Method  \n",
+       "0           group  \n",
+       "1      Smart-seq2  \n",
+       "2      Smart-seq2  \n",
+       "3      Smart-seq2  \n",
+       "4      Smart-seq2  \n",
+       "...           ...  \n",
+       "31017     inDrops  \n",
+       "31018     inDrops  \n",
+       "31019     inDrops  \n",
+       "31020     inDrops  \n",
+       "31021     inDrops  \n",
+       "\n",
+       "[31022 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extra_meta_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(33694, 44433)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counts.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ensembl_ids = [x.split('_')[0] for x in genes]\n",
+    "gene_symbols = [x.split('_')[1] for x in genes]\n",
+    "adata = sc.AnnData(\n",
+    "    X=counts.T.tocsr().astype(np.float32),\n",
+    "    obs=pd.DataFrame(index=cells),\n",
+    "    var=pd.DataFrame(index=ensembl_ids, data={'gene_symbols': gene_symbols}),\n",
+    ")\n",
+    "adata.obs.index.name = 'cell_id'\n",
+    "\n",
+    "# subset anndata to only cells in extra_meta_df\n",
+    "adata = adata[adata.obs.index.isin(extra_meta_df['NAME'].values[1:])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extended_obs = pd.merge(left=adata.obs, right=meta_df, how='left', left_index=True, right_on='Name')\n",
+    "extended_obs = pd.merge(left=adata.obs, right=extra_meta_df, how='left', left_on='cell_id', right_on='NAME')\n",
+    "assert len(adata) == len(extended_obs)\n",
+    "adata.obs = extended_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "harmony_df = pd.read_csv(harmony_path, sep='\\t', )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.obs = pd.merge(left=adata.obs, right=harmony_df, how='left', left_on='NAME', right_on='NAME')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "harmony_n2 = np.zeros((len(adata.obs), 2))\n",
+    "harmony_n2[:, 0] = np.asarray(adata.obs['X'].values).astype(np.float32)\n",
+    "harmony_n2[:, 1] = np.asarray(adata.obs['Y'].values).astype(np.float32)\n",
+    "adata.obsm['X_harmony'] = harmony_n2\n",
+    "\n",
+    "# drop X and Y from adata.obs\n",
+    "adata.obs = adata.obs.drop(columns=['X', 'Y'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'CellType'}, xlabel='X_harmony1', ylabel='X_harmony2'>"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.embedding(adata, basis='X_harmony', color='CellType', show=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.write(os.path.join(root_path, 'pbmc_adata.h5ad'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Playground"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "_adata = adata.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sc.pp.normalize_total(adata, target_sum=1e4)\n",
+    "sc.pp.log1p(adata)\n",
+    "sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)\n",
+    "adata = adata[:, adata.var['highly_variable']]\n",
+    "sc.pp.scale(adata, max_value=10)\n",
+    "sc.tl.pca(adata, svd_solver='arpack')\n",
+    "sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n",
+    "sc.tl.umap(adata)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Axes: title={'center': 'CellType'}, xlabel='X_umap1', ylabel='X_umap2'>,\n",
+       " <Axes: title={'center': 'Method'}, xlabel='X_umap1', ylabel='X_umap2'>]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1455.6x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.embedding(adata, basis='X_umap', color=['CellType', 'Method'], show=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
index f2e28ebe..9803628a 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/01_generate_resources.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cl_ontology_owl_file_url = \"https://github.com/obophenotype/cell-ontology/releases/download/v2024-01-04/cl.owl\"\n",
+    "# cl_ontology_owl_file_url = \"https://github.com/obophenotype/cell-ontology/releases/download/v2024-01-04/cl.owl\"\n",
+    "cl_ontology_owl_file_url = \"https://github.com/obophenotype/cell-ontology/releases/download/v2025-02-13/cl.owl\"\n",
     "cl_datastore_terms_csv = os.path.join(ONTOLOGY_ROOT_PATH, \"datastore_cl_terms.csv\")\n",
     "\n",
     "cl_prefix = \"CL_\"\n",
@@ -97,13 +98,9 @@
      "output_type": "stream",
      "text": [
       "INFO:root:Loading ontology ...\n",
-      "* Owlready2 * Warning: ignoring cyclic subclass of/subproperty of, involving:\n",
-      "  http://purl.obolibrary.org/obo/CHEBI_36080\n",
-      "  http://purl.obolibrary.org/obo/PR_000000001\n",
-      "\n",
-      "INFO:root:All classes: 2914\n",
+      "INFO:root:All classes: 3060\n",
       "INFO:root:Building nx graph from OWL ontology...\n",
-      "INFO:root:Filtered classes: 1026\n",
+      "INFO:root:Filtered classes: 1038\n",
       "INFO:root:Building nx graph from OWL ontology...\n"
      ]
     }
@@ -191,7 +188,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Generating cell type ontology benchmarking resource from CL ontology...\n",
+      "INFO:root:Generating cell type ontology benchmarking resource from CL ontology...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "INFO:root:Writing output file to /home/mehrtash/data/data/cellariumgpt_artifacts/ontology/cl_benchmarking_resource.pkl\n"
      ]
     }
@@ -222,6 +225,7 @@
       "cell\n",
       "neuron\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
@@ -229,6 +233,7 @@
       "sensory neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -243,14 +248,17 @@
       "\t cell\n",
       "\t experimentally modified cell in vitro\n",
       "\t cell in vitro\n",
-      "fibroblast neural crest derived\n",
+      "neural crest derived fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "migratory neural crest cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t multi fate stem cell\n",
@@ -258,12 +266,15 @@
       "\t neural crest cell\n",
       "fibroblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "neuronal receptor cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -271,6 +282,7 @@
       "\t electrically signaling cell\n",
       "sensory receptor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "embryonic cell (metazoa)\n",
       "\t cell\n",
       "experimentally modified cell in vitro\n",
@@ -280,6 +292,7 @@
       "\t cell\n",
       "stem cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "male germ cell\n",
       "\t cell\n",
@@ -288,6 +301,8 @@
       "germ cell\n",
       "\t cell\n",
       "\t germ line cell\n",
+      "eukaryotic cell\n",
+      "\t cell\n",
       "haploid cell\n",
       "\t cell\n",
       "sperm\n",
@@ -295,6 +310,7 @@
       "\t germ line cell\n",
       "\t male germ cell\n",
       "\t germ cell\n",
+      "\t eukaryotic cell\n",
       "\t haploid cell\n",
       "\t male gamete\n",
       "\t gamete\n",
@@ -309,18 +325,18 @@
       "\t cell\n",
       "\t germ line cell\n",
       "\t germ cell\n",
+      "\t eukaryotic cell\n",
       "oocyte\n",
       "\t cell\n",
       "\t germ line cell\n",
       "\t germ cell\n",
-      "\t female germ cell\n",
       "\t eukaryotic cell\n",
-      "eukaryotic cell\n",
-      "\t cell\n",
+      "\t female germ cell\n",
       "oogonial cell\n",
       "\t cell\n",
       "\t germ line cell\n",
       "\t germ cell\n",
+      "\t eukaryotic cell\n",
       "\t female germ cell\n",
       "primordial germ cell\n",
       "\t cell\n",
@@ -330,21 +346,32 @@
       "\t cell\n",
       "smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
       "\t non-striated muscle cell\n",
       "\t visceral muscle cell\n",
       "\t electrically active cell\n",
+      "central nervous system neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "neural crest derived neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "glioblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "non-terminally differentiated cell\n",
@@ -358,30 +385,36 @@
       "surface ectodermal cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
       "\t ectodermal cell\n",
       "exocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "precursor cell\n",
       "\t cell\n",
       "single fate stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "somatic stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "epithelial fate stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
       "hematopoietic stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "\t hematopoietic cell\n",
@@ -394,9 +427,11 @@
       "\t hematopoietic cell\n",
       "progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "erythroid progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -407,10 +442,12 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "erythroid lineage cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t myeloid cell\n",
       "myeloid lineage restricted progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -418,6 +455,7 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "megakaryocyte-erythroid progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -426,6 +464,7 @@
       "\t hematopoietic oligopotent progenitor cell\n",
       "monopoietic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -434,9 +473,11 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "myeloid cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "macrophage dendritic cell progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -463,6 +504,7 @@
       "\t nucleate cell\n",
       "neutrophil progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -488,6 +530,7 @@
       "\t biogenic amine secreting cell\n",
       "common lymphoid progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -495,24 +538,30 @@
       "\t hematopoietic oligopotent progenitor cell\n",
       "neural stem cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "neural cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "multi fate stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "common myeloid progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "\t hematopoietic oligopotent progenitor cell\n",
       "hematopoietic oligopotent progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "granulocyte monocyte progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -528,37 +577,47 @@
       "\t cell\n",
       "myoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t muscle precursor cell\n",
       "muscle precursor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
-      "connective tissue cell\n",
-      "\t cell\n",
-      "mesenchymal stem cell\n",
+      "stromal cell\n",
       "\t cell\n",
-      "\t stem cell\n",
-      "\t precursor cell\n",
-      "\t multi fate stem cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
+      "connective tissue cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "ecto-epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "secretory epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t secretory cell\n",
       "\t epithelial cell\n",
       "neurecto-epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "osteoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t connective tissue cell\n",
       "preosteoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
@@ -566,20 +625,38 @@
       "\t bone cell\n",
       "ciliated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "ependymal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
       "\t epithelial cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t glial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
+      "\t neuron associated cell\n",
+      "epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "ciliated epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ciliated cell\n",
       "\t epithelial cell\n",
-      "epithelial cell\n",
+      "glial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "radial glial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t glial cell\n",
+      "\t neuron associated cell\n",
       "leukocyte\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -588,70 +665,93 @@
       "\t nucleate cell\n",
       "duct epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "branched duct epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "blood vessel endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "squamous epithelial cell\n",
+      "endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
       "endothelial cell of vascular tree\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "embryonic blood vessel endothelial progenitor cell\n",
-      "\t cell\n",
-      "\t embryonic cell (metazoa)\n",
-      "\t mesodermal cell\n",
       "non-branched duct epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t meso-epithelial cell\n",
       "meso-epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "barrier cell\n",
       "\t cell\n",
       "columnar/cuboidal epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "squamous epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "mesothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t lining cell\n",
       "lining cell\n",
       "\t cell\n",
       "\t barrier cell\n",
       "stratified epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "circulating cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "blood cell\n",
       "\t cell\n",
       "\t hematopoietic cell\n",
       "epithelial cell of lung\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "epithelial cell of lower respiratory tract\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "epithelial cell of pancreas\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "endo-epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "T cell\n",
       "\t cell\n",
@@ -674,6 +774,7 @@
       "\t single nucleate cell\n",
       "pro-T cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -758,6 +859,7 @@
       "\t defensive cell\n",
       "bone cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "monocyte\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -788,6 +890,7 @@
       "\t nucleate cell\n",
       "neuron associated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "neutrophil\n",
       "\t cell\n",
@@ -820,10 +923,13 @@
       "sensory epithelial cell\n",
       "\t cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
@@ -831,6 +937,7 @@
       "motor neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t efferent neuron\n",
       "\t electrically responsive cell\n",
@@ -839,6 +946,7 @@
       "efferent neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
@@ -846,53 +954,61 @@
       "afferent neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "bipolar neuron\n",
+      "peripheral nervous system neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "multipolar neuron\n",
+      "bipolar neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
+      "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "autonomic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t peripheral nervous system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "peripheral nervous system neuron\n",
+      "cholinergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "cholinergic neuron\n",
+      "catecholaminergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "catecholaminergic neuron\n",
+      "visual system neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -920,22 +1036,16 @@
       "ectodermal cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
-      "endothelial cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
-      "\t barrier cell\n",
-      "\t lining cell\n",
-      "CNS neuron (sensu Vertebrata)\n",
+      "\t eukaryotic cell\n",
+      "mesodermal cell\n",
       "\t cell\n",
-      "\t neuron\n",
-      "\t neural cell\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
-      "\t electrically signaling cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
       "GABAergic interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -947,6 +1057,8 @@
       "cerebellar Golgi cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -955,14 +1067,14 @@
       "\t cerebellar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "cerebellar inhibitory GABAergic interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -970,44 +1082,43 @@
       "\t cerebellar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "granule cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "Purkinje cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t efferent neuron\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t cerebellar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "cerebellar neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "stellate neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t multipolar neuron\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -1016,37 +1127,41 @@
       "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t neuron associated cell\n",
-      "glial cell\n",
-      "\t cell\n",
-      "\t neural cell\n",
-      "\t neuron associated cell\n",
       "macroglial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "astrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "oligodendrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t myelinating glial cell\n",
       "myelinating glial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "oligodendrocyte precursor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
+      "\t neural cell\n",
+      "\t neuron associated cell\n",
+      "\t neuron associated cell (sensu Vertebrata)\n",
       "microglial cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -1054,6 +1169,7 @@
       "\t myeloid cell\n",
       "\t professional antigen presenting cell\n",
       "\t neural cell\n",
+      "\t glial cell\n",
       "\t leukocyte\n",
       "\t tissue-resident macrophage\n",
       "\t myeloid leukocyte\n",
@@ -1062,7 +1178,6 @@
       "\t phagocyte\n",
       "\t mononuclear phagocyte\n",
       "\t mononuclear cell\n",
-      "\t glial cell\n",
       "\t central nervous system macrophage\n",
       "\t stuff accumulating cell\n",
       "\t motile cell\n",
@@ -1090,79 +1205,84 @@
       "\t single nucleate cell\n",
       "\t defensive cell\n",
       "\t macrophage\n",
-      "mesodermal cell\n",
-      "\t cell\n",
-      "\t embryonic cell (metazoa)\n",
       "gut endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "corneal endothelial cell\n",
       "\t cell\n",
-      "\t ecto-epithelial cell\n",
-      "\t neurecto-epithelial cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
-      "\t columnar/cuboidal epithelial cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "fibrocyte\n",
+      "mesenchymal stem cell\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
-      "\t stromal cell\n",
-      "stromal cell\n",
+      "\t stem cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
+      "\t multi fate stem cell\n",
+      "\t connective tissue cell\n",
+      "fibrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "adipocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t stuff accumulating cell\n",
       "stuff accumulating cell\n",
       "\t cell\n",
       "chondrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
-      "\t connective tissue cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t glycosaminoglycan secreting cell\n",
       "\t collagen secreting cell\n",
       "\t extracellular matrix secreting cell\n",
       "\t carbohydrate secreting cell\n",
       "glycosaminoglycan secreting cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
-      "\t connective tissue cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t extracellular matrix secreting cell\n",
       "\t carbohydrate secreting cell\n",
       "collagen secreting cell\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t extracellular matrix secreting cell\n",
       "pigment cell\n",
       "\t cell\n",
       "\t stuff accumulating cell\n",
       "melanocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t stuff accumulating cell\n",
       "\t pigment cell\n",
       "visual pigment cell\n",
       "\t cell\n",
       "\t stuff accumulating cell\n",
       "\t pigment cell\n",
-      "glandular epithelial cell\n",
+      "glandular secretory epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "extracellular matrix secreting cell\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "carbohydrate secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
@@ -1171,50 +1291,59 @@
       "\t secretory cell\n",
       "peptic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t protein secreting cell\n",
-      "\t glandular cell of stomach\n",
       "\t endo-epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t protein secreting cell\n",
+      "\t glandular epithelial cell of stomach\n",
       "\t epithelial cell of stomach\n",
-      "glandular cell of stomach\n",
+      "\t epithelial cell of alimentary canal\n",
+      "glandular epithelial cell of stomach\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
+      "\t glandular secretory epithelial cell\n",
       "\t epithelial cell of stomach\n",
+      "\t epithelial cell of alimentary canal\n",
       "surfactant secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
       "club cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t surfactant secreting cell\n",
       "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "epithelial cell of tracheobronchial tree\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "seromucus secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
       "goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t mucus secreting cell\n",
       "mucus secreting cell\n",
@@ -1223,28 +1352,35 @@
       "\t seromucus secreting cell\n",
       "parietal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t glandular cell of stomach\n",
       "\t endo-epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t glandular epithelial cell of stomach\n",
       "\t epithelial cell of stomach\n",
+      "\t epithelial cell of alimentary canal\n",
       "endocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "neuroendocrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
@@ -1256,10 +1392,12 @@
       "chromaffin cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural crest derived neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
@@ -1273,9 +1411,11 @@
       "amine precursor uptake and decarboxylation cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
@@ -1290,15 +1430,18 @@
       "\t secretory cell\n",
       "insulin secreting cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t protein secreting cell\n",
       "\t peptide hormone secreting cell\n",
       "type B pancreatic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t protein secreting cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
@@ -1307,14 +1450,17 @@
       "\t pancreatic endocrine cell\n",
       "pancreatic endocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "progenitor cell of endocrine pancreas\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "glucagon secreting cell\n",
@@ -1323,10 +1469,12 @@
       "\t peptide hormone secreting cell\n",
       "pancreatic A cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -1335,9 +1483,10 @@
       "\t type A enteroendocrine cell\n",
       "type A enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -1348,44 +1497,71 @@
       "\t peptide hormone secreting cell\n",
       "pancreatic D cell\n",
       "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
       "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t efferent neuron\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
+      "\t neuroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
       "\t pancreatic endocrine cell\n",
       "\t somatostatin secreting cell\n",
       "\t type D enteroendocrine cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "type D enteroendocrine cell\n",
       "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
       "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
+      "\t neuroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
       "\t somatostatin secreting cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "steroid hormone secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
       "stromal cell of ovary\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "testosterone secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
       "Leydig cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t testosterone secreting cell\n",
       "\t interstitial cell\n",
       "interstitial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "hepatocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t polyploid cell\n",
       "\t epithelial cell\n",
       "\t endopolyploid cell\n",
@@ -1395,12 +1571,14 @@
       "hepatoblast\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "contractile cell\n",
       "\t cell\n",
       "pericyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
       "\t supporting cell\n",
@@ -1408,16 +1586,18 @@
       "\t mural cell\n",
       "myoepithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t contractile cell\n",
       "myofibroblast cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
       "muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
@@ -1426,8 +1606,10 @@
       "\t electrically active cell\n",
       "cell of skeletal muscle\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "slow muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -1437,10 +1619,12 @@
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t myotube\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "\t skeletal muscle fiber\n",
       "extrafusal muscle fiber\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -1449,10 +1633,12 @@
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t myotube\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "\t skeletal muscle fiber\n",
       "fast muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -1462,28 +1648,33 @@
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t myotube\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "\t skeletal muscle fiber\n",
       "non-striated muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "visceral muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "smooth muscle myoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t myoblast\n",
       "\t muscle precursor cell\n",
       "cardiac muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -1494,22 +1685,17 @@
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "somatosensory neuron\n",
-      "\t cell\n",
-      "\t neuron\n",
-      "\t neural cell\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
-      "\t electrically signaling cell\n",
       "auditory hair cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -1521,6 +1707,7 @@
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -1530,12 +1717,14 @@
       "chemoreceptor cell\n",
       "\t cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "olfactory receptor cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -1545,21 +1734,19 @@
       "taste receptor cell\n",
       "\t cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t sensory epithelial cell\n",
       "\t chemoreceptor cell\n",
-      "\t endo-epithelial cell\n",
-      "endo-epithelial cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
       "photoreceptor cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
-      "\t neuronal receptor cell\n",
-      "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -1569,12 +1756,14 @@
       "\t cell\n",
       "Sertoli cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t mesothelial cell\n",
       "\t lining cell\n",
       "\t protein secreting cell\n",
@@ -1589,11 +1778,12 @@
       "\t protein secreting cell\n",
       "seminiferous tubule epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t mesothelial cell\n",
       "\t lining cell\n",
       "insulating cell\n",
@@ -1601,23 +1791,26 @@
       "\t barrier cell\n",
       "myelinating Schwann cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t glial cell\n",
       "\t barrier cell\n",
       "\t neuron associated cell\n",
-      "\t glial cell\n",
       "\t myelinating glial cell\n",
       "\t insulating cell\n",
       "\t Schwann cell\n",
       "Schwann cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "immature Schwann cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t Schwann cell\n",
       "motile cell\n",
       "\t cell\n",
@@ -1627,12 +1820,15 @@
       "\t cell\n",
       "single nucleate cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t nucleate cell\n",
       "multinucleate cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t nucleate cell\n",
       "erythrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
@@ -1642,11 +1838,13 @@
       "\t cell\n",
       "reticulocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
       "platelet\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t myeloid cell\n",
       "\t secretory cell\n",
@@ -1660,6 +1858,7 @@
       "\t biogenic amine secreting cell\n",
       "megakaryocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t myeloid cell\n",
       "defensive cell\n",
@@ -1694,6 +1893,7 @@
       "\t single nucleate cell\n",
       "keratinizing barrier epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -1703,6 +1903,7 @@
       "\t keratin accumulating cell\n",
       "stratified squamous epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
       "\t stratified epithelial cell\n",
@@ -1711,8 +1912,7 @@
       "\t stuff accumulating cell\n",
       "brush border epithelial cell\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "Merkel cell\n",
@@ -1722,8 +1922,8 @@
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t stratified epithelial cell\n",
       "\t neuron associated cell\n",
       "\t neuron associated cell (sensu Vertebrata)\n",
@@ -1736,6 +1936,7 @@
       "\t general ecto-epithelial cell\n",
       "keratinocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -1748,12 +1949,14 @@
       "\t general ecto-epithelial cell\n",
       "transitional epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "primary sensory neuron (sensu Teleostei)\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
       "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -1763,6 +1966,7 @@
       "eurydendroid cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t efferent neuron\n",
       "\t electrically responsive cell\n",
@@ -1774,8 +1978,10 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t photoreceptor cell\n",
       "\t electrically active cell\n",
@@ -1790,13 +1996,15 @@
       "\t stuff accumulating cell\n",
       "tracheal epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t respiratory tract epithelial cell\n",
       "epidermal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t general ecto-epithelial cell\n",
@@ -1807,28 +2015,45 @@
       "milk secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
-      "sebum secreting cell\n",
+      "sebocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t epidermal cell\n",
+      "\t epithelial cell of skin gland\n",
       "\t sebaceous gland cell\n",
+      "\t general ecto-epithelial cell\n",
+      "epithelial cell of skin gland\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t general ecto-epithelial cell\n",
       "sebaceous gland cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "seminal vesicle glandular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "pneumocyte\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t epithelial cell of lung\n",
-      "\t epithelial cell of alveolus of lung\n",
-      "epithelial cell of alveolus of lung\n",
+      "\t glandular secretory epithelial cell\n",
+      "pulmonary alveolar epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t epithelial cell of lung\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "lysozyme secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
@@ -1836,6 +2061,7 @@
       "metanephric mesenchyme stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t connective tissue cell\n",
@@ -1844,10 +2070,12 @@
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "mesenchymal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t motile cell\n",
       "biogenic amine secreting cell\n",
       "\t cell\n",
@@ -1859,11 +2087,14 @@
       "\t pigment cell\n",
       "extraembryonic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "trophoblast cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t extraembryonic cell\n",
       "sphincter associated smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -1873,6 +2104,7 @@
       "\t electrically active cell\n",
       "vascular associated smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -1885,16 +2117,19 @@
       "\t cell\n",
       "general ecto-epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "myotube\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "attachment cell\n",
       "\t cell\n",
@@ -1911,9 +2146,10 @@
       "\t blood cell\n",
       "tendon cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t fibrocyte\n",
-      "\t stromal cell\n",
       "\t supporting cell\n",
       "\t attachment cell\n",
       "plasmatocyte\n",
@@ -1926,65 +2162,57 @@
       "ganglion interneuron\n",
       "\t cell\n",
       "\t neuron\n",
-      "\t neural cell\n",
-      "\t interneuron\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
-      "\t electrically signaling cell\n",
-      "CNS interneuron\n",
-      "\t cell\n",
-      "\t neuron\n",
-      "\t neural cell\n",
-      "\t interneuron\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
+      "\t eukaryotic cell\n",
       "\t central nervous system neuron\n",
-      "\t electrically signaling cell\n",
-      "central nervous system neuron\n",
-      "\t cell\n",
-      "\t neuron\n",
       "\t neural cell\n",
+      "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
+      "obsolete CNS interneuron\n",
       "electrically signaling cell\n",
       "\t cell\n",
       "\t electrically active cell\n",
+      "syncytial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t nucleate cell\n",
+      "\t multinucleate cell\n",
       "excretory cell\n",
       "\t cell\n",
-      "\t secretory cell\n",
       "reticular cell\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "follicular dendritic cell\n",
       "\t cell\n",
       "\t defensive cell\n",
       "striated muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "epithelial cell of parathyroid gland\n",
-      "\t cell\n",
-      "\t secretory cell\n",
-      "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t endo-epithelial cell\n",
-      "\t parathyroid glandular cell\n",
       "preadipocyte\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "brown adipocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t adipocyte\n",
       "\t stuff accumulating cell\n",
       "brown preadipocyte\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t preadipocyte\n",
       "dendritic cell\n",
@@ -2047,19 +2275,23 @@
       "\t biogenic amine secreting cell\n",
       "cardiac myoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t myoblast\n",
       "\t muscle precursor cell\n",
       "neural progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "follicular epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "ovarian surface epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "CD4-positive helper T cell\n",
       "\t cell\n",
@@ -2108,6 +2340,8 @@
       "inhibitory interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -2115,29 +2349,28 @@
       "\t electrically signaling cell\n",
       "granulosa cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t steroid hormone secreting cell\n",
       "\t follicular epithelial cell\n",
+      "\t ovarian surface epithelial cell\n",
       "\t follicular cell of ovary\n",
       "follicular cell of ovary\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t follicular epithelial cell\n",
+      "\t ovarian surface epithelial cell\n",
       "theca cell\n",
       "\t cell\n",
-      "\t secretory cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
-      "\t steroid hormone secreting cell\n",
+      "\t connective tissue cell\n",
       "\t stromal cell of ovary\n",
-      "\t androgen secreting cell\n",
-      "androgen secreting cell\n",
-      "\t cell\n",
-      "\t secretory cell\n",
-      "\t steroid hormone secreting cell\n",
       "enkephalin secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
@@ -2149,53 +2382,59 @@
       "\t peptide hormone secreting cell\n",
       "type G enteroendocrine cell\n",
       "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
       "\t secretory cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t glandular cell of stomach\n",
-      "\t endocrine cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t efferent neuron\n",
+      "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
+      "\t neuroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
-      "\t endo-epithelial cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "\t enkephalin secreting cell\n",
       "\t endorphin secreting cell\n",
       "\t gastrin secreting cell\n",
-      "\t mucous cell of stomach\n",
-      "\t epithelial cell of alimentary canal\n",
-      "\t epithelial cell of stomach\n",
       "gastrin secreting cell\n",
       "\t cell\n",
       "\t secretory cell\n",
       "\t peptide hormone secreting cell\n",
-      "mucous cell of stomach\n",
-      "\t cell\n",
-      "\t secretory cell\n",
-      "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t glandular cell of stomach\n",
-      "\t endo-epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
-      "\t epithelial cell of stomach\n",
       "paneth cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t protein secreting cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t protein secreting cell\n",
       "\t lysozyme secreting cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "intestinal epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
-      "paracrine cell\n",
+      "transit amplifying cell of gut\n",
       "\t cell\n",
-      "\t secretory cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t transit amplifying cell\n",
       "cardiac muscle myoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t myoblast\n",
@@ -2204,8 +2443,10 @@
       "\t cardiocyte\n",
       "cardiocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "skeletal muscle fiber\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -2214,6 +2455,7 @@
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t myotube\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "perineuronal satellite cell\n",
       "\t cell\n",
@@ -2223,12 +2465,15 @@
       "\t neuron associated cell (sensu Vertebrata)\n",
       "syncytiotrophoblast cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t extraembryonic cell\n",
       "\t trophoblast cell\n",
+      "\t syncytial cell\n",
       "pigmented epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
@@ -2237,7 +2482,19 @@
       "\t cell\n",
       "\t neuron\n",
       "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "spinal cord motor neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
+      "\t motor neuron\n",
+      "\t efferent neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -2291,12 +2548,14 @@
       "\t mature T cell\n",
       "proerythroblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
       "\t nucleate cell\n",
       "erythroblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t progenitor cell\n",
@@ -2304,6 +2563,7 @@
       "\t myeloid cell\n",
       "megakaryocyte progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2311,27 +2571,37 @@
       "\t myeloid lineage restricted progenitor cell\n",
       "\t myeloid cell\n",
       "\t hematopoietic lineage restricted progenitor cell\n",
-      "neuronal brush cell\n",
+      "obsolete neuronal brush cell\n",
+      "unipolar brush cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
       "CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
+      "\t progenitor cell\n",
       "\t hematopoietic oligopotent progenitor cell\n",
       "\t hematopoietic oligopotent progenitor cell, lineage-negative\n",
       "bone marrow hematopoietic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t bone cell\n",
       "\t bone marrow cell\n",
       "promonocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2343,6 +2613,8 @@
       "amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -2351,17 +2623,19 @@
       "\t retinal cell\n",
       "retinal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "retinal ganglion cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "promyelocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2375,6 +2649,7 @@
       "\t migratory neural crest cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t multi fate stem cell\n",
@@ -2387,6 +2662,7 @@
       "\t migratory neural crest cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t multi fate stem cell\n",
@@ -2398,13 +2674,16 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t photoreceptor cell\n",
       "\t electrically active cell\n",
       "\t eye photoreceptor cell\n",
       "\t electrically signaling cell\n",
+      "\t retinal cell\n",
       "\t camera-type eye photoreceptor cell\n",
       "camera-type eye photoreceptor cell\n",
       "\t cell\n",
@@ -2412,20 +2691,25 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t photoreceptor cell\n",
       "\t electrically active cell\n",
       "\t eye photoreceptor cell\n",
       "\t electrically signaling cell\n",
+      "\t retinal cell\n",
       "retinal progenitor cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "corneal epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -2433,15 +2717,16 @@
       "type EC enteroendocrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural crest derived neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t efferent neuron\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t neuroendocrine cell\n",
@@ -2449,18 +2734,12 @@
       "\t amine precursor uptake and decarboxylation cell\n",
       "\t peptide hormone secreting cell\n",
       "\t electrically responsive cell\n",
-      "\t endo-epithelial cell\n",
       "\t electrically active cell\n",
       "\t serotonin secreting cell\n",
       "\t biogenic amine secreting cell\n",
       "\t electrically signaling cell\n",
       "\t enkephalin secreting cell\n",
       "\t endorphin secreting cell\n",
-      "\t epithelial cell of alimentary canal\n",
-      "epithelial cell of alimentary canal\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
       "cell in vitro\n",
       "\t cell\n",
       "immature neutrophil\n",
@@ -2477,6 +2756,7 @@
       "\t nucleate cell\n",
       "myelocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2525,23 +2805,24 @@
       "\t macrophage\n",
       "enterocyte\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t gut absorptive cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "gut absorptive cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "cochlea auditory hair cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -2553,12 +2834,14 @@
       "\t ear hair cell\n",
       "skeletal muscle satellite cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t muscle precursor cell\n",
       "\t cell of skeletal muscle\n",
       "enucleate erythrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
@@ -2568,6 +2851,7 @@
       "\t oxygen accumulating cell\n",
       "enucleated reticulocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
@@ -2576,8 +2860,9 @@
       "pyramidal neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t multipolar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -2587,13 +2872,16 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t photoreceptor cell\n",
       "\t electrically active cell\n",
       "\t eye photoreceptor cell\n",
       "\t electrically signaling cell\n",
+      "\t retinal cell\n",
       "\t camera-type eye photoreceptor cell\n",
       "ear hair cell\n",
       "\t cell\n",
@@ -2601,6 +2889,7 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
       "\t electrically responsive cell\n",
@@ -2610,6 +2899,7 @@
       "\t electrically signaling cell\n",
       "granulocytopoietic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2618,6 +2908,7 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "basophil mast progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -2629,6 +2920,7 @@
       "GABAergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
@@ -2636,9 +2928,11 @@
       "\t electrically signaling cell\n",
       "acinar cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t protein secreting cell\n",
       "natural killer cell\n",
       "\t cell\n",
@@ -2695,60 +2989,68 @@
       "hepatic stellate cell\n",
       "\t cell\n",
       "\t fibroblast\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t extracellular matrix secreting cell\n",
       "Mueller cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
-      "\t retinal cell\n",
       "\t radial glial cell\n",
-      "radial glial cell\n",
-      "\t cell\n",
-      "\t neural cell\n",
       "\t neuron associated cell\n",
-      "\t glial cell\n",
+      "\t retinal cell\n",
       "pituitary gland cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "tanycyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t ciliated cell\n",
+      "\t ependymal cell\n",
+      "\t epithelial cell\n",
+      "\t ciliated epithelial cell\n",
       "\t glial cell\n",
-      "\t macroglial cell\n",
-      "\t astrocyte\n",
-      "\t ependymoglial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t neuron associated cell\n",
       "Bergmann glial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "astrocyte of the forebrain\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "\t brain macroglial cell\n",
       "basal cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
       "\t epithelial fate stem cell\n",
       "juxtaglomerular complex cell\n",
       "\t cell\n",
-      "\t kidney cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cortical cell\n",
+      "\t kidney cell\n",
       "prickle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -2762,6 +3064,7 @@
       "\t general ecto-epithelial cell\n",
       "mesangial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
       "\t pericyte\n",
@@ -2770,62 +3073,73 @@
       "\t mural cell\n",
       "mucous neck cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t mucus secreting cell\n",
       "pinealocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t endocrine cell\n",
       "podocyte\n",
       "\t cell\n",
-      "\t secretory cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t excretory cell\n",
       "\t renal filtration cell\n",
       "\t epithelial cell of glomerular capsule\n",
-      "\t glomerular cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney glomerular epithelial cell\n",
+      "\t glomerular cell\n",
       "\t kidney corpuscule cell\n",
       "renal filtration cell\n",
       "\t cell\n",
-      "\t secretory cell\n",
+      "\t eukaryotic cell\n",
       "\t excretory cell\n",
       "epithelial cell of glomerular capsule\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t glomerular cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney glomerular epithelial cell\n",
+      "\t glomerular cell\n",
       "\t kidney corpuscule cell\n",
       "cardiac valve cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t cardiocyte\n",
-      "fenestrated cell\n",
+      "fenestrated endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
       "mural cell\n",
       "\t cell\n",
       "\t perivascular cell\n",
       "glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
@@ -2833,44 +3147,42 @@
       "\t electrically signaling cell\n",
       "M cell of gut\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t defensive cell\n",
-      "\t epithelial cell of alimentary canal\n",
+      "\t intestinal epithelial cell\n",
       "\t transporting cell\n",
-      "ependymoglial cell\n",
-      "\t cell\n",
-      "\t neural cell\n",
-      "\t neuron associated cell\n",
-      "\t glial cell\n",
-      "\t macroglial cell\n",
-      "\t astrocyte\n",
+      "\t epithelial cell of alimentary canal\n",
       "non-myelinating Schwann cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t Schwann cell\n",
       "Cajal-Retzius cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "PP cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
       "dopaminergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t catecholaminergic neuron\n",
@@ -2879,38 +3191,44 @@
       "\t electrically signaling cell\n",
       "endothelial tip cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "choroid plexus epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t ciliated cell\n",
       "\t ependymal cell\n",
-      "\t ciliated epithelial cell\n",
       "\t epithelial cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t glial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
+      "\t neuron associated cell\n",
+      "\t brush border epithelial cell\n",
       "\t transporting cell\n",
       "leptomeningeal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t connective tissue cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t extracellular matrix secreting cell\n",
       "urothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t transitional epithelial cell\n",
       "retina horizontal cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -2919,215 +3237,193 @@
       "retinal bipolar neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
-      "\t visual system neuron\n",
-      "visual system neuron\n",
-      "\t cell\n",
-      "\t neuron\n",
-      "\t neural cell\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
-      "\t electrically signaling cell\n",
       "ON-bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "OFF-bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "rod bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "cone retinal bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "type 1 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 2 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 3 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 4 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 5 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 6 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 7 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 8 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "type 9 cone bipolar cell (sensu Mus)\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "myeloid dendritic cell\n",
       "\t cell\n",
@@ -3578,6 +3874,7 @@
       "\t thymocyte\n",
       "DN1 thymic pro-T cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -3671,6 +3968,7 @@
       "\t lymphocyte of B lineage, CD19-positive\n",
       "pro-B cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -3796,6 +4094,7 @@
       "\t innate lymphoid cell\n",
       "lymphoid lineage restricted progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -3803,14 +4102,17 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "hematopoietic multipotent progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "hematopoietic lineage restricted progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "common dendritic progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "\t myeloid cell\n",
@@ -3868,6 +4170,20 @@
       "\t mature B cell\n",
       "\t B cell, CD19-positive\n",
       "\t lymphocyte of B lineage, CD19-positive\n",
+      "olfactory epithelial cell\n",
+      "\t cell\n",
+      "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t ecto-epithelial cell\n",
+      "\t neurecto-epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t sensory epithelial cell\n",
+      "\t chemoreceptor cell\n",
+      "\t epithelial cell of upper respiratory tract\n",
+      "\t respiratory tract epithelial cell\n",
       "classical monocyte\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -3983,6 +4299,28 @@
       "\t single nucleate cell\n",
       "\t defensive cell\n",
       "\t macrophage\n",
+      "\t border associated macrophage\n",
+      "border associated macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t neural cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t central nervous system macrophage\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
       "myeloid suppressor cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -4013,6 +4351,7 @@
       "\t elicited macrophage\n",
       "early T lineage precursor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -4418,6 +4757,7 @@
       "\t mature T cell\n",
       "early lymphoid progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -4656,11 +4996,13 @@
       "\t long lived plasma cell\n",
       "hematopoietic oligopotent progenitor cell, lineage-negative\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "\t hematopoietic oligopotent progenitor cell\n",
       "pre-conventional dendritic cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
       "\t myeloid cell\n",
@@ -4685,6 +5027,7 @@
       "CD34-positive, CD38-negative hematopoietic stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "\t hematopoietic stem cell\n",
@@ -4693,8 +5036,10 @@
       "\t progenitor cell\n",
       "CD115-positive monocyte OR common dendritic progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
+      "\t myeloid cell\n",
       "\t hematopoietic lineage restricted progenitor cell\n",
       "CD115-positive monocyte\n",
       "\t cell\n",
@@ -4722,26 +5067,27 @@
       "cerebellar granule cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t granule cell\n",
       "\t cerebellar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebellum glutamatergic neuron\n",
       "cerebellum glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t cerebellar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "cerebellar granule cell precursor\n",
@@ -4754,20 +5100,24 @@
       "cerebral cortex neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
+      "\t neuron of the forebrain\n",
       "hippocampal neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "T-helper 22 cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -5032,6 +5382,7 @@
       "\t single nucleate cell\n",
       "erythroid progenitor cell, mammalian\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -5192,6 +5543,7 @@
       "\t mature T cell\n",
       "fraction A pre-pro B cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -5201,6 +5553,7 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "early pro-B cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -5244,45 +5597,62 @@
       "\t defensive cell\n",
       "\t classical monocyte\n",
       "\t CD14-positive monocyte\n",
-      "type I pneumocyte\n",
+      "pulmonary alveolar type 1 cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t epithelial cell of lung\n",
-      "\t pneumocyte\n",
-      "\t epithelial cell of alveolus of lung\n",
-      "type II pneumocyte\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t pulmonary alveolar epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "pulmonary alveolar type 2 cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t epithelial cell of lung\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t surfactant secreting cell\n",
-      "\t pneumocyte\n",
-      "\t epithelial cell of alveolus of lung\n",
+      "\t pulmonary alveolar epithelial cell\n",
       "\t lung secretory cell\n",
+      "\t respiratory tract epithelial cell\n",
       "lung secretory cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "pancreatic acinar cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t protein secreting cell\n",
       "\t acinar cell\n",
       "\t pancreas exocrine glandular cell\n",
       "\t epithelial cell of exocrine pancreas\n",
       "pancreas exocrine glandular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t epithelial cell of exocrine pancreas\n",
       "specialized cardiac myocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5292,25 +5662,26 @@
       "\t cardiocyte\n",
       "enterocyte of epithelium of large intestine\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t enterocyte\n",
       "\t gut absorptive cell\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "epithelial cell of large intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "nodal myocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5319,45 +5690,56 @@
       "\t striated muscle cell\n",
       "\t cardiocyte\n",
       "\t specialized cardiac myocyte\n",
-      "brush cell of trachebronchial tree\n",
+      "brush cell of tracheobronchial tree\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
       "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
       "\t brush cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "brush cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "pancreatic ductal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t branched duct epithelial cell\n",
       "\t epithelial cell of pancreas\n",
+      "\t endo-epithelial cell\n",
       "\t epithelial cell of exocrine pancreas\n",
       "epithelial cell of exocrine pancreas\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
+      "\t endo-epithelial cell\n",
       "interstitial cell of Cajal\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "bone marrow cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t bone cell\n",
       "interstitial cell of ovary\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t stromal cell of ovary\n",
       "transversely striated visceral muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5367,19 +5749,24 @@
       "\t striated visceral muscle cell\n",
       "cortical cell of adrenal gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t steroid hormone secreting cell\n",
       "\t adrenal gland glandular cell\n",
       "adrenal gland glandular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "regular cardiac myocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5389,10 +5776,12 @@
       "\t cardiocyte\n",
       "type I cell of adrenal cortex\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t steroid hormone secreting cell\n",
       "\t mineralocorticoid secreting cell\n",
       "\t cortical cell of adrenal gland\n",
@@ -5434,6 +5823,7 @@
       "\t lymphocyte of B lineage, CD19-positive\n",
       "regular atrial cardiac myocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5444,6 +5834,7 @@
       "\t regular cardiac myocyte\n",
       "regular ventricular cardiac myocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -5454,72 +5845,82 @@
       "\t regular cardiac myocyte\n",
       "endothelial cell of lymphatic vessel\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "vascular lymphangioblast\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t perivascular cell\n",
       "\t lymphangioblast\n",
       "epithelial cell of sweat gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t epidermal cell\n",
-      "\t general ecto-epithelial cell\n",
       "\t epithelial cell of skin gland\n",
+      "\t general ecto-epithelial cell\n",
       "capillary endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
+      "\t squamous endothelial cell\n",
       "\t microvascular endothelial cell\n",
+      "squamous endothelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
       "microvascular endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "ciliated columnar cell of tracheobronchial tree\n",
+      "multiciliated columnar cell of tracheobronchial tree\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
-      "\t multi-ciliated epithelial cell\n",
-      "\t respiratory ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
-      "multi-ciliated epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t respiratory tract multiciliated cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t multiciliated epithelial cell\n",
+      "respiratory tract multiciliated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
       "\t epithelial cell\n",
-      "respiratory ciliated cell\n",
-      "\t cell\n",
-      "\t ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t multiciliated epithelial cell\n",
       "epithelial cell of uterus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
+      "\t meso-epithelial cell\n",
       "late promyelocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -5531,6 +5932,7 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "early promyelocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t hematopoietic cell\n",
       "\t hematopoietic precursor cell\n",
@@ -5542,20 +5944,19 @@
       "\t hematopoietic lineage restricted progenitor cell\n",
       "endosteal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t bone cell\n",
-      "olfactory epithelial cell\n",
+      "epithelial cell of upper respiratory tract\n",
       "\t cell\n",
-      "\t sensory receptor cell\n",
-      "\t ecto-epithelial cell\n",
-      "\t neurecto-epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t columnar/cuboidal epithelial cell\n",
-      "\t sensory epithelial cell\n",
-      "\t chemoreceptor cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "renal interstitial pericyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t interstitial cell\n",
       "\t contractile cell\n",
@@ -5567,18 +5968,43 @@
       "\t kidney interstitial cell\n",
       "epithelial cell of stomach\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
+      "epithelial cell of alimentary canal\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "foveolar cell of stomach\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t mucus secreting cell\n",
+      "\t epithelial cell of stomach\n",
       "\t epithelial cell of alimentary canal\n",
+      "\t mucous cell of stomach\n",
+      "mucous cell of stomach\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t seromucus secreting cell\n",
+      "\t mucus secreting cell\n",
       "\t epithelial cell of stomach\n",
+      "\t epithelial cell of alimentary canal\n",
       "basal cell of epidermis\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
@@ -5600,34 +6026,25 @@
       "stem cell of epidermis\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "glomerular endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t fenestrated cell\n",
-      "\t glomerular cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
-      "\t kidney corpuscule cell\n",
-      "glomerular cell\n",
+      "\t kidney cell\n",
+      "kidney cortical cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
-      "\t kidney corpuscule cell\n",
       "granular cell of epidermis\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -5639,14 +6056,22 @@
       "\t keratinocyte\n",
       "\t epidermal cell\n",
       "\t general ecto-epithelial cell\n",
+      "epithelial cell of parathyroid gland\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "epithelial cell of thyroid gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "renal beta-intercalated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
@@ -5656,62 +6081,66 @@
       "\t epithelial cell of nephron\n",
       "renal intercalated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t epithelial cell of nephron\n",
-      "epithelial cell of lower respiratory tract\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
-      "\t respiratory epithelial cell\n",
-      "brush cell of epithelium proper of large intestine\n",
+      "tuft cell of large intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
       "\t brush cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t intestinal tuft cell\n",
       "intestinal tuft cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t brush cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "brush cell of trachea\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
       "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
       "\t tracheal epithelial cell\n",
-      "\t brush cell of trachebronchial tree\n",
+      "\t brush cell of tracheobronchial tree\n",
       "\t brush cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "bronchial epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t respiratory tract epithelial cell\n",
       "epithelial cell of esophagus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "vertebrate lens cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t stuff accumulating cell\n",
       "\t crystallin accumulating cell\n",
       "anterior lens cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t stuff accumulating cell\n",
@@ -5720,6 +6149,7 @@
       "\t lens epithelial cell\n",
       "lens epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t stuff accumulating cell\n",
@@ -5727,6 +6157,7 @@
       "\t vertebrate lens cell\n",
       "secondary lens fiber\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t stuff accumulating cell\n",
       "\t crystallin accumulating cell\n",
@@ -5734,37 +6165,44 @@
       "\t lens fiber cell\n",
       "lens fiber cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t stuff accumulating cell\n",
       "\t crystallin accumulating cell\n",
       "\t vertebrate lens cell\n",
       "lactocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t milk secreting cell\n",
       "\t general ecto-epithelial cell\n",
       "\t mammary gland epithelial cell\n",
       "\t mammary gland glandular cell\n",
       "epithelial cell of prostate\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "basal cell of prostate epithelium\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of prostate\n",
       "luminal cell of prostate epithelium\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of prostate\n",
       "basal epithelial cell of prostatic duct\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t endo-epithelial cell\n",
@@ -5773,10 +6211,13 @@
       "pulmonary interstitial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t interstitial cell\n",
       "smooth muscle cell of sphincter of pupil\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -5788,6 +6229,7 @@
       "peripheral blood stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "\t hematopoietic stem cell\n",
@@ -5798,6 +6240,7 @@
       "intestinal crypt stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t epithelial cell\n",
@@ -5806,76 +6249,92 @@
       "\t epithelial cell of alimentary canal\n",
       "epithelial cell of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "stromal cell of endometrium\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "decidual cell\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "\t extraembryonic cell\n",
       "thyroid follicular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t epithelial cell of thyroid gland\n",
       "neuroepithelial stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
-      "parathyroid glandular cell\n",
-      "\t cell\n",
-      "\t secretory cell\n",
-      "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "endothelial cell of sinusoid\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "enteroendocrine cell of colon\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
+      "\t epithelial cell of large intestine\n",
       "\t epithelial cell of alimentary canal\n",
+      "\t intestinal enteroendocrine cell\n",
+      "\t colon epithelial cell\n",
+      "colon glandular cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t intestinal epithelial cell\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t glandular cell of the large intestine\n",
-      "\t intestinal enteroendocrine cell\n",
-      "\t colon glandular cell\n",
       "\t colon epithelial cell\n",
       "enteroendocrine cell of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "\t intestinal enteroendocrine cell\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "P/D1 enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -5886,10 +6345,12 @@
       "\t peptide hormone secreting cell\n",
       "pancreatic PP cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -5897,40 +6358,44 @@
       "\t PP cell\n",
       "type I enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
       "GIP cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "\t enteroendocrine cell of small intestine\n",
       "\t intestinal enteroendocrine cell\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "type L enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
       "type N enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -5940,20 +6405,24 @@
       "\t peptide hormone secreting cell\n",
       "epithelial cell of thymus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "cortical thymic epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of thymus\n",
       "medullary thymic epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of thymus\n",
       "pigmented ciliary epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
@@ -5961,24 +6430,27 @@
       "\t pigmented epithelial cell\n",
       "non-pigmented ciliary epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "epithelial cell of distal tubule\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "nephron tubule epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
@@ -5986,59 +6458,73 @@
       "\t kidney tubule cell\n",
       "kidney cortex tubule cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "epithelial cell of proximal tubule\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
-      "\t columnar/cuboidal epithelial cell\n",
-      "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
-      "epithelial cell of skin gland\n",
+      "brush border cell of the proximal tubule\n",
       "\t cell\n",
-      "\t ecto-epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t general ecto-epithelial cell\n",
+      "\t meso-epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t nephron tubule epithelial cell\n",
+      "\t kidney cortex tubule cell\n",
+      "\t epithelial cell of proximal tubule\n",
+      "\t kidney epithelial cell\n",
+      "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney tubule cell\n",
       "embryonic stem cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
-      "myoepithelial cell of mammary gland\n",
+      "basal-myoepithelial cell of mammary gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t contractile cell\n",
       "\t myoepithelial cell\n",
       "\t mammary gland epithelial cell\n",
       "mammary gland epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "mammary gland glandular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t mammary gland epithelial cell\n",
       "luminal adaptive secretory precursor cell of mammary gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t epithelial cell\n",
@@ -6048,37 +6534,43 @@
       "\t luminal epithelial cell of mammary gland\n",
       "luminal epithelial cell of mammary gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t branched duct epithelial cell\n",
       "\t mammary gland epithelial cell\n",
       "progenitor cell of mammary luminal epithelium\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "basal epithelial cell of tracheobronchial tree\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
-      "ciliated cell of the bronchus\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t respiratory tract epithelial cell\n",
+      "multiciliated epithelial cell of the bronchus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
       "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
-      "\t ciliated columnar cell of tracheobronchial tree\n",
-      "\t multi-ciliated epithelial cell\n",
-      "\t respiratory ciliated cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
       "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of tracheobronchial tree\n",
+      "\t multiciliated columnar cell of tracheobronchial tree\n",
+      "\t respiratory tract multiciliated cell\n",
       "\t bronchial epithelial cell\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t multiciliated epithelial cell\n",
       "keratinocyte stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t multi fate stem cell\n",
@@ -6118,30 +6610,44 @@
       "\t motile cell\n",
       "\t nucleate cell\n",
       "\t single nucleate cell\n",
+      "\t extraembryonic cell\n",
       "\t natural killer cell\n",
       "\t group 1 innate lymphoid cell\n",
       "\t mature natural killer cell\n",
       "\t innate lymphoid cell\n",
+      "\t uterine natural killer cell\n",
+      "uterine natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
       "endocardial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t cardiocyte\n",
       "\t cardiac endothelial cell\n",
       "cardiac endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t cardiocyte\n",
       "gestational hematopoietic stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "\t hematopoietic stem cell\n",
@@ -6150,28 +6656,34 @@
       "\t progenitor cell\n",
       "primitive red blood cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
       "\t primitive erythroid lineage cell\n",
       "primitive erythroid lineage cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
       "primitive erythroid progenitor\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t hematopoietic cell\n",
       "\t erythroid lineage cell\n",
       "\t myeloid cell\n",
       "\t primitive erythroid lineage cell\n",
       "keratocyte\n",
       "\t cell\n",
-      "\t fibroblast neural crest derived\n",
+      "\t neural crest derived fibroblast\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "myometrial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6182,6 +6694,7 @@
       "\t uterine smooth muscle cell\n",
       "uterine smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6189,22 +6702,25 @@
       "\t non-striated muscle cell\n",
       "\t visceral muscle cell\n",
       "\t electrically active cell\n",
-      "respiratory epithelial cell\n",
+      "respiratory tract epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
-      "respiratory goblet cell\n",
+      "respiratory tract goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
+      "\t respiratory tract epithelial cell\n",
       "Schwann cell precursor\n",
       "\t cell\n",
-      "\t glioblast\n",
-      "\t non-terminally differentiated cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "CD141-positive myeloid dendritic cell\n",
@@ -6297,6 +6813,14 @@
       "pancreatic stellate cell\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
+      "\t connective tissue cell\n",
+      "\t stromal cell of pancreas\n",
+      "stromal cell of pancreas\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "epidermal Langerhans cell\n",
       "\t cell\n",
@@ -6366,31 +6890,40 @@
       "\t macrophage\n",
       "nasal mucosa goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t respiratory goblet cell\n",
+      "\t epithelial cell of upper respiratory tract\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t respiratory tract goblet cell\n",
       "melanocyte of skin\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t stuff accumulating cell\n",
       "\t pigment cell\n",
       "\t melanocyte\n",
       "hair follicle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "auditory epithelial cell\n",
       "\t cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t sensory epithelial cell\n",
       "trophoblast giant cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t extraembryonic cell\n",
       "\t trophoblast cell\n",
       "fetal cardiomyocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -6400,10 +6933,12 @@
       "\t cardiocyte\n",
       "adventitial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t supporting cell\n",
       "enteric smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6413,31 +6948,38 @@
       "\t electrically active cell\n",
       "kidney epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t kidney cell\n",
       "kidney cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "subcutaneous adipocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t adipocyte\n",
       "\t stuff accumulating cell\n",
       "epithelial cell of amnion\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "intrahepatic cholangiocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t branched duct epithelial cell\n",
       "\t cholangiocyte\n",
       "cholangiocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t branched duct epithelial cell\n",
       "aortic smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6450,6 +6992,7 @@
       "\t blood vessel smooth muscle cell\n",
       "blood vessel smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6461,55 +7004,64 @@
       "\t perivascular cell\n",
       "endothelial cell of artery\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "vein endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "aortic endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t endothelial cell of artery\n",
+      "embryonic blood vessel endothelial progenitor cell\n",
+      "\t cell\n",
+      "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
+      "\t mesodermal cell\n",
       "fibroblast of cardiac tissue\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t cardiocyte\n",
       "skin fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "fibroblast of lung\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "fibroblast of lymphatic vessel\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "fibroblast of mammary gland\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "iris pigment epithelial cell\n",
       "\t cell\n",
@@ -6521,12 +7073,14 @@
       "mesenchymal stem cell of adipose tissue\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t connective tissue cell\n",
       "\t mesenchymal stem cell\n",
       "central nervous system pericyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
@@ -6534,37 +7088,32 @@
       "\t supporting cell\n",
       "\t perivascular cell\n",
       "\t mural cell\n",
-      "placental epithelial cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t extraembryonic cell\n",
       "fibroblast of breast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "renal cortical epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
       "\t kidney cortical cell\n",
-      "kidney cortical cell\n",
-      "\t cell\n",
+      "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "retinal blood vessel endothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t epithelial cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t retinal cell\n",
       "retinal pigment epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t epithelial cell\n",
       "\t stuff accumulating cell\n",
@@ -6573,6 +7122,7 @@
       "\t retinal cell\n",
       "smooth muscle cell of the pulmonary artery\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6585,9 +7135,11 @@
       "\t blood vessel smooth muscle cell\n",
       "bladder cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t lower urinary tract cell\n",
       "bronchial smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6598,6 +7150,7 @@
       "\t tracheobronchial smooth muscle cell\n",
       "tracheobronchial smooth muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6607,6 +7160,7 @@
       "\t electrically active cell\n",
       "smooth muscle cell of trachea\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -6617,23 +7171,26 @@
       "\t tracheobronchial smooth muscle cell\n",
       "astrocyte of the cerebellum\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "\t brain macroglial cell\n",
       "brain macroglial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "astrocyte of the cerebral cortex\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "\t astrocyte of the forebrain\n",
@@ -6641,64 +7198,95 @@
       "\t cerebral cortex glial cell\n",
       "cerebral cortex glial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "migratory enteric neural crest cell\n",
       "\t cell\n",
       "\t migratory neural crest cell\n",
       "\t embryonic cell (metazoa)\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t multi fate stem cell\n",
       "\t motile cell\n",
       "\t neural crest cell\n",
+      "neuron of the forebrain\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "adipocyte of breast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t adipocyte\n",
       "\t stuff accumulating cell\n",
       "gingival epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t oral mucosa squamous cell\n",
+      "oral mucosa squamous cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "prostate stromal cell\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
+      "\t connective tissue cell\n",
       "acinar cell of salivary gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t protein secreting cell\n",
       "\t endo-epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t protein secreting cell\n",
       "\t acinar cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t salivary gland glandular cell\n",
+      "\t salivary gland cell\n",
       "salivary gland glandular cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
+      "\t salivary gland cell\n",
       "immature astrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "mature astrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "mature microglial cell\n",
@@ -6708,6 +7296,7 @@
       "\t myeloid cell\n",
       "\t professional antigen presenting cell\n",
       "\t neural cell\n",
+      "\t glial cell\n",
       "\t leukocyte\n",
       "\t tissue-resident macrophage\n",
       "\t myeloid leukocyte\n",
@@ -6716,7 +7305,6 @@
       "\t phagocyte\n",
       "\t mononuclear phagocyte\n",
       "\t mononuclear cell\n",
-      "\t glial cell\n",
       "\t microglial cell\n",
       "\t central nervous system macrophage\n",
       "\t stuff accumulating cell\n",
@@ -6728,51 +7316,52 @@
       "respiratory basal cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
       "\t epithelial fate stem cell\n",
-      "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
       "\t basal cell\n",
-      "\t respiratory epithelial cell\n",
       "glandular cell of esophagus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of esophagus\n",
       "endothelial cell of venule\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "glandular cell of the large intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "endothelial stalk cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "tongue muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -6781,23 +7370,26 @@
       "\t nucleate cell\n",
       "\t multinucleate cell\n",
       "\t myotube\n",
+      "\t syncytial cell\n",
       "\t striated muscle cell\n",
       "\t skeletal muscle fiber\n",
       "intestinal enteroendocrine cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "bistratified retinal ganglion cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -6806,6 +7398,8 @@
       "bistratified cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
@@ -6813,8 +7407,8 @@
       "ON retinal ganglion cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -6822,8 +7416,8 @@
       "OFF retinal ganglion cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -6834,82 +7428,83 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t photoreceptor cell\n",
       "\t electrically active cell\n",
       "\t eye photoreceptor cell\n",
       "\t electrically signaling cell\n",
+      "\t retinal cell\n",
       "\t retinal cone cell\n",
       "\t camera-type eye photoreceptor cell\n",
       "type 3a cone bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "\t type 3 cone bipolar cell (sensu Mus)\n",
       "type 3b cone bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "\t type 3 cone bipolar cell (sensu Mus)\n",
       "type 5a cone bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "\t type 5 cone bipolar cell (sensu Mus)\n",
       "type 5b cone bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t cone retinal bipolar cell\n",
       "\t type 5 cone bipolar cell (sensu Mus)\n",
       "H1 horizontal cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -6919,6 +7514,8 @@
       "H2 horizontal cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -6928,6 +7525,8 @@
       "A2 amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -6944,6 +7543,8 @@
       "bistratified retinal amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -6955,6 +7556,8 @@
       "narrow field retinal amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
@@ -6965,6 +7568,8 @@
       "glycinergic amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -6977,6 +7582,8 @@
       "starburst amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -6993,6 +7600,8 @@
       "GABAergic amacrine cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -7006,20 +7615,24 @@
       "\t GABAergic neuron\n",
       "ionocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "renal principal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "epithelial cell of nephron\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "renal alpha-intercalated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
@@ -7027,12 +7640,21 @@
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t epithelial cell of nephron\n",
+      "multiciliated epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t ciliated cell\n",
+      "\t epithelial cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
       "pancreatic epsilon cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of pancreas\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t endocrine cell\n",
       "\t enteroendocrine cell\n",
       "\t peptide hormone secreting cell\n",
@@ -7041,12 +7663,14 @@
       "lymphangioblast\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t perivascular cell\n",
       "mesenchymal lymphangioblast\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t connective tissue cell\n",
@@ -7056,6 +7680,7 @@
       "cranial motor neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t motor neuron\n",
       "\t efferent neuron\n",
@@ -7065,6 +7690,7 @@
       "visceromotor neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t motor neuron\n",
       "\t efferent neuron\n",
@@ -7074,26 +7700,30 @@
       "parasympathetic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t autonomic neuron\n",
       "\t peripheral nervous system neuron\n",
+      "\t autonomic neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "notochordal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "enteric neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural crest derived neuron\n",
       "\t neural cell\n",
-      "\t autonomic neuron\n",
       "\t peripheral nervous system neuron\n",
+      "\t autonomic neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "striated visceral muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -7103,6 +7733,7 @@
       "skeletal muscle satellite stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t non-terminally differentiated cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
@@ -7113,6 +7744,7 @@
       "inhibitory motor neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t motor neuron\n",
       "\t efferent neuron\n",
@@ -7122,122 +7754,148 @@
       "cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "cerebral cortex GABAergic interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "extravillous trophoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t extraembryonic cell\n",
       "\t trophoblast cell\n",
       "primary motor cortex pyramidal cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t multipolar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t pyramidal neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cerebral cortex pyramidal neuron\n",
       "L5 extratelencephalic projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t multipolar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t pyramidal neuron\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cerebral cortex pyramidal neuron\n",
       "\t extratelencephalic-projecting glutamatergic cortical neuron\n",
+      "peripheral sensory neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t sensory neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t afferent neuron\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
       "inflammatory cell\n",
       "\t cell\n",
       "salivary gland cell\n",
       "\t cell\n",
-      "small intestine glandular cell\n",
+      "\t eukaryotic cell\n",
+      "small intestine secretory cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "paneth cell of colon\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t protein secreting cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t protein secreting cell\n",
       "\t lysozyme secreting cell\n",
       "\t paneth cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
-      "\t glandular cell of the large intestine\n",
-      "\t colon glandular cell\n",
-      "\t colon epithelial cell\n",
-      "colon glandular cell\n",
-      "\t cell\n",
-      "\t secretory cell\n",
-      "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t endo-epithelial cell\n",
-      "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
-      "\t epithelial cell of large intestine\n",
+      "\t colon glandular cell\n",
       "\t glandular cell of the large intestine\n",
       "\t colon epithelial cell\n",
       "transit amplifying cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "transit amplifying cell of colon\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t transit amplifying cell of gut\n",
+      "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t transit amplifying cell\n",
       "transit amplifying cell of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t transit amplifying cell of gut\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t epithelial cell of small intestine\n",
       "\t transit amplifying cell\n",
       "intestinal crypt stem cell of large intestine\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t intestinal crypt stem cell\n",
       "intestinal crypt stem cell of small intestine\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t epithelial cell\n",
@@ -7248,15 +7906,9 @@
       "\t epithelial cell of small intestine\n",
       "stromal cell of lamina propria of small intestine\n",
       "\t cell\n",
-      "\t connective tissue cell\n",
+      "\t eukaryotic cell\n",
       "\t stromal cell\n",
-      "colon epithelial cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
-      "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
-      "\t epithelial cell of large intestine\n",
+      "\t connective tissue cell\n",
       "colon macrophage\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -7277,60 +7929,69 @@
       "\t macrophage\n",
       "colon goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
-      "\t glandular cell of the large intestine\n",
-      "\t colon glandular cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t colon epithelial cell\n",
       "\t large intestine goblet cell\n",
       "\t intestine goblet cell\n",
+      "colon epithelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "large intestine goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
-      "\t glandular cell of the large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t intestine goblet cell\n",
       "tuft cell of colon\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
       "\t brush cell\n",
-      "\t brush cell of epithelium proper of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
+      "\t tuft cell of large intestine\n",
       "\t intestinal tuft cell\n",
-      "\t colon epithelial cell\n",
       "intestinal crypt stem cell of colon\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t multi fate stem cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t intestinal crypt stem cell\n",
-      "\t colon epithelial cell\n",
+      "\t intestinal crypt stem cell of large intestine\n",
       "smooth muscle cell of large intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -7341,6 +8002,7 @@
       "\t enteric smooth muscle cell\n",
       "lung pericyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
       "\t pericyte\n",
@@ -7349,22 +8011,19 @@
       "\t mural cell\n",
       "endothelial cell of placenta\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t extraembryonic cell\n",
-      "\t placental epithelial cell\n",
       "endothelial cell of uterus\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "fibro/adipogenic progenitor cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t motile cell\n",
@@ -7418,6 +8077,7 @@
       "\t regulatory T cell\n",
       "hair follicular keratinocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -7430,49 +8090,43 @@
       "\t epidermal cell\n",
       "\t general ecto-epithelial cell\n",
       "\t hair follicle cell\n",
+      "obsolete epithelial cell of alveolus of lung\n",
       "cardiac blood vessel endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t cardiocyte\n",
       "\t cardiac endothelial cell\n",
       "cardiac neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t peripheral nervous system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "spinal cord motor neuron\n",
-      "\t cell\n",
-      "\t neuron\n",
-      "\t neural cell\n",
-      "\t motor neuron\n",
-      "\t efferent neuron\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
-      "\t electrically signaling cell\n",
       "lateral mesodermal cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
       "\t mesodermal cell\n",
       "intermediate mesodermal cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
       "\t mesodermal cell\n",
       "mesothelial cell of epicardium\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t mesothelial cell\n",
       "\t lining cell\n",
       "\t cardiocyte\n",
@@ -7506,157 +8160,205 @@
       "skeletal muscle fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t cell of skeletal muscle\n",
       "\t muscle fibroblast\n",
       "muscle fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "olfactory ensheathing cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
-      "monocyte-derived dendritic cell\n",
+      "\t neuron associated cell\n",
+      "dermal microvascular endothelial cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
-      "\t hematopoietic cell\n",
-      "\t myeloid cell\n",
-      "\t professional antigen presenting cell\n",
-      "\t leukocyte\n",
-      "\t myeloid leukocyte\n",
-      "\t phagocyte (sensu Vertebrata)\n",
-      "\t phagocyte\n",
-      "\t mononuclear phagocyte\n",
-      "\t mononuclear cell\n",
-      "\t stuff accumulating cell\n",
-      "\t motile cell\n",
-      "\t nucleate cell\n",
-      "\t single nucleate cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "\t microvascular endothelial cell\n",
+      "\t dermis blood vessel endothelial cell\n",
+      "dermis blood vessel endothelial cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
+      "\t barrier cell\n",
+      "\t lining cell\n",
+      "monocyte-derived dendritic cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
       "\t defensive cell\n",
       "\t dendritic cell\n",
       "\t conventional dendritic cell\n",
       "\t myeloid dendritic cell\n",
       "chorionic trophoblast cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t extraembryonic cell\n",
       "\t trophoblast cell\n",
       "sympathetic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t autonomic neuron\n",
       "\t peripheral nervous system neuron\n",
+      "\t autonomic neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "spiral ganglion neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t peripheral nervous system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "forebrain radial glial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
       "\t radial glial cell\n",
+      "\t neuron associated cell\n",
       "pulmonary ionocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of lung\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "\t ionocyte\n",
       "splanchnic mesodermal cell\n",
       "\t cell\n",
       "\t embryonic cell (metazoa)\n",
+      "\t eukaryotic cell\n",
       "\t mesodermal cell\n",
       "\t lateral mesodermal cell\n",
       "epithelial cell of urethra\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t urethra cell\n",
       "\t lower urinary tract cell\n",
       "hillock cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "tracheobronchial serous cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t seromucus secreting cell\n",
       "\t serous secreting cell\n",
       "tracheobronchial goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t epithelial cell of tracheobronchial tree\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
-      "\t respiratory goblet cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t respiratory tract goblet cell\n",
       "endothelial cell of periportal hepatic sinusoid\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t endothelial cell of sinusoid\n",
       "\t endothelial cell of hepatic sinusoid\n",
       "endothelial cell of hepatic sinusoid\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t endothelial cell of sinusoid\n",
       "endothelial cell of pericentral hepatic sinusoid\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
+      "\t endothelial cell of vascular tree\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t endothelial cell of sinusoid\n",
       "\t endothelial cell of hepatic sinusoid\n",
       "periportal region hepatocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t polyploid cell\n",
       "\t epithelial cell\n",
       "\t hepatocyte\n",
       "\t endopolyploid cell\n",
       "midzonal region hepatocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t polyploid cell\n",
       "\t epithelial cell\n",
       "\t hepatocyte\n",
       "\t endopolyploid cell\n",
       "centrilobular region hepatocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t polyploid cell\n",
       "\t epithelial cell\n",
       "\t hepatocyte\n",
       "\t endopolyploid cell\n",
       "intestine goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "kidney tubule cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "forebrain neuroblast\n",
       "\t cell\n",
@@ -7667,28 +8369,33 @@
       "\t neural cell\n",
       "lung goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
       "\t epithelial cell of lung\n",
-      "\t glandular epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
       "\t lung secretory cell\n",
-      "\t respiratory goblet cell\n",
-      "lung neuroendocrine cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t respiratory tract goblet cell\n",
+      "pulmonary neuroendocrine cell\n",
       "\t cell\n",
       "\t neuron\n",
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t ecto-epithelial cell\n",
+      "\t secretory epithelial cell\n",
       "\t neurecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
-      "\t epithelial cell of lung\n",
       "\t sensory epithelial cell\n",
       "\t efferent neuron\n",
       "\t afferent neuron\n",
@@ -7701,12 +8408,22 @@
       "\t biogenic amine secreting cell\n",
       "\t electrically signaling cell\n",
       "\t lung secretory cell\n",
-      "lung ciliated cell\n",
+      "lung multiciliated epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ciliated cell\n",
-      "\t respiratory ciliated cell\n",
+      "\t epithelial cell\n",
+      "\t ciliated epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t epithelial cell of lung\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract multiciliated cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t multiciliated epithelial cell\n",
       "smooth muscle cell of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -7717,6 +8434,7 @@
       "\t enteric smooth muscle cell\n",
       "smooth muscle fiber of ileum\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -7728,27 +8446,39 @@
       "\t smooth muscle cell of small intestine\n",
       "urethra cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t lower urinary tract cell\n",
       "fibroblast of connective tissue of prostate\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
+      "\t prostate stromal cell\n",
       "fibroblast of connective tissue of nonglandular part of prostate\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
+      "\t prostate stromal cell\n",
       "fibroblast of connective tissue of glandular part of prostate\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
+      "\t prostate stromal cell\n",
       "epicardial adipocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t adipocyte\n",
       "\t stuff accumulating cell\n",
       "\t cardiocyte\n",
       "adipocyte of epicardial fat of left ventricle\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t connective tissue cell\n",
       "\t adipocyte\n",
       "\t stuff accumulating cell\n",
@@ -7756,198 +8486,201 @@
       "\t epicardial adipocyte\n",
       "bronchial goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t epithelial cell of tracheobronchial tree\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
       "\t bronchial epithelial cell\n",
-      "\t respiratory epithelial cell\n",
-      "\t respiratory goblet cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t respiratory tract goblet cell\n",
       "\t tracheobronchial goblet cell\n",
       "small intestine goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "\t intestine goblet cell\n",
       "intestinal villus goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "\t intestine goblet cell\n",
       "\t small intestine goblet cell\n",
-      "duodenum glandular cell\n",
+      "duodenum secretory cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "ileal goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t endo-epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
-      "\t small intestine glandular cell\n",
+      "\t small intestine secretory cell\n",
       "\t intestine goblet cell\n",
       "\t small intestine goblet cell\n",
       "\t intestinal villus goblet cell\n",
       "tracheal goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t epithelial cell of lower respiratory tract\n",
+      "\t endo-epithelial cell\n",
       "\t epithelial cell of tracheobronchial tree\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
-      "\t endo-epithelial cell\n",
       "\t tracheal epithelial cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
-      "\t respiratory goblet cell\n",
+      "\t respiratory tract epithelial cell\n",
+      "\t respiratory tract goblet cell\n",
       "\t tracheobronchial goblet cell\n",
       "serous cell of epithelium of trachea\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t seromucus secreting cell\n",
       "\t serous secreting cell\n",
       "\t tracheobronchial serous cell\n",
       "serous cell of epithelium of bronchus\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t seromucus secreting cell\n",
       "\t serous secreting cell\n",
       "\t tracheobronchial serous cell\n",
       "enterocyte of epithelium of small intestine\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t enterocyte\n",
       "\t gut absorptive cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "enterocyte of epithelium proper of small intestine\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t enterocyte\n",
       "\t gut absorptive cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "\t enterocyte of epithelium of small intestine\n",
       "enterocyte of epithelium proper of ileum\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t enterocyte\n",
       "\t gut absorptive cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "\t enterocyte of epithelium of small intestine\n",
       "\t enterocyte of epithelium proper of small intestine\n",
       "paneth cell of epithelium of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
-      "\t protein secreting cell\n",
       "\t endo-epithelial cell\n",
+      "\t glandular secretory epithelial cell\n",
+      "\t protein secreting cell\n",
       "\t lysozyme secreting cell\n",
       "\t paneth cell\n",
       "\t intestinal epithelial cell\n",
       "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
-      "\t small intestine glandular cell\n",
-      "enterocyte of colon\n",
+      "\t small intestine secretory cell\n",
+      "colonocyte\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t brush border epithelial cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t enterocyte\n",
       "\t gut absorptive cell\n",
       "\t enterocyte of epithelium of large intestine\n",
       "\t epithelial cell of large intestine\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t colon epithelial cell\n",
       "basal cell of epithelium of trachea\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
       "\t epithelial fate stem cell\n",
-      "\t epithelial cell\n",
-      "\t epithelial cell of tracheobronchial tree\n",
-      "\t endo-epithelial cell\n",
-      "\t tracheal epithelial cell\n",
       "\t basal cell\n",
-      "\t epithelial cell of lower respiratory tract\n",
-      "\t respiratory epithelial cell\n",
       "\t respiratory basal cell\n",
       "microfold cell of epithelium of small intestine\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
       "\t defensive cell\n",
       "\t intestinal epithelial cell\n",
-      "\t epithelial cell of alimentary canal\n",
       "\t transporting cell\n",
       "\t M cell of gut\n",
+      "\t epithelial cell of alimentary canal\n",
       "\t epithelial cell of small intestine\n",
       "ventricular cardiac muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -7957,6 +8690,7 @@
       "\t cardiocyte\n",
       "myocyte of sinoatrial node\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
       "\t electrically responsive cell\n",
@@ -7971,25 +8705,26 @@
       "\t striated visceral muscle cell\n",
       "endothelial cell of arteriole\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "conjunctival epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "epithelial cell of lacrimal drainage system\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
       "\t meso-epithelial cell\n",
       "epithelial cell of lacrimal sac\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
@@ -7998,6 +8733,7 @@
       "\t epithelial cell of nasolacrimal duct\n",
       "epithelial cell of nasolacrimal duct\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
@@ -8005,6 +8741,7 @@
       "\t epithelial cell of lacrimal drainage system\n",
       "bladder urothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t transitional epithelial cell\n",
       "\t urothelial cell\n",
@@ -8012,6 +8749,7 @@
       "\t lower urinary tract cell\n",
       "ciliary muscle cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -8022,6 +8760,7 @@
       "epithelial cell of stratum germinativum of esophagus\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
@@ -8029,28 +8768,32 @@
       "\t basal cell\n",
       "kidney glomerular epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t glomerular cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
+      "\t glomerular cell\n",
       "\t kidney corpuscule cell\n",
       "parietal epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t epithelial cell of glomerular capsule\n",
-      "\t glomerular cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney glomerular epithelial cell\n",
+      "\t glomerular cell\n",
       "\t kidney corpuscule cell\n",
       "epithelial cell of intermediate tubule\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney epithelial cell\n",
@@ -8060,6 +8803,7 @@
       "\t kidney loop of Henle epithelial cell\n",
       "kidney loop of Henle epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney epithelial cell\n",
@@ -8068,6 +8812,7 @@
       "\t kidney tubule cell\n",
       "kidney collecting duct epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
@@ -8078,9 +8823,11 @@
       "\t kidney collecting duct cell\n",
       "kidney collecting duct cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "smooth muscle cell of prostate\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t smooth muscle cell\n",
       "\t contractile cell\n",
       "\t muscle cell\n",
@@ -8090,59 +8837,80 @@
       "\t electrically active cell\n",
       "mesothelial cell of pleura\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t mesothelial cell\n",
       "\t lining cell\n",
       "mesothelial cell of visceral pleura\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
-      "\t squamous epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t barrier cell\n",
+      "\t squamous epithelial cell\n",
       "\t mesothelial cell\n",
       "\t lining cell\n",
-      "\t epithelial cell of lung\n",
       "\t mesothelial cell of pleura\n",
       "kidney interstitial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t interstitial cell\n",
       "\t kidney cell\n",
       "kidney medulla cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "kidney pelvis cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t kidney cell\n",
+      "glomerular cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t kidney cortical cell\n",
+      "\t kidney epithelial cell\n",
       "\t kidney cell\n",
+      "\t renal cortical epithelial cell\n",
+      "\t epithelial cell of nephron\n",
+      "\t kidney corpuscule cell\n",
       "kidney inner medulla cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "\t kidney medulla cell\n",
       "kidney outer medulla cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "\t kidney medulla cell\n",
       "papillary tips cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
       "\t kidney medulla cell\n",
       "\t kidney pelvis cell\n",
       "\t kidney inner medulla cell\n",
       "lower urinary tract cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "kidney corpuscule cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "kidney interstitial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t interstitial cell\n",
       "\t kidney cell\n",
@@ -8194,6 +8962,7 @@
       "\t kidney interstitial cell\n",
       "kidney pelvis urothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t transitional epithelial cell\n",
       "\t urothelial cell\n",
@@ -8202,6 +8971,7 @@
       "\t kidney pelvis cell\n",
       "kidney collecting duct principal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
@@ -8215,6 +8985,7 @@
       "\t kidney collecting duct cell\n",
       "kidney collecting duct intercalated cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t non-branched duct epithelial cell\n",
@@ -8228,47 +8999,45 @@
       "\t kidney collecting duct cell\n",
       "kidney connecting tubule epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "kidney proximal convoluted tubule epithelial cell\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t epithelial cell of proximal tubule\n",
+      "\t brush border cell of the proximal tubule\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "kidney proximal straight tubule epithelial cell\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
-      "\t columnar/cuboidal epithelial cell\n",
-      "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t epithelial cell of proximal tubule\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
@@ -8277,6 +9046,7 @@
       "\t kidney loop of Henle descending limb epithelial cell\n",
       "kidney loop of Henle descending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney epithelial cell\n",
@@ -8286,238 +9056,184 @@
       "\t kidney loop of Henle epithelial cell\n",
       "kidney distal convoluted tubule epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "macula densa epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t juxtaglomerular complex cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "kidney blood vessel cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
       "kidney arterial blood vessel cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
       "\t kidney blood vessel cell\n",
       "kidney capillary endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
-      "\t kidney medulla cell\n",
-      "\t kidney blood vessel cell\n",
-      "kidney venous blood vessel cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t capillary endothelial cell\n",
+      "\t squamous endothelial cell\n",
+      "\t microvascular endothelial cell\n",
       "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
       "\t kidney blood vessel cell\n",
       "glomerular capillary endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t fenestrated cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t capillary endothelial cell\n",
+      "\t squamous endothelial cell\n",
       "\t microvascular endothelial cell\n",
       "\t glomerular endothelial cell\n",
-      "\t glomerular cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
-      "\t kidney glomerular epithelial cell\n",
+      "\t kidney cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney corpuscule cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney capillary endothelial cell\n",
       "kidney afferent arteriole cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
+      "\t kidney cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney corpuscule cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney arterial blood vessel cell\n",
       "kidney efferent arteriole cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
+      "\t kidney cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney corpuscule cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney arterial blood vessel cell\n",
       "kidney loop of Henle ascending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
       "peritubular capillary endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
+      "\t fenestrated endothelial cell\n",
       "\t capillary endothelial cell\n",
+      "\t squamous endothelial cell\n",
       "\t microvascular endothelial cell\n",
-      "\t kidney epithelial cell\n",
       "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney capillary endothelial cell\n",
       "vasa recta cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t microvascular endothelial cell\n",
       "\t kidney cell\n",
-      "\t vein endothelial cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney blood vessel cell\n",
-      "\t kidney capillary endothelial cell\n",
-      "\t kidney venous blood vessel cell\n",
       "kidney cortex artery cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t kidney cortical cell\n",
+      "\t kidney cell\n",
       "\t kidney medulla cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney arterial blood vessel cell\n",
       "kidney afferent arteriole endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t endothelial cell of artery\n",
-      "\t renal cortical epithelial cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
+      "\t kidney cell\n",
       "\t endothelial cell of arteriole\n",
       "\t kidney medulla cell\n",
-      "\t kidney corpuscule cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney arterial blood vessel cell\n",
       "\t kidney afferent arteriole cell\n",
       "kidney efferent arteriole endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
-      "\t kidney cell\n",
-      "\t renal cortical epithelial cell\n",
       "\t kidney cortical cell\n",
-      "\t epithelial cell of nephron\n",
+      "\t kidney cell\n",
       "\t endothelial cell of arteriole\n",
       "\t kidney medulla cell\n",
-      "\t kidney corpuscule cell\n",
       "\t kidney blood vessel cell\n",
       "\t kidney arterial blood vessel cell\n",
       "\t kidney efferent arteriole cell\n",
       "kidney loop of Henle thick ascending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
@@ -8525,15 +9241,16 @@
       "\t kidney loop of Henle ascending limb epithelial cell\n",
       "kidney loop of Henle thin ascending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
@@ -8542,15 +9259,16 @@
       "\t kidney loop of Henle ascending limb epithelial cell\n",
       "kidney loop of Henle medullary thick ascending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
@@ -8560,15 +9278,16 @@
       "\t kidney loop of Henle thick ascending limb epithelial cell\n",
       "kidney loop of Henle cortical thick ascending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t epithelial cell of distal tubule\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney loop of Henle epithelial cell\n",
@@ -8577,6 +9296,7 @@
       "\t kidney loop of Henle thick ascending limb epithelial cell\n",
       "kidney loop of Henle thin descending limb epithelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney epithelial cell\n",
@@ -8590,46 +9310,31 @@
       "\t kidney loop of Henle descending limb epithelial cell\n",
       "vasa recta ascending limb cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t microvascular endothelial cell\n",
       "\t kidney cell\n",
-      "\t vein endothelial cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney blood vessel cell\n",
-      "\t kidney capillary endothelial cell\n",
-      "\t kidney venous blood vessel cell\n",
       "\t vasa recta cell\n",
       "vasa recta descending limb cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
-      "\t kidney epithelial cell\n",
+      "\t microvascular endothelial cell\n",
       "\t kidney cell\n",
-      "\t vein endothelial cell\n",
-      "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t kidney medulla cell\n",
-      "\t kidney blood vessel cell\n",
-      "\t kidney capillary endothelial cell\n",
-      "\t kidney venous blood vessel cell\n",
       "\t vasa recta cell\n",
       "urethra urothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t transitional epithelial cell\n",
       "\t urothelial cell\n",
@@ -8642,28 +9347,43 @@
       "\t sensory neuron\n",
       "\t neuronal receptor cell\n",
       "\t sensory receptor cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t afferent neuron\n",
+      "\t peripheral nervous system neuron\n",
       "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t peripheral sensory neuron\n",
       "\t somatosensory neuron\n",
+      "somatosensory neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
+      "\t peripheral nervous system neuron\n",
+      "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "medium spiny neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t GABAergic neuron\n",
       "hypothalamus cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "glycinergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
@@ -8671,43 +9391,39 @@
       "\t electrically signaling cell\n",
       "lung endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t epithelial cell of lung\n",
-      "\t endothelial cell\n",
       "pulmonary artery endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t endothelial cell of artery\n",
       "cerebral cortex pyramidal neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t multipolar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t pyramidal neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "cerebral cortex endothelial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "peripheral blood mononuclear cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -8737,56 +9453,56 @@
       "\t lymphocyte of B lineage, CD19-positive\n",
       "dermis lymphatic vessel endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t endothelial cell of lymphatic vessel\n",
       "lung microvascular endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t epithelial cell of lung\n",
-      "\t endothelial cell\n",
       "\t microvascular endothelial cell\n",
       "\t lung endothelial cell\n",
       "endothelial cell of coronary artery\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t cardiocyte\n",
       "\t cardiac endothelial cell\n",
       "\t endothelial cell of artery\n",
       "\t cardiac blood vessel endothelial cell\n",
       "dermis microvascular lymphatic vessel endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t blood vessel endothelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t endothelial cell of lymphatic vessel\n",
+      "\t microvascular endothelial cell\n",
+      "\t dermal microvascular endothelial cell\n",
+      "\t dermis blood vessel endothelial cell\n",
       "\t dermis lymphatic vessel endothelial cell\n",
       "embryonic fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "brain pericyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t connective tissue cell\n",
       "\t contractile cell\n",
@@ -8798,12 +9514,13 @@
       "brainstem motor neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t motor neuron\n",
       "\t efferent neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "hepatic pit cell\n",
       "\t cell\n",
@@ -8818,6 +9535,20 @@
       "\t natural killer cell\n",
       "\t group 1 innate lymphoid cell\n",
       "\t innate lymphoid cell\n",
+      "\t liver-resident natural killer cell\n",
+      "liver-resident natural killer cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t leukocyte\n",
+      "\t lymphocyte\n",
+      "\t mononuclear cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t natural killer cell\n",
+      "\t group 1 innate lymphoid cell\n",
+      "\t innate lymphoid cell\n",
       "liver dendritic cell\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
@@ -8836,24 +9567,25 @@
       "\t dendritic cell\n",
       "prostate gland microvascular endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t blood vessel endothelial cell\n",
-      "\t squamous epithelial cell\n",
+      "\t endothelial cell\n",
       "\t endothelial cell of vascular tree\n",
-      "\t meso-epithelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t microvascular endothelial cell\n",
       "placental villous trophoblast\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t extraembryonic cell\n",
       "\t trophoblast cell\n",
       "conjunctiva goblet cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
+      "\t secretory epithelial cell\n",
       "\t epithelial cell\n",
-      "\t glandular epithelial cell\n",
+      "\t squamous epithelial cell\n",
       "\t seromucus secreting cell\n",
       "\t goblet cell\n",
       "\t mucus secreting cell\n",
@@ -8861,11 +9593,14 @@
       "bronchus fibroblast of lung\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t fibroblast of lung\n",
       "cord blood hematopoietic stem cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t somatic stem cell\n",
       "\t hematopoietic stem cell\n",
@@ -8894,197 +9629,288 @@
       "\t single nucleate cell\n",
       "\t defensive cell\n",
       "\t macrophage\n",
-      "vip GABAergic cortical interneuron\n",
+      "\t placental resident macrophage\n",
+      "placental resident macrophage\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t hematopoietic cell\n",
+      "\t myeloid cell\n",
+      "\t professional antigen presenting cell\n",
+      "\t leukocyte\n",
+      "\t tissue-resident macrophage\n",
+      "\t myeloid leukocyte\n",
+      "\t phagocyte (sensu Vertebrata)\n",
+      "\t phagocyte\n",
+      "\t mononuclear phagocyte\n",
+      "\t mononuclear cell\n",
+      "\t stuff accumulating cell\n",
+      "\t motile cell\n",
+      "\t nucleate cell\n",
+      "\t single nucleate cell\n",
+      "\t defensive cell\n",
+      "\t macrophage\n",
+      "VIP GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t caudal ganglionic eminence derived cortical interneuron\n",
+      "\t caudal ganglionic eminence derived neuron\n",
       "intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "extratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t glutamatergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
+      "lamp5 GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
-      "\t glutamatergic neuron\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
-      "lamp5 GABAergic cortical interneuron\n",
+      "\t neuron of the forebrain\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "\t caudal ganglionic eminence derived cortical interneuron\n",
+      "\t caudal ganglionic eminence derived neuron\n",
+      "caudal ganglionic eminence derived cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t caudal ganglionic eminence derived neuron\n",
       "near-projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "corticothalamic-projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "sncg GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t caudal ganglionic eminence derived cortical interneuron\n",
+      "\t caudal ganglionic eminence derived neuron\n",
       "sst GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
+      "\t electrically signaling cell\n",
+      "\t inhibitory interneuron\n",
+      "\t GABAergic neuron\n",
+      "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
+      "\t cortical interneuron\n",
+      "\t cerebral cortex GABAergic interneuron\n",
+      "\t medial ganglionic eminence derived GABAergic cortical interneuron\n",
+      "\t medial ganglionic eminence derived interneuron\n",
+      "medial ganglionic eminence derived GABAergic cortical interneuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t central nervous system neuron\n",
+      "\t neural cell\n",
+      "\t secretory cell\n",
+      "\t interneuron\n",
+      "\t GABAergic interneuron\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t medial ganglionic eminence derived interneuron\n",
       "pvalb GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t medial ganglionic eminence derived GABAergic cortical interneuron\n",
+      "\t medial ganglionic eminence derived interneuron\n",
       "sst chodl GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
       "\t sst GABAergic cortical interneuron\n",
+      "\t medial ganglionic eminence derived GABAergic cortical interneuron\n",
+      "\t medial ganglionic eminence derived interneuron\n",
       "direct pathway medium spiny neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t GABAergic neuron\n",
       "\t medium spiny neuron\n",
       "indirect pathway medium spiny neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t GABAergic neuron\n",
       "\t medium spiny neuron\n",
       "chandelier pvalb GABAergic cortical interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
       "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t cortical interneuron\n",
       "\t cerebral cortex GABAergic interneuron\n",
+      "\t medial ganglionic eminence derived GABAergic cortical interneuron\n",
       "\t pvalb GABAergic cortical interneuron\n",
       "\t chandelier cell\n",
+      "\t medial ganglionic eminence derived interneuron\n",
       "chandelier cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t interneuron\n",
@@ -9097,169 +9923,209 @@
       "L6b glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "L6 corticothalamic-projecting glutamatergic cortical neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t corticothalamic-projecting glutamatergic cortical neuron\n",
       "L5/6 near-projecting glutamatergic neuron of the primary motor cortex\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t near-projecting glutamatergic cortical neuron\n",
       "\t L5/6 near-projecting glutamatergic neuron\n",
       "L5/6 near-projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t near-projecting glutamatergic cortical neuron\n",
       "L2/3 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "\t L2/3 intratelencephalic projecting glutamatergic neuron\n",
       "L2/3 intratelencephalic projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "L4/5 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "\t L4/5 intratelencephalic projecting glutamatergic neuron\n",
       "L4/5 intratelencephalic projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t multipolar neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t pyramidal neuron\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t primary motor cortex pyramidal cell\n",
       "\t cerebral cortex pyramidal neuron\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "\t L6 intratelencephalic projecting glutamatergic neuron\n",
+      "\t untufted pyramidal neuron\n",
       "L6 intratelencephalic projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
+      "untufted pyramidal neuron\n",
+      "\t cell\n",
+      "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
+      "\t neural cell\n",
+      "\t electrically responsive cell\n",
+      "\t electrically active cell\n",
+      "\t electrically signaling cell\n",
+      "\t pyramidal neuron\n",
       "vascular leptomeningeal cell\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t mesothelial fibroblast of the leptomeninx\n",
       "\t mesothelial fibroblast\n",
       "mesothelial fibroblast of the leptomeninx\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t mesothelial fibroblast\n",
       "mesothelial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "vascular leptomeningeal cell (Mmus)\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t vascular leptomeningeal cell\n",
       "\t mesothelial fibroblast of the leptomeninx\n",
@@ -9270,70 +10136,58 @@
       "\t neural cell\n",
       "\t neuron associated cell\n",
       "\t neuron associated cell (sensu Vertebrata)\n",
-      "caudal ganglionic eminence derived interneuron\n",
+      "medial ganglionic eminence derived interneuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
-      "meis2 expressing cortical GABAergic cell\n",
+      "caudal ganglionic eminence derived neuron\n",
       "\t cell\n",
       "\t neuron\n",
-      "\t neural cell\n",
-      "\t secretory cell\n",
-      "\t electrically responsive cell\n",
-      "\t electrically active cell\n",
+      "\t eukaryotic cell\n",
       "\t central nervous system neuron\n",
-      "\t electrically signaling cell\n",
-      "\t GABAergic neuron\n",
-      "\t cerebral cortex neuron\n",
-      "thalamic excitatory neuron\n",
-      "\t cell\n",
-      "\t neuron\n",
       "\t neural cell\n",
-      "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
-      "\t glutamatergic neuron\n",
-      "caudal ganglionic eminence derived GABAergic cortical interneuron\n",
+      "meis2 expressing cortical GABAergic cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t GABAergic interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
-      "\t inhibitory interneuron\n",
       "\t GABAergic neuron\n",
       "\t cerebral cortex neuron\n",
-      "\t cortical interneuron\n",
-      "\t cerebral cortex GABAergic interneuron\n",
-      "\t caudal ganglionic eminence derived interneuron\n",
-      "brain vascular cell\n",
-      "\t cell\n",
-      "\t neural cell\n",
-      "unipolar brush cell\n",
+      "\t neuron of the forebrain\n",
+      "thalamic excitatory neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t CNS interneuron\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
+      "\t neuron of the forebrain\n",
+      "obsolete caudal ganglionic eminence derived GABAergic cortical interneuron\n",
+      "brain vascular cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t neural cell\n",
       "hypendymal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t ecto-epithelial cell\n",
       "\t neurecto-epithelial cell\n",
@@ -9342,8 +10196,8 @@
       "midget ganglion cell of retina\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -9351,8 +10205,8 @@
       "parasol ganglion cell of retina\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -9360,112 +10214,141 @@
       "alveolar type 1 fibroblast cell\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t interstitial cell\n",
       "\t pulmonary interstitial fibroblast\n",
       "\t fibroblast of lung\n",
-      "alveolar type 2 fibroblast cell\n",
+      "alveolar adventitial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t interstitial cell\n",
       "\t pulmonary interstitial fibroblast\n",
       "\t fibroblast of lung\n",
       "epithelial cell of proximal tubule segment 1\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t epithelial cell of proximal tubule\n",
+      "\t brush border cell of the proximal tubule\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
+      "\t kidney proximal convoluted tubule epithelial cell\n",
       "epithelial cell of proximal tubule segment 3\n",
       "\t cell\n",
-      "\t ciliated cell\n",
-      "\t ciliated epithelial cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t meso-epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
       "\t brush border epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t epithelial cell of proximal tubule\n",
+      "\t brush border cell of the proximal tubule\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
+      "\t kidney loop of Henle epithelial cell\n",
+      "\t kidney medulla cell\n",
+      "\t kidney outer medulla cell\n",
+      "\t kidney proximal straight tubule epithelial cell\n",
+      "\t kidney loop of Henle descending limb epithelial cell\n",
       "kidney connecting tubule principal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t columnar/cuboidal epithelial cell\n",
+      "\t kidney cortical cell\n",
       "\t nephron tubule epithelial cell\n",
       "\t kidney cortex tubule cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "\t renal cortical epithelial cell\n",
-      "\t kidney cortical cell\n",
       "\t renal principal cell\n",
       "\t epithelial cell of nephron\n",
       "\t kidney tubule cell\n",
       "\t kidney connecting tubule epithelial cell\n",
-      "respiratory hillock cell\n",
+      "respiratory tract hillock cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "\t hillock cell\n",
-      "BEST4+ intestinal epithelial cell, human\n",
+      "BEST4+ enterocyte\n",
       "\t cell\n",
       "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t columnar/cuboidal epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t brush border epithelial cell\n",
+      "\t intestinal epithelial cell\n",
+      "\t enterocyte\n",
       "\t gut absorptive cell\n",
+      "\t epithelial cell of alimentary canal\n",
       "valve interstitial cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t secretory cell\n",
       "\t interstitial cell\n",
       "\t cardiocyte\n",
       "\t cardiac valve cell\n",
       "valve endothelial cell\n",
       "\t cell\n",
-      "\t epithelial cell\n",
-      "\t meso-epithelial cell\n",
+      "\t eukaryotic cell\n",
+      "\t endothelial cell\n",
       "\t barrier cell\n",
       "\t lining cell\n",
-      "\t endothelial cell\n",
       "\t cardiocyte\n",
       "\t cardiac valve cell\n",
       "\t cardiac endothelial cell\n",
+      "deuterosomal cell\n",
+      "\t cell\n",
+      "\t eukaryotic cell\n",
+      "\t epithelial cell\n",
+      "\t endo-epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "L4 intratelencephalic projecting glutamatergic neuron\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
       "\t secretory cell\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
-      "\t central nervous system neuron\n",
       "\t electrically signaling cell\n",
       "\t glutamatergic neuron\n",
       "\t cerebral cortex neuron\n",
+      "\t neuron of the forebrain\n",
       "\t intratelencephalic-projecting glutamatergic cortical neuron\n",
       "\t L2/3-6 intratelencephalic projecting glutamatergic neuron\n",
       "ureteric bud cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t kidney epithelial cell\n",
       "\t kidney cell\n",
       "serous secreting cell of bronchus submucosal gland\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t exocrine cell\n",
       "\t secretory cell\n",
       "\t seromucus secreting cell\n",
@@ -9473,6 +10356,7 @@
       "\t tracheobronchial serous cell\n",
       "suprabasal keratinocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t ecto-epithelial cell\n",
       "\t epithelial cell\n",
       "\t squamous epithelial cell\n",
@@ -9486,30 +10370,31 @@
       "\t general ecto-epithelial cell\n",
       "retinal astrocyte\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
-      "\t neuron associated cell\n",
       "\t glial cell\n",
+      "\t neuron associated cell\n",
       "\t macroglial cell\n",
       "\t astrocyte\n",
       "\t retinal cell\n",
       "ON-blue cone bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "airway submucosal gland duct basal cell\n",
       "\t cell\n",
       "\t stem cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t single fate stem cell\n",
       "\t somatic stem cell\n",
@@ -9518,162 +10403,156 @@
       "perichondrial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "lung perichondrial fibroblast\n",
       "\t cell\n",
       "\t fibroblast\n",
+      "\t eukaryotic cell\n",
+      "\t stromal cell\n",
       "\t connective tissue cell\n",
       "\t fibroblast of lung\n",
       "\t perichondrial fibroblast\n",
       "diffuse bipolar 1 cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "diffuse bipolar 2 cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "diffuse bipolar 3a cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "diffuse bipolar 3b cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "diffuse bipolar 4 cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "diffuse bipolar 6 cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "flat midget bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "invaginating midget bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "giant bipolar cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t ON-bipolar cell\n",
       "OFFx cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
       "\t neural cell\n",
       "\t secretory cell\n",
-      "\t interneuron\n",
-      "\t bipolar neuron\n",
+      "\t visual system neuron\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
       "\t retinal cell\n",
       "\t glutamatergic neuron\n",
       "\t retinal bipolar neuron\n",
-      "\t visual system neuron\n",
       "\t OFF-bipolar cell\n",
       "lung resident memory CD4-positive, alpha-beta T cell\n",
       "\t cell\n",
@@ -9751,23 +10630,20 @@
       "\t defensive cell\n",
       "\t macrophage\n",
       "\t lung macrophage\n",
-      "deuterosomal cell\n",
-      "\t cell\n",
-      "\t epithelial cell\n",
-      "\t endo-epithelial cell\n",
-      "\t respiratory epithelial cell\n",
-      "respiratory suprabasal cell\n",
+      "respiratory tract suprabasal cell\n",
       "\t cell\n",
+      "\t eukaryotic cell\n",
       "\t precursor cell\n",
       "\t progenitor cell\n",
       "\t epithelial cell\n",
       "\t endo-epithelial cell\n",
-      "\t respiratory epithelial cell\n",
+      "\t respiratory tract epithelial cell\n",
       "small bistratified retinal ganglion cell\n",
       "\t cell\n",
       "\t neuron\n",
+      "\t eukaryotic cell\n",
+      "\t central nervous system neuron\n",
       "\t neural cell\n",
-      "\t CNS neuron (sensu Vertebrata)\n",
       "\t electrically responsive cell\n",
       "\t electrically active cell\n",
       "\t electrically signaling cell\n",
@@ -9776,12 +10652,10 @@
       "\t bistratified cell\n",
       "luminal hormone-sensing cell of mammary gland\n",
       "\t cell\n",
-      "\t exocrine cell\n",
-      "\t secretory cell\n",
+      "\t eukaryotic cell\n",
       "\t epithelial cell\n",
       "\t duct epithelial cell\n",
       "\t branched duct epithelial cell\n",
-      "\t paracrine cell\n",
       "\t mammary gland epithelial cell\n",
       "\t luminal epithelial cell of mammary gland\n"
      ]
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
index 8f7f9175..8058f17a 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,325 +83,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "465c2a5da48849358b18b84c369656f5",
-       "version_major": 2,
-       "version_minor": 0
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8b4fad6da1f4933a0c19d8323aae0a2",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5389bc4351240d69eb06f947daf334e",
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-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "66bb900c11914db994d6568817a8683a",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d643b97294324c9b96df7bf5405f0804",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/110 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:21: RuntimeWarning: Mean of empty slice.\n",
-      "  recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-      "/opt/conda/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "/var/tmp/ipykernel_2595801/2475547255.py:22: RuntimeWarning: Mean of empty slice.\n",
-      "  precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from collections import defaultdict\n",
     "from tqdm.auto import tqdm\n",
     "\n",
     "results = defaultdict(list)\n",
+    "eps = 1e-8\n",
     "\n",
     "for prefix_idx in tqdm(range(len(PREFIX_LIST))):\n",
     "    prefix = PREFIX_LIST[prefix_idx]\n",
@@ -413,27 +103,47 @@
     "        results[\"val_adata_id\"].append(val_adata_id)\n",
     "\n",
     "        for key in [\"cell_type\", \"disease\", \"development_stage\", \"tissue\", \"sex\"]:\n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
     "            for n_hops in range(N_HOPS_DICT[key] + 1):\n",
     "                metric = f\"{key}_hop_{n_hops}\"\n",
     "                sensitivity_col_name = f\"{metric}_sensitivity\"\n",
     "                specificity_col_name = f\"{metric}_specificity\"\n",
     "                try:\n",
-    "                    recall = meta_adata.obs[sensitivity_col_name].dropna().values.mean()\n",
-    "                    precision = meta_adata.obs[specificity_col_name].dropna().values.mean()\n",
+    "                    new_obs = meta_adata.obs.dropna(subset=[sensitivity_col_name, specificity_col_name])\n",
+    "                    if len(new_obs) == 0:\n",
+    "                        print(f\"Error processing {key} for val_adata_id {val_adata_id} and prefix {prefix}\")\n",
+    "                    recall = new_obs[sensitivity_col_name].values.mean()\n",
+    "                    precision = new_obs[specificity_col_name].values.mean()\n",
     "                    f1 = 2 * (precision * recall) / (precision + recall)\n",
+    "                    logodds = np.log(new_obs[sensitivity_col_name].values + eps) - np.log(1 - new_obs[specificity_col_name].values + eps)\n",
+    "                    logodds = logodds.mean()\n",
     "                except:\n",
+    "                    print(f\"Error processing {key} for val_adata_id {val_adata_id} and prefix {prefix}\")\n",
     "                    recall = np.nan\n",
     "                    precision = np.nan\n",
     "                    f1 = np.nan\n",
-    "                \n",
+    "                    logodds = np.nan\n",
+    "\n",
+    "                # truth_names = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "                # truth_indices = np.asarray(list(map(mapper.get, truth_names)))\n",
+    "                # weights = np.ones_like(truth_indices)\n",
+    "                # bad_indices = [idx for idx in range(len(truth_names)) if truth_names[idx] not in mapper]\n",
+    "                # weights[bad_indices] = 0\n",
+    "                # truth_indices[bad_indices] = 0\n",
+    "                # loss = - weights * meta_adata.obsm[f\"{key}_class_logits\"][np.arange(len(meta_adata)), truth_indices.astype(int)]\n",
+    "                # loss = loss.mean()\n",
+    "\n",
+    "                # results[f\"{metric}_loss\"].append(loss)\n",
     "                results[f\"{metric}_recall\"].append(recall)\n",
     "                results[f\"{metric}_precision\"].append(precision)\n",
-    "                results[f\"{metric}_f1\"].append(f1)"
+    "                results[f\"{metric}_f1\"].append(f1)\n",
+    "                results[f\"{metric}_logodds\"].append(logodds)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,110 +152,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.24239872085189104"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results_df[results_df[\"prefix\"] == PREFIX_LIST[3]][\"sex_hop_0_f1\"].std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prefix\n",
-      "val_adata_id\n",
-      "cell_type_hop_0_recall\n",
-      "cell_type_hop_0_precision\n",
-      "cell_type_hop_0_f1\n",
-      "cell_type_hop_1_recall\n",
-      "cell_type_hop_1_precision\n",
-      "cell_type_hop_1_f1\n",
-      "cell_type_hop_2_recall\n",
-      "cell_type_hop_2_precision\n",
-      "cell_type_hop_2_f1\n",
-      "cell_type_hop_3_recall\n",
-      "cell_type_hop_3_precision\n",
-      "cell_type_hop_3_f1\n",
-      "disease_hop_0_recall\n",
-      "disease_hop_0_precision\n",
-      "disease_hop_0_f1\n",
-      "disease_hop_1_recall\n",
-      "disease_hop_1_precision\n",
-      "disease_hop_1_f1\n",
-      "disease_hop_2_recall\n",
-      "disease_hop_2_precision\n",
-      "disease_hop_2_f1\n",
-      "disease_hop_3_recall\n",
-      "disease_hop_3_precision\n",
-      "disease_hop_3_f1\n",
-      "development_stage_hop_0_recall\n",
-      "development_stage_hop_0_precision\n",
-      "development_stage_hop_0_f1\n",
-      "development_stage_hop_1_recall\n",
-      "development_stage_hop_1_precision\n",
-      "development_stage_hop_1_f1\n",
-      "development_stage_hop_2_recall\n",
-      "development_stage_hop_2_precision\n",
-      "development_stage_hop_2_f1\n",
-      "development_stage_hop_3_recall\n",
-      "development_stage_hop_3_precision\n",
-      "development_stage_hop_3_f1\n",
-      "tissue_hop_0_recall\n",
-      "tissue_hop_0_precision\n",
-      "tissue_hop_0_f1\n",
-      "tissue_hop_1_recall\n",
-      "tissue_hop_1_precision\n",
-      "tissue_hop_1_f1\n",
-      "tissue_hop_2_recall\n",
-      "tissue_hop_2_precision\n",
-      "tissue_hop_2_f1\n",
-      "tissue_hop_3_recall\n",
-      "tissue_hop_3_precision\n",
-      "tissue_hop_3_f1\n",
-      "sex_hop_0_recall\n",
-      "sex_hop_0_precision\n",
-      "sex_hop_0_f1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k in results_df.keys():\n",
-    "    print(k)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/tmp/ipykernel_2595801/2074687837.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/2074687837.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  greens = cm.get_cmap('Greens', len(cell_metrics))\n",
-      "/var/tmp/ipykernel_2595801/2074687837.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/2074687837.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  reds = cm.get_cmap('Reds', len(disease_metrics))\n",
-      "/var/tmp/ipykernel_2595801/2074687837.py:62: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/2074687837.py:62: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  blues = cm.get_cmap('Blues', len(tissue_metrics))\n",
-      "/var/tmp/ipykernel_2595801/2074687837.py:68: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/2074687837.py:68: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  purples = cm.get_cmap('Purples', len(dev_metrics))\n"
      ]
     },
@@ -660,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -769,22 +389,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/tmp/ipykernel_2595801/137335904.py:42: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/137335904.py:42: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/137335904.py:48: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/137335904.py:48: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/137335904.py:54: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/137335904.py:54: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/137335904.py:60: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/137335904.py:60: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/137335904.py:84: FutureWarning: \n",
+      "/var/tmp/ipykernel_13649/137335904.py:84: FutureWarning: \n",
       "\n",
       "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:black'` for the same effect.\n",
       "\n",
@@ -793,7 +413,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -919,28 +539,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/tmp/ipykernel_2595801/582546310.py:22: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/1115505766.py:37: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/582546310.py:28: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/1115505766.py:43: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/582546310.py:34: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/1115505766.py:49: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_2595801/582546310.py:40: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/var/tmp/ipykernel_13649/1115505766.py:55: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 800x2000 with 20 Axes>"
+       "<Figure size 800x1500 with 20 Axes>"
       ]
      },
      "metadata": {},
@@ -953,15 +573,12 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib import cm\n",
     "\n",
-    "# Example PREFIX_LIST (adjust as needed)\n",
-    "PREFIX_LIST = ['prefixA', 'prefixB', 'prefixC', 'prefixD']\n",
-    "\n",
     "# -------------------------------\n",
     "# User-specified options:\n",
     "# Choose which metric to plot: 'f1', 'sensitivity', or 'specificity'\n",
     "value_to_plot = 'f1'  \n",
     "# Flag to select summary statistic: True uses mean/std, False uses median/IQR.\n",
-    "use_mean = True  \n",
+    "use_mean = False  \n",
     "# -------------------------------\n",
     "\n",
     "# Define metric groups and available hops.\n",
@@ -1027,7 +644,7 @@
     "n_rows = len(metric_groups)\n",
     "max_cols = max(len(hops) for hops in metric_groups.values())\n",
     "\n",
-    "fig, axes = plt.subplots(n_rows, max_cols, figsize=(4 * max_cols, 4 * n_rows), sharey=True)\n",
+    "fig, axes = plt.subplots(n_rows, max_cols, figsize=(2 * max_cols, 3 * n_rows), sharey=True)\n",
     "\n",
     "# Loop over each metric group (row) and each hop (column).\n",
     "for row_idx, (group, hops) in enumerate(metric_groups.items()):\n",
@@ -1081,7 +698,179 @@
     "\n",
     "        # Label the y-axis with the metric group on the leftmost panel.\n",
     "        if col_idx == 0:\n",
-    "            ax.set_ylabel(group, fontsize=12, fontweight='bold')\n",
+    "            ax.set_ylabel(f\"{group} ({value_to_plot})\", fontsize=12, fontweight='bold')\n",
+    "        # Label the x-axis on the bottom row.\n",
+    "        if row_idx == n_rows - 1:\n",
+    "            ax.set_xlabel('Prefix')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_14815/3149745032.py:38: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_14815/3149745032.py:44: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_14815/3149745032.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_14815/3149745032.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# -------------------------------\n",
+    "# User-specified options:\n",
+    "# Choose which metric to plot: 'f1', 'sensitivity', or 'specificity'\n",
+    "value_to_plot = 'loss'  \n",
+    "# Flag to select summary statistic: True uses mean/std, False uses median/IQR.\n",
+    "use_mean = True\n",
+    "_PREFIX_LIST = PREFIX_LIST[:3]\n",
+    "# -------------------------------\n",
+    "\n",
+    "# Define metric groups and available hops.\n",
+    "# For 'sex', only hop 0 is available.\n",
+    "metric_groups = {\n",
+    "    'cell_type': [0],\n",
+    "    'disease': [0],\n",
+    "    'tissue': [0],\n",
+    "    'development_stage': [0],\n",
+    "    'sex': [0]\n",
+    "}\n",
+    "\n",
+    "# Build the list of metric columns (e.g., \"cell_type_hop_0_f1\")\n",
+    "selected_metrics = []\n",
+    "for group, hops in metric_groups.items():\n",
+    "    for hop in hops:\n",
+    "        selected_metrics.append(f\"{group}_hop_{hop}_{value_to_plot}\")\n",
+    "\n",
+    "# Precompute a color palette for each metric column.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell_type: use Greens.\n",
+    "cell_metrics = [f\"cell_type_hop_{hop}_{value_to_plot}\" for hop in metric_groups['cell_type']]\n",
+    "n = len(cell_metrics)\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For disease: use Reds.\n",
+    "disease_metrics = [f\"disease_hop_{hop}_{value_to_plot}\" for hop in metric_groups['disease']]\n",
+    "n = len(disease_metrics)\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For tissue: use Blues.\n",
+    "tissue_metrics = [f\"tissue_hop_{hop}_{value_to_plot}\" for hop in metric_groups['tissue']]\n",
+    "n = len(tissue_metrics)\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For development_stage: use Purples.\n",
+    "dev_metrics = [f\"development_stage_hop_{hop}_{value_to_plot}\" for hop in metric_groups['development_stage']]\n",
+    "n = len(dev_metrics)\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For sex: assign a fixed color (e.g., orange).\n",
+    "sex_metrics = [f\"sex_hop_{hop}_{value_to_plot}\" for hop in metric_groups['sex']]\n",
+    "for m in sex_metrics:\n",
+    "    palette[m] = 'orange'\n",
+    "\n",
+    "# Melt the dataframe into long format for the selected metrics.\n",
+    "# Assume your original dataframe is named `df`.\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'],\n",
+    "    value_vars=selected_metrics,\n",
+    "    var_name='metric',\n",
+    "    value_name=value_to_plot\n",
+    ")\n",
+    "# Drop any rows with NaN values for the chosen metric.\n",
+    "df_long = df_long.dropna(subset=[value_to_plot])\n",
+    "\n",
+    "# Determine grid size: one row per metric group, columns = max hops among groups.\n",
+    "n_rows = len(metric_groups)\n",
+    "max_cols = max(len(hops) for hops in metric_groups.values())\n",
+    "\n",
+    "fig, axes = plt.subplots(1, n_rows, figsize=(2 * n_rows, 4), sharey=True)\n",
+    "if max_cols == 1:\n",
+    "    axes = axes.reshape(-1, 1)\n",
+    "\n",
+    "# Loop over each metric group (row) and each hop (column).\n",
+    "for row_idx, (group, hops) in enumerate(metric_groups.items()):\n",
+    "    for col_idx in range(max_cols):\n",
+    "        # Turn off extra panels.\n",
+    "        if col_idx >= len(hops):\n",
+    "            axes[col_idx, row_idx].axis('off')\n",
+    "            continue\n",
+    "\n",
+    "        hop = hops[col_idx]\n",
+    "        metric_name = f\"{group}_hop_{hop}_{value_to_plot}\"\n",
+    "        ax = axes[row_idx, col_idx]\n",
+    "\n",
+    "        # Filter data for the current metric and drop any NaNs.\n",
+    "        df_metric = df_long[df_long['metric'] == metric_name].dropna(subset=[value_to_plot])\n",
+    "\n",
+    "        # Compute summary statistics per prefix.\n",
+    "        if use_mean:\n",
+    "            stats = df_metric.groupby('prefix')[value_to_plot].agg(['mean', 'std']).reindex(_PREFIX_LIST)\n",
+    "            y_vals = stats['mean']\n",
+    "            y_err = stats['std']\n",
+    "        else:\n",
+    "            stats = df_metric.groupby('prefix').agg(\n",
+    "                median=(value_to_plot, np.median),\n",
+    "                q1=(value_to_plot, lambda x: x.quantile(0.25)),\n",
+    "                q3=(value_to_plot, lambda x: x.quantile(0.75))\n",
+    "            ).reindex(_PREFIX_LIST)\n",
+    "            y_vals = stats['median']\n",
+    "            y_err_lower = y_vals - stats['q1']\n",
+    "            y_err_upper = stats['q3'] - y_vals\n",
+    "            y_err = [y_err_lower.values, y_err_upper.values]\n",
+    "\n",
+    "        x = np.arange(len(_PREFIX_LIST))\n",
+    "        # Retrieve the precomputed color (default to gray if not found).\n",
+    "        line_color = palette.get(metric_name, 'gray')\n",
+    "\n",
+    "        # Plot black markers with error bars.\n",
+    "        ax.errorbar(\n",
+    "            x, y_vals, yerr=y_err,\n",
+    "            fmt='o', color='black', ecolor='black',\n",
+    "            capsize=4, markersize=6, linestyle='None'\n",
+    "        )\n",
+    "        # Connect the markers with a line in the precomputed color.\n",
+    "        ax.plot(x, y_vals, color=line_color, linestyle='-', linewidth=2)\n",
+    "\n",
+    "        ax.set_xticks(x)\n",
+    "        ax.set_xticklabels(_PREFIX_LIST, rotation=90)\n",
+    "        # ax.set_ylim(0, 1)\n",
+    "        # Set the title to show the hop value.\n",
+    "\n",
+    "        # Label the y-axis with the metric group on the leftmost panel.\n",
+    "        if col_idx == 0:\n",
+    "            ax.set_ylabel(f\"{group} ({value_to_plot})\", fontsize=12, fontweight='bold')\n",
     "        # Label the x-axis on the bottom row.\n",
     "        if row_idx == n_rows - 1:\n",
     "            ax.set_xlabel('Prefix')\n",
@@ -1089,6 +878,13 @@
     "plt.tight_layout()\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
new file mode 100644
index 00000000..34f728e0
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
@@ -0,0 +1,1198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "### Analyze metadata predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "METADATA_PREDICTIONS_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\")\n",
+    "PREFIX_LIST = [\n",
+    "    \"10M_001_bs1536\",\n",
+    "    \"19M_001_bs2048\",\n",
+    "    \"30M_001_bs2560\",\n",
+    "    \"100M_long_run_last\"\n",
+    "]\n",
+    "VAL_ADATA_IDX_RANGE = np.arange(1, 111)\n",
+    "N_HOPS_DICT = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "def load_predictions_anndata(val_adata_idx: int, prefix_idx: int) -> sc.AnnData:\n",
+    "    path = os.path.join(\n",
+    "        METADATA_PREDICTIONS_ROOT_PATH,\n",
+    "        PREFIX_LIST[prefix_idx],\n",
+    "        f\"extract_{VAL_ADATA_IDX_RANGE[val_adata_idx]}_metadata_prediction_scores.h5ad\")\n",
+    "    return sc.read_h5ad(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology_resource_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"ontology\")\n",
+    "\n",
+    "logger.info(\"Loading ontology resources ...\")\n",
+    "ontology_benchmarking_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_benchmarking_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_benchmarking_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_benchmarking_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_benchmarking_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_benchmarking_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "ontology_propagation_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_propagation_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_propagation_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_propagation_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_propagation_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_propagation_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "# Define number of hops for each metadata key\n",
+    "n_hops_dict = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "# Load the benchmarking ontology resources\n",
+    "ontology_benchmarking_resource_dicts = {}\n",
+    "for meta_key, path in ontology_benchmarking_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_benchmarking_resource_dicts[meta_key] = pickle.load(f)\n",
+    "\n",
+    "# Load the propagation ontology resources into a separate dictionary\n",
+    "ontology_propagation_resource_dicts = {}\n",
+    "for meta_key, path in ontology_propagation_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_propagation_resource_dicts[meta_key] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_meta_adata_to_query_obj_for_scoring(\n",
+    "        meta_adata: sc.AnnData,\n",
+    "        metadata_key: str,\n",
+    "        scores_col_name_suffix: str = \"class_probs\",\n",
+    "        ontology_term_ids_uns_key_name_suffix: str = \"ontology_term_ids\",\n",
+    "        ontology_term_id_obs_col_name_suffix: str = \"ontology_term_id\",\n",
+    "    ) -> t.Tuple[t.List[dict], t.List[str]]:\n",
+    "    scores_col_name = f\"{metadata_key}_{scores_col_name_suffix}\"\n",
+    "    ontology_term_ids_uns_key_name = f\"{metadata_key}_{ontology_term_ids_uns_key_name_suffix}\"\n",
+    "    ontology_term_id_obs_col_name = f\"{metadata_key}_{ontology_term_id_obs_col_name_suffix}\"\n",
+    "    assert scores_col_name in meta_adata.obsm\n",
+    "    assert ontology_term_ids_uns_key_name in meta_adata.uns\n",
+    "    assert ontology_term_id_obs_col_name in meta_adata.obs.columns\n",
+    "    query_objs = []\n",
+    "    ground_truth_ontology_term_ids = []\n",
+    "    obs_index = meta_adata.obs.index.values\n",
+    "    for i_cell in range(len(meta_adata)):\n",
+    "        obs_row = meta_adata.obs.iloc[i_cell]\n",
+    "        ground_truth_ontology_term_id = obs_row[ontology_term_id_obs_col_name]\n",
+    "        query_obj = dict()\n",
+    "        query_obj[\"query_cell_id\"] = obs_index[i_cell]\n",
+    "        query_obj[\"matches\"] = []\n",
+    "        for ontology_term_id, score in zip(\n",
+    "                meta_adata.uns[ontology_term_ids_uns_key_name],\n",
+    "                meta_adata.obsm[scores_col_name][i_cell, :]):\n",
+    "            query_obj[\"matches\"].append({\n",
+    "                \"ontology_term_id\": ontology_term_id,\n",
+    "                \"score\": score,\n",
+    "            })\n",
+    "        query_objs.append(query_obj)\n",
+    "        ground_truth_ontology_term_ids.append(ground_truth_ontology_term_id)\n",
+    "    return query_objs, ground_truth_ontology_term_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ab0f156fa004e8f9ada679709548284",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "511ad396b0444e0b8e9499ac03f97f4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ec92f47d82d45fb8798a100cbe7a877",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16b983e50b5a4a97b338afb256c2786e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc2aa6c8e4fe4739814f109c5409d476",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "eps = 1e-8\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(PREFIX_LIST))):\n",
+    "    prefix = PREFIX_LIST[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(VAL_ADATA_IDX_RANGE))):\n",
+    "        val_adata_id = VAL_ADATA_IDX_RANGE[val_adata_idx]\n",
+    "        meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx)\n",
+    "\n",
+    "        results[\"prefix\"].append(prefix)\n",
+    "        results[\"val_adata_id\"].append(val_adata_id)\n",
+    "\n",
+    "        for key in [\"cell_type\", \"disease\", \"development_stage\", \"tissue\", \"sex\"]:\n",
+    "\n",
+    "            # Calculate accuracy\n",
+    "            query_objs, ground_truth_ontology_term_ids = convert_meta_adata_to_query_obj_for_scoring(\n",
+    "                meta_adata=meta_adata,\n",
+    "                metadata_key=key)\n",
+    "            results_df = calculate_metrics_for_prediction_output(\n",
+    "                ground_truth_ontology_term_ids=ground_truth_ontology_term_ids,\n",
+    "                model_predictions=query_objs,\n",
+    "                ontology_resource=ontology_benchmarking_resource_dicts[key],\n",
+    "                num_hops=n_hops_dict[key],\n",
+    "                metric_style=\"hop_k_call\").dropna()\n",
+    "            \n",
+    "            for n_hops in range(N_HOPS_DICT[key] + 1):\n",
+    "                hop_accuracy = results_df[f\"hop_{n_hops}_call\"].mean()\n",
+    "                results[f\"{key}_hop_{n_hops}_accuracy\"].append(hop_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Coarse tissue for each validation dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ff8860043e7443e99b50d6bacf2b7761",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "validation_meta_df = pd.read_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))\n",
+    "\n",
+    "# Stephen's validatdion table\n",
+    "# what we have called intestine is actually \"small intestine\"\n",
+    "tissue_ontology_term_id_to_coarse_name_map = dict()\n",
+    "for row in validation_meta_df['tissue_name_ont_coarsename_coarseont'].values:\n",
+    "    split_row = row.strip(\"()\").split(', ')\n",
+    "    coarse_name = split_row[2].strip(\"'\")\n",
+    "    if coarse_name == \"intestine\":\n",
+    "        coarse_name = \"small intestine\"\n",
+    "    tissue_ontology_term_id_to_coarse_name_map[split_row[1].strip(\"'\")] = coarse_name\n",
+    "\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_adata_id_to_coarse_tissue_map = dict()\n",
+    "for val_idx in tqdm(range(1, 111)):\n",
+    "    \n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    obs_df = val_adata.obs\n",
+    "    val_adata_id_to_coarse_tissue_map[val_idx] = obs_df['tissue_ontology_term_id'].map(\n",
+    "        tissue_ontology_term_id_to_coarse_name_map).iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['coarse_tissue'] = df['val_adata_id'].map(val_adata_id_to_coarse_tissue_map)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "coarse_tissue\n",
+       "blood              136\n",
+       "brain              112\n",
+       "lung                60\n",
+       "heart               36\n",
+       "small intestine     32\n",
+       "liver               24\n",
+       "kidney              20\n",
+       "thymus               8\n",
+       "nose                 8\n",
+       "eye                  4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master_df['coarse_tissue'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = master_df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df.to_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"metadata_prediction_scores.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>cell_type_hop_0_accuracy</th>\n",
+       "      <th>cell_type_hop_1_accuracy</th>\n",
+       "      <th>cell_type_hop_2_accuracy</th>\n",
+       "      <th>cell_type_hop_3_accuracy</th>\n",
+       "      <th>disease_hop_0_accuracy</th>\n",
+       "      <th>disease_hop_1_accuracy</th>\n",
+       "      <th>disease_hop_2_accuracy</th>\n",
+       "      <th>disease_hop_3_accuracy</th>\n",
+       "      <th>development_stage_hop_0_accuracy</th>\n",
+       "      <th>development_stage_hop_1_accuracy</th>\n",
+       "      <th>development_stage_hop_2_accuracy</th>\n",
+       "      <th>development_stage_hop_3_accuracy</th>\n",
+       "      <th>tissue_hop_0_accuracy</th>\n",
+       "      <th>tissue_hop_1_accuracy</th>\n",
+       "      <th>tissue_hop_2_accuracy</th>\n",
+       "      <th>tissue_hop_3_accuracy</th>\n",
+       "      <th>sex_hop_0_accuracy</th>\n",
+       "      <th>coarse_tissue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.626214</td>\n",
+       "      <td>0.632147</td>\n",
+       "      <td>0.647789</td>\n",
+       "      <td>0.906688</td>\n",
+       "      <td>0.8950</td>\n",
+       "      <td>0.8950</td>\n",
+       "      <td>0.8950</td>\n",
+       "      <td>0.8950</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0795</td>\n",
+       "      <td>0.0795</td>\n",
+       "      <td>0.951000</td>\n",
+       "      <td>brain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.554500</td>\n",
+       "      <td>0.844500</td>\n",
+       "      <td>0.985000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.131500</td>\n",
+       "      <td>blood</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.716000</td>\n",
+       "      <td>0.847000</td>\n",
+       "      <td>0.968000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0040</td>\n",
+       "      <td>0.004000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.249500</td>\n",
+       "      <td>blood</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.579492</td>\n",
+       "      <td>0.863283</td>\n",
+       "      <td>0.895391</td>\n",
+       "      <td>0.924909</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.893500</td>\n",
+       "      <td>small intestine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.635500</td>\n",
+       "      <td>0.859000</td>\n",
+       "      <td>0.970000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0045</td>\n",
+       "      <td>0.004500</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.148000</td>\n",
+       "      <td>blood</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>100M_long_run_last</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0.665000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>0.782000</td>\n",
+       "      <td>0.815500</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0035</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.901000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.724500</td>\n",
+       "      <td>lung</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>100M_long_run_last</td>\n",
+       "      <td>107</td>\n",
+       "      <td>0.580040</td>\n",
+       "      <td>0.743083</td>\n",
+       "      <td>0.821146</td>\n",
+       "      <td>0.928854</td>\n",
+       "      <td>0.9070</td>\n",
+       "      <td>0.9070</td>\n",
+       "      <td>0.9070</td>\n",
+       "      <td>0.9070</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1275</td>\n",
+       "      <td>0.127500</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.925500</td>\n",
+       "      <td>lung</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>100M_long_run_last</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0.408931</td>\n",
+       "      <td>0.678026</td>\n",
+       "      <td>0.922444</td>\n",
+       "      <td>0.982374</td>\n",
+       "      <td>0.9815</td>\n",
+       "      <td>0.9815</td>\n",
+       "      <td>0.9815</td>\n",
+       "      <td>0.9815</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0230</td>\n",
+       "      <td>0.023000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.537000</td>\n",
+       "      <td>lung</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>100M_long_run_last</td>\n",
+       "      <td>109</td>\n",
+       "      <td>0.397308</td>\n",
+       "      <td>0.531890</td>\n",
+       "      <td>0.581042</td>\n",
+       "      <td>0.599766</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.939146</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>0.857226</td>\n",
+       "      <td>brain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>100M_long_run_last</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.265000</td>\n",
+       "      <td>0.287000</td>\n",
+       "      <td>0.394000</td>\n",
+       "      <td>0.998000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0095</td>\n",
+       "      <td>0.2360</td>\n",
+       "      <td>0.940000</td>\n",
+       "      <td>brain</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 prefix  val_adata_id  cell_type_hop_0_accuracy  \\\n",
+       "0        10M_001_bs1536             1                  0.626214   \n",
+       "1        10M_001_bs1536             2                  0.554500   \n",
+       "2        10M_001_bs1536             3                  0.716000   \n",
+       "3        10M_001_bs1536             4                  0.579492   \n",
+       "4        10M_001_bs1536             5                  0.635500   \n",
+       "..                  ...           ...                       ...   \n",
+       "435  100M_long_run_last           106                  0.665000   \n",
+       "436  100M_long_run_last           107                  0.580040   \n",
+       "437  100M_long_run_last           108                  0.408931   \n",
+       "438  100M_long_run_last           109                  0.397308   \n",
+       "439  100M_long_run_last           110                  0.265000   \n",
+       "\n",
+       "     cell_type_hop_1_accuracy  cell_type_hop_2_accuracy  \\\n",
+       "0                    0.632147                  0.647789   \n",
+       "1                    0.844500                  0.985000   \n",
+       "2                    0.847000                  0.968000   \n",
+       "3                    0.863283                  0.895391   \n",
+       "4                    0.859000                  0.970000   \n",
+       "..                        ...                       ...   \n",
+       "435                  0.700000                  0.782000   \n",
+       "436                  0.743083                  0.821146   \n",
+       "437                  0.678026                  0.922444   \n",
+       "438                  0.531890                  0.581042   \n",
+       "439                  0.287000                  0.394000   \n",
+       "\n",
+       "     cell_type_hop_3_accuracy  disease_hop_0_accuracy  disease_hop_1_accuracy  \\\n",
+       "0                    0.906688                  0.8950                  0.8950   \n",
+       "1                    1.000000                  0.0000                  0.0000   \n",
+       "2                    1.000000                  0.0000                  0.0000   \n",
+       "3                    0.924909                  0.0000                  0.0000   \n",
+       "4                    1.000000                  0.0000                  0.0000   \n",
+       "..                        ...                     ...                     ...   \n",
+       "435                  0.815500                  0.0000                  0.0000   \n",
+       "436                  0.928854                  0.9070                  0.9070   \n",
+       "437                  0.982374                  0.9815                  0.9815   \n",
+       "438                  0.599766                  1.0000                  1.0000   \n",
+       "439                  0.998000                  0.0000                  0.0000   \n",
+       "\n",
+       "     disease_hop_2_accuracy  disease_hop_3_accuracy  \\\n",
+       "0                    0.8950                  0.8950   \n",
+       "1                    0.0000                  0.0000   \n",
+       "2                    0.0000                  0.0000   \n",
+       "3                    0.0000                  1.0000   \n",
+       "4                    0.0000                  0.0000   \n",
+       "..                      ...                     ...   \n",
+       "435                  0.0000                  0.0000   \n",
+       "436                  0.9070                  0.9070   \n",
+       "437                  0.9815                  0.9815   \n",
+       "438                  1.0000                  1.0000   \n",
+       "439                  0.0000                  0.0000   \n",
+       "\n",
+       "     development_stage_hop_0_accuracy  development_stage_hop_1_accuracy  \\\n",
+       "0                              0.0000                               1.0   \n",
+       "1                              0.0000                               1.0   \n",
+       "2                              0.0000                               1.0   \n",
+       "3                              0.0000                               1.0   \n",
+       "4                              0.0000                               1.0   \n",
+       "..                                ...                               ...   \n",
+       "435                            0.0035                               1.0   \n",
+       "436                            0.0000                               1.0   \n",
+       "437                            0.0000                               1.0   \n",
+       "438                            0.0000                               1.0   \n",
+       "439                               NaN                               NaN   \n",
+       "\n",
+       "     development_stage_hop_2_accuracy  development_stage_hop_3_accuracy  \\\n",
+       "0                                 1.0                               1.0   \n",
+       "1                                 1.0                               1.0   \n",
+       "2                                 1.0                               1.0   \n",
+       "3                                 1.0                               1.0   \n",
+       "4                                 1.0                               1.0   \n",
+       "..                                ...                               ...   \n",
+       "435                               1.0                               1.0   \n",
+       "436                               1.0                               1.0   \n",
+       "437                               1.0                               1.0   \n",
+       "438                               1.0                               1.0   \n",
+       "439                               NaN                               NaN   \n",
+       "\n",
+       "     tissue_hop_0_accuracy  tissue_hop_1_accuracy  tissue_hop_2_accuracy  \\\n",
+       "0                   0.0000               0.000000                 0.0795   \n",
+       "1                   0.0000               0.000000                 1.0000   \n",
+       "2                   0.0040               0.004000                 1.0000   \n",
+       "3                   0.0000               0.000000                 1.0000   \n",
+       "4                   0.0045               0.004500                 1.0000   \n",
+       "..                     ...                    ...                    ...   \n",
+       "435                 0.0000               0.901000                 1.0000   \n",
+       "436                 0.1275               0.127500                 1.0000   \n",
+       "437                 0.0230               0.023000                 1.0000   \n",
+       "438                 0.0000               0.939146                 1.0000   \n",
+       "439                 0.0000               0.000000                 0.0095   \n",
+       "\n",
+       "     tissue_hop_3_accuracy  sex_hop_0_accuracy    coarse_tissue  \n",
+       "0                   0.0795            0.951000            brain  \n",
+       "1                   1.0000            0.131500            blood  \n",
+       "2                   1.0000            0.249500            blood  \n",
+       "3                   1.0000            0.893500  small intestine  \n",
+       "4                   1.0000            0.148000            blood  \n",
+       "..                     ...                 ...              ...  \n",
+       "435                 1.0000            0.724500             lung  \n",
+       "436                 1.0000            0.925500             lung  \n",
+       "437                 1.0000            0.537000             lung  \n",
+       "438                 1.0000            0.857226            brain  \n",
+       "439                 0.2360            0.940000            brain  \n",
+       "\n",
+       "[440 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_119159/3100616606.py:39: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_119159/3100616606.py:45: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_119159/3100616606.py:51: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+      "/var/tmp/ipykernel_119159/3100616606.py:57: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1500 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "# -------------------------------\n",
+    "# User-specified options:\n",
+    "# Choose which metric to plot: 'f1', 'sensitivity', or 'specificity'\n",
+    "value_to_plot = 'accuracy'  \n",
+    "# Flag to select summary statistic: True uses mean/std, False uses median/IQR.\n",
+    "use_mean = True\n",
+    "# df = master_df[master_df['coarse_tissue'] == 'blood'].copy()\n",
+    "# df = master_df.copy()\n",
+    "# -------------------------------\n",
+    "\n",
+    "# Define metric groups and available hops.\n",
+    "# For 'sex', only hop 0 is available.\n",
+    "metric_groups = {\n",
+    "    'cell_type': [0, 1, 2, 3],\n",
+    "    'disease': [0, 1, 2, 3],\n",
+    "    'tissue': [0, 1, 2, 3],\n",
+    "    'development_stage': [0, 1, 2, 3],\n",
+    "    'sex': [0]\n",
+    "}\n",
+    "\n",
+    "# Build the list of metric columns (e.g., \"cell_type_hop_0_f1\")\n",
+    "selected_metrics = []\n",
+    "for group, hops in metric_groups.items():\n",
+    "    for hop in hops:\n",
+    "        selected_metrics.append(f\"{group}_hop_{hop}_{value_to_plot}\")\n",
+    "\n",
+    "# Precompute a color palette for each metric column.\n",
+    "palette = {}\n",
+    "\n",
+    "# For cell_type: use Greens.\n",
+    "cell_metrics = [f\"cell_type_hop_{hop}_{value_to_plot}\" for hop in metric_groups['cell_type']]\n",
+    "n = len(cell_metrics)\n",
+    "for i, m in enumerate(cell_metrics):\n",
+    "    palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For disease: use Reds.\n",
+    "disease_metrics = [f\"disease_hop_{hop}_{value_to_plot}\" for hop in metric_groups['disease']]\n",
+    "n = len(disease_metrics)\n",
+    "for i, m in enumerate(disease_metrics):\n",
+    "    palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For tissue: use Blues.\n",
+    "tissue_metrics = [f\"tissue_hop_{hop}_{value_to_plot}\" for hop in metric_groups['tissue']]\n",
+    "n = len(tissue_metrics)\n",
+    "for i, m in enumerate(tissue_metrics):\n",
+    "    palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For development_stage: use Purples.\n",
+    "dev_metrics = [f\"development_stage_hop_{hop}_{value_to_plot}\" for hop in metric_groups['development_stage']]\n",
+    "n = len(dev_metrics)\n",
+    "for i, m in enumerate(dev_metrics):\n",
+    "    palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
+    "\n",
+    "# For sex: assign a fixed color (e.g., orange).\n",
+    "sex_metrics = [f\"sex_hop_{hop}_{value_to_plot}\" for hop in metric_groups['sex']]\n",
+    "for m in sex_metrics:\n",
+    "    palette[m] = 'orange'\n",
+    "\n",
+    "# Melt the dataframe into long format for the selected metrics.\n",
+    "# Assume your original dataframe is named `df`.\n",
+    "df_long = df.melt(\n",
+    "    id_vars=['prefix', 'val_adata_id'],\n",
+    "    value_vars=selected_metrics,\n",
+    "    var_name='metric',\n",
+    "    value_name=value_to_plot\n",
+    ")\n",
+    "# Drop any rows with NaN values for the chosen metric.\n",
+    "df_long = df_long.dropna(subset=[value_to_plot])\n",
+    "\n",
+    "# Determine grid size: one row per metric group, columns = max hops among groups.\n",
+    "n_rows = len(metric_groups)\n",
+    "max_cols = max(len(hops) for hops in metric_groups.values())\n",
+    "\n",
+    "fig, axes = plt.subplots(n_rows, max_cols, figsize=(2 * max_cols, 3 * n_rows), sharey=True)\n",
+    "\n",
+    "# Loop over each metric group (row) and each hop (column).\n",
+    "for row_idx, (group, hops) in enumerate(metric_groups.items()):\n",
+    "    for col_idx in range(max_cols):\n",
+    "        # Turn off extra panels.\n",
+    "        if col_idx >= len(hops):\n",
+    "            axes[row_idx, col_idx].axis('off')\n",
+    "            continue\n",
+    "\n",
+    "        hop = hops[col_idx]\n",
+    "        metric_name = f\"{group}_hop_{hop}_{value_to_plot}\"\n",
+    "        ax = axes[row_idx, col_idx]\n",
+    "\n",
+    "        # Filter data for the current metric and drop any NaNs.\n",
+    "        df_metric = df_long[df_long['metric'] == metric_name].dropna(subset=[value_to_plot])\n",
+    "\n",
+    "        # Compute summary statistics per prefix.\n",
+    "        if use_mean:\n",
+    "            stats = df_metric.groupby('prefix')[value_to_plot].agg(['mean', 'std']).reindex(PREFIX_LIST)\n",
+    "            y_vals = stats['mean']\n",
+    "            y_err = stats['std']\n",
+    "        else:\n",
+    "            stats = df_metric.groupby('prefix').agg(\n",
+    "                median=(value_to_plot, np.median),\n",
+    "                q1=(value_to_plot, lambda x: x.quantile(0.25)),\n",
+    "                q3=(value_to_plot, lambda x: x.quantile(0.75))\n",
+    "            ).reindex(PREFIX_LIST)\n",
+    "            y_vals = stats['median']\n",
+    "            y_err_lower = y_vals - stats['q1']\n",
+    "            y_err_upper = stats['q3'] - y_vals\n",
+    "            y_err = [y_err_lower.values, y_err_upper.values]\n",
+    "\n",
+    "        x = np.arange(len(PREFIX_LIST))\n",
+    "        # Retrieve the precomputed color (default to gray if not found).\n",
+    "        line_color = palette.get(metric_name, 'gray')\n",
+    "\n",
+    "        # Plot black markers with error bars.\n",
+    "        ax.errorbar(\n",
+    "            x, y_vals, yerr=y_err,\n",
+    "            fmt='o', color='black', ecolor='black',\n",
+    "            capsize=4, markersize=6, linestyle='None'\n",
+    "        )\n",
+    "        # Connect the markers with a line in the precomputed color.\n",
+    "        ax.plot(x, y_vals, color=line_color, linestyle='-', linewidth=2)\n",
+    "\n",
+    "        ax.set_xticks(x)\n",
+    "        ax.set_xticklabels(PREFIX_LIST, rotation=90)\n",
+    "        ax.set_ylim(0, 1)\n",
+    "        # Set the title to show the hop value.\n",
+    "        ax.set_title(f\"hop {hop}\")\n",
+    "\n",
+    "        # Label the y-axis with the metric group on the leftmost panel.\n",
+    "        if col_idx == 0:\n",
+    "            ax.set_ylabel(f\"{group} ({value_to_plot})\", fontsize=12, fontweight='bold')\n",
+    "        # Label the x-axis on the bottom row.\n",
+    "        if row_idx == n_rows - 1:\n",
+    "            ax.set_xlabel('Prefix')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Playground"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_indices = np.argmax(meta_adata.obsm['cell_type_class_probs'], -1)\n",
+    "best_probs = np.max(meta_adata.obsm['cell_type_class_probs'], -1)\n",
+    "best_names = meta_adata.uns['cell_type_ontology_term_ids'][best_indices]\n",
+    "best_labels = list(map(ontology_propagation_resource_dicts[metadata_key]['ontology_term_id_to_label'].get, best_names))\n",
+    "term_id_to_idx_map = {term_id: idx for idx, term_id in enumerate(meta_adata.uns[f'{metadata_key}_ontology_term_ids'])}\n",
+    "gt_indices = list(map(term_id_to_idx_map.get, meta_adata.obs[f'{metadata_key}_ontology_term_id'].values))\n",
+    "bad_indices = [i for i, idx in enumerate(gt_indices) if idx is None]\n",
+    "for i in bad_indices:\n",
+    "    gt_indices[i] = 0\n",
+    "gt_probs = meta_adata.obsm[f'{metadata_key}_class_probs'][np.arange(len(gt_indices)), gt_indices]\n",
+    "gt_probs[bad_indices] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_obs = meta_adata.obs.copy()\n",
+    "new_obs['best_label'] = best_labels\n",
+    "new_obs['best_prob'] = best_probs\n",
+    "new_obs['gt_prob'] = gt_probs\n",
+    "new_obs['hop_2_inclusion_score'] = results_df[\"hop_2_inclusion_score\"].values\n",
+    "new_obs = new_obs[[metadata_key, 'best_label', 'best_prob', 'gt_prob', 'hop_2_inclusion_score', 'cell_type_hop_2_sensitivity']]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_obs['cell_type_hop_2_sensitivity'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show all rows in a context manager\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(new_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for query_obj in query_objs:\n",
+    "    if query_obj[\"query_cell_id\"] == \"26540767\":\n",
+    "        break\n",
+    "term_id_to_score_map = {match[\"ontology_term_id\"]: match[\"score\"] for match in query_obj[\"matches\"]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_type_label = 'oligodendrocyte precursor cell'\n",
+    "cell_type_label_to_term_id = {\n",
+    "    label: term_id\n",
+    "    for term_id, label in ontology_propagation_resource_dicts[metadata_key]['ontology_term_id_to_label'].items()}\n",
+    "cell_type_term_id = cell_type_label_to_term_id[cell_type_label]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_hop_inclusion_scores\n",
+    "\n",
+    "calculate_hop_inclusion_scores(query_obj, cell_type_term_id, 3, ontology_benchmarking_resource_dicts[metadata_key])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i_hop in range(4):\n",
+    "\n",
+    "    nodes = ontology_benchmarking_resource_dicts[metadata_key][cell_type_term_id][f'hop_{i_hop}']['nodes']\n",
+    "    ancestors = ontology_benchmarking_resource_dicts[metadata_key][cell_type_term_id][f'hop_{i_hop}']['all_ancestors']\n",
+    "    descendants = ontology_benchmarking_resource_dicts[metadata_key][cell_type_term_id][f'hop_{i_hop}']['all_descendants']\n",
+    "\n",
+    "\n",
+    "    print(f\"All nodes (hop {i_hop}):\")\n",
+    "    total_score = 0\n",
+    "    for node in nodes:\n",
+    "        label = ontology_propagation_resource_dicts[metadata_key]['ontology_term_id_to_label'][node]\n",
+    "        score = term_id_to_score_map[node] if node in term_id_to_score_map else 0\n",
+    "        total_score += score\n",
+    "        print(f\" - {label} ({score:.3f})\")\n",
+    "    print(f\"Total score: {total_score:.3f}\")\n",
+    "    print('')\n",
+    "\n",
+    "    \n",
+    "    print(f\"All ancestors (hop {i_hop}):\")\n",
+    "    total_score = 0\n",
+    "    for ancestor in ancestors:\n",
+    "        label = ontology_propagation_resource_dicts[metadata_key]['ontology_term_id_to_label'][ancestor]\n",
+    "        score = term_id_to_score_map[ancestor] if ancestor in term_id_to_score_map else 0\n",
+    "        total_score += score\n",
+    "        print(f\" - {label} ({score:.3f})\")\n",
+    "    print(f\"Total score: {total_score:.3f}\")\n",
+    "    print('')\n",
+    "\n",
+    "    print(f\"All descendants (hop {i_hop}):\")\n",
+    "    total_score = 0\n",
+    "    for descendant in descendants:\n",
+    "        label = ontology_propagation_resource_dicts[metadata_key]['ontology_term_id_to_label'][descendant]\n",
+    "        score = term_id_to_score_map[descendant] if descendant in term_id_to_score_map else 0\n",
+    "        total_score += score\n",
+    "        print(f\" - {label} ({score:.3f})\")\n",
+    "    print(f\"Total score: {total_score:.3f}\")\n",
+    "    print('')\n",
+    "    \n",
+    "    print('-----------------------\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb
new file mode 100644
index 00000000..86df5330
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "cuda_device_index = 0\n",
+    "val_adata_index = 1\n",
+    "\n",
+    "checkpoint_path = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ref_adata_path = \"/home/mehrtash/data/data/extract_0.h5ad\"\n",
+    "gene_info_path = \"/home/mehrtash/data/gene_info/gene_info.tsv\"\n",
+    "adata_path = f\"/home/mehrtash/data/data/cellariumgpt_validation/extract_{val_adata_index}.h5ad\"\n",
+    "ontology_resource_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/ontology\"\n",
+    "output_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last\"\n",
+    "\n",
+    "rng_seed = 42\n",
+    "n_cells = 10000\n",
+    "n_genes = 4_091\n",
+    "gene_selection_method = \"random\"  # \"random\" or \"highly_expressed\"\n",
+    "chunk_size = 16\n",
+    "\n",
+    "os.makedirs(output_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-27 17:36:09,593 - __main__ - INFO - Loading ontology resources ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ontology resources\n",
+    "logger.info(\"Loading ontology resources ...\")\n",
+    "\n",
+    "ontology_benchmarking_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_benchmarking_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_benchmarking_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_benchmarking_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_benchmarking_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_benchmarking_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "ontology_propagation_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_propagation_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_propagation_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_propagation_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_propagation_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_propagation_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "n_hops_dict = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "ontology_benchmarking_resource_dicts = {}\n",
+    "for meta_key, path in ontology_benchmarking_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_benchmarking_resource_dicts[meta_key] = pickle.load(f)\n",
+    "\n",
+    "ontology_propagation_resource_dicts = {}\n",
+    "for meta_key, path in ontology_propagation_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_propagation_resource_dicts[meta_key] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-27 17:36:10,323 - __main__ - INFO - Loading the model checkpoint from /home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "logger.info(f\"Loading the model checkpoint from {checkpoint_path} ...\")\n",
+    "\n",
+    "device = torch.device(f\"cuda:{cuda_device_index}\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "    ref_adata_path=ref_adata_path,\n",
+    "    gene_info_tsv_path=gene_info_path,\n",
+    "    device=device,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load a reference adata\n",
+    "adata = sc.read_h5ad(ref_adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-27 17:36:17,330 - __main__ - INFO - Using 4091 random genes (seed = 42).\n",
+      "2025-02-27 17:36:17,331 - __main__ - INFO - Using 10000 random cells (seed = 42).\n"
+     ]
+    }
+   ],
+   "source": [
+    "if n_genes is not None:\n",
+    "    if gene_selection_method == \"highly_expressed\":\n",
+    "        logger.info(f\"Using {n_genes} highly expressed genes.\")\n",
+    "        X_g = np.asarray(adata.X.sum(0)).flatten()\n",
+    "        highly_expressed_gene_indices = np.argsort(X_g)[-n_genes:]\n",
+    "        highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]\n",
+    "        adata = adata[:, highly_expressed_gene_ids]\n",
+    "        n_rand_prompt_vars = None\n",
+    "        torch_rng = None\n",
+    "    elif gene_selection_method == \"random\":\n",
+    "        logger.info(f\"Using {n_genes} random genes (seed = {rng_seed}).\")\n",
+    "        torch_rng = torch.Generator().manual_seed(rng_seed)\n",
+    "        n_rand_prompt_vars = n_genes\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unknown gene selection method: {gene_selection_method}\")\n",
+    "else:\n",
+    "    logger.info(f\"Using all genes.\")\n",
+    "\n",
+    "if n_cells is None:\n",
+    "    logger.info(f\"Using all cells.\")\n",
+    "else:\n",
+    "    n_cells = min(n_cells, len(adata))\n",
+    "    logger.info(f\"Using {n_cells} random cells (seed = {rng_seed}).\")\n",
+    "    rng = np.random.RandomState(rng_seed)\n",
+    "    adata = adata[rng.choice(len(adata), n_cells, replace=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-27 17:36:22,307 - __main__ - INFO - Predicting metadata for 10000 cells ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6df03c36d8d747cc8fafe37eeacd55bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/625 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "logger.info(f\"Predicting metadata for {len(adata)} cells ...\")\n",
+    "preds = ctx.predict_metadata_chunked(\n",
+    "    adata=adata,\n",
+    "    chunk_size=chunk_size,\n",
+    "    n_rand_prompt_vars=n_rand_prompt_vars,\n",
+    "    rng=torch_rng)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata_key = 'cell_type'\n",
+    "probs = np.exp(preds[metadata_key])\n",
+    "ontology_names = np.asarray(ctx.metadata_ontology_infos[metadata_key]['names'])\n",
+    "ontology_labels = np.asarray(ctx.metadata_ontology_infos[metadata_key]['labels'])\n",
+    "names_to_labels_map = {n: l for n, l in zip(ontology_names, ontology_labels)}\n",
+    "\n",
+    "best_indices = np.argmax(probs, -1)\n",
+    "best_probs = np.max(probs, -1)\n",
+    "best_names = ontology_names[best_indices]\n",
+    "best_labels = list(map(names_to_labels_map.get, best_names))\n",
+    "\n",
+    "new_obs = adata.obs.copy()\n",
+    "new_obs['predicted_' + metadata_key] = best_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cell_type xent loss: 3.58 +/- 2.81\n",
+      "development_stage xent loss: 7.35 +/- 2.68\n",
+      "disease xent loss: 3.51 +/- 5.61\n",
+      "tissue xent loss: 5.31 +/- 3.37\n",
+      "sex xent loss: 1.00 +/- 1.52\n"
+     ]
+    }
+   ],
+   "source": [
+    "for metadata_key in ['cell_type', 'development_stage', 'disease', 'tissue', 'sex']:\n",
+    "    truth_term_ids = adata.obs[f\"{metadata_key}_ontology_term_id\"].values\n",
+    "\n",
+    "    vocab_names = ctx.metadata_ontology_infos[metadata_key][\"names\"]\n",
+    "    vocab_names_to_idx = {v: i for i, v in enumerate(vocab_names)}\n",
+    "    vocab_names_to_idx[\"unknown\"] = -1\n",
+    "    truth_vocab_indices = np.array([vocab_names_to_idx[t] for t in truth_term_ids])\n",
+    "    truth_logprobs = preds[metadata_key][np.arange(len(preds[metadata_key])), truth_vocab_indices]\n",
+    "    good_indices = np.where(truth_vocab_indices != -1)[0]\n",
+    "    truth_logprobs = truth_logprobs[good_indices]\n",
+    "    mean_truth_logprobs = -truth_logprobs.mean()\n",
+    "    std_truth_logprobs = truth_logprobs.std()\n",
+    "    print(f\"{metadata_key} xent loss: {mean_truth_logprobs:.2f} +/- {std_truth_logprobs:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From be707a06957149f4cd966bcf5ade11b91d192b2c Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 28 Feb 2025 22:52:02 +0000
Subject: [PATCH 51/64] new

---
 .../03_analyze_metadata_predictions.ipynb     | 142 +----
 ...alyze_metadata_predictions_new_style.ipynb | 595 +-----------------
 .../05_loss_scales.ipynb                      | 403 +++++++++++-
 ...ata_predictions_new_style_no_compute.ipynb | 561 +++++++++++++++++
 scripts/metadata_prediction_analysis.py       |  60 +-
 5 files changed, 1045 insertions(+), 716 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb

diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
index 8058f17a..3a5f5812 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/03_analyze_metadata_predictions.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,34 +152,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_13649/2074687837.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  greens = cm.get_cmap('Greens', len(cell_metrics))\n",
-      "/var/tmp/ipykernel_13649/2074687837.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  reds = cm.get_cmap('Reds', len(disease_metrics))\n",
-      "/var/tmp/ipykernel_13649/2074687837.py:62: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  blues = cm.get_cmap('Blues', len(tissue_metrics))\n",
-      "/var/tmp/ipykernel_13649/2074687837.py:68: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  purples = cm.get_cmap('Purples', len(dev_metrics))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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aunSp2rZtKy8vrxTHrl+/XpcvX1bnzp1tXldXV1fVrl1bmzZtyvC47du3V5EiRdI85sKFC/rhhx/Uu3dvlShRwmZfZu9MCQAA4Ahcdgcg16tVq5bNB9fOnTurWrVqioqK0n/+8x+5u7vr2LFjKlasmHx9fW3aJl/GduzYMes2d3d3LViwQA888IA8PDwUHR2d6gc0FxcXmwBJunN3L+nOeiypuXDhghISElSuXLkU+ypUqKCkpCSdOHFClSpVytjJ/7/k+u/W79q1axUfH5+hsCI1lStXVpMmTbLUNiPMZrNGjx6t0aNH68yZM/r+++/1xhtv6NNPP5Wbm5sWL16sCxcu6OrVqzaXn6Xm2LFjql27dort//xa/7OPUqVK2RwXExMj6U4odTdXrlxRwYIFNXXqVPXo0UNhYWGqUaOGWrVqpe7du6f4vribiIgIm+dlypSRi4uLzfdP9+7dtWzZMv34449q2LChNmzYoHPnzqlbt24ZGiM8PFzvvfeeTCaTPDw8FBERoaCgoBTH/ft1uHDhgi5fvqx3331X7777bqp9Jy9en1llypTRuHHjNHLkSN1///165ZVXMtTu77//1vjx4/XJJ5+kGPvKlSspjv930FKwYEFJ0qVLl+Tn56djx47JxcVFZcqUsTkutfdRRnTp0kUzZszQsGHDtHXr1hQL6CdL/h575JFHUt3v5+eX4TH//XVLzeHDhyUp3fcOAACAsxA+AchzXFxc9PDDD+uNN95QTExMpoMcSVq7dq2kO7MrYmJiMvQBD/YREhKiJ598Uu3bt1elSpX06aefplg42Z7+vVB68qymadOmqWrVqqm28fHxkXRnTaEGDRroyy+/1Lp16zRt2jS9/vrr+uKLL9SyZctM15JayNm8eXMVLVpUixcvVsOGDbV48WIFBwdnOAz09vbO0LF3ex2eeuqpuwZxVapUyVANqVm3bp2kO+t4xcbGZmgmXceOHbV161aNHDlSVatWlY+Pj5KSktSiRYtU1zn65xpt/2Rk4KYAWdG5c2eNGjVK/fr1U2BgoJo1a5bqccm1fvTRR6med1rrnv1bVhf6BwAAyE0InwDkSbdv35YkxcXFSbqzqPWGDRt07do1m9lPBw4csO5P9ttvv2nChAnq1auXdu/erb59++r333+Xv7+/zRhJSUk6fPiwdbaTJP3111+S7sw2SU2RIkXk5eWlP//8M8W+AwcOyMXFRWFhYZIydzlMcv1367dw4cJZnvXkLG5ubqpSpYpiYmJ08eJFBQUFyc/PT3/88Uea7UqWLHnX1yF5f1qSZ8H4+fllKLQJCQnRoEGDNGjQIJ0/f17Vq1fXq6++mqHw6d/B5sGDB5WUlGTz/ePq6qouXbpo4cKFev3117VixQr169fvrsGKvSTf/S0xMdHus97mz5+v9evX69VXX9WUKVP09NNP66uvvkqzzaVLl7Rx40aNHz9eY8aMsW5PnkWUFSVLllRSUpIOHTpkM9spte+fjChRooTq16+vzZs3a+DAgXcNkZK/x4KCgtJ9be1xWVzyTLz03jsAAADOwppPAPKcW7duad26dXJ3d7deatWqVSslJiZqzpw5NsfOmjVLJpPJGhTcunVLPXv2VLFixfTGG29o4cKFOnfunIYNG5bqWP/szzAMzZkzR25ubmrcuHGqx7u6uqpZs2b66quvbC6tOnfunJYuXaoHH3zQeslNcliUvOZRWkJCQlS1alUtWrTI5vg//vhD69atU6tWrdLtw1liYmJ0/PjxFNsvX76sbdu2qWDBgipSpIhcXFzUrl07ffPNN/r5559THJ88m6VVq1b673//q23btln3xcfH691331V4eLjNukapqVGjhsqUKaPp06dbw8t/unDhgqQ7ay/9+1KvoKAgFStWTBaLJf0T1531yv7prbfekqQUwVW3bt106dIlPf300ynu5Ogorq6uat++vT7//PNUQ4vk1yGzjhw5opEjR6p9+/Z66aWXNH36dH399dfWO/ylVY+UctbS7Nmzs1SH9L/X+c0337Rbn5MmTdLYsWOt63elpnnz5vLz89PkyZN169atFPv/+dpm5ufA3RQpUkQNGzbUggULUrzXHDULDAAAIDOY+QQg11u9erV1Vsv58+e1dOlSxcTE6MUXX7QGOW3atNHDDz+s0aNH6+jRo4qMjNS6dev01VdfaejQodaZCJMmTdLu3bu1ceNG+fr6qkqVKhozZoxefvlldejQwSbE8fDw0Jo1a9SjRw/Vrl1bq1ev1sqVK/XSSy+luQDwpEmTtH79ej344IMaNGiQChQooHfeeUcWi0VTp061Hle1alW5urrq9ddf15UrV2Q2m/XII4+kul6PdOcysZYtW6pu3brq06ePrl+/rrfeekv+/v4aN25cdl/mNB07dsx6K/jkYGjSpEmS7swuSWt9oj179qhLly5q2bKlGjRooEKFCunUqVNatGiRTp8+rdmzZ1uDh8mTJ2vdunVq1KiR+vfvrwoVKujMmTP67LPP9NNPPykgIEAvvviiPv74Y7Vs2VJDhgxRoUKFtGjRIh05ckSff/65XFzS/ruKi4uL3n//fbVs2VKVKlVSr169VLx4cZ06dUqbNm2Sn5+fvvnmG127dk2hoaHq0KGDIiMj5ePjow0bNmjnzp2aMWNGhl63I0eO6NFHH1WLFi20bds2LV68WF26dFFkZKTNcdWqVdP999+vzz77TBUqVFD16tUz1H92vfbaa9q0aZNq166tfv36qWLFivr777/1yy+/aMOGDfr7778z1Z9hGOrdu7c8PT01b948SdLTTz+tzz//XM8++6yaNGmiYsWKpdrWz89PDRs21NSpU3Xr1i0VL15c69at05EjR7J8flWrVlXnzp319ttv68qVK6pXr542btyogwcPZrnPRo0aqVGjRmke4+fnp3nz5qlbt26qXr26nnzySRUpUkTHjx/XypUrVb9+fWuwXaNGDUl3FkVv3ry5XF1d9eSTT2a6rjfffFMPPvigqlevrv79+6tUqVI6evSoVq5cqd27d2e6PwAAALty1m32ACA90dHRhiSbh4eHh1G1alVj3rx5KW4hfu3aNWPYsGFGsWLFDDc3NyMiIsKYNm2a9bhdu3YZBQoUMJ555hmbdrdv3zYeeOABo1ixYsalS5cMwzCst04/dOiQ0axZM8PLy8soWrSoMXbsWCMxMdGmvVK5Zfsvv/xiNG/e3PDx8TG8vLyMhx9+2Ni6dWuKc3zvvfeM0qVLG66uriluWZ+aDRs2GPXr1zc8PT0NPz8/o02bNsa+fftsjtm0aZMhyfjss8/S7CszxyYfl9qjUaNGabY9d+6c8dprrxmNGjUyQkJCjAIFChgFCxY0HnnkEWP58uUpjj927JjRvXt3o0iRIobZbDZKly5tDB482LBYLNZjDh06ZHTo0MEICAgwPDw8jFq1ahnffvttps7t119/NR5//HEjMDDQMJvNRsmSJY2OHTsaGzduNAzDMCwWizFy5EgjMjLS8PX1Nby9vY3IyEjj7bffTvN8DcMwxo4da0gy9u3bZ3To0MHw9fU1ChYsaERFRRnXr19Ptc3UqVMNScbkyZPT7T9Zo0aNjEqVKqV7nCRj8ODBqe47d+6cMXjwYCMsLMxwc3MzgoODjcaNGxvvvvtuun0kv8bJ37dvvPGGIcn4/PPPbY47fvy44efnZ7Rq1SrNOk+ePGk89thjRkBAgOHv72888cQTxunTp1O8x5Jf3wsXLti0T/6ZceTIEeu269evG0OGDDECAwMNb29vo02bNsaJEydSfd/+25EjRwxJxrRp09I8Lvnnxb9t2rTJaN68ueHv7294eHgYZcqUMXr27Gn8/PPP1mNu375tPPPMM0aRIkUMk8lkJP9qltbYyfuio6Nttv/xxx/W18/Dw8MoV66c8corr6RZOwAAQE4wGQbzsQHg33r27Knly5enelkW4AhvvPGGhg0bpqNHj6a4ixsAAACQl7HmEwAATmYYhj744AM1atSI4AkAAAD3HNZ8AgDASeLj4/X1119r06ZN+v3339O9IxwAAACQFxE+AQDgJBcuXFCXLl0UEBCgl156SY8++qizSwIAAADsjjWfAAAAAAAA4DCs+QQAAAAAAACHIXwCAAAAAACAw+S7NZ+SkpJ0+vRp+fr6ymQyObscAAAAAPmcYRi6du2aihUrJhcX5gcAuPfku/Dp9OnTCgsLc3YZAAAAAGDjxIkTCg0NdXYZAGB3+S588vX1lXTnB7ufn5+TqwEAAACQ3129elVhYWHWzyoAcK/Jd+FT8qV2fn5+hE8AAAAAcg2WBQFwr+KCYgAAAAAAADgM4RMAAAAAAAAchvAJAAAAAAAADpPv1nwCAAAAANhfYmKibt265ewyAOQQNzc3ubq6ZuhYwicAAAAAQJYZhqGzZ8/q8uXLzi4FQA4LCAhQcHBwujdMIHwCAAAAAGRZcvAUFBQkLy8v7toH5AOGYSghIUHnz5+XJIWEhKR5POETAAAAACBLEhMTrcFTYGCgs8sBkIM8PT0lSefPn1dQUFCal+Cx4DgAAAAAIEuS13jy8vJyciUAnCH5vZ/eem+ETwAAAACAbOFSOyB/yuh7n/AJAAAAAAAADkP4BAAAAAAAHM5kMmnFihXOLgNOQPgEAAAAAEA+0bNnT5lMJg0YMCDFvsGDB8tkMqlnz54Z6mvz5s0ymUy6fPlyho4/c+aMWrZsmYlqca8gfAIAAAAAIB8JCwvTJ598ouvXr1u33bhxQ0uXLlWJEiXsPt7NmzclScHBwTKbzXbvH7kf4RMAAAAAAPlI9erVFRYWpi+++MK67YsvvlCJEiVUrVo167akpCRNmTJFpUqVkqenpyIjI7V8+XJJ0tGjR/Xwww9LkgoWLGgzY+qhhx5SVFSUhg4dqsKFC6t58+aSUl52d/LkSXXu3FmFChWSt7e3atasqR07djj47OEMBZxdAAAAAAAAyFm9e/dWdHS0unbtKklasGCBevXqpc2bN1uPmTJlihYvXqz58+crIiJCP/zwg5566ikVKVJEDz74oD7//HO1b99ef/75p/z8/OTp6Wltu2jRIg0cOFBbtmxJdfy4uDg1atRIxYsX19dff63g4GD98ssvSkpKcuh5wzkInwAAAAAAyGeeeuopjRo1SseOHZMkbdmyRZ988ok1fLJYLJo8ebI2bNigunXrSpJKly6tn376Se+8844aNWqkQoUKSZKCgoIUEBBg039ERISmTp161/GXLl2qCxcuaOfOndZ+7rvvPjufJXILp15298MPP6hNmzYqVqxYhle937x5s6pXry6z2az77rtPCxcudHidAAAAAADcS4oUKaLWrVtr4cKFio6OVuvWrVW4cGHr/oMHDyohIUFNmzaVj4+P9fHhhx/q0KFD6fZfo0aNNPfv3r1b1apVswZPuLc5deZTfHy8IiMj1bt3bz3++OPpHn/kyBG1bt1aAwYM0JIlS7Rx40b17dtXISEh1mtIAQAAAABA+nr37q2oqChJ0ty5c232xcXFSZJWrlyp4sWL2+zLyKLh3t7eae7/5yV6uPc5NXxq2bJlpm6zOH/+fJUqVUozZsyQJFWoUEE//fSTZs2aRfgEAAAAAEAmtGjRQjdv3pTJZErxmbpixYoym806fvy4GjVqlGp7d3d3SVJiYmKmx65SpYref/99/f3338x+ygfy1JpP27ZtU5MmTWy2NW/eXEOHDr1rG4vFIovFYn1+9epVR5WXayUkJCgmJibb/URERMjLyyvVfSdPnlRsbGyq+27cuKETJ05ke/ywsDB5eHikuu/s2bO6du3aXdtaLBadO3cu2zUULVr0ril/mTJlbO4MgbwlP7xPpDu3uU3+JcEZNQQGBio0NDTbY8A5cuJ9khPj54Yasjo+cj9nf4/wPgGQGa6urtq/f7/13//k6+urESNGaNiwYUpKStKDDz6oK1euaMuWLfLz81OPHj1UsmRJmUwmffvtt2rVqpU8PT3l4+OTobE7d+6syZMnq127dpoyZYpCQkL066+/qlixYtY1pnDvyFPh09mzZ1W0aFGbbUWLFtXVq1d1/fr1VKftTZkyRePHj8+pEnOlmJiYFKFdVmzYsEGRkZEptp88eVJ16tSVxXIj22PkZa6urvr555/5YJ1Fzv5FlfdJznB3N2vHju28T/IoR79PpLRD2piYGA0cODDb40vSvHnzFBERkeq+tEJae9WQ1viEtHlbfnmfpPV9mhOvAQD78fPzu+u+iRMnqkiRIpoyZYoOHz6sgIAAVa9eXS+99JIkqXjx4ho/frxefPFF9erVS927d8/wuszu7u5at26dnnvuObVq1Uq3b99WxYoVU1z+h3uDyTAMw9lFSJLJZNKXX36pdu3a3fWYsmXLqlevXho1apR126pVq9S6dWslJCSkGj6lNvMpLCxMV65cSfNNZk/O/lCd3vjJv6Sk9QtGWuPv2bPnzi8YgVUlt1RSbiNRun0903WnUMBTMrmm3H79vHTlLxVJrCx3I/WUPUmJuq3s11BAnnJRyhpumuJ0wfV3fknKBuv3UTZl9Wtwz79PJOnG39KlfZIp+8NkVVohrbN/ViJ9jn6f5J6Q1iTJeb8emc0e2r59GwFUFuX2mazHjx/XlClTNGrUKJUoUeKux91tFum5c+fUq1dv3bxpSaVVznF3N2vhwmgFBQWl2Ofo10C690Laq1evyt/fP0c/o9jLjRs3dOTIEZUqVSrN2dcA7k0Z/RmQp2Y+BQcHp7h06ty5c/Lz87vrYmVmszlDi6E5krP/+uPl5ZWhdhEREdkLTtx8JHf/1Pc58ktw685CeL5GMXnKOdcKXzf+1gX9ftf9fKhOX0REhDZs2HDX/Zn5UJsV9/z7JJlJd/1lP/nDQHal9WGiTJky/KU8D3P0+yQ2NvZO8HS3kDYnZOAPGo500xSnC5bfFRsbe099sM5JOTGTtW6dOrphyV74k+2ft858n9z4Wzcv7VOXLl2y1U12XgNCWgDIW/JU+FS3bl2tWrXKZtv69etz/fWgzv5QDefjQ3X6ciz8gRo3bpzqa5iQkKDGjRtnu/+shqT8rExfvgmy0wppHe3//6BxwfXuf1BA7ubonyWxsbG6YbGo8i2TvI2cn0p60ZSkg25y/vvEJBVPrCuzkfOzdCymqzpl2UZICwB5iFPDp7i4OB08eND6/MiRI9q9e7cKFSqkEiVKaNSoUTp16pQ+/PBDSdKAAQM0Z84cPf/88+rdu7e+++47ffrpp1q5cqWzTiFDcuJDdVrTv9OT/EEmqx9o7LWo5b2MD9XICzL6s8rZ4+fnAJIgO+cUv11XZjnhQ7Wu6lSBbTk+7r0kp36WeBsm+TkhfIo3OfeyUBvOKiOXnD4AIOOcGj79/PPPevjhh63Phw8fLknq0aOHFi5cqDNnzuj48ePW/aVKldLKlSs1bNgwvfHGGwoNDdX777+f4paQ+Y29pn/ba3FKpMSHagD2QJCdg5z1+d6Ja7IBmUVQCgDIKKeGTw899JDSWu88tVXyH3roIf36668OrCrvyTXTvwEADkWQnQNcCkiGdMrVeR+qzWYPBQYGOm18IKOYIQgAyKg8teYT0pbfp39bTFedVobFdNU5AyNTuDwVQLpcPSRT2reYT0tGZ5+l5V67ixfuXWb5Oe1mLwCAvIXwCXlfLvgrtcRfqnM7Lk8FkBnZnT3G7DMAAID/IXxC3pfNv1JL/KU6P+DyVAD3gnxzx0MAAHBPIXyC/fz/7amdNa49/srMX6rvffn98lQAeRt3PAQAAHkR4RPsJ3a3sysAAOCexh0PAeQV2VlrMyty+iqEnj176vLly1qxYoWkOzfGqlq1qmbPnp1jNdjbvXAOhmHo6aef1vLly3Xp0iX9+uuvqlq1qrPLggifYE+BVSU3n5wf91YcwRcAIF/gjocA8oKTJ0+qTp06smRzrc3MMJvN2r59e55YBiM8PFxDhw7V0KFDnV1KrrN3716NGTNGu3bt0rFjxzRr1qxMvU5r1qzRwoULtXnzZpUuXVqFCxfWDz/8oGnTpmnXrl06c+aMvvzyS7Vr185h54DUET7Bftx8JHd/Z1cBAAAAwIliY2NlsVh0I8RNSWYXh4/nYkmSzlgUGxubJ8In3F1CQoJKly6tJ554QsOGDct0+0OHDikkJET16tWzbouPj1dkZKR69+6txx9/3J7lIhMc/5MAAADgHpGQkKA9e/bc9ZG8GHhMTEyaxyUkJDj5TIB7V3rv04w+eJ9mX5LZRUkeOfDIYsCVlJSkqVOn6r777pPZbFaJEiX06quvSpJOnDihjh07KiAgQIUKFVLbtm119OjRbL8mDz30kI4dO6Zhw4bJZDLJZDIpPj5efn5+Wr58uc2xK1askLe3t65du6ajR4/KZDLpk08+Ub169eTh4aH7779f33//vU2bP/74Qy1btpSPj4+KFi2qbt266eLFi5l6TZ5//nkVKlRIwcHBGjdunM3+48ePq23btvLx8ZGfn586duyoc+fOWfePGzdOVatW1TvvvKOwsDB5eXmpY8eOunLlSobGf+CBBzRt2jQ9+eSTMpvNGa5bunMp5DPPPKPjx4/LZDIpPDxcktSyZUtNmjRJjz32WKb6g30x8wkAACCDMrrg98CBA9Pcz4LfgOOwMD8yatSoUXrvvfc0a9YsPfjggzpz5owOHDigW7duqXnz5qpbt65+/PFHFShQQJMmTVKLFi3022+/yd3dPctjfvHFF4qMjFT//v3Vr18/SZK3t7eefPJJRUdHq0OHDtZjk5/7+vpa188aOXKkZs+erYoVK2rmzJlq06aNjhw5osDAQF2+fFmPPPKI+vbtq1mzZun69et64YUX1LFjR3333XcZqm/RokUaPny4duzYoW3btqlnz56qX7++mjZtqqSkJGvw9P333+v27dsaPHiwOnXqpM2bN1v7OHjwoD799FN98803unr1qvr06aNBgwZpyZIlWX7dMuKNN95QmTJl9O6772rnzp1ydXV16HjIHMInAACADEpvwe/M9APAMViYHxlx7do1vfHGG5ozZ4569OghSSpTpowefPBBLV68WElJSXr//fdlMt25S3J0dLQCAgK0efNmNWvWLMvjFipUSK6urvL19VVwcLB1e9++fVWvXj2dOXNGISEhOn/+vFatWpXiezkqKkrt27eXJM2bN09r1qzRBx98oOeff15z5sxRtWrVNHnyZOvxCxYsUFhYmP766y+VLVs23fqqVKmisWPHSrrzHpgzZ442btyopk2bauPGjfr999915MgRhYWFSZI+/PBDVapUSTt37tQDDzwgSbpx44Y+/PBDFS9eXJL01ltvqXXr1poxY4bNOdubv7+/fH195erq6tBxkDWETwAAABmU0QW/gfzAYroqGU4aNw0szI+M2L9/vywWixo3bpxi3549e3Tw4EH5+vrabL9x44YOHTrkkHpq1aqlSpUqadGiRXrxxRe1ePFilSxZUg0bNrQ5rm7dutZ/FyhQQDVr1tT+/futdW/atEk+PilvAnXo0KEMh0//lByESXdes7CwMGvwJEkVK1ZUQECA9u/fbw2fSpQoYQ2ekmtOSkrSn3/+SSiUjxE+IV9ISEiwrsORmn+u0ZGWiIgIeXl52bU2AACAPMWlgGRIp1y3Oa0Es9lDgYGBThsfeZ+np+dd98XFxalGjRqpXiZWpEgRh9XUt29fzZ07Vy+++KKio6PVq1cv68yrjIiLi1ObNm30+uuvp9gXEhKSoT7c3NxsnptMJiUlJWW4BuBuCJ+QL7BGBwAAgJ24ekgmpXvZ2t1k9LK3tAQGBnJXM2RLRESEPD09tXHjRvXt29dmX/Xq1bVs2TIFBQXJz8/P7mO7u7srMTExxfannnpKzz//vN58803t27fPejngP23fvt06G+r27dvatWuXoqKirHV//vnnCg8PV4EC9v+oX6FCBZ04cUInTpywzn7at2+fLl++rIoVK1qPO378uE6fPq1ixYpZa3ZxcVG5cuXsXhPyDsIn5Aus0QEAAGBf2b1sjcve4EweHh564YUX9Pzzz8vd3V3169fXhQsXtHfvXnXt2lXTpk1T27ZtNWHCBIWGhurYsWP64osv9Pzzz2c7+AwPD9cPP/xgvaNb4cKFJUkFCxbU448/rpEjR6pZs2apjjN37lxFRESoQoUKmjVrli5duqTevXtLkgYPHqz33ntPnTt3tt6x7uDBg/rkk0/0/vvvZ3sB7iZNmqhy5crq2rWrZs+erdu3b2vQoEFq1KiRatasaT3Ow8NDPXr00PTp03X16lUNGTJEHTt2zNAldzdv3tS+ffus/z516pR2794tHx8f3XfffVmqOy4uTgcPHrQ+P3LkiHbv3q1ChQqpRIkSWeoTmUf4hHyBNToAAACAnOViyZnLtbI6ziuvvKICBQpozJgxOn36tEJCQjRgwAB5eXnphx9+0AsvvKDHH39c165dU/HixdW4cWO7zISaMGGCnn76aZUpU0YWi0WG8b/F0/r06aOlS5daA6V/e+211/Taa69p9+7duu+++/T1119bw6tixYppy5YteuGFF9SsWTNZLBaVLFlSLVq0kIuLS7brNplM+uqrr/TMM8+oYcOGcnFxUYsWLfTWW2/ZHHfffffp8ccfV6tWrfT333/rP//5j95+++0MjXH69GlVq1bN+nz69OmaPn26GjVqZHNHvcz4+eef9fDDD1ufDx8+XJLUo0cPLVy4MEt9IvMInwAAAAAAdhMYGCiz2SydseTYmGazOdPrgLm4uGj06NEaPXp0in3BwcFatGjRXdv+O7TITDBSp04d7dmzJ9V9p06dUmBgoNq2bZvq/goVKmjHjh137TsiIkJffPFFhmv5p9TOYcWKFTbPS5Qooa+++irdvgYOHJjukiapCQ8PtwnjMmvo0KEaOnSozbaHHnooW33CPgifAAAAAAB2Exoaqu3btys2NjbHxszr64AlJCTozJkzeu211/T000/L3d3d2SUBdkX4BAAAAACwq9DQ0DwdBmXVjz/+qJYtW951f1xcXKrbp06dqldffVUNGzbUqFGj7F7X8ePHbRYF/7d9+/blyPpHPj4+d923evVqNWjQ4K77c8s5IGsInwAAAAAAsIOaNWtq9+7dmW43btw4jRs37q77s3s5WrFixdKsK/nOdNmV3nmkVUPx4sXT7DunzgGOQfgEAAAAAIAdeHp6ZvmubI5UoECBXFFXdmrILeeArMn+kvcAAAAAAADAXRA+AQAAAAAAwGG47A4AAACA3SQkJCgmJuau+5P3pXWMdOeW8V5eXnatDQDgHIRPAAAAAOwmJiZGTZo0Sfe4gQMHprl/w4YNioyMtFdZAAAnInwCAAAAYDcRERHasGGDXfoBANwbCJ8AAAAA2I2XlxczlqCTJ08qNjY2x8YLDAxUaGhotvt56KGHVLVqVc2ePVvh4eEaOnSohg4dmv0Cnejo0aMqVaqUfv31V1WtWtXZ5WTZli1bNGDAAB04cECtW7fWihUrnF0SMoHwCQAAAABgNydPnlTdOnV0w2LJsTE9zGZt277dLgFUsp07d8rb29tu/eV3Tz/9tDZs2KDTp0/Lx8dH9erV0+uvv67y5ctnqP3w4cNVtWpVrV69Wj4+PpKkIUOGaMuWLfrjjz9UoUIF7d6924FngOwgfAIAAAAA2E1sbKxuWCyqfMskb8Pk8PHiTYZ+l0WxsbF2DZ+KFClit74g1ahRQ127dlWJEiX0999/a9y4cWrWrJmOHDkiV1fXdNsfOnRIAwYMSPE17t27t3bs2KHffvvNUaXDDlycXQAAAAAA4N7jbZjklwOPrAZc8fHx6t69u3x8fBQSEqIZM2bY7A8PD9fs2bMlSYZhaNy4cSpRooTMZrOKFSumIUOGWI+1WCwaMWKEihcvLm9vb9WuXVubN2+27o+NjVXnzp1VvHhxeXl5qXLlyvr4449txlu+fLkqV64sT09PBQYGqkmTJoqPj7fuf//991WhQgV5eHiofPnyevvttzN1vocPH9bDDz9svTR227ZtNvs///xzVapUSWazWeHh4am+HhMnTlTnzp3l7e2t4sWLa+7cuRkev3///mrYsKHCw8NVvXp1TZo0SSdOnNDRo0fTbHf06FGZTCbFxsaqd+/eMplMWrhwoSTpzTff1ODBg1W6dOkM1wHnIHwCAAAAAOQ7I0eO1Pfff6+vvvpK69at0+bNm/XLL7+keuznn3+uWbNm6Z133lFMTIxWrFihypUrW/dHRUVp27Zt+uSTT/Tbb7/piSeeUIsWLRQTEyNJunHjhmrUqKGVK1fqjz/+UP/+/dWtWzf997//lSSdOXNGnTt3Vu/evbV//35t3rxZjz/+uAzDkCQtWbJEY8aM0auvvqr9+/dr8uTJeuWVV7Ro0aIMn+/o0aM1YsQI7d69W2XLllXnzp11+/ZtSdKuXbvUsWNHPfnkk/r99981btw4vfLKK9aQJ9m0adMUGRmpX3/9VS+++KKeffZZrV+/PsM1JIuPj1d0dLRKlSqlsLCwNI8NCwvTmTNn5Ofnp9mzZ+vMmTPq1KlTpseEc3HZHQAAAJDX3IrLn2MDdhIXF6cPPvhAixcvVuPGjSVJixYtuutle8ePH1dwcLCaNGkiNzc3lShRQrVq1bLui46O1vHjx1WsWDFJ0ogRI7RmzRpFR0dr8uTJKl68uEaMGGHt75lnntHatWv16aefqlatWjpz5oxu376txx9/XCVLlpQkm3Br7NixmjFjhh5//HFJUqlSpbRv3z6988476tGjR4bOecSIEWrdurUkafz48apUqZIOHjyo8uXLa+bMmWrcuLFeeeUVSVLZsmW1b98+TZs2TT179rT2Ub9+fb344ovWY7Zs2aJZs2apadOmGarh7bff1vPPP6/4+HiVK1dO69evl7u7e5ptXF1dFRwcLJPJJH9/fwUHB2doLOQuhE8AAABAXhO729kVAHnaoUOHdPPmTdWuXdu6rVChQipXrlyqxz/xxBOaPXu2SpcurRYtWqhVq1Zq06aNChQooN9//12JiYkqW7asTRuLxaLAwEBJUmJioiZPnqxPP/1Up06d0s2bN2WxWOTl5SVJioyMVOPGjVW5cmU1b95czZo1U4cOHVSwYEHFx8fr0KFD6tOnj/r162ft//bt2/L398/wOVepUsX675CQEEnS+fPnVb58ee3fv19t27a1Ob5+/fqaPXu2EhMTrWsy1a1b1+aYunXrWi9NzIiuXbuqadOmOnPmjKZPn66OHTtqy5Yt8vDwyHAfyJsInwAAAIC8JrCq5ObjnLFvxRF+Id8JCwvTn3/+qQ0bNmj9+vUaNGiQpk2bpu+//15xcXFydXXVrl27UiycnXxXtmnTpumNN97Q7NmzVblyZXl7e2vo0KG6efOmpDuze9avX6+tW7dq3bp1euuttzR69Gjt2LHDGlC99957NmFZcruMcnNzs/7bZLqzTlZSUlLmX4xs8Pf3l7+/vyIiIlSnTh0VLFhQX375pTp37pyjdSDnET4BAAAAeY2bj+Se8RkPAGyVKVNGbm5u2rFjh0qUKCFJunTpkv766y81atQo1Taenp5q06aN2rRpo8GDB6t8+fL6/fffVa1aNSUmJur8+fNq0KBBqm23bNmitm3b6qmnnpJ0J/T566+/VLFiResxJpNJ9evXV/369TVmzBiVLFlSX375pYYPH65ixYrp8OHD6tq1q51fiTsqVKigLVu2pKi5bNmyNgHX9u3bbY7Zvn27KlSokKUxDcOQYRiyWCxZao+8hfAJAABYnTx5UrGxsVlqm7yoavJ/s9oeAABH8/HxUZ8+fTRy5EgFBgYqKChIo0ePlotL6vfkWrhwoRITE1W7dm15eXlp8eLF8vT0VMmSJRUYGKiuXbuqe/fumjFjhqpVq6YLFy5o48aNqlKlilq3bq2IiAgtX75cW7duVcGCBTVz5kydO3fOGj7t2LFDGzduVLNmzRQUFKQdO3bowoUL1mBn/PjxGjJkiPz9/dWiRQtZLBb9/PPPunTpkoYPH57t1+O5557TAw88oIkTJ6pTp07atm2b5syZk+KOelu2bNHUqVPVrl07rV+/Xp999plWrlyZbv+HDx/WsmXL1KxZMxUpUkQnT57Ua6+9Jk9PT7Vq1SrLdR88eFBxcXE6e/asrl+/rt27d0uSKlasmO5aUshZhE8AkJNYIBa52MmTJ1W3Th3dyOZfIAcOHGinigAAeVm8ycjV40ybNk1xcXFq06aNfH199dxzz+nKlSupHhsQEKDXXntNw4cPV2JioipXrqxvvvnGuqZTdHS0Jk2apOeee06nTp1S4cKFVadOHf3nP/+RJL388ss6fPiwmjdvLi8vL/Xv31/t2rWzjufn56cffvhBs2fP1tWrV1WyZEnNmDFDLVu2lCT17dtXXl5emjZtmkaOHClvb29VrlxZQ4cOzdK5/1v16tX16aefasyYMZo4caJCQkI0YcIEm8XGpTsh1c8//6zx48fLz89PM2fOVPPmzdPt38PDQz/++KNmz56tS5cuqWjRomrYsKG2bt2qoKCgLNfdt29fff/999bn1apVkyQdOXJE4eHhWe4X9kf4BAA5iTUykIvFxsbqhsWiyrdM8jZMOT7+RVOSDrqlfxwAIHcLDAyUh9ms32WRlDMBlIfZbA2CMsrHx0cfffSRPvroI+u2kSNHWv999OhR67/btWundu3a3bUvNzc3jR8/XuPHj091f6FChbRixYq7tq9QoYLWrFmTZr1dunRRly5d0jwmNeHh4TIM269DQEBAim3t27dX+/bt0+zLz89Pn376aaZrKFasmFatWpXpdv90+fLlFNs2b96crT6RcwifACAnsUAs8gBvwyQ/J4RP8SaTcupDCgDAcUJDQ7Vt+/YsX8adFYGBgQoNDc2x8QBkDuETAOQkFogFAAD5QGhoKGFQDpk8ebImT56c6r4GDRpo9erVDq9hyZIlevrpp1PdV7JkSe3duzfN9gMGDNDixYtT3ffUU09p/vz52a4RzkX4BAAAAABAHjVgwAB17Ngx1X2enp52G+eflyH+26OPPqratWunus/NLf1r6idMmKARI0akus/Pzy9D9SF3I3wCAAAAACCPKlSokAoVKuTUGnx9feXr65vl9kFBQdlaeBy5X+r3kQQAAAAAAADsgPAJAAAAAAAADkP4BAAAAAAAAIchfAIAAAAAAIDDED4BAAAAAADAYbjbHQAAAADArk6ePKnY2NgcGy8wMFChoaHZ6mPz5s16+OGHdenSJQUEBNinMAcJDw/X0KFDNXToUGeXkmVnz55Vt27dtHXrVrm5ueny5cvOLgkORPgEAAAAALCbkydPqk6durJYbuTYmGazh7Zv35apAOqhhx5S1apVNXv2bElSvXr1dObMGfn7+zuoynvLvHnzNG/ePB09elSSVKlSJY0ZM0YtW7bMUPtZs2bpzJkz2r17t/U1f/fdd7V06VL98ssvunbtWp4IApExhE8AAAAA8pTszKqJiYmx+W9W2GOWzb0sNjb2TvAUWFVy83H8gLfiZIndrdjY2Gx9Xdzd3RUcHGzHwu5toaGheu211xQRESHDMLRo0SK1bdtWv/76qypVqpRu+0OHDqlGjRqKiIiwbktISFCLFi3UokULjRo1ypHlI4cRPgF2wi9BAAAAjnfy5EnVq1tH129YstXPwIEDs9zW08Osrdu287tXetx8JPfcOYuoZ8+e+v777/X999/rjTfekCRFR0erV69e1tk2x44dU1RUlH766SfdvHlT4eHhmjZtmlq1aqVLly4pKipK69atU1xcnEJDQ/XSSy+pV69eqV6+t3v3blWrVk1HjhxReHi4JOmnn37SqFGj9PPPP6tw4cJ67LHHNGXKFHl7e2foHBISEtS7d2999tlnKliwoF5++WX179/fuv/333/Xs88+q23btsnLy0vt27fXzJkz5ePjY30NLl++rGrVqmnOnDmyWCzq0qWL3nzzTbm7u6c7fps2bWyev/rqq5o3b562b9+ebvgUHh6uY8eOSZI+/PBD9ejRQwsXLrReRrh58+YMvQbIOwifADvglyAAAICcERsbq+s3LJrQyaxSQTl//6Qj55M0Zpkl27Ns4FxvvPGG/vrrL91///2aMGGCJGnv3r02xwwePFg3b97UDz/8IG9vb+3bt88a3Lzyyivat2+fVq9ercKFC+vgwYO6fv16hsc/dOiQWrRooUmTJmnBggW6cOGCoqKiFBUVpejo6Az1MWPGDE2cOFEvvfSSli9froEDB6pRo0YqV66c4uPj1bx5c9WtW1c7d+7U+fPn1bdvX0VFRWnhwoXWPjZu3CgPDw9t3rxZR48eVa9evRQYGKhXX301w+ciSYmJifrss88UHx+vunXrpnv8zp071b17d/n5+emNN96Qp6dnpsZD3kP4BNgBvwQBAADkrFJBLipf3NXZZSCP8vf3l7u7u7y8vKyX2h04cMDmmOPHj6t9+/aqXLmyJKl06dI2+6pVq6aaNWtKknU2U0ZNmTJFXbt2tc70iYiI0JtvvqlGjRpp3rx58vDwSLePVq1aadCgQZKkF154QbNmzdKmTZtUrlw5LV26VDdu3NCHH35onUk1Z84ctWnTRq+//rqKFi0q6c6lhgsWLJCXl5cqVaqkCRMmaOTIkZo4caJcXNL/XPP777+rbt26unHjhnx8fPTll1+qYsWK6bYrUqSIzGazPD09udQxnyB8AuyIX4IAAACAe8OQIUM0cOBArVu3Tk2aNFH79u1VpUoVSXeuWGjfvr1++eUXNWvWTO3atVO9evUy3PeePXv022+/acmSJdZthmEoKSlJR44cUYUKFdLtI7kWSTKZTAoODtb58+clSfv371dkZKTNJXz169dXUlKS/vzzT2v4FBkZKS8vL+sxdevWVVxcnE6cOKGSJUumW0O5cuW0e/duXblyRcuXL1ePHj30/fffZyiAQv6S81M0AAAAAADI5fr27avDhw+rW7du+v3331WzZk299dZbkqSWLVvq2LFjGjZsmE6fPq3GjRtrxIgRkmSdMWQYhrWvW7du2fQdFxenp59+Wrt377Y+9uzZo5iYGJUpUyZD9bm5udk8N5lMSkpKyvL5ZoW7u7vuu+8+1ahRQ1OmTFFkZKR1DS3gn5j5BAAAcpdbcflzbABAjnJ3d1diYmKax4SFhWnAgAEaMGCARo0apffee0/PPPOMpDuXjvXo0UM9evRQgwYNNHLkSE2fPl1FihSRJJ05c0YFCxaUdGfB8X+qXr269u3bp/vuu8/+JyapQoUKWrhwoeLj462zn7Zs2SIXFxeVK1fOetyePXt0/fp165pL27dvl4+Pj8LCwrI0blJSkiyW7K2Di3sT4RMAAMhdYnc7uwIAQD4QHh6uHTt26OjRo/Lx8Ukxa2jo0KFq2bKlypYtq0uXLmnTpk3Wy+HGjBmjGjVqqFKlSrJYLPr222+t++677z6FhYVp3LhxevXVV/XXX39pxowZNn2/8MILqlOnjqKiotS3b1/rgubr16/XnDlzsn1uXbt21dixY9WjRw+NGzdOFy5c0DPPPKNu3bpZL7mTpJs3b6pPnz56+eWXdfToUY0dO1ZRUVEZWu9p1KhRatmypUqUKKFr165p6dKl2rx5s9auXZvlus+ePauzZ8/q4MGDku6sKeXr66sSJUqoUKFCWe4Xzkf4BAAAcpfAqnduz+0Mt+IIvwDAXnJqNmkWxxkxYoR69OihihUr6vr16ynuMpeYmKjBgwfr5MmT8vPzU4sWLTRr1ixJd2ZNjRo1SkePHpWnp6caNGigTz75RNKdy+E+/vhjDRw4UFWqVNEDDzygSZMm6YknnrD2XaVKFX3//fcaPXq0GjRoIMMwVKZMGXXq1CmLL4ItLy8vrV27Vs8++6weeOABeXl5qX379po5c6bNcY0bN1ZERIQaNmwoi8Wizp07a9y4cRka4/z58+revbvOnDkjf39/ValSRWvXrlXTpk2zXPf8+fM1fvx46/OGDRtKkqKjo9WzZ88s9wvnI3wCAAC5i5uP5O7v7CoAAFkUGBgos9lDlhwM881mDwUGBmaqTdmyZbVt2zabbf8MOJLXd0rNyy+/rJdffvmu++vXr6/ffvvNZts/14CSpAceeEDr1q3LRMX/c/To0RTb/n1pX+XKlfXdd9+l29f48eNtAp+M+uCDDzLd5p9WrFiRYtu4ceMyHH4hbyF8AgAAAADYTWhoqLZv36bY2NgcGzMwMFChoaE5Nh6AzCF8AgAAAADYVWhoKGFQFv34449q2bLlXffHxTn+csbjx4+rYsWKd92/b98+lShR4q77lyxZoqeffjrVfSVLltTevXuzXSPyFsInAAAAAAByiZo1a6a4hM4RFi5ceNd9xYoVS7OGYsWKpdn3o48+qtq1a6e6z83NLSPl4R5D+AQAAAAAQC7h6emp++67z6k1FChQIFs1+Pr6ytfX144VIa9L//6JAAAAAAAAQBYRPgEAAAAAAMBhCJ8AAAAAAADgMIRPAAAAAAAAcBjCJwAAAAAAADgMd7sDAAAAANjVyZMnFRsbm2PjBQYGKjQ0NNv9PPTQQ6patapmz56d/aIyoGfPnrp8+bJWrFiRI+PBPsaNG6cVK1Zo9+7dzi4lW8aNG6d58+bp/Pnz+vLLL9WuXTuHjUX4BAAAAACwm5MnT6pOnbqyWG7k2Jhms4e2b99mlwAKWWfvMM1kMjk8FMmr/vzzTw0YMED79u3TlStXVKxYMXXp0kVjx46Vm5tbuu3379+v8ePH68svv1SdOnVUsGBB7d27V2PGjNGuXbt07NgxzZo1S0OHDrVLvYRPAAAAAAC7iY2NlcVyQ8UT68ps+Dl8PIvpqk5Ztik2NpbwCfmGm5ubunfvrurVqysgIEB79uxRv379lJSUpMmTJ6fb/tChQ5Kktm3bymQySZISEhJUunRpPfHEExo2bJhd62XNJwAAAACA3ZkNP3mqkMMfWQ244uPj1b17d/n4+CgkJEQzZsyw2W+xWDRixAgVL15c3t7eql27tjZv3ixJunr1qjw9PbV69WqbNl9++aV8fX2VkJAgSTpx4oQ6duyogIAAFSpUSG3bttXRo0fvWpPFYtGQIUMUFBQkDw8PPfjgg9q5c6d1/+bNm2UymbRy5UpVqVJFHh4eqlOnjv744w/rMQsXLlRAQIC+/fZblStXTl5eXurQoYMSEhK0aNEihYeHq2DBghoyZIgSExMzdL7/7Hft2rWqUKGCfHx81KJFC505c0bSnUu4Fi1apK+++komk0kmk8mmfWpu3rypqKgohYSEyMPDQyVLltSUKVMkSeHh4ZKkxx57TCaTyfr80KFDatu2rYoWLSofHx898MAD2rBhg02/Z86cUevWreXp6alSpUpp6dKlCg8Pt7mc8vLly+rbt6+KFCkiPz8/PfLII9qzZ0+a9f7bRx99pPDwcPn7++vJJ5/UtWvXbF7P7H4t01K6dGn16tVLkZGRKlmypB599FF17dpVP/74Y7ptx40bpzZt2kiSXFxcrOHTAw88oGnTpunJJ5+U2WzOzEuRLmY+AQAA5CLZWSclJibG5r9ZYa91UwAgtxs5cqS+//57ffXVVwoKCtJLL72kX375RVWrVpUkRUVFad++ffrkk09UrFgxffnll2rRooV+//13RURE6D//+Y+WLl2qli1bWvtcsmSJ2rVrJy8vL926dUvNmzdX3bp19eOPP6pAgQKaNGmSWrRood9++03u7u4panr++ef1+eefa9GiRSpZsqSmTp2q5s2b6+DBgypUqJBN7W+88YaCg4P10ksvqU2bNvrrr7+sl1slJCTozTff1CeffKJr167p8ccf12OPPaaAgACtWrVKhw8fVvv27VW/fn116tQpQ+eb3O/06dP10UcfycXFRU899ZRGjBihJUuWaMSIEdq/f7+uXr2q6OhoSbKpOTVvvvmmvv76a3366acqUaKETpw4oRMnTkiSdu7cqaCgIEVHR6tFixZydXWVJMXFxalVq1Z69dVXZTab9eGHH6pNmzb6888/VaJECUlS9+7ddfHiRW3evFlubm4aPny4zp8/bzP2E088YQ0Q/f399c4776hx48b666+/0q1buhOCrVixQt9++60uXbqkjh076rXXXtOrr75q169lRh08eFBr1qzR448/nu6xI0aMUHh4uHr16mUNDx2N8AkAACCXOHnypOrVraPrNyzZ6mfgwIFZbuvpYdbWbdsJoADc0+Li4vTBBx9o8eLFaty4sSRp0aJF1p99x48fV3R0tI4fP65ixYpJuvOBfc2aNYqOjtbkyZPVtWtXdevWTQkJCfLy8tLVq1e1cuVKffnll5KkZcuWKSkpSe+//751Zkl0dLQCAgK0efNmNWvWzKam+Ph4zZs3TwsXLrQGWu+9957Wr1+vDz74QCNHjrQeO3bsWDVt2tSm7i+//FIdO3aUJN26dUvz5s1TmTJlJEkdOnTQRx99pHPnzsnHx0cVK1bUww8/rE2bNqlTp04ZOt/kfufPn2/tNyoqShMmTJAk+fj4yNPTUxaLRcHBwRn6Ohw/flwRERF68MEHZTKZVLJkSeu+IkWKSJICAgJs+ouMjFRkZKT1+cSJE/Xll1/q66+/VlRUlA4cOKANGzZo586dqlmzpiTp/ffftwZokvTTTz/pv//9r86fP2+d4TN9+nStWLFCy5cvV//+/dOtPSkpSQsXLpSvr68kqVu3btq4caNeffVVu34t01OvXj398ssvslgs6t+/v/XrkRYfHx8FBARIUoa/VtlF+AQAAJBLxMbG6voNiyZ0MqtUUM6vjnDkfJLGLLOwbgqAe96hQ4d08+ZN1a5d27qtUKFCKleunCTp999/V2JiosqWLWvTzmKxKDAwUJLUqlUrubm56euvv9aTTz6pzz//XH5+fmrSpIkkac+ePTp48KA1nEh248YN63o7/67p1q1bql+/vnWbm5ubatWqpf3799scW7du3RR1//MYLy8va0AkSUWLFlV4eLh8fHxstiXPBsrI+abWb0hISIoZRZnRs2dPNW3aVOXKlVOLFi30n//8J0Uo929xcXEaN26cVq5cqTNnzuj27du6fv26jh8/LunOQtwFChRQ9erVrW3uu+8+FSxY0Pp8z549iouLszk3Sbp+/XqqX5vUhIeH23xt//la2PNrmZ5ly5bp2rVr2rNnj0aOHKnp06fr+eefz3D7nEL4BAAAkMuUCnJR+eKuzi4DAPKtuLg4ubq6ateuXdbLvZIlBzju7u7q0KGDli5dqieffFJLly5Vp06dVKBAAWsfNWrU0JIlS1L0nzyrx1H+fcmWyWRKdVtSUpKkjJ3v3fo1DCPLdVavXl1HjhzR6tWrtWHDBnXs2FFNmjTR8uXL79pmxIgRWr9+vaZPn6777rtPnp6e6tChg27evJnhcePi4hQSEpLqmlTJM4LSk9brmZPCwsIkSRUrVlRiYqL69++v5557LsXX0dkInwAAAAAA+UqZMmXk5uamHTt2WNcJunTpkv766y81atRI1apVU2Jios6fP68GDRrctZ+uXbuqadOm2rt3r7777jtNmjTJuq969epatmyZgoKC5OeX/qLoZcqUkbu7u7Zs2WK9/OzWrVvauXNnitvdb9++PUXdFSpUyOzLYJXR802Pu7u7zSLmGeHn56dOnTqpU6dO6tChg1q0aKG///5bhQoVkpubW4r+tmzZop49e+qxxx6TdCdI+uci7uXKldPt27f166+/qkaNGpLurId06dIl6zHVq1fX2bNnVaBAAetC5vbkrK9lUlKSbt26paSkJMKne1F2FgaVsr84aHYWFQUAAADyoqPnc36GgTPHhX35+PioT58+GjlypAIDAxUUFKTRo0fLxeXOJc9ly5ZV165d1b17d82YMUPVqlXThQsXtHHjRlWpUkWtW7eWJDVs2FDBwcHq2rWrSpUqZXMZX9euXTVt2jS1bdtWEyZMUGhoqI4dO6YvvvhCzz//fIrLm729vTVw4ECNHDlShQoVUokSJTR16lQlJCSoT58+NsdOmDBBgYGBKlq0qEaPHq3ChQurXbt2WX49Mnq+6QkPD9fatWv1559/KjAwUP7+/mkunD1z5kyFhISoWrVqcnFx0Weffabg4GDr7KPw8HBt3LhR9evXl9lsVsGCBRUREaEvvvhCbdq0kclk0iuvvGIz46h8+fJq0qSJ+vfvr3nz5snNzU3PPfecPD09rWtvNWnSRHXr1lW7du00depUlS1bVqdPn9bKlSv12GOPWdeKyqqc+FouWbJEbm5uqly5ssxms37++WeNGjVKnTp1yvRi5clu3rypffv2Wf996tQp7d69Wz4+Prrvvvuy1GcywqdsOnnypOrUqSOLJXsLg0rZWxwUAAAAyE9eWZb937/hWBbTVSnrV2RlbpwsmDZtmuLi4tSmTRv5+vrqueee05UrV6z7o6OjNWnSJD333HM6deqUChcurDp16ug///mP9RiTyaTOnTtr6tSpGjNmjE3/Xl5e+uGHH/TCCy/o8ccf17Vr11S8eHE1btz4rjOhXnvtNSUlJalbt266du2aatasqbVr19qsV5R83LPPPquYmBhVrVpV33zzTap3z8uMjJxvevr166fNmzerZs2aiouL06ZNm/TQQw/d9XhfX19NnTpVMTExcnV11QMPPKBVq1ZZQ8AZM2Zo+PDheu+991S8eHEdPXpUM2fOVO/evVWvXj0VLlxYL7zwgq5etf0e+PDDD9WnTx9rODhlyhTt3btXHh4eku583VatWqXRo0erV69eunDhgoKDg9WwYUMVLVo08y9eKhz9tSxQoIBef/11/fXXXzIMQyVLllRUVJSGDRuW5ZpPnz6tatWqWZ9Pnz5d06dPV6NGjVK9RDEzTEZ2LtDMg65evSp/f39duXIlQ1Mf07Nnzx41adJEN0LclGTO+YVBJck1LlHmi7dV56aL/AxTjo9/xiVJv7sZUvCDkrt/jo+vm1eksz9pw4YNNnc9yEnJ3wcfPePplDU6DpxKVLe3rjv1NXC05Nc4q+eY3D7fvk+kXPFeyY7sfg/kB7xP7MDJ7xP+f+J4vE/sIJe8TyZ2MivcCQvzHz2fpFeWWex6/vb+jJKTbty4oSNHjqhUqVLWD/Z3/kBfVxbLjRyrw2z20Pbt2+75myVs3rxZDz/8sC5dupThtYlw53syLCxMGzZssN7d0Nnula9laj8DUsPMJztJMrsoycM54ZPJwtRfAAAA5C/hLMyfa4WGhmr79m3ZWpokswIDA+/54AkZ99133ykuLk6VK1fWmTNn9Pzzzys8PFwNGzZ0dmn5ltPDp7lz52ratGk6e/asIiMj9dZbb6lWrVp3PX727NmaN2+ejh8/rsKFC6tDhw6aMmVKmgkbAAAAACDnhIaGEgZBkydP1uTJk1Pd16BBA61evdoh4966dUsvvfSSDh8+LF9fX9WrV8+6RlJGVKpUSceOHUt13zvvvKOuXbvas9xUtWzZUj/++GOq+1566SW99NJLabb/510K/2316tXZWlg+K5waPi1btkzDhw/X/PnzVbt2bc2ePVvNmzfXn3/+qaCgoBTHL126VC+++KIWLFigevXq6a+//lLPnj1lMpk0c+ZMJ5wBAAAAAAA546GHHlJeWjlnwIAB6tixY6r7PD09HTZu8+bN1bx58yy3X7VqlW7dupXqPnutCZXe1/L999/X9evXU91XqFChdPvfvXv3XfcVL1483fb25tTwaebMmerXr5969eolSZo/f75WrlypBQsW6MUXX0xx/NatW1W/fn116dJF0p2V7zt37qwdO3bkaN0AAAAAACBthQoVylBQktuULFnS2SVkOyDK7t3p7M05ixTpzm37du3apSZNmvyvGBcXNWnSRNu2bUu1Tb169bRr1y7997//lSQdPnxYq1atUqtWrXKkZgAAAAAAAGSO02Y+Xbx4UYmJiSmmrBUtWlQHDhxItU2XLl108eJFPfjggzIMQ7dv39aAAQPSvNbRYrHIYvnfbVj/fQtGAAAAAAAAOI7TZj5lxebNmzV58mS9/fbb+uWXX/TFF19o5cqVmjhx4l3bTJkyRf7+/tZHWFhYDlYMAAAAAACQvzlt5lPhwoXl6uqqc+fO2Ww/d+6cgoODU23zyiuvqFu3burbt68kqXLlyoqPj1f//v01evRoubikzNJGjRql4cOHW59fvXqVAAoAAAAAACCHOG3mk7u7u2rUqKGNGzdatyUlJWnjxo2qW7duqm0SEhJSBEyurq6SdNdV4s1ms/z8/GweAAAAAAAAyBlOvdvd8OHD1aNHD9WsWVO1atXS7NmzFR8fb737Xffu3VW8eHFNmTJFktSmTRvNnDlT1apVU+3atXXw4EG98soratOmjTWEAgAAAAA418mTJxUbG5tj4wUGBio0NDTHxsuqo0ePqlSpUvr1119VtWpVZ5eTZVu2bNGAAQN04MABtW7dWitWrHB2ScjlnBo+derUSRcuXNCYMWN09uxZVa1aVWvWrLEuQn78+HGbmU4vv/yyTCaTXn75ZZ06dUpFihRRmzZt9OqrrzrrFAAAAAAA/3Dy5EnVq1tH129Y0j/YTjw9zNq6bXueCKBygxs3bui5557TJ598IovFoubNm+vtt99OcUOwuxk+fLiqVq2q1atXy8fHR5I0ZMgQbdmyRX/88YcqVKig3bt3O/AMkNc4NXySpKioKEVFRaW6b/PmzTbPCxQooLFjx2rs2LE5UBkAAAAAILNiY2N1/YZFEzqZVSrI8Su9HDmfpDHLLIqNjSV8yqBhw4Zp5cqV+uyzz+Tv76+oqCg9/vjj2rJlS4baHzp0SAMGDEjxevfu3Vs7duzQb7/95oiykYc5PXwCAOQf2ZmCHxMTY/PfrMgrU/IBALgXlApyUfniuXd5lOXLl2v8+PE6ePCgvLy8VK1aNX311Vfy9vbW+++/rxkzZujIkSMKDw/XkCFDNGjQIEl3Apaff/5ZO3fulNls1s2bN1W7dm1VrlxZH374YYbGPnz4sIYNG6Y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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import seaborn as sns\n",
@@ -280,20 +255,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import seaborn as sns\n",
@@ -389,39 +353,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_13649/137335904.py:42: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/137335904.py:48: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/137335904.py:54: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/137335904.py:60: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/137335904.py:84: FutureWarning: \n",
-      "\n",
-      "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:black'` for the same effect.\n",
-      "\n",
-      "  sns.stripplot(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import seaborn as sns\n",
@@ -539,34 +473,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_13649/1115505766.py:37: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/1115505766.py:43: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/1115505766.py:49: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_13649/1115505766.py:55: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x1500 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -709,34 +618,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_14815/3149745032.py:38: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_14815/3149745032.py:44: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_14815/3149745032.py:50: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_14815/3149745032.py:56: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
index 34f728e0..cb740190 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/04_analyze_metadata_predictions_new_style.ipynb
@@ -170,80 +170,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ab0f156fa004e8f9ada679709548284",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ec92f47d82d45fb8798a100cbe7a877",
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16b983e50b5a4a97b338afb256c2786e",
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc2aa6c8e4fe4739814f109c5409d476",
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from collections import defaultdict\n",
     "from tqdm.auto import tqdm\n",
@@ -281,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,24 +226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff8860043e7443e99b50d6bacf2b7761",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "validation_meta_df = pd.read_csv(\n",
     "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))\n",
@@ -344,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,38 +283,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "coarse_tissue\n",
-       "blood              136\n",
-       "brain              112\n",
-       "lung                60\n",
-       "heart               36\n",
-       "small intestine     32\n",
-       "liver               24\n",
-       "kidney              20\n",
-       "thymus               8\n",
-       "nose                 8\n",
-       "eye                  4\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "master_df['coarse_tissue'].value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,463 +306,34 @@
    "outputs": [],
    "source": [
     "master_df.to_csv(\n",
-    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"metadata_prediction_scores.csv\"), index=False)"
+    "    os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\", \"metadata_prediction_scores.csv\"), index=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>prefix</th>\n",
-       "      <th>val_adata_id</th>\n",
-       "      <th>cell_type_hop_0_accuracy</th>\n",
-       "      <th>cell_type_hop_1_accuracy</th>\n",
-       "      <th>cell_type_hop_2_accuracy</th>\n",
-       "      <th>cell_type_hop_3_accuracy</th>\n",
-       "      <th>disease_hop_0_accuracy</th>\n",
-       "      <th>disease_hop_1_accuracy</th>\n",
-       "      <th>disease_hop_2_accuracy</th>\n",
-       "      <th>disease_hop_3_accuracy</th>\n",
-       "      <th>development_stage_hop_0_accuracy</th>\n",
-       "      <th>development_stage_hop_1_accuracy</th>\n",
-       "      <th>development_stage_hop_2_accuracy</th>\n",
-       "      <th>development_stage_hop_3_accuracy</th>\n",
-       "      <th>tissue_hop_0_accuracy</th>\n",
-       "      <th>tissue_hop_1_accuracy</th>\n",
-       "      <th>tissue_hop_2_accuracy</th>\n",
-       "      <th>tissue_hop_3_accuracy</th>\n",
-       "      <th>sex_hop_0_accuracy</th>\n",
-       "      <th>coarse_tissue</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10M_001_bs1536</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.626214</td>\n",
-       "      <td>0.632147</td>\n",
-       "      <td>0.647789</td>\n",
-       "      <td>0.906688</td>\n",
-       "      <td>0.8950</td>\n",
-       "      <td>0.8950</td>\n",
-       "      <td>0.8950</td>\n",
-       "      <td>0.8950</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0795</td>\n",
-       "      <td>0.0795</td>\n",
-       "      <td>0.951000</td>\n",
-       "      <td>brain</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10M_001_bs1536</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.554500</td>\n",
-       "      <td>0.844500</td>\n",
-       "      <td>0.985000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.131500</td>\n",
-       "      <td>blood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10M_001_bs1536</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.716000</td>\n",
-       "      <td>0.847000</td>\n",
-       "      <td>0.968000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0040</td>\n",
-       "      <td>0.004000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.249500</td>\n",
-       "      <td>blood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10M_001_bs1536</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.579492</td>\n",
-       "      <td>0.863283</td>\n",
-       "      <td>0.895391</td>\n",
-       "      <td>0.924909</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.893500</td>\n",
-       "      <td>small intestine</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10M_001_bs1536</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.635500</td>\n",
-       "      <td>0.859000</td>\n",
-       "      <td>0.970000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0045</td>\n",
-       "      <td>0.004500</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.148000</td>\n",
-       "      <td>blood</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>435</th>\n",
-       "      <td>100M_long_run_last</td>\n",
-       "      <td>106</td>\n",
-       "      <td>0.665000</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.782000</td>\n",
-       "      <td>0.815500</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0035</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.901000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.724500</td>\n",
-       "      <td>lung</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>436</th>\n",
-       "      <td>100M_long_run_last</td>\n",
-       "      <td>107</td>\n",
-       "      <td>0.580040</td>\n",
-       "      <td>0.743083</td>\n",
-       "      <td>0.821146</td>\n",
-       "      <td>0.928854</td>\n",
-       "      <td>0.9070</td>\n",
-       "      <td>0.9070</td>\n",
-       "      <td>0.9070</td>\n",
-       "      <td>0.9070</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1275</td>\n",
-       "      <td>0.127500</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.925500</td>\n",
-       "      <td>lung</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>437</th>\n",
-       "      <td>100M_long_run_last</td>\n",
-       "      <td>108</td>\n",
-       "      <td>0.408931</td>\n",
-       "      <td>0.678026</td>\n",
-       "      <td>0.922444</td>\n",
-       "      <td>0.982374</td>\n",
-       "      <td>0.9815</td>\n",
-       "      <td>0.9815</td>\n",
-       "      <td>0.9815</td>\n",
-       "      <td>0.9815</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0230</td>\n",
-       "      <td>0.023000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.537000</td>\n",
-       "      <td>lung</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>438</th>\n",
-       "      <td>100M_long_run_last</td>\n",
-       "      <td>109</td>\n",
-       "      <td>0.397308</td>\n",
-       "      <td>0.531890</td>\n",
-       "      <td>0.581042</td>\n",
-       "      <td>0.599766</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.939146</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.857226</td>\n",
-       "      <td>brain</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>439</th>\n",
-       "      <td>100M_long_run_last</td>\n",
-       "      <td>110</td>\n",
-       "      <td>0.265000</td>\n",
-       "      <td>0.287000</td>\n",
-       "      <td>0.394000</td>\n",
-       "      <td>0.998000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>0.2360</td>\n",
-       "      <td>0.940000</td>\n",
-       "      <td>brain</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>440 rows × 20 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 prefix  val_adata_id  cell_type_hop_0_accuracy  \\\n",
-       "0        10M_001_bs1536             1                  0.626214   \n",
-       "1        10M_001_bs1536             2                  0.554500   \n",
-       "2        10M_001_bs1536             3                  0.716000   \n",
-       "3        10M_001_bs1536             4                  0.579492   \n",
-       "4        10M_001_bs1536             5                  0.635500   \n",
-       "..                  ...           ...                       ...   \n",
-       "435  100M_long_run_last           106                  0.665000   \n",
-       "436  100M_long_run_last           107                  0.580040   \n",
-       "437  100M_long_run_last           108                  0.408931   \n",
-       "438  100M_long_run_last           109                  0.397308   \n",
-       "439  100M_long_run_last           110                  0.265000   \n",
-       "\n",
-       "     cell_type_hop_1_accuracy  cell_type_hop_2_accuracy  \\\n",
-       "0                    0.632147                  0.647789   \n",
-       "1                    0.844500                  0.985000   \n",
-       "2                    0.847000                  0.968000   \n",
-       "3                    0.863283                  0.895391   \n",
-       "4                    0.859000                  0.970000   \n",
-       "..                        ...                       ...   \n",
-       "435                  0.700000                  0.782000   \n",
-       "436                  0.743083                  0.821146   \n",
-       "437                  0.678026                  0.922444   \n",
-       "438                  0.531890                  0.581042   \n",
-       "439                  0.287000                  0.394000   \n",
-       "\n",
-       "     cell_type_hop_3_accuracy  disease_hop_0_accuracy  disease_hop_1_accuracy  \\\n",
-       "0                    0.906688                  0.8950                  0.8950   \n",
-       "1                    1.000000                  0.0000                  0.0000   \n",
-       "2                    1.000000                  0.0000                  0.0000   \n",
-       "3                    0.924909                  0.0000                  0.0000   \n",
-       "4                    1.000000                  0.0000                  0.0000   \n",
-       "..                        ...                     ...                     ...   \n",
-       "435                  0.815500                  0.0000                  0.0000   \n",
-       "436                  0.928854                  0.9070                  0.9070   \n",
-       "437                  0.982374                  0.9815                  0.9815   \n",
-       "438                  0.599766                  1.0000                  1.0000   \n",
-       "439                  0.998000                  0.0000                  0.0000   \n",
-       "\n",
-       "     disease_hop_2_accuracy  disease_hop_3_accuracy  \\\n",
-       "0                    0.8950                  0.8950   \n",
-       "1                    0.0000                  0.0000   \n",
-       "2                    0.0000                  0.0000   \n",
-       "3                    0.0000                  1.0000   \n",
-       "4                    0.0000                  0.0000   \n",
-       "..                      ...                     ...   \n",
-       "435                  0.0000                  0.0000   \n",
-       "436                  0.9070                  0.9070   \n",
-       "437                  0.9815                  0.9815   \n",
-       "438                  1.0000                  1.0000   \n",
-       "439                  0.0000                  0.0000   \n",
-       "\n",
-       "     development_stage_hop_0_accuracy  development_stage_hop_1_accuracy  \\\n",
-       "0                              0.0000                               1.0   \n",
-       "1                              0.0000                               1.0   \n",
-       "2                              0.0000                               1.0   \n",
-       "3                              0.0000                               1.0   \n",
-       "4                              0.0000                               1.0   \n",
-       "..                                ...                               ...   \n",
-       "435                            0.0035                               1.0   \n",
-       "436                            0.0000                               1.0   \n",
-       "437                            0.0000                               1.0   \n",
-       "438                            0.0000                               1.0   \n",
-       "439                               NaN                               NaN   \n",
-       "\n",
-       "     development_stage_hop_2_accuracy  development_stage_hop_3_accuracy  \\\n",
-       "0                                 1.0                               1.0   \n",
-       "1                                 1.0                               1.0   \n",
-       "2                                 1.0                               1.0   \n",
-       "3                                 1.0                               1.0   \n",
-       "4                                 1.0                               1.0   \n",
-       "..                                ...                               ...   \n",
-       "435                               1.0                               1.0   \n",
-       "436                               1.0                               1.0   \n",
-       "437                               1.0                               1.0   \n",
-       "438                               1.0                               1.0   \n",
-       "439                               NaN                               NaN   \n",
-       "\n",
-       "     tissue_hop_0_accuracy  tissue_hop_1_accuracy  tissue_hop_2_accuracy  \\\n",
-       "0                   0.0000               0.000000                 0.0795   \n",
-       "1                   0.0000               0.000000                 1.0000   \n",
-       "2                   0.0040               0.004000                 1.0000   \n",
-       "3                   0.0000               0.000000                 1.0000   \n",
-       "4                   0.0045               0.004500                 1.0000   \n",
-       "..                     ...                    ...                    ...   \n",
-       "435                 0.0000               0.901000                 1.0000   \n",
-       "436                 0.1275               0.127500                 1.0000   \n",
-       "437                 0.0230               0.023000                 1.0000   \n",
-       "438                 0.0000               0.939146                 1.0000   \n",
-       "439                 0.0000               0.000000                 0.0095   \n",
-       "\n",
-       "     tissue_hop_3_accuracy  sex_hop_0_accuracy    coarse_tissue  \n",
-       "0                   0.0795            0.951000            brain  \n",
-       "1                   1.0000            0.131500            blood  \n",
-       "2                   1.0000            0.249500            blood  \n",
-       "3                   1.0000            0.893500  small intestine  \n",
-       "4                   1.0000            0.148000            blood  \n",
-       "..                     ...                 ...              ...  \n",
-       "435                 1.0000            0.724500             lung  \n",
-       "436                 1.0000            0.925500             lung  \n",
-       "437                 1.0000            0.537000             lung  \n",
-       "438                 1.0000            0.857226            brain  \n",
-       "439                 0.2360            0.940000            brain  \n",
-       "\n",
-       "[440 rows x 20 columns]"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df"
+    "master_df = pd.read_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\", \"metadata_prediction_scores.csv\")\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/tmp/ipykernel_119159/3100616606.py:39: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Greens')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_119159/3100616606.py:45: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Reds')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_119159/3100616606.py:51: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Blues')((i + 1) / (n + 1))\n",
-      "/var/tmp/ipykernel_119159/3100616606.py:57: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
-      "  palette[m] = cm.get_cmap('Purples')((i + 1) / (n + 1))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x1500 with 20 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "master_df[master_df['prefix'] == PREFIX_LIST[0]]['cell_type_hop_0_accuracy'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -880,11 +343,11 @@
     "# -------------------------------\n",
     "# User-specified options:\n",
     "# Choose which metric to plot: 'f1', 'sensitivity', or 'specificity'\n",
-    "value_to_plot = 'accuracy'  \n",
+    "value_to_plot = 'accuracy'\n",
     "# Flag to select summary statistic: True uses mean/std, False uses median/IQR.\n",
     "use_mean = True\n",
     "# df = master_df[master_df['coarse_tissue'] == 'blood'].copy()\n",
-    "# df = master_df.copy()\n",
+    "df = master_df.copy()\n",
     "# -------------------------------\n",
     "\n",
     "# Define metric groups and available hops.\n",
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb
index 86df5330..641531e6 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/05_loss_scales.ipynb
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,15 +258,408 @@
     "best_labels = list(map(names_to_labels_map.get, best_names))\n",
     "\n",
     "new_obs = adata.obs.copy()\n",
-    "new_obs['predicted_' + metadata_key] = best_names"
+    "new_obs['predicted_' + metadata_key] = best_labels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>predicted_cell_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>51973649</th>\n",
+       "      <td>neural cell</td>\n",
+       "      <td>neuron</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162899660</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>endothelial cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11126346</th>\n",
+       "      <td>respiratory epithelial cell</td>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3561915</th>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>keratinocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6584585</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86231550</th>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113834925</th>\n",
+       "      <td>Purkinje cell</td>\n",
+       "      <td>ciliated cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113494677</th>\n",
+       "      <td>cortical cell of adrenal gland</td>\n",
+       "      <td>ciliated cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33264029</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21357920</th>\n",
+       "      <td>basal cell</td>\n",
+       "      <td>mesothelial cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3156818</th>\n",
+       "      <td>secretory cell</td>\n",
+       "      <td>platelet</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27128798</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>mucosal invariant T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77027308</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>macrophage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62320806</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>microglial cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53191367</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53340161</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>memory B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150849580</th>\n",
+       "      <td>T cell</td>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111535086</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>naive B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131413815</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>CD1c-positive myeloid dendritic cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117555252</th>\n",
+       "      <td>erythrocyte</td>\n",
+       "      <td>erythrocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11817178</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18143362</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5169402</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>mucosal invariant T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153626269</th>\n",
+       "      <td>CD14-positive monocyte</td>\n",
+       "      <td>macrophage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29866064</th>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>keratinocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11195909</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114260517</th>\n",
+       "      <td>inhibitory interneuron</td>\n",
+       "      <td>ciliated cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69453897</th>\n",
+       "      <td>pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>pvalb GABAergic cortical interneuron</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15150795</th>\n",
+       "      <td>endothelial cell of vascular tree</td>\n",
+       "      <td>endothelial cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21240438</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74874817</th>\n",
+       "      <td>monocyte</td>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26685568</th>\n",
+       "      <td>uterine smooth muscle cell</td>\n",
+       "      <td>pericyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5232410</th>\n",
+       "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116146692</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>astrocyte</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40708447</th>\n",
+       "      <td>melanocyte of skin</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58764949</th>\n",
+       "      <td>plasma cell</td>\n",
+       "      <td>plasma cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45876734</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>effector memory CD8-positive, alpha-beta T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46236211</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>hematopoietic precursor cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69450841</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91111304</th>\n",
+       "      <td>Mueller cell</td>\n",
+       "      <td>Mueller cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35275331</th>\n",
+       "      <td>double negative thymocyte</td>\n",
+       "      <td>progenitor cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169158064</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>macrophage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114003261</th>\n",
+       "      <td>Purkinje cell</td>\n",
+       "      <td>ciliated cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40938483</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>neutrophil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5216909</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45357807</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>professional antigen presenting cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82552174</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3244891</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>neuron</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13567310</th>\n",
+       "      <td>plasmablast</td>\n",
+       "      <td>B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66535832</th>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   cell_type  \\\n",
+       "51973649                                         neural cell   \n",
+       "162899660                                             neuron   \n",
+       "11126346                         respiratory epithelial cell   \n",
+       "3561915                                      epithelial cell   \n",
+       "6584585    naive thymus-derived CD4-positive, alpha-beta ...   \n",
+       "86231550                lamp5 GABAergic cortical interneuron   \n",
+       "113834925                                      Purkinje cell   \n",
+       "113494677                     cortical cell of adrenal gland   \n",
+       "33264029                      oligodendrocyte precursor cell   \n",
+       "21357920                                          basal cell   \n",
+       "3156818                                       secretory cell   \n",
+       "27128798                     CD8-positive, alpha-beta T cell   \n",
+       "77027308                                 alveolar macrophage   \n",
+       "62320806                                     microglial cell   \n",
+       "53191367                                 alveolar macrophage   \n",
+       "53340161                                              B cell   \n",
+       "150849580                                             T cell   \n",
+       "111535086                                             B cell   \n",
+       "131413815                                         macrophage   \n",
+       "117555252                                        erythrocyte   \n",
+       "11817178   naive thymus-derived CD4-positive, alpha-beta ...   \n",
+       "18143362                                        naive B cell   \n",
+       "5169402                                   gamma-delta T cell   \n",
+       "153626269                             CD14-positive monocyte   \n",
+       "29866064                                     epithelial cell   \n",
+       "11195909                                              B cell   \n",
+       "114260517                             inhibitory interneuron   \n",
+       "69453897                pvalb GABAergic cortical interneuron   \n",
+       "15150795                   endothelial cell of vascular tree   \n",
+       "21240438                                              neuron   \n",
+       "74874817                                            monocyte   \n",
+       "26685568                          uterine smooth muscle cell   \n",
+       "5232410               CD4-positive, alpha-beta memory T cell   \n",
+       "116146692                                          astrocyte   \n",
+       "40708447                                  melanocyte of skin   \n",
+       "58764949                                         plasma cell   \n",
+       "45876734                                 natural killer cell   \n",
+       "46236211                                 natural killer cell   \n",
+       "69450841   L2/3-6 intratelencephalic projecting glutamate...   \n",
+       "91111304                                        Mueller cell   \n",
+       "35275331                           double negative thymocyte   \n",
+       "169158064                                         macrophage   \n",
+       "114003261                                      Purkinje cell   \n",
+       "40938483                                              B cell   \n",
+       "5216909                                        memory B cell   \n",
+       "45357807                                          enterocyte   \n",
+       "82552174   naive thymus-derived CD4-positive, alpha-beta ...   \n",
+       "3244891                                               neuron   \n",
+       "13567310                                         plasmablast   \n",
+       "66535832                lamp5 GABAergic cortical interneuron   \n",
+       "\n",
+       "                                         predicted_cell_type  \n",
+       "51973649                                              neuron  \n",
+       "162899660                                   endothelial cell  \n",
+       "11126346                                  type II pneumocyte  \n",
+       "3561915                                         keratinocyte  \n",
+       "6584585    naive thymus-derived CD4-positive, alpha-beta ...  \n",
+       "86231550                lamp5 GABAergic cortical interneuron  \n",
+       "113834925                                      ciliated cell  \n",
+       "113494677                                      ciliated cell  \n",
+       "33264029                      oligodendrocyte precursor cell  \n",
+       "21357920                                    mesothelial cell  \n",
+       "3156818                                             platelet  \n",
+       "27128798                            mucosal invariant T cell  \n",
+       "77027308                                          macrophage  \n",
+       "62320806                                     microglial cell  \n",
+       "53191367                                 alveolar macrophage  \n",
+       "53340161                                       memory B cell  \n",
+       "150849580                    CD4-positive, alpha-beta T cell  \n",
+       "111535086                                       naive B cell  \n",
+       "131413815               CD1c-positive myeloid dendritic cell  \n",
+       "117555252                                        erythrocyte  \n",
+       "11817178                                              B cell  \n",
+       "18143362                                              B cell  \n",
+       "5169402                             mucosal invariant T cell  \n",
+       "153626269                                         macrophage  \n",
+       "29866064                                        keratinocyte  \n",
+       "11195909                                              B cell  \n",
+       "114260517                                      ciliated cell  \n",
+       "69453897                pvalb GABAergic cortical interneuron  \n",
+       "15150795                                    endothelial cell  \n",
+       "21240438   L2/3-6 intratelencephalic projecting glutamate...  \n",
+       "74874817                              non-classical monocyte  \n",
+       "26685568                                            pericyte  \n",
+       "5232410    naive thymus-derived CD4-positive, alpha-beta ...  \n",
+       "116146692                                          astrocyte  \n",
+       "40708447                                     epithelial cell  \n",
+       "58764949                                         plasma cell  \n",
+       "45876734     effector memory CD8-positive, alpha-beta T cell  \n",
+       "46236211                        hematopoietic precursor cell  \n",
+       "69450841   L2/3-6 intratelencephalic projecting glutamate...  \n",
+       "91111304                                        Mueller cell  \n",
+       "35275331                                     progenitor cell  \n",
+       "169158064                                         macrophage  \n",
+       "114003261                                      ciliated cell  \n",
+       "40938483                                          neutrophil  \n",
+       "5216909                                               B cell  \n",
+       "45357807                professional antigen presenting cell  \n",
+       "82552174                     CD4-positive, alpha-beta T cell  \n",
+       "3244891                                               neuron  \n",
+       "13567310                                              B cell  \n",
+       "66535832                lamp5 GABAergic cortical interneuron  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs[[metadata_key, 'predicted_' + metadata_key]].head(50)"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
new file mode 100644
index 00000000..46d3754d
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "### Analyze metadata predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "METADATA_PREDICTIONS_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\")\n",
+    "PREFIX_LIST = [\n",
+    "    \"10M_001_bs1536\",\n",
+    "    \"19M_001_bs2048\",\n",
+    "    \"30M_001_bs2560\",\n",
+    "    \"100M_long_run_last\"\n",
+    "]\n",
+    "VAL_ADATA_IDX_RANGE = np.arange(1, 111)\n",
+    "N_HOPS_DICT = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "def load_predictions_anndata(val_adata_idx: int, prefix_idx: int) -> sc.AnnData:\n",
+    "    path = os.path.join(\n",
+    "        METADATA_PREDICTIONS_ROOT_PATH,\n",
+    "        PREFIX_LIST[prefix_idx],\n",
+    "        f\"extract_{VAL_ADATA_IDX_RANGE[val_adata_idx]}_metadata_prediction_scores.h5ad\")\n",
+    "    return sc.read_h5ad(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "top_k_list = [1, 3, 5, 10]\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(PREFIX_LIST))):\n",
+    "    prefix = PREFIX_LIST[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(VAL_ADATA_IDX_RANGE))):\n",
+    "        val_adata_id = VAL_ADATA_IDX_RANGE[val_adata_idx]\n",
+    "        meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx)\n",
+    "        results[\"prefix\"].append(prefix)\n",
+    "        results[\"val_adata_id\"].append(val_adata_id)\n",
+    "\n",
+    "        for key in [\"cell_type\", \"disease\", \"development_stage\", \"tissue\", \"sex\"]:            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            # logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
+    "            labels_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = ~np.isnan(labels_n)\n",
+    "            probs_nk = probs_nk[valid_indices]\n",
+    "            labels_n = labels_n[valid_indices].astype(int)  \n",
+    "\n",
+    "            if len(labels_n) == 0:\n",
+    "                for top_k in top_k_list:\n",
+    "                    results[f\"{key}_top_{top_k}_accuracy\"].append(np.nan)\n",
+    "                # results[f\"{key}_auc\"].append(np.nan)\n",
+    "                # results[f\"{key}_loss\"].append(np.nan)\n",
+    "                continue\n",
+    "\n",
+    "            # get top k predictions\n",
+    "            for top_k in top_k_list:\n",
+    "                top_k_indices = np.argsort(probs_nk, axis=1)[:, -top_k:]\n",
+    "                top_k_probs = np.take_along_axis(probs_nk, top_k_indices, axis=1)\n",
+    "\n",
+    "                # calculate top k accuracy\n",
+    "                pred_label_n = np.any(top_k_indices == labels_n[:, None], axis=1).astype(int)\n",
+    "                top_k_accuracy = np.mean(pred_label_n)\n",
+    "                results[f\"{key}_top_{top_k}_accuracy\"].append(top_k_accuracy)\n",
+    "\n",
+    "            # # Assume classifier.classes_ gives the full set of classes.\n",
+    "            # classes = np.arange(probs_nk.shape[1])\n",
+    "            # label_bin_nk = label_binarize(labels_n, classes=classes)\n",
+    "            # if len(classes) == 2:\n",
+    "            #     # binary AUC\n",
+    "            #     auc = roc_auc_score(labels_n, probs_nk[:, 1])\n",
+    "            # else:\n",
+    "            #     auc = roc_auc_score(label_bin_nk, probs_nk, multi_class='ovr', average='micro')\n",
+    "            # results[f\"{key}_auc\"].append(auc)\n",
+    "\n",
+    "            # # loss\n",
+    "            # truth_names = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "            # truth_indices = np.asarray(list(map(mapper.get, truth_names)))\n",
+    "            # weights = np.ones_like(truth_indices)\n",
+    "            # bad_indices = [idx for idx in range(len(truth_names)) if truth_names[idx] not in mapper]\n",
+    "            # weights[bad_indices] = 0\n",
+    "            # truth_indices[bad_indices] = 0\n",
+    "            # loss = - weights * meta_adata.obsm[f\"{key}_class_logits\"][np.arange(len(meta_adata)), truth_indices.astype(int)]\n",
+    "            # loss = loss.mean()\n",
+    "            # results[f\"{key}_loss\"].append(loss)\n",
+    "\n",
+    "            # for n_hops in range(N_HOPS_DICT[key] + 1):\n",
+    "            #     # accuracy\n",
+    "            #     hop_accuracy = meta_adata.obs[f\"{key}_hop_{n_hops}_call\"].dropna().mean()\n",
+    "            #     results[f\"{key}_hop_{n_hops}_accuracy\"].append(hop_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_meta_df = pd.read_csv(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))\n",
+    "\n",
+    "# Stephen's validatdion table\n",
+    "# what we have called intestine is actually \"small intestine\"\n",
+    "tissue_ontology_term_id_to_coarse_name_map = dict()\n",
+    "for row in validation_meta_df['tissue_name_ont_coarsename_coarseont'].values:\n",
+    "    split_row = row.strip(\"()\").split(', ')\n",
+    "    coarse_name = split_row[2].strip(\"'\")\n",
+    "    if coarse_name == \"intestine\":\n",
+    "        coarse_name = \"small intestine\"\n",
+    "    tissue_ontology_term_id_to_coarse_name_map[split_row[1].strip(\"'\")] = coarse_name\n",
+    "\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "val_adata_id_to_coarse_tissue_map = dict()\n",
+    "for val_idx in tqdm(range(1, 111)):\n",
+    "    \n",
+    "    val_adata_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    obs_df = val_adata.obs\n",
+    "    val_adata_id_to_coarse_tissue_map[val_idx] = obs_df['tissue_ontology_term_id'].map(\n",
+    "        tissue_ontology_term_id_to_coarse_name_map).iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['coarse_tissue'] = df['val_adata_id'].map(val_adata_id_to_coarse_tissue_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_PREFIX_LIST = PREFIX_LIST[:3]\n",
+    "df = df[df['prefix'].isin(_PREFIX_LIST)].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = 'cell_type'\n",
+    "\n",
+    "metrics = [\"top_1_accuracy\", \"top_3_accuracy\", \"top_5_accuracy\", \"top_10_accuracy\"]\n",
+    "n_metrics = len(metrics)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, n_metrics, figsize=(2.5 * n_metrics, 4))\n",
+    "\n",
+    "for i_metric, metric in enumerate(metrics):\n",
+    "    ax = axs[i_metric]\n",
+    "\n",
+    "    full_metric = f\"{key}_{metric}\"\n",
+    "    df_metric = df[[full_metric, \"prefix\"]].copy()\n",
+    "    df_metric = df_metric.dropna()\n",
+    "    \n",
+    "    stats = df_metric.groupby('prefix').agg(\n",
+    "        median=(full_metric, np.median),\n",
+    "        mean=(full_metric, np.mean),\n",
+    "        q1=(full_metric, lambda x: x.quantile(0.25)),\n",
+    "        q3=(full_metric, lambda x: x.quantile(0.75))\n",
+    "    ).reindex(_PREFIX_LIST)\n",
+    "    y_vals = stats['mean']\n",
+    "    y_err_lower = y_vals - stats['q1']\n",
+    "    y_err_upper = stats['q3'] - y_vals\n",
+    "    y_err = [y_err_lower.values, y_err_upper.values]\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        np.arange(len(_PREFIX_LIST)), y_vals, yerr=y_err,\n",
+    "        fmt='o', color='black', ecolor='black',\n",
+    "        capsize=4, markersize=6, linestyle='None'\n",
+    "    )\n",
+    "\n",
+    "    ax.plot(np.arange(len(_PREFIX_LIST)), y_vals)\n",
+    "\n",
+    "    # rotate the x-axis labels by 90 degrees\n",
+    "    ax.set_xticks(np.arange(len(_PREFIX_LIST)))\n",
+    "    ax.set_xticklabels(_PREFIX_LIST, rotation=90)\n",
+    "    ax.set_title(key)\n",
+    "    ax.set_ylabel(metric)\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = [\"cell_type_top_10_accuracy\", \"tissue_top_10_accuracy\", \"disease_top_10_accuracy\", \"sex_top_1_accuracy\"]\n",
+    "n_metrics = len(metrics)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, n_metrics, figsize=(2.5 * n_metrics, 4))\n",
+    "\n",
+    "for i_metric, metric in enumerate(metrics):\n",
+    "    ax = axs[i_metric]\n",
+    "\n",
+    "    df_metric = df[[metric, \"prefix\"]].copy()\n",
+    "    df_metric = df_metric.dropna()\n",
+    "    \n",
+    "    stats = df_metric.groupby('prefix').agg(\n",
+    "        median=(metric, np.median),\n",
+    "        mean=(metric, np.mean),\n",
+    "        q1=(metric, lambda x: x.quantile(0.25)),\n",
+    "        q3=(metric, lambda x: x.quantile(0.75))\n",
+    "    ).reindex(_PREFIX_LIST)\n",
+    "    y_vals = stats['mean']\n",
+    "    y_err_lower = y_vals - stats['q1']\n",
+    "    y_err_upper = stats['q3'] - y_vals\n",
+    "    y_err = [y_err_lower.values, y_err_upper.values]\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        np.arange(len(_PREFIX_LIST)), y_vals, yerr=y_err,\n",
+    "        fmt='o', color='black', ecolor='black',\n",
+    "        capsize=4, markersize=6, linestyle='None'\n",
+    "    )\n",
+    "\n",
+    "    ax.plot(np.arange(len(_PREFIX_LIST)), y_vals)\n",
+    "\n",
+    "    # rotate the x-axis labels by 90 degrees\n",
+    "    ax.set_xticks(np.arange(len(_PREFIX_LIST)))\n",
+    "    ax.set_xticklabels(_PREFIX_LIST, rotation=90)\n",
+    "    ax.set_title(metric)\n",
+    "    ax.set_ylim((-0.05, 1.05))\n",
+    "    # ax.set_ylabel()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = [\"cell_type_top_10_accuracy\", \"tissue_top_10_accuracy\", \"disease_top_10_accuracy\", \"sex_top_1_accuracy\"]\n",
+    "n_metrics = len(metrics)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, n_metrics, figsize=(2.5 * n_metrics, 4))\n",
+    "\n",
+    "for i_metric, metric in enumerate(metrics):\n",
+    "    ax = axs[i_metric]\n",
+    "\n",
+    "    df_metric = df[[metric, \"prefix\"]].copy()\n",
+    "    df_metric = df_metric.dropna()\n",
+    "    \n",
+    "    stats = df_metric.groupby('prefix').agg(\n",
+    "        median=(metric, np.median),\n",
+    "        mean=(metric, np.mean),\n",
+    "        std=(metric, np.std),\n",
+    "        count=(metric, 'count'),\n",
+    "        q1=(metric, lambda x: x.quantile(0.25)),\n",
+    "        q3=(metric, lambda x: x.quantile(0.75))\n",
+    "    ).reindex(_PREFIX_LIST)\n",
+    "    y_vals = stats['mean']\n",
+    "    y_err = stats['std'] / np.sqrt(stats['count'])\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        np.arange(len(_PREFIX_LIST)), y_vals, yerr=y_err,\n",
+    "        fmt='o', color='black', ecolor='black',\n",
+    "        capsize=4, markersize=6, linestyle='None'\n",
+    "    )\n",
+    "\n",
+    "    ax.plot(np.arange(len(_PREFIX_LIST)), y_vals)\n",
+    "\n",
+    "    # rotate the x-axis labels by 90 degrees\n",
+    "    ax.set_xticks(np.arange(len(_PREFIX_LIST)))\n",
+    "    ax.set_xticklabels(_PREFIX_LIST, rotation=90)\n",
+    "    ax.set_title(metric)\n",
+    "    # ax.set_ylim((-0.05, 1.05))\n",
+    "    # ax.set_ylabel()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for metric in metrics:\n",
+    "    fig, ax = plt.subplots(figsize=(6,5))\n",
+    "\n",
+    "    # --- Half Violin Plot (the \"raincloud\" part) ---\n",
+    "    sns.violinplot(\n",
+    "        x=\"prefix\", \n",
+    "        y=metric, \n",
+    "        data=df,\n",
+    "        hue=True,\n",
+    "        hue_order=[True, False],\n",
+    "        split=True,\n",
+    "        bw=0.2,\n",
+    "        ax=ax\n",
+    "    )\n",
+    "    ax.legend_ = None\n",
+    "    # Title etc.\n",
+    "    ax.set_title(f\"Distribution of {metric} by prefix\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _format_axis(ax, category, horizontal=False, keep_variable_axis=True):\n",
+    "\n",
+    "    # Remove the axis lines\n",
+    "    ax.spines[\"top\"].set_visible(False)\n",
+    "    ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "    if horizontal:\n",
+    "        ax.set_ylabel(None)\n",
+    "        lim = ax.get_ylim()\n",
+    "        ax.set_yticks([(lim[0] + lim[1]) / 2])\n",
+    "        ax.set_yticklabels([category])\n",
+    "        if not keep_variable_axis:\n",
+    "            ax.get_xaxis().set_visible(False)\n",
+    "            ax.spines[\"bottom\"].set_visible(False)\n",
+    "    else:\n",
+    "        ax.set_xlabel(None)\n",
+    "        lim = ax.get_xlim()\n",
+    "        ax.set_xticks([(lim[0] + lim[1]) / 2])\n",
+    "        ax.set_xticklabels([category], rotation=90)\n",
+    "        if not keep_variable_axis:\n",
+    "            ax.get_yaxis().set_visible(False)\n",
+    "            ax.spines[\"left\"].set_visible(False)\n",
+    "\n",
+    "def categorical_kde_plot(\n",
+    "    df,\n",
+    "    variable,\n",
+    "    category,\n",
+    "    category_order=None,\n",
+    "    horizontal=False,\n",
+    "    rug=True,\n",
+    "    figsize=None,\n",
+    "):\n",
+    "    \"\"\"Draw a categorical KDE plot\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df: pd.DataFrame\n",
+    "        The data to plot\n",
+    "    variable: str\n",
+    "        The column in the `df` to plot (continuous variable)\n",
+    "    category: str\n",
+    "        The column in the `df` to use for grouping (categorical variable)\n",
+    "    horizontal: bool\n",
+    "        If True, draw density plots horizontally. Otherwise, draw them\n",
+    "        vertically.\n",
+    "    rug: bool\n",
+    "        If True, add also a sns.rugplot.\n",
+    "    figsize: tuple or None\n",
+    "        If None, use default figsize of (7, 1*len(categories))\n",
+    "        If tuple, use that figsize. Given to plt.subplots as an argument.\n",
+    "    \"\"\"\n",
+    "    if category_order is None:\n",
+    "        categories = list(df[category].unique())\n",
+    "    else:\n",
+    "        categories = category_order[:]\n",
+    "\n",
+    "    # figsize = (7, 1.0 * len(categories))\n",
+    "\n",
+    "    fig, axes = plt.subplots(\n",
+    "        nrows=len(categories) if horizontal else 1,\n",
+    "        ncols=1 if horizontal else len(categories),\n",
+    "        figsize=figsize[::-1] if not horizontal else figsize,\n",
+    "        sharex=horizontal,\n",
+    "        sharey=not horizontal,\n",
+    "    )\n",
+    "\n",
+    "    for i, (cat, ax) in enumerate(zip(categories, axes)):\n",
+    "        sns.kdeplot(\n",
+    "            data=df[df[category] == cat],\n",
+    "            x=variable if horizontal else None,\n",
+    "            y=None if horizontal else variable,\n",
+    "            # kde kwargs\n",
+    "            bw_adjust=0.5,\n",
+    "            clip_on=False,\n",
+    "            fill=True,\n",
+    "            alpha=1,\n",
+    "            cut=0,\n",
+    "            linewidth=1.5,\n",
+    "            ax=ax,\n",
+    "            color=\"lightslategray\",\n",
+    "        )\n",
+    "\n",
+    "        keep_variable_axis = (i == len(fig.axes) - 1) if horizontal else (i == 0)\n",
+    "\n",
+    "        if rug:\n",
+    "            sns.rugplot(\n",
+    "                data=df[df[category] == cat],\n",
+    "                x=variable if horizontal else None,\n",
+    "                y=None if horizontal else variable,\n",
+    "                ax=ax,\n",
+    "                color=\"black\",\n",
+    "                height=0.1,\n",
+    "            )\n",
+    "\n",
+    "        _format_axis(\n",
+    "            ax,\n",
+    "            cat,\n",
+    "            horizontal,\n",
+    "            keep_variable_axis=keep_variable_axis,\n",
+    "        )\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for coarse_tissue in df['coarse_tissue'].unique():\n",
+    "    for metric in metrics:\n",
+    "        fig = categorical_kde_plot(\n",
+    "            df[df['coarse_tissue'] == coarse_tissue],\n",
+    "            metric,\n",
+    "            \"prefix\",\n",
+    "            horizontal=False,\n",
+    "            figsize=(4, 4),\n",
+    "        )\n",
+    "\n",
+    "        fig.gca().set_title(f\"{coarse_tissue} - {metric}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
index dd4a53b3..50ae4849 100644
--- a/scripts/metadata_prediction_analysis.py
+++ b/scripts/metadata_prediction_analysis.py
@@ -50,21 +50,29 @@ def propagate_probs_over_ontology(
 
 def convert_meta_adata_to_query_obj_for_scoring(
         meta_adata: sc.AnnData,
-        metadata_key: str):
-    assert metadata_key + "_propagated_class_probs" in meta_adata.obsm
-    assert metadata_key + "_propagated_ontology_term_ids" in meta_adata.uns
+        metadata_key: str,
+        scores_col_name_suffix: str = "class_probs",
+        ontology_term_ids_uns_key_name_suffix: str = "ontology_term_ids",
+        ontology_term_id_obs_col_name_suffix: str = "ontology_term_id",
+    ) -> t.Tuple[t.List[dict], t.List[str]]:
+    scores_col_name = f"{metadata_key}_{scores_col_name_suffix}"
+    ontology_term_ids_uns_key_name = f"{metadata_key}_{ontology_term_ids_uns_key_name_suffix}"
+    ontology_term_id_obs_col_name = f"{metadata_key}_{ontology_term_id_obs_col_name_suffix}"
+    assert scores_col_name in meta_adata.obsm
+    assert ontology_term_ids_uns_key_name in meta_adata.uns
+    assert ontology_term_id_obs_col_name in meta_adata.obs.columns
     query_objs = []
     ground_truth_ontology_term_ids = []
     obs_index = meta_adata.obs.index.values
     for i_cell in range(len(meta_adata)):
         obs_row = meta_adata.obs.iloc[i_cell]
-        ground_truth_ontology_term_id = obs_row[metadata_key + "_ontology_term_id"]
+        ground_truth_ontology_term_id = obs_row[ontology_term_id_obs_col_name]
         query_obj = dict()
         query_obj["query_cell_id"] = obs_index[i_cell]
         query_obj["matches"] = []
         for ontology_term_id, score in zip(
-                meta_adata.uns[metadata_key + "_propagated_ontology_term_ids"],
-                meta_adata.obsm[metadata_key + "_propagated_class_probs"][i_cell]):
+                meta_adata.uns[ontology_term_ids_uns_key_name],
+                meta_adata.obsm[scores_col_name][i_cell, :]):
             query_obj["matches"].append({
                 "ontology_term_id": ontology_term_id,
                 "score": score,
@@ -82,8 +90,9 @@ def main():
         help="CUDA device index to use (default: 0)"
     )
     parser.add_argument(
-        "--val_adata_index", type=int, default=1,
-        help="Index to choose the validation AnnData file (default: 1)"
+        "--val_adata_path", type=str,
+        default="/home/mehrtash/data/data/extract_0.h5ad",
+        help="Validation AnnData path"
     )
     parser.add_argument(
         "--checkpoint_path", type=str,
@@ -131,12 +140,13 @@ def main():
         "--chunk_size", type=int, default=16,
         help="Chunk size for processing (default: 16)"
     )
+    parser.add_argument(
+        "--metric_style", type=str, default="hop_k_call",
+        help="Performance metric style: 'hop_k_sensitivity_precision', 'hop_k_inclusion', or 'hop_k_call' (default: hop_k_call)"
+    )
 
     args = parser.parse_args()
 
-    # Compute the validation AnnData file path using the provided index
-    adata_path = f"/home/mehrtash/data/data/cellariumgpt_validation/extract_{args.val_adata_index}.h5ad"
-
     os.makedirs(args.output_path, exist_ok=True)
 
     logger.info("Loading ontology resources ...")
@@ -188,8 +198,8 @@ def main():
         attention_backend="mem_efficient",
         verbose=False
     )
-    logger.info(f"Loading the validation AnnData object from {adata_path} ...")
-    adata = sc.read_h5ad(adata_path)
+    logger.info(f"Loading the validation AnnData object from {args.val_adata_path} ...")
+    adata = sc.read_h5ad(args.val_adata_path)
     
     # Updated gene selection logic to support both "highly_expressed" and "random"
     if args.n_genes is not None:
@@ -251,17 +261,34 @@ def main():
         )
     meta_keys = ontology_benchmarking_resource_dicts.keys()
 
+    if args.metric_style in {"hop_k_sensitivity_precision"}:
+        logger.info(f"Calculating performance metrics using {args.metric_style} style ...")
+        scores_col_name_suffix = "propagated_class_probs"
+        ontology_term_ids_uns_key_name_suffix = "propagated_ontology_term_ids"
+        ontology_term_id_obs_col_name_suffix: str = "ontology_term_id"
+    elif args.metric_style in {"hop_k_inclusion", "hop_k_call"}:
+        logger.info(f"Calculating performance metrics using {args.metric_style} style ...")
+        scores_col_name_suffix = "class_probs"
+        ontology_term_ids_uns_key_name_suffix = "ontology_term_ids"
+        ontology_term_id_obs_col_name_suffix: str = "ontology_term_id"
+    else:
+        raise ValueError(f"Unknown metric style: {args.metric_style}")
+
     results_dfs = []
     for meta_key in meta_keys:
         logger.info(f"Calculating performance metrics for {meta_key} ...")
         query_objs, ground_truth_ontology_term_ids = convert_meta_adata_to_query_obj_for_scoring(
             meta_adata=meta_adata,
-            metadata_key=meta_key)
+            metadata_key=meta_key,
+            scores_col_name_suffix=scores_col_name_suffix,
+            ontology_term_ids_uns_key_name_suffix=ontology_term_ids_uns_key_name_suffix,
+            ontology_term_id_obs_col_name_suffix=ontology_term_id_obs_col_name_suffix)
         results_df = calculate_metrics_for_prediction_output(
             model_predictions=query_objs,
             ground_truth_ontology_term_ids=ground_truth_ontology_term_ids,
             ontology_resource=ontology_benchmarking_resource_dicts[meta_key],
-            num_hops=n_hops_dict[meta_key])
+            num_hops=n_hops_dict[meta_key],
+            metric_style=args.metric_style)
         results_df.columns = [
             f"{meta_key}_{col}" if col != "query_cell_id" else col 
             for col in results_df.columns
@@ -271,8 +298,9 @@ def main():
     final_results_df = pd.concat(results_dfs, axis=1)
     meta_adata.obs.index.name = "query_cell_id"
     meta_adata.obs = pd.concat([meta_adata.obs, final_results_df], axis=1)
+    output_prefix = os.path.splitext(os.path.basename(args.val_adata_path))[0]
     meta_adata_output_file_path = os.path.join(
-        args.output_path, f"extract_{args.val_adata_index}_metadata_prediction_scores.h5ad"
+        args.output_path, f"{output_prefix}_metadata_prediction_scores.h5ad"
     )
     logger.info(f"Saving the results to {meta_adata_output_file_path} ...")
     meta_adata.write_h5ad(meta_adata_output_file_path)

From c7c5e5436f1b7c951bc9b76ef688076e305ccd33 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sun, 2 Mar 2025 22:54:01 +0000
Subject: [PATCH 52/64] updates to metadata prediction to include fixed genes

---
 .../inference/cellarium_gpt_inference.py      |   55 +-
 ...ata_predictions_new_style_no_compute.ipynb |  127 +-
 ...w_metadata_prediction_implementation.ipynb | 1389 +++++++++++++++++
 .../xx_annotate_genes.ipynb                   |  395 +++++
 scripts/metadata_prediction_analysis.py       |   56 +-
 5 files changed, 1978 insertions(+), 44 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index c250e991..96b66aab 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -173,6 +173,8 @@ def generate_tokens_from_adata(
             obs_index: int | list[int] | None,
             query_var_names: list[str],
             n_rand_prompt_vars: int | None = None,
+            rand_prompt_var_names_sublist: list[str] | None = None,
+            fixed_prompt_var_names_sublist: list[str] | None = None,
             rng: torch.Generator | None = None,
             query_total_mrna_umis: float | None = None,
             metadata_prompt_masks_dict: dict[str, bool] | None = None,
@@ -196,7 +198,6 @@ def generate_tokens_from_adata(
         # generate gene ids and masks
         n_cells = len(adata)
         adata_var_names = adata.var_names
-        assert all([var_name in self.var_name_to_index_map for var_name in adata_var_names])
         assert all([var_name in self.var_name_to_index_map for var_name in query_var_names])
         query_var_index = [self.var_name_to_index_map[var_name] for var_name in query_var_names]
 
@@ -205,6 +206,8 @@ def generate_tokens_from_adata(
 
         if n_rand_prompt_vars is None:  # using all genes in the AnnData as prompt
 
+            assert all([var_name in self.var_name_to_index_map for var_name in adata_var_names])
+
             prompt_var_index = [self.var_name_to_index_map[var_name] for var_name in adata_var_names]
             n_prompt_vars = len(prompt_var_index)
             n_query_vars = len(query_var_index)
@@ -231,33 +234,55 @@ def generate_tokens_from_adata(
 
         else:
 
-            assert 0 <= n_rand_prompt_vars <= adata.shape[1]
+            assert rand_prompt_var_names_sublist is not None
+            assert fixed_prompt_var_names_sublist is not None
+            assert all([var_name in self.var_name_to_index_map for var_name in rand_prompt_var_names_sublist])
+            assert all([var_name in self.var_name_to_index_map for var_name in fixed_prompt_var_names_sublist])
+            assert set(rand_prompt_var_names_sublist) & set(fixed_prompt_var_names_sublist) == set()
+            assert 0 <= n_rand_prompt_vars <= len(rand_prompt_var_names_sublist)
             assert rng is not None
 
-            n_prompt_vars = n_rand_prompt_vars
+            n_fixed_prompt_vars = len(fixed_prompt_var_names_sublist)
+            rand_prompt_sublist_size = len(rand_prompt_var_names_sublist)
+            n_prompt_vars = n_rand_prompt_vars + n_fixed_prompt_vars
             n_query_vars = len(query_var_index)
             n_total_vars = n_prompt_vars + n_query_vars
 
             # gene id
-            adata_var_index = torch.tensor(
-                [self.var_name_to_index_map[var_name] for var_name in adata_var_names],
+            rand_prompt_sublist_var_index = torch.tensor(
+                [self.var_name_to_index_map[var_name] for var_name in rand_prompt_var_names_sublist],
                 dtype=torch.int64, device=cpu_device)
-            rand_prompt_adata_indices_np = torch.argsort(
-                torch.rand((n_cells, n_prompt_vars), generator=rng, device=cpu_device), dim=-1)
-            rand_prompt_vars_np = adata_var_index[rand_prompt_adata_indices_np]
+            fixed_prompt_sublist_var_index = torch.tensor(
+                [self.var_name_to_index_map[var_name] for var_name in fixed_prompt_var_names_sublist],
+                dtype=torch.int64, device=cpu_device)
+            rand_prompt_sublist_indices_np = torch.argsort(
+                torch.rand((n_cells, rand_prompt_sublist_size), generator=rng, device=cpu_device), dim=-1)[:, :n_rand_prompt_vars]
+            rand_prompt_vars_nr = rand_prompt_sublist_var_index[rand_prompt_sublist_indices_np]
+            fixed_prompt_vars_nf = fixed_prompt_sublist_var_index[None, :].expand(n_cells, n_fixed_prompt_vars)
             query_vars_nq = torch.tensor(
                 query_var_index, dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_query_vars)
-            gene_ids_nc = torch.cat([rand_prompt_vars_np, query_vars_nq], dim=1)
+            prompt_vars_np = torch.cat([rand_prompt_vars_nr, fixed_prompt_vars_nf], dim=1)
+            gene_ids_nc = torch.cat([prompt_vars_np, query_vars_nq], dim=-1)
 
             # gene value
+            adata_var_index = torch.tensor(
+                [self.var_name_to_index_map[var_name] for var_name in adata_var_names],
+                dtype=torch.int64, device=cpu_device)
+            
             try:
                 _prompt_X_ng = np.asarray(adata.X.todense())
             except AttributeError:
                 _prompt_X_ng = adata.X
+
+            # re-layout the count matrix so that the var dimension is the same as the model var names
             prompt_X_ng = torch.tensor(_prompt_X_ng, dtype=torch.float32, device=cpu_device)
-            prompt_gene_value_np = prompt_X_ng[
+            prompt_X_nv = torch.zeros((n_cells, len(self.model_var_names)), dtype=torch.float32, device=cpu_device)
+            prompt_X_nv[:, adata_var_index] = prompt_X_ng
+            
+            # select values
+            prompt_gene_value_np = prompt_X_nv[
                 torch.arange(n_cells, device=cpu_device)[:, None],
-                rand_prompt_adata_indices_np]
+                prompt_vars_np]
             query_gene_value_nq = torch.zeros(
                 n_cells, n_query_vars, dtype=torch.float32, device=cpu_device)            
             gene_value_nc = torch.cat([prompt_gene_value_np, query_gene_value_nq], dim=1)
@@ -876,6 +901,8 @@ def predict_metadata_chunked(
             adata: sc.AnnData,
             chunk_size: int = 128,
             n_rand_prompt_vars: int | None = None,
+            rand_prompt_var_names_sublist: list[str] | None = None,
+            fixed_prompt_var_names_sublist: list[str] | None = None,
             rng: torch.Generator | None = None
         ) -> dict[str, np.ndarray]:
         metadata_prediction_chunks_dict = []
@@ -887,6 +914,8 @@ def predict_metadata_chunked(
             metadata_prediction_dict = self.predict_metadata(
                 adata=adata[first:last],
                 n_rand_prompt_vars=n_rand_prompt_vars,
+                rand_prompt_var_names_sublist=rand_prompt_var_names_sublist,
+                fixed_prompt_var_names_sublist=fixed_prompt_var_names_sublist,
                 rng=rng)
             metadata_prediction_chunks_dict.append(metadata_prediction_dict)
         metadata_prediction_dict = dict()
@@ -900,6 +929,8 @@ def predict_metadata(
             self,
             adata: sc.AnnData,
             n_rand_prompt_vars: int | None = None,
+            rand_prompt_var_names_sublist: list[str] | None = None,
+            fixed_prompt_var_names_sublist: list[str] | None = None,
             rng: torch.Generator | None = None,
         ) -> dict[str, np.ndarray]:
         # tokenize the given anndata: no query genes, just metadata (which will be included as query by default)
@@ -908,6 +939,8 @@ def predict_metadata(
             obs_index=None,
             query_var_names=[],
             n_rand_prompt_vars=n_rand_prompt_vars,
+            rand_prompt_var_names_sublist=rand_prompt_var_names_sublist,
+            fixed_prompt_var_names_sublist=fixed_prompt_var_names_sublist,
             rng=rng,
         )
 
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
index 46d3754d..2d34463e 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,17 +52,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "ROOT_PATH = \"/home/mehrtash/data\"\n",
-    "METADATA_PREDICTIONS_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"metadata_predictions\")\n",
+    "METADATA_PREDICTIONS_ROOT_PATH = os.path.join(ROOT_PATH, \"data\", \"metadata_predictions_rand_4091\")\n",
     "PREFIX_LIST = [\n",
     "    \"10M_001_bs1536\",\n",
     "    \"19M_001_bs2048\",\n",
     "    \"30M_001_bs2560\",\n",
-    "    \"100M_long_run_last\"\n",
+    "    \"59M_001_bs3072\"\n",
     "]\n",
     "VAL_ADATA_IDX_RANGE = np.arange(1, 111)\n",
     "N_HOPS_DICT = {\n",
@@ -85,7 +85,78 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b921408d0d154ef2a87369a94e8efa90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae15d3aa0768485dba47ee1827809daa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d4a80774dfe475ab2b5fe1a50173da1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9b9213c24ec403bb30746e8b2e5f016",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bf3581cc4ca546a48bfd60143d84f3e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from collections import defaultdict\n",
     "from tqdm.auto import tqdm\n",
@@ -109,7 +180,7 @@
     "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
     "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
     "\n",
-    "            # logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
     "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
     "            labels_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
     "\n",
@@ -122,7 +193,7 @@
     "                for top_k in top_k_list:\n",
     "                    results[f\"{key}_top_{top_k}_accuracy\"].append(np.nan)\n",
     "                # results[f\"{key}_auc\"].append(np.nan)\n",
-    "                # results[f\"{key}_loss\"].append(np.nan)\n",
+    "                results[f\"{key}_loss\"].append(np.nan)\n",
     "                continue\n",
     "\n",
     "            # get top k predictions\n",
@@ -145,16 +216,16 @@
     "            #     auc = roc_auc_score(label_bin_nk, probs_nk, multi_class='ovr', average='micro')\n",
     "            # results[f\"{key}_auc\"].append(auc)\n",
     "\n",
-    "            # # loss\n",
-    "            # truth_names = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
-    "            # truth_indices = np.asarray(list(map(mapper.get, truth_names)))\n",
-    "            # weights = np.ones_like(truth_indices)\n",
-    "            # bad_indices = [idx for idx in range(len(truth_names)) if truth_names[idx] not in mapper]\n",
-    "            # weights[bad_indices] = 0\n",
-    "            # truth_indices[bad_indices] = 0\n",
-    "            # loss = - weights * meta_adata.obsm[f\"{key}_class_logits\"][np.arange(len(meta_adata)), truth_indices.astype(int)]\n",
-    "            # loss = loss.mean()\n",
-    "            # results[f\"{key}_loss\"].append(loss)\n",
+    "            # loss\n",
+    "            truth_names = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "            truth_indices = np.asarray(list(map(mapper.get, truth_names)))\n",
+    "            weights = np.ones_like(truth_indices)\n",
+    "            bad_indices = [idx for idx in range(len(truth_names)) if truth_names[idx] not in mapper]\n",
+    "            weights[bad_indices] = 0\n",
+    "            truth_indices[bad_indices] = 0\n",
+    "            loss = - weights * meta_adata.obsm[f\"{key}_class_logits\"][np.arange(len(meta_adata)), truth_indices.astype(int)]\n",
+    "            loss = loss.mean()\n",
+    "            results[f\"{key}_loss\"].append(loss)\n",
     "\n",
     "            # for n_hops in range(N_HOPS_DICT[key] + 1):\n",
     "            #     # accuracy\n",
@@ -164,9 +235,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "All arrays must be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:709\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    703\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[1;32m    704\u001b[0m         data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[1;32m    705\u001b[0m     )\n\u001b[1;32m    707\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[0;32m--> 709\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[1;32m    711\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:481\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m    477\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    478\u001b[0m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[1;32m    479\u001b[0m         arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[0;32m--> 481\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:115\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[1;32m    113\u001b[0m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 115\u001b[0m         index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m         index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:655\u001b[0m, in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    653\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[1;32m    658\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    659\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    660\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: All arrays must be of the same length"
+     ]
+    }
+   ],
    "source": [
     "df = pd.DataFrame(results)"
    ]
@@ -227,7 +314,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "_PREFIX_LIST = PREFIX_LIST[:3]\n",
+    "_PREFIX_LIST = PREFIX_LIST\n",
     "df = df[df['prefix'].isin(_PREFIX_LIST)].copy()"
    ]
   },
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
new file mode 100644
index 00000000..fa2edb4a
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
@@ -0,0 +1,1389 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "cuda_device_index = 0\n",
+    "val_adata_index = 1\n",
+    "\n",
+    "checkpoint_path = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ref_adata_path = \"/home/mehrtash/data/data/extract_0.h5ad\"\n",
+    "gene_info_path = \"/home/mehrtash/data/gene_info/gene_info.tsv\"\n",
+    "adata_path = f\"/home/mehrtash/data/data/cellariumgpt_validation/extract_{val_adata_index}.h5ad\"\n",
+    "ontology_resource_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/ontology\"\n",
+    "rand_prompt_vars_sublist_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/autosomal_gene_ids.txt\"\n",
+    "fixed_prompt_vars_sublist_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/empty_gene_ids.txt\"\n",
+    "output_path = \"/home/mehrtash/data/data/cellariumgpt_artifacts/metadata_predictions/100M_long_run_last\"\n",
+    "\n",
+    "rng_seed = 42\n",
+    "n_cells = 1000\n",
+    "n_genes = 4_091\n",
+    "gene_selection_method = \"highly_expressed\"  # \"random\" or \"highly_expressed\"\n",
+    "chunk_size = 16\n",
+    "\n",
+    "os.makedirs(output_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-02 22:37:14,887 - __main__ - INFO - Loading ontology resources ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ontology resources\n",
+    "logger.info(\"Loading ontology resources ...\")\n",
+    "\n",
+    "ontology_benchmarking_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_benchmarking_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_benchmarking_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_benchmarking_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_benchmarking_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_benchmarking_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "ontology_propagation_resource_path_dict = {\n",
+    "    'cell_type': os.path.join(ontology_resource_path, 'cl_propagation_resource.pkl'),\n",
+    "    'development_stage': os.path.join(ontology_resource_path, 'hsapdv_propagation_resource.pkl'),\n",
+    "    'disease': os.path.join(ontology_resource_path, 'mondo_propagation_resource.pkl'),\n",
+    "    'tissue': os.path.join(ontology_resource_path, 'uberon_propagation_resource.pkl'),\n",
+    "    'sex': os.path.join(ontology_resource_path, 'sex_propagation_resource.pkl'),\n",
+    "}\n",
+    "\n",
+    "n_hops_dict = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "ontology_benchmarking_resource_dicts = {}\n",
+    "for meta_key, path in ontology_benchmarking_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_benchmarking_resource_dicts[meta_key] = pickle.load(f)\n",
+    "\n",
+    "ontology_propagation_resource_dicts = {}\n",
+    "for meta_key, path in ontology_propagation_resource_path_dict.items():\n",
+    "    with open(path, \"rb\") as f:\n",
+    "        ontology_propagation_resource_dicts[meta_key] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-02 22:37:16,275 - __main__ - INFO - Loading the model checkpoint from /home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "logger.info(f\"Loading the model checkpoint from {checkpoint_path} ...\")\n",
+    "\n",
+    "device = torch.device(f\"cuda:{cuda_device_index}\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "    ref_adata_path=ref_adata_path,\n",
+    "    gene_info_tsv_path=gene_info_path,\n",
+    "    device=device,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-02 22:37:20,519 - __main__ - INFO - Loading the validation AnnData object from /home/mehrtash/data/data/cellariumgpt_validation/extract_1.h5ad ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "logger.info(f\"Loading the validation AnnData object from {adata_path} ...\")\n",
+    "adata = sc.read_h5ad(adata_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-02 22:37:23,433 - __main__ - INFO - Starting with 0 fixed genes.\n",
+      "2025-03-02 22:37:23,433 - __main__ - INFO - In addition, using up to 4091 highly expressed genes.\n",
+      "2025-03-02 22:37:23,475 - __main__ - INFO - Selected 4091 genes.\n",
+      "2025-03-02 22:37:23,476 - __main__ - INFO - Using 1000 random cells (seed = 42).\n"
+     ]
+    }
+   ],
+   "source": [
+    "if n_genes is not None:\n",
+    "\n",
+    "    with open(fixed_prompt_vars_sublist_path, \"r\") as f:\n",
+    "        fixed_prompt_var_names_sublist = f.read().splitlines()\n",
+    "    logger.info(f\"Starting with {len(fixed_prompt_var_names_sublist)} fixed genes.\")\n",
+    "\n",
+    "    if gene_selection_method == \"highly_expressed\":\n",
+    "        logger.info(f\"In addition, using up to {n_genes} highly expressed genes.\")\n",
+    "        X_g = np.asarray(adata.X.sum(0)).flatten()\n",
+    "        highly_expressed_gene_indices = np.argsort(X_g)[::-1]\n",
+    "        selected_gene_set = set(fixed_prompt_var_names_sublist)\n",
+    "        target_n_genes = n_genes + len(fixed_prompt_var_names_sublist)\n",
+    "        for idx in highly_expressed_gene_indices:\n",
+    "            if len(selected_gene_set) >= target_n_genes:\n",
+    "                break\n",
+    "            gene_id = adata.var_names[highly_expressed_gene_indices[idx]]\n",
+    "            selected_gene_set.add(gene_id)\n",
+    "        logger.info(f\"Selected {len(selected_gene_set)} genes.\")\n",
+    "        fixed_prompt_var_names_sublist = list(selected_gene_set)\n",
+    "        rand_prompt_var_names_sublist = []\n",
+    "        n_rand_prompt_vars = 0\n",
+    "        torch_rng = torch.Generator().manual_seed(rng_seed)\n",
+    "    \n",
+    "    elif gene_selection_method == \"random\":\n",
+    "        logger.info(f\"In addition, using {n_genes} random genes (seed = {rng_seed}).\")\n",
+    "        torch_rng = torch.Generator().manual_seed(rng_seed)\n",
+    "        n_rand_prompt_vars = n_genes\n",
+    "        with open(rand_prompt_vars_sublist_path, \"r\") as f:\n",
+    "            rand_prompt_var_names_sublist = f.read().splitlines()\n",
+    "    \n",
+    "    else:\n",
+    "        raise ValueError(f\"Unknown gene selection method: {gene_selection_method}\")\n",
+    "else:\n",
+    "    logger.info(f\"Using all genes.\")\n",
+    "    n_rand_prompt_vars = None\n",
+    "    rand_prompt_var_names_sublist = None\n",
+    "    fixed_prompt_var_names_sublist = None\n",
+    "    \n",
+    "if n_cells is None:\n",
+    "    logger.info(f\"Using all cells.\")\n",
+    "else:\n",
+    "    n_cells = min(n_cells, len(adata))\n",
+    "    logger.info(f\"Using {n_cells} random cells (seed = {rng_seed}).\")\n",
+    "    rng = np.random.RandomState(rng_seed)\n",
+    "    adata = adata[rng.choice(len(adata), n_cells, replace=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-03-02 22:37:25,800 - __main__ - INFO - Predicting metadata for 1000 cells ...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8539546975c146a2ae0b5ae928853a05",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/63 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "logger.info(f\"Predicting metadata for {len(adata)} cells ...\")\n",
+    "preds = ctx.predict_metadata_chunked(\n",
+    "    adata=adata,\n",
+    "    chunk_size=chunk_size,\n",
+    "    n_rand_prompt_vars=n_rand_prompt_vars,\n",
+    "    rand_prompt_var_names_sublist=rand_prompt_var_names_sublist,\n",
+    "    fixed_prompt_var_names_sublist=fixed_prompt_var_names_sublist,\n",
+    "    rng=torch_rng)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/tmp/ipykernel_1041600/427261693.py:4: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
+      "  adata.obs[key + \"_pred\"] = best_label\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in preds.keys():\n",
+    "    best_call_n = np.argmax(preds[key], -1)\n",
+    "    best_label = [ctx.metadata_ontology_infos[key][\"labels\"][idx] for idx in best_call_n]\n",
+    "    adata.obs[key + \"_pred\"] = best_label\n",
+    "\n",
+    "cols = []\n",
+    "for key in preds.keys():\n",
+    "    cols.append(key)\n",
+    "    cols.append(key + \"_pred\")\n",
+    "new_obs = adata.obs[cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>cell_type_pred</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>tissue_pred</th>\n",
+       "      <th>development_stage</th>\n",
+       "      <th>development_stage_pred</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>disease_pred</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>sex_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>31589848</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31594264</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31601455</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31589769</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593942</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>23-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31598517</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599161</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31601198</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599402</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31594186</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31590646</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600383</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31592385</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31588945</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31590959</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31592015</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31588921</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31592421</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31592116</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>17th week post-fertilization stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31591598</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31588345</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593570</th>\n",
+       "      <td>Bergmann glial cell</td>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600178</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599815</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31598814</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>12th week post-fertilization stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593104</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600997</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>51-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31598473</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>51-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600098</th>\n",
+       "      <td>differentiation-committed oligodendrocyte prec...</td>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>12th week post-fertilization stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599509</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>kidney loop of Henle epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31598433</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>12th week post-fertilization stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599851</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593301</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600830</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31589187</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31600725</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>kidney loop of Henle epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31588821</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31591031</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31599410</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31591972</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593634</th>\n",
+       "      <td>differentiation-committed oligodendrocyte prec...</td>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31601467</th>\n",
+       "      <td>macroglial cell</td>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31601214</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31589523</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31589177</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31601204</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>51-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>male</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31590685</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31589072</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31593664</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>cerebellum</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31590994</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>temporal lobe</td>\n",
+       "      <td>52-year-old human stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>female</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  cell_type  \\\n",
+       "31589848                                    oligodendrocyte   \n",
+       "31594264                                    oligodendrocyte   \n",
+       "31601455                                            unknown   \n",
+       "31589769                                    oligodendrocyte   \n",
+       "31593942                                            unknown   \n",
+       "31598517                            cerebellar granule cell   \n",
+       "31599161                            cerebellar granule cell   \n",
+       "31601198                            cerebellar granule cell   \n",
+       "31599402                            cerebellar granule cell   \n",
+       "31594186                                    microglial cell   \n",
+       "31590646                                    oligodendrocyte   \n",
+       "31600383                            cerebellar granule cell   \n",
+       "31592385                                    oligodendrocyte   \n",
+       "31588945                                    oligodendrocyte   \n",
+       "31590959                                    oligodendrocyte   \n",
+       "31592015                                    oligodendrocyte   \n",
+       "31588921                                    oligodendrocyte   \n",
+       "31592421                                    oligodendrocyte   \n",
+       "31592116                                            unknown   \n",
+       "31591598                                    oligodendrocyte   \n",
+       "31588345                                    oligodendrocyte   \n",
+       "31593570                                Bergmann glial cell   \n",
+       "31600178                            cerebellar granule cell   \n",
+       "31599815                            cerebellar granule cell   \n",
+       "31598814                            cerebellar granule cell   \n",
+       "31593104                                    oligodendrocyte   \n",
+       "31600997                            cerebellar granule cell   \n",
+       "31598473                                        interneuron   \n",
+       "31600098  differentiation-committed oligodendrocyte prec...   \n",
+       "31599509                            cerebellar granule cell   \n",
+       "31598433                                        interneuron   \n",
+       "31599851                            cerebellar granule cell   \n",
+       "31593301                                    oligodendrocyte   \n",
+       "31600830                            cerebellar granule cell   \n",
+       "31589187                                    oligodendrocyte   \n",
+       "31600725                            cerebellar granule cell   \n",
+       "31588821                                    oligodendrocyte   \n",
+       "31591031                                    oligodendrocyte   \n",
+       "31599410                            cerebellar granule cell   \n",
+       "31591972                                    oligodendrocyte   \n",
+       "31593634  differentiation-committed oligodendrocyte prec...   \n",
+       "31601467                                    macroglial cell   \n",
+       "31601214                            cerebellar granule cell   \n",
+       "31589523                                            unknown   \n",
+       "31589177                                            unknown   \n",
+       "31601204                            cerebellar granule cell   \n",
+       "31590685                                    oligodendrocyte   \n",
+       "31589072                                    oligodendrocyte   \n",
+       "31593664                                    oligodendrocyte   \n",
+       "31590994                                    oligodendrocyte   \n",
+       "\n",
+       "                                cell_type_pred           tissue  \\\n",
+       "31589848                       oligodendrocyte  dentate nucleus   \n",
+       "31594264                       oligodendrocyte  dentate nucleus   \n",
+       "31601455               cerebellar granule cell  dentate nucleus   \n",
+       "31589769                       oligodendrocyte  dentate nucleus   \n",
+       "31593942                                neuron  dentate nucleus   \n",
+       "31598517                       epithelial cell  dentate nucleus   \n",
+       "31599161            stratified epithelial cell  dentate nucleus   \n",
+       "31601198                                neuron  dentate nucleus   \n",
+       "31599402            stratified epithelial cell  dentate nucleus   \n",
+       "31594186                            macrophage  dentate nucleus   \n",
+       "31590646                       oligodendrocyte  dentate nucleus   \n",
+       "31600383       monocyte-derived dendritic cell  dentate nucleus   \n",
+       "31592385                       oligodendrocyte  dentate nucleus   \n",
+       "31588945                       oligodendrocyte  dentate nucleus   \n",
+       "31590959                       oligodendrocyte  dentate nucleus   \n",
+       "31592015                       oligodendrocyte  dentate nucleus   \n",
+       "31588921                       oligodendrocyte  dentate nucleus   \n",
+       "31592421                       oligodendrocyte  dentate nucleus   \n",
+       "31592116                      GABAergic neuron  dentate nucleus   \n",
+       "31591598                       oligodendrocyte  dentate nucleus   \n",
+       "31588345                       oligodendrocyte  dentate nucleus   \n",
+       "31593570                             astrocyte  dentate nucleus   \n",
+       "31600178                       epithelial cell  dentate nucleus   \n",
+       "31599815       monocyte-derived dendritic cell  dentate nucleus   \n",
+       "31598814       monocyte-derived dendritic cell  dentate nucleus   \n",
+       "31593104                       oligodendrocyte  dentate nucleus   \n",
+       "31600997                      GABAergic neuron  dentate nucleus   \n",
+       "31598473                      endothelial cell  dentate nucleus   \n",
+       "31600098        oligodendrocyte precursor cell  dentate nucleus   \n",
+       "31599509  kidney loop of Henle epithelial cell  dentate nucleus   \n",
+       "31598433                                neuron  dentate nucleus   \n",
+       "31599851            stratified epithelial cell  dentate nucleus   \n",
+       "31593301                       oligodendrocyte  dentate nucleus   \n",
+       "31600830                      GABAergic neuron  dentate nucleus   \n",
+       "31589187                       oligodendrocyte  dentate nucleus   \n",
+       "31600725  kidney loop of Henle epithelial cell  dentate nucleus   \n",
+       "31588821                       oligodendrocyte  dentate nucleus   \n",
+       "31591031                       oligodendrocyte  dentate nucleus   \n",
+       "31599410            stratified epithelial cell  dentate nucleus   \n",
+       "31591972                       oligodendrocyte  dentate nucleus   \n",
+       "31593634        oligodendrocyte precursor cell  dentate nucleus   \n",
+       "31601467                             astrocyte  dentate nucleus   \n",
+       "31601214                      GABAergic neuron  dentate nucleus   \n",
+       "31589523       monocyte-derived dendritic cell  dentate nucleus   \n",
+       "31589177       monocyte-derived dendritic cell  dentate nucleus   \n",
+       "31601204                      GABAergic neuron  dentate nucleus   \n",
+       "31590685                       oligodendrocyte  dentate nucleus   \n",
+       "31589072                       oligodendrocyte  dentate nucleus   \n",
+       "31593664                       oligodendrocyte  dentate nucleus   \n",
+       "31590994                       oligodendrocyte  dentate nucleus   \n",
+       "\n",
+       "            tissue_pred        development_stage  \\\n",
+       "31589848  temporal lobe  52-year-old human stage   \n",
+       "31594264     cerebellum  52-year-old human stage   \n",
+       "31601455     cerebellum  52-year-old human stage   \n",
+       "31589769  temporal lobe  52-year-old human stage   \n",
+       "31593942     cerebellum  52-year-old human stage   \n",
+       "31598517     cerebellum  52-year-old human stage   \n",
+       "31599161     cerebellum  52-year-old human stage   \n",
+       "31601198     cerebellum  52-year-old human stage   \n",
+       "31599402     cerebellum  52-year-old human stage   \n",
+       "31594186  temporal lobe  52-year-old human stage   \n",
+       "31590646  temporal lobe  52-year-old human stage   \n",
+       "31600383     cerebellum  52-year-old human stage   \n",
+       "31592385     cerebellum  52-year-old human stage   \n",
+       "31588945  temporal lobe  52-year-old human stage   \n",
+       "31590959  temporal lobe  52-year-old human stage   \n",
+       "31592015  temporal lobe  52-year-old human stage   \n",
+       "31588921  temporal lobe  52-year-old human stage   \n",
+       "31592421     cerebellum  52-year-old human stage   \n",
+       "31592116     cerebellum  52-year-old human stage   \n",
+       "31591598  temporal lobe  52-year-old human stage   \n",
+       "31588345  temporal lobe  52-year-old human stage   \n",
+       "31593570     cerebellum  52-year-old human stage   \n",
+       "31600178     cerebellum  52-year-old human stage   \n",
+       "31599815     cerebellum  52-year-old human stage   \n",
+       "31598814     cerebellum  52-year-old human stage   \n",
+       "31593104     cerebellum  52-year-old human stage   \n",
+       "31600997     cerebellum  52-year-old human stage   \n",
+       "31598473     cerebellum  52-year-old human stage   \n",
+       "31600098     cerebellum  52-year-old human stage   \n",
+       "31599509     cerebellum  52-year-old human stage   \n",
+       "31598433     cerebellum  52-year-old human stage   \n",
+       "31599851     cerebellum  52-year-old human stage   \n",
+       "31593301     cerebellum  52-year-old human stage   \n",
+       "31600830     cerebellum  52-year-old human stage   \n",
+       "31589187  temporal lobe  52-year-old human stage   \n",
+       "31600725     cerebellum  52-year-old human stage   \n",
+       "31588821  temporal lobe  52-year-old human stage   \n",
+       "31591031     cerebellum  52-year-old human stage   \n",
+       "31599410     cerebellum  52-year-old human stage   \n",
+       "31591972     cerebellum  52-year-old human stage   \n",
+       "31593634     cerebellum  52-year-old human stage   \n",
+       "31601467     cerebellum  52-year-old human stage   \n",
+       "31601214     cerebellum  52-year-old human stage   \n",
+       "31589523     cerebellum  52-year-old human stage   \n",
+       "31589177     cerebellum  52-year-old human stage   \n",
+       "31601204     cerebellum  52-year-old human stage   \n",
+       "31590685     cerebellum  52-year-old human stage   \n",
+       "31589072  temporal lobe  52-year-old human stage   \n",
+       "31593664     cerebellum  52-year-old human stage   \n",
+       "31590994  temporal lobe  52-year-old human stage   \n",
+       "\n",
+       "                      development_stage_pred disease disease_pred   sex  \\\n",
+       "31589848                   61-year-old stage  normal       normal  male   \n",
+       "31594264                   61-year-old stage  normal       normal  male   \n",
+       "31601455                   61-year-old stage  normal       normal  male   \n",
+       "31589769                   61-year-old stage  normal       normal  male   \n",
+       "31593942                   23-year-old stage  normal       normal  male   \n",
+       "31598517                   61-year-old stage  normal       normal  male   \n",
+       "31599161                   61-year-old stage  normal       normal  male   \n",
+       "31601198                   61-year-old stage  normal       normal  male   \n",
+       "31599402                   61-year-old stage  normal       normal  male   \n",
+       "31594186                   61-year-old stage  normal       normal  male   \n",
+       "31590646                   61-year-old stage  normal       normal  male   \n",
+       "31600383                   61-year-old stage  normal       normal  male   \n",
+       "31592385                   61-year-old stage  normal       normal  male   \n",
+       "31588945                   61-year-old stage  normal       normal  male   \n",
+       "31590959                   61-year-old stage  normal       normal  male   \n",
+       "31592015                   61-year-old stage  normal       normal  male   \n",
+       "31588921                   61-year-old stage  normal       normal  male   \n",
+       "31592421                   61-year-old stage  normal       normal  male   \n",
+       "31592116  17th week post-fertilization stage  normal       normal  male   \n",
+       "31591598                   61-year-old stage  normal       normal  male   \n",
+       "31588345                   61-year-old stage  normal       normal  male   \n",
+       "31593570                   61-year-old stage  normal       normal  male   \n",
+       "31600178                   61-year-old stage  normal       normal  male   \n",
+       "31599815                   61-year-old stage  normal       normal  male   \n",
+       "31598814  12th week post-fertilization stage  normal       normal  male   \n",
+       "31593104                   61-year-old stage  normal       normal  male   \n",
+       "31600997                   51-year-old stage  normal       normal  male   \n",
+       "31598473                   51-year-old stage  normal       normal  male   \n",
+       "31600098  12th week post-fertilization stage  normal       normal  male   \n",
+       "31599509                   61-year-old stage  normal       normal  male   \n",
+       "31598433  12th week post-fertilization stage  normal       normal  male   \n",
+       "31599851                   61-year-old stage  normal       normal  male   \n",
+       "31593301                   61-year-old stage  normal       normal  male   \n",
+       "31600830                   61-year-old stage  normal       normal  male   \n",
+       "31589187                   61-year-old stage  normal       normal  male   \n",
+       "31600725                   61-year-old stage  normal       normal  male   \n",
+       "31588821                   61-year-old stage  normal       normal  male   \n",
+       "31591031                   61-year-old stage  normal       normal  male   \n",
+       "31599410                   61-year-old stage  normal       normal  male   \n",
+       "31591972                   61-year-old stage  normal       normal  male   \n",
+       "31593634                   61-year-old stage  normal       normal  male   \n",
+       "31601467                   61-year-old stage  normal       normal  male   \n",
+       "31601214                   61-year-old stage  normal       normal  male   \n",
+       "31589523                   61-year-old stage  normal       normal  male   \n",
+       "31589177                   61-year-old stage  normal       normal  male   \n",
+       "31601204                   51-year-old stage  normal       normal  male   \n",
+       "31590685                   61-year-old stage  normal       normal  male   \n",
+       "31589072                   61-year-old stage  normal       normal  male   \n",
+       "31593664                   61-year-old stage  normal       normal  male   \n",
+       "31590994                   61-year-old stage  normal       normal  male   \n",
+       "\n",
+       "         sex_pred  \n",
+       "31589848   female  \n",
+       "31594264   female  \n",
+       "31601455   female  \n",
+       "31589769   female  \n",
+       "31593942   female  \n",
+       "31598517     male  \n",
+       "31599161   female  \n",
+       "31601198   female  \n",
+       "31599402     male  \n",
+       "31594186     male  \n",
+       "31590646   female  \n",
+       "31600383     male  \n",
+       "31592385   female  \n",
+       "31588945     male  \n",
+       "31590959   female  \n",
+       "31592015   female  \n",
+       "31588921     male  \n",
+       "31592421   female  \n",
+       "31592116     male  \n",
+       "31591598   female  \n",
+       "31588345     male  \n",
+       "31593570     male  \n",
+       "31600178   female  \n",
+       "31599815   female  \n",
+       "31598814     male  \n",
+       "31593104   female  \n",
+       "31600997     male  \n",
+       "31598473     male  \n",
+       "31600098     male  \n",
+       "31599509     male  \n",
+       "31598433     male  \n",
+       "31599851     male  \n",
+       "31593301   female  \n",
+       "31600830     male  \n",
+       "31589187     male  \n",
+       "31600725   female  \n",
+       "31588821     male  \n",
+       "31591031   female  \n",
+       "31599410   female  \n",
+       "31591972   female  \n",
+       "31593634     male  \n",
+       "31601467     male  \n",
+       "31601214     male  \n",
+       "31589523     male  \n",
+       "31589177     male  \n",
+       "31601204     male  \n",
+       "31590685   female  \n",
+       "31589072   female  \n",
+       "31593664   female  \n",
+       "31590994   female  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs.head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_adata = adata[adata.obs['cell_type'] == 'cerebellar granule cell'].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pp.normalize_total(_adata, target_sum=1e4)\n",
+    "# sc.pp.log1p(_adata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr_g = np.asarray(_adata.X.mean(0)).flatten()\n",
+    "order = np.argsort(expr_g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MALAT1 1176.4282\n",
+      "KCNIP4 51.889442\n",
+      "RBFOX1 39.08155\n",
+      "NRXN1 37.05956\n",
+      "ZNF385D 31.274553\n",
+      "NRXN3 29.412807\n",
+      "RALYL 28.302118\n",
+      "FGF14 27.987516\n",
+      "CDH18 26.29924\n",
+      "KCND2 25.589275\n",
+      "SYT1 25.392553\n",
+      "CADM2 24.365475\n",
+      "GRIK2 22.64451\n",
+      "FSTL5 20.709612\n",
+      "ADGRB3 20.459864\n",
+      "CADPS2 19.70784\n",
+      "DPP6 19.099762\n",
+      "CHN2 18.342234\n",
+      "TIAM1 17.454777\n",
+      "JMJD1C 16.945967\n",
+      "RIMS1 16.903675\n",
+      "SNHG14 16.197824\n",
+      "STXBP5L 15.808228\n",
+      "SNAP25 15.755832\n",
+      "ANKS1B 15.605362\n",
+      "GRID2 15.461536\n",
+      "KAZN 15.290413\n",
+      "MSRA 15.083475\n",
+      "CALM1 13.966373\n",
+      "DLGAP1 13.554987\n",
+      "RORA 13.512486\n",
+      "NTM 13.479536\n",
+      "NLGN1 13.344391\n",
+      "CALN1 13.213117\n",
+      "CACNA1A 12.965487\n",
+      "LINC00486 12.807418\n",
+      "NFIA 12.51874\n",
+      "SYNE1 12.420801\n",
+      "ZFPM2 12.312247\n",
+      "ANK3 12.283244\n",
+      "RBFOX3 11.96194\n",
+      "CA10 11.820011\n",
+      "UNC13C 11.696393\n",
+      "ZBTB20 11.657179\n",
+      "NKAIN2 11.602788\n",
+      "MAP1B 11.503829\n",
+      "ERC1 11.317675\n",
+      "OPCML 11.300125\n",
+      "ABLIM1 11.08845\n",
+      "CAMK4 11.029335\n"
+     ]
+    }
+   ],
+   "source": [
+    "for o in order[::-1][:50]:\n",
+    "    gene_id = _adata.var_names[o]\n",
+    "    expr = expr_g[o]\n",
+    "    gene_name = ctx.gene_id_to_gene_symbol_map[gene_id]\n",
+    "    print(gene_name, expr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb
new file mode 100644
index 00000000..9d59f5ac
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# arguments\n",
+    "cuda_device_index = 0\n",
+    "val_adata_index = 1\n",
+    "\n",
+    "checkpoint_path = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ref_adata_path = \"/home/mehrtash/data/data/extract_0.h5ad\"\n",
+    "gene_info_path = \"/home/mehrtash/data/gene_info/gene_info.tsv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cuda_device_index = 0\n",
+    "device = torch.device(f\"cuda:{cuda_device_index}\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=checkpoint_path,\n",
+    "    ref_adata_path=ref_adata_path,\n",
+    "    gene_info_tsv_path=gene_info_path,\n",
+    "    device=device,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['ENSG00000187642', 'ENSG00000078808', 'ENSG00000272106', ...,\n",
+       "       'ENSG00000228836', 'ENSG00000231937', 'ENSG00000268916'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ctx.model_var_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_genes_with_info_set = set(ctx.gene_info_df['ENSEMBL Gene ID'].values)\n",
+    "gene_id_to_biotype_map = dict(zip(ctx.gene_info_df['ENSEMBL Gene ID'], ctx.gene_info_df['Gene Biotype']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(int,\n",
+       "            {'protein_coding': 19250,\n",
+       "             'lncRNA': 16411,\n",
+       "             'IG_V_gene': 145,\n",
+       "             'IG_V_pseudogene': 187,\n",
+       "             'missing': 224,\n",
+       "             'TR_V_gene': 106,\n",
+       "             'TR_V_pseudogene': 33,\n",
+       "             'TR_J_gene': 79,\n",
+       "             'IG_D_gene': 37,\n",
+       "             'IG_J_gene': 18,\n",
+       "             'TR_J_pseudogene': 4,\n",
+       "             'IG_J_pseudogene': 3,\n",
+       "             'IG_C_pseudogene': 9,\n",
+       "             'transcribed_unitary_pseudogene': 17,\n",
+       "             'artifact': 17,\n",
+       "             'transcribed_unprocessed_pseudogene': 29,\n",
+       "             'processed_pseudogene': 2,\n",
+       "             'IG_C_gene': 14,\n",
+       "             'TR_C_gene': 6,\n",
+       "             'TR_D_gene': 4,\n",
+       "             'TEC': 1,\n",
+       "             'transcribed_processed_pseudogene': 2,\n",
+       "             'unitary_pseudogene': 1,\n",
+       "             'unprocessed_pseudogene': 2})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# what is the breakdown by type?\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "counts_dict = defaultdict(int)\n",
+    "for var_name in ctx.model_var_names:\n",
+    "    if var_name not in all_genes_with_info_set:\n",
+    "        counts_dict[\"missing\"] += 1\n",
+    "    else:\n",
+    "        biotype = gene_id_to_biotype_map[var_name]\n",
+    "        counts_dict[biotype] += 1\n",
+    "\n",
+    "counts_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Gene stable ID contig\n",
+      "0      ENSG00000210049     MT\n",
+      "1      ENSG00000211459     MT\n",
+      "2      ENSG00000210077     MT\n",
+      "3      ENSG00000210082     MT\n",
+      "4      ENSG00000209082     MT\n",
+      "...                ...    ...\n",
+      "86397  ENSG00000235358      1\n",
+      "86398  ENSG00000228067      1\n",
+      "86399  ENSG00000293271      1\n",
+      "86400  ENSG00000310526      1\n",
+      "86401  ENSG00000241860      1\n",
+      "\n",
+      "[86402 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pybiomart import Dataset\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Connect to the human gene dataset\n",
+    "dataset = Dataset(name='hsapiens_gene_ensembl', host='www.ensembl.org')\n",
+    "\n",
+    "# Query the dataset for all gene IDs and their chromosome names\n",
+    "result = dataset.query(attributes=['ensembl_gene_id', 'chromosome_name'])\n",
+    "\n",
+    "# Optionally, rename the chromosome column for clarity\n",
+    "result.rename(columns={'Chromosome/scaffold name': 'contig'}, inplace=True)\n",
+    "\n",
+    "print(result)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contig\n",
+       "1                  7095\n",
+       "2                  5685\n",
+       "6                  4230\n",
+       "11                 4170\n",
+       "3                  4161\n",
+       "                   ... \n",
+       "KI270720.1            1\n",
+       "KI270718.1            1\n",
+       "GL000216.2            1\n",
+       "HSCHR17_12_CTG4       1\n",
+       "HSCHR4_4_CTG12        1\n",
+       "Name: count, Length: 528, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['contig'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_id_to_contig_map = dict(zip(result['Gene stable ID'], result['contig']))\n",
+    "model_genes_df = pd.DataFrame(ctx.model_var_names, columns=['ensembl_gene_id'])\n",
+    "model_genes_df['contig'] = model_genes_df['ensembl_gene_id'].map(gene_id_to_contig_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "contig\n",
+       "1             3327\n",
+       "2             2450\n",
+       "11            2025\n",
+       "19            1995\n",
+       "17            1938\n",
+       "12            1857\n",
+       "3             1820\n",
+       "6             1764\n",
+       "5             1736\n",
+       "7             1633\n",
+       "16            1605\n",
+       "4             1477\n",
+       "8             1435\n",
+       "14            1428\n",
+       "10            1351\n",
+       "9             1268\n",
+       "15            1225\n",
+       "NaN           1135\n",
+       "X             1128\n",
+       "20             941\n",
+       "22             880\n",
+       "13             761\n",
+       "18             740\n",
+       "21             538\n",
+       "Y              107\n",
+       "MT              13\n",
+       "KI270728.1       4\n",
+       "KI270727.1       3\n",
+       "KI270734.1       3\n",
+       "GL000194.1       2\n",
+       "KI270713.1       2\n",
+       "KI270726.1       2\n",
+       "GL000219.1       1\n",
+       "GL000195.1       1\n",
+       "KI270721.1       1\n",
+       "KI270731.1       1\n",
+       "KI270711.1       1\n",
+       "GL000009.2       1\n",
+       "GL000213.1       1\n",
+       "GL000218.1       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_genes_df['contig'].value_counts(dropna=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of autosomal genes: 34194\n",
+      "Number of sex genes: 1235\n"
+     ]
+    }
+   ],
+   "source": [
+    "autosomal_gene_ids = model_genes_df[\n",
+    "    model_genes_df['contig'].isin({str(i) for i in range(1, 23)})]['ensembl_gene_id'].values.tolist()\n",
+    "sex_gene_ids = model_genes_df[\n",
+    "    model_genes_df['contig'].isin({'X', 'Y'})]['ensembl_gene_id'].values.tolist()\n",
+    "print(f\"Number of autosomal genes: {len(autosomal_gene_ids)}\")\n",
+    "print(f\"Number of sex genes: {len(sex_gene_ids)}\")\n",
+    "assert set(autosomal_gene_ids).intersection(sex_gene_ids) == set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write the gene lists to file\n",
+    "with open(\"/home/mehrtash/data/data/cellariumgpt_artifacts/autosomal_gene_ids.txt\", 'w') as f:\n",
+    "    for gene_id in autosomal_gene_ids:\n",
+    "        f.write(f\"{gene_id}\\n\")\n",
+    "\n",
+    "with open(\"/home/mehrtash/data/data/cellariumgpt_artifacts/sex_gene_ids.txt\", 'w') as f:\n",
+    "    for gene_id in sex_gene_ids:\n",
+    "        f.write(f\"{gene_id}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6957"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "8192 - 1235"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8192"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "6957 + 1235"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
index 50ae4849..3700d778 100644
--- a/scripts/metadata_prediction_analysis.py
+++ b/scripts/metadata_prediction_analysis.py
@@ -136,6 +136,16 @@ def main():
         "--gene_selection_method", type=str, default="random",
         help="Gene selection method: 'random' or 'highly_expressed' (default: random)"
     )
+    parser.add_argument(
+        "--rand_prompt_vars_sublist_path", type=str,
+        default="/home/mehrtash/data/data/cellariumgpt_artifacts/autosomal_gene_ids.txt",
+        help="Gene list to select random genes from."
+    )
+    parser.add_argument(
+        "--fixed_prompt_vars_sublist_path", type=str,
+        default="/home/mehrtash/data/data/cellariumgpt_artifacts/sex_gene_ids.txt",
+        help="Gene list to select fixed genes from."
+    )
     parser.add_argument(
         "--chunk_size", type=int, default=16,
         help="Chunk size for processing (default: 16)"
@@ -201,39 +211,59 @@ def main():
     logger.info(f"Loading the validation AnnData object from {args.val_adata_path} ...")
     adata = sc.read_h5ad(args.val_adata_path)
     
-    # Updated gene selection logic to support both "highly_expressed" and "random"
     if args.n_genes is not None:
+
+        with open(args.fixed_prompt_vars_sublist_path, "r") as f:
+            fixed_prompt_var_names_sublist = f.read().splitlines()
+        logger.info(f"Starting with {len(fixed_prompt_var_names_sublist)} fixed genes.")
+
         if args.gene_selection_method == "highly_expressed":
-            logger.info(f"Using {args.n_genes} highly expressed genes.")
+            logger.info(f"In addition, using up to {args.n_genes} highly expressed genes.")
             X_g = np.asarray(adata.X.sum(0)).flatten()
-            highly_expressed_gene_indices = np.argsort(X_g)[-args.n_genes:]
-            highly_expressed_gene_ids = adata.var_names[highly_expressed_gene_indices]
-            adata = adata[:, highly_expressed_gene_ids]
-            n_rand_prompt_vars = None
-            torch_rng = None
+            highly_expressed_gene_indices = np.argsort(X_g)[::-1]
+            selected_gene_set = set(fixed_prompt_var_names_sublist)
+            target_n_genes = args.n_genes + len(fixed_prompt_var_names_sublist)
+            for idx in highly_expressed_gene_indices:
+                if len(selected_gene_set) >= target_n_genes:
+                    break
+                gene_id = adata.var_names[highly_expressed_gene_indices[idx]]
+                selected_gene_set.add(gene_id)
+            logger.info(f"Selected {len(selected_gene_set)} genes.")
+            fixed_prompt_var_names_sublist = list(selected_gene_set)
+            rand_prompt_var_names_sublist = []
+            n_rand_prompt_vars = 0
+            torch_rng = torch.Generator().manual_seed(args.rng_seed)
+        
         elif args.gene_selection_method == "random":
-            logger.info(f"Using {args.n_genes} random genes (seed = {args.rng_seed}).")
+            logger.info(f"In addition, using {args.n_genes} random genes (seed = {args.rng_seed}).")
             torch_rng = torch.Generator().manual_seed(args.rng_seed)
             n_rand_prompt_vars = args.n_genes
+            with open(args.rand_prompt_vars_sublist_path, "r") as f:
+                rand_prompt_var_names_sublist = f.read().splitlines()
+        
         else:
             raise ValueError(f"Unknown gene selection method: {args.gene_selection_method}")
     else:
-        logger.info("Using all genes.")
+        logger.info(f"Using all genes.")
         n_rand_prompt_vars = None
-        torch_rng = None
-
+        rand_prompt_var_names_sublist = None
+        fixed_prompt_var_names_sublist = None
+        
     if args.n_cells is None:
-        logger.info("Using all cells.")
+        logger.info(f"Using all cells.")
     else:
         n_cells = min(args.n_cells, len(adata))
         logger.info(f"Using {n_cells} random cells (seed = {args.rng_seed}).")
         rng = np.random.RandomState(args.rng_seed)
         adata = adata[rng.choice(len(adata), n_cells, replace=False)]
+
     logger.info(f"Predicting metadata for {len(adata)} cells ...")
     preds = ctx.predict_metadata_chunked(
-        adata, 
+        adata=adata,
         chunk_size=args.chunk_size,
         n_rand_prompt_vars=n_rand_prompt_vars,
+        rand_prompt_var_names_sublist=rand_prompt_var_names_sublist,
+        fixed_prompt_var_names_sublist=fixed_prompt_var_names_sublist,
         rng=torch_rng
     )
 

From 6a4b5d0bc55a0c72d08a9ae2934b0750698defef Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sun, 2 Mar 2025 22:56:54 +0000
Subject: [PATCH 53/64] foundry changes

---
 scripts/linear_response_analysis.py     | 24 ++++++++++++------------
 scripts/run_linear_response_analysis.sh | 10 +++++-----
 2 files changed, 17 insertions(+), 17 deletions(-)
 mode change 100644 => 100755 scripts/run_linear_response_analysis.sh

diff --git a/scripts/linear_response_analysis.py b/scripts/linear_response_analysis.py
index 3d137090..d22ec526 100644
--- a/scripts/linear_response_analysis.py
+++ b/scripts/linear_response_analysis.py
@@ -181,24 +181,24 @@ def main():
         "cell_index": args.cell_index,
         
         # Expression range outputs
-        "x_lo_q": gex_range_dict['x_lo_q'].cpu().numpy(),
-        "x_hi_q": gex_range_dict['x_hi_q'].cpu().numpy(),
-        "range_q": gex_range_dict['range_q'].cpu().numpy(),
-        "gene_logits_qk": gex_range_dict['gene_logits_qk'].cpu().numpy(),
-        "gene_logits_mode_q": gex_range_dict['gene_logits_mode_q'].cpu().numpy(),
-        "gene_marginal_mean_q": gex_range_dict['gene_marginal_mean_q'].cpu().numpy(),
-        "gene_marginal_std_q": gex_range_dict['gene_marginal_std_q'].cpu().numpy(),
+        "x_lo_q": gex_range_dict['x_lo_q'].cpu().numpy().astype(np.float32),
+        "x_hi_q": gex_range_dict['x_hi_q'].cpu().numpy().astype(np.float32),
+        "range_q": gex_range_dict['range_q'].cpu().numpy().astype(np.float32),
+        "gene_logits_qk": gex_range_dict['gene_logits_qk'].cpu().numpy().astype(np.float32),
+        "gene_logits_mode_q": gex_range_dict['gene_logits_mode_q'].cpu().numpy().astype(np.float32),
+        "gene_marginal_mean_q": gex_range_dict['gene_marginal_mean_q'].cpu().numpy().astype(np.float32),
+        "gene_marginal_std_q": gex_range_dict['gene_marginal_std_q'].cpu().numpy().astype(np.float32),
         
         # Dose response outputs
         "query_gene_ids": query_gene_ids,
         "perturb_gene_ids": query_gene_ids,
-        "doses_pi": dose_response_dict['doses_pi'],
-        "responses_mean_pqi": dose_response_dict['responses_mean_pqi'],
+        # "doses_pi": dose_response_dict['doses_pi'],
+        # "responses_mean_pqi": dose_response_dict['responses_mean_pqi'],
         
         # Linear regression outputs
-        "slope_qp": slope_qp,
-        "intercept_qp": intercept_qp,
-        "r_squared_qp": r_squared_qp,
+        "slope_qp": slope_qp.astype(np.float32),
+        # "intercept_qp": intercept_qp,
+        "r_squared_qp": r_squared_qp.astype(np.float32),
     }
     
     print("Saving output ...")
diff --git a/scripts/run_linear_response_analysis.sh b/scripts/run_linear_response_analysis.sh
old mode 100644
new mode 100755
index fc525b42..268a0f6a
--- a/scripts/run_linear_response_analysis.sh
+++ b/scripts/run_linear_response_analysis.sh
@@ -12,11 +12,11 @@ fi
 CUDA_DEVICE_INDEX="$1"
 
 # Default parameters (feel free to modify these defaults)
-CHECKPOINT_PATH="/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt"
-REF_ADATA_PATH="/home/mehrtash/data/data/extract_0.h5ad"
-GENE_INFO_TSV_PATH="/home/mehrtash/data/gene_info/gene_info.tsv"
-VALIDATION_ADATA_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad"
-OUTPUT_PATH="/home/mehrtash/data/data/linear_response/100M_long_run_last_checkpoint"
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt"
+REF_ADATA_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/data/extract_0.h5ad"
+GENE_INFO_TSV_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/gene_info/gene_info.tsv"
+VALIDATION_ADATA_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/cell_types_for_validation_filtered.h5ad"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/linear_response/59M_001_bs3072"
 
 QUERY_CHUNK_SIZE=1000
 TOTAL_PROB_MASS=0.5

From 24d7a9f16a35b850e6da5e29ebcad1349bba6156 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Sun, 2 Mar 2025 22:58:51 +0000
Subject: [PATCH 54/64] foundry scripts

---
 .../run_metadata_prediction_analysis.sh       | 66 +++++++++++++++++++
 .../run_metadata_prediction_analysis_multi.sh | 27 ++++++++
 2 files changed, 93 insertions(+)
 create mode 100755 scripts/foundry/run_metadata_prediction_analysis.sh
 create mode 100755 scripts/foundry/run_metadata_prediction_analysis_multi.sh

diff --git a/scripts/foundry/run_metadata_prediction_analysis.sh b/scripts/foundry/run_metadata_prediction_analysis.sh
new file mode 100755
index 00000000..678c5353
--- /dev/null
+++ b/scripts/foundry/run_metadata_prediction_analysis.sh
@@ -0,0 +1,66 @@
+#!/bin/bash
+# run_metadata_prediction_analysis.sh
+
+# Usage check: All three arguments are required.
+if [ "$#" -ne 3 ]; then
+    echo "Usage: $0 <GPU_ID> <CHECKPOINT_PATH> <OUTPUT_PATH>"
+    exit 1
+fi
+
+GPU_ID=$1
+
+# ----------------------------
+# Default parameters (CLI args)
+# ----------------------------
+CHECKPOINT_PATH=$2
+REF_ADATA_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/data/extract_0.h5ad"
+GENE_INFO_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/gene_info/gene_info.tsv"
+ONTOLOGY_RESOURCE_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/ontology"
+VAL_ADATA_ROOT_PATH="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files"
+VAL_ADATA_FILE_LIST="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files/files.txt"
+OUTPUT_PATH=$3
+RNG_SEED=42
+N_CELLS=2000
+N_GENES=4091
+GENE_SELECTION_METHOD="random"
+CHUNK_SIZE=128
+METRIC_STYLE="hop_k_call"
+
+# ----------------------------
+# Infer the total number of non-empty lines in VAL_ADATA_FILE_LIST
+# ----------------------------
+TOTAL_INDICES=$(grep -cve '^\s*$' "${VAL_ADATA_FILE_LIST}")
+echo "Total number of validation files: ${TOTAL_INDICES}"
+
+NUM_GPUS=8
+
+# Calculate the start and end indices for the given GPU.
+# Indices range from 1 to TOTAL_INDICES (inclusive).
+START_INDEX=$(python3 -c "import math; print(int(math.floor(${GPU_ID} * ${TOTAL_INDICES} / ${NUM_GPUS}))+1)")
+END_INDEX=$(python3 -c "import math; print(int(math.floor((${GPU_ID}+1) * ${TOTAL_INDICES} / ${NUM_GPUS})))")
+
+echo "GPU ${GPU_ID} processing validation files from line ${START_INDEX} to ${END_INDEX}"
+
+# ----------------------------
+# Loop over the assigned lines and run the Python CLI tool
+# ----------------------------
+for i in $(seq ${START_INDEX} ${END_INDEX}); do
+    # Read the i-th non-empty line from VAL_ADATA_FILE_LIST
+    file=$(sed -n "${i}p" "${VAL_ADATA_FILE_LIST}")
+    val_adata_path="${VAL_ADATA_ROOT_PATH}/${file}"
+    echo "Running metadata prediction for file ${val_adata_path} on GPU ${GPU_ID}"
+    python3 ../metadata_prediction_analysis.py \
+        --cuda_device_index ${GPU_ID} \
+        --val_adata_path "${val_adata_path}" \
+        --checkpoint_path "${CHECKPOINT_PATH}" \
+        --ref_adata_path "${REF_ADATA_PATH}" \
+        --gene_info_path "${GENE_INFO_PATH}" \
+        --ontology_resource_path "${ONTOLOGY_RESOURCE_PATH}" \
+        --output_path "${OUTPUT_PATH}" \
+        --rng_seed ${RNG_SEED} \
+        --n_cells ${N_CELLS} \
+        --n_genes ${N_GENES} \
+        --gene_selection_method ${GENE_SELECTION_METHOD} \
+        --chunk_size ${CHUNK_SIZE} \
+        --metric_style ${METRIC_STYLE}
+done
diff --git a/scripts/foundry/run_metadata_prediction_analysis_multi.sh b/scripts/foundry/run_metadata_prediction_analysis_multi.sh
new file mode 100755
index 00000000..81fca4e1
--- /dev/null
+++ b/scripts/foundry/run_metadata_prediction_analysis_multi.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+# Check if an argument was provided
+if [ "$#" -ne 1 ]; then
+  echo "Usage: $0 <argument>"
+  exit 1
+fi
+
+arg="$1"
+
+CHECKPOINT_PATH_1="/mnt/cellariumgpt-xfer/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt"
+OUTPUT_PATH_1="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/10M_001_bs1536"
+
+CHECKPOINT_PATH_2="/mnt/cellariumgpt-xfer/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt"
+OUTPUT_PATH_2="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/19M_001_bs2048"
+
+CHECKPOINT_PATH_3="/mnt/cellariumgpt-xfer/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt"
+OUTPUT_PATH_3="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/30M_001_bs2560"
+
+CHECKPOINT_PATH_4="/mnt/cellariumgpt-xfer/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt"
+OUTPUT_PATH_4="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/59M_001_bs3072"
+
+# Call the scripts with the provided argument
+./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_1} ${OUTPUT_PATH_1}
+./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_2} ${OUTPUT_PATH_2}
+./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_3} ${OUTPUT_PATH_3}
+./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_4} ${OUTPUT_PATH_4}

From f31534f2ea1299a05d5060d66dcbc528daa4b891 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Mon, 3 Mar 2025 16:07:18 +0000
Subject: [PATCH 55/64] foundry scripts

---
 .../run_metadata_prediction_analysis_multi.sh | 40 +++++++----
 ...n_metadata_prediction_analysis_with_XY.sh} | 10 ++-
 ...metadata_prediction_analysis_without_XY.sh | 70 +++++++++++++++++++
 3 files changed, 104 insertions(+), 16 deletions(-)
 rename scripts/foundry/{run_metadata_prediction_analysis.sh => run_metadata_prediction_analysis_with_XY.sh} (85%)
 create mode 100755 scripts/foundry/run_metadata_prediction_analysis_without_XY.sh

diff --git a/scripts/foundry/run_metadata_prediction_analysis_multi.sh b/scripts/foundry/run_metadata_prediction_analysis_multi.sh
index 81fca4e1..1b06c256 100755
--- a/scripts/foundry/run_metadata_prediction_analysis_multi.sh
+++ b/scripts/foundry/run_metadata_prediction_analysis_multi.sh
@@ -8,20 +8,34 @@ fi
 
 arg="$1"
 
-CHECKPOINT_PATH_1="/mnt/cellariumgpt-xfer/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt"
-OUTPUT_PATH_1="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/10M_001_bs1536"
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_with_XY/10M_001_bs1536"
+./run_metadata_prediction_analysis_with_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
 
-CHECKPOINT_PATH_2="/mnt/cellariumgpt-xfer/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt"
-OUTPUT_PATH_2="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/19M_001_bs2048"
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_with_XY/19M_001_bs2048"
+./run_metadata_prediction_analysis_with_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
 
-CHECKPOINT_PATH_3="/mnt/cellariumgpt-xfer/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt"
-OUTPUT_PATH_3="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/30M_001_bs2560"
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_with_XY/30M_001_bs2560"
+./run_metadata_prediction_analysis_with_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
 
-CHECKPOINT_PATH_4="/mnt/cellariumgpt-xfer/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt"
-OUTPUT_PATH_4="/mnt/cellariumgpt-inference/metadata_predictions_rand_4091/59M_001_bs3072"
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_with_XY/59M_001_bs3072"
+./run_metadata_prediction_analysis_with_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
 
-# Call the scripts with the provided argument
-./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_1} ${OUTPUT_PATH_1}
-./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_2} ${OUTPUT_PATH_2}
-./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_3} ${OUTPUT_PATH_3}
-./run_metadata_prediction_analysis.sh "$arg" ${CHECKPOINT_PATH_4} ${OUTPUT_PATH_4}
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_without_XY/10M_001_bs1536"
+./run_metadata_prediction_analysis_without_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
+
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_without_XY/19M_001_bs2048"
+./run_metadata_prediction_analysis_without_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
+
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_without_XY/30M_001_bs2560"
+./run_metadata_prediction_analysis_without_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
+
+CHECKPOINT_PATH="/mnt/cellariumgpt-xfer/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt"
+OUTPUT_PATH="/mnt/cellariumgpt-inference/metadata_predictions_high__4096_without_XY/59M_001_bs3072"
+./run_metadata_prediction_analysis_without_XY.sh "$arg" ${CHECKPOINT_PATH} ${OUTPUT_PATH}
diff --git a/scripts/foundry/run_metadata_prediction_analysis.sh b/scripts/foundry/run_metadata_prediction_analysis_with_XY.sh
similarity index 85%
rename from scripts/foundry/run_metadata_prediction_analysis.sh
rename to scripts/foundry/run_metadata_prediction_analysis_with_XY.sh
index 678c5353..f3928c5c 100755
--- a/scripts/foundry/run_metadata_prediction_analysis.sh
+++ b/scripts/foundry/run_metadata_prediction_analysis_with_XY.sh
@@ -16,14 +16,16 @@ CHECKPOINT_PATH=$2
 REF_ADATA_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/data/extract_0.h5ad"
 GENE_INFO_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/gene_info/gene_info.tsv"
 ONTOLOGY_RESOURCE_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/ontology"
+FIXED_PROMPT_VARS_SUBLIST_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/sex_gene_ids.txt"
+RAND_PROMPT_VARS_SUBLIST_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/autosomal_gene_ids.txt"
 VAL_ADATA_ROOT_PATH="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files"
 VAL_ADATA_FILE_LIST="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files/files.txt"
 OUTPUT_PATH=$3
 RNG_SEED=42
 N_CELLS=2000
-N_GENES=4091
-GENE_SELECTION_METHOD="random"
-CHUNK_SIZE=128
+N_GENES=4096
+GENE_SELECTION_METHOD="highly_expressed"
+CHUNK_SIZE=64
 METRIC_STYLE="hop_k_call"
 
 # ----------------------------
@@ -61,6 +63,8 @@ for i in $(seq ${START_INDEX} ${END_INDEX}); do
         --n_cells ${N_CELLS} \
         --n_genes ${N_GENES} \
         --gene_selection_method ${GENE_SELECTION_METHOD} \
+        --fixed_prompt_vars_sublist_path ${FIXED_PROMPT_VARS_SUBLIST_PATH} \
+        --rand_prompt_vars_sublist_path ${RAND_PROMPT_VARS_SUBLIST_PATH} \
         --chunk_size ${CHUNK_SIZE} \
         --metric_style ${METRIC_STYLE}
 done
diff --git a/scripts/foundry/run_metadata_prediction_analysis_without_XY.sh b/scripts/foundry/run_metadata_prediction_analysis_without_XY.sh
new file mode 100755
index 00000000..1618e364
--- /dev/null
+++ b/scripts/foundry/run_metadata_prediction_analysis_without_XY.sh
@@ -0,0 +1,70 @@
+#!/bin/bash
+# run_metadata_prediction_analysis.sh
+
+# Usage check: All three arguments are required.
+if [ "$#" -ne 3 ]; then
+    echo "Usage: $0 <GPU_ID> <CHECKPOINT_PATH> <OUTPUT_PATH>"
+    exit 1
+fi
+
+GPU_ID=$1
+
+# ----------------------------
+# Default parameters (CLI args)
+# ----------------------------
+CHECKPOINT_PATH=$2
+REF_ADATA_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/data/extract_0.h5ad"
+GENE_INFO_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/gene_info/gene_info.tsv"
+ONTOLOGY_RESOURCE_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/ontology"
+FIXED_PROMPT_VARS_SUBLIST_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/empty_gene_ids.txt"
+RAND_PROMPT_VARS_SUBLIST_PATH="/mnt/cellariumgpt-xfer/mb-ml-dev-vm/cellariumgpt_artifacts/autosomal_gene_ids.txt"
+VAL_ADATA_ROOT_PATH="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files"
+VAL_ADATA_FILE_LIST="/mnt/cellariumgpt-training-data/cellariumgpt_validation_set/extract_files/files.txt"
+OUTPUT_PATH=$3
+RNG_SEED=42
+N_CELLS=2000
+N_GENES=4096
+GENE_SELECTION_METHOD="highly_expressed"
+CHUNK_SIZE=64
+METRIC_STYLE="hop_k_call"
+
+# ----------------------------
+# Infer the total number of non-empty lines in VAL_ADATA_FILE_LIST
+# ----------------------------
+TOTAL_INDICES=$(grep -cve '^\s*$' "${VAL_ADATA_FILE_LIST}")
+echo "Total number of validation files: ${TOTAL_INDICES}"
+
+NUM_GPUS=8
+
+# Calculate the start and end indices for the given GPU.
+# Indices range from 1 to TOTAL_INDICES (inclusive).
+START_INDEX=$(python3 -c "import math; print(int(math.floor(${GPU_ID} * ${TOTAL_INDICES} / ${NUM_GPUS}))+1)")
+END_INDEX=$(python3 -c "import math; print(int(math.floor((${GPU_ID}+1) * ${TOTAL_INDICES} / ${NUM_GPUS})))")
+
+echo "GPU ${GPU_ID} processing validation files from line ${START_INDEX} to ${END_INDEX}"
+
+# ----------------------------
+# Loop over the assigned lines and run the Python CLI tool
+# ----------------------------
+for i in $(seq ${START_INDEX} ${END_INDEX}); do
+    # Read the i-th non-empty line from VAL_ADATA_FILE_LIST
+    file=$(sed -n "${i}p" "${VAL_ADATA_FILE_LIST}")
+    val_adata_path="${VAL_ADATA_ROOT_PATH}/${file}"
+    echo "Running metadata prediction for file ${val_adata_path} on GPU ${GPU_ID}"
+    python3 ../metadata_prediction_analysis.py \
+        --cuda_device_index ${GPU_ID} \
+        --val_adata_path "${val_adata_path}" \
+        --checkpoint_path "${CHECKPOINT_PATH}" \
+        --ref_adata_path "${REF_ADATA_PATH}" \
+        --gene_info_path "${GENE_INFO_PATH}" \
+        --ontology_resource_path "${ONTOLOGY_RESOURCE_PATH}" \
+        --output_path "${OUTPUT_PATH}" \
+        --rng_seed ${RNG_SEED} \
+        --n_cells ${N_CELLS} \
+        --n_genes ${N_GENES} \
+        --gene_selection_method ${GENE_SELECTION_METHOD} \
+        --fixed_prompt_vars_sublist_path ${FIXED_PROMPT_VARS_SUBLIST_PATH} \
+        --rand_prompt_vars_sublist_path ${RAND_PROMPT_VARS_SUBLIST_PATH} \
+        --chunk_size ${CHUNK_SIZE} \
+        --metric_style ${METRIC_STYLE}
+done

From 73a9d7dab01ef9cbb55099ab531cec6fce7aa9e3 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Sun, 9 Mar 2025 15:26:50 +0000
Subject: [PATCH 56/64] prediction writer

---
 .../ml/callbacks/cellarium_gpt_prompt_size.py | 186 +++++++
 cellarium/ml/core/datamodule.py               |   7 +
 cellarium/ml/models/cellarium_gpt.py          | 186 -------
 .../ml/transforms/cellarium_gpt_tokenizer.py  | 478 +-----------------
 4 files changed, 219 insertions(+), 638 deletions(-)
 create mode 100644 cellarium/ml/callbacks/cellarium_gpt_prompt_size.py

diff --git a/cellarium/ml/callbacks/cellarium_gpt_prompt_size.py b/cellarium/ml/callbacks/cellarium_gpt_prompt_size.py
new file mode 100644
index 00000000..1e579f97
--- /dev/null
+++ b/cellarium/ml/callbacks/cellarium_gpt_prompt_size.py
@@ -0,0 +1,186 @@
+# Copyright Contributors to the Cellarium project.
+# SPDX-License-Identifier: BSD-3-Clause
+
+import os
+from collections.abc import Sequence
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from queue import Queue
+
+import lightning.pytorch as pl
+import numpy as np
+import pandas as pd
+import torch
+from torch.utils._pytree import tree_map
+
+
+def write_prediction(
+    prediction: torch.Tensor,
+    obs_names_n: np.ndarray,
+    output_dir: Path | str,
+    postfix: int | str,
+    gzip: bool = True,
+    executor: ThreadPoolExecutor | None = None,
+) -> None:
+    """
+    Write prediction to a CSV file.
+
+    Args:
+        prediction:
+            The prediction to write.
+        obs_names_n:
+            The IDs of the cells.
+        output_dir:
+            The directory to write the prediction to.
+        postfix:
+            A postfix to add to the CSV file name.
+        gzip:
+            Whether to compress the CSV file using gzip.
+        executor:
+            The executor used to write the prediction. If ``None``, no executor will be used.
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir, exist_ok=True)
+    # move to cpu using tree_map
+    prediction = tree_map(lambda x: x.cpu() if isinstance(x, torch.Tensor) else x, prediction)
+    df = pd.DataFrame(prediction)
+    df.insert(0, "obs_names_n", obs_names_n)
+    output_path = os.path.join(output_dir, f"batch_{postfix}.csv" + (".gz" if gzip else ""))
+    to_csv_kwargs: dict[str, str | bool] = {"header": False, "index": False}
+    if gzip:
+        to_csv_kwargs |= {"compression": "gzip"}
+
+    def _write_csv(frame: pd.DataFrame, path: str) -> None:
+        frame.to_csv(path, **to_csv_kwargs)
+
+    if executor is None:
+        _write_csv(df, output_path)
+    else:
+        executor.submit(_write_csv, df, output_path)
+
+
+class BoundedThreadPoolExecutor(ThreadPoolExecutor):
+    """ThreadPoolExecutor with a bounded queue for task submissions.
+    This class is used to prevent the queue from growing indefinitely when tasks are submitted,
+    which can lead to an out-of-memory error.
+    """
+
+    def __init__(self, max_workers: int, max_queue_size: int):
+        # Use a bounded queue for task submissions
+        self._queue: Queue = Queue(max_queue_size)
+        super().__init__(max_workers=max_workers)
+
+    def submit(self, fn, /, *args, **kwargs):
+        # Block if the queue is full to prevent task overload
+        self._queue.put(None)
+        future = super().submit(fn, *args, **kwargs)
+
+        # When the task completes, remove a marker from the queue
+        def done_callback(_):
+            self._queue.get()
+
+        future.add_done_callback(done_callback)
+        return future
+
+
+class PredictionWriter(pl.callbacks.BasePredictionWriter):
+    """
+    Write predictions to a CSV file. The CSV file will have the same number of rows as the
+    number of predictions, and the number of columns will be the same as the prediction size.
+    The first column will be the ID of each cell.
+
+    .. note::
+        To prevent an out-of-memory error, set the ``return_predictions`` argument of the
+        :class:`~lightning.pytorch.Trainer` to ``False``. This is accomplished in the config
+        file by including ``return_predictions: false`` at indent level 0. For example,
+
+        .. code-block:: yaml
+
+            trainer:
+              ...
+            model:
+              ...
+            data:
+              ...
+            return_predictions: false
+
+    Args:
+        output_dir:
+            The directory to write the predictions to.
+        prediction_size:
+            The size of the prediction. If ``None``, the entire prediction will be
+            written. If not ``None``, only the first ``prediction_size`` columns will be written.
+        key:
+            PredictionWriter will write this key from the output of `predict()`.
+        gzip:
+            Whether to compress the CSV file using gzip.
+        max_threadpool_workers:
+            The maximum number of threads to use to write the predictions using a ThreadPoolExecutor.
+    """
+
+    def __init__(
+        self,
+        output_dir: Path | str,
+        gzip: bool = True,
+        max_threadpool_workers: int = 8,
+    ) -> None:
+        super().__init__(write_interval="batch")
+        self.output_dir = output_dir
+        self.executor = BoundedThreadPoolExecutor(
+            max_workers=max_threadpool_workers,
+            max_queue_size=max_threadpool_workers * 2,
+        )
+        self.gzip = gzip
+
+    def __del__(self):
+        """Ensure the executor shuts down on object deletion."""
+        self.executor.shutdown(wait=True)
+
+    def write_on_batch_end(
+        self,
+        trainer: pl.Trainer,
+        pl_module: pl.LightningModule,
+        prediction: dict[str, torch.Tensor],
+        batch_indices: Sequence[int] | None,
+        batch: dict[str, np.ndarray | torch.Tensor],
+        batch_idx: int,
+        dataloader_idx: int,
+    ) -> None:
+        gene_prompt_size_n = batch["gene_prompt_size_n"]
+        gene_query_size_n = batch["gene_query_size_n"]
+        total_mrna_umis_downsampled_n = batch["total_mrna_umis_downsampled_n"]
+
+        label_nc_dict = batch["label_nc_dict"]
+        logits_nck_dict = prediction
+        label_weight_nc_dict = batch["label_weight_nc_dict"]
+
+        loss_n_dict = {}
+        loss_fn = torch.nn.CrossEntropyLoss(reduction="none")
+        # Make sure that label_nc_dict is created by concatenating the gene_value and metadata labels
+        # in the same order as the embeddings.
+        for key, label_nc in label_nc_dict.items():
+            logits_nck = logits_nck_dict[key]
+            assert isinstance(logits_nck, torch.Tensor)
+            label_weight_nc = label_weight_nc_dict[key]
+            assert isinstance(label_weight_nc, torch.Tensor)
+            loss_nc = loss_fn(logits_nck.view(label_nc.numel(), -1), label_nc.view(-1).long()).reshape(label_nc.shape)
+            loss_n_dict[key] = torch.sum(loss_nc * label_weight_nc, dim=-1) / label_weight_nc.sum(dim=-1)
+
+        loss_n_dict["prompt_size"] = gene_prompt_size_n.cpu().int()
+        loss_n_dict["query_size"] = gene_query_size_n.cpu().int()
+        loss_n_dict["total_mrna_umis_downsampled"] = total_mrna_umis_downsampled_n.cpu()
+
+        if "obs_names_n" not in batch.keys():
+            raise ValueError(
+                "PredictionWriter callback requires the batch_key 'obs_names_n'. Add this to the YAML config."
+            )
+        assert isinstance(batch["obs_names_n"], np.ndarray)
+
+        write_prediction(
+            prediction=loss_n_dict,
+            obs_names_n=batch["obs_names_n"],
+            output_dir=self.output_dir,
+            postfix=batch_idx * trainer.world_size + trainer.global_rank,
+            gzip=self.gzip,
+            executor=self.executor,
+        )
diff --git a/cellarium/ml/core/datamodule.py b/cellarium/ml/core/datamodule.py
index 02b9fe10..8bb6527f 100644
--- a/cellarium/ml/core/datamodule.py
+++ b/cellarium/ml/core/datamodule.py
@@ -113,6 +113,7 @@ def __init__(
         drop_incomplete_batch: bool = False,
         train_size: float | int | None = None,
         val_size: float | int | None = None,
+        pred_size: float | int | None = None,
         worker_seed: int | None = None,
         test_mode: bool = False,
         # DataLoader args
@@ -134,6 +135,10 @@ def __init__(
         self.shuffle_seed = shuffle_seed
         self.drop_last_indices = drop_last_indices
         self.n_train, self.n_val = train_val_split(len(dadc), train_size, val_size)
+        if pred_size is not None:
+            _, self.n_pred = train_val_split(len(dadc), None, pred_size)
+        else:
+            self.n_pred = len(dadc)
         self.worker_seed = worker_seed
         self.test_mode = test_mode
         # DataLoader args
@@ -196,6 +201,8 @@ def setup(self, stage: str | None = None) -> None:
                 drop_incomplete_batch=self.drop_incomplete_batch,
                 worker_seed=self.worker_seed,
                 test_mode=self.test_mode,
+                start_idx=len(self.dadc) - self.n_pred,
+                end_idx=len(self.dadc),
             )
 
         if stage == "test":
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index da506e40..8168a5ba 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -5,7 +5,6 @@
 
 import lightning.pytorch as pl
 import numpy as np
-import pandas as pd
 import torch
 from torch import nn
 from torch.nn.attention.flex_attention import BlockMask, create_block_mask
@@ -374,188 +373,3 @@ def validate(
         )
 
         pl_module.log_dict(loss_dict, sync_dist=True, on_epoch=True, batch_size=n)
-
-    # def test(
-    #     self,
-    #     trainer: pl.Trainer,
-    #     pl_module: pl.LightningModule,
-    #     batch_idx: int,
-    #     token_value_nc_dict: dict[str, torch.Tensor],
-    #     token_mask_nc_dict: dict[str, torch.Tensor],
-    #     prompt_mask_nc: torch.Tensor,
-    #     label_nc_dict: dict[str, torch.Tensor],
-    #     label_weight_nc_dict: dict[str, torch.Tensor],
-    #     obs_names_n: np.ndarray,
-    #     total_mrna_umis_n: torch.Tensor,
-    #     total_mrna_umis_downsampled_n: torch.Tensor,
-    #     cell_type_n: np.ndarray,
-    #     gene_prompt_size_n: torch.Tensor,
-    #     gene_query_size_n: torch.Tensor,
-    #     gene_query_nnz_size_n: torch.Tensor,
-    # ) -> None:
-    #     logits_nck_dict = self.predict(
-    #         token_value_nc_dict=token_value_nc_dict,
-    #         token_mask_nc_dict=token_mask_nc_dict,
-    #         prompt_mask_nc=prompt_mask_nc,
-    #     )
-
-    #     loss_fn = nn.CrossEntropyLoss(reduction="none")
-    #     key = "gene_value"
-    #     label_nc = label_nc_dict[key]
-    #     logits_nck = logits_nck_dict[key]
-    #     assert isinstance(logits_nck, torch.Tensor)
-    #     label_weight_nc = label_weight_nc_dict[key]
-    #     assert isinstance(label_weight_nc, torch.Tensor)
-    #     loss_nc = loss_fn(logits_nck.view(label_nc.numel(), -1), label_nc.view(-1).long()).reshape(label_nc.shape)
-    #     loss_n = torch.sum(loss_nc * label_weight_nc, dim=-1) / label_weight_nc.sum(dim=-1)
-    #     nnz_label_weight_nc = (label_nc > 0).float() * label_weight_nc
-    #     loss_nnz_n = torch.sum(loss_nc * nnz_label_weight_nc, dim=-1) / nnz_label_weight_nc.sum(dim=-1)
-
-    #     df = pd.DataFrame(
-    #         data={
-    #             # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
-    #             # "query_size": label_weight_nc.sum(-1).cpu().int(),
-    #             # "total_mrna_umis": total_mrna_umis_n.cpu(),
-    #             # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-    #             # "cell_type": cell_type_n.cpu(),
-    #             # "loss": loss_n.cpu(),
-    #             # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
-    #             # "loss_nnz": loss_nnz_n.cpu(),
-    #             "prompt_size": gene_prompt_size_n.cpu(),
-    #             "query_size": gene_query_size_n.cpu().int(),
-    #             "total_mrna_umis": total_mrna_umis_n.cpu(),
-    #             "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-    #             "cell_type": cell_type_n.cpu(),
-    #             "loss": loss_n.cpu(),
-    #             "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
-    #             "loss_nnz": loss_nnz_n.cpu(),
-    #         },
-    #         index=obs_names_n,
-    #     )
-    #     postfix = batch_idx * trainer.world_size + trainer.global_rank
-    #     output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_prefix_0_seed_3113024_all_measured/batch_{postfix}.csv"
-    #     # output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_all_prefixes_seed_3112024_all_measured/batch_{postfix}.csv"
-    #     df.to_csv(output_path)
-
-    # def test(
-    #     self,
-    #     trainer: pl.Trainer,
-    #     pl_module: pl.LightningModule,
-    #     batch_idx: int,
-    #     token_value_nc_dict: dict[str, torch.Tensor],
-    #     token_mask_nc_dict: dict[str, torch.Tensor],
-    #     prompt_mask_nc: torch.Tensor,
-    #     label_nc_dict: dict[str, torch.Tensor],
-    #     label_weight_nc_dict: dict[str, torch.Tensor],
-    #     obs_names_n: np.ndarray,
-    #     total_mrna_umis_n: torch.Tensor,
-    #     total_mrna_umis_downsampled_n: torch.Tensor,
-    #     cell_type_n: np.ndarray,
-    #     gene_prompt_size_n: torch.Tensor,
-    #     gene_query_size_n: torch.Tensor,
-    #     gene_query_nnz_size_n: torch.Tensor,
-    #     measured_query_mask_nc: torch.Tensor,
-    #     unmeasured_query_mask_nc: torch.Tensor,
-    # ) -> None:
-    #     logits_nck_dict = self.predict(
-    #         token_value_nc_dict=token_value_nc_dict,
-    #         token_mask_nc_dict=token_mask_nc_dict,
-    #         prompt_mask_nc=prompt_mask_nc,
-    #     )
-
-    #     key = "gene_value"
-    #     logits_nck = logits_nck_dict[key]
-    #     k = logits_nck.shape[2]
-    #     value_k = torch.arange(k, dtype=torch.float32, device=logits_nck.device)
-    #     assert isinstance(logits_nck, torch.Tensor)
-    #     probs_nck = logits_nck.softmax(-1)
-    #     mean_nc = probs_nck @ value_k
-    #     measured_mean_n = (mean_nc * measured_query_mask_nc).sum(-1) / measured_query_mask_nc.sum(-1)
-    #     unmeasured_mean_n = (mean_nc * unmeasured_query_mask_nc).sum(-1) / unmeasured_query_mask_nc.sum(-1)
-
-    #     df = pd.DataFrame(
-    #         data={
-    #             # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
-    #             # "query_size": label_weight_nc.sum(-1).cpu().int(),
-    #             # "total_mrna_umis": total_mrna_umis_n.cpu(),
-    #             # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-    #             # "cell_type": cell_type_n.cpu(),
-    #             # "loss": loss_n.cpu(),
-    #             # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
-    #             # "loss_nnz": loss_nnz_n.cpu(),
-    #             "prompt_size": gene_prompt_size_n.cpu(),
-    #             "query_size": gene_query_size_n.cpu().int(),
-    #             "total_mrna_umis": total_mrna_umis_n.cpu(),
-    #             "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-    #             "cell_type": cell_type_n.cpu(),
-    #             "measured_mean": measured_mean_n.cpu(),
-    #             "measured_size": measured_query_mask_nc.sum(-1).cpu(),
-    #             "unmeasured_mean": unmeasured_mean_n.cpu(),
-    #             "unmeasured_size": unmeasured_query_mask_nc.sum(-1).cpu(),
-    #             # "loss": loss_n.cpu(),
-    #             # "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
-    #             # "loss_nnz": loss_nnz_n.cpu(),
-    #         },
-    #         index=obs_names_n,
-    #     )
-    #     postfix = batch_idx * trainer.world_size + trainer.global_rank
-    #     output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/unmeasured/batch_{postfix}.csv"
-    #     df.to_csv(output_path)
-
-    # predict cell type
-    def test(
-        self,
-        trainer: pl.Trainer,
-        pl_module: pl.LightningModule,
-        batch_idx: int,
-        token_value_nc_dict: dict[str, torch.Tensor],
-        token_mask_nc_dict: dict[str, torch.Tensor],
-        prompt_mask_nc: torch.Tensor,
-        label_nc_dict: dict[str, torch.Tensor],
-        label_weight_nc_dict: dict[str, torch.Tensor],
-        obs_names_n: np.ndarray,
-        total_mrna_umis_n: torch.Tensor,
-        total_mrna_umis_downsampled_n: torch.Tensor,
-        cell_type_n: np.ndarray,
-        gene_prompt_size_n: torch.Tensor,
-        gene_query_size_n: torch.Tensor,
-        gene_query_nnz_size_n: torch.Tensor,
-    ) -> None:
-        logits_nck_dict = self.predict(
-            token_value_nc_dict=token_value_nc_dict,
-            token_mask_nc_dict=token_mask_nc_dict,
-            prompt_mask_nc=prompt_mask_nc,
-        )
-        import pdb; pdb.set_trace()
-
-        key = "cell_type"
-        label_nc = label_nc_dict[key]
-        logits_nck = logits_nck_dict[key]
-        probs_nck = logits_nck.softmax(-1)
-        pred_label_nc = probs_nck.argmax(-1)
-
-        df = pd.DataFrame(
-            data={
-                # "prompt_size": prompt_mask_nc.sum(-1).cpu(),
-                # "query_size": label_weight_nc.sum(-1).cpu().int(),
-                # "total_mrna_umis": total_mrna_umis_n.cpu(),
-                # # "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-                # "cell_type": cell_type_n.cpu(),
-                # "loss": loss_n.cpu(),
-                # "query_size_nnz": nnz_label_weight_nc.sum(-1).cpu().int(),
-                # "loss_nnz": loss_nnz_n.cpu(),
-                "prompt_size": gene_prompt_size_n.cpu(),
-                "query_size": gene_query_size_n.cpu().int(),
-                "total_mrna_umis": total_mrna_umis_n.cpu(),
-                "total_mrna_umis_downsampled": total_mrna_umis_downsampled_n.cpu(),
-                "cell_type": cell_type_n.cpu(),
-                "loss": loss_n.cpu(),
-                "query_size_nnz": gene_query_nnz_size_n.cpu().int(),
-                "loss_nnz": loss_nnz_n.cpu(),
-            },
-            index=obs_names_n,
-        )
-        postfix = batch_idx * trainer.world_size + trainer.global_rank
-        output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_prefix_0_seed_3113024_all_measured/batch_{postfix}.csv"
-        # output_path = f"/mnt/cellariumgpt-training-data/info_theor/out/cell_state_all_prefixes_seed_3112024_all_measured/batch_{postfix}.csv"
-        df.to_csv(output_path)
\ No newline at end of file
diff --git a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
index e0b5cd25..79d983c5 100644
--- a/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
+++ b/cellarium/ml/transforms/cellarium_gpt_tokenizer.py
@@ -283,6 +283,7 @@ def forward(
 
         ## gene value ##
         gene_value_nj = gene_token_nj_dict.pop("gene_value")
+        gene_prompt_mask_nj = gene_value_nj >= 0
         total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
         device = gene_value_nj.device
         n, j = gene_value_nj.shape
@@ -293,7 +294,6 @@ def forward(
         gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
         gene_value_nj = torch.binomial(gene_value_nj, gene_downsample_p_nj)
 
-        gene_prompt_mask_nj = gene_value_nj >= 0
         gene_query_mask_nj = ~gene_prompt_mask_nj
         gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
         gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
@@ -374,8 +374,8 @@ class CellariumGPTTestTokenizer(torch.nn.Module):
     Args:
         context_len:
             Context length.
-        gene_downsample_fraction:
-            Fraction of genes to downsample.
+        gene_downsample_p:
+            Gene downsample probability.
         min_total_mrna_umis:
             Minimum total mRNA UMIs.
         max_total_mrna_umis:
@@ -398,7 +398,7 @@ class CellariumGPTTestTokenizer(torch.nn.Module):
     def __init__(
         self,
         context_len: int,
-        gene_downsample_fraction: float,
+        gene_downsample_p: float,
         min_total_mrna_umis: int,
         max_total_mrna_umis: int,
         gene_vocab_sizes: dict[str, int],
@@ -411,7 +411,7 @@ def __init__(
     ) -> None:
         super().__init__()
         self.context_len = context_len
-        self.gene_downsample_fraction = gene_downsample_fraction
+        self.gene_downsample_p = gene_downsample_p
         self.min_total_mrna_umis = min_total_mrna_umis
         self.max_total_mrna_umis = max_total_mrna_umis
         self.gene_vocab_sizes = gene_vocab_sizes
@@ -422,426 +422,6 @@ def __init__(
         self.metadata_prompt_token_list = metadata_prompt_token_list
         self.obs_names_rng = obs_names_rng
 
-    # def forward(
-    #     self,
-    #     metadata_token_n_dict: dict[str, torch.Tensor],
-    #     gene_token_n_dict: dict[str, torch.Tensor],
-    #     gene_token_ng_dict: dict[str, torch.Tensor],
-    #     obs_names_n: np.ndarray | None = None,
-    # ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
-    #     ### GENE TOKENS ###
-    #     n, g = gene_token_ng_dict["gene_value"].shape
-    #     m = len(metadata_token_n_dict)
-    #     c = self.context_len
-    #     # gene context length
-    #     j = c - m
-    #     device = gene_token_ng_dict["gene_value"].device
-
-    #     ## gene measurement tokens (assay, suspension type, etc.) ##
-    #     gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
-
-    #     ## gene id ##
-    #     gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
-
-    #     if self.obs_names_rng:
-    #         rng_n = [torch.Generator(device=device) for _ in range(n)]
-    #         [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
-    #         shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
-    #     else:
-    #         shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
-    #     shuffle_idx_nj = shuffle_idx_ng[:, :j]
-
-    #     for key, gene_token_ng in gene_token_ng_dict.items():
-    #         gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
-
-    #     ## gene value ##
-    #     gene_value_nj = gene_token_nj_dict.pop("gene_value")
-    #     total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
-    #     # downsample gene values
-    #     max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
-    #     downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
-    #     if self.gene_downsample_fraction > 0:
-    #         gene_downsample_p_nj = torch.minimum(
-    #             torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
-    #             torch.tensor(1.0, device=device),
-    #         )
-    #         downsampled_total_mrna_umis_nj = torch.lerp(
-    #             torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
-    #             downsampled_total_mrna_umis_nj,
-    #             gene_downsample_p_nj,
-    #         )
-    #     gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
-    #     gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
-    #     total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
-    #     if self.prefix_len is not None:
-    #         prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
-    #     else:
-    #         # sample prefix length
-    #         # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
-    #         max_prefix_len = j - 1 - 2048
-    #         prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
-    #         # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
-    #         # prefix_len_weights[0] = 1 / 10
-    #         prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
-    #     # create prompt and query masks
-    #     # query_len_n = j - prefix_len_n
-    #     # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
-    #     gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
-    #     gene_prompt_mask_nj = ~gene_query_mask_nj
-    #     if "measured_genes_mask" in gene_token_nj_dict:
-    #         measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
-    #         gene_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
-    #         gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
-
-    #     gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
-    #     gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
-    #     gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
-
-    #     gene_token_value_nc_dict = {
-    #         key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
-    #         for key, gene_token_nj in gene_token_nj_dict.items()
-    #     }
-    #     gene_token_mask_nc = torch.cat(
-    #         [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
-    #         dim=1,
-    #     )
-    #     gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
-
-    #     # gene label
-    #     gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
-    #     gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
-
-    #     ### METADATA TOKENS ###
-
-    #     ## metadata tokens ##
-    #     # assign token codes based on the ontology info
-    #     # token values not in the ontology are treated as unmeasured and assigned a code value of -1
-    #     for key, ontology_info in self.ontology_infos.items():
-    #         if key in metadata_token_n_dict:
-    #             assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
-    #             metadata_token_n_dict[key] = torch.tensor(
-    #                 pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
-    #                 dtype=torch.int,
-    #             )
-    #     # create metadata query and prompt masks
-    #     if self.metadata_prompt_token_list is not None:
-    #         metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
-    #         for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
-    #             if metadata_token in self.metadata_prompt_token_list:
-    #                 metadata_prompt_mask_nm[:, metadata_token_idx] = True
-    #     else:
-    #         metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
-    #         metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
-    #         shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
-    #         metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
-    #     metadata_query_mask_nm = ~metadata_prompt_mask_nm
-    #     metadata_measured_mask_nm = torch.stack(
-    #         [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
-    #     ).bool()
-    #     metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
-    #     metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
-    #     # clamp unmeasured tokens to 0 in order to avoid error during embedding
-    #     # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
-    #     for key, metadata_token_n in metadata_token_n_dict.items():
-    #         metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
-    #     # metadata labels
-    #     metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
-    #     if self.ontology_downsample_p != 0:
-    #         # downsample metadata based on ontology
-    #         for key, ontology_info in self.ontology_infos.items():
-    #             if "shortest_distances_matrix" not in ontology_info:
-    #                 continue
-    #             if key not in metadata_token_n_dict:
-    #                 continue
-    #             metadata_token_n = metadata_token_n_dict[key]
-    #             shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
-    #             ontology_weights = (
-    #                 self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
-    #             )
-    #             metadata_token_n_dict[key] = (
-    #                 torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
-    #             )
-    #     # impute mask token for unmeasured metadata
-    #     # mask token is the last token in the vocabulary
-    #     for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
-    #         metadata_token_n_dict[key] = torch.where(
-    #             metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
-    #         ).int()
-
-    #     block_metadata_token_nm = torch.block_diag(
-    #         *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
-    #     )
-    #     metadata_token_value_nc_dict = {
-    #         key: torch.cat(
-    #             [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
-    #             dim=1,
-    #         )
-    #         for i, key in enumerate(metadata_token_n_dict)
-    #     }
-    #     block_metadata_token_mask_nm = torch.block_diag(
-    #         *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
-    #     )
-    #     metadata_token_mask_nc_dict = {
-    #         key: torch.cat(
-    #             [
-    #                 torch.zeros((n, j), dtype=torch.bool, device=device),
-    #                 block_metadata_token_mask_nm[n * i : n * (i + 1)],
-    #             ],
-    #             dim=1,
-    #         )
-    #         for i, key in enumerate(metadata_token_n_dict)
-    #     }
-
-    #     ### PROMPT MASK ###
-    #     prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
-
-    #     ### LABELS ###
-    #     block_label_nc = torch.block_diag(
-    #         gene_label_nj,
-    #         *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
-    #     )
-    #     label_nc_dict = {
-    #         key: block_label_nc[n * i : n * (i + 1)]
-    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
-    #     }
-
-    #     ### LABEL WEIGHTS ###
-    #     block_label_weight_nc = torch.block_diag(
-    #         gene_query_mask_nj,
-    #         *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
-    #     )
-    #     label_weight_nc_dict = {
-    #         key: block_label_weight_nc[n * i : n * (i + 1)]
-    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
-    #     }
-
-    #     return {
-    #         "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
-    #         "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
-    #         "prompt_mask_nc": prompt_mask_nc,
-    #         "label_nc_dict": label_nc_dict,
-    #         "label_weight_nc_dict": label_weight_nc_dict,
-    #         "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
-    #         "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
-    #         "cell_type_n": metadata_token_n_dict["cell_type"],
-    #         "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
-    #         "gene_query_size_n": gene_query_mask_nj.sum(-1),
-    #         "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
-    #     }
-
-    # def forward(
-    #     self,
-    #     metadata_token_n_dict: dict[str, torch.Tensor],
-    #     gene_token_n_dict: dict[str, torch.Tensor],
-    #     gene_token_ng_dict: dict[str, torch.Tensor],
-    #     obs_names_n: np.ndarray | None = None,
-    # ) -> dict[str, dict[str, torch.Tensor] | torch.Tensor]:
-    #     ### GENE TOKENS ###
-    #     n, g = gene_token_ng_dict["gene_value"].shape
-    #     m = len(metadata_token_n_dict)
-    #     c = self.context_len
-    #     # gene context length
-    #     j = c - m
-    #     device = gene_token_ng_dict["gene_value"].device
-
-    #     ## gene measurement tokens (assay, suspension type, etc.) ##
-    #     gene_token_nj_dict = {key: gene_token_n_dict[key][:, None].expand(-1, j).int() for key in gene_token_n_dict}
-
-    #     ## gene id ##
-    #     gene_token_ng_dict["gene_id"] = torch.arange(g, device=device).expand(n, g)
-
-    #     if self.obs_names_rng:
-    #         rng_n = [torch.Generator(device=device) for _ in range(n)]
-    #         [rng.manual_seed(int(obs_name)) for rng, obs_name in zip(rng_n, obs_names_n)]
-    #         shuffle_idx_ng = torch.stack([torch.randperm(g, generator=rng, device=device) for rng in rng_n])
-    #     else:
-    #         shuffle_idx_ng = torch.argsort(torch.rand((n, g), dtype=torch.float32, device=device), dim=-1)
-    #     shuffle_idx_nj = shuffle_idx_ng[:, :j]
-
-    #     for key, gene_token_ng in gene_token_ng_dict.items():
-    #         gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
-
-    #     ## gene value ##
-    #     gene_value_nj = gene_token_nj_dict.pop("gene_value")
-    #     total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
-    #     # downsample gene values
-    #     max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
-    #     downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
-    #     if self.gene_downsample_fraction > 0:
-    #         gene_downsample_p_nj = torch.minimum(
-    #             torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
-    #             torch.tensor(1.0, device=device),
-    #         )
-    #         downsampled_total_mrna_umis_nj = torch.lerp(
-    #             torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
-    #             downsampled_total_mrna_umis_nj,
-    #             gene_downsample_p_nj,
-    #         )
-    #     gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
-    #     gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
-    #     total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
-    #     if self.prefix_len is not None:
-    #         prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
-    #     else:
-    #         # sample prefix length
-    #         # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
-    #         max_prefix_len = j - 1 - 2048
-    #         prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
-    #         # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
-    #         # prefix_len_weights[0] = 1 / 10
-    #         prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
-    #     # create prompt and query masks
-    #     # query_len_n = j - prefix_len_n
-    #     # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
-    #     gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
-    #     gene_prompt_mask_nj = ~gene_query_mask_nj
-    #     if "measured_genes_mask" in gene_token_nj_dict:
-    #         measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
-    #         gene_query_mask_nj = gene_query_mask_nj
-    #         gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
-    #         measured_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
-    #         unmeasured_query_mask_nj = gene_query_mask_nj & (~measured_genes_mask_nj)
-
-    #     gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
-    #     gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
-    #     gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
-    #     measured_query_mask_nc = torch.cat([measured_query_mask_nj, torch.zeros((n, m), device=device, dtype=torch.float32)], dim=1)
-    #     unmeasured_query_mask_nc = torch.cat([unmeasured_query_mask_nj, torch.zeros((n, m), device=device, dtype=torch.float32)], dim=1)
-
-    #     gene_token_value_nc_dict = {
-    #         key: torch.cat([gene_token_nj, torch.zeros((n, m), device=device, dtype=gene_token_nj.dtype)], dim=1)
-    #         for key, gene_token_nj in gene_token_nj_dict.items()
-    #     }
-    #     gene_token_mask_nc = torch.cat(
-    #         [torch.ones((n, j), dtype=torch.bool, device=device), torch.zeros((n, m), dtype=torch.bool, device=device)],
-    #         dim=1,
-    #     )
-    #     gene_token_mask_nc_dict = {key: gene_token_mask_nc for key in gene_token_nj_dict}
-
-    #     # gene label
-    #     gene_value_vocab_size = self.gene_vocab_sizes["gene_value"]
-    #     gene_label_nj = gene_value_nj.clamp(0, gene_value_vocab_size - 1).int()
-
-    #     ### METADATA TOKENS ###
-
-    #     ## metadata tokens ##
-    #     # assign token codes based on the ontology info
-    #     # token values not in the ontology are treated as unmeasured and assigned a code value of -1
-    #     for key, ontology_info in self.ontology_infos.items():
-    #         if key in metadata_token_n_dict:
-    #             assert self.metadata_vocab_sizes[key] == len(ontology_info["names"])
-    #             metadata_token_n_dict[key] = torch.tensor(
-    #                 pd.Categorical(metadata_token_n_dict[key], categories=ontology_info["names"]).codes,
-    #                 dtype=torch.int,
-    #             )
-    #     # create metadata query and prompt masks
-    #     if self.metadata_prompt_token_list is not None:
-    #         metadata_prompt_mask_nm = torch.zeros((n, m), dtype=torch.bool, device=device)
-    #         for metadata_token_idx, metadata_token in enumerate(metadata_token_n_dict):
-    #             if metadata_token in self.metadata_prompt_token_list:
-    #                 metadata_prompt_mask_nm[:, metadata_token_idx] = True
-    #     else:
-    #         metadata_prefix_len_n = torch.randint(0, m + 1, (n,), device=device)
-    #         metadata_prefix_mask_nm = torch.arange(m, device=device) < metadata_prefix_len_n[:, None]
-    #         shuffle_idx_nm = torch.argsort(torch.rand_like(metadata_prefix_mask_nm, dtype=torch.float32), dim=-1)
-    #         metadata_prompt_mask_nm = torch.gather(metadata_prefix_mask_nm, dim=-1, index=shuffle_idx_nm)
-    #     metadata_query_mask_nm = ~metadata_prompt_mask_nm
-    #     metadata_measured_mask_nm = torch.stack(
-    #         [metadata_token_n >= 0 for metadata_token_n in metadata_token_n_dict.values()], dim=1
-    #     ).bool()
-    #     metadata_query_mask_nm = metadata_query_mask_nm & metadata_measured_mask_nm
-    #     metadata_prompt_mask_nm = metadata_prompt_mask_nm & metadata_measured_mask_nm
-    #     # clamp unmeasured tokens to 0 in order to avoid error during embedding
-    #     # the value of unmeasured tokens doesn't matter since they will be masked out by the attention mask
-    #     for key, metadata_token_n in metadata_token_n_dict.items():
-    #         metadata_token_n_dict[key] = metadata_token_n.clamp(0).int()
-    #     # metadata labels
-    #     metadata_label_n_dict = {key: metadata_token_n_dict[key].clone() for key in metadata_token_n_dict}
-    #     if self.ontology_downsample_p != 0:
-    #         # downsample metadata based on ontology
-    #         for key, ontology_info in self.ontology_infos.items():
-    #             if "shortest_distances_matrix" not in ontology_info:
-    #                 continue
-    #             if key not in metadata_token_n_dict:
-    #                 continue
-    #             metadata_token_n = metadata_token_n_dict[key]
-    #             shortest_distances_matrix = ontology_info["shortest_distances_matrix"]
-    #             ontology_weights = (
-    #                 self.ontology_downsample_p * (1 - self.ontology_downsample_p) ** shortest_distances_matrix
-    #             )
-    #             metadata_token_n_dict[key] = (
-    #                 torch.multinomial(ontology_weights[metadata_token_n], num_samples=1).squeeze(-1).int()
-    #             )
-    #     # impute mask token for unmeasured metadata
-    #     # mask token is the last token in the vocabulary
-    #     for i, (key, metadata_token_n) in enumerate(metadata_token_n_dict.items()):
-    #         metadata_token_n_dict[key] = torch.where(
-    #             metadata_query_mask_nm[:, i], self.metadata_vocab_sizes[key], metadata_token_n
-    #         ).int()
-
-    #     block_metadata_token_nm = torch.block_diag(
-    #         *[metadata_token_n_dict[key].unsqueeze(-1) for key in metadata_token_n_dict],
-    #     )
-    #     metadata_token_value_nc_dict = {
-    #         key: torch.cat(
-    #             [torch.zeros((n, j), dtype=torch.int, device=device), block_metadata_token_nm[n * i : n * (i + 1)]],
-    #             dim=1,
-    #         )
-    #         for i, key in enumerate(metadata_token_n_dict)
-    #     }
-    #     block_metadata_token_mask_nm = torch.block_diag(
-    #         *[torch.ones((n, 1), dtype=torch.bool, device=device) for _ in metadata_token_n_dict],
-    #     )
-    #     metadata_token_mask_nc_dict = {
-    #         key: torch.cat(
-    #             [
-    #                 torch.zeros((n, j), dtype=torch.bool, device=device),
-    #                 block_metadata_token_mask_nm[n * i : n * (i + 1)],
-    #             ],
-    #             dim=1,
-    #         )
-    #         for i, key in enumerate(metadata_token_n_dict)
-    #     }
-
-    #     ### PROMPT MASK ###
-    #     prompt_mask_nc = torch.cat([gene_prompt_mask_nj, metadata_prompt_mask_nm], dim=1)
-
-    #     ### LABELS ###
-    #     block_label_nc = torch.block_diag(
-    #         gene_label_nj,
-    #         *[metadata_label_n.unsqueeze(-1) for metadata_label_n in metadata_label_n_dict.values()],
-    #     )
-    #     label_nc_dict = {
-    #         key: block_label_nc[n * i : n * (i + 1)]
-    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
-    #     }
-
-    #     ### LABEL WEIGHTS ###
-    #     block_label_weight_nc = torch.block_diag(
-    #         gene_query_mask_nj,
-    #         *[metadata_query_mask_nm[:, i].unsqueeze(-1).float() for i in range(m)],
-    #     )
-    #     label_weight_nc_dict = {
-    #         key: block_label_weight_nc[n * i : n * (i + 1)]
-    #         for i, key in enumerate(["gene_value"] + list(metadata_token_n_dict))
-    #     }
-
-    #     return {
-    #         "token_value_nc_dict": gene_token_value_nc_dict | metadata_token_value_nc_dict,
-    #         "token_mask_nc_dict": gene_token_mask_nc_dict | metadata_token_mask_nc_dict,
-    #         "prompt_mask_nc": prompt_mask_nc,
-    #         "label_nc_dict": label_nc_dict,
-    #         "label_weight_nc_dict": label_weight_nc_dict,
-    #         "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
-    #         "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
-    #         "cell_type_n": metadata_token_n_dict["cell_type"],
-    #         "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
-    #         "gene_query_size_n": gene_query_mask_nj.sum(-1),
-    #         "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
-    #         "measured_query_mask_nc": measured_query_mask_nc,
-    #         "unmeasured_query_mask_nc": unmeasured_query_mask_nc,
-    #     }
-
     def forward(
         self,
         metadata_token_n_dict: dict[str, torch.Tensor],
@@ -874,45 +454,42 @@ def forward(
         for key, gene_token_ng in gene_token_ng_dict.items():
             gene_token_nj_dict[key] = torch.gather(gene_token_ng, dim=-1, index=shuffle_idx_nj)
 
-        ## gene value ##
-        gene_value_nj = gene_token_nj_dict.pop("gene_value")
-        total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
-        # downsample gene values
-        max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
-        downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
-        if self.gene_downsample_fraction > 0:
-            gene_downsample_p_nj = torch.minimum(
-                torch.rand((n, 1), device=device) / self.gene_downsample_fraction,
-                torch.tensor(1.0, device=device),
-            )
-            downsampled_total_mrna_umis_nj = torch.lerp(
-                torch.full_like(gene_downsample_p_nj, self.min_total_mrna_umis),
-                downsampled_total_mrna_umis_nj,
-                gene_downsample_p_nj,
-            )
-        gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
-        gene_value_nj = torch.round(gene_value_nj * gene_downsample_p_nj)
-        total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
         if self.prefix_len is not None:
             prefix_len_n = torch.full((n,), self.prefix_len, dtype=torch.float32)
         else:
             # sample prefix length
             # prefix_len_weights = [1, max_prefix_len / 2, max_prefix_len / 3, ..., max_prefix_len / max_prefix_len]
-            max_prefix_len = j - 1 - 2048
+            max_prefix_len = j - 1
             prefix_len_weights = torch.ones(max_prefix_len + 1, dtype=torch.float32)
             # prefix_len_weights = 1 / torch.arange(max_prefix_len + 1, dtype=torch.float32)
             # prefix_len_weights[0] = 1 / 10
             prefix_len_n = torch.multinomial(prefix_len_weights, n, replacement=True)
         # create prompt and query masks
-        # query_len_n = j - prefix_len_n
-        # gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
-        gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
+        query_len_n = j - prefix_len_n
+        gene_query_mask_nj = torch.arange(j, device=device) < query_len_n[:, None].expand(n, -1)
+        # gene_query_mask_nj = torch.arange(j, device=device) >= prefix_len_n[:, None].expand(n, -1)
         gene_prompt_mask_nj = ~gene_query_mask_nj
         if "measured_genes_mask" in gene_token_nj_dict:
             measured_genes_mask_nj = gene_token_nj_dict.pop("measured_genes_mask")
-            gene_query_mask_nj = gene_query_mask_nj
+            gene_query_mask_nj = gene_query_mask_nj & measured_genes_mask_nj
             gene_prompt_mask_nj = gene_prompt_mask_nj & measured_genes_mask_nj
 
+        ## gene value ##
+        gene_value_nj = gene_token_nj_dict.pop("gene_value")
+        total_mrna_umis_nj = gene_token_nj_dict.pop("total_mrna_umis")
+        # downsample gene values
+        max_total_mrna_umis = torch.tensor(self.max_total_mrna_umis, device=device)
+        downsampled_total_mrna_umis_nj = torch.minimum(total_mrna_umis_nj, max_total_mrna_umis).float()
+        # only downsample prompt genes
+        downsampled_total_mrna_umis_nj = torch.where(
+            gene_prompt_mask_nj,
+            downsampled_total_mrna_umis_nj * self.gene_downsample_p,
+            downsampled_total_mrna_umis_nj,
+        )
+        gene_downsample_p_nj = downsampled_total_mrna_umis_nj / total_mrna_umis_nj
+        gene_value_nj = torch.binomial(gene_value_nj, gene_downsample_p_nj)
+        total_mrna_umis_nj = torch.round(downsampled_total_mrna_umis_nj)
+
         gene_token_nj_dict["gene_value"] = torch.log1p(gene_value_nj) * gene_prompt_mask_nj.float()
         gene_token_nj_dict["gene_query_mask"] = gene_query_mask_nj.float()
         gene_token_nj_dict["total_mrna_umis"] = torch.log1p(total_mrna_umis_nj)
@@ -1042,10 +619,7 @@ def forward(
             "prompt_mask_nc": prompt_mask_nc,
             "label_nc_dict": label_nc_dict,
             "label_weight_nc_dict": label_weight_nc_dict,
-            "total_mrna_umis_n": torch.minimum(gene_token_n_dict["total_mrna_umis"], max_total_mrna_umis),
             "total_mrna_umis_downsampled_n": total_mrna_umis_nj[:, 0],
-            "cell_type_n": metadata_token_n_dict["cell_type"],
             "gene_prompt_size_n": gene_prompt_mask_nj.sum(-1),
             "gene_query_size_n": gene_query_mask_nj.sum(-1),
-            "gene_query_nnz_size_n": (gene_query_mask_nj & (gene_label_nj > 0)).sum(-1),
         }
\ No newline at end of file

From 6c08fb2ef422ed0ab74f072372d229e334b77b81 Mon Sep 17 00:00:00 2001
From: Yerdos Ordabayev <yordabay@broadinstitute.org>
Date: Sun, 9 Mar 2025 21:40:37 +0000
Subject: [PATCH 57/64] ontology notebooks

---
 .../training/cell_type_ontology.ipynb         | 1252 ++++++++
 .../cellarium_gpt/training/cell_vocab.ipynb   | 2570 +++++++++++++++++
 .../training/development_stage_ontology.ipynb |  854 ++++++
 .../training/disease_ontology.ipynb           |  868 ++++++
 .../cellarium_gpt/training/sex_ontology.ipynb |  318 ++
 .../training/tissue_ontology.ipynb            |  720 +++++
 6 files changed, 6582 insertions(+)
 create mode 100644 notebooks/cellarium_gpt/training/cell_type_ontology.ipynb
 create mode 100644 notebooks/cellarium_gpt/training/cell_vocab.ipynb
 create mode 100644 notebooks/cellarium_gpt/training/development_stage_ontology.ipynb
 create mode 100644 notebooks/cellarium_gpt/training/disease_ontology.ipynb
 create mode 100644 notebooks/cellarium_gpt/training/sex_ontology.ipynb
 create mode 100644 notebooks/cellarium_gpt/training/tissue_ontology.ipynb

diff --git a/notebooks/cellarium_gpt/training/cell_type_ontology.ipynb b/notebooks/cellarium_gpt/training/cell_type_ontology.ipynb
new file mode 100644
index 00000000..07a7f91b
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/cell_type_ontology.ipynb
@@ -0,0 +1,1252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# cell_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "# query_format = \"\"\"\n",
+    "#     select {column_name}, count(*) as num_cells \n",
+    "#     from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "#     group by {column_name}\n",
+    "#     order by num_cells desc\n",
+    "# \"\"\"\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `dsp-cell-annotation-service.cas_2024_05_16_dataset.cellarium_gpt_train_set_remake__extract_cell_info_randomized`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_shortest_path_lengths_from_source(graph, source: str) -> t.Dict[str, float]:\n",
+    "#     # Perform a topological sort of the graph\n",
+    "#     topo_order = list(nx.topological_sort(graph))\n",
+    "\n",
+    "#     # Initialize distances with infinity for all nodes except the source\n",
+    "#     distances = {node: float(\"inf\") for node in graph.nodes()}\n",
+    "#     distances[source] = 0\n",
+    "\n",
+    "#     # Process nodes in reverse topological order\n",
+    "#     for node in reversed(topo_order):\n",
+    "#         if distances[node] != float(\"inf\"):  # Only process reachable nodes\n",
+    "#             for neighbor in graph.predecessors(node):\n",
+    "#                 if distances[neighbor] > distances[node] + 1:\n",
+    "#                     distances[neighbor] = distances[node] + 1\n",
+    "\n",
+    "#     return distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_longest_path_lengths_from_source(graph, source: str) -> t.Dict[str, float]:\n",
+    "#     # Perform a topological sort of the graph\n",
+    "#     topo_order = list(nx.topological_sort(graph))\n",
+    "\n",
+    "#     # Initialize distances with -infinity for all nodes except the source\n",
+    "#     distances = {node: float(\"-inf\") for node in graph.nodes()}\n",
+    "#     distances[source] = 0\n",
+    "\n",
+    "#     # Process nodes in reverse topological order\n",
+    "#     for node in reversed(topo_order):\n",
+    "#         if distances[node] != float(\"-inf\"):  # Only process reachable nodes\n",
+    "#             for neighbor in graph.predecessors(node):\n",
+    "#                 if distances[neighbor] < distances[node] + 1:\n",
+    "#                     distances[neighbor] = distances[node] + 1\n",
+    "\n",
+    "#     return distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "#     \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "#     .. note:\n",
+    "#         The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "#     \"\"\"\n",
+    "#     n_nodes = len(graph.nodes)\n",
+    "\n",
+    "#     distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "#     for name, self_idx in names_to_idx_map.items():\n",
+    "#         for ancestor_name, distance in get_shortest_path_lengths_from_source(graph, name).items():\n",
+    "#             if distance == float(\"inf\"):\n",
+    "#                 continue\n",
+    "#             ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "#             distance_matrix[self_idx, ancestor_idx] = distance\n",
+    "\n",
+    "#     return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def get_longest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "#     \"\"\"Returns a sparse matrix representation of longest distances.\n",
+    "\n",
+    "#     .. note:\n",
+    "#         The matrix element (i, j) = d iff d is the longest distance between i and j.\n",
+    "#     \"\"\"\n",
+    "#     n_nodes = len(graph.nodes)\n",
+    "\n",
+    "#     distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "#     for name, self_idx in names_to_idx_map.items():\n",
+    "#         for ancestor_name, distance in get_longest_path_lengths_from_source(graph, name).items():\n",
+    "#             if distance == float(\"-inf\"):\n",
+    "#                 continue\n",
+    "#             ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "#             distance_matrix[self_idx, ancestor_idx] = distance\n",
+    "\n",
+    "#     return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>2938728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CL:4023040</td>\n",
+       "      <td>1695623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>1294620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CL:0000624</td>\n",
+       "      <td>1238275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CL:0000625</td>\n",
+       "      <td>1179803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>CL:0011101</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>CL:0000103</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>CL:0000019</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>CL:0002280</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>CL:0000218</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>671 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    cell_type_ontology_term_id  num_cells\n",
+       "0                   CL:0000540    2938728\n",
+       "1                   CL:4023040    1695623\n",
+       "2                   CL:0000128    1294620\n",
+       "3                   CL:0000624    1238275\n",
+       "4                   CL:0000625    1179803\n",
+       "..                         ...        ...\n",
+       "666                 CL:0011101         14\n",
+       "667                 CL:0000103         12\n",
+       "668                 CL:0000019         11\n",
+       "669                 CL:0002280         10\n",
+       "670                 CL:0000218          8\n",
+       "\n",
+       "[671 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"cell_type_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(38307243)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_counts[\"num_cells\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>2938728</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>1695623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>1294620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>1238275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>1179803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>666</th>\n",
+       "      <td>bone marrow cell</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>667</th>\n",
+       "      <td>bipolar neuron</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>sperm</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>669</th>\n",
+       "      <td>type N enteroendocrine cell</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>myelinating Schwann cell</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>671 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type  num_cells\n",
+       "0                                               neuron    2938728\n",
+       "1    L2/3-6 intratelencephalic projecting glutamate...    1695623\n",
+       "2                                      oligodendrocyte    1294620\n",
+       "3                      CD4-positive, alpha-beta T cell    1238275\n",
+       "4                      CD8-positive, alpha-beta T cell    1179803\n",
+       "..                                                 ...        ...\n",
+       "666                                   bone marrow cell         14\n",
+       "667                                     bipolar neuron         12\n",
+       "668                                              sperm         11\n",
+       "669                        type N enteroendocrine cell         10\n",
+       "670                           myelinating Schwann cell          8\n",
+       "\n",
+       "[671 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"cell_type\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `cell_type`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL', 'unknown'}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/obophenotype/cell-ontology/releases/download/v2024-01-04/cl.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"CL_\"\n",
+    "\n",
+    "# the 'cell' node\n",
+    "CELL_ROOT_NODE = \"CL:0000000\"\n",
+    "\n",
+    "# the 'eukaryotic cell' node\n",
+    "EUKARYOTIC_CELL_ROOT_NODE = \"CL:0000255\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `cell_type`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(labels_counts[query_label]) - set(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    # for prop in self_class.get_class_properties():\n",
+    "    #     if PARTOF_RELATIONSHIP in prop.name:\n",
+    "    #         for related_term in prop[self_class]:\n",
+    "    #             if related_term.name.startswith(PREFIX):\n",
+    "    #                 graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [1, 0, 0, ..., 1, 0, 0],\n",
+       "       [1, 0, 0, ..., 0, 1, 0],\n",
+       "       [1, 0, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "670"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"unknown\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "890"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {CELL_ROOT_NODE, EUKARYOTIC_CELL_ROOT_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"cell_type_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/1128136463.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_counts_map = {label: count for label, count in zip(labels_counts[query_label], labels_counts[\"num_cells\"])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "    data={\n",
+    "        \"cell_type\": new_labels,\n",
+    "        \"num_cells\": [labels_counts_map.get(label, 0) for label in new_labels],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "220"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_ancestors_idx = [idx for idx, label in enumerate(new_labels) if labels_counts_map.get(label, 0) == 0]\n",
+    "len(empty_ancestors_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_cells = torch.tensor(df[\"num_cells\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.6097561 , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.03160556, 0.79013906, 0.        , ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.80128205, ..., 0.        , 0.        ,\n",
+       "        0.        ],\n",
+       "       ...,\n",
+       "       [0.        , 0.        , 0.        , ..., 0.80128205, 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.        , ..., 0.        , 0.67204301,\n",
+       "        0.        ],\n",
+       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+       "        0.57603687]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 0.8\n",
+    "probs = (1 - p) ** new_shortest_distances_matrix * p\n",
+    "probs = probs / probs.sum(axis=1, keepdims=True)\n",
+    "probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/3989491769.py:1: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n",
+      "  df[\"new_num_cells\"] = (num_cells[:, None] * probs).sum(axis=0).int().numpy()\n"
+     ]
+    }
+   ],
+   "source": [
+    "df[\"new_num_cells\"] = (num_cells[:, None] * probs).sum(axis=0).int().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>num_cells</th>\n",
+       "      <th>new_num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>2938728</td>\n",
+       "      <td>1952112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sensory neuron</td>\n",
+       "      <td>1605</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>primary cultured cell</td>\n",
+       "      <td>80</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cultured cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>fibroblast neural crest derived</td>\n",
+       "      <td>0</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>885</th>\n",
+       "      <td>metallothionein-positive alveolar macrophage</td>\n",
+       "      <td>357</td>\n",
+       "      <td>275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>lung interstitial macrophage</td>\n",
+       "      <td>302</td>\n",
+       "      <td>180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>deuterosomal cell</td>\n",
+       "      <td>67</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>respiratory suprabasal cell</td>\n",
+       "      <td>1792</td>\n",
+       "      <td>1204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>luminal hormone-sensing cell of mammary gland</td>\n",
+       "      <td>196476</td>\n",
+       "      <td>113177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>890 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         cell_type  num_cells  new_num_cells\n",
+       "0                                           neuron    2938728        1952112\n",
+       "1                                   sensory neuron       1605           1799\n",
+       "2                            primary cultured cell         80             64\n",
+       "3                                    cultured cell          0             12\n",
+       "4                  fibroblast neural crest derived          0             95\n",
+       "..                                             ...        ...            ...\n",
+       "885   metallothionein-positive alveolar macrophage        357            275\n",
+       "886                   lung interstitial macrophage        302            180\n",
+       "887                              deuterosomal cell         67             53\n",
+       "888                    respiratory suprabasal cell       1792           1204\n",
+       "889  luminal hormone-sensing cell of mammary gland     196476         113177\n",
+       "\n",
+       "[890 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([], [])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.sort_values(by=\"num_cells\", ascending=False).plot.bar(x=\"cell_type\", y=[\"num_cells\", \"new_num_cells\"], figsize=(20, 10))\n",
+    "# remove x-ticks label\n",
+    "plt.xticks([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 10000000.0)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_df = df.copy()\n",
+    "_df[\"num_cells\"] = 1 + df[\"num_cells\"]\n",
+    "_df[\"new_num_cells\"] = 1 + df[\"new_num_cells\"]\n",
+    "_df.plot.scatter(x=\"num_cells\", y=\"new_num_cells\", figsize=(5, 5), s=1)\n",
+    "plt.plot([0, 1e7], [0, 1e7], color=\"red\")\n",
+    "plt.loglog()\n",
+    "plt.xlim(0.1, 1e7)\n",
+    "plt.ylim(0.1, 1e7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## combine ontologies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2984670/2248219605.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  tissue_onotology_data = torch.load(\"tissue_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  development_stage_onotology_data = torch.load(\"development_stage_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  disease_onotology_data = torch.load(\"disease_ontology_data.pt\")\n",
+      "/tmp/ipykernel_2984670/2248219605.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  sex_onotology_data = torch.load(\"sex_ontology_data.pt\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data = torch.load(\"cell_type_ontology_data.pt\")\n",
+    "tissue_onotology_data = torch.load(\"tissue_ontology_data.pt\")\n",
+    "development_stage_onotology_data = torch.load(\"development_stage_ontology_data.pt\")\n",
+    "disease_onotology_data = torch.load(\"disease_ontology_data.pt\")\n",
+    "sex_onotology_data = torch.load(\"sex_ontology_data.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology_infos = {\n",
+    "    \"cell_type\": cell_type_ontology_data,\n",
+    "    \"tissue\": tissue_onotology_data,\n",
+    "    \"development_stage\": development_stage_onotology_data,\n",
+    "    \"disease\": disease_onotology_data,\n",
+    "    \"sex\": sex_onotology_data,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['CL:0000540', 'CL:0000101', 'CL:0000001']"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_type_ontology_data[\"names\"][:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(ontology_infos, \"ontology_infos.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cellarium",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellarium_gpt/training/cell_vocab.ipynb b/notebooks/cellarium_gpt/training/cell_vocab.ipynb
new file mode 100644
index 00000000..1007c91d
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/cell_vocab.ipynb
@@ -0,0 +1,2570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cell vocabulary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cell level *biological* tokens:\n",
+    "- `cell_type`\n",
+    "- `donor_id`?\n",
+    "- `development_stage`\n",
+    "- `disease`\n",
+    "- `sex`\n",
+    "- `tissue`\n",
+    "\n",
+    "scRNA-seq *measurement* tokens consist of gene specific measurement (ID and count) and context (assay, library size, temperature, etc):\n",
+    "- gene count `X`\n",
+    "- gene Ensembl ID `var_names`\n",
+    "- `suspension_type`\n",
+    "- `assay`\n",
+    "- `total_mrna_umis`\n",
+    "- `dataset_id`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from cellarium.ml.data import read_h5ad_file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/cellarium/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata = read_h5ad_file(\"gs://cellarium-file-system/curriculum/homo_sap_all_diseases/extract_files/extract_0.h5ad\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 10000 entries, 734010876 to 1488009346\n",
+      "Data columns (total 22 columns):\n",
+      " #   Column                                    Non-Null Count  Dtype   \n",
+      "---  ------                                    --------------  -----   \n",
+      " 0   cell_type                                 10000 non-null  category\n",
+      " 1   cas_ingest_id                             10000 non-null  category\n",
+      " 2   assay_ontology_term_id                    10000 non-null  category\n",
+      " 3   cell_type_ontology_term_id                10000 non-null  category\n",
+      " 4   development_stage_ontology_term_id        10000 non-null  category\n",
+      " 5   disease_ontology_term_id                  10000 non-null  category\n",
+      " 6   donor_id                                  10000 non-null  category\n",
+      " 7   is_primary_data                           10000 non-null  bool    \n",
+      " 8   organism_ontology_term_id                 10000 non-null  category\n",
+      " 9   self_reported_ethnicity_ontology_term_id  10000 non-null  category\n",
+      " 10  sex_ontology_term_id                      10000 non-null  category\n",
+      " 11  suspension_type                           10000 non-null  category\n",
+      " 12  tissue_ontology_term_id                   10000 non-null  category\n",
+      " 13  assay                                     10000 non-null  category\n",
+      " 14  development_stage                         10000 non-null  category\n",
+      " 15  disease                                   10000 non-null  category\n",
+      " 16  organism                                  10000 non-null  category\n",
+      " 17  self_reported_ethnicity                   10000 non-null  category\n",
+      " 18  sex                                       10000 non-null  category\n",
+      " 19  tissue                                    10000 non-null  category\n",
+      " 20  total_mrna_umis                           10000 non-null  int64   \n",
+      " 21  dataset_id                                10000 non-null  category\n",
+      "dtypes: bool(1), category(20), int64(1)\n",
+      "memory usage: 894.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata.obs.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `cell_type`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      10000\n",
+       "unique       385\n",
+       "top       neuron\n",
+       "freq         774\n",
+       "Name: cell_type, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"cell_type\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "cell_type\n",
+       "neuron                             774\n",
+       "glutamatergic neuron               427\n",
+       "CD4-positive, alpha-beta T cell    392\n",
+       "CD8-positive, alpha-beta T cell    358\n",
+       "classical monocyte                 270\n",
+       "                                  ... \n",
+       "pancreatic endocrine cell            0\n",
+       "glioblast                            0\n",
+       "primary sensory neuron               0\n",
+       "basophil mast progenitor cell        0\n",
+       "endosteal cell                       0\n",
+       "Name: count, Length: 666, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell_type_freq = adata.obs[\"cell_type\"].value_counts()\n",
+    "cell_type_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'cell_type frequencies'}, xlabel='cell_type'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cell_type_freq[cell_type_freq > 50].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"cell_type frequencies\",\n",
+    "    legend=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "most_freq_cell_types = cell_type_freq[cell_type_freq > 50].index\n",
+    "filtered_df = adata.obs.loc[adata.obs[\"cell_type\"].isin(most_freq_cell_types)]\n",
+    "\n",
+    "filtered_df.groupby(\"cell_type\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(10, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `donor_id`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count          10000\n",
+       "unique          2837\n",
+       "top       H18.30.002\n",
+       "freq             370\n",
+       "Name: donor_id, dtype: object"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"donor_id\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "donor_id\n",
+       "H18.30.002    370\n",
+       "H19.30.001    310\n",
+       "H19.30.002    272\n",
+       "H27471        223\n",
+       "H27431        205\n",
+       "             ... \n",
+       "144_144         1\n",
+       "Hrv51           1\n",
+       "Hrv49           1\n",
+       "Hrv47           1\n",
+       "subject_4       1\n",
+       "Name: count, Length: 2837, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "donor_id_freq = adata.obs[\"donor_id\"].value_counts()\n",
+    "donor_id_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['H18.30.002',\n",
+       " 'H19.30.001',\n",
+       " 'H19.30.002',\n",
+       " 'H27471',\n",
+       " 'H27431',\n",
+       " 'H27474',\n",
+       " 'H27477',\n",
+       " 'H27472',\n",
+       " 'H27634',\n",
+       " 'TSP14',\n",
+       " 'H27464',\n",
+       " 'H27098',\n",
+       " 'D496',\n",
+       " 'H27058',\n",
+       " 'F51',\n",
+       " '21wpc donor',\n",
+       " 'D2',\n",
+       " 'TSP2',\n",
+       " 'H3',\n",
+       " '19D015',\n",
+       " 'GW25',\n",
+       " 'H18.30.001',\n",
+       " 'F61',\n",
+       " 'H5',\n",
+       " 'H27552',\n",
+       " '356C',\n",
+       " '130064',\n",
+       " '19D013',\n",
+       " 'GW18_2',\n",
+       " 'H27423',\n",
+       " 'D6',\n",
+       " 'D503',\n",
+       " 'GW16',\n",
+       " '2',\n",
+       " 'H26547',\n",
+       " 'TSP7',\n",
+       " 'GW18',\n",
+       " 'H27473',\n",
+       " '110814',\n",
+       " 'P-M004',\n",
+       " 'Donor30',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747D239',\n",
+       " 'D3',\n",
+       " 'ICC_C_0003',\n",
+       " 'C41',\n",
+       " 'H06',\n",
+       " 'GTEX-1HSMQ',\n",
+       " 'SPECTRUM-OV-110',\n",
+       " '3',\n",
+       " 'H27771',\n",
+       " 'H26350',\n",
+       " 'Donor2',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NU_CZI01',\n",
+       " '104689',\n",
+       " 'SPECTRUM-OV-118',\n",
+       " '120974',\n",
+       " 'D11',\n",
+       " 'H02',\n",
+       " 'HBCA_Donor_38',\n",
+       " '109829',\n",
+       " 'P2',\n",
+       " 'D4',\n",
+       " 'SPECTRUM-OV-007',\n",
+       " 'TSP4',\n",
+       " 'TSP1',\n",
+       " 'Donor8',\n",
+       " 'SPECTRUM-OV-009',\n",
+       " 'H27772',\n",
+       " '240day donor',\n",
+       " 'F50',\n",
+       " 'F73',\n",
+       " 'F45',\n",
+       " 'F41',\n",
+       " 'Donor5',\n",
+       " 'Donor1',\n",
+       " 'CV-003',\n",
+       " 'BM3',\n",
+       " '166301',\n",
+       " '109373',\n",
+       " '1',\n",
+       " '640C',\n",
+       " 'SPECTRUM-OV-107',\n",
+       " 'SPECTRUM-OV-037',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NU_CZI02',\n",
+       " 'R-00646_01',\n",
+       " 'A43',\n",
+       " '367C',\n",
+       " 'SPECTRUM-OV-026',\n",
+       " 'SPECTRUM-OV-065',\n",
+       " 'SPECTRUM-OV-075',\n",
+       " 'P-M009',\n",
+       " 'P8',\n",
+       " 'T06',\n",
+       " 'H11',\n",
+       " '19D014',\n",
+       " '328C',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_5',\n",
+       " 'H2',\n",
+       " 'A36',\n",
+       " 'F122',\n",
+       " 'P-M007',\n",
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+       " '130084',\n",
+       " 'Donor9',\n",
+       " 'P7',\n",
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+       " 'P3',\n",
+       " 'H21.33.027',\n",
+       " 'H27870',\n",
+       " 'H20.33.013',\n",
+       " 'rGBM-04',\n",
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+       " 'HBCA_Donor_33',\n",
+       " 'H6',\n",
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+       " 'H20.33.039',\n",
+       " '290b',\n",
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+       " 'H20.33.014',\n",
+       " 'H27876',\n",
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+       " 'HBCA_Donor_15',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_7',\n",
+       " 'GTEX-145ME',\n",
+       " 'SPECTRUM-OV-003',\n",
+       " 'GW20',\n",
+       " 'Rep_C_1094',\n",
+       " 'Donor12',\n",
+       " 'GRO-09',\n",
+       " 'F71',\n",
+       " 'Donor31',\n",
+       " 'SPECTRUM-OV-077',\n",
+       " 'Donor23',\n",
+       " 'Leader_Merad_2021_706',\n",
+       " 'SPECTRUM-OV-081',\n",
+       " 'SPECTRUM-OV-083',\n",
+       " 'SPECTRUM-OV-115',\n",
+       " 'SPECTRUM-OV-116',\n",
+       " 'TTx',\n",
+       " 'donor_2',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747Case_1',\n",
+       " 'A32 (411C)',\n",
+       " '104152',\n",
+       " 'H03',\n",
+       " 'N00023',\n",
+       " 'P5',\n",
+       " 'NU_CZI02',\n",
+       " 'H84',\n",
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+       " '637C',\n",
+       " 'P6',\n",
+       " 'P1',\n",
+       " 'H27798',\n",
+       " 'H27915',\n",
+       " 'HBCA_Donor_44',\n",
+       " 'H21.33.047',\n",
+       " 'HBCA_Donor_46',\n",
+       " 'HDBR15233',\n",
+       " 'HGR0000051',\n",
+       " 'HGR0000101',\n",
+       " 'NU_CZI01',\n",
+       " 'He_Fan_2021_P2',\n",
+       " 'Hrv93',\n",
+       " 'H25',\n",
+       " '0',\n",
+       " 'Patient_1',\n",
+       " 'H20.33.005',\n",
+       " 'H20.33.043',\n",
+       " 'SPECTRUM-OV-041',\n",
+       " 'Patient_3',\n",
+       " 'Patient_2',\n",
+       " 'CV-011',\n",
+       " 'HC-502',\n",
+       " 'CV-001',\n",
+       " 'C70',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747PMB_1',\n",
+       " 'C3N-01816',\n",
+       " 'BT363',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747PMB_2',\n",
+       " 'H16',\n",
+       " 'P-S053',\n",
+       " 'GW20_34',\n",
+       " 'A29',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747Patient_1',\n",
+       " '154787',\n",
+       " 'HT-234',\n",
+       " 'H12',\n",
+       " 'S00052',\n",
+       " 'Patient_4',\n",
+       " 'ndGBM-02',\n",
+       " 'L04',\n",
+       " 'LONZA3038099',\n",
+       " 'Leader_Merad_2021_695',\n",
+       " 'ac21',\n",
+       " 'D6_mother',\n",
+       " 'F72',\n",
+       " 'D7',\n",
+       " 'F133',\n",
+       " 'F66',\n",
+       " 'SPECTRUM-OV-042',\n",
+       " 'H33',\n",
+       " 'H00054',\n",
+       " 'FL-074',\n",
+       " 'P-S038',\n",
+       " 'F38',\n",
+       " 'F29',\n",
+       " 'Donor49',\n",
+       " 'P-M044',\n",
+       " 'P-M040',\n",
+       " 'H21.33.025',\n",
+       " 'P9',\n",
+       " 'H21.33.007',\n",
+       " 'H4',\n",
+       " 'H20.33.017',\n",
+       " 'DP1',\n",
+       " 'H20.33.004',\n",
+       " 'HBCA_Donor_8',\n",
+       " 'D372',\n",
+       " 'D353',\n",
+       " 'HBCA_Donor_21',\n",
+       " 'RBG33508_F90_F103_F107',\n",
+       " 'HTA8_1029',\n",
+       " 'A31',\n",
+       " 'H21.33.019',\n",
+       " 'Rep_C_1107',\n",
+       " 'HBCA_Donor_20',\n",
+       " 'HBCA_Donor_6',\n",
+       " 'H27909',\n",
+       " 'Rep_C_1033',\n",
+       " 'C-8944',\n",
+       " 'H27295',\n",
+       " 'H80',\n",
+       " 'Rep_C_1001',\n",
+       " '10',\n",
+       " 'Donor34',\n",
+       " 'Donor38',\n",
+       " 'Donor42',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_4837472020-3173-NC004',\n",
+       " 'SPECTRUM-OV-082',\n",
+       " 'Kim_Lee_2020_P0008',\n",
+       " '3593',\n",
+       " 'C-8884',\n",
+       " 'H21.33.041',\n",
+       " 'H21.33.040',\n",
+       " 'H21.33.039',\n",
+       " 'A39 (440C)',\n",
+       " 'H21.33.022',\n",
+       " 'HBCA_Donor_27',\n",
+       " 'HBCA_Donor_29',\n",
+       " 'Donor11',\n",
+       " 'S00056',\n",
+       " '1014',\n",
+       " 'D9_mother',\n",
+       " 'D17',\n",
+       " 'HGR0000124',\n",
+       " 'SAMN15049048',\n",
+       " 'HGR0000391',\n",
+       " 'D1',\n",
+       " 'Chen_Zhang_2020_NSCLC-11',\n",
+       " '124246',\n",
+       " 'hb30',\n",
+       " 'S00045',\n",
+       " '19D016',\n",
+       " 'Chen_Zhang_2020_NSCLC-8',\n",
+       " 'S00028',\n",
+       " 'CVID29',\n",
+       " 'Hrv33',\n",
+       " '158108',\n",
+       " 'HBCA_Donor_49',\n",
+       " 'ICC_C_0001',\n",
+       " 'HBCA_Donor_48',\n",
+       " 'TSP8',\n",
+       " 'HBCA_Donor_40',\n",
+       " 'X192',\n",
+       " '1019',\n",
+       " 'Leader_Merad_2021_564',\n",
+       " 'F21',\n",
+       " 'H20.33.041',\n",
+       " 'H20.33.040',\n",
+       " 'BT364',\n",
+       " 'F67',\n",
+       " 'BPH389',\n",
+       " 'P11',\n",
+       " 'F83',\n",
+       " 'P19',\n",
+       " 'H19',\n",
+       " 'SPECTRUM-OV-024',\n",
+       " 'BCP5',\n",
+       " 'H10',\n",
+       " 'H09',\n",
+       " 'Aged2',\n",
+       " 'P-M020',\n",
+       " 'H1',\n",
+       " 'P-HC013',\n",
+       " 'G05078',\n",
+       " 'P-M010',\n",
+       " 'F30',\n",
+       " 'H20.33.028',\n",
+       " 'H21.33.015',\n",
+       " 'Emp3',\n",
+       " 'P-S044',\n",
+       " 'Leader_Merad_2021_593',\n",
+       " 'Leader_Merad_2021_626',\n",
+       " 'H21.33.005',\n",
+       " 'SPECTRUM-OV-068',\n",
+       " 'SPECTRUM-OV-025',\n",
+       " 'H21.33.043',\n",
+       " 'H21.33.011',\n",
+       " 'H21.33.012',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747D353',\n",
+       " 'HBCA_Donor_50',\n",
+       " 'Subject7',\n",
+       " 'H21.33.014',\n",
+       " 'D339',\n",
+       " 'HC-009',\n",
+       " 'H08',\n",
+       " '302C',\n",
+       " 'SPECTRUM-OV-008',\n",
+       " 'H07',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SSc_4',\n",
+       " 'T07',\n",
+       " 'H21.33.035',\n",
+       " 'H21.33.029',\n",
+       " 'G05153',\n",
+       " 'HC-504',\n",
+       " 'GTEX-15RIE',\n",
+       " 'GRO-10',\n",
+       " 'SPECTRUM-OV-071',\n",
+       " '117351',\n",
+       " '1243',\n",
+       " 'AN13',\n",
+       " 'H00067',\n",
+       " 'H49',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747BAL019',\n",
+       " 'H41',\n",
+       " '9',\n",
+       " 'H21.33.006',\n",
+       " '8',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747GRO-10',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747GRO-09',\n",
+       " 'Donor25',\n",
+       " '31-10000',\n",
+       " 'H20.33.034',\n",
+       " 'H21.33.008',\n",
+       " 'HBCA_Donor_43',\n",
+       " 'HBCA_Donor_11',\n",
+       " 'HBCA_Donor_17',\n",
+       " 'H21.33.021',\n",
+       " '198',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747GRO-04',\n",
+       " 'H20.33.024',\n",
+       " 'H35',\n",
+       " 'HC-543',\n",
+       " 'H20.33.018',\n",
+       " 'HBCA_Donor_32',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SAMN12574699',\n",
+       " 'Donor7',\n",
+       " 'H20.33.025',\n",
+       " 'hb53',\n",
+       " 'D8_3453',\n",
+       " 'A37',\n",
+       " 'P-S049',\n",
+       " 'Leader_Merad_2021_581',\n",
+       " 'AAQ',\n",
+       " 'Leader_Merad_2021_378',\n",
+       " 'LONZA3038016',\n",
+       " 'P-S085',\n",
+       " 'P-S088',\n",
+       " 'LB3872T',\n",
+       " 'A34 (417C)',\n",
+       " 'C-8921',\n",
+       " 'PP1',\n",
+       " 'C-8905',\n",
+       " 'A30',\n",
+       " 'donor_1',\n",
+       " 'A26 (386C)',\n",
+       " 'R3',\n",
+       " 'R4',\n",
+       " 'A16',\n",
+       " 'C3N-02188',\n",
+       " 'ac13',\n",
+       " 'pat9',\n",
+       " 'P-S021',\n",
+       " 'AP7',\n",
+       " 'AP9',\n",
+       " 'P-HC015',\n",
+       " 'cov17',\n",
+       " 'P87',\n",
+       " 'P79',\n",
+       " 'P-M036',\n",
+       " 'P18',\n",
+       " 'P13',\n",
+       " 'P-M067',\n",
+       " 'NP44',\n",
+       " 'BRC2251',\n",
+       " 'pooled',\n",
+       " 'NP20',\n",
+       " 'NP17',\n",
+       " 'NP13',\n",
+       " 'P-S017',\n",
+       " 'VUHD106',\n",
+       " 'GTEX-1I1GU',\n",
+       " 'RU675',\n",
+       " '180844',\n",
+       " '165697',\n",
+       " '148519',\n",
+       " 'IGTB469',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747Wunderink2021_subject_A',\n",
+       " '197396',\n",
+       " 'IGTB514',\n",
+       " 'IGTB670',\n",
+       " 'S00058',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_4',\n",
+       " 'S00081',\n",
+       " 'GTEX-1ICG6',\n",
+       " 'Rep_C_1101',\n",
+       " 'HGR0000429',\n",
+       " 'Rep_C_1044',\n",
+       " 'UKIM-V-2_P15',\n",
+       " 'D5',\n",
+       " 'HGR0000083',\n",
+       " 'RU1293',\n",
+       " 'C-8943',\n",
+       " 'Rep_C_1003',\n",
+       " 'D3_3203',\n",
+       " 'Rep_C_1014',\n",
+       " 'G799',\n",
+       " 'ac20',\n",
+       " 'F69',\n",
+       " 'GRO-04',\n",
+       " 'BPH340',\n",
+       " 'Chen_Zhang_2020_NSCLC-10',\n",
+       " 'F88',\n",
+       " 'AP12',\n",
+       " 'BCP6',\n",
+       " 'SK-010',\n",
+       " '32-10003',\n",
+       " 'G05097',\n",
+       " 'Aged3',\n",
+       " 'AP10',\n",
+       " 'G1003',\n",
+       " 'BPH511',\n",
+       " 'Adams_Kaminski_2020_153CO',\n",
+       " 'AP11',\n",
+       " 'CVID34',\n",
+       " 'TSP9',\n",
+       " 'CV-056',\n",
+       " 'Donor22',\n",
+       " 'SPECTRUM-OV-105',\n",
+       " 'C-8937',\n",
+       " 'Donor48',\n",
+       " 'Donor32',\n",
+       " 'VUHD94',\n",
+       " '29-10006',\n",
+       " 'SPECTRUM-OV-053',\n",
+       " 'C-8885',\n",
+       " 'D5_3296',\n",
+       " 'VUHD68',\n",
+       " 'CONTROL7',\n",
+       " 'C-8821',\n",
+       " 'SPECTRUM-OV-036',\n",
+       " 'Donor15',\n",
+       " 'C53ctr',\n",
+       " 'C3N-03186',\n",
+       " 'C3N-02784',\n",
+       " 'UKIM-V-2_P7',\n",
+       " 'C3',\n",
+       " 'ac11',\n",
+       " 'UKIM-V-2_P6',\n",
+       " 'TSP10',\n",
+       " 'ac14',\n",
+       " 'DT4',\n",
+       " 'E99',\n",
+       " 'H36',\n",
+       " 'GTEX-13N11',\n",
+       " 'PC6',\n",
+       " 'PC10',\n",
+       " 'L07cov',\n",
+       " 'PP5',\n",
+       " 'PP8',\n",
+       " 'PP13',\n",
+       " 'R1',\n",
+       " 'R2',\n",
+       " 'R5',\n",
+       " 'R266',\n",
+       " 'R-00646_04',\n",
+       " 'KR_SGI_H025',\n",
+       " 'RU1108',\n",
+       " 'Rep_C_1023',\n",
+       " 'Rep_C_1025',\n",
+       " 'Rep_C_1031',\n",
+       " 'JP_RIK_H045',\n",
+       " 'Rep_C_1039',\n",
+       " 'Rep_C_1045',\n",
+       " 'Rep_C_1052',\n",
+       " 'IGTB1650',\n",
+       " 'Rep_C_1131',\n",
+       " 'Rep_C_1154',\n",
+       " 'IGTB508',\n",
+       " 'IGTB498',\n",
+       " 'IGTB195',\n",
+       " 'IC_H02',\n",
+       " 'S00005',\n",
+       " 'L22cov',\n",
+       " 'LB3933T',\n",
+       " 'GTEX-15CHR',\n",
+       " 'LONZA3038097',\n",
+       " 'P-HC018',\n",
+       " 'P-HC017',\n",
+       " 'P-HC014',\n",
+       " 'P-HC005',\n",
+       " 'P108',\n",
+       " 'P-M026',\n",
+       " 'P97',\n",
+       " 'P-M032',\n",
+       " 'P-M034',\n",
+       " 'P76',\n",
+       " 'P49',\n",
+       " 'P21',\n",
+       " 'P15',\n",
+       " 'P-M064',\n",
+       " 'P-M074',\n",
+       " 'NP37',\n",
+       " 'P-M078',\n",
+       " 'NP16',\n",
+       " 'NGCII531',\n",
+       " 'NGCII512',\n",
+       " 'NGCII511',\n",
+       " 'P-S037',\n",
+       " 'P-S047',\n",
+       " 'P-S060',\n",
+       " 'Leader_Merad_2021_800',\n",
+       " 'Leader_Merad_2021_406',\n",
+       " 'Lambrechts_Thienpont_2018_6653_8',\n",
+       " 'S00006',\n",
+       " 'Hrv30',\n",
+       " 'S00033',\n",
+       " 'He_Fan_2021_P4',\n",
+       " 'H37',\n",
+       " 'H34',\n",
+       " 'H31',\n",
+       " 'H28',\n",
+       " 'H27',\n",
+       " 'H21.33.042',\n",
+       " 'H21.33.017',\n",
+       " 'H21',\n",
+       " 'H20.33.046',\n",
+       " 'H20.33.044',\n",
+       " 'H20.33.035',\n",
+       " 'H20.33.033',\n",
+       " 'H20.33.029',\n",
+       " 'H20.33.020',\n",
+       " 'H20.33.002',\n",
+       " '302c',\n",
+       " 'H17',\n",
+       " 'H14',\n",
+       " 'H13',\n",
+       " 'H7',\n",
+       " '325C',\n",
+       " 'H01',\n",
+       " '337C',\n",
+       " 'GW22T',\n",
+       " 'SG_HEL_H378',\n",
+       " 'GW14',\n",
+       " 'GTEX-16BQI',\n",
+       " 'H38',\n",
+       " 'H00053',\n",
+       " 'H00058',\n",
+       " 'HC-014',\n",
+       " 'He_Fan_2021_P1',\n",
+       " 'S00042',\n",
+       " 'HTA8_1031',\n",
+       " 'HT-228',\n",
+       " 'HT-187',\n",
+       " 'S00109',\n",
+       " 'HGR0000144',\n",
+       " 'HGR0000042',\n",
+       " 'HDBR15280',\n",
+       " 'S1100A',\n",
+       " 'HC-538',\n",
+       " 'HC-520',\n",
+       " 'SB19PCW',\n",
+       " 'H67',\n",
+       " 'HC-006',\n",
+       " 'HBCA_Donor_51',\n",
+       " 'HBCA_Donor_47',\n",
+       " 'HBCA_Donor_45',\n",
+       " 'HBCA_Donor_34',\n",
+       " 'HBCA_Donor_31',\n",
+       " 'HBCA_Donor_23',\n",
+       " 'HBCA_Donor_18',\n",
+       " 'H27799',\n",
+       " 'H27432',\n",
+       " 'H79',\n",
+       " 'H00070',\n",
+       " 'AP4',\n",
+       " 'P-M008',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747muc10380',\n",
+       " 'A35',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_4837472020-3173-NC005',\n",
+       " '1078',\n",
+       " '128624',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_6',\n",
+       " '1068',\n",
+       " '1961',\n",
+       " 'sanes2022_Hu0218',\n",
+       " 'sanes2022_Hu0220',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_8',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747muc10381',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747Wunderink2021_subject_8',\n",
+       " '128346',\n",
+       " '1514',\n",
+       " '128400',\n",
+       " 'pooled [sanes2022_Hu0220,sanes2022_Hu0218]',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NP31',\n",
+       " 'subject_3',\n",
+       " '148824',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NP32',\n",
+       " 'donor_3',\n",
+       " 'subject_1',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NML1',\n",
+       " '1326',\n",
+       " '3535',\n",
+       " '1857',\n",
+       " '1262',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SAMN15153960',\n",
+       " 'HBCA_Donor_35',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SAMN12574693',\n",
+       " 'HBCA_Donor_25',\n",
+       " 'HBCA_Donor_37',\n",
+       " 'HBCA_Donor_39',\n",
+       " 'AN12',\n",
+       " 'HBCA_Donor_19',\n",
+       " 'HBCA_Donor_16',\n",
+       " 'HBCA_Donor_12',\n",
+       " 'HBCA_Donor_5',\n",
+       " 'HBCA_Donor_41',\n",
+       " 'HBCA_Donor_2',\n",
+       " '1130',\n",
+       " 'H81',\n",
+       " 'H78',\n",
+       " '17D013',\n",
+       " 'H55',\n",
+       " 'H53',\n",
+       " 'H40',\n",
+       " 'H39',\n",
+       " 'SG_HEL_H08a',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SAMN15153963',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SAMN15153962',\n",
+       " 'SF11247',\n",
+       " 'S00113',\n",
+       " 'HGR0000102',\n",
+       " 'HGR0000115',\n",
+       " 'HGR0000134',\n",
+       " 'HGR0000135',\n",
+       " 'HGR0000392',\n",
+       " 'HPAP045',\n",
+       " 'S00094',\n",
+       " 'HT-172',\n",
+       " 'HT-182',\n",
+       " 'HT-184',\n",
+       " 'HT-236',\n",
+       " 'S00076',\n",
+       " 'S00072',\n",
+       " 'S00067',\n",
+       " 'HGR0000069',\n",
+       " 'S00124',\n",
+       " 'HDBR15279',\n",
+       " 'AP1',\n",
+       " '1051',\n",
+       " 'H23',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SCD1',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747SSc_1',\n",
+       " 'HBCA_Donor_52',\n",
+       " 'HBCA_Donor_54',\n",
+       " 'HC-500',\n",
+       " 'HDBR15168',\n",
+       " 'HC-508',\n",
+       " 'HC-519',\n",
+       " 'HC-541',\n",
+       " 'S1130',\n",
+       " 'HCA_54_ref',\n",
+       " 'HDBR14853',\n",
+       " 'H24',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747NML2',\n",
+       " 'H21.33.046',\n",
+       " '32-10074',\n",
+       " 'F86',\n",
+       " 'FL-025',\n",
+       " 'FL-029',\n",
+       " 'FL-030',\n",
+       " '32-10034',\n",
+       " 'FL-045',\n",
+       " 'FLARE009',\n",
+       " '2119',\n",
+       " 'FLARE014',\n",
+       " 'G549',\n",
+       " '2020-3173-NC006',\n",
+       " 'SK-013',\n",
+       " 'G05064',\n",
+       " 'G05077',\n",
+       " 'F81',\n",
+       " '390c',\n",
+       " 'GRO-03',\n",
+       " 'SPECTRUM-OV-070',\n",
+       " 'SPECTRUM-OV-112',\n",
+       " '105598',\n",
+       " 'Donor41',\n",
+       " 'Donor44',\n",
+       " 'SPECTRUM-OV-090',\n",
+       " '102141',\n",
+       " 'SPECTRUM-OV-067',\n",
+       " 'F64',\n",
+       " 'SPECTRUM-OV-054',\n",
+       " 'SPECTRUM-OV-052',\n",
+       " 'Emp1',\n",
+       " '2135',\n",
+       " 'SPECTRUM-OV-049',\n",
+       " '31-10013',\n",
+       " 'SK-007',\n",
+       " 'SHD5',\n",
+       " 'H21.33.044',\n",
+       " 'H21.33.002',\n",
+       " '296C',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747GRO-03',\n",
+       " 'H20.33.019',\n",
+       " 'H20.33.027',\n",
+       " 'H20.33.030',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747IPF3',\n",
+       " 'H21.33.003',\n",
+       " '298C',\n",
+       " 'H21.33.009',\n",
+       " 'H21.33.010',\n",
+       " 'S00059',\n",
+       " 'H21.33.016',\n",
+       " 'H21.33.026',\n",
+       " '1334',\n",
+       " 'H20.33.015',\n",
+       " 'H20.33.012',\n",
+       " 'GTEX-1CAMS',\n",
+       " '1791',\n",
+       " '1909',\n",
+       " '1888',\n",
+       " 'SHD1',\n",
+       " '362C',\n",
+       " 'Goveia_Carmeliet_2020_patient_41',\n",
+       " '1804',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747D322',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747DD073R',\n",
+       " '1768',\n",
+       " '315_316',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747D363',\n",
+       " 'H18',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747D372',\n",
+       " 'H20.33.001',\n",
+       " 'HTA8_1023',\n",
+       " 'S00039',\n",
+       " 'S00050',\n",
+       " 'rGBM-05',\n",
+       " 'NGCII543',\n",
+       " 'P-S016',\n",
+       " 'NP24',\n",
+       " 'NP27',\n",
+       " 'NP30',\n",
+       " 'pooled [D9,D7,D8,D10,D6]',\n",
+       " 'NP35',\n",
+       " 'P-M076',\n",
+       " 'NP39',\n",
+       " 'pooled [sanes2022_Hu0216,sanes2022_Hu0235]',\n",
+       " 'P-M070',\n",
+       " 'P02',\n",
+       " 'P04',\n",
+       " 'rGBM-02',\n",
+       " 'P16',\n",
+       " 'NGCII541',\n",
+       " 'NGCII533',\n",
+       " 'NGCII509',\n",
+       " 'N10',\n",
+       " 'ndGBM-07',\n",
+       " 'P-S065',\n",
+       " 'ndGBM-10',\n",
+       " 'MGH124',\n",
+       " 'P-S051',\n",
+       " 'P-S048',\n",
+       " 'P-S046',\n",
+       " 'NGCII498',\n",
+       " 'P-S042',\n",
+       " 'N00028',\n",
+       " 'P-S036',\n",
+       " 'N00044',\n",
+       " 'P-S033',\n",
+       " 'ND7',\n",
+       " 'P17',\n",
+       " 's11',\n",
+       " 'HTA11_3410',\n",
+       " 'P-M059',\n",
+       " 'sanes2022_Pt14',\n",
+       " 'P92',\n",
+       " 'P-M031',\n",
+       " 'P-M030',\n",
+       " 'P96',\n",
+       " 'P99',\n",
+       " 'scalp32',\n",
+       " 'P-M025',\n",
+       " 'P-M024',\n",
+       " 'P124',\n",
+       " 'P294',\n",
+       " 'P322',\n",
+       " 'P-HC006',\n",
+       " 'P-HC010',\n",
+       " '614_615',\n",
+       " 'P89',\n",
+       " 'P88',\n",
+       " 'P86',\n",
+       " 'P54',\n",
+       " 'P-M058',\n",
+       " 'P36',\n",
+       " 'P46',\n",
+       " 'P47',\n",
+       " 'sanes2022_Hu0216',\n",
+       " 'P50',\n",
+       " 'P-M043',\n",
+       " 'P80',\n",
+       " 'P60',\n",
+       " 'P-M041',\n",
+       " 'P63',\n",
+       " 'P64',\n",
+       " 'P-M038',\n",
+       " 'P70',\n",
+       " '791_792',\n",
+       " 'P-S076',\n",
+       " 'Lambrechts_Thienpont_2018_6653_6',\n",
+       " 'Lambrechts_Thienpont_2018_6149v2_5',\n",
+       " 'IGTB1827',\n",
+       " '7',\n",
+       " 'Rep_C_1095',\n",
+       " 'IGTB1921',\n",
+       " 'IGTB1952',\n",
+       " 'IGTB2065',\n",
+       " 'Rep_C_1078',\n",
+       " 'Rep_C_1054',\n",
+       " 'JK142',\n",
+       " 'Rep_C_1053',\n",
+       " 'Rep_C_1050',\n",
+       " '892_893',\n",
+       " 'Rep_C_1021',\n",
+       " 'Rep_C_1016',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747muc9826',\n",
+       " 'Rep_C_1130',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747lambrechts_3',\n",
+       " 'Rep_C_1143',\n",
+       " 'Hrv34',\n",
+       " 'S00048',\n",
+       " 'HTA11_6801',\n",
+       " 'S00041',\n",
+       " 'Donor24',\n",
+       " 'He_Fan_2021_P3',\n",
+       " 'Hrv3',\n",
+       " 'Hrv40',\n",
+       " 'Rep_C_1148',\n",
+       " 'Hrv41',\n",
+       " 'homosapiens_None_2023_None_sikkemalisa_002_d10_1101_2022_03_10_483747Wunderink2021_subject_3',\n",
+       " 'Hrv92',\n",
+       " 'Hrv99',\n",
+       " 'ICC-C-0177',\n",
+       " 'IGTB826',\n",
+       " 'Rep_C_1010',\n",
+       " 'Rep_C_1005',\n",
+       " 'Rep_C_1002',\n",
+       " 'LB3864T',\n",
+       " 'PP4',\n",
+       " 'PC24',\n",
+       " 'L09cov',\n",
+       " 'nCoV 2',\n",
+       " 'LB3667T',\n",
+       " 'LB3853T',\n",
+       " 'P-S086',\n",
+       " 'lafy_donor_4',\n",
+       " 'ndGBM-03',\n",
+       " 'P-S084',\n",
+       " 'P-S081',\n",
+       " 'P-S079',\n",
+       " 'LONZA3038306',\n",
+       " 'P-S077',\n",
+       " 'lafy_donor_6',\n",
+       " 'PP15',\n",
+       " 'RU1170',\n",
+       " 'QZY9VQng',\n",
+       " 'KR_SGI_H001',\n",
+       " 'RU1134',\n",
+       " 'RBG33503_F96',\n",
+       " 'KR_SGI_H056',\n",
+       " 'KR_SGI_H067',\n",
+       " 'KR_SGI_H075',\n",
+       " 'Q59',\n",
+       " 'Kim_Lee_2020_P1028',\n",
+       " '34-10187',\n",
+       " 'KR_SGI_H119',\n",
+       " 'KR_SGI_H144',\n",
+       " 'Kim_Lee_2020_P0009',\n",
+       " 'Kim_Lee_2020_P0031',\n",
+       " 'Kim_Lee_2020_P1010',\n",
+       " '109389',\n",
+       " 'HC-022',\n",
+       " 'donor-PIKE',\n",
+       " 'donor 3',\n",
+       " '18_18',\n",
+       " 'VUHD071',\n",
+       " 'C-8822',\n",
+       " 'hb17',\n",
+       " 'D046',\n",
+       " 'CONTROL',\n",
+       " 'D35',\n",
+       " '191305',\n",
+       " 'AML3',\n",
+       " 'ABK',\n",
+       " 'donor 7',\n",
+       " 'fore9',\n",
+       " '195045',\n",
+       " 'C-8827',\n",
+       " 'BRC2256',\n",
+       " 'D326',\n",
+       " 'AML1',\n",
+       " 'donor-COST',\n",
+       " 'BCP8',\n",
+       " 'AP8',\n",
+       " 'BCP9',\n",
+       " 'C3L-02705',\n",
+       " 'VUHD67',\n",
+       " 'C57ctr',\n",
+       " '20-649_N',\n",
+       " 'TSP12',\n",
+       " 'AAP',\n",
+       " 'D367',\n",
+       " 'X194',\n",
+       " 'D363',\n",
+       " 'T03',\n",
+       " '183146',\n",
+       " 'D344',\n",
+       " 'Adams_Kaminski_2020_003C',\n",
+       " 'br53epi',\n",
+       " 'COV-02-0094',\n",
+       " 'C-8828',\n",
+       " 'D001-12',\n",
+       " 'healthy_4',\n",
+       " 'C-8899',\n",
+       " 'control_3',\n",
+       " 'Chen_Zhang_2020_NSCLC-5',\n",
+       " 'C-8903',\n",
+       " 'cov03',\n",
+       " 'donor-TREE',\n",
+       " 'Control-084',\n",
+       " 'Adams_Kaminski_2020_222C',\n",
+       " 'A13',\n",
+       " 'cov01',\n",
+       " 'C-8913',\n",
+       " 'C-8918',\n",
+       " 'Chen_Zhang_2020_NSCLC-7',\n",
+       " 'C-8896',\n",
+       " 'ac15',\n",
+       " 'T176',\n",
+       " 'D7_3391',\n",
+       " 'control_1',\n",
+       " 'Adams_Kaminski_2020_056CO',\n",
+       " 'C-8882',\n",
+       " '158891',\n",
+       " 'VUHD98',\n",
+       " 'D7_mother',\n",
+       " 'AN2',\n",
+       " 'D4_3295',\n",
+       " 'C-8886',\n",
+       " 'CVID18',\n",
+       " 'Adams_Kaminski_2020_133C',\n",
+       " 'CVID25',\n",
+       " 'C-8928',\n",
+       " 'Chen_Zhang_2020_NSCLC-2',\n",
+       " 'BM1',\n",
+       " 'healthy_6',\n",
+       " 'AAT',\n",
+       " 'AP6',\n",
+       " 'ACB',\n",
+       " 'Donor10',\n",
+       " 'ac02',\n",
+       " 'CV19-I-O-71',\n",
+       " 'C3N-01798',\n",
+       " 'abd4',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "donor_id_freq.index.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'donor_id frequencies'}, xlabel='donor_id'>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "donor_id_freq[donor_id_freq > 20].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"donor_id frequencies\",\n",
+    "    legend=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"donor_id\",\n",
+    "    s=1,\n",
+    "    figsize=(5, 4),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `suspension_type`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     10000\n",
+       "unique        2\n",
+       "top        cell\n",
+       "freq       6754\n",
+       "Name: suspension_type, dtype: object"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"suspension_type\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "suspension_type\n",
+       "cell       6754\n",
+       "nucleus    3246\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "suspension_type_freq = adata.obs[\"suspension_type\"].value_counts()\n",
+    "suspension_type_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "suspension_type_freq.plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(5, 4),\n",
+    "    title=\"suspension_type frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"suspension_type\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 2),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"donor_id\",\n",
+    "    y=\"suspension_type\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 2),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.groupby(\"suspension_type\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(3, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `assay`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count         10000\n",
+       "unique           19\n",
+       "top       10x 3' v3\n",
+       "freq           3690\n",
+       "Name: assay, dtype: object"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"assay\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "assay\n",
+       "10x 3' v3                                   3690\n",
+       "10x 3' v2                                   2219\n",
+       "10x 5' v1                                   1198\n",
+       "sci-RNA-seq                                 1182\n",
+       "10x 5' v2                                    820\n",
+       "10x 3' transcription profiling               209\n",
+       "microwell-seq                                183\n",
+       "10x 5' transcription profiling               181\n",
+       "Drop-seq                                      71\n",
+       "BD Rhapsody Whole Transcriptome Analysis      49\n",
+       "10x multiome                                  46\n",
+       "BD Rhapsody Targeted mRNA                     36\n",
+       "single-cell RNA sequencing                    31\n",
+       "Seq-Well                                      29\n",
+       "Smart-seq2                                    24\n",
+       "10x 3' v1                                     14\n",
+       "single cell library construction              10\n",
+       "inDrop                                         7\n",
+       "STRT-seq                                       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "assay_freq = adata.obs[\"assay\"].value_counts()\n",
+    "assay_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "assay_freq.plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"assay frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"assay\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 4),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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meN3d3eHk5ITOnTtjwYIFqFOnDlJTU3HgwAF06dLlg+omIiIiIiIqCkfUEH3GYmNj4eDgAAcHBwDAuHHj4ODggGnTpol5Jk6ciNGjR2P48OFo3LgxsrKyEBYWBlVVVQDAmDFjoK6ujjlz5gAAbG1tMWfOHIwYMQL37t0r13g3btyInJwcNGrUSOxAKk6zZs0wcuRI9OrVC9WrV8eCBQsK5JFIJNizZw+qVq2K7777Du7u7qhduzZ27NjxQbFKJBIcPHgQ3333HQYNGoQ6deqgd+/euH37doHFmYmIiIiIiMqLRHh7wQgiIio3mZmZ0NLSQkZGBjQ1NSs6HCIiIiIiqiCl+W7AETVERERERERERJUEO2qIiIiIiIiIiCoJdtQQEREREREREVUS7KghIiIiIiIiIqok2FFDRERERERERFRJsKOGiMrFo0eP4O3tjVq1akFFRQX6+vrw8PDA7NmzIZFIit0iIiIQFBQk7isoKMDAwAC9evXCnTt3kJKS8t46goKCKvoSEBERERERfTClig6AiL4M3bp1w6tXr7B582bUrl0bDx48QHh4OOrVq4e0tDQx35gxY5CZmYlNmzaJaTo6OkhJSYGmpiaSkpIgCAJu3boFHx8f9OjRA1FRUXJ1LFy4EGFhYTh69KiYpqWl9WlOlIiIiIiI6CPiiBoi+mDp6ek4deoU5s+fD1dXV5iYmKBJkybw9/dHx44doa+vL25SqVQccZO/KSsrAwAkEgn09fVhYGCAZs2aYciQITh79iyePXsml18mk0FJSalAve/auXMnbG1tIZVKoaurC3d3dzx79kw8vn79etjY2EBVVRXW1tZYuXKlXPmzZ8/CwcEBqqqqcHR0xO7duyGRSBAfH1/odcjOzkZmZqbcRkRERGWz9cxtOM87hq1nbld0KEREnxQ7aojog8lkMshkMoSGhiI7O7tc6nz48CF2794NRUVFKCoqlrp8Wloa+vTpg8GDByMxMRERERHo2rUrBEEAAISEhGDatGmYPXs2EhMTMWfOHEydOhWbN28GAGRlZaF9+/aoW7cuzp8/j4CAAIwfP77YNufOnQstLS1xMzY2Lv2JExEREQBgVUQy7qW/wKqI5IoOhYjok+LUJyL6YEpKSggKCsKwYcOwevVqNGzYEC1atEDv3r1hZ2dX4noyMjIgk8kgCAKeP38OAPD19YW6unqpY0pLS0NOTg66du0KExMTAICtra14fPr06Vi0aBG6du0KADAzM8PVq1exZs0aDBw4EL/99hvy8vKwYcMGqKqqol69evi///s/eHt7F9mmv78/xo0bJ+5nZmays4aIiKiMvF3MsSoiGd4u5hUdChHRJ8URNURULrp164bU1FTs3bsXbdq0QUREBBo2bFiqRX41NDQQHx+P2NhYLFq0CA0bNsTs2bPfW+7UqVPiqB6ZTIaQkBDY29vDzc0Ntra26NGjB9atW4cnT54AAJ49e4bk5GQMGTJErtysWbOQnPzmf+0SExNhZ2cHVVVVsR0nJ6di41BRUYGmpqbcRkRERGXT7xsTRPq1RL9vTCo6FCKiT4ojaoio3KiqqqJVq1Zo1aoVpk6diqFDh2L69Onw8vIqUXkFBQVYWFgAAGxsbJCcnAxvb29s2bKl2HKOjo5y68bo6elBUVERR44cQVRUFA4fPozly5dj8uTJiImJgZqaGgBg3bp1aNq0qVxdZZlmRUREREREVF44ooaIPpq6devKLd5bWn5+ftixYwcuXLhQbD6pVAoLCwtx09DQAPBmcWJnZ2fMmDEDcXFxUFZWxu7du6GnpwdDQ0P8/fffcuUsLCxgZmYG4E1H0cWLF/Hy5UuxnTNnzpT5XIiIiIiIiEqCI2qI6IP9+++/6NGjBwYPHgw7OztoaGggNjYWCxYsQKdOncpcr7GxMbp06YJp06Zh//79pSobExOD8PBwtG7dGjVq1EBMTAwePXoEGxsbAMCMGTPg6+sLLS0ttGnTBtnZ2YiNjcWTJ08wbtw4fP/995g8eTKGDRsGf39/pKSkYOHChWU+FyIiIiIiopJgRw0RfTCZTIamTZtiyZIlSE5OxuvXr2FsbIxhw4bhf//73wfV/eOPP8LJyQlnz55FkyZNSlxOU1MTJ0+eRGBgIDIzM2FiYoJFixbB09MTADB06FCoqanhl19+wYQJE6Curg5bW1uMHTtWPKd9+/Zh5MiRcHBwQN26dTF//nx069btg86HiIiIiIioOBIh/1m1RERUrJSUFJiZmSEuLg4NGjR4b/7MzExoaWkhIyODCwsTEREREX3FSvPdgGvUEBERERERERFVEuyoISIiIiIiIiKqJLhGDRFRCZmamoKzRYmIiIiI6GPiiBqir0xERAQkEgnS09MrOpRSMTU1RWBgoLgvkUgQGhpaYfEQERERERF9DOyoIfrKNGvWDGlpadDS0qroUIiIiIiIiOgd7Kgh+sooKytDX18fEomkTOVfvXpVzhERERERERFRPnbUEH3mXFxcMHr0aIwdOxZVq1aFnp4e1q1bh2fPnmHQoEHQ0NCAhYUFDh06BKDwqU+RkZFwcXGBmpoaqlatCg8PDzx58kSsf9SoURg7diyqVasGDw8PAMCJEyfQpEkTqKiowMDAAH5+fsjJyQEA7N+/H9ra2sjNzQUAxMfHQyKRwM/PT2xz6NCh6Nevn7h/+vRpfPvtt5BKpTA2Noavry+ePXtW5uvy5MkT9O3bF9WrV4dUKoWlpSU2bdokHr979y569uwJbW1t6OjooFOnTkhJSRGP5+bmYty4cdDW1oauri4mTpyIgQMHonPnzmWOiYiIiIiI6H3YUUP0Bdi8eTOqVauGs2fPYvTo0fD29kaPHj3QrFkzXLhwAa1bt0b//v3x/PnzAmXj4+Ph5uaGunXrIjo6GqdPn0aHDh3ETpb8+pWVlREZGYnVq1fj3r17aNu2LRo3boyEhASsWrUKGzZswKxZswAA3377LZ4+fYq4uDgAbzp1qlWrhoiICLHOEydOwMXFBQCQnJyMNm3aoFu3brh48SJ27NiB06dPY9SoUWW+JlOnTsXVq1dx6NAhJCYmYtWqVahWrRoA4PXr1/Dw8ICGhgZOnTqFyMhIyGQytGnTRhwxtGjRIgQFBWHjxo04ffo0Hj9+jN27dxfbZnZ2NjIzM+W2r0XH5adh6ncAHZefLnDMd1sczP0PwHdbXAVERkRfA1O/A6XaCvtdRZVHSf/d2HrmNpznHcPWM7c/UWRERJ+GROAjTIg+ay4uLsjNzcWpU6cAvBkJoqWlha5duyI4OBgAcP/+fRgYGCA6OhovX76Eq6srnjx5Am1tbXz//fe4c+cOTp8u/I9WFxcXZGZm4sKFC2La5MmT8eeffyIxMVGcQrVy5UpMmjQJGRkZUFBQQKNGjdCnTx+MHz8eXbp0QePGjTFjxgz8+++/yMjIQM2aNXH9+nVYWlpi6NChUFRUxJo1a8Q2Tp8+jRYtWuDZs2dQVVWFqakpxo4di7FjxwJ4s5jw7t27ixzh0rFjR1SrVg0bN24scGzr1q2YNWuWXPyvXr2CtrY2QkND0bp1axgaGuLHH3/EhAkTAAA5OTkwMzNDo0aNilzEOCAgADNmzCiQnpGRAU1NzULLfClM/Q6IP6fMayd3zNz/AHIFQFECJM9t925RIqIP9vbvoJJ693cVVR4l/XfDed4x3Et/ASNtKSL9Wn7CCImISi8zMxNaWlol+m7AETVEXwA7OzvxZ0VFRejq6sLW1lZM09PTAwA8fPiwQNn8ETXFadSokdx+YmIinJyc5Na5cXZ2RlZWFv7v//4PANCiRQtERERAEAScOnUKXbt2hY2NDU6fPo0TJ07A0NAQlpaWAICEhAQEBQVBJpOJm4eHB/Ly8nDr1q33nr+np6dYrl69egAAb29vbN++HQ0aNMDEiRMRFRUl5k9ISMDNmzehoaEhltPR0cHLly+RnJyMjIwMpKWloWnTpmIZJSUlODo6FhuHv78/MjIyxO3u3bvvjf1LYWekJff6tnZ2hlCUvHklIqoMCvtdRZVHSf/d8HYxh5G2FN4u5p8oMiKiT0OpogMgog9XpUoVuX2JRCKXlt+hkpeXV6CsVCp9b/3q6uqljsnFxQUbN25EQkICqlSpAmtra7i4uCAiIgJPnjxBixYtxLxZWVkYMWIEfH19C9RTq1at97a1fv16vHjxAsB/18LT0xO3b9/GwYMHceTIEbi5ueGHH37AwoULkZWVhUaNGiEkJKRAXdWrVy/1ueZTUVGBiopKmct/zvaObl7ksWV9HLCsj8MnjIaIvjYcHfNlKem/G/2+MUG/b0w+QURERJ8WR9QQfeXs7OwQHh5eqjI2NjaIjo7G2zMnIyMjoaGhgZo1awL4b52aJUuWiJ0y+R01ERER4vo0ANCwYUNcvXoVFhYWBTZlZeX3xmNkZCTmNzH57w+26tWrY+DAgdi6dSsCAwOxdu1asb0bN26gRo0aBdrT0tKClpYWDAwMEBMTI9aVk5OD8+fPl+o6ERERERERlRY7aoi+cv7+/jh37hx8fHxw8eJFXLt2DatWrcI///xTZBkfHx/cvXsXo0ePxrVr17Bnzx5Mnz4d48aNg4LCm18rVatWhZ2dHUJCQsROme+++w4XLlzA9evX5UbUTJo0CVFRURg1ahTi4+Nx48YN7Nmz54MWE542bRr27NmDmzdv4sqVK9i/fz9sbGwAAH379kW1atXQqVMnnDp1Crdu3UJERAR8fX3FqVtjxozBvHnzEBoaimvXrsHHx0fuSVlEREREREQfAztqiL5yderUweHDh5GQkIAmTZrAyckJe/bsgZJS0TMjjYyMcPDgQZw9exb29vYYOXIkhgwZgilTpsjla9GiBXJzc8WOGh0dHdStWxf6+vqwsrIS89nZ2eHEiRO4fv06vv32Wzg4OGDatGkwNCz7mibKysrw9/eHnZ0dvvvuOygqKmL79u0AADU1NZw8eRK1atUS184ZMmQIXr58KS7s9dNPP6F///4YOHAgnJycoKGhgS5dupQ5HiIiIiIiopLgU5+IiErIy8sL6enpRT716V2lWdmdiIiIiIi+XHzqExERERERERHRZ4gdNURERERERERElQQfz01EVEJBQUEVHQIREREREX3hOKKGiIiIiIiIiKiSYEcNFcrLywudO3cu1zpTUlIgkUgQHx9frvWWRkREBCQSifiY5aCgIGhraxeZ/92YS1ueKo+PcU8TERERERGVN059okItXboUfCBYQc2aNUNaWhq0tLQqOpTPiouLCxo0aIDAwMCP3lZKSgrMzMwQFxeHBg0aiOm8p4mIiIiI6HPAjhoqFDsiCqesrAx9ff0PquPVq1dQVlYuU9nXr1+jSpUqH9R+ZSUIAnJzc6Gk9HF+LfGeJiIiIiKizwGnPn3Fdu7cCVtbW0ilUujq6sLd3R3Pnj0DUHCaiIuLC3x9fTFx4kTo6OhAX18fAQEBcvVdu3YNzZs3h6qqKurWrYujR49CIpEgNDS0yBguX74MT09PyGQy6OnpoX///vjnn3+KjTsyMhIuLi5QU1ND1apV4eHhgSdPngAA8vLyMHfuXJiZmUEqlcLe3h47d+4s0/UpzLtTn/KFhobC0tISqqqq8PDwwN27d8VjAQEBaNCgAdavXw8zMzOoqqoCAMLCwtC8eXNoa2tDV1cX7du3R3Jyslguf9rVjh070KJFC6iqqmLt2rXQ1NQscE6hoaFQV1fH06dPC407Ly8PCxYsgIWFBVRUVFCrVi3Mnj1bPH7p0iW0bNlSvBeGDx+OrKws8Xj+/bBw4UIYGBhAV1cXP/zwA16/fi3mWblypXgN9PT00L17d7HsiRMnsHTpUkgkEkgkEqSkpIjX8tChQ2jUqBFUVFRw+vTpQqcojR07Fi4uLiU6HzMzMwCAg4MDJBKJWO7derOzs+Hr64saNWpAVVUVzZs3x7lz5wq81+Hh4XB0dISamhqaNWuGpKSkQq8xERERERFReWBHzVcqLS0Nffr0weDBg5GYmIiIiAh07dq12Kkhmzdvhrq6OmJiYrBgwQLMnDkTR44cAQDk5uaic+fOUFNTQ0xMDNauXYvJkycXG0N6ejpatmwJBwcHxMbGIiwsDA8ePEDPnj2LLBMfHw83NzfUrVsX0dHROH36NDp06IDc3FwAwNy5cxEcHIzVq1fjypUr+PHHH9GvXz+cOHGiDFepZJ4/f47Zs2cjODgYkZGRSE9PR+/eveXy3Lx5E3/++Sd27dolrnfz7NkzjBs3DrGxsQgPD4eCggK6dOmCvLw8ubJ+fn4YM2YMEhMT0bVrV/Tu3RubNm2Sy7Np0yZ0794dGhoahcbo7++PefPmYerUqbh69Sp+++036OnpiXF4eHigatWqOHfuHP744w8cPXoUo0aNkqvj+PHjSE5OxvHjx7F582YEBQWJT0GKjY2Fr68vZs6ciaSkJISFheG7774D8GbKkZOTE4YNG4a0tDSkpaXB2NhY7vzmzZuHxMRE2NnZleiaF3c+Z8+eBQAcPXoUaWlp2LVrV6F1TJw4EX/++Sc2b96MCxcuwMLCAh4eHnj8+LFcvsmTJ2PRokWIjY2FkpISBg8eXGRc2dnZyMzMlNuI6Ouw9cxt2Ew9BDO/A/DdFlemOjouPw1TvwPouPx0ofU7zzuGrWduAwB8t8XBzO8AbKaGiWlERET0hRDoq3T+/HkBgJCSklLo8YEDBwqdOnUS91u0aCE0b95cLk/jxo2FSZMmCYIgCIcOHRKUlJSEtLQ08fiRI0cEAMLu3bsFQRCEW7duCQCEuLg4QRAE4eeffxZat24tV+fdu3cFAEJSUlKhcfXp00dwdnYu9NjLly8FNTU1ISoqSi59yJAhQp8+fQRBEITjx48LAIQnT54IgiAImzZtErS0tAqtr7CYCysPQDhz5oxYJjExUQAgxMTECIIgCNOnTxeqVKkiPHz4sMh2BEEQHj16JAAQLl26JNd2YGCgXL6YmBhBUVFRSE1NFQRBEB48eCAoKSkJERERhdabmZkpqKioCOvWrSv0+Nq1a4WqVasKWVlZYtqBAwcEBQUF4f79+4IgvLkfTExMhJycHDFPjx49hF69egmCIAh//vmnoKmpKWRmZhbaRosWLYQxY8bIpeVfy9DQULn0d+89QRCEMWPGCC1atCjR+bz7nhVWb1ZWllClShUhJCREPP7q1SvB0NBQWLBggVx8R48elbsuAIQXL14U2vb06dMFAAW2jIyMQvMT0Zej2dxwwWTSfsFk0n6htt/+MtWRX95kUsHy+fU3mxsuCIIg1Pb7L29+GhEREVVeGRkZJf5uwBE1Xyl7e3u4ubnB1tYWPXr0wLp168TpQ0V5d7SDgYEBHj58CABISkqCsbGx3PotTZo0Kba+hIQEHD9+HDKZTNysra0BQG4K0NvyR9QU5ubNm3j+/DlatWolV2dwcHCR9ZUHJSUlNG7cWNy3traGtrY2EhMTxTQTExNUr15drtyNGzfQp08f1K5dG5qamjA1NQUA3LlzRy6fo6Oj3H6TJk1Qr149bN68GQCwdetWmJiYiCNY3pWYmIjs7Owir1tiYiLs7e2hrq4upjk7OyMvL09umk+9evWgqKgo7r/9/rdq1QomJiaoXbs2+vfvj5CQEDx//rzQ9t717vm9z/vOpySSk5Px+vVrODs7i2lVqlRBkyZN5N43QP6+NzAwAADxvN/l7++PjIwMcXt7ChwRfdm8XcwhraIACYB2doZlqsPOSEvu9d36jbSl8HYxB/5/GxIA0iqKYhoRERF9GbiY8FdKUVERR44cQVRUFA4fPozly5dj8uTJiImJEdf4eNe7i9hKJJIC03RKIysrCx06dMD8+fMLHMv/QvwuqVRabH0AcODAARgZGckdU1FRKXOc5eHtTpB8HTp0gImJCdatWwdDQ0Pk5eWhfv36ePXq1XvLDh06FL/++iv8/PywadMmDBo0CBKJpNC2i7tmpVHc+6+hoYELFy4gIiIChw8fxrRp0xAQEIBz58699/Hl756fgoJCgSl4b6+FU17nU1Jvn3f+NS7qvldRUanwe42IKka/b0zQ7xuTD6pj7+jmJa5/WR8HLOvj8EHtERERUeXEETVfMYlEAmdnZ8yYMQNxcXFQVlbG7t27y1SXlZUV7t69iwcPHohpby/MWpiGDRviypUrMDU1hYWFhdxWWOcE8GZ0Q3h4eKHH6tatCxUVFdy5c6dAfW+viVLecnJyEBsbK+4nJSUhPT0dNjY2RZb5999/kZSUhClTpsDNzQ02NjbvHdH0tn79+uH27dtYtmwZrl69ioEDBxaZ19LSElKptMjrZmNjg4SEBHEhaeDNgs0KCgqwsrIqcUxKSkpwd3fHggULcPHiRaSkpODYsWMA3jwtK38dofepXr060tLS5NLy1/UpyfnkP1GruPbMzc2hrKyMyMhIMe3169c4d+4c6tatW6I4iYiIiIiIPgZ21HylYmJiMGfOHMTGxuLOnTvYtWsXHj16VGznQnFatWoFc3NzDBw4EBcvXkRkZCSmTJkCAEWO9Pjhhx/w+PFj9OnTB+fOnUNycjL++usvDBo0qMgv2f7+/jh37hx8fHxw8eJFXLt2DatWrcI///wDDQ0NjB8/Hj/++CM2b96M5ORkXLhwAcuXLxenCX0MVapUwejRoxETE4Pz58/Dy8sL33zzTbFTv6pWrQpdXV2sXbsWN2/exLFjxzBu3LgSt1m1alV07doVEyZMQOvWrVGzZs0i86qqqmLSpEmYOHGiOA3szJkz2LBhAwCgb9++UFVVxcCBA3H58mUcP34co0ePRv/+/cUFet9n//79WLZsGeLj43H79m0EBwcjLy9P7OgxNTVFTEwMUlJS8M8//xQ7Eqtly5aIjY1FcHAwbty4genTp+Py5cslPp8aNWpAKpWKi1NnZGQUaENdXR3e3t6YMGECwsLCcPXqVQwbNgzPnz/HkCFDSnTOREREREREHwM7ar5SmpqaOHnyJNq2bYs6depgypQpWLRoETw9PctUn6KiIkJDQ5GVlYXGjRtj6NCh4lOf8h9H/S5DQ0NERkYiNzcXrVu3hq2tLcaOHQttbW0oKBR+a9apUweHDx9GQkICmjRpAicnJ+zZswdKSm9m8f3888+YOnUq5s6dCxsbG7Rp0wYHDhwocjpXeVBTU8OkSZPw/fffw9nZGTKZDDt27Ci2jIKCArZv347z58+jfv36+PHHH/HLL7+Uqt0hQ4bg1atXxT6FKN/UqVPx008/Ydq0abCxsUGvXr3EdVbU1NTw119/4fHjx2jcuDG6d+8ONzc3rFixosSxaGtrY9euXWjZsiVsbGywevVqbNu2DfXq1QMAjB8/HoqKiqhbty6qV69eYB2et3l4eGDq1KmYOHEiGjdujKdPn2LAgAElPh8lJSUsW7YMa9asgaGhITp16lRoO/PmzUO3bt3Qv39/NGzYEDdv3sRff/2FqlWrlvi8iYiIiIiIyptEeHcxCKJyEhkZiebNm+PmzZswN+dCh+Vty5Yt+PHHH5GamipO96HKJTMzE1paWsjIyICmpmZFh0NERERERBWkNN8NuJgwlZvdu3dDJpPB0tISN2/exJgxY+Ds7MxOmnL2/PlzpKWlYd68eRgxYgQ7aYiIiIiIiL4gnPpE5ebp06f44YcfYG1tDS8vLzRu3Bh79uyp6LC+OAsWLIC1tTX09fXh7+9f0eEQERERERFROeLUJyKij4RTn4iIiIiICCjddwOOqCGiCufi4oKxY8dWdBhEREREREQVjmvUEFGF27VrF6pUqVLi/G8/8l1NTQ2GhoZwdnbG6NGj0ahRo48RIhERERER0SfBETVEVOF0dHSgoaFRqjKbNm1CWloarly5gl9//RVZWVlo2rQpgoODiyyTm5uLvLy8Dw2XiIiIiIjoo2FHDRFVuLenPpmammLOnDkYPHgwNDQ0UKtWLaxdu7ZAGW1tbejr68PU1BStW7fGzp070bdvX4waNQpPnjwBAAQFBUFbWxt79+5F3bp1oaKigjt37uDJkycYMGAAqlatCjU1NXh6euLGjRti3fnlQkNDYWlpCVVVVXh4eODu3buf5HoQEREREdHXix01RFTpLFq0CI6OjoiLi4OPjw+8vb2RlJT03nI//vgjnj59iiNHjohpz58/x/z587F+/XpcuXIFNWrUgJeXF2JjY7F3715ER0dDEAS0bdsWr1+/lis3e/ZsBAcHIzIyEunp6ejdu3ex7WdnZyMzM1NuI6Ivk++2OJj7H4DvtrhyqW/rmdtwnncMW8/cLnPb5R0TERERVQx21BBRpdO2bVv4+PjAwsICkyZNQrVq1XD8+PH3lrO2tgYApKSkiGmvX7/GypUr0axZM1hZWeHevXvYu3cv1q9fj2+//Rb29vYICQnBvXv3EBoaKlduxYoVcHJyQqNGjbB582ZERUXh7NmzRbY/d+5caGlpiZuxsXGZrwERVW4HLqYiV3jzWh5WRSTjXvoLrIpILnPb5R0TERERVQx21BBRpWNnZyf+LJFIoK+vj4cPH763nCAIYpl8ysrKcvUlJiZCSUkJTZs2FdN0dXVhZWWFxMREMU1JSQmNGzcW962traGtrS2X513+/v7IyMgQN06VIvpytbMzhKLkzWt58HYxh5G2FN4u5mVuu7xjIiIioorBpz4RUaXz7hOgJBJJiRYBzu9EMTMzE9OkUqlcx83HpKKiAhUVlU/SFhFVrGV9HLCsj0O51dfvGxP0+8bkg9ou75iIiIioYnBEDRF9MQIDA6GpqQl3d/ci89jY2CAnJwcxMTFi2r///oukpCTUrVtXTMvJyUFsbKy4n5SUhPT0dNjY2Hyc4ImIiIiIiMCOGiL6TKWnp+P+/fu4ffs2jhw5gu7du+O3337DqlWroK2tXWQ5S0tLdOrUCcOGDcPp06eRkJCAfv36wcjICJ06dRLzValSBaNHj0ZMTAzOnz8PLy8vfPPNN2jSpMknODsiIiIiIvpaceoTEX2WBg0aBABQVVWFkZERmjdvjrNnz6Jhw4bvLbtp0yaMGTMG7du3x6tXr/Ddd9/h4MGDclOu1NTUMGnSJHz//fe4d+8evv32W2zYsOGjnQ8REREREREASIT81TeJiAgAEBQUhLFjxyI9Pf2D6snMzISWlhYyMjKgqalZPsEREREREdFnpzTfDTj1iYiIiIiIiIiokmBHDRERERERERFRJcGOGiKid3h5eX3wtCciIiIiIqKyYEcNEREREREREVElwY4aos9EUFBQsY+dLqmUlBRIJBLEx8d/cF0VRRAEDB8+HDo6OuK5uLi4YOzYsWIeU1NTBAYGivsSiQShoaGfPFYiIiIiIqLSYEcNldjJkyfRoUMHGBoaFvmlVxAETJs2DQYGBpBKpXB3d8eNGzc+qN2IiAhIJJIC2/3798U8Xl5eCAgIKLKOgIAANGjQ4IPiqGi9evXC9evXS1XGy8sLnTt3lkszNjZGWloa6tevX47RfVphYWEICgrC/v37xXPZtWsXfv755yLLpKWlwdPT8xNGSUREREREVHrsqKESe/bsGezt7fHrr78WmWfBggVYtmwZVq9ejZiYGKirq8PDwwMvX7784PaTkpKQlpYmbjVq1PjgOt/1+vXrcq+zPLx+/RpSqbRczllRURH6+vpQUlIqh8jK16tXr0qULzk5GQYGBmjWrJl4Ljo6OtDQ0CiyjL6+PlRUVMorVCIiIiIioo+CHTVUYp6enpg1axa6dOlS6HFBEBAYGIgpU6agU6dOsLOzQ3BwMFJTU8XRN8HBwZDJZHKjbHx8fGBtbY3nz58X236NGjWgr68vbgoKJbt9g4KCMGPGDCQkJIijcYKCggC8mQ6zatUqdOzYEerq6pg9ezZyc3MxZMgQmJmZQSqVwsrKCkuXLpWrM3+kysKFC2FgYABdXV388MMPch09K1euhKWlJVRVVaGnp4fu3buLx/Ly8rBgwQJYWFhARUUFtWrVwuzZswH8NzVpx44daNGiBVRVVRESElJg6lP+KKE1a9bA2NgYampq6NmzJzIyMsTjmzdvxp49e8TzjoiIKHTq04kTJ9CkSROoqKjAwMAAfn5+yMnJEY+7uLjA19cXEydOhI6ODvT19YsdwfT2NZoxYwaqV68OTU1NjBw5Uq4zxsXFBaNGjcLYsWNRrVo1eHh4vDceLy8vjB49Gnfu3IFEIoGpqalY19tTn9719iiw/Guwa9cuuLq6Qk1NDfb29oiOjpYrs27dOvHadunSBYsXLy6X6WdERERERERFqXz/pU6frVu3buH+/ftwd3cX07S0tNC0aVNER0ejd+/eGDBgAPbv34++ffsiKioKf/31F9avX4/o6GioqakVW3+DBg2QnZ2N+vXrIyAgAM7OziWKq1evXrh8+TLCwsJw9OhRMa58AQEBmDdvHgIDA6GkpIS8vDzUrFkTf/zxB3R1dREVFYXhw4fDwMAAPXv2FMsdP34cBgYGOH78OG7evIlevXqhQYMGGDZsGGJjY+Hr64stW7agWbNmePz4MU6dOiWW9ff3x7p167BkyRI0b94caWlpuHbtmlzcfn5+WLRoERwcHKCqqoq//vqrwLndvHkTv//+O/bt24fMzEwMGTIEPj4+CAkJwfjx45GYmIjMzExs2rQJAKCjo4PU1FS5Ou7du4e2bdvCy8sLwcHBuHbtGoYNGwZVVVW5zpjNmzdj3LhxiImJQXR0NLy8vODs7IxWrVoVee3Dw8OhqqoqdhANGjQIurq6YqdUfr3e3t6IjIwsUTxLly6Fubk51q5di3PnzkFRUbHI9t9n8uTJWLhwISwtLTF58mT06dMHN2/ehJKSEiIjIzFy5EjMnz8fHTt2xNGjRzF16tRi68vOzkZ2dra4n5mZWebYiL4Ulv87iNd5QpnLK0qAGZ3qo983JuUYFREREVHlxY4aKjf5a8bo6enJpevp6cmtJ7NmzRrY2dnB19cXu3btQkBAABo1alRkvQYGBli9ejUcHR2RnZ2N9evXw8XFBTExMWjYsCEAiCNkCiOVSiGTyaCkpAR9ff0Cx7///nsMGjRILm3GjBniz2ZmZoiOjsbvv/8u11FTtWpVrFixAoqKirC2tka7du0QHh6OYcOG4c6dO1BXV0f79u2hoaEBExMTODg4AACePn2KpUuXYsWKFRg4cCAAwNzcHM2bN5eLYezYsejatWuR5wUAL1++RHBwMIyMjAAAy5cvR7t27bBo0SLo6+tDKpUiOzu70PPOt3LlShgbG2PFihWQSCSwtrZGamoqJk2ahGnTpokjl+zs7DB9+nQAgKWlJVasWIHw8PBiO2qUlZWxceNGqKmpoV69epg5cyYmTJiAn3/+WazX0tISCxYsEMtMnjy52Hi0tLSgoaEhTuH6EOPHj0e7du0AvHnP69Wrh5s3b8La2hrLly+Hp6cnxo8fDwCoU6cOoqKisH///iLrmzt3rty9Q0T4oE4aAMgVgFURyeyoISIioq8Gpz7RJ1e1alVs2LABq1atgrm5Ofz8/IrNb2VlhREjRqBRo0Zo1qwZNm7ciGbNmmHJkiXlEo+jo2OBtF9//RWNGjVC9erVIZPJsHbtWty5c0cuT7169eRGcxgYGODhw4cAgFatWsHExAS1a9dG//79ERISIk7tSkxMRHZ2Ntzc3Eod17tq1aoldtIAgJOTE/Ly8pCUlPTesvkSExPh5OQEiUQipjk7OyMrKwv/93//J6bZ2dnJlXv7fItib28vN1LKyckJWVlZuHv3rpj2biddSeMpD2+fk4GBAQCI55SUlIQmTZrI5X93/13+/v7IyMgQt7fPk+hrVUVB8v5MxVCUAN4u5uUUDREREVHlxxE1VG7yRzc8ePBA/NKbv//uE5dOnjwJRUVFpKWl4dmzZ8UuAluYJk2a4PTp0x8cMwCoq6vL7W/fvh3jx4/HokWL4OTkBA0NDfzyyy+IiYmRy1elShW5fYlEgry8PACAhoYGLly4gIiICBw+fBjTpk1DQEAAzp07B6lUWqa4Klpx5/shKvI83z6n/I6hDzknFRUVLlhM9I4bc9pWdAhEREREnxWOqKFyY2ZmBn19fYSHh4tpmZmZiImJgZOTk5gWFRWF+fPnY9++fZDJZBg1alSp24qPj5frDHofZWVl5ObmlihvZGQkmjVrBh8fHzg4OMDCwgLJycmljlFJSQnu7u5YsGABLl68iJSUFBw7dgyWlpaQSqVy16ms7ty5I7fmzJkzZ6CgoAArKysAJTtvGxsbREdHQxD+m54QGRkJDQ0N1KxZ84PiS0hIwIsXL+Tik8lkMDY2rpB4SsPKygrnzp2TS3t3n4iIiIiIqLyxo4ZKLCsrC/Hx8eLTgm7duoX4+HhxSpBEIsHYsWMxa9Ys7N27F5cuXcKAAQNgaGiIzp07A3izPkv//v3h6+sLT09PhISEYMeOHdi5c2eR7QYGBmLPnj24efMmLl++jLFjx+LYsWP44YcfShy7qampGO8///wjt+DruywtLREbG4u//voL169fx9SpU0v9BX3//v1YtmwZ4uPjcfv2bQQHByMvLw9WVlZQVVXFpEmTMHHiRAQHByM5ORlnzpzBhg0bStUGAKiqqmLgwIFISEjAqVOn4Ovri549e4qjm0xNTXHx4kUkJSXhn3/+KfTx4z4+Prh79y5Gjx6Na9euYc+ePZg+fTrGjRtX4idrFeXVq1cYMmQIrl69ioMHD2L69OkYNWpUsfV+zHhKY/To0Th48CAWL16MGzduYM2aNTh06JDclCwiIiIiIqLyxo4aKrHY2Fg4ODiIi+KOGzcODg4OmDZtmphn4sSJGD16NIYPH47GjRsjKysLYWFhUFVVBQCMGTMG6urqmDNnDgDA1tYWc+bMwYgRI3Dv3r1C23316hV++ukn2NraokWLFkhISMDRo0ffu8bL27p164Y2bdrA1dUV1atXx7Zt24rMO2LECHTt2hW9evVC06ZN8e+//8LHx6fEbQGAtrY2du3ahZYtW8LGxgarV6/Gtm3bUK9ePQDA1KlT8dNPP2HatGmwsbFBr1693rveS2EsLCzQtWtXtG3bFq1bt4adnR1WrlwpHh82bBisrKzg6OiI6tWri09WepuRkREOHjyIs2fPwt7eHiNHjsSQIUMwZcqUUsfzLjc3N1haWuK7775Dr1690LFjx/c+1vtjxlMazs7OWL16NRYvXgx7e3uEhYXhxx9/FO9lIiIiIiKij0EivD2/gIg+GwEBAQgNDRVHOFU2Xl5eSE9PR2hoaEWHUm6GDRuGa9euyT1qvTiZmZnQ0tJCRkYGNDU1P3J0RERERERUWZXmuwEXEyYiKsLChQvRqlUrqKur49ChQ9i8ebPciCUiIiIiIqLyxo4aIqIinD17FgsWLMDTp09Ru3ZtLFu2DEOHDq3osIiIiIiI6AvGqU9ERB8Jpz4RERERERFQuu8GXEyY6DMRFBQEbW3tD64nJSUFEomk0q5tUxKCIGD48OHQ0dERz8XFxQVjx44V85iamiIwMFDcl0gkX9R6OURERERE9GViRw2V2MmTJ9GhQwcYGhoW+aVXEARMmzYNBgYGkEqlcHd3x40bNz6o3dOnT8PZ2Rm6urqQSqWwtrbGkiVL5PJ4eXkV+zShgIAANGjQ4IPiqGi9evXC9evXS1XGy8tLfDR6PmNjY6SlpaF+/frlGN2nFRYWhqCgIOzfv188l127duHnn38uskxaWho8PT0/YZRERERERESlx44aKrFnz57B3t4ev/76a5F5FixYgGXLlmH16tWIiYmBuro6PDw88PLlyzK3q66ujlGjRuHkyZNITEzElClTMGXKFKxdu7bMdRbl9evX5V5neXj9+jWkUilq1KjxwXUpKipCX18fSkqVb4mqV69elShfcnIyDAwM0KxZM/FcdHR0oKGhUWQZfX19qKiolFeoREREREREHwU7aqjEPD09MWvWLHTp0qXQ44IgIDAwEFOmTEGnTp1gZ2eH4OBgpKamiqNvgoODIZPJ5EbZ+Pj4wNraGs+fPy+0XgcHB/Tp0wf16tWDqakp+vXrBw8PjxI/IjkoKAgzZsxAQkICJBIJJBIJgoKCALyZDrNq1Sp07NgR6urqmD17NnJzczFkyBCYmZlBKpXCysoKS5culaszf6TKwoULYWBgAF1dXfzwww9yHT0rV66EpaUlVFVVoaenh+7du4vH8vLysGDBAlhYWEBFRQW1atXC7NmzAfw3NWnHjh1o0aIFVFVVERISUmDqU/4ooTVr1sDY2Bhqamro2bMnMjIyxOObN2/Gnj17xPOOiIgodOrTiRMn0KRJE6ioqMDAwAB+fn7IyckRj7u4uMDX1xcTJ06Ejo4O9PX1ix3B9PY1mjFjBqpXrw5NTU2MHDlSrjPGxcUFo0aNwtixY1GtWjV4eHi8Nx4vLy+MHj0ad+7cgUQigampqVjX21Of3vX2KLD8a7Br1y64urpCTU0N9vb2iI6Oliuzbt068dp26dIFixcvLpfpZ0REREREREWpfP+lTp+tW7du4f79+3B3dxfTtLS00LRpU0RHR6N3794YMGAA9u/fj759+yIqKgp//fUX1q9fj+joaKipqZWonbi4OERFRWHWrFklyt+rVy9cvnwZYWFhOHr0qBhXvoCAAMybNw+BgYFQUlJCXl4eatasiT/++AO6urqIiorC8OHDYWBggJ49e4rljh8/DgMDAxw/fhw3b95Er1690KBBAwwbNgyxsbHw9fXFli1b0KxZMzx+/FiuY8nf3x/r1q3DkiVL0Lx5c6SlpeHatWtycfv5+WHRokVwcHCAqqoq/vrrrwLndvPmTfz+++/Yt28fMjMzMWTIEPj4+CAkJATjx49HYmIiMjMzsWnTJgCAjo4OUlNT5eq4d+8e2rZtCy8vLwQHB+PatWsYNmwYVFVV5TpjNm/ejHHjxiEmJgbR0dHw8vKCs7MzWrVqVeS1Dw8Ph6qqqthBNGjQIOjq6oqdUvn1ent7IzIyskTxLF26FObm5li7di3OnTsHRUXFItt/n8mTJ2PhwoWwtLTE5MmT0adPH9y8eRNKSkqIjIzEyJEjMX/+fHTs2BFHjx7F1KlTi60vOzsb2dnZ4n5mZmaZYyMqCd9tcThwMRXt7AyxrI9DRYdD9NFsPXMbqyKS4e1iDgBYFZGMRiZVcf72E3i7mKPfNyYVHCHR56/j8tO4eC9DLk0C4Na8dnCeF4576W9GyBtpqyLSz03M02DGX0h/kQNtqRLip3uUqK3CPtP8LH86lv87iNd55ftMoY72/FukPLGjhsrN/fv3AQB6enpy6Xp6euIxAFizZg3s7Ozg6+uLXbt2ISAgAI0aNXpv/TVr1sSjR4+Qk5ODgIAAucck54+QKYxUKoVMJoOSkhL09fULHP/+++8xaNAgubQZM2aIP5uZmSE6Ohq///67XEdN1apVsWLFCigqKsLa2hrt2rVDeHg4hg0bhjt37kBdXR3t27eHhoYGTExM4ODw5hfX06dPsXTpUqxYsQIDBw4EAJibm6N58+ZyMYwdOxZdu3Yt9pq8fPkSwcHBMDIyAgAsX74c7dq1w6JFi6Cvrw+pVIrs7OxCzzvfypUrYWxsjBUrVkAikcDa2hqpqamYNGkSpk2bBgWFNwPv7OzsMH36dACApaUlVqxYgfDw8GI7apSVlbFx40aoqamhXr16mDlzJiZMmICff/5ZrNfS0hILFiwQy0yePLnYeLS0tKChoSFO4foQ48ePR7t27QC8ec/r1auHmzdvwtraGsuXL4enpyfGjx8PAKhTpw6ioqKwf//+IuubO3eu3L1D9LEduJiKXOHNK/84oi/Zqohk3Et/gVURyQCAe+kvcD/jBXKFN8f45Y7ow73bSQMA+V/l8ztp3v0ZANJf5Mi9lkRhn2l+lj+d8u6kAfi3SHnj1Cf65KpWrYoNGzZg1apVMDc3h5+fX4nKnTp1CrGxsVi9ejUCAwOxbdu2conH0dGxQNqvv/6KRo0aoXr16pDJZFi7di3u3Lkjl6devXpyozkMDAzw8OFDAECrVq1gYmKC2rVro3///ggJCRGndiUmJiI7Oxtubm4oTmFxvatWrVpiJw0AODk5IS8vD0lJSe8tmy8xMRFOTk6QSCRimrOzM7KysvB///d/YpqdnZ1cubfPtyj29vZyI6WcnJyQlZWFu3fvimnvdtKVNJ7y8PY5GRgYAIB4TklJSWjSpIlc/nf33+Xv74+MjAxxe/s8iT6GdnaGUJS8eSX6knm7mMNIWwpvF3Px53Z2hmIaEX04OyOtAmn5f40ZaauKaW//DADaUiW515Io7DPNz/KnU0VB8v5MpcS/RcoXR9RQuckf3fDgwQPxS2/+/rtPXDp58iQUFRWRlpaGZ8+eFbsIbD4zMzMAgK2tLR48eICAgAD06dPng+NWV1eX29++fTvGjx+PRYsWwcnJCRoaGvjll18QExMjl69KlSpy+xKJBHl5eQAADQ0NXLhwARERETh8+DCmTZuGgIAAnDt3DlKptExxVbTizvdDVOR5vn1O+R1DH3JOKioqXLCYPqllfRz4v1f0Vej3jYnc/7Tzf92Jyt/e0c2LPPb2VKd3lXS609v4ma5YN+a0regQ6D04oobKjZmZGfT19REeHi6mZWZmIiYmBk5OTmJaVFQU5s+fj3379kEmk2HUqFGlbisvL09uLZD3UVZWRm5ubonyRkZGolmzZvDx8YGDgwMsLCyQnJxc6hiVlJTg7u6OBQsW4OLFi0hJScGxY8dgaWkJqVQqd53K6s6dO3Jrzpw5cwYKCgqwsrICULLztrGxQXR0NAThvyGQkZGR0NDQQM2aNT8ovoSEBLx48UIuPplMBmNj4wqJpzSsrKxw7tw5ubR394mIiIiIiMobO2qoxLKyshAfHy8+LejWrVuIj48XpwRJJBKMHTsWs2bNwt69e3Hp0iUMGDAAhoaG6Ny5M4A367P0798fvr6+8PT0REhICHbs2IGdO3cW2e6vv/6Kffv24caNG7hx4wY2bNiAhQsXol+/fiWO3dTUVIz3n3/+KbaTx9LSErGxsfjrr79w/fp1TJ06tdRf0Pfv349ly5YhPj4et2/fRnBwMPLy8mBlZQVVVVVMmjQJEydORHBwMJKTk3HmzBls2LChVG0AgKqqKgYOHIiEhAScOnUKvr6+6Nmzpzi6ydTUFBcvXkRSUhL++eefQh8/7uPjg7t372L06NG4du0a9uzZg+nTp2PcuHHiOjJl9erVKwwZMgRXr17FwYMHMX36dIwaNarYej9mPKUxevRoHDx4EIsXL8aNGzewZs0aHDp0SG5KFhERERERUXnj1CcqsdjYWLi6uor748aNAwAMHDhQXMx34sSJePbsGYYPH4709HQ0b94cYWFhUFV9M5d1zJgxUFdXx5w5cwC8mcY0Z84cjBgxAk5OTnLrreTLy8uDv78/bt26BSUlJZibm2P+/PkYMWJEiWPv1q2b+Cjm9PR0bNq0CV5eXoXmHTFiBOLi4tCrVy9IJBL06dMHPj4+OHToUInb09bWFhdKfvnyJSwtLbFt2zbUq1cPADB16lQoKSlh2rRpSE1NhYGBAUaOHFni+vNZWFiga9euaNu2LR4/foz27dtj5cqV4vFhw4YhIiICjo6OyMrKwvHjx8XHWeczMjLCwYMHMWHCBNjb20NHRwdDhgzBlClTSh3Pu9zc3GBpaYnvvvsO2dnZ6NOnz3sf6/0x4ykNZ2dnrF69GjNmzMCUKVPg4eGBH3/8EStWrPikcRARERER0ddFIrw9v4CIPhsBAQEIDQ0VRzhVNl5eXkhPT0doaGhFh1Juhg0bhmvXrsk9ar04mZmZ0NLSQkZGBjQ1NT9ydEREREREVFmV5rsBR9QQERVh4cKFaNWqFdTV1XHo0CFs3rxZbsQSERERERFReWNHDRFREc6ePYsFCxbg6dOnqF27NpYtW4ahQ4dWdFhERERERPQF49QnIqKPhFOfiIiIiIgIKN13Az71iYiIiIiIiIiokmBHDRERERERERFRJcGOGqKv3KNHj+Dt7Y1atWpBRUUF+vr68PDwQGRk5CePJSUlBRKJpFI8ySohIQF9+vSBsbExpFIpbGxssHTp0ooOi4iIiIiIvnBcTJjoK9etWze8evUKmzdvRu3atfHgwQOEh4fj33///aRxvHr16pO29z7nz59HjRo1sHXrVhgbGyMqKgrDhw+HoqIiRo0aVdHhERERERHRF6pMI2r+/vvv8o6DiCpAeno6Tp06hfnz58PV1RUmJiZo0qQJ/P390bFjRwCARCLBmjVr0L59e6ipqcHGxgbR0dG4efMmXFxcoK6ujmbNmiE5OVmsNzk5GZ06dYKenh5kMhkaN26Mo0ePyrVtamqKn3/+GQMGDICmpiaGDx8OMzMzAICDgwMkEglcXFyKjH3nzp2wtbWFVCqFrq4u3N3d8ezZM/H4+vXrYWNjA1VVVVhbWxd4rPbZs2fh4OAAVVVVODo6Yvfu3XKjeQYPHoylS5eiRYsWqF27Nvr164dBgwZh165dH3LJiYiIiIiIilWmjhoLCwu4urpi69atePnyZXnHRESfiEwmg0wmQ2hoKLKzs4vMl9+hEh8fD2tra3z//fcYMWIE/P39ERsbC0EQ5EaZZGVloW3btggPD0dcXBzatGmDDh064M6dO3L1Lly4EPb29oiLi8PUqVNx9uxZAMDRo0eRlpZWZKdIWloa+vTpg8GDByMxMRERERHo2rUr8h9iFxISgmnTpmH27NlITEzEnDlzMHXqVGzevFmMr3379qhbty7Onz+PgIAAjB8//r3XKyMjAzo6OkUez87ORmZmptz2Odl65jac5x3D1jO3KzqUSsd3WxzM/Q/Ad1tcRYfyyX3N5/6hCrt2pfmcFZZ365nbsJl6CGZ+/9XLzy5VlAYz/oKp3wFxK+nvCd6zRETFK1NHzYULF2BnZ4dx48ZBX18fI0aMEL9gEdHnQ0lJCUFBQdi8eTO0tbXh7OyM//3vf7h48aJcvkGDBqFnz56oU6cOJk2ahJSUFPTt2xceHh6wsbHBmDFjEBERIea3t7fHiBEjUL9+fVhaWuLnn3+Gubk59u7dK1dvy5Yt8dNPP8Hc3Bzm5uaoXr06AEBXVxf6+vpFdoqkpaUhJycHXbt2hampKWxtbeHj4wOZTAYAmD59OhYtWoSuXbvCzMwMXbt2xY8//og1a9YAAH777Tfk5eVhw4YNqFevHtq3b48JEyYUe62ioqKwY8cODB8+vMg8c+fOhZaWlrgZGxsXW2dlsyoiGffSX2BVRPL7M39lDlxMRa7w5vVr8zWf+4cq7NqV5nNWWN5VEcl48ToPAv6rl59dqijpL3Lk9kv6e4L3LBFR8crUUdOgQQMsXboUqamp2LhxI9LS0tC8eXPUr18fixcvxqNHj8o7TiL6SLp164bU1FTs3bsXbdq0QUREBBo2bIigoCAxj52dnfiznp4eAMDW1lYu7eXLl+IIkqysLIwfPx42NjbQ1taGTCZDYmJigRE1jo6O743v1KlT4sgfmUyGkJAQ2Nvbw83NDba2tujRowfWrVuHJ0+eAACePXuG5ORkDBkyRK7crFmzxOlZiYmJsLOzg6qqqtiOk5NTkTFcvnwZnTp1wvTp09G6desi8/n7+yMjI0Pc7t69+97zq0y8XcxhpC2Ft4t5RYdS6bSzM4Si5M3r1+ZrPvcPVdi1K83nrLC83i7mkFZRgAT/1cvPLlUUban8cpcl/T3Be5aIqHgSIX+uwAfIzs7GypUr4e/vj1evXkFZWRk9e/bE/PnzYWBgUB5xEtEnNHToUBw5cgS3b9+GRCLB7t270blzZwBvnsxkZmaGuLg4NGjQAAAQEREBV1dXPHnyBNra2hg5ciSOHDmChQsXwsLCAlKpFN27d4eLiwsCAwMBvFmjZuzYsRg7dqzYbmF1v3jxAvfu3RPz6OnpQUNDA4IgICoqCocPH8bu3btx//59xMTEQE1NDfr6+ti6dSuaNm0qd16KioowMzPDjz/+iISEBBw7dkw8lpCQgAYNGsi1DQBXr16Fq6srhg4ditmzZ5fqOmZmZkJLSwsZGRnQ1NQsVVkiIiIiIvpylOa7wQc9njs2NhY+Pj4wMDDA4sWLMX78eCQnJ+PIkSNITU1Fp06dPqR6IqogdevWlVuYt7QiIyPh5eWFLl26wNbWFvr6+khJSXlvOWVlZQBAbm6umCaVSmFhYSFuGhoaAN4scuzs7IwZM2YgLi4OysrK2L17N/T09GBoaIi///5brpyFhYW4WLGNjQ0uXrwot8bWmTNnCsRz5coVuLq6YuDAgaXupCEiIiIiIiqLMj2ee/Hixdi0aROSkpLQtm1bBAcHo23btlBQeNPvY2ZmhqCgIJiampZnrERUzv7991/06NEDgwcPhp2dHTQ0NBAbG4sFCxZ8UEerpaUldu3ahQ4dOkAikWDq1KnIy8t7b7kaNWpAKpUiLCwMNWvWhKqqKrS0tArki4mJQXh4OFq3bo0aNWogJiYGjx49go2NDQBgxowZ8PX1hZaWFtq0aYPs7GzExsbiyZMnGDduHL7//ntMnjwZw4YNg7+/P1JSUrBw4UK5Ni5fvoyWLVvCw8MD48aNw/379wG8GZWTv5YOERERERFReStTR82qVaswePBgeHl5FTm1qUaNGtiwYcMHBUdEH5dMJkPTpk2xZMkSJCcn4/Xr1zA2NsawYcPwv//9r8z1Ll68GIMHD0azZs1QrVo1TJo0qURPQFJSUsKyZcswc+ZMTJs2Dd9++63cIsX5NDU1cfLkSQQGBiIzMxMmJiZYtGgRPD09AbyZuqWmpoZffvkFEyZMgLq6OmxtbcVpVjKZDPv27cPIkSPh4OCAunXrYv78+ejWrZvYxs6dO/Ho0SNs3boVW7duFdNNTExKNDqIiIiIiIioLMpljRoios9dYevjfCiuUUNEREREREDpvhuUaURNvufPn+POnTt49eqVXPrbT4ghIiIiIiIiIqKSKVNHzaNHj+Dl5YWwsLBCj7+9ECgREREREREREZVMmZ76NHbsWGRkZCAmJkZc+HPz5s2wtLTE3r17yztGIqKPztTUFIIglNu0JyIiIiIiorIo04iaY8eOYc+ePXB0dISCggJMTEzQqlUraGpqYu7cuWjXrl15x0lERERERERE9MUr04iaZ8+eoUaNGgCAqlWr4tGjRwAAW1tbXLhwofyiIyIiIiIiIiL6ipSpo8bKygpJSUkAAHt7e6xZswb37t3D6tWri3xcNxF9eU6ePIkOHTrA0NAQEokEoaGhBfIIgoBp06bBwMAAUqkU7u7uuHHjxge1GxERAYlEUmC7f/++mMfLywsBAQFlbiMhIQF9+vSBsbExpFIpbGxssHTp0g+Km4iIiIiI6H3KNPVpzJgxSEtLAwBMnz4dbdq0QUhICJSVlREUFFSe8RFRJfbs2TPY29tj8ODB6Nq1a6F5FixYgGXLlmHz5s0wMzPD1KlT4eHhgatXr0JVVfWD2k9KSpJ7tF3+SL/ycP78edSoUQNbt26FsbExoqKiMHz4cCgqKmLUqFHl1g4REREREdHbytRR069fP/HnRo0a4fbt27h27Rpq1aqFatWqlVtwRFS5eXp6wtPTs8jjgiAgMDAQU6ZMQadOnQAAwcHB0NPTQ2hoKHr37o3g4GD4+PggLi4OlpaWAAAfHx8cO3YMFy5cgJqaWpH116hRA9ra2qWO+/r167CyskJiYiKsra3F9CVLlmDFihVITk7G4MGD5crUrl0b0dHR2LVrV5EdNdnZ2cjOzhb3MzMzSx3b56Dj8tO4eC8DdkZa2Du6eUWHQ1+JrWduY1VEMrxdzNHvGxPxPpT8/+Md7A0BAPsSUqFaRQGT29VFv29MStVGbf8DyBP+25dWUcCL13kF8qXM41p89HUy8zuA/I/IrM71S/0Z+1zYTD0k99nXliohfrpHudX/7u+ayib/d5+Rtioi/dyKzJd/naRVFJD4c9F/D77r3d/nJfHue/Kled+1pq9PmaY+vUtFRQUKCgpQVFQsj+qI6Atx69Yt3L9/H+7u7mKalpYWmjZtiujoaADAgAED0LZtW/Tt2xc5OTk4cOAA1q9fj5CQkGI7aQCgQYMGMDAwQKtWrRAZGVniuOrUqQNHR0eEhITIpYeEhOD7778vslxGRgZ0dHSKPD537lxoaWmJm7GxcYlj+pxcvJch90r0KayKSMa99BdYFZEM4L/7T/j/24GLqThwMRUCgBev88R8pfHuF6cv+UsBUVm8/REpy2fsc/HuZz/9RU651l+ZO2mA/87/XvrLEuUr7e/Kd3+flyamL9X7rjV9fcr8eO4NGzYAAHJzc/Hdd9+hYcOGMDY2RkRERHnGR0Sfsfw1Y/T09OTS9fT05NaTWbNmDdLS0uDr64shQ4YgICAAjRo1KrJeAwMDrF69Gn/++Sf+/PNPGBsbw8XFRW4x86CgoGLXqOnbty+2bdsm7l+/fh3nz59H3759C80fFRWFHTt2YPjw4UXW6e/vj4yMDHG7e/dukXk/Z3ZGWnKvRJ+Ct4s5jLSl8HYxB/Df/Sf5/1s7O0O0szOEBG/+Nzg/X2koSOT3pVXK5f+ziL4Yb39EyvIZ+1y8+9nXlpZpEkKR3v1dU9nkn7+RdvFT1PPzlfZ35bu/z0sT05fqfdeavj4SQRBK3adbs2ZNhIaGwtHREaGhofjhhx9w/PhxbNmyBceOHSvV/2wT0ZdBIpFg9+7d6Ny5s5gWFRUFZ2dnpKamyi003rNnT0gkEuzYsUNMO3z4MDw8PNCsWTOcOnUKCgql+we5RYsWqFWrFrZs2VKi/Pfv30fNmjVx+vRpfPPNN5g+fTr279+P8+fPF8h7+fJluLq6YsyYMZgyZUqJY8rMzISWlhYyMjLk1tIhIiIiIqKvS2m+G5Spa/Kff/6Bvr4+AODgwYPo0aMH6tSpg8GDB+PSpUtlqZKIvkD5vycePHggl/7gwQPxWL6TJ09CUVERaWlpePbsWanbatKkCW7evFmq2Fq2bInffvsNAPDbb78VOprm6tWrcHNzw/Dhw0vVSUNERERERFQWZeqo0dPTw9WrV5Gbm4uwsDC0atUKAPD8+XOuU0NEIjMzM+jr6yM8PFxMy8zMRExMDJycnMS0qKgozJ8/H/v27YNMJivTU5Xi4+PlRu2URN++fbFjxw5ER0fj77//Ru/eveWOX7lyBa6urhg4cCBmz55d6piIiIiIiIhKq0wTLgcNGoSePXvCwMAAEolEXCg0JiZG7gkqRPRly8rKkhvFcuvWLcTHx0NHRwe1atWCRCLB2LFjMWvWLFhaWoqP5zY0NBSnSD19+hT9+/eHr68vPD09UbNmTTRu3BgdOnRA9+7dC203MDAQZmZmqFevHl6+fIn169fj2LFjOHz4cKni79q1K7y9veHt7Q1XV1cYGhqKxy5fvoyWLVvCw8MD48aNE9fUUVRURPXq1Ut5pYiIiIiIiEqmTB01AQEBqF+/Pu7evYsePXpARUUFwJsvMH5+fuUaIBFVXrGxsXB1dRX3x40bBwAYOHAggoKCAAATJ07Es2fPMHz4cKSnp6N58+YICwuDquqbRdPGjBkDdXV1zJkzBwBga2uLOXPmYMSIEXBycoKRkVGBdl+9eoWffvoJ9+7dg5qaGuzs7HD06FG5WEpCQ0MDHTp0wO+//46NGzfKHdu5cycePXqErVu3YuvWrWK6iYkJUlJSStUOERERERFRSZVpMeHCpKenQ1tbuzyqIiL6InAxYSIiIiIiAj7BYsLz58+Xe1pLz549oauri5o1a+LixYtlqZKIiIiIiIiI6KtXpo6a1atXw9jYGABw5MgRHDlyBIcOHUKbNm0wfvz4cg2QiIiIiIiIiOhrUaY1au7fvy921Ozfvx89e/ZE69atYWpqiqZNm5ZrgEREREREREREX4syjaipWrUq7t69CwAICwsTn/okCAJyc3PLLzoiIiIiIiIioq9ImTpqunbtiu+//x6tWrXCv//+C09PTwBAXFwcLCwsyjVAIqr8vLy8IJFIIJFIUKVKFejp6aFVq1bYuHEj8vLyKjo8IiIiIiKiz0aZOmqWLFmCUaNGoW7dujhy5AhkMhkAIC0tDT4+PuUaIBF9Htq0aYO0tDSkpKTg0KFDcHV1xZgxY9C+fXvk5OQUWub169efOEoiIiIiIqLKrUwdNVWqVMH48eMxYsQIPHjwAHv37sXevXthbm6OGjVqlHeMRPQZUFFRgb6+PoyMjNCwYUP873//w549e3Do0CEEBQUBACQSCVatWoWOHTtCXV0ds2fPBgCsWrUK5ubmUFZWhpWVFbZs2SJXd345T09PSKVS1K5dGzt37iw2nidPnqBv376oXr06pFIpLC0tsWnTJvH43bt30bNnT2hra0NHRwedOnVCSkqKeDw3Nxfjxo2DtrY2dHV1MXHiRAwcOBCdO3cul+tFRERERERUmDItJvz333+ja9euuHTpEoA3a9MAb75MAeA6NUQEAGjZsiXs7e2xa9cuDB06FAAQEBCAefPmITAwEEpKSti9ezfGjBmDwMBAuLu7Y//+/Rg0aBBq1qwJV1dXsa6pU6di3rx5WLp0KbZs2YLevXvj0qVLsLGxKbTtqVOn4urVqzh06BCqVauGmzdv4sWLFwDejOTx8PCAk5MTTp06BSUlJcyaNQtt2rTBxYsXoaysjEWLFiEoKAgbN26EjY0NFi1ahN27d6Nly5ZFnm92djays7PF/czMzPK4jJ+Eqd+BQtNT5rX7xJF8XFvP3MaqiGR4u5ij3zcmFR0OfUE+9r3Fe5c+B+V9n249cxsL/0oCAIz3sOK9/4k4zwvHvfSXkFZRwKucPLSzM8SyPg6Fvh++2+Jw4GKqmCe/rJG2KrxdLLDwryRkvnyNPAEw0lYFIEEjk6o4ef2RXD0Af88Rva1MI2rGjBkDU1NTPHjwAGpqarhy5QpOnjwJR0dHRERElHOIRPQ5s7a2lhup8v3332PQoEGoXbs2atWqhYULF8LLyws+Pj6oU6cOxo0bh65du2LhwoVy9fTo0QNDhw5FnTp18PPPP8PR0RHLly8vst07d+7AwcEBjo6OMDU1hbu7Ozp06AAA2LFjB/Ly8rB+/XrY2trCxsYGmzZtwp07d8TfYYGBgfD390fXrl1hY2OD1atXQ0tLq9hznTt3LrS0tMQt/+l4VHmsikjGvfQXWBWRXNGh0BfmY99bvHfpc1De9+mqiGSkv3iN9Bevee9/QvfSXwIAXrzOQ64AHLiYCqDw9+PAxVS5PPll76W/FPPnCf/Vey/9BQ5cTC30feXvOaL/lKmjJjo6GjNnzkS1atWgoKAABQUFNG/eHHPnzoWvr295x0hEnzFBEMTRdgDg6OgodzwxMRHOzs5yac7OzkhMTJRLc3JyKrCfn8fT0xMymQwymQz16tUDAHh7e2P79u1o0KABJk6ciKioKLFsQkICbt68CQ0NDbGcjo4OXr58ieTkZGRkZCAtLQ1NmzYVyygpKRWI/V3+/v7IyMgQt/yn41Hl4e1iDiNtKbxdzCs6FPrCfOx7i/cufQ7K+z71djGHtrQKtKVVeO9/Qm9GvgDSKgpQlADt7AwBFP5+tLMzlMuTX/bNiJo3+RUk/9VrpC1FOzvDQt9X/p4j+k+Zpj7l5uZCQ0MDAFCtWjWkpqbCysoKJiYmSEpKKtcAiejzlpiYCDMzM3FfXV293NtYv369OK2pSpUqAN503ty+fRsHDx7EkSNH4Obmhh9++AELFy5EVlYWGjVqhJCQkAJ1Va9evcxxqKioQEVFpczlK9KXNsWpKP2+MeFwavooPva9xXuXPgflfZ/yvq8YkX5uhaYX9n4s6+OAZX0ciixbmveP7zfRf8o0oqZ+/fpISEgAADRt2hQLFixAZGQkZs6cidq1a5drgET0+Tp27BguXbqEbt26FZnHxsYGkZGRcmmRkZGoW7euXNqZM2cK7OevT2NkZAQLCwtYWFjAxOS/f+CrV6+OgQMHYuvWrQgMDMTatWsBAA0bNsSNGzdQo0YNsVz+lj9tycDAADExMWJdOTk5OH/+fNkuBBERERERUQmVaUTNlClT8OzZMwDAzJkz0b59e3z77bfQ1dXFjh07yjVAIvo8ZGdn4/79+8jNzcWDBw8QFhaGuXPnon379hgwYECR5SZMmICePXvCwcEB7u7u2LdvH3bt2oWjR4/K5fvjjz/g6OiI5s2bIyQkBGfPnsWGDRuKrHfatGlo1KgR6tWrh+zsbOzfv1/s2Onbty9++eUXdOrUCTNnzkTNmjVx+/Zt7Nq1CxMnTkTNmjUxZswYzJs3D5aWlrC2tsbixYuRnp5eLteKiIiIiIioKGXqqPHw8BB/trCwwLVr1/D48WNUrVpVbi0KIvp6hIWFwcDAAEpKSqhatSrs7e2xbNkyDBw4EAoKRQ/e69y5M5YuXYqFCxdizJgxMDMzw6ZNm+Di4iKXb8aMGdi+fTt8fHxgYGCAbdu2FRh18zZlZWX4+/sjJSUFUqkU3377LbZv3w4AUFNTw8mTJzFp0iR07doVT58+hZGREdzc3KCpqQkA+Omnn5CWlibGP3jwYHTp0gUZGRkffrGIiIiIiIiKIBHyn61NRFRJSSQS7N69G507d67QOLy8vJCeno7Q0NAS5c/MzISWlhYyMjLEDiAiIiIiIvr6lOa7QZnWqCEiIiIiIiIiovLHjhoiIiIiIiIiokqiTGvUEBF9SpVlhmZQUFBFh0BERERERF84jqghIiIiIiIiIqok2FFDRERERERERFRJsKOGiMrs5MmT6NChAwwNDSGRSAp9GpIgCJg2bRoMDAwglUrh7u6OGzdufFC7p0+fhrOzM3R1dSGVSmFtbY0lS5bI5fHy8kJAQMAHtePr64tGjRpBRUUFDRo0+KC6iIiIiIiISoIdNURUZs+ePYO9vT1+/fXXIvMsWLAAy5Ytw+rVqxETEwN1dXV4eHjg5cuXZW5XXV0do0aNwsmTJ5GYmIgpU6ZgypQpWLt2bZnrLMrgwYPRq1evcq+XiIiIiIioMOyoIaIy8/T0xKxZs9ClS5dCjwuCgMDAQEyZMgWdOnWCnZ0dgoODkZqaKo6+CQ4Ohkwmkxtl4+PjA2trazx//rzQeh0cHNCnTx/Uq1cPpqam6NevHzw8PHDq1KkSxX39+nVIJBJcu3ZNLn3JkiUwNzcX95ctW4YffvgBtWvXLlG92dnZyMzMlNs+Bx2Xn4ap3wF0XH4aW8/chvO8Y9h65naZ6vLdFgdz/wPw3RZX6rap/H3o+1kZVeZzKmtspf3cEBER0ZeNHTVE9NHcunUL9+/fh7u7u5impaWFpk2bIjo6GgAwYMAAtG3bFn379kVOTg4OHDiA9evXIyQkBGpqaiVqJy4uDlFRUWjRokWJ8tepUweOjo4ICQmRSw8JCcH3339fwrMraO7cudDS0hI3Y2PjMtf1KV28lyG+ropIxr30F1gVkVymug5cTEWu8Oa1tG1T+fvQ97MyqsznVNbYSvu5ISIioi8bO2qI6KO5f/8+AEBPT08uXU9PTzwGAGvWrEFaWhp8fX0xZMgQBAQEoFGjRu+tv2bNmlBRUYGjoyN++OEHDB06VDwWFBRU7Bo1ffv2xbZt28T969ev4/z58+jbt29JT68Af39/ZGRkiNvdu3fLXNenZGekJb56u5jDSFsKbxfz95QqXDs7QyhK3ryWtm0qfx/6flZGlfmcyhpbaT83RERE9GWTCIIgVHQQRPT5k0gk2L17Nzp37iymRUVFwdnZGampqTAwMBDTe/bsCYlEgh07dohphw8fhoeHB5o1a4ZTp05BQeH9/ci3bt1CVlYWzpw5Az8/P6xYsQJ9+vQpUbz3799HzZo1cfr0aXzzzTeYPn069u/fj/PnzxfIGxAQgNDQUMTHx5eo7nyZmZnQ0tJCRkYGNDU1S1WWiIiIiIi+HKX5bsARNUT00ejr6wMAHjx4IJf+4MED8Vi+kydPQlFREWlpaXj27FmJ6jczM4OtrS2GDRuGH3/8sVRPedLX10fLli3x22+/AQB+++23DxpNQ0REREREVB7YUUNEH42ZmRn09fURHh4upmVmZiImJgZOTk5iWlRUFObPn499+/ZBJpNh1KhRpW4rLy8P2dnZpSrTt29f7NixA9HR0fj777/Ru3fvUrdLRERERERUnpQqOgAi+nxlZWXh5s2b4v6tW7cQHx8PHR0d1KpVCxKJBGPHjsWsWbNgaWkJMzMzTJ06FYaGhuIUqadPn6J///7w9fWFp6cnatasicaNG6NDhw7o3r17oe3++uuvqFWrFqytrQG8GY2zcOFC+Pr6lir+rl27wtvbG97e3nB1dYWhofz6EDdv3kRWVhbu37+PFy9eiFOf6tatC2Vl5VK1RUREREREVBLsqCGiMouNjYWrq6u4P27cOADAwIEDERQUBACYOHEinj17huHDhyM9PR3NmzdHWFgYVFVVAQBjxoyBuro65syZAwCwtbXFnDlzMGLECDg5OcHIyKhAu3l5efD398etW7egpKQEc3NzzJ8/HyNGjChV/BoaGujQoQN+//13bNy4scDxoUOH4sSJE+K+g4MDgDcdUqampqVqi4iIiIiIqCS4mDAR0UfCxYSJiIiIiAjgYsJERERERERERJ8ldtQQEREREREREVUS7KghIiIiIiIiIqok2FEDICgoCNra2hUdRpkEBASgQYMGn7zdiIgISCQSpKenf1A9EokEoaGh5RLT5ywlJQUSiUR8qhCVTnlfP1NTUwQGBpZLXURERERERKVRoR01Xl5ekEgk4qarq4s2bdrg4sWLcvnezqOurg5LS0t4eXnh/Pnz723D1NRULKumpgZbW1usX7/+Y53SZ6d3795o06aNXFpYWBgkEgkCAgLk0gMCAlCrVq1PGF3xAgIC5O6NwrbPhbGxMdLS0lC/fv0Sl/mcOhjnzp0LRUVF/PLLLxUdSomcO3cOw4cPr+gwiIiIiIjoK1ThI2ratGmDtLQ0pKWlITw8HEpKSmjfvn2BfJs2bUJaWhquXLmCX3/9FVlZWWjatCmCg4Pf28bMmTORlpaGy5cvo1+/fhg2bBgOHTr0MU7ns+Pq6orIyEjk5OSIacePH4exsTEiIiLk8h4/flzuUcwVbfz48eK9k5aWhpo1a4rvdf72tlevXlVQpMV79eoVFBUVoa+vDyUlpYoO56PYuHEjJk6cWOgjsCuj6tWrQ01NraLDICIiIiKir1CFd9SoqKhAX18f+vr6aNCgAfz8/HD37l08evRILp+2tjb09fVhamqK1q1bY+fOnejbty9GjRqFJ0+eFNuGhoYG9PX1Ubt2bUyaNAk6Ojo4cuRIgXx//fUXbGxsIJPJxA6kfOfOnUOrVq1QrVo1aGlpoUWLFrhw4YJceYlEglWrVsHT0xNSqRS1a9fGzp07xeOvXr3CqFGjYGBgAFVVVZiYmGDu3Lni8Tt37qBTp06QyWTQ1NREz5498eDBA7k25s2bBz09PWhoaGDIkCF4+fKleOzkyZOoUqUK7t+/L1dm7Nix+Pbbbwu9Nq6ursjKykJsbKyYFhERAT8/P8TExIj1v3z5EjExMQU6as6fPw9HR0eoqamhWbNmSEpKkju+atUqmJubQ1lZGVZWVtiyZUuhceS7e/cuevbsCW1tbejo6KBTp05ISUkpNK9MJhPvHX19fSgqKorvtb6+Pnr37o1Ro0Zh7NixqFatGjw8PAAAixcvhq2tLdTV1WFsbAwfHx9kZWWJ9eaPVCnufoiIiECTJk2grq4ObW1tODs74/bt2+Lxffv2oXHjxlBVVUW1atXQpUsX8ZipqSl+/vlnDBgwAJqamhg+fHiBqTv5U8sOHDgAOzs7qKqq4ptvvsHly5fF44MGDUJGRoY4eih/BNSTJ08wYMAAVK1aFWpqavD09MSNGzcKnN/+/fthZWUFNTU1dO/eHc+fP8fmzZthamqKqlWrwtfXF7m5uWK57OxsjB8/HkZGRlBXV0fTpk0LdOYV5sSJE3jx4gVmzpyJzMxMREVFyR3Pn763ZcsWmJqaQktLC71798bTp0/FPGFhYWjevDm0tbWhq6uL9u3bIzk5udD2BEGAhYUFFi5cKJceHx8PiUSCmzdvQhAEcYSYiooKDA0N4evrK/ce5U99el9eIiIiIiKi8lThHTVvy8rKwtatW2FhYQFdXd335v/xxx/x9OnTQjtdCpOXl4c///wTT548gbKystyx58+fY+HChdiyZQtOnjyJO3fuYPz48eLxp0+fYuDAgTh9+jTOnDkDS0tLtG3bVu7LJABMnToV3bp1Q0JCAvr27YvevXsjMTERALBs2TLs3bsXv//+O5KSkhASEgJTU1Mxtk6dOuHx48c4ceIEjhw5gr///hu9evUS6/79998REBCAOXPmIDY2FgYGBli5cqV4/LvvvkPt2rXlOkNev36NkJAQDB48uNBrUqdOHRgaGuL48ePieV64cAE9evSAqakpoqOjAQBRUVHIzs4u0FEzefJkLFq0CLGxsVBSUpJrZ/fu3RgzZgx++uknXL58GSNGjMCgQYPEtt71+vVreHh4QENDA6dOnUJkZKTYSVLW0TCbN2+GsrIyIiMjsXr1agCAgoICli1bhitXrmDz5s04duwYJk6cKFeuuPshJycHnTt3RosWLXDx4kVER0dj+PDh4lSrAwcOoEuXLmjbti3i4uIQHh6OJk2ayNW/cOFC2NvbIy4uDlOnTi0y/gkTJmDRokU4d+4cqlevjg4dOuD169do1qwZAgMDoampKY4eyo/Py8sLsbGx2Lt3L6KjoyEIAtq2bYvXr1/Lnd+yZcuwfft2hIWFISIiAl26dMHBgwdx8OBBbNmyBWvWrJHraBw1ahSio6Oxfft2XLx4ET169ECbNm3kOoEKs2HDBvTp0wdVqlRBnz59sGHDhgJ5kpOTERoaiv3792P//v04ceIE5s2bJx5/9uwZxo0bh9jYWISHh0NBQQFdunRBXl5egbokEgkGDx6MTZs2yaVv2rQJ3333HSwsLPDnn39iyZIlWLNmDW7cuIHQ0FDY2toWGn9p8mZnZyMzM1Nuq+zM/A7A1O8AzPwOVHQoRJ+drWduw3nesf//Gg5TvwNwnhcOAGgw4y/xs2X61vb2fsflp9Fx+Wlx33dbHADAd1sczN7K23H56SLb9t0WJ8ZA9LXw3RYHc///PjNERF8coQINHDhQUFRUFNTV1QV1dXUBgGBgYCCcP39eLh8AYffu3QXKv3jxQgAgzJ8/v8g2TExMBGVlZUFdXV1QUlISAAg6OjrCjRs3xDybNm0SAAg3b94U03799VdBT0+vyHpzc3MFDQ0NYd++fXJxjhw5Ui5f06ZNBW9vb0EQBGH06NFCy5Ythby8vAL1HT58WFBUVBTu3Lkjpl25ckUAIJw9e1YQBEFwcnISfHx8CtRvb28v7s+fP1+wsbER9//8809BJpMJWVlZRZ5L3759hdatWwuCIAgHDhwQ6tatKwiCIAwfPlyYNm2aIAiCMHXqVMHMzEwsc/z4cQGAcPToUTHtwIEDAgDhxYsXgiAIQrNmzYRhw4bJtdWjRw+hbdu24v7b7+2WLVsEKysrueuTnZ0tSKVS4a+//ioy/nwmJibCkiVLxP0WLVoIDg4O7y33xx9/CLq6uuL+++6Hf//9VwAgREREFFqfk5OT0Ldv32Lj7Ny5s1zarVu3BABCXFycIAj/Xd/t27eLef79919BKpUKO3bsEOPU0tKSq+f69esCACEyMlJM++effwSpVCr8/vvvRZ7fiBEjBDU1NeHp06dimoeHhzBixAhBEATh9u3bgqKionDv3j259tzc3AR/f/8izzUjI0OQSqVCfHy8IAiCEBcXJ8hkMrl2pk+fLqipqQmZmZli2oQJE4SmTZsWWe+jR48EAMKlS5cEQSh4/e7duycoKioKMTExgiAIwqtXr4Rq1aoJQUFBgiAIwqJFi4Q6deoIr169KrT+t++l9+V92/Tp0wUABbaMjIz3lq0oJpP2ixsRlU6zueGCyaT94uvbn6W390u61fZ7U7a2X8FjRbWdn7fZ3PBPeu5EFSn/vs//zBARfQ4yMjJK/N2gwkfUuLq6Ij4+HvHx8Th79iw8PDzg6ekpN42kKIIgAMB7F42dMGEC4uPjcezYMTRt2hRLliyBhYWFXB41NTWYm5uL+wYGBnj48KG4/+DBAwwbNgyWlpbQ0tKCpqYmsrKycOfOHbl6nJycCuznj6jx8vJCfHw8rKys4Ovri8OHD4v5EhMTYWxsDGNjYzGtbt260NbWFssnJiaiadOmxbbn5eWFmzdv4syZMwDeTHPp2bMn1NXVi7w+Li4uiIyMxOvXrxEREQEXFxcAQIsWLcSpLREREYWuT2NnZyd3zQCI1y0xMRHOzs5y+Z2dncXzeVdCQgJu3rwJDQ0NyGQyyGQy6Ojo4OXLl0VOc3mfRo0aFUg7evQo3NzcYGRkBA0NDfTv3x///vsvnj9/LuYp7n7Q0dGBl5cXPDw80KFDByxdulRuWlR8fDzc3NyKjcvR0bFE8b/9/uro6MDKyqrI6we8ueZKSkpy94murm6Bcu+en56eHkxNTSGTyeTS8s/50qVLyM3NRZ06dcT3RiaT4cSJE8W+N9u2bYO5uTns7e0BAA0aNICJiQl27Nghl8/U1BQaGhri/rufvxs3bqBPnz6oXbs2NDU1xZFo737+8hkaGqJdu3bimjj79u1DdnY2evToAQDo0aMHXrx4gdq1a2PYsGHYvXu33DpNbytNXn9/f2RkZIjb3bt3i7w2lYXknVciKjlvF3MYaUv//6sqAIiv2tI3a469+9l6e9/OSAt2Rlrifjs7Q/FV8lbet/O823Y7O0MxBqKvRTs7QyhK/vvMEBF9aSp85VJ1dXW5TpP169dDS0sL69atw6xZs4otm//F08zMrNh81apVg4WFBSwsLPDHH3/A1tYWjo6OqFu3rpinSpUqcmUkEonYEQQAAwcOxL///oulS5fCxMQEKioqcHJyKtWUnIYNG+LWrVs4dOgQjh49ip49e8Ld3V1uesmHqlGjBjp06IBNmzbBzMwMhw4deu86Iq6urnj27BnOnTuH48ePY8KECQDedNQMHjwYjx8/RkxMDEaMGFGg7NvXLb/DrLDpKCWRlZWFRo0aISQkpMCx6tWrl6nOdzuoUlJS0L59e3h7e2P27NnQ0dHB6dOnMWTIELx69UpcQPZ998OmTZvg6+uLsLAw7NixA1OmTMGRI0fwzTffQCqVljquT62w8yssLf+9zMrKgqKiIs6fPw9FRUW5fG937rxrw4YNuHLlitwiyXl5edi4cSOGDBlSbDxv30cdOnSAiYkJ1q1bB0NDQ+Tl5aF+/frFfv6GDh2K/v37Y8mSJdi0aRN69eolvr/GxsZISkrC0aNHceTIEfj4+OCXX37BiRMnCsRSmrwqKipQUVEpMqbK6Na8dhUdAtFnq983Juj3jYn489vip3uUud5lfRywrI9Didsm+tqU5DNCRPQ5q/ARNe+SSCRQUFDAixcv3ps3f40Od3f3EtdvbGyMXr16wd/fv1RxRUZGwtfXF23btkW9evWgoqKCf/75p0C+/JEsb+/b2NiI+5qamujVqxfWrVuHHTt24M8//8Tjx49hY2ODu3fvyv0P/NWrV5Geni52KNnY2CAmJqbY9oA3X1B37NiBtWvXwtzcvMColneZm5vD2NgYe/fuRXx8PFq0aAEAMDIygpGRERYtWoRXr16V+olPNjY2iIyMlEuLjIyU6yB7W8OGDXHjxg3UqFFD7FjL37S0Cv5vYlmcP38eeXl5WLRoEb755hvUqVMHqampZarLwcEB/v7+iIqKQv369fHbb78BeDPKKDw8vFziffv9ffLkCa5fvy7eT8rKynKL/QJvrnlOTo7cffLvv/8iKSmpyOteEg4ODsjNzcXDhw8LvDf6+vqFlrl06RJiY2MREREhjpqLj49HREQEoqOjce3atRK1nR//lClT4ObmBhsbm/cuIA4Abdu2hbq6OlatWoWwsLAC6zRJpVJ06NABy5YtE2O6dOlSoXWVJi8REREREdGHqPARNdnZ2eJTip48eYIVK1YgKysLHTp0kMuXnp6O+/fvIzs7G9evX8eaNWsQGhqK4OBgaGtrl6rNMWPGoH79+oiNjS3xFBRLS0ts2bIFjo6OyMzMxIQJEwodOfHHH3/A0dERzZs3R0hICM6ePSsunrp48WIYGBjAwcEBCgoK+OOPP6Cvrw9tbW24u7vD1tYWffv2RWBgIHJycuDj44MWLVqIMY4ZMwZeXl5wdHSEs7MzQkJCcOXKFdSuXVsuBg8PD2hqamLWrFmYOXNmic7P1dUVK1euhIWFBfT09MT0Fi1aYPny5eKiw6UxYcIE9OzZEw4ODnB3d8e+ffuwa9cuHD16tND8ffv2xS+//IJOnTph5syZqFmzJm7fvo1du3Zh4sSJqFmzZqnaL4yFhQVev36N5cuXo0OHDnKLDJfUrVu3sHbtWnTs2BGGhoZISkrCjRs3MGDAAADA9OnT4ebmBnNzc/Tu3Rs5OTk4ePAgJk2aVOp4Z86cCV1dXejp6WHy5MmoVq0aOnfuDODNdKGsrCyEh4fD3t4eampqsLS0RKdOnTBs2DCsWbMGGhoa8PPzg5GRETp16lTq9vPVqVMHffv2xYABA7Bo0SI4ODjg0aNHCA8Ph52dHdq1KzgqY8OGDWjSpAm+++67AscaN26MDRs24Jdffnlv21WrVoWuri7Wrl0LAwMD3LlzB35+fu8tp6ioCC8vL/j7+8PS0lJuGllQUBByc3PRtGlTqKmpYevWrZBKpTAxKfi/06XJS0RERERE9KEqfERNWFgYDAwMYGBggKZNm+LcuXP4448/xHVS8g0aNAgGBgawtraGt7c3ZDIZzp49i++//77UbdatWxetW7fGtGnTSlxmw4YNePLkCRo2bIj+/fvD19cXNWrUKJBvxowZ2L59O+zs7BAcHIxt27aJIxk0NDSwYMECODo6onHjxkhJScHBgwehoKAAiUSCPXv2oGrVqvjuu+/g7u6O2rVry63l0atXL0ydOhUTJ05Eo0aNcPv2bXh7exeIQUFBAV5eXsjNzRU7D97H1dUVT58+LXDdW7RogadPn5Z6NA0AdO7cGUuXLsXChQtRr149rFmzBps2bSrQRj41NTWcPHkStWrVQteuXWFjYyM+glxTU7PU7RfG3t4eixcvxvz581G/fn2EhITIPSK9JNTU1HDt2jV069YNderUwfDhw/HDDz+IU8NcXFzwxx9/YO/evWjQoAFatmyJs2fPlineefPmYcyYMWjUqBHu37+Pffv2iU8sa9asGUaOHIlevXqhevXqWLBgAYA307IaNWqE9u3bw8nJCYIg4ODBgwWm6ZTWpk2bMGDAAPz000+wsrJC586dce7cOdSqVatA3levXmHr1q3o1q1boXV169YNwcHBck+iKoqCggK2b9+O8+fPo379+vjxxx9L1MEDQJzSNmjQILl0bW1trFu3Ds7OzrCzs8PRo0exb9++Qp82V5q8REREREREH0oivL3wBn0QiUSC3bt3iyMeKtKQIUPw6NEj7N27t6JDoTLIX7z5yZMnpR4xRv85deoU3NzccPfuXbmRYp9KZmYmtLS0kJGRUW6djURERERE9PkpzXeDCp/6ROUrIyMDly5dwm+//cZOGvpqZWdn49GjRwgICECPHj0qpJOGiIiIiIioLCp86hOVr06dOqF169YYOXIkWrVqVdHhEFWIbdu2wcTEBOnp6eKUMCIiIiIios8Bpz4REX0knPpERERERERA6b4bcEQNEREREREREVElwY4aIqJiuLi4YOzYseK+qakpAgMDKyweIiIiIiL6srGjhojK3aNHj+Dt7Y1atWpBRUUF+vr68PDwQGRk5Edr08DAAPPmzZNL8/Pzg0QiQUREhFy6i4sL+vfv/9FiISIiIiIiKit21BBRuevWrRvi4uKwefNmXL9+HXv37oWLiwv+/fffj9ami4tLgQ6Z48ePw9jYWC795cuXOHPmDFq2bPnRYiEiIiIiIiordtQQUblKT0/HqVOnMH/+fLi6usLExARNmjSBv78/OnbsKOYZOnQoqlevDk1NTbRs2RIJCQly9cybNw96enrQ0NDAkCFD4OfnhwYNGhTZrqurKyIjI5GTkwMAePr0KeLi4jBp0iS5jpro6GhkZ2fD1dUVAHD58mV4enpCJpNBT08P/fv3xz///FO+F4WIiIiIiKiE2FFDROVKJpNBJpMhNDQU2dnZhebp0aMHHj58iEOHDuH8+fNo2LAh3Nzc8PjxYwDA77//joCAAMyZMwexsbEwMDDAypUri23X1dUVWVlZOHfuHADg1KlTqFOnDrp164aYmBi8fPkSwJtRNqampjA1NUV6ejpatmwJBwcHxMbGIiwsDA8ePEDPnj3LdO7Z2dnIzMyU2yor321xMPc/AN9tccXmc54XDlO/A3CeF17o8a1nbsN53jFsPXP7vW2+nbes5cozb76Oy0/D1O8AOi4/XeIy9EZZrjcREVVe7/5ez9/33RYnpuf/DZH/N4Kp35u/J97926KwfyOKqp//jhDJY0cNEZUrJSUlBAUFYfPmzdDW1oazszP+97//4eLFiwCA06dP4+zZs/jjjz/g6OgIS0tLLFy4ENra2ti5cycAIDAwEEOGDMGQIUNgZWWFWbNmoW7dusW2a2lpCSMjI3H0TEREBFq0aAF9fX3UqlUL0dHRYnr+aJoVK1bAwcEBc+bMgbW1NRwcHLBx40YcP34c169fL/W5z507F1paWuJmbGxc6jo+lQMXU5ErvHktzr30l3Kv71oVkYx76S+wKiL5vW2+nbes5cozb76L9zLkXqnkynK9iYio8nr393r+/oGLqWJ6/t8Qb/9tcOBiaoG/LQr7N6Ko+vnvCJE8dtQQUbnr1q0bUlNTsXfvXrRp0wYRERFo2LAhgoKCkJCQgKysLOjq6oqjb2QyGW7duoXk5Df/SCcmJqJp06ZydTo5OYk/nzp1Sq5sSEgIAPl1aiIiIuDi4gIAaNGiBSIiIvDixQvExMSIHTUJCQk4fvy4XF3W1tYAIMZSGv7+/sjIyBC3u3fvlrqOT6WdnSEUJW9ei2OkrSr3+i5vF3MYaUvh7fL/2rv7uBrv/w/gr+tU6nQv++pOKokyJWQWtjKNuWluvhuR0cZYWLG5yVcILexrXzebxYiGZmzGDMNma+4qQtqsr0jhl4wvulVJ5/r9YefaOXU6nU5F217Px+M8juu6Pjfvz+e6znV1Pq7rc9zqrFM1rb75GjOtkrejldo76U6f/iYiouar+nlduTzY20Far/wbQvVvg8HeDjX+ttB0jaitfF5HiNQJoiiKTzoIIvrrmzhxIr777jtMmTIFH374YY2JfwHA2toaTz31FFq2bInVq1dj3Lhx0rYZM2bgxx9/RHp6OsrKypCXlydtU85lEx8fj4iICFy9ehV2dnbIy8tD69atkZiYiPXr12PhwoUIDAzE//3f/8HR0REDBw6Eqakpli9fXiMWe3t7mJmZISAgAD4+PtJPcru4uGD69OlqP9ldm6KiIlhZWaGwsBCWlpb17jMiIiIiIvprqM93A8PHFBMR/c116tQJe/bsQbdu3XDz5k0YGhrCxcVFY1pPT0+kpqaqDdSkpKRI/5bL5Wjfvn2NfH379kVpaSn+85//wN3dHa1btwYAPP/885gwYQK+/fZb6REpAOjWrRt27doFFxcXGBrydEhERERERE8eH30iokZ1584dvPDCC9i2bRsyMjKQk5ODL774Au+//z6GDh2KwMBA+Pn5YdiwYTh8+DByc3Nx8uRJzJs3D2lpaQCAiIgIbNq0CZs3b0ZWVhYWLlyICxcu1Fl3u3bt0LZtW3z44Yfw9/eX1js5OcHBwQGffPKJ9NgTAEydOhV3797F6NGjcfr0aWRnZ+PQoUN4/fXXUVVV1fidQ0REREREVAcO1BBRozI3N0fPnj2xcuVKPP/88+jcuTPmz5+PN998Ex999BEEQcCBAwfw/PPP4/XXX0eHDh0QHByMq1evwtbWFgAwatQozJ8/H7Nnz0b37t1x9epVhIWF6VR/3759UVxcLM1Po+Tv74/i4mK1gRoHBwecOHECVVVV6N+/P7y8vDB9+nRYW1tDJuPpkYiIiIiIHj/OUUNEfwrR0dHYs2cP0tPTn3QoOuMcNUREREREBNTvuwH/y5iIiIiIiIiIqJngQA0RERERERERUTPBR5+IiJoIH30iIiIiIiKAjz5RMxYaGophw4Y1apm5ubkQBOGJz10SEBCA6dOnS8suLi5YtWrVE4uH1DXFsUdERERERNTYOFBDj9Xq1auRkJDwpMNoFqKjoyEIAgRBgIGBAZycnDBp0iTcvXtXLZ2LiwsEQUBKSora+unTp9f4ZSMA+L//+z+0aNECnTt3bsrw/3R47BERERER0Z8BB2rosbKysoK1tfWTDqPZePrpp5Gfn49r165h8+bNOHjwoMafoTYxMcGcOXN0KjMhIQEjR45EUVERUlNTGzvkPy0ee0RERERE9GfAgRpqdF9++SW8vLwgl8vRqlUrBAYGorS0FEDNx08CAgIQHh6O2bNnw8bGBnZ2doiOjlYr77///S/69OkDExMTdOrUCd9//z0EQcCePXtqjeGXX37BwIEDYW5uDltbW7z22mv43//+pzXuiooKzJkzB05OTjA2Nkb79u0RHx/foDLrYmhoCDs7Ozg6OiIwMBCvvvoqvvvuuxrpJk2ahJSUFBw4cEBreaIoYvPmzXjttdcwZswYtfhro21/AcDGjRvh6ekJExMTeHh44OOPP1bLf+rUKXTt2hUmJibw9fXF7t271R5FS0hIqDFAsmfPHgiCoLbu66+/Rrdu3WBiYoJ27dph0aJFePjwobRdEARs3LgRw4cPh6mpKdzd3bF37161Mi5cuIAhQ4bA0tISFhYWeO6555CdnQ3g8R17REREREREDcGBGmpU+fn5GD16NN544w1kZmYiKSkJI0aMgLY5qz/99FOYmZkhNTUV77//PhYvXiwNVlRVVWHYsGEwNTVFamoqPvnkE8ybN09rDAUFBXjhhRfQtWtXpKWl4eDBg/jtt98wcuRIrfnGjRuH7du3Y82aNcjMzMT69ethbm7eoDLrIzc3F4cOHUKLFi1qbHN1dcVbb72FuXPnQqFQ1FrGjz/+iPv37yMwMBBjx47F559/rjboUl1d+ysxMRELFizAe++9h8zMTMTGxmL+/Pn49NNPAQAlJSUYMmQIOnXqhDNnziA6OhozZ86sd9uPHTuGcePGISIiAr/++ivWr1+PhIQEvPfee2rpFi1ahJEjRyIjIwODBg1CSEiI9KhYXl4enn/+eRgbG+OHH37AmTNn8MYbb6gN9lTX2MdeRUUFioqK1F7NUfj2c3Cbux/h28/h5Q+PwyVyP17+8HiNbUrbUq6i97IfsC3laq1l6pKmvrGprnON3A/P+d9iW8pVrXVpa5s+Gqtduparqf26qC1fQ/bdtpSr8Fl0GD6LDkvbmqo/iIiIiOgPHKihRpWfn4+HDx9ixIgRcHFxgZeXF6ZMmSINeGji7e2NhQsXwt3dHePGjYOvry+OHDkCAPjuu++QnZ2NLVu2oEuXLujTp0+NL+/VffTRR+jatStiY2Ph4eGBrl27YtOmTfjxxx+RlZWlMU9WVhZ27tyJTZs2Yfjw4WjXrh369euHUaNG6V2mLn7++WeYm5tDLpfD1dUVFy5cqPURp6ioKOTk5CAxMbHW8uLj4xEcHAwDAwN07twZ7dq1wxdffFFr+rr218KFC/HBBx9gxIgRcHV1xYgRIzBjxgysX78eAPDZZ59BoVAgPj4eTz/9NIYMGYJZs2bVux8WLVqEyMhIjB8/Hu3atcOLL76IJUuWSPUohYaGYvTo0Wjfvj1iY2NRUlKCU6dOAQDWrl0LKysrfP755/D19UWHDh3w+uuvo2PHjrXW29jH3tKlS2FlZSW9nJyc6t0Xj8P+jBuoEh+9Z+QVAoD0rrpNKS4pG3kFZYhLyq61TF3S1Dc21XUigLJKBeKSsrXWpa1t+misdularqb266K2fA3Zd3FJ2Sgoq0RBWaW0ran6g4iIiIj+wIEaalRdunRBv3794OXlhVdffRUbNmzAvXv3tObx9vZWW7a3t8etW7cAABcvXoSTkxPs7Oyk7c8884zW8s6fP48ff/wR5ubm0svDwwMAkJ2djcTERLVtx44dQ3p6OgwMDODv769Xmfrq2LEj0tPTcfr0acyZMwcDBgzA22+/rTHtP/7xD8ycORMLFizAgwcPamwvKCjAV199hbFjx0rrxo4dq/XxJ237q7S0FNnZ2ZgwYYJau2NiYqQ2Z2ZmwtvbGyYmJlKZfn5+9e6H8+fPY/HixWr1vPnmm8jPz8f9+/eldKrHipmZGSwtLaVjJT09Hc899xyMjIx0rrexj725c+eisLBQel2/fl3nWB6nwd4OMBAevXs7WgGA9K66TSkswA2O1nKEBbjVWqYuaeobm+o6AYDcSIawADetdWlrmz4aq126lqup/bqoLV9D9l1YgBus5UawlhtJ25qqP4iIiIjoD4ZPOgD6azEwMMB3332HkydP4vDhw/jwww8xb948pKamwtXVVWOe6l+sBUHQ+nhPXUpKShAUFITly5fX2GZvbw+FQoGePXtK6xwdHfH99983qEx9tWjRAu3btwcALFu2DIMHD8aiRYuwZMkSjenfeecdfPzxxzXmiQEe3d1SXl6u1jZRFKFQKJCVlYUOHTrUyKNtf5mamgIANmzYoFamMp+uZDJZjUffKisr1ZZLSkqwaNEijBgxokZ+1UEgbceKXC7XOSZdytOHsbExjI2N9c7/uKwZ3RVrRnfVedvYZ50x9llnrWXqkkbf2GqLSdf8DdFY7dK1XH3jry1fQ/adpvVN1R9ERERE9AfeUUONThAE9O7dG4sWLcK5c+fQokUL7N69W6+yOnbsiOvXr+O3336T1p0+fVprnm7duuHChQtwcXFB+/bt1V5mZmawsLBQWyeXy+Hl5QWFQoGffvpJrzIbS1RUFFasWIEbNzQ/9mBubo758+fjvffeQ3Fxsdq2+Ph4vPvuu0hPT5de58+fx3PPPYdNmzbVWmdt+8vW1hYODg64cuVKjTYrB908PT2RkZGB8vJyqbzqPyP+j3/8A8XFxWpz5SgnGlbq1q0bLl68WKOe9u3bQybT7TTl7e2NY8eO1RgE0pc+xx4REREREVFDcaCGGlVqaipiY2ORlpaGa9eu4auvvsLt27fh6empV3kvvvgi3NzcMH78eGRkZODEiROIiooCgBq/GqQ0depU3L17F6NHj8bp06eRnZ2NQ4cO4fXXX0dVVZXGPC4uLhg/fjzeeOMN7NmzBzk5OUhKSsLOnTv1LlMffn5+8Pb2RmxsbK1pJk2aBCsrK3z22WfSuvT0dJw9exYTJ05E586d1V6jR4/Gp59+qnFS3br216JFi7B06VKsWbMGWVlZ+Pnnn7F582b85z//AQCMGTMGgiDgzTffxK+//ooDBw5gxYoVanX07NkTpqam+Ne//oXs7Gx89tlnSEhIUEuzYMECbNmyBYsWLcKFCxeQmZmJzz//XNrXupg2bRqKiooQHByMtLQ0XLp0CVu3bsXFixd1LkOVPsceERERERFRQ3GghhqVpaUljh49ikGDBqFDhw6IiorCBx98gIEDB+pVnoGBAfbs2YOSkhL06NEDEydOlH55R/WRGFUODg44ceIEqqqq0L9/f3h5eWH69OmwtrbWendGXFwcXnnlFUyZMgUeHh548803pbtA9C1THzNmzMDGjRtrnd/EyMgIS5YsUbuLJT4+Hp06dZLmzVE1fPhw3Lp1S+NPe9e1vyZOnIiNGzdi8+bN8PLygr+/PxISEqQ7aszNzfHNN9/g559/RteuXTFv3rwaj4fZ2Nhg27ZtOHDgALy8vLB9+/YaP4M9YMAA7Nu3D4cPH0aPHj3w7LPPYuXKlXB21v0Ri1atWuGHH35ASUkJ/P390b17d2zYsKFec9ao0ufYIyIiIiIiaihB1Pa7yUTN0IkTJ9CnTx9cvnwZbm6c0LK5yc3NhaurK86dOwcfH58nHU6jqu+xV1RUBCsrKxQWFsLS0vIxREhERERERM1Rfb4bcDJhavZ2794Nc3NzuLu74/Lly4iIiEDv3r05SENNjsceERERERE9bhyooWavuLgYc+bMwbVr1/DUU08hMDAQH3zwwZMOi/4GeOwREREREdHjxkefiIiaCB99IiIiIiIioH7fDTiZMBERERERERFRM8GBGiIiIiIiIiKiZoIDNUSkt6NHjyIoKAgODg4QBAF79uypkUYURSxYsAD29vaQy+UIDAzEpUuXGlTv8ePH0bt3b7Rq1QpyuRweHh5YuXKlWprQ0NAaPwNeH+fPn8fo0aPh5OQEuVwOT09PrF69ukFxExERERER1YWTCROR3kpLS9GlSxe88cYbGDFihMY077//PtasWYNPP/0Urq6umD9/PgYMGIBff/0VJiYmetVrZmaGadOmwdvbG2ZmZjh+/DgmT54MMzMzTJo0qSFNkpw5cwatW7fGtm3b4OTkhJMnT2LSpEkwMDDAtGnTGqUOIiIiIiKi6jiZMBE1CkEQsHv3bgwbNkxaJ4oiHBwc8O6772LmzJkAgMLCQtja2iIhIQHBwcHYsmULpkyZgnPnzsHd3R0AMGXKFPzwww84e/YsTE1Ndap/xIgRMDMzw9atWwE8uqPGxcVF4101WVlZ6NixIzIzM+Hh4SGtX7lyJT766CNkZ2drrGPq1KnIzMzEDz/8oFNMnEyYiIiIiIiA+n034B01RNRkcnJycPPmTQQGBkrrrKys0LNnTyQnJyM4OBjjxo3Dvn37EBISgpMnT+LQoUPYuHEjkpOTdR6kOXfuHE6ePImYmBid0nfo0AG+vr5ITEzEkiVLpPWJiYkYM2ZMrfkKCwthY2NT6/aKigpUVFRIy0VFRTrF8zhtS7mKuKRs5BWUNXldRjIBlQrt/xcgAGjM/y14uYsD9p6/IS3LjWSwMWuBvIJyrfmMZAIA1Ii3tjbIBGDx0M7Yefo6MvIK6xWjLv2ipOwf1X7ydrTCLzcKoRAfte/BQwUGeztgzeiu8Fl0CAVlD6W8QV0c8IyrDRbtvVBrnaply41kmDe4E+KSLiOvoByO1iY4EdkPL394HBl5hfB2tAIAZOQV1mhH9X1pLTdEQdlDqQx9tJu7Hzp2lUbejlbY+3Yf/QuopveyI3UeS49LY392moOGHCu6Uh7LpD8BQM6ywRq3bUu5iqg9v2jcJhMgfZ6rnz/kRjKUVSqk9+qMZAIeKkQY/v6uPC9ayY0wc0DHGnXmqsSnev5SPR8or4etzFrUekwYyQRcih2klj4swA0ApPNqbecZ1XpH9nCS8o591lljXc1FXec55bkdeHTNfcbVptZ9Xj1fcflDPO1ghTulD6R+XHHoIgrLKqXzmZFMgJmxocb9Cjye8wTVn+rfH6pyazlXPC7bUq5ixaGLAICZAzo2+8+fKg7UEFGTuXnzJgDA1tZWbb2tra20DQDWr18Pb29vhIeH46uvvkJ0dDS6d+9eZ/lt2rTB7du38fDhQ0RHR2PixInStoSEBK15Q0JC8NFHH0kDNVlZWThz5gy2bdumMf3JkyexY8cO7N+/v9Yyly5dikWLFtUZ95P0uAZpgJqDHpo09hfN/Rk31JbLKhU6fbGuLdba1itE/ftS10Ea4I/+Uc2h+oVC+YVmf8YNrBndVe2PJPH39Weu3tNap+qWskrF7+161GfKd2WdqnVXL7N6DcpYGjKw0ZBBGgCN/oW8uQzSAH+9QRrg8fQvB2kaTtuxF5ek+Y5UQP3zXP38oTyXaRqkUU2vmk8EUFBWqbVOQPP5SxlrXkGZ1vO4an3K9Mr6lNtqO6ZU671T+kDK29y/KNb1OVS9ziivMbpQ5lP2i7IfC8oq1dJVKkSt+7U5nYfpD5oGaZqDuKRs6Rj7M3z+VHEyYSJ64lq2bIn4+HjExcXBzc0NkZGROuU7duwY0tLSsG7dOqxatQrbt2/Xuc7g4GDk5uYiJSUFwKO7abp166b2KJTSL7/8gqFDh2LhwoXo379/rWXOnTsXhYWF0uv69es6x/O4hAW4wdFa/ljqUt6lok3dKepnsLeD2rLcSAZH67rnQjKSCRrjra0NMuFRXyrvMKkPXfpFSaj2Djy6S0RZhNxIBgPhj3Zbyw3V8g72dkBYgJvWOlW3yI1kvx8jj/pM+a5sp7ejlfTv6mVWr0EZiy79X5t6dJVG+uwfbRrSlsbW2J+d5uBx9G9jHxN/R9qOPeVdEpqofp6rnz/kRjK19+qMZAIElXdlHNZyI611Aurnr+qxOlrLtR4TqnEq04cFuKmdV2vLr1qvat7mrq7Poep1RnmN0YW13BAGwqP+UO1Ha7mR2jFlJBO07tfmdB6mP6geF82J8hjT5VzR3HCOGiJqFJrmqLly5Qrc3Nxw7tw5+Pj4SOv9/f3h4+Oj9itKUVFRWLZsGdq2bYvz58/DwsKiXvXHxMRg69atuHjxos55+vfvDw8PD6xZswbu7u4ICwvDO++8o5bm119/Rd++fTFx4kS899579YqJc9QQERERERFQv+8GvKOGiJqMq6sr7OzscOTIEWldUVERUlNT4efnJ607efIkli9fjm+++Qbm5uZ6/aqSQqFQmx9GFyEhIdixYweSk5Nx5coVBAcHq22/cOEC+vbti/Hjx9d7kIaIiIiIiEgfzfMeJSL6UygpKcHly5el5ZycHKSnp8PGxgZt27aFIAiYPn06YmJi4O7uLv08t4ODg3TnTXFxMV577TWEh4dj4MCBaNOmDXr06IGgoCC88sorGutdu3Yt2rZtKz2mdPToUaxYsQLh4eH1in/EiBEICwtDWFgY+vbtCweHPx6b+eWXX/DCCy9gwIABeOedd6Q5dQwMDPCPf/yjXvUQERERERHpigM1RKS3tLQ09O3bV1pWPjY0fvx4aTLf2bNno7S0FJMmTUJBQQH69OmDgwcPwsTk0TPGERERMDMzQ2xsLADAy8sLsbGxmDx5Mvz8/ODo6FijXoVCgblz5yInJweGhoZwc3PD8uXLMXny5HrFb2FhgaCgIOzcuRObNm1S2/bll1/i9u3b2LZtm9oEw87OzsjNza1XPURERERERLriHDVERE2Ec9QQERERERHAOWqIiIiIiIiIiP6UOFBDRERERERERNRMcKCGiIiIiIiIiKiZ4EANEREREREREVEzwYEaItLb0aNHERQUBAcHBwiCgD179tRII4oiFixYAHt7e8jlcgQGBuLSpUsNqjcpKQmCINR4KX9CGwBCQ0MRHR3doHrCw8PRvXt3GBsbw8fHp0FlERERERER6YIDNUSkt9LSUnTp0gVr166tNc3777+PNWvWYN26dUhNTYWZmRkGDBiA8vLyBtd/8eJF5OfnS6/WrVs3uMzq3njjDYwaNarRyyUiIiIiItKEAzVEpLeBAwciJiYGw4cP17hdFEWsWrUKUVFRGDp0KLy9vbFlyxbcuHFDuvtmy5YtMDc3V7vLZsqUKfDw8MD9+/e11t+6dWvY2dlJL5lMt1NaVlYWBEHAf//7X7X1K1euhJubm7S8Zs0aTJ06Fe3atdOpXCIiIiIiooYyfNIBENFfV05ODm7evInAwEBpnZWVFXr27Ink5GQEBwdj3Lhx2LdvH0JCQnDy5EkcOnQIGzduRHJyMkxNTbWW7+Pjg4qKCnTu3BnR0dHo3bu3TnF16NABvr6+SExMxJIlS6T1iYmJGDNmjH6NBVBRUYGKigppuaioSO+ymsLLHx5HRl5hvfJ4O1ph79t90HvZEeQVPLoLytHaRPp3bYxkAioVIoxkAi7FDsK2lKuIS8pGfmEZFKKG2Lo4YO/5G1rLdLQ2wa2iClQqRHg7WgGA1B65kQxllQqt+QUAosq/ZQIgEx7FqVrHjYJyCAIgioCV3BAFZQ+l7dZyQxSXP4SFifp6VTHDOmPss85Sfyv7UHV5ZA8nrDh0ERUPq6R8xoYGmDmgI8Y+64zw7efwzfkbEARI/aUav7LNL3ayq9Fv1nJDVDxU1OgPR2sThAW0R1xSNsIC3DD2WWcAwLaUq3hv/69SermRDO6tLXDhRqHUzup1q5ZZWlGFiodVeFglSvscAB4qRAR1ccCa0V019pMufBYdqrWflfW3MjNWOw7KKxUwlAkwMzaU+lNXLpH7dUpnLTdE+sIB0nL49nPYn3EDg70ftVd5vKv2c12ql/F3o2vf1yV32eBGKYc0q20/xQzrjLiky8grKIejtQlORPbTmE55bjMxkmHe4E4AoPNnRfVzVZ98tcWh6ZpjLTdCacVD6brwcgPPYbXR1I/Ka0dzoHo+OnP1rrRfwwLaY8HXv9S4jst/358rDl0EAJgZG0h/JxjJBBgaCGrXJJkAGGi4/iqvJ6rXQ2o+lH8vlFcq1K7vjXX+bijl31uqVGPTdm5qjnhHDRE1GeWcMba2tmrrbW1t1eaTWb9+PfLz8xEeHo4JEyYgOjoa3bt3r7Vce3t7rFu3Drt27cKuXbvg5OSEgIAAnD17VkqTkJCgdY6akJAQbN++XVrOysrCmTNnEBISUt9mSpYuXQorKyvp5eTkpHdZTaG+gzSqeVQHZuoapAEg/fGlfI9LykZegeZBGgDYn6F9kEZZr7K8jLxCtfbUNUgDqA80iACqRKj9kaisQ8SjwRERqDFIUFD2EFVizfWq4pKypRhre49LykZBWSXKKhXSq6CsUsq7P+OGFIem+JVt1tRvBWUPNfZHXkG5tB+U9SjjVU1fVqlARl6hWjtr2W3IKyiX2qG6zysVIkTotl+10dbPyvqrHwfi7zGo9mdjqx7X/owbqBL/aK+mfq5L9TKI/kweHfOPrg3arhHKc1tZpQJxSdn1+qyoptXnM1Y9Dk0KyirVrguP8/PYVOcrfaiej1T3a1xStsbruHJ/FpRVoqCsUu0YqFSINa5Jilquv8rrSVOev0l/yr8XGuP63hTq+jtXl79fmxMO1BDRE9eyZUvEx8cjLi4Obm5uiIyM1Jq+Y8eOmDx5Mrp3745evXph06ZN6NWrF1auXKlzncHBwcjNzUVKSgqAR3fTdOvWDR4eHnq3Y+7cuSgsLJRe169f17uspqC8C0WfPI7WJtI61X/XRnlXhfI9LMANjtZy/L5Yw2BvhzrLdLQ2kcrzdrRSa4/cqO7LmVDt3wbCH/Gp1qG820bAozsnVFnLDWEg1FyvSvm/vcr4NL2HBbjBWm4EuZFMelnLjaS8g70dpDg0xa9ss6Z+s5YbauyPR/8b+mg/KOtRxquaXm4kg7ejlVo7a9ltcLQ2kdqhus+NZAIE6LZftdHWz8r6qx8Hwu8xqPZnY6se12BvBxgIf7RXUz/XpXoZRH8mj475R9cGbdcI5blNbiRDWIBbvT4rqmn1+YxVj0MTa7mR2nXhcX4em+p8pQ/V85Hqfg0LcNN4HVfuT2u5EazlRmrHgJFMqHFNktVy/VVeT5ry/E36U/690BjX96ZQ19+5uvz92pwIoijW9h9lREQ6EwQBu3fvxrBhw6R1V65cgZubG86dO6f2q0n+/v7w8fHB6tWrpXVRUVFYtmwZ2rZti/Pnz8PCwqJe9c+aNQvHjx9HcnKyznn69+8PDw8PrFmzBu7u7ggLC8M777xTI110dDT27NmD9PT0esVUVFQEKysrFBYWwtLSsl55iYiIiIjor6M+3w14Rw0RNRlXV1fY2dnhyJEj0rqioiKkpqbCz89PWnfy5EksX74c33zzDczNzTFt2rR615Weng57e/t65QkJCcGOHTuQnJyMK1euIDg4uN71EhERERERNSZOJkxEeispKcHly5el5ZycHKSnp8PGxgZt27aFIAiYPn06YmJi4O7uDldXV8yfPx8ODg7SnTfFxcV47bXXEB4ejoEDB6JNmzbo0aMHgoKC8Morr2isd9WqVXB1dcXTTz+N8vJybNy4ET/88AMOHz5cr/hHjBiBsLAwhIWFoW/fvnBwUL+N8/LlyygpKcHNmzdRVlYm3VHTqVMntGjRol51ERERERER6YIDNUSkt7S0NPTt21daVj42NH78eCQkJAAAZs+ejdLSUkyaNAkFBQXo06cPDh48CBOTR8+JRkREwMzMDLGxsQAALy8vxMbGYvLkyfDz84Ojo2ONeh88eIB3330XeXl5MDU1hbe3N77//nu1WHRhYWGBoKAg7Ny5E5s2baqxfeLEifjpp5+k5a5dH81un5OTAxcXl3rVRUREREREpAvOUUNE1EQ4Rw0REREREQGco4aIiIiIiIiI6E+JAzVERERERERERM0EB2qIiIiIiIiIiJoJDtQQERERERERETUTHKghIiIiIiIiImomOFBDRHo7evQogoKC4ODgAEEQsGfPnhppRFHEggULYG9vD7lcjsDAQFy6dKlB9R4/fhy9e/dGq1atIJfL4eHhgZUrV6qlCQ0NRXR0tN513LlzBy+99BIcHBxgbGwMJycnTJs2DUVFRQ2KnYiIiIiISBsO1BCR3kpLS9GlSxesXbu21jTvv/8+1qxZg3Xr1iE1NRVmZmYYMGAAysvL9a7XzMwM06ZNw9GjR5GZmYmoqChERUXhk08+0bvM6mQyGYYOHYq9e/ciKysLCQkJ+P777/HWW281Wh1ERERERETVcaCGiPQ2cOBAxMTEYPjw4Rq3i6KIVatWISoqCkOHDoW3tze2bNmCGzduSHffbNmyBebm5mp32UyZMgUeHh64f/++xnK7du2K0aNH4+mnn4aLiwvGjh2LAQMG4NixYzrFnZWVBUEQ8N///ldt/cqVK+Hm5gYAaNmyJcLCwuDr6wtnZ2f069cPU6ZM0bkOIiIiIiIifRg+6QCI6K8rJycHN2/eRGBgoLTOysoKPXv2RHJyMoKDgzFu3Djs27cPISEhOHnyJA4dOoSNGzciOTkZpqamOtVz7tw5nDx5EjExMTql79ChA3x9fZGYmIglS5ZI6xMTEzFmzBiNeW7cuIGvvvoK/v7+tZZbUVGBiooKabm5PSblErlf77xyIxnKKhWNEkfMsM6Yv+cXiI1SGiAAjVaWNnX1gaO1CW4VVaBSoR6NtdwQBWUPNeYxkgmoEkUo6miAo7UJ8gr0vwtNSVsbjGRCjdh18XIXB+w9f6POOgQAOcsG17t818j90v6VCcCVpZrL2JZyFXFJ2QgLcMPYZ50bdLy/3MUBa0Z3BaD+uaneR6rpVPVedkTaX47WJujubIP9GTcw2Ltmes/536KsUgG5kQyZSwbCZ9EhteNFuZ7oSVM9ro1kAi7FDmrQ56w58Ha0gstTZtI5zNvRCndKH0jnkXZz99d5ftaXo7UJTkT2AwC8/OFxZOQVwlpuiOLyhxjs7YAzV+8ir6BcShe+/Rz2Z9yACEgxvdzFAWeu3pPirX4eVD2/vNjJDvszbuBphz/aCADv7f8V5ZUKODTwOiMTAHsruVS3sk1KciMZ5g3upFan1e/XR0drE9wtfaDx2qE81v7KntTnSNs1tT6U+9rb0UptnwOPPlN73+7T4Dr+jnhHDRE1mZs3bwIAbG1t1dbb2tpK2wBg/fr1yM/PR3h4OCZMmIDo6Gh07969zvLbtGkDY2Nj+Pr6YurUqZg4caK0LSEhQescNSEhIdi+fbu0nJWVhTNnziAkJEQt3ejRo2FqagpHR0dYWlpi48aNtZa5dOlSWFlZSS8nJ6c62/Bn0ViDNAAQl5TdqAMrj2OQBqi7D/IKyjUOdNQ2SAMAlYq6B2mUZTcGbW3QZ5AGAPZn3FBbrq0OffeTaj5tIcYlZSOvoAxxSdl61vSH6m1Sqt5HtaVT3V95BeXYn3EDVaLm9Mr+Ur5XP14a87NH1BCqx7W+54vmJiOvUO1zmZFXqHYeacpmqvan8sttQdlD6Vyh3K58V55HVGN6lO6PeKufB1XPL8r8qm2MS8pGWaUCIhp+nVGIUKu7+hf2skpFjTqV57u8gvJaz3V/lWOtOWqsrlXu6+r7vLZ1pBsO1BDRE9eyZUvEx8cjLi4Obm5uiIyM1CnfsWPHkJaWhnXr1mHVqlVqAy91CQ4ORm5uLlJSUgA8upumW7du8PDwUEu3cuVKnD17Fl9//TWys7Pxzjvv1Frm3LlzUVhYKL2uX7+uczzNndyo8S4XYQFuEBqtNDRqWdrU1QeO1iYwktWMxlpe+82rRjIBGrJoLLsxaGuDpth1MdjbQac69N1Pqvm0hRgW4AZHa7n0v8QNUb1NStX7qLZ0qvvL0doEg70dYCBoTq/sL+V79eOlMT97RA2helzre75obrwdrdQ+l96OVmrnkaZspmp/ejtaAXj0+VeeK5Tble/K84hqTI/S/RFv9fOg6vlFmV+1jWEBbpAbySCg4dcZmQC1upVtUpIbyWrUqTzfOVqb1Hqu+6sca81RY3Wtcl9X3+e1rSPdCKIocpiSiBpMEATs3r0bw4YNk9ZduXIFbm5uOHfuHHx8fKT1/v7+8PHxwerVq6V1UVFRWLZsGdq2bYvz58/DwsKiXvXHxMRg69atuHjxos55+vfvDw8PD6xZswbu7u4ICwvTOhBz/PhxPPfcc7hx4wbs7e3rLL+oqAhWVlYoLCyEpaWlznEREREREdFfS32+G/C/aYioybi6usLOzg5HjhyR1hUVFSE1NRV+fn7SupMnT2L58uX45ptvYG5ujmnTptW7LoVCoTY/jC5CQkKwY8cOJCcn48qVKwgODq6zDgD1roeIiIiIiEhXnEyYiPRWUlKCy5cvS8s5OTlIT0+HjY0N2rZtC0EQMH36dMTExMDd3R2urq6YP38+HBwcpDtviouL8dprryE8PBwDBw5EmzZt0KNHDwQFBeGVV17RWO/atWvRtm1b6TGlo0ePYsWKFQgPD69X/CNGjEBYWBjCwsLQt29fODj8cfvzgQMH8Ntvv6FHjx4wNzfHhQsXMGvWLPTu3RsuLi716ygiIiIiIiIdcaCGiPSWlpaGvn37SsvKx4bGjx+PhIQEAMDs2bNRWlqKSZMmoaCgAH369MHBgwdhYvLoWeiIiAiYmZkhNjYWAODl5YXY2FhMnjwZfn5+cHR0rFGvQqHA3LlzkZOTA0NDQ7i5uWH58uWYPHlyveK3sLBAUFAQdu7ciU2bNqltk8vl2LBhA2bMmIGKigo4OTlhxIgROs+fQ0REREREpA/OUUNE1EQ4Rw0REREREQGco4aIiIiIiIiI6E+JAzVERERERERERM0EB2qIiIiIiIiIiJoJDtQQERERERERETUTHKihZichIQHW1tZPOgy9REdHw8fH50mH0awlJSVBEAQUFBQ86VCIiIiIiIiaHQ7UkCQ0NBSCIEivVq1a4aWXXkJGRoZaOtU0ZmZmcHd3R2hoKM6cOVNnHS4uLlJeU1NTeHl5YePGjU3VpD8V5QCGtldSUtITja05D64kJCRI/SSTyWBvb49Ro0bh2rVraukCAgIgCAI+//xztfWrVq2Ci4tLjXLLyspgY2ODp556ChUVFU3ZBCIiIiIiIg7UkLqXXnoJ+fn5yM/Px5EjR2BoaIghQ4bUSLd582bk5+fjwoULWLt2LUpKStCzZ09s2bKlzjoWL16M/Px8/PLLLxg7dizefPNNfPvtt03RnD+VXr16SX2fn5+PkSNHqu2P/Px89OrVq15lVlZWNlG0zZOlpSXy8/ORl5eHXbt24eLFi3j11VdrpDMxMUFUVJRO/bNr1y48/fTT8PDwwJ49e5ogaiIiIiIioj9woIbUGBsbw87ODnZ2dvDx8UFkZCSuX7+O27dvq6WztraGnZ0dXFxc0L9/f3z55ZcICQnBtGnTcO/ePa11WFhYwM7ODu3atcOcOXNgY2OD7777rka6Q4cOwdPTE+bm5tKAhdLp06fx4osv4qmnnoKVlRX8/f1x9uxZtfyCICAuLg4DBw6EXC5Hu3bt8OWXX0rbHzx4gGnTpsHe3h4mJiZwdnbG0qVLpe3Xrl3D0KFDYW5uDktLS4wcORK//fabWh3Lli2Dra0tLCwsMGHCBJSXl0vbjh49CiMjI9y8eVMtz/Tp0/Hcc8/VaG+LFi2kvrezs4NcLlfbH+np6XjhhRdgbW2NVq1aYciQIcjOzpby5+bmQhAE7NixA/7+/jAxMUFiYiIePnyI8PBwKd+cOXMwfvx4DBs2TMqrUCiwdOlSuLq6Qi6Xo0uXLlJf5ebmom/fvgCAli1bQhAEhIaG1plP6cCBA+jQoQPkcjn69u2L3NzcGm2vThAErF+/HkOGDIGpqSk8PT2RnJyMy5cvIyAgAGZmZujVq5da+5X57OzsYG9vj169emHChAk4deoUioqK1NKNHj0aBQUF2LBhQ52xxMfHY+zYsRg7dizi4+O1pq2oqEBRUZHaq7nwWXQILpH74bPokNr6lz88DpfI/Xj5w+ONVpcuZXrO/xYukfvhOb9+g7S1lb0t5Sp6L/sB21Ku6hWzNvUpu/eyI3CJ3I/ey47U2Ba+/Rzc5u5H+PZz9aq/rv5srLbrU05THD9EREREf3ccqKFalZSUYNu2bWjfvj1atWpVZ/oZM2aguLhY46CLJgqFArt27cK9e/fQokULtW3379/HihUrsHXrVhw9ehTXrl3DzJkzpe3FxcUYP348jh8/jpSUFLi7u2PQoEEoLi5WK2f+/Pn45z//ifPnzyMkJATBwcHIzMwEAKxZswZ79+7Fzp07cfHiRSQmJkqPvigUCgwdOhR3797FTz/9hO+++w5XrlzBqFGjpLJ37tyJ6OhoxMbGIi0tDfb29vj444+l7c8//zzatWuHrVu3SusqKyuRmJiIN954Q6c+UlVaWop33nkHaWlpOHLkCGQyGYYPHw6FQqGWLjIyEhEREcjMzMSAAQOwfPlyJCYmYvPmzThx4gSKiopq3BmydOlSbNmyBevWrcOFCxcwY8YMjB07Fj/99BOcnJywa9cuAMDFixeRn5+P1atX15kPAK5fv44RI0YgKCgI6enpmDhxIiIjI3Vq75IlSzBu3Dikp6fDw8MDY8aMweTJkzF37lykpaVBFEVMmzat1vy3bt3C7t27YWBgAAMDA7VtlpaWmDdvHhYvXozS0tJay8jOzkZycjJGjhyJkSNH4tixY7h6tfYvsUuXLoWVlZX0cnJy0qmtj0NB2UO1d6WMvEK198agS5lllQq194aWHZeUjbyCMsQlZWvK1iD1KTuvoFztXdX+jBuoEh+910dd/dlYbdennKY4foiIiIj+9kSi340fP140MDAQzczMRDMzMxGAaG9vL545c0YtHQBx9+7dNfKXlZWJAMTly5fXWoezs7PYokUL0czMTDQ0NBQBiDY2NuKlS5ekNJs3bxYBiJcvX5bWrV27VrS1ta213KqqKtHCwkL85ptv1OJ866231NL17NlTDAsLE0VRFN9++23xhRdeEBUKRY3yDh8+LBoYGIjXrl2T1l24cEEEIJ46dUoURVH08/MTp0yZUqP8Ll26SMvLly8XPT09peVdu3aJ5ubmYklJSa1tURo/frw4dOjQWrffvn1bBCD+/PPPoiiKYk5OjghAXLVqlVo6W1tb8d///re0/PDhQ7Ft27ZS2eXl5aKpqal48uRJtXwTJkwQR48eLYqiKP74448iAPHevXvSdl3yzZ07V+zUqZPa9jlz5tQoqzoAYlRUlLScnJwsAhDj4+Olddu3bxdNTEykZeVxY2ZmJpqamooARABieHi4Wtn+/v5iRESEWF5eLjo7O4uLFy8WRVEUV65cKTo7O6ul/de//iUOGzZMWh46dKi4cOHCWuMuLy8XCwsLpdf169dFAGJhYWGteR6XLtEHRec5+8Qu0QfV1getOSY6z9knBq051mh16VKmR9QB0XnOPtEj6kCjlL01OVfstfSIuDU5V6+YtalP2b2Wfi86z9kn9lr6fY1tb392VmwXuU98+7Oz9aq/rv5srLbrU05THD9EREREf0WFhYU6fzcwfNwDQ9S89e3bF3FxcQCAe/fu4eOPP8bAgQNx6tQpODs7a80riiKAR4+faDNr1iyEhoYiPz8fs2bNwpQpU9C+fXu1NKampnBzc5OW7e3tcevWLWn5t99+Q1RUFJKSknDr1i1UVVXh/v37NSaO9fPzq7Gcnp4O4NHkyS+++CI6duyIl156CUOGDEH//v0BAJmZmXByclK7I6JTp06wtrZGZmYmevTogczMTLz11ls1yv/xxx+l5dDQUERFRSElJQXPPvssEhISMHLkSJiZmWntI00uXbqEBQsWIDU1Ff/73/+kO2muXbuGzp07S+l8fX2lfxcWFuK3337DM888I60zMDBA9+7dpfyXL1/G/fv38eKLL6rV9+DBA3Tt2rXWeHTJl5mZiZ49e6ptr75PauPt7S3929bWFgDg5eWltq68vBxFRUWwtLQE8OixurNnz6KyshLffvstEhMT8d5772ks39jYGIsXL8bbb7+NsLCwGturqqrw6aefSncPAcDYsWMxc+ZMLFiwADJZzRsSjY2NYWxsrFP7Hrf0hQM0rt/7dp9Gr0uXMjOXDGzUssc+64yxz2o/R+mrPmWfiOxX67Y1o7tizejaP1O1qas/G6vt+pTTFMcPERER0d8dB2pIjZmZmdqgycaNG2FlZYUNGzYgJiZGa17lI0Wurq5a0z311FNo37492rdvjy+++AJeXl7w9fVFp06dpDRGRkZqeQRBkAaCAGD8+PG4c+cOVq9eDWdnZxgbG8PPzw8PHjzQua3dunVDTk4Ovv32W3z//fcYOXIkAgMDa8yx0hCtW7dGUFAQNm/eDFdXV3z77bd6/3JTUFAQnJ2dsWHDBjg4OEChUKBz58412lzfQaCSkhIAwP79++Ho6Ki2Tdugg775dKV6DCgH/zStU330SyaTScevp6cnsrOzERYWpvb4maqxY8dixYoViImJqfGLT4cOHUJeXp7a427AowGcI0eO1BigIiIiIiIiagyco4a0Uv7UcVlZWZ1pV61aBUtLSwQGBupcvpOTE0aNGoW5c+fWK64TJ04gPDwcgwYNwtNPPw1jY2P873//q5EuJSWlxrKnp6e0bGlpiVGjRmHDhg3YsWMHdu3ahbt378LT0xPXr1/H9evXpbS//vorCgoKpAElT09PpKamaq0PACZOnIgdO3bgk08+gZubG3r37l2vtgLAnTt3cPHiRURFRaFfv37w9PSsc9JmALCysoKtrS1Onz4trauqqlKbeLlTp04wNjbGtWvXpAE05Ut5R5FyDqGqqqp65fP09MSpU6fUYtLUR00lMjISO3bsqDHRtJJMJsPSpUsRFxdXY5Lj+Ph4BAcHIz09Xe0VHBxc56TCRERERERE+uIdNaSmoqJC+pWie/fu4aOPPkJJSQmCgoLU0hUUFODmzZuoqKhAVlYW1q9fjz179mDLli2wtrauV50RERHo3Lkz0tLS1B7b0cbd3R1bt26Fr68vioqKMGvWLMjl8hrpvvjiC/j6+qJPnz5ITEzEqVOnpC/Z//nPf2Bvb4+uXbtCJpPhiy++gJ2dHaytrREYGAgvLy+EhIRg1apVePjwIaZMmQJ/f38pxoiICISGhsLX1xe9e/dGYmIiLly4gHbt2qnFMGDAAFhaWiImJgaLFy+uV98otWzZEq1atcInn3wCe3t7XLt2TedJed9++20sXboU7du3h4eHBz788EPcu3dPuiPFwsICM2fOxIwZM6BQKNCnTx8UFhbixIkTsLS0xPjx4+Hs7AxBELBv3z4MGjQIcrlcp3xvvfUWPvjgA8yaNQsTJ07EmTNnkJCQoFcf6MPJyQnDhw/HggULsG/fPo1pBg8ejJ49e2L9+vXSI1a3b9/GN998g71796o9VgYA48aNw/Dhw3H37l3Y2Ng0eRuIiIiIiOjvhXfUkJqDBw/C3t4e9vb26NmzJ06fPo0vvvgCAQEBaulef/112Nvbw8PDA2FhYTA3N8epU6cwZsyYetfZqVMn9O/fHwsWLNA5T3x8PO7du4du3brhtddeQ3h4OFq3bl0j3aJFi/D555/D29sbW7Zswfbt26U7YiwsLPD+++/D19cXPXr0QG5uLg4cOACZTAZBEPD111+jZcuWeP755xEYGIh27dphx44dUtmjRo3C/PnzMXv2bHTv3h1Xr17VONeJTCZDaGgoqqqqMG7cuHr3j7KMzz//HGfOnEHnzp0xY8YM/Pvf/9Yp75w5czB69GiMGzcOfn5+MDc3x4ABA2BiYiKlWbJkCebPn4+lS5fC09MTL730Evbv3y89xubo6IhFixYhMjIStra20q8t1ZWvbdu22LVrF/bs2YMuXbpg3bp1iI2N1asP9DVjxgzs37+/xp09qpYvX6720+pbtmyBmZkZ+vWrOd9Iv379IJfLsW3btiaJl4iIiIiI/t4EUXXiD6K/EEEQsHv3bgwbNuxJh4IJEybg9u3b2Lt375MOBQqFAp6enhg5ciSWLFnypMP5SysqKoKVlRUKCwulCY+JiIiIiOjvpz7fDfjoE1ETKiwsxM8//4zPPvvsiQ3SXL16FYcPH4a/vz8qKirw0UcfIScnR6+7n4iIiIiIiKhpcaCGqAkNHToUp06dwltvvfXEfiVIJpMhISEBM2fOhCiK6Ny5M77//nu1SZWJiIiIiIioeeCjT0RETYSPPhEREREREVC/7wacTJiIiIiIiIiIqJngQA0RERERERERUTPBOWqIiJqI8snSoqKiJxwJERERERE9ScrvBLrMPsOBGiKiJlJcXAwAcHJyesKREBERERFRc1BcXAwrKyutaTiZMBFRE1EoFLhx4wYsLCwgCMKTDoeIiIiIiJ4QURRRXFwMBwcHyGTaZ6HhQA0RERERERERUTPByYSJiIiIiIiIiJoJDtQQERERERERETUTHKghIiIiIiIiImomOFBDRERERERERNRMcKCGiIiIiP72AgICMH369CcdhlYJCQmwtrbWmiY6Oho+Pj6PJR4iImoaHKghIiIiIvoTGDVqFLKysp50GERE1MQMn3QAREREREQEVFZWwsjIqNbtcrkccrn8MUZERERPAu+oISIiIqK/ldLSUowbNw7m5uawt7fHBx98oLb93r17GDduHFq2bAlTU1MMHDgQly5dkrYrH0E6dOgQPD09YW5ujpdeegn5+flSGoVCgcWLF6NNmzYwNjaGj48PDh48KG3Pzc2FIAjYsWMH/P39YWJigsTERK1xa3r0admyZbC1tYWFhQUmTJiA8vLyBvQMERE1BxyoISIiIqK/lVmzZuGnn37C119/jcOHDyMpKQlnz56VtoeGhiItLQ179+5FcnIyRFHEoEGDUFlZKaW5f/8+VqxYga1bt+Lo0aO4du0aZs6cKW1fvXo1PvjgA6xYsQIZGRkYMGAAXn75ZbUBHwCIjIxEREQEMjMzMWDAgHq1Y+fOnYiOjkZsbCzS0tJgb2+Pjz/+WM9eISKi5kIQRVF80kEQERERET0OJSUlaNWqFbZt24ZXX30VAHD37l20adMGkyZNwtSpU9GhQwecOHECvXr1AgDcuXMHTk5O+PTTT/Hqq68iISEBr7/+Oi5fvgw3NzcAwMcff4zFixfj5s2bAABHR0dMnToV//rXv6S6n3nmGfTo0QNr165Fbm4uXF1dsWrVKkREROgUe0JCAqZPn46CggIAQK9evdC1a1esXbtWSvPss8+ivLwc6enpDe0qIiJ6QnhHDRERERH9bWRnZ+PBgwfo2bOntM7GxgYdO3YEAGRmZsLQ0FBte6tWrdCxY0dkZmZK60xNTaVBGgCwt7fHrVu3AABFRUW4ceMGevfurVZ379691coAAF9fX73bkpmZqRYnAPj5+eldHhERNQ8cqCEiIiIiqqfqk/4KggB9blQ3MzNrrJCIiOgvggM1RERERPS34ebmBiMjI6Smpkrr7t27J/3staenJx4+fKi2/c6dO7h48SI6deqkUx2WlpZwcHDAiRMn1NafOHFC5zJ04enpqRYnAKSkpDRa+URE9GTw57mJiIiI6G/D3NwcEyZMwKxZs9CqVSu0bt0a8+bNg0z26P8v3d3dMXToULz55ptYv349LCwsEBkZCUdHRwwdOlTnembNmoWFCxfCzc0NPj4+2Lx5M9LT0+v8Zaf6iIiIQGhoKHx9fdG7d28kJibiwoULaNeuXaPVQUREjx8HaoiIiIjob+Xf//43SkpKEBQUBAsLC7z77rsoLCyUtm/evBkREREYMmQIHjx4gOeffx4HDhyo8biTNuHh4SgsLMS7776LW7duoVOnTti7dy/c3d0brR2jRo1CdnY2Zs+ejfLycvzzn/9EWFgYDh061Gh1EBHR48dffSIiIiIiIiIiaiY4Rw0RERERERERUTPBgRoiIiIiomZg4MCBMDc31/iKjY190uEREdFjwkefiIiIiIiagby8PJSVlWncZmNjAxsbm8ccERERPQkcqCEiIiIiIiIiaib46BMRERERERERUTPBgRoiIiIiIiIiomaCAzVERERERERERM0EB2qIiIiIiIiIiJoJDtQQERERERERETUTHKghIiIiIiIiImomOFBDRERERERERNRM/D8gj2ama7JiKQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"donor_id\",\n",
+    "    y=\"assay\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 4),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.groupby(\"assay\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(10, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `development_stage`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       10000\n",
+       "unique        152\n",
+       "top       unknown\n",
+       "freq          545\n",
+       "Name: development_stage, dtype: object"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"development_stage\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "development_stage\n",
+       "unknown                                     545\n",
+       "15th week post-fertilization human stage    542\n",
+       "50-year-old human stage                     438\n",
+       "42-year-old human stage                     340\n",
+       "human adult stage                           324\n",
+       "                                           ... \n",
+       "31st week post-fertilization human stage      1\n",
+       "91-year-old human stage                       1\n",
+       "immature stage                                1\n",
+       "95-year-old human stage                       1\n",
+       "1-month-old human stage                       1\n",
+       "Name: count, Length: 152, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "development_stage_freq = adata.obs[\"development_stage\"].value_counts()\n",
+    "development_stage_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "development_stage_freq[development_stage_freq > 100].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"development_stage frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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btvcjlx9FLj8q5DznT4o7moEJ0hcZbZTTc2AKOp+XSiWXH7Md3eCdWQH3uWrCHGZCzQlJg+aw6m5Cn1QKbK6yoR3MjdCzHgLUp3p55vNXYn93h81MDKAAE1FS9RsXJ4m9zp2zU1m6QiW7lR8Hog7UKxa3IZtJI5tJCznP+WFK+3lMqJdMwAFTNVdAdTnnq5LN1NmObvDOrID7XDVhDjOhZo2kXCKsupvQJ5UCm6tsaJuz9fSshwD1KVFpkHN+DFCyLYIgCIIgCIIoQs75BkNSO8I0yHafICoDepbjg/q+eqGxjw7auMQA2UISpkG2+wRRGdCzHB/U99ULjX100MaFCAWSPsSPTDQZ0gISRGVAz3J8UN9XLzT20VH1Pi5hJQ1auWkPevqG0NHehHXL5tt+a+08EdqTTXTrvU1PZLSoexsGcyNoztZjZ+dlLuc4+/GE3Tavfo+KWat7MFEoOhT/6Y6OSO/NxsZKbQro96mHCf1mAnE/eyLPlolY17T93cHnPHuGrOgol6F7nFWfn3ldvcjlx5DN1OGxtUsC1SFom9wS61Urca7jScW6DrCAANX8PiHkIB8XQcJS74mGIDQ1DKkXIqFz3aQPYbfNhNCPqiFcdeA0JiLRZEzoNxOI+9kzOSx1lIT97OgeZ9XnRzSMtQhB2+SWWK9aiXMdrwTofUKERdVvXMJS74mGIDQ1DKkXIqFz3fx4wm6bCaEfVUO46oAfk5oUhCReJvSbCcT97JkcljpKwn52nMY5SKI+1edHNIy1CEHnrltivWolznW8EqD3CREWVW8qRhAEQRBtq3swXhAzrSQIgiD0QqZiMSPjnB5E0rd0/Q60dvZg6fodKtWMHKe2Bmn/ou5taO3swaLubTqrWXW4jYEJQRa8xlilfia0STeiz8GcNVvR2tmDOWu2RlSzYKiOld91Tr9Xo9ZRtn/DeHY27hrAvK4HMa/rwYp6Jolg73aCcIM2LiEhY28cxL+gb3DYdjQdp7YGaT/5BOjBbQzi9vkAvMdYpX4mtEk3os9BfnTCdjQd1bHyu87p93XL5qP/jo6qciaW7d8wnp0N2/uRy48ilx+tqGeSIN9JIhxo4xISMvbGXpI+PwmpjE18mJJm0bKd2qoi6WSaJmaPHdQnQLfmKogmKA6NgNsYxO3zAXj7AajUj7+mEjQwoutAJl1jO5qO6vzzu87p90qYB7LI9m8Y68GKxW3IZtLIZtLG+nUSaoSlxdT1rFbjM18JkI+L4fiFGXULO+yEzLmyhFm2E7rDr5pUXtR9aTph9wf1NwHQPCCIpKDrWaVn3izIx0UQ03fufhLSBS1TUZsqHv0IV3pe4I7yyPRhe3MjgGLEl9bOHszr6lW+r7U8dgxKEMl2HFoONw2RCc/H9IZJtqMMIjbWJmiVGGH5dFjPm7NmK2Z2mmF37lfvKH1yTJoHUUE+CEQS0fWsVuMzXwlUvcYl6Tt3UyQGOjQWKm3RrSnRRdIiFLn1ownPR5C+TNo4qPaT6HXsPMCMPvGrt6nPd6WQtOeDIIjKhTQugoS1cw9LksVLKE2RGOjIP6HSFlNzDyQtQpGbhshrTGS0A0HmqUpfsudvRmN9osYhLJ8O63mZdA1SMKNP/OqdNJ+cpJG0dYogkgz51Oih6jUuYRGWJMsUDQtPXPUiiaEeVPrR1LkI0LwgCIIgzMLkd6YJkMZFEKdoUjp2xTMa621HXfASSpFoWFHs8oNI1INop+Y2NdqOppC0/DJu89Vr7kSl7VOJ+GbqvIgbNi/bVveUxtRtjE2QDpr2HJnQJ25QTiOCMBtTLGSSTtVvXJzyoLBY9bf3PGn7oJZZ5A8Mj9iOuli+sAU7Oy/F8oUtrvVnsA3B7T1PhJ63gq+XDCzW++a9Q9IbmGdeetV2jAKRD+m48stY52hrZ0/pPz/c6uuVtyHImHvBP2cquYr+fOg129GrfJMI2zmfje94AejavA+A+xibkO/G6zkSabPusQ6rT3TUM4qcRnw92Ttm6fodWNT9S6zctMfYZ4sg4sb6zjT5PWQ6Vb9xcYLtikdGx23Jk2QW+ahsh72iYbENwcjohNG7fNZXAKSTVY2MjtuOUSDyIc3aw45RofvDKg4JUdgfzCZ8kLsRVsJFhtUHbXSiaCXsNsYmSAeZ65qTC5tIm5PyPOiop46cRn7w9WTvmL7BYQzm8ujpGzL22SIIkzD5PWQ6yhuXXC6Hb3zjG1i9ejUOHToEAHj00UcxODiorXJRsPSC4kfz0gtObDDYrviqC+ybD5lF/sKZ0zCjMYMLZ04LVD+/Xfnmmy7G/u4ObL7p4rLf2IbgqguaQpGM62L3wCGMF4oOuLKbPTZGV10QnXOpSOjkuJxeVT+snJ4DIDytihd8G7z62+35WLVkNpqzGaxaMtu3fJMI2zl/Z+dlZf3pNsbLF7ZgxeI2bNjeH5tU8Narz0dzNoNbrz6/7DeRNuse67CeBx31VKmb7DV8Pdk6197ciOZsBh3tTcY+W0Tl4GRN4PetxH43RSto8nvIdJSc8/v6+vC3f/u3aGxsxP79+/HUU09h1qxZ+OxnP4s///nP+Pa3v+15fTU455sQRjYpVGLI02oYNxOgfg4X6l+CIEzD6ZvBb61iv9emipYdtKaZR6jO+Z/85CfxgQ98AM888wzq60+YHlx55ZV4+OGHVYpMFCJJ0aJMkBTEVjJsO0uv8tlv2UwdgGChlE2DjduClqlGSHcqFZJahQv1LxEnvJTcFGk5YR5+axX7nbSCyUdJ49LY2IhHH30UbW1tmDJlCvbu3YtZs2ZhYGAAs2fPxsiIt0Ny0jUupmkIgkhFw5aoepVfDdLcamgjQRBEGPBScpKWE0TlEqrGZfLkyXjllVfK/v7000/j1FNPVSkyUZiWFC2IVDRsiapX+UmS5qpqppLURoIgCJPgpeQkLScIQknjcuONN+LgwYO47777MG3aNPT19aG2thbXXHMNLrnkEtx9992e1ydd40JUH6Q5IQiCIAiCCIdQNS533XUXXn31VZx22mnI5/N429vehrPOOgtTpkzB7bffrlxp0wiSGNELit+dPCpBc0LzjiAIgiCIJKOkcWHs2LEDfX19ePXVV/GmN70Jf/u3fyt0XVI0Lm2re0p2tf136PNlIek9EQc07wiCIAiCMJFQNS6Miy++GB/5yEdw8803C29akoSOXBxOUm6TpPckha8eTJp3BJFEaL0kCIKIFyWNy7p165wLS6VQX1+Ps846C5dccglqa2sdz0uKxkUHpku5Ta8fQRCEKdB6SRAEEQ6ie4M6lcK//OUv4y9/+Qtee+01TJ06FQDw8ssv46STTsLJJ5+Ml156CbNmzcJDDz2EM844Q60FEbNy0x709A2ho70JF86chg3b+7FicZtvRuGl63egb3AY7c2Ntuz1G3cNYMP2fixoKfbPisVtpb+JlCuKTJnWNq5bNr9UL3Z9VOjsB6c2xVEPnfUyiTD7KEl1iIpqamsSiWO9JIikEtZ7kH13FXO/pTzXS+u32O6Bl7GgZSoefvovAIBVS2bTOptAlDQumzZtwte//nV84xvfQFtbcQH/4x//iH/+53/Ghz/8YSxatAjvfe97MWPGDPzwhz8su95EjcvMzh7wHeEmVbN+XHz2J4+X/m7N6cIkc9lMGg2T67BicRvu7H0KufwoUgAKKCZc3Nl5WekalfwwMhJAr/LdNmBB4T/ENu4awNqfPm6LxR8kLw7vhyTz4ScrPeUXQK97sHox+LEWYVH3NgzmRqSu1f3h69RHbv0Q1kf3vK4HkcuPIptJ47G1l2srNyg68zmxvjtydAy5/Khjf4e90WdrYArAs1x7TN5QzevqRS4/hmymDquWnGtsPWXg+1tVsJYU3Oal00en39obVd2c0PEedXrWVN4FJuDlJ+y3prA2A8DSC+x9b117Ae+8Pm65gPyuc0P3txIrz43mbD1WLD7L1lf89yrbwFmfj98+ewg9fUOY29SIg0eOxfoMiRKqj8tnP/tZfPnLXy5tWgDgrLPOwp133onVq1fjDW94A774xS9i586dKsXHQn263KzNTaq2YXs/BnN5bNjej/bmRgAoHa3XNmczAFA6l8EmHHsog6DLb4E9OF4PkArWvmL/ZouHDqkl74fE388L2b5jZff0Dfneg9WLoTLW7BqZa2XaL4JTH7n1g+57VxOs7wC49reOfu3pG8J4oXjkKXBHp/qZOLa5/FjpaHI9ZeDbYR23SmmjFbd5ya81ImtvVHVzQsd71Gl8Vd4FJuDlJ+w3j61t5fuefW81Z+t93+FOuYCymTSymbTSN4jubyW/cgZzI2V9xa/Rg7mRsueDzdu+weHYnyHdKG1cXnjhBYyNjZX9fWxsDAcOHAAANDU14fDhw8FqFwGLurehtbMHk+tSpY0Gg9+NLl2/4/hOv1B6WDbfdDH2d3eU7byXL2zBzs5LsWrJ7NK57P+zmaKFXnGXHAx2H5Gd89ILiovI0gvKFxG3DZgTTg6qbqGj+Q/fBS1TkQIwqc556sk6vl44cxpmNGZw4cxpjvfzQqbvrGXPbWpEbQolM0An1i2bj/47OkpjrDLWKteqbmTdxs+pj/gXAbtXWM7/7LlZtWS2cL2TBuu7VUtml/qbtW16wyTffmVr09L1OzzvM7ep+HzX1KTKnrMUd3Sqn4kmUmw9zWbqQq9nVM75fDusH4BxjoXo8ybST2zOLurehkl1NUih/APXhASUXh/ffDtl3qNuOI1vkPdInLD3oJOmym8eW9vK9z377trZeZnvO5y9w9Ytm186Prb2cjy29nIljYOOMXYqz42ixsXeV/wazTZw1ueDzdv25sbYnyHdKJmKdXR04MCBA/jGN76B+fOLE3LPnj34p3/6J8yYMQM/+9nP8MADD+CWW27B73//+7LrTTIVkzH30GkakmSczIdEQ0eza4FyNa2K42sczrKV6KAbVujvsElqvUWQaZvo2uT1/BH+VOKzL4PsOu/VTzLmPqZS7fOBIHQSqqnYPffcg2nTpmHBggWYPHkyJk+ejDe/+c2YNm0a7rnnHgDAySefjLvuukut9hEiI8lgO+NMukZIugk4S55mre5Ba2cPZq22L9yiUlORe4R5rZOkRDR09PSGSQCAdE0K0xsmoW11D2Z2FvvjheG8qyTArZ5xSB913jOoBJeXgKqWxyTx7BgWIvWTaYOOkOWqhC19l2mbqBRwxeK2QGYSUWFq2OGo1htT2y86J0X6ic3VbKbOV4NtKiZrIpOOqc+ACfXS9d5PKoESUP7hD3/A008/DQCYPXs2Zs8uN+VwwiSNiwoymhcniYzb9aoanSBSn6glRlaJHQCbAzvg3u5KlWwFbRcvAVUtL6r+FblPUsY6KfVMItXet9XU/mpqKyGOqfPChHrpeu+bRiQJKM8991wsXboUS5cuFd60mIrMjlVEuulln16Tsh9lynUiiNRHh8Ro464BzOt6EPO6HvTtP6vEjv0/64Y03yGa62kiQdvFS0BVy4uqf0Xuw59jqh9Lpc5JhteaGLaEr9L71o+o2m/Cs1XtY0044zUv2Lxdun4HFnX/Eis37YlM42DCfNX13k8qyhqX559/Hps3b8af//xnHDt2zPbbl770Jc9rTdS46N6xVrLtPQ/ZzRNhUk3Pkkl4rYmVIuGrdujZIpIIn26AhTim9SjZhKpx2bZtG2bPno0NGzbgrrvuwkMPPYR7770X3/zmN/HYY4+p1jlWdO9Y2Y54blNj5BIBL8KQlCbFbt6JarMNTSJx+rFEhcw8VJmz7Jql63cIS9i91sRqkvBV8hqRxGerkseDEMMtYlY1rEeEosblwgsvxBVXXIGuri5MmTIFe/fuxWmnnYb3v//9ePvb344VK1Z4Xm+ixiUs+ORHcUsESFJqh/qDMAGZeRgk+h6DJOzi0BphFjQeBFGZhKpxefLJJ3H99dcDAOrq6pDP53HyySfj1ltvxRe+8AW1Glcobjkv4q5P3PXQRVAbbZX+YLl/FnVvU7onQfDIzEOVOcuuaW9uTJyEPW4qbc1MOjQeBFHdKGlcZsyYgYceeghz5szBeeedh+7ubixduhR79+7FokWL8Oqrr3peX00aFyJc4rDRpnw+BEEQBEEQ+ghV47Jw4ULs2FHMNXLllVfiU5/6FG6//Xb84z/+IxYuXKhW45gIK6oKH/UiLHvceV29aO3swbyu3lDK143VPlk1b42VSXU1tqOs/bOKvXRSshibZAsepC5BnlGn+5rUL06EWb+Vm/ag9XjeJPbcmd4fSYfvXx3rXiVB848gCBmUNi5f+tKX8H/+z/8BAHR1deGyyy7D97//fbS2tpYSUCaFnr4hjBeKR51s3lsst29wGIO5PDZs79daPiOXH7MdnYjCtEn043LD9v5Sf/QNDgMo9tGi7l+WNmEy9cyPTtiO1vJ5nF6QXue7sbPzMuzv7sDOzstczzEhzKhM28L6eGDl3tn7lG9d3OrAnqXNe+WfUXbfO3ufKv1NZcyjYuOuAaz96ePS9RP9GLb2IXv+2P3W/vRxtUoTnvDzzbru6SRpGwBW364H9mEwl8ftPU/GXSWCAJC8Z6naUNq4zJo1C+3t7QCAhoYG/Od//if6+vrwox/9CC0tLVorGDZRRFWJ2x53MDdiO4aB6AbQap/M8tWka1IYzOVLm68g9fSyf3b6YA3LXjqsDbEMMm0L62OelQv4PwdRbShMtpHfsL2/ZPooU78gH8MsrCifDJbQAz/fVPN1+WHyhtwJVt/R4xNvZHQ85hoRRJGkPUvVhvLG5eDBg2V/z+VymDVrVuBKRcn+vx7BeKF4ZOjYbWczdaXjzs5LsXzhiQ2dzt380guakAKQSdeUlcfuw+qiy7TJSZtQk0rZjm4sX9hS6o/NN12M/d0dWLt0LpqzGWTSxenI6isCS1rplbyS9cOClqllH6zW+ugkjA2x7LyRaRvbXFgjT+mAfbStWjLbty5uGwrrsyTLJeecitpU8cgIa8xF8NPEsT7ouvp8qfqJmi9anxN+XTDd9DGp8PONrXubb7pY630WtExFbap4FCVOyfL0hkkATszJJpp/FUdSNRcmC7cIxY3L/v37MT5eLh05evQoBgcHA1cqSpwklTp2214mXDp38+uWzUdTNoP86ERZeew+DZPTvqZNMjhpE0YnCrajDOzFPq1hMgCgYXJa+Fr+vk59y/62e+DlyD5Y1y2bj/47OrBu2XxtZSZRCiSzSXA79/DImO0ow+6BlzFeKB5NwE8Tp76pSnFHZ575/JWl/2drk4jpI2E+KnM9zjVl31DxncvW7gPD4VkEEPGQxHcWEK9wi/BHauOyefNmbN68GQDQ29tb+vfmzZvx4x//GLfddhtaW1vDqGdoOElzjxwdtR1VYFKkFFCSsDLpA8A+7guu0gh2rkjiSnfpQMF2DCr9YNfPbSoPqSr22XTCFr+1s1zqrCIxZAJkdnTqC/a3BS1Thdvv5DPApOVn37LFNyBCGD4uslIgE/xs/BDxzZjb1Gg7WvGb015zit17Ufc2rVJBrzrxbeHHSPUZlZkboqZKMnUhh/P4UZESW58PndJxkbKYVro5W5/YEN1J1Si41dvp78HeIwXuGAxWF+YPK7Le8G2y/jup41ftSNleXHPNNQCAVCqFG264wfZbOp1Ga2sr7rrrLm2ViwInaa6Iw7sfTIpUAEoS1t0DL9tMcZgvx4bt/WU7eyapODCcx3jB+RzG8oUtjr/xvi1W6YeKJMHqr8CHHhZdnqyarZ6+IZtGQkViyBQ87OjUF+xvLHGZSPudNHFMWj5+PIK41/ywStZ1aV3cxllHHViC1Fq/nadmRHwzDh45Zjta8ZvTXnOK3dPrOVTBq058W/gxUn1GZeaGqImSTF3CcjgnxJFdHwD788HeTzqeA5G5s27ZfK0a6TgI+k6NC7d6O/09yLtMt38tqwt794qsN3ybeC1QEsev2pHSuExMTGBiYgJnnnkmXnrppdK/JyYmcPToUTz11FN4xzveEVZdQ8Hpg9vNpl5md878NRgzGutLEjFmS57N1LlKg0UTV3pJQ3jJalC7TR12n1Y7eiZhYxqMoVxeWuMiQ1CpNJMQMm2al89FFEEf/JCpQ1wO2iLSf69x8xtTr9/ZPZuz9Vrsmb18qdzqw4+R9fcwNGZMUumk8eSR0YCG5XDu1QdxSEtVNEsmS3WZn8n0hklC6yMfoZLvD5FngMHOXbp+h/GaYTdUrARMwG2snf4e5F0WxD/RCVYXVp7IesO3yfpv8mVJJkoJKJ3I5XLIZrNC55qUgNIpmSCTzDdnM9jZeWnpd7e/O8ESIzKsCRJZOUzKLVKe332iTMDohGhSRqc+tF4LyPUHJYPUA/VjcGTWBxHCeLat4+xXru72qODVB3HUT+U5MaEf3ZCdY3z7+X/LtJWdy4j7HaaCyWNrAqZ8nxDJQHRvoLQN/sIXvoDW1la85z3vAQC8+93vxo9+9COcfvrp2LJlCy644AK1WsdAJl2D/OgEalLlmw0naQRTM7Z29iAF4FmXl1dHexN6+oYwo7EeB4ZHbNKKBS1TcWA4j7lNjTh45FjpPiovxblNjegbHHa0/+dZun4H+gaH0d7cWGYuMmfNVuRHJ5BJ1+DJ267Axl0D2LC9HysWtwmpUFk/8pomvhzWh9a+TdekMDpRQE0KOL0xmPRDtt5hs3LTHvT0DaGjvclYswh7dLgYK+KC15jy/Rvl+J99yxbbvGVSV13SO7aGeEk6ZdubzdSVzCz4cvn1Z8XiNnRtLubYWLp+h/YoWFbcnhOvPnBaS8Kog3VtbG9uLK2hoqjUk1+Pw0JkjllpztZjMDdS0pzz/SHTVnbu9IZJ2Dc0bKsDGwv2/iygaA4cdn844fWM6Z6Dbszs7EEBKH1zJOG9AnjPr1mrezBRKL5z/pSgTY3Xd5QKrB/csK7ZjOZsPXZ2XlZ6B6VrUrbAK16Y9o2kgpLGZebMmfjud7+Liy66CL/4xS9w3XXX4fvf/z7uu+8+/PnPf8aDDz7oeb1JGhcGv2kB3DcPQaXTblKasKV5XuUHkZzpqp8OTJOAJUHiZHodvcaUr3uU4x9EU6gLne11Wh+i0sSZMAfd6hCHNrLaNaBO72NG1P1hwjuFnw8mPC9BSeoc111v/j0iipPGUwQT5rMbonsDpXDIBw4cwBlnnAEA+NnPfobrrrsOl19+OW6++WY88sgjajWOGWY7KYJoBC0eGftfUZxsNGetLtqwz1ptfyC87NCZpoQdddl+ipSj0wbcNJtVE3xd/DC9jl5j6uUjEjbM16kmFV+S2bDbG5bvCo8Jc9CtDvzaGAVx3NMk+IhjTBMcR3+Y8E7hvzlMeF6CwkcETQq610S/9jv5BzGNp0geOx4T5nNQlDQuTU1N+OEPf4iLLroIs2fPxuc+9zm8+93vxlNPPYW3vOUteOWVVzyvN1HjEgVR7XSTJskwWQJAEARBEARBhEuoGpdrr70W73vf+/B3f/d3OHjwIK64omhzumfPHpx11llqNTYIHTkJnKLhuO10vXK2zFmzFa2dPZizZqvwvZMmyTBBAiATwUlEQxREi8SuZRF8KDeGOyZHbFIhCbl3TCap80FHvZPa9iRDz6s3uvuH5jgBKG5cvvzlL+NjH/sYzjvvPPziF7/AySefDAB44YUX8JGPfERrBeNAR04CpwzZbtlYmcN/T99QWZbZ/OiE7Sjy4P7pjg7s7+5IjMObCVlq/TKaWxHJBhwkYzC7lsW+Nz03RpwvE52ZmU14KcrMQ6KcpGbq1lHvpLY9ydDz6o3u/qE5TgCKG5d0Oo1Vq1bhK1/5CubPPxHR4hOf+ARuvPHG0r87OjrwwgsvBK9lxOiwYZSxQfXK2cLbOtODGw4q4+WlIQqiReLz/YTtXxCUOOekTm2dCc9WJdiux4kJ2lsVdNQ7qW1PMvS8eqO7f2iOE4BiOGRRHn74YeTzef8TDeOZlw7bjm54hZXb/9cjGC8ADz/9EtpW99jCFs7r6kUuP4Zspg6PrV0ilfGYD7/oFZrPxLB3G3cN4M7epwAAZ047CfuGhjGprhhKmYX4k0EmHKBMfwTtO5Us1oz7HnnuuM9PMTHidW85o+wc3eEw+XkkUz4/J3XXzWss3PpZpg7sXBZSXPSlaOLzpZMkti/Ic5d0VNqexDE2iQtnTsPugZdx4cxpcVfFSB5++qXSd5AONmz/IwZzI9iw/Y+2+eo1j/nfROZ82M8FPXfBqM6QJT7w5llueElomXlPLj9WpiplMbn52NwideHNqrzM2kyQIPNs2N6PXH4Uufwo+gaHMV440TZmGiXD6PEA6KNegdAt93brD16l7XVu2P3KxnIwNyJcX133ZEeZ8vk5aYJ5gEwd2Ln7hoalTBbDmAcmmZ6YuH5UKnH1NY1xMKj/vJH51hGBfSPw3woy7+uwTb1FoHkTDNq4COBm++6ltmQh7JiDvDVBJPuNHfnyrf/2C4vJzInY0cqClqmoTaGUGC8oXkEEWjt7Sv85XcPOPXTkaOm39uZG1KZOtC2bqZP2MZAJBzi9YZLtaK0bU2nPbWr0DVktoq4++5YtaO3swdm3bBFuC4OZhjGNi0gYYCdkfDZ480g2X0USm/L30W8+UeCOJ3ALpCHTPzMai8+ObFvDMFuw9rvM+Ime63SemwOt7vVDBR2BUpIAW5OAQqR+VtYxjtrHK4r7hX0PMl3yRndYb7fvHa9x4H8L29RbBJo3wVAKhyzKlClTsHfvXsyaNcv2d9PDIetIxsgn0PK6li9f5n5e5+oOM8zKq00B4wV7m9xCMPN18ArVHHZYZJFkhbrqYEJI6iBt0TUHdSCTOFUGr/nsd01YbbWWD0D7GDid55bMzoQw5SY8R1Eg877Qiep8033vsO5nwhyuZqj/CRlCDYdcbajsjpnk1ElqzksRvSQCbpoMhpdUlC83qPTJK4iA3zXsXF7b5HWuCPO6etHa2YN5Xb2+5/JaBOv9WOjhI0dHfevAzl3Uvc31nCAhqZm2ZtbqHi3jJdKfvNSdXbugZaqrlk3lPl64zU+vOSMSSMNNo8m0akHms26sWkGZe4me63Sem3bKBKlgVAkwgXhD27IxaG9ujLTPre+PqMc7ivuZMIerGd39z96987p6Xa1UiMqHNC4O6JDyeUkaZMr3O9cUyXhcklGZ+0Y1Jn738oPfpEYhrfKTustoJVRx6zO3uqmWa7IUMGhbCXWqse9NfhYIgsft3UjzuDIIVePy8MMPY2ys3NlqbGwMDz/8cOnft9xyC6ZNS160DR12mV6SBp1SxDCksipEKRm14iWN5/Fqv5evkMq5Qfqa+evUpBCqtNCq+fOTustoJVRx67OgPjMqNs5xQeFV46Ma+97kZ4EgeNg7N5upS8yaTuhHSeNSW1uLF154Aaeddprt7wcPHsRpp52G8fFxz+tN17gQRDVQLf4DBEEQBEGYTagal0KhgFSq3Hj/4MGDaGhoUCmyqojTlpogGHFpyYhyyEabIAiCIPyRSkB57bXXAgBSqRQ+8IEPYPLkyaXfxsfH0dfXh4suukhvDSsQa64GHQn6CEIFPmEpER/WuP6UkIwgCIIgnJHSuDQ2NqKxsRGFQgFTpkwp/buxsREzZszAhz/8YWzcuDGsulYMum2pSVpLEMmGbLTjJ8g6SmswQRBENCj5uHR1dWHVqlXKZmGm+7is3LQHPX1D6GhvUtaIbNw1gA3b+7FicZuvBHXp+h3oGxxGe3OjtBScXQuUR3zS0Q5R5qzZivzoBDLpGjx52xWu53n1i0o/zOvqRS4/hmymDo+tXSJ0jVcdRMZN5Jwo+14Hbm2Smcci5S9omYrdAy8HLo/h18+66h8VYdd3464B3N7zBEZGJ3DVBd5z05Q5HNUYRpX3yAuR9UxnfyTt+ZDh7Fu2YHSigHRNCnNOP0X5HUtUD5X8PCSBUH1c1q5dW9G+LFZTLlWsph9+sI0HO8pgvYaX1upohyj50Qnb0Q2vflHph1x+zHYUwasOIuMmck6Ufa8DtzbJzGOR8nv6hrSUx/DrZ131j4qw67thez/yoxMowH9umjKHoxrDIFovXRozkfVMZ38k7fmQYXSiUDoGeccS1UMlPw+VhNLG5cUXX8Q//MM/oKmpCXV1daitrbX9l3RmNNbbjip4JYbkYc7RKRQjPc1Zs1X4PlYHa15C4GSSFlZgABbCN+2TcdGpX5iZBQt1KOMsLhMOmeH1kSHyASJyTtJCq7q1SdcHWVhhlf36OWkmWOHX94SC3W9umjKHoxrD5QtbsLPzUiVJa5BrrYisZzr7I2nPhwzWdxIFIkkeKzftKSXf9kr2rJNKfh4qCSVTsSuuuAJ//vOf8bGPfQynn356WYSxq6++2vN6003FdCQiUzEdiCI8bVhJ1kTb63QeJY8iiGigENgEQSQB9q3CoPWq8gnVVGzHjh347ne/ixUrVuCaa67B1Vdfbfsv6ejQuJyQbJbvC920Hk6JL/2cPmWcQjfuGsCkulqkoF+KKiqpcNK4yGineGQ0SKyvVm7aE6ojrSmOujq0aya0Jew6mBSePOy6MIlzuibl259RjL0J86tSUOlL6n8iClTmmfUbhU/2PK+rF62dPZjX1Vsq/+xbtpQSKqvek0gGShuXM844AwqKmsRwYHjEdlRhMDdiO1pxsx1/8rYrsL+7w+bc7mdzKWOTWbRvH0dTNqPd2VbUVGL3wMsYLxSPXn8TRcYOPyw/C7f7xG0nG7WvVliEXQdTfDmiqMvmmy5GczaD0YmCb39GMfYmzK9KQaUvqf+JKFCZZ+uWzcf+7g7s7+7Azs7LbL/xvmAbtveXfJqYHxPN7cpFaeNy9913o7OzE/v379dcnehhu/Szb9lS+psOjYuT9oQxpb7OduSxSgr8NRnumh0eXfabTpKMRd3bhGxRnerA/ragZaq0hISpkscF9tFh+Vk43ScFYDCXL0mE4kDGR2HOmq2O/lVR2vy6SciCaORE7je3qdEIXw5A3a9EVLq4cdcAhnJ5AMCRo6Oe54bV71b85peIBipKyarOe+mu9/SGSbaj1z2Z1nlBy9TYbPqt7SfpuBmENQ663yO8L9iKxW0lnyamVSZ/lcpFycdl6tSpeO211zA2NoaTTjoJ6XTa9vuhQ4c8rzfJx8XJ5luHH4iXLbmfnbmMz0ccNutO9dNRD1P9glQwtV5umFBft/EPyweqknyrZH3MGF5jbUL/iKzFUdZT571011umr2pTRWFPnGNrbT+A2OcaYcYzT1Qvofq43H333fj617+Ob37zm/jqV7+KL3/5y7b/ko6b1JO3q/TCK4qJX4QTGUkBkzikgMikVU71YzaovC0qL8HxkuiISAz9MEVyJxplTYYw28aqqbG6NkTq7ibh1zEvnDBRIqc6xuJ9dEJOxbTBUWu6ZBDRQMmMY3AfInENtx+659/cpkbb0eueTlpnkbnHNOtz1mxF2+qiP4HTNSJlnZirBRw5OoZsJm3Es2jKOyQOolgT3fq3mvudkEM8hqyFG264QXc9jGLdsvmOPiAyOUO8klz5JcBavrBFOKzm4ZFiXQoo2nRGkTTJqX68DSrDame6fGFL2b+t7Bsath1FYJLD2pTz/eJi4rgic0KjL1iYbUtxR92I1N3N10llXogg85xFheoYi/aR1efu2NiE5z2D+J7pwm0ttiIzjlYfIhU/Py/fRVl0z7+DR47ZjrL3FJl7rN0sX5fVn8B6jUhZbK6yMpuzGSOeR1PeIXEQxZro1r/V3O+EHEoaFwDo7+/HZz/7WSxbtgwvvfQSAGDr1q3Yt2+ftsqZhkzOkKXrd9giXFjRKVnoaG9CCkXpqYiUJCyphpskk5fgeEl0VOz7eSmjKVL0MHJghNk2GV8hhkyUNpG6u0n4VfrSpEhhMqiOsWgfWbW87Nywc/iYRNDn0uR8IEHHS+R6plHPpGtQmyr2g+rcYWPhVkZcVOK8N4mkrzdB3i2ivsCEN0o+Lr/61a9wxRVXYNGiRXj44Yfx5JNPYtasWeju7sbvfvc7/PCHP/S83nQfl7DLjdOONKx7h5Ufxg+yydWDynOg215e51jGNR8JgiCIyiXIu8UEX1KTCdXHpbOzE5/73Ofwi1/8ApMmnbCpvvTSS7Fr1y6VIo0giCaC12R4md7wkmUv7YyOulj/LmNL7FaOk8RBVJIp4yckUg5zNB6yOBxHTVDpflS2vUHu4xQBiEUk8orSJnNPN4mbl5+U27NjStZ3whmyZzcDGgfCCmkE/AnybnHzBSbkUNq4/P73v8c73/nOsr+fdtpp+Otf/xq4UnHBx/x2+yB1+vjmY4ZbXTj5D6vNe4t21pv3FnM1MDthdgzKnb1PYTCXx529T9n+vmF7P3L5URwe8Q6Daj3f2ib2kntgb3muiXXL5qP/jo4yu3H+xWj1EwqyYeP9jMLOKuS1OfHKvSGyqdEZb97rQyTIfazXsv/fPfAydnZeinXL5rvm8JG5p1suIL4M67/dnh23+QiY+bGWVNM2VSjHghnEMQ6VnCgzCUIoL3T6cBHl7Oy8zDEvDSGH0sYlm83ihRdeKPv7nj170NzcHLhSUcJslZuz9WXSXrcPUicnfT/7TK9NSVR20ysWt5XMekReVHyb2Euu/rh9s4jEgX8xWjVRQTZsvJ9RWNGwGF6bEy8JjEhCQZ22vV4fIl738fPfsl4rU18dbfPyk1J5dkz8aDYpCWYUJMWevdKJYxwqOVFmVPUM6z6kEfCn2tZqE1HycVm1ahV+85vf4Ac/+AHOOeccPProo3jxxRdx/fXX4/rrr8fatWs9rzfJx8WLlZv2oKdvCB3tTTbJ7byuXuTyY8hm6vDY2iWO127cNXB8USlgMDeC9ubGUjSxWat7MFEofmz/KQT7e3bvFYvbyqTXXr8FKVf0Guu/73vkOfQNDtv6xlTc5kJY16kiM0bVam8b5BkIi6jnCUHEhY73iKlEVc+k9EclQmt1eIjuDZQ2LseOHcNHP/pRfOtb38L4+Djq6uowPj6O973vffjWt76F2tpaLZUjCCI8qnXjQhAEQRCEWYTqnD9p0iT813/9F/r7+/Gzn/0MGzduxB/+8Ad85zvf8d20mIZux/iwy1XBVPtgmXC6JmHS2KrA+j0p6J6/UT0PpvqtiLbf1HWDIVI/ds7S9TuMHAuG6X1tMjrWY+uzSmNBhA3NsWAo53EBgDPPPBNXXnklrrvuOpx99tm66hQpuh3jwy5XBVPtg1m9evqGjKyfGyaNrQqs35uzGezv7jBe26J7/kb1PJhqCy3aflPXDYZI/dg5fYPDRo4Fw/S+Nhkd67H1WaWxIMKG5lgwlDYuhUIBP/jBD/CRj3wE73rXu3Dttdfa/ksSYTmjySSrdEJ0R+4k1eWvtTpg6tjpO5WhIl1m9WLhdBe0TI092oxIebqDKUQtmXdyyA2rDjrGZ3rDJNsx6P2jckg2MSSzTEh00x3oRerHzmlvbjRuLKzo6GuVZ03Xcx+nBFlkPfZrp/VZdRsLUzWoYROk3WHNi6RrLExfW01Hycfl//2//4evfe1r+Ju/+Ru8/vWvRyplD+l07733el5vko+LqQkZRevldB+va3W016kMHQn/VOqme/ziSGhpQrLEsOqgoz+D1I0SlNqh/qhcVMZW13Nv+rzS0U4T1uk4MHH9NX2+EWqE6uPyne98B/fffz+2bt2Kb33rW7j33ntt/yUJlsCQHQE9kpW5TY22oyyiO3Inqa7XtWGEpwWAGY31tqMXrH+Xrt/hqhkSp8AdgyFTB13JNFUk8yISJ5l5PFGwH3WhY74F0Vw4aWuS7qMUBJL0VS4qY6tLK2j6vNLRThM1qFEQpN1hzQvT55sb1aq1042SxmXmzJnYunUrzj33XKWbmqRxcYqsFJf2IMnI9Bk7lxGkj+KMjBXnvUXml8yYVGqEMac+qNS2EgRBEOZSrVo7UULVuPzbv/0burq6kM/n/U9OIEzCMLepUdmOUkUi0NrZU/rPRLykBTJSGXZue3MjmrMZHDpyVFlzEVbyThGNhogfU1j2wSLzS2ZM0sczeKbDzuQZMU59IDJnkm5D7cbZt2xBa2cPzr5li9R1SemPpNSTIAhvVm7aU/oekl2vTKVatXa6UdK45PN5vPOd78TOnTvR2tqKdDpt+/3RRx/1vN4kjYsXUWtNTJcEhyUtMLHdusbeRPtgwp9K7XvVZy0p/ZGUehIE4Q1vmWHKtwERHqJ7A6WQVzfccAN2796N5cuXOzrnJwmnDLQzO3tKHhOqdpROZbDy+cyrrA5WFnX/MnBWXN3Zdec2NaJvcFjZb4ev14KWqdg98DIy6RrkRyeUIrAtXb8DfYPDaG9uxOabLraVH6TdKxa3lcoQuScPG+MZjfU4MDyibB/M+kllPsj0g1ubTMjQPK+rF7n8GLKZOjy2dkkk9/Qa/zARmVtBSNekMDpRkNasxdUfsiSlnmFgUkZvE9YNItl0tDdh895i+HJ+veLfCRt3DeDO3qcAAKuWzLbNOZqLlYeSxqWhoQG9vb24+GK1F6tJGpdZq3swUQBqUsCfNNjAs4fE6uwPFB+8Zz5/JYATm5oUgGe7O3D2LVtKHxPPfP5KzOt6ELn8KLKZNB5be7nQ/ZweSplyRGDlZdI1mNYw2XZPp350Ys6arciPTgAotr+AEz4uMguMUz+zsQpb6ioyP/gxDoJMe6x9aM3X4nedW5vilGB7jTFD5UNf5QPP6ZowPhRV156wNzxx4bUmVOIHSZBxVNHuhtWHIu8e9vxMqa9DLj8W6tyt1OdDN9Y17cKZ0wLPjUXd2zCYG0Fzth47Oy8LXD+3dwJ7TwHlPrOkhU0Oofq4nHHGGbFvOHShO5qS06YFAEYtN6hP19iO7LdRhUrEkchoZHSi7J6i/cg2LUCx/VaNlkxb3PoZMCPiCD/GQZBpj7UPw4ogFxVeY8xQST6nkhjS6ZowEkyq+holPSmqG15rQiUmcQsyjir283H2IXt+cvkxAOHO3Up9PnSjOxHnYG7EdgwKqxNbHZmVxorFbchm0o75qUz4HiD0ovRVddddd+Hmm2/G/v37NVcnepw+FII4fLOHhD1QrNjmbH3JcfTvzpuB5mwGf3feDCzq/mXp3pnjH7mXnHMqalPFox8LWqaiNlU88qxaMhvN2QxWLZkt3Q4rrN6XnHMqmrMZXHVBU9lCkOKOblh/n9Yw6fjC+Ecs6v4lFrRMFV5gWLtZ/2YsG4TlC1uws/NSIa2NiiOvX1tXbtqDkdEJZNK1+EzHeVJlW1nUvQ2tnT3YsP2PQu0B7Iu0aD94wZfh1GdBwguLBCBgyWHZ82i9xu1Z9SpXJVS500dhGI6Wa5fORXM2g7VL50rNz7CCVMRN2KHdTYOtYxkFgce6ZfPRf0eHlPYvrD4Uefew54e9K8Ocu0l/PqIKoyuSiFOGoIm4eVidbrvmfOzv7iiZDi9f2ILH1l6Ox9ZeXva+0/EeJMxCyVRs6tSpeO211zA2NoaTTjqpzDn/0KFDntebZCoWVjhkp5C/AGwqS6t601oHGdVmFGpQkXuI1sMrYppK4klGlM7vftfpCmIQZdAC0Xs5tT1IPVXmVtD5mBTTgaTUk9CHiYFKiPhJahhdWsMIGUJ1zr/77rtV62UcNSmUfDMYzOZ2Sr26lIA5smczdTg8MlbSiBwYzmMwly+9oJqzmdIHOKuCl4MpbzO6oGUqDgznbRoX3bb3fH2c7IVfGM7bjiJkM8V+LkrUU5JO6Pb9do1AgAi+72Qcea19eujIUQAoHXnY2I8XivdUte1tztaX6iuK1WYdgC0IAutXJ7t2p+fAqVw2z6x91t7cWJoPIvWyjq3IGPBz3HqNX7lOc4r/je+fMPHzKbD+Hoaj+cpNe/DA3iHUp2vxmY45nu3V7f+g4sMWh/+K172D+Eo4lcsHKmFrYjZTJx2QQ6XPrO257i1nSF/P31OlDpXkqxRWWzram0rvHx42huw9ChQs742UbWyYA/sl55zq+07QwfTjVhXWBMBBYD6ymXQNnrztCi1lEslDOapYpeDkm8FsbtlRhYNHjgEADo+MYbwA7B54GQBsWhgA2Nl5aWkTw35avrDFdfHgbUZ3D7xsKx+w26nq2Ljw9XGyF1bxFTo8MuboCL5he7/v4snbzIr4B/F959XPPNY+ZWNo9dexwsbeqZ4yqGx4eLvkwVweB4bzGC+g1K/Wc1j7/cbP6m/CS85EPuCc7gmIjQE/x63XuM0Zdo7T7/xvfP+EiVs/OP0ehnlDT98QCgDyo+O+7fWrqywy5em+twxe9w7iK+FULvsbm4O1xwUHh0fGkMuPSbVfpc+s7Tl45Jj09fw9VeoQ51jrJqy2rFs23/VdzsaQf9ewf1vHJpcfBXDiPRZk3ETYNzRsOwaFvXPd3r1EdaDsOTw+Po4f/vCHuO2223DbbbfhRz/6EcbG1D/048LJpliHPSyTMMxorC/ZiTL7TJE6uMGk7+zoZIcadpIjp/4RdSq2/sz7GMjY1LJ7s/JEbGj5vpPB2qfsXpl0jaMPworFbTbfpiix9iH7/452u0+SUz/7zcGg9s5Brg/i5yByLd8/YRKkvjroaG9CCkAmXet7Dy//ORVk2han/4rXvXX4P1rL5ecgf5Rpv0qfWdujcj1/jY4ykkwcbWFj2Jytt/kEsn9bx4Y5sIu8E3Sg+1skiA8YUTko+bjs27cPS5cuxYEDBzB7dtH57umnn8app56KBx54AOeff77n9Sb5uIRlg5lUm9QoqBS710ppB0C29UQ5lTS/CYIgCLMJNRzyjTfeiLlz5+L555/Ho48+ikcffRTPPfcc2tvb8eEPf1i50nFw5Oio7aiLGY31tiNxAp3SHRZ5a1H3Ng01k0O3RDpOmIZIMgqvsahGjEs6OttdSZJwgiCIuKnW95JulHxcHnvsMfzud7/D1KknPtimTp2K22+/HW95y1u0VS4KdPizOHFgeMR2JE4g41vih+448TI4+RclFd35jOKmkuzmZdDZbp3PKUEQRLVTre8l3ShpXM455xy8+OKLZX9/6aWXcNZZZwWuVJSo+D3MWbMVrZ09mLNmq+sOmtl2zm1qLPvd7Zp5Xb1o7ezBvK5eqTYkfRd/9i1b0NrZg7Nv2SJ9bRC/laCISKSTMja64+3HBetvkZxAKmNj6niKtjto/U1tP5FsaF7JUS39FadFRRiQFlsPSj4uW7Zswc0334x/+7d/w8KFCwEAu3btwq233oru7m5cfPGJKENOdmom+bioYPUHYOGM3ezAnezE3WzHVf0Mkm6LnhT/CpUw00kfm6Th1N9uoT5VxsbU8RStV9D6m9r+SqOSQgSLQPNKjmrpr6R8GxB6CNXH5R3veAeeeOIJXHfddWhpaUFLSwuuu+46PP7447jqqqswdepUZLNZmylZJWGNbKESJcjtGlWpd9J38aIRyeLGGhJZlKSPTdJw6m8+RLTXuSrlm4BoveKMDkeI4zZnKxWaV3JUS3/FaVFBmIuSXchDDz2kux6xM7OzB7zqyW2Hb40l7mcHfmfvH5DLj+HO3j+U5ZHgUfW3EbVFX7lpDzbvLX50L73ArjUQkWzoTi7GEmexDctpp0wWKlO1LkFY1L2tlL/FL7QjnwSy0giShE83IvPALZHjfY88h8FcHvc98pzwHLI+a7zmNU7puA5/FN2JaysFPnGtF9Ykf6uWzFYek5deGbEdWdmVmtzRb/7KjkGQNgdJqhnWMzSvq7eUmPSxtUvw22cP4cBwHr999pDR4+qFyJiqJm8OE7f3n+rYs35wopg+o1D2e00KOL0xE2nyZJNQ0ri87W1vE/4vKYTlkxyW878KVk2BjNaAoSIF9LqGJc5iySNlHOyjlkha6+a3KFnrVomS0yBJ+HTD969Tfy9f2OKYzFFnO5Iyxl7zUUSjWInz2Q+ZACAsyV8uPxqoj9iaaE2sq3v9TRKyYxCkzSJrihsqWnkR+O+IsO4TJXEG1gmC23tDdUy82j+Yyzv+PlEo/tbTN1QRz7csyll8crkc7rrrLtx444248cYb8eUvfxnDw/F/yMjCHO1FYA5xIjBHe4ZTwiQdDnYrN+1B2+oerNy0x/dcq6aAT/4oAlNPL2iZWlZvt8ACTipt1m6m/mUaFxl1MAtF/NIrI6VACX59yQcBkOl/EZW11UE6k67BUC6P6Q2TtKr0/eps/V2mfUvX70BrZw+Wrt/he66OBK26CJIAT2c7dI1x2E63Xv0jkiwuKhMVk5yPZcxVrEn+RPrIrZ1OifbCMG3U3c8y7yMZZMfArc0i7Q2ypoSV/Jk3Iw87ybQuvN4rSTUDc3tvqI6JV/utCUWt1KQQefJkk1Byzv/d736HJUuWIJPJ4MILLwQAPPLII8jn83jwwQfxpje9yfN6k5zzZZy/ZBzi+M2Q0zU6HOxkE13quKdTGWH1o18ZPF5l8nXU7eBoLe/AcD6UBKR+dbb+DkBpvpITZHxUi9OtH9XSD3G3U/f9TU+8HHd/Vxv0XiFkCNU5/xOf+ASWLl2K/fv34/7778f999+PZ599Fu94xzvw8Y9/XLXOsZDijlZ46YyM5MVants1buXJSMGm1NfZjn7okJg6lSHqYL9y0x4M5fLIpGu11MGKX7v4OqpK5tx+s5YXlkRMJhiETILMpARIkCUsCXBYVIvTrR/V0g9xt1N3El3TNQG6+9skzaCJVOp7hYgXJY1LJpPBnj17cO6559r+/sQTT+DNb34zXnvtNc/rk6JxCSKdCSJpUNXsxCnREJW06ZbIhdV+rzFIitQuifNIN6ZLgAkiTpKylpkK9Z83lfpeIcIhVI3LKaecgj//+c9lf3/uuecwZcoUlSJjwykEMZPSBvFPcLJRZtKZlZv22KQ0vNRGRArm5CfCS32cpEFh+dXMaKy3Hd1gvqbjBQSSgrN2MDmOjEBHRJsyvWGSbQys1xw5OgoApaMTMj4jQeDbYp1jB4aZKV3B85ooUbm3ShIydp+5TY2hSYDD0OZY+0dH+TL97TaXRK51qqtX/UlSXYTvB5UxD9KXujUu1UbcGrOghK2RZt9VmXQNPe+ENpQ2Lu95z3vwoQ99CN///vfx3HPP4bnnnsP3vvc93HjjjVi2bJnuOobK4ZEx2xE4ER1i39CwYzQiEY6NTdiOwInoJHwkCD5qye6BlzFeKB7dYNcAKTRnMxidKJRFlnCKhqIjyoxT9IwDwyO2oxvWT+ggEVFYO1h5ExJ6Q68+YL/tGxq2jYH1GpFIcVFF3nKLgMPGCCiPWhJnpCGVe6tEn2H3OXjkGPrv6AglvG8YkX2s/aOjfJn+1h1Nyav+lRLtKih8P6iMeZC+FHnXEO64RSxMCmFHJ2uYnAZQ/A6i553QhdLG5c4778S1116L66+/Hq2trWhtbcUHPvABvOtd78IXvvAF3XUMFRZda0p9XUnywL6B+W9hXjrhJR1l5VqjdzHpzIkPyqJEnJd6iUhxrOfw57O6HDpytKwlOiRETnbMk+pqbEc3rLau7HoW2W3Omq3CdWDtkNG4MMn9kaOjrtHRWLksWgc7Z0HLVCl/GBnb3iASU7cIONax4SPaxSklVLm3SvSZKNoYhj3/9IZJpaNM+W6SU6e1YWZnD+as+XnZfFNZhxhOdfWqv2jZpmlmvN4BKvD94PTekC1D9f4ibfE7x9o/fucyrfSi7m2Y1/Ug5nU9qHWcrVpvN2sHmbbJnpcEwvZJYmvK3KZGLeuxyrcCUXko+bgwXnvtNfT3F3fQbW1tOOmkk4SuM8nHhY9OVZtCaWMB2O0yeXt5ryhSXravYUe3YvW0ErZ9aRBb1qiuVel3kQhqJkRZcyJI3xDxoOqTI+tjBpRH4DPRXt+0Onm9A3RHJoy6varroRVr/8xozHie65SGQGe7+QSxg7l86f0exHfRtDlpMrr7it5TlU2oPi6Mk046CW984xvxxje+UXjTYhrMTyFdkypJHpz8U4ByrYJXFCmm7XjplRFP6YyfNJ+Hl/Y4+VKwujBZf1gRPax1CRI9xCuyW9B6WeEl9ysWtyGTrsVQLu9q4yur/QJg88Vwyt+jcg8Vwsq3EpZdtJs020tKWmmoSkBFJfX23+3SDRPt9a0aKJ44JN/8+Ojus6jHwPrMidzbzyfG2j9+5bF1yepfqtPXxrr+8Zp0mSifqucR5fNl466BMu2azHPs9m1GVBfCGpdrr71WuND777/f83eTNC5hSaV5aVK6NoVnbr/S9rd5XQ8ilx9FJl2LaQ2TsGJxm6+tLC/B8KpT2NIJa12sWiuve23cNYAN2/uxYnEb7nvkuTIfEB0aFzcpj/Xeyxe2YOOuAXz2J48D0Bt1ih/7uCRDi7q3YTA3guZsPXZ2XlbWfisycyWsSF1u0mxGuiaFZz5/pUcJ5Xi1uZJga0k2k8Zjay93Pe/sz2zBqEUdu7+7w+g+mtnZgwKKQo1nNUZ9JIoEzQMWZO6wa48cHUMuXxQg0lhWFvx8sa7p/N9o7AntGpfGxkbh/yoVpkwQUSrwp4zytlsWRkbHhR3XTJL2qNTF6kgaluO6W72cnI8ZOm18TckEzDu163KIDssu2k2azRiVicBwHHICt2Ndh9K1xVXK5D6qPy5ZrXeQsJq0FiYV2WeZ7/Mgc+dEgBkgm0kjm0nTWFYYTj6Y/FjTc0zIIpa1EMC9994bZj1iozlbX5JKu7Fy0x709A3h/KZGHDxyTOgBe2NzY9mH+azVPY7Rr5qy9QBSQuUuX9hik2w51f/sW7bYPvKsv8lI4WXqcmfvH5DLj9nU/k4w0zynrPdAUfLvJF1l8PWtSRUjilk3k3wfMdg92XHF4jbftqv0jzX61dIL/D8I2HiJaBRYfaY3TMK+oWF0tDfZImZZxzebqbONibW9PCmcMBxa1P1Lz/buHjh0PBLRIdvfl67fgb7BYbQ3N2LzTRf7tptn3bL5trawcXSyhfcbF2s/HRjOe5qg8M+ECDLzgq0f1rGas2Yr8qMTyKRr8ORtV3ieK8Il55yKnr4hXHLOqZ7nsfUCAObMKEq0FrRMdewjtzr6EXQeWPlMx3muc/a+R57DYC6P+x55rqRBtZ7rNz4qfa06Pm7o1HapzGPrM8fqsqBlKnYPvOxYJ35t9VpT/GDXHjpyFPnRCWQzdUp9IFJvXnMXtZbR634icypq3w5dzzA/X5zezfzfTNYA+8GPk8jcJORRNhQcGxvD//7v/+JrX/saDh8+DAAYGhrCq6++qq1yUSASxlclPPK+oXJtgpvA+MDwiHJIRaf6Wzct+7s7bC+xsKTwIiGCRX4HyqO5WeHry5qqIIwXCmUZtH9EPm7YeIloFFh9+gaHHcNYWseXhaJkR6/2Wu/s11638MRRhYAG/MfFLay1E0HCLauGCs6PTtiOXueKIB7W9sQOn61Rbte61dEPnfPAa87y97GOicj4qPS17vCxOrVdKvPYqS58uH4vgoQDZtey+SXybnBCpN58pNCotYxe9ws7JLEKUa7lPCZrgGVReaYIf5Q2LgMDA3jjG9+Iq6++Gh/96Efxl7/8BQDwhS98AatWrdJawbCZUl9nO1phTmPW5IqiTmROiRidLMyCmtywa60JHa3aB95xn0+4qUtN6xaylg9faO0DmVDGDFbf6Q2T0Lb6hHTDWoYOp11WhkzgBCfmdfXa/u3k2C5jgsja397snFjROg4iiTKdcHK+tfapm4NkWMEAnODnQXmggOInypT6Ot/xCzvcspM5jlsfqprhidbHOq7MUd/tWllHWDZH2Pl+2lcRvAJB8PPN2g6RxIoqfS2aaFcUvu+DrF1OyZT9cHLO93Jg5+GDw3gF1HALvsHWPdUgMm71ntfVi9bOnrI12HpNVOZJXvcLOySxCmGt5SLzu5JMx1SeKcIfpXDI11xzDaZMmYJ77rkH06dPx969ezFr1ixs374d//RP/4RnnnnG8/qkOOczpzEWQtErlCKPU0hi3ond6Z4q+Dk1W+8TliOcW7l8/1r/be1X2dCiXiGfReui0h5R3O7l5AxrWjhkwDtULh/6O2y82uHmXExhM51xco4No2xAz/xQDQQR1jMVVmAKRpB6q1wbtD1uoeGd3pV+7yndfeoUDpkcwOOFHPEJL0INh/zrX/8an/3sZzFpkj1EZWtrKwYHB1WKjA0vyQK/W5bZNTMpCpMiZTN1pfKYREyHE/fGXQOYVFeLFMqdmln5rG0rN+3BUC6PTLpG+85fVGprlQoGCS3KrmXlWvvSrRwZyXpQqY+b9NNJuhaWhEmmvWyONGfrHeviFfpbF27SOC9JspsEPErtT5Jwco7VWbaXNlCFsDVQUdVHlCD1Vrk2aHvctF5O70q39V7nfLFiXTfCmg+VlIwyCnSNA7PkOPuWLcIh82msKgcljcvUqVOxc+dOnHfeeZgyZUpJ47Jjxw78/d//PV588UXP603SuCQdFS1FVJJy0yBpj9m4jY/XuFX7nCYIIj7onRIPvIWAiDUMjZX5hKpxufzyy3H33XeX/p1KpfDqq69i7dq1uPJKuTwLcRPlLjyMxH0ittwME2xpdfU3K2fp+h2+farLX0WGsJI0yiDT10HGRVdb3aRxXnPchDlNJA+SvhI6qCR/jCTBLC3SNSlhaxgaq8pBSePy/PPPY8mSJSgUCnjmmWfw5je/Gc888wxe97rX4eGHH8Zpp53meb1JGpcod+FhSIeTJkXQVV8Z++g4+sgETYBMu4P0kcl2/wThBM0pgiAIsxDdGyiFfXnDG96AvXv34nvf+x76+vrw6quv4kMf+hDe//73I5PJ+BdgFMV922AuX1I/WvNfPLZ2ieuVG3cN4M7epwAAq5bMtoWEZDH10zUpjE4U0N7ciLlNxdwuNakUWjt7SvH2WR6PFIqRpWRyBLjlYHBCJc6/KKKx11csbsOdvU/hyNExbNw1gOULW0px7Gc01uPA8IhQ+1kOgJdeGcHoRAGT6tyVh0FyDVhh9bQGBXBz/J7RWMyXoRJ9SF/sdz4IqDsy84ino72plIdAFpF5o1I3nblEdOCWqyGOeoo+qybkUwirDrrWBN1E3ee689KYxMpNe/DA3iHUp2vwmY7zhPvThHlfKehe3/iACyyfmfXbAYBtTlMulcpDyVRsZGQEdXV1WL58Ob74xS/iP/7jP3DjjTcmcNPiHPNeNCfJhu39yOVHkcuPlsXnZuWy3Bx9g8M4eOSY7W/8OQVAOp67eP6G4HH+vRCNvb58YQsaJtfZ+oxtBgZzI8LtZzkAWN955ZoIkmvACr9p8UIkP5AbumK/y4y3zDziWbdsPvrv6FD68BGZNyp1izMPgRNuuRriqKfos2pCPoWw6qBrTdBN1H1uYg4RXfT0DaGA4rtBpj9NmPeVQpjrmzWfmfXbgZ/TlEul8lDauJx22mm44YYb8Itf/AITE3LJyUzDKeqQaDx8rwg9/LXpmlTJVt9K2+qeUj6TFOTzuixomYoUgENHjpbstfnY+gxrdKlF3duE7yHC9IZJtqMbG3cN4MjRMVufMT8F1mcsv4Tb9VbbdJnIWUF9MVg9RWBt8GqLG7piv8tE1lLN+WJFxW9AxO5Yxo+LYVpUMTdfHGs0N9HoOEER7U+Vfg8KP4eitEs3we+FtXdBy9RI6lLJPmId7U1IAdJRNE30hTBhbqoQ5jpsjUjXnK0vzWO36HUi71Ovfk7qGFQiSj4uP/7xj/E///M/6OnpQWNjI97znvdg+fLlePOb3yx0vUk+LmHhlEvFKY8LEMw3wCkvg1cOi7DyW4j6OXjZlovYnc/rehC5/CiymTQeW3u5lFpfly+GiMld0mzodcyLqHMEqWKiKYhX/ouw7uV3jzjmcJzPjcy9w55DSVs/iHCh+RANQb9PiGCEGlXsne98J37wgx/gxRdfxOc//3k88cQTWLhwIc455xzceuutypWuJJxytvB/y6RrAku7nLQ+XlIOlSzhIohK7rykWSqSLhm1vi7poogJlolSOy9Y0mrF5NUAwmuz7nJNNAWJMsOyaH/GMYfjfG5k7h32HEra+kGEC82HaND9fUKEg9LGhTFlyhR88IMfxIMPPoi+vj40NDSgq6tLV90iwcl8aF5XL1o7ezCvqzdw+auWnIv93R3Y2XlZya76knNOQ20K+LvzZij7BjB+++whHB4ZxSXnnFqS/G2+6eJilvrXNZS1bcXis44/fGf5li2jGr1w5jTMaMzgwpnThK9d1L0NrZ09QmZrbJzOnHYSmrMZrFoy+3h7xBcTvo6qiGz+fvvsIRwYzuO3zx4KdK+ouPXq89GczeDWq89XLoPNbwChq9RF56bTeSa+gKx9Z8q8icMPJE7fE5l7hz2HTPXBSSpJN/Oh+VCEJZ6cs2ZrKOV79TONgTkE2riMjIzgvvvuwzXXXIM3velNOHToED796U/rqlskODknijrne+ElkdPpEOlVltNvMpLCIOe6XWv9O6+5EOmzfUPDtsVDZjHRJSXd2XlZaTPqRtKcXnUuyrql0U7lBXEwN/kFFMW8MVHjlDRMnkNEOTTnKwMWhMcrGA9R+ShtXHp7e3HDDTfg9a9/PVasWIHXv/71ePDBBzEwMIDu7m7ddQyVmlTKdgRgc5YH1Jy6nSRyTOrD7sVC+Lo504vAwu06hd11chCXkRTKnHvoyFHb0e3aFYvbkEnXYCiXLyWRymbqfBNE8mZerC9nre5Ba2cPzr5li28ddTkbi0jvTHJ61an9EilLtzTaadxMNncKguq8EV2jlq7fUfKJ0+l0T06tyURG6x3Wved19YY+P3SvAzSnxaB+IsJAKY/LO9/5TrzjHe/At7/9bVx55ZVIp9O66xUZLJwuOwLl2S+sUlBRs67lC1vKpHFM6sNgUoMgIQO9wu6y8Mvs6FYvN2TO5SUhbtcuX9iCtT99HAUAx8YmsL+7wxZgwM3pbd2y+ba+5/vSOn5uBAn5a8UqvXPrH76+cSJSX9HQySJlycwbEZzGTfQeuusSNqrzRnSNsq4xQZ8DK17zQmTOEPEQZoh80Xvn8mPI5cdCnR+61wGa02JQPxFhoKRxefHFF3Hffffh6quvTvSmBXAOfcz7MASRnlslDk6hghd1/7J0H5WQgU4aIyZ9nd4wyVfKpKJNcrrGLYQ0L3HZuGsANcc9wE9ogoqbjpdeGbFpnrw0UUyCxlot4lQuGrLZDz/pnZOPlJfkSUXj5lYeK2tR97bS7yLSRjYHmfbLTUIWhwYj7LC8YUsFRcpn56iGQxZdo6xrjF9/Wp9zv3WCnFrVYBrjWat7/E8OAeu7LmrpOLs3C1Kj6/mWed5U26pjTsvUIYhVRpzofvZFU1Wo4jQmMuPEazBJ4xQOSuGQAaC/vx/33nsv+vv78ZWvfAWnnXYatm7dijPPPBNz5871vLYawiEzrCH0DgznHRMYBgmv5xTGVibsr0qIYJlr+BCCfuGbrW0RCdErE6JQVzhkP5zq7VVPlVDEbuXxfSk7t0wM+Rh2nUwo38RwyNbnBUAkz061EVZ4ehXievZ131fmeYtznZOpg0nzpJJxGpMg42TCPEsSoYZD/tWvfoU3vvGN+M1vfoP7778fr776KgBg7969WLt2rVqNY4LtkOes2VqSKOqQbrAygEJJ4uCUjNAv2Zjfjt1JAsHuM6OxvkzbwZfFJLVzmxqF6+Ak3XWrJy9xsWo7mISNSd6Y1oTXQHlpol56ZcR29MLNT0ZWGiI6JgBKvjdekieVENVu5VkTGqZrUhjM5YXmMWuTl59R2Lj1q47kmF73CVsjIKIxYnWYUs/mjpI8KVB9+H5h60hNKoW5TY1anh3Cjo4w5AwTtAgm3FekPBO0gDJ1MC2hrihJWyecxkRmnPh3uQnzrBJR0ri89a1vxbvf/W588pOfxJQpU7B3717MmjULv/3tb3Httdfi+eef97zeJI0LL6FmEk+GqnTDS+puvVf/Hd67cpUdu5v0VvU+OiVYTIoLlNdLRdocRBKlKg0RuU6mXmFJZUyogwwiWiQd0sao22qaZNWtPiLaUb8yiPigMSFMg+YkIUOoGpff//73eOc731n299NOOw1//etfVYqMDd7OtqO9qUy6oeIHwq7NpGtK2hsm6WQSeSbR1G0f7pbMbsXiNlcpfNA6iPohWLU07NwgyfdkJJa8Jk1VGiJyXfp4hdICFQtLKiMjpQvbj0QEt37QnTQ1aimYaZJVr4h//HrBJ7f1K8MUkibp1YHpY+KGyvtVlqTPhyj6KAySOie9SPpcqgSUNC5veMMbcN999+Giiy6yaVx+/OMfY9WqVejv946VbpLGRYQgvhFWCWpzNhOZHbtIfXRKdGUkKzqlMDJjQ3bC7pBkjKgkaD4nhyh8D5M+H6LyzyT8SfpcMplQNS7vfe978S//8i84cOAAUqkUJiYmsHPnTqxatQrXX3+9cqXjYGZnMarLTAcHcYZKVDHmO8Mk7u3NjYE0C7oIS6IrE7FLp3SfmZ05BT3gsfp/6JCYVJLkhZkEWU0Zk4yXn1qcuSuSRJLnt4ykN8ntjJow+iqKnFdJl/yblBes2gkyl86+ZYtw3jnCHSWNy7Fjx/DRj34U3/rWtzA+Po66ujqMjY3h/e9/P771rW+htrbW83qTNC4qUviNuwawYXs/Vixuc41N7lWuyPVJQyXSWDaTRsPkukD9oDMilyhs/I4cHUMuP5oYyYvXvKs0bZRXeyqtrWFRLZLFammnDqivCEIdevd4E6rGZdKkSfiv//ov/OlPf8LPfvYzbNy4EU899RS+853v+G5aTCPFHUWwJlVyw8suX+T6pCEjEWISCwCB+0Fl/IJK36zJL5MkxavEeeeGl2ZRt89MpZJ0KbUo1dJOHVBfEYQ6Mr6vhDvCGpdPfvKTwoV+6Utf8vzdJI0LY+WmPejpG0JHe5Nv5mqZc52k3EvX70Df4DDamxux+aaLtbVBhKi1PV59xX4rAJgoFAMZPHnbFaHXyYpKf8hcIzNXVJCpSyVq+niibOO8rl7k8mPIpGswrWFyRfcrES3V8KwSRBIx8dk0sU4qaNe47Nmzx/bfPffcg6997WvYvn07tm/fjq9//eu455578Nhjj+mof+T09A1hvFA8+rF74GWMF4pHP5yk3PuGhm3HKIla6u7Vr6wfJ45vnfOjE5HUyYpKfyxf2IKdnZcKLRAy80oFmfrL1DupRDm/c/kxAMV5Wy2aLCIaqkk7ShBJwsRn08Q6hYnwxuWhhx4q/XfVVVfhbW97G55//nk8+uijePTRR/Hcc8/hb/7mb9DRkSy7vXldvWjt7EFNKlUydfJz3hVRl7MyDh05WnJEZ46NLJkbS/rIzmWOxBt3DWBe14OY1/WgrxOkrLOkDqdVkXuy8I0zGuttJmTMafrsz2wpJTtkatOaFFxDPvL3ZP+es2YrWjt7MK+rV6j9PHx/iLSNzRmve/LzyivBZ5BQl17jKVOuqMO6V5ms75au36E1dCcb4zlrtrrek08mOb1hknAdVB2OWVjzTLrG85kKGso0Kudx/j46EvHGRVh9FlWfqJhkqbRZ9ppKC2SQ1DDDYaF7fHWXZ4Jzu4nmkibWKUyUfFzuuusu3HHHHZg69URUqKlTp+Jzn/sc7rrrLm2ViwImNR2dKKD/jg6sWzYfg7liFnZ25BGRXLNr86MTJe0M2xUfPHIM/Xd04OCRYxjM5Uvn9g0WNTAbtvcjlx9FLj/qu4OW3WnLSN3dyha5J9M0HBgeKfUrcKKNo+MF7B54GTs7L8Uzn78S+7s7kAJctRP8Pdm/mZaGjaMsfH+ItI3dy+ue/LxiY+1UbhCtjNd4ypTrN+dFymR91zc4rFXLxMbYSSPHjxfrj31D4nVQlVY9tnYJ9nd34MnbrvB8poJq3aKSpvH3Yc8qOyaJsPosqj5R0Y6qtFn2mkqT7IatEU8ausdXd3mjx80z2DEOTLRcMLFOYaK0cXnllVfwl7/8pezvf/nLX3D48OHAlYoSJjVlR0DOeddNYsOuzWbqSjthpwRvzdmM7X6Lun+JBS1TXRO/8cjstL0k107tEE1U53Stm7M+a2ttCqXrmRRzSn2dq4O/W98xFzddvm4i/ek0Z/zO8So3jFCXG3cNYFJdDVIQK1d0znvVlbWxvblRa3sy6Rrb0emefL/K9GnYyTdl6iLzHOqGv0+SgxiYkNQ1aoIkKxa9ptIkuxRm2I7u8dVdntWpvfV4KguGn4XIyk17bMdK0RpWI0rhkK+//nr8+te/xl133YULL7wQAPCb3/wGn/70p/F//+//xX//9397Xm+ic74qOhNDhR1q0isUX5B2qIRCtrYxSIhACi/oDIUtFcekvjIp0ZxJ/UIQBMFweu+7rVfs7yzxd5wJwAlvQg2H/J//+Z+44oor8L73vQ8tLS1oaWnB+973Prz97W/Hf/zHfyhXOk6YpJPt4q0PhhcqEhs3LU1Q6YSfPalVYs37ijC/G9YOGdtUrz7gy7FKt5m9qlP9RIlKArpy0x7M7OzBnDU/l5LUqCQ7lOl7t3PDlIyabOeu0nfM10q2r7w0mKrEKQHm1yXrHDJ5zHXh9qzG4QdBvhd2wp5/XuXrWI+JcnT3lZ+FCEv8HTQBuO56s2d9Fvf9yf5j7xnmV+vlYyfjH51klDQujCNHjqC/v2i72NbWhoaGBqHrTNS4MEmnlbAk+GFJVUUkpPw5flKKoFIJr/s5ZWk3VWtinR8yfRJ2gsw4pOImS+Kj7LtK0/Z5rUsmj7ku3MYzDi2YSZo3Ewh7/nmVb/p6nFSS2le66+307SmC0zuH1Q1IpkYpVI0Lo6GhAe3t7WhvbxfetJgG71+hCy+JmU6pqvU+IhGy3HxFvPxYRCQMbtE+nO6XzaRx5OhYmV+KitaE7+ewJA4d7U1IAcika7GgZapvf7A+Yz4uMn4CMtoSr3NlJEMyEl7R+nmVKXM/mXbo6jsRvHxvVFGV5ole53Uevy5Zzw1Lg2eShNrNp8faLyoaVBWCviNUtARBbP+DRDRj7+DWTvf1IGzfGq/yo1xTqgkT+0pkHuuuN3vW3fx0re8X9j3h9q3Evq9E/KOTTCCNiyomaVzCkppGJTGLQkoqUo5MP+qUWPDtj0LioKLZigOZOoQxX73KDOoXVamotlX0OtOkx0kb26Ro2VTGOYjtv8o4WtdqBmmYiDhJ2npUaUSicakEgkTO8dqdz2gsljelvs53By8b7cvK3KZG29EK8yeZ3jBJSZLG7j29YZJvFDEmLeClBk65IU68rAqO54jAJJ9MxVqTKt7YS+KgK8+BiMQl7EhVIsjUYVJdje2oQxLuJTV2+k3F98strwYra+n6HbFL9GX6cnrDJNtRFNGxtmo8eT83vn5RzGETpa5emBxpTVVDxvsAqOSOYX5iMvmT2Pyy9iVF94oX6xzS8Q5Imu9Q0tajasU9nmvVkOKO4lhjlPPxsw8MF/Nh5PJjyOXHcHvPE6Vzlq7fgb7BYbQ3N2LzTRd75qmwxplnuVCsHDxyzHa0wjLTs5wWTvV0Y1H3tlJOj31DwzYpGKvT5r1D2Lx3CO3NjTi9seizcnpjxrOPrPkPBnMjNgnm7T1PCtePzzfC4rovX9jiWobXeLmhcg1wou93D7wsfI1uZOrAz0HVdltZt2y+45x1+81trnuNqVteDVaWNTdSXDHuZfpy39Cw7SiK6FgvX9hSVh+3+kUxh93GduWmPejpG0JHe1NpLrA1qTlbj52dlwmV71SOKBt3DWDD9n6sWNxWqqPofePAOo4yOR28ni/RewLAzs5LS5pUt/eVFTa/gJTR2qtqgs+7EvQd4LX2yayLOt5HIqg8C/z3HBE+Va9xCbLDFsnNwRixbEr4jy0vW3k/W2cR21wVSZp1Y8Dfm29b3+CwcM4XLz+WkdFx4frx/kgi205deQ5EkmqZILmRqQMvSY6j/ip2/W5R5VhZ7c2NiRoHVd+GIHb4os9ulDglBhRNkupXjihJS7YYx3jx95SZvyaskYQd65joGJ9q8B1KcqLepFL1Pi5h4yTxc9uhz1rdg4lC0dzqTxHb+fJ1EpFuBpU0zOvqRS4/hnRNChOFgpRUlElDF7RMxe6Bl21S0bBxksQSBKGPODQu/HNNzzlBEH6QxkUfonsD2rgYRJyOn0lxOiUIgggDcswlCIKID9G9QdX7uDhJ7jds/6OwZM/tg99rF+4mOaxJoaRxCQOvzUlztr5UJ1mqTTKpIvk1naSOoZuWUkTSTpIywkpx7e830hyFIAi7760f6ZpUyfeVUXv8G4v99XPXnJ+o9x1RpOo3LsyO+cBwvuTArmJLzeNl9+hWftTmYXbCCVJQieiYH6aR1DFk7yXu/eQb1AIg22TCThAndYIgwkfmnctvWgCUJXpM2vuOKFL1zvksJOPcphNOvDoSy3mVwZIIZdI1jom/wkrO53U9C2epO0gBDwtj3La6RzhZXpDEaF7lqpbHxo8dnZjX1YvWzh7M6+oNvT46cAt9a0LdvHALw+0VJpzh5tgvQlj9YlJ/m1QXgiCSR5xrSNrBdKU2ZRfNxqldpfVVnarXuLCQjAePHCvZNa/96eMAgGNj5eGJedz8QbxCHDdMTiOXH8OxsYkybc/yhS1C0mKGjKTcqa58OEsVZCSVTGIiEp7ZSRumQzoSVLvAxq9hctr1nFx+zHYMsz46cAt9a0LdvHDTUnqFCWcEMQ8Lq19M6m+T6kIQRPIIcw1Jui8ura/qVL3GZej4Rzs7btw1UJbUUAUmxc1m6kraE7bDnt4wCbWpYnJKoJis0qqxYMkr2dELL22HyI5eJcygU7mtnT2l/7xgWooUgENHjtquO/uWLbZyWd1YP1iT8rHEg/x9RRJeBdEuAWKJ+Zi0x0nqwyM6BrISGpnz3dokMz90JRvjkdFAMsIOn+mUyFFXuSr1lulfvj/drpVJhqkyRiK4JRiNi7ClpFFLYXWNGytnUfc2aWsBpoXXPcYk0Y4f3Uls3TTsScTkEM+mU/UblwJ3tMbsd7KRFGXzTRdjf3cHDo+MlbQnbIfNEkIyafyB4RFbwjCWvJIdvVi+sMU12ZhIHgKv690Ikt+AaSmaspkybdToRMFWLqsb6wdrUj43vwSvurHfdg+8LN1mKyKJ+SaOB+ubEAjaJzoGsv0uc75bm2Tmh0jfq8wZlVwcKvNahuULW9AwuQ65/KjWPB+q9ZbpX74/3a6VSYYZJF+KF6b5IYWd2yXq3DG6xo2VM5gbES6PtZVp4XWPcdLy8FQiupPYsgTXfKLrJBL2O6qSqfqNixUmjWf4+bh4SauYFIlpbeY2NZZ22HObGlGbKkbyqk0VNSvWckTs8xkbdw1gXteDmNf1YEmypEuz4EYQSYFViuvkI+IknXFKaubml6Ar4ZUXIuWwcQ+itVO5r1XKKCMx5yVjKv5FXvULInmTeR4YIvUPKpE9cnTUdpTB7d6qEnCZuc33p9vYyCQTVE2c6UcQPyQvVMc+Ck2eavkqbZpUV2M7qt6LjT9b00WeVdZWFslS9xiHOVaVqs1h3y2Lure5rkX8373WrBPvnoKWdZi0FARAeVzKTJuas8WdvEg8/7bVPRgvFD+2+zlbe6dyWVl8vgC+HJl8Auxc6z3iyEcgmgfGqc+8+krXfaMmrnpZx575BjnNT6/rrHOoNlX0Rwo6l4LMSZVrReof9DkJMsZu9/ZaU3ThNtbVlL+kEtus0ibVOex2r0rsVycqtZ3W+cDWTn4t4tcorzWL/cYIYx0mKgfRvUHVa1x4W8npDZOEd/VeUkamrUnXpMrK4iWcfDnW+/v5jjBb+2wmXboHu35By9TIpEIikbaAE3474wWUJDRefQU4S2SYlIeNX7omVTrHTYLjJYWXkfqInKsjMp0K1rkjo6ng5yQrp6O9KVQtlVi/8wad4vfzqn9Q6R2TFKvkPnK7t4p2SRa3sebrEpbfShT4zasofImiRkWrKbpu87j1X7VIxCu1ndY1ze37hv+713cQ0+S5vduByu1LIjyqXuMys7PH9jnEHkC/5HV+WDca7KFktoy8lMsrWZ6fRMzr2nldDyKXH0U2k8Zjay9XaocTTskKRSV3vESn/46OUnlAAYO5kbKEgE4SGV6Sw8hm0hjOj6Jw/P+t7faSwvP38ErI6Cch2rhrAGt/+rgWTQVfrkySyDlrtiI/OoFMugZP3naF57ln37IFoxMFpGtSeObzV2qprwgi0jZTtWphoCp9FEm4KXsPtjamADzr0+/WuQlAeJ76zWnVZK9hSXFNlg6r1G3Omp8jPzqOTLoWT9729pBraIeNbSZdg2NjE4Het4SZVNPaTQSHNC6C1B+XiNek7JsWnY6mfg6CQe4XllOsF7qcHpmExs9J00kiw6Q8VpiZn9tO3EsKz9/Dq41+EqIN2/tLqnOdUiTZfh85HvxgxCEkNw8LRBEkIIUKItK2sPwcTERV+iizDojeoz5dazt6YZ2bMvPU71zVZK9hSXFNlg6r1G1kdNx2jBI2pvnRicjfYQRBJJeqz+MyrWESBnMjOL3RLtFj0kvghDRzblMjDh45JiRJTNekbB+BLwzncfZntmCUUxO0re7BjMZ6HBgecVS1+kkpWHHjhaL0cvnCFixdvwN9g8PIZupQmwIuOefU0vm8FFVGUss4dOSo7egFk6guaJmK3QMv2/pl894hbN7r/7JyyhOzbtl8rFs23ybR2dl5KVZu2oMH9g6hPl2DVUtm267p2rwPoxMFvPTKSJlWgb/HgpapODCcVwzjWGzfjMb6Upmy2hInVixuK5XhNm5OJoUa4wMAcJfuz1rdU8pgz4QAF86c5tlua7+7lfvMS4dtR0C8P2W0TmEh8oxZ1xgekbZatcR+/OtPH8dEAVjzk8c9y53WkMZgbhzTGtK+dbDOzTt7/wBALGiB9TonmrP1JY0LD7+2WOvGP898/dkayWt3/bi95wnkRydwe88T0s+x0zxw0yip1O+3zx7CgeE8fvvsIeG6XXVB+bzh+8r6798+e8jWBrd54fR3/m9sbBmygUx4q4YjR0eRy485aufY2lSTcs/9pBPW1kNHjiI/OoFspg6PrV1i+82pf/lx48vJpGswrWFyoPeITP1F7+N2fjZTh1x+TNoU0RRk+4Gfk05rExGcqte4OEn01i2bj/47OkovFybN7BscFpYk8pLriQLKNi1AccNxYHjEdj9VWL2YxiKXHysLRch7C6hobLySazrVaTCXR0/fEAZz+dAl+rsHXkYBwLSGyWULhYxWwSuMo4qUWIeWyho+UWbcdHe5272t9+FDgKuE6WU4zTfRcmXmaliIjJXXGiPSVn7N8oKNUwHe2mDrPParg3VuyiRf9QsJurPzMuzv7nA0E+PXFq/+4euvGmY5yHxymgduGiWV+qms5U7zhu8r679FQ2k7/Z3/GxtbRpB3w2AuX5pvTto5VnRUCmXWVjZPrM+CV//6lZMfnYgkxLOu0PuPrV2C/d0dpU1b0gjy3hZdmwh5qn7jImKGwsyS2psbhVXxrDwmQ0rXpJDmbZsAzSFEC2jt7Ck5hDdn68vqm+KOKmFMnRw63RyVefMsEcmLSNJGN7zMJXQlhfQzyXCaU8Gc08uRGTc+QICT07VMYi+3e1uvZb/LmK+4lesU6MCtXD5hoe4gCSrjJTJWXmuMbvMkNk4pQHgey9TBbS1QCbHthUwACb7+quaHQeaT0zxw6yuV+ukKSc33lfXfXoFkvMrwOlc1QIAV67vFSTsXdeJC1lY2T6xt8+pfv3Iy6ZpITBVl1xyTTSiDEKRdOoPbEHaq3jm/kiBHuOQRh7NvFCF34yLsZ8Bk52zT0R1imyAIgqgcRPcGyTQ8jBgV/wRmu2zlc9ecjw3b+/HCcL5kb3t6Y0aqXJ55Xb0lNTSzJ/WS0qnadvvh5kvgZVsOFE0halJ2Fb7VHphHxSeHIfNR6zXmrM+96ik6Z/xs/FXq53eOkz+EVxQ8Hrd+FLHV9irXbWyd5qxbee3Njbb55VRmkGcgmO/TCXT4POnCrS7WvvPzVbLi5rfB5rrV7tsPp2fNy7dFhajGwqQxFyGq+lr9Lm87/o7UfU+ZCHlhwNacdE0Ka5fOtbWNzXH2HnRal0goGQ1B1xb+Oyhpz3xSqHpTMRFU7Byd7GxZOVZ726D2j1bb2cMjY9jf3eH5MaZq2+2Hm+23l205+3/e7tjLNj6qKGpeYy5iwy86Z/xs/IOU73aOnz+E6pwUsdVWia7nNGfdytt808W2Z8CpzCDPgJfvkwy6IvPpwK0u1r6Tqa+b3wab6+uWzRee807PmoxviwhRjYVJYy5CVPW1+l2GdU/etzNq2FozOlEoaxub2+w9qPvdTIgTdG3hv4OS9swnharfuPA28UC5HTuzc5RJ6Ohkr8vKsdrbNmczmN4wSTjRG183q621iG2zjO20jD0/7zvD8LItZ//P2x172Y/zNtYzjyfnnOmSoFMVL9tWEZvssG1+RcpXSUgHePs9OMHmyYKWqYGSjKn4uLg9k6xOc5say8oMEl5Z17iKlBNVskO3uliTYerwcVHBa+zDTo7qxKLubWjt7MGi7m2h3kcFlflibY/be89aX9F7eCUv5X2drITVR27vp6CI9gdba9I1KVcfH/YerKSw7yYnbHUi6NrCr1eV6vsTN1Xv4+KkgnWzY5exb5c5V8bngC83TJt7mbJ1qLJV2kIqdHeiGr+w/T686qbjWTWVuNsQ9/1NqYMVk9eboOtnczbje73oPbzeabyvEyOJPnemzU/ToP4hZKAElILIRICSkWDLnCsTDYYvN8wdfdTSgqRIJ5IiRZLpzyDRksIeNy8NiUxUo6QRdxvivr8pdbCiU5ukG5W+smqPVbS4bmuh1zuNl2q3N5drRZOCafPTPOI20iMqkarXuMgQlsYlrDpESVySyDgiZJk6BgRBEDLIrmVRavyJ5GOyhpIwD9K4BMBNisRLV7wk7+xcJ/8V3v7Xy2669bgPB1sAvCQ8fH3YfRZ1b/O1N45Ke2C9H/t/a/vd6sLXk/2bZVueVFfjeq4ITr5Obvhp0zbuGiiN2Zw1W6XvE8SO3jq3vOzMeXT6Csn0v0gdZdohUh9dcz5ovZxQrZvonGHnta3u8e2PpGgWrfBrisjzXA24j6WcRFwmBwlBBPEn9EJkbdKdN4owh6rfuDh9sP3rTx/HYC6Pf/3p47Zz+QhQXQ/sw2Auj64H9pU9SPc98hwGc3n0DQ5jvAA8sPdEVKPNe4vRejYf/5tTFB5WHo9XFCo+ggW7z2BupFQH/gGOOuqFtW/v7H0Kg7m8rf2DuTzu7H3Kt23s3yzbskhWdf5D0zpm1khTfovcw0//BeOF4pEvh92fwUdZE4loZe0P2QXXGgmKn2de6FTos3F1GkcekShxDxxvB3uGZF9aa4/POZFM1TKEEeFOpu+suEXycjtvvIDSPWSyn+tG9+aI1Zm1kyI0FXEbS+u8EdmI8+8f9p6775Hnwqu8BpK4CY8DGQGeCHyUx6AwgQS/pjuhO/ogYQ5Vv3Fx+mCzhiv2YvS4Z+HoeME17C+j3sN3wCmrLytPBi/pV20KqE/Xlj3AUUvMnPqWtbvWI+SLm6RP5FwG/6FpHbNS1JfalPQix4+9V1+KSKCY/XxNSj40sa7s2VEhUt/6dK3tKBMKmo15bQrapcQm9bWo74XT73H6CeneHLE6s3ZWUoSmILiNpXU9UtmIhxVeXzcUllYM08fTKnjxW5t0Rx8kzIESUDrQnK0vJVCzwiezsya7u+4tZ9gSCbLfimXYQyBm0jWlJEUAcOvV59uuXblpD4ZyeWTSNfi782Z4JmvjE7S5JePbfNPFtmRIjOULW6RziLAEc5l0DY6NTaCjvaks8Z8b1r5dsfgsW3ImVm7D5Nqy62TqyZ/L2j23qRH7hoZLH5rW5I/sfKc+4rnknFPR0zeES845tawcdv/fPnuoLMkjACHJE0va51UXt8RW65bNL+Vn2f/XI0JjoptVS2YLJ9W01pfBt21aQxqDuXFMa0gD8E7aaU0gBgDTGyaVxpz1k8qcd6qbU92DItN3VqyJHv3O4+eVW39YpelhJU/zGkuGTBI30bGttsRwbv1iXY+s7zc3+H4TWfdN6GuReUaUJ/ANiu6xt34/+PlUqazzJsxVwp+qd84PK2xxEGTuE4fzWyvnB6GrP2TawsZNpA66HUhFygvbaVV3+SY5UfJtU5kXYTkPV5MzsilzIow+r6Zx1IlKv1FfVy9JG/uk1bfSIOd8QZyc6FVCPOqA2ZcyR/OaVMrXJtcpjC0rZ86arTZ7VVU7X972mWmiMuka1KaKCermrNmKmZ3+jspedtQyjnxs3ERCafolK5SFbZjcTPk27hrAS68UVdrTGyYFupcbuhMYOpkrBkV2vvGJLNl4sTqlBSoXtvNw2OZTYTj8i8KPl0ii1Shw63Ov+eU39+JwKg/bzyIKPw6VfiMH/upFNQmyH2GtkzRXk0HVa1wYVi3HjEb/RFxhwGsyAPjWwUlC4FTO/u4OZWmCnwbIqv3w077EEb6YoUua4ieJlumPMIk6gWiQ+zudz2vVdNbNVEx6PuKsiwhe88tEyWnStLAEEZSw5qTpaxOhBmlcBOG1HHObGktS8iDScib9Wrp+h3AoYqZpaM7WI5tJI5tJYyiXR2tnD2atdg5T6yQhYOUwLQz7t6o0gdc08dKOFYvbSlLxGY3ezsFzm4p1qUmlSmEK+ZDPIoiEeubxGtdZq3ts/RxEemmVLvn1hxNMU8aHUrYiEsJWRtoVhnTdq7+d6u+WXFUFa3Qcv7E0KeKQaGCQMODXB/assqNpeK1nKmtd2PMgbGluUOm2X/tlpdwmPVdhUQ1tDEJYGheTAqMQ0VP1zvksegYLW3vwyDEcGC5KefcNqUfWOBGWs1hWT99QmSOvNdLJ8oUtjo7b7GPe7UPGyQHNzQFc1SmZd0K2Rp9Zt2w+li9swdrjoaMPDHuHYz145BgAYHSiUCpHBXYtizLi1L88bDydxpX/YOTHxoqf1H/3wMul//frDyfYXORDKVtxqx8fPWe8YK+PG4dHxmxHHXj1t1P9dw+8bKsvm68yG1qGNTrOwSPHXMfSrS5xEWeeaX59YM8qO5qG13qmstaFPQ+CBIUQgX9+ZPFrP7/uBy2vEqiGNgYh6Jx0I4zAKERyqHqNi1XLwaRhfhoGEZiUmWkiptTXlcqZ19WL1s4eHDk66iuN8PM9cEo85xWL3SuXiShMi2DVJrANiNtGhN2X+X6k4KyNEO1nNkYMLwk1ayM7hSWttML3s5d0lNfO8KxY3IZMugYpqEmEnPyWnO7hF8JWRsLrN35W3J4H/u9eUjGnurnVl/m2WH1c/Oat9bk+cnQM2UzatR/CkgqK1tVKGL5G6qhto6Ly09Et7TbZvt2pT/n2B62/3/UiUm5rnUTr47ee6oC9c+d19WotN4w5s3HXQJnPqIgW3kRM1Hyaem9CHPJxEUDFnpJdY6U25fxhGMT+08k3wctfgW+Lig2qU3/4+UjI9IdMP4v4Zuj2lTAl2pJOZNrk9jyEZXfsNEdF523SIsCZ5KegOs+jsj83qa/CxqlPTWy/Sp2iWE+TtGY7+Ugmqf5BiXNem/hMVRPk4yKIk3aC97lgkqa5TY3Cu3H+gzybqSv7GxOqDh73Y2H3CyqxtEblau3swdm3bAFQlNqwOlhzmVglIl4SB1ZHVsaUenFLQ15DArhrSQrH72XVIsngJom0sqj7lyVJH+sfK0HGoCgx+7lQlDVV+DYyzdvZt2wpzWd+HstIk6zX8s+Hm+RVViLLwz+L7FynKG5sTPlnh8dJ4sf7RwEFX41LEEmcnNRRTcsh48fjNFd0ImN/LtOvMhqGsPyaRNYFlbLdymVlzW0qj56oQ9pvratbva11459RXhOgEsExCi2jKRHyRHDS2Ito4XWg4m/qBT+nRJ6fODWfJmtdiRNUvcZFRmOhGqUJKG5SRDp6f3eHtjwu/G8y2gmnNrpFK/Orh18ZXvhJl5zu69YGr3v7aab87mnFKccMS1qqC69cJ074RZXzmivWv+uut1cdRDRl1mtE+9lJ+wd4az7DlsSxxGfW9sr0t1/92O/ZTBq5/GjZ7073ikLKG5Y2SrQ/dEdYVC3brdwotYEAHO9lrZv1ufF6p5DkOpnofuaTFqmQiBfSuAiikjtEdjdemwLqHSQlqeO/8eiKmMH7BgTxnXAiaHZdp7wctakT9WP5YmRxawO7XQrFj1Sv/CBeY+DXj0WJWW1Jo8YcWnXCt5H1FWuL09gEkSbpyqTsVQf+WZSNKibaz2xsm7P1qE0V7+fXL2FL4timxWv8vPCrn1Nf+t3LybdINzL9qvNcXREWdZXtVm7Y807EJ85aN/4ZdVsLSXJNAOXzgKKBETqoeo2LEys37UFP35BWKTmTqLIHmP2/NRKJqrTDWrZfZBO3c2d29qCA4kf9sxHb0Orsb2v7ujbvw+hEAemaFJ75/JWaaitHGHOJKEd3P0c9bjLPcBBoProT1RgQBOHPvK5e5PJjSAG47Zrz6ZmsAkT3BrRxcSAudabqxkWHCUWczn86+9vaPlWzG4KoVJMGMuFxh/qGIMzB+k1Cz2R1QKZigjg5i+lIvCbj3M0c2FRxc4h0CqHopsJPcUeR+lodL1Wc2XnHU5ngB+xa5mDMO4euWNxWZurC11vVQVd3yMQg5XldK1OuU1htHQRxvmYECWWq2reVatKwYnEbspk0jhwd830OoggNalL4UTJvksek8dONV1oBQg6VecICKaQAeiYJG1W/cbEm1WLoSLzmVK4bzL69OZvB/u4Oae3A8oUt2Nl5KXYPvGxLPuiUyJCdy6tdnz1+XxEzMT7JISDXXr6cg0eOof+ODluiQNFrR4+HJmNttLbvmc9fif3dHSUzMb7eTu2QqbfsdWGU53WtTLkskSc76kKmDm7n5vJjtmNY97eybtl89N/RUXHmVMsXtqBhch1y+VHf50D3PHciinuI4rY2Eu6YNH66sSaxJYKhMk8eW7uk9E1CzyRhpeo3LkyyWpNKlaS6OiRvMlobXZI+vpywQig61VdFQs2Xo+J8ywceCOt+XuU4ISNhCjL+XtfKlMuc+1UDIqjUT/TcIPOYpOjliD4HYSfm9Lo3kQwqefxkAvcQ3uieJ0wbtqh7GxZ1/xIrN+0JrNknkgP5uBwn7DCARHVB468P6st4oH4nCMJE+HD9LFR30pL8EnbIx0UQ3r8kheDJBxd1/xJHjhZzJRw5Ouq7u2eJEGet1pP0STciEgpRKQbzV2DJGRd1/7Lki+Pmw+BVdli+GUHYuGsAL71SNLma3jAp9Hu5JfiK20bbWpcgUi7rsxQGJkngdPks6YDN3bDncBiY5Icmgt87x6Q5ShCiBE2m7QbTgjVn69GczaCjvSmUsOqEmVT9xoVP+laAmr8GX57VLt/PtpNlkHfLJB83IvapojasrF9YPw/m8iX/FDcfBq+yw/LNCMKG7f0l35t9Q+HaR/N9Y527cdtoW+sSxBY+iI+LCCbZ6evyWdIBm7thz+EwMMkPTQS/d45Jc5QgRAnyLeXF5psuxv7uDuzsvAw7Oy/FumXzhf3TyJct+VT9xoVJE5mvRHO2Xspfg0kUmCYBKKA5m7H5DLDdfRhSsygkcSISikNHjtqOblj9FOY2FRP/sb+xKCIy92fXuF1rRVdf+ZWzYnFbKTrblHr/egXBK8GXjG9IkMhdbljrEsRfwi86nChu15kkgVP1WVLRePqdqyO6YlBUx1r3mIY9R/zeObr96oJcowurn8K8rgcxr+tB0igZCK81kZkzNamU7UgQOqh6H5eg+RrY9VbcfGTcbCuD1MEUe01RHyFWX0BPbHYdOWzCuGeceXFU6hB2fYP0PV831bJMeVbCQLRtMrkRTOgvE+qQFFT6Ks7+5f0UAMrXYSL894nMnDHhPUgkB/JxEYRJE8cLxYfM+p+fXebGXQNlmxZme8mkEkvX7yhJK5jUbHrDJJsEg0nbvPKYuPkrOEmymYRkUfc2232sbdMNk4Zb7+OEtZ689F0l58cLw8VNEPMp8SqDl1qq2t+KaJfC1gSJ5N5gQ1IjIOzi8/jolsT6SYy9xoLXHAWNBsfnOzKJoBoGv7bJ5EYIS8ovQxzaMKc2Oa3npqHSV0H9mIKMv9VPIZtJI5tJG6H1VKGSfZB4baDMPJN5B4kQhmUAkTyqfuPyuIf99ua93naZd/Y+Zfv3/u4ObL7pYgDA7T1PYjCXR9/gMMYLwAOWstjf+PKfeemwqx2zm7/Cz47bkP7MYkPK7EoHcyOlewdJcCni5D0hqLiz1vMXT7yIttXFchd1/xJ39j7l2H6nlwKz+WZ+QcynRMYWXNX+1ik/Do+MX4aK/bpf7o07e5+S8p0qcEfdNvV+dsVeY8H3t4iNMtsIsbm1cdeAa74jk1Dtd9G2OeVGcNs0ivRz2L4XcdijO7WJ/Y2t3bpt9nV8+Kr0VVA/JrZms3ehTDtaX9dwXOg2DauWzEbD5HDNasOkkn2Q+JxWMvNMt/9u2P6OUVHJG90oqPqNS1gO8SOj47Z/16drywIBMNhH28johKskwy2mvNPCwCQkzdl61KaA+nSN431FEXHyZvf0w1rPkdFxjBeK5VrNx/j2O70UmNSHx0saxJejmh1dRpsigoqk1O0ap34RqScvGYta0q07Uz17ptjccpo7Jkp3g9ZN5fogDrQm96UqTm1if2tvbtQ6Txlxffjqfu5UBUdJ//CvxOdAB7pz4eh+98ZF0ud73FS9j8u8rl7X3XsKRQl0NlOHhslFNfbyhS1lduLs71aWrt+BvsHhUhmZdA0+03Fe2eZlf3cHVm7aU1rErX+XrX9zth47Oy8rO4eVP7epEQePHHOsr5ctKmtLNlOHwyNj6GhvUs4oPrOzBwUU+7YpW4/B3Agy6RocG5twLXfjrgFs2N7vWG/W/mymDo+tXeJ5b69yZBApZ1H3NgzmRlzHRFed3K6XKXfjroGSxHTVktm+57P50N7cWNIwAmLzTAVWrtP8cGunSF3c2uFVrvXvn/3J46W/s48/1edCBmu9W1/X4No3TqzctAcP7B1CfboWn+mYg+ULW8rWnxSAZ7s7sHHXAG7veQIjoxO46oJi+W5joevZUinPaxxlcHtmVcuXfQat5ybFN4Cvt0ib2TnTGyZh39AwOtqbsP+vRxz7WPe8UkF0LQ8T63vz2e4OzFmzFfnRCaRrUjjtlPpY+wdQG6ekzHEeHXPSWsaG7X/0jIqazdRhOD8G64d6Jl2DJ2+7omxuOq3ZbrB1LV2bwtqr5hoRZY18XARpmJx2/Y1NFK+Qxm4q04NHjtnKyI9OlFSsPEwVq4K1/m6Tf/fAyxgvFM0BVHb5LPTg4ZGxwGYSVpOkA8PF+uZHJzBeKNbTCS/VNDN98du0+JUjg4i0ZGfnZaVwjUHLUrlepq0btvcjlx9Fw+Q6ofPdNHBeWo4g8KYKfN2d7sWu2XzTxa794KVJ9DPF4/8ehvmQG9Z6y2pLevqGUACQHx0vtYFff6zmgvnRiVLocna90/3iDD+sK+y3W2h11fJl2pDUEK18vWXMCw8eOVZ6rt1M1kyQTJsQcp835WVms6MThdj7BzBjnKJCR1utz4nfvMpxmxbgxPjzc9NpzXaDrWej44XEjVvVb1y8nBKtYXqtjq9WmK0iS6bIbBb5ctMe3mksiaIKKxa3lcx7WAhmZrPOEjseOTqK5mwGc5salUPSAt5mBaI2m1ZV74zGettvQ7m8tN2njIM9X0dVO1O/hIgbdw1gzpqtpSSbXoRpGiTaPha+eTCXF3J6dFP/s/nR3tyo1WzCqx1Bwix7mTG4BXKY3jDJsW26zG1E5rO13rKmPh3tTUgByKRry9rAB2iwrmEsiInb/eIMP6zLHMUawt6tfJn1JmrzobCS/UURqCOqeaWC27yIEv7ZZN8m6ZqU1v5RnUNRjZPb91bQc2XQ3Va/eZXN1IH/emTjz8/NFYvbkEnXIAX/dwJb19K1qcSZOFa9qRgfzpj5BziF+3MKA8j+VpsqSl3Zb3y51lDHvIpUt8rULURzmKEvVcp2qicgFxJTJpQ0X0fV/vAbL2vIZ9Uw2zqolLCVXu2IKpxr0LDppt1Hpi4AhallmDQ+PGHVjUJSVw8mz2/A/Xsr6LmEGZCpmCD8pmVBy9TSRycfYndBy1RXp82O9ibbb/wHuVW74CchtEbxYlqTOWu22s7xkoIx6RW/K2dS6cFcvqw8FZimqGhnmS+V7VVPa3ucHPrdJBlukiDWz+woEg6Zac6cxlMEPiGi0338pB7WvmNYE7I5SYpkpGErN+3BUC5/fA4UfKPCWYky3KuINJdJ/l96ZaSsz9zmHR/aXHS+888b6/MZjfXSWpWZx+8900ObyrdfRoPiFe3PaX5Z/962usdV88jaPKmuuH7UpPxDJ8eBl0TVrU1B57XI+MQVMUi3oz0jbGl6HP1FUZ2cCWsO6cLteyvouVFD8y8YyQ7NoBkmhWfwIXbZOVaWL2wRsktm/hwAfB08nWyq+dC7VjtL/v7rls139Aew+pB4hfIVRcT2l6+nNbQtq6dVyuMmEbHa1ns5nHn1Cxsrq0ZERQLD5sWoS0g6kTnh1HdsvNnfDgznMV5AqS2ifQCc8GU4NjZRKk/URl/0HjrwGi8Gs3tn/a1iay463/nQy6zPDwyPSEsgebt0J/j2uz27Tnj5Xrg9m+zf1nnF14G1mfVBCjDS94LVm39OrL+pPDteiIyPyJwOA5m5I4PoO06VOPorrjEynbDmkC5k5mLY8zYINP+CUfUaFx7rrjxdk3KUzJ99yxa0dvbg7Fu2CJfLbMRFsGpk+OR71nqqhNBlsLYFSXooYvvL19PaHhlptqgkSKRfrOeISmKtbddhU+/Ud9aEbE6SIhlpmPVcVm42UyfU1iglbiLj5aZBBMptv93gnx+/89gxiARSpG5BpNle89Dt2WT/rrVoUfg68OHUkyh9dWtTFG0xwS8jSYj0F0s8ePYtW7RozpI+RiSxTzZJn39xU/U+Ll642fbK+AOYah+sUi/dbTHBnla0DqaOowwm9DdBEIQsfPCaal/DKuF9RBA85OMiidVenNmBs2hc/K7Y6uPgJ/lg9vle0cviQGfSQ1VMsKfVqckxHRP6m6gsSPJLBEVkDrFolOmaFK1hqIz3EUGoQhqX47iFIw6qUSEpN0EQlQpJfomg0BwiCAIgjYsWePtwJ8nQoSNHbUf+XJY7xSohCltK6Va+NcqSTF14PxB2zdL1O8rsjVX8f7yiI/nBbJ9F8o9UGzKRlIKMQdjzOUjdnKhmLcHKTXsws7MHc9b83DefkUg/xRlxKo5xrLT1xtqHQfozyLVBcjERZlPNay0RHlW/cWEPlhN81nOnjKnWKETWB/TO3qcwmMvjz4deK8v6/dmfPI7BXB6f/cnjnnUqHp3DmjKcPk5ZPe/sfcp30WD1vLP3Kddz+GzZ7Jq+weHS31md/SJuOX2EWqMjyS5yufyY7ehFWAnawiLooi+TVd0tQpVIHcLOmuwVPUtlTKspyzMPizaXHx0vtd9tDRBZG8LO+O41VnGMo8x6kwSsfRikP4Ncu3vgZYwX7FEviXiQeefE+W6QEWbpSjxNmEPVb1ysoY798JMu6npArQ+7X8hhp49TVk9ddXLzjUjXnrA3Fu1Hp49QawZX2foy22d29ELmQ94Egi76OnxaROoQttTdK3qWyphWs314R3sTUgAy6dpEtN9rrOIYR5n1JglY+zBIf8Z1LaEXmXdOnO8GL2EWD1/PahZcVQpVv3HhTbys8Lv53z57CAeG8/jts4dKf2M5CFOwJ0+85JxTUZsCjo6Nl2lM2DUu+QttD7tfyGGWJG5SXY1Ne7Sz81KsWjLbd9Fg56xaMrvsN1behTOn2bRGrG1XnH966e/8poW92N1CKNemUDI1w/G+mzPjFGGTAVZuw+Tifc6c1uB7DQtJLROa2uveMuZ1Kqgs+ta67f/rEYwXgP1/PeJ7nds8EzHj0CV1d+vXzTddjP3dHY75j+IMOBCG5C5saeDDT79U0riwdYw9z5ecc6rtXK+1QRd+7fWaW2Fre5x4bO0S7O/uwGNrl0R2zzCx9qFIf7J1rRjE5sS4qYwFG/v7Hnmu7L0aFSR9t+P1zuHfaSLvJ93PKLNAYQGSRFIS8PWkjXLyqXrnfKfQxm7hjmUc7a1JDvmydDojWuvanM1odXJ0q6fT3936jD/XqV8YtaliYjyR+nv1r2x7ZBEpJ66gDNa6WftHtW+idJyN6l5RzgMTyrRifU7Z3IzTOZocs5MFW9cYQcaNX8PjCGBD808cEwINyaSiIJIHOecL4qT6dzNNkZHusl09K9cqyda547dKynVLEtzKk7mPk7Qjm0kjm0mjvbkYuKC9udE1kZxfuaz9KpIXVWSSJkatCbDWTSZRpo6xDkpU94pyHphQphXrOsfmZpwSSJJ+Jgu2rrE1O8i4sbFn74E4tKY0/8QxIZy+SNJrovKpeo0LQRAEQRAEQRDxQRoXgiAIgiAIgiAqBtq4EARBEARBEARhPLRxIQiCIAiCIAjCeGjjQhAEQRAEQRCE8dDGhSAIgiAIgiAI46GNC0EQBEEQBEEQxkMbF4IgCIIgCIIgjIc2LgRBEARBEARBGA9tXAiCIAiCIAiCMB7auBAEQRAEQRAEYTx1cdy0UCgAAF555ZU4bk8QBEEQBEEQhCGwPQHbI7gRy8bl8OHDAIAzzjgjjtsTBEEQBEEQBGEYhw8fRmNjo+vvqYLf1iYEJiYmMDQ0hClTpiCVSkV9e4IgCIIgCIIgDKFQKODw4cNoampCTY27J0ssGxeCIAiCIAiCIAgZyDmfIAiCIAiCIAjjoY0LQRAEQRAEQRDGQxsXgiAIgiAIgiCMhzYuBEEQBEEQBEEYD21cCIIgiMB84AMfwDXXXFP69+LFi/Hxj388tvoQBEEQlQdtXAiCIIhYaW1txd133x13NQiCIAjDoY0LQRAEQRAEQRDGQxsXgiAIAkAxOfAXv/hFnHXWWZg8eTLOPPNM3H777QCA5557Dtdddx2y2SymTZuGq6++Gvv37w98z8WLF2NgYACf+MQnkEqlkEqlcOTIEZxyyin44Q9/aDv3Jz/5CRoaGnD48GHs378fqVQK3/ve93DRRRehvr4e559/Pn71q1/Zrnn88cdxxRVX4OSTT8brX/96/MM//AP++te/Bq43QRAEET20cSEIgiAAAKtXr0Z3dzfWrFmDJ554Av/zP/+D17/+9RgdHcWSJUswZcoU/PrXv8bOnTtx8skn4+1vfzuOHTsW6J73338/3vCGN+DWW2/FCy+8gBdeeAENDQ1473vfi3vvvdd27r333ot3vetdmDJlSulvn/70p/GpT30Ke/bswVvf+lZcddVVOHjwIAAgl8vh0ksvxfz58/G73/0OP//5z/Hiiy/iuuuuC1RngiAIIh7q4q4AQRAEET+HDx/GV77yFXz1q1/FDTfcAABoa2vDxRdfjI0bN2JiYgLf+MY3kEqlABQ3EdlsFtu3b8fll1+ufN9p06ahtrYWU6ZMwYwZM0p/v/HGG3HRRRfhhRdewOmnn46XXnoJW7Zswf/+7//arv/Yxz6Gv//7vwcAbNiwAT//+c9xzz334Oabb8ZXv/pVzJ8/H5///OdL53/zm9/EGWecgaeffhrnnHOOcr0JgiCI6CGNC0EQBIEnn3wSR48exWWXXVb22969e/HHP/4RU6ZMwcknn4yTTz4Z06ZNw8jICPr7+0Opz4UXXoi5c+fiv//7vwEAGzduREtLCy655BLbeW9961tL/19XV4c3v/nNePLJJ0v1fuihh0p1Pvnkk3HuuecCQGj1JgiCIMKDNC4EQRAEMpmM62+vvvoqFixYgO9+97tlv5166qmh1enGG2/Ev//7v6OzsxP33nsvPvjBD5Y0PiK8+uqruOqqq/CFL3yh7LfTTz9dZ1UJgiCICCCNC0EQBIGzzz4bmUwG27ZtK/vtTW96E5555hmcdtppOOuss2z/NTY2Br73pEmTMD4+Xvb35cuXY2BgAOvWrcMTTzxRMmGzsmvXrtL/j42NYffu3ZgzZ06p3vv27UNra2tZvRsaGgLXmyAIgogW2rgQBEEQqK+vx7/8y7/g5ptvxre//W309/dj165duOeee/D+978fr3vd63D11Vfj17/+NZ599lls374dK1euxPPPPx/43q2trXj44YcxODhoi/g1depUXHvttfj0pz+Nyy+/HG94wxvKrv33f/93/PjHP8Yf/vAHfPSjH8XLL7+Mf/zHfwQAfPSjH8WhQ4ewbNkyPPLII+jv70dvby8++MEPOm6UCIIgCLOhjQtBEAQBAFizZg0+9alP4V//9V8xZ84cvOc978FLL72Ek046CQ8//DDOPPNMXHvttZgzZw4+9KEPYWRkBKecckrg+956663Yv38/2traykzPPvShD+HYsWOlzQhPd3c3uru7ccEFF2DHjh3YvHkzXve61wEAmpqasHPnToyPj+Pyyy/HG9/4Rnz84x9HNptFTQ29/giCIJJGqlAoFOKuBEEQBEE48Z3vfAef+MQnMDQ0hEmTJpX+vn//fsycORN79uzBvHnz4qsgQRAEERnknE8QBEEYx2uvvYYXXngB3d3d+Od//mfbpoUgCIKoTkhXThAEQYTGr3/9a1s4Yv4/N774xS/i3HPPxYwZM7B69eoIa0wQBEGYCpmKEQRBEKGRz+cxODjo+vtZZ50VYW0IgiCIJEMbF4IgCIIgCIIgjIdMxQiCIAiCIAiCMB7auBAEQRAEQRAEYTy0cSEIgiAIgiAIwnho40IQBEEQBEEQhPHQxoUgCIIgCIIgCOOhjQtBEARBEARBEMZDGxeCIAiCIAiCIIzn/wNe7HnmBMLgPQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"development_stage\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 4),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "most_freq_dev_stage = development_stage_freq[development_stage_freq > 100].index\n",
+    "filtered_df = adata.obs.loc[adata.obs[\"development_stage\"].isin(most_freq_dev_stage)]\n",
+    "\n",
+    "filtered_df.groupby(\"development_stage\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(10, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `disease`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      10000\n",
+       "unique        60\n",
+       "top       normal\n",
+       "freq        6581\n",
+       "Name: disease, dtype: object"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"disease\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "disease\n",
+       "normal                                             6581\n",
+       "COVID-19                                           1088\n",
+       "glioblastoma                                        319\n",
+       "malignant ovarian serous tumor                      281\n",
+       "lung adenocarcinoma                                 269\n",
+       "systemic lupus erythematosus                        199\n",
+       "Crohn disease                                       171\n",
+       "dilated cardiomyopathy                              147\n",
+       "dementia                                            146\n",
+       "squamous cell lung carcinoma                         57\n",
+       "Alzheimer disease                                    54\n",
+       "myocardial infarction                                50\n",
+       "breast cancer                                        49\n",
+       "B-cell non-Hodgkin lymphoma                          41\n",
+       "non-small cell lung carcinoma                        35\n",
+       "gastric cancer                                       35\n",
+       "follicular lymphoma                                  35\n",
+       "arrhythmogenic right ventricular cardiomyopathy      31\n",
+       "post-COVID-19 disorder                               31\n",
+       "respiratory system disorder                          30\n",
+       "acute kidney failure                                 30\n",
+       "chronic kidney disease                               25\n",
+       "small cell lung carcinoma                            24\n",
+       "common variable immunodeficiency                     22\n",
+       "benign prostatic hyperplasia                         21\n",
+       "chronic obstructive pulmonary disease                16\n",
+       "interstitial lung disease                            14\n",
+       "pulmonary fibrosis                                   14\n",
+       "blastoma                                             12\n",
+       "acute myeloid leukemia                               11\n",
+       "lung large cell carcinoma                            11\n",
+       "pneumonia                                            10\n",
+       "pilocytic astrocytoma                                10\n",
+       "pulmonary emphysema                                  10\n",
+       "tubular adenoma                                       9\n",
+       "gastritis                                             9\n",
+       "chronic rhinitis                                      9\n",
+       "lymphangioleiomyomatosis                              8\n",
+       "non-compaction cardiomyopathy                         8\n",
+       "Crohn ileitis                                         7\n",
+       "influenza                                             6\n",
+       "cystic fibrosis                                       6\n",
+       "colon sessile serrated adenoma/polyp                  6\n",
+       "clear cell renal carcinoma                            6\n",
+       "type 1 diabetes mellitus                              5\n",
+       "Down syndrome                                         5\n",
+       "type 2 diabetes mellitus                              5\n",
+       "adenocarcinoma                                        4\n",
+       "pleomorphic carcinoma                                 4\n",
+       "trisomy 18                                            4\n",
+       "hydrosalpinx                                          4\n",
+       "stomach disorder                                      3\n",
+       "colorectal cancer                                     3\n",
+       "hyperplastic polyp                                    2\n",
+       "tubulovillous adenoma                                 2\n",
+       "cataract                                              2\n",
+       "chromophobe renal cell carcinoma                      1\n",
+       "acute promyelocytic leukemia                          1\n",
+       "Wilms tumor                                           1\n",
+       "Barrett esophagus                                     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "disease_freq = adata.obs[\"disease\"].value_counts()\n",
+    "disease_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disease_freq[disease_freq > 20].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"disease frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"disease\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 10),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "most_freq_disease = disease_freq[disease_freq > 20].index\n",
+    "filtered_df = adata.obs.loc[adata.obs[\"disease\"].isin(most_freq_disease)]\n",
+    "\n",
+    "filtered_df.groupby(\"disease\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(10, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `sex`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sex\n",
+       "male       5057\n",
+       "female     4323\n",
+       "unknown     620\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sex_freq = adata.obs[\"sex\"].value_counts()\n",
+    "sex_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sex_freq.plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(5, 4),\n",
+    "    title=\"sex frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"sex\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 2),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.groupby(\"sex\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(5, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## `tissue`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     10000\n",
+       "unique      204\n",
+       "top       blood\n",
+       "freq       2528\n",
+       "Name: tissue, dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"tissue\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tissue\n",
+       "blood                       2528\n",
+       "lung                         737\n",
+       "telencephalon                490\n",
+       "breast                       432\n",
+       "cerebral cortex              380\n",
+       "                            ... \n",
+       "caecum epithelium              1\n",
+       "myometrium                     1\n",
+       "jejunum                        1\n",
+       "bone spine                     1\n",
+       "temporoparietal junction       1\n",
+       "Name: count, Length: 204, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tissue_freq = adata.obs[\"tissue\"].value_counts()\n",
+    "tissue_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ob0RERERE9P4wKDkKDQ1FQEAAjh49il27duHFixdo2LAhnj59qrdf7969cfv2beU2ffp05b7k5GQ0bdoUiYmJOHz4MFauXImQkBCMGTNG2efatWto2rQp6tSpg/DwcAwcOBC9evXCjh07jHy5REREREREWbM0ZOft27fr/RwSEoL8+fPj5MmT8PX1Vbbb2NjAzc0tyxg7d+7E33//jT///BOurq4oX748Jk6ciOHDh2PcuHGwsrLC4sWL4eXlhVmzZgEASpUqhYMHD2LOnDnw8/PLMm5CQgISEhKUn2NjYw15aURERERE9J4zas1RTEwMACBv3rx623/66Se4uLigTJkyGDlyJOLj45X7jhw5grJly8LV1VXZ5ufnh9jYWJw/f17Zp379+nox/fz8cOTIkWzHMnXqVDg4OCg3Dw8PY14aERERERG9Zwy6cpReSkoKBg4ciBo1aqBMmTLK9o4dO6Jw4cJwd3fHmTNnMHz4cFy6dAkbNmwAANy5c0cvMQKg/Hznzp2X7hMbG4tnz54hd+7cmcYzcuRIDB48WPk5NjaWCRIREREREeWY6uQoICAA586dw8GDB/W29+nTR/l32bJlUaBAAdSrVw9XrlxBkSJF1I/0FXQ6HXQ6ncniExERERHRu03VtLrAwEBs2bIFe/fuxQcffPDSfatWrQoAiIyMBAC4ubnh7t27evuk/Zy2Tim7fezt7bO8akRERERERGQsg5IjEUFgYCA2btyIPXv2wMvL65WPCQ8PBwAUKFAAAFC9enWcPXsW9+7dU/bZtWsX7O3t4ePjo+yze/duvTi7du1C9erVDRkuERERERFRjhmUHAUEBODHH3/Ezz//DDs7O9y5cwd37tzBs2fPAABXrlzBxIkTcfLkSVy/fh2//fYbunbtCl9fX3z44YcAgIYNG8LHxwddunTB6dOnsWPHDowaNQoBAQHKtLi+ffvi6tWrGDZsGC5evIhvv/0Wa9aswaBBgzR++URERERERKkMSo4WLVqEmJgY1K5dGwUKFFBuv/76KwDAysoKf/75Jxo2bIiSJUtiyJAhaNOmDX7//XclhoWFBbZs2QILCwtUr14dnTt3RteuXTFhwgRlHy8vL2zduhW7du1CuXLlMGvWLHz//ffZlvEmIiIiIiIylpmIyOsehCnExsbCwcEBMTExsLe3V7Z7jtia4xjXpzU1xdCIiIiIiOg/lF1ukJFRfY6IiIiIiIjeFUyOiIiIiIiIwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAiAgcnR1KlT8dFHH8HOzg758+dHy5YtcenSJb19nj9/joCAADg7OyNPnjxo06YN7t69q7dPVFQUmjZtChsbG+TPnx/BwcFISkrS22ffvn2oWLEidDodihYtipCQEHWvkIiIiIiIKAcMSo5CQ0MREBCAo0ePYteuXXjx4gUaNmyIp0+fKvsMGjQIv//+O9auXYvQ0FDcunULrVu3Vu5PTk5G06ZNkZiYiMOHD2PlypUICQnBmDFjlH2uXbuGpk2bok6dOggPD8fAgQPRq1cv7NixQ4OXTERERERElJmZiIjaB9+/fx/58+dHaGgofH19ERMTg3z58uHnn3/GZ599BgC4ePEiSpUqhSNHjqBatWrYtm0bPv30U9y6dQuurq4AgMWLF2P48OG4f/8+rKysMHz4cGzduhXnzp1Tnqt9+/aIjo7G9u3bsxxLQkICEhISlJ9jY2Ph4eGBmJgY2NvbK9s9R2zN8eu7Pq2pQb8PIiIiIiJ688TGxsLBwSFTbpCRUWuOYmJiAAB58+YFAJw8eRIvXrxA/fr1lX1KliyJQoUK4ciRIwCAI0eOoGzZskpiBAB+fn6IjY3F+fPnlX3Sx0jbJy1GVqZOnQoHBwfl5uHhYcxLIyIiIiKi94zq5CglJQUDBw5EjRo1UKZMGQDAnTt3YGVlBUdHR719XV1dcefOHWWf9IlR2v1p971sn9jYWDx79izL8YwcORIxMTHK7ebNm2pfGhERERERvYcs1T4wICAA586dw8GDB7Ucj2o6nQ46ne51D4OIiIiIiN5Sqq4cBQYGYsuWLdi7dy8++OADZbubmxsSExMRHR2tt//du3fh5uam7JOxel3az6/ax97eHrlz51YzZCIiIiIiopcyKDkSEQQGBmLjxo3Ys2cPvLy89O6vVKkScuXKhd27dyvbLl26hKioKFSvXh0AUL16dZw9exb37t1T9tm1axfs7e3h4+Oj7JM+Rto+aTGIiIiIiIi0ZtC0uoCAAPz888/YvHkz7OzslDVCDg4OyJ07NxwcHODv74/Bgwcjb968sLe3R1BQEKpXr45q1aoBABo2bAgfHx906dIF06dPx507dzBq1CgEBAQo0+L69u2Lb775BsOGDUPPnj2xZ88erFmzBlu35rzSHBERERERkSEMunK0aNEixMTEoHbt2ihQoIBy+/XXX5V95syZg08//RRt2rSBr68v3NzcsGHDBuV+CwsLbNmyBRYWFqhevTo6d+6Mrl27YsKECco+Xl5e2Lp1K3bt2oVy5cph1qxZ+P777+Hn56fBSyYiIiIiIsrMqD5Hb7LsapmzzxERERER0fvlP+lzRERERERE9K5gckRERERERAQmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAFQkR/v370ezZs3g7u4OMzMzbNq0Se/+7t27w8zMTO/WqFEjvX0ePXqETp06wd7eHo6OjvD390dcXJzePmfOnMEnn3wCa2treHh4YPr06Ya/OiIiIiIiohwyODl6+vQpypUrh4ULF2a7T6NGjXD79m3ltnr1ar37O3XqhPPnz2PXrl3YsmUL9u/fjz59+ij3x8bGomHDhihcuDBOnjyJGTNmYNy4cVi6dKmhwyUiIiIiIsoRS0Mf0LhxYzRu3Pil++h0Ori5uWV534ULF7B9+3YcP34clStXBgAsWLAATZo0wcyZM+Hu7o6ffvoJiYmJWL58OaysrFC6dGmEh4dj9uzZeklUegkJCUhISFB+jo2NNfSlERERERHRe8wka4727duH/Pnzo0SJEujXrx8ePnyo3HfkyBE4OjoqiREA1K9fH+bm5jh27Jiyj6+vL6ysrJR9/Pz8cOnSJTx+/DjL55w6dSocHByUm4eHhyleGhERERERvaM0T44aNWqEH374Abt378bXX3+N0NBQNG7cGMnJyQCAO3fuIH/+/HqPsbS0RN68eXHnzh1lH1dXV7190n5O2yejkSNHIiYmRrndvHlT65dGRERERETvMIOn1b1K+/btlX+XLVsWH374IYoUKYJ9+/ahXr16Wj+dQqfTQafTmSw+ERERERG920xeytvb2xsuLi6IjIwEALi5ueHevXt6+yQlJeHRo0fKOiU3NzfcvXtXb5+0n7Nby0RERERERGQMkydH//zzDx4+fIgCBQoAAKpXr47o6GicPHlS2WfPnj1ISUlB1apVlX3279+PFy9eKPvs2rULJUqUgJOTk6mHTERERERE7yGDk6O4uDiEh4cjPDwcAHDt2jWEh4cjKioKcXFxCA4OxtGjR3H9+nXs3r0bLVq0QNGiReHn5wcAKFWqFBo1aoTevXvjr7/+wqFDhxAYGIj27dvD3d0dANCxY0dYWVnB398f58+fx6+//op58+Zh8ODB2r1yIiIiIiKidAxOjk6cOIEKFSqgQoUKAIDBgwejQoUKGDNmDCwsLHDmzBk0b94cxYsXh7+/PypVqoQDBw7orQf66aefULJkSdSrVw9NmjRBzZo19XoYOTg4YOfOnbh27RoqVaqEIUOGYMyYMdmW8SYiIiIiIjKWmYjI6x6EKcTGxsLBwQExMTGwt7dXtnuO2JrjGNenNTXF0IiIiIiI6D+UXW6QkcnXHBEREREREb0NmBwRERERERGByREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAVydH+/fvRrFkzuLu7w8zMDJs2bdK7X0QwZswYFChQALlz50b9+vURERGht8+jR4/QqVMn2Nvbw9HREf7+/oiLi9Pb58yZM/jkk09gbW0NDw8PTJ8+3fBXR0RERERElEMGJ0dPnz5FuXLlsHDhwizvnz59OubPn4/Fixfj2LFjsLW1hZ+fH54/f67s06lTJ5w/fx67du3Cli1bsH//fvTp00e5PzY2Fg0bNkThwoVx8uRJzJgxA+PGjcPSpUtVvEQiIiIiIqJXMxMRUf1gMzNs3LgRLVu2BJB61cjd3R1DhgzB0KFDAQAxMTFwdXVFSEgI2rdvjwsXLsDHxwfHjx9H5cqVAQDbt29HkyZN8M8//8Dd3R2LFi3CV199hTt37sDKygoAMGLECGzatAkXL17MciwJCQlISEhQfo6NjYWHhwdiYmJgb2+vbPccsTXHr+/6tKYG/T6IiIiIiOjNExsbCwcHh0y5QUaarjm6du0a7ty5g/r16yvbHBwcULVqVRw5cgQAcOTIETg6OiqJEQDUr18f5ubmOHbsmLKPr6+vkhgBgJ+fHy5duoTHjx9n+dxTp06Fg4ODcvPw8NDypRERERER0TtO0+Tozp07AABXV1e97a6ursp9d+7cQf78+fXut7S0RN68efX2ySpG+ufIaOTIkYiJiVFuN2/eNP4FERERERHRe8PydQ9AKzqdDjqd7nUPg4iIiIiI3lKaXjlyc3MDANy9e1dv+927d5X73NzccO/ePb37k5KS8OjRI719soqR/jmIiIiIiIi0pGly5OXlBTc3N+zevVvZFhsbi2PHjqF69eoAgOrVqyM6OhonT55U9tmzZw9SUlJQtWpVZZ/9+/fjxYsXyj67du1CiRIl4OTkpOWQiYiIiIiIAKhIjuLi4hAeHo7w8HAAqUUYwsPDERUVBTMzMwwcOBCTJk3Cb7/9hrNnz6Jr165wd3dXKtqVKlUKjRo1Qu/evfHXX3/h0KFDCAwMRPv27eHu7g4A6NixI6ysrODv74/z58/j119/xbx58zB48GDNXjgREREREVF6Bq85OnHiBOrUqaP8nJawdOvWDSEhIRg2bBiePn2KPn36IDo6GjVr1sT27dthbW2tPOann35CYGAg6tWrB3Nzc7Rp0wbz589X7ndwcMDOnTsREBCASpUqwcXFBWPGjNHrhURERERERKQlo/ocvcmyq2XOPkdERERERO+X19LniIiIiIiI6G3F5IiIiIiIiAhMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIAACWr3sA7wLPEVtzvO/1aU1NOBIiIiIiIlKLV46IiIiIiIjA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgAmSI7GjRsHMzMzvVvJkiWV+58/f46AgAA4OzsjT548aNOmDe7evasXIyoqCk2bNoWNjQ3y58+P4OBgJCUlaT1UIiIiIiIihaUpgpYuXRp//vnn/z2J5f89zaBBg7B161asXbsWDg4OCAwMROvWrXHo0CEAQHJyMpo2bQo3NzccPnwYt2/fRteuXZErVy5MmTLFFMMlIiIiIiIyTXJkaWkJNze3TNtjYmKwbNky/Pzzz6hbty4AYMWKFShVqhSOHj2KatWqYefOnfj777/x559/wtXVFeXLl8fEiRMxfPhwjBs3DlZWVlk+Z0JCAhISEpSfY2NjTfHSiIiIiIjoHWWSNUcRERFwd3eHt7c3OnXqhKioKADAyZMn8eLFC9SvX1/Zt2TJkihUqBCOHDkCADhy5AjKli0LV1dXZR8/Pz/Exsbi/Pnz2T7n1KlT4eDgoNw8PDxM8dKIiIiIiOgdpXlyVLVqVYSEhGD79u1YtGgRrl27hk8++QRPnjzBnTt3YGVlBUdHR73HuLq64s6dOwCAO3fu6CVGafen3ZedkSNHIiYmRrndvHlT2xdGRERERETvNM2n1TVu3Fj594cffoiqVauicOHCWLNmDXLnzq310yl0Oh10Op3J4v/XPEdszdF+16c1NfFIiIiIiIjeDyYv5e3o6IjixYsjMjISbm5uSExMRHR0tN4+d+/eVdYoubm5Zapel/ZzVuuYiIiIiIiItGDy5CguLg5XrlxBgQIFUKlSJeTKlQu7d+9W7r906RKioqJQvXp1AED16tVx9uxZ3Lt3T9ln165dsLe3h4+Pj6mHS0RERERE7ynNp9UNHToUzZo1Q+HChXHr1i2MHTsWFhYW6NChAxwcHODv74/Bgwcjb968sLe3R1BQEKpXr45q1aoBABo2bAgfHx906dIF06dPx507dzBq1CgEBAS8U9PmiIiIiIjozaJ5cvTPP/+gQ4cOePjwIfLly4eaNWvi6NGjyJcvHwBgzpw5MDc3R5s2bZCQkAA/Pz98++23yuMtLCywZcsW9OvXD9WrV4etrS26deuGCRMmaD1UIiIiIiIihebJ0S+//PLS+62trbFw4UIsXLgw230KFy6MP/74Q+uhERERERERZcskTWDpzcQKeERERERE2TN5QQYiIiIiIqK3AZMjIiIiIiIiMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAKxWR0ZiBTwiIiIielfwyhERERERERF45YjeQLwaRURERESvA68cERERERERgVeO6D3Bq1FERERE9Cq8ckRERERERAQmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAwPJ1D4DobeU5YmuO9rs+ramJR0JEREREWuCVIyIiIiIiIjA5IiIiIiIiAsBpdURvFE7VIyIiInp9eOWIiIiIiIgITI6IiIiIiIgAcFod0TvPFFP1OP2PiIiI3kW8ckRERERERAReOSKiN0BOr0QBOb8a9T7HJCIiInWYHBERvWOYxBEREanD5IiIiF6LtyWJY2JIRPT+YHJERET0H2NREyKiNxOTIyIionfA21KZkokhEb3JmBwRERHRW40JFxFphckRERERUQa8akb0fmJyRERERPSWeluSOCaG9LZgckREREREb5W3pTIlY775MTMyV/UoIiIiIiKidwyTIyIiIiIiIrzhydHChQvh6ekJa2trVK1aFX/99dfrHhIREREREb2j3tjk6Ndff8XgwYMxduxYhIWFoVy5cvDz88O9e/de99CIiIiIiOgd9MYWZJg9ezZ69+6NHj16AAAWL16MrVu3Yvny5RgxYkSm/RMSEpCQkKD8HBMTAwCIjY3V2y8lIT7HY8j42Oy8zpg5jceYjPkmx3zX/i4ZkzG1ivmu/a0zJmNqFfNd+1tnTNPHTPtZRF76ODN51R6vQWJiImxsbLBu3Tq0bNlS2d6tWzdER0dj8+bNmR4zbtw4jB8//j8cJRERERERvU1u3ryJDz74INv738grRw8ePEBycjJcXV31tru6uuLixYtZPmbkyJEYPHiw8nNKSgoePXoEZ2dnmJmZZftcsbGx8PDwwM2bN2Fvb6/J+BmTMRmTMd/1mG/DGBmTMRmTMRnzzYj3JsQUETx58gTu7u4v3e+NTI7U0Ol00Ol0etscHR1z/Hh7e3vN/qMYkzEZkzHfl5hvwxgZkzEZkzEZ882I97pjOjg4vHKfN7Igg4uLCywsLHD37l297Xfv3oWbm9trGhUREREREb3L3sjkyMrKCpUqVcLu3buVbSkpKdi9ezeqV6/+GkdGRERERETvqjd2Wt3gwYPRrVs3VK5cGVWqVMHcuXPx9OlTpXqdVnQ6HcaOHZtpSh5jMiZjMiZj/nfxGJMxGZMxGfPNifk2jNFUMd/IanVpvvnmG8yYMQN37txB+fLlMX/+fFStWvV1D4uIiIiIiN5Bb3RyRERERERE9F95I9ccERERERER/deYHBEREREREYHJEREREREREQAmR0RERERERACYHNF74tmzZ4iPj1d+vnHjBubOnYudO3caFTcpKQl//vknlixZgidPngAAbt26hbi4OKPikjbGjh2LGzduvO5hkArveq2gPXv24Pnz5yaLHxcXh9jYWL3bmyQ6Ohrff/89Ro4ciUePHgEAwsLC8O+//77mkZne+/zaTclUf0+xsbHYtGkTLly4YJL4WklOTkZ4eDgeP36sOkZKSgouX76MgwcPYv/+/Xq3N8XL/p9v376tzZMIkRHGjh0rycnJmbZHR0dL+/btX8OIstagQQNZtGiRiIg8fvxYXF1d5YMPPhBra2v59ttvVcW8fv26lCxZUmxsbMTCwkKuXLkiIiL9+/eXL774IsdxWrVqJTExMcq/X3ZTIykpSUJDQ+Xx48eqHm9K5cuXlwoVKuTopka5cuXEwsJC6tatKz/99JM8f/5c41egrfPnz8u2bdtk8+bNerd3Vbdu3SQuLi7T9mvXrknNmjVfw4hyLikpSU6dOiWPHj1S9XhbW1vR6XRSs2ZNGTVqlOzatUvi4+ONGtPVq1elSZMmYmNjI+bm5srNzMxMzM3NVcW8c+eOdO7cWQoUKCAWFhZ6cdXGPH36tOTLl0+KFi0qlpaWymfnV199JV26dFEVs3DhwjJ+/Hi5ceOGqsf/V0zx2t9nycnJMmHCBHF3d9f7Hh41apR8//33qmK2bdtWFixYICIi8fHxUqxYMcmVK5dYWlrKunXrVMU0xftzwIABymtMSkqSGjVqiJmZmdja2srevXsNjnfkyBHx8vJSPjPS39T+rZtCqVKl5NSpU5m2r1u3TlxcXDR5jje2CSylioiIwN69e3Hv3j2kpKTo3TdmzBiD49WtWxcbNmyAo6Oj3vbY2Fi0bNkSe/bsMSjesmXLsHPnTvz444/w9vYGAOzbtw9du3aFm5ubweNL7/nz5zhz5kyWr7158+YGxQoLC8OcOXMAAOvWrYOrqytOnTqF9evXY8yYMejXr5/B4xswYAAqV66M06dPw9nZWdneqlUr9O7dO8dxHBwcYGZmpvxbaxYWFmjYsCEuXLiQ6f/9dWvZsqXy7+fPn+Pbb7+Fj48PqlevDgA4evQozp8/jy+//FJV/PDwcJw6dQorVqzAgAEDEBAQgPbt26Nnz5746KOPchznt99+y/G+hr43AeDq1ato1aoVzp49CzMzM+WqSdr7Ijk52eCYplKrVi34+/ujbdu2yJ07t1GxTp8+jQ8//BA//vij8n++cuVK9O/fH3Xr1jUqttafnQMHDkTZsmXh7++P5ORk1KpVC4cPH4aNjQ22bNmC2rVrGxTv8ePH+OuvvxAaGorQ0FDMnTsXiYmJqFy5MurUqYNJkyYZPMbOnTtDRLB8+XK4uroq7x9jdO/eHVFRURg9ejQKFCigSczBgweje/fumD59Ouzs7JTtTZo0QceOHVXFHDhwIEJCQjBhwgTUqVMH/v7+aNWqlaaNIbVgiteeXmJiIq5du4YiRYrA0tK4Q7znz59jwYIF2f4dhYWFGRVfC5MmTcLKlSsxffp0ve/dMmXKYO7cufD39zc45v79+/HVV18BADZu3AgRQXR0NFauXIlJkyahTZs2Bsc0xftz3bp16Ny5MwDg999/x7Vr13Dx4kWsWrUKX331FQ4dOmRQvL59+6Jy5crYunWrZn/r6V25cgUrVqzAlStXMG/ePOTPnx/btm1DoUKFULp06RzHqV27NqpVq4bx48dj+PDhePr0KQICArBmzRpMnjxZm8FqkmK9JTKejX3ZTQ1HR0dxcnLKdMubN6+4u7uLr6+vLF++PMfxli5dKhYWFuLq6irlypWT8uXLKze1Z9LNzMzk7t27mbbfvXtXLC0tDY736NEjadu2rdjZ2cnSpUtl6NChkitXLvnf//4nL168UDVGEZFt27ZJvnz5Mp29UHsGI3fu3MoZm7Zt28q4ceNERCQqKkpy586taox58+aVixcviohInjx5lDNW165dUx3TVCpVqiR//vmnpjGTkpLk+++/lw4dOki9evWkTp06ejdD+fv7y6hRozJtHzNmjPTo0cPo8SYmJsr69evl008/lVy5cknZsmVl7ty5Eh0d/crHZvUezPizMWfSP/30U2nRooXcv39f8uTJI3///bccOHBAqlSpIvv371cVM82zZ8/k2LFj8vvvv2vyOTdgwADJly+f2NvbS69eveTIkSOqx5aYmChDhw4VKysrGTlypLRt21by5MkjS5cuVR1TxDSfnQULFpTjx4+LiMjGjRvF3d1dLl26JKNGjZKPP/7YqPGKiJw7d066desmlpaWqt9Htra2ymeSVvLkyZPlWVpj2NvbS2RkpBI/7bPz+vXrotPpjIp98uRJCQoKEhcXF3FycpKAgAA5efKkUTHj4uJk69atsmjRIpk3b57ezVCmeu1Pnz6Vnj17ioWFhd4VlMDAQJk6daqqmB07dhQXFxfp27evjB07VsaNG6d3U0PrK5FFihRRvtvS/z4vXLggjo6OqsZobW0tUVFRIiLSpUsXGT58uIiI3LhxQ2xtbVXFTKPl+1On08nNmzdFRKR3794yYMAAEUm9gmxnZ2dwPBsbG4mIiFA1llfZt2+f5M6dW+rXry9WVlbK/9PUqVOlTZs2BsfbsmWLuLm5Sc2aNaVIkSJSrlw5OXv2rGbjfa+SI1Mf4MyePVucnZ2lc+fOMn/+fJk/f7507txZXFxcZPLkydKrVy/R6XQ5/uIvVKiQTJs2TdVYMjp9+rScPn1azMzMZO/evcrPp0+flrCwMJkyZYoULlxYdfyRI0eKmZmZ5MqVS5OD8KJFi8qXX34pd+7cMTqWiEjZsmVl3rx5EhUVJfb29nL48GERETlx4oS4urqqiuno6Cjnz58XEf0P5QMHDkj+/PlVj/XFixeya9cuWbx4scTGxoqIyL///itPnjxRHXPbtm1Svnx5+f333+XWrVsSExOjd1MjICBAbG1tpV27djJgwAAZOHCg3s1Q9vb2cvny5UzbL1++LPb29qrGmF5CQoL88ssv0rBhQ7G0tBRfX18pWrSo2NnZyS+//JLjOLt27ZKKFSvK9u3bld/f9u3bpXLlyrJz505VY3N2dpbTp0+LSOrvIe0Ad/fu3VK+fHlVMUW0P8mQ5sWLF7J+/Xpp3ry55MqVS0qVKiUzZsxQ/fc6ZswY5fMj7W/TGFp+dqbR+kDk0qVLsmTJEunQoYO4u7uLs7OztGzZUubOnSvh4eGqxli7dm3ZtWuXqsdmp1SpUhIWFqZpzHz58ikx03927ty5Uz744ANNniMxMVHmzp0rOp1OzM3NpVy5crJs2TJJSUkxKE5YWJi4ubmJvb29WFhYKH9Ptra24uXlZfC4TPXa+/fvL5UqVZIDBw6Ira2tEnfTpk2qP0Ps7e3l4MGDqseUlUaNGomPj498++23snHjRtm0aZPezVDW1tZy/fp1EdH/fZ4/f151IlOsWDH59ddfJS4uTvLlyye7d+8WEZHw8HBxdnZWFTMjLd6fhQoVkh07dkhSUpJ4eHjIli1bRCT1RIuaxLBOnTqybds2gx+XE9WqVZNZs2aJiP7/07Fjx6RgwYIGx0tOTpYvv/xS+d7Yvn27puN9r5Kj9ExxgNO6dWtlXUt6ixcvltatW4uIyPz586VMmTI5imdnZ6e8gYyVPvnL6kDJxsZGli1bpir2/PnzxcbGRjp27CglSpQQHx8f1V/uaezs7JSza1pYu3at5MqVS8zNzaVBgwbK9ilTpkijRo1UxWzXrp307t1bRFL/2K9evSpPnjyRunXrSvfu3VXF1GodU0bZnQQw5iDZ2dlZtm7dqnpMGbm6usqKFSsybV+xYoVRyeaJEyckICBA8ubNKwUKFJDhw4frnR2bP3++QfFLly4tBw4cyLR9//79UrJkSVVjdHR0lKtXr4qIiLe3t+zZs0dERCIjI426Cqn1SYas3L17VyZOnCjW1taSK1cuadGihXIw8SqJiYkyePBg0el08r///U98fX3Fzc3N6PeVlp+dabQ+EDEzM5P8+fPL5MmT5fTp0wYftGclMjJS6tevLyEhIXLixAm9k2BpybehduzYIQ0bNpRr164ZPb40/v7+0rJlS0lMTFQ+O2/cuCEVKlRQkk61EhMT5ddff5VGjRqJhYWF1KhRQ5YvXy4TJkwQV1dX6dChg0HxatWqJb1795bk5GTloC4qKkp8fX1l/fr1Bo/PVK+9UKFCylXc9AefERERqpJ3kdTEWO37JjtaX4msWLGirFq1Somd9rrHjx+vet3iwoULxdLSUhwdHaVcuXLKuur58+dL7dq1jRqvlu/PsWPHioODg5QsWVIKFSqkrKldtmyZVKtWLUcx0n8+bNiwQXx8fGTFihWafX6ksbW1Vb7jMs60MfSKaWRkpFSpUkUKFSokO3fulK+++kqsrKwkODhYEhMTjRpnmvc2OTLFAY6trW2WlyQjIiKUMxiRkZFiY2OTo3g9e/bMMtlS4/r163Lt2jUxMzOT48ePy/Xr15XbrVu3JCkpSVVcPz8/cXZ2lrVr14pI6uLFvn37irW1tXz99deqx9ujRw/Viymzc/v2bQkLC9MrIHHs2DG5cOGCqng3b94UHx8fKVWqlFhaWkq1atXE2dlZSpQokeXUxZxo0aKFdO7cWRISEvQ+QPbu3StFixZVFVMk9ZL2y25qFChQQC5duqR6TBlNnTpVrK2tJSgoSFatWiWrVq2SwMBAsbGxUT0tpEyZMmJpaSlNmjSRjRs3Zvk+v3//vpiZmeU4prW1dZaX70+fPi3W1taqxlmzZk3ZuHGjiIh06NBBGjVqJAcPHpSuXbtK6dKlVcUU0f4kQ0bHjh2Tvn37iqOjoxQqVEjGjBkj/v7+kjt3bhkyZMgrH//hhx9K0aJFlYO6lJQUmTZtmuh0OunXr5/qcWn52ZlGiwOR9AYMGCAVKlQQnU4n1atXl5EjR8qOHTvk6dOnqseYtqA6qxkSak+CODo6ipWVlZibm0uePHkyTRtXIzo6WurXry+Ojo5iYWEhHh4ekitXLvH19c2yQEdOnDx5UgIDA8XZ2Vny5csnQ4YMyfTZfvbsWYP/Rh0cHJQruQ4ODvL333+LiMjRo0elRIkSBo/TFK9dJHXqeNr3RfrvjvDwcNVX3v/44w9p1KiRcmVGC1pfidy0aZM4ODjItGnTxMbGRmbMmCG9evUSKysr1Se6RUSOHz8uGzZs0JuxsWXLFtVX0kz1/ly7dq3Mnj1buaotIhISEpLjq3BZzaLS8vMjTcGCBeXQoUMiov/+3LBhg3h7exsUK0+ePPL555/rFZk6dOiQFClSxKiZFumZibzj9VKzkTt3bhw/fhxlypTR237mzBlUrVoVz549MzhmoUKFMGjQIAwaNEhv+5w5czBnzhxERUXhzJkzaNiwIe7cufPKeFOnTsXs2bPRtGlTlC1bFrly5dK7v3///gaPUWsNGjTAypUr4e7urrd969at6NWrl+qyivHx8Wjbti3y5cv3xr52ILWU9y+//IIzZ84gLi4OFStWRKdOnVQvVHd2dsbhw4dRokQJ2NnZ4fTp0/D29sb169fh4+OjV478dZs1axauXr2Kb775RrOFm2vWrMG8efOUkqmlSpXCgAED0K5dO1XxJk6ciJ49e6JgwYKajA8AfH19YW1tjVWrVsHV1RUAcPfuXXTt2hXPnz9HaGiowTF37NiBp0+fonXr1oiMjMSnn36Ky5cvw9nZGb/++qvq4gQ9e/ZEjRo1VC1Kzs69e/ewatUqrFixAhEREWjWrBl69eoFPz8/5X1w8OBBNGrU6JUl7f39/TF//nzY2trqbT916hS6dOmCc+fOqRqjqT47161bh5s3b6Jt27b44IMPAKQWkHB0dESLFi1UxYyOjsaBAweUwgznz59HhQoVDF5MDQA+Pj4oVaoUhg0blmVBhsKFCxscc+XKlS+9v1u3bgbHTHPw4EG9z8769eurjmVhYYEGDRrA398fLVu2zPR/DgBPnz5FYGAgVqxYkeO4+fLlw+HDh1GsWDEUL14cCxYsgJ+fHy5evIhKlSrh6dOnqsar5WsHUj+X2rZti6CgINjZ2eHMmTPw8vJCUFAQIiIisH37doNj3r9/H+3atcP+/fthY2OT6XeaVobcEDt37sSsWbOwZMkSeHp6Gvz4rBw4cAATJkzA6dOnld/nmDFj0LBhQ03ia8FU709jGdLqQs3nR5qhQ4fi2LFjWLt2LYoXL46wsDDle7Nr164YO3ZsjmOtWrUKXbp0ybT9yZMnGDhwIJYtW6Z6nGne2+TIFAc43333Hfr164cmTZqgSpUqAIDjx4/jjz/+wOLFi+Hv749Zs2bhr7/+wq+//vrKeF5eXtneZ2ZmhqtXrxo8xpUrV8LFxQVNmzYFAAwbNgxLly6Fj48PVq9ebdSbP6MHDx7AxcVF1WOXLVuGvn37wtraGs7Oznpf8mpee506dV56AG9olT5TcXJywqFDh+Dj46OXHB08eBBt2rTB3bt3Vcc+cOAAlixZgqtXr2Lt2rUoWLAgVq1aBS8vL9SsWdPgeK1atcLevXuRN29elC5dOtOH/YYNG1SP9U0WGRmJVq1a4fLly/Dw8AAA3Lx5E8WKFcOmTZtQtGhRTZ7n0aNHcHJyMirxNMVJBisrKxQpUgQ9e/ZE9+7dkS9fvkz7xMbGokWLFti7d6/qsSckJKiu5GSKz05TefjwIUJDQ7F3717s27cPf//9N5ycnPDgwQODY9na2uL06dOavQffJjdu3ND0+ytNw4YN0b17d3Ts2BG9e/fGmTNn0L9/f6xatQqPHz/GsWPHNH9ONQ4ePIjGjRujc+fOCAkJwRdffIG///4bhw8fRmhoKCpVqmRwzPr16yMqKgr+/v5ZJttqEmMnJyfEx8cjKSlJs4RLaz179nzp/cuXLzc4plbvz/nz56NPnz6wtrbG/PnzX7rvm3ISGUitohgQEICQkBAkJyfD0tISycnJ6NixI0JCQmBhYaEqplaVGTN6b5MjUx3gHDp0CN988w0uXboEAChRogSCgoLw8ccfazZ2Y5QoUQKLFi1C3bp1ceTIEdSrVw9z587Fli1bYGlpqeqANu2g+8qVK1i3bp3RB90A4Obmhv79+2PEiBEwNze+V3HGq3kvXrxAeHg4zp07h27dumHevHk5imPqks6ff/45HBwcsHTpUuXsX758+dCiRQsUKlRI9Rml9evXo0uXLujUqRNWrVqFv//+G97e3vjmm2/wxx9/4I8//jA4Zo8ePV56v9qxJiYmZlk2tlChQqri/fPPP/jtt98QFRWFxMREvftmz56tKqaIYNeuXbh48SKA1Ctc9evX17z0qbG0PskApP69f/LJJ5qNcdWqVVi8eDGuXbuGI0eOoHDhwpg7dy68vLxUX40xhQkTJrz0fkPLg/fv318vGfL19UWtWrVQu3ZtlC1bVtV7qVmzZujevbuqUsMvk5ycrNcEs3Tp0mjevLlBBzSvOpBLT+1BXXR0NNatW4crV64gODgYefPmRVhYGFxdXVVfPT5x4gSePHmCOnXq4N69e+jatatyJWn58uUoV67cK2P8F68dSC2VPG3aNL0rKMOHD0fZsmVVxbOxscGRI0dy9BpzylRXIrX83mjVqpXezy9evMC5c+cQHR2ttEMxlLe3N44fP67X9gNIfc9WrFgxx5/FXl5eOHHiBJydnTU/CTR16lS4urpmSg6XL1+O+/fvY/jw4QbFy0pUVBTOnTuHuLg4VKhQAcWKFTM4xrNnzxAYGKi8ly5fvgxvb28EBQXhgw8+0GSc7+2aI5HU+e07duxQSnLu3LlTk0WxppCSkqLJ2NKXtB42bJjSdO7cuXOqmmetW7dOcufOrVTiS5tHumDBAmncuLHqcTo5OZl0rUSasWPH5mhtRJrs5uVqVQnMFOuYRFKbra5cuVJE9Of7hoWFqa7Wp7XLly9LzZo1M5V2Neb3+eeff4qNjY2y9qh8+fLi6OgoDg4OqsqNm8qzZ89k+vTp0rhxY6lUqZImDXBFUotcTJ48OctGzcbQqqLit99+Ky4uLjJp0iS9NRMrVqwweuGzSGqFwosXLxrVViBN+nLg5cuXl9KlS4uNjY3Y29ur+j/67LPPZMGCBZqWn12yZIl4eHjI2LFjZd26dZqUbo+IiJBixYqJjY2N8n60sbGREiVKGPQZ7enpqXeztbUVMzMzZe2SMRXgRFLX/Lm4uLyRzVUzvvbsbmpfu6lUqFDBqFL9/wVTfG9kJTk5Wfr06aN6LXV2bVTu3LkjVlZWxg5PE4ULF1bWBaV39OhR8fT0fA0jypopKjNm9N5eOTKVlJQUREZGZnkGw9fX1+B4P/zwA2bMmIGIiAgAQPHixREcHJzlfMucyJ8/P3bs2IEKFSqgQoUKGDx4MLp06YIrV66gXLlyr1wjkFGFChUwaNAgdO3aVW8a2KlTp9C4ceMcra3KyqBBg5AvXz7873//U/X4nIqMjESVKlXeiEv5abRexwSkngH8+++/4enpqff/dPXqVfj4+OD58+cavgJ1atSoAUtLS4wYMSLLBnRqzl5WqVIFjRs3xvjx45XXnT9/fnTq1AmNGjVS1fwXSJ0bHhoamuXVKDVnfjt16oSdO3fis88+y3L6iiHzsdPLmzcvjh8/jiJFiqh6fFZu3LiBRo0aISoqCgkJCcpZuwEDBiAhIQGLFy/OcSwfHx9MmTIFLVu21Htfnjt3DrVr11Y1tQxInU4YFBSU5ZnFggULYsSIEariZhQbG4vu3bujVatWqj+TtfSyq+xmZmaqmgk3adIEIoKffvoJefPmBZA6FbBz584wNzfH1q1bDY75888/49tvv8WyZctQokQJAMClS5fQu3dvfPHFF+jUqZPBMevVq4dKlSopzVXT3kuHDx9Gx44dcf36dYNjvk3CwsKQK1cu5SrR5s2bsWLFCvj4+GDcuHGwsrIyOObOnTsxfvx4TJ48Octpufb29jmKExsbq+wbGxv70n1zGjONKb43snPp0iXUrl3boLXUabNNWrZsiZUrV+o1ek9OTsbu3buxa9cuZbbR62RtbY0LFy5kuiKlxXFCcnIyQkJCsHv37iyPjw1Z2lC4cGH8+uuvqFatmt7femRkJCpWrPjK91hOaDtJ7y0TGhqKmTNnKlMFfHx8EBwcrHrKyNGjR9GxY0fcuHEDGXNONV9Ms2fPxujRoxEYGIgaNWoASJ1X3LdvXzx48CDTVLGcaNCgAXr16oUKFSrg8uXLaNKkCQDg/PnzqhZHXrp0Kcukz8HBAdHR0QbHS5OcnIzp06djx44d+PDDDzN9KKudDpXRkSNHYG1trUksrVhaWipdr7Xi5uaGyMjITP/HBw8ehLe3d47jVKxYEbt374aTkxMqVKjw0qk/hnZPDw8Px8mTJ1GyZEmDHvcyFy5cwOrVqwGk/l6fPXuGPHnyYMKECWjRooWq5OjUqVNo0qQJ4uPj8fTpU+TNmxcPHjyAjY0N8ufPryo52rJlC/744w/l71wr3bp1w6+//qrpSYYBAwagcuXKOH36tN4UkVatWul1qM+Ja9euoUKFCpm263Q61QvdAWDkyJE4ffo09u3bh0aNGinb69evj3HjxmmWHNnb22P8+PFo1qyZquToypUrmDt3rt530IABA1QnsxkPOLQQGhqKo0ePKokRkFo4Ztq0aarfr6NHj8a6deuUxAhInfI9Z84cfPbZZ6qSoxMnTmDp0qWZthcsWFD1STogdS3y0KFDlYO6jN/tahJOU/jiiy8wYsQIlC1bFlevXsXnn3+O1q1bY+3atYiPj8fcuXMNjpn2t1OvXj297SJi0DGNk5MTbt++jfz588PR0THL7w1DY6YxxfdGdq5cuYKkpCSDHtOyZUsAqceAGacM5sqVC56enpg1a1aO4w0ePDjH+xp6nOTh4YFDhw5lSo4OHTqUqeiWoQYMGICQkBA0bdoUZcqUMWoK+v3795E/f/5M258+farZ1Pb3Njn68ccf0aNHD7Ru3Vo5mDl48CDq1auHkJAQdOzY0eCYffv2ReXKlbF169Ysz2AYasGCBVi0aBG6du2qbGvevDlKly6NcePGqUqOFi5ciFGjRuHmzZtYv369cnBz8uRJdOjQweB4Wh10Z3T27FnloCljxSo1v9fWrVvr/SwiuH37Nk6cOIHRo0erGmP//v1RtGjRTAfD33zzDSIjI3P8ZWTqdUwA0Lt3bwwYMADLly+HmZkZbt26hSNHjmDo0KEGvf4WLVooi+TTPvS14uPjo/pKQXZsbW2VKzsFChTAlStXULp0aQBQ/VyDBg1Cs2bNsHjxYjg4OODo0aPIlSsXOnfujAEDBqiKWbBgQdjZ2al67MuY4iTDgQMHcPjw4Uxnoj09PfHvv/8aFMvLywvh4eGZFipv374dpUqVMnhsaTZt2qScWUz/eVG6dGlcuXJFddysxMTEICYmxuDH7dixA82bN0f58uWVJOPQoUMoXbo0fv/9dzRo0EDTcaql0+nw5MmTTNvj4uJUXY0AgNu3b2d5kJmcnKy66IxOp8vyjPHly5ezLBqSU927d0dUVBRGjx6tyfd6mzZtUKVKlUzrIqZPn47jx49j7dq1quJevnwZ5cuXBwCsXbsWtWrVws8//4xDhw6hffv2qpIjYwqqpLdnzx4ludYqZhpTfG9kTEDSjhe2bt1q8JqotBMWXl5eOH78uOoiVWlOnTqVo/3UvE979+6NgQMH4sWLF0qF1N27d2PYsGEYMmSIwfHS++WXX7BmzRrlhLwx0o6zg4KCAPzfa/3+++9RvXp1o+MDeH/XHJUsWVJmz56dafusWbNU9zmysbHJss+RWjqdLst4ly9fNrhplqlMmTJFfHx85OjRo2JnZycHDhyQH3/8UfLlyyfz589/3cNTdO/eXe/Ws2dPGT58uOzYsUN1THd3dzlx4kSm7SdPnjSo47Op1zGJpK5ZmzRpkjLP38zMTKytrWXUqFGqY2pt9+7dUr16ddm7d688ePBAac6cdlOjRYsWsnTpUhERGTJkiBQtWlQmTZokFStWlHr16qmKqXXfExHT9BMREaldu3a2N7VrrhwdHeX8+fMior9+7cCBAwY36/3uu++kYMGC8ssvv4itra2sXr1aeZ+uXr1a1fhETNPzJW1tatpt7ty5Mnz4cHF3dze4sahI6hqm4cOHZ9o+fPhw1evMxo8f/9KbGl26dJHSpUvL0aNHlbWvR44ckTJlyki3bt1Uxfz000+lQoUKcvLkSWXbiRMnpGLFitKsWTNVMU3VXFXrpqUuLi5y5syZTNvPnDljVLNrOzs7uXz5soiI1K9fX+bOnSsiIjdu3FDdf+1tYIrvjYyflXXr1pXPP/9clixZosn6xTdVSkqKDBs2TKytrZW1WzY2Nqo/O9LTsi/igQMHJE+ePEpPzQEDBkiDBg3E1tY2y2MyNd7b5MjKyirbhq1qE486derItm3bjB2aonTp0jJ58uRM2ydOnChlypQxKvbTp0/lwoULRndAfhsOupOSkiQ0NFQePXqkadzskldj3kOmlpCQIOfPn5djx44ZvHje1NIngVotrL1y5Yryvo6Li5MvvvhCypYtK61bt1adiLi4uCgHIcWKFZPt27eLiMiFCxdy3OA5o3v37knt2rU1bbRpKu3atZPevXuLiCgHoU+ePJG6detK9+7dDY73448/StGiRZX//4IFCxrdAPqTTz5RTs6kjVFEJDAwUPz8/FTFzLh43tvbW6pWrSojR45UilIYQqfTKe+j9C5duqT680ProhEiIo8fP5bmzZuLmZmZWFlZKQ1hW7ZsKdHR0api3rt3Txo3bpwpZuPGjVUXnjFVc1Wtm5ZaW1srJ1fSu3DhglFJTJ06daRr167yww8/SK5cuZTvpn379knhwoVVxQwNDX3pzRhaHYNo/b2RkpIiN27ckPj4eIMfm9G8efPk2bNnyr9fdnuTPHnyRP766y85e/as0uzaWDNnzpQvv/xSs6JnkZGR0qtXL/noo4+kVKlS0qlTpyxPOqj13hZkKFq0KIKDg/HFF1/obV+8eDFmzZqlFEAwxMaNGzFq1CgEBwdnuXjxww8/NCje+vXr8fnnn6N+/fp60y52796NNWvWZCo3mRP3799H9+7ds20Ip3b+dGJiIiIjIxEXFwcfHx/kyZNHVZw0Wvclym6hoTHKlCmDvn37IjAwUG972nTIv//+W7PnMtaPP/6I1q1bw8bGRrOYycnJmDNnDtasWZNlYQJDi1y8qrdYrVq1DB6jKZii74kp+omYyj///AM/Pz+ICCIiIlC5cmVERETAxcUF+/fvz3IueE7Ex8cjLi5O9ePTM0XPF615eHhg9uzZaNu2rd72NWvWYOjQoYiKitLkebQqGhEREaFXul6LXkqXL1/GhQsXYGZmhpIlS6J48eJGx9S6uarWTUurVKmCTz/9NFPp93HjxuH333/HyZMnVcU9c+YMOnXqhKioKAwePFgp4hIUFISHDx/i559/NjhmVgU+0n82qTleuH//Pnr06IFt27Zleb+hMbX+3khJSYG1tTXOnz+vqsx0eqYsu53mxIkT2X4Hv0m9Bt+6voiapVlvmW+//VasrKykb9++8sMPP8gPP/wgX3zxheh0Olm8eLGqmNlNhTLmzPeJEyekU6dOUrFiRalYsaJ06tTJqLNYHTt2lBo1asjx48fF1tZWdu7cKatWrZISJUrIli1bDI7Xo0ePLM+axsXFSY8ePVSPc+DAgXq3gIAAqVGjhjg4OEj//v0NjlepUiX5888/VY8nK8uWLZPcuXPLmDFjZN++fbJv3z4ZPXq02NjYKFO5DBUUFJTlWaQFCxYYNTXExcVFbG1tpUOHDrJ161ZJSkpSHSvN6NGjpUCBAjJz5kyxtraWiRMnir+/vzg7O78xZ8JGjx4te/bsUc7eaeH48eOyZ88eERG5e/eu+Pn5iZ2dnVSsWFHCw8NVxcydO7fqx75M2vS57G5qvXjxQlatWiXBwcHSr18/+e677zQ506olU59ZNNb48ePF0dFRpk2bJvv375f9+/fL1KlTxdHRUSZMmKDpc505c0b11QNT06pNhak4OjoqV7a0uKr722+/iaWlpXTt2lVCQkIkJCREunTpIpaWlrJx40bNx//s2TNJTExU9djo6Gi92/3792Xnzp1StWpV1d+nWh+DmIKPj88bX8JcRGT16tWSK1cu+fTTT8XKyko+/fRTKV68uDg4OKi6ii+S+v0WHBwsn3/+ubRq1UrvZoyMSxsy3gxhbm6e5RXmBw8eaFa+/b29cgSkXumZNWuWUimoVKlSCA4OVt148MaNGy+93xTduw1VoEABbN68GVWqVIG9vT1OnDiB4sWL47fffsP06dNx8OBBg+JZWFgoVWjSe/DgAdzc3Ayu7PIq48aNQ1xcHGbOnGnQ47Zv346RI0di4sSJqFSpEmxtbfXuN7R8aJpFixZh8uTJuHXrFoDURenjxo3TK6JhiIIFC+K3337LdGY7LCwMzZs3xz///KMqblJSErZv347Vq1dj8+bNsLGxQdu2bdGpUyfVDYqLFCmC+fPno2nTprCzs0N4eLiy7ejRo6rOVAKpVxCyOgtm6JVXILU645EjR5CUlISPPvpIabJZo0YNo0qja61ixYr49ttvUa1aNU3jatX8WEuvqnKYnqEVD03p+fPnWLBgAfbu3ZtlKVpDxyoimDt3LmbNmqV8fri7uyM4OBj9+/fXtKHwwYMH0axZMzx+/DhH+w8ePBgTJ06Era3tK6tjqa0cqnWbCiB18Xh2pYKXL1+uKqYpmpZu3boVU6ZMQXh4OHLnzo0PP/wQY8eO1eTq+MmTJ/WqH1asWNHomBmFhoZi8ODBqq5yaXEMcubMGZQpUwbm5uY4c+bMS/dV873x+++/Y/r06Vi0aBHKlClj8OOzcvDgQdSsWVOTWGk+/PBDfPHFFwgICFBKWnt5eeGLL75AgQIFMH78eIPi/fLLL+jatSv8/Pywc+dONGzYEJcvX8bdu3fRqlUr1Y3dtWZubo47d+5kOu68desWihQpgmfPnhn9HO91cvQmMqQ+u5oDent7e5w5cwaenp4oXLgwfv75Z9SoUQPXrl1D6dKlER8fn+NxigicnJwQERGhVw0oOTkZv//+O0aMGKF86WtFbV+i9NMD0h90iMryoRndv38fuXPnNno6obW1Nc6dO5dpykpkZCTKlCmjST+i+Ph4bNy4ET///DP+/PNPfPDBB6oqeNna2uLChQsoVKgQChQogK1btyqdvitUqGBwBS+tp1ukSUpKwrFjx7B//36Ehobi8OHDSEhIwEcffWTwyQBT0aqfSE6pPcmQJiIiItskIeN0oYwM+cJW298JSC27u2LFCly9ehVz585F/vz5sW3bNhQqVEipWGgILXtRJSUl4eeff4afnx9cXV2VanDGViycP3++3s/y/6tsrVq1SqlelhN16tTBxo0b4ejoiDp16rx0XzXVx7JrU7Fw4UJMmjRJVSXW8ePHY8KECahcuXKWVeU2btxocMy3yb179/D5558jNDQUjo6OAIDo6GjUqVMHv/zyi1EV+zK6ePEiKleubHBfRECbY5D0B8fm5uYwMzPLVGIdUN/by8nJCfHx8UhKSoKVlVWmE2lq+iJaWVmhYMGC6NChAzp16qTqMygjW1tbpQ2Ls7Mz9u3bh7Jly+LChQuoW7euQf2YAO2TLa2lfb4NGjQIEydO1DveSk5Oxv79+3H9+vUcV/R7mfe2lHea9GdZSpcunWXPjZz64YcfXnp/Tq4mZNcDID1jDuhLlCiBS5cuwdPTE+XKlVPmUS9evBgFChTIcZy0cZqZmWU5T9zMzMwkf0hq+xLt2bNH0zOxGWn1xVO0aFFs37490zqmbdu2GVUaPT0bGxv4+fnh8ePHuHHjhvL+N9QHH3yA27dvo1ChQihSpAh27tyJihUr4vjx40q5b0MMHDgQ0dHROHbsGGrXro2NGzfi7t27mDRpkkF9IDKytLREjRo1kC9fPuTNmxd2dnbYtGmTsn7CUKboe6JVP5Gc6ty5M6pUqaIqOfruu+/Qr18/uLi4wM3NTe/vyszM7JXJkTEJT06FhoaicePGqFGjBvbv349JkyYhf/78OH36NJYtW4Z169YZHFPLXlSWlpbo27ev8renVRn3OXPm6P1sbm6OfPnyoVu3bhg5cmSO46RPeLQuvQyYpk3F4sWLERISokkzXlM2LTWVoKAgxMXF4fz580oZ/L///hvdunVD//79lX5vhsh4VSYt2Z42bZpSNtxQWhyDXLt2TfnOvXbtmqpxvIyasuevcuvWLfzyyy9YvXo1pk2bhg8//BCdOnVChw4d8MEHH6iK6eTkpJxYKViwIM6dO4eyZcsiOjo6xye607ty5QqaNm0KIDWZS+sbNGjQINStW9fgYzqt+yKmfb6JCBYvXgwLCwvlPisrK+V9pIX3Njm6d+8e2rdvj3379ml2liVjj5MXL14gPj4eVlZWsLGxyVFyZIovovQGDBignE0YO3YsGjVqhJ9++glWVlYICQnJcZy9e/dCRFC3bl2sX79er0GglZUVChcubFTTMK37EtWuXVv1WNIzZRNUIHU6S2BgIO7fv6/XZ2DWrFlGf2CnXTH66aefsHv3bnh4eKBDhw6qDhSB1AWWu3fvRtWqVREUFITOnTtj2bJliIqKUnVws2fPHmzevBmVK1eGubk5ChcujAYNGsDe3h5Tp05VPrQNsXTpUuzbtw+hoaFISEjAJ598gtq1a2PUqFGqplsA2vc9AUyfvGdkTPPjSZMmYfLkyZn6tBjjxIkTelOBjC2YMGLECEyaNAmDBw/WSzzq1q2Lb775RlVMrXtRValSBadOndJ0urUpDhR79uyJefPmZXrtT58+RVBQkKrpardv385yOu/HH39s8NnuNImJiaqnCGekddPSvHnz4vLly3BxcYGTk9NL/9bVXJUAUqeO//nnn3r9wXx8fLBw4UI0bNhQVczy5ctneVWmWrVqqqcpanEMkv5v5saNG/j4449haal/OJuUlITDhw+r+vsyRQEcFxcXBAYGIjAwENeuXcPPP/+MlStXYuTIkfD19TW4yBQA+Pr6YteuXShbtizatm2LAQMGYM+ePdi1a1emE205oXWylb4vYosWLYz+jkv7fEt/ZdtU3ttpdZ9//jmuXr2KH374IdNZlqJFi6o6y5KViIgI9OvXD8HBwfDz89Mkppbi4+Nx8eJFFCpUyODmZElJSejduzcmTJgADw8PTcfVo0cPvZ/TzoDWrVtX1Qe9l5cXevToge7du6NQoUKqxzV+/HgEBwfDxsbmlWdR1J4h13odEwC0b98eW7ZsgY2NDdq1a4dOnTpp1yzt/zt69CgOHz6MYsWKoVmzZgY/Xqspn+mlvW+GDBmCL7/80uhpj0DqWf4DBw6oPnP6X3rVSQY171F7e3uEh4drciXzn3/+QYcOHXDo0CG9k1Qff/wxfvnlF9VnVPPkyYOzZ8/Cy8tLmR7i7e2N69evo2TJkqqmp27btg3z58/H4sWLNUlo1qxZg5EjR2LQoEFZroNUk7ybIpExxbrSMmXKoGPHjvjf//6nt33SpEn49ddfcfbsWYNjDh8+HHny5FHd1Du90NBQ1KhRA5aWlppUQ1u5ciXat28PnU5nkjVMQPafS6dOnUKtWrUMmrKfJuM66rTPU7UnVrJizDEIkP378+HDh8ifP7/qq+5p03KvXLmCefPmGT0tN6Pk5GRs27YNo0ePxpkzZ1SN89GjR3j+/Dnc3d2RkpKC6dOnK9/Bo0aNgpOTk0HxOnbsiMqVKytrDhcsWIAWLVpg165dqFix4htRUe7FixcoWbIktmzZYlSj8FfSpKzDW8je3l7++uuvTNuPHTsmDg4Omj7X8ePHVTeHFNGuH0B6CQkJcvHiRaMbmuXJk0euXbtmVIz/wpw5c6RcuXJiYWEh9evXl9WrVxtVvz+td9Ljx4+1G2QG9+7d06wXUceOHTWrUicikpiYKD169FD6x2ihcuXKSs+gZs2aSZcuXeSff/6RYcOGibe3t6qYGzdulEGDBkmFChXE2tpaqlevLiNHjpQdO3bI06dPVcXUuu+JSGoPnfHjx8uNGzc0jWuK5sc9e/aURYsWaTI+Pz8/qVq1ql7fl4sXL0r16tVV9yMSESlYsKAcOnRIRPSbwG7YsEH1e0nrXlSmqG6aXRWn+/fvi4WFhUGxYmJiJDo6WszMzCQyMlKvseajR49k5cqVUqBAAVXjXLdunVhYWIifn59MmDBBJkyYIH5+fmJpaSkbNmxQFbN///7i6Ogovr6+EhgYKIMGDdK7qXXjxo0sq+ml9cMxxIsXL2TlypVy584d1ePJTvPmzcXX11f+/fdfZds///wjtWrVkpYtW6qKuXLlyiy/JxMSEmTlypUGx0tMTBRvb2+lcbYWzMzM5N69e5m2X7p0Sezs7FTF3Ldvn+TOnVvq168vVlZWyufH1KlTpU2bNkaN9+DBg9KvXz/Jly+f2NnZSefOnVX1xzTFe+nhw4fK+yc5OVmmTp0qzZo1k8GDBxvdJ9LLy0sePHiQafvjx4/Fy8vLoFju7u6avoey8t4mR9l1vQ4LC1P9B5WdU6dOqYp57949adq0aabmZmk3NZ4+fSo9e/YUCwsLsbCwUP7oAwMDZerUqQbHa968uYSEhKgay+tw8uRJCQoKEhcXF3FycpKAgAC9Lu2G0Ol0miYHbxt7e3tNX/+qVatkxYoVIpJawt7FxUXMzc3F2tpafvnlF6PjR0dHy++//y5du3aVXLlyqW60uWPHDmnYsKGmJwW0Tt5NacqUKeLi4iLdunWTmTNnGtXM0NraOstE88SJE5I7d27VYxwyZIjUrFlTbt++LXZ2dhIRESEHDx4Ub29vGTdunKqY9erVk2LFism0adNkxYoVShnmtJuhrl+//tKbIUyRyGTVWDP9zcLCQiZNmmRQzPS0blNRu3btbG/GlK3Xumxw7ty5VTegfpmoqCgpX7685MqVS7y9vcXb21ssLS2lQoUKcvPmTVUxTVEyWasD27Ty0ubm5tKkSRO9ktPNmzcXT09P1SdYqlWrJrNmzRIR/ZMrx44dk4IFC6qKOWLECPH09BQrKytp2rSp/Pzzz6pP0KUx1XvJFMzMzLJ8L925c0dy5cplUKzJkydLt27djD65/zLv7ZqjunXrYsCAAVi9erWyNubff//FoEGDVM3VBIDffvtN72f5/1NYvvnmG1WLeE2xQH3kyJE4ffo09u3bpywCB1KbUI4bNw4jRowwKF7jxo0xYsQInD17NsupIc2bN89xrFfNxU5P7bzsihUromLFipg1axa+/fZbDB8+HIsWLULZsmXRv39/9OjRI8djKFOmDK5evappY1kvL6+XPr8hjeLmz5+PPn36wNraOlMVq4z69++f47hpWrZsiU2bNqlaX5SVzp07K/+uVKkSbty4YdR0izQPHz5EaGgo9u3bh3379uH8+fNwcnLCJ598oire559/jvj4eBQpUgQ2NjaZKsupeW8OHDgQAwcORFhYGEJCQhAUFIQvv/wSHTt2RM+ePY0qxxsdHY1169bhypUrCA4ORt68eREWFgZXV1cULFjQ4HhLly5Fnjx5EBoammnKkZmZmUHvJQ8PD7x48SLT9uTkZKPWLE6ZMgUBAQHw8PBAcnIyfHx8kJycjI4dO2LUqFGqYh4+fBhHjhxBuXLlVI8rzYsXL1C3bl3NpoaYokCOqdeVVqpUCT/++KPqx2dkqvW68v/XFmUUFxenanqZKdaaAal/S2FhYdi9e7deexJjmuBm99r/+ecfODg4qIoZEBCAr7/+Gt9//32mdUKGSHt+EYGdnZ1eRTkrKytUq1YNvXv3VhX77NmzWVZ2zJ8/Px48eKAq5v79+xEcHIx27doZ9X2WXpUqVRAeHq75e+nevXtZViJVM9U3/XHxjh079N43ycnJ2L17t8HHUMePH8fu3buxc+dOlC1bNtNxpxbT/97bNUc3b95E8+bNcf78eWW9zM2bN1GmTBn89ttvqua6Z+wmbWZmpqyTmTVrlkHV4ADtexIBqQsZf/31V1SrVk1vLn5kZCQqVqxo8LzkrDpopzG0ytar5mKnp3Ze9osXL7Bx40asWLECu3btQrVq1eDv749//vkHCxcuRN26dXNc7tYUvZMy9p158eIFTp06he3btyM4ONig5NXU3bnTkvR69epl+frVJFxaSytr6uTkBF9fX9SuXRu1atVSXYwBME3fk4xevHihJO8vXrxQlbwDqdWm6tWrB0dHR1y/fh2XLl2Ct7c3Ro0ahaioqFdW2DS1zZs3Y8qUKVi4cCEqV64MILU4Q1BQEIYPH46WLVsaFT8qKgrnzp1DXFwcKlSoYFTHe617URUsWDDTAnq1QkNDTZbI3LhxAx4eHi/9rFcjJSUFkZGRWR6E+fr6avpcaqT1d5o3bx569+4NGxsb5b7k5GQcO3YMFhYWOHTokEFxTbHWLI1WfZ7Sig2dPn0apUuX1ktikpOTce3aNTRq1Ahr1qwxeIxphXzy5MmjyYHt+PHjMXTo0ExxjPHBBx9gzZo1+Pjjj/WOkzZu3IihQ4eqan1hClq/l06ePIlu3brhwoULmYpwqK2amva5kVVhj1y5csHT0xOzZs3Cp59+muOYGdekZ6RFP6b3NjkCUs84/Pnnn0pJX2PPsmjNFAvUbWxscO7cOXh7e+v90Z8+fRq+vr4G96Z5W4SFhWHFihVYvXo1zM3N0bVrV/Tq1QslS5ZU9jl37hw++uijHDcQM3XvpPQWLlyIEydOvDFN2ABoknC9qsFkemqaTS5cuBC1atXSrJGfqWmZvAOpV4QrVqyI6dOn6/29Hz58GB07dsT169dN92JyIH0/kbQDsLR/Z/yiV3u1WCta96KaMmUKLl++bPQZ9PRMlcgA2jZnPnr0KDp27IgbN24YdRCWseDIyxh60J3W3yk0NBTVq1eHlZWVcl9a2eChQ4canHBn9X+TduBozPeGln2e0q4yjh8/HkOGDNErZJP22tu0aaP3O8kprQ9snz17BhFRktcbN25g48aN8PHxUV2lb+jQoTh27BjWrl2L4sWLIywsDHfv3kXXrl3RtWtXo9oR/P3331n+HRkyyyaN1u+lcuXKoUiRIhg+fHiWvdyMuULl5eWF48ePa3bVzNTe6+RIC6Y8uPvoo48wadIk+Pn5oXnz5nB0dMTUqVMxf/58ZZqMoXx9fdG2bVsEBQXBzs4OZ86cgZeXF4KCghAREYHt27cbHFMrpmyAa2FhgQYNGsDf3x8tW7bMdGADpFZ0CgwMzPGHsxZVjHLq6tWrKF++vEG/o5y+N83MzIzqIwRAOcAxtFTnqxpMpjEzMzO41Kkpq9okJydj06ZNej3Smjdvrtd3wRCmSN6B1KknYWFhKFKkiF5ydOPGDZQoUSLHVdtM9Tmn5dViUyfa6c+Apqf2QETrM+hpHj9+jGXLlumVRu/Ro4fe1SRDmKI5c/ny5VG8eHGMHz8+ywP5nE7ZSn+gLSLYuHEjHBwclKuQJ0+eRHR0NFq3bq36xFKPHj0wb948zfoZZawAl5HaA9ACBQpg+vTpmvR5SrNy5Up8/vnnmlan01rDhg3RunVr9O3bF9HR0ShRogSsrKzw4MEDzJ49G/369TM4ZmJiIgICAhASEoLk5GRYWloq03JDQkJUfc5fvXoVrVq1wtmzZ/WuoqS999X8HWn9XrKzs8OpU6cyNaE3lejoaKPKcd+/fx+XLl0CkNo/S8tGx+/VmqNXrbtIL6dTgnLaiVdNfXetehKlN2XKFDRu3Bh///03kpKSMG/ePPz99984fPjwKw/2sxMaGoqZM2fqfRkHBwcbvKbDlA1wr169+soPCltbW4O+QL28vODh4ZHlwdLNmzcNGt+rrFu3zuCDG1O+N9MsW7YMc+bMQUREBACgWLFiGDhwIHr16pWjx5uyr1euXLlUlWx+lcjISDRp0gT//vsvSpQoAQCYOnUqPDw8sHXrVhQpUsTgmB999BEaNGiARYsWZZu8e3l5oX379gbF1el0WSbUly9fNuiLJON7KSwsDElJScrrv3z5MiwsLAzuT6RlPxFTv9+17kXl6OiINm3aaBYPSF3X0KxZM70EYf78+ZgwYQJ+//13VdPVTLH2NSIiAuvWrTP6ICz95/Xw4cPRrl07veaQycnJ+PLLL41KbNKeIzIyEleuXIGvry9y586d7XqcVzFFXx5A2z5PadL+PhMTE7OcqmdMW4x79+7pHdhmLMWdU2FhYUpz0HXr1sHNzQ2nTp3C+vXrMWbMGFXJkZWVFb777juMGTMGZ8+e1WRa7oABA+Dl5aWssfnrr7/w8OFDDBkyRFUzbkD791K9evVw+vRpkyRHX3/9NTw9PfH5558DANq2bYv169ejQIEC+OOPPwxay5nWmuCHH35Q3pMWFhbo2rUrFixYoDcFVjWTlXp4A3l6euboZmhZwf/K06dP5eTJk3L//n2j4ly5ckV69eolH330kZQqVUo6deokZ86cURVr1apVYmlpKe3atVOqVbVr105y5colP/30k0Gx9u3bl+Pbm8AUlXzKly8vFSpUUG7ly5cXNzc3sbCwkCVLlhg7ZE2NHj1abG1tZcSIEbJ582bZvHmzjBgxQvLkySOjR49+3cMTEdNUtWncuLE0atRIHj58qGx78OCBNGrUSJo0aaIqpqkqDvn7+0vLli0lMTFR8uTJI1evXpUbN25IhQoVZMCAAapizpo1S5o1a6ZX2vXRo0fSokULmTlzpqqYd+/elbNnz2raruB9VKZMGendu7deyf6kpCTp06ePlClTRlVMNzc3OXbsmIiI2NnZyaVLl0REZPPmzVKjRg1VMevUqaOqfPHLuLi46JWET3Px4kXJmzev6rgPHz6UunXrKtX70iqX9ejRQwYPHmxwPFN8b4iIDBs2TCZMmKD68Vm5fPmy1KxZM1OlQmPKzcfExEjnzp3F0tJSKWFvaWkpnTp1kujoaIPj5c6dWymp3rZtW6UaZVRUlOqKl+PHj8+yklx8fLyMHz9eVUxnZ2flM83e3l55r+7evVvKly+vKqbW76X79+9LkyZNZNy4cbJu3Trlez3tZgxPT0+lvcLOnTvF0dFRduzYIf7+/tKgQQODYvXp00e8vb3ljz/+UKpybt26VYoUKSJ9+/Y1apxp3qvkKDspKSlZ9jF415iiN03JkiVl9uzZmbbPmjVLSpYsqdnzGMvR0TFTXxInJyfJmzevuLu7i6+vryxfvtygmNn1V7h+/brY2NioGue4ceP0bhMmTJBFixbJhQsXVMUzJRcXF/n5558zbf/555/F2dnZ4HgtW7bUK8eadmvdurV07NhRxowZk+XBz6ti2tnZSYECBaRhw4aZYqthY2OT5cmE8PBwsbW1VRUzTUJCgty8eVNu3Lihd1MrOjpa6tevL46OjmJhYSEeHh6SK1cu8fX1lbi4OFUx3d3d5dy5c5m2nz171uBy0SdOnJDSpUsrB1wZe/5oISoqSqKiooyOY6peVFqytrbONkGwtrZWFdPOzk4pW1+oUCE5ePCgiIhcvXpV9cHnhg0bxMfHR1asWCEnTpzQJCl2dHSUTZs2Zdq+adMmcXR0VBVTRKRLly7i5+cnN2/e1CvrvH37dvHx8TE4nin68oiYps/Txx9/LL6+vvLHH3/IqVOnJDw8XO+mRrt27aRYsWKyfft25cB2+/btUqJECfn8888Njle2bFmZN2+eREVFib29vRw+fFhEUj9bXF1dVY3RFAmso6Ojcuzl7e0te/bsERGRyMhI1X9HWr+XfvvtN3FwcMi2B5sxrK2tlc/h/v37S58+fZSxGvr36ezsLHv37s20fc+ePeLi4mLUONO8V9PqMjJ2SpCpJScnIyQkJNvqM4auwciVKxfWr1+vSQfxNFevXkWzZs0ybW/evHmm7ueGOnDgAJYsWYKrV69i7dq1KFiwIFatWgUvLy/UrFnToFhjxozB5MmT0bhxY1SpUgUA8Ndff2H79u0ICAjAtWvX0K9fPyQlJb2y/Gfa2gYzMzOMHj06yypGGbuU55QxCz3/ay9evFCm7qRXqVIlJCUlGRzPwcEBmzZtgqOjozI9KywsDNHR0WjYsCF+/fVXfP3119i9e3eOS+ObYuqSTqfDkydPMm2Pi4tTtUAZSJ2W5u/vj8OHD+ttFyMXaTs4OGDXrl04ePAgzpw5g7i4OFSsWNGowjOxsbG4f/9+pu3379/P8vfyMj179kTx4sWxbNmyLBcAq5WUlITx48dj/vz5iIuLAwDkyZMHQUFBGDt2bJbTFl9lwIABCAkJwYQJE1CnTh34+/ujVatW0Ol0qse5bt06rFmzJssF2mFhYQbHq1ixIi5cuKBMd0xz4cIF1SXIS5QogUuXLsHT0xPlypXDkiVL4OnpicWLFxtcgTVN2t9kz549lW3GFiXo0aMH/P39ceXKFeUz/tixY5g2bdoriwC8zM6dO7Fjx45MFWyLFSv2yjUf6aUVjzAzM0P37t313jfJyck4c+aMUdPizpw5o3zvnDt3Tu8+tX9X4eHhOHnypN7aR2Nt2bIFO3bs0PsO9/Pzw3fffafXXiSnxowZg44dO2LQoEGoW7cuqlevDiD1/61ChQqqxijZTJk8ffq06rV7ZcqUwenTp+Hl5YWqVati+vTpsLKywtKlS+Ht7W1QLFO9l4KCgtC5c2eMHj0arq6uBj/+ZZycnHDz5k14eHhg+/btmDRpEoDU37Whf+/x8fFZji9//vyqCpVl5b1NjsaMGYPZs2cjKChI+WM6cuQIBg0ahKioKEyYMOE1j/D/voybNm2KMmXKaHLgoHVvGg8PD+zevTvTHNU///xTKZGuxvr169GlSxd06tQJYWFhSEhIAADExMRgypQp+OOPPwyKd/DgQUyaNAl9+/bV275kyRLs3LkT69evx4cffoj58+e/MjlKW9sgIjh79mymKkblypXD0KFDDRpfem96ids0Xbp0waJFizItbl+6dCk6depkcDw3Nzd07NgR33zzjbL4PSUlBQMGDICdnR1++eUX9O3bF8OHD89xGXtTVPf79NNP0adPHyxbtkzvIKxv376qKg4BqQd2lpaW2LJlS5YL1I1Vs2ZNg08oZKdVq1bo0aMHZs2apff6g4ODDaoeBqSeXFm/fr3mc9yDgoKwYcMGTJ8+Xe/zfdy4cXj48CEWLVpkcEyte1HNnz8fX331Fbp3747NmzejR48euHLlCo4fP46AgACDxwekrpUdMGAAIiMjlZLjR48excKFCzFt2jScOXNG2TenVeZMsfb12rVrqh73MjNnzoSbmxtmzZqljLdAgQIIDg7GkCFDVMd9+vRplmsYHj16ZFBibMq+PIBp1m/6+Pio7uuTHWdn5ywLbjg4OMDJycngeJ999hlq1qyJ27dv650AqFevHlq1amVQrLRei2n9wtJ/DicnJyMuLi7TMUROjRo1Ck+fPgUATJgwAZ9++ik++eQTODs749dffzUolqneSw8fPsSgQYM0T4yA1ISuY8eOKFasGB4+fIjGjRsDgKoCENWrV8fYsWPxww8/KMVCnj17hvHjxyuf90bT5PrTW0jrKUGm4OzsLFu3btU05sSJE8XR0VHatGkjU6ZMMaq7vYjIt99+K1ZWVtK3b1/54Ycf5IcffpAvvvhCdDqdLF68WPU4y5cvLytXrhQR/Q7VYWFhqi6V29raSkRERKbtERERylSoyMhIg6bDde/eXWJiYgwey8scOXJEvLy8TDrNyBjpp2kEBQWJnZ2dlC5dWvz9/cXf31/KlCkj9vb2EhgYaHBsFxcXZT1DepcuXVL+Js+cOSMODg7GvgyjPH78WJo3by5mZmZiZWUlVlZWYmZmJi1btpTHjx+rimljY6PZ1MmMf9Mvu6nx9OlT6devn+h0OmUNgpWVlfTr18/gqXotWrSQdevWqRrHy9jb28sff/yRafvWrVvF3t5ek+dITEyUuXPnKr+HcuXKybJly3I8RbtEiRLKd1D6z7jRo0dLQECAqjFlNR0m42eIsZ8lWq19NaW06VpaaNy4sYwaNUpERFm3l5ycLG3btpU2bdoYHG/cuHGqp7T+13bv3i3Vq1eXvXv3yoMHD5TfqzG/3yVLlkj9+vXl9u3byrbbt29Lw4YNjTpmiIiIkO3bt0t8fLyIiKqlEiEhIbJixQoxMzOTefPmSUhIiHL7+eeflSl7Wnn48KFRSzq0fi917dpVvvvuO83ipZeYmCgzZsyQ/v37S1hYmLJ99uzZBj/n2bNnxd3dXZydnaVu3bpSt25dcXZ2loIFC2Y55VuN97aUt6OjI44fP56p+sjly5dRpUoVREdHv56BpePu7o59+/Zl2fFcLVM0A924cSNmzZql15U7ODgYLVq0UD1OGxsb/P333/D09NQrQXz16lX4+PgYXIWsUKFCGDRoUKYrZnPmzMGcOXMQFRWFM2fOoGHDhrhz547qcRtLqxK3pmLK0ttOTk5YuXJlpqsvv/32G7p164bHjx8jIiICVapUwePHj7ONU7FiRezevRtOTk5KM8PsqJm6lCYyMlLvPW/M1Y+PPvoIc+bM0eTqTk67jav9e0/z9OlTpZ1AkSJFVDVhfPDgAbp164YqVaqgTJkymaa7qb0Slz9/foSGhmYq4X7hwgX4+vpmOS0wp7TqRWVjY4MLFy6gcOHCyJ8/P3bt2oVy5cohIiIC1apVw8OHDw0emyHTvIzpWfI+OX/+POrWrYuKFStiz549SvP4R48e4dChQ6qqU74ttCpfn/FzOCIiAgkJCUq1u6ioKOh0OhQrVszgz+SHDx+iXbt22Lt3L8zMzBAREQFvb2/07NkTTk5OqioqhoaG4uOPP1Y1/TY7MTExSE5OzjQt79GjR7C0tNSsVLwxJk+ejLlz56Jp06ZZ9nJ7Exq7p4mPj8dPP/2k16e0U6dOelfRjPHeTqvTekqQKQwZMgTz5s3DN998o9k0m/TTGURlb5qMWrVqZfDl61dxc3NDZGQkPD099bYfPHjQ4Pm5ADB69Gj069cPe/fuVaYCHT9+HH/88QcWL14MANi1a5dBvYmePn2KadOmZbsmTM2Bp1Ylbk3FlKW3u3TpAn9/f/zvf//DRx99BCD1/2jKlCno2rUrgNQvrdKlS780TosWLZTpLi1bttR8nFn100n7Yra2tkbRokXRokWLV85NT19i++uvv8awYcMwZcoUoxuMmmLKUlZsbW1VNQBN78iRIzh06FCWPXSMWWsVGBiIiRMnYsWKFcp7ISEhAZMnT0ZgYKCqmFn1opozZ47eeoxWrVop791XcXNzw6NHj1C4cGEUKlQIR48eRbly5XDt2rVMjVFzyhQJj4hg3bp12Lt3b5afc2r7MWnB1CdCXrx4gf79++P333/Hrl27YGdnh7i4OLRu3RoBAQGq1lzdvXsXQ4cOVb43Mv5fa9k83Fhafd6b4nM4zaBBg5ArVy5ERUXpnQz5/PPPMXjwYFXJkZeXlzI1MytqSpi3b98ezZo1w5dffqm3fc2aNfjtt98MXioAaP9e+v7775EnTx6EhoZmau1iZmZmdHK0atUqZR35kSNHULhwYcydOxdeXl4Gn0y3sbExahrqq7xXV47SH9QkJSUhJCQEhQoVUuZmHzt2DFFRUUqt9Nch45z9PXv2IG/evChdunSmAya1X0paFqI4fvw4UlJSULVqVb3tx44dg4WFRZYL9nNi6tSp+PHHH7F8+XI0aNAAf/zxB27cuIFBgwZh9OjRCAoKMjjmoUOH8M033+j1VggKClK9CLZDhw4IDQ1Fly5dsrzKM2DAAINj1q1bF8OGDVO1MPVtl5ycjGnTpuGbb77B3bt3AQCurq4ICgrC8OHDYWFhgaioKJibm2daHP1fqlOnDsLCwpCcnJypz0/JkiVx6dIlmJmZ4eDBg/Dx8ck2jrm5ud57RrJYBGzoGVog9XNu4sSJsLW1fWljVEOa/7Zu3RohISGwt7d/5boiQz6XPD098emnn2q+ADitwapOp1PWIZw+fRqJiYmoV6+eqvFq3Ui6V69e8PDwwNixY7Fw4UIEBwejRo0aOHHiBFq3bo1ly5blaFwZrVq1CosXL8a1a9eMPgABUj/HlixZgjp16mRZNMMU6/pyavz48QgODoaNjQ3GjRv30uRIbbGbfPny4fDhw0b1uEmvcePGiIqKQmBgYJbfG8bMuHgfubm5YceOHShXrlymWSYffvihUpDFEBk/mzNSk8DmzZsXhw4dynQ1++LFi6hRo4aqK8Vv03tp0aJFGDNmDAYOHIjJkyfj3Llz8Pb2RkhICFauXGlQIj516lS4urrqFXQBgOXLl+P+/fsYPny40eN9r64cZWwSmFYRK21qiIuLC1xcXHD+/Pn/fGxpMk6b0vqKjNaFKAICAjBs2LBMydG///6Lr7/+GseOHVM1zhEjRiAlJQX16tVDfHw8fH19odPpMHToUFWJEQDUqFEjx1XOcmLbtm3YunWrpjGDgoIwZMgQ3LlzJ8srCMaeqX+TWVhY4KuvvsJXX32lXFXJeMXEmKaDWkm7KrRixQplfDExMejVqxdq1qyJ3r17K9WTduzYkW0cU12FO3XqFF68eKH8OzuGXDF2cHBQ9n/Z1E5Dr0KbagFwVlUKjSkQA2jfSHrp0qXKVZiAgAA4Ozvj8OHDaN68Ob744gtVY8x4AJJ2EOfo6Ii5c+eqOlhatWoVNmzYgCZNmqgakymlT3jGjRuX7X7GnAPu3Lkzli1bhmnTpqmOkd7Bgwdx4MAB1RVN/2taVo01Ba0KZqSX8XPzxYsXOHXqFGbPno3JkyeripmQkJBlFdcXL17g2bNnqmKa8r2k1cyiNAsWLMB3332Hli1b6v0tVa5c2eACVkuWLMly6nLp0qXRvn17TZKj97Ygw/tK60IUtra2ykLi9K5evSp58uRRNcb0EhIS5Pz583Ls2DF58uSJ0fG05OnpKX///bemMU21iPp9lZSUJDNmzJCPPvpIXF1dM/W5UsPd3V3Onz+fafu5c+fE3d1dREROnjz5xhR20VpWnx9phg4dalAsUy0Ajo+P11uofO3aNZkzZ45s377d6Nha96LSUqlSpWTjxo0iol/k4ezZs6rfj56enibrs6bl73L69OlZbk9KSpL27durHmNgYKDY29tLpUqVpE+fPkb3DypVqpTegvQ32bp16yR37tzSq1cv0el0yvtpwYIF0rhx4xzHya7PYFY3Q2ldMONltmzZIrVq1VL12Nq1a2dZqOjLL7+UmjVrqoppivfSypUrpUyZMqLT6USn00nZsmXlhx9+MDqutbW10uw8/WfT5cuXDe7BptPpsuzXeeXKFdHpdEaPVeQ973P0trh3757eVLD8+fOrjqV1bxqdToe7d+9mWgd0+/ZtWFoa//aysrKCnZ0d7OzskCdPHqPjaWnixIkYM2YMVq5cmeWZKzX+q/Ui74vx48fj+++/x5AhQzBq1Ch89dVXuH79OjZt2oQxY8aoihkTE4N79+5lmjJ3//595YqXo6Njpr41L7NixQrkyZMHbdu21du+du1axMfHo1u3bqrGagr9+vWDo6OjUoo1zeDBg7F69WrMmDEjx7GKFy+OkSNH4uDBg5ouAG7RogVat26Nvn37Ijo6GtWqVUOuXLnw4MEDzJ49G/369TM4pil6UT1//hxnzpzJci2PmmIU165dy7K3i06nU8oIG2rcuHEYP348li9frtli54iICPTs2VPT3+WMGTOQN29e+Pv7K9uSk5PRvn37TH1/DHHu3DmlTPvly5f17lNzVn3u3LkYMWKE0i/qTTZp0iQsXrwYXbt2xS+//KJsr1GjhtKnJifmzp2r/Pvhw4eYNGkS/Pz89Gav7NixQ1UPxunTp6NevXo4ceIEEhMTMWzYML2CGVoqUaIEjh8/ruqxkyZNQv369XH69Gllau/u3btx/Phx7Ny5U1VMrd9Ls2fPxujRoxEYGKjMiDl48CD69u2LBw8eGNUCxsvLC+Hh4Zmuvm/fvj3TVMNX8fDwwKFDhzIVHzp06BDc3d1Vj1GPJikWmURMTIx07txZLC0tlasIlpaW0qlTJ4mOjlYVM61zdkZDhgyRL7/80uB47du3l1q1aumN5/Hjx1KrVi1p27atqjGKiLx48UJGjRol9vb2Srlge3t7+eqrryQxMVF1XC2VL19e7OzsJE+ePFKmTBmpUKGC3o1eP29vb9myZYuIpJ6tioyMFJHUctcdOnRQFbNjx47i5eUlGzZskJs3b8rNmzdlw4YN4u3tLZ07dxYRkdWrV0ulSpVyHLNYsWJKx/T09u3bJ8WLF1c1TlPZsmWLODg4yIEDB5RtgYGBUqBAAYOvMHh6emZ78/LyUj1GZ2dnpaTrd999Jx9++KEkJyfLmjVrpGTJkqpifvzxx+Lr6yt//PGHnDp1SsLDw/Vuhtq2bZvky5dP0270pUqVkk2bNomI/tnZ+fPnq/5Mio+PFz8/P00/57T+XYqI/PXXX+Lo6Chr164VkdTvkFatWkmpUqX0yka/bo6OjmJlZSXm5uaSJ08eTa5mm0ru3Lnl2rVrIqL/fjLmDH3r1q1lwYIFmbYvWLBAWrRooSpmdHS0TJo0Sdq2bSuNGzeWr776Sm7duqUqlohkKlkeHR0tFy5ckM8//1zKlSunOu6pU6ekY8eO4uPjI5UqVZIePXrI5cuXVcfT+r3k6emptFBJLyQkRDw9PVWPUyT1c7hgwYLyyy+/iK2traxevVomTZqk/NsQX3/9tTg7O8vy5cvl+vXrcv36dVm2bJk4OzvLlClTjBpnGl45eoP17t0bp06dwpYtW/TOsAwYMABffPGF3pmcl0m/KNvMzAzff/89du7cmWUhCkPNnDkTvr6+KFy4sHLGMjw8HK6urli1apXB8dKYoomj1kxZgYe0kbZ2CwDy5MmDmJgYAFCKAKixZMkSDBo0CO3bt1eutlpaWqJbt26YM2cOAKBkyZL4/vvvcxwzKioqyxLchQsXRlRUlKpxmkrTpk3x7bffonnz5ti1axeWLVuGzZs3q2o7YKorpfHx8bCzswMA7Ny5E61bt4a5uTmqVatmULnr9MLDw3Hy5Em96nTGCAoKQtu2bTFmzBjN1lwNHjwYAQEBeP78OUQEf/31F1avXo2pU6ca9H5Mr1u3bjh58iQ6d+6cZUEGNbT+XQKp5fDXr1+Pli1bwsrKCsuWLUNkZCT27t1rkqaWaqW/ivKm07pqLADs2LEDX3/9dabtjRo1wogRIwyOFxUVBQ8PD3z11VdZ3qdmnaqjo2OWxXE8PDxyfNyVlfLly+Onn35S/fiMtH4v3b59O8sCVR9//PFLq/flRK9evZA7d26MGjUK8fHx6NixI9zd3TFv3jy0b9/eoFjBwcF4+PAhvvzyS2WGhrW1NYYPH46RI0caNU6FJikWmYSNjY3e2dk0+/fvN6hZae3atXN0q1OnjqpxxsXFyZIlS+TLL7+UIUOGyMqVK42+uvNfNHGkd1/x4sXl6NGjIiJSo0YNmTp1qoiI/PLLL5IvXz6jYj958kROnz4tp0+fNno9nIeHh2zevDnT9k2bNknBggWNim0qCxcuFJ1OJx988EGWDZYNlZKSYlRDxPTKli0r8+bNk6ioKLG3t1eaN544cUJVE2kRkcqVK2f5eayWnZ2dciVTSz/++KMULVpUuQpVsGBB+f7771XHy+57yBha/y7T27hxo1haWkrZsmXf6Ea1b4MpU6aIj4+PHD16VOzs7OTAgQPy448/Sr58+WT+/PmqYhYqVEhmzpyZafvMmTOlUKFCBsczNzeXu3fvZtr+4MED1Vdg9+3bp3fbv3+/XLhwQV68eKEq3tuidOnSMnny5EzbJ06cKGXKlNHseZ4+fZrl/5mhnjx5In/99ZecPXtWnj9/rsHI/g+vHL3BnJ2ds6wM5eDgACcnpxzHMWVvGiC1QlOfPn00janT6bKcQ+vl5QUrK6scxXhVyeH01JZFj46Oxrp163DlyhUEBwcjb968CAsLg6urKwoWLKgq5vtk/vz5Od5XzfqTtJLOVatWRVBQkFJ5Kioqyqj500DqlSitqgd26NAB/fv3h52dHXx9fQGk9nQaMGCAwWfVTCG7kuD58uVDxYoV8e233yrbMvaOe5UffvgBM2bMUFoLFC9eHMHBwejSpYvq8Y4ZM0apGFivXj3l6vPOnTuzXJOTHVP1ogKAzz77DPv27dO8iWinTp3QqVMnxMfHIy4uzqg1qkDq/H4tGlSa4neZ3Wd8vnz54OjoqPe99Dr7MWV05coVrFixAleuXMG8efOQP39+bNu2DYUKFXplH7f/kimqxo4fPx69evXCvn37lCq3x44dw/bt2/Hdd98ZHE+yaIEAAHFxcbC2tlY1RkP6Hb5uWr6Xxo8fj88//xz79+9X1hwdOnQIu3fvxpo1a4waZ926dbFhwwY4OjrCxsZGWacdGxuLli1bGtw0Hkj9Ds5pXzlDvVd9jt42S5cuxdq1a7Fq1Sq4ubkBSJ0m1K1bN7Ru3Vp1ude3wYQJE3Dx4sVMTRz9/f1RrFixHPWs6NGjR46fT02vjjNnzqB+/fpwcHDA9evXcenSJXh7e2PUqFGIiorCDz/8YHDM901WU8myYmZmpqqpbkZHjx5VepY0a9bM6HhaSUxMRJcuXbB27VqlkElKSgq6du2KxYsX5/iEgKnUqVMnR/uZmZkZ9CWX3QLghQsXYtKkSUYlsHfu3MHt27dRrlw5mJubAwD++usv2Nvb53g6l6l6UQGpU//atm2LfPnyaVaM4tmzZxAR5cDjxo0b2LhxI3x8fNCwYUOD4wHA1q1bsWDBAixevNioRd+m+F2a+jPeFEJDQ9G4cWPUqFED+/fvx4ULF+Dt7Y1p06bhxIkTWLdu3eseYiaJiYmIjIxEXFwcfHx8jC6OdOzYMcyfPx8XLlwAAJQqVQr9+/fP1BLkZdJO2MybNw+9e/fWK4qUnJys9FrUuijDm8QU76WTJ09izpw5ev83Q4YMMeikUlbMzc1x586dTCdr7t27h4IFCyrtJ94UTI7eYBUqVEBkZCQSEhKUebNRUVHQ6XSZGtKp6f79psl4FvDPP//Mtonjm3AWsH79+qhYsSKmT5+u13zu8OHD6NixI65fv/66h0hvmcuXL+P06dPInTs3ypYt+8q+Om87Ly8vjB8/PtN6x5UrV2LcuHGvvXpj+i7x169fh4eHBywsLPT2SUlJQVRUlMEVBZctW4a+ffvC2toazs7OeomC2pMBDRs21KvSV6JECVhZWRlVpc/JyQnx8fFISkqCjY3N/2vvzuNqTP//gb9OJe1ptbaqoZXSmOxLhphR2SlbsqeyJWamUnYfhTCyRGIsTbJTSJayaxOlVckyWccktF2/P/p1fx0ndO7u45zqej4ePR65z+k673SW+7qv9/V+C0ziXr16VadxPv2//JaGdOVeWN26dcOoUaMwf/58vs+NmzdvYvjw4SgqKhJ3iA1CzQWbS5cuoVu3bnwXkGRlZaGvr4+FCxdy1rxXEjWE51JaWhqA6v1WFy5cgLq6OnNbZWUlYmJisG3bNok7X6KTIwkWEBBQ5/uy7f4tSRraVUBVVVUkJSWhffv2fG9MBQUF6NChAz58+FCncdTU1Oq8ybmuJyIU1RDIyckhPT0dRkZGfMezs7NhYWFR59fQ9yAtLY2nT58KXPl8+fIltLW1hV45atWqFTw9PbF48WJmZau+NDU1cenSJZiZmWHnzp3YtGkTkpOTcfjwYfj5+TFXg4WxZ8+er94uSWXmGwIlJSXcvXsXBgYGfJ8bDx8+RMeOHSXqOf/hwwds2rQJ8fHxtZabr+tF2bdv3zKpkp+mV9ZG2BROV1dXbNy4kZPUz4aG6+cS1+9xAP+KcW3TDXl5eWzatAlTpkwRemxRonuOJFhjmPAIQ9QTnqioKERGRqKwsFCgBw2blbfmzZvX+kaflZUFLS2tOo/TkKoXiVpRURGOHz9e699I2L0sDUllZSXCw8MRFxdX60kIm3zshsDIyAiRkZH47bff+I4fOnRI4q74cr23oaysDGPGjOFsYgRwX6WvvLwcly5dgq+vb51TYOvq9evXCAsLYyZspqamcHV15buy3Bi1aNECT58+Ffj/TE5Olrh9qm5ubjh79ixGjhyJrl27sq5UqKamxpx011YJDmCfnsrVeUNISAimT58OOTk5pgJefSszinrfM9fPpS+tlXz8+JF1and+fj4IIcyK1qfnRrKystDW1hZYjZcEdHIk4Zryhv+KigpcvHgRubm5cHZ2hrKyMp48eQIVFRWhc55DQkLw+++/Y/LkyTh27BhcXV2Rm5uLW7duwd3dnVV8Dg4OCAwMZDYq8ng8FBYWwsfHByNGjKjzOPTKa7W4uDg4ODjA0NAQmZmZMDc3x8OHD0EIYZowNlZeXl4IDw/HL7/8AnNzc07KJTcEotwAzJWavQ08Hg++vr617m3o3Lmz0ONOmjQJhw4dEpgY1oeRkRGOHj2KYcOGITY2ltmzVVxczOrKerNmzXD48GHWZe+/5PLlyxg6dChUVVWZpuQhISEIDAzEiRMnmKIkjdHYsWPh4+ODv//+GzweD1VVVUhMTMTChQtZtdMQpZMnT+L06dPMa5OtT9OpuC4Q9e7dO6xevfqLF5bqmp46f/58jB07FnJycjAwMKh1BUVYtRXU4hJXz6Wawkg1rV4+Pb+qrKzE5cuXWZfdr0kN//zvIqzjx4/X+b5sGmh/jqbVSTBJ3fD/PdLACgoKYG9vj8LCQnz8+BFZWVkwNDSEl5cXPn78iNDQUKHG69ixI/z9/TFu3Di+5Wc/Pz+8evUKmzdvFjrGf//9FyNHjsTt27fx33//oU2bNnj27Bm6deuG06dPQ1FRUegxP/XhwweB1ZPGnDrQtWtXDB48GAEBAczfSFtbGy4uLrC3t2e1X6Kh0NTUREREBIYMGSLuUL47UW0A5oqo9jZ4enoiIiICnTp1gqWlpcBeHjYrpVFRUXB2dkZlZSXs7Oxw9uxZAMCqVatw+fJlnDlzRugxJ02ahM6dO9e7uuOnLCws0K1bN2zdupW5alxZWYnZs2fj6tWruHv3LmePJWnKysrg7u6O8PBwVFZWQkZGBpWVlXB2dkZ4eLhEXUU3NTXFwYMHOavKWePDhw9IS0urdTIj7IntuHHjcOnSJUyYMAGtW7cWODfx8vKq0zi6urpYsmQJhgwZAgMDA9y+fRuamppfvK8k4Oq5VLPyVFBQgHbt2vH9XM17XGBgoFAFM2qTnZ39xRRNPz+/r/5sXVfY2aw+1joOnRxJLknd8P+tHPRPsV0VcXJygrKyMsLCwqChocH87hcvXsS0adOYsr91paCggIyMDOjp6UFbWxvnzp1Dp06dkJ2dDVtbW7x8+ZJVnEB1da20tDSUlJTA2toaAwYMYD3Wu3fv4OPjg8jIyFpj4uJFL6mUlZWRkpKC9u3bQ01NDQkJCTAzM0NqaiocHR1ZPd8nTZoENzc3ib8S3aZNG1ZNVKnvh+u9DV+rAChs1b9PcVGl71PLly9HUFAQ7Ozs0KVLF4GLPmyq6snLyyMlJQUdOnTgO/7gwQN07twZ79+/r9M46urqyMrKgqamJqZMmYKNGzcyaYWSrrCwEOnp6SgpKYGVlZXEpZECwJkzZxASEoLQ0FDOisPExMRg4sSJePHihcBtbE5sW7RogVOnTtV7dWv79u3w8PBgGnvXhm3qn6g9evQId+/erfdzqV+/foiOjhaqVUxd7dixA7NmzYKmpiZatWolUIBG4oqKcdo1ieKUiooK0yRQSUmJ5ObmEkIIefjwIWnevLk4QxM5dXV1kpmZSQjh/93z8/OJvLy80OMZGBiQpKQkQgghXbp0IaGhoYQQQmJjY4mamhqrGAsLC1n93NfMnj2bmJiYkKioKCIvL0927dpFli1bRtq1a0f27dvH+eNJkpYtW5L79+8TQggxMTFhmqKmpKQQRUVFVmM6OjqSZs2aESMjI7JixQpSVFTEWbxcWrduHZk9ezZnTVAbilOnTpGYmBiB4zExMbU2gaa+P319/S9+GRgYsBqze/fu5MiRIwLHjxw5Qn766ac6j6OoqMh8NkhJSZHi4mJW8XxPomp+KwrFxcWkb9++REpKiigpKRE1NTW+LzaMjIzI7NmzybNnzziJUV9fn/ncqK+3b9+Su3fvEh6PR+Li4khKSkqtX2z9/fffZNSoUeSnn34iVlZWfF+SqKKigiQnJ5NXr17VeyxdXV2yevVqDqL6PuieIwnG1Yb/74XLNLCqqqpar84UFRWxujLYv39/HD9+HFZWVnB1dcW8efMQFRWF27dvC7Vp8lP6+vro2bMnxo8fj5EjR3JyteXEiROIiIhA37594erqil69esHIyAh6enr466+/4OLiUu/HkFS2trZISEiAiYkJhgwZggULFuDu3buIjo6Gra0tqzGPHj2K58+fY+/evdizZw/8/f0xYMAAuLm5wdHRUSCVSVwSEhIQHx+PM2fOwMzMTCAuSShdLwqLFy/G6tWrBY4TQrB48WIMHjxYDFFRnxJFOXVPT094eXkhJyeHeW1fv34dW7ZswerVq5nyvwC+mtLVrVs3ODk5oUuXLiCEwNPTE/Ly8rXed9euXdz+Eiz1798fbdu2xbhx4zB+/HiYmpqKO6QvGjduHB4/foyVK1eiZcuWnOyF/OeffzB//ny0bNmSgwiBZcuWwc/PD3v27OHbD8iGsrIyzM3NsXv3bvTo0YPpscgFUex7HjFiBLp27QofHx++42vXrsWtW7fw999/CzXe3LlzYWFhATc3N1RWVqJ37964du0aFBQUcPLkSfTt25dVnEB1AZZRo0ax/vnPvXv3DpcuXaq1eBOb1ezP0bQ6CTZ16lS8fPkSkZGRUFdXR1paGqSlpeHk5ITevXtLRJUzUaWBjRkzBqqqqti+fTuUlZWRlpYGLS0tODo6QldXV+gKNVVVVaiqqmIabB48eJBpBjpjxgxWlViSk5Oxf/9+HDx4EM+fP4e9vT3Gjx+PoUOHsn5TVVJSwv3796Grq4t27dohOjoaXbt2RX5+PiwsLFBSUsJq3IYgLy8PJSUlsLS0xLt377BgwQLmbxQcHMxJWkdSUhJ2797NbDodP348Zs+eLfaUlm+VsZeE0vWiIC8vj4yMDIHmog8fPoSZmRnevXsnnsCoWtWcLtT3JPlb+wd4PF6dUpj++ecfrF+/Hrm5uYiOjsagQYO++N575MiResXMlRcvXuDgwYM4cOAArl27BktLS7i4uGDcuHFo166duMPjo6CggGvXrjG9BrkwZcoU9OjRA25ubpyMZ2VlhdzcXBBCoK+vL3BhqT7pWnfu3OGrplifwkCi2PespaWFCxcuwMLCgu/43bt3MWDAAPzzzz9Cjde2bVscO3YMNjY2OHr0KNzd3REfH4+9e/fiwoUL9Wqo6+bmhh9//BEzZ85kPUaN5ORkDBkyBKWlpXj37h3U1dXx4sULKCgoQFtbm5OG8XRyJMFEveGfCzUvnmXLlmHChAnYsmULHj9+jG3btmH16tWsVzoePXoEe3t7EEKQnZ0NGxsbZGdnQ1NTE5cvXxaqikxFRQVWrlyJKVOmiOTDhxCCixcvYv/+/Th8+DCqqqowfPhwVlcqLS0tsWnTJvTp0wcDBgxA586dsW7dOoSEhGDt2rUS0dStoXr69CkiIiKwe/duFBUVYcSIEXj8+DEuXbqEtWvXcrrhnKqbVq1aYf/+/ejfvz/f8fPnz8PZ2RnFxcViioz6VEREBP73v/8xez1/+OEHeHt7Y8KECazGE6aseF0vitRsotfQ0GAVkzjk5+dj//79OHDgADIzM9G7d2+JKttvbW2NP//8k/XKfW1KS0sxatQoaGlpwcLCQmAyI+xV/2/1g2TTEqW4uBhjx47FxYsX0aJFCwDVlYP79euHgwcPssrcEcW+5y/t3cvMzISVlVWd9+7VkJOTQ05ODtq1a4fp06dDQUEBGzZsQH5+Pjp16vTNHlVfs2rVKgQHB+OXX36p99+9b9+++OGHHxAaGgpVVVWkpqaiWbNmGD9+PLy8vFhnA/ERUzofJYSEhASyZcsWsmbNGnLu3Dlxh8NHR0eHxMfHE0IIUVZWJtnZ2YQQQiIiIsjgwYPrNXZ5eTnZt28f8fb2JrNmzSI7duwgpaWlrMZSVFQk+fn59YqnLu7cuUM6d+5MpKSkWP18cHAw2bhxIyGEkHPnzhE5OTnSvHlzIiUlRTZs2MBlqBLr48eP5NGjR6SgoIDvi42ysjISFRVFfvnlF9KsWTPSpUsXsnXrVvLvv/8y94mOjiYtWrTgKnzWysvLyblz50hoaCh5+/YtIYSQx48fk//++0/MkYnO9OnTiYWFBbO3khBCsrOziaWlJXFzcxNjZA1PWVkZcXV1JXl5eZyOGxQURBQUFMiiRYvIsWPHyLFjx4i3tzdRUFAgwcHBnD5WU1RRUUFOnDhRr88NUYmNjSXdu3cn8fHx5MWLF+Tff//l+2Jj586dREZGhigpKRE9PT1O9rBxbfTo0cTGxoZvL9O9e/eIjY0NGTt2LKsxRbHv+ccffyQBAQECx/39/Ym1tbXQ4+nq6pLY2FhSUVFBdHR0yMmTJwkhhKSnp9f7M5LLvYuqqqrMnnRVVVXm73T9+nXSoUOHesVZg06OqHpRVFRkTlzbtm1Lbty4QQghJC8vj/Um+rKyMmJoaMjZJktCCHFwcCDh4eGcjfepR48ekTVr1pBOnToRaWlp0rNnT7J161ZOxn748CE5fPgwSU1N5WQ8SfbgwQPSs2dPIiUlxffF4/FYnzRoaGgQNTU1Mnv2bJKcnFzrfV6/fk309fXrEXn9PXz4kHTs2JEoKCgQaWlpZpO5p6cnmTFjhlhjE6U3b94QW1tbIiMjw3xQysjIkH79+pHXr1+LO7wGR0VFhfPJkb6+PtmzZ4/A8fDw8Hq9bh4/fkwOHTpENm3aRDZu3Mj3xdbFixfJr7/+Stq3b0/at29Phg4dSi5fvsx6PFFKSEggs2bNIlpaWkRZWZmMHz+enDlzRtxh8eHxeMwXV+/JLVu2JCtWrCCVlZUcR8sdFRUVcvPmTYHjN27cIKqqqqzGdHNzI0uXLiWEELJ582YiLy9PBgwYQFq0aEGmTJnCaszjx48TGRkZMnHiRBIeHk7Cw8PJhAkTiIyMTK0FT77F39+fqKqqko4dOxJdXV3y4cMHQgghYWFhxNbWllWMoqCpqUmysrIIIYQYGxszRX0yMjKIgoICJ49BCzJIME9PTxgZGQksN27evBk5OTkSsefI0NAQ+fn50NXVRceOHREZGYmuXbvixIkTzHK0sJo1a4YPHz5wGufgwYOxePFi3L17t9ZytGyahm3btg379+9HYmIiOnbsCBcXFxw7doz13pjy8nLY29sjNDSU2QOjp6fHWQlVSefq6goZGRmcPHmy1n4VbKxfvx6jRo2CnJzcF+/TokULkWw6F4aXlxdsbGyQmprKlxY0bNgwTJs2TYyRiZaqqiquXr2Kc+fOITU1FfLy8rC0tJT40utc2bt3L0JDQ5Gfn49r165BT08PGzZsgIGBARwdHYUez8nJCUePHuU0RfTp06fo3r27wPHu3bvj6dOnrMYMDw9n9npqaGgIlPVls6F63759cHV1xfDhw5mfT0xMhJ2dHcLDw+Hs7MwqVq4tWbIEBw8exJMnT/Dzzz9j48aNcHR0rHcxAVHgumErUN2bZ8yYMXXuW1ObT0u4f6vvIptei1VVVbUW62nWrBnrZqbbt29nftbd3R0aGhq4evUqHBwcMGPGDFZjDh06FEePHsXKlSsRFRXFvH+eP38effr0EXq8pUuXwtzcHI8ePcKoUaOY/XvS0tJYvHix0OPNnz8fy5Ytg6KiItNMuzY8Hg9BQUF1HtfKygq3bt2CsbEx+vTpAz8/P7x48QJ79+6Fubm50HHWGhMhdM+RpGrbti2OHz+OLl268B1PSkqCg4ODROw/Wb9+PaSlpeHp6Ynz589j6NChIISgvLwcwcHBdW7A9rmVK1ciKysLO3fuZIoo1MfX3ojZ9i3Q0dHBuHHj4OLiwtmGVS0tLaYIQVOjqKiIO3fusO7E/bny8nImJ5urN0xRqfmg7NChA99m3YcPH8LU1BSlpaXiDpHi2NatW+Hn54e5c+dixYoVSE9Ph6GhIcLDw7Fnzx5WJ6ai6Elkbm4OZ2dn/PbbbwKPdejQIVYNW3V0dDBz5kwsWbKkXifJnzIxMcH06dMFJobBwcHYsWMHs7Fe3Hr06AEXFxeMHj36i01GJcmbN28QFhbGV5jAzc0NqqqqrMabN28etLS0BJ5PwtizZw/Gjh2L5s2bf7PvIptei46Ojnjz5g0OHDiANm3aAAAeP34MFxcXqKmpSUxxD0nXr18/HDlyBC1atOC0r1vNPvx+/fqhuLgYEydOZM6bwsLC0Llz53rHTidHEkxOTg7p6ekwMjLiO56TkwNzc3POV1e4UFBQgDt37sDIyKheXbWHDRuGuLg4KCkpwcLCQuBDXhJKG5P/X02JS/PmzUPz5s1rLW/c2P34449Yv349evbsydmYhoaGOHLkCKfVlkRBTU0NiYmJMDU15ZscJSQkYMSIEUJXHaIkn6mpKVauXMk0vK75m6enp6Nv3761Nsn8lppO97Xh8XisqjgdPnwYY8aMwYABA5hGm4mJiYiLi0NkZCSGDRsm9JgaGhq4efMm2rdvL/TPfknz5s1x7969BvV5Kelu374Ne3t7yMnJoWvXrgCAW7du4f379zh79iyr6m2enp6IiIhAp06dYGlpKbBCExwczEns9fHo0SM4ODjg3r170NHRYY6Zm5vj+PHjrAo7xcTEQElJifl827JlC3bs2AFTU1Ns2bJFJI1XKfZoWp0EMzIyQkxMDObMmcN3/MyZMzA0NBRTVP9HlGlgLVq0wIgRI+o9jihxPTECqivr7dq1C+fPn6/1yq8kfHCIypo1a7Bo0SKsXLmy1mo2bHpm/f777/jtt9+wd+9eqKurcxUq5wYOHIgNGzZg+/btAKqfWyUlJfD398eQIUPEHB0lCvn5+bCyshI43rx5c9YlzEWRHjpixAjcuHED69evx9GjRwFUr9LcvHmz1vjrws3NDX///TerVJ0v0dHRQVxcnMDk6Pz588wJLiWcefPmYejQodixYweTwVFRUYGpU6di7ty5uHz5stBj3r17l3nepKen890mis9UNnR0dJCUlITz588jMzMTQPVzfsCAAazH9Pb2xpo1awBU/x/Mnz8fCxYsQHx8PObPn99o2zWIQv/+/REdHS2wdePt27dwcnLipOIjXTmSYLt27cKcOXPg7e3NlLqNi4tDUFAQNmzYIBF7ERpSGtilS5ewbt06vvQAb29v9OrVS8yR/R8ul54bmpr0ms8/IEkd+p18iZWVFXJyclBeXg49PT2ByWZ9emBwicvS9VTDYGpqilWrVsHR0ZFv5WjTpk3YvXu3WJ+bn+4VuHz5Mrp3785JenONyspK/Prrr3j//n2tF0LYXATaunUr5s6diylTpjB7pBITExEeHo6NGzey3tfRlMnLyyM5OVkg1fn+/fuwsbGh6b5CUFJSQnp6OvT19bF06VKkp6cjKioKSUlJGDJkCJ49eybuEBsMKSkpPHv2TOBzsbi4GG3btkV5eXm9H4OuHEmwKVOm4OPHj1ixYgWWLVsGANDX18fWrVsxceJEMUdXbfz48QgLCxNJGlhFRQUuXryI3NxcODs7Q1lZGU+ePIGKigqUlJSEGquhbNYVxQbYhkIUv7uTkxPnY4qCjo4OUlNTcejQIaSmpqKkpARubm5wcXGBvLy8uMOjRGD+/Plwd3fHhw8fQAjBzZs3ceDAAaxatQo7d+5kPW5RURGOHz9ea+f4uk46Nm3aBB8fHygqKqJfv354+vQppxP0VatWITY2lunP8nlBBjZmzZqFVq1aISgoCJGRkQCqr/YfOnSIVXELqnq1vrCwUGBy9OjRIygrK4spqoZJVlaWmUyeP3+eOYdTV1evV/+g+hL1hRAupaWlMd/fv3+fb0JZWVmJmJgYtG3blpPHoitHDcTz588hLy8v9KRA1Dw8PBAREQFjY2NO08AKCgpgb2+PwsJCfPz4EVlZWTA0NISXlxc+fvyI0NBQocZrKJt1P/Xo0SMAoCkhjVx5eTk6duyIkydPwsTERNzhfFenT5+GtLQ0Bg0axHc8NjYWVVVVGDx4sJgi+z7++usvLF26FLm5uQCANm3aICAgAG5ubqzGi4uLg4ODAwwNDZGZmQlzc3M8fPgQhBBYW1vXeeXZ2NgYo0ePxsCBA5lN1V/aE8GmsqCamhrWr1+PyZMnC/2z1Pfj6emJI0eOYN26dXyrcd7e3hgxYoREVMxtKBwcHFBWVoYePXpg2bJlyM/PR9u2bXH27FnMmTMHWVlZYomrWbNmKCoqQsuWLSEtLc35hRAuSUlJMRdPapu6yMvLY9OmTZgyZUq9H4tOjqh6EVUaWM0m5bCwMGhoaDApJxcvXsS0adOYTu11JYrNuqLIe62oqEBAQABCQkJQUlICoHo53sPDA/7+/rWWF21Mrly5gm3btiEvLw9///032rZti71798LAwKBehRru3LnDTIDNzMxY75UQlbZt2+L8+fNNbnJkaWmJ1atXC+yriomJgY+PD1JTU8UU2fdVWlqKkpKSep+UdO3aFYMHD0ZAQACTqqetrQ0XFxfY29tj1qxZdRrn6NGjmDlzJoqLi8Hj8Wo9EQHYV/ps1aoVrly50iDSsblmaGiIW7du8ZXsB6qrwllbW7MqmiEqZWVl8Pb2RmhoKCoqKgBUn0zPmjULq1evZko9U99WWFiI2bNn49GjR/D09GQugMybNw+VlZUICQlhNW59V4pFfSGESwUFBSCEwNDQEDdv3oSWlhZzm6ysLLS1tSEtLc3JY9HJkQT7559/sHDhQsTFxaG4uFjgA4rNh1JDwXVpYyMjI3h7ewvknYeGhiIoKEjoyRYgmrzXWbNmITo6GoGBgejWrRsA4Nq1a1i6dCmcnJywdetWocdsKA4fPowJEybAxcUFe/fuxf3792FoaIjNmzfj9OnTOH36tNBjFhcXY+zYsbh48SIziX3z5g369euHgwcP8r25ihPXpesbCnl5eWRkZEBfX5/v+MOHD2FmZsa6MEFTpaysjJSUFLRv3x5qampISEiAmZkZUlNT4ejoiIcPHwo1XklJCVRUVPDgwYMvTtzYlHRetWoVnj59yvqEsCH70ufGP//8A11dXXz8+FFMkX1ZaWkps7rZvn17sfdkGj58eJ3vKwmVbUWFi5ViUV8IaaiazqdwAzR58mQUFhbC19eXs6aYosRlGlhVVVWtL8SioiJWuc4LFiyAp6cnUlJSat2sKwxR5r3u378fBw8e5EsnsrS0ZHoqNebJ0fLlyxEaGoqJEyfi4MGDzPEePXpg+fLlrMb08PDAf//9h3v37jGrMvfv38ekSZPg6emJAwcOcBJ7fd26dQtxcXE4e/asxJauFwVVVVXk5eUJTI5ycnIE/g8aG1Fc/FJUVGSuHrdu3Rq5ubkwMzMDAFalwZWUlBAfHw8DAwNOJ+03b97EhQsXcPLkSZiZmQmsiDfG5/vx48eZ72NjY/kmlZWVlYiLixN4HUgKBQUFWFhYiDsMBtseS2x8+PBBYEWGTeVUUYy5ZMkSLFy4kFkpPnz4MN9KcV04OTnBycmpThdCJEl2djbi4+NRXFws0JjXz8+v3uPTyZEES0hIwJUrVzhpaCUqokoD47q0MZebdTt37gwejwcej8dUEfxUTd4rG82bN6/1A9LAwACysrKsxmwoHjx4UOuyvaqqKt68ecNqzJiYGIF0tZq+EgMHDmQbKucaQul6UXB0dMTcuXNx5MgRpudNTk4OFixYAAcHBzFHJ1qiuPhla2uLhIQEmJiYYMiQIViwYAHu3r2L6Oho2NrashqzT58+9Y7rcy1atBDq6n9jUFMchsfjCTQmbdasGfT19REUFCSGyBoeUZe9Li0txaJFixAZGYmXL18K3M7mwsW7d+/g4+PD6ZgZGRnMBT4ZGRm8f/8eSkpKCAwMhKOjY53TaAHRXQgRhR07dmDWrFnQ1NREq1atBAq6cDE5AqEklomJCUlKShJ3GF81c+ZMoq2tTUJDQ0lqaipJTU0loaGhpFWrVmTmzJmsx3306BExNTUlJiYmREZGhtja2hINDQ3yww8/kH/++YfD30A4//77L8nPzyf5+fmEx+ORW7dukYcPHzJfT548IRUVFazHDwgIIOPGjSMfPnxgjn348IG4uLiQpUuXcvErSCwDAwNy7tw5QgghSkpKJDc3lxBCyJ49e4iJiQmrMZWUlEhycrLA8aSkJKKsrMw6Voobb968Iba2tkRGRobo6+sTfX19IiMjQ/r160dev34t7vBE6kvPzfrIzc0lqamphBBCSkpKyIwZM4iFhQUZPnw4efjwIaePJWnev3//xduePHnyHSP5On19ffL8+XNxh0F9xezZs4mJiQmJiooi8vLyZNeuXWTZsmWkXbt2ZN++fRIzZsuWLcn9+/cJIdXni8eOHSOEEJKSkkIUFRVZjVlRUUGioqLIsmXLyLJly8jhw4frdU4jCrq6umT16tUifQy650iCnT17FkFBQdi2bZvELrerqqoKpIEB1VWoxo0bh3///Zf12BUVFXylja2tretd2vj27dt8fY66dOki1M9/Ws2lZvPi5wUZhPX5FdTz58+jefPm6NSpEwAgNTUVZWVlsLOza5TpJjVWrVqFffv2YdeuXfj5559x+vRpFBQUYN68efD19YWHh4fQYzo6OuLNmzc4cOAA2rRpAwB4/PgxXFxcoKamhiNHjnD9a1BCIoTg3LlzSE1Nhby8PCwtLcW+8fd7MDU1xV9//SVxxUG+B39/f0yZMoWThuE1TE1NsX//foFMi8OHD2PmzJl4/vw5Z49FiZeVlVWdV1rZ9AvT1dVFREQE+vbtCxUVFSQlJcHIyAh79+7FgQMHWO1/FcWYTk5O+OWXXzBt2jQsXLgQx44dw+TJkxEdHQ01NTWcP39eqPFycnLwyy+/oKioiCmz/+DBA+jo6ODUqVPM6r64qaioICUlBYaGhiJ7DMleO2vixowZg9LSUmYD5Ocpaq9evRJTZP9HVGlgq1atQsuWLTFlyhS4uLgwx3ft2oXnz5/Dx8dHqPGKioowbtw4JCYm8m3M7969Ow4ePIh27drVaRwlJSW8fPkS2trauHz5MifNxj7Pn/48vaqplPJevHgxqqqqYGdnh9LSUvTu3RvNmzfHwoULWU2MAGDz5s1wcHCAvr4+8//46NEjmJubY9++fVyGX29RUVGIjIysteqQpDSrFQUej4eBAwdKVJrj97BhwwYsXrxYoi9+icqxY8ewYsUK9OnTB25ubhgxYkS9K5/17dsXtra2CAgIgI+PD969ewd3d3dERkZixYoVHEXOjbi4OGav2ef7JXbt2iWmqBoOUfeve/XqFXPiraKiwpxr9ezZU6hUNVGPGRwczGxnCAgIQElJCQ4dOgRjY2NWbVQ8PT1haGiIa9euQV1dHQDw8uVLjB8/Hp6enjh16hSrOLk2atQonD17FjNnzhTZY9CVIwm2Z8+er97+ed6yOAQGBiIzMxO7d+9mPtw+fvwINzc3GBsbw9/fn9W4+vr62L9/P1M8ocaNGzcwduxY5OfnCzWevb093rx5gz179vBdEXF1dYWKigpiYmLqNM6IESOQmJgIExMTXLp0Cd27d//iJJBtGfOmqLKyEomJibC0tISCggJycnJQUlICU1PTevf2IoTg/PnzyMzMBFC912zAgAFchM2ZkJAQ/P7775g8eTK2b98OV1dX5Obm4tatW3B3d5e4k7v6CAkJwfTp0yEnJ/fNamU1DZsbIzU1NZSWlqKioqJeF7/U1NTqfBVdEi6o1UhOTsbu3btx4MABVFRUYOzYsZgyZQp+/PFH1mOeOnUKU6dOhZGREZ4+fQolJSXs27cP5ubmHEZePwEBAQgMDISNjU2te83oarb4WVpaYtOmTejTpw8GDBiAzp07Y926dQgJCcHatWtRVFQkEWNyTVFREdevXxcovpGamooePXowEzFxW7VqFYKDg/HLL7/AwsJC4L2Ti88NOjmihPY90sDk5OSQkZEBAwMDvuN5eXkwNTUVui+RvLw8rl69KpDCcufOHfTq1avOpcHfv3+PPXv2IDc3F0FBQZg2bdoXy5quX79eqBhrVFRU4OLFi8jNzYWzszOUlZXx5MkTqKioSFwTYC596W9eHxERERgzZozAVemysjIcPHiQ6VIubh07doS/vz/GjRvHV7rez88Pr169wubNm8UdImcMDAxw+/ZtaGhofPVvzePxJKrnC9e4uvj1rXHYjPk9SyWXl5fjxIkT2L17N2JjY9GxY0e4ublh8uTJQlclq6qqgoeHB7Zu3QoZGRmcOHFCoMGwuLVu3Rpr167FhAkTxB0K9QXr16+HtLQ0PD09cf78eQwdOhSEEJSXlyM4OBheXl4SMSZQnQETFRWF3NxceHt7Q11dHUlJSWjZsqXQVXPV1dVx8uRJgYvSiYmJGDp0qMRcXPkenxs0rU7C5ebmYvfu3cjNzcXGjRuhra2NM2fOQFdXlynR+r19jzQwHR0dJCYmCrwIEhMTmb0jwo5XWwpcZWWlUOPJy8szS7m3b9/GmjVr6r3n6FMFBQWwt7dHYWEhPn78iJ9//hnKyspYs2YNPn78iNDQUM4eS9KYm5sjLy+P08mRq6sr7O3tBUqT/vfff3B1dZWYyVFhYSHzgSQvL4///vsPADBhwgTY2to2qsnRp6u+wq4ANyZcrfyLIoPge5ZKrjlBLCsrAyEEampq2Lx5M3x9fbFjxw6MGTOmTuPUXEx69uwZYmNjcenSJTg4OMDLywsrVqyQmAbaZWVlAiefFHuVlZVYv379F1OS2ZzQz5s3j/l+wIAByMzMxJ07d2BkZARLS0tWcYpizLS0NAwYMACqqqp4+PAhpk2bBnV1dURHR6OwsBARERFCjffrr79i+vTpCAsLQ9euXQFUZ+vMnDlToqqHfpfPDZGWe6Dq5eLFi0ReXp4MGDCAyMrKMtW7Vq1aRUaMGCHm6ERrzZo1RENDg+zatYupBBcWFkY0NDTIypUrhR7v6NGjpGvXruTWrVvMsVu3bhFbW1ty5MiResX68eNHkpmZScrLy+s1DiGEODo6kvHjx5OPHz/yVWyLj48nRkZG9R5fkp05c4Z07tyZnDhxgjx58oT8+++/fF9s8Hg8UlxcLHA8JSWFqKmp1TdkzhgYGDCVKbt06UJCQ0MJIYTExsZKVJwUtz6vDBUdHV2vylAFBQVf/ZIkt2/fJu7u7kRdXZ20bt2a+Pj4kOzsbOb2kJAQoq2tXefxlJSUyJgxY/iqHCYmJpL27duTzp07cxl6vSxatIgEBgaKO4xGw9fXl7Ru3ZqsW7eOyMnJkWXLlhE3NzeioaFBNm7cyPnjvXv3jvMx2bKzsyPe3t6EEP4Kr4mJiURPT0/o8V6/fk0cHBwIj8cjsrKyRFZWlkhJSREnJyfy5s0bLkPnTFVVFamqquJ8XJpWJ8G6deuGUaNGYf78+XypNjdv3sTw4cMlIkcVEE0aGCEEixcvRkhICHMlSE5ODj4+Pqxq2H+a319Tw7/m+8+bTdb1StP79+8xZ84cJq0lKysLhoaG8PDwQNu2bbF48WKh49TQ0MDVq1fRoUMHvr/5w4cPYWpqWuf0v4ZISkqK+f7TPHxCiNDduWuqGaWmpsLMzIyvb0NlZSXy8/Nhb2/P9LwSt6lTp0JHRwf+/v7YsmULvL290aNHD9y+fRvDhw9HWFiYuEMUCUIIoqKivtjMrzFXZ8zJycGQIUPw+PFjzipDSUlJfXX/kaR0uLewsEBmZiYGDhyIadOmYejQoZCWlua7z4sXL6CtrS3wnPiSvXv31pqq9t9//2Hu3LlifQ3Nnz+f+b6qqgp79uyBpaUlLC0tBVa02Gykb8rat2+PkJAQ/PLLL1BWVkZKSgpz7Pr169i/f7/QY9rZ2SEiIkIgLe3mzZsYP348srKyWMXKdSEOVVVVJCUloX379nznCwUFBejQoYPQ2w9q5OTkMFV9TUxMYGRkxGocUYqIiMD//vc/ZGdnAwB++OEHeHt7c5auStPqJNjdu3drfWFra2uz6nYuCqJKA+PxeFizZg18fX2RkZEBeXl5GBsbs65otGHDBlY/9zWLFy9GamoqLl68yNeNesCAAVi6dCmryVFVVVWtJzBFRUVQVlauV7ySLj4+nrOxaqoZpaSkYNCgQXyTdFlZWejr60tU09Xt27czH5bu7u7MJNnBwQEzZswQc3SiM3fuXGzbtg39+vVDy5YtOWmE2lB4enqiffv2uH79OmeVoZKTk/n+XV5ejuTkZAQHB9erqAfXlRRHjx6NKVOmfHVPhKamZp0nRgCYk6KysjLk5+ejffv2kJGRgbKystgvLnz+d6kpN56ens53vCk9/7ny7NkzpoCAkpIS0z7k119/ha+vL6sx5eTkYGlpiT///BNjxoxBVVUVAgMDsXLlSsyePZvVmN8qxMFG8+bN8fbtW4HjWVlZ0NLSYj2ukZGRRE6IagQHB8PX1xdz5sxBjx49AAAJCQmYOXMmXrx4wZfCyBrna1EUZ9q2bUsSExMJIfxLptHR0cTQ0FCcoTGachqYrq4uuXbtGiGE/++TnZ3NusHo6NGjybRp05gx8/LyyH///Uf69+9PJk+ezE3gTUh4ePhXm0NS4qWmpkZOnTol7jDEQkFBgaSlpQkcr08Dxy85efIk6dOnD6uf3bhxI1FSUiJz5swhsrKyZMaMGWTAgAFEVVWV/Pbbb5zGWR+lpaVkypQpRFpamkhLSzPvx3PmzBF5w0hKfH744Qdy/fp1QgghPXr0IKtWrSKEEHLw4EGipaXFetzNmzcTBQUFMm7cONKtWzfSpk0bEhsby3q8Vq1akYiICNY/Xxs3Nzfi5OREysrKmPOFgoICYmVlRby8vDh9LEmir69P9uzZI3A8PDyc6Ovrc/IYdOVIgo0dOxY+Pj74+++/wePxUFVVhcTERCxcuFBiNpJfuXIFV69eFShnra+vj8ePH4spqi8rLi6udUmbzYbI58+fC2z0B4B3796xviq0bt062NvbMxX5nJ2dkZ2dDU1NTRw4cIDVmJIsLS2tzvdl8zeShHL3dfX69WuEhYXxNSl2dXVlVhUaI1VVVZE28pNkzZs3ZwpvfKqkpKRePeJq06FDB9y6dYvVz/7555/Yvn07xo0bh/DwcCxatIivkmJdfZpa9i1sUsu+tZIvbG88qmEYNmwY4uLi8NNPP8HDwwPjx49HWFgYCgsL67WC4O7ujqKiIqxZswYyMjK4ePFivQppiKIQR1BQEEaOHAltbW28f/8effr0wbNnz9CtW7dG1f7hc0+fPq31/7J79+54+vQpJ49BJ0cSbOXKlXB3d4eOjg4qKythamqKyspKODs7448//hB3eAAaThrYnTt3MGnSJGRkZIB8ts1O2P0sNWxsbHDq1CmmQWnNhGjnzp3o1q0bqzh1dHSQmpqKQ4cOITU1FSUlJXBzc4OLiwvk5eVZjSnJOnfuDB6Px+wr+ho2fyNRVDIShcuXL8PBwQEqKiqwsbEBUN0PKDAwECdOnEDv3r3FHKFoLF26FAEBAdi1a1ejfH5/jSgqQ32eYkMIwdOnT7F06VIYGxuzGpOrSoqfp5Z9CdsLS0ePHsWhQ4dga2vLN4aZmRlyc3NZjSkKw4YNq/V35PF4kJOTg5GREZydnZl9aNTXrV69mvl+zJgx0NXVxbVr12BsbIyhQ4eyGvP169eYOnUq4uLisG3bNly6dAkDBw7E2rVrWafVTZ06Ffv372ed6lcbVVVVnDt3DgkJCUhLS0NJSQmsra0lro8f14yMjBAZGYnffvuN73hNA1xOcLL+RIlUQUEBOXXqFDl06BDJysoSdzh8GkoamKWlJRk2bBi5fv06yc/PZyrg1XyxceXKFaKkpERmzpxJ5OTkiJeXF/n555+JoqIiuX37ttDjlZWVEUNDQ3L//n1W8TREn/4Njhw5Qtq3b09CQ0NJamoqSU1NJaGhocTY2Jh1RcHvXcmILXNzczJt2jS+SmUVFRVk+vTpxNzcXIyRiVZpaSkZNGgQUVJSIubm5sTKyorvqzETRWUoHo9HpKSk+L54PB7R1dUlV69eZTVmQ6mkKC8vz6TSfZrmnJKSQlRUVMQZGp9JkyYRVVVVoqenR4YPH06GDx9O9PX1SYsWLcjo0aNJhw4dSPPmzUlCQoK4Q22y2rRpQ3r06EHy8vKYYwcPHiTq6upkyJAhrMb09PQkLVq0IL179yZz5swh8+bN4/tio7CwkNXPNXRRUVFEWlqaDBo0iAQGBpLAwEAyaNAgIiMjQ6Kjozl5DFqtjqqXR48ewd7eHoQQZGdnw8bGhkkDu3z5cq1pZ+KgrKyM5ORkzjcZ5ubmYvXq1cwqj7W1NXx8fAQ6TNdV27Ztcf78eZiYmHAaZ0PQtWtXLF26FEOGDOE7fvr0afj6+uLOnTtCjymKSkaiIC8vj5SUFIGrxQ8ePEDnzp3x/v17MUUmWqNHj0Z8fDxGjhxZa0EGf39/MUX2/WRnZyMjIwM8Hq/elaEuXrzI938oJSUFLS0tGBkZ8VVsFEZDqaTYu3dvjBo1Ch4eHlBWVkZaWhoMDAzg4eGB7OxsxMTEiDtEANXpf2/fvsXmzZuZCp1VVVXw8vKCsrIyVqxYgZkzZ+LevXtISEgQc7QNw5MnT5CQkFBryrynp6fQ4y1btgy///47XwVVoDojxtXVFefOnRN6zH79+n3xNh6PhwsXLgg9prS0NHr27Inx48dj5MiRUFNTE3qMT+nr62PKlCmYPHkydHV16zWWqCUlJSE4OJivqt6CBQtgZWXFyfh0ciRhRJ2XLQoVFRV8aWDW1tYSlwbm5OSECRMmSFSFstqsXLkSWVlZ2LlzJ+uTmYZKXl4eSUlJAhPDjIwMWFtbs5ogKCoqIiMjA7q6umjdujVOnToFa2tr5OXlwcrKiqlsJG49evSAt7c3U2WvxtGjR7F69Wpcv35dPIGJmKKiImJjY9GzZ09xhyJWNR/DklitrKqqClVVVcz70cGDB3H16lUYGxtjxowZnO+PYishIQGDBw/G+PHjER4ejhkzZuD+/fu4evUqLl26hC5duog7RACAlpYWEhMT8cMPP/Adz8rKQvfu3fHixQvcvXsXvXr1wps3b8QTZANS87eWlZWFhoYG32uIx+MhLy+vXuN/+PABcnJy9Q1TJJKTk7F//34cPHgQz58/h729PcaPH4+hQ4eyquy7YcMGhIeHIz09Hf369YObmxuGDRvGukqwKJSXl2PGjBnw9fXltGH85+jkSMJ87erCp9heaeBSeXk5OnbsiJMnT0r8SseLFy8wadIkdO3aFebm5gK9Jeqa419b2cwvUVFRESpG4P82lyopKcHCwkKgB1Nj7vtibW0Nc3Nz7Ny5kznhKisrw9SpU5Gens6qZHCHDh0QERGBn376CT179sSvv/6KxYsX49ChQ/Dw8EBxcTHXvwYrhw4dwqJFi+Dh4QFbW1sAwPXr17FlyxasXr2a7/XFtpu6JOrYsSMiIyMb1e8kjLCwMKxfv57p1WFsbIy5c+di6tSprMZbtWoVWrZsiSlTpvAd37VrF54/f97oixJwvZIvCmpqatizZ4/AZ87x48cxadIkvH79GtnZ2ejatStev34tpigbDh0dHcycORNLliwRWOlhq6qqCitWrEBoaCj++ecfpoehr68v9PX14ebmxsnjcIUQgosXL2L//v04fPgwqqqqMHz4cFa9k4DqVZnw8HAcOHCA2ec+ZcoUWFtbcxw5O6qqqkhJSRHp5IjuOaLqpU2bNg1ij8zx48eJqqoq4fF4Al9SUlJ1Hqe2nP7acvyFGfNTkydP/upXY3bjxg2ira1NtLS0iJ2dHbGzsyNaWlpEW1ub3Lhxg9WYPj4+ZMWKFYSQ6pxxGRkZYmRkRGRlZYmPjw+X4ddLbc/Lz5+j9XleSaqTJ0+SQYMGkfz8fHGH8t35+voSRUVFsnjxYnLs2DFy7NgxsnjxYqKkpER8fX1Zjamnp8e0f/jU9evX61Xi9tWrV+R///sfmTJlCpkyZQpZt24defnyJevxmjIPDw+iqalJgoODyZUrV8iVK1dIcHAw0dTUJJ6enoQQQnbs2EF69Ogh5kgbBnV1dZKTk8PpmAEBAcTQ0JDs27ePby/bwYMHia2tLaePxbU7d+6Qzp07c/JZUVZWRjZs2ECaN29OpKSkSKdOnUhYWBipqqriIFL2Jk6cSIKDg0X6GHTlqAHIyclBbm4uevfuDXl5+TpV9vpeGkoamL6+PtMUrmXLlqzHuXTpUp3v26dPH9aP01S9e/cOf/31FzIzMwFU5xE7OzsLrKCxdf36dSYliG0lI1EoKCio83319PREGMn3paamhtLSUlRUVEBBQUFgRVdSqgmKgpaWFkJCQjBu3Di+4wcOHICHhwerRt9ycnLIyMgQuKKal5fHtAcQVm2VFO/cuYM3b95IVCVFaWlpPH36VGCf68uXL6Gtrc2q2qUoVFZWYvXq1di8eTP++ecfAEDLli3h4eEBHx8fSEtLo7CwEFJSUmjXrp2Yo5V8ixYtgrq6Oqum619iZGSEbdu2wc7ODsrKykhNTYWhoSEyMzPRrVs3iVvRKyoqwv79+7F//36kp6ejW7ducHFxwcyZM1mNV15ejiNHjmD37t04d+4cbG1t4ebmhqKiImzZsgX9+/cX637d5cuXIygoCHZ2dujSpYvA+QGbfWafo5MjCfby5UtmwzKPx0N2djYMDQ0xZcoUqKmpISgoSNwhNpg0sE8340u6iooKXLx4Ebm5uXB2doaysjKePHkCFRUVKCkpiTu8BuN75SZT7O3Zs+ertzekPlXCatGiBW7duiVQejYrKwtdu3Zltd/E2NgY/v7+GD9+PN/xvXv3wt/fn9X+CwsLC3Tr1g1bt26FtLQ0gOoT/NmzZ+Pq1au4e/eu0GOKgpSUFJ49eyYwOXry5Anat28vkUVNatK02aRgU9UqKyvx66+/4v3797CwsBC4wMJmb7a8vDwyMzOhp6fHNzm6f/8+unbtipKSEq7Cr5dt27Zh//79SExMRMeOHeHi4gJnZ2fWF9CSkpKwe/duHDhwAFJSUpg4cSKmTp2Kjh07MvdJT0/Hjz/+KNbX09c+z7nYZwbQPkcSbd68eWjWrBkKCwv59hyMGTMG8+fPl4jJUYsWLSS+yAEADB8+HPHx8RI/OSooKIC9vT0KCwvx8eNH/Pzzz1BWVsaaNWvw8eNHhIaGijtEkbt//36tPYmE7f3SrFkzHD58mNO+EhS3GvPk51smTJiArVu3Cpy8bd++HS4uLqzGnDZtGubOnYvy8nL0798fABAXF4dFixZhwYIFrMbMyclBVFQUMzECqldp5s+fj4iICFZjcikkJARA9UnRzp07+S4gVVZW4vLly3wnd5KETorqb9WqVYiNjWUqfX5ekIENU1NTXLlyRWCSERUVxVk1NC4sX74c48aNQ0hICDp16lTv8X788Uf8/PPP2Lp1K5ycnAQmmkD1xGTs2LH1fixhvX37lnm95Ofni/zx6ORIgp09exaxsbECS+vGxsZCpeGI0u7du8UdQp388MMPWLJkCRISEmq9usTFMiwXvLy8YGNjg9TUVGhoaDDHhw0bhmnTpokxMtHLy8vDsGHDcPfuXaYxLPB/H3Bs0mKcnJxw9OjRenVKp76PDx8+CEyIG9vJ46fVSGtO5s+ePcsU4bhx4wYKCwsxceJEVuN7e3vj5cuXmD17NvN/KScnBx8fHyxZsoTVmNbW1sjIyBAoM5+RkcHJCVl9rV+/HkD1pvTQ0FC+SZysrCz09fXFflHJ2toacXFxUFNTg5WV1VdP2tkUnmnKgoKCsGvXLkyePJmzMf38/DBp0iQ8fvwYVVVViI6OxoMHDxAREYGTJ09y9jj1VVhYyOkWi7y8vG+uOikqKorlvE9NTY1Jm+3fvz+io6PRokULkT0enRxJsHfv3kFBQUHg+KtXrySqtGJDSAOruaJ46dIlgX1DPB5PYiZHV65cwdWrVwXK4+rr6+Px48diiur78PLygoGBAeLi4mBgYICbN2/i5cuXWLBgAdatW8dqTGNjYwQGBiIxMVFkuckUe+/evYOPjw8iIyPx8uVLgdslZZ8IV5KTk/n+XVNeOjc3FwCgqakJTU1N3Lt3j9X4PB4Pa9asga+vLzIyMiAvLw9jY+N6fV54enrCy8sLOTk5tVZSTEtLY+4rjqqDNVeR+/XrhyNHjoj0hIktR0dH5m/webl+qn6aN2+OHj16cDqmo6MjTpw4gcDAQCgqKsLPzw/W1tY4ceIEfv75Z04fqz54PB7evHmDsLAwpt+Pqakp3NzcoKqqKvR4kryfVUlJidk/ePHiRZSXl4v08eieIwk2ZMgQdOnSBcuWLWOa2unp6WHs2LGoqqpCVFSUuEMUSAOrKXnp5eXVZNLAuKSmpobExESYmpry5TonJCRgxIgRzAbexkhTUxMXLlyApaUlVFVVcfPmTXTo0AEXLlzAggULBE4s6+J75CZT7Lm7uyM+Ph7Lli3DhAkTsGXLFjx+/Bjbtm3D6tWrWaeXUdz5VnnkmlVeHo8ntslsQ2krUVlZicTERFhaWkrkJK4hWrVqFZ4+fcqkVzYlt2/fxqBBgyAvL4+uXbsCAG7duoX379/j7NmzdSq9raamVufVJ3EWyBkxYgQSExNhYmKCS5cuoXv37l/sscZFmxu6ciTB1q5dCzs7O9y+fRtlZWVYtGgR7t27h1evXiExMVHc4QFoeGlgZWVlyM/PR/v27etdXe9LS7tv376Fk5MTqxfowIEDsWHDBmzfvh1A9YlHSUkJ/P39MWTIkHrFK+kqKyuhrKwMoHqi9OTJE3To0AF6enp48OABqzG/R24yxd6JEycQERGBvn37wtXVFb169YKRkRH09PTw119/0clRHQwfPhzh4eFQUVHB8OHDv3pfNgVyGsJrqFmzZqwq8X1v0tLSGDhwIDIyMujkiCM3b97EhQsXcPLkSZiZmQmkzNenKFRZWRmKi4tRVVXFd1xXV5f1mFyaN28eHBwcsGPHDuZ8pqKiAlOnTsXcuXNx+fLlb46xYcMG5vuXL19i+fLlGDRoELp16wYAuHbtGmJjY8W+d3ffvn3Ys2cPcnNzcenSJZiZmdWaWcUVOjmSYCoqKsjIyMDWrVuhrKyMkpISDB8+HO7u7iJfUqyrhpIGVlpaCg8PD6Y6Vs0Kl4eHB9q2bcuqDOjFixcF9kgA1Xsnrly5wirOoKAgDBo0iCm76+zsjOzsbGhqauLAgQOsxmwozM3NkZqaCgMDA/z0009Yu3YtZGVlsX37dhgaGoo7PJH60tU7Ho8HOTk5GBkZYfLkyXB1dRVDdKLz6tUr5m+roqLCXJns2bMnZs2aJc7QGgxVVVXmucMmleZbJDnV5lPu7u5Ys2aNxLeVMDc3R15eHq2gyZEWLVp886KAsLKzszFlyhRcvXqV77i4V0g/d/v2bb6JEQDIyMhg0aJFTNn9b/m0KM6IESMQGBiIOXPmMMc8PT2xefNmnD9/Xqx7d+Xl5ZnS5Ldv38aaNWvonqOmysDAAE+fPsXvv//Od/zly5do166dRLxAq6qqao2jqKiIWQWQBEuWLEFqaiouXrwIe3t75viAAQOwdOlSoSZHn+bY379/H8+ePWP+XVlZiZiYGLRt25ZVnO3atUNqaioOHTrEdHl3c3ODi4sL5OXlWY3ZUPzxxx949+4dACAwMBC//vorevXqBQ0NDRw6dKjO48yfPx/Lli2DoqIi3wb42rAp8yoKfn5+WLFiBQYPHsykR9y8eRMxMTFwd3dHfn4+Zs2ahYqKColckWXL0NAQ+fn50NXVRceOHREZGYmuXbvixIkT9Mp6HdVsjiaEICAgAFpaWpy/Vzx58gQJCQm1XkWXlH17t27dQlxcHM6ePSvRbSWWL1+OhQsXYtmyZbXug2xsRUhETRTFASZPngwZGRmcPHkSrVu3lpi+kp9TUVFBYWGhQDXGR48esTr/io2NxZo1awSO29vbc9pHqr7i4+MBcJsJ9Dk6OZJgX9oOVlJSAjk5ue8cTe0aShrY0aNHcejQIdja2vK90ZmZmTGboeuqc+fO4PF44PF4TLncT8nLy2PTpk2sY5WRkYGLi0uTSykaNGgQ872RkREyMzPx6tUroXKigepN7zUrq1/bpyRJH3gJCQlYvny5QNO+bdu24ezZszh8+DAsLS0REhLSqCZHrq6uSE1NRZ8+fbB48WIMHToUmzdvRnl5ucRMXBsKQgiMjIxw7949gd5J9REeHo4ZM2ZAVlYWGhoaAqWSJWVy1FDaStR8Ljo4OPD9X0raqkRD8f79exBCmBSrgoICHDlyBKamphg4cCCrMVNSUnDnzh2JLQFfY8yYMXBzc8O6devQvXt3AEBiYiK8vb0FmkvXhYaGBo4dOyZQ9v/YsWN82ybE7f3795gzZw6nmUCfowUZJFDN1e6NGzdi2rRpfHmVlZWVuHHjBqSlpSVi31FRUREGDRoEQgiys7NhY2PDpIFdvnxZoCGfuCgoKCA9PR2GhoZ8hQ5SU1PRu3dv/Pvvv3Ueq6CgAIQQGBoa4ubNm9DS0mJuk5WVhba2Nl85WWGsWrUKLVu2xJQpU/iO79q1C8+fP4ePjw+rcSnJpqSkhJSUFBgZGfEdz8nJQefOnVFSUoLc3FxYWloyq2uNUUFBAe7cuQMjIyOxVD5r6MzMzBAWFsZUleOCjo4OZs6ciSVLlnyzOAP1bZ9XS/1cnz59vlMkjcPAgQMxfPhwzJw5E2/evEGHDh0gKyuLFy9eIDg4mFV67o8//oj169ejZ8+eIoiYO2VlZfD29kZoaCgqKioAVO+/mzVrFlavXi10lcrw8HBMnToVgwcPxk8//QSgur1ATEwMduzYwWm59Prw8vJCYmIiNmzYAHt7e6SlpcHQ0BDHjh3D0qVLWRVvEkAoidO3b1/St29fwuPxSPfu3Zl/9+3blwwcOJBMnz6dZGVliTtMRnl5Odm3bx/x9vYms2bNIjt27CClpaXiDotPr169SEhICCGEECUlJZKXl0cIIWTOnDlk0KBBQo9XVlZGJk+ezIzDFT09PZKYmChw/Pr160RfX5/Tx2pqCgsLSWFhobjDqJWOjg4JDg4WOB4cHEx0dHQIIYSkpqaSli1bfu/QqAbk+PHjpGfPnuTu3bucjamurk5ycnI4G0/UiouLyZUrV8iVK1dIcXGxuMOhRExDQ4Okp6cTQgjZsWMHsbS0JJWVlSQyMpJ07NiR1ZhxcXGkW7duJD4+nrx48YL8+++/fF/ilJqaSiorK/mOvXv3jqSlpZG0tDTy7t27eo1//fp14uzsTKysrIiVlRVxdnYm169fr9eYXNPV1SXXrl0jhFSfz+Xm5hJCCMnOzibKysqcPAZdOZJgrq6u2LhxI81B5kBCQgIGDx6M8ePHM2ki9+/fx9WrV3Hp0iWm34gwWrRogeTkZE431srJySEjI0NgzLy8PKZIA1V3FRUVCAgIQEhICEpKSgBUr9J4eHjA39+/1g7g4rBjxw7MmjULQ4YM4SvJevr0aYSGhsLNzQ1BQUG4efOmUPuvJJEwJXclJWWroVBTU0NpaSkqKiogKysrsPeITSneRYsWQV1dXaL2HNTm3bt38PDwQEREBLMvSlpaGhMnTsSmTZtEWtmKEh8FBQVkZmZCV1cXo0ePhpmZGfz9/fHo0SN06NABpaWlQo9Zs0L6eeo1kYDUR2lpaaYZqqGhIW7duiVRKW/fA5eZQF9C9xxJMHF0IRZWQ0kD69mzJ1JSUrB69WpYWFgwPQCuXbsGCwsLVmM6Ojri6NGjnFZw0dHRQWJiosDkKDExEW3atOHscZoKDw8PREdHY+3atXylSZcuXYqXL19i69atYo6w2rRp02BqaorNmzczG8c7dOjA9HMAIJAH3lCtX7++TveTpP0sDcX69es530u3atUq/Prrr4iJiYGFhYXABQVJ2Rs2f/58XLp0CSdOnGCagiYkJMDT0xMLFiyQmNc6xS0jIyMcPXoUw4YNQ2xsLPN5XFxczPrCcs2Gf0nUokUL5OfnQ1tbGw8fPhQokFJflZWVOHr0KNNU1szMDA4ODqy3CoiCjY0NTp06BQ8PDwD/N4nduXMn8zlfX3TliKoXfX197N+/nzmBq3Hjxg2MHTu2QfTIYGv58uUICgqCnZ1drVWH2JzYrV27FmvXrsX//vc/pthDXFwcFi1ahAULFmDJkiWcxN5UqKqq4uDBgxg8eDDf8dOnT2PcuHGcXGGiqMZs+fLl8PPzQ4cOHdCyZUuBggxcNFzkgqamJqKiotC3b1++4/Hx8Rg9ejSeP38unsAokYqKioKzszMqKythZ2eHs2fPAqie1F++fBlnzpwRc4Tcmj59OiIiItC6dWsUFhaiXbt2X5y4CNvkPCcnB7/88guKiorQoUMHAMCDBw+go6ODU6dOoX379vWOnwuiyAT6HJ0cUfXSUNLAPl2K/tTLly+hra3Napn8a+l0PB5P6DcmoHrZfvHixQgJCWF6KMnJycHHxwd+fn5Cj9fUaWtr49KlSzAxMeE7npGRgd69e0vUCVNVVRVycnJqLZfcu3dvMUVFNSSieJ9TU1PD+vXrJWYz9pcoKCjgzp07Aq/1e/fuoWvXro26kElT9+zZMzx9+hSdOnViUuJu3rwJFRWVOlec+7RFx7eIu1hMTEwMcnJy4OnpicDAwC+W7fby8hJq3CFDhoAQgr/++gvq6uoAqt87xo8fDykpKZw6daresXMlLy8Pq1atYlqeWFtbw8fHh3Um0Ofo5IiqF2NjY/j7+2P8+PF8x/fu3Qt/f39WEwRRkJKSwrNnzwROGp48eYL27dvj/fv3YoqsdiUlJcjIyIC8vDyMjY2FrjpDVQsMDERmZiZ2797N/B9+/PgRbm5uzHNXEly/fh3Ozs5MJcRPiTvHXdSKiopw/PhxFBYWCjRVlpSUrYZCFO9zrVq1wpUrVzgtDy4KdnZ20NDQQEREBNPq4v3795g0aRJevXqF8+fPiznCav3790d0dLRAH6+3b9/CyclJYlbimhopKSnweDxmX9HXSMr7saurK0JCQjjrKamoqIjr168LTDBSU1PRo0cPZt+uOJWXl2PGjBnw9fUVaSNluueIqpdp06Zh7ty5KC8vrzUNTNxqNn/zeDzs3LkTSkpKzG2VlZW4fPmyRPYyUFJSwo8//ijuMBq85ORkxMXFoV27dujUqROA6jf6srIy2NnZ8XVWF2eTyJkzZzJ51JLcdJBrcXFxcHBwgKGhITIzM2Fubo6HDx+CEAJra2txh9dgiPJ9zsvLC5s2bRKqkIY4bNy4EYMGDRJ4rcvJySE2NlbM0f2fixcvClwEAIAPHz7gypUrYoiIAsC3BSA5ORkLFy6Et7c3317VoKAgrF27VlwhCuB6X3rz5s3x33//CRwvKSmBrKwsp4/FVrNmzXD48GH4+vqK9HHoyhFVL5KeBlZzZaGgoEAgN1dWVhb6+voIDAxkavoLi171lmyurq51vq84C6AoKioiNTVVoM9RY9e1a1cMHjwYAQEBTNUhbW1tuLi4wN7enlWPkqZIlO9zw4YNw4ULF6ChoQEzMzOBggzivKjwudLSUvz111/IzMwEAJiYmMDFxUWgap841KRtde7cGRcuXGDSloDqCWxMTAy2bduGhw8fiilCqkbXrl2xdOlSgUb2p0+fhq+vL+7cuSOmyERr4sSJSEpKQlhYGFM19caNG5g2bRq6dOmC8PBw8Qb4/02aNAmdO3fmtBjW5+jkiOKEpKeB9evXD9HR0VBTU+NszG9d9abpEVRd9e/fH4sWLYK9vb24Q/mulJWVkZKSgvbt20NNTQ0JCQkwMzNDamoqHB0d6YmikETxPvetCwwNoaqqJKhJ2wIgkDoLAPLy8ti0aZNA5Vfq+5OXl0dSUlKte1Wtra0lLg2fK2/evMGkSZNw4sQJ5iJIRUUFHBwcEB4eDlVVVTFHWE0UxbA+RydHFMUSveot+d6/fw9CCNPjpKCgAEeOHIGpqSkGDhwo5uj+z5EjR/DHH3/A29u71nLJ4t4ALCqtWrVCfHw8TExMYGpqitWrV8PBwUGictyphkHS20rU7Cc0NDTEzZs3oaWlxdwmKysLbW1tiSqX3JRZW1vD3NwcO3fuZNLJysrKMHXqVKSnpyMpKUnMEYpWdnY2MjIywOPxYGJiInEZDaIohiUwDp0cURQ79Kq35Bs4cCCGDx+OmTNn4s2bN+jQoQNkZWXx4sULBAcHS8wEtqbC0qc+3RwsKRuAuebk5IRffvkF06ZNw8KFC3Hs2DFMnjyZWf2QlE30DUVlZSXCw8MRFxdXa9XDxrya3RDaSpSXl2P69Onw8/MT6WZyqn5u3ryJoUOHghDCXJhKS0sDj8fDiRMnmJSzxqxmatBU9r9+jhZkoCiWFBUVmX1GrVu3Rm5uLszMzAAAL168EGdo1P+XlJTENB2NiopCq1atkJycjMOHD8PPz09iJkeScOImDsHBwczqUEBAAEpKSnDo0CEYGxvTPXsseHl5ITw8HL/88gvMzc05O7GJiopCZGRkrXsrJeUq+rNnz9C6dWuB41paWnj69KkYIhLUrFkzHDlyRCL241Jf1rVrV+Tl5fHtXxszZgycnZ0FUrgam7CwMKxfvx7Z2dkAqisSz507F1OnThVzZLUT1SSOTo4oiiVbW1skJCTAxMQEQ4YMwYIFC3D37l1ER0fD1tZW3OFRqN6gXVPm9OzZsxg+fDikpKRga2uLgoICMUf3f/T09MQdglgYGhoy3ysqKiI0NFSM0TR8Bw8eRGRkpMBG8voICQnB77//jsmTJ+PYsWNwdXVFbm4ubt26BXd3d84ep750dHSQmJgosCKTmJiINm3aiCkqQY6Ojjh69KhIN5NT9aeoqIjp06eLO4zvys/PD8HBwfDw8OCr0jdv3jwUFhYiMDBQzBH+H1FP4ujkiKJYole9JZ+RkRGOHj2KYcOGITY2ljkhKS4uhoqKipij+z8RERFfvX3ixInfKRLxKCsrqzUNTFdXV0wRNUyysrKc7w/4888/sX37dowbNw7h4eFYtGgRDA0N4efnh1evXnH6WPUh6W0lahgbGyMwMBCJiYki20xOceP+/fu1rpY6ODiIKSLR2rp1K3bs2IFx48YxxxwcHGBpaQkPDw+JmRx9j0kc3XNENRlXrlzBtm3bkJubi6ioKLRt2xZ79+6FgYEBevbsKe7wKBGIioqCs7MzKisr0b9/f5w7dw5A9ebty5cv48yZM2KOsNrn1cXKy8tRWloKWVlZKCgoSNRJKJeysrLg5uaGq1ev8h1v7HutRCUoKAh5eXnYvHkzZ2kmCgoKyMjIgJ6eHrS1tXHu3Dl06tQJ2dnZsLW1xcuXLzl5nPqS9LYSNb7HZnKqfvLy8jBs2DDcvXuX2fsJ/F/qVmN9X2rRogVu3bol0PA5KysLXbt2xZs3b8QT2Ge0tLQQEhLCN4kDgAMHDsDDw4OTbQ105YhqEg4fPowJEybAxcUFycnJ+PjxIwDg33//xcqVK3H69GlW47558wZRUVHIzc2Ft7c31NXVkZSUhJYtW6Jt27Zc/goUCyNHjkTPnj3x9OlTpjEkANjZ2WHYsGFijIzf69evBY5lZ2dj1qxZ8Pb2FkNE34erqytkZGRw8uTJJtX8VlQSEhIQHx+PM2fOcNaTqFWrVnj16hX09PSgq6uL69evo1OnTsjPz6+1JLW48Hg8rFmzBr6+vhLdVqKp7i9sSLy8vGBgYIC4uDgYGBjg5s2bePnyJRYsWIB169aJOzyRmTBhArZu3SqQ+bJ9+3a4uLiIKSpB5eXlsLGxETjepUsXVFRUcPIYdOWIahKsrKwwb948TJw4kSm7bWhoiOTkZAwePBjPnj0Tesy0tDQMGDAAqqqqePjwIR48eABDQ0P88ccfKCws/GaqFPX95OTkIDc3F71794a8vDyzMiHpbt++jfHjxzObghsbRUVF3LlzBx07dhR3KI2CKHoSTZ06FTo6OvD398eWLVvg7e2NHj164Pbt2xg+fDjCwsLYhktREklTUxMXLlyApaUlVFVVcfPmTXTo0AEXLlzAggULkJycLO4QOTN//nzm+4qKCoSHh0NXV5fZN33jxg0UFhZi4sSJ2LRpk7jC5OPh4YFmzZoJTOIWLlyI9+/fY8uWLfV+DLpyRDUJDx48QO/evQWOq6qqsl4qnj9/PiZPnoy1a9cym/4BYMiQIXB2dmYbKsWhly9fYvTo0YiPjwePx0N2djYMDQ3h5uYGNTU1BAUFiTvEr5KRkcGTJ0/EHYbImJqa0sqOHBJFQ9bt27cze8Hc3d2hoaGBq1evwsHBATNmzOD88ZqCoqIiHD9+vNb9LHS/qvhVVlYyn+mampp48uQJOnToAD09PTx48EDM0XHr84lely5dAAC5ubkAqn9/TU1N3Lt377vH9jVhYWE4e/ZsrZO4Tyd8bF9PdHJENQmtWrVCTk4O9PX1+Y4nJCTwVcwSxq1bt7Bt2zaB423btmW1EkVxb968eWjWrBkKCwv5up2PGTMG8+fPl5jJ0fHjx/n+TQjB06dPsXnzZvTo0UNMUYnG27dvme/XrFmDRYsWYeXKlbU2v5WkohkNRUVFBS5evIjc3Fw4OztDWVkZT548gYqKCpSUlIQeT0pKiq8P19ixYzF27FguQ25S4uLi4ODgAENDQ2RmZsLc3BwPHz4EIQTW1tbiDo8CYG5ujtTUVBgYGOCnn37C2rVrISsri+3bt7M+X5BU8fHx4g5BaOnp6cxr5fNJXHp6OnO/+mSH0MkR1SRMmzYNXl5e2LVrF3g8Hp48eYJr165h4cKF8PX1ZTVm8+bN+U70amRlZfF1P6fE5+zZs4iNjUW7du34jhsbG0tUKW8nJye+f/N4PGhpaaF///4SM4HjSosWLfg+tAghsLOz47sPLcjATkFBAezt7VFYWIiPHz/i559/hrKyMtasWYOPHz+yKpW+e/duKCkpYdSoUXzH//77b5SWlmLSpElchd8kLFmyBAsXLkRAQACUlZVx+PBhaGtrw8XFBfb29uIOjwLwxx9/4N27dwCAwMBA/Prrr+jVqxc0NDRw6NAhMUdHfY8JHZ0cUU3C4sWLUVVVBTs7O5SWlqJ3795o3rw5Fi5cCA8PD1ZjOjg4IDAwEJGRkQCqT2gLCwvh4+ODESNGcBk+xdK7d++goKAgcPzVq1cStVH78xLWjVlDvFLZUHh5ecHGxgapqanQ0NBgjg8bNgzTpk1jNeaqVatqXSHX1tbG9OnT6eRISBkZGThw4ACA6rTZ9+/fQ0lJCYGBgXB0dJSYxtRN2aBBg5jvjYyMkJmZiVevXkFNTa1B7FWl6o9Ojqgmgcfj4ffff4e3tzdycnJQUlICU1NTVmkmNYKCgjBy5Ehoa2vj/fv36NOnD549e4Zu3bphxYoVHEZPsdWrVy9ERERg2bJlAKqfB1VVVVi7di369esn5uiapj59+jDfFxYWQkdHR+CEgxCCR48efe/QGrwrV67g6tWrkJWV5Tuur6+Px48fsxqzsLCw1vLTenp6KCwsZDVmU6aoqMjsM2rdujVyc3NhZmYGAHT/nQRTV1cXdwjUd0QnR1STIisrC1NTU07GUlVVxblz55CQkIC0tDSUlJTA2toaAwYM4GR8qv7Wrl0LOzs73L59G2VlZVi0aBHu3buHV69eITExUayxfbpp9Fsa6yZtAwMDPH36FNra2nzHX716BQMDA5pWJ6Sqqqpa/8+Kior4isYIQ1tbG2lpaQL7NT9fnaLqxtbWFgkJCTAxMcGQIUOwYMEC3L17F9HR0czmcoqixItOjqhGa/jw4XW+L5v+HzV69uxJm8hKKHNzc2RlZWHz5s1QVlZGSUkJhg8fDnd3d7Ru3VqssdW1HGxjTuP4Ukn1kpISyMnJiSGihm3gwIHYsGEDtm/fDqD6uVNSUgJ/f38MGTKE1Zjjxo2Dp6cnlJWVmYqfly5dgpeXFy3MwEJwcDBKSkoAAAEBASgpKcGhQ4dgbGzcaC+CUFRDQ/scUY3Wt3p+fIptCdy4uDjExcWhuLhYYN/Irl27WI1JcaO8vBz29vYIDQ0V6PhNiVfNqtnGjRsxbdo0vn1hlZWVuHHjBqSlpcW+utfQFBUVYdCgQSCEIDs7GzY2NsjOzoampiYuX74ssEJXF2VlZZgwYQL+/vtvyMhUX0+tqqrCxIkTERoaKpDCR1EU1dDRyRFFsRQQEIDAwEDY2NigdevWAlfAjxw5IqbIqBpaWlq4evUqnRxJmJr9XpcuXUK3bt34TrBlZWWhr6+PhQsX0r8bCxUVFTh48CBfqq+Liwvk5eXrNW5WVhZSU1MhLy8PCwsL6OnpcRRx0/PmzRtERUUhNzcX3t7eUFdXR1JSElq2bIm2bduKOzyKavLo5IiiWGrdujXWrl2LCRMmiDsU6gvmzZuH5s2bY/Xq1eIOhaqFq6srNm7cSPsZceTDhw8iTUesOV1ozKmeopaWloYBAwZAVVUVDx8+xIMHD2BoaIg//vgDhYWFiIiIEHeIFNXk0T1HVKNlZWVV5w/xpKQkoccvKytD9+7dhf456vupqKjArl27cP78eXTp0gWKiop8t9Mcf/Fim85K1U5bWxvDhg3D+PHjYWdnx9e8tT4iIiLwv//9D9nZ2QCAH374Ad7e3vTCEAvz58/H5MmTsXbtWr4iGUOGDIGzs7MYI6MoqgadHFGN1qeNNT98+IA///wTpqam6NatGwDg+vXruHfvHmbPns1q/KlTp2L//v2sm8hSovdpJ+2srCy+2+jVb6qx2bNnD/bv3w9HR0eoqqpizJgxGD9+PGxsbFiPGRwcDF9fX8yZMwc9evQAACQkJGDmzJl48eIF5s2bx1X4TcKtW7dq7RvVtm1bPHv2TAwRURT1OZpWRzUJU6dORevWrZl+NzX8/f3x6NGjOhdP+LT8clVVFfbs2QNLS0tYWlqiWbNmfPelqxIURYnDf//9h6ioKBw4cAAXLlyAoaEhxo8fDz8/P6HHMjAwQEBAACZOnMh3fM+ePVi6dCny8/O5CrtJ0NbWRmxsLKysrKCsrIzU1FQYGhri3LlzmDJlCu3vRVESgE6OqCZBVVUVt2/fFtjgXVPR6d9//63TOHVtHMrj8XDhwgWh46QoiuLS/fv34eLigrS0NFZ9o+Tk5JCeng4jIyO+49nZ2bCwsMCHDx+4CrVJmDp1Kl6+fInIyEioq6sjLS0N0tLScHJyQu/evbFhwwZxh0hRTR5Nq6OaBHl5eSQmJgpMjhITE4XawBwfH891aBRFUZz68OEDjh8/jv379yMmJgYtW7aEt7c3q7GMjIwQGRmJ3377je94TW8eSjhBQUEYOXIktLW18f79e/Tp0wfPnj1Dt27dsGLFCnGHR1EU6OSIaiLmzp2LWbNmISkpCV27dgUA3LhxA7t27aJ7hiiKahRiY2Oxf/9+HD16FDIyMhg5ciTOnj3LNG9lIyAgAGPGjMHly5eZPUeJiYmIi4tDZGQkV6E3Gaqqqjh37hwSEhL4yq0PGDBA3KFRFPX/0bQ6qsmIjIzExo0bkZGRAQAwMTGBl5cXRo8eLebIKIqi6k9BQQFDhw6Fs7MzhgwZIrAPkq07d+5g/fr1fO+dCxYsgJWVFSfjUxRFSRI6OaIoiqKoBq6iogJbt27FqFGj0KpVK3GHQ31FXFwc4uLiUFxcjKqqKr7b6lociKIo0aGTI4qiKIpqBBQUFJCRkQE9PT1Ox62srMSRI0eYlSNTU1M4OjpCRoZm5gsrICAAgYGBsLGxQevWrQVaChw5ckRMkVEUVYNOjqhGS01Nrc69bF69eiXiaCiKokSrb9++mDt3Ll+Pt/q6d+8eHBwc8OzZM3To0AFAdc8wLS0tnDhxAubm5pw9VlPQunVrrF27ljbQpSgJRi/7UI3WpyVRX758ieXLl2PQoEFME9hr164hNjaWFmSgKKpRmD17NhYsWICioiJ06dIFioqKfLdbWloKPebUqVNhZmaG27dvQ01NDQDw+vVrTJ48GdOnT8fVq1c5ib2pKCsrQ/fu3cUdBkVRX0FXjqgmYcSIEejXrx/mzJnDd3zz5s04f/48jh49Kp7AKIqiOCIlJSVwjMfjgRACHo/Hqs+RvLw8bt++DTMzM77j6enp+PHHH/H+/XvW8TZFPj4+UFJSohflKEqC0ZUjqkmIjY3FmjVrBI7b29tj8eLFYoiIoiiKW/n5+ZyP+cMPP+Cff/4RmBwVFxcLNIalajd//nzm+6qqKmzfvh3nz5+HpaWlQEXB4ODg7x0eRVGfoZMjqknQ0NDAsWPHsGDBAr7jx44dg4aGhpiioiiK4g7XhRgAYNWqVfD09MTSpUtha2sLALh+/ToCAwOxZs0avH37lrmviooK54/fGCQnJ/P9u3PnzgCqV98+Vdc9shRFiRZNq6OahPDwcEydOhWDBw/GTz/9BKC6CWxMTAx27NiByZMnizdAiqIoDuzduxehoaHIz8/HtWvXoKenhw0bNsDAwACOjo5Cj/dpql7NyXvNacOn/2abtkdRFCVp6MoR1SRMnjwZJiYmCAkJQXR0NIDqRoYJCQnMZImiKKoh27p1K/z8/DB37lysWLGCmay0aNECGzZsYDU5io+P5zpMiqIoiUZXjiiKoiiqETA1NcXKlSvh5OQEZWVlpKamwtDQEOnp6ejbty9evHgh7hApiqIkHl05ohqtt2/fMjnwn+bF14bmylMU1dDl5+fDyspK4Hjz5s3x7t071uO+fv0aYWFhfE1gXV1doa6uznpMiqIoSSVY95OiGgk1NTUUFxcDqE4rUVNTE/iqOU5RFNXQGRgYICUlReB4TEwMTExMWI15+fJl6OvrIyQkBK9fv8br168REhICAwMDXL58uZ4RUxRFSR66ckQ1WhcuXGCubNK8eYqiGrv58+fD3d0dHz58ACEEN2/exIEDB7Bq1Srs3LmT1Zju7u4YM2YMtm7dCmlpaQBAZWUlZs+eDXd3d9y9e5fLX4GiKErs6J4jiqIoimok/vrrLyxduhS5ubkAgDZt2iAgIABubm6sxpOXl0dKSgo6dOjAd/zBgwfo3LkzbQJLUVSjQ1eOqCbjw4cPSEtLQ3FxMaqqqvhuc3BwEFNUFEVR3HFxcYGLiwtKS0tRUlICbW3teo1nbW2NjIwMgclRRkYGOnXqVK+xKYqiJBGdHFFNQkxMDCZOnFhrtSban4OiqMagf//+iI6ORosWLaCgoAAFBQUA1QVpnJyccOHCBaHH9PT0hJeXF3JycviawG7ZsgWrV69GWloac19LS0tufhGKoigxoml1VJNgbGyMgQMHws/PDy1bthR3OBRFUZyTkpLCs2fPBFaLiouL0bZtW5SXl7Ma82t4PB5tAktRVKNCV46oJuGff/7B/Pnz6cSIoqhG59PVm/v37+PZs2fMvysrKxETE4O2bduyGjs/P7/e8VEURTUkdHJENQkjR47ExYsX0b59e3GHQlEUxanOnTuDx+OBx+Ohf//+ArfLy8tj06ZNrMbW09Orb3gURVENCk2ro5qE0tJSjBo1ClpaWrCwsECzZs34bvf09BRTZBRFUfVTUFAAQggMDQ1x8+ZNaGlpMbfJyspCW1ubKcNdF8ePH8fgwYPRrFkzHD9+/Kv3pcVsKIpqbOjkiGoSwsLCMHPmTMjJyUFDQwM8Ho+5jcfjIS8vT4zRURRFSY5P9y59bc8R3WdEUVRjRCdHVJPQqlUreHp6YvHixd/cYExRFNVQZWdnIz4+vtaWBX5+fmKKiqIoquGgkyOqSVBXV8etW7foniOKohqtHTt2YNasWdDU1ESrVq0EVsiTkpLEGB1FUVTDQCdHVJMwb948aGlp4bfffhN3KBRFUSKhp6eH2bNnw8fHh9Nx4+LiEBcXV+tq1K5duzh9LIqiKHGj1eqoJqGyshJr165FbGwsLC0tBQoyBAcHiykyiqIobrx+/RqjRo3idMyAgAAEBgbCxsYGrVu35luNoiiKaozoyhHVJPTr1++Lt/F4PFad4ymKoiSJm5sbfvzxR8ycOZOzMVu3bo21a9diwoQJnI1JURQlyejKEdUkxMfHizsEiqIokTIyMoKvry+uX7/OWcuCsrIydO/enasQKYqiJB5dOaIoiqKoRsDAwOCLt7FtWeDj4wMlJSX4+vrWJzSKoqgGg06OKIqiKIpizJ8/n/m+qqoKe/bsgaWlJd2vSVFUk0AnRxRFURRFMb62R/NTdL8mRVGNEZ0cURRFUVQDNX/+fCxbtgyKiop8Kz61oas8FEVR30YLMlAURVFUA5WcnIzy8nLm+y+hJbgpiqLqhq4cURRFURRFURRFAZASdwAURVEURVEURVGSgE6OKIqiKIqiKIqiQCdHFEVRFEVRFEVRAOjkiKIoiqIoiqIoCgCdHFEURVEURVEURQGgkyOKoiiqAbh48SJ4PB7evHkj7lAoiqKoRoyW8qYoiqIkTt++fdG5c2ds2LABAFBWVoZXr16hZcuWtGcPRVEUJTK0CSxFURQl8WRlZdGqVStxh0FRFEU1cjStjqIoipIokydPxqVLl7Bx40bweDzweDyEh4fzpdUVFBRg6NChUFNTg6KiIszMzHD69GkAwOvXr+Hi4gItLS3Iy8vD2NgYu3fvBlB7el5KSgp4PB4ePnzIHEtISECvXr0gLy8PHR0deHp64t27d9/rv4CiKIoSE7pyRFEURUmUjRs3IisrC+bm5ggMDAQA3Lt3j+8+7u7uKCsrw+XLl6GoqIj79+9DSUkJAODr64v79+/jzJkz0NTURE5ODt6/f1/nx8/NzYW9vT2WL1+OXbt24fnz55gzZw7mzJnDTLIoiqKoxolOjiiKoiiJoqqqCllZWSgoKDCpdJmZmXz3KSwsxIgRI2BhYQEAMDQ05LvNysoKNjY2AAB9fX2hHn/VqlVwcXHB3LlzAQDGxsYICQlBnz59sHXrVsjJybH8zSiKoihJR9PqKIqiqAbH09MTy5cvR48ePeDv74+0tDTmtlmzZuHgwYPo3LkzFi1ahKtXrwo1dmpqKsLDw6GkpMR8DRo0CFVVVcjPz+f6V6EoiqIkCJ0cURRFUQ3O1KlTkZeXhwkTJuDu3buwsbHBpk2bAACDBw9GQUEB5s2bhydPnsDOzg4LFy4EAEhJVX/sfVqotby8nG/skpISzJgxAykpKcxXamoqsrOz0b59++/0G1IURVHiQCdHFEVRlMSRlZVFZWXlV++jo6ODmTNnIjo6GgsWLMCOHTuY27S0tDBp0iTs27cPGzZswPbt25njAPD06VPmvikpKXzjWltb4/79+zAyMhL4kpWV5eg3pCiKoiQRnRxRFEVREkdfXx83btzAw4cP8eLFC1RVVfHdPnfuXMTGxiI/Px9JSUmIj4+HiYkJAMDPzw/Hjh1DTk4O7t27h5MnTzK3GRkZQUdHB0uXLkV2djZOnTqFoKAgvrF9fHxw9epVzJkzBykpKcjOzsaxY8cwZ86c7/PLUxRFUWJDJ0cURVGUxFm4cCGkpaVhamoKLS0tFBYW8t1eWVkJd3d3mJiYwN7eHj/88AP+/PNPANWrTkuWLIGlpSV69+4NaWlpHDx4EADQrFkzHDhwAJmZmbC0tMSaNWuwfPlyvrEtLS1x6dIlZGVloVevXrCysoKfnx/atGnzfX55iqIoSmx45NPEa4qiKIqiKIqiqCaKrhxRFEVRFEVRFEWBTo4oiqIoiqIoiqIA0MkRRVEURVEURVEUADo5oiiKoiiKoiiKAkAnRxRFURRFURRFUQDo5IiiKIqiKIqiKAoAnRxRFEVRFEVRFEUBoJMjiqIoiqIoiqIoAHRyRFEURVEURVEUBYBOjiiKoiiKoiiKogDQyRFFURRFURRFURQA4P8B/q1TiMS5g78AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tissue_freq[tissue_freq > 50].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"tissue frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "most_freq_tissues = tissue_freq[tissue_freq > 50].index\n",
+    "filtered_df = adata.obs.loc[adata.obs[\"tissue\"].isin(most_freq_tissues)]\n",
+    "\n",
+    "filtered_df.groupby(\"tissue\", observed=True).boxplot(\n",
+    "    column=\"total_mrna_umis\",\n",
+    "    figsize=(10, 4),\n",
+    "    rot=90,\n",
+    "    xlabel=None,\n",
+    "    ylabel=None,\n",
+    "    subplots=False,\n",
+    "    grid=False,\n",
+    ")\n",
+    "plt.ylim(0, 5e4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## `total_mrna_umis`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    1.000000e+04\n",
+       "mean     8.834597e+03\n",
+       "std      5.143560e+04\n",
+       "min      1.520000e+02\n",
+       "25%      1.657750e+03\n",
+       "50%      3.543000e+03\n",
+       "75%      7.368000e+03\n",
+       "max      2.161352e+06\n",
+       "Name: total_mrna_umis, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"total_mrna_umis\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs[\"total_mrna_umis\"].hist(\n",
+    "    bins=2000,\n",
+    "    figsize=(5, 4),\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "plt.xlim(0, 6e4)\n",
+    "plt.xlabel(\"total_mrna_umis\")\n",
+    "plt.ylabel(\"frequency\")\n",
+    "plt.axvline(np.mean(adata.obs[\"total_mrna_umis\"]), color=\"red\", label=\"mean\")\n",
+    "plt.axvline(np.median(adata.obs[\"total_mrna_umis\"]), color=\"orange\", label=\"median\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `dataset_id`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count                                    10000\n",
+       "unique                                     262\n",
+       "top       f7c1c579-2dc0-47e2-ba19-8165c5a0e353\n",
+       "freq                                      1182\n",
+       "Name: dataset_id, dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs[\"dataset_id\"].describe(include=object)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset_id\n",
+       "f7c1c579-2dc0-47e2-ba19-8165c5a0e353    1182\n",
+       "9dbab10c-118d-496b-966a-67f1763a6b7d     399\n",
+       "3faad104-2ab8-4434-816d-474d8d2641db     372\n",
+       "218acb0f-9f2f-4f76-b90b-15a4b7c7f629     368\n",
+       "c2876b1b-06d8-4d96-a56b-5304f815b99a     351\n",
+       "                                        ... \n",
+       "ee195b7d-184d-4dfa-9b1c-51a7e601ac11       1\n",
+       "d5452b83-7c3d-4d7c-ab7a-c7fece7196c5       1\n",
+       "4e74d843-8d7a-497a-918a-f079ef141b6d       1\n",
+       "e04daea4-4412-45b5-989e-76a9be070a89       1\n",
+       "c4dd26a8-d956-4bee-a233-44b573f2ce27       1\n",
+       "Name: count, Length: 262, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_id_freq = adata.obs[\"dataset_id\"].value_counts()\n",
+    "dataset_id_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3rtF+q1atktDQULG2tjZpWu/Hp/I2mDlzpgQFBYmdnZ1UrFhRduzYkeNvyMuTpvLOawrqc+fOSadOncTX11dsbGykYMGC0qhRI1m+fLnRfkeOHJGaNWuKvb29FCxYUMaOHSvffvttjv+fjIwM+eSTTyR//vzi6Ogo0dHRcvbs2RxTeYtkTaM9ZMgQKVq0qNja2kr+/PklMjJSpkyZImlpaU9tP3KZNvzYsWPSvHlzcXNzE3t7eylRooQMHz5c2f74VN4GW7dulejoaHF1dRV7e3sJDg6Wd999Vw4dOiQiIrdv35Y+ffpIyZIlxcnJSVxdXSU8PFx+/PHHXN9XIqJXgU7EzJGbREREBCCry1+XLl1w4cIFFkElInoFccwREREREREROOaIiIg0dv/+/acWrfXw8DAq9EpERPQiMDkiIiJNLVu2DF26dHniPlu3bkWtWrWeT4OIiIjywDFHRESkqb///hvHjx9/4j4VKlSAu7v7c2oRERFR7pgcERERERERgRMyEBERERERAfgPjznKzMzE9evXkS9fPuh0uhfdHCIiIiIiekFEBHfu3IGfnx/0+rzvD/1nk6Pr16/D39//RTeDiIiIiIheEleuXEGhQoXy3P6fTY7y5csHIOsNcHFxecGtISIiIiKiFyUlJQX+/v5KjpCX/2xyZOhK5+LiwuSIiIiIiIieOtyGEzIQERERERGByREREREREREAJkdEREREREQAVCRHO3bsQOPGjeHn5wedToeVK1cq29LT0/HJJ5+gTJkycHJygp+fHzp16oTr168bxUhMTET79u3h4uICNzc3dO3aFampqUb7HDlyBNWrV4e9vT38/f0xadIkdX8hERERERHRMzA5Obp79y7eeOMNzJgxI8e2e/fu4Y8//sDw4cPxxx9/YMWKFTh16hSaNGlitF/79u1x/PhxbNq0CWvWrMGOHTvQvXt3ZXtKSgrq1auHwoULIy4uDpMnT8aoUaMwd+5cFX8iERERERHR0+lERFQ/WafDL7/8gmbNmuW5z8GDB1G5cmVcunQJAQEBOHHiBEJDQ3Hw4EFUrFgRALBhwwa89dZbuHr1Kvz8/DBr1iwMHToUCQkJsLW1BQAMHjwYK1euxMmTJ5+pbSkpKXB1dUVycjJnqyMiIiIieo09a26g+Zij5ORk6HQ6uLm5AQD27t0LNzc3JTECgKioKOj1euzfv1/Zp0aNGkpiBADR0dE4deoU/v3331xf5+HDh0hJSTFaiIiIiIiInpWmydGDBw/wySefoG3btkqGlpCQAG9vb6P9rK2t4eHhgYSEBGUfHx8fo30Mjw37PG78+PFwdXVVFn9/f0v/OURERERE9B+mWXKUnp6OVq1aQUQwa9YsrV5GMWTIECQnJyvLlStXNH9NIiIiIiL677DWIqghMbp06RK2bNli1K/P19cXN2/eNNr/0aNHSExMhK+vr7LPjRs3jPYxPDbs8zg7OzvY2dlZ8s8gIiIiIqLXiMXvHBkSozNnzuD333+Hp6en0faIiAgkJSUhLi5OWbdlyxZkZmYiPDxc2WfHjh1IT09X9tm0aRNKlCgBd3d3SzeZiIiIiIjI9OQoNTUV8fHxiI+PBwBcuHAB8fHxuHz5MtLT0/HOO+/g0KFDWLJkCTIyMpCQkICEhASkpaUBAEJCQlC/fn1069YNBw4cwO7duxETE4M2bdrAz88PANCuXTvY2tqia9euOH78OJYtW4apU6eif//+lvvLiYiIiIiIsjF5Ku9t27ahdu3aOdZ37twZo0aNQlBQUK7P27p1K2rVqgUgqwhsTEwMVq9eDb1ejxYtWmDatGlwdnZW9j9y5Aj69OmDgwcPIn/+/Pjggw/wySefPHM785quL3Dw2meOcXFCw2fel4iIiIiIXk7POpW3WXWOXmZMjoiIiIiICHiJ6hwRERERERG9CpgcERERERERgckRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERABXJ0Y4dO9C4cWP4+flBp9Nh5cqVRttFBCNGjECBAgXg4OCAqKgonDlzxmifxMREtG/fHi4uLnBzc0PXrl2RmppqtM+RI0dQvXp12Nvbw9/fH5MmTTL9ryMiIiIiInpGJidHd+/exRtvvIEZM2bkun3SpEmYNm0aZs+ejf3798PJyQnR0dF48OCBsk/79u1x/PhxbNq0CWvWrMGOHTvQvXt3ZXtKSgrq1auHwoULIy4uDpMnT8aoUaMwd+5cFX8iERERERHR0+lERFQ/WafDL7/8gmbNmgHIumvk5+eHAQMG4OOPPwYAJCcnw8fHBwsWLECbNm1w4sQJhIaG4uDBg6hYsSIAYMOGDXjrrbdw9epV+Pn5YdasWRg6dCgSEhJga2sLABg8eDBWrlyJkydPPlPbUlJS4OrqiuTkZLi4uCjrAwevfea/7+KEhs+8LxERERERvZzyyg0eZ9ExRxcuXEBCQgKioqKUda6urggPD8fevXsBAHv37oWbm5uSGAFAVFQU9Ho99u/fr+xTo0YNJTECgOjoaJw6dQr//vtvrq/98OFDpKSkGC1ERERERETPyqLJUUJCAgDAx8fHaL2Pj4+yLSEhAd7e3kbbra2t4eHhYbRPbjGyv8bjxo8fD1dXV2Xx9/c3/w8iIiIiIqLXxn9mtrohQ4YgOTlZWa5cufKim0RERERERK8QiyZHvr6+AIAbN24Yrb9x44ayzdfXFzdv3jTa/ujRIyQmJhrtk1uM7K/xODs7O7i4uBgtREREREREz8qiyVFQUBB8fX2xefNmZV1KSgr279+PiIgIAEBERASSkpIQFxen7LNlyxZkZmYiPDxc2WfHjh1IT09X9tm0aRNKlCgBd3d3SzaZiIiIiIgIgIrkKDU1FfHx8YiPjweQNQlDfHw8Ll++DJ1Oh759+2LcuHH49ddfcfToUXTq1Al+fn7KjHYhISGoX78+unXrhgMHDmD37t2IiYlBmzZt4OfnBwBo164dbG1t0bVrVxw/fhzLli3D1KlT0b9/f4v94URERERERNlZm/qEQ4cOoXbt2spjQ8LSuXNnLFiwAIMGDcLdu3fRvXt3JCUloVq1atiwYQPs7e2V5yxZsgQxMTGoW7cu9Ho9WrRogWnTpinbXV1dsXHjRvTp0wcVKlRA/vz5MWLECKNaSERERERERJZkVp2jlxnrHBEREREREfCC6hwRERERERG9qpgcERERERERgckRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEQIPkKCMjA8OHD0dQUBAcHBwQHByMsWPHQkSUfUQEI0aMQIECBeDg4ICoqCicOXPGKE5iYiLat28PFxcXuLm5oWvXrkhNTbV0c4mIiIiIiABokBxNnDgRs2bNwtdff40TJ05g4sSJmDRpEqZPn67sM2nSJEybNg2zZ8/G/v374eTkhOjoaDx48EDZp3379jh+/Dg2bdqENWvWYMeOHejevbulm0tERERERAQA0En2WzoW0KhRI/j4+ODbb79V1rVo0QIODg5YvHgxRAR+fn4YMGAAPv74YwBAcnIyfHx8sGDBArRp0wYnTpxAaGgoDh48iIoVKwIANmzYgLfeegtXr16Fn5/fU9uRkpICV1dXJCcnw8XFRVkfOHjtM/8tFyc0fOZ9iYiIiIjo5ZRXbvA4i985ioyMxObNm3H69GkAwOHDh7Fr1y40aNAAAHDhwgUkJCQgKipKeY6rqyvCw8Oxd+9eAMDevXvh5uamJEYAEBUVBb1ej/379+f6ug8fPkRKSorRQkRERERE9KysLR1w8ODBSElJQcmSJWFlZYWMjAx89tlnaN++PQAgISEBAODj42P0PB8fH2VbQkICvL29jRtqbQ0PDw9ln8eNHz8eo0ePtvSfQ0RERERErwmL3zn68ccfsWTJEnz//ff4448/sHDhQkyZMgULFy609EsZGTJkCJKTk5XlypUrmr4eERERERH9t1j8ztHAgQMxePBgtGnTBgBQpkwZXLp0CePHj0fnzp3h6+sLALhx4wYKFCigPO/GjRsICwsDAPj6+uLmzZtGcR89eoTExETl+Y+zs7ODnZ2dpf8cIiIiIiJ6TVj8ztG9e/eg1xuHtbKyQmZmJgAgKCgIvr6+2Lx5s7I9JSUF+/fvR0REBAAgIiICSUlJiIuLU/bZsmULMjMzER4ebukmExERERERWf7OUePGjfHZZ58hICAApUqVwp9//on//e9/eO+99wAAOp0Offv2xbhx41CsWDEEBQVh+PDh8PPzQ7NmzQAAISEhqF+/Prp164bZs2cjPT0dMTExaNOmzTPNVEdERERERGQqiydH06dPx/Dhw9G7d2/cvHkTfn5+6NGjB0aMGKHsM2jQINy9exfdu3dHUlISqlWrhg0bNsDe3l7ZZ8mSJYiJiUHdunWh1+vRokULTJs2zdLNJSIiIiIiAqBBnaOXBescERERERER8ALrHBEREREREb2KmBwRERERERGByREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAjZKja9euoUOHDvD09ISDgwPKlCmDQ4cOKdtFBCNGjECBAgXg4OCAqKgonDlzxihGYmIi2rdvDxcXF7i5uaFr165ITU3VorlERERERESWT47+/fdfVK1aFTY2Nli/fj3++usvfPHFF3B3d1f2mTRpEqZNm4bZs2dj//79cHJyQnR0NB48eKDs0759exw/fhybNm3CmjVrsGPHDnTv3t3SzSUiIiIiIgIA6ERELBlw8ODB2L17N3bu3JnrdhGBn58fBgwYgI8//hgAkJycDB8fHyxYsABt2rTBiRMnEBoaioMHD6JixYoAgA0bNuCtt97C1atX4efn99R2pKSkwNXVFcnJyXBxcVHWBw5e+8x/y8UJDZ95XyIiIiIiejnllRs8zuJ3jn799VdUrFgRLVu2hLe3N8qVK4d58+Yp2y9cuICEhARERUUp61xdXREeHo69e/cCAPbu3Qs3NzclMQKAqKgo6PV67N+/P9fXffjwIVJSUowWIiIiIiKiZ2Xx5Oj8+fOYNWsWihUrht9++w29evXChx9+iIULFwIAEhISAAA+Pj5Gz/Px8VG2JSQkwNvb22i7tbU1PDw8lH0eN378eLi6uiqLv7+/pf80IiIiIiL6D7N4cpSZmYny5cvj888/R7ly5dC9e3d069YNs2fPtvRLGRkyZAiSk5OV5cqVK5q+HhERERER/bdYPDkqUKAAQkNDjdaFhITg8uXLAABfX18AwI0bN4z2uXHjhrLN19cXN2/eNNr+6NEjJCYmKvs8zs7ODi4uLkYLERERERHRs7J4clS1alWcOnXKaN3p06dRuHBhAEBQUBB8fX2xefNmZXtKSgr279+PiIgIAEBERASSkpIQFxen7LNlyxZkZmYiPDzc0k0mIiIiIiKCtaUD9uvXD5GRkfj888/RqlUrHDhwAHPnzsXcuXMBADqdDn379sW4ceNQrFgxBAUFYfjw4fDz80OzZs0AZN1pql+/vtIdLz09HTExMWjTps0zzVRHRERERERkKosnR5UqVcIvv/yCIUOGYMyYMQgKCsJXX32F9u3bK/sMGjQId+/eRffu3ZGUlIRq1aphw4YNsLe3V/ZZsmQJYmJiULduXej1erRo0QLTpk2zdHOJiIiIiIgAaFDn6GXBOkdERERERAS8wDpHREREREREryImR0RERERERGByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBOA5JEcTJkyATqdD3759lXUPHjxAnz594OnpCWdnZ7Ro0QI3btwwet7ly5fRsGFDODo6wtvbGwMHDsSjR4+0bi4REREREb2mNE2ODh48iDlz5qBs2bJG6/v164fVq1fjp59+wvbt23H9+nW8/fbbyvaMjAw0bNgQaWlp2LNnDxYuXIgFCxZgxIgRWjaXiIiIiIheY5olR6mpqWjfvj3mzZsHd3d3ZX1ycjK+/fZb/O9//0OdOnVQoUIFxMbGYs+ePdi3bx8AYOPGjfjrr7+wePFihIWFoUGDBhg7dixmzJiBtLQ0rZpMRERERESvMc2Soz59+qBhw4aIiooyWh8XF4f09HSj9SVLlkRAQAD27t0LANi7dy/KlCkDHx8fZZ/o6GikpKTg+PHjub7ew4cPkZKSYrQQERERERE9K2stgi5duhR//PEHDh48mGNbQkICbG1t4ebmZrTex8cHCQkJyj7ZEyPDdsO23IwfPx6jR4+2QOvVCxy89pn3vTihoYYtISIiIiIiU1n8ztGVK1fw0UcfYcmSJbC3t7d0+DwNGTIEycnJynLlypXn9tpERERERPTqs3hyFBcXh5s3b6J8+fKwtraGtbU1tm/fjmnTpsHa2ho+Pj5IS0tDUlKS0fNu3LgBX19fAICvr2+O2esMjw37PM7Ozg4uLi5GCxERERER0bOyeHJUt25dHD16FPHx8cpSsWJFtG/fXvm3jY0NNm/erDzn1KlTuHz5MiIiIgAAEREROHr0KG7evKnss2nTJri4uCA0NNTSTSYiIiIiIrL8mKN8+fKhdOnSRuucnJzg6emprO/atSv69+8PDw8PuLi44IMPPkBERASqVKkCAKhXrx5CQ0PRsWNHTJo0CQkJCRg2bBj69OkDOzs7SzeZiIiIiIhImwkZnubLL7+EXq9HixYt8PDhQ0RHR2PmzJnKdisrK6xZswa9evVCREQEnJyc0LlzZ4wZM+ZFNJeIiIiIiF4DzyU52rZtm9Fje3t7zJgxAzNmzMjzOYULF8a6des0bhkREREREVGWF3LniEzDKcKJiIiIiLTH5Og1xqSLiIiIiOj/WXy2OiIiIiIiolcRkyMiIiIiIiIwOSIiIiIiIgLAMUekAY5lIiIiIqJXEe8cERERERERgckRERERERERAHaro1cIu+sRERERkZZ454iIiIiIiAhMjoiIiIiIiACwWx0Ru+sREREREQDeOSIiIiIiIgLAO0dEmuEdKSIiIqJXC5MjolcQEy8iIiIiy2O3OiIiIiIiIjA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgA6xwRUTasn0RERESvM945IiIiIiIiApMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIAgPWLbgAR/fcFDl77zPtenNBQw5YQERER5Y3JERG9sph0ERERkSWxWx0RERERERGYHBEREREREQFgckRERERERARAg+Ro/PjxqFSpEvLlywdvb280a9YMp06dMtrnwYMH6NOnDzw9PeHs7IwWLVrgxo0bRvtcvnwZDRs2hKOjI7y9vTFw4EA8evTI0s0lIiIiIiICoEFytH37dvTp0wf79u3Dpk2bkJ6ejnr16uHu3bvKPv369cPq1avx008/Yfv27bh+/TrefvttZXtGRgYaNmyItLQ07NmzBwsXLsSCBQswYsQISzeXiIiIiIgIgAaz1W3YsMHo8YIFC+Dt7Y24uDjUqFEDycnJ+Pbbb/H999+jTp06AIDY2FiEhIRg3759qFKlCjZu3Ii//voLv//+O3x8fBAWFoaxY8fik08+wahRo2Bra2vpZhMRERER0WtO8zFHycnJAAAPDw8AQFxcHNLT0xEVFaXsU7JkSQQEBGDv3r0AgL1796JMmTLw8fFR9omOjkZKSgqOHz+e6+s8fPgQKSkpRgsREREREdGz0rTOUWZmJvr27YuqVauidOnSAICEhATY2trCzc3NaF8fHx8kJCQo+2RPjAzbDdtyM378eIwePdrCfwERvY5YP4mIiOj1pOmdoz59+uDYsWNYunSpli8DABgyZAiSk5OV5cqVK5q/JhERERER/XdoducoJiYGa9aswY4dO1CoUCFlva+vL9LS0pCUlGR09+jGjRvw9fVV9jlw4IBRPMNsdoZ9HmdnZwc7OzsL/xVERJbDO1JEREQvN4vfORIRxMTE4JdffsGWLVsQFBRktL1ChQqwsbHB5s2blXWnTp3C5cuXERERAQCIiIjA0aNHcfPmTWWfTZs2wcXFBaGhoZZuMhERERERkeXvHPXp0wfff/89Vq1ahXz58iljhFxdXeHg4ABXV1d07doV/fv3h4eHB1xcXPDBBx8gIiICVapUAQDUq1cPoaGh6NixIyZNmoSEhAQMGzYMffr04d0hIiIiIiLShMWTo1mzZgEAatWqZbQ+NjYW7777LgDgyy+/hF6vR4sWLfDw4UNER0dj5syZyr5WVlZYs2YNevXqhYiICDg5OaFz584YM2aMpZtLRPTK07K7HrsCEhHR68TiyZGIPHUfe3t7zJgxAzNmzMhzn8KFC2PdunWWbBoREREREVGeNK9zRERERERE9CrQtM4RERFRbthdj4iIXka8c0RERERERAQmR0RERERERACYHBEREREREQFgckRERERERASAEzIQEdF/CCd6ICIiczA5IiIiegomXURErwcmR0RERC+IVkkXkzkiInU45oiIiIiIiAhMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAcCpvIiIiekacIpyI/ut454iIiIiIiAi8c0REREQvAd6VIqKXAe8cERERERERgckRERERERERACZHREREREREADjmiIiIiP7DOJaJiEzBO0dERERERERgckRERERERASA3eqIiIiITMbuekT/TUyOiIiIiF4STLqIXiwmR0RERET/cUy6iJ4NkyMiIiIiUkWrpIvJHL0oTI6IiIiI6LXBhI6ehMkREREREdFL6lVL5l71JJHJERERERERvdSeV9LFOkdERERERERgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAF7y5GjGjBkIDAyEvb09wsPDceDAgRfdJCIiIiIi+o96aZOjZcuWoX///hg5ciT++OMPvPHGG4iOjsbNmzdfdNOIiIiIiOg/6KVNjv73v/+hW7du6NKlC0JDQzF79mw4Ojpi/vz5L7ppRERERET0H2T9ohuQm7S0NMTFxWHIkCHKOr1ej6ioKOzduzfX5zx8+BAPHz5UHicnJwMAUlJSjPbLfHjvmdvx+HOfRqvYjMu4zys24zLu84rNuIz7vGIzLuM+r9iM+3LHNawTkSc+VydP2+MFuH79OgoWLIg9e/YgIiJCWT9o0CBs374d+/fvz/GcUaNGYfTo0c+zmURERERE9Aq5cuUKChUqlOf2l/LOkRpDhgxB//79lceZmZlITEyEp6cndDrdE5+bkpICf39/XLlyBS4uLhZrE+NqG1fL2IzLuM8rNuMy7vOKzbiM+7xiMy7jPq/YpsQVEdy5cwd+fn5P3O+lTI7y588PKysr3Lhxw2j9jRs34Ovrm+tz7OzsYGdnZ7TOzc3NpNd1cXGx+MHAuNrH1TI24zLu84rNuIz7vGIzLuM+r9iMy7jPK/azxnV1dX3qPi/lhAy2traoUKECNm/erKzLzMzE5s2bjbrZERERERERWcpLeecIAPr374/OnTujYsWKqFy5Mr766ivcvXsXXbp0edFNIyIiIiKi/6CXNjlq3bo1bt26hREjRiAhIQFhYWHYsGEDfHx8LP5adnZ2GDlyZI5ueYz7csfVMjbjMu7zis24jPu8YjMu4z6v2IzLuM8rthZxX8rZ6oiIiIiIiJ63l3LMERERERER0fPG5IiIiIiIiAhMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiHKRkpLyoptARET03HG2OsLly5fx999/Q6/Xo0iRIvD09HzRTXqqV7HNr7uMjAxYWVkpj/fv34+HDx8iIiICNjY2L7BlT5aRkYHbt29Dr9fDy8vrRTfnqSzVXisrK/z999/w9vZGnTp1sGLFCri5uVmuoa+opKQkLF++HOfOncPAgQPh4eGBP/74Az4+PihYsOCLbh7REz169AjHjx9HQkICAMDX1xehoaEv9Xfw83Ljxg08fPgQAQEBFomXlpaGCxcuIDg4GNbWL23lnFfK7du3kT9/fu1fSF4zt2/fli1btsg///wjIiK3bt2SCRMmyOjRo+Wvv/5SFbNfv37PvJjr4MGD8t1338l3330nBw8eNCvWjBkzJCAgQPR6vdFStWpVOXTokNltFRE5fvy4rF+/XlatWmW0vMxtfh4yMzNVP1frv3Pt2rWyYcOGHOs3bNgg69atMzne9evXpWrVqmJlZSU1atSQxMREadiwoeh0OtHpdFK8eHG5fv26yXEfPHggaWlpyuOzZ8/Kp59+Kh06dJChQ4fK+fPnTY6Z3Zo1a6R69epiZ2enHGeurq7SoUMHuXTpklmxP/jgA5k6dWqO9dOnT5ePPvropWivi4uL8p2o0+nk5s2bqtqVl/j4ePn222/l3LlzIiJy7Ngx6dWrl/To0SPX489U3333nURGRkqBAgXk4sWLIiLy5ZdfysqVK1XHPHz4sHh5eUnRokXF2tpaafvQoUOlY8eOZrX3wIEDsm/fvhzr9+3bZ/Z3/Y4dO6R9+/ZSpUoVuXr1qohkvT87d+40K64lafl5Xrt2rXTt2lUGDhwoJ06cMNqWmJgotWvXNjnmlClTlOPK0rT4bGRkZMjQoUPFzc1N+e41LG5ubjJs2DDJyMgwu+3//vuvzJs3TwYPHqycZ8XFxSnH3csgJSVF2rdvLwEBAdKpUyd5+PCh9O7dW3Q6nej1eqlRo4YkJyerjn/37l157733xMrKSqysrJT/x5iYGBk/frzJ8WJiYmTHjh2q2/NfotfrpU6dOrJkyRJ58OCBZq/zWiVH+/fvF1dXV9HpdOLu7i6HDh2SoKAgKVasmAQHB4uDg4PExcWZHLdWrVpGi4uLizg6Okq5cuWkXLly4uTkJC4uLqq+gA2uXLki1apVU9ru7u4uOp1OqlatKleuXDE53uTJk8XPz0+mT58u8+bNk5CQEBkzZoysX79eOnbsKI6Ojmb9IJ87d07Kli2rfNkYvoQNJ21qaN3mvCQmJsrChQtNft6DBw9kwIABUr16dZkwYYKIiIwdO1acnJzEyclJ2rZtq+oLWKfTSXBwsHz22Wdy7do1k5//NGXKlJG1a9fmWL9+/XopW7asyfE6duwokZGR8uuvv0rr1q0lMjJSqlevLlevXpVLly5J1apVpU+fPibHrVmzpvz0008iIrJr1y6xs7OTsmXLSuvWraVcuXLi6Ogoe/bsMTmuSNaJY758+WTAgAEydOhQ8fX1lcGDB8usWbOkZs2akj9/fjl9+rSq2CIifn5+uSa5cXFxUrBgwZeivW+//bb4+PhIrVq1lO+a2rVr57qY6ueffxYrKyvx9PQUZ2dn2bRpk7i5uUlUVJRER0eLlZWVLFmyxOS4BjNnzpT8+fPLuHHjxMHBQTk5iY2NlVq1aqmOW7duXRk4cKCIiDg7Oytxd+/eLYULF1YdV0SkUqVKyvGc3c8//yyVK1dWHXf58uXi4OAg77//vtjZ2Sltnj59ujRo0EB1XIN///1X5s6dK8OGDZN58+ZJUlKSqjhafZ6XLFkiVlZW0rBhQ6lWrZrY29vL4sWLle0JCQmqfpN0Op1YWVlJVFSULF26VB4+fGhyjNxo9dkYOHCgeHl5yezZs+XChQty7949uXfvnly4cEHmzJkj3t7eMmjQILPabumLB6VLl5YxY8bI5cuXzWrX42JiYqRkyZIybdo0qVWrljRt2lRKly4tu3btku3bt0toaKh8+umnquN/+OGHUqFCBdm5c6c4OTkp78PKlSslLCzM5HiGc6dixYrJhAkT5O+//1bdtif56aefpGXLlhIeHq6cvxoWS0tISJDRo0eb/DydTif169cXW1tbcXd3l5iYGPnzzz8t3r7XKjmKioqS999/X1JSUmTy5MlSqFAhef/995XtXbp0kWbNmpn1Gl988YU0btxYEhMTlXWJiYnStGlTmTJliuq40dHREh4eLidPnlTWnTx5UiIiIiQ6OtrkeIGBgUZ3AU6dOiWenp6Snp4uIlkf7jfffFN1exs1aiRNmzaVW7duibOzs/z111+yc+dOqVy5suorIFq3OS/x8fGqfjz79esnfn5+MmDAAAkJCZHevXtLQECALF68WL7//nspWrSofPDBBybH1el00q1bN/H29hZra2tp2LCh/PLLL/Lo0SOTY+XG3t5eLly4kGP9hQsXxNHR0eR4BQoUkL1794qIyD///CM6nU5+//13ZfvmzZulSJEiJsd1cXFRTvhr1qyZ487ssGHDpGrVqibHFREpWbKkLF26VHl88OBBKVSokHLHr3Xr1tK8eXNVsUVE7Ozs5MyZMznWnzlzRuzs7F6K9t67d09mzZolH3/8seh0Ounevbv07ds318VU5cuXl3HjxomIyA8//CBubm4yZswYZfuUKVNUnUQYhISEyC+//CIixknM0aNHxdPTU3VcFxcXOXv2bI64Fy9eVPX/ll32k6jszp8/L87OzqrjhoWFKRd3srf5jz/+EB8fH5PjNW/eXElijh07Jvnz5xcvLy8JDw8XHx8f8fX1VdULQ6vPc1hYmNFd2mXLlomTk5N88803ImJechQbGytNmzYVGxsb8fT0lI8++kiOHj1qcqzstPps+Pj4PPGu04YNG8Tb29v0Bmdj6YsHOp1OPD09xcrKSqKjo2X58uXK7705/P39ZcuWLSIicu3aNdHpdLJ69Wpl+5o1a6REiRKq4wcEBCi/ednfhzNnzki+fPlMjmf4zfzoo48kf/78YmNjI02aNJHVq1db5G6fiMjUqVPF2dlZYmJixNbWVnr06CFRUVHi6upqVqKYF7XnVTqdTm7cuCG3bt2SKVOmSGhoqOj1eilfvrzMnDnTrDt+2b1WyZG7u7vypZ2WliZ6vV7279+vbFd71TY7Pz8/OXbsWI71R48elQIFCqiOa29vL3/88UeO9YcOHRIHBweT4zk6OhqdAGdmZoq1tbXSvSk+Pt6sH2RPT085fPiwiGT96BmSus2bN6s+6dGqzcnJyU9cdu7cqepD7O/vL5s2bRKRrDtper3eqEvPxo0bVf9g3LhxQ9LT02X58uXy1ltviZWVlfj4+MigQYPk1KlTJsfMzsfHRzZv3pxj/aZNm8TLy8vkePb29kZX/pycnIwSg0uXLqk6hp2cnJQuMj4+PhIfH2+0/ezZs6qPYQcHhxwJorW1tXKnbv/+/eLm5qYqtohIqVKlZPr06TnWT5s2TUJCQkyOp3V7a9WqJf/++6/q5z/OyclJaW9mZqbY2NjIkSNHlO3nzp0z6/vH3t5e6fKU/eTk9OnTYm9vrzqul5eX8j2cPe7GjRulUKFCquOKiHh4eOR6Z2T37t1m/d9lPzayt/ncuXOqEjp3d3flc9egQQNp166dctckLS1NunbtKvXq1TM5rlafZycnpxxd8rZs2SLOzs4ya9Yss5KjGzduiIjIjRs3ZOLEiVKyZEnR6/VSqVIlmTt3rqSkpKhqrxafDUdHR6M4jzt8+LA4OTmZHDc7S1880Ol0cu3aNfnll1+kcePGYm1tLV5eXjJgwADVwyBEsi5OZf9NcnR0NPrdvHjxoqoLgQbZ71Znfx/i4+PFxcXF5HjZj7W0tDRZtmyZchfRz89PPv3001wvtpmiRIkS8v333+do8/Dhw1X17Dh8+PATl2XLlpn9uTPYs2ePvPfee5IvXz5xdHQ0u4uziMhrNVtdWloaHBwcAAA2NjZwdHQ0GtiVP39+/PPPP2a9RkpKCm7dupVj/a1bt3Dnzh3Vcf39/ZGenp5jfUZGBvz8/EyOV7x4cWzatEl5vHXrVtja2sLX1xcAYG9vD51Op7q9GRkZyJcvH4Cs9/X69esAgMKFC+PUqVOqYmrVZjc3N7i7u+e51KhRQ1V7b9++jeLFiwMAihQpAisrKxQtWlTZXqxYsVyPlWdlbW2NFi1aYO3atbh06RL69OmD5cuXIyQkRHWbAaBp06bo27cvzp07p6w7e/YsBgwYgCZNmpgcz9vbG3///bfyOCYmBh4eHsrjf//9F05OTibHDQ8Px+rVqwEAwcHBOHz4sNH2+Ph4o9cxRWBgIA4dOqQ8/uOPP6DX6+Hj4wMA8PDwyPXz+Kz69++PQYMGYeTIkdi+fTu2b9+OESNGYPDgwejXr99L196tW7cqkzFI1kU11bEAIF++fMp3bVJSEh49emT03fvPP//A2dlZdfygoCDEx8fnWL9hwwaEhISojtukSROMGTNGeS91Oh0uX76MTz75BC1atFAdFwDq1auHIUOGIDk5WVmXlJSETz/9FG+++abquL6+vjh79myO9bt27UKRIkVMjvfgwQNl8H58fDw+/vhj2NraAsj6XR00aBD2799vclytPs8uLi64ceOG0bratWtjzZo1GDhwIKZPn25yzMd5e3tj0KBBOHHiBLZt24bQ0FD069cPBQoUMDmWVp+NWrVq4eOPP8bt27dzbLt9+zY++eQT1KpVy+S42dnZ2eU6y+Xp06dVTw5jbW2NZs2a4ddff8Xly5fRr18//PrrryhdujQiIyMxf/58k2N6enoa/fY2bdrUaLKZ1NRU2NnZqWovAFSsWBFr165VHhvOS7755htERESojgtkfcZatWqFDRs24Pz58+jWrRuWLFmCEiVKmBX38uXLiIyMBAA4ODgo56sdO3bEDz/8YHK8sLAwlCtXDmFhYTmWcuXKoU2bNqramds5XkREBL799lv8/fffmDZtmtG5i2pmp1evkJIlSxpdEV+zZo3cu3dPebxv3z6zr/517NhRAgMD5eeff5YrV67IlStXZPny5RIUFCSdOnVSHXflypVSuXJlozE1Bw8elCpVqijdR0yxbNkysbGxkVatWkmnTp3E2dlZBg8erGyfPXu2REREqG5vtWrVlHa1bdtW6tevL7t27ZJOnTpJqVKlVMXUqs0uLi4yceJE2bZtW67LvHnzVF3hKFGihNLV6cCBA2Jrayvz589Xti9dulSKFStmcly9Xp/jykl2v//+u7Rr187kuAZJSUlSpUoVsba2lsDAQAkMDBRra2upXbu2qrsHTZo0ka+++irP7V9//bXUqVPH5Lh79uwRV1dXGTlypEyfPl3y588vw4YNkyVLlsiIESPEzc1NJk6caHJcQ5tcXV1l0KBBMmLECPHz85OuXbsq2xcvXmx2P+yZM2dKwYIFlfF4QUFBqsa2Pa/2fvPNN1KqVCmxtbUVW1tbKVWqlMybN09VrA4dOkh4eLgsXrxYGjduLNHR0VKlShU5ceKEnDx5UmrWrCnvvPOO6rbOmzdPChYsKEuXLhUnJyf54YcfZNy4ccq/1UpKSpKoqChxc3MTKysr8ff3FxsbG6lRo4akpqaqjisicvXqVSlSpIi4uroq41fd3NykRIkSZo25+PzzzyU0NFT27dsn+fLlk507d8rixYvFy8tLpk2bZnK88PBwmTt3roiIlCtXLsfvz8aNG8XX19fkuFp9nps2bSojRozIddvWrVvFyclJ1ff7076Hk5OTlffJFFp9Ni5fviylS5cWa2trKVeunNSvX1/q168v5cqVE2traylbtqzZY3u6du0qzZo1k7S0NHF2dpbz58/LpUuXpFy5cqommnnSe7x161bp0KGDqrtd9evXl9mzZ+e5PTY2ViIjI02Oa7Bz505xdnaWnj17ir29vXz00Ufy5ptvipOTk6oJlXK7W5JdZmambNy4UXV7RUSCgoKUu+IVKlRQ3p/ffvtN3N3dTY7n6ekp3377rVy8eDHXZe3atRa7c6SF1yo5GjVq1BN/GD/99FN5++23zXqNu3fvSq9evYxmjLK1tZVevXqZ/OPp5uamTL7g7u4utra2Srzs/1Zz4IqIrFu3Ttq1ayctWrTI8SV++/ZtuX37tqq4Iln9l3/++WcRyepnW6JECdHpdJI/f/5cu2y9yDbXqlXriT+68fHxotPpTI775Zdfir29vURFRYm7u7tMmzZNfH19ZdCgQTJ48GBxdXU16kv+rJ7Hl0NmZqb89ttvMmnSJJk+fbps375ds9fav3+/6n76e/bskSpVquSYfalgwYJPTMiexcyZMyUyMlIqVKggn376qdy/f1/Zdvr06RyzXql18+ZNuXPnjtlxtGzv8OHDxcnJSQYPHqzMODl48GBxdnaW4cOHmxwvISFB3nzzTXF2dpbo6GhJSkqSmJgYo4HHhu45ai1evFiKFi1qdEwYxpmYa9euXTJjxgyZOHGi0nXWElJTU2XOnDnSu3dvGTBggCxcuNBoBjc1MjMzlcTQ8F7Y29vLsGHDVMVbs2aNeHh4SGxsrMTGxkpgYKB88803snv3bpk/f774+/sr405MpcXnedu2bfL555/nuX3Lli3y7rvvmhxXq+9hLT8bGRkZsm7dOhkxYoR0795dunfvLiNGjJD169dbZOyKpS8ePMt7rGaMyT///PPEC33r1q2TrVu3mhw3u7Nnz8r7778vlSpVkpCQEGnfvv0TuzU+SWBgoFnnY8+ia9euMmrUKBHJutjm4OCg/F++9957JserV6+ejB07Ns/tas+rFixYoOksdQasc5TNvXv3YGVlZdbtVIO7d+8qt/aCg4NVdRtauHDhM+/buXNnk+M/b4mJiXB3dzeru54W5s2bh/v37+PDDz/MdfuNGzcwe/ZsjBw50uTY33//Pfbu3YvIyEi0bdsW27Ztw4gRI3Dv3j00btwYw4cPh15vWu/W7du3o2rVqqybkM2tW7dw/vx5ZGZmokCBAggMDHzRTXomN2/eVLqZlixZ8qWto+Tl5YVp06ahbdu2Rut/+OEHfPDBB7l21VHj/PnzuHfvHkqWLGmx4/vevXtITU2Ft7e3ReK9qtLS0nD27FmkpqYiNDTUrG6LP//8M/r27Yvr168bdbG0s7NDz549MWXKFKOaZqZ6VT/PWjp37hzu379v0c+GVnbv3o3Dhw8jNTUV5cuXR1RUlKo4Xbp0wbRp05Qu+qSdzMxMZGZmKsfW0qVLsWfPHhQrVgw9evRQus4+q19++QV3795Fhw4dct3+77//4tdff31pz12ZHNErIy4uDhUqVHjRzfhPu3//PuLi4uDh4YHQ0FCjbQ8ePMCPP/6ITp06qY5/6tQpTJ8+HSdOnAAAhISE4IMPPjC7v/TzYqkigXfu3EHv3r3xww8/IDMzE0BW0dXWrVtjxowZcHV1tURzkZ6ebpHijm5ubjh48CCKFStmtP706dOoXLkykpKSzH4NLS1YsADNmzc3633V+rMB5P75iImJQcmSJc2Kq4WMjAz88ccfRklMhQoVXsoT2YyMDFy6dAmBgYHQ6/V4+PAhVq1ahczMTNSuXVsZm2cOrQt+iohZFxZ//vlnNGjQAI6OjhZs1atLRHDx4kX4+/vD2toaaWlp+OWXX/Dw4UO89dZbJhcazW2sVV5cXFxMba6Rs2fP4ty5c6hRowYcHBzMPjZeRbGxsXB2dkbLli2N1v/000+4d++e+UmX5vemXmLXrl2TESNGSLt27WTAgAFmdTvRYj7+p82iln0xVVpamgwcOFCCg4OlUqVK8u233xptVzuDj0jW7dKOHTtKUFCQ2Nvbi6Ojo5QuXVqGDRtm1jSLWtf30VJSUpKcPHlSTp48qboOSHZaFDM+deqUFC5c2KgQXvb32ZxjQiSr3oq1tbVUqVJFKYocEREh1tbWsnz5cpNiTZ069ZkXNbQuEtiqVSspVqyYbNiwQfkMb9iwQUqUKCGtW7c2Od6yZcuM6qxMnz5dKZbs6empqp5EdjExMbkWsR4wYID07t3brNgiWd3J5s+fL59++qlMnz7d4l1IbGxszJrdSuvPhohlPx8G8fHxMnbsWJkxY4bcunXLaFtycrJ06dLFpHjlypVTylSMHj1a7t69q6pdeVm9erUMHz5cdu3aJSJZs5s2aNBAoqOjZc6cOapiHj58WHx9fUWv10vp0qWVsTdOTk7i7Ows7u7uRrPWmsrSBT/zYu4xrNPpxMXFRbp165ZrsWFz/fXXXzJ//nzlPOrEiRPSs2dP6dKli1ld6bVw8uRJKVy4sOj1eilatKicP39eKlSoIE5OTuLo6KiqLlz2Oo5PW9S6ffu21K1bV3ktw7HWpUsX6d+/v+q4N27ckM2bNyvnJgkJCTJx4kQZP3682VPTa6VYsWLKdOzZbdu2TYoXL252/NcqOXJwcFCqvB8/flxcXV2laNGi0rJlSylZsqQ4Ojoq00+bSov5+LX8sI0cOVJ8fHxk8uTJMnToUHF1dZXu3bsr2xMSElT1B92wYYM4ODhIixYtpEOHDuLo6CgxMTHyySefSNGiRSU4OFh1ATMt6vtofZJtKFb7+P9XSEiI6vEPWhUzbtasmTRs2FBu3bolZ86ckYYNG0pQUJBcunRJRMw/ASxSpEiu41NGjBhhcp0jw0QRhsUwniJ7gWQnJycJCgpS1VatiwQ6OjrKzp07c6zfsWOHqilksw9cnj9/vtjb28uIESNk7dq1yngTUydPMJyg9+vXTz744APJly+flCpVSrp27Spdu3aV0qVLi4uLi8TExJjc3pCQECWxv3z5sgQGBoqrq6tUqlRJPDw8xNvbO8f0y88i+xjN7ItOpxNXV1flsam0/myIWPbzIZI1kNowcUZAQIB4enoanUyoabO9vb1SdFyv1yu/p5Ywe/Zssba2lgoVKoiLi4ssWrRI8uXLJ++//7706NFDHBwcVI07io6OlnfeeUeOHj0qH330kYSEhEjLli0lLS1N0tPTpUOHDhIVFaW63ZYu+Jn9c5d90ev10qlTJ+WxqXQ6nYwZM0bKlSsnOp1OSpUqJV9++aVFLkSsX79ebG1txcPDQ+zt7WX9+vXi5eUlUVFRUqdOHbGyslKdIM2YMUPq1q0rLVu2NKqPJ5J1UVDNd3zTpk2lSZMmcuTIEenbt6+EhIRI06ZNJS0tTR48eCCNGzeWDh06mBQz+yROCxYsUApxZx+jWaBAAVmwYIHJ7TXo2LGjREdHy5UrV4ym296wYYOEhoaqimmYlESn04mvr6/Ex8dLoUKFpFixYlKiRAmxs7OT3377TVXs48ePS69evSQsLEx8fX3F19dXwsLCpFevXnL8+HFVMQ3s7OzyrMdoTrkGg9cqOco+uK9p06bSuHFjJYHJyMiQNm3aSKNGjVTHtvR8/Fp+2IoWLWpU9OzMmTNStGhReffddyUzM1P1j31YWJjMmjVLebxx40YpWbKkiGTdrapbt66qwa8i2tT30fIke9KkSeLo6CiDBw+WrVu3yl9//SV//fWXbN26VYYMGSJOTk4yefJkk+NqVczY29vbaMBoZmam9OzZUwICAuTcuXNmnwA6ODjkWovh9OnTquocGSxZskSqVq2ao0By9erVZfHixapial0k0N/fP9fBuYcPH1ZVay37d1vlypVl0qRJRttnzpxp8mx1hhnTnrbUrl3brPa2b99eIiMjlauWd+7ckaioKGnbtq3JcZ2dnaVhw4ayYMECZYmNjRUrKyv57LPPlHWm0vqzIWL5z0dERISSwGdmZsrEiRPF2dlZ1q9fLyLqkqMqVapIVFSUjBo1SnQ6nQwcOFBGjx6d62Kq0NBQZZKdLVu2iL29vcyYMUPZHhsbq6oGWPb6hvfu3RMrKyujO0XHjh0zqzCwFgU/w8LCcnzOdDqdVKpUySKfuUOHDkmvXr3Ezc1N7OzspGXLlmbNdhYRESFDhw4VkazCte7u7kYXjwYPHqyqQPvUqVPF0dFR+vTpIx06dBBbW1ujyTXUfu68vLzkzz//FJGsu9Y6nc7oYtXu3bslICDA5LgGderUUWoGZbdkyRKpWbOm6rjZ6389XrNMbY2qatWqSZ8+feTOnTsyefJkKViwoFFdo48//ljVzH3r1q0TW1tbqVKliowcOVJmzpwpM2fOlJEjR0pkZKTY2dk9sSjx0/j7+8uqVatyrF+5cqXZ9UpFXuPkyN/fX3bs2GG0/Y8//lBdqPXxWVWuX78un3/+uRQrVkz0er1ERETk6LpmCkt/2HIrGnn16lUpXry4tG/fXq5du6bqS8fe3j5HoVYbGxulUOuOHTtUFRIVyX3mmqtXr8qYMWOkSJEiotfrpXr16qpii1j+JDsgIECWLVuW5/alS5eKv7+/yXG1KmacL1++XBP5Pn36SKFChWTHjh1mnQA2aNDAaCpzg/nz56sqGmlQpEiRPAskBwYGqoqpdZHAOXPmSFRUlNFd1L///lvq1av3xClm86LT6ZSr+Pnz58+1gKaaEzWtZP8sFylSJMeJ2e7du1V9Ns6cOSOVKlWSTp06Gc0AaG1tbdaVSq0/GyKW/3xkL8hpsGTJEnFycpLVq1erOrE8efKktG7dWipWrKh0UwsLC8uxqJk23sHBQbkTJ5LVjSx7l54LFy6o+sy5ubkpXaTS0tLEysrK6M76iRMnVM/4ami3JQt+jh8/XoKCgnLcaTH3GM7t9/P+/fvy3XffSa1atUSv16v+vnRxcVES+4yMDLG2tjb6Tj569Kj4+PiYHDc0NFSWLFmiPN69e7d4eXkpd1jVJkePH2vOzs5Gn5XLly+rKlqbPX5u3fJOnTpl1oVAZ2dnJW72Y+3gwYPi4eGhKmb274n09HSxtrZWEkeRrIszrq6uJsctW7bsE2cyHTlypJQpU8bkuAaDBg2SwoULy5YtW+TRo0fy6NEj2bx5sxQuXFgGDBigOq7Ba5UcZe8GULhw4Rxd6M6fP6/6dpxW8/EbWPrDFhQUlOMWtUjWVfLixYvLm2++qepLJzg42OhqwJkzZ8TKykoZD3H+/HnVXw5a1/ex9Em2vb39E+8aHj9+XNV7kb2Cuojxl6SIyKVLl1Qdx5UqVZLvvvsu1219+vQRNzc3k48Jw13OVatWyaxZs8TLy0v69OkjixYtkkWLFkmfPn3E29vb6G6jqRwcHOTAgQM51u/fv1/1sebn52d0AtW2bVujY+/YsWMmn1AZThoNi7Ozs9jY2EhwcLAEBweLjY2NODs7qzqx1Ol08t1338mqVaukUKFCsmfPHqPtx44dU3WilpetW7ca1YgzVfZkzs/PL0e/9osXL6r+Lk5PT5dBgwZJcHCwMnbF3BNLLT4bItp+Pry8vHKtqfLDDz+Io6OjzJo1y6yEztJTWRuSTJH/v1u7du1aZfu2bdtU1SGsW7eudO3aVa5evSqjR4+WokWLGo216t27t1kX1apXr67UizLU9hHJ6pobHR2tKuaBAwekePHiMmDAAGUqd3OP4af9fp45c0Z1V+HHE/HHf5PUfp5zu4hrSLQGDx6sOjkKDg42ulM0c+ZMSUlJUR7HxcWpqtVlULx48Vynsx84cKBZ42EaNGigTMFvONYyMjKkZcuW0qJFC1Ux8+fPL8eOHRORrPFzer1euRMqktWbIX/+/CbHtbe3N7rQ/LiTJ0+a1f3t4cOH0qpVK9HpdGJjYyM2NjZiZWUlXbp0MRp/q9ZrlRzpdDqldpCNjY0sWrTIaPvGjRtVXznRaj5+A0t/2Lp27Zrn3PVXr16VokWLqvrSGT16tBQqVEhmzZol8+fPl9KlS0vz5s2V7StWrFDdN1br+j6WPsmuXr26dOrUKdexZ48ePZJOnTpJjRo1TI6rVTHjzz//XBo0aJDn9l69epk8Du3xeiV5LeacpDVq1EjKlStnlMwcOnRIypcvL40bN1YVU4sigaNGjXrmxVSPv5/jxo0z2v7NN9+YXQQ2O0sMDi9TpoySJD4+4cD27dvN7hqxefNmCQgIkCFDhoiNjY1ZJ5ZafDZEtP18vPnmm3l22/3+++/FxsbG7LtdltSnTx8pVqyYjBs3TipXriydO3eWkiVLyvr162XDhg1SpkwZVfVWDhw4IJ6enqLX68XLy0uOHTsm4eHh4uvrK35+fuLg4JDrhcJnZemCnwZ37tyRTp06SdmyZeXo0aNmH8Na/n6WLVtW6a4pkpXAZP/d27Fjh6qu6bn18BHJurDo4+MjnTp1UnUM9+jR44ljMMePHy9vvfWWyXEN1q5dK/b29lK6dGlljGaZMmXE3t7eKOE31dGjR8Xb21vq168vtra28s4770hISIj4+Piorn3VtGlTadSokezatUu6d+8uFStWlIYNG0pqaqrcvXtX3nnnHalfv77JcUuWLClffPFFntu/+OILs7qmG5w+fVp+/PFHWb16tVy8eNHseAavVXKUvR/6ggULjLJjEZExY8aoGugoIvLuu+8aXXmwNEt/2C5evPjE/p7Xrl1T1TffcNXWz89PPD09pV27dkazJO3fv191QdFt27aZPcnFk1j6JNswS5Knp6c0b95cevbsKT179pTmzZuLp6enFChQQNVMMM+jmPGr5ObNm9KgQQPR6XRGBZIbNGig+mTgeRQJfJ5Wr16tqn939jtd2RedTichISHKY1M9ngw+3raPP/5Y2rRpY3Lcx92+fVuaN28ubm5uT7yK+V+0YsUK6du3b57blyxZIrVq1bLY6wUFBZk8w1d2qamp0q1bNyldurR0795dHj58KJMnTxZbW1vR6XRSq1Yt1Z/n1NRUOXTokNLV8v79+/LNN9/I9OnTLXJcWLLg5+N++OEH8fHxEb1eb1ZydPHiRYsUes3NrFmzZM2aNXluHzJkiHTt2tXkuG3bts3zGD527Jh4eXlpkuCfP39eGQqg1uXLl2XIkCHSvHlzad68uXz66acWmc04KSlJxo0bJy1btpQGDRrI0KFDzWrr6dOnpVixYsp3+tWrV6VJkyZibW2tjJ1XM8HTjz/+KNbW1tK4cWOZOnWqLF26VJYuXSpTp06VJk2aiK2trepZOJ8H1jl6hVy9ehWzZs0yqoHRs2dP+Pv7v+CW/TfcunULnTt3xoYNG5TaMI8ePUJ0dDQWLFigqojknTt3sHjxYuzbtw8JCQkAAF9fX0RERKBdu3Zm1zvIjSWLGb+sUlJScrx3Z86cUT4bJUuWRPHixV9E01QZPXo0+vTpY3JtDa3Z2NggKioKVapUUdaJCMaOHYuePXsqnwk1BZJJO9OmTUP37t1hb2+Py5cvw9/f32J1UKZNm5br+v79+2PQoEHw9fUFgDyLapvqwYMHSE9PfynrJz0vV69eRVxcHKKiolQVlH9VHTlyBHFxcejSpUuu248dO4aff/6Z3z8W8M8//8DT01N5vHnzZty/fx8RERFG602xZ88eTJs2DXv37s1x/vPRRx8hIiLC5JiNGzdGq1at8M4778DBwUFVu54FkyNk/dhnZmaaVdH7cYcOHTJKYipWrGix2Jbw8OFD6PV6JQk4d+4c5s+fj8uXL6Nw4cLo2rUrgoKCzHqNjIwM3L59G3q9Hl5eXpZodq6KFCmC3377LUeBymfxXzvJtpRDhw7hxx9/xOXLl5GWlma0bcWKFSbHW7NmDQ4cOIDo6GhUrVoVW7ZswZQpU5CZmYm3334b3bt3NymelZUV/v77b3h7e6NOnTpYsWIF3NzcTG7X0xw4cCDXL/bKlSuripdboUARgZeXF3bt2qUU+zQlaT5y5Mgz71u2bNln3hfIqnTfuXNntG/fHiNHjoRerweQlTQdPnw4RzHUl8m5c+cQGxuL8+fP46uvvoK3tzfWr1+PgIAAlCpVyuR4hw8fxurVq+Hh4YFWrVoZJbIpKSno27cv5s+fb8k/AUBW4eE5c+ZgxIgRz/wca2trXL9+Hd7e3kafFUvQ6/UoWLBgjkKnly5dgp+fH2xsbKDT6XD+/HmLvJ7W7t69i7i4ONSoUUPV89etWwcrKytER0cbrf/tt9+QmZmJBg0aWKKZZskroc2NpZLal92JEyewb98+REREoGTJkjh58iSmTp2Khw8fokOHDqhTp45J8bT8Hn4atcdw+fLlsXnzZri7u2PMmDH4+OOPX/oiwXq9HlZWVnByckLbtm3x/vvvo0KFCpZ/oRd41+q5S09Pl6FDh0qNGjVkxIgRIvL/0y3b2toqxR7NceXKFalWrVqO6aCrVq2q1IewpNTUVFXd1GrWrCk//fSTiIjs2rVL7OzspGzZstK6dWspV66cODo65hjU/azWrFkj1atXFzs7O6Wuj6urq3To0MFohhhT5VV/yMrKSoYMGaKqHlH2Qaq1a9d+Yleql5UlixmLZHXjsLGxkUaNGomtra00atRIihcvLq6urqqmYdeifomLi4sy5iX74H5LuXHjhvI5Lly4sFSuXFkqV66sFAKtVq2aqi4+edUpM4wrUTO+5PHnWrr4YFJSkrRp00bCw8OVfu3mDg4X0a5YtEhWF1wHBweJiooSW1tbZXD4+PHjVQ1c1qJm0LOKj483Oba/v7/MnDlTLl68KDqdTuLi4uTSpUu5Lqbq0aOHhIWF5RhzZoljYvr06dKxY0el2/B3330nISEhUqJECRkyZIgm3arVvL/ZlSlTJteu7evXr5eyZcuaHE+LgvJa14XL7uDBgzJw4EBp3bq10qXMsJgjMTFRJk+eLO+995689957MnnyZKVWmqm0qMuU1/dw9nGD5haBzYvaY/jxumVajum2FJ1OJ8ePH5cvv/xSypQpI3q9Xt544w2ZPn26UqDaEl6r5GjYsGHi4+Mj/fv3l9DQUOnZs6f4+/vL4sWLZeHChVKwYEGZOHGiWa8RHR0t4eHhOaaDjoiIUD1zzZOo/VC4uLgo/cNr1qyZY6zVsGHDpGrVqibH/e677yRfvnwyYMAAGTp0qFKbadasWVKzZk1VlacNdDqdFCpUKMcXvU6nk4IFC0pgYKDJX+5anmSnpaXJwIEDJTg4WCpVqpRjKndzpiHVqpixSNaP/ddffy0i/z/rUGZmpnTr1k25qGAKLeqXvP322+Lj46PU/6hatarUrl0710WNFi1aSERERK7jEU6ePCmRkZHyzjvvmBy3YMGC0rBhQ9myZYtSw2zr1q1iZWUlsbGxyjpTXLx4UVl++eUXCQ4OltmzZ8vhw4fl8OHDMnv2bClWrJj88ssvJrc3u/nz54uvr6/MmTPH7MHhWhaLFsmqx2MYDJx95qz9+/ermuhBi5pBBob/p7yWZcuWmRx7zpw5yti7pyXkaqxYsUL8/f1l+vTpyjpzk6OxY8dKvnz5pEWLFuLr6ysTJkwQT09PGTdunHz++efi5eWl6vvnacxNjh4vX2GgdupxLQrKZ6dFXTgDS19YM9i+fbu4urqKv7+/kmgFBASIi4uLqovDWtRlel7fw7lRewxrWbdMJGusfNeuXWXgwIE5LqYkJiaaXa9LJOs7vXv37uLq6ioODg7Stm1b1QWHs3utkqMiRYoohRzPnDkjer1eli5dqmxftmyZlC5d2qzXsLe3z3M6aHPmt8+L2g+Fk5OTcoche2Exg7Nnz4qzs7PJcUuWLGn0nh48eFAKFSokmZmZIiLK1SQ1tLhiqeVJ9siRI8XHx0cmT54sQ4cOFVdXV+nevbuyPSEhQfUMV1oVMxbJqulj+LH38PBQBhb/9ddfqqY31aJ+yb1792TWrFny8ccfi06nk+7du0vfvn1zXdRwdnbO9XNscOjQIVWfj3/++UeaNWsmtWvXlqtXryrrLXHVXSRryuncrmKvXbtWypcvb3b806dPS6VKlZSrd2ppWSxaJOv7zTClcvbk6MKFC6rql2hRM8gg+xXn3GapU5vEpKSkyNGjR0Wn08nmzZslPj4+10Wtq1evSp06daR+/fry999/m30MBwcHy88//ywiWb9rVlZWRifrK1askKJFi5oc13B3JK/FxcXFrOTIx8cn15OxTZs2qarpp0VB+ey0qAtnYOkLawalS5eWbt26yaNHj5R1jx49ku7du6s6Z9OqLpOBpb+HtTqGtaxbtmTJErGyspKGDRtKtWrVxN7e3ujzrPY7M69ZF+/evSuxsbFSrVo1i9yde62SI3t7e6Nb1fb29kZdkM6fP292ocRixYoZFeQ02L9/vwQHB5scT6sPRZ06dWTSpEkiIhIZGSkLFy402r58+XJVFaJzq0tgbW0t165dE5Gs98HNzc3kuAaWvmKp5Ul20aJFlWRcJCshL1q0qLz77ruSmZlpkS8HSxczFsm6u2FIiMqUKaMUH96zZ4+qWjla1S8xqFWrlsW7Q3p6ej7xDs7WrVvF09NTdfyZM2eKn5+f8t5aKjnKq7bWX3/9ZVZNiewyMjIkKSlJueChhpbFokWyjuHdu3eLiHFytGLFCilSpIjJ8bSsGeTp6Snffvut0ZXn7MvatWvN+rFfsGCBPHjwQPXznyQzM1M+//xz8fX1FSsrK7OO4dwuohjqr4ioL7zs6OgoAwYMyDFbrWEZPXq0We9v9+7dpUyZMkbJ85kzZ6Rs2bKqZmjTuqC8FnXhDCx9Yc0gr5o5amvlaFWXycDS38NaH8Milp/qPSwszGiYw7Jly8TJyUm++eYbEbF8cpRd9oLtar1WyZGPj4/R9JqRkZFGV29PnDhhdqHElStXSuXKleXgwYPKuoMHD0qVKlVU3U7V6kOxZ88ecXV1lZEjR8r06dMlf/78MmzYMFmyZImMGDFC3NzcVHUxDAkJUcYyiWQVU7O1tVWu+Jw5c8asYrgilr9iaWDpk+zcEsWrV69K8eLFpX379nLt2jVV/3daFjMWyZo+1dAlacyYMeLl5SXvv/++FC5cWNVdP63ql2ipd+/eUrhwYVmxYoXR+Jfk5GRZsWKFBAYGSkxMjFmvcfz4cXnjjTekbdu2FjuGy5UrJx07djQaO/nw4UPp2LGjRescmUvLYtEiIgMGDJBq1arJ33//Lfny5ZMzZ87Irl27pEiRIqrqSGlZM6hevXoyduzYPLfHx8erusP8PB06dEi++uors/r8BwUFKd0UT58+LXq9Xn788Udl+9q1a1Xd2YiMjHzimEZzu9UlJSVJlSpVxNraWunqbW1trXoMq9YF5bWoC2dg6QtrBpGRkbmeP/3yyy8SHh5ucjyt6jIZWPp7WOtjWAvZ794bbNmyRZydnWXWrFmqkyMtLobm5rVKjmrXrv3E2j0//vijVKhQweS4hsKyhsXQ1zt7zRVbW1txd3c3ObaWH4o9e/ZIlSpVcnTlKFiwoMkD5A2+/vprcXV1lUGDBsmIESPEz8/P6OrZ4sWLLXKSZskrlloJCgrKtbjgtWvXpHjx4vLmm2+qvnKiVTFjkayuX4Y7fRkZGTJ+/Hhp3Lix9O/fX9XJj5b1S44fPy69evWSsLAw8fX1FV9fXwkLC5NevXqZdUw8ePBAevbsqXx+7e3txd7eXvks9+rVyyJX4x8+fCj9+vWTsLCwHD8kauzfv1+8vb3Fy8tL6tatK3Xr1hUvLy/x9vbO9Y72kzz+vfakxVRaFosWyXpf33//fbG2tlYqqOv1eunQoYNR15xnpWXNoBUrVuT4DGeXmJioquaciMiMGTOkbt260rJlyxzfRbdu3bLIAHxLGTZsmHIhJigoSAYPHiwBAQEya9YsmT17tvj7+6uqQ/jZZ589MSG+fPmyWV04RbJ+j3777TeZNGmSTJ8+XXUtPxHtC8prURfOwJIX1rKPu1u6dKkEBATI5MmTZefOnbJz506ZPHmyBAYGGnXjf1Za1WUysOT3sIh2x/CqVaueeTFVgQIFctQSFcnqKeLs7CxDhw596RK67F6rqbxPnz4NGxubPKeo/v7772FtbY1WrVqZFHfhwoXPvG/nzp1Niv35558jPT09z3n8r1y5ghEjRiA2NtakuNndunUL58+fR2ZmJgoUKIDAwEDVsQBg1qxZWLx4MR4+fIjo6GgMHz4c9vb2ALKmyc7IyFCmLDZXXFwcdu3ahU6dOsHd3d0iMa9fv445c+bg7NmzKFCgAN5//31V7X3//fchIvj2229zbLt27Rpq1aqF8+fPIyMjw6S4jx9vJUqUMKpDM3bsWPz777/43//+Z3KbLS23qdINzK1fsn79ejRr1gzly5dHdHQ0fHx8AGRNfbxp0ybExcVh1apVOabYNbX9cXFxRlN5V6hQQZP6VJZy9+5dLFmyBCdPngSQVUqgXbt2JtdHyX6c/fPPPxg3bhyio6OV2hR79+7Fb7/9huHDh6Nfv34mxX706BGGDh1q9D0xdepUZXrsAwcO4MGDB6qnVza4cuUKjh49itTUVJQrV07VdP9a1gzS0rRp0zBkyBB06dIFycnJ+PHHHzFq1CgMGTIEQNbnxM/Pz+TvH4MWLVqgcuXK+OSTT4zWT5o0CQcPHsRPP/1kUrzMzExMmDABe/fuRWRkJAYPHoxly5Zh0KBBuHfvHho3boyvv/76pa/zIyJmHR9dunTBtGnTNK/rpEXJisTERDx48AB+fn7IzMzEpEmTsGfPHhQrVgzDhg0z6Tdar9dDp9PhaaeoOp1O9TGsJUt9D2vJUJrB4PH3O/txbOp73KxZM7zxxhsYPXp0jm3btm1Do0aNcP/+/Zfy/w7A6zWVN2Vxd3eXW7duiYhIly5dJCUl5QW36MXSava3ixcvGnUdety1a9dMviK8atUqSUtLM7ktz+rRo0dy7tw5pZL6gwcPZNmyZbJ06VJJSEgwOZ6WU6WXLVtWhg8fnuf2kSNHSpkyZSz2epaUV6X6jIwMk6dXLleunHJHb/To0XL37l2z2/e4t99+22icn8H06dOladOmFn+9l4mVlZVyDL8q092KZM0SuWTJEuXx7t27xcvLS/nMmDv9eP78+Y26qRscOXJEvL29Vcd91dnY2Fhk4oTXXV5j8HJbyHybNm2S8uXLy4YNGyQ5OVmSk5Nlw4YNUrFiRdm4caPJ8bZt2yaff/55ntu3bNmi6m6Xs7OzvPfee8qYUq28tsnR2bNnZejQodKmTRvlx27dunVGA0DNkZ6eLhs3bpRvvvlGfv/9d1VdObTi5OSkDD7MPn5FCwcPHpTvvvtOvvvuO6NxWGo0atRIvvvuO7l3756FWpdF69nfLCn7/5elT9QOHz4sBQoUUGatuXz5spQuXVqcnJzE2dlZ3N3dcx3I+yRaTpWe1yBdA7WDdQ1Wr14tw4cPl127domIyObNm6VBgwYSHR0tc+bMURUzOTlZWrZsKfb29uLt7S3Dhw83+m5Qc8L6PGpVODk5KbM7ZWfuGMLz58/nOrX/6dOnc50e+VlYulaOljWDtGqzSO5jHg2zcA0ePNjs5Civz9+JEycsNvmHpdy+fVu2bNmi1MW5deuWTJgwQUaPHq06kenXr1+ui16vl06dOimPLSUhIcGsY0zkxdSRellpVWdNi2PtaRITE3NMqmWqUqVKyc6dO3Os37FjhzKT6MtAp9NJqVKlRKfTScmSJWXKlCmanMO+lsmRpQsEiojExMQoM5NduXJFSpYsKVZWVuLj4yNWVlZSpkwZo8kfzBUUFKS6XlBUVJSUKVNG3n33XdHpdNKmTRvp0qVLrotaWhTD1el0Ym1tLa6urtKzZ89cZ49SG1fL2d82b94so0ePlp49e0rv3r1lypQpqv/vfHx85Ndff1XabckvhejoaHnnnXfk6NGj8tFHH0lISIi0bNlS0tLSJD09XTp06CBRUVEmxdRyqvSSJUsq/dtz88UXX0iJEiVMjiuiTeFaEZEPP/xQihcvLj/99JPMmzdPChcuLA0bNlQG7qqZ3l3rWhUiIgEBATJlypQc66dMmaJqVkuDGjVq5Hr3dNGiRVKzZk2T42lRK0frmkFa1ffJ7btMJOvuuI+Pj3Tq1Mms5KhSpUq5HlMjR45UPW28Fies+/fvF1dXV+W36NChQxIUFCTFihWT4OBgcXBwMJqc4FnpdDoJCwuTWrVqGS06nU4qVaoktWrVUvW9lpKSIu3bt5eAgAClMH3v3r2VY6xGjRqq3gst60hpUdNPy/EwWtVZ0+pYexpLTMhgb29vVGLD4PDhw5pc7EhPT1eV7BvO1+Lj4yUmJkY8PDzE1tZW3n77bVm3bp1Zs6hm91omR5YuECiSddJqOLBatWolUVFRSte1f/75Rxo1aqSqaOTUqVNzXaysrGTIkCHKY1MkJCTIJ598Iu+8844yELNZs2a5LmppUQxXq8rIWs3+duPGDalcubLo9XqxtrYWvV4vFSpUUCaRGDhwoMkxR44cmaPadl6Lqdzd3ZUrW/fu3RMrKyujwaPHjh0zefpqLadK//HHH8Xa2loaN24sU6dOlaVLl8rSpUtl6tSp0qRJE7G1tZXly5ebHFdEm8K1IllJxtatW5XHt27dksqVK0u9evXkwYMHqk4itKxVYRAbGytWVlbSqFEjGTt2rIwdO1YaNWok1tbWEhsbqzquYSa5x505c0ZcXV1NjqdVrRwtawZp1ea2bdvm+bk6duyYeHl5mXVC9euvv4q1tbV06tRJmT21Y8eOYm1trWpmVq1OWKOiouT999+XlJQUmTx5shQqVEjef/99ZXuXLl1U/daNHz9egoKCctQ4MnfmyZiYGClZsqRMmzZNatWqJU2bNpXSpUvLrl27ZPv27RIaGmpUsPRZaXWciWhT0y+vml/ZH6v9rdOqzppWx5qhm1tey86dO81OjqpXry5vvvmmUff5hIQEqVevntSoUcOs2LlRm9A9PmHJgwcP5Pvvv5e6deuKXq+XQoUKPbG7/bN6LZMjSxcIFMnKug0xCxUqlGNGkqNHj0r+/PlNjqvT6aRQoULKFKGGxTCrXGBgoFkzDgUGBsrt27dVPz8vWhTD1aoyslazv7Vu3VqaNWsmycnJ8uDBA4mJiZFOnTqJSNbdJE9PT1V3H06cOCGrV68WnU4nCxYskJUrV+a6mMrNzU25o5WWliZWVlZGV7lOnDihamYyAy2m4Ny9e7e0bt1aAgIClJmXAgICpHXr1rJnzx7VcbUoXGuI+/isdCkpKRIRESF16tSR8+fPm/UjZ+laFdnt27dP2rVrJ+XKlZNy5cpJu3btZN++fWbFdHFxyfN7Qk2RXa1q5RhoUTNIqzYfPnxY5s+fn+f2o0ePqprWPLs1a9ZIZGSkODo6iqenp9SuXfuJ9cGeRKsT1uwXfdLS0kSv1xv9PsfFxam+KHrgwAEpXry4DBgwQBkLam5y5O/vL1u2bBGR/68Ll71e3po1a1TdEdfys6FVTT8DS4+H0arOmlbH2tMuiJpz59rgzJkzUrp0abG1tZXg4GAJDg4WW1tbKVWqVK4XsMylNjl6UtfxCxcuyLBhw8Tf39/c5r2eyZGlCwSKZA0ON0wpGRISIps2bTLavmfPHvHw8DA5bo8ePSQsLCxHX1VL1UXRiqWL4YpoVxn58dpRj08/OWbMGFV9x11cXIx+fFJTU8XGxkbpErFo0SLV3b5EREaNGmXRwfd169aVrl27ytWrV2X06NFStGhRo66VvXv3lurVq1vs9V5mWhWuLVGiRK6V0+/cuSMRERHyxhtvvNTTm1pao0aNpGXLlkbjrh49eiQtWrSQ+vXrmxxPq1o52duW3f79+2Xv3r1mJUxat/lVodUJq5OTk1Hcxwt+Xrp0yaxuQ3fu3JFOnTpJ2bJl5ejRo2JjY2PWb7OdnZ1RsXpHR0ejopZqkxgtjzOtavoZWHo8jFZ11rQ61lxcXGTixImybdu2XJd58+ZZ5HfDMCW9oUfSxo0bVXdTM1xEy2spWbKkZkVgLdG17rVMjixdIFAkq9tJoUKFZOvWrcogx99//12uXbsmW7ZskTJlyhjdXjXFihUrxN/f32i2KEvcus+tP7qlWLoYrsjzq4xsKV5eXkb/R/fu3RO9Xq8M1Dx37pzqO5WPe/DggZw9e9ask7QDBw6Ip6en6PV68fLykmPHjkl4eLj4+vqKn5+fODg45Fq3yRRXrlyRGTNmyCeffJJjILMlWOJ9ENGucO0HH3yQZ/falJQUCQ8Pt9iP3JYtW2Tu3LmyevVqTWc4NMfx48fF09NTgoOD5d1335V3331XgoODxcvLK9f+70+jVa2cixcvSoUKFcTKykrq168vycnJEhUVpXTxKVKkiOrvHq3a/Lh///1X5s6dK8OGDZN58+ZJUlKSRWLOmzdPhgwZonyvxcXFqRpfq9UJa8mSJY16FKxZs8ZoUp99+/aputDxuB9++EF8fHxEr9eb9dvs5+dndMe+bdu2Rr97x44dU3UHX8vjTKuafgaWHg+jVZ01rY61WrVqycSJE/Pc/jIWirazs5POnTvLqFGjcl169Oih6piw9EXhvLyWyZGlCwQafPHFF+Lo6CgODg45BvA2a9ZM7ty5ozr21atXpU6dOlK/fn35+++/zU6ODLdhixUrJhMmTFDVl/txWhbDFXl+lZFFLHOS3bx5c2nRooWkpqZKWlqa9O3b16hP9759+8TX19fkuLGxsUqXsfv378t7770nVlZWytimHj16qG53amqqHDp0SDlW79+/L998841Mnz79iTPDPYvff/9dGWBtbW0tYWFh4ubmJq6urqoGLmv9PmhRuDYxMfGJM2KmpKSo6pbUoEED5WT3n3/+kfDwcNHpdMq4kpIlS6qavEOLgdaPu3btmgwZMkTeeustadGihYwePVo50TZVRkaGfPbZZ9KoUSP5/PPPJTMzU3744Qfx9/cXT09PeffddyU1NdXkuC1atJCaNWvK6tWrpVWrVlK1alWpVauWXL16Va5fvy7R0dGqx2hq1ebmzZvLTz/9JCJZJ9T58+cXLy8vCQ8PFx8fH/H19TVr9qzDhw+Ll5eXFC1aVKytrZUr5EOHDpWOHTuaHE+rE9ZRo0Yps7Pl5tNPP5W3337b5Li5uXLliqxcuVLV/5dB/fr1Zfbs2Xluj42NlcjISJPjanWciYh07do1z4tFV69elaJFi5r1PWHp8TDp6ekyaNAg8fPzE09PT2nXrp0yRlwk646wmkK+Wh1rc+fOfeLY8oSEBLO6yN66dUsmTpwozZo1kypVqkiVKlWkWbNmMmnSJNWTPlWoUEFmzpyZ5/Y///zzpe4l8VomRwaXLl2StWvXyrJly1TPHva4f//9V3788UeZMGGCfP755xIbG2ux2JmZmfL5558rA/rNTY5+//13+eijjyR//vxiY2MjTZo0kdWrV+dZg+VpHu+e9qTlZaLVSfa5c+ckODhYrK2txcbGRtzc3Iy6W8bGxsrgwYNNjhsUFKSM9fj4448lMDBQVqxYISdOnJCVK1dK8eLFVU32oLVKlSopsyEZuhvcuXNHmjRp8sQv0by8iPfh/v37L2VdsOx3VXv16iWhoaHK2KYrV65IhQoVpGfPnibH1WKg9avIy8tL/vzzTxERSUpKEp1OZ9TNJy4uTnx8fF5Q63Ln7u4uJ06cEJGs5Lldu3bKXZi0tDTp2rWr1KtXT3X8unXrKp+v7N2Hdu/eLYULFzY5nlYnrE9z9+5dsy6CJSUl5ZrM//PPP6pmlfvnn3+eeBFw3bp1RpO6PIvDhw+r/l1/FlrU9MvueY+H0Yq5x5oWDhw4IO7u7lKwYEHp3LmzDBo0SAYNGiSdO3eWQoUKiYeHh6oyLB9++KF89NFHeW4/e/as1KpVy4yWZ8leNmfTpk0WK5vzWidHr6pDhw7JV199ZdYMbdlPptLS0mTZsmUSHR0tVlZW4ufnJ59++ulL+6Vj6dooWp5k3717V3777TdZvXq10Q+9Oezs7JSBtcWLF1f6kRts377drOmVH1e7dm2LFNpzdnaWs2fPikjWXUbDHZT4+HhVJ1PP+314HhISElRNuZ3981yiRIkc09v+/vvvqiZu0Wqg9fLly59L1whLyZcvn5JsZmRkiLW1tdHsdGfOnJF8+fJZ5LUePHhgkRMoBwcH5fNWoECBHBNfnDp1StWMgAYuLi5K/OzJ0cWLFy3WXfhVUL9+faOZLA1mzZolDRo0eAEtyin7IPagoCBNJmHSmiXHw+TGUl2yXzXh4eHSvXv3XN/LzMxM6d69u1SpUuUFtCx3z6tszmuXHB0/flx69eolYWFh4uvrK76+vhIWFia9evWy6AQHd+/elRMnTsjhw4eNlpdFXuN3Ll26JCNHjpTChQtb7JZnWlqanD592iJ93EUsXxvlVTvJLly4sDKbUcGCBXNc1fnrr79UFebMq4aElZWVfP3116prShj4+Pgo3XhCQkKUWPHx8araq9X78Ditx0llZ870pobuD97e3jm67qk9YdVqoLVOpxMXFxfp1q2b2TPePU6LWjlVqlSRYcOGiYjI/PnzlUKqBmPGjJEKFSqobvPGjRulQYMG4ubmpnTFdnNzkwYNGuSY3OdZhYeHK9PRlytXLsdYz40bN6rq1mvg5eWlJFzZk6ONGzdaZAyPJV2/fl0WLVoka9euVe6eGaSmpppVAyz7DGXZnThxQtUkTAbnzp2ThQsXyoQJE2TSpEmyfPly1cevh4eH8jmzdH28JzGnHqOWtOyS/SSXL19WXT9Si6LkIlnjuQx3mHPzshV11qpszuNeq+Ro3bp1YmtrK1WqVJGRI0fKzJkzZebMmTJy5EiJjIwUOzu7J94afhY3b96Ut956y2K1Z+Li4oym/v3uu+8kMjJSChUqJFWrVn1i/9YnedrkBpmZmaqmyJw4caIyAPHRo0cyYMAAZbyRtbW1dOnSxezB4ZaujaLlSbYWX2iffvqpREREyL///iuDBw+Wxo0bK2OE7t69K61atVLVXSa3WhK51ZpQq2nTpsrJ2oABA6Ro0aIybtw4KV++vNStW9fkeFq9D9lZepzU4xdLHl+WLVumOtl46623pHnz5uLu7m50t0cka3ybmm5fWg201ul0MmbMGClXrpxS8fzLL780+4q2VrVyNmzYIPb29mJrayv29vayfft2KV68uFSuXFmqVKkiVlZWsmzZMlVtXrBggVhbW0ubNm0kNjZW1q1bJ+vWrZPY2Fhp27at2NjYyHfffWdy3DVr1oiHh4fExsZKbGysBAYGyjfffCO7d++W+fPni7+/v1ndTrt27SrNmjWTtLQ0cXZ2lvPnz8ulS5ekXLlyT+xOkxtDHa5nWUx14MABcXNzExcXF3FwcJCiRYsaXTwwd9yco6OjHDlyJMf6I0eOqJpAIjU1Vd555x2j71xDV3pnZ2f5+uuvTY7ZrVs3sbOzk8DAQNHr9RIQECBBQUG5LmpoUY/xWai90/6iuqarvfilVVFykaxyLgsXLsxz+8KFC1X17MjN+fPnZePGjaom2zHQqmzO416r5Khs2bJPLA41cuRIKVOmjFmv0a5dO6lataocPHhQnJycZOPGjcqUzWvWrDE5XtmyZZUrh/PmzRMHBwf58MMPZdasWdK3b19xdnbOMUj6WWhV3yj77fvJkyeLu7u7zJ8/X44fPy6LFy8Wb2/vJ8668iwsXRtFq5Nsrb7QHj58KE2aNBF3d3d58803lavjxYoVEycnJwkICFA1c1b9+vWlYcOGOZJmS00bf+7cOeXuaWpqqvTo0UPKlCkjb7/9tqpue1q9D9lZepzUkxJQc+pVGGZ6MyyPn6gPHDhQVfFlrQZaZ784c+jQIenVq5e4ubmJnZ2dtGzZUtWFGRHtauWIZNXQWL58uXInLSEhQYYPHy4DBgxQLq6oUaxYsSee8M6YMUN1cc7ly5dLoUKFchxz9vb20rdvX7P65yclJUlUVJS4ubmJlZWV+Pv7i42NjdSoUcPkgf3ZZ7EaPHiwuLi4SJUqVZQ7tBEREeLi4qJqjGZUVJR06dJFMjIyJCUlRXr16iWenp7Kb4i5yVGtWrUkJiYmx/revXtLtWrVTI7XvXt3qVq1qhw9elTOnDkj77zzjgwaNEju3r0r3377rTg6OsqSJUtMjrt+/XqZPn266HQ6GTt2rHz11Ve5LmpoXY8xL2qTDa16i+TV+8KwfPnll6raq1VRchGRr7/+Wuzs7OTDDz+UVatWyb59+2Tfvn2yatUq+fDDD8XBwSHXbqNP06tXL+U86t69e9KiRQuj37jatWurmqRMq7I5j3utkiN7e/snzrh18uRJs28f+vr6Kplsvnz5lJOzVatWSdWqVU2O5+DgoJw4litXTvmAGCxZskTVDD5ayX7SU65cuRx3RxYvXiylSpUy6zUsXRtFq5NsLb/QRLJ+7Hr37i3169eXevXqSefOnWXu3LlmzZT0v//9T/z9/Y3uPJiTHE2dOlXu378vIlldNi3ZR9xAi/fBwNLjpDw9PeXbb7+Vixcv5rqsXbtWkxl8UlNTlf8HU2g10Dq3O9f379+X7777TmrVqiV6vV5VzRWtauVoyc7OTtPfpUePHsmBAwdk6dKl8v3338vWrVstOqnIzp07ZcaMGTJx4kTVXQCz69q1q9KFMbsRI0ao6pLk7u6e4/t7/Pjx4u7uLgcOHDA7Odq1a5fY29tL9erVlQSvevXqYm9vr6pcRv78+eXQoUPK48TERLG3t1fG6H399dcSFhamur3vvvuuxSeV0aoeo1Z32rXqLaJV7wutipIbLF26VMLDw5UZnHU6nVhbW0t4eLjqO+LZL5QPGTJEChUqJFu2bJG7d+/Krl27JDg4WNXFDi3L5mT3WiVHJUuWlC+++CLP7V988YVZRTlFshIiw49zQECA0p1KbY0GT09P5YvS29vbaBCwSNaMH2riGlh6pp3s/Zk9PT1z3D49f/68WR9iEcvXRjGw9Em21l9oWvnzzz8lNDRUunfvLnfv3jXrB87Kykr5gnxSZeuXlaXHSdWrV0/Gjh2b5/aXsV6FFp52LJw5c0Y+/fRTk+NqVSsn+8mqpZUvX/6JXXgGDRok5cuX1+z1XzYuLi55Trjj4uJicjx3d/dcx/tOnjxZ3NzcZMWKFWZfkPjzzz+lXbt2EhoaKhUqVJAuXbqoHmvj5uZm9Ny0tDSxtrZWfldPnz5tkTEglpr4w0CLeoxa3WnXqreIn5+frFy5Ms/taqev1qoo+ePS0tLk+vXrcv36dbOHP2S/AFa6dGn5/vvvjbavWrVKihcvriq2lmVzDKzxGhkzZgzatWuHbdu2ISoqCj4+PgCAGzduYPPmzdiwYQO+//57s16jRIkSOHXqFAIDA/HGG29gzpw5CAwMxOzZs1GgQAGT4zVo0ACzZs3CN998g5o1a2L58uV44403lO0//vgjihYtqrq9bdq0QePGjdG7d2+j9T/++CN+/fVXrFu3zuSY8+bNg7OzM2xtbZGYmGi07c6dO7Czs1PdXgAIDQ3FkSNH8PXXX+Pw4cNwcHBAp06dEBMTAw8PD9Vx69evj/r165vVtuw8PT1x6dIlBAQE4Pr163j06BEuX76M0qVLAwAuXbpkVnsB4PLly/j777+h1+tRpEgReHp6mt3usLAwHDp0CP369UNYWBhERHUsPz8//Pzzz3jrrbcgIrh69SoePHiQ674BAQEmxb59+zby58+vum3PokqVKti1axdCQkLw1ltvYcCAATh69ChWrFiBKlWqmByvZ8+euHv3bp7bAwICEBsba1LM/v37P/O+//vf/0yKnd2iRYswe/ZsXLhwAXv37kXhwoXx1VdfISgoCE2bNjUp1tOOqaJFi+Kzzz4zuY2dOnXC+++/j6FDh8LOzg7/+9//0KRJE9ja2gIA4uPjERQUZHLcSpUqoUiRInjvvffw7rvvws/Pz+QYefniiy/QqFEjbNiwIdffpfPnz2Pt2rUWe70iRYrgt99+Q7FixVQ9/+rVq7C3t1c+ezt37sTs2bNx+fJlFC5cGH369EFERITq9jk4OGD37t052rd7927Y29ubHK906dLYs2cPypYta7T+448/RmZmJtq2bau6rQZhYWFYsmSJ2XGArGNt6tSp+PrrrwEAU6dOhZeXF7y8vAAAqampcHZ2VhV706ZN+PLLL7F3716kpKQAAFxcXBAREYH+/fsjKipKdbubN2+OypUro1OnTli7dq3J32O58fDwwKRJk1C3bt1ctx8/fhyNGzc2Oe7IkSNx7NgxFClSBBUrVsTOnTvh4+ODggUL4vr16/D09MSmTZtMjluhQgXExcXl+X2o0+lU/Z42bdoUXbt2RefOnfHrr7+iU6dOGDBgAPR6PXQ6HQYOHIh69eqZHPdxNjY2qs5T86LT6QAACQkJOT5/b7zxBq5cuaIqbv/+/fHee+9h06ZNOH/+PDIzM1GgQAFUrVpV9fdaDmanV6+Y3bt3S+vWrSUgIEApThoQECCtW7dWZi8xx6JFiyQ2NlZEsq425s+fX/R6vdjb2yv9JE1x7do1CQwMlBo1akj//v3FwcFBqlWrJt26dZMaNWqIra2t0RUEU1l6pp3ChQsb9Tn+8ssvjbZ/9dVXL9W0kFrq06ePFCtWTMaNGyeVK1eWzp07S8mSJWX9+vWyYcMGKVOmTJ7jOZ5mxowZEhAQkGPCj6pVq1r0KveqVaukb9++qu/4zJkzJ8eVnccXtVf/9Hq91KlTR5YsWaLZ9KuWHielhVq1ahktLi4u4ujoqAxgd3JyEhcXF1UTSBjMnDlT8ufPL+PGjRMHBwdlZrLY2FhVtSouXryoSRdLrWrl6HQ66datm3h7e4u1tbU0bNhQfvnlF4vV1Lhw4YIMGjRIatSoIcWLF5fixYtLjRo15JNPPlFVnkBEu0HylStXVrrdrly5UvR6vTRp0kQ++eQTad68udjY2OSYEMQU48ePF3t7e/nggw9k0aJFsmjRIomJiRFHR0cZP368yfHmzZsnHTp0yHP7hAkTVHXhfJr09HSjngPPKi4uTjw8PMTX11c5T8k+8dLXX38tnTp1MjmuVhN/PM6S9Ri1vtNu6d4iO3bsyDF+KbvU1FRVRb61KkpuEB8fL2PHjpUZM2bkKDmSnJysqjurTqeTHj16SL9+/cTb2zvHONK4uDiTJ05o3ry50qNp4cKFmk67/tolR8/b3bt3JS4uzqwaN//++6988sknEhoaqsyYVLhwYWnXrp2q4lzZWXqmnafZu3dvrpMpmGL//v3y1VdfyeDBg2Xw4MHy1VdfyYEDByzUwpzUDvrU6gtt8uTJ4ufnJ9OnT5d58+ZJSEiIjBkzRtavXy8dO3YUR0dHs48Lg/v375t9EpuSkiJHjx4VnU4nmzdvlvj4+FwXU+l0Oqlfv77Y2tqKu7u7xMTEKIU6X1dffPGFNG7c2KgGWmJiojRt2lSmTJmiOm5ISIgyFXT2aZuPHj0qnp6eZrU5Oy0SJkswdBFJT0+X5cuXy1tvvaXU1hg0aJDZE39oQatB8k5OTspsUeHh4TJhwgSj7dOnT1c1q1x2y5Ytk8jISHF3dxd3d3eJjIxUPfbhRVH7uyGSNfX43LlzZfr06RYrMaLlxB+5sUQ9xhUrVsiiRYvy3J6YmPjSFZV/nixRlPy3335TCuoGBASIp6en0QQzasfk1axZ0+ii3bx584y2jx071uTSK9nHj2rdTZ/J0WvO0jPtaOnGjRtSrVo10el0UrhwYalcubJUrlxZChcuLDqdTqpVq6bJh8XS40DM/UILDAyUdevWKY9PnTolnp6ekp6eLiJZlanffPNN1fEzMjJkzJgx4ufnJ1ZWVsqJ8LBhw+Sbb75RHXfBggUWvdJjOGG9deuWTJkyRUJDQ0Wv10v58uVl5syZqmuCZHfgwIFc6/Ds27fPYglodubUwRDJ6vP+eI0jkawkpkCBAqrj2tvbK3fKsidHlhr/YGBjY5PrnWxzZWZmmnWXJ7cJJK5evSpjxoyRIkWKiF6vl+rVq5vbTCOjRo0y66KaVoPkXV1dlbup3t7eOcbznD171uSxlM9j4pbnzZzkSAtaT/zxKkpPT5f4+HjZsGGDbNiwQeLj480ea/OqiYiIUMZ3ZmZmysSJE8XZ2Vm5A2buhCV5OXfunFy5csWk55QpU0Y6d+4sCxYsEJ1OJ9OnT5eFCxfmupjrtU2OfvrpJ2nZsqWEh4ebXUfBYOPGjTJixAjZvHmziGRNB1m/fn2pXbu2zJ8/31JNtyhLz7TzNImJiaoP3BYtWkhERESuX/AnT56UyMhIVcW/mjdv/sSlTp06L9WPnKOjY44ZuaytrZUrKvHx8aqmNDcYPXq0FClSRBYvXmzUhWrp0qUvVZfI3E5Y9+zZI++9957ky5dPHB0dpWPHjma9RqVKleSnn37Ksf7nn3+WypUrmxU7N+aeUDk7O8vWrVtzrN+yZYtZx0RISIgy0Dh7cjRt2jRV35mPF9M1LHq9Xjp16qS6yG56eroMHTpUatSooUzBPmnSJHF0dBRbW1vp1KlTjiKgz+JpVyl///13adeunclxRbK6rTy+JCUliY2Njezfv19Zp4YWg+SbNGmizDIVHR2do2vevHnzpFixYibF1HrilrVr10rXrl1l4MCBOQpeJiYmqupy+rR6TCVLlrTI78bx48dl/fr1OaaFNpWWE39oVY9RKxkZGTJ06FBxc3PLMdGDm5ubDBs2TDIyMkyO+/hx++eff0qnTp0kMjJSWrRoket3syWYc1HNxcVFmZHVYMmSJeLk5CSrV6/WLDlSY/fu3RIeHq4MV3F1dRU3N7cci7u7u9mv9VomR1OnThVnZ2eJiYkRW1tb6dGjh0RFRYmrq6uqGZJEssYaWVtbS/ny5cXZ2VliY2PFzc1N3n//fXnvvffE1tY215OsZ6FFf9DsLDnTztOYc/Ln7Oz8xC55auscWVtbS4MGDXLUijEsTZo0Ud1mLRLmsLAwoyndN2/eLI6OjsrV1pMnT0q+fPlUxRbJmvHLUPgz+4nwiRMnxM3NTXXcR48eyeTJk6VSpUri4+OjdJkxLKZ60klUamqqfPPNNxIZGam6vSJZXYgMf39258+fV3WsaVUHw6Bjx44SGBgoP//8s1y5ckWuXLkiy5cvl6CgIFXjFAzmzZsnBQsWlKVLl4qTk5P88MMPMm7cOOXfptLpdBIWFpZjvJROp5NKlSpJrVq1VJ2wDhs2THx8fKR///4SGhoqPXv2FH9/f1m8eLEsXLhQChYsqKrO2tOKZpvjaWPxzC2+fPXqValTp47Ur19f/v77b7OTo7/++ks8PT2lU6dOMnbsWHF2dpYOHTrIZ599Jp06dRI7Oztl3O2z8vf3l5kzZ8rFixdFp9NJXFycXLp0KdfFVEuWLBErKytp2LChVKtWTezt7WXx4sXKdrUnf3Z2dtK5c2ejGk3Zlx49epj1/3bu3DkpW7Zsjhnb1BaU37p1qzg5OUmZMmWkX79+MmHCBJkwYYL069dPypYtK87OzqrG44loV49RJKuL4aJFi2Tt2rU5LmykpqaqKgI7cOBA8fLyktmzZ8uFCxfk3r17cu/ePblw4YLMmTNHvL29ZdCgQSbHzf6btHv3brGxsZGaNWvKwIED5c033xRra2vV7/GTmHNe5eXlles45R9++EEcHR1l1qxZqmLHxMRocoHdQMvvZJH/Y++946K43u/xuwXYXZbm0nsvCgYrCAqoKCj23sAgqKioUSMaCwY7oknsGhWiYjfEEpXYFSuWQCyg2CvG2EVFyvn9wXfnswsLce/MGP35Pq/XvOLuxGevuzN37vPc85zzhSZHHh4ejKyg6uJv8uTJGDZsGFVMX19fpoK2f/9+SKVS/PDDD8z5uXPnUvkc8cUH5QuaqqCqR1ZWFvV4FQpFjc2Mhw4doup/8PHxqZEuRiu/yVfCvGnTJujo6KBHjx6IioqCXC5X8wtYtmwZmjRponVcJaqjUF26dIlKvlqJyZMnw8rKCnPnzoVEIsG0adMQExMDhUJB1RjO9+QIALVq1dIo1HL8+HGqRJEvHwwlioqKMGTIEOjp6TGLKF1dXQwZMoS171N6ejpcXV2ZcdrY2FDTLGfNmgUnJyemaKAE24W7s7MzIwZQUFAAoVCoJoSzadMmeHt7ax338OHDDG2Va9jY2CAiIgIHDx7E4cOHcfjwYRw6dAgikQhpaWnMe2zAZZM8UEGd69WrFwwMDJjrQUdHBwEBAUxvmjbgU7hF9dkMVFwD+vr6zLVL+wxt0KBBjUbQtM8NJdq1a4eOHTvi8ePHkMvluHz5MrKystC4cWPqRScfwh8Af36M2dnZMDY2hqGhIaRSKVxdXdVow7S/nYWFRY3+bZmZmTA3N9c6ruozqVWrVlUEl0aOHIkWLVpoHZfPolqrVq2QkpKi8dz69euho6NDbfQtFArh5uaG2bNn4+HDh1Tjqw58ifoo8UUmR6o3spmZGdMMfvXqVWpnXdUmVaCCO6/Kxc7Ly6NauPPFB+XLt0O1ssXlAw6o6INycHBARkaGGs3kxYsXyMjIgKOjo8b+qX/D119/jaFDh1Z7/vLly1RqRnwlzACwe/du9OnTB127dq3yIPrnn3/wzz//UMUFKugXyiZY1eQoKSmJVR+as7Mzfv/9dyaucit//vz56N27t9bxuO5h0oRevXohODgYz58/Z9579uwZgoOD0b17d63j8eWDURmvX79mjBK5MMNVRVFRESdJaXZ2Ntzd3TFmzBiG5882OZJIJLhz547aa1Ua1Y0bN7TeVa1Xrx7TVJ6UlMSYcXKFJ0+eoFOnTmjevDnu3bvHvM/2u9AELprkVVFeXo7CwkJOfFH4Em6p/GwG/o9munTpUupn6IgRIzBy5Mhqz1+7do1KyVEJhULBrCEMDQ0ZOvmBAwdYmcDyAb78GENDQxEdHY2ysjK8fPkSQ4YMgUKhYBgktL9ddUJUSuTm5lKbwCrnRisrK5w8eVLt/MWLF7VWaFPG5auolpGRgW+++aba8+vWraO6jgUCAfbv34+RI0fC1NQUOjo66NChA3bu3ElFWdSEo0ePom/fvvD392fmzjVr1iArK4t17C8yOXJycmJurgYNGmDZsmUAKnZpaLmKxsbGar0wqotKgN78lC8+qEAggIuLC2bMmIH79+9r/ferg6GhIZKTk5lqZ+VjxYoV1Dfxu3fvEBcXx1QYJRIJJBKJWnWcZrH87t07zhc8AH8JM9/Ytm0bjIyMMHv2bMhkMqSkpCA2Nha6urpV5Di1gUwmY2gxlpaWOHfuHIAK+oi25o4fS9Lz3r17cHZ2hpGREUP9MjY2hoeHh9oi/EPRvn17TJ48udrzXIp/3Llzh2qMH4K0tDS1hJEWr169QlRUFOrWrYsLFy5AR0eHVUJgYWGhtugJCAhQSzjy8vK0vtYkEgnTOMynQtKSJUtgbW3NsBr4SI4+B3Bd9NC0SAUqdgPlcjkmTpz4SbEvlDA2NmaeH87OzgxjhK3xe2UUFhZS0RVV0a9fP8TExAAAunfvjkmTJqmdnzlzJnx8fLSOa2JiUkUJctasWTAxMUF2djb1+qdt27Zo3bq1RsGTx48fIzw8HBEREVrHFQgEuHbtGl68eKG2zlSCRqwE+HhFNS6hmii+f/8emzZtQlhYGEQiEaytrTFhwgQUFBRQx9+6dSukUiliY2Ohp6fHrLcXLlyINm3asB7/F5kcxcTE4PvvvwdQ4RkglUoRGhoKY2Njat+Zhg0bql28L168UNvy27dvH5UbMF98UL58O0JCQmrk9HOx+Hvx4gUOHjyI9evXY/369Th48CAnymRcg4+E+d9oi6oHGxw9ehShoaEwMzODVCpFYGAg/vjjD1Yx3d3dGeW3wMBAxrNk48aNMDMz0yrWx5T0fP36NZYvX46hQ4dizJgxWL16NXWVnC8fDCVKSkowadIkGBoaMru1hoaGmDhxIqcqTFyrym3YsAEWFhYQCoWsEoLmzZvXKO27efNmNGjQQKuY/v7+CA0Nxffffw+BQICxY8ciKSlJ48EWly5dwldffYXevXtzmhzdvXsXixcvxrhx46qIYNCOc8iQIfD19YWlpSUsLS3h6+uLIUOGsB4z1wpiHTt2ZMQ5KkPZh/OpLSwBoGnTpgxFsXfv3ggPD8exY8cQFRWFOnXqaB3v5cuX6Nu3L+zt7RlhkqFDhzK7DkFBQdTPDb78GE1MTKqoIQIVdhbGxsbIyMig+u3u3LkDb29viMVi1KtXD+Hh4QgPD0e9evUgFotRt25dqsKSKnNGIBBUYXVs376dSi79YxXVnj17hhUrVmD8+PF48uQJgAqxDdUC04eiOtr77du3MWXKFDg4OLC673x9fRlxL9W11fnz52FhYUEdV4kvMjkqKytT449v2LABw4cPx4IFC6iUjICKrcmaGu1mzZpVpZryIeCTD8qHb8fPP/9cY/9IYWEhk5h+aqicGJ46dQpHjhyhfjDzkTD/G21R9fjUMG7cOMyYMQNARUIkFovh6uoKXV1djBs3TqtYH1PS83NCXFwczM3NsWzZMoZWt2zZMlhaWiIuLk7reJVFM5SHQCCAkZERtZiGJty9exfbtm1jRQO8cuVKFQqVKtatW6e1X05+fj569uyJhg0bQigUwtvbG76+vlUOtt4+ShQXF2PUqFHw9fWt8d/yodi/fz9kMhmzGPT19YWxsTGMjIyoRC92794NXV1d+Pv7Y8qUKViyZAmWLFmCKVOmICAgAHp6ejX2c1QHvhTEDh8+jJkzZ1Z7/uDBg/j666+1jlsZRUVFyMvLY+475UGLzMxM/PrrrwAq+uc8PDwgEAhgampapVfvQxAfHw9PT08sWLAAISEh6NixI7y9vXHs2DEcOXIEtWvXphakAvjxY2zWrBmWLl2q8VxycjLTW0mDsrIy7N69G4mJiRg0aBAGDRqExMRE7Nmzh5r2VZkpU3kd9dNPP2HOnDlax+W7qAZUUAnNzMzg6uoKsVjMJBsTJ06kUn39t57g8vJyVkwUqVTK9MmpJkfXr1+Hnp4edVwlvsjk6HMCn3zQj+3bwTcKCwupqrcPHjxAYGAgRCIRgoKC8PTpU0RERDAPZnd3d2aXQhvwkTCrTry//PILLC0tMX78eKYxc/z48bCysuLUGI8L2oUmnDx5EvPmzcOOHTu0/rsfQ9KzuLgYmzZtwjfffINevXqhV69e+Oabb7B582bqIgrfMDQ0VPPAUmLXrl1a08mAiodOREQEfvnlF+ZIS0uDSCTCjBkzmPe4wqFDh/DmzRvO4nGNjyECwjUaNWrE7JwoFxGvXr1Chw4dahQUqA5169atsYo9ZcoUKgoVXwpifOPvv/9GRETERylSPXnyhLoJ3c7OjqHm3b9/HwKBgBEvAYDff/8dHh4enIyTK6xYsQL9+vWr9vzs2bOp+oH5wOfu1dWyZUtG6l012Th+/DgcHBy0jufo6Miq9/nf4OTkxCgkqo539erV8PLyYh3/i0+Obty4gb179+LChQucx3737h2uXbvGe9M4Dfj07fivQCtnGRkZiYCAAOzYsQM9e/ZEQEAAmjVrhnv37uH27dsIDAykVjHkEy1atGD6E1Sxbt06rZ2nAX5pF3yCjwVrQUEBnJ2dIZFIEBwcjB49eqBHjx4IDg6GRCKBq6ur1nzpf/PTUj1oYWZmppHudvnyZapG4IKCAjRq1AhRUVF49eoV8z5f/TBc0vWuXbuGiRMnolevXsz1sXv3bo0muZ8yXr9+zUr+V1X8xNjYmPn35+TkUC16JBIJL2aifCmIKcE1M0CJPn36IDAwEGfOnIG+vj727t2LtWvXwsPDgxGg+RSgp6enRhWTyWRqOxu3bt2i6of53FB5Z6+mQxvw7dXFN1T721WTjVu3bnGyE8M1Zs6cidq1a+PUqVMwMDBAVlYW0tPTYWZmhgULFrCO/0UlR0OGDGEe8G/evEHXrl3VFNSaN2+utgDQBmlpaYzk79u3bzFgwACIRCIIhUKIxWIMHjz4k0qS+KyA7ty5E5MnT8axY8cAVKjrtGnTBmFhYVi+fDl13H+byDZt2kSVHKk27D558oRRWVHiwIEDcHZ2ph53ZXDVzC6VSjX6UV25coWqYZdv2kV1YGMMDPAj6RkaGoqOHTtqTAZfvHiBjh07onXr1lrFVPXO6t+/PwwNDWFnZ8ckRPb29jA0NGRF8UlKSkLv3r3V5pp3796hb9++1HTWkpISJCQkwMXFhbmn2SZH1RlnCgQCeHl5sTbkPnz4MNNLqquryzzoZ82aha5du1LFzMnJwapVq5hYFy9exJAhQzB48GAqGpk2n8tmB8LCwoJJOL28vBgD0ZycHCpFLk9PT8ybN6/a8/PmzaPageBLQezBgwcICAjgnBmghKWlJU6fPg0AMDAwYBKO7du3U6mR8mUkam1tzYjgABV9TKqfdfHiRVY77Tk5OYiMjISTkxMkEglD5Zw0aRInRbXS0lIUFhbi77//ZhWnsn+Yao9Q5fe0AZ9eXao4c+YMxo4di549e3JWVAMqCmtKAQnV5Gjv3r2wtbVlFRuoKLRlZmYyzAC2z+zy8nLGa0/520kkEqr2FU34opIj1Wz+u+++g62tLQ4ePIiioiIcO3YMLi4uan4x2sDJyYlpNv/222/h6OiIjIwM5OXlYdu2bXB3d6/RnVoTlLSgDzm0BV++HcuWLYNYLEaDBg1gaGiItWvXwsDAALGxsRg8eDCkUil++uknqtg1yVmykQmvLP+rr6+vtitw+/ZtTtWBuKqOV3dNjR07lkr847+iXbBd/KWmpmLz5s1V3t+8eTM15Usqlda4m/zXX3+xuiYSEhIQGxurVs0uLS3FoEGD8O2332oVq/ID0sDAAKampmjZsiVatmwJU1NTGBoasn54HjhwAPb29vjuu+9Yq8qJxWKEh4ermWZOmTIFQqEQQ4cOZd6jhb+/P7OAV33Qnz59GjY2NlrH+/XXXyESiaBQKCCXy7Fv3z4YGxsjNDSUUWBat24d9XhrAtv7o2PHjkxj+JgxY+Dq6orp06ejfv36aNmypdbxNm/eDLFYjPbt22P+/PnYuHEjNm7ciPnz56NDhw7Q1dXF1q1btY7Ll4IY38wAAwMDpvfB3t6eKSDcuHGDao7gy0g0PDycUebVhLS0NGrT7MzMTEilUnTt2hX9+vWDTCZDfHw8xo0bB1dXV7i4uFD73Pz+++9o1qyZmnebkZER+vXrR5Vo3Lp1izl+++03uLi4VOnRdHNz09qvi0+vLiU2bNgAHR0dtGvXDrq6umjXrh3c3d1hZGTEum8uJiYGnTp1wvv37yGXy3Hjxg3cvn0b9erVq1Gy/t/wzz//oGXLlsy/XTkXR0dHY/To0VrFys3NrdIPVlxcjEuXLuH06dPUmxua8EUlR6q7Jd7e3lUoSdu3b6daVAIVW9bKG9Xd3b1K89yRI0dgb2+vVUxVnv+8efNgYmKCXr16Yf78+Zg/fz569eoFExMTNe+c/xq1a9dmHsQHDx6ERCLB4sWLmfNpaWnUfFCFQoFVq1apTW6qx65du6gmHnt7e6byB1QIByiVWoCKxQkNJYnvZvZdu3ZBIpHA29sbMTExiImJgY+PDyQSCZUyEF+0Cz6NgQHAzc1NzRhZicOHD1Pfz1ZWVmqJYWXs2LEDVlZWVLEBwNTUVCM1KT8/X2uvNdUdqX872OKff/5B586dqygxagtlMSoxMVHtYccVXU9VRl81Obp58yYVRaR+/fqYPn06gIoFirGxMaZOncqcnzt3LrX3zL8VvpTKg7S4fv06QxF6/fo1Bg8eDB8fH3Tp0oXx+9MWx48fR8+ePWFvbw9dXV3o6urC3t4ePXv21Gia/CHgS0GMb2ZAw4YNmZ3D9u3bIzIyEvfu3UNCQgJVXL6MRJ88eYJnz55Ve3737t3Uu1K+vr5qwgl79+6Fp6cngAoZ55YtW1LNP2vWrIGBgQHGjBmDiRMnMj22S5cuRXBwMExNTTWyJz4UjRo10vis3LVrF+rXr691PL68upTw8fHBokWLAPzfvFZeXo6BAwdWq8j4oXj+/Dmj2iwSiWBnZwcdHR0EBQWxEsmJjIxEWFgY7t69qzYXZ2Zmam0MrFo4cHJy4rWn6YtLjpRbsqamplW457du3aKuBjs4ODALNBsbmyrqLJcvX6aiBCjRpUsXLFy4sMr7CxcuRMeOHanjAhULsmHDhqFFixZo0aIFhg0bRr3wkUqlatUcHR0dtQr8zZs3qXnNrVu3xrRp06o9Tytn2aFDhxp3sxYtWkT1MPoYzex3797FhAkTmF2DCRMmUHvb8EW74NMYGKhI6jS5u9+8eZOq9wEAJk+ezBQecnNzUVhYiMLCQuTm5uKHH35ArVq1MGXKFOoxGxsba/St2LZtG4yNjanjfk54/vw5evXqBT8/P4brzlVyZGNjg+PHjwNQT44yMjKoFqz6+vrMNVZeXg4dHR01Ctj169chl8upxiqTyTBmzBi1eUL1SEpK+iTVJ/kAHwpifDMD1q5di7S0NAAVJrtKkRiJRIKNGzdqHY8vI1E+IZFI1OZg5T2ipCsePXpUa7sGoILCqfodnjlzBra2tgwlS0ktYzPu6no0aZ8dAH8G5TKZjPmea9WqxcxBly9fhqWlJSefkZWVhcWLFyM5OZkRPGADCwsLJiGsrCqn7Zq4Vq1aDENLdT3PB7645Gjw4MEYNWoUzM3Nq8gInjt3jnrSmTBhApo0aYJnz55h/PjxaN++PbPFV1RUhB49emjdo6CKyhO6EgUFBaySrq1bt0IsFsPf35/xvWjSpAnEYjEVNcLW1hZHjx4F8H/ULNXKzOHDh6n5qxkZGVi7dm21558+fcqpcpYSp0+fphLs+NjN7GzBF+2CT2NgoIIOqOyjUMW2bduoKFRKzJ49G1ZWVlW8K6ysrGr08voQjBo1CgqFAvPmzUNWVhaysrIwd+5cmJqaUnvP/BcoKSlhzaFPTU2FpaUlli9fzpqup8SYMWPQtGlTPHz4EAYGBigoKMCxY8fg7OxMRdeztLRk/OaePn0KgUCgVmXPzs6mXpwEBATUWJxhS6vjG48ePcLRo0dx9OjRT7IJnS9mQHUoKirCuXPnNNIDPwR8GInyJUKghIuLi1rfXUFBAUQiEaPqSUsxVJVrVkIsFjPG9adPn2ZVTKpXrx4iIyPV1EeLi4sRGRnJmTQ/l7CxsWESIh8fH4b9dOLECSo10o8BuVzO7O6pJkdnzpzRmiUxcOBA6OnpwdHREUKhEPb29nByctJ4sMUXlRwFBwczLvchISFYsWKF2vlp06ZRqXwBFTdUhw4dYGJiglatWjENiW5ubtDX14e9vT21dxBQMcHPnTu3yvtz587Vmq6nCmdnZ42yrImJiVQV1mHDhsHNzQ3Tp09H48aN0b9/f3h6emLPnj3IzMyEj48PtdHu5wg+mtk1gQvVRb5oF3wbAyckJDA7t6WlpSgtLcWBAwfg4OCAMWPGUMdV4saNGzhx4gROnDjBie8MUFEhT05OhrW1NdM3Z21tjeTkZCoj5tOnT6v9vZ07dyIoKAjW1tZo0KABb35PXC3cr169ikaNGkEgEHBybxQXFyM2NhZisRgCgYDxg+vXrx/V99uvXz/4+fkhPT0d7du3R1hYGPz9/ZGXl4f8/HwEBwejW7duVGOdMWNGjQnbnTt3tKYk8dmvqsSLFy/Qr18/iEQi5hoWi8Xo27cvleDM1q1bUVRURD2e6sAXM4Av8GEkWp0IAVfS40lJSbC1tcXSpUuRmpoKb29vtR2djIwMrSlUQIWAyJYtW5jX586dg66uLnMPsy0Onz59Gubm5jAzM2N6NM3MzGBubq6WUH8IPsY917t3b6aXcurUqTAzM0NsbCwcHBxY95QCFSrFERERcHZ2hrOzMyIiIljvHrVp04YRSVD2MpWVlaF79+5U4jh79uzBwoULIRAIMG3aNPz0008aD7b4opKjf8P169dx9+5dVjH27NmDoUOHIjw8HK1bt0b//v3x888/s+JsAmDoWO3atcO0adMwbdo0tGvXDmKxmNnSp4FUKtW4I3X16lWqSs/r168xcOBAeHt7Y9CgQSguLkZKSgp0dXUhEAgQEhLySVYXgQppyFWrVlV5f9WqVZg9ezar2Fw2s2tSXVQVpmCjusgH+DYGLi4uRo8ePZhFsI6ODkQiEaKjoz9ZPyJVKHuv2ECVi71jxw4IhUJERUVh8eLFTJKQkZHBxXDVwOWuRllZGZ4/f86p8uDt27exa9cubNq0iVVvQmFhIVq1agW5XI6wsDA8f/4c8fHxzD3n5ubGUAM/BXyMftUePXrAzc0NmZmZzDWcmZkJDw8P9OzZU+t4AoEAhoaGGDhwIEOd+RigZQao4u7du1i8eDHGjRvHMDCUh7bgw0iULxECJZRFQGtraygUCvTp00dt5+z06dNUIhKLFi2CkZEREhISkJiYCGtra8TExDDn09PTWe/wvH79GsuXL2d+L9r12se45548ecLsmpWVlWHWrFlo3749Ro8ejadPn1LHBYDFixdDLBarjbl3797Q0dFh+pxocOHCBZibmyM8PBy6urro1q0bvLy8YGFhwWrO/Prrr/Hy5Uvqv/9v+F9y9Bnh1KlT6NOnDyNz26dPH9YPkTZt2iA1NbXK+6mpqVrTAGta4L19+5b1hXzu3Dm1yv2aNWsQEBAAW1tbBAYGYsOGDaziOzg4MH0Kqjh16hQnRnNcNbPzqbpYE9j6rfCNq1evYvPmzdi5cyd1o3lldOnSRWNinJycTL1TwAdU+xSaNm1a5fefMWMG/P39tY5bneS28vD09GSVHJWUlCAnJweZmZnIzMxETk4Oa9+Zj4lr167hwoULvCh/cgW++lVlMhmysrKqvH/06FGqvlKBQICpU6cyku516tTBjz/+yGvTNRfYv38/I1stFovh6+sLY2NjGBkZoXnz5v/18KqAaxGC7du383rPLlmyBAEBAWjQoAEmTJjAGK0CFXN+Xl4eb59NCz57xPmCjY2NxjEvWrQI1tbWrGI/f/4c06dPR/fu3dGmTRtMnDiRlXx+Zdy5c4e617o6CACAfGEYMWIEcXV1JSNGjFB7f9GiReTatWvkp59+0jrmP//8Q0xNTTkaIb/YsWMH8+cHDx6QxMRE0qNHD+Lv708IIeTUqVNky5YtJCkpicTFxX1wXJFIRB4+fEjMzc1JixYtSEZGBjE2NuZs3F999RWZN28eCQ0NJStXriQjRowgAwcOJF5eXuTKlStk5cqVZP78+WTAgAFU8SUSCcnLyyNOTk5q79+4cYPUrl2bvHv3jot/BmsIhUJSWFhIzM3NiY+PD5kwYQLp3bs3c37Hjh1k7Nix5MqVK5x+bm5uLqlfvz4pKyvjNO6nDDMzM3Lw4EHi4+Oj9v6FCxdIaGgoefTokdYxd+/eTTIyMkitWrVIdHQ08fLyYs49e/aMdO3alRw8eFCrmKrXhIWFBdm9ezdp0KABc/7KlSvE39+fPHv2TKu4EomE9OrVq8o9ocTDhw/JihUrtL4mysvLSWJiIlm8eDF58eKF2jkjIyMSHx9PkpKSiFAo/OCYo0eP/uD/94cffvjg/7cmACACgYCTWNXh2bNnZOfOnSQqKorq78vlcpKTk0NcXV3V3r927Rrx9fUlr1+/poprb29Pdu3aVeXe+Ouvv0jbtm3JvXv3tIqneg2fO3eOrFq1imzYsIG8ffuWdOjQgQwcOJC0atWKaqxKXLlyhSxcuJDk5eURQgjx8vIiw4cPJx4eHtQxGzduTNq0aUOSkpKIgYEByc3NJebm5qRv374kPDycDBkyhNWYlSgpKSE6Ojqs40ilUnL+/Hm1eYcQQvLy8kj9+vXJ27dvtYonEolIYWEhMTMzU1sDfA64d+8eMTY2JnK5XO39kpIScvLkSRIUFEQVl697rqysjIhEIub16dOnSXFxMWnSpAnra6O6MRcUFJB69epRj5kvlJaWkqSkJLJgwQJmbHK5nAwfPpxMmTKF/b3Caar1mcDa2ppprlXFuXPnqBu4hUIhWrRogXXr1nGuUlJaWorr168zaj3v3r3Dpk2bsGHDBhQWFmodT5NPUHXeQdrA0NCQUX7hQ0lEKpUyOwL16tWrwsVet24dFa9ZCVdXV42CD2vWrKFu8OP6twP4VV2sCWwoVG/evMGqVasQHR2N8PBwtG3bFvHx8WqSujTYu3cvEhMTceDAAQAVkvnh4eFo3ry5xh1RbSGRSDTu8uXl5VGpGa1btw4ikQgRERFo2rQpJBIJ0tPTmfOFhYVU37FSICA3NxcODg7Izs5WO5+fn0+lptagQQMsWbKk2vN//vkn1XjHjh0LMzMzLFu2DDdv3sSbN2/w5s0b3Lx5E8uXL4e5uTkSEhK0iqnaTxoSEgJDQ0PIZDJml0tfXx+GhoacVvO58iyrCWypi3z1qy5fvhyhoaFq/jUPHz5E69ataxR2qQ6ajMnfvn2LNWvWICQkBEKhkNUOPtfiQ0rI5XKGHmRsbMzMxzk5OXBwcNA63qZNm9TowAsXLoS9vT2EQiEUCgWSkpKoxwpwL0JgYWGBHTt2AOBfQQyo2C3av3+/xnaAD8WDBw/QqFEjCIVCiEQiREZGqlHRaedhJbi+5x48eIDAwEDejIyBin4mTXTNlJQUKpqsJnDRG61EXFwczM3Nq9BDLS0tERcXxzr+F5kc6enpVav8RuOBAVRMCkpOpYmJCeLj4/Hnn3+yHGmFyoyVlRWEQiG8vb0ZLwh9fX3I5XKYmJhUWQj9V+jSpQssLCwQEhICgUCAwMBANG/eXONBA4VCwSS15ubmVfwCrl27xiopSE5OhkKhQGpqKsPPXrVqFRQKBWbOnKl1PL5+O75UF/nyWykoKICDgwPMzc1hZ2cHgUCAiIgI+Pn5QSQSoXv37lS0pLVr10IsFqN+/fqQy+VIS0uDsbExYmNjMWDAAOjq6qo189KgUaNGGhcjU6ZMoaKg+Pr6qvVfbdq0Cfr6+li5ciUAdsmRqkHyjz/+qHZ+w4YNVIWDESNG1GgAeO3aNYSEhGgd18LCQk3dqjIyMzNhbm6udVwl5s2bh/bt26vx8J8+fYqOHTtqXLT8Gyr3kSgPZW8XbX8JwL8PGJf9qr6+vmq0SrlcDh0dHbi4uMDFxQU6OjqQy+VUi2xVurAmFBQUYMKECVrHVYJr8SElLCwsmATZy8uLUc7MycmhEgtQ/R5SU1MhkUiQmJiIXbt2Yfr06dDX168iJqUNuBQhACrmwg8ReqC5hmfOnMkU0J4+fcqYiSo/Lzw8vEYRoeoQFRUFPz8/nDlzBvv27UODBg3QsGFDZr4oLCxkJRLEdY84X0bGyt6i+fPnY9q0aTAyMkLbtm2ZMUdERMDY2LhGC5XqwHdvtKGhIXbv3l3l/V27dnGi3PdF0uq8vb1JXFwciY+PV3t/4cKFZOnSpeTy5ctax1RSAoRCIVm9ejVJTU0l+fn5xNfXl8TGxpK+ffsSQ0NDreOGh4cTAwMDMmXKFLJy5Uqyd+9e4u3tTdatW0cEAgGJjo4mhYWFZN++fVrH1gSwoIm8ffuWrF69mly/fp3MmzePDBw4kMhkMo3/748//qh1/MjISKKnp0dWrlxJevToQTw8PMi0adOY87NmzSIbNmwgf/31F9X4AZDx48eTBQsWkPfv3xNCKmhF48aNI4mJiVrH4+u3CwkJUfuN+vbtS2JjY5nX06dPJ/v37yeHDx/WKq6+vj4ZMmRIFZqMErdv3yZJSUlaU6jatm1L7O3tydKlS4lAICDJycnkyJEjZPfu3aSgoIC0bt2a9O/fn3z//fdaxa1Xrx6Jjo4mI0aMIAcOHCDt27cnM2bMIKNGjSKEEDJv3jzy22+/kWPHjmkVVxU7d+4kXbp0IX369CEtWrQghBBy4MABsmHDBrJlyxbSqVMnreLJ5XJy4cIFNZraoUOHSIcOHUhKSgrp3Lkzsba21vo7vn37dpXPUSgUzOs1a9YQQgg1NYtr6Ovrk1OnTlV7rf31118kICCAmsphY2ND9u7dS+rUqaP2/sWLF0nr1q3JgwcPtIonFArJV199VYUmfOTIEdKwYUOir69PBAKB1nRIZeya5lzlnMyGznr69GmyYMECNTrZiBEjiJ+fn1ZxkpKSPvj/nTJlilaxVWl1fEAmk5G//vpLI23oq6++Im/evKGK26lTJxIREUEGDhxIvv32W7J9+3by9ddfk4yMDGJiYkL279+vVTzV78HPz49069aNjB07ljm/dOlSsmLFCnL+/Hmq8RJCSFFREVm3bh3Jz88nhFRcD3369CH6+vpU8fLz88m1a9dIhw4dSFpaWrV0+o4dO2oV187OjuzYsYPUq1ePDBw4kKFbKqn0cXFxpE6dOmTlypVaxbWxsSG//fYbady4MSGEkOLiYtK9e3dy9+5dcuDAAVJSUkI1D6uCq3uOEEKsra1JRkYG8ff3J0+fPiWmpqZk3759pGXLloQQQg4ePEgGDhxIrl+/rlXc6ujSlSEQCMiNGze0iq1KsZwwYQJZu3YtWbNmDfHz8yN//vkn6d+/P+nevTuZNWuWVnGVMDc3J0eOHNFIDw0KCiKPHz+misuAdXr1GWLVqlWQSqVITExkFGEmT54MmUxWhar1odBECThx4gQGDBgAAwMDyGQyREZGah3XxMSEqUq9efMGIpFIrbpz8eJFKBQKqjFrAlc0kZCQEKqKTk24f/8+HB0dERQUhNGjR0MqlaJp06YYOHAggoKCoKurq7HRVFu8evUK2dnZuHDhAiuK5Mf+7ZSgVV3ky29FJpOpKYUVFxdDR0eHabTetm0bFV1GX19fTaBDR0dHzacjLy+Pk+/3999/R0BAAGQyGRQKBZo3b47Dhw9TxdJk6ghUKFTJ5XJMnDjxk/a04Qpt27ZF69atNXrBPH78GOHh4YiIiKCOL5fLNcrOHzx4kIpeOGvWLDg5OTH0TSW4kOXn2wfsc8GtW7c4VSqsDC7Fh1Rx/fp1Zt55/fo1Bg8eDB8fH3Tp0oVKGKYybVoTQ8LAwIB6vHzi+++/51SOXU9Pj/kOHR0dqwgCnT17FlZWVlrH1dfXr6JeWVJSgk6dOqFu3br466+/Pql7jm8jYz6guib29vZmPJmU2L59O9zd3anjJyUloXfv3mprtHfv3qFv376s1G+VELNLrT5PDBgwgBQXF5MZM2YwOw+Ojo5k6dKl1JVVTZW/Jk2akCZNmpAFCxaQjRs3ktTUVK3jAiBiccXPVPm/hFRk5+Xl5VrHra55uaysjMyePZupOtM2Lh86dEjt9c2bN4mdnZ3a2LWFtbU1+fPPP8ns2bPJzp07CQCSnZ1N7t69SwIDA8nx48dJw4YNqeMrIZfLSaNGjVjH4eu3+zc4OztT/b2IiAjy/Pnzas/XqlWL6v4wNjYmr169Yl6/efOGlJaWEl1dXUIIIXXr1iUPHz7UOq6Ojg6zw0cIIXp6emqNtXp6elo3F2tCREQEiYiIYB2HkIrm7T179jDiJ0oEBweTnTt3knbt2rGKX1hYSE6fPk0KCwsJIYRYWloSPz8/YmlpSR3z77//JhcvXiQNGjQgRkZG5NGjR2T16tWkvLycREREVLv7UxOWLVtG2rZtS6ysrIiPjw+xsLAghBDy6NEjcuHCBVK7dm3y+++/U4+5c+fOJDo6msybN4+pDp8+fZqMHTuWdOnSRet448ePJy1btiT9+vUj7du3J7NmzeKkOZ4QQurXr08IqbgGNMHY2JiAI4JHSUkJuXXrFjE3NydGRkas4z1//pxs3bqVXL9+nYwdO5bUqlWLnD9/nlhYWBAbGxutYjk4OLAeT2Woig916NCBjBs3jpw7d06j+BAtVOdbfX19smzZMvoB/z9kZmYSIyMjIpFIquxovXv3jprdkZubS86dO0dCQkKIs7MzuXTpElm8eDEpLy8nnTt3JmFhYazGHRUVRe7fv0/c3NzU3i8oKCA6OjrE0dFRq3gODg7k4sWLxMHBgQgEgirrB5FIRIqKirQep7OzM/nrr7/UxikWi8mWLVtI9+7dWc/DXMPc3Jw8fPiQ2NnZEUIIiY+PJ7Vq1WLOP3v2jHrXrzLev39Pbt68SVxcXFit1wj5v3VxYWEhqVu3rtq5r776ity9e1ereJXn7v379xNbW1vy1VdfEUIqru/3798zO2qswDq9+szx999/c+IJo2nniAu0bNkSMTExuHfvHpKSkuDq6oro6Gjm/NChQ9GsWTOt4woEAvj6+lZpYhYIBGjUqBFCQkI+u8ZlNlixYgWioqKYyuLGjRvh6ekJJycnJCYmUsXk67dTxevXr7Fr1y4sXbpUjT9ck6/Qx0b//v0RHByMvLw83LhxAz179lTrSTh8+DDs7Oy0jtuwYUNs27aNef3ixQu1yvO+fftYVab4wOHDh2vsXzt48KDWhp9AxXXQt29fiEQiiMVimJubw9zcHGKxGCKRCP369aOq6B46dAj6+voQCASwtLRETk4ObG1t4ebmBg8PD+jp6eGPP/7QOi5Q4dOxe/duJCYmYtCgQRg0aBASExOxZ88eRsCEFkVFRRgyZAj09PSYfgddXV0MGTKElefcq1evEBUVhbp16+LChQusPcsA/nzAkpOT8ebNGwAVwjBjxoyBrq4uhEIhxGIxoqOjWUkw5+bmwszMDK6urhCLxbh+/ToAYOLEiVQsCVVwNa/xJT6kiuzsbI2WGqdOncKZM2e0jld5bNOnT1c7v3LlSqqerl9//RUikQgKhQJyuRz79u2DsbExQkNDERYWBpFIhHXr1mkdVxVBQUH45Zdfqry/du1aBAcHax0vJSUFXl5eKCgowLx589CkSRNG/OLGjRsICQmhslRISEiodrewpKQEHTp0oLom3r9/j7Fjx8LFxQWNGjWq4ptI21P6MYyMi4qKMGDAAIhEIohEIuZ+jo+Px6xZs7SOx0dv9Ndff/3BB1t88ckRV/jll184V6kDKiZehUIBoVAIMzMzXLx4EX5+frC0tIS1tTWkUimV4hdfNJHOnTtrPIRCIUJDQ5nXnxJ+/PFH6Ovro0uXLrCyssL06dOhUCgwffp0JCUlwdDQEMuXL9c6Ll+/nRLnz5+HpaUlDA0NIRKJYGZmBoFAAH19fWp1PT7w6NEj+Pv7M4sQBwcHnD9/njm/ZcsWLFiwQOu4GRkZNfouzZo1i3Hm1gYfw+mca8TExDCGnEr3eKBiUfzHH3/A3d0dsbGxWsdt2rQphg0bhlevXiElJQU2NjZqjb/ffvstAgICOPk38IHXr18zSkZsjbhVsWHDBlhYWEAoFLJOjviCamN/SkoKTExMkJqaikuXLiE9PR3m5uZITk6mjt+yZUuMHTsWQAWNUbmYOn78OJVKmxKfy7ymRKNGjTQKv/z6669o3Lgx55+3c+fOGsVMqkP9+vWZRGvDhg0wNjbG1KlTmfNz586Fr68vq7EZGBhUK3ZlZGREFXP48OHQ0dGBp6cnJBIJU+gQCoVo2LChmlrih6KkpKRGX8aSkhIqSuSUKVNgYWGBlJQUTJw4EUZGRhg0aBBznq3QQ3Xgwsh4xIgRaNCgAbKysqCvr8/cz9u2baO6LoKDg9UK75VFRKZNm0aVMH8sfFGCDNnZ2aRBgwaMTvzvv/9OUlJSyLVr14iVlRUZMWLEJ9OwrIqioiKSn59PPDw8iFwuJ+/evSPr1q0jb9++Ja1ataL2aDhz5kwVmoiOjg7Jzc0ltWvXpoopFApJUFBQlUa/NWvWkA4dOjCNmmlpaVrF1YYKk5GRoVVsLy8vMnnyZNKnTx/y559/ksaNG5Nly5aRmJgYQgghq1atIkuXLiVnz57VKi4h/P12hFQIM7i7u5Nly5YRIyMjkpubS3R0dEi/fv3IyJEjqehDhFQIW1hYWFTxi0pNTSWPHz8m48aNo4pbUFBAiouLiaenJ+vtej6xevVq5s9Pnjwh06dPJ2FhYaRJkyaEEEJOnjxJ/vjjDzJ58mRG/EFbFBUVkXPnzpGHDx8SoVBInJ2dSf369anpMiYmJmTXrl0kICBA4/njx4+Tdu3aae1zZGRkRM6fP09cXFxIaWkpkUql5MyZM8TX15cQUvGbNmrUqEY6Zk3Izs4mJ0+eVKMBBgQEcEJrJaTCV+T69eskKCiISKVSTn2J7t69S86fP09CQ0M5o7RwCdXG/vr165O4uDgyaNAg5vy6devIrFmzyMWLF6niq14bSn8fZ2dncvv2beLh4UHtC8fXvMYX5HI5+euvv6rQmW/evEnq1q2rRin+LyGXy8nFixeJo6MjAUD09PTIuXPnGFrsjRs3yFdffcVqvEZGRuTw4cOkXr16au8rqXy0sfPy8sjvv/9Obty4QcrLy4mVlRUJDAwkoaGhvPuMaQM3Nzfy448/MrS8a9eukTZt2pCmTZuS1NRU8vfff7MWeuALDg4OZNOmTcTf31/tfr527RqpX78+efnyJaefd+PGDaKrq0tsbW1Zxfn7778ZT0cPDw/uBF3+29zs40K1krZjxw5GhnXx4sWIjY2FWCxGRkYGL5/9+vXrGqvc/xW4pols2LABtra2VRpf2e5IqW6X9u/fH4aGhrCzs2N2ouzt7WFoaEi1nSqVSnH79m3mtZ6enpp3UEFBAYyNjanHzheMjIwYDx4jIyOGtnjq1Cl4eHhQx3VwcMDx48ervH/q1ClWPiN849GjRzh69CiOHj3KGcWVa6fz0tJSjB07FjKZjKF7KekzDg4OjF+ItjA0NKyRwpOdnU0lb6rqo1VUVAShUKgmKJGbm0slG//o0SM0bdqU+Xc3btwYjRs3hoODAwQCAZo2bcrqN/znn3/QokULZrdSWQWNjo7G6NGjqeMq8e7dO86YAnfv3lUTpjh69Cj69OmDpk2bom/fvjhx4gRVXNXGfoVCUaWyfOPGDchkMupxm5mZMTvAqjtHe/fuha2tLXVcvua14cOHa6TlLVy4sEa5+n9DrVq1NP5Gx48f5+W5QbuWsLS0ZKwwnj59ynijKZGdnQ1LS0tWY2vXrh26d+9eZfe6a9euCA8PZxWbT9y/fx+JiYno06cPxowZg7y8PKo4UqkUN2/eVHvv3r17cHd3R9++fXH//n1OhR6cnJyqCEvQQiqVMvew6v2ck5PDiTQ213jx4gX69esHsVjMPEPFYjH69u2L58+fs47/RSVHqn1BTZs2xfjx49XOz5gxA/7+/rx8No3Sl5IO8iEHW3BJE7l58yYCAwPRpUsXxjeAC1UnJRISEhAbG1tlAh40aBC+/fZbreMpFAq1fihbW1u1LfWCggIqhavKKCoqQl5eHme/nampKTMxKilVQIVKG5tFj56enpoKnBLXr1+n9gF78OAB1q5di127dqmZDwIVD3s2xoYvX77kbZKsrAqkREFBAZWHybhx4+Dl5YWdO3di3759CAoKQnJyMvLy8jB58mTqHp4+ffqgXr16anRFJc6fP48GDRqgb9++Wsft2LEj2rVrh2PHjmHQoEFo2LAhIiIi8Pr1axQVFaFbt25Ui56uXbuiSZMmGg128/PzERAQQNVLoERkZCTCwsJw9+5dtQd9ZmYmtVH03r170aZNGxgbGzOJrbGxMdq0aYN9+/ZRj7Vx48bYuXMngAoKi1AoRIcOHTBu3Dh07twZOjo6zHltIBAIMGPGDMyfPx9WVlZVFtS5ubmsqKExMTHo1KkT3r9/D7lcjhs3buD27duoV68eq2SDr3mND/N3AOjVqxeCg4PV5ppnz54hODgY3bt3p45bHWhVQ/v16wc/Pz+kp6ejffv2CAsLg7+/P/Ly8pCfn4/g4GBW9xwAXLp0CQqFAi4uLkwx08XFBWZmZpwYfwIVxsCVvcC0hVQqZQoHly5dgpGREVxdXdG9e3d4enpCJpNRPZudnJw0UuXv378Pd3d3tGrViuq3q9x3pzxEIhG+++47TvqMmzVrxtDblfczUNFzFBYWpnW8+Ph4HD16lNWYakKPHj2Y+UF5HWRmZsLDw4MT09ovNjkyNzevMlHm5+fztkNAM6GpGjtyba6mCXfu3MG2bds44eaXlZUhMTERdnZ2yMzM5KRxWQlTU9NqF1W1atXSOl5gYCA2btxY7fmdO3fC29tb67hK/P3334iIiOD8t2vVqhXTQBsbG4vGjRsjPT0dYWFhrLjurq6uWLt2bZX316xZQ8X5z87OhrGxMQwNDSGVSuHq6qq2M8fWjZzPSZJrp3MrKyu1B8a9e/cgl8uZXYipU6eiSZMmWsd9+vQpwsPDIRAIUKtWLXh6esLT0xO1atWCUChEmzZtqKT1r169Cjc3NwgEAnh5eeHevXvo0KEDxGIxxGIxzMzMcO7cOa3jyuVyjYmcEmfPnmVVkLCwsGAkkFWTo+vXr1Mltb/88gvEYjF69eqFtLQ07N69G7t370ZaWhp69+4NHR0drFmzhmqsqpL0fn5+mD17ttr5hQsXUjXgOzg4wNHRkTkqGwP/9NNPrIqBz58/R2hoKIyNjSESiWBnZwcdHR0EBQWxeobwNa/xYf4OVNzDzs7OMDIyYvorjI2N4eHhoSa/zBVok6PCwkK0atUKcrkcYWFheP78OeLj45n1hZubGyN2wAb379/Hd999h7Zt26Jr165ISkrCkydPWMV8/fo1hg0bBjMzM06eoaprwY4dO6J9+/aMEXlZWRl69eqFdu3aaR03JiYGAwYM0Hju3r17cHV1pR6vra2t2v3s6OgIgUAAGxsbODo6su7Hy8rKglwuR1xcHCQSCUaOHIlWrVpBX19fY1HhQ8asvK5mz55N1RtWE2QyGbKysqq8f/ToUVZFFCW+uOTo0KFDyM3NhYODA7Kzs9XO5+fnUz+Q/61x29DQUOub4tatW8zx22+/wcXFBcuWLWN2HJYtWwY3Nzf89ttvVGP+GMjKyoKTkxOnjcvGxsZqKmVKbNu2jSq5PXbsGP78889qzy9evFgjtepD0adPHwQGBuLMmTPQ19fH3r17sXbtWnh4eOD333+njnvmzBkcPHgQQAVNKSwsDAYGBqhfv34VbwxtkJycDIVCgdTUVOb6W7VqFRQKRY1Ka9UhNDQU0dHRKCsrw8uXLzFkyBAoFApmccw2OeJzkuTa6dzAwIBZqAMVD2KxWMw8OC5dusRqzHl5eUhNTcXMmTMxc+ZMpKamUlNEVKH0pFJi//792LlzZ5X3PxQKhaJGr6hDhw6x8qiSy+XM7oNqcnTmzBmqAoqbmxsWLVpU7fnFixfD1dWVaqxGRkZMldrc3LxKxfratWucPOwr4+TJkzUmqB+KrKwsLF68GMnJyax20JTga16rU6eOxnl8wYIF8PLyoo4LVCzely9fjqFDh2LMmDFYvXo1tRIgH2uJmnD9+nVcuHCBSQ4+RQwdOhReXl7YunUrpFIpUlNTMW3aNNja2iI9PV3reKrJkZ2dXZUdjvPnz1P5J926datGsYz79+9rVPP7NwwePBi+vr5VFH+5ZOQAFXNNbGwsGjVqBC8vL/Tt2xd//fUXVSyBQID9+/dj5MiRMDU1hY6ODjp06ICdO3eyViMFKn43TWPLzc1ltROsxBeXHKly/CtX0jZs2EBNuZDJZBgzZgx++eUXjUdSUhKrCa1Ro0YaDU537dqF+vXrU8cFKh5GY8eORc+ePauozHGBV69eIScnhzOO/qhRo6BQKDBv3jxkZWUhKysLc+fOhampKUaNGsXJZ3AJS0tLxvzVwMAAV65cAVBhghYYGPhfDk0jysvLkZCQwCgDCYVCyGQyauqbiYkJ829WYtasWTAxMUF2djbr5IjvSfLUqVMMba1evXro06ePRuneD0FAQICaNK9SNUqJCxcufFIqeHxh6NChcHBwQEZGhhot5sWLF8jIyICjoyPi4+Op47dp04ZRKlRSRMrKytC9e3d07dpV63h6enoad6uVyM/Ph0QioRprhw4dGIp3WFhYFXrMihUr4ObmRhX7c8H27dtZyYp/CPgwf+cDfK8lPga4ppDb2dkx/VGqinhr1qxBmzZttI4nFAoZWp2Dg0OVsd24cYP6fuYLGRkZsLOzU0vwuU6OuIRqAvr+/Xts2rSJkYy3trbGhAkTNO7kfiiWL1+O0NBQtR2phw8fonXr1li2bBnr8X9RyZHqTsytW7eqVD1Xr16N1atXU8UOCAioUYeeditcCYlEotEn6PLly6xu4g0bNkBHRwft2rWDrq4u2rVrB3d3dxgZGbHWin/+/Dny8/ORn5/PSYOcEmVlZUhOToa1tTWT6FpbWyM5OVmtD+lTgYGBAdOkaW9vj2PHjgGomIDZulrz9R0DFUltdnY2Lly4wCqxNTEx0fhgTElJgbGxMTIyMljdG3xPklxi//790NPTQ+PGjREUFASxWKxWpElJSWHtV5Gfn49hw4ahRYsWaNGiBYYNG8bJ7pEq2DYCv3v3DnFxcYwkr0QiUZPpHTJkCKtr7sKFCzA3N0d4eDh0dXXRrVs3eHl5wcLCgoo6VL9+fUa2WhMSEhKoi1SXL1+GQqFAVFQUpk2bBrlcjn79+mHGjBmIioqCnp4e1S7l3LlzqzSHc4XHjx8jOTkZnTp1gr+/P/z9/dGpUyfMmTOHWXRqA9XFqqpwEtdYsmQJbGxsmOeGk5MT9TNfibt372r0Snz//j2VcAKfawmuf7fK4ItCrq+vz4gm2djYMMXGGzduUNFkBQIBY9mgo6NThUa+d+9e1uJDmubhmgosH4J79+6hRYsWCA8Px8OHDzlPjkpLS7FlyxZMnToVU6dOxdatW6l3FKvz/rx9+zamTJkCBwcHra8JX19fpkhZr149yOVy6OjowMXFBS4uLtDR0YFcLqeiIVcZP/DlSHnziZkzZ5KSkhIyZcoUjefv3r1LEhMTtZawVqJ+/frE29ubrFy5kujq6hJCKpyMY2NjycWLF8n58+ep4tatW5cMHjyYDBs2jJFvdHJyIoMHDyZWVlZUzuErV64kP/zwAyOviP8nn+vh4UHGjBnDSGRzAaW8pKGhIdXfNzEx+WAp0KdPn1J9RqNGjRg5aKWc+axZs8iCBQsYd3ltUfk7VoKP75gQwkoCOSgoiPTp04fExcVVOTdnzhySmJhISkpKtJI3rVevntp4lDLh9vb2hBBC7ty5Q/T09Iibmxv1vVEZJSUl5NatW8Tc3JwYGRlRx8nNzSWbN28mxcXFJCwsjLRq1YqT8RFCyK+//kp69epFGjZsyEiPnzp1ipw5c4Zs3LiRdO3aVat4CxYs0Pj+6NGjSUJCArG0tCSEEDJixAiq8b58+ZKcO3dOTcq7QYMG1PezKl68eEEWLVpEcnNzyevXr0n9+vXJsGHDiJWVldaxDh8+TNq1a0ecnZ1JaGgosbCwIIQQ8ujRI3LgwAFy48YNsmvXLhIUFEQ11uvXr5NJkyaRXbt2kdevXxNCCBGLxaRRo0Zk7NixpFOnTlrHFAqFRCgUkubNm5PY2FjSuXNn5tnBBmfOnCFhYWFEJpNp/C7evHlD/vjjD9KwYcMPjmlpaUlWrFhB2rdvT4RCIXn06BExMzNjPdbq8PjxYyKVSolcLqeO8fDhQ9KxY0dy7tw5IhAISJ8+fciSJUuYmI8ePaKSbeZrLcHH71YZffv2Jbdv3yY//fQTCQkJIb/99ht59OgRmT59Opk3bx6JiIigilu3bl2ycOFCEhwcTEJDQ4mvry+ZO3cuWbBgAZkzZw65d++eVvFU7RoIqXhu+vv7M6+nTZtGnj17Rn744Qeq8XI9D6sCAJk9ezZZsGABefz4Mfnrr7+orVdUcenSJdKhQwdSWFjIWIxcvXqVmJmZkZ07dxJvb2+t4qlaCVT379i/f79Wzz9t1qPV3T8fDNbp1WeMkpIS7N27FytXrsS+ffs+yV0HJU6fPg1zc3OYmZmhZcuWaNmyJczMzGBubs5UUWggk8mY6mKtWrUYetLly5epZD3nzJkDmUyG8ePH49ChQ7h8+TIuX76MQ4cO4bvvvoO+vj5SUlKox6sJhYWF1M1+qnSFefPmwcTEBL169WLUX3r16gUTExP88MMP1ONbu3YtU/k9e/YsTE1NmWp5TUIQ1YGv7/jdu3cYM2YMmjVrxjSFT5s2Dfr6+tDX10fv3r2plIFWrFiBfv36VXt+9uzZWlfpvv/++w8+aJCcnIw3b94AqKimjRkzhtnlEIvFiI6O5p0GRANnZ2dMnjy5yvuJiYlwdnbWOh4fjcAmJiaMdHV0dDRevnyp9bj+C9y8eRMJCQkICgqCu7s73N3dERQUhHHjxnG2Q1NeXo7CwkI8ePCA9fUlEAiQlpaGjh07QkdHBwqFAiNHjmStGubn54dBgwahvLy8yrny8nIMGjRIa6GHKVOmfJDw0KdEJ4uKioKfnx/OnDmDffv2oUGDBmjYsCGj0MqX4Sct+PjdKoMvCvkPP/zA0E337dsHiUQCPT09CIXCGnfZ/itwPQ9rwtmzZ/HTTz8x1xtb+Pv7o3379mrxnj59ig4dOlCJBDk6OlL3pH4K+KKSo/j4eEYS9e7du/D09IRIJIKFhQVEIhF8fHxw7949rePWq1ePuaCSkpJQVFTE6biVUDZ9jho1CqNGjcLPP//MWlnOxsaGSYh8fHywfv16AMCJEyeotO3t7e2xadOmas9v3LgRdnZ2VGN98uQJunbtCjs7O8TFxaG0tBQxMTHMQ7VJkyZ48OABVWyAe0+b6lBUVIRz586peZtoA76+41GjRsHa2hpjxoyBl5cXhg4dCnt7e6Snp2P9+vVwdXXF8OHDqcb8uUGV2pOSkgITExOkpqbi0qVLSE9Ph7m5OZKTk7WOu3XrVt7mB6BColYTj/vq1atUFE4+GoFV3ddV6VRcIjU1FZs3b67y/ubNm6kaoj9HqNJaHj16hOTkZHh6ekIoFKJRo0b4+eefqRJTiURSI00zLy+Piuqdl5eHnTt3QiAQ4JdffsG2bds0HtpCLpdjwIABGr3b2MDa2lqtMPnu3Tu0b98evr6+ePLkCeteSq7B1++mCj4p5Kq4efMmfv31V05sTJR49+4drl27xklvNNfzsCaUl5drTHRpIZFI1FRklbhw4cIn13+lCYWFhWp+lWzxRSVHFhYWTNWsR48eCA0NZRaoT548Qbt27ah0/iUSCe7evQuAX740H+jduzfmzZsHoEJG2MzMDLGxsXBwcKASZKiuN0qJS5cuUU8OAwYMgLe3NxYuXIjg4GB07NgRdevWxbFjx3DixAk0atQIUVFRVLEB7j1t+AJf37GdnR2jNnX9+nUIhUK1xcjevXvh4OCgddzPEaoLy3r16mH58uVq59PT01GnTh2quIaGhhg4cCC1qENNaNOmTRUDZqAiWWjdujVVTK4bgUNDQ+Hj44Ovv/4aAoEAvXr1QnR0tMaDFm5ubozimSoOHz4Md3d3rePRFjK0QZcuXarIeAMVu5g0z6XqOP9Hjx5F//79mR1hbeHo6Fhjn87q1atZzRPff/89pwUEgUCAOnXqQCAQwNPTE3PnzuUkIdfX16/Sd1dSUoJOnTqhbt26+OuvvzhJji5duoQ9e/Zg+/btaoe24Pt3A4CGDRsyam3t27dHZGQk7t27h4SEBM52TLhAWloaY9z79u1bDBgwACKRiGEGDB48mFWSxMc8rMTKlStRp04d6OrqQldXF3Xq1MGKFStYxQSAunXr4sCBA1XeP3DgACsrE67x8uVL9O3bF/b29oiKikJxcTGGDh3KFMmDgoKoGC6V8UUlRxKJhPGTsLW1rUJHu3DhApXbu7+/P0JDQ/H9999DIBBg7NixSEpK0nhwBa6ckZ88eYL79+8DqBA6mDVrFtq3b4/Ro0dTbdc2a9YMUVFRGpv4SktLERUVhaCgIKqxWllZMdU/JWVh7969zPljx46xUifj2tMGqNh1ioyMxIYNGwBUqOt4eXnBw8MD3333HVWzI1/fsVQqVau86OjoqFWSbt68qbWkcOUGypoOWpSWliIlJQWNGjWChYVFFelbGggEAmYBpVAoqlCRbty4QSWvLBAIMHXqVNSrV49ZtP3444+s6AeqC6alS5fCzMwMw4YNw9q1a7F27VoMGzYM5ubmWLp0KfVncNkIXFhYiHHjxqFbt26MB1OnTp00HrTQ09PTSHW7efMmVRVUKBSiRYsWWLduHWeqm5VhamqqUXXxr7/+grm5udbx/q1Q9+LFCyqVtkWLFkFPTw8jRozA9u3bcerUKZw6dQrbt2/HiBEjIJVKsXjxYq3j8gVlkpiTk4P4+HjUqlULurq66NKlC3bv3k1dfffx8cHWrVurvK9MkOzt7VklR9evX0fdunWrqOzS0gs/xu/GNYVcFfv370dERAScnZ3h7OyMiIgIaul4Jycnpjj17bffwtHRERkZGcjLy8O2bdvg7u5eowCLJnyMeXjy5MnQ19fH+PHjmc8aP3485HK5Rhrfv0HVSHfXrl2oU6cOtmzZgrt37+Lu3bvYsmULfHx8NColfwhycnIQGRkJJycnSCQSyGQyeHt7Y9KkSdQJTHx8PDw9PbFgwQKEhISgY8eO8Pb2xrFjx3DkyBHUrl0bEyZMoIqtii8qOapbty5zg3p5eVW5sU6cOEHlgZGfn4+ePXuiYcOGEAqF8Pb2hq+vb5WDZgH4MZyRuURubi4sLS2hUCjQuXNnxMXFIS4uDp07d4ZCoYCVlRU1510mk+HWrVvMax0dHbVYtMo1SnDtaTNt2jQYGBiga9eusLS0xOzZs6FQKDB9+nTMnDkTZmZmSExM1DouX9+xh4cHc39kZ2dDV1dXrfq1ceNGrSWFVXt/xo8fD0NDQ/j7+zPU0CZNmsDQ0JCRMqbB5MmTYWVlhblz50IikWDatGmIiYmBQqGgvjcEAgFmzJiB+fPnw8rKqorqVG5uLlXipVrNP3v2LIYMGQJjY2Po6emhe/fuasm+NjE/5GBbxS4vL8fMmTNhaWkJkUjEiUoSX7x0Ozs7jdX1bdu2URVQBAIBo3xnYmKC+Pj4Gr3RaCCRSDSqWdHSnarbOeICGzduhJ+fH8RiMXN9icVi+Pn51Uj5/VBs2bIF3bt3h5+fH+siSuXv4d27d1i/fj1atmwJoVAIW1tbqoVlQkJCtbsAJSUl6NChA6t7rl27dujYsSMeP34MuVyOy5cvIysrC40bN67iy/Oh4Pt3qwy2FHIlFi9ezJgwK9c8SvPlmvzHqoOenh5TCHR3d8eePXvUzh85ckTrgujHmIdNTU2Z1gdVrF+/nsoXrnKfX+UEnM2YMzMzIZVK0bVrV/Tr1w8ymQzx8fEYN24cXF1d4eLiQtUrbmdnx7AC7t+/D4FAwLTLAMDvv/8ODw8PreNWxheVHKWlpcHW1haHDh1iKvj79+/H/fv3cfDgQfj4+CA2NpbVZ3D9QPoYzshKvH//HlevXmUtCf3y5UssWbIEUVFRaN26NVq3bo2oqCgsXbqU1XbnV199xUyEu3fvhoGBAUMJBIClS5ey3v7l0tPGxcUFv/76K4CKCopIJFIzrMvIyKA2juTjO/7xxx8hkUgQGhoKExMTLFiwAJaWlkhISMD48eNhZGSEqVOnUsUGKtzDld4zqkhMTGRFoXJ2dmbMdOVyOSPVrHyA0sDBwUHtfqvsifbTTz9RNS9rmh/evn2LNWvWICQkBEKhkLWELN/guhGYDyQkJMDBwQEHDx5EaWkpSktLceDAATg4OGDMmDFax1P+bo8fP8bcuXNRu3ZtCIVC1K9fH0uWLOGExtGoUSON7IIpU6aw9rLjC+/fv8eDBw84EZBQYv78+ZDL5YiPj4euri4GDx6M0NBQGBkZUVWEa9pBu3nzJiZNmkTVo1lSUlLj715SUqJWzNMWCoWC6akxNDRkEucDBw7A19eXOi7Az+/GJ2xsbDT2Ay9atAjW1tZax1PODcrYZ86cUTt/+fLlT4pKr4SRkZFGxtCVK1dgZGSkdTyl39eHHNrC19dXbZds79698PT0BFBx/bVs2ZLKLkZPTw937txhXstkMjUfxVu3bnFimv1FJUcAMG/ePMhkMkilUkZ9Snl06tRJo1/Bfwm+nJE/RzWu9PR0iEQiuLq6Qk9PD1u2bIG1tTV69OiBXr16QVdXl6qKxBf+jabG1U3MJdatW4f4+HimOnXo0CE0a9YMDRo0wPfff8/K2drQ0FDjxH716lUq8Q8lZDIZ8z1bWlri3LlzACpoKWzi1oSTJ0/i/PnzWv+9f6M6FRQUcEIJ+Bxw+fJlpKamMk3ieXl5iIuLQ3R0tEbuuzYoLi5Gjx49IBAIoKOjAx0dHYhEIkRHR6O4uFjreJqS2hMnTmDAgAEwMDCATCZDZGQkqzHv2LEDYrEYUVFRjIJmZGQkxGIxfvvtN1axPyd4eHgw849cLmfEOyZPnoxhw4ZpHe9DCpZcNrZzBWNjY6YNwNnZmVnMX7t2jVNxA67w4MEDTJ48Gc2bN4enpydq166Ndu3aYeXKlayVgKvrB7569SpVEjNhwgQ0adIEz549w/jx49G+fXtm7VdUVIQePXqw7g3iA/Hx8RqN7seMGYOhQ4f+ByOqHhKJRI3aXF5eDh0dHUY06+jRozAzM9M6rrW1NfOMByr65lXv74sXL3JipP7FJUcA8OzZM2zevBmzZ8/GzJkzkZaWxrp/Z+7cuayqRDWBD2dkvtS4lKg8GZ46dQpHjhxhnXAdO3YMc+fOZXqPLl26hMjISHTt2pVTFaq3b9+q8XFpKsNOTk7Mdv3Vq1chFArVFLR27drFepeAS9M2vmFhYaGRnpiWlkbVU6GEu7s7s7sXGBiIWbNmAaigkNBMvnyCD6pTddRbTQcN+Oib27NnD3R1dVGrVi1IJBLs2bMHZmZmCA0NRYsWLSASiVgnSEBFRXXz5s3YuXMnq/m5pqT29evXWLlyJQICAqjjK/H7778jICAAMpkMCoUCzZs3p6rafgju3LlDvWObk5ODadOmYfHixVUoUy9evGC1EyyVSpnfyszMDDk5OQAq5lAa2jvXAg9KnDt3jklegIr7IiAgALa2tggMDGTuF1o0bdqUSYp79+6N8PBwHDt2DFFRUVRiMP+Ga9euoXnz5lR/98yZMzAyMkKDBg3QtGlTiEQiREZGomfPnjA2NkZAQAAryf7evXtjzpw5Vd5PSUlBz549tY5XXFyMDh06wMTEBK1atWL6Ydzc3KCvrw97e3u13QgacNUjpaSgjxo1CsOHD4eBgQHq1KmDmJgYxMTEwNvbG4aGhoiPj2c1Xq4VPl1cXBhxDqCi8CcSiZjiFK2CYXh4eI3m7mlpaZzMxV9kcsQHBAIBRCIRQkNDsXHjRqrqZE3g2hmZLzWuBw8eIDAwECKRCEFBQXj69CkiIiIY7qq7uzsruW0+UVRUhGHDhsHMzIwTf41JkyYx6n9OTk4YP3487O3tsXTpUixbtgx2dnYaq0AfiosXL8LJyQkymYyhAerr68PR0ZG1l4kSXMqbzpo1CxKJBMOHD2eaVOPj4yGTyZiEhgbjxo3DjBkzAFQkRGKxGK6urtDV1cW4ceNYj5tLp/Nbt25xXqWuTLnV19eHQCBgBCkEAgH09fWp6Ld89c01adIEEydOBABs2LABJiYmajtm48ePR6tWrbSOyxf47N/5r5CTk0M1r/3xxx+MSpa9vT0UCoWaMiBbCWsnJydmV7ZBgwbMQuiPP/7gpCLMFerWrcssdlesWAGpVIoRI0Zg6dKl+OabbyCXy7Fq1Srq+JmZmQwtu6CgAB4eHhAIBDA1NeWkcFAZtNcDUFGUUvWUW7t2Lfz8/ABUeOX4+vpixIgRWsVULepMmzYNRkZGaNu2LdMPHBERAWNjY0ybNo1qzEBFkWbo0KEIDw9H69at0b9/f04sUrjskQoJCfmggzaxVYJrhc+kpCTY2tpi6dKlSE1Nhbe3t5oCckZGBmrXrq113CdPnuDZs2fVnt+9ezcOHTqkddzK+F9ypILXr19Xabz+UPBluKcKLhui+VLjioyMREBAAHbs2IGePXsiICAAzZo1w71793D79m0EBgZSUSNUUVJSgpycHGRmZiIzMxM5OTmcUACHDh0KLy8vbN26FVKpFKmpqZg2bRpsbW3VeoU+FGVlZZgxYwbatWuHmTNnory8HBs2bICdnR0UCgW+/vprVpMw16ZtfMubAsCmTZsQEBDALNwDAgI4bwQ+efIk5s2bhx07drCOtXXrVojF4ioiEmKxWKNS1X+NdevWITAwUC15y8/PR7NmzaiuYb765gwNDRmaTFlZGcRisRpN8cKFC7CwsNA6LlCxmzxkyBD4+vrC0tISlpaW8PX1xZAhQ6jnzF9++YU3lbrqwNa3o7L0c+Xjxx9/pFoMN2nShElky8vLkZycDLlczuySs02OYmJimIX2okWLIJVKERoaCmNjYwwYMIA6rhJcmb+r7nDVq1evivLfunXrqBZ/NeHJkyfUxZV/21VOSEig/t2kUilDfwQq7mkdHR0UFhYCqOg30bY3qHLRp7qDq55rLsF1j9THANcKnyUlJUhISIC1tTUUCgX69Omjtst8+vRp6vX2x8D/kiMVsKmc8GW4pwlcNETzpcZlZWWFkydPAqiYyAUCAfbv38+cP3DgALXfQVlZGSZOnAhjY+MqCjDGxsaYNGkSq54YOzs7puJgYGDALN7WrFmDNm3aaBUrNzeX1Vg+BFybtvEhb/q5gy+n8+HDh2ukuS1cuBAjR46kjuvs7KyxF+rs2bNUFE6++uYMDQ0Z4QxAvbdEGZfmGt69ezd0dXXh7++PKVOmYMmSJViyZAmmTJmCgIAA6OnpqVE9PgXw5dtRWQKaK+Wsyr8dUJEI6OvrY+fOnayTo7KyMjWq5oYNGzB8+HAsWLCAipHBl/m7QqHA2bNnAQDm5uYM/U+JT603SCAQwNrautokw9ramvp3c3BwYAxfgQoGiUAgYPqaaRfYnyu47pH6GOBa4fO/AptNDlX8LzlSAVfJkSrYGu7xBb7UuCQSiZqSSOVJ4vbt29QPjLFjx8LMzAzLli3DzZs38ebNG7x58wY3b97E8uXLYW5ujoSEBKrYyrEqF4I2NjaMDxaNRLhqj4KTkxMvcsVcm7bxIW9aGbdv38apU6eQnZ2NJ0+esIrFZ5+fEnw5nVtbWzMLK1WcO3eO1YNIKpUiOzu7yvunT5+mGi9ffXN169ZVu74uXLigtiA+evQoVUW4bt26NcoyT5kyBT4+PlrHVcXRo0fRt29f+Pv7M4vqNWvWICsriyoeX74d1tbWaibOlfHnn39SPe/MzMw0XrsbNmyATCbD0qVLOTE/5Qp8mb/369cPMTExAIDu3btXUeKcOXOm1tda586dP/jQFo6OjjXu0tNeDwAwcuRIeHt7Y8+ePTh48CCaN2+OkJAQ5nxmZiZcXFyoYvOFxYsXo2XLlujevbtaAReoMH1msyPFZY8Un9eEKrhW+KwO5eXlrAU6agKbdbwqvqjkqLI5ZOXD0NCQ+kvly3CvXbt2WLNmDVOB+VigVeOyt7dXM9cdN26c2iI4JyeHymgXqHjI1VT1zczMZNXY7+PjwzQ/t2zZkpkQ5s+fr/WCtVatWswujCqFkS34NG3jU9508eLFjCmi6hEYGKhxofUh4LvPD+DP6VxPT09j0lVQUAA9PT3quO3atUO9evXU1HzOnj2L+vXro3379lrH46tvbunSpYz8uiZ89913zMJTG1TnFaREfn4+qwq2knIbGxsLPT09Zrdr4cKFWu8uK8GXb0f79u1rTBRzcnIgEAi0jtuqVSukpKRoPLd+/Xro6OjwkhzRVoT5Mn+/f/8+HB0dERQUhNGjR0MqlaJp06YYOHAggoKCoKurq/U8/PXXXzNH//79YWhoCDs7O2bxa29vD0NDQyoJ5K5du9ZYPKS9HgDg1atX6NGjB+OfFBAQoCZW8ccff2hs9v8QPHjwAGvXrsWuXbuqzPGvX7/WKH//b5g/fz5kMhmGDRuGfv36QVdXFzNnzmTO0+x+8tUjxec1oQquFT5LSkowceJEBAUFMX2pc+bMgUwmg66uLrNLzjX+lxxRQCaTYcyYMYxMauUjKSmJ850jtlCatBkZGSEuLo56Ifmx0KFDB/z000/Vnl+0aBFatGhBFVsmk2l0kFciNzeX1e7cDz/8wFCd9u3bB4lEAj09PQiFwhr/TZowcOBA6OnpwdHREUKhEPb29nByctJ4aAM+Tdv4kjdNSUmBtbU1Fi5ciBUrVsDLywtTp07Fnj17EBkZCZlMViUR+xDw1ef3MZzO69Spo5GTvmDBAnh5eVHH/fvvv9GmTRsIBALo6uoy8vxt2rShmp/47pvjGp6enmreZ5Uxb948VgaBvr6+WL16NQB1KuD58+epe6T48u04evRold1fVbx+/ZpKCS8jIwPffPNNtefXrVuntmvAFWgXPXyZvwMVyrfjxo1D7dq1IZFIoKurCwcHB/Tp04dqTlNFQkICYmNj1arspaWlGDRoEL799lut4126dKnGMb1//571Tvzbt285tUPJzs6GsbExDA0NIZVK4erqqkbrpaVw1q5dG+vWrWNeHz9+HGZmZkwxgSbux+iR4vqa0ASuFD4nTZoECwsLjB49GrVr10ZcXBzs7OyQnp6O1atXw8bGhkoRmc9NDlV8UclRQEBAjYtcrjJOLiEQCHDp0iX8+OOP8PHxgVAoxFdffYWFCxfyasL49OlTZhHAJU6fPk29eG3bti1at26t0W378ePHCA8PR0REBNshMrh16xZ+/fVXxohPW+zZswcLFy6EQCDAtGnT8NNPP2k8tAGfpm18yZs6Ojpi9+7dzOsrV65AoVAwNKoRI0ZQKZPx1ef3MZzOV61aBalUisTEROb3mjx5MmQyGdUOc2VcvXqVSfDYStJ+Tti8eTPEYjHat2+P+fPnY+PGjdi4cSPmz5+PDh06QFdXl5WQhlQqZZqWVZOj69evU+/4fSzfjs8dtM/nj2H+zgdMTU017oLm5+dTJ3OfG0JDQxEdHY2ysjK8fPkSQ4YMgUKhYFgttMmR6n2shFIEZvz48az75vjCx7omysvLWauqOjs7MzvgBQUFEAqFTJECqBBnoqH/87nJoYovKjmaMWOGmtxkZdy5c4f11iTXqLwjdfr0aQwaNAhGRkaQSqXo3bv3JyfryRfu3LkDb29viMVi1KtXD+Hh4QgPD0e9evUgFotRt25dtQrsp4Kvv/6aMzGOjwGu5U1lMlkVMzixWMxIuufk5EAul2sd93Pr86uMJUuWwMbGhkm2nJyceClI8IFDhw6xpvrm5OQgMjISTk5OTCLu7e2NSZMmUQkQKHH8+HH07NkT9vb2zO6Zvb09evbsyagx0sLJyYnZeVBNjlavXk294/exfDu4BteqoXxWhD8383egwgRWU8/Ytm3bYGxs/B+MqHpUnof//PNPREVFISAgAF27dqWWVjYxMalS3Jk1axZMTEyQnZ1NncTY2dnh6NGjVd6/dOkSLCwsEBUVxcn6p7i4GPn5+Zx5D/J9TaxcuRJ16tRh5s06depgxYoVVLEq959LJBLG8Buo6OU2MDDQOu7H2uT4opIjPsGXIVx1C8CioiKkpaWhadOmVBdCZYPTykdWVhYnF1h5eTkOHjyIn3/+GTt37mQtuV1WVobdu3cjMTERgwYNwqBBg5CYmIg9e/awUod78+YNsrKyNEr9vn379pNftN64cQN79+7lVDqeK/j6+qrthhw4cAAymYypTOXn51NNknz1+VWHt2/fchZLFX///TfrxRnfhpSVoaOjg8uXL1P//czMTEilUnTt2hX9+vWDTCZDfHw8xo0bB1dXV7i4uODhw4ccjpgbzJw5E7Vr18apU6dgYGCArKwspKenw8zMDAsWLKCKyadvBx9mrXyphvJdEebD/L0msDFVBSrMPxUKBebNm4esrCxkZWVh7ty5MDU1pfbH27VrF2JiYjB27Fi1hSpQwRahHa/qXHz8+HHo6OggODgYY8eORatWrSAWi6n6xUxMTDQyN1JSUmBsbIyMjAyqa6J3797VUkMvXrzIeB3SoqioiLHCEIlETBElPj6elacfH9eEEpMnT4a+vj7Gjx/PsA7Gjx8PuVxeY+9idbCwsFBrgwgICFBThczLy4OhoaHWcT/WJsf/kiOOwJch3If0MtHQZir3rlQ+aGlDbdq0wfPnzwFUPPT9/PwgEAiYycbT05MzcQKucOXKFTg4OKhJ56oa1XKxxX7mzBmMHTsWPXv2ZK0wM2TIEGYx/ebNG3Tt2lWN6tW8eXPWi21VVTm2SnubNm2Cjo4OevTogaioKMjlcowfP545v2zZMipfpo9hzFlaWoqpU6fC2tpa7SE3adIkrFy5krPPKSwsZJUM8DX/KM2FKx8CgQBeXl7Ma23h6+ur1rO1d+9eeHp6AqjofWjZsiWnu/hpaWnMvMQG5eXlmD59OmO0KxAIIJFIqiiVfQrgy6yVL9XQz5H2XhPYjresrAzJycmwtrZmrjVra2skJydTqX2tW7cOIpEIERERaNq0KSQSiZpnGZvnnOpc3KpVqyp+VCNHjqTqNW7WrFm1vZ3JyclMT7C2yM3N1Si0o8SFCxdqXID/G0aMGIEGDRogKysL+vr6zHNj27Zt8PX1pY7L9TWhClNTU6xfv77K++vXr4dCodA6XvPmzfHLL79Ue37z5s1o0KCB1nE/Fr645CgnJwerVq1iLtaLFy9iyJAhGDx4MCv/C74M4UJCQmqsKtLC0NAQycnJ1farrFixgmrSUZ0khwwZgtq1azMV7bt376JBgwaIi4tjPX6lnG6TJk1Yy+l26tQJERERePz4MQoKChAREQEnJydG1pptcrRhwwbo6OigXbt20NXVRbt27eDu7g4jIyOqBaBqle67776Dra0tDh48iKKiIhw7dgwuLi5qyYc24ENVDqiofvfp0wddu3atcm/8888/vEidc4GkpCQ4OzsjPT1dzehw48aNVFL3T548QdeuXWFnZ4e4uDiUlpYiJiaGSWybNGmilph/KPiaf8RiMcLDw/H9998zx5QpUyAUCjF06FDmPW0hkUiqUC11dHSYf/vRo0dhZmamddzqwHanqzKKi4tx6dIlnD59mjNKVmpqqkZFr82bN9e4yKgOfJm18qUa+rFp782bN2fVcM6nqWplKBkdbODr66vmrbZp0ybo6+szRR6ukiNVr0MlLl68SKUIuGLFCvTr16/a87Nnz6ayEuAb9vb2zHegSr8tKCigYkloAhfXhCqMjIw07qReuXIFRkZGWse7cuWKGpuhMtatW8e5ATyX+KKSo19//RUikQgKhQJyuRz79u2DsbExQkNDERYWBpFIpKZgog0+N0O4kJCQGpVCaGU9VSdJDw+PKqZi+/fvZ+1ozbWcrrm5udr2b3l5OeLi4mBvb4/r16+zTo58fHywaNEiAP83UZaXl2PgwIGMxKU2UP2Ovb29q1R7tm/fDnd3d63j8qUq9zFx7NgxvHv3jrN4Li4ujAeG6kMuLy+PiuM9YMAAeHt7Y+HChQgODkbHjh1Rt25dHDt2DCdOnECjRo0QFRWldVy+5h9lsp2YmKhGlRKLxRopqB8KFxcXtQV2QUEBRCIRI+1648YNqvFW168iEAhgZGTEvOYC69ev51Spz83NTW1nR4nDhw9T3c98mbXyrRrKNVTVJ1UPkUiERYsWMa+1BZ+mqnxAX1+/ymL14MGDkMvlWLp0Kevk6Nq1a3jx4gWcnJyq2IBcu3aNSnHxY6GoqAh5eXnIzc1VO2ihWkhTfW7k5ORQUcmqA5eeQfHx8RqpeWPGjMHQoUM5+Qy+YGBgoGYizgW+qOSofv36mD59OoCKar6xsTGmTp3KnJ87dy71licfhnA1gS1N5Oeff1arIlVGYWEhVUVY1dPH3NxcTXYTqFCAY+PjAnAvp2tgYKCxqjxs2DDY2tri6NGjrB5yqoIEtWrVYhYWly9fhqWlpdbxVL9jU1NTjd8xzcKSL1U5TWjbti3VDsm/getJUiKRMNVl1Wvt0qVLVAtAKysrHD9+HEDFPSYQCLB3717m/LFjx6hMYPmcf54/f45evXrBz8+PWWyzTY6SkpJga2uLpUuXIjU1Fd7e3moU04yMDKqdLrlcjoiICLVelbS0NIhEIsyYMYN5jwtwfa3p6elVUdACgJs3b1J5M/Fl1vqxVUPZQrkry7XyJJ+mqjXh8uXLVAVGTTs6QEXyLZfLMXHiRFbJkSolv/LO9fbt2+Hq6koVWxWPHj3C0aNHcfToUU4o1X///TciIiKqbS+gRbNmzZgeRLlcziSl8fHxCAsL0zoeX55Bo0aNYo7hw4fDwMAAderUQUxMDGJiYuDt7Q1DQ0PEx8drHftj2s6oPpu5wheVHOnr6zMPHyWNQ7UCdv36dSrVLIAfQ7iawDVNhCsIBAK0bdsWnTt3homJiZqZIQCcOnWK2g9ECa7ldBs1aoQ1a9ZoPDds2DAYGxuzmihtbGyY68zHx4fZ6Tlx4gRVFUkgEGDw4MEYNWoUzM3N1RbXQEVzPg2FgS9VOU3gYzLjI279+vWxdu3aKrGTkpLQtGlTrePJZDI1Ko+Ojo6aiMaNGzeokq6PMf+kpqbC0tISy5cvh46ODqvkqKSkBAkJCbC2toZCoUCfPn3UFtunT5+mauAuKChgdt9U6W5skzlN4Ppas7Oz07iDsW3bNqqEmS+z1o+pGspFAqpM1iovptleE3yaqtYE2l6mjh07VstUOHToEPT19amfc5Vp+ZX7oH/66SfMmTOHKjYAvHz5Ev369WNMZpX+j3379mVVJO7Tpw8CAwNx5swZ6OvrY+/evVi7di08PDxqNKn+N2RlZUEulyMuLg4SiQQjR45Eq1atoK+vT5U08OUZFBIS8kEHjVCHQCCAi4sLZsyYgfv372v997XB/5IjlrC0tGQuzKdPn0IgEKipAGVnZ1NV8pXgwxDuY9JE7t69y0rxDVB3c/7666+rVNbGjh1LVTlRBddyujNnzqyRjjdkyBBWD7nevXszxpRTp06FmZkZYmNj4eDgQCXIEBwcrDZxVZbanDZtGoKDg7WOy5eqnCZ8LsnRtm3bYGRkhNmzZ0MmkyElJQWxsbHQ1dWtkpR+CL766iuGYrl7924YGBiomZYuXbqUyvsB4NeQUomrV6+iYcOGjP/apwhl4uXi4oJjx44B+DySo4SEBDg4OODgwYMoLS1FaWkpDhw4AAcHB4wZM0breHyatfKlGloZXH3HP/zwA+zs7NSKdWyvCb5MVVWr+ZqOfv36USUxhw8fxsyZM6s9f/DgwU/OykSJHj16wM3NDZmZmUyvTWZmJjw8PNCzZ0/quJaWljh9+jSAikRcmdRt374dgYGBrMZ87do1xMbGolGjRvDy8kLfvn1rpKPWBL48g/iEQCDAwIEDYW5uDrFYjIiICPz222+c0QBVERcXp3Enmw2+qOSoX79+8PPzQ3p6Otq3b4+wsDD4+/sjLy8P+fn5CA4ORrdu3f7rYarhc6aJaMLr169ZSyLzIafLJ548ecJUTsrKyjBr1iy0b98eo0eP5sXI9/r167h7967Wf48vVTlNqFOnDi+eVOvWreO0DwSoEAcIDQ2FmZkZpFIpAgMD8ccff1DFSk9Ph0gkgqurK/T09LBlyxZYW1ujR48e6NWrF3R1dZnk6VNFWVkZnj9/ztoksDJmzZrFqfjMgQMHYG9vj++++471TpcmZGVlcSrvXlxcjB49ekAgEEBHRwc6OjoQiUSIjo6mosz8/wFcJqB//vknateujUGDBqGoqIiXhJkLCIVC1K9fv9oqfsOGDT+pXqbqMGTIEM4WrDKZTKPY0tGjR1n1MhkYGDBsCXt7e6aYQtvzyBf48gyqDlz0Uyp7o0tKSrB161a0bdsWIpEIFhYWSEhI+OTNyb+o5KiwsBCtWrWCXC5HWFgYnj9/jvj4eIYv6+bmVqWBlQ24uMA+Z5qIElw3yX8MOV2um635Bhe7fsDnqyoHVNwrmZmZjDkp14t3rnDs2DHMnTuX6T26dOkSIiMj0bVrV84KHQB/1zCXix5V8FGc+eeff9C5c2cYGxtrdJb/FHH16lVs3rwZO3fuZKWmpgl8/Xbe3t68FDu4rgi/efMGgwcPhpubG0QiEefPUC4SfHd3d4bKqwlc9jLx1fsJcHs/29nZadx1yc3NpaKcKtGwYUNGGKZ9+/aIjIzEvXv3kJCQAGdnZ+q4quDiO+bLM6g6cPHbabLauHfvHqZOnQpnZ2cIhUI0a9aMKvbjx4+RnJyMTp06wd/fH/7+/ujUqRPmzJnDmVXMF5UcVYfr16/jwoULnLkYK8HV5PC50kSU4GtHig85XSX4GvPnIkTwMcBWSleJf/75By1btmSKHMrvITo6GqNHj2YdXwm+FpZ8ga9rgq+4fM0/bOHr61ut31PlgytwXVBS4kv77arD9u3b8c0333Duk8bF99unT58a6ZBc9jLx+btxGXv58uUIDQ1V84J7+PAhWrdujWXLllHHXbt2LdLS0gBUCAiYmppCKBRCIpGo0dbYgIvv4WN7BnEx5n8zad+/fz/69Omjddzs7GyYmJjAxsYG/fv3R0JCAhISEtC/f3/Y2tqiVq1anNDIxeQLx71794ijoyMRCoWcxwbASRyxWEySk5NJWFgY6dOnD+nbty8RCAScxFbFhAkTSK1atTiPy9X3UBm6urokNzeXdOjQgejr63Mam68xHz16lLx9+5bzuHyNNyIigqxcuZJYWVlRx9ixY4fG948ePUp+//13YmdnRwghpEOHDlTxR40aRcRiMblz5w7x8vJi3u/ZsycZPXo0mTdvHlXcykhPTyfffvstMTU15SSeEkOHDiVTp07lPC5f1wRfcfkC22u4U6dOzJ/fvXtHlixZQmrXrk2aNGlCCCHk1KlT5NKlS2To0KFcDJcQQkibNm1ITk4OcXZ25iwmIZ/Pb3fgwAHy448/kry8PEIIIV5eXuSbb74hoaGhnMSvX78+iYiIICKRiJN4SnDx/c6bN48UFxdXe/6rr74i5eXlrD/nU0e9evXU1jkFBQXE3t6e2NvbE0IIuXPnDtHT0yOPHz8mgwcPpvqMfv36MX9u0KABuX37NsnPzyf29vacz8dssGzZMqKjo1Pt+ZKSEpKQkPARR/Tv+Ld7oWXLlqRly5Zaxx0+fDjp3r07WbZsWZV1MAASFxdHhg8fTk6ePKl1bFV88clR7dq1eXkI8YEWLVqQ8+fPk4EDBxJ9fX3OJ/bx48dzGu9jYPDgwcTPz++z+P0+R3CRzHXq1IkIBAKNk+Xw4cMJIYQIBAJSVlZGFX/v3r3kjz/+ILa2tmrvu7m5kdu3b1PF1AS+FpZ8JV2fGy5fvkxsbGw4j8v2Gp4yZQrz59jYWDJixAgybdq0Kv/P3bt3qT+jMj6XJEaJZs2aEalUykmsJUuWkJEjR5Ju3bqRkSNHEkIqEtC2bduSH3/8kQwbNoz1Z3zKz31LS8uP9lkODg41LrrZ4NWrV6z+vmpR4mPg+PHjpGHDhqR+/fqcxuXiO3Z3d6/xfJ8+fVjFr4w9e/YQa2trVjEOHTrES7E9NzeX/PLLLxo3CAQCARk1ahSpV68e+w9ivff0mYPPbWWuG3b5wsqVK1GnTh3o6upCV1cXderUqaKAxgZ8NMkrwdfvl5WVxQuthS8hgpkzZ3LazK4EF98vX1K6SsjlcsbZW3W8Z86cQa1atVjHV/2cz4mSxNc1zAfOnDmDNWvWYM2aNZwbDXP5/RoaGmp0kb969SqnnP/PjabGJWxsbLBw4cIq7y9atAjW1tacfAZf3++dO3d4UeP63Ci9QIVa39WrV1lJbX9MfG7UdD77xT5lODo6Mj6XmrB69Wo4ODiw/pwvfueITzRt2pSXuFxQnZRITEwkP/zwAxk+fDhDEzl58iQZNWoUuXPnDpk6dSrrz2jWrBl5+vQpK+rby5cviaGhIeuxfAj+/vtvAoBkZ2cTDw8PYm5uzlnsixcvchZLFXzt+nFR9dqzZw/58ccfScOGDcmSJUtIu3btOBpdBZo1a0bWrFnDVPMFAgEpLy8nc+bMIc2bN+fsc9hWQj8WXrx4QQoLC4mZmRl59+4d0dPTYxWvrKyM3L59m6EfFxcXk+3bt5Py8nLSvHlzYmFhQR373r17pHfv3uT48ePE2NiYEELI8+fPSUBAANm4cWOV3UAacFkdl0ql5Pjx48TNzU3t/ePHjxOJRMLJZxBCyPLly1l9rx+C0tJS8uDBA4am9CGojiKrCbQ02efPn5Pw8PAq77du3ZqMGzeOKubHgpIizDW43F3Ozs4mJ0+eJIWFhYSQip2qJk2akMaNG1PHnDNnDhk+fDiRSqWkrKyMjBs3jixcuJCUlpYSoVBIIiMjyfLlyzm5Dz8HGvKNGzfIsWPHyMOHD4lQKCTOzs6kVatWnK5huGB1VKYu1oTz58+z+ixCCDE0NGS9Y/vtt9+SQYMGkXPnzpGWLVsy8+SjR4/IgQMHyIoVK8jcuXNZj/WL3znio+K+ZcsWdO/eHX5+frw07HJZ9TI1NWVMSVWxfv16KBQK6rglJSWYNGkSDA0NGbdpQ0NDTJw4Ee/fv9c6nmpzX/PmzZnfjMvdOb6M5qpTkSsrK8Pt27ep4wL87/pxCb6kdC9cuABzc3OEh4dDV1cX3bp1g5eXFywsLDhVn/zUsWLFCnh5eVVxeffy8sLKlSupYubm5sLKygpCoZBRJPP29oa+vj7kcjlMTEyQnZ1NPeawsDD4+fmpKcnl5+ejSZMmrP3Q+MCsWbMgkUgwfPhwrF27FmvXrkV8fDxkMhlmzZrF6WdxLRBUGTRmosp5UXkIhcIqr5UHLXr37q3RMDQlJYWVp40q2D73jY2Nq/UgrHxwAS6e+Y8ePULTpk0hEAjg4OCAxo0bo3HjxnBwcIBAIEDTpk2pBSpUn88pKSkwMTFBamoqLl26hPT0dJibm1OZlGrCpywq8vr1a3Tr1k3tfrC0tIRIJIJcLufUpoGL8X7//ffMMX78eBgaGsLf35/x1GrSpAkMDQ3VLD3+6zEDwMaNG+Hn51dlrebn51fFW5MWX3xyxDXmz58PuVyO+Ph46OrqYvDgwQgNDYWRkREmTJjAyWdwmRwZGRlppIlcuXIFRkZG1HHj4uJgbm6OZcuWITc3F7m5uVi2bBksLS0RFxendTxDQ0NcvnwZQMUDmiu5RlVwbTT34sULdO/eHRKJBObm5pg8ebIa5aKwsJDVImLy5MnQ19fH+PHjsX37dmzfvh3jx4+HXC7H5MmTqeMCFf4Rffv2RZMmTRjJ0DVr1mj0mtAGfEnpPn/+HNOnT0f37t3Rpk0bTJw4kRXl4P379xg7dixcXFzQqFEjrFq1Su0829/u4cOH2LZtG5YtW4Zly5Zh27ZtakpM2mLOnDmQyWQYP348Dh06hMuXL+Py5cs4dOgQvvvuO+jr6yMlJUXruGFhYejWrRsuXLiAkSNHwsvLC927d8f79+9RUlKCfv36ITQ0lHrcEokE58+fr/L+2bNnNbbB3AABAABJREFUqX1GHj16hAMHDjAFjcLCQiQnJ2PWrFnUJoyq2LRpEwICApjFb0BAAKsH8p49e5hxlZWVYerUqbC2toZQKISNjQ1mzZrFiyw9TXKkin379qF+/fpV5suGDRtqbZA8f/585pg2bRqMjIzQtm1bTJs2DdOmTUNERASMjY0xbdo06vFyCVXfwXnz5sHExAS9evVi/g29evWCiYkJfvjhB04+j4tnfteuXdGkSRONkvb5+fkICAig9nlUlW2uV68eli9frnY+PT0dderUoYpdGXxRIrmg/w8aNAiBgYG4cOECCgoK0K1bNyQkJKCoqAirVq2CTCbDunXrOBkv1xT9mJgYjXYoiYmJiI6O5uQzuP7t3r9/jwcPHuDBgwdURfea8MUlRw8ePMDkyZPRvHlzeHp6onbt2mjXrh1WrlzJCVfYw8OD2YlRvRAmT56MYcOGsY4PcHtTxMfHY9SoUVXeHzNmDIYOHUod19DQELt3767y/q5du6i4+V26dIGFhQVCQkIgEAgQGBiI5s2bazxowbXR3IgRI+Du7o4tW7ZgxYoVcHBwQEREBGPoWFhYyEqSla9dv61bt0IqlSI2NhZ6enrMNbxw4UK0adOGOq4q+JLS5QpTpkyBhYUFUlJSMHHiRBgZGWHQoEHMedrf7vXr1+jbty9EIhHEYjHMzc0ZB3GRSIR+/fqhqKhI67j29vY1LtA3btwIOzs7reOamJgwRYk3b95AJBIxjvIAcPHiRVbXmpubm1o8JU6fPg0XFxet4x06dIjxP7O0tEROTg5sbW3h5uYGDw8P6OnpURv48gUPDw8cPXoUQMWOhkKhwA8//IA9e/bgp59+goWFBWbPnq113H+THff09GSVHNWpU6fa+dLT01OrWI6Ojh90ODk5UY117969SExMxIEDBwAAR44cQXh4OJo3b47U1FSqmEp06dJFY4/UwoUL0bFjR1axuYRcLtdYiFDi7NmzkMvlVLFVC5YKhQIXLlxQO3/jxg1WZq2q4CM54sojz9TUFGfPnmVeP336FBKJhJnTFy1aBF9fX/YD5gEfo5+Sa98yPvFF9RydPXuWhIaGEldXVyKVSklBQQHp06cPef/+Pfn2229JamoqyczMJAYGBtSfcefOHRIQEEAIqeCnK/sUIiMjib+/P1m0aBHrfwfbvpXRo0czfxYIBGTlypVk7969xN/fnxBCyOnTp8mdO3dIVFQU9Wfo6ekRR0fHKu87OTkRXV1dreOlp6eT1atXk+vXr5MjR46QOnXqEJlMRj0+TVAoFMTIyKjK+0ZGRsTExETreNu2bSOrV68mISEhhJAK9Z2IiAjSvn17hrvPRpK9pKSENGzYsMr7DRo0IKWlpdRxp0+fTpYtW0aioqLIxo0bmfcDAwPJ9OnTtYpVq1YtcvXqVWJqakoGDBhA5s+fTwwMDEiHDh2oexI04dmzZ2TVqlWM9G/t2rVJdHQ0tVrOunXryMqVK5n+qK+//pq0adOGREdHk9TUVEII3W83cuRIkp2dTXbt2kVCQ0MZxcmysjJy4MABMnz4cDJy5EiyYsUKreL+/fffxMfHp9rzPj4+5J9//tF6vACIWFzxmKj8X0IIEYlErGSFU1JSyPDhw8nixYuZa/ns2bNk5MiRVLzxyZMnk6+//prMnj2bLFu2jERERJBOnTox8+7YsWNJUlISad26NfWYVVFSUsK6j+LWrVvEwcGBEELI+vXrydKlS0n37t0JIYSEh4cTV1dX8s0332jdb3P58mXSq1cv4uTkpPH8w4cPydWrV6nHff36daZPTBVGRkbk1q1bWsW6efMm9Tj+Denp6SQ6OprUrVuX/PDDD2ThwoVk1KhRpFu3bqS8vJzExcURAwMD0q1bN6r4f/zxB0lOTq7yfnh4OKte0KKiInLu3Dm1vpX69etTPzP09PTIy5cvqz3/6tUrVv2JK1asIHK5nOjq6pKnT59yGrtyLK7w5MkT0rNnT3Lw4EEiEAhIQUEBcXZ2JjExMcTExERrG4jS0lK1viK5XE5KS0tJUVERkclkpHXr1uTbb7+lHi8f/WJKfIx+yqVLl3ISR4mzZ8+SzZs3kzt37pD379+rncvIyGAX/L/Ozj4mAgMD8f333zOv165dCz8/PwAVGb6vry9GjBjB6jOcnJyY6kyDBg0Yg7I//vhDa/6xko72IYc2CAkJ+aCDzS5MUlISevfuraaW9e7dO/Tt21ftN6BBSEgIL8psXBvNSaVS3LhxQ+29ly9fokmTJmjRogVu3LjBqnLL166fVCrFzZs3AahX6a5fvw49PT2tYunr6zN/XygU8kKHPHLkCAwNDWFnZ4fOnTujc+fOsLe3h6GhIY4cOUIVU/U7UOLevXtwd3dH3759cf/+farfztjYGMePH6/2/LFjx2BsbKx13GbNmiEqKkpjn0ppaSmioqIQFBSkddyWLVsiJiYG9+7dQ1JSElxdXdUoFkOHDtXa5bxyv4auri6EQiHTN6f8M02/hqGhIdNnVlJSArFYjD///JM5f/XqVSq68KZNm5gdX6BiV8De3h5CoRAKhQJJSUlax1TCysoKJ0+eBABYWFhUqe5fvXqVimLYoEEDLFmypNrzf/75J6v5p1mzZmjVqhUKCwuZ9woLC9G6dWuqa6063Lhxg1X/la+vL+bPnw+gwnhSKpWq0d3mzp2LwMBA6vj29vaYO3dulffnzp0Le3t7reOVlZVh7NixkMlkTP+Wsq/CwcEBO3bsoBrn0KFD4eDggIyMDLx48YJ5/8WLF8jIyICjoyPi4+OpYjs4OKjt8P34449q53/66Sf4+/tTxa6Mt2/fMjRO5UGLyMhIhIWF4e7du2rPuszMTNSuXVvreK1atVJjCKWkpMDKyop5ff78eZiammodl89+MSX46Kd8/PgxkpOT0alTJ/j7+8Pf3x+dOnXCnDlzWK8FNmzYAB0dHbRr1w66urpo164d3N3dYWRkhK+//ppVbOALo9VJpVK17diysjLo6Ogwk/vevXtZS4XGxMQwi/9FixZBKpUiNDQUxsbGGDBggFaxVBteVRtdK79m84DjEsqFqfIwMDCAqakpWrZsiZYtW8LU1BSGhobo3Lnzfz1UBr6+vmpUE7lcDh0dHbi4uMDFxQU6OjqQy+VUYhoeHh7YtWtXlfdfvXqFJk2a4KuvvtL6t1M2So4aNQrDhw+HgYEB6tSpg5iYGMTExMDb2xuGhobUDzmgIsHft28fAPXkaPXq1fDy8tIqVmhoKHx8fPD1119DIBCgV69eiI6O1njQwtvbGwMHDlSjxZaWlmLQoEHw9vamiunk5IT9+/dXef/+/ftwd3dHq1atqO47Q0PDGqWqs7OzqSgMubm5sLS0hEKhQOfOnREXF4e4uDh07twZCoUCVlZWVaguH4Ls7GwoFAoIhUKYmZnh4sWL8PPzg6WlJaytrSGVSjV+TzVBtV/j3w5tYWpqiosXLwIAioqKIBQKmcQDqPieaBYnqg3nqampkEgkSExMxK5duzB9+nTo6+tTC6EMHToU7dq1Y67Z2NhYNVrP8OHD0aRJE63jjhgxAiNHjqz2/LVr1xASEkIzZAAVVCRvb2/o6uoy86VSFKagoIA6bmXo6Ogw1E4a6OvrqxWpdHR01AqKeXl5rKihaWlpEIlEaNeuHdMj1a5dO4jFYqSlpWkdb9y4cfDy8sLOnTuxb98+BAUFITk5GXl5eZg8eTI1NfTdu3eIi4tjChASiQQSiYQpRgwZMoQ36f+TJ0/WSOn7NxQVFWHYsGEwMzOrsvZhs/6xsLBATk4OgKqFQH19fa3jnTt3DrVq1YKlpSXs7e2hq6uLDRs2MOcXLVqEqKgorePy2S+mCi77KbOzs2FiYgIbGxv0798fCQkJSEhIQP/+/WFra4tatWqxsm3w8fFhBC6Uv115eTkGDhyIxMRE6rhKCIDPzG2OBRwdHcm6detIYGAgIaSCVmBjY0OKioqIVColt27dIl5eXqzkEcvLy0l5eTlDPdm4cSM5ceIEcXNzI4MHD9aKUqZqYPnnn3+Sb7/9lowdO1ZNcnvevHlkzpw5rA3TlI7cbLa+o6OjP/j/TUtLo/qMffv2kWPHjpHg4GDSokULcvToUTJr1ixSXFxMIiMjtRoDIYQkJSV98P+ragb5IRgxYgR5+PAh2bJlS5Vzr169Iq1atSJnzpzRyvz0Q6WpBQIBOXjw4AfHVcWsWbNIeno6SU1NJa1atSK7d+8mt2/fJqNGjSKTJ09mjFs/BI8ePSI//vgjuX79OsnIyCBhYWHVXmO//fYb1XilUinJyckhHh4eau9fuXKF+Pr6Ut3PsbGxBABZtWpVlXP3798nISEh5MaNG1ob1/bt25fk5eWRVatWVTGq+/PPP8nAgQOJp6cnSU9P13rMr169Iunp6eTUqVNVaBd9+vShlpEtKioi+fn5xMPDg8jlcvLu3Tuybt068vbtW9KqVasq3/t/iU6dOpGysjIyfvx4smbNGnL+/HliYWFBNm3aRAQCAenfvz95/fo12bNnj1ZxhUIhKSwsJObm5sTPz49069aNjB07ljm/dOlSsmLFCiq52xcvXpDQ0FDy/Plz0qRJE7JlyxZiYWFB3N3dybVr18jTp0/JH3/8Qfz8/LSOzTcAkH379pH8/HxCCCFeXl4kNDSUivrVpUsXje9v376dtGjRgqG7a0uXMTExIadOnWKuUwMDA5Kbm8vICd+8eZN4e3uToqIircesxOnTp8mCBQsYWq+XlxcZMWIE1W9mbW1NNm3aRJo1a0YIqZhvPD09yT///EP09PTItGnTyJ49e8iJEyeoxvry5Uty9uxZ8ujRI0JIxRzRoEGDj2aVQYNhw4aRQ4cOkWnTppHIyEiyePFicv/+fbJ8+XIye/Zs0rdvX6q4BgYG5Pz588TNzU3tujh79iwJCwsjT5480Trmw4cPye+//06Ki4tJixYtSO3atanGVnmcR48erdbc9Ny5cyQkJOSTspvw9/cnX331FVm2bFmV+QAAiYuLI3/99Rc5efIkVXx9fX1y6dIl4ujoSBQKBTl8+DDx8fEheXl5pEWLFuThw4fs/gGs06vPCCNHjoS3tzf27NmDgwcPonnz5mqVs8zMTKom4I+BRo0aadyF2LVrF+rXr08Vc+/evWjTpg2MjY2ZCoyxsTHatGnD7Bx8Sli7di3EYjHq168PuVyOtLQ0GBsbIzY2FgMGDICuri62bNmiVcz58+czUuC3b9+uVnabBk+fPmWq2Jrw8uVLHD58mLPP4wrl5eVMNVxJ55BIJBqVbLSBo6Mj/vnnH45G+X8ICAjAb7/9VuX93377jaHNaotbt24hMzOz2vP379+n2tl4+vQpwsPDIRAIUKtWLXh6esLT0xO1atWCUChEmzZteKGMfqqoLIJz6tQpHDlyhFp56OrVq3Bzc4NAIICXlxfu3buHDh06QCwWQywWw8zMDOfOndM6rmrDuampKVNtVuLatWswMDCgGjNQobq0dOlStG3bFp6ennB3d0dwcDAmTJiAu3fvUsf9WHj79i1rRT2BQIDg4GB8/fXXaodQKESnTp2Y19qiYcOG2LZtG/P6xYsXamPdt28f3N3dWY2dS1SWqi4rK4NYLGbo3pcuXeJM3IBLk/p//vkHBw8exJMnTwBUUKpmz56NpKQkVjt/AGBnZ4dDhw4BqPh+lDuTa9asYSUS1KZNG+a5JpfLcePGDZSVlaF79+7o2rUrqzFzCYVCUeNa4dChQ6x2PzXh+++/ZyWeIJFIkJeXV+35vLw8SCQS6vg2NjaMyqePjw8jTnXixAlOBCS+qOTo1atX6NGjB6ONHhAQoLbd/scff2Dz5s2cfNazZ8/w888/Y9KkSVi5ciVrl2iJRKJxgrl8+TLVBfbLL79ALBajV69eSEtLw+7du7F7926kpaWhd+/e0NHRwZo1a1iNWYl3797h2rVrrLfs+eCOi0Qihi6jSp1hCxMTE2ZiiY6OxsuXLzmJ+zFRXFyMS5cu4fTp03j16tV/PRw1qPbbbdy4Efb29khJSUFWVhaysrKQkpICR0dHbNy48b8eqkbk5eUhNTUVM2fOxMyZM5Gamlrjg+RDsWvXLo1JXWZmpkb1SG2glHf39/dnLe/+4MEDBAQEQCQSISgoCE+fPkVERASTjLu7u7OSYq+chO/fvx87d+6kTs4FAgHWrFmD7du3w9bWFidOnFA7f/HiRc4UnbgEn7LmqrLjIpGIWdArn3naYsOGDbC1ta2iHsfWDy0jI6PG3sNZs2axKvxwfc8FBARg+vTpzOsNGzao9SFeuHCBlX9SaWkpp78bUKEuaWRkBIFAABMTE5w9exZOTk5wc3ODi4sLpFIpVVFCCX19fcYT0MbGhlG4vHHjBhX9TQk+PPJycnKwatUqZm158eJFDBkyBIMHD66x4FYT+OwXq9y/9eLFCzx//hw6Ojo4ffo0dV+Xo6MjVq9eXe351atXw8HBgWrMQIUf2rx58wAAU6dOhZmZGWJjY+Hg4MBJ68YXlRwp8fbtW84Xe507d2Z2LS5evAhTU1OYmZnBz88PFhYWsLS0ZFU9qVevHiIjI9WagouLixEZGUnVD+Pm5lajIdnixYvh6uqqddy0tDRm4fD27VsMGDAAIpEIQqEQYrEYgwcPpk6S+OCO29nZYcmSJbh16xYEAgHOnTuH27dvazy0HSvfQgTV4dq1a6zENCqDKzPK4cOHM8mtKhYuXFhjb4QmaDKg1HRw0Y+3Zs0aBAQEwMrKCrdu3QIA/Pjjj2rV6E8FPj4+GneY9+zZg7p161LH5VrePTIyEgEBAdixYwd69uyJgIAANGvWDPfu3cPt27cRGBjImfUBF6h8XakuXoEKM2auTL4BbgpKfMuaJyUlwdnZGenp6Wr9vBs3bqRuvr958yYCAwPRpUsXPH36FAD75IhvcH3P7d+/H3p6emjcuDGCgoIgFovVBA5SUlLQokUL6vHy8buFhoYiNjYWL1++REpKCmxtbREbG8ucj46ORqdOnajH7OPjw+yctGzZEmPGjAFQwfywsbGhjgtw65H366+/QiQSQaFQQC6XY9++fTA2NkZoaCjCwsIgEomofI747BfT1MOl2ttO+xxdtGgR9PT0MGLECGzfvh2nTp3CqVOnsH37dowYMQJSqRSLFy+mGjMAPHnyBPfv3wdQUaiZNWsW2rdvj9GjRzNzBxt8kckRHzAxMWEqv23atEGfPn2YROb9+/eIiYlB69atqeOfPn0a5ubmMDMzYwQOzMzMYG5urtEn5N+gp6ensblPifz8fKodKScnJ5w6dQoA8O2338LR0REZGRnIy8vDtm3b4O7ujrFjx2odF6hQuVIdc2W/AxovheXLlzMTzr9NEtrgYwgRVAdac0e+zSitra3VPCCUOHfunNYPuFu3bn3wwQZLliyBqakppk+frraQSEtLY9XMXh2ePn1aY7Xt3yCRSKqo7AEVi042VBxfX19mXKr33fnz52FhYaF1PFWFtidPnkAgEKgJOxw4cADOzs7U460MJycnjR4eXGHnzp3UVWG+CkpNmzbFsGHD8OrVK6SkpMDGxkYt4fz2228REBBANWYAcHFxYX4z1WsiLy+PSnFRibKyMiQmJsLOzg6ZmZnQ0dHhLDkqKSnBvn37sGzZMmY3//79+6yKpXzcczk5OZgwYQLGjBmjtaHuv4GP303VD+39+/cQCoVq6xKaOV4VP/zwA1NY27dvHyQSCfT09CAUCvHTTz9Rx+Ua9evXZwonyh2/qVOnMufnzp3LyufoxYsXOHjwINavX4/169fj4MGDrNT6gIqduIiICBw8eBCHDx/G4cOHcejQIYhEIqSlpTHv0WDjxo3w8/Nj2FoCgQBisRh+fn6sjLM/Bv6XHKmATcVdKpUy27BWVlZVlFmuXLlCJSGritevX2P58uWMWtnPP/9M7ehcv379GpOUhIQEql4mPT09ZpfF3d0de/bsUTt/5MgRKnlTgD/u+MuXL3HhwgUIBAIcOHAAOTk5Gg9tUFhYiHHjxqFbt25ML0mnTp00HtpC1U1e05GQkECVHPFlRqmEnp6eRhWrgoICrSXCPxa8vLyYfibVhcSFCxc453gD9ImtEhYWFozRpSr27dsHMzMz6rhcyrsDFQtKVSNrfX19tWvj9u3bVPLV1d0TIpEI3333HfP6UwJfBSW+ZM2VkEgkTPFB9Zq4dOkSK6qTEllZWXBycoJQKOQkObp16xY8PT0hk8nU6GQjRozA4MGDqePydc/xBT5+N319fbUEsXLh8vbt26z6Syrj1q1b+PXXX7W2MamM1NRUja0UmzdvpuopVf0eysvLoaOjo0ZfvX79OrXRLl948uQJOnXqhObNmzN0aYDbHdv379/jwYMHePDgAXU/aU3gUt5dif8lRypgszDx8/PDzz//DKCCAle5QXzv3r2wtLRkO0TOoKRc+Pj4YNSoUZg9ezZmz56NUaNGoW7dupDL5VQeMQ4ODjh48CCAiopEZanGy5cvU0/AfHPHf/nlF16kTLkWIhAIBLC2tq7WRV6526MtVBNbb2/vKg+N33//nYpqqUSdOnU0OskvWLBAa4lwJZT8buWDmAt+tyqqW0hcvXqV6mGvid+temRlZbFKjgYNGgQfHx81vnxBQQHq1q2LmJgY6rhcyrsDFd4wqpXlcePGMY3cQMXvSiO5LRAIYGtrW+WeEAgEsLGxgaOjI5ycnLSO+294/fo1tacWXwUlvmTNlahfvz7Wrl0LQP2aSEpKQtOmTanjquLVq1fIycnhZF7u2LEj+vXrh+LiYrXxHjp0iNW8xtc9B1TsJKekpGDAgAEYMGAAUlJS1O4TGvDxu3l6eqoliL///jvevHnDvD516hRsbW1ZjJofuLm5MesVVRw+fJiq0GppacmwI54+fQqBQMAISQAV8tZ8rAMLCwtZea0BFSwJa2trRtiAazprQUEBMjMzmeuCrYALX/LuSnxRyRFfFXegYjKoVasW0tLSkJaWBkdHR6xcuRLHjx9Hamoq7OzsqKp/7969U8u0r127hgkTJqBfv36YOHFiFZNRbXDz5k0kJCQgKCgI7u7ucHd3R1BQEMaNG6eRJvAhmDBhApo0aYJnz55h/PjxaN++PUNZKCoqQo8ePVjRCz8Gzpw5gzVr1mDNmjWsdPj5gqOjY41b0rTmjnyZUSqxatUqSKVSJCYmMlv1kydPhkwmYwoL2oAvfrcqvLy8mN1K1YXEggULqHpMNHmUsaVwquL58+fw9/eHWCxmEgOxWIzmzZuzUsGbOXMmateujVOnTsHAwABZWVlIT0+HmZkZFixYoHW8Dh061EiHWbRoEVVfxeDBg+Hr61ulv5PvvhU2hTW+CkodO3ZEu3btcOzYMQwaNAgNGzZEREQEXr9+jaKiInTr1g3h4eFUYwaAbdu2wcjICLNnz4ZMJkNKSgpiY2Ohq6vLKRWssLBQzZibFrVq1WJo2ar38s2bN1nNa3zdc3yYWwP8/G7ff/+9mp9PZUyYMAFdunShir1z505MnjwZx44dA1BBuW3Tpg3CwsKwfPlyqphK6OnpVUuJpCl+9evXD35+fkhPT0f79u0RFhYGf39/5OXlIT8/H8HBwZz4EVUGW8aBEpcuXcJXX32F3r17czZn/vPPP2jRogXzbFPed9HR0Rg9ejR13KFDh8LLy4vph01NTcW0adNga2uL9PR01uP+opIjviruSmzduhW2trZVGsUlEgm++eabKrK1H4Lg4GBG6OHYsWPQ09ND3bp10bNnT9SrVw8ymayKctJ/ieLiYnTo0AEmJiZo1aoVJBIJZDIZ3NzcoK+vD3t7e1y5coXTz+Tq4Xnv3j3GhVppgiYQCBAYGEgtp8vHxN61a1ckJCRUez4nJwcCgUDruHyZUapiyZIlsLGxYe4NJycn6h4bvvndALBixQrY2Nhg48aN0NfXx4YNGxiZ85oWA9XB0NAQycnJTHJY+VixYgXrh1x5eTn++OMPzJkzBwsXLmS1kFKNyYe8e3U4ffo0lWktULHDbGdnp7ZL+SknR3wVlPiSNVfF0aNHERoaCjMzM0ilUgQGBlKLPDx58gRdu3aFnZ0d4uLiUFpaipiYGGZR1aRJE1YKhsbGxsw1oJocZWVlwdzcnDouwM89x4e5tRJc/m4fgqKiIqrdv2XLlkEsFqNBgwYwNDTE2rVrYWBggNjYWAwePBhSqZRVz5GdnR22b99e5f1t27ZR9UgVFhaiVatWkMvlCAsLw/PnzxEfH89cw25ublQqeKrqrJqOTZs2cZIcARVruFGjRsHX15dV8V2JyMhIhIWF4e7du2r3XWZmJmrXrk0dly95dyW+qOSIr4q7KkpLS5GdnY2NGzdi/fr1OHToECsZZ0NDQ6aRODg4GKNGjVI7P2nSJK3lqyvj9u3bOHXqFLKzszmjf+3ZswdDhw5FeHg4Wrdujf79+7PqkQL4f3iGhYXBz89PTfQhPz8fTZo0QVhYmNbx+JrYL126VOOO1vv376mECJ4/f46GDRvC1dUVkZGRkEgkcHBwQKtWreDk5AQjIyOmN4It/v77b9aKkR+L352eng5XV1cmKbCxsaGWvA0JCUFycnK152kT2+rAlrpQGZ+yvLsq7t27hxYtWiA8PBwPHz5knRwpiyXVHYaGhtTPDr4LSlzLmvOFAQMGwNvbGwsXLkRwcDA6duyIunXr4tixYzhx4gQaNWqEqKgo6vg9evTAwIEDAfyfp82rV6/QokULKv8kviGRSDSKJtGKJX2OqF27NsMqOHjwICQSiZrCWVpaGjUlG6jorVbu3JaWlqK0tBQHDhyAg4MDo4jHBa5fv44LFy5QK7/WpM7KBeOAT1hYWDA925X7Vdn0JvIl767EF5Uc8VVx5xP6+vqMCp7qRabEtWvXqBeAixcvhr29fRVqT2BgoEZVsf8afD88JRJJFSoZAJw9e5aKdsH3xM4HPiczyo/N7y4qKmLtg/Xzzz/XKAhQWFiI77//ntVnqEJHR4e1AaMquOSNFxcXY9OmTfjmm2/Qq1cv9OrVC9988w02b96sZllAi/LycsycOROWlpYQiUSskiOZTIYxY8bgl19+0XgkJSWxXpzwUVD6mGDbaG1lZYXjx48DqLgPBAKBGs3r2LFjrBTP7t69i9q1a8PLywtisRj+/v5QKBTw8PCgvq/fvHmDrKwsjdfW27dvWSlP8mFurQpV+jgXz/sHDx5g7dq12LVrV5X79/Xr11Q9MVKpVM1GQ0dHR21Hma0KZ3FxMXr06AGBQAAdHR3o6OhAJBIhOjqakzmIKygUCqxatapaRdZdu3Zxnhw1b96ctdorUJEQKQv8qsnRmTNnUKtWLeq4fMq7A19YcsRXxR3grzeoRYsWmDNnDoCKybLyZLt161aqZt2UlBRYW1tj4cKFWLFiBby8vDB16lTs2bMHkZGRkMlknPbbcKFQwvfD083NTaMs+unTp+Hi4qJ1PL4n9sooLy+nom5+TGzZsgXdu3eHn58f6tWrp3Zoi4/B775x44ZGCeirV69S9+XxAaWCZeVDKBQiKiqKeU0LrnnjBQUFcHZ2hkQiQXBwMHr06IEePXogODgYEokErq6uGpUNaXD27Fn89NNPrLwvAgICatzl5YrzzyX4eiZt2rRJbeG4cOFCpsimUCioG8NlMpna87fyfMlFRbikpATp6ekYO3YshgwZghUrVqgJB2iDK1euwMHBgbkngoKCGN8VoOIZpe018THMre/evcs5fTw7OxvGxsYwNDSEVCqFq6srIwYC0H0XAGBra8soqN6/fx8CgUDNU+rw4cOcCD1cvXoVmzdvxs6dOzlJCLj09AOA1q1bY9q0adWeZ1PY3759u8ZDJBJh0aJFzGtatGnThqFfK3dsy8rK0L17d3Tt2pU6Lt/y7l9UcsQn+OoNOnHiBIyMjDBlyhQsXLgQpqammDRpEtatW4fExEQYGxvXSNOpDo6Ojmru3VeuXIFCoWC2fUeMGIFWrVppHZevByfA/8Nz27ZtaNy4sVpSeObMGfj7+2us4P0b+JrYS0pKMHHiRAQFBSExMREAMGfOHMhkMujq6iIqKuqTqnopMX/+fMjlcsTHx0NXVxeDBw9GaGgojIyMMGHCBK3j8cXvVkVQUJBGSde1a9ciODiYVWwuIRAI4Ovri5CQELVDIBCgUaNGCAkJYWUMzDVvPDQ0FB07dtQoufrixQt07NjxkxJumTFjRo07enfu3GFNzapc2Dh9+jROnjxJrdTG1zNJKBQyOy2pqamQSCRITEzErl27mL60FStWaB33q6++YozJd+/eDQMDA8ybN485v3TpUta9NlyiU6dOiIiIwOPHj1FQUICIiAg4OTkxBTGahOBjmFtzTR8HKu7n6OholJWV4eXLlxgyZAgUCgXDxKBNjoYNGwY3NzdMnz4djRs3Rv/+/eHp6Yk9e/YgMzMTPj4+GDBgANWY+QSXnn5ARR+lUmFQE54+fUolPQ582DXH5nq7cOECzM3NER4eDl1dXXTr1g1eXl6wsLBg/XxWBVfy7kr8LzniCHz2Bp04cQL+/v5VLlgbGxvqDFkmk6lVvsvLyyEWi5menZycHCq6Hl8PToCfh6exsbFa74DSEFZXV1ftzyYmJlqPl6+JfdKkSbCwsMDo0aNRu3ZtxMXFwc7ODunp6Vi9ejVsbGyoEuZ/w+XLl1nJIHt4eDAyoaoL7MmTJ6uZU7IFW363KlQbPVVRUFDA2rcMqKCbpKamYsKECVi4cCF1H8isWbPg5ORUxW+FKyECrnnjUqm0RsGFv/76i1pBbO/evUhMTGS+iyNHjiA8PBzNmzdHamoqVUw+cevWLTRo0AAikQjh4eF48eIFQkNDmXne2dmZqueIr2eSQCBg5vjGjRszzAYllixZQrUTnJ6eDpFIBFdXV+jp6WHLli2wtrZGjx490KtXL+jq6jLzPxdgawxsbm6u1uNYXl6OuLg42Nvb4/r161QJwccwt+aaPg5U9ORVvkZnzZoFExMTZGdnUydHr1+/xsCBA+Ht7Y1BgwahuLgYKSkp0NXVhUAgQEhICDUlcuHChYiMjGSEddasWQMvLy94eHjgu+++Y/X8+Jw8/cLDwxEREVHle+RSxOb58+eYPn06unfvjjZt2mDixIms+sM/Br7Y5IhLeg/Ab2+QEn///TdOnTqFEydOsKb0+Pr6qsknHzhwADKZjOkhyM/Ph4GBgdZx+XpwAvw8PKvrIdB0aAu+JnZnZ2fs3LkTQMVkKxQK1WgWmzZt4qXCypY6JJVKmYe6mZkZc49cvXqVFfeYTxgaGla7kKC5n728vBifkjt37sDR0RFGRkZo1KjR/8fem4fVtP7//6+129Vu2pVdqTQXipApFAqRZM5MmcnM6ZjHDCk5HPOstzHTMYWSeRbl1DuEyOzkOOYyRD1/f3Tt9dm7do69Bm9+vo/rWtc5ey1e+7b2Wvd9v2aUK1cOVlZWnMOdLl26hEqVKiEiIoINpxJqgRM6btzGxoZ9hjWxf/9+2NjYaC1306ZNkEqlqFWrFoyNjREXFwczMzMMGDAA/fr1g56eHutN+V4ICQmBn58fEhIS0KVLF/j6+sLf3x+PHj3CkydPEBgYyKlZtFhrEsMw+PvvvwEU91LSJJfL2gEUe7jmz5/Phk9fu3YNoaGhCAkJ4WwZF6sxsImJicZ8vmHDhrFRA99bqCUgfPg4UKwcabLYx8bGwszMDLt37xb0Xrx//55XoatZs2bBxMQEISEhsLa2RnR0NBQKBWbPno2oqChYWlqyURlcEKOnn5gsWLAA9vb2anOy2BU++SBmeXclP6VyJHR4DyBebpBYbN++Hbq6uujSpQvCwsJgbGyMCRMmsNdXrlzJqWyzmAsnIM7i+a3hO7HLZDI8ePBA7bNyEwQUhxdyucdl5a0oj169evFa4JydnVlFo3bt2li5ciUA4PDhw5w8c6rk5eXh4MGDWLFiRalNEB9at26Nzp07lyqnGxISwqlHjKrxoGfPnvDx8cGrV68AFDe8DAgIQPfu3TmP9+3btwgLC0P16tWRmZkJXV1dQRY4oePGp06dCnNzcyxYsAAZGRnIzc1Fbm4uMjIysGDBApQrVw7Tp0/XWq6Xlxf7mx89ehQGBgZYsGABe33+/Pm8q3sCxfNOYmJiqTh9LlhaWuLPP/8EUGxhZRgGZ86cYa+npaWhfPnyWssVa01iGAYbN27Evn37YGdnVyo07+rVq5DL5VrLFQuxGgPXrVsXGzdu1Hht2LBhMDMz4zRfitnbEBA+fBwAGjVqhBUrVmi8FhMTw+aCfC+4urrijz/+AFBs9NPR0VHrjbN7925ejYGF7ukHiO8R//PPP1GlShUMGjQI+fn5gihHT58+xbFjx9g1Ljc3FzExMZg7d66a11UbxC7vruSnVI7ECO8RKzfo3+DTGfnQoUPo0aMHQkJCSr2w//zzD6cQnx9t4SzJ58+fsXPnTsycORMzZ87Erl27BAnPEpLy5curTSw+Pj549OgR+zkrK4vTPZZIJKhVq1apvBXlUadOHV4LXP/+/dm8jaVLl8LAwAABAQEwMzPjFTd+5coVWFtbQy6XQ0dHB5aWlmAYBkZGRrzCAIHiTbBCoYCrqyv69OmDPn36wNXVFZaWlpz68KgqRy4uLqWaLp47dw729va8xgwU930qX748JBKJIMqRGHHj0dHRsLGxUWuMyzAMbGxsOM+VRkZGaptHXV1dNYt2VlYWFAoFJ9lAcRhh9erVS8Xp8+nKbmJiwo65sLAQUqlUzaiUnZ3Nydgh1ppUMrxb2WtMydq1azlHBygRsr2EWI2Bo6KivthPZciQIZyS5MXIFRMzfBwo7gfXq1evMq9HR0fDycmJk2xVHj9+jGnTpqFHjx6IiIhQMwpqg6ZiSaoFJO7du8e7WJKQPf2+lUf83bt3GDx4MCpWrMi7wueJEyfYvnjW1tZIT0+HnZ0dKlasiMqVK0NfX59Tb61vVQX4p1SOxArvESM36N/43qokfYuFU4nQpUivXr0KFxcXGBoasiGWRkZGcHJy4tyQ8smTJ5g6dSqaNGkCd3d3VKlSBa1bt8batWs5V5Zr0qTJF71kO3bsQO3atbWWW6lSpS8mffLtA1ZYWKimaMbHx2PEiBFYvHgxrwISfn5+GDhwIAoLC1ljx4MHD9C4cWPWOsiHx48fY+LEiWjVqhVCQkIQGRnJhsZpi6pn1dbWttRzde/ePcF6mDx48AB79+4VrB+RWHHjOTk5OH/+PM6fP8/bKm5mZqaWaK5q/FJ+F59NT+vWrdGuXTs8e/YMxsbGuH79Os6cOQNvb2+2+Iq21K9fn/XKrV+/HuXLl1fz4s+cOZPT+wz8b9akhIQEJCUlcfq7YrWX+F80BuaKGLliYoaPi4mBgQE7X167dg2mpqZwc3ND586d4e7uDkNDQ04J+M7OzkhMTARQvO+TSCTYsWMHe/3gwYOCKHOAMD39vpVHXMm+ffswevRoXq0rGjZsiGHDhuHt27eIjY1FhQoV1JwPv/76K3x8fLSW+62qAP+UypGY4T2AsLlB36Izcklvyc6dO0XzlvBZOJWIUYoUKN6ktGnTRq3k74sXL9C2bVtOIYaXL1+GqakpateujYYNG0JHRwehoaHo2rUrzMzM4OPjwym87ubNm1/cRG7ZsuWLzY7LokePHhg9enSZ17/HPmAAYGpqym6ITU1NWQvxxYsXUbly5f/l0ErBMAyqVauGmjVrwtjYGLt27VK7furUKUF6NKgidCPY75k6depg79697OfXr1+r/fuPHDmCSpUqcZavUCjYzZhcLmefu2PHjsHLy4uTzKSkJMhkMujp6UEmk+HUqVOoVKkSvL29Ub9+fejo6HB6n1URck0SC7HbSwjdGFgTDx48UAt55sK3yF8Wk8+fPyM3N5dVavig6mlv164d2rRpw+5NCgsL0a1bN7Ru3VpruVOmTIGlpSUGDBgAZ2dnTJgwAQ4ODlixYgVWrlwJe3t7Xq0PlDx9+hSnT5/G6dOned0PsT3iYiCXy9mogk+fPkEqlbLhw0CxUsqlqNG3Ku/+UypHYoX3iIHYnZGvXr0KZ2dnQb0lYiNGKVKgOHdH1bWuJDMzk5M139fXV63876ZNm9jmfS9evICXlxdGjhzJebxC89dffwnS4+Fr8PT05L2JUGJhYcFaWitWrMgq31lZWbwtSHfu3MGGDRsQHR2NefPm4Y8//tBYfvprmTFjhtpR0lDw66+/olu3blrL/fDhAyIiItCoUSNER0cDKE46NjIygpGREbp378553GLEjavy8OFDLFu2DOPHjy+V56Ytu3fvxqlTp8q8PnfuXNZLwwUzMzN2k+Li4oLjx48DKN6wcq30BRRbO3ft2sUqLrm5uZg6dSoiIiLY7/ie+NIzwXXdEKu9hCpCNgZW8unTJ0yZMgVyuZz1dMnlckyePJlTfz8x85c/f/6MO3fuoLCwEEDxvLF9+3bEx8cjNzeXk0wlBw4cQKNGjdj8IolEAlNTU/Tq1UvN0q8NqsqRvb19Ke/slStXOBVuKSwsxJw5c9C6dWtERUWhqKgI8fHxsLe3h0KhQJ8+fXg1X37z5g169eoFHR0ddr8mlUrRs2dP9p3RBrE94u/evcO6devQt29ftGzZEq1atcLw4cNx9OhRzjItLCzY/VR+fj4kEgkuXLjAXs/IyICFhYXWcr9VefefUjkSK7xHjAdM7M7IQntLSpKfn4+srKxSHi8+iFGKFACqV69eqhQyUGwV5lL9zcDAQG0CKywshK6uLrsIJScnw9bWlvN4gW/r9ROSkpM7H5o3b44tW7YAAAYMGABvb29s3rwZgYGB8Pb25iQzLy8PnTp1UjNEKDdUxsbGgpYUFoIxY8bA1tYWERER8PDwwNChQ+Hg4IDNmzdj69atcHNzw4gRI7SWK1bcuJKjR4/C0NAQnp6ekEql8PLygpmZGUxNTXn1ZRKLhg0bsknr3bt3R8uWLXH27FmEhYWhatWq/9vBaUCMNUmsZ0Ks9hKaEKIxsJLw8HBYWVlh5cqV7Pq2cuVKWFtbIzw8XGt5YuWKZWRkwNraGhKJhDVOeXp6wsjICMbGxjA3N9dYxe5r2LhxI0xMTBAREYHJkyfD2toaEyZMwIoVK+Dn56dmwNIGiUTCelwcHR1L7R1ycnIEC0MWki5durCGutevX+P169dISkpC5cqV0bVrV63liekRz87OhqOjI6ysrGBvbw+GYRAcHIx69epBR0cHnTt35rSnaNeuHVq3bo2zZ89i0KBBqFOnDoKDg5GXl4f8/Hx06tSJU1EjMcu7q/JTKkdiINYDJmZnZEB4b4mSv//+G8HBwaVix/kkLisRshSpcuJ6/fo1Dh48iKpVq2Lnzp14+PAhHj58iJ07d6JatWpqbtuvxdHRkS01CRTnHzEMw3Zkv3v3Lq97LEaOlCqqOV18wlk0IaRydPnyZda6/vTpUwQGBsLExAS1atUqFZLytQwaNAi+vr7IzMxEdnY2OnXqhHHjxiE/Px/r1q2DoaEhq5AJBZ/wN3t7exw5cgRAsbdLIpGoLabJyclwdHTUWq5YceNK6taty5bMVT4Tb9++Rdu2bbF8+XLOcpWohrUIsWAmJSWxeWzZ2dmoXLkyGIaBhYWFRsOKNuTl5eHUqVPYtm0bduzYgbS0NF7PhFhrkljPhFjtJTRRVFQkWLipXC5X83gpOXjwIOfiQ2LkigUGBqJTp07IzMzEqFGj4OHhgc6dO6OgoACfPn1Cr169EBAQwEm2u7u7WjuJy5cvw87Ojr3HXbt2RYcOHbSWyzAMW0xCV1e3VE5scnKyYLlBQoREKjE0NFSrOKnk9OnTnDw8YnrEg4KCMHjwYPa3io6OZguN3Lp1C05OTpwqh966dQsVK1YEwzDw8PDAo0eP0LZtW0ilUkilUlhaWiItLY3TmDXBtwpwSX5a5ejFixeIjY1Fv3790K9fP8yfP59zkjUg3gMmZmdkQHhviZIePXrA19cXly9fhpGREZKTk7Fp0yZUrlwZBw4c4CwXELYUqWqlKU3Vp/h0iB41ahQ8PT2RmJiI48ePo0mTJvD392evJyUlce4rAYjn9RMrp0uVoKCg77oJnIWFhVoC+IsXLyCTyZCfnw+gOByXS46JWOFv/1Z9iWuSqlhx40qMjY1Z+WZmZuyY09PTOSlzSpRhLVKpVJCwli/x/PlzXhvtwsJCjB07FgYGBqXmHUdHR+zfv5+TXLHWJLGeCbHaS6iydu1aVK1ala3SVrVqVc5NyZVYWlpq7Hd0/fp1TmFDqgiZK2Zubs6O8927d9DR0VEzMl69epVz3oqBgUGp8UmlUjx+/BhAseHSzMxMa7kli0WohmUBxcVK+OQGCR0SqcTe3l5jyHFGRobgOaV8MTQ0VPPqffz4Ebq6umyVyL179/JSQEtWmzx69CgSEhJ4V6FU5ezZs/jw4YNg8oCfVDk6deoUTE1NYW9vjw4dOqBDhw5wcHCAXC7/onb+JcR+wIRETG+JEmtra3biNTExYbtn79u3j1NVFbFKkSp7EHzNoS1v375Fly5d2A2aj4+Pmrfk8OHDahVytEUsr59YOV3fgrlz5+Lly5e85ZiZmam9zwUFBZBKpWyIx61btzjdY7HC3ypXrsxabi9dugQ9PT213hfbtm1DxYoVtZYrVty4kvLly7MbNg8PD7ZXUHp6OoyMjDjLFTqsRRNbt27llZegZPz48fDw8EBCQgKOHDmCxo0bIyYmBllZWZg6dSqvMDUx1iQxnwkx2ksomTp1KoyMjDBhwgS2L9WECRNgbGyMqVOncpYbGRmJ7t27q23OPnz4gJ49e6rlnP6vUZ3TCgoKoKOjo2a5z8rK4lyQysPDQ62UdFpaGvT09NiKrNnZ2bzeZ7EQOiRSyapVqxAQEIC//vqLPffXX3+hRYsWbAEwrghZ8AIorpqq+hy8fPkSDMOwXpicnBzo6+sL8l1iYWJiIlgkipKfUjny9PTEwIEDSzV2HDRoEGdvyY/0gInpLVFiYmLCWpIcHBzY8LKcnBxOeUE/ailSoNjdK1Q5ZVXE8vqJldOlRMgGmiURapJs3ry5WqhQbGysWuLvlStXOG0AxQp/W7hwIWQyGQICAmBubo7FixfD2toa48aNw4QJE2BqaoqZM2dqLVesuHFV+cpNcEREBNzc3DB79mzUqlULzZo14yxX6LAWTQj1rNnY2Kglmj969AjGxsbsZnvmzJmcPCZirUliPxNiYWFhwfY3VGXr1q1ae0yURlXlYWJiAgsLCzRr1gzNmjWDhYUF5HI5p1AyQJxcsWbNmqF///549OgRIiMj4ebmhr59+7LXhw4dikaNGnGSvXTpUpiammLcuHGYNm0abG1t0b9/f/b65s2bBWvhISRChkR6eXmx4e3KaqS6urpwdXWFq6srdHV1YWxszPk+iFHwAgB69+4NPz8/ZGVlIScnh+2npeTkyZO8eu8dO3YMkZGRCA8Px9ChQzF//nxO+WdfQsgwfSVS+gm5ffs27dq1i3R0dNhzOjo69Msvv9DGjRs5yWzevDn98ssvtHLlStLX16eJEyeSl5cXmZiYEBHRgwcPyMrKipPspUuX0qVLl6hVq1bUrVs32rRpE82dO5eKioqoY8eONHPmTJJKv/6nPHHiBKdxaEPlypXp5s2b5OTkRDVq1KBVq1aRk5MTrVy5kmxsbLSW17t3bxFGWTbVqlWjQ4cOkb29PW9ZMpmM/f/4+Hhq27YtGRkZcZL15s0b9v/nzp1LI0eOpBkzZlD9+vWJiOjixYs0c+ZMiomJ4Txee3t7+vTpU6nzhYWFZGtry1luTk4OdejQgTIzM4lhGAJAREQMw7Dy+aKUyZfo6Ghq3rw5/fHHH6Snp0e5ubm0YcMG9vr58+epVatWWsv9559/qFKlSkRE5OLiQjo6OuTm5sZer1ixIj179kxruaNHjyYrKyu6cOEC9evXj7p3707VqlWjadOm0bt372jMmDE0efJkreXGxsZScHAwNWrUiNzd3enIkSM0dOhQMjMzIyIic3NzSkpK0lqukgULFlBeXh4REUVGRlJeXh5t376dKlasSAsWLOAsV6FQkKmpaanzpqamZG5uzlmuKkI9a3l5eVShQgX2s42NDX348IFevnxJ1tbWFBISQtHR0VrLFWtNEvuZUCU4OJjWrl3Lac0oyadPn6hOnTqlzteuXZs+f/6slaySz1ZISIjaZz7rxu3btykgIIDev39P+vr69OjRI2rVqhVdvnyZVqxYQR07dqStW7dqteYTFa8XQUFBFBcXRwqFgk6cOEH9+/cnGxsbkkgk9PLlS0pISOA05mHDhpFEIqHNmzfTx48fqU+fPjR16lT2ure3N23dupWTbCW7du2iHTt20IMHD6igoEDt2pUrVzjJ1NfXJycnp1LnnZ2dSU9PTytZ7du35zSGr2HTpk00bNgwGjRoEDVu3JjWrVtHffr0IUdHR9q2bRvVrl2bzp8/TxUrVtRa9rx586hdu3ZUpUoVYhiG7O3tac+ePez1Z8+e0dixY7WW+/fff1ObNm0oNTWVJBIJFRUVUc2aNWn37t00fvx4+uWXX2jevHlay/1mCKpq/SD4+PhozE3Zs2cPW2pZW54+fcomUEokEjg6OqpZ33fu3InFixdrLXfWrFkwMTFBSEgIrK2tER0dDYVCgdmzZyMqKgqWlpZsQvP3xKZNmxAXFweg2ONgYWEBiUQCmUymlrjJl1atWomSuyKGJQLgb23+Fl4/IXO6VBGjgWZJhPzdnjx5gtWrV2PJkiWC9UMRK/xNbL5F3LiQiBnWokSoZ83Hx0etWXZ8fLxafkZmZiancCex1iQl3+KZEPJ9Hj58uMb8lIiICAwdOlSQ7xACsXLFgOKiH6mpqWwkw/v377F27VosWbJELYz6e2PRokUwNjbG8OHDoaenh8GDByMgIACmpqaYNGkSZ7k/SkikWAUvVLl16xYyMzMFq3bbtWtXtG/fHq9fv8aHDx8wfPhwhIWFASj2JikUCsEaUW/ZskWQEGdVfhrlSLWM9LZt2+Dg4IDY2FicOXMGZ86cQWxsLJycnHhv3IV+wFxdXdnqSOnp6dDR0cHmzZvZ67t374abm5sg3yVk75mS5OfnIy0tDc+ePRNUrlhKzPcqV6wcKbFyulQRo4FmSR48eMD28PgeESv8TRNC5V99K4YMGcJrfhA7rKUkZ86cESQJ+OjRo9DX14e3tzcaN24MqVSKhQsXstdjY2PRtGlTzvKFXpO+JXznS9WeWSNGjICJiQmqVq2K/v37o3///vD09IRcLsfw4cMFGa8Q79yPlL/8Jfi+z6pUrlyZDYlUfSamTp2qFv78NYgdElkSIe6DWAUvykKIAgdyuVwtJzovLw+6urpswSFlga7vFQYQKDbgO0cikaiF8pQFwzCChPcQEZ07d47q1KlD+vr6nGUYGhrSjRs3yMHBgYiI9PT06M8//6SqVasSEdH9+/epSpUqlJ+fz3u8JiYmlJGRQS4uLrxlqSLEfSgLscbcqlUrWrdunSDhHKqINV6+qIaM/RtcQxzNzc3pypUr5OzsTK6urrR27Vpq0qQJ3blzh6pVq0bv3r3jJFeV7OxsevDgATk6OqqFqwmBXC6n9PR03r/d1q1b6cKFC+Tj40Pdu3enkydPsuFvbdq0oalTp5JEIvluxvujyI2MjPzqPzt9+nRO36Hk77//pps3bxJRcQgx15BpJRkZGbRjxw76+PEjBQYGUvPmzXnJKwux5mKxnglPT09KTEzkHKbWpEmTr/pzDMPQ8ePHOX2HKkLchwoVKlBCQgLVqlWLiIhevXpF5cqVo9evX5OJiQndvXuXPDw86MOHD7zHK2T4eEmEfCYMDQ0pKyuLHB0dycrKio4cOUI1atSg7Oxsql+/Pj1//vyrZfXt2/er/2xcXByX4aohxH2oUqUKzZw5kzp16kRExWGEDRo0oHfv3pGOjg7dvn2bvLy82BDl72HMVlZWdPLkSapSpQoREb1//56MjY3p2bNnVK5cOcrJyaEqVarweo5TU1PLDLXcvXs3Z7lERD9NztHdu3e/+XcGBQXxfsCsra3p+vXr5ODgQNnZ2VRYWEjXr19nlaNr167xXpjFRoj7UBaOjo6kq6sruNxDhw4JLpOIKDExUS2/QCj4LnLfIqfL09OTMjIyyNnZmerVq0fz5s0jPT09Wr16NadnY+7cueTt7U3NmjWjly9fUufOndkNDsMw1KJFC4qPj2dzIfgilB2pR48e1KNHD/azv78/nT59WhDZqohl9/pe5fJVeL6Gt2/f0tChQ2nbtm2sEU1HR4e6du1Ky5Yt05jn9DXUqFGDatSoIeRQNSLWXCzWM3H16lVef/9b5NeqIsR9EDN/uST37t3TmF8qBEI+E9bW1vTixQtydHQkBwcHunjxItWoUYPu3r2r9fcIofBogxD3YdiwYTRgwAC6fPkyyWQyWrt2LYWGhrJ58ykpKWwuqxAIMeaGDRvStGnTaMOGDaSnp0eTJk0iFxcXKleuHBEV5zLxyf/ctm0bhYWFUWBgICUnJ1OLFi3o1q1b9PTpU+rQoQPv8f80YXX/C4QIzZoyZQosLS0xYMAAODs7Y8KECXBwcMCKFSuwcuVK2Nvb86rzr4pYvWfEClETko8fP2L79u0YPXo0unXrhm7dumH06NHYsWMHPn78KOh3xcXFCd5rRax7LGROl9ANNO3s7NgcigEDBqBmzZq4cuUK3r9/j/T0dNSvX1+tYhJfxLrHYoW/fa+hod9arpDhPd+iRDggXojzj/bb5eTkIDk5WZCm1qoIVYa9JELcB7FzxVQRc20WUnb//v3ZHKClS5fCwMAAAQEBMDMzQ79+/QT5ju99Hl6+fDl8fHxQu3ZtTJo0Ce/fv2ev3bp1C1lZWby/Q4kQY75z5w5cXV0hlUqhq6sLMzMztlIrULwPUu1jpi3VqlXD0qVL1cZbVFSEgQMHCpKH/9MrR2LUR1cixANWWFiIOXPmoHXr1oiKikJRURHi4+Nhb28PhUKBPn36iDLJC4nQE3BKSgp+//13TJgwARMmTMDvv/+u1sxOW7Kzs+Hi4gKZTAY/Pz906dIFXbp0gZ+fH2QyGdzc3JCdnS3Y+HV1dTU2DeTDj7bpUcKngaa+vj7u3bsHAHBycirVoyw1NVWt/DZfwsPDBc+ZA8Sbg8TKvxLrPoiFkPf3W5QIB36891mIZ2LIkCFsoYB3794hJCRErcBMkyZNBGuJIOY7p9oihA/fIlfse2/GraSwsFDtPsTHx2PEiBFYvHixYMZLMfeCPxpCFTjIz89HcnIyEhISBF8zDA0N2TyscuXKsU13r1+/Dmtra97yf3rlSMzNnxgVNITiw4cPah2gb9++jUmTJqFXr16YPHkycnJyBPsuoe7D06dP0bBhQ7ZzvLe3N7y9veHo6AiGYdCwYUM8ffpUa7kBAQFo164dmyioyuvXr9GuXTu0aNFCa7mqBQ5UD4ZhYGpqyn4Wgh/R68d30a9UqRIOHDgAAHB2dsa5c+fUrv/5559a96r4XyDmPZ4xY8YPpciIgZD3197+23S+F+uZ+J7XJIlEws7fEydOhJ2dHY4fP478/HycPXsWrq6uvCzNqoj5zqmuq0IhRIL8tyAvLw+nTp3Ctm3bsGPHDqSmpnI2fn1rhH4mPn/+jJ07d2LmzJmYOXMmdu7cKaiiK6RH/Fvx8OFDwQx2FSpUYOfiatWqsQU7zp8/L8i6//+UIxEnyQcPHogSGiFESICfnx/b0frs2bPQ19dH9erV2QZghoaGOH/+vBDDBcB/IwwAISEhaNCggcaSozdu3ICPjw86deqktVwDA4Mvhm3897//5dT81NjYGMHBwWoNauPi4qCjo4M5c+Z8t01rValatSrvZzgxMZGdxAoLCzFz5kzY2tpCIpGgQoUKmDt3LqcFNDY2Fh4eHsjOzsZvv/2GBg0a4Pbt2wCKw3H8/f05PQ9fQ25uLiIjI7X6OxkZGRoXBiHmIGWIl+rx6tUr6OrqIiUlhT3HBTEaUhYUFGDs2LFwdXVF3bp1sW7dOrXrubm5vMrRqyLkHP8tSoQD379F/927dzhz5ozGEvfv37/Hhg0btJbJMAyrHHl6epZq2Lpv3z5UqlSJ24BLIMQzsX37djWvxZIlS+Dg4ACJRAKFQqH1/PAl+Ho1Sjbc/tLBhcLCQowdOxaGhoal2ko4Ojpi//79nMdeEr4hp2LOw0quXr0KFxcXGBoastUzjYyM4OTkJFiIqJCerjVr1iAsLIxtK7Ft2za4u7vD2dlZ0FYxQo65e/fu+O233wAUN8pWpp84OjoKUmnwp1eOhA4R+fTpE6ZMmQK5XM5OEnK5HJMnTxbMoiTEAyaXy9lyoX5+fqXylqZMmQJfX1+t5Yq1EQaKJy/V2OuSpKamwtjYWGu5NjY2SEhIKPP6/v37OYVnZWdno27duggLC1MLB5FKpYL1zdHEixcvOG1OxKJy5cpsH6OoqCgoFAosWLAAiYmJ+P3331G+fHlER0dzkj1ixAjo6urC3d0dMpmMLTkukUhQp04dtU2skKSnp2u9eVe1jDs7O7OleYUIxVHtfVWyD5bqf7UlOzsbjo6OsLKygr29PRiGQXBwMOrVqwcdHR107tyZk+Fj+vTpKF++PGJjYzF58mSYmppi0KBB7PXc3FwwDKO1XDH41iXChSQ9PR2zZs3CsmXLSq1zr1+/Rt++fbWWefPmTdZbL5FI0LhxY7akMMBdsWUYBn///TcAwMLCQq0MMADcu3ePk5FKE0KUYVd9n9evXw+ZTIZp06bh4MGDmD17NoyMjLBmzRohhst7465UVFTDFEt+Vh5cGD9+PDw8PJCQkIAjR46gcePGiImJQVZWFqZOnQp9fX0cPnyY8/hV4XsvxJyHldSvXx9t2rTBixcv2HMvXrxA27Zt0aBBA0G+QyhlbuHChTAyMkLHjh1hY2OD2bNns700IyMjIZfLsWrVKgFGLKwC+vz5c3beKSwsxNy5c9GmTRv88ssvavedKz+9ciQ04eHhsLKywsqVK9m+SitXroS1tTXCw8MF+Q4hHjAjIyM2ga98+fJIT09Xu3779m1OioaYG2GFQvHF/j0nTpyAQqHQWu7UqVNhbm6OBQsWICMjA7m5ucjNzUVGRgYWLFiAcuXKcW669+nTJ4wbNw6urq44e/YsAPGVIy4bd1XWr1+PHTt2lDq/Y8cOTp4ufX193L9/H0Cx1a+k7AMHDvDq1XX9+nXMmzcP4eHhGDRoEKZPn47k5GRe4RyqfdE0Hdu3b9f6HpcrVw4XL14EoL4RFIIKFSogODgYx48fZ/tcnThxAjo6OoiLi+PU+woQryGlm5ubmkEiOzsbbm5u6NOnD4qKinh7joQM75kxY8ZXH1wQK8T58OHD0NPTQ9WqVeHg4ACFQoHjx4+z17ne4/bt2yM4OBjPnj1DdnY2goOD4ezszL7jfJSjwYMHY8yYMbCyskJycrLa9bS0NFhYWGgttyxyc3N5GU9UPV3e3t6YN2+e2vXly5cLpjALuak8cuQIatWqVaqoSJ06dUrd86/FxsZGrZH3o0ePYGxszCqgM2fO/G6UAjHnYSUymayUcg8UN3SWyWSCfIdQz4S7uzu2bNkCALhy5QqkUinWrl3LXl+7di1q167N+3sAYfqWKaOmTp06JWo+3k+nHIkRDqCKXC7HoUOHSp0/ePCgYPkPQrwUTZs2ZSdzHx+fUv/uXbt2wcHBQWu5Ym6Ehw4dCkdHR+zevVstROj169fYvXs3nJycODfzi46Oho2NjZoVjWEY2NjYICYmhpNMVY4dOwYHBwdMnDgRurq6vJQjTSFUqseZM2d4bSwrVqyotolScvLkSU5hLTY2Nrhw4QKAYkW8pPfv1q1bglmEhUKTdbWk1VXbezxw4EDo6+vDyckJEokEDg4OcHZ21nhoy/Pnz9G+fXs0adIEjx49Ys/zVcTFakipqanho0ePUKlSJfTs2ROPHz/m9Ax/y/AeoRArxLlBgwaYNGkSAKCoqAgxMTEwNjZGYmIiAO5KjJWVlVreVVFREcLDw+Hg4IA7d+5wluvn5wd/f3/2KOl1mTVrFvz8/LSW+/z5c4SEhMDe3h7h4eH4/Pkz+vfvz77DDRo04BTCWNLTpcnAaGJiorVcTQiZK1a1atUyi4q4u7tzklkymqWwsBBSqZRVPq9duyZYwRK+IadizsNKqlevrrEC67Fjx+Dp6clZrhgYGBiwezageA+nqthlZ2cL1mA2KiqKV0VAqVSK3NxcAOoeQDH4qZQjscIBVLG0tNRYiez69euCWb2ECAk4f/48TE1NMX36dCxZsgQWFhaYMmUKtmzZgmnTpsHMzIyTUiDmRvjDhw8IDw9nw6ZkMplaKNWQIUN435ecnBycP38e58+fF7QoBQD8888/6NChA8zMzDTmTX0tJcMgygql4oq+vn6pjSsA3L17l5PVa+jQoWjdujU+f/6MQYMGYcCAAWpW/BEjRnC2Kn7+/Bl37txhY8g/fPiA7du3Iz4+np1EuaBQKLBu3Trcu3dP43Hw4EFO9zgxMRFLliwBwzCYNWsWfv/9d40HV5YvXw5bW1s2X4OvcmRra4u0tDT288uXL8EwDN68eQOg+H3R19fXWq6zs7PGnKXHjx+jUqVKaN68Oaf7+y3De27duoWjR4/yrmQpVoizXC5nc/CUbNmyBUZGRkhISOC83pmYmGhc44YNGwY7OzucPn1asHwxVe7cuYOHDx9q/ff69esHT09PLFmyBH5+fmjXrh2qV6+Os2fP4vz582zos7YwDIONGzdi3759sLOzK6XAXr16VTCDqJD5yzKZTGPeS0ZGBmevho+PD2bPns1+jo+PV9tQZ2ZmClZ8SAjEmIdVDZQHDx5E1apVsXPnTjx8+BAPHz7Ezp07Ua1aNRw8eJDX2G/fvo3Jkyeje/furHJw6NAhjZ6qr0GhUKi9z3Z2dmwVWKBYOeISRSQGbm5umDRpEk6ePAmGYbB3716cOnVK48GXn0o5EiscQJXIyEh0795dbZP+4cMH9OzZk3PYhSaE6JVz/vx5tp+C6lGhQgXOGzQxN8JKXr9+jWPHjmHr1q3YunUrjh8/zjnZ/EdELpcjJiaGDZUqeaxZs4bXc2xvb68xMXfv3r2cKnK9evUKderUgZubG0JDQyGTyeDo6IjmzZvD2dkZpqambJiDNmRkZMDGxgYSiYRN0vX09ISRkRGMjY1hbm6OS5cuaS0XAFq0aIFZs2aVeT09PZ1XTkyfPn1YBUNorl27hho1aqB79+68laPevXvDz88PWVlZyMnJYb0ZSk6ePAl7e3ut5fbv37/M/iSPHj2Cm5sbp2dYrPCeqKgoVpl78eIFmjVrpuZJbNmyJWeLqFghzpaWlkhNTS11Pj4+HoaGhlixYgWne1y3bl1s3LhR47Vhw4bBzMxMFOWIKzY2NmwlS2Uum2r42NmzZznNayXXTVXlACgOR+ITVidW/nKjRo3QvHlzNeNRbm4uWrRogcaNG3OSefToUejr68Pb2xuNGzeGVCrFwoUL2euxsbFo2rQp5zEDwMaNG+Hj4wMbGxt2875w4ULs3buXs0wh5+GSRsuS+Vyqn7ly8uRJtseTnp4e662bO3cuQkJCOMn09fXFtm3byryekJAgiLfr8ePHmDZtGnr06IGIiAhOfZn27NmD8uXLfzGyg+89VvJTKUdihQN06NBB7TAxMYGFhQWaNWuGZs2awcLCAnK5XJAKGkqE7JXz999/4+LFizh//rxGj4E2iLURFpsnT55g6tSpaNKkCdzd3VGlShW0bt0aa9euFSxJ89OnT0hOTsbatWtx9OhRznL9/f2/6NXju3EfN24cHB0dcfz4cXz+/BmfP3/GsWPH4OjoiIiICE4yCwoKsGLFCrRq1Qru7u6oVKkS/Pz8MGnSJE7WYAAIDAxEp06dkJmZiVGjRsHDwwOdO3dGQUEBPn36hF69eiEgIICT7N27d2PTpk1lXn/x4oUglQazs7ORlJSEd+/eAYBgZW8/fvyIMWPGwMvLi5cHVKyGlPfu3UNSUlKZ1x8/fszp/ooV3iNmw2GxQpybN2+O2NhYjde2bt0KXV1dTutdVFQUm3emiSFDhnCef65fv47169ezG6esrCyEh4ejb9++nBpFA8WhoaqWcF1dXTXPSU5ODoyMjDjJ/hIJCQlffMb/DbHyl7Ozs+Hp6Qk9PT22qIgyN42PFzQ9PR2TJk1CREQE59ylsli+fDksLCwwe/ZsyGQy9h2Pi4uDv7+/oN/FlbKMlZoOrtSvX5+t0KaaXpGSksK5lcDZs2fx559/lnl92bJlWLJkidZyDQwM2LDTa9euwdTUFG5ubujcuTPc3d1haGiIjIwMTmN++/YtGIbBrVu38OrVK40HXxgAoJ8EuVxOKSkp5OHhoXZ++PDhtG/fPtq6dSv5+/tTYWGhVnL79u371X82Li5OK9nlypXTeP7Vq1ckl8tJIpEQEdGLFy+0kismnz59onXr1lFCQgLl5ORQUVER2djYkK+vLw0ZMoTs7Oy0lrl48eKv/rMjR47USnZqaioFBASQm5sbGRgY0IULF6hHjx5UUFBAhw8fpipVqlBSUhKZmJhoJXfEiBEUGBhIrVu3pkePHlHz5s0pOzubLCws6J9//qEqVapQYmIiVahQQSu5a9asoffv35f573z69CmtXLmSpk+frpVcJQUFBRQaGko7d+4kqVRKRERFRUUUFhZGK1euJD09PU5yhaZcuXJ07tw58vDwoPfv35OJiQmdP3+evL29iYjo2rVr5OfnR//888//eKSlefHiBXXu3JlOnDhBDMNQdnY2ubi4UL9+/cjc3Jx+++23//UQ1cjOzqaPHz+Su7s7+0x8j/j6+lKrVq1o8uTJRES0bds2GjJkCL18+ZKIiK5evUqNGzfWer6UyWR08+ZNcnR0JGdnZ9qwYQM1btyYvZ6WlkZt2rShJ0+eaD3mCxcuUFBQEI0ePZosLCwoMjKSwsPDycPDg27evEmLFy+miRMn0rhx47SSu2fPHjp9+jQtXLhQ4/WtW7fSmjVr6MSJE1qPWQySkpKoXbt2ZGxsTO/evaM9e/ZQWFgY1ahRg4qKiujUqVOUnJxMTZs21Uqul5cXDRw4kIYNG0aJiYnUtWtXmjFjBv3yyy9ERLRy5UpatmwZZWZmivHP4oypqSlt27aNgoKC1M4fOnSIunfvTq9fv+YsGwAdOXKEbty4QUREHh4eFBAQQAzD8BqzWFSpUoWioqKoffv2ZGJiQhkZGeTi4kJXr14lf39/XnN8amoq7dixgx48eEAFBQVq13bv3s136IJibGxMmZmZ5OzsrHYf7t27R+7u7vThw4f/9RBZJBIJ5ebmkpWVFbVv356Kiopo9+7dJJVKqaioiHr27El5eXmUkJDASf6pU6fI19dXvPWIt3r1A/GjhQMA4vXKGT58uFr4yfeOk5OT2mFkZASGYdQaqxoZGXFKovT19VULedy0aRPq1asHoNhD4OXlhZEjR2ott3z58qyFskuXLggICGDL6T5//hytW7cWrQ+PENy6dQs7duxAQkKCmuVVCD58+IDbt2/zyhEzMzNjczUKCgqgo6Ojlh+TlZX1XcW5qxIaGorAwEA8fPhQzQKYlJSEKlWqCPIdOTk5SE5OFqyvhphcu3YNiYmJvHuuiBXeI3bDYTFCnL81J06cYD2gXGjQoAEmT54MoDj0z9zcnC0oAQATJkxA8+bNtZa7efNm6OjowM3NDfr6+ti5cydsbW3RpUsXdOvWDXp6eli6dCnncQPFxQx69uyJ+vXrswVRNm7cqLHwwdfyLfKXxeDFixeIjY1Fv3790K9fP8TGxuL58+e8ZMpkMnYNUp0vb926xav6W3x8PHR1ddG6dWvo6emhdevWqFSpEkxNTdGnTx/Ocu/cuYMNGzYgOjoa8+bNw65duwQJ/a9QoQI796jeh927d8PFxYWX7EuXLmmM6rl48SIuX76stTzVao729val9ptXrlzh1CJFycGDBzV6ZpOSkjQWRdOWn0o5EjMcQBNCbADF6pWjDJOpWLEioqOjRekH8/nzZ+Tm5gpeKnPLli3w9fVVK2pw48YNNGrUCJs3b9ZanoGBQalQHF1dXTYmOzk5Gba2tlrLlclkbEiTnZ0dUlJS1K5nZmZ+14ucUMTFxbHJyu/fv0e/fv2go6MDiUQCqVSKwYMHc3pHmjVrhv79++PRo0eIjIyEm5ubWt+WoUOHolGjRlrLHTNmzFcfXFHNLVFd5O7cucMpxGfIkCHs/PDu3TuEhISoxV83adJEbf7QhmvXrmHIkCHw8vKCtbU1rK2t4eXlhSFDhvAuSX/nzh1Ur169VAw5n54rYoT3fKuGw0KGOAvZv+9r4BvqLZfL2bAuZTikaghnZmYmypcvz0n22bNnMX/+fHZjee3aNYSGhiIkJIR3eOyuXbtgYGCAAQMGQF9fn32XlyxZ8sX9xr8hRv6yGGGLqpw6dQpyuRz29vZsmoGDgwPkcjmvJHkPDw82t0h1vly8eDGvvK5q1aqxirFSblFREQYOHMip+WleXh46deqkNo9ZW1tDR0cHxsbGvJXwiIgINGzYEH/99RdMTEyQnZ2Ns2fPwsXFhXdOe926ddmqmar88ccf8Pb21lqeRCJh936Ojo6lQuhycnJ4KbZlFbdITExE9erVOctV8lMpR2Ii1gYQEKdXDsMwOHr0KEaNGgULCwvo6uqibdu2SEhI0Ng9WhsOHDiARo0aQV9fn93kmJqaolevXmolI7ni4uKisRlsamoqp7LCjo6O7H0FivOPGIZhraBcq7RVr16dTXT08PDAkSNH1K6fP38e5cqV01ouUBzPPnXqVHbcx44dQ1BQEAIDAwVr2CZEAiVQbGlXWqR+/fVXODk5Yffu3cjKysLevXtRqVIljB07Vmu5ly5dgkKhgEQigaWlJa5evYp69erB2toatra2MDAw0FgR7d9QLSfs7+8PuVxeqtO5XC5HkyZNtJatxNjYmPV6qS72ly9f5vRMqJY1nThxIuzs7HD8+HHk5+fj7NmzcHV1xYQJE7SWe+jQIejp6aF+/fqYPn06li9fjuXLl2P69Onw8fGBvr4+r7yK1q1bo127dnj27BmMjY1x/fp1nDlzBt7e3t+dZ/t/1XCYK0qleMuWLbyreKqi2hBX9WAYBh4eHuxnbSlZXa9ky4p79+5pPQ8vWrQI79+/BwDcv39fsJw+Vby8vNg8MdUxX7lyRWtlTsz85cTEROjp6aFcuXKQyWRITEyEpaUlAgIC0LRpU+jo6PBWkDw9PTFw4EC1fFplcSY+Sf1r1qxBhQoVsG3bNhgZGSE+Pp5ttBsfH89ZrqGhIWuEKFeuHJuTfv36dVhbW2stb9CgQfD19UVmZiays7PRqVMnjBs3Dvn5+Vi3bh0MDQ3ZnkJc+PjxIwYMGACpVAqGYdi8wV69evHOjTYyMtLYIiYnJ4dTQRiGYWBmZgZzc3Po6uqWyuFNTk7mtF9TIpPJyqyqK0TZ+J9aORIyGVqsDaAqQvbKUXV5FhQUYPv27QgMDISOjg5sbW0xadIkTsmZGzduhImJCSIiIjB58mRYW1tjwoQJWLFiBfz8/GBhYaHWN4ULBgYGGquQpaSkcCoTPmrUKHh6eiIxMRHHjx9HkyZN1JI8k5KS4OrqqrXcuLg42NnZ4cSJE9i4cSM8PDxw9OhRPH78GMePH0e1atUwYMAAreWuXLkSUqkUtWvXhlwux6ZNm2BiYoIBAwZg8ODBMDAw4BSKI1YCpWrvq0qVKrF9VpScOnWKU8I5UGypS01NZb0i79+/x9q1a7FkyRJe5dKV/Pbbbxo7nbdr1w7z58/nLDcoKAhTpkwBULyhysnJQWFhITp37syp6pDq++zp6cmW8layb98+Tj2qqlevjqlTp5Z5ffr06ahWrZrWcpUoFAr2mZLL5exvduzYMXh5eXGWW5aBp7CwkJeBRoyGww8fPlTz8pw+fRo9evRAw4YN0bNnT049joDiZ6Jly5bQ09ODubk5hg8f/sXE669FKpWiZcuWas1vp0+fDolEgqFDh3JuiFu9enW1uSEzM1OtyePp06e1DpvW0dFh3wux+qKo9uwq6QXWtsx9nz59vvrQFrHCFlWRyWQa590bN27wbn66efNmuLm5qYWcqjYr5UKFChVYhahatWrsvHn+/HlOYbIWFhZqFSJfvHgBmUyG/Px8AMDSpUt5zWtKHjx4gIMHD2L79u2891NKypUrp3GuOXfuHKc+R6qpIP/5z3/YFi9KZs6cyTv6QpMyf+TIEVhaWnKWq+SnVI7++ecfthSrRCJhJ7O+ffvil19+4SRTzA2gKkL2ytG0UNy/fx/Tp0+Ho6Mjp7AWd3d3tbKQly9fhp2dHbt56Nq1K++qfa1bt0bNmjXV8ktSU1NRq1YttGnTRmt5b9++RZcuXVhrjI+Pj1qFr8OHD5dqZvu1/PbbbzA0NISBgQFrZVYe7du35xTqVKVKFaxevRoAcPz4cchkMixbtoy9HhcXBw8PD63lqj4T7dq1Q5s2bdjNSWFhIbp164bWrVtrLVdZ+Q4oXoxKxi9fv35dlGpRQmBra1tmp3M+8dKZmZmwsrJiN6+dOnWCh4cHypcvX6o3zddQsiFlyTHfu3ePk+GgrM2OEr6bHjMzM/Zdc3FxYZ+T27dvcxrv69ev0blzZ8hkMlhZWWHq1KlqFlUh2jUIjbe3NxISEgAUl8uXSCRo27Ytxo8fjw4dOkBXV5e9rg3K9/nZs2eYP38+qlSpAolEglq1amH58uWccyCUnshp06apKaF8oxlWrFjB5nVpYuLEiVpXBLS3t8fy5ctx7949MAyDtLQ03L9/X+PBFWdnZzYqQFU52rBhA6d5WCzEDFtU4uPjgz179pQ6v2fPHjaPly/5+fmCKbndu3dnq7/NnDkTlpaWGDBgABwdHTntU1TzYIFiw7NUKmXnZr45UiXJyclRMyDwoVu3bvDz81Or9Pby5Uv4+fmhc+fOgnyHkAwaNAjVqlVTWy+zs7NRvXp1zpVDVfkplSMxkqF/tA1gWcqRkqKiIk7x+qpWNCVSqZRttpuSksK72/Lff/+NoKAgMAwDPT09VukICgriNWm+f/+ec17Gl3j58iV27NiB6OhoREVFIS4ujpe1p2RH65Klabm6lcVKoJw0aRIaNGiAly9fYsKECWjTpg17n/Pz89GlSxe0aNFCa7m7du1iLXJiYWxsjBMnTpQ6f/z4cd6N8V69eoVZs2ahc+fOCAoKwuTJkzl3fmcYBoMHD8aYMWNgZWVV6t1NS0vjlN/m7u7Obh408dtvv6Fy5cpay1XSsGFDdjPVvXt3tGzZEmfPnkVYWBiqVq2qtbyRI0eiUqVK2LlzJ9asWQNHR0cEBwfj48ePAP6vz402qDZ3/LeDC0ZGRqyCWK9ePURHR6tdX7JkCacwNU1z/Pnz59GvXz+YmJjA0NAQoaGhnMb86tUrdOvWDfXq1WM3J3yVIzFYtWpVKaOU0E2zo6KiUKVKFVy8eBEmJiY4c+YMNm/eDEtLS05l7jUhRP6yGGGLANgy4xkZGdi2bRscHBwQGxuLM2fO4MyZM4iNjYWTk9MXe+n8r3j+/Dm7NyksLMTcuXPRpk0b/PLLL2rRAl9L8+bNMWzYMPZzbGys2pp55coVQfOMhWzp8ujRI7i4uMDU1JQNKTczM0PlypUFa0AsJK9evUL9+vUhlUrZQl1SqRRNmjTh3HNOlZ9SORI6GRoQbwOopGQ86cWLF3Hq1CnOzeCcnJzwzz//cB5PWXh4eKgl9aWlpUFPT48df3Z2tmBK4q1bt9iqVjdv3uQt79WrVxqr6jx//vy7ajKr7EIPFOcFMQyjlph48uRJ2NnZaS1XrATKjx8/om3btjA3N0fz5s0hk8lgaGiIihUrwsjICA4ODpx+P4ZhIJfLMXDgQNF6Z4WGhsLJyQl//PEH2+l8165dcHZ2RlhYmCjfyQU/Pz+1PKk1a9aoXZ81axb8/Py0lrtjxw5IpVK0adMGixYtwrZt27Bt2zYsWrQIbdu2hZ6eHnbt2sV53ElJSfjjjz8AFM8NlStXBsMwsLCw4JT/4ODgoKbMPnv2DN7e3mjRogU+fPjAyXNUsrnjlw4umJqasu+alZVVqffu9u3bnIwdXwojy8vLw9q1a+Hj46P9gFVYv349rK2tsWrVKt6h3mLx5s0bZGZmgmEYHDt2DOnp6RoPrhQVFbH5L8qQL5lMxobNaotY+ctihC0C//d+lNWQk09jzpSUFLW9T0JCAho3bgxbW1vUrl27VE+w/zVpaWkoV64crK2t4eDgAD09PbWcqKVLl3JaN0rmoikPiUSCgIAA9jNf8vLysGrVKgwdOhQRERHYsGEDpz2ml5dXmbmJJQ8+FBUV4fDhw5g3bx6WLFnCq+hHSX5K5UjoZGhAvA3gkydP4OvrCx0dHTRu3BgvXrxAcHAwO+FUqlRJa2tzRkaGYI1NS7J06VKYmppi3LhxmDZtGmxtbdVcnJs3b+b9QohFy5Yt1cLTlKxYsYJX1SFA2PKmw4YNQ8WKFTF79mx4e3ujd+/ecHd3R2JiIpKSklCtWjX069dPa7liJ1AmJiZi6NChaNmyJVq0aIHevXtj9erVyMvL4ySPYRjMnDmTTQavWrUqFi5cKKjSn5+fjyFDhqgVF9HT08OQIUM4j1uJsvxvgwYNBCv/WxZ37tzh3Gz33Llz6Nq1K7vY6+npwcHBAV27duWcD/Mlnj9/zjmHx8DAoFTT2zdv3qBBgwZo2rQpcnJytN6kqTZv/M9//sPmUSoNMxMmTICNjQ3nqmdt27Zli2UEBgZi0aJFatfXrFmDihUrai3336IDhOLWrVuoW7cuGIbhrRylp6cjNDQUzs7O7Brq6emJKVOm8DZQ/ec//xG0MEVJPn78iGvXriElJYVXBIJY+ctihC0CxR6nrz20RVXB379/PyQSCcLCwrBs2TK2MMHu3bu1lqskLy8Pp06dwrZt27Bjxw6kpqbyLtrx5MkTrF69GkuWLBHMWMAwDPz8/ErlnSnD87nmoYmFaj7ihAkTIJfLUb9+fbbKa4MGDSCXyzkVCSoLoYut/JTKkdDJ0KoIvQEMDQ2Fj48P9u/fj65du8LHxweNGjXCo0ePcP/+ffj6+qq5cb8GVQ+Bs7Oz4B6k5cuXw8fHB7Vr18akSZPYakFA8ULKtepZ69atsXHjRl69NL6Eubm5Rhd1VlYWZ6UZEL68aV5eHgYOHAhPT08MGjQIHz9+RGxsLPT09MAwDPz9/TltisROoBQa1c1famoqhgwZAjMzM+jr66Nz586cyzhnZGSUSujPy8tjQ0f4KkWAeOV/f2QePHjAO3yjcuXKGsu7vn37Fg0aNECNGjV4hVA1bdq0VLELoLi9ABfPHFAccq1QKBAWFoZZs2bB2NgYvXr1wpw5cxAWFgZ9fX3ExcVpLVdsZUCVwsJCvHr1itcGJSkpCQYGBggJCUGvXr1gaGiI4cOHY/z48XBzc4Orq6tgFQFv3bqFo0ePcio6JDbfKn/5R0B1jm/YsGGpzfScOXNQv359reUWFhZi7NixMDAwUAuvZBgGjo6O2L9/vyDjF4r4+HjY2dlh/fr1aueFCmX9559/cPz4cdZg++zZM0RHRyMyMpJ32F7//v01elGnTZum1nqDL0KGGAI/qXIkdDK0mNjY2LCb1OfPn7MluJUcO3ZM6+Zf5cqVYy1Tqonc3zsMw0AqlcLU1BTh4eFqVWGEwNDQkK1co8p///tfTsnhSoQsb/ol6+n79+/x5s0bzuP81syYMYNXLxZNlvH3799j48aN8Pf3h0Qi4eTpUrVWimE8AIQt/wuIV/FMFTH6ln369AlTpkyBXC5nNylyuRyTJ0/mFM4xYsSIMvsNvXnzBvXq1eOlHBkYGGjMF7x58yavOeL27dvo1q0bTExM2E2arq5umQnu/3/Ey8sLK1asYD8nJyfD3d0dQHFie7NmzThZx6Oiotg188WLF2wxJmWoV8uWLTnnKKSnp2PWrFlYtmxZqbns9evXnDZ/3yp/Wchqvf/GixcvOIXAqc7xVlZWpdb8GzducMphHj9+PDw8PJCQkIAjR46gcePGiImJQVZWFqZOnQp9fX0cPnxYK5klG1h/6eDC3bt34evri44dO7L5UEIoRykpKTA1NQXDMDA3N0dqaiqcnZ1RsWJFuLq6wsDAQK34lbbI5XKNc+atW7c4VQQsq+eg0qvItwehkp9SOQKK80tmz54tSDJ0Se7fv4+LFy/i0qVLvDdWMplMzZpqZGSkZu26f/++1ovywIEDoa+vDycnJ0gkEjg4OMDZ2Vnj8T2hDNtYuHAhqlWrBolEgho1amDJkiWckidL4u/vj+HDh5c6P3ToUDRs2JCzXCHLm6pu3IVKPBQbTYnrr169gq6uLlJSUjgns/9bad7s7Gy1UrVfy7cwHghZ/hcQr+IZIG7fsvDwcFhZWWHlypWsZ27lypWwtrZGeHi41vJevHihsbqgkjdv3uDkyZOcx1tWSNPYsWM5lUovSVFREXJzc/HkyRPO+aRfizKkSGjS09M5KaAl+5YUFRVBV1eXXZdPnz7NqUSvnZ0dW5VtwIABqFmzJq5cuYL3798jPT0d9evX5xROdvjwYejp6aFq1apwcHCAQqFglRqAe2VEsfOXxajW+29wfSYYhsGJEyeQkZEBR0fHUi08bty4wakwjo2NjVrBoUePHsHY2Jj1tM6cORMNGjTQeqwlc6xKfuaTmwgUe7ymTZsGe3t7JCUlCZLnFxAQgAEDBuDNmzeIjY2FnZ2dWouRvn37on379pzlly9fXqPnOy4uDlZWVlrLYxgGXl5epfoRMgyDunXrwt/fn1cPQiU/rXIkBsuWLYODg0OpJF1fX1/OXg4HBwekpKSwn8ePH6+Wq5Kens6p+kliYiKWLFkChmEwa9Ys/P777xoPbVDmq3zNwYWSnoKUlBQMGjQIpqamMDAwQPfu3Xk1sTt79ixkMhkaNWrExss2atQIMpmMV0NKIcubyuVy1nUs5MZdzATKf6sSxTVZV6ycim9hPBC6/K9YFc/E7lsml8tx6NChUucPHjzIyaooNgcPHoRMJoOnpyf69++P/v37o1q1apDJZBrD+b5nuG5Yv0authUBAcDV1VWtoXB2djZ0dHTYSoM5OTmcvHP6+vpsvouTk1MphTA1NZVTFc4GDRqwxpeioiLExMTA2NiYDYPjqhyJlb+sRIxqvf9WxfHMmTOc53hVJWPhwoVq1+Pj4zmN2cTERK1Sn7K0uTJs89q1a7waiR45cgS1atVCUlISew+SkpJQp04dzuHeqpw5cwbOzs6QSCS8lSPVdIKCggJIJBK1PWdaWhoqVKjAWf7cuXMhk8kwYsQIbNq0CZs2bcLw4cNhaGiIuXPncpLn7Oxcaq8ndLVMKf1E/PHHHxQUFESGhoaCy54/fz4tXLiQJk6cSDKZjBYsWEDdu3enunXr0tatW6lx48Z06tQpqlOnjlZyvby86MKFC+Tt7U1ERNHR0WrXz549S9WrV9d6vC1btiQiorS0NBo1ahSZmJhoLaMkv//+O/v/z58/p9mzZ1NgYCA1aNCAiIguXLhAhw8fpqlTp/L+LiIib29v8vb2poULF9KOHTto3bp11Lx5cyosLOQkz9fXly5cuECxsbG0Y8cOMjAwoOrVq9O6deuoYsWKWsn673//y/7/yJEjadSoUXT79m2qX78+ERFdvHiRli1bVur3/DcCAgKoSZMm5OHhQUREHTp0ID09PY1/9vjx418tt3379uz/f/jwgZYvX05VqlRhf7uLFy/StWvXaOjQoVqNl4jIxsaGvLy8KCIigiQSCRERAaCAgABau3YtOTs7ay2TiOju3btkaWnJ6e9+idWrV1PHjh3p9u3bNHLkSBo4cKAg74cqAwcOpFGjRtH69euJYRh68uQJXbhwgX799VdO74dUKqW3b98SUfF9CQoKUrseFBRE48eP11puVFQUrVmzhrp27UpExc9Jhw4d6MGDBxQeHk7dunWj8ePH0+7du7WWTUSkr69PTk5Opc47OzuX+Vx/LceOHaOFCxdSVlYWERF5eHjQ6NGjKSAggLPMVq1aUXZ2Nq1YsYKV26ZNGwoPDyd7e3vOco8cOUJnz54lPz8/atq0KZ0+fZrmzp1LHz9+pNDQUOrbty9n2ULTsWPHL15//fo1MQyjtdywsDAaMGAATZ48mfT19WnBggXUtm1b9jlIT0/nNFc4OjrS1atXydHRkRiGIalUfdujo6ND+fn5Wsu9du0abdq0iYiIGIahcePGkZ2dHXXq1Im2bdtGdevW1VomEZGenh7t27ePkpKSKCEhgXR0dKioqIhsbGzI19eXevToQUZGRpxkExElJyfT4cOHyc7OTu18xYoV6f79+5xkmpmZffE3B8Dpmbh7967aZ2NjY7XPBQUFnOa1atWqUXx8PE2ePJmIiHbs2EHGxsZkbW1NRERFRUWkr6+vtVwlo0ePppUrV1LDhg3Zc4GBgWRoaEiDBg1i5w6uNGzYkP773//SnTt3yNXVlZesgoICMjAwICIiXV1dMjQ0JAsLC/a6hYUFPX/+nLP8CRMmkIuLCy1atIg2b95MRMVzcVxcHHXp0oWTvGbNmlGvXr2oTZs2NHfuXNLV1eU8vjIRTM36ARCz9K+Tk5OaBfTmzZtQKBRsqcyRI0fy7j6tiZSUFLUeN3w5ceKEIAUPOnbsiCVLlpQ6v2TJErRr146TzK/xFAhR0lsIxCpv+u7dO6xYsQK//vorGIbBoEGDMHr0aI0HV4ROoHz+/Dnat2+PJk2asFXZAPH6oggZO9+nTx9R8riELv8rVsUzsfuWRUZGonv37mqFAz58+ICePXtixowZnOUuW7YMUqkU3bp1w6JFi7Bo0SJ0794durq6WLp0KWe5YrBp0yZIpVLUqlULxsbGiIuLg5mZGQYMGIB+/fpBT09PrT3C1/Jv3ntlnpe2SKVSBAUFlaqcpTzatm3LSe6nT58wbtw42NraQqFQoEePHmp5PCkpKZzCAGNjY+Hh4YHs7Gz89ttvaNCgAZtbnJOTA39//zLz1L6EpaWlxoiQ+Ph4GBoaYsWKFVrfh0WLFrEFjO7fv1+qMIwQiFGtVy6XIyYmRq2yo+qxZs2a76r58tGjR6Gvrw9vb280btwYUqlUzSsVGxuLpk2bcpYvk8k07ssyMjIEawIrRO8roLiXnaoX5sCBA2p7wIsXL3JqDSI2b9++RVhYGKpXr47MzEzBWwn8dMqRWKV/DQ0NS8VLS6VSNl46PT2dd9PIb4FQFT9K5kYp4dPnyN/f/5vm2PTp04fdBGqLmOVNlYh1P4ROoFSyfPly2NrastW+xFKOhK5ao4pQxgMlQpX/FavimRh9y0r26jAxMYGFhQWaNWuGZs2awcLCAnK5nFffjgoVKmg0zixduhS2trac5ZakSZMmvN5hoDikVanMHj16FAYGBliwYAF7ff78+fD19dVarqGhISIiIkpVoVQekZGRnDas1apVw9q1a8u8/ueff35XG2GguFCHrq4u3N3dIZPJ2JL8EokEderU4VQFr3nz5oiNjdV4bevWrdDV1dX6Pujo6LAGwH/LqeSKGNV6/f39ERMTU+Z1rqGWmigoKMCtW7fw6tUrXnLS09MxadIkRERECBLqpkqjRo3QvHlz5Obmsudyc3PRokULNG7cWGt5YvW+AooLI6n2YirJpEmT0LFjR06ylajm4XNtYVIW8fHxKF++vCAhhqr8VGF1RESDBw+mqVOnUlpaGq1bt44iIyNpwoQJ1LZtWxo4cCA1b96ck9xKlSrRkSNHaODAgUREdOLECdLT02PdtDKZjJNbuSxcXFzo8OHDWod7KalVq5bG858/f6aQkBCSyWRERHTlyhVO8hUKBe3bt48iIiLUzu/bt48UCgUnmSdOnOD09/4N1RA4VbZs2ULt2rUjFxcXIiKtwhcdHR0FGduXKHk/7t69S/b29qXCRrTFwMCAzp07V+rZOnfuHPtccGHIkCHk5+dHPXr0oISEBF5jJCL65ZdfNJ4vLCyk6Oho9jlbsGAB7+9S0qJFC8rIyGDDGvmip6dHVapUoZMnT5KOjg5nOR4eHpSSkkJTpkyhefPmUX5+Pm3ZsoWkUinVrVuXtm3bphY6+bUMGzaMBgwYQJcvXyaZTEZr166l0NBQdqwpKSlUqVIlrWSampqqfQ4JCVH7zCc8TcmrV6/Y0GFVWrRowSkMZ//+/RrPnz59mg4cOMCOuW3btlrLzs7OpjZt2hARUbNmzejz58/UrFkz9npwcDDNnTtXa7leXl5kb29PvXv31ng9IyODIiMjtZZbu3ZtunLlCvXv31/jdX19fXJwcNBabln85z//oQ4dOpR6brRh8eLFNGTIEDpw4ADl5OSohakFBARwWpuHDBlCp0+f1nite/fuBIDWrFmjlUxbW1v6448/qFWrVgSAHj16RB8+fND4Z7ne43nz5lGzZs0oNTWVCgoKaNy4cXTt2jV68eIFnTt3jpPMHj160Pv378u8bm1tTdOnT+c01hEjRpCBgQEVFhbS+PHjacmSJfT582eSSCQUGhpKq1at4hRWVaNGDapRo4bWf+9rWL9+PXXo0IEcHBzYueHhw4dUsWJF2rt3r9byZs6cSfHx8URENHXqVDp+/Djt3LmTPDw86ObNmzRu3DiaOnUqzZs3T2vZ//a7TJ48mfPatHz5coqJiaFHjx6pnW/QoAEtWrSIateuzUmuKt26daOGDRtSWlqaoPsuBgAEk/adI5FIKDc3l6ysrNhzHz58oJ07d9L69evp9OnT5ODgUCrO9WvYsWMH9erVizp06EAymYx2795Nw4cPZxe1VatW0YYNG+j8+fNayV28eLHG87/88guNGzeOVb5GjhyplVxdXV0KCAhgc2CIiuOCZ82aReHh4ew94jKhERUvaAMGDKCgoCCqV68eERVvpJKSkmjNmjXUp08fTnLL+i4+i6dEIiGGYUjTq6A8zzAM51ymkvBVbMtCT09PkI17dHQ0RUZG0sCBA9lct5SUFFq/fj1NnTqVJkyYwEt+QUEBTZgwgU6cOEG7d+/mnHMkkUioRo0aZGZmpnZemdtnZGREDMNolXulpCzjQXp6Orm7u/M2HpREqN+OqPg9/vvvv6moqIgsLCx4x2OvWLGCNm/eTB8/fqTAwECaOnUq++/Pzs6mwsJCcnd35z1uIenRowfVrFmTxo4dq3Z+/vz5lJqaStu2bdNK3pfmCCVc5whzc3O6ePEiVa5cmYiITExMKCMjgzXK3L17lzw9PbXOi4mKiqJPnz6VOYc/fPiQpk2bRnFxcVrJ/fjxIxUWFoqSu6sJId+N753Vq1fTiBEj6PPnz2X+GSHWo9evX9PSpUspIyOD8vLyqFatWjRs2DCysbHhLFMMdHR06K+//iIrKyuaP38+RUVF0W+//Ub16tWjP//8k3755ReKiIigcePGCfJ9TZs2pbi4OEE22QDoyJEjdOPGDSIqNmBxVcRlMhndunWLHBwcqHLlyrRo0SI148/p06cpNDSUc86YJvgaW/8tD/+PP/7glIevifj4eGrbti2vXLyS/FTKkeqLponbt29TXFwczZkzh5P8xMREtU2E0otERGxCm7ZeE4lEQhUqVCj1gN6/f59sbW1JV1eXGIahnJwcreSeO3eOevfuTT179qTp06ezifK6urqUkZFBVapU0UqeJlJSUmjx4sVqCdEjR45klSWh4Lt4enl5kZ2dHc2fP59NTARAFStWpMTERFaJ0XbCFEuxLSshet++fdS0aVO2eADXJHmiYmV/0aJFar/dqFGjOCVQikV0dDStXr2a1q5dS02bNmXPC/EMi2U8+NZKl1DexG8FH0OH6vv25s0bmj9/Pvn6+qoVFTl37hxFRETQlClTtJIdFBREOjo6tH79erX1Q4hnrW7dujRlyhRq164dO3YTExN2E3X06FEaNmwY3bx5k/N3/AiUK1dO4/lXr16RXC5n16gXL14I8n19+/alOXPmkK2trSDylERGRtKwYcPUktq14e3bt3T//n2qXr06HT16tMw9g1heDyEBx0IMSlQN2rVq1aLw8HAaNGgQe33Lli00d+5cunr1qlZyy/IEd+zYkRYtWsTLE6wJvvfBycmJ4uLiqEmTJmRnZ0d79+5VUyqysrKobt26lJeXJ8RwiYj/vsrZ2ZmWL1/OFge6desW+fj4UG5uLkmlUho1ahRlZWVRcnIy77HK5XJKT09nDUpC8FMpR5o8R9874eHhlJKSQlu3blV7SIVYlF+/fk3h4eF09+5d2rJlC7m6ugqqHAmNWIunMrTgyJEjtHnzZqpZsyYR8b/HYim2EomEGjduXMrjsnHjRmrbti3rSdHWIvwtqVatGh06dIh3CNXly5dLVa0R4hkWy3ggtse2JGJZ3fluAMuCz3i/1gPJ5Z0jIlq4cCEtXLiQli9fTq1btyYiYebhPXv2kEKhoMaNG2u8Hh0dTfn5+TRr1izO3/Et4KtsmJiYkJ+fH3Xu3Jk9B4AGDBhAM2fOpAoVKhARlRkmWBZlhU3XqVOHduzYwSlsmqhYiS0JALK0tKSzZ8+y3lS5XK6VXCUbNmygbt268aqa9jUI6S0pCd/5RyKR0NOnT8nS0pIsLCzo5MmT5OnpyV7n6lUV0xOsCb73YfLkyXTixAk6dOgQxcTE0LVr12jr1q1kbGxM7969o759+9KrV6/o8OHDWssWy9hqZGRE165dY6uRAiA9PT168OAB2djYUEZGBjVs2JCtssqHkt52IfgxzIkCIVbp3y/Bd8FYuXIl7dmzhwIDA2ncuHE0fPhwwcZmampK8fHxFBcXRw0bNqTIyEhB86JUEcJK9+nTpzIXz3HjxrGLp7bo6enR77//TomJidS2bVsaOnQop7yEkgwaNKhMxTY5OZnzhmrr1q00duxY6t27t1qJ382bN9OcOXMEV2zF2Ajfu3ePPn36xFtO3bp1KS0tjYYNG0Z16tShLVu2CPIM+/r6UlpaGoWHh5OPjw9rPODLyZMnqXfv3uTt7a2mdM2ZM4eGDRvG+bcra4ErLCykkSNHcl7gytoAzpkzh4KCgtgyy9puAMsydHz+/JkaNGjAydDBJRxaG8aMGUNNmjShnj17UkJCAi1cuFAQuR06dPjidb5hrCXhG9YrRo4mEdGff/5JPXr0oOPHj9OyZcvYss0DBw6k9u3bc343vLy8ytwIh4SEcA5TMzc313geADVo0IB3+FtJJZDvPCxm3pyY+Z9r1qwhY2Nj0tPTKzUfvH37lpPyGBgYKIonWKz7MH36dLp69Sq5uLhQnTp16MyZM1S+fHmqUKECPXnyhBQKBR05coTTmPfu3avR2EpUXDqda7rCt87DF5qfSjkqyzLC1+VJJN6CQVS8eHp7e1NYWBgdPHhQcI9A3759qWHDhtSjR48vxjp/DWLeB7EWTyVBQUGUmppKffv2pcTERF6yiMRTbLt160b169enXr160YEDB2jt2rVlLtTaINZGWGyMjY1pw4YNtG3bNgoICBDM2ieG8UAspUusBU6sDaBYho6yECq80MvLi1JTU2nMmDHk5eX1RcszX/jmUpYV1vvgwQOKi4vjHNYrlrLh5uZG58+fp8mTJ5OXlxdt2LCBfH19tZKhierVq39V2LS2iNW/Tax5uH379mX+biNGjCAi7t6S33//XWP+JwDKyspi8z+1xcHBgS1qoa+vT1euXFHzsJ44cYLN1dOGxMREWrhwIdWpU0fNE8wXse6DmL2vxDK2Tpw4kXr16kVHjx5l8/BHjhzJ/vtLegH5kJiYKPia8VOV8i4LIUr/fqmvjfI83/KmRUVFiIqKgrW1NXR0dAQvg1xYWIhXr17x6hMj9n1Q9sJwdXXF2bNnAYhTEnrRokVo3749Hj58yFvWo0eP0LRpU7Rs2RJ//fWXYOMtLCzEtGnTYG9vj6SkJN51/iUSicZDyGdYSVBQEFvmXkgePnyIvXv3Ii8vT1C5t27dQp06dcAwjGDP2vr162FtbY1Vq1bx/u3i4+NhZ2eH9evXq53n+6xVqFABwcHBOH78ONuz5MSJE9DR0UFcXBx7Tluys7NRt25dhIWFqZUw/5HKu+/btw+jR48WpdwywH/MDMPAzs4OTk5OagfDMKhQoQKcnJzg7OystdwaNWogODgYWVlZbDuCu3fvQiqV4siRI7xbFADAsWPH4ODggIkTJ/J+Nz5+/IhRo0ahSpUquHLlCnue77MmVv82sebhli1bIjg4uNTzKsQ7N3fuXDg7O6v1yxFK9pe4cOGC2m+qLX/++SeqVKmCQYMGIT8/n/d4/1f3gS93796Fr68vOnbsiBcvXgAQZsyHDh1Cjx49EBISgtWrV6td++effwRpo6MkNzeXU0n+sviplKMxY8ZoPCQSCcLCwtjPXPgWC4aS1NRU/P777+xDLBRDhgxRa7rHhW91H4RcPL8FYiq2Z86cgbOzM+86/2JthL8lW7duFVwxUiKE8aAkQipdYixwYjbwFcPQUbKHkvKQSCQICAhgPwvBw4cP2X5PfCirSSvDMDA1NWU/a8vgwYPh5eVVSsHie4/FUjZK8s8//6BDhw4wMzPDjRs3eMs7dOgQ7OzsEBUVhcLCQsHGK3T/NjHn4QULFsDe3h4JCQnsOaHuw6VLl1CpUiVERESgoKBAUNli8u7dOwwePBgVK1YUZG3+lvdhxowZvPdsSoQ2torF8+fPERISAnt7e4SHh+Pz58/o378/azRo0KCBIIbXn0o5YhgGXl5e8Pf3VzsYhkHdunXh7++PJk2acJL9rRYMJTk5Ofj06ZOgMk1MTNhu2Vz5lvdB6MVTFSHuhSbS0tJEUWzfvn2L9PR0fPz4kbMMMTfCQLFCGxkZifDwcAwdOhTz58/X2GyWD2L9boAwxgNNCKl0ibXAidnAV0hDB8Mw8PPzQ58+fdQOiUSC9u3bs5+FQKhnzdjYGMHBwWoNWuPi4qCjo4M5c+aw57iwe/du2NvbqzXEFeq3E0vZEJPc3FwEBQWhUaNGgo732rVrqFGjBrp37/5dGyQA4b0lqrx9+xZhYWGoXr06MjMzRdtgOzs7C752COkJFvo+vH79utTx6tUr6OrqIiUlhT0nBEIZW8uiT58+ePz4Mee/369fP3h6emLJkiXw8/NDu3btUL16dZw9exbnz59nIxL48lMpR9/C5fmtFgwxwkSMjY0F21j+iAunKkLeC1XOnj3LuZP1l5g7dy5evnwpiCyhN8JPnz6Ft7c3281bIpGgdu3arBdt7NixgowbEO93A8RTvMRQusRY4ITcAJZEKEOHWOGFmhDqWRM7xFCssF5APGVDSatWrUQJvxUybFrJx48fMWbMGHh5eSEnJ4e3PDENEkJ7S0oSHx+P8uXL855/Fi1apPHQ0dHBxIkT2c9CIJQnWBWh7sO3DHkHhDG2ZmRkaDx0dXWxZ88e9rO22NjY4Ny5cwCK5x+GYZCcnMxeP3v2LCpUqMB53Ep+KuUI+DYuTyEXDDHCRPbt28f+21URemMp9sIJiLd4irXJFmuDLbRcITfCXbt2Rfv27fH69Wt8+PABw4cPZy07x44dg0KhwO+//y7IuMVUjn60Z0KIBa4kQm8AxUCs+PmSCPk8iJ1LKXa+qhjKBiDu+/wjIKZBAhA3b06I/E+x8uY0IdY8LMR9+NYh70IYW8XKPzc0NFRLzdDV1UVmZib7OScnB0ZGRrzGDvyEyhHw7Vy/QiwYYoSJSCQS/P333+z/i5VQrESshRMQb/EMDw8XJYRKrPGKIVeojbBcLsfVq1fZz3l5edDV1WXDADZt2oTKlSvzHi9Q7DERwzMH/Fi/HSCsN/FbIKSh41vEz0dFRQl+f8XOpRQrrFcsfjSDhKenJx48eCCoTDENEmJ4S5QIkf8pVt6cJsR61oS4D2KHWpZEiPdDrPzzGjVqYOnSpQCKI5RMTEzw22+/sddXrFgBT09PXmMHflLlSIlQLk8xESNMpHz58ti/fz+AYuVLqSj9iPxolsUfbYMtBJaWlmrP6rt37yCRSPD8+XMAwJ07d6Cvry/Y98XFxeHVq1eCyROb/7cBLEaM+yB2/LwYiJlLKVZYr1jPWtWqVX+YZ01MuWIhZo6mULLFzJtT5UeYh8UMtVRFiHshVv755s2boaOjAzc3N+jr62Pnzp2wtbVFly5d0K1bN+jp6bHKEx8kwhYG/7Ho1q0bpaam0u7du0XpDi2Xyzl1YlelW7dudObMGVq3bh2FhITQy5cveY8rPDyc2rVrRzo6OsQwDFlbW5OOjo7GQwiEuA9l4ejoSLq6urzlvH//ntavX0/9+vWjoKAgCg4OphEjRtCxY8cEGOX/sWrVKipfvrygMomIrl+/znaiFpJq1arRw4cPeclo2LAhTZs2jfLz8+nTp080adIkcnFxYRuBPnv2TJA+TUoGDRpET548EUye2Lx9+1bQzt5KIFIPHqEa+H4LGjZsSP/973/pypUr5ObmxkvWX3/9RZs3b6ZDhw5RQUGB2rX8/HyaOXMmL/lKFAoF7d69m16+fMmph8uXCAoKosePHwsqk0i8Z+3q1atsc9KfGSHmYU2I9bsJKbtDhw504cIF2rNnDwUFBVFubq4gcksyadKkMptT80HIezxkyBA6cuQIxcTEUI8ePQSTKwZ6enr0+++/0/z586lt27Y0d+5cKioq4i23Z8+edOrUKQoPD6fjx49Tp06d6MiRI6Svr0+fPn2i1atX07Bhw3h/z0/VBFYTZ86cobZt23JuoPUlhHopnJyc6PTp0xQZGUk1atSgNWvW8GpGOWPGDOrWrRvdvn2b2rZtS3FxcaWalgmJmBPw1atXecu4ffs2BQQE0Pv370lfX58ePXpErVq1osuXL9OKFSuoY8eOtHXrVt4NJG/fvk0KhUKtYaAQHaJTU1MpKyuLiIg8PDyoTp06vGUqEWIjPH/+fGrRogWZmZkRwzBkZGREO3fuZK9nZWVRnz59tJZb1kL2+fNnatCgAXufS3ZV/xo+ffpEkydPpt27d1O5cuUoPDyc+vXrx15/+vQp2draCtZwtm/fvjRnzhyytbUVRN73zOLFi2nQoEEkk8nowYMHZG9vTwzDCGboKMnSpUspPDycbZ7JhcuXL1OLFi2oqKiIPn36RBUqVKC9e/dS1apViYgoLy+PIiMjadq0aYKMOTg4mNauXUs2NjaCyFMi5lwsNO/evaMHDx6UUkS5NBAvSa9evURpZt2oUSO20ayQ/EgGCTGoUKECHT16lKKjo6lmzZqiPMcTJ04UXKYYVKlShS5dukQTJkwgT09PUZ6369evC7YWBQUFUWpqKvXt25cSExMFkenr66vWILpKlSq0ceNGQWSz8PY9/eCI6Vb+EcJEZsyYgfz8fAFGVjZC34c7d+5gw4YNiI6Oxrx587Br1y5eZSyDgoIwePBgtpRydHQ0goKCABT3oXFycsL06dM5y//nn3/QrFkzNgFReS/69u2LX375hbPchw8fomHDhmAYRq03iq+vr2D5XUL9dvn5+UhOTkZCQoJguVxilkCePn06ypcvj9jYWEyePBmmpqYYNGgQe11ZJUdbxKrgo4kHDx6Ikk/At4Gvjo4Om+f4LXIehZjjAwIC0LdvXxQWFuLNmzcYMmQIFAoFGy6Sm5sraLWoHy3sS8gczb///hvBwcFlVuj6GRHrdxMjb07JmTNn8P79e8HlCpU39+TJE0ydOhVNmjSBu7s7qlSpgtatW2Pt2rWCzpti5sH+aAidf/7y5UusWbMGEyZMYMP009LS1PKyuPLTK0dixgiLldQvRhUqMRHqPuTl5aFTp05qFU+U1ZeMjY05x5kaGhqq9Uz4+PEjdHV12e7Ne/fuhZOTE+dxh4aGIjAwEA8fPlR73pKSklClShXOcgMDA1GvXj21vIQbN26gQYMGCAwM5CxXFb4bYXNzc/a379u3L968eSPIuABxSyC7ubmpNUrMzs6Gm5sb+vTpg6KiIs6bYbEq+Khy//59XLx4EZcuXRK0A7lQ2NvbY/ny5bh37x4YhkFaWhru37+v8RACIeZ4c3Nz3Lx5U+3c3LlzYW5ujkuXLv0wytGWLVtEa5IsFD169ICvry8uX74MIyMjJCcns0VbDhw4wEv20aNHERwcDBcXF7i4uCA4OBhHjhzhJTMqKgrr1q0rdX7dunWIjo7mJVsJ33n4/08IkTd3+fJlmJqaonbt2mjYsCF0dHQQGhqKrl27wszMDD4+PoKuVWLkwQqR+2lmZlZmE+qSx/dGRkYGLC0t4ebmBqlUys6XkydPRmhoKG/5/085+sESKAHhqlAdPHgQ/fv3x9ixY0tVg3nx4gXnhrhiMWjQIPj6+iIzMxPZ2dno1KkTxo0bh/z8fKxbtw6GhobYsmWL1nJtbW2RlpbGfn758iUYhmEnx5ycHF4FA8qXL4/09HQA6s/bnTt3eJWclMlkaomOSlJTU2FgYMBZrpAYGRmx/17VKolCIVYJZAMDA9y9e1ft3KNHj1CpUiX07NkTjx8/5rQZFquCDwAsW7YMDg4OpSztvr6+SE1N5STz33jx4gU2bNig1d9ZtWoV9PT0yvQMCN23QyjlSJNHLzY2FmZmZti9e7egypFYhQiEIiEhAVOnTmXfuWPHjiEoKAiBgYFYtWoVL9nW1tZISUkBUOz1Uyql+/btg6+vL2e5y5Ytg1QqRbdu3dj+ON27d4euri6vBG5HR0e274oqFy9e5GVUE4uOHTtqVNpiYmLQqVMn3vJ37tyJzp07o169eqhZs6baIQRCeIJ9fX0xY8YM9vOmTZtQr149AMVzmpeXF0aOHMnrO1T5XvtSqkZd/PbbbzA3N1d7P7p16wZzc3MsWLBAkDELGanVrFkztkei6r04d+4cHB0decv/6ZUjIVyeZTUq03QIgRAP2JYtW6Cjo4Pg4GA0bNgQMpkMmzdvZq/zsYSKtXBaWFiobfJevHgBmUzGhgUuXboUXl5eWsvt3bs3/Pz8kJWVhZycHHTt2lVtIj958iTs7e05j9vY2Jj1TKm+xJcvX0a5cuU4y61YsSK7iVAlJSUFrq6unGSmp6dj3bp17BivXr2KIUOGYPDgwUhKStJaXkBAAKpVq4Y+ffqAYRh069YNffv21XjwQegSyM7Ozjh69Gip848fP0alSpXQvHlzTu+HWBV8YmNjYWtriyVLlmDNmjXw8PDAzJkzkZiYiNDQUBgaGuLy5cuc5ZdFeno6p/vw5s0bZGZmgmEYHDt2DOnp6RoPIRAivLBRo0ZYsWKFxmsxMTHQ19f/LkO+0tPTERoaCmdnZ8hkMhgaGsLT0xNTpkzhHIq8cuVKSKVS1K5dG3K5HJs2bYKJiQkGDBiAwYMHw8DAgFffMhMTE9Yw4eDgwK4jOTk5vIw+FSpUUKt4pmTp0qWwtbXlLFdfX19jiW0+VTj/+ecfHD9+nA0VevbsGaKjoxEZGcl7k21hYYH//ve/pc7/97//hZWVFS/ZixYtgrGxMYYPHw49PT0MHjwYAQEBMDU1xaRJk3jJViKEUmBgYKAmo7CwELq6usjNzQUAJCcnc3omyvK6MAwDU1NTQb0wQhv2O3bsqPH9WLJkCdq1ayfIdwg5Zrlcjtu3b5eSe+/ePUGq3/60ytGrV69w48YN3Lhxg7e7s2SDMiMjo1J5IEZGRoI1KxPiAfPy8lJT1rZv3w4jIyOsXbsWAHflSMyF08zMTC38raCgAFKplPVG3Lp1CzKZTGu5T58+Rf369VlrtaOjo9rGdefOnVi8eDGnMQPFIRFTpkwBUPzb5eTkoLCwEJ07d0ZISAhnuXv37oW3t7fapvfy5cuoX78+9uzZo7W8P/74Azo6OlAoFDA2NsaRI0dgZmaGgIAABAYGQkdHR2vPXG5uLsaPH49OnTpBIpEgKCgI7du313jwRcgSyP3790e/fv00Xnv06BHc3Nx4bYYPHToEOzs7REVFobCwkLdy5OTkhEOHDrGfb968CYVCgU+fPgEARo4ciebNm2st9/Xr1188zpw5w+s+/Oc//xElHv/SpUu4ePFiqfMXL17kpCSuWbMGvXr1KvN6dHQ0Jy9BWTlomg5tSUpKgoGBAUJCQtCrVy8YGhpi+PDhGD9+PNzc3ODq6oq//vpLa7lVqlTB6tWrAQDHjx+HTCbDsmXL2OtxcXHw8PDQWq6SOnXqsIaYNm3aIDQ0FI8ePcK4cePg4uLCWa6RkRGys7NLnb916xYvD76bmxs2bdpU6vzGjRs5rfkpKSkwNTVl9xCpqalwdnZGxYoV4erqCgMDA7VIB22RyWQa58esrCxO66cqlStXZstMq+5Tpk6dimHDhvGSrUSI/Y+joyOrdAPF+UcMw+Ddu3cAihtJc7kXYuTBjhkzhg2FPXXqFDunCx1qWdb7kZ2dLUhTVUBY5cjS0pLdp6nKTU5Ohp2dHW/5P51ypLSqlgzj8PDwYBUDPmzZsgW+vr6l8kAaNWqk5pnhgxAPmJGRUSlr1/Hjx2FsbIwVK1ZwVo7EXDibN2+uNsHGxsbCxsaG/XzlyhVYWFhwkg0UL5KZmZns5CMUmZmZsLKyQsuWLaGnp4dOnTrBw8MD5cuXZy0fX0vJGGFleJKenp7a/3OxTtWqVQuzZ88GUNxfy8zMDDNnzmSvz58/n5NnTomTk9N3mQOjiXv37n3RU/b48WPOxR6U5ObmIigoCI0aNeKtHBkaGqqFARYVFUEqlbKLZ3p6OoyNjbWWqzQYiB3+dvnyZWzcuBEbN24UxMNVt25d7Ny5s9T5P/74A97e3rzlC0XJXLOS95VPIQIvLy81b1dycjLc3d0BFBuWmjVrpnUDcaDY6q6aD1ayQ/3du3dhaGiotVwlmzZtQlxcHIDiEGELCwtIJBLIZDJs27aNs9zu3btj3rx5pc7Hxsaia9eunOXGxMRAoVBg/fr1bFjsunXroFAoEBUVpbW8gIAADBgwAG/evEFsbCzs7OwwYMAA9nrfvn15GZPq1q2LyMjIUuenT5+OWrVqcZYLFD8byrBgS0tL1vt769YtXlESqgiRNzdq1Ch4enoiMTERx48fR5MmTeDv789eT0pK4hR9IUYerFQqZT1aYhawcXBwwPz580udnz9/PhwcHAT5DiHz8Pv374/27dujoKCANTrfv38fNWvWxKhRo3jL/6mUo3nz5sHQ0BATJkzAiRMncP36dVy/fh0nTpzAxIkTYWRkhNjYWF7f4eLiUmYeiFDxxw8ePEBhYSEvGTY2Nrhw4UKp8ydPnoSxsTEmT57MaUEWc+FMS0tDuXLlYG1tDQcHB+jp6SE+Pp69vnTpUoSFhXGSLTavXr3C7Nmz0blzZwQFBWHy5MmcrD6qFql/O7TFyMiI3WAXFRVBV1dXLfzizp07nDbY35KCggLcunXrh2oCK0QFHy8vL9YoARSHGRoaGrIVGG/cuAETExOt5crlcsTExODkyZMajzVr1vBSjh49eiRKxUXVXDdVcnJyvqtnWLmZvnfvHvbs2QNXV1esXLmS9RatXLkSFStW5OQJlslkpRRmXV1ddt45ffo0LC0ttZZrZ2eH06dPAyg2EjAMg4MHD7LXT548KYjlVkl+fj7S0tI4bapUQ9pnzZoFU1NTtGrVCrNmzcKsWbMQHBwMMzMzzJo1i/P4ioqKMG7cOMhkMlaRNTQ01KiAfA3m5uZs6FxBQQEkEola+HRaWhoqVKjAebz79++HVCpFWFgYu1aEhoZCKpVyes5UcXZ2Zvc/tWvXxsqVKwEAhw8f/q6S+t++fYsuXbpAKpWCYRj4+PioGYsPHz6MHTt2cJItdB6sm5sbJk2ahJMnT4JhGOzduxenTp3SePBB6eFq3bo1+360bt0aUqmUNVZ8T7x69QoBAQEwMzODjo4O7O3toauri8aNGwtSdOanUo4cHBywffv2Mq9v27aNV24JUKwcXLp0qdT5lJQUXvHSJWPmU1JScOHCBc4hKe3atcO0adM0Xjtx4gSMjIw4bXrEXjifPHmC1atXY8mSJYJ3hhY7UfV7x9rams3pevHiBRiGwYkTJ9jrly5dgrW1NSfZYuShxcTEsGEQnz9/RkREBOs9k0ql6Nu3LwoKCjjJ/vDhg9rfvX37NiZNmoRevXph8uTJGnMM/pds374durq66NKlC8LCwmBsbIwJEyaw11euXIkGDRpoLdff3x8xMTFlXk9PT+dU0lyJWBUXy5Urh/Pnz5c6f+7cOZiZmXGWC4iXcF63bl21uVLJwYMHOVn0XV1d1byf2dnZ0NHRYauccs3hGTZsGCpWrIjZs2fD29sbvXv3hru7OxITE5GUlIRq1aqVGZL6rSkZ8l7WIUTI+9u3b3Hp0iVkZmbyChVVNVIBpSNF7t+/zzv87cCBA/Dx8YGhoSEUCgWaNGmCkydP8pIJFFvzlYUOli5dCgMDA3YDy+WZ8PT0xMyZM0UrTvL+/Xs1D4+QCJUHu2fPHpQvX/6LlU6F8uBfvHgRPXr0YOezHj16aAxP5kJeXh7Wr1+PSZMmYcmSJYJFkZw9exbLli1DTEwM78qTqvxUypFMJvtiMuO1a9d4V/lq3bo1atasqRYTnJqailq1aqFNmzZay7t37x5q164NHR0dtGzZEq9fv0ZAQAD7Qri4uJQqMfs1nDx58osu/+PHj3MKufiRFs6SiJmoChRbQLOysnjnEohFr169UK9ePWzevBlt2rRBYGAg6tevj6ysLNy4cQN+fn6clESx8tBUQwxiY2Nhbm6O9evX49q1a9i8eTOsrKy+uLH/En5+fmxY1tmzZ6Gvr4/q1auzxToMDQ01br6/lmPHjiEyMhLh4eEYOnQofvvtN7V8Oi4cOnQIPXr0QEhIiJoXCSjOx+KyGK1evfqLhWRyc3PVqj5pi1gVF7t16wY/Pz81D+LLly/h5+eHzp07c5YrZsJ5WevT9evXOW2GIyMjYWdnhxUrVmD9+vXw9PREhw4d2Ou7d+/m1EogLy8PAwcOhKenJwYNGoSPHz8iNjYWenp6YBgG/v7+nEN/njx5gk2bNuHgwYOlWlXk5eVp7Y35kTzIStzd3XHs2DH284EDB1gjEFC8gRXSMyckhYWFamHp8fHxGDFiBBYvXsyp9QjDMFAoFNDR0UFgYCB27doleNi7mAiZB/v27VswDMNGRmg6vic8PDzYgiIPHjyAk5MTTE1NUbduXZQrVw5WVlbfnZFRlZ9KOWrUqBHCwsI0vlyfP39GWFgYGjduzOs7/v77bwQFBYFhGLU8kKCgIE4LRkhICPz8/JCQkIAuXbrA19cX/v7+ePToEZ48eYLAwECt44/LSvATAjEXTkDcKj5iJaoK3dSwoKAAY8eOhaurK+rWrVuqxwbXfLHc3Fw0b94cxsbGCAwMxKtXrzB8+HDWKlWxYkWtc6QA8fLQGIZhn6WaNWuW8kBt3rwZVatW1VouUBxOplRW/Pz8MGbMGLXrU6ZM4VRW+OnTp/D29ma9WxKJBLVr12b7dSlLk/4siFFxESgO13NxcYGpqSn8/f3h7+8PMzMzVK5cmZcVWsyE85o1ayI0NFRtE/nx40eEhoZy8kopw3tsbW2hUCjQo0cPtdC0lJQUrUNxvlTh7v3797x6w1y6dAlmZmaQy+UwMDCAm5sbrl69yl7nMq+ptg9o0qSJaA1PNXH79m1O7TBmzJihFi5ekkmTJqFjx458hqZGbm6uYD3FhIZhGDx+/Bh79uxBmzZtIJVKYWlpiYiICN7rvRiVHMXm5MmToiiHnz9/xp07d9h0jQ8fPmD79u2Ij49n8520RXV97tmzJ3x8fFgF7u3btwgICED37t2F+QeokJubyzmkVZWfSjnKyMiAtbU1FAoFOnTogPDwcISHh6NDhw5QKBSwsbFRy4/hw61bt7Bv3z7s27ePk2dHiaWlJf78808AxVYwhmFw5swZ9npaWhrKly+vlcxvleCnCt+FExC/io9YiapCNzWcPn06ypcvj9jYWEyePBmmpqYYNGgQez03N5dXqFNJ7ty5w6tQhVh5aAzDsBsfhUJR6t3NycnhnN9mZGSErKwsAOp9qpTcvn2bU+5K165d0b59e7x+/RofPnzA8OHD2Ty5Y8eOQaFQ8CqD/KMhdMVFVfLy8rBq1SoMHToUERER2LBhA+cwSyViJpynpKTAysoKlpaWaNasGZo1awZLS0tYWVlpVCD/F6iuF0IrGwEBAejbty8KCwvx5s0bDBkyBAqFgvUsclGO5HI5u4lWnS++BVzL3P8b+fn5nML23rx5g549e8LBwQFhYWH4+PEjhg4dyhq/GjduLKhSkJOTg+TkZF57KtUNNlDsWYyKikLFihUhkUjQoEEDjQ14/w2xKjlq4v3796WqfHIlLy8Pp06dwrZt27Bjxw6kpqayeaVcycjIgI2NDSQSCdtY1tPTE0ZGRjA2NmYbXWuL6m/n4uKC5ORktevnzp3jncaiCaHeu59KOQKKJ4jly5cjLCwMLVq0QIsWLRAWFoYVK1Z8l9YCExMT1vWoLPmrulHLzs7WOtH6WyX4CY3YVXzESlQVuqmhm5sbEhIS2M/Z2dlwc3NDnz59UFRUxKtHlRiIlYfGMAzmzJmDRYsWwcbGptTzmpGRwTkJuGnTpmxlKx8fn1KNTnft2sWpgo9cLlezhufl5UFXV5ede5RKs7aI5U1U5ejRowgODoaLiwtcXFwQHBzMKcZbzIqLYiN2wrlSoRszZgzGjBmD1atX804uzsnJ0RiyeevWrVKNjv8NMZUNc3PzUobEuXPnspszLs9wx44dUb58efj7+7OFPpo0aaLx0JZ/62k4bty472oeHj58ONzd3bF48WL4+/ujXbt28PT0xNmzZ3Hq1ClUqVKFc2jokCFD2Nydd+/eISQkRK3qYpMmTTjl9nzJeHvixAn06tWLU5lpsSo5KsnPz8ewYcNgaWkpSLRIYWEhxo4dC0NDQ7WKlgzDwNHREfv37+c81sDAQHTq1AmZmZkYNWoUPDw80LlzZxQUFODTp0/o1asXAgICtJarOj/Y2tqWUpLv3bvHKSLn31oebN++XZD3Tko/GSYmJjRkyBAaMmSI4LKXLl1Kly5dolatWlG3bt1o06ZNNHfuXCoqKqKOHTvSzJkzSSrV7pZXrVqV1q9fT7NmzaINGzaQQqGgbdu2UY0aNYiIKD4+nipVqqSVzNjYWAoPD6e5c+cSwzDUoUMHjX+OYRgqLCz8arm//PLLV//ZBQsWfPWfVZKWlkaLFy8mExMTGjVqFI0fP54GDhzIXh8+fDi1bdtWa7lK2rRpQ3v37qWoqCjatWsXGRgYUPXq1eno0aPk5+fHWW5+fj5ZWVkREZG5uTk9e/aMKlWqRNWqVaMrV65oLe/x48fk6enJfnZzc6OTJ09S06ZNKTQ0lObNm8d5rAcOHKBLly5RYGAg+fr60vHjx2n+/PnsMzxo0CCtZbZr14769+9PvXv3pv3791NYWBhFRESQRCIhhmFo7Nix1KJFC63lOjg40Jo1a4iISF9fn65cuUKNGzdmr584cYIqV66stVwiotmzZ1NQUBDl5+dT9+7dKSIigrKzs8nDw4Nu3rxJixcvpokTJ2otV19fnxiGYT9LJBIqLCykz58/ExGRj48P3bt3T2u5c+bMoY0bN9Kvv/5Kr169ol9++YVSUlJo1apV7J8BoLVcJcuXL6dRo0ZRp06daNSoUUREdPHiRWrVqhUtXLiQhg0b9tWyfv/9d87j0Jbr16/TgwcPqKCgQO0813miadOmtH//fqpZsyb17duXxowZQ7t27aLU1FTq2LGj1vJq1apFx44dI3Nzc5o5cyb9+uuvnN6xL9GnTx/q168fVaxYUe18SkoKrV27lk6ePPnVsgICAqhJkybk4eFBREQdOnQgPT09jX/2+PHjWo/1w4cPap8nTJhAUqmUWrRoQevXr9da3ubNm2nDhg10584dOnXqFFWtWpUMDQ21lqOJ0aNHk42NTZn//pLP3Ncg5hq6b98+2rBhAzVp0oRCQkLIzs6O9u/fT76+vkRENG/ePIqIiKA5c+ZoJZeIaNWqVTRjxgwyNjamWbNmUUpKCh09epTq1atHf/75J/Xu3ZvmzJlDc+fO1Urul+Ysf39/8vf3pzdv3mg93hs3blDLli3ZzwEBAXTnzh3666+/yMbGhqZPn04hISFay1UyduxYOnHiBK1YsYJCQ0Np2bJl9PjxY1q1ahVFR0drLW/SpEl04MAB2r59O8lkMpo1axYFBwdT27ZtaevWrdS5c2fav38/p3X00qVLdO7cOfLw8KC5c+fS0qVL6T//+Q/p6uoSUfE7yHX/06xZM5JKpfTmzRu6efOm2r7l/v37pFAotJbp5eVFDMNofDaU51XXWM7wVq9+QA4ePKixh0lSUpJaI0VtmDVrFkxMTBASEgJra2tER0dDoVBg9uzZiIqKgqWlZZnV4b5EUlISZDIZ9PT0IJPJcOrUKVSqVAne3t6oX78+dHR0vliB70sIneCnjO1XHnK5HIaGhmzlEyMjI8jlck5WOuDbVPERA6GbGjo7O+Po0aOlzj9+/BiVKlVC8+bNv6sGvmLnoZXFhQsXNCb7fy3nz59nmwOrHhUqVOAc+tahQweEhIQgLy8PBQUFGD16NNzc3NjrFy9e5FQRUGxvYoUKFTR2T1+6dCmnTvJic+fOHVSvXr1UhSeullslQiecy2QytmS5WCHOJiYmZTZ3NDU11UrWu3fvsGLFCvz6669gGAaDBg3C6NGjNR7a0qhRIzVrvioxMTHQ19fn9dv5+/sLGgbo5OT0xbX3zz//1Hq8Yq6h+vr6avl2hoaGap66e/fucQ5DVg2h8vT0ZPPylOzbtw+VKlXSWm6fPn14h+NrQqxKjkrs7e3ZSq+q79/GjRsRFBSktTwbGxs2+gIozqk0NjZmwytnzpzJqRopUOzJV3qWCwoKoKOjo5aekJWVxckrPmPGDLWj5J77119/Rbdu3bSWq1AosG7dOrU2CKrHwYMH/19YHVeqVaumsWRqYmIiqlevzkmmq6sr/vjjDwDFMY86OjpqTV93796ttgnShrt372LXrl2sYpCbm4spU6YgIiICx48f5yRTiVgJfr/99hvatGmDFy9esOdevHiBdu3aaWw09jV8iyo+L1++xJo1azBx4kS26ENaWhoePXrEWabQTQ379+9fZsW/R48ewc3N7btr4KsJIfLQvgV///03Ll68iPPnz2sdhlSSO3fuwNXVFVKpFLq6ujAzM1MLTYuLi1Mrwf21GBgYlBrbo0ePUKlSJfTs2ROPHz/mtWCU1T391q1bvLqnixFDDxRXDW3Xrh2ePXsGY2NjXL9+HWfOnIG3t7faJuN/Tf369REQEIAZM2aAYRiMHTsWkZGRGg+uyOXyMisC8un5JLSysWbNGvTq1avM69HR0YL1ChSCkJAQjBs3rszrfMvcC72G2traqm16u3fvrqaMX716lXNoqGoIlYWFhVroMFCseGmrbCxatAjv378HUGz4FGJeUCJWJUclRkZGbJ5thQoV2LD6nJwcTvOliYmJmiFYmWKhzIu6du0aZ8W2WbNm6N+/Px49eoTIyEi4ubmhb9++7PWhQ4eiUaNGWsnMyMgo1X5GKFq0aPHFvmR83zslP6VyVLI5nhI+DUo1JZ2rThB8rDJiItbmxNbWttQECQCZmZmwsbHhJFPsKj4ZGRmwtLSEm5sbpFIpOxlNnjwZoaGhnOWWhE9TQ6D4WdLk+VTy+PFjTk1gxWzgKwbz58/nraz8L8jPz0dycjISEhIE6xYuljdRSffu3dkcLFViY2PRtWtXreV9/vwZY8eOhYGBgeAx9ECxdVFZJl8ul7NVKI8dOwYvLy9esl+8eIHY2Fj069cP/fr1w/z581lDirbcuHEDXbt2RZ06ddiEaC8vr1IHnx5KrVu3RufOndU2K58/f0ZISAhatmzJWe6PgDJ362sObbl27ZpaIZGSFBQUsMU7uCD0GtqyZUs2R04TcXFx8PHx0VouUKwcDR48GGPGjIGVlVWp5Pu0tDRYWFhoJVNHR4dV3oT2qopRyVGVatWqsX2jmjVrhoiICADFCh+XBr4+Pj6YPXs2+zk+Pl6tX1tmZiZnxfbSpUtQKBSQSCSwtLTE1atXUa9ePVhbW8PW1hYGBgYa15YvoVol0tnZWbCeRkCx4rpp06Yyr7948YLT/qckP6VyVL58eTUPhJIjR45w6hgOFD8AiYmJAIqtqRKJRK3D8sGDB3lbvT5//oydO3di5syZmDlzJq+a/2Im+AHFIW+qDUSVHD9+XLQO9Vyr+Chp1qwZW0pZNWTv3LlzcHR0FGKIKCoqEtQCJiRiN/AVuskuwzDQ0dFBQEAAtm3bxims6Uu8e/cOZ86c0di87/3796WKNPwvEcObqJpcPmvWLJiamqJVq1Zs9/Tg4GCYmZl90YpXFuPHj4eHhwcSEhJw5MgRNG7cGDExMcjKysLUqVOhr6+Pw4cPay1XiZmZGVvIxsXFhfWw3759m1e4zKlTp2Bqagp7e3t06NABHTp0gIODA+RyOe8CNiUrcwnFtWvXoFAo4Orqij59+qBPnz5wdXWFpaUl7+qsDx8+xLJlyzB+/HjeyoYYiB3qLSZCr6HPnz//oqfv0KFDGr/va/Dz81O7z2vWrFG7PmvWLPj5+Wkl097eHsuXL8e9e/fAMAzS0tJw//59jcf3xoIFC9j+cEeOHIFMJmPDQrmEZB89ehT6+vrw9vZG48aNIZVKsXDhQvZ6bGwsmjZtynm8eXl5SE1NZYtmvH//HmvXrsWSJUs49WcqV64c2zz2W1eJFIqfUjkaNGgQqlWrptazJTs7G9WrV0f//v05yZwyZQosLS0xYMAAODs7Y8KECXBwcMCKFSuwcuVK2Nvb81owrl69ChcXl1ITu5OTE6cFTuzNSWhoKJycnPDHH3/g4cOHePjwIXbt2gVnZ2e2dDFfPn78iBs3bggWFiiXy9lnQlU5unfvHvT19XnJXrt2LapWrcpW5KpatWqpBeRr+fDhg1pJ4tu3b2PSpEno1asXJk+ezLmxmtgNfIVussswDOLi4tCuXTvo6upCoVBg1KhRgpTjv3nzJhwdHdXK3D558oS9LkT1t7y8PBw8eBArVqwoVelKW8TwJjo5OX3V4ezsrPV4xYyhB4CGDRuyFSa7d++Oli1b4uzZswgLC+Pc+woozqcYOHBgKS/MoEGD4OnpyVmu2Dx+/BgTJ05Eq1atEBISgsjISM7eLiVHjx5le8NIpVJ4eXnBzMwMpqamnJQNY2Nj9OvXD+fOneM1rrIQI9RbyadPn3DkyBGsXLmSDRV+/PgxpwptSr7FGvqtuHPnDptb97WsWrWKrV5Z1qGcn/nw6tUr3LhxAzdu3BCtkeq9e/fwxx9/8Gr6np6ejkmTJiEiIqKUZ+57Y+DAgdDX14eTkxMkEgkcHBzg7Oys8fhe+SmVo1evXqF+/fqQSqXsAi+VSnn1bSgsLMScOXPQunVrREVFoaioCPHx8bC3t4dCoUCfPn14lWStX7++xom9bdu2nDYRYm9O8vPzMWTIENZaoizPO2TIEN6lafPz89GvXz/o6OhAR0eHVWKGDx+OuXPncpZraWnJxuarKkfJycm8PCZTp06FkZERJkyYwPa+mjBhAoyNjTF16lSt5fn5+WHnzp0AgLNnz0JfXx/Vq1dH165dUbNmTRgaGuL8+fNayxW7cILQTXZVLe1Pnz5FTEwM3N3dIZFIULduXaxevZpzTlP79u0RHByMZ8+eITs7G8HBwXB2dmatlHyVoytXrsDa2hpyuRw6OjqwtLQEwzAwMjL6rhcMoRAzhh4oLmSjzAHNzs5G5cqVwTAMLCwsNEYNfC1lPcM3btzgXQwmKipKY8+WdevWafS4/q+pW7cuW2RIOV++ffsWbdu2xfLly7WWxzAMqlatCoZh4O7ujvnz5wtqcRYj1Bso3vi6u7vD0NBQbT0aOXIkBg8ezFmumGuoEiF6EZWFMl+ID2/evEFmZiYYhsGxY8eQnp6u8eDCmjVr4OHhUUrh8vDwwNq1a3mP/Ufg38piqx7akpiYiCVLloBhGMyaNQu///67xoMvjx8/xrRp09CjRw9ERESw/Qn58lMqR0BxeNPhw4cxb948LFmy5Lvr6VMSmUxW5sTOZVEWe3OiJC8vj325hJrQR44cidq1a+PMmTMwMjJi/x179+7llU/Qv39/tG/fHgUFBTA2NkZOTg7u37+PmjVrYtSoUZzlWlhYlKreAwBbt26FQqHQWp5cLmery/j5+ZXySE6ZMoVT/6SyEKpwgtBNdssKQzp9+jR69+4NIyMjzsUCrKys1LxcRUVFCA8Ph4ODA+7cucNbOfLz88PAgQNRWFjIbiwfPHiAxo0bs5t6Lhw7dgyRkZEIDw/H0KFDMX/+fI09bv7XiBlDXxbPnz/nHdLq4+OjsefZnj17UK9ePV6yHR0dNXpNLl68KEghgvz8fGRlZfHe9CgxNjZmPe1mZmbs+pSens4pDFn5Pqenp2P48OEoV64c9PT00LFjRxw6dIj3bydWqHe7du3Qq1cvfPz4Uc2oduLECc5FmFQRag0VqxeRKp8/f8bMmTNha2urpihOmTKFl8Lxn//8h1fIfEnmzZsHQ0NDTJgwASdOnMD169dx/fp1nDhxAhMnToSRkRFiY2M5yX727BliYmLQvn171K9fH/Xr10f79u0xb948zsr+rl27kJ+fz+nvfgnVap5f8tDxWeuErjhoYGDA3sdr167B1NQUbm5u6Ny5M2uk4DOvKflplaMfjerVq2u0eB47doxTOMe33pwIWRHPwcEBFy5cAKDu4eHSEFeVV69eISAgAGZmZtDR0YG9vT10dXXRuHFjXouSqampxg3qzZs3tS6lCxRXwlFaR8qXL1/Kcnb79m1B87pycnIE+f2EbrL7b0m6r1+/ZqvvaYuJiQnb7FKVYcOGsblZfBYMU1NT1gNhamrKftfFixc5NYF9+vQpvL29IZFIIJVKIZFIULt2bVhbW0NHR4fNpdOWtLQ0tTDNjRs3wsfHB3Z2dvD19f1igZQvIXYMvZCoKhLbtm2Dg4MDYmNjcebMGZw5cwaxsbFwcnLiVHlSFX19fY0hsXfu3OEV1vv3338jODhY8E1P+fLl2efWw8MD+/btA1CsHHExSpQ0dnz48AFbt25Fs2bNIJFIYGdnx8nTrkSsMLVy5cqx77LqenT37l1e+W1CozpfTpw4EXZ2djh+/Djy8/Nx9uxZuLq6cqqUqUpkZCRcXFywefNmGBgYsPdi27ZtqF+/Pu9/g5Lc3FxeuUYODg5fLMO+bds22Nvbay330qVLMDc3R4UKFdC7d2+MGzcO48aNQ+/evWFnZ4dy5cp9sYhHWTAMA7lcjoEDB7K5PEKgWgJ7z549cHV1xcqVK9n5buXKlahYsSKn9VksVOeJdu3aoU2bNuz+pLCwEN26dUPr1q15f89Poxz9WzdrPvH+X8Pt27e1jsN+/fo1exw8eBBVq1bFzp072Yl9586dZZYl/zfE2pwkJiayFvfCwkLWiiSRSFChQgXMnTuXtwVQddJVXYzS09Mhl8t5yQaKQ9WWLVuGmJgYtTLLXBk+fLjGfLOIiAgMHTpUa3lNmzZlK4f5+PiUKgywa9cuODg4cBusBnR1dTUqClw4cOAAfHx8YGhoCIVCgSZNmrBVfbRFrAR2oNjLtXHjRo3Xhg0bBjMzM14bSwsLC1ZhrlixIpsvlJWVxclj27VrV7Rv3x6vX7/Ghw8fMHz4cHbDd+zYMSgUCk4hDNWrV2ffgTVr1sDAwAAjR47EihUrMHr0aBgbG2sMBfsahI6hVxZI+JpDG0r2Sirr4Jv74ObmprEK08aNG3mFWvbo0QO+vr64fPkyjIyMkJycjE2bNqFy5co4cOAAZ7nt2rVjjQ8RERFwc3PD7NmzUatWLTRr1kxreV8ydty9exdTpkzhtGFVIlaYmpmZGVu0RXU9OnPmDKdcSk9PT8ycOVOtJ5EQiNWLSBVXV1e2spnqvcjKylIzvn4tb968Qc+ePeHg4ICwsDB8/PgRQ4cOVcsFff36tdZyZTLZF9e0a9eucVJs69Wrh0GDBmnc4xQVFWHQoEGclESGYTBz5kzUrFmTDT9duHChoFXg6tatq3EvefDgQU6RHUDxmhEWFob169cDKFY63d3d4ezszKnvJ6D+HNvb25dqzXDlyhVeYbJKfhrlqGQSsZGRERiGgbm5OczNzUWP909PT9d68Szp6izZyJDvoixGgl/lypXZhzUqKgoKhQILFixAYmIifv/9d5QvX553/HyjRo2wePFiAGDD34BiJSQwMJDfP0AgVKs2jRgxAiYmJqhatSr69++P/v37w9PTE3K5HMOHD9da9vnz52Fqaorp06djyZIlsLCwwJQpU7BlyxZMmzYNZmZmiImJ0VpuWRtJiUSCgIAAThvLH5WoqKgvNusbMmQIr14KzZs3x5YtWwAAAwYMgLe3NzZv3ozAwEB4e3trLU8ul6uF3ebl5UFXV5fdOCg3w9piYGDAliOuWbNmKU/cli1bePUDERJlJbY+ffqgd+/ekMvlGqvK9enTRyu5ZTUb1HTwISYmBgqFAuvXr2flrVu3DgqFAlFRUZzlWltbs31WTExM2Maf+/bt4xV+e+fOHTZ8JS8vD4MHD0a1atXQsWNHTvfia4wdQlT6FDrUu0uXLhg4cCCA/1uP3r59i6ZNm2r9rAHF90GhUEBHRweBgYG8qtKWlCt0L6KSyGQy9rdXVY6uXbvGyZs4fPhwuLu7Y/HixfD390e7du3g6emJs2fP4tSpU6hSpQomTZqktdxGjRohLCxM4339/PkzwsLC0LhxY63lymSyL+a8CJFfm5qaiiFDhsDMzAz6+vro3LmzIPu3shTG69evcxrzwoULYWRkhI4dO8LGxgazZ8+GQqHA7NmzERkZCblcjlWrVmktV7VMuKOjY6kQupycHN75n8BPpBypsmXLFvj6+qol1t64cQONGjVSa9yqDf/mjRo3bpzWSszJkye/+vhe0NfXZ93dnp6eauXMgWLPAd847DNnzsDY2Bjh4eGQyWQYNWoUmjdvDiMjI6Smpmotb/jw4YI3hixZQrasg2sJ2fPnz6N+/fqlrNcVKlTgnOTIMAz8/PzUNpl9+vSBRCJB+/bt2c9CMGTIEMF6/JREqDBAMbl8+TJbXvrp06cIDAyEiYkJatWqxSnB2NLSUq3k+Lt37yCRSNiKZFxDsxQKBftOWVlZaQzhFDJ0qEmTJryVDAAYN24cBgwYoLGq3K+//spbvhgUFRVh3LhxkMlkrAHM0NCQVwNYoFghUvYDc3BwwNmzZwEUvyffU9jXjBkzRMmrEJuHDx+iSpUq8PDwgFQqRf369aFQKFC5cmVOnm2GYfD48WPs2bMHbdq0gVQqhaWlJSIiInh58MXoRVSSWrVqsd5PVeUoMjISDRs21Fqevb09O08q20skJCSw1w8cOMDJ6JORkQFra2soFAp06NAB4eHhCA8PR4cOHaBQKGBjY8OpSIWTk9MXWzxs2LCBVz6eKu/fv8fGjRvh7+8PiUTCOy+xZs2aCA0NVWuJ8fHjR4SGhnLqs+bu7s4aAK9cuQKpVKqWd7Z27VrUrl1ba7kMw8DMzAzm5ubQ1dUt5W1PTk4WJEfzp1SOXFxcyuwYzvWmMgwDW1vbMsvdKkPLvmc8PT15u/JtbGzYfKDy5cuXus+3bt0SZEG+ffs2BgwYgLp168LDwwM9e/bUWCb6a1B63ypWrIjo6Gi2KMWPwN9//42LFy/i/PnzvBuixsfHw87OjnWBK5FKpRp7/fChZEEQIREyDPBHoUOHDggJCUFeXh4KCgowevRoNSPExYsXYW1trbXcXr16se0NOnfujClTpqhdj4qKQrVq1bSWq6zaWPLQ0dHB0qVL2c9csbCwKLOqXLly5TjJ/Oeff3D8+HFW4Xz27Bmio6MRGRkp6PP29u1bXLp0CZmZmYIkodepU4cN22zTpg1CQ0Px6NEjjBs3Di4uLrzlK1FtL/C9kZ6ejlmzZmHZsmWljDKvX79G3759ecn/9OkTNm3ahLFjx2LIkCFYs2YN3r17x0lWyY3wkydPEBUVhYoVK0IikaBBgwacQlnF6EVUkr1798LU1BTR0dEwNDREbGwsBgwYAD09PU7eDX19fbU9iaGhIev5BIq9XVwLR7158wbLly9HWFgYWrRogRYtWiAsLAwrVqzgFKoHAEuXLoW+vj5GjhyJffv24eLFi7h48SL27duHkSNHwsDAAMuWLdNa7r/l12ZnZ3PyoKmSkpICKysrWFpaolmzZmjWrBksLS1hZWXFep61oWRTeX19fTVvZXZ2NqdQS2W+svJQ7jeVzJw5U5A+az+lcmRgYIBLly6VOp+SksJ54+7k5PTFBL8///xTEOVI6IpDqqhaergydOhQtG7dmrXSDhgwQC0UYsSIEbxKhIsBwzA4evQoRo0aBQsLC+jq6qJt27ZISEhAYWHh/3p4X40QISd3796Fr68vOnbsyJaNF0M5EuJZEzsMMD09HaGhoXB2doZMJmN7ukyZMoXz4lkWcXFxvHps3LlzB66urpBKpdDV1YWZmZlavlxcXBynZOvHjx/DyckJjRs3xi+//AIDAwM0bNgQAwcOROPGjaGnp8cp5/Fr8nj4zJdmZmbYu3dvqfN79+7ltCCnpKTA1NSUDcVOTU2Fs7MzKlasCFdXVxgYGCAtLY3zeMVk06ZNiIuLA1BsALSwsIBEIoFMJuNURGL79u1q1uUlS5bAwcEBEokECoWCl6fr8+fPuHPnDjvvfvjwAdu3b0d8fDxyc3M5yTx8+DDbW87BwQEKhYL1RgDC9CwTki9thE+cOIFevXpxrsL5Jbj0ItLE6dOnERAQAEtLSxgYGMDX15dzz0RbW1u196p79+5q9+bq1auCV7Xky7Zt21CvXj1IpVJ2LpNKpahXr94X94hfQsz8WlXy8vKwatUqNiVg9erVnENPFQqFmtHIzs5OLSogOztb0MJRQvNTKketW7dGzZo11V661NRU1KpVC23atOEkMyQkBOPGjSvzenp6Oq8cBbEqDqkixIb11atXqFOnDtzc3BAaGgqZTAZHR0c0b94czs7OMDU15V1tJS8vD6dOncK2bduwY8cOpKWl8VIMVCeegoICbN++HYGBgdDR0YGtrS0mTZqE7OxsTrLFtliqIpTHpLCwENOmTYO9vT2SkpKgq6vLWTnat2+fRouyEM+amGGASUlJMDAwQEhICHr16gVDQ0MMHz4c48ePh5ubG1xdXQX1MArx2+Xn5+Pw4cNISEgQNGTx5cuXGD9+PKpUqQKZTAY9PT04OjqiR48enCovAUDLli0RHBxcasEXShEfM2YMFAoFfvvtN7aq3Pz582FhYcHJqhgQEIABAwbgzZs3iI2NhZ2dHQYMGMBe79u3L9q3b895vN9ynsjPz0daWhrnZ0R1875+/XrIZDJMmzYNBw8exOzZs2FkZMSpwXVGRgZsbGwgkUjYKAZPT08YGRnB2NgY5ubmGo2a/0aDBg1Yq3pRURFiYmJgbGyMxMREAMIoRzdu3MCwYcPQtGlTNG3aFMOGDePcb+VrNsJCG2e+V1q2bImVK1eWeT0uLg4+Pj6c5R88eFBj8+ykpCQcOnSIs1ygeC/x5MkTPHnyhLdX9d69e4IYP78lvr6+XzS+JCQkCNY4e+7cuZx7lJbFT6kc/f333wgKCgLDMNDT02O7MAcFBXHWzq9du/bFjUJBQQGvWHohKw6NGTOGtQacOnWKzc8ICgrCkydPOI9RSUFBAVasWIFWrVrB3d0dlSpVgp+fHyZNmsTLMlVYWIixY8fC0NCwVFEKR0dH7N+/n5Pcshaj+/fvY/r06XB0dOS0eIplsVQt9qB6SCQShIWFsZ/5cubMGTg7O0MikXDesKomT/5baIC2iBkG6OXlhRUrVrCfk5OT4e7uDqD4+W7WrBknxUtZAKbkwTAMTE1N2c8/AwsWLIC9vb1aDoFQylFhYSFiYmJga2vLzhG2traIiYlRy0P6WszNzVnltaCgABKJRC3UJC0tDRUqVOA01h/Ns6E6X3p7e7OVM5UsX76cU45CYGAgOnXqhMzMTIwaNQoeHh7o3LkzCv6/9t47Korzff9/ZneBpZd16R2kCb5BRBBUUEksoFhiI4IaOxhjNCL2GpSgsaCCJTYsGHsMYu8aJBqxKygWrIkaC1hAuH5/8Nv57MKC7hSiX+d1zpwTdsLN4+7szHO36y4tRVlZGXr37o3w8HCN7RoZGdHzmBSsW7cO+vr62LlzJ+v3d/PmzXSvkeLe27RpU0gkEmzevFlje1zPhflQHj58yLq/Dah8bubk5CA3N5cuQWXKkydPat307tq1S+3sqg+lJrXf7OxsNGzYkLFddXzszk1RUZHaOVelpaWM5oAeP34cZ8+erfH8okWLkJqaqrFddfBRpv9ZOkcK8vPz6dp25TrWjxEuFYckEgldosD1hpVPxowZA09PT+zcuRP79u1DixYtkJycjCtXrmDixInQ0dFhlL5/X6SuoqKCUb00XxFLiqLg6+tbTdyBoigEBASwEnqoysuXL5GXl8e498HCwoJ2WpXVkriCrzJAqVSq0sNVUVEBLS0tOnhw9OhRyOVyje0aGBggIiJCpWZ65cqVEIvF+PHHH+nXmHD58mWsWLGCjlhfuXIFQ4YMQb9+/dTOSPsYOHv2LLy8vDBo0CCUlJSw+uxqylIqxiGwQV9fX+V6qJr5vH37NmOFJL4zG126dFGrEJqcnIyvvvpKY3tVVc/UiXQwmTen7IC+evUKYrFYxQG9ePEio6HZcrlcrVDPhg0boKenh7S0NFbvr7Ozs9r5S5MmTeK0p4tvmCjqKrNo0SK6vFL5CAkJYSSUVBdUvc8ruHnzJqNepjdv3mDUqFFo3rw5/Z2bPn06PZC8V69ejO5FfIhGKbh//z4CAgIgEokgFosRExOj4iR9bMEZdXBRiVKVz9o5+pTgUnHI1dUV48aNw+HDh0FRFLZv344jR46oPdjw7NkzXL16FVevXmXVT6HAyspK5QZx9+5dGBgY0Bv3adOmMepncnR05HRegAK+IpYzZ86Ek5NTtQ0v171Bjx49wtGjR3H06FHGDvTkyZM/aPo2m5svl2WAClxcXFTKLQoKCiAWi+leC6ZKXwUFBQgICEBsbKzKA4jtZ5ednQ1tbW2YmZlBKpUiOzsbcrkc4eHhaNWqFcRiMWsH6c8//8To0aPRo0cPVnODqvLq1SsMHjwY9evXh1gs/iizlB4eHirv3++//67ScJ+TkwNbW1tGtvnObNSrV0+tWM358+cZzeGhKApr1qzBjh07YGtri5MnT6qcv3jxIqN5cyYmJvTsr9LSUojFYpXS9ytXrjDKqn7xxRdISUlRe279+vXQ0tJi9f7q6uqqLbvmSnyIK6r2KVc9Nm7cyPh9SElJgbW1NVJTU7Fs2TJ4enpi2rRpyM7ORkxMDPT09BiX4FZUVKgokL59+xaZmZlYvXo16/JhCwsLtffFffv2MQp+ff/997C2tsaoUaPg6emJuLg42NvbY+3atVi/fj1cXV3x7bffamyXT9Go2NhYBAYG4s8//8S+ffvg7++Pxo0b08HGhw8fsmoJyc3NVdtKkZOTw/iaqIrgHHHA/fv3kZGRgaysLJWmUqCyl4WLtLI6Ll++zGqGEpeKQ9u2bYOFhcV7m6KZ3igVN0fl0jeRSARPT08VKUdNqZo6LS8vh0QioW8Uly5dYqxcwwd8Rixzc3Ph5uaGUaNG0dFyrpyjFy9eoHfv3tUaSr/++mtGTu6VK1ewc+dOUBSFVatWYfv27WoPtnBRBqhg6tSpsLW1RVpaGlasWAFvb28VJ2Dr1q2M5/uUlZUhISEBLi4udJCD7WfXtGlTjB8/HkDl9WVqaqqiXpSYmIgvvviCsf0NGzZAS0sLkZGR0NbWRmRkJNzc3GBsbMyZvPuOHTswYsQIxk4Nn1nKKVOmYMOGDTWeHzduHLp06cLINt+ZDalUqla5j83MFeVjxowZKueXL1/OqKyudevW6N+/P+7evYupU6fC1dVVpdcqLi4OzZs319ju1q1bMWLEiBrPr1u3DmFhYRrbVdCuXbtqpb1AZT/Wl19+ydjuu3fvkJKSgoCAAFhYWFQrxdWU2p73ys9pJjg6Oqr06Fy7dg0ymYx2aIYPH87o/nP16lW6rN3V1RWFhYXw9/eHvr4+9PT0VIZpM2HQoEHw8fFRCU4UFBSgYcOGtEqnJtjZ2dFCODdu3IBIJFJ5tu3du5exlDdfolHW1tYqGdo3b96gQ4cO8PX1xZMnT1gHZwICArBp06Zqr2/ZsoXRTD913Llzh3PxrM/KOcrNzYWJiQmMjIygq6sLV1dXFWlBPtOHbFPWXCsOAZUlUxRFIT8/H8+ePVN7aMpPP/0EPT09JCYm4tChQ7h8+TIuX76MQ4cOYezYsdDX168xivc+goODVR7EGzZsUFGeunDhAmf9GuvXr2c9IJDviOXLly8RGxuLhg0b4sKFC5xkTIDKoYb169fH7t276ZKk3bt3w93dHT169NDIlnKpU13MMVGUAVYNfGiKwoGxtraGTCZDdHS0SpTy1KlTrDOrBw4cgL29PcaOHcv6szMyMqKj14qggbKM/oULF2BhYcHYvo+PDxYuXAjg/6J0FRUVGDhwIONJ51UpKipi1A+koC6ylDVRUlLCuPSU7/tEQECA2qDf5MmT0ahRI8Z2a2Lnzp1qm9zfR25uLmQyGUQiEeRyOS5evIjAwEBYWlrC2toaurq62L9/P+frZYKy/HxaWhrkcjni4+ORkZGBjIwMxMfHw9zcXKVvUVMmTpwIKysrzJ49G1KpFNOnT0f//v0hk8kwf/58je3JZDL88ssvNQ4wzsrKYnyd6enpVStDlkgkdBlyXl4eI2WyqKgodOzYEefPn8eIESPg6emJqKgolJaW0pv43r17M1ozUFndEhQUBIlEQo9dkUgkaNmyJaMG/6ry1VpaWip7TKblenyKRunr61dzMMvKytCpUyc0bNgQ58+fZ3X/0dfXV5vVKSwsZKVWV1ZWhry8POzevRu7d+9GXl4ep+MEPivnKDw8HP369UN5eTlevHiBoUOHQiaT0ZsINs5RTU3yiqN3796cPpTZKg4pOHz4MKcDM+3t7WuVq8zMzISdnR0j2/v374eOjg6aNGmCFi1aQCKRYO7cufT5lJQUtGrVipHtqnDR4Md3xFLBhg0bYGFhwUnGBKh80B07dqza60ePHtX4xs5nqZM6+FCt4ZPHjx+jc+fOMDExURvd/1CqlmZVLTO4desWq6nhypsfMzMzukzr8uXLjOYnqYOL71xdZSmByoZjLuYQ8X2f+O233yCRSBAbG0v3tMXExEAikWDbtm2M7fJBcXExTp8+TZecvn79GsuXL0dqaiqr70dV2A6hrk2Cnis5emdnZ1psycDAgP5+z58/H7169dLY3pdffonp06fXeJ6Noq6vry+WLl1K/3zgwAHo6enRIgRXr15l1Icml8vppv7i4mJQFKXybDpx4gTs7e0ZrVlBRUUF9uzZg59++gmpqamsgl7u7u50sDo3Nxfa2toqWcXMzEzUr19fY7t8iUYBlYEvdcIhCgdJ0UfGFDMzs2qlt0DlZ8dkrEJ5eTnGjx8PExOTat83ExMTTJgwgZMs0mflHJmamlYTXpg5cyYtEcrGORKJRGjUqFG1JnnF0bhx44+yqe3BgwfYvn070tPTkZ6eju3bt7OqZ5VKpbVKEl+6dIlVHXZeXh7GjRuHUaNGMRJJ+FD4qGHlk6KiImzfvp11tguoLA1Q16Nw7tw5jRW5+BZkqAqfw2U/ZserYcOGdAM/UJkpUg56HD16lFVZr42NDX1N+Pj4YP369QCAkydPMuovUQeX37m6yFLyea1xze+//47g4GDo6elBJpOhZcuWOHz4MGf2uRggXpd8Cp+dnp4enYWwtLSk+69u3LjB6Du3detWZGRk1Hj+6dOnjMVgNm7cCC0tLXTv3h2xsbEwMDBQmauWnp7OqB+4aiZG2UkEKsupdHR0GK25KkVFRaw31XPnzoVUKkV4eDhMTU2xYMECWFpaIiEhAYmJiTA2Nsa0adM0tsuXaBQAJCQk1Fj+WVZWho4dO7Lau/bs2ROhoaEqlUj//vsvQkND0a1bN43tjR49GnK5HOnp6bh58yZevXqFV69e4ebNm1iyZAnMzc1rHavzoXx2zpG6gakpKSkwMTHB1q1bGV8Ebm5utd542AyB5aNPqri4GF9//TXEYjEkEgnMzc1hbm4OiUQCsViM3r17M9pcNG/eHLGxsWqzUe/evUNsbCxatGihsd26hi/niG3Esia4KANUsGTJEoSHh6s4yQ8ePMCXX35Z68wJddR1qROfTi1fG6r27duzltBPS0urVdJ/7NixjGroFfTq1Qtz5swBUCl8IpfLMWDAADg4OLAWZFDA12fHl1PL57XG132CL/h6L/hyuvhaLxcbbAVubm50I3tISAhmzpwJoDL7wEQsgG927dqF6OhodO3aVSWLBFRmyJmIHrm4uKhkihYvXqwic37mzJmPKnMNVGZ6hw0bRgeQDh06hObNm8Pf3x9TpkxhdH3wJRoFVDpAtSnolZWVsRpDc/fuXTg7O8PY2JhOFpiYmMDd3Z3Rd9vCwqLWst3du3czEpqpymflHDVv3rzGGuDk5GTo6Ogw3qRFR0fXWhrBNGXNV59U//796b4S5Tr/d+/eYc+ePXBzc1MZcvihnDt3DpaWlpDJZOjcuTOGDBmCIUOGoHPnzpDJZLCyssKFCxc0tlsTfD08jx07htevX3Nul68NNlu7vr6+8PPzow8DAwNoaWnBxcUFLi4u0NLSgoGBAaNG67osdeJzw8qX7U8hS/nkyRPcu3cPQGVZw8yZM9GhQweMHDmSVjViS1JSEi9ODF/fuU/REefL6frUvht82eXycxszZgx+/PFHAJUOkUQigaurK7S1tTFmzBhO/gZXpaF8MXjw4FoHCs+cORPt27fn5G99CvfhuoLr66K4uBhLlixBXFwcRo0ahdWrVzPuD9LT01Nb2aLg3Llz0NfXZ7pUGgoAyGfC8uXLyZEjR0hGRoba88nJySQ9PZ3cvHlTY9sPHz4kb9++JQ4ODmyXqcIXX3xB7OzsyPLly0lJSQkZM2YM+fXXX8m+ffuIn58fefToEbG2tibl5eUa2TU1NSVZWVkkODhY7fkTJ06QyMhI8u+//2q85pcvX5K1a9eSnJwc8vDhQ0IIIZaWlqRp06YkOjqaGBkZaWyzJgwNDcm5c+eIs7MzJ/bKy8vJ48ePiUgkInK5nBObynC9Xq7sTp069YP/38mTJzP+G6NHjyZ6enqMfv9DKCoqItbW1kQsFnNu+2P97Gpiw4YNpGPHjkRfX59Tu58afL2/69evJ1FRUby8v3yt2cjIiOTl5XFut3379uSXX34hVlZWnNrl633gCz7X+8cff5A//viD1K9fn3To0IETm3xdD4QQEhERQZYvX875NaHMzZs3iVQq5eRv8PXZzZo1iwwZMoSYmJhwapfP+zuf1wVbIiIiyLt378i6detIvXr1VM49fvyYxMTEELFYTH7//Xd2f4i1eyXAK3z1SRkZGdWqMZ+bm8tZLwGfcBXt+f3339G8eXM6eygSiWBsbIzevXur1Dyz5VOLhAr8H3fu3GGlplYTDRo04CX7+TGXAQJAamoqYmJiaInsNWvWwNPTE+7u7hg7dixnQjGf4ndDuE9U0q5dO06uNQDVvrs5OTk4cuQIpwpXn9r7+ylm2rksXVTmU8tc89k39zFnVu/cuQNvb29IJBL4+fmhbdu2aNu2Lfz8/CCRSNCwYUNOnqcidq7Vp8+sWbPIs2fPeLEdFxdHHj9+zNrOmzdvVH5OTEwk48aNI19++SU5efIkI5uRkZFk0KBB5OzZs9XOnT17lgwdOpSz6FRERAR58OABJ7aq0rx5c6Krq8vKRkZGBunVqxdp0qQJ+eGHH4i5uTlJSEggs2bNIkVFRcTf358UFBRwst6XL1/yEo3Jzs4mNjY2nNvl6hqujStXrvDynpw7d47TDJKdnR0vGamLFy8SOzs7zu2Cp6KAo0ePktevX7OyMWPGDDJu3Djy6tUr8v3335Pk5GTy/fffk6+//pr06dOHLF++nEyfPp2T9V6+fJnzjH5Vbty4QVq1asWZPb7uE1xy9+5dUlxcXO31srIycvToUU7+xq5du1hnBR48eECaNWtGdHR0SGhoKPn3339JZGQkadq0KQkLCyPe3t6cPZ/GjRtHzMzMOLGljJGRESksLOTc7qeIl5cXuXXrFud2x44dy3l2hxD+7sN82eUTLtZsZ2dHzp07R3777TfSoUMHYm9vT+zt7UmHDh3Izp07ydmzZ7l5nrJ2rz5x+PS+ubDNV5/U06dP0bZtW1AUBTMzM3h4eMDDwwNmZmYQiURo164dZ1GUjz2a5uHhoTIr6s8//4StrS0tQ9qjRw+Nm84V84E+5GDDs2fPcPXqVVy9epXRXKr3UReqTmxngNVml81kb4DbQYzvm1CvfHDBx5x9cHFxwZYtWwBUfk5isRhr166lz2/duhWurq6s/kZdwsU1zLVyKF/cv38fAQEBEIlEEIvFiImJoaW3AW7mBa5ZswbBwcGwsrKim8Hnzp3LqDcxJiYGwcHB+O2339CjRw8EBwejefPmuHv3Lm7fvo2QkBDEx8ezWi/f8PVdXrduHWdCPlXhKyPO1Xtx//59TJw4ES1btoSHhwe8vLwQGRmJ5cuXc1oh8DHfh2uCr+viY98LKiNh71592oBH75sL27GxseTIkSNkyJAh1c4lJCQQACQ9PV1ju6ampiQ7O5tcvXqV/PHHH9V6gzw8PFivnS8OHjxIjh8/Th48eEBEIhFxdnYmHTt2JPXr12dk7/bt2yQwMJD+uXHjxuThw4fkwYMHxNramowcOZK0adNGI5smJiaEoqgP+n817RcjpLJ/7ueffybXrl1Ted3d3Z2MGjWK9O/fX2Ob6uDiGh45cmSt5//55x9Gdrt06VLr+efPn3/wZ1ATU6dOJcuXLyejRo0iEyZMIOPHjye3bt0i27dvJ5MmTdLIlq+vL6EoigBQWVfVnwlhdk1Uha9sooODA9HS0mJl4/79+6Rx48aEEEL+97//EZFIRHx9fenzjRo1Ivfv32dke/HixWTr1q3EzMyMDB48mLRu3Zo+9/jxY9KkSRONo/ALFiyo9fy9e/cYrZUQQkpKSsjgwYNJZmYmoSiKzjw8ffqUACC9evUiS5Ys0ahX78WLFx/8/2raA5qYmEhEIhE5deoUefbsGUlMTCQtW7Yke/fuJaampoQQdveNtLQ0MmnSJDJixAgyY8YM+rtgYmJC5s2bR6KiojSyt3//frJ161YSFBREQkJCSL169ci+ffvo78a0adPIwIEDNbL5vnuaMj///LNGtuuK69evE5lMRkSiygIidfchNly8eJEzW1xz+vRpEh4eTlxdXYmuri4pKCgg0dHRpLS0lPzwww9kxYoVZPfu3cTQ0JD137p8+TKxtrbmYNWq8HV/J4SQ6Oho8u7dO87t9u7dm5Oe87///ptcvHiR+Pv7E2NjY/Lo0SOyevVqUlFRQSIiIoiPjw/7xf5nbtlHwqdYc/upwVUE6dGjR2jSpAlEIhEkEglEIhH8/f1haWkJsViM0aNHM7Lr6emJTZs20T+fOXMG2tradPSooKBAY/WTw4cP08eqVatgaWmJxMREeqp6YmIirKysGM2V+Omnn6Cnp4fExEQcOnQIly9fxuXLl3Ho0CGMHTsW+vr6SElJ0diuOri4hvmaASaRSNCuXTv07dtX7cF2PgPA7SBG5Wn027Ztg4uLC9LT0+lsUXp6OurXr//RDebkAycnJ3ouU35+PkQiEX799Vf6fFZWFhwdHTW2O3/+fOjp6SE+Ph69e/eGtrY2kpKS6PNMsxoURcHa2hqOjo5qD2tra8bXGh/KoR8qn89kzdbW1jh16hT985s3b9ChQwf4+vriyZMnrDNHnp6e9HdA+f5z4cIFyGQyje1JpVKV54++vj4KCgron2/fvq3x7L2q9zAjIyPo6enRap/6+vowMjJCy5YtNV6vOoYMGcKZyuDjx4/RunVr+hpRvL/9+vXDyJEjOfkbAFBYWIi9e/dyqk4LcNMbFBISgilTptA/Z2RkIDAwEEBlVY2vry+GDx/O6m8o8/DhQ056lwsLC5Gfn1/t9fz8fHpIt6ZkZ2fT6m/l5eWYNm0afT+zsbHBzJkz6SoatnChAHzo0CHo6+uDoihYWloiLy8Ptra2qF+/Ptzd3aGjo4M9e/aw/jufvXPEV5M1n3A9t+Pq1auIj49Hq1at0KpVK8THx+PKlSuc2eeKHj16oFOnTnj+/DnevHmDYcOGITY2FkDlRG6ZTIZ58+ZpbHfhwoUwNjZGQkICJk2aBGtra5WZMGvXrmUkYa2gVatW9MwDZdatW4fQ0FCN7dnb22Pjxo01ns/MzISdnZ3GdvmCrxlgPj4+WL58Oed2leF6EKOCgIAAZGVlVXs9KysLjRo10tiegYEBvvnmG5w4cYLxmj6EkpISXLlyhXUZ4IQJE+hZSU5OTkhMTIS9vT3S0tKQnp4OOzs7fP/99xrb9fLywrp16+ifT5w4AblcjokTJwJg7hw5OjrW+p1jc62ZmJjU+rkdP35c40nyfAZn9PX1q23QysrK0KlTJzRs2BDnz59n9b2TSqV0KZ2yc5Sfnw+pVKqxPXt7exVnbsyYMXjy5An9c15eHurVq8d4vXPmzEGHDh1UJO2fPn2KqKgozJ49m7FdvoiJiUGbNm1QVFSk8v7u3r0bXl5ejGwOHTqULq189eoVunbtCoqiaAesZcuWKqWX/zW6uroqQb/y8nJoaWnh4cOHAIC9e/fC2tpaY7svXrzA119/DXt7e8TGxuLt27eIi4uj34cWLVqwKqVv0aKF2u9sRkYGo70EALi7u+Po0aMAKh1PmUyGn3/+GdnZ2Zg3bx4sLCwwa9YsxmtWdrjEYjH9vk+YMKHW53dNNGvWDPHx8Xj58iVSUlJgY2OjUhb7ww8/IDg4mPF6FXy2ztHt27eRk5OD3NxcTodrZWVlqR1QtXv3buzatYuTv8FlH8jmzZshkUgQFBSE77//Ht9//z2aNm0KiUSCzZs3s7L99OlTpKSk4JtvvsE333yDlJQUlYeSphgZGanMeCouLoaWlhZ9s8nIyIC7uzsj24sXL0ZwcDD8/f0xbtw4lQhHfn4+K2dRV1dXbbTn2rVrGkcsgcrNw+XLl2s8f+nSJUZ21VFaWor8/HxW/Ux8zQDr27cv4uLiajx/+fJlRtkHZfgaxFjTZ3j58mVGG0CKotCgQQNQFAUPDw/Mnj0bf//9N+P1VeXvv/9GREQEZ9mH8vJy/Pjjj4iMjERSUhIqKiqwYcMG2NnZQSaToW/fvoxq3nV1datFUC9cuAALCwskJiYydo66du1a69R1Nv1tfCuHch2c8fHxUftsUDhI9vb2rDNHit4i5c37ggULGAWpOnbsWGvQbOHChWjVqhWzxaIyk6b8XFJw4cIFWFlZMbL56tUr/PLLL+jXrx/atm2L9u3bY9iwYdi/fz/jdSqwsLBAXl4eANX398aNG4znw4hEIjx69AhA5cBpW1tbHDx4ECUlJTh+/DhcXFyQmJjIeu337t3DpEmTEB0djVGjRjF+Ljs4OOD48eP0z/fv3wdFUXj16hUA4ObNm4zuw8OGDYOHhwcWLFiAsLAwREVFwdvbG8ePH8eRI0fg5eWFcePGMVozULn3U856KigoKICxsTEjmzo6OnQA0NvbWyWDD1Qq+bLp/5w6dSqcnZ2xdu1aFac0MzMTQUFBGtszMjKiKzjKysogkUhw9uxZ+nx+fj7j90KZz845WrRoEX3zVj5CQkJw+vRp1vZ9fHzURoSzs7PRsGFD1vYBbsv1nJ2d6aiqMpMmTYKzszNju0eOHIGxsTHs7OzQuXNndO7cGfb29jAyMsKRI0cY2ZTL5bh06RL986tXryASiWiH68aNG9DR0dHI5vz582lH6Pbt27xIhLq5uakt+Rs9ejTc3Nw0tte8eXPExsaqlTp+9+4dYmNj0aJFC43tJicn0w+Hd+/eYdSoUdDW1qbLGPv168dI9vbBgwesJmzXxJs3b1BSUsK5XWX4GsTo5+eHmJgYvH37ln7t7du3iImJYbQBpCgKjx49Ql5eHoYNGwYzMzNoa2ujS5cu2LVrF+uyiOjoaISEhODPP/+Evr4+9u7dSwcjFGWHHwN2dnZ0FFSZS5cuwcLCArGxsYw27pcuXarVgSktLWV8jUdHR8PPzw9//fVXtXN//fUX/P398fXXXzOyDXAfnElISMCXX36p9lxZWRnrctZly5bBxsYGmZmZ0NfXx4YNGzBjxgz6v7nm1KlTrEq/DAwMcOjQoWqvHzx4EAYGBhrbKygogIODA8zNzWFnZweKohAREYHAwECIxWJ069aNlcy9gYEBfT0o7yX+/PNPmJmZMbKpuP8AlRvsqs74jh07GD3rdHV16SDPpUuXYGxsDFdXV3Tr1g0eHh7Q09NjlLn+7rvv4O3tjezsbBw8eBAtW7ZEWFgYfX737t1wcXHR2K6dnR0OHjwIoNKRoygKO3fupM///vvvjAO4QKVjoO4+cfr0aUbXGgBYWVnhjz/+AFDpOFe1n5+fzyrY6uLiQjv1ytfblStXNM6IA0C9evXoYERJSQlEIhG9fqBS+IhNJljBZ+UcpaSkwNraGqmpqVi2bBk8PT0xbdo0ZGdnIyYmBnp6erU+AD8EqVSqtvbz5s2b0NPTY2VbAZfOka6urtpIBNsvhLe3NwYOHFithn7QoEHw9vZmZLNz587o2rUriouLUVpaihEjRqhENHJycmBpaamRTbFYTN/UlaNfXJKVlQWpVApvb2/0798f/fv3h4+PD6RSqVpH+n2cO3cOlpaWkMlk6Ny5M4YMGYIhQ4agc+fOkMlksLKyYvSwV/73p6SkwNTUFCtWrMClS5ewdu1amJubIzk5WWO7/y/xxx9/YM6cOfjtt99Y2Tl16hTMzc0hl8vRunVrtG7dGnK5HObm5iolQB+K8uYEqHQc169fj9atW0MkEsHW1lZtEORDsbS0pNdlaGhIz17bsWMHQkJCGNtVBxtHrlevXjVmKS9evAi5XM6LMiIb+FYO5To4U1ZWVmtpUFlZGetgyNq1a+Hq6kqXZtnY2DAqwakLYmJi4OjoiC1btqCoqAhFRUXYvHkznJyc6LJvTWjXrh0GDx5Mfw9mzZqFdu3aAah8Ljs6OmLy5MmM19uuXTtMmDABQOVeorCwEOXl5ejWrRu6du3KyCZFUbQTo7x5VXDr1i1G+wnl+1pUVBQ6dOhAO4bl5eXo2bMnIiMjNbb78uVLdO/eHRKJBBRFITg4WGVPtWfPnmoZlA9BR0dHpb9NT09PZU7lrVu3WO0DIyMj0a1bt2r7qq5du6Jt27aMbMbFxSEyMpLenw0YMEDlHvztt9+iadOmjNdcU5nspUuXGGUqo6KiEBkZiePHj2PQoEFo3LgxIiIiUFxcjJKSEnz11VeM3wtlPivnyNHRUaW07dq1a5DJZPSXbfjw4fjiiy9Y/Q0LCwscOHCg2uv79u1jVYajzJ07dzjLcLRr1w4rVqyo9vqKFStqjA5+CFKpFFevXq32+tWrVxmlq4HKzJCLiwskEgm0tLRgYmKCffv20edXrlypcerezs4Oixcvxq1bt0BRFM6cOYPbt2+rPdhQVFSEcePG0Vm0cePGsRKpePHiBRYvXozY2Fh8+eWX+PLLLxEbG4u0tDTGNc3KDyI/Pz8sWbJE5fzatWvRoEEDxmsGgH///RfLli1DYmIinfE7c+YM7t69y8qugtevX3Mqlc4nxcXFWLJkCV3OunTpUsbyqbU59jdv3sSECRNY9aEZGhrSQR97e3u6JKWwsJDRpufNmzcYNWoUmjdvTtezT58+HXp6etDX10evXr0YfXbnzp1Tez9TcOHCBZVGbCbwJZ9/5coVrFixAklJSUhKSsKKFSs46f3kOjhTl5SUlHAWsEpKSsIvv/xS7fVffvmFVU9FSUkJhg4dqjJAXFtbG0OHDmX0fdbT01PJ9L19+xZaWlp0+f/27dtZlQtfuHAB5ubmaNu2LbS1tfHVV1/B09MTFhYWdLmSplAUhcGDB+P777+Hubk59u7dq3L+zJkzjKL5ys8kdVnhv/76i3HpIlD5vOCyF8ra2pruSwUqgzXK1+/Fixc1HgGhzKVLlyCTyeDi4kILD7m4uEAulzPOfj579gyNGzeGq6srYmJiIJVK4eDggC+++AJOTk4wNjamS8uZ0KhRI7rnWNk5mjp1Kpo1a6axvfz8fNSvXx8URcHT0xN3795Fx44dIZFIIJFIIJfLVT4DpnxWzpGenp5KVqeiogISiYSewJ2Xl8c4Nalg0KBB8PHxUbnJFBQUoGHDhipN/myZMmUKY/UaRVPujh07kJaWBrlcjvj4eGRkZCAjIwPx8fEwNzevcb7ShxAcHKxWdWvbtm20KgwTSkpKsGfPHuzcuZMT9Z4lS5bQpWM1HYpmyo+B77//nn7gcj3dXTn6J5PJqt1sCwsLWUW9zp07B7lcDldXV0gkEvomOX78eMTExDC2W1JSgvj4eDorwLYfBgBmz57NSykgX1TNHKmDTUamcePGdC9lhw4dEBMTg7t37yIhIYFR+e33338Pa2trjBo1Cp6enoiLi4O9vT3Wrl2L9evXw9XVFd9++y3j9fKBotqg6vXl6en50WY1FNy5cwdjx47lLDhz6dIlDB06FL6+vrC0tISlpSV8fX0xdOhQldJnJvChyAVU9pmoE77Iyclh3ZsIVAY7FAIlbGbEVN1g//vvv6AoCi9evABQ+f5oWj5elWfPnmHGjBno1q0b2rVrh/Hjx9P7ICaEhoaqqPctW7ZM5fz06dMZ9beJRCL6meTg4FCthK6wsJBxsFWZgoIC7N69my4rZ3qvbNu2LdLT02s8v3LlStZiAffu3cPYsWPRvn17dO3aFVOnTmXVyw1UlgWnpaWhffv28PDwgJubG0JDQzFu3DgUFRWxsr19+3YYGxtj1qxZ0NPTQ0pKCgYMGABtbe1qTrQmVNUK2L9/P3bu3MmZhsBn5Rz5+vpi6dKl9M8HDhyAnp4e/UW4evUqDA0NWf2NZ8+eISgoCBKJhJZ5lUgkaNmyJaPSCHVDQ589ewYtLS2cOnWKUXRcUarwvkPTjaWyelVmZibs7e2RkpKCY8eO4dixY0hJSYGjo6PKwNWPgRcvXuDChQugKAoHDhxAXl6e2oMpNWX5ysvLNc5ISSQSWlGH6zJAiqLw448/Yv78+bCysqrWG3bu3DlWUa/WrVvT5T3KEaQTJ07AwcGBsd24uDh4enpi8+bN0NXVxYoVKzB9+nTY2tqqDBbVBIqiIBaLER4ejszMTJXeIC6pqKjAwYMHsXTpUvz++++Mnd0pU6bw2n+VkZGBlStXAqisb69Xrx5EIhGkUimj77OdnR2d9b1x4wZEIpHKgM+9e/eyuiYUcKWuV1fy+Z+CcuiuXbugra2NoKAgTJ48GYsXL8bixYsxefJkBAcHQ0dHR60o0YfChyIXUFnyVFhYWO11Jr2qyjx79kzt5vTJkyeMsp99+vRBaGgorly5gsLCQvTo0UOlD/Hw4cMaZ4E7d+5Mr2X16tV48+aNxutiw40bNxhtsimKgomJCUxNTaGlpVVN9XTv3r2sHNvHjx+jVatWnMmaP3nypNZ93q5du9T2p/2/ztGjRxEeHg65XA5dXV2EhIRwIrfNJ5+Vc7Rx40ZoaWmhe/fuiI2NhYGBgUoZVnp6OqvaSgUVFRXYs2cPfvrpJ6SmpjIWIADw3mzGx5TVUF4Tl06XMq9evcKxY8fURidfv36N1atXM7a9atUqTh8az58/R7du3SCVSmFubo6JEyeq1AozUc5ydXXFuHHjcPjwYVAUhe3bt+PIkSNqD01xcHBQmd0yd+5clfPz5s1jpC6jQFllRtk5unXrFqvNiZ2dHf3AUVbzWbNmDV2rrykURWHlypWIioqClpYWZDIZvvvuO9YzO9q1a0eXYj158gSBgYGgKIrOenl4eHCqMscXJSUlOHPmDOPsra6urkpgQEtLS6VPgW2PJtfqenUhn8+lcmhVZ7C2Q1MaNmxYa//a5MmT4ePjo7FdBXwocgGV9051IwXWrFkDJycnxnbbtm2LRYsWVXs9LS2N0f3n0aNHCAoKop+VDg4OKk3ymzZtwoIFCzSyqaWlRWeG+Oqt5YNVq1apHMqN9wAwbdo0RpL/CviQNeeDzZs38y4+VJWKioqPcszN7NmzWWWQP5TPyjkCKj336OhodO3aVSWLBFRGEbiU9eYCGxsbRERE4ODBg/TcikOHDkEsFmPlypX0ax8DykMu33cw4dq1a3BwcFCZGaBcCsB2+CDAbU/M8OHD4ebmhk2bNmHZsmVwcHBAREQEnYV4+PChxvK/27Ztg4WFxXsdUT4c5j/++EOtUs6HIpfL6d9XfhDt3bsXtra2jO3q6+vTG20bGxtaOKCwsJCxNK1ymdqjR4+QnJwMDw8PiEQiBAQEYOnSpXSZC1O7Q4cOhZeXFx3NLioqgr+/P4YMGaKRTXXZ5ZqOjwV3d3c645SbmwttbW2VXqHMzEzUr1+fsX2u1fXqQj6fS+XQqsEz5aBa1dc0paZ+UgVs+koBfhS5gEo1TplMhhUrVtDPoV9++QUymUxlULCmmJqaqr02rly5wlj9DagsI7xw4QIrZToFPj4+6NOnD1atWgWKopCamorVq1erPfjg4cOHmDp1Ki+22cCHrPmjR49w4MABOgj28OFDJCcnY+bMmfSwVU2hKApGRkYYOHAgq/4fdZSVlWH8+PFo0aIFJk2aBOD/MuXa2tr0vCauYDsQt66qOj4754hP/vnnHyQnJ6NTp04ICgpCUFAQOnXqhJ9++olxNPjJkyfo1KkTWrZsqbJBl0gkrGu7gUolkvnz51d7PTU1Fd999x1r+1zSqVMnRERE4J9//kFBQQEiIiLg5OREf9HYOkdc98TY29urpND/+ecfNGnSBF9++SXevHnDar0vX74ERVH0DCJ1x8dG//790alTJ5SWltIqSbdv34afnx+ra83Hx4cOELRu3RqjRo0CUCnTbmNjw8hmTT08R48eRZ8+faCvr8/o4als193dHTt27FA5v3//fo2j2FU3u7UdTEhNTUVMTAwto7xmzRp4enrC3d0dY8eOZbR5mzt3LqRSKcLDw2FqaooFCxbA0tISCQkJSExMhLGxMaZNm8ZovQD36np8yecrw6VyqHIgatu2bXBxcUF6ejqdLUpPT0f9+vXV9oW+Dw8PD8yZM6fG83PmzGElV8yHIhdQGQlPSEiAVCqlvw96enqsN+16enpqN73nz5/nbN4cW06cOIHAwEC6HNbY2BgmJibVDjZl07WRl5fH6tnMVx8a17Lmhw4dgr6+PiiKgqWlJfLy8mBra4v69evD3d0dOjo6jMrJKIrCtGnT4OfnR8+0mzt3LifB/AkTJsDCwgIjR46El5cXhgwZAjs7O6xduxarV6+GjY0NI5Vavgbi8lXVUZXPyjniYo5RTeTm5sLU1BQ2Njbo06cPEhISkJCQgD59+sDW1hZmZmasZMIXL14Ma2tren4AV86RtbW12vflzJkzjDeWtfH06VPG0Slzc3OVh1BFRQWGDBkCe3t73Lhxg7Vz1KpVK057YnR1davVuL948QJNmzZFq1atUFhYyGq9hw8f5iSq+KGw+eyAytr88PBwmJiYQCwWw87ODlpaWmjRogWrBuaff/6ZdvD37dsHqVRKK0fVNvyxNt5XevL8+fNqmecPQVn0wtzcXK3kraYlhors8eHDh7Fq1SpYWloiMTGRFl1JTEyElZWV2j6O9zF9+nQYGhqia9eusLS0xKxZsyCTyTBjxgwkJSVBLpfT0UZNWbduHYYNG0bf0w4dOoTmzZvD398fU6ZMYaXIybW6Hl/y+crwpRwaEBCgVpUuKysLjRo10tjer7/+ColEgg4dOmD+/PnIzMxEZmYm5s+fj44dO0JbW5vVAHE+FLmUefnyJXJzc3HhwgVOyqjDwsIwbNiwaq/HxcVprMalKKf8kIMpHyLeoinvK93cuHEjq2cdX31oXMuaN2vWDPHx8Xj58iVSUlJgY2OD+Ph4+vwPP/zASJBB+TM7ffo0hg4dChMTE+jo6KBbt26shA2cnZ3pWUwFBQUQiUQqfaQbN25kNH6Fr4G4fFV1VOWzco4oioKLiwt+/PFH3Lt3j1PbgYGBGDRokFqVk4qKCgwaNIhVvwZQ+dD43//+h169enHmHOno6NRY381WEUcdbCJIhoaGassX4uPjYWtri6NHj7K6AXPdE+Pu7q52U/Ly5Us0bdoU//vf/xitl08nvzbYRv8UHD9+HIsWLUJycrKKFDtX3Lp1C1u2bGHUT6GAjw2Ewm779u3RuXNnmJqaqgwIBCqVsywsLBjbb9WqVbUBjEClI8JkE+Hi4oItW7YAqPz8xWKxisjF1q1bWU1P5wuu1fUAfuTz60I5tKaSwMuXLzMufztx4gR69OgBe3t7aGtrQ1tbG/b29ujRowdOnjzJeK0K+FDkUubOnTus1PqUOX78OKRSKZo3b44pU6ZgypQpaN68OaRSqdqBxLWhrPgWFhYGIyMj6Onpwc/PD35+ftDX14eRkRFatmzJeL23bt1iPRS6KrWVeXPRG81XHxrXsubKe4iysjJIJBKcPXuWPp+fn89oveqeR69fv8aaNWsQFhYGkUjEWJhCKpWqfBekUqmKCExhYSEjoTK+BuLyVdVRlc/OORo4cCDMzc0hkUgQERGBbdu2cdJ0VvWCqsqVK1c4kZx8+/Ytvv/+e/j6+qpV3tGUBg0aIDU1tdrrCxYsgKenp8b23tfzcOzYMcY3yYCAAKxZs0btufj4eJiYmLC6AXPdE/Ptt9/iq6++UnvuxYsXCAwMZLRevpx8Pj87gUr69OlDR8P79u1brcl/9OjRaNOmDWP7urq6astPrl27xihj8j7hBLZDDYHKTcS+ffuQnp5OR/zu3bvHav4I1+p6fMGXcqgyfn5+iImJUanNf/v2LWJiYlRU0P5fp6ysDBMmTICRkRFdVmdkZITx48ezHolw9uxZREdHw8vLC/7+/ujXr5/a76EmzJkzBx06dMDTp0/p154+fYqoqCjMnj2bkc2KigoUFhbSFQdv375FZmYmVq9ezWo0hkwmwy+//FJjj3FWVhbrwCUffWgAt7LmygNwS0pKIBKJVEQkzp07x2je0/sqGQoKChhlYYDKvivlipzg4GCVFo4rV67AyMhIY7t8DcTlq6qjKp+dc/To0SOUlZVh8+bNaN++PcRiMSwsLJCQkKDywWmKo6NjrSVHq1ev5kSalmt++eUX6OrqYtKkSXRpzsSJE6Gnp8e4bIivmUFJSUm1qv8MHTpUY4EDZbjuiXn69Gm1sillXrx4wUhMgy8nn8/Pjg+VwaKiIpUH+tGjRxEdHY1mzZrh66+/5iSCzSUfkl0oLi7G69evGf8NNzc3ujRUmdGjR8PNzU1je05OTsjOzgZQGfUUiUQqk+OzsrJYSeneunULHh4e0NPTg1gspgMSw4cPx+DBgxnbrQpbdT1lrl+/jvHjx6sMeNy1a1et3/X/klOnTsHc3BxyuRytW7dG69atIZfLYW5uTvdlfYxwJcOuYMiQITA3N6/We2VpaamxCEpdYG1trfaaunDhAqPBp1evXoWDgwNEIhFcXV1RWFgIf39/6OvrQ09PD/Xq1WPs0H355ZeYPn16jefz8vJYPZv56kPjmqioKERGRuL48eMYNGgQGjdujIiICBQXF6OkpARfffUVo/XyVckAAC1btqy15PrXX3+Fv7+/xnb5GojL53uhzGfpHClz9+5dTJs2Dc7OzhCJRGjevDkj2wsXLoSOjg6GDx+OHTt2ICcnBzk5OdixYweGDx8OXV1dtXKfH8qpU6cwb948JCYmIjExEfPmzUNubi5je8osXrwYNjY2dJTSycmJcW+JkZERkpOTVfoglI9ly5Z9tNkHvnpiuIYvJ5+vz06dyqByxotpr1iTJk3oNP327dshEonQsWNHjBkzBp07d4aWlla1srUPga+6f+WIF9O5Z+8jKysLUqkU3t7e6N+/P/r37w8fHx9IpVK1JZ7vY8KECZDL5RgwYACcnJyQmJgIe3t7pKWlIT09HXZ2dqz6H6KiotC7d2+8fftWJVt76NChj7Jc7/Dhw9DV1UV4eDi0tbXp9c6cOZNRj0JdUVxcjCVLltDX7dKlSxnf0wwMDPDNN9+oHajKBVzLsCswMjLCrl27qr2elZXFKDIOVG7Qb9y4QffHvXnzBhs3bsSGDRvoeXRMMTAwUDsT5+DBg4yyJVFRUejYsSPOnz+PESNGwNPTE1FRUSgtLcWbN2/QoUMH9O7dm9Fat27dqlYmXcHTp08Z9Twq4LoPjS9p7Pz8fNSvXx8URcHT0xN3795Fx44dIZFIIJFIIJfLVRyGD4WPUkgF165dq7UKad26dbWOMaiJuhiIyyeflXP0vnTc/v37ER0dzdh+ZmYmAgMDIZFIaEdDIpEgMDCQ0cUFVDacNWvWDBRFwcHBAU2aNEGTJk3ozWazZs0486L//vtvVqUsQGXNdG3KJmwjSOrgWpTg2LFjvPbEKGAqb8qXk8/XZ8eXyqC+vj59Uw8MDMSsWbNUzqempjIqG+Kr7t/IyIju/VAWZuCaO3fuYNy4cejcuTM6d+6McePGMe6vKC8vx48//ojIyEgkJSWhoqICGzZsgJ2dHWQyGfr27csqcGBmZkbLQis7Rzdv3mSs9LV3715MmjQJBw4cAAAcOXIEbdu2RcuWLdUKHmhCUFAQrdSmvN5Tp06xErDZuXMnJk6cSAtHHDhwAO3atUObNm2wZMkSVmvmGoVaFkVR8PDwwOzZszm9lrmWYVcgl8tr7L1iUup07tw5WFlZQSQSwdvbG3fu3IG3tzf09fVhYGAAU1NTVgHMmJgYODo6YsuWLSgqKkJRURE2b94MJycnxMbGamxPLpfT/S/FxcWgKArHjh2jz584cQL29vaM18s3XPah8SmNDaCaitz+/fuxc+fOj25UDJ986gNxPyvnqK7ScaWlpbh//z7u37/Pupa5a9euaNq0qdq5ElevXkVwcHCNfS3/BUuXLlUrDa7g4cOHmDJlCiPb2dnZdG1seXk5pk2bBmtra4hEItjY2GDmzJm8RVf4gKnAAV9OPl+fHV8qg8bGxnSZjbm5ebWSm+vXr7Puh+Gy7r9Lly6wsLBAWFgYKIpCSEgIWrZsqfb4GDh37hwrxbgPwcTEhC61VHY2jh07BnNzc43tZWRkQCKRoFGjRjAwMMDKlSthYmKCAQMG4JtvvoG2tjY2bdrEeL3KDnlVZ46pgE16ejokEgn8/f1hZGSEjIwMGBoaYsCAARg8eDB0dXU1Vl1UFnp436EpiudoXl4ehg0bBjMzM2hra6NLly7YtWsX63sw1zLsCqZOnYpevXqpKNS9efMGX3/9NaP7Wps2bfDVV1/hwoUL+O677+Dp6Ylu3bqhtLQUZWVl6N27N8LDwxmvt6SkBEOHDqWVN0UiEbS1tTF06FBGAYmq/YMGBgYqogN37tzhRYTpY4RPaey6oLi4GCtWrMC4ceOQmprKybqrluXn5OTgyJEjrPewfHD//n1kZGQgKyur2pyj4uJiTmZqfVbOUV1LH3OBgYFBrYM3uWhI3LRpE7p164bAwEA6Oq44Pibc3d1p9Z+kpCTIZDL8/PPPyM7Oxrx582BhYVEte/Ah8NW7wpe8aV05+VzBl8pgx44dkZiYCKByo1LVsVu2bBmrQaIAt3X/r169QlpaGn744QdQFIVBgwZhxIgRag+mrFixQqUnSMGvv/6qcVmLshPu5OTEy8ahe/fuGDhwIID/k9J9+fIlWrVqhb59+2psz9fXl74O9u/fD11dXfz888/0+dmzZ7PaYNvY2NDlZMrO0datWxmr4Hl5edH9nQcPHoRUKlUpwV65cqXG4jg1KYZVfY2L+8+bN2+wfv16tG7dGiKRCLa2tmqH2X4oXMqwK7KnisPQ0BD16tWje6/q1asHIyMjdO7cWeN1Kg9/ffXqFcRisUoP18WLFyGTyTS2W5Xi4mL6mcEmS+vi4qKSKVq8eLGK5PGZM2dgaWnJ2D5fGVu+hqryIY395s0bFWfi+vXrGDduHHr37o3x48czFtHy9PSks2R37tyBo6MjjI2NERAQADMzM5ibmzO2ff/+fYSEhEAsFqNFixZ4+vQpIiIi6PuEm5sbY4EKZbjqIczNzYWJiQmMjIygq6sLV1dXlWc025EuCj4r5+i/5Pr164wiwjKZrNam/UOHDrG6Ac+fPx8GBgYYNmwYtLW1MXjwYISHh8PY2Jix+glf6Ojo0JEvb2/vapvA33//nVGfAl+9K3zJm35qTj5fKoOXL1+GTCZDbGwspk+fDgMDA/Tu3Rs//vgjYmNjoaOjQyuWMYXrun8FYWFhvPQc1a9fn5ZPVebw4cMaCzKYmZnRJSd8lQEWFRXBy8sLnp6ekEgkCAoKgkwmg7u7O6MAgHJmB6hU11N+AF+5coXV/XLUqFFo1qwZHjx4QMsLHz9+HM7Ozowz4uoUAZX7KG7evMkqA7pv3z40atQIu3fvppUnd+/ejcaNGzPaBNaWub558yYmTJgAOzs7xuvlUoZdWRnyfYemmJiY0AIGpaWlEIvFKv0kV65c4WyoKhfS44MHD8ayZctqPD9z5ky0b9+ekW2+MrZ8DlXlQxo7NDSU/nceP34cOjo6aNiwIXr06AE/Pz/o6ekxCrYqr/frr79GcHAw7Sy+fPkS4eHh6NWrl8Z2gcryzeDgYPz222/o0aMHgoOD0bx5c9y9exe3b99GSEiIyqwmTeG6hzA8PBz9+vVDeXk5Xrx4gaFDh0Imk9FJBME54oCKigocPHgQS5cuxc6dO3lNHzItoYqLi4ODgwO2bt2qonb1/PlzbN26FY6OjmoH0H0o7u7u9FwU5UjoxIkTWX0hgMrIbUREBJydneHs7IyIiAhWPTxWVla0LKaFhUW1jBqTSfIAf70rfMub8sE///yD5ORkdOrUCUFBQQgKCkKnTp3w008/Md4c86kyeP36dfTs2ROGhoa046mlpYXg4GBs27aNkU1luK775xsdHR21E+Nv3ryp8SiBgQMHQkdHB46OjhCJRLC3t4eTk5Pagw1lZWVYu3YtRo8ejaFDh2LZsmV49eoVI1smJiYqJcjK9zSgMvvAxtF4+/YtBgwYQPeVamlpQSQSoXfv3ozVIhXZU+D/5oEoi2ccPnyY0SgBBQ0aNFDJGCg4evQoPDw8NLb3IZlrNqV1n4oMe+vWrdG/f3/cvXsXU6dOhXJOLAwAAFhQSURBVKurK/r160efj4uLYyzwBPArPa6OwsJCxhkCvjK2fA1V5Usa28jIiHaYQ0NDq4nVTJgwgdH7oPydc3Z2rhbUOHHiBOOAhPK+6smTJ6AoCvv376fPHzhwgHFWHOC+h9DU1LSa6NTMmTPpHj/BOWJAu3btaG/7yZMnCAwMBEVRkMvlEIlE8PDwYLwBnD9/fq1HQkICow/szZs3GDJkCLS1tekHhFQqVak/ZjPlW1dXF7du3QJQ2bCZl5cHoNLRMDMzY2x30aJFkEgk6NmzJ/0e9OrVC1paWli4cCEjm3FxcYiMjMS7d+8waNAgDBgwQOUh/O2336Jp06Ya2+Wrd4VvedOqODk5sZqtkZubC1NTU9jY2KBPnz5ISEhAQkIC+vTpA1tbW5iZmeHPP//kbL1cUlFRgYcPH3LS56cM13X/Z86cUclsrFmzBsHBwbC1tUVISAg2bNjAar12dnZq+0i2b9/OSDAgOzsbqampoCgK06dPx7x589QeHwuNGzfG9u3b6Z+fP3+uco/Yt28fI0nzqty+fRtZWVnYuHEj63k28fHxqF+/PmbMmIEmTZqgT58+8PDwQHZ2Nnbv3g0fHx988803jO1LpVK1il7nzp1jNHtvypQpvCh91QSXMuxckpubC5lMBpFIBLlcjosXLyIwMBCWlpawtraGrq6uyiZTUz4l6XG+MrZ1OVSVC/T19el5lxYWFvR+SsH169cZVRwoZ+6tra2rfZ9v3brFeI5m1SGw+vr6KgN3b9++zVgcB+C+h9DU1FRtOV5KSgpMTEywdetWTpwjCfmM2L17N3n79i0hhJAJEyaQly9fkhs3bhAnJydy9+5d0qlTJzJp0iSSlpamse0RI0YQKysroq2trfZ8aWkpozXr6OiQtLQ0kpycTM6cOUMePnxICCHE0tKS+Pv7EyMjI0Z2FVhaWpKnT58SBwcHYm9vT3Jycsj//vc/cvPmTQKAsd2kpCQyd+5cMmzYMPq14cOHk5CQEJKUlETi4+MZ2QwPDyceHh6kadOmZNOmTWTfvn3Ezc2NXL9+nTx9+pTs2bNHY7uhoaFkw4YNpGHDhsTPz48cPnyYNGzYkD5/6NAhYmNjo7HdIUOGkJKSkhrP29vbk5UrV2psd8GCBWpfv3PnDlm5ciWxtLQkhFS+35rw7bffkm7dupH09HRCUZTKOQBkyJAh5NtvvyV//PGHxmsmhJDnz5+T8vJyYmZmpvL606dPiUQiYXUtUxRFLCwsGP9+Tejp6ZHFixeTlJQUcuPGDUIIIS4uLkRfX5+RvX79+pE5c+YQJycnsnz5cjJ8+HAycOBAEhMTQ65du0YGDhxIXr16Rb755htG9nv16kWGDx9ODA0NSYsWLQghhBw5coR89913pGfPnhrba9u2LSGEkDNnzpDvvvuOGBoaMlrX+wBADh8+TK5fv06sra3Jl19+SbS0tDS2M27cOGJqakr/XPWaOn36NOnevTvr9drb2xN7e3vWdgghJDk5mZSWlpLMzEwSHBxMUlNTyYIFC0hUVBQpKysjoaGhZObMmYztBwQEkJEjR5KMjAz6O/Lo0SMyevRo0qRJE43tTZ48mfFamKCnp0caNWrEysauXbvI1q1biZmZGenXrx/x9PSkz/3777+ka9eu5ODBgxrZDAgIILdv3yZXr14l7u7uxMDAgBw+fJisW7eOvH79mnzxxRfE3d2d8ZrXr19PMjMzSbt27ejXGjZsSOzs7EivXr002qfU9MxQh6bPDUII0dLSUtnj6OjoEAMDA5WfX79+rbFdbW1t8ubNG0JI5R6qoqKC/pkQQl6/fs3oPnHz5k0il8s1/r33ERgYSHbu3Ek8PDyIi4sLOXfuHPnf//5Hn8/Ly6v2/PtQWrduTSQSCXnx4gW5du0a8fb2ps/dvn2byGQyRnbNzc3JgwcPiJ2dHSGEkGHDhqms8d9//2X8vCOEkJKSEmJubk4IIcTU1JT8888/xM3Njfj4+JC//vpLY3ve3t7k5MmTKns0Qgj54YcfSEVFBenVqxfjtarA2r36hFCOFri7u1eLsO7fv59xiYijo2Otct1nz5796EqogMrBp4pa+YULF9IzPExMTFhFK6tGHxTk5+dDX1+fsd3S0lKkpaWhffv28PDwgJubG0JDQzFu3DgUFRUxslkXvStcQlEUbG1t4ejoqHJQFAUbGxs4Ojoyuo6lUikd9VLHlStXGEengMq5B+pmfaWlpdVadlcbfMo284FyptbPz6/aoOV169bBy8uLsf23b9+ie/fudMmXlpYWxGIx+vXrV03V57+Ezyw+X9y/fx8TJ05Ey5Yt4eHhAS8vL0RGRmL58uWsBzCr4/Xr1yoN80wpKCiAt7c3tLW14eLiAhcXF2hra6NBgwZq79EfAl/zfVJTUxETE0NnUNesWQNPT0+4u7tj7NixjHot161bB7FYjIiICDRr1gxSqRRr166lz3NVhsM1XEqPV31WKPp4TE1NYWpqCoqioK+vz3j/w1fGlq+hqnxx8uRJGBsbY/LkyUhNTUW9evUwYcIErFu3DpMmTYKJiUmt4zJqYsqUKSqHoidPwQ8//ICePXsyWnPHjh1rzf4vXLgQrVq1YmQb4LaHEKgUWqptHtesWbNYDSZX8Nk5R4oHrrm5eTUVqlu3bjGWsuzatSsSEhJqPM/HfB+A+awcBeXl5SoPnA0bNuDbb7/FggULWG2mevXqhZ9++qna6ykpKejRowdju3zBd+8KlwwePBi+vr7VHpwSiYSWRWaCo6NjrcN/V69eDQcHB8b2ldWdlLly5QqjEk6+moCrKlzVdmiKTCbD6dOnAVTeg9SVXbApYVCQn5+PX3/9FTt37qSdMT5gKjSjHKgaOnQovLy86LKcoqIi+Pv7c1o6tHLlStoZY8Kff/4JY2Nj+Pv7o1mzZhCLxYiJiUGPHj1gYmKC4OBgThwZvqioqMCePXvoEue9e/cy7gvia77P9OnTYWhoiK5du8LS0hKzZs2CTCbDjBkzkJSUBLlcjkmTJmlsV7kfBgA2btwIfX19LF++HAB754hLhUhluJYeV7Bu3TqEhISo9OZdvXoVzZs3V3EaNWHr1q04cuRIjednzpyJCRMmaGyXr6GqQKXC4LFjx9Q+M1+/fl3rs7A2Tp48iaCgoGoiTDY2Nh9VCfKHcurUKUaDdhVw2UO4Y8eOOpMW/+yco/bt26Nz584wNTWtpkCWk5MDCwsLRrYvXbpUaz9GaWkpL5sUpkIPfKDcYzV9+nQYGxujffv2mD59OqZPn46IiAiYmJjU2ofzX8N17wpf0eatW7fCzs4Oqamp9GtsnaOFCxdCR0cHw4cPx44dO5CTk4OcnBzs2LEDw4cPh66urtrMz4eip6enVnr1/PnzjBwCvpqAlRWs+vTpAyMjI9jZ2dEOkb29PYyMjBgpXPXu3Rv9+/cHAHTr1q3ahiEpKQk+Pj4a2/2vYHr/4TOLrw4tLS21jvmHEhISorIhzcjIQGBgIIDKuVe+vr4YPnw4Y/uXLl3C0KFD4evrC0tLS1haWsLX1xdDhw5l9Z3mA77m+7i4uGDLli0AKq8rsVisslnfunUrIzXSqv0wwP+pTaalpbF2jrhUiORTelyBs7Oz2vEgp0+f5iTizgdcD1W9du0aHBwcaMXYFi1aqIhRcJFN/Pvvv5GTk4OTJ0+qFcnhik9ptiPArodQJBLRCY73iWqwhQJYNJZ8YvTr10/l53bt2qnUnyckJJDz58+T3bt31/XSauT8+fO1nr969Srp1asXKS8v19j233//TS5evEj8/f2JsbExefToEVm9ejWpqKggkZGRKjWtH4KTk9MH/X8URZHCwkKN10sIP7XjfHH69GkSHh5OXF1dia6uLvnjjz9IdHQ0KS0tJXv27CFeXl5k9+7djPs47t27R2JjY4m2tjZZuXIlsbOzI+fOnSNeXl6M17xx40Yyd+5ccubMGfqaEovFxN/fn4wcOZJVv0bLli2Jt7c3SU1NVXk9Pj6enD9/nhw7dkwjewYGBuTChQv0daetrU1Onz5N1yJfvXqVNGvWjDx+/JjxmseMGUOePn1K0tPTiVgsJoQQUl5eTuLi4oiRkRFJSUnRyN79+/dJSEgIsbe3J40bNyZpaWnE39+feHp6kmvXrpGcnByybds20r59e43Xum/fPnL8+HESGhpKWrVqRY4ePUpmzpxJ3r59S2JiYqrd/z6E9/Uq3Lt3j8yePVvj+49IJCKPHj0icrmcWFhYkIMHD5IGDRrQ52/fvk3c3d1V+gs+hJrq+Z89e0aMjIyISCQihFT2uWmCnp4euXjxInF2diaEEFJRUUGkUikpKioiFhYWZN++faRv377k3r17GtklhJDs7GzSqVMn0qhRI9KmTRuVvqB9+/aRM2fOkB07dpA2bdpobPv3338nubm5pE2bNiQkJIQcPHiQzJ49m1RUVJAuXbqQQYMGaWzTzMyMnDhxgnh6epLXr18TQ0NDcvLkSbp/6dKlSyQ0NFTj752enh65evUq3culra1Nzp49S18Xt2/fJl5eXrX2carD2tqabN26lQQFBam8fuTIERIZGUm+++47MnPmTEbPUEIIkUql5OrVq8TR0VHl9Vu3btHv0YeiyXeUSb8qIZXv85EjR0hAQIDK67m5uSQsLIy8evWKkV11rFq1inTu3JkYGxtzZpMLOnfuTMrKysiqVavIs2fPyIgRI8jly5fJ4cOHib29PXn06BGxtrZmfE0o8+bNGyKVSlnZePv2LRk/fjzJzc0lERERZMyYMWTGjBlk1qxZhBBCOnbsSNLT01n17R48eJAcP36cPHjwgIhEIuLs7Ew6duxI6tevz2rtXGJpaUmWLVtGOnTooPIM4QXe3K5PkOLiYrx+/ZoTW48ePcLRo0dx9OhRVt4tX7Ny+JofwCd1UTteVFSERYsWYcyYMfj+++9VDk3hO9oMVEaNkpKSYGlpCbFYzFmUubS0FPfv3+dU/e348eOQSqVo3rw5XTvdvHlzSKVSWspYE/iWbQaAevXqqfwNBVevXmWs5vjvv/9izJgx8PLyglQqhba2NhwcHBAdHc1YDZCvEkOKomBtbV2tZ0FxWFtbM84c8ZHFNzAwQEREBFatWkUfK1euhFgsxo8//ki/pikODg70MFKgMiNMURQtOc5EKl1Bw4YNax2aOnnyZEbZxPT0dEgkEvj7+8PIyAgZGRkwNDTEgAEDMHjwYOjq6jIq8+Frvo+TkxOys7MBVJZTiUQilXK1rKwsRpmNqKioGsvxFM9BNs8NrhUi+SYyMhJ+fn4qn9np06fRqFEjdOjQgdO/xTZjW5VLly4hOzsbO3bsUDk0xdzcXKWKoaKiAkOGDIG9vT1u3LjBei/x7t07TJs2DdbW1hCLxfRzacKECXQ5pyZ8//33sLa2xqhRo+Dp6Ym4uDjY29tj7dq1WL9+PVxdXfHtt98yWuujR4/QpEkTiEQiSCQSiEQi+Pv703uK0aNHM7ILcF85M3nyZJUB1rUdbBGcI4558eIFevfuTc/BoCgKEokEX3/9NaOad75m5fA1P4BP+K4d379/P/T09ODt7Q2JRAJfX1+YmJjA2NiYUV+Frq6uyma9vLwcWlpadMPy3r17YW1tzXi9ypw+fRrz5s3D06dPObGngIvhg8qcPXsWvXr1gpeXF/z9/dGvXz/GUsh1IdtsYmKi8jcUbN++HSYmJqxscwlfJYZ8Cc1UHcBZ9W+MHj0abdq00dhuQUEBAgICEBsbi5cvX9Kvsy05/e677+Dt7Y3s7GwcPHgQLVu2RFhYGH1+9+7dcHFxYWRbKpWqdcAVXL16lZHj5eXlRQt+HDx4EFKpVKUsduXKlfD09NTYLl/zfSZMmAC5XI4BAwbAyckJiYmJsLe3R1paGtLT02FnZ8coSHX48GEkJSXVeP7gwYOMSmQVJCQkwMHBAQcPHsS7d+/w7t07HDhwAA4ODhg1ahRju3zx999/o127dqAoCtra2vSYkHbt2jEO5CqEHaoeFEXB2NiY/pkpN27cQMOGDasFi5luhA0NDdU6bfHx8fTcMTZ7ialTp8LZ2Rlr165V2QdkZmYiKChIY3t2dnb0jMgbN25AJBKpPJf27t3LuB+4R48e6NSpE54/f443b95g2LBh9Ay/AwcOQCaTMQqi8NWneeXKFezcuRMURWHVqlXYvn272oMtn6Vz1KVLl2qDPgEgOTkZX331FSvb3bt3R/369atNI3d3d2ckRMDXrBy+5gfwOceF79rxgIAAOsKoyEK8fPkSHTt2xOLFizW2x2e0mU/qevggU/hqAlbm+++/h0wmw5w5c3Ds2DEcO3YMs2fPRr169Rht1BQ8e/YMT548qfb6kydPVIY9fyh8zRn5r4Rm2GTxy8rKkJCQABcXF/r7x9Y5evnyJbp3704HvYKDg1Xe7z179qhtyv8QPDw8MGfOnBrPz5kzB+7u7hrb1dXVxe3bt+mftbS0VBqrb968ySizytd8n/Lycvz444+IjIxEUlISKioqsGHDBtjZ2UEmk6Fv376MZovxDZcKkb6+vvDz8/uggy35+fl05qXqUE1N4StjqyAyMhJRUVH4559/YGBggMuXL+PYsWNo0qQJo6qDgIAArFmzRu25+Ph4mJiYsNpLuLi40N8B5YqGK1euMAqqqfsuKwuKMf0uA5V7QWVbxcXF0NLSop9DioGtmsJ35Qzf89Y+S+eoXr16NTaGm5ubs7Ktp6dX4zRyJhfv1q1bkZGRUeP5p0+fMrrp1KtXj/5ClJSUQCQS0VOSgUpFIk3lQoHKEhFFhGPZsmXQ1dXF8OHDkZaWhhEjRsDAwAC//PKLxnYB1UnOyhw+fBgGBgYYP348qxuagYEB7TCamJjQ709eXh6jqAyf0WZl7t27h0mTJiE6OhqjRo2qVY77Q+Bz+OC7d++wadMmTJs2DdOmTcPmzZsZyfPWFeXl5UhOToa1tTUdrbS2tkZycjIrQQ2uZc35KjH8r4RmuODAgQOwt7fH2LFjoaWlxUnJ6evXr1UyUlzw66+/QiKRoEOHDpg/fz4yMzORmZmJ+fPno2PHjtDW1sbmzZs1tquIgAOV9wiKopCVlUWfP3z4MGxtbRmtubi4GKdPn6bfi9evX2P58uVITU2tNQv2X6BQh+QbLhQileWaExMTYWRkhKCgILq0u2nTpjAyMkJiYiLHq2cHXxlbBTKZjA72GBkZ0dfYgQMH4Ovrq7G9pKSkWu+zQ4cOZRX0kUql9DWgfC++dOkSo1Em7u7utLJbbm4utLW1VUZVZGZmon79+ozWKpfLVT6jV69eQSQS0cG7GzduMFJxrsvKGYC9ImlVPkvnqKYyBrZzXIDK9Kc6x+vcuXMfVf0xX/MD+JzjwnftuIWFBZ1q9/T0pGuZ8/LyGN3Q+Io26+rq0ootly5dgrGxMVxdXdGtWzd4eHhAT09P7QTpD8XIyAi7du2q9npWVhaMjIwY27148SKcnZ2hp6dHRz/19fXh6OjISipUmTdv3uD69esq8rdcocgEcwHXsuZ1UWLIJZGRkVizZg2dReWLx48fo3PnztWcR67gyrE/ceIEevToAXt7e7rUyd7eHj169MDJkycZ2YyPj0f9+vUxY8YMNGnSBH369IGHhweys7Oxe/du+Pj4sJpl96lAURRcXFzw448/4t69e//1cj6Y/v37q818T5o0SaWMURP4mCOlgI+MrQITExP62ens7EyrA3I1+oBrGjVqRAe1lZ2jqVOnolmzZhrbmzt3LqRSKcLDw2FqaooFCxbA0tISCQkJSExMhLGxMaZNm8ZorZ07d0bXrl1RXFyM0tJSjBgxQkUVMicnB5aWlhrbrevKGa772z5L5yggIEDtbKDJkyejUaNGrGwvWbIE4eHhePDgAf3agwcP8OWXXyI9PZ2VbS7ha34An3Nc+K4dj4qKop25UaNGwdXVFTNmzECjRo3QunVrxna5jjYryyBHRUWhQ4cO9EOtvLwcPXv2RGRkJGP7XA4fVCYoKAgdOnRQ6Yt6+vQpOnbsiKZNm2psb+XKlfTG8fXr1/jmm28gFovpxtLBgwfz4iRxAdey5nVRYghwKzQjkUhgbGyMIUOG1Fl0nynZ2dn051VeXk43W4tEItjY2GDmzJkfnaRucXExBg4cCG9vbwwaNAhv375FSkoKtLW1QVEUwsLCNP4MFZnkDzmYkJeXh5iYGDg5OUEqldI9oBMmTGAcmKAoCgMHDoS5uTkkEgkiIiKwbds2Tgb33r9/HxkZGcjKyqpWQldcXMxqBqGRkZHafsz8/HxGQSq+5khVhY+MbbNmzeh5g7169ULbtm1x/PhxxMbGokGDBqztc8327dthbGyMWbNmQU9PDykpKRgwYAC0tbWxd+9eRjbXrVuHYcOGYf369QAqA8LNmzeHv78/pkyZQg9k1pQbN27AxcUFEokEWlpaMDExoat/gMrnLJNMJV+VM3z3tyn4LJ2j3377DRKJBLGxsXQtbExMDCQSCaOBn1XrhA0MDKClpUVPI9fS0oKBgYHGdcJV1dJqO5jC9fyAT3mOy40bN+iHenFxMQYPHgwfHx906dKF09KhQ4cOsYqYKztHdnZ21Wqu//rrL1hZWTG2z9fwQalUWm3wMgBcuHCBUQTJyckJOTk5ACoFRBwdHbF161ZcuXIF27dvh5ubGyulHQWbNm1Ct27dEBgYyFndf1hYGIYNG1bt9bi4OEaRRb7hWmiGoihcunQJc+fOhY+PD0QiEf73v/8hNTWVE1GR4uJiHDlyBJmZmfj1119x5swZVs6Lu7s7/T1LSkqCTCbDzz//jOzsbMybNw8WFhZq+1j/K2pzJF6/fs14YG1VlVQu1aJ2794NXV1ddO3aFb1794aenh6GDRuGMWPGwNXVFS4uLipBR03W/OjRI5SVlWHz5s1o3749xGIxLCwskJCQwLjfJjc3FyYmJjAyMoKuri5cXV1V7m9se2AtLCzo4ZnKrFy5klH5P19zpNTBdcZ29+7d9NoLCgrg7u4OiqJQr149HDhwgJFNPhxxZY4ePYrw8HDI5XLo6uoiJCSEkQJwXQw+LSkpwd69e7Fz505G84fUwVflDN/9bQo+S+cIAH7//XcEBwdDT08PMpkMLVu2xOHDhxnZUq4Tft+hCWFhYSqHkZFRtZIkIyMjRkpqfHHv3j04OjqiRYsWGDlyJHR1ddGsWTMMHDgQLVq0gLa2tkrd++cI2/Sv8iA0BweHalHawsJCjZ2Nuhg+2LBhQ7UPsgMHDsDb21tjezo6OnSTqpubGy0DrODIkSOwt7dnttj/n/nz58PAwADDhg2DtrY2Bg8ejPDwcBgbG2PcuHGM7XIta843XAvNKDv4QOUU9kGDBsHY2Bi6urro1asXo01PeXk5Ro8eDT09PXqTrnDmHBwc8Ntvv2lsE1C91ry9vas91H///XdWG8usrCz0798fo0ePrnZvePr0qcb3eOUBiS1btsS///7LeG3KKCulbtu2DS4uLtV6E+vXr884yJiWlkb/vHfvXnh4eACo7G1r3bo1o8qAqtcaANy9exfTpk2Ds7MzRCIRI3W98PBw9OvXD+Xl5Xjx4gWGDh0KmUxGD1hl6xzNnDkTUqkU3377LTIyMpCRkYFhw4ZBT08PM2fO1Nje+5r6b926xXr0QV3y5MkTxgEPvhxxPqjLwacKuBppo7DFZeUM3/1tCj5b5+hTY86cOWpLkqKiojB79mzO/97Tp0+xevVqRr/LxxwXoPIBOXr0aLi4uCAgIKCasAMXc44U5OfnY//+/bRAAxNqUhlSlDIyzT5QFAUTExOYmppCS0urmmDH3r17NZ4HUlVaubZDExQb6efPnyMrKwsNGjTApk2bUFRUhKKiImzatAk+Pj6MHGaFfC4A2NjYVLu2Ll++zKhXTBl3d3e6jEG5dnzixIkq0vdMOHv2LKKjo1nLmteFwhXXQjPqNqxAZQRz5cqVaNasGaPv8pgxY+Dp6YmdO3di3759aNGiBZKTk3HlyhVMnDiR8fw2ZTEYCwsLegOsID8/n3G5MB/z24yMjGgni6IoenPFJQEBAWq/t1lZWYzK06VSKW7evEn/XFFRAS0tLdy/fx9A5bUml8s1tvu+DeX+/fsRHR2tsV1TU9NqWaeZM2fC1NQUubm5nDyPNm7ciODgYLpUKDg4uFZp/drga46UOsrKyrB3714sX74c+/fv56SEkUv4csRr4uHDhyqOqSZYWFjQQR2+vsuAarkwF7OZ+ITP/jYFgnP0iWBtbV1jSRKbEqqayMvL48zR4IrJkyfDwsICKSkpGD9+PIyNjTFo0CD6/MOHDxkpzCQlJdGym0+fPkXr1q1V5ii0bduWUeRVIpGgbdu2KpnDyZMnQyQSIS4ujlE2EYBKOnnVqlXVFPymTZvGqtSSS6qW31SdT6H8s6aMGzcOTZs2xb///ovExER06NCBjiSVlJSge/fu+PLLL1mtX1lgRC6X0z10+fn5jIfAck1dKFxxLTRTk3OkDJNyJysrK5XM2927d2FgYECXiE6bNo1Rf1tcXBwiIyPx7t07DBo0CAMGDFCJWn/77beM7AL8zG/r0qULLCwsEBYWBoqiEBISgpYtW6o9mCKVSmvsTWRSJuvi4oLdu3fTPxcUFEAsFtO9PIWFhYwc0A+51phgamqqtrcqJSUFJiYm2Lp160f1DOVrjhQADBs2jB7kXFRUBA8PD7p0USwWw8fHB3fv3mVkOy8vD9OnT8eiRYuqlXw9f/6ckTgFX474ixcv8PXXX8Pe3h6xsbF4+/Yt4uLi6GdcixYtNC7Zq6vBp1zPZgIqFYtjY2NpZb3MzEx4eHjAycnpo+1vUyA4R0pw4RC8e/cOKSkpCAgIgIWFRbWmMaYYGBjg0KFD1V5XzPnRFOWIvrrj2LFjH9WNHQBcXV3pGzBQ+fB0dXVF3759UVFRwThSZ2trS0eCBwwYAD8/P/z11194/fo18vLyEBQURPdRacLx48fh4uKCSZMmqTRLch3h+Jg5fPjwBx+a8vbtW3Ts2BGmpqb44osv6Lrx+vXrQ19fH/b29qzndzg5OdHXhr+/Py2qsmfPHk6aPrmGD4UrgHuhmbCwMM5KvZQxNDSsJh8rkUjodV+6dIlRpuvZs2do3LgxXF1dERMTA6lUCgcHB3zxxRdwcnKCsbEx3f+mKXzMb3v16hXS0tLwww8/gKIoDBo0CCNGjFB7MMXPzw8xMTEqQgRv375FTEwMoyzl1KlTYWtri7S0NKxYsQLe3t4qZbxbt25lpHR6+PBhXsYFNG/eXCX7oExycjJ0dHRYP0Nv376NnJwc5Obmqp2Lpgl8zpGysLCgFUe7d++O8PBw2pF58uQJIiMjGc2Q3LNnD7S1tdGgQQPY29tDJpPR1QIA82oRvhzxYcOGwcPDAwsWLEBYWBiioqLg7e2N48eP48iRI/Dy8mJUjl0Xg0+5ns00d+5c6Ovro0uXLrCyssKMGTNoAZCpU6fCyMgIS5YsYbVmgD9FUsE5UoKLgYYTJ06ElZUVZs+eDalUiunTp6N///6QyWQq0UFNiYmJgaOjI7Zs2UKXJG3evBlOTk70NGNNeF8kgmk039vbG9OmTcOdO3c0/t33oaurqxLtASojw25ubvj6669x7949RmvW0dGhswOOjo7VVL9Onz7NODv37Nkz9OzZE4GBgXSJHpfOUVlZGfbt24f09HS6yfrevXuMa3y57n2oytGjR/H1118jKCiIjiSuWbNGbcnWh5KdnY24uDi0bdsWX375Jfr06YOlS5dyMjCyf//+dHZv4cKF0NXVRXh4OExMTBjJIPNdGsqlwhVfQjN8EhwcjBkzZtA/b9iwQeXBfuHCBcZObWlpKdLS0tC+fXt4eHjAzc0NoaGhGDduHIqKihivme/5bXw5oqdOnYK5uTnkcjndmyiXy2Fubo5Tp05pbE9RKmNtbQ2ZTIbo6GiVTMGpU6dqVWSsa5YtW4bevXvXeH7WrFmMy9QWLVoEe3v7as/lkJCQj1LZUSqV0g6+ra1ttc//woULjJROmzZtSjsTFRUVSE5OhoGBAV0eyPR+yZcjbmdnRztvitliygHd33//ndFAVQV8Dj7lejaTh4cH1q1bB6BSJEoikaiU5y1fvhz+/v4crJwfPivnqGrTedWjVatWrCM9zs7O+P333wGoDhWdP38+evXqxdhuSUkJhg4dSkejRCIRtLW1MXToUEabQCMjIyQnJ9cYxV+2bBmj94KiKMhkMojFYrRp04bTIZ9OTk5qJ6/fu3cPbm5u+OKLLxit2c3Njf7MnJyccOLECZXzZ8+eZTXfBwBWrFgBS0tLLFmyhLP0761bt+i5Rso1wsOHD8fgwYM1tsdH74Mymzdvhq6uLgYMGAAdHR16vampqYwGn9YF5eXlKtfvhg0b8O2332LBggXVpHs/BL5KQxVwqXDFl9AMn+zfvx86Ojpo0qQJWrRoAYlEgrlz59LnU1JS0KpVq/9ugWrge35bVQoLCzm7JxcXF2PJkiV0CSdXQQmuefv2LTZu3IgRI0agZ8+e6NmzJ0aMGIFff/2V0feYT1JSUmBtbY3U1FQsW7YMnp6emDZtGrKzsxETEwM9PT1WvbvPnj1Tm4V68uQJY5W2hg0b0kNKPT09VaSgAeDkyZOMypCNjIyq9f2uW7cO+vr62LlzJ+NnEl+OuI6OjkpgWE9PT6V64WMWveB6NlNVARAdHR2V1pCCggJGGSmgskLrxo0bdEXOmzdvsHHjRmzYsIEeMsuWz8o5kkgkaNeuXY2N5h07dmT9ENLT06MvCEtLS3pW0I0bN1hvsIHKh5FCGYjNQygsLAzJyck1nmeaRaMoCvfu3cO2bdvQoUMHembSqFGjWA/o6t+/f43R+rt378LV1ZXR55eSkgJPT08UFBRgzpw5aNq0KX1DLiwsRFhYGKOSgKrk5+cjICCAljFmS1RUFHr37o23b9+q3MwOHTrESDmLj96HqvYVIh/K6/3rr79gYWHB2O6nBF+loQq4VrjiCwMDA3zzzTfVAhFckJeXh3HjxmHUqFGMZ4rUBtfZWr7nt1WF62GJfLNy5UpGcvEKCgoK4OzsDKlUitDQUHTv3h3du3dHaGgopFIpXF1dUVBQwOGK2eHo6KgyhPvatWuQyWS0Qzt8+HB88cUXjO23bdsWixYtqvZ6Wloa4yDVypUrYWtri0OHDtGDZffv34979+7h4MGD8PHxwYABAzS2K5fL1WbKNmzYAD09PaSlpX1U5f/W1tYq8yF79eql0u928eJFRplrPu+XCriezSSTyVTuM7a2tiojUQoKChi1hJw7dw5WVlYQiUTw9vbGnTt34O3tDX19fRgYGNCCKGz5rJwjHx+fWlU3zp49y/qL5ubmRteeh4SE0BuSzMxMRg1+fLF06dJay/wePnzIKCJctfn1/v37SEpKQv369SESidC0adNqpUQfyq1bt1TqhKty7949xvr23377LbS0tODh4QGpVEpn5kQiERo3bsyZrGd5eTmePXvGycBIMzMzusZW2dm4efMmo3ppPnoflFEui1Re740bN6Cjo8PYLl99fgqePn2KlJQUfPPNN/jmm28we/ZsxvX/fJWGKsOlwhVfUBSFBg0agKIoeHh4YPbs2bypMHEJ19laPqmpQkIkEiE8PJz+mSkHDhzA1KlTMWTIEMTFxWHOnDm8OBlsnbnw8HBERUWpzYo8f/4cUVFRrIVbuJyFpqenV00sQCKR0GIBeXl5jDaVCkxNTdW+n1euXGElMjNnzhzo6elBV1eXfnYqjk6dOjEKHnzxxRdISUlRe279+vXQ0tL6qJyjtm3b1tqDuXLlSgQHB2tst67ul1zNZgIq97+KbKI6du7cyWiER5s2bfDVV1/hwoUL+O677+Dp6Ylu3bqhtLQUZWVl6N27N8LDwxmtWZnPyjnq27cv4uLiajx/+fJl1lKWY8aMwY8//gig0iGSSCRwdXWFtrY2xowZw8r2n3/+idGjR6NHjx7VHngfC7XJph46dAi9e/dmLa/MF5cvX8ZPP/2EIUOGYNCgQZg8eTL27t3L2pHJzc1V26ydk5PDqjzCxMSEzkApOxvHjh1jNCSQ794HJycnutxCeb2rV6+Gp6cnY7t89fkBlbOSjI2NYWdnR3/X7O3tYWRkxKjsgq/SUD5QyMV/yKEpiiBKXl4ehg0bBjMzM2hra6NLly7YtWsXJ8EDoHKswNKlSzFhwgQsW7aMVQYC4D5b+z5hHOVDUyiKQmhoaLUKCcVmlYk0PwA8evQITZo0gUgkgkQigUgkgr+/PywtLSEWixkPXq7p2qIoCsbGxoyvNV1dXVosQB3nz59nLMMOcD8LzdfXF0uXLqV/PnDgAPT09OjvxNWrV2FoaMh4vXp6emqVJ9m+D0Dl9+3XX3/FrFmzkJSUhJUrVzIaT6Bg69attYqGrFu3DmFhYRrZ5HP0wZMnT2rt79u1a5daYa33UVf3Sy45fvw4zp49W+P5RYsWITU1VWO7ys79q1evIBaLVXrcLl68CJlMprHdqnxWztGbN294a2ariZMnT2LOnDmMhw8q2LBhA7S0tBAZGQltbW1ERkbCzc0NxsbGnJZcsOVDZFPZTp8uKipSG4UqLS39qBp2FQQEBGDTpk3VXt+yZQuaNGnC2G737t0xcOBAAJXORmFhIV6+fIlWrVoxuib47n1ISkqCl5cXcnJyYGhoiGPHjmHt2rWQy+VYsGABY7t89fkBlQIjAwcOVJnToZBzZhL14qs0lA+U5eLnzJkDU1NT9OzZE/Pnz8f8+fPRs2dPmJqa4ueff9bYdtX7xJs3b7B+/Xq0bt0aIpEItra2mDhxosZ2O3fuTH/XLl68iHr16kEulyMwMBAWFhawtLRklYXgOlv7oRK9TK6JDRs2wNbWlpbRVcBWEKZHjx7o1KkTnj9/jjdv3mDYsGG0KNCBAwcgk8kwb948je0aGPAz+d7KykqllLUqv/32G6txGFzPQtu4cSO0tLTQvXt3xMbGwsDAQEWGPz09nbFsPFBZUj9s2LBqr8fFxTHqLfnUqIvRB1zD1/2yNkpLSzm1xxUmJia0w11aWgqxWKxSynjlyhVOKkY+K+foU8bHxwcLFy4E8H834IqKCgwcOJCxXvyrV69w7NgxtQ/K169fMxoC27dvX7oOn2vu37+PgIAAiEQiiMVixMTEqDhJXAzdu379OsaPH69SK7xr1y61M6Y+FH19fRV5YQWFhYWsyiOKiorg5eUFT09PSCQSBAUFQSaTwd3dndFcD757HyoqKjBjxgzo6+vT842kUqla6WlN4LPPTyqVqpUHvXr1KqNZLnyWhgL8lRh26dJFbZQvNTUVUVFRGturLcN88+ZNTJgwAXZ2dhrbNTU1xZUrVwAA7dq1Q3R0NN1wX1paiv79+7MqoeI6W6ssgrNq1SpYWloiMTERO3bswI4dO5CYmAgrKyvG18TNmzcREhKCLl260APE2TpHRkZGKvfD4uJiaGlp0UGvjIwMRopcBQUFCAjgfvL9xIkTaSf+3LlzePjwIR4+fIhz587h559/hpmZGSZPnszYPh+z0Hbt2oXo6Gh07dpVJYsEVEoXP378mPF6jx8/DqlUiubNm9NOQvPmzSGVSlVmhDGlahlySkoKaxlyoDIrtWzZMiQmJtL2zpw5w3h+EsDt6ANFL/iHHJrC1/0SqHTGlUVJUlNTaaVEmUyGqVOnMrJbdSYVV7Ru3Rr9+/fH3bt3MXXqVLi6uqp8VnFxcWjevDnrv/NZOkdZWVlqNyi7d+9WaYRkytWrVxEfH49WrVqhVatWiI+PZ62/rlyHbGZmRqfFL1++DEtLS43tXbt2DQ4ODirDyRQ1zQA3jgbXxMbGIjAwEH/++Sf27dsHf39/NG7cmH7os1X6Onz4MC3VrK2tTW98Zs6cia5duzK2a2ZmhpMnT1Z7/cSJE4zVWhSUlZUhIyMDo0ePxtChQ7Fs2TK8evWKlU2+efv2LS5duoRTp04xbmJXhs8+v+DgYGzbtq3a69u2bUNgYCAr23zAV4mhvr6+2n6SgoICRmWyH5JhZlIqoqurS2cOrays6BlVCq5duwZjY2ON7SrgOlurTKtWregMhDLr1q1DaGgoY7vl5eWYNGkS7OzssHv3btZqmXK5XOX3X716BZFIRG9Y2fQQKlTEXFy4nXw/a9YsWFlZVRtAbWVlVasw0Yfwqc1CAyr7q6Ojo+Hl5QV/f3/069ePVfmbgiNHjsDIyIizMmQF586dg1wuh6urKyQSCf1sHj9+PGJiYhjb5XL0geLa+pBssKbwdb8EVB2vFStWQCqVYtKkScjKyqKDmcuWLWNkt1WrVli3bh09hJsLcnNzIZPJIBKJIJfLcfHiRTRp0gSWlpawtraGrq6u2tJ1TfksnSMfHx9kZWVVez07OxsNGzZkZXvz5s10FF85TSuRSLB582bGdm1sbGiHyMfHh36Injx5klF0vFOnToiIiMA///yDgoICREREwMnJiY7As3WOlMuQgEppzD/++IPVl8Ta2lqltvTNmzfo0KEDfH198eTJE9ZrDgoKwpw5cwCoRoVPnToFGxsbxnZ79uyJ0NBQlX6Hf//9F6GhoejWrRtju3VBaWkp8vPzWfdq8AnXfX7KEb7MzEzY29sjJSUFx44dw7Fjx5CSkgJHR8dam001xcnJiZPNCV8lhvb29pg9e3a112fPng17e3uN7fE1ryMwMJCOtPv5+VVzbPfu3csomKSA62ytMrq6umqvgWvXrrHuBQEqs1tOTk4QiUSsnI3OnTuja9euKC4uRmlpKUaMGKHSb5WTk8PqPQYqy/Ps7bmffF9YWIiTJ0/i5MmT1cRnmML1LLRPGa7LkBW0bt2a7mVTfjafOHECDg4OjO1yOfrg1q1b9LFt2za4uLggPT2dfpakp6ejfv36aoNt74PP+UbKjleTJk3w008/qZxfvHgxo/4riqLQtm1baGtrw9TUFMOGDau1B0kTiouLcfr0aTq4+vr1a/zyyy9ITU3lbBDsZ+kcSaXSaopRQGV6kq0GvbOzs9raz0mTJsHZ2Zmx3V69etEb92nTpkEul2PAgAFwcHBgJMhgbm6u0pRZUVGBIUOGwN7eHjdu3GDsaNy6dQv+/v4Qi8Vo27Ytnj9/jvDwcLqMytnZWUX3XxP09fWrbR7KysrQqVMnNGzYEOfPn2flHCmrtVXtJ2Cjpnb37l04OzvD2NgYYWFhCAsLg4mJCdzd3RkPy+VjbkdycjKddXr37h1GjRpFqw5JJBL069fvo61DVoZtn59yBLC2g8m1pujXqXqIxWKMHTuW/pkpfJUYKno/IiMjMX36dEyfPh2RkZGQSCRqNxf/Fb///jvMzMywcuVKrFy5Eo6Ojli+fDlOnDiBFStWwM7OjrFggIKysjKsXbuW82ytm5ub2rWNHj0abm5urO0DwMuXL5GXl8dqts+NGzfg4uICiUQCLS0tmJiYqMy1WblyJSf9Go8fP0bnztxPvucarmeh8TEomk/hD2W4LkNWoDzvSPnZfOvWLVbPZr5GHwQEBKgNwGdlZaFRo0aM7fIBRVG08l29evXoslAF169fZyQAonC6/vnnH8yePRteXl4QiURo1KgRFi9ezPpaA/5vqHzTpk05Gyqv4LN0jiwsLHDgwIFqr+/bt491GY6urq7a8pP8/HxW0b8nT57g3r17ACpvxjNnzkSHDh0wcuRIuqxMEwwNDdU2JsfHx8PW1hZHjx5ltPnr2rUrQkNDsXPnTnTv3h0hISEICwvD3bt3cf/+fbRp0wadOnXS2C5QmTFTl31TOEiKOlmm2NjY0HMElG/AW7duZeXYAv83LDEuLg6jRo3C6tWrGTsafM3tUE6vp6SkwNTUFCtWrMClS5ewdu1amJubsy5B+RRQjgC+79AUiqJga2sLR0dHlYOiKNjY2MDR0RFOTk6M185niWFOTg6io6NpJafo6Gi1KowfgrrZJVyxefNm2NraVnNwpVIpRowYUS2r/bGQlZUFqVQKb29v9O/fH/3794ePjw+kUqnajZYmcD2bqaSkBHv37sXOnTt56y3gAkX1xoccHwt8DIrmU/hDGb7KkOVyOV26qPxs3rt3L2xtbRnbBfgZfSCVStXury5fvszKSax678rJycGRI0dYBS0pisKaNWuwY8cO2NraVmsBuHjxIqPAmrpSwJMnT+Kbb76BoaEh9PT0WJVE8j1U/rN0jgYNGgQfHx+VycsFBQVo2LAh+vfvz8p2u3btqqkDAZW1nGxnKXBJQEAA1qxZo/ZcfHw8TExMGN0o5XI5nTp99uwZKIpS8eLPnDnDeOBnQkJCje9hWVkZ6yG+o0aNQrNmzfDgwQMYGhqioKAAx48fh7OzM6OZT3zB19wO5ZuZn58flixZonJ+7dq1aNCgAbNF8wwffX58MHjwYPj6+lZ7cHLRVwHwO0qASyiKgouLC3788Uc66MMl7969Q25uLjIzM7F+/XocOnSIF6EYrsohFRQVFWHcuHF0v8a4ceMYZ5cVfEqzmary+vVrVpkNRaZecRgZGUFPT4928PX19WFkZISWLVtqZJfP5ns+BkXzKfxRF2XI/fv3R6dOnVBaWkr3+t2+fRt+fn747rvvGNvlCz8/P8TExKhkDt++fYuYmBhGJWr3799HSEgIxGIxWrRogadPnyIiIoIO/Li5uan0jGtC1YqIGTNmqJxfvnw5ozXXJiJRXFyM5cuXM5r5pIDvofKfpXP07NkzBAUFQSKR0JFbiUSCli1b1qpRXxOKG8yOHTuQlpYGuVyO+Ph4Ok0bHx8Pc3NzpKWlcbJ+tg8MoFJWuTbveujQoYzEDQwNDenStPLyckgkEpU0bUFBAaMU7fPnz1FWVlbrv7WsrIxRNF/B27dvMWDAAEgkElAURQ+Y6927N6No85s3b1QiOtevX8e4cePQu3dvjB8/nnHNO19zO5TT6zKZrNrfKCwsZF12ygd89fkpU1FRgYMHD2Lp0qXYuXMnq0jd1q1bYWdnp6L+xpVzVJU//viDk1ECynBx/6EoCgMHDoS5uTkkEgkiIiKwbdu2jzarw3c5JJ9wPZtJmf379yMiIgLOzs5wdnZGRESESokdE0pKShAfHw+5XM5pZmPOnDno0KGDSqXF06dPERUVpbafrjb4bL7ne1A018IffJYhK3j27BndxyUWi2FnZwctLS20aNECxcXFjO0C/6eCN3bsWM5U8E6dOgVzc3PI5XK0bt0arVu3hlwuh7m5uUrf9IcSExOD4OBg/Pbbb+jRoweCg4PRvHlz3L17F7dv30ZISAgj2fgPYefOnbUqrNbEh4hIsIGvofIKPkvnCKjc7OzZswc//fQTUlNTWSmpvO+mwMXNga8HBtcEBQXR0pgrVqyAhYWFSv35tGnT4O/vr7Fd5SgEUyf2Q7lz5w6ysrKwceNGVlHh0NBQeubK8ePHoaOjg4YNG6JHjx7w8/ODnp6eWhW798HX3A6KovDjjz9i/vz5sLKyqvadOHfu3EepvsRHn1+7du1oEYonT54gMDAQFEXR3z8PDw9WE8rv3r2LVq1aoW3btnjw4AFvzhFXcH3/UTw4y8rKsHnzZrRv3x5isRgWFhZISEhg3JfIV0CC73LIiooKFBYW0r0rb9++RWZmJlavXs26bI3r2UwKFi1aBIlEojL7qlevXtDS0qLHTjAhLi4Onp6edNnMihUrMH36dNja2mLt2rWM7VpbW6sdyXDhwgWN75d8Nt/zPSiaa+EPPsuQq3L8+HEsWrQIycnJrJ1wgD8VPOD/SukVAbulS5cyduSUB7Q/efIEFEWpXCMHDhxgXfrPNatWreJUpa4qfA2VV/DZOkefGnw9MLhm9+7dkEql0NbWhlQqxZEjR+Dm5oYmTZogKCgIYrGYUT2vkZERXYqknOGoC+7cuaPxzANAVSY0NDS0Wl37hAkTEBISorFdvuZ2ODg4qGz85s6dq3J+3rx5CAoK0tgu3/DR56cc9Ro6dCi8vLzojXVRURH8/f0xZMgQ5otG5YY4KSkJlpaWEIvFvDpHT58+ZTS3TAHX9x91UcW7d+9i2rRpcHZ2hkgkYjSrgq+ABJ/lkFevXoWDgwNEIhFcXV1RWFgIf39/6OvrQ09PD/Xq1WMVpOF6NpMCGxsbtbOvFi5cCGtra8Z27ezscOjQIQCgy5uBykZrNr0EBgYGtF1lDh48yGreHNfN93wPiq4L4Y9PBb5U8LhGKpWqlNdWHa1w+/ZtVoEOPoMzfMHXUHkFn5VzNGzYME6GnP0X8PXA4IObN29i8+bNdMrz4cOHmDhxIkaNGoWDBw8ystmlSxdYWFggLCwMFEUhJCQELVu2VHtwTV5eHqOHkb6+Pj2Q0sLCQq0KDNOHctW5HYrsJBdzO2rijz/+qDY35mOAjz4/5c27u7s7duzYoXJ+//79rDIFypw+fRrz5s1jJKzyoTC9hhVwff+prR4dqHx/o6OjNbbLV0AC4K8cMioqCh07dsT58+cxYsQIeHp6IioqCqWlpfS4gt69ezO2z9dspppmX+Xn5zOafaVsV6G4aGNjQ5chFRYWsrIbExMDR0dHbNmyBUVFRSgqKsLmzZvh5OSE2NhYxna5br7ne1B0TcIfOjo6rIU/AO77P3fu3ImJEyfSc68OHDiAdu3aoU2bNtX6YjWFLxW8mmAapLK3t1cpxxszZozKYN28vDzUq1eP0Zr4Ds6sWLECv/76a7XXf/31V1bXMV9D5RVQAEA+E0QiEaEoiri4uJD+/fuTPn36EEtLS9Z2FyxY8MH/7/Dhwxn9DQMDA3L58mVib29PbG1tydatW0mTJk3IzZs3iY+PDykuLmZk91Ph9evXZPXq1eTGjRtkzpw5ZODAgURPT0/t/zt37lyNbP/222+1ni8sLCSjRo0i5eXlGtlt3bo1adu2LRk9ejQJCQkhgwcPJrGxsfT5LVu2kJEjR5Lbt29rZFeZmzdvkocPHxIAxMrKijg5OTG29Smh/Jndv3+fTJo0iXTv3p0EBQURQgjJyckhmzZtIlOnTiVDhgzR2L5IJCKPHj0icrmcWFhYkIMHD5IGDRrQ52/fvk3c3d3Jmzdv2P9jOODFixe1nj9//jwJDQ3V+BpWwPX9RyQSkYcPHxJzc3NG66ltnadPnyYeHh7E0tKS7Nmzh/zvf/+jz9+4cYP4+vqSly9fMrJ/7949EhsbS7S1tcnKlSuJnZ0dOXfuHPHy8mK8ZnNzc7J3717i6+tLSkpKiKGhITl69Chp1qwZIYSQkydPkl69ejG+T9y9e5e0adOGACAFBQWkcePGpKCggMhkMnLs2DHGn0F0dDTx8/Mjo0ePVnl99uzZ5PTp0yQzM5OR3YYNG5LU1FQSGhpKwsPDia+vL5k9ezZZsGAB+emnn8jdu3c/2Nb58+eJt7c3EYlE5NWrV+SHH34gK1asIGVlZYQQQiQSCenfvz9JSUkh+vr6jNbbqFEj4u3tTZYvX060tbUJIYSUlpaSAQMGkIsXL5K//vqLkV0+uXv3LklLSyNXrlwhhBDi6elJhgwZQuzs7FjZ3bJlC+nZsydp3Lgxadq0KSGk8l78559/kszMTNK1a1eN7C1ZsoQMGzaM/O9//yMFBQVk0aJFJC4ujvTo0YOIxWKyZs0aMnPmTPLdd98xWq+5uTnZs2cP8fPzI4aGhuTcuXPE2dmZ7Nu3j3zzzTekqKiIkd2aOHfuHGnUqJHG9+GoqCjSqlWrGv+dixYtIlu3biUHDhzQeE2dOnUiAMiMGTPIihUryJ49e4ibmxvZtGkTqaioIN26dSPGxsYkIyNDY9uEEOLm5kaWLFlCWrZsqfL6kSNHyKBBg8i1a9cY2VVQWlpKrl+/ToqLi4mXlxcxMDBgZY+GExfrE0FRp/ndd9+hXr160NLSQseOHbFz506Ul5cztlu1Dl3hySqkISmKgr6+PqtIs4+PDw4fPgygMhU8atQoAJWNwmwGlH6KhIWFcdpz9CENpUyi7idPnoSxsTEmT56M1NRU1KtXDxMmTMC6deswadIkmJiYsM7yLF++HA0aNIC2tja0tbXRoEEDRtOslSkqKlIr8VtaWsqqN49L+O7zoygK7du3R+fOnWFqalqtxysnJ4exIk5qaipiYmKwYcMGAJXZF09PT7i7u2Ps2LEqM1M0WW9tTeFsex65vv8cPnyY0b/zfbRq1YoeYhgcHFwtSrt582ZGQ2uV4bocUldXl86UAKrDe4HKsl62UWyuZjMpi1FMnz4dxsbGaN++PT37KiIiAiYmJpg+fTrjtf7888+0uMW+ffsglUqho6MDkUiEefPmaWRLOUPp5OSEx48fo7i4mO4NYtvMD9TcfC+Xyxk1378PtiWyQOV8mOjoaAQFBXE6H4br/k8vLy96qPPBgwchlUqxaNEi+vzKlStZ9ZdwrYL3vhlSx44d46VH/NSpU7WKNNWGssJwcXFxNYXhEydOsLpn6ujo1DhXlI2sOd98ds6R4kZZWlqKjRs3ok2bNhCLxbC2tsa4ceMYzYdRZt26dQgJCVFJI1+9ehXNmzdn1RvE5QODT/gYYKfM/fv3MWHCBLRs2RIeHh7w8vJCZGQkli9fzljpytraGtu3b6/x/NmzZxmv+eTJkwgKCqq2abexsWH9uU2cOBH6+vrVJFkNDAzUPqDex/379xEQEACRSASxWIyYmBgVJ4ntZ/cp0bdvX5Wjap/c6NGj0aZNG43tTp8+HYaGhujatSssLS0xa9YsyGQyzJgxA0lJSZDL5Zg0aZLGdo2MjJCcnKwi2at8LFu2jNVnx9f9Jykpqdo9AgB++eUXzJo1S2N7dRGQUHDmzBlOyiFdXFxUNiOLFy9WkR0/c+YMLC0tWf0NALh06RKys7NV1FWrlou+j6qBwJoOrkpOgcoSpy1btjCSxTYzM6NncfHZq8pl8/37YFsiy+d8GK77P6sGDrS0tFScgJs3b7JSUOVaBY/vIJUCLr7LCvgOztjZ2ald2/bt2z/qwP5n6xwpc/v2bUyePJmuu2SDs7Oz2r6M06dPw9HRkZVtZdg8MPiEjwF2Cv78808YGxvD398fzZo1ozfwPXr0gImJCYKDgxnNMunQoUOtzkReXh7jNSv4+++/kZOTg5MnT6qNojChXr16aiVZ169fD5lMprG92NhYBAYG4s8//8S+ffvg7++Pxo0b05s/Np8dX/zzzz9ITk5Gp06dEBQUhKCgIHTq1Ak//fQTr42kxcXFeP36tca/5+Ligi1btgCovK7EYrFK0GTr1q2M5JXDwsJq3fRzcQ0rw9X9x8HBgR68rExOTg7j+yXXAQm+B4kOHjy41mzvzJkz0b59e0a2gUpp24YNG9YoP/3/MgMHDoSOjg4cHR0hEolgb28PJycntQdTuHbw+c4+8Dkfhuv+T8VAeqCy14qiKJW+qMOHD7MeAgtUipNwoYLHd5BK3XdZ+fvMBL6DMwkJCXBwcMDBgwfx7t07vHv3DgcOHICDgwNdgfAxIjhHSlRUVGDv3r2s/oauri5yc3OrvX7q1CmNIyempqb0Bq9fv368DDHkGj4G2CkICQlRGcaakZFBT91++vQpfH19MXz4cI3tHj16FNnZ2TWeLy4upkuK2FBcXIwVK1Zg3LhxWLhwIR4/fszKnrGxcY2SrMbGxhrbs7a2VikDUTSD+/r64smTJx9d5ig3NxempqawsbFBnz59kJCQgISEBPTp0we2trYwMzPDn3/++V8vUwV1kVBleeFbt24xioQuXbq01o3/w4cPP6pBxgp0dHTUymtzMauCq4AEX4NEP5TCwkLGAx4BIDIyElFRUfjnn39gYGCAS5cu4dixY2jSpAkvAkVM1T0B4NWrVzh27JjaUsXXr18zKifLzs5GamoqKIrC9OnTMW/ePLUHU7h28PnOPnA9H4bPOY/x8fGoX78+ZsyYgSZNmqBPnz7w8PBAdnY2du/eDR8fnxqV/ZigpaWlVlzjQ+E7SFX1u3z58mXW32W+gzNv375F9+7d6dmRWlpaEIvF6NevH69S32z5bJyjvLw82Nrast6Qvo/IyEj4+fnhzJkz9GunT59Go0aN0KFDB41s6evr0zcukUhUp/LVTOFzgJ2uri79fgCVQ2a1tLTw8OFDAMDevXtZSchyjaenJ60oc+fOHTg6OsLY2BgBAQEwMzODubk547krQKX6orpo9ahRoxAXF6exPX19/WrOVllZGTp16oSGDRvi/PnzH5VzFBgYiEGDBqGioqLauYqKCgwaNIix9PiZM2dUPps1a9YgODgYtra2CAkJofuFNMXJyYl2xPPz8yESiVSUfLKysjjNMHMBHxtWBa6ursjIyKj2+po1axhH82vLJrK9h3I5SLQmlIMoqamprJ9ZMpmMzvAZGRnRJd8HDhyAr68v6/VWhWnZ17Vr1+Dg4EBv/lu0aKHiFLINzvTt25eXACPXDj7f2Qeu58Pw2f9ZXFyMgQMHwtvbG4MGDcLbt2+RkpICbW1tUBSFsLAwRoNGa8r8ikQixMbGMs4EL126tNZh0GyDVHX9XQbYB2cU5Ofn49dff8XOnTs5mXnFN5+Nc6SuMZMP/v77b7Rr1w4URdFN8hRFoV27dhp/icPDw+Hj44O+ffuCoij07NkT/fr1U3t8LPA5wM7BwYGW8wQqe2QoiqIbiz+2Bj/lTOXXX3+N4OBgerDoy5cvER4ejl69emlkU/lG/u2338LQ0BANGjSgJVm9vb1hZGSEYcOGabxeHx8fbN68udrrCgfJ3t7+o3KOpFIpLZWujitXrjC+Hho2bEhvIJYtWwZdXV0MHz4caWlpGDFiBAwMDNSW0ryPCRMmQC6XY8CAAXByckJiYiLs7e2RlpaG9PR02NnZMS7P4sMp4HvDmpycDJlMhhUrVtCDIn/55RfIZDIkJSVpbI/vbCKXg0QV8B1EMTExoX/f2dmZHqdw/fp1Rn0gVfscqh5z585ldE106tQJERER+Oeff1BQUICIiAg4OTnRmdaPLXOtgGsHn+/sA9/zYeqC169fs3J0KYqCr69vtawwRVEICAhAWFgYb5lgNnD9XVYH18GZmmCTYa4LPhvnqK4aMxXk5+fTDwum094fPnyIMWPG4KuvvqIdrE6dOqk9Phb4HGD33XffwdvbG9nZ2Th48CBatmyJsLAw+vzu3bvh4uLCyDYfKDtHzs7O1Uo2T5w4ATs7O41sVr2Z13QwubEnJCTUWBdeVlaGjh07flSbE0dHx1qzFqtXr2Y8yE9XV5eObvn5+dGKSQrWrVsHLy8vje2Wl5fjxx9/RGRkJJKSklBRUYENGzbAzs4OMpkMffv2ZdQIzJdTwPeGtaKiAgkJCZBKpXTZkJ6eHqZOncrIHp/ZRICfQaJ8BFGUadasGbZt2wYA6NWrF9q2bYvjx48jNjYWDRo0YLRePtQ9zc3Ncf78efrniooKDBkyBPb29rhx48ZH6xxx7eDzXSLLx3yY/2qGJNMN9syZM+Hk5IQDBw6ovM7F3DI+4fq7DPAfnKkJtsIifPPZOEcDBw6EtrY2r42Z9+/fx8SJEzlVUlPg6OjIe0kgF/A5wO7ly5fo3r07JBIJKIpCcHCwypd2z549aoeN/VcoO+HW1tbVpDZv3br1UWW6ysrK8Pz5c5XXKioq6I1mWVnZR5UOX7hwIXR0dDB8+HDs2LEDOTk5yMnJwY4dOzB8+HDo6uqqyL5qgkwmw+nTpwFUbtrUDfDlKlLHBXw5BXW1YX358iVyc3Nx4cIFVnXofGYTAX4GifIRRFFm9+7dtAhIQUEB3N3dQVEU6tWrV21z+CHwpe5paGiott8jPj6ebsz/GDdTXDv4dcXbt29x6dIlnDp1Su3oBk1QOMT169fHrFmz8ODBA45WWTtsNti5ublwc3PDqFGjUFpaCoAb54jPMmSuv8sAf8EZvjLMdcVn4xwB/DZm8qWkBlRO2l6xYgVdX3rlyhUMGTIE/fr1Y/yF4BuuFXyUef36NeubeV1AURR8fHzg5+cHAwODaiVrR44c+WilLPmYn8QHmZmZCAwMpB1miqIgkUgQGBhYTX5bE3r37o3+/fsDALp161YtqpqUlAQfHx9Wa+cSvpyCT23Dymc2EQBKSkowdOhQWsZcJBJBW1sbQ4cOZSzd/F8EUZ48eaLWkf4Q+FL3DAgIwJo1a9Sei4+Ph4mJyUd1rVWFKwcf4Ldvjg/4miHJ9wb75cuXiI2NRcOGDXHhwgVoaWmxco74LkNWB5vvMsBfcIavDHNd8Vk5Rwr4aMzkS0ktOzsb2traMDMzg1QqRXZ2NuRyOcLDw9GqVSuIxeKP0kHiQ6L3U2PKlCkqR9WM2g8//ICePXv+R6urGa7nJ9UFpaWluH//Pu7fv09HAdlw7949ODo6okWLFhg5ciR0dXXRrFkzDBw4EC1atIC2traKpOx/DV9Owae2YeUzm6gMl4NEP7UgCl/qnklJSbXO2Rk6dOhHN0qADz5FFU6+ZkjW1QZ7w4YNsLCwgEgkYuUcfYp9c3wFZ/icH1kXfJbOER/wpaTWtGlTjB8/HkDlF9jU1BTjxo2jzycmJuKLL75guXru4VOiV4BfuJ6f9Kny77//YsyYMfDy8oJUKoW2tjYcHBwQHR390W1O+HIKPsUNK1/ZRL74VIMoAvzAd98cH/A1Q7IuN9hFRUXYvn07q2DHp9g3x1dwpi7mR/KJ4BxxBF9KakZGRnTEpby8HBKJRGXI7IULF1gPbuMDPiR6BeoGrucnCdQNn5pTwDdcZxMFBOoCvvvm+ICvGZKf2gb7UytDBvgLztTV/Ei+kBABTujUqRMZMmQISUlJITo6OmT69OkkNDSU6OrqEkIIuXbtGrGxsWFkm6IoQgghIpGISKVSYmxsTJ8zNDQkz58/Z/8P4JiBAweSESNGkLKyMtKqVStCCCEHDhwgCQkJZNSoUf/x6gRqIyYmhqSlpZGff/5Z5fWlS5eSr7/++j9alcD76NGjB+nRowcpKysjjx8/JoQQUq9ePaKlpfUfr+y/QUtLi1hZWf3XyxAQ0AhLS0uSm5tLPDw81J7Pzc0lFhYWdbyq2nFwcCBisbjG8xRFkS+++EJju6NHjyYlJSU1nnd1dSWHDh3S2C5feHh4kNOnTxNPT0+V1xcuXEgIIaRjx47/xbJqZfLkybWeT0lJYWS3efPmtZ7X19cnoaGhjGzXBYJzxBEzZswgDx48IB06dCDl5eWkadOmZO3atfR5iqLIzJkzNbbr6OhICgoKiIuLCyGEkD/++IPY29vT5+/cufNRbgBGjx5Nnjx5QuLi4khpaSkhhBCpVErGjBlDxo4d+x+vTqAqI0eOpP+boiiyfPlysnfvXhIUFEQIIeTUqVPkzp07JDY29r9aosAHIjgFAgKfLj/88AMZNGgQOXPmDGndujXtCD169IgcOHCALFu2jMyePfs/XqUqN2/e5MXup7bB7ty5M9mwYQOJiYmpdm7hwoWkoqKCpKen/wcrE9AUCgD+60X8v8SbN2/Iu3fviIGBASf20tPTiZ2dHYmIiFB7fty4ceTvv/8my5cv5+TvcU1xcTG5cuUK0dXVJfXr1yc6Ojr/9ZIE1NCyZcsP+v8oiiIHDx7keTUCAgICny8bN24kc+fOJWfOnCHl5eWEEELEYjHx9/cnI0eOJN27d/+PVygg8P82gnMkICAgICAgIPCRIZTICgj8NwjOkYCAgICAgICAgICAACFE9F8vQEBAQEBAQEBAQEBA4GNAcI4EBAQEBAQEBAQEBASI4BwJCAgICAgICAgICAgQQgTnSEBAQECgDgkLCyMjRoz4r5fBORRFke3bt9d4/tatW4SiKJKXl1dnaxIQEBAQ0BzBORIQEBAQ+Cg5fPgwoSiKPHv2rE7/7pQpU4ivr69Gv/PgwQPSrl07fhYkICAgIFBnCENgBQQEBAQEWGJpaflfL0FAQEBAgAOEzJGAgICAAC+UlJSQ2NhYYmBgQKysrMicOXNUzmdkZJDGjRsTQ0NDYmlpSaKjo8nff/9NCKksQ1MMJzY1NSUURZG+ffsSQgjZvXs3adasGTExMSEymYxERkaSGzdu0HZLS0vJsGHDiJWVFZFKpcTBwYHMnDmTPv/s2TMyYMAAIpfLiZGREWnVqhU5d+4cIYSQVatWkalTp5Jz584RiqIIRVFk1apV7/23Vi2ry83NJX5+fkQqlZLGjRuTs2fPMnkLBQQEBATqGME5EhAQEBDghdGjR5MjR46QHTt2kL1795LDhw+Tv/76iz5fVlZGpk+fTs6dO0e2b99Obt26RTtAdnZ2ZMuWLYQQQq5du0YePHhA5s+fTwipdLpGjhxJTp8+TQ4cOEBEIhHp3LkzqaioIIQQsmDBAvLbb7+RX3/9lVy7do2sW7eOODo60n+3W7du5O+//ybZ2dnkzJkzpFGjRqR169bk6dOnpEePHmTUqFGkQYMG5MGDB+TBgwekR48eGv27i4uLSWRkJPHy8iJnzpwhU6ZMIT/88AOLd1JAQEBAoK4QyuoEBAQEBDinuLiY/PLLL2Tt2rWkdevWhBBCVq9eTWxtben/55tvvqH/29nZmSxYsIAEBASQ4uJiYmBgQMzMzAghhJibmxMTExP6/+3atavK31qxYgWRy+Xk8uXLxNvbm9y5c4fUr1+fNGvWjFAURRwcHOj/9/jx4yQ3N5f8/fffREdHhxBCyOzZs8n27dvJ5s2byaBBg4iBgQGRSCSMS+XWr19PKioqyC+//EKkUilp0KABuXv3Lhk6dCgjewICAgICdYeQORIQEBAQ4JwbN26Q0tJSEhgYSL9mZmZG3N3d6Z/PnDlDOnToQOzt7YmhoSEJDQ0lhBBy586dWm0XFBSQXr16EWdnZ2JkZERnhRS/17dvX5KXl0fc3d3J8OHDyd69e+nfPXfuHCkuLiYymYwYGBjQx82bN1VK89hw5coV0rBhQyKVSunXmjZtyoltAQEBAQF+ETJHAgICAgJ1TklJCWnTpg1p06YNWbduHZHL5eTOnTukTZs2pLS0tNbf7dChA3FwcCDLli0j1tbWpKKignh7e9O/16hRI3Lz5k2SnZ1N9u/fT7p3707Cw8PJ5s2bSXFxMbGysiKHDx+uZlc5OyUgICAg8HkiOEcCAgICApzj4uJCtLS0yKlTp4i9vT0hhJB///2X5Ofnk9DQUHL16lXy5MkTMmvWLGJnZ0cIIeT06dMqNrS1tQkhhJSXl9OvPXnyhFy7do0sW7aMNG/enBBSWSpXFSMjI9KjRw/So0cP8tVXX5G2bduSp0+fkkaNGpGHDx8SiUSi0odU9e8q/01N8fT0JBkZGeTNmzd09ignJ4exPQEBAQGBukMoqxMQEBAQ4BwDAwPSv39/Mnr0aHLw4EFy8eJF0rdvXyISVT527O3tiba2NklNTSWFhYXkt99+I9OnT1ex4eDgQCiKIr///jv5559/SHFxMTE1NSUymYwsXbqUXL9+nRw8eJCMHDlS5fd+/vlnsmHDBnL16lWSn59PNm3aRCwtLYmJiQkJDw8nTZs2JZ06dSJ79+4lt27dIidPniTjx4+nnTNHR0dy8+ZNkpeXRx4/fkzevn2r0b89OjqaUBRFBg4cSC5fvkx27dpFZs+ezeLdFBAQEBCoKwTnSEBAQECAF1JSUkjz5s1Jhw4dSHh4OGnWrBnx9/cnhBAil8vJqlWryKZNm4iXlxeZNWtWNQfCxsaGTJ06lSQmJhILCwsybNgwIhKJSGZmJjlz5gzx9vYm33//PUlJSVH5PUNDQ/LTTz+Rxo0bk4CAAHLr1i2ya9cuIhKJCEVRZNeuXaRFixakX79+xM3NjfTs2ZPcvn2bWFhYEEIqBR/atm1LWrZsSeRyOdmwYYNG/24DAwOyc+dOcuHCBeLn50fGjx9PkpOTWbyTAgICAgJ1BQUA//UiBAQEBAQEBAQEBAQE/muEzJGAgICAgICAgICAgAARnCMBAQEBAYFaWbdunYrst/LRoEGD/3p5AgICAgIcIpTVCQgICAgI1MLLly/Jo0eP1J7T0tJSGTIrICAgIPBpIzhHAgICAgICAgICAgICRCirExAQEBAQEBAQEBAQIIQIzpGAgICAgICAgICAgAAhRHCOBAQEBAQEBAQEBAQECCGCcyQgICAgICAgICAgIEAIEZwjAQEBAQEBAQEBAQEBQojgHAkICAgICAgICAgICBBCBOdIQEBAQEBAQEBAQECAECI4RwICAgICAgICAgICAoQQQv4/5SkrJGFnRh4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset_id_freq[dataset_id_freq > 50].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"dataset_id frequencies\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata.obs.plot.scatter(\n",
+    "    x=\"cell_type\",\n",
+    "    y=\"dataset_id\",\n",
+    "    s=1,\n",
+    "    figsize=(10, 4),\n",
+    ")\n",
+    "plt.xticks([])\n",
+    "plt.yticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2999499/1662577740.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  unique_dataset_id_freq = adata.obs.groupby(\"cell_type\")[\"dataset_id\"].nunique().sort_values(ascending=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# number of unique dataset_id for each cell_type\n",
+    "unique_dataset_id_freq = adata.obs.groupby(\"cell_type\")[\"dataset_id\"].nunique().sort_values(ascending=False)\n",
+    "unique_dataset_id_freq[unique_dataset_id_freq > 5].plot(\n",
+    "    kind=\"bar\",\n",
+    "    figsize=(10, 4),\n",
+    "    title=\"number of unique dataset_id for each cell_type\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cellarium",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellarium_gpt/training/development_stage_ontology.ipynb b/notebooks/cellarium_gpt/training/development_stage_ontology.ipynb
new file mode 100644
index 00000000..a1de1457
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/development_stage_ontology.ipynb
@@ -0,0 +1,854 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# development_stage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>development_stage_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2645739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>HsapDv:0000095</td>\n",
+       "      <td>1768880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>HsapDv:0000144</td>\n",
+       "      <td>1768121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HsapDv:0000123</td>\n",
+       "      <td>1411475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HsapDv:0000136</td>\n",
+       "      <td>1396533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>HsapDv:0000219</td>\n",
+       "      <td>2240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>HsapDv:0000223</td>\n",
+       "      <td>976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>HsapDv:0000221</td>\n",
+       "      <td>930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>HsapDv:0000220</td>\n",
+       "      <td>867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>HsapDv:0000192</td>\n",
+       "      <td>509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>174 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    development_stage_ontology_term_id  num_cells\n",
+       "0                              unknown    2645739\n",
+       "1                       HsapDv:0000095    1768880\n",
+       "2                       HsapDv:0000144    1768121\n",
+       "3                       HsapDv:0000123    1411475\n",
+       "4                       HsapDv:0000136    1396533\n",
+       "..                                 ...        ...\n",
+       "169                     HsapDv:0000219       2240\n",
+       "170                     HsapDv:0000223        976\n",
+       "171                     HsapDv:0000221        930\n",
+       "172                     HsapDv:0000220        867\n",
+       "173                     HsapDv:0000192        509\n",
+       "\n",
+       "[174 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"development_stage_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>development_stage</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2645739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>80 year-old and over human stage</td>\n",
+       "      <td>1768880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50-year-old human stage</td>\n",
+       "      <td>1768121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29-year-old human stage</td>\n",
+       "      <td>1411475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>42-year-old human stage</td>\n",
+       "      <td>1396533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>93-year-old human stage</td>\n",
+       "      <td>2240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>97-year-old human stage</td>\n",
+       "      <td>976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>95-year-old human stage</td>\n",
+       "      <td>930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>94-year-old human stage</td>\n",
+       "      <td>867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>19-month-old human stage</td>\n",
+       "      <td>509</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>174 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    development_stage  num_cells\n",
+       "0                             unknown    2645739\n",
+       "1    80 year-old and over human stage    1768880\n",
+       "2             50-year-old human stage    1768121\n",
+       "3             29-year-old human stage    1411475\n",
+       "4             42-year-old human stage    1396533\n",
+       "..                                ...        ...\n",
+       "169           93-year-old human stage       2240\n",
+       "170           97-year-old human stage        976\n",
+       "171           95-year-old human stage        930\n",
+       "172           94-year-old human stage        867\n",
+       "173          19-month-old human stage        509\n",
+       "\n",
+       "[174 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"development_stage\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `development_stage`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'HsapDv', 'unknown'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"http://purl.obolibrary.org/obo/hsapdv.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"HsapDv_\"\n",
+    "\n",
+    "# the 'life cycle stage' node\n",
+    "LIFE_CYCLE_STAGE_NODE = \"HsapDv:0000000\"\n",
+    "\n",
+    "# the 'life cycle' node\n",
+    "LIFE_CYCLE_NODE = \"HsapDv:0000001\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['infant stage',\n",
+       " 'adult stage',\n",
+       " 'young adult stage',\n",
+       " 'middle aged stage',\n",
+       " '1-month-old stage']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these are the labels that are not unique\n",
+    "non_unique_labels = []\n",
+    "for i in range(len(labels) - 1):\n",
+    "    if labels[i] in labels[i + 1 :]:\n",
+    "        non_unique_labels.append(labels[i])\n",
+    "non_unique_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extract_names_to_labels_map = dict(zip(names_counts[query_name], labels_counts[query_label]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `development_stage`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'unknown'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [1, 1, 0, ..., 1, 0, 0],\n",
+       "       [1, 1, 0, ..., 1, 1, 0],\n",
+       "       [1, 1, 0, ..., 1, 1, 1]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [ 1.,  0., inf, ..., inf, inf, inf],\n",
+       "       [ 1.,  2.,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [ 1.,  2., inf, ...,  0., inf, inf],\n",
+       "       [ 1.,  3., inf, ...,  1.,  0., inf],\n",
+       "       [ 1.,  4., inf, ...,  2.,  1.,  0.]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "110"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.index(\"15-year-old stage\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'15-year-old stage'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels[110]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['life cycle stage',\n",
+       " 'life cycle',\n",
+       " '15-year-old stage',\n",
+       " '15-19 year-old',\n",
+       " 'prime adult stage',\n",
+       " 'adult stage',\n",
+       " 'young adult stage',\n",
+       " 'postnatal stage']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[labels[idx] for idx in ancestors_matrix[110].nonzero()[0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "173"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"unknown\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "191"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {LIFE_CYCLE_NODE, LIFE_CYCLE_STAGE_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"development_stage_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='development_stage'>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellarium_gpt/training/disease_ontology.ipynb b/notebooks/cellarium_gpt/training/disease_ontology.ipynb
new file mode 100644
index 00000000..6761dbd4
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/disease_ontology.ipynb
@@ -0,0 +1,868 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# disease"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>disease_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PATO:0000461</td>\n",
+       "      <td>25905102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MONDO:0100096</td>\n",
+       "      <td>4109684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MONDO:0001627</td>\n",
+       "      <td>1135641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MONDO:0018177</td>\n",
+       "      <td>1001237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MONDO:0007915</td>\n",
+       "      <td>708987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>MONDO:0012419</td>\n",
+       "      <td>1614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>MONDO:0005348</td>\n",
+       "      <td>1227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>MONDO:0004994</td>\n",
+       "      <td>1027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>MONDO:0019562</td>\n",
+       "      <td>1018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>MONDO:0002513</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   disease_ontology_term_id  num_cells\n",
+       "0              PATO:0000461   25905102\n",
+       "1             MONDO:0100096    4109684\n",
+       "2             MONDO:0001627    1135641\n",
+       "3             MONDO:0018177    1001237\n",
+       "4             MONDO:0007915     708987\n",
+       "..                      ...        ...\n",
+       "93            MONDO:0012419       1614\n",
+       "94            MONDO:0005348       1227\n",
+       "95            MONDO:0004994       1027\n",
+       "96            MONDO:0019562       1018\n",
+       "97            MONDO:0002513         57\n",
+       "\n",
+       "[98 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"disease_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>disease</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>normal</td>\n",
+       "      <td>25905102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>COVID-19</td>\n",
+       "      <td>4109684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>dementia</td>\n",
+       "      <td>1135641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>glioblastoma</td>\n",
+       "      <td>1001237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>systemic lupus erythematosus</td>\n",
+       "      <td>708987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>age related macular degeneration 7</td>\n",
+       "      <td>1614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>keloid</td>\n",
+       "      <td>1227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>cardiomyopathy</td>\n",
+       "      <td>1027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>localized scleroderma</td>\n",
+       "      <td>1018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>kidney benign neoplasm</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               disease  num_cells\n",
+       "0                               normal   25905102\n",
+       "1                             COVID-19    4109684\n",
+       "2                             dementia    1135641\n",
+       "3                         glioblastoma    1001237\n",
+       "4         systemic lupus erythematosus     708987\n",
+       "..                                 ...        ...\n",
+       "93  age related macular degeneration 7       1614\n",
+       "94                              keloid       1227\n",
+       "95                      cardiomyopathy       1027\n",
+       "96               localized scleroderma       1018\n",
+       "97              kidney benign neoplasm         57\n",
+       "\n",
+       "[98 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"disease\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `disease`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MONDO', 'PATO'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/monarch-initiative/mondo/releases/download/v2024-01-03/mondo.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"MONDO_\"\n",
+    "\n",
+    "# # the 'life cycle stage' node\n",
+    "# LIFE_CYCLE_STAGE_NODE = \"HsapDv_0000000\"\n",
+    "\n",
+    "# # the 'life cycle' node\n",
+    "# LIFE_CYCLE_NODE = \"HsapDv_0000001\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['obsolete coronary heart disease',\n",
+       " 'obsolete deafness, autosomal recessive',\n",
+       " 'obsolete Wilms tumor',\n",
+       " 'obsolete emphysema',\n",
+       " 'obsolete gastric cancer',\n",
+       " 'obsolete microcephalic primordial dwarfism',\n",
+       " 'obsolete familial adenomatous polyposis',\n",
+       " 'obsolete Heimler syndrome',\n",
+       " 'obsolete epilepsy, familial focal, with variable foci',\n",
+       " 'obsolete Frontometaphyseal dysplasia',\n",
+       " 'obsolete JMP syndrome',\n",
+       " 'obsolete Walker-Warburg syndrome',\n",
+       " 'obsolete marginal zone B-cell lymphoma',\n",
+       " 'obsolete splenic marginal zone lymphoma',\n",
+       " 'obsolete small cell neuroendocrine carcinoma',\n",
+       " 'obsolete primary bone dysplasia with disorganized development of skeletal components',\n",
+       " 'obsolete nut midline carcinoma',\n",
+       " 'obsolete craniodiaphyseal dysplasia',\n",
+       " 'obsolete herpes simplex virus keratitis',\n",
+       " 'obsolete meningococcal meningitis',\n",
+       " 'obsolete chronic neutrophilic leukemia',\n",
+       " 'obsolete Friedreich ataxia',\n",
+       " 'obsolete ovarian cancer',\n",
+       " 'obsolete thymoma type AB',\n",
+       " 'obsolete thymic carcinoma',\n",
+       " 'obsolete craniopharyngioma',\n",
+       " 'obsolete juvenile xanthogranuloma',\n",
+       " 'obsolete ampulla of vater carcinoma',\n",
+       " 'obsolete fibrolamellar carcinoma',\n",
+       " 'obsolete syringocystadenoma papilliferum',\n",
+       " 'obsolete cerebellar liponeurocytoma',\n",
+       " 'obsolete hepatoblastoma',\n",
+       " 'obsolete mitochondrial myopathy',\n",
+       " 'obsolete acute megakaryoblastic leukemia',\n",
+       " 'obsolete Wiskott-Aldrich syndrome',\n",
+       " 'obsolete adrenocortical carcinoma',\n",
+       " 'obsolete lactose intolerance',\n",
+       " 'obsolete carcinoid syndrome',\n",
+       " 'obsolete pigmented villonodular synovitis',\n",
+       " 'obsolete apnea, central sleep',\n",
+       " 'obsolete primary bone dysplasia with multiple joint dislocations',\n",
+       " 'obsolete primary bone dysplasia with increased bone density',\n",
+       " 'obsolete vibratory angioedema',\n",
+       " 'obsolete anus, imperforate',\n",
+       " 'obsolete SC phocomelia syndrome',\n",
+       " 'obsolete testicular regression syndrome',\n",
+       " 'obsolete Amish infantile epilepsy syndrome',\n",
+       " 'obsolete microcephaly, short stature, and impaired glucose metabolism',\n",
+       " 'obsolete Pierre Robin syndrome associated with collagen disease',\n",
+       " 'obsolete Pierre Robin syndrome associated with a chromosomal anomaly',\n",
+       " 'obsolete Pierre Robin syndrome associated with branchial archs anomalies',\n",
+       " 'obsolete Pierre Robin syndrome associated with bone disease',\n",
+       " 'obsolete autosomal ichthyosis syndrome with other associated signs',\n",
+       " 'obsolete rare bone disease related to a common gene or pathway defect',\n",
+       " 'obsolete rare disorder with female infertility due to a congenital hypogonadotropic hypogonadism',\n",
+       " 'obsolete type 11 collagen-related bone disorder',\n",
+       " 'obsolete dysostosis with predominant craniofacial involvement',\n",
+       " 'obsolete syndrome with synostosis or other joint formation defect',\n",
+       " 'obsolete deafness hyperuricemia neurologic ataxia',\n",
+       " 'obsolete overgrowth or tall stature syndrome with skeletal involvement',\n",
+       " 'obsolete dysostosis with brachydactyly without extraskeletal manifestations',\n",
+       " 'obsolete dysostosis with brachydactyly with extraskeletal manifestations',\n",
+       " 'obsolete ectrodactyly with and without other manifestations']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# these are the labels that are not unique\n",
+    "non_unique_labels = []\n",
+    "for i in range(len(labels) - 1):\n",
+    "    if labels[i] in labels[i + 1 :]:\n",
+    "        non_unique_labels.append(labels[i])\n",
+    "non_unique_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extract_names_to_labels_map = dict(zip(names_counts[query_name], labels_counts[query_label]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx + 1 for idx, name in enumerate(names)}\n",
+    "names_to_idx_map[\"PATO:0000461\"] = 0  # Add the normal cell type\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx + 1: name for idx, name in enumerate(names)}\n",
+    "idx_to_names_map[0] = \"PATO:0000461\"  # Add the normal cell type\n",
+    "idx_to_labels_map = {idx + 1: label for idx, label in enumerate(labels)}\n",
+    "idx_to_labels_map[0] = \"normal\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `disease`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PATO:0000461'}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "graph.add_node(\"PATO:0000461\")  # Add the normal cell type\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    substitute = str(substitute)\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 1, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 1, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [inf,  0., inf, ..., inf, inf, inf],\n",
+       "       [inf, inf,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [inf, inf, inf, ...,  0., inf, inf],\n",
+       "       [inf, inf, inf, ..., inf,  0., inf],\n",
+       "       [inf, inf, inf, ..., inf, inf,  0.]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "98"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "350"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"disease_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "252"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(new_labels) - set(labels_counts[query_label].values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='disease'>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyJAQBAAAAAACyVNbcDWiJ1j/pnlrfn3neHl9QSwAAAAAAgJq4EgQAAAAAAMiSEAQAAAAAAMiSEAQAAAAAAMiSZ4I0A88UAQAAAACAtc+VIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJaEIAAAAAAAQJbKmrsBNNz6J91T6/szz9tjrdYDAAAAAEBL4EoQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS0IQAAAAAAAgS2XN3QBanvVPuqfG92aet8cX2BIAAAAAAKiZK0EAAAAAAIAsCUEAAAAAAIAsuR0WX6jabqUV4XZaAAAAAAA0HVeCAAAAAAAAWXIlCC2KK0kAAAAAAKgvV4IAAAAAAABZEoIAAAAAAABZcjss/q0Uezut5qx3KzAAAAAAgIZpVAhy2WWXxfnnnx9z586NIUOGxG9+85vYaqutauz/lltuiVNPPTVmzpwZgwYNiv/+7/+O3XffvdGNBhquJQdAa7tegAQAAAAAeWpwCHLzzTfHscceGxMmTIitt946xo8fH8OHD4/XX389evbsuVr/TzzxROy///4xbty42HPPPWPixImx9957x/PPPx+bbLJJk0wEwNr0ZQ5wvuz1LbntX/Z64R0AAABA3Rocglx00UVx+OGHxyGHHBIRERMmTIh77rknrr766jjppJNW6//iiy+OESNGxM9//vOIiDjrrLPiwQcfjEsvvTQmTJhQZPMBgDX5Mgc4X/b6ltz2L3t9S257feoBAAD48mlQCLJ06dKYOnVqnHzyyYXXSktLY9iwYfHkk0+usebJJ5+MY489ttprw4cPjzvvvLPG8VRVVUVVVVXh/wULFkRExMKFCwuvraxaUmtbP9vv5xVTq772+pbc9i97fUtu+5e9viW3/cte35Lb/mWvb8lt/7LXt+S2f9nrW3Lb61O/yen31/r+y2cOX2v1zTnull7fktv+Za9vyW3/ste35LZ/2etbctu/7PUtue1f9vqW3Pb61APUZNUxWkqp1v5KUl19fMa7774bffv2jSeeeCK23XbbwusnnHBCPPLII/H000+vVtOmTZv4/e9/H/vvv3/htcsvvzzOPPPMmDdv3hrHc8YZZ8SZZ55Z32YBAAAAAAD/hmbPnh3rrrtuje836sHoa9vJJ59c7eqRlStXxvvvvx/dunWLkpKS1fpfuHBh9OvXL2bPnh0VFRUNGlcxtep99j67f7/6ltz25q5vyW1v7vqW3PaWXt+S297c9S257S29viW3vbnrW3LbW3p9S257c9e35La39PqW3Pbmrm/JbW/u+pbc9pZe35Lb3tz1LbntLb2+Jbe9PvUppfjoo49inXXWqXU4DQpBunfvHq1atVrtCo558+ZF796911jTu3fvBvUfEVFeXh7l5eXVXuvcuXOd7auoqGjUh1lsrXqfvc/u36++Jbe9uetbctubu74lt72l17fktjd3fUtue0uvb8ltb+76ltz2ll7fktve3PUtue0tvb4lt72561ty25u7viW3vaXXt+S2N3d9S257S69vyW2vq76ysrLO+tKGjKxNmzax5ZZbxqRJkwqvrVy5MiZNmlTt9lifte2221brPyLiwQcfrLF/AAAAAACAptDg22Ede+yxcdBBB8XXv/712GqrrWL8+PGxePHiOOSQQyIiYvTo0dG3b98YN25cREQcffTRseOOO8aFF14Ye+yxR9x0003x3HPPxRVXXNG0UwIAAAAAAPAZDQ5Bvv/978d7770Xp512WsydOzeGDh0a9913X/Tq1SsiImbNmhWlpf93gck3v/nNmDhxYpxyyinxi1/8IgYNGhR33nlnbLLJJk02EeXl5XH66aevdguttV2r3mfvs/v3q2/JbW/u+pbc9uaub8ltb+n1LbntzV3fktve0utbctubu74lt72l17fktjd3fUtue0uvb8ltb+76ltz25q5vyW1v6fUtue3NXd+S297S61ty25uifpWSlFIqaggAAAAAAABfQg16JggAAAAAAEBLIQQBAAAAAACyJAQBAAAAAACyJAQBgCa2fPnyGDt2bLzzzjvN3RQaIKUUs2bNik8++aS5mwIAAC3eihUrYsqUKfHhhx82d1NanH/nY5Ply5fHddddF/PmzWt0veNxPk8I0sItXLgw7rzzznj11VcbVLdkyZJ47bXX4sUXX6zW1WX69OmNbWpERHz44Ydx1VVXxcknnxzvv/9+REQ8//zz8Y9//KPO2o8//jiWLFlS+P/tt9+O8ePHxwMPPNCgNjR22os1e/bsaivgZ555Jo455pi44oor1vq4I4r77FuyZcuWxaGHHhozZswoeliffPJJLFy4sFpXl/vuuy8ee+yxwv+XXXZZDB06NA444ID44IMPim7Tl9nDDz9c9DDeeuutOOWUU2L//feP+fPnR0TEvffeG6+88krRw67NsmXLoqysLF5++eVG1ZeVlcX5558fy5cvb3QbWvoy++ijj8Z//ud/xrbbblto8/XXX19teVibGjPvpJRi4MCBMXv27C+kjWty0EEHxZQpU4oezptvvhn3339/fPzxxxHx6bQ1VGPWeTlZsWJFTJs2Lft1dUTz76PkpLH75l8Wxcz3Df2RxnzXeMXuYxX72Rd7XOa759/R888/Hy+99FLh/7vuuiv23nvv+MUvfhFLly5t0LCaY1vT2OOyVq1axW677fal2Z9qSYFCUxybrL/++jF27NiYNWtWE7Zs7SsrK4uf/OQnjf6+muJ4fJX58+fHyy+//IX9hjh16tS1NuyW5JNPPolnnnkm7r777vjTn/5UrWussiZsX4sya9asmDNnTpSWlsaAAQOiW7duzd2kevmP//iP2GGHHeLII4+Mjz/+OL7+9a/HzJkzI6UUN910U3z3u9+ttf69996LQw45JO699941vr9ixYpa6wcOHBg77rhjjBkzJr73ve9F27Zt6932F198MYYNGxaVlZUxc+bMOPzww6Nr165x++23x6xZs+K6666rtX7UqFGx7777xk9+8pP48MMPY+utt47WrVvHP//5z7joooviv/7rv2qtL3baV6xYEb/+9a/jj3/8Y8yaNWu1HZVVP1LW5IADDogf/ehHceCBB8bcuXNj1113ja997Wtx4403xty5c+O0006rtf6NN96Ihx9+OObPnx8rV66s9l5dtcV+9hERzz33XI3Tfvvtt9dZf/3118eECRNixowZ8eSTT0b//v1j/PjxscEGG8SoUaPqrL/11ltrHP/zzz9fY13r1q3jtttui1NPPbXOcazJkiVL4oQTTog//vGP8a9//Wu19+uab37+85/Hf//3f0dExEsvvRTHHXdcHHvssfHwww/HscceG9dcc02dbVi8eHE88sgja5z2o446qs76hx9+OHbeeec6+6tJY7/7ESNGxLrrrhuHHHJIHHTQQdGvX78GjfeRRx6JkSNHxnbbbRdTpkyJc845J3r27BkvvPBC/O53v4tbb721zmEUM9+st956dX6/tfn2t78djzzySKy//voNrm2KZbax077KO++8E3/605/WWH/RRRfVWnvbbbfFgQceGD/84Q/jr3/9a1RVVUVExIIFC+Lcc8+NP//5z3WOv5h1TmPnndLS0hg0aFD861//ikGDBtXZxtr87W9/W2Pbv/Od79Rat2DBghg2bFj079+/sOz07du33uP917/+Fd///vfjoYceipKSknjjjTdiwIABMWbMmOjSpUtceOGFtdYXs84rdjvZFIrZ1hxzzDGx6aabxpgxY2LFihWx4447xhNPPBHt27ePu+++O3baaac6x1/strK5FLuPElHcOiOi+G1dROOXu9mzZ0dJSUmsu+66EfHpD7ITJ06MjTfeOH70ox/VWlvsvnmxbS9WsfP9ypUr45xzzokJEybEvHnz4u9//3sMGDAgTj311Fh//fVjzJgxNdYWO981xTqn2G1lcyl2H6vYz77Y47KmWOc0hWKWu2Jqi91WFFO/ePHiOO+882LSpElrPK6s7aTHZcuWxVe/+tW4++67Y/DgwXW2sybNtb5bkxUrVsRLL70U/fv3jy5dutTZ/+mnnx6HHnpo9O/fv8Hj+vGPfxwnnXRSbLrppjF9+vT4wQ9+EPvss0/ccsstsWTJkhg/fnyNtU2xrSlmH6nY47JNNtkkpk+fHhtssEGd7VyTefPmxfHHH1+Ybz9/ck9dx2zFbKsiit9WNHYfpymOTY455pi49tprY+zYsbHzzjvHmDFjYp999ony8vJ61T/00EPx2GOPVfv99Dvf+U6d7XnhhRdi6tSpsdNOO8WAAQPilVdeicsuuyxWrlwZ++yzTwwfPrzOcW+11VYxbdq0Ri1vEcUdj0d8GkYcdNBB8eqrrxbmuZKSkkgpRUlJSZ3z3dixY+P444+P9u3bV3v9448/jvPPP7/Gbd03vvGNGDBgQBx66KFx8MEHxzrrrNOo9hfroIMOijFjxsQOO+zQ6GE0dn1/3333xejRo+Of//znau/V57OvUfo3c9lll6X11lsvlZaWVuu222679Nxzz9V7OLfcckvab7/90tZbb50233zzal19LFq0KN1zzz3pt7/9bbr44ourdbXp1atXmjZtWkoppRtvvDENHDgwLV68OF1++eVp6NChdY73gAMOSNttt1169tlnU4cOHdIDDzyQrr/++rTRRhulu+++u876v/71r+moo45KPXr0SJWVlelHP/pRevrpp+s1zbvsskv6+c9/nlJKqWPHjumtt95KKaX0+OOPp/79+9dZ361bt/Tyyy+nlFK68sor02abbZZWrFiR/vjHP6avfvWrddYXO+2nnnpq6tOnT7rgggtS27Zt01lnnZXGjBmTunXrVuf3llJKnTt3Tq+99lpKKaWLL744ffOb30wppXT//fenDTbYoNbaK664IrVq1Sr16tUrDRkyJA0dOrTQ1WeeK/az/8Mf/pBat26d9txzz9SmTZu05557pq985SupsrIyHXzwwXXWX3755al79+7p7LPPTu3atSuM/5prrkk77bRTnfUXX3xx6tixYzryyCNTmzZt0o9//OM0bNiwVFlZmX7xi1/UWT969Oh00UUX1dnfmvz0pz9NgwcPTrfeemtq165duvrqq9NZZ52V1l133XTDDTfUWd+hQ4c0Y8aMlFJKp59+evrud7+bUkpp6tSpqVevXnXWP//886l3796poqIitWrVKvXo0SOVlJSkDh061DnfrNKmTZs0YMCAdNZZZ6VZs2bVq2aVYr779957L1100UVpyJAhqaysLO22227p5ptvTlVVVfUa9zbbbJMuvPDClFL1+fbpp59Offv2rbO+2PnmqquuSrvvvnv617/+Va/2ft5vf/vb1Lt373TccceliRMnprvuuqtaV5til9lip/0vf/lLat++fdpkk01SWVlZGjp0aOrcuXOqrKxMO++8c531Q4cOTb///e9Xa//zzz9fr/m+2HVOMfPOn/70p7T99tunl156qc7xrMlbb72VNttss1RSUpJKS0tTSUlJ4e/S0tJ6DWP+/PnpwgsvTJtttlkqKytLI0aMSLfccktaunRpnbUHHnhgGj58eJo9e3a1ab/vvvvSxhtvXGd9Meu8YreTxSp2W9O3b9/07LPPppRSuuOOO9I666yTXn/99XTKKacUttm1KXa+/azFixenV199Nb3wwgvVurrce++96dFHHy38f+mll6YhQ4ak/fffP73//vs11hWzj5JS8euMYrd1xS5322+/fbruuutSSinNmTMnVVRUpG233TZ17949nXnmmbXWFrtv3hTrjIceeqjG9yZMmFBrbbHz/ZlnnpkGDBiQbrjhhmrL3U033ZS22WabWmuLne+KXecUu61MKaUlS5akxYsXF/6fOXNm+vWvf53uv//+etXPnj07XXbZZenEE09M/+///b9qXW2K3ccq9rMv9ris2PEXq5jlrthltthtRbH1P/jBD1KfPn3SCSeckH7961+n8ePHV+vqss4666S//e1vdfa3Jk2xvtthhx3S73//+7RkyZJGteHoo49OV111VUoppeXLl6ftttuusL15+OGH66wfMmRIatWqVfr2t7+dbrzxxvTJJ5/Ue9wVFRXpzTffTCmldN5556XddtstpZTSY489ltZdd91aa4vd1hS7j1Tscdm9996bhg4dmv73f/83vfvuu2nBggXVurqMGDEibbzxxunyyy9Pd9xxR7rzzjurdXUpZltV7Lai2H2cYo9NVpk6dWr62c9+lrp37566dOmSjjjiiDR16tQa+583b17aaqutUmlpaSorK0ulpaVpyy23TL17906tWrUqHKuuyW233ZZatWqVunXrljp27JgefPDB1Llz5zRs2LA0fPjw1KpVq3TjjTfW2eabb745DRgwIP3mN79JTzzxRIP3i4s5Hk8ppc022yzts88+6amnnkozZsxIM2fOrNbVpbS0NM2bN2+11//5z3/Wus4rKSlJhx9+eOrZs2cqKytLe+yxR7rjjjvS8uXL6xxnUxo1alRq3bp1GjhwYDrnnHPSO++8U+/aYtf3AwcOTD/96U/T3Llzi5mE1bSoEOTzYUFt3Zqcf/75aZ111km/+c1v0pVXXpkGDx6cxo4dm+6999504IEHpvbt2xcOAupqR3OtBNu2bVv4EfPAAw9MJ554Ykoppbfffjt16NChznH37t27EFp06tQpvf766ymllO6666603Xbb1Vm/yrJly9Jtt92W9tprr9S6dev0ta99LV144YVp/vz5NdZ8dqP/2Q3nzJkzU3l5eZ3jbNeuXXr77bdTSintt99+6YwzzkgppTRr1qzUrl27OuuLnfYBAwYUwpKOHTsWpuXiiy9O+++/f531n/0xfK+99krnnXdeSunT765t27a11q633nqF/huj2M9+0003TZdeemm1+pUrV6bDDz88nXbaaXXWDx48ON1xxx2rjf+ll15K3bp1q7N+o402ShMnTlyt/tRTT01HHHFEnfVnnXVW6ty5c/rud7+bzj333AYFj/369SvsEHfq1Cm98cYbKaWUrrvuujRy5Mg6x92lS5f0yiuvpJRS2m677dL//M//pJRSmjFjRr3m2x133DEdfvjhacWKFYVpnzVrVtphhx3SbbfdVmd9SsUdKBf73a8yderUdOSRR6Zu3bqlbt26pZ/97GeFHfmadOjQIU2fPr3auFP69LOrz3xb7HwzdOjQ1LFjx1ReXp6+8pWvNDjwXrWhX1NX14a/2GW22Gn/xje+Ufh+V9V/9NFH6Tvf+U66/PLL66xv165dYX332fG/9dZbX8g6p5h5p3PnzqlNmzaptLQ0tW3bNnXp0qVaV5c999wzjRo1Kr333nupY8eO6W9/+1t69NFH01ZbbZWmTJlSZ/3nrVp22rZtm7p3756OOeaY9Pe//73G/j97kPz5z74++wnFrPOK3U5+3iuvvJLuvffeeh+wFLutKS8vT7Nnz04ppXT44Yeno48+OqWU0vTp01OnTp3qrG+K9eX8+fPTHnvssdrJOvU9YNhkk03SPffck1JK6cUXX0zl5eXp5JNPTttss02tP44Vs4+SUvHrjGK3dcUud8X8IFvsvnlTrDPatGmTjj/++GpB6XvvvZf23HPP1Llz51pri53vN9xww/SXv/wlpVR9uXv11VfrHHex812x65xit5UppbTrrrum3/72tymllD744IPUq1evtO6666a2bdvWOe8XGx6u0th9rGI++2KPy4od/yqNPTGxmOWu2GW22G1FsfWVlZXpscceq7O/mpxzzjnpoIMOSsuWLWtwbVOs744++ujUo0ePVFFRkQ477LD05JNPNqgNxQa/KX36e86qH5M7d+6cfvKTn6RnnnmmzrpOnToV9uGGDRtWCJ3qM98Xu60pdh+p2OOyzx8Hrerqc1y0apx//etf6+yvJsVsq4rdVhS7j1PsscnnLV26NI0fPz6Vl5en0tLSNGTIkPS73/0urVy5slp/3//+99Pee++dFixYkD755JN05JFHptGjR6eUUpo0aVLq1q1bjcHpFltskc4+++yU0qfBbefOndPYsWML719wwQX1Cu9qOo6u73xTzPF4Sp9+36uOhRqjpKRkjb+RTpo0KXXv3r3Wunnz5qVly5alW2+9Ne2+++6Fk6JPOOGEwm+ZNencufNq80lNXV0ae3Jesev7Tp06FfbrmlKLCkHWX3/9enU1Haysv/766c9//nPh/9dffz1169atsAE/6qij0q677lpnO5pzJTho0KB08803p0WLFqUePXqkSZMmpZRSmjZtWr02Xp06dSrscK633nqFHaDp06fXa4f18z755JN00UUXpfLy8lRSUpLKy8vTgQcemN59993V+u3Ro0d6/vnnU0rVP7cHHnigzjMfUvp0h+/iiy9Os2bNShUVFemJJ55IKaX03HPP1evM4mKnvX379oWd/d69exdS87feeitVVFTUWb/VVlulE088MU2ZMiW1bdu2cHDy5JNP1nn2RKdOnQqfV2MU+9m3b9++8Nl17do1vfjiiymllP72t7+l3r1711nftm3bQlL+2fH//e9/r/eB1qr6Hj16FD67v//976lr16511jdmfbFKhw4dCt973759C0Ha9OnT67XDuddee6Xhw4ensWPHptatWxfS8/vvvz8NGjSozvrKysrCDzOVlZWFM6+eeuqptNFGG9VZ/3kNPVAu9rv/rH/84x/p9NNPT+Xl5alDhw6pVatWafvtty+cSfh5ffv2TY8//nhKqfp8c/vtt6cBAwbUOb5i55szzjij1m5tKnaZLXbaP/tjUufOnQvf0bRp0+p1JcoGG2yQHnzwwdXa//vf/z4NHjy4zvpi57ti5p1rr7221q4u3bp1K5yZVFFRUVh+J02aVK+d/c96991303nnnZc22mij1KFDhzR69Oi0yy67pLKyshqvbuvYsWPhAPuz0/7ss8/W67svZp1X7HZylcaeOVTstma99dZL999/f1q+fHnq169f4cfVl19+uc4D5JSaZn1Z7FWrjb36sJh9lJSKX2cUu60rdrkr5gfZYvfNm2Kd8fjjj6cNN9wwDRkyJL3yyivp7rvvTr169Uo77LBDnWcqFjvf17TcvfLKK3WuM4qd74pd5xS7rUypuCsiig0PP6uh+1jFfvbFHpcVO/6UijsxsZjlrthltthtRbH166+/fqOv5Egppb333jt16tQp9enTJ+22225pn332qdbVpqn2kVadkPmd73wntW7dOg0ePDidf/759TpjuNjg97OWLl2abrvttrTnnnum1q1bp0033TSNHz8+ffjhh2vsf+edd06jR49O1113XWrdunXhx9XJkyfXua0sdltT7D5SscdlkydPrrWry+DBgwvHRo1RzLaq2G1Fsfs4xR6brLJ06dJ08803pxEjRqRWrVql7bbbLl199dVp7NixqVevXqudPFBRUVFtG7Jo0aLUunXrwpU7q/ZP1+Sz+1UrV65MrVu3LqyrUvp0O92xY8c62/z5Ky8aeiVGsUaNGpVuvfXWBtetCiFKS0tXCyQqKipSaWlp+ulPf1pj/aoQ5LPeeeedNHbs2DRgwIBUWlqavvWtb9VY/9n548ILL0xdunRJP/jBDwonAf/gBz9IXbp0afDdUhpycl6x6/tDDjmkcNVeU2pRIUixPrvDkNKnC2NZWVnhB/tp06bVa0FszpXgZZddlsrKylLnzp3TkCFD0ooVK1JKKV1yySX1uozx61//errvvvtSSp8e5B144IHpnXfeSSeccEK9Nl6rPPvss+m//uu/UpcuXdK6666bfvnLX6bp06enKVOmpF122SV94xvfWK1mzJgxae+9905Lly5NHTt2TNOnT09vv/122nzzzQs7H7W55ZZbUuvWrVNpaWm1sOrcc89NI0aMWOvT/pWvfCU99dRTKaVPz+gfN25cSunTSyh79OhRZ/3DDz+cOnfunEpLS9MhhxxSeP3kk0+uc4fx0EMPLZxl1hjFfvZ9+/YtbLQ23XTTQgj4xBNP1Osgc/DgwYXLVD+703HJJZfU64z6DTbYoLDTs+WWWxZu7XD//fc36uyHhth0000LO2a77LJLOu6441JKnx541ecg7e2330577LFH2myzzaqtxI855pj0s5/9rM767t27FzYsgwYNKszDr776amrfvn2Dpyelhh0oF/vdL126NN1yyy1p5MiRqaysLG2zzTbpyiuvTIsWLUozZsxIP/zhD2v8Ufy4445L22+/fZozZ07hjPTHHnssDRgwoF4hRHPON5/38ccfN6j/YpfZYqe9V69ehW3T4MGDC2ffT5s2rV7h37nnnps23njj9NRTT6VOnTqlRx99NN1www2pR48e6ZJLLqmzvtj5rth5pxidO3cunCk3YMCAwm1q3nzzzXoF7kuXLk233npr2mOPPVLr1q3TlltumX77299Wu1XA7bffXuOPkyNHjkynnHJKSikV5p0VK1ak/fbbr/CDeG2KWecVu51cpbFnDhW7rTn99NNTZWVl+upXv5rWW2+9wi0ufve739V5q4SUip9vUyr+qtXGXn1YzD5KSsWvM4rd1hW73BXzg2yx++bFtn2Vjz76KP3whz9M5eXlqXXr1um8885b7azONSl2vt9iiy3S9ddfn1KqvtydeeaZafvtt6+1ttj5rth1TlPsJxRzRUSx4WEx+1jFfvbFHpcVO/6UijsxsZjlrthltthtRbH1119/ffre975X7TZuDXHwwQfX2tWmqdZ3nzVv3rx01llnpbZt26bWrVunUaNGFQKCNSk2+P2sqqqqdNNNN6XddtstlZWVpR122CENHDgwderUKd10002r9f/CCy+kTTbZJFVUVFTbHz3yyCPrvHqt2G1NsftIzblvndKn6+Xddtut2u95DVHMtqrYbcXaOJ5viM+eBNmjR4903HHHpVdffbVaPy+99NJqYViPHj0K+5QpfXr7x9LS0sKtomu7wr93796Fxw28//77qaSkpNrt5p555pkGn1BZrIYej6f06VW1u+++ezrjjDPSrbfeWu+r06+99tp0zTXXpJKSknTxxRdXCyUmTpxYOHGgJjXdRmuVv/zlL+mAAw6o1zTsu+++6Te/+c1qr//mN79Jo0aNqtcwUmr4yXnFru8XL16cdt9993TQQQelCy64oEF3c6nNv1UIMnTo0HTFFVcU/p80aVJq37594QDhtddeq1f639wrwWeffTbdfvvt6aOPPiq8dvfdd9frstbrr78+XXPNNSmlT8/U6d69e+GyujVtqD/vwgsvTJtssklhB+N///d/CxvgVWbPnp1atWq1Wu2HH36Yhg0bljp37pxatWqV+vXrl1q3bp122GGHtGjRojrHndKn92l+/vnnq43z6aefXm0lvibFTvuJJ56YzjnnnJTSpwdXZWVlaeDAgalNmzaFy1Hrsnz58tXuyT1jxoxaV3ApfXpA0b1790avAIr97Pfff//CPUDHjh2bevTokQ477LDUv3//eh2oXHnllalv377ppptuSh06dEh/+MMf0tlnn134uy5jxowp7FxdeumlqV27doXpOfTQQ+usX6Wqqiq99tprDbp8+6KLLip8xg8++GBq27Zt4dLR+tw3t1i77rpr4X6Zhx12WNpqq63SDTfckIYPH5622mqreg+nsQfKxXz3q3a2unbtmo4++ug13sd0zpw5qaSkZI31VVVV6bDDDktlZWWppKSkcLD9n//5n/W6H2ZTzDcffPBBuvLKK9NJJ51U2OGbOnVqve6HuXz58jR27Ni0zjrrpFatWhV2tk855ZQ6z2oodpktdtpHjRpV2F4ed9xxaeDAgenss89OW2yxRdpll13qrF+5cmVhGV91Fn/btm0LP87Xpdh1TrHzzptvvpl++ctfph/84AeF9fOf//znGs+o/aztt9++cLuB/fffP40YMSI99thjafTo0elrX/tanfXdunVLXbp0ST/96U9rvOz/gw8+SOuvv/4a33vppZdSz54904gRI1KbNm3S9773vTR48ODUq1evel1SXMw6rym2kyk1/syhYrc1KX36w95FF11UOEs0pU8PZOpzv+li59uUir9qtZirDxu7j5JS8euMYrd1xS53xf4gW8y+ebFtX2Xq1Klpo402ShtuuGFq165dOuSQQ+q9f13MfH/nnXemysrKdN5556X27dun888/Px122GGpTZs26YEHHqizvpj5rth1TlPsJxRzRUQx4WGx+1gpFffZrxp+Y4/LmmL8xZyYWMxyV+wyW+y2otj6oUOHpk6dOqWOHTumTTbZpFHPN22splrfrfL000+nn/zkJ6lz585pvfXWS6eddloaM2ZMateuXeEkjs8rNvhN6dPl+4gjjkhdu3ZNffr0SSeeeGK1W+ZccsklqWfPnvWejo8//rhez30rZltT7D5SsfvWKX26/3rBBRekMWPGpDFjxqSLLrqoxqtmPu+zt4Tq2LFjg2/pU8y2qthtRVMczxdzbFJaWpqGDx+e/vjHP9Y4ny1atGi1EHOfffZJ3/3ud9OiRYvS0qVL0zHHHJMGDhxYeP+pp56qMcj4z//8z7T11lunG264obBvus0226RXX301vfbaa2nHHXdM3/ve9+o17dddd1365je/mfr06VNY5//617+u1z5KMcfjKX36PJbKyspG305r8uTJjbp14JquBGmsDh06rPGWXm+88Uad+xrFnJxX7Pr+qquuSmVlZaljx46pf//+DbqbS21adAjS0AfJ3Xzzzal169bpP/7jP9Lo0aNTx44d00knnVR4f8KECWnbbbetc7xfhpVgU1m8eHGaOnVqeu+99+rV/8CBA9O55567xttdrVJVVVXrZXmPPfZYuuyyy9J///d/F26X0hwaOu2f98QTT6QLL7ww/elPf2rilq2umNs5fVZjP/t//etf6R//+EdKKaUVK1akcePGpb322isde+yxtT5o9bNuuOGGNHDgwMJGo2/fvvW+vG3FihXVNh5/+MMf0s9+9rN0ySWX1Ou5FosXL06HHnpoatWqVbWN35FHHlk4a7C+Zs6cmW677bZaH8T12Y3C5x/61tCHwD377LOF1HzevHlp+PDhqVOnTmmLLbao837PqxRzoFzMd//tb387TZw4sdYHBi5btqzOS6BnzZqV7rnnnnTzzTfX+iyEzyt2vnnhhRdSjx490sCBA1NZWVlhvvnlL3+ZDjzwwDrri3kA3yqNXWaLnfa33nqrMI8vWrQo/fjHP06bbrpp2nfffRt06XFVVVV65ZVX0tNPP13tgK0uTbHOSalx887kyZML2/U2bdoUvrdx48bV60qK++67r3BryzfeeCNttNFGqaSkJHXv3r3WMxNXue666xp1ptJnffjhh+nss89O++23Xxo5cmT65S9/Wet2uzb1WefVpLHbyWLOHCpmW1Ospphvi71qtdirDxur2HVGsdu6Ype7lIr/QbaxmqLt48aNS23atElHHnlk+vjjj9NLL72Uhg4dmgYMGFDn2YZNYcqUKWnYsGGpR48eqV27dmm77bar94PBm1JD1znFbitTKu6KiGLCw6bax2rJijkxsZjlrthltthtRbH1TXG712XLlqUHH3wwTZgwIS1cuDCl9OmV5nXt6zXF+m7evHnpggsuSF/72tdSmzZt0ne/+9107733Vrvy7dFHH631x71igt9Vz/DZfffda3xQ8XvvvVdrANlcmmIfqbHHZatuy9q3b9/CrdPWXXfd1K1bt1ofzr1KU9wSqrHbqmK3FcXu4xR7bNLY20a99dZbacMNN0xlZWWpdevWqXPnztWOR6+55ppqv6d+1ty5c9Ouu+6aOnbsmIYPH54+/PDDdOSRRxbCg0GDBtXr5KzLL788de/ePZ199tnVjqevueaael0BVezxeP/+/dMRRxzR6Idz33PPPYX9+s+67777qj2q4fMaG56syXrrrZcuuOCC1V6/4IIL0nrrrVdrbTEn5xW7vu/Vq1c655xzVjvpvlglKaUULdCkSZPiO9/5TgwYMCBee+212GSTTWLmzJmRUootttgiHnrooTXW3XvvvXHDDTdEVVVVDB8+PA4//PDCe//6178iIqJbt261jnvlypWxcuXKKCsri4iIm266KZ544okYNGhQ/PjHP442bdrUWv/cc8/FRx99FDvvvHPMnz8/Ro8eXai/+uqrY8iQITXWHnroobUO++qrr671/S+bDz/8MDp37lzj+/vuu29ce+21UVFREfvuu2+tw7r99tubuHXF22KLLWLSpEnRpUuX2HzzzaOkpKTGfp9//vkvsGV1f/Zry5IlS2LRokXRs2fPL2ycRx99dDz++OMxfvz4GDFiRLz44osxYMCAuOuuu+KMM86Iv/71r006vlatWsWcOXOiZ8+eUVpausbvPaUUJSUlsWLFiiYd95rssssucdhhh8W+++4b5eXla+xn+fLl8fjjj8eOO+641tvTWCtWrIiXXnop+vfvH126dFnr4xs2bFhsscUW8atf/So6deoUL7zwQgwYMCCeeOKJOOCAA2LmzJm11g8cODD+53/+J3bZZZdq9a+99lpsu+228cEHH6yxbtmyZdGuXbuYNm1abLLJJmthyr54CxcujIceeig22mijGDx48Bc+/obMO9tuu23st99+ceyxx1b73p555pnYd99945133mnw+N9///3o0qVLrduAVRYsWBArVqyIrl27rjaMsrKyqKioaPD4W5pvfetbcdxxx8Xee+8dBxxwQHzwwQdxyimnxBVXXBFTp06Nl19+uc5h1Hdbc8kll8SPfvSjaNu2bVxyySW19nvUUUc1aDoa44Ybbojly5fHwQcfHFOnTo0RI0bE+++/H23atIlrr702vv/97zfZuL7M+yhNoSHLXTHWxr55Q9vep0+fuPrqq2PkyJGF15YtWxa/+MUv4pJLLomqqqoaa8eOHVvrsE877bT6Nbqecpzv5s6dG3PmzIkhQ4ZEaWlpREQ888wzUVFREV/96ldrrJs+fXosWrQoNttss1i8eHEcd9xxhWPCiy66KPr379+k7Sz2s2/K47INNtig1vFPnz691vqIiMMOOyz69esXp59+elx22WXx85//PLbbbrt47rnnYt99943f/e53dQ7js4pZZ3xR65svg7fffjtGjBgRs2bNiqqqqvj73/8eAwYMiKOPPjqqqqpiwoQJDRpeQz+7Nm3axIYbbhiHHnpoHHzwwdGjR4/V+lm4cGGMGjUqHn744Qa1pT7OOuusOPTQQ6Nv37716r9r167x97//Pbp3717ndL7//vvV/j/22GPjrLPOig4dOsSxxx5b63guuuiiGt9bvnx5TJw4MYYPHx69evVqkuPxhh6Xfetb34qBAwfGlVdeWfgdbfny5XHYYYfF9OnTY8qUKY1uS12WL18e5557bhx66KGx7rrrrrXxrC3FHpvMnj07SkpKCtP+zDPPxMSJE2PjjTeOH/3oR7XWLlmyJB5//PGoqqqKbbbZJrp3717UtEyfPj2WLFkSX/3qVwvzQW023njjOPfcc2PvvfeuNu0vv/xy7LTTTvHPf/6z1vrGHo+v0qlTp5g2bVpsuOGGDZrOVTbbbLM477zzYvfdd6/2+n333RcnnnhivPDCC40abkNce+21cdhhh8XIkSNj6623joiIp59+Ou6777648sor4+CDD66x9vrrr4/99tsv2rZt2yRtacj6vmvXrvHss882+rOvSd1z3ZfUySefHMcff3yceeaZ0alTp7jtttuiZ8+e8cMf/jBGjBhRY93IkSOrHSB8Vl3hxyqlpaWFHdyIiB/84Afx/8n787ia1vd/HH82aJ4pGRtIRIhwzJR5KHFkyJAyHWTOcBDFwTGPxxQaHIQjHEMhyUwDZUqDUhwzoTI0XL8/+u713rs9rLX3vR2v8/k9H4/1eNTa+1rrXmvfwzXd13Po0KGC2+7q6sr9bWVlhZiYGMGylQdpSUkJ7t27h4KCAri5ufHKExGOHDmC+Ph4vHr1CuXl5RKfCwkkFBQU4NatWzLlR40aJVfu999/h62tLWfAe3t746+//oK1tTVOnz4tM/hjamrKDRBTU1PetimCOp79n3/+wZUrV2TKy3KOeHp6ck7nAQMGqN54MYjilsoo2aq8+48fP3LOto8fPyq8Pp9T7vPnzyAiGBgYwMDAAK9fv8aGDRvg5OSEHj16CHqGL1++IC0tTea79/DwUCh77NgxREVF4aeffpJ4b40bN0Z2drbU91mdYhcuXOAcmN9D+VYWcXFxvN/R1tZWGAB59eqVzHfftGlThdfNzMyUO+b4HCvTp0+Hs7Mz/P39UVZWhs6dO+PatWswMDDAyZMn0aVLF8UPBbZ+k5iYiB07dkidr1WrFl68eMF772fPnqF+/fpS58vLy1FSUiJXrkqVKqhbt67SAbK0tDQ0adIEmpqaSEtLU/hdvt9NHIWFhVLvjm/Me3t7o1OnTpgyZQo+f/6MVq1aIScnB0SEgwcPYtCgQVIy6pxzWPrO3bt3sX//fqnzVlZWvIq2PFQOaCjC0KFD0b9/f0yaNEni/KFDh3DixAmcPn1aofzevXthZGSEwYMHS5w/fPgwiouLMXr0aN42JCYmyh23ioxsQPl1UhYWLlyIoqIiABUO2n79+qFjx46oWrUqoqKi5MqpstasX78ePj4+0NPTw/r16+VeW0NDQ3D7VZ0vAWDEiBHc3y1btsSTJ0+Qnp6OunXryjU8VR074jqKp6en2px3qswZ3wN8405dznhW3VwWlJkzgIp5q3L/qFKlClavXo1+/foplI2Ojpb4v6SkBDk5OdDW1ka9evWUCoII+e3V3e9Y5xwWPUEEa2trWFtbS5xr3bo1r5y9vT33t6GhodLOY2V1LFa7RNwuMzExYfrtpk+fLvF/SUkJbt++jZiYGAQGBgq6xs6dO7nnnjx5MqpWrYpr167Bw8MDEyZMULpNyo47ZWRZdRx16kgiJCcn4+HDhwAqbCIXFxdBctOmTYOrqytSU1Ml/CdeXl4SCaZCocx7JyLExcXB1dUV+vr6cr9nYmIiYYOpM+Fh0aJFgtsLVOgZxsbG3N/KjJvbt29zNoOipD2+a2pra2PixInc7y3Sk5QBq12WlJQkEQARtWvOnDkS/jFFKCsrw7FjxyT6rYeHB7S0tBTKaWtrY9WqVQp9VXxQx1qhKlhtk+HDh2P8+PEYOXIkXrx4ge7du6Nx48b4888/8eLFC4XrvIGBAbp3787UfnGIr3tCkJOTI3Nu0tXV5WwFRVDVHhdh4MCBiI+PV9kRn5mZCScnJ6nzDRs2RFZWlkrXVBa+vr5o1KgRNm3axPk8GzVqhCtXrnBBEXkYOXKkWtuizHw/evRoREVF4ddff1VrG/6zO0HEI3Lm5ua4cuUKGjdujNTUVHh6evJm6MpCaWkp/vnnH9StW1fqs+/lXFIHysvL8csvv6BevXqYM2eOwu9OmzYNO3bsQNeuXVG9enWpBXPv3r0K5f/++2/4+PigsLBQSvnV0NCQyl4Qh52dHf7880+0a9cO586dg7e3N6KionDo0CHk5eXh7NmzAp5WdbA+e1hYGLfTp2rVqlLPLiRjiQURERFYvXo1MjMzAQANGjRAYGCgoIlJlXevzt0MPXr0wMCBAzFx4kQUFBTA0dEROjo6ePPmDdatW4dffvlFoXxMTAxGjRolc5EXcn8DAwPcu3cP9vb2EhkAqamp6NSpEz58+CDxfTs7OyQlJaFq1aqws7OTe10hv3teXh7q1Kkj9f6ICPn5+TLnG3G8ffsWQUFBco1cRWNOhPDwcFSrVg19+/YFAMyZMwc7d+6Ek5MTDhw4oDDTMDk5GaNHj8bDhw9Rebnge/e7du3CL7/8gmrVqsHa2lpqzPBleNauXRvHjh2Dq6srjh07hkmTJuHixYuIjIzEhQsXcPXqVYXyrP3GysoKsbGxcHFxkeg3586dg5+fH/Lz8xXKt2zZEjNmzMCIESMk5ENCQnDu3DlcvnxZruzu3btx9OhRREZGClYWNDU18eLFC4kxK2uJF/LsOTk5mDJlCi5evIgvX75w54WOeWtra8TGxqJZs2bYv38/Fi9ejNTUVISHh2Pnzp0yDTl1zjksfad27do4dOgQ2rVrJ/G7RUdHY/bs2TIDp+rMjrWwsMDVq1eldsykp6ejffv23K5VeWjQoAG31okjISEB48ePx6NHjxTKL1++HAsXLoSjo6PUWqmhoSF3py3wfddJIZlDrGsNK1jmSxEuXbqEhg0bSmVnlpSU4Pr16+jUqZOUzP/C7kPWOUOVtY513AUHByMwMBAGBgZYsmSJwr61ePFihdevDGV08y9fvmDz5s1yn/1H7Ib4+PEjfH194eXlxatnsv72LGCdc1TVE77HTnVlg4esOtb/KrZu3YqkpCReu4wVLONOFVnWeVqd8/yrV68wdOhQXLx4kasKUFBQgK5du+LgwYMyd1aIQxRscnR0lNCTcnNz4eTkhOLiYrmyrPNdeXk59PT0cP/+fTg4OCj8rjhYbTu+XRji4EsW+VHo0qULpk+frnJiJqtdVr16dURGRkolpcTGxmLUqFF4+fKlQvmsrCz06dMHz549g6OjIwDg0aNHqFOnDk6dOsXrpPb09MTAgQMFJQNVBqtNyWrPq2KbiMPc3Bw3btyAo6MjNm3ahKioKFy9ehVnz57FxIkTVdbPX758iR07dqi0Y1SorJOTE1asWAFPT0+JZ9+8eTP27t3LO2ew2OMA8Ntvv2HDhg3o27cvnJ2dUaVKFYnP+YKm1tbW2L9/v1RSzPnz5zF8+HC8evVKofyPRlFREVauXIm4uDiZfVdR32Gd76dOnYqIiAg0a9YMTZs2lXr3qs61/9mdIIaGhvj27RuAii3g2dnZaNy4MQConKl5//59tGjRQuYk1rx5c8651Lx5cybnkjqcmuLQ1NTEzJkz0aVLF15DKzIyEkePHpXajiUUs2bNgp+fH5YvX6509sCLFy9Qp04dAMDJkyfh7e2NHj16wNbWljcCCUhmeAIVW3Gjo6MF7yZgffZFixYhKCgI8+fPl9gJJBQs2xDXrVuHRYsWYcqUKWjfvj0A4MqVK5g4cSLevHmDGTNmKJRX5d2rczdDSkoKl2F75MgRWFtb4/bt2/jrr78QFBTE65gKCAjA4MGDERQUhOrVqyt9f1dXV5w6dQoBAQEA/i9bJjQ0FG3btpX6fk5Ojsy/VYGdnR1ntIjj3bt3sLOz450vRo4ciaysLPj7+8sM3gnB8uXLsW3bNgDA9evXsXXrVqxfvx4nT57EjBkzFBrofn5+aNCgAXbv3q30/ZctW4bffvsNc+fOVbrNQMVcLsqsPH36NLy9vdGgQQP4+flh48aNvPKs/cbDwwMhISE4dOgQgIp+k5eXh7lz58rcyVAZQUFBGD16NJ49e4by8nIcPXoUjx49QkREBE6ePKlQdsuWLcjKykLNmjVhY2MDQ0NDic9lKQ05OTmc4crab0eMGAEiwp49e1Tqdx8+fODmj5iYGAwaNAgGBgbo27ev3AxPdc45LH1n6NChmDt3Lg4fPgwNDQ2Ul5fj6tWrmD17ttwMMnXuWvz69StKS0ulzpeUlODz58+88nl5eTINfBsbG+Tl5fHKb9y4EXv27FG4NVoeWNdJRRASDGRda0JCQjB79mwp/ebz589YvXo1r6HGMl+K0KVLF1SvXh3R0dH46aefuPPv3r1D165dZa4Z6hg79vb2SExMlNoVXVBQgBYtWvAayKxzhiprHeu4Ew9sLFmyRGl5RVBGN/f398fZs2fx888/o3Xr1ir1GxYjVRZMTEwQHByM/v378wZBWH571n7HOueoqieoa85nCSCx6lgsdgkAuLm54ejRo1KldT9+/IgBAwYoDJgrQu/evTF//ny5QRB1JSayjDtVZFnnaXXqSAEBAfj06RPu37/PJVw8ePAAo0ePxtSpU3HgwAGF8uXl5TL75tOnT7kdD/LAOt9pamrCwcEBb9++VSoIwmrbCS2dzPc8KSkpqFKlCpydnQEAx48fx969e+Hk5IQlS5bwllUXh6jUbMOGDRWW3RNh0qRJmDVrFp4+fYqWLVtK2RZ8ibysdtmQIUPg7++PNWvWoF27dgCAq1evIjAwEMOGDeOVnzp1KurVq4cbN25wY+Ht27cYMWIEpk6dilOnTimU7927N+bNm4e7d+/KfH5FuzlYbUpWe14V20QcJSUl3C7A8+fPc8/asGFDPH/+XOnnEeHFixcIDg5WKQgiVHbmzJmYPHkyvnz5AiLCrVu3cODAAaxYsQKhoaG892Gxx4EKf5GRkRESEhKQkJAg8ZmQXeKenp6YPn06oqOjuUBdVlYWZs2a9V13EKlr9+DYsWORkJCAkSNHokaNGt99rRTH3bt3uV1AlUshM+0iVivDyL8IFiI5ebhz5w5pamrK/Cw3N5cj28rNzVV48KF3797k4OBAK1eupL1796pE6lQZp06domrVqvF+z9bWlh4+fKjSPYiIDAwMODIhZVGjRg26evUqERE1aNCADh06RERE6enpZGxszCvfvXt32rZtGxFVkO9YWVlR7dq1SU9Pj/744w9eedZnt7CwEETeJA8dOnSgiIgIIqogoTY2Nqa2bdtStWrVKDg4WKGsra0thYeHS50PCwuTSUJUGazvnhX6+vr05MkTIiIaPHgwR7qXl5fHS3JLRGRsbMz07i9fvkxGRkY0ceJE0tPTo2nTplH37t3J0NCQkpKSFMoGBwdTUVGR1Pni4mLe342ISENDg169eiV1Pjc3lwwMDHjljYyMBBOgy4P4+58zZw5H6n3v3j3eecPIyIgyMzNVuq+xsbHK8wVRBYlXbGwslZaWUp06dejkyZNEVNFuMzMzQfdn6TcFBQXUrVs3MjMzIy0tLapTpw5VqVKFOnXqRIWFhYKuoSoBHytpZUJCgkwytZKSEkpISOCVNzQ0pPT0dN7vyYODgwNFRUVRYWEhWVpacuRnd+7coapVq/LKP3nyRILgUoTy8nKuLysCS9/5+vUrjR07lrS1tUlDQ4MjvB0xYoRM8kt1o0uXLjRlyhSp85MmTaIOHTrwytepU4eOHz8udf7YsWNUq1YtXnlra2uliC7FwbpOivD582datWoV9e7dm1q2bEkuLi4ShzywrjWampoySbDfvHkjVz8UB8t8KYKGhgZNnz6dDAwMaO/evdz5Fy9eCCJZVXXsaGhoyHz2Fy9eUJUqVXjvyzpnqGOtY4GdnR29efNG6vz79+/Jzs5OpWsK1c1NTEzoypUrKt1DhKFDh1KNGjVozpw5tH79etqwYYPEoQouX74saK1l+e1Z+x3rnMOqJ7CiXbt21LZtWzp48CDFx8fTxYsXJQ5FYNWxWOwSIvm/3cuXL0lbW1vldv3+++9kY2Mj6L4igl0RybP4wTdns4w7dYzZHwkTExO6deuW1PmbN2+Sqakpr7y3tzeNGzeOiCrm7sePH9OnT5/Izc2NfH19ee/N+u5OnDhBHTp0oLt376okz2rbscDV1ZWOHDlCRBWk07q6ujRs2DCqX78+TZs2TaHs4MGDafPmzVxbHRwcqEqVKqStrc1dUxHkjRMh44WI3S77+vUrTZ06lXR0dEhTU5M0NTVJV1eXpk+fTl++fOGVNzAwoLS0NKnzd+7cIUNDQ155Wc8vdL5gXStYdRxW26R169Y0d+5cunTpEunp6XFtuX79ukLbIDU1VeERFRUl992xyFbGvn37qH79+tzvVatWLQoNDRUkS6S6Pa4OFBQU0E8//UTa2tpka2tLtra2pK2tTV27dqX3799/t/uK2zSiPl75ENL3TU1N/59bK/+zO0HWrVuHwsJCABXb2QsLCxEVFcURyclCixYtFF5TUYaleLmYJ0+eoF27dlJEPqWlpbh27Rovid3ly5dx5coVhQTo8lB5KyYR4fnz5zh16pSgrX1LlixBcHAw9uzZo7COpjz07NkTSUlJStfyAyq2jg8fPpzL3hBxs9y+fVtmnb7KYM3wZH12f39/HD58GPPmzVNaFqiIXorqAx86dAjOzs4S2xAVRcGfP3/OZUyIo127doKi96zvHmCrg1m/fn0cO3YMXl5eiI2N5XauvHr1SlDd2p9//hkXL15UuRZjhw4dcOfOHaxcuRLOzs44e/YsWrRogevXr3OZOPIQHByMiRMnSmUGFxcXK8xeEI1VDQ0NLFq0SEK+rKwMN2/eRPPmzXnb3rBhQ0HZ34pgZGSEt2/fom7dujh79izXNj09Pd5ru7u7IzU1VXA/EcfgwYO5/q0KxowZA29vby7roFu3bgAqiLyEZDyx9htTU1OcO3cOV65cQVpaGgoLC9GiRQuuHULQsWNHnDt3Tul7K1t6pTK6du0qcwfShw8f5GaTi6NVq1bIz8/ntpsri+nTp8PHxwdGRkawsbHh6gRfunSJd8wB7DuoWPqOjo4Odu3ahUWLFuHevXsoLCyEi4uL4IxD1l2Ly5YtQ7du3ZCamgp3d3cAFbw+iYmJgspGDhs2DFOnToWxsTFXOikhIQHTpk0TxF82Y8YMbN26FRs2bOD9bmWwrpPi11Elc4h1raH/L/u6MlJTUwXtRGGZL0XQ0NDA/Pnz0bFjR4waNQppaWlYu3Yt9xkflB07J06c4P6OjY2VyGovKytDXFycwtIhIrDOGaxrHeu4y83NlTmvfP36lZdwlFU3r1WrFm/2NB/OnDmDU6dOcbuFlUHl+vii9kdGRsrlURSHKr+9uvod65zDqicAbH0vNTUVycnJKo0bVh1LVbtEfPfFgwcPJDjSysrKEBMTI4gwujIPDxHhxYsXeP36Nf744w+5cura9coy7tQxZlXl2FSHfHl5uVRZEaCCR6jytWRh7dq16NmzJ5ycnPDlyxcMHz4cmZmZqFatGu8uEnW8u1GjRqG4uBjNmjWDjo6OlF3PV1VDVdtOHcjIyODsv8OHD6Nz587Yv38/rl69iqFDhyrUvS5duoQFCxYAqOByIiIUFBQgPDwcy5Yt492lzrpLnNUu09HRwcaNG7FixQqufFO9evUEVxfR1dXFp0+fpM4XFhYK2kEjpG/LA+tawarjiGyToKAg3L17V2nb5Pfff4eXlxdWr16N0aNHc37IEydOKOSv4quAI09vZpWtDB8fH/j4+KC4uBiFhYVSOi4fVLXHZaGsrAx3796FjY0NzM3Neb9vamqKa9eu4dy5c0hNTYW+vj6aNm0qs7ytInz79k3mfC+vtLq6dg+am5urzJeljvleHMrufpOH/ywniCrQ09PD0KFD5SrVz58/x65du3idK+I1OcXx9u1bWFlZCXIubd68WaLMgVBUrvGtqakJS0tLuLm5wc/PTyowUxmfP3+Gl5cXrl69CltbWykFSFaJFXFD5fXr1wgJCcGYMWNk1sRT5AwvKSnBxo0bkZ+fD19fX25rk4gsbOzYsQrbbmBgwJGDent7o3Hjxli8eDFneCmqP6rqs4ujrKwM/fr1w+fPn2U+O19NOiMjI9y7dw+2trbw8PBA+/btMXfuXOTl5cHR0VHhwtikSRMMHz5cihRo2bJliIqKwt27dxXem/Xds9bBPHLkCIYPH46ysjK4u7tzjrwVK1bg0qVLOHPmjEL54uJiDB48GJaWlirVYmSBpqYmXr58KVUf98KFCxgyZAhev34tU040VhMSEtC2bVsJ5UxHRwe2traYPXs2r/KSmJiIefPmISgoCE2aNJF6diGOPR8fH6Snp8PFxQUHDhxAXl4eqlatihMnTuDXX3+V2l4ojjdv3mD06NFo3bq1zPtXHvPizpSioiKsW7dO5RqaQEXfyc/Px+DBg7mSDeHh4TAzM4Onp6dC2R/Zb3405PXbjIwMuLq68m6Lzc7OxsSJEzFixAiZv7sQ7qvk5GTk5eWhe/fuMDIyAgCcOnUKZmZmvI46ee1/8uQJnJycBBHhsfQdFqiDl+LOnTtYvXo17ty5wynL8+fPF2TsfPv2DSNHjsThw4c5naC8vByjRo3C9u3beQ3F8vJy9O3bFxkZGXBycpL67RWVz2NdJ0UwNTXF6dOnlXboqrrWiLhGPnz4IMV3VlZWhsLCQkycOBFbt25VeH9l50tZEOf2uX37Njw9PeHk5ISNGzfCycmJd71VduyISgjJMlSrVKkCW1tbrF27lpdcm3XOYF3rVB13Ih13wIABCA8Pl+mMP3funEIuHVbd/MyZM9i0aRO2b9/Om0glD3Z2djh9+rQUl5BQWXGIt3/+/Pm8Bqwqv726+h3rnKMOPYFlzu/atSsWLFggOLlCnTqWqnaJOB+FLDeCvr4+Nm/eDD8/P4X3Dw4OlrqupaUlunTpwuTcEAqWccc6Zlk4NtUh7+npiYKCAhw4cAA1a9YEUEEe7OPjA3Nzc0RHR/M+Q2lpKaKiopCamsolCfn4+PAmGqpjvgsPD1f4OV/wWVXbThxJSUkct6aoRLsIivQkExMTJCcnw8HBAd27d0e/fv0wbdo0Qf4AfX19ZGRkoE6dOhg1ahRq1qyJlStXIi8vD05OTlyC8PfEj9KtgYrgV0pKCnbv3s057m/evIlx48ahZcuWCAsL+273Zl0r1GHPs6KsrAwfP36UcNzn5ubCwMBAblChWrVqWLVqFZeUVRn3799H//79ZeqmLLLiyMnJQWlpqZQNlJmZyekL3xPTp0+Hs7Mz/P39UVZWhk6dOuH69eswMDDAyZMnuUS/74XMzEz4+fnh2rVrEufpX+BdA4B9+/bh+PHjCA8PV5oOgXW+9/b2RqdOnTBlyhR8/vwZzZo1Q25uLogIBw8eFFSeXBb+nwiCCCWSc3V1hb+/v1xl9M6dO2jZsqXKBqZQ59KPnAS9vb0RHx+Pn3/+WWY9QlnZx0Jr7H7vQdi0aVOMHTsWXl5eaNKkCWJiYtC2bVskJyejb9++EplIsqDKs4tj2bJlCAoKUoksFgDatGmDrl27om/fvujRowdu3LiBZs2a4caNG/j5558VZhr+9ddfGDJkCLp168Y5ha5evYq4uDgcOnQIXl5eCu/NCgcHB/To0UPlOphARd3H58+fo1mzZlyfunXrFkxMTHiNnd27d2PixInQ09NTifjy9OnT0NLSQs+ePSXOx8bGory8XGamo7qcYmPGjMHGjRtVHteZmZkYPny4VJBOmYWvoKAACxcuRH5+Pn755Rf06tULQEWf19HR4bKKZOHvv//GyJEjZc5rsu4vJHNTJMtCkiwEqvSbyhmxiiBL4eUjbhaHIiNVHumlCIrIWoGKGsO9evXi6r+KZNLS0uDo6IiYmBiFbbtx4waGDx+O3Nxc7px41s73mutFGdUbN27EuHHjZO6g0tLS4iVfVPW+QsDnVKtWrRoSEhLQuHFjhIaGYvPmzRK7Fh8+fMjaXEHIyMjgMo6cnZ0FK59TpkxBaGgounbtKnOtVERWy7pOiuDk5ISDBw8KCrZVhiprTXh4OIgIfn5+2LBhg4QjXBS0lsUfVRnKzpeyIB4EET3PgAED8PTpUzx//lzuNVjHjp2dHRITE1GtWjXeNsoC65zButapOu7U5YxnwevXr+Ht7Y1Lly7BwMBAyi4QwhXIYqSyguW3Z+13rHMOq34JsM35ygaQ1KljqWqXPHnyBEQEe3t73Lp1S8Ie1tHRgZWVFbS0tAS1Ux3IzMyUy7OpKKOfZdyxjtkGDRqgT58+KnFsqkM+Pz8fHh4euH//PscZmZ+fjyZNmuDEiROcc/t7QB3znapQl2138OBBjBo1Cj179sTZs2fRo0cPZGRk4OXLl/Dy8lKoJ7m5uaFOnTro1q0b/P398eDBA9SvXx8JCQkYPXq0xDxaGQ0aNMCyZcvQt29f2NnZ4eDBg3Bzc+N2DvPx4kZERCj8XMgOJBawkiQXFBRg9OjR+Pvvv7l+U1paCg8PD4SFhfHyM4WEhCj8XNF8wbpWsOo4gwYNQuvWraU4oFatWoXExEQcPnxYobwIr1694pI6HB0deXdU9OzZEx07dsTChQtlfp6amgoXFxeZu2xYZMXRuXNn+Pn5SQU39+3bh9DQUFy8eFFKRl32OFBBSn/s2DG4urri2LFjmDx5MuLj4xEZGYkLFy4IskmLioqQkJAgM2jKF0Br3749tLW1MW/ePJmcHEKrCxUUFGD37t2cTtK4cWP4+fnJHDeVd2pmZWWBiJROJGed762trREbG4tmzZph//79WLx4MVJTUxEeHo6dO3cK5mqqjP9sEEQVIrlp06ZBQ0ND7jbD7OxsjB07Vu52IXU5l1gmQdbt/oaGhoiNjUWHDh14v/u98ODBA5kTAF+GpHiGp5ubG7elTehuAtZnNzc3x/r161UiiwWAixcvwsvLCx8/fsTo0aOxZ88eAMCvv/6K9PR0hVkjQEVW9fr167mJq1GjRpg1axa3q0MIVH33JiYmuH37NlO5ABZYW1tj6tSpmDdvnkrEl02bNsXKlSvRp08fifMxMTGYO3cuUlNTpWTU5RRjRevWraGtrY1p06bJdEh27tz5u97f1tYW/fr1w6JFi1QOgLGARWlQpd9UdjC8fv0axcXFHPFnQUEBlzEjS+EVz057+/Ytli1bhp49e3J95fr164iNjcWiRYu4Uj2ycPz4cYn/S0pKcPv2bYSHhyM4OBj+/v4y5caMGcO1w9vbWyIjT9Rvx40bx+twcnJyQqNGjTBnzhyZ/U6IQ/3p06c4ceKEzN9OXiBBXTuoAOX6TuVM7pSUFJSWlnIlSjIyMqClpYWWLVvyOtVYdy0CFbsxsrKyZBqJym6fVhbGxsY4ePAg+vbtq7Qs6zopgjoyRVVBQkIC2rVrJ7NMiBCoY74cM2YMNm3aJJF9//XrV4wfPx6XLl2SW85CnWNHFbDOGaxrHeu4Y3HGsxJEd+vWDXl5eXIJU4WU1HJxcUF2drZKRqqI1Lbyjo+ioiIEBARw+qo8qGO9UBWscw6rfgmw9b0flXAAsNslrJCXNKihoQFdXV1B5W127dqFX375BdWqVYO1tbWUY1JRv2cZd6xj1tDQEHfv3lWpvLQ65IEKv8P58+eRnp4OoMKuFLojacWKFahevbrUbp89e/bg9evXUo5acahjvsvLy1P4ubzyMOqy7Zo2bYoJEyZg8uTJMDY2RmpqKuzs7DBhwgTUqFFDapeTONLS0uDj44O8vDzMnDmTS8IMCAjA27dvsX//frmyf/zxB6ZNm8aVmk1JSYGmpiY2b96Mo0eP8pa8qVy6p6SkBMXFxdDR0YGBgYGgABSLXebj48OVOlUlIVWEzMxMiX4rtARpZZ9JSUkJcnJyoK2tjXr16imcL1jXClYdx9LSEhcuXJAqKXz37l1069YNL1++VCj/6dMnTJo0CQcPHuTWFS0tLQwZMgRbt26VG0CKjo5GUVERRowYIfPz9+/f48SJEzLHLYusOExMTJCSkiL1O2dlZcHV1RUFBQVSMuqyx4GKakJZWVmoXbs2xo8fDwMDA2zYsAE5OTlo1qwZbwL87du30adPHxQXF6OoqAgWFhZ48+aNQn+COAwNDZGcnMy0QzIpKQk9e/aEvr4+t4sqMTERnz9/5srEi0PRHFYZisYt63z/3Xa/fS+yke8NFiI5VeHr60u+vr6koaFBQ4YM4f739fWl8ePH0/Lly+n169e812nVqpXKbWclB3d0dKTU1FRhDywD4eHhMomrvn79KpO4WxzZ2dnUtGlTKRI7ETGPEDx//pxSUlKorKyMO3fz5k1BhOesz169enWVyWJFKC0tpXfv3kmcy8nJkUksqE6wvvsxY8YoRT4lC4mJiRQYGEhDhgwhLy8viYMP5ubmTGRkenp6lJOTI3U+JyeHl5z84sWL9O3bN6Xu5+XlRR8+fOD+VnTwQV9fn4lsVoR3797R6tWryc/Pj/z8/Gj16tX09u1bXjkjIyOV3z0r8WBKSgpZW1uTiYkJaWlpkaWlJWloaJChoaEgolrWfvPnn39S+/btJd5/eno6dezYkfbt28crP3DgQI7AUBybN28mT09Pldvk4eHB+70lS5YIJm+XBQMDAyaC5/Pnz5OBgQE1adKEtLW1qXnz5mRmZkampqbUtWtXXnlfX19uDKkClr6zdu1a6t+/v8Rc/e7dO/L09KQ1a9bw3tvZ2Zk2btxIeXl5ZGJiQteuXSMioqSkJKpevTqv/PXr18nOzk4m2auQ+bq0tJRCQ0Np2LBh5O7uTl27dpU4+FC3bl1Ba6osqGOdJCJ69eoVdenShTQ1NcnIyIjMzc0lDkVQdq0R72cfPnxQePCBZb5UF5QZOxs3bqTPnz9zfys6+MA6Z7CudazjjgWsBNH6+vrMpPBLlixReCiCOHmmOF6/fk1aWlq891b2t1dnv2Odc1j1BCK2vteoUSMaOHAg3bhxg3Jycig3N1fiUAR1kDsra5ccP36c04mPHz+u8OCDPKJW0VG3bl0KCgqSsPkqo27durRy5UpBz1oZLOOOdcx6eXlRVFTUD5NnhY2NDV29elXq/I0bN8jW1lahrDrmO76+wwdVbDtxGBgYcHalhYUFR9b94MEDsra2Vumanz9/FtSmxMREOnr0KH369Ik7d/LkSZXJhzMyMsjd3Z1iYmJ4v8tql/0vkiR/+PCBvLy8KCIiQuH3WNcKVh1HT09PpvzDhw9JT0+PV97b25scHBwoJiaG02ljYmLI0dGRhgwZonK7/g2YmJhQSkqK1PmkpCQyMjLilWe1x+vWrUuxsbFUWlpKderUoZMnTxIR0b1798jMzIxXvnPnzjRu3DgqKysjIyMjys7Opry8POrUqRP99ddfvPKurq50+fJl3u8pQocOHcjX15dKSkq4cyUlJTR69Gjq2LEj07UVgXW+d3BwoKioKCosLCRLS0uKi4sjIqI7d+5Q1apVVb7ufzYIYmhoqBbHoCpgdS6xTIJVq1ale/fuERHRrl27qGnTplRWVkaHDh2ihg0b8sqfPHmSevbsKdMhLATyDKU3b97wKh39+vUjT09Pev36NRkZGdGDBw/o8uXL1Lp1a7p06ZJS7cjPz6f8/HylZFifffny5RQQEKCSrDhevXpFly9fpsuXL9OrV6/kfk+djhnWd19UVER9+vSh0aNH05o1a5Q2Ug8cOEBVqlShfv36kY6ODvXr148aNGhApqam5Ovryys/ffp0+u2333i/Jw/Vq1fnJk1xnDt3jiwtLXnly8rK6NGjR3T58mVKSEiQOGTB19eXPn78yP2t6OBDx44d6dy5c7zfU4SEhAQyMTGhOnXqcM7AunXrkomJidxnEGHUqFG0a9cule7LMl8QsSsNrP3G3t5ertLFZ+QRVaxTshxDmZmZZGhoqFKbsrOzVZZVBv369aMjR46oLN+qVSsKCgoiIuJ+u0+fPpGHh4eggD0rWPpOzZo1uXVWHHfv3qUaNWrw3vvw4cNUpUoV0tTUpO7du3Pnly9fTr169eKVb9asGQ0ePJgePHhA79+/p4KCAomDD5MnTyZDQ0Py9vamadOm0fTp0yUOPuzZs4e8vb1lOtf4oK510t3dnRwcHGjlypW0d+9eCgsLkzjkQZW1RnyekudYERqAYpkvxfH+/XuKjY2lyMhICg8P5w4+I11Z2Nra0ps3b7i/5R1CnBuscwbrWqfKuGN1xqemplJqaippaGhQfHw8939qaiqlpKTQ8uXLycbGhrftLi4udP36deUfmhEfPnyggoIC0tDQoKysLAm98t27dxQeHi5ozlP2t1dnv2Odc1j1BCLJvtetWzeJtvHN+SzBQ1YdSwShdgmRZMCvcpBe2YB9eHg41a5dmxYuXEgnTpygEydO0MKFC6lOnTq0Y8cOWrZsGZmZmSn8fYyNjSk7O1vws4qDZdyxjtnQ0FCqW7cuLV68mI4cOaJ0AEkVeXUGH3V1denx48dS57Ozs0lXV1ehrDrmuzt37kgciYmJtHPnTmrYsKFcHU+ddnWtWrW4wIezszPt37+fiIiuXbtGJiYmgp4hKSmJIiMjKTIykpKTkwXJiKO8vJzKy8uVlpOFxMREcnR05P0eq13WqFEjpRNSZ8yYwfndZsyYofBQFWlpabxrNetawarjtGrVSmZwe/HixdSiRQteeQMDA5mO9EuXLvEmhFbGlStXZCZFfy/069ePBg8eTKWlpdy50tJSGjRokCC7itUeX7x4MZmamlLDhg2pbt263LPv3r2bfvrpJ155U1NTzvdrampKDx48IKKKoLGQcRcXF0dt27al+Ph4evPmjdLzFVFFEE1Wgtv9+/dJX19foaydnR2ns4nj/fv3vHoa63y/detW0tbWJjMzM2rWrBmXFLFp0ybq0qWLytf9zwZBunTpwuwYJCJq0qQJ5eXlqaFFwsEyCerr69OTJ0+IiGjw4MFcdldeXh5vByYiMjMzIx0dHZWyK4kqFF5ZCvKdO3d45atWrcotfCYmJtxkEBcXR82bN+e9d1lZGQUHB5OJiQnnmDA1NaWQkBCFWUIisD77gAEDyMTEhOzs7Khfv35KZ/QXFhbSmDFjSEtLizMStLW1yc/PT6azSZ2OGdZ3HxoaStra2mRkZEQ2NjZKG6nOzs60ZcsWIvo/h2h5eTmNGzeOc5QqQkBAAJmamlKnTp1oypQpSis948ePJ2dnZ4kMjszMTGratCn5+/srlGXNymbFoUOHyMnJifbu3UtJSUkSThahimSTJk1o3LhxUsrD+PHjqUmTJgplly1bRtWqVVMpACZvvoiLi6Nq1arxtptVaWDtN/r6+nTr1i2p8zdv3hQ039atW1fmzoE1a9ZQ3bp1eeUro7i4mKZNm0YNGjTg/e6LFy9oxIgRVKNGDdLS0lI6S27Hjh1Up04dlQ108Yx4MzMzLqhw584dQU7BwsJCWrhwIbVt25bq1atHdnZ2EgcfWPqOkZERxcfHS52/cOGCoIwjIrZdi6wZ9VWrVqVTp06pLN+8eXMyNjYmIyMjatKkCbm4uEgcisC6ToqgauaQKmvNxYsXucyoyjtzld1lzDJfinDixAkyNjYmDQ0NMjU1JTMzM+6Qp6uoc/ehqmCdM9Sx1ik77sSd8ZV1GyF6jrhuJssRbGBgQLt37+Ztd2xsLLVr147JwFUFfNnUWlpatGzZMt7rsP72LGCdc1j1BBFUnfNZgoesOpaydom64ebmJnM3Q1RUFLm5uRERUUREhMI128/Pj6uQoCxYxh3rmGUNIKkir87gY/369SkyMlLqfEREBK/895zvTp48SZ07d5b5mTrt6mHDhtHatWuJiCgkJIQsLS1p7NixZGNjwzvvvHz5krp06UIaGhqcD0JDQ4Pc3Nx4g5BEFcHDJk2akK6uLunq6pKzszNzgsTt27fJ2NiY93usdtnp06epV69evLvcxNGlSxd6//4997eiQ1VcvnyZN6Ofda1g1XFOnDhB2traNGrUKC4haOTIkaStrU3R0dG88nXq1OECd+JITU2lWrVq8cqLgyX4rAru379PVatWpXr16nFJpPXq1SNLS0u6e/cur7w67PHDhw/TunXrJJKww8LC6NixY7yy1apV43asinbjEFXs4hESgKpcwUXZ+YqIyMrKimJjY6XOx8TEkJWVFe/9ZSVcvHjxgqpUqaJQVh3zfVJSklp3vxERaatWROvHIzQ0FBMnTsSzZ88EEcnJQ25uLkpKSpS698uXLzF79mzExcXh1atXUkSKfPVbAwICMG3aNAQGBsLZ2VmpttevXx/Hjh2Dl5cXYmNjuRp2r169EkS8LI8PhQ8ichwNDQ24u7tDW/v/uk5ZWRlycnI4smV5KCsr4+oNV6tWDf/88w8cHR1hY2PDETQpwoIFC7B7926sXLmSIwe/cuUKlixZgi9fvuC3335TKK/qs4tgZmbG8cKogpkzZyIhIQF///23RPunTp2KWbNmYdu2bRLfv3DhAiwsLACAt8YnH9Tx7oODg1Wug5mdnc3Vl9fR0UFRURE0NDQwY8YMuLm58dYdvHv3LlfH8969exKfCSG9WrVqFXr16oWGDRtyZH9Pnz5Fx44dsWbNGoWyEydOhKurK06dOiWTjOp7Y8iQIQAgUXdX2XrRWVlZOHLkiARRpZaWFmbOnMlLkhcaGgojIyMkJCQgISFB4jMNDQ2F5OAaGhpo0KCBXOJBPlSpUoXrb1ZWVsjLy0OjRo1gamqK/Px8XnnWfuPu7o4JEyYgNDSUq5WZnJyMX375RVDd5ODgYIwdOxYXL15EmzZtAAA3b95ETEwMdu3apVC2MqEbEeHTp08wMDDAvn37eO/t6+uLvLw8LFq0SKV+K/p9ZJEICul3hoaGXK3gGjVqIDs7G40bNwYAXuJGABg7diwSEhIwcuRIldrP0ne8vLwwZswYrF27lqubevPmTQQGBgpeA6ytrWFtbS1xTnQtPrRp0wZZWVmCaxxXho6OjsqyADBgwACVZVnXSREaNmyIz58/Ky2nylojXoeZlWNJlfmyMmbNmgU/Pz+lCG9NTU25MVKZ7FVZfPv2DTk5OahXr56ErscH1jlDHWudsuNOnF9FERmtInlSA0G0SH92d3eXOM/37BYWFsjIyEC1atV4SUBl1XqPj48HEcHNzQ1//fUXp3OK2m9jY4OaNWvytp/1t2cB65zDqieIIOp7IjLx2rVrC5rz+/fvjxkzZuDu3bsybUJZnH3q0rGUtUvUjWvXrmH79u1S511cXHD9+nUAQIcOHRTyP9SvXx+LFi3CjRs3ZL4/RXOuquOOVRYALxEwH1SRF5/v5HFLCcW4ceMwffp0lJSUwM3NDQAQFxeHOXPmYNasWQplWd+dIjg6OiIxMVHmZ+J29YULF5jWyS1btnCctAsWLECVKlVw7do1DBo0SC4JtAgBAQEoLCzE/fv30ahRIwAVnJ2jR4/G1KlTceDAAbmy69atw6JFizBlyhSJMTtx4kS8efOGl9vgxIkTEv8TEZ4/f44tW7Zw11MEVrvM1dUVX758gb29vWCSZHEfCKs/ZNOmTRL/i54/MjISvXv3VijLulaw6jj9+/fHsWPHsHz5chw5cgT6+vpo2rQpzp8/L0h3XbhwIWbOnInIyEhOT3rx4gUCAwOxaNEiXnlxVPZ9fm84OTkhLS0NW7ZsQWpqKvT19TFq1ChMmTJFQm+RBxZ7XISff/5Z6pwQ/iKgYk1LTEyEg4MDOnfujKCgILx58waRkZFo0qQJrzxrvwcq+p+/vz/WrFmDdu3aAQCuXr2KwMBADBs2TKaM+HwRGxsrwRtTVlaGuLg4KS7VylDHfN+yZUu0bNlS4pwqvJXi+M8So6uLSE5EZqUMsVjv3r2Rl5eHKVOmyHTOeHp6KpSX5UQW2nZWcnBVIXIcBAcHY9asWTAyMuI+ExGJDRo0SCGJXceOHTFr1iwMGDAAw4cPx/v377Fw4ULs3LkTycnJUgtKZdSsWRPbt2+XMgiOHz+OSZMm4dmzZwxP+P1RrVo1HDlyBF26dJE4Hx8fD29vb7x+/VqubF5eHurUqSPV14gI+fn5cgngRGB99xYWFkhMTFSZGL127do4c+YMnJ2d0bRpU8yfPx/Dhg3D9evX0atXL3z48EGl6yoDIsK5c+e4xbNp06aCCIYNDQ2RmpqqslNRFECsDA0NDejp6aF+/frw9fWVImYW4cmTJwqvL4RwtH379ggMDJRybh47dgwrV67EjRs3eK+hDNRFPNijRw/4+vpi+PDhGDduHNLS0jB16lRERkbi/fv3uHnzplrbXRmvX7/G6NGjERMTwynqpaWl6NmzJ8LCwmBlZcV7jZs3b2LTpk14+PAhgAoCv6lTp3JKmDyEhYVJ9BtNTU1YWlqiTZs2UsSGsmBsbIzLly+jefPmvN/9HhgwYAD69u2LcePGYfbs2Th+/Dh8fX1x9OhRmJub4/z58wrlzczMcOrUKUFGmSyw9J3i4mLMnj0be/bs4ZIktLW14e/vj9WrV8PQ0JD3/klJSTh06JBM4kg+stno6GgsXLhQpUQJAFi7di0eP36MLVu2/OtBW3Xh7NmzCA4Oxm+//SbzHchL+mBda9LS0mSeF83XdevWha6urmoPJRDqILxVBcXFxQgICODIJDMyMmBvb4+AgADUqlUL8+bN+673V8dap+q4KykpQcOGDXHy5EnOKfVvonLArDLkOTjCw8MxdOhQ6OrqSpCAyoIiY/3JkyeoW7fuD5kvysrKEBYWxiWWVXbu8pHK/y+gvLwcy5Ytw9q1azmSTmNjY8yaNQsLFixQmDyk6DN5NqG6dCwWu0SEuLg4ub+diGhdHho0aICBAwdi5cqVEufnzZuH6OhoPHr0CElJSfD09JRr4ylywGhoaCgknFV13LHK/i8gJCQEs2fPlgq0f/78GatXr0ZQUJBCeSLCvHnzsGnTJm6u1dPTw9y5c3ll1fHuKhMRi5zZS5YsQXp6Ou7cucN7jR8FU1NTnD9/Hq1atZI4f+vWLfTo0UMmybMIdnZ2CA4OxqhRoyTOh4eHY8mSJbzBrcrzjYaGBiwtLeHm5oa1a9eiRo0aCuVZ7TJWkmQ/Pz9s3LiRS+wUoaioCAEBAbxzTuX5QmRbubm5Yf78+VLXVSfUoeOwwMXFBVlZWfj69SvnN8rLy4Ouri4cHBwkvquIIB5QzX/6o6GqPS5CUVEREhISZOqXfAlOSUlJ+PTpE7p27YpXr15h1KhRuHbtGhwcHLBnzx40a9ZMtYdSAt++fUNgYCC2b9+O0tJSABVBzV9++QUrV66UaduI5guRn1ocVapUga2tLdauXYt+/frJva865vunT5/ixIkTMt/9unXreOVl4T8bBHFyckKjRo0wZ84cmZOo0ImkT58+2L17N++kLw5W5xLrJPjixQs8f/4czZo14zrnrVu3YGJigoYNG/Lev6ysDMeOHeMmgcaNG8PDw0NQtlp4eDiGDBkCPT093u9WRmxsLIqKijBw4EBkZWWhX79+yMjIQNWqVREVFcVlksiDnp4e0tLS0KBBA4nzjx49QvPmzQVljbI8uwivX7/mdk84OjpKZP4pgoGBAZKTk6UM7Pv376N169YoKiqSK6ulpYXnz59LOV3fvn0LKysr3qAf67ufMWMGLC0t8euvv/I8pWwMHz4crq6umDlzJpYuXYrNmzfD09MT586dQ4sWLXidguIQz7L7N+Dm5oY5c+bw7nSSh/nz52Pbtm1wdnbmsgITExORlpYGX19fPHjwAHFxcTh69ChvAFVVREVFYc6cOQgICMBPP/0EoCKQvHXrVqxcuVKiTypysIqWC6GOkoSEBLRr107KgSkU6lQalO03ogCjpaUlnj59ys0ZDRs2lJqDZKGkpAQTJkzAokWLeLMkvgecnJzw559/cllL/zYeP36MwsJCNG3aFEVFRZg1axb3261bt453nbOzs8Pp06dVdkiq2nfKyspw9epVODs7Q0dHB9nZ2QCAevXqCQp+AMDBgwcxatQo9OzZE2fPnkWPHj2QkZGBly9fwsvLC3v37lUoz5IoAVTsZImPj4eFhQUaN24sNf6EzrfJyckSa6UyfUnVdVIEccVbHHzvgHWt0dTUVDi/ValSBUOGDMGOHTt49SBl50sRBg4ciKFDh8Lb21spORHc3Nxw9OhRmJmZSZz/+PEjBgwYINepPG3aNFy9ehUbNmxAr169kJaWBnt7exw/fhxLlizB7du3Bbfhy5cvKumJLGAdd7Vq1cL58+cFzzknTpxA7969UaVKFans2sqQlc3/o5GWloYmTZpAU1NTbvBPBKG761XBlClTEBYWhr59+8pMLFu/fr2g67DOOYDq+uX8+fOxe/duBAcHS+1UHzduHO9OdVXBqmOx2CVARWJcSEgIXF1dZf520dHRCuVPnDiBwYMHo2HDhpxDOCkpCenp6Thy5Aj69euHbdu2ITMzU2VHx/8yEhISsGbNGm6ddXJyQmBgIDp27Pjd5VntShEKCwvx8OFD6Ovrw8HB4bsnCYgga60mItSpUwcHDx7kDQI6ODjAx8cHPj4+Ug5goSgvL0dWVpbMAKCiJDt5fqTbt2+jc+fOUgEecejp6eHevXtSiXmZmZlwdnbmdqd8L7DaZQYGBrh+/brKTl95/fbNmzewtrbmnLvfG/+2L0Ic3759k9nn+BJi+apuiGPx4sUKP9+/fz88PT0F20XqQEFBAW7duiXz2SsHBcWhDnv89u3b6NOnD4qLi1FUVAQLCwu8efMGBgYGsLKyUhhsF/kTrKysmPTigoIC7N69W8Iu8/Pzk0iCEILi4mIJ21bIjnM7OzskJiaiWrVqSt2rpKQEvXr1wvbt21WeZ+Pi4uDh4QF7e3ukp6ejSZMmyM3NBRGhRYsWqifKqFxI6weDtWY2Cxo1aiSTLPffhirk4JmZmeTg4EAGBgZcfW8DAwNydHSU4Ev4t/D27VvBpF6tW7eWSX44ZcoUatOmDa8867Oz1s51c3OjwYMHc6R0RBU1/gcPHkzu7u4KZeXV/c3NzVWazEoEZd49ax3Mt2/f0rNnz4iogttlxYoV1L9/f5o5cya9e/eOV56VD4aI6Pz58zR//nzy9/enMWPGSByKcPToUaYanmPHjqWQkBCp80uXLqWxY8cSEVFQUBC1bNlS7jWysrJoypQp5O7uTu7u7hQQEKDUeFVUO1hUT1JRXUl11J/9/Pnzv1bnXASWflNWVkZVqlThaniqAhMTE5nEkfJQuW8pOvgQGxtLPXr0oJycHJXbf/HiRerXrx/Vq1eP6tWrR/3796dLly6pfD1lEBkZST///PO/Upe8MuQRfgoFKwdSbm6uwoMPonq58g4+vHz5krp27apSvWp11ZhXlZuDda05duwYOTo6UmhoKKWlpVFaWhqFhoZSo0aN6ODBg7Rv3z6qXbs2zZo1S+41WOdLVsJcebV7X758Sdra2nLl6taty5EXivotUYXuJKRWeGlpKYWEhFDNmjVJS0uLk1+4cCGFhobyyhOxrXWs4+63336j0aNHc/wwfBB/z6z1/YkqCCbXrFlD/v7+5O/vT+vWraOCggJBsiKUlpbS4cOHKSQkhEJCQujIkSNyn6dy++Xxmghtv6rrBSuHEeucow79skaNGjLH5rFjx6hmzZpKP5MqUEXHYrFLiIisra2ZuQhycnJo3rx5HIfLvHnzVNZbVCGKZhl3LLKRkZGkra1N3t7eHGeUt7c3ValShf7888/vLs/KJ8MK1vmusk5w6dIlevjwoeD5e926deTq6kqamprk6upKGzZsoOfPnwu+PwtfpIeHB3Xq1InTVYiInj59Sp07d6YBAwYolG3cuLFMcu6lS5fycjz+L0BVkuQPHz5QQUEBaWhoUFZWlsQ89+7dOwoPD6caNWp8hxb/H9SxVrDoOBkZGdShQwcmXghWlJeX0+vXr2WSZCuCjY0NBQcHc7zGykIVrjxxKGuPV0bnzp1p3LhxVFZWxumXeXl51KlTJ/rrr78UyqrDn5CYmEgWFhZUq1Ytbq2sXbs2Va1alZKTk1W+7r8BcT4UVdCqVStOhxe9+0+fPpGHhwf98ccfKl/3PxsEYSGSi4uLo8mTJ1Pfvn2pX79+FBAQQAkJCYLl1eFcUnUSZJ2Ae/fuTb169aK3b99y5968eUO9evWiPn368MqXlpbS6tWrqVWrVlS9enWlycWJKgzqmJgYKi4uJiISrLBevHiRDA0NqVGjRuTn50d+fn7UqFEjMjIyEmRosT77+PHjyd7enk6fPs0tvKdOnaJ69erRxIkTeeXT0tKoZs2aVLVqVXJzcyM3NzeqWrUq1apViyMNrgxRkEFTU5MmTJggEXiYOnUqtWnThtq1a8d7bxFUffeKSMi6du0q+P6qYt68eWRpaUl//PEH5wTeunUrWVpa0q+//sorv2TJEtLU1KTWrVuTp6cnDRgwQOJQBFWCBuIwMTGRGbDNzMwkExMTIqogxpJHuBwTE0M6OjrUunVr7rdv3bo16erq0tmzZ3nvT8TvVFXkYF27di0ZGBjQnDlzOCdcYGAgGRgY0Lp16xTet6ioiCZPnkyWlpYyCQiFoKSkhM6dO0fbt2+njx8/EhHRs2fPJMix5IG13zg5OamkrIswatQo3nckDkWOKGWdUmZmZqSjo0OamppkZGSk9FzNamATVRi5u3btonnz5nHzbnJyMj19+pRXloWcWwRV+07Lli3p/Pnzgu4hCwYGBpx+YGFhwRERPnjwgKytrVW+7r8Fb29vcnV15QgviSpICV1dXWno0KEKZVnXyR+NVq1acYSF4oiJiaFWrVoREVF0dDTZ29vLlGeZL0VQdeyL5jgNDQ2Kj4+XCJqmpKTQ8uXLycbGRq68vr4+F7gQD4LcuXOHW6sUITg4mOzt7Wnfvn0S1zp48CD99NNPvPKsax3ruBswYAAZGxtTjRo1qEePHv8aoTyRegzce/fukb29vUSij6GhIdna2sokDs3NzeV0QNbAK8t6UaNGDXr06JGgZ5QF1jmHVU8gqgicy3qG9PR00tPT45VXNYDEqmOpYpeIw8LC4ock0FWGqoFnlnHHOmYbNmwoc01Yu3YtNWzY8LvJi5yGmpqa3N+iQ+RbmDRpEu/9CwsLaeHChdS2bVuqV68e2dnZSRyK8L/k0Hv06BEFBQWRg4MDaWtrU/fu3Sk8PJxXrlmzZjR48GB68OABvX//ngoKCiQORcjLy6PmzZtTlSpVyN7enuzt7alKlSrk4uLCm9x65MgR0tLSop49e3LB7p49e5K2tjYdPXpUpkzlxEVFhxCw2GWqkiSL9B95h5aWFi1btkymbOW1XNGhCKxrBauO065dO+rUqROdPn2abt++TXfu3JE4hEBVu+z58+c0cuRIMjU15d65mZkZjRkzhl68eMF73/Xr11OzZs1IS0uLunXrRgcOHKAvX74IajNRBZn4tGnTVE6MU9YerwxTU1NKT0/n/hbZRzdu3CBHR0deeVZ/QocOHcjX11ciyFtSUkKjR4+mjh07ypVTV98nqpjzT506Rdu2beP0PNGhCNOnT6e5c+cKf9hKMDIy4vQMMzMzTje5c+eOQpuGD//Zclg7d+7EsmXL4OfnJ5hIDqgg79u5cyfMzc3RoEEDEBEyMzNRUFCASZMmYfPmzbz3Njc3R3FxMUpLSwWTOokjNjYWHh4eaN68Obdt+urVq0hNTcXff/+N7t27y5Vl3XJtaGjIkceJIzU1Fe3bt+dq2cpDUFAQQkNDMWvWLCxcuBALFixAbm4ujh07hqCgIIU18d6+fQtvb2/Ex8dDQ0MDmZmZsLe3h5+fH8zNzbF27VqF9waAf/75B1u3bkV6ejqAinp+kyZNEkTcyPrs6qidW1xcjD///FOi/T4+PtDX15f5fRFPREJCAtq2bSvBuSKq+zt79mzeLWbqePesyM7Oxt69e5GdnY2NGzfCysoKZ86cQd26dTnCZHlg5YOpUaMGVq1ahZEjRyrdbtbyddWrV8fq1aultmpGREQgMDAQL1++xIMHD9C5c2eZfcjFxQU9e/aUWTP57NmzvHU7WcFSf3by5MmIj4/H0qVLMXLkSGzduhXPnj3Djh07sHLlSvj4+Ci895MnT9CrVy/k5eXh69evXI36adOm4evXrzIJNcXB2m/+/vtvrFq1Ctu2bRNEXFYZohrh7u7uaNmypdS24crzJV9fEwdfv2OpDw9UzE3jx4+XIllct24ddu3axW3HlYe0tDR069YNpqamyM3NxaNHj2Bvb4+FCxciLy8PERERCuX5tm3zbdVm6TsxMTGYP38+li5dKvN3k8dHIYI6OJAiIyOxfft25OTk4Pr167CxscGGDRtgZ2cnqGxeaWkpLl68iOzsbAwfPhzGxsb4559/YGJiIsHpJQss9arVsU6KoOrWb5a1Rl9fH7dv35YqLZqeng4XFxd8/vwZubm5cHJyQnFxsZQ8a71uFoiXB5Gl2uvr62Pz5s0SpJzi6NSpEwYPHoyAgAAYGxsjLS0NdnZ2CAgIQGZmJmJiYhTev379+tixYwfc3d0l6kWnp6ejbdu2eP/+vUJ51rWOddyNGTNG4ed85bRY0LFjR9SvXx+7du3iyOhLS0sxduxYPH78GJcuXeK9Rtu2bWFpaYnw8HCON+r9+/fw9fXF69evce3ate/Wfpb1gpXDiHXOUQffYJs2bdCmTRsp0t2AgAAkJiYq5F3bt28fxowZg4EDB0rYhNHR0QgLC8Pw4cPlyrLqWIDydok45s6dCyMjI6VJdWW1QVatbyFl2OQRRW/duhXLli1TSBTNMu5Yx6yuri7u378vVdYoKysLTZo04S1rpKq8uvhkhg0bhoSEBIwcOVJmKbRp06bJlVX13SlTgtDIyAgNGzYU5B8Q4caNG/jll1+QlpbGWw6MlS+SiHD+/HmJcdetWzdBssnJyVi/fr0Et8GsWbPkliyVxzlZGRoaGrylZVjtMlVLnSYkJICI4Obmhr/++kuCDFtHRwc2NjZyf2u+tV0citZ51rWCVccxNDREcnKyoNL3sqCqXfbx40c0b94chYWF8PHxQcOGDUFEePDgAQ4cOABzc3OkpKTw2hZABddIWFgYDhw4gLKyMgwfPhx+fn5o0aIF77OzcOUpa49XhqWlJVf2rUGDBti8eTN69uyJ9PR0tGzZkrd0JKs/QZ5t8uDBA7i6usq0RwD19X2WcmABAQGIiIiAg4ODzHfPV+rS2toa8fHxaNSoEZycnLBy5Up4eHgI9t/Khcrhkx8MVTLljh49Sjo6OrR3716JDPiysjLavXs36ejoCCo1EBYWpvDgQ/PmzWVGxObOncub4cq65drc3JyuXr0qdf7KlSuCsoPt7e3p5MmTRCQZmdu4cSMNGzZMoezIkSOpZ8+elJ+fL5FlGBMTQ05OTgplv337Rm5ubkzbqVifXV9fXyIzVoR79+7xlqT69u0b2dvby5QXAl9fX6byQSzvXhwsu3j09fWpW7dupKOjw91/xYoVNGjQIF551iy7H5mttnTpUtLX16epU6dSZGQkRUZG0tSpU8nAwIDLWlm3bh1169ZNpryurq7Mfv/o0SPS1dUV3I6IiAhq164d1ahRg8vsXL9+PR07dkyhnK6ursydLBkZGbz3r1OnDsXHxxMRkbGxMXediIgI6t27N2+bPT09acSIEfT161eJfhsfH0/169fnlWftN+K7KfT09JTeTWFrayv34MuU+9HQ0dGRu4NJSL9zd3enwMBAIpLMKr969SpT5oZQsPSdyvqEslvOhw0bRmvXriUiopCQELK0tKSxY8eSjY2NoGybP/74g6pVq0bLli2TyKjfu3cvdenShVc+NzeXGjZsSAYGBhJliaZOnUoTJkzglTcyMqLbt29LnU9JSeEti8SyTopD1UxR1rWmefPmNHr0aPr69St37tu3bzR69Ghq3rw5EVXoDLa2tjLlWeZLWRAvU8OH3NxcysnJIQ0NDUpMTJTI5P/nn3+otLRUofzly5fJyMiIJk6cSHp6ejRt2jTq3r07GRoaUlJSEu/99fT0uLVFfMzdv3+fDA0NeeVZ1zrWcacsKmfCKTr4oKenRw8fPpQ6f//+fdLX1xfUHj09PZnZ+3fv3pW53lUus6bo4APLejFgwAAyNTUlOzs76tevn9LZiaxzDqueQMS2U51lRwCLjsVqlxBVrClmZmYql8p99eoV9e3bV252txDY2trKzNwPCwuTO0+LwDLuWMdsvXr1aPv27VLnt23bJki/ZZW/ePGi4NJRsmBqakpXrlxRSVbVdye0BKF4WTwh2d83b96kadOmkbW1NRkYGNCQIUN4Zbp27Upnzpzh/V5lfPv2jbS0tGTuzvsvgNUuU7XUqQi5ubmCS0+pG6xrBauO4+rqSpcvXxbWWBlQ1S4LCQmh+vXryyyf9/LlS6pfv77MEm2K8O3bN9qwYQPp6uqSpqYmNWvWjHbv3i3Xr+Tl5UVRUVFK3UMcrPZ49+7duV2tY8eOpdatW9O+ffuoZ8+e1Lp1a155Vn+ClZUVxcbGSp2PiYkhKysrXnlWsJQDY60m4+npSTt37iQiolmzZlH9+vVp2bJl1KJFC0FlO+XhPxsEUQX9+/enefPmyf18zpw55OHh8d3bwTIJsk7AI0eOpMaNG9ONGze42qnXr1+nJk2a0OjRo3nlDQwMuHp+1tbWnDMiOzubt1xC9erVue164pNvdna2IAOZtaYc67Oz1s6tWbOmysZGQUGBRBkvEd6+fSsoOML67t+8eUNubm6cE1AkP2bMGJo5cyav/E8//cQ5J8Tvf/PmTapVqxavPCsfzJw5c2TycgiFqgEEEfbt20c//fQTt9j99NNPEiUiiouL5Tq7ateuTYcOHZI6HxUVRXXq1BF0fxanKkv9WUNDQ26+qFWrFt28eZOIiB4/fiyo31lYWHDbT8X7TU5OjiAjk7XfsAa8lcXx48fp27dv3N8sTimiirKLCxYsoKFDh3KG4+nTpwWVuWA1sE1MTLjAo/hvl5ubK9gZzFJOi6XvsBpprLwUjRo1oujoaKm23717l6pWrcorz2qkstSrZl0nRVB16zfrWnP16lWqWrUqWVpacuVKraysqGrVqtxW9oiICFq1apVMeXXU61YHt4aqyMrKorFjx1KrVq2oUaNG5OPjw5WV4kOLFi0oMjKSiCTffXBwMHXo0IFXnnWtYx13Xbt2pffv30ud//Dhg0xDTZFRrayBrQ4Dt2nTphQXFyd1Pi4uTmbfE+JEFBr4ZVkvWDmMWOccVj1BhGfPntGvv/5KAwcOpIEDB9KCBQsk5lB5YAkgsepYLHYJEbtzY/jw4dS+fXtKTEwkQ0NDOnv2LEVGRpKjoyOXcMcHlsAzy7hjHbN//PEH6ejo0MSJEykiIoIiIiJowoQJpKurK3MsqVv+1KlTcks/nj59mlfe1tZW5b7zbzj0vn79Snv37pVbCrFyGawePXpQeHi4wpJO4iUmWfgi7ezsBJcvIiKpklGKju8NVrtMXSgqKqKHDx8qzZfIAta1glXHiYuLo7Zt2ypdSkwEVe2yNm3a0J49e+R+vnv3bkElT4kqgh9RUVHUq1cv0tLSovbt29OePXsoJCSEqlevLjehmpUrjxWJiYl04cIFIqoI/PTs2ZOMjY2pRYsWgsYyqz8hICCAateuTQcPHqS8vDzKy8ujAwcOUO3atWnatGmCn0PVUnas5cBYkJ2dzY3twsJCmjBhAjk7O9PAgQMFlWuVh/9kEETVKLq4gigLN27cEGQkE7E5l1gmQdYJ+P379+Th4UEaGhqko6PDRSUHDBggiJCsQYMGdOPGDSIiat++Pa1YsYKIKmo+W1paKpQ1MjLighjik68o45MPrDXlWJ/97t27TLVzlSXdFEevXr1o69atUue3bdsmKKOe9d2z7iQxNDTkCKkqK01CHKKsfDAs2WqsWdmsCA4OJjMzM1q5ciVdunSJLl26RCtWrCAzMzPBgR0Wp6oq9WdFcHZ25pzG7u7uHJnwxo0bBc21ZmZmdP/+fal2X758WZChxNpv1AkhhJ3qJNplzYhnNbAtLS0pJSWFiCR/u7Nnz1Lt2rV55VNTU8nS0pLq169P2tranPyCBQto5MiRvPKsfedHQl5GfUZGhuCdbyxGKku9atZ1UgRVM0VZ1xoioo8fP9K2bdu49UHcWOADy3wpAiu3BlFFX9mxYwctXbqUgoODJY7vhWPHjpGpqSmtXLmSDAwMaPXq1TR27FjS0dERVO9aHWsdC1QllFcH1GHgnjp1iho3bkyHDx+m/Px8ys/Pp8OHD5OzszOdOnXquzrKWNcLFrDOOerQE548eSJ3fecjgmUJILHqWCx2iTpgbW3N2eXGxsZckt/x48epffv2gq7BEnhmGXfqGLNHjx6l9u3bk4WFBVlYWFD79u0FJ1exyovmhco4c+YMNW3alFc+MjKSfv75Z5Vq9KvLoceHjx8/yrXxNDQ0qHXr1rRhwwZBnAYiGUW8fUJ3DIeGhlKfPn1kJjcquq+QQxa8vLy4eZ+VF4BVt05ISFB48EGV3WMuLi5cIkTz5s2lOAaF8g2yrhWsOo6sHerK7FJX1S4zNzfnbApZePjwIe9uhuTkZJoyZQqXZDRr1iwpHV/erlUidptYHELs8f81fP36laZOncr5LjU1NUlXV5emT58umFuFpUqAeCK6g4MDF0B/+PCh4F3+qlaT+V7QVq2I1o9FlSpVULduXd56jZXx5s0b1K5dW+7ntWvXxtu3b3mvk5CQgN69e6N9+/a4dOkSfvvtN1hZWSE1NRW7d+/GkSNHFMqPGzcO48ePx+PHj9GuXTsAFfVff//9d8ycOVOh7KpVq9C3b1+cP3+eq9l5/fp15Ofn4/Tp07xtNzMzw/Hjx5GZmSlRh1JoTUsvLy/ExcWhTZs2CAgIwIgRI7B7927k5eUprLsKVNQAjYiIwNKlSwFU1IMsLy/HqlWrBNWrLC0txZ49e3D+/HmVasqxPnuTJk2QmZkpUTt32LBhgmvnJiYmIi4uDmfPnoWzs7NU+48ePSpX9ubNmzKfr0uXLliwYAHvvVnf/dmzZxEbGys1fhwcHATxGJiZmeH58+ews7OTOH/79m3UqlWLV75z587IyMiQ4IMZOHCgYD6YtLQ0NG/eHABw7949ic/4alBv3rwZu3btwoABAyTqeLq6umL27Nm89xbh27dvePXqFcrLyyXO161bV6HcokWLYGxsjLVr12L+/PkAKuqSLlmyhLeGpQg5OTkya8Xq6ury1rEcNGgQbt68ifXr1+PYsWMAKsbNrVu35NafFWHMmDFITU1F586dMW/ePPTv3x9btmxBSUkJ73gFgB49emDDhg3YuXMngIrfqrCwEIsXL0afPn145Vn7DQCUlZXh2LFjErwEHh4e0NLSEiQfERGB1atXIzMzEwDQoEEDBAYGyuSnEe8blfuJspg3bx6WLVuGmTNnwtjYmDvv5uaGLVu28Mr/8ssvsLa2xtq1a3Ho0CEAFb97VFSUIE4KDw8PhISEcLIaGhrIy8vD3LlzMWjQIF75mTNnwtfXF6tWrZJof58+fRTWSBeBte8oy0fx8eNH3muKwMcpYmdnhzt37kjxvsTExKBRo0a81y8vL5epHz19+lTiXcpDnTp1kJKSolK9atZ1UgQTExPk5eVJ1b/Nz89X+Aysaw0AGBsbY+LEiYLbKg6W+VKEiIgI7Ny5E+7u7hLtaNasGfdOFWHXrl345ZdfUK1aNVhbW0uscRoaGggKCuL+V2e/9fT0xN9//42QkBAYGhoiKCgILVq04OW6E0GVtU4d7U9LS+P+fvDgAV68eMH9X1ZWhpiYGMF9R1WsWbMGGhoaGDVqFEpLSwFU2Dq//PKLVP1weejXrx8AwNvbW4obpn///tz/iuquqwrW9YIFrHOOOvQEOzs7PH/+HFZWVhLn3759Czs7O4Xve9asWZg6dSru3LkjYROGhYVh48aNCu/LqmOx2CWV8fTpUwBQaGdXRlFREffOzM3N8fr1azRo0ADOzs6C+e6Cg4MxZMgQXLp0SYJTJS4ujuuL8sAy7tQxZr28vODl5SXou+qWz8zMhJOTk9T5hg0bIisri1d+7dq1yM7ORvXq1WFrayvFj6ro91PHuxM9Q3x8vEzbKigoCMbGxnLHwaNHj3j5NCtDXZxeW7ZsQVZWFmrWrAkbGxupcVf53cXHx3N/5+bmYt68efD19ZXwA4WHh2PFihUy72dqasqtCXycanxg1a0rczeJriEC39o0ffp0FBQU4ObNm+jSpQuio6Px8uVLjvNBFjw9PaGrqwsAGDBgAG8b5YF1rWC158X7gSpQ1S77+PEjzMzM5H5uZmbGq4u1atUK3bt3x7Zt2zBgwACp+QKoWEeHDh0qU57VJgaUs8e/J758+SLFf8WnX+vo6GDjxo1YsWIFsrOzAQD16tWDgYGB4PtOmzYNrq6uSE1NRdWqVbnzXl5eGDdunEJZFxcXJCYmwsHBAZ07d0ZQUBDevHmDyMhIXo4TebzE/v7+/xovsSz8Z4nRd+/ejaNHjyIyMlKCHEkRNDU18fLlS1haWsr8/OXLl6hZsybvBNy2bVsMHjyYcy6JyB9v3bqFgQMHcoqgPBARNmzYgLVr1+Kff/4BUDEJBgYGYurUqbxOWRZycHXj+vXruH79OhwcHDgjSx7u3bsHd3d3tGjRAhcuXICHhwfu37+Pd+/e4erVq6hXr55CeUXOeiFkXj8aLKSb8kjd7969izZt2sglRBKB9d0bGxsjJSUFDg4OEn0+KSkJPXv25A0ezp49Gzdv3sThw4fRoEEDpKSk4OXLlxg1ahRGjRrFS3L8I6Gvr4/09HTY2NhIPHtmZiaaNm2Kz58/K5TPzMyEn5+fFCmpKs6IT58+AYAgR6Y4nJycsGLFCnh6eko8w+bNm7F3797vTq4uQm5uLlJSUlC/fn1BhJdPnz5Fz549QUTIzMyEq6srMjMzUa1aNVy6dEnK4aBuZGVloU+fPnj27BkcHR0BVBhOderUwalTp3jHDQthJyuMjIxw9+5d2NnZSfzmubm5aNiwIS/pJis+fPiAn3/+GUlJSfj06RNq1qyJFy9eoG3btjh9+rSU0VcZpqamSElJQb169STa/+TJEzg6OvK2n6XviOY1fX19tG7dGkCFs+jz5884e/asTAI/cVJqeRA65kNDQ7FkyRKsXbsW/v7+CA0NRXZ2NlasWIHQ0FC5RoIIQ4YMgampKXbu3MkRXFtaWsLT0xN169b9rgTP6sLUqVMRHR2NNWvWSDgGAwMDMWjQIGzYsEGmnDrWGj7HyveGvDXnwYMHaN26NS8JoI2NDSZNmoS5c+fy3kud/VadELrWqaP9qhLK8yUtiUOIQxqoIIhW1cBNSEgQ/N3OnTtLkXgrgiIHTWlpKZYvXw4/Pz/BDvAWLVogLi4O5ubmcHFxUfgbfm/9JC8vD3Xq1JHZhry8PN5EFUC+ffnkyRM4OTnxJptER0dj7dq1EkTHgYGBSgeQlNWxWOwSoMIxJXI+iuYlY2NjzJo1CwsWLOBIkOWhVatWWLZsGXr27AkPDw+YmZlhxYoV2LRpE44cOcKNBT4oSxRdGSzjjkVWhMLCQqm1hs8pxipvbW2N/fv3w83NTeL8+fPnMXz4cLx69UqhfHBwsMLPhay1LO+OL9j/b9k1qoDl3bm7u2Ps2LEYNmyYxPn9+/dj586duHjxojqaKBesdtmHDx8k/i8pKcHt27exaNEi/Pbbb3B3d1coX6NGDRw/fhytW7eGiYkJkpKS0KBBA5w4cQKrVq3ClStXmJ/x34Cq9jwLVLXLtLS08OLFCyb/6ZMnT6QSu/5NqGKP8+km4uCbb4qKijB37lwcOnRIps/s39Cvq1atimvXrsHR0VHKL+Dk5KTQlyjqM127dsWrV68watQojih+z549aNasmVzZUaNG4dWrVwgNDUWjRo24+8bGxmLmzJm4f/++lIy5ubngd//u3TtB36uM/2wQxMXFBVlZWSgpKREURQcqlNTx48fLXWSLi4uxa9cu3o6oTueSspMgq7JeVlaGsLAwxMXFyTTwv3cg4cOHD9iyZQtSU1NRWFiIFi1aYPLkyahRo8Z3vS+gnmf/Uc6Rrl27okmTJti8ebPE+cmTJyMtLQ2XL1/mvQbLu+/Tpw9atmyJpUuXck41GxsbDB06FOXl5by7n759+4bJkycjLCwMZWVl0NbWRllZGYYPH46wsDBBWfUFBQW4deuWzHc/atQoXnlVwRpAaN++PbS1tTFv3jzUqFFDauwqWjjUBVananl5ObKysmS++06dOn3PpqO0tBRRUVES/VaZrHKWftOnTx8QEf78808u2P727VuMGDECmpqaOHXqlEJ5Ozs7BAcHS90nPDwcS5Ys4c0qKyoqQkJCAvLy8qSyRviyhmrXro1Dhw6hXbt2Ev02Ojoas2fPFuxcUHUHkwhXr16V+O2E7CYAACsrK8TGxsLFxUWi/efOnYOfnx/y8/N5r6Fq3+nYsSPq16+PXbt2QVtbm7vW2LFj8fjxY1y6dElKRlkHJB/+/PNPLFmyhPudatasieDgYPj7+/PKqiN4GBcXJ3et3LNnj0JZdayT3759Q2BgILZv3y4zU1SU1SdLjmWtUcWxos7dFADQsmVLzJgxAyNGjJDo+yEhITh37hzvem9iYoI7d+7A3t6e917q7Lf5+fnQ0NDgHOG3bt3C/v374eTkhPHjxwu+jzJQR/ufPHkCIuKSmcQNfR0dHVhZWcnsN0J20QL/u0k6lXdLyYOGhgYeP36s8DtGRka4d+8ebG1tBV0zODgYgYGBMDAwUIszlWXO0dLSkruLw8rKSqFNKAqEbdy4EePGjZOwL8vKynDz5k1oaWnh6tWrvM/wX8T8+fOxe/duBAcHSziWlixZgnHjxuG3335TKL9v3z6UlpbC19cXycnJ6NWrF969ewcdHR2EhYVhyJAh/8Zj/BDk5ORgypQpuHjxooTfQGjQmVV+woQJuH79OqKjo7mEnqysLAwaNAitWrVCaGgow9Mphp+fHzZu3Cjl+ygqKkJAQACvjgEoF+wXwcLCAhkZGahWrRqvk02IY+3Ro0fYvHmzRPAtICCAS5r6HjAwMEBqaqrULpaMjAw0b96cNylSHWC1y2QhISEBM2fORHJyssLvmZiYIC0tDba2trCxscH+/fvRvn175OTkoHHjxt/9+X+UL0IcxcXFMu1CIYFvoGKOTktLE2yXaWpqSuwmqgwiwsePHwU78lUN+rLYxKrY43y6iTj49JTJkycjPj4eS5cuxciRI7F161Y8e/YMO3bswMqVK+Hj4yMlM3DgQISFhcHExAQDBw5UeH0huzbNzc1x9epVODk5SdgWV65cwaBBg/Dy5Uvea6gCa2trxMbGolmzZhL3ffz4MZo2bSozsSs8PFzw9UePHq1Su/6zQRBVlOYuXboIiirxbTdjdS65ubnh6NGjUlvLPn78iAEDBig0lliUdQCYMmUKwsLC0LdvX5kO2fXr1yuUB37Mol8ZIgdYnTp1BMuwPrs6sk5KS0tx8eJFZGdnY/jw4TA2NsY///wDExMTGBkZyZW7evUqunXrhlatWnFZEnFxcUhMTMTZs2fRsWNH3nuzgHUniQj5+fm4e/cuCgsL4eLiIngr8t9//w0fHx8UFhbCxMRE6t0LUVaTkpJw6NAhmYunosWDNYBgaGiI5ORkqbIuQvHy5UvMnj2bc0hWnrKFKh2qOlVv3LiB4cOHc44icQgxtOLi4qQy9KZPny7YGc4C1n4jbwdWamoq2rdvz5uRraenh3v37kmV3MvMzISzs7PCgPnt27fRp08fFBcXo6ioCBYWFnjz5g0MDAxgZWXF65RizYhX5w4mEQoKChRuqRbH2LFj8fbtWxw6dAgWFhZIS0uDlpYWBgwYgE6dOsndCaAO6Ovr4/bt21Jj9sGDB3B1df1XjEwRiouLUVhYqPSup9LSUhw8eFDC0BFqpAYHByMkJASurq4y18ro6Gi5surOzlQ1U1TVtUYVx4q6d1McP34co0ePxvz58xESEoLg4GA8evQIEREROHnyJG9pKX9/f7Rq1Urlkl6qomPHjhg/fjxGjhyJFy9eoEGDBlypooCAAF5ntLrWuv8iioqKsHLlSrmBR3nzvXgpLz4IdZCoAk9PTwwcOFBlY5QFrHMOyy4OUSAsISEBbdu2hY6ODveZjo4ObG1tMXv2bEHzj6oJB+rQsV69eoVHjx4BABwdHQWvNzVr1sT27dvh4eEhcf748eOYNGkSnj17JrgNQMV8n56ejrp166JatWpKyb569Urm+1PU71Udd6yyQEWCFBFh2rRpqF69utQawhd0ZpX/8OEDevXqhaSkJC5w/fTpU3Ts2FGmj0KdkOfLePPmDaytrbnEB0VQJtgvQnh4OIYOHQpdXV1eJxvfXPbXX39h6NChcHV15cpS3bhxA4mJiTh48CBv2deCggJut1NgYCAsLCyQkpKC6tWrKyy/6OjoCE9PT6xatUri/Jw5c3D8+HFuHCvCkSNH5NrDP2oHTXp6OlxdXXntKtbdY2VlZVi/fr3c51dkF7LalKw6zuvXrzFmzBicOXNG7rN9Dwh1SCsaM6xBW1abmMUeVwfq1q2LiIgIdOnSBSYmJtyOzcjISBw4cEAmpcGYMWOwadMmGBsbw9fXV6GdIWSHv7e3N8zMzJiqBIjrCg0bNpS7O0gcrNVkvhu+P+3I/3uYNWsWdejQgZ4/f07GxsaUmZlJV65cIXt7e1qyZAmvPAv5ooaGBr169UrqfG5uriBimqpVq8okQhOKI0eOkLa2Nv30008caWjbtm1JW1ubjhw5olD2zJkzdPnyZe7/LVu2ULNmzWjYsGEcYZUilJSU0MKFC8nExIQjBTIxMaEFCxbQt2/feOVZn71u3bq0cuVKleVZCImIiO7cuUPDhw8nJycnatmyJY0ZM4YjKeID67snIiooKKBly5bR4MGDqXfv3rRgwQL6559/BMlWRmlpKd2+fVvwvR0cHGjatGkqEfARER04cICqVKlC/fr1Ix0dHerXrx81aNCATE1NydfXl1d+3759VL9+fY6Eq1atWhQaGiro3q6urhLvXln06tWLnJyc6I8//qDo6Gg6duyYxKEsioqKZM4/8tCsWTMaPHgwPXjwgN6/f08FBQUShyJs3bqVtLW1aejQobRx40bauHEjDRs2jKpUqUJbtmzhvXdYWBidPHmS+z8wMJBMTU2pbdu2HHG0IrD2G3Nzc7p69arU+StXrvCSwBGxEXZ27tyZxo0bR2VlZRyBXV5eHnXq1In++usv3nt//fqVxo4dS9ra2qShoUFVqlQhTU1NGjFiBJWWlvLKt2vXjjp16kSnT5+m27dv0507dyQOPqxcuZIOHjzI/T948GDS1NSkmjVrCpIvKCigbt26kZmZGWlpaVGdOnWoSpUq1KlTJyosLOSVZ+k7VlZWFBsbK3U+JiZGMKn6u3fvaPXq1Rx54po1awSTYP5oWFtbU0REhEqyrOukCGPGjJFJRl5YWEhjxowRfB1l1xpjY2NubRaKixcvCj6E4tKlS9StWzeytLQkfX19at++vcw+KQvLly+natWq0ejRo2nNmjXc3Cs6xJGamkplZWXc34oOPpiZmXHkmRs3bqR27doREVFsbCzZ2dnxyqtjrWMZd6zrDZHqxI9Dhw6lGjVq0Jw5c2j9+vW0YcMGiUMe+Ih6lSUO/fr1K6WnpytNlL1t2zaytramWbNm0f79++n48eMSh9B75+fn05MnTyQOPqg654hsGE1NTZowYQL3/4wZM2jq1KnUpk0brg/zwdfXV2XC+YyMDOrQoYNKZLesOtaHDx9oxIgRnJ6goaFB2tra5OPjw6vfERHp6upyZObiSE9Pl0twq24kJSVR48aNZY4Dvven6rhjlSUiMjQ0VEg2/L3liSrmp9jYWFq1ahVt3rxZEDG1CKWlpbR69Wpq1aoVVa9enczNzSUOWfjw4QMVFBSQhoYGZWVl0YcPH7jj3bt3FB4eTjVq1BB0fz8/P9q2bZvg9qob9vb2tGjRIqnzQUFBZG9vr1A2NTWVLC0tqX79+qStrc3pHAsWLKCRI0cqlD116hTp6elRkyZNyN/fn/z9/cnZ2Zn09PQE+Tg2btxIRkZGNGXKFNLR0aEJEyZQt27dyNTUlH799VdeedZ1srJecefOHTpz5gx17tyZ2rdvzysfGRlJe/fuJaKKsV+tWjXS1NQkPT09CZtDHhYtWkQ1atSgNWvWkJ6eHi1dupT8/f2patWqUvpRZbDalKw6zvDhw6l9+/aUmJhIhoaGdPbsWYqMjCRHR0eJ30QclfU/Rcf3RLt27aht27Z08OBBio+PV1o3ZrWJWexxcSQmJlJERARFRERQUlKSYDlDQ0NOn6lVqxbdvHmTiIgeP35MhoaGgq/Dgvz8fHJycqJGjRpxvtyqVatSgwYNeP1CHz9+VFlX6N27Ny1cuJCIiIyMjOjx48dUVlZGgwcPpkGDBglqe2lpKR0+fJhCQkIoJCSEjhw5orSeWhn/+SBIUlISRUZGUmRkJKWkpPwr91TVuSSa8DU0NCg+Pl5iEUhJSaHly5eTjY2NTFl1Kes1atSQqbAKBcui36RJE26BTktLIx0dHZo/fz799NNPghzREydOJCsrK9q+fTv33rZv307W1tY0ceJEXnnWZ1fFOSIOT09PGjFiBH39+pWbwImI4uPjqX79+nLlvn37RmPGjKHHjx+rfG/Wd3/hwgW5nwkxtKZNm8YFDUpLS6l9+/akoaFBhoaGFB8fzytvYGDA9O6dnZ25dorefXl5OY0bN46CgoIEX0fZAAIRUVxcHLVt25bi4+PpzZs3Ekq/EKPZyMiIbt++rdQ91QkDAwPKzMxUSbZWrVq0efNmqfNbtmyhmjVr8so3aNCA4uLiiIjo2rVrpK+vTzt27KD+/fuTl5cXrzxrvxk5ciQ1btyYbty4QeXl5VReXk7Xr1+nJk2a0OjRo3nljxw5QlpaWtSzZ09u4e7Zsydpa2vT0aNHFcqamppyBq6pqSk9ePCAiIhu3LhBjo6Ogp/hyZMndOrUKYqKihIcNCWqeHcPHz4U/P3KsLW15QJIZ8+eJTMzM4qNjSV/f3/q3r274OtcvnyZtm7dSr///judO3dOsBxL3wkICKDatWvTwYMHKS8vj/Ly8ujAgQNUu3ZtmjZtGu+9ExISyMTEhOrUqUNeXl7k5eVFdevWJRMTE7lOBhcXF85R37x5c3JxcZF7yEJlp6Oigw8WFhaUlZXF+z1ZYF0nRdDU1JQ5175+/Zq0tLTkyrGuNT/asaIO2Nrayj0qByPEk3IUOdSFONENDQ0pJyeHiIj69+/POaafPHkiyCHKutapMu7EwTJnvHnzhtzc3Lh3JRoDY8aMoZkzZ/Le29TUlK5cuSLgKSWRm5sr+FCEoqIi8vPzIy0tLYkknSlTptCKFSt428ESgHn06JHKQQAi1eecLl26UJcuXUhDQ4PatWvH/d+lSxfq0aMHjR8/Xqk1U1WwJByw6lje3t7k4OBAMTExnE4aExNDjo6ONGTIEF751q1bU0BAgNT5KVOmUJs2bXjlS0tLKTQ0lIYNG0bu7u7UtWtXiUMImjZtSl5eXnTjxg3KyclRqt+rOu5YZYkq+p8yOo065b99+0ZaWlp09+5dle+vijNZNKblHVpaWrRs2TJB91cm2C8LlW0x0fHx40f6+vUrr7y+vr5M2ygjI4P09fUVyrq7u1NgYCARkYQ/4OrVq3L9QOLIy8uj+fPnc+vcr7/+Snl5ebxyRESOjo60f/9+qXsvWrSIJk+ezCvPapfJ0zPatm2rks1RVFREycnJ9Pr1a0Hft7e35wIGRkZGnK4rCiArAqtNyarjWFtbc85zY2Njzqd1/PhxuQEkRfqgIt1Q3WAN2rLaxCz2OFFFAKFDhw6koaHBBXo1NDSoffv2lJ+fzyvv7OzMBXvc3d1p1qxZRFTR72rVqsUr37VrV3r//r3U+Q8fPgheK/fv308lJSUUGRlJgYGB9Msvv9CuXbuouLiYZs+erVCWRVe4e/cuWVlZUa9evUhHR4d+/vlnatSoEVWvXl2QrXnv3j2yt7cnAwMDzg42NDQkW1tbpjXsPxsEefnyJXXt2lWqM7q5ucncKUFEEoEDRYdQKOtcEl/8ZRkKBgYGtHv3bpmy6lLW16xZQ5MmTRKcnVYZLIu+uIG8ePFiLvqXnJxM1atX5723iYkJnT59Wur8qVOnyMTEhFee9dlZnSMWFhbcBC6ueOTk5PC+OxMTE6YgCOu7NzMzkxnx3rBhAxkbG/PK16pVixITE4mIKDo6mgtILVy4UFDwzsvLi6Kioni/Jw8GBgbc81tYWFBaWhoRET148ICsra1Vvq4QiDsCVDHwGzVqpFKAl8+RyudUFaFr16505swZpe9PVNHv5M0XQjIf9PX1ucyJOXPmcBlS9+7do2rVqvHKs/ab9+/fk4eHB2loaJCOjg7p6OiQpqYmDRgwQFCWJFFFoN7Hx4datGhBLVq0IB8fH0G/Z7Vq1bg5XaR4EBE9fPhQ0K4/VrDuYNLT0+OMsqlTp9L48eOJqMLhZWZmppY2KgJL3/n69StNnTqV+701NTVJV1eXpk+fTl++fOG9d5MmTWjcuHESSRGlpaU0fvx4uRlHS5Ys4bLLlixZovCQBVmOR1nnhMw5c+bMoZCQEN7vyQLrOsmaKcq61rA6VogqdnH4+PhQ27Zt6enTp0REFBERwTSevhdyc3M5nYjFiU5U4RCdO3cuXbp0ifT09DgH7vXr1wUZeaqudSKoMu7EwTJnjBw5knr27En5+fkS+l1MTAw5OTnx3tvW1pYz6n8Epk6dSi1btqTLly+ToaEh1/5jx45R8+bNv+u9WXcdss45LLs4xJGYmEiBgYE0ZMgQzjkpOhSBJeGAVccyMDCQOS9dunRJkJ5x8eJFMjQ0pEaNGnG7rxo1akRGRkZ06dIlXvnJkyeToaEheXt707Rp02j69OkShxAYGRmpnKjDMu5Yx2xWVhZ169aNwsLCKCkpSemdd6zydnZ2gsaXPKjiTL548SLFx8eThoYGHT16VCIT/Nq1a/Ts2TPB92d16PIFZOrWrUtBQUHcTsnK6N27N+3Zs0fq/J49e6hHjx4K721iYsK9L/H1Ijc3l3R1dXnbzgJ9fX1uPbe0tOT6QEZGBllYWAiSZ7HLKusVeXl59PnzZ0Ft//btG9nb2zONOwMDA6791tbWlJycTERE2dnZvL4kVpuSVccxNjbmfBl169blgrCPHz/m9SOxwMzMTGqnl6xDEViDvuqwiVW1x4mIevbsSW3atJEI5KSnp1Pbtm2pZ8+evPLr1q3jbIhz586Rnp4e6erqkqampqCdgyxVhEQwNTWV6UedMWMGry+MVVdgqSbz008/Uf/+/SV29L979448PDyobdu2gq4hC9o/pggXOwICAvDp0yfcv38fjRo1AlBRr3v06NGYOnUqDhw4ICVz+/Zt3usKZaIHKuq7CSWHBSrq4ZEK5IvA//GUjBkzBhs3bhREICQLV65cQXx8PM6cOYPGjRujSpUqEp/zEet06dIFly9flqqpd+XKFV5eCh0dHa6W+vnz5zlyIgsLC0HEorq6ujJJF+3s7CTq8MoD67PXr18fixYt4jgCKsvzkTKVl5fLrHn49OlTKXK4yhgwYACOHTuGGTNmKPyePLC++9WrV6N37964dOkSVyd/7dq1CAkJ4SWHBv6vzisAnD59Gt7e3mjQoAFHjseHvn37IjAwEA8ePJD57ivXI64Mc3NzfPr0CQBQq1Yt3Lt3D87OzigoKOCt7//27VsEBQXJJd3kqwHKxzHEhw0bNmDevHnYsWOHYNJRoKLPqArxOuMBAQGYNWsWXrx4IfPdK6q37OHhgejoaAQGBkqcP378OPr168fbDiMjI7x9+xZ169bF2bNnORJSPT09fP78mVeetd+YmZnh+PHjyMrKkqi3XXn+U4SWLVti3759gr8vgouLCxITE+Hg4IDOnTsjKCgIb968QWRkJJo0acIrT0Q4cuSI3H7LN9/9/vvvmDNnDpYvXy7z3fGtQebm5sjPz0edOnUQExODZcuWce0SWrc2MTFRbvvXrVunUJal7+jo6GDjxo1YsWKFSnwUWVlZOHLkiMR6rqWlhZkzZyIiIkKmjDhHixAi4MoQfz/nz5/H3LlzsXz5cq5W9fXr17Fw4UIsX76c91pfvnzBzp07cf78eTRt2lTqt6/87jdt2sT9zbpOmpmZQUNDAxoaGmjQoIHU5xoaGgo54VjXmp07d8LIyAgJCQlSpNsaGhq87f/rr78wcuRI+Pj4ICUlBV+/fgVQUX99+fLlMuv+VgYfx4gydZ/p/6s5Le96NjY2Mv9WBb///ju8vLywevVqjB49Gs2aNQMAnDhxAq1bt+aVV3WtE0GVcScOljnj7NmziI2N5Wrri+Dg4IAnT57w3nvp0qUICgpCeHi44HlGHh48eCCz1rmi9e7YsWOIiorCTz/9JNFXGjduzFtjnRV37txh4k1jnXM6deokJaMsDh48iFGjRqFnz544e/YsevTogYyMDLx8+RJeXl4KZZ2cnPDmzRuV7suqY1WtWhWmpqZS501NTWFubs4r37lzZ2RkZGDr1q1IT08HUEHmOmnSJNSsWZNX/uDBgzh06BD69OnD+115cHd3R2pqqlJ6mQgs4451zL5+/RrZ2dkYM2YMd05DQ0NwjXxW+QULFuDXX39FZGQkLCwslG6/yCYAKubODx8+AAD69euHRYsWyZQR8ZRkZ2fD1tZWKZ9LZcgiMlYGYWFhWLBgAXx9fbn16datWwgPD8fChQvx+vVrrFmzBrq6uvj111+l5D08PDB37lwkJyfjp59+AlDBCXL48GEEBwfjxIkTEt8Vh66urkzbOyMjQ2aN/bS0NDRp0gSampq8PFB83E/W1tZ49+4dbGxsULduXdy4cQPNmjXjfFR8YLXLEhISMGTIEOjq6kqc//btGzePykOVKlWYuRtq166N58+fo27duqhXrx7Onj2LFi1aIDExUapNlcFqU7LqOI6Ojnj06BFsbW3RrFkz7jrbt29HjRo1lL6eUKxfv55prAIV/KoTJ07Es2fP0KRJE6V8CQC7TQyobo8DFf322rVrEvzHjo6O2Lx5syBeXnH/Xbdu3ZCeno7k5GTUr19f4bOLj/cHDx7gxYsX3P9lZWWIiYlRyCEkjj///BPDhg3DyZMn0aFDBwAV+pHIV6AIrLqCqakpFixYIKidlXHnzh0kJSVJ3Mfc3By//fYbWrVqpdI1gf8wMbqpqSnOnz8v9fC3bt1Cjx49UFBQ8N3uzepc+pEQV5ZkgY8UZ/v27QgKCoK3t7fMRV9c6a28GHh4eODbt29o3749li5dipycHNSqVQtnz57FlClTkJGRofDeISEhSE9Px969e7mF6uvXr/D394eDgwOv44j12e3s7OR+pqGhwUvKNGTIEJiamqpESLRs2TKsXbsW7u7uaNmyJQwNDSU+5zPyWN89AKxatQqbNm3ClStXEBUVxTl02rdvzytrY2ODXbt2wd3dHXZ2dti2bRv69u2L+/fvo0OHDnj//r1CeU1NTbmfCVH2hw8fDldXV8ycORNLly7F5s2b4enpiXPnzqFFixYKx2yfPn2QlZUFf39/meSD35sI1NzcHMXFxSgtLYWBgYGU4iCEFF5ZiJxw8pYHoYbWsmXLsGbNGrRv316COPDq1auYNWuWhCNdVh/28fFBeno6XFxccODAAeTl5aFq1ao4ceIEfv31V9y7d4/3OeRBFXLvsrIy3L17FzY2NoIW/ZSUFFSpUoUzFI8fP469e/fCyckJS5YsURi8TUpKwqdPn9C1a1e8evUKo0aNwrVr1+Dg4IA9e/ZwDkZ5mDZtGnbs2IGuXbvK7Ld8853o3VWWE2pgT5kyBSdPnoSDgwNu376N3NxcGBkZ4eDBg1i1ahUv+eLy5cuxcOFCODo6SrVfQ0MDFy5cUCjP2nfE8fHjR1y4cAGOjo5c0oUitG/fHoGBgVKByGPHjmHlypW4ceOGQvnExESUl5ejTZs2Eudv3rwJLS0tuLq6KpRv0qQJtm/fzim5Ily+fBnjx4/nAnryICL7lQVZ717R2lhZlm+dTEhIABHBzc0Nf/31l4RzRkdHBzY2Ngqda6xrDStcXFwwY8YMjBo1SoIA8Pbt2+jdu7eEASMPx48fl/i/pKQEt2/fRnh4OIKDg+Hv7897jYiICKxevRqZmZkAgAYNGiAwMBAjR45U7cEEoqysDB8/fpSYH3NzcznySkVgXetYxx3LnMFK/Oji4oLs7GwQEWxtbaWeXQhZ7ePHj+Hl5YW7d+9KrN+iuVPRnG1gYIB79+7B3t5eov2pqano1KkT5+AUh3jwkw+KdNRWrVph/fr1UvOVULDq5tWrV8fnz58xePBg+Pv7o127dkq3oWnTppgwYQImT57MvT87OztMmDABNWrUUBi4vXDhAhegVjbhgFXH2rlzJw4fPozIyEguePzixQuMHj0aAwcOxIQJE5R6D8qiZs2auHjxosyAt1C8efMGo0ePRuvWrWU61xQ5JlnGHeuYdXJyQqNGjTBnzhyZOhpfUJpV3sXFBVlZWSgpKYGNjY2UXcnXfkdHR0RERKBNmzbo0KED+vXrh3nz5iEqKgoBAQF49eqVXFk7OzuMGTMGvr6+SiWTimw4Q0NDzvkuCxoaGli7dq3Ca7m7u2PChAnw9vaWOH/o0CHs2LEDcXFxiIyMxG+//cYF+MShyL6o3JbKc+/YsWPx9u1bHDp0CBYWFkhLS4OWlhYGDBiATp06YcOGDVL3evHiBaysrBTaZ0J087Fjx6JOnTpYvHgxtm7disDAQLRv3x5JSUkYOHAgdu/erVCeVbfW0tLC8+fPpfSBt2/fwsrKirf9y5cvR0ZGBkJDQ6GtrXwu97x582BiYoJff/0VUVFRGDFiBGxtbZGXl4cZM2Zg5cqVcmVZbUpWHWffvn0oLS2Fr68vkpOT0atXL7x79w46OjoICwvDkCFDpGSEjhlAfnJZWVmZ3CRtobhx4waGDx+O3Nxc7pwyQVtFNvHu3bvRvHlzhfIs9jhQoUPv27dPKqHn1q1bGD58OLKyshTKR0REqBT8E0+KkjXm9fX1sXnzZvj5+Sm8vwj79+/HlClTcO7cOezevRvHjx9HfHw87xqsrK7AF6wVB18ArFmzZli/fj3c3Nwkzl+4cAHTpk3D3bt3Bd9LHP/ZIIixsTEuX74s1elv376Nzp07y4ywz549G2PHjlU520gEVudSeHg4qlWrhr59+wIA5syZg507d8LJyQkHDhzgVVySkpJw6NAhmZle3zsAw7Lo5+XlYdKkScjPz8fUqVM5Q37GjBkoKyuTaVANHDhQ4v/z589DV1eXcwCmpqbi27dvcHd3/58OPgEVOz569uwJIkJmZiZcXV2RmZmJatWq4dKlSwodBKxGnirvXhbmzp2L3bt3o6ysDGfOnOECYXxYsmQJNmzYgBo1aqC4uBgZGRnQ1dXFnj17sGvXLly/fl3QdVTFu3fv8OXLF9SsWRPl5eVYtWoVt3guXLhQoUPb2NgYV65c4XU686G4uFjmmOWb/MPDwxV+/j2CMEIyV0VQNF+xOkcLCgqwcOFC5Ofn45dffkGvXr0AVGTK6+joqJxVIBTTp0+Hs7Mz/P39UVZWhs6dO+PatWswMDDAyZMn0aVLF4XyrVq1wrx58zBo0CA8fvwYTk5OGDhwIBITE9G3b18pQ+fEiRPo3bs3c1YqULHLa9++fSpnWFbOgq8MUUafPJSUlGDjxo3Iz8+Hr68vXFxcAFRkFBkbG2Ps2LEK5atXr47ff/8dvr6+SrVbBJa+4+3tjU6dOmHKlCn4/PkzmjVrhtzcXBARDh48iEGDBim8d1RUFObMmYOAgACJZIGtW7di5cqVEoEUWeO/devWmDNnDn7++WeJ80ePHsXvv/+OmzdvKry/vr4+EhMTpbKj0tLS0KZNG0HZej8aT548Qd26dZXOPvvRa42BgQEePHgAW1tbCWeyaPyzZDHu378fUVFRUkGSyli3bh0WLVqEKVOmcEkKV65cwdatW7Fs2TKVd5TyYfHixfDz81N5RwnrWsc67ljmjD59+qBly5ZYunQpl+RiY2ODoUOHory8HEeOHFHYdkVOclEb+NC/f39oaWkhNDQUdnZ2uHXrFt6+fYtZs2ZhzZo1CrMVO3XqhMGDByMgIIBrv52dHQICApCZmYmYmBgpmcrr++vXr1FcXAwzMzMAFe9TFPyqvL6L22hJSUkqBwHUgdLSUvz9998ICwvDmTNnYG9vjzFjxmD06NGcsc8HQ0ND3L9/H7a2tqhatSouXrwIZ2dnPHz4EG5ubnj+/LlcWZaEA1V0LBcXF4l7ZWZm4uvXr5wzOi8vD7q6unBwcBAUfPvy5QvS0tJkJgXyZUavXbsWjx8/xpYtW1TONP77778xcuRImXY/3/tjGXesY9bQ0FDlHSzqkGdtP4szecOGDQgLC8O9e/fQtWtX+Pv7w8vLizcTv2vXroiOjoaZmZnSyRqVoa+vj7S0NDg4OEicz8zMRLNmzVBcXIycnBw0btyYt2KAsvjw4QN+/vlnzrFbs2ZNvHjxAm3btsXp06elAlLi+hCffca3/paXl6O8vJwLIBw8eJCzhydMmMDrDGa1yzQ1NfHy5UupHS+pqano2rUrbyDAy8sLcXFxMDIygrOzs9S7UtYXdOPGDe75+/fvr5SsslC3PV9cXIz09HTUrVsX1apVk/kddYyZGjVqYPTo0VzSsSpgDdqyQll7vDKOHz+O5cuXY+vWrVwiWlJSEgICAjB37lze6huqBv+ePHmichUhefjjjz8wc+ZMWFpaIj4+XtAaIgqay9MVxJGSkiIRrBX/rWXtTucLgJ0+fRpz5szBkiVLJHT7kJAQrFy5UiKBRhl98T8bBPH09ERBQQEOHDjAZQU+e/YMPj4+MDc3R3R0tJSMg4MDHj9+jDZt2mDs2LEYMmSI1OQpBKzOJUdHR2zbtg1ubm64fv063N3dsWHDBpw8eRLa2toKJ3C+Ldd8AZj/Gvh2b4jjv/DspaWlOHjwINLS0lBYWIgWLVrAx8cH+vr6P7ppUpAXGFmzZg06deokEQ3n24kCAEeOHEF+fj4GDx7MlYwIDw+HmZkZPD091dPo74BWrVph8+bNggM+lfH69WuMGTMGZ86ckfm5srsRhMLCwgIZGRmoVq0azM3NFRqY32M3yf8LqF27No4dOwZXV1ccO3YMkyZNwsWLFxEZGYkLFy7g6tWrCuVNTU2RkpKCevXq4ffff8eFCxcQGxuLq1evYujQocjPz5f4vpaWFl68eAFLS0u5CpNQ2NnZ4cyZM8xB/x+FGjVq4NKlSyor3CywtrZGbGwsmjVrhv3792Px4sVITU1FeHg4du7cyVtaky9ZgC/7ycjICGlpabC3t5c4n5OTg6ZNm3Kl/eShU6dO0NPTQ2RkJKpXrw4AePnyJUaNGoUvX77wBrjUBb5STIoQExMDIyMjTrndunUrdu3aBScnJ2zdulVh4Jp1rXn69ClOnDghM2jNV4bN3t4eO3fuRLdu3SSCIBEREVi5ciUePHjAe395ePz4MZo2bYrCwkKF37Ozs0NwcLBUZll4eDiWLFnCXEZEHpo3b4579+6hc+fO8Pf3x6BBg3idWuoE67hjwb179+Du7o4WLVrgwoUL8PDwwP379/Hu3TtcvXoV9erVU+v9ZKFatWq4cOECmjZtClNTU9y6dQuOjo64cOECZs2apXDeunLlCnr37o0RI0YgLCwMEyZMwIMHD3Dt2jUkJCSgZcuWCu+9f/9+/PHHH9i9ezdXLuLRo0cYN24cJkyYAB8fH4nvVy75VtlQFj/3vXQkWXj58iX27duH8PBwpKeno1evXvD390f//v0V9q/atWvjzJkzcHZ2RtOmTTF//nwMGzYM169fR69evWTupBGBNeFAWfA5v8XB5wiPiYnBqFGjZJbzkvfbVU5uu3DhAiwsLFQqUwwAtra2XAkm0Xr3X0D//v3h6+vLm1TxveTVDVWcySkpKQgLC8OBAwdQVlaG4cOHw8/PDy1atPjOra3I7B44cKBUsGbevHmIjo7Go0ePkJSUBE9PTzx79kzhtb58+QI9PT2l23DlyhUJf0C3bt2UvoYQDBw4EGFhYTAxMZGbkf69IQq+pqamonHjxhK7OMrKypCTk4NevXrh0KFDCq+jSlWPFi1aIC4uDubm5ggJCcHs2bOZy07+/xOWLl2K8PBw5OTkoF27dvD394e3t7dS75A1aJuTk4PS0lKZQcsqVarwlhdT1h6vDPFdPKK+K/q7si9Zlk+FNfinKuTt/jl8+DBatGghoZsqsm+U1RvEg7W3b9/G7NmzERgYKFGiee3atVi1ahVvAElc96q8K0b8f2X1xf9sECQ/P58zMurUqQOgIiLl7OyMEydOSNXlFeHSpUvYs2cP/vrrLwDA4MGDMXbsWKW2P7M6lwwMDLjI7dy5c/H8+XNERETg/v376NKlC16/fi1XVpUt1+KTf+UMoMoQkvWjDD5+/MhF5fi4J75Hthfrs6tjG+GPAuu7V2eJE1WwadMmjB8/Hnp6erw7VfiCMKdPn4aWlhZ69uwpcf7s2bMoKytD79695comJiZi3rx5CAoKkrnVnq/f+vj44MmTJ9iwYQO6dOmC6OhovHz5kitxJtoRJg51jJvw8HAMHToUurq6zNknjx49wubNmyV4MQICAiRqY/JBqEOUtfatOvuNnp4esrKyULt2bYwfPx4GBgbYsGEDcnJy0KxZM0G/TXJyMhwcHNC9e3f069cP06ZNQ15eHhwdHaUy8q2trbFr1y7O4SJLYRKK8PBwxMTEYM+ePYKDrKzvXnwni3g9ZFngyxBdtWoV/vnnH97sHHGoq26yvr4+MjIyUKdOHYwaNQo1a9bEypUrkZeXBycnJ14nNOtOqqpVq+LkyZOcsijCtWvX0LdvX96STllZWfDy8uKeAajQmRwcHHDs2DGZRoi4kVzZSVUZfI6p3bt3Y/369VwpJgcHB0yfPp139484nJ2d8fvvv6NPnz64e/cuXF1dMWvWLMTHx6Nhw4bfLekhLi4OHh4esLe3R3p6Opo0acLtAhI5uBVhxYoV2LdvH/bs2YPu3bvj9OnTePLkCWbMmIFFixYhICBApXZ9/vwZ8+fPx5kzZ/Do0SOF39XT08O9e/ekfufMzEw4Ozsz19RWhNu3b2Pv3r04cOAASktLMXToUPj5+cmt26tOHVFdOxhV3bX54cMHbNmyBampqZxTa/Lkyd+1Vrc4zM3NkZKSAjs7O9SrVw+hoaHo2rUrsrOz4ezszJvNnJ2djZUrV0q0f+7cuVz5CEWoV68ejhw5wu34EyE5ORk///yzVOBNmUCsrCDA99TNb968iT179iA8PBw1atTA+/fvYW5ujr1798rd/clSclVdYAk6qwoHBwf06NEDQUFBggMQ6k5uMzY2xp07d/6VQKM6sXPnTixbtgx+fn4q8QuwyrNixYoVqF69ulQZlj179uD169eYO3eu4GuVlJTgjz/+wNy5c1FSUgJnZ2dMnToVY8aM+W79+cSJExg8eDAaNmzIrU9JSUlIT0/HkSNH0K9fP2zbtg2ZmZky55CysjIsX74c27dvx8uXL5GRkQF7e3ssWrQItra2gspWqors7Gxs2LCBs8mcnJwwbdo0uWNAR0cHT548QY0aNVRKsFKHbi3yTwUHB2PWrFkwMjKSaJ+trS0GDRokiONVWejr6yMzMxO1a9dW+vlZbUp16jiDBg1C69atpcbWqlWrkJiYiMOHDyuUF4fI6S+yEYTg4sWL2Lt3L/766y9oaWnB29sbY8eOlSrdKwusQdvOnTvDz89Pyl+xb98+hIaG4uLFiwrllbXHK4PPjyIO8TaqK/gngrKcb4p2/4hDyO45VdG6dWssWbJEavPA6dOnsWjRIiQnJyuUZ9UX5eE/GwQBKhS+uLg4Ccec0Ch6UVERoqKisHfvXly9ehWOjo7w9/fHyJEjeRU5VZxL4rCyskJsbCxcXFzg4uKCmTNnYuTIkcjOzkazZs0UOlhU2XIdHByMwMBAGBgYqGXLfUJCAtasWSOx+AYGBsrcai++2Mgj+/ye2V6sz866jZDPESgOPoVV2ezUH/3uWRUHOzs7JCUloWrVqszlwJo2bYqVK1dKTcAxMTGYO3cuUlNT5cpmZmZi+PDhUkEyoe+uRo0aOH78OFq3bg0TExMkJSWhQYMGOHHiBFatWoUrV65Iyfzo304cf/31F4YOHQpXV1eJmtOJiYmCSgMpW5uer/YtXzavOvsNK7+Am5sb6tSpg27dusHf3x8PHjxA/fr1kZCQgNGjR0vURgUqSvmEhIQIMvr4fvfPnz/Dy8sLV69eFVyvmrXucGV5eRDSb8vLy9G3b19kZGTAyclJUIYoa98RoUGDBli2bBn69u0LOzs7HDx4EG5ubkhNTYW7u7tCEtuSkhJMmDABixYtEhxIroxhw4bh+fPnOH78OEdEV1BQgAEDBsDKykqQskxEOHfuHFfLWqQfyetbY8aMwaZNm2BsbMzEnxUUFIR169YhICBAIuNny5YtmDFjBkJCQnjbDlTshrl37x5sbW2xZMkS3Lt3D0eOHEFKSgr69Okjwa2hzsBn69at0bt3bwQHB3OJJlZWVvDx8UGvXr3wyy+/KJQnIixfvhwrVqzgnM66urqYPXs2li5dKujZK+/cIyJ8+vQJBgYG2LdvH6+u0KRJEwwfPlyKzHXZsmWIiorirZ/77ds3maVtlKndXlJSgr///ht79+5FbGwsGjZsCH9/f/j6+kqQK/4vrXWvX7+Gr6+vzNJPgPp3bap7t2bHjh0xa9YsDBgwAMOHD8f79++xcOFC7Ny5E8nJyUrxICkLAwMDJCQkyORp7NKli9wATElJCXr16oXt27crtetPnWVxgIodIJGRkdi7dy8eP36MAQMGwN/fH926dUNRURFCQkJw8OBBuYE2ZUuuqpPo+Efx/wAVjqXbt2//0ADE6NGj0bFjR8FBdpZxp84xy6ojscqXlZVh/fr1cstr87Xf1tYW+/fvl0oivXnzJoYOHSpox2FJSQmio6Oxd+9enDt3Dj/99BP8/f3x9OlTbN26FW5ubti/fz/vdVRFTk4OduzYwfFiOjo6YsKECYJIq0NCQhAeHo6QkBCMGzeO41SKiorChg0bpEpvqotDKTY2Fh4eHmjevDlX7vLq1atITU3F33//je7du0vJNG3aFC1atEDXrl05XU+ew10WN4G6dGugwoc2ZMgQlXbOiFBaWoqLFy8iOzsbw4cPh7GxMf755x+YmJhIBFdEaNu2LbezODg4GLNnz5b5PaBChxUHq02pTh3H0tISFy5ckEpMuHv3Lrp164aXL18qlC8tLUVwcDA2bdrE+RqNjIwQEBCAxYsXCy7FXFhYiIMHDyIsLAzXrl1Do0aN4O/vrzAhgTVoa2JigpSUFKnknqysLLi6uvJyQStrj6sL6gr+sXC+qRPJycmc/7dx48ZSiS+yoK+vj5SUFClOzYcPH6JFixY/rETzfzoIEhcXh7i4OJnG2p49ewRfJysrC3v37sX27dtRWFiIr1+/Kvy+Ks4lcbCQSrFsuVYH9u3bhzFjxmDgwIESi290dDTCwsIwfPhwie8nJCSgffv20NbWVmnL94/cxaIOsHCoiEOV7FTWd88KdTqjWaGvr4+HDx9KKba5ublo3LgxioqK5Mq2bt0a2tramDZtmsw6lnzvzsTEBGlpabC1tYWNjQ3279+P9u3bK6w1+z1+u7KyMkRHR0sELz09PXmJ5erVqwcfHx8p5+XixYuxb98+ZGdny5VVpTa9OmvfsoKVXyAtLQ0+Pj7Iy8vDzJkzuUBrQEAA3r59K9O4S09PR1ZWFjw8PLB3716uvnpl8JX18fb2Rnx8PH7++WeZ/VZW0Pd/6d1PmTKFy2QWyr2lrvb/8ccfmDZtGoyMjGBjY8PVN928eTOOHj2K+Ph4hdc2NTXFnTt3VA6CPHv2DJ06dcLbt285BfPOnTuoXr06zp07p1Tm1r8NS0tLbNq0CcOGDZM4f+DAAQQEBCgMIInDwsICV65cgZOTEzp06IBRo0Zh/PjxyM3NhZOTk8S8qc61Rjyr2NzcHFeuXEHjxo2RmpoKT09PwYbSt2/fkJWVhcLCQjg5Ock1uGUhLCxMor9ramrC0tISbdq0UVgGTIS//voLQ4YMQbdu3ST0tLi4OBw6dAheXl4y5TIzM+Hn54dr165JnFclCPHt2zdER0djz549uHDhAtq1a4d//vkHL1++xK5duzgCz++x1imbKSeCKrs2xaEsN4L4bs3Kv3llCKkVHhsbi6KiIgwcOBBZWVno168fMjIyULVqVURFRUmRSsrCq1evZLafzxHfv39/PHv2DKGhoVwpm+TkZIwfPx61atVSmBBkaWnJBQx+BPr374/Y2Fg0aNAAY8eOxahRo2BhYSHxnVevXsHa2lrqvagKdREdq6JjqdOR7+fnh/bt2zNnvb969Yrb4ebo6KhUlvpvv/2GDRs2oG/fvjKda5UdyizjTt1j9kciKCgIoaGhmDVrFhYuXIgFCxYgNzcXx44dQ1BQkKCd0g8fPpRac4XwX6WkpHA7BjU1NTFq1CgpztZ79+6hVatW/7McZvXr18eOHTvg7u4uUfoyPT0dbdu2lUqSUld1BRcXF/Ts2VNmGa+zZ8/K9IVcu3YNM2fORHZ2Nt69ewdjY2OZfVdDQ0PmmP9fsg2ePHmCXr16IS8vD1+/fuV24EybNg1fv37F9u3bpWQePXqExYsXIzs7GykpKXBycpJp+2poaKjdl6ROHUdfXx937tyRqsIg8inyjZVffvkFR48eRUhIiESS0pIlSzBgwABs27ZNwBNJ4tSpUxg1ahQKCgoUrlWsQVtTU1NcvHhR5m7TLl268JYJVsUer4yysjIcO3ZMIgjg4eEhiJODNfjHwvmmDrx69QpDhw7FxYsXJXjfunbtioMHDyqsWtGiRQs0adIEoaGhXLDn27dvGDt2LO7duydozBUUFGD37t0S797Pz08iqUpp0H8US5YsIU1NTWrdujV5enrSgAEDJA6hKCwspD179lD79u1JQ0ODGjZsyCszePBgqlatGk2cOJEWL15MS5YskTj48P79e5o8eTJ5eHjQmTNnuPNBQUG0bNkyhbLDhg2jtWvXEhFRSEgIWVpa0tixY8nGxoa8vLx4733r1i26ceOG1PkbN25QYmIir3zDhg1p3bp1UufXrl0r6N0piyVLllBRURH3t6KDD6zPXlBQQG/fvpU6//btW/rw4YOAp1EdrVq1oqCgICIiMjIyouzsbPr06RN5eHjQH3/88V3vTUQ0ZswYhcf/OqpXr05xcXFS58+dO0eWlpYKZfX19Sk9PV3le7u6ulJMTAwREfXv359GjhxJT58+pTlz5pC9vb3K11UG9+7dI3t7ezIwMCAXFxdycXEhQ0NDsrW1pbt37yqU1dfXp8zMTKnzGRkZpK+vr1DW1taWwsPDpc6HhYWRra2tcg/xg3D48GFat24d5efnc+fCwsLo2LFjKl/z8+fP9O3bN4XfEZ/7VIGBgQFdvnxZZfkfDSMjIzp58uQPu39iYiIdPXqUPn36xJ07efIkXblyhVd21KhRMtdJZVBYWEg7duygSZMm0axZsyg8PJy3z4jj/PnzNH/+fPL391d6vn78+DFlZGRInc/IyKCcnByFsqampjJlHz16RKampkKbT/3796eePXtSSEgIValShZ4+fUpERLGxseTg4CD4OsqievXq9ODBAyIiatSoER0/fpyIiO7cuUOGhoa88nv27KHi4uLv1j6hSEpKIh8fH2rRogW1aNGCfHx8KCUlRaFMu3btqFOnTnT69Gm6ffs23blzR+IQet/JkyeThYUF1ahRg+bOnSuxfmzatImsrKyYnk0esrOzqWnTpqShoUGampqkoaHB/a2pqckrb21tTTdv3iQiImNjY3r06BERER0/fpzat2+vUPbMmTNkaWnJ3VP8EHLv74W3b99SeXk57/eSkpKocePGEu9Nmfa/evWKevfuTRoaGqSjo0M6OjqkqalJvXv3ppcvXyqUnT59Os2dO1fwM1UGq27u5+dH165dU/id8vJyys3N5b3Wy5cv6e7du5SamipxVEZubi73u+Tm5io8FEEVHSssLIy+fPnC/a3o4ENRURH16dOHRo8eTWvWrKGNGzdKHHz48OEDjRgxgrS1tbn+pq2tTT4+PlRQUMArT1TxDuQddnZ2gq7x/4+wt7fndCwjIyPKysoiIqKNGzfSsGHDeOXr169PkZGRUucjIiJ437umpib17NmTDh06JFevKSwsJF9fX952sOD9+/cUGxtLkZGRFB4eLnHwQU9PjxufIruciOj+/fuCdAVVoaurK1fH0tXV5ZXX0NDgnZP/l+Hp6UkjRoygr1+/Srz3+Ph4ql+/Pq/8f/n5W7VqRcHBwVLnFy9eTC1atOCVNzExodOnT0udP3XqFJmYmAhuR1FREe3du5c6depEmpqa5ODgQCtWrBAsrwr69etHgwcPptLSUu5caWkpDRo0iHr16qXydYXY40REmZmZ5ODgIOFHMTAwIEdHR27u/J6oWrUqp0uYmJhwfqm4uDhq3rz5d7+/t7c3ubq6cvYRUcVc5+rqSkOHDlUoe/PmTbKysiJLS0tyd3cnd3d3srS0JCsrK07nVoTExESysLCgWrVqkZeXF3l5eVHt2rWpatWqlJycrPIz/Wd3gtSoUQOrVq1SebvvlStXsGfPHhw5cgREhMGDB8Pf35/LpFEEQ0NDxMbGSrDR/1tQdst1ZbRu3Rpz5szBzz//LHH+6NGj+P3333Hz5k2F8rq6urh//77M7WhNmjSRyvzg2+YtDr76/nl5eahdu7bg3RWVwfrsvXv3Rv/+/TFp0iSJ89u3b8eJEydw+vRpldolBKpkp7K+e3FUzhwtKSnBvXv3UFBQADc3t+9e75i1DuaECRNw/fp1REdHc9v2s7KyMGjQILRq1QqhoaFyZTt16oSgoCCVCev27duH0tJS+Pr6Ijk5Gb169cK7d++go6ODsLAwLiNWHvbu3QsjIyMMHjxY4vzhw4dRXFwsKNusbdu2sLS0RHh4ODdHvH//Hr6+vnj9+rVU5q84+vTpg8GDB0uVyNm7dy8OHjyI2NhYubKq1KZXZwk5ddZP/a+hYcOGOHToEO/YlgdV6j2ra7s/UJFNJiqjIxTq7DssEGWOu7u7o2XLllKkeXzPzorg4GCEhITA1dUVNWrUkMr4i46OVijPUns3ICAAVapUkSrROHv2bHz+/Blbt24V9Ax5eXmYNGkS8vPzMXXqVC7TeMaMGSgrK1OqrymDAQMGoG/fvhg3bhxmz56N48ePw9fXF0f/f+x9d1QUWfP2M+ScwUAQEUWSiqJrWANGRAXD6iomBLMoJpRdI6iIAXNWREFXVMy6CiYQMQIKuCZEBcyYF1BJ9f3BN/1jmNA906C77/s+5/Q5cGeq7+2eG+rWrarn8GEYGhri3LlzMuVr1aqFL1++MDolV7656lyvFYW2tjZSU1MV5rtzdnbG/fv30b17d4wZM4bxXKuMt2/fwszMTKJHPd+1jq+nnCJRm0Iowo1QGXx4y6Th8+fPuHDhAho3bsz6mzZt2hQNGjTA7NmzJUbecfXuffjwIZOCr3HjxmjUqBGrzOTJkxEVFYWGDRtKnC/ZOD1+pG4uRGpqKkaOHIl79+6JRXTUZCo3RXSsynwqly5dQtu2bVkjgqUhIiIC48ePh4aGBoyNjUX6DZfIu19//RW3bt3C+vXrRTyTAwIC0KxZM8TExCjULq7gM+74jlm21JBV0/JUt7y2tjbu3bsHKysr1KlTB6dOnULz5s3x+PFjuLi4sGaWWL58OZYvX44VK1YwUWbnz5/HrFmzMGPGDPz2229SZXNycmo8YoANJ06cwNChQ1FQUAA9PT2xvssWBdWiRQtMmzYNw4YNE4kECQkJwdmzZ5GUlFQj7ba0tMSqVavE1skDBw5g5syZyM3NlSlfOaqDK/4pujVQwZl35coV2NnZibx3SVHC1Q2+e0q+Os6JEyfQv39/eHt7i4y5ffv24eDBg6wE02ZmZkhMTJSYlqhDhw4yOYmBioiinTt34uDBgygtLcUvv/wCPz8/dOjQQaZcdeDu3bvo0KEDDAwMGF0uKSmJ0XOcnJxqtH4PDw8QEfbu3ctEir579w7Dhg2DkpISTp06JVOeb/pBvpxvfKGvr49z585JTHnavXt31nRkhYWF2Lt3r0iKZm9vbzF9TxLat28PW1tbbN++XYSUfvTo0Xj8+DEuXbqk2EMpfHzyg2FkZCT3yduLFy9o6dKlZGdnRwKBgNq0aUPbt28X8fTkAjs7O4mePfKisLCQ7t27x+oxVJ3Q1tZmTs0r4/Hjx6Sjo8Mq36BBA9qyZYtY+ebNmyWewFf2yBN640m7JEFZWZk5sVdSUuJ1es/32Q0NDUVOQIW4d+8eGRkZscpPnjxZomfU+vXrKSAgQKasIt6pfN89G8rKymjs2LG0bNkyiZ9PmzaN88UGExMTysjIECvPyMjg5FX68eNHat26NamoqDAeYioqKuTm5kYfPnyQKXvgwAFycHCgyMhISklJ4T1eCwsLKTU1lfLz8zl9v2HDhnThwgWx8oSEBGrUqBGne2hoaNCdO3fEyjMzM0lDQ0Om7ObNm8nU1JQmTZpE0dHRFB0dTZMmTSIzMzPavHkzHTt2jLmqwtHRkZYsWSJWvmjRInJycpJYnyQv1Kr/c+23ivSbtWvX0pcvX5i/ZV2SYGhoyPy2BgYGZGhoKPWqSZw8eZJ69OjB6rkvDfXq1aPk5GSx8mvXrkn1MK3qhamtrU0CgYB5XoFAQNra2py8M3fu3EmDBg2SKxqGT9+ZNm0aFRQUMH/zma8U8Uw9duwY441UeUxJuthQu3ZtioqKYv2eNOjq6kqM/srKymKN5vD39yc9PT1ydHQkPz8/8vPzIycnJ9LT0yN/f3+53iNXVOdak52dzczrBQUFNG7cOHJ2dqb+/ftz8gQvKSmhw4cPk6enJ6mqqpKdnR2FhYXRy5cvZcpJ6q/yRBV8+vSJ8yUNrq6uvKLHQkJCmIgdRcB3rePrKccnalNXV5eXN6CzszOdOnVKrPz06dPUpEkTTvcYOHAgrV+/noiIioqKqGHDhqSqqkoqKioUGxsrU1ZHR0fimP8e6NSpk9TLzc2NVf5H6uZCNGnShPr160fXrl2jJ0+eyBXNERoaShEREWLlERERFBYWJlNWER1LRUWFXr16RUT891W1atWiJUuWUFlZmULy0iJWL126RFpaWgq3iyv4jDu+Y7ZZs2Yil6OjI2lpaZGenh65uLjUuHyjRo2Y7Ajt2rVjPLljYmJYI+SJKqKjZs2aRRoaGoxepaWlJdFTvSr4ZmaoDjRs2JACAgIUjrg+evQo6evrU1hYGGlpadGKFSto9OjRpKamRvHx8TJl+/fvL3FsL1u2jH755ReZssHBwWRgYEBhYWF06dIlunTpEi1dupQMDAwoJCREoWdhQ3Xuy/jCwMCA/vrrLyISjcBJSkqqsShTIfjaIqpjP3/y5Elq27YtaWlpkbGxMbm5uVFCQgIn2eDgYBoyZAgTCUhE9PXrVxo6dKjMjCrLli2jxo0bMxl4tm7dSp8/f+ZUZ2UkJCRQ7969qUGDBtSgQQPq06cPXbp0ibP88+fP6bfffiMPDw8aMGAABQcHS4wCFaI69+NaWloSf3uuUeLz5s2jOnXq0MqVK0lDQ4MWLVpEfn5+ZGxszClq8ueff6YjR44QUUVWIHd3d7p8+TKNGDGCHB0dWeX5QkdHh27duiVWnpaWRrq6ujVat4aGBt27d0+s/K+//mLNSCIL/9pDkFmzZsk92SsrK5OZmRnNmDFDosLMFXyNS2/evCEPDw9eBmmuIddVYWRkJDHsOzk5mQwMDFjlN23aRGpqajR+/HiKioqiqKgoGjduHKmrq0s8HKm8CThy5AhziCJs75YtW6hhw4bMwK4KS0tL2rRpEz19+pQEAgGlpqZSTk6OxKumn13aBJiRkcFpENatW5dSUlLEylNTU8nc3FymrJeXF23bto2IiGbMmEG2tra0ePFiat68OXXp0kWiDN93zwX379+n2rVrS/ys6mZWT09PLB2Tnp4ep02uhoaGxJRU9+7dYzXiC1FeXk5xcXG0fPlyWr9+PSUmJnKSk2aI+l5pLtTV1SXONU+ePOH87E2aNJGYDuz8+fNSN8pCsBnlZBnnYmNjSVlZmUlrExISQj169CAVFRU6fPgw1w3EPwABAABJREFUa7vPnj1LzZs3pzNnzjAGvDNnzpCrqyvrJoNIsX5jbW1Nb9++Zf6W15hdnakm+MDAwIBJSaKjoyO3wqeurk6PHz8WK8/OzuYUcr93715q166dyPu/f/8+tW/fnvbs2cMq36xZM9LV1SUdHR1ycnJi5g3hxQZ5+06nTp2YA1G+RjlFUDlEXxEjeGUo4iRSGXp6ehJTJ6WkpLA6DMh6d4q+xy9fvsg05FfnWlOdePXqFa1cuZKcnZ1JVVWV+vTpQ0ePHpVoMGRLicNmUOXi7MCmY54/f57atGlDFy9epLdv33I+PKku8F3rDAwMmDnLxsaGMTY8evSIk44WHR1NkZGRRFTR101MTEggEJC6ujrFxMTIlB01ahTt2LGDtQ5p0NDQkPrsXI3BtWrVYtKW7d27l2xtbamwsJA2bdrEegjk5eXFelAiC6WlpbRjxw4aMmQIdenShdzc3ESumsSP1M2F4HOIpIjDgRCK6Fi2trb0+++/U0JCAgkEAjp69CglJiZKvNhgaGjIa62xtLSU+Nulp6dzfvd80vXyGXfVMWar4tOnT9SvXz+FnRjkkZ89ezZzgBYTE0MqKipka2tLampqcqWn+/vvv+nGjRuUmZkpYlyVhZYtW9LBgwfFyg8dOkStWrXiXDcfaGlpSXSMlAeXLl2irl27kqmpKWlqalK7du0oLi6OVY6PMb28vJxWrVpF5ubmjF5obm5Oa9as4ZT6kC/47suqorS0lG7dukXv37/n9P1BgwbRmDFjiKhi3n38+DH9/fff1Llz5xpPn8bXFlEd+3k+6Nu3L+nq6pKJiQmTlsjExIT09PSYNEPCqzJMTExo6tSprOmzZSE6OppUVFRo0KBBjCPhoEGDSFVVlfbu3cv30SSiOvfjhoaGEtfpy5cvc9pT800/eObMGTp06BARVTikCR36TUxMJNp3qhuenp7UoUMHev78OVP27Nkz6tixIycaikePHpG/vz/T76ZMmcJZdzAzM5M4r545c4bXwee/9hBkypQpZGBgQB06dBDzLpTm7Xfo0CEqKSnhXTdf45K3tze1a9eObt68Sdra2hQfH0/R0dFkZ2fHmgOdb97ewYMHU8eOHUVyrX748IE6duxIAwcOZH94Ijp8+DC1a9eOjIyMyMjIiNq1a8cpP37Lli0les2cOnVKai7DrVu3Mu9a2vW9nr1Tp07k7+8vVj5x4kT6+eefWeXV1dWleteyGRX5eqcq8u654NSpU2RiYsL6vfDwcOrTp4+IkvP+/Xvy8vKilStXssrzzYPJB4oYpKrTM9nS0lKi9/fRo0c5bxJPnTpFjo6OdPDgQcrLy6O8vDw6ePAg48lWk4YuRXLTC+Ho6CjVS5ALB9GP7Dc/GnwVPj75nokqFD5phnQufDB8OaD49p3qwLdv3+j+/fvVonfIA0WcRCqjpnLvyoOCggKaNGkSmZqaymXI57vWVDeuXbtGY8eOJXV1dbK2tiZ9fX2ytramixcvinzPxcWFaXNwcLDc3qkJCQnMtWvXLqpduzYFBQUx0UNBQUFUp04dmWNfklenPDoWX0M437Wuuj3l5Ina5MuNwIe3TAgNDQ3Kzc0lIqLhw4czhsycnBxWT8X8/Hzy8PCghQsXUmxsrNzRZ5MmTSJtbW0aNGgQBQQE0NSpU0WumsSP1M2F4HOIxNfhQF4d68iRI1SrVi3W6DMuY37q1KkSI1G4YuvWrdS1a1eRSLmXL19S9+7dJTrWSUJVTtBevXpRvXr1SF9fn5Urk8+4q44xKwkZGRlUr1697y5/9epVCg8Pp+PHjytcN1fwzcxQHejXrx/t37//u9RVFdXh2EdE9PnzZ4U88vmAr24dEBDAOAyUlpYynLza2tpiepEk5OXlkYODA9nb25OKigq1bt2ajI2Nyc7Orsa5PvjuKatjP88HPj4+nK/KqMyZ8eHDB7p58ybdvHmTNZNGZXxvTuHqxvDhw8nR0ZGuXbtG5eXlVF5eTlevXiUnJycaOXIkq7yWlhbjsF27dm2GyyI7O1suPpbK4Mr5Vh3Izc2lZs2akaqqKtnY2JCNjQ2pqqqSi4uLCGeqJJw5c4bU1NSoVatWjO2rVatWpK6uzungdPLkyWRhYUExMTGUm5tLubm5tG/fPrKwsOAcrSsJiiUB/QcgIyMDzZo1AwDcuXNH5DNpeQ779+8PoCL33r59+/Dw4UMAQKNGjeDt7S3GFSENa9asUazR/x8XLlzAsWPH4OrqCiUlJdSrVw/dunWDnp4eli5dil69ekmV9fX1RaNGjRARESExby8bVqxYgY4dO6JevXpwcXEBANy+fRu1atVCdHQ0p3v069dPjCOCCzIzM1G/fn2x8vr16+Pu3bsSZcaOHYshQ4YgJycHTZo0wblz52BsbCx33QD/Z1+8eDG6du2K9PR0dOnSBUBFLsabN28iPj6eVd7W1hZnzpyBv7+/SPnp06dhY2Mj9v3KfCgqKipwdnYGUJHHdcuWLaz1VYYi774ypk+fLvI/EeHly5c4deoUJ06K8PBwxMfHi3DWGBoaYvHixejevTtmzJghU37evHno378/srOzJebBrEkokrf21q1bnL7HZfwOGTIEU6ZMga6uLpN3MzExEQEBARg8eDCnenr37g0AGDRoEFMn/f+81X369GH+r4nc1S1atMCePXsUks3OzoaBgYFYub6+vkQenKr4Ef3m8+fPnL+rp6cn9bPHjx9LnBe4gsu4lIUxY8Zg6tSpKCkpkZjvmQ0vX75EaWmpWHlZWRlev37NKr9gwQL5G10JfPsOHxQVFWHy5MnYvXs3gIo8+TY2Npg8eTLMzc0RFBQkJmNkZISHDx/CxMQEvr6+WLt2LXR1dRWq/+vXr9i2bRvOnTuHJk2aQFVVVeRzthz7YWFh6NixI+zs7CTm3v0emDVrFi5evIjNmzdj+PDh2LhxI54/f46tW7ciLCxMqpwia42hoSFnXYotby8AvH79GtHR0YiMjMTjx4/Rt29fnDx5El27dkVhYSFCQkIwcuRI5OTkMDL37t1DYWEhDA0NERwcjPHjx0NLS4tTm4AKHhchQkJCsGrVKgwZMoQp8/T0hLOzM7Zt2yZ1brh48SLn+iQhICAAu3btQq9eveDk5CS3fsp3rZs7dy4KCwsBVLyD3r17o3379jA2NubELVBVzxFCIBBAQ0MDtra28PLyYvJBV8a+ffsQHx8PDQ0NJCQkiOWXZ+MB8vLywtSpU8V4y2bMmME5x7qlpSWuXr0KIyMjnDlzhnnmDx8+QENDQ6bs1atXkZycjNOnT4t9xkUviImJwYEDB+Dh4cGprdWJ762bS8KOHTswcuRI3LlzB05OTmJzrqzf0NLSEsnJyWI6enJyMurWrctat7w6Vt++fdG3b1+GC+HBgwcwMzPjLF8ZZWVlWL58OeLi4hRaazZv3oxHjx7BysoKVlZWACr4oNTV1ZGfn4+tW7cy301LS5N4D0kcV+Xl5ZgwYQIzlqSBz7irjjErCZ8+fWLl41BUvnnz5jh//jwMDQ0REhKCmTNnMutM69at0bp1a4XrlQfq6up4/fq12Ph6+fKlwvw08qJXr14IDAzE3bt34ezsLNeYBYCbN2+ivLwcP/30k0j59evXoaysDFdXV6myzs7O2L9/vxhvS0xMDBwcHGTW++TJE5SWlqJhw4YiOmJWVhZUVVVhbW0tU54v+OrWsbGxGDZsGIAKjosnT57g/v37iI6Oxpw5c5CcnCxT3sLCAunp6YiJiUFGRgYKCgrg5+eHoUOHQlNTU5FH4gy+e8rq2M/zQWRkpEJyqqqqePr0KSZNmoS4uDjGfiAQCODu7o4NGzaw9rvHjx8z9obK8PT0xO+//65Qu9hQXftxoMImN3LkSLRp04aZK0pLS+Hp6Ym1a9ey3t/CwgIvX76ElZUVGjRogPj4eDRv3hw3b96Eurq6TNmSkhJoamri9u3bItwnknTRmoKlpSXS0tJw7tw5EV4PLny5QUFBmDZtmtjeLSgoCLNnz0a3bt1kyq9cuRICgQAjRoxg7AqqqqqYMGGCzP0gG/61xOiKoLy8HIMHD0ZsbCwaNWrEEAXeu3cPjx49wsCBA7Fv3z65N27ygg/5oq6uLm7duiVGgicPhOQ06enp0NTURJMmTTBkyBAxBUASRo4cqTAJUvPmzeHk5IQdO3ZATU0NAFBcXIzRo0fjzp07UhVcIXbv3o3BgwezThaywOfZASA9PR3Lly/H7du3GfnffvsNDRs2ZJXduXMn/P39ERgYKLJ4hoeHY82aNRgzZozI91VUVPDixQuYmZlBWVkZL1++VHijwvfdu7m5ifyvpKQEU1NTdO7cGb6+vqxKq66uLk6cOIFOnTqJlF+8eBGenp74+++/WZ/h1KlTCA0NFXn3CxYsEDH+1BSio6OxZcsWPHnyBFevXkW9evWwZs0a1K9fH15eXjVad3FxMYYPH46DBw8y77m8vBwjRozAli1bmN9TFhITEznXJ+l93rx5ExcvXsSbN2/ECG1lbXI7duwIPz8/DBw4UCHltEOHDtDQ0EB0dDRDNvv69WuMGDECX79+5fRc8vYbaYYwSZD07EpKSpzXEFmGJSUlJeb9/fLLL6xGLEnIzs5GZGQksrOzsXbtWpiZmeH06dOwsrKCo6OjTFkiQlBQENatW8cQuGloaGD27NmshJtAxeHa8+fPsWPHDjRv3hxABXns2LFjYW5uzolo8ePHj4iNjUV2djYCAwNhZGSEtLQ01KpVC+bm5jJl5e07QicJLjh8+LDMzwMCApCcnIw1a9bA3d0dGRkZsLGxwbFjx7Bw4UKJh6Q6OjrM95SVlfHq1SuYmppyblNlVJ2vK0MgEHA6yHjx4gU2bNggslb6+/uzKt1fv37F+vXrpc4XbGuNEFZWVoiKikKnTp2gp6eHtLQ02NraIjo6Gvv27ZNKdqzIWiM8rOICLuTccXFxaNSoEUaPHo0RI0aIvbM3b96gdu3aIu+mTZs20NHRwc8//4zg4GDMnDkTOjo6EutgG39aWlpIT08X00sePnyIZs2a1RiBoomJCaKiohQ2hFfHWlcV79+/53zI5ebmhrS0NJSVlcHOzg5AxTtTVlZG48aN8eDBAwgEAly+fFnMUFW7dm1MmTIFQUFBUFJSkrudnz59gru7O1JSUmBhYQEAePbsGdq3b4/Dhw9LNDpVxaZNmxAQEAAdHR3Uq1cPaWlpUFJSwvr163H48GGZh1zW1tbo3bs35s2bpxCxe926dZGQkMCJCL0m8D11c0k4ceIEhg8fLtHownaIxIdgmq+OlZiYiHbt2ilseOa71gQHB3OuS17HiAcPHqBTp054+fKl1O/wGXd8x+y6detE/hc6l0VHR6Njx474448/ql1eU1MTWVlZsLCw4L2v5IMhQ4bg5cuXOHbsGPT19QFU6Ht9+/aFmZkZDhw4UONtkDVPczn4bdWqFWbNmiXmQHv48GEsW7YM169flyrLh+C6Y8eO8PX1FdNF9uzZgx07diAhIUFmuzt37iyxf37+/Bl9+/ZlHbN892UaGhp49OgRLCwsMHbsWGhpaWHNmjV48uQJmjZtKpfhWhHwsWMB/GwRNaHjyIvS0lIkJCQgOzsb3t7e0NXVxYsXL6CnpydV58zLy0PLli2hqqqKiRMnMsTqd+/exebNm1FaWoqbN28y86Ak2NraIjAwEOPGjRMp37JlC8LDw5GVlVV9D/n/UV378crIysoSOQTgao8NCgqCnp4efv/9d+zfvx/Dhg2DtbU1cnNzJR4QVIWNjQ2OHDmCpk2bcqqvusHHMVNDQwOZmZkS9yRNmjTB169fOd2nqKgI2dnZAIAGDRrI5SgmEQrHkPwLsWrVKjIyMqITJ06IfXbs2DEyMjKi1atXc7rXo0ePaM6cOTR48GAm/O7PP/+USD5cFXzIF/mEXBcXF5ONjQ0vPhQvLy9SVVUlW1tbWrJkiVwkmNevXyczMzMyNTVlcsKZmpqSmZkZXb9+XeE2cQHfZy8uLqZRo0ZJDFmXB5s2bRLJ41m/fn3avXu3xO9WJx/Kj3z3RBVhhNbW1nTo0CEmHVNsbCzVr1+fRowYIVO2pKSEgoODWcPtagqbNm0iExMTWrx4MWlqajIh3JGRkdSpUye57iV8dq4oLy+nnJwcKioqoocPH9KBAwfoxIkTnFKgVcWlS5do6NCh1Lp1a2bcRkVFsRLhLlmyhAQCATVu3Jg6duwoV17/gIAAMjU1JT09PRo9ejRdvXpVrjZnZWWRk5MTqampMURqampq5OjoyJp/W9F+w5dfoDrS0hAR3bp1i6ZMmUKmpqakr69PY8eOlWusJiQkkKamJnXt2pXU1NSYfrt06VIaMGCATNnS0lJKTEyk9+/fK5TvmaiC+6pnz54kEAhITU2NSWvYs2dPTiHr6enpZGpqSra2tqSiosK0f86cOTR8+HBWeXn7TuUw8JEjR5Kenh5ZWloy+XGtrKxIT0+PU85hKysrpq9XJm7MysqSSiDXtWtXcnZ2Jh8fHxIIBDR48GCFcpzzRXFxMXXu3JkePnyokLy3tzeZmJjQ+PHjacGCBXKnMRNCW1ubWdvMzc2Zvv/48WOZqX34rDXVAV9fX4ncY5VRXl4uNoffv3+ffv31V3J1dSUlJSVycnISI75t1qwZZ8LbwMBAsfLAwEBW8s0PHz5QXFwcRUdH0+7du0UuNtSpU4cePHjA+j1JqI61btSoURJTgxQUFHAaN6tXr6b+/fuLpIX8+PEj/fLLL7RmzRoqLCwkLy8v6t69u5gsX24EIsV5yyrj5s2bdPjwYfr777+ZspMnT9Lly5dlylXOUa0IVq5cSRMnTvxuqRmE+BG6uSTUq1ePJk2axBCOywM+BNN8daz/ZHBN18tn3PGRrcozZ2NjQz/99BP99ttvnFIcKSLfunVr6tq1Ky1cuJAEAgEFBgZScHCwxKsm8ezZM7KxsSF9fX1G1zYwMCA7Ozsmpd8/HXxTeilKcK2rqys1fZ++vj6rfGX+ucp4/fo1qaiosMrz2ZcRVejHcXFxVFpaSpaWlkwa+Dt37nDiZyUievjwIW3dupUWLVokd79V1I7F1xZRnft5RfH06VNq3LgxaWlpkbKyMtN/p0yZQuPGjZMq5+vrSx06dKAvX76IfVZUVEQdOnQgPz8/mXXLyylcHaiu/XhlVFeaY3nTD+7YsYM8PDxkEsHXJAQCAXXq1Imio6Ml9gNZsLCwoAMHDoiV79+/nywtLTnfJysri86cOUNFRUVERLz1zf+qQxBnZ2eKiIiQ+vmOHTvI2dmZ9T58jEtEkskXlZSUSENDg5V8kW/e3rp16/I6BCGqMG6Fh4dTkyZNSEVFhdzd3engwYMiOQOloaCggLZu3crkhNu2bRsVFBTwag9X8H12PT093hstId68eSOySZWE6uRDIeL/7ktKSujs2bO0ZcsWRsF+/vw563MQVeTLnjBhAqmrqzNtV1NTowkTJnBqg7a2tkQyse8Be3t7Js94ZYNmZmYmGRsbs8qXlZVRcHAw6enpMc+ur69PISEhEglyq8qqqqoqbJAUIjY2ljQ1NWn06NGkrq7OPMP69eupZ8+eMmXNzMyY+UoRlJSU0KFDh8jT05NUVVXJ3t6eVqxYwdlYINxkCnOrx8fHc174+PYbvvwCnTt3pj/++EOsfO/evdSxY0dObRC+vz59+pCqqio5OjpSeHg4vXnzRqZc69atKTw8nIhE++3169c55Z6VlqdcXjx8+JBZn+QxkHbp0oUx5FZuf3JyMud814r2nVmzZtHo0aPFODHGjh1LM2fOZJWvfFhaue23b9+Wmvv11atXNHv2bPrll1+Yw6Kquc6FF1coqjCamJgoPOfo6emxGly5wNnZmTEIdOnShWbMmEFEFSSCsvov37WGqOK3jo2NpUWLFtGiRYvo8OHDIn2hpiHNSMEVp06dIg0NDXJyciI/Pz/y8/MjZ2dn0tDQkMgNJsTx48dJV1eXBAIB6evrk4GBAXNx4bvjYwivjrVOSUlJ4nvLz88nZWVlVvm6devSX3/9JVZ+584dqlu3LhFVkGVLWvf5cCMUFxeTsrIyL9LRqhDmrOaKESNG0Pbt2xWur2/fvqSvr0/169en3r17yyRZrW58b91cEhQ9RKoOhwO+OtY/ASkpKRQdHU3R0dGcOeOEqMqzN3XqVPr1119JR0eHJk2aJFWOz7iriTH7PVCdh+18IdyTTpw4kWbMmEG7d+/mZEf4p8DIyEiiw0NycjJnY74i0NPTk8q3J+vwJT09ndLT00kgENDFixeZ/9PT0yktLY1CQ0NrXLcmquDP0NfXp8aNG5OVlRUz10VERFDr1q1Z5bdt20bKyspUq1Ytatq0qUL9VlE7Fp89ZXXt54kUN8R7eXnRsGHD6Nu3byJ7k4sXL5Ktra1Uubp168p0mExMTKQ6deqw1q8opzARkZubm0QOkk+fPnHinOO7Hy8sLCRfX19SVlYWOUDy9/enpUuXssqHhoZKtEFHRERQWFgYq3yzZs1IR0eH1NXVqVGjRoxjpvCqaUhyzLx27Ron2eDgYDIwMKCwsDC6dOkSXbp0iZYuXUoGBgac+Cvfvn1LnTt3Zmyewnc/atQomj59usLP9F91CKKhoSHTa/7p06ecCKn4GpeqQh7yxePHj5O+vr7CJHZLliyhkSNHVhtRa2pqKvn7+5OGhgaZmJjQ1KlTq2WCrwnwffYRI0ZIJHXiiqKiIhGy06dPn9Lq1aspLi5Oqsznz58pMzOTBAIBnT9/nm7fvi3xqmko6j1QFQUFBYzSJc8BjKenp1wn9VVx6tQpJvqqMs6cOUN//vmnTFkNDQ3GU6PyeH/48CGn+SIoKIhMTU1p06ZNzLNv3LiRTE1N6ffff2eVd3Bw4O3d16xZM8arsfIzpKWlUa1atWTK1q5du9rG9OvXr2nRokWkoaFBqqqq5OXlJZFYsrrAt9/UrVtXYnRfZmYmJ4VPU1NT4rt78OABaWpqytWWr1+/0qpVq0hdXZ0EAgGpq6vT8OHD6cWLFxK/r62tzRiGKv/mT5484US22qJFCzp37pxcbaxO6OnpMUalyu1/+vQpZ7JaRWFiYiKRtPL+/ftkZGTEKt++fXtat24dEVW0Xfg7+Pv7U48ePVjlra2t6e3bt3K2+v/AV2GcOnUqQ6osL+zt7Sk9PV0h2cpYtWoVQyh99uxZ0tDQYA421qxZwyqv6FqTlZVFDRs2FIn+0tLSIjs7O85Gzhs3btCyZctoxowZYga674W8vDz67bffGCP077//zuph27BhQwoICJCLlL2qsZuPIVzRte7Tp0/08eNHEggE9OjRI/r06RNzvX//nnbv3s1pvpZGzHrx4kXGuJSdnS0xmmvy5Mmkr69PHTp0IH9/f7l/9/r161eLLrd7925ycnIidXV1UldXJ2dnZ4qKimKVW7x4MZmYmChM7C4PyWpV8DUO8NXNhXjz5g0lJSVRUlISp/1Y1TYoeohUXQ4HRN9fxxJGBEu7uLTXzc2NBAIBGRoakqGhIQkEAurcuTOro0flNlS+OnfuTL/++itt3bqVdb/HZ9xV15jl692qqDzfw/Z/I9auXct4MVed4+Sd8wYPHkwdO3akjx8/MmUfPnygjh070sCBA2XK5ubmikQUXL9+nQICAmjr1q2s9fbu3ZsGDhwo5qQzYMAAcnd3lyon1AeFDpRVLy0tLZmOwtWJgwcP0qpVq0Tewa5duzgZxK2srDitC1whjx2L756S736eryHeyMiI2dtU3RfK2pOqqanJjIDJy8ur8X0Z3wgmvvvxKVOmUIsWLSgpKUkkCuzo0aPUrFkzVvl69epRcnKyWPm1a9fI2tqaVb5qVL2iUfZ8oYhjZnl5Oa1atUok2tbc3JzWrFnDab0aPnw49ejRg/Ly8kT67ZkzZ8jBwUHhZ/nXEqMrAk1NTXz8+JEhX6uKz58/c8q7npmZKTHXppmZGd6+fSt3u7S0tJic6WyYPHkyhg0bpnDe3ps3b+L8+fOIj4+Hs7MztLW1RT5ny3VeGS9fvsTZs2dx9uxZKCsrw8PDA5mZmXBwcMDy5csxbdo0udtXk+D77A0bNkRISAiSk5PRokULMXkuxJf9+/fH+PHj8fHjR7Rq1Qpqamp4+/YtVq1ahQkTJojJ6OrqwsnJCZGRkWjXrh0vPhQ+CAgIgKurK9LT00WI6fv168cpX7IQ2traaNKkidz19+zZE0FBQcjMzJT47tkI7IKCgiTmW6T/z3vQs2dPqbL169fH7du3xQjSz5w5w+TFlIXdu3djx44dIm1s0qQJzM3NMXHiRCxZskSmfFhYGAIDA7F582YRQix58ODBA4n5T/X19fHx40eZstOmTcPGjRuxZs0aheoW4saNG4iMjERMTAzMzMzg4+OD58+fo3fv3pg4cSJWrlzJ6/6SwLfffP78Gfn5+WLl+fn5nHhsLC0tsX37dixfvlykfMeOHbC0tOTwBEBKSgp27tyJmJgYaGtrY+bMmfDz88OzZ88QHBwMLy8v3LhxQ0zOwMAAL1++FCNbvXXrFiufBlBBNjtz5kwsWrRI4rtjI5HjC3V1dYm5gR8+fKgwVwZXlJaW4v79+wwvgBD3798X47iQhNDQUPTs2RN3795FaWkp1q5di7t37+LKlSuceGyePHnC/P3161e5+WCmTZsGVVVV5ObmisxRv/76K6ZPn47w8HCZ8qWlpdi5cyfOnTsn8beXxQMUHh6O2bNnY8uWLWJzprzPIETXrl1x//59pKamwtbWltMaouhaM2XKFDRo0ADXrl1juDzevXuHYcOGYcqUKTh16pRM+dDQUMydOxd2dnaoVauWGEE2V2RlZUnlVeHCyWNhYYHQ0FDO9QHA8+fPMWXKFLny7ArzuQvRr18/ueqsDEXXOgMDAwgEAggEAomcFAKBgBP3gJeXF3x9fREeHo6WLVsCqNAbZ86cyeRov3HjhsQ6MjMz4eLiAgC4c+eOWP1smDNnDn7//XdER0crTHa5atUqzJs3D/7+/mjXrh0A4PLlyxg/fjzevn0rUyffsWMHdHR0kJiYKDZHcSF2V5RwFQC2bt0qcU/l6OiIwYMHY/bs2TLl+ermhYWFmDx5MqKiopixpqysjBEjRmD9+vWcxkOjRo3w22+/4fLlyxJJlmW1wcnJCY8fPxZbq+XFj9CxmjVrJvJ/SUkJbt++jTt37rDyJwEVe9q///4bf/31l0ie+ZEjR2LKlCnYt28f6z1kcd2wgc+44ztm3717h0GDBuHixYsQCATIysqCjY0N/Pz8YGhoyLpO85XnosvUJLKzs7FmzRrcu3cPAODg4ICAgABWMns+WL16NYYOHQoNDQ2sXr1a6ve4zHkrV65Ehw4dUK9ePWbuv337NmrVqoXo6GiZst7e3hg7diyGDx+OV69eoWvXrnBycsLevXvx6tUrmWt8WFgYOnbsCDs7O7Rv3x4AkJSUhM+fP8vk83jy5AmICDY2Nrhx44aIHq2mpsbwj34PVOVRAdj51oT48OEDBg4cWC3tkNeOxXdPyXc//9tvvyE9PR0JCQlwd3dnyrt27YqFCxciKChIpnx5eblE7otnz55BV1dXqlydOnVw9+5dqZwfd+7cQe3atWXWnZeXB4FAwNzjxo0b+OOPP+Dg4ICxY8dKlcvIyGD+vnv3Ll69esX8X1ZWhjNnznDa0/Ldjx89ehT79+9H69atRXQ6R0dHhqdCFl69eoU6deqIlZuamsrkrRJCXk6smoKKigr69++PXr16YdOmTfjtt98wc+ZM/P777xg0aBCWLVsm8pylpaX4448/4O3tjWnTpjG2E1n9rSri4+MRFxcn1v8aNmyInJwcxR9G4eOTfyE8PDxo/PjxUj8fN24ca2oYoor81MLTvMonUocPH5bK6VHVK0zWJQt88/by8dYiqggBjo2NpV69epGqqiq1aNGCNm/eLJJD+fDhwzUSCnrhwgVe8nyfvWr+1cpX/fr1WeWNjY0Zr/Lt27dTkyZNqKysjA4cOECNGzfm9Azfvn2jvLw8uTlB+EJR74HqgiSvFXkioDQ0NCSGsD558oS0tLRkym7fvp3Mzc0pJiaGtLW1ad++fbR48WLmbzaoq6tLTAN0//59TpEkBgYGTFo0DQ0NxltOeHFB/fr16ezZs0Qk+vvt3r2b7O3tZcqWlZWRu7s72djYyO3d+/r1a1q5ciU5OjqSmpoaDRgwgE6fPi1y8i/0qqgJ8O03fPkFFE1LQ1SRisvJyYnx5jxx4oRY+rS8vDypaV5mzJhBP//8M718+ZLJIXz58mWysbHh5DVS9V0pkoKPD/z8/Khv375UXFzMRFPk5OSQi4sLBQQE1Gjd06ZNI2NjYwoPD2c8g1euXEkmJiacvfkfPXpEo0ePppYtW5K9vT0NHTqUMjIyOMmWlZVRSEgI1a1bV8Tba+7cubRjxw5W+Vq1ajEeqpXHe3Z2NqexVtWzVh4eoDdv3lCnTp1ISUmJdHR0FJqvfiS0tLQk/k63b9/m9O74pg8kqp50Dx8+fKCVK1cy886qVatEPFYloV+/frR//35ebecDRde6hIQEunjxIgkEAjp8+LBIHugrV67Q8+fPOdX/999/0+jRo0XSkKqpqdGYMWOYaKJbt27RrVu3quNxRVAdqQ6sra0l8ljs2rWLk6chH8yfP1/h3ObSIiGys7M5eZfy1c3Hjh1LNjY29OeffzIRRKdOnaIGDRrI3DNWVxtOnz5NzZo1oxMnTtCLFy9EIpkq760kga+O9fHjR4k5xt+9e8datywsWLCASWEoC3p6enTjxg2x8uvXr3PiNxBC0XS9fMYd3zHL17u1Orxj+XAr8MGZM2dITU2NWrVqxdg+WrVqRerq6hQfH1+jdVcnFE3pZWBgwOyp165dS23btiUiori4OE5z1vPnz+m3334jDw8PGjBgAAUHB3PiCiguLiYfH59qizxTFHyiZX19fWnz5s0K183HjsV3T8l3P68I32BlDBo0iMaMGcPIP378mP7++2/q3LmzTDtYQEAAOTs7S/T2f/36NTVp0oR1X/bzzz8zUanCfWmbNm3IxMRE5nxTXRFMfPbjRIqlOa4MW1tbio6OFiuPioriNOaJKvT67du3U1BQEDPeU1NT5eJn5oubN2/ShAkTyNDQkCwsLGjOnDn0+PFjunTpEnXp0oVatmwpJqOpqcmL+0ZHR4eJ4qn87m/evMkpO4M0/FdFgsyZMwedOnXCu3fvMHPmTDRu3BhEhHv37iE8PBzHjh3j5FEi9Ew6ePAgBAIBysvLkZycjJkzZ2LEiBESZW7dusWpjWweY/3798fFixcV9pTg460FVJwGl5eXY8iQIbhx44aYFxAAuLm5wcDAgFc9kuDu7g4LCwuMGjUKI0eO5OxJLQTfZ6/snasIioqKmJPP+Ph49O/fH0pKSmjdujXrSWZWVhZ8fX1x5coVkXIigkAgkHiyX51Q1HugOuvnA319fTx+/BjW1tYi5Y8ePRLz5KiK0aNHQ1NTE3PnzkVRURG8vb1Rt25drF27FoMHD2atu2nTptiwYQPWrVsnUr5hwwY0bdqUVZ5vBAYAjBkzBgEBAdi5cycEAgFevHiBq1evYubMmZg3b55M2SlTpuDixYtwc3ODsbGxXN7MFhYWaNCgAXx9feHj4yPRg79JkyaM1211g2+/2bJlC2bOnAlvb2+UlJQAqPCC8PPzw4oVK1jlPTw8kJWVhc2bNzPebn369MH48eNZ56/Nmzcz702S9whQEX0YEREh8bPQ0FBMmjQJlpaWKCsrg4ODA8rKyuDt7Y25c+eytp2Pd2V1IDw8HL/88gvMzMzw5csXdOzYEa9evUKbNm1Yo6f4YuXKlahduzbCw8MZD506deogMDAQM2bM4HSPBg0aYPv27QrVv3jxYuzevRvLly8XibRzcnLCmjVr4OfnJ1O+sLBQovfy+/fvOUUT8vnthwwZgufPnyM0NFQsEoINVedIWWDz0lQU6urqEqO8CgoKoKamxiqvpKTEeOErisWLF2PJkiWsHvDSkJKSgh49ekBTUxOtWrUCUBElsGTJEsTHx4tEHh8/fpz5u1evXggMDMTdu3clerOzeTkK8ebNGzx48AAAYGdnBzMzM05yiq51HTt2BFCho1lZWcnV5ypDR0cH27dvx+rVq/H48WMAgI2NDXR0dJjvSNJ5qwPCSBM+ePnyJdq2bStW3rZtW06ehnxw7NgxLFmyBB07doSfnx8GDBjAOXLZ0tISycnJYpEQycnJqFu3Lqs8X9380KFDiI2NRadOnZgyDw8PaGpqYtCgQdi8eXONtsHDwwNAxfiq3He56Pd8dazBgwejT58+mDhxokj5gQMHcPz4cfz555+KPBKGDRuGVq1asUaflJeXi80zAKCqqspZf8vJyYG7uztyc3Px7ds3dOvWDbq6uli2bBm+ffuGLVu2SJXlM+74jlm+3q185bdv344JEybAxMQEtWvXFota5BJxqCiCgoIwbdo0sQj9oKAgzJ49G926dauxuoWYPn26xHKBQAANDQ3Y2trCy8tLZpSPtra2TC92aSgpKWHmx3PnzjFra+PGjWXO1SUlJXB3d8eWLVvkjvQEKsbVkSNHavS3ZQPfaFlbW1vMmzcP165dkzvqDuBnx+K7p+S7n8/Pz5eoTxUWFnJ6d+Hh4ejRowccHBzw9etXeHt7IysrCyYmJjKj7hYsWIA///wTDRo0wLBhw0Tsp3/88Qdq167N2qfu3LnD6KMHDhyAs7MzkpOTER8fj/Hjx0uVr64IJj77cQBwdXXFqVOnMHnyZAD/11d37NiBNm3asMqPGTMGU6dORUlJCTp37gwAOH/+PGbNmsVpX5mRkYGuXbtCX18fT58+xZgxY2BkZITDhw8jNzcXUVFRrPfgg1WrViEyMhIPHjyAh4cHoqKi4OHhASUlJQAVWVN27dolZmcDgFatWuHWrVsKZwdo3749oqKisGjRIgBgbO/Lly+Hm5ubws/0XxUJQlRxuiskIq98GRsbU2xsLKd7fPv2jUaPHk0qKiokEAhIVVWVBAIBDRs2rMbJM/nm7eWLqKgoJp/m90Z+fj6tWrWKmjZtSioqKtS9e3fav38/ffv27Ye0R144OzvT2rVrKTc3l/T09BhCtZSUFFZehrZt21KHDh3ozz//pFu3bn13ThBFvQf+KRg7diw5OzuLRFFlZWVRkyZNyM/Pj/N9CgsL5c6hm5CQQNra2mRvb0++vr7k6+tL9vb2pKOjQ5cuXZLrXoqivLyciV4Rek9oaGjQ3LlzWWV1dHTo5MmTCtX7vZ6vpqEov8A/ATk5OXTq1Cnav3//P5avSRaSkpJo48aNtGzZMiaa6XuCizeuJJSWltLBgwcpJCSEQkJCKDY2ljMfVYMGDRg+lspeL/fu3eMUZdmzZ09mbAvn67KyMho4cCANGDBA7meRB5qamgqvSbK8qeX17lYUw4cPJ0dHR7p27RpDLn316lVycnKikSNHssovW7aMd6SSrq4u85srgp9//pl8fHxE+ltJSQmNHDmS2rdvL/JdWZ6N8no5fvr0iYYNG8boxgKBgFRUVGjo0KGsUSjVhUuXLtHQoUOpTZs2jHdcVFSUTFLP/xQ4OjpKJGdftGgROTk51Xj9aWlpNHnyZDIxMSEDAwMaP368RC//qli2bBkZGxvTzp076enTp/T06VOKiIggY2NjCg0NrfF2a2pq0t27d8XK79y5wxopLAnyktJXjlySdMkCXx3L0NBQ4rPfu3ePl4dlVFQUJx4eT09P6tChg0i01rNnz6hjx47Ut29fTnUpSvb7o8HXu5WvfHVzK8gDdXV1qfn5a5pbQIhOnTqRnp4eaWtrU/Pmzal58+ako6ND+vr69NNPP5GBgQEZGhrSX3/9JfUeikbStGrVimbPnk2XLl0iDQ0NRme6evUqK7esiYkJL12+ujiUFAXfaFm+uuGPtGPxBV++QaIKXXDPnj0UGBhIEyZMoO3btzN8QrLw/v17Gj9+PMPbJORxGjduHKcopMqk8n369GHmnpycHE5ZMX40kpKSSEdHh8aPH08aGhoUEBBA3bp1I21tbUpJSWGVLy8vp1mzZpGGhgZje9bS0uIcddelSxcKDAwkItH5Pjk5merVq6fwc3GFra0thYaGSuUhJaqwj0vizNm/fz/Z2NjQ+vXr6cqVK4w9RXixITMzk8zMzMjd3Z3U1NTol19+IXt7e6pVqxav7EgCIiLFj1D+nSgqKkJcXByysrIAVORy7d69u1x5kIGK/HaZmZkoKCiAi4sLGjZsKHdb8vLyAIBzVIOsnLECgYDxXpOF2NhYHDhwALm5uSguLhb5LC0tjVM7+KC4uFhinmtpXC2SkJaWhsjISObk2tvbG35+fqye9Xyf/dmzZzh+/LhEeVl50oV1e3t7o6ysDF26dEF8fDwAYOnSpbh06RJOnz4tVVZbWxupqalo3LgxaxtlQdF3/+zZM/To0QNEhKysLLi6ujLeA5cuXZLp6VlSUoJx48Zh3rx5vHIeFxYWIjExUeK7Z/P8+PTpE9zd3ZGSksJ4TT179gzt27fH4cOHayRyqTJevHiBjRs34v79+wAAe3t7TJw4kZOXY2V8/fpV7Nnl4WYoLi7Go0ePUFBQAAcHBxEPV2moV68e4uLiePc9RSD0Kh04cCA0NTUVugeffvOjkZSUhK1btyI7OxuxsbEwNzdHdHQ06tevj59//vm7tKGoqEjiu2PjW7C2tma8U+WZ26sLkvhEgIp1Ul1dnZNXv6L466+/4OnpiVevXjG8IkIukxMnTrDmAtbU1MT9+/dRr1496OrqIj09HTY2Nrh79y5atWqFgoICmfJ37txBly5d0Lx5c1y4cAGenp7466+/8P79eyQnJ3OKJE1JSZG6Vsriz2revDk2bdqE1q1bs9bBFUI1VVEPf3nw8eNHjBw5EidOnGA8DEtLS+Hp6Yldu3aJcWBURXl5OXr16oWHDx/CwcFBzEuRC++an58fWrZsifHjxyv0DJqamrh165bYnH337l24urqiqKhIofuy4ddff8WtW7ewfv16xjPu6tWrCAgIQLNmzRATE8P5XoqsdYcOHcLw4cMxdOhQREdH4+7du7CxscGGDRvw559/KuzV/m/BoUOH8Ouvv6Jr165MNFJycjLOnz+PAwcO8OJrkQclJSU4ceIEIiMjGd3Bz88PPj4+EscP/X9utnXr1jG/uYaGBmbPns3ZY5mPbt6lSxcYGxsjKiqK4V/68uULRo4ciffv3+PcuXOc2hAVFYUVK1aI7C0DAwMxfPhwTvI/Atra2oxHdWVkZmbip59+Yp0r+vfvL/I/EeHly5dISUnBvHnzWPOY5+XlMeuTcB+cl5cHJycnHD9+XGoO+sowNjbGlStXYGdnJ7JePn36FA4ODjU23/GFh4cHWrRogUWLFkFXVxcZGRmoV68eBg8ejPLycsTGxtaovJ6eHm7fvg0bG5vqfCxOsLS0xKpVq8S4HQ4cOICZM2ciNze3xtuwZs0aJCUlITIykllbPn36hNGjR+Pnn3/GmDFj4O3tjS9fviAuLk5Mni2SRpZNISEhAf369cPnz58xcuRI7Ny5EwDw+++/4/79+zL1hGnTpkFdXV0izyUXLF68GOHh4ejSpYtCHErKysp4+fKl2L7/3bt3MDMzY81MUadOHVy6dEkhmxlflJSUQFNTE7dv31aYY7O69pSK6DiXL19Gz549MWzYMOzatQvjxo0T4Rts0aIF9wdREETEcGWamppy1st/+uknuLm5oVevXujevTuuXbuGpk2b4tq1a/jll1/w7NkzmfK7d++GiYkJevXqBQCYNWsWtm3bBgcHB+zbt48XByFXZGdnIywsDOnp6SgoKEDz5s0xe/ZssfVTFgoKCnDv3j1oamqiYcOGnCNm9fX1kZaWhgYNGoisczk5ObCzs8PXr18VfawahzBapDIEAoFc2Ww+ffqEDRs2iLz7SZMmSc2UwQX/VYcgFy5cgL+/P65duyY20Xz69Alt27bFli1bGKKpypAWNikJbAp3aWkpgoODsW7dOsaYoaOjg8mTJ2PBggUSQ4OrC+vWrcOcOXPg4+ODbdu2YdSoUcjOzsbNmzcxadIk1jQjX79+xfr166USdspa9Ks7pdOLFy+wbds2hIWFQUVFBV+/fkWbNm2wZcsWODo6in2f77OfP38enp6esLGxwf379+Hk5ISnT5+CiBhjExtevXqFly9fomnTpsykcOPGDejp6ck0Mrds2RKrV69W2PBZHe++tLQUMTExyMjIYCagoUOHcjJO6+vr4/bt2wofgty6dQseHh4oKipCYWEhjIyM8PbtW2hpacHMzIzT4R8R4ezZs0hPT4empiaaNGkikSwcAFxcXDgv7DV9cFhYWIjZs2fjwIEDePfundjnNZ0KLTIyEmfOnEFkZKTcB8Xv3r3D/Pnzpc4X79+/lyk/depU/PHHH/j27RsGDRoEPz8/uYyr1dFvfhQUMehV5zqVn5+PUaNGST2cZet3a9aswa5du3Dnzh24ubnBz88P/fr1k6nwVWc6JCUlJZlj2MLCAj4+PliwYIFEBY0P2rRpA1NTU+zevRuGhoYAKsgcfXx8kJ+fLzYPV0WLFi0wbdo0DBs2TETZDQkJwdmzZ5GUlMTaBj4KY0xMDEaMGIEePXogPj4e3bt3x8OHD/H69Wv069dPZmrJ+Ph4BAcHY8mSJRJTFchzaBsREYHVq1czRsWGDRti6tSpGD16tEy5Fy9e4PLlyxLnHK6b1KysLJFDa1tbW05y/v7+2LFjB9zc3CSmA+OSlnPp0qVYtWoVevXqpVC6ByExa/fu3UXK4+LiMGLECLx+/ZrTs8gLbW1txMXFiekpSUlJcHd3R2FhoUx5vmudi4sLpk2bhhEjRoiMm1u3bqFnz54ihJr/NJSVlWH16tVSDx7Z1koh0tLSsGrVKibdg729PWbMmMEQ934PFBcX48iRI9i5cycuXLiAtm3b4sWLF3j9+jW2b9+OX3/9VaKcosYBvrr5nTt30KNHD3z79o1xpEpPT4eGhgbi4uIk7ieqQhop/caNG7F48WKZpPRCKOJwwFfHcnNzg5OTE9avXy9SPmnSJGRkZLCuNaNGjRL5X0lJCaampujcubPY/CMNRIRz586JzLddu3blJAsAhoaGSE5OhoODg8i4v3z5MgYMGCBzvuMz7viOWb7OCnzl+R6280FISAhWr16NoKAgJoVfcnIyli1bhunTp7Om6a0OmJub4+zZs3BwcBAp/+uvv9C9e3c8f/4caWlp6N69O96+fSsmX69ePUycOFHhtJVlZWX4/PkzoyMCwNOnT5n9iTRMnjwZUVFRaNiwocRDDDbdnq8zrZKSEl69eiXWxhcvXqBBgwb48uWLTPnly5fjxYsXvFNDFRcX48mTJ2jQoAFUVLhn97exscGRI0c4paOuCr57yurYz1eHIV5efPnyBWfPnoWbm5tYCvTPnz8jISEBPXr0kLlm8zn4AypSq27evBmdO3fG1atX0aVLF6xZswYnT56EiooKJwejfzPMzMwQFxcHFxcXkXXu7Nmz8PX1ZZzqaxJfv35FRkaGRF1DVrpctvSM3+MASyIUjiH5F6JPnz4yQwDXrl0rNfy2Kjmonp4eaWlpMQRo2trapKenx0oaSkQ0fvx4MjMzoy1btjChQFu2bKHatWtzJuH79u0b3b9/n3N6DSHs7Ozojz/+ICLRcKp58+bRpEmTWOW9vb3JxMSExo8fTwsWLKCFCxeKXLJQHSmdiouL6eDBg9SzZ09SUVGh1q1b0/bt26mgoICePHlCQ4cOlUr0zPfZW7ZsSfPnzxeR//vvv8nT05M2bdrEqf2K4vz589SmTRu6ePEivX37Vi7iRKIfn06Lb/htx44dacyYMVRWVsa8+9zcXOrQoQMdOnSoGltagcp9OigoiPT09Kh169YMcVubNm1IT0+PgoKCON3vw4cPFBcXR9HR0bR7926Riw0TJ04ke3t7io2NJU1NTdq5cyctWrSILCwsaM+ePXwflRXNmjUjXV1d0tHRIScnJ7nIH3v27EkNGzaksLAwioyMpF27dolcXFBSUkKHDh0iT09PUlVVJXt7e1qxYgW9evWKVfZ795vqRLNmzZj+UXm+SktLk5o+rzrXKW9vb2rXrh3dvHmTtLW1KT4+nqKjo8nOzk6u9GipqalMihRDQ0OaNGkSpaamSvxu1dB2Yfo2IWmgQCAgbW1tTiHvu3fvJgsLC5o7dy4dP36cjh8/TnPnziVLS0vaunUrLV68mAwMDCSmj+ELDQ0NunPnjlh5ZmYmp7Dvo0ePkr6+PoWFhZGWlhatWLGCIWz+HqShzs7OtGHDBiL6v75XXl5OY8aMYdZAaaicPqnyxTWlkhDz5s0jbW1tCgoKomPHjtGxY8coKCiIdHR0aN68eVLlIiMjSU1NjXR0dKhevXrfLY2WEHzSBwrBN93D5MmTycLCgmJiYig3N5dyc3Np3759ZGFhwZqqKyEhgXr37k0NGjSgBg0aUJ8+fTin3LG0tJRIKp+ens6a4oOI/1qnqanJpFuoPGdyJdj+kZg3bx7VqVOHVq5cSRoaGrRo0SLy8/MjY2NjTqlui4uLadSoUT+U8DYlJYUmTZpERkZGVKdOHZo9ezZlZWUxn69bt47MzMyqvd7q0M0LCwtp27ZtNH36dJo+fTrnFCFC8CGlf/PmDfXq1UtszhRessBXx7p8+TJpaGhQ+/btGZ23ffv2pKGh8cPSmX748EGu7/NJ18tn3PEds0QVxPSLFy+mgQMHUs+ePWnOnDky041Up3xoaOgPS69dXl5Oq1atInNzc0ZnMDc3pzVr1siVSo4PtLW16eLFi2LlFy9eJB0dHSKqWDukEU7zTVupKKrq+ZUvLrq9ohD2CyUlJVqyZIlIX1m1ahX17duXmjVrxnqfsrIycnd3JxsbG+rduzf169dP5GJDYWEh+fr6krKyMikrKzO/gb+/Py1dupRVfseOHeTh4cEphVNV8N1T/uj9vKJYs2YNde7cWernXbp0YfYMslBaWkrv378XKXvy5AmnNOOampqUk5NDRESzZs2i4cOHE1FF2koTExNW+epAWVkZPXjwgJKSkigxMVHkqmn4+flR3759qbi4mFnncnJyyMXFhXcKXi44ffo0mZiYKJwuly/42NGk4b/qEMTKykpi7lMh7t27R5aWlqz3CQ8Ppz59+ogM5Pfv35OXlxetXLmSVV5PT4/+/PNPsfJTp06Rnp6eTFm+k7+mpiY9ffqUiIhMTU0ZA/jDhw855RDV09Ojy5cvs35PErS0tOjevXsKyRJVPKOxsTEZGRlRQEAAZWZmin3n5cuXJBAIJMrzfXYdHR0m95yBgQFj5Lp9+3aN5+Pja1hS5N0LDU9cLjYsWrSIDAwMaMCAARQaGiq3sq2vr0/3799n/haO42vXrpGdnZ1EmbVr1zJ5P6vWJ0/9fn5+Erkz5s+fT6NGjWJt+/Hjx0lXV5cEAgHp6+uTgYEBcxkaGrLKW1paMoq6rq4uY1SIioqinj17ssrzRdWDTnkOPnV0dKr1kO3169e0aNEi0tDQIFVVVfLy8qLz589L/b4i/eafAr4GPb7rVO3aten69etEVNHvHjx4QEQV80K7du3kfRwqLi6mNWvWkLq6OikpKVHTpk0pIiJC6oZ379691K5dO+b3IyK6f/8+tW/fntNmoXPnzrR//36x8v379zPKfFRUVI30gyZNmkjsl+fPn+ecm//SpUvUtWtXMjU1JU1NTWrXrh3FxcVxbsP79+9pxYoVDA/RypUrOW/6tLS0mL5nZGTEGLbv3r1LtWvXlil78eJFhfPbV4aJiQnjtFAZf/zxBxkbG0uVs7CwoMWLF1NZWRnnuiqjtLSUduzYQUOGDKEuXbqQm5ubyMUGKysrXnpOdeDbt280ZcoUUlNTY3QFdXV1mjp1Kn39+lWqXHR0NKmoqNCgQYOYtXHQoEGkqqpKe/fuZa1369at1LVrV3r58iVT9vLlS+revTtt2bKFVZ7vWle/fn2GN6jynLl7926pzjHVhV27dokcfgUGBpK+vj61adOG0TtlwcbGhpGvrGuuXbuWhgwZwqkNenp6Ch+CnD59WoQ3ZcOGDdS0aVMaMmSImNFCEpycnEhFRYU8PDzoyJEjEvkR8/PzGf28X79+jANPVSOYvEaxH6mbC6Guri5y4CPEw4cPWddrPg4H1aFj3bp1i7y9vcnBwYFatGhBo0aNkotz4MOHD7R9+3YKCgpi1pjU1FSGk0cWwsLCKCYmhvl/4MCBpKSkRHXr1uX8XHl5eeTg4ED29vaMc5yxsTHZ2dmxGtf4jLvqGLM/Ej+Kd6ukpIR2797NODJ9/vyZPn/+XGP1SYO3tzfVr1+fDh8+THl5eZSXl0eHDx8mGxsbGjZsGBER7du3j1q0aCFR3tfXlzZv3vw9m/xDIewbAoGALC0tRfpLo0aNqHv37nTt2jXW+0yaNInU1dXJ3d2dRo4cST4+PiIXG6ZMmUItWrSgpKQk0tbWZtb5o0ePcjqEadasGeno6JC6ujo1atRILsc+vntKvjpOTk6OzKum0LJlSzp+/LjUz0+cOEEtW7assfqJKux2aWlpRFTxG0ZFRRER0aNHj0hbW7tG6yaq4OupX78+Y3v73ocAHz9+pK5du5KBgQEpKyuTpaUlqaqqUocOHb4LV6mtrS1NnDiRkwOqJERFRVHbtm2pTp06jE68evVqOnr0KKssXzuaNPxXHYJIU1KFyMrK4uSlWbduXalenlyI4ExNTSUexty9e5f1NJPv5F+/fn1mEmnRogWzMY2Li+PUkezt7TmR2EiCq6srL3LKzp070x9//CFzE19SUiLV0ML32WvVqsX8bvb29ozx//bt2zU+AfMhTiRS7N1XJ2EqX2W7MhFcw4YN6cyZM0RUcXApjbjS2tqa3r59y7t+PT09iRvChw8fsh5aCtsbEBBAhYWFrN+VBG1tbUa5MTc3ZwzTjx8//i4LPx+4urrS1atXq+Ve169fp/Hjx5OBgQFZWVnR/Pnzyc/PjzQ1NWnGjBkSZRTpN9WN0tJSevXqFb1580YuOb4GPb7rlK6uLmMIt7KyYg6/Hz9+TJqamlwfg4qLi2n//v3k7u5OysrK1K5dO9q5cyeFhIRQrVq1pBoLbGxsmPm6MlJSUlg9a4kqojGkjVth++V9Fq44deoUOTo60sGDB5nN9cGDB8nZ2ZlOnTolVxSfIkhMTCQ9PT2ytLRkDIlWVlakp6fHyWPJ3NycOfhwdnZmDiOuXLnCac6rDujr60slTtXX15cqZ2RkxIsob9KkSaStrU2DBg2igIAAmjp1qsjFhp07d9KgQYPknu+nTZvGbGSEEYeSrunTp3O+Z2FhIWVkZFBGRgan9jRu3FhixGZ4eDg1btyYVV5oXFBVVWUiSVRVVUlHR4eToYHvWhcaGkoODg507do10tXVpaSkJNqzZw+ZmpoyZKI1hUaNGjEHn1euXCEtLS3aunUr9enTh5MhX0tLi3n22rVrM9Fy2dnZnMccn4hbJycnOnXqFBERZWRkkLq6Ov3222/UunVrToapkJAQTkZvIXx8fBjDZ1UjmLxGMb66eWhoKEVERIiVR0REcCaO5kNKz8fhoDp1LEWQnp5OJiYmZGtrSyoqKoyeMmfOHMZTVxasra0pOTmZiIji4+PJwMCA4uLiyM/Pj7p168a5HSUlJRQdHS032S+fcVcdY5avd2tNeMd+D1R2SvxR+Pvvv5kIW6GzgJqaGo0ZM4ZZi2/dukW3bt2SKP8jI2n4Ii8vjzZu3EizZ88W0zHY0KlTJ7mjtSqDb7SslZUVM+dV3htlZWVJjdqpDD6OfXz3lHx1HEnOsFyjBvnAwMBA5iFLTk4OGRgYiJW7uLgwThTNmjUT0wO5Hj4RVRxaNm/enPz8/EhLS4ux7xw7dowcHR1Z5d3c3CT220+fPnFycGratCkNHDiQ7t69Sx8+fKCPHz+KXN8LSUlJtHHjRlq2bBljI/ge0NXVVXhvtWnTJjIxMaHFixeTpqYmM2YjIyOpU6dOrPJ87WjSwD2J3n8AzM3NcefOHam5nTMyMjjly/78+TNDClQZ+fn5+Pvvv1nl/f39sWjRIkRGRjL58759+4YlS5bA399fpuzRo0exf/9+tG7dWiTXtKOjI7Kzs1nr7ty5M44fPw4XFxeMGjUK06ZNQ2xsLFJSUsQI7iQhPDwcs2fPxpYtW+TO4bZs2TLMmjULoaGhcucKLykpQb169dC6dWuZOQdVVFTQsWNHiZ/xffbWrVvj8uXLsLe3h4eHB2bMmIHMzEwcPny4WklgJUHaM3GFIu++ar4/Pnjy5AkveRcXF9y8eRMNGzZEx44dMX/+fLx9+xbR0dFSyc0q18mnfk1NTSQnJ4uRuCUnJzMkmrLw/PlzTJkyRW4+DSFsbGzw5MkTWFlZoXHjxjhw4ABatWqFEydO1DihOwCMHDkSfn5+UvlTZGHTpk0ICgrC/Pnz4eTkJDc/wJs3bxAdHY3IyEhkZWWhT58+2LdvH3r06MHMfz4+PnB3d8fKlSvF5BXpN1WhKL/AqVOnsGzZMty4cQMlJSUAAF1dXfTp0wdLlixhJQsfM2YMAgICsHPnTggEArx48QJXr17FzJkzOeVL5rtO2dnZ4cGDB7C2tkbTpk2xdetWWFtbY8uWLZzWybS0NERGRmLfvn1QUlLCiBEjsHr1ahHuo379+qFly5YS5V++fInS0lKx8rKyMk6cBpaWloiIiBAjj4yIiGAIWN+9eyeSj7kybt68KTXPOlvO5d69ewMABg0axPRT+v/0a3369GH+V4QLiwsmTZqEX3/9FZs3b4aysjKAivc2ceJETJo0CZmZmTLlO3TogLNnz8LZ2RkDBw5EQEAALly4gLNnz6JLly4yZevXr49Ro0bBx8eHtY/LwvDhw7F582axd71t2zYMHTpUqpyfnx8OHjyIoKAgheqNiYnBgQMH4OHhoZD8unXrkJ2djVq1asHa2lpszpPGIXXr1i1mnrh165bU+8tDDq+lpSVXnujHjx8z/bMyPD098fvvv7PK9+3bl3NdksB3rQsKCkJ5eTm6dOmCoqIidOjQAerq6pg5cyYmT57Mq21syMvLY/YWR48exYABAzB27Fi0a9cOnTp1YpW3sLDAy5cvYWVlhQYNGiA+Ph7NmzfHzZs3OXNjNGzYECEhIUhOTpab8PbJkydMbvxDhw6hd+/eCA0NRVpaGqexUHVNKisrQ2ZmJurVqydxjq3MjcOFJ0cW+OrmW7duxR9//CFW7ujoiMGDB3PK+R8cHIxff/0Vly5dkkhKLwuFhYVMfn1DQ0Pk5+ejUaNGcHZ2ZuWcU0TH+vz5M1P++fNnmfdn09GmT5+OUaNGYfny5SK54j08PODt7S1TFqjgSRSuxydPnsSgQYPQvXt3WFtb46effmKVF0JFRQXDhg3j/H0h+Iw7vmP2xIkTGDp0KAoKCqCnpydGrD1ixIgalf+RaNWqFW7duvXjcsGjgot1+/btWL16NcPlYGNjAx0dHeY7zZo1kyq/bds26OjoIDExEYmJiSKfCQQCuUiy5UVKSopULho2bgQ2DiVZKCkpQW5uLl6+fKnw/tPIyIiVr0YW8vPzJXKmFBYWctKPFixYoHDdfPeUfHWcqrphSUkJbt26hVWrVrHy2grx8eNHxMbGIjs7G4GBgTAyMkJaWhpq1aoFc3NziTKlpaXIz8+XqtPn5+dL3K95eXkxcyFf/XDjxo2YO3cu8vLycOjQIRgbGwMAUlNTMWTIEFb5hIQEsbECVPBccOFZzMrKQmxsLGd+wJrCzz//rDA/MB/88ssvSEhIUGjsrl+/Htu3b0ffvn1F9uSurq6YOXMmqzxfO5o0/Fcdgnh4eGDevHlwd3cXM15++fIFCxYsYIwXstCvXz+MGjUK4eHhaNWqFQDg+vXrCAwMlGpMr1p+7tw5WFhYiJDwFRcXsxoY+E7+27ZtYww6kyZNgrGxMa5cuQJPT0+MGzeOVd7V1RVfv36FjY0NtLS0xBRuWURwQqK7qs/IxRikqqqKQ4cO8SJL4/vsq1atYojsg4ODUVBQgP3796Nhw4asRjFFkJGRAScnJygpKSEjI0Pmd2URJwL83n11QlEis9DQUMZwu2TJEowYMQITJkxAw4YNERERIVO2pKQEjRs3xsmTJ2Fvby93m6dOnYoJEyYgLS1NZLzv3LmTU3/s0aMHUlJSYGNjI3fdQAXxZHp6Ojp27IigoCD06dMHGzZsQElJSY30u6r49OkTunbtinr16mHUqFEYOXKkVEWpKgwMDPD582d07txZpJxrv7OwsECDBg3g6+sLHx8fmJqain2nSZMmUg3pfPoNAOzatQvjxo2DmpoajI2NxTaZ0jY50dHRmDRpEsaOHYsOHTogIiICPj4+qFevHmJiYtCiRQtcuXJF7GCtMvga9BRZpyojICAAL1++BFCxaXB3d8fevXuhpqaGXbt2scq3bNkS3bp1w+bNm9G3b1+xtQKoMJgPHjxYonyXLl0wbtw47Nixg9mYpaamYsKECZxIU1euXImBAwfi9OnTTP9ISUnB/fv3ERsbC6DioEMSSW9oaCjmzp0LOzs7MXJrLuvshQsX5DJWAxXGL64ybISrjx49QmxsLHMAAgDKysqYPn06oqKiWO+/YcMGfP36FQAwZ84cqKqq4sqVKxgwYADmzp0rUzYgIAC7du1CSEgI3Nzc4Ofnh379+nEyCk2fPp35WyAQYMeOHYiPj2cMmdevX0dubq5M487SpUvRu3dvnDlzRuKBP9ucqaamxmuTo+hG7+LFixL/5or+/ftj165d0NPTYx3f0owklpaWOH/+vNjznzt3jjFUygIf4wLAf60TCASYM2cOAgMD8ejRIxQUFMDBwUHEqFVT0NHRwbt372BlZYX4+HimL2toaLASxQIV8/X58+fx008/YfLkyRg2bBgiIiKQm5vLiVQbqDjgNTAwQGpqKlJTU0U+YzPKqampoaioCEDF7y0cY0ZGRqyGcqBCT3J2doafnx/KysrQsWNHXLlyBVpaWjh58iSngyBFwVc3f/XqlcSDfVNTU2YNZMOAAQNw/fp1rF69GkePHgVQQfB948YNVlJ6Pg4HiuhYhoaGePnyJczMzGBgYCBx3eGqo928eRNbt24VKzc3N8erV69kygrbkpeXB0tLS5w5cwaLFy9m6pdnX5KVlSXVaWH+/PlS5fiMO75jdsaMGfD19UVoaKhCxh1F5KdPn45FixZBW1tbZL2VhJrcX0ycOBEzZszAs2fPJB7Ysu1pqxM6OjoK1cfXuU+Ir1+/cnKoEyImJgYjRoxAjx49EB8fj+7du+Phw4d4/fo1+vXrxyr/22+/YebMmQgODoauri4OHToEMzMzDB06FO7u7jJlVVVVGd1QUSxcuBALFixAZGSkQv3e1dUVp06dYvZBwvlrx44daNOmDef7pKam4t69ewAqDrzZ5mmA/56Sr44jiczd1dUVdevWxYoVK1h1v4yMDHTt2hX6+vp4+vQpxowZAyMjIxw+fBi5ublS9weOjo44d+4cWrRoIfHz+Ph4ODo6ipULdcKysjK4ubmhSZMmCh+eGRgYYMOGDWLlwcHBMuUq28/u3r0rsi6VlZXhzJkznGwaP/30Ex49evRDD0HOnz+P8+fPS1znhETzNYUNGzZg4MCBSEpKkri3YnOykTS+1NXVUVhYyFo3XzuaNAhI6J74X4DXr1+jefPmUFZWhr+/P+zs7AAA9+/fx8aNG1FWVsachspCUVERZs6ciZ07dzKeeyoqKvDz88OKFSvEFnOgYuLjClleUR06dMDAgQMxefJk6OrqIiMjA/Xr18fkyZORlZWFM2fOcK5HEXTt2hW5ubnw8/MTMw4BFV7j0lDVU6Iq2KIdRo4ciWbNmnHeEP4TcPz4cc7f9fT0FPlfSUkJr169gpmZGZSUlCAQCCBpuHLZqPB993xRVFSEyZMnY/fu3QCAhw8fwsbGBpMnT4a5ubnCXrtcYW5ujnPnzil0CAIABw4cwNq1axmFyd7eHgEBARg0aJDE71f+3fPz8xESEoJRo0ZJXDiq/u5syMnJQWpqKmxtbb/bRiE/Px/R0dHYvXs37t69i65du8LPzw9eXl4SjdtCtGrVCioqKggICJA4X7D1u6SkJLRv375ankERWFpaYvz48fjtt9+gpKTEWc7e3h4LFy5kDOwpKSno168fcnNzIRAIMHjwYBQXF7N6bAEVB4eKGPQUWafY7nf//n1YWVnBxMRE5nfLysqwZ88eeHp6So20YEN+fj5GjhyJM2fOMH2stLQUPXr0wK5duyQ6A1TFkydPsHXrVjx8+BBAhbFp3LhxsLa2lilXq1YtLFu2DD4+Pgq1XREI50agIkJl8eLF6NGjB7Opu3r1KuLi4jBv3jzWNbBdu3YIDAwUM8gfPXoUYWFhuHbtWrW3vyrS0tKwa9cu7Nu3D2VlZfD29oavr69MT0M3NzdO9xYIBLhw4YLEzxYvXoz58+dLPcCSJidEeHg4Hj9+jA0bNsh9kPUjMWrUKKxbtw66urqs+qY0HXPz5s2YOnUqfH190bZtWwAV3uy7du3C2rVrOTmLCFFQUCC2SWPzKq+KH7HWKYqhQ4fi/v37cHFxwb59+5CbmwtjY2McP34cv//+O+7cuSPX/a5du8YclEuKzqlueHp6ori4GO3atcOiRYvw5MkTmJubIz4+Hv7+/swcKg0WFhY4evQoXF1dcfToUUyaNAkXL15EdHQ0Lly4gOTkZKmyr1+/xsyZM5nNfVU9t6addBo2bIgFCxaIRRJER0djwYIFjJd4TWHPnj0oLS2Fj48PUlNT4e7ujvfv3zMOB5IO6oVQRMdKTExEu3btoKKiwntvYGZmhri4OLi4uEBXVxfp6emwsbHB2bNn4evri7y8PJny/v7+OHnyJBo2bIhbt27h6dOn0NHRQUxMDJYvX84aCQMA27dvx4QJE2BiYoLatWuLzflc7iEEn3Enr6y2tjYyMzMVNuwoIu/m5oYjR47AwMBA5nrLZa3kA0n6tHCP+z0d86oDijj3lZeXY8mSJdiyZQtev37N7InnzZsHa2tr+Pn5SZVt0qQJxo0bh0mTJjFjrn79+hg3bhzq1KnDahTW1dXF7du30aBBAxgaGuLy5ctwdHREeno6vLy88PTpU5nyoaGhePjwIXbs2CGXM6MQLi4uyM7OBhHJFS0rxOXLl9GzZ08MGzaMcVS7e/curly5gsTERKmGeiHevHmDwYMHIyEhgTHIf/z4EW5uboiJiZHobFdTqC4d59GjR2jatCmrQblr165o3rw5E7knnK+vXLkCb29vqb/9tm3bMH36dMTExIg5i584cQJDhgzBqlWrMHbsWKl1a2ho4N69e6hfv77cz1cZRUVFEiOgpL0/of0MgEQbmqamJtavXw9fX1+Z9R45cgRz585FYGCgRFtOTeuowcHBCAkJgaurK+rUqSO21h85cqRG64+IiMD48eOhoaEh0SlUlp7k4OCApUuXwsvLS6TfrV+/HpGRkaxjPiIiolrtaAyqNbnWvwBPnz6lnj17ihDbKCkpUc+ePeUmFCwoKKD09HRKT0//LqQ0RBW54HR0dGj8+PGkoaFBAQEB1K1bN9LW1qaUlBRW+Z07d9KBAwfEyg8cOEC7du1ildfU1KxWomN5wJdcm++z37hxQyLp17Vr1+jmzZsSZfjwajx9+pQhDX769KnM658Ovlw2jx8/lprfX8hbIAtLliyhkSNHUklJidxtVwTVyafyT0Nqair5+/uThoYGmZiY0NSpU6WSaGpqaooQWyuCkpISOnv2LG3ZsoXJIf78+XP6+++/WWX59htF+QUqk5oLoaKiQs+fPyeiCn4TSflTawI/Yp0iquDgUpSktzIePnxIx44do2PHjjG50msatWvXlosYtiqsra0pODhYYaLC/v370/r168XK169fT15eXqzyMTExZGVlRStWrKCkpCRKSkqiFStWkLW1NcXExDD9QRq/16lTp5hcx5URFxdHf/75p1zPUlxcTGvWrCF1dXVSUlKipk2bUkREBLO2VTcMDAwoMjJSLpmqRMz6+vpUv3596t27t9wkzT8a5eXllJOTwykfviQcPnyY2rVrR0ZGRmRkZETt2rXjRFxIVDHfenh4kJaWlkie6u+11n358oWWL19OPXv2pBYtWsidc5oPPnz4QJMmTSJPT086ffo0Uz5//nxavHhxjdZdHcjJyaFevXpRkyZNaMeOHUz51KlTafLkyazy6urqlJeXR0REY8aMoYCAACKq6BNsedrd3d3JwcGBNm3aREeOHKGjR4+KXGxQRDevjGXLlpGxsTHt3LmT0akjIiLI2NiYQkNDWeWJiDp06EC7d+9WeNxVRmFhIaWmplJ+fj7rd/nqWDk5ORLnYuE8wgY/Pz/q27cvFRcXk46ODj1+/JhycnLIxcWF6QOyUFxcTCtWrKApU6aIcICtWrWKtm/fzukZrKysOHO3/JPQr18/2r9//w+T/5H4t+9piSrGqa+vLykrK5OysjKzr/X396elS5fKlA0ODiYbGxvas2ePSI78mJgYat26tUxZLS0tZn9hZGTE8LfdvXuXateuzdpuvhxKffv2JV1dXapTpw51795dbh2JDyeHEI8ePaLRo0dTy5Ytyd7enoYOHcq8BzYMGjSIXF1dRbh5//rrL3J1daXBgwfLlOW7p+SLynyCnz59oo8fP9K9e/fo119/paZNm7LK6+npMXvaynwqT58+JXV1dZmyQ4cOJYFAQPb29tS3b1/q27cvNW7cmJSUlFjfG1EFF++5c+fYH1IK3rx5Qx4eHnLzoTx9+pSePHlCAoGAbt68KTLPvHjxgkpLSznVL82G873029q1azNk8D8CtWrVoiVLllBZWZncstu3bydzc3OKiYkhbW1t2rdvHy1evJj5mw01ZUf7rzsEEeL9+/d048YNun79OkPa873x5s0bxkAhD2kun8m/YcOGdOHCBbHyhIQEatSoEau8i4sLLxI+PiRufMm1+T57y5Yt6eDBg2Llhw4dolatWrHK/2h8+PCBVq5cSX5+fuTn50erVq36bmROfInMOnToIPGgKjo6mjp27Mgqz1dp+5GYPHmyxEO+9evXc9pkVidevHhBYWFhZGdnR9ra2jRixAjq0qULqaioSCRkbd++PS/irqdPn1Ljxo1JS0tLZJMxZcoUGjduHKs8334TGBjIupmRBHt7e5G5IjU1ldTU1BhlKysri3WzUVBQQHPnzqU2bdpQgwYNqH79+iJXTaN///4SDQvLli2jX375hVWer8JbHVB0vVm2bBmvsbV69Wpq2rQpKSsrU9euXWnfvn309etXzvLa2tqUlZUlVs6l3xCxH8KyKe5CAveqOH36NDVp0oTTMxQXF9P+/fvJ3d2dlJWVqV27drRz504KCQmhWrVq0ZAhQzjdR17UqlVL7gMsNmJmeUiafzTKyspIVVVV7ndQUlJCwcHBjCFbEbRt25batGlDMTExdPHiRUpISBC52MB3rfP29iYTExMaP348LViwQCHjyo+CouTcVUltZV01CSsrK4qLi6PS0lKytLRkiG/v3LnDeuCvo6MjlXyYC/jq5uXl5TRr1izS0NBgDCpaWloUHBzMuQ0BAQFkampKenp6NHr06O9GVs5Xx1JSUqLXr1+Llb99+5aTceHjx4/UtWtXMjAwIGVlZbK0tCRVVVXq0KHDd3O60NXVZXRDeaHouOMrS0S0Y8cOsrKyogULFlBsbCzj7CG8alr+RyIxMVGiU1pJSQklJib+gBbJDz7OfQ0aNGD048p74nv37rHOl+bm5ozNx9nZmf744w8iIrpy5Qrp6emxttvLy4u2bdtGREQzZswgW1tbWrx4MTVv3py6dOnCKv9v15H09PToxo0bYuXXr18nfX19mbJ895R8dRxJxOgCgYCsrKzoypUrrPKmpqbMYXPlfhcfH08WFhas8vv37ycvLy9ycHAge3t78vLy4nwQe/r0aWrWrBmdOHGCXrx4IXagwwZvb29q164d3bx5k7S1tSk+Pp6io6PJzs6O0Tekobi4mHx8fHg55v3og1tFnTKrC4aGhrzq37NnD9na2jL7UHNzcxGHmx+B/9pDkB+JgoICGjVqFCkrKzOdQUVFhXx9famwsLBG61ZXV5d4Wv3kyRPS0NBglY+Li6O2bdvSxYsX6e3bt3JNYsePHyddXV0SCASkr69PBgYGzGVoaKjoI3EG32evrORUxuPHj0lHR6c6mlhjuHnzJhkZGZG5uTlj+LewsCBjY2NKTU2t8fore7pUXnhv377NSWnT1dWVahRkU1qI/t1KW926dSVGeaWmppK5uXmN119cXEyxsbHUq1cvUlVVpRYtWtDmzZtFxvvhw4clKu4HDhwgBwcHioyMpJSUFBEPdGle6JXh5eVFw4YNo2/fvon0m4sXL5KtrS2rPN9+U1paSu7u7tSxY0fy9/fnbFTasGED6evr06xZs2j+/PlUt25d8vPzYz7fs2cPq2fy4MGDqU6dOjRr1ixavXo1rVmzRuSqaZiYmEg8XM/IyCAzMzNWeb4KL1/wWW/KysrI3d2dbGxseEUDpKam0uTJk8nExIQMDQ1p0qRJnOZbKysrWrlypVj5ypUrycrKilWeTVlnU9w1NDSkrpVaWloy6xZGihkbG5OpqSnNmDGD7t27J/KdzMxMTmuuIggNDeXkuf6fDAcHB4WMsNra2ry8GbW1tXl5pfNd6/T09Ojy5csK188Xly5doqFDh1KbNm3o2bNnREQUFRVFSUlJrLL16tWj5ORksfJr166RtbW1VLlOnTqJXHp6eqSlpcVEv2hra5Oenh65ubmxtuHRo0c0Z84cGjx4MGMY//PPP+nOnTussgsWLCB9fX1q3LgxWVlZMYe+ERERrJ7N9vb2IlEA8qK6dPO///6bbty4QZmZmXIdWgtRUlJChw4dIk9PT1JVVSV7e3tasWIFvXr1SqYcH4cDvjqWQCCQ6IT39OlT1rm+MpKSkmjjxo20bNkyuQ9loqKiqF27dlSnTh1mTVq9ejXnCDRfX1/avHmzXHUKoei44ytLxN+79d8cZc738O2fAD7OfRoaGkxfryz7119/sTq6DBkyhMLDw4mIKCQkhExNTWn06NFUr149TvppdnY2MzcUFBTQuHHjyNnZmfr37/+viMKpupcQXp8/f6Zv376xyks7dE9LS2P93fjuKfnqOFUdSy5dukT37t3jnOWCb+QeH1Sdn+SNFK5duzZdv36diCp+B2FmgGPHjlG7du1Y5fX19aslO8GPwqxZsygkJOSH1T916lRasmQJ7/sUFhZKnPt/BP6riNH/KZg+fToSExNx4sQJtGvXDkBFjsMpU6ZgxowZ2Lx5s0z58vJyPHr0SCIxTocOHWTKmpmZISMjQywnenp6OoyNjVnbLiTNUoRgmy8JHF/wfXZ1dXW8fv1aLP/qy5cvOefFLCwsRGJiosR8hrJIhfhi2rRp8PT0xPbt25m2lpaWYvTo0Zg6dSouXbokU/7PP/+EsrIyevToIVIeFxeH8vJy9OzZU6Y8XyIzgUDAkJFVxqdPnzjljpXFs1PTmDJlCmxtbcV+3w0bNuDRo0dYs2aNTPl3795BX19frFxPTw9v376tzqZKRJ06dVBeXo4hQ4bgxo0baNasmdh33NzcJJKdCXNZV861KU/e36SkJFy5cgVqamoi5dbW1nj+/Dlr2/n2m6VLlyIuLo7hj+JKkD1p0iQoKSlhz549+PbtG3x8fDBv3jzm81atWuGPP/6QWffp06dx6tQpZo343igoKBB770AFMSIXolwPDw8AFbk6K78rrr89X/BZb6ZMmYKLFy/Czc1NLPepPGjevDmaN2+O8PBwbNq0CbNnz8bmzZvh7OyMKVOmYNSoURLvHRwcjNGjRyMhIQE//fQTgApS8DNnzmD79u2s9darV0+h9gqhr6+Px48fi62Vjx49YuWSadmyJbp164bNmzejb9++EjmD6tevj8GDB/NqozTcuHEDFy5cwMmTJ+Ho6ChWPxsPz5MnT1BaWoqGDRuKlGdlZUFVVZWVT+afgLCwMAQGBmLz5s1wcnLiLNelSxckJiYq/IwtW7ZEXl4eM1/KC75rnbm5OXR1dRWqmy8OHTqE4cOHY+jQoUhLS8O3b98AVKw1oaGh+PPPP2XKK0rOffHiRebvVatWQVdXF7t372a4mD58+IBRo0axcmslJiaiZ8+eaNeuHS5duoQlS5bAzMwM6enpiIiIQGxsrEz5hQsXwsnJCXl5eRg4cCDU1dUBAMrKyqycb2vWrEFQUBBDCC4vqkM3ByoIklu2bCl3/UKoqKigf//+6N+/P968eYNt27Zh3rx5+P333+Hh4YEpU6aIEZgDwKVLl7Bw4UKx8p49eyI8PFxmnYrqWEJSbIFAgHnz5omskWVlZbh+/bpEXU8afv75Z/z888+cvy/E5s2bMX/+fEydOhVLlixh2mtgYIA1a9bAy8uL9R62traYN28erl27Jjdhq6Ljjq8sALH9u7zgK/8jIeyfVfHu3Tu5+ep+FPLz8yVy0xUWFrLqjA4ODkhKShLT1WJjY1kJujds2MCQk8+ZMweqqqq4cuUKBgwYgLlz57K2u/I8qa2tjS1btrDK8IWhoSFnPfr9+/cyPzcwMJB5LwsLC/j4+GDBggUSuWc6d+6MgIAA7Nu3D3Xr1gUAPH/+HNOmTROzbVUF3z0lXx2HL39reHg4fvnlF5iZmeHLly/o2LEjXr16hTZt2mDJkiW87s2GyrqKIigsLGTGm6GhIfLz89GoUSM4Oztz4n3y8vLC0aNH/1W8wpXx9etXbNu2DefOnUOTJk3E1rlVq1bVaP1lZWVYvnw54uLieNWvpaX1Q2zAkvC/Q5AfgEOHDiE2NhadOnViyjw8PKCpqYlBgwbJPAS5du0avL29kZOTI0bww8WwNGTIEEyZMgW6urrMgUliYiICAgI4GSX4TGLPnz/HlClTeHX+Z8+e4fjx4xIPEdgGIN9n7969O3777TccO3aMWcQ+fvyI33//Hd26dWOVv3XrFjw8PFBUVITCwkIYGRnh7du30NLSgpmZWY0egqSkpIgcgAAVG7ZZs2bB1dWVVT4oKAhhYWFi5USEoKAg1kOQ0NBQ9OzZE3fv3kVpaSnWrl0rQmTGhg4dOmDp0qXYt28flJWVAVRMyEuXLuW08ercuTMOHz4sZqj//Pkz+vbtW6MEgIcOHRIhSheibdu2CAsLYz0EsbW1xZkzZ+Dv7y9Sfvr0aYUJFeXB6tWrMXDgQGhoaEj9joGBAZ48eSJWLqlMHpSXl0uc0549e8bJ2MW334SHh2Pnzp0KEWRPmDABEyZMkPhZVQOrJBgaGsLIyEjueqsLzs7O2L9/P+bPny9SHhMTAwcHB1Z5vgovX/BZb3bv3o1Dhw6hV69evNpQUlKCI0eOIDIyEmfPnkXr1q3h5+eHZ8+e4ffff8e5c+ckHob5+PjA3t4e69atY4z29vb2uHz5MnMowobo6Ghs2bIFT548wdWrV1GvXj2sWbMG9evXZzUseXl5YerUqThy5AgaNGgAoOIAZMaMGawEdI8fP2Y9hNHW1q6xg2kDAwP0799fYXkfHx/4+vqKjdHr169jx44dSEhI4NnCmseIESNQVFSEpk2bQk1NDZqamiKfSzMy9OzZE0FBQcjMzESLFi3EjFFsv/2OHTswfvx4PH/+HE5OTnITR/Jd68LDwzF79mxs2bKF90GgvFi8eDG2bNmCESNGICYmhilv164dFi9ezCpvaWmJ5ORkMdLQ5ORkxlDDhvDwcMTHxzMHIEDFOrJ48WJ0794dM2bMkCobFBSExYsXY/r06SJra+fOnbFhwwZO9f/yyy9iZSNHjmSV+/XXX1FUVIQGDRpAS0tLrN+wGcX46uaFhYUICwtjiNmrGpflJUa/ceMGIiMjERMTAzMzM/j4+OD58+fo3bs3Jk6ciJUrV4p8n4/DgaI61q1btwBU6PCZmZki9aupqaFp06aYOXMmp3udP39e6rvbuXOnTNn169dj+/bt6Nu3r8gew9XVlXP927Ztg46ODhITE8X2EwKBQObeis+4q44x+98G4dosEAjg4+PDHJYCFbp5RkYG2rZt+6OaJxf4OPfNnz8fI0eOxPPnz1FeXo7Dhw/jwYMHiIqKwsmTJ2XKVt4XKCkpsR4yS8LHjx8RGxuL7OxsBAYGwsjICGlpaahVqxbMzc1Z5WNjY3HgwAGJthhJBmm2fa482LVrF+bMmQMfHx+0atUKQMWcu3v3bsydOxf5+flYuXIl1NXV8fvvv4vJb9iwAZ6enrC2toalpSUAIC8vD05OTtizZ4/MuvnuKfnqOJJsCUBF39PQ0ICtra1M4nF9fX2cPXsWly9fRkZGBgoKCtC8eXN07dqVtW6+qF+/PiwtLcUOsIgIeXl5rPJ2dnZ48OABrK2t0bRpU8ZpYsuWLRIPo6uiYcOGCAkJQXJyskT9tiZtcNWBjIwMxjHhzp07Ip8p6qgnDzIzM5kDWi71u7i4cG4Xl0OsmsD/DkF+AIqKilCrVi2xcjMzMxQVFcmUHT9+PLPw1qlTR+6Ov2jRIjx9+hRdunRhDOLl5eUYMWIEQkNDWeX5nEL36NEDKSkpChtuz58/D09PT9jY2OD+/ftwcnLC06dPQURo3rw5qzzfZ1+5ciU6dOiAevXqMRPB7du3UatWLURHR7PKT5s2DX369MGWLVugr6+Pa9euQVVVFcOGDUNAQACrPB/o6ekhNzcXjRs3FinPy8vjZEzOysqSaPhs3LgxHj16xCr/888/4/bt2wgLC4OzszPi4+PRvHlzXL16Fc7Ozqzyy5YtQ4cOHWBnZ8d4NCYlJeHz58+cDjASEhLEFDWg4mQ9KSmJVZ4P+Hp+TJ8+Hf7+/sjPz2e8CM+fP4/w8PBqVSylYfjw4QrL8jVEde/eHWvWrMG2bdsAVCy0BQUFWLBgARNpIAt8+426uvoPi8RYtGgR5s+fj927d/8Qr4l58+ahf//+yM7OFul3+/btw8GDB1nl+Xos8QWf9cbIyIgx/iuCtLQ0REZGYt++fVBSUsKIESOwevVqkfm3X79+Mr2Of/rpJ+zdu1eh+vl61y5fvhzu7u5o3LgxLCwsAFQcPLZv317MgFcV39v4XBV8D1du3bolccy3bt1abOMqhNCrmgtq2lsLUNzgMHHiRACS28jFySY/Px/Z2dkYNWqUiBzX6C++a52rqyu+fv0KGxsbhYzpfPDgwQOJkdj6+vr4+PEjq/yYMWMwdepUlJSUiDz7rFmzZB5eVMbnz5+Rn58vVp6fny/Re7UyMjMzJR7ImpmZ1XjEKV89hq9uPnr0aCQmJmL48OEK7asA4M2bN4iOjkZkZCSysrLQp08f7Nu3Dz169GDu5+PjA3d3d7E5lI/DgaLzrdBJYdSoUVi7di309PQUuk9wcDBCQkLg6uqq0Lt78uSJRM93dXV1FBYWcr6HEELnQK7t4DPuqmPMnj9/HqtXr8a9e/cAVDg7TJ06lbNRkq/894ZwL0RE0NXVFTmgV1NTQ+vWrTFmzJgf1Ty5wMe5z8vLCydOnEBISAi0tbUxf/58NG/eHCdOnGA9uE1LS4Oqqiqzdz527BgiIyPh4OCAhQsXSjxQrYyMjAx07doV+vr6ePr0KcaMGQMjIyMcPnwYubm5iIqKkim/bt065hDi2LFjGDVqFLKzs3Hz5k1MmjRJogyXw3Cu2L17N8LDwzFo0CCmrE+fPnB2dsbWrVtx/vx5WFlZYcmSJRIPQSwtLZGWloZz587h/v37ACrGDZcxw3dPyVfH6du3L6NTVUZlPevnn3/G0aNHRZwhqkLRyD0+qF+/Pl6+fCkWPfX+/XvUr1+fVT8MCAhgIuwWLFgAd3d37N27F2pqati1axdr/RERETAwMEBqaipSU1NFPmM7LP8n4Ec7Fspbf9++fWumIdUIAVUdSf9DjaNLly4wNjZGVFQU41395csXjBw5Eu/fv8e5c+ekymprayM9PR22tra82vDw4UOkp6dDU1MTzs7OcinSHz9+REREBKN0OTo6wtfXV6Kht/KpdX5+PkJCQjBq1CiJIctsXoatWrVCz549ERwcDF1dXaSnp8PMzAxDhw6Fu7u7VK/rqsjKysLt27cVevbCwkLs3buXeXdNmjTBkCFDJKb8qAoDAwNcv34ddnZ2MDAwwNWrV2Fvb4/r169j5MiRzGJcE5gyZQqOHDmClStXMl42ycnJCAwMxIABA1gX39q1a+OPP/4QC+U/d+4cvL298ebNm5pqOoMXL15gw4YNIu/e399fprd8RkYGAKBZs2a4cOGCyHfLyspw5swZbN26FU+fPhWRq06jlpOTE8aPHy9mQFu/fj02b96Mu3fvstaxefNmLFmyBC9evABQkQ5q4cKFGDFiBOd2ygN5PKnZ0stkZ2djzZo1zHzh4OCAgIAATkbmZ8+eoUePHiAiZGVlwdXVFVlZWTAxMcGlS5ckhqJXhSL9RoilS5fi5cuXWLduHet35UF6ejqaN28uU+lzcXFBdnY2iAjW1tZic4wkzwlpXkKSwDbfAsCpU6cQGhrKzJdNmjTBggUL5DrgKCoqkugtxuYVbm1tDV9fX/j4+MDKyopzfUJEREQovN5ERkbizJkziIyMVOgASllZGd26dYOfn5/UlFCFhYXw9/eXarQvKyvD0aNHRdZZT09PxvtMFhwcHBAaGoq+ffsya6WNjQ3u3LmDTp06cTJqEhHOnj0rMm6kpduszlQH1YHS0lIkJCQgOzsb3t7e0NXVxYsXL6CnpwcdHR2Zsvr6+khISBAzzKWmpqJTp04Sjclubm6c2iUQCGo06vBHw8HBAfb29pg1axZq1aol1ie46Fp81rquXbsiNzcXfn5+EuuvTkNMVdjY2GDbtm3o2rWryJiLiopCWFgY6zovjKpdt24dM1dqaGhg9uzZYsZxaRgxYgSSkpIQHh7OeMhev34dgYGBaN++PXbv3i1V1sLCAgcOHEDbtm1F2n/kyBHMnDkT2dnZHN/EjwFf3Zxv6kk1NTU0aNCAWbNMTU3FvvP582d4eXmJGRNOnDiB/v37w9vbW6LDAZsxgY+OJamNFy5cQOPGjcWcpiShTp06WL58ucLOMg4ODli6dCm8vLxE+t369esRGRnJ2UM0IiICq1evRlZWFoAKr9+pU6di9OjRMuX4jDu+Y3bTpk0ICAjAL7/8wkQOXLt2DbGxsVi9erVUgzIf+erWERVFcHAwZs6c+a9JfSUN2dnZCAsLQ3p6OuNVP3v2bE7OfYqiZcuWCAoKwoABA/D48WM4ODigf//+uHnzJnr16sW6n+/atSuaN2+O5cuXi4y5K1euwNvbW2w/XBWNGzfGggULMGTIEBH5+fPn4/379xIjBz9//swctLJFt7EdyGpqaiIjI0NiytKmTZuiqKgIT548gaOjo0TH4qioKPz6668iUUgAUFxcjJiYGFZdg8+eEuCn45w/fx5z5szBkiVLRKJg5s2bh7lz50JfXx/jxo3DTz/9hIiICKn3+BEHp0pKSnj9+rXY2piTkwMHBwfOh95CFBUV4f79+7CysoKJiUl1NvUfjUePHiE7OxsdOnSApqam1NSCNYlnz54BAOMk92/F/w5BfgDu3LmDHj164Nu3b2jatCmACqOYhoYG4uLi4OjoKFW2c+fOmDVrFsPNwQfyeswAFWmVevToAU1NTWYCvnnzJr58+cJ491eGpHyMksDFS1BXVxe3b99GgwYNYGhoiMuXL8PR0RHp6enw8vJiXbh/NExNTXHlyhU0bNgQjRo1wvr169GjRw/cv38fLVq0EFsAqtOwVFxcjMDAQGzZsgWlpaUAKkLtJ0yYgLCwMDFloCrGjRuHq1eviqVHGTBgAFq2bIkdO3ZwauebN28khsyzGUQVhZKSEvMOJU11mpqaWL9+vUg+ZaB6jVo7d+6Ev78/AgMDJXp+yOP1lJ+fD01NTVZDHl9U9uRlgyzP67i4OHh6eqJZs2aMgSE5ORnp6emcPJ6ACoNmTEyMSOju0KFDxVK81AT69euHCxcuwNjYWCF+AWlIT0+Hi4uLzJzOwcHBMu+xYMECsbLqnG/5Ij8/H6NGjcLp06clfs5W/5o1a7Br1y7cuXMHbm5u8PPzQ79+/VjnKiFkvQu251fkAKoycnJyeEVEPHr0CL169cKzZ88YfoUHDx7A0tISp06dYjVuaWpq4v79+6hXr57IJjUrKwtNmjTBly9fFG6bJMgyrlZFTRqigYp37+7ujtzcXHz79g0PHz6EjY0NAgIC8O3bN9b813369IGmpqZYuoNff/0VhYWFUvvzPw3Z2dmIjIxEdnY21q5dCzMzM5w+fRpWVlYydUw+qC4nHUCxtU5LSwtXr15l9OrviaVLl2LPnj3YuXMnunXrhj///BM5OTmYNm0a5s2bx6RMYUNBQQHu3bsHTU1NNGzYkPN8B1QYBWbOnImdO3eipKQEQEXaUz8/P6xYsUKmwXHmzJm4fv06Dh48iEaNGiEtLQ2vX7/GiBEjMGLECInrTXWCz6EvX9SvXx9//vkn7O3tFb5HUlISK++KLCjqcMBXxxo0aBA6dOgAf39/fPnyBU2bNmUi7GNiYjBgwACZ8sbGxrhx44bCkZM7duzAwoULER4eDj8/P+zYsQPZ2dlYunQpduzYwSlV8fz587Fq1SpMnjyZOQy4evUqNmzYgGnTpiEkJIT1HnzGnaKyFhYWCAoKEnOQ2rhxI0JDQ1l57xSR/6foiF++fAERMU4mOTk5OHLkCBwcHNC9e/caq/c/Afr6+khLS0ODBg2wbNkyXLhwAXFxcUhOTsbgwYNZUwtVlq+sH+bk5MDOzo7hG5EGLS0t3Lt3D/Xq1YOZmRnOnj2Lpk2bIisrC61bt8a7d+/EZJSVlZkogMr78srgGjHaqFEj9O/fXyxFd1BQEI4cOYIHDx4gJSUFXl5eEsdA5bZUxrt372BmZlbjeyMhFNFxnJycsG3bNrGUccnJyRg7diz++usvnDt3Dr6+vsjNzRWT53vwqgiETqVr167FmDFjJPJPKSsrIzk5mdP9iouL8eTJEzRo0EAuzi++uHnzJsrLy8VSEgvbzyW1PB+8e/cOgwYNwsWLFyEQCJCVlQUbGxv4+vrC0NCQlT+ML8rLy7F48WKEh4ejoKAAQIVddsaMGZgzZ47MtaU63h0fPmxp+N8hyA9CUVER9u7dKxKKJ82wJ/RmByo2tnPnzkVgYKBE71YuxuSoqCisWLGC8Zhp1KgRAgMDOXnytG/fHra2thIJth8/fsxKsM0HtWvXxsWLF2Fvbw8HBweEhYXB09MT6enpaNeuHTMo/6no3r07fHx84O3tjTFjxiAjIwNTpkxBdHQ0Pnz4gOvXr4t8vyYMS0VFRYxHnzD/Mhd8+vQJ7u7uSElJEUuPIolroypSU1MxcuRI3Lt3TyEuG0Uh5M6xsbHBjRs3RDwQ1NTUYGZm9l022d87kuOfAhcXF/To0UOishofH//D8kByBdthkLQDILZImk+fPiEhIeG7Kds/AkOHDkVOTg7WrFmDTp064ciRI3j9+jWjRHHl20hLS8OuXbuwb98+lJWVwdvbG76+vpxSICoKRQ6gJKG4uFiiwsYW2eLh4QEiwt69exnvsnfv3mHYsGFQUlLCqVOnZMpXl3ftvxHC6JeIiAgYGxszz56QkIAxY8Yweo803L17Fx06dICBgYHEdAfyEI3/KFQlub537x5sbGwQFhaGlJQUMZLrCxcuwN/fH9euXRPzwvz06RPatm2LzZs3s240+vTpAx8fH1bDaU2hefPm2LRpE1q3bv3d6yYihIaGYunSpYz3qbq6OmbOnIlFixZ917YUFhaK6HlcvK2Li4sxadIk7Nq1C2VlZVBRUWHm2127dtWonvTo0SN4eHjg+fPnCh368sWePXtw7NixH5Z6kg/46li1a9dGXFwcmjZtij/++AMLFixAeno6du/ejW3btjHcIdIwe/Zs6OjoYN68eQo/w969e7Fw4UKmz9atWxfBwcHw8/PjJG9qaop169ZhyJAhIuX79u3D5MmTazydm6LQ0dHB7du3xQ6Ns7Ky4OLiwrqn5Sv/I9G9e3f0798f48ePx8ePH2FnZwc1NTW8ffsWq1at4pzZ4UdDHsNcdTk26unpITU1FQ0bNkS3bt3Qu3dvBAQEIDc3F3Z2dqxOLmZmZoiLi4OLi4uIfnj27Fn4+vqyHqLY2Njg0KFDcHFxgaurK8aMGYNx48YhPj4egwcPltj2xMREtGvXDioqKkhISJD5HtgOfo8fP46BAweicePGTErZlJQU3L9/H7Gxsejduzc2b96MrKwsiZkapEUkpKenw83N7btEKysKTU1N3Lx5U0wPzczMRKtWrfDlyxfk5OTA3t5eYhQM34PXfv36SfztKnOSeHt7M+s48H9OpYmJiWjTpo0Y/5S1tTVmzpzJypVZVFSEyZMnM7YxoYPT5MmTYW5uzokbhw+vcKtWrTBr1iwx7rPDhw9j2bJlYja86saIESPw5s0b7NixA/b29sy4jYuLw/Tp0/HXX3/VaP2//fYbIiIiEBwczDhcXL58GQsXLsSYMWOwZMkSqbJ83x1fPmxp+N8hyL8AwlNzaT+VPDmXV61ahXnz5sHf31+kE2/cuBGLFy/GtGnTZMpramri1q1bYmHSd+/ehaurKyunCR/07dsXvXr1wpgxYzBz5kwcO3YMPj4+OHz4MAwNDWWmEfsnICUlBX///Tfc3Nzw5s0bjBgxgokM2blz53f1XhQadOzs7Dh7v8mTHqUqmjZtigYNGmD27NkKp8n4T8D3iuT4p0BDQwOZmZliys3Dhw/RpEkTVo8joGJDd/HiRYmbDK5pQr43VFVV0a1bN4ncT0DFBufkyZP/0YcgderUwbFjx9CqVSvo6ekhJSUFjRo1wvHjx7F8+XJcvnxZrvuVlJRg06ZNmD17NkpKSuDs7IwpU6Zg1KhR3z0UmA0PHz6En58frly5IlLOdZ3W1tbGtWvXxFIqcD3wrw7v2n8rjI2NceXKFdjZ2Yls8J8+fQoHBwdOOoq86Q6qM31gdaBNmzYYOHAgQ3ItfAc3btxA//79mVB2ITw9PeHm5iZV/1u3bh0uXryII0eOyKx327ZtWLx4MXx9fRVKecoX8fHxCA4OxpIlSyTWryjvgTwoLi7Go0ePUFBQAAcHh++61kdGRmLw4MG8oiTz8vKQmZmJgoICuLi4sBomKkNRTz2+h758wTfyD6ho7/z586XqKlwMa6mpqSKRMJK4MqqCr46lqamJhw8fwtLSEiNGjEDdunURFhaG3NxcODg4sK41AQEBiIqKQpMmTdCkSROxdycPB1JRUREKCgo4pTmtDAMDA9y8eVPiO2jVqhUnTp4fAW9vb7i4uCAwMFCkfOXKlUhJSUFMTEyNyv9ImJiYIDExEY6OjtixYwfWr1+PW7du4dChQ5g/fz4zDv7JkNcwV12OjZ07d4alpSW6du0KPz8/3L17F7a2tkhMTMTIkSNZs2KMHj0a7969w4EDB2BkZISMjAwoKyujb9++6NChA2s6rdGjR8PS0hILFizAxo0bERgYiHbt2iElJQX9+/eXmobpyZMnMkm75cGTJ0+wdetWPHz4EEAFafa4ceNgbW0tVUZI1Jyeng5HR0eRKIKysjI8efIE7u7uOHDgQLW0sSbw888/Q1dXF1FRUcwhTn5+PkaMGIHCwkJcunQJ586dw6RJk/DgwQMxeb4Hpz4+Pjh69CgMDAzQokULABXr48ePH9G9e3ekp6fj6dOnOH/+vFh6Sb78UwEBAUhOTsaaNWvg7u6OjIwM2NjY4NixY1i4cCHrgT0brzBbVg8dHR2mzsp48uQJmjRpwsq7xheVHRYq6/WPHz9GkyZNavzQu27dutiyZYuYHn/s2DFMnDhR5gEa33fXrFkzNGrUCMHBwRK5xyTRMXDB/4jRvxOOHz+Onj17QlVVlTUnZ9UOVpn0jS+EPASVPdA9PT3h6OiIhQsXsh6C8CXYTkxMxMqVK0Vy1wrzFbNh1apVzCAPDg5GQUEB9u/fj4YNG34XslE+ICKYmZkxp/dmZmY4c+aMQvf6+vWr2Ak226JSNeTd1dVVrpB3oEKp6969u0Khyo8fP8ahQ4eqJU2GIti9ezdMTEwY7/NZs2Zh27ZtcHBwwL59+1gPYVJSUnDgwAGJ3gPyGLUk5Yn+T4apqSlu374ttjm9ffs2p43u9u3bMWHCBJiYmKB27doiC59AIPguhyCK8AvY29tjwIABUj0Zb9++jZMnT4qVVze3QmFhIRITEyX225omgSssLGR+Y0NDQ+Tn56NRo0ZwdnaWKxKhpKQER44cQWRkJM6ePYvWrVvDz88Pz549w++//45z585JJPRlS4HB1nc+fvyI2NhYZGdnIzAwEEZGRkhLS0OtWrVgbm4uU3bUqFFQUVHByZMnFSKLVVdXl6gUFhQUsJJeAhWbVE1NTcydOxdFRUXw9vZG3bp1sXbt2v/oAxCgwhAr6ZDp2bNnnHQUoELZDw0N5Vynogp4TUFekuv09HQsW7ZM6v26d+8uRuYsCePHjwcgeex9jxR8wjSxXbp0ESnnevjIB76+vli7di10dXVFyKwLCwsxefJk7Ny5s8bqFiIoKAgBAQEYOHAg/Pz8xFJmyEJISAhmzpwJS0tLWFpaMuVfvnzBihUrWOdLPp56iYmJuHbtmsgho7GxMcLCwnjxdHBFdRB4Dh8+HI8ePZLKRyMLb968weDBg5GQkMBEVX/8+BFubm6IiYmRqTfy1bEsLS1x9epVGBkZ4cyZM4zh/MOHDwxnpSxkZGSgWbNmACpSPVeGvOuelpaWQpE4w4cPx+bNm8X2gdu2bcPQoUPlvt/3goODA5YsWYKEhASR1DTJycmYMWOGCBedJH2Nrzzw43TEoqIiZj2Oj49H//79oaSkhNatWyMnJ6fG6q1OjB8/Hq6urjh16hQnPa+6UoGuWbMGQ4cOxdGjRzFnzhxmXx0bG8tpzg8PD8cvv/wCMzMzfPnyBR07dsSrV6/Qpk0bmd7cQmzbto055J00aRLjeOLp6Ylx48ZJlWvQoAHq1asHNzc3dO7cGZ06dVKYU6B+/fpi0W9sEM7zt2/fRo8ePUT2b8KIhB8VxcoVERER8PLygoWFBbNO5+XlMYcBQMU+Ye7cuRLlPT09ceTIEbGD02PHjqF3796s9deuXRve3t7YsGEDk/6ovLwcAQEB0NXVRUxMDMaPH4/Zs2eLObrJSp3NBUePHsX+/fvRunVrkbHm6OjIibPst99+w8yZMxle4UOHDonwCrNBXV0dr1+/FjPkv3z58ruk5SosLJS4Pr5//16u9I2K4v379xJ5who3bsxqj+D77rKyshAbG1v9NkT6H74LBAIBvX79mvlb2qWkpCTzPomJiVRSUiJWXlJSQomJiaztUFdXp6ysLLHyhw8fkrq6Oqv85MmTycLCgmJiYig3N5dyc3Np3759ZGFhQQEBATJlo6OjSUVFhQYNGkRr166ltWvX0qBBg0hVVZX27t3LWve/GWVlZaSqqkoPHz5USL6goIAmTZpEpqampKSkJHaxoVatWnT79m0iItq7dy/Z2tpSYWEhbdq0iZo1ayZRZu3atfTlyxfmb1kXG7y8vCg2NlaOJ65eNGrUiM6fP09ERFeuXCFNTU3aunUr9enTh/r16ydTdt++faSqqkq9e/cmNTU16t27NzVq1Ij09fXJx8fnezT/X4vg4GAyMDCgsLAwunTpEl26dImWLl1KBgYGFBISwipvZWVFYWFh36GlkvH06VNq3LgxaWlpkbKyMmVnZxMR0ZQpU2jcuHFS5Xx8fGjixIlSP7979y5ZW1uLle/atYu5wsPDydDQkAYPHsyMs8GDB5OhoSGtWrWKte1paWlUu3Zt0tPTI2VlZTI1NSWBQEDa2tpUv359Dk/PD66urnTmzBkiIurTpw8NHz6cnj17RrNmzSIbGxtW+dTUVPL39ydjY2MyNTWlGTNm0L1790S+k5mZSRoaGhLlmzVrJnI5OjqSlpYW6enpkYuLi8y609PTydTUlGxtbUlFRYX53efMmUPDhw9nbbuWlpZYW+XB8OHDydHRka5du0bl5eVUXl5OV69eJScnJxo5cqRc9yosLGR0j/8GDBo0iMaMGUNERDo6OvT48WP6+++/qXPnzv8187W5uTklJycTUcU7EPbfw4cPSxx70vRCIbKysqSOs38SEhISZF41CSUlJYnjLD8/n5SVlWu0biFKSkro8OHD5OnpSaqqqmRnZ0dhYWH08uVLVllp7X/79i0nHbNp06Y0cOBAunv3Ln348IE+fvwocsmCoaEh018r4/Lly2RoaMha9z8BOjo6jI4tLwYNGkSurq509+5dpuyvv/4iV1dXGjx4sExZvjrWxo0bSUVFhQwMDKhp06ZUVlZGRETr1q2jTp06KfQ83xv+/v6kp6dHjo6O5OfnR35+fuTk5ER6enrk7+9P06ZNY65/EqytrTld0vQ1vvI/Ukd0dnamtWvXUm5uLunp6dGVK1eIiCglJYVq1apVo3VXF7S0tGSum2woLS2lgwcPUkhICIWEhFBsbKxE+w5XfPnyhYqLizl/PykpiTZu3EjLli2js2fPKlwvV1y8eJEWLFhAHTt2JA0NDVJSUiJbW1saO3Ys7du3j169esX5XpcuXaKhQ4dSmzZt6NmzZ0REFBUVRUlJSayyu3btYmwb/0aUlZXR6dOnmX3hmTNnmHlbEirbahYtWkT6+vrk4eFBixYtokWLFlGvXr3IwMCAFi1axFq3iYkJPXjwQKz8wYMHZGxsTEREGRkZpK+vr/DzSYOmpiajy1bWa2/fvk16enqs8jo6OvTo0SMiIjIwMKA7d+4w8vXq1WOVHzx4MHXs2FFEn/nw4QN17NiRBg4cKO/jyI2ePXvS3Llziej/9jZlZWU0cOBAGjBgQI3X36pVK5o8ebJYub+/P/30008yZfm+Ozc3Nzp9+rT8jWbB/w5B/mXgu1FxdHSkJUuWiJUvWrSInJycWOW/fftGU6ZMITU1NcYAr66uTlOnTqWvX7/KlG3cuLFE4114eDg1btyYte7KbcjLy6OcnByRSxLS09M5XzUNBwcHunr1qkKyEydOJHt7e4qNjSVNTU3auXMnLVq0iCwsLGjPnj2s8hoaGpSbm0tEFQa22bNnExFRTk4OaWtrS5Sxtramt2/fMn/Lq2BXRn5+Pnl4eNDChQspNjaWjh07JnLVNDQ1NZk+MmvWLMaQeefOHTIxMZEp6+zsTBs2bCCi/1t4y8vLacyYMTR//vyabfi/HOXl5bRq1SoyNzdnDnrNzc1pzZo1VF5eziqvq6vLKDqKwM3NjT58+CBW/unTJ3Jzc2OV9/LyomHDhtG3b99ElK6LFy+Sra2tVLmvX79SYWGhwu0mIurfvz+tX79erHz9+vXk5eXFKt+xY0caM2YMlZWVMW3Pzc2lDh060KFDh3i1jQuio6MpMjKSiCo2tiYmJqSkpEQaGhoUExPDKq+kpEQ9evSgAwcOSN3YFRQUyGXY/vTpE/Xr14+ioqJkfq9Lly4UGBhIRKLKdnJyMidl2dXVldNmTBo+fPhAnp6eJBAISE1NjVlv+/bty2pQVBSfPn3ifP2TkZeXRw4ODmRvb08qKirUunVrMjY2Jjs7u+92GFRSUkJnz56lLVu20OfPn4mI6Pnz5/T3339/l/pnzJhBP//8M718+ZJ0dXUpKyuLLl++TDY2NrRw4UKx79vY2NCRI0ek3u/QoUNyG8UUMTLs3r1boh757ds32r17t9z3+x749OkTffz4kQQCAT169EhknLx//552795NderUYb0PXwenqnj16hWtXLmSnJ2dSVVVlfr06UNHjx6VaiwRCAT05s0bsfLz58+z6khE/AyC1XnoywcpKSkUHR1N0dHRlJaWJpesq6urwvq9np4e3bhxQ6z8+vXrrMYkvjoWEdHNmzfp8OHDIvPTyZMn6fLly3I9h9Ax7nujU6dOnC4uOp88qO4x+73xI3XEgwcPkqqqKikpKVG3bt2Y8tDQUHJ3d6/RuqsLfAxzd+7cIRsbG9LS0iIXFxdycXEhbW1tsra2pszMzGpuafVDeAjRunVruQ8hiCr0g/Pnz9O8efOoffv2pK6uTkpKSuTg4MAqK7SDjB49mtTV1Rn9fP369dSzZ0/FH4ojfrR+Jy/4HpZWhoGBgUSbzbFjx8jAwICIKpyqhX9XJ9q3b0/r1q0jov87BCCqMML36NGDVb5WrVqMo4G9vT3zHLdv35ZqB6uMZ8+ekY2NDenr6zNrioGBAdnZ2X2XdS8zM5PMzMzI3d2d1NTU6JdffiF7e3uqVasWc7hTk0hISCBtbW2yt7cnX19f8vX1JXt7e9LR0aFLly7JlOX77g4fPkwODg4UGRlJKSkp1Wa//R8nyL8M0gidHj58CFdXV3z+/Fmm/KFDh/Drr7+ia9euTJh5cnIyzp8/jwMHDqBfv36c2qEIwba6ujr++usvsXCmR48ewcnJiTV3rSJ51vnyqVRnepoTJ05g+fLl2Lx5s9zkqlZWVoiKikKnTp2gp6eHtLQ02NraIjo6Gvv27cOff/4pU75Ro0ZYvHgxevXqhfr16yMmJgadO3dGeno6unTpUuPEgSdOnMDw4cMl9k8uaSqEuTwlyQrJuHx8fBgCrqqoTATn4uKC6dOnY/jw4cjOzkbTpk1l5lLU1tbGX3/9BWtraxgbGyMhIQHOzs64d+8eOnfujJcvX7I8PT9UDmmvjMrP3qFDhxohLi0pKYG7uzu2bNkiV25wSRCm9+GakgYA/Pz80LJlSybNirxQUlLCq1evxNJCvHnzBubm5igpKZEpXx38AopCWu7WR48eoVmzZqz5Pw0MDHD9+nXY2dnBwMAAV69ehb29Pa5fv46RI0fi/v37MuWnT58usbxyv/Py8pLKk1AVRUVFuH//PqysrGBiYsL6/ZycnBrhCsrMzESfPn1k5k3W19dHWloaGjRoIPK75+TkwM7OTuJaVXlu+3/snXlcTdv//18nNI8alKGJpJG63XANIZKhZJYQlVmSZA6FMiWFK0STIbPMGcoUIUNUUhqUoZDrXoWrYf3+6Hf2t9MZ9u4M4nM9H4/zeLA76+y1z9nDWuv9fr9e6enpWL58OYKDg0XyJsjLy6N+JxMTE4GlwPzukbzgJUfGflYKQtCz8keiuroahw4dQkZGBioqKmBtbQ03NzeRvBKY8uLFCzg6OqK4uBj//vsvZd7o4+ODf//9F5GRkRLvAy+T6+rqari5ufE0ufb29sbVq1dx7949LgmcL1++wNbWFn379uX7LGJTU1OD4OBgREZGoqysjDr2gIAA6Ovr0xodN2vWDG/evOG6X5eXl0NLS+uHPO/orhsWi4XAwEAsW7ZM4OdI4tjv3LmDvXv3IjY2Fjo6Ovjrr7+gpqaG6Oho9OnTB8D/jXH//vtvKCsrcxxLTU0NKioqMGPGDGzfvl3gvvr164eFCxcykpVoyMePH+Hu7o7Tp09T98rq6mo4OzsjJiaGp9ycOMfmoshRsbl37x4WL16MFStWwNzcvFH3fCUlJdy4cYOSlWLz8OFD2NnZ0c7r2AgzxkpJSeE7bmZCdXU1AgMDERERQY1JFBUV4e3tjZUrV3J9Dz8aISEhaNWqFTw8PDi27927F+/evcOiRYv4tv0Z71f1EXWMKCqlpaV48+YNOnfuTEnr3L17F8rKyjxlV340Tpw4geXLl8Pf35/nOM/S0pJv2+7du0NTUxOxsbFQU1MDUCdBN3nyZLx7945rnUPcXLlyBVeuXOHpX0Qn3Xjs2DFMnDgRbm5uiI+PR3Z2NgwNDbFt2zacO3eOdj2iPt++fUNqairOnz+PnTt3oqKigtF6gK+vLyZNmsQxPn/48CEGDRqE0tJSge1ramoQFhbGV+Ja0PNCmPEd3bipPkwk6ET57URl7ty5OHjwIJYuXUqZ0t+7dw/BwcEYP348wsPDERUVhZiYmEb7PtJx8+ZNDBo0CBMmTEBMTAymT5+O7Oxs3Lp1C9euXaM8SvghDl/hyspK7N+/n8Mv0NXV9bs95/7++2/Kr5A9t5k9ezZ0dHS+y/5fv36N7du3c8xLZ82ahdatW9O2FeW7Yz8f6tMYP2x+/PIE+U6IehNkG2+yWCxMnjyZQ/+tpqYGjx8/ZqQFOXLkSNy5cwdhYWE4efIkgLqT+O7du4xM+NjIy8tzmbbS0a5dO1y5coVrIefy5cscGsT8EEZnXVQ/FTqDsMYwadIkfP78GZ07d4a0tDTXgoygB++HDx8oLT1lZWXqvT179sTMmTNp9z1v3jy4ublBUVERenp61AT4+vXrjH5Htl50w2AXU71ob29vTJgwAQEBAXzNogXh6OiIHTt2wMLCAra2tgDqHryPHz/G5MmTkZ2djf79++P48eMYNmwYV/sBAwbAy8sLVlZWyM3NxeDBgwGACm4IQk1NjZpctmnTBpmZmbCwsMDHjx8lugjOJiwsDO/evcPnz585Bsvy8vJQVFTE27dvYWhoiJSUFEbXUWNo0aIFHj9+LJbPaszEnE2HDh0QEBBAmUQ3fFjyGzDW73N2djbHoLimpgYXLlyg9XUAxOMvwGbIkCGIiopiPFhRV1dHYmIi/Pz8OLYnJiZCXV2dtn2LFi2ogYOWlhaKi4thYmICFRUVlJSU0LZ/+PAhHjx4gJqaGhgbGwOoC0Q3a9YMnTp1wp9//gk/Pz/cvHmTQwOfH/Ly8rC2tqZ9HxtJBECAukHk33//LfA9MjIyPBeecnNz+S6IqaqqcjyTCCEiexMYGRkxDj7W17X/+vUr/vzzT5iamnJohWdlZWHWrFk826ekpDDaDx2VlZVYt24d30laQUGBWPbDj+vXr+OPP/6Am5sbhx58dXU1rl+/LtCgWRz4+PjAxsYGGRkZHNfp8OHDMXXqVInum420tDR2796NFStW4MmTJ6isrISVlRXfINry5ctx/PhxdOzYEXPmzKGu95ycHGzfvh01NTW0i/gAsHbtWsTGxmLDhg0cx2pubo4tW7bQBkHY10dDXr58+cP5rrBJSUkBIQT9+vXDsWPHOILC0tLS0NPTYzRB5Hfs5eXlUFBQYNyfsrIyxMfHIzo6GgUFBXBxccGZM2fQv39/VFZWIigoCO7u7pTu/pYtW0AIgYeHBwIDAzm+Z7ZOOvseIghvb2/4+fmhtLS0UQuChBD8888/SEhIwKtXryi/QLqgrzjH5t7e3vj06ROysrJgYmICoG7c4O7uTi360KGqqop//vkH/fr149jO5J7fr18/+Pj44ODBg9S58urVK/j6+nI9QwQhzBjL0dERbdu2xZQpU+Du7t7oMaS3tzeOHz+ODRs2UOfJ7du3sWrVKpSXl2PHjh0C28fFxWHs2LFcmubfvn1DQkICh3+lJNi5cydP/yQzMzOMGzdOYBBEXNdsUyHqGFFUtLW1oa2tzbGNPb/7GWD7RzQMoAH0yX2PHj1Ceno6NacD6uaaa9eupRaXJUVgYCCCgoJgY2MjlGfdmjVrEBkZiUmTJlEeQgDQo0cPrFmzRmDbb9++IS0tDSkpKbh69Sru3LmDdu3aoXfv3ti2bRvs7Oxo9//s2TOe4zgVFRV8/PiRtn1gYCCioqLg5+eH5cuXY9myZSgqKsLJkydp1zKEGd+FhYXR9gmoO2fogiCi/nb1YScHN+YzwsLC0KpVK2zYsAFlZWUAgFatWsHX15e6Vzo4OAiVDEFHz549kZGRgZCQEFhYWODixYuwtrbG7du3Ga1jicNXWEFBAdOmTRPpOERBRUWF0VhcUrRu3ZqRbxAvRPnuxOmNXZ9flSDfCQMDA0bvY7FYPBcJpkyZAqDO4HnMmDEcC+jsicrUqVMZZdg2lhEjRiAmJgbKyspUMIYfgkyid+zYgXnz5sHDw4MK2KSmpiImJgbh4eECDbWAugvo/v37P0WGCC9iY2MF/l2QaZqlpSW2bt0KOzs79O/fH126dMGmTZsQERGBDRs24OXLl7T7T09PR0lJCQYMGEAZgp09exaqqqq05pOiZjwpKSnh0aNHaN++PW0/eTF16lTo6uoiICCAY/uaNWvw4sUL7N69GytXrsTZs2eRnp7O1f7jx49Yvnw5SkpKMHPmTOoBvXLlSkhLSwt8qIwfPx42NjaYP38+Vq9eja1bt2LYsGG4dOkSrK2taY3RRa3kOHjwIHbt2oWoqCjq+3v+/DmmT5+OadOmoUePHhg3bhy0tbVx9OhRgX0RBl9fX8jIyDA2obO2tsaVK1egpqZGm51OZ5At6L7J714JcGbn8nrEycnJYevWrTwnL/UZO3YsVFRUsGvXLigpKeHx48fQ1NTEsGHDoKur2yijt/oZS0yIiYmBl5cXBg0ahK5duwKoy+y9cOECdu/ejcmTJwts7+DggMmTJ2P8+PGYOnUqHj9+jLlz5yI+Ph5//fUX7ty5I7D9li1bcOPGDURHR1NZrH///Te8vLzQs2dPTJ06FePHj8eXL1+QlJTE1b6mpgYxMTF8F8OTk5O52ogzu7fhdUcIwZs3bxAfHw87OzueCx9svLy8UF5ejsOHD6Nly5Z4/PgxmjVrBhcXF/Tu3ZvnAty1a9cY9RsA7URPmO+uYf91dHSwevVqju0rV65ESUmJRLPFXF1dce3aNUycOJHnJM3Hx0di+wZEf1atXLkSHh4eQgfhmrJ6rD579uxBWFgY8vLyANQF1ObNmwcvLy+e73/x4gVmzpyJpKQkjsnxwIEDsX37dkZj2A4dOmDnzp2wt7fnOPacnBx0794df/31F8927OdERkYGzMzMOIwSa2pqUFhYCEdHRxw+fLixX8N348WLF9DV1W30ogR7TJ2YmAhHR0eeCU7Gxsa4cOEC7Wc5OTkhKSkJHTt2hJeXFyZNmsRVqff27Vtoa2tz3VOuXbuGHj16CG3wKWymXm1tLWRlZZGVlSVytamwqKio4PLly1yLj3fv3oWDgwOjhTVbW1s0b94cPj4+PI3RBd3zS0pK4OzsjKysLA6zW3Nzc5w6dYrLPFicY6z3798jPj4esbGxyMrKQr9+/eDp6QkXFxdIS0vTHTZUVFSQkJCAQYMGcWw/d+4cXF1daRMOmrqaQlZWFk+fPuW6vxUUFMDU1JRn1ac4r9mmRNQxoqikp6fzzcanm1f9CNAZuAsaQ3Tu3BlhYWFcQdPk5GT4+PjgyZMnYukjL3R0dLBhwwZMnDhRqPby8vLIzs6Gvr4+x3Ne0DUD1AV779y5AwMDA9jZ2aFXr16ws7NrdBa7oaEhdu3ahf79+3PsPy4uDuvWrUN2drbA9u3bt0dERASGDBnCsTYRERGBtLQ0gXODph7fifrbAXWB540bN1Jjw44dO8Lf37/Rn8lOFGNa2S4KVVVVmD59OgICAhivp4qDU6dOYdCgQWjRogVOnTol8L3Ozs4S7Qu/hFT2WpKurq7YDdIfP34Mc3NzSElJ0SbECqp8i42NhYaGBoYMGQIAWLhwIXbt2gVTU1McPHhQYkmPdPyqBPlOiBrFYi+26evrY8GCBSJlmdTW1uL58+c8F1f4RdfZg2xRsvFmzpwJbW1thIaGUpNZExMTHDp0iGf2fkNMTU0bLdtEd9OqT2NuYF+/fuUatNE9CAQFOeiYMmUKMjIyYGdnh8WLF8PJyQnbtm1DVVUV4wi2jY0NbGxsOLaxb0h08Mt4ysjIYCSHM2LECKSkpAgdBDl8+DDu37/PtX3cuHH47bffsHv3bri6uvL9LlRVVbFt2zau7YGBgbT73rZtGzWoW7ZsGVq0aIFbt25h5MiRWL58OW17USs5li9fjmPHjnF8dx06dMCmTZswcuRIFBQUYMOGDVRWkriprq7G3r17cfnyZfz2229c956G3/mwYcOoB3H97HRhEPa+WVhYCEIIDA0NcffuXY7sfWlpaWhpaTGSDwsNDcXAgQOpgf348eORl5cHDQ0NRtmhojB58mSYmJggIiKCmhCamJjg5s2bVFBEEMHBwVQF09q1azFp0iTMnDkTRkZGjBbBN27ciEuXLnHc11RUVLBq1So4ODjAx8cHK1asgIODA8/2Pj4+iImJwZAhQ2Bubs5ocbB+cKG8vBxr1qzBwIEDOTJMk5KSuIKhvGiYfSUlJQVNTU24u7tjyZIlAtuGhoZi1KhR0NLSwpcvX2BnZ4fS0lJ0796dbxYMkww2pgjz3dXnyJEjPIPBEyZMgI2NDc/fvzEVX4IGu+fPn8fZs2dpA+uSQtTs3MTERKxduxZ2dnbw9PTEyJEjGzWxEGf1mLCsWLECmzdvhre3N8e14+vri+LiYgQFBXG10dPTw7lz5/DXX3/h+fPnIITAyMiII1OVjlevXvHM3q+trRUoPch+Tjx69AgDBw6kkjSA/0vykdTzTVzo6enhxo0b2LlzJwoKCnDkyBG0adMG8fHxMDAwQM+ePXm2Y4+pCSFQUlLiSnDq1q0b4woiLS0tXLt2TWDlhqamJs/nqp2dHfLz8xEdHY38/HyEh4dDS0sL58+fh66uLszMzATuW9hntZSUFIyMjFBeXt6oIAhTiSiAfmxeW1vLU5KhRYsWXHMkfmRmZuLhw4dUFVVjaNeuHR48eIDLly9zyEz079+f5/vFOcbS0NCAr68vfH198eDBA0RHR2PWrFmYNWsWxo8fD09PT3Tu3JlvexkZGZ7V1AYGBoyCKE1d/dWuXTukpqZyLaylpqbyreAS5zXblIg6RhQFdpXPwIEDcfHiRTg4OCA3NxdlZWWMZbmbGvbCXXZ2Nlcgh8ViCVzYCwkJwdy5c7Fq1Sp069YNQF21blBQENavX89xf2t4/xJVwu7bt2+MlEP4oa2tjefPn3Nd9zdv3hSY5HXjxg3o6OigX79+6NOnD+zs7BhVtTdk6tSp8PHxwd69e8FisfD69Wvcvn0bCxYsYDQ3YFcrAnXSfexA7dChQ2nbN/X4TtTfbvPmzQgICMCcOXOoMfrNmzcxY8YMvH//Hr6+vow/63sEP9i0aNECx44dY/T7CuLjx484evQo8vPz4e/vj5YtW+LBgwdo1aoVT3UIFxcXSlJb0LP2e8gEd+nShSu5s/6zs0WLFhg7dix27tzJJWsryj7Zx8/eP6/EUrrjDw4OpqpCb9++jW3btmHLli04c+YMfH19aYPe/NZy6ycTCxUcE9pN5BdC4+vry/M1f/58snTpUrJ3715SXl7Os+2KFStIUVGR0Pu+ffs2MTAwIFJSUpSJHvvFxFhdWKqqqkhgYCApKSkR+jOuXLlCunfvTlJSUsj79+8ZmbU2PEZ+LybHXlFRQWbPnk00NTUpU/j6LyY8f/6cLFu2jIwbN44yaT137hzJzMxk/kUQQoqKisixY8cYGwJVV1eTqKgo4urqSuzt7Unfvn05XvxQVVUlampqREpKivo3+6WsrEykpKTIrFmzaPe/Zs0aoqGhQdzd3cmmTZtIeHg4x4sOLS0tnsaosbGxREtLixBCSFZWFq2BZ2VlJXn69CljU6WqqioSGxtLSktLafvIjwMHDpA+ffpwGFfl5eWRfv36kYSEBFJSUkJ69OhBRo4cybO9nJwcuXfvHtf2u3fvEjk5OUIIIYWFhYyMvYThexpN/ohUVVWRffv2EX9/fzJz5kyye/du8vnz50Z/jpmZ2XczDa2trSUvXrwQypyYjYKCAklJSeHanpKSQhQVFQkhhOTn5xMlJSWe7dXV1cnZs2eF3r+oxvDi4ObNm2T79u1k/fr15NKlS99ln4SI/t21atWKMqWvT3R0NHW/bAj7OSjqs1JfX58yH/yeDB8+nAwfPpxISUmRwYMHU/8fPnw4cXZ2Jvr6+ozMEwkh5MGDB8Tb25toaGgQVVVVMmPGDJ7GxbwYM2YMmTp1KiHk/8wbP336RPr160cmT54s9PE1Bg0NDXLgwAGu7QcOHCDq6uoS26+1tTWJj48nhBDKaJcQQgIDA0nPnj1p28fExPA0Rv8ZENWsddWqVaSiokLkfly+fJksWbKEeHp6kilTpnC8BHH16lUiJydH+vfvT6Slpan+h4SE8B2biItTp06Rnj17NsoQmH0vEvRiOrZ3dnYmvXv3Jq9evaK2vXz5ktjZ2REXFxdG/enVq9d3fUZIilevXpGVK1cSGRkZoqCgQJo1a0Z69uzJd44SGBhIXF1dOa7br1+/Ejc3N7Jq1Sq+++nSpQuxsrIiUlJSxMLCgjKHtrKyIpaWlkRJSYmMHj1a7MfXkPXr1xN1dXWyd+9eUlRURIqKisiePXuIuro6CQ4OFthWXNfsfxELCwuybds2Qsj/PStqa2vJ1KlTyYoVK5q4d8zIz88nlpaWXGMnJmsCDcdUvNrzu39JS0sTQ0NDsnr1aqHmFAsXLiRBQUGNbscmODiYmJqakrS0NKKkpERu3LhB9u3bRzQ1NSnjal5UVFSQ8+fPk0WLFhFbW1siLS1NzM3NyezZs8mRI0fI27dvGe2/traWrFmzhigoKFDfmaysLFm+fDmj9h07diRpaWmEEEJ69OhBQkJCCCGEJCQkEE1NTYFthRnf8Vvz4/WiQ9TfTl9fn+daSkxMDNHX16dtX1paSiZMmEB0dHRIs2bNhFoHE5ZJkyaRzZs3C90+IyODaGpqkg4dOpDmzZtTY5xly5aRiRMniqubEuPkyZPE2NiYREVFkcePH5PHjx+TqKgoYmJiQhISEsi+fftI27ZtiZ+fn9j2WVRURGpra6l/C3oJQk5Ojrx48YIQUncOs7/vzMxM2nU7QvjPT+vfJ3v37k0+fPjQqOP7JYfVBPTt21eg1vqzZ8/AYrF4aq1bWVnhyZMnQmcpdunSBR07dkRgYCBPqQpJZt4oKioiMzOT1oOBH+xy+4Z9Jt/JrHX27NlISUnB6tWrMXHiRGzfvh2vXr3Czp07sW7dOg79cV5cu3YNgwYNQo8ePXD9+nU8ffoUhoaGWLduHdLT0yUiZcRmzpw5VGYxr9+dn2ZlbGwspRe9ZcsWofWihZU1YrNmzRoEBwdj6tSpHGZcUVFRWLp0KZYtW4awsDCcO3cOly5d4mr/7t07TJ48mW95uqBzR15eHk+fPhW6XK99+/Y4duwYT9NLdiUHu7KEl8n6kCFDUFpaiqioKMq35+HDh5g6dSq0tbVx5swZnD59GkuXLpVoCfX3gp8hNy/oqqBELcFk+ws0lAiprq7GrVu3xO4v8M8//1DZNXSZroKycMQhMeLm5obbt28jNDSU45pbsGAB/vjjD8THxyMhIQGbNm3iWXXQunVrXL16FR07dhRq/6Iaw4tCU+uUi/rdrVu3DoGBgZg6dSqlsc02SQ4ICMDixYu52tBJO9RH0HWzb98+JCYmIjY2lstDSpJIQjK0qqoKp0+fRnR0NJKSktCpUyd4enpi8uTJfMdKL1++xMCBA0EIQV5eHmxsbKjqsevXr3PJvkgCVVVV3Lt3j+vaz83Nha2tLSN5H2FITEykKq2CgoIQGBiIZ8+eIS4uDmfOnMGAAQMEti8pKQGLxaLkf+7evYsDBw7A1NSUr5YwnRxQfeikgURBVLPWL1++gBBCXTMvXrzAiRMnYGpqyrfariHs75yfXviJEyf4tu3evTtGjx6N+fPnc/T/7t27GDFiBK3kqq6uLpXd26dPn0ZV/aqpqeHz58+orq5m7JcnTvnBxspR8eLIkSNYtWqVUCbJQNOa3VZVVSExMRF79+7FpUuXYGNjA09PT7i6uuLdu3dYvnw5Hjx4wFNmZvjw4bhy5QpkZGSoipGMjAx8+/aNy8+kfrYnuwo7MDAQfn5+fKu/mFSTiAIhBIsXL0ZERASVyS8rK4tFixbR+gMIc82Kq+KSzcePH3H37l2e542kxymioKCgQHkyqqur4+rVq7CwsMDTp0/Rr18/nnOhHw0nJyc0a9YMUVFRMDAwwJ07d/Dhwwf4+flh06ZN6NWrF9+2oty/hJGwqz+vqq2tRWxsLCwtLWFpacl1r6KbVxFCEBwcjJCQEEr+SUZGBgsWLOCSYBXEp0+fcPPmTcofJCMjA0ZGRsjMzGTU/tu3b3j+/DkqKipgamrKcQ8RxOLFi6GsrIylS5fi0KFDmDBhAvT19VFcXAxfX1+B0s/CjO+YVu2wWCxaqVsfHx/ExcUJ/dvJysoiMzOTa16Vl5cHCwsLvlJmbAYNGoTi4mLMmTOH5xiDiaqLsKxZswahoaGwt7fnqUpB56fSv39/WFtbY8OGDRxjnFu3bmH8+PEoKioS2L6p54W2trZYvXo1Bg4cyLGdrY5w9+5dnDx5En5+fsjPz5doXxqLlpYWkpKSYGVlBSsrK8yfPx8TJ05Efn4+OnfuTDufv3LlCpYtW4a1a9dSc9q7d+8iICAAy5cvh4qKCqZPn46uXbtiz549jPv1KwjSBIiqtf7w4UNER0fj4MGDqK6uxrhx4+Dh4cHITEtBQQEZGRkCDQcbIq5J5rBhwzBixAihZaHoBg2NkSP5+vVro8vFdHV1ERcXhz59+kBZWRkPHjxAhw4dEB8fj4MHD+LcuXMC2zd2khkREYFp06ZBVlaWr68EG7qbv4aGBuLi4ihD8MYiql60ONi/fz+2bduGZ8+eAQCMjY3h7e2N8ePHA6ibkLBL4xri5uaGFy9eYMuWLejTpw9OnDiBsrIy6qEqSBasT58+8PX1FfrhLi8vj+vXr3NJkd27dw92dnb4/PkzioqKYG5uzvNBUFpaiokTJ+LKlSvUgKe6uhr29vaIj49Hq1atkJKSgqqqKsaLJZJEVF+HhgPGBw8eoLq6mitg/Ntvv9EOGI2NjbFjxw7069cPt2/fhr29PVWC2bx5c9oSTFH1qvPy8pCYmIiioiKwWCwYGhpi2LBhfEvG6++vvq9JfZgGfc3MzLBnzx6q1L6xVFRUwNfXF3FxcaiurgYANG/eHO7u7ggLC4OCggIePXoEAFwBPqBOUqqgoADbtm0TyrxPT08Pc+fO5TKGDw0NRUREBM9FezrPqvoI+u2bWqdc1O8OqJMQDA8P5zAa9vHxwZgxY8TZVS6srKyQn58PQgj09fW5JmmSXIgG6hbVRJUMZfPt2zecOHECe/fuRXJyMv744w+8fv0aZWVl2L17N8aOHcuzXXV1NQ4dOoSMjAxUVFTA2toabm5uXIu7ksLb2xstWrTgmhAvWLAAX758wfbt2yW27xs3biAoKIjj2AXJ5tWnV69emDZtGiZOnIjS0lJ07NgR5ubmyMvLg7e3N89FSSaSlmxWrlzZqGNpDMLqpLNxcHDAiBEjMGPGDHz8+BHGxsaQlpbG+/fvsXnzZsycOZO2D6LohSsqKuLJkycwMDDg0jrv1KkTbf/37duH69ev4+rVq3j+/DnatGkDOzs7KigiKBgvil+euCCEMJaj4oWwnigAvdltw+CVOL2zvL29cfDgQRBCMHHiRHh5ecHc3JzjPaWlpWjdujVPaTB28JkJvDzUYmNjMW7cOLFrmTeWiooKPH36FHJycjAyMmLUH2GuWfa4jt/SC9NzBgBOnz4NNzc3VFRUQFlZmeOcYLFYPH97cfrJiELbtm1x/vx5WFhYwNLSEkuWLIGrqytu374NR0dHWi+ZHwENDQ0kJyfD0tISKioquHv3LoyNjZGcnAw/Pz88fPhQ4n1gS9ix5Xn5SdiJcyGejbBBCDa1tbW4d+8eUlJSkJKSgps3b+Lr168SH183JC0tDbdu3YKRkRGcnJxo319dXY2EhAQ8fvz4u4/vBP2OTH47c3NzjB8/HkuXLuXYvmbNGhw6dIg2kVJJSQk3btzgOeeTNKIm06qoqODBgwdo3749xxjnxYsXMDY2ph3jNPW8UE5ODg8fPuTyRc7JyYGVlRW+fPkidm8acVkKuLm5Uf08ePAgiouLoa6ujlOnTmHp0qW0gU9zc3Ps2rWLSwouNTUV06ZNQ1ZWFi5fvgwPDw8UFxcz7vMvT5AmQFStdXYkLTQ0lMpS7NGjB6Msxa5du+L58+eNCoLU18H7+vUr/vzzT5iamlIVAGlpacjKysKsWbMEfs6gQYOwePFiPHnyhGcUl86TQ1TN9ZqaGgQHByMyMhJlZWXIzc2FoaEhAgICoK+vD09PT4HtP3z4QC1eKisrUwPMnj17MpqgPnnyhKfhlpaWFk+vk7CwMLi5uUFWVpZvpQZQd/OnC4JIS0s36jdviJKSEp4+fUrpaCYmJiI6OhqmpqZYtWqVxLO1gLqbqKBqG0EDkOTkZCQmJsLGxgZSUlLQ09PDgAEDoKysjJCQEIFBkFmzZmH+/PkoKSnhed7SZWv17dsX06dP56rkmDlzJmWKx1584IW2tjYuXbqEnJwc5ObmAqhb3K+vPS2KPiwTGmNgKKqvQ0pKCvXvzZs3Q0lJCbGxsRx+KlOmTBGYZcWmpKSEOu9PnjyJUaNGUWbyffr0oW1PRPAXCAkJwYoVK1BbWwstLS0QQvDu3TssWrQIwcHBWLBgAVeb5ORkymMnOTlZ6AVwoK4awN/fHzt27OBa1GCCoqIidu/ejbCwMGpwaWhoyDHRETQQZmd4nT9/HmZmZlyL4XQBqMDAQHh5eeHq1as8jeF5Ia5KRn6/+/fSKRf1uwOAMWPGiBTwiI+PR2RkJAoLC3H79m3o6elhy5YtMDAwEBgQFlWjXlQWLlzIscAkTEb9/fv3qYUFGRkZTJo0Cdu3b6fuJVu3bsXcuXP5BkGaN29O+7ySNHv27MHFixepIOidO3dQXFyMSZMmcWSFMvUUY0qvXr14VmMyITMzk8ryOnz4MCwsLJCamoqLFy9ixowZPIMgkgxsNAZhddLZPHjwgBrnHT16FNra2nj48CGOHTuGFStWMBpjiqIXrqqqijdv3nCNQx4+fMhTK7shEyZMwIQJEwAAb968wbVr13DmzBnMmjWLr446G3EEOYTxY6kPi8XCgAEDaKuV+CGK72NkZCRiYmIYB6/E6Z2VnZ2NrVu3YsSIEXwX/jU0NDjGZfXhFdhoDIGBgRg6dCjXvj9+/Ahra2vahS1xoaioyCiRsD7CXLOi+oPWx8/PDx4eHggODmZcdSlOPxlR6N27Ny5dugQLCwuMHj0aPj4+SE5OxqVLl7gqiH5UampqKB8IDQ0NvH79GsbGxtDT06OS9eojLpPh+lhbW0NbWxvq6upYt24d9u7diz///BPdu3dHZGQk5eXE7/oVBWlpaS61EkHU1tYiPT0dV69eRUpKClJTU1FZWYk2bdqgb9++2L59O9+5rLgSnHgZbHfr1q1RyWLNmzennnXC8vz5c+Tn56N3796Qk5PjO+doiKi/Y2BgIMaOHYvr169TniCpqam4cuUK5dUriHbt2vEN4EoaUe+dMjIyPBUWcnNzOXxD+dHU88JOnTph3bp12LVrF7XmVlVVhXXr1lGBkVevXqFVq1Zi22fDZ0TDAH7970PQGG/79u1Yvnw5SkpKcOzYMcoL6P79+3B1daXtR35+Pk/1C2VlZWqMYGRk1Gjf6F+eIE2AqFrrbP7991+SkJBAHBwcSPPmzUnv3r1Jhw4diJKSEklISODZ5vjx48TU1JRER0eT9PR0xt4IbDw9PXnqLq5YsYJWc1hUTw5CCPnrr7/Ipk2biKenJ/H09CSbN28mHz9+ZNQ2MDCQGBoakn379hE5OTlKDzAhIYF069aNtr2FhQW5evUqIYQQe3t7SncvPDyctGnThrZ9mzZtSGpqKiGEUy/7+PHjxNDQkNExCMumTZvIrFmzKG2/xmJjY0OOHj1KCKk7N2VkZIirqyvp0KED8fHx4dnG19eX0ssVVQeTzb///ktKSkrIixcvOF50KCkpkcLCQkIIIbq6uuTmzZuEEEIKCgooXw1+8DtfmZ63b968If379ycsFotIS0sTaWlpIiUlRQYMGEB5jSQnJ5OkpCTaz2oKDh48SFq0aEGGDh1KpKWlydChQ0nHjh2JiooKrca9qL4OrVu35qlF/eTJE6Kjo0PbXlNTkzx48IAQUqdBHRcXRwip8+YR5KEiqr9AcnIykZKSIitXruTQqCwvLycBAQGkWbNm5Nq1awL7/u3bN75/e/funcC2hNT5+bDPNVlZWQ4/HzU1Ndr2ojJ58mSBLyakpaWR8ePHU1rh48ePp7R8JYG4dMoLCgpIbm4u1/bc3FzqPiQIcXx3ovDnn38SDQ0NsmbNGo5nZXR0NOnTp4/E9y8KAwYMIDt27CCE1I0XtLS0SNu2bYmsrCz5888/adubm5uT5s2bk8GDB5MTJ06Q6upqrve8e/eOsFgsnu2Dg4PJnj17uLbv2bOHrFu3rpFHIxyCfJx+ZE8nBQUF6vpwcnKivq8XL14QWVnZJuwZPcLqpLOpr5k8evRoyk+huLiYdozCRhS9cD8/P9KzZ0/y5s0boqSkRPLy8sjNmzeJoaGhQG+H+lRWVpKkpCSyZMkS0q1bNyIjI0O6dOlC5s2bR9u2pqaGPHv2jNy4cYNcu3aN40WHqH4sTU3Lli05POMaw4/gnSUKLBaL8kesT2lpKZGWlpbIPocPH075SNYf2/F6CUIc16woyMvLU+f6z0Z5eTnlwVNTU0NCQkKIk5MTmT9/fqN13ZuKnj17khMnThBCCHF1dSWOjo7k5s2bZNKkScTMzIzr/fXPdUEebEzmld++fSNHjhwhgwYNIs2bNyfdunUju3fvJhUVFaSwsJC4ubkRExMTsR4vm4qKCrJ8+XLSvXt30r59e2JgYMDx4oeSkhKRkpIirVu3Jm5ubiQqKorxfa/++Nfd3Z0oKyuTdu3aUdeprq4uUVZWZjQ+VlZWJgUFBYyPtyG5ublk586dZPXq1SQwMJDjRcf79+9Jv379qN+Zff1OmTKFzJ8/v1H9KCkpEcpnNz09nbi5uRFra2tibW1N3NzcqHkyHUlJScTBwYHRPEaS1NbWNno9y9PTk7i4uJBv375Rfi4vXrwgVlZWfNexCPlx/KtSU1OJuro60dTUJPb29sTe3p5oaWkRdXV1cvv2bUIIIXFxcWTDhg0S2f+lS5eItbU1uXDhAuXFfOHCBWJjY0MuXrwokX2y6dGjB3F0dOTwDXr79i1xdHQkvXr1ovrXsWPHRn3uLzmsJkBUrXVeWYpeXl4cWYpr1qxBWVkZV1tRSraBukzb9PR0rtJ2tjaiJEtY09PTMXDgQMjJyVHZgvfu3cOXL19w8eJFWFtbC2zfoUMH7Ny5E/b29hylcDk5OejevTv++usvge3DwsLQrFkzzJ07F5cvX4aTkxMIIaiqqsLmzZvh4+MjsP2CBQtw584dHDlyBB07dsSDBw9QVlaGSZMmYdKkSRLNaBw+fDhSUlLQsmVLoTKL65cRrl+/HsnJyUhKSkJqairGjRuHkpISrjZ9+/bFiRMnoKqqKnIJZ15eHjw8PHDr1i2O7UzP299//53KlnN2doaqqipCQkIQERGBo0ePCtRPpNPKZ+oVIqiSQxA1NTWIiYnhqxfNtHRZWCwtLTF9+nTMnj2bum4MDAwwffp06OjoCJQjEdXXQUlJCadPn+aq2khJSYGzszM+ffoksL2wJZii+guMHTsWqqqq2LlzJ8+/T5s2DZ8+faJK2HkxcuRIHD16lCvzpKysDPb29rTlo6JKjFRWVmLdunV8z7vvlaH5PRGXTrmdnR08PDy4vuN9+/YhKioKV69eFXvfxYmpqSmCg4Ph4uLC8azMzMxEnz59Gp9t8x3R0NDAtWvXYGZmhqioKGzdupUjO5ctD8aP1atXw8PDg1H2Oy/09fVx4MABroz8O3fuYNy4cWLNBP5fo2vXrujbty+GDBkCBwcHpKWloXPnzkhLS8OoUaNofSlqamoQFhbGt2qRThpIFIiIOumWlpbw8vLC8OHDYW5ujgsXLqB79+64f/8+5QvGC3FpvX/79g2zZ89GTEwMampq0Lx5c9TU1GD8+PGIiYlBs2bNBPb/jz/+wMOHD2FiYkJ5g/Tu3Zuq4BREWloaxo8fjxcvXnBlmTIZ34nqxyIu8vPzsWXLFuoeY2pqCh8fH1p/lEWLFkFRUZFR5UZDRB1jhYSEoFWrVvDw8ODYvnfvXqpyVRKwZTZcXFwQGxvLkUlbU1ODK1eu4NKlSzwz6kVlypQpiIiIgJKSEq2cl6BKF2Gv2YZkZ2fzvF/RqSOMGDEC48aNk7jE5S94k5SUhMrKSowYMQLPnz/H0KFDkZubC3V1dRw6dIiq9Gfz4sUL6OrqgsViiTSvFFXCTlRcXV1x7do1TJw4kad8H7+1kJ07d6Jv375Ce92xWbRoET58+IDIyEjquVRTU4NZs2ZBWVkZGzduFNje3d0dXbp0ga+vb6P3vXv3bsycORMaGhrQ1tbmkqCjk5CbNGkS3r59i6ioKJiYmFDPqqSkJMyfPx9ZWVkC29fW1lIy3ux7u5KSEvz8/LBs2TKea3zipL5/l7y8PNcYQ5JjLKCuwjksLAx5eXkA6rL/582bBy8vL9q2f//9N0aNGoX09HR8+vQJrVu3RmlpKbp3745z587xVXf4UfyrgDofnf3793OsJY0fP56qSJMk5ubmiIyM5KqsvXHjBqZNm0Y7txKFZ8+eYdiwYSgsLOTwbTM0NERiYiI6duyIkydP4tOnT42Sg/0lh9UE7Ny5E76+vhg3bhxPrXWgruwpKiqKq62FhQVycnLg4OCAPXv2UMZc9XF1deX7EBJ1Ai4nJ4fU1FSuIEhqamqjPTYai6+vL5ydnbF7927Km6K6uhpeXl6YN28erl+/LrD9q1eveEpC1dbWoqqqitH+2fTv3x85OTm4f/8+OnTowKh0NTg4GLNnz0a7du1QU1MDU1NTapK5fPlyrveL0yBaVVUVw4cPZ/x5DSGEUIOpy5cvY+jQoQDqSiP5LYjVL9sUtYRz8uTJaN68Oc6cOcNz0EWHj48PZbS3cuVKODo6Yv/+/ZCWlkZMTIzAtsIaojekU6dOXFqOTPDx8aFM7c3NzUWSSBKG/Px8Si5MWloalZWVYLFY8PX1Rb9+/QQGQdTV1ZGYmMjl65CYmEiVQwpi+PDhmDJlCkJDQzkMnv39/RmVRwtbgsme+Orr6wvlL3D37l3Ex8fz/fvEiRNpTdSKi4vh5eXFYfL15s0b9OvXjypxF4SoEiNeXl4CJzpMeffuHYePD5OyYzY1NTU4efIkNbgyMzODs7Mz7YKcsLAD0fr6+iLplD98+JAqNa9Pt27dMGfOHJH6+D0oLCykpPvqIyMjg8rKSq7tLVu2RG5uLjQ0NGj16iU9Sfr8+TM1Ibh48SJGjBgBKSkpdOvWjXbhoaqqCjExMRg1apTQQZDS0lLo6OhwbdfU1PwpzF6bkvXr12P48OHYuHEj3N3dKV3zU6dOUfd/QQQGBiIqKgp+fn5Yvnw5li1bhqKiIpw8eZLW5FhUWCwWli1bBn9/f6F00lesWIHx48fD19cX9vb2lLTRxYsXeV6LbBrqzrMlChsGyenu39LS0ti9ezcCAgKQmZmJiooKWFlZCfTyqE9OTg4UFBSocY6JiQmjAAgAzJgxAzY2Njh79qxQz5pnz56hd+/eXNtVVFTw8ePHRn2WsCQlJcHZ2RldunThkBkxMzPD6dOnBcpsff36Fbt27cLly5cbHbwSdYy1c+dOnjK9ZmZmGDdunMSCIGyZDRaLxTVWadGiBfT19REaGiqRfdcPbIgi5yXsNcumoKAAw4cPx5MnTzhkRtjnP13wb8iQIfD390d2djYsLCy4zhteQRRx+sn816lvUNyhQwfk5OTgw4cPfL/j+nPJFy9e4I8//uDy2ayursatW7cEzjtFlbATlfPnz+Ps2bM8x7iCmD59ulj2v3fvXty8eZNjHtCsWTPMnz8ff/zxB20QxMjICEFBQUhNTW20wfaaNWuwdu1aoe+LFy9eRFJSEtq2bcvVJ7rxKQAsW7YMe/bswbp166jv/+bNm1i1ahW+fv2KtWvXcrXhJQHFD16SQ/WpL8X4vVmxYgU2b94Mb29vDulHX19fFBcXIygoSGB7FRUVXLp0CTdv3uTwc6Hz/qo/Lxw7dqzE1zoFoaSkhBkzZjTJvvPz86Gqqsq1XUVFhdZUXlSMjY2RnZ2NixcvcgSABgwYQAX+hJJ3FEOVyi+E5NOnT5QM1adPnxi1CQoKIi9fvpRwz/gTEhJCZGVlibe3N4mPjyfx8fFkzpw5RF5enoSEhPBsc+XKFWJiYkKVH9fn48ePxNTUlFHJu6ysLHn69CnX9qysLEalx9bW1iQ+Pp4QwilHFRgYSHr27EnbXlwUFxeTs2fPkkOHDvGUTGHTULpCWVmZyMvLU2V4CgoKRFlZ+btIWvTt25dMmjSJxMXFkRYtWpC8vDxCCCFXr14lenp6jD8nLy+PXLhwgXz+/JkQQhiXM8rLy/P87YWlsrKS3L9/n5GsECF18klz5syhShC9vb0Zl/FWV1eTqKgo4urqSuzt7Unfvn05XnSoq6uTs2fPMtqXJGjTpg15/PgxIaROEu7AgQOEEEJu3bpFlJWVBbaNjo4mzZo1I0OHDiWrV68mq1evJkOHDiXNmzcn0dHRtPuurKwkM2fOJDIyMkRKSopISUkRaWlpMnPmTEpqTZJ8/vyZVFZWUv8vKioiYWFhtNJlcnJyAsuUS0pKaOVd3r59Szp16kTJxb169Yp07NiRjB49mtTU1DDqf3V1NTl69Cj13R8/fpynvA8vVFRUKNk4YaioqCBTpkwhzZo1o8r8mzdvTjw8PDi+U37k5eWRjh07ctzz5OXlibGxsdDSIUwpLi7m+P3u3LlDfHx8yM6dOxm1V1ZW5llenp6eTkleCkJfX59LYoCJ3IC4MDExISdPniSEcD4rIyIiiJWVFdf7Y2JiyNevXwkhddd8TEwM35eksbCwIOHh4aS4uJgoKyuTW7duEULqvvtWrVrRtm/dujXJzs4Wev8dOnSgxhn1iYuL+y6/3c9OdXU1lxxKYWEhT8mchhgaGpIzZ84QQurOW/Z9Ijw8nLi6uoq/s3woLi4mxcXFjW735s0b8uDBA477+507d8Q69mGCMFITtbW1JCMjg4SHh5MRI0YQDQ0N0rp1a+Lq6kp27dolsK28vDw1phQGAwMDcunSJUII5/0qNjZWYpIwDenSpQtZtGgR1/ZFixbxvGfWRxTJOlHHWDIyMjylYdiyt5JGX1+f8Tj8R0SUa3bo0KFk2LBh5N27d0RRUZFkZ2eTGzduEFtbW3L9+nXa9sJITNd/FoeGhhI1NTUybtw4Eh4eTsLDw8m4ceOImpoa2bx5M/Mv4ReNRkpKiucz7f379wLlsL59+0amTJkikpyTqOjr64s0RhIVVVVVanxan5MnTxJVVVXa9vr6+nxfdGM0JSUlkSToFBUVqXWf+s+qe/fukZYtW9K219HRIYmJiVzbT548SVq3bs2zDft+IOjVGFn6pkJDQ4Naf6jPgQMHiLq6ehP06PsTFxdHevToQXR0dEhRUREhhJDNmzfzvB7ETa9evThk3Ampk610cHAgvXv3lvj+JcGvIMhPxLdv34ihoaHID5+4uDjyxx9/cFxEYWFhjC+iQ4cOkT/++IPSlv/jjz/IoUOH+L7fyclJ4IAqPDycuLi40O5XS0uL5+LjhQsXiJaWFm37kydPEhUVFbJu3ToiLy9PNm7cSLy8vIi0tDRjPbvLly+TJUuWEE9PTzJlyhSOFx2BgYE8F/8+f/5MqyUZGhpKnJycOBYHPnz4QIYNG0Y2bdrEqO+ikJGRQczNzYmysjKHPvScOXMYLS6IqoNpY2NDbty4IfwBiMCFCxeItLQ0sbW1pTxMbG1tiYyMDKPzZvbs2URBQYGMGTOG+Pj4kHnz5nG86NDR0SHPnj0Tx6EIhaurKwkNDSWE1AVhNTU1iZeXF9HT06PVTCZEPL4OFRUVVMBYmOBHZWUlefr0aaM9kIT1F+Cnc82mtLSU0YCzuLiY6OrqEl9fX2JkZETGjh3LOIiRl5dHjIyMhA4iiDrRmTZtGjE0NCTnzp2j9EPPnj1L2rdvT2bMmEHbftCgQcTR0ZGUl5dT296/f08cHR3J4MGDhe4XE3r27En5x7A18rt37040NDQY6f4OHTqUjB49muO3qq6uJiNHjiSOjo607bds2cLx2rhxIxk/fjxp2bIl32QDXvz7778kJyeHVFVVMW5DCCG7d+8mbdq0IQkJCURBQYEcPHiQrFmzhvq3sLCD35LkyJEjpEWLFpTvEpvg4GBG3/3atWuJu7t7o78zNuvXryfq6upk7969pKioiBQVFZE9e/YQdXV1EhwcLNRn/iwkJyc36f7l5eUpjX5tbW1y//59Qkjdgi5dwF5UqqqqyPLly4mysjK1sKCsrEyWLVsm0N/pRyIqKoqYmZlR3mVmZmZk9+7djf6c2tpacu/ePeLu7k6aN29O+6zr27cvOX/+vLDdFtmPpXfv3iQ2Nlak+5OMjAzPpKZnz55JPJggyhhL1KCtOD0pvnz5IrbPYkppaSmZMGEC0dHRIc2aNeNaHJQk6urq1DhUWVmZ5OTkEELqEge7dOki0X0T8vP7yfzMsFgsDn17Ns+ePaP1gxXV00JU4uPjyahRoxglM0kCX19foq6uTkJDQ8mNGzfIjRs3yKZNm4iGhkajPEaFwcPDg5oTCsOgQYMoX122L0VNTQ0ZPXo0GTlyJG17GRkZnusBOTk5fBPrrl69yvjFBFGS60RBRUWF7zNWRUWF0WeIsoZXXV1NNm7cSH7//XfSqlWr7+6zWd+rUVZW9rt7Nebm5hJzc3MiLS1N2rdvT9q3b0+NE0VJYuFHeHg4NSZgB+n5vYTlVxDkJ0PULEVRDE+rqqpIYGBgo42YdHV1Bfb56dOnpF27drSf4+3tTdq2bUsSEhKoTLuDBw+Stm3bCjQ1qs/169dJ//79iaamJpGTkyM9evRgbEi9atUqIiUlRWxtbcmwYcOIi4sLx4sOYTM/CBHOINrKyooKmrCNnfi9hOXLly+MJvgTJ04kAwcOJCUlJRzZDxcuXCCmpqa07a9cuUK6d+9OUlJSyPv376lFVfaLDn6G7PPnzydLly4le/fu5VhsrY8oGX6EiF7JIaqpvagIa2D4I2QsvX37lgwePJhv9gsd6urq1HW3e/duYmlpSWpqasjhw4dJp06d+LZjsVhk7dq1fB/Ya9asYTzBfvbsGdHS0iJubm6NOgdEDSKIOtFRV1cnKSkpXNuTk5OJhoYGbXt5eXmqAqk+jx49EmhqLw5UVVWpBYnw8HDyxx9/EELqTAGZLAxlZWURdXV10r59e8rMsX379kRTU5M8efJE6H5t27aNkfFjZWUl8fDwIM2aNSPNmjWj7rdz5sxhHETZt28f6dChA5VZ2qZNGxIVFUXbztvbm+f2ioqK72aqLkp2rouLC1FSUiI6OjrEwcGhUUa5hNQtAC9cuJDIyspS9xl5eXlGwbOfHWlpaWJoaEhWr14tVCUEIXVBrNGjR5OuXbs2epzSsWNHavG3R48e1LmekJBANDU1heoPU2bMmEG0tLRIZGQkFWSPjIwk2trajIK+hNRlg/r7+5OxY8c2+rwTlYCAAKKgoEAWL15MEhMTSWJiIlm8eDFRVFQkAQEBtO3v379PJeuoqamR5s2bEysrK+Lr68szwap+MsLx48eJqakpiY6OJunp6Y1OVqitraWCtOz7laysLLXYRIePjw/R1NQkysrKxMvLizIZbQxt27Ylhw8f5tp+6NAhRvMbQhpfKS2OMZaoQVsWi0X69OlD4uPjhQpi1NTUkKCgINK6dWuOZ9Xy5csZPW9ExdHRkZiampI///yTnDhxgpw8eZLjRYco16yqqir12xkaGlJB5OfPn38XY3UFBQWei1d5eXkSH2P9V2GfG1JSUmTw4MEc54uzszPR19cnAwcOFPgZkyZN+u6VOg3XEJSUlIiioiIxNzcX23oCU2pqasj69etJ69atqft969atyfr16yW+GB8cHEw0NDSIu7s72bRpU6MXY588eUK0tLSIo6MjkZaWJqNGjSImJiakVatWjJLTbG1teY6x58yZQ7p27SrUMTUGUZPrRGHOnDk8g1x+fn5k1qxZtO1FXcMLCAggOjo6ZNOmTURWVpasXr2aeHp6EnV1dZEW4pliYmJCTpw4QQjhrCJ68uTJd6uEqa2tJUlJSdT5fvHiRYmtTenr65P3799T/xa2eksQv4zRfzKCg4ORm5uLqKgoLi1JJohqeKqoqIjMzEzo6+sz3qesrCwyMzN5+nEAdQZ+FhYW+PLli8DP+fbtG/z9/REZGUl5qbRo0QIzZ87EunXrhNZvZ4qOjg42bNjQKNOd+khJSaGsrIxLEz85ORljx47Fu3fv+LYVxiA6MDAQ/v7+kJeXx6pVqwTqwErSlB0AtLW1kZSUhM6dO3OcdwUFBbC0tKQ1b2Rr/jU8BsLQGL1v37548OABampqKEPy3NxcNGvWDJ06dcKzZ8/AYrFw8+ZNmJqacrSVlZXFkydPuLSxc3NzYWlpia9fvwrcd+vWrXH16lWhzeBENbVvSlRUVPDo0SMYGBgwbjNixAjExMRAWVmZ1veD7tjd3Nzw4sULbNmyBX369MGJEydQVlZGGcuxvU74IS8vj5ycHOjq6mLMmDEwMzPDypUrUVJSAmNjY8oAtyH6+vqMdJcbejTx0xL+/PkzZGRkODRw6fSaFRQUkJaWBgsLC47tGRkZ6NGjB+01Z2Vlhfz8fBBCoK+vz3Xe0RkAysvL4/79+zAxMeHYnpWVBVtbW57eEvVp2bIlzpw5w2UwnZqaCicnJ57HHxERIfAz6yNI97f+c87Z2Rk9evTAokWLUFxcDGNjY9pnFQC8fv0a27ZtQ0ZGBuTk5GBpaYk5c+agZcuWjPvYkIKCAnTp0oVW49fHxwepqanYsmULHB0d8fjxY8pAbtWqVVw+AoL4/PkzKioqoKWlxej97du3x4QJEzi8giorK+Ho6AigzkRPkkRHR2PcuHGQk5MTqr0oRrn1qaiowNOnTyEnJwcjIyOJj09+BN6/f4/4+HjExsYiKysL/fr1g6enJ1xcXBiZRkZERGDZsmWYPHkydu3ahSlTpiA/Px/37t3D7Nmzeepd12fx4sVQVlbG0qVLcejQIUyYMAH6+vooLi6Gr68v1q1bJ65D5UJFRQUJCQkYNGgQx/Zz587B1dUVf//9t8D2CQkJmDRpEgYOHIiLFy/CwcEBubm5KCsrw/Dhw0XyLmCCpqYmIiIiuLyyDh48CG9vb9q5QfPmzWFlZQU7OzvKFL2+2XVDpKSkOHwQGsL+G5PxHZtv374J5ccC1Gnxnzp1CrGxsTh//jw6dOgADw8PTJw4Ea1ataJtHxQUhLCwMCxevJh6ZqWmpmL9+vWYP3++QNPz8vJyjBkzBikpKWCxWMjLy4OhoSE8PDygpqYm0BtDmDFWfQghWLx4MSIiIihjbllZWSxatIiRj86jR48QHR2NgwcP4tu3bxg7diw8PT0ZefgAdd9bbGwsgoKCMHXqVGRmZsLQ0BCHDh3Cli1bcPv2baGOiylKSkq4ceMG5aXTGES9Znv16gU/Pz+4uLhg/Pjx+Ouvv7B8+XLs2rUL9+/f5/L14cW1a9ewadMmyjfN1NQU/v7+6NWrF21bPT09zJ07l8tPJjQ0FBEREYw8CoTl77//Rk1NDdd46MOHD2jevDmtN8HPCnt8ERsbizFjxnCMU9gmy1OnToWGhgbfz2DPX+zt7RvtaSEsgrwfGyLp9YT6sMfCdOfL/PnzsXr1aigoKND6rQryYBJ0n2WxWCgoKBD42UDduc+eG7B9KWbPns3TS64h165dw5AhQ6Crq8vhi1FSUoJz584xuu5FYfDgwSCEYP/+/dS1W15ejgkTJkBKSgpnz54V6/7q/1bV1dWIiYmBrq4uunXrBqDOI7S4uBiTJk3C1q1bBX6WqGt47du3R0REBIYMGQIlJSU8evSI2paWlsbTW0ucyMnJIScnB3p6ehzraHl5ebC0tGQ0LxWWqqoqyMnJ4dGjRzA3N2fUxsrKirH/FN16gqT4ZYz+k3Hv3j1cuXIFFy9ehIWFBdfDj25RsLGGpw2xt7fHtWvXGhUEadOmjcAgyOPHjxnd/KWlpREeHo6QkBDk5+cDqLspycvLM+rHvXv3UFtbi65du3Jsv3PnDpo1awYbGxuB7b99+8a1IMcE9sImi8VCx44dOW4KNTU1qKiooDU6EsYguv5AZNWqVXw/m0kctKamBmFhYTh8+DCKi4upyRIbugXZyspKnr/Thw8fGC0OiWrwNmzYMLRs2RLR0dHUYOnvv/+Gl5cXevbsialTp1IGh0lJSRxtNTU18ejRI64gyKNHjxgtDPr5+SE8PBzbtm0TylxaVFN7YRCXkZqLiwtOnjwJX19fxp+noqJCfU+CFlGYkJycjMTERNjY2EBKSgp6enoYMGAAlJWVERISQhsE6dChA06ePInhw4cjKSmJOo63b98KPG5hTcLEaTonIyPDMzhaUVHBaEFSKJOxenTv3h0rV65EXFwcZST35csXBAYGUoN3QQwdOhTTpk3Dnj17OO55M2bM4Gn4CQBhYWGM+sZisQROFM3MzBAZGYkhQ4bg0qVLWL16NYC6wAYTs1mgLvgZHBzM6L1MOXr0KKMgysmTJ3Ho0CF069aN455jZmZGPTsFUVhYiOrqahgZGUFeXp66d+fl5VGmtfy4ePEievXqBTU1NcybNw+fPn3CwIED0bx5c5w/f57+IEVk8eLF8PHxwejRo+Hp6dnoZ7a4FpsVFRXx+++/i+WzfhY0NDTg6+sLX19fPHjwANHR0Zg1axZmzZqF8ePHw9PTkzI758Wff/6JXbt2wdXVFTExMVi4cCEMDQ2xYsUKRia99YMcY8eOha6uLm7fvg0jIyM4OTmJ5Rj5ISMjw/O6MDAwYHS/DQ4ORlhYGGbPng0lJSWEh4fDwMAA06dPZzQ+FpWqqiqeY+DffvuNSjoSxIcPHxq1cNkwAUAU6i+o1k9iacyCavPmzTFixAiMGDECb9++xa5duxAQEIClS5di8ODBmDt3Lvr168e3fUBAAJSUlBAaGoolS5YAqHsGrFq1inZR0tfXFy1atEBxcTFH0sDYsWMxf/58gUEQYcZY9WGxWFi/fj0CAgKECtp26dIF4eHhCA0NxalTpxATE4OePXuiY8eOVBCpYeJXfeLi4rBr1y7Y29tzzIM6d+6MnJwcoY6pMbRr147RHIgXol6zy5cvp+bdQUFBGDp0KHr16gV1dXUcOnSItv2+ffswZcoUjBgxgjrHUlNTYW9vj5iYGIwfP15g+8DAQHh5eeHq1avUvPjOnTu4cOECdu/eTbt/URg3bhycnJwwa9Ysju2HDx/GqVOncO7cOYnuv6lgjy/09fWxYMECrjUcJuzZsweqqqq4f/8+7t+/z/E3urGtsHzPwEZjYPrMefjwIaqqqqh/84Nuji6O55aKigqWLVsmVFs7Ozvk5uZi+/bt1P1xxIgRmDVrFlq3bi1y3+i4du0a0tLSOOYh6urqHEbt4qThb/Xbb78BADWP0dDQgIaGBrKysmg/S9g1PDalpaVUUqGioiKV2DJ06FCBSQ7iwsDAAI8ePYKenh7H9gsXLnAlG4qbFi1aQFdXl3FCCsC5hvD161f8+eefMDU1peb/aWlpyMrK4noGfFdEK1b5xfeGLa3B70VHYw1PG7Jjxw6ira1N/Pz8yIEDB6iyefaLF3PmzCHm5uY8S6U/f/5MzM3N+UpoiJPff/+dHDlyhGv7sWPHiK2tLW37hQsXkqCgoEbvNyYmhkRHRxMWi0XCw8M5jOkOHDhAGbcKQlSD6A0bNvDcXl1dTcaNG0fbXtQyQFF1MEWldevWJCsri2t7ZmYmZSZ2//59niWFgYGBRFVVlaxbt45cv36dXL9+nYSEhBBVVVVG54OLiwtRUVEhBgYGZOjQod9d5kIYxGWktnr1aqKqqkpGjhxJgoODxabjyBQlJSVSWFhICKmT5WMbfRcUFDCSGxDVX6ApmThxIjEzMyNpaWmU0e3t27eJubk5cXd3l/j+nzx5Qlq3bk3U1dVJv379SL9+/Yi6ujpp06YNT2m/hvz111/E2dmZsFgsSqNeSkqKuLi4kI8fP0q07ykpKURVVZVISUlxaMUuWbKE7zWbkZFByS81lHNprLxLQ+mBLl26EG1tbdKsWTNG5uz1pS7rP+cfPXrEyBuhd+/ePE3M4+PjiZ2dHW37jIwM0rJlSxIeHk66detG7OzshPLyEYaqqipy/Phx4uzsTFq0aEGMjY3JunXryJs3b77L/n/xf7x69YqsXLmSyMjIEAUFBdKsWTPSs2dPvte/nJwc5VOnqalJHj16RAip0yJmYhralAQGBhJXV1fy9etXatvXr1+Jm5sbh48aP+Tl5alnVcuWLSkpwOzsbKKtrS2RPtdHVKkJNunp6SQ+Pp7Ex8dTniySxtHRkWzfvp1r+44dO8igQYMa9Vl37twhM2bMIKqqqkRXV5esWLGCeHp6Ejk5OeLn58foM/755x/yzz//MN5nq1atqHO9/v06Pz+fVpaoqcdYDfn69SvZvHkzkZGRISwWi8jIyJCJEyeS169f83y/rKwsdc3XP/asrKzvIsmUlJREHBwcqGuvMUjimi0vL2csL9KpUyeeskihoaEC5VrrIw7PPmFQU1PjKZP99OnTH/5e/4tfCMvevXt5yiYePnyY55i7IS9evOB7f2D7oUkSNTU1kpqayrX95s2b38UXQxSEXcNj05Ryq4RIzquRKVFRUWTw4MF8peMF4enpyVOedMWKFYz9WKKiooirqyuxt7cnffv25XgJyy85rP8YUVFRWLVqFUJDQ+Hp6YmoqCjk5+cjJCQEUVFRGDdunMD2bFkiXvArWy8rK4O1tTWaNWuGOXPmUHJEOTk52L59O2pqavDgwQPakvOvX79i69atSElJwdu3b1FbW8vxd7pyKkVFRUoWpD6FhYWwtLTkmTVdHx8fH8TFxcHS0hKWlpZc8jCCSiiBugj6H3/8wdWOjpqaGqSmpsLCwgLS0tIcVTBMs0i0tLQQEhICT09Pjs8dN24cMjMzqVJqfohaBpiZmQl7e3tYW1sjOTkZzs7OyMrKwocPH5Camor27dsLbH/9+nWBf+/du7fAvysqKuLMmTNccmJXr16Fk5MTPn36xFdqhhCCLVu2IDQ0FK9fvwZQl+Hn7++PuXPn0maOiEte5Xty7do1xu+1s7Pj+zdRS4cPHjzIJc/Bxt/fHxs3bhTY/vfff8eaNWswcOBAODs7Q1VVFSEhIYiIiMDRo0cZZcWXlpbizZs36Ny5M3X/u3v3LpSVldGpUyeebZjKMkkiY4vNx48f4e7ujtOnT1P3nOrqajg7OyMmJkbkKhsmfP78Gfv376cylkxMTODm5tYoqaK8vDw8ffoULBYLJiYmfCsKxU1NTQ3++ecfqKmpUduKioogLy/PswJMSkoKpaWl0NLSEij1wkTepaH0gJSUFDQ1NdGnTx++51x9evfujdGjR8Pb2xtKSkp4/PgxDAwM4O3tjby8PFy4cEFge2VlZTx48IDru37+/DlsbGzw8eNH2j7cvn0bAwYMQNeuXXHmzBmh5alEoaysDPv27UNsbCxycnLg6OgIT09PODk5CRzLHD16lG/VY1OVbf8sVFVVITExEXv37sWlS5dgY2MDT09PuLq64t27d1i+fDkePHiA7OxsrraGhoY4duwYrKysYGNjg6lTp2L69Om4ePEixo0bx6gaJC8vj+8YkYm8T2NoWIV7+fJlyMjIUNUuGRkZ+PbtG+zt7WmrtNu2bYvz58/DwsIClpaWWLJkCVxdXXH79m04OjrSymkJgzilJt6+fYuxY8fi2rVrUFVVBVD3DOrbty8SEhIEVgOEhISgVatW8PDw4Ni+d+9evHv3DosWLRK475YtWyI1NZUrGzInJwc9evRAeXk5bd/j4+MRHR2NvLw8ODk5wcvLCwMHDqTGdzdv3oSjoyOtjKQwKCkp4cGDBzAyMuKQuUhPT8fAgQMF9l8c8iziID09HXv37kVCQgIUFBTg7u4OT09PvHz5EoGBgfjnn39w9+5drna//fYbfH19MWHCBI5jDwoKwqVLlyQin9hQdrSyshLV1dWQl5fnmp8Juuc0xTVbHxkZGWRlZfF8TpubmwuU6q2qqsL06dMREBAgtJSaKPCTa33y5Am6du3KV2r2Z+ZnkIf5r1FSUgKgriKMH+KS0gKAjh07YufOnejbty/H9mvXrmHatGl49uyZwPbNmjXDmzdvuOYg5eXl0NLSalSmvjBMmjQJDx484KrQnzp1Kn777TfExMRIdP+Npf7vVVtbi9jYWKHX8JpSbpXN/v37sWrVKmrtonXr1ggMDORY25MUVlZWeP78OaqqqqCnp8e1/ijonqWiooL09HQuRZW8vDzY2NjQPivnzJmDmJgYDBkyBDo6Olz3UaYqEA35JYf1E1JdXY2rV68iPz8f48ePh5KSEl6/fg1lZWVaDVwvLy/Iyclh+fLl+Pz5M8aPH4/WrVsjPDycNgACgGtSyYRWrVrh1q1bmDlzJpYsWUItDLFYLAwcOBDbt29npLnr6emJixcvYtSoUbC1tW20tJCMjAzKysq4giBv3rxh5K/y+PFjSje2oVYrk77UXyz++vUr1+IKv7LOZs2awcHBAU+fPoWBgQEsLS1p99WQs2fPwsHBASoqKhg1ahSqq6sxZswY5OTkMJKaErUM0NzcHLm5udi2bRuUlJRQUVGBESNGMNbBbBi8AMAlKyaIYcOGwcPDA6GhoZREyb1797BgwQKqZO/u3bs8fTtYLBYl8cEOlCkpKdH2mY2oQQ4DAwOB55ckJrmCAhuNQdTS4ZkzZ0JVVZVLZ93X1xcJCQm0QRAfHx+8efMGQF05t6OjI/bv3w9paWlGgzW2v4C2tjbHdjq9ayYPZEmVrbNRVVVFYmKi0EEE9kI+P5gMtuXl5TF16lTGfeaFkZER1efG3vNfvnyJU6dO8VzMFjTgXblyJTw8PLjKjgXJQBUWFlILfaKe96JKDwQHB2PQoEHIzs5GdXU1wsPDkZ2djVu3bjEKcLJYLJ5JAWzZmYbwm9zLyMjg9evXHGXy33Ny36pVK/Ts2RO5ubnIzc3FkydP4O7uDjU1NURHR/N8rtT3pUhMTOTypfgFf7y9vXHw4EEQQjBx4kRs2LCBQz9YQUEBmzZt4ivb0K9fP5w6dQpWVlaYMmUKfH19cfToUaSnp9P6QwHA7t27MXPmTGhoaEBbW5vjnGSxWGIPgjQMJI8cOZLj/4IWVhrSu3dvXLp0CRYWFhg9ejR8fHyQnJyMS5cuwd7eXiz9bYg4pSa8vb1RUVGBrKwsKhiRnZ0Nd3d3zJ07FwcPHuTbdufOnTwTaczMzDBu3DjaIMi///7LU7KrqqqKkU5227Zt0b59e3h4eGDy5Mk8AzaWlpZc8nbW1ta4cuUK1NTUaBc4Bd33evXqhbi4OEp2kcVioba2Fhs2bOBaLGuIOGXFhGHz5s2Ijo7Gs2fPMHjwYMTFxWHw4MFUkNnAwAAxMTF8n50rVqyAu7s7Xr16hdraWhw/fhzPnj1DXFwczpw5I5E+i0t2VJhrVpyed+3atcOVK1e4xnSXL1+mvfe0aNECx44d+y4yLrywtbXFrl27uIKrkZGR1H3ofw1RJWbrI+zY9hd1a2iBgYGIiIiggtqKiorw9vbGypUruRbH60tpPXjwgO99nsn8pLi4mGfQUU9PD8XFxbTtyf/3yWpIRUUFJTssSSIiIuDu7o7u3btzJdeFh4dLfP+NpeEYR5Q1vKaUW2Xj5uYGNze3Rns1igNR7l9ycnJITU3lCoKkpqYyOm8TEhJw+PBhDB48WOg+8OJXJchPxosXL+Do6Iji4mL8+++/yM3NhaGhIXx8fPDvv/8iMjKS8Wc1xUX0119/4fnz5yCEwMjIiCPLlg4VFRWcO3dOaN1BV1dXvHnzBomJidTk9ePHj3BxcYGWlhYOHz4s1Ocy5fPnz1i4cCEOHz7MM7NL0KKijY0N1q9fL9JkODk5GS4uLti3bx/27NmD58+fIzk5mVEAytjYGHFxcejatSt69uyJoUOHYvHixTh06BC8vb3x9u1bofvFhIZR4qqqKjx8+BABAQFYu3Yt7fdSUVEBX19fxMXFUZPl5s2bw93dHWFhYVBQUMCjR48AgK9B4tu3b6ksjU6dOgnMbBQnDQcW7GO/cOEC/P39sXjxYonuX9QqHFE4e/Ys3NzccObMGfTs2RNA3WLL8ePHceXKFUZZ8fX5/PkzZXQuyHiQTatWrfDlyxeh/QV+FOoHnpmSmJjI8X/2eRcbG8s48+T169e4efMmz6xsJgGgPXv2ICwsDHl5eQDqAiLz5s2Dl5cXbdsrV67A2dkZhoaGyMnJgbm5OYqKikAIoSrS+NGlSxdkZmbCzs4Onp6eGDlyZKOMra9fv44//viDK7heXV2NW7duMb5m3r59y/O7YxIIz8/Px7p16zjMFxctWsSVeckLJycnyMnJ4eDBg2jWrBmAuufT2LFjUVlZyeXt8aOZZpaVlVGZ3QUFBXBxcYGnpyf69++PyspKBAUFISEhgafxa6dOnbBy5Uq4urpyZCazfSm2bdsm8f7/rNjb28PLywsjRozge71UV1cjNTWVZ6C9trYWtbW11HWTkJCAW7duwcjICNOnT6f11tDT08OsWbNoF81/RD58+ICvX7+idevW1AI4+9iXL1/eqLFyU6CiooLLly9zBQru3r0LBwcHgdVjsrKyVJJPfQoKCmBqaiowox0A+vbtC3Nzc64F1dmzZ+Px48e01QQ3btwQylA2MDAQ/v7+kJeXp70HCrrviVop3ZQYGRlRwSN+CU3fvn3DwYMH4e7uzvPvN27cQFBQEMezasWKFXBwcJBk10VGmGt2ypQpiIiIgJKSEiZPnixwTEaXQLVjxw7MmzcPHh4e1Ng0NTUVMTExCA8Px/Tp0wW2d3d3R5cuXYT2kxGF1NRU9O/fH7///js1f7ty5Qru3btH+Yr9gjeijG1/dqqqquDo6IjIyEiuBVWmzJw5E8ePH0dQUBCHufiqVavg4uKCHTt2iLPLHOjq6mLbtm1cvoaJiYmYPXs2Xr58ybMdu6IhPDwcU6dO5fBYrampobxtU1NTudpKogIpLy+Po8L/e1Xo/+LnZN26dQgMDMTUqVM5Koj27t2LgIAA2nWs1q1b4+rVqzwTlUXhVxDkJ8PFxQVKSkrYs2cP1NXVqQn61atXMXXqVGqxSJJUVlbi2rVrPDMQJJnZbGpqioSEBKEqIQDg1atX6N27N8rLyylz+EePHqFVq1a4dOkS46y958+fIz8/H71794acnBzfyHxDZs+ejZSUFKxevRoTJ07E9u3b8erVK+zcuRPr1q2Dm5sb37YXLlzAkiVLsHr1avz2229cZWhMzcFOnjyJ0aNHw8TEBMnJyYwWggHxlAF+/PgRd+/e5bmoN2nSJEb9aMi1a9cwf/58LnM4flRUVFCVE4aGhrSVUwDw6dMnzJo1CwcPHqT63axZM4wdOxbbt2/nKSskrgxBQWzfvh3p6ekSl9PiJRvTmCocUTOWDhw4gDlz5uDSpUvYs2cPEhMTkZKS0qiH4bdv31BYWIj27dszqvpiU11djdOnTyMmJgbnz5+HoaEhpkyZAnd3d67qkPps27YNEydO/C6SU4IQJYjAjwMHDuDQoUNcQZKGxMTEUAuX6urqXFnZdBVMK1aswObNm+Ht7c0xUdm2bRt8fX0RFBQksL2trS0GDRqEwMBAajFbS0sLbm5ucHR0xMyZMwW2f/jwIaKjo3Hw4EFUV1dj3Lhx8PDwYGR2LWrJ+v379+Hu7o6nT59ySWoxkdMSlezsbPTu3RuqqqrUYsSNGzfwzz//IDk5mSO7/0fDyckJSUlJ6NixI7y8vDBp0iQuM/m3b99CW1ubZ2WrvLw8nj59Cj09PWhpaeHSpUvo3Lkz8vLy0K1bN1ppHTbZ2dk873kNJ7//KzS1vApQNw569OgRV7XvLySPkpISbty4wZVE8vDhQ9jZ2XHJjNbHyMgIK1euxIQJEzi2x8fHY+XKlbTPCnEsqIpSYS8O/v77b2zbto0jEMC0Urops8KLioqgq6vLNU4khKCkpAS6urp821ZXVyM4OBgeHh5o27atRPspiJqaGpw4cYKSBTY1NcWwYcMaNVZsKk6cOIHQ0FCq7yYmJvD398ewYcNo265ZswahoaGwt7fnOa+U5HweqJt/b9y4EY8ePYKcnBwlKSbs4vZ/BVHHtj87mpqaVLBRGFRUVJCQkMClMHDu3Dm4urryleapqqqCnJwcHj16JPQYeNGiRTh06BCio6OpZKhr167Bw8MDo0aNwqZNm3i2Y1cEXrt2Dd27d+dICJGWloa+vj4WLFjA8zv50ZKUfmbi4+MRGRmJwsJC3L59G3p6etiyZQsMDAwY3XMby/+ShN7hw4cRHh7O8azy8fHBmDFjaNuGhoaioKAA27Zta7QihCB+BUF+MtTV1XHr1i0YGxtzZCkWFRXB1NSUp46mOC+ihw8fYvDgwfj8+TMqKyvRsmVLvH//ntJJl6T+7Pnz5xEREYHIyEgumRKmVFZWYv/+/cjIyKAGXa6urox8OsrLyzFmzBikpKSAxWIhLy8PhoaG8PDwgJqaGkJDQwW219XVRVxcHPr06cOhuR4fH4+DBw/i3LlzfNvWn2DU/y3ZARhei2L8Sq3T0tLQoUMHjgAIXdl1Q27fvt2oMsDTp0/Dzc0NFRUVUFZW5loQZaL1zYucnBzY2NhIRKeZzdixY/Hw4UNs3bqVYzHWx8cHXbp0QUJCAlcbcWUICoKfh4m4EaUKR1wZS3/++Sfmz58PTU1NpKSkMM46+fz5M7y9vREbGwsAVOWct7c32rRp06gqmsb4C6ioqKCqqgouLi7w8vJCv379GO+nIcIGXUUNIvCjoKAAlpaWtNdcu3btMGPGDCxZskSg/wI/NDU1ERERweUJc/DgQXh7e+P9+/cC29f3LlJTU8PNmzdhZmaGjIwMDBs2DEVFRYz6UVVVhdOnTyM6OhpJSUno1KkTPD09MXnyZL5BLikpKZSVlXFVi+Xm5sLGxob2mu3cuTPat2+PRYsWoVWrVly/N93zTxy6wa9fv6YW5djPyjlz5nAFFH40PD094eXlRZ3zvCCEoLi4mOf3KKovRUFBAYYPH44nT55w+MKwf0NJB7CaEhUVFTx69EikIMiNGzewc+dO5Ofn4+jRo2jTpg3i4+NhYGBAVQPyw9PTE7///jtmzJgh9P5FQVQvmfz8fERHRyM/Px/h4eHQ0tLC+fPnoaurCzMzM0l1myI9PZ1v/+nGiMOGDcPHjx9x8OBBSu7s1atXcHNzg5qaGk6cOMG37YYNG7BhwwZs3LiRelZeuXIFCxcuhJ+fH5YsWULbd1EWVMVVYf/x40fKa8zf3x8tW7ak/A7btGnD6DMaS1NnhYv6rFFUVERmZqZAqUlJkpWVBWdnZ5SWllJ+lbm5udDU1MTp06dpFztFuWb79euH48ePUx46bP755x+4uLhI/Lf7Ufxk/ouIIjcrrrFtY6DzwqiPpAOvvr6+kJGREdqDQUtLC9euXePykHr69Cl69+6Nd+/e8W1raGiIEydOUL5fjeXbt2+YOHEijhw5QgVZa2trMWnSJERGRtJWu06ZMgXh4eGME1/FwY/02zclO3bswIoVKzBv3jysXbsWmZmZMDQ0RExMDGJjYxlJyzeW+utHX79+xZ9//glTU1NqfpOWloasrCzMmjULISEhYt9/fWpqahAWFsZ3jCjsOh4Thg8fjpSUFLRs2RJmZmZca7aNXcNk8+OnOfyCg9raWp4Px5cvX/L1Kaiv40Z3EdHh6+sLJycnREZGQkVFBWlpaWjRogUmTJgAHx8f4Q6KITY2Nvj69SsMDQ0bbWDHRkFBAdOmTRNq/76+vmjRogWKi4s5Hp5jx47F/PnzaYMgHz58oDIUlZWVqf727NmTNnNDmJsrvwW6gQMHNvqzGtK9e3eBi0wN8fPzg4eHB4KDgznKOJny+PFjjv8TQvDmzRusW7eOr3yVuDhz5gySkpI4FmAGDhyI3bt3w9HRkWeb+oENSWVWHD169LssSPI6jwYMGABpaWnaKpwlS5ZgwYIFVMbSsWPHODKWeMFvwKWpqQlra2v8+eef1Da6AdeSJUuQkZGBq1evcuyvf//+WLVqVaOCII3xFygtLcWRI0cQHR2NAQMGQFdXl5KMYFpxVl5ejrFjxyI5OZkj6Orp6cko6Lpjxw7s3r2bI4jg7OwMS0tLeHt7CxUE+fLlCyIiIhgt6Hz+/Bnjxo0TKgAC1AUfbGxsuLb/9ttvPPXfG6KgoEAN0nR0dJCfn08tStAFUOpDCEFVVRW+ffsGQgjU1NSwbds2BAQEYPfu3Rg7diz1XnbgmcViYfLkyRySQDU1NXj8+DEjSbWCggIcO3ZM6BJzfrkt//77L+0ki03r1q0RHBzc6H03NJ1lw2KxICsriw4dOmDy5MmYMmVKoz+bCXv27OHa9vHjR46FJhaLxTeQJKovhY+PDwwMDHDlyhUYGBjg7t27KC8vh5+fH98sv/8VXFxccPLkSaHlVY4dO4aJEyfCzc0NDx8+xL///gugLhAfHBwsMFEEADp06ICAgADKcLfhGFGSmc2ieslcu3YNgwYNQo8ePXD9+nWsXbsWWlpayMjIwJ49e3D06FGJ9R2okx6bNGkSBg4ciIsXL8LBwQG5ubkoKyvD8OHDaduzJT709fWpZ1xJSQnMzc2xb98+gW39/f1RXl6OWbNmUfdsWVlZLFq0iFEABKiTMNy/fz+j9zbEx8cHNjY2yMjIgLq6OrV9+PDhjD2tHj9+jP79+0NFRQVFRUWYOnUqWrZsiePHj6O4uBhxcXFc72eKoOp3YcZY4oTfs4apRr29vT2uXbvWZEEQLy8vmJmZIT09nZKv+uuvvzB58mRMmzYNt27d4ttW1Gv26tWrXAtJQN08XRKG8A353n4y//zzD7V4S5cI8j0XeZuChkHhhnKzghDX2LYxNPRW4Ic4s7T5UV1djb179+Ly5cs8K5jo5oVz5szB6tWrER0dTY3R//33X6xduxZz5swR2HbZsmVYunQp4uPjhZp/S0tL49ChQ1izZg0VsLewsGCc2Ctp5Qde/Ei/fVOydetW7N69Gy4uLhwBOBsbGyxYsEAi+6y/fuTl5YW5c+dS3mH131NSUiKR/dcnMDAQUVFR8PPzw/Lly7Fs2TIUFRXh5MmTjPz22EkiBQUFWLBgQaOSRFRVVRmNQxvLr0qQn4yxY8dCRUUFu3btgpKSEh4/fgxNTU0MGzYMurq6tDdILy8v6Ojo8L2I9u7dK7C9qqoq7ty5A2NjY6iqquL27dswMTHBnTt34O7uTmkESoL+/fujuLgYnp6ePLNj+enNigttbW0kJSWhc+fOHFU4TDOjLS0tsXXrVtjZ2aF///7o0qULNm3ahIiICGzYsIGvFqSosMvSNTU1IScnx7jdqVOnGL+XTuJDQUEBT548EVqmgp010/B21a1bN+zdu7fR3hCNQVdXF2fPnuXS0n/8+DEGDx5M+7uVlJSAxWJR5f53797FgQMHYGpqyigg17CSixCC0tJSvHv3Dn/++afQQT1RYVKFI0zGEp0RKBsWi0WbKaenp4dDhw6hW7duHNfs8+fPYW1tzaiKRhR/AaBuQTsmJgZxcXF4+fIl+vfvD09PT7i4uAisQJs0aRLevn2LqKgomJiYUH1PSkrC/Pnzac1qVVVVce/ePa5M2NzcXNja2grUaAe4F7MJIfj06RPk5eWxb98+2mt+4cKFaNmypdCeNd7e3mjRogXXhGbBggX48uULtm/fLrC9i4sLhgwZgqlTp2LBggVITEzE5MmTcfz4caipqeHy5csC29+/f5+Sw5KRkcGkSZPg5eVFBSa2bt2KNWvWoKysjGrDXtiPjY3FmDFjOO637JL1qVOn0soQuri4YOLEiVxGy3REREQAqAvYr169mkPGpaamBtevX0dRURHjSY0whIWFYe3atRg0aBCl/Xr37l1cuHABvr6+KCwsRHx8PLZu3cp4gbExrF+/Hvr6+lRwasyYMTh27Bi0tbVx7tw52gw+UX0pNDQ0kJycDEtLS6ioqODu3bswNjZGcnIy/Pz8JPrdNzWiyqtYWVnB19cXkyZN4rhfP3z4EIMGDUJpaanA9k2Z2Syql0z37t0xevRozJ8/n6P93bt3MWLECImND9lYWlpi+vTpmD17NrV/AwMDTJ8+HTo6OowkNQghuHz5ModWeP/+/Rn3oaKiAk+fPoWcnByMjIwa5cMkCsJU2Dekf//+sLa2xoYNGzg+49atWxg/fjzXWKf+mLbhcxZgLjnaFFnhgGga9fWJjIxEYGAg3NzceN4zJC0fKCcnh/T0dK6qjczMTPz+++/48uUL37bCXrPsAFiXLl2QnJzMsZhaU1ODCxcuYOfOnTx/u5YtWyI3NxcaGhp8Ew7YSDIzVxjqVw3xq4QQpG7wX4CJ3KyoY9ufHUFzRCbzwuHDh+PKlSuQkZGhxoMZGRn49u0bl7JBwwxzKysrPH/+HFVVVdDT0+O6X/3okkRNmc3/syMnJ4ecnBzo6elx3O/z8vJgaWkp8FkhDlRUVJCens41p8/Ly4ONjQ1fGTdx0b59e0RERGDIkCEc446IiAikpaXhwIEDfNs2TBJ59uwZDA0NsXz5cp5JIt+LX5UgPxmhoaEYOHAgZRY4fvx45OXlQUNDAwcPHqRtf+TIEaSnp3NtnzBhAmxsbGiDIC1atKAye7W0tKiqCBUVFYlHIm/duoXbt28LXYYoKpWVlTyrGD58+MBosjZlyhRkZGTAzs4OixcvhpOTE7Zt24aqqirGJYSfP3/m+eASlClGCEGHDh2QlZXVKA3N+hVEgmAyYB04cCDS09OFDoI0zFiSkpKCpqYmo0wzUVm+fDnmz5+P+Ph4ygeitLQU/v7+CAgIoG0/fvx4TJs2DRMnTkRpaSn69+8Pc3Nz7N+/H6WlpbQR9Ia/A/vY+/TpI9HgDxtRqnCEyVgSZ0npu3fvuGQagLprmUnWSn1/galTp3L5CygoKMDPzw8bN27k+xmGhoYICgpCYGAgLl++jJiYGEyePBkKCgp4+/Yt33YXL15EUlISl1a2kZER34BLfSZOnIgdO3Zw3Vt27dol0H+ITVhYGMd3xD7vunbtysikNyQkBEOHDsWFCxd4ZmXzuufVrwJisViIiorCxYsX0a1bNwB1RmrFxcWMPIQ2b95MBegCAwNRUVGBQ4cOwcjIiPZ+a2FhgZycHDg4OGDPnj1wcnKiDMLZuLq6clU/spMQ2Pq8DSdITImKioK7uzsyMzNhbm7O9d3xWxgKCwsDUHeNRkZGcvSZHYRhKu0iLDdv3sSaNWu4JIl27tyJixcv4tixY7C0tERERIREgiCRkZFURvilS5dw6dIlnD9/HocPH4a/vz8uXrwosL2UlBRH9dK4ceMwbtw4xvuvqamhqnI1NDTw+vVrGBsbQ09PD8+ePRPiiH4e9uzZA1VVVdy/f5+rQpDFYtEGQZ49e0bpZNdHRUWFNmgLfP/M5voUFxdTVV5ycnL49OkTgLr7cLdu3WiDIE+ePOE5idTS0pJYdm998vPzMWTIEAB19wr2M9LX1xf9+vVjFARhsVgYMGAABgwYIFQfFBUVGXkuiRthKuwbcu/ePezcuZNre5s2bXgG7+qfqw8fPsSCBQvg7+/PIV0ZGhqKDRs2CNxvU2SFs/sM1D1rnjx5wqVR37lzZ0bZsWwFAl7P5O+xGN6xY0eUlZVxBUHevn1LW4kp7DXbpUsXsFgssFgsnlKpcnJy2Lp1K8+2YWFh1DnZcIwmDN/TT6Z+wEcS0jH/C3Tr1o02sU2Use3/AqKeO6qqqlwJRkwr9Jmui/yoiJrN/1/GwMAAjx494qrauXDhApe0miSQk5NDamoq1zpeamrqd1kLKy0tpZKBFRUVqaDL0KFDadfC5s+fj8mTJ1NJImwGDx6M8ePHM9q/JHzbfgVBfjLatm2LjIwMHDp0iDLQ8/T0hJubG6Msf1EvIisrKyq72M7ODitWrMD79+8RHx8vcbPUTp06STzSKohevXohLi6OqqJhsViora3Fhg0bGGWv15eI6N+/P3JycnD//n106NCB1uz93bt3mDJlCs6fP8/z74ImClJSUjAyMkJ5eXmjgiC8TGMbQ/1KkiFDhsDf3x/Z2dk8F0Tpsr2E9YARBzt27MDz58+hq6tLmTwWFxdDRkYG796945j48soCyczMpDKiDx8+DAsLC6SmpuLixYuYMWMG7cCjqY3K2BM2flU4gujWrRtu3rwJExMTDB48GH5+fnjy5AmOHz9OLWxLEhsbG5w9exbe3t4A/i+7MioqipGcG1s7VtB7NTU1GS2+sVgsNG/enPouq6qqBL5f1KArULcoyS+IUD/gwGviNHnyZEb74EdISAiSkpIone2GPkC8aJgl/9tvvwGoW6AD6haVNTQ0aKtgAHAEXBUUFBq1+D9mzBh4eHgILNHV0NDge48U9Zq9ffs2UlNTed7vBS0Msc/Dvn37UlmB35ukpCSsX7+ea7u9vT38/PwA1A18ha0QoqO0tJSa0J45cwZjxoyBg4MD9PX10bVrV55tHj9+DHNzc0hJSdHK1NA9q83Nzaks+q5du2LDhg2QlpbGrl27/ucNu0UNQmhra+P58+dc0jg3b9784b87bW1tfPjwAXp6etDV1UVaWho6d+6MwsJCvpJB9VFVVcWbN2+4qlkePnwoMT+J+qipqVGBmzZt2iAzMxMWFhb4+PEjbSVEbW0tYmJicPz4cRQVFYHFYsHAwACjRo3CxIkTf3iZDAcHB2zZsgW7du0CUHePraiowMqVKzF48GBGnyEjI8OzspTtL9GQ+mPa0aNHIyIigmNflpaWaNeuHQICAgQuvDXVGIu9ECmqRr2o8wxRCQkJwdy5c7Fq1Srq+0pLS0NQUBDWr1/P8Zs2PEZhr1n2PYFdNVL//JCWloaWlhZX0gWb+ooHoo7R6PxkxI2dnR31bwMDA7Rr147r3sBWLvgvwlRuVpSxrbgQxT+qqRFFUqqp5+Oisn//fuzevRtDhgzBqlWr4Orqivbt28PS0hJpaWkSlQz92Zk/fz5mz56Nr1+/ghCCu3fv4uDBgwgJCUFUVJTE9z9v3jzMnDkTDx48oNaU7ty5g7179zJKyBWVtm3b4s2bN9DV1UX79u1x8eJFWFtb4969e7RrEo1NEmlIQ9+2AQMGQElJCevXr2+Ub1tDfgVBfkKaN28ONzc3Rtm8DRH1IgoODqYmSmvXrsWkSZMwc+ZMGBkZ8dTiFifr1q2Dn58f1q5dy3MhXdIaohs2bIC9vT3S09Px7ds3LFy4EFlZWfjw4QNtyTcv9PT0GC/uz5s3Dx8/fsSdO3fQp08fnDhxAmVlZZT8BB3r1q2Dv78/duzYIVHKRbMAAQAASURBVPFgFRteEzdePgT8FvXY0i5MkOSDW9TMj6qqKuoBcfnyZSrg06lTJ7x584Znm8aYnUv6vBelCqepM5aCg4MxaNAgZGdno7q6GuHh4cjOzsatW7dw7do12vai+gsAdXJo0dHRiImJQXFxMXr37o3du3fTSh2JGnTNzMykJrINgwiZmZkc/WcjLp1yoK5qce/evY2aqP8o2YENn4U1NTV48uQJ9PT0GAcWRDFJ9vb2xoQJExAQEIBWrVo1rvNo2u+xZcuWOH36NJcvxOnTp6ks0MrKSsYZ1o1FTU0NJSUlaNeuHS5cuIA1a9YAqFtc4Rc86tKlC0pLS6GlpcU36Aswy0xevnw5KisrAdQ974YOHYpevXpBXV0dhw4dEvHo/reZOnUqfHx8sHfvXrBYLLx+/Rq3b9/GggUL+I5P58+fj9WrV0NBQYHWwFOSzxxRvWTGjRuHRYsW4ciRI9S9PjU1FQsWLGBU+SYqvXv3xqVLl2BhYYHRo0fDx8cHycnJuHTpEpdESH0IIXB2dqak5iwsLEAIwdOnTymJlpMnT0q8/6IgaoU9UJfIExQUhMOHDwOou1cUFxdj0aJFtM/6J0+e8JRyMzAwQHZ2tsC2TT3GEmVBsaqqCnJycnj06NF3m5c0ZOjQoQDqEh/YYyH2vd/JyYn6P697v7DXLHu8KGoASFRT+qb0kzEwMODZ9w8fPsDAwOB/Xg6LTm6WCd++fcPbt2+5ziN2sp6kENU/ShyIEoT58uULCCFUktmLFy9w4sQJmJqawsHBQWDbe/fuoba2liuhhi3/x8vH8EdClGz+/zpeXl6Qk5PD8uXL8fnzZ4wfPx6tW7dGeHh4o6rFhWXx4sUwNDREeHg4dY8wMTFBdHQ0xowZI/H9s2XkunbtSs1R9+zZg+LiYlofwMYmiTREHL5tvPjlCfIf5PDhwwgPD8fTp08B1F1EPj4+3+UiEgW2RAWvzJHvpSH6999/Y9u2bVQVjrW1NWbPng0dHR2e7xfXQr6Ojg4SExNha2sLZWVlpKeno2PHjjh16hQ2bNiAmzdvCvxsNTU1fP78GdXV1ZCWluaqGmKiA3nlyhWEhYVxnDfz5s1rlOZzYxCk710fSWt9i0rXrl3Rt29fDBkyBA4ODlSGaFpaGkaNGsVTN5ifXm59/uvauUwpKChASEgIxzW7aNEiLo8XXgjrL/Dt2zccP34ce/fuRXJyMnR0dODu7g4PDw/GGc2ZmZmwt7eHtbU1kpOT4ezszBF0bd++PfMvgSH8vHcawuS809bWxo0bNxpVfSYq4tLLnjdvHiwsLODp6YmamhrY2dnh1q1bkJeXx5kzZ9CnTx+B/ahvkrxr1y4uk+S1a9cKbF9fb1VYvqfMRX12796NmTNnYvDgwVSixb1793Du3DlERkbC09MToaGhuHv3rkSCAnPmzMGZM2dgZGSEhw8foqioCIqKikhISMCGDRt4BqBevHgBXV1dsFgsWqk5YaoSP3z4QHs+/q8gynlHCEFwcDBCQkKo6gMZGRksWLCAy8eOTd++fXHixAmoqqrSBoclGRwU1Uvm27dvmD17NmJiYlBTU4PmzZujpqYG48ePR0xMDN/McHHx4cMHfP36Fa1bt6aC7ez+L1++nG/wNzo6Gj4+PkhMTOT6/pOTk+Hi4oJt27Z9l0DO8+fPkZ+fj969e0NOTo7Lb0MQ1dXVSEhIwOPHj6lxAtMKe6BubjBq1Cikp6fj06dPaN26NUpLS9G9e3ecO3dOoDSitbU1zM3NERUVRZ0n3759g5eXFzIzM384nfkRI0YgJiYGysrKtAE+ugVJQ0NDnDhxoslkjpkkw7CpX8kACHfNnjp1CoMGDUKLFi1ofRfpKuSlpKSo4H19Xr9+jfbt29MqJzSVnwy772VlZVwLYC9evICpqSmVSPC/SmxsLMf/GyM3m5ubC09PT9y6dYtj+/eaE4rDP0oU6IIwdIFZBwcHjBgxAjNmzMDHjx9hbGwMaWlpvH//Hps3b8bMmTP5trW1tcXChQsxatQoju3Hjx/H+vXrcefOHbEco6QwNjZGXFwcunbtip49e2Lo0KFYvHgxDh06BG9vb4ESzf9lqqurceDAAQwcOBCtWrXC58+fUVFRwVNu+79CWloaNUZkJwzww8vLC+Xl5Th8+DBatmyJx48fo1mzZnBxcUHv3r2xZcsWge3F4dvGi19BkF80isLCQlRXV/M05mnRogWXjIE4oRusNhygAtzZFoIQtChWVVUFR0dHREZGNmpRT1wL+crKynj8+DH09fWhp6eHAwcOoEePHigsLISZmRntDaDhgKshdKbyf/75J3x8fDBq1ChKGigtLQ1Hjx5FWFgYZs+eLbB9cXExWrVqxVUyxy59lnTmirioqKjgyrqhq8S4evUqhg8fjn/++Qfu7u6UhNTSpUuRk5PDc5IoysRM3MydOxcdOnTgCtJt27YNz58/F/jwasqsmaqqKkyfPh0BAQGMr8OGGBgYYP/+/fjjjz9w6dIljBkzBocOHaIykPj5C7Rs2RKfP3/G0KFD4enpiYEDB3L4DDClsUFXUWHiNcKGbjE4JCQEb968aVQgWFRiY2Mxbtw4yMjIICYmRuC9X9A9r23btjh58iRsbGxw8uRJzJ49GykpKYiPj0dycjJt5Z+oJsnu7u7o1asXvLy8BB8wH+hkLngZR1pZWTF+VtItyqWmpmLbtm2UB4axsTG8vb0pzwRJUlVVhfDwcJSUlGDy5MmwsrIC8H9a6sJ+p7+gR5jzjk1NTQ1SU1NhaWkJeXl5PH/+HBUVFTA1NRVa7/dngT0O0tTUxPv37/HkyRNUVFTAysrquwaRhcHBwQH9+vXjK28XHByMa9euISkpSWJ9KC8vx9ixY5GcnAwWi4W8vDwYGhrCw8MDampqjKqlxcXNmzc5AilMkoTu3r0LJycnEEKoCkt2VeaZM2eoYDIvmmKMNWXKFEREREBJSQmTJ08W+NygW5Dcs2cPjh8/jvj4eA6/te9BdXU1goOD4eHhweW9Roew12z9wIWgMaGgxWz2mMrX1xerV6/muD/W1NTg+vXrKCoq4pIXbYi2tjZSUlJgYmICU1NTrFu3Ds7OzsjIyECPHj2oCiNxwq7WCw8Px9SpUzkkX2tqaqjzVhh1hf8KPXr0QPPmzbF48WLo6OhwXX+SDigqKCggKysL+vr6UFdXx9WrV2FhYYGnT5+iX79+fBUOxIWoQRgNDQ1cu3YNZmZmiIqKwtatW/Hw4UMcO3YMK1asoBI9eaGoqIjHjx9zJbMVFhbC0tKSUkqpjzgr7EVl8eLFUFZWxtKlS3Ho0CFMmDAB+vr6VDb/unXrJLr/nxl5eXk8ffq0SeXZm5KQkBC0atUKHh4eHNv37t2Ld+/eYdGiRXzbipIkAtSt5aampsLU1JRjTn3z5k2MHDkSZWVlQh3TryDILxqFnZ0dPDw8uBaQ9u3bh6ioKFy9erVpOsYHusX/+tAFAjQ1Namo5/fm999/x5o1azBw4EA4OztDVVUVISEhiIiIwNGjRym5G0nRtm1bLF68GHPmzOHYvn37dgQHB+PVq1cC20tJScHExASnTp3iyG4uKytD69atf+hqhsLCQsyZMwdXr17F169fqe2NybqpqanBP//8w5HhU1RUBHl5+R8+k6BNmzY4deoU5c/A5sGDB3B2duZZycKmqbNmVFRU8OjRI6GDIHJycsjNzUW7du3g4+ODr1+/YufOncjNzUXXrl3x119/8Wy3efNmTJw4kVGZJz+Ki4t5aiaz/0YXOPz69Su2bt2KlJQUniXzks4uHT58OJKTk6Gurg4zMzMu+cIfWTdYVlYWz58/R9u2bTFt2jTIy8tjy5YtKCwsROfOnWnl6uoPlrW0tHDp0iV07twZeXl56NatG8rLywW2X7t2LbZs2YIhQ4bwlH6kk/+ztbXFoEGDKJmLjIwMDpkLXplujcne+9l1kRtCl5FbH7rs3Ka+7poSYc67+sjKyuLp06dC3689PDwQHh7OJbVWWVkJb29vWg+rxiIuL5na2lrIysoiKyvru44v//nnHyqJg+6exi/ZQ1tbGxcuXECXLl14/v3hw4cYNGgQI91nYZk0aRLevn2LqKgomJiYUBPkpKQkzJ8/n5GHVF5eHt9r9nsYxlZWVmL//v3IyckBUFdpPX78eNrFgaYeY4mKlZUVnj9/jqqqKujp6XEdr6Tvl0pKSnjy5EmjE/ia6poF/i+57sWLF2jbti1HxYm0tDT09fURFBTE1wOLjYuLC4YMGYKpU6diwYIFSExMpCTs1NTUcPnyZbH3nV0txvbaq18hx+77ggULfvjgrzj4+vUrHj9+zPOeI2icoaCggPv376NTp06S7iJP2rZti/Pnz8PCwgKWlpZYsmQJXF1dcfv2bTg6OlISS5JC1CCMvLw8cnJyoKurizFjxsDMzAwrV65ESUkJjI2NBSaVqqur48yZM1w+kbdu3cKQIUN4zgnpKuzZf2sKZYfGZPP/1+nTpw/mzZsnskT6z4q+vj4OHDjAlcx2584djBs3jpEnoDBJIgAwduxYqKioYNeuXVBSUsLjx4+hqamJYcOGQVdXV2hZzl+eIP8xampqEBYWxldLkU4W6eHDh+jRowfX9m7dunEtkIsDUSPodIGNxsDWv2uKSLmPjw/1YF+5ciUcHR2xf/9+SEtLIyYmplGf9fXrV67fna6a4ePHjzw1Yh0cHARGf+tjYmICW1tbHD58mENj+kePw06YMAGEEOzduxetWrUSStKkWbNmqK6upmTLjI2NJVo1JU7Ky8uhoqLCtV1ZWRnv378X2DY7O5unwaKVlRWt1jUgWuYBUDfJO3nyJK1eJT+E8RcA/i/b7d69ezh48CByc3MBAB07dsT48eMZZWfy00wuLy9npJns6emJixcvYtSoUbC1tf3uUjyqqqqMtPAlhSh62a1atUJ2djZ0dHRw4cIF7NixAwDw+fNnRrI0opokR0VFQVFREdeuXeOqCmOxWLRBkKdPn1Ja9s2bN8eXL1+gqKiIoKAgDBs2jOditCiBjR/Jw0gYmE5qmExSm/q6a0qEOe/qY25ujoKCAqGDILGxsVi3bh1XEOTLly+Ii4sTexBEXF4yUlJSMDIyQnl5+Xdd/FNTU6PukaqqqjzPVbrFmQ8fPgj0LWrVqhXfZAFxcfHiRSQlJXFl8xsZGTGqbmRL+GloaEBbW5vje2CxWHyDIOL0rVNQUEDPnj2hq6tLjc+vXLkCQPCCqKhjLFFZs2YN3NzchL5mm3pBqV+/frh27Vqjx+PiuGbj4uIwduxYrgr5b9++UZI/vGAvNvXt25cKWAhDU/jJsCUJp0yZgvDw8B9yPPA9uHDhAiZOnMgzIYbueWFqako795IkwvpHiQs1NTWq4qJNmzbIzMyEhYUFPn78yEgWp0OHDjh58iSGDx+OpKQkan749u1b2vPRwcEBS5YsQWJiIjUv/vjxI5YuXYoBAwbwbMNkcfh7wEsdoVu3bujWrVsT9+znYNasWfDz88PLly/x22+/cQXsJV3F09SUlpbyVKHQ1NSkDTyy1WB69uyJnj17UtuZqsGIw7eNJ+QXPxXFxcWkpKSE+v+dO3eIj48P2blzJ6P2AQEBREdHh2zatInIysqS1atXE09PT6Kurk7Cw8Np2ysrK5MHDx5wbU9PTyeKiorMD4QhLBaLSElJERaLJfAlJSXVqM/98uUL+fvvvzledMyZM4coKyuT3377jUybNo34+vpyvL4nlZWV5P79++Tdu3eM3l9RUUFmz55NNDU1iZSUFNeLDldXV7Jhwwau7Rs3biRjx46lbS8lJUXKysrI5s2biYyMDHWulZaWNvq3+94oKCiQnJwcodtXVFSQKVOmkGbNmlHna/PmzYmHhweprKwUY08lg5mZGdm6dSvX9oiICGJiYiKwbcuWLcmtW7e4tqemphJVVVXafevp6ZHU1FSu7WlpaURfX5+2/erVq4mqqioZOXIkCQ4OJuHh4RwvOmbPnk309PRI//79ibq6Ovn06RMhhJCDBw8SKysrgW39/f0Ji8UiSkpKpHPnzqRz585EUVGRSElJkYULF9Lum8Vikbdv33JtLyoqIvLy8rTtlZWVyc2bN2nf96NSUVEhUnsWi0XKysq4tr969YrIysoKbLty5UqioqJCOnXqRHR1dcnXr18JIYTs2bOHdOvWjXbfnp6eZNWqVYQQQrZt20bk5ORI//79iaqqKvHw8BDiaBpHq1atSHZ2NiGEEBMTE5KYmEgIIeTRo0dEQUFB7PtjP4OZvP7X+dmvO1EQ9bw7f/486dKlCzl9+jR5/fo14zHa33//TT5+/EhYLBZ5/vw5R5sPHz6Q2NhYoqOjI56DrEdRURGpra2l/i3oRcepU6dIz549yZMnT8TeT35cvXqVVFVVUf8W9OKHlJQUz+cUm+8xxlNUVCS5ubnUv/Pz8wkhhNy7d4+0bNmStr2uri5Zt25do/err6/P6GVgYCDwc/Lz84mlpSXHfIfpPVPUMZaoWFpaEikpKdK9e3eyfft2xnOSH4UdO3YQbW1t4ufnRw4cOEASExM5XoIQ9Zplz4sa8v79+//5Z+XevXvJ58+fm7obTUaHDh3IrFmzSGlpaaPbXrlyhXTv3p2kpKSQ9+/fN3otQ1TKy8vJq1evCCGE1NTUkJCQEOLk5ETmz59PPnz4IPH9u7q6ktDQUEIIIUFBQURTU5N4eXkRPT09Mnz4cNr2R44cIS1atCBSUlKkf//+1Pbg4GDi6OgosO3Lly+JoaEhUVFRIX369CF9+vQhqqqqxNjYmBQXF4t2YN8BZWVlUlBQ0NTd+Cnht/YozBrkz0iHDh1IfHw81/a4uDjaMQ6LxSKmpqbk+fPnHNsbMz6sqqoi8fHxxN/fn8ycOZPs3r1b5GfILzmsn4xevXph2rRpmDhxIkpLS2FsbAwzMzPk5eXB29ubtmy7ffv2iIiIwJAhQzhM0SIiIpCWloYDBw4IbO/k5AQ5OTkcPHiQyoitqanB2LFjUVlZifPnz4vtWAHxatRXVlZi0aJFOHz4MM/sC7oMT0GmmywWS6DedVPD1rRfvXo1Jk6ciO3bt+PVq1fYuXMn1q1bBzc3N6429bPc/vnnH2zatAk9evTg8ARJTU2Fn58fli9fLnD/9XVwz58/D1dXV4wePRorVqyAvr7+Dy2H1bdvXyxbtkxoA/jp06fj8uXL2LZtG1VFdfPmTcydOxcDBgygssx/VPbu3Ys5c+bA398f/fr1A1CXoRgaGootW7Zg6tSpfNu6urrizZs3XFkzLi4u0NLSwuHDhwXum588SkFBAZURIAhB2Yl0PjyA8P4CsbGxmDFjBjZu3Ijp06dTckZVVVXYsWMHFi1ahJ07d/LM9BOXZrKpqSkSEhJ+2uwURUVFjBkzBh4eHhyZI3SISy/76NGjKCkpwejRo6kM49jYWKiqqmLYsGEC24pqkiwqospcNLZitH61SlFRERYvXozJkydTz4rbt28jNjYWISEhYq3O/BH52a87URD1vKuvkV8/G5/QVCOw5Sb4wWKxEBgYiGXLljXyiL4fampq+Pz5M6qrqyEtLc1lyE1Xpd1USElJYdCgQVzZ7Gz+/fdfXLhwQaJjvMGDB+O3337D6tWrKakEPT09jBs3DrW1tTh69KjA9srKynj06BGXzvv3wsnJCc2aNUNUVBQMDAxw584dfPjwAX5+fti0aRN69erFt62oYyxxkJWVhf379yMhIQEvX77EgAED4ObmBhcXF47xy4+IsL4cgOjXLD9z8IyMDPTt25fRNf/y5UucOnWK53OarpqjKT37WrVqhS9fvmD06NHw9PT8Ln5hPxLKysp4+PAhhzw0U9jnbMNnHt1z8n+FDx8+4OvXr2jdujVqa2uxYcMGany9fPlyRpVRpaWlePPmDTp37kx9n3fv3oWysjKtzBhbujAjIwNycnKwtLSEq6srl2ytILKzs3les3Ryq6Li7u6OLl26CK2O8F+Gbj1Skl4hVVVV6NSpE86cOQMTExOJ7UcQGzZswIYNG7Bx40aOtaCFCxfCz88PS5Ys4dtWSkoKI0aMQEpKCocaTFlZGXR0dLjkAL8Xv4IgPxlqampIS0uDsbExIiIicOjQIaSmpuLixYuYMWMG7aKegoICnj59Cl1dXejo6ODs2bOwtrZGQUEBrKysaLUcs7Oz0bt3b6iqqlID8xs3buCff/5BcnIyzM3NxXas4kaYQMCPwsiRI2Fra8sl/7Nhwwbcu3cPR44cEdheV1cXcXFx6NOnD5SVlfHgwQN06NAB8fHxOHjwIM6dO8fVRlym7gBnEASoO4+cnZ2hoKCAzMzMH3rQlp+fjxkzZmDChAkwNzfnGujQLXZpaGjg6NGj6NOnD8f2lJQUjBkzBu/evRN3l8XOjh07sHbtWrx+/RpAnTbkqlWr+Jbrs3n16hV69+6N8vJyKoDw6NEjtGrVCpcuXUK7du0EtjcyMsLKlSsxYcIEju3x8fFYuXIl7XnXVNja2sLV1ZXvQHPz5s1ISEjA3bt3uf4mLs3k8+fPIyIiApGRkd/NyE2c5tonT55ETEwMzp07B319fXh4eGDSpElo3bq1wHbi0ssWBX5+LoRh6S8hBEePHuWrUU/np1JQUICKigpYWlqisrISfn5+1CRx8+bNtOfDihUrEBUVRQW4ly1bhqKiIpw8eRIrVqwQKO1ib28PLy8vuLq6cmw/cOAAdu3a9cP5hgF1gbNp06ZBVlaWVuKGTtamKa67HwVRz7uG0m8NsbOz49uOEIJ+/frh2LFjHAbL0tLS0NPTo71vCIM4vWTo/OskETwUh2HrlClTGLUXVrOZCZmZmbC3t4e1tTWSk5Ph7OyMrKwsfPjwAampqbQLjZ6envj9998xY8YMsfSHPa1m+izU0NBAcnIyLC0toaKigrt378LY2BjJycnw8/MTGLAXdYwlblJTU3HgwAEcOXIEX79+pZVKFFWiuSkR9pplj5MyMjJgZmZGJUsAdd9HYWEhHB0daQNYV65cgbOzMwwNDZGTkwNzc3MUFRWBEEJdC4JoSj+Z6upqnD59GjExMTh//jwMDQ0xZcoUuLu7Q1tbW2L7/VHw8PBAjx494Onp2ei2wj4nxYUoUrM/Es+fP0d+fj569+4NOTk5KogkSQoKCjB8+HA8efKEQz6TvV9Jf3dr1qxBaGgo7O3teUo60Y1vf9F0tGnTBpcvX26yIAghBIsXL0ZERAT1nJaVlcWiRYtoE/DZ94z9+/djyZIl2LBhA+bOndsoX+Bnz55h69atePr0KYA6if05c+aI5I30Kwjyk6GoqIjMzEzo6+vD2dkZPXr0wKJFi1BcXAxjY2N8+fJFYHtjY2PExcWha9eu6NmzJ4YOHYrFixfj0KFD8Pb2xtu3b2n78Pr1a2zbto0jCj5nzhyOiackiIuLE/h3ugVZYQIBPwqamppITk6GhYUFx/YnT56gf//+KCsrE9heUVER2dnZ0NXVRdu2bXH8+HHY2tqisLAQFhYWlC6spOjbty9OnDgBVVVValt5eTlGjBiBGzdu8IwCi2OCLg7S0tIwfvx4FBUVUdsaY2QmLy+P+/fvcz24srKyYGtri8rKSkl0WyK8e/cOcnJyHNn1dIiSNSNK5kFDGrswIQoKCgp48uQJ38zSgoICWFhYCPztRdVMfvfuHcaMGYPr169DXl6e6/uWxOKCJMy13717h/j4eMTExODp06cYOHAgPDw84OzszLF40BBR9bJFQdRJoo+PD3bu3Im+ffvy9CGS5IIiIFrFqLy8PDIyMriCdLm5uejSpQsjzebvjYGBAdLT06Guri5y9VhTXHe/qOPFixfQ1dX9bj4sDbPIG3qC1O/Hj7gwVN+wle47+xH7X5+///6bmpewTTdnz57NU8O6ISEhIdi8eTOGDBkCCwsLrmuW6cJQXFwcNm7ciLy8PAB1HmD+/v6YOHGiwHZqamp48OABDAwM0L59e0RFRaFv377Iz8+HhYUF7T1THJnJ4uLRo0fYt28fEhISUF5eTjsnFSXg/rPCHicFBgbCz8+PYzzNTtQYOXIkbcWora0tBg0ahMDAQCgpKSEjIwNaWlpwc3ODo6MjrQeToqIiHj9+zDVOLSwshKWlJeW7IGnKysqwb98+xMbGIicnB46OjvD09ISTk5PASp2fmc+fP2P06NHQ1NQU6Z7TFDRMamTz+vVrtG/fnvaaFwZx+s6Vl5djzJgxSElJAYvFQl5eHgwNDeHh4QE1NTWEhoYKbJ+fn48tW7ZQi7Gmpqbw8fFhVNXTsOrv7t27KC8vZ1T1Jw5EHd/+lxHVo1RUgoODkZubi6ioKIFzX0lTUVGBp0+fQk5ODkZGRnyrgOsjqhrMsWPHMG7cONjY2HCo0dy7dw8JCQkYOXKkcAcjkpjWL747tra2ZNGiReT69etEVlaWPHr0iBBCyO3bt0mbNm1o2y9atIisXbuWEEJIQkICad68OenQoQORlpYmixYtkmjfRUVVVZXjpaCgQFgsFpGRkSFqamq07RUUFMiLFy8IIYS0adOG3LlzhxBCSEFBgUR00huyd+9ecvjwYa7thw8fJjExMQLbysrK8vSlePr0Ka2+PSGEWFhYULrO9vb2xM/PjxBCSHh4OKPzpj61tbWUDrYkofOD+V5ajCYmJmTEiBEkLS2NFBYWNlrru1+/fmT06NHky5cv1LbPnz+T0aNHE3t7e55tunTpQqysrBi9/pepra0lCxcuJLKyspQ+try8PAkMDGT8GVFRUcTMzIxIS0sTaWlpYmZmRnbv3i3BXhOipKREnj59yvfvOTk5RElJSaJ9sLe3J0ZGRmTdunUkOjqaxMTEcLx4oaqqStTU1Bi9moKIiAgiIyNDWCwW0dTUJAEBAT+kr46ofi5qamrk7NmzkugaI+Tl5alnpba2Nrl//z4hpE67XllZWWDbjh07En9/f67t/v7+pGPHjuLvLPmxzlthrrv/Nf79919SUlJCXrx4wfFiwocPH8jGjRuJh4cH8fDwIJs2bSLl5eWM2p4/f57cuHGD+v+2bdtI586diaurq8S1yi9dukSsra3JhQsXKG32CxcuEBsbG3Lx4kVGn1FdXU2OHDlCgoKCSFBQEDl69Cjl2SEJ6o9jTpw4Qdq3b08iIyNJRkYGycjIIJGRkcTIyIicOHFCYn34ERDFz4NNaGgokZeXJwsXLqT8JPz9/Ym8vDzZvHmzwLY9e/akvmNXV1fi6OhIbt68SSZNmkTMzMxEPTyJU1BQQNasWUNMTU1Js2bNSL9+/UhUVBT5+PEjbVtDQ0Ny5swZQkidnwtbMzw8PJy4urpKtN9srl69SoYOHUrat29P2rdvT5ycnMj169cZtRXlmo2JiaH8xoSh/velqqpKMjMzCSF1Hkx6enq07ZvaT6Y+aWlpZNq0aURGRobo6+sTFRUVoq+vT1JSUr5rP74XUVFRpHnz5kRRUZHo6ek1+p7z119/kU2bNhFPT0/i6elJNm/ezOh6EwW2l6KUlBRZu3Yth7/i5s2biYuLC+nSpYtE9s3Ed47pesDEiRPJwIEDSUlJCYeH1IULF4ipqanAthcuXCDS0tLE1taW8oO1tbUlMjIyjJ7z6urqJCMjgxBS58/BXte5cuWKxL67X4gHUT1KRcXFxYUoKSkRHR0d4uDgQIYPH87x+pFp6NGZlZVF2rdvT3mK0WFoaEgCAgK4tq9YsYIYGhoK3a+mCyX9QijWr1+P4cOHY+PGjXB3d0fnzp0B1JXl29ra0rZft24d9e+xY8dCT0+PkitwcnKSWL/FwV9//cW1LS8vDzNnzoS/vz9te0NDQxQWFkJXVxedOnXC4cOHYWtri9OnT3NUKEiKkJAQ7Ny5k2u7lpYWpk2bJlDuwMLCAocOHeIqOUtISICpqSntvqdMmYKMjAzY2dlh8eLFcHJywrZt21BVVUWrG8umsVluomZuFBYWMm4vSV68eIFTp06hQ4cOQrXfsmULHB0d0bZtW+p6zcjIgKysLJKSkni2cXFxEba7YsHa2hpXrlyBmpoarcQRnayRKLBYLKxfvx4BAQGNzjwA6rIMN2/eDG9vbw5/Al9fXxQXFyMoKEgi/ba2tsb+/fuxevVqnn+Pj4+HtbW1wM9gV77wg07q4NatW7h9+zZ1zjFhy5YtjN/7vSgrK0NsbCxiYmLw4sULjBo1Cp6ennj58iXWr1+PtLQ0XLx4EUCdn8rq1auhoKBAeavwg+l9rzGw98lisRAQEMDTz6VLly60n6OiotJoffqWLVsiNzcXGhoaUFNTE3jN0lUjtG3bFm/evIGuri7at2+PixcvwtraGvfu3aO99sLCwjBy5EicP3+ekhy7e/cu8vLycOzYsUYdE1Mkdd4SIarHhLnu/lfIzc2Fp6cnbt26xbGdMKyavH79OpycnKCiokLp0UdERCAoKAinT59G7969Bbb39/fH+vXrAdRVyc6fPx9+fn5ISUnB/PnzJVpBNW/ePERGRnL4Fw0cOBDy8vKYNm0alTXKj6ysLDg7O1Nef0DdeF9TUxOnT5+WiNRsfXmy0aNHIyIiAoMHD6a2WVpaol27dggICGjyMYkg+FUNs1gsyMrKQldXV+B9Sxxjza1bt2LHjh0cFenOzs4wMzPDqlWrBGqwL1++nKoKDQoKwtChQ9GrVy+oq6vj0KFDIvdNknTr1g337t2DpaUlpkyZAldXV7Rp04Zx+9LSUqrCXVFRkZJkHjp0KAICAiTS5/rs27cPU6ZMwYgRI6js+9TUVNjb2yMmJgbjx4/n21bUa7Zfv3549+4d5Tl29+5dHDhwAKamppg2bRpt3xUUFChZEh0dHeTn58PMzAwA8P79e9r2Dg4OWLJkCZefzNKlSzFgwADa9qJSVlaG+Ph4REdHo6CgAC4uLjhz5gz69++PyspKBAUFwd3dvVHeoD8Ly5YtQ2BgIBYvXtzoapf09HQMHDgQcnJy1LrP5s2bsXbtWmqsJgnCwsIA1D3PIyMjeUrNRkZGSmTfKSkpYvusixcvIikpibru2BgZGdGea4sXL4avry/HWhp7+6JFi2ivm5qaGigpKQGok0F8/fo1jI2Noaenh2fPnglxNMIjzPj2v0xpaSnPylJNTU28efNG4vtXVVUVvuKhibGzs+OobDQ1NcWdO3cwYsQIjuppfrx584an2s+ECROwceNG4TsmdPjkF01GdXU1V1ZbYWEhR5Ttv8S9e/eIsbEx7fs2b95MwsPDCSF1WXuysrJERkaGSElJkS1btki6m0RGRoYUFhZybS8sLKSt5jh16hRp3rw5mTRpEpVROnHiRNK8eXOhsvSKiorIsWPHqIwEOoTJchNn5kZTMnToUHL06FGRPqOyspLs2rWLzJ8/n8yfP5/s3r2bfP78WUw9FD+rVq2iMuxXrVol8PUjo6GhQQ4cOMC1/cCBA0RdXV1i+z19+jRp1qwZ8ff3J6WlpdT2N2/ekAULFpDmzZuT06dPC/yMefPmcbxmz55NevToQVRUVMjcuXNp+2BlZUVu374t8rEIC931T8exY8fI0KFDSYsWLUjnzp3J1q1byV9//cXxnufPn5MWLVpQ/+/Tpw/1Hjs7O9KnTx++L0nA/mwWi0X++OMPjv05ODiQadOmkdzcXNrPiYmJIePGjWvUPaJ+VmnD6oPGViOIWjFaUlJClixZQmUoLV26lBQXFzM+lqZGlOqxpr7umpI//viD9O7dm5w7d448fPiQPHr0iONFh7m5OZk6dSqprq6mtlVXV5Np06YRc3Nz2vYKCgrUGGvlypVk5MiRhBBC7t+/T1q1aiXcQTFEVlaWPHnyhGt7RkYGo2rdbt26EScnJ46x/YcPH4izszPp3r27WPvKC1lZWZKdnc21PTs7m1H/m5L6z5r6lcLs1/9j78zjofr+P/4aZN+XtNoiWYv2vbSntC8ksrRHSUqrj1b1SYv0SUqhz6dVqbSgUooSEtJmq2hf1ZeU7fz+6OH+jBkzY2auoTwfj3k8uHPPPedyl3Pey+stJSVFHBwcmLJxhY2UlBTJyclh2Z6dnU2kpKTqfbxPnz41SMa1oKxatYo8fPiQ7/YdO3YkSUlJhBBC+vbtS7Zs2UII+fXe0dDQEMoYOdGpUye2axh/f3/SqVMnjm0FvWf79etHwsPDCSG/5oYKCgqkd+/eRF1dnads53HjxpHg4GBCCCGenp5EX1+fbNy4kVhaWtaZZV6Tly9fEj09PaKkpETNU5SVlYmhoSHt7+vquZ2JiQnZuXMn22y/d+/eEQaDQes4RIWKigqVxVNf+vXrR2bNmsWUcVReXk4cHR1J//79hTXEOhk0aBDtmZV0Ii8vT83Da2aCpKSkEFVVVY5tpaSk2M7hnz59ytNzvjFk/YWFhRFTU1MiJSVFpKSkiJmZGfUcaqZu9PX1yZEjR1i2h4eH85wx2gx/jBo1ihw6dIhl+6FDh8jw4cP5Pm5zTZBmmjzp6ekYMGBAvTIPgF8R/vfu3YO+vj6tNSWq0dLSQmBgIEuBzHPnzmHhwoV4+fIlx/YXL17E5s2bkZ6eTun++vj4cCyCVlVVhb///hvnz59HWVkZhgwZAh8fH8jIyNRr7Lq6uvD19WXxxIaFheGvv/5iG0nHrXhbTdidgzCLjgpCcHAwNm7cCGdnZ7barZz6Li8vR6dOnXDhwgWRFbNqakycOBGhoaFQVFTExIkTOe7LrUC0srIyUlJS2NYn6NGjB4qKiji2d3R0hIuLC9cIZHbs2bMHy5YtQ0VFBRVl9/XrV0hISGDbtm1YvHhxvY8JAH/99ReKi4uxfft2jvvFxsbC19cXmzZtYnvdssu+Eqbu7rlz55h+Ly8vx/379xEWFgZfX1+uBSGVlJQwffp0uLq6onv37mz3KS0txbZt23iuL1If8vLycPjwYeTl5WH37t2UlqmWlhYVbVkXgtZzKS0txYQJE5CYmAgdHR2W/x2d2VfsuHPnDu7cudMkMkZr8uPHD5ZCu9z+J3VljwUGBsLDw4Nr9hg/993vgpycHO7du8d3oUIZGRmkp6dTUdXVPH36FF26dOGqNa6qqoqEhAQYGxujX79+cHBwwJw5c/D8+XMYGxvTWo9mwIABkJaWxpEjR6CpqQngV6Szg4MDfvz4wXU+JCMjg9TUVJZnS1ZWFrp3706LznpNLC0tYWpqioMHD1IRe2VlZXB1dUVWVlaDP3Pqw7lz57BixQp4eXlRkdHJycnw9/eHj48PKioq4O3tjWnTplHvTW6ZgjXhJWvQ1NQUdnZ2WLVqFdP2jRs34sSJE3jw4EE9zujPwdvbG4qKili1ahVOnDgBe3t76OjooKCggG3EtbCRkpLCw4cPWTK9c3NzYWpqih8/ftTZVtB7VkVFBUlJSTA0NERAQABOnDiBxMRExMbGYt68eVz1+fPz81FcXAxzc3OUlJTA09OTUnbYsWMHU6ZXXYiqnoyLiwtcXV2pdyw7CCEoKCjg6TyaGh4eHtDQ0GB5XvCCjIwM7t+/z/KeffToEbp169Yo664Jk5s3b3L8ntt6bfTo0ejatSs2bNgABQUFZGZmQltbG9OnT0dVVRUiIiLqbNu+fXvs2LEDU6ZMYdp+8uRJLFu2DAUFBRz7jomJQUlJCSZOnIjc3FyMGTMG2dnZVNYfNwUAQdmxYwfWrl2LRYsWoW/fvgCAhIQE7N27Fxs3buSYsfinI8wapX8C3759o9Y73GwL7NZFNW2Ar1+/xrp16zB16lT06tULwK+aIKdOnYKvry/mzZvH1xibnSBNDF1dXY6pa79zUaPaRnFCCN68eYPAwEC0b98ely9fpr1PTnAzxK9YsQInTpzA4cOHqZd0fHw8nJ2dMXnyZK5GTX7YsGED/vrrLwwdOhQyMjKIiYmBra0tDh06VK/jSEtLIysri2WhkJOTAzMzM44LBW5kZWWxTRvnNUWYF5kNQeA0Dl76btu2La5evVovJwg3OZuaNNZCu5WVlUhMTIS5uXm95OacnJwQEBAABQUFODk5cdyXm7yJm5sbWrRowWLEWLZsGUpLS7F3716O7cePH49Lly5BW1sbTk5OcHR0rJfUw8uXL3Hq1CkmCblJkyahffv2PB+jNrm5uejRowfX/3v1dVv7OiIcpGmqC+VyglN7Xjh69ChOnDjB4iSpzffv35nkpOqLs7Mzdu/eTaWeV1NSUgI3NzeOz8D4+HiMGjUKffv2xc2bN/H48WPo6enBz88PqampHBdJ7Pj27Rvi4uLQqVMnngzE1UUbJ0+ezLYwOi9On8rKSkRGRjIVbxw3blyDFNQrKipCSEgI1beJiQmcnZ0pZyCdlJSUYMWKFTh58iQ+ffrE8j2361ZDQwMBAQGwtbVl2n7s2DG4ublxlRnh5777XejevTt27tzJJAlVH/r27QsvLy8W6aWzZ8/Cz88PSUlJHNvb2NigrKwMffv2xYYNG/Ds2TO0bdsWsbGxWLRoEbKzs/kaFy/k5uZiwoQJyM7Opp7vhYWFMDAwwNmzZ7nKaXbu3Bk7d+5kMYLExcVh8eLFtBvRk5OTMXbsWBBCqKCgzMxMMBgMREVF8SS3Kyp69OiBDRs2YMSIEUzbY2JisHbtWiQnJ+Ps2bPw9PREXl4eAGDw4MFM+6alpaGiooJywGVnZ0NcXBxdu3blKj0J/CrcOW3aNAwdOpQyLiUmJuLatWs4efIkJkyYIIxTbRTQKTvZ0A53fX19eHl5Ye7cuUzbg4KC4O/vT83d2CHoPSsvL4+srCzo6OjAxsYGffv2xYoVK1BQUABDQ0OOThR+59fNNA7c3d0RHh6Ozp07w9zcnMXpxOm+0dTUxJEjRzB8+HCm7TExMXBwcMC7d++EPl5RS83WhN2avOZ8i9scKysrC0OGDIGlpSXi4uJgY2ODhw8f4vPnz0hMTORY4Hz9+vXYuXMnvL290adPHwC/nvNbt27F0qVL+ZLw+/z5c73W/ILAT0BrM78ghMDb2xsBAQFUcJW0tDRWrFjBIlVPFxERETh58iQKCgpYArwaW6CKuLg43rx5g5YtW9ZpW+Bmj+AFQdZVzU6QJsbu3buZfq+Oro2OjoaXlxe8vb1FNDL6qX1DMBgMaGhowMrKCv7+/my1+mpz7do1XLt2De/fv0dVVRXTd+yMYsK8CcvKyjBz5kycOnWKMkRVVVXBwcEBQUFBTHp5dXHv3j0mw5KFhQXH/Q0MDLBs2TJqgn/16lVYW1ujtLS0Xjqkwo5y+9///odjx47h4MGDuHfv3m9tGNq8eTOys7Nx8OBBng2QYWFhPB+fUy0ZfhGWE0ZaWhqPHz+Grq6usIbGlZoT9IqKCoSGhkJLS4uKHrh79y4KCgrg4OCAPXv2cD3ehw8fcOTIEYSFheHRo0cYOnQoXFxcMG7cONqj5dhx5MgRrFixAq9fv+a4H7fIY3bZV4Jmb/FCfn4+zM3NUVxczHE/BwcHDB48GAMGDOC4KKmLmhOwmnz8+BGtWrVCRUVFnW179+6NKVOmYOnSpVBQUEBGRgb09PSQnJyMiRMncs3amzp1KgYMGIBFixahtLQUnTt3xvPnz0EIwfHjx7nqusrJySEmJoZvYzI7rfLs7GyetcrDwsKgrq4Oa2trAMDy5csRHBwMY2NjHDt2jGNkJju96pSUFJSWltKqV13NwoULcf36dWzYsAEzZ87E3r178erVK+zfvx9+fn6YMWMGx/aCZo/xc9/9LsTFxWHNmjXYvHkzz1kwNes5PH78GMuXL4ebmxtTtNfevXvh5+eHadOmcey/oKAACxYsQGFhIdzd3alsMw8PD1RWViIgIEDQU+QIIQRXrlzBkydPAABGRkYYOnRone/SmtFxCQkJWL58Of766y+mc1+/fj38/PyYanXQRXVUeM3x29nZQU5Ojva+BaGuyOgnT57AwsICpaWlHLOBduzYgRs3biAsLAwqKioAftUgdHJyQv/+/eHp6cnTOO7du4edO3dSc3QjIyN4enpynacLg7KyMrbrGi0tLaH3NXjwYERGRkJZWZnFmVQbYWr508G+ffuwZMkSODs7Mxk1Q0NDsXv3bhbniDDv2Z49e2Lw4MGwtrbG8OHDkZSUhM6dOyMpKQmTJ0/mOs8Qxvw6Ly8Pu3btYgqWWLx4MV9zrvrg7u4OfX19qg5LNYGBgcjNzW2U9emECaf7hsFgcHS8uru7IzIyEtu3b2e6Zr28vDBp0iRa/na83vPcxi4MqusGVVNtB1u7di02bdqEIUOG8HSMwMBAZGRkoLi4GJaWlli4cCFXOxIhBLt27YK/vz+1BmvTpg28vLzg7u7e6Otr0BnQ+qdQXFzMV41SQQkICMDq1asxa9YsBAcHw8nJCXl5eUhJScHChQuxadOmBhkHr8THx6Nv376QkJBotOuiZifIb8LevXuRmppKS+FHboWRa9LYPJE18fX1xfr169GtWze0bt2a5ZwiIyMbZBzZ2dlU6rGZmRlPqb7v37/H9OnTcePGDSrqp6ioCIMHD8bx48ehoaHBtp2UlBRyc3OZIs+lpaWRm5vLUhSME8KKcrt58yZCQkJw+vRptGnTBhMnTsSkSZPqlLthx48fPyAtLc3z/sKEn74nTJiAa9euQV5eHmZmZiwGBW6STqJAWE6Ybt26YevWrTxNSoUFt0V5NfxM1tPS0nD48GEcPHgQ8vLysLe3x4IFC1gMpvfu3cOyZctw7tw5FsPf169fMX78eOzatYtj8eTaUmDVmW+pqalYu3YtLRJQdFNaWoqVK1fi8uXLXIsAurq64ubNm8jNzUXbtm0xcOBADBo0CAMHDmT5e9fk27dvIIRARUUFOTk5TM/GyspKREVFwdvbm6MTSV5eHg8ePICuri6TE+T58+fo1KkT14VCq1atEBMTg86dO+Po0aPw8fFBRkYGwsLCEBwcjPv373Ns36lTJ5w8eZJvmcbevXtDQ0ODxag3a9YsfPjwgaVwdW0MDQ2xb98+WFlZ4c6dOxgyZAh27dqFCxcuQEJCguMzq3///tDX18eBAwcop29FRQVcXV2Rn5/PVcpAULS0tBAeHo5BgwZBUVERaWlp0NfXx5EjR3Ds2DFcunSJY3tBs8f+ZATJPuO2FPkds2hqR8eRWoVKa/7+u527MLGwsEDnzp0RHBxMBROVl5dj9uzZyMjIwP3795GYmAh7e3u2ka7V2ULsZI2GDx/ONeBAlOTk5MDZ2Znlmd5UMs8+ffoENTU1AL8ypw4cOIDS0lLY2Nigf//+DTKGyMhI+Pv7MzmvvLy8MG7cOJZ9hXnP3rhxAxMmTMC3b9/g6OhIBeKtWrUKT5484bo2EHR+HRMTAxsbG3Tp0oVpXZeRkYGoqChai6O3bdsW58+fR9euXZm2p6WlwcbGhqsD6E+mrKwMXl5eCAoKooJ5WrRogfnz58PPz6/BjLKNjfj4eCxduhT37t2rc5/y8nKMHDkSQUFBHNcRvPC///0PAFiyzTlRUlICPz+/OoNx6VZzaZZtbLp06tQJPj4+sLW1ZVqXrlu3Dp8/f0ZgYKCoh9jkoF8XoZkGYdSoUVi5ciUtTpCa0gQ/fvzAP//8A2NjY0rLMykpCQ8fPsSCBQuE3ndd1J548kJQUBBCQ0Mxc+ZMuobFEx07dkTHjh3r1cbNzQ3/+9//8PDhQ0pW6dGjR3B0dIS7uzuOHTvGtl1FRQWL0b5FixYoLy+vV/+TJk3C3bt3sXPnTpw9exbAr4VCcnIy1yi3t2/fIjQ0FCEhIfj27RumTp2Knz9/4uzZszA2Nuap/8rKSmzevBlBQUF49+4dsrOzoaenh7Vr10JHR4drfQFBELRvZWVlrpHf3BCkPgE/CCu7ZOPGjVi2bBk2bNiArl27sjiAuOnjv3v3DsuWLaMmjLUNZewWmXRFHr558wZXrlzBlStXIC4ujtGjR+PBgwcwNjbGtm3bmLRU/f39YWVlxfb8lJSUMGzYMPz999/4999/6+yvtnSQmJgYDA0NsX79epY0+Lq4desW9u/fj/z8fJw6dQpt27bFkSNHoKury1OWgSDta2cTEULwv//9D7KyshzPu5qDBw8CAF69eoWbN28iPj4e/v7+mDt3Llq3bl3nIllZWRkMBgMMBoPtc5bBYMDX15dj38rKynjz5g1LhOX9+/d5kkP7+vUrVFVVAQDR0dGYNGkSZGVlYW1tDS8vL67t/f39sXz5cgQFBUFHR4fr/rVJT09Hamoq5QABfv0/Nm3axJPDubCwkIoUO3v2LCZPnow5c+agb9++GDRoEMe2qampTA4QAJCQkMDy5cvRrVu3ep9Lffn8+TP09PQA/Hq+VGeq9evXD/Pnz2fbpmb2GIPBwMGDBxEbG8s2e4wb0dHRkJeXp+6PvXv34sCBAzA2NsbevXuZ/ie/G/w8e4Utv9DQ70pBaOxR8k2FvXv3wsbGBu3ataMcxw8ePEBlZSUuXLgA4Jdxqa41yrdv3/DhwweW7R8+fKAMXXW1E0TzWhjMmjULEhISuHDhAtvgLrrhV3bywYMHGDt2LCUZd/z4cYwcORIlJSUQExPDzp07ERERwSKNRwcTJkzgOZBLWPcsIQR6enooKChARUUF03thzpw5PEmBCjq/9vb2Zlt3xdvbGytWrKDVCfLp0ye28piKiopcJSf/dCQlJbF7925s2bKFkvfr0KGDQPKxvwOamppcg6tatGjBlH0qCPVxflTj6uqK+Ph4zJw5UyTPa19fX0ybNg03b95kG9DaTN2I2oFVUFBAZX7JyMhQc5OZM2eiV69ejc4JUp/7rCHqMrOj2QnymxAREUEZXThRl8wNg8GAtLQ09PX1MWvWLCYt/poRx66urnB3d8eGDRuY2vv4+KCwsFCAM+CNkJAQ7Ny5k9JpNTAwwJIlS+Dq6sq1bVlZGfUA4ZX6yCfUTusFhKejGR0dzVJXotqowskgSgjBrFmzmCJDfvz4gXnz5jFNmHnJRujatStPxsuajB07Fjdv3oS1tTV27dqFkSNHQlxcHEFBQfU6zqZNmxAWFoZt27Zh9uzZ1HZTU1Ps2rWLVieIoH0L6pisXZ9g06ZNaNmyJTIyMhASElLv+gS8IKwC2dVyADY2NiwGcV4i5WbNmoWCggKsXbtWJBPG8vJynD9/HocPH0ZsbCzMzc2xZMkS2NnZUecdGRkJZ2dnJifI3bt3OUoTjh07ljLy14Wg183p06cxc+ZMzJgxA2lpafj58yeAXwb6zZs3c42IF7R97ZR8MTExaGhooGfPnvUyBKuoqEBNTQ0qKipQVlaGhIREnZlvwC8jBSEEVlZWOH36NNN7UVJSEtra2mjTpg3HPqdPn44VK1bg1KlTYDAYqKqqQmJiIpYtW8aTIbx9+/a4c+cOVFVVER0djePHjwP4lY3BSyaZvb09vn//Ti1sa8sKcasH07FjR7x7947F6Pv+/XuutQmAX5kwnz59gpaWFmJjY6l3l7S0NNdir4qKiigoKGCRpiksLORr0Vhf9PT08OzZM2hpaVEZNT169EBUVFSd2um1M3Oqo1OrDQzq6upQV1fHw4cPufbv5eWFrVu3Avhl6Fu6dCk8PT1x/fp1LF26lJZAlcYCPyntwix6K4p3pSD8ztJoDUmfPn3w7Nkz/Pfff1TdlylTpsDOzo565nAKfpowYQKcnJzg7+9PSfjdvXsXXl5eLBmZNVFRUaEkF6ud77WhOyMjPT0d9+7d46nWFB2EhYXBz8+P5dleWlqK8PDwOp0gy5cvh5mZGf777z8cOXIEY8aMgbW1NQ4cOADgV+CXn59fgzhBqikuLmYxbNWe3wrrniWEQF9fHw8fPmSJSOc18EHQ+fXjx4/ZGj6dnZ1pl6PS19dHdHQ0Fi1axLT98uXLVBBDM5yRlZWFmZlZg/cramNwbeNqdZa8n58funTpwrW9vb09QkJCWJx/dSFMNZTLly/j4sWLlAOioREkoPVPR9QOrFatWuHz58/Q1taGlpYWJZ/47NkzrpnUoqBLly6NPsu72QnSxKj9MCaE4O3bt/jw4QP++ecfru3XrVuHTZs2YdSoUdRkPzk5GdHR0Vi4cCGePXuG+fPno6KigsngW82pU6eQmprKst3e3h7dunWrd8Ht+rBu3Trs2LEDbm5uVBbKnTt34OHhgYKCAqxfv55je1dXVxw9erRehat27tzJ034MBoOtE+T+/ftU1gUnCRRuD9Oqqiq29QdatGjBMgGpCbuIfnt7e459cRpDbm4u20lPdaH32ly+fBnu7u6YP3++QKmn4eHhCA4OxpAhQzBv3jxqe+fOnSn9aroQRt8VFRW4ceMG8vLyqIX569evoaioCHl5eY5tvb29sXHjRqo+QTVWVla0ef7rWtDXhJeFlqBRcwkJCbh16xZPE1s6aN26NaqqqmBra4vk5GS24xg8eDCLcfXVq1ccDb7y8vJ48+YNx75TUlJQVVWFnj17Mm2/e/cuxMXFuUbVb9y4EUFBQXBwcKCM8MCv4sMbN27k2FYY7QXNJlq1ahVu3LiB+/fvw8jICAMHDoS3tzcGDBjA0YlSbaSoNoTzM1HdvHkzFi5ciPbt26OyshLGxsaorKyEnZ0d1qxZw7X9kiVLMGPGDMjLy0NbW5vKnrh58yZPi1ZBDRBbtmyBu7s7W63yrVu3Mjk52Tkxhw0bBldXV1hYWCA7O5sytjx8+JCrgWbatGlwcXFhq1ddu9g4HTg5OSEjI4O6XsaOHYvAwECUl5fXGWggzIj8Z8+eURmOp0+fxtixY7F582akpaU1SF0HUVNUVISQkBCm2mXOzs5so36FjSjelc2IlvLycnTq1AkXLlxgmp/Vh6CgICxbtgx2dnbUfF1CQgIuLi74+++/62wXFxdHOdlFldVjbGwsksj5atnJ6gzPms79yspKXLp0iaUeV01SUlIQFxcHc3NzSspswYIFlKRezbpAdPLs2TMsWrQIN27cYJK5pNt5JSYmBgMDA3z69InvtZGg15yGhgbS09NZ+k9PT+f4vxMGS5cuxaJFi/DhwweqsPy1a9fg7+//29cDEZQfP35gz549uH79Otv1ON2y5KI2BtdlXO3VqxdPNqiKigocOnQIV69eZZtBVXueKEw1FBUVFZ4ClumEn4DWZkTvwLKyssL58+dhYWEBJycneHh4ICIiAqmpqRyDNUSFsLO86aDZCdLEqB0VUx1dO2jQIJ4igRISErBx40aWxcL+/fsRGxuL06dPw9zcHAEBAWydIDIyMkhMTGSZNCUmJtJep2Hfvn04cOAAkyHFxsYG5ubmcHNz4+oE+fHjB4KDg3H16lWYm5uzOBXYGUgEvYlrTlIFmbBaWVlh8eLFOHbsGBXF/OrVK3h4eHDUgxVW1GlSUhLs7Ozw4sULlokHp4VCQkICQkJC0LVrVxgZGWHmzJmYPn16vft/9eoV2wjmqqqqekt7NXTfL168wMiRI1FQUICfP39i2LBhUFBQwNatW/Hz50+uWTEPHjzA0aNHWba3bNmStsWvsBb0gkbNtW/fXqQRDjt37sSUKVM4PtuUlZVZnhMaGhp4+vRpnQUrnzx5AnV1dY59L1y4EMuXL2dxgrx69Qpbt27F3bt3ObZ/+vQpW+ekkpIS1+LOwmgP/Mp8qGkQNTY2hpOTE0+LAD8/P2hoaMDHxwcTJ06st4Tg48ePUVhYyJcskaSkJA4cOIC1a9ciKysLxcXFsLCw4NlYsWDBAvTo0QOFhYUYNmwYZdjR09NrEAfSmDFjAPwq0F5bq3zs2LHU73U9u/fu3Ys1a9agsLAQp0+fpjTb7927x9WRsX37djAYDDg4OLDVq6abmhlZQ4cOxZMnT3Dv3j3o6+s3SMq1pKQkVXz56tWrVOaQqqpqvTLsmiKpqakYMWIEZGRkqCCbHTt2YNOmTYiNjYWlpSWt/YviXdmMaGnRooXAxVxlZWXxzz//4O+//2aSl+FWEL7m/EZUWT1bt27F8uXLsXnzZpiZmbGsa+iS4RJUdvLz589o1aoVgF9BIXJycizyjZykyISFvb09CCE4dOgQNDU1G9Sg6+fnBy8vL+zbtw+mpqb1bi/oNTd79mzMmTMH+fn5TAELW7du5apcICjOzs74+fMnNm3aRClL6OjoYN++fTxl2/7JuLi4IDY2FpMnT0aPHj0a3AkhamNw7fVWtR2MVxtUVlYWNRepzhysht3fUphqKBs2bMC6desQFhYmMvmyyspKREZGMq3Lxo0bxyRh2wwronZgBQcHUw7PhQsXQk1NDbdv34aNjQ3mzp0rsnHVhTCzvOmiuTD6H4a8vDzS09NZjLq5ubno0qULiouLkZeXB3Nzc5SUlLC09/Pzg6+vL2bPns2UNn7o0CGsXbuWowSMoCgrKyMlJYXFEJWdnY0ePXpwNcxxKphcnyLJZWVlePbsGTp06NBgL43CwkLY2Njg4cOHVJHzwsJCmJqa4vz58/Uqcs4PXbp0QceOHeHr68s28oNblGdJSQlOnDiBQ4cOITk5GZWVldixYwecnZ15kkjp2rUrPDw8YG9vz1QQav369bhy5Qpu3bol0PnR2ff48eOhoKCAkJAQqKmpUe1v3LiB2bNnU9JuddGuXTucPHkSffr0Yeo/MjISy5YtoxbtjZnv37+joKAAZWVlTNu5GSVjY2Ph7++P/fv381UbQRDKy8shIyOD9PT0ei9QnZyckJuby/baIISgf//+MDAw4OiklJeXR2ZmJos0wLNnz2Bubs7VQKCnp4fg4GAMHTqU6boJDw+Hn58fHj16RGv7mzdvYuzYsVBSUqKyVu7du4eioiJERUXVmT1WTUZGBuLj43Hjxg3cunULkpKSVHH0QYMGcXWKmJmZYevWrVTtlm7dulGyRJ06deL4t09ISOCpZgov8Fq/Spj68vHx8TyPjy7j3ffv3/9IvWobGxuUlZWhb9++2LBhA549e0YVXl60aBHLovt3on///tDX12eqCVNRUQFXV1fk5+fj5s2btPbfGN+VFRUVeP36NbS0tBq87z+FzZs3Izs7GwcPHhSZIacu/etqqWEtLS1aChZXO9hrv1/ozmSIj48XSHZSTEwM7969o6QtFRQUkJmZSQWOvHv3Dm3atKFdJkNeXh737t2DoaEhrf2wQ0VFBd+/f0dFRQUkJSUhIyPD9D072cvMzEyYmppCTEyMq+Y6t/k1IQS7du2Cv78/Xr9+DQBo06YNvLy84O7u3mDG9Q8fPkBGRoZrVnwzv1BSUsKlS5dE5oTQ1dXFpUuXmOS5/xSUlJSQmprKYofKyclBt27d8PXrV47tLSwskJeXB0IIdHR0WJzWdGfxPHz4EDY2Nnj79i31zMvOzoaGhgaioqL4csb+Kfz77784d+6cSB1YTZ1Hjx6xtQXZ2NhwbDdw4EC4uLhgypQpLO9JQWh2+zVBBPHiqqqqIioqiilaEgCioqKoSWxJSUmdhmlvb2/o6elh9+7dVDqdkZERDh8+jKlTpwpyWlyZOXMm9u3bx5KxERwcjBkzZnBtL2h0+/fv3+Hm5oawsDAAoApku7m5oW3btlwdQILoaLZv3x5paWm4evUqJcFkZGSEoUOHCnROvJKTk4OIiAie9OTZIScnB2dnZzg7O+Pp06eUHqe3tzeGDRuG8+fPc2y/bt06ODo64tWrV6iqqsKZM2fw9OlThIeHU4Uv6ULQvm/duoXbt29DUlKSabuOjg5evXrFtb2g9QmEAb8SJx8+fICTkxMuX77M9ntuC9xp06YJVBtBEFq0aAEtLS2+FuFr1qxB165d0bNnT3h6elKTzSdPnsDf3x/Z2dkIDQ3leAwpKSm8e/eOxQny5s0bnp71s2fPxuLFi3Ho0CEwGAy8fv0ad+7cwbJly3iSBBS0/cKFCzFt2jTs27cP4uLiAH79vxcsWICFCxfiwYMHHNt37twZnTt3pmQGMzIysHPnTixcuBBVVVVc/y+CyBJZWVmhbdu2sLW1hb29PXWc+hAeHo6///6bcnJ27NgRXl5edWrTC1NfXliODX6dl4Do9Krd3d2hr6/PIk8ZGBiI3Nxc2qU2AgMDsWDBAkRERGDfvn1o27YtgF/RkyNHjqS1b1GTmprK5AABfskKLV++nKt8nzBoDO/K2jx8+BCWlpYi0zzmhX/++QdnzpyBqqoq5s6dy5Rd/PHjR/To0YN2nXdBSElJwbVr1xAbGwszMzOWDA52Ne8mTpyI0NBQKCoqcpWS4KVmXrVES120aNEC06ZNw/79+4WaNS8qGS5hyE7WrFdYu1ZhdQ0yuunevTsKCwtF4gTh513UpUsXvH37Fi1btuSouc7LPIHBYMDDwwMeHh5UUE1D1O2qyYcPH6hi1p06deKaId0M0LZt2wb/P9VE1NkMopzjCaqG0pA1jtjh6uoKExMTpKamUpl3X758waxZszBnzhzcvn1bpONrzPj7+yMvLw+ampoN5sBqCsXFeSE/Px8TJkzAgwcPmN5Z1fMGbu8qCwsLLFu2DG5ubpg6dSpcXFyEIpfZnAnSxHj48CHGjh2Ld+/e8eXFPXDgAObPn4/Ro0dTmRwpKSm4dOkSgoKC4OLiAn9/fyQnJ+PEiRO0n099cHNzQ3h4ONq3b09d/Hfv3kVBQQEcHByYHkiciozn5uYiLy8PAwYMgIyMDGVY4sbixYuRmJhIFfiujtI+d+4c/vrrL441PwDA1taWo47m4sWL62ybn58v0mJxVlZWWL58uVCNOJWVlYiKisKhQ4e4OkGAX86E9evXIyMjA8XFxbC0tMS6des4FoYXFoL0raKigsTERBgbGzNFpyYkJGDSpEl49+4dx/ZlZWVYuHAhQkNDUVlZCQkJCao+QWhoKGVgpgt2EicpKSkoLS3lKnEyY8YMvHjxArt27cKgQYMQGRmJd+/eYePGjfD394e1tTXHvqsdjnUhqGwQN0JCQnDmzBkcOXKk3mmwqampmDVrFh49esQkSWRsbIzDhw+je/fuHNvb2trizZs3OHfuHOVsKioqwvjx49GyZUu2BS1rQgjB5s2bsWXLFkqeR0pKCsuWLWNJ5aajfXUWTW3jwtOnT9GlSxeuBbYJIbh//z5u3LiBGzduICEhAd++fYO5uTkGDhzItV6TqqoqEhISYGxsjH79+sHBwQFz5szB8+fPYWxsTJ0TOz5+/Ijjx4/j2LFjuHPnDszNzTFjxgzY2trylHW3Y8cOrF27FosWLaKi9RISErB3715s3LiRJQgB+BVZ27dvX0hISHDN5ODFySFIbYYPHz5g1qxZiI6OZvs9uwkrr5q0vBgUBaFt27Y4f/48Vdy8mrS0NNjY2ODly5e09v8no6mpiSNHjrC8F2NiYuDg4MD1XVeb/Px8lJaWwsjIiIp454So35XsyMjI4NkJIkjtMH4JCAjAypUr4eTkhK9fv+LkyZP466+/sHLlSgANF5EvCE5OThy/Z5f15+TkhICAACgoKPDVvjbnzp3DihUr4OXlxVRv0d/fHz4+PqioqIC3tzemTZuG7du3cz1eY0YY2Qjc/ubVCEvSty7y8vIwb9482Nvbw9TUlMWwVV/D0rdv3/Dff/8hJCSEbf1MQXnx4gXldHrx4gXHfbnJkfj4+MDZ2VkksiUlJSXUmr46IFBcXBwODg7Ys2dPc6Q1By5fvoyAgAAEBQWJ5H8n6mwGUc7xRKmGIgxkZGSQmpoKExMTpu1ZWVno3r0713XZnwwneUeAWTZNWIiJiTX64uK8MHbsWIiLi+PgwYPQ1dVFcnIyPn36BE9PT2zfvh39+/fneoyKigqcP38eYWFhuHz5MvT19eHs7IyZM2dCU1OTr3E1O0GaGL1794aGhgbCwsJYvLgfPnzgyYubmJiIwMBAKvrC0NAQbm5ulCYoN4qKihAREYH8/HwsW7YMqqqqSEtLg6amJhX1SAec5KxqUpe01adPnzB16lRcv34dDAYDOTk50NPTg7OzM1RUVODv78/xuNra2jhx4gR69erFZMzOzc2FpaUlVwkTZWVlvnU0xcTEqHSwyZMn015/BWD2QOfl5WHNmjXw8vJiqzvcmD3QombatGlQUlJCcHAwlfKvoaGBcePGQUtLi+dFXmFhIR48eFDv+gSCIojESevWrXHu3Dn06NEDioqKSE1NRceOHXH+/Hls27YNCQkJDXIO/GJhYYHc3FyUl5dDW1ubJcKUl8l+eno6cnJyQAhBx44deS7y/urVKwwYMACfPn2ChYUFdSxNTU1cuXKFksXjRllZGXJzc1FcXAxjY+N6G9P4bd+3b194eXmxRD6dPXsWfn5+SEpK4theRUUFxcXF6Ny5MyWD1b9/f5Yi9HUxduxYlJeXCyxL9OzZMxw9ehTHjh3DkydPMGDAAK7Sibq6uvD19WWJPg8LC8Nff/1Fe8E4QRyXAH/Oy9pGraNHj2Ls2LEsEYt0G7WkpaWRlZXFVvLT1NSUa/2AkpISrrUAeOXHjx8sWTR0afQ3Btzd3REZGYnt27czacx7eXlh0qRJdUZolpeXY+PGjUhLS0OvXr3g7e0Ne3t7ytFraGiIS5cu8SyJ2JDvSm73UmlpKbKzs7kuUGvXDqvONF68eDFPtcP4xcTEBKtXr4adnR0A4Pbt2xg/fjzmzZuH9evXNwknSGOgR48e2LBhA0aMGMG0PSYmBmvXrkVycjLOnj0LT09PocuyCeLw5gcxMTEqG4GTkaaxG2aA/693+Pz5c2pb9fnUZ/zXr1/HoUOHcObMGSgpKWHChAnYu3cv13Z5eXk4fPgw8vLysHv3brRs2RKXL1+GlpYWi6FS2HTp0gVZWVnU2nLSpEm0SLaxY+7cubh69SoCAwOZAkXc3d0xbNgw7Nu3r0HG0RT58OEDpk6dips3bzZ4hjwgGmNwTQSd4wnKyZMnsXv3bup5a2RkhMWLF/OshlJtQ8vLy4OXl1eD2dCAXxn2O3fuhJWVFdP2uLg4LF68mGuGfjMNCzdHd00acw0OdXV1xMXFwdzcHEpKSkhOToahoSHi4uLg6enJNYi8Nu/fv0dwcDA2bdqEyspKjB49Gu7u7izXNVdIM00KaWlpkpWVxbL9wYMHRFpamvb+MzIyiIaGBtHX1ycSEhIkLy+PEELI6tWrycyZM2nvXxBmzpxJRowYQQoLC4m8vDw19ujoaGJsbMy1vYyMDNWmZvv09HSiqKjItb2Ojg559OgRX2O/f/8+cXd3JxoaGkRJSYnMmTOHJCUl8XUsXmEwGERMTIwwGAy2n+rvxMTEaB0HIYQkJyezPd+kpCSSkpJCa9+6urrk48ePLNu/fPlCdHV1ubYvLCwkxsbGxMjIiEhISJBevXoRNTU1YmhoSN69e0fHkIWKtLQ0efz4Mcv2hw8fEhkZGY5tFRQUyLNnzwghhGhpaZGEhARCCCH5+flc21ZTUVFBIiIiyIYNG8iGDRvImTNnSEVFRf1Ogk/++usvjp/6kJCQQH78+FGvNsXFxWT//v1kwYIFxNPTk4SFhZGysjKe2jo5OZFv376xPaaTkxPX9kVFReTTp08s2z99+kS+fv3Ktf3x48eJlpYW+fvvv8mtW7fIrVu3yN9//010dHTI8ePHSUZGBvVhx4ULF3jqpy5evHhBxowZQ8zNzcnBgwep7UuWLCFubm71OlZFRQWJiooiXbp04el5JyUlRXJycli2Z2dnEykpqXr1zQ/9+vUjs2bNIuXl5dS28vJy4ujoSPr378+1fatWrcjdu3cJIb/u4adPnxJCCDl37hzp27cvT2Oo+Y5sSExMTMiePXtYtgcEBBAjIyOu7eXk5IiTkxO5desWX/0XFxeThQsXEg0NDSImJsby+Z35+fMncXd3J5KSktT5SklJkSVLlnB89i1dupRoaGgQV1dXoqenR2xsbIihoSE5fvw4OXnyJDEzMyN2dnZc+/f19SUlJSUs279//058fX0FOre6kJKSIo6OjnW+I+bOncvT/33cuHHE3t6e/Pz5k+neuX79OtHX16dl7IT8mtdWv6OrefDgAdHU1CTe3t7k7du3v/11e/To0Tq/W7ZsGU/HqGue9PjxY2pt9uzZM57nPbySkpJCVFVVSdu2bcmECRPIhAkTSLt27Yiamhq5d++eUPuq5vnz56Sqqor6mdOnsWNkZEQmTpxIkpKSyLNnz+o1/pcvX5KNGzeSDh06EDU1NSImJkaOHz9O/W24cePGDSIjI0OGDh1KJCUlqXt+y5YtZNKkSTwd48mTJ2ThwoXEysqKWFlZkYULF5InT57w1JYQQtLS0oibmxtRV1cnysrKZN68eSQ5OZnn9vyipqZGrl+/zrI9Li6OqKur095/U2bIkCHEwMCA+Pn5kcOHD5PQ0FCmz++OoHM8USJqG9rFixeJiYkJOXXqFCksLCSFhYXk1KlTxMzMjFy8eJF8/fqV+jTTjLBQVlYm+fn5hBBC9PT0SFxcHCGEkNzc3HrPie7evUvmzZtHlJWViZaWFlm3bh1xcXEhMjIyxNPTs17HanaCNDHMzc3JtWvXWLZfu3aNmJqa8nSMyspK8vTpU3Lr1i0SHx/P9OHGkCFDiJeXFyGE2ciRmJhItLW1eT8REaCpqUnS09MJIcxjz8vLI3Jyclzb9+/fnwQEBFDtq2/oRYsWkREjRnBtf+TIETJ58mS2i3ReKS8vJ6dPnyZjx44lLVq0ICYmJsTf35+8f/+e72PWBbfFTUMudLp3705OnTrFsv306dOkR48etPbNYDDYOivevn1LJCUleTpGeXk5OXLkCPHy8iLz588nBw4cIN+/f+ep7cSJE4mfnx/L9q1bt5LJkyfzdAxBaNmyJYmJiWHZHh0dTVq2bMmxbbdu3Uh0dDQhhJCxY8eSmTNnkpcvX5Lly5cTPT09rn3n5OQQAwMDIisrSywsLIiFhQWRlZUlhoaGJDc3l78TEhEKCgoNahQWExNje91++PCBiIuLc20/cuRIsnfvXpbt+/btI6NGjeLavi7naX2dqDk5OSQ6Opq6X3gxMJSXl5OwsDDy5s0brvtyIiEhgcyfP59oaGgQBQUFYm9vTy5fvsy1nYmJCdm0aRPL9g0bNvD8nhYEQRyXhAjHeSkqJ0hISAiRkZEh69atIzdu3CA3btwga9euJbKysiQ4OJhr+8jISDJu3DjSokULYmBgQLZs2UJevXrFc/8LFiwgRkZGJCIigsjIyJBDhw6RDRs2kHbt2pF///1XkFNrMpSUlJDMzEySmZnJ03xHS0uLXLx4kRBCyNOnTwmDwSCXLl2ivr9x4wZp27Yt1+PU9cz7+PEjbYb8rl27kn/++afO7+/fv89T36qqqpTxsua9Q4fhvCbt27cnN2/eZNn+8OFDoqmpSRwcHJqEE+TUqVNkypQppGfPntRcofrDDSUlJabrrZolS5aQVq1a8dR/ly5diKOjI/n58ye1raysjDg6OpIuXboQQn69T3R0dHg8I94Q1OH9pyMrK8s2YIETERERZNSoUUROTo5MnjyZnD17lvz8+ZNISEiQhw8f8nycXr16EX9/f0II8z1/9+5dnp53ERERVGCVh4cH8fDwIL179yYSEhIkIiKiXudUVlZGTp8+TcaMGUNatGhBzMzMyK5du0hRUVG9jsMrMjIybIMCs7KyiKysLC19/i7IyMhQtgxR8eXLF3LgwAHi7e1NBUvdu3ePvHz5kva+BZ3jCYOfP3+SwsJC8uLFC6YPN0RtQ6u9BqsZ6Frz96bwzm9oKioqyN9//026d+9ONDU1iYqKCtOHbsLCwjh+GjP9+vUjkZGRhBBCbG1tyciRI0lCQgJxcHAgJiYmXNu/e/eObN++nZiYmBBJSUkyadIkcvnyZSZ7wK1bt3iy5dak2QnSxBDUi3vnzh2iq6vLNsKfl4eeoqIiZXys+QB//vx5g0S4CoK8vDzJzs6mfq4ee3UkFTdu3bpF5OXlybx584i0tDRZvHgxGTZsGJGTkyOpqals23Tp0oVpMaagoEDk5eWJqalpvRdqNfnx4wfZsWMHkZKSIgwGg0hJSZGZM2eS169f1+s4TQU5OTm2BrX8/HwiLy9PS5/nzp0j586dIwwGg4SHh1O/nzt3jpw5c4YsXLiQdOzYkZa+a6Kurk4yMzNZtmdmZnJ1QggDNzc30q5dO3L8+HFSUFBACgoKyLFjx0i7du3I4sWLObY9cuQIOXz4MCGEkNTUVKKurk7ExMSItLQ0OX78ONe+R40aRUaOHMmUkfDx40cycuRIMnr0aEFOq16kpqaSI0eOkCNHjpC0tDS+jsGvUfjhw4fk8uXLTNffuXPn6tz/69evpKioiDAYDJKbm8v0Tvj8+TMJCwsjrVu35tqviooK20Xq48ePeXpeCupE/fjxI7GysqLeTdV/OycnJ7J06VKu/cvIyPDtoPX29iY6OjpEUlKSWFtbk6NHj9bLeR0REUHExcXJiBEjyPr168n69evJiBEjiISEBDlz5gxfY6oPgjguCRHceUmI6JwghBDyzz//kLZt21JzG11d3XovEt6/f0/8/f2JmZkZkZCQINbW1uT06dNMxkZ2tG/fnopwVVBQoAxs4eHhPDkP/0QkJCSYjCfS0tLUXI0QQl6/fs2T45bBYLANCLl27Rpt0cXu7u4c34O5ublk0KBBXI+jrKxMGVBr3ju3bt2i9T1va2tLlixZwva7rKwsKqOpMbN7924iLy9PFi1aRCQlJcncuXPJ0KFDiZKSElm1ahXX9hcuXCBKSkpM2V+LFi0ibdq0YetMZkdiYiJRU1MjGhoaZMiQIWTIkCGkZcuWRE1Njdy5c4cQ8usZsG3bNv5Osg4EdXgLSmhoKLlw4QL1u5eXF1FSUiK9e/duEpkgY8aMqbfDQFxcnKxatYol07a+ThA5OTkqmK6245OX9bSenh5Zu3Yty/Z169bx/J6u5ufPn+T48eNk+PDhREJCggwYMIDo6+sTBQUFnubq9cXKyopMmTKFlJaWUtu+f/9OpkyZQoYMGSL0/n4nLCwsqGeKKBB1NgMhwpnj8UN2djbp168fS4ZvU7GhVTuNePk0w8zatWtJ69atyfbt24m0tDTZsGEDcXFxIWpqamT37t2096+srMz0kZOTo+x/DeGEEYTo6Ghy+vRpQsivwEZDQ0PCYDCIuro628D+2rRo0YJ06tSJbNu2rc6g769fv/I0165JsxOkiSGoF7dz585kypQp5NGjR+TLly+kqKiI6cMNDQ0NyghY8wEeGxtL2rVrJ7wTpYFRo0aRNWvWEEL+P5OjsrKSTJkyhefU49zcXOLq6kq6d+9OjIyMyIwZM9gaqKvhJqdTX2mdlJQUMn/+fKKiokLatWtHVq9eTfLz88nNmzfJkCFDSPfu3Xk6Dj8ImnYtCKqqquT27dss2xMTE4mysjItfdaOWK/5kZSUJB07diRRUVFs29Y2WHP6cENaWprt37mmzAKd8Ctxwo6SkhJy79498uHDB572l5WVZXt/paen19vjzw/v3r0jgwcPJgwGg4r2YDAYxMrKqt7ZV/U1Cufl5RFzc3OWa5CbrE7Nfdh9xMXFycaNG7n2X9ffPjMzs0GMK4LKFw4cOJCKPKkvffr0IXv37uX5OmXHvXv3yIwZM4ilpSWxtLQkM2bM4NuBVl8EcVwSIrjzkhDROkGqef/+Pfnf//4n8HECAgKogAMNDQ2ydu3aOp1icnJyVERg27ZtKVmx/Pz8BnlmiZLS0lKybds2MmrUKNK1a1eeAz1qZ1vWvna4STIpKysTFRUVIiYmRv1c/VFUVCRiYmJkwYIFwjlJmpg6dSqZPXs2IeT/56f/+9//iJWVFZk1axZt/WZkZJBDhw7V+f2DBw/qLf3Y0BgaGlKSVjWvnbVr15KFCxfydIz//vuPqKiokNTUVDJ//nzSpk0bSgaQV759+0b27dtHReUHBQWxlaQUJoI6vAWlY8eOlBHj9u3bREZGhuzfv5+MHTuWTJgwgfb+BWX//v2kffv2xMfHh0RERPA0P58zZw5RUlIiffr0Ifv27SOfP38mhNTfCdK2bVuSmJhICGG+bs+cOcOTE0NGRqZO2U1e52ipqalk4cKFRFVVlbRu3ZqsWLGC6ZgBAQG0XEcPHjwgbdq0IWpqatSaUk1NjbRt25at5Hcz/09MTAzp06cPuX79Ovn48SNToFNDyBiJOpuhJsKa4/FKnz59yIABA8ilS5fI/fv3SXp6OtOHG03Zhvano6enRzn85eXlKWfW7t27ia2trUjGlJ2dTYYMGUIFrTUlPn36xLN0JLtsZWHQ7ARpYgjqxeUn9bcmLi4uZPz48aSsrIxaqL148YJYWFjwZFwRJQ8ePCAtW7YkI0eOJJKSkmTy5MnEyMiIaGpqNnppHX9/f2JqakpatGhBxo0bR6KiokhlZSXTPoWFhTxFS/KDMNOu+WH69Olk4MCBTI66L1++kIEDB5IpU6bQ2reOjk69jaHcpIDqk33VvXt3tnrmPj4+xNLSsl7j4pWMjAyW66u+EifCQEVFhVok1iQhIaFBIh+mTp1KunXrxpQR8fDhQ9KtWzcyffr0eh3rv//+I8XFxTzvP2bMGDJu3Djy4cMHIi8vTx49ekRu3bpFevTowXFCcOPGDXL9+nXCYDDImTNnmN4Jt2/f5lnaZ9CgQWTRokUs2xcsWED69evH83nwi6DyhSdOnCB6enpkz5495Pbt20w1SOqqQyIMysrKiJOTExXhyS/l5eXkypUrTIa0V69e8bTgE6bjkhDenJe1jUfV0gT1dfo2Ft6+fUu2bt1KjIyMiKysLJkxYwaJi4sj4eHhxMTEhAwbNoxtOzMzM2r+NWTIEEqjdvfu3TxJnDRl7OzsiLq6Opk3bx7x8fHhOdCjdrZl7WsnLCyM47syNDSUHD58mDAYDLJ7924mffSjR4+yDaBobDT12mGipGbWn4aGBvXeyM7O5ilrsZq9e/cSKSkp0q5dO4HWSQ2JoA5vQZGRkaGcvsuXL6ciwbOysppEbQd+5+ffv38noaGhZMCAAURKSorY2NgQcXFx8uDBA5779vT0JP369SNv3ryhsgYTEhKInp4eT47HUaNGsXVgHjp0iAwfPpxre1NTUyIhIUFGjx5NIiMj2dba+/DhA2EwGLydUD0pKSkhwcHBZOnSpWTp0qX1kgn+k6kdENXQMkaizmYQJbKysjxnB7KjMdjQSktLyd27d0lUVFSTnZ+LAllZWepd16pVK6rmVl5eHk91gekiJSWFGBoaiqx/XhC0xighgq2J64JBCCH1K6XeTFPGysoKy5cvx8iRI/lq//XrV0yePBmpqan43//+hzZt2uDt27fo3bs3Ll26BDk5OSGPWLh8/foVgYGByMjIQHFxMSwtLbFw4UK0bt2aa9tLly5BXFwcI0aMYNoeExODqqoqjBo1iqcxpKam4vHjxwAAY2NjdO3alWsbAwMDODs7Y9asWXWOtaysDMeOHYOjoyNP46gPHTp0wIwZM7B+/Xqm7T4+Pvj333+Rl5cn9D5r8urVKwwYMACfPn2ChYUFACA9PR2ampq4cuUK2rdvT2v/oiQqKgoTJ06EnZ0drKysAADXrl3DsWPHcOrUKYwfP17ofYqLi+PNmzdo2bIl9PT0kJKSAjU1NZ7aLl26lOd+duzYwfF7BwcHpKWlISQkBD169AAA3L17F7Nnz0bXrl0RGhrKc1/8oKSkhKtXr6J79+5M25OTkzF8+HAUFRVxbO/s7Izdu3dDQUGBaXtJSQnc3Nxw6NChOtuqq6sjLi4O5ubmUFJSQnJyMgwNDREXFwdPT0/cv3+fY98vXrxA+/btISYmxvkk6yAxMRFDhw5F9+7dMWTIEAC/rruUlBTExsaif//+fB2XVxQUFJCWlgYDAwMoKCggIyMDenp6SE1NxYgRI/Dp0yeO7dmdN4PBACEEDAYDlZWVTN+dP38eo0aNQosWLXD+/HmOx7axseH4vZKSEtLT06Grq8txv7p48eIFRo4ciYKCAvz8+RPZ2dnQ09PD4sWL8fPnTwQFBfF0nO/fv1PP5g4dOkBWVrbeY6meIjIYDI778XKdsfu7CwNLS0tcu3YNKioqsLCw4DjWtLQ0jsc6c+YMDh8+jJiYGBgbG8PV1RX29vZQVlam9snLy4ORkRHKyspY2u/cuRPi4uJwd3fH1atXMXbsWBBCUF5ejh07dmDx4sV8n2djR0lJCZcuXULfvn3r1U5Y1058fDz69OmDFi1a1Kv/xkJFRQWOHz+OzMxMan46Y8YMyMjIiHpojRo9PT2cPn0aFhYW6NatG2bPno25c+ciNjYW06dPx+fPn1na1DVPOXXqFCwtLdGhQwdqG7d5SjU5OTm4fv063r9/j6qqKqbv1q1bV48z4p2ysjJ4eXkhKCgIFRUVAIAWLVpg/vz58PPzg5SUFC39VtOyZUvExMTAwsICFhYWWLp0KWbOnIm8vDx07twZxcXFtPbfGMjJycHhw4cRFhaG4uJiWFtbY/LkyZg4cSLHdmVlZVi4cCFCQ0NRWVkJCQkJVFZWws7ODqGhoRAXF+fYPigoCOvWrcPUqVPRq1cvAEBSUhJOnToFX19ftGnThtqX3Zxlw4YNcHZ2Rtu2bfk462ZERXx8PMfvBw4cSGv/Ne/5mnPzK1euwNnZGYWFhbT2DwARERE4efIkCgoKWOZh3OZ4gtC9e3fs3LkT/fr146u9qG1o0dHRcHBwwMePH1m+o2t+/rtgaGiI8PBw9OzZE/369cOYMWPg7e2NEydOwM3NDe/fvxfJuNLT0zFgwAB8+/ZNJP3zwqhRozB27FgsWLCAaXtQUBDOnz+PS5cucWwvrDVxbZqdIE2Mmzdvcvx+wIABHL+PjIzEmjVr4OXlBTMzM5bForm5OU/jSExMZHIkDB06lKd2oqK8vBwjR45EUFAQDAwM+DqGubk5/Pz8MHr0aKbt0dHRWLFiBTIyMji2f/nyJWxtbZGYmEgZVIqKitCnTx8cP34c7dq142tcDYGsrCwyMzOhr6/PtD0nJwedO3fG9+/faR9DSUkJ/vvvP2RkZEBGRgbm5uawtbVtEINHfHw8tm/fzuS88vLyot0QXM3FixexefNmpKenU+fu4+ND22RXTU0Nly5dQs+ePSEmJoZ3795BQ0ODp7aDBw/maT8Gg4G4uDiO+xQVFcHR0RFRUVHU/7miogI2NjYIDQ2FkpIST33xi4KCAm7duoUuXbowbb9//z4GDhzIddJR05lUk48fP6JVq1aU0YIdKioqSEtLg66uLjp06ICDBw9i8ODByMvLg5mZGU/3XFFREUJCQqjr1sTEBM7Ozjz/3dLT0/H3338zXXcrV67k+xlaH0aPHo2uXbtiw4YNUFBQQGZmJrS1tTF9+nRUVVUhIiKCY/sXL15w/F5bW5vpdzExMbx9+xYtW7bkaJTlZaHg6OiILl26wMPDg+N+dTF+/HgoKCggJCQEampq1CLzxo0bmD17NnJycvg6bn0ICQnBzp07qb4MDAywZMkSuLq60t53ffH19YWXlxdkZWXh6+vLcV8fHx+O3yspKcHW1hYuLi4szs9qSktLsW3bNq7HAn5dh/fu3YO+vj7P86umirGxMY4fPy7S86yqqkJubi5bQzS3+bEo+fHjB6SlpUU9jCaJq6sr2rdvDx8fH+zduxdeXl7o27cvUlNTMXHiRISEhLC0EeY8BQAOHDiA+fPnQ11dHa1atWJyxDIYDFoNc4BwHN78MGPGDDx58gQWFhY4duwYCgoKoKamhvPnz2PVqlXIyspqkHE0BqqqqnDx4kWEhITg8uXL+PnzJ0/tCgsL8eDBAxQXF8PCwoLn+RWvAS6NxbjJLbikJtwCTZoRHa6urvj06RNOnjwJVVVVZGZmQlxcHOPHj8eAAQOwa9cuWvsPCAjA6tWrMWvWLAQHB8PJyQl5eXlISUnBwoULsWnTJtr6jouLw5o1a7B582a2NjRFRUWejiMqG5qBgQGGDx+OdevWQVNTs0H6/F3w9vaGoqIiVq1ahRMnTsDe3h46OjooKCiAh4cH/Pz8aO2/9vOTEII3b94gMDAQ7du3x+XLl2ntXxBUVVWRmJgIIyMjpu1PnjxB3759uQY10rUmbnaCNDHqim6thttEp77RsbUJDw/HtGnTWKKLysrKcPz4cTg4OHBsL0o0NDRw+/Ztvg14MjIyePz4MXR0dJi2P3/+HCYmJigpKeHYfuTIkSgqKkJYWBgMDQ0BAE+fPoWTkxMUFRURHR3Nsf2PHz+QmZnJdnFP94Rx9OjRmDJlCpycnJi2Hz58GMePH0dMTAyt/YuSf//9F05OTpg4cSIV4ZqYmIjIyEiEhobCzs6OY3t3d3fo6+vD3d2daXtgYCByc3NpnzDyw5w5cxAeHo7WrVujoKAA7dq1qzMqLT8/n5YxEEJQWFgIDQ0NvHr1ijLkGxkZsTjj6GLcuHEoKirCsWPHqKi6V69eYcaMGVBRUUFkZCTbdt++fQMhBCoqKsjJyWFyIFVWViIqKgre3t54/fp1nX33798fnp6eGD9+POzs7PDlyxesWbMGwcHBuHfvHlfjQnXGhIyMDJVFk5KSgtLSUsTGxsLS0rK+f44GJSsrC0OGDIGlpSXi4uJgY2ODhw8f4vPnz0hMTGSK1G1sbNy4Ef7+/hgyZAi6du3KEt1V+1lQGzU1Ndy+fRuGhoZMkXbPnz+HsbEx7U7ndevWYceOHXBzc0Pv3r0BAHfu3EFgYCA8PDxYMgJ/FyoqKhAcHIxJkyY1LxD54PLlywgICEBQUBCLk7EhSEpKgp2dHV68eIHaS5vGYgisC0VFRUyYMAH29vYYMmQI3xl8fyJVVVWoqqqChIQEAOD48ePUXH/u3LmQlJSkfQza2tpYsGABVqxYQXtfjYmioiKsWbMGhYWFmD9/PqUy4OPjA0lJSaxevVrEI+QOHUFO79+/Zwl+qc369euxbNkyFodVaWkp/v77b9qyh6ohhCAiIqLO7KUzZ84Itb+m5rRpTGRmZvK8L91BCKLOZujUqRN8fHxga2vLND9et24dPn/+jMDAQNr6rr6Ga2cb82JDKy8vh4yMDNLT02FqakrbGDmhqKiI+/fvN+r1U1Phzp07uHPnDgwMDDB27Fja+6v9/GQwGNDQ0ICVlRX8/f15UrQRFXJyckhKSoKZmRnT9gcPHqBnz55c17R0rYmbnSBNjK9fvzL9Xl5ejvv372Pt2rXYtGkTJVtSF/WNjq1NXZHNnz59QsuWLRv1xMXDwwNSUlJ8e2tbtWqFo0ePUpJE1Vy9ehV2dnZcU+FkZGRw+/ZtSs6pmnv37qF///4cb+Lo6GjMnDmTrbe0ISaMgqZdN2WMjIwwZ84clqjuHTt24MCBA9TCqS7atm2L8+fPs8iepaWlwcbGBi9fvhT6mIVBdHQ0cnNz4e7ujvXr17NIOlVDl7xLVVUVpKWl8fDhwwbJPGBHYWEhZXyvllwrLCyEqakpzp8/X2f2lpiYGEdJHgaDAV9fX44GgpiYGJSUlGDixInIzc3FmDFjkJ2dDTU1NZw4cYLlOVSb/v37Q19fHwcOHKCMQxUVFXB1dUV+fj7XrEJhUFRUhIiICOTl5cHLywuqqqpIS0uDpqYmTxIMgsgXAsCRI0cQFBSEZ8+e4c6dO9DW1sauXbugq6uLcePG1dlOUGc/JxksBoPB1XGooqKCxMREGBsbM034EhISMGnSJLx7945je0HR0NBAQEAAbG1tmbYfO3YMbm5ubFPp2aGoqIj09HTo6enRMUxakJWVxePHj+tlxA8ICOB5X24OsKbMhw8fMHXqVNy8eROysrIsUZLsZImESZcuXdCxY0f4+vqidevWLM9gujMHBSEyMhJHjx7FxYsXoaSkhGnTpsHe3h7dunUT9dCa4YGGfNZNnDgRoaGhUFRU5Cq5JGxD9u+GoEFOgkDHerqoqIhJupETixcvxv79+zF48GBoamqyPC8PHz5c7/6boYfqNQU3k11DOpASEhKYpBsbKpuh5hytZcuWuHLlCjp37oycnBz06tWLa1S5IAgqRaanp4fIyEh07txZmMPiGWdnZ/Tt2xcuLi4i6b+ZP5PBgwfD1NQUe/bsYdq+cOFCZGZm4tatWxzb07UmbnaC/CbEx8dj6dKluHfvHq391CWNk5GRgcGDB9O+yBUENzc3hIeHw8DAgG10Ljfd37lz5+LOnTuIjIykvOi5ubmYNGkSunfvjoMHD3Js37FjR/z7779UVHY1ycnJsLOzQ25ubp1tRZ3C+CdH8EhJSeHhw4cs2Qe5ubkwNTXFjx8/OLaXlpZGVlYW3+1FjZOTEwICAup0gnBi8ODBHJ0B3GQmTExMEBISQjneRAEhBFevXsWTJ08A/HKKcZvsx8fHgxACKysrnD59GqqqqtR3kpKS0NbWZnIc8srnz5+hoqLCtT4D8Mvpev/+fXTq1Ilp+6NHj9CtWzfaswkyMzMxdOhQKCkp4fnz53j69Cn09PSwZs0aFBQUIDw8nK/jvnz5EuvXr0dwcDDH/fbt24d169ZhyZIl2LRpE7KysqCnp4fQ0FCEhYXh+vXrdbYVtbN/2rRpUFJSQnBwMCUFpqGhgXHjxkFLS4t244SysjJSUlJYnI/Z2dno0aMH11o41dScrDYUdd0fDAYD0tLS0NfXx6xZs1iyGqsZNGgQlixZUq9aS7zWfuHFAdaUGTp0KAoKCuDi4sLWqEZHvbKayMnJISMjo8EyBWtTUVGBGzduIC8vD3Z2dlBQUMDr16+hqKgIeXl5no7xv//9DxERETh27Bji4uKgp6cHe3t72qPCAeGMv6FoTJHRACj5vHnz5tHeV8052axZszjOBxrCkF1UVITk5GSWbAIGg4GZM2fS3r8gCBrkJAh1rafj4uIwbdo0fPjwgWP7rVu3QkdHB9OmTQMATJkyBadPn0br1q1x6dIlroZWVVVV/PvvvywSz800PrgFsNZEFFmYDQk/NaAaCyEhIThz5gyOHDnCtC5sKL5//44pU6ZAQ0ODrZzX7xykIwyePn2KPXv2MClTuLm5UeouzbBH0BqjdK2Jm50gvwlPnjxBt27duBah42Z4qivCtbrYaEZGBkxMTKjIYuCXvMuzZ88wcuRInDx5sv6DbyA4aQDzovv79etXjBw5EqmpqVQE+MuXL9G/f3+cOXOGa/TNuXPnsHnzZuzdu5eK7ktNTYWbmxtWrFjB0ejSnMIoOvT19eHl5YW5c+cybQ8KCoK/vz9XLUJTU1PMmzcPixYtYtq+Z88e7Nu3D48ePRL6mBsLtReW5eXlSE9PR1ZWFhwdHbF7926O7aOiorBt2zbs27dPJOnDgmYEvHjxAlpaWjw5LYSNpqYmjhw5guHDhzNtj4mJgYODA+3ZBEOHDoWlpSW2bdvGZAy/ffs27Ozs8Pz5c76Om5GRAUtLS66OCGNjY2zevJnSEq3uPysrC4MGDeKYzSBMZz+vhcVr8vLlS4wYMQKEEOTk5KBbt27IycmBuro6bt68yVVmQ1Dc3NzQokULlsCAZcuWobS0FHv37uXpOKJwguzcuRObNm3CqFGjqICD5ORkREdHw8PDA8+ePcORI0ewZ88ezJ49m6X9yZMnsXLlSnh4eLANlvjd63oIgqysLO7cuSOyKEcrKyssX76ckuRpSOgo3Pjo0SPMmDEDmZmZtDte6So8SReNLTJ6y5Yt2LFjB6ytrf8o41JUVBRmzJiB4uJiKCoqstRCacwGSUDwICd+qHbUf/36leVvVllZieLiYsybN4/re1ZXVxf//fcf+vTpgytXrmDq1Kk4ceIEVTA6NjaWa/vLly+zBMrQRUBAAObMmQNpaWmu2ZO/6/3yO1DX/65moMmAAQPqlFAWFH5qQAmTW7duYf/+/cjPz8epU6fQtm1bHDlyBLq6ulwLpltYWCA3Nxfl5eXQ1tZmmV/SXTsqJCQE8+bNg7S0NNTU1Fie179zkI6gnD59GtOnT0e3bt0omeCkpCSkpKTg+PHjmDRpEq39T5o0CT169GCR3Ny2bRtSUlJw6tQpWvsXlPT0dGzbto2pri+vNUbpWhM3O0GaGLWjn6oL4/j5+aGiogIJCQkc26uoqDD9Xl5eju/fv0NSUhKysrJ1Tliri436+vrC09OTKSpMUlISOjo6mDRpUoNo74oSQgiuXLnCdBPzWmxTRUUF379/R0VFBZM8jYSEBMuLsPb/oTmFUXTs27cPS5YsgbOzM/r06QPgl1c7NDQUu3fvZnGO1ObQoUNYtGgRvLy8KAmja9euwd/fH7t27WJriPvd+euvv1BcXIzt27dz3K/mPSMpKQkZGRmm7+leYAuaERAdHQ15eXlqYrx3714cOHAAxsbG2Lt3L8vzWJi4u7sjMjIS27dvZ7puvby8MGnSJNpr0SgpKSEtLQ0dOnRgMoa/ePEChoaGfBsXeHWCyMjI4MmTJ9DW1mbqPycnB+bm5igtLWVpI0xnv6CFxSsqKnD8+HEmuYEZM2aw3APCYunSpUx9h4aGQktLi8rCunv3LgoKCuDg4MCS0lwX8+fPx4YNG6Curk7LmNkxadIkDBs2jCUie//+/YiNjcXp06exZ88eBAcH48GDByztBa2bVhN+HGBNGUtLS/zzzz8iy9yLjIzEmjVr4OXlxdYQTacDS1iFG3/8+IHz58/j6NGjiI6OhqamJmxtbWkvuklX4Um6aGyR0YJKIPKLlZUV2yCsb9++Yfz48TwVdReEjh07YvTo0di8eXODFWMXJoIGOfFDWFgYCCFwdnbGrl27mGT6qtfT1UY2TsjIyCA7Oxvt27fH4sWL8ePHD+zfvx/Z2dno2bMnvnz5wnUc0dHROHToEG3zipro6uoiNTUVampqIrtffgf4DWYVFrq6uvjw4QO+f/9OrWG+fPkCWVlZyMvL4/3799DT08P169cpGWFhIsoaUKdPn8bMmTMxY8YMHDlyBI8ePYKenh4CAwNx6dIlXLp0iWP7altaXfj4+AhzuCy0atUK7u7u8Pb2bq47Vk86dOiAGTNmsNRE9PHxwb///ou8vDxa+9fQ0EBcXBzbuhpDhw6lPbBR1NCxJm52gjQx6op+6tWrFw4dOsRXREdOTg7mz58PLy8vjBgxguO+YWFhmDZtGqSlpevdz59OWFgYz/vWlo1oDCmMKSkpdRbQ4yYlJiiFhYVgMBhUBk5ycjKOHj0KY2NjzJkzh9a+gV/GFX9/f6YUSC8vL451BWqyb98+bNq0iSqEraOjg7/++ov2yWpjJTc3Fz169ODqxOB2z9AtryJoRoCZmRm2bt2K0aNH48GDB+jWrRs8PT1x/fp1dOrUiVapirKyMnh5eSEoKAgVFRUAgBYtWmD+/Pnw8/NjyW4RNi1btkRMTAwsLCyYnBBXrlyBs7MzCgsL+TpufTJBtmzZgnHjxjH1v2fPHhw+fJhtxJWwnP1NsbA4p0zJmvCSNSlK5OXlkZ6ezjayt0uXLiguLkZeXh7Mzc1RUlLC0l7QummA4A6wpkpsbCx8fX2xadMmtvMURUVFWvsXpgOrvghauDEmJgZHjx7F2bNnISEhgcmTJ2PGjBk8B9mIevzNiAYxMTG8ffuWJVDj/fv3aNu2LcrLy2ntX05ODg8ePGhSdZ9qImiQkyDEx8ejT58+LM9JXmnTpg0iIiLQp08fGBoaYuPGjZgyZQqePn2K7t2749u3bxzbl5aWYsKECUhMTISOjg7LOOiOSm+GP/gNZhUWx44dQ3BwMA4ePMgkDT537lzMmTMHffv2xfTp09GqVStERETQOpaGxsLCAh4eHnBwcGB6T96/fx+jRo3C27dvRT1EjqiqqiIlJaVZVYQPZGVlkZmZybK2yMnJQefOnWmfI8nIyCA9PZ1FeuvJkyewsLBgG9jXmKisrMTZs2cpO5qJiQlsbGxoyxjjBQnuuzTTmHj27BnT72JiYtDQ0BDIKWFgYAA/Pz/Y29tTuvd1QbfR8XdGkL/dsWPHEBsbC2lpady4cYMlhZFuJ8jmzZuxZs0aGBoasmh9N0SUq52dHebMmYOZM2fi7du3GDZsGExMTPDff//h7du3tOtlT5gwARMmTOC7/fz58zF//nx8+PABMjIyXPW1a0Zlc4NuBxQd3Llzh6dnlqieN9UZAQwGA0OGDKkzI4Abz549g7GxMYBfEURjx47F5s2bkZaWRrsOs6SkJHbv3o0tW7ZQESodOnTgGK3JrchqTbgVXLWxscH69euprAkGg4GCggKsWLGC9rRh4Nc9tHDhQvz48QOEECQnJ+PYsWPYsmVLnfWbqqOwdHR0MH36dL4dRfv27cOBAweYCovb2NjA3Nwcbm5ubJ0g58+f5/n4NjY2fI2LE5xqpNSXa9eu4dq1a2wd5ocOHRJaP+xQVVVFVFQUixRfVFQUpcFcUlJSZ40jQaPG63KAeXh4oKCgoFE6wIRF9TOxWvO3Gk5OCF5rHAHcM/9qz48bkqqqKrbn9/LlS57qaU2YMAFjxoxBeHg4Ro8ezbdhlF8EHX8zDUtNVYBHjx4xGd8qKysRHR2Ntm3b0j6OESNGIDU1tck6QebPn49WrVrB39+fmqsYGRnhxIkTbIOchPm8GjhwIKqqqpCdnc32XcnNATpx4kTY2dnBwMAAnz59wqhRowAA9+/f56kukqOjI+7duwd7e3u2NZwaij8tY1JQ2GX41AxmpZs1a9bg9OnTTIZ0fX19bN++HZMmTUJ+fj62bdtG6zz/x48fyMzMZHvf0DE/rubp06ds70slJSWea+WJEkdHR5w4cQKrVq0S9VCaHIMGDcKtW7dYnq0JCQlca1oIAzMzM5w4cYLF3nX8+HHKztBYyc3NhbW1NV6+fEk5cbZs2YL27dvj4sWLPDnlcnJy6gzE5tcG2OwEaWLQldYtISFBRalzorKyEjt37qQ0R8vKypi+b+z6r6KGX0/o6tWr4evrK7IUxt27d+PQoUOYNWtWg/cNAFlZWZS++8mTJ2FqaorExETExsZi3rx5tDpB9PT0kJKSAjU1NabtRUVFsLS05Jq2XVpaCkIIZGVloaGhgRcvXuDgwYMwNjZmqddQzf3793kaW2NfNNQ2qlfL96WmpmLt2rU8HaOqqgq5ubl8LRL5pbo+T3p6OkaMGFFnRgA3JCUlqeiQq1evUpk/qqqqXKP0BMXZ2Rm7d++GgoICU/psSUkJ3Nzc2Bqja8oyCIq/vz8mT56Mli1borS0FAMHDsTbt2/Ru3dvbNq0qc523BwxvC40XF1dISMjgzVr1uD79++ws7NDmzZtsHv3bkyfPp1jWysrK3z48IHvzLPy8nKq7lNNunbtSmXl1IbXQtwNpXHPL76+vli/fj26deuG1q1bN/gzau3atZg/fz6uX79OvTNSUlJw6dIlqq7BlStXMHDgQI7HefToEds5DrcFNj8OsN8FfhxpwpTlE2VB2OHDh2PXrl0IDg4G8Os+LS4uho+PD08O73fv3onU2SDo+Jv55TA6f/482+eGsINVunTpQgVqVMus1kRGRoZn2UJBsLa2hpeXFx49esQ2+4tOg6SwqE+QkzCfV0lJSbCzs8OLFy9Y1B14ec/v3LkTOjo6KCwsxLZt26h56ps3b7BgwQKu/V+8eBExMTFc6xjQxZ+aMUkH9QlmFZQ3b96wncdWVFRQztg2bdrgf//7Hy39R0dHw8HBgW1dP7rnx61atUJubi50dHSYtickJDQJR3BlZSW2bduGmJgYmJubszyvm2JQZUNhY2ODFStW4N69e5Tka1JSEk6dOgVfX1+mQDY63ntr167FxIkTkZeXxyStfuzYsUZfD8Td3R16enq4c+cOFYz26dMn2Nvbw93dHRcvXuTY/sCBA5g/fz7U1dXRqlUrlkBsfm2AzXJYTQBuBcRqwi0joHa0abVRMjAwEO3bt8fly5c5tl+3bh0OHjwIT09PrFmzBqtXr8bz589x9uxZrFu3rrmYGQdyc3MxevRovHr1ivKEPn36lCdPqKhTGFu3bo2bN2/yVMCIDuTl5ZGVlQUdHR3Y2Nigb9++WLFiBQoKCmBoaEhrGmBdcgPv3r2DlpYWfv78ybH98OHDMXHiRMybNw9FRUUwNDSEpKQkPn78iB07dmD+/Pm0jV3UODk5Mf1enblmZWVVpwOoJoIuEgVFUPk/GxsblJWVoW/fvtiwYQOePXuGtm3bIjY2FosWLUJ2draQR/z/1FXP5OPHj2jVqlWdxnhhk5CQwKThOXToUI77175m6qI+UmLfv39HcXExz8XT+vfvz5R51rFjR5iamiInJwdubm5cJ1zCKizeFGndujW2bduGmTNnimwMiYmJCAwMxNOnTwEAhoaGcHNzo+ROOJGfn48JEybgwYMHTNKj1ZNubs8cZWVlpKSksLwrs7Oz0aNHjyYRLdiUOH/+PEaNGoUWLVpwzaai0yDLT+HGb9++URJh3JzidEuJ0VV48k/h2rVrsLGxgZ6eHp48eQJTU1M8f/4chBBYWloKXUKwek6kp6eH5ORkJslOSUlJtGzZskFkJjgFZTV2h301RUVFiIiIQH5+PpYtWwZVVVWkpaVBU1OT1myaLl26oGPHjvD19WUbMCDMoBR2dOrUCSdPnqS1VlJdNEXJ0MZOeno6BgwYQHuAlbW1Nd6+fYuDBw/CwsICwK/AvdmzZ6NVq1a4cOECoqKisGrVKrZ11wTFwMAAw4cPx7p166CpqSn043Niy5Yt+Pfff3Ho0CEMGzYMly5dwosXL+Dh4YG1a9fCzc2tQcdTXzjJ3jZ2qVtRw2sAMp3vvYsXL2Lz5s1IT0+n6hL7+PhwDeoSNXJyckhKSmKpZ5KRkYG+ffuiuLiYY3ttbW0sWLCApSi8oDQ7QZoAnAqI1YSXYmK1b2IGg0EZJf39/dG6dWuO7Tt06ICAgABYW1tDQUEB6enp1LakpCQcPXqUp7GKgpKSEpYC5LxSUVGBo0ePYsSIEXy/dEePHg1CCP777z8WT6iYmBhHT6iHhwc0NDRElsK4bds2vH79mvZiynXRs2dPDB48GNbW1hg+fDiSkpLQuXNnJCUlYfLkyXj58qXQ+6w2qIwfPx5hYWFMC5LKykpcu3YNV65coQxtdaGuro74+HiYmJjg4MGD2LNnD+7fv4/Tp09j3bp1VFZQM6yIepEoKAUFBViwYAEKCwvh7u4OFxcXAL/u58rKyno5uHnl27dvIIRARUUFOTk5TMaRyspKREVFwdvbm6fMP0EoLCykpSgirwhSMFZFRQVJSUkwNDREQEAATpw4wZR5xu096+bmhvDwcLRv355tYfGa0Ve/W+SVmpoakpOTm6zm8NixYyEuLo6DBw9CV1cXycnJ+PTpEzw9PbF9+3auae9/mgOspiwPN+pjbPvx4wdLND07R0DNIAVRG2TrW7ixpqO6ut5fbRqinkk1dBSe/FPo0aMHRo0aBV9fX0orvmXLlpgxYwZGjhz5Wwe7NGUyMzMxdOhQKCkp4fnz53j69Cn09PSwZs0aFBQUcC1CXQ2vz6uayMnJISMjgyfpKk7wm7V48eJF7NmzB0FBQSyR7XSjoaGBgIAApoxJ4Jf0s5ubG9so/2Z+IWgwq6C8ffsWM2fOxLVr16i5bEVFBYYMGYIjR45AU1MT169fR3l5OU/BbvVFUVER9+/fF8kckxCCzZs3Y8uWLVSWv5SUFJYtW4YNGzY0+HiaaaYpoKqqigsXLrAEoiUmJmLs2LFcVYQUFRWRnp4u9GyrZidIM/VCTk4Ojx8/hpaWFlq3bo2LFy9SkkAWFhb4+vWrqIdYJ/Ly8pg6dSqcnZ35Sv+VlZXF48eP+ZZcEMQT6u7ujvDwcHTu3FkkKYxVVVWwtrZGdnY2jI2NWfrnVh9AUG7cuIEJEybg27dvcHR0pKR8Vq1ahSdPntDSf7VBpWY0cDUtWrSAjo4O/P39MWbMGI7HkZWVxZMnT6ClpYWpU6fCxMQEPj4+KCwshKGhIdtiWsKszSBKUlJSUFVVhZ49ezJtv3v3LsTFxdlKBtVEWItEfmmK8n91GdOqYTAY8PX1xerVq1m+q66FwgvcimaKi4ujX79+sLe3x+TJk1mKOdKNIAVjBc08E0aR8WvXrmHnzp2Uk9TIyAhLlizhmkkjalasWAF5eXme5e7oQJACfOrq6oiLi4O5uTmUlJSQnJwMQ0NDxMXFwdPTk6tU4Z/mAKt+3nBbSvBiyC8pKcGKFStw8uRJfPr0ieX7phBVXh/i4+PRt29fSEhIID4+nuO+jT3S70+nZlCYiooKEhISYGJigoyMDIwbNw7Pnz+npd8tW7ZAU1MTzs7OTNsPHTqEDx8+CD1ykhM/fvwQqD6lKBg6dCgsLS2xbds2pkLHt2/fhp2dHcf/m6DPKysrKyxfvpyn+nLsEDRrUUVFBd+/f0dFRQVkZWVZ1nV0zm+bMyb5R9BgVmHx5MkTKpvd0NCQpWAzXTg7O6Nv375UUJkoKCsrQ25uLoqLi2FsbMy1zie79s+ePUOHDh2Yak42FLm5ucjLy8OAAQMgIyNDBVs00wwdODg4IC0tDSEhIZRM8d27dzF79mx07doVoaGhHNu7uLige/fumDdvnlDH1VwTpJl60a5dO7x58wZaWlro0KEDYmNjYWlpiZSUFL6LyDYU//77L0JDQ2FlZQUdHR04OzvDwcEBbdq04al9jx49kJ6ezrcTREpKiq1GZnFxMSQlJTm2ffDgAZV2mpWVxfRdQ7y43N3dcf36dQwePBhqamoN/rIcNGgQPn78iG/fvjEZU+fMmcOx0LMgVNef0NXVRUpKCtTV1fk6jr6+Ps6ePYsJEyYgJiaGKtj7/v37OiPFGnuGA68sXLgQy5cvZ3GCvHr1Clu3bsXdu3c5tu/Zsydyc3NF5gTx9fXlKP/HC4IYZPnh+vXrIITAysoKp0+fprLOgF8yGdra2nU+83itS8ELqampOHr0KNavXw83NzeMHDkS9vb2GDt2LK3vCmEUjDUxMUFQUBCsra1x5coVKsLr9evXLLWB2CFokfF//vkHixcvxuTJk7F48WIAv6ThRo8ejZ07d2LhwoUCHZ9Ofvz4geDgYFy9elUkDnt2spP1KcBXWVlJ1WZQV1fH69evYWhoCG1tba5Zf8Cv97OlpSUAIC8vjzqOuro607v7d1lwCrMY+fLly3H9+nXs27cPM2fOxN69e/Hq1Svs378ffn5+QuuHLp4+fYo9e/YwOS4XLVqETp06sd2/pmNDV1cX7du3Z7kuCCEoLCykb9A1qO/4Gwt1FatmMBiQlpaGvr4+Zs2axbPUIj/IyclRQRKtW7dGXl4eTExMAIDWqPb9+/ezzcA3MTHB9OnTaXeCVFZWYvPmzQgKCsK7d++QnZ0NPT09rF27Fjo6OiI1VPJCSkoK9u/fz7K9bdu2THMHdgj6vHJzc4Onpyfevn3Ltp4Kt8y5xYsXQ1dXF9euXWObtcgNUWX2A8DMmTOxb98+lvlAcHAwZsyYIaJRNQ1q10YUFZ06dRLJuyEwMBBTpkzBrVu32N43DSHLLikpCUVFRSgqKtbLAfL9+3e4ubkhLCwMAKjnpZubG9q2bQtvb2+6hgzgl/rI1KlTcf36dTAYDOTk5EBPTw8uLi5QUVGBv78/rf03wz9NMSizmoCAADg6OqJ3795M2WM2NjbYvXs31/b6+vpYu3YtFUgutHueNPPb4+HhwfOHGytWrCCbNm0ihBBy/PhxIiEhQfT19YmkpCRZsWIF3aciFN6/f0/8/f2JmZkZkZCQINbW1uT06dOkvLycY7sTJ04QPT09smfPHnL79m2SkZHB9OHGzJkziYmJCUlKSiJVVVWkqqqK3Llzh5iamhJHR0chnR09yMvLkwsXLoh6GE2SU6dOkRYtWhAxMTEybNgwavvmzZvJyJEjRTgy+pGTkyN5eXks2/Pz84m8vDzbNjXvqTNnzhBjY2Ny+PBhkpqaWu97TlD09PSo615eXp7k5uYSQgjZvXs3sbW15do+JyeHGBgYEFlZWWJhYUEsLCyIrKwsMTQ0pI5FF8+fPyeVlZW09sELVVVVJC4ujri6uhIVFRWipKREnJycaOuPwWAQMTExIiYmRhgMBstHVlaWhISEcDzG9evXibKyMhETE2Ma68qVK8mECRNoG3s1bdu2JXv27GHZHhgYSNq0aUN7/4IwaNCgOj+DBw+mvf9Ro0aRkSNHkk+fPlHbPn78SEaOHElGjx7NtX2/fv1IZGQkIYQQW1tbMnLkSJKQkEAcHByIiYkJXcNuhhDSvn17cv36dUIIIQoKCiQnJ4cQQkh4eDgZNWqUCEfGnYiICCIhIUF69epFzad79+5NJCQkSEREBNf2YmJi5N27dyzbP378SMTExOgYMhOCjl+U7Nixg6ipqRF7e3sSEBBAAgICiL29PVFXVyebNm0irq6uREpKigQHB9M2hnHjxlHH9/T0JPr6+mTjxo3E0tKSDBkyhLZ+paSkSH5+Psv2vLw8IiUlRVu/1fj6+hI9PT3y77//EhkZGWq+d/z4cdKrVy/a+xcUDQ0NkpaWRgj5NcerHn9sbCxp164dx7aCPq/YzU+q5y283PNqamrUPFhRUZE8efKEEELItWvXSJcuXbi2FyWLFi0iioqKxMTEhLi4uBAXFxdiampKFBUVyaJFi+pll/iTqbYlNCQVFRXk4MGDxNbWlgwZMoQMHjyY6UM3Bw8eJBISEkReXp5oa2sTHR0d6qOrq0tr35WVlcTX15coKipS6wwlJSWyfv16ntZb7u7upGvXruTWrVtM6+OzZ882yD07c+ZMMmLECFJYWMj0vIuOjibGxsa0998M/6xdu5a0bt2abN++nUhLS5MNGzYQFxcXoqamRnbv3i3q4fFEdnY2OX/+PDl//jz1vuSFmvd47Y8g93yzHNYfgDCkOerizp07uHPnDgwMDDB27Fh+hidS9uzZAy8vL5SVlUFdXR3z5s2Dt7c32+wCdnrT1SnIvEg9FBUVwdHREVFRUSye0NDQ0EYd/a+trY2YmBiRRQTq6upyjJzlptEvKNeuXcO1a9fw/v17liicamkuTrx9+xZv3rxB586dqesoOTkZioqKjT7KUhDU1NRw4cIFqvBhNbdv34a1tTW+fPnC0oabvEp97jlBEVT+T5A6QMKgqKgIycnJbK9bBwcHWvtmR1paGlxcXJCZmUnb/05YBWMrKytZMs+eP38OWVlZ2osEy8vLIz09nSUDKicnBxYWFlyLyP3JCFqALyYmBiUlJZg4cSJyc3MxZswYZGdnQ01NDSdOnICVlRWdw2/ScNPP5/bMkZeXx6NHj6ClpYV27drhzJkz6NGjB549ewYzM7NGfd136NABM2bMYCno6+Pjg3///ZfKCqoLMTExvHv3jul5Bfx6nhkbG6OkpEToY66JoOMXJZMmTcKwYcNYpBL279+P2NhYnD59Gnv27EFwcDAthXqBX3PQ4uJimJubo6SkBJ6enrh9+zYMDAywY8cOvjPIuWFgYAAfHx/Y29szbT9y5Ah8fHxonxvr6+tj//79GDJkCJOc1JMnT9C7d2+2c7zGhKurKz59+oSTJ09CVVUVmZmZEBcXx/jx4zFgwACO2RKCPq9evHjB8Xtu14yKigrS0tKgq6uLDh064ODBgxg8eDDy8vJgZmbGVmq3sUCnXeJPICQkBDt37kROTg6AX8+BJUuWwNXVlfa+Fy1ahNDQUFhbW7Ot1bhz505a+2/VqhXc3d3h7e3Nc7FqYbFy5UqEhITA19cXffv2BQAkJCTgr7/+wuzZs7Fp0yaO7bW1tXHixAn06tWL6XmZm5sLS0tL2ovat2rVCjExMejcuTNT//n5+TA3N2/Uc6w/naZck7mx0iyH9QcgqDQHJ3r37s1i4GzsvHv3DmFhYQgNDcWLFy8wefJkuLi44OXLl9i6dSuSkpIQGxvL0k4Q2QdCCL59+4bjx4/j1atXTHIDopL6qQ9//fUXfHx8cPjwYdrkpzixZMkSpt/Ly8tx//59REdHw8vLi9a+fX19sX79enTr1o3thI8XWrVqhVatWjFtq9ZF5IWIiIg6UyC51WYQJcOHD8fKlStx7tw5yslXVFSEVatWYdiwYWzbCFNeRVAElf+Lj49HUlISkySVmpoa/Pz8qAk0XURFRWHGjBkoLi6GoqIi03XLYDC4GiSFlXr78uVLHD16FEePHkVWVhZ69+5Na3HoasOBoJIBhBDcu3cPeXl5sLOzg4KCAiQlJRvk+WdjY4PIyEiWZ9u5c+e41iBqTLx8+RLAr/uooRBEdhIARowYQf2sr6+PJ0+e4PPnz3VK7jTz/1RLt1VTXl6O79+/U/cNt2eOnp4enj17Bi0tLXTq1AknT55Ejx49EBUVBWVlZRpHLjhv3rxhe3729vb4+++/62y3dOlSAL+eyWvXrmV6vlRWVuLu3bvo0qWL0MdbG37H3xiIiYnB1q1bWbYPGTIEnp6eAH4FJNApNVKzYKecnByCgoJo66sms2fPxpIlS1BeXk45aK9du4bly5dT504nr169YruGqaqq4lh3q7Hg7++PyZMno2XLligtLcXAgQPx9u1b9O7dm6tBU9DnlaCOMVNTU2RkZEBXVxc9e/bEtm3bICkpieDgYKEXkBU2dNolfnfWrVuHHTt2wM3NjbK/3LlzBx4eHigoKGBxZAub48eP4+TJkxg9ejSt/dRFWVkZpk2b1uAOEAAICwvDwYMHYWNjQ20zNzdH27ZtsWDBAq7PjA8fPrANoiopKWmQ+WVJSQnbNcznz58bvaT9n061bCLwywFfHYQ5ZswYkdZgrIvquS0viKo+YrMTpBmunD9/nud9a74YGhtnzpzB4cOHERMTA2NjYyxYsAD29vZMk9U+ffrAyMiIbXtBJqyEEOjr6+Phw4cwMDBoEo6PmgQEBCAvLw+amprQ0dFh0eOj2xBf27hSzd69e5Gamkpr30FBQQgNDcXMmTNp7acuAgICsHr1asyaNQvnzp2Dk5MT8vLykJKS0qhrAwDA9u3bMWDAAGhra1M1bdLT06GpqYkjR46wbUNXxCQ/TJgwAdeuXUPPnj3h5uYGe3t7hISEoKCggKrtwglBDbKC4OnpCWdnZ2zevJkvw72g9VCqtcoTExPRqVMnzJgxA+fOnWvQ/29OTg6uX7/ONhOG0zm8ePECI0eOREFBAX7+/Ilhw4ZBQUEBW7duxc+fP2kxcAUEBFA/GxsbY9OmTbhx4wa1yE1KSkJiYmKDGLYEoaqqChs3boS/vz8VVaagoABPT0+sXr2a9oXrmDFjMGfOHJYCfPPmzeN7flLTidlM3bCL+s7JycH8+fN5ClZwcnJCRkYGBg4cCG9vb4wdOxaBgYEoLy9v9EXkBw0ahFu3brHM7RISEtC/f/86292/fx/ArznigwcPmN4LkpKS6Ny5M5YtW0bPoGvA7/gbA6qqqoiKimJ5J0dFRVH3bklJCVXrh07KysrYvm+0tLRo6c/LywufPn3CggULqEAFaWlprFixAitXrqSlz5oYGxvj1q1bLO/1iIgIas7XmFFSUsKVK1eQkJCAzMxMFBcXw9LSEkOHDuXalp/n1fnz5zFq1Ci0aNGC69qa2/tqzZo1VIbY+vXrMWbMGPTv35/KWmzm92Tfvn04cOAAbG1tqW02NjYwNzeHm5sb7U4QSUlJkdowHB0dceLECaxatarB+/78+TNb9YZOnTrxFBjWrVs3XLx4EW5ubgD+vzbcwYMHGySguH///ggPD6fqHDIYDFRVVWHbtm08Z2f9qYiLi+PNmzcsTqxPnz6hZcuWtCtTNLWazNVzW27U5fxrCCdKsxxWE+Pw4cOQl5fHlClTmLafOnUK379/h6Ojo9D75NVo0RDyNIKgpKQEW1tbuLi4oHv37mz3KS0txbZt2+Dj41PncR49esQ2MprbhNXExAQhISHo1atX/QcvYnx9fTl+z+nvRSf5+fno0qULrSmkampqSE5O5lpQly46deoEHx8f2NraMqWvrlu3Dp8/f0ZgYKBIxsUrJSUl+O+//5CRkQEZGRmYm5vD1taWxZHGji1btkBTUxPOzs5M2w8dOoQPHz7QXvSzNklJSZTEBS/yfw4ODkhLS2MxyM6ePRtdu3ZFaGgobWOVk5PDgwcP+I4IFDT1tn379rC1tcWMGTPQuXNnvsYgCAcOHMD8+fOhrq6OVq1asWTCcHLcjh8/HgoKCggJCYGamhp1z924cQOzZ8+mJAiEia6uLk/7MRgM2iVOBEFQuQBBEVR28sePH9izZ0+dzrPGnHnXWElNTYW9vT2ePHlSr3YvXrzAvXv3oK+vz7VIMABcunQJ4uLiTNk8wK9MgaqqKowaNape/deHoKAgrFu3DlOnTqXmeElJSTh16hR8fX3Rpk0bal92c0UnJyfs3r0bioqKtI2RE4KOX5RUP+tHjx5NvWdTUlJw6dIlBAUFwcXFBf7+/khOTqbNOJydnQ0XFxfcvn2baXtDSXcWFxfj8ePHkJGRgYGBQYMZRc6dOwdHR0esXLkS69evh6+vL54+fYrw8HBcuHChzozf3xFenldiYmJ4+/YtWrZsyXFtze8105y1+PujrKyMlJQUGBgYMG3Pzs5Gjx49UFRURGv//v7+yM/PR2BgoEiuM3d3d4SHh6Nz584wNzdnWUvSGTDRs2dP9OzZkyloCQDc3NyQkpKCpKQkju0TEhIwatQo2NvbIzQ0FHPnzsWjR49w+/ZtxMfHo2vXrrSNHQCysrIwZMgQWFpaIi4uDjY2Nnj48CE+f/6MxMREkdk5mgI1n901ef36NTp06IDS0lJa+/f29oaioiJWrVqFEydOwN7eHjo6OlRQpp+fH639NzS1nXJpaWmoqKiAoaEhgF/PO3FxcXTt2pVvycRmJ0gTo2PHjti/fz/LxREfH485c+bg6dOnIhpZ46aiogLBwcGYNGkSNDU1+TpGfn4+JkyYgAcPHjDVLKieBHCbsEZFRWHbtm3Yt28fTE1N+RpDM8xs27YN//zzD54/f05bHytWrIC8vLzI0g1lZWXx+PFjaGtro2XLlrhy5Qo6d+6MnJwc9OrVC58+fRLJuBoCHR0dHD16FH369GHafvfuXUyfPp1W6azy8nLMnTsXa9eu5dlAXRtR1gGaOHEipk+fjqlTp/LVXtB6KNXGH1Ghra2NBQsW8OUoU1NTw+3bt2FoaMjkeHz+/DmMjY150trmNwulqdOmTRsEBQWxGErPnTuHBQsW4NWrVw0yjpycHMroXh/ZyRkzZiA2NhaTJ0+GpqYmyzUsKod/UyY9PR0DBgygXe/a3Nwcfn5+LDId0dHRWLFiBTIyMmjru6kHCzX18ScmJiIwMJBaAxkaGsLNzY1l7kAXffv2hYSEBLy9vdnKpooiEKChuHXrFtavX4+MjAwqk2LdunUYPny4qIfGltoGTE64u7vTOJLGQW5uLvLy8jBgwADIyMiIfO7WDGfc3NzQokULFmP/smXLUFpaSqvcLPArQ/769etQVVWFiYkJixPizJkztPbPKWOB7hoy8fHxsLa2hpaWFpMUWWFhIS5dusRT1mReXh78/PyYnpcrVqxgqWNHF1+/fkVgYCBT/wsXLkTr1q0bpP+mRvX7wsPDAxs2bIC8vDz1XWVlJW7evInnz5/znPkgLJp6Teb6sGPHDty4cQNhYWFUnc4vX77AyckJ/fv351shodkJ0sSQlpbGkydPoKOjw7T9+fPnMDIyot0T2ZSpaUzmh7Fjx0JcXBwHDx6Erq4ukpOT8enTJ3h6emL79u1cX34qKir4/v07KioqICkpCRkZGabvedXYFwWOjo5wcXHBgAEDRNK/hYUF06ScEIK3b9/iw4cP+OeffzBnzhza+l68eDHCw8Nhbm7OV9RJSUkJ5OTk+O5fT08Pp0+fhoWFBbp164bZs2dj7ty5iI2NxfTp0xvddSPMdH9paWk8fvyYxQmRn58PY2Nj/PjxQ+DxckJJSQnp6el8O0GqycnJwePHj8FgMBqsDlBISAjWr18PJycnmJmZsVy33P72hoaGCA8PR8+ePdGvXz+MGTMG3t7eOHHiBNzc3PD+/XuWNpmZmTA1NYWYmBgyMzM5Hp+XyG5BUFRURHp6Ol+ZMCoqKkhMTISxsTGTEyQhIQGTJk3Cu3fvOLYXJAulqSMtLY3MzEx07NiRafvTp0/RpUuXRj9HUVJSwqVLl2iv2fM7Uvt5TwjBmzdvEBgYiPbt2+Py5ctcj3Ht2jVcu3aNrfPw0KFDHNvKyMjg8ePHbOfHJiYmtBcXF5TU1NQ6azDRbVhqRjDk5ORw7949tlIpdNN83dQPYWZdCvK8EjWfPn3CtGnTEBcXBwaDgZycHOjp6cHZ2RkqKirw9/cX9RCbYYObmxvCw8PRvn17Kmvv7t27KCgogIODA9Ncn46sCCcnJ47fHz58WOh9NiZev36NvXv3MgXZLFiwgClbsrFSUFCA9u3bs3VyFhQU0Cbb2JSpfl+8ePEC7dq1g7i4OPWdpKQkdHR0sH79evTs2VNUQ/ztadu2LWJjY2FiYsK0PSsrC8OHD8fr16/5Om5zTZAmRsuWLZGZmcmyyMvIyICamhotfQYEBGDOnDmQlpbmGkHTmKNmevTogfv37/PtBLlz5w7i4uKgrq4OMTExiImJoV+/ftiyZQvc3d25eoF37tzZZKNrvn79iqFDh0JbWxtOTk5wdHRE27ZtG6z/8ePHM/0uJiYGDQ0NDBo0iPZFZ2ZmJlWYNCsri+k7Xv6fmpqamDp1KpydndGvX796929lZYXz58/DwsICTk5O8PDwQEREBFJTUzFx4sR6H49uxo8fT6WM1v6/1YSXiNL27dsjMTGRZdGamJjYIBPO8ePH4+zZszzV/+BEzTpADfUMmD17NgCw1Qfm5W/PTz2ULl26UP/7Ll26MGXMVffbUPIgU6ZMQWxsLObNm1fvtsOHD8euXbsQHBwM4Ne4i4uL4ePjw1MxyI0bN2LTpk0CybW9fPkS58+fZ2vYasz1ETp37ozAwECWuUJgYCBt0dDC1I5t27Ztg9QO+B2p/bxnMBjQ0NCAlZUVTwY1X19frF+/Ht26dWMbTc8NJSUl5Ofns8yPc3NzBQpE4IX8/HyBihEfP34cDg4OGDFiBGJjYzF8+HBkZ2fj3bt3mDBhghBHyh5Bxy9qqqqqkJuby9YY3RDBO8bGxvj48SPt/dRG1NdNYWEhGAwG2rVrBwBITk7G0aNHYWxsTGtwkiAIK4NY0OeVqPHw8ICEhAQKCgqYamFOmzYNS5cubXaCNFKysrJgaWkJ4FdWAQCoq6tDXV2daY1K1/X4uzs56qK8vBwjR45EUFCQQLKulZWViIyMxOPHjwH8eneMGzcOEhL0m2R1dXXrrGuhq6vbKLM8RU31+2Lw4ME4c+YMlYkgCl6/fo2EhAS285zGbH8VlG/fvuHDhw8s2z98+MC27irPkGaaFMuXLyfa2tokLi6OVFRUkIqKCnLt2jWira1NPD09aelTR0eHfPz4kfq5ro+uri4t/QuLEydOED09PbJnzx5y+/ZtkpGRwfThhrKyMsnPzyeEEKKnp0fi4uIIIYTk5uYSGRkZWsfeGHj//j3x9/cn5ubmREJCgowcOZKcOnWKlJWViXpojZrIyEgybtw40qJFC2JgYEC2bNlCXr16xXP7yspKUl5eTv1+7Ngx4ubmRgICAsjPnz/pGHKjYevWrURNTY0cOnSIPH/+nDx//pyEhIQQNTU1snnzZtr737BhA1FWViaTJk0imzdvJrt372b68MLBgweJiYkJkZSUJJKSksTExIQcOHCA5pELn9u3bxN/f39y/vz5Ovd5/vw5qaqqon7m9KGbzZs3E3V1deLo6Ei2b99er/9dYWEhMTY2JkZGRkRCQoL06tWLqKmpEUNDQ/Lu3TuufSsoKJC8vDy+x3716lUiKytLTE1NiYSEBOnSpQtRVlYmSkpKZPDgwXwftyG4ceMGkZOTI0ZGRsTZ2Zk4OzsTIyMjIi8vT27evElLn4MGDeLpw8vf7tKlS2TkyJENco02w0yrVq1IeHg43+3nzJlDzMzMSG5uLrUtJyeHmJubExcXF2EMsU4YDAYZNGgQOXLkCCktLa13ezMzMxIYGEgIIUReXp7k5eWRqqoqMnv2bLJu3TphD5cFQccvSu7cuUN0dXWJmJgYYTAYTB8xMTHa+v369Sv1uXbtGunduze5fv06+fjxI9N3X79+pW0Mor5u+vXrR92zb968IQoKCqR3795EXV2d+Pr60t6/MKmqqqLmL7wg6PNK1GhqapL09HRCyP9fO4QQkpeXR+Tk5EQ5tGaaAO/fvye3bt0it27dIu/fvxf1cBoEdXV1kp2dzXf7rKwsoqenR2RlZYmFhQWxsLAgcnJyREdHhzx48ECII2UPg8Fg+796/vw5kZWVpb3/Zvjn8OHDRFJSksjLyxNtbe0mZX8VlJkzZxIdHR1y+vRpUlhYSAoLC0lERATR1dUlDg4OfB+32QnSxPj58yeZOnUqYTAYpEWLFqRFixZEXFycODk5/fYGUUGpvTiqXiDxulDq168fiYyMJIQQYmtrS0aOHEkSEhKIg4MDMTEx4dpeTEyMrQHt48ePtC7U6ODevXtk0aJFRFpamqirq5MlS5YINDHghYqKChIREUE2bNhANmzYQM6cOUMqKipo7bM21Q9ffqh2IpmZmREJCQlibW1NTp8+zeTg+J0oKysjVlZWAl0XVVVVZPny5URaWpqIiYkRMTExIisr22CLa0GdvmvXriVycnLE29ubnDt3jpw7d454e3sTeXl5snbt2gY4A9ERHx/P9touLy8n8fHxtPcv6P+uvLycHDlyhHh5eZH58+eTAwcOkO/fv/PUt7OzM9m3bx/fY+/evTtlwKo2Tvzvf/8jNjY25J9//uH7uA3Fq1evyKpVq8jEiRPJxIkTyerVq+vl+BUl79+/J4MGDSJiYmJEXl6eqKioMH2a4Y36GhQJIURVVZXJgVFfioqKSK9evYiEhAR1r0tISJDBgweTL1++8H1cXrh//z5xd3cnGhoaRElJicyZM4fcvXuX5/aysrLk2bNnhJBff4fMzExCCCGPHj0irVq1omPITAg6flHSuXNnMmXKFPLo0SPy5csXUlRUxPShi+q1Q/Wn9u/1WV/wi6ivG2VlZfLkyRNCCCG7d+8mffr0IYQQEhMT02QMM/wGqgj6vBI18vLy1Py8phMkJSWFqKqqinJozTRiiouLiZOTExEXF6dsKRISEsTZ2ZmUlJSIeni0smTJErJixQq+2/fq1YuMHTuWfP78mdr2+fNnYmNjQ3r37i2MIbLFw8ODeHh4EDExMTJ37lzqdw8PD+Lu7k569uxJPbubYU9FRQU5ePAgsbW1JUOGDCGDBw9m+tBNu3btyMaNG0llZSXtfTU2SkpKyPz584mUlBQ1t5KUlCTz588nxcXFfB+3uSZIEyU7OxsZGRmQkZGBmZkZ3xJPfxIvXrzg+D23v2FMTAxKSkowceJE5ObmYsyYMcjOzoaamhpOnDgBKysrju3FxMQoqZiavH79Gh06dGj0WunVvHnzBuHh4Th8+DBevnyJSZMm4dWrV4iPj8e2bdsElg5iR25uLkaPHo1Xr17B0NAQwC+N+fbt2+PixYvo0KGD0PuspqqqChs3boS/vz+Ki4sBAAoKCvD09MTq1at5Lihakz179sDLywtlZWVQV1fHvHnz4O3tDVlZWbb7//jxA5mZmWxTILnVdhAlGhoauH37NgwMDAQ6TnFxMR4/fgwZGRkYGBhASkpKSCOkFw0NDQQEBMDW1pZp+7Fjx+Dm5ka7fEZ8fDy2b9/OlHbt5eXFU/E+QLDUW3Fx8TrTrlu2bPlbp11v2bIFO3bsgLW1Ndt6LNz+dgoKCkhPT0eHDh2goqKChIQEmJiYICMjA+PGjcPz589pHP3vw8uXLwGAkmrhhaFDh6KgoAAuLi5sC6M7OjoKdYy/GyEhIdi5cydycnIA/JICXLJkCVxdXbm2XbFiBeTl5bF27Vq++yeE4MqVK9T82NzcvEFrmVVUVOD8+fMIDQ1FdHQ0OnbsCGdnZ8ycORMaGhp1tmvXrh0uX74MMzMzmJubY+XKlbC1tcWdO3cwcuRIfP36tVGPX5TIyckhIyOjQWpt1SQ+Pp7nfQcOHEjLGER93cjLyyMrKws6OjqwsbFB3759sWLFChQUFMDQ0LDRr2vWrVuHHTt2wM3NjanQcWBgIDw8PNjKiVYjjOeVKBk9ejS6du2KDRs2QEFBAZmZmdDW1sb06dNRVVWFiIgIUQ+xmUbI3LlzcfXqVQQGBlK10xISEuDu7o5hw4Zh3759Ih4hfVTXYzEwMEDXrl1ZZDa5ya3KyMggNTWVbW2D7t270/a8rC4mHx8fj969e0NSUpL6rrquxbJlywReq//OLFq0CKGhobC2tmYrf7hz505a+1dTU0NycjKt9q7GTklJCSUB2KFDB4FlbpudIM1wRZh6278bnz9/hoqKCkftzWptdA8PD2zYsAHy8vLUd5WVlbh58yaeP3/OtaaIKCkvL8f58+dx+PBhxMbGwtzcHK6urrCzs4OioiIAIDIyEs7Ozvjy5YvQ+x89ejQIIfjvv/+gqqoK4Jcx1d7eHmJiYrh48aLQ+6xm5cqVCAkJga+vL9OE76+//sLs2bN51gZ99+4dwsLCEBoaihcvXmDChAlwcXHBy5cvsXXrVrRp0waxsbEs7aKjo+Hg4MDWYN4QtRUEwcPDA1JSUvDz8xP1UESCsrIyUlJSWCaW2dnZ6NGjB4qKimjr+99//4WTkxMmTpxIXbeJiYmIjIxEaGgo7OzsOLYPDQ3F3LlzISkpCTU1NZbi3twKhoqJieHdu3cshrPs7Gx069YN37594/PM6Kd2gedqGAwGpKWloa+vz7G4KqfvePnbtWrVCtevX4eRkRGMjY3h5+cHGxsbZGRkoG/fvpQztrFSVFSE5ORkts4zBwcHWvsW1GktKyuLO3fu0Fa/5HdGEIMiACxevBjh4eEwNzeHubk5i/OwKc0vf/78iX/++QcrV65EWVkZJCUlMXXqVGzduhWtW7dm2d/Ozg7dunXD0qVLsWHDBuzZswfjxo3DlStXYGlp2eAFrus7flFiZWWF5cuXY+TIkaIeSoMj6uumZ8+eGDx4MKytrTF8+HAkJSWhc+fOSEpKwuTJkylHdGNFkEAVQZ9Xly5dgri4OEaMGMG0PSYmBlVVVRg1alQ9z6Z+ZGVlYciQIbC0tERcXBxsbGzw8OFDfP78GYmJiX+0sa2ZulFXV0dERAQGDRrEtP369euYOnUqW+3+34VqZwI7GAwG4uLiOLbv3Lkzdu7cyRI0GxcXh8WLF+PBgwdCGWddODk5Yffu3ZTdphneUVdXR3h4OE91Ielg+fLlUFVVhbe3t0j6/x1pdoI0Aaont3JyclwdEnQsEms/9NPS0lBRUUFF5GdnZ0NcXBxdu3bl+gJoDDx69IhtsVleI+pzc3ORl5eHAQMGQEZGhir0WxfVBrEXL16gXbt2EBcXp76r9sCvX78ePXv25ONsGgZ1dXVUVVXB1tYWs2fPpgqF16SoqAgWFhZCKzpYEzk5OSQlJcHMzIxpe0MYBdu0aYOgoCCW6+PcuXNYsGABXr16xbH9mTNncPjwYcTExMDY2Biurq6wt7eHsrIytU9eXh6MjIxYrkngVyTt8OHDsW7dOmhqagrlnBoKQaNmRIEwn7dubm5o0aIFy37Lli1DaWkp9u7dK/B468LIyAhz5sxhyczasWMHDhw4QGWH1EX79u0xb948rFy5sl7ZThMnTgTw6/4YOXIkU9ZOZWUlMjMzYWhoiOjo6HqcDW8I638nJibGUtQdYC7s3q9fP5w9e5aWInnjx4+HtbU1Zs+ejWXLluHcuXOYNWsWVZTv6tWrQu9TWERFRWHGjBkoLi6GoqIii/Ps8+fPtPYvqNPa0tIS//zzD3r16kXrOH9HBM1848fAEBAQgDlz5kBaWpoKOKmLhigcmZqaikOHDuH48eOQk5ODo6MjFezg6+uLb9++ITk5maXd58+f8ePHD7Rp0wZVVVXYtm0blUW5Zs2aBivGye/4RUlkZCTWrFkDLy8vtpl35ubmDTKOL1++ICQkhCnz0snJiQrcoQNRXzc3btzAhAkT8O3bNzg6OuLQoUMAgFWrVuHJkycN7ryrL4IEqghqEDU3N4efnx+LUS06OhorVqxARkYG9xMQkK9fvyIwMBAZGRkoLi6GpaUlFi5c2Ogcnc00HmRlZXHv3j0YGRkxbX/48CF69OiBkpISEY2scVIz4CshIQHLly/HX3/9Rc0xk5KSsH79erbPgmYaD23atMGNGzfQsWNHkfRfWVmJMWPGoLS0lO08pzHaUwRh4sSJCA0NhaKiImVXqAt+5xnNTpAmwODBgxEZGQllZWWBJ12CsmPHDty4cQNhYWHU5PrLly9wcnJC//794enpSWv/gpCfn48JEybgwYMHTAauaiMNt4j6T58+YerUqbh+/ToYDAZycnKgp6cHZ2dnqKiowN/fn2P7wYMHU0aspsaRI0cwZcoUSEtLi6R/VVVVXLhwAX369GHanpiYiLFjx9JqWJOWlkZmZibLi+/p06fo0qUL1/RVJSUl2NrawsXFBd27d2e7T2lpKbZt2wYfHx+W7xQVFXH//v0mGZXF6XkF/IocamwI83lb7QRq3749NeG9e/cuCgoK4ODgwDSJEfYERkpKCg8fPmSRCMnNzYWpqSl+/PjBsT2/qbdOTk4AgLCwMEydOhUyMjLUd9VO39mzZ0NdXb1ex+UFYf3vrl27htWrV2PTpk3o0aMHACA5ORlr167FmjVroKSkhLlz56Jnz54ICQkR+nnk5+ejuLgY5ubmKCkpgaenJ2XY2rFjR6OWv+zYsSNGjx6NzZs31ynvRyeCOq1jY2Ph6+uLTZs2sV1oNEfQ1Y0oMt90dXWRmpoKNTU1gTOwBGHHjh04fPgwnj59itGjR8PV1RWjR49mciC/fPkSOjo6qKiooG0c/NKUx8/OSV/TYd0Q2bI3b97E2LFjoaSkhG7dugEA7t27h6KiIkRFRTWoJFtDQQhBYWEhVFRUUFFRwbS2ef78OWRlZVnkMBsbogxUkZGRwePHj6Gjo8O0/fnz5zAxMaHdmFxQUID27duzDeIrKCiAlpYWrf030zQZMmQI1NTUEB4eTtkESktL4ejoiM+fPzfqIB1RUB1UVU1t21PN3xuzssOfjr+/P/Lz8xEYGMgx8JkuNm7ciHXr1sHQ0JBFqrch7L8NjZOTEwICAqCgoEDZFeri8OHDfPXR7ARppl60bdsWsbGxbPUMhw8fjtevX4toZNwZO3YsxMXFcfDgQejq6iI5ORmfPn2Cp6cntm/fzlUn38HBAe/fv8fBgwdhZGSEjIwM6OnpISYmBkuXLsXDhw95Hkvtl2AznHFwcEBaWhpCQkIoo+Tdu3cxe/ZsdO3aFaGhobT13bNnT/Ts2ZMlytTNzQ0pKSlISkqqs21FRQWCg4MxadIkvrM4nJ2d0bdvX7i4uPDVvhnRwc0JVA0dExh9fX14eXlh7ty5TNuDgoLg7+9PafbXhaCpt76+vvDy8hKJIVxQTE1NERwczNbpOmfOHDx8+BBXr16Fs7MzCgoKWNrXlYVSU05r3LhxtEYIiwo5OTk8ePAAenp6IulfUKd1tUG19ru5IQ2qTRVRGhRFjYGBAZydnTFr1qw6o6jLyspw7NgxtnVl6pIHZDAYkJKSYtLwpgNBxy9KBK33JwzMzMzQu3dv7Nu3j8r2rqysxIIFC3D79m1aZU4qKysRGRnJlIEybtw4SEhI0NYn8Et6UFpaGg8fPmyyWvKiDFRp1aoVjh49yiKNc/XqVdjZ2eH9+/dC7a82f3Ldtmb4JysrCyNGjMDPnz8p2dCMjAxIS0sjJiaGxT70OzF48GCOdht267jGUDuqGcGZMGECrl+/DlVVVZiYmLAESNGd9aiiooKdO3di1qxZtPbzJ9HsBGmmXigoKCAqKoqtFqSNjQ3+97//iWZgPKCuro64uDiYm5tDSUkJycnJMDQ0RFxcHDw9PbnW5GjVqhViYmLQuXNnKCgoUE6Q/Px8mJub8yTJFB4ejr///psyQHbs2BFeXl6YOXOmUM5RmHBLP6sJ3Q//oqIiODo6IioqinrxVFRUwMbGBqGhoVBSUqKt7/j4eFhbW0NLS4tJ57ywsBCXLl3i6jyTlZXF48eP+V6If//+HVOmTIGGhgZfRZZFibOzM3bv3g0FBQWm7SUlJXBzc6OkE5oChYWFAH7JRDUF9u3bhyVLlsDZ2Zky5icmJiI0NBS7d+9mcY7URtDU22fPnqGiooLFOJKTk4MWLVqwRD82JmRkZJCSkgJTU1Om7Q8ePECPHj1QWlqKFy9ewMjICN+/f2dpP3jwYKSlpaGyspJFNrJTp054+vQpGAwGEhISYGxs3CDn1FBMnDgR06dPx9SpU0XSvyBOa4D7grV5kVo3/BgUhZXyXl5ejk6dOuHChQssMh0NwfPnz6GlpcWSlVAdLc8tqrp2tGht2rVrh1mzZsHHx6de8oS8Iuj4/3RkZGSQnp5OPe+r4dX5yi8PHz6EjY0N3r59y/Su0dDQQFRUFMs7TNiYmJggJCSkycoH1jdQRZgSHXPnzsWdO3cQGRlJZdzm5uZi0qRJ6N69Ow4ePMjbSfBJXXXbXrx4AWNj42ZZo2bq5Pv37/jvv//w5MkTAL/kd2fMmMGU+f07UlteuLy8HOnp6cjKyoKjoyN2794topE1Qzd0ZSPwSqtWrXDr1q0mG3AgCMeOHWOR2a3Gy8sLf//9N1/HpTdMpBmh8+PHD+zZswfXr19nW3Q0LS2N1v4nTJgAJycn+Pv7M0Xke3l51ctoLgoqKyspY6y6ujpev34NQ0NDaGtr4+nTp1zbl5SUsI1q/vz5M5PufV3s2LEDa9euxaJFi5i0yufNm4ePHz+yvFxFDZ2OhfqirKyMc+fOIScnh2nSVVvqhw4GDhyI7Oxs7N27l+p74sSJWLBgAdq0acO1fY8ePXD//n2+nSDHjh1DbGwspKWlcePGDZYUyMbsBAkLC4Ofnx+LE6S0tBTh4eGN3glSUVEBX19fBAQEUE5OeXl5uLm5wcfHh8Ux0JiYP38+WrVqBX9/f5w8eRLAr3vmxIkTGDduHNf2W7ZsQUxMDGVYqX3dcWPWrFlwdnZmmbDdvXsXBw8exI0bN+pxNg1L165d4eXlhfDwcMpA8OHDByxfvpyStMvJyanTIVad5XH48GFKPunr169wdXVFv379MHv2bNjZ2cHDwwMxMTEAfkn+ZWdnQ11dHSoqKhz/xnTX1RAEa2treHl54dGjR2ydZ7zW3uKXbdu2wdraGlevXmXrtOZGs5ODf7KysmBpaQngV50r4NdcS11dHVlZWdR+Na9tJSUl6ndB5hwtWrTgKvFHJx06dGAbVf3582fo6upyjaoODQ3F6tWrMWvWLCYJvrCwMKxZswYfPnzA9u3bISUlhVWrVjW68YuS8PBwjt87ODjQPgZLS0s8fvyYxQny+PFjKlqaDlxdXWFiYoLU1FQmieJZs2Zhzpw5uH37Nm19A4Cfnx+8vLywb98+2h0udFBfSdb/Y+/O42pM//+Bv05IKyWVLS2KaLE1ZkwkMbYhy4wZW0XWGVOGrJ8hy1gaM3YzlhFi7NvYl1GEGFnLUlrJkK2EytJy//7w63wdJ0q6z31OvZ6Ph8eD+z6n64U659z3dV3vd2m9XgGv36s6deoEe3t71KlTB8DrknOtW7fGb7/99lFf+30KdqrKZDJMmTJF4bo2Ly8PZ8+eLbTvI1EBPT09DB06VOoYKrdgwYJCj0+bNq3YvUkzMjIUekc5ODjA19dXre65kDKxJzmKMmrUKCxZsqTI3ndl0XfffQcjIyN07txZ4fjo0aOxefPmEk+CcCeIhunfvz+OHDmCr7/+WqkmHIBCewqUpuzsbIwdOxarV69GTk4OAKBixYoYPHgwfv31V6XGx+qkoGdJjx490K9fPzx+/BiTJ0/GypUrceHCBYWL9MJ06dIFzZs3x88//wxDQ0NER0fD0tISffr0QX5+PrZv3/7e51tbW2P69OlKF2QhISGYNm2aKA3F6ePk5OSgU6dOWL58eYln37du3YpJkyZh9OjRhTYHL6ppZ40aNeDv74+JEyeKsgJUDE+fPoUgCDA2NkZ8fLzCSrO8vDzs3bsXEydOVOvyecDrN96dO3dixowZCjdUp02bhh49emDZsmUSJyxcbm4uZs+eDV9fX/nF9Yf62K23VapUwcWLFwvtSeLi4iJKf4DScuPGDXTv3h3JycnyiY7bt2/DxsYGu3fvRv369fH333/j2bNnhe7iq127Nv755x+lXR7Xrl1Dhw4dcOfOHVy8eBEdOnSQN4sOCQlBnz59ULlyZYSEhLw3n7qVo3nT+16jVFVO6u7duwqT1g0bNnzvpHV0dDQcHR2hpaWF6Ojo935tVTVZpg83e/ZsxMXFYdWqVaKXAnqblpYW7t27pzSJUNxV1e3atcPw4cOVdlBt3boVK1asQGhoKNavX49Zs2bJv6/VKb+U3u6zl5OTg+zsbGhra0NPT08lk8ZbtmzB+PHj4efnp9Dw9vfff0dQUJDC7qTSfA3R1dXF+fPnCy1R/Mknn4i2A6WAsbExsrOzkZubC21tbaWV4Oo8Ya8OBEHAP//8g6ioKOjq6sLZ2Vn0/jEFu1/Cw8PRsmVLhVJ7BX3bxo4dWy5XHFPR5syZA3Nzc/j6+iocX716NR4+fIgJEyZIlEw6CQkJaNGiRZGvd+fPn0fHjh2hq6srX+xw7tw5PH/+HEeOHJEvIiF6W8+ePREWFgYTExNJynFJaf/+/ejfvz/27duHVq1aAXi983znzp0IDQ2Fvb19ib4uJ0E0TNWqVXHgwAH5TgKpZGVlyVf61atXT60nPwocPnwYWVlZ6NWrFxISEtC1a1fExcXBxMQEW7ZsUarL+rarV6+iXbt2aNasGcLCwuDp6Ylr164hPT0dERERRTYQ1tHRwdWrV5VuCsbHx8PJyUnSVYzqThAEbN++/Z07oMR88Tc1NZU3JS6Jj23aWa1aNZw7d06jGqMXVdpDJpNh+vTp+Omnn1SY6sNVrVoVmzdvVlp9cODAAfTt2xdPnjyRKFnRDAwMcPXq1RKXnfrYrbdVq1bF8ePH0bRpU4XjFy5cgLu7u1qXTgRe1zs/cuQI4uLiAAANGjTAF198UayJSAMDA+zbt0+pbOTx48fRrVs3PHv2DElJSWjSpIlSL4Dc3Fxs3LgRHTt2LHEfofKsJM1e37wBXPDaVdhHY/YEUW89e/ZEaGgoDAwM4OTkpPS5VIzPCQWrqhctWoShQ4cWuqq6QoUKiIiIeO/X0dXVRXR0dKHlAxs3bozs7GwkJyfDwcGh0BJ8UudXN/Hx8fjuu+8wbtw4dOzYUfTxinpfEKtRe+PGjbFgwQKl65ewsDCMGjVK1F4kADR6wr68GzRoEBYtWiTfrUpUHFZWVti4caNSz7yzZ8+iT58+5XJB5/r16zFhwoQiF/a1bt0atra2+PPPP+ULNXJzczFkyBAkJSXhxIkTqohLJbR9+3Zs3boVKSkpePXqlcI5sSvxSF2OS2obN27EDz/8gH/++QfBwcHYvXs3jh07ptT/8UOwHJaGqV27tlJpGSno6+tr3IrINy+EbG1tERsbi/T09CJLjxRwdHREXFwcli5dCkNDQ2RmZqJXr14YOXLkOxtJvsnW1hZbt25VKmWwZcsWrrgpwo8//ogVK1agbdu2he6AEtOAAQMQHByMoKCgEj3/Yz8Q+vj4YMuWLaKUwBDLsWPHIAgCPDw8sGPHDoUG0Nra2rC0tCxWKTGpVa5cudBJBGtra9Eb1X6sdu3aITw8vMSTIB+79dbNzQ1z5szBpk2bFBrFzpkzR76SQ51paWmhU6dO6NSp0wc/t3v37vD19cW8efPk5bPOnTuHsWPHokePHgBel7op7MNbxYoVMWLECPlWefow1tbW72z2+q6yPsnJyfLdauXxAl5dpKWlITAw8J2LHYpaZWlkZISvvvpKzIhKCnrJCYKAK1euKK2qbty4McaOHVvk17GwsCj0c0ZwcLB8N1paWprSroePVVr51Y2dnR2CgoIwYMAAUXbOvE2q1405c+bA398f06ZNU9iBMmPGDPzyyy8Kk+xi3Owuz5McJXm9Wrx4MYYNGwYdHZ0iP1uJXeq2rN80I3Hcu3ev0HsepqamSE1NlSCR6rxd9l0QBKSmpuL8+fOYMmVKkc8/f/68wgQI8Poz//jx4+Hi4lLqean0LF68WF6ydPfu3Rg0aBASExNx7tw5jBw5UvTxy/vrdb9+/ZCRkQFXV1eYmpoiPDz8o0vicyeIhjl48CAWL16M5cuXl7jHAEljx44d+Pbbb9G+fXv5Tp6IiAiEhoZi69at6Nmzp8QJ1Ve1atXw119/oUuXLiofu6DZq52dXaHlrIpqEP2x/P39sW7dOjRu3BjOzs4f3KBaSrdu3YKFhYXGlPF624wZMxAbG4s1a9bI+/68fPkSgwcPhp2dnejlBz/G8uXLMX36dPTv37/Q79uiejN87Nbb69evw83NDUZGRmjdujUA4OTJk3j69CnCwsLUrn54ad6cyMzMxOjRo7Fu3Trk5uYCeH2h4+PjgwULFkBfXx+XL18GgEJrb7u7u+PHH3+UT5hQ8X1Ms9ecnBwMHz4cU6ZMgbW1tdhR6S1dunRBQkICBg8eXOhiB3W+4fqxq6r37NmD3r17w97eXj5xev78ecTGxmL79u3o2rUrli1bhvj4eFHe88viqvDLly/Dzc1NabddWfLmZ6uCn5eCy/o3/yzmLrbExESsWbMGiYmJWLRoEczMzHDw4EHUrVtXqUxXWVKS1ytra2ucP38eJiYm732PkclkSEpKKvXMbyqq+kFYWJio45NmKrj2GTBggMLx9evXY+rUqaJ/30rp7dX4WlpaMDU1hYeHBzp06FDk883NzbF+/Xqlxx4+fBje3t64f/9+qeal0mNvb4+pU6eib9++MDQ0RFRUFGxsbBAYGIj09HQsXbpU6ohlSsEu5bdt27YNzZo1U6iOUtLPxJwE0TAPHz7EN998gxMnTkBPT0/pxhTrryr6kGbthd3UK6o++JuKszPmwoULWLBggXyVb8OGDREQEKBUMoYUWVtb4+DBgyWu+/cxCurnFkYmkxX7QuH69euFbqEs6mZ0aY0vlYyMDERGRha6Uk4VDUs/RkF5lcqVK8ubm0ZFReHVq1do166dwmPVrR7nx/ZmKI2tt3fv3sXSpUsV6l3/8MMPCjuD1IUYNycyMzPlj7WxsYGBgUGxnvexfYTKo9Iq61O1alVcvnyZkyASMDQ0xKlTp0rcSNrDwwM7d+6EkZGRwvGnT5+iR48eav9emZycjBUrViiU4Bs+fHiJd/OVF3v27FH4c8Hq3KVLl8LCwgIHDx5USY67d+/i1KlThX7WEWtVf3h4eLEf26ZNG1HG79y5M1xdXXHixAnExMTAxsYGQUFBOH/+fJG9EjVZSV6vnjx5ojYNkEePHq3w55ycHFy+fBlXr16Fj48PFi1aJFEyUmdz587F3Llz8euvv8on0kJDQzF+/HgEBARg0qRJEidUX/7+/ti1axd+++03eTmxiIgIjBs3Dl999RUWLlwobUB6Jz09PcTExMDS0hJmZmb4559/0LhxY8THx+Ozzz5DWlqa1BHLlPfd+3rTx9wH4ySIhmnfvj1SUlI0cqWcFIq6kfemwm7qva8++JvKU63wFy9eQEdHR6VjhoSE4NChQ1i9erVS40UxvNko92MlJSWhZ8+euHLlisL3UsHPbln+vtm7dy/69++PzMxMVKlSReH1SiaTqf2k7ce+fhCVxMf2ESqPSqvZq4+PD5o0aaJ0g4jE98knn2DJkiXysj4f6l3NvR88eIDatWsjJyenNGKSmnn79VImk8lX586bN69Y5Wo/1tq1azF8+HBoa2vDxMRE6bNOWV0d3bJlS/Tu3RtjxoxRWB0bGRmJXr164b///pM6omhK8npVoUIFebnGd03aSm3atGnIzMzEb7/9JnUUUkOCIGDixIlYvHixfFGfjo4OJkyYgMDAQInTiS8jIwPbt29HYmIixo0bh2rVquHixYswNzdH7dq13/vcV69eYdy4cVi+fLl8l3ilSpXw3XffISgoSF5xgNSPjY0NduzYgaZNm8LFxQVDhw7F8OHDceTIEfTp00ft72eQMk6CaBg9PT2cOXOmxCvl6MPcunWr2I8tqjxZmzZtMHjwYPTu3VslN/JLU35+PmbNmoXly5fj/v37iIuLg42NDaZMmQIrKysMHjxY1PGfP3+Onj17IiIiAlZWVko7oEq7IdWbFyo2NjY4d+4cTExMSvS1unXrhgoVKmDVqlWwtrZGZGQk0tLSEBAQgN9++01eKqi4CsoJ2dvbS7Iz5kPUr18fXbp0wezZsxVWZpM4qlWrhri4OFSvXh2+vr5YtGhRiXtIPX/+HIIgyP/fbt26hV27dqFRo0bF2vZdVIM/Nze3EuVSV7169cLatWtRpUqVIncgFrVrqKj3HZbCfLePLeszc+ZMzJs3D+3atSt0F47YddrLs3PnzmHixIkIDAyEo6Oj0vv8u/5PC3bsNmnSBGFhYQo7zfLy8nDo0CGsWLECN2/eFC17aTh58iRWrFiBpKQkbNu2DbVr18b69ethbW2tEX2UyjMLCwuMGDECkyZNUnn5zxcvXiA6OrrQHShF7TT+WAYGBrhy5Qqsra0VJkFu3rwJe3t7vHjxQtTxpVSS16uqVavi33//RcOGDd9ZulFqCQkJaNGiBW/q0XtlZmYiJiYGurq6sLOzKxc38KOjo9GuXTsYGRnh5s2buHHjBmxsbDB58mSkpKRg3bp1xfo62dnZSExMBADUq1eP18caYMiQIbCwsMDUqVPx+++/Y9y4cXB1dcX58+fRq1cvBAcHSx2RPhAbo2sYe3t7PH/+XOoY5UZp3mxq2rQpxo4dCz8/P3zzzTcYPHhwiVc8qtrMmTMREhKCuXPnYujQofLjjo6OWLhwoeiTID4+Prhw4QIGDBigksboRkZGSE5OhpmZGW7evKl0Yfkhzpw5g7CwMFSvXh1aWlrQ0tJCq1at5A0tCxqTvss333wDNzc3/PDDD3j+/DlcXFxw8+ZNCIKAzZs3q7wR7Ie4c+cO/P39NfYD3sdOBKjaq1ev8PTpU1SvXh0hISH45ZdfSjwJ0r17d/Tq1QsjRoxARkYGWrRoAW1tbTx69Ajz58/Hd999997nu7u7Kx178+dW3XYzvKv+aGEKqz9atWpV+d/vY8tdaNokx4fU3Be758DH7sgKDg6GkZERLly4gAsXLiick8lknAQRkZGREZ4+fapUq76oHVBNmjSBTCaDTCYrtM69rq4ulixZIkrm0rJjxw54eXmhf//+uHjxIl6+fAngdfmc2bNn48CBAxInpPfJzs5Gnz59VD4BcujQIXh7e+PRo0dK51Sxa9DIyAipqalK5QMvXbpU5KpoTVeS16v27dujbdu2aNiwIYDXJVff3LX4JqnK9505c0blu/1J8xgYGMj7V5UXY8aMwaBBgzB37lyFa6suXbqgX79+xf46enp6cHJyEiMiiWTlypXye0EjR46EiYkJTp8+DU9PTwwfPlzidFQSnATRMEFBQQgICMCsWbPg5ORU7JVy5VXTpk2LfcO8qN0Ec+bMgbm5OXx9fRWOr169Gg8fPsSECRPe+/yFCxfit99+w549exASEgI3NzfY2trC19cXXl5eMDc3L1ZOKaxbtw4rV65Eu3btMGLECPnxxo0bIzY2VvTx9+/fj8OHD6tsNeRXX32FNm3aoGbNmpDJZHBxcUGFChUKfWxRZQ7y8vLkH5aqV6+Ou3fvokGDBrC0tMSNGzeKzHLixAn89NNPAIBdu3ZBEARkZGQgJCQEM2fOVOtJkI4dO+L8+fOwsbGROkqJfOxEgKq1bNkSPXr0QPPmzSEIAvz9/d+562z16tXv/VoXL17EggULAADbt29HjRo1cOnSJezYsQOBgYFF/t0fP36s8OecnBxcunQJU6ZMwaxZsz7gb6Uab09GXrx4Ebm5uWjQoAEAIC4uDhUqVEDz5s0Lff6bN99LqzRaSfsIqZqRkVGR77OqLOV1/vx5bN26tdB/u6J24SQnJ4sZjd6jf//+qFSpEjZu3PhBix2Sk5MhCIK8DM+bK6u1tbVhZmb2zvdvdTFz5kwsX74c3t7e2Lx5s/y4q6srZs6cKWEy9fSxk9albfDgwdi2bRsmTpwo+lhv8vPzQ+/evREYGCjJNUSfPn0wYcIEbNu2DTKZDPn5+YiIiMDYsWPVvufbxyrJ69Vff/2FkJAQJCYmIjw8HA4ODpItEnp7x2pBL53z589jypQpkmQiUmfnzp3DihUrlI7Xrl0b9+7dK/L5WVlZCAoKQmhoaKE798pq2cSyoGARa4E+ffqgT58+oo65ePHiYj+WC7Q+HCdBNEynTp0AQKkpL2uFF65Hjx6l9rVWrFiBjRs3Kh13cHCQXwgUpWLFiujVqxd69eqFBw8eYOXKlZgyZQr+97//oUuXLvD39y90JaPU7ty5A1tbW6Xj+fn5KqmzbWFhodIJvpUrV6JXr15ISEiAv78/hg4dWuIV9Y6OjoiKioK1tTU+/fRTzJ07F9ra2li5cmWxJgeePHkiL+9x6NAhfPXVV9DT08OXX36JcePGlSiTqhRkvH79eqGTtup2M/dtHzsRoGp//fUXFixYgMTERMhkMjx58qTE5Siys7Pl3/NHjhxBr169oKWlhc8++6xYZQIL2w3xxRdfQFtbG2PGjFFaZS+1Y8eOyX8/f/58GBoaIiQkBMbGxgBeT+oMGjTog8vXlYSm9RF6899Oaps3b4a3tzc6duyII0eOoEOHDoiLi8P9+/fRs2fPD/pab/+7k7iuXr2KS5cuyScei6tg59TH7NiU2o0bNwotEVi1alVkZGSoPpCaK2oHbQFV/ezOmTMHXbt2xaFDhwr9rCPWRMz9+/cxZswYyRZRzZ49GyNHjoSFhQXy8vLQqFEj5OXloV+/fpg8ebIkmVSlJK9Xurq68oVk58+fxy+//CJZT5C3P6NpaWmhQYMGmDFjhlrudCaSWuXKlQvd+RwXF1essnZDhgxBeHg4vLy85IssSXNkZGQgMjKy0AksMSb9C+4/FHj48CGys7Pl7xkZGRnQ09ODmZkZJ0FKQiCNcvz48ff+IvFUrlxZSEpKUjqemJgoVK5c+YO+1tmzZ4URI0YIRkZGQt26dYXAwEBh8ODBgq6urhAQEFBakUtNs2bNhPXr1wuCIAgGBgZCYmKiIAiCMH36dKFVq1aij79v3z6hY8eOQnJysuhjvW3gwIHC06dPS/z8Q4cOCTt27BAEQRDi4+OFBg0aCDKZTKhevboQGhpa5PPt7OyELVu2CJmZmYKpqan8OZcvXxZMTExKnEsVZDLZO39paWlJHa9Iurq6wq1btwRBEITevXsL06ZNEwRBEFJSUgRdXV0poxXJyspKePToUYmf7+TkJCxatEhISUkRqlSpIpw+fVoQBEE4f/68YG5uXuKvGxMTI+jr65f4+apQq1Yt4erVq0rHr1y5ItSsWbPI59+7d08YMGCAULNmTaFChQqClpaWwq+idO3aVejevbvw8OFDwcDAQLh+/bpw8uRJoUWLFsKJEydK9HcqL5ycnISlS5cKgvB/71X5+fnC0KFDhcDAwGJ9jVWrVgkODg6Ctra2oK2tLTg4OAh//vmnmLFJEITWrVsL//zzT4mfv3btWmHfvn3yP48bN06oWrWq0LJlS+HmzZulEVE01tbW8r/7m5+xQkJChIYNG0oZjYrh559/FmQymWBvby+0adNGcHd3l/9q27ataOMOGjRIWLVqlWhfv7hu3bol7N+/X9iyZYsQFxcndRyV+NjXKyLSLIMHDxZ69OghvHr1SjAwMBCSkpKEW7duCU2bNhVGjRpV5POrVq0qnDp1SvygVOr27NkjGBoaCjKZTKhatapgZGQk/2VsbCz6+Bs2bBBcXV2F2NhY+bHY2FihdevWwl9//SX6+GURG6MTFZOdnR2mTp2KAQMGKBxfv349pk6dWuQ2xgcPHmD9+vVYs2YN4uPj0a1bNwwZMgQdO3aUrwY4deoUOnXqhMzMTNH+HiWxe/du+Pj4YNKkSZgxYwamT5+OGzduYN26ddi3bx+++OILUcc3NjZGdnY2cnNzoaenp7TKTtMa+KWnp8PY2LhYq0D++OMPjBo1CgYGBqhbty4uXboELS0tLFmyBDt37lSrVdhljbOzM4YMGYKePXvC0dERhw4dQsuWLXHhwgV8+eWXxdr+rKm2b9+Ofv36IS8vD+3atcORI0cAvF7xeuLECRw8ePC9zy9oVlxA+P+lFoKCgpCbm4tTp06Jlv1jGRoaYu/evUp9TY4dOwZPT088e/bsvc/v3LkzUlJS8MMPPxS62qt79+7vfX716tURFhYGZ2dnVK1aFZGRkWjQoAHCwsIQEBBQ7FXQqvL2//X7ODs7i5gE0NfXx7Vr12BlZQUTExMcP34cTk5OiImJgYeHB1JTU9/7/MDAQMyfPx9+fn5o2bIlgNc10pcuXYrRo0djxowZouYvz7Zt24Zp06Zh3Lhxha6mL+p7p0GDBli2bBk8PDxw5swZtGvXDgsXLsS+fftQsWLFIkuhSWnOnDn466+/sHr1anzxxRc4cOAAbt26hdGjR2PKlCnw8/OTOiK9h7GxMRYsWICBAweqdNzs7Gz07t0bpqamhf7MqHJ1qFDOds6V5PVqzJgx+Pnnn6Gvr19kSTexy7idO3cO+fn5+PTTTxWOnz17FhUqVICLi4uo4xNpmidPnuDrr7/G+fPn8ezZM9SqVQv37t1Dy5YtceDAAejr67/3+dbW1jhw4IC8JxBpjvr166NLly6YPXu2JCUM69Wrh+3bt6Np06YKxy9cuICvv/6apXxLQto5GCqJEydOCP379xdatmwp/Pfff4IgCMK6deuEkydPSpxM/RgbGwsPHz4UBEGQz9a+61dRfvnlF8HExERYvXq1cPPmTeHmzZtCcHCwYGJiIsyePbvI51eqVEmwt7cX5s6dKzx48KDQxzx58kRwd3f/sL+kipw4cUJo3769YGpqKujq6gqurq7C4cOHVTL22rVr3/urrDt//rywc+dO4dmzZ/Jj+/bt06gVJc+fP5c6wgfbtm2bUKlSJUFLS0to3769/Pjs2bOFTp06SZhMNVJTU4WLFy8KeXl58mNnz54VYmJiinxuwW6ft3cAtWzZsljPl5KXl5dgZWUl7NixQ7h9+7Zw+/ZtYfv27YK1tbXg7e1d5PMNDAyES5culXh8IyMj+a5DGxsbISwsTBAEQUhISFDLHUjv+r+WYvdX7dq1hejoaEEQXu8K2bhxoyAIgnD69GmhSpUqRT6/evXq8ue8aePGjWq/807Tvet7prjfO2/u3Bs/frzg5eUlCIIgXL16Vahevbqo2T9Wfn6+MHPmTEFfX1/+d9fR0REmT54sdTQqBnNzc0l2QKxatUqoWLGiYGBgIFhaWgpWVlbyX9bW1irLUB53zpXk9crd3V14/Pix/Pfv+iXm7qECn3zyibBt2zal4zt27BBatGgh+vhEmurkyZPC77//Lvzyyy8ftBts/fr1wtdffy1kZWWJmI7EoKenJ9+hKwVdXV0hMjJS6fjZs2fV8rpQE3AniIbZsWMHvLy80L9/f6xfvx7Xr1+HjY0Nli5digMHDuDAgQNSR1QrISEh6NOnDypXroyQkJD3PtbHx+e95wVBwMSJE7F48WJ5s1UdHR1MmDABgYGBRWY5efKkSurJk3p58eIFlixZgmPHjhVaR/LixYvF/loRERFwcXFB5cqVSzumKPLy8jB79mwsX74c9+/fR1xcHGxsbDBlyhRYWVlh8ODBUkcs0r1795CamorGjRvLm6JFRkaiSpUqsLe3lzid+nq7b4iWlhZMTU2ho6MjUaLiy87OxtixY7F69Wp5z6OKFSti8ODB+PXXX4tc7dWoUSNs2LBBacVOcbVu3RoBAQHo0aMH+vXrh8ePH2Py5MlYuXIlLly4gKtXr5bo64qlOD1iChT0bxBLv3794OLiIl9xu2TJEnTv3h3//PMPmjVrVuRuACMjI5w7dw52dnYKx+Pi4tCiRQv2ZxBRUd9HRX3vmJmZ4fDhw2jatCmaNm2KMWPGwMvLC4mJiWjcuLHa7bAtzKtXr5CQkIDMzEw0atQIBgYGUkeiYpgzZw5SU1M/qJFpaahRowb8/f0xceJEhaatqlKed8597OuV1AwMDBAdHa3UmzA5ORnOzs5F7ngloqI1bdpUYXdcQkICBEGAlZWV0u6xD7kfQKrVq1cv9OnTB998840k43fr1g137tzBqlWr0KxZMwCvd4EMGzYMtWvXxp49eyTJpck4CaJhmjZtitGjR8Pb2xuGhoaIioqCjY0NLl26hM6dO5fp8izqIjMzEzExMdDV1YWdnZ3G3JD+GDY2Njh37hxMTEwUjmdkZKBZs2ZFlgIrTS9evJBPQhVQZdP0D9W/f38cOXIEX3/9NczNzZVKBUydOrXYX6tKlSq4fPlysRqqq4MZM2YgJCQEM2bMwNChQ3H16lXY2Nhgy5YtWLhwIc6cOSN1xGJJSEhAYmIi3NzcoKurC0EQyk3Jh5LIyclBp06dsHz5cqWbyZokKysLiYmJAF5vRS5q8qPAkSNHMG/ePKxYsQJWVlYfPO7hw4eRlZWFXr16ISEhAV27dkVcXBxMTEywZcsWeHh4fPDXLC/S09Px4sUL1KpVC/n5+Zg7dy5Onz4NOzs7TJ48Wd7o/l38/PxQqVIlpVIkY8eOxfPnz/H777+LGZ8+Qv/+/REbG4umTZti06ZNSElJgYmJCfbs2YP//e9/ajd5SGVHz549ERYWBhMTEzg4OCjd3BKrFFu1atVw7tw51KtXT5SvXxRTU1MsXrwYffv2VTi+adMm+Pn54dGjR5Lk0gRPnjxBXl4eqlWrpnA8PT0dFStWFP26xsTEBPv27ZNPXhU4ffo0vvzySzx+/FjU8Yk0UWhoKEJDQwtd1Lh69Wqlx0+fPr3YX/tD7geQagUHB2PGjBkYNGhQoeUPPT09RR3/4cOH8PHxwaFDh+Rj5+bmomPHjli7di3MzMxEHb8s4iSIhtHT08P169dhZWWlMAmSlJSERo0a4cWLF1JHVGt5eXnYtWsXYmJiALxesdu9e3dUrFhR9LHT0tIQGBj4zh0B6tzXQktLC/fu3VN6kb1//z7q1q2Lly9fijp+VlYWJkyYgK1btyItLU3pfF5enqjjf4yqVaviwIEDcHV1/eiv9ebPvCawtbXFihUr0K5dO4XssbGxaNmypdpfZKWlpeGbb77BsWPHIJPJEB8fDxsbG/j6+sLY2Bjz5s2TOqLaMjU1ld98Lm/E6GH0IX2EpLZ+/XosX74cycnJOHPmDCwtLbFw4UJYW1sX2Q9Fan5+fli3bh0sLCzw2WefAXhdIz0lJQXe3t4K/5di12wvb9atW/fe897e3u89n5GRgcmTJ+P27dv47rvv0KlTJwCvbyxoa2vjp59+KrWspS0rKwtBQUHvvLmiyoUm9OEGDRr03vNr1qwRZdzRo0fD1NQU//vf/0T5+kUpzzvnPvb1qnPnzujWrRu+//57hePLly/Hnj17RK/s0LdvX6SmpmL37t2oWrUqgNevoT169ICZmRm2bt0q6vhEmmb69OmYMWMGXFxcCu33t2vXLomSkdjet9NSJpOp7D5UXFwcYmNjAQD29vaoX7++SsYti8S/80ulqkaNGkhISFBaXXrq1CmNuTEqlWvXrsHT0xP37t1DgwYNAAC//PILTE1NsXfvXjg6Ooo6vpeXFxISEjB48OBCdwSooze31x0+fFj+QRl4PfEQGhpaopXOH2r8+PE4duwYli1bBi8vL/z++++4c+cOVqxYgaCgINHH/xi1a9eGoaGh1DEkcefOHdja2iodz8/Pl5cZUmejR49GpUqVkJKSotDI7ttvv8WYMWPUehKkQoUKSE1NVZq4TEtLg5mZmegf2AYMGIDg4GC1//kUw8KFCz/q+X/99Rd69uypsPPk7dWi6mrZsmUIDAzEjz/+iFmzZsm/z4yMjLBw4UKVTYI8ePCg0JvJRTXXvnr1qnyrecEuoOrVq6N69eoKOwk04f1b04waNUrhzzk5OcjOzoa2tjb09PSKvKloZGSEpUuXKh3/kJWYUhkyZAjCw8Ph5eVV6M0VUm9iTXIUJS8vD3PnzsXhw4fh7OysNOEu9kStl5cXli1bpjTOypUr0b9/f1HHltrHvl6dPXu20P8fd3d3lUzY/vbbb3Bzc4OlpaW8dOfly5dhbm6O9evXiz4+kaZZvnw51q5dCy8vr4/6OufPn1dYjNu8efPSiEcievtaQipWVlYQBAH16tVTyQLusoz/ehpm6NChGDVqFFavXg2ZTIa7d+/izJkzGDt2LKZMmSJ1PLU2ZMgQODg44Pz58/KSGI8fP8bAgQMxbNgwnD59WtTxT548iVOnTqFx48aijlOaevToAeD1DZ+3e6ZUqlQJVlZWKrkRvHfvXqxbtw7u7u4YNGgQWrduDVtbW1haWmLDhg1qfbE1b948TJgwAcuXL//oGsErVqyAubl5KSUTX6NGjXDy5Emlv/f27dtL3C9BlY4cOYLDhw+jTp06Csft7Ow+qA+CFN61yfPly5fQ1tYWffzc3FysXr0aR48eRfPmzZVKSZXlVfRF9ZcqyujRozFixAh4enpiwIAB6NixIypUqFBK6cS1ZMkS/Pnnn+jRo4fCBJiLiwvGjh0r+vgXLlyAj48PYmJilH4GirNa69ixY2LGo/cobGdgfHw8vvvuO4wbN67YXyc7OxspKSlKZTOLmgCT0sGDB7F///5S2TFKqjd16lT4+vqqvA/ElStX5J+l3i73pqqJtODgYBw5cqTQnXNjxoyRP66sved/7OvVy5cvkZubq3Q8JycHz58/L5WM71O7dm1ER0djw4YNiIqKgq6uLgYNGoS+ffsqTaYR0eueXZ9//nmJn//ff/+hb9++iIiIgJGREYDXu68+//xzbN68Welak6hAdnY2/Pz85P2NC3qs+vn5oXbt2pg4caLECTUPJ0E0zMSJE5Gfn4927dohOzsbbm5uqFy5MsaOHQs/Pz+p46m1y5cvK0yAAK/LlsyaNQuffPKJ6OPb29ur5INtaSqY+ba2tsa5c+dQvXp1SXKkp6fLdzpVqVJFXk6mVatW+O677yTJVFwuLi548eIFbGxsPro0Tr9+/Uo7nqgCAwPh4+ODO3fuID8/Hzt37sSNGzewbt067Nu3T+p4RcrKyoKenp7S8fT0dLXtBVTQmFUmk2HVqlUKjXXz8vJw4sQJlTR0f3NFfVxcnOjjqZvExESsWbMGiYmJWLRoEczMzHDw4EHUrVsXDg4O731uamoqDh06hE2bNuGbb76Bnp4eevfujf79+3/UBZgqJCcnFzrBWblyZWRlZYk+vq+vL+rXr4/g4GCN2XFJ72ZnZ4egoCAMGDBAXgLgXR4+fIiBAwfi0KFDhZ5X57KZxsbGGrPbi5Tt3r0bs2bNQps2bTB48GB89dVXKvmMIPWkLXfOKfqQ16sWLVpg5cqVWLJkicLx5cuXq2xluL6+PoYNG6aSsYg03ZAhQ7Bx48YSLzoeMmQIcnJyEBMTI69IcuPGDQwaNAhDhgx552cXkl7BtfXbZDIZdHR0YGtrCzc3N9EWrE2aNAlRUVE4fvy4vNQrALRv3x7Tpk3jJEgJcBJEw8hkMvz0008YN24cEhISkJmZiUaNGinc6KLC1a9fH/fv31e6AfXgwYNCS/aUtj/++AMTJ05EYGAgHB0dlW6Gq3Nz7+TkZEnHt7GxQXJyMurWrQt7e3ts3boVLVq0wN69e+WrKdRV3759cefOHcyePbtEN+U0uVZ49+7dsXfvXsyYMQP6+voIDAxEs2bNsHfvXnzxxRdSxytS69atsW7dOvz8888AXr/+FjRbbtu2rcTpCrdgwQIAr3eCLF++XOEDmba2NqysrLB8+XLRc0h9c0ZK4eHh6Ny5M1xdXXHixAnMmjULZmZmiIqKQnBwMLZv3/7e51esWBFdu3ZF165dkZ2djV27dmHjxo1o27Yt6tSpI7/ZpI6sra1x+fJlpRXRhw4dUigpJ5akpCTs2LFDJe/ppBoVK1bE3bt3i3zcjz/+iCdPnuDs2bNwd3fHrl27cP/+fcycOVOtSxcCwM8//4zAwECEhIQUOvFO6u3y5cu4dOkS1qxZg1GjRmHkyJHo06cPfH19VbLICni9yhiASlcTl+f3+Xcp7uvVzJkz0b59e0RFRaFdu3YAXjddPnfuHI4cOSJ2TLnr168XunNO7Ea/RJrmxYsXWLlyJY4ePVqi8oPh4eE4ffq0fAIEABo0aIAlS5agdevWomSm0rFgwQI8fPgQ2dnZCtVk9PT0YGBggAcPHsDGxgbHjh2DhYVFqY//999/Y8uWLfjss88U7iM5ODio9TWhOuMkiIbx9fXFokWLYGhoiEaNGsmPZ2Vlwc/PD6tXr5YwnXqbM2cO/P39MW3aNPm27X///RczZszAL7/8gqdPn8ofK8aEhJGREZ4+fQoPDw+F44IgqLSpUknMmDHjvecDAwNFHX/QoEGIiopCmzZtMHHiRHTr1g1Lly5FTk6O2m+xP336NM6cOVPiMmiaXiu8devW+Oeff6SOUSJz585Fu3btcP78ebx69Qrjx4/HtWvXkJ6ejoiICKnjFapgwrJt27bYuXOnws43VXrzvepN5eG9auLEiZg5cybGjBmj8Pf38PAotGfB++jp6aFjx454/Pgxbt26Ja8jrK7GjBmDkSNH4sWLFxAEAZGRkdi0aRPmzJmDVatWiT5+u3btEBUVxUkQDfRmDzLg9Wej1NRULF26tFhlosLCwrB79264uLhAS0sLlpaW+OKLL1ClShXMmTMHX375pVjRP9q8efOQmJgIc3NzWFlZKd1cuXjxokTJqLiaNm2Kpk2bYt68edi7dy/WrFkDV1dX2NvbY/DgwRg4cKBCX73SkJ+fL5/ky8zMBAAYGhoiICAAP/3003ubuZaGNWvWoE+fPtDV1RV1HHX0sa9Xrq6uOHPmDObOnYutW7dCV1cXzs7OCA4OVmo0L4akpCT07NkTV65cgUwmk5ePLLjGUOdrUiIpREdHo0mTJgBKVn7QwsKi0H6YeXl5qFWrVqlkJHHMnj0bK1euxKpVq1CvXj0AQEJCAoYPH45hw4bB1dUVffr0wejRo4tc6FYSDx8+VOrxCby+pta0+0LqQia8q3A4qaV3Nbt99OgRatSoUWh9UXrtzYuBgheMtz/0iTkh0aJFC1SsWBGjRo0qdEdAmzZtSn3M0vJ2eZOcnBwkJyejYsWKqFevnsov0G/duoULFy7A1tZWret8A0CzZs3wxx9/yCfePpSRkZHG1gofMmQIBgwYAHd3d6mjlNiTJ0+wdOlSREVFITMzE82aNcPIkSNRs2ZNqaOptfL8XmVgYIArV67A2toahoaGiIqKgo2NDW7evAl7e3u8ePGiyK9RsANkw4YNCA0NhYWFBfr27Yv+/furpJzZx9iwYQOmTZsmX51Uq1YtTJ8+HYMHDxZ97EePHsHHxwctWrQodMclV7eqr7dv2MpkMpiamsLDwwPz5s0r8jW3SpUqiI6OhpWVFSwtLbFx40a4uroiOTkZDg4OyM7OFjP+RymqefvUqVNVlIQ+1qtXr7Br1y6sXr0aYWFh+Pzzz3H37l3cv38ff/75J7799ttSG2vSpEkIDg7G9OnT5Z8RT506hWnTpmHo0KGYNWtWqY1VGHNzczx//hy9e/fG4MGD1b5cY2n62NcrqXXr1g0VKlTAqlWrYG1tjcjISKSlpSEgIAC//fYbV6YTlbLdu3dj9uzZ+P333+Hi4gLgdZN0Pz8/TJgwQd6HldRPvXr1sGPHDvkkWIFLly7hq6++QlJSEk6fPo2vvvoKqamppT6+m5sbevfuDT8/PxgaGiI6OhrW1tbw8/NDfHw8S6mVAHeCaIinT59CEAQIgoBnz55BR0dHfi4vLw8HDhwodIaQ/o/U27avXr2KS5cuKWyD1BSXLl1SOvb06VMMHDgQPXv2VHkeS0tLlTegLKmgoCAEBARg1qxZcHJy+uAyaJpcK/zhw4fo1KkTTE1N0adPH/Tv31/pA4S6ysnJQadOnbB8+XL89NNPUsf5YL6+vu89L9ZODL5XvZ64TE1NhbW1tcLxS5cuoXbt2kU+v0+fPti3bx/09PTwzTffYMqUKWjZsqVYcUtd//790b9/f2RnZyMzM1Ol/99nzpxBREQEDh48qHRO3Xdclndvl3r8UA0aNMCNGzdgZWWFxo0bY8WKFfLyf+p+Q5KTHJrvwoULWLNmDTZt2oTKlSvD29sbv//+u3xX2pIlS+Dv71+qkyAhISFYtWqVwuSus7Mzateuje+//170SZA7d+5g7969WLt2Ldzd3WFjY4NBgwbBx8cHNWrUEHVsqX3s6xXwf73DkpKSsHDhwg/qHfaxzpw5g7CwMFSvXh1aWlrQ0tJCq1at5FUTCrvuI6KSGzhwILKzs/Hpp5+iYsXXt2Bzc3NRsWJF+Pr6Kly3fUi/UBJfampqoYv3cnNzce/ePQCvF3w9e/ZMlPFnz56Nzp074/r168jNzcWiRYtw/fp1nD59GuHh4aKMWdZxJ4iG0NLSeu92J5lMhunTp2vkzbryws3NDYGBgWjfvr3UUUrNlStX0K1bN9y8eVPUcaQux/UxClaLvf3zW9xdR3/99Rd2796tsbXCHz9+jG3btmHjxo04efIk7O3t0b9/f/Tr1w9WVlZSx3svU1NTnD59WiWlCUrb25OTOTk5uHr1KjIyMuDh4YGdO3eKMi7fq4CxY8fi7Nmz2LZtG+rXr4+LFy/i/v378Pb2hre3d5E3PAsmETp27Chakz2xJCcnIzc3V+lnJj4+HpUqVRL9Z97Kygpdu3bFlClTYG5uLupYpF7++usv5ObmYuDAgbhw4QI6deqE9PR0aGtrY+3ataV681lM33//PWbMmIHq1atLHYWKycnJCTExMejYsSOGDh0qX2X/pkePHsHMzKxUbp4X0NHRQXR0NOrXr69w/MaNG2jSpAmeP39eamMV5f79+/jrr78QEhKC2NhYdOrUCYMHD0a3bt1EL8ulid7uHRYTEwMbGxsEBQXh/PnzopRUeZOxsTEuXrwIa2tr1KtXD6tWrULbtm2RmJgIJycntd45R6SJQkJCiv1YHx8fEZPQh/ryyy9x7949rFq1Sl4d5dKlSxg6dChq1KiBffv2Ye/evfjf//6HK1euiJIhMTERQUFBCpUpJkyYACcnJ1HGK+s4CaIhwsPDIQgCPDw8sGPHDoWV4dra2rC0tGQ9wWLKzs4utAmc2GWVtm3bhmnTpmHcuHGF7ghQ97JOhTl16hS6deuGx48fizqOupXj+hBFzdAXVQatadOmSExMhCAIGl8r/L///sOmTZuwevVqxMfHq31JpNGjR6Ny5coICgqSOkqpyM/Px3fffYd69eph/PjxoozB96rX5VBGjhyJtWvXIi8vDxUrVkReXh769euHtWvXatzExodo06YNfH19lS7g/vrrL6xatQrHjx8XdXxDQ0NcvnxZXrOX1NuYMWOK/dgP7f+VnZ2N2NhY1K1bV6MmFKpUqYLLly/DxsZG6ihUTD///DN8fX2LtdOvNH366af49NNPsXjxYoXjfn5+OHfuHP7991+V5jl79ixWr16NkJAQ1KxZE48fP4axsTHWrFmj0WVRC5Tm61XLli3Ru3dvee+wgrKZkZGR6NWrl7zRvVhat26NgIAA9OjRA/369cPjx48xefJkrFy5EhcuXFDqeUBEVF7du3cPXl5eCA0Nld+Hyc3NRbt27bB+/XqYm5vj2LFjyMnJQYcOHSROS8XBSRANc+vWLdStW5dNcErg4cOHGDRoUKFlMgDxm8AVthKqoBmdupfpePsCq6AB4Pr169GmTRts3LhR5ZneLMfl5eWl8vFVpazUCs/JycH+/fvx119/Yf/+/ahWrRru3Lkjdaz38vPzw7p162BnZ4fmzZtDX19f4fyH3pRTBzdu3IC7u7soNUvfxPcqICUlBVevXkVmZiaaNm2qkTuKPlSVKlVw8eJFpcbkCQkJcHFxQUZGhqjj+/j4oHXr1hgyZIio41DpaNu2bbEeJ5PJEBYWJnIa9fDmDVFSX2JO4BVXeHg4vvzyS9StW1deMvHMmTO4ffs2Dhw4oJK+Dvfv38f69evlZZ169OiBwYMHo3379sjKysKMGTOwefNm3Lp1S/QsYivN16vS6B32MQ4fPoysrCz06tULCQkJ6Nq1K+Li4mBiYoItW7bAw8ND1PGJyqO8vDz8/fffiImJAQA4ODjA09OzTC+OKktiY2MRFxcH4HUJVlWVuH9Xn820tDSYmZmp9T1EdcWeIBomJiYGt2/fRqtWrQAAv//+O/788080atQIv//+O4yNjSVOqL5+/PFHZGRk4OzZs3B3d8euXbtw//59zJw5E/PmzRN9/OTkZNHHEMuCBQsU/qylpQVTU1P4+Phg0qRJkmSqUqUKpk+fjm7duqndJEh0dDQcHR2hpaWF6Ojo9z62qB1AmjLJ8S7Hjh3Dxo0bsWPHDuTn56NXr17Yt2+fRlxgXb16Fc2aNQMA+YeeApp6cz8xMVElO3D4XgXUrVsXFhYWADT3++VDyWSyQmviPnnyRCUf0uvXr49Jkybh1KlThe649Pf3Fz0DFV9p9mp7101pmUwGHR0d2Nraonv37hrbY4vUS3F7Joj52t+mTRvcuHEDf/zxB2JjYwEAvXr1wvfff6+SHZfdunXD4cOHUb9+fQwdOhTe3t4KP1/6+voICAjAr7/+KnoWVSjN16uP7R32sTp27Cj/va2tLWJjY5Geng5jY+Ny83mFSJUSEhLQpUsX3LlzR37zfM6cObCwsMD+/fu5g1kD2Nvbw97eXuXjvmvPwsuXL6Gtra3iNGUDd4JoGCcnJ/zyyy/o0qULrly5AhcXFwQEBODYsWOwt7fHmjVrpI6otmrWrIndu3ejRYsWqFKlCs6fP4/69etjz549mDt3Lk6dOiV1RPpAqirH9aG0tLRw7949mJmZyXskFPZS+yE7gC5cuKCwcuTtEmHqqHbt2khPT0enTp3Qv39/dOvWDZUrV5Y6Vrnw9g3Bgt1b+/fvh4+PD5YuXSrq+OX9vSo4OBgLFixAfHw8AMDOzg4//vhjmd+h0K1bN+jq6mLTpk3ylW15eXn49ttvkZWV9c6dmKXl7RtKb5LJZEhKShJ1fJJO27ZtcfHiReTl5clvMMTFxaFChQqwt7fHjRs3IJPJcOrUKTRq1EjitESab/DgwRgyZIh8F0phBEFASkoKLC0tVZhM/X1s7zAi0ixdunSBIAjYsGGDfLI4LS0NAwYMgJaWFvbv3y9xQnrTmDFj8PPPP0NfX7/InZ9i7fYsqMQyevRo/PzzzzAwMJCfy8vLw4kTJ3Dz5s1iL8qg/8NJEA1jYGCAq1evwsrKCtOmTcPVq1exfft2XLx4EV26dMG9e/ekjqi2qlSpgujoaFhZWcHS0hIbN26Eq6srkpOT4eDgoLImcNevXy+0J4mnp6dKxi+JglW8b6+gTE9PR8WKFVGlShVRx1fHclzv82YpoKJKABR1YfjgwQP06dMHx48fh5GREQAgIyMDbdu2xebNm2FqalpasUvdn3/+id69e8tzk+q8XbahYPeWh4cHfH19UbGiuBtBy/N7VWBgIObPnw8/Pz+FEiVLly7F6NGjMWPGDIkTiuf69etwc3ODkZGRvBTLyZMn8fTpU4SFhcHR0VHihFRWLVy4ECdPnsSaNWvkn0mePHmCIUOGoFWrVhg6dCj69euH58+f4/DhwxKnVZaYmCgvKbRw4UKYmZnh4MGDqFu3LhwcHKSOR2pozZo1MDAwQO/evRWOb9u2DdnZ2Wyuq8bKc+8wovJIX18f//77r1Ij66ioKLi6uiIzM1OiZFSYtm3bYteuXTAyMnpvKUQxy7UWLOy6desW6tSpo/C+oK2tDSsrK8yYMQOffvqpKOOXZZwE0TDVqlWTr2Jr1aoVvL29MWzYMNy8eRONGjVS2Y18TfTJJ59g5syZ6NixIzw9PWFkZIQ5c+Zg8eLF2L59OxITE0UdPykpCT179sSVK1cUdgYUbDtW53p+nTt3Rrdu3fD9998rHF++fDn27NmDAwcOiDr+26t737yhO2nSJBgaGoo6fknl5ORg+PDhmDJlyntXKL/Pt99+i6SkJKxbtw4NGzYE8PpGo4+PD2xtbbFp06bSjExUKsrze5WpqSkWL16Mvn37KhzftGkT/Pz88OjRoyK/Rn5+PhISEvDgwQPk5+crnHNzcyvVvKXt7t27WLp0KaKioqCrqwtnZ2f88MMPKi1D9OrVKyQnJ6NevXqiT/iReqhduzb++ecfpV0e165dQ4cOHXDnzh1cvHgRHTp0KNbPoCqFh4ejc+fOcHV1xYkTJxATEwMbGxsEBQXh/Pnz2L59u9QRSQ3Vr18fK1asULpBEx4ejmHDhuHGjRuiZwgNDUVoaGih71WrV68WfXxNVx57hxGVR9WqVcO+ffvw+eefKxyPiIhAt27dkJ6eLlEyUndt27bFzp07y0UpaVXhlaGGadWqFcaMGQNXV1dERkZiy5YtAF5v+a9Tp47E6dTbqFGj5M2Ap06dik6dOmHDhg3Q1tbG2rVrVTK+tbU1QkNDYW1tjcjISKSlpSEgIAC//fab6ON/jLNnzxa61c/d3R0//fST6ONraj+VSpUqYceOHZgyZUqJv8ahQ4dw9OhR+QQIAHlfhQ4dOpRGTCrDHj58KL8R0qBBA5XtHCrP71U5OTlwcXFROt68efNi9WP5999/0a9fP9y6dUupjN6HlNCTSq1atTB79mxJxs7Ozoafnx9CQkIAvP5+s7GxgZ+fH2rXro2JEydKkovE9+TJEzx48EBpEuThw4d4+vQpgNd1+N/ehasOJk6ciJkzZ2LMmDEKizo8PDxEL11ImislJaXQBTaWlpZISUkRffzp06djxowZcHFxQc2aNdlLogTKY+8wovKoa9euGDZsGIKDg9GiRQsAr++vjBgxQq2rgZD0SrMfFb3GSRANs3TpUnz//ffYvn07li1bJm+edvDgQXTq1EnidOptwIAB8t83b94ct27dQmxsLOrWrYvq1auLPv6ZM2cQFhaG6tWrQ0tLC1paWmjVqhXmzJkDf39/ta7n9/Lly0Jv3uXk5OD58+cSJNIcPXr0wN9//43Ro0eX6Pn5+flKzX2B1xMsb6+6IyqQlZUFPz8/rFu3Tv59UqFCBXh7e2PJkiXQ09MTdfzy/F7l5eWFZcuWKU0cr1y5Ev379y/y+SNGjICLiwv279+v0TeWnJyccODAAfkNHlWYNGkSoqKicPz4cYXvs/bt22PatGmcBCnDunfvDl9fX8ybNw+ffPIJAODcuXMYO3YsevToAQCIjIxE/fr1JUxZuCtXrhRa1tPMzEztdq2Q+jAzM5OX+X1TVFQUTExMRB9/+fLlWLt2Lby8vEQfqywqr73DiMqjxYsXw8fHBy1btpTFLLrOAABm1ElEQVRf1+fm5sLT0xOLFi2SOB29rVevXsV+7M6dO0VM8tp///2HPXv2FFpSX6yeJGUZJ0E0TN26dbFv3z6l4wsWLJAgjeYSBAG6urpo1qyZysbMy8uTr/CrXr067t69iwYNGsDS0lIlW9Y/RosWLbBy5UosWbJE4fjy5cvRvHlz0cfv2bNnsW8EquKN6EPY2dlhxowZiIiIQPPmzaGvr69w3t/f/73P9/DwwKhRo7Bp0ybUqlULAHDnzh2MHj0a7dq1Ey03abYxY8YgPDwce/fuhaurKwDg1KlT8Pf3R0BAAJYtWybq+OXtverNpnkymQyrVq3CkSNH8NlnnwF4vdorJSUF3t7eRX6t+Ph4bN++Hba2tqLlVYWbN28iJydHpWP+/fff2LJlCz777DOF9wwHBwfRS16StFasWIHRo0ejT58+8kUbFStWhI+Pj/x1x97eHqtWrZIyZqGMjIyQmpqqtKr/0qVL8glkorf17dsX/v7+MDQ0lJdJDA8Px6hRo9CnTx/Rx3/16pVSaRcqnnf1Dhs9ejRSUlLKdO8wovLIyMgIu3fvRnx8PGJjYwEADRs21PjP+mVV1apVpY4gFxoaCk9PT9jY2CA2NhaOjo64efMmBEFQ6b3MsoQ9QTRMmzZtMHjwYPTu3Ru6urpSx9E4Uq66ad26NQICAtCjRw/069cPjx8/xuTJk7Fy5UpcuHABV69eFT1DSUVERKB9+/b45JNP5DfeQ0NDce7cORw5ckTeAFcsAwcOxK5du1C1alV5mZkLFy7gyZMn6NGjh8LNrjVr1oia5UO9rxeITCZDUlLSe59/+/ZteHp64tq1a/IV1bdv34ajoyP27Nmj1qWFTpw4gc8//1ypJn9ubi5Onz6t9r0NNFn16tWxfft2uLu7Kxw/duwYvvnmGzx8+FD0DHl5edi1axdiYmIAvP6w36NHjzLZo+F9TfPeVJwGeh4eHhg/frzG75gxNDREVFQUbGxsVDamnp4erl69ChsbG4Xxo6Ki4ObmhidPnqgsC0kjMzNT/r5qY2MDAwMDiRMVbezYsTh79iy2bduG+vXr4+LFi7h//z68vb3h7e2NqVOnSh2R1NCrV6/g5eWFbdu2yd9X8/Pz4e3tjeXLl0NbW1vU8SdMmAADA4OPKvlaXpVG7zAiIir7WrRogc6dO2P69OnyaxszMzP0798fnTp1wnfffSd1RI3DSRAN8+OPP2Ljxo14+fIlvvnmGwwePFi+0pTe712rbpYuXYrRo0eLvurm8OHDyMrKQq9evZCQkICuXbsiLi4OJiYm2LJlCzw8PEQd/2NdvnwZv/76Ky5fvixvdjtp0iSVNPGbMGEC0tPTsXz5clSoUAHA6xus33//PapUqYJff/1V9AxSEgQBR48eVVg50r59e4lTFa1ChQpITU2FmZmZwvG0tDSYmZmpfW8DTaanp4cLFy4o9JIBXjcJbtGiBbKyskQd/9q1a+jWrRvu37+PBg0aAHjdn8HU1BR79+6Fo6OjqONrsl27dmHy5MkYN24cnJyclMrhOTs7S5Tsw3Tp0gXBwcGoWbOmysZ0c3ND79694efnB0NDQ0RHR8Pa2hp+fn6Ij4/HoUOHVJaFqLhevXqFkSNHYu3atcjLy0PFihWRl5eHfv36Ye3atfLPPUSFiY+Pl382d3JygqWlpWhjvbnrMT8/HyEhIXB2doazs7PSexVLdLybkZERzp07p3QNFRcXhxYtWiAjI0OaYEQkCkEQsH37dhw7dgwPHjxQKmmtbpUsSH0YGhri8uXLqFevHoyNjXHq1Ck4ODggKioK3bt3x82bN6WOqHE4CaKBcnNzsWfPHoSEhODgwYOwtbWFr68vvLy8YG5uLnU8taWOq27S09NhbGyssTXfVcXU1BSnTp2S30wtcOPGDXz++edIS0uTKNmHKXi5LS//31paWrh//75SM+64uDi4uLjIm9VS6WvXrh1MTEywbt066OjoAACeP38OHx8fpKen4+jRo6KO37JlS5iamiIkJATGxsYAgMePH2PgwIF4+PAhTp8+Ler4mkxLS0vpmEwmgyAIGtEYXUqnTp1C586dMWDAAKxduxbDhw/H9evXcfr0aYSHh6ukfCNRSaWkpODq1avIzMxE06ZNVbLIhMqGiIgIuLi4oHLlyqKOU5q7HsszPz8/VKpUSWmiaOzYsXj+/Dl+//13iZIRkRhGjRqFFStWoG3btjA3N1e6F6BulSxI0fbt27F169ZCe3JcvHhR1LFr1KiBY8eOoWHDhmjUqBGCgoLg6emJqKgouLq6IjMzU9Txy6KyV5OiHKhYsSJ69eqFXr164cGDB1i5ciWmTJmC//3vf+jSpQv8/f3VfleBFHJycuSllN7UvHnzQpt+q0K1atUkGfdjvHjxQunFv0qVKqKOmZubi9jYWKVJkNjYWI1oDv6xZdhCQ0MRGhpa6MqR1atXl3rej1XQTEwmk2HgwIEKF+V5eXmIjo5mHWmRLVq0CB07dkSdOnXQuHFjAK+bpero6ODw4cOij3/58mWcP39ePgECAMbGxpg1a5a8aTEVLjk5WeoIJZaWliZvyHv79m38+eefeP78OTw9PUUvmwgArVq1wuXLlxEUFAQnJyccOXIEzZo1w5kzZ+Dk5CT6+EQfo27duqhbt67UMUgDde7cGZcvXxa9/OCxY8dE/frlSXBw8Dt7h72544Y7aog03/r167Fz50506dJF6ij0gRYvXoyffvoJAwcOxO7duzFo0CAkJibi3LlzGDlypOjjf/bZZzh16hQaNmyILl26ICAgAFeuXMHOnTtZEaiEOAmiwSIjI7FmzRps3rwZZmZmGDhwIO7cuYOuXbvi+++/x2+//SZ1RLXi5eWFZcuWKX2YXLlyJfr37y9RKs2QnZ2N8ePHY+vWrYXuuhB7ZfKgQYMwePBgJCYmokWLFgBeXywEBQVh0KBBoo79sT62+eH06dMxY8YMuLi4oGbNmhqxi6SgmZggCDA0NFToX6StrY3PPvsMQ4cOlSpeueDo6Ij4+Hhs2LBBXkatb9++6N+/v0r6SdWvXx/379+Hg4ODwvEHDx6wCWARxCxlIpYrV66gW7duuH37Nuzs7LB582Z06tQJWVlZ0NLSwoIFC7B9+3b06NFD9Cz16tXDn3/+Kfo4RB/jzZucReFNUCoKCztolqtXr8ob2iYmJgJ43cutevXqCj0iNeEzPxEVrWrVqirtkUel548//sDKlSvRt29frF27FuPHj4eNjQ0CAwORnp4u+vjz58+X7/aYPn06MjMzsWXLFtjZ2fHzYQmxHJaGefDgAdavX481a9YgPj4e3bp1w5AhQ9CxY0f5B6VTp06hU6dO3Br1Fj8/P6xbtw4WFhaFrrp5s5YtX1AUjRw5EseOHcPPP/8MLy8v/P7777hz5w5WrFiBoKAg0SeR8vPz8dtvv2HRokVITU0FANSsWROjRo1CQECAWtfL/tgybDVr1sTcuXPh5eUlZkxRTJ8+HWPHjoW+vr7UUUgF3ixvdurUKYwfPx7Tpk2Tv97++++/mDFjBoKCgrgSqgiJiYlYuHChvKl8o0aNMGrUKNSrV0/iZIXr3LkzKlasiIkTJ2L9+vXYt28fOnbsKJ+M8PPzw4ULF/Dvv/+KniU/Px8JCQmF7pxzc3MTfXyi4mBZISpNBc1SeZONiEj9hISE4NChQ1i9erVKFqNR6dHT00NMTAwsLS1hZmaGf/75B40bN0Z8fDw+++wzjSnLTv+HkyAaRltbG/Xq1YOvry8GDhyoVGsfeH0jqnv37tyy/BZecJZc3bp1sW7dOri7u6NKlSq4ePEibG1tsX79emzatAkHDhxQWZaCG61il+AqLR/b/NDExASRkZFqe/OT1Fd8fPw7G/AFBgaW+nhaWloKqxbf7oHz5p/Z1+LdDh8+DE9PTzRp0gSurq4AXtd7j4qKwt69e/HFF19InFBZ9erVERYWBmdnZ2RmZqJKlSo4d+6cvAdHbGwsPvvsM9Gbvf7777/o168fbt26pbQymt93RFRWbdy4Ed27d+eiEw319OlThIWFwd7eHvb29lLHIaJS9vz5c/Ts2RMRERGwsrJSWHwLiN9XgkrOxsYGO3bsQNOmTeHi4oKhQ4di+PDhOHLkCPr06aOS3SBUujgJomFOnjypkrraRG8yMDDA9evXUbduXdSpUwc7d+5EixYtkJycDCcnJ9F3HT1//hyCIEBPTw8AcOvWLezatQuNGjVChw4dRB37Y31s88MJEybAwMAAU6ZMETOmKO7fv4+xY8fK+5m8/XbDG5Li+fPPP/Hdd9+hevXqqFGjhsLkhEwmE+XDdnh4eLEf26ZNm1Ifv6xo2rQpOnbsiKCgIIXjEydOxJEjR9TyQklLSwv37t2DmZkZAOVVyffv30etWrVE/5lv0qQJ6tevj+nTpxdaPrCgVB+Rurp9+zYAwMLCQuIkRCSWb775Bm5ubvjhhx/w/PlzNG7cGDdv3oQgCNi8eTO++uorqSMSUSn65ptvcOzYMXz99deFNkafOnWqRMmoKEOGDIGFhQWmTp2K33//HePGjYOrqyvOnz+PXr16ITg4uNTHNDY2LnY5RE7CfDj2BNEwLi4uyM7O1sibwaS5bGxskJycjLp168Le3h5bt25FixYtsHfvXhgZGYk+fvfu3dGrVy+MGDECGRkZaNGiBbS1tfHo0SPMnz8f3333negZPsbHND988eIFVq5ciaNHj8LZ2Vlp5Yg6l24bOHAgUlJSMGXKFI3pZ1JWzJw5E7NmzcKECRNUNiYnNkpHTEwMtm7dqnTc19cXCxcuVH2gYnr751uKn/f4+Hhs376dfWdIo+Tm5mL69OlYvHixfFGJgYEB/Pz8MHXqVKX3fSIAyMrKQlBQkHyhyds7PpOSkiRKRkU5ceIEfvrpJwDArl27IAgCMjIyEBISgpkzZ3IShKiM2b9/Pw4fPoxWrVpJHYU+0MqVK+XvryNHjoSJiQlOnz4NT09PDB8+XJQx1fl6ryzgJIiGeftm8KeffopKlSppzM1gKfFioeQGDRqEqKgotGnTBhMnTkS3bt2wdOlS5OTkqOQm/MWLF7FgwQIAwPbt21GjRg1cunQJO3bsQGBgoFp/339s88Po6Gg0adJE/rXepO6TCqdOncLJkyfl+Ul1Hj9+jN69e0uaISMjA8HBwfK+Fg4ODvD19eVq/CKYmpri8uXLSiX0Ll++LN9poY4GDhyIypUrA3g9eTtixAh5aZaXL1+qJMOnn36KhIQEToKQRvHz88POnTsxd+5ctGzZEgBw5swZTJs2DWlpaVi2bJnECUkdDRkyBOHh4fDy8uJCEw3z5MkTVKtWDQBw6NAhfPXVV9DT08OXX36JcePGSZyOiEqbhYWFxpTyJkVaWlrQ0tKS/7lPnz7o06ePqGP6+PiI+vXLO06CaJi3bwabm5trzM1gqfFioeRGjx4t/3379u0RGxuLCxcuwNbWFs7OzqKPn52dDUNDQwDAkSNH0KtXL2hpaeGzzz7DrVu3RB//Y3xsbx5N7u1jYWGhVAKLVKN37944cuQIRowYIcn458+fR8eOHaGrq4sWLVoAeL1radasWThy5Ih8YpCUDR06FMOGDUNSUhI+//xzAK97gvzyyy8KO8fUydsf1gcMGKD0GG9vb1HGjo6Olv/ez88PAQEBuHfvHpycnJRW0Kvi/YroQ23cuBGbN29G586d5cecnZ1hYWGBvn37chKECnXw4EHs379f3juKNIeFhQXOnDmDatWq4dChQ9i8eTOA1wtYdHR0JE5HRKVt3rx5GD9+PJYvXw4rKyup49AHevHiBaKjowtdSO3p6Sn6+ImJiVizZg0SExOxaNEimJmZ4eDBg6hbty4cHBxEH7+sYU8QDaOnp4fY2FjUrVsX33zzDRwcHDB16lTcvn0bDRo0QHZ2ttQR1ZaRkREvFjSUs7MzhgwZgp49e8LR0RGHDh1Cy5YtceHCBXz55Ze4d++e1BGpEEeOHMG8efOwYsUKfuBTgcWLF8t/n5WVhfnz5+PLL78s9Gawv7+/qFlat24NW1tb/Pnnn6hY8fV6i9zcXAwZMgRJSUk4ceKEqONrMkEQsHDhQsybNw93794FANSqVQvjxo2Dv78/J/DfoqWlBZlM9s4J14JzbIxO6srMzAzh4eFo2LChwvGYmBi4ubnh4cOHEiUjdWZtbY0DBw4ofd+Q+vvjjz8watQoGBgYoG7durh06RK0tLSwZMkS7Ny5U6MXQBGRMmNjY2RnZyM3Nxd6enpK12Xs66C+Dh06BG9vbzx69EjpnCquLcLDw9G5c2e4urrixIkTiImJgY2NDYKCgnD+/Hls375d1PHLIk6CaBjeDC45Xixoru3bt6Nfv37Iy8tDu3btcOTIEQDAnDlzcOLECRw8eFDihFTg7UZeWVlZ/MCnItbW1sV6nEwmE738n66uLi5dugR7e3uF49evX5f3tqKiPXv2DADkO+FI2YfsBrS0tBQxCVHJzJgxA7GxsVizZo28pNzLly8xePBg2NnZsWEqFeqvv/7C7t27ERISIu8VSZrjwoULSElJwRdffAEDAwMAr/sGGBkZccEeURkTEhLy3vMsf6S+7Ozs0KFDBwQGBsLc3Fzl47ds2RK9e/fGmDFjYGhoiKioKNjY2CAyMhK9evXCf//9p/JMmo6TIBqGN4NLjhcLmu3evXtITU1F48aN5XUZIyMjUaVKFaUbrSSdoj7kvYkf+Mouc3NzrF+/Hh06dFA4fvjwYXh7e+P+/fsSJSMiUi89e/ZEaGgoKleujMaNGwMAoqKi8OrVK7Rr107hsTt37pQiIqmhpk2bIjExEYIgwMrKSmmhycWLFyVKRh8iIiICLi4u8glQIiJSH1WqVMGlS5dQr149ScY3MDDAlStXYG1trTAJcvPmTdjb2+PFixeS5NJk7AmiYb7++mu0atVKfjO4QLt27dCzZ08Jk6m/efPmITExEebm5rxY0EA1atRAjRo1FI4V9Bog9cGJDfUwY8YMjB07VmnC9/nz5/j1118RGBgo6vjffvstBg8ejN9++02hr8W4cePQt29fUcfWdGlpaQgMDMSxY8cKrT3LHVREZYuRkRG++uorhWMWFhYSpSFN0aNHD6kjUCno3LkzLl++DBsbG6mjEJGI8vLy8PfffyMmJgYA4ODgAE9PT1SoUEHiZPQ+X3/9NY4fPy7ZJIiRkRFSU1OVKj5cunQJtWvXliSTpuNOECo3pk+f/t7zLDdAVLqePn1a6HGZTIbKlStDW1tbxYnKjwoVKiA1NRVmZmYKx9PS0mBmZiZ6/dJXr15h3LhxWL58OXJzcwEAlSpVwnfffYegoCCueHyPLl26ICEhAYMHD4a5ublSDxBONBIREZUNb67sJaKyKSEhAV26dMGdO3fQoEEDAMCNGzdgYWGB/fv3S3aDnYqWnZ2N3r17w9TUVJI+m2PHjsXZs2exbds21K9fHxcvXsT9+/fh7e0Nb29v3sMsAU6CEFGh3nUDuzBVqlQRMQlpqoKGxe9Sp04dDBw4EFOnTpWXOKPSoaWlhfv378PU1FTheFhYGL799luVNdrNzs5GYmIiAKBevXosRVgMhoaGOHXqlMJuTyIiosJcuHBBYWVx06ZNJU5EH4KTIERlX5cuXSAIAjZs2IBq1aoBeL0wbcCAAdDS0sL+/fslTkjvEhwcjBEjRkBHRwcmJiYK9zZU0Wfz1atXGDlyJNauXYu8vDxUrFgReXl56NevH9auXcudRCXASRAqd3ixUDxF3cAGAEEQIJPJRF9VTppp3bp1+OmnnzBw4EB56bLIyEiEhIRg8uTJePjwIX777TeMGzcO//vf/yROWzYUNKZ/8uQJqlSpovAznJeXh8zMTIwYMQK///67hCnpfT755BMsWbIEn332mdRRiEgkzZo1Q2hoKIyNjdG0adP3ft5iuVYqzIMHD9CnTx8cP34cRkZGAICMjAy0bdsWmzdvVloEQepp48aN6N69O/T19aWOQkQi0dfXx7///gsnJyeF41FRUXB1dUVmZqZEyagoNWrUgL+/PyZOnKjyRZuCIOD27dswNTXFo0ePcOXKFWRmZqJp06aws7NTaZayhD1BqNzgxcKHOXbsmNQRSMOFhIRg3rx5+Oabb+THunXrBicnJ6xYsQKhoaGoW7cuZs2axUmQUrJw4UIIggBfX19Mnz4dVatWlZ/T1taGlZUVWrZsKWFCKsoff/yBiRMnIjAwEI6Ojkrbrrnz7t1sbGxw7tw5mJiYKBzPyMhAs2bNRF+tRVRc3bt3l5cFZG8HKgk/Pz88e/YM165dQ8OGDQEA169fh4+PD/z9/bFp0yaJE1Jx9OvXT+oIRCSyypUr49mzZ0rHMzMzWR5azb169QrffvutJFUrBEGAra0trl27Bjs7O/aLKyXcCULlxrfffoukpCSsW7dO6WLB1taWFwtEpUxXVxfR0dFKKxXi4+PRuHFjZGdnIzk5GQ4ODsjOzpYoZdkUHh6Ozz//XOkGOqm/+Ph49OvXT2n1N3feFU1LSwv37t1T6oVz//591K1bFy9fvpQoGVHh8vLyEBERAWdnZ/kCHaLiqFq1Ko4ePYpPPvlE4XhkZCQ6dOiAjIwMaYJRkbKyshAUFITQ0FA8ePAA+fn5Cuc5YU9Utnh7e+PixYsIDg6WV0c4e/Yshg4diubNm2Pt2rXSBqR3Gj16NExNTSVbsOng4IDg4GBWCChF3AlC5cahQ4dw9OhR+QQIADRq1Ai///47OnToIGEy9RQdHV3sxzo7O4uYhDSVhYUFgoODERQUpHA8ODhYvpIhLS0NxsbGUsQrc54+fSrfJdC0aVM8f/4cz58/L/Sx3E2gvvr3749KlSph48aNhTZGJ2V79uyR//7w4cMKO6Dy8vIQGhoKKysrCZIRvV+FChXQoUMHxMTEcBKEPkh+fn6hCx0qVaqkdFOd1MuQIUMQHh4OLy8v1KxZk+/zRGXc4sWL4ePjg5YtW8pft3Nzc+Hp6YlFixZJnI7eJy8vD3PnzsXhw4fh7Oys9L47f/58UccPCgrCuHHjsGzZMjg6Ooo6VnnBnSBUbhgaGuLkyZNo0qSJwvFLly6hTZs2H9QIvDwo6AlS1EsEVybTu+zZswe9e/eGvb29fKXi+fPnERsbi+3bt6Nr165YtmwZ4uPjRf8AUR5UqFABqampMDMze2dPH+4mUH96enq4dOkSGjRoIHUUjVGwRb2w96xKlSrBysoK8+bNQ9euXaWIR/ReLi4u+OWXX9CuXTupo5AG6d69OzIyMrBp0ybUqlULAHDnzh30798fxsbG2LVrl8QJ6V2MjIywf/9+uLq6Sh2FiET2Zl+HO3fuyHvTNmzYELa2thKno6K0bdv2nedkMhnCwsJEHd/Y2BjZ2dnIzc2FtrY2dHV1Fc6np6eLOn5ZxEkQKjd4sfBhbt26VezHWlpaipiENFlycjJWrlyJGzduAAAaNGiA4cOHc1W2CMLDw+Hq6oqKFSvi+PHj711Z2KZNG1GzzJkzB+bm5vD19VU4vnr1ajx8+BATJkwQdXxN5ubmhsDAQLRv317qKBrH2toa586dQ/Xq1aWOQlRshw4dwqRJk/Dzzz+jefPmSg2SuXOPCnP79m14enri2rVr8t21t2/fhqOjI/bs2YM6depInJDexdraGgcOHFCoTkBEZVN+fj50dHTkfR2IPkRISMh7z/v4+KgoSdnBSRAqN3ixQESkGlZWVti4cSM+//xzheNnz55Fnz59kJycLFEy9bdt2zZMmzYN48aNg5OTk9K2a5YfJCpb3my2+ebkNXfuUVEEQcDRo0cRGxsL4PXKYk6gq7+//voLu3fvRkhICPT09KSOQ0QiY18HIvXBSRAqV3ixUHLr16/H8uXLkZycjDNnzsDS0hILFy6EtbU1unfvLnU8UhPR0dFwdHSElpZWkX1leDNXPG5ubnB3d0ebNm3g6uoKHR0dlY6vo6ODmJgYWFtbKxxPSkpCo0aN8OLFC5Xm0SRv3hAtUFDmiTdEixYaGvrOZrOrV6+WKBXRu4WHh7/3vNg794hItZo2bYrExEQIggArKyulxQ4XL16UKBkRiWHv3r2YO3cu+zoQqQE2RqdyRSaT4YsvvsAXX3whdRSNsmzZMgQGBuLHH3/ErFmz5DfhjIyMsHDhQk6CkFyTJk1w7949mJmZoUmTJu/sK8ObueLq0KEDTpw4gfnz5yM3NxcuLi4KkyJirzy0sLBARESE0iRIRESEvBwhFY67ZEpu+vTpmDFjBlxcXNhsljSGtbU1LCwslL5fC+qIE70LJ301U48ePaSOQEQq5O3tjezsbDRu3Jh9HYgkxp0gVK7wYqFkGjVqhNmzZ6NHjx4wNDREVFQUbGxscPXqVbi7u+PRo0dSRyQ1cevWLdStWxcymazIvjLsJSO+3NxcnDt3DuHh4Th+/DjCwsKgpaUl+k6MuXPnYu7cufj111/h4eEB4PXr7/jx4xEQEIBJkyaJOj6VTzVr1sTcuXPh5eUldRSiYqtQoQJSU1NhZmamcDwtLQ1mZmZcMECFKmrSl70OiYjUA/s6EKkP7gShcoMrREsuOTkZTZs2VTpeuXJlZGVlSZCI1FXBxEZOTg6mT5+OKVOmKO0GINVJSkrClStXEBUVhejoaBgaGsLNzU30cceNG4e0tDR8//33ePXqFYDXJbImTJjACZBiuHv3Lk6dOlXohL2/v79EqdTfq1evlPrQEKm7glJ3b8vMzFR5KUPSHMuXL8fatWs56avBLly4gJiYGACvewYUdq1FRJqPkxxE6oM7Qajc4ArRkmvUqBHmzJmD7t27K+wEWbJkCdasWcPatVSoqlWr4vLly5wEkUC/fv0QHh6Oly9fws3NDW3atIG7uzucnZ1VOgGcmZmJmJgY6Orqws7ODpUrV1bZ2Jpq7dq1GD58OLS1tWFiYqLw/yWTyZCUlCRhOvU2YcIEGBgYYMqUKVJHISrSmDFjAACLFi3C0KFDFcoU5uXl4ezZs6hQoQIiIiKkikhqzMTEBJGRkahXr57UUegDPXjwAH369MHx48dhZGQEAMjIyEDbtm2xefNmmJqaShuQiEpdfn4+EhISCl3gpIoFaqTZEhISkJiYCDc3N+jq6r5zAQ0VjTtBqNzgCtGSGzNmDEaOHIkXL15AEARERkZi06ZNmDNnDlatWiV1PFJTPXr0wN9//43Ro0dLHaXc2bx5M6pXr44hQ4bAw8MDrVq1Er0PSGEMDAzwySefqHxcTTZlyhQEBgZi0qRJhTZJp3d78eIFVq5ciaNHj8LZ2Vmp2ez8+fMlSkak7NKlSwBe7wS5cuUKtLW15ee0tbXRuHFjjB07Vqp4pOaGDBmCjRs3ctJXA/n5+eHZs2e4du0aGjZsCAC4fv06fHx84O/vj02bNkmckIhK07///ot+/frh1q1bSr0y2SeT3ictLQ3ffvstwsLCIJPJEB8fDxsbGwwePBjGxsaYN2+e1BE1DneCULnBFaIfZ8OGDZg2bRoSExMBALVq1cL06dMxePBgiZORupo5cybmzZuHdu3aoXnz5tDX11c4z7I+4nn8+DFOnjyJ48ePIzw8HDExMWjSpAnc3d3h7u6ODh06lPqYvXr1wtq1a1GlShX06tXrvY/duXNnqY9fVnB1b8m1bdv2nedkMhnCwsJUmIaoeAYNGoRFixahSpUqUkchDTJq1CisW7cOzs7OnPTVMFWrVsXRo0eVFolERkaiQ4cOyMjIkCYYEYmiSZMmqF+/PqZPn15oWfaqVatKlIzUnbe3Nx48eIBVq1ahYcOG8ooshw8fxpgxY3Dt2jWpI2ocToJQucGLhdKRnZ2NzMxMpQaeRG97XxkslvVRrYSEBMycORMbNmxAfn6+KCuOBg0ahMWLF8PQ0BCDBg1672PXrFlT6uOXFePHj0e1atUwceJEqaMQEZGa4qSv5jI0NMTJkyfRpEkTheOXLl1CmzZt8PTpU2mCEZEo9PX1ERUVBVtbW6mjkIapUaMGDh8+jMaNGyuUpU9KSoKzszMyMzOljqhxOAlC5QYvFkouOTkZubm5sLOzUzgeHx+PSpUqwcrKSppgRFSotLQ0hIeH4/jx4zh+/DiuX78OIyMjeX+QUaNGSR2R3iEvLw9du3bF8+fP4eTkxAl7IiKiMqR79+7IyMjApk2bUKtWLQDAnTt30L9/fxgbG2PXrl0SJySi0uTh4YHx48ejU6dOUkchDWNoaIiLFy/Czs5OYRLk/Pnz6NixI9LS0qSOqHHYE4TKjWPHjkkdQWMNHDgQvr6+SpMgZ8+exapVq3D8+HFpghFRoczMzFC9enW0bt0aQ4cOhbu7O5ycnKSORcUwZ84cHD58GA0aNAAApcbo9H7nz5/H1q1bkZKSglevXimcYxk2IiKS2tKlS+Hp6QkrKytYWFgAAG7fvg1HR0f89ddfEqcjotIQHR0t/72fnx8CAgJw7969Qhc4OTs7qzoeaYjWrVtj3bp1+PnnnwG8vhbMz8/H3Llz37vIm96NO0GIqEhVqlTBxYsXlbZwJiQkwMXFhbVridTMtWvX4ODgINn49+/fx9ixYxEaGooHDx4oNQFkA8B3MzY2xoIFCzBw4ECpo2iczZs3w9vbGx07dsSRI0fQoUMHxMXF4f79++jZsyfLsBERkVoQBAFHjx5FbGwsAKBhw4Zo3769xKmIqLRoaWlBJpMpXQMVKDjHxuj0PlevXkW7du3QrFkzhIWFwdPTE9euXUN6ejoiIiLYQ7IEuBOEiIokk8nw7NkzpeNPnjzhmzaRGpJyAgR4vXssJSUFU6ZMKbQBIL1b5cqV4erqKnUMjTR79mwsWLAAI0eOhKGhIRYtWgRra2sMHz4cNWvWlDoeERERgNfXVl988QW++OILqaMQkQiSk5OljkBlgKOjI+Li4rB06VIYGhoiMzMTvXr1wsiRI3ltU0LcCUJERerWrRt0dXWxadMmVKhQAcDrldzffvstsrKycPDgQYkTEpE6eVfTTyranDlzkJqaisWLF0sdRePo6+vj2rVrsLKygomJCY4fPw4nJyfExMTAw8MDqampUkckIiJCaGiofLdsfn6+wrnVq1dLlIqIiKhs404QIirSL7/8Ajc3NzRo0ACtW7cGAJw8eRJPnz5lQ3kiUmJhYfHO7d/0fpGRkQgLC8O+ffvg4OCgVDeYfS3ezdjYWL5rsXbt2rh69SqcnJyQkZGB7OxsidMREREB06dPx4wZM+Di4sLdskTlwJw5c2Bubg5fX1+F46tXr8bDhw8xYcIEiZKRunuzt8ybZDIZdHR0ULduXVSuXFnFqTQbd4IQUbHcvXsXS5cuRVRUFHR1deHs7IwffvgB1apVkzoaqak1a9bAwMAAvXv3Vji+bds2ZGdnw8fHR6JkJLYjR45g3rx5WLFiBaysrKSOo1EGDRr03vPsa/Fu/fr1g4uLC8aMGYOff/4ZS5YsQffu3fHPP/+gWbNmnEAiIiLJ1axZE3PnzoWXl5fUUYhIBaysrLBx40Z8/vnnCsfPnj2LPn36sHQWvVNBbxkA8gWGb06cV6pUCd9++y1WrFgBHR0dSTJqGk6CEBGRKOrXr48VK1agbdu2CsfDw8MxbNgw3LhxQ6JkZVtOTg46deqE5cuXw87OTpIMxsbGyM7ORm5uLvT09JR2M6Snp0uSi8q29PR0vHjxArVq1UJ+fj7mzp2L06dPw87ODpMnT4axsbHUEYmIqJwzMTFBZGQkG9oSlRM6OjqIiYmBtbW1wvGkpCQ0atQIL168kCgZqbvdu3djwoQJGDduHFq0aAHgddWAefPmYerUqcjNzcXEiRPx7bff4rfffpM4rWZgOSwi+iBOTk44cOAALCwspI5Cai4lJUXpwx4AWFpaIiUlRYJE5UOlSpXeuXVWVRYuXCjp+GXBw4cP5ROFDRo0gKmpqcSJ1N+bOxO1tLQwceJECdMQEREpGzJkCDZu3IgpU6ZIHYWIVMDCwgIRERFK18URERGoVauWRKlIE8yaNQuLFi1Cx44d5cecnJxQp04dTJkyBZGRkdDX10dAQAAnQYqJkyBE9EFu3ryJnJwcqWOQBjAzM0N0dLRSOaSoqCiYmJhIE6qcGDBgAIKDgxEUFCTJ+Cx1VnJZWVnw8/PDunXr5M1SK1SoAG9vbyxZsgR6enoSJ1Rv+fn5SEhIKLTZrJubm0SpiIiIXnvx4gVWrlyJo0ePwtnZWWm37Pz58yVKRkRiGDp0KH788Ufk5OTAw8MDABAaGorx48cjICBA4nSkzq5cuQJLS0ul45aWlrhy5QoAoEmTJkhNTVV1NI3FSRAiIhJF37594e/vD0NDQ/nNx/DwcIwaNQp9+vSROF3Zlpubi9WrV+Po0aNo3rw59PX1Fc6r4gI7Ly8Pf//9N2JiYgAADg4O8PT0RIUKFUQfW5ONGTMG4eHh2Lt3L1xdXQEAp06dgr+/PwICArBs2TKJE6qvf//9F/369cOtW7fwdrVXmUyGvLw8iZIRERG9Fh0djSZNmgAArl69qnCOTdKJyp5x48YhLS0N33//PV69egXgdYmsCRMmYNKkSRKnI3Vmb2+PoKAgrFy5Etra2gBel74OCgqCvb09AODOnTswNzeXMqZGYU8QIvogXbp0QXBwMGrWrCl1FFJzr169gpeXF7Zt24aKFV/Puefn58Pb2xvLly+Xv5FT6Xu7D8ubZDIZwsLCRB0/ISEBXbp0wZ07d9CgQQMAwI0bN2BhYYH9+/ezDvZ7VK9eHdu3b4e7u7vC8WPHjuGbb77Bw4cPpQmmAZo0aYL69etj+vTpqFmzptLNpKpVq0qUjIiIiIjKs8zMTMTExEBXVxd2dnaoXLmy1JFIzZ0+fRqenp7Q0tKCs7MzgNe7Q/Ly8rBv3z589tlnWL9+Pe7du4dx48ZJnFYzcBKEiIhEFRcXh6ioKOjq6sLJyanQLZ1UtnTp0gWCIGDDhg3yPg1paWkYMGAAtLS0sH//fokTqi89PT1cuHABDRs2VDh+7do1tGjRAllZWRIlU3/6+vqIioqCra2t1FGIiIiIiIg+yrNnz7BhwwbExcUBeN0rsl+/fjA0NJQ4mWbiJAgRFSktLU3ew+H27dv4888/8fz5c3h6eqJ169YSpyOi9/nvv/8AAHXq1FHZmPr6+vj333/h5OSkcDwqKgqurq7IzMxUWRZN065dO5iYmGDdunXQ0dEBADx//hw+Pj5IT0/H0aNHJU6ovjw8PDB+/Hh06tRJ6ihERERERESkRtgThIje6cqVK+jWrRtu374NOzs7bN68GZ06dUJWVha0tLSwYMECbN++HT169JA6KqmJMWPG4Oeff4a+vj7GjBnz3sey8aN48vPzMXPmTMybN08+4WBoaIiAgAD89NNP0NLSEnX8ypUr49mzZ0rHMzMzWQatCIsWLULHjh1Rp04dNG7cGMDrySMdHR0cPnxY4nTqzc/PDwEBAbh37x6cnJyUms0WbCMnIiIiIiLSBNevX0dKSoq8p0wBT09PiRJpLu4EIaJ36ty5MypWrIiJEydi/fr12LdvHzp27Ig///wTwOsbThcuXMC///4rcVJSF23btsWuXbtgZGQkeV+K8mzSpEkIDg7G9OnTFZprT5s2DUOHDsWsWbNEHd/b2xsXL15EcHAwWrRoAQA4e/Yshg4diubNm2Pt2rWijq/psrOzsWHDBsTGxgIAGjZsiP79+0NXV1fiZOqtsMk9mUwGQRDYGJ2IiIiIiDRGUlISevbsiStXrsivaQDI+x7y2ubDcRKEiN6pevXqCAsLg7OzMzIzM1GlShWcO3cOzZs3BwDExsbis88+Q0ZGhrRBiUhBrVq1sHz5cqXVIbt378b333+PO3fuiDp+RkYGfHx8sHfvXvlq/NzcXHh6emLt2rVsUE2iuHXr1nvPsx8RERERERFpgm7duqFChQpYtWoVrK2tERkZibS0NAQEBOC3335jafoSYDksInqn9PR01KhRAwBgYGAAfX19GBsby88bGxsXWvKGiKSVnp4Oe3t7peP29vZIT08XdWxBEPD06VNs3rwZd+7cQUxMDIDXuxnYsLpoc+bMgbm5OXx9fRWOr169Gg8fPsSECRMkSqb+OMlBRERERERlwZkzZxAWFobq1atDS0sLWlpaaNWqFebMmQN/f39cunRJ6ogah5MgRPReBVvt3vVnonfJyspCUFAQQkND8eDBA+Tn5yucT0pKkihZ2de4cWMsXboUixcvVji+dOlSeZ8JsQiCAFtbW1y7dg12dnac+PhAK1aswMaNG5WOOzg4oE+fPpwEeY89e/YUelwmk0FHRwe2trawtrZWcSoiIiIiIqIPk5eXB0NDQwCvq7TcvXsXDRo0gKWlJW7cuCFxOs3ESRAieq+BAweicuXKAIAXL15gxIgR0NfXBwC8fPlSymik5oYMGYLw8HB4eXmhZs2anEBToblz5+LLL7/E0aNH0bJlSwCvV5Lcvn0bBw4cEHVsLS0t2NnZIS0tDXZ2dqKOVRbdu3cPNWvWVDpuamqK1NRUCRJpjh49eijUyy3wZl+QVq1a4e+//1bY1UhERERERKROHB0dERUVBWtra3z66aeYO3cutLW1sXLlStjY2EgdTyOxJwgRvdOgQYOK9bg1a9aInIQ0kZGREfbv3y9vzE2qdffuXfz+++8KzbW///571KpVS/Sx9+7di7lz52LZsmVwdHQUfbyyxM7ODlOnTsWAAQMUjq9fvx5Tp07lDqr3CA0NxU8//YRZs2ahRYsWAIDIyEhMmTIFkydPRtWqVTF8+HB8+umnCA4OljgtERERERFR4Q4fPoysrCz06tULCQkJ6Nq1K+Li4mBiYoItW7bAw8ND6ogah5MgREQkCmtraxw4cAANGzaUOgqpmLGxMbKzs5GbmwttbW3o6uoqnBe7L4kmmzt3LubOnYtff/1V/sE2NDQU48ePR0BAACZNmiRxQvXl6OiIlStX4vPPP1c4HhERgWHDhuHatWs4evQofH19kZKSIlFKIiIiIiKiD5eeng5jY2NW2SghlsMiIiJR/PzzzwgMDERISAj09PSkjkMqtHDhQqkjaKxx48YhLS0N33//PV69egUA0NHRwYQJEzgBUoTExERUqVJF6XiVKlXkO2js7Ozw6NEjVUcjIiIiIiL6KNWqVZM6gkbjThAiIhJF06ZNkZiYCEEQYGVlhUqVKimcv3jxokTJiNRfZmYmYmJioKurCzs7O3lvJnq3Vq1awdDQEOvWrYOpqSkA4OHDh/D29kZWVhZOnDiBo0ePYuTIkWwmSEREREREVI5wJwgREYmiR48eUkcgCeXn5yMhIQEPHjxAfn6+wjk3NzeJUmkOAwMDfPLJJ1LH0CjBwcHo3r076tSpAwsLCwDA7du3YWNjg927dwN4Pbk0efJkKWMSERERERGRinEnCBEREZWqf//9F/369cOtW7fw9scMmUyGvLw8iZJRWZefn48jR44gLi4OANCgQQN88cUX0NLSkjgZERERERERSYWTIEREJJqMjAxs374diYmJGDduHKpVq4aLFy/C3NwctWvXljoeiaRJkyaoX78+pk+fjpo1ayo1bqtatapEyYiIiIiIiIiovOEkCBERiSI6Ohrt27dH1apVcfPmTdy4cQM2NjaYPHkyUlJSsG7dOqkjlln379/H2LFjERoaigcPHijtxhB7J4a+vj6ioqJga2sr6jhEixcvxrBhw6Cjo4PFixe/97H+/v4qSkVERERERETqhJMgREQkivbt26NZs2aYO3cuDA0NERUVBRsbG5w+fRr9+vXDzZs3pY5YZnXu3BkpKSn44YcfCt2J0b17d1HH9/DwwPjx49GpUydRxyGytrbG+fPnYWJiAmtr63c+TiaTISkpSYXJiIiIiIiISF1wEoSIiERRtWpVXLx4EfXq1VOYBLl16xYaNGiAFy9eSB2xzDI0NMTJkyfRpEkTlY0ZHR0t/31iYiImT56McePGwcnJCZUqVVJ4rLOzs8pyEREREREREVH5VlHqAEREVDZVrlwZT58+VToeFxcHU1NTCRKVHxYWFkolsMTWpEkTyGQyhXF9fX3lvy84x8boRERERERERKRK3AlCRESiGDJkCNLS0rB161ZUq1YN0dHRqFChAnr06AE3NzcsXLhQ6ohl1pEjRzBv3jysWLECVlZWKhnz1q1bxX6spaWliEmoPBkzZkyxHzt//nwRkxAREREREZG64iQIERGJ4smTJ/j6669x/vx5PHv2DLVq1cK9e/fQsmVLHDhwAPr6+lJHLFOMjY0Ven9kZWUhNzcXenp6SuWo0tPTVR2PSBRt27Yt1uNkMhnCwsJETkNERERERETqiJMgREQkqlOnTiE6OhqZmZlo1qwZ2rdvL3WkMikkJKTYj/Xx8RExCTBnzhyYm5srlMMCgNWrV+Phw4eYMGGCqOMTERERERERERXgJAgRERGVKisrK2zcuBGff/65wvGzZ8+iT58+SE5OligZEREREREREZU3bIxORESiWLx4caHHZTIZdHR0YGtrCzc3N1SoUEHFycq+ChUqIDU1FWZmZgrH09LSYGZmJnpj8nv37qFmzZpKx01NTZGamirq2FS+nT9/Hlu3bkVKSgpevXqlcG7nzp0SpSIiIiIiIiIpcRKEiIhEsWDBAjx8+BDZ2dkwNjYGADx+/Bh6enowMDDAgwcPYGNjg2PHjsHCwkLitGXLuzZ5vnz5Etra2qKPb2FhgYiICFhbWyscj4iIQK1atUQfn8qnzZs3w9vbGx07dsSRI0fQoUMHxMXF4f79++jZs6fU8YiIiIiIiEginAQhIiJRzJ49GytXrsSqVatQr149AEBCQgKGDx+OYcOGwdXVFX369MHo0aOxfft2idOWDQW7b2QyGVatWgUDAwP5uby8PJw4cQL29vai5xg6dCh+/PFH5OTkwMPDAwAQGhqK8ePHIyAgQPTxqXyaPXs2FixYgJEjR8LQ0BCLFi2CtbU1hg8fXujOJCIiIiIiIiof2BOEiIhEUa9ePezYsQNNmjRROH7p0iV89dVXSEpKwunTp/HVV1+xRFIpKdh5cevWLdSpU0eh1Ji2tjasrKwwY8YMfPrpp6LmEAQBEydOxOLFi+UliXR0dDBhwgQEBgaKOjaVX/r6+rh27RqsrKxgYmKC48ePw8nJCTExMfDw8ODrDBERERERUTnFnSBERCSK1NRU5ObmKh3Pzc3FvXv3AAC1atXCs2fPVB2tzCpoON62bVvs3LlTXoZM1WQyGX755RdMmTIFMTEx0NXVhZ2dHSpXrixJHiofjI2N5a8ntWvXxtWrV+Hk5ISMjAxkZ2dLnI6IiIiIiIikoiV1ACIiKpvatm2L4cOH49KlS/Jjly5dwnfffScvkXTlyhWlvhH08Y4dOybZBMibDAwM8Mknn8DR0ZETICQ6Nzc3/PPPPwCA3r17Y9SoURg6dCj69u2Ldu3aSZyOiIiIiIiIpMJyWEREJIp79+7By8sLoaGhqFSpEoDXu0DatWuH9evXw9zcHMeOHUNOTg46dOggcVrNN2bMGPz888/Q19fHmDFj3vvY+fPnqygVkeqkp6fjxYsXqFWrFvLz8zF37lycPn0adnZ2mDx5slpMDBIREREREZHqcRKEiIhEFRsbi7i4OABAgwYN0KBBA4kTlU1t27bFrl27YGRkhLZt277zcTKZDGFhYSpMRkREREREREQkHU6CEBGR6AreamQymcRJiKise/DgAR48eID8/HyF487OzhIlIiIiIiIiIimxJwgREYlm3bp1cHJygq6uLnR1deHs7Iz169dLHavMCwsLw8uXL6WOQaRSFy5cgKOjI2rWrAlnZ2c0adJE/qtp06ZSxyMiIiIiIiKJVJQ6ABERlU3z58/HlClT8MMPP8DV1RUAcOrUKYwYMQKPHj3C6NGjJU5Ydnl6eiI3NxeffPIJ3N3d0aZNG7i6ukJXV1fqaESi8fX1Rf369REcHAxzc3PuPCMiIiIiIiIALIdFREQisba2xvTp0+Ht7a1wPCQkBNOmTUNycrJEycq+nJwcREZGIjw8HOHh4Th9+jRevXoFFxcXtG3bFjNnzpQ6IlGpMzQ0xKVLl2Brayt1FCIiIiIiIlIjnAQhIiJR6Ojo4OrVq0o3JOPj4+Hk5IQXL15IlKz8uXbtGn799Vds2LAB+fn5yMvLkzoSUanr0aMHvLy88NVXX0kdhYiIiIiIiNQIy2EREZEobG1tsXXrVvzvf/9TOL5lyxbY2dlJlKp8iIuLw/Hjx3H8+HGEh4fj5cuXaN26NX777Te4u7tLHY9IFKtWrYKPjw+uXr0KR0dHVKpUSeG8p6enRMmIiIiIiIhIStwJQkREotixYwe+/fZbtG/fXt4TJCIiAqGhodi6dSt69uwpccKyS0tLC6amphg1ahS6du0KJycn9kegMm/v3r3w8vLC06dPlc7JZDLugCIiIiIiIiqnOAlCRESiuXDhAhYsWICYmBgAQMOGDREQEICmTZtKnKxs+/HHH3HixAlcv34dzZo1g7u7O9zd3dGqVSvo6elJHY9IFFZWVujatSumTJkCc3NzqeMQERERERGRmuAkCBERURmVkZGBkydPyhukX7t2DU2bNkVERITU0YhKnaGhIS5fvox69epJHYWIiIiIiIjUCHuCEBGRqB48eIAHDx4gPz9f4bizs7NEicqPvLw85OTk4OXLl3jx4gVevnyJGzduSB2LSBS9evXCsWPHOAlCRERERERECjgJQkREorhw4QJ8fHwQExODtzcdsj6/uPz9/XH8+HFcv34dxsbGcHNzw9ChQ+Hu7g4nJyep4xGJon79+pg0aRJOnToFJycnpcbo/v7+EiUjIiIiIiIiKbEcFhERiaJx48aoV68eJkyYAHNzc6XG3JaWlhIlK/t69+6NNm3awN3dHY6OjlLHIVIJa2vrd56TyWRISkpSYRoiIiIiIiJSF5wEISIiURgaGuLSpUuwtbWVOgoREREREREREZVTWlIHICKisqldu3aIioqSOgYRlTOvXr3CjRs3kJubK3UUIiIiIiIiUgPcCUJERKJ49OgRfHx80KJFCzg6OirV5/f09JQoGRGVRdnZ2fDz80NISAgAIC4uDjY2NvDz80Pt2rUxceJEiRMSERERERGRFNgYnYiIRHHmzBlERETg4MGDSufYGJ2IStukSZMQFRWF48ePo1OnTvLj7du3x7Rp0zgJQkREREREVE6xHBYREYnCz88PAwYMQGpqKvLz8xV+cQKEiErb33//jaVLl6JVq1aQyWTy4w4ODkhMTJQwGREREREREUmJO0GIiEgUaWlpGD16NMzNzaWOUq7Fx8cjJSUFlpaWbFJPZdrDhw9hZmamdDwrK0thUoSIiIiIiIjKF+4EISIiUfTq1QvHjh2TOka5MmfOHISGhgIAHj9+jPbt26NBgwb44osv0KBBA3Tu3BkZGRnShiQSiYuLC/bv3y//c8HEx6pVq9CyZUupYhEREREREZHEuBOEiIhEUb9+fUyaNAmnTp2Ck5OTUmN0f39/iZKVXX/88Ye8F8L48eORnp6OCxcuoGHDhrhx4wZGjBiBsWPHYtWqVRInJSp9s2fPRufOnXH9+nXk5uZi0aJFuH79Ok6fPo3w8HCp4xEREREREZFEZIIgCFKHICKissfa2vqd52QyGZKSklSYpnzQ0dHBjRs3YGlpCWtra4SEhMDNzU1+/sKFC+jWrRvu3r0rYUoi8SQlJWHOnDmIiopCZmYmmjVrhgkTJsDJyUnqaERERERERCQR7gQhIiJRJCcnSx2h3LG0tMTVq1dhaWkJmUyGihUV3+YrVKiArKwsidIRiScnJwfDhw/HlClT8Oeff0odh4iIiIiIiNQIe4IQERGVEUOHDsW4ceOQkJCAH374AWPHjkViYiKA15NSo0ePRocOHSROSVT6KlWqhB07dkgdg4iIiIiIiNQQy2EREZEo8vLysHbtWoSGhuLBgwfIz89XOB8WFiZRsrLN398fy5cvR7169XDz5k28evUKFStWRG5uLpo1a4a9e/eiRo0aUsckKnU+Pj5o0qQJRo8eLXUUIiIiIiIiUiMsh0VERKIYNWoU1q5diy+//BKOjo6QyWRSRyoXFi9ejO+++w779u1DUlIS8vPzUbNmTbi6uqJ9+/b8f6Ayy87ODjNmzEBERASaN28OfX19hfP+/v4SJSMiIiIiIiIpcScIERGJonr16li3bh26dOkidRQiKgesra3feU4mkyEpKUmFaYiIiIiIiEhdcCcIERGJQltbG7a2tlLHIKJyIjk5WeoIREREREREpIbYGJ2IiEQREBCARYsWgRsOVScnJwfjx4+Hra0tWrRogdWrVyucv3//PipUqCBROiLVEQSBrz1EREREREQEgDtBiIioFPXq1Uvhz2FhYTh48CAcHBxQqVIlhXM7d+5UZbRyYdasWVi3bh3Gjh2LjIwMjBkzBmfPnsWKFSvkj+GNYSrLgoODsWDBAsTHxwN43Sfkxx9/xJAhQyRORkRERERERFLhJAgREZWaqlWrKvy5Z8+eEiUpnzZs2IBVq1aha9euAICBAweic+fOGDRokHxXCBujU1kVGBiI+fPnw8/PDy1btgQAnDlzBqNHj0ZKSgpmzJghcUIiIiIiIiKSAhujExERlRF6enq4fv06rKys5Mfu3LkDDw8PfPLJJ5g7dy4sLCyQl5cnXUgikZiammLx4sXo27evwvFNmzbBz88Pjx49kigZERERERERSYk9QYiISBQeHh7IyMhQOv706VN4eHioPlA5UKNGDSQmJiocq127No4dO4Zz585h4MCB0gQjUoGcnBy4uLgoHW/evDlyc3MlSERERERERETqgJMgREQkiuPHj+PVq1dKx1+8eIGTJ09KkKjs8/DwwMaNG5WO16pVC2FhYUhOTpYgFZFqeHl5YdmyZUrHV65cif79+0uQiIiIiIiIiNQBe4IQEVGpio6Olv/++vXruHfvnvzPeXl5OHToEGrXri1FtDJvypQpiI2NLfRc7dq1ER4ejn/++UfFqYhUJzg4GEeOHMFnn30GADh79ixSUlLg7e2NMWPGyB83f/58qSISERERERGRirEnCBERlSotLS158+3C3mJ0dXWxZMkS+Pr6qjoaEZVhbdu2LdbjZDIZwsLCRE5DRERERERE6oKTIEREVKpu3boFQRBgY2ODyMhImJqays9pa2vDzMwMFSpUkDAhERERERERERGVF5wEISIiUWRlZUFfX1/qGEREREREREREVI6xMToREYnC3Nwcvr6+OHXqlNRRiIiIiIiIiIionOIkCBERieKvv/5Ceno6PDw8UL9+fQQFBeHu3btSxyIiIiIiIiIionKE5bCIiEhUDx8+xPr167F27VrExMSgY8eO8PX1haenJypWrCh1vDLp9u3bkMlkqFOnDgAgMjISGzduRKNGjTBs2DCJ0xERERERERERqQ4nQYiISGWWLFmCcePG4dWrV6hevTpGjBiBiRMnQk9PT+poZUrr1q0xbNgweHl54d69e2jQoAEcHBwQHx8PPz8/BAYGSh2RiIiIiIiIiEglWA6LiIhEdf/+fcydOxeNGjXCxIkT8fXXXyM0NBTz5s3Dzp070aNHD6kjljlXr15FixYtAABbt26Fo6MjTp8+jQ0bNmDt2rXShiMiIiIiIiIiUiHWISEiIlHs3LkTa9asweHDh9GoUSN8//33GDBgAIyMjOSP+fzzz9GwYUPpQpZROTk5qFy5MgDg6NGj8PT0BADY29sjNTVVymhERERERERERCrFnSBERCSKQYMGoVatWoiIiMDly5fxww8/KEyAAECtWrXw008/SROwDHNwcMDy5ctx8uRJ/PPPP+jUqRMA4O7duzAxMZE4HRERERERERGR6rAnCBERiSI7O5u9PiRy/Phx9OzZE0+fPoWPjw9Wr14NAPjf//6H2NhY7Ny5U+KERERERERERESqwUkQIiISTX5+PhISEvDgwQPk5+crnHNzc5MoVfmQl5eHp0+fwtjYWH7s5s2b0NPTg5mZmYTJiIiIiIiIiIhUh5MgREQkin///Rf9+vXDrVu38PZbjUwmQ15enkTJiIiIiIiIiIiovOAkCBERiaJJkyaoX78+pk+fjpo1a0Imkymcr1q1qkTJyqZmzZohNDQUxsbGaNq0qdK/95suXryowmRERERERERERNKpKHUAIiIqm+Lj47F9+3bY2tpKHaVc6N69OypXrgwA6NGjh7RhiIiIiIiIiIjUBHeCEBGRKDw8PDB+/Hh06tRJ6ihERERERERERFROcScIERGVmujoaPnv/fz8EBAQgHv37sHJyQmVKlVSeKyzs7Oq45Ubt2/fhkwmQ506dQAAkZGR2LhxIxo1aoRhw4ZJnI6IiIiIiIiISHW4E4SIiEqNlpYWZDKZUiP0AgXn2BhdXK1bt8awYcPg5eWFe/fuoX79+nB0dER8fDz8/PwQGBgodUQiIiIiIiIiIpXgThAiIio1ycnJUkcgAFevXkWLFi0AAFu3boWTkxMiIiJw5MgRjBgxgpMgRERERERERFRucBKEiIhKjaWlJQAgJycH9vb22LdvHxo2bChxqvInJydH3iT96NGj8PT0BADY29sjNTVVymhERERERERERCqlJXUAIiIqeypVqoQXL15IHaPccnBwwPLly3Hy5En8888/8ub0d+/ehYmJicTpiIiIiIiIiIhUh5MgREQkipEjR+KXX35Bbm6u1FHKnV9++QUrVqyAu7s7+vbti8aNGwMA9uzZIy+TRURERERERERUHrAxOhERiaJnz54IDQ2FgYEBnJycoK+vr3B+586dEiUrH/Ly8vD06VMYGxvLj928eRN6enowMzOTMBkRERERERERkeqwJwgREYnCyMgIX331ldQxyq0KFSogNzcXp06dAgA0aNAAVlZW0oYiIiIiIiIiIlIxToIQEVGpy83NRdu2bdGhQwfUqFFD6jjlTlZWFvz8/LBu3Trk5+cDeD0p4u3tjSVLlkBPT0/ihEREREREREREqsGeIEREVOoqVqyIESNG4OXLl1JHKZfGjBmD8PBw7N27FxkZGcjIyMDu3bsRHh6OgIAAqeMREREREREREakMe4IQEZEo3N3d8eOPP6JHjx5SRyl3qlevju3bt8Pd3V3h+LFjx/DNN9/g4cOH0gQjIiIiIiIiIlIxlsMiIiJRfP/99wgICMB///2H5s2bKzVGd3Z2lihZ2ZednQ1zc3Ol42ZmZsjOzpYgERERERERERGRNLgThIiIRKGlpVxxUSaTQRAEyGQy5OXlSZCqfGjXrh1MTEywbt066OjoAACeP38OHx8fpKen4+jRoxInJCIiIiIiIiJSDU6CEBGRKG7duvXe85aWlipKUv5cuXIFnTp1wsuXL9G4cWMAQFRUFHR0dHD48GE4ODhInJCIiIiIiIiISDU4CUJERFQGZWdnY8OGDYiNjQUANGzYEP3794eurq7EyYiIiIiIiIiIVIeTIEREJKrr168jJSUFr169Ujju6ekpUaKyLScnB/b29ti3bx8aNmwodRwiIiIiIiIiIkmxMToREYkiKSkJPXv2xJUrV+S9QIDXfUEAsCeISCpVqoQXL15IHYOIiIiIiIiISC0od60lIiIqBaNGjYK1tTUePHgAPT09XLt2DSdOnICLiwuOHz8udbwybeTIkfjll1+Qm5srdRQiIiIiIiIiIkmxHBYREYmievXqCAsLg7OzM6pWrYrIyEg0aNAAYWFh+H/t3XuQluV9N/Dvs8Dqcl7AbYIHDrIBjIvdaiWBWhOaUUoBo23aECN24mGcxgIBmaRNisdYR0W3NlNT6xTFpPEUk8YxqRqMoFJQhCFoqKhYQMSssNq4YEAenvePTJiXwfedzASe295+PjPMwHVfz+537n92mO9e12/evHlZs2ZN0RFL6+yzz86SJUvSt2/ftLW1pU+fPgc8f+CBBwpKBgAAAFBfrsMC4LCoVqvp169fkl8VIq+99lpGjx6dYcOG5YUXXig4XbkNHDgwf/qnf1p0DAAAAIDCKUEAOCxOPPHErF27NiNGjMj48eNz/fXXp7GxMbfddltGjhxZdLxSW7RoUdERAAAAAN4XXIcFwGHx8MMPZ+fOnTnnnHPy0ksvZerUqdmwYUMGDx6ce+65J5MmTSo6Yqnt3bs3jz/+eF5++eV87nOfS79+/fLaa6+lf//+6du3b9HxAAAAAOpCCQJA3XR1daW5uTmVSqXoKKW2adOmTJ48OZs3b87u3buzYcOGjBw5MrNnz87u3bvzzW9+s+iIAAAAAHXRUHQAAD44Bg0apACpg9mzZ+eUU07Jm2++maampv3rvx6YDgAAAPBBYSYIAJTME088keXLl6exsfGA9eHDh2fr1q0FpQIAAACoPydBAKBk9u3bl2q1etD6q6++mn79+hWQCAAAAKAYShAAKJkzzjgjHR0d+/9dqVTS3d2dyy+/PFOmTCkuGAAAAECdGYwOACXz6quv5swzz0ytVsuLL76YU045JS+++GKGDBmSZcuWpaWlpeiIAAAAAHWhBAGAEtq7d2/uvvvu/PSnP013d3d+7/d+L+eee+4Bg9IBAAAAyk4JAgAAAAAAlFLPogMAAL+9H/zgB7/x3unTpx/GJAAAAADvH06CAEAJNDQ0/Eb7KpVKqtXqYU4DAAAA8P6gBAEAAAAAAErpN/u1UQAAAAAAgP9llCAAUDKzZs3KLbfcctD6N77xjcyZM6f+gQAAAAAKogQBgJL57ne/m4kTJx60PmHChNx///0FJAIAAAAohhIEAEpmx44dGTBgwEHr/fv3z/bt2wtIBAAAAFAMJQgAlMyoUaPyH//xHwet/+hHP8rIkSMLSAQAAABQjJ5FBwAADq25c+fm0ksvzRtvvJFJkyYlSZYsWZKFCxemo6Oj2HAAAAAAdVSp1Wq1okMAAIfWrbfemq9//et57bXXkiTDhw/PFVdckZkzZxacDAAAAKB+lCAAUGJvvPFGmpqa0rdv36KjAAAAANSdmSAAUDLvvPNOdu3alSQ56qijsmPHjnR0dOSRRx4pOBkAAABAfSlBAKBkzjrrrCxevDhJ8tZbb+XUU0/NwoULc9ZZZ+XWW28tOB0AAABA/ShBAKBkVq9endNOOy1Jcv/99+dDH/pQNm3alMWLF+eWW24pOB0AAABA/ShBAKBkdu3alX79+iVJHnnkkZxzzjlpaGjIxz72sWzatKngdAAAAAD1owQBgJIZNWpUvv/972fLli15+OGHc8YZZyRJOjs7079//4LTAQAAANSPEgQASmbBggW57LLLMnz48IwfPz4f//jHk/zqVEh7e3vB6QAAAADqp1Kr1WpFhwAADq3XX38927Zty0knnZSGhl/9zsPTTz+d/v37Z8yYMQWnAwAAAKgPJQgAAAAAAFBKrsMCAAAAAABKSQkCAAAAAACUkhIEAAAAAAAoJSUIAAAAAABQSj2LDgAA/PZ+8IMf/MZ7p0+ffhiTAAAAALx/VGq1Wq3oEADAb6eh4Tc73FmpVFKtVg9zGgAAAID3ByUIAAAAAABQSmaCAAAAAAAApWQmCACU0M6dO7N06dJs3rw5e/bsOeDZrFmzCkoFAAAAUF+uwwKAklmzZk2mTJmSXbt2ZefOnRk0aFC2b9+e3r17p6WlJRs3biw6IgAAAEBduA4LAErmS1/6UqZNm5Y333wzTU1NWbFiRTZt2pSTTz45N954Y9HxAAAAAOrGSRAAKJmBAwdm5cqVGT16dAYOHJj//M//zNixY7Ny5cqcf/75+a//+q+iIwIAAADUhZMgAFAyvXr1SkPDr37Et7S0ZPPmzUmSAQMGZMuWLUVGAwAAAKgrg9EBoGTa29vzzDPPpLW1NaeffnoWLFiQ7du356677sqJJ55YdDwAAACAunEdFgCUzKpVq/L222/nk5/8ZDo7OzNz5swsX748ra2t+dd//decdNJJRUcEAAAAqAslCACUSK1Wy5YtW9LS0pIjjzyy6DgAAAAAhTITBABKpFarZdSoUWZ/AAAAAEQJAgCl0tDQkNbW1uzYsaPoKAAAAACFU4IAQMlcd911mT9/fp577rmiowAAAAAUykwQACiZ5ubm7Nq1K3v37k1jY2OampoOeN7V1VVQMgAAAID66ll0AADg0Oro6Cg6AgAAAMD7gpMgAAAAAABAKZkJAgAl9PLLL+drX/taZsyYkc7OziTJj370ozz//PMFJwMAAACoHyUIAJTM0qVL09bWlpUrV+aBBx5Id3d3kmTt2rW5/PLLC04HAAAAUD9KEAAoma985Su55ppr8uijj6axsXH/+qRJk7JixYoCkwEAAADUlxIEAEpm3bp1Ofvssw9ab2lpyfbt2wtIBAAAAFAMJQgAlMzAgQOzbdu2g9bXrFmTo48+uoBEAAAAAMVQggBAyXz2s5/Nl7/85bz++uupVCrZt29fnnrqqVx22WWZOXNm0fEAAAAA6qZSq9VqRYcAAA6dPXv25Itf/GLuuOOOVKvV9OzZM9VqNZ/73Odyxx13pEePHkVHBAAAAKgLJQgAlNSWLVuybt26dHd3p729Pa2trUVHAgAAAKgr12EBQMlcddVV2bVrV4499thMmTIlf/7nf57W1ta88847ueqqq4qOBwAAAFA3ToIAQMn06NEj27ZtS0tLywHrO3bsSEtLS6rVakHJAAAAAOrLSRAAKJlarZZKpXLQ+tq1azNo0KACEgEAAAAUo2fRAQCAQ6O5uTmVSiWVSiUf+chHDihCqtVquru7c8kllxSYEAAAAKC+XIcFACVx5513plar5Qtf+EI6OjoyYMCA/c8aGxszfPjwfPzjHy8wIQAAAEB9KUEAoGSWLl2aCRMmpFevXkVHAQAAACiUEgQASuyXv/xl9uzZc8Ba//79C0oDAAAAUF8GowNAyezatSuXXnppWlpa0qdPnzQ3Nx/wBwAAAOCDQgkCACUzf/78PPbYY7n11ltzxBFH5Pbbb8+VV16ZoUOHZvHixUXHAwAAAKgb12EBQMkcd9xxWbx4cT7xiU+kf//+Wb16dUaNGpW77ror3/nOd/LDH/6w6IgAAAAAdeEkCACUTFdXV0aOHJnkV/M/urq6kiR/8Ad/kGXLlhUZDQAAAKCulCAAUDIjR47MK6+8kiQZM2ZM7r333iTJgw8+mIEDBxaYDAAAAKC+XIcFACVz8803p0ePHpk1a1Z+/OMfZ9q0aanVann33Xdz0003Zfbs2UVHBAAAAKgLJQgAlNymTZvy7LPPZtSoURk3blzRcQAAAADqRgkCAAAAAACUUs+iAwAAv71bbrnlN947a9asw5gEAAAA4P3DSRAAKIERI0b8RvsqlUo2btx4mNMAAAAAvD8oQQAAAAAAgFJqKDoAAAAAAADA4aAEAQAAAAAASkkJAgAAAAAAlJISBAAAAAAAKCUlCAAAAAAAUEpKEAAomUWLFuW+++47aP2+++7LnXfeWUAiAAAAgGIoQQCgZP7+7/8+Q4YMOWi9paUl1157bQGJAAAAAIqhBAGAktm8eXNGjBhx0PqwYcOyefPmAhIBAAAAFEMJAgAl09LSkp/+9KcHra9duzaDBw8uIBEAAABAMZQgAFAyM2bMyKxZs/KTn/wk1Wo11Wo1jz32WGbPnp3PfvazRccDAAAAqJtKrVarFR0CADh09uzZk/POOy/33XdfevbsmSTZt29fZs6cmW9+85tpbGwsOCEAAABAfShBAKCkNmzYkLVr16apqSltbW0ZNmxY0ZEAAAAA6koJAgAAAAAAlFLPogMAAL+9uXPn5uqrr06fPn0yd+7c/+/em266qU6pAAAAAIqlBAGAElizZk3efffd/X//f6lUKvWKBAAAAFA412EBAAAAAACl1FB0AAAAAAAAgMPBdVgAUDI7d+7MddddlyVLlqSzszP79u074PnGjRsLSgYAAABQX0oQACiZCy+8MEuXLs15552XD3/4w+aAAAAAAB9YZoIAQMkMHDgwDz30UCZOnFh0FAAAAIBCmQkCACXT3NycQYMGFR0DAAAAoHBKEAAomauvvjoLFizIrl27io4CAAAAUCjXYQFACbS3tx8w++Oll15KrVbL8OHD06tXrwP2rl69ut7xAAAAAAphMDoAlMCnP/3poiMAAAAAvO84CQIAAAAAAJSSkyAAUFKrVq3K+vXrkyQnnHBCTj755IITAQAAANSXEgQASubVV1/NjBkz8tRTT2XgwIFJkrfeeisTJkzI3XffnWOOOabYgAAAAAB10lB0AADg0Lrwwgvz7rvvZv369enq6kpXV1fWr1+fffv25cILLyw6HgAAAEDdmAkCACXT1NSU5cuXp729/YD1Z599Nqeddlp27dpVUDIAAACA+nISBABK5thjj82777570Hq1Ws3QoUMLSAQAAABQDCUIAJTMDTfckL/+67/OqlWr9q+tWrUqs2fPzo033lhgMgAAAID6ch0WAJRMc3Nzdu3alb1796Znz55Jsv/vffr0OWBvV1dXEREBAAAA6qJn0QEAgEOro6Oj6AgAAAAA7wtOggAAAAAAAKXkJAgAlFC1Ws33v//9rF+/Pkny0Y9+NNOnT0+PHj0KTgYAAABQP06CAEDJvPTSS5kyZUq2bt2a0aNHJ0leeOGFHHvssXnooYdy/PHHF5wQAAAAoD6UIABQMlOmTEmtVsu3v/3tDBo0KEmyY8eOfP7zn09DQ0MeeuihghMCAAAA1IcSBABKpk+fPlmxYkXa2toOWF+7dm0mTpyY7u7ugpIBAAAA1FdD0QEAgEPriCOOyNtvv33Qend3dxobGwtIBAAAAFAMJQgAlMzUqVNz8cUXZ+XKlanVaqnValmxYkUuueSSTJ8+veh4AAAAAHXjOiwAKJm33nor559/fh588MH06tUrSbJ3795Mnz49d9xxRwYMGFBwQgAAAID6UIIAQInUarVs2bIlRx11VLZu3Zr169cnScaOHZtRo0YVnA4AAACgvpQgAFAi+/bty5FHHpnnn38+ra2tRccBAAAAKJSZIABQIg0NDWltbc2OHTuKjgIAAABQOCUIAJTMddddl/nz5+e5554rOgoAAABAoVyHBQAl09zcnF27dmXv3r1pbGxMU1PTAc+7uroKSgYAAABQXz2LDgAAHFo333xzKpVK0TEAAAAACuckCAAAAAAAUEpmggBAyfTo0SOdnZ0Hre/YsSM9evQoIBEAAABAMZQgAFAy/69Dnrt3705jY2Od0wAAAAAUx0wQACiJW265JUlSqVRy++23p2/fvvufVavVLFu2LGPGjCkqHgAAAEDdmQkCACUxYsSIJMmmTZtyzDHHHHD1VWNjY4YPH56rrroq48ePLyoiAAAAQF0pQQCgZD75yU/mgQceSHNzc9FRAAAAAAqlBAGAEvv1j/lKpVJwEgAAAID6MxgdAEpo8eLFaWtrS1NTU5qamjJu3LjcddddRccCAAAAqCuD0QGgZG666ab83d/9XS699NJMnDgxSfLkk0/mkksuyfbt2/OlL32p4IQAAAAA9eE6LAAomREjRuTKK6/MzJkzD1i/8847c8UVV+SVV14pKBkAAABAfbkOCwBKZtu2bZkwYcJB6xMmTMi2bdsKSAQAAABQDCUIAJTMqFGjcu+99x60fs8996S1tbWARAAAAADFMBMEAErmyiuvzF/8xV9k2bJl+2eCPPXUU1myZMl7liMAAAAAZWUmCACU0LPPPpubb74569evT5KMHTs28+bNS3t7e8HJAAAAAOpHCQIAAAAAAJSSmSAAUDKnn356Fi9enHfeeafoKAAAAACFUoIAQMm0t7fnsssuy4c+9KFcdNFFWbFiRdGRAAAAAAqhBAGAkuno6Mhrr72WRYsWpbOzM3/4h3+YE044ITfeeGN+/vOfFx0PAAAAoG7MBAGAkuvs7Mxtt92Wr3/966lWq5kyZUpmzZqVSZMmFR0NAAAA4LByEgQASuzpp5/O5ZdfnoULF6alpSV/8zd/kyFDhmTq1Km57LLLio4HAAAAcFg5CQIAJdPZ2Zm77rorixYtyosvvphp06blwgsvzJlnnplKpZIkefLJJzN58uR0d3cXnBYAAADg8OlZdAAA4NA65phjcvzxx+cLX/hC/vIv/zJHHXXUQXvGjRuX3//93y8gHQAAAED9OAkCACXzxBNP5LTTTis6BgAAAEDhlCAAAAAAAEApuQ4LAEpmx44dWbBgQX7yk5+ks7Mz+/btO+B5V1dXQckAAAAA6ksJAgAlc9555+Wll17KBRdckN/5nd/ZPwwdAAAA4IPGdVgAUDL9+vXLk08+mZNOOqnoKAAAAACFaig6AABwaI0ZMybvvPNO0TEAAAAACuckCACUzDPPPJOvfOUrWbBgQU488cT06tXrgOf9+/cvKBkAAABAfZkJAgAlM3DgwPziF7/IpEmTDliv1WqpVCqpVqsFJQMAAACoLyUIAJTMueeem169euXf/u3fDEYHAAAAPtBchwUAJdO7d++sWbMmo0ePLjoKAAAAQKEMRgeAkjnllFOyZcuWomMAAAAAFM5JEAAomfvuuy9XXHFF5s+fn7a2toMGo48bN66gZAAAAAD1pQQBgJJpaDj4oGelUjEYHQAAAPjAMRgdAErmlVdeKToCAAAAwPuCkyAAAAAAAEApOQkCACX1s5/9LJs3b86ePXsOWJ8+fXpBiQAAAADqSwkCACWzcePGnH322Vm3bt3+WSDJr+aCJDETBAAAAPjAOHhyKgDwv9rs2bMzYsSIdHZ2pnfv3nn++eezbNmynHLKKXn88ceLjgcAAABQN2aCAEDJDBkyJI899ljGjRuXAQMG5Omnn87o0aPz2GOPZd68eVmzZk3REQEAAADqwkkQACiZarWafv36JflVIfLaa68lSYYNG5YXXnihyGgAAAAAdWUmCACUzIknnpi1a9dmxIgRGT9+fK6//vo0Njbmtttuy8iRI4uOBwAAAFA3rsMCgJJ5+OGHs3Pnzpxzzjl56aWXMnXq1GzYsCGDBw/OPffck0mTJhUdEQAAAKAulCAA8AHQ1dWV5ubmVCqVoqMAAAAA1I0SBAAAAAAAKCWD0QEAAAAAgFJSggAAAAAAAKWkBAEAAAAAAEpJCQIAAAAAAJSSEgQAADisPvGJT2TOnDlJkuHDh6ejo6PQPAAAwAdHz6IDAAAAHxzPPPNM+vTpU3QMAADgA0IJAgAA1M1RRx1VdAQAAOADxHVYAADAIbNz587MnDkzffv2zYc//OEsXLjwgOf/93VYtVotV1xxRY477rgcccQRGTp0aGbNmrV/7+7du3PZZZfl6KOPTp8+fTJ+/Pg8/vjj+5/v2LEjM2bMyNFHH53evXunra0t3/nOdw74fvfff3/a2trS1NSUwYMH51Of+lR27ty5//ntt9+esWPH5sgjj8yYMWPyT//0T4f+pQAAAIVxEgQAADhk5s+fn6VLl+bf//3f09LSkr/927/N6tWr87u/+7sH7f3ud7+bm2++OXfffXc++tGP5vXXX8/atWv3P7/00kvzs5/9LHfffXeGDh2a733ve5k8eXLWrVuX1tbW/PKXv8zJJ5+cL3/5y+nfv38eeuihnHfeeTn++ONz6qmnZtu2bZkxY0auv/76nH322Xn77bfzxBNPpFarJUm+/e1vZ8GCBfnGN76R9vb2rFmzJhdddFH69OmT888/v16vDAAAOIwqtV//DwAAAOC30N3dncGDB+db3/pWPvOZzyRJurq6cswxx+Tiiy9OR0dHhg8fnjlz5mTOnDm56aab8s///M957rnn0qtXrwO+1ubNmzNy5Mhs3rw5Q4cO3b/+qU99Kqeeemquvfba98wwderUjBkzJjfeeGNWr16dk08+Of/93/+dYcOGHbR31KhRufrqqzNjxoz9a9dcc01++MMfZvny5YfilQAAAAVzEgQAADgkXn755ezZsyfjx4/fvzZo0KCMHj36Pfd/5jOfSUdHR0aOHJnJkydnypQpmTZtWnr27Jl169alWq3mIx/5yAGf2b17dwYPHpwkqVarufbaa3Pvvfdm69at2bNnT3bv3p3evXsnSU466aT80R/9Udra2nLmmWfmjDPOyJ/92Z+lubk5O3fuzMsvv5wLLrggF1100f6vv3fv3gwYMOBQvxoAAKAgShAAAKAQxx57bF544YX8+Mc/zqOPPpq/+qu/yg033JClS5emu7s7PXr0yLPPPpsePXoc8Lm+ffsmSW644Yb8wz/8Qzo6OtLW1pY+ffpkzpw52bNnT5KkR48eefTRR7N8+fI88sgj+cd//Md89atfzcqVK/cXJf/yL/9yQGnz688BAADloAQBAAAOieOPPz69evXKypUrc9xxxyVJ3nzzzWzYsCGnn376e36mqakp06ZNy7Rp0/LFL34xY8aMybp169Le3p5qtZrOzs6cdtpp7/nZp556KmeddVY+//nPJ0n27duXDRs25IQTTti/p1KpZOLEiZk4cWIWLFiQYcOG5Xvf+17mzp2boUOHZuPGjTn33HMP8ZsAAADeL5QgAADAIdG3b99ccMEFmT9/fgYPHpyWlpZ89atfTUNDw3vuv+OOO1KtVjN+/Pj07t073/rWt9LU1JRhw4Zl8ODBOffcczNz5swsXLgw7e3teeONN7JkyZKMGzcuf/Inf5LW1tbcf//9Wb58eZqbm3PTTTfl5z//+f4SZOXKlVmyZEnOOOOMtLS0ZOXKlXnjjTcyduzYJMmVV16ZWbNmZcCAAZk8eXJ2796dVatW5c0338zcuXPr9t4AAIDDRwkCAAAcMjfccEO6u7szbdq09OvXL/Pmzcv//M//vOfegQMH5rrrrsvcuXNTrVbT1taWBx98cP/Mj0WLFuWaa67JvHnzsnXr1gwZMiQf+9jHMnXq1CTJ1772tWzcuDFnnnlmevfunYsvvjif/vSn93+//v37Z9myZeno6MgvfvGLDBs2LAsXLswf//EfJ0kuvPDC9O7dOzfccEPmz5+fPn36pK2tLXPmzDn8LwoAAKiLSq1WqxUdAgAAAAAA4FB773PpAAAAAAAA/8spQQAAAAAAgFJSggAAAAAAAKWkBAEAAAAAAEpJCQIAAAAAAJSSEgQAAAAAACglJQgAAAAAAFBKShAAAAAAAKCUlCAAAAAAAEApKUEAAAAAAIBSUoIAAAAAAACl9H8AnpOPCM7tn8IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellarium_gpt/training/sex_ontology.ipynb b/notebooks/cellarium_gpt/training/sex_ontology.ipynb
new file mode 100644
index 00000000..40545000
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/sex_ontology.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# sex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>19181831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PATO:0000383</td>\n",
+       "      <td>16267559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2857853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sex_ontology_term_id  num_cells\n",
+       "0         PATO:0000384   19181831\n",
+       "1         PATO:0000383   16267559\n",
+       "2              unknown    2857853"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"sex_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sex</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>male</td>\n",
+       "      <td>19181831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>16267559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>unknown</td>\n",
+       "      <td>2857853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sex  num_cells\n",
+       "0     male   19181831\n",
+       "1   female   16267559\n",
+       "2  unknown    2857853"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"sex\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='sex'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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IiRo8pPPy8uDk5GTQ5uTkhKKiIty/f7/afWJjY6HRaPSLm5tbQ5dJRCQkIe/uiI6ORmFhoX65fv263CUREcnC6DFpYzk7OyM/P9+gLT8/H3Z2drCysqp2H7VaDbVa3dClEREJr8F70n5+fkhMTDRoO3DgAPz8/Br6q4mIGj2jQ7qkpASZmZnIzMwE8OgWu8zMTOTk5AB4NFQRGhqq337y5Mm4cuUKZs2ahYsXL+Kzzz7D119/jRkzZtTPGRARNWFGh3RaWhq0Wi20Wi0AIDIyElqtFjExMQCA3NxcfWADgIeHB3bv3o0DBw7A19cXy5cvx7p163j7HRFRLRg9Jh0YGAhJkmpcX93ThIGBgcjIyDD2q4iIFE/IuzuIiOgRhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcAY0kREAmNIExEJjCFNRCQwhjQRkcDqFNKrV69G+/btYWlpib59++LkyZM1brtx40aoVCqDxdLSss4FExEpidEhvX37dkRGRmL+/PlIT0+Hr68vgoODUVBQUOM+dnZ2yM3N1S/Xrl17rqKJiJTC6JCOj4/HpEmTMHHiRHTt2hVr1qyBtbU11q9fX+M+KpUKzs7O+sXJyem5iiYiUgqjQrqiogKnTp1CUFDQ/w5gZoagoCCkpqbWuF9JSQnc3d3h5uaGUaNG4dy5c0/9nvLychQVFRksRERKZFRI3759G5WVlVV6wk5OTsjLy6t2n06dOmH9+vX47rvv8OWXX0Kn06F///747bffavye2NhYaDQa/eLm5mZMmURETUaD393h5+eH0NBQ9OjRAwEBAfjmm2/Qpk0bfP755zXuEx0djcLCQv1y/fr1hi6TiEhIFsZs3Lp1a5ibmyM/P9+gPT8/H87OzrU6RrNmzaDVapGVlVXjNmq1Gmq12pjSiIiaJKN60s2bN0evXr2QmJiob9PpdEhMTISfn1+tjlFZWYmzZ8/CxcXFuEqJiBTIqJ40AERGRiIsLAy9e/dGnz59sGLFCpSWlmLixIkAgNDQULRt2xaxsbEAgEWLFqFfv37w8vLC3bt3sWzZMly7dg3vvvtu/Z4JEVETZHRIjxs3Drdu3UJMTAzy8vLQo0cP7N27V38xMScnB2Zm/+ug//HHH5g0aRLy8vLQsmVL9OrVC8eOHUPXrl3r7yyIiJoolSRJktxFPEtRURE0Gg0KCwthZ2fXoN/V/oPdDXp8U7q6dLjcJdSrpvTbAE3v9yHj1DbXOHcHEZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNJERAIz+h2HRER/xlebNRz2pImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoHVKaRXr16N9u3bw9LSEn379sXJkyefuv2OHTvQuXNnWFpaonv37tizZ0+diiUiUhqjQ3r79u2IjIzE/PnzkZ6eDl9fXwQHB6OgoKDa7Y8dO4a33noL4eHhyMjIwOjRozF69Gj88ssvz108EVFTZ3RIx8fHY9KkSZg4cSK6du2KNWvWwNraGuvXr692+08++QTDhg3D+++/jy5dumDx4sXo2bMnVq1a9dzFExE1dRbGbFxRUYFTp04hOjpa32ZmZoagoCCkpqZWu09qaioiIyMN2oKDg5GQkFDj95SXl6O8vFz/ubCwEABQVFRkTLl1oiu/1+DfYSqm+O9lSk3ptwGa1u/D36bu3yFJ0lO3Myqkb9++jcrKSjg5ORm0Ozk54eLFi9Xuk5eXV+32eXl5NX5PbGwsFi5cWKXdzc3NmHIVT7NC7groafj7iMuUv01xcTE0Gk2N640KaVOJjo426H3rdDrcuXMHrVq1gkqlkrGy51dUVAQ3Nzdcv34ddnZ2cpdDf8LfR1xN7beRJAnFxcVwdXV96nZGhXTr1q1hbm6O/Px8g/b8/Hw4OztXu4+zs7NR2wOAWq2GWq02aLO3tzemVOHZ2dk1if/Rmir+PuJqSr/N03rQjxl14bB58+bo1asXEhMT9W06nQ6JiYnw8/Ordh8/Pz+D7QHgwIEDNW5PRET/Y/RwR2RkJMLCwtC7d2/06dMHK1asQGlpKSZOnAgACA0NRdu2bREbGwsAiIiIQEBAAJYvX47hw4dj27ZtSEtLw9q1a+v3TIiImiCjQ3rcuHG4desWYmJikJeXhx49emDv3r36i4M5OTkwM/tfB71///7YunUr5s6dizlz5sDb2xsJCQno1q1b/Z1FI6JWqzF//vwqwzkkBv4+4lLqb6OSnnX/BxERyYZzdxARCYwhTUQkMIY0EZHAGNJERAJjSBMRCYwhTQQgKysL+/btw/379wE8e9IbIlNhSJvQ4cOH8de//hV+fn64ceMGAGDz5s04cuSIzJUp1++//46goCB07NgRr776KnJzcwEA4eHhmDlzpszVETGkTWbXrl0IDg6GlZUVMjIy9FOxFhYW4qOPPpK5OuWaMWMGLCwskJOTA2tra337uHHjsHfvXhkrIwAoLS3FvHnz0L9/f3h5ecHT09NgUQIhZ8FripYsWYI1a9YgNDQU27Zt07f7+/tjyZIlMlambPv378e+ffvQrl07g3Zvb29cu3ZNpqrosXfffRfJycmYMGECXFxcGv0smHXBkDaRS5cuYdCgQVXaNRoN7t69a/qCCMCjntqTPejH7ty5o7jHj0X0008/Yffu3fD395e7FNlwuMNEnJ2dkZWVVaX9yJEjivlnm4gGDhyITZs26T+rVCrodDrExcVh8ODBMlZGANCyZUs4ODjIXYas2JM2kUmTJiEiIgLr16+HSqXCzZs3kZqaiqioKMybN0/u8hQrLi4OQ4YMQVpaGioqKjBr1iycO3cOd+7cwdGjR+UuT/EWL16MmJgYfPHFF9X+i0cJOMGSiUiShI8++gixsbG4d+/R++DUajWioqKwePFimatTtsLCQqxatQqnT59GSUkJevbsiSlTpsDFxUXu0hRPq9Xi8uXLkCQJ7du3R7NmzQzWp6eny1SZ6TCkTayiogJZWVkoKSlB165dYWNjI3dJRMKq7l2nT5o/f76JKpEPQ5oU58yZM7Xe1sfHpwErIXo2hnQDGjNmTK23/eabbxqwEnqSmZkZVCrVM58qVKlUqKysNFFVVJ2YmBgMHjwYfn5+sLS0lLscWfDCYQOqzUsmyfSys7PlLoFqKTU1FfHx8Xj48CFeeuklBAQEIDAwEP7+/rCyspK7PJNgT5qIhPbw4UOcOHECKSkpSE5OxrFjx1BeXo6XXnpJEVMqsCdNBOD8+fPIyclBRUWFQftrr70mU0X0mIWFBfz9/dGmTRs4ODjA1tYWCQkJuHjxotylmQR70ia0c+dOfP3119WGgRJuJRLRlStX8Prrr+Ps2bMG49SPHz/mmLS81q5di6SkJCQnJ6O8vBwDBw5EYGAgAgMD4ePjo4jHxPnEoYmsXLkSEydOhJOTEzIyMtCnTx+0atUKV65cQUhIiNzlKVZERAQ8PDxQUFAAa2trnDt3DikpKejduzeSkpLkLk/xJk+ejMTERERERODq1av49ttvERERAV9fX0UENMCetMl07twZ8+fPx1tvvQVbW1ucPn0anp6eiImJwZ07d7Bq1Sq5S1Sk1q1b4+DBg/Dx8YFGo8HJkyfRqVMnHDx4EDNnzkRGRobcJSpaQkICUlJSkJSUhAsXLkCr1ep70gMGDFDEU4jsSZtITk4O+vfvDwCwsrJCcXExAGDChAn46quv5CxN0SorK2FrawvgUWDfvHkTAODu7o5Lly7JWRoBGD16NOLj45Geno68vDzMmTMHN27cwIgRIxQzpwcvHJqIs7Mz7ty5A3d3d7zwwgs4fvw4fH19kZ2dzbeAyKhbt244ffo0PDw80LdvX8TFxaF58+ZYu3YtJ74SxO+//47k5GQkJSUhKSkJ586dQ8uWLTFw4EC5SzMJhrSJvPzyy/j++++h1WoxceJEzJgxAzt37kRaWppRD71Q/Zo7dy5KS0sBAIsWLcKIESMwcOBAtGrVCtu3b5e5OurevTsuXLiAli1bYtCgQZg0aRICAgIU9SQox6RNRKfTQafTwcLi0d+L27dvx9GjR+Ht7Y3JkydXmTiG5HPnzh20bNlSMRemRLZ69WoEBASgW7ducpciG4a0CZWVleHMmTMoKCiATqfTt6tUKowcOVLGyojE9+fbI5WCwx0msnfvXkyYMAG///57lXWcI0I+ZWVl+PTTT3Ho0KEqf3kCvH9dBJs2bcKyZcvw66+/AgA6duyI999/HxMmTJC5MtNgSJvIe++9hzfffBMxMTFwcnKSuxz6r/DwcOzfvx9jx45Fnz59FNdLE118fDzmzZuHqVOn6l+hdeTIEUyePBm3b9/GjBkzZK6w4XG4w0Ts7OyQkZGBDh06yF0KPUGj0WDPnj2KfoeeyDw8PLBw4UKEhoYatH/xxRdYsGCBIibL4n3SJjJ27Fg+wSagtm3b6u+TJvHk5ubqny94Uv/+/ZGbmytDRabHnrSJ3Lt3D2+88QbatGmD7t27V7mbY9q0aTJVpmw//fQTVq5ciTVr1sDd3V3ucuhPunXrhrfffhtz5swxaF+yZAm2b9+Os2fPylSZ6XBM2kS++uor7N+/H5aWlkhKSjIY+1SpVAxpmfTu3RtlZWXw9PSEtbV1lb8879y5I1NlBDx6fda4ceOQkpKiH5I6evQoEhMT8fXXX8tcnWmwJ20izs7OmDZtGj744AOYmXGUSRRBQUHIyclBeHg4nJycqlw4DAsLk6kyeuzUqVP4+OOPceHCBQBAly5dMHPmTGi1WpkrMw2GtIk4ODjg559/5oVDwVhbWyM1NRW+vr5yl0JULQ53mEhYWBi2b99eZWyN5NW5c2fcv39f7jLoKXQ6HbKysqq9j33QoEEyVWU6DGkTqaysRFxcHPbt2wcfH58qY5/x8fEyVaZsS5cuxcyZM/Hhhx9We0HXzs5OpsoIAI4fP463334b165dqzIRmVIeAuNwh4kMHjy4xnUqlQoHDx40YTX02OPrA38ei5YkSTEhILIePXqgY8eOWLhwIVxcXKr8Tkp42TNDmhQtOTn5qesDAgJMVAlVp0WLFjh9+jS8vLzkLkU2HO4gRWMIi61v377IyspiSBMp2eHDh/H555/jypUr2LFjB9q2bYvNmzfDw8MDAwYMkLs8RXvvvfcwc+ZM5OXlVXvNQAnzSnO4gxRt165dmDBhAsaPH4/Nmzfj/Pnz8PT0xKpVq7Bnzx7s2bNH7hIVrbpnCh6/1V0p1wwY0qRoWq0WM2bMQGhoqMELgjMyMhASEoK8vDy5S1S0a9euPXW9Eh7l53AHKdqlS5eqvddWo9Hg7t27pi+IDLRs2bLG2yCzsrJMXI08+HwyKZqzs3O1f9iPHDnCF9EKYPjw4SgrK6vSfunSJQQGBpq+IBkwpEnRJk2ahIiICJw4cQIqlQo3b97Eli1bEBUVhX/84x9yl6d4NjY2GDNmDB4+fKhvu3DhAgIDA/GXv/xFxspMh2PSpDhnzpxBt27d9BelPvzwQ8TGxuLevXsAALVajaioKCxevFjOMgnA/fv3ERQUhHbt2mHbtm04d+4chgwZgvHjxyvmKV2GNCmOubk5cnNz4ejoCE9PT/z888+wtbVFVlYWSkpK0LVrV9jY2MhdJv3X3bt3ERgYCG9vb6SkpCA0NBTLli2TuyyTYUiT4rRq1Qp79uxB3759YWZmhvz8fLRp00busui/ioqKqrTl5ubilVdewYgRI7B06VJ9uxLmVmFIk+L8/e9/x6ZNm+Di4oKcnBy0a9cO5ubm1W575coVE1dHZmZm1b4Q+HFUKe0+ad6CR4qzdu1ajBkzBllZWZg2bRomTZrE9xwK5NChQ3KXIBT2pEnRJk6ciJUrVzKkSVgMaSIS2t27d3Hy5MlqJ/0PDQ2VqSrTYUgTkbB++OEHjB8/HiUlJbCzs6vyAmclvCiYIU1EwurYsSNeffVVfPTRR7C2tpa7HFkwpIlIWC1atMDZs2cV/Yg+HwsnImEFBwcjLS1N7jJkxVvwiEhYw4cPx/vvv4/z589XO+n/a6+9JlNlpsPhDiISVnWT/j+mlIdZGNJERALjcAcRCWvRokU1rlOpVJg3b54Jq5EHe9JEJCytVmvw+cGDB8jOzoaFhQU6dOiA9PR0mSozHfakiUhYGRkZVdqKiorwzjvv4PXXX5ehItNjT5qIGp2zZ89i5MiRuHr1qtylNDjeJ01EjU5hYSEKCwvlLsMkONxBRMJauXKlwWdJkpCbm4vNmzcjJCREpqpMi8MdRCQsDw8Pg89mZmZo06YNXn75ZURHRytiilmGNBGRwDgmTUQkMIY0EZHAGNJERAJjSBMRCYwhTUQkMIY0EZHAGNKkGDt37kT37t1hZWWFVq1aISgoCKWlpQCAdevWoUuXLrC0tETnzp3x2Wef6ff729/+Bh8fH5SXlwMAKioqoNVqFfGmahKARKQAN2/elCwsLKT4+HgpOztbOnPmjLR69WqpuLhY+vLLLyUXFxdp165d0pUrV6Rdu3ZJDg4O0saNGyVJkqTi4mLJ09NTmj59uiRJkhQVFSW1b99eKiwslPOUSCEY0qQIp06dkgBIV69erbKuQ4cO0tatWw3aFi9eLPn5+ek/Hzt2TGrWrJk0b948ycLCQjp8+HCD10wkSZLEJw5JESorKxEcHIyTJ08iODgYQ4cOxdixY9G8eXPY2NjAysrK4FVNDx8+hEajQX5+vr5tzpw5iI2NxezZs7F06VI5ToMUiBMskSKYm5vjwIEDOHbsGPbv349PP/0U//d//4cffvgBAPCvf/0Lffv2rbLPYzqdDkePHoW5uTmysrJMWjspGy8ckmKoVCr4+/tj4cKFyMjIQPPmzXH06FG4urriypUr8PLyMlienNxn2bJluHjxIpKTk7F3715s2LBBxjMhJWFPmhThxIkTSExMxNChQ+Ho6IgTJ07g1q1b6NKlCxYuXIhp06ZBo9Fg2LBhKC8vR1paGv744w9ERkYiIyMDMTEx2LlzJ/z9/REfH4+IiAgEBATA09NT7lOjJo5j0qQIFy5cwIwZM5Ceno6ioiK4u7vjvffew9SpUwEAW7duxbJly3D+/Hm0aNEC3bt3x/Tp0xESEoJevXphwIAB+Pzzz/XHGzVqFG7fvo2UlBSDYRGi+saQJiISGMekiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigf0/scctQIgGH5gAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 400x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels_counts.plot.bar(x=query_label, y=\"num_cells\", figsize=(4, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = names_counts[query_name].values.tolist()\n",
+    "new_labels = labels_counts[query_label].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['PATO:0000384', 'PATO:0000383']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove unknown\n",
+    "new_labels = new_labels[:2]\n",
+    "new_names = new_names[:2]\n",
+    "new_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "    },\n",
+    "    \"sex_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellarium_gpt/training/tissue_ontology.ipynb b/notebooks/cellarium_gpt/training/tissue_ontology.ipynb
new file mode 100644
index 00000000..2c41934d
--- /dev/null
+++ b/notebooks/cellarium_gpt/training/tissue_ontology.ipynb
@@ -0,0 +1,720 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# tissue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import typing as t\n",
+    "from functools import lru_cache\n",
+    "from logging import log\n",
+    "\n",
+    "import networkx as nx\n",
+    "import owlready2\n",
+    "from scipy import sparse as sp\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pandas_gbq\n",
+    "from google.cloud import bigquery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query template format\n",
+    "query_format = \"\"\"\n",
+    "    select {column_name}, count(*) as num_cells \n",
+    "    from `cas_2024_05_16_dataset.human_cellariumgpt_extract__extract_cell_info`\n",
+    "    group by {column_name}\n",
+    "    order by num_cells desc\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_ancestors_csr_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> sp.csr_matrix:\n",
+    "    \"\"\"Returns a sparse matrix representation of ancestors.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = 1 iff j is an ancetor of i.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    row = []\n",
+    "    col = []\n",
+    "    data = []\n",
+    "\n",
+    "    for name, self_idx in names_to_idx_map.items():\n",
+    "        row.append(self_idx)\n",
+    "        col.append(self_idx)\n",
+    "        data.append(1)\n",
+    "        for ancestor_name in nx.ancestors(graph, name):\n",
+    "            ancestor_idx = names_to_idx_map[ancestor_name]\n",
+    "            row.append(self_idx)\n",
+    "            col.append(ancestor_idx)\n",
+    "            data.append(1)\n",
+    "\n",
+    "    ancestors_csr_matrix = sp.csr_matrix((data, (row, col)), shape=(n_nodes, n_nodes))\n",
+    "    return ancestors_csr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_shortest_distances_matrix(graph, names_to_idx_map: t.Dict[str, int]) -> np.ndarray:\n",
+    "    \"\"\"Returns a sparse matrix representation of shortest distances.\n",
+    "\n",
+    "    .. note:\n",
+    "        The matrix element (i, j) = d iff d is the shortest distance between i and j.\n",
+    "    \"\"\"\n",
+    "    n_nodes = len(graph.nodes)\n",
+    "\n",
+    "    distance_matrix = np.full((n_nodes, n_nodes), np.inf)\n",
+    "\n",
+    "    for target, value in dict(nx.all_pairs_shortest_path_length(graph)).items():\n",
+    "        for source, distance in value.items():\n",
+    "            source_idx = names_to_idx_map[source]\n",
+    "            target_idx = names_to_idx_map[target]\n",
+    "            distance_matrix[source_idx, target_idx] = distance\n",
+    "            distance_matrix[target_idx, source_idx] = distance\n",
+    "\n",
+    "    return distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## extract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tissue_ontology_term_id</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>UBERON:0000178</td>\n",
+       "      <td>9446186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>UBERON:0002048</td>\n",
+       "      <td>2412727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>UBERON:0000310</td>\n",
+       "      <td>2269902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UBERON:0009834</td>\n",
+       "      <td>1591509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>UBERON:0002771</td>\n",
+       "      <td>1317262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  tissue_ontology_term_id  num_cells\n",
+       "0          UBERON:0000178    9446186\n",
+       "1          UBERON:0002048    2412727\n",
+       "2          UBERON:0000310    2269902\n",
+       "3          UBERON:0009834    1591509\n",
+       "4          UBERON:0002771    1317262"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_name = \"tissue_ontology_term_id\"\n",
+    "names_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_name),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "names_counts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yordabay/anaconda3/envs/ontology/lib/python3.10/site-packages/google/cloud/bigquery/table.py:2379: UserWarning: A progress bar was requested, but there was an error loading the tqdm library. Please install tqdm to use the progress bar functionality.\n",
+      "  record_batch = self.to_arrow(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>num_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>blood</td>\n",
+       "      <td>9446186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>lung</td>\n",
+       "      <td>2412727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>breast</td>\n",
+       "      <td>2269902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>dorsolateral prefrontal cortex</td>\n",
+       "      <td>1591509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>middle temporal gyrus</td>\n",
+       "      <td>1317262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>left parietal lobe</td>\n",
+       "      <td>163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>vault of skull</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>kidney blood vessel</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>skin of face</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>skin of shoulder</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>264 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             tissue  num_cells\n",
+       "0                             blood    9446186\n",
+       "1                              lung    2412727\n",
+       "2                            breast    2269902\n",
+       "3    dorsolateral prefrontal cortex    1591509\n",
+       "4             middle temporal gyrus    1317262\n",
+       "..                              ...        ...\n",
+       "259              left parietal lobe        163\n",
+       "260                  vault of skull        123\n",
+       "261             kidney blood vessel         47\n",
+       "262                    skin of face         37\n",
+       "263                skin of shoulder         37\n",
+       "\n",
+       "[264 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_label = \"tissue\"\n",
+    "labels_counts = pandas_gbq.read_gbq(\n",
+    "    query_format.format(column_name=query_label),\n",
+    "    project_id=\"dsp-cell-annotation-service\",\n",
+    ")\n",
+    "labels_counts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many different ontologies are used for `tissue`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL', 'UBERON'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set([value[0] for value in names_counts[query_name].str.split(\":\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ontology"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Used in CZ CELLxGENE schema v5:\n",
+    "# https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/5.0.0/schema.md\n",
+    "OWL_PATH = \"https://github.com/obophenotype/uberon/releases/download/v2024-01-18/uberon.owl\"\n",
+    "\n",
+    "# only keep nodes with the following prefix when parsing CL ontology\n",
+    "PREFIX = \"UBERON_\"\n",
+    "\n",
+    "# the 'cell' node\n",
+    "LIFE_CYCLE_NODE = \"UBERON:0000104\"\n",
+    "\n",
+    "# the 'eukaryotic cell' node\n",
+    "ANATOMICAL_ENTITY_NODE = \"UBERON:0001062\"\n",
+    "\n",
+    "# relationships we need\n",
+    "PARTOF_RELATIONSHIP = \"BFO_0000050\"  # part_of"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ontology = owlready2.get_ontology(OWL_PATH).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_classes = list(ontology.classes())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# only keep CL classes with a singleton label\n",
+    "classes = list(\n",
+    "    _class for _class in all_classes if _class.name.startswith(PREFIX) and len(_class.label) == 1\n",
+    ")\n",
+    "\n",
+    "names = [_class.name.replace(\"_\", \":\") for _class in classes]\n",
+    "labels = [str(_class.label[0]) for _class in classes]\n",
+    "assert len(set(names)) == len(classes)\n",
+    "# assert len(set(labels)) == len(classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes_set = set(classes)\n",
+    "names_to_labels_map = {name: label for name, label in zip(names, labels)}\n",
+    "names_to_idx_map = {name: idx for idx, name in enumerate(names)}\n",
+    "# labels_to_names_map = {label: name for name, label in zip(names, labels)}\n",
+    "# labels_to_idx_map = {label: idx for idx, label in enumerate(labels)}\n",
+    "idx_to_names_map = {idx: name for idx, name in enumerate(names)}\n",
+    "idx_to_labels_map = {idx: label for idx, label in enumerate(labels)}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are all `tissue`s covered by the ontology tree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'embryonic stem cell'}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(labels_counts[query_label]) - set(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'CL:0002322'}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(names_counts[query_name]) - set(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15567"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build a networkx graph from CL\n",
+    "graph = nx.DiGraph(name=\"CL graph\")\n",
+    "\n",
+    "for _class in classes:\n",
+    "    graph.add_node(_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "for self_class in classes:\n",
+    "    # parents\n",
+    "    for parent_class in ontology.get_parents_of(self_class):\n",
+    "        if parent_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(parent_class.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "    # children\n",
+    "    for child_class in ontology.get_children_of(self_class):\n",
+    "        if child_class not in classes_set:\n",
+    "            continue\n",
+    "        graph.add_edge(self_class.name.replace(\"_\", \":\"), child_class.name.replace(\"_\", \":\"))\n",
+    "    # part of\n",
+    "    for prop in self_class.get_class_properties():\n",
+    "        if PARTOF_RELATIONSHIP in prop.name:\n",
+    "            for related_term in prop[self_class]:\n",
+    "                if related_term.name.startswith(PREFIX):\n",
+    "                    graph.add_edge(related_term.name.replace(\"_\", \":\"), self_class.name.replace(\"_\", \":\"))\n",
+    "\n",
+    "    # deprecated terms (WHY???!!)\n",
+    "    if \"deprecated\" in [prop.name for prop in self_class.get_class_properties()]:\n",
+    "        for prop in self_class.get_class_properties():\n",
+    "            if \"consider\" in prop.name:\n",
+    "                for substitute in prop[self_class]:\n",
+    "                    if substitute.startswith(PREFIX):\n",
+    "                        graph.add_edge(substitute, self_class.name.replace(\"_\", \":\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 0],\n",
+       "       [0, 1, 1, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 1, 0, ..., 1, 0, 0],\n",
+       "       [0, 1, 0, ..., 0, 1, 0],\n",
+       "       [0, 1, 0, ..., 0, 0, 1]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ancestors_matrix = get_ancestors_csr_matrix(graph, names_to_idx_map).toarray()\n",
+    "ancestors_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
+       "       [inf,  0., inf, ..., inf, inf, inf],\n",
+       "       [inf,  4.,  0., ..., inf, inf, inf],\n",
+       "       ...,\n",
+       "       [inf,  6., inf, ...,  0., inf, inf],\n",
+       "       [inf,  6., inf, ..., inf,  0., inf],\n",
+       "       [inf,  5., inf, ..., inf, inf,  0.]])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shortest_distances_matrix = get_shortest_distances_matrix(graph, names_to_idx_map)\n",
+    "shortest_distances_matrix = np.where(ancestors_matrix, shortest_distances_matrix, np.inf)\n",
+    "shortest_distances_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only leave nodes that exist in the data and their ancestors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "263"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extract_names_set = set(names_counts[query_name]) - set([\"CL:0002322\"])\n",
+    "extract_idx = [idx for name, idx in names_to_idx_map.items() if name in extract_names_set]\n",
+    "assert len(extract_idx) == len(extract_names_set)\n",
+    "len(extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "822"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_extract_idx = ancestors_matrix[extract_idx].any(axis=0).nonzero()[0].tolist()\n",
+    "new_extract_idx = [idx for idx in new_extract_idx if idx_to_names_map[idx] not in {LIFE_CYCLE_NODE, ANATOMICAL_ENTITY_NODE}]\n",
+    "len(new_extract_idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_names = [idx_to_names_map[idx] for idx in new_extract_idx]\n",
+    "new_labels = [idx_to_labels_map[idx] for idx in new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_shortest_distances_matrix = shortest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "# new_longest_distances_matrix = longest_distances_matrix[new_extract_idx][:, new_extract_idx]\n",
+    "new_ancestors_matrix = ancestors_matrix[new_extract_idx][:, new_extract_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save({\n",
+    "        \"names\": new_names,\n",
+    "        \"labels\": new_labels,\n",
+    "        \"shortest_distances_matrix\": torch.tensor(new_shortest_distances_matrix, dtype=torch.float32),\n",
+    "        # \"longest_distances_matrix\": torch.tensor(new_longest_distances_matrix, dtype=torch.float32),\n",
+    "        \"ancestors_matrix\": torch.tensor(new_ancestors_matrix, dtype=torch.int32),\n",
+    "    },\n",
+    "    \"tissue_ontology_data.pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ontology",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From e79fe044a5d474a2354db262ed2b6a59fe1481ee Mon Sep 17 00:00:00 2001
From: Neil Mallinar <nmallinar@gmail.com>
Date: Mon, 10 Mar 2025 15:07:54 -0500
Subject: [PATCH 58/64] checkin activations work

---
 .gitignore                                    |     1 +
 cellarium/ml/layers/transformer.py            |    22 +
 cellarium/ml/lr_schedulers/__init__.py        |     3 +-
 cellarium/ml/models/cellarium_gpt.py          |    48 +-
 .../inference/cellarium_gpt_inference.py      |    39 +
 .../00_fixed_cell_type_embeddings.ipynb       | 11212 ++++++++++++++++
 .../01_extract_cell_type_activations.ipynb    |   254 +
 .../02_extract_cell_type_activations.py       |   168 +
 notebooks/embeddings/run1.sh                  |    19 +
 notebooks/embeddings/sandbox.ipynb            |  1495 +++
 10 files changed, 13259 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/embeddings/00_fixed_cell_type_embeddings.ipynb
 create mode 100644 notebooks/embeddings/01_extract_cell_type_activations.ipynb
 create mode 100644 notebooks/embeddings/02_extract_cell_type_activations.py
 create mode 100644 notebooks/embeddings/run1.sh
 create mode 100644 notebooks/embeddings/sandbox.ipynb

diff --git a/.gitignore b/.gitignore
index 1629b225..8b38d673 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 __pycache__/
 runs/
+configs/
 .ipynb_checkpoints
 .mypy_cache/
 .ruff_cache/
diff --git a/cellarium/ml/layers/transformer.py b/cellarium/ml/layers/transformer.py
index 7f68fc51..b999bcde 100644
--- a/cellarium/ml/layers/transformer.py
+++ b/cellarium/ml/layers/transformer.py
@@ -178,6 +178,28 @@ def __init__(
     def _reset_parameters(self) -> None:
         self.ln.reset_parameters()
 
+    def forward_all_hidden_states(
+        self,
+        hidden_state_ncd,
+        attention_mask_ncc,
+        to_cpu=True
+    ):
+        hidden_states = []
+        for block in self.blocks:
+            hidden_state_ncd = block(hidden_state_ncd, attention_mask_ncc)
+            if to_cpu:
+                hidden_states.append(hidden_state_ncd.detach().cpu())
+            else:
+                hidden_states.append(hidden_state_ncd.detach())
+
+        if to_cpu:
+            hidden_states.append(self.ln(hidden_state_ncd).detach().cpu())
+        else:
+            hidden_states.append(self.ln(hidden_state_ncd).detach())
+
+        return hidden_states
+
+
     def forward(
         self,
         hidden_state_ncd: torch.Tensor,
diff --git a/cellarium/ml/lr_schedulers/__init__.py b/cellarium/ml/lr_schedulers/__init__.py
index f10125d5..d487f84b 100644
--- a/cellarium/ml/lr_schedulers/__init__.py
+++ b/cellarium/ml/lr_schedulers/__init__.py
@@ -2,5 +2,6 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from cellarium.ml.lr_schedulers.linear_lr import LinearLR
+from torch.optim.lr_scheduler import CosineAnnealingLR
 
-__all__ = ["LinearLR"]
+__all__ = ["LinearLR", "CosineAnnealingLR"]
diff --git a/cellarium/ml/models/cellarium_gpt.py b/cellarium/ml/models/cellarium_gpt.py
index 927e71c9..447762d2 100644
--- a/cellarium/ml/models/cellarium_gpt.py
+++ b/cellarium/ml/models/cellarium_gpt.py
@@ -568,7 +568,53 @@ def set_attention_backend(self, attention_backend: str) -> None:
         self.attention_backend = attention_backend
         for block in self.transformer.blocks:
             block.attention.attention_backend = attention_backend
-    
+
+    def get_embeddings(
+        self,
+        gene_tokens_nc: dict[str, torch.Tensor],
+        metadata_tokens_n: dict[str, torch.Tensor],
+        prompt_mask_nc: torch.Tensor | None,
+        to_cpu = True
+    ) -> dict[str, torch.Tensor]:
+        # embed the gene IDs, values, and total mRNA UMIs
+        embedding_ncd = torch.cat(
+            [
+                self.gene_embedding(gene_tokens_nc),
+                self.metadata_embedding(metadata_tokens_n),
+            ],
+            dim=1,
+        )
+
+        # create attention mask
+        if self.attention_backend == "flex":
+
+            def prompt_diagonal_mask_mod(b, h, q_idx, kv_idx):
+                return prompt_mask_nc[b, kv_idx] | (q_idx == kv_idx)
+
+            n, c = prompt_mask_nc.shape
+            attention_mask_ncc = create_block_mask(
+                prompt_diagonal_mask_mod, B=n, H=None, Q_LEN=c, KV_LEN=c, BLOCK_SIZE=c, device=prompt_mask_nc.device, _compile=False)
+        else:
+            attention_mask_ncc = prompt_diagonal_mask(prompt_mask_nc)
+
+        # transformer blocks
+        hidden_state_ncd = embedding_ncd * self.embeddings_scale
+
+        hidden_states = self.transformer.forward_all_hidden_states(hidden_state_ncd, attention_mask_ncc, 
+                                                                   to_cpu=to_cpu)
+        # hidden_state_ncd = self.transformer(hidden_state_ncd, attention_mask_ncc)
+
+        return hidden_states
+
+        # # compute logits
+        # logits_nck = {}
+        # if predict_keys is None:
+        #     predict_keys = ["gene_value"] + list(metadata_tokens_n)
+        # for key in predict_keys:
+        #     logits_nck[key] = self.head[key](hidden_state_ncd) * self.output_logits_scale
+
+        # return logits_nck 
+
     def predict(
         self,
         gene_tokens_nc: dict[str, torch.Tensor],
diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index db3034ae..3faa8814 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -555,6 +555,45 @@ def get_gene_value_logits_from_tokens(
 
         return gene_logits_nqk
      
+    def get_embeddings_from_tokens(
+            self,
+            tokens_dict: dict,
+            context_indices: dict,
+            max_counts: int | None = None,
+            to_cpu = True
+        ) -> t.Tuple[torch.Tensor, torch.Tensor]:
+    
+        # convert to cuda
+        tokens_dict = self.gpt_pipeline.transfer_batch_to_device(tokens_dict, self.device, 0)
+        
+        # get model predictions
+        hidden_states = self.gpt_pipeline.model.get_embeddings(
+            gene_tokens_nc=tokens_dict["gene_tokens_nc"],
+            metadata_tokens_n=tokens_dict["metadata_tokens_n"],
+            prompt_mask_nc=tokens_dict["prompt_mask_nc"],
+            to_cpu = to_cpu
+        )
+
+        # note: we use `q` to denote query genes
+        # query_gene_indices = torch.tensor(context_indices['query_genes'], device=self.device, dtype=torch.int64)
+        # gene_hidden_states_nqd = hidden_states_ncd[:, query_gene_indices, :]
+
+        return hidden_states
+
+        # gene_logits_nqk = logits_dict['gene_value'][:, query_gene_indices, :]
+        
+        # if max_counts is None:
+        #     max_counts = gene_logits_nqk.shape[-1]
+        # else:
+        #     assert max_counts > 0
+        # gene_logits_nqk = gene_logits_nqk[:, :, :max_counts]  # truncate to max_counts
+        # gene_logits_nqk = gene_logits_nqk - torch.logsumexp(gene_logits_nqk, dim=-1, keepdim=True)  # renormalize
+
+        # return gene_logits_nqk
+
+        # gene_logits_nqk = self.get_gene_value_logits_from_tokens(tokens_dict, context_indices, max_counts)
+
+        # pass
 
     def get_marginal_mean_std_from_tokens(
             self,
diff --git a/notebooks/embeddings/00_fixed_cell_type_embeddings.ipynb b/notebooks/embeddings/00_fixed_cell_type_embeddings.ipynb
new file mode 100644
index 00000000..4bec9278
--- /dev/null
+++ b/notebooks/embeddings/00_fixed_cell_type_embeddings.ipynb
@@ -0,0 +1,11212 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract cell type embeddings from fixed  embedding layer of the trained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import math\n",
+    "import torch\n",
+    "import pickle\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.express as px\n",
+    "from braceexpand import braceexpand\n",
+    "from tqdm import tqdm\n",
+    "import umap\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "# DEVICE = torch.device('cuda:1')\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/mb-ml-dev-vm\"\n",
+    "TRAIN_ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/human_cellariumgpt_v2/extract_files\"\n",
+    "CHECKPOINT_PATH = \"/work/hdd/bbjr/mallina1/cellarium/models/latest/epoch=5-step=452000.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "ONTOLOGY_INFO_PATH = os.path.join(ROOT_PATH, \"ontology_infos.pt\")\n",
+    "ONTOLOGY_DIR_PATH = os.path.join(ROOT_PATH, \"ontology\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_290983/398718590.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  ontology_infos = torch.load(ONTOLOGY_INFO_PATH)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ontology_infos = torch.load(ONTOLOGY_INFO_PATH)\n",
+    "cell_type_labels = ontology_infos['cell_type']['labels']\n",
+    "cell_type_names = ontology_infos['cell_type']['names']\n",
+    "\n",
+    "with open(os.path.join(ONTOLOGY_DIR_PATH, 'cl_benchmarking_resource.pkl'), 'rb') as fp:\n",
+    "    cell_type_benchmarking = pickle.load(fp)\n",
+    "\n",
+    "# with open(os.path.join(ONTOLOGY_DIR_PATH, 'cl_propagation_resource.pkl'), 'rb') as fp:\n",
+    "#     cell_type_propagation = pickle.load(fp)\n",
+    "\n",
+    "cl_label2name = {}\n",
+    "cl_name2label = {}\n",
+    "\n",
+    "for idx in range(len(cell_type_labels)):\n",
+    "    cl_label2name[cell_type_labels[idx]] = cell_type_names[idx]\n",
+    "    cl_name2label[cell_type_names[idx]] = cell_type_labels[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['CL:0000000', 'CL:0000540', 'CL:0000101', 'CL:0000001', 'CL:0000010', 'CL:0000005', 'CL:0000333', 'CL:0000057', 'CL:0000006', 'CL:0000197', 'CL:0002321', 'CL:0000578', 'CL:0000039', 'CL:0000034', 'CL:0000015', 'CL:0000586', 'CL:0000413', 'CL:0000019', 'CL:0000408', 'CL:0000021', 'CL:0000023', 'CL:0000255', 'CL:0000024', 'CL:0000670', 'CL:0000412', 'CL:0000192', 'CL:0000029', 'CL:0000030', 'CL:0000055', 'CL:0000031', 'CL:0000114', 'CL:0000152', 'CL:0011115', 'CL:0000035', 'CL:0000723', 'CL:0000036', 'CL:0000037', 'CL:0000988', 'CL:0008001', 'CL:0011026', 'CL:0000038', 'CL:0000764', 'CL:0000839', 'CL:0000050', 'CL:0002194', 'CL:0000763', 'CL:0002009', 'CL:0000771', 'CL:0000145', 'CL:0000834', 'CL:0000767', 'CL:0002274', 'CL:0000051', 'CL:0000047', 'CL:0002319', 'CL:0000048', 'CL:0000049', 'CL:0002032', 'CL:0000557', 'CL:0000151', 'CL:0000056', 'CL:0000680', 'CL:0002320', 'CL:0000134', 'CL:0002077', 'CL:0000710', 'CL:0000062', 'CL:0007010', 'CL:0000064', 'CL:0000065', 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'CL:0000931', 'CL:0000934', 'CL:0000936', 'CL:0000938', 'CL:0000939', 'CL:0000940', 'CL:0000974', 'CL:0000972', 'CL:0000957', 'CL:0000955', 'CL:0000956', 'CL:0000970', 'CL:0000979', 'CL:0000982', 'CL:0000984', 'CL:0000985', 'CL:0000986', 'CL:0000987', 'CL:0001060', 'CL:0002010', 'CL:0002465', 'CL:0001024', 'CL:0001019', 'CL:0001022', 'CL:0001031', 'CL:2000028', 'CL:0002362', 'CL:0010012', 'CL:0002608', 'CL:0001042', 'CL:0001043', 'CL:0001044', 'CL:0002677', 'CL:0002678', 'CL:0001049', 'CL:0001050', 'CL:0001054', 'CL:0002397', 'CL:0001056', 'CL:0001057', 'CL:0001058', 'CL:0001061', 'CL:0001062', 'CL:4030002', 'CL:0001063', 'CL:0001064', 'CL:0001065', 'CL:0001066', 'CL:0001068', 'CL:0001069', 'CL:0001071', 'CL:0001074', 'CL:0001076', 'CL:0001077', 'CL:0001078', 'CL:0001079', 'CL:0001080', 'CL:0001081', 'CL:0001200', 'CL:0002038', 'CL:0002045', 'CL:0002046', 'CL:0002048', 'CL:0002057', 'CL:0002062', 'CL:0002063', 'CL:1000272', 'CL:0002064', 'CL:1001599', 'CL:0002086', 'CL:0002071', 'CL:0002253', 'CL:0002072', 'CL:0002075', 'CL:0002204', 'CL:0002079', 'CL:1001433', 'CL:0002088', 'CL:0002092', 'CL:0002094', 'CL:0008009', 'CL:0002097', 'CL:1001601', 'CL:0002098', 'CL:0002099', 'CL:0002102', 'CL:0002117', 'CL:0002129', 'CL:0002131', 'CL:0002138', 'CL:0005022', 'CL:1000448', 'CL:0002144', 'CL:2000008', 'CL:0002145', 'CL:0005012', 'CL:4030034', 'CL:0002149', 'CL:0002151', 'CL:0002154', 'CL:0002157', 'CL:0002167', 'CL:1001318', 'CL:0002178', 'CL:0002179', 'CL:0002187', 'CL:1000428', 'CL:0002188', 'CL:1000746', 'CL:0002189', 'CL:0002257', 'CL:0002201', 'CL:0005010', 'CL:0002632', 'CL:0002203', 'CL:0019032', 'CL:0002207', 'CL:0002328', 'CL:0002252', 'CL:0002222', 'CL:0002223', 'CL:0002224', 'CL:0002225', 'CL:0011004', 'CL:0002325', 'CL:0002231', 'CL:0002341', 'CL:0002340', 'CL:0002236', 'CL:0002241', 'CL:0002243', 'CL:0002246', 'CL:0002250', 'CL:0002254', 'CL:0002255', 'CL:2000002', 'CL:0002258', 'CL:0002259', 'CL:1001593', 'CL:0002262', 'CL:0009042', 'CL:0009006', 'CL:0002268', 'CL:0002269', 'CL:0002275', 'CL:0002277', 'CL:0002278', 'CL:0002279', 'CL:0002280', 'CL:0005018', 'CL:0002293', 'CL:0002364', 'CL:0002365', 'CL:0002303', 'CL:0002304', 'CL:0002305', 'CL:1000494', 'CL:1000615', 'CL:0002306', 'CL:0002308', 'CL:0002322', 'CL:0002324', 'CL:0002327', 'CL:1001586', 'CL:4033057', 'CL:0002326', 'CL:0009116', 'CL:0002329', 'CL:0002332', 'CL:0002337', 'CL:0002338', 'CL:0002343', 'CL:0002350', 'CL:0010008', 'CL:0002352', 'CL:0002355', 'CL:0002417', 'CL:0002361', 'CL:0002363', 'CL:0002366', 'CL:0002601', 'CL:0002368', 'CL:0002370', 'CL:0002375', 'CL:0002394', 'CL:0002395', 'CL:0002396', 'CL:0002399', 'CL:0002410', 'CL:0002457', 'CL:0002470', 'CL:0002477', 'CL:0002480', 'CL:1000458', 'CL:0002559', 'CL:0002491', 'CL:0002488', 'CL:0002495', 'CL:0002503', 'CL:0002504', 'CL:0002518', 'CL:1000497', 'CL:0002521', 'CL:0002536', 'CL:0002538', 'CL:1000488', 'CL:0002539', 'CL:0019018', 'CL:1000413', 'CL:0002543', 'CL:0002544', 'CL:0002548', 'CL:0002620', 'CL:0002553', 'CL:0002554', 'CL:0002555', 'CL:0002565', 'CL:0002570', 'CL:0002575', 'CL:0002577', 'CL:4006000', 'CL:0002584', 'CL:0002681', 'CL:0002585', 'CL:0002586', 'CL:0002591', 'CL:1001319', 'CL:0002598', 'CL:0019019', 'CL:0002600', 'CL:0002603', 'CL:2000005', 'CL:0002605', 'CL:1001579', 'CL:0002607', 'CL:0002617', 'CL:0002621', 'CL:0002622', 'CL:0002623', 'CL:1001596', 'CL:0002626', 'CL:0002627', 'CL:0002629', 'CL:0002633', 'CL:0002657', 'CL:1000414', 'CL:0002658', 'CL:0002671', 'CL:0002673', 'CL:1001516', 'CL:0003001', 'CL:0004247', 'CL:4023032', 'CL:4023033', 'CL:0003050', 'CL:0004213', 'CL:0004214', 'CL:0004215', 'CL:0004216', 'CL:0004217', 'CL:0004218', 'CL:0004219', 'CL:0004250', 'CL:0004251', 'CL:4030028', 'CL:0004232', 'CL:4030027', 'CL:0005006', 'CL:0005009', 'CL:1000449', 'CL:0005011', 'CL:0005019', 'CL:0005020', 'CL:0005021', 'CL:0015000', 'CL:0005025', 'CL:0011102', 'CL:0007005', 'CL:0007011', 'CL:0008008', 'CL:0008011', 'CL:0008015', 'CL:0008031', 'CL:0010011', 'CL:0008036', 'CL:2000049', 'CL:4023041', 'CL:0009002', 'CL:0009005', 'CL:1001598', 'CL:0009009', 'CL:1001588', 'CL:0009010', 'CL:0009011', 'CL:0009012', 'CL:0009016', 'CL:0009017', 'CL:0009022', 'CL:0011108', 'CL:0009038', 'CL:0009039', 'CL:1000320', 'CL:0009041', 'CL:0009043', 'CL:1000279', 'CL:0009089', 'CL:0009092', 'CL:0009095', 'CL:0009099', 'CL:0009111', 'CL:0009112', 'CL:0009113', 'CL:2000092', 'CL:0010006', 'CL:0010022', 'CL:0011001', 'CL:0011010', 'CL:0011011', 'CL:0011019', 'CL:0011024', 'CL:0011025', 'CL:0011027', 'CL:1001609', 'CL:0011028', 'CL:0011031', 'CL:0011101', 'CL:0011103', 'CL:0011113', 'CL:0013000', 'CL:0017000', 'CL:0017001', 'CL:1000296', 'CL:4030024', 'CL:0019001', 'CL:0019003', 'CL:0019021', 'CL:1000398', 'CL:0019022', 'CL:0019026', 'CL:0019028', 'CL:0019029', 'CL:0019031', 'CL:1000507', 'CL:1000042', 'CL:1000143', 'CL:1000223', 'CL:1000271', 'CL:1000275', 'CL:1000278', 'CL:1001320', 'CL:1000299', 'CL:1000304', 'CL:1000305', 'CL:1000309', 'CL:1000311', 'CL:1000312', 'CL:1000495', 'CL:1000317', 'CL:1001589', 'CL:1000326', 'CL:1000329', 'CL:1000330', 'CL:1000331', 'CL:1000334', 'CL:1000339', 'CL:1000342', 'CL:1000343', 'CL:1000347', 'CL:1000348', 'CL:1000353', 'CL:2000046', 'CL:1000409', 'CL:1000412', 'CL:1000432', 'CL:1000435', 'CL:1000436', 'CL:1000437', 'CL:1001428', 'CL:1000443', 'CL:1000447', 'CL:1000510', 'CL:1000452', 'CL:1000453', 'CL:1000909', 'CL:1000454', 'CL:1001225', 'CL:1000487', 'CL:1000491', 'CL:1000493', 'CL:1000500', 'CL:1000504', 'CL:1000505', 'CL:1000617', 'CL:1000616', 'CL:1000597', 'CL:1000600', 'CL:1000612', 'CL:1000692', 'CL:1000695', 'CL:1000698', 'CL:1000703', 'CL:1001431', 'CL:1001432', 'CL:1000768', 'CL:1000838', 'CL:1000839', 'CL:1001021', 'CL:1000849', 'CL:1000850', 'CL:1000854', 'CL:1000891', 'CL:1000892', 'CL:1000893', 'CL:1001005', 'CL:1001006', 'CL:1001009', 'CL:1001016', 'CL:1001033', 'CL:1001036', 'CL:1001045', 'CL:1001096', 'CL:1001099', 'CL:1001106', 'CL:1001107', 'CL:1001108', 'CL:1001109', 'CL:1001111', 'CL:1001131', 'CL:1001285', 'CL:1001430', 'CL:1001451', 'CL:1001474', 'CL:2000030', 'CL:1001509', 'CL:1001567', 'CL:1001568', 'CL:4023111', 'CL:1001602', 'CL:2000001', 'CL:2000006', 'CL:2000011', 'CL:2000016', 'CL:2000018', 'CL:2000041', 'CL:2000042', 'CL:2000043', 'CL:2000047', 'CL:2000054', 'CL:2000055', 'CL:2000059', 'CL:2000060', 'CL:2000084', 'CL:2000093', 'CL:2000095', 'CL:3000001', 'CL:4023016', 'CL:4023008', 'CL:4023009', 'CL:4023011', 'CL:4023012', 'CL:4023013', 'CL:4023015', 'CL:4023017', 'CL:4023018', 'CL:4023121', 'CL:4023026', 'CL:4023029', 'CL:4023036', 'CL:4023083', 'CL:4023038', 'CL:4023040', 'CL:4023042', 'CL:4023043', 'CL:4030067', 'CL:4023047', 'CL:4030059', 'CL:4023048', 'CL:4030062', 'CL:4023050', 'CL:4030065', 'CL:4023051', 'CL:4023058', 'CL:4023054', 'CL:4023056', 'CL:4023059', 'CL:4023064', 'CL:4023065', 'CL:4023068', 'CL:4023070', 'CL:4023072', 'CL:4023161', 'CL:4023181', 'CL:4023188', 'CL:4023189', 'CL:4028004', 'CL:4028006', 'CL:4030009', 'CL:4030011', 'CL:4030018', 'CL:4030023', 'CL:4030026', 'CL:4030032', 'CL:4030033', 'CL:4030063', 'CL:4030066', 'CL:4033005', 'CL:4033013', 'CL:4033015', 'CL:4033019', 'CL:4033024', 'CL:4033025', 'CL:4033026', 'CL:4033027', 'CL:4033028', 'CL:4033029', 'CL:4033030', 'CL:4033031', 'CL:4033032', 'CL:4033033', 'CL:4033034', 'CL:4033035', 'CL:4033036', 'CL:4033038', 'CL:4033039', 'CL:4033042', 'CL:4033043', 'CL:4033044', 'CL:4033048', 'CL:4033053', 'CL:4033058'])\n",
+      "CL:0000540 neuron\n",
+      "['neuron', 'sensory neuron', 'primary cultured cell', 'cultured cell', 'fibroblast neural crest derived', 'migratory neural crest cell', 'fibroblast', 'neuronal receptor cell', 'sensory receptor cell', 'embryonic cell (metazoa)', 'experimentally modified cell in vitro', 'germ line cell', 'stem cell', 'male germ cell', 'germ cell', 'haploid cell', 'sperm', 'male gamete', 'primordial germ cell', 'polyploid cell', 'smooth muscle cell', 'neural crest derived neuron', 'glioblast', 'non-terminally differentiated cell', 'neuroblast (sensu Vertebrata)', 'surface ectodermal cell', 'exocrine cell', 'precursor cell', 'single fate stem cell', 'somatic stem cell', 'epithelial fate stem cell', 'hematopoietic stem cell', 'hematopoietic cell', 'hematopoietic precursor cell', 'progenitor cell', 'erythroid progenitor cell', 'erythroid lineage cell', 'myeloid lineage restricted progenitor cell', 'megakaryocyte-erythroid progenitor cell', 'monopoietic cell', 'myeloid cell', 'macrophage dendritic cell progenitor', 'eosinophil', 'professional antigen presenting cell', 'neutrophil progenitor cell', 'basophil', 'histamine secreting cell', 'common lymphoid progenitor', 'neural cell', 'multi fate stem cell', 'common myeloid progenitor', 'hematopoietic oligopotent progenitor cell', 'granulocyte monocyte progenitor cell', 'secretory cell', 'myoblast', 'muscle precursor cell', 'connective tissue cell', 'mesenchymal stem cell', 'ecto-epithelial cell', 'neurecto-epithelial cell', 'osteoblast', 'preosteoblast', 'ciliated cell', 'ependymal cell', 'ciliated epithelial cell', 'epithelial cell', 'leukocyte', 'duct epithelial cell', 'branched duct epithelial cell', 'blood vessel endothelial cell', 'squamous epithelial cell', 'endothelial cell of vascular tree', 'non-branched duct epithelial cell', 'meso-epithelial cell', 'barrier cell', 'columnar/cuboidal epithelial cell', 'mesothelial cell', 'lining cell', 'stratified epithelial cell', 'circulating cell', 'blood cell', 'epithelial cell of lung', 'epithelial cell of pancreas', 'T cell', 'lymphocyte', 'pro-T cell', 'lymphocyte of B lineage', 'Kupffer cell', 'tissue-resident macrophage', 'osteoclast', 'myeloid leukocyte', 'phagocyte (sensu Vertebrata)', 'bone cell', 'monocyte', 'granulocyte', 'neuron associated cell', 'neutrophil', 'phagocyte', 'mast cell', 'sensory epithelial cell', 'interneuron', 'motor neuron', 'efferent neuron', 'afferent neuron', 'bipolar neuron', 'multipolar neuron', 'autonomic neuron', 'peripheral nervous system neuron', 'cholinergic neuron', 'catecholaminergic neuron', 'mononuclear phagocyte', 'mononuclear cell', 'ectodermal cell', 'endothelial cell', 'CNS neuron (sensu Vertebrata)', 'GABAergic interneuron', 'granule cell', 'Purkinje cell', 'cerebellar neuron', 'stellate neuron', 'neuron associated cell (sensu Vertebrata)', 'glial cell', 'macroglial cell', 'astrocyte', 'oligodendrocyte', 'myelinating glial cell', 'oligodendrocyte precursor cell', 'microglial cell', 'central nervous system macrophage', 'mesodermal cell', 'gut endothelial cell', 'corneal endothelial cell', 'fibrocyte', 'stromal cell', 'adipocyte', 'stuff accumulating cell', 'chondrocyte', 'glycosaminoglycan secreting cell', 'collagen secreting cell', 'pigment cell', 'melanocyte', 'visual pigment cell', 'glandular epithelial cell', 'extracellular matrix secreting cell', 'carbohydrate secreting cell', 'protein secreting cell', 'peptic cell', 'glandular cell of stomach', 'surfactant secreting cell', 'club cell', 'epithelial cell of tracheobronchial tree', 'seromucus secreting cell', 'goblet cell', 'mucus secreting cell', 'parietal cell', 'endocrine cell', 'enteroendocrine cell', 'neuroendocrine cell', 'chromaffin cell', 'amine precursor uptake and decarboxylation cell', 'peptide hormone secreting cell', 'insulin secreting cell', 'type B pancreatic cell', 'pancreatic endocrine cell', 'progenitor cell of endocrine pancreas', 'glucagon secreting cell', 'pancreatic A cell', 'type A enteroendocrine cell', 'somatostatin secreting cell', 'pancreatic D cell', 'type D enteroendocrine cell', 'steroid hormone secreting cell', 'stromal cell of ovary', 'testosterone secreting cell', 'Leydig cell', 'interstitial cell', 'hepatocyte', 'endopolyploid cell', 'hepatoblast', 'contractile cell', 'pericyte', 'myoepithelial cell', 'myofibroblast cell', 'muscle cell', 'electrically responsive cell', 'cell of skeletal muscle', 'slow muscle cell', 'extrafusal muscle fiber', 'fast muscle cell', 'non-striated muscle cell', 'visceral muscle cell', 'smooth muscle myoblast', 'cardiac muscle cell', 'chemoreceptor cell', 'taste receptor cell', 'endo-epithelial cell', 'photoreceptor cell', 'electrically active cell', 'Sertoli cell', 'supporting cell', 'androgen binding protein secreting cell', 'seminiferous tubule epithelial cell', 'insulating cell', 'myelinating Schwann cell', 'Schwann cell', 'immature Schwann cell', 'motile cell', 'anucleate cell', 'nucleate cell', 'single nucleate cell', 'multinucleate cell', 'erythrocyte', 'oxygen accumulating cell', 'reticulocyte', 'platelet', 'serotonin secreting cell', 'megakaryocyte', 'defensive cell', 'macrophage', 'B cell', 'keratinizing barrier epithelial cell', 'stratified squamous epithelial cell', 'keratin accumulating cell', 'brush border epithelial cell', 'Merkel cell', 'keratinocyte', 'transitional epithelial cell', 'primary sensory neuron (sensu Teleostei)', 'eurydendroid cell', 'eye photoreceptor cell', 'gamete', 'crystallin accumulating cell', 'tracheal epithelial cell', 'epidermal cell', 'serous secreting cell', 'milk secreting cell', 'sebum secreting cell', 'sebaceous gland cell', 'pneumocyte', 'epithelial cell of alveolus of lung', 'lysozyme secreting cell', 'neural crest cell', 'mesenchymal cell', 'biogenic amine secreting cell', 'extraembryonic cell', 'trophoblast cell', 'sphincter associated smooth muscle cell', 'vascular associated smooth muscle cell', 'perivascular cell', 'general ecto-epithelial cell', 'myotube', 'attachment cell', 'tendon cell', 'ganglion interneuron', 'CNS interneuron', 'central nervous system neuron', 'electrically signaling cell', 'excretory cell', 'reticular cell', 'follicular dendritic cell', 'striated muscle cell', 'preadipocyte', 'dendritic cell', 'Langerhans cell', 'conventional dendritic cell', 'noradrenergic cell', 'cardiac myoblast', 'neural progenitor cell', 'follicular epithelial cell', 'ovarian surface epithelial cell', 'CD4-positive helper T cell', 'CD4-positive, alpha-beta T cell', 'activated CD4-positive, alpha-beta T cell', 'inhibitory interneuron', 'granulosa cell', 'follicular cell of ovary', 'theca cell', 'androgen secreting cell', 'enkephalin secreting cell', 'endorphin secreting cell', 'type G enteroendocrine cell', 'gastrin secreting cell', 'mucous cell of stomach', 'paneth cell', 'intestinal epithelial cell', 'paracrine cell', 'cardiac muscle myoblast', 'cardiocyte', 'skeletal muscle fiber', 'syncytiotrophoblast cell', 'pigmented epithelial cell', 'primary neuron (sensu Teleostei)', 'T-helper 1 cell', 'T-helper 2 cell', 'CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell', 'proerythroblast', 'erythroblast', 'neuronal brush cell', 'CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor', 'bone marrow hematopoietic cell', 'promonocyte', 'amacrine cell', 'retinal cell', 'retinal ganglion cell', 'promyelocyte', 'cardiac mesenchymal cell', 'migratory cardiac neural crest cell', 'retinal cone cell', 'camera-type eye photoreceptor cell', 'corneal epithelial cell', 'type EC enteroendocrine cell', 'epithelial cell of alimentary canal', 'cell in vitro', 'immature neutrophil', 'myelocyte', 'alveolar macrophage', 'lung macrophage', 'enterocyte', 'gut absorptive cell', 'skeletal muscle satellite cell', 'enucleate erythrocyte', 'enucleated reticulocyte', 'pyramidal neuron', 'retinal rod cell', 'granulocytopoietic cell', 'basophil mast progenitor cell', 'GABAergic neuron', 'acinar cell', 'natural killer cell', 'group 1 innate lymphoid cell', 'mature alpha-beta T cell', 'CD8-positive, alpha-beta T cell', 'transporting cell', 'hepatic stellate cell', 'Mueller cell', 'radial glial cell', 'Bergmann glial cell', 'astrocyte of the forebrain', 'basal cell', 'prickle cell', 'mesangial cell', 'mucous neck cell', 'podocyte', 'renal filtration cell', 'epithelial cell of glomerular capsule', 'fenestrated cell', 'mural cell', 'glutamatergic neuron', 'M cell of gut', 'non-myelinating Schwann cell', 'Cajal-Retzius cell', 'PP cell', 'dopaminergic neuron', 'endothelial tip cell', 'choroid plexus epithelial cell', 'urothelial cell', 'retina horizontal cell', 'retinal bipolar neuron', 'visual system neuron', 'ON-bipolar cell', 'OFF-bipolar cell', 'rod bipolar cell', 'cone retinal bipolar cell', 'myeloid dendritic cell', 'plasmacytoid dendritic cell', 'mature B cell', 'B cell, CD19-positive', 'transitional stage B cell', 'immature B cell', 'precursor B cell', 'plasma cell', 'antibody secreting cell', 'plasmablast', 'memory B cell', 'naive B cell', 'alpha-beta T cell', 'gamma-delta T cell', 'immature alpha-beta T cell', 'immature T cell', 'mature T cell', 'CD4-positive, CD25-positive, alpha-beta regulatory T cell', 'regulatory T cell', 'alpha-beta intraepithelial T cell', 'CD4-positive, alpha-beta thymocyte', 'CD8-positive, alpha-beta cytotoxic T cell', 'activated CD8-positive, alpha-beta T cell', 'CD8-alpha-beta-positive, alpha-beta intraepithelial T cell', 'intraepithelial lymphocyte', 'DN3 thymocyte', 'mature gamma-delta T cell', 'gamma-delta intraepithelial T cell', 'CD8-alpha alpha positive, gamma-delta intraepithelial T cell', 'thymocyte', 'DN4 thymocyte', 'double negative thymocyte', 'DN1 thymic pro-T cell', 'double-positive, alpha-beta thymocyte', 'CD8-positive, alpha-beta thymocyte', 'memory T cell', 'naive T cell', 'mature NK T cell', 'small pre-B-II cell', 'pro-B cell', 'B-1 B cell', 'B-1a B cell', 'B-1b B cell', 'B-2 B cell', 'immature natural killer cell', 'immature innate lymphoid cell', 'mature natural killer cell', 'lymphoid lineage restricted progenitor cell', 'hematopoietic multipotent progenitor cell', 'hematopoietic lineage restricted progenitor cell', 'common dendritic progenitor', 'mature conventional dendritic cell', 'follicular B cell', 'germinal center B cell', 'classical monocyte', 'elicited macrophage', 'inflammatory macrophage', 'non-classical monocyte', 'intermediate monocyte', 'meningeal macrophage', 'alternatively activated macrophage', 'early T lineage precursor', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'CD4-positive, alpha-beta memory T cell', 'T-helper 17 cell', 'naive thymus-derived CD8-positive, alpha-beta T cell', 'natural T-regulatory cell', 'central memory CD4-positive, alpha-beta T cell', 'CD4-positive, alpha-beta memory T cell, CD45RO-positive', 'effector memory CD4-positive, alpha-beta T cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, alpha-beta memory T cell, CD45RO-positive', 'CD8-positive, alpha-beta cytokine secreting effector T cell', 'CD8-positive, alpha-beta memory T cell', 'cytotoxic T cell', 'effector T cell', 'helper T cell', 'effector memory CD8-positive, alpha-beta T cell', 'immature NK T cell', 'CD8-alpha-alpha-positive, alpha-beta intraepithelial T cell', 'Tc1 cell', 'type I NK T cell', 'type II NK T cell', 'activated type II NK T cell', 'CD4-positive, alpha-beta cytotoxic T cell', 'early lymphoid progenitor', 'CD16-negative, CD56-bright natural killer cell, human', 'CD16-positive, CD56-dim natural killer cell, human', 'mucosal invariant T cell', 'long lived plasma cell', 'class switched memory B cell', 'large pre-B-II cell', 'pre-B-II cell', 'pre-B-I cell', 'unswitched memory B cell', 'IgG memory B cell', 'IgG plasmablast', 'IgA plasmablast', 'IgG plasma cell', 'IgM plasma cell', 'IgA plasma cell', 'hematopoietic oligopotent progenitor cell, lineage-negative', 'pre-conventional dendritic cell', 'CD11b-positive dendritic cell', 'CD34-positive, CD38-negative hematopoietic stem cell', 'CD115-positive monocyte OR common dendritic progenitor', 'CD115-positive monocyte', 'cerebellar granule cell', 'cerebellum glutamatergic neuron', 'cerebellar granule cell precursor', 'cerebral cortex neuron', 'T-helper 22 cell', 'activated CD4-positive, alpha-beta T cell, human', 'effector CD4-positive, alpha-beta T cell', 'naive regulatory T cell', 'memory regulatory T cell', 'activated CD8-positive, alpha-beta T cell, human', 'effector CD8-positive, alpha-beta T cell', 'CD14-positive monocyte', 'CD14-positive, CD16-positive monocyte', 'dendritic cell, human', 'myeloid dendritic cell, human', 'plasmacytoid dendritic cell, human', 'abnormal cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated', 'neoplastic cell', 'malignant cell', 'innate lymphoid cell', 'erythroid progenitor cell, mammalian', 'ILC1', 'group 2 innate lymphoid cell', 'group 3 innate lymphoid cell', 'CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human', 'ILC1, human', 'group 3 innate lymphoid cell, human', 'NKp44-positive group 3 innate lymphoid cell, human', 'NKp44-negative group 3 innate lymphoid cell, human', 'group 2 innate lymphoid cell, human', 'lymphocyte of B lineage, CD19-positive', 'T follicular helper cell', 'fraction A pre-pro B cell', 'early pro-B cell', 'late pro-B cell', 'CD14-positive, CD16-negative classical monocyte', 'type I pneumocyte', 'type II pneumocyte', 'lung secretory cell', 'pancreatic acinar cell', 'pancreas exocrine glandular cell', 'enterocyte of epithelium of large intestine', 'epithelial cell of large intestine', 'brush cell of trachebronchial tree', 'brush cell', 'pancreatic ductal cell', 'epithelial cell of exocrine pancreas', 'interstitial cell of Cajal', 'bone marrow cell', 'interstitial cell of ovary', 'cortical cell of adrenal gland', 'adrenal gland glandular cell', 'regular cardiac myocyte', 'IgG-negative class switched memory B cell', 'regular atrial cardiac myocyte', 'regular ventricular cardiac myocyte', 'endothelial cell of lymphatic vessel', 'vascular lymphangioblast', 'epithelial cell of sweat gland', 'capillary endothelial cell', 'microvascular endothelial cell', 'ciliated columnar cell of tracheobronchial tree', 'multi-ciliated epithelial cell', 'respiratory ciliated cell', 'epithelial cell of uterus', 'endosteal cell', 'renal interstitial pericyte', 'epithelial cell of stomach', 'foveolar cell of stomach', 'basal cell of epidermis', 'stem cell of epidermis', 'glomerular endothelial cell', 'glomerular cell', 'granular cell of epidermis', 'epithelial cell of thyroid gland', 'renal beta-intercalated cell', 'renal intercalated cell', 'epithelial cell of lower respiratory tract', 'brush cell of epithelium proper of large intestine', 'intestinal tuft cell', 'brush cell of trachea', 'bronchial epithelial cell', 'epithelial cell of esophagus', 'vertebrate lens cell', 'anterior lens cell', 'lens epithelial cell', 'secondary lens fiber', 'lens fiber cell', 'lactocyte', 'epithelial cell of prostate', 'basal cell of prostate epithelium', 'luminal cell of prostate epithelium', 'basal epithelial cell of prostatic duct', 'pulmonary interstitial fibroblast', 'smooth muscle cell of sphincter of pupil', 'peripheral blood stem cell', 'intestinal crypt stem cell', 'epithelial cell of small intestine', 'stromal cell of endometrium', 'decidual cell', 'thyroid follicular cell', 'endothelial cell of sinusoid', 'enteroendocrine cell of colon', 'enteroendocrine cell of small intestine', 'P/D1 enteroendocrine cell', 'vasoactive intestinal peptide secreting cell', 'pancreatic PP cell', 'type I enteroendocrine cell', 'GIP cell', 'type L enteroendocrine cell', 'type N enteroendocrine cell', 'ghrelin secreting cell', 'epithelial cell of thymus', 'cortical thymic epithelial cell', 'medullary thymic epithelial cell', 'pigmented ciliary epithelial cell', 'non-pigmented ciliary epithelial cell', 'epithelial cell of distal tubule', 'nephron tubule epithelial cell', 'kidney cortex tubule cell', 'epithelial cell of proximal tubule', 'epithelial cell of skin gland', 'embryonic stem cell', 'myoepithelial cell of mammary gland', 'mammary gland epithelial cell', 'mammary gland glandular cell', 'luminal adaptive secretory precursor cell of mammary gland', 'luminal epithelial cell of mammary gland', 'progenitor cell of mammary luminal epithelium', 'basal epithelial cell of tracheobronchial tree', 'decidual natural killer cell, human', 'endocardial cell', 'cardiac endothelial cell', 'gestational hematopoietic stem cell', 'primitive red blood cell', 'primitive erythroid lineage cell', 'keratocyte', 'myometrial cell', 'uterine smooth muscle cell', 'respiratory epithelial cell', 'respiratory goblet cell', 'Schwann cell precursor', 'CD141-positive myeloid dendritic cell', 'Gr1-high classical monocyte', 'CD14-low, CD16-positive monocyte', 'CD1c-positive myeloid dendritic cell', 'pancreatic stellate cell', 'epidermal Langerhans cell', 'MHC-II-positive classical monocyte', 'nasal mucosa goblet cell', 'melanocyte of skin', 'hair follicle cell', 'trophoblast giant cell', 'fetal cardiomyocyte', 'adventitial cell', 'enteric smooth muscle cell', 'kidney epithelial cell', 'kidney cell', 'subcutaneous adipocyte', 'intrahepatic cholangiocyte', 'cholangiocyte', 'blood vessel smooth muscle cell', 'endothelial cell of artery', 'vein endothelial cell', 'fibroblast of cardiac tissue', 'skin fibroblast', 'fibroblast of lung', 'fibroblast of mammary gland', 'placental epithelial cell', 'fibroblast of breast', 'renal cortical epithelial cell', 'kidney cortical cell', 'retinal blood vessel endothelial cell', 'retinal pigment epithelial cell', 'smooth muscle cell of the pulmonary artery', 'bladder cell', 'bronchial smooth muscle cell', 'tracheobronchial smooth muscle cell', 'brain macroglial cell', 'astrocyte of the cerebral cortex', 'cerebral cortex glial cell', 'migratory enteric neural crest cell', 'adipocyte of breast', 'prostate stromal cell', 'acinar cell of salivary gland', 'salivary gland glandular cell', 'immature astrocyte', 'mature astrocyte', 'mature microglial cell', 'respiratory basal cell', 'glandular cell of esophagus', 'endothelial cell of venule', 'glandular cell of the large intestine', 'tongue muscle cell', 'intestinal enteroendocrine cell', 'S cone cell', 'H1 horizontal cell', 'H2 horizontal cell', 'glycinergic amacrine cell', 'starburst amacrine cell', 'GABAergic amacrine cell', 'ionocyte', 'renal principal cell', 'epithelial cell of nephron', 'renal alpha-intercalated cell', 'pancreatic epsilon cell', 'lymphangioblast', 'mesenchymal lymphangioblast', 'visceromotor neuron', 'enteric neuron', 'skeletal muscle satellite stem cell', 'inhibitory motor neuron', 'cortical interneuron', 'cerebral cortex GABAergic interneuron', 'extravillous trophoblast', 'primary motor cortex pyramidal cell', 'L5 extratelencephalic projecting glutamatergic cortical neuron', 'inflammatory cell', 'salivary gland cell', 'small intestine glandular cell', 'paneth cell of colon', 'colon glandular cell', 'transit amplifying cell', 'transit amplifying cell of colon', 'transit amplifying cell of small intestine', 'intestinal crypt stem cell of large intestine', 'intestinal crypt stem cell of small intestine', 'colon epithelial cell', 'colon macrophage', 'colon goblet cell', 'large intestine goblet cell', 'tuft cell of colon', 'intestinal crypt stem cell of colon', 'lung pericyte', 'endothelial cell of placenta', 'endothelial cell of uterus', 'fibro/adipogenic progenitor cell', 'centrocyte', 'centroblast', 'T follicular regulatory cell', 'hair follicular keratinocyte', 'cardiac neuron', 'mesothelial cell of epicardium', 'double negative T regulatory cell', 'exhausted T cell', 'skeletal muscle fibroblast', 'muscle fibroblast', 'monocyte-derived dendritic cell', 'chorionic trophoblast cell', 'sympathetic neuron', 'forebrain radial glial cell', 'pulmonary ionocyte', 'epithelial cell of urethra', 'hillock cell', 'tracheobronchial serous cell', 'tracheobronchial goblet cell', 'endothelial cell of periportal hepatic sinusoid', 'endothelial cell of hepatic sinusoid', 'endothelial cell of pericentral hepatic sinusoid', 'periportal region hepatocyte', 'midzonal region hepatocyte', 'centrilobular region hepatocyte', 'intestine goblet cell', 'kidney tubule cell', 'forebrain neuroblast', 'lung goblet cell', 'lung neuroendocrine cell', 'lung ciliated cell', 'smooth muscle cell of small intestine', 'smooth muscle fiber of ileum', 'urethra cell', 'fibroblast of connective tissue of nonglandular part of prostate', 'fibroblast of connective tissue of glandular part of prostate', 'epicardial adipocyte', 'adipocyte of epicardial fat of left ventricle', 'bronchial goblet cell', 'small intestine goblet cell', 'intestinal villus goblet cell', 'duodenum glandular cell', 'ileal goblet cell', 'tracheal goblet cell', 'serous cell of epithelium of trachea', 'serous cell of epithelium of bronchus', 'enterocyte of epithelium of small intestine', 'enterocyte of epithelium proper of small intestine', 'enterocyte of epithelium proper of ileum', 'paneth cell of epithelium of small intestine', 'enterocyte of colon', 'basal cell of epithelium of trachea', 'microfold cell of epithelium of small intestine', 'ventricular cardiac muscle cell', 'conjunctival epithelial cell', 'epithelial cell of lacrimal drainage system', 'epithelial cell of lacrimal sac', 'epithelial cell of nasolacrimal duct', 'bladder urothelial cell', 'ciliary muscle cell', 'epithelial cell of stratum germinativum of esophagus', 'kidney glomerular epithelial cell', 'parietal epithelial cell', 'epithelial cell of intermediate tubule', 'kidney loop of Henle epithelial cell', 'kidney collecting duct epithelial cell', 'kidney collecting duct cell', 'smooth muscle cell of prostate', 'mesothelial cell of pleura', 'kidney interstitial cell', 'kidney medulla cell', 'kidney pelvis cell', 'kidney inner medulla cell', 'kidney outer medulla cell', 'papillary tips cell', 'lower urinary tract cell', 'kidney corpuscule cell', 'kidney interstitial fibroblast', 'kidney interstitial alternatively activated macrophage', 'kidney resident macrophage', 'kidney pelvis urothelial cell', 'kidney collecting duct principal cell', 'kidney collecting duct intercalated cell', 'kidney connecting tubule epithelial cell', 'kidney loop of Henle descending limb epithelial cell', 'kidney distal convoluted tubule epithelial cell', 'kidney blood vessel cell', 'kidney capillary endothelial cell', 'kidney venous blood vessel cell', 'glomerular capillary endothelial cell', 'kidney loop of Henle ascending limb epithelial cell', 'vasa recta cell', 'kidney loop of Henle thick ascending limb epithelial cell', 'kidney loop of Henle thin ascending limb epithelial cell', 'kidney loop of Henle thin descending limb epithelial cell', 'vasa recta ascending limb cell', 'vasa recta descending limb cell', 'medium spiny neuron', 'glycinergic neuron', 'lung endothelial cell', 'pulmonary artery endothelial cell', 'cerebral cortex pyramidal neuron', 'cerebral cortex endothelial cell', 'peripheral blood mononuclear cell', 'tonsil germinal center B cell', 'dermis lymphatic vessel endothelial cell', 'lung microvascular endothelial cell', 'dermis microvascular lymphatic vessel endothelial cell', 'embryonic fibroblast', 'brainstem motor neuron', 'hepatic pit cell', 'liver dendritic cell', 'prostate gland microvascular endothelial cell', 'placental villous trophoblast', 'bronchus fibroblast of lung', 'cord blood hematopoietic stem cell', 'Hofbauer cell', 'vip GABAergic cortical interneuron', 'intratelencephalic-projecting glutamatergic cortical neuron', 'extratelencephalic-projecting glutamatergic cortical neuron', 'lamp5 GABAergic cortical interneuron', 'near-projecting glutamatergic cortical neuron', 'corticothalamic-projecting glutamatergic cortical neuron', 'sncg GABAergic cortical interneuron', 'sst GABAergic cortical interneuron', 'pvalb GABAergic cortical interneuron', 'sst chodl GABAergic cortical interneuron', 'direct pathway medium spiny neuron', 'indirect pathway medium spiny neuron', 'chandelier pvalb GABAergic cortical interneuron', 'chandelier cell', 'L6b glutamatergic cortical neuron', 'L2/3-6 intratelencephalic projecting glutamatergic neuron', 'L6 corticothalamic-projecting glutamatergic cortical neuron', 'L5/6 near-projecting glutamatergic neuron of the primary motor cortex', 'L5/6 near-projecting glutamatergic neuron', 'L2/3 intratelencephalic projecting glutamatergic neuron', 'L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex', 'L6 intratelencephalic projecting glutamatergic neuron', 'vascular leptomeningeal cell', 'mesothelial fibroblast of the leptomeninx', 'mesothelial fibroblast', 'differentiation-committed oligodendrocyte precursor', 'caudal ganglionic eminence derived interneuron', 'caudal ganglionic eminence derived GABAergic cortical interneuron', 'brain vascular cell', 'unipolar brush cell', 'midget ganglion cell of retina', 'parasol ganglion cell of retina', 'alveolar type 1 fibroblast cell', 'alveolar type 2 fibroblast cell', 'epithelial cell of proximal tubule segment 1', 'epithelial cell of proximal tubule segment 3', 'kidney connecting tubule principal cell', 'respiratory hillock cell', 'BEST4+ intestinal epithelial cell, human', 'L4 intratelencephalic projecting glutamatergic neuron', 'serous secreting cell of bronchus submucosal gland', 'suprabasal keratinocyte', 'retinal astrocyte', 'ON-blue cone bipolar cell', 'airway submucosal gland duct basal cell', 'perichondrial fibroblast', 'lung perichondrial fibroblast', 'diffuse bipolar 1 cell', 'diffuse bipolar 2 cell', 'diffuse bipolar 3a cell', 'diffuse bipolar 3b cell', 'diffuse bipolar 4 cell', 'diffuse bipolar 6 cell', 'flat midget bipolar cell', 'invaginating midget bipolar cell', 'giant bipolar cell', 'OFFx cell', 'lung resident memory CD4-positive, alpha-beta T cell', 'lung resident memory CD8-positive, alpha-beta T cell', 'metallothionein-positive alveolar macrophage', 'lung interstitial macrophage', 'deuterosomal cell', 'respiratory suprabasal cell', 'luminal hormone-sensing cell of mammary gland']\n",
+      "dict_keys(['all_ancestors', 'all_descendants', 'hop_0', 'hop_1', 'hop_2', 'hop_3'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cell_type_benchmarking.keys())\n",
+    "print(cell_type_names[0], cell_type_labels[0])\n",
+    "print(cell_type_labels)\n",
+    "\n",
+    "cl_name2label['CL:0000540']\n",
+    "\n",
+    "print(cell_type_benchmarking['CL:0000101'].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_type_embedding = ctx.gpt_pipeline.model.metadata_embedding.E['cell_type'].weight.cpu().detach().numpy()\n",
+    "cell_type_embedding.shape\n",
+    "\n",
+    "cell_type_embedding = cell_type_embedding[:len(cell_type_labels),:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# np.save('/u/mallina1/cell_type_embedding.npy', cell_type_embedding)\n",
+    "\n",
+    "# Prep for CSV data format that is used in Poincare VAE codebase\n",
+    "header = [f'feature_{idx}' for idx in range(512)] + ['cell_type_idx']\n",
+    "header = ','.join(header)\n",
+    "np.savetxt('/u/mallina1/cell_type_embedding.csv',\n",
+    "           np.concatenate((cell_type_embedding, np.arange(cell_type_embedding.shape[0]).reshape(-1, 1)), axis=1), \n",
+    "           delimiter=',',\n",
+    "           header=header,\n",
+    "           comments=\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=2)\n",
+    "X_ct_pca = pca.fit_transform(cell_type_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "neuron"
+          ],
+          [
+           "sensory neuron"
+          ],
+          [
+           "primary cultured cell"
+          ],
+          [
+           "cultured cell"
+          ],
+          [
+           "fibroblast neural crest derived"
+          ],
+          [
+           "migratory neural crest cell"
+          ],
+          [
+           "fibroblast"
+          ],
+          [
+           "neuronal receptor cell"
+          ],
+          [
+           "sensory receptor cell"
+          ],
+          [
+           "embryonic cell (metazoa)"
+          ],
+          [
+           "experimentally modified cell in vitro"
+          ],
+          [
+           "germ line cell"
+          ],
+          [
+           "stem cell"
+          ],
+          [
+           "male germ cell"
+          ],
+          [
+           "germ cell"
+          ],
+          [
+           "haploid cell"
+          ],
+          [
+           "sperm"
+          ],
+          [
+           "male gamete"
+          ],
+          [
+           "primordial germ cell"
+          ],
+          [
+           "polyploid cell"
+          ],
+          [
+           "smooth muscle cell"
+          ],
+          [
+           "neural crest derived neuron"
+          ],
+          [
+           "glioblast"
+          ],
+          [
+           "non-terminally differentiated cell"
+          ],
+          [
+           "neuroblast (sensu Vertebrata)"
+          ],
+          [
+           "surface ectodermal cell"
+          ],
+          [
+           "exocrine cell"
+          ],
+          [
+           "precursor cell"
+          ],
+          [
+           "single fate stem cell"
+          ],
+          [
+           "somatic stem cell"
+          ],
+          [
+           "epithelial fate stem cell"
+          ],
+          [
+           "hematopoietic stem cell"
+          ],
+          [
+           "hematopoietic cell"
+          ],
+          [
+           "hematopoietic precursor cell"
+          ],
+          [
+           "progenitor cell"
+          ],
+          [
+           "erythroid progenitor cell"
+          ],
+          [
+           "erythroid lineage cell"
+          ],
+          [
+           "myeloid lineage restricted progenitor cell"
+          ],
+          [
+           "megakaryocyte-erythroid progenitor cell"
+          ],
+          [
+           "monopoietic cell"
+          ],
+          [
+           "myeloid cell"
+          ],
+          [
+           "macrophage dendritic cell progenitor"
+          ],
+          [
+           "eosinophil"
+          ],
+          [
+           "professional antigen presenting cell"
+          ],
+          [
+           "neutrophil progenitor cell"
+          ],
+          [
+           "basophil"
+          ],
+          [
+           "histamine secreting cell"
+          ],
+          [
+           "common lymphoid progenitor"
+          ],
+          [
+           "neural cell"
+          ],
+          [
+           "multi fate stem cell"
+          ],
+          [
+           "common myeloid progenitor"
+          ],
+          [
+           "hematopoietic oligopotent progenitor cell"
+          ],
+          [
+           "granulocyte monocyte progenitor cell"
+          ],
+          [
+           "secretory cell"
+          ],
+          [
+           "myoblast"
+          ],
+          [
+           "muscle precursor cell"
+          ],
+          [
+           "connective tissue cell"
+          ],
+          [
+           "mesenchymal stem cell"
+          ],
+          [
+           "ecto-epithelial cell"
+          ],
+          [
+           "neurecto-epithelial cell"
+          ],
+          [
+           "osteoblast"
+          ],
+          [
+           "preosteoblast"
+          ],
+          [
+           "ciliated cell"
+          ],
+          [
+           "ependymal cell"
+          ],
+          [
+           "ciliated epithelial cell"
+          ],
+          [
+           "epithelial cell"
+          ],
+          [
+           "leukocyte"
+          ],
+          [
+           "duct epithelial cell"
+          ],
+          [
+           "branched duct epithelial cell"
+          ],
+          [
+           "blood vessel endothelial cell"
+          ],
+          [
+           "squamous epithelial cell"
+          ],
+          [
+           "endothelial cell of vascular tree"
+          ],
+          [
+           "non-branched duct epithelial cell"
+          ],
+          [
+           "meso-epithelial cell"
+          ],
+          [
+           "barrier cell"
+          ],
+          [
+           "columnar/cuboidal epithelial cell"
+          ],
+          [
+           "mesothelial cell"
+          ],
+          [
+           "lining cell"
+          ],
+          [
+           "stratified epithelial cell"
+          ],
+          [
+           "circulating cell"
+          ],
+          [
+           "blood cell"
+          ],
+          [
+           "epithelial cell of lung"
+          ],
+          [
+           "epithelial cell of pancreas"
+          ],
+          [
+           "T cell"
+          ],
+          [
+           "lymphocyte"
+          ],
+          [
+           "pro-T cell"
+          ],
+          [
+           "lymphocyte of B lineage"
+          ],
+          [
+           "Kupffer cell"
+          ],
+          [
+           "tissue-resident macrophage"
+          ],
+          [
+           "osteoclast"
+          ],
+          [
+           "myeloid leukocyte"
+          ],
+          [
+           "phagocyte (sensu Vertebrata)"
+          ],
+          [
+           "bone cell"
+          ],
+          [
+           "monocyte"
+          ],
+          [
+           "granulocyte"
+          ],
+          [
+           "neuron associated cell"
+          ],
+          [
+           "neutrophil"
+          ],
+          [
+           "phagocyte"
+          ],
+          [
+           "mast cell"
+          ],
+          [
+           "sensory epithelial cell"
+          ],
+          [
+           "interneuron"
+          ],
+          [
+           "motor neuron"
+          ],
+          [
+           "efferent neuron"
+          ],
+          [
+           "afferent neuron"
+          ],
+          [
+           "bipolar neuron"
+          ],
+          [
+           "multipolar neuron"
+          ],
+          [
+           "autonomic neuron"
+          ],
+          [
+           "peripheral nervous system neuron"
+          ],
+          [
+           "cholinergic neuron"
+          ],
+          [
+           "catecholaminergic neuron"
+          ],
+          [
+           "mononuclear phagocyte"
+          ],
+          [
+           "mononuclear cell"
+          ],
+          [
+           "ectodermal cell"
+          ],
+          [
+           "endothelial cell"
+          ],
+          [
+           "CNS neuron (sensu Vertebrata)"
+          ],
+          [
+           "GABAergic interneuron"
+          ],
+          [
+           "granule cell"
+          ],
+          [
+           "Purkinje cell"
+          ],
+          [
+           "cerebellar neuron"
+          ],
+          [
+           "stellate neuron"
+          ],
+          [
+           "neuron associated cell (sensu Vertebrata)"
+          ],
+          [
+           "glial cell"
+          ],
+          [
+           "macroglial cell"
+          ],
+          [
+           "astrocyte"
+          ],
+          [
+           "oligodendrocyte"
+          ],
+          [
+           "myelinating glial cell"
+          ],
+          [
+           "oligodendrocyte precursor cell"
+          ],
+          [
+           "microglial cell"
+          ],
+          [
+           "central nervous system macrophage"
+          ],
+          [
+           "mesodermal cell"
+          ],
+          [
+           "gut endothelial cell"
+          ],
+          [
+           "corneal endothelial cell"
+          ],
+          [
+           "fibrocyte"
+          ],
+          [
+           "stromal cell"
+          ],
+          [
+           "adipocyte"
+          ],
+          [
+           "stuff accumulating cell"
+          ],
+          [
+           "chondrocyte"
+          ],
+          [
+           "glycosaminoglycan secreting cell"
+          ],
+          [
+           "collagen secreting cell"
+          ],
+          [
+           "pigment cell"
+          ],
+          [
+           "melanocyte"
+          ],
+          [
+           "visual pigment cell"
+          ],
+          [
+           "glandular epithelial cell"
+          ],
+          [
+           "extracellular matrix secreting cell"
+          ],
+          [
+           "carbohydrate secreting cell"
+          ],
+          [
+           "protein secreting cell"
+          ],
+          [
+           "peptic cell"
+          ],
+          [
+           "glandular cell of stomach"
+          ],
+          [
+           "surfactant secreting cell"
+          ],
+          [
+           "club cell"
+          ],
+          [
+           "epithelial cell of tracheobronchial tree"
+          ],
+          [
+           "seromucus secreting cell"
+          ],
+          [
+           "goblet cell"
+          ],
+          [
+           "mucus secreting cell"
+          ],
+          [
+           "parietal cell"
+          ],
+          [
+           "endocrine cell"
+          ],
+          [
+           "enteroendocrine cell"
+          ],
+          [
+           "neuroendocrine cell"
+          ],
+          [
+           "chromaffin cell"
+          ],
+          [
+           "amine precursor uptake and decarboxylation cell"
+          ],
+          [
+           "peptide hormone secreting cell"
+          ],
+          [
+           "insulin secreting cell"
+          ],
+          [
+           "type B pancreatic cell"
+          ],
+          [
+           "pancreatic endocrine cell"
+          ],
+          [
+           "progenitor cell of endocrine pancreas"
+          ],
+          [
+           "glucagon secreting cell"
+          ],
+          [
+           "pancreatic A cell"
+          ],
+          [
+           "type A enteroendocrine cell"
+          ],
+          [
+           "somatostatin secreting cell"
+          ],
+          [
+           "pancreatic D cell"
+          ],
+          [
+           "type D enteroendocrine cell"
+          ],
+          [
+           "steroid hormone secreting cell"
+          ],
+          [
+           "stromal cell of ovary"
+          ],
+          [
+           "testosterone secreting cell"
+          ],
+          [
+           "Leydig cell"
+          ],
+          [
+           "interstitial cell"
+          ],
+          [
+           "hepatocyte"
+          ],
+          [
+           "endopolyploid cell"
+          ],
+          [
+           "hepatoblast"
+          ],
+          [
+           "contractile cell"
+          ],
+          [
+           "pericyte"
+          ],
+          [
+           "myoepithelial cell"
+          ],
+          [
+           "myofibroblast cell"
+          ],
+          [
+           "muscle cell"
+          ],
+          [
+           "electrically responsive cell"
+          ],
+          [
+           "cell of skeletal muscle"
+          ],
+          [
+           "slow muscle cell"
+          ],
+          [
+           "extrafusal muscle fiber"
+          ],
+          [
+           "fast muscle cell"
+          ],
+          [
+           "non-striated muscle cell"
+          ],
+          [
+           "visceral muscle cell"
+          ],
+          [
+           "smooth muscle myoblast"
+          ],
+          [
+           "cardiac muscle cell"
+          ],
+          [
+           "chemoreceptor cell"
+          ],
+          [
+           "taste receptor cell"
+          ],
+          [
+           "endo-epithelial cell"
+          ],
+          [
+           "photoreceptor cell"
+          ],
+          [
+           "electrically active cell"
+          ],
+          [
+           "Sertoli cell"
+          ],
+          [
+           "supporting cell"
+          ],
+          [
+           "androgen binding protein secreting cell"
+          ],
+          [
+           "seminiferous tubule epithelial cell"
+          ],
+          [
+           "insulating cell"
+          ],
+          [
+           "myelinating Schwann cell"
+          ],
+          [
+           "Schwann cell"
+          ],
+          [
+           "immature Schwann cell"
+          ],
+          [
+           "motile cell"
+          ],
+          [
+           "anucleate cell"
+          ],
+          [
+           "nucleate cell"
+          ],
+          [
+           "single nucleate cell"
+          ],
+          [
+           "multinucleate cell"
+          ],
+          [
+           "erythrocyte"
+          ],
+          [
+           "oxygen accumulating cell"
+          ],
+          [
+           "reticulocyte"
+          ],
+          [
+           "platelet"
+          ],
+          [
+           "serotonin secreting cell"
+          ],
+          [
+           "megakaryocyte"
+          ],
+          [
+           "defensive cell"
+          ],
+          [
+           "macrophage"
+          ],
+          [
+           "B cell"
+          ],
+          [
+           "keratinizing barrier epithelial cell"
+          ],
+          [
+           "stratified squamous epithelial cell"
+          ],
+          [
+           "keratin accumulating cell"
+          ],
+          [
+           "brush border epithelial cell"
+          ],
+          [
+           "Merkel cell"
+          ],
+          [
+           "keratinocyte"
+          ],
+          [
+           "transitional epithelial cell"
+          ],
+          [
+           "primary sensory neuron (sensu Teleostei)"
+          ],
+          [
+           "eurydendroid cell"
+          ],
+          [
+           "eye photoreceptor cell"
+          ],
+          [
+           "gamete"
+          ],
+          [
+           "crystallin accumulating cell"
+          ],
+          [
+           "tracheal epithelial cell"
+          ],
+          [
+           "epidermal cell"
+          ],
+          [
+           "serous secreting cell"
+          ],
+          [
+           "milk secreting cell"
+          ],
+          [
+           "sebum secreting cell"
+          ],
+          [
+           "sebaceous gland cell"
+          ],
+          [
+           "pneumocyte"
+          ],
+          [
+           "epithelial cell of alveolus of lung"
+          ],
+          [
+           "lysozyme secreting cell"
+          ],
+          [
+           "neural crest cell"
+          ],
+          [
+           "mesenchymal cell"
+          ],
+          [
+           "biogenic amine secreting cell"
+          ],
+          [
+           "extraembryonic cell"
+          ],
+          [
+           "trophoblast cell"
+          ],
+          [
+           "sphincter associated smooth muscle cell"
+          ],
+          [
+           "vascular associated smooth muscle cell"
+          ],
+          [
+           "perivascular cell"
+          ],
+          [
+           "general ecto-epithelial cell"
+          ],
+          [
+           "myotube"
+          ],
+          [
+           "attachment cell"
+          ],
+          [
+           "tendon cell"
+          ],
+          [
+           "ganglion interneuron"
+          ],
+          [
+           "CNS interneuron"
+          ],
+          [
+           "central nervous system neuron"
+          ],
+          [
+           "electrically signaling cell"
+          ],
+          [
+           "excretory cell"
+          ],
+          [
+           "reticular cell"
+          ],
+          [
+           "follicular dendritic cell"
+          ],
+          [
+           "striated muscle cell"
+          ],
+          [
+           "preadipocyte"
+          ],
+          [
+           "dendritic cell"
+          ],
+          [
+           "Langerhans cell"
+          ],
+          [
+           "conventional dendritic cell"
+          ],
+          [
+           "noradrenergic cell"
+          ],
+          [
+           "cardiac myoblast"
+          ],
+          [
+           "neural progenitor cell"
+          ],
+          [
+           "follicular epithelial cell"
+          ],
+          [
+           "ovarian surface epithelial cell"
+          ],
+          [
+           "CD4-positive helper T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "activated CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "inhibitory interneuron"
+          ],
+          [
+           "granulosa cell"
+          ],
+          [
+           "follicular cell of ovary"
+          ],
+          [
+           "theca cell"
+          ],
+          [
+           "androgen secreting cell"
+          ],
+          [
+           "enkephalin secreting cell"
+          ],
+          [
+           "endorphin secreting cell"
+          ],
+          [
+           "type G enteroendocrine cell"
+          ],
+          [
+           "gastrin secreting cell"
+          ],
+          [
+           "mucous cell of stomach"
+          ],
+          [
+           "paneth cell"
+          ],
+          [
+           "intestinal epithelial cell"
+          ],
+          [
+           "paracrine cell"
+          ],
+          [
+           "cardiac muscle myoblast"
+          ],
+          [
+           "cardiocyte"
+          ],
+          [
+           "skeletal muscle fiber"
+          ],
+          [
+           "syncytiotrophoblast cell"
+          ],
+          [
+           "pigmented epithelial cell"
+          ],
+          [
+           "primary neuron (sensu Teleostei)"
+          ],
+          [
+           "T-helper 1 cell"
+          ],
+          [
+           "T-helper 2 cell"
+          ],
+          [
+           "CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell"
+          ],
+          [
+           "proerythroblast"
+          ],
+          [
+           "erythroblast"
+          ],
+          [
+           "neuronal brush cell"
+          ],
+          [
+           "CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor"
+          ],
+          [
+           "bone marrow hematopoietic cell"
+          ],
+          [
+           "promonocyte"
+          ],
+          [
+           "amacrine cell"
+          ],
+          [
+           "retinal cell"
+          ],
+          [
+           "retinal ganglion cell"
+          ],
+          [
+           "promyelocyte"
+          ],
+          [
+           "cardiac mesenchymal cell"
+          ],
+          [
+           "migratory cardiac neural crest cell"
+          ],
+          [
+           "retinal cone cell"
+          ],
+          [
+           "camera-type eye photoreceptor cell"
+          ],
+          [
+           "corneal epithelial cell"
+          ],
+          [
+           "type EC enteroendocrine cell"
+          ],
+          [
+           "epithelial cell of alimentary canal"
+          ],
+          [
+           "cell in vitro"
+          ],
+          [
+           "immature neutrophil"
+          ],
+          [
+           "myelocyte"
+          ],
+          [
+           "alveolar macrophage"
+          ],
+          [
+           "lung macrophage"
+          ],
+          [
+           "enterocyte"
+          ],
+          [
+           "gut absorptive cell"
+          ],
+          [
+           "skeletal muscle satellite cell"
+          ],
+          [
+           "enucleate erythrocyte"
+          ],
+          [
+           "enucleated reticulocyte"
+          ],
+          [
+           "pyramidal neuron"
+          ],
+          [
+           "retinal rod cell"
+          ],
+          [
+           "granulocytopoietic cell"
+          ],
+          [
+           "basophil mast progenitor cell"
+          ],
+          [
+           "GABAergic neuron"
+          ],
+          [
+           "acinar cell"
+          ],
+          [
+           "natural killer cell"
+          ],
+          [
+           "group 1 innate lymphoid cell"
+          ],
+          [
+           "mature alpha-beta T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "transporting cell"
+          ],
+          [
+           "hepatic stellate cell"
+          ],
+          [
+           "Mueller cell"
+          ],
+          [
+           "radial glial cell"
+          ],
+          [
+           "Bergmann glial cell"
+          ],
+          [
+           "astrocyte of the forebrain"
+          ],
+          [
+           "basal cell"
+          ],
+          [
+           "prickle cell"
+          ],
+          [
+           "mesangial cell"
+          ],
+          [
+           "mucous neck cell"
+          ],
+          [
+           "podocyte"
+          ],
+          [
+           "renal filtration cell"
+          ],
+          [
+           "epithelial cell of glomerular capsule"
+          ],
+          [
+           "fenestrated cell"
+          ],
+          [
+           "mural cell"
+          ],
+          [
+           "glutamatergic neuron"
+          ],
+          [
+           "M cell of gut"
+          ],
+          [
+           "non-myelinating Schwann cell"
+          ],
+          [
+           "Cajal-Retzius cell"
+          ],
+          [
+           "PP cell"
+          ],
+          [
+           "dopaminergic neuron"
+          ],
+          [
+           "endothelial tip cell"
+          ],
+          [
+           "choroid plexus epithelial cell"
+          ],
+          [
+           "urothelial cell"
+          ],
+          [
+           "retina horizontal cell"
+          ],
+          [
+           "retinal bipolar neuron"
+          ],
+          [
+           "visual system neuron"
+          ],
+          [
+           "ON-bipolar cell"
+          ],
+          [
+           "OFF-bipolar cell"
+          ],
+          [
+           "rod bipolar cell"
+          ],
+          [
+           "cone retinal bipolar cell"
+          ],
+          [
+           "myeloid dendritic cell"
+          ],
+          [
+           "plasmacytoid dendritic cell"
+          ],
+          [
+           "mature B cell"
+          ],
+          [
+           "B cell, CD19-positive"
+          ],
+          [
+           "transitional stage B cell"
+          ],
+          [
+           "immature B cell"
+          ],
+          [
+           "precursor B cell"
+          ],
+          [
+           "plasma cell"
+          ],
+          [
+           "antibody secreting cell"
+          ],
+          [
+           "plasmablast"
+          ],
+          [
+           "memory B cell"
+          ],
+          [
+           "naive B cell"
+          ],
+          [
+           "alpha-beta T cell"
+          ],
+          [
+           "gamma-delta T cell"
+          ],
+          [
+           "immature alpha-beta T cell"
+          ],
+          [
+           "immature T cell"
+          ],
+          [
+           "mature T cell"
+          ],
+          [
+           "CD4-positive, CD25-positive, alpha-beta regulatory T cell"
+          ],
+          [
+           "regulatory T cell"
+          ],
+          [
+           "alpha-beta intraepithelial T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta thymocyte"
+          ],
+          [
+           "CD8-positive, alpha-beta cytotoxic T cell"
+          ],
+          [
+           "activated CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD8-alpha-beta-positive, alpha-beta intraepithelial T cell"
+          ],
+          [
+           "intraepithelial lymphocyte"
+          ],
+          [
+           "DN3 thymocyte"
+          ],
+          [
+           "mature gamma-delta T cell"
+          ],
+          [
+           "gamma-delta intraepithelial T cell"
+          ],
+          [
+           "CD8-alpha alpha positive, gamma-delta intraepithelial T cell"
+          ],
+          [
+           "thymocyte"
+          ],
+          [
+           "DN4 thymocyte"
+          ],
+          [
+           "double negative thymocyte"
+          ],
+          [
+           "DN1 thymic pro-T cell"
+          ],
+          [
+           "double-positive, alpha-beta thymocyte"
+          ],
+          [
+           "CD8-positive, alpha-beta thymocyte"
+          ],
+          [
+           "memory T cell"
+          ],
+          [
+           "naive T cell"
+          ],
+          [
+           "mature NK T cell"
+          ],
+          [
+           "small pre-B-II cell"
+          ],
+          [
+           "pro-B cell"
+          ],
+          [
+           "B-1 B cell"
+          ],
+          [
+           "B-1a B cell"
+          ],
+          [
+           "B-1b B cell"
+          ],
+          [
+           "B-2 B cell"
+          ],
+          [
+           "immature natural killer cell"
+          ],
+          [
+           "immature innate lymphoid cell"
+          ],
+          [
+           "mature natural killer cell"
+          ],
+          [
+           "lymphoid lineage restricted progenitor cell"
+          ],
+          [
+           "hematopoietic multipotent progenitor cell"
+          ],
+          [
+           "hematopoietic lineage restricted progenitor cell"
+          ],
+          [
+           "common dendritic progenitor"
+          ],
+          [
+           "mature conventional dendritic cell"
+          ],
+          [
+           "follicular B cell"
+          ],
+          [
+           "germinal center B cell"
+          ],
+          [
+           "classical monocyte"
+          ],
+          [
+           "elicited macrophage"
+          ],
+          [
+           "inflammatory macrophage"
+          ],
+          [
+           "non-classical monocyte"
+          ],
+          [
+           "intermediate monocyte"
+          ],
+          [
+           "meningeal macrophage"
+          ],
+          [
+           "alternatively activated macrophage"
+          ],
+          [
+           "early T lineage precursor"
+          ],
+          [
+           "naive thymus-derived CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta memory T cell"
+          ],
+          [
+           "T-helper 17 cell"
+          ],
+          [
+           "naive thymus-derived CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "natural T-regulatory cell"
+          ],
+          [
+           "central memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta memory T cell, CD45RO-positive"
+          ],
+          [
+           "effector memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "central memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta memory T cell, CD45RO-positive"
+          ],
+          [
+           "CD8-positive, alpha-beta cytokine secreting effector T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta memory T cell"
+          ],
+          [
+           "cytotoxic T cell"
+          ],
+          [
+           "effector T cell"
+          ],
+          [
+           "helper T cell"
+          ],
+          [
+           "effector memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "immature NK T cell"
+          ],
+          [
+           "CD8-alpha-alpha-positive, alpha-beta intraepithelial T cell"
+          ],
+          [
+           "Tc1 cell"
+          ],
+          [
+           "type I NK T cell"
+          ],
+          [
+           "type II NK T cell"
+          ],
+          [
+           "activated type II NK T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta cytotoxic T cell"
+          ],
+          [
+           "early lymphoid progenitor"
+          ],
+          [
+           "CD16-negative, CD56-bright natural killer cell, human"
+          ],
+          [
+           "CD16-positive, CD56-dim natural killer cell, human"
+          ],
+          [
+           "mucosal invariant T cell"
+          ],
+          [
+           "long lived plasma cell"
+          ],
+          [
+           "class switched memory B cell"
+          ],
+          [
+           "large pre-B-II cell"
+          ],
+          [
+           "pre-B-II cell"
+          ],
+          [
+           "pre-B-I cell"
+          ],
+          [
+           "unswitched memory B cell"
+          ],
+          [
+           "IgG memory B cell"
+          ],
+          [
+           "IgG plasmablast"
+          ],
+          [
+           "IgA plasmablast"
+          ],
+          [
+           "IgG plasma cell"
+          ],
+          [
+           "IgM plasma cell"
+          ],
+          [
+           "IgA plasma cell"
+          ],
+          [
+           "hematopoietic oligopotent progenitor cell, lineage-negative"
+          ],
+          [
+           "pre-conventional dendritic cell"
+          ],
+          [
+           "CD11b-positive dendritic cell"
+          ],
+          [
+           "CD34-positive, CD38-negative hematopoietic stem cell"
+          ],
+          [
+           "CD115-positive monocyte OR common dendritic progenitor"
+          ],
+          [
+           "CD115-positive monocyte"
+          ],
+          [
+           "cerebellar granule cell"
+          ],
+          [
+           "cerebellum glutamatergic neuron"
+          ],
+          [
+           "cerebellar granule cell precursor"
+          ],
+          [
+           "cerebral cortex neuron"
+          ],
+          [
+           "T-helper 22 cell"
+          ],
+          [
+           "activated CD4-positive, alpha-beta T cell, human"
+          ],
+          [
+           "effector CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "naive regulatory T cell"
+          ],
+          [
+           "memory regulatory T cell"
+          ],
+          [
+           "activated CD8-positive, alpha-beta T cell, human"
+          ],
+          [
+           "effector CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD14-positive monocyte"
+          ],
+          [
+           "CD14-positive, CD16-positive monocyte"
+          ],
+          [
+           "dendritic cell, human"
+          ],
+          [
+           "myeloid dendritic cell, human"
+          ],
+          [
+           "plasmacytoid dendritic cell, human"
+          ],
+          [
+           "abnormal cell"
+          ],
+          [
+           "effector memory CD8-positive, alpha-beta T cell, terminally differentiated"
+          ],
+          [
+           "effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated"
+          ],
+          [
+           "neoplastic cell"
+          ],
+          [
+           "malignant cell"
+          ],
+          [
+           "innate lymphoid cell"
+          ],
+          [
+           "erythroid progenitor cell, mammalian"
+          ],
+          [
+           "ILC1"
+          ],
+          [
+           "group 2 innate lymphoid cell"
+          ],
+          [
+           "group 3 innate lymphoid cell"
+          ],
+          [
+           "CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human"
+          ],
+          [
+           "ILC1, human"
+          ],
+          [
+           "group 3 innate lymphoid cell, human"
+          ],
+          [
+           "NKp44-positive group 3 innate lymphoid cell, human"
+          ],
+          [
+           "NKp44-negative group 3 innate lymphoid cell, human"
+          ],
+          [
+           "group 2 innate lymphoid cell, human"
+          ],
+          [
+           "lymphocyte of B lineage, CD19-positive"
+          ],
+          [
+           "T follicular helper cell"
+          ],
+          [
+           "fraction A pre-pro B cell"
+          ],
+          [
+           "early pro-B cell"
+          ],
+          [
+           "late pro-B cell"
+          ],
+          [
+           "CD14-positive, CD16-negative classical monocyte"
+          ],
+          [
+           "type I pneumocyte"
+          ],
+          [
+           "type II pneumocyte"
+          ],
+          [
+           "lung secretory cell"
+          ],
+          [
+           "pancreatic acinar cell"
+          ],
+          [
+           "pancreas exocrine glandular cell"
+          ],
+          [
+           "enterocyte of epithelium of large intestine"
+          ],
+          [
+           "epithelial cell of large intestine"
+          ],
+          [
+           "brush cell of trachebronchial tree"
+          ],
+          [
+           "brush cell"
+          ],
+          [
+           "pancreatic ductal cell"
+          ],
+          [
+           "epithelial cell of exocrine pancreas"
+          ],
+          [
+           "interstitial cell of Cajal"
+          ],
+          [
+           "bone marrow cell"
+          ],
+          [
+           "interstitial cell of ovary"
+          ],
+          [
+           "cortical cell of adrenal gland"
+          ],
+          [
+           "adrenal gland glandular cell"
+          ],
+          [
+           "regular cardiac myocyte"
+          ],
+          [
+           "IgG-negative class switched memory B cell"
+          ],
+          [
+           "regular atrial cardiac myocyte"
+          ],
+          [
+           "regular ventricular cardiac myocyte"
+          ],
+          [
+           "endothelial cell of lymphatic vessel"
+          ],
+          [
+           "vascular lymphangioblast"
+          ],
+          [
+           "epithelial cell of sweat gland"
+          ],
+          [
+           "capillary endothelial cell"
+          ],
+          [
+           "microvascular endothelial cell"
+          ],
+          [
+           "ciliated columnar cell of tracheobronchial tree"
+          ],
+          [
+           "multi-ciliated epithelial cell"
+          ],
+          [
+           "respiratory ciliated cell"
+          ],
+          [
+           "epithelial cell of uterus"
+          ],
+          [
+           "endosteal cell"
+          ],
+          [
+           "renal interstitial pericyte"
+          ],
+          [
+           "epithelial cell of stomach"
+          ],
+          [
+           "foveolar cell of stomach"
+          ],
+          [
+           "basal cell of epidermis"
+          ],
+          [
+           "stem cell of epidermis"
+          ],
+          [
+           "glomerular endothelial cell"
+          ],
+          [
+           "glomerular cell"
+          ],
+          [
+           "granular cell of epidermis"
+          ],
+          [
+           "epithelial cell of thyroid gland"
+          ],
+          [
+           "renal beta-intercalated cell"
+          ],
+          [
+           "renal intercalated cell"
+          ],
+          [
+           "epithelial cell of lower respiratory tract"
+          ],
+          [
+           "brush cell of epithelium proper of large intestine"
+          ],
+          [
+           "intestinal tuft cell"
+          ],
+          [
+           "brush cell of trachea"
+          ],
+          [
+           "bronchial epithelial cell"
+          ],
+          [
+           "epithelial cell of esophagus"
+          ],
+          [
+           "vertebrate lens cell"
+          ],
+          [
+           "anterior lens cell"
+          ],
+          [
+           "lens epithelial cell"
+          ],
+          [
+           "secondary lens fiber"
+          ],
+          [
+           "lens fiber cell"
+          ],
+          [
+           "lactocyte"
+          ],
+          [
+           "epithelial cell of prostate"
+          ],
+          [
+           "basal cell of prostate epithelium"
+          ],
+          [
+           "luminal cell of prostate epithelium"
+          ],
+          [
+           "basal epithelial cell of prostatic duct"
+          ],
+          [
+           "pulmonary interstitial fibroblast"
+          ],
+          [
+           "smooth muscle cell of sphincter of pupil"
+          ],
+          [
+           "peripheral blood stem cell"
+          ],
+          [
+           "intestinal crypt stem cell"
+          ],
+          [
+           "epithelial cell of small intestine"
+          ],
+          [
+           "stromal cell of endometrium"
+          ],
+          [
+           "decidual cell"
+          ],
+          [
+           "thyroid follicular cell"
+          ],
+          [
+           "endothelial cell of sinusoid"
+          ],
+          [
+           "enteroendocrine cell of colon"
+          ],
+          [
+           "enteroendocrine cell of small intestine"
+          ],
+          [
+           "P/D1 enteroendocrine cell"
+          ],
+          [
+           "vasoactive intestinal peptide secreting cell"
+          ],
+          [
+           "pancreatic PP cell"
+          ],
+          [
+           "type I enteroendocrine cell"
+          ],
+          [
+           "GIP cell"
+          ],
+          [
+           "type L enteroendocrine cell"
+          ],
+          [
+           "type N enteroendocrine cell"
+          ],
+          [
+           "ghrelin secreting cell"
+          ],
+          [
+           "epithelial cell of thymus"
+          ],
+          [
+           "cortical thymic epithelial cell"
+          ],
+          [
+           "medullary thymic epithelial cell"
+          ],
+          [
+           "pigmented ciliary epithelial cell"
+          ],
+          [
+           "non-pigmented ciliary epithelial cell"
+          ],
+          [
+           "epithelial cell of distal tubule"
+          ],
+          [
+           "nephron tubule epithelial cell"
+          ],
+          [
+           "kidney cortex tubule cell"
+          ],
+          [
+           "epithelial cell of proximal tubule"
+          ],
+          [
+           "epithelial cell of skin gland"
+          ],
+          [
+           "embryonic stem cell"
+          ],
+          [
+           "myoepithelial cell of mammary gland"
+          ],
+          [
+           "mammary gland epithelial cell"
+          ],
+          [
+           "mammary gland glandular cell"
+          ],
+          [
+           "luminal adaptive secretory precursor cell of mammary gland"
+          ],
+          [
+           "luminal epithelial cell of mammary gland"
+          ],
+          [
+           "progenitor cell of mammary luminal epithelium"
+          ],
+          [
+           "basal epithelial cell of tracheobronchial tree"
+          ],
+          [
+           "decidual natural killer cell, human"
+          ],
+          [
+           "endocardial cell"
+          ],
+          [
+           "cardiac endothelial cell"
+          ],
+          [
+           "gestational hematopoietic stem cell"
+          ],
+          [
+           "primitive red blood cell"
+          ],
+          [
+           "primitive erythroid lineage cell"
+          ],
+          [
+           "keratocyte"
+          ],
+          [
+           "myometrial cell"
+          ],
+          [
+           "uterine smooth muscle cell"
+          ],
+          [
+           "respiratory epithelial cell"
+          ],
+          [
+           "respiratory goblet cell"
+          ],
+          [
+           "Schwann cell precursor"
+          ],
+          [
+           "CD141-positive myeloid dendritic cell"
+          ],
+          [
+           "Gr1-high classical monocyte"
+          ],
+          [
+           "CD14-low, CD16-positive monocyte"
+          ],
+          [
+           "CD1c-positive myeloid dendritic cell"
+          ],
+          [
+           "pancreatic stellate cell"
+          ],
+          [
+           "epidermal Langerhans cell"
+          ],
+          [
+           "MHC-II-positive classical monocyte"
+          ],
+          [
+           "nasal mucosa goblet cell"
+          ],
+          [
+           "melanocyte of skin"
+          ],
+          [
+           "hair follicle cell"
+          ],
+          [
+           "trophoblast giant cell"
+          ],
+          [
+           "fetal cardiomyocyte"
+          ],
+          [
+           "adventitial cell"
+          ],
+          [
+           "enteric smooth muscle cell"
+          ],
+          [
+           "kidney epithelial cell"
+          ],
+          [
+           "kidney cell"
+          ],
+          [
+           "subcutaneous adipocyte"
+          ],
+          [
+           "intrahepatic cholangiocyte"
+          ],
+          [
+           "cholangiocyte"
+          ],
+          [
+           "blood vessel smooth muscle cell"
+          ],
+          [
+           "endothelial cell of artery"
+          ],
+          [
+           "vein endothelial cell"
+          ],
+          [
+           "fibroblast of cardiac tissue"
+          ],
+          [
+           "skin fibroblast"
+          ],
+          [
+           "fibroblast of lung"
+          ],
+          [
+           "fibroblast of mammary gland"
+          ],
+          [
+           "placental epithelial cell"
+          ],
+          [
+           "fibroblast of breast"
+          ],
+          [
+           "renal cortical epithelial cell"
+          ],
+          [
+           "kidney cortical cell"
+          ],
+          [
+           "retinal blood vessel endothelial cell"
+          ],
+          [
+           "retinal pigment epithelial cell"
+          ],
+          [
+           "smooth muscle cell of the pulmonary artery"
+          ],
+          [
+           "bladder cell"
+          ],
+          [
+           "bronchial smooth muscle cell"
+          ],
+          [
+           "tracheobronchial smooth muscle cell"
+          ],
+          [
+           "brain macroglial cell"
+          ],
+          [
+           "astrocyte of the cerebral cortex"
+          ],
+          [
+           "cerebral cortex glial cell"
+          ],
+          [
+           "migratory enteric neural crest cell"
+          ],
+          [
+           "adipocyte of breast"
+          ],
+          [
+           "prostate stromal cell"
+          ],
+          [
+           "acinar cell of salivary gland"
+          ],
+          [
+           "salivary gland glandular cell"
+          ],
+          [
+           "immature astrocyte"
+          ],
+          [
+           "mature astrocyte"
+          ],
+          [
+           "mature microglial cell"
+          ],
+          [
+           "respiratory basal cell"
+          ],
+          [
+           "glandular cell of esophagus"
+          ],
+          [
+           "endothelial cell of venule"
+          ],
+          [
+           "glandular cell of the large intestine"
+          ],
+          [
+           "tongue muscle cell"
+          ],
+          [
+           "intestinal enteroendocrine cell"
+          ],
+          [
+           "S cone cell"
+          ],
+          [
+           "H1 horizontal cell"
+          ],
+          [
+           "H2 horizontal cell"
+          ],
+          [
+           "glycinergic amacrine cell"
+          ],
+          [
+           "starburst amacrine cell"
+          ],
+          [
+           "GABAergic amacrine cell"
+          ],
+          [
+           "ionocyte"
+          ],
+          [
+           "renal principal cell"
+          ],
+          [
+           "epithelial cell of nephron"
+          ],
+          [
+           "renal alpha-intercalated cell"
+          ],
+          [
+           "pancreatic epsilon cell"
+          ],
+          [
+           "lymphangioblast"
+          ],
+          [
+           "mesenchymal lymphangioblast"
+          ],
+          [
+           "visceromotor neuron"
+          ],
+          [
+           "enteric neuron"
+          ],
+          [
+           "skeletal muscle satellite stem cell"
+          ],
+          [
+           "inhibitory motor neuron"
+          ],
+          [
+           "cortical interneuron"
+          ],
+          [
+           "cerebral cortex GABAergic interneuron"
+          ],
+          [
+           "extravillous trophoblast"
+          ],
+          [
+           "primary motor cortex pyramidal cell"
+          ],
+          [
+           "L5 extratelencephalic projecting glutamatergic cortical neuron"
+          ],
+          [
+           "inflammatory cell"
+          ],
+          [
+           "salivary gland cell"
+          ],
+          [
+           "small intestine glandular cell"
+          ],
+          [
+           "paneth cell of colon"
+          ],
+          [
+           "colon glandular cell"
+          ],
+          [
+           "transit amplifying cell"
+          ],
+          [
+           "transit amplifying cell of colon"
+          ],
+          [
+           "transit amplifying cell of small intestine"
+          ],
+          [
+           "intestinal crypt stem cell of large intestine"
+          ],
+          [
+           "intestinal crypt stem cell of small intestine"
+          ],
+          [
+           "colon epithelial cell"
+          ],
+          [
+           "colon macrophage"
+          ],
+          [
+           "colon goblet cell"
+          ],
+          [
+           "large intestine goblet cell"
+          ],
+          [
+           "tuft cell of colon"
+          ],
+          [
+           "intestinal crypt stem cell of colon"
+          ],
+          [
+           "lung pericyte"
+          ],
+          [
+           "endothelial cell of placenta"
+          ],
+          [
+           "endothelial cell of uterus"
+          ],
+          [
+           "fibro/adipogenic progenitor cell"
+          ],
+          [
+           "centrocyte"
+          ],
+          [
+           "centroblast"
+          ],
+          [
+           "T follicular regulatory cell"
+          ],
+          [
+           "hair follicular keratinocyte"
+          ],
+          [
+           "cardiac neuron"
+          ],
+          [
+           "mesothelial cell of epicardium"
+          ],
+          [
+           "double negative T regulatory cell"
+          ],
+          [
+           "exhausted T cell"
+          ],
+          [
+           "skeletal muscle fibroblast"
+          ],
+          [
+           "muscle fibroblast"
+          ],
+          [
+           "monocyte-derived dendritic cell"
+          ],
+          [
+           "chorionic trophoblast cell"
+          ],
+          [
+           "sympathetic neuron"
+          ],
+          [
+           "forebrain radial glial cell"
+          ],
+          [
+           "pulmonary ionocyte"
+          ],
+          [
+           "epithelial cell of urethra"
+          ],
+          [
+           "hillock cell"
+          ],
+          [
+           "tracheobronchial serous cell"
+          ],
+          [
+           "tracheobronchial goblet cell"
+          ],
+          [
+           "endothelial cell of periportal hepatic sinusoid"
+          ],
+          [
+           "endothelial cell of hepatic sinusoid"
+          ],
+          [
+           "endothelial cell of pericentral hepatic sinusoid"
+          ],
+          [
+           "periportal region hepatocyte"
+          ],
+          [
+           "midzonal region hepatocyte"
+          ],
+          [
+           "centrilobular region hepatocyte"
+          ],
+          [
+           "intestine goblet cell"
+          ],
+          [
+           "kidney tubule cell"
+          ],
+          [
+           "forebrain neuroblast"
+          ],
+          [
+           "lung goblet cell"
+          ],
+          [
+           "lung neuroendocrine cell"
+          ],
+          [
+           "lung ciliated cell"
+          ],
+          [
+           "smooth muscle cell of small intestine"
+          ],
+          [
+           "smooth muscle fiber of ileum"
+          ],
+          [
+           "urethra cell"
+          ],
+          [
+           "fibroblast of connective tissue of nonglandular part of prostate"
+          ],
+          [
+           "fibroblast of connective tissue of glandular part of prostate"
+          ],
+          [
+           "epicardial adipocyte"
+          ],
+          [
+           "adipocyte of epicardial fat of left ventricle"
+          ],
+          [
+           "bronchial goblet cell"
+          ],
+          [
+           "small intestine goblet cell"
+          ],
+          [
+           "intestinal villus goblet cell"
+          ],
+          [
+           "duodenum glandular cell"
+          ],
+          [
+           "ileal goblet cell"
+          ],
+          [
+           "tracheal goblet cell"
+          ],
+          [
+           "serous cell of epithelium of trachea"
+          ],
+          [
+           "serous cell of epithelium of bronchus"
+          ],
+          [
+           "enterocyte of epithelium of small intestine"
+          ],
+          [
+           "enterocyte of epithelium proper of small intestine"
+          ],
+          [
+           "enterocyte of epithelium proper of ileum"
+          ],
+          [
+           "paneth cell of epithelium of small intestine"
+          ],
+          [
+           "enterocyte of colon"
+          ],
+          [
+           "basal cell of epithelium of trachea"
+          ],
+          [
+           "microfold cell of epithelium of small intestine"
+          ],
+          [
+           "ventricular cardiac muscle cell"
+          ],
+          [
+           "conjunctival epithelial cell"
+          ],
+          [
+           "epithelial cell of lacrimal drainage system"
+          ],
+          [
+           "epithelial cell of lacrimal sac"
+          ],
+          [
+           "epithelial cell of nasolacrimal duct"
+          ],
+          [
+           "bladder urothelial cell"
+          ],
+          [
+           "ciliary muscle cell"
+          ],
+          [
+           "epithelial cell of stratum germinativum of esophagus"
+          ],
+          [
+           "kidney glomerular epithelial cell"
+          ],
+          [
+           "parietal epithelial cell"
+          ],
+          [
+           "epithelial cell of intermediate tubule"
+          ],
+          [
+           "kidney loop of Henle epithelial cell"
+          ],
+          [
+           "kidney collecting duct epithelial cell"
+          ],
+          [
+           "kidney collecting duct cell"
+          ],
+          [
+           "smooth muscle cell of prostate"
+          ],
+          [
+           "mesothelial cell of pleura"
+          ],
+          [
+           "kidney interstitial cell"
+          ],
+          [
+           "kidney medulla cell"
+          ],
+          [
+           "kidney pelvis cell"
+          ],
+          [
+           "kidney inner medulla cell"
+          ],
+          [
+           "kidney outer medulla cell"
+          ],
+          [
+           "papillary tips cell"
+          ],
+          [
+           "lower urinary tract cell"
+          ],
+          [
+           "kidney corpuscule cell"
+          ],
+          [
+           "kidney interstitial fibroblast"
+          ],
+          [
+           "kidney interstitial alternatively activated macrophage"
+          ],
+          [
+           "kidney resident macrophage"
+          ],
+          [
+           "kidney pelvis urothelial cell"
+          ],
+          [
+           "kidney collecting duct principal cell"
+          ],
+          [
+           "kidney collecting duct intercalated cell"
+          ],
+          [
+           "kidney connecting tubule epithelial cell"
+          ],
+          [
+           "kidney loop of Henle descending limb epithelial cell"
+          ],
+          [
+           "kidney distal convoluted tubule epithelial cell"
+          ],
+          [
+           "kidney blood vessel cell"
+          ],
+          [
+           "kidney capillary endothelial cell"
+          ],
+          [
+           "kidney venous blood vessel cell"
+          ],
+          [
+           "glomerular capillary endothelial cell"
+          ],
+          [
+           "kidney loop of Henle ascending limb epithelial cell"
+          ],
+          [
+           "vasa recta cell"
+          ],
+          [
+           "kidney loop of Henle thick ascending limb epithelial cell"
+          ],
+          [
+           "kidney loop of Henle thin ascending limb epithelial cell"
+          ],
+          [
+           "kidney loop of Henle thin descending limb epithelial cell"
+          ],
+          [
+           "vasa recta ascending limb cell"
+          ],
+          [
+           "vasa recta descending limb cell"
+          ],
+          [
+           "medium spiny neuron"
+          ],
+          [
+           "glycinergic neuron"
+          ],
+          [
+           "lung endothelial cell"
+          ],
+          [
+           "pulmonary artery endothelial cell"
+          ],
+          [
+           "cerebral cortex pyramidal neuron"
+          ],
+          [
+           "cerebral cortex endothelial cell"
+          ],
+          [
+           "peripheral blood mononuclear cell"
+          ],
+          [
+           "tonsil germinal center B cell"
+          ],
+          [
+           "dermis lymphatic vessel endothelial cell"
+          ],
+          [
+           "lung microvascular endothelial cell"
+          ],
+          [
+           "dermis microvascular lymphatic vessel endothelial cell"
+          ],
+          [
+           "embryonic fibroblast"
+          ],
+          [
+           "brainstem motor neuron"
+          ],
+          [
+           "hepatic pit cell"
+          ],
+          [
+           "liver dendritic cell"
+          ],
+          [
+           "prostate gland microvascular endothelial cell"
+          ],
+          [
+           "placental villous trophoblast"
+          ],
+          [
+           "bronchus fibroblast of lung"
+          ],
+          [
+           "cord blood hematopoietic stem cell"
+          ],
+          [
+           "Hofbauer cell"
+          ],
+          [
+           "vip GABAergic cortical interneuron"
+          ],
+          [
+           "intratelencephalic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "extratelencephalic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "lamp5 GABAergic cortical interneuron"
+          ],
+          [
+           "near-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "corticothalamic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "sncg GABAergic cortical interneuron"
+          ],
+          [
+           "sst GABAergic cortical interneuron"
+          ],
+          [
+           "pvalb GABAergic cortical interneuron"
+          ],
+          [
+           "sst chodl GABAergic cortical interneuron"
+          ],
+          [
+           "direct pathway medium spiny neuron"
+          ],
+          [
+           "indirect pathway medium spiny neuron"
+          ],
+          [
+           "chandelier pvalb GABAergic cortical interneuron"
+          ],
+          [
+           "chandelier cell"
+          ],
+          [
+           "L6b glutamatergic cortical neuron"
+          ],
+          [
+           "L2/3-6 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "L6 corticothalamic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "L5/6 near-projecting glutamatergic neuron of the primary motor cortex"
+          ],
+          [
+           "L5/6 near-projecting glutamatergic neuron"
+          ],
+          [
+           "L2/3 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex"
+          ],
+          [
+           "L6 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "vascular leptomeningeal cell"
+          ],
+          [
+           "mesothelial fibroblast of the leptomeninx"
+          ],
+          [
+           "mesothelial fibroblast"
+          ],
+          [
+           "differentiation-committed oligodendrocyte precursor"
+          ],
+          [
+           "caudal ganglionic eminence derived interneuron"
+          ],
+          [
+           "caudal ganglionic eminence derived GABAergic cortical interneuron"
+          ],
+          [
+           "brain vascular cell"
+          ],
+          [
+           "unipolar brush cell"
+          ],
+          [
+           "midget ganglion cell of retina"
+          ],
+          [
+           "parasol ganglion cell of retina"
+          ],
+          [
+           "alveolar type 1 fibroblast cell"
+          ],
+          [
+           "alveolar type 2 fibroblast cell"
+          ],
+          [
+           "epithelial cell of proximal tubule segment 1"
+          ],
+          [
+           "epithelial cell of proximal tubule segment 3"
+          ],
+          [
+           "kidney connecting tubule principal cell"
+          ],
+          [
+           "respiratory hillock cell"
+          ],
+          [
+           "BEST4+ intestinal epithelial cell, human"
+          ],
+          [
+           "L4 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "serous secreting cell of bronchus submucosal gland"
+          ],
+          [
+           "suprabasal keratinocyte"
+          ],
+          [
+           "retinal astrocyte"
+          ],
+          [
+           "ON-blue cone bipolar cell"
+          ],
+          [
+           "airway submucosal gland duct basal cell"
+          ],
+          [
+           "perichondrial fibroblast"
+          ],
+          [
+           "lung perichondrial fibroblast"
+          ],
+          [
+           "diffuse bipolar 1 cell"
+          ],
+          [
+           "diffuse bipolar 2 cell"
+          ],
+          [
+           "diffuse bipolar 3a cell"
+          ],
+          [
+           "diffuse bipolar 3b cell"
+          ],
+          [
+           "diffuse bipolar 4 cell"
+          ],
+          [
+           "diffuse bipolar 6 cell"
+          ],
+          [
+           "flat midget bipolar cell"
+          ],
+          [
+           "invaginating midget bipolar cell"
+          ],
+          [
+           "giant bipolar cell"
+          ],
+          [
+           "OFFx cell"
+          ],
+          [
+           "lung resident memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "lung resident memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "metallothionein-positive alveolar macrophage"
+          ],
+          [
+           "lung interstitial macrophage"
+          ],
+          [
+           "deuterosomal cell"
+          ],
+          [
+           "respiratory suprabasal cell"
+          ],
+          [
+           "luminal hormone-sensing cell of mammary gland"
+          ]
+         ],
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+         "legendgroup": "",
+         "marker": {
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killer cell, human\"],[\"endocardial cell\"],[\"cardiac endothelial cell\"],[\"gestational hematopoietic stem cell\"],[\"primitive red blood cell\"],[\"primitive erythroid lineage cell\"],[\"keratocyte\"],[\"myometrial cell\"],[\"uterine smooth muscle cell\"],[\"respiratory epithelial cell\"],[\"respiratory goblet cell\"],[\"Schwann cell precursor\"],[\"CD141-positive myeloid dendritic cell\"],[\"Gr1-high classical monocyte\"],[\"CD14-low, CD16-positive monocyte\"],[\"CD1c-positive myeloid dendritic cell\"],[\"pancreatic stellate cell\"],[\"epidermal Langerhans cell\"],[\"MHC-II-positive classical monocyte\"],[\"nasal mucosa goblet cell\"],[\"melanocyte of skin\"],[\"hair follicle cell\"],[\"trophoblast giant cell\"],[\"fetal cardiomyocyte\"],[\"adventitial cell\"],[\"enteric smooth muscle cell\"],[\"kidney epithelial cell\"],[\"kidney cell\"],[\"subcutaneous adipocyte\"],[\"intrahepatic cholangiocyte\"],[\"cholangiocyte\"],[\"blood vessel smooth muscle cell\"],[\"endothelial cell of artery\"],[\"vein endothelial cell\"],[\"fibroblast of cardiac tissue\"],[\"skin fibroblast\"],[\"fibroblast of lung\"],[\"fibroblast of mammary gland\"],[\"placental epithelial cell\"],[\"fibroblast of breast\"],[\"renal cortical epithelial cell\"],[\"kidney cortical cell\"],[\"retinal blood vessel endothelial cell\"],[\"retinal pigment epithelial cell\"],[\"smooth muscle cell of the pulmonary artery\"],[\"bladder cell\"],[\"bronchial smooth muscle cell\"],[\"tracheobronchial smooth muscle cell\"],[\"brain macroglial cell\"],[\"astrocyte of the cerebral cortex\"],[\"cerebral cortex glial cell\"],[\"migratory enteric neural crest cell\"],[\"adipocyte of breast\"],[\"prostate stromal cell\"],[\"acinar cell of salivary gland\"],[\"salivary gland glandular cell\"],[\"immature astrocyte\"],[\"mature astrocyte\"],[\"mature microglial cell\"],[\"respiratory basal cell\"],[\"glandular cell of esophagus\"],[\"endothelial cell of venule\"],[\"glandular cell of the large intestine\"],[\"tongue muscle cell\"],[\"intestinal enteroendocrine cell\"],[\"S cone cell\"],[\"H1 horizontal cell\"],[\"H2 horizontal cell\"],[\"glycinergic amacrine cell\"],[\"starburst amacrine cell\"],[\"GABAergic amacrine cell\"],[\"ionocyte\"],[\"renal principal cell\"],[\"epithelial cell of nephron\"],[\"renal alpha-intercalated cell\"],[\"pancreatic epsilon cell\"],[\"lymphangioblast\"],[\"mesenchymal lymphangioblast\"],[\"visceromotor neuron\"],[\"enteric neuron\"],[\"skeletal muscle satellite stem cell\"],[\"inhibitory motor neuron\"],[\"cortical interneuron\"],[\"cerebral cortex GABAergic interneuron\"],[\"extravillous trophoblast\"],[\"primary motor cortex pyramidal cell\"],[\"L5 extratelencephalic projecting glutamatergic cortical neuron\"],[\"inflammatory cell\"],[\"salivary gland cell\"],[\"small intestine glandular cell\"],[\"paneth cell of colon\"],[\"colon glandular cell\"],[\"transit amplifying cell\"],[\"transit amplifying cell of colon\"],[\"transit amplifying cell of small intestine\"],[\"intestinal crypt stem cell of large intestine\"],[\"intestinal crypt stem cell of small intestine\"],[\"colon epithelial cell\"],[\"colon macrophage\"],[\"colon goblet cell\"],[\"large intestine goblet cell\"],[\"tuft cell of colon\"],[\"intestinal crypt stem cell of colon\"],[\"lung pericyte\"],[\"endothelial cell of placenta\"],[\"endothelial cell of uterus\"],[\"fibro\\u002fadipogenic progenitor cell\"],[\"centrocyte\"],[\"centroblast\"],[\"T follicular regulatory cell\"],[\"hair follicular keratinocyte\"],[\"cardiac neuron\"],[\"mesothelial cell of epicardium\"],[\"double negative T regulatory cell\"],[\"exhausted T cell\"],[\"skeletal muscle fibroblast\"],[\"muscle fibroblast\"],[\"monocyte-derived dendritic cell\"],[\"chorionic trophoblast cell\"],[\"sympathetic neuron\"],[\"forebrain radial glial cell\"],[\"pulmonary ionocyte\"],[\"epithelial cell of urethra\"],[\"hillock cell\"],[\"tracheobronchial serous cell\"],[\"tracheobronchial goblet cell\"],[\"endothelial cell of periportal hepatic sinusoid\"],[\"endothelial cell of hepatic sinusoid\"],[\"endothelial cell of pericentral hepatic sinusoid\"],[\"periportal region hepatocyte\"],[\"midzonal region hepatocyte\"],[\"centrilobular region hepatocyte\"],[\"intestine goblet cell\"],[\"kidney tubule cell\"],[\"forebrain neuroblast\"],[\"lung goblet cell\"],[\"lung neuroendocrine cell\"],[\"lung ciliated cell\"],[\"smooth muscle cell of small intestine\"],[\"smooth muscle fiber of ileum\"],[\"urethra cell\"],[\"fibroblast of connective tissue of nonglandular part of prostate\"],[\"fibroblast of connective tissue of glandular part of prostate\"],[\"epicardial adipocyte\"],[\"adipocyte of epicardial fat of left ventricle\"],[\"bronchial goblet cell\"],[\"small intestine goblet cell\"],[\"intestinal villus goblet cell\"],[\"duodenum glandular cell\"],[\"ileal goblet cell\"],[\"tracheal goblet cell\"],[\"serous cell of epithelium of trachea\"],[\"serous cell of epithelium of bronchus\"],[\"enterocyte of epithelium of small intestine\"],[\"enterocyte of epithelium proper of small intestine\"],[\"enterocyte of epithelium proper of ileum\"],[\"paneth cell of epithelium of small intestine\"],[\"enterocyte of colon\"],[\"basal cell of epithelium of trachea\"],[\"microfold cell of epithelium of small intestine\"],[\"ventricular cardiac muscle cell\"],[\"conjunctival epithelial cell\"],[\"epithelial cell of lacrimal drainage system\"],[\"epithelial cell of lacrimal sac\"],[\"epithelial cell of nasolacrimal duct\"],[\"bladder urothelial cell\"],[\"ciliary muscle cell\"],[\"epithelial cell of stratum germinativum of esophagus\"],[\"kidney glomerular epithelial cell\"],[\"parietal epithelial cell\"],[\"epithelial cell of intermediate tubule\"],[\"kidney loop of Henle epithelial cell\"],[\"kidney collecting duct epithelial cell\"],[\"kidney collecting duct cell\"],[\"smooth muscle cell of prostate\"],[\"mesothelial cell of pleura\"],[\"kidney interstitial cell\"],[\"kidney medulla 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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"x\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"y\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"height\":500,\"width\":500},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('cb0e89f9-f3cb-4fa2-bb99-5a4b67d34ca7');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plt.scatter(X_ct_pca[:,0], X_ct_pca[:,1])\n",
+    "df = pd.DataFrame({'x': X_ct_pca[:,0], 'y': X_ct_pca[:, 1], 'label': cell_type_labels})\n",
+    "\n",
+    "fig = px.scatter(df, x='x', y='y', hover_data='label', width=500, height=500)\n",
+    "fig.show()\n",
+    "fig.write_html(\"pca_cell_type_emb.html\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reducer = umap.UMAP(n_components=2, n_neighbors=30, min_dist=0.0, metric='euclidean')\n",
+    "X_ct_umap = reducer.fit_transform(cell_type_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "neuron"
+          ],
+          [
+           "sensory neuron"
+          ],
+          [
+           "primary cultured cell"
+          ],
+          [
+           "cultured cell"
+          ],
+          [
+           "fibroblast neural crest derived"
+          ],
+          [
+           "migratory neural crest cell"
+          ],
+          [
+           "fibroblast"
+          ],
+          [
+           "neuronal receptor cell"
+          ],
+          [
+           "sensory receptor cell"
+          ],
+          [
+           "embryonic cell (metazoa)"
+          ],
+          [
+           "experimentally modified cell in vitro"
+          ],
+          [
+           "germ line cell"
+          ],
+          [
+           "stem cell"
+          ],
+          [
+           "male germ cell"
+          ],
+          [
+           "germ cell"
+          ],
+          [
+           "haploid cell"
+          ],
+          [
+           "sperm"
+          ],
+          [
+           "male gamete"
+          ],
+          [
+           "primordial germ cell"
+          ],
+          [
+           "polyploid cell"
+          ],
+          [
+           "smooth muscle cell"
+          ],
+          [
+           "neural crest derived neuron"
+          ],
+          [
+           "glioblast"
+          ],
+          [
+           "non-terminally differentiated cell"
+          ],
+          [
+           "neuroblast (sensu Vertebrata)"
+          ],
+          [
+           "surface ectodermal cell"
+          ],
+          [
+           "exocrine cell"
+          ],
+          [
+           "precursor cell"
+          ],
+          [
+           "single fate stem cell"
+          ],
+          [
+           "somatic stem cell"
+          ],
+          [
+           "epithelial fate stem cell"
+          ],
+          [
+           "hematopoietic stem cell"
+          ],
+          [
+           "hematopoietic cell"
+          ],
+          [
+           "hematopoietic precursor cell"
+          ],
+          [
+           "progenitor cell"
+          ],
+          [
+           "erythroid progenitor cell"
+          ],
+          [
+           "erythroid lineage cell"
+          ],
+          [
+           "myeloid lineage restricted progenitor cell"
+          ],
+          [
+           "megakaryocyte-erythroid progenitor cell"
+          ],
+          [
+           "monopoietic cell"
+          ],
+          [
+           "myeloid cell"
+          ],
+          [
+           "macrophage dendritic cell progenitor"
+          ],
+          [
+           "eosinophil"
+          ],
+          [
+           "professional antigen presenting cell"
+          ],
+          [
+           "neutrophil progenitor cell"
+          ],
+          [
+           "basophil"
+          ],
+          [
+           "histamine secreting cell"
+          ],
+          [
+           "common lymphoid progenitor"
+          ],
+          [
+           "neural cell"
+          ],
+          [
+           "multi fate stem cell"
+          ],
+          [
+           "common myeloid progenitor"
+          ],
+          [
+           "hematopoietic oligopotent progenitor cell"
+          ],
+          [
+           "granulocyte monocyte progenitor cell"
+          ],
+          [
+           "secretory cell"
+          ],
+          [
+           "myoblast"
+          ],
+          [
+           "muscle precursor cell"
+          ],
+          [
+           "connective tissue cell"
+          ],
+          [
+           "mesenchymal stem cell"
+          ],
+          [
+           "ecto-epithelial cell"
+          ],
+          [
+           "neurecto-epithelial cell"
+          ],
+          [
+           "osteoblast"
+          ],
+          [
+           "preosteoblast"
+          ],
+          [
+           "ciliated cell"
+          ],
+          [
+           "ependymal cell"
+          ],
+          [
+           "ciliated epithelial cell"
+          ],
+          [
+           "epithelial cell"
+          ],
+          [
+           "leukocyte"
+          ],
+          [
+           "duct epithelial cell"
+          ],
+          [
+           "branched duct epithelial cell"
+          ],
+          [
+           "blood vessel endothelial cell"
+          ],
+          [
+           "squamous epithelial cell"
+          ],
+          [
+           "endothelial cell of vascular tree"
+          ],
+          [
+           "non-branched duct epithelial cell"
+          ],
+          [
+           "meso-epithelial cell"
+          ],
+          [
+           "barrier cell"
+          ],
+          [
+           "columnar/cuboidal epithelial cell"
+          ],
+          [
+           "mesothelial cell"
+          ],
+          [
+           "lining cell"
+          ],
+          [
+           "stratified epithelial cell"
+          ],
+          [
+           "circulating cell"
+          ],
+          [
+           "blood cell"
+          ],
+          [
+           "epithelial cell of lung"
+          ],
+          [
+           "epithelial cell of pancreas"
+          ],
+          [
+           "T cell"
+          ],
+          [
+           "lymphocyte"
+          ],
+          [
+           "pro-T cell"
+          ],
+          [
+           "lymphocyte of B lineage"
+          ],
+          [
+           "Kupffer cell"
+          ],
+          [
+           "tissue-resident macrophage"
+          ],
+          [
+           "osteoclast"
+          ],
+          [
+           "myeloid leukocyte"
+          ],
+          [
+           "phagocyte (sensu Vertebrata)"
+          ],
+          [
+           "bone cell"
+          ],
+          [
+           "monocyte"
+          ],
+          [
+           "granulocyte"
+          ],
+          [
+           "neuron associated cell"
+          ],
+          [
+           "neutrophil"
+          ],
+          [
+           "phagocyte"
+          ],
+          [
+           "mast cell"
+          ],
+          [
+           "sensory epithelial cell"
+          ],
+          [
+           "interneuron"
+          ],
+          [
+           "motor neuron"
+          ],
+          [
+           "efferent neuron"
+          ],
+          [
+           "afferent neuron"
+          ],
+          [
+           "bipolar neuron"
+          ],
+          [
+           "multipolar neuron"
+          ],
+          [
+           "autonomic neuron"
+          ],
+          [
+           "peripheral nervous system neuron"
+          ],
+          [
+           "cholinergic neuron"
+          ],
+          [
+           "catecholaminergic neuron"
+          ],
+          [
+           "mononuclear phagocyte"
+          ],
+          [
+           "mononuclear cell"
+          ],
+          [
+           "ectodermal cell"
+          ],
+          [
+           "endothelial cell"
+          ],
+          [
+           "CNS neuron (sensu Vertebrata)"
+          ],
+          [
+           "GABAergic interneuron"
+          ],
+          [
+           "granule cell"
+          ],
+          [
+           "Purkinje cell"
+          ],
+          [
+           "cerebellar neuron"
+          ],
+          [
+           "stellate neuron"
+          ],
+          [
+           "neuron associated cell (sensu Vertebrata)"
+          ],
+          [
+           "glial cell"
+          ],
+          [
+           "macroglial cell"
+          ],
+          [
+           "oligodendrocyte"
+          ],
+          [
+           "myelinating glial cell"
+          ],
+          [
+           "oligodendrocyte precursor cell"
+          ],
+          [
+           "microglial cell"
+          ],
+          [
+           "central nervous system macrophage"
+          ],
+          [
+           "mesodermal cell"
+          ],
+          [
+           "gut endothelial cell"
+          ],
+          [
+           "corneal endothelial cell"
+          ],
+          [
+           "fibrocyte"
+          ],
+          [
+           "stromal cell"
+          ],
+          [
+           "adipocyte"
+          ],
+          [
+           "stuff accumulating cell"
+          ],
+          [
+           "chondrocyte"
+          ],
+          [
+           "glycosaminoglycan secreting cell"
+          ],
+          [
+           "collagen secreting cell"
+          ],
+          [
+           "pigment cell"
+          ],
+          [
+           "melanocyte"
+          ],
+          [
+           "visual pigment cell"
+          ],
+          [
+           "glandular epithelial cell"
+          ],
+          [
+           "extracellular matrix secreting cell"
+          ],
+          [
+           "carbohydrate secreting cell"
+          ],
+          [
+           "protein secreting cell"
+          ],
+          [
+           "peptic cell"
+          ],
+          [
+           "glandular cell of stomach"
+          ],
+          [
+           "surfactant secreting cell"
+          ],
+          [
+           "club cell"
+          ],
+          [
+           "epithelial cell of tracheobronchial tree"
+          ],
+          [
+           "seromucus secreting cell"
+          ],
+          [
+           "goblet cell"
+          ],
+          [
+           "mucus secreting cell"
+          ],
+          [
+           "parietal cell"
+          ],
+          [
+           "endocrine cell"
+          ],
+          [
+           "enteroendocrine cell"
+          ],
+          [
+           "neuroendocrine cell"
+          ],
+          [
+           "chromaffin cell"
+          ],
+          [
+           "amine precursor uptake and decarboxylation cell"
+          ],
+          [
+           "peptide hormone secreting cell"
+          ],
+          [
+           "insulin secreting cell"
+          ],
+          [
+           "type B pancreatic cell"
+          ],
+          [
+           "pancreatic endocrine cell"
+          ],
+          [
+           "progenitor cell of endocrine pancreas"
+          ],
+          [
+           "glucagon secreting cell"
+          ],
+          [
+           "pancreatic A cell"
+          ],
+          [
+           "type A enteroendocrine cell"
+          ],
+          [
+           "somatostatin secreting cell"
+          ],
+          [
+           "pancreatic D cell"
+          ],
+          [
+           "type D enteroendocrine cell"
+          ],
+          [
+           "steroid hormone secreting cell"
+          ],
+          [
+           "stromal cell of ovary"
+          ],
+          [
+           "testosterone secreting cell"
+          ],
+          [
+           "Leydig cell"
+          ],
+          [
+           "interstitial cell"
+          ],
+          [
+           "hepatocyte"
+          ],
+          [
+           "endopolyploid cell"
+          ],
+          [
+           "hepatoblast"
+          ],
+          [
+           "contractile cell"
+          ],
+          [
+           "pericyte"
+          ],
+          [
+           "myoepithelial cell"
+          ],
+          [
+           "myofibroblast cell"
+          ],
+          [
+           "muscle cell"
+          ],
+          [
+           "electrically responsive cell"
+          ],
+          [
+           "cell of skeletal muscle"
+          ],
+          [
+           "slow muscle cell"
+          ],
+          [
+           "extrafusal muscle fiber"
+          ],
+          [
+           "fast muscle cell"
+          ],
+          [
+           "non-striated muscle cell"
+          ],
+          [
+           "visceral muscle cell"
+          ],
+          [
+           "smooth muscle myoblast"
+          ],
+          [
+           "cardiac muscle cell"
+          ],
+          [
+           "chemoreceptor cell"
+          ],
+          [
+           "taste receptor cell"
+          ],
+          [
+           "endo-epithelial cell"
+          ],
+          [
+           "photoreceptor cell"
+          ],
+          [
+           "electrically active cell"
+          ],
+          [
+           "Sertoli cell"
+          ],
+          [
+           "supporting cell"
+          ],
+          [
+           "androgen binding protein secreting cell"
+          ],
+          [
+           "seminiferous tubule epithelial cell"
+          ],
+          [
+           "insulating cell"
+          ],
+          [
+           "myelinating Schwann cell"
+          ],
+          [
+           "Schwann cell"
+          ],
+          [
+           "immature Schwann cell"
+          ],
+          [
+           "motile cell"
+          ],
+          [
+           "anucleate cell"
+          ],
+          [
+           "nucleate cell"
+          ],
+          [
+           "single nucleate cell"
+          ],
+          [
+           "multinucleate cell"
+          ],
+          [
+           "erythrocyte"
+          ],
+          [
+           "oxygen accumulating cell"
+          ],
+          [
+           "reticulocyte"
+          ],
+          [
+           "platelet"
+          ],
+          [
+           "serotonin secreting cell"
+          ],
+          [
+           "megakaryocyte"
+          ],
+          [
+           "defensive cell"
+          ],
+          [
+           "macrophage"
+          ],
+          [
+           "B cell"
+          ],
+          [
+           "keratinizing barrier epithelial cell"
+          ],
+          [
+           "stratified squamous epithelial cell"
+          ],
+          [
+           "keratin accumulating cell"
+          ],
+          [
+           "brush border epithelial cell"
+          ],
+          [
+           "Merkel cell"
+          ],
+          [
+           "keratinocyte"
+          ],
+          [
+           "transitional epithelial cell"
+          ],
+          [
+           "primary sensory neuron (sensu Teleostei)"
+          ],
+          [
+           "eurydendroid cell"
+          ],
+          [
+           "eye photoreceptor cell"
+          ],
+          [
+           "gamete"
+          ],
+          [
+           "crystallin accumulating cell"
+          ],
+          [
+           "tracheal epithelial cell"
+          ],
+          [
+           "epidermal cell"
+          ],
+          [
+           "serous secreting cell"
+          ],
+          [
+           "milk secreting cell"
+          ],
+          [
+           "sebum secreting cell"
+          ],
+          [
+           "sebaceous gland cell"
+          ],
+          [
+           "pneumocyte"
+          ],
+          [
+           "epithelial cell of alveolus of lung"
+          ],
+          [
+           "lysozyme secreting cell"
+          ],
+          [
+           "neural crest cell"
+          ],
+          [
+           "mesenchymal cell"
+          ],
+          [
+           "biogenic amine secreting cell"
+          ],
+          [
+           "extraembryonic cell"
+          ],
+          [
+           "trophoblast cell"
+          ],
+          [
+           "sphincter associated smooth muscle cell"
+          ],
+          [
+           "vascular associated smooth muscle cell"
+          ],
+          [
+           "perivascular cell"
+          ],
+          [
+           "general ecto-epithelial cell"
+          ],
+          [
+           "myotube"
+          ],
+          [
+           "attachment cell"
+          ],
+          [
+           "tendon cell"
+          ],
+          [
+           "ganglion interneuron"
+          ],
+          [
+           "CNS interneuron"
+          ],
+          [
+           "central nervous system neuron"
+          ],
+          [
+           "electrically signaling cell"
+          ],
+          [
+           "excretory cell"
+          ],
+          [
+           "reticular cell"
+          ],
+          [
+           "follicular dendritic cell"
+          ],
+          [
+           "striated muscle cell"
+          ],
+          [
+           "preadipocyte"
+          ],
+          [
+           "dendritic cell"
+          ],
+          [
+           "Langerhans cell"
+          ],
+          [
+           "conventional dendritic cell"
+          ],
+          [
+           "noradrenergic cell"
+          ],
+          [
+           "cardiac myoblast"
+          ],
+          [
+           "neural progenitor cell"
+          ],
+          [
+           "follicular epithelial cell"
+          ],
+          [
+           "ovarian surface epithelial cell"
+          ],
+          [
+           "CD4-positive helper T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "activated CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "inhibitory interneuron"
+          ],
+          [
+           "granulosa cell"
+          ],
+          [
+           "follicular cell of ovary"
+          ],
+          [
+           "theca cell"
+          ],
+          [
+           "androgen secreting cell"
+          ],
+          [
+           "enkephalin secreting cell"
+          ],
+          [
+           "endorphin secreting cell"
+          ],
+          [
+           "type G enteroendocrine cell"
+          ],
+          [
+           "gastrin secreting cell"
+          ],
+          [
+           "mucous cell of stomach"
+          ],
+          [
+           "paneth cell"
+          ],
+          [
+           "intestinal epithelial cell"
+          ],
+          [
+           "paracrine cell"
+          ],
+          [
+           "cardiac muscle myoblast"
+          ],
+          [
+           "cardiocyte"
+          ],
+          [
+           "skeletal muscle fiber"
+          ],
+          [
+           "syncytiotrophoblast cell"
+          ],
+          [
+           "pigmented epithelial cell"
+          ],
+          [
+           "primary neuron (sensu Teleostei)"
+          ],
+          [
+           "T-helper 1 cell"
+          ],
+          [
+           "T-helper 2 cell"
+          ],
+          [
+           "CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell"
+          ],
+          [
+           "proerythroblast"
+          ],
+          [
+           "erythroblast"
+          ],
+          [
+           "neuronal brush cell"
+          ],
+          [
+           "CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor"
+          ],
+          [
+           "bone marrow hematopoietic cell"
+          ],
+          [
+           "promonocyte"
+          ],
+          [
+           "amacrine cell"
+          ],
+          [
+           "retinal cell"
+          ],
+          [
+           "retinal ganglion cell"
+          ],
+          [
+           "promyelocyte"
+          ],
+          [
+           "cardiac mesenchymal cell"
+          ],
+          [
+           "migratory cardiac neural crest cell"
+          ],
+          [
+           "retinal cone cell"
+          ],
+          [
+           "camera-type eye photoreceptor cell"
+          ],
+          [
+           "corneal epithelial cell"
+          ],
+          [
+           "type EC enteroendocrine cell"
+          ],
+          [
+           "epithelial cell of alimentary canal"
+          ],
+          [
+           "cell in vitro"
+          ],
+          [
+           "immature neutrophil"
+          ],
+          [
+           "myelocyte"
+          ],
+          [
+           "alveolar macrophage"
+          ],
+          [
+           "lung macrophage"
+          ],
+          [
+           "enterocyte"
+          ],
+          [
+           "gut absorptive cell"
+          ],
+          [
+           "skeletal muscle satellite cell"
+          ],
+          [
+           "enucleate erythrocyte"
+          ],
+          [
+           "enucleated reticulocyte"
+          ],
+          [
+           "pyramidal neuron"
+          ],
+          [
+           "retinal rod cell"
+          ],
+          [
+           "granulocytopoietic cell"
+          ],
+          [
+           "basophil mast progenitor cell"
+          ],
+          [
+           "GABAergic neuron"
+          ],
+          [
+           "acinar cell"
+          ],
+          [
+           "natural killer cell"
+          ],
+          [
+           "group 1 innate lymphoid cell"
+          ],
+          [
+           "mature alpha-beta T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "transporting cell"
+          ],
+          [
+           "hepatic stellate cell"
+          ],
+          [
+           "Mueller cell"
+          ],
+          [
+           "radial glial cell"
+          ],
+          [
+           "basal cell"
+          ],
+          [
+           "prickle cell"
+          ],
+          [
+           "mesangial cell"
+          ],
+          [
+           "mucous neck cell"
+          ],
+          [
+           "podocyte"
+          ],
+          [
+           "renal filtration cell"
+          ],
+          [
+           "epithelial cell of glomerular capsule"
+          ],
+          [
+           "fenestrated cell"
+          ],
+          [
+           "mural cell"
+          ],
+          [
+           "glutamatergic neuron"
+          ],
+          [
+           "M cell of gut"
+          ],
+          [
+           "non-myelinating Schwann cell"
+          ],
+          [
+           "Cajal-Retzius cell"
+          ],
+          [
+           "PP cell"
+          ],
+          [
+           "dopaminergic neuron"
+          ],
+          [
+           "endothelial tip cell"
+          ],
+          [
+           "choroid plexus epithelial cell"
+          ],
+          [
+           "urothelial cell"
+          ],
+          [
+           "retina horizontal cell"
+          ],
+          [
+           "retinal bipolar neuron"
+          ],
+          [
+           "visual system neuron"
+          ],
+          [
+           "ON-bipolar cell"
+          ],
+          [
+           "OFF-bipolar cell"
+          ],
+          [
+           "rod bipolar cell"
+          ],
+          [
+           "cone retinal bipolar cell"
+          ],
+          [
+           "myeloid dendritic cell"
+          ],
+          [
+           "plasmacytoid dendritic cell"
+          ],
+          [
+           "mature B cell"
+          ],
+          [
+           "B cell, CD19-positive"
+          ],
+          [
+           "transitional stage B cell"
+          ],
+          [
+           "immature B cell"
+          ],
+          [
+           "precursor B cell"
+          ],
+          [
+           "plasma cell"
+          ],
+          [
+           "antibody secreting cell"
+          ],
+          [
+           "plasmablast"
+          ],
+          [
+           "memory B cell"
+          ],
+          [
+           "naive B cell"
+          ],
+          [
+           "alpha-beta T cell"
+          ],
+          [
+           "gamma-delta T cell"
+          ],
+          [
+           "immature alpha-beta T cell"
+          ],
+          [
+           "immature T cell"
+          ],
+          [
+           "mature T cell"
+          ],
+          [
+           "CD4-positive, CD25-positive, alpha-beta regulatory T cell"
+          ],
+          [
+           "regulatory T cell"
+          ],
+          [
+           "alpha-beta intraepithelial T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta thymocyte"
+          ],
+          [
+           "CD8-positive, alpha-beta cytotoxic T cell"
+          ],
+          [
+           "activated CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD8-alpha-beta-positive, alpha-beta intraepithelial T cell"
+          ],
+          [
+           "intraepithelial lymphocyte"
+          ],
+          [
+           "DN3 thymocyte"
+          ],
+          [
+           "mature gamma-delta T cell"
+          ],
+          [
+           "gamma-delta intraepithelial T cell"
+          ],
+          [
+           "CD8-alpha alpha positive, gamma-delta intraepithelial T cell"
+          ],
+          [
+           "thymocyte"
+          ],
+          [
+           "DN4 thymocyte"
+          ],
+          [
+           "double negative thymocyte"
+          ],
+          [
+           "DN1 thymic pro-T cell"
+          ],
+          [
+           "double-positive, alpha-beta thymocyte"
+          ],
+          [
+           "CD8-positive, alpha-beta thymocyte"
+          ],
+          [
+           "memory T cell"
+          ],
+          [
+           "naive T cell"
+          ],
+          [
+           "mature NK T cell"
+          ],
+          [
+           "small pre-B-II cell"
+          ],
+          [
+           "pro-B cell"
+          ],
+          [
+           "B-1 B cell"
+          ],
+          [
+           "B-1a B cell"
+          ],
+          [
+           "B-1b B cell"
+          ],
+          [
+           "B-2 B cell"
+          ],
+          [
+           "immature natural killer cell"
+          ],
+          [
+           "immature innate lymphoid cell"
+          ],
+          [
+           "mature natural killer cell"
+          ],
+          [
+           "lymphoid lineage restricted progenitor cell"
+          ],
+          [
+           "hematopoietic multipotent progenitor cell"
+          ],
+          [
+           "hematopoietic lineage restricted progenitor cell"
+          ],
+          [
+           "common dendritic progenitor"
+          ],
+          [
+           "mature conventional dendritic cell"
+          ],
+          [
+           "follicular B cell"
+          ],
+          [
+           "germinal center B cell"
+          ],
+          [
+           "classical monocyte"
+          ],
+          [
+           "elicited macrophage"
+          ],
+          [
+           "inflammatory macrophage"
+          ],
+          [
+           "non-classical monocyte"
+          ],
+          [
+           "intermediate monocyte"
+          ],
+          [
+           "meningeal macrophage"
+          ],
+          [
+           "alternatively activated macrophage"
+          ],
+          [
+           "early T lineage precursor"
+          ],
+          [
+           "naive thymus-derived CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta memory T cell"
+          ],
+          [
+           "T-helper 17 cell"
+          ],
+          [
+           "naive thymus-derived CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "natural T-regulatory cell"
+          ],
+          [
+           "central memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta memory T cell, CD45RO-positive"
+          ],
+          [
+           "effector memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "central memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta memory T cell, CD45RO-positive"
+          ],
+          [
+           "CD8-positive, alpha-beta cytokine secreting effector T cell"
+          ],
+          [
+           "CD8-positive, alpha-beta memory T cell"
+          ],
+          [
+           "cytotoxic T cell"
+          ],
+          [
+           "effector T cell"
+          ],
+          [
+           "helper T cell"
+          ],
+          [
+           "effector memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "immature NK T cell"
+          ],
+          [
+           "CD8-alpha-alpha-positive, alpha-beta intraepithelial T cell"
+          ],
+          [
+           "Tc1 cell"
+          ],
+          [
+           "type I NK T cell"
+          ],
+          [
+           "type II NK T cell"
+          ],
+          [
+           "activated type II NK T cell"
+          ],
+          [
+           "CD4-positive, alpha-beta cytotoxic T cell"
+          ],
+          [
+           "early lymphoid progenitor"
+          ],
+          [
+           "CD16-negative, CD56-bright natural killer cell, human"
+          ],
+          [
+           "CD16-positive, CD56-dim natural killer cell, human"
+          ],
+          [
+           "mucosal invariant T cell"
+          ],
+          [
+           "long lived plasma cell"
+          ],
+          [
+           "class switched memory B cell"
+          ],
+          [
+           "large pre-B-II cell"
+          ],
+          [
+           "pre-B-II cell"
+          ],
+          [
+           "pre-B-I cell"
+          ],
+          [
+           "unswitched memory B cell"
+          ],
+          [
+           "IgG memory B cell"
+          ],
+          [
+           "IgG plasmablast"
+          ],
+          [
+           "IgA plasmablast"
+          ],
+          [
+           "IgG plasma cell"
+          ],
+          [
+           "IgM plasma cell"
+          ],
+          [
+           "IgA plasma cell"
+          ],
+          [
+           "hematopoietic oligopotent progenitor cell, lineage-negative"
+          ],
+          [
+           "pre-conventional dendritic cell"
+          ],
+          [
+           "CD11b-positive dendritic cell"
+          ],
+          [
+           "CD34-positive, CD38-negative hematopoietic stem cell"
+          ],
+          [
+           "CD115-positive monocyte OR common dendritic progenitor"
+          ],
+          [
+           "CD115-positive monocyte"
+          ],
+          [
+           "cerebellar granule cell"
+          ],
+          [
+           "cerebellum glutamatergic neuron"
+          ],
+          [
+           "cerebellar granule cell precursor"
+          ],
+          [
+           "cerebral cortex neuron"
+          ],
+          [
+           "T-helper 22 cell"
+          ],
+          [
+           "activated CD4-positive, alpha-beta T cell, human"
+          ],
+          [
+           "effector CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "naive regulatory T cell"
+          ],
+          [
+           "memory regulatory T cell"
+          ],
+          [
+           "activated CD8-positive, alpha-beta T cell, human"
+          ],
+          [
+           "effector CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "CD14-positive monocyte"
+          ],
+          [
+           "CD14-positive, CD16-positive monocyte"
+          ],
+          [
+           "dendritic cell, human"
+          ],
+          [
+           "myeloid dendritic cell, human"
+          ],
+          [
+           "plasmacytoid dendritic cell, human"
+          ],
+          [
+           "abnormal cell"
+          ],
+          [
+           "effector memory CD8-positive, alpha-beta T cell, terminally differentiated"
+          ],
+          [
+           "effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated"
+          ],
+          [
+           "neoplastic cell"
+          ],
+          [
+           "malignant cell"
+          ],
+          [
+           "innate lymphoid cell"
+          ],
+          [
+           "erythroid progenitor cell, mammalian"
+          ],
+          [
+           "ILC1"
+          ],
+          [
+           "group 2 innate lymphoid cell"
+          ],
+          [
+           "group 3 innate lymphoid cell"
+          ],
+          [
+           "CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human"
+          ],
+          [
+           "ILC1, human"
+          ],
+          [
+           "group 3 innate lymphoid cell, human"
+          ],
+          [
+           "NKp44-positive group 3 innate lymphoid cell, human"
+          ],
+          [
+           "NKp44-negative group 3 innate lymphoid cell, human"
+          ],
+          [
+           "group 2 innate lymphoid cell, human"
+          ],
+          [
+           "lymphocyte of B lineage, CD19-positive"
+          ],
+          [
+           "T follicular helper cell"
+          ],
+          [
+           "fraction A pre-pro B cell"
+          ],
+          [
+           "early pro-B cell"
+          ],
+          [
+           "late pro-B cell"
+          ],
+          [
+           "CD14-positive, CD16-negative classical monocyte"
+          ],
+          [
+           "type I pneumocyte"
+          ],
+          [
+           "type II pneumocyte"
+          ],
+          [
+           "lung secretory cell"
+          ],
+          [
+           "pancreatic acinar cell"
+          ],
+          [
+           "pancreas exocrine glandular cell"
+          ],
+          [
+           "enterocyte of epithelium of large intestine"
+          ],
+          [
+           "epithelial cell of large intestine"
+          ],
+          [
+           "brush cell of trachebronchial tree"
+          ],
+          [
+           "brush cell"
+          ],
+          [
+           "pancreatic ductal cell"
+          ],
+          [
+           "epithelial cell of exocrine pancreas"
+          ],
+          [
+           "interstitial cell of Cajal"
+          ],
+          [
+           "bone marrow cell"
+          ],
+          [
+           "interstitial cell of ovary"
+          ],
+          [
+           "cortical cell of adrenal gland"
+          ],
+          [
+           "adrenal gland glandular cell"
+          ],
+          [
+           "regular cardiac myocyte"
+          ],
+          [
+           "IgG-negative class switched memory B cell"
+          ],
+          [
+           "regular atrial cardiac myocyte"
+          ],
+          [
+           "regular ventricular cardiac myocyte"
+          ],
+          [
+           "endothelial cell of lymphatic vessel"
+          ],
+          [
+           "vascular lymphangioblast"
+          ],
+          [
+           "epithelial cell of sweat gland"
+          ],
+          [
+           "capillary endothelial cell"
+          ],
+          [
+           "microvascular endothelial cell"
+          ],
+          [
+           "ciliated columnar cell of tracheobronchial tree"
+          ],
+          [
+           "multi-ciliated epithelial cell"
+          ],
+          [
+           "respiratory ciliated cell"
+          ],
+          [
+           "epithelial cell of uterus"
+          ],
+          [
+           "endosteal cell"
+          ],
+          [
+           "renal interstitial pericyte"
+          ],
+          [
+           "epithelial cell of stomach"
+          ],
+          [
+           "foveolar cell of stomach"
+          ],
+          [
+           "basal cell of epidermis"
+          ],
+          [
+           "stem cell of epidermis"
+          ],
+          [
+           "glomerular endothelial cell"
+          ],
+          [
+           "glomerular cell"
+          ],
+          [
+           "granular cell of epidermis"
+          ],
+          [
+           "epithelial cell of thyroid gland"
+          ],
+          [
+           "renal beta-intercalated cell"
+          ],
+          [
+           "renal intercalated cell"
+          ],
+          [
+           "epithelial cell of lower respiratory tract"
+          ],
+          [
+           "brush cell of epithelium proper of large intestine"
+          ],
+          [
+           "intestinal tuft cell"
+          ],
+          [
+           "brush cell of trachea"
+          ],
+          [
+           "bronchial epithelial cell"
+          ],
+          [
+           "epithelial cell of esophagus"
+          ],
+          [
+           "vertebrate lens cell"
+          ],
+          [
+           "anterior lens cell"
+          ],
+          [
+           "lens epithelial cell"
+          ],
+          [
+           "secondary lens fiber"
+          ],
+          [
+           "lens fiber cell"
+          ],
+          [
+           "lactocyte"
+          ],
+          [
+           "epithelial cell of prostate"
+          ],
+          [
+           "basal cell of prostate epithelium"
+          ],
+          [
+           "luminal cell of prostate epithelium"
+          ],
+          [
+           "basal epithelial cell of prostatic duct"
+          ],
+          [
+           "pulmonary interstitial fibroblast"
+          ],
+          [
+           "smooth muscle cell of sphincter of pupil"
+          ],
+          [
+           "peripheral blood stem cell"
+          ],
+          [
+           "intestinal crypt stem cell"
+          ],
+          [
+           "epithelial cell of small intestine"
+          ],
+          [
+           "stromal cell of endometrium"
+          ],
+          [
+           "decidual cell"
+          ],
+          [
+           "thyroid follicular cell"
+          ],
+          [
+           "endothelial cell of sinusoid"
+          ],
+          [
+           "enteroendocrine cell of colon"
+          ],
+          [
+           "enteroendocrine cell of small intestine"
+          ],
+          [
+           "P/D1 enteroendocrine cell"
+          ],
+          [
+           "vasoactive intestinal peptide secreting cell"
+          ],
+          [
+           "pancreatic PP cell"
+          ],
+          [
+           "type I enteroendocrine cell"
+          ],
+          [
+           "GIP cell"
+          ],
+          [
+           "type L enteroendocrine cell"
+          ],
+          [
+           "type N enteroendocrine cell"
+          ],
+          [
+           "ghrelin secreting cell"
+          ],
+          [
+           "epithelial cell of thymus"
+          ],
+          [
+           "cortical thymic epithelial cell"
+          ],
+          [
+           "medullary thymic epithelial cell"
+          ],
+          [
+           "pigmented ciliary epithelial cell"
+          ],
+          [
+           "non-pigmented ciliary epithelial cell"
+          ],
+          [
+           "epithelial cell of distal tubule"
+          ],
+          [
+           "nephron tubule epithelial cell"
+          ],
+          [
+           "kidney cortex tubule cell"
+          ],
+          [
+           "epithelial cell of proximal tubule"
+          ],
+          [
+           "epithelial cell of skin gland"
+          ],
+          [
+           "embryonic stem cell"
+          ],
+          [
+           "myoepithelial cell of mammary gland"
+          ],
+          [
+           "mammary gland epithelial cell"
+          ],
+          [
+           "mammary gland glandular cell"
+          ],
+          [
+           "luminal adaptive secretory precursor cell of mammary gland"
+          ],
+          [
+           "luminal epithelial cell of mammary gland"
+          ],
+          [
+           "progenitor cell of mammary luminal epithelium"
+          ],
+          [
+           "basal epithelial cell of tracheobronchial tree"
+          ],
+          [
+           "decidual natural killer cell, human"
+          ],
+          [
+           "endocardial cell"
+          ],
+          [
+           "cardiac endothelial cell"
+          ],
+          [
+           "gestational hematopoietic stem cell"
+          ],
+          [
+           "primitive red blood cell"
+          ],
+          [
+           "primitive erythroid lineage cell"
+          ],
+          [
+           "keratocyte"
+          ],
+          [
+           "myometrial cell"
+          ],
+          [
+           "uterine smooth muscle cell"
+          ],
+          [
+           "respiratory epithelial cell"
+          ],
+          [
+           "respiratory goblet cell"
+          ],
+          [
+           "Schwann cell precursor"
+          ],
+          [
+           "CD141-positive myeloid dendritic cell"
+          ],
+          [
+           "Gr1-high classical monocyte"
+          ],
+          [
+           "CD14-low, CD16-positive monocyte"
+          ],
+          [
+           "CD1c-positive myeloid dendritic cell"
+          ],
+          [
+           "pancreatic stellate cell"
+          ],
+          [
+           "epidermal Langerhans cell"
+          ],
+          [
+           "MHC-II-positive classical monocyte"
+          ],
+          [
+           "nasal mucosa goblet cell"
+          ],
+          [
+           "melanocyte of skin"
+          ],
+          [
+           "hair follicle cell"
+          ],
+          [
+           "trophoblast giant cell"
+          ],
+          [
+           "fetal cardiomyocyte"
+          ],
+          [
+           "adventitial cell"
+          ],
+          [
+           "enteric smooth muscle cell"
+          ],
+          [
+           "kidney epithelial cell"
+          ],
+          [
+           "kidney cell"
+          ],
+          [
+           "subcutaneous adipocyte"
+          ],
+          [
+           "intrahepatic cholangiocyte"
+          ],
+          [
+           "cholangiocyte"
+          ],
+          [
+           "blood vessel smooth muscle cell"
+          ],
+          [
+           "endothelial cell of artery"
+          ],
+          [
+           "vein endothelial cell"
+          ],
+          [
+           "fibroblast of cardiac tissue"
+          ],
+          [
+           "skin fibroblast"
+          ],
+          [
+           "fibroblast of lung"
+          ],
+          [
+           "fibroblast of mammary gland"
+          ],
+          [
+           "placental epithelial cell"
+          ],
+          [
+           "fibroblast of breast"
+          ],
+          [
+           "renal cortical epithelial cell"
+          ],
+          [
+           "kidney cortical cell"
+          ],
+          [
+           "retinal blood vessel endothelial cell"
+          ],
+          [
+           "retinal pigment epithelial cell"
+          ],
+          [
+           "smooth muscle cell of the pulmonary artery"
+          ],
+          [
+           "bladder cell"
+          ],
+          [
+           "bronchial smooth muscle cell"
+          ],
+          [
+           "tracheobronchial smooth muscle cell"
+          ],
+          [
+           "brain macroglial cell"
+          ],
+          [
+           "cerebral cortex glial cell"
+          ],
+          [
+           "migratory enteric neural crest cell"
+          ],
+          [
+           "adipocyte of breast"
+          ],
+          [
+           "prostate stromal cell"
+          ],
+          [
+           "acinar cell of salivary gland"
+          ],
+          [
+           "salivary gland glandular cell"
+          ],
+          [
+           "mature microglial cell"
+          ],
+          [
+           "respiratory basal cell"
+          ],
+          [
+           "glandular cell of esophagus"
+          ],
+          [
+           "endothelial cell of venule"
+          ],
+          [
+           "glandular cell of the large intestine"
+          ],
+          [
+           "tongue muscle cell"
+          ],
+          [
+           "intestinal enteroendocrine cell"
+          ],
+          [
+           "S cone cell"
+          ],
+          [
+           "H1 horizontal cell"
+          ],
+          [
+           "H2 horizontal cell"
+          ],
+          [
+           "glycinergic amacrine cell"
+          ],
+          [
+           "starburst amacrine cell"
+          ],
+          [
+           "GABAergic amacrine cell"
+          ],
+          [
+           "ionocyte"
+          ],
+          [
+           "renal principal cell"
+          ],
+          [
+           "epithelial cell of nephron"
+          ],
+          [
+           "renal alpha-intercalated cell"
+          ],
+          [
+           "pancreatic epsilon cell"
+          ],
+          [
+           "lymphangioblast"
+          ],
+          [
+           "mesenchymal lymphangioblast"
+          ],
+          [
+           "visceromotor neuron"
+          ],
+          [
+           "enteric neuron"
+          ],
+          [
+           "skeletal muscle satellite stem cell"
+          ],
+          [
+           "inhibitory motor neuron"
+          ],
+          [
+           "cortical interneuron"
+          ],
+          [
+           "cerebral cortex GABAergic interneuron"
+          ],
+          [
+           "extravillous trophoblast"
+          ],
+          [
+           "primary motor cortex pyramidal cell"
+          ],
+          [
+           "L5 extratelencephalic projecting glutamatergic cortical neuron"
+          ],
+          [
+           "inflammatory cell"
+          ],
+          [
+           "salivary gland cell"
+          ],
+          [
+           "small intestine glandular cell"
+          ],
+          [
+           "paneth cell of colon"
+          ],
+          [
+           "colon glandular cell"
+          ],
+          [
+           "transit amplifying cell"
+          ],
+          [
+           "transit amplifying cell of colon"
+          ],
+          [
+           "transit amplifying cell of small intestine"
+          ],
+          [
+           "intestinal crypt stem cell of large intestine"
+          ],
+          [
+           "intestinal crypt stem cell of small intestine"
+          ],
+          [
+           "colon epithelial cell"
+          ],
+          [
+           "colon macrophage"
+          ],
+          [
+           "colon goblet cell"
+          ],
+          [
+           "large intestine goblet cell"
+          ],
+          [
+           "tuft cell of colon"
+          ],
+          [
+           "intestinal crypt stem cell of colon"
+          ],
+          [
+           "lung pericyte"
+          ],
+          [
+           "endothelial cell of placenta"
+          ],
+          [
+           "endothelial cell of uterus"
+          ],
+          [
+           "fibro/adipogenic progenitor cell"
+          ],
+          [
+           "centrocyte"
+          ],
+          [
+           "centroblast"
+          ],
+          [
+           "T follicular regulatory cell"
+          ],
+          [
+           "hair follicular keratinocyte"
+          ],
+          [
+           "cardiac neuron"
+          ],
+          [
+           "mesothelial cell of epicardium"
+          ],
+          [
+           "double negative T regulatory cell"
+          ],
+          [
+           "exhausted T cell"
+          ],
+          [
+           "skeletal muscle fibroblast"
+          ],
+          [
+           "muscle fibroblast"
+          ],
+          [
+           "monocyte-derived dendritic cell"
+          ],
+          [
+           "chorionic trophoblast cell"
+          ],
+          [
+           "sympathetic neuron"
+          ],
+          [
+           "forebrain radial glial cell"
+          ],
+          [
+           "pulmonary ionocyte"
+          ],
+          [
+           "epithelial cell of urethra"
+          ],
+          [
+           "hillock cell"
+          ],
+          [
+           "tracheobronchial serous cell"
+          ],
+          [
+           "tracheobronchial goblet cell"
+          ],
+          [
+           "endothelial cell of periportal hepatic sinusoid"
+          ],
+          [
+           "endothelial cell of hepatic sinusoid"
+          ],
+          [
+           "endothelial cell of pericentral hepatic sinusoid"
+          ],
+          [
+           "periportal region hepatocyte"
+          ],
+          [
+           "midzonal region hepatocyte"
+          ],
+          [
+           "centrilobular region hepatocyte"
+          ],
+          [
+           "intestine goblet cell"
+          ],
+          [
+           "kidney tubule cell"
+          ],
+          [
+           "forebrain neuroblast"
+          ],
+          [
+           "lung goblet cell"
+          ],
+          [
+           "lung neuroendocrine cell"
+          ],
+          [
+           "lung ciliated cell"
+          ],
+          [
+           "smooth muscle cell of small intestine"
+          ],
+          [
+           "smooth muscle fiber of ileum"
+          ],
+          [
+           "urethra cell"
+          ],
+          [
+           "fibroblast of connective tissue of nonglandular part of prostate"
+          ],
+          [
+           "fibroblast of connective tissue of glandular part of prostate"
+          ],
+          [
+           "epicardial adipocyte"
+          ],
+          [
+           "adipocyte of epicardial fat of left ventricle"
+          ],
+          [
+           "bronchial goblet cell"
+          ],
+          [
+           "small intestine goblet cell"
+          ],
+          [
+           "intestinal villus goblet cell"
+          ],
+          [
+           "duodenum glandular cell"
+          ],
+          [
+           "ileal goblet cell"
+          ],
+          [
+           "tracheal goblet cell"
+          ],
+          [
+           "serous cell of epithelium of trachea"
+          ],
+          [
+           "serous cell of epithelium of bronchus"
+          ],
+          [
+           "enterocyte of epithelium of small intestine"
+          ],
+          [
+           "enterocyte of epithelium proper of small intestine"
+          ],
+          [
+           "enterocyte of epithelium proper of ileum"
+          ],
+          [
+           "paneth cell of epithelium of small intestine"
+          ],
+          [
+           "enterocyte of colon"
+          ],
+          [
+           "basal cell of epithelium of trachea"
+          ],
+          [
+           "microfold cell of epithelium of small intestine"
+          ],
+          [
+           "ventricular cardiac muscle cell"
+          ],
+          [
+           "conjunctival epithelial cell"
+          ],
+          [
+           "epithelial cell of lacrimal drainage system"
+          ],
+          [
+           "epithelial cell of lacrimal sac"
+          ],
+          [
+           "epithelial cell of nasolacrimal duct"
+          ],
+          [
+           "bladder urothelial cell"
+          ],
+          [
+           "ciliary muscle cell"
+          ],
+          [
+           "epithelial cell of stratum germinativum of esophagus"
+          ],
+          [
+           "kidney glomerular epithelial cell"
+          ],
+          [
+           "parietal epithelial cell"
+          ],
+          [
+           "epithelial cell of intermediate tubule"
+          ],
+          [
+           "kidney loop of Henle epithelial cell"
+          ],
+          [
+           "kidney collecting duct epithelial cell"
+          ],
+          [
+           "kidney collecting duct cell"
+          ],
+          [
+           "smooth muscle cell of prostate"
+          ],
+          [
+           "mesothelial cell of pleura"
+          ],
+          [
+           "kidney interstitial cell"
+          ],
+          [
+           "kidney medulla cell"
+          ],
+          [
+           "kidney pelvis cell"
+          ],
+          [
+           "kidney inner medulla cell"
+          ],
+          [
+           "kidney outer medulla cell"
+          ],
+          [
+           "papillary tips cell"
+          ],
+          [
+           "lower urinary tract cell"
+          ],
+          [
+           "kidney corpuscule cell"
+          ],
+          [
+           "kidney interstitial fibroblast"
+          ],
+          [
+           "kidney interstitial alternatively activated macrophage"
+          ],
+          [
+           "kidney resident macrophage"
+          ],
+          [
+           "kidney pelvis urothelial cell"
+          ],
+          [
+           "kidney collecting duct principal cell"
+          ],
+          [
+           "kidney collecting duct intercalated cell"
+          ],
+          [
+           "kidney connecting tubule epithelial cell"
+          ],
+          [
+           "kidney loop of Henle descending limb epithelial cell"
+          ],
+          [
+           "kidney distal convoluted tubule epithelial cell"
+          ],
+          [
+           "kidney blood vessel cell"
+          ],
+          [
+           "kidney capillary endothelial cell"
+          ],
+          [
+           "kidney venous blood vessel cell"
+          ],
+          [
+           "glomerular capillary endothelial cell"
+          ],
+          [
+           "kidney loop of Henle ascending limb epithelial cell"
+          ],
+          [
+           "vasa recta cell"
+          ],
+          [
+           "kidney loop of Henle thick ascending limb epithelial cell"
+          ],
+          [
+           "kidney loop of Henle thin ascending limb epithelial cell"
+          ],
+          [
+           "kidney loop of Henle thin descending limb epithelial cell"
+          ],
+          [
+           "vasa recta ascending limb cell"
+          ],
+          [
+           "vasa recta descending limb cell"
+          ],
+          [
+           "medium spiny neuron"
+          ],
+          [
+           "glycinergic neuron"
+          ],
+          [
+           "lung endothelial cell"
+          ],
+          [
+           "pulmonary artery endothelial cell"
+          ],
+          [
+           "cerebral cortex pyramidal neuron"
+          ],
+          [
+           "cerebral cortex endothelial cell"
+          ],
+          [
+           "peripheral blood mononuclear cell"
+          ],
+          [
+           "tonsil germinal center B cell"
+          ],
+          [
+           "dermis lymphatic vessel endothelial cell"
+          ],
+          [
+           "lung microvascular endothelial cell"
+          ],
+          [
+           "dermis microvascular lymphatic vessel endothelial cell"
+          ],
+          [
+           "embryonic fibroblast"
+          ],
+          [
+           "brainstem motor neuron"
+          ],
+          [
+           "hepatic pit cell"
+          ],
+          [
+           "liver dendritic cell"
+          ],
+          [
+           "prostate gland microvascular endothelial cell"
+          ],
+          [
+           "placental villous trophoblast"
+          ],
+          [
+           "bronchus fibroblast of lung"
+          ],
+          [
+           "cord blood hematopoietic stem cell"
+          ],
+          [
+           "Hofbauer cell"
+          ],
+          [
+           "vip GABAergic cortical interneuron"
+          ],
+          [
+           "intratelencephalic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "extratelencephalic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "lamp5 GABAergic cortical interneuron"
+          ],
+          [
+           "near-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "corticothalamic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "sncg GABAergic cortical interneuron"
+          ],
+          [
+           "sst GABAergic cortical interneuron"
+          ],
+          [
+           "pvalb GABAergic cortical interneuron"
+          ],
+          [
+           "sst chodl GABAergic cortical interneuron"
+          ],
+          [
+           "direct pathway medium spiny neuron"
+          ],
+          [
+           "indirect pathway medium spiny neuron"
+          ],
+          [
+           "chandelier pvalb GABAergic cortical interneuron"
+          ],
+          [
+           "chandelier cell"
+          ],
+          [
+           "L6b glutamatergic cortical neuron"
+          ],
+          [
+           "L2/3-6 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "L6 corticothalamic-projecting glutamatergic cortical neuron"
+          ],
+          [
+           "L5/6 near-projecting glutamatergic neuron of the primary motor cortex"
+          ],
+          [
+           "L5/6 near-projecting glutamatergic neuron"
+          ],
+          [
+           "L2/3 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex"
+          ],
+          [
+           "L6 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "vascular leptomeningeal cell"
+          ],
+          [
+           "mesothelial fibroblast of the leptomeninx"
+          ],
+          [
+           "mesothelial fibroblast"
+          ],
+          [
+           "differentiation-committed oligodendrocyte precursor"
+          ],
+          [
+           "caudal ganglionic eminence derived interneuron"
+          ],
+          [
+           "caudal ganglionic eminence derived GABAergic cortical interneuron"
+          ],
+          [
+           "brain vascular cell"
+          ],
+          [
+           "unipolar brush cell"
+          ],
+          [
+           "midget ganglion cell of retina"
+          ],
+          [
+           "parasol ganglion cell of retina"
+          ],
+          [
+           "alveolar type 1 fibroblast cell"
+          ],
+          [
+           "alveolar type 2 fibroblast cell"
+          ],
+          [
+           "epithelial cell of proximal tubule segment 1"
+          ],
+          [
+           "epithelial cell of proximal tubule segment 3"
+          ],
+          [
+           "kidney connecting tubule principal cell"
+          ],
+          [
+           "respiratory hillock cell"
+          ],
+          [
+           "BEST4+ intestinal epithelial cell, human"
+          ],
+          [
+           "L4 intratelencephalic projecting glutamatergic neuron"
+          ],
+          [
+           "serous secreting cell of bronchus submucosal gland"
+          ],
+          [
+           "suprabasal keratinocyte"
+          ],
+          [
+           "ON-blue cone bipolar cell"
+          ],
+          [
+           "airway submucosal gland duct basal cell"
+          ],
+          [
+           "perichondrial fibroblast"
+          ],
+          [
+           "lung perichondrial fibroblast"
+          ],
+          [
+           "diffuse bipolar 1 cell"
+          ],
+          [
+           "diffuse bipolar 2 cell"
+          ],
+          [
+           "diffuse bipolar 3a cell"
+          ],
+          [
+           "diffuse bipolar 3b cell"
+          ],
+          [
+           "diffuse bipolar 4 cell"
+          ],
+          [
+           "diffuse bipolar 6 cell"
+          ],
+          [
+           "flat midget bipolar cell"
+          ],
+          [
+           "invaginating midget bipolar cell"
+          ],
+          [
+           "giant bipolar cell"
+          ],
+          [
+           "OFFx cell"
+          ],
+          [
+           "lung resident memory CD4-positive, alpha-beta T cell"
+          ],
+          [
+           "lung resident memory CD8-positive, alpha-beta T cell"
+          ],
+          [
+           "metallothionein-positive alveolar macrophage"
+          ],
+          [
+           "lung interstitial macrophage"
+          ],
+          [
+           "deuterosomal cell"
+          ],
+          [
+           "respiratory suprabasal cell"
+          ],
+          [
+           "luminal hormone-sensing cell of mammary gland"
+          ]
+         ],
+         "hovertemplate": "astrocyte descendants=NOT IN ASTROCYTE DESCENDANTS<br>x=%{x}<br>y=%{y}<br>label=%{customdata[0]}<extra></extra>",
+         "legendgroup": "NOT IN ASTROCYTE DESCENDANTS",
+         "marker": {
+          "color": "#636efa",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "NOT IN ASTROCYTE DESCENDANTS",
+         "orientation": "v",
+         "showlegend": true,
+         "type": "scatter",
+         "x": [
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macrophage\"],[\"osteoclast\"],[\"myeloid leukocyte\"],[\"phagocyte (sensu Vertebrata)\"],[\"bone cell\"],[\"monocyte\"],[\"granulocyte\"],[\"neuron associated cell\"],[\"neutrophil\"],[\"phagocyte\"],[\"mast cell\"],[\"sensory epithelial cell\"],[\"interneuron\"],[\"motor neuron\"],[\"efferent neuron\"],[\"afferent neuron\"],[\"bipolar neuron\"],[\"multipolar neuron\"],[\"autonomic neuron\"],[\"peripheral nervous system neuron\"],[\"cholinergic neuron\"],[\"catecholaminergic neuron\"],[\"mononuclear phagocyte\"],[\"mononuclear cell\"],[\"ectodermal cell\"],[\"endothelial cell\"],[\"CNS neuron (sensu Vertebrata)\"],[\"GABAergic interneuron\"],[\"granule cell\"],[\"Purkinje cell\"],[\"cerebellar neuron\"],[\"stellate neuron\"],[\"neuron associated cell (sensu Vertebrata)\"],[\"glial cell\"],[\"macroglial cell\"],[\"oligodendrocyte\"],[\"myelinating glial cell\"],[\"oligodendrocyte precursor cell\"],[\"microglial cell\"],[\"central nervous system macrophage\"],[\"mesodermal cell\"],[\"gut endothelial cell\"],[\"corneal endothelial cell\"],[\"fibrocyte\"],[\"stromal cell\"],[\"adipocyte\"],[\"stuff accumulating cell\"],[\"chondrocyte\"],[\"glycosaminoglycan secreting cell\"],[\"collagen secreting cell\"],[\"pigment cell\"],[\"melanocyte\"],[\"visual pigment cell\"],[\"glandular epithelial cell\"],[\"extracellular matrix secreting cell\"],[\"carbohydrate secreting cell\"],[\"protein secreting cell\"],[\"peptic cell\"],[\"glandular cell of stomach\"],[\"surfactant secreting cell\"],[\"club cell\"],[\"epithelial cell of tracheobronchial tree\"],[\"seromucus secreting cell\"],[\"goblet cell\"],[\"mucus secreting cell\"],[\"parietal cell\"],[\"endocrine cell\"],[\"enteroendocrine cell\"],[\"neuroendocrine cell\"],[\"chromaffin cell\"],[\"amine precursor uptake and decarboxylation cell\"],[\"peptide hormone secreting cell\"],[\"insulin secreting cell\"],[\"type B pancreatic cell\"],[\"pancreatic endocrine cell\"],[\"progenitor cell of endocrine pancreas\"],[\"glucagon secreting cell\"],[\"pancreatic A cell\"],[\"type A enteroendocrine cell\"],[\"somatostatin secreting cell\"],[\"pancreatic D cell\"],[\"type D enteroendocrine cell\"],[\"steroid hormone secreting cell\"],[\"stromal cell of ovary\"],[\"testosterone secreting cell\"],[\"Leydig cell\"],[\"interstitial cell\"],[\"hepatocyte\"],[\"endopolyploid cell\"],[\"hepatoblast\"],[\"contractile cell\"],[\"pericyte\"],[\"myoepithelial cell\"],[\"myofibroblast cell\"],[\"muscle cell\"],[\"electrically responsive cell\"],[\"cell of skeletal muscle\"],[\"slow muscle cell\"],[\"extrafusal muscle fiber\"],[\"fast muscle cell\"],[\"non-striated muscle cell\"],[\"visceral muscle cell\"],[\"smooth muscle myoblast\"],[\"cardiac muscle cell\"],[\"chemoreceptor cell\"],[\"taste receptor cell\"],[\"endo-epithelial cell\"],[\"photoreceptor cell\"],[\"electrically active cell\"],[\"Sertoli cell\"],[\"supporting cell\"],[\"androgen binding protein secreting cell\"],[\"seminiferous tubule epithelial cell\"],[\"insulating cell\"],[\"myelinating Schwann cell\"],[\"Schwann cell\"],[\"immature Schwann cell\"],[\"motile cell\"],[\"anucleate cell\"],[\"nucleate cell\"],[\"single nucleate cell\"],[\"multinucleate cell\"],[\"erythrocyte\"],[\"oxygen accumulating cell\"],[\"reticulocyte\"],[\"platelet\"],[\"serotonin secreting cell\"],[\"megakaryocyte\"],[\"defensive cell\"],[\"macrophage\"],[\"B cell\"],[\"keratinizing barrier epithelial cell\"],[\"stratified squamous epithelial cell\"],[\"keratin accumulating cell\"],[\"brush border epithelial cell\"],[\"Merkel cell\"],[\"keratinocyte\"],[\"transitional epithelial cell\"],[\"primary sensory neuron (sensu Teleostei)\"],[\"eurydendroid cell\"],[\"eye photoreceptor cell\"],[\"gamete\"],[\"crystallin accumulating cell\"],[\"tracheal epithelial cell\"],[\"epidermal cell\"],[\"serous secreting cell\"],[\"milk secreting cell\"],[\"sebum secreting cell\"],[\"sebaceous gland cell\"],[\"pneumocyte\"],[\"epithelial cell of alveolus of lung\"],[\"lysozyme secreting cell\"],[\"neural crest cell\"],[\"mesenchymal cell\"],[\"biogenic amine secreting cell\"],[\"extraembryonic cell\"],[\"trophoblast cell\"],[\"sphincter associated smooth muscle cell\"],[\"vascular associated smooth muscle cell\"],[\"perivascular cell\"],[\"general ecto-epithelial cell\"],[\"myotube\"],[\"attachment cell\"],[\"tendon cell\"],[\"ganglion interneuron\"],[\"CNS interneuron\"],[\"central nervous system neuron\"],[\"electrically signaling cell\"],[\"excretory cell\"],[\"reticular cell\"],[\"follicular dendritic cell\"],[\"striated muscle cell\"],[\"preadipocyte\"],[\"dendritic cell\"],[\"Langerhans cell\"],[\"conventional dendritic cell\"],[\"noradrenergic cell\"],[\"cardiac myoblast\"],[\"neural progenitor cell\"],[\"follicular epithelial cell\"],[\"ovarian surface epithelial cell\"],[\"CD4-positive helper T cell\"],[\"CD4-positive, alpha-beta T cell\"],[\"activated CD4-positive, alpha-beta T cell\"],[\"inhibitory interneuron\"],[\"granulosa cell\"],[\"follicular cell of ovary\"],[\"theca cell\"],[\"androgen secreting cell\"],[\"enkephalin secreting cell\"],[\"endorphin secreting cell\"],[\"type G enteroendocrine cell\"],[\"gastrin secreting cell\"],[\"mucous cell of stomach\"],[\"paneth cell\"],[\"intestinal epithelial cell\"],[\"paracrine cell\"],[\"cardiac muscle myoblast\"],[\"cardiocyte\"],[\"skeletal muscle fiber\"],[\"syncytiotrophoblast cell\"],[\"pigmented epithelial cell\"],[\"primary neuron (sensu Teleostei)\"],[\"T-helper 1 cell\"],[\"T-helper 2 cell\"],[\"CD4-positive, CXCR3-negative, CCR6-negative, alpha-beta T cell\"],[\"proerythroblast\"],[\"erythroblast\"],[\"neuronal brush cell\"],[\"CD7-negative lymphoid progenitor OR granulocyte monocyte progenitor\"],[\"bone marrow hematopoietic cell\"],[\"promonocyte\"],[\"amacrine cell\"],[\"retinal cell\"],[\"retinal ganglion cell\"],[\"promyelocyte\"],[\"cardiac mesenchymal cell\"],[\"migratory cardiac neural crest cell\"],[\"retinal cone cell\"],[\"camera-type eye photoreceptor cell\"],[\"corneal epithelial cell\"],[\"type EC enteroendocrine cell\"],[\"epithelial cell of alimentary canal\"],[\"cell in vitro\"],[\"immature neutrophil\"],[\"myelocyte\"],[\"alveolar macrophage\"],[\"lung macrophage\"],[\"enterocyte\"],[\"gut absorptive cell\"],[\"skeletal muscle satellite cell\"],[\"enucleate erythrocyte\"],[\"enucleated reticulocyte\"],[\"pyramidal neuron\"],[\"retinal rod cell\"],[\"granulocytopoietic cell\"],[\"basophil mast progenitor cell\"],[\"GABAergic neuron\"],[\"acinar cell\"],[\"natural killer cell\"],[\"group 1 innate lymphoid cell\"],[\"mature alpha-beta T cell\"],[\"CD8-positive, alpha-beta T cell\"],[\"transporting cell\"],[\"hepatic stellate cell\"],[\"Mueller cell\"],[\"radial glial cell\"],[\"basal cell\"],[\"prickle cell\"],[\"mesangial cell\"],[\"mucous neck cell\"],[\"podocyte\"],[\"renal filtration cell\"],[\"epithelial cell of glomerular capsule\"],[\"fenestrated cell\"],[\"mural cell\"],[\"glutamatergic neuron\"],[\"M cell of gut\"],[\"non-myelinating Schwann cell\"],[\"Cajal-Retzius cell\"],[\"PP cell\"],[\"dopaminergic neuron\"],[\"endothelial tip cell\"],[\"choroid plexus epithelial cell\"],[\"urothelial cell\"],[\"retina horizontal cell\"],[\"retinal bipolar neuron\"],[\"visual system neuron\"],[\"ON-bipolar cell\"],[\"OFF-bipolar cell\"],[\"rod bipolar cell\"],[\"cone retinal bipolar cell\"],[\"myeloid dendritic cell\"],[\"plasmacytoid dendritic cell\"],[\"mature B cell\"],[\"B cell, CD19-positive\"],[\"transitional stage B cell\"],[\"immature B cell\"],[\"precursor B cell\"],[\"plasma cell\"],[\"antibody secreting cell\"],[\"plasmablast\"],[\"memory B cell\"],[\"naive B cell\"],[\"alpha-beta T cell\"],[\"gamma-delta T cell\"],[\"immature alpha-beta T cell\"],[\"immature T cell\"],[\"mature T cell\"],[\"CD4-positive, CD25-positive, alpha-beta regulatory T cell\"],[\"regulatory T cell\"],[\"alpha-beta intraepithelial T cell\"],[\"CD4-positive, alpha-beta thymocyte\"],[\"CD8-positive, alpha-beta cytotoxic T cell\"],[\"activated CD8-positive, alpha-beta T cell\"],[\"CD8-alpha-beta-positive, alpha-beta intraepithelial T cell\"],[\"intraepithelial lymphocyte\"],[\"DN3 thymocyte\"],[\"mature gamma-delta T cell\"],[\"gamma-delta intraepithelial T cell\"],[\"CD8-alpha alpha positive, gamma-delta intraepithelial T cell\"],[\"thymocyte\"],[\"DN4 thymocyte\"],[\"double negative thymocyte\"],[\"DN1 thymic pro-T cell\"],[\"double-positive, alpha-beta thymocyte\"],[\"CD8-positive, alpha-beta thymocyte\"],[\"memory T cell\"],[\"naive T cell\"],[\"mature NK T cell\"],[\"small pre-B-II cell\"],[\"pro-B cell\"],[\"B-1 B cell\"],[\"B-1a B cell\"],[\"B-1b B cell\"],[\"B-2 B cell\"],[\"immature natural killer cell\"],[\"immature innate lymphoid cell\"],[\"mature natural killer cell\"],[\"lymphoid lineage restricted progenitor cell\"],[\"hematopoietic multipotent progenitor cell\"],[\"hematopoietic lineage restricted progenitor cell\"],[\"common dendritic progenitor\"],[\"mature conventional dendritic cell\"],[\"follicular B cell\"],[\"germinal center B cell\"],[\"classical monocyte\"],[\"elicited macrophage\"],[\"inflammatory macrophage\"],[\"non-classical monocyte\"],[\"intermediate monocyte\"],[\"meningeal macrophage\"],[\"alternatively activated macrophage\"],[\"early T lineage precursor\"],[\"naive thymus-derived CD4-positive, alpha-beta T cell\"],[\"CD4-positive, alpha-beta memory T cell\"],[\"T-helper 17 cell\"],[\"naive thymus-derived CD8-positive, alpha-beta T cell\"],[\"natural T-regulatory cell\"],[\"central memory CD4-positive, alpha-beta T cell\"],[\"CD4-positive, alpha-beta memory T cell, CD45RO-positive\"],[\"effector memory CD4-positive, alpha-beta T cell\"],[\"central memory CD8-positive, alpha-beta T cell\"],[\"CD8-positive, alpha-beta memory T cell, CD45RO-positive\"],[\"CD8-positive, alpha-beta cytokine secreting effector T cell\"],[\"CD8-positive, alpha-beta memory T cell\"],[\"cytotoxic T cell\"],[\"effector T cell\"],[\"helper T cell\"],[\"effector memory CD8-positive, alpha-beta T cell\"],[\"immature NK T cell\"],[\"CD8-alpha-alpha-positive, alpha-beta intraepithelial T cell\"],[\"Tc1 cell\"],[\"type I NK T cell\"],[\"type II NK T cell\"],[\"activated type II NK T cell\"],[\"CD4-positive, alpha-beta cytotoxic T cell\"],[\"early lymphoid progenitor\"],[\"CD16-negative, CD56-bright natural killer cell, human\"],[\"CD16-positive, CD56-dim natural killer cell, human\"],[\"mucosal invariant T cell\"],[\"long lived plasma cell\"],[\"class switched memory B cell\"],[\"large pre-B-II cell\"],[\"pre-B-II cell\"],[\"pre-B-I cell\"],[\"unswitched memory B cell\"],[\"IgG memory B cell\"],[\"IgG plasmablast\"],[\"IgA plasmablast\"],[\"IgG plasma cell\"],[\"IgM plasma cell\"],[\"IgA plasma cell\"],[\"hematopoietic oligopotent progenitor cell, lineage-negative\"],[\"pre-conventional dendritic cell\"],[\"CD11b-positive dendritic cell\"],[\"CD34-positive, CD38-negative hematopoietic stem cell\"],[\"CD115-positive monocyte OR common dendritic progenitor\"],[\"CD115-positive monocyte\"],[\"cerebellar granule cell\"],[\"cerebellum glutamatergic neuron\"],[\"cerebellar granule cell precursor\"],[\"cerebral cortex neuron\"],[\"T-helper 22 cell\"],[\"activated CD4-positive, alpha-beta T cell, human\"],[\"effector CD4-positive, alpha-beta T cell\"],[\"naive regulatory T cell\"],[\"memory regulatory T cell\"],[\"activated CD8-positive, alpha-beta T cell, human\"],[\"effector CD8-positive, alpha-beta T cell\"],[\"CD14-positive monocyte\"],[\"CD14-positive, CD16-positive monocyte\"],[\"dendritic cell, human\"],[\"myeloid dendritic cell, human\"],[\"plasmacytoid dendritic cell, human\"],[\"abnormal cell\"],[\"effector memory CD8-positive, alpha-beta T cell, terminally differentiated\"],[\"effector memory CD45RA-positive, alpha-beta T cell, terminally differentiated\"],[\"neoplastic cell\"],[\"malignant cell\"],[\"innate lymphoid cell\"],[\"erythroid progenitor cell, mammalian\"],[\"ILC1\"],[\"group 2 innate lymphoid cell\"],[\"group 3 innate lymphoid cell\"],[\"CD34-positive, CD56-positive, CD117-positive common innate lymphoid precursor, human\"],[\"ILC1, human\"],[\"group 3 innate lymphoid cell, human\"],[\"NKp44-positive group 3 innate lymphoid cell, human\"],[\"NKp44-negative group 3 innate lymphoid cell, human\"],[\"group 2 innate lymphoid cell, human\"],[\"lymphocyte of B lineage, CD19-positive\"],[\"T follicular helper cell\"],[\"fraction A pre-pro B cell\"],[\"early pro-B cell\"],[\"late pro-B cell\"],[\"CD14-positive, CD16-negative classical monocyte\"],[\"type I pneumocyte\"],[\"type II pneumocyte\"],[\"lung secretory cell\"],[\"pancreatic acinar cell\"],[\"pancreas exocrine glandular cell\"],[\"enterocyte of epithelium of large intestine\"],[\"epithelial cell of large intestine\"],[\"brush cell of trachebronchial tree\"],[\"brush cell\"],[\"pancreatic ductal cell\"],[\"epithelial cell of exocrine pancreas\"],[\"interstitial cell of Cajal\"],[\"bone marrow cell\"],[\"interstitial cell of ovary\"],[\"cortical cell of adrenal gland\"],[\"adrenal gland glandular cell\"],[\"regular cardiac myocyte\"],[\"IgG-negative class switched memory B cell\"],[\"regular atrial cardiac myocyte\"],[\"regular ventricular cardiac myocyte\"],[\"endothelial cell of lymphatic vessel\"],[\"vascular lymphangioblast\"],[\"epithelial cell of sweat gland\"],[\"capillary endothelial cell\"],[\"microvascular endothelial cell\"],[\"ciliated columnar cell of tracheobronchial tree\"],[\"multi-ciliated epithelial cell\"],[\"respiratory ciliated cell\"],[\"epithelial cell of uterus\"],[\"endosteal cell\"],[\"renal interstitial pericyte\"],[\"epithelial cell of stomach\"],[\"foveolar cell of stomach\"],[\"basal cell of epidermis\"],[\"stem cell of epidermis\"],[\"glomerular endothelial cell\"],[\"glomerular cell\"],[\"granular cell of epidermis\"],[\"epithelial cell of thyroid gland\"],[\"renal beta-intercalated cell\"],[\"renal intercalated cell\"],[\"epithelial cell of lower respiratory tract\"],[\"brush cell of epithelium proper of large intestine\"],[\"intestinal tuft cell\"],[\"brush cell of trachea\"],[\"bronchial epithelial cell\"],[\"epithelial cell of esophagus\"],[\"vertebrate lens cell\"],[\"anterior lens cell\"],[\"lens epithelial cell\"],[\"secondary lens fiber\"],[\"lens fiber cell\"],[\"lactocyte\"],[\"epithelial cell of prostate\"],[\"basal cell of prostate epithelium\"],[\"luminal cell of prostate epithelium\"],[\"basal epithelial cell of prostatic duct\"],[\"pulmonary interstitial fibroblast\"],[\"smooth muscle cell of sphincter of pupil\"],[\"peripheral blood stem cell\"],[\"intestinal crypt stem cell\"],[\"epithelial cell of small intestine\"],[\"stromal cell of endometrium\"],[\"decidual cell\"],[\"thyroid follicular cell\"],[\"endothelial cell of sinusoid\"],[\"enteroendocrine cell of colon\"],[\"enteroendocrine cell of small intestine\"],[\"P\\u002fD1 enteroendocrine cell\"],[\"vasoactive intestinal peptide secreting cell\"],[\"pancreatic PP cell\"],[\"type I enteroendocrine cell\"],[\"GIP cell\"],[\"type L enteroendocrine cell\"],[\"type N enteroendocrine cell\"],[\"ghrelin secreting cell\"],[\"epithelial cell of thymus\"],[\"cortical thymic epithelial cell\"],[\"medullary thymic epithelial cell\"],[\"pigmented ciliary epithelial cell\"],[\"non-pigmented ciliary epithelial cell\"],[\"epithelial cell of distal tubule\"],[\"nephron tubule epithelial cell\"],[\"kidney cortex tubule cell\"],[\"epithelial cell of proximal tubule\"],[\"epithelial cell of skin gland\"],[\"embryonic stem cell\"],[\"myoepithelial cell of mammary gland\"],[\"mammary gland epithelial cell\"],[\"mammary gland glandular cell\"],[\"luminal adaptive secretory precursor cell of mammary gland\"],[\"luminal epithelial cell of mammary gland\"],[\"progenitor cell of mammary luminal epithelium\"],[\"basal epithelial cell of tracheobronchial tree\"],[\"decidual natural killer cell, human\"],[\"endocardial cell\"],[\"cardiac endothelial cell\"],[\"gestational hematopoietic stem cell\"],[\"primitive red blood cell\"],[\"primitive erythroid lineage cell\"],[\"keratocyte\"],[\"myometrial cell\"],[\"uterine smooth muscle cell\"],[\"respiratory epithelial cell\"],[\"respiratory goblet cell\"],[\"Schwann cell precursor\"],[\"CD141-positive myeloid dendritic cell\"],[\"Gr1-high classical monocyte\"],[\"CD14-low, CD16-positive monocyte\"],[\"CD1c-positive myeloid dendritic cell\"],[\"pancreatic stellate cell\"],[\"epidermal Langerhans cell\"],[\"MHC-II-positive classical monocyte\"],[\"nasal mucosa goblet cell\"],[\"melanocyte of skin\"],[\"hair follicle cell\"],[\"trophoblast giant cell\"],[\"fetal cardiomyocyte\"],[\"adventitial cell\"],[\"enteric smooth muscle cell\"],[\"kidney epithelial cell\"],[\"kidney cell\"],[\"subcutaneous adipocyte\"],[\"intrahepatic cholangiocyte\"],[\"cholangiocyte\"],[\"blood vessel smooth muscle cell\"],[\"endothelial cell of artery\"],[\"vein endothelial cell\"],[\"fibroblast of cardiac tissue\"],[\"skin fibroblast\"],[\"fibroblast of lung\"],[\"fibroblast of mammary gland\"],[\"placental epithelial cell\"],[\"fibroblast of breast\"],[\"renal cortical epithelial cell\"],[\"kidney cortical cell\"],[\"retinal blood vessel endothelial cell\"],[\"retinal pigment epithelial cell\"],[\"smooth muscle cell of the pulmonary artery\"],[\"bladder cell\"],[\"bronchial smooth muscle cell\"],[\"tracheobronchial smooth muscle cell\"],[\"brain macroglial cell\"],[\"cerebral cortex glial cell\"],[\"migratory enteric neural crest cell\"],[\"adipocyte of breast\"],[\"prostate stromal cell\"],[\"acinar cell of salivary gland\"],[\"salivary gland glandular cell\"],[\"mature microglial cell\"],[\"respiratory basal cell\"],[\"glandular cell of esophagus\"],[\"endothelial cell of venule\"],[\"glandular cell of the large intestine\"],[\"tongue muscle cell\"],[\"intestinal enteroendocrine cell\"],[\"S cone cell\"],[\"H1 horizontal cell\"],[\"H2 horizontal cell\"],[\"glycinergic amacrine cell\"],[\"starburst amacrine cell\"],[\"GABAergic amacrine cell\"],[\"ionocyte\"],[\"renal principal cell\"],[\"epithelial cell of nephron\"],[\"renal alpha-intercalated cell\"],[\"pancreatic epsilon cell\"],[\"lymphangioblast\"],[\"mesenchymal lymphangioblast\"],[\"visceromotor neuron\"],[\"enteric neuron\"],[\"skeletal muscle satellite stem cell\"],[\"inhibitory motor neuron\"],[\"cortical interneuron\"],[\"cerebral cortex GABAergic interneuron\"],[\"extravillous trophoblast\"],[\"primary motor cortex pyramidal cell\"],[\"L5 extratelencephalic projecting glutamatergic cortical neuron\"],[\"inflammatory cell\"],[\"salivary gland cell\"],[\"small intestine glandular cell\"],[\"paneth cell of colon\"],[\"colon glandular cell\"],[\"transit amplifying cell\"],[\"transit amplifying cell of colon\"],[\"transit amplifying cell of small intestine\"],[\"intestinal crypt stem cell of large intestine\"],[\"intestinal crypt stem cell of small intestine\"],[\"colon epithelial cell\"],[\"colon macrophage\"],[\"colon goblet cell\"],[\"large intestine goblet cell\"],[\"tuft cell of colon\"],[\"intestinal crypt stem cell of colon\"],[\"lung pericyte\"],[\"endothelial cell of placenta\"],[\"endothelial cell of uterus\"],[\"fibro\\u002fadipogenic progenitor cell\"],[\"centrocyte\"],[\"centroblast\"],[\"T follicular regulatory cell\"],[\"hair follicular keratinocyte\"],[\"cardiac neuron\"],[\"mesothelial cell of epicardium\"],[\"double negative T regulatory cell\"],[\"exhausted T cell\"],[\"skeletal muscle fibroblast\"],[\"muscle fibroblast\"],[\"monocyte-derived dendritic cell\"],[\"chorionic trophoblast cell\"],[\"sympathetic neuron\"],[\"forebrain radial glial cell\"],[\"pulmonary ionocyte\"],[\"epithelial cell of urethra\"],[\"hillock cell\"],[\"tracheobronchial serous cell\"],[\"tracheobronchial goblet cell\"],[\"endothelial cell of periportal hepatic sinusoid\"],[\"endothelial cell of hepatic sinusoid\"],[\"endothelial cell of pericentral hepatic sinusoid\"],[\"periportal region hepatocyte\"],[\"midzonal region hepatocyte\"],[\"centrilobular region hepatocyte\"],[\"intestine goblet cell\"],[\"kidney tubule cell\"],[\"forebrain neuroblast\"],[\"lung goblet cell\"],[\"lung neuroendocrine cell\"],[\"lung ciliated cell\"],[\"smooth muscle cell of small intestine\"],[\"smooth muscle fiber of ileum\"],[\"urethra cell\"],[\"fibroblast of connective tissue of nonglandular part of prostate\"],[\"fibroblast of connective tissue of glandular part of prostate\"],[\"epicardial adipocyte\"],[\"adipocyte of epicardial fat of left ventricle\"],[\"bronchial goblet cell\"],[\"small intestine goblet cell\"],[\"intestinal villus goblet cell\"],[\"duodenum glandular cell\"],[\"ileal goblet cell\"],[\"tracheal goblet cell\"],[\"serous cell of epithelium of trachea\"],[\"serous cell of epithelium of bronchus\"],[\"enterocyte of epithelium of small intestine\"],[\"enterocyte of epithelium proper of small intestine\"],[\"enterocyte of epithelium proper of ileum\"],[\"paneth cell of epithelium of small intestine\"],[\"enterocyte of colon\"],[\"basal cell of epithelium of trachea\"],[\"microfold cell of epithelium of small intestine\"],[\"ventricular cardiac muscle cell\"],[\"conjunctival epithelial cell\"],[\"epithelial cell of lacrimal drainage system\"],[\"epithelial cell of lacrimal sac\"],[\"epithelial cell of nasolacrimal duct\"],[\"bladder urothelial cell\"],[\"ciliary muscle cell\"],[\"epithelial cell of stratum germinativum of esophagus\"],[\"kidney glomerular epithelial cell\"],[\"parietal epithelial cell\"],[\"epithelial cell of intermediate tubule\"],[\"kidney loop of Henle epithelial cell\"],[\"kidney collecting duct epithelial cell\"],[\"kidney collecting duct cell\"],[\"smooth muscle cell of prostate\"],[\"mesothelial cell of pleura\"],[\"kidney interstitial cell\"],[\"kidney medulla cell\"],[\"kidney pelvis cell\"],[\"kidney inner medulla cell\"],[\"kidney outer medulla cell\"],[\"papillary tips cell\"],[\"lower urinary tract cell\"],[\"kidney corpuscule cell\"],[\"kidney interstitial fibroblast\"],[\"kidney interstitial alternatively activated macrophage\"],[\"kidney resident macrophage\"],[\"kidney pelvis urothelial cell\"],[\"kidney collecting duct principal cell\"],[\"kidney collecting duct intercalated cell\"],[\"kidney connecting tubule epithelial cell\"],[\"kidney loop of Henle descending limb epithelial cell\"],[\"kidney distal convoluted tubule epithelial cell\"],[\"kidney blood vessel cell\"],[\"kidney capillary endothelial cell\"],[\"kidney venous blood vessel cell\"],[\"glomerular capillary endothelial cell\"],[\"kidney loop of Henle ascending limb epithelial cell\"],[\"vasa recta cell\"],[\"kidney loop of Henle thick ascending limb epithelial cell\"],[\"kidney loop of Henle thin ascending limb epithelial cell\"],[\"kidney loop of Henle thin descending limb epithelial cell\"],[\"vasa recta ascending limb cell\"],[\"vasa recta descending limb cell\"],[\"medium spiny neuron\"],[\"glycinergic neuron\"],[\"lung endothelial cell\"],[\"pulmonary artery endothelial cell\"],[\"cerebral cortex pyramidal neuron\"],[\"cerebral cortex endothelial cell\"],[\"peripheral blood mononuclear cell\"],[\"tonsil germinal center B cell\"],[\"dermis lymphatic vessel endothelial cell\"],[\"lung microvascular endothelial cell\"],[\"dermis microvascular lymphatic vessel endothelial cell\"],[\"embryonic fibroblast\"],[\"brainstem motor neuron\"],[\"hepatic pit cell\"],[\"liver dendritic cell\"],[\"prostate gland microvascular endothelial cell\"],[\"placental villous trophoblast\"],[\"bronchus fibroblast of lung\"],[\"cord blood hematopoietic stem cell\"],[\"Hofbauer cell\"],[\"vip GABAergic cortical interneuron\"],[\"intratelencephalic-projecting glutamatergic cortical neuron\"],[\"extratelencephalic-projecting glutamatergic cortical neuron\"],[\"lamp5 GABAergic cortical interneuron\"],[\"near-projecting glutamatergic cortical neuron\"],[\"corticothalamic-projecting glutamatergic cortical neuron\"],[\"sncg GABAergic cortical interneuron\"],[\"sst GABAergic cortical interneuron\"],[\"pvalb GABAergic cortical interneuron\"],[\"sst chodl GABAergic cortical interneuron\"],[\"direct pathway medium spiny neuron\"],[\"indirect pathway medium spiny neuron\"],[\"chandelier pvalb GABAergic cortical interneuron\"],[\"chandelier cell\"],[\"L6b glutamatergic cortical neuron\"],[\"L2\\u002f3-6 intratelencephalic projecting glutamatergic neuron\"],[\"L6 corticothalamic-projecting glutamatergic cortical neuron\"],[\"L5\\u002f6 near-projecting glutamatergic neuron of the primary motor cortex\"],[\"L5\\u002f6 near-projecting glutamatergic neuron\"],[\"L2\\u002f3 intratelencephalic projecting glutamatergic neuron\"],[\"L6 intratelencephalic projecting glutamatergic neuron of the primary motor cortex\"],[\"L6 intratelencephalic projecting glutamatergic neuron\"],[\"vascular leptomeningeal cell\"],[\"mesothelial fibroblast of the leptomeninx\"],[\"mesothelial fibroblast\"],[\"differentiation-committed 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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenu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descendants\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"height\":500,\"width\":750},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('06f3c356-f408-4155-88ea-7d31fc80e33b');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plt.scatter(X_ct_umap[:,0], X_ct_umap[:,1])\n",
+    "\n",
+    "label_to_color = 'astrocyte'\n",
+    "cl_name = cl_label2name[label_to_color]\n",
+    "descendants = cell_type_benchmarking[cl_name]['all_descendants']\n",
+    "pd_tree_col = []\n",
+    "pd_tree_size_col = []\n",
+    "for cl_label in cell_type_labels:\n",
+    "    if cl_label2name[cl_label] in descendants or cl_label == label_to_color:\n",
+    "        pd_tree_col.append(cl_label)\n",
+    "        pd_tree_size_col.append(2.0)\n",
+    "    else:\n",
+    "        pd_tree_col.append(f'NOT IN {label_to_color.upper()} DESCENDANTS')\n",
+    "        pd_tree_size_col.append(0.5)\n",
+    "\n",
+    "df = pd.DataFrame({'x': X_ct_umap[:,0], 'y': X_ct_umap[:, 1], 'label': cell_type_labels, f'{label_to_color} descendants': pd_tree_col})\n",
+    "\n",
+    "fig = px.scatter(df, x='x', y='y', hover_data='label', width=750, height=500, color=f'{label_to_color} descendants')\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_290983/2620288918.py:2: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
+      "  plt.scatter(hyperbolic_mapper.embedding_.T[0],\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f00faa4d420>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 293
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hyperbolic_mapper = umap.UMAP(output_metric='hyperboloid').fit(cell_type_embedding)\n",
+    "plt.scatter(hyperbolic_mapper.embedding_.T[0],\n",
+    "            hyperbolic_mapper.embedding_.T[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = hyperbolic_mapper.embedding_[:, 0]\n",
+    "y = hyperbolic_mapper.embedding_[:, 1]\n",
+    "z = np.sqrt(1 + np.sum(hyperbolic_mapper.embedding_**2, axis=1))\n",
+    "\n",
+    "disk_x = x / (1 + z)\n",
+    "disk_y = y / (1 + z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Circle at 0x7f00fa6b7550>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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y8Y6T+YFAeT7ZiBEjcOzYMbz++usICQlBTk4Ohg8fjgEDBiAnJ0eG0RJ5HwZjRFYYdwo68gV+/o5sPPHt7/IPykNsycyT5DzVApV4pH2DKkGUOkiJ0Z2isWlKkkOBGCCuX2XbhrUx6fuD2PiXNL+XL5qflu30kqWRWq3GrFmzcPz4cQwfPhwAsHHjRrRo0QLz5s1DWZkvZvsROY7BGJEFQnYK/paZL2j2wNssTM/Got2nJTnXrZJSPNKhoamJ+NLkjlj7bFfsm94Lbw1u6XDyuJHQfpX7z1wVtBnB38xenylqWb5hw4b47rvvsH79ekRFReHGjRt49tln0b17d2g0GglHSuRdGIwRWSB0p+D8tGyfziETEqTaU6jTm5qId20SjrYNwiSvh0XSqdwjVIgBAwbgr7/+wtNPPw0ASE9PR+vWrfHee+9Br/ffjTHkvxiMEVUidqfg1ztPSTgazyI0SLVFysBLaBNyctyKP84i/3oRdp+8hG92ncY3u04h4+Qlp3cX16xZE/PmzcP27dvRpEkT6HQ6TJs2DZ06dcLhw4dlGj2RZ+LXT6JKxO4UXHvwHF574G6fm92Ro5yFvUKuznB1uQ1/VaQvQ8e3t6ByTK4MUCChYRjuaxqOFnfWQp0aQeWbKuzcB926dcPhw4cxa9YsfPTRR9i/fz/uvfdeTJs2DTNmzEBwcLB8vwyRh+DMGFElYncKFunLLDa09nZylLOwV8jVGa4ut+HPLE2OlpYZ8Pvpq5jzWxaSF/+BIfN24563fsNTS/7A4bMFNs9XvXp1vP/++9izZw9atWoFvV6P2bNnIyEhAXv27JHldyDyJAzGiCqRol6VLxaElbqchSOFXJ3hq0VfvVmxvgwb/8rDg5/tQu//24EfD52zeW+0b98ef/zxB9544w0EBgbi77//RpcuXfD888+jsLDQhSMnci0GY0SVxEWGIiRQ3K3ha0uUgDRBqjlHCrk6Q+rxkbSy8m/8r0fmr1Z7ZAJAUFAQZs6ciQMHDqBDhw4wGAyYO3cuWrduja1bt7p41ESuwWCMqBJ1sAoDW90p6hz1a4VINBrPIaaoamUv923mcP0wR0k5PpJPcakBqXty0MdOq6WWLVti9+7d+Oijj1CtWjVkZ2fj/vvvxxNPPIFr1665cMRE8mMwRmRB00hxSeX/XCuSaCSeQ0xR1cq6NAmX5DzmpBwfucbs9ZmYuGy/1b9XKpWYOnUqjhw5gu7duwMAFi5ciLvvvhs//fSTi0ZJJD8GY0QW/H7qqqjH+2LOGCC8qGplxy9cl+Q8lY3pHAOFQpZTk0x+OnIBjV9dj2mrjmC3lRIZTZo0wZYtWzB//nzUrFkT//zzD/71r39hxIgRuHjxohtGTSQtBmNElRTq9Nh18pKoc/hizhhQvhQ4rkuM6PP8Z9VRwa11bImLDMX0AfFOPaZ3fF3Jx0HOKS0Dvvv9LB5dsBdt3vgVzy3bXyWnLCAgABMmTMBff/2FBx54AADw3Xff4e6778by5cvZeJy8GoMxoko0eVoUlQjvlResVEhWO8sTvdi3GQKV4qefZq/PlCUgS06MxYyB8XZnyIxNyBc81t6h48k19GUG/HzkgtWcsqioKKxbtw5Lly5FnTp1cOnSJTz66KN48MEHceHCBTeMmEg8BmNElYgtkRARGuyzM2NA+azf8PYNJDlXygZxvQ6tSU6MxaYpSRjdKbpKUr9CgSpNyG0dT+4ze32mxX6vCoUCjz76KDIzM/HII48AAH766Se0adMGmzZtcvUwiURTGDi3K0pUVBQAIDc3180jIbGOHDkCACgNa4Ah83YLPk+wMgAHZvb26YBMk6dF3zlpFot/Omt0p2i8NbilBGeyrFCnR1b+DRTq9AjR5kIVEIA2bVo7dPySPWew8U/Otrjbk91i8Up/68vPP/74I5KTk3HpUnl6QVpaGmrWrIk2bdq4aojkBsb37Natrd/PznDn5zlnxogqEVtnTFfqmxX4zcVFhmL6QOdys6wx9jqUi3kT8kBlgN3lSPPjp/aO4/KlB5i/I9vmkvaDDz6Iw4cPo0ePHgAArVaLixcv4uTJk64aIpEoDMaIKlEHq9C1sbjSC766m9KcMTdLrCJ9Gbq+t9WhtjmuJmRDAMlj9nrbS9r169fHb7/9htmzZ0OhUKC4uBj33HMPli5d6sJREgnDYIzIgn+1rS/q8b68RGkuOTEWvz6fhE6xd4g6T0mpwdQ2Z/Bnu2TJIxPK0Q0BJL9Jyw/Y/HulUonp06cjIiICKpUKWq0Wo0aNwtixY3Hjhm/PVpN3YzBGZEGv+EgECdwxqA5S+vRuysriIkPxn37NJTvfobMF6DMnDe/8UjVx212Y4O8Zjl24gVEL99oN1oOCglC3bl0MGzYMAPDtt9+iXbt2OHjwoCuGSeQ0BmNEFqiDVfi3wB2DQxOi/GZmzEiOVkTzd2TjiW9/l/ScYsRFhuKtwS2xb3ovrH22Kwa2Ftcyi4TZeeKS3VZKQHldshUrVmD+/PkICQmBRqNBp06dMHfuXNYkI4/DYIzICiHV3BUKYHTnaHkG5MHkakX0W2a+xdIG7qQOVqF6kBLrj5x391D8mrWyF+YUCgUmTJiAP/74Ay1atEBxcTGef/55DBo0iJX7yaMwGCOyQkjy9vQB8YiLDJVpRJ5NrlZE89OyPSqHDABeXnnE3UMglL82Xlp52O5xLVq0wO+//46nnnoKALB+/Xq0adMG27Ztk3uIRA5hMEZkg7PV3I1FRP2RnDsPv955SpbzCnH4bAEOediuT3+24o9cjFiQYfe4atWq4fPPP8fKlSsRFhaG8+fP4/7778eMGTOg1/v+7mfybAzGiOywlbytDlJWqebuz+Taebj24DmPKRfy/sZj7h4CVZJx8goe/HSnQ8cOGzYMhw8fRteuXWEwGJCSkoKkpCScPn1a3kES2eBfWcZEAhmTt6f1b26qzq4OVqFp3Rp+l6xvT3JiLJLiIrAk4wxW/HEWRXrhfT6NivTlhXTbNggTP0ARCnV67Dl12a1jIMsO517DiC8zsHxCZ7vHNmzYENu3b8ebb76J2bNnIyMjA23btsXChQvx0EMPuWC0RBVxZozICebV2ds2CGMgZoUxeE17uYeobgbmPGFm7HBuAUrFx5Ykk4zsKw6XRFGpVHjzzTexdetW1K9fH9euXcPDDz+MJ598Ejdv3pR5pEQVMRgjItnUrRmCh9pFSXIuTwh8NRdYONTTzd+RjZLSMpSUlmFn1iUczLlqM5Dv3r07Dh8+jEGDBgEAvvzyS7Rv3x5Hjx511ZCJGIwRkbyk2mW5fN8ZD9hVyfpU3uDyjWJcvlGMUV/txZB5u9EhZTNmrD1q9fUTHh6OH3/8ER9//DGCgoLw999/o0OHDliwYIGLR07+isEYEclKql2W3/+ei75z7Rf7lFOzev5ZtsTbFRaXInVPjs3Xj0KhwKRJk7Bnzx7ExcWhqKgIEyZMwDPPPIPi4mIXj5j8DYMxIpKdVE3FDYbyYp/uCshaR4VBFcAmld7KkdfPPffcg/3792P48OEAgM8//xy9e/dGfn6+q4ZJfojBGBFJqlCnx8Gcq1XydYxNxaNqVxN9jZQNmW5ZslQHq9CvZT2XX5ekZe/1U6NGDSxfvhzvvPMOFAoF0tLS0L59exw6dMh1gyS/wmCMiCShydNixtqj6JCyGUPm7baYrxMXGYqd/+mJ8V1jRF3LYACWZJyRZuBOmnR/U7dcl6TjyOtHoVBg2rRp+Omnn1CzZk3k5OSgS5cu+OGHH1w0SvInDMaISLSF6dnoOzcNqXtyUFhcWuHvLOXrzBzUAr8+n4RhIvpZrj6Q65ZyF3GRoZIsuZJ7Ofr6GThwIPbu3Yu4uDjcunULw4cPx/Tp01FWxhonJB0GY0QkysL0bMxenwmDnY2GlfN14iJDMaqT8KbqhcWlyMp3T6kJU6cBt1ydpODM66d58+bYu3cv+vXrBwB4++238eCDD+LatWtyDpH8CIMxIhJMk6dFygbHimwamefrFOpK7RxtmzsLwSYnxmLT8+VtsqQqbEuu9dZPfzl8bFhYGH7++We8/PLLAICff/4ZnTp1QlZWllzDIz/CdxAiEmxxxmm7M2KVmefrqIOVdo62zd2FYI2dBvbP6I3RHRu6dSzkvP05Bfj1rwsOH69UKvHee+9h6dKlCAkJwbFjx9ChQwds2rRJxlGSP2AwRkSCFOr0WHPgnKDHGvN14iJDqzRfd5Q6SImmdWsIeqzU1MEqDJWo0wC51lvr/3aoUr+5Rx99FDt37kRUVBQKCgowYMAAfPjhhzA4+82E6H8YjBH9j8EAh1uoUPkSZeVkfUcZ83XUwSoMEZjEPzQhyu0zY+bELrmSe5y9csvhSv3m2rVrhz/++ANdu3ZFWVkZXnrpJYwePRq3bt1ywajJ1zAYI79nLMmQry2q0EKl/ezfHH5j9kdS5XsJaZekUACjOwtP/peD2CVXZykUwKNcGpWUI5X6zUVGRmLr1q144oknAABLly5FUlIScnNz5R4q+RgGY+TXFqZno8+c8pIMlVcYbpaUIXVPDvrMcW8LHk8lVb6XkHZJ0wfEIy7Ss1oTiVlydYY6SInRnaKxaUoSHubSqCyc6fQQFBSE+fPnY968eVCpVPjjjz9w7733Yvfu3S4YKfkKBmPkt4wlGRzhzhY8nkrKfC9TqQg7M2QKBTBjYDySE2MFXVdOYpZcHdErvi7WPtsV+6b3wluDWyIuMtRlAaC/mr0+06EEf4VCgaeffhqbN29GeHg48vLy0L17d3z11VcuGCX5AgZj5Jc0eVqHAzGj2evd04LHU0md75WcGItNU8pLRVQOMMxngzwxEDMSsuTqCIUCeLlfc7RtEFbh300drMIDbepLf0EymbBkv8NfxLp164bff/8dbdq0QUlJCZKTkzFx4kSUlJTIPErydp6T/UrkQv/dohH0uE+2ZOGTRxMkHo33GtM5Bkv3Vl3itcVWvpexVMS0/s2RlX8DhTo91MEqNK1bw6OS9a0xLrk6G+jbY2lZVpOnxeKM01h3UNiOVnLc7PWZKNaX4ZkeTeweGxMTg127dmHcuHFYsWIFPv30U/z5559YsWIFwsPDXTBa8kacGSO/U6jTY+OfeYIe+8ufF7jL0oxc+V7qYBXaNghD1ybhVWaDPF1yYixe7ttMknOZL8uaN2B/Y91fplzHW3q25XGF9zcdx2Nf73FodlytVuP7779HSkoKFAoFtm/fjvbt2+Po0aMuGCl5I+95hyOSyOHcAujLhNUD0pcZcCT3Gjo3riPxqLyXcdkwZYPtlkgKRXkg5snLjFJ5pkcT/H3+On4+cl7Q49VBSgxNiDLNIM5YexRrDpwTXEqEpLFDcxk7NGkO5S0qFAq8+uqraNWqFUaOHInTp08jMTER69atQ1JSkotGTN6CwRj5Hc0Fcf0Ms/K0DMYqSU6MRVJcBJZknCkv6GoWNJgHFlLvgCzU6cvrnelKoQ5Wlie0e8gs2qT7m2L90fPOLeEC+PiRe3B/fF2og1VYmJ5tN8gl1zMuQzvyxWLQoEHYs2cP+vfvj5ycHPTp0wfLli3D0KFD5R4meRHZ3rW2bduGb7/9Fjt37sT58+cRFBSEqKgo9OvXDxMmTEDTpk0lv6ZGo8HXX3+NHTt2ICsrC9euXUNQUBDq1q2L1q1b41//+hceffRRVKtWTfJrkzcR98m2dO8ZjOkSI81QvIi9wMeV+V7GfKnKs0XqICWGJNyFMZ1j3F76Qkj+2PSB8fhX2/KEfGd2+5LrzV6fiaS4CIdeZ3fffTcyMjLQr18/HD16FA899BDmzZuHp556ygUjJW+gMEjcv0Gn0yE5ORmpqalWjwkJCcG7776LyZMnS3JNg8GAGTNm4P3334debzufp2HDhli2bBm6du0qybWjosrr/LDIn/fIOHkJIxbsrfL/vxwYAQCYsP6i3XP0jq+LBY+1l3xsnsjTAh9HZousLYkeOXIEANC6dWs5h1iBkPFq8rToOzeNM2ICOXMvizG6UzTeGtzS4eMLCgrw4IMPIi0tDQAwc+ZMzJo1Cwo5tuD6AanvZ3d+nkv6ddVgMGDkyJFYtWoVAKBGjRoYP3482rdvD51Oh02bNmHlypUoKirClClTEBgYiGeeeUb0dV955RW89957pj/37NkTffv2RYMGDXD9+nVkZmbi22+/RUFBAXJyctC7d2/8/vvvaNGihehrk/c5mFMg+hy/Zebj178uoE+LeuIH5MFsBRLGauVL9+a4LBfM0dkiY9FOwLGlJDkJWcIV0oCdXG/V/rOY1r+5wzO/YWFh2LRpE0aOHInVq1fjzTffxIULF/DZZ59BpfKM5XVyD0lnxpYsWYIxY8YAACIiIrBjxw7Ex1fcabVixQoMHz4cBoMBwcHBOHbsGGJiYgRf8+zZs2jUqBFKS0uhVCqxdu1aPPDAA1WOu379OgYNGmT6RjJs2DCsXLlS8HWNODPmXWzNODj7bTousgZ+fb6blMPzKM4uk8lZjLVQp8fmzDxM+e6QU4vMCgWwaUqSKdBx5cyYpWVdAHaXcAt1enRI2cxkfRFcNTMGAGuf7Yq2DcKcekxpaSmee+45fPHFFwCAwYMHY9myZUyhcRJnxiwwGAx47bXXTH/+9NNPqwRiAPDwww9j27Zt+Pzzz6HT6fDGG29g0aJFgq/766+/orS0/E1ryJAhFgMxAKhZsyY+/fRT05O2fft2wdck7yXljIMm7wbyrxehbs0QaU7oQTR5WqRscC5fKWV9JtrH3IE2Tn4w2RuHpSVSRxkMwJKMM04tJYnlyLKurQ9vMQ3YyfWOX7judDCmVCoxb9483HnnnXj99dexdu1a9OnTB+vWrUPt2rXlGSh5NMnqjO3cuRNnzpwBAERHR+Ohhx6yeuwLL7xg+nnVqlXQ6XSCr5uXd7teVFxcnM1jzf/+xg1xO+rI+xTq9FhzQNoCmds18n/zdgchQasBwLDPd2PG2qM4fLbAVBPrYM5VQbXZFqZno+/c8lpaYoKT1QdyXVYbztaYHW1CLbYBO7nW9uPC3gMUCgVmzpyJ+fPnIyAgADt37kRiYiJXWfyUZDNjGzZsMP3cr18/BARYj/MaN26MuLg4aDQaaLVapKWloXfv3oKuGxkZafr5+PHjNo81/3vmi/kfOWYcNBd8rz2SmKBVX2ZA6p4cpO7JqfD/nU32l3InYWFxKbLybzg9ewE4VzpDqnw2JUtxe5UdmoumZWcjZ143EyZMQN26dfHII4/gr7/+QpcuXbBp0yaLK0vkuyQLxoxrtwDQoUMHu8d36NABGo3G9FihwdiAAQMQFBSE4uJirFmzBj/99BMGDRpU5bjr169j4sSJpj+/+OKLgq5H3kuOGYe/zl+X/JzuJkfQ6miyvzE3LEXikg7Ozow5s4PUOGZng8eUDRVLIxivuXo/Z0a8yU2zYP/w2QJ8vv0EtmsuoqjkdmcEe19GBg8ejN9++w3/+te/cPbsWXTt2hU///wzunTp4spfhdxIsgT+Jk2a4OTJkwCArVu3okePHjaPnzlzJt566y0AQHJyMhYsWCD42p9++ikmTZoE469y//33o2/fvoiKioJWq0VmZia++eYbFBQUIDAwEB9++CEmTZrk8PmNSX2WnD9/HuHh4fjtt98Ej59co6S0DJdvFFv9+7CQ8imJgiLH28soFEDd0BBZmkO7S7G+DFcKrf87SSE0RFVhpkBfZsBNnR63Skpl2UUYVj0QIYFKU8PmwMBAq8cW6vTQFtkP3qoHKwEDRI25epASNasFOnxNcoyQe1kMdbAKOn0p9KX2XwiVX/vmSkpKcOnSJZSWlkKhUKBOnToICfG9nFSpOHI/O6N3794IDAz07gT+q1evmn52pBmq+TEFBQWirv3cc88hOjoaL774IjQaDbZs2YItW7ZUOEahUOC5557DxIkT7eaWkW9SBQRAoYCkH/YGA6AvK0OgD60tuSKw1BbpERyohCpA4ZJARKcvQ0ig0u5xzozlpgQzrbdKSqEMUDAQ83LOzLxqi/S4WVKK2tWDoAqoeLMFBgaibt26uHTpEkpKSnD58mWEhYVBrVZLPWTyMJIFY1rt7dwZR7bnmh9z/br4pZ6BAwciODgYL7zwAv78888qf28wGPD111/j+vXreP/99yvkmtljK0o2zpq5sogkCTdj7dEq+UxGQrfDL03uiHZN7H8B8RauKq0wulM0outUd0mVeXWQEvum98LJ438DsHy/avK0eJCFVn2CK0tbiGGtHMzly5fxwAMPYM+ePQCAlJQUvPLKKywOW4nUpS2kmmETwie+zufm5qJz587o27cvzp49iw8++ABZWVnQ6XS4fv060tPTMWLECNy8eROLFy9Ghw4dLAZs5PvGdI6RfObHU3ohSkUdrMKQhLtkv87K/bkua/djTOK3xZcKrVaecSHPNHt9psWdtXXq1MGWLVtMpZqmT5+OSZMmmco4ke+RLBgLDb2dlHjr1i27x5sfU7NmTcHXzc/PR8eOHbFv3z6EhYVhz549ePHFF9GkSRMEBQUhNDQU9913H5YtW4Z3330XAJCTk4OhQ4ea1pvJfxj7BUpFHaRE07o1JDufp5AjaK3sVolrP1hsLSXJUfbEnYJUAVj+RCcsTe6Itc92xY/PdsXoTtFQB1VcqmXM5n6z12dCk1d1V3b16tWxZs0ajBs3DkB5bvSIESNElYIizyVZMBYWFmb6+dKlS3aPNz/G/LHOSklJwT///AOgfIdk8+bNrR770ksvmfLFsrKysG7dOsHXJe+VnBiLGQPjJQk2hiZE+dzMWKFOj0KdHo92aOjuoUjK1vPka4VWbxaXolqQEl2bhKNtgzC0aRCGtwa3xL7pvbD22a4Y1bEhFADKfGQm0Ns9lbrf4v9XqVT46quv8OqrrwIo72DTv39/XLt2zZXDIxeQLBgzD4JOnTpl93jzY2wFUPb8+OOPpp/79Olj89iAgAD06tXL9OeMjAzB1yXvlpwYi01TkjC6U7SoJZ3RnaMBlAcwYoucupsmT4sZa4+iQ8pmDJm3G0v3Ws6t80b2ZjB9sdCqpdegOliFP05fQereHKfaSpG8si8WotdH2y3+nUKhQEpKCv773/9CoVBg27Zt6NatG86fP+/aQZKsJPtK37p1a1Ph13379mH8+PE2j9+3b1+FxwplnBUDHJthq1Wrluln800H5H/iIkMxrX9zrDqQC72AWZEgZQBuFZdixtqjDtWjcgVnik2aP+bDTcexaPdp1wzSDezNYKqD7e+09DaWfl8hba7INU5cLMSILzOwfEJni38/ceJEREZGYvTo0Th8+DASExOxfft2m6WXyHtIFowNGDDAlJO1ceNGlJWVWa3Cf/LkSVPB19DQUCQmJgq+bmhoKK5cuQKgPBesadOmNo83tmwCHCvBQb5Nk6fFTYHLU8WlZXjws10W/87RIqdScaZIaeXH/PB7LopLXVOPyR0UitszmNbERYZCHaT0maVKazOBvrRJwRdlZF/Br39dQJ8W9Sz+/b///W+Eh4fjwQcfxMmTJ9G9e3cGZD5CsmXKrl27omHD8hyTM2fOYOXKlVaP/eijj0w/Dx06VFRRO/NZtaVLl9o89sqVK1i/fr3pzx07dhR8XfINci9PGVvf2OpFKJaQfojmj/HlQAwApg+Itzs76aodpK5iaSbQ1zYp+Kop3x+y+fc9e/bEL7/8gho1apgCMvaz9H6SBWMBAQF48803TX+eOHEijh07VuW4lStX4osvvgAABAcHY+bMmVbP2b17dygUCigUCsyaNcviMaNGjTL9/M033+DLL7+0eNz169cxfPhwU+JjVFSU4BZM5DtctTyVssHyjimxjP0Q7c12mAeFjj7Gk6kceOdSKKzXcbLEFTtIXcHaTKCvbVLwVTeLS/Ha2qM2j7nvvvsYkPkYSeuMjRkzBoMHDwZQXnKiQ4cOmDJlCpYuXYqvv/4aw4cPx7///W9T26IPPvgAsbHilm/Gjh2Lbt26ASgv7Prkk08iMTERH330EVasWIHU1FTTLsrNmzcDAJRKJT7//HOHitOSbzMuT8nNYACWZJyxf6ATNHlap3s4zl6fKXnfR3fQ/28yTxWgqLIBQx2kxOhO0dg0Jcmp5WGpy564i7WZQF/cpOCrluzJsfvljQGZb5F0T75CocDy5csxfvx4LF++HFqtFh9//HGV44KDg/H2229XaNwtlFKpxE8//YQnnngC33//PQBg586d2Llzp8Xjw8PDsXDhQlMxPfJvxuUpa1X5pbT6QC6m9W8uSSkMTZ4Wzy07IGhHnBdPiFWh/19tBgWAkR0b4qF7G6Bp3RqC/42NwVvK+kyv+3dSKGDKT7S8kUP+Lx0K+Nbry52+3nkK7w6zvbnNGJD179+fOWReTvICSSEhIVi2bBmSk5PxzTffYNeuXTh//jyCgoIQFRWFvn374sknn5S0P2RoaCi+++47vPDCC0hNTcXu3buRnZ2N69evIzAwEHXq1EGbNm3Qv39/jBo1qsKOSqIxnWNcUsbBWAW+bYMwQbsejYzLjHSbAUDq3hzEhKvRtkGYqHMlJ8YiKS4Cr6w+iv1nrtp/gIRUAQoEqQIqbCpRBykxNCEKiU3DkZ51CasP5FbZpDE0Icq0NGltd+8Dbe5EtUClbMV2ZwyMx6GzV/HzkQuynN/frDl4Dq89cLfd9wVLAdm2bdvQoEEDF42UpKAwGLw5c8T9jN9AOD3s3RamZ6MhygsRy9nP7r1hrXD03DXBpTAYiNmmUACbpiRZ/Td0tpfdvG0n8P6m406NQR2kxF21q0GTZ7v9kiWjO0VjWv/myMq/gUKdHupgVZWZvkKd3uLfL0zPRsoG1+cCGmfkRnRo6JKepo7wlt6U9gy+pz7mDr/HoWN37dqFfv364caNG2jcuLFfBGRS96Z05+e5b5UOJxIoOTEWGb8XQFtku1ir2GWY/6yynJjrSCkM1oiyz5ib99bglpKc75keTdDr7kgsyThjdUbqoXZRMAAVgqNzBbfQ18mm48bEe3WwyubsnqW/d0eQHqQMwPD2DTC6czTiIkPLix17QCDmS9Ye/AeFRXoseKy93WO7du2KjRs3ol+/fjh58iR69OjhFwGZr2AwRvQ/6mAVggPLk79tffA+umCPbB86xl2PAKoEZKwR5Rgpc/OA8sT+twa3tDtjVfkx0wfEOxUgOVKCwxJ3BenFpWWIrlPdNObjF1hEWw6/ZebjnV8y8Up/+5tLGJB5LwZjRGZUAQq7H7yuSPhP2ZCJpLgI0wcda0Q5zjw3T0r2ZqwqM20GsLN0aJ54L4Q7g3Tz1+n24/nuGcT/qAIUUAYAOr3vfWOZvyMbwxKiHArWGZB5J0lLWxD5CuMHr7HRsvkMiCvqUVUuhcEaUc7xlN6g5j1QK5dQEVqCw5y7g3Tj67RQp0eaxr35WfoyA74Y1Q5BSh8oFmfB1zvt93w2MgZkxrIXPXr0wNmzZ2UcHYnFmTEiJ8VFhqJX87r4LVPemQDz5TYpa0T5Q/kBqZYopSBkmdNRnhCkrz6QiwGt6uFmifs7OWzOzEdxqW++utcecmx3pVHlGTJj2QvOkHkmzowROUmTp8XmY/IvyRiX2wDpOgXMGBiP6QM9u7CpQgG83LeZ4GK81voyuput2VahPKGQa2FxqaCdo3JwRYkadykqKcOGo+edeoz5DFl2dja6d+/OGTIPxWCMyEmuzNExLreJ7RSgwO3WQMmJsZgxMN4trX8UCuDL0e3sLts906OJ4F6Rlvoy+ipXtfOyR6d3f1DoD15aeQSDPtnpVGs1BmTewT/esYgk4uocHWNQIaZTQFxkDXz6aEKF5F9jYVNLJRvkNH1APPq0qIc+LerZXbYzFuMVUh5CLDFFeV3JGKS7e6myWqBnBIX+4Oi5a+gzJw0P3xuFDx5q49Bjunbtik2bNqFv376mgIxLlp7F895diDyYK3N0Ki+3CQlOAFQJxIws5TLlXr1ptRaaWJWbdtvbnejK8hBGmjwtFmecFlyUVwgxgZ8r23lZHUOQEnfWYp9fV1vxRy4O5xTg16ndHDq+S5cuDMg8GJcpiZzgyhydysttQhpZzxhoPzgxz2V6oHV9WRqnVwtU4vTlQqeWVwA4vKSqUFQN9py1MD0bfeemIXVPTpWA21iUt+/cNCxMzxZ8DXOaPC1mrD2KDimbMWTeboz6ai+GzNuNDimbMWPtUYf/rVyxu9eWxKYRHrNc6m80+Tcw4ssMh483BmRcsvQ8DMaInOCqDx1ry21yByfGmRYhmtezHvTdKhEezMhdHgK4XcHe3qyjsSiv2IDMocBvThrmbTth91xCgnQpbfzrAh7/5neoAnyzpISny8i+gl//crwfqKWAjO383I/LlEROcFWOjq3lNlv5XuZNo4UupwldDj3mQAV2Wx0GbJG7PISzFewrF+V1hqOtiwwA3t90HFuO5eGdoa1tXsvRArNycXVZi+pBSuhKSuGjVSyc9v6m4+jTop7Dx1desuzXrx/S09NRu3ZtGUdJtnBmjMgJYmaO2kXXlmxGyxic7JveC2uf7YqlyR2x9tmu2De9F94a3FJUXpMrZlpSNmQ6vWQJyFMeQsju2MpFeR2lydM63UNy/5kC9Jljf0bR1gxioI8UQlUA+O8j92DhY/cyEDNzIv8G8q8XOfWYLl26YMOGDQgODsZff/2FQYMG4datWzKNkOxhMEbkJCE5OgoF8M7QVpIvt8kRnADOLYe2iw5z+vxCgxmpidkdu/pArtOV/l9ZdUTQtQDHlkctBenjusagxEcil+kD4/GvtvVR5v76sh5nu4AOCImJifjuu+8QEBCAXbt24ZFHHoFe7xndK/wNgzEiJwmZOTIuO8o5oyVEoU6PgzlXsTPrEg7mXEWhTm/6f83r1cTc4W3xSPsGVoPHtc90xbHzwhpECwlmpCZmd6x5UV5HHD5bgP05BYKuZeTojKIxSM88fx2Ldp0WdU1PUHnGmBsGqsq75tzMmNHgwYPxxRdfAADWrVuHp556CgZ3NTv1Y8wZIxJAbBNoZ5tOS81aCQdjEra+7PYvpQ5S4oE2d6Jrk3DUUQdXyNU6mHNVdDDjzn8HsbtjnQkmP99uPxnfHuOM4luDW9o9VkguXGXVg5Qo1pdVeD3IYVCbOxFWLcjhHMi4yFAEKQNQXMopMqO9p65gosDHPvHEE8jLy8Nrr72Gr776CvXq1cPs2bMlHR/ZxmCMSCC5E+nlsjA922oQaelDt7C4FN//nosf/sitEli6MpiRg9gZFkeXhQt1emw/Lk0jbfOepbYI7RTRv2U9jOoUDXWwCjeL9Xh0wV6BI3WMQgFM7NkUcZGhDm/QWL4vh4FYJTtPXIImTyv4/Wb69Om4cOECPvvsM6SkpCAyMhITJwoN78hZDMaIRJBzl58cHN3JZ4mlnZCuCmbkImZ3bJAywOEemJo8LYr00gQPjswoismFS9NcxIcPt4E6WIWdWZcEjtJx5juHHZkxlmLGz1d9siULnzyaIOixCoUCH3/8MfLy8rBy5UpMnjwZkZGR+Pe//y3xKMkS5owRSUBsIr2l3C2pSfUhZp63JKZnpic09BazO7a4tAzL9zlW+V7qYsH2Xh9S5cLJmZsltBaeK3vDeptf/rwg6r1DqVQiNTUVPXr0gMFgwKhRo7BlyxYJR0jWMBgjciOpqrA7QqoPMfOdkGKCGU9p6C2mgr3jCfXSBjX2/t2kWj4W26DeEjGFel3dG9aSQA8ubqsvM+BI7jVR5wgODsaaNWvQtm1blJSUYMiQIThw4IBEIyRrGIwRuYmUVdjtKdTp8cPv0rU9Md8JKbTUhxQNvaUgpq6aoyU6pAxqHJlRlGr5WB2sQo/mEaLOZRSiCsDyJzqK2jnsyt6wlqiDlNg1radpJ/Qb/2rhtrFYkyXBF7hatWrhl19+QaNGjaDVatG/f3+cPHlSgtGRNQzGiNzA4fY7KK+uPezzXaJmyTZn5qFYwlpT5ktZYkp9eIoRHRoiSGBhVEdKdIiZQazMkRlFqZaPF6ZnY/0Rx1vt2FKkL0O1IJWo2VBX9oa1pPmdNVG3ZogpJaF1VC23jseS3Ks3JTlPvXr18OuvvyIiIgL5+fno06cP8vLyJDk3VcVgjMjFhORuOVqF3Zp1h/4R9DhbzAMQVzb0loMmTys4WHW03pgUDb0dnVGUYvnY9IVB0Fkssxa0Opoz6e76YvvPXK1wD8ZFhqKayrM+Rg+dFbdMaa5Jkyb45ZdfTH0s+/fvj+vXr0t2frrN/QkbRH5GTO6WkL6OhTo9dp2Ufldc5RkOby31AbimRIdxBlHoblbAuRlFIT1GjcGeXDsWK79mrNW7UwcpMSThLozpHFOlvpgresPaYt6XdPm+HNySaJesVA7nFph2dUuhXbt2WL16NQYOHIiDBw9i6NChWL9+PYKDgyU5P5VjMEbkQlIkIDvbpFqTp0WRxI2creUteVupDyNXlegQ2tDbWvFgW4QEf8Zg7z+rjki+YzEksGIpEFv17ow5k0v35lT4vY0zfql7HNvFKgeDAfh65yk0qVtDVGAtF52+TPJiyr1798bixYsxYsQIbNmyBWPGjMHy5csREOBZs4LezHPfHYl8kBQJyM5UYQfkybOxl7fk7g4DzhIz4+JsiQ5bM4iWzj00IQoPtYtCmcGAnVmXoA5Wlo/XgQDQ2U4RSXER+M+qw/j+91yHfx9H3dck3DRmR+vdWaptJ2TGT2rfSbgZRg6//XUeN4r0Tr1W7HnkkUeQn5+PyZMn44cffkDdunXx3//+Fwqxa+8EgMEYkUtJFRg5WoUdkD7PxriUVajTlweXulJJ3/TdQcyMi5ASHdZmEOvXCsE/14pMf1YAWLH/LB5dsMehZTxLHF0+TtNcRN+5abIFOf9qU57DJmQJ1Hw2WIrlXl/32fZsfLa9PLctSKnAA23uxFPdmohOEZg0aRIuXLiAd955B59++inuvPNOvPrqq1IM2e955zsnkZeSKjBypq9jXGQoVAEKyfoLTkiMdSrPx1uIybESytIMYt2aIQCELeNZY2/5WExnBkcEKRW4P74uAGE5k5Vng5MTY3FRq8P8NGEbWvxJcakBqw/8g9UH/sHD7aLwwcNtRJ0vJSUFFy5cwKJFizB9+nRERkbi8ccfl2i0/osLvkQuJGW9KVf3dVQogN7xdfFlerbt2mhzhe/6dCdPKtHhcOmT/y3jOfrvbalThCvaCw1v3xDqYJWonEnzEiIL07PxpRe+xtxtxf5c9Pm/HaLOoVAo8OWXX+KBBx4AAEyYMAHr1q2TYnh+jcEYkQtJWW/K0aUxTZ5W1KxYiCoAoztFY0JSLH7LzJc8QPAknlCiQ+gyntA6dHK3FzKfPZSiTZOjgSpZpsm/gRELMkSdQ6VS4fvvv0fnzp1RVlaG4cOHY9euXRKN0D8xGCNyMSnqTTmTNC42T+2zkQkY3TkaXzq5JCQmQLDGFT08kxNjsWlKEkZ3iq4yiymmlY+jxCzjOcsV7YXMZw/FvhaPX7jOJuESyDh5Bb/+Ja6Yb/Xq1fHzzz8jPj4eRUVFeOCBB3Ds2DGJRuh/mDNG5GJSJCA7kzQuNk+tTo1gSfJ8xHC2HpVY7irRIXYZz9FNHUauaC+UFHe7nZLY1+K24xc5IyaR2esz0adFPVHnuOOOO7Bp0yZ06dIFubm5GDRoEPbu3Ys77rhDolH6D86MEbmBaTlMwGOdTRoX2xqnfq0QSfJ8hHKoh6dMeWqWcqzkJMUynlOPcUF7IfMZOzGvxeqBAUjTXJRqWH4v58pN5F8vEn2eBg0a4KeffkL16tVx4sQJPPzwwygpKZFghP6FwRiRmyQnxmLT80loF13bqcc5mzQutjXOuYJbLg0QzMmVyO6pjl8Qt6zrbODrivZC5gG5mNdiUlxd3HRj5X1ftEnkUqVR27ZtkZqaCgDYunUrJk2aBAOnMJ3CYIzIjeIiQ7Hq6S54uW8zu8eKSRoXkqd2u56Y/K2CLHF1Irsn2H48X9TjhdQ7k2p3rzWVA/IxnWOcPodCAXRvFmH/QHLKCRFflCobMmQI3n77bQDAF198gc8++0yyc/sDBmNEHuCZHk3w6/PyJY2LKdsgVasgZ5PvXZnI7gkKdXpRy3DVnewEAEi7u9cW8+dayO84fUA8mtXzvtp1ni5Y4ibn06ZNw6hRowAAU6ZMwa+//irp+X0ZE/iJPITcSePOtsYxHi+2VZACwIy1R51Kvnd1Irsn0ORpcVNED9FucRGCfmdXtBdytg2SOeNscKFO7/Ym4b4momaw0y22bFEoFFiwYAFOnDiBPXv24N///jf27NmD5s2bSzRi3+Vd71ZEfkDOvo6OtsYxD47EtApqfmdNDJ63y+kq8lIksntTb0xAfDK90GU8udsLGcuwCC0uqy8rMwUMD7Spj+89vC+kN0lZf7sUhVQ7k0NCQrBmzRp06NABZ8+e5Q5LBzEYI/IzQmbghM6e7D9z1e4xlppBuytPzZ3ELgc3q1dT8GMdnTUVwliG5b9bNILO/e4vx00/V5N4WY1uc7bFli316tXDunXr0LVrV9MOy40bNyIwMFDCEfsWvrKJvIAcxU6dKdsgJOfMWebJ91LlqXkTsSVInM0Xq8xWsdtqqgBE16nm9DmNm0DmbTuBn4+I37l3Sy98GdebtKwvPLAWS6qdydxh6Rzve8ci8iOuLnZqi5yzJ0DFIrFi89TEBiZCFOr05curulJBOThiloOdKQJsi71Z03nbTuD9Tcftn+h/pg+IR5rmolOPkdKT3WJxU1daZUnek6mDlPj+yc7YmpmP9zYeQ27BLbeMI2VDJpLiIkS9vxh3WL766qv44osv0KJFCzz33HMSjtJ3MBgj8lAL07OtBj5SLik4w1bOmRSMyffnCm7hrtrVoMlzfuu9VIGJo6QMmIUsBztbBNgR1vIWn+nRBEGqAIc3gSTFRaDv3DRJx1ZZtUAlbpVU/HevHqRE9WAVXkksn801Bpcr/jiLpXttB7sKBTC2SwwW7Tot57Ct6hlfF+/8klnl9eRqUnXQmDZtGv7++2+kpqZiypQpaNGiBXr06CHRKH2HwsB5Q1GioqIAALm5uW4eCYl15MgRAEDr1q1lu4ajsyfO7jqTq2m1LYfPFuDBz6RvDjyuawy+2S2sebVCAWyakmQ1+JH6ObYVMJuPyZmA2Ruee02e1qFNIDPWHhU00+eMR9o3wCMdGlaYxTt5/G8Alp9nR8Z+V1g1dEjZ7DWzaXIJUgbg4Mzeor/cFBUVISkpCb///jvq1KmDP/74AzExMaLHJ/X97M7Pc86MEbmAM7MnQoudil1ScNaK/fLsahMzI+FsdwIxHA2aLG1QsEVoCRJXcmQTiCuakAPAT4f/wWsP3O1wwODoBhahS8a+pLi0DFuO5eFfbcTVogsJCcHq1atx7733Ii8vD0OGDMGuXbtQvXp1iUbq/ZjATyQzZ3srekOxU1d90DpKTHcCIeTuDmArmV6KIsBSsbUJxBVNyAHhLbfsbWAR0rXCF6079I8k54mKisLq1asRGBiIQ4cO4fHHH2dCvxnOjBHJyNnZk2J9mVcUO3XVB60j4iJr4NNHEwTNiAlNuhcTMDuagyN3EWC5uaIJ+e1rCd9dbO01IHf9NW+xM+uS6bUnVpcuXfDZZ59hwoQJ+O6773DPPffg5ZdflmCU3s/z72giLyVk9uSDTcch9LuiK4uduvKD1p5zV2/hrjDnyi6ISbp3dXcAOYsAy8kVTchvX8v5jzJHXgPJibE4d/UWFu0+LeFovUuRvkzS95UnnngCBw4cwBdffIFp06ahdevW6NevnyTn9mZcpiSSiaDZE5HXdFWxU1d+0Nrj7DJVoU5vd9m4z5w0zNt2wuLjpegO4A9c0YQcEFbKxJnUgRf7NkO1QM95vbuD1O8rH3/8Me677z4YDAaMGDECJ05Yvtf8CYMxIhm4K6fKVctXrvqgdZSjHxaFOj20RXqHguT3Nx3HsHm7quR5+WN3ACFc1YTc2VImxtQBe68BY+rA8n05GNZO/t/Dk0n9vhIUFISVK1ciKioKBQUFePDBB6HVOpZP6asYjBHJwB05Va4sduqqD1pHOfJhocnTQlvkXCC0P6cAfeekVahG7o/dAYSSOwne2RprgncqN43w62T+3ScuSX7OyMhIrFmzBsHBwfj7778xZswYlJX5R4cFSxiMEcnAHTlVri52KvUHrSpA2MkcDUIXZ5wWdH4DKraHcXfbIm8idxstZ0uZCN14kZ51SfZ2YJ7sg1+PO7wT2Bn33nsvFixYAABYu3YtZs+eLfk1vAWDMSIZuDqnSo4q7Pa4ol+lIxwJQqVYNjaWphAzK+jqgNkTJCfG4slu0pbgEFLKROzGixEdGmLGwHi/nCGTs3TO6NGj8fzzzwMAXn/9dWzYsEGW63g6BmNEMhA7e/Jy32ZOPcaVxU7NJSfGSvYBpS8zwNnTOBqESrFsbDAA7288hoM5V/FwuwZO/87uCJg9wcL0bHyZZr/p9PiuMVj7bFcsTe6I/45oi0faN5C0xpoUGy9s1X8LUvr2x+nqA7my5Tu+//776NmzJwBgzJgxftnRxr++ohG5iNimz872AHRn8U/zfpUr/jiLIr3wvI+RHRsi1U7vQHOOBqFSLRtvzszH5sx8qIOUSGgQhv05BQ4/1l0Bszs509rp612nUT+smum1/K82d+G1B+6WrMaaVBsvbNV/W74vx+49663kLJ2jUqmwdOlStG3bFnl5eXj00UexdetWqFT+E6L4dihP5EZCcqrMZ0+8pQo7cPsDatG49qLO89C9DRyaaXN2mUrqZePC4lKHAzFXdwfwFFJ0KbBXJd8ZUm+8sDQ2W/dsiCoA/VrUQ78WkaLG4U5v/fSXbOeuV68eUlNToVAokJ6ejjfffFO2a3ki/wk7iVxMSAXvyrMn3laFvXVUGNRBSkHLQcbk9rYNwkwzbfYaUTtK7lIc7aJr49j565KM1VMI7U5g5IouBc6IiwyFKkABfZnz01aqAIXDGy+M9+xD7aLw+Y6T2H48H0UlZSjSl2HjXxe8umbZ/pwCvPNLJl7pL0+uaK9evTB9+nTMnj0bs2fPRlJSEnr16iXLtTyN572bE/kQqZo+e0sVdjHLs4Pa1K/w4T+tf3PJgtDbSfe3nH6sIw7kXMXaZ7rCAHh8wGyPmO4ERnJ2KRAbJLrCwvRsq/f8rRLP6V4hxPwd2RiWECXbF4zXX38dO3bsQHp6OkaNGoVDhw6hXr16slzLk3jWK5jIB5nnVEk10+PJxnSOwdK9OU7Pivx46B989/tZ05/NP/ylCETHdI7BaY08fQYNBmDl/lxZZnRcad62E3h/03GLf2esTL90b47dPEUpkuUrP+dig0RNnlbQrBhQvrnE0XwpZ/LkvNXXO0/h3WGtZTm3SqXCsmXLTPljo0aNwqZNm6BUeu+MoiOYM0bkAsali33Te5l2jK19tiv2Te+Ftwa39JlADBBe8qLyjEHltjRSjCs0RL7vn3LuNpObJk+LYfN2WQ3EzBkr09t6TqTuUuBM+yK5xnT4bIHd51dInpw3WnvonKyv9aioKHz77bcAgC1btuDdd9+V7VqegsEYkQtJmZDsyaQseeHIh7+j1MEq2QIyb+07uTA9G33npDm1MxSommxvTspkeWfbF1l7nYgd0+vr/kKHlM2Ysfao1d9bSJ6cnPq3rGdxM4HYKhxFJWWyv9YHDhyIF198EQAwc+ZMpKeny3o9d2MwRkSysLWzrJrK+bceWx/+zlAHq/Dr80loF11b9Lkq87aZMVOgI+CxtgqBStWlQOiOTEvLkVJs4rA1C+eufrS2NLyjepUZ+XFdY1AqQdchV7zWU1JS0LFjR5SVlWHEiBG4dEn6tkyegsEYEZkU6vQ4mHMVO7Mu4WDOVdFvuNaWZ/91j/MV7KWsAh4XGYpVT3fBy32bOV1o1hZvmumUYknN2tKsVF0KhO7IvCnxmCxdo/IsnDv60dozPy3b1DWibYMwZJ6/jkW7Tktybkuv9b//uYYXfjiEsV/vwws/HMLf/1wTdY2goCB89913qFWrFs6dO4exY8f6bP9K73nnICLZSLGDzhbz3aCFOj1+PvyPoPPY22nnrGd6NEGvuyMtbq5wlrf1nZRiSc1WIVAhGznM6+yJmWm6VVKK0JBAScZkS8qGTCTFRSAuMtQt/Wgd8dyyA/j00QQAkCyfrfJr/f2NmViYfgrFpRX/YVcdOIcgpQLJiY3wcj9h5TBiYmLw9ddfY9iwYVi/fj3mzJmDF154QdT4PRFnxoj8nCPJ0X3mSJNED0iz005KlWfvesXXFXQeb+o7KeWSmrXZUyEbOczr7Il5nRgMgN7CDIrU/VTNZ2td3Y/WUZq8G+g7Jw2vrD4iWRBq/lpvP/s3zNueXSUQMyouNWDe9my0n/2b8OsNHYrnnnsOADBt2jTs3btX8Lk8FYMxIj/maHI0UL4s844E36yl3mknFePs3cv9mvt830kpl9RsBaCObuSw1KVA7OvE2ms6OTHW6d6vthiXauUuLCyGAcD+MwWSne/emNrYmXUJbd/YhIs3ih16zMUbxaICsg8//BAJCQnQ6/UYPnw4rl69KvhcnojBGJGfEpIzND8tG+/8cvsxQnLMpG5LIzWxMzreQKolNUeWZoW29RL7OrEVAHZuXEfUuc0ZZ2ulzEnzZEHKAEz+7hBGfbUXBbec+2J08UYx3t8o7AtdcHAwvv/+e4SGhuLMmTNITk4WdB5P5R1z6kQkOaE5Q/N3lC9XGpe6nM0xM84giGmZJDepOid4KqmW1BxdmhXS1kvM60ShAFQB1ucapM7vMn4JkTonzRMVi9yKuTD9tOD8sSZNmuDLL7/EiBEjsHr1arzwwguoUcN78jRt4cwYkR8SmzM0f0e24AKcUu20k5s3NWp3lhRLakKWZp2psyfmdVItUGlzZkzq/C7z3yOhYZik5/Y1xaVlonZZPvLII3jiiScAANeuXUNJSYlUQ3MrzowR+SG5t+Ebt/4DsBisiN1p5yre1qjdUWJ6iBq5YmlW6Oukup3nJi4yFNWDlLgpwT1gnK211Y/S4jgBQfXdfMGi3afxwUNtBD9+7ty5yMjIgMFgwOXLl6HVahEa6j1pApZwZozIB9nL5XLVNnxrhVq9LS/LFzsnjOkcI7hDQuVke7kIfZ2oAmz/YupgFZKahosZmsnQhCgs35fj8EYYAGgXXRtzH2kryfW90SWtTtTjq1evju+//x4KhQJ6vR5PPvkkDF6+Nuz97yhEZOJovTBXbcM3bv231EDb1/OyCnV6HMktwPELWgAKxNWrgTZRnhPIGQMdZ5pat4uujXeGtnJpUCzkdXLkyBG75+3erC42/pUnamwKBZDYNBxPpu536nEHcq5i5gN3C86J83bhocGiz3H33Xfj+vXruHLlCpYvX477778fjz/+uASjcw/PeFcgItFsLZMYc7mW7s3B9AHxGNGhocs+CGwVak1OjEVSXITFoqvqICWGJkRhdOdor9qpqMnT4r9bsrDxzwtV2vKoAhTo1zISk+6P84jfyeFAB8BLfZvhmR5NXDOwSuR4nTSrJ/7ff/qAeKRlXRTUJWDl/lzRS8XealyXGEnOU716dRQVFQEAJk6ciI4dO6Jly6pf/LyBwuDtc3tuFhUVBQDIzc1180hILOO36datW7t5JM4z1gtz1IyB8Th9udBlHwRrn+1qsUq7uUKdXva8LLmfY2eeB1ct9TlCk6f1moDYkdeJI89zoU6PDimbBX8heblvMzzWJUbwOdRBSix7ohMe/GyXoOsLFRdZA1n5N9y24zNIGQBNSn9JznXkyBEYDAb8+9//hkajwd133419+/ZBrVYLOp87P885M0bk5TR5WqcCMaB8JmT+qHYuC8Ycqz+mshuweTJnA2JbGxxczZs2Kkj1OhGziWFQ6zvxTI8m5fmYIrpJ3CwuRZAyQHS5CGd88FAb5F0vwoQlzi2tSiU5MUbS8ykUCvzwww/o2LEj/v77b7z44ov4/PPPJb2GKzCB3wtJ3cyZvNsrq+3nx1RmMADpWZcwY6B0rWFs8bQPdKlp8rRIcTIgBsoDMksbHNzFFzcqWGJ8D21Zv5bTj1UogIn3N/3fecQt8+88cdGlgdjdd4aiTYMwpGVddNk1zUXUCBJcY8yWNm3a4MMPPwQAfPHFF9i8ebPk15Cbb95pPkruZs7kfQ6fLRDc5mT1gVzsm94LF7U6zE+Tpu+kJd7WQFuIxRmnBZcpsLbBgaRn7T3UGea7esVuhPls20lRj3fWO0NbS9qX1BkRNYLw+4zesp3/mWeewerVq7Ft2zY8/vjjOHr0KGrWrCnb9aTGmTEv4UgzZ1uFNsk3fb79hODHGtu4vDIgHk92k2+pzJsaaAtRqNNjtYgPt1X7z3J22wVsvYc6alyXGDSvV9O0IuHJ/SgteXTBHrzwwyGX7uAMUgbgme6xsgZiABAQEICvv/4aNWrUQE5ODl588UVZryc1332H9CGO5qLYK7RJvqVQp8d2jbjlBmMQ8Er/eETUCEbK+kxJC1F6WwNtITR5WlHFQ2+WlCEr/4ZX58t5Omfz+cxVUwWgQZ3qOHvlFhbtPo1Fu08DuL0i0aN5BH4+ckHC0cqnsLhUdDkPe9pE1cId6iCEhwZjXJcY3C1gKViomJgYfPjhh3jqqaewYMECDBs2DH379nXZ9cVgMObhhDRzTtmQiaS4CC5Z+jhNnhZFJeLyTcxnrBwpH1A9SOnUkqa3NdAWQooCupwZk4+Q91AFgHeHtcLxC1os2nUamrwbVY4xrkgIrJsrqbDqgbhZVIJi16WfVeEp9QAnTJiAlStXYvPmzXj88cfx559/IiwszK1jcgSDMQ9VqNNDk6fFp1tPCKphwzwU3yc2CAhRBVTJ5XJkV11EaLDPFmoVQooCur68jOtuizNOO/8eCuDTLSdwtuCWQ8e6W8FN9/Vn9LTyJwqFAl999RVatmyJc+fOYerUqfj666/dPSy7+A7gYaRIMAVsF9ok3yA2COjRvK7V14et8gHeWKjV+OWmVF8GhQKmIFMKYvscVg+sGhSTNMQkqzsSiDnLl/pR9oqvi+d6NvXI8icNGzbEnDlzkJycjEWLFmHYsGEYOHCgu4dlk2f9C/o5ZxvN2mJMzmYeiu8yJg8LDdqf6tZY1LW9oS5V5S83Xw6MAAB0SNks2Q5kdbAKQ0VUUh/WroFH/Zv5Ek2e1qXJ6oPa3ImwakFWv6S0qF8T01Yfddl45JRx8jI+fuQep167xi9FhbpSqIOV/2vNJs9rf/z48Vi5ciU2btyIJ554An/99Rdq164ty7WkwHcADyEmwdQa5qH4NjFFK9tF10YbiQpnemrA70x7KLHLqWM6x2DpnhxBsx6+vsHBnaTI53PG1sx87Jvey+qXlIM5V106Hjk584XfHWWZFAoFFixYgJYtW+L8+fOYPHkyFi9eLOk1pMTSFh5ASIKpI/ht2/eN6RwDhYAM4neGtpJ+MDJzptix8cuNvVlm4w5ksSVh4iJDMV1AAd0ZA31/g4M7SZHP5wxjgGKteG5cZCiqB/rOx675PWjt/nRnWaaoqCh8/PHHAIAlS5bgxx9/lPwaUuGntQcQkmBqjz8U2qT/BQED4p3uS+lNAYCz36rdtQPZOLvmjb0pfZXYpXwhbH1JUAerkBQXIXt5CVdRB6uw5+RlzN2swf4zV1FSdvuDTB2kRPM7Qx0qSi1nWaYxY8Zg5cqV+Pnnn/Hkk0/ivvvuQ506dSS9hhR8J0T3UnJVQ/b1Qpt0W3JiLGYMjLc7Q6ZQeF8A4Mi36j5z0vDGur9MH4KCds/9bweyWMmJsfj1+SQManMnVAFVnxBVgAKDWt+JX59P8qrnwVsZl/JdfU1bujer66KRyCtYFYDkb/bhkQV7sOfUlQqBGFB+fzrbHSRlg/TtwRQKBebPn4/atWsjLy8PEydOlPT8UuGntZvJkWDqD4U2qSJv2+HoSCKvM3mUi3afxtK9ORiScBfWHRT25UaqHchxkaH4ZEQCCofqcSS3AJq8G1AAaBoZitZRtfglycXGdI7B0r05kq8+WOLIikSzep5xD4ql05dBp5e2sJlcZZnq16+PTz75BKNGjcLy5csxbNgwDBs2TNJriMV3BTeTI8HUHwptUlXesMPR0SVHIUuNxaVl+P73s4LHJvUOZHWwCp0bh6Nz43BJzkfCCFnKF8qRFYm7wqrJPg5neVLJDbnKMj366KNYsWIFfvzxRzz99NNISkpCRESEpNcQg8uUbiZ1gumojg25/OHnrCUPu5szibxy5FE6gjuQfZOjS/liOLoicU6G+mViqIOUeKlvM3cPw8T4pUhqCoUCX3zxBe644w5cvHgRzz77rOTXEIPBmJtJ3Wj2oXsbSHYuIqk4u7vxh99zXTOwSjwlcPUXzuyQFSs5MRabpiRhdKdoWZp792pe16EViRX73fPatiaxaQRaR4VhXJcYdw/FRK7XQb169fDZZ58BAFasWIEffvhBlusIwXceNxNTK6rKubiDkjyQ0CVHV+P94zruqDsFVF3K/3RrFjZn5kty7t8y87EwPdvmyoQmT4tle8W/10tp418XsPGv8kbn1QKVaHBHNZy9fBO3zPLB1EFK9G9ZDytl2GxmyebMPHRtIs/y/vDhw7Fy5UqsWrUKzzzzDLp164bIyEhZruUMzox5gDGdYyRpNssdlOSJ3LXk6CzeP67hzrpTRsal/Jf7NZe00be93YCLM05LeDXp3SophSbvBor0ZRjfNQZLkzti7bNdsW96L7RvdIfLxlHenF3aXZVGCoUC8+bNQ3h4OC5fvoynn34aBg94g2Iw5gHiIkPxaMeGos/DHZTkaeQq3SI17kB2DVcX47VHaLFea2yVSPGWewEoT+b/etdpZJ6/bso7vVro2mbkn2zNku3cdevWxbx58wAAa9aswfLly2W7lqMYjHmIh9pFiXr8uK7l0/quzMEgssfVvQGF4g5k+QktxivXDImRMblfKqsP5Fp83/WWe8Gc+b9/bXWgS6+9/sh5WT+/Hn74YQwfPhwA8Nxzz+H8+fOyXcsRDMY8hJhE/iBlAAa3vQsz1h4tb4A8bzdGfbUXQ+btRoeUzZix9qjsb2hElri6N6AQTybFcgeyC7izGK899or1OsPSbsBCnR6HzxaIOq87GAzA//2mQaFOjx4uLlZbZgA2HJU3QPr0009Rt25dXL16FU8++aSs17JHtmBs27ZtGDt2LJo0aQK1Wo3atWujVatWeOmll5CVJd/0IwDs3bsXU6dORdu2bREREYHg4GDUr18fCQkJeOqpp/Ddd9+huLhY1jE4S0yl6FZRtTB43i635mAQWeLq3oCVBSrtf7B+mZ7Ne0NmYpbozGea5Jz5NxbrPfx6H7w6oLmocxnHpcnTmr4kz/rpbymG6XIb/7yA9rN/w3+3ZiG6TnWXXvuHP4TXDXREeHg4vvjiCwDATz/9hJs3b8p6PVskz1bV6XRITk5Gampqhf9/8+ZNFBQU4M8//8Snn36Kd999F5MnT5b02hcvXsRzzz1ncbvq+fPncf78eRw8eBDz58/HqVOnEBMTI+n1xRJaKXr/mat2j5Gz9xeRNWJ6AwYpFSguFZdYW+LA43lvyE/MEl1hcSm2HMvDvlNXXLL7Uh2sQvsYccnq6mAVFqZnI2WD/fw4b3CzpEySHf/OOny2wFS8Wi5DhgzByJEjsXTpUly7dg3Vq7s24DSS9Dc0GAwYOXIkVq1aBQCoUaMGxo8fj/bt20On02HTpk1YuXIlioqKMGXKFAQGBuKZZ56R5Nq5ubno1asXjh8/DgBo1KgRBg8ejBYtWqBWrVrQarU4ceIEtm7din379klyTam5olK0FA2RiRwlpnTL8PYNEV2nuss+0HhvyEfscvWk5Ycsn/d/M/9L9+Zg+gDp+q6K+RKhDlJi94lLeH/TcUnG4s+KSw2SdsWwZs6cOdiwYQOuXrU/sSEXSYOx1NRUUyAWERGBHTt2ID7+dmLk448/jhUrVmD48OEwGAyYOnUqBgwYIHqGqqSkBA8++CCOHz8OhUKBlJQUvPTSS1CpLP96+fn5qFWrlqhrysX4ZmLvA0ihABIahGF/ToFT55er9xeRNUJmfI27G+MiQ009N7//PUf0TJkt/nxvONIrVAy5l6ulmt00/3dIbBqOjX/lOX2OnvF18cGvDMQAoH5YCC5cK0KZiNvWFZvQIiIi8O6777o1b0yynDGDwYDXXnvN9OdPP/20QiBm9PDDD+Opp54CUL6k+cYbb4i+9nvvvYcDBw4AAGbPno1XXnnFaiAGlG9rDQ4OFn1dudiqFK0OUmJ0p2isfaYrjl0QlpRvbbcPkRyMM77OMN/daCzUeXBmH4zvar8mnwLlS5xC+Nu9YZ7TJOfGH6k7jVgjdPelpX8HIYEYAFy/VewTS5P2dI8LR1yk5SLJDe+ojk8euQe7p92PzrF1RF3HVbX/kpOTERjo2h2j5hQGiaqdpaenIykpCQAQHR2N7OxsBARYjvVOnjyJJk2aAABCQ0Nx8eJFwcFRUVERoqKicPnyZURHR+PkyZNQKl2XNBwVVV6SIjdXnhYXhTq9xabPB3OuYsi83YLPu/bZrrJP/XqbI0eOAABat27t5pH4JkdyaBQK2F1u0uRpsSTjTHngVCl/aGhCFO6NqY3J3x2y+NgvB5Y3Bp6w/qLV8/vLvSHV8+GoGWuPuiTvaHSnaDwcW1493pF7WercLlWAAnoxU0FeQh2kxL7pvVCo02O75iKuFhajtjoI3eMiULdmiOm4w2cL8OBnuwRdI0gZgIMze1sNyKR+z46MjERgYKBsn+e2SBZybtiwwfRzv379rAZiANC4cWPExcVBo9FAq9UiLS0NvXv3FnTdNWvW4PLlywCAkSNHujQQcwVjpejKxOZg+NO3f/IMyYmxpiVHa4GUcWnSlsotbSp/UdmZdUnUOL3x3nB2mdFYfNUeKTc3CN2g5KzVB3LxUKP6DjUFd/TfwRn+EIgBt0t4tG0Qhn/b6IncpkEYosKqIVdAg/Ti0jKcK7jlsjxOd86MSRaMGSNUAOjQoYPd4zt06ACNRmN6rNBgbMeOHaafO3XqBIPBgNTUVCxevBhHjhxBQUEB6tSpg7Zt22LIkCF47LHHEBQUJOhankRsDgbbvpA72AuknGHti4o/3RtCejwKLb4qdnODKzYoAeVBgr6sDIFK21k4Qv4dqCJHvrho8rSCAjEjf8njlCxnzLiLESjfyWiP+THHjh0TfF3znZE1a9ZEz549MWbMGGzevBn5+fkoLi7G+fPn8csvv2DChAlo2bIl/v7bO+u9mBOTg8GGyORuxkCqa5NwU7sVqfjLvSG0x6M7i68aq93bm7US2y/Skd/PW3qmerItmfbz6sT241z5x1mvnK12lmQ5Y3Xq1MGVK1cAlM90tWrVyubx//3vf011xh566CGsWLFC0HXvvPNOXLhQ3nG+WbNmOH78ONRqNcaNG4cOHTogICAABw8exFdffYWCggIA5Tsn9u/fjwYNrE+tmjPmhVly/vx5hIeH47fffhM0fjGu3yrBTQFbr6sHKVGzmvumYz1VSUl57zV3TlWTNKzdG2Eh5d8/C4rKLD7OW+6NQp0e2iLHP6BCQ1RQB6tgMAD52iJBQYhCAdQNDXFo+c8efZkBN3V63CoprTAWhQKoFqhEoCoA124K74VYp3p5MG7tXhbz70AVhYcGW+1aINW/c50aQRZnOqV+z+7du7f354xptbd3sFSrVs3u8ebHXL9+XfB1zeuCHD9+HA0bNsTWrVvRuHFj0/8fOXIkpk6divvvvx/Hjh3DxYsX8eyzz2LdunWCr+sJqgerhAVjXrQMQySEL98b+jKDU4EYAGiL9AgOVMJgMAj+YDQY4NDynyNUAQrUrBaI0JBA6MvKYDCUB2KqgAAoFOXXuq4oETZWB4JF4zVJvJs6vdUvMFL9O5eUSvO682Se/85jR+WJva+++qpCIGZUv359LF26FO3atQNQ3vrgxIkTpl2dttiKko2zZu7agedsAuqMgfHo05ZVxi3hbkrfYunesLWb0lvujfJdidZ3g1ozulM0+raohwnf7xV87aXJHdGuSbjgxztDzO7L1Y9EQRWgsHov78y6JOrfwZZBbe7Ez0fO+02wZ9xVaSnVQKp/586xd2D5hIQq/1/q92x3ropIFmqGht5O7Lx1y36ynvkxNWvWlOS6TZo0Qa9evawem5CQgI4dO5r+vHnzZsHX9RQO52Aoyj9s2O6F/IUv3htiezza2OTuEFdubhjTOUbwY2/ayTGSqwitQgFM7NnU6bp63sxSY3Qjqf6dD5yRtg+pJ5IsGAsLCzP9fOmS/a3l5seYP9ZZtWvXNv1877332j3e/JgTJ04Ivq4ncaRI7KYpSV7xYUMkJV+7N8T2eNx+PB/BKmFv+67e3HBXWDUECVyaqpyLVplcRWiNxYqTE2PxaMeGkp/fU1kLlOIiQxEi8PVmTve/tki+TLKvOc2bN0d2dvmunVOnTqFHjx42jz916lSFx4q5rjGocqTFkXngJyZXzdNIWTKAyJeY3xvH/v4TBkN5YVdvvDfE1hf8Mu2U/YOsGJoQ5dJ/L02eFsWlljda2GPMb7NGTM9USywVx324XRSW7XV9c213sPa6UAer0L1ZhOBuBuY4M+Yg8zVbRxpxmx8jZr23bdu2pp+vXbtm93jjjkrAseDN28hZMoDIm6mDVQhUBiBIFeC194bcPR6tUSiAh9pF4WDOVezMuoSDOfIvG4kNPO3lbI3pHCN6Z6it2VVXtYCyR+jsoqNCVAE2Z0yf7m4/L9sR3ni/OkOy327AgAF49913AQAbN25EWVmZzXZIxoKvoaGhSExMFHzdBx54ALNnzwYA/PHHH3aPNz+mWbNmgq9LRORqxg94oUuVQiU0CMOjC/Y4XFhWCmIDT3uBlpAitC/3bYYuTcIdWnmQevbNWcbZuhEdGmLLsTxMWn5IlusMvucum4FSmwZhaNcwDPtzCgRfw5vq/wklWcjctWtXNGxYvkZ+5swZrFy50uqxH330kennoUOHIiQkxOqx9nTs2BFNmzYFUJ4DZisp/8CBA9i7t3xnh1KpRN++fQVfl4jI1Ywf8K62P6fAqcKyUhAzs2Qsk2FNoU6PgzlX0bxeTYxzpPn8/zZ5PNOjiVMrD1LMvlkby4/PdkXvu+tWmfmqPFunDlZh36kr0g7CzPj77Bd5f2eYuN2Orl4idwfJgrGAgAC8+eabpj9PnDjRYmX9lStX4osvvgAABAcHY+bMmVbP2b17dygUCigUCsyaNcvqccYZOQB4/PHHTblr5s6fP4+RI0ea/jxy5EiHi74SEVVm/EB31bKdkRwf8JWpg5RoF13b/oG43b9S6oBMTOBZLVBp8d9Ik6fFjLVH0SFlM4bM241RX+3Fol2nEaIKQFxkDVQLlHaTh3H2zRlPJsXa3HAyf1Q7HDp7FcM+343f/s6vkFenVACJTcMr9Hg9fLYA3+076/TYHfFwuyiHZkXjIkMxY6CwHaYKBTC6c7Sgx3oTSUPNMWPGYO3atVi7di3y8/PRoUMHjB8/Hu3bt4dOp8OmTZuwYsUKU22wDz74ALGx4ncxDR06FGPHjsU333yDnJwctG7dGuPHj69QgX/hwoWmfLHGjRtj7ty5oq9LRP5HSD9IKcnZ4zFEFYBF49qjepAKg+ftcuqxUvSvrExIc3GFwnLx3oXp2UjZkGnxXLf0ZdDkle/WG9c1Br3iIyXbAGUM4qxd23zc5psALG3GWr4vBxOW7Ld6jlIDsPGvPGz8K88U/KSsz4RcJc9CAh2fuTT+Xs6+bo07VH2dpMGYQqHA8uXLMX78eCxfvhxarRYff/xxleOCg4Px9ttvY+LEiZJde+HChahRowY+++wzFBYW4pNPPrF4XJcuXbBixYoKJTGIiBxh6wPduGy3dG9OlZ11UktOjMXFGzrM3yHtbFSRvgzVglRYsf+s4P6VUjZ1FhJ4Th8QD1VAxTIIzhTHXrTrNO4Kqybp85ecGIukuAgsyTiD1QdyqwTxQxOiKsxmAbc3Ywn5HQDngx4hVh/IxbT+zR0OWI3/Dq+sPor9Z67aPNbSDtVCnb68vIuuFOpgJQIM9nMDvYXki7AhISFYtmwZkpOT8c0332DXrl04f/48goKCEBUVhb59++LJJ59EXFycpNdVKpX45JNPMGbMGCxatAhbt27FP//8g5KSEtStWxcdO3bEiBEjMHjwYCh85dkjIpdx9MPQuGwHwO4HeuUPl7jIUIc/2ORaFr18QyeqsKwzH86OEDKzZKzMDpTPZKZscC4wkWOWT0z5IU2e1iXBlbOMBV/Ng0Z74iJDserpLjh8tgBf7DiJbcfyUaS/vdRqKTi1Nhu94IEIVAtUQpOn9frZM9ky4nr27ImePXuKOsf27dudfkz79u3Rvn17UdclIjIn9Qe62KVOMZX47bl8o1hUYVlnP5wdIWRmyWhxxmmPmOUzqjzj5YjFGaclH4dUKn8pcPQLRpsGYfh8VDsU6vQ2g1Nbs9EGA3CzuBR956bJPhstN9/enkBEJAEpP9ClWOoUU4nfFnWQEneog0SdQ64ZOyEzS2LbR0k9yydEoU6P1fut90d2N+O/j9AvGLaCUzlmoz2Vb7dBJyISSewHunlwYvxwsRfY2duhKLYgqjVDE6JQp4a4YEzu4MWZwtZi20d5QgseTZ4WN0uEdSKQm7H+18L0bPSdm4bUPTmSlUAROhutydM69RhPwWCMiMgGqT7QpfxwkaMSv7GEgJj6Xp5WnFNs0OoJLXjkCrylMDQhCsv35UjyBaMyMbPR3ojBGBGRDVJ9oEv54SJHqx1jCQEx9b08rTin2KDVE34Xd7XAskfxv5pmcsxeSTkb7S0YjBER2SDFB7rUHy5SVuI3VnQ3z7URUljWE4tzig1aPWHffVxkKKoHet5H9fQB8UjLuijL7JUvLC87y/OeYSIiDyLFsp0cHy5iK/FXC7Td5NrZyvGeWJxTbNC60gMS59XBKgxtF+WSa7WLrm33NWUM3kd0aCjb7JUvLC87y/1zsETk18TU2nIFMQ2fjct2cny4CCmI+kDrOzHknrtQp0aw3fpWQivHe5qH2zUQ3KzbU3ZUjukcI3vDcQWAd4a2AgCHSogczLkqWwkUX1hedpb3jZiIfIK72wo5Q2hbHuOynVwfLnIHTGLqe3mKMmfX0czIVTfNWcbejnIWfp0+8PbMpiMlROScvTLORgsJ9jxtE4mjGIwRkct5SlshRwlty2P8cJPzw0XugElM5XhP4CtLXkJ7OzriyW6xVe4ze8Vp5Zy9kmI22tt434iJyKt5ayFHMbNQcn+4uCJgElI53hP40pKXMfCeufZP7Dl1RZJzPpkUi1f6O5cfCMg/eyV2NtrbMIGfiFzG2ws5JifGYtOUJIzuFF0lqV8dZD0hHnDNDkVnCqL6C1+qmwaU/z7/6d9cknPNGBiPV5zcqGEkdwkUX9lE4ijeqUTkMp7WJ1AIobNQYpc6SRgxs5LN76zpkQGtmFkpAHikfQOMv6+R6NdWUtMIQf+uiU3DHTrOVzaROIIzY0TkEr5WyFHILFRyYixmDIx3uHyAN3+4eBKhZUD2n7nqVAsfVxEzK/VI+wZ4d1hrSYL8tKyLgh6XnnXJ4WNtzUYrFED1IKXV2Whv4nkhPxH5JClqbXljzlJlvrBD0dsImZU0StmQiaS4CLc8H4U6PQ7nFkBz4QYAA5rVC0XrqPLAX2hO1fj7Gkk2Nlc1Ybc2G624kgOFAj5xrzAYIyKX8JVdbVLw9h2K3ig5MRa/HD2P/TkFTj3OHcvkmjwt/rtFg41/5kFfVjHaUgUo0K9lPUy6v6lbl73d8eWq8iaSI1flrb3mSlymJCKX8KVdbVJhwr3rFOr0OHZB2EYQVy6TL0zPRp85afj5yIUqgRgA6MsM+PnIefSZkwYAblv25pcraTEYIyKX8LVdbeRdvKHfoaNlX4xmr89E9qVCzB3eFo+0b+D0Dl8x+OVKWvzXICKX8MdCjuQ5PH0mR5OnFZTTtmxvDpbtzYE6SIkH2tyJrk3CUUcdLPuyt9gdnZ7QhN2TcGaMiFzGFbW2iCzx9JmcxRmnRT2+sLgU3/+ei8nfHULm+euyL3v7QhN2T8JgjIhcxt8KOZLn8ORl8kKdHqslCk6MnStcUZLj4XYNBD/WE8vVuBODMSJyKdbaIneQu2K8GJo8LW6WlEl6Tld0rpCiCTuVYzBGRC4npq0QkVCeukwuNp/NEmNJDjl5eh6eN2FGLBG5BWttkat5aksqsfls1jhbXNVZnp6H5034L0FEblW5kCORnDyx32FcZCiqBwZIvlQpd+cKMTsqWa6mIi5TEhGRX/G0ZXJ1sApD20XJcm45lwLF5OHVVgdxZswM/yWIiMjveNIyeaFOj9Z3hQGQvr2P3L9LUtMIQbUDc6/ewju/ZOKV/s7trvZVDMaIiMhvuXOZXJOnxeKM01i1/xxulUifxO+KpcC0rIuCHzt/RzaGJUSxdA24TElERORyC9Oz0XduGlL35MgSiAHyl+SQoj7a1ztPSTQa78ZgjIiIyIWMPShFlOmyyxUlOaSoj7b20DmWuACDMSIiIpfR5GmRssH5HpTOckVJDinqoxWVlLH4K5gzRkRE5DKLM07LPiPmqpIcUtVH48wYgzEiIiKXKNTpsebAOVnOHaQMwPD2DTC6c7TLEuKlqo/GEhcMxoiIiFxCk6cVVCDVnnFdY/Bin2YuD2qM9dGElLYwnYPFXwEwGCMvV6jTl7/B6UqhDlaWV4Tmtywi8kBS96BsF10b7wxt5dbSEGM6x4gKxuTe8ekt+C9AXslYn2fNgXMVvmmqg5QYknAXxnSOYe0aIpKVrS+Dlv9OmhyrzrF3YFr/eLTxgDZicZGhmDHQuX6fRq7Y8ektGIyR11mYnm21r1xhcSlS9+Rg6d4clyWxEpF/sfVlsEfzCAAKbDuWX+XvHmhzJ6oFKkXVFaseGICFj7X3qNkk4/usswGZK3Z8egvPeTaJHGCsz2OPwXD7jYEBGRFJxd6XwZ+PXLD4uMLiUnz/u7gCqQAwrF0DjwrEjJITY5EUF4Ep3x3E3+e1No915Y5Pb+F5zyiRFULq86RsyERSXAS/fRGRaI5+GZSLpy/rxUWGYsPkJBw+W4BPt53AjuMXUVx6e6elOkiJoQlRLt3x6S0YjJHXEFKfx2AAlmScwVuDW8ozKCLyC64q1mqLtyzrtWkQhgVj7kWhTu/2JuzeghX4ySsU6vRY9YewKf7VB3JZVJCIRJG7WKstCgUwY6D3LesZm7B3bRKOtg3CGIjZwGCMvMKHvx7HLb2wwoKFxaVst0FEgslVrLWaKgCPtG+AaoGWd1lWUwVgdKdobJqS5HWBGDmHYSp5vIXp2Vi067Soc3BmjIiEkqtY6y19GR7p0BCvPXA3svJv4PINHS4XFqOOOgh1agRzWc+P8FkmjyZVngbf0IhIKKmLtVY8t960nEf+i8uU5NGkyNNguw0iEkOqYq2Wz80visRgjDyYVHkabLdBRGLERYZCHSR9QMYvimTEYIw8lhR5Gp5el4eIPJ86WIUhCXdJfl5+USQjBmPksaTI0/CWujxE5NnGdI6BQiHd+fhFkcwxGCOPJTZPY1zXGG4HJyJJxEWGYvqAeMnOxy+KZI7zo+SxjHkaQpYqg1UBeLFPMxlGRdYU6vTlS8u6UqiDleXPH5dgyIcYv9xZ603pCPZlJEv4Tkkey5inkbonx+nH6vRlWL4vh294LqDJ02JxxmmsOXCuQuCsDlJiSMJdGNM5hjMA5DOMDbGXZJwp7+5R6TXfs3ldQAFszcyv8nfsy0jWMBgjjzamcwyW7s0R9C3U2NCXAZl8FqZnW50lKCwuReqeHCzdm8OZAPIpcZGheGtwS0zr39xq70X2ZSRn8JVBHs2Yp2EMrJyVsiETSXER/CYqg4Xp2Q49LwYDA2PyTbaKtbKQKzmDCfzk8ZITYzFjoLDEWYMBWJJxRuIRkZDOCCkbMqHJ08o0IiIi78VgjLzCiA4NUU0l7OW6+kAue1NKTEhnBAbGRESWMRgjr6DJ0+KWvkzQYwuLS5GVf0PiEfkvMZ0RGBgTEVXFYIy8gtgCsAwApCOmMwIDYyKiqhiMkVcQWwCWu5ikw8CYiEhaDMbIK4hp1MtmvNJiYExEJC0GY+QVxDTqZTNeaTEwJiKSFoMx8hpCGvWyGa/0GBgTEUmLwRh5DSGNetmMVx4MjImIpMNgjLyKsQCsI4HAjIFswSMXBsZERNJhMEZeJzkxFpumJGF0p2irhWCrBSpx+nIhK77LyNHAWKFgYExEZAuTN8grxUWGIrpOdRRZKQR7q4RNql0hOTEWSXERWJJxprygq1n9MXWQEkMTojC6c7TFGbFCnb68ZpmuFOpgZfnGAOaTEZEf4jsfeSU2qfYccZGheGtwS0zr3xxZ+TdQqNNDHaxC07o1LAZXmjwtFmecxpoD56oEb0MS7sKYzjFcziQiv8JgjLyO0CbVSXER/JCXkTpYhbYNwmweszA9GykbMi32tSws5mwmEfkn5oyR12GTau9knM2099wZZzMXpme7ZmBERG7GYIy8CptUeyehs5ncgEFE/oDBGHkVNqn2TpzNJCKyjsEYeRU2qfY+nM0kIrKNwRh5FTap9j6czSQiso3BGHkVNqn2PpzNJCKyjcEYeaxCnR4Hc65iZ9YlHMy5aqpfxSbV3oWzmUREtvFdjjyOvaKgSU0jsHRvjlMJ4WxS7T7G2UwhS5WczSQif8CZMfIoC9Oz0XduGlL35FT58DYWBX0ydT96Na/r1HnZpNp9OJtJRGQbgzHyGM4UBf0tMx+94+uySbWXGNM5xu5zVRlnM4nIXzAYI48gpCjo5mP5mD+qHUZ3iq6S1K8OUmJ0p2hsmpLEQMwDxEWGYvqAeKcew9lMIvIXnP8njyC0KGh61iWnmlST+xiDYmu9KY0UCrA3JRH5FX5akduJLQo6rX9zh5pUk/slJ8YiKS4CSzLOlBd0rbRBY2hCFEZ3juaMGBH5FQZj5HZSFAVlIOY94iJDOZtJRGSG73zkdiwK6p84m0lEVI4J/OR2LApKRET+jJ9i5HYsCkpERJYU6vTlqSy6UqiDleWfFz74Bdz3fiPyOsaioKl7cpx+LIuCEhH5nsNnC/D59hPYdvwidPoy0/+vHqTE0IS7MKZzjPsGJwMuU5JHYFFQIiLS5GkxbN4uPPjZLmz8K69CIAYAN//XiaXvnDSfyhdmMEYegUVBiYj828L0bPSZk4b9OQV2jzUA0BbpfSYg4/oOeQwWBZWev+RbEJF3M7bDc5a2qPw9ztu/mPNdmTwKi4JKQ5OnxeKM01i9Pxc3S8zyLQIDMLRdFMZ0juG/IRF5BCHt8Mx9sjULn4xIkHBErsdgjDwOi4KKY+sb5s2SMqTuyUHqnhw2UCcijyCkHZ65X45eQOFQvVd/PnjvyMnnsSio85yZ6jcex4CMiNxFTDs8I32ZAUdyC9C5cbhEo3I9JvAT+QhNntbpnIvZ6zOhydPKNCIiItvEtMOreJ4bEozGfRiMEfmI/27RCHrcJ1uyJB4JEZFjxLbDM3KyMpLHYTBG5AMKdXps/DNP0GN/+fOCz2wPJyLvIrYdnlFTL9+QxGCMyAcczi2AvkxYBmx5vsU1iUdERGSfsR2eGKoABVpH1ZJoRO7BYIzIB2guiMuXyGLeGBG5gbEdnhj9W9bz6p2UAIMxIh8hYl+46EcTEQknpB2euYn3N5VuMG4iWzC2bds2jB07Fk2aNIFarUbt2rXRqlUrvPTSS8jKcl3C8KuvvgqFQmH6b+zYsS67NpGrNKsnLl8iLrKGRCMhInKOkHZ4RqEhKp8oYC35vJ5Op0NycjJSU1Mr/P+bN2+ioKAAf/75Jz799FO8++67mDx5stSXr+D333/H+++/L+s1iDxB66gwqAIUgvLGyvMtwqQfFHk0tsoiT+JoOzxzoSEqn3nNSvpbGAwGjBw5EqtWrQIA1KhRA+PHj0f79u2h0+mwadMmrFy5EkVFRZgyZQoCAwPxzDPPSDkEE51Oh3HjxqG0tBRqtRqFhYWyXIfIE6iDVejXsh5+PnLe6cf2b+X9+RbkOGOrrDUHzlVpNzYk4S62yiK3MW+Ht3J/Lm6VVC17EaJS4OF7G2J052gU5Z1ywyjlIek7cGpqqikQi4iIwI4dOxAff3vq8fHHH8eKFSswfPhwGAwGTJ06FQMGDEBMTIyUwwAAzJo1C3/99Rdq1qyJl156Ca+99prk1yDyJJPubyooGJvY0/vzLcgxC9Ozrc48FBaXInVPDpbuzcH0AWyVRe5RuR3e5Rs6XC4sRh11EOrUCK7QFu+IsGo+HkmynDGDwVAh4Pn0008rBGJGDz/8MJ566ikA5bNXb7zxhlRDMNm3bx8++OADAMAHH3yAqKgoya9B5GniIkMxY6BzeRczBsZzFsRPGFtl2VsCMhjKOzMsTM92zcCILDC2w7s/PhL/vrcB7o+PRNsGYT47iy9ZMLZz506cOXMGABAdHY2HHnrI6rEvvPCC6edVq1ZBp9NJNQzodDqMHTsWpaWl6NGjB5544gnJzk3k6ZITYzFjYLzdatQKgI3C/YgmT4uUDc61ykrZwFZZRK4iWTC2YcMG08/9+vVDQID1Uzdu3BhxcXEAAK1Wi7S0NKmGgZkzZyIzMxPVq1fHggULoBCzX5bICyUnxmLT80kY3Ska1SsVU6wepMToTtHY9HwSAzE/sjjjtMNJ0UYGA/DJVrbKInIFyeb7jhw5Yvq5Q4cOdo/v0KEDNBqN6bG9e/cWPYa9e/fio48+AgC89dZbaNy4sehzEnmjynkXhTo91MGqCvkW5B8KdXqsOXBO0GN/Onwe8fVO4JkeTSQeFRGZk+xd+fjx46afGzVqZPd482OOHTsm+vpFRUWm5cmOHTtiypQpos9J5O2MeRfkvzR52gq7Jp31/qbjCFIFcCaVSEaSBWNXr141/RweHm73ePNjCgoKRF//tddew7FjxxAUFISvvvrK5jKps2xtADh//jzCw8MrzAySdyopKQEAPpc+zB+f41J9Gb4cGCHyLJdw4JAWqgDvSPvwx+fZH0n9PJeUlCAwMFCSczlLsohFq72d6FmtWjW7x5sfc/36dVHXzsjIwP/93/8BAGbMmIEWLVqIOh8Rka+QKm32pk4vzYmIqAqvTx4pKirCuHHjUFZWhtatW2PatGmSXyM3N9fq3xlnzVq3bi35dcm1jN+u/P259OXK7P74HBfq9OiQslnUUiVQXhR23/ReXvFa8Mfn2R9J/Ty7a1YMkDAYCw0NxZUrVwAAt27dsnu8+TE1a9YUfN3p06fj+PHjUCqV+Prrr936j0nkzViZ3Tepg1UYknAXUvfkiDpPYXEpsvJvMAeRSAaSLVOGhYWZfr506ZLd482PMX+sM3bv3o25c+cCKK9d1q5dO0HnIfJ3C9Oz0XduGlL35FSZQTFWZu87N42FQL3UmM4xkixXFnKpkkgWks2MNW/eHNnZ5W/Up06dQo8ePWwef+rU7Z5SzZs3F3TNBQsWoKysDEqlEoGBgZg9e7bF4w4ePGj6+ciRI6bjQkJC8OKLLwq6NpGvMFZmt8dYmR0Ad9Z5mbjIUEwfEO/Q82yLNyxREnkjye6s1q1bmwq/7tu3D+PHj7d5/L59+yo8VgjD/6oYlpaWIiUlxaHHHDx40BSc1apVi8EY+TWhldmT4iK4ZOllkhNjUawvw/ubjts/2AJ1kBJN69aQeFREBEi4TDlgwADTzxs3bkRZWZnVY0+ePGkq+BoaGorExESphkFEThBamX1Jxhl5BkSyeqZHEzzQup6gxw5NiOLMGJFMJAvGunbtioYNGwIAzpw5g5UrV1o91lglHwCGDh2KkJAQQdf85ptvYDAY7P63aNEi02Mee+wx0/+Xor4ZkbcSU5l99YFc5g95qUn3xzmdP6ZQAKM7R8szICKSLhgLCAjAm2++afrzxIkTLVbWX7lyJb744gsAQHBwMGbOnGn1nN27d4dCoYBCocCsWbOkGioRQVxlduPOOvI+xvwxZ0xIiuWyNJGMJJ1zHjNmDNauXYu1a9ciPz8fHTp0wPjx49G+fXvodDps2rQJK1asMOV6ffDBB4iNZSIwkTsU6sTVneLMmPcybsBI2ZDp0DL1l2nZiKgRzI0bRDKRNBhTKBRYvnw5xo8fj+XLl0Or1eLjjz+uclxwcDDefvttTJw4UcrLE5ET1MFKkY9n/pA3S06MxUWtDvPT7Jcr4U5aInlJ18Dxf0JCQrBs2TJs2bIFo0ePRmxsLKpVq4ZatWqhRYsWmDp1Ko4cOYKpU6dKfWkickJcZCjUQcICMu6s836aPC2+dLJuXMqGTGjytPYPJCKnyPbVtmfPnujZs6eoc2zfvl2SsYwdOxZjx46V5FxEvkJMZXburPN+YnbSvjW4pTyDIvJTks+MEZH3EFKZnTvrvB930hJ5FgZjRH5MyM666QPiubPOy3EnLZFn4ToDkZ9zdGedQlEeiDGB2/txJy2RZ2EwRkRIToxFUlwElmScKV+GMps1UQcpMTQhCqM7R3NGzEdwJy2RZ+EdRUQAypcs3xrcEtP6N0dW/g0U6vRQB6vQtG4Nfvj6GONOWiFLldxJSyQ9vsMSUQXqYBXaNghz9zBIRtxJS+RZmMBPROSHuJOWyHMwGCMi8kPcSUvkOTjXTETkp7iTlsgzMBgjIvJj3ElL5H4MxoiI/Bx30hK5F+8yIh9WqNOXV1vXlUIdrCwvacAPV7KCO2mJ3IPvykQ+SJOnxeKM01hz4FyVZachCXdhTOcYLjsREXkIBmNEPmZherbVhOzC4lKk7snB0r05TMgmIvIQDMaIfMjC9GzMXp9p9ziDAabjGJAREbkX64wR+QhNnhYpG+wHYuZSNmRCk6eVaUREROQIBmNEPmJxxmmbtaIsMRiAJRln5BkQERE5hMEYkQ8o1Omx5sA5QY9dfSAXhTq9xCMiIiJHMRgj8gGaPG2FXZPOKCwuRVb+DYlHREREjmIwRuQDCnXCArHbj+fMGBGRuzAYI/IB6mClyMdzYzURkbswGCPyAXGRoVAHCQvI1EFKNK1bQ+IRERGRoxiMEfkAdbAKQxLuEvTYoQlRnBkjInIjBmNEPmJM5xgoFM49RqEARneOlmdARETkEAZjRD4iLjIU0wfEO/WY6QPi2aOSiMjNuDZB5EOMrY2s9aY0UijA3pRERB6CwRiRj0lOjEVSXASWZJwpL+hqVn9MHaTE0IQojO4czRkxIiIPwWCMyAfFRYbircEtMa1/c2Tl30ChTg91sApN69Zgsj4RkYfhuzKRD1MHq9C2QZi7h0FERDYwgZ+IiIjIjRiMEREREbkRgzEiIiIiN2IwRkRERORGDMaIiIiI3IjBGBEREZEbMRgjIiIiciPWGSMiIgBAoU4PTZ4WhbpSqIOViIsMZZFgIhfgXUZE5Oc0eVoszjiNNQfOVWmfNSThLozpHMP2WUQyYjBGROTHFqZnW20sX1hcitQ9OVi6N4eN5YlkxGCMiMhPLUzPxuz1mXaPMxhgOo4BGZH0mMBPROSHNHlapGywH4iZS9mQCU2eVqYREfkvBmNERH5occZpi0uTthgMwJKMM/IMiMiPMRgjIvIzhTo91hw4J+ixqw/kolCnl3hERP6NwRgRkZ/R5Gkr7Jp0RmFxKbLyb0g8IiL/xmCMiMjPFOqEBWK3H8+ZMSIpMRgjIvIz6mClyMdzIz6RlBiMERH5mbjIUKiDhAVk6iAlmtatIfGIiPwbgzEiIj+jDlZhSMJdgh47NCGKM2NEEmMwRkTkh8Z0joFC4dxjFApgdOdoeQZE5McYjBER+aG4yFBMHxDv1GOmD4hnj0oiGXCumYjITxlbG1nrTWmkUIC9KYlkxGCMvF6hTl9eN0lXCnWwsjw5mTktRA5JToxFUlwElmScKS/oalZ/TB2kxNCEKIzuHM0ZMSIZ8ROLvJYmT4vFGaex5sC5Kh8gQxLuwpjOMfwAIXJAXGQo3hrcEtP6N0dW/g0U6vRQB6vQtG4NfrEhcgHeZeSVFqZnW11aKSwuReqeHCzdm8OlFSInqINVaNsgzN3DIPI7DMbI6yxMz8bs9Zl2jzMYYDqOARkREXkq7qYkr6LJ0yJlg/1AzFzKhkxo8rQyjYiIiEgcBmPksEKdHgdzrmJn1iUczLnqlv50izNO29z1ZYnBACzJOCPPgIiIiETiMiXZ5SmJ8oU6PdYcOCfosasP5GJa/+ZMRiYiIo/DmTGyaWF6NvrOTUPqnpwKgRhwO1G+79w0LEzPln0smjxtlTE4qrC4FFn5NyQeERERkXicJiCrPC1RvlAnLBC7/XjXL6sSERHZw5kxssgTE+XVwUqRj+d3DyIi8jwMxsgiT0yUj4sMhTpIWECmDlKiad0aEo+IiIhIPAZjVIXYRHm5lgPVwSoMSbhL0GOHJkQBgNt3gxIREVXGdRuqQopEebmqeI/pHIOle3OcnrW7erMYHVI2s20SERF5HM6MURWenCgfFxmK6QPinX7cz0fOu3w3qCfUZSMiIs/HmTGqwtMT5Y07Nq31pnSWcTfo8mF3SjJ2T6nLRkRE3oEzY1SFNyTKJyfGYtOUJIzuFF1lrOogJQa1vhMKJ8+pLdJDXyYuuvOkumxEROQdODNGVRgT5VP35Dj92KEJUS4rIREXGYq3BrfEtP7NkZV/A4U6PdTBKjStWwPv/JIJIWHVTRFLiZ5Wl43IHQp1+vK8U10p1MHK8i93LCtDZBPvELJISKK8QgGM7hxt8e/kfINWB6sqbBgQsxv0VkmpKahzhtC6bElxEVyyJJ/A5Xki4RiMkUXGRHlHZnqMpg+Ir/Jm6443aDG7QQ0GCNoNKqYu21uDWzr3QCIPszA922oOp3F5funeHEwfEM/ZYCILmDNGViUnxmLGwHgo7CRfKRTAjIFV32TdlT/l6t2gnlqXjcgVjMvz9r6MGJfnmS9JVBWDMbLJXqL86E7R2DQlyWIg5q43aFfvBmUDc/JXntg2jcgbcZmS7LKVKG8pcHF3/pRxN6iQAEmhgNO7QT25LhuRnLg8TyQNBmPksMqJ8taIeYOe1r+5KdE/IABQACgtg1NJ/2J2g1YLVDo9M+bpddmI5CB2eX5a/+Z87RP9D+8EkpSYN+jl+3Kw6kAublqZ0XIm6V9o26TqAj4cxMzEsYE5eStPbptG5G2YM0aSEvMGrS8zWA3EAOeS/oW0TQoNUUEV4GypWPENzDk7QN6Iy/NE0mEwRpIS+wbtCEeT/p3dDSomKBrTOcbudSxd11pdNiJPx+V5IukwGCNJiX2DdoYju7KE7gZ1lpCZOEt12Yi8hTe0TSPyFvxqQpISkz/lLEd3ZTm7G1QoRxuYKxRg8Uvyet7SNo3IG3BmjCQlJn9KCGeKphp3g3ZtEo62DcJk+TBw1UwckSfg8jyRNPjVhCQndCejEJ64K8tVM3FE7iZV2zQif8eZMZKckPwpMTx1V5YrZuKI3E1s2zQi4swYycTR/CkpMMghcq/kxFgkxUVgScaZ8tQBs5xRdZASQxOiMLpzNGfEiKzgpxjJxvgGPWn5QRy7IE8vOu7KIvIMXJ4nEo53CMmmUKdH5vnrsgViAHdlEXkaR9umEdFt/BQjyWnytPhq5yn8eOgcikrKZLsOd2UREZEvYDBGknrnl0zM32G7Mr5UuCuLiIh8AYMxkswT3/6O3zLzZb8Oi6YSEVCeCqHJ06JQVwp1sLK86DTTFsgL8VVLknhnQ6bsgRh3ZRERUJ4KsTjjNNYcOFdl5+aQhLswNMYAVYCT1WiJ3IjBGImmydNifpr0S5P9WkTisS4xKDOAu7KICACwMD3basmcwuJSpO7JQVKdCISG8L2CvAdfrSTaVzulD8SClAH46N9tGXwRkcnC9GyHq/1ri/RYmJ7NdAbyCqzAT6IU6vT48eA/kp93ePsGDMSIyESTp0XKBsfbLgHlRac1efKV1iGSCoMxEkWTp0WRXtryFSxZQUSVLc447XQ3D4MBWJJxRp4BEUmIwRiJUqgrtX+Qk1iygojMFer0WHPgnKDHrj6Qa7d/baFOj4M5V7Ez6xIO5lz12H635Lu4DkSiqIOVkp2LJSuIyBJNnrbCrklnFBaXIiv/hsWuAPZ2ZY7pHMMvhuQSDMZIlLjIUKiDlILfKI36t6yH53vH8Y2PiKoQOwNvaabLkV2ZS/fm8AsiuYRsy5Tbtm3D2LFj0aRJE6jVatSuXRutWrXCSy+9hKysLEmvdeXKFSxfvhxPPfUUOnXqhPDwcAQGBqJmzZpo1qwZRo4ciXXr1qG0VPolNX+nDlZhSMJdos5RPUiJDx9uw0CMiCwSOwNfeTOQcVemvRw0gwGYvT4TC9Nd01WE/JfkM2M6nQ7JyclITU2t8P9v3ryJgoIC/Pnnn/j000/x7rvvYvLkyaKvN3XqVHzyySfQ66t+89FqtdBqtdBoNFi2bBkSEhKQmpqK+Ph40del28Z0jsHSvTlOJ9caDWOzbyKyQcwMvDpIiaZ1a5j+LHRXZlJcBL8wkmwknRkzGAwYOXKkKRCrUaMGJk2ahCVLlmDhwoV4+OGHoVAoUFRUhClTpmDevHmir/n333+bArFGjRrh8ccfx2effYbvv/8eX3/9NZ544gmo1WoAwIEDB9CtWzecOHFC9HXptrjIUEwfICzA5c5JIrJHzAz80Epf9v67JYu7MsnjSDodkZqailWrVgEAIiIisGPHjgqzUI8//jhWrFiB4cOHw2AwYOrUqRgwYABiYmIEXzMgIADDhg3DlClTcN9991X5+3HjxuGVV15B3759kZWVhYsXL+Lpp5/Gb7/9JviaVJUxp8LRgoxG3DlJRI4QMgNv/mWvUKfHOxsy8fOR84Kuv/pALqb1b85ZfJKFZDNjBoMBr732munPn376qcXlwIcffhhPPfUUgPIlzTfeeEPUdVNTU7Fy5UqLgZhRo0aN8P3335v+vHnzZpw5w285UktOjMWvzyehXXRtu8cqFMCMgUyMJSLHCJmBNx4/Y+1R3PPmb0jdmyP4+sZdmURykCwY27lzpynAiY6OxkMPPWT12BdeeMH086pVq6DT6QRf94477nDouHvuuQfNmjUz/fnIkSOCr0nWxUWGYtXTXfDjs13Rv2U9hARWfImpg5QY3Skam6YkMRAjIqckJ8ZixsB4KBzoAW7sTdl3bhpS9+SguFR8cWrWHyO5SDbfumHDBtPP/fr1Q0CA9TivcePGiIuLg0ajgVarRVpaGnr37i3VUKyqWbOm6eebN2/Kfj1/1qZBGD4f1Q6FOj2y8m+gUKdns28iEi05MRZJcRFYknGmvKBrpfpgQxOiEB5aCl1JqdNpE/bwvYvkItkry3ymqUOHDnaP79ChAzQajemxcgdjxcXFpusBEJWnRo5TB6ssFlskIhIqLjIUbw1uiWn9m1v8snfg0GFoi6Sdxaq8K5NISpIFY8ePHzf93KhRI7vHmx9z7NgxqYZh1bJly3Dt2jUAQL169dC+fXuHHxsVFWX1786fP4/w8HAue/qAkpISAFzC9mV8jn1PAIBQALgJnLxa/v/K9HqEhQTgy4ERkl2nepASJ4//Ldn5SDyp7+eSkhIEBgZKci5nSZYzdvXqVdPP4eHhdo83P6agoECqYViUl5eHl156yfTn6dOn21xGJSIi72QwAAYILHpoQ3UuUZKMJHt1abVa08/VqlWze7z5MdevX5dqGFXodDoMGTIEly5dAgDcd999pt2cjsrNzbX6d8ZZs9atWwsfJHkE47crPpe+i8+x7zuYcxVXz5R3eZmw/qIk5+wdXxcLHmsjyblIOlLfz+6aFQNkbIfkCUpLSzFq1ChkZGQAAO6880589913UKn4DYeIyBeJ7WNpyeZj+dDkae0fSCSQZMFYaOjtwp23bt2ye7z5Mea7HKVSVlaGsWPHYuXKlQDK88S2bt2Ku+4S10eRiIg815bMPMnPyQr8JDfJgrGwsDDTz8YlQVvMjzF/rBTKysowbtw4U1smYyDWvHlzSa9DRESeQ5OnxaLdp2U59+oDuawzRrKRLBgzD3ROnTpl93jzY6QMkkpLS/HYY49h8eLFAMqXJrdt28bm4EREPm5xxmnZzs0K/CQnyYIx8wS6ffv22T3e/Bipku9KS0sxevRo04xY/fr1sX37ds6IERH5uEKdHmsOnJP9GkRykCwYGzBggOnnjRs3oqzMeuuJkydPmgqwhoaGIjExUfT19Xo9Hn30USxfvhxA+S7HHTt2IC4uTvS5iYjIs2nytBWq8cuBFfhJLpIFY127dkXDhg0BAGfOnDElzlvy0UcfmX4eOnQoQkJCRF1br9djxIgR+OGHHwAADRs2xI4dO9CkSRNR5yUiIu8gxy5Kc6zAT3KSLBgLCAjAm2++afrzxIkTLVbWX7lyJb744gsAQHBwMGbOnGn1nN27d4dCoYBCocCsWbMsHqPX6/HII4+Ygr+YmBjs2LEDsbFsQk1E5C/UwUpZzz80IYozYyQbSV9ZY8aMwdq1a7F27Vrk5+ejQ4cOGD9+PNq3bw+dTodNmzZhxYoVMBjKqyN/8MEHooOmcePGYdWqVQDKC7Y9//zzOHToEA4dOmTzcc2bN2cuGRGRj4iLDIU6SCnLUqVCAYzuHC35eYmMJA3GFAoFli9fjvHjx2P58uXQarX4+OOPqxwXHByMt99+GxMnThR9zfT0dNPPJSUlmDx5skOPe/31163OthERkXdRB6swJOEupO7Jkfzc0wfEIy4y1P6BRAJJXoE/JCQEy5Ytw5YtWzB69GjExsaiWrVqqFWrFlq0aIGpU6fiyJEjmDp1qtSXJiIiPzamcwwUEp5PAWDGwHgkJzLtheQl2wJ4z5490bNnT1Hn2L59u91jTp8+LeoaRETkG+IiQ/FS32YArog+lypAgVVPd0GbBmGiz0Vkj0/3piQiIv/yTI8mCFSJ/2gb0aEhAzFyGQZjRETkU2pVCxT1eCbsk6sxGCMiIp+iClAgNER4Fg4T9snVGIwREZHPUQerMGNgPBROZPQrFEzYJ/dgBTsiIvJJyYmxSIqLwJKMM1h9INdqDTJ1kBJDE6IwunM0Z8TILRiMERGRz4qLDMVbg1tiWv/myMq/gUKdHgEKBQADygzlM2hN69ZgdX1yK776iIjI56mDVWjL3ZHkoZgzRkRERORGDMaIiIiI3IjBGBEREZEbMRgjIiIiciMGY0RERERuxGCMiIiIyI0YjBERERG5EYMxIiIiIjdiMEZERETkRgzGiIiIiNyIwRgRERGRGykMBoPB3YPwZkFBQSgtLcWdd97p7qGQSCUlJQCAwMBAN4+E5MLn2D/wefYPUj/P58+fh1KpRHFxsSTncwZnxkQKDAyEUql09zBIApcuXcKlS5fcPQySEZ9j/8Dn2T9I/TwrlUq3BfCcGSP6n6ioKABAbm6um0dCcuFz7B/4PPsHX3qeOTNGRERE5EYMxoiIiIjciMEYERERkRsxGCMiIiJyIwZjRERERG7EYIyIiIjIjVjagoiIiMiNODNGRERE5EYMxoiIiIjciMEYERERkRsxGCMiIiJyIwZjRERERG7EYIyIiIjIjRiMEREREbkRgzEiIiIiN2IwRkRERORGDMbIY23btg1jx45FkyZNoFarUbt2bbRq1QovvfQSsrKyZLvu2rVr8fDDD6NRo0aoVq0awsPD0a5dO8yaNQv//POPU+cqLS3FkiVLMHDgQDRo0ADBwcGIjIxE165d8cEHH6CgoECeX8KLuPJ5vnLlCpYvX46nnnoKnTp1Qnh4OAIDA1GzZk00a9YMI0eOxLp161BaWurQ+RQKhVP/3bhxQ9Lfx1u48jnu3r27U8/Jzz//7NB5eS/b56rneezYsU7fe8b/rHH7vWwg8jBFRUWGUaNGGQBY/S8kJMQwd+5cSa975coVQ9++fW1et1atWobvv//eofOdPn3a0L59e5vnq1+/vmHr1q2S/h7ewtXP8/PPP29QqVQ2r2f8LyEhwfD333/bPacj5zL/T6vVSvK7eAt33MvdunVz6jn56aef7J6T97Jtrn6eH3vsMafvPQCG2NhYq+d0972sshakEbmDwWDAyJEjsWrVKgBAjRo1MH78eLRv3x46nQ6bNm3CypUrUVRUhClTpiAwMBDPPPOM6OsWFRXhgQcewO7duwEAERERSE5ORsuWLXH9+nWsWbMGv/76K65du4ZHH30U1apVw6BBg6ye79KlS+jTpw80Gg0AoGHDhkhOTkbTpk2Rn5+PZcuWYe/evfjnn38waNAgbNu2De3btxf9e3gLdzzPf//9N/R6PQCgUaNG6NmzJxISEhAeHo7CwkJkZGRg2bJlKCwsxIEDB9CtWzfs3r0bTZo0sXvuFi1aYPbs2XaPq1atmqjfwZu46142t2bNGrvH2LvveC/b5o7nedKkSRg8eLBDx06cOBG5ubkAgOTkZLvHu+1eljS0IxJp8eLFpm8eERERFmcnfvjhB4NCoTAAMAQHBxtOnTol+rpvvvmm6bpxcXGGc+fOVTnmww8/NB1Tt25dw7Vr16yeb/z48aZju3btWuXYsrIyw8SJE03HtGjRwqDX60X/Ht7CHc9z//79DcOGDTOkp6dbPSY7O9vQtGlT09h69epl85zG47p16yZqbL7IXfey+cyYFHgv2+au59kRR48eNY1NpVIZzp8/b/VYd9/LDMbIY5SVlRmio6NNN4Wt5cCnn37adNzYsWNFXffatWsGtVptOt/evXutHtu/f3/TcbNmzbJ4jEajMQQEBJjeeM6cOWPxuJKSEkOrVq1M5/vmm29E/R7ewl3P8+XLlx067sCBAxWWI06fPm31WHe/gXsqdz3HBoO0wRjvZdvc+Tw7YtKkSaZrDhkyxOax7r6XGYyRx0hLSzPdENHR0YbS0lKrx544ccJ0bGhoqKGoqEjwdc2/2d133302j928eXOFGTRLzGfZRo0aZfN8CxcuNB3bu3dvwb+DN3HX8+yMZs2ama67bt06q8e5+w3cU7nzOZYyGOO9bJsn38u3bt0y1K5d23TNX375xebx7r6XuZuSPMaGDRtMP/fr1w8BAdZfno0bN0ZcXBwAQKvVIi0tTZLrDhw40Oax3bp1g1qtBgBoNBqLO4ScOd+AAQNMP2/btg23bt1yaMzezF3PszNq1qxp+vnmzZsuuaYv8Ybn2BG8l23z5Od51apVuHr1KoDyPL8+ffrIej2xGIyRxzhy5Ijp5w4dOtg93vwY88fKeV2VSoV77rnH6nUNBgP+/PNPh8935513IioqCgCg1+vx999/Ozxub+Wu59lRxcXFpmRtAIiJibH7mOPHjyMpKQkREREIDAxEeHg47rnnHjz33HPYt2+fjKP1TJ7yHD/wwAOIiopCcHAwatWqhbi4OIwePRqrV69GWVmZzcfyXrbPU55nSxYsWGD6+fHHH7cZKJpz173MYIw8xvHjx00/N2rUyO7x5sccO3ZM0DUNBkOF2S2x1z137pyp/oxSqUSDBg1Enc8XueN5dsayZctw7do1AEC9evUc2hl34cIFpKen49KlS9Dr9bh8+TIOHTqEzz77DB07dsSDDz6IS5cuyT10j+Epz/H69etx7tw5FBcX4/r168jKykJqaiqGDRuGVq1a4dChQ1Yfy3vZPk95nivTaDTYsWMHgPLn7vHHH3f4se66l1nagjyGcUoZAMLDw+0eb36M0IKLN27cQElJiWTXNf8datWqhcDAQFHn80XueJ4dlZeXh5deesn05+nTp9v9Rh0TE4NevXqhTZs2qFu3LkpKSnD69Gls2LDBVCpl3bp16NKlC3bv3u3Q7+zt3P0c165dG7169cK9996LqKgoqFQqnD9/HmlpaVi3bp1p5qpr167YsmULOnXqZPN34L1smbufZ2sWLlxo+rl///646667HHqcO+9lBmPkMbRarelnR2q4mB9z/fp10deU4rrO/g72zueL3PE8O0Kn02HIkCGmb7333XcfnnrqKZuP2bZtG7p3727x76ZPn47169dj1KhRKCgoQFZWFh5//HH8+OOPUg/d47jzOX7nnXfQrl07BAUFVfm7yZMn4/jx43jooYfw559/4ubNmxg2bBg0Go0pF9SI97J9nngvl5SU4NtvvzX9ecKECQ49zt33MpcpicjvlZaWYtSoUcjIyABQnv/z3XffQaWy/X3V2pu30cCBA7FixQrTn9etW4cDBw6IHi9Z17lzZ4uBmFGzZs3w22+/mWY1/vnnH8yfP99VwyOZ/fjjj8jPzwcA3HXXXRU2Vtji7nuZwRh5jNDQUNPPjuxEMj/GfPeb0GtKcV1nfwd75/NF7niebSkrK8PYsWOxcuVKAOV5Ylu3bnV4acOeXr164f777zf9ed26dZKc15N52nNcWb169TB58mTTny09J7yX7fPE59k8cX/cuHFQKpWSnVvOe5nBGHmMsLAw08+OJEiaH2P+WGfUqFGjwuyH2Oua//natWum9jtCz+eL3PE8W1NWVoZx48YhNTUVwO1ArHnz5pJep2fPnqaf/WGXnSc9x9bYe054L9vnac/z6dOn8dtvvwEAAgICHGp/5Cy57mUGY+QxzD8AT506Zfd482OEfngqFApT7RsprhsVFYUaNWoAKF/6ysnJEXU+X+SO59mS0tJSPPbYY1i8eDGA8qXJbdu2IT4+XrJrGEVERJh+9ofEbk95jm2x95zwXrbP057nr776CuX1W4HevXsjOjpa8mvIdS8zGCOP0bp1a9PPjtRzMT/G/LFyXlev1+PgwYNWr6tQKNCyZUuHz3f+/HlTE1ulUom7777b4XF7K3c9z+ZKS0sxevRo04xY/fr1sX37dtk+QM1nBGrXri3LNTyJJzzH9th7Tngv2+dJz3NpaSkWLVpk+rOjifvOkuteZjBGHsM80XLjxo02izKePHnSVJgzNDQUiYmJklzXvKK0JTt27EBhYSEAoGnTpmjatKmo85n/fY8ePRzeteXN3PU8G+n1ejz66KNYvnw5gPIZkB07dlSYIZXatm3bTD83a9ZMtut4Cnc/x45w5DnhvWybJz3PGzZswLlz5wAAkZGRGDRokKTnN5LtXnZLEyYiC0pLSw0NGzZ0uunsY489Juq6BQUFghqFv/766xaPOX78eIXmwjk5ORaPq9xceNGiRaJ+D2/hrufZYCj/N3/ooYdM52zYsKHh5MmTos9ry9atWys0Ht+3b5+s1/ME7nyOHZGXl2eIiIgwXff999+3eBzvZds86XkeNGiQ6fz/+c9/JD+/wSDvvcxgjDzKN998Y3qh161b15CZmVnlmBUrVhgUCoXpDdLWh6l502BrwZPBYDDMmjXLdFyzZs0M586dq3LMhx9+aDomPDzcUFBQYPV8Y8eOrdB8/Nq1axX+vqyszDBp0iTTMfHx8YaSkhKr5/M17nieS0pKDMOGDTMdFxMTYzh16pTg3+E///mP3UBu06ZNFZoVDxgwQPD1vI07nuO5c+cadu7caXNcWVlZhjZt2pjOFRkZabh+/brV43kv2+au92xzubm5BqVSaQBgUCgUhhMnTjj1O3jCvcyir+RRxowZg7Vr12Lt2rXIz89Hhw4dMH78eLRv3x46nQ6bNm3CihUrTEmaH3zwAWJjY0Vf9+WXX8bGjRuxZ88eHD9+HG3btsUTTzyBli1b4vr161izZg02bdoEoDwfZMGCBahVq5bV873//vvYuXMnTpw4gZ07d6J169ZITk5GkyZNcPHiRSxbtgx79uwBAFSvXh2LFi2yW9PKl7jjeR43bhxWrVoFAAgMDMTzzz+PQ4cO2WyJA5QnGlvKJfviiy/w3nvv4d5778V9992H5s2bo3bt2igtLcWZM2ewYcMGpKenm45v3Lgxvv76a1G/gzdxx3O8bds2TJkyBY0aNUKvXr3QsmVLREREQKVS4cKFC0hLS8OPP/5o6rpRrVo1rFixokqJG3O8l21z13u2uUWLFqG0tBRA+RJx48aNnXq8R9zLkoZ2RBK4deuWYcSIERWmgyv/FxwcbPjoo4/snsuZb1mXL1829O7d2+Z1a9asaVi2bJlDv0d2drahXbt2Ns9Xr149w+bNmx06n69x9fMcHR1t81rW/rN2vlq1ajl8jgcffNBw4cIFEf9a3snVz/GDDz7o8HPSokULwx9//OHQ78F72TZ3vWcbDOUzkzExMabHfPfdd06P3xPuZf8J38lrhISEYNmyZUhOTsY333yDXbt24fz58wgKCkJUVBT69u2LJ598UvKE6zvuuAO//vor1qxZg6VLl+L3339HXl4e1Go1oqOj8cADD+DJJ590uBhoo0aNsHfvXqSmpuL777/HkSNHcPHiRdSqVQuNGzfG4MGDMWHCBL/YXWeJu55nqfz666/IyMjA3r17kZmZiUuXLuHy5csoKytDWFgYGjdujM6dO2PUqFFo27atu4frFq5+jv+/vTu2cRAKoig6boAKaIMeyBD0RUVUQEYJxEikpHZgO7XlZN8G5xTws5GuJvgzz3MNw1Druta2bXUcR53nWdd1VdM01bZtdV1X0zRV3/df746+meXPkrO8LEvt+15Vz9uX4zj+/MZ/mOXb/f7aHQIA8Od8bQEAECTGAACCxBgAQJAYAwAIEmMAAEFiDAAgSIwBAASJMQCAIDEGABAkxgAAgsQYAECQGAMACBJjAABBYgwAIEiMAQAEiTEAgCAxBgAQJMYAAILEGABAkBgDAAh6APHdMyIoLM0EAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 305
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.scatter(disk_x, disk_y)\n",
+    "boundary = plt.Circle((0,0), 1, fc='none', ec='k')\n",
+    "ax.add_artist(boundary)\n",
+    "# ax.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/embeddings/01_extract_cell_type_activations.ipynb b/notebooks/embeddings/01_extract_cell_type_activations.ipynb
new file mode 100644
index 00000000..5882ea57
--- /dev/null
+++ b/notebooks/embeddings/01_extract_cell_type_activations.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract cell type embeddings from layer prior to classification heads.\n",
+    "\n",
+    "Prompting with all genes and no metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-25 14:14:20.497059: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2025-02-25 14:14:21.920141: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import math\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from braceexpand import braceexpand\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "# DEVICE = torch.device('cuda:1')\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/mb-ml-dev-vm\"\n",
+    "TRAIN_ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/human_cellariumgpt_v2/extract_files\"\n",
+    "CHECKPOINT_PATH = \"/work/hdd/bbjr/mallina1/cellarium/models/latest/epoch=5-step=452000.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the names of the 100 most expressed cell types in the first train chunk and filter down on these. To be updated later with a custom list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['neuron', 'L2/3-6 intratelencephalic projecting glutamatergic neuron', 'glutamatergic neuron', 'oligodendrocyte', 'CD4-positive, alpha-beta T cell', 'CD8-positive, alpha-beta T cell', 'B cell', 'classical monocyte', 'natural killer cell', 'fibroblast']\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "train_fnames = list(braceexpand('extract_{0..10}.h5ad'))\n",
+    "\n",
+    "tr_adata = sc.read_h5ad(os.path.join(TRAIN_ROOT_PATH, train_fnames[0]))\n",
+    "cell_type_filter_list = list(tr_adata.obs['cell_type'].value_counts().to_dict().keys())[:11]\n",
+    "cell_type_filter_list.remove('unknown')\n",
+    "\n",
+    "print(cell_type_filter_list)\n",
+    "\n",
+    "cell_type_filter_list = set(cell_type_filter_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 41%|████████████████████████████████████████▌                                                         | 436/1053 [2:15:25<3:11:15, 18.60s/it]"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mCanceled future for execute_request message before replies were done"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mCanceled future for execute_request message before replies were done. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "metadata_prompt_dict = {\n",
+    "    \"cell_type\": False,\n",
+    "    \"tissue\": False,\n",
+    "    \"disease\": False,\n",
+    "    \"sex\": False,\n",
+    "    \"development_stage\": False\n",
+    "}\n",
+    "\n",
+    "train_fnames = train_fnames[:1]\n",
+    "\n",
+    "cell_type_embeddings = []\n",
+    "disease_embeddings = []\n",
+    "\n",
+    "susp_type_labels = []\n",
+    "assay_labels = []\n",
+    "cell_type_labels = []\n",
+    "disease_labels = []\n",
+    "\n",
+    "batch_size = 3\n",
+    "for train_fname in train_fnames:\n",
+    "    tr_adata = sc.read_h5ad(os.path.join(TRAIN_ROOT_PATH, train_fname))\n",
+    "\n",
+    "    # only preserve rows where the cell type is in the list we want\n",
+    "    tr_adata_filtered = tr_adata[tr_adata.obs.cell_type.isin(cell_type_filter_list)]\n",
+    "\n",
+    "    for obs_idx in tqdm(range(0, tr_adata_filtered.obs.shape[0], batch_size)):\n",
+    "        try:\n",
+    "            tokens_dict, context_indices = ctx.generate_tokens_from_adata(tr_adata_filtered, obs_index=[obs_idx, obs_idx+1, obs_idx+2], query_var_names=[],\n",
+    "                                                                        metadata_prompt_masks_dict=metadata_prompt_dict)\n",
+    "        except:\n",
+    "            # incomplete batch, stop iterating\n",
+    "            break\n",
+    "\n",
+    "        query_cell_type_idx = context_indices['query_cell_type']\n",
+    "        query_disease_idx = context_indices['query_disease']\n",
+    "\n",
+    "        with torch.inference_mode():\n",
+    "            hidden_states_ncd, _ = ctx.get_embeddings_from_tokens(tokens_dict, context_indices)\n",
+    "            hidden_states_ncd = hidden_states_ncd.cpu()\n",
+    "\n",
+    "        cell_type_embeddings.append(hidden_states_ncd[:, query_cell_type_idx, :])\n",
+    "        disease_embeddings.append(hidden_states_ncd[:, query_disease_idx, :])\n",
+    "\n",
+    "        cell_type_labels.append(tr_adata_filtered.obs.iloc[obs_idx].cell_type)\n",
+    "        disease_labels.append(tr_adata_filtered.obs.iloc[obs_idx].disease)\n",
+    "        susp_type_labels.append(tr_adata_filtered.obs.iloc[obs_idx].suspension_type)\n",
+    "        assay_labels.append(tr_adata_filtered.obs.iloc[obs_idx].assay)\n",
+    "\n",
+    "        # if len(cell_type_embeddings) >= math.ceil(1000/batch_size):\n",
+    "        #     break\n",
+    "\n",
+    "cell_type_embeddings = torch.cat(cell_type_embeddings, dim=0).squeeze()\n",
+    "disease_embeddings = torch.cat(disease_embeddings, dim=0).squeeze()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'cell_type_embeddings' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcell_type_embeddings\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m      2\u001b[0m disease_embeddings\u001b[38;5;241m.\u001b[39mshape\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'cell_type_embeddings' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "cell_type_embeddings.shape\n",
+    "disease_embeddings.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/embeddings/02_extract_cell_type_activations.py b/notebooks/embeddings/02_extract_cell_type_activations.py
new file mode 100644
index 00000000..14370c08
--- /dev/null
+++ b/notebooks/embeddings/02_extract_cell_type_activations.py
@@ -0,0 +1,168 @@
+import os
+import argparse
+import math
+import torch
+import pickle
+import warnings
+import numpy as np
+import scanpy as sc
+import pandas as pd
+import matplotlib.pyplot as plt
+from braceexpand import braceexpand
+from tqdm import tqdm
+import multiprocessing as mp
+
+# for flex attention
+import torch._dynamo
+import torch.multiprocessing as mp 
+torch._dynamo.config.suppress_errors = True
+
+sc.set_figure_params(figsize=(4, 4))
+
+from cellarium.ml.utilities.inference.cellarium_gpt_inference import \
+    CellariumGPTInferenceContext, \
+    GeneNetworkAnalysisBase
+
+DEVICES = [torch.device(f'cuda:{idx}') for idx in range(torch.cuda.device_count())]
+
+ROOT_PATH = "/work/hdd/bbjr/mallina1/data/mb-ml-dev-vm"
+TRAIN_ROOT_PATH = "/work/hdd/bbjr/mallina1/data/human_cellariumgpt_v2/extract_files"
+CHECKPOINT_PATH = "/work/hdd/bbjr/mallina1/cellarium/models/latest/epoch=5-step=504000.ckpt"
+OUTPUT_PATH = "/work/hdd/bbjr/mallina1/data/human_cellariumgpt_v2/activations"
+
+REF_ADATA_PATH = os.path.join(ROOT_PATH, "data", "extract_0.h5ad")
+GENE_INFO_PATH = os.path.join(ROOT_PATH, "gene_info", "gene_info.tsv")
+
+def process_arg_on_gpu(arg):
+    metadata_prompt_dict = {
+        "cell_type": False,
+        "tissue": False,
+        "disease": False,
+        "sex": False,
+        "development_stage": False
+    }
+    (train_fname, ctx, tr_adata, indices, batch_size, output_fp) = arg
+
+    for batch_idx in tqdm(range(0, len(indices), batch_size)):
+        obs_index = [indices[batch_idx]]
+        if len(indices) > batch_idx + 1:
+            obs_index.append(indices[batch_idx+1])
+        if len(indices) > batch_idx + 2:
+            obs_index.append(indices[batch_idx+2])
+
+        tokens_dict, context_indices = ctx.generate_tokens_from_adata(tr_adata, 
+                                                                      obs_index=obs_index, 
+                                                                      query_var_names=[],
+                                                                      metadata_prompt_masks_dict=metadata_prompt_dict)
+        query_cell_type_idx = context_indices['query_cell_type']
+        query_disease_idx = context_indices['query_disease']
+        query_tissue_idx = context_indices['query_tissue']
+        query_sex_idx = context_indices['query_sex']
+        query_development_stage_idx = context_indices['query_development_stage']
+
+        with torch.inference_mode():
+            hidden_states = ctx.get_embeddings_from_tokens(tokens_dict, context_indices, to_cpu=True)
+
+        # shape: (layers, batch_size, context_length, hidden_dim)
+        hidden_states = np.stack(hidden_states)
+
+        if os.path.exists(output_fp):
+            curr_data = np.load(output_fp, allow_pickle=True)
+
+            curr_cell_type_activations = curr_data['cell_type_activations']
+            curr_disease_activations = curr_data['disease_activations']
+            curr_tissue_activations = curr_data['tissue_activations']
+            curr_sex_activations = curr_data['sex_activations']
+            curr_development_stage_activations = curr_data['development_stage_activations']
+            
+            curr_metadata = curr_data['metadata'].item()
+            
+            curr_cell_type_activations = np.concatenate((curr_cell_type_activations, hidden_states[:, :, query_cell_type_idx, :].squeeze()), axis=1)
+            curr_disease_activations = np.concatenate((curr_disease_activations, hidden_states[:, :, query_disease_idx, :].squeeze()), axis=1)
+            curr_tissue_activations = np.concatenate((curr_tissue_activations, hidden_states[:, :, query_tissue_idx, :].squeeze()), axis=1)
+            curr_sex_activations = np.concatenate((curr_sex_activations, hidden_states[:, :, query_sex_idx, :].squeeze()), axis=1)
+            curr_development_stage_activations = np.concatenate((curr_development_stage_activations, hidden_states[:, :, query_development_stage_idx, :].squeeze()), axis=1)            
+            
+            curr_data.close()
+        else:
+            curr_cell_type_activations = hidden_states[:, :, query_cell_type_idx, :].squeeze()
+            curr_disease_activations = hidden_states[:, :, query_disease_idx, :].squeeze()
+            curr_tissue_activations = hidden_states[:, :, query_tissue_idx, :].squeeze()
+            curr_sex_activations = hidden_states[:, :, query_sex_idx, :].squeeze()
+            curr_development_stage_activations = hidden_states[:, :, query_development_stage_idx, :].squeeze()
+            curr_metadata = {
+                'cell_type_labels': [],
+                'disease_labels': [],
+                'suspension_type_labels': [],
+                'assay_labels': [],
+                'sex_labels': []
+            }
+
+        for idx, jdx in enumerate(obs_index):
+            cell_type_label = tr_adata.obs.iloc[jdx].cell_type
+            disease_label = tr_adata.obs.iloc[jdx].disease
+            susp_type_label = tr_adata.obs.iloc[jdx].suspension_type
+            assay_label = tr_adata.obs.iloc[jdx].assay
+            sex_label = tr_adata.obs.iloc[jdx].sex
+
+            curr_metadata['cell_type_labels'].append(cell_type_label)
+            curr_metadata['disease_labels'].append(disease_label)
+            curr_metadata['suspension_type_labels'].append(susp_type_label)
+            curr_metadata['assay_labels'].append(assay_label)
+            curr_metadata['sex_labels'].append(sex_label)
+
+        np.savez(output_fp, cell_type_activations=curr_cell_type_activations, 
+                            disease_activations=curr_disease_activations,
+                            sex_activations=curr_sex_activations,
+                            tissue_activations=curr_tissue_activations,
+                            development_stage_activations=curr_development_stage_activations,
+                            metadata=curr_metadata)
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--train_fname', default='extract_0.h5ad', type=str)
+    cfg = parser.parse_args()
+
+    mp.set_start_method('spawn')
+    contexts = []
+    for DEVICE in DEVICES:
+        ctx = CellariumGPTInferenceContext(
+            cellarium_gpt_ckpt_path=CHECKPOINT_PATH,
+            ref_adata_path=REF_ADATA_PATH,
+            gene_info_tsv_path=GENE_INFO_PATH,
+            device=DEVICE,
+            attention_backend="mem_efficient"
+        )
+        contexts.append(ctx)
+
+    # train_fnames = list(braceexpand('extract_{0..1}.h5ad'))
+    # train_fnames = ['extract_0.h5ad']
+    train_fnames = [cfg.train_fname]
+
+    batch_size = 3
+    for train_fname in train_fnames:
+        print(f'Processing {train_fname}...')
+        tr_adata = sc.read_h5ad(os.path.join(TRAIN_ROOT_PATH, train_fname))
+        total_n = tr_adata.obs.shape[0]
+
+        per_gpu_n = math.floor(total_n / len(DEVICES))
+        leftover_n = total_n % len(DEVICES)
+        args = []
+        start_idx = 0
+        for idx in range(len(DEVICES)):
+            indices = list(range(start_idx, start_idx + per_gpu_n))
+            start_idx += per_gpu_n
+            if leftover_n > 0 and idx == len(DEVICES) - 1:
+                indices += [x for x in range(start_idx, total_n)]
+
+            output_fp = os.path.join(OUTPUT_PATH, f'activations_{train_fname[:-5]}_part_{idx}.npz')
+            arg = (train_fname, contexts[idx], tr_adata, indices, batch_size, output_fp)
+
+            args.append(arg)
+
+        with mp.Pool(len(DEVICES)) as p:
+            # results = list(tqdm(p.imap(process_arg_on_gpu, args), total=len(DEVICES)))
+            p.map(process_arg_on_gpu, args)
+
+if __name__=='__main__':
+    main();
diff --git a/notebooks/embeddings/run1.sh b/notebooks/embeddings/run1.sh
new file mode 100644
index 00000000..aac629d7
--- /dev/null
+++ b/notebooks/embeddings/run1.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+#SBATCH -J nmallina-cellarium
+#SBATCH -o /work/hdd/bbjr/mallina1/nn-cellarium-logs/%j.log
+
+#SBATCH -p gpuA100x4
+#SBATCH --account bbjr-delta-gpu
+#SBATCH --nodes 1
+#SBATCH --tasks 1
+#SBATCH --tasks-per-node 1
+#SBATCH --cpus-per-task 16
+#SBATCH --mem 32G
+#SBATCH --gpus-per-node 4
+#SBATCH --time 05:00:00
+
+source /u/mallina1/envs/torch_jax2/bin/activate
+cd /u/mallina1/research/cellarium-ml/notebooks/embeddings
+
+fname=$1
+python 02_extract_cell_type_activations.py --train_fname ${fname}
\ No newline at end of file
diff --git a/notebooks/embeddings/sandbox.ipynb b/notebooks/embeddings/sandbox.ipynb
new file mode 100644
index 00000000..a1622b19
--- /dev/null
+++ b/notebooks/embeddings/sandbox.ipynb
@@ -0,0 +1,1495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedding analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-02-24 15:33:50.783326: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2025-02-24 15:33:52.148590: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for flex attention\n",
+    "import torch._dynamo\n",
+    "torch._dynamo.config.suppress_errors = True\n",
+    "\n",
+    "# DEVICE = torch.device('cuda:1')\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/mb-ml-dev-vm\"\n",
+    "TRAIN_ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/human_cellariumgpt_v2/extract_files\"\n",
+    "CHECKPOINT_PATH = \"/work/hdd/bbjr/mallina1/cellarium/models/latest/epoch=5-step=452000.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tr0 = sc.read_h5ad(os.path.join(TRAIN_ROOT_PATH, 'extract_0.h5ad'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "original_cell_id                                                          CCTATTAGTCAAAGAT-46\n",
+       "cell_type                                   naive thymus-derived CD4-positive, alpha-beta ...\n",
+       "assay_ontology_term_id                                                            EFO:0009899\n",
+       "cell_type_ontology_term_id                                                         CL:0000895\n",
+       "development_stage_ontology_term_id                                             HsapDv:0000206\n",
+       "disease_ontology_term_id                                                         PATO:0000461\n",
+       "donor_id                                                                              106_106\n",
+       "is_primary_data                                                                          True\n",
+       "organism_ontology_term_id                                                      NCBITaxon:9606\n",
+       "self_reported_ethnicity_ontology_term_id                                       HANCESTRO:0005\n",
+       "sex_ontology_term_id                                                             PATO:0000384\n",
+       "suspension_type                                                                          cell\n",
+       "tissue_ontology_term_id                                                        UBERON:0000178\n",
+       "assay                                                                               10x 3' v2\n",
+       "development_stage                                                     80-year-old human stage\n",
+       "disease                                                                                normal\n",
+       "organism                                                                         Homo sapiens\n",
+       "self_reported_ethnicity                                                              European\n",
+       "sex                                                                                      male\n",
+       "tissue                                                                                  blood\n",
+       "total_mrna_umis                                                                          3214\n",
+       "cell_type_distance_from_root                                                              9.0\n",
+       "bq_row_index                                                                         10042488\n",
+       "donor_dataset_concat                            106_106##3faad104-2ab8-4434-816d-474d8d2641db\n",
+       "farm_finger                                                              -9223371973394485188\n",
+       "dataset_id                                               3faad104-2ab8-4434-816d-474d8d2641db\n",
+       "dataset_version_id                                       c2054d78-3e98-4313-a9e1-60c9a6100bee\n",
+       "cas_ingest_id                                                             cas-ingest-9cf3b1ab\n",
+       "Name: 12024477, dtype: object"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tr0.obs['cell_type'].value_counts()\n",
+    "tr0.obs.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ENSG00000187642', 'ENSG00000078808', 'ENSG00000272106', 'ENSG00000162585', 'ENSG00000272088', 'ENSG00000204624', 'ENSG00000162490', 'ENSG00000177000', 'ENSG00000011021', 'ENSG00000120949']\n",
+      "36601\n"
+     ]
+    }
+   ],
+   "source": [
+    "gene_names = tr0.var_names.tolist()\n",
+    "print(gene_names[:10])\n",
+    "print(len(gene_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 36601])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# manually select prompt metadata\n",
+    "custom_prompt_metadata_dict = {\n",
+    "    \"cell_type\": False,\n",
+    "    \"tissue\": False,\n",
+    "    \"disease\": True,\n",
+    "    \"sex\": False,\n",
+    "    \"development_stage\": False\n",
+    "}\n",
+    "\n",
+    "# idx = 0\n",
+    "\n",
+    "# # prompt metadata comes from sample and process function?\n",
+    "# samp = tr0.obs.iloc[idx]\n",
+    "# assay = samp.assay\n",
+    "# suspension_type = samp.suspension_type\n",
+    "# total_mrna_umis = samp.total_mrna_umis\n",
+    "# prompt_metadata_dict = {\n",
+    "#     \"cell_type\": samp.cell_type,\n",
+    "#     \"tissue\": samp.tissue,\n",
+    "#     \"disease\": samp.disease,\n",
+    "#     \"sex\": samp.sex\n",
+    "# }\n",
+    "\n",
+    "# metadata_prompt_masks_dict, metadata_dict = ctx.process_user_metadata(\n",
+    "#     assay, suspension_type, prompt_metadata_dict, total_mrna_umis)\n",
+    "\n",
+    "# print(metadata_prompt_masks_dict)\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "    tokens_dict, context_indices = ctx.generate_tokens_from_adata(tr0, obs_index=[0], query_var_names=gene_names[:5000],\n",
+    "                                                                  metadata_prompt_masks_dict=custom_prompt_metadata_dict)\n",
+    "    # tokens_dict, context_indices = ctx.generate_tokens_from_adata(tr0, obs_index=[0], query_var_names=[],\n",
+    "    #                                                               metadata_prompt_masks_dict=custom_prompt_metadata_dict)\n",
+    "    print(tokens_dict['gene_tokens_nc']['assay'].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query cell type idx: 36601\n",
+      "prompt disease idx: 36604\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(f\"prompt cell type idx: {context_indices[f'prompt_cell_type']}\")\n",
+    "except:\n",
+    "    print(f\"query cell type idx: {context_indices[f'query_cell_type']}\")\n",
+    "\n",
+    "try:\n",
+    "    print(f\"prompt disease idx: {context_indices[f'prompt_disease']}\")\n",
+    "except:\n",
+    "    print(f\"query disease idx: {context_indices[f'query_disease']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'cell_type': tensor([890], dtype=torch.int32), 'tissue': tensor([822], dtype=torch.int32), 'development_stage': tensor([191], dtype=torch.int32), 'disease': tensor([0], dtype=torch.int32), 'sex': tensor([2], dtype=torch.int32)}\n",
+      "tensor([890], dtype=torch.int32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokens_dict['metadata_tokens_n'])\n",
+    "print(tokens_dict['metadata_tokens_n']['cell_type'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.inference_mode():\n",
+    "    hidden_states_ncd, gene_hidden_states_nqd = ctx.get_embeddings_from_tokens(tokens_dict, context_indices)\n",
+    "    gene_hidden_states_nqd = gene_hidden_states_nqd.cpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 0, 512])\n",
+      "torch.Size([1, 36606, 512])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(gene_hidden_states_nqd.shape)\n",
+    "print(hidden_states_ncd.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OUTDATED BELOW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ROOT_PATH = \"/mnt/cellariumgpt-xfer/mb-ml-dev-vm\"\n",
+    "# CHECKPOINT_PATH = \"/mnt/cellariumgpt-xfer/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "ROOT_PATH = \"/work/hdd/bbjr/mallina1/data/mb-ml-dev-vm\"\n",
+    "CHECKPOINT_PATH = \"/work/hdd/bbjr/mallina1/cellarium/models/latest/epoch=5-step=452000.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing embedding dims from cell type prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_gene_ids_path = os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_validation\", \"query_gene_ids.csv\")\n",
+    "query_gene_ids = pd.read_csv(query_gene_ids_path, header=None).values.flatten().tolist()[:5000]  # NOTE\n",
+    "\n",
+    "assay = \"10x 3' v3\"\n",
+    "suspension_type = \"cell\"\n",
+    "prompt_metadata_dict = {\n",
+    "    \"cell_type\": \"neuron\",\n",
+    "    \"tissue\": \"blood\"\n",
+    "}\n",
+    "total_mrna_umis = 5_000\n",
+    "\n",
+    "with torch.inference_mode():\n",
+    "    tokens_dict, context_indices, _, _ = ctx.generate_gene_tokens_by_metadata(\n",
+    "        assay=assay,\n",
+    "        suspension_type=suspension_type,\n",
+    "        prompt_metadata_dict=prompt_metadata_dict,\n",
+    "        total_mrna_umis=total_mrna_umis,\n",
+    "        query_gene_ids=query_gene_ids,\n",
+    "        perturb_gene_ids=None\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'gene_tokens_nc': {'assay': tensor([[1, 1, 1,  ..., 1, 1, 1]]),\n",
+       "  'suspension_type': tensor([[1, 1, 1,  ..., 1, 1, 1]]),\n",
+       "  'gene_id': tensor([[    0,  3269,  1251,  ..., 27863,  3842, 22118]]),\n",
+       "  'gene_value': tensor([[[0.0000, 1.0000, 8.5174],\n",
+       "           [0.0000, 1.0000, 8.5174],\n",
+       "           [0.0000, 1.0000, 8.5174],\n",
+       "           ...,\n",
+       "           [0.0000, 1.0000, 8.5174],\n",
+       "           [0.0000, 1.0000, 8.5174],\n",
+       "           [0.0000, 1.0000, 8.5174]]])},\n",
+       " 'metadata_tokens_n': {'cell_type': tensor([0], dtype=torch.int32),\n",
+       "  'tissue': tensor([65], dtype=torch.int32),\n",
+       "  'development_stage': tensor([191], dtype=torch.int32),\n",
+       "  'disease': tensor([350], dtype=torch.int32),\n",
+       "  'sex': tensor([2], dtype=torch.int32)},\n",
+       " 'prompt_mask_nc': tensor([[False, False, False,  ..., False, False, False]])}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokens_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 5001])\n",
+      "torch.Size([1, 5001, 3])\n",
+      "torch.Size([1, 5006])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokens_dict['gene_tokens_nc']['gene_id'].shape)\n",
+    "print(tokens_dict['gene_tokens_nc']['gene_value'].shape)\n",
+    "print(tokens_dict['prompt_mask_nc'].shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'prompt_genes': [0],\n",
+       " 'query_genes': [1,\n",
+       "  2,\n",
+       "  3,\n",
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+       "  921,\n",
+       "  922,\n",
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+       "  924,\n",
+       "  925,\n",
+       "  926,\n",
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+       "  928,\n",
+       "  929,\n",
+       "  930,\n",
+       "  931,\n",
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+       "  933,\n",
+       "  934,\n",
+       "  935,\n",
+       "  936,\n",
+       "  937,\n",
+       "  938,\n",
+       "  939,\n",
+       "  940,\n",
+       "  941,\n",
+       "  942,\n",
+       "  943,\n",
+       "  944,\n",
+       "  945,\n",
+       "  946,\n",
+       "  947,\n",
+       "  948,\n",
+       "  949,\n",
+       "  950,\n",
+       "  951,\n",
+       "  952,\n",
+       "  953,\n",
+       "  954,\n",
+       "  955,\n",
+       "  956,\n",
+       "  957,\n",
+       "  958,\n",
+       "  959,\n",
+       "  960,\n",
+       "  961,\n",
+       "  962,\n",
+       "  963,\n",
+       "  964,\n",
+       "  965,\n",
+       "  966,\n",
+       "  967,\n",
+       "  968,\n",
+       "  969,\n",
+       "  970,\n",
+       "  971,\n",
+       "  972,\n",
+       "  973,\n",
+       "  974,\n",
+       "  975,\n",
+       "  976,\n",
+       "  977,\n",
+       "  978,\n",
+       "  979,\n",
+       "  980,\n",
+       "  981,\n",
+       "  982,\n",
+       "  983,\n",
+       "  984,\n",
+       "  985,\n",
+       "  986,\n",
+       "  987,\n",
+       "  988,\n",
+       "  989,\n",
+       "  990,\n",
+       "  991,\n",
+       "  992,\n",
+       "  993,\n",
+       "  994,\n",
+       "  995,\n",
+       "  996,\n",
+       "  997,\n",
+       "  998,\n",
+       "  999,\n",
+       "  1000,\n",
+       "  ...],\n",
+       " 'prompt_cell_type': 5001,\n",
+       " 'prompt_tissue': 5002,\n",
+       " 'query_development_stage': 5003,\n",
+       " 'query_disease': 5004,\n",
+       " 'query_sex': 5005}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(context_indices['query_genes'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.inference_mode():\n",
+    "    hidden_states_ncd, gene_hidden_states_nqd = ctx.get_embeddings_from_tokens(tokens_dict, context_indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 5006, 512]) torch.Size([1, 5000, 512])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(hidden_states_ncd.shape, gene_hidden_states_nqd.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 7296e06e661fbcd1c54ac4ddd9f845936610634a Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Tue, 11 Mar 2025 16:26:56 +0000
Subject: [PATCH 59/64] bugfix in assay code

---
 .../inference/cellarium_gpt_inference.py      | 58 ++++++++++++++++---
 scripts/metadata_prediction_analysis.py       |  6 +-
 2 files changed, 54 insertions(+), 10 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 96b66aab..297c4b35 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -100,7 +100,7 @@ def __init__(
         self._adata = sc.read_h5ad(ref_adata_path)
 
         # get gene ontology infos from the reference anndata
-        self.gene_ontology_infos = self._get_gene_ontology_infos(self._adata)
+        self.gene_ontology_infos = self._get_gene_ontology_infos()
 
         # load the model
         self.device = device
@@ -153,16 +153,60 @@ def _instantiate_predict_tokenizer(self) -> PredictTokenizer:
             ontology_infos=self.metadata_ontology_infos,
         )    
             
-    def _get_gene_ontology_infos(self, adata: AnnData) -> dict:
+    def _get_gene_ontology_infos(self) -> dict:
         gene_ontology_infos = dict()
 
+        assay_labels = [
+            "Seq-Well",
+            "10x 3' v3",
+            "SPLiT-seq",
+            "Smart-seq v4",
+            "Drop-seq",
+            "sci-RNA-seq",
+            "10x 5' v2",
+            "10x 5' transcription profiling",
+            "inDrop",
+            "microwell-seq",
+            "10x multiome",
+            "10x 3' v1",
+            "ScaleBio single cell RNA sequencing",
+            "Smart-seq2",
+            "10x 3' transcription profiling",
+            "Seq-Well S3",
+            "10x 3' v2",
+            "MARS-seq",
+            "10x 5' v1"
+        ]
+
+        assay_ontology_term_ids = [
+            "EFO:0008919",
+            "EFO:0009922",
+            "EFO:0009919",
+            "EFO:0700016",
+            "EFO:0008722",
+            "EFO:0010550",
+            "EFO:0009900",
+            "EFO:0030004",
+            "EFO:0008780",
+            "EFO:0030002",
+            "EFO:0030059",
+            "EFO:0009901",
+            "EFO:0022490",
+            "EFO:0008931",
+            "EFO:0030003",
+            "EFO:0030019",
+            "EFO:0009899",
+            "EFO:0008796",
+            "EFO:0011025"
+        ]
+
         gene_ontology_infos["assay_ontology_term_id"] = dict()
-        gene_ontology_infos["assay_ontology_term_id"]["names"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
-        gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(adata.obs['assay'].cat.categories)
+        gene_ontology_infos["assay_ontology_term_id"]["names"] = assay_ontology_term_ids
+        gene_ontology_infos["assay_ontology_term_id"]["labels"] = assay_labels
 
         gene_ontology_infos["suspension_type"] = dict()
-        gene_ontology_infos["suspension_type"]["names"] = list(adata.obs['suspension_type'].cat.categories)
-        gene_ontology_infos["suspension_type"]["labels"] = list(adata.obs['suspension_type'].cat.categories)
+        gene_ontology_infos["suspension_type"]["names"] = ["nucleus", "cell"]
+        gene_ontology_infos["suspension_type"]["labels"] = ["nucleus", "cell"]
 
         return gene_ontology_infos
 
@@ -261,7 +305,7 @@ def generate_tokens_from_adata(
             fixed_prompt_vars_nf = fixed_prompt_sublist_var_index[None, :].expand(n_cells, n_fixed_prompt_vars)
             query_vars_nq = torch.tensor(
                 query_var_index, dtype=torch.int64, device=cpu_device)[None, :].expand(n_cells, n_query_vars)
-            prompt_vars_np = torch.cat([rand_prompt_vars_nr, fixed_prompt_vars_nf], dim=1)
+            prompt_vars_np = torch.cat([rand_prompt_vars_nr, fixed_prompt_vars_nf], dim=-1)
             gene_ids_nc = torch.cat([prompt_vars_np, query_vars_nq], dim=-1)
 
             # gene value
diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
index 3700d778..e2c6f788 100644
--- a/scripts/metadata_prediction_analysis.py
+++ b/scripts/metadata_prediction_analysis.py
@@ -124,12 +124,12 @@ def main():
         help="Random number generator seed (default: 42)"
     )
     parser.add_argument(
-        "--n_cells", type=int, default=1000,
-        help="Number of cells to use (default: 1000)"
+        "--n_cells", type=int, default=None,
+        help="Number of cells to use (default: None, meaning all)"
     )
     # Updated default for n_genes to match the new notebook and added new argument for gene selection method
     parser.add_argument(
-        "--n_genes", type=int, default=4091,
+        "--n_genes", type=int, default=None,
         help="Number of genes to use. For 'highly_expressed', selects top genes; for 'random', selects random genes. Set to None to use all."
     )
     parser.add_argument(

From 220827999e58e43687379128ac35a715d69c9ede Mon Sep 17 00:00:00 2001
From: Neil Mallinar <nmallinar@gmail.com>
Date: Tue, 11 Mar 2025 13:53:36 -0500
Subject: [PATCH 60/64] update cellarium gpt inference context with assay id
 fix

---
 .../inference/cellarium_gpt_inference.py      | 56 +++++++++++++++++--
 1 file changed, 50 insertions(+), 6 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 3faa8814..2abfd4b4 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -100,7 +100,7 @@ def __init__(
         self._adata = sc.read_h5ad(ref_adata_path)
 
         # get gene ontology infos from the reference anndata
-        self.gene_ontology_infos = self._get_gene_ontology_infos(self._adata)
+        self.gene_ontology_infos = self._get_gene_ontology_infos()
 
         # load the model
         self.device = device
@@ -153,16 +153,60 @@ def _instantiate_predict_tokenizer(self) -> PredictTokenizer:
             ontology_infos=self.metadata_ontology_infos,
         )    
             
-    def _get_gene_ontology_infos(self, adata: AnnData) -> dict:
+    def _get_gene_ontology_infos(self) -> dict:
         gene_ontology_infos = dict()
 
+        assay_labels = [
+            "Seq-Well",
+            "10x 3' v3",
+            "SPLiT-seq",
+            "Smart-seq v4",
+            "Drop-seq",
+            "sci-RNA-seq",
+            "10x 5' v2",
+            "10x 5' transcription profiling",
+            "inDrop",
+            "microwell-seq",
+            "10x multiome",
+            "10x 3' v1",
+            "ScaleBio single cell RNA sequencing",
+            "Smart-seq2",
+            "10x 3' transcription profiling",
+            "Seq-Well S3",
+            "10x 3' v2",
+            "MARS-seq",
+            "10x 5' v1"
+        ]
+
+        assay_ontology_term_ids = [
+            "EFO:0008919",
+            "EFO:0009922",
+            "EFO:0009919",
+            "EFO:0700016",
+            "EFO:0008722",
+            "EFO:0010550",
+            "EFO:0009900",
+            "EFO:0030004",
+            "EFO:0008780",
+            "EFO:0030002",
+            "EFO:0030059",
+            "EFO:0009901",
+            "EFO:0022490",
+            "EFO:0008931",
+            "EFO:0030003",
+            "EFO:0030019",
+            "EFO:0009899",
+            "EFO:0008796",
+            "EFO:0011025"
+        ]
+
         gene_ontology_infos["assay_ontology_term_id"] = dict()
-        gene_ontology_infos["assay_ontology_term_id"]["names"] = list(adata.obs['assay_ontology_term_id'].cat.categories)
-        gene_ontology_infos["assay_ontology_term_id"]["labels"] = list(adata.obs['assay'].cat.categories)
+        gene_ontology_infos["assay_ontology_term_id"]["names"] = assay_ontology_term_ids
+        gene_ontology_infos["assay_ontology_term_id"]["labels"] = assay_labels
 
         gene_ontology_infos["suspension_type"] = dict()
-        gene_ontology_infos["suspension_type"]["names"] = list(adata.obs['suspension_type'].cat.categories)
-        gene_ontology_infos["suspension_type"]["labels"] = list(adata.obs['suspension_type'].cat.categories)
+        gene_ontology_infos["suspension_type"]["names"] = ["nucleus", "cell"]
+        gene_ontology_infos["suspension_type"]["labels"] = ["nucleus", "cell"]
 
         return gene_ontology_infos
 

From 05e59af7de73566475dd51b9f816193fd7dfb60b Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Wed, 12 Mar 2025 17:43:46 +0000
Subject: [PATCH 61/64] latest

---
 .../inference/cellarium_gpt_inference.py      |     5 +-
 ...on_by_cell_type_prompting_comparison.ipynb |    41 +-
 ...ata_predictions_new_style_no_compute.ipynb |   499 +-
 ...w_metadata_prediction_implementation.ipynb |   466 +-
 ...8_analyze_metadata_predictions_final.ipynb | 13071 ++++++++++++++++
 .../09_disease_case_studies.ipynb             | 12184 ++++++++++++++
 .../xx_annotate_genes.ipynb                   |    82 +-
 .../xx_metadata_prediction_debug.ipynb        |   298 +
 .../xx_mean_expression_debug.ipynb            |  1785 +++
 scripts/run_all.sh                            |     7 +
 .../run_metadata_prediction_analysis_deep.sh  |    69 +
 11 files changed, 28141 insertions(+), 366 deletions(-)
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/08_analyze_metadata_predictions_final.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/09_disease_case_studies.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/metadata_benchmarking/xx_metadata_prediction_debug.ipynb
 create mode 100644 notebooks/cellariumgpt_inference/xx_mean_expression_debug.ipynb
 create mode 100755 scripts/run_all.sh
 create mode 100755 scripts/run_metadata_prediction_analysis_deep.sh

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 297c4b35..9ceef8b3 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -90,7 +90,7 @@ def __init__(
             ref_adata_path: str,
             gene_info_tsv_path: str,
             device: torch.device,
-            attention_backend: str = "mem_efficient",
+            attention_backend: str | None = "mem_efficient",
             verbose: bool = True):
         
         # for logging
@@ -110,7 +110,8 @@ def __init__(
         self.gpt_pipeline.model.gene_categories = np.asarray(self._adata.var_names)
         
         # change attention backend to memory efficient
-        self.gpt_pipeline.model.set_attention_backend(attention_backend)
+        if attention_backend is not None:
+            self.gpt_pipeline.model.set_attention_backend(attention_backend)
 
         # gene info related
         self.model_var_names = np.asarray(self._adata.var_names)
diff --git a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
index 0757b2ec..f03911e7 100644
--- a/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
+++ b/notebooks/cellariumgpt_inference/11_mean_expression_by_cell_type_prompting_comparison.ipynb
@@ -36,15 +36,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
     "ROOT_PATH = \"/home/mehrtash/data\"\n",
-    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
     "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\"\n",
     "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\"\n",
     "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/98M_001_bs4608/epoch=6-step=63560__updated.ckpt\"\n",
+    "\n",
     "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
     "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
     "\n",
@@ -60,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +85,7 @@
        "    var: 'feature_id', 'feature_name'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -93,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -911,13 +914,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05fe5d496a5e4e8cb4de3abb788f85df",
+       "model_id": "bfed247963ee41e9a6620599b4d0e946",
        "version_major": 2,
        "version_minor": 0
       },
@@ -931,7 +934,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0467e8fa80c54bd1bd8d348a7c045659",
+       "model_id": "1e73ee07cbdf494094f6b23794720ae3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -945,7 +948,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "440a33e515ab4a95a64dd87b9f3196bf",
+       "model_id": "904ec2e65f484d42ba7344a7a8b5002c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -959,7 +962,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bfbd03775a6541f6b49af116a37c3f54",
+       "model_id": "325a8578f2684d8fa0bae8cefd43b876",
        "version_major": 2,
        "version_minor": 0
       },
@@ -973,7 +976,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d9a57afba654b0c9f49537021141101",
+       "model_id": "9e81387681ae42ecac5cfc1eb0d37e53",
        "version_major": 2,
        "version_minor": 0
       },
@@ -987,7 +990,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d4224c8a093401f9b2e82aff6d79b2b",
+       "model_id": "8ff683fe01a841cd92d31396badc2471",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1001,7 +1004,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0982dc17172b48409e6cfd994fbb7520",
+       "model_id": "f34e8a4ff4c64aa29c209dbcbbbaa318",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1053,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1073,12 +1076,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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XBQCA39i0aZM6dOig+Ph4Va1aVatXr9Z1111ndlkAAPiNQ4cOKTo6Wnv27FGRIkU0f/58tWvXzuyyAADwG/RHAQDgWVbvj2LADwDAZ3366aeui9n//ve/evzxx02uCAAA/xITE+O6mH388cc1adIknnQHAIAbnThxQkOGDJFhGIqMjNTixYtVokQJs8sCAMBvOBwOPfbYY4qPj1etWrW0evVq1alTx+yyAADwK8OHD9eePXtUrFgxLV68WC1atDC7JAAA/Ar9UQAAeJbV+6MY8AMA8FlPPfWUtm3bpubNm6tv375mlwMAgN+JjIzUhAkTdODAAY0fP55ptgEAcLOKFStqwYIFmjhxombNmqWiRYuaXRIAAH4lKChICxYs0IABA/T555+revXqZpcEAIDf+eijj3Tq1Cm98soratq0qdnlAADgd+iPAgDAs6zeH8WAHwCAT0lNTVVoaKgkyW6367PPPjO5IgAA/EtaWpqCg4NdF69PP/20yRUBAOBfDMNQWlqa69o2Ojpa0dHRJlcFAIB/ufR95Lp162rt2rUmVwQAgH+5NGtLlCih5cuXm1wRAAD+hf4oAAA8y5/6o6wzFxEAwO8dO3ZMt956q2bMmGF2KQAA+KXk5GTde++9Gjp0qAzDMLscAAD8jmEYGj58uDp27Kjk5GSzywEAwC/Nnj1bN954o/755x+zSwEAwC/9+uuvuu666/TTTz+ZXQoAAH6J/igAADzL3/qjGPADv7d8+XL17dtXDRs2VKlSpVS6dGndcssteu+992i8AHzI4cOHFRUVpe3bt2vQoEE6deqU2SUByCOyFrCGpKQkdenSRYsWLdL777+vjRs3ml0SgDwiawFrMAxDQ4cO1dixY7Vq1SpNnjzZ7JIA5BFZC1jHtGnT9OCDD2r37t0aNmyY2eUAyCOyFrCOTZs2qVWrVjpw4ID69+8vp9NpdkkA8oi8BayB/ijAushawBr8sT+KAT/wSevWrZPNZsvyX2hoqCpUqKBmzZpp+PDh2r9/f47b+uyzz7Rz50498sgjmjlzpt577z0FBQXp2WefVevWrZWenu6l78qzvv/+e91///2qUaOGihQpokqVKqlVq1aaMmWKHA6HW/d1+PBhvfTSS2rUqJFKliypkiVLqlGjRnrppZd0+PDhHNd1OBxatGiRXn31VXXs2FENGzZUhQoVFBISolKlSqlRo0Z67LHHtGnTJrfWDN+2b98+RUZG6u+//1axYsW0aNEilS9f3uyyAL9G1uYfWQsrO3/+vDp06KCVK1dKkiZNmqTmzZubXBXg38ja/LNK1kpSixYtsv39Xvlv/vz5bq0dvsnpdOqJJ57Q+++/L0l67LHHNGTIEJOrAvwbWZt/3szaKy1YsOCy39GoUaMKtJ3Dhw+rZMmSru20aNHCrXXCt/33v//VI488IsMwFBkZqUmTJpldEuDXyNr8s2LWJiUlafz48WratKnKly+v8PBw1alTR/369WN2lwC0bt06tW3bVvHx8apVq5YWLlwou522IsCTyNv881beJiUl6YMPPlB0dLQqVKig0NBQ174mT56stLS0Am/7119/VXBwsOt33bdvX7fVDd9GfxTgfWRt/lnlni39UciK3/ZHGbCsqlWrGlWrVs3XOqmpqcaOHTuMHTt2GKmpqR6qrPDWrl1rSMrTv7CwMGPSpEnZbuuff/656muJiYlGzZo1DUnG3LlzPfmteFx6errRr1+/HH9G//73v43jx4+7ZX/fffedUbJkyWz3VapUKWP+/PnZrh8XF5fn322vXr2M5OTkfNdoleMcGXbt2mVUrVrVkGSULFnS+OGHH7y274KcRxFYCnqMWOE8RNbmHVl7NSsc47goLi7OuO222wxJhs1mM6ZOneq1fZO1yA1ZS9YahvWy1jAMIyoqKs+/3++++65AdVrhOEeGtLQ0o3fv3q7f+VNPPWU4nU6v7JusRV746/vIZG3eeTtrrxQbG2tUrlz5sv29+uqrBdpW+/btL9tOVFRUgeuywnGOi8aPH+/6vbdp08ZITEz02r7JW+SGrCVrrZq1u3btMq655ppsa7bb7cbIkSMLXJcVjnNctHz5cqNIkSKGJKNevXrGoUOHvLZvsha58desNQzyNj+8mbe//vqrUatWrRz3ddNNNxXoXJmammrccMMNl22rT58+BarTKsc5MtAfBV/GPVuy1jCsd8+W/ihcyZ/7o3gUB3xe165dtW3bNte/H3/8UV9++aVatmwpSUpJSdGgQYO0cOHCLNevUqXKVV8rWrSobr31VknSnj17PFe8FzzzzDOaNm2aJKlBgwaaPn26fv75Zy1YsEDt27eXJP3888/q1KmTUlJSCrWvTZs2qWfPnoqPj1eRIkU0YsQIbdiwQRs2bNCIESMUFhamc+fOqUePHvrxxx+z3U5ERIS6du2q119/XTNnztSaNWv0yy+/aPHixXrttddUvXp1SdJXX32l/v37F6pm+LZt27YpMjJS//zzjyIiIrR69Wo1bdrU7LKAgEPW5oyshZWdPn1arVq10k8//aSgoCB99dVX6tevn9llAQGHrM2ZFbM2U5UqVS773Wb1Lzo6ulA1w7elpaWpV69e+vzzzyVJL7/8siZMmCCbzWZyZUBgIWtz5s2szcrQoUN17NgxVaxYsVDbmT59upYvX17o7cB6xowZo2effVaS1KlTJy1cuFBFixY1uSogsJC1ObNi1p45c0bt27fX33//LUnq1auXli9frp9++kmTJk1StWrV5HQ69dprr2n8+PFurxm+ZcGCBerSpYuSk5PVsGFDbdiwwXUfAYD3kLc581be7tu3T23bttWBAwckSZ07d9Z3332nrVu3auXKlRo0aJCCg4P1+++/q127doqPj8/X9seMGaNt27ZxbRtg6I8CfANZmzMr3rOlPwqZ/L4/ymNDieBxgfIEi5yeYvDyyy9f9uSEvDp9+rQRERFhSDLWrVvnhorN8fPPPxs2m82QZDRq1MiIj4+/7HWn02n06dPH9TN69913C7wvh8Nh3Hjjja6nOa1ateqqZVauXGnY7XZDkvGvf/3LcDgcVy3jdDqz/PqlEhISjH//+9+uurdt25avWq1ynAe6pKQko0qVKoYko3z58sYff/zh9Rp4ggVyEyhPsCBrs0fWZs0KxzgytGnTxpBkhISEGPPmzfP6/sla5IasJWutmLWGcXGGn5o1axa4ntxY4TiHYQwbNsx1fP7nP//x2sw+mcha5IW/vo9M1uaNN7M2K8uWLTMkGUWLFjWmTp2ar1kHLnX06FHX72P27Nmu7TDDj39KdziNj9ftMfpP/9l4bPSHrt939+7djZSUFK/XQ94iN2QtWWvFrB00aFCOy/7zzz9GpUqVDElGeHi4ceTIkXzXZoXjPJBl5m33N78x7EHBrv/DJ0+e9HotZC1y469ZaxjkbV55M2/vvvtu13ZefvnlLJdZsGCBq54XX3wxz9v+888/jZCQEMNutxszZ85khp8AQX8UrIB7tmStFe/Z0h+FS/l7fxQz/MDSXnnlFddT3H7//XedPHky13UMw9CAAQMUFxenu+66S1FRUZ4u02PeeustGYYhSfrwww9VokSJy1632WyaOHGi6+tvv/22nE5ngfa1dOlS/fnnn5Kk3r17Z/mE4tatW+vhhx+WJP3xxx9atmzZVcvYbDbZ7TmfeooVK6ahQ4e6Pl+/fn2BaoZvCw8P18cff6yaNWtqw4YNuvHGG80uCUAWyFqyFtY2YcIEVa9eXQsWLNA999xjdjkAskDWWi9rgUs9//zzuummmzRu3DiNHDmSmX0AH0TWei9rr3T+/HkNHDhQkvSf//xHtWvXLvC2nnzyScXFxalTp07q0aOHW+qD75oSs09jl+3Sqp0ntTyhum6Jaq+HHnpIs2bNUmhoqNnlAbgCWWu9rI2NjdUnn3wiSapbt65Gjhx51TJVqlTR2LFjJUkXLlzQhAkT3FIzfEdm3m6JK6KSTR9QrQb/0po1a1S+fHmzSwOQBfLWO3l75swZ16wO1atX12uvvZblcl26dNG9994rSZo4cWKeZvlxOBx65JFHlJaWpieffFJ33HFHvuuDNdEfBVgDWWu9e7b0R+FS/t4fxYAfWFp4eLgaNmzo+vzQoUO5rjNixAjNnz9f1atXd00/Z0VJSUmuEKtXr162fyyULFlS9913nyTpxIkTiomJKdD+vvnmG9fHAwYMyHa5S6e9u3Sd/Lr0D4bk5OQCbwe+rXPnztq9e7fq169vdikAskHWkrWwtoYNG+rvv/9Whw4dzC4FQDbIWv/NWgSGMmXKaPPmzXr++efNLgVANsha72XtlV588UUdOnRIt9xyi5555pkCb+frr7/W/PnzVbx4cX300UduqQ2+bcuBWNfHNnuQ/vXwK5o+fbqCg4NNrApAdsha62XtwoULlZaWJknq16+fgoKCslyuZ8+eKlasmCTp22+/LXS98C2X5m2ppj3U4tlJioiIMLEiADkhb72Tt1u2bHE1L7dr1y7Ha5C77rpLUsZ91kWLFuW67XfeeUe//PKLqlWrpjfeeCPftcHa6I8CfB9Z67/3bOmPCgz+3h/FgB9Y3qVvQGb3ZmSmCRMm6I033lDZsmW1fPlyVahQwdPlecwvv/yipKQkSVKLFi1yXLZly5aujws6QnXDhg2SMv6w+fe//53tcrfffrvCw8MLtS9JmjlzpuvjBg0aFHg78C0rVqxQz549XTcRJCksLMzEigDkBVlL1sI6/vrrL7Vr106nTp1yfY2sBXwfWWv9rD1+/Lj++usvnTp1yvX0K/ins2fPqmPHjvr9999dXyNrAd9H1nonay+1bt06TZ48WcHBwZoyZUquP/fsnD59WkOGDJEkvfHGG6pevXqha4Nvczgc2jVnnBJ3bXR97bZ6FQp8DAHwDrLWWlmbeX0s5Vx3kSJFdPvtt0uS9u/fn6eGN1jDhAkTdGz1jMu+dvs1lUyqBkBekbeez9vY2IuDIStWrJjjspUqXTxvrlu3Lsdld+/erVGjRkmSPvroo6tmTYD/oT8KsCay1vr3bLNCf5R/CrT+KAb8wNIcDof++usv1+c5TVP+4YcfaujQoSpfvrzWrFlz2WhcK/rf//7n+ji37+XS13fs2JHvfSUmJurAgQOSpGuuuUYhISHZLhsSEqJ69epJkg4ePKgLFy7kaR8Oh0NHjx7VihUr1LlzZ1fINmrUSO3atct3zfA9CxcuVJcuXfT1119r+PDhZpcDII/I2gxkLaxg+/btioyM1Pfff6+ePXuaXQ6APCJrM1g1a48dO6by5curcuXKuu6661ShQgVVqFBBvXr10tatW/NdJ3zb6dOnFR0draVLl+quu+5SYmKi2SUByAOyNoOns/ZSSUlJGjBggAzD0LPPPqubb765wNsaMmSITp06pdtvv12DBg0qVF3wfWlpaerVq5d+WDpXsYvfUeNSiRrWob4GNK9jdmkAckDWZrBS1ppVN3zDmDFjNHToUC39YpLahO1V6wYVyFvAAsjbDJ7OreLFi7s+Pnv2bI7LXvr6pTVeyel0qn///kpOTtb999+vzp0757suWAv9UYA1kbUZrHrP9lL0R/m/QOyPYsAPLO2zzz5TXFycJKlt27YqXbp0lsu99957GjJkiGrWrKmNGzfqxhtvLPS+R40aJZvNVuh/06dPL9D+L32CUm5PNbz09YI8eenIkSOuJxTn5QmKmcs4nU4dPnw42+VOnz7t+jkEBweratWqat++vRYvXixJuvbaa/Xtt9/y5D4/MGfOHN17771KTU3Vv/71Lz3//PNmlwQgj8jaDGQtfN3WrVvVokULnThxQuXLl9f48ePNLglAHpG1GayatampqTp9+vRlXzt9+rRmzpypJk2a6NVXX813rfBNx48fV8uWLbV161aFhIToww8/VLFixcwuC0AekLUZPJ21lxoxYoT27t2runXrup5gXBALFy7U7NmzFRISok8//VR2O7e0/FlKSoq6d++ur7/+WpL06isjNffl+zQwqq6C7DaTqwOQE7I2g5WyNnP/xYoVU0RERI7LurNumMswDA0fPlwjRoyQJN17772aNPxxTenThLwFLIC8zeDpvL20iTm3mQQuff3gwYPZLvfhhx/qhx9+UEREhCZOnJjvmmAt9EcB1kXWZrDqPVv6owJHoPZHBZtdAJBfCQkJ2rt3r6ZOnaqPPvpIklSyZEm9+eabWS4/YcIEPfvssypevLhGjx6tQ4cOXRY0VapUseQI2/Pnz7s+vvQJE1m59PVL1/PEvtyxv/DwcI0dO1YDBgygecYPfP755+rXr5+cTqeaNGmi5cuXq0yZMmaXBSAHZG0GshZWsXnzZrVv317nzp1TlSpVtGrVKqYhBnwcWZvBylkbERGhwYMH66677tJNN92kcuXK6cKFC9q+fbtmzJihKVOmyOl06j//+Y+KFCmiYcOG5btm+I4jR44oOjpaf/31l4oUKaJvv/1Wd911l9llAcgBWZvBm1mbafPmzXr//fclSZMnT1Z4eHiBtnP27Fk98cQTkqSXX35ZjRo1KnBN8H1JSUm655579P3330uS3n77bb3wwgsmVwUgJ2RtBqtmbeb+vfFeNHyDYRh67rnn9N5770mSevXqpenTpys4mJYhwJeRtxm8mbf16tVTo0aNtH37dtd7vX369Llque3bt+vzzz/PdV/79+/X//3f/0mS3nnnHVWsWDHfNcE66I8CrIeszWDle7a5oT/KvwRyfxRX7/B5M2bM0IwZM7J9/fbbb9fEiROznap8/vz5kjLCuW/fvle93qdPnwKNbH3yySfVvXv3fK93pWrVqhVovUunpwsNDc1x2bCwsCzX88S+8rO/iIgIbdu2TVLGNHonT55UTEyMJk+erP/7v//T7t279e677xb4pjDMN3nyZD3++OOSpGbNmmnJkiUqWbKkyVUBuBJZmzWyFlawfv16derUSQkJCapRo4bWrFmjunXrml0WgCuQtVmzctbOmzdPNtvlT74NCQlR06ZN1bRpU3Xq1En33HOPHA6HRo0apZ49e6p27dr5rhvm279/v6Kjo7V//34VLVpUixYtUqtWrcwuC8AVyNqseTNrpYwZWh555BE5nU717dtX0dHRBdqOJD377LM6evSorrvuOg0fPrzA24HvO3/+vLp06aJ169ZJkj744AMNHjzY3KIAXIWszZpVszZz/+58Lxq+y+l0atCgQfr4448lSQMGDNDHH3/Mk60BH0TeZs3befvmm2+qc+fOMgxDAwYM0N9//61+/fqpRo0aio2N1cKFCzVs2DAlJycrJCREaWlpSkpKynJbjz76qBITE9WyZUs98sgjBaoH1kB/FGANZG3WrHzPNhP9Uf4v0PujGPCDAnM4DU2J2actB2LVpFYZDWhex+vTPJcoUUKDBg1SkyZNsl0m8yaRu1WoUEEVKlTwyLbzokiRIq6PU1NTc1w2JSXF9XFBAis/+8rP/oKCgq56MmObNm301FNPqW3btvrvf/+rLVu2aP369SpatGi+64a5vvzyS9fFbHR0tBYsWMAoaSCfyFqytrD7I2v926+//qoOHTrowoULqlu3rtasWaMaNWqYXRZgKWQtWVvQ/V052OdKnTt31lNPPaX33ntPqampmj59ukaPHp2PiuELzpw5o8jISB05ckQlSpTQsmXLdOedd5pdFmA5ZuctWZvB01krSaNHj9bOnTtVoUIFvfvuuwXahiR9//33mjZtmmw2mz799NPLburCvzidTnXq1EkbNmxw/b779+9vdlmA5ZC1ZG1+FSlSRElJSW59Lxq+67nnnnMN9hkyZIgmTJggu91uclWAtZidtRJ5m8kbeduxY0e99957eu6555Senq4xY8ZozJgxVy03btw4vf322zp16pRKlChx1euffPKJVq9erSJFiuiTTz4pUC2wBvqjgMIjawMna+mPQkHQHyVxFY8CmxKzT2OX7dKqnSc1dtkuTYnZ55H9dO3aVdu2bdO2bdv0559/auXKlRo9erTKlSun8+fP6+GHH9aHH37okX37sksvFhMSEnJc9tLXs7rIdOe+3LG/cuXK6YsvvpAk/fLLL9lOkQjf1rZtW9WvX18dO3bU4sWLuZgFCoCsNRdZC193ww03qFWrVmrQoIE2bNgQcBezgDuQteby56yVMp7ilSkmJqZA24C5ypQpo4cfflgRERFavXo1g32AAvJG3pK1WfNm1v72228aN26cJOn9999XmTJl8r2NzDoee+wxSdJjjz2m5s2bF2g7sAa73a5BgwYpNDRUX375JYN9gAIia81jxay9dP/euj72Bw6nocnr92rAjC2avH6vHE7D7JLyrHfv3ipdurRefPFFvf/++wz2AQqA95HN5c28zfT0009r06ZNuueee1S8eHHX1202m5o1a6bvv/9ezz//vM6fPy9JV+XykSNH9MILL0iSXn31VdWrV6/AtcD30R8FFB5Zay5/vmdLf5R/oD/Kj2f4OX/+vE6dOqVTp07JZrOpfPnyKl++/GV/hKNwthyIverzgVHunx6rdOnSl428vOGGG9S6dWv16dNHt99+u44fP67nnntOzZo100033eT2/Wfn5MmTOnnyZKG3U61aNZUuXTrf6116wjp8+HCOy176ekFOdNWrV5fNZpNhGLnu69L92Wy2Ak8TeP311+uaa67R33//ra+//lr/+c9/CrQdmKdChQpav369SpcunafpF2E9ZK3nkbVkbW77I2sDW2hoqL755hslJCSoXLlyZpcDDyBrPY+sJWtz219hsvbaa691fXz8+PECbQPmstlsGjNmjJ588skCHwfwfeSt53kjb8narHkza998802lp6e71p09e/ZVy+zYscP18fbt213LXHfddbr55pslSV988YUOHjyooKAg3XzzzVlu51InT550LRMREaF27drlu3aY6/7771fTpk3JWj9G1noeWUvWZspL1mbu/8SJE0pMTFRcXJwiIiI8Vre/yGxAlKRVOzOOOU+8h+QJN998s7Zt26aqVavmOmMxrIms9TzeRw6cvL3Uv//9b82bN0/p6ek6fvy4Lly4oMqVK7v+bx0+fFjJycmSMu67XuqDDz5QfHy8SpYsqWrVqmWZ26dOnXJ9vH//ftcy1apVU7NmzQpVO7yL/ij/R9Z6HlkbOFlLfxQKgv4oPxrwc/r0aS1YsEDr16/X+vXrdeTIkSyXq169uiIjIxUVFaWuXbsG7C/eHZrUKuN6Myvzc2+qWbOmJk+erK5duyo1NVVDhw7V2rVrvbb/jz76SKNHjy70dqZNm6a+ffvme71LLxYvffM2K5e+3rBhw3zvq1ixYqpZs6YOHDigv//+W2lpaQoJCcly2bS0NO3Zs0eSVKtWrUJNf1euXDn9/fffOnDgQIG3Ae8xDEOjR49Wjx491KBBA0kydapJuB9Z631kLVmbFbI2sH3xxRcqU6aMOnbsKCljuuNLpzyGtZG13kfWkrVZcVfWOhwO18fBwX7zFpzf27x5s3788UcNHTpUUuFuIMA3kbfeZ2bekrXey9qUlBRJ0qFDh/TAAw/kuvy3336rb7/9VlLGk5Mzm5Azt+NwOPT444/nup2dO3e69vevf/2LAT8WcOTIEU2cOFFjx45VUFCQJJG1foas9T6ylqzNSnZZK2XUvWXLFlddOc1mWti6/YW3GhDdISkpScOHD9crr7ziGsxF1voXstb7eB85cPI2K8HBwVmeRzOzVJJuv/32y17LzO34+Hg9/PDDue5jw4YN2rBhg6SM2ScY8OPb6I/yf2St95G1gZO19Echr+iPupzl5+pdvXq1evTooWrVqumxxx7TV199pcOHD8swjCz/HTp0SF999ZUee+wxVatWTT179tTq1avN/jYsaUDzOhrWob5aN6igYR3qa0DzOl6voUuXLoqKipIkrVu3TsuWLfN6DWZp0qSJwsPDJWV87zm59I+PyMjIAu0v8+d84cIF/fzzz9kut3nzZl24cKFQ+8r0zz//SBKj4S3A6XRq0KBBGj16tFq3bs3Tq/0MWWsestZcZC18zeTJk9W7d2/de++9+vHHH80uB25E1pqHrDWXv2ftn3/+6fq4atWqBd4OvGf9+vVq06aNnn32WU2ZMsXscuBm5K15zM5bstZ7WQvk5sCBA4qMjNS4ceM0ePBgs8uBm5G15iFrzWPVrM38fUk5152cnKzNmzdLymiuCuQZfq5sOPR2A2JeJSQkqGPHjpowYYLuuusupaenm10S3IisNY/ZWSuRt76Yt19//bWkjAcG9ejRw6P7gu+gP8q/kbXmIWvN5e/3bCX6o6yG/qgsGBa1adMmo2nTpobdbjdsNpths9mMsLAw4/bbbzeefvppY+LEicZXX31lLF++3Fi6dKnx5ZdfGhMnTjSeeuop47bbbjPCwsJc69ntduPOO+80Nm3aZPa3lS9Vq1Y1qlatmq91UlNTjR07dhg7duwwUlNTPVRZ4a1du9aQZEgy+vTpk+Oy69atcy172223eadAH9GtWzfX975u3boslzl37pxRsmRJQ5JRoUIFIz09vUD7WrhwoWtfffv2zXa5Pn36uJZbtGhRgfZlGIbx448/urbTokWLfK1rlePcX6Snpxt9+/Z1/b6GDBliOBwOs8vKk4KcRwMJWVvwY8QK5yGyNm/I2qxZ4Rj3N++9957r9xUdHW0kJCSYXVKekLU5I2vJ2kxkrX9m7YMPPujazttvv53v9a1wnPuTFStWGOHh4YYko27dusaBAwfMLilPyNrckbf++z4yWZs33sza3Fz6O3v11VcLta3M7URFRRV4G1Y4zv3JX3/9ZVSrVs2QZJQoUcKIiYkxu6Q8I29zRtaStYZB1lota0+fPm2EhIS4rn+yq2fatGmu7T333HP5rscKx3lepTucxsfr9hj9p/9sfLxuj5HucJpd0lXOnj1r3HHHHYYkw2azGVOmTDG7pDwja3NG1vpv1hoGeZtXvpS3hmEYv/zyi2G32w1JRseOHQu0jf379+f5d58dqxzn/sKb/VHu/tuDrM0ZWcs920xkrX/es6U/ylroj8qaJQf83Hvvva5wLV68uPHQQw8Zy5cvN1JSUvK8jZSUFGPp0qXGgw8+aBQvXtwVtt27d/dg5e7FBe1FLVq0cC2/dOlSzxfoI3766SfX933DDTcY8fHxl73udDovC71x48ZluZ1Lf97Z3SB1OBzGDTfcYEgy7Ha7sXr16quWWbVqleuC9oYbbsjyombOnDnGiRMncvy+Dhw4YFxzzTWumqZPn57j8leyynHuD1JTU40ePXq4flcvvvii4XT63hvc2eGCNntkbQYuaC8ia8naS1nhGPcnb7zxhut3dddddxlJSUlml5RnZG32yNoMZO1FZK11snbVqlXGmTNncvy+3n33XVc9JUuWNE6dOpXj8lmxwnHuLxYuXGiEhoYakoz69esb//zzj9kl5RlZmzPyNoO/vo9M1uaNN7M2Nwz4CVzbt283KlWqZEgyIiIijJ9//tnskvKFvM0eWZuBrM1A1lora5988skcl/3nn39c5+4iRYoYhw4dync9VjjO/cXp06eNW2+91ZBkBAUFGV999ZXZJeULWZs9sjaDv2atYZC3eeXNvHU6nTm+P7h7927XwwyKFStm7N27t0DfEwN+rMXb/VEfr9tj1Hxpsevfx+v2FGp7ZG32yNoM3LO9iKy1zj1b+qP8D/1R2bPkgB+bzWZUqVLFmDhxopGYmFjo7SUmJhoTJkwwKleubNjtdjdU6B1c0F4UyCNrL30ztkGDBsaMGTOMLVu2GAsXLjQ6dOjgeu2WW24xLly4kOU28voGckxMjKsBpkiRIsbIkSONmJgYIyYmxhg5cqRRpEgRQ5IRGhqa7dP5unbtaoSFhRn33HOP8cEHHxirV682tm7davz888/GN998YzzxxBNG8eLFLztp5/cCySrHudUlJycbd999t+t3NWrUKEsN9jEMLmhzQtZm4IL2IrKWrL2UFY5xf+B0Oo0RI0a4flfdunXL1xuLvoCszR5Zm4GsvYistU7W9unTxyhatKhx3333GR999JGxdu1a47fffjM2bdpkfPrpp0azZs1ctdhstgI32VjhOPcHc+bMMYKDgw1Jxo033pjrjQFfQ9bmjLzN4K/vI5O1eefNrM0JA34C09atW41y5coZkoxy5coZv//+u9kl5Rt5mz2yNgNZm4GstVbWnjx50qhRo4Zr+V69ehkrVqwwfvrpJ+Ojjz5yNTJLBZu11jCscZz7g+PHj7ua5UJCQoxvvvnG7JLyjazNHlmbwV+z1jDI2/zwVt6mpaUZISEhxt13321MnjzZ2Lhxo/Hrr78aixcvNgYPHux6DzkkJMT49ttvC/z9MODHOszoj+o//efLBvz0n164B2eQtdkjazNwz/YistY692zpj/If9EflzpIDfiZMmJDtyaIwkpOTjQkTJrh9u57CBe3loqKiXOssW7bMswX6kPT0dKN3796u7z2rf40bNzaOHTuW7Tby8wbyN998Y5QoUSLbfZUoUSLHC9quXbvmWGvmP7vdbjzxxBNGcnJyvn8mVjnOrSwlJcVo37696/f11ltvmV1SgXBBmz2yNgMXtJcja8naTFY4xv3Biy++6Pp9Pfjgg0ZaWprZJeUbWZs9sjYDWXs5stYaWXvp06ty+hcREWHMmTOnoD8WSxznVvfVV1+5ng7WuHHjXGdu8kVkbc7I2wz++j4yWZt33s7avGyDAT+BYcuWLUbp0qUNSUblypWN//3vf2aXVCDkbfbI2gxk7UVkrbWydseOHUa9evWyrdlmsxnDhg0rUC2GYY3j3OqOHTtmXHfddYYkIywszFi8eLHZJRUIWZs9sjaDv2atYZC3+eGtvE1LS8v1vd+qVasWetYHBvxYg1n9Uczw4z1kbQbu2V6OrLXGPVv6o/wH/VG5s8uCnn76aRUpUsTt2w0LC9PTTz/t9u3CO0aNGuX6ePTo0eYV4mVBQUGaMWOGli1bpnvvvVfVqlVTaGioypcvrxYtWmjy5Mn68ccfValSJbfs795779X27dv1/PPPq0GDBipevLiKFy+uhg0b6vnnn9f27dvVrVu3bNf/73//qylTpqhPnz5q3LixqlevriJFiigsLEyVKlVSixYtNGLECO3YsUMfffSRwsLC3FI33CskJET16tWTJH3wwQd68cUXTa4I7kbWIitkLVkL76pfv74k6ZFHHtHnn3+u4OBgkyuCO5G1yApZa42sffnllzVhwgQ98MADuvHGG1W5cmWFhYUpPDxc1apVU8eOHTVx4kQdOHBA9913n1tqhmfUrl1b4eHhuvPOO7Vq1SqVKVPG7JLgZuQtrkTWeidrgUyVKlVSRESEatSooQ0bNqhhw4ZmlwQ3I2txJbLWWlnboEED/fHHH3rnnXd02223qUyZMgoLC1OtWrXUu3dvbdq0SW+88YbZZSIHpUqVUpUqVVS0aFEtWbJEHTt2NLskuBlZi6yQt57N2+DgYM2YMUN9+vRRw4YNVaZMGYWEhKhy5cpq1aqVJk6cqF27dqlDhw5u+s7gy8zqjxrQvI6Gdaiv1g0qaFiH+hrQvI5X9huIyFpkhay1xj1b+qP8B/1RubMZhmGYXQQKplq1apKkI0eO5HmdtLQ07dmzR5JUr149hYSEeKQ2wEwc597hdDq1ceNGRUZGml1KgRXkPIrAUtBjhPMQ/B3HuPds2LBBzZo1k91uyWc1kLXIFVkLZI/j3Dt++eUX1a9fX8WLFze7lAIha5EXvI8MXM0wDJ04m6jjRw4q/kKa/v2vhioSFmp2WX7pwIEDstlsqlmzptmlFBh5i9yQtUDWOM69IyEhQbt27VLjxo3NLqXAyFrkhqwFssZx7h30RyEQcM8WyBrHuPfQH5Uza/5UAABeFxsbq9mzZ7s+t9vtlr6YBQDA16SlpenTTz+V0+l0fS0yMtKyF7MAAPiizz//XOfPn3d93rhxY8sO9gEAFNzphBSdOJ8sp9PQsXPJ+uqng2aX5DeWL1+uffv2uT6vVauWpQf7AADga3bs2KF169a5Pi9evLilB/sAAOBr6I8CAMCz6I/KP8v+ZNauXatHHnlEjzzyiHbu3Jnn9Xbu3Olab+PGjR6sEAD8x8mTJ9WiRQs98MADmjJlitnlwEvIWgDwnpSUFPXo0UOPPfaYBg8eLCZiDQxkLQB4j2EYeuWVV9SnTx917txZFy5cMLskeAl5CyAriSmOyz7//dBZcwrxM9988406d+6s6OhoHT582Oxy4CVkLQB4z++//66oqCh17NhRP/zwg9nlwEvIWgDwHvqjAhNZCwDeQ39UwVhywI/T6dQTTzyhGTNmyOFwqEGDBnlet0GDBnI4HJo+fboGDx7swSoBwD/8888/ioqK0rZt2xQaGqpKlSqZXRK8gKwFAO+5cOGC7rnnHn333XeSpBo1ashms5lcFTyNrAUA7zEMQy+++KJee+01SVKVKlUUHBxsclXwBvIWQHaKhQVd9vlNNUqbU4ibOJyGJq/fqwEztmjy+r1yOL1/k/TLL79Ujx49lJ6ernLlyqlYsWJerwHeR9YCgPf8/PPPatmypU6fPq2SJUsqIiLC7JLgBWQtAHiPmf1RvnBdH6jIWgDwHvqjCs6SA35Wrlypv/76S8WKFdO7776b7/XHjx+vEiVKaNu2bVqzZo0HKgQA/3Dw4EFFRkZq165dCg8P1+LFi9WpUyezy4IXkLUA4B2JiYnq1KmTli1bJkl6//339fLLL5tcFbyBrAUA73A6nRoyZIjeeecdSVK/fv30xRdfKCQkxOTK4A3kLYDslCsepoolishut6lyqSLqdVtNs0sqlCkx+zR22S6t2nlSY5ft0pSYfd7d/5Qp6t27t5xOp5o2bapVq1apTJkyXq0B5iBrAcA7YmJi1Lp1a509e1bVq1fXhg0b1LBhQ7PLgheQtQDgHWb3R5l9XR/IyNqL4pLSGHAGwGPojyocSw74mTdvniSpd+/eKleuXL7XL1u2rPr06SPDMDR37lx3lwcAfmHPnj1q3ry59u3bp+LFi2v58uVq06aN2WXBS8haAPC8+Ph4tWvXTmvWrJHNZtPkyZP11FNPmV0WvISsBQDPczgceuyxxzRp0iRJ0pNPPqkpU6YoKCgolzXhL8hbANmx2WwqWzxMYcF2lS8RpiC7tZ8iuOVAbI6fe9IHH3ygRx99VIZhqGXLllqxYoVKlSrltf3DXGQtAHjeqlWr1L59e50/f1516tTRhg0bdM0115hdFryErAUAz/OF/igzr+sDHVl7UUqagwFnADyC/qjCs+SAn59//lk2m02dO3cu8DYyR2D/9NNP7ioLAPzGjh07FBkZqcOHD6t06dJatWqVIiMjzS4LXkTWAoBnxcbGqnXr1vrhhx9kt9v1+eef67HHHjO7LHgRWQsAnpWenq7evXvrs88+kyQ9//zz+vDDD2W3W/LtUBQQeQsgUDSpVSbHzz3l7bffdt2Y7dChg5YsWaLixYt7Zd/wDWQtAHjWkiVL1KlTJyUlJem6667Thg0bVKtWLbPLgheRtQDgWb7SH2XWdT3I2qww4AyAO9Ef5R7BZhdQEIcOHZIkXXfddQXexrXXXispYzpGAMDlTpw4obi4OJUrV04rV67UTTfdZHZJ8DKyFgA8Kz4+XkePHlVwcLBmzZql7t27m10SvIysBQDPSklJ0f79+yVJr7zyikaNGiWbzdqzNyD/yFsAgWJA8zqSMhoymtQq4/rckwzD0O7duyVJ99xzj2bNmqWwsDCP7xe+hawFAM86cOCAUlJSdMMNN2jlypWqWLGi2SXBy8haAPAsX+mPMuO6HhnI2qsx4AyAO9Ef5R6WHPBz/vx5SVJERESBt1G6dGlJUkJCgjtKAgC/0rJlSy1YsEBVq1bV9ddfb3Y5MAFZCwCeVatWLa1Zs0Z79uzRXXfdZXY5MAFZCwCeVaxYMS1btkzz5s1Tv379zC4HJiFvAQSKILtNA6PqamBUXa/t02az6ZNPPlHjxo01YMAAhYSEeG3f8B1kLQB41qBBg1SiRAl17NhRZcuWNbscmICsBQDP8pX+KDOu65GBrL0oLCRIwzrUZ8AZALeiP8o97GYXUBCZARkbW/Cp4+Li4iRJJUuWdEdJAGB5v/32my5cuOD6vG3btgz2CWBkLQC436FDh3TkyBHX59deey0XswGMrAUA90tMTNQff/zh+rxUqVIM9glw5C0AuJfT6dSPP/7o+jwoKEhPPPEEg30CGFkLAO73448/yjAM1+e9e/dmsE8AI2sBwP3oj8KlyNqLIoqGaGBUXQXZbWaXAsDi6I9yP0sO+KlUqZKkjD++Cipz3cxtAUAgW716tZo1a6Z7771XKSkpZpcDH0DWXhSXlKbJ6/fK4TRyXxgAsrF37141b95c0dHROnHihNnlwAeQtQDgXvHx8Wrfvr0iIyP166+/ml0OfAR5CwDu43A4NHDgQN15552aNWuW2eXAR5C1AOBeH374oZo2baqXX375skE/CFxkLQC4F/1RuBJZCwDuRX+UZ1hywE+zZs1kGIbmzp1b4G3MmTNHNptNzZo1c2NlAGA9S5cuVceOHZWUlKS9e/e6njqAwEbWXpSS5tDYZbs0JWaf2aUAsKidO3eqefPmOnTokE6cOKHDhw+bXRJ8AFkLAO4TGxur1q1ba+PGjUpISNDu3bvNLgk+grwFAPdIT09X7969NWXKFBmGUagmGPgXshYA3GfcuHEaMmSIJOmPP/5Qenq6yRXBF5C1AOA+9EchK2QtALgP/VGeY8kBP127dpUkzZ07Vxs3bsz3+jExMa6AztxWoLDbL/7KnU6niZUAnnPp045sNqaYzMm8efN09913KyUlRY0aNdKGDRt42oBF/fbbb3r++ecVFRWlhg0bqm7dupe9fvz4cc2bN0+LFy/O0/bI2qttOZD36XsvPffwBDb4I7I27/744w9FRUXp2LFjKlu2rNasWaPGjRubXRZ8AFlbOFzbIhBcemxfeszjcidPnlTLli21ZcsWBQcHa/bs2XrwwQfNLgs+grwtOLIWgYCszZvU1FT16NFDM2fOlCSNGDFCb731lslVwVeQtQVH1iIQkLV5YxiGRo8erRdffFFSxvlwwYIFCgkJMbky+AKytuDIWgQC7tnmHf1RyA5ZWzj0R8HfkbV5R3+UZ1nyXZV27dqpcePGcjqduvvuu/MVtDExMbrnnnskSbfeeqvat2/vqTJ9kt1ud13UJiYmmlwN4BkXLlyQlHG8BwUFmVyN75o5c6buv/9+paWl6ZZbbtG6detUsWJFs8tCPiUnJ6tfv35q3Lix3nvvPcXExGjXrl06cODAZcsVK1ZM/fr1U9euXbV9+/Zct0vWXq1JrTJ5XjYoKMiVt5nnJMCfkLV5s2XLFrVs2VKnTp1SxYoVtX79et1yyy1mlwUfQdYWDte2CASZx/alxzsud/ToUbVo0UJ//vmnQkND9e233+q+++4zuyz4EPK24MhaBAKyNnfJycm65557NG/ePEnSmDFj9Nprr3FzGy5kbcGRtQgEZG3uDMPQsGHDNGrUKElSjx49NHfuXIWFhZlbGHwGWVtwZC0CAfds84b+KOSErL0oLilNk9fvlcOZ94E79EfB35G1eUN/lOdZ9l2VTz/9VMWLF1dcXJxatGihPn36aOPGjVlOa5yenq6NGzeqT58+atWqlWJjY1WsWDF9+umnJlRuLpvNplKlSkmSYmNjlZaWZnJFgHs5nU7XlKvFixfnxmM2pk6dqoceekgOh0N33HGHVq9erbJly5pdFgqgR48e+vzzz2UYhqKiovTMM89kuVyJEiV0zz33yDAM1w363JC1GcJCgjSsQ30NaF4nz+vYbDYVL15ckhQXF8dTo+BXyNq82bhxo6KjoxUXF6dq1appw4YNuv76680uCz6GrC04rm3h79LS0hQbmzHDZKlSpcjbLBw8eFCRkZHauXOnwsPDtWjRInXp0sXssuCDyNuCIWvh78ja3CUmJqpTp05aunSpJOm9997T//3f/5lcFXwRWVswZC38HVmbO6fTqaeffto1c17fvn311VdfMbMPrkLWFgxZC3/HPdu8oT8KeUHWZkhJc2jssl2aErMvz+vQHwV/RtbmDf1R3mEzLDyP2vfff6/7779f8fHxrv9IoaGhqlOnjkqXLi1JOnv2rPbt26fU1FRJGU9IKVGihGbPnq0OHTqYVbpbVKtWTZJ05MiRfK2XkpKi/fv3yzAMBQUFqXTp0ipWrJhCQkI4IcGyDMPQhQsXFBcX5xpVW716ddcflLgoPT1dt99+u3799Ve1aNFCixYtCtifU0HPo75i7ty56tGjh4oUKaJ58+apffv2SkxMVIkSJWSz2eRwOC5bfs6cOerZs6eio6O1cuXKPO2DrC34MZKQkKDDhw9LksLDwxUREaHw8HCyFpZF1uZP//79NXXqVNWuXVurV69W7dq1zS7JFFbPWm8gawt+jHBtC39jGIbS0tKUmJios2fPyuFwyGazqXbt2jzZNwsfffSRBg0apGLFimnJkiWKiooyuyRTkLV5Q97yPjIgkbX5FRMTo+joaKWlpenjjz/WwIEDzS7JNORt7shashaQyNr8Onr0qG6++WadPHlSTzzxhD788MOAnQmJrM0dWUvWApm4Z5t39EddRNbmjqytpuPnklVt0Ay1blBBU/o0yfO69EfB35C1+UN/VAZPZ62lB/xI0p49e/T4449rzZo1rq9dGRSXfostW7bUxx9/rGuuucZrNXpKYQ6OpKQkHTly5KpmcMBfVKxYUWXKlDG7DJ91+vRpjR49Wm+99ZaKFi1qdjmmsfoF7V133aUVK1Zo9OjRGjFihCTlOOBn9+7datCggapWreq60MoLsrbgx0hsbKxOnDjhzpIAn0HW5iwtLU0vvPCCnn/+ede5JBBZPWu9hawt+DHCtS38WVBQkKpVqxbQ12y5efPNNxUVFaU77rjD7FJMQ9bmHXnL+8jAlcja3M2bN0/nz59Xnz59zC7FVORt3pC1ZC1wJbI2d9u2bdPcuXM1evTogG6IJGvzhqwla4GscM82Z/RHZSBr8ybQszZzwM+wDvU1MKpuvtanPwr+jKzNGf1RGRjwk0dbt27VN998o5iYGO3fv981PXSZMmVUu3ZtNW/eXN26dVPjxo0vW2/v3r2qWzd/4eQrCntwpKWl6ezZs0pMTHSNQgSszG63q3jx4ipVqhSjaa9gGIaOHz+uypUrm12KT7H6BW3FihV1+vRp7dy5U9dee62knAf8xMbGqly5cgoLCyvQeZ+sLZiEhASdO3dOCQkJTF0LyyNrc3bs2DGy9gpWz1pvI2sLhmtb+Jvw8HAVK1ZMpUuXVkhIiNnl+JRjx46pUqVKAd0EdSWyNv/I2/wja+FvyNrsnTlzRiVKlFBoaKjZpfgU8jZ/yNr8I2vhb8ja7KWmpur8+fMqW7as2aX4FLI2f8ja/CNr4W+4Z5s9+qOyRtbmT6BmbVxSmsZ/t0kDmtdRkD3/92Hoj4I/IWtzRn/U1Rjwk4U5c+bo/vvvL/R2du3apdatW1v2Dxl3HhxOp1NOp1MWPBwASRmj6YOCgmj6yYJhGBo2bJimTJmi9evX6/rrrze7JJ9h9QvasLAwpaenKzY2VqVKlZKU84CfM2fOqHz58ipSpIiSkpJy3DZZm8Gdx4hhGHI4HGQtLIuszdnMmTPVv39/zZo1S3fffbfZ5fgMq2etJ5G1Gdx9jHBtCyuz2Wyy2+2y2+1ml+KTtmzZonbt2mno0KEaOXKk2eX4DLI2Z+RtBt5HBjLYbDYZsmnqDwe05UCsmtQqU+AGBn909OhRtW7dWg0aNNDXX3+t4OBgs0vyGeRt9sjaDGQtkIHr2pwlJyere/fuOnTokNatW8cToi9B1maPrM1A1gIXcc82e/RHZY+szR5Zm4H+KOAisjZn9EdlzdNZa8l36x9++GGFh4erc+fOBd7G9u3bFR0drdOnT7uxMuvijTfAPzmdTj3zzDP64IMPJElTp07Vu+++a3JVcJfSpUvr9OnTOn36tGvAT0727dsnSSpfvnyuy5K17mez2WiUAPzU1KlTNWDAABmGoQkTJqhr165c+CNXZK1ncG0L+KcffvhBd911l+Lj4zV58mQNHjxYERERZpcFCyBv3Y+shdVNXr9XY5ftkiSt2nlSkjQwyppPXnWnQ4cOKTo6Wnv27NH+/fu1bds23XzzzWaXBQsga92PrAX8U2Jiou6++26tWrVKkrRs2TL16tXL5KpgBWSt+5G1gH+iPwoFRda6H/1RgP+iP8o8lryCSUtL0/33368VK1YUaP2tW7eqZcuWOnXqlJsrAwDf4XA4NHDgQNfF7OOPP65x48aZXBXc6YYbbpAkrVu3Lk/Lz58/X5LUpEmTXJclawEgbyZNmqT+/fvLMAxFRUVp0aJFXMwiT8haAMibNWvWqG3btoqPj1etWrUUExPDYB/kGXkLWI/DaWjy+r0aMGOLJq/fK4fTvU8C3XIgNsfPA9HevXsVGRmpPXv2qFixYlq+fDmDfZBnZC0A5C4+Pl4dOnRwDfb5+OOPGeyDPCNrASB39EehMMhaAMgb+qPMZckBP1WqVFFKSoq6deumtWvX5mvdzZs3q3Xr1jpz5oxsNpsmTZrkoSoBwDzp6enq27evpkyZIkl65pln9NFHH/GkGj9z9913yzAMjRkzRufOnctx2R07dmjixImy2Wzq3r17rtsmawEgd++8844GDx4sSWrbtq2WLl2qEiVKmFwVrIKsBYDcLVu2TB07dlRSUpKuvfZaxcTEqHbt2maXBQshbwHrmRKzT2OX7dKqnSc1dtkuTYnZ59btN6lVJsfPA82uXbsUGRmpgwcPqlSpUlq5cqWioqLMLgsWQtYCQM7i4uLUpk0bxcTEyG63a/r06Ro4cKDZZcFCyFoAyBn9UYHnu+++U/fu3VWzZk0VLVr0qplkDh06pPHjx+vDDz/M0/bIWgDIHf1R5rPkXzarV69WxYoVdeHCBXXp0kU//PBDntaLiYlRu3btdPbsWdntdk2ZMkWPP/64h6sFAO9KTU1Vz5499eWXX0qS/u///k/jx49nNK0fevTRR1WrVi0dPHhQkZGRWrt2rQzj8qeenj17Vp988omioqKUlJSkG264QT169Mh122QtAGTPMAz95z//0QsvvCBJ6tKlixYuXKiiRYuaXBmshKwFgJx999136tq1q5KTk3X99ddr/fr1qlatmtllwWLIW8B6PD0Dz4DmdTSsQ321blBBwzrU14Dmddy6fSv5888/FRkZqaNHj6ps2bJas2aN7rjjDrPLgsWQtQCQvVOnTqlVq1b6+eefFRwcrFmzZqlPnz5mlwWLIWsBIHv0RwWW2NhYtW7dWt27d9e8efN0+PBhJScnX9UnVbFiRb355pt6+umn9dNPP+W6XbIWALJHf5TvsOSAn+uuu04rV65UuXLllJiYqLvuuktbtmzJcZ3Vq1frrrvu0vnz5xUcHKzPP/9c/fr181LFAOA9c+bM0bfffitJev311zVmzBguZv1UWFiYFi1apDJlymjbtm1q3bq1Klas6Pp9ly9fXuXKldMTTzyhM2fOqEKFCvr222/zdDyQtQCQve3bt2v06NGSpPvuu0/ffPONwsLCTK4KVkPWAkD2EhISNHDgQKWlpenmm2/WunXrVKlSJbPLggWRt4D1eHoGniC7TQOj6mpKnyYaGFVXQfbAfN/UMAwNHjxYp06dUsWKFbVu3TrdcsstZpcFCyJrASB7b7zxhn7//XeFhobqm2++0f333292SbAgshYAsmd2f5TDaWjy+r0aMGOLJq/fK4fTyH0lFIjD4VDHjh21Zs0ahYaGqm/fvho/fnyWy4aFhem+++6TYRhasGBBrtsmawEge/RH+Q5LDviRpEaNGmnFihWKiIjQ+fPn1a5dO/3xxx9ZLrt06VJ16dJFiYmJCg0N1axZs/Tggw96uWIA8I5evXrpueee07vvvqvhw4ebXQ487Prrr9fvv/+uLl26SJIuXLggwzBkGIbOnDkjp9MpwzDUqVMnbdmyRXXr1s3ztslaAMjaDTfcoBkzZqhv376aOXOmQkJCzC4JFkXWAkDWihcvriVLlqht27Zas2aNypUrZ3ZJsDDyFrAWf5qBx5cbf2w2m77++mu1aNFC69evV6NGjcwuCRZG1gJA1saOHauuXbtq4cKF6tq1q9nlwMLIWgDImtn9UVNi9mnssl1atfOkxi7bpSkx+7xeQ6CYOnWqfvrpJ5UuXVqbN2/W1KlT9eijj2a7fNu2bSUpz7P1kLUAkDX6o3yHzbhyTjuL2bJli9q0aaP4+HiVK1dO69atU8OGDV2vz58/Xw888IBSUlIUFhamOXPmqHPnziZW7D7VqlWTJB05csTkSgCYLTU1VaGhoWaXYTn+dh49cOCAVq9erV27duncuXMqXry46tSpo7Zt2+raa68t8HbJWv85RgAUnNPplNPpVHBwsNmlWArn0bwhazlGAGTg2jb/OI/mHXnLcQJ42+T1ezV22S7X58M61NfAqLw/jMcTyNqC4TyaN4GetXFJaRr/3SYNaF4nYGcPA0DWFhRZmzeBnrUSxwgAz2atw2loSsw+bTkQqya1yuT6t/2AGVu0audJ1+etG1TQlD5NPFJbYVn9PNqyZUtt2LBB77//vgYPHixJSkxMVIkSJWSz2eRwOC5bfu/evbrmmmtUsWJFHTt2LM/7IWute4wAcB/6owrG0+dRy87wk6lJkyZasmSJihUrptOnTys6Olp//fWXJOnrr79Wjx49lJKSoqJFi2r+/Pl+E7AAkCkuLk6RkZF67733zC4FJqtVq5b69++vcePG6ZNPPtH48eM1ePDgQg32kchaAEhPT1ffvn31yCOPyOl0ml0O/BBZCwDS+PHj1bx5c8XHx5tdCvwUeQsEFl+YXWfLgdgcP/e25cuXq0GDBvr7779NrQP+K9CzNiXNwVO9gQC3a9cuNWjQQN9//73ZpcBPBXrWAoCn+6PyO2NPk1plcvwc7rNt2zZJUqdOnfK0fNmyZSVlHDP5QdYCCHT0R/kuyw/4kaQ777xTixYtUnh4uE6ePKno6Gi99dZbeuihh5SWlqbixYtr0aJFateundmlAoBbnTp1Sq1atdJPP/2kF198UXv37jW7JHjRhg0btGHDBuVnsr7MdfKLrAUQqFJTU/XAAw/oiy++0BdffKGFCxeaXRL8FFkLIJC9/vrreu655/Tzzz9r3LhxZpcDP0beAv7t0kE+A2ZsyVeTjif4UuPP/Pnz1aVLF+3bt09PP/20aXXA/5G15g/uA2CObdu2KSoqSvv27VP//v2VnJxsdknwU2QtgEDljf6o/D64Y0DzOhrWob5aN6igYR3qa0DzOm6vCRnOnz8vSYqIiMjT8mlpaZJUoNkpyFoAgYr+KN9mM/LTJezjvv/+e3Xt2lWpqamSJMMwVKJECS1dulR33nmnydW5H9PoAYHt2LFjat26tXbs2KGQkBDNmTNHd999t9llWYrVz6N2u112u13x8fEqWrRorss7HA6FhITIbrcrPT29QPskawEEkuTkZN1///1atGiRJOm1117TiBEjTK7KWjiP5h9ZCyCQGIahESNG6I033pAk3Xffffryyy8VGhpqcmXWwXm0YMhbwD9NXr9XY5ftyvK11g0qaEqfJl6tx+E0NCVmn7YciFWTWmU0oHkdBdltXq1ByngCba9eveRwOHTTTTdp5cqVKleunNfrsDLOo/kXiFl7/Fyyqg2aoWEd6mtgVF2zSwLgRb/++qvatm2r2NhYVahQQStXrtSNN95odlmWQtbmXyBmrcQxAgQqb/VHXfm+gj/9bW/182iVKlV04sQJ/e9//1P9+vUlSYmJiSpRooRsNpscDsdly2/cuFGRkZGqXbt2gQeHkbUAAgn9UYXn6fOoX8zwk6lt27b65ptvFBwcLMMwFBERoVWrVvllwAIIbIcPH1ZUVJR27NihIkWKaOHChQz2CVAFGbdbmLG+ZC2AQJGUlKQuXbq4LmbfeecdLmbhFWQtgEBhGIaGDh3qGuzz8MMPa+bMmQz2gVeQt4B/yunJu2bMrhNkt2lgVF1N6dNEA6PqmjLYZ/r06XrwwQflcDh02223ac2aNQz2gVcEYtaGhQTxVG8gAG3atEmtWrVSbGysqlSpovXr1zPYB14RiFkLIDB5sz+KGXt81y233CJJWrFiRZ6Wnz17tiTpjjvuKPA+yVoAgYL+KGvI/5x1PqBVq1Y5vl66dGmdPn1apUqV0ksvvZTjsjabTatXr3ZneQDgUfv27VN0dLQOHDigYsWKadGiRWrZsqXZZcECMp9oERQUlOuyZC2AQHb+/Hl16tRJGzZskCRNmjRJTz75pMlVwd+QtQACmdPp1BNPPKFPPvlEkvTYY4/pv//9r+x2v3o2EXwAeQsElia1ymjVzpOuz1teV15Bdptrdp1A89FHH2nQoEGSpMjISC1evFglSpQwuSr4G7L2ooiiIX7z9G8AebN27Vp17txZiYmJqlmzplavXq26dTkPwL3IWgCBzNv9UZkP7uDvet9z//33a+nSpXrjjTd07733umZRyMq6dev0ySefyGazqVevXrlum6wFEMjoj7IOm1GYx/ybxG63y2Yr/JPQDMPIcko/q2AaPSDwOBwO3XDDDdq5c6dKliypZcuWqWnTpmaXZVlWP49m5uH58+dVtGjRXJffuXOnrr/+epUrV04nT57McVmyNoPVjxEABdOrVy/NnDlTdrtdn332mfr27Wt2SZbFeTR7ZG0GjhEgMI0fP17PPfecJOnpp5/We++955ZzYiDiPJoz8jYDxwkChcNpaErMPm05EOsa5GPGrDq+YN26da4mqDZt2mj+/Pl5ev8QWeM8mj2yNgPHCBB4jh8/rrp16yopKUn16tXTmjVrVL16dbPLsizOo9kjazNwjACBh/4o97L6edTpdKpJkyb67bffVKVKFb322muKiopSvXr1XH1Te/bs0ezZszVhwgSlpKQoKipKa9asyXXbZG0Gqx8jAAqG/ij38fR51JIz/NSoUYMmAAABKSgoSJMnT1avXr303Xff6dZbbzW7JHhR5kjqK23cuFFFihTJdj2Hw6GjR49q0qRJstlsuummm3LdF1kLIJC98cYb+umnn/T666+rZ8+eZpcDP0XWAghkAwcO1Pz589WsWTONGTOG8yE8hrwFAgtP4r0oKipKAwcO1D///KO5c+fm+N4hUBhkLYBAValSJY0fP14ffPCBVq5cqcqVK5tdEvwUWQsgUNEfhUvZ7XYtXLhQLVu21J49ezRgwABJcmXkpTMaG4ahBg0a6Ouvv87TtslaAIGM/ijrsOQMP8jAqFogcKWkpCgsLMzsMizPaufRK58qkRnheb3wzHyaxNy5c9WtWzeP1OhvrHaMAHAfstY9OI8iNxwjQOAia92D8yjyguMECExOp1MOh0MhISFml2J5nEeRG44RIHBxbesenEeRG44RIHCRte7hL+fR8+fPa+TIkfrss8+UmJh41ethYWF69NFHNWbMmMsGASF3/nKMAMg/stY9PH0etXtkqwAAt9m8ebO6du2qpKQk19cI2MBlGIbrn81mk81mu+xr2f2TpEaNGmnq1KkM9gGAKxw5ckRt27bVwYMHXV8jawEAcJ+kpCR169ZNMTExrq+RtQAAuI9hGHruuef0ySefuL5mt9sZ7AMAgBvNmDFDgwcP1qXP1OXaFgAA97myP8rhNDR98xENmLFFk9fvlcPJc+0DXYkSJTRhwgSdOHFCK1eu1AcffKDXX39dEyZM0MKFC3Xy5ElNnDiRwT4AkA36o6wr2OwCAADZW79+vTp16qSEhAQNHjxYU6dONbskmGj//v2ujw3DUJ06dWSz2fS///1PRYsWzXa9kJAQlSlTRkWKFPFGmQBgKfv371d0dLT279+vrl27auvWrbLbeS4CAADucv78eXXu3Fnr16/X6tWrtWfPHpUvX97ssgAAfsThNDQlZp+2HIhVk1plNKB5HQXZ8zYjtj9wOp0aNGiQPv74Y9lsNjVq1EhNmzY1uywAgB8pSNb6Wz5//PHHeuKJJyRJDRo00KBBg0yuCAAA/5JVf9SUmH0au2yXJGnVzpOSpIFRdc0sEz6iaNGiio6OVnR0tNmlAIBl0B9lbZYf8LN161atXLlSR44ckWEYqlq1qlq1aqXbbrvN7NIAoFBWrFihu+++W8nJyapXr55GjRpldkkwWc2aNS/7vEaNGrLb7apdu7ZHB/OQtQD81V9//aVWrVrpn3/+UcmSJTVp0iQuZmEKshaAvzp79qw6dOigzZs3y2az6b333mOwD0xD3gL+K5AbgBwOh/r3768ZM2ZIkoYMGaI77rjD5KoQqMhawH8VJGv9KZ8nTJigoUOHSpJat26tvn37mlsQAhZZC8BfZdcfteVA7GXLbTkQa9m/J1A4n3/+uSTpoYceyvP9/Mx1evfunef9kLUA/BX9UdZn2QE/Z8+e1UMPPaRly5Zl+XqLFi00a9YsVahQwcuVAUDhLVy4UPfdd59SU1PVsGFDrVq1SpUrVza7LPiYAwcOeHT7ZC0Af7Z9+3a1bt1aJ06cUEREhL7//ns1btzY7LIQYMhaAP7s9OnTatu2rX777TcFBQXpiy++0AMPPGB2WQhA5C3g/wK1ASgtLU0PP/ywvv76a0nSSy+9pLFjx8pms+7sCbAmsjZDYkq6BszY4hczmQBXKkjW+ks+v/HGGxo+fLgkqWPHjvrmm288+hA+ICtkLQB/llN/VJNaZVwDhzM/z4kvzTDoS7X4g759+8put6t79+4qWrRorss7HA7XOnkZ8EPWAvBn9Ef5B0sOz3I4HOrYsaOWLVsmwzCy/Ldu3Tq1a9dOqampZpcLAPkyZ84c3XvvvUpNTdW//vUvrVu3jsE+yNLHH3+s8+fPe2TbZC0Af7Z161a1aNFCJ06cUPny5bVu3TouZuF1ZC0Af3b8+HG1bNlSv/32m0JCQjRnzhwG+8AU5C0QGK5s+MmtAcgfpKSk6L777nMN9hk9ejSDfWAKsvai88npWrXzpMYu26UpMfvMLgdwq4JkrdXz2TAMjRgxwjXY595779W8efMCcrCPw2lo8vq9GjBjiyav3yuH0zC7pIBC1gLwZ7n1Rw1oXkfDOtRX6wYVNKxDfQ1oXifH7WXOMOgLf5f7Ui3+wjDy/zdIXtYhawH4M/qj/IclB/x8/fXX+vHHH2UYhq699lpNnz5dW7Zs0a+//qovvvhCjRo1kmEY+vPPPzV16lSzywWAPFuyZIkeeOABpaen69///rfWrl2r8uXLm10WfNSTTz6pSpUqqXfv3lq7dq1bt03WAvBXf//9t1q1aqUzZ86ocuXKWr9+vW688Uazy0IAImsB+KukpCS1aNFC27dvV1hYmObPn69u3bqZXRYCFHkLBIb8NgD5gwcffFALFiyQJL399tt65ZVXGOwDU5C1WbtyZhPA6gqStVbP57Fjx2rMmDGSpF69emn27NkKDQ01uSpz0LBsLrIWgL/KS39UkN2mgVF1NaVPEw2MqpvrDDlZzTBoFl+qJRClpaVJkoKDg3NdlqwF4K/oj/IvNqMgQ19N1qVLFy1evFh33nmn1qxZo5CQkMtedzgc6tChg1atWqUWLVpozZo1JlXqWdWqVZMkHTlyxORKALhLfHy8WrdurbCwMC1ZskQlS5Y0uyS/ZvXzaFBQkAzDcN3Mr127tvr166c+ffq4vreCImszWP0YAXC1zOm7N2zYoNWrV6tevXpml+TXOI9mj6zNwDEC+Kfx48dr5MiRWrhwoaKjo80ux69Z5TwaFBTktm3ZbDalp6fnaVnyNoNVjhOYy+E0NCVmn7YciFWTWmU0oHmdXBtp/LEGq/j+++/VpUsXvfPOOxo8eLDZ5ZjKG8cN59HskbUZqlWrpuPnklVt0AxJ0rAO9TUwqq7JVQGe58/Z/ffffysyMlKdOnXSxx9/7NZrGqsZMGOLVu086fq8dYMKmtKniVv3QdZmj6zNwDECWEte/kbwRH/U5PV7NXbZLtfnZv5d7ku1SNY/j9rtdtlsNp0/f15FixbNdflff/1VTZo0UeXKlfXPP//kuCxZm8HqxwiAq9Ef5V2ePo9acsBP9erVdfToUS1fvlxt2rTJcplNmzapWbNmKlWqlOLi4rxcoXcQsoB/iouLU2hoqIoVK2Z2KX7P6ufRw4cPa9q0aZoxY4b2798vKaPxyW63q02bNnrkkUfUtWvXqy5G84KszVCtWjXFJaVp/Heb/OpGFRDo0tPTderUqcumhIdnWD1rPYmszcAxAvivI0eOFPpBBMidVc6jdrv7Jpq32WxyOBx5Wpa8zWCV4wTm8oVGFF+owUrI2gzeOG44j2aPrM1QrVo1Jaak6953FvndoAcgJ/6e3f/8848qV67s1usZKyJrzUXWZuAYAawlr9nh7v4oXxqM7Eu1SNY7j37++eeXfd63b1/ZbDZ9/PHHCgsLy3Y9h8Oho0ePasaMGdq7d686derkmiE5O2RtBqsdIwDyhv4o72HATxbCw8OVmpqqkydPqmzZslkuk5KSovDwcNlsNqWmpvrlE1eqVaumk+dT1GvCEk1+uLFCgwP7jSbAqt555x1FRUWpSRP3PgkJufOni5V169Zp6tSpmjdvnpKSklyz/pQpU0YPPfSQ+vXrl68pGcnaDJc+mdHfblQBgWTRokWKj49Xr169zC4l4PhT1robWZuBYwTwD9u3b9d3332nESNGuK5F4B1WOY/OmDHDrdvr06dPnpYjbzNY5TiBubzx5HYr1OCrzpw5ozFjxmjs2LE5NrYEImYdMBdZm4FjBIHKn7I7LS1N//d//6dnn32WRqgrMJueucjaDBwjgLVk9zcC/VHmsdp5NHNGn0yZLc55vf9gGIbsdrtrVp6ckLUZrHaMAMga/VHm8fR5NNgjW/WwlJQU2Ww2hYeHZ7vMpTc8UlJS8jSVnxU5nYbW7j6lgV/8omn9/m12OQDywTAMjRw5UmPGjFFERIR+/vlnps1DgbVo0UItWrTQpEmTNHv2bE2bNk2bN2/WmTNnNHHiRE2cOFG33HKL+vfvrwcffDDX6ZDJ2qttORDLgB/AgubOnasHH3xQTqdTZcqUUYcOHcwuCZBE1gLwH7/99pvatGmjM2fOKCQkRC+//LLZJcEH5XWAjruRt0DeNalV5rJmnCa1ynh0f1k1jnq7Bqs4ceKEWrdure3bt+vw4cOaO3eu2SX5FI4bc5G1QGDzl3NwSkqKevTooQULFmjp0qXasmUL56pLBNltGhhVl3tkJiFrAVjRlX8jNK4ZoREjRhSoP+rK9w/63Vlb037Y7zMz58BzLp3HIHOgT25zGwQHB6tcuXJq3Lixhg4dmutgH4msBeA/6I/yb5Yc8IOrbfvnnNklAMgHwzD0/PPPa/z48ZKkDh06qFatWuYWBb9QokQJPfroo3r00Ue1e/duTZ06VV9++aWOHTumrVu3auvWrXruuefUrVs3PfLII2rZsqXZJVuGVW9UAYHsiy++UN++feV0OtWkSRPddtttZpcEAIBf2bx5s9q3b69z586pcuXK6tq1q9klAQAKaEDzOpJ0WcOMJ02J2aexy3ZJkqsJyNs1WMGRI0cUHR2tv/76S2FhYerXr5/ZJfkcjhsAMI8/nIOTkpLUrVs3rVixQpLUt29fGjgBACikS/9GaFwzQrsXfqQJ770nKf/9UVe+f7B53xmt3X3K9bkkBqX6IafTednnmTP+JCQk8LcaAGSB/ij/Zze7gMLI6xR9eV3Oym6oWsrsEgDkkdPp1KBBg1yDffr376/PP/9cwcGMwYR7XXfddXrrrbe0e/du9ezZ0/WkiwsXLmjmzJlq3bq16tevr6lTp2b7FAyyVgoLCdKwDvUteaMKCGSffPKJ+vTpI6fTqWbNmmnVqlUqU4aBe/A9ZC0Aq9qwYYPatGmjc+fOqUaNGtqwYYMaNGhgdllAlshbIHeZT26f0qeJBkbV9fjTcbcciL3qc2/X4OsOHDigyMhI/fXXXypatKiWLFmiu+66y+yyfA7HjW8ga4HAZPVzcEJCgjp27Oga7PPBBx/ohRdeMLkqIGtkLQAryfwb4ZOHb9Wfc8a7BvsUpD/qyvcPrnwo+pWvwz9FRkYqMjJSQUFBHtsHWQvAquiPCgyW7i5v3759nkI8p+VsNptWr17t7tK8xm63qeV15TX54cZmlwIgDxwOhwYMGKDp06dLkgYPHqz3339fdrulx1/CR/3yyy+aOnWqZs+erXPnMt70MAxDNWvWVHh4uHbt2qW//vpLjz76qKZOnaolS5aoVKnLB5CStVJE0RCeCANYzPvvv69nnnlGktSqVSstXLhQxYoVM7coIBtkLQArWrlypbp27aoLFy6obt26Wr16tWrWrGl2WUC2yFvA99xas4zrSbyZn+Oiv//+W61atdKRI0dUokQJLV26VM2aNTO7LCBbZC2ATA6noSkx+y6b9ccXBwKdO3dOHTp00I8//iibzaZPPvlEAwYMMLssIFtkLQBfkpe8d1d/VJNal79/cEPVUq4ZfjJfh/9bt26dx/dB1gKwIvqjAoelB/xs3Lgxx9czR9Nmt5xhGJYfcVuhRJim9fu32WUAyAOn06mHHnpIs2fPliS98MILeuuttyx/HoJvOX36tL744gtNmzZN//vf/yRl5F1ISIi6dOmiRx99VG3atJHNZtNPP/2k8ePHa+7cufrxxx/16quvasKECZdtj6wFYDXjxo3Tiy++KP0/e/cdFcX59QH8O0uVpoJooqgUuyYxKtYgIliw9xYFFfzZoiZqTIzGGqPGEjVq1CCo2HuCCiqogB2xxG6odkFAadJ25/2Dd1d22WULuztb7uecnBOW2ZkLLnNnnrn3eQD06tULhw8fRpUqVTiOihDZKNcSQvRNeHg4BgwYgKKiIjRp0gRRUVGoXbs212ERHTd+/Hi17YthGGzfvl2p91C+JUSzVCvslVxtWvrq04ocb1wnF4RcStb5wmJFPXr0CF5eXnj9+jWqV6+O06dPw93dneuwCKkQ5VpCiFBQbBKWhz8CAFFxrq5Nqvbu3Tv4+PggPj4eJiYm2LlzJ77++muuwyIGoqioCGfOnEFcXBzS09NRWFgodg9bXFyMnJwcmJiYlJuIsSKUawkhukRevldnfVSghysAVDgGQAzfjz/+iICAADRs2FBjx6BcSwjRN1QfZVz0suGnXr16lBwJIXqHx+OhadOmAICFCxdi4cKFdC4jaiEQCBAeHi5apae4uBgsW1ok0aRJEwQEBMDf3x81atQQe1+7du1w4MABeHh4YPr06Th+/Lio4YdyLSFEXzVs2BAmJibo378/9u3bB3Nzc65DIkQqyrWEEH1Vt25d2Nraok6dOjh79ixq1qzJdUhED+zYsUMteU/40FTRhh/Kt4RohzKFvcJmne0Xk8Vej0/NUvl4V5MyRLP76mphsTIcHR1Ro0YNlJSUIDIyEl988QXXIREiE+VaQrRLH1bPiUvJLPe1ruVlGxsb1K9fH3fu3MH+/fsxePBgrkMiBmLnzp348ccfkZZWek0q7R72xYsXaNiwIXg8Hp4+fYpatWpVuE/KtYQQXSQv36uzPsqEx2Cip5vY/iW/Jobvt99+w6pVq9CxY0cEBARg2LBhsLKyUsu+KdcSQvQV1UcZF71s+ElJSeE6BJ2RlV+MrdGJOjmYRwgp7+eff4anpyc8PT25DoUYgCdPniA4OBihoaF4/fo1gNKBYysrKwwZMgSBgYH46quv5O5n3LhxmD59Ol68eCF6jXItIURfDRgwANHR0WjXrh1MTfXydocYCcq1hBB91aJFC5w7dw5OTk6wt7fnOhyiJzp37szJQ1PKt4Roh2Shz6H456IZdiWLkss265Tl7qx4TpE83t0X78t9f6Knm1hRdOv61QEwiE/V3QJpIQcHB0RGRiIzM1NUIEWIrqJcS4h26cPqOe7O9qLYhF/rGlNTU+zbtw83btxAx44duQ6HGIhly5ZhwYIFYFkW1tbWaNKkCeLj48tt5+zsjO7duyMiIgKHDx/G1KlTK9wv5VpCiC5SJN9TfRRRJxsbG+Tm5uLSpUu4fPkyZsyYgWHDhmHcuHGVvp6jXEsI0VdUH2Vc6F9YzxUW80WDero2mEcIAXJzc3HkyBH4+/sDKF3ek25mibo0adIEDMOIVvNp1aoVAgMDMWrUKNjZ2Sm8H2trawClKwURQoi+EQgECA4Ohr+/P8zMzAAAnTp14jgqQgghxLAcPHgQXbt2Fa0a+vnnn3McEdE3Fy5c4DoEQogGSRb6JKTlIig2CQDKFSVLNuvUsDHHBA9XUYOQKsf7rE5V0Qo/wu8D0ouiy/6/Lj1TiY2NRdWqVUU5tlatWnJneyeEEGJ89GH1HGFOL9vwqwtSUlJw//599O7dGwBgbm5OzT5EbeLi4vDzzz+DYRjMmTMHCxcuBJ/Pl/m8dsCAAQgPD0dUVJTchh9CCNFF0vI91UcRTXrz5g0OHz6M4OBgxMTEICcnB8HBwQgODkbjxo0xfvx4+Pn5oWbNmlyHSgghGkP1UcaNx3UAqliyZAmWLFmCkpISrkPRGZKDe4QQ7r1//x49evTA2LFjsXLlSq7DIQbKzs4OkydPRnx8PG7cuIFJkyYp1ewjdP78eZw7d070NeVaQog+4PP5mDBhAiZMmAA/Pz/w+XyuQyJEYZRrCSH6YsOGDRg+fDh69OiBd+/ecR0OIUqhfEuIdgR6uKJBTWux1+JSMqUWJUvO+jvBwxUTPd2UWm0n0MMVc32bwKdpTcz1bYKtY9qIfV228EgWXXqmEhkZiR49eqBbt2549Kj86keE6DLKtYRol2Qe1cXVc0x4DCZ6uiHI313pHK8p//33Hzp37oyBAwfi1KlTXIdDDNCGDRsAAKNHj8aKFStQpUqVCle5bdOmDQDg3r17cvdNuZYQwjW+gMXW6EQE7ozD1uhE8AVsuXyfm5NN9VFEo6pUqYIxY8bg/PnzSEhIwPz581G3bl2wLItHjx7hhx9+QN26dTFw4ECEhYUpNeEx5VpCiD6g+iiilw0/ixYtwuLFi1FUVMR1KArp3bs3GIYBwzAYO3asRo6hi4N5hBizjIwMeHt74/Lly+DxeKhTpw7XIREDtHPnTrx69QqbNm3Cl19+Wal9SS6lTLmWEKLriouLMWbMGAQHBwMAnJycwOPp5e0NMVKUawkh+mDlypWYMWMGgNKVBiwsLDiOiBDlUL4lRDtMeAyGtq4r9pq7s73UomTJZh1VZv2XLCwyN+VJLSyu6LmJrjxTOXHiBPr06YMPHz6gevXqsLW15TokQpRCuZYQ7VJHHjU2Dx48QOfOnfHs2TNYW1vDwcGB65CIAYqJiQHDMJg+fbpC2zs5OQEAXr16JXdbyrWEEK4JV8+NfJiG5eGPRCv6ClF9FNE2FxcXLFmyBMnJyThz5gyGDx8OCwsLFBcX459//sGAAQPg5OSEH3/8EU+ePJG7P8q1hBBdR/VRBABMuQ7A0O3bt0+js8RYmJnQYB4hOiYtLQ0+Pj64e/cuTE1NsW/fPgwZMoTrsIgBGjNmDNch6ARN51pCiO4pLCzEyJEjcezYMQDAggULsGjRogpnzCOEqI5yLSHGh2VZLFq0CEuWLAEADBw4EPv27aOGH6JR2dnZiI+PR3p6OgoKCuDn58d1SFpF+Zbou7Kr6ggbe4TKviZs1pno6SZzX3wBi20xSThy8zkAFoNbOeF/nZVfIWBcJxdcTcrA3Rfv0aK2Hdq6OODm06xy8XHl8OHDGDlyJEpKSvDZZ5/h7NmzqFWrFtdhEWKwKNcSQ6BIHjUmfAGLoNikctcaQrdv30a3bt3w9u1b1KhRA2fPnkXLli25C5gYrDdv3gAA3NwU+9s0MzMDAL0pLFYU5VpCDJO01XuF1yL6UB8l73qB6C+GYeDj4wMfHx+8f/8ee/fuRXBwMOLj4/H69WusWrUKq1atQocOHRAYGIjhw4ejSpUqXIddKZrOtVn5xaUretHfCSE6g+qjiBC1eGlQZmYmvv32W1StWhVNmzbVyDGqW5npzFLYhBDgxYsX8PT0xN27d2Fubo5jx47p3M0sIYZEG7mWEKJbPnz4gIEDB4puZpcvX47FixfTzSwhGkK5lhDjw7Is5syZI2r2GTlyJA4cOEDNPkRjHj58iP79+8PBwQE+Pj4YOXIkxo0bJ7bN48eP8fnnn8Pd3R0fPnzgKFLNoXxLDIHkqjsmPEb02tYxbQAAE0NvYGt0IvgCtsJ9BcUmYWXEIySk5SIhLQ8rIx6Xm0FYESGXknH+cTre5hbhwpO3MOEx5VYB4sru3bsxfPhwlJSUoHXr1jh//jw1+xCiQZRrCVE/voAtLQbcGadQfteEilYcuH79Ory8vPD27Vt88sknuHDhAjX7EI2xsrICAOTn5yu0vXBln+rVq2ssJm2jXEuI4ZK2ei+gm/VR0q5P5K1QRAxD1apVMXnyZBw/fhwjRowQvc6yLC5fvoyAgADUqVMHCxYsUDhf6xpt5NrCYj79nRCiQ6g+ipRFDT8aNHPmTKSlpWH58uWoWbMm1+EQQjQsNTUVnTt3xqNHj1ClShWcOHECffr04TosYsBiY2NhYmICV1dXCASCCrfl8/lwdXWFqakprly5oqUINY9yLSHGJTc3F71790Z4eDgAYP369fjxxx85jooQw0a5lhDjIhAI8M0332D16tUAgHHjxiE0NFQ08ywh6hYREYG2bdvixIkT4PP5YFkWLFu+ULBx48YwMzPDzZs38ffff3MQqWZRviWGTtniGsnZg2W9Jo+0WYh1wV9//QU/Pz8IBAJ07NgRUVFRcHBw4DosQgwa5VpC1E8Ximdl5frY2Fj4+Pjg3bt3qFu3LmJiYtC8eXOtx0eMh6tr6eqRN27cUGj7qKgoAECLFi00FpO2Ua4lxHAFerhirm8T+DStibm+TRDo4aqz9VHSrk8krxcOxT/ntGGZqF9xcTEOHz6MXr16wdnZGQcOHABQ2uzz1VdfoWfPnuDxeHj37h2WLVuGli1b4uXLlxxHrTxt5lrJvxtdaPYnxNhQfRSRpNWGn+TkZFy/fh0xMTHaPCwnIiMjsXPnTrRr1w4TJ07kOhxCiBZkZGTg7du3sLGxwenTp9GtWzeuQyIG7uDBg2BZFmPHjgWPV3FKNzExwfjx4yEQCEQ3t/qOci0hxicvLw8vXrwAwzDYtm0bpk+fznVIhBg0LnItDRgTwq2SkhIkJycDAKZMmYKgoCCYmJhwHBUxVC9fvsSwYcOQl5eHTp064cKFC3jz5o3M7YcPHw6WZXH69GktRql5dG9LDI206zllG28kZw+W9Zo8smYh5lpCQgJYloWXlxdOnz6NqlWrch0SIQaNci0hmqELjbUVrTiQm5sLV1dXxMTEoGHDhlqPjRiXHj16gGVZrF27Vu622dnZWLNmDRiGQa9evbQQneZRriXEsElb0VdX66OkXZ9IXi8kpOXSaj8G4s6dO5gxYwZq166N4cOHIyIiAnw+HzVq1MCsWbPw8OFDxMTE4NSpU0hNTcX3338Pc3NzJCYmYt68eVyHrxRt51rJvxtdaPYnxNhQfRSRZKrpA6SlpWHZsmXYu3cvMjNLL6oYhkFJSYlomydPnmD27NmwsLDAvn37YGqq8bA06sOHD5g4cSJMTU2xdetWuUXYlZGVX1z68MzDFSY8WqaLEC61atUK4eHhMDExQbt27bgOhxiBixcvgmEYhQdPunXrhgULFiA2NlbDkWmeNnMtIUR31KpVC1FRUbh69SrnS8ITYui0mWvzCksQuDMO7s72ELAsVkY8BgBEPkwDAEz0dNPYsQkh4szNzXHkyBHs2rUL//vf/2hJeKJRa9asQW5uLtq3b49z587B1NQUeXl5Mrfv2LEjAODmzZvaClHj6N6WGCJhAQDw8XrO3dle9P/CrysS6OEKAQscufkcAIvBrZwQ6OGqdCzC9wgLfFTZhyasWLECrq6u8PPzQ5UqVbgOhxCDRrmWEM1RNr9rgqxcP2LECJiYmKBjx46oU6eO1uMixmf69OnYsGEDYmNj4e/vj02bNkkdU3n06BHGjh2L1NRUODg4YMKECRwMJR+RAAEAAElEQVREq16UawnRHr6AFa1Y4+5sj3GdXBByKVksD2qrdlDT9VGSP6vkzybr+9KuT8peL6Rk5CEh7eP4Y1xKJj2D0jPv3r3Dnj17EBwcjNu3bwMoXcmHx+Ohe/fuCAwMRP/+/cvVH9euXRsrV65E69atMWLECJw9e5aD6FWjzVxrYWYiWsmrLGnNdPS3Q4hmUX0UkaTRzpr4+Hj07dsXb968AcvKnpW3UaNG+O+///DkyROEh4ejb9++Cu1/0qRJlW4OYhgG27dvr9Q+JC1YsABJSUmYPXs2vvjiC7XuW1JhMV/08IySKCHad+/ePTg5OaFatWoAPhafEKINz58/BwCFZyZzcyvNEy9evFD4GJRrqbmWEK6lpaXh/fv3onOdk5MT3cwSg0K5FsgpKEHkwzREPkxDg5rWYt+jAWNCNK+oqAh37tyBu7s7AKBKlSo0GyvRioiICDAMg4ULFyqUC11dSx8yPn36VOljUb4lRHukFQBsHdNG9P+KNN6Y8BhM7uKGyV1KrwPlFfpUtJ+Jnm5KXU+qeqyKsCyLy5cvo1OnTgBKzyeUa4mhoVxLiHEomydb17fHDz0bIz41i7PG2rK5/urVqwDrDKB0ldqhQ4dqPR5ivGrVqoXg4GCMGDECu3fvxtGjR9GhQwfR94cOHYrExET8+++/EAgEMDExwa5du2Bra6vwMSjXEkIkJ9i4mpSB84/TRV8Dmq0d1GZ9lLTJRMr+bLK+L60ZuOz1wtboRNH7AN1ZCZjId+bMGQQHB+Off/5BYWGhqBa5bt26GDduHMaPH4969erJ3U+/fv0AAK9evSr3Pcq1QHUrM6nnEV1o9ifEGFB9FKmIxhp+3r17hz59+uDNmzdo2LAh5s2bh2bNmqFt27ZStx82bBiWLl2KU6dOKdzws2fPHrXEqs4ke/PmTfz++++oV68eFi1apLb9ykNFUIRo3/Xr19GjRw80bdoUZ86cgY2NDdchESOTm5sLAArPui3c7t27dwofg3ItNdcSwqWXL1/C29sbOTk5iImJERV5EsKlgoICJCYm4v3792Ir10rTuXNnufujXCtJ/LqGBowJ0awPHz5gyJAhOHfuHE6ePImuXbtyHRIxIqmpqQCANm3aKLS9tXVpU2hFqwDJQvmWEO2RVgCgSuNNWfIKfdRJ3cdiWRY//PADVq1ahT/++APffPONWuIkRNdQriXEOEjmybm+TRDk785xVKXnIH9/f/j5+SEoKIhWGCGcGDp0KKytrTF+/HikpaUhMjJS9L2jR4+KCpNr1aqFHTt2oEePHkrtn3ItIURygo27L96X+76m7pW1XR8lbzURWd+XN/6gqysBE/l69uwJhmHAsizMzMzQt29fBAYGokePHgrXTAGApaWlzO9RrpWN/nYI0TyqjyLyaKzh5/fff8ebN2/QtGlTXLlyBXZ2dhU+jPX09MTSpUsRFxen8DEqWjWIC3w+HxMmTACfz8fGjRtFD6Erw8nJSeb3Xr16BcaqOgAqgiJE2y5evIhevXohJycHz58/R3p6OjX8EK2rUaMGXr58if/++w8ODg5yt//vv/8AAPb2iucMyrUfc+315Axq+CFEi1JTU+Ht7Y3ExERUqVIFKSkpdENLOHXmzBksX74cly9fltvoA5Q22iqyHeXaj7kWAAa3cgKPoQFjQrQhLy8P/fr1w7lz5wAADx8+pIYfolXKPIgFPk5eocwMyEKUb1/h008/rfQxCFGEIgUAyq6iI6/QR53UeSyBQIDp06dj06ZNAIBbt26BZVmlz3+E6APKtZRriXHQZk5W1Pbt2zFhwgSwLIvHjx/jw4cPavmbJ0QVvXr1QmpqKvbv34/IyEg8evQI79+/h42NDVxdXdGjRw+MHj0aVapUUXrflGsp1xIiOcHGZ3Wqilb4EX5fE7ioj5K3moiqq41UdkISwq2GDRsiICAA/v7+qFmzpsr7SU5Olvo65VrZuZb+dgjRLKqPIorQWMPPP//8A4ZhsGTJEtjZ2cndvlGjRgBkJ1RpcnNzYWVlpXKM6rZ27VrcvHkTAwcOVHiVosqyMDPBXN8mVARFiBZFRUWhX79+yM/Ph4uLC86dOwdnZ2euwyJGyN3dHX///TdCQ0PRvn17uduHhoYCAFq1aqXwMSjXfiTQrXt7QgxaQkICvL298fTpU9jY2ODEiRPw9PTkOixixFasWIF58+YpNdCr6LaUawFbS1P4NK0pVuxJA8aEaFZ2djZ69eqFS5cugWEYbNmyBf/73/+4DosYmdq1ayMhIQEPHz7EV199JXf7a9euAYBKDzko3xKiWXwBi20xiThy8zkABoNbOWHrmDYym3iUXUVH1UIeVajrWHw+HxMnThTN6jp58mRs3LiRmn2IwaJcS4hx0GZOVsTGjRsxbdo0AECXLl0QFhZGzT6EcxYWFvD394e/v79a90u5lhAiOcHGuE4uCLmUrNEJ1Liqj5I3mQitNmJ8oqOj4eHhoZZ91a9fX+rrlGsJIVyg+iiiKI01/CQlJQGAQg9rAYiagnJycjQVkkYlJSVh0aJFsLW1xYYNG9S23+fPn8v8nrDjlgqhCNGeU6dOYdCgQSgsLETjxo0RFRWFOnXqcB0WMVJDhw7F8ePHsW3bNnTt2hWDBw+Wue2xY8ewbds2MAyD4cOHazFK9eEq175+XwAA4FFBBiFa8fDhQ3h7e+PVq1eoWrUqIiIiFGpqJERTrl+/jnnz5gEAfvjhB/j5+aF58+ZgGAb//vsviouLcffuXYSGhiIyMhLNmzdHSEgIHB0dOY5ceVzl2rxC+SshEULUJzMzEz179kRcXBx4PB5CQkLg5+fHdVjECHl6eiIhIQHBwcFyx5BZlsX69evBMAy8vb21FKFmcDmOTIimBMUmYWXEY9HXKyMegcfIfnah7OoA2izkUcexSkpK4O/vj7179wIAZs6cidWrV1OzDyFaQrmWEM3RpeLaVatWYc6cOQCAnj174ujRoyqtmkJUo+yKjcSwUK4lhBvCydICPVwRFJuEKXvi4e5sX+GEG5XBZX2UvNVEaLUR46OuZh99wWWupes8QrSH6qOIMjTW8FNcXAwAMDc3V2h7YaOPvs64MnPmTOTn52PZsmWoVq0acnNzxb7P5/MBlD7oEX7PysoKPB5P67ESQlRz9OhRjBgxAsXFxfjss89w9uxZ1KpVi+uwiBEbMWIE1qxZg5s3b2LYsGH4+uuv4e/vj5YtW8LW1hY5OTm4ffs2du7cib1790IgEODLL7/E6NGjuQ5dJVzn2rYu3M5UR4gxuHPnDrp164b09HQ4ODjgzJkzSq1KRogmbNq0CSzLYsiQIVi+fLnY91xcXGBlZYWWLVtizJgx2LFjByZMmICAgADRKgT6hKtcm1NQgsiHaQrN7k4IqZz09HR069YNd+7cgampKfbu3YuhQ4dyHRYxUlOnTsX27duxa9cufPXVVxg/frzU7UpKSjBjxgxcuXIFpqammDx5spYjVS+u720J0QTJBh7ha7Ku65RdHUCbhTyVPVZRURFGjRqFI0eOAADmz5+PJUuWULMPIVpEuZYYCl0stNOF4lqWZbFkyRIsWrQIANC/f38cOHAAFhYWnMVkjJRdsdHQ8Xg88Hg8ZGdnK7QyAJ/Ph5mZGXg8HkpK9G8yJMq1hHBLG+dgqo8iuoZlWTx79gwAULdu3QrHWQQCgaixpV69elqJT924zLV0nUeIdlB9FFGWxu6mPvnkEwBAYmKiQtvfvHkTgP4m2ZSUFADAvHnzYGtrW+6/ixcvAgD27Nkjeu3ff//lMGJCiDJYlsXmzZtRXFyMVq1a4fz583QzSzjHMAyOHz8OV1dXsCyLPXv2oHv37qhZsyaqVKmCmjVronv37tizZw8EAgHc3Nzw999/622BAVe51sLMBHN9m9Ay0IRowd69e5Geno5atWrhwoULdDNLdMLFixfBMAz+97//lfsey7JiX48dOxbTpk3DvXv3sH79em2FqDa6cF8rrViUEKI+58+fx507d2Bubo4jR45Qsw/h1BdffIHZs2dDIBBgwoQJ8PT0xNq1a0Xf37RpE2bOnIkGDRpgy5YtYBgGixcvRv369TmMuvJ0Id8Sogy+gMXW6EQE7ozD1uhE8AVsuW2kNexU1MQT6OGKub5N4NO0psGNuTx69Ajh4eEAgGXLlmHp0qV6OxZHiL6iXEsMhbDQLvJhGpaHP0JQbJJa9qtIbtdl79+/R0hICABg+PDhOHToEDX7cEDaio3GTnKsWFPv0QWUawnhlqbPwVQfRXTR6dOn4eLigt69e8sdZ+HxeOjduzdcXFwQGRmppQjVi8tcS9d5hGgH1UcRZWms4adTp04AgH379im0/datW8EwDDw9PTUVEiGEqIxhGBw7dgxTp05FVFQUHBwcuA6JEAClS6rGx8dj0qRJMDc3B8uy5f6zsLDA1KlTER8fT8udq6C6lRkmerpxPnMeIcZg+fLlmDNnDmJiYtCiRQuuwyEEAPD69WsAQKNGjUSvCWdHKiwsLLf9uHHjwLIsDh06pJ0ADYy82d0JIZUzbNgwbNq0CWFhYejXrx/X4RCClStXYs6cOQCA2NhYLFq0SPTAdvr06Vi/fj2ePn0KlmXx008/Ye7cuVyGS4hRUqTYONDDFT/0bIwGNa3RoKYNfuhZcROPcHWAIH93gxtz+fzzzxEWFob169fjp59+4joco6XvxeyEEAJortBOU41E2lKtWjWcO3cOs2fPxp49e2BmZsZ1SEZJcgyPxvSUI2z0ocZwQkhFZN3XtK4vfs6V/LqyqD6K6KKDBw+CZVn4+/srtL3wee3Bgwc1HJnhoes8QrSD6qOIsjTW8BMYGAiWZbFx40acPXu2wm1XrlyJU6dOAYDUWZP1we3bt6UWWQv/EzYy+fv7i15r2bJlpY+blV9MDysI0aBXr16J/t/W1hYbN25EtWrVuAuIECmqVq2KzZs3Iy0tDSdPnsSGDRvwyy+/YMOGDTh16hTS0tLwxx9/wM7OjutQK4WrXEsI0ayyuZbH42HlypVijRWEcE0gEAAAbGxsRK8J/z8tLa3c9nXq1AEAJCXpV7EEwF2utbU0NcjZ3QnRFa9fvxadywBgypQp6N69O4cRESJuxYoVuHHjBvz8/FC7dm2x3GNvb4+RI0ciLi4OS5cu5TpUtaB7W6JvFCk2NuExmNylASJndkHkTE9M7qLbTTzqbgbJzs5GXl6e6OuuXbti+vTplQ2TVIK+F7OTyqFcSwyFpgrttDVjtzrzLZ/Px5s3b0Rfu7q6YtWqVTAxMVFHqEQFhrxiozYIx5XLjjnrE8q1hGiH7PsayZyqnppBruujaOIGUpG4uDgwDAMvLy+Fthdud+3aNU2GpTFc5lq6ziNEc6g+ilSGqaZ27OnpidGjR2P37t3o1asXRo0aha5du4q+f+rUKSQkJGD//v24du0aGIbBN998o1CnWr169cDj8USzKhuzwmI+loc/AgBM9HTjOBpCDMuqVauwZMkSnDlzBh06dOA6HELksrW1ha+vr1r2Rbn2I2FzbaCHq8LFKnwBi6DYJMSlZMLd2V6p9xJiTE6dOoUhQ4Zg8+bNGDt2LNfhECJVrVq18OzZM7x58wb29qWFFfXr18e9e/dw+/ZtNGnSRGx74RLrBQUFFe6Xcu1H1hamCPJ35zoMQgzSw4cP4e3tjaFDh2LdunU0cyzRWV9++SV27NgBAMjLy8P79+9hY2NT6YkrKN8SopiKxjHcne0R+fBjo7u7s71Gxz20MaYiLJoCIPrZVH2+kpmZiZ49e8LW1hYnTpxAlSpV1BYnUZ20YnZ6hqYZlGsJ0RxhYV3ZnKgO0nK7Jqgr35aUlGDs2LG4fPkyYmNjRZPtEG4JV2yk/CpO3rgLy7J4+fIllixZAgBo3Lix3H1SriXEeMm6r4lPzRJ7XfJrVehCfZQ679WJ4Xn+/DkAwMXFRaHt69evDwB4+fKl3G0p14qj6zxCNIPqo0hlaazhBwCCgoJQVFSEgwcPYvfu3di9e7foBrdv374APi5VO3r0aKxdu1ah/QoLqMhH15MzKMkSoiYsy2LJkiVYtGgRAODPP/+khh9idCjXfiRsrr2c+BbBY9sqVGRCg1GEyHf06FGMGDECxcXFWLduHb7++muYmZlxHRYh5bRs2RLPnj3Dixcv0LRpUwBAx44dcffuXWzduhUjRowQ23716tUA5A84U679KK+wBIE746hJlhA1u3PnDrp164b09HTs2bMH33//PZycnLgOixC5rK2tYW1trZZ9Ub4lRDEVjWNIKzZWx7iHrMYebYypqKsZJD09Hd26dcOdO3dgamqKa9euoUuXLmqKklSGtorZCeVaQjRJU4V2krl9XCcXbI1OVHuzrTrybVFREUaNGoUjR44AAA4fPowZM2ZUOjZCKkva6lIsyyq1Yg/DMBg6dKjc7SjXEmI8JO+TW9evLvW+Rt79jjITaehSfRRN3EAqUlRUBKB05UdFCLfLzc2Vuy3l2vJokmNC1Ivqo4g6aLThx9zcHPv378eIESOwbt06XLlyBcXFxaLv83g8tG/fHrNmzcKgQYM0GYrBKxFwHQEhhoFlWcydOxcrV64EAAwbNgzbt2/nOCpCFJORkYErV64gJSUFOTk5sLW1hbOzMzp27ChakYCoLvrJWwTFJik0qESDUYRUbO/evfDz8wOfz0erVq1w+vRpupklOsvX1xdhYWG4evUqfHx8AACBgYHYtm0bYmJi0LlzZwwdOhR8Ph///PMPoqOjwTAMhg8fznHk+iOnoASRD9OoSZYQNbpx4wa6d++OrKws1KpVC5GRkdTsQwghRKaKxjGkFRurOu5RtliAL2Bx/nE6APHGHm2MqaijGeTVq1fw9vbGw4cPYW5ujkOHDul9s48hFXNoalUMQggxBJK5fWt0okaabSubbwsKCjBkyBCcPHkSAPDLL79Qsw/RGcKJjRV9XZKJiQlGjx6N7777Tp1hEUL0nOQEGD/0bIK5vk3K3dfIu99RdCINXauPookbSEVq1aqF1NRU3Lt3D15eXnK3v3//PgDA0dFR06EZJJrkmBD1ofoooi4abfgRGjBgAAYMGIAPHz4gKSkJ79+/h42NDZydnWFnZ6f0/r788kssXrwY/fr1U2ucx44dw5IlS3Dr1i217hcALly4oPZ9lnXvxXuN7p8QYyAQCPDtt9/ijz/+AAD4+/tj+/btUmfoIUSXpKam4vvvv8fx48elzmZhYmKCQYMG4bfffkO9evUU2iflWukULTKhwShCZAsODkZgYCBYlkX79u0RHh6OatWqcR0WITL169cPP/74I06fPo358+cDAFq3bo0ff/wRy5cvx6VLl3Dp0iXR9izLom3btpgzZ06F+6VcKx01yRJSeZcuXUKvXr2QnZ0NJycnREVFoVGjRlyHRUg5T548waRJk2BnZ4ejR4+Cx+PJ3JbP52PQoEHIzc3FX3/9BVdXxQq3Kd8SohhlxzFUHfcoWywgSXgdqI0xlco2gzx9+hTe3t5ISEhAlSpVcPz4cXTv3l3tcWqbIRVzaGpVDFIe5VpCdJsizZyKNtsq2xhamXybl5eHAQMGIDIyEgCwdu1aaowgOiUkJETs63HjxoFhGPz555+wsLCQ+T4zMzPUqFEDX375pcIFyJRrCTEekjk5PjUTQf7u5fKyvPsdRXK7LtZH0cQNpCLt27dHamoq/vrrL4UafrZt2wYAaNeundxtKdeWR5McE6IeVB9F1EkrDT9CVapUQfPmzSu9nzt37mDgwIH48ssvMX36dAwaNEippXHLys3NxeHDh7FhwwbcuXOn0rFxJb+ohOsQCNFrfD4fkyZNQlBQEABg0qRJ2LRpU4XFJoTogitXroiK+WTNGlVSUoJDhw7h9OnTCA8PR/v27eXul3KtdIoWmdBgFCHSbdq0Cd988w0AwNPTE2FhYbC1teU4KkIq9umnnyIrK6vc68uWLcPnn3+OzZs34/bt2ygsLISrqyuGDx+OOXPmwNLSssL9Uq6VjppkCamcc+fOoW/fvsjPz4ezszPOnTsHFxcXrsMiRKr9+/fjwoULmDJlitzxFxMTE9SrVw+bN2/GgQMHMHfuXIWOQfmWEMVUNI4hrbhX1XEPyWKBsoTXgdoYU6lMM0hiYiK6du2Kp0+fwtraGidPnoSnp6faY+QCFXMQVVCuJUS3KdLMqWizrbKNoarm2+zsbPTu3RsXL14EAPz555+YNGmSUvsgRNP8/f3Fvh43bhwAYPTo0bCyslLrsSjXEqIafVzBVFpOVuXnkJfbdbU+iiZuIBUZPXo09u/fjwMHDqBNmzaYOXOmzG3XrVuH/fv3g2EYjB49Wu6+KdeWR5McE1J5VB9F1I1hFV1TVknjx48HwzDYsmWLQstPsSyLgIAAMAwjd3nI06dPY/bs2bh//z4YhkGVKlXQr18/+Pr6om3btmjcuHGF73/06BGuX7+OU6dOISwsDAUFBWBZFp999hlWrVqlNzOxOTk54fX7AjhN3QnXGlY4N1t+9zIhRLrTp0+jZ8+eAIBvv/0Wa9euBcPo9s0+qTwnJycAwPPnzzmORDVZWVlo1KgRMjIyYGZmhoCAAAwfPhzNmzeHra0tcnNzce/ePRw8eBBBQUEoLi6Go6MjHj9+LLdbnHJtqbK51rORI4LHuuv8QCAhuurZs2do1KgRCgoK0L17dxw7dkztD76I7tH3XKtJlGtLOTk5Ia+wBINXh+nNQzdCdFVxcTGaNm2KxMRENGrUCFFRUaLzMDFc+pxrO3XqhKtXr+LUqVPo0aOH3O3Pnj2LHj16oFOnToiNjVXoGJRvS+nz54Rwb2t0otiqPHN9m6hcfCO5L6/GjjDhMXp1HTho0CAcO3YMVatWRXh4ODp06MB1SGqjzn9rQ0PnUdko15aizwhRBBeFx4E748QK9byb1ERbF/tyjbWKxCW5L5+mNRHk7672mJctW4b58+eDx+MhODi4XGMFMUz6fh5NTU0FANSvX1/t+6ZcW0rfPyNE+/Tx/kbatYLkSrmK/BzyrjmoPso4GcJ5tGfPnjhz5gwYhkGnTp3g7++Pli1bwtbWFjk5Obh9+zZ27dqFixcvgmVZ+Pj44MyZM3L3S7m2VNlntq3rVwfAID5Vf5omCdElVB9lnDSdazXW8MPj8cAwDHJychT6oPL5fJiZmYFhGPD5fLnbCwQCbN++Hb/++itSU1PFLjzt7Ozg4uICe3t72Nvbg2VZZGZmIisrC0lJScjJyQEA0UoIrq6umDdvHsaOHatXF7Bli5C7NqmJ4LHqH1AjxJisWLECOTk5+OWXX/TqXEBUp+83tIsWLcKSJUtQtWpVnD59Gm3btpW5bVxcHLp3747s7GwsWLAACxculLt/yrXiuda7iSO2j5X9OyaEyHfy5Ens2LEDu3fvhoWFBdfhEC3Q91yraZRr6TNCiLo9fPgQ33zzDfbs2YNPPvmE63CIFujzedTJyQmvXr3Cs2fPULt2bbnbv3z5Ek5OTqhbt66omEoRlG/1+3NC1E/ZYmPJ4t4GNW1w+tvOKj3k18cZliW9e/cOw4YNw4oVK9CqVSuuw1ErQ/j30RQ6j1aMci19RohiuCg8ltZse/5xukoxaCt+Pp+PsWPHok+fPhg+fLja9090k76fRx89eoQmTZpobP+Ua/X/M0K0T1uNqpqmqZ+D6qOMjyGcR9+9ewdfX19cu3atws8ty7Jo3749wsPDUbVqVYX2TblWvD4K0I9GSUJ0GdVHGR9q+JGDZVmcPn0aW7ZsQXh4OIqLi8W+L0yakj+mubk5+vTpg4kTJ6Jbt24KH0+XlE2yP/RsgsldKMESoozi4mKYmprq1cU1US99v6Ft3bo1bt++jXXr1mHatGlyt//jjz8wY8YMtGrVCjdu3FD4OJRrS3OtV2NHhIz72PBDRRCEyMeyLEpKShRa8ZMYJn3Ptdpi7LkWoM8IIZVRVFQEc3NzrsMgHNHn86ilpSWKi4uRkZEhdxVaoPSBrr29PSwsLPDhwwelj0f5Vj8/J0T9lC3WldweUGxlnsqOm+jSuAvlWkLnUcVQrqXPCKkYF4XHkvn0enIGoh59bPhRJgZN5mbKtUTfz6MmJiZo3749AgICMHz4cFhbW2vkOJRr9fczQrRPH1f4kebPC4lYGfHx51C1PlAb9VG6dB9PyjOU82hxcTF+//13bNiwAS9fviz3/Tp16uDbb7/FjBkzYGpqqvT+jT3Xlm340ddGSUK4QvVRRNO5VvmspiH5+fkAoHQnG8Mw6NmzJ3r27In8/HxcuXIFMTExePDgAdLT05Geng6GYeDo6AhHR0e0aNECnTt3Rvv27WFpaamJH4UjGunbIsRg5efnY8CAAXB3d8eyZcu4DocQlSQlJQEA+vfvr9D2/fv3x4wZM5CYmKjUcSjXluJJDH6VXT5b+JBOHwcJCdEUlmXx3XffISEhAUePHqUHtoRUgHItIURVISEhWLNmDc6dO4eaNWtyHQ4hSqlatSrevn2Lly9fKtTwI3yAq2rhFOVbQkrFpWSKfX0o/lmFRTiBHq44FP8MCWl5oteEKwNUNB4iOW4iYFnwGEbhwh9dGXe5fPkyRo0ahWPHjuHLL7/U+vEJ0SeUawmpmLuzvVjDj7uzvcLvVbWA1oTHYKKnm1gOLdvwo0wM0valCsmfpXt9U/Ty7YnFixdj2LBhldo3IVxhWRZXr17F1atXMWPGDAwbNgzjxo3DV199pdbjUK4lRHGBHq4AIJY79ZNkPaDy9YHaqo/Slft4YtjMzMwwZ84czJkzBw8fPkRiYiKys7NhZ2eHBg0aVHrFPcq1Hylzr6Bu1EBI9O0zQPVRRBt0puHn3LlzAIDatWurvA8rKyt4e3vD29tbXWHpjRspWVyHQIjeyMnJQZ8+fRATE4OzZ89i0KBBaN26NddhEaK0goICAIoXOwm3KywsVPmYxpxr20jczEoWyMSlZNKAFSH/TyAQYPLkydi2bRsAYNeuXQgMDOQ4KkLkc3UtfeDDMIyoQVb4mrLK7kMZxpxrCSHK2bx5M6ZOnQoAWLBgAbZs2cJxRIQop1mzZoiJicE///yDZs2ayd3+n3/+AQA0bty40semfEuMmWSxcUJaHoJik8TGNCQfqA5u5YSVEY+l7k/WeIjkuMmRm89FTUOKFP7owrjL+fPn0bdvX+Tl5eF///sfrl+/TqvFE6IgyrXEGChbgFSZwmN1FdCWjaF1fXsIWBaBO+O0WkBV9mcJv/Ivfjq+EJlvXmDixIno3r27QpMB6Ct9K1ojitu5cyd27NiBCxcuIC8vDzt27MCOHTvQoEEDBAQEwM/PD5988olaj0m5lpCKqatRlWvxqVkVfi2LMOdcevgMV7fMwX934jReH6UL9/HEuDRt2hRNmzbV2P6NOdc6WJtz2ihJDYREnz4DVB9FtEVtDT/jx4+X+vqkSZMqXB6Pz+fj5cuXuHjxIhiGQZcuXdQVklHhs7TCDyGKePfuHXr27Ilr166Bx+Nh+/bt1OxD9NYnn3yCp0+f4vbt2wrdYN66dQsAUKtWLU2HZqDEc21lZuMjxJCVlJRg/PjxCA0NBQDMmDEDAQEBHEdFiGJSUlIAQKyIT/iasqgQkBCiSWvXrsWsWbMAAN26dcPatWs5jogQ5fXq1QvR0dH47bffMHjwYDRs2FDmtgkJCVi1ahUYhkHfvn21GCUhhqd0xZ7nSEjLFb0mWYQj+UD1+x6N4dXYEXdfvEfVKmZITP+42o+s8RDJcRNA/PpYXuEP1+MuERERGDhwIAoKCtCgQQMcPXqUrvEJIYSIUbYAqTKFx8oU0FbUVFI2hq3RiVge/ljh+NVF+LMUZzzDm/3zwM/NhJ2dHU6ePGnQzT6AfhWtEeWMGTMGY8aMQUpKCkJCQrBr1y6kpqbiv//+w9y5czF//nz4+vpi/Pjx6NOnD0xMTLgOmRCiAxRpBFX13jgoNgnLjt3Am4MLUfTqMRiGh6/Gz8eN3GpoKWA10nDK9X08IUR9qlubcdqYTg2ERF8+A1QfRbRJbQ0/O3bsKPewg2VZ7NmzR+572f9vVnF0dMS8efPUFZJReZaZJ38jQozc27dv0b17d9y6dQsmJibYvXs3RowYwXVYhKjMw8MDu3fvxoIFC+Dh4VHhcpBFRUVYuHAhGIaBh4eHFqM0HJKz5RjOMuCEqE9RURFGjx6NQ4cOAQDmzp2LZcuWUVEU0RsLFy5U6DVCCOEKy7JYtmwZfv75ZwBAnz59cOjQIVhaWnIcGSHKmzx5MlavXo23b9+iY8eOWLlyJUaNGiX2eS4oKMDevXvx448/IisrCzVq1MCUKVM4jJoQ/WfCYzC0tZOo2BQoX4Qj+UD12K2Pq/O8zS2CV2NHmPCYCsdDJMdNBCywMkL2MeW9X5vjLsePH8ewYcNQXFyMZs2aITIyEp9++qnWjk8IIUQ/aLMASVYBrbRCYcmmkkPxzzG0tVO5IuKK4tfkSjTuzvY4FXMdb/bPhyD/HaztquHcuUijmKBRX4rWiOqcnZ2xePFiLF68GFFRUQgODsbx48fx4cMHnDhxAidOnICjoyP8/Pwwbtw4ja5KQAjhjqJ5VJFGUFXvjWPvJuHN/nkoepMIMDw49JmFpzXaio6nifxD9RNEm+7cuYPY2FikpKQgJycHtra2cHZ2hoeHB7744guuw9N7g1s5cXp8aiAk+vAZoPooom1qa/jp3Lmz2Ac1OjoaDMOgU6dOFc5OYWZmhho1aqBNmzbw8/NDjRo11BKPQCDAv//+Wy6pf/bZZwY5WwbD8LgOgRCd9urVK/j4+ODBgwcwNzfHgQMHMGDAAK7DIqRSpkyZgt27d+Pq1avw9vbGhg0b8OWXX5bb7vbt25gxYwauXLkChmEwdepUtRzf2HKt5M2DoSwDToi6FBQUYNiwYQgLCwMALF26FPPnz+c4KkKUo2sNP8aWawkhFWNZFvPmzcPy5csBAEOHDsXu3bsrbPwnRJfZ2NjgwIED8PX1RUZGBiZMmIApU6agYcOGsLW1RU5ODv777z8UFxeDZVlYWFjg0KFDsLOzU2sclG+JMaqoCIcvYMEXsBLvEH9IacJjsHVMGwTFJmFi6A2pBUyS4yZ8AQseo3jhD1fjLgcOHMDXX38NPp+Pli1b4syZM3B0dNRqDIQYGsq1xFBpswBJVu6WVigs2VSSkJYrtbi3ovg1uRLNl1ZZyD5c2uxjW90BMefPoeUXn6tl37qgoiJvfShaI+rj7e0Nb29vZGdnY+/evQgJCUFcXBzS0tKwZs0arFmzBu3atUNAQACGDx8OGxsblY9FuZYQ3aJoHlWkEVSVe+NXr17h3Joppc0+JqZw7PcDrBp1qPA46kD1E0Qb4uLiMHXqVMTHx8vcpk2bNti0aRPatGmjtuMaS67l8Rh4NXZEwFfcNuxRAyHR9c8A1UcRLqit4efChQtiX/N4pQ0oERERsLKyUtdh5Hr79i0WL16M0NBQ5OTklPu+ra0t/Pz8sGDBArU1F+kCrrtqCdFlLMti6NChePDgASwtLXHs2DH07NmT67AIqbT27dtj1qxZWLNmDS5fvow2bdrAxcUFzZo1g62tLXJzc3H//n0kJyeL3jN79my0a9euUsc1tlxrYWaCub5NdO7mgRBd88MPP4huZlevXo1Zs2ZxHBEh+svYci0hRDGhoaGiZp8xY8YgODgYpqZqG9ojhBNdunRBbGwsxo4diwcPHqCoqAj3798vt12LFi2wY8cOtGrVSm3HpnxLjFlFRThBsUk4/zhd9LVXY0e0dXEotzqPsoXA0hqAtkYnamTlAFXdvXsXo0aNgkAgQLt27RAeHo7q1atzGhMh+oxyLTF02ixAkpW7pRUKSzaVlP1e2fdXFL+mVqLJyclB716+yMt+hzp16iAqKgqNGzeu9H51SUXXSLpetEY0w87ODpMmTcKkSZPw4MEDLFmyBAcPHgQAXLt2DdeuXcO3336Lr7/+GjNnzkSjRo0U3jflWkJ0k6J5VBONoML6qFcpCTAzt0DXb37Dpy3ai93nU8Mp0VfHjx/H8OHDUVJSApYtnazG2toaNjY2yM3NRV5e6erUcXFx6NSpEw4dOoR+/fpV6pjGlmsFAhbnH6dj+8UkTO7SgLM4qIGQ6PpngOqjCBcYVpj91GzRokVgGAbz5s3TWvHBlStX0K9fP2RmZqKiH4thGDg4OCAsLKzSRc9ccnJyQlZ+MdYeu6wTD8MI0WW3b99G3759sWvXLnh5eXEdDtERTk6lzZLPnz/nOJLKWblyJRYtWoTCwkIAEFtxT5gPLSwssHjxYsyZM6dSxzLGXAvo/2eEEG1IT0+Hl5cXpkyZgilTpnAdDtERdB5VHuVaQogsRUVFGDJkCD799FP8+eefosl2iHEzpPPo2bNnERkZicTERGRnZ8POzg4NGjRAt27d4O3trdZjUb4l+q6imeMrK3BnnFjBkU/TmqLVfMoeb2LojXLbBfm7K3ycrdGJomJYAJjr20QnHuDOnz8fsbGxOHHiBGxtbbkOh+gIOo8qj3ItIdohmU8b1LT5/4lCWRy5+RwJaXmi7ymTazWZpw8dOoS5c+fizJkzcHU1vIYXaddSylwjGStDP48WFxfj2LFjCAkJQWRkJAQCAViWBcMwYBgGAoEADMOAx+Nh9uzZoglfKkK5lhDuybo3VzSPVvbeXtb7JeujNDmGQPSHvp9HX7x4gUaNGuHDhw+oVq0afvzxRwwfPhz169cXbZOamoqDBw9ixYoVyMrKgpWVFZ48eYLatWurdExjzLWv3xfAaepONKhpjdPfetK5gxAZqD6KSKPpXKuxhh9te/HiBZo3b47s7GwwDIOBAwdi8ODBaNasmaiL98GDBzhy5AiOHTsGlmVRrVo13Lt3T+WkzjV9vxAjRNsKCwthYWHBdRhEhxjSeTQ9PR2hoaGIjY1Famqq2BKyHh4eGD16NBwdHSt1DMq1hBB5KNcSSXQeVQ7lWnH0EIqQ8oqKimBmZibW5E+MG+Va5VG+JYZAk0W4iu67sjEoWgyr7WtClmVRVFRE97ZEDJ1HlUO5Vhzd2xJNEn6+DsU/K9fcE+jhqvJnT9OfW0MeR9bVpmZdZ6i59tatWwgJCcHevXuRlZUlKhb+5JNPMHbsWAQEBMDKygo7d+7Eli1b8PTpUzAMg3Xr1mHatGky90u5lhDdIOucr63rv4pyjiHnWiG6zlaOvp9HZ8+ejbVr16J27dqIjY2Fi4uLzG2Tk5Ph4eGBV69eYebMmVi1apXSxzPWXPux4ccGQ1s70XUtIRUwhlxLlKO3DT/t27dHQEAARowYoZVZ0KZOnYo///wTNjY2OHbsWIWzPkZGRmLQoEHIy8vD5MmTsXHjRo3Hpwn6fiFGiCbdvXsX33//Pfbt24fq1atzHQ7RUfp+Hn369CkAoHr16pRrNUTfPyOEaNLbt28xevRorF27Fs2aNeM6HKKj9O08umTJErXub8GCBUptb8y59uc90SrPgkeIuujaA8Li4mKMGzcOw4cPR9++fTmLg+g2fcu1usCY8y19TgyHZLOMdxNHtHVxUEsOUzQfVnY7bTUWybNs2TIAwLx589S2T2J46DyqHMq14ujelmiDLqwqIyvn//333zh8+DBCQkJgamqq1Zi4oGtjC/rCkHJtVlYWdu/ejZCQENy5cwdAaVO5iYkJevbsicDAQPTp0wcmJiZi7ysqKsLEiROxc+dONG3aFPfv35d5DMq1hOgGVfKvvDyhTB4RHr8oPQVZ54MxZPZvCJ2i3lWydRldZytH38+jLVq0wMOHDxEcHAx/f3+52+/cuRPjxo1D8+bNcffuXaWPZ6y5Vtjw80PPJohPzeT8HoMQXUH1UUQRms61GhtRuX79OuLi4vDdd99hyJAhGD9+PDp37qypw+HUqVNgGAaLFy+uMMECgI+PDxYtWoTZs2fj1KlTGotJG7Lyi7E1OpEGiggp4+bNm+jWrRsyMzPh5+eHsLAwrkMiRCOcnZ3B4/Fw6tQpdO/eXePHM9ZcSwgp7/Xr1/Dx8cH9+/fRq1cvPH78mGauIAZh0aJFal01Q9mGH2PNtXmFJaKHMsKB44mebohLyRTbLi4lkx7WEI0Kik2S+lnkQkFBAYYNG4awsDAcPnwYDx8+rHDGOkKI4ow13xLD4u5sL/bAXcBCbTnMhMdgoqeb3Pcrup2s/Bro4QoAYkVM0mjqmpBlWcyfPx+//vorAODzzz+nBltC1IRyrTi6tyXqJq0QWPLawN3ZXutxScv51V7fwOjRo1FSUgI3NzcsWrRI63Fpm6LXSMSwsCyL06dPIzg4GGFhYSgqKhKt5uPs7Izx48dj/PjxFc74b25ujhUrVmDnzp1ISkqq8HiUawnRDarkX3lj0MqMUbs72+Pk+ctIO/AzBAU5uLN7GWAgDT+KND7RdbZxEU6IrGhtlHC71NRUlY5nrLnWwsykzGqh4PwegxBdQPVRRFdorOHHy8sLFy5cQH5+PkJDQxEaGgo3NzeMGzcO/v7+al+67vXr1wCAQYMGKbT9kCFDMHv2bLx69UqtcWhbYTFfdKFPF62EAFeuXIGvry/ev3+P2rVrq7QsJyH6wsrKCh8+fECrVq20cjxjzbXUXEuIuGfPnsHb2xv//fcfLC0t8eeff9LNLDEY9erVU2vDj7KMNdcW8cUXHhY+lNGFYhViXHTlAWF+fj4GDhyIM2fOAAB++eUXavYhRI2MNd8SwyLZLHM9Wfkcpq3Z52XlV0WLYTVxTciyLGbNmoXff/8dADB69Gj4+vpWer+EkFKUa8XRvS1RF2HuPhT/DAlpeQA+FuAp2kgrbX/quhaQzPl7d+/CxeBfIBAI0LZtW8yYMUPlfROi6+rVq4eXL18CKL3WNDc3x4ABAxAYGAgfHx+F91OrVi0Apav9VIRyLSHaJStnKpJ/Jd97PTlD7PuS9+/KjFE3N32D94dLm32q1qiFfUGbKvuj6gxFGp/oOtu48Pl8AFB4xUjhSnoCgUCl4xlrrq1uZSb6W1PlHoMQQ0P1UUSXaKzhJyoqCqmpqQgJCcGuXbuQkpKChIQEzJ8/HwsWLECPHj0QEBCAvn37qmXpZnt7e7x+/Ro2NjYKbW9tbS16nyG4nkxd6oRcuHABffr0QV5eHurVq4dz587BzY3+LojhcnJywn///YfCwkKtHM9Yc6285lptFcgQoguSk5PRtWtXpKSkwMrKCmFhYejatSvXYRGiNikpKZwe31hzrbmJeN4UPpShgWSibbrwgDAnJwd9+/ZFdHQ0AGDjxo2YOnWq1uMgxJAZa77NKyxB4M44um81EJKFRQJWvIFaHbMKq0tl86u6rwkFAgGmTp2KLVu2AAAmTJiALVu2gMfjVWq/hJCPjDXXykL3tkRdyubusoSFwMquKqOOa4Gyz0f4go/XIzm3TiHmzGYAgIeHB06cOAE7Ozul9k2IPnnx4gUAoFmzZggICICfnx8cHBxU2teCBQvkTkpFuZYQ7ZKVM+VNZMEXsAjcGYfzj9NF7/Vq7Ci2jeQ9sqL30ML6qA8GWh+lSOMTXWcblzp16iAxMRGXL19G//795W5/5coVAFB5UQLKtbRyJSFUH0V0jUafYNSvXx+LFi1CUlISoqKiMGrUKFhaWoLP5yM8PBxDhgxBnTp1MGvWLNy/f79Sx2rfvj0A4ObNmwptf+PGDbH36TvJB3qEGJvTp0/D19cXeXl5cHNzQ2xsrEHdzBIijXDm0aioKK0cz9hzreSgkpBwgC/yYRqWhz9CUGySliMjRDuePHkCDw8PpKSkwNbWFmfOnKGbWULUzFhzrbWFKeb6NoFP05qiZeKBjwPJQf7uoodnhGhSoIer1M+itrx79w7du3dHdHQ0GIbB9u3bqdmHEA0w1nybU1BC960Gpux4xPnH6fBq7KhUDpNWPKMqvoAtXR15Zxy2RieKFfxWNr+q85qQz+dj/Pjxomaf6dOnY+vWrdTsQ4iaGWuuBSD1XEj3tkRdZOVqVSerUMe1gLTrkU+eRiHz/5t9fHx8EB4eTs0+xOCNGzcOly5dwr179/Ddd9+p3OwDAIsWLcLChQsr3MaYcy0hXJCVM2XdCwtf77EuRtTsI8RjmArvkRW5hzaG+ihpjVCS6DrbuHh5eYFlWfz00094//59hdtmZ2fjp59+AsMwKtczUK4lxLhRfRTRRVp7iuHl5YXdu3fj9evX+PPPP9G2bVuwLIv09HSsW7cOn3/+Odq1a4e//voLOTk5Su9/5syZ4PF4+Omnn5Cfn1/hth8+fMC8efNgYmKCmTNnqvoj6RS6ZiXG7NKlS+jXrx8KCgrQtGlTxMTEoF69elyHRYjGzZo1C9WqVcPPP/8sWiZek4w918p6aKbOAhlCdNXr16/RuXNnvHjxAtWrV0dUVBQ6derEdViEGBxjzrX0UIboAi4fEPL5fHTv3h1Xr16FiYkJ9uzZg/Hjx2vt+IQYE2POt0J032oYJP8dTXiMUjlMkeIZRVU0GYouFeBMmTIFO3fuBAD88MMPWLdundzZ0wkhyjPWXJtXWEITQxGNkszVDWraVGqyCnVcC0hej/wXfQzX9v0OAOjduzfCwsJEs50TYsi2b9+ODh06aO14xpprCeGKrJwp615Y+HpCWm65fbV1sa/wHlnePbSx1EdxPTkX0T3ffvstTExM8OjRI7i7u+PYsWMoKSkR26akpATHjx9H27Zt8fDhQ5iYmGDGjBkqHY9yLSHGi+qjiK7S+rRltra2mDhxIq5cuYIHDx5g9uzZqFWrFliWxY0bNzBp0iR8+umnGDt2rGhpPUV06tQJW7Zswd27d9GxY0ecPn0arMSqNyzLIiIiAh06dMD9+/exZcsWg/lDbOui+gwhhOi71q1bw8PDA1988QUuXLig8nKchOgbJycnREREgGVZtGzZEqtXr8aDBw9QUFCgkeMZa661MDOpcBBJnQUyhOiqWrVqYcSIEXB0dMT58+fh7u7OdUiEGCRjzbWEEMDExARTp06FhYUFDh48iJEjR3IdEiEGi/It3bcaisqOR6izeEZfJkMZN24cbGxssHjxYixfvpyafQjREGPNtUV88Z9RV8+FRH9J5u7T33auVDOtOq4FJK8/+vTpA1dXVwwePBhHjx6FpaWlSrERQipmrLmWEG0TrtRzPTkDXo0d4d1EPGdKXu8din8OvoCVeR3YpbFjpRtXjKU+SpcmDyG6oWnTpli9ejVYlkViYiKGDBkCOzs7fP755+jUqRO++OIL2NnZYfDgwXjy5AkAYM2aNWjatKlKxzPWXJtXWAKftRfgszYaf14QX8WbEGNB9VFEVzGsZCbSoqKiIhw9ehTbtm1DdHQ0AIgSo/BBS6dOnfDHH3/giy++qHBfwuWy/vvvP7x48QIMw8Da2hoNGzaEjY0NcnNz8d9//yEvLw8AUKdOHTRo0EDm/hiGQVRUVKV/Rk1ycnJCWk4hvl53ElvHtIG5qdb7twjRGXl5eSgsLIS9PRUsEMU5OTkBAJ4/f85xJKoxMTER/T/LsgoXKTAMU26mC0UYa64FKv6M8AUsgmKTEJeSCXdnewR6uKplwElT+yVEVSzL4uXLl6hTpw7XoRA9ou+5FgBiYmKUfo+pqSns7OxQp04dVK9eXeH3Ua4lhDx//lz0d0GIIug8qjxjzbd5hSUYvDqM7i8NiC6NG2yNTsTy8Eeir+f6NsFETzfR17oUq7HnWl36t9AnlG+VY8y5tmrAdtFrkudCQrRFkXO95DbjOrkg5FKy0vlB2rHS096gRo0aMDU11dSPSAyQvufaJ0+eYNKkSbCzs8PRo0fB48mu3eHz+Rg0aBByc3Px119/wdVV+eJ/Y821gP5+Roh+Eea3Q/HPxVbpETb7CHMfX8Di/ON0sffO9W0CAGL3yEI/9GyCyV0qf31I9VFEFYZyHj148CBmzJiBN2/eyNymVq1aWL9+PYYNG6bycYw1175+XwCnqTtFr3k1doQJj1HoHoHGnIghofooogpN51pOGn5u3ryJ4OBg7Nu3D+/evQNQ+gdSp04d0QxrO3bswKNHpRe/lpaWiI6OrrBTjsfjgWGYcp20qmIYBnw+Xy370pSySZYGjYmx2bZtG9zc3ODt7c11KESP6fsNbUWDxRVRNccZa64FuPmMyCuWIUTToqOjcf/+fUyZMoXrUIge0/dcC3zMf6pydnbGiBEj8O2338LR0VGhY1GuJcQ4JCcnY9u2bVi2bJnK1/aE6Mt5VNhA6+HhwfmKGpRvCVE/eQ/0uRrjyMnJwc8//4ylS5fC1tZW48fTBzTepBo6jyrHmHPtz3uiqbiJcE6Rc73kNl6NHcUKlhXNDwKBAD///DPGjx8PNzfKJ0R1+p5rlyxZgkWLFmHKlCnYuHGj3O2nTZuGzZs345dffsHcuXOVPp4x51p9/YwQ/SKZJ4V8mtaEu7O92PccrM2QkVcs+rqGjTkCvnIFwGL7xWS8zS0Sfa9BTWs4O1grfa1I9VFEHQzpPFpcXIywsDDExsYiNTUVOTk5sLW1hbOzMzw8PNCnTx+YmZlV6hjGmmslG37KknePIO0eI8jfne6LiV6g+iiiDprOtVqbViUzMxOhoaEICQnB3bt3AZQ2+ZiamqJ3794IDAyEr6+vqMBhzpw5CA8Px7Rp05CUlIT58+fj9OnTMvfv5+fH+cNiLl1PzqCHMsRorFu3Dt999x2srKwQGxuLVq1acR0SIZwICQnR6vGMNddm5Rdja3Si1h/QSi71HZeSSbmeaM2ZM2cwYMAAfPjwAdWqVcOoUaO4DokQTlVmMDc5ORkrVqxAUFAQjh8/jg4dOsjc1lhzLSHG6MmTJ/D29sbz589RXFyM1atXcx0SIRrVpUsX8Hg8ZGdnw8rKitNYKN8SIk4ds2+a8BhM9HSTOm7BF7A4FC/+gEsbYxzv3r1Dr169cOXKFdy5cwdRUVHUYAsabyLaYcy5Vta5kOgPLmal5gtYbItJwpGbzwGwGNzKCf/r7CZ2XGXiUuRcL7nN3Rfv5b6nXNx8PgICArBz507s3r0bN2/ehIODg7wflxCDdPr0aTAMg759+yq0fb9+/bBp0yacOnVKpYYfY861hGiSMN/+FZsk9fvuzvblcigg/rf4NrcIKyMeYa5vE0zwcBUrfk9Iy0NCWh4iH6YBgELXjVQfRUh5ZmZmGDRoEAYNGqSxY1CuLU/ePYLk+fH843QExSbRPTLReVQfRfSFRht+WJZFeHg4QkJCEBYWhuLiYlGhVIMGDRAQEICxY8eiVq1aUt/v6+uLvXv3on379rhx40aFx9qxY4e6w9crJQLxr2mJPGKofv31V8ybNw8A4OXlhWbNmnEcESHc8ff31+rxjDXXFhbzRQNx2rwRdXe2Fw32Cb8mRBvCwsIwZMgQFBUVoWnTpujSpQvXIRHCqeTkZKSmpiIgIADJyckYPnw4+vfvjyZNmoiWbH/8+DH+/vtvHDhwAC4uLggKCoK9vT0SEhJw/Phx7NmzB+np6ejXrx8eP34Me3vp53RjzbWEGJt79+7Bx8cHb968QfXq1TF8+HCuQyJEKzhYaF4qyreEiAuKTRKNeyhTdKTM/hPScsVe0/QYR0ZGBrp3746bN2/CxMQEEyZMoGaf/0fjTUQbKNcSfabpvCjNtphErIx4LPp6ZcRj8BgGgR6uouf9fAErWoFHXlyKnOslt/msTlWxFX7k5Yfi4mKMGTMGBw4cAACMGDFC5ngXIcYgNTUVAPDZZ58ptH3z5s0BAE+fPlXpeJRriSSqEVOPstcBZTWoaY2hresi0MMVAMRyaEZeEbwaO+Lui/diq/kcuvEMA1vVQYOa1gAYgAUS0j/eG0sWzkv7N1y5YjnVRxHCEWPNtbaWpqLzllP1KrigxD2C5D0GQBPNEN1H9VFEn2is4eenn37Crl278OrVKwClD3QtLS0xaNAgBAYGKvyH0aZNGwCls7ER2e5JzLrDxWAkMW6aHkBgWRY///wzli1bBgAYPHgw9u7dC3Nzc7UdgxBCKqLtG1HhgGHZ8yohmnbo0CGMGjUKJSUl+OKLL3DmzBnUrFmT67AI4VS1atXg5eWFd+/eISYmBh07diy3zeeff46hQ4fim2++Qa9evTBu3DjEx8ejRYsWGDBgAMaPHw9fX19kZmbijz/+wMKFCzn4SQghuuDWrVvo1q0bMjIy4OjoiLNnz+KLL77gOixCCCFaoAsFWNJiUGQVgMrELrn/BjWtVRrjUDSGN2/ewMfHB/fu3YOZmRn27duHwYMHK308Q0XjTYQQUjF1rIRWNme1rl8dAIP4VNn5q3RlH+lxSCs6lheXIud6yW3GdXJByKVkhfJDYWEhhg8fjr///hsAsGjRIixYsIBmQCdG7e3btwCg8Iq2wu3S0tLkbEmIYqhGTD0krwNq2JhjgoerWP4O9HDFofhnSEjLE21398X7cs2zCel5WHX6iehrr8aOYg0/koXzZf8Nzz54g7+D1iJ892YAVB9FCAC0b98eAQEBGDFiBGxtbbkOx2AV8VmxBkfJsbiKBHq44mpShlITCRDCJaqPIvpGYw0/K1asEP3/Z599hgkTJmD06NGoVq2aUvvh8XioV68ezb4mR3ZBsdjX6hiMJEQZmhxAYFkWs2fPxtq1awEAo0aNws6dO2FqqtFFygghREzr+vbYGp2otcIcEx6DiZ5ulL+J1oSGhmLs2LEQCARwd3dHREQEzcpICIDffvsNqampWL9+vdRmn7Lat2+PJUuWYPr06fjtt9/w66+/AgA6d+6MWbNm4ZdffsGpU6eo4YcQI3X16lX07NkT79+/x6effoqoqCg0bdqU67AIIYRoiS4UYEmLQZFVACoTu+T+h7auq9J4iiIxPH/+HN7e3njy5AksLCxw5MgR9O7dW+ljGTIabyKEkIqpYyU0aTmr7P+XPweXz4vuzvblnvdLfl8WRc710rZRJD/k5+dj0KBBOH36NABg5cqVmDNnToXvIcQYVK1aFW/fvsXLly8Vqol6+fIlAMDa2lrDkRFjQTVi6iF5HTDBw7Xc79GEx2Bo67piTblvc4tw/nE6HKzNkZFXBGl4DDDXt4nMwnnhvyHLssg6vx3hcccBUH0UIULXr19HXFwcvvvuOwwZMgTjx49H586duQ7L4BQW80XnN+H9gaL5xITHIMjfXakmIUK4QvVRRB9prIvGxsYGEyZMwLVr13Dnzh188803Sjf7CKWkpCApKUm9ARoYG3MTsa8lB/moW5ZomrQBBHWZNm2aqNknICAAu3btoptZQojWWJiZYK5vEwAsloc/QuTDNCwPf4SgWLo2IYZj+/bt8Pf3h0AgwFdffYXIyEi6mSXk/x09ehQA0L9/f4W2F2537NgxsdeFs4onJCSoMTrDkFdYgsCdcdganQi+gOU6HEI04tKlS+jWrRvev3+PevXqISYmhpp9CCHEyGhy/LQyMQR6uGKubxP4NK2Jub5NpD6IVyZ2voDF1uhE0fXduE4ucvevauxlPXv2DJ07d8aTJ09gZWWFkydPUrMPIYQQpSmSF+WpKE/GpWSWy5WDvqwjtk2Xxo4I9HAt93zfq7GjWFyS+9H0mMqHDx/Qu3dvUbPPH3/8Qc0+hPy/Zs2aAQD++ecfhbYXbte4cWONxUSMC9WIqYei1wGBHq7wauxY7vXq1mYy991Gzr+J8N8sK3ILcv6/2Yfqowj5yMvLC0BpA3poaCi8vLzQqFEjLF++XNRIS9Tn4I1nKt1fCCcWCPJ3x0RPN62vbk6IIqg+iugrjV0Rvn79WuHlarnw4sULbN++HQCwYMECjqOpvM/rVhf7WpGluglRJ3XMeCWLcIDsm2++wfr162nFL2KUXF3Vdx5nGAaJiYlq258shpJrq1uZYaKnGwJ3xom9TjMjEUPi5uYGCwsLdOzYEf/88w/NKkdIGU+fPgUAhe9vhdsJ3ydUr149AEBubq7aYjOUXJtTUILIh2mIfJgGAQtM7kL5lRieOnXqoFq1aqhVqxaioqJQv359rkMihCjIUPIt4Z4mx08rE4MiqwAoE7uslXjkjaHwBWy5GUDLFgXIi6F69eqoXbs23r59i5MnT8LDw6PC4xFCdAflWqJL1LESmmTOkvyeZK78oWfjcjP+m/AYqc/7y+bGrdGJMle/k5dXVWFhYYEGDRogOjoa27ZtQ2BgYKX2R4gh6dWrF6Kjo/Hbb79h8ODBaNiwocxtExISsGrVKjAMg759+2olPsq1ho9qxDSjonz6LOtDue0Ht3ICj2EQl5KJ1vXtAbCIT82Cu7M9BCzE8jZfwCIuJRN3X7zHZ3WqYvPXrQEAO182x5WbJzF16lRs2LBB4/VRmrhmIEQToqKikJqaipCQEOzatQspKSlISEjA/PnzsWDBAvTo0QMBAQHo27cvJ01yhpZrE9PzsC0mEZO7NOA6FELUjuqjiL5iWJY1yulzr127hg4dOoBhGPD5fK7DUYmTkxNevy+A09Sd8G5SE9vHunMdEjFimr4JjImJgYeHBxiGbiyJ+jg5OQEAnj9/znEk8ikykMMwDKSldcnXtZX7DCXXAsDPe6JxKP4ZEtLyRN+b69uEGn6IQbl27Ro+//xzVKlShetQiAHRp1wrS40aNZCVlYXTp0/Dx8dH7vZnz55Fjx49YG9vj7dv34pef/PmDT799FM4OjrizZs3aonNUHKt8L4WANxqWCFqthfHURGiGf/99x+sra1Ru3ZtrkMhBkRfci2PxwPDMNiyZQssLCwqvT8/Pz81RKUYQ8m3gO5/TgydtopoKjqOqjEo877AnXFiRc4+TWsiyF/+s4uyRctA+XEXRWJ4//49kpOT0bJlS7nHI0QZdB7VLMq1xNCUzVmt61cHwCA+9WP+mhh6Q6VcKaminCsvr6qKz+fj8uXL1FhL1E7fz6O5ublwc3PD27dvYW9vj5UrV2LUqFGwtLQUbVNQUIC9e/fixx9/xNu3b1GjRg0kJCTAzs5O4/FRriVEPr6AReDOOJx/nC56zauxo9jXbo7WcHawhoBlxV4HgC6NamD72LYK3ys7WJsjI69I7Fgh49oC0G59lKauGYjuMbTz6Pnz57F9+3YcO3YMHz58EP291KhRA6NHj8b48ePRvHlzrcVjKLm27DPbBjWtETmzC7dBEaIhVB9FNEHTuVZj7ay5ubk4evQoGIbB119/XWGhMp/Px969e8GyLIYMGaLTKwPpKnlLfxKiaeqY8UqosLAQoaGhCAgIEF2Qd+7cudL7JUSfLVy4UOb3du3aheTkZJiZmcHT0xPNmjWDjY0NcnNz8eDBA0RHR6OoqAiurq4YM2aMFqM2DHmFJWKDXA1q2mBoayeaGYnoNZZlERISghEjRoiuvdu1a8dxVITopjZt2uDMmTOYP38+vvrqK7GHtJIKCwsxf/58MAyDNm3aiH3vwYMHAICaNWtqNF599+p9AdchEKI2f//9N1q1aoW6desCQIWzuxJiLCZNmlTpfTAMo9WGH0LURZ3jpxU1v8haYUfae4DS4h55jTzKxK7qSkZxKZnlvi57PGkx3Lp1Cx8+fEDHjh0BAFWrVqVmH0IIIZyTnjc//r+6Vv2raD/y8qqi3rx5g5iYGAwdOhQAYGJiQs0+hEhhY2ODAwcOwNfXFxkZGZgwYQKmTJmChg0bwtbWFjk5Ofjvv/9QXFwMlmVhYWGBQ4cOaaXZhxBjUpmJNoJik8o18dx98V7s68T0PCSm50Gadq41KjyWZN7+UFzaEMCWFCP3/jn8a91H9D1t1kep65qBEG3z8vKCl5cXcnJysHfvXuzYsQPXrl1Deno61q1bh3Xr1qFNmzYIDAzEiBEjYGtry3XIeogmZVcGrZimu6g+ihgKjTX8HD16FOPGjYOPj4/c4mITExPs2bMHZ8+ehZmZGUaOHKmpsAyWgBVwHQIhavHhwwcMGjQIERERePz4MVatWsV1SIToBFkNP2PHjkVycjLGjBmDtWvXwsHBodw2GRkZ+O6777B7924kJydjx44dGo7WsOQUlKBqma+dHaxokIvoNZZl8f3332PNmjU4cOAA/vnnH7XMsk6IoZo2bRrOnDmDuLg4dOrUCcuXL4ePj4/YpBYsy+Ls2bOYO3cubt26BYZhMG3aNLH9HD9+HAzDoH379tr+EfQLrehJDERoaCjGjh0LNzc3xMTE4JNPPuE6JEJ0gpEuNk+I2ggfHB+Kf46EtFwA4k09gOxiHWmNQACkNgdVhrCRSLKxSB5li5+vXr2Knj17gmVZREVFlWu4J8RYlZSUwNRUPY9/N2/ejClTpqhlX4ToG3UXa0mu+vNDzyZiq/6ooqKcK5lX+QIWfAGr1M/w4sULeHt748mTJygqKsLXX3+tUpyEGIsuXbogNjYWY8eOxYMHD1BUVIT79++X265FixbYsWMHWrVqxUGUhBg2WRNgKELyXhoAlBnGik/NRNkGX0mSeftK4lucu/8C6cd+RUFyPD5FJoDuih9QQfKuadTViEwIV2xtbTFx4kRMnDgRjx49QnBwMHbv3o3Xr1/jxo0buHHjBr777jsMGTIEEydORIcOHbgOWW8MbuXEdQh6pTI5iGgO1UcRQ6LRhh8ACjfvjBo1CmfOnMGhQ4fkvicmJqbS8QlnVzYUx269wFQvmimW6Lfc3Fz069cP58+fBwDUr1+f44gI0W179uzBrl27MGzYMOzcuVPmdg4ODti1a5do9Sxvb2+5zbiUa2WjQS6izwQCAaZNm4bNmzcDKF1OVF1FIIQYqt69e+O7777D77//jtu3b8PX1xdVqlSBm5sbbGxskJeXh4SEBHz48EH0nhkzZqB3796ir7OysnDw4EFYWVmJXqdcK90ndh9XUKKZkIi+2rZtGyZNmgSWZVGzZk1aDp6QMk6dOqXVvwnKt+IUWcmF6LayD47LKjsDr6xiHWmNQBXtR1WqrmSkTKNQTEwMevfujdzcXNSrVw/VqlWrTMiEGJSRI0fiwIEDYpNUqGLt2rX4/vvv5Tb8UK4lhkrdxVqS+5vr2wRB/u6i76syBlJRzg30cMXVpAzRSgXnH6cjKDZJ4Z8hJSUF3t7eSEpKgpWVFU1iQYiC2rRpg3v37uHs2bOIjIxEYmIisrOzYWdnhwYNGqBbt27w9vZWap+UawlRnLKr1ZTNv3xB+e6ejLwime91c7QWW+1HXg2BZN4e+oUjmq+aioLkeABAYE/NrDQg75pG1Uk7CNFFrq6uaNWqFW7cuIE3b94AKC32z8/PR2hoKEJDQ9GpUyf88ccf+OKLL0Tvo1xbnpW5CQAoPWmAMaMV03QP1UcRQ6OxT68wiSnaFSuc5fjhw4dyt+3SpQsYmvVXDE2OSfTd+/fv0atXL1y+fBkMw2Dbtm0IDAzkOixCdNpff/0FhmEwe/ZshbafM2cODh06hL/++ktuww/lWuka1LShQS6it/h8PiZMmICQkBAAwNSpU7Fhw4ZKF4AQYgzWrFmDpk2bYu7cucjIyEB+fj7u3r1bbjt7e3v8+uuv+N///if2evXq1fHq1Sux1yjXSlf2N0IzIRF9tGHDBsyYMQMA0LVrV/z999+wsbHhOCpCdEfnzp1hZWWlteNRvv0or7CE8qoBkNakA4gXFskq1pHVCKSOmXzV0ahdUdFy2f3bvH2IbT9PwocPH+Dq6opz587RxFGElHHkyBH4+/sjNDRU5X38+uuvmD9/vkI5lHLtR1n5xdganUhNtQZC3cVa8van7jEQEx5T7nOo6M/w33//wdvbG8+ePYOllTW8ZqxFgqkzulCxHyEK69atG7p166aWfVGuJURxsu57Zd2zSk6q4dXYESY8BikZ+aJVdYHSGoH69lUgYAEew6Ctiz3GdXJByKVklRpl3r9/j769e+H1o3iN10fJuwZRddIOQnTJzZs3ERwcjH379uHdu3cASht96tSpg3HjxsHGxgY7duzAo0ePcPHiRXTo0AHR0dFwdy9twKdcW15+ER8rI0rPj5O70PlBEbRimm6h+ihiiDTW8PPixQsAUHi2l1q1agEAXr58qfAxWOpyEalrr70H5YSoW0ZGBnr06IH4+HjweDzs3LkTo0eP5josQnTevXv3AABubordXLm6lg4yKTOzBOVacUNbO9EDLaKXiouL4efnh/379wMAZs+ejd9++40GrghRQmBgIMaMGYOwsDBcunQJKSkpyM3NhY2NDerXr49OnTqhX79+Si8BTblW3LsPRaLZomgmJKJvVq5ciR9//BEA4OvriyNHjtDqPoToCMq3QBFf/HdAeVU/ST44blDTGkNb1xUrLJJVrFPRrL2VnclX043awv3nJ1xH+vHlAL8YTZo0QWRkJOrUqaO24xBiKPbu3QsLCwsEBQUp/d4FCxZg2bJlAIDatWsr/D7KtUBhMV90LqQcq/8qW6wlWVjcun71CvdX2TEQ4fGuJ2eIipEFEn+XivwMDx48gLe3N16/fg0rGzvYDVyIe4I6uEefbUI4R7mWEPlk3fdK3rNeTcpAkL97ufzLY0rzZUpGntjrQ1s7Sc2BqjTKSKuPGjnqa42tykwF6MRQZWZmIjQ0FCEhIaJJGlmWhampKXr37o3AwED4+vqKivvnzJmD8PBwTJs2DUlJSZg/fz5Onz4ttk/KtRCfmRHAkZvPqeFHQbRimu6g+ihiqDTW8GNmZoaCggLk5uaiatWqcrfPzS3tjBcIBHK3rVKlCgoKCtC7d28MGTJEpfgSExPxyy+/qPReXWRCJyOip9LS0uDj44O7d+/C1NQU+/btU/nvmhBj8+HDBwClTbbVq1eXu72wGbegoEDutpRry7My49ENGdFLhYWFGDlyJI4dOwagtHBj0aJFdDNLiAosLCwwZMgQtVyvUq6VLiOvGEGxSZjo6UYPoojeYFkWixYtwpIlSwAAAwcOxL59+5RuACSEqB/l24/MTcSv/ymv6idpD45lFQFJm8FYWjGSOmby1XSjdlxKJvIeXcTbsFWAgA/7ug0QHR2NmjVrKrUfdaxERIiuGz58OA4cOICQkBBYWlpi48aNCr93zpw5WLNmDViWRf369REVFSX3PZRry7uenGHwTRHGcD6tbLGWZGHxDz2bYK5vk3L7E/4uUzLyxd6v7LWa5AoFQsKVCmT9DGX/LT8pfo2tc8fj7du3qFGjBr6atg638quJtqWGcUKUk52djadPnyInJwe2traoV68e7OzslN4P5VpCFCdrAgzJe9bzj9MRFJtU7hmEgIVYPm1Q0wZDWzuVy9uqXgPJqo/aGp2olkk0pMVHBejEkLAsi/DwcISEhCAsLAzFxcWiJp0GDRogICAAY8eOFS08IMnX1xd79+5F+/btcePGDdHrlGvLKNfzRE1QiqIV03QD1UcRQ6axhh8nJyc8fPgQV69exeDBg+Vuf/XqVQCKzRbVqlUrXL58GQDg7++vUnzXrl0zjCT7/9q60ANaop/evXuHtLQ0mJub48iRI+jTpw/XIRGiN9zc3HD//n1s3boVf/zxh9zt//zzT9H75KFcW94nVS3Vti9jeCBKdEdBQQGePn0KAFi+fLlo5QFCCLco135kKlGALCwgoQdRRF+wLIuEhAQAwMiRI7Fz506YmZlxHBUhBKB8W5a1hanUIlOiX5R5cKzpVXfK0nSjtruzPY68TwMEfJh/0hDzNu1TutkH0O7vhBCu7N69G4WFhTh+/Dj+/PNPWFhYYM2aNXLfN336dGzatAksy8LNzQ1RUVGoV6+e3PdRri1PYAT1UMZwPlW2WEtyzP96snhhcXxqJoL83cvtT7JRR7h637hOLkrN9C9ZyFz25wjyd5f5vrLH/5B0C1nv3uOTTz5BVFQUYtMtcKtMbNQwToh8fD4fW7duxV9//YW7d++KrRTAMAw+++wzTJw4ERMmTICJiYlC+6RcSwwNF8/JJe9ZgdLcuXVMG9H/S8vfzg5WmOjpBr6AxdboRByKf4aEtNLVf1S5BpJVH6WuSTRkXaNRAToxBD/99BN27dqFV69eASh9LmNpaYlBgwYhMDAQXbp0UWg/bdqU/t2/e/dO9BrlWtkGt3LiOgRClEL1UcSQ8TS1Y09PT7Asi99++w18Pr/Cbfl8vmjJLEWSb9u2bcGyLOLi4tQUrf4yNWHQoKY1BCwLvjGMIBOD06hRI0RGRuLkyZPU7EOIkkaMGAGWZbF582YsWrQIJSUlUrcrKSnBokWL8Oeff4JhGIwcOVLuvinXlpf0Nh9BsUmir4UDe4E747A1OlGpPCwcbIt8mIbl4Y/E9kuIulWtWhWnT5/Grl276GaWEDXJzs7GvXv3cOXKFdy7dw/Z2dlK74Ny7UeSKVRYQCIsrhEWxFBzLNFVPB4PO3bswObNmxEaGkrNPoToEMq34iivGhdpBUOaEujhirm+TeDdxBFejR1xPTlT6bESeftfsegneAT8jJVBBzGjdyuV9qPN3wkhXDExMcGBAwfQq1cvsCyLdevWYd68eRW+Z+LEiaJmn8aNGyM6OlqhZh+Acq00PCOYNZbOp+VJjvkLWPEcKKtZRvJ35+xgjYmebgi5lKzUMwRZ+5fXpFP2+FVcW6PrNysQExODZs2aifK7T9OamOvbhBrGCZHj9evXaN++PaZNm4Z///0XAoEALMuK/hMIBLhz5w6mTp2KDh064M2bNwrtl3ItMTRcPCcP9HBFl8aOYq99Wa96ucYjycm2hXlUGLOw2UdI2WsgWfVRkvla1SZbukYjhmzFihV4+fIlWJZFixYtsGHDBrx8+RK7d+9WuNkHKH2eU69ePdSvX1/0GuVa6bwaO+J/nalZkOgXqo8ihkxjK/xMnjwZW7ZswY0bNzBs2DAEBwejatWq5bbLzs5GQEAA4uLiwOPxMGXKFLn7btu2LQAgPT0dT58+VXjQ2RCV8FkkpOVhZcRj8BiGOvKJXkhISICtra1oCc0WLVqgRYsWHEdFiP6ZNWsW9u/fj3v37mHp0qXYtm0b+vTpg6ZNm8LGxga5ubl4+PAhTp48idevXwMo/XubOXOm3H1TrpWu7Gw6lZnFUF2z9BAiy/v37/Hs2TNRfnVwcMCYMWM4jooQ/abu2Rkp134kKFMIam9tpvQstoRwgc/nIy4uDu3btwcAmJmZYfLkyRxHRQiRRPmWGDN1rLqj6MzLwkZtAKKxkqhHlV/x4dKlS+jYsWOZVRaWqLwvQPMrERGiK8zMzHD06FH06dMHkZGRWLFiBSwtLfHzzz+LbceyLMaNG4fQ0FBR0dTZs2dFz24UQbm2vNSMvNKJogz4XpbOp+VJjvnzGCi0uqKs36WyzxCE+7+enAEBW9p41tZF/qqOjgXPwZYwYExLJ64YPKA/GjYsPY6yqxwRYsz4fD569+6N27dvg2VZNGvWDEOHDkXz5s1ha2uL3Nxc3Lt3D4cPH8b9+/cRHx+PPn364Nq1a+DxKp6nmXItMTRcPCc34TFo5+KAC4/TxY4r/FqYiwM9XCFggSM3nwNgRZNvy2qcUeQaSJH6KGG+ruyqzHSNRgyZjY0NRo4cicDAQLi7y17BUhEpKSliX1OuLa9BTRsE+bsb7D0tMSxUH0WMhcYaflq0aIHZs2dj1apVOH78OCIjI9GvXz+0bNkStra2yMnJwe3bt/HPP/8gNzcXADBz5kx88cUXcvctTLLCzlpVkqyjoyP8/PzAGNAsS1QsTPTBgwcP4OPjAwcHB5w/fx41atTgOiRC9JaFhQXOnTuHoUOHIjo6Gq9fv8b27dvLbScsSPb09MShQ4dgbm4ud9+Ua6UrOyim6mAkX1B+VT4abCPqlJmZiR49eiApKQkXLlzAZ599xnVIhOi9169fo2/fvrh58yYAiDX7CL8Wzs4YHByMsLAwuQVSlGulyy0oEc1iCyjfVEuINpSUlMDf3x8HDhzAoUOHMHDgQK5DIkTnJScnAwCsrKy0elzKt8RYSGvMUUfBkLKTnaizcOu3337DDz/8gEWLFmHhwoUq7UOSuoqoCNEH5ubm+Oeff9CzZ0/ExMRg0aJFqFKlCmbPng0AEAgE+Prrr3Hw4EGwLIsvv/wSZ86cgYODg1LHoVxbXkJ6nujcaaj3snQ+LU+ywLati4OoWaaiBlpZv0tlCnbL7r+ti4No//Iad0+ePInfZ4xEE/fOaDV2Mdo1qEn/loSoKDg4GLdu3QKPx8OaNWvw7bfflttm8ODBWLhwIdavX49Zs2bh5s2bCA4ORmBgYIX7plxLDA1XTSnxqeL3q/devBf7Wnj/ymOAhLTSOsaVEY9x5OZz1K0uPp7VoKYNhrZ2kps3Fa2PUleTLV2jEUP2+vVrjY0tU64tb2hrJ2r2IXqB6qOIMdFYww9QupReSUkJfv/9d+Tk5GDv3r3Yu3ev2DbCQqnZs2fjt99+U2i/Li4uEAgElYrN1dUVO3bsqNQ+dA0VCxNdd/v2bXTr1g1v375FUVERXr16RQ0/hFRSjRo1cP78eRw9ehQhISG4dOkS3r17J/p+tWrV0KlTJ4wfP16pQkTKtWUwgJkJA6dqlriW9BZA6WCZqoORQbFJOF9m9iCvxo402EbUJi0tDd26dcO///4LU1NTJCQk0A0tIZWkqdkZKddKV9feilbCIzqtqKgII0eOxNGjRwEA//77LzX8EKKA+vXrc3JcyrfEWMhqzKlswZCy12XqKNxiWRaLFy/G4sWLAZSOKfP5fIVW0ZSHViqomKIrOhH9YWlpiZMnT6J79+64cuUKfvjhB1haWmLSpEkYOnQo/vnnH7Asi7Zt2yIiIgLVqlVT+hiUa2Uz5HtZQzufquP8V1GBrbQ8HejhKnbMrWPaiB1TmYJdWdcBFTXuHjlyBCNHjkRxcTH4715h1YBGSjf8EUI+OnDgABiGwfTp06U2+5Q1Y8YMpKSkYP369di/f7/chh/KtcTQcNWUInm/+lmdqmLP7GWtspeQloeEtDy4OVrj/YdifFanKraOaQNz04pX5+KiPsrQrtEIKUuTE0lRrv3IwowHr8aOuJ5cei6UdW9EY0hEF1B9FDE2Gm34YRgGa9aswYgRI7Bu3TpERUUhLe3jxXPNmjXRrVs3zJgxA23atNFkKAavQU1rKhYmOu369evo0aMH3r17h1q1aiEqKgrNmzfnOixCDMagQYMwaNAgAKVLVebm5sLGxgZVq1blODIDwALFfBbJGR+QnPEB5x6/xdWkDPCY0mYdHsOgrYvig5GSg4QmPIZufIlavHz5Et7e3nj06BHMzc1x+PBh9O3bl+uwCNF7mpydkYizMuPhxDQP7LqSIvbgTbg6HuVLwrWCggIMHjwYp06dAgD8+uuvmDt3LsdREUIIIepdWacsZRt4Klu4xbIsfvzxR9HkcCNGjMCuXbvU0uxD5FN2RSeiH6ytrREREYGuXbsiPj4eM2bMwF9//YW7d+8CADp16oRTp07B1taW40gND03UqD/Ucf6rqMBWWp4GUOExpe1PVlGdrOsAWa/v2bMH/v7+4PP5aNWqlUqrexHVUGGk4fr3338BAAEBAQptHxgYiPXr14veR4gx4aopRfJ+dVwnF4RcSpa7yp5QYnoeAOD843SEXEquMH6qjyKE6CtzE56oGTLqkex7IxpDIlyj+ihijDTa8CPk7u6OPXv2AAByc3ORnZ0NOzs72NjYaOPwRmFwK1pGj+iuixcvolevXsjJyYGTkxOioqLQqFEjrsMixGBVrVqVGn00rOxsP3N9myh148rVMuXEsKWmpsLb2xuJiYmoUqUK/v77b3Tr1o3rsAgxCJqcnZGIm+bdCFXMTRDo4YqrSRmifHv+cTqCYpM0MlBMhRZEUXl5eejfvz+ioqIAAOvWrcOMGTM4jooQQiqPcqFhUPdYg/BzcT05Q6nJTipTuCUQCDBjxgxs3LgRADB27FgEBQVRs48W0UqbhsvW1hZnz56Fl5cX7ty5I2r26dKlC8LCwmBtbc1xhIbBwszk/8+ZQFsXB5qoUY9o+vwnLU8rc0xhXj4U/wwJaaXFxmWL6mRdB0h7ffv27ZgwYQJYlkWHDh1w6tQplVb3IqqhwkjD9f79ewBAnTp1FNq+du3aAIDs7GyNxUSIsVB0XEPa/aq0+1fhNdyh+OdISMuVesyK8jbVRxGiWe/evcPhw4dx7do1vHr1Ch8+fADLsjK3ZxhG9FyHyFfEF/9dyjrf0RgS4RLVRxFjpZWGn7JsbGyo0UcDKrhuIYRT586dQ9++fZGfnw8XFxdERUXBxcWF67AIIURtlL1x5WqZcmK4EhMT0bVrVzx9+hQ2NjY4ceIEPD09uQ6LEINBszNqU+mNrbTV7zQ1UEyFFkQR2dnZ6N27Ny5evAiGYbBlyxb873//4zosQghRC8qFukXVBix1jzWU/VwAyk92oiw+n49JkyYhKCgIADB58mRs3LgRPB5PY8c0ZKp+jmiSGsPw9OlTmd8LCgpC//798erVK7Rs2RJ//vknMjIykJGRIfM99erV00SYBqm6lRlCxrXlOgytMaSmYU2f/2TlaUWPKZmXhYRjJbL2L/n6h9un8N230wEAjVq2Q3jEaVS1o9W9tIkKIw2Xvb090tLSkJSUhFatWsndPjk5GQBQvXp1TYdGiMGTHNfYFpOEQA8X/K+zm9xrE1nXM8L8KvweX8CKTQgqK29TfRQhmrV7925MmzZN1DBbUaOPEMPo5z0KV8xNxH9fss53NIZEuEL1UcSYab3hh2jG0VsvMMWrAddhEFLOX3/9hfz8fDRq1AhRUVFwcnLiOiRCDJZAIEBWVhby8/Pl3tjSg1rVuTlai5bsBpS/cTXhMWIP2gDo9cNQwr1jx47h6dOnqFq1KiIiItC+fXuuQyLEoNDsjNpzJP656CGctgaKqdCCKOLatWu4cuUKeDweQkJC4Ofnx3VIhBCiNpQLdYuqDViVWVlHGk1/LiSLqrydGBw5cgQAMHPmTKxevVpuQYYhFZqrm7zPkazfHU1SYxicnZ0VKmi6ffs2mjVrVuE2DMOgpKREXaERA2NITcOaPv9Jy9PKHFMyLwsJx0pkXQeUfb2goABuY/8AAFi6tMKHrt9j/600TPSkhh9tosJIw9W6dWuEh4fjjz/+QEhIiNzthatatm7dWtOhEWJwJO9nrieLN69n5BVhZcRj8BhG7rWJ5PWMgGXBYxix/DzR003qPZQ0VB9FiOZERETA398fLMuCYRh8+eWXaNCgAapUqcJ1aAbF2sIUc32b4HpyJgQsKzrHSo670RgS4QrVRxFjppWGn8zMTISGhiI2NhYpKSnIycmBra0tnJ2d4eHhgTFjxsDevnKDGV27dlX6PaamprCzs4OTkxNat26N/v37w87OrlJxcEd2YTc9+CJc2rFjBxwdHTFv3jzUqlWL63AIMUhBQUEIDg7GrVu3UFRUJHd7VR/UUq4FGjha49SMzgi5lFypG1dDehhKuDdr1izk5+ejT58+Cs0cRwhRjjZnZzT2XJuQnoeg2KQKZ6dVNyq0IIro1q0bdu7cCTMzMwwbNozrcAghlWTs+VYS5ULdoisNWJr+XJQbF/FtgjNnziAiIgLz5s1TqFmBxlZkk/c5kvW7U3fjGOGOIrMca5Kx5tqs/GJsjU40muewupKz1EHW+U+Tz9iVOedK5uUGNW0wtLWTUmMllpaW8Px2PU6E/onqXcaDMTXT638zfUWFkYbr66+/xqlTp7Br1y7UqFEDy5Ytg7m5ebntiouLMX/+fOzYsQMMw2D06NEqHc9Ycy0hQPn7Ga/GjlK3UyTPSV7PHLn5HAlpeaJ9A4rfK/EFLDqN/xn/vhVg7JSZ+LS2YpPICd9LdX2EVGzFihVgWRaNGzfG8ePH0bhxY40ez5hzrfBcJzzXRj1Kx6H456J7EBMeQ2NIhDNUH0WMmcYbfn7//XfMnz8fBQUFAMQHmW/duoVjx47hp59+wq+//ooZM2aofJwLFy6I/r/sw6Cyx5P3uqWlJb7//nv8/PPPMDExUTkWLgz80knmDQA9+CLa9urVK3z66acAAAsLC2zYsIHjiAgxTMXFxejXrx/OnDkDQPMPco091wKl7bXquHE1pIehhBtlcy3DMFiwYAHHERFiuLQ5OyPl2o85UVsDxVRoQWRJT09H9erVYWpaOnT29ddfcxwRIURdKN+Ko1yoW3SlAUvTn4u4lEywJUUQlBTBxNKm9BrQ3x1t2rRRah+SX9PYSil5nyP63Rm2hQsXch2C0ebawmK+6Hmsqn9T+lTsqSs5S5N05Rm7tLysyOeCZVm8fv1aNI7s2bIRLr+aKPq+If6b6ToqjDRcI0eORFBQEM6fP4+1a9di165d6Nu3L5o1awZbW1vk5ubi/v37OHHiBNLT0wGUFhKPGDFCpeMZa64lBCh/P8NjGHg1dsT5x+liryuS5ySvZwDx/Hoo/rnc/Ct8ZhsUm4TVUclAaz9suvYWdvZJCp/vdeWagxBddvPmTTAMg02bNmm82QegXCt5rk1Iy630/S4hqqL6KEJKabThZ968eaLuWgCoX78+mjdvLrqhvXfvHlJTU/HhwwfMnDkT6enp+OWXX1Q6lp+fHxiGQWxsLJKSkgAATZo0QZMmTWBjY4Pc3Fw8fvwYDx8+BAC4urriq6++Qm5uLhISEnD37l18+PABS5cuRVJSEnbt2qWeX4LWsDJvAOjhDdGm4OBgTJ06FcePH0ePHj24DocQg7Zu3TqcPn0aANCyZUv4+fmhcePGsLKy0sjxKNcCiWVWHlBW2Ye1fIF4cxY9WCPKuHjxInr16oWFCxdi1qxZXIdDiMHT5uyMlGuBW0+zMDbkOkwYoK2LA8Z1cim3sp46i52o0IJIk5qaCm9vb7Rr1w67du3SuwcxhJCKUb4VZ4i5UJ+KpSWp2mij7p9Z05+Lz2tZYs/CxRAU5qPWiF9UGhcxhkJzVcn7HNHvzrDpQsOPsefayjyH1adiT0NvGuYLWByKfyb22qH4Z5xcX6iSlwUCAWbMmIFDhw4hJiYGjRo1Mvh/M12iz9ejRHXHjx/H8OHDERERgfT0dKmTRwlrp3x9fbF//36Vj2XsuZYYN8n7mbYupefZbTFJOHLzOQAWg1s5YVwnF2yNThSdi6U9a5DMjQIWWBnxSLTvhLRcJKTlyrwuK1sfFfe6/EQLZbevKDdQXR8h8gmf02hrRQ9jz7XlGyJL0fmJaBvVRxHyEcNqaDmAuLg4tGvXDgDQtm1bbNiwAW3btpW63fTp03Ht2jUwDIOrV6/C3d1dpWMuXboUixYtgq+vL37//Xc0bNiw3DYJCQmYOXMmTp48iYULF4q6/ZKSkjBt2jSEh4eDYRiEh4eje/fuKsWhLU5OTnj9vgBOU3eigaM1nGtYiyVan6Y1EeTvjq3RiaLBYQCY69uEEi/RiE2bNuGbb74BUDpIdfLkSbFudkJ0jZOTEwDg+fPnHEeimpYtW+Lu3bsYOHAgDh06pJW/N2POtULC/KosyXzs1dgRJjyGHvYQpZw7dw59+/ZFfn4+XFxc8O+//8LGxobrsAiRSd9zrZC3tzfOnz8PhmFQo0aNCmdnZFkW3t7eOHv2rErHolwrTnJmPrqfJZqWmJiIrl274unTp7C2tsb169fRrFkzrsMiRCZDybXaZoz5Niu/GGuPXTaK+09jHA/Xp585OzsbvXr3xqWLFwEAE5dswqZ5k5X+XFIhrWKk/Z4A0O9OSZRvlWeMuVZ4b1uZcd/AnXFSn/US7ZPMrZJ0Odfy+XxMmjQJQUFBAID58+dj6dKlHEdlWORdh2jq2syQr38MKdf+/fff2L59Oy5duoSsrCzR69WrV8dXX32FwMBA9O3bt9LHMcZcCxjGZ4QoRta9zLaYxP9v7GEwuJUT/tdZ/FwofN+h+OdISMsVvd6lUQ1cePJW9PX3PRrBlMeTea+UkpGHhLQ80fYNalrD2cFatO2WPzeL1Uf1m7MBKyIei7aXPPfLyg18AYvAnXFiz0Ea1LTB0NZOBnWeJ9zT9/NomzZtcOvWLSQkJMDFxUUrxzTGXCscQxY2SR6KfyZ2LtTl+yBieKg+iugbTedajTX8+Pv7IzQ0FG3btsWFCxdgaWkpc9vCwkJ4enoiLi4OY8aMwY4dO5Q+3pkzZ9CzZ0/4+vrixIkTFRY9syyLPn36ICIiAidPnkTPnj0BlA5+eXh44Nq1axgyZAgOHDigdBzaVHbw2MrcBNO6NhSbaaDszYGhDvwQ3bF69Wp8//33AIAePXrg6NGjGltlhBB10fcbWmtraxQUFOD27dv47LPPNH48Y821aTmFqD15h+g1ZW9ghXn4r9gkvM0tEr2uzoe1lOuNw6lTpzBo0CAUFhaiUaNGiIqKEp3HCNFV+p5rhXJyckSzMwKQmgMlZ2e0tbVV+jjGmmsravipYWOusfxJiKSHDx/C29sbr169QtWqVREeHo4OHTpwHRYhFTKUXKtNxp5vjeGhrDEUS0uOA1xPzkDUo4/FQbr6M2dmZqJnz56Ii4sDj8dDcHAw/P39uQ7LoOlTM5guo3yrHGPNtVn5xRi2JqxSk1bQ36zukLyesDI3QX4RX/S1rubakpISjB07Fnv27AEAfPfdd1izZg1N0Khm8v5WNXU9asjnCEPNtdnZ2cjJyYGtrS3s7OzUtl9jzbWA4X1GiGzSznkA5J4HZTXtSuZyB2szZOQVy9xXRc2/rd9fxNEtKwB8rI+ysKxS4fN6WbmhouMY0nmecE/fz6MbNmzAt99+izVr1uC7777T+PGMNddKjiFTLRLhCtVHEX2k6VzL08heAcTExIBhGCxdurTCZh8AsLCwwJIlS8CyLGJiYlQ63vr168EwDObNmyd3wEq4Hcuy+OOPP0Svm5iYYM6cOWBZFteuXVMpDq7kF/EhYAWY69sEPk1rYq5vE9HypBNDbwAAto5pg4mebpR0iVqxLIslS5aImn369++Pv//+m5p9CNECCwsLAEC9evW0cjxjzbUCQWkBt5ujFeb6NhHN7sMXsPjzQiJ81kbDZ+0F/HkhAXxB+T7qoNgkLA9/JFasDJQugasuwmNEPkzD8vBHCIpNUtu+iW44duwYBgwYgMLCQrRo0QIxMTF0M0uIFtna2uLUqVM4duwY+vTpg2rVqoFlWdF/1apVQ9++ffH333/j5MmTKjX7AMabayvyWZ2qYl+rM38SUtadO3fg6emJV69ewcHBAefOnaNmH0I4UFRUhPHjxyMgIEBjxzD2fBuXksl1CBoneb0g7fqBL2CxNToRgTvjsDU6Uer9vC6THAeQDF8Xr5nS09PRtWtXxMXFwdTUFPv27aNmHy2Q/Js3hnOAsTt37pxa/1OFMefaZ1kfxL5W9m8u0MNV7FmvcCyaaJ9kLm3nohvXFxUdo6ioCCNGjBA1+8ybN4+afTREXn5V5HpUE8cl3HF1dYWbmxtOnjwp9rqdnR3q1Kmj1mYfwLhzLTEe0s55kq/9FZtULh/KOjdWMTMR+/pDsUDs60Pxz8Xya9nrsgY1S1cTYFkW7y7tEzX7lK2PMuExmOjphiB/d6m1ebJyQ0XncjrPE/LRpEmT0LFjRyxatAhXr17V+PGMPddeSy49/8g7txGiCVQfRYh0ppra8evXrwEArVq1Umj71q1bi71PWfHx8QCApk2bKrS9cDvh+4Tatm0LAEhLSyv3Hl137NYLRM7sIuruLzsLgHCWAC46/6nT13CxLIu5c+di5cqVAIBhw4Zh9+7dMDMz4zgyQoxDs2bNcOXKFbx48QJVq1aV/4ZKMvZcyzCMWB4Nik0SW1lvZcRj8CS2AcoPxNWwMccED1e1PqyVNuBJs/0Yjr1798LPzw98Ph+tWrXC6dOnUaNGDa7DIsQo9e/fH/379wegmdkZjT3XAoCDtTk+c6oKEwZo6+IgWjK+7P0kIeoWFxeHHj16ICsrC7Vq1UJkZCRatGjBdViEGKXi4mLs2LEDDMNg+/btGjmGsedbXWwEUbdxnVxwNSkDd1+8x2d1qmJcJ5dy2wgbZgBux85VJTkOwGMYzPVtIvOaiesx+levXsHb2xsPHz6Eubk5Dh48KLquJprl7mwvNou0MZwDjJ2Pj4/aivsZhkFJSYnS7zPWXFtYzEdCWq7Ya8r+zQmLqfQpJxkqYS4V5k5FxiekXV8EeriqNQfLuoYpKCjAkCFDRM0Gv/zyC+bNm6fycUjFJPMrX8AicGec6N9Y8vOjrvEsyuu668WLFygpKUHLli21cjxjzbXEuEie81rXL13dtqy3uUWivCi8fpJ8X4Oa1hjaui74Aha/nX4ser2tiz0ulFmZMSEtFwlpuWL5Vfjf1uhE/HrqId5F70T2tcMAlK+PkpUbJOOV/B0QQkqZm5sjLCwM48ePh6enJ0aMGIHu3bujdu3aMDExqfC9nTt3Vvp4xp5rk9NzuA6BGCmqjyJENo01/FhaWqKoqAh5eXlwcHCQu31eXh6Aj6sVKCs7OxsAkJmZierVq8vdPjMzU+x9QtbW1gAg90JAF2XlFYMvYEWDhLpS/KvvD0+JbDdu3MBvv/0GAPD398f27dv18m+HEH01YcIEXL58GXv27MGyZcs0fjzKteIP4aTNqHM9OaNcjpMcpJvg4ar2PEgPeQxXVlYWpk6dCj6fj/bt2yM8PBzVqlXjOixCjIqrqysYhsGGDRvQu3dv0et2dnZqn5mRci3wZb1qCPJ3F3uNip2IJrEsi8mTJyMrKwt16tRBVFQUGjduzHVYhBANMtZ8a2FmYjQrBYRcSsb5/y/aOf84HSGXkuVOzqFvE2dIjgO0dbGv8JqJ6zH6RYsW4eHDh7C0tMTx48fRo0cPrR3b2Gmq4JjoNpbldtUyY821Qg1qWsPZwZr+5vSctOYreeMTslZfUWcOlnUNs2vXLlGzz9q1a/Hdd9+pfAwiX9n8yhewomtPaUXimjounWN0yyeffILnz5+rXO+kLGPPtcQ4SJ7zBOzH860kYSNQXEomWte3xw89GyM+NUus2VZYTyetmTclIw8JaXmi/UmOEQR6uCLl0b9YseoIAMDPzx/BwdLro2RNuCGrsbvsz9m6fnUADOJT6TxPiDTm5ub4/PPPcfLkSezevRu7d++W+x5VJ7Iw9lybkvGh3GtcTyhEDB/VRxFSMY01/Li6uuL27dsICwvD1KlT5W4fFhYmep8q6tati4SEBOzduxc///yz3O337t0LAOWW+nr16hUAwNHRUaU4uJSRV4Sg2CSZsxYoU/yrzgSt7w9PiWzu7u7YunUrbt26hY0bN4LH43EdEiFGxd/fHydOnMCqVavw2WefYcSIERo9nrHn2kFf1hHLj2WXBhcSsOVzqHAmYckHMOrMtfSQx3BVr14dYWFhWL58Ofbv3w9bW1uuQyLE6GhzdkZjz7WAZptWaSCaSMMwDI4ePYqxY8di+/btcHEpvwoEIcSwGGu+rW5lZjRjsoqMR+v7xBnKjgNwPUb/+++/48WLF5g9eza6dOmiteMSWi3EGJ0/f17l9yYlJeHXX39FUlJSpZqGjDXXCg1tXZf+5oyUtOsLdedgacfgC1gIGnVFU++h+PLzzzB9xrcq758opmx+DdwZJ/Y9TV5nUV7XXV999RX279+PW7duoVu3bho/nrHnWmIcJM95kufbsgSseIPtXN8m5SYWk3YOFd5Lp2TkiW0rOUZgwmOwfOJAuPLk10cpO+GG9HM7necJkZSdnQ0fHx/Ex8drZZILY8+1kr9h4YqW0hrdCVEXqo8ipGIaa/jp1asXbt26hZ9//hkdO3bEl19+KXPbO3fuYMGCBWAYBn369FHpeP3798fq1avx66+/omHDhhUWPR88eBDLli0DwzAYMGCA2PcuXrwIAHpb4FF2AKkyxb/qnPFP3x+eEnElJSXg8Xiim9cJEyZwHBEhxmvXrl2ifPv1119j48aN8PX1VWjJWj8/P6WPZ+y5ls+yYvkRAByszZGRVyT6mscwMnOoZB5VZ66lhzyGp6ioCObm5gBKHxQJZ2ckhGifNmdnNNZca2rCoEFNawxu5aTRplWuZ7YnuqVsrq1Xrx7OnTvHcUSEEG0x1nwLGE/zqyLj0fo+cYay4wBcjNGXzbVWVlY4ceKExo9JCAE8PT2Vfs/Lly+xdOlShISEoLi4GCzLomrVqpg5c6ZKMRhrrjWm1fSIdLKuL9SZg4X7vJ6cieLiIlxPzsDVpIzSwrs2/rgEiE0QqgxjuVZUN6qFIAAwffp0HDx4EEuWLIGXlxdMTTVWigXAeHOtLqNzqOZJnm/dHK3x/kMxPqtTFQwj/rtWtPlS8tl/g5o2GNr643MKVeqjuJ5wQ53oc010ycqVK3Hjxg0AwNdff40RI0bAzc0NVapU0cjxjD3XVjHjlZsQWXKVNX0+vxHdQvVRhCiGYTXU8pqZmYmGDRvi3bt3MDc3x//+9z8MHToUzZo1g62tLXJzc3H//n0cPnwY27ZtQ0FBAezt7fHkyRPY2ys/CJKVlYUWLVrg1atXYBgGbdu2Rf/+/dG4cWPY2NggLy8Pjx8/xt9//41r166BZVl8+umnuHfvntiyex06dMC1a9ewfPly/PDDD+r8laidk5MTXr8vgNPUnaLX5vo2UUsiDdwZJ3aj5NO0ZrnZDxRFNwCGo6ioCKNGjYK9vT22bt1a7qaZEH0jnFnh+fPnHEeiGh6Pp9LfoapL1hp7rrW3MkOr+tXF8mODmtZiy3vP9W2CuJRMhXKoOnIt5VjDw7Isli5dirNnzyIiIkK0xDMh+krfcy1QOmi8f/9+REREaHx2RmPNtVn5xVh77LLG85g673OJfjt27Bi+//57REZGwtnZmetwCKkUQ8i1ZeXl5cHW1hYMw4DP52vkGMaabwHg5z3RYoUsXo0dEeTvbnD3kXSvXJ62fyf//vsv+vfvjx07dqjUfED0kyH/7RlavhVKS0vDr7/+im3btqGwsBAsy8La2hrTp0/H999/j2rVqqm0X2PNtXmFJRi06h8I2NKJodq6GNbfgT7QxfOQOmMqu6+8d5k48usU2LbsCdsve4ltp+p4x9boRLFrRXXVHRg6Xfzc6SNDyLXbtm3DN998g44dO2L16tVo06aNxo5lrLkW0N3PCJ1DNa+iwnOvxo5iX5dOMFYXAIv41CyZ5+eKnhmUrY/a/OcWbL+YrNC53pA+C4b0sxDdP4/K06hRIyQmJmLu3Ln45ZdfNH48Y821wvqob30aoIqZqdg5QBKdE0hlUX0UMTSazrUam1bC3t4ex44dQ58+fZCbm4uNGzdi48aNUrdlWRY2NjY4fvy4Ss0+QOlyXufOnUOfPn2QmJiI69ev4/r16zKP5+bmhrCwMLEEm5WVhSFDhmDIkCEYNmyYSnFwyauxo9pmjVLnTDS06oBhKCgowNChQ0UzMQ4ZMgTdu3fnOCpCiCp9u6r2+hp7rs3KLy63nPfgVk7gMYzcGfukPfBRR66llQoMC8uy+Omnn7BixQoAwIYNGzB37lyOoyKEaHN2RmPNtYXFfCwPfwQBy2JylwYaOw7NuEoAYN++fRgzZgz4fD5mz56Nw4cPcx0SIUTLjDXfAuVnmT3/OF3lGeB1GY1Hl6fN38mNGzfQo0cPZGZmIjAwEA8fPtT4DOdEN9A4lf7IzMzEb7/9hk2bNiE/Px8sy8LS0hKTJ0/G3LlzUaNGjUrt31hzbU5BCaIefSw0jXpEfwfapivnIWnPA+TFoUjTiPDnK8nNRNr++SjOeIqsc0Go0qAtTG0//t2qOt6hrhUJjK0Bhq49CQB07doVAODg4IDY2Fi0a9cODg4OcHV1hZWVlcz3MQyDqKgopY9nrLlWlxnSqi66quz5NnBnnNj3eExp4fmh+OdISMtFQloeVkZ8LFKXdV0g65mBZH1UlUYd8fdbxwr3JaTvKwqXRZ9rokuExdOBgYFaOZ6x59qbqVkwNzWR+X111ikb2/0DKUX1UYQoT6NPWTp37oxbt25h9uzZCAsLg0AgKLeNiYkJ+vXrh1WrVsHVtXJJoHHjxrh79y42btyIHTt24MGDB+W2adq0KcaOHYtvvvmm3JJ+1atXx6xZsyoVA1eqmPHUOhujIjcglGyNR15eHgYMGIDIyEgAwJo1a6jZhxAdIC2vapox51oWEK3mU3Y5b+HgopC0HCrtQaM6BvtokM1wsCyLb7/9Fhs2bAAA+Pn54fvvvxfbhq69COFGu3btsGnTJnzzzTfw8fHR+OyMxpxrj9x8rtGGn3GdXHA1KQN3X7zHZ3WqYlwnF40di+imkJAQBAQEgGVZtG/fHkFBQVyHRIhR2rVrl8zvFRYWiv4/NDRU5oQVfn5+lYrBWPOtZCELQPeR+kbX7wsvX74MX19fZGdno06dOjhx4gQ1++g5ZT5zNE6l+3JycrB69WqsX78eOTk5YFkW5ubmCAgIwPz58/Hpp5+q7VjGmmsl0d+BdmnrPMQXsNgWk4gjN58DYDC4lRP+1/nj+VGVxiNF3hOXkomS7DS82T8PJVmvwJiaw3HgPJja1oBXY0eY8JhKFRara6IUXWm8IkSbLly4AIZhxO5h3759i7dv31b4PoZR/Vqecq1uocmmtEvy9y1gS5+9x6VkIiEtV+p7pF0XSHteL60+6oFDc+BtWoX7EjKkRlD6XBNd4uDggJcvX8LOzk5rxzTmXHs5MRPf92gsdg6QvOeQNj6kytilvPsHXR8PJcpTpD6KEFKexp+0uLm54dixY0hPT8fly5eRmpqKnJwc2NrawtnZGR07dqz0TFFlWVpaYvbs2Zg9ezbevXuH1NRU5ObmwsbGBvXq1RProjUkDMOoNZEpcgNCg3XGIScnB71790ZsbCwAYPPmzZg8eTLHURFCuGSsubas+valN+oTQ2+Uu6GUlkNlPWis7GAfDbIZBoFAgMmTJ2Pbtm0AgIkTJ2Lz5s3g8Xhi29G1FyHc0PbsjIDx5tqsvGLwBaUPxTUxcBtyKRnnH5fOtnz+cTpCLiXTedSIbN68GVOnTgVQOkHNiRMnYGtry3FUhBinsWPHyi1qYlkWY8eOlfn9yjb8AMaZbwM9XHE1KUOUDwG6j9Q3unxfeP78efTt2xd5eXlwdnZGVFRUpSd5I9xT5jNH41S6Kz8/H+vXr8eaNWuQlZUFlmVhYmICPz8/LFiwAPXr19fIcY0x10pS9u+ACpkqR1vnoaDYJKyMeCz6emXEI/CYj+dHVRqPFHmPs3kuXu/5EfzsNDBmluj+3Vo4NWujts+KulYkoAZQYoz8/Pwq1byjKsq1usOQVnXRZcJrpevJmXBztEZieulkncIVjKVNdCLUur49tkYnlrvOKvu8XlZ91NboRKO816HPNdElHh4eOHDgAO7du4fOnTtr7bjGmmsFAlbqOaCyzTvSyLt/0OXxUKI8ReujCCHlaazhZ8mSJQCA3r17o3Xr1nB0dET//v01dTipqlWrhmrVqmn1mFyxNOWBL2C1OuhLg3WGLysrC76+vrh27Rp4PB62b99eYbEFIcT4GFOuLSslIx9Rj0oLpBS5oZQcXOQLWLXkbRpk038lJSUYP348QkNDAQAzZszA77//LvXBEF17EcINLmZnLMuYcm1GXhECd8bhWdYH0Sx8kQ/TcDUpQ+6MUYqg86jxWrt2rWgWtW7duuH48eMVNuwRQjSrc+fOMvMkn8/HxYsXwTCMVh/cGku+NeExCPJ3L1dITLRDHUXcktczf8UmAQDnBeEREREYOHAgCgoK0LBhQ0RFRaFu3bqcxUPUR5lraBqn0j2FhYXYtGkTVq5cibdv34JlWfB4PIwYMQKLFy9GgwaaW2FVkjHkWltLU3g3cYSABXgMg7Yuyv8dUCFTecrkT02eh8rGkZKRX+77Zc+PqjQeyXvPo0eP8Ofs0eBnp8GsijW+XRWC5ZOHaH1CUEVQAygxRjt27OA6BKPItbrMkFZ10WVlr5UkxaVkYuuYNqL/b13fHgCL+NQsuDvbQ8CyWB5e2rAr7TqrovooY73Xoc810SWzZ8/GkSNHsHTpUpw5c4aTRlujyrX//+uVPAeUbbwUsCx4DNDG2R4Ag/jU8vdKijyLlXf/QM93DYcy9VGEkPI01vCzePFiAMCgQYM0dQhSRmZ+MYJik7SazGiwzvCNGzcO165dg4mJCXbv3o0RI0ZwHRIhRI7k5GSkp6ejoKBAq8VRxsDK3AQd3RzAF7BisyEDFd9Q8gUsBCzgYG2OjLwiAB9nGaps3jaWQTZDntlyxYoVopvZuXPnYtmyZTJvZunaixBucDU7o7GSzLFlX6tssROdR43TqVOnRM0+ffv2xcGDB2FpaclxVIQYtwsXLsj8Xm5uLuzs7ACUrhZC1M9Y7iN1kTqKuCWvZ97mFon2ydW/6dOnT9G/f38UFRWhefPmiIyMxCeffMJJLET9lLmGpvOL7igpKcHWrVuxfPlyvHr1SjSBxcCBA7FkyRI0b96c4wgNk7WFKbaPbVupfVAhU3nK5E9NnIeEY9OH4p+LJieRpuz5UZGiYMkx73GdXGS+p6ioCL169cLLly9hb2+PM2fOoHXr1ur6EaWqzJi8sRZFE0IIFwz5Gao0ktdKZbk721d4LRC4M67cvv6PvTMPi6p8+/j3zLAogyKrJiirYZaVIGgZouJapqZimruiZnvpm5m2WWnarzQzDcPcU0PNMtdAVDJXtNIUlE3BDRgQBVRg5rx/jDPMObMPs8/9ua6uZM5zznlmYM73ue/nXpTHKcdHTXj/K/zJPIr7h/LUdgIiCMLyREVF4ccff0RSUhIGDRqEJUuWIDycvpPmQsrKbLGkuDCOzkhZWYdTZeTFktWhz16sLvuB9ncdB0PiowiCUMVsCT++vr4Qi8UIDAw01y00IpFI8PvvvyMzMxOFhYW4c+cOmjVrhpCQEMTFxWHgwIEQCoUWn5e5sbTTl5x1js///vc//PPPP1i8eDGGDBli7ekQBKGBkpISfP755/jpp59QXi5zcjEMg/r6esWYixcvYubMmXB3d8emTZvg4tK4JYAzam2XUB+kjI/B5DUnVI5pMyhTMvNVDF6ANmsNwZErW77xxhvYuXMnnn/+ecydO1frWFp7EYR1sFZ1RmfUWn1ojH7Sc9Q56d+/P8aOHYt79+5hw4YNcHNzs/aUCILQgqU3d0hvCUtiiiBu+frlh8x8lFXVNupapqJt27ZYuHAh1q1bh/3798PPz88q8yDMA62h7Y9Vq1bhs88+w5UrVxSJPgMGDMBnn32GTp06WXw+pLWGQYFMqlg7CUpTJ4Fwf48Ha1cGw6KCOM9HfYKCNfm81Z3j5uaG5ORkJCUlYefOnXj88ccb+a5U4QeMS1kWC/dq7oKgDQqKNgxnC9YnTA9prXPjyHuo6uCvlXpG+kMoYFRsFXXPVl3rLHl8VP/Js7Cnqg1wocQpPlOCsBd69eoFQBaTvHv3buzevRthYWFo3bq1Vp1jGAbp6emNurezaq3cFlPWmYgAT53n+YrcwDBAx0AvRWEDbeiyH8g35TgYEh9FEIQqZkv4ad++PY4cOYLi4mJ4e3ub6zYq7NixA6+99hquX7+u9vjixYvRunVrLFu2DIMHD7bYvCyBpZ2+5KxzfCIiIpCdnQ13d3drT4UgCA1kZWXh+eefx82bNxUbuOp4+OGHcenSJVy8eBF79uzB888/b/Q9nU1rGQYI9xdh+WhZxTwp72MO9xdpNSg1VRqKDvZG8qE82sTRA2tv6pqT5s2b4/Dhw3ppLa29CMJ5cDat1UREgAhtvD04XX/kdq8xwRD0HHVOBAIBfvzxRwBodNI7QRCOBektYWmMCeJWt+aRr2WUg4+tFRAun985726YtLAvvH18rTIPwnzQGtr+mDJlChiGAcuyiIiIwIcffoinn34aAJCfn2/w9cLCjA+kcTatraipQ7JSFXhjoEAmGcr6J+E55C2lefI5/JCp/nszonPbRj0bNfm8ld97dLAPABZZlysQExKGC9k58Ghqno61/IDxiACR2vkRpsfZgvWdgdraWuzfvx8nT55EaWkp7t+/j1WrVimO19XV4c6dOxAKhfDy8mrUvZxNawlVbH0P1dRJjerWSuqup+7ZqmudJY+PenXzv4CS7Z6aVUz7+ARhAxw8eFBh68rJy8tDXl6e1vMaW2DKmbU2JsRHTbyT5rg0OeJqWZGijJxSrD5S0GhdIt+U42BIfBRBEKqYLdLhpZdewp9//ol169bhyy+/NNdtOCxfvhyvv/46AIBlWbi5uSEiIgLNmjVDVVUVLl26hNraWly9ehVDhw7FsmXLMH36dIvMzWwwgIerEK/0DHdapy9hOvLz8/Hyyy9jzZo1aN26NQCQwBKEDXPr1i0MHDgQN2/eRLt27TBnzhx06NABsbGxasePGDECn376KXbv3m10wo8zai3LAnml1VjzVwGm94iAgOcQCPEVaXXw8QN6IgJESIxuAykL2sTRE0eqbHnnzh2MHj0ac+bMQZcuXQCQ1hIEwcUZtZZPRIAnhkUFAWBxqrACPSP9IWCA2FBfhd1LwRCEJqRSKV577TV07doV48aNA0CJPgRBqEJ6S/CxRGV1Y4K4Na15rB0Q/v333+PSpUto9/x0fKGo/g+4urlbZE1GlfAJQjcMwyAvLw/jx49v1DWUu8gbgjNq7f06ieKZbeyz0B4CmSzxDOZ31dFUvd+caOrsI/fvN3Yemnze8vveu/IvfsraCf/n/w+Mi5vZfR+qgXzc36k9++RtHVsP1icMY+3atXjvvfdQUiL7zrIsC4ZhOAk/V69eRbt27SAQCHDlyhW0bNnSqHs5o9YSqtj6Hqqp/fj6rpU0PVuVz9UUH8X/THNLqpCSmU/PZoKwMuPGjbN4d3hn1tpwPw+FzaP8TBwWFYQTBeWcYo09HvZDlzA/ZF0uR6G4Grkl1Ypjxqxtye/nOFB8FEGYFrNFPEybNg2pqalYsmQJ2rVrh6lTp5rrVgCAnJwcvPnmm2BZFqGhoViwYAGGDBkCNzc3xZi6ujrs2LED77//PvLy8vDmm2+iV69eiIyMNOvczAoL1NRJkHW5goSNaBQ5OTlISEjA1atXMWzYMPz1118WXygTBGEYixcvxs2bN/HII4/g6NGjaN68OaqrqzWOj4+Px6effoqTJ08adT+n1doH/JCZj6ndwxEb6oP07AaDNjZUu+NUU6WhpLXc3wNt4mjG2oFMpuLWrVsYMGAAjh07hr/++gt5eXmNrt5GEIT5qaqqwvbt28EwDEaPHg2BQKBxrEQiwU8//QSWZTF8+HB4eHgYdC9n11o5Ib4eEDDAgj05itd6RvorNumS4sIoGIJQS319PSZPnox169YhOTkZHTt2RKdOnaw9LYIgbAzSW0IdlkgmNiaIW9Oax5oB4V9//TVmzJgBAIirFAF+XVTmZ24o+ZsgtKOtE7wlcHatPVHg2PapJZ7BfP0TChikjI8x6T0MnYOHmxCtWzTFsKggkwScafJ5nywsx938LJT+8jnY+lpUZKyCT5/pimPm+tviBzcPiwqCgLF/n7w9YOvB+oT+fP755/jwww/BsixEIhHat2+PrKwslXEhISHo27cv9u7di61bt+LVV181+F7OrrVEA7a+h6qPH19dYDeARgV763q2aouPSooLQ2pWESdgXd7xj4LOCcJ6rFmzxqL3c3atHRHTVhbXpEZnpnYP1/CMDkfyobxGdyUnv59jQPFRBGF6zJbws379erz44ou4dOkSpk+fjqVLl+LZZ59FWFiYzqAneQVWQ1iyZAkkEgkiIyNx5MgR+PioioWrqysSExORkJCAbt264eLFi/jmm2+wfPlyg+9naxwvkLU0J8OCMIazZ8+id+/eKCkpgbe3N7799ltK9iEIO+C3334DwzCYN28emjdvrnP8ww8/DAAoKCgw6n7OrrXl1XVYeTgPU7vLDElNjlN1Tkl1QTi0iaM/9lDZUhdlZWXo27cvzpw5A6FQiG+//ZaMWYKwE7Zv346JEyeid+/eGDt2rNaxQqEQGzduxB9//AFXV1eMGjXKoHs5u9bKUdciXl4pSq6djdVRqg7leNTV1WH06NFITU0FALz77rt48sknrTspgiBsEtJbQh22mkxsSt+BKdY/n332GT744AMAQMeuPSFs9wxQUWeS+RmCrf6+CMIWWL16tbWn4PRa+3fRLSQfynNYO9PQZ7Ax+mMLvnP+HGpqJcgtqcLCvdkQMI0PONPk83YtzkLJ9k8BST1cfdui+VMvcuZkLjQVLiN9NT+2HqxP6MfJkyfxwQcfgGEYvPvuu/joo48gkUg07t8OGTIEe/bsQXp6ulEJP86utUQDtr6Hqo+mqwvsBmB0sLdEykLKAhEBngBYRbKu/Ngna/fgy7fG4t7tcrXxUUIBg8ToNpyA9bKq2kZ3ciQIwr5wZq0VMMCRSyWY2C1Ure2mTXtMsbYlv5/9Q/FRBGEezJbwM2HCBM6C+MKFC7hw4YLO8xiGMSrhJz09HQzDYP78+WoFVhkfHx98/vnnGD58ONLT0w2+ly1SUyuhFqKEUZw+fRp9+vRBeXk5AgIC8Mcff+Dxxx+39rQIgtCD/HxZJZlnnnlGr/Fyp/KdO3eMup+zay0AfHsgFwKGUSTxqNus1LfaBG3iOA83btxA79698d9//8HV1RVbtmzBCy+8YO1pEQShJ9u3bwcAvZN3XnrpJezfvx+pqakGJ/yQ1gI+Hq5qW8Qrk5pVjJ2vPYNj+WKcvVqJjoFemNgt1KD7UHUox+LevXsYMWIEdu7cCQCYN28e5s6dS4UsCIJQC+mt8ThywqwtBBarw5S+g8asf1iWxdy5czF//nwAQFR8f5TFTMPtB8k+EQGeSIwOsphvw1Z/XwRhC4wfP97aU3B6rRVXO3ZAqKHPYGP0xxZ858pzKBRXc6r8myvgbMuWLUj5+HVAUg+ftg9j1jfr0ayFD7IuV5j9c7D1gHFHhj57x2Dp0qUAgDFjxuCLL74AAFRXV2sc37lzZwDAuXPnjLqfs2stYT/oo+nqArvVjdH3OZmSmY+FexuSdQQMo/BdfLjqN3zxxlhI792BwKMFXv3fesX3EQBq66WYtv4U/i2+hXB/ESrv1qGsqlZlHo7sHyEIQoYza62UBQ7nluOpBel4PMhLpSijtuexKda25Pezbyg+iiDMh9kSfgDjWsYb22b+6tWrAPQPepaPk5/nCHyTdgnjngpBUzehtadC2AlHjx7FgAEDUFlZidatWyM9PR3t27e39rQIgtCTujpZUIVyy1htyBN9RCKRUfcjrZUl2C7Yk43UrCIkRreBRMpi0b4cADLjVsoCWZf1qzZBmzjOQVFRERISEnDp0iU0adIEv/zyC/r372/taREEYQDnz58HADz11FN6je/atSsA6FXwgg9pLcAwMo2c2C1UkdDj1dQVeaUNG+S5JVV4ZWOWwsGckVOKVX/mQ8Awem+wUXUox6GmpgYvvPAC9u/fDwD48ssvMXPmTCvPiiAIYxGJRJBKpWa9B+mt8ThywqwtBBarw5S+A2PXPyzL4p133sGSJUsAyIIYXXu+ggMXG64X4uth0b8FW/19EQQhg7RWhqPamYY+g43RH1vwnSvPIflQHqfKvzkCztauXYtJkyZBKpUiNjYWe/fuhbe3t8nvQxCEeTh8+DAYhsEbb7yh1/igoCAAwPXr1426H2ktYS/wNV0iZZF8KI+zjtAU2G1ssDd/7XGiQAwA+D3tIPb+701I71VD6OmDliM/x3VhAGfstPWnFPsO4uo6hPuLOAk/8nk4sn+EIOwFsViMo0ePorCwEHfu3EGzZs0QEhKCp59+WmeCjj6Q1sqKWcifiXJ+yJQVpjZnoiP5/ewXio8iCPNitoQfc2/M8hEKZUku9fX1eo2XSCQAAIFAYLY5WZqaOgkGfpuJ9Bk9rD0Vwg44d+4c+vTpg+rqagQHByM9PR3h4WSAEoQ90apVK1y+fBl5eXmcyjOaOH36NACgbdu2Rt2PtLaB3JJqLNiTDVch14BNycxHUlwoVZsgAAC3b99G9+7dUVhYCJFIhN9++w29evWy9rQIgjAQuWO2VatWeo1v2bIlAODatWsG34u0FpBKZUVAVh8pUDiRy6pq4Styhbi6TjHu7NVKznnbThcrqu3qs8FG1aEcA5ZlOck+y5Ytw6uvvmrlWREEYeuQ3hqPIyfM2kJgsanQVGnY2PXPnDlzFMk+U6ZMwffff48fMgs4CT+WXks50u+LIBwRZ9VaF56vuE7CQiJlHa7au6HPYEewv80dcLZ161ZMmDABABAXF4fff/8dzZs3N+k9CIIwLzdv3gQAveMtXF1dAQC1tbU6RqrHWbWWsH/4iTLSB0XBw/09cKumHt4ebpCywORnQgEYp738tYeUBT5Ztxc31s8EW3cPwuYBaDnyc7h6P6SyLuHvO9yqqcXsAe1V5uHI/hGCsHUuX76M//u//8OOHTsU+qaMUCjE0KFDsWjRIqNjo+TXAUhr+ZRVmb+jLfn97BOKjyII8+MwCiMX6LS0NL3Gy8c1RthtkaLyGmtPgbAT2rdvjwEDBiAiIgKHDx+mZB+CsEO6desGANi0aZNe45OTk8EwDOLj4426H2mtKnUSbmdCcXUtAAazB7RH70cCMHtAe6o24cQ0b94cSUlJaN68Ofbt20fGLEHYKfLN16qqKr3Gy8cZUwSDtBa4fa8eSWtP4udTRZzXW3hwOxp2DPTinckNoOJvuPFJigsjvXYAGIbBa6+9Bnd3d6xatYqSfQiC0AvSW+PhB8LYW8CuvJJw0tqTSD6UB4mU1X2SFa7ZWOQBVGkXSrBgTzZSlCpvGrP+GTNmDPz8/PDGG28gOTkZAoGA1lIEQWjFWbWWLwGHLpYiae1Jm9IIa+AImiEPOEsZH4Np8eEmT+Lq1asXnnjiCfTu3Rt79uyhZB+CsEM8PDwAyDpR64O8s4+xnbycVWvtEVu0Ga0J32+/7XQxFu7NQV5pDcTVtcgtrcLCvdlYfaTAaO3lrz0EDANX3zZoGhYNF++H0Gr0F3D1fgjh/iJM7BbKOZe/7/B4UAu187B3/whB2CtHjx7Fk08+iW3btqG+vh4sy6r8V19fj9TUVDzxxBM4duyY0fcirW2gZ6Q//Dy5+7S69mEJ54PiowjC/DhMwk+fPn3Asizef/99FBUVaR1bXFyMOXPmgGEY9OvXz0IztAwuAgEZiYReuLi4YOPGjThy5IhDLjYJwhlISkoCy7JYtmwZ/vjjD61jFy5ciN27dwMApk6datT9SGv1Y9WfskCa5LGdzbL5R9gXc+bMwfnz5xUJegRB2B9BQUEAoLdTWD6udevWBt+LtBaQsLLKfnml1ZzX2/h4YPaA9kho74+ekf5gIHMwJ7SXbdoNiwrijNe1wWbuYB3Ccjz//PPIy8vDpEmTrD0VgiDsBGfV24qaukb7je09YFdTIoytXdPQgDD++BMFYs5x+Qa8seufDh064O+//8aSJUvAMEyjrmUsFCRHEPaFs2qtVM2zKSOn1KQaYY+YUzMcRR98fHyQlpaGnTt3QiQSWWUOjvJZEoS1CAuT2UanTp3Sa3x6ejoA4LHHHjPqfs6qtfaIOWxGe0bVb69+XdCYQHL+2iM21AeMQAi/52ei1egv4dI8AACQV1qN1UcKADToIMMwCPcXwVfkip6R/kge21ntPezdP0IQ9khFRQUGDRqEyspKuLi44OWXX0ZGRgZKSkpw9+5dlJaWIiMjA9OnT4eLiwsqKysxePBg3Lp1y6j7kdYCEQGemD2gPVLGx2AK7zlHiY6EOig+iiDMi9kSfgQCAVxcXPSuYCGRSBTnGMM777yDJk2a4Nq1a+jUqRMWL16M4uJizpji4mIsWbIEUVFRKC4uRpMmTfD2228bdT9bpaZOotZI5Dvpauul5LRzQn7++Wds27ZN8bObmxsCAgKsOCOCIBpDfHw8xowZg7q6Ojz77LMYP348tm7dqji+e/duLF26FE8//TTef/99RQV0Y53HpLX6IW9h6+wOW2fl9OnTWLhwIee1wMBAK82GIAhTEB8fD5ZlsWjRIrWt4ZWRSCRYtGgRGIZBjx49DL4Xaa1mhAzzYHPOFxk5pTiQU4qMnFLEhvpgWnw4pnanDTZn4caNG5gxYwbq6uoUr5HWEoTjcefOHZw6dQpZWVkoLS016bWdVW/va/AbG4K9J8zyA4Y0BRAZEvCq7zUNQd+AMPk8+y05xBnPn66hG/D379/HO++8g7KyMsVrgYGBimSfxmJMQDEFyRGEfeGsWqsLqoDcgKmSS+xVH1iWxfz58/Hvv/8qXvPz80OTJk2sNid7/SwJwlbo168fWJbF119/rXPs7du38dVXX4FhGDz77LNG3Y+01n4wh81ozyTFhWFW/0hEBIgQ4e8JKat+DSCRsor/jF0zyOOj5Mk57R7yhlDUgjNG/vuQ6+CBbFkxsqndw7F6YizcXNSHVcr9I/KEoGnrT1HsHUGYmW+++QZisRheXl7IzMzE8uXLER8fDz8/P7i7u8PX1xfx8fH47rvv8Oeff8LLywtlZWX45ptvjLqfs2utq4DB7jfiFD5g5UTHWf3bQ8qyFHdMUHwUQVgYs3b4YTUszE19DgC0adMG69atg0AgQHl5OWbOnIng4GA0b94cgYGB8PLyQnBwMGbMmIGysjIIhUKsW7dOUa3Z0eAbiXwn3bT1p8hp52SsW7cOo0aNwqhRo3Dw4EFrT4cgCBORkpKCESNGQCKRYMOGDZg0aZIiAOP555/H22+/jWPHjoFlWYwePVovR7MmSGtV8XATajzm7A5bZ+TYsWPo1asX3nvvPSxdutTa0yEIwkRMnz4dDMPg1KlTGDFiBCorK9WOu337NkaOHImTJ0+CYRi88sorBt+LtFYzsaGyQFW+vqZmFUMiZe0+AJnQj+LiYsTHx+Prr79GUlKStadDEIQRZGdnY8aMGXjuuecwceJETtEKACgrK8Po0aPh6+uLLl26IDY2Fq1atUJMTAz27t1rkjk4u95+k34JKw7mOuVGLD/xJTrYR23gkCEBr/xrKv9sbGCSvgFh8nnmlnA7IxaV1yAiwBMRASLM6h9pUCJ0TU0NBg0ahMWLF6Nv376ora3V+1x9MSagmILkCMK+cHatFTCAr8gN8Q/7c16nCsgNmCq5xBh9sHYnG5ZlMXPmTMyZMwe9e/fGlStXLHp/TZDWEkTjeOONNyASiZCZmYnx48ejqqpK7bjs7Gz07dsXly9fho+PD6ZMmWLU/Zxda+0JbTajMyIUMBAwDHJLqpFbWoX80gZ71kfkqvh3Rk4pUjLzjV4zKMdHZR4+hGnx4dj3Vnf0jFS/PjNWBylhliAsx86dO8EwDObNm4fY2FitY2NiYjBv3jywLIudO3cadT9n19o6KavoggZwC0EJGGDh3hx69jk5FB9FEJbHrAk/hiBP9GlMlbjhw4fj0KFDePLJJ8GyLFiWRVVVFa5fv447d+4oXuvUqRMOHz6MYcOGmWr6NgffSOQbI2evVmo9TjgWycnJGD9+PKRSKbp06YKoqChrT4kgCBPh5uaGzZs3Y/v27YiLi4OLi4tC71iWBcMweOqpp7B161asW7cOQqHmBBV9IK2V4Spk4CtyQ+fgFhrHOLvD1tk4dOgQ+vTpg8rKSrRt29boqmwEQdgejz32GGbOnAmWZbFjxw60bdsWY8eOxVdffYWVK1fiq6++wtixY9GmTRts374dgKzq0xNPPGHU/Zxda4UM4CtyRZifB3pE+iOhPbdjD19fc0uqsPJwnjWmSliYgoICdO/eHRcvXoSHhwfGjRtn7SkRBGEgqampePLJJ7FkyRLs3bsX69atw4svvoiRI0cCAKqrq9G3b19s3rwZ9fX1HNs2KysLAwcORHJysknm4sx6W1MrwcK9OU65EatciXL2gPYAWLWBOYYE+vCvqZxcY2zgj74BYZrmlVtajdySKuSWVEPAMHonQt+5cwfPPfcc9u/fDwCYNGkS3Nzc9DrXEIwJpKIgOYKwP5xZa6UsMLV7GH6cEEOdaDVgquQSfZN5lbFmYK5UKsWrr76qKMw2ePBgRfVjaycikdYSRONo2bIlfvzxRwDAhg0b8NBDD+GFF15QHE9MTERUVBQee+wxnDhxQhEU3KxZM6Pv6cxaa09osxmdFU26z7dcTxSIVcaeKBDr1EtN8VFCAYOU8dz12cRuoUg+lIdCMbeQhr46SAmzBGE58vNl6/bBgwfrNV4+Li/P+D1EZ9da+TNNImWx4mAeen99CL2/PoifT11RO45wHig+iiCsg4u1JyCnpKQEAODp6dmo6zz99NM4ffo0Tp8+jczMTFy+fBl37txBs2bNEBISgri4OHTq1MkUU7ZJwv090NZHhBMFMiFNiguDUMAgJsQHaRdKFOM6BnohI6dU8TM57RyXJUuWKNpFJiQk4Ndff4VIJLLyrAiCMDVDhgzBkCFDcPfuXeTn56OyshKenp4ICQlB8+bNTXovZ9XaJi4CBHk3RUVNHcTVtRBX1+LwJTFnTESACCG+IsSE+JDD1onYv3+/4vsXHh6O9PR0BAcHW3taBEGYkC+++AL19fVYvHgx7ty5g59++gk//fQTZ4y8iMXMmTOxaNGiRt3PWbUWACQsIK6ug7i6DvllNegZ6a+wawGZjbvycB7E1XWKc7adLsb0HhHWmjJhAS5evIiEhAQUFxejWbNm2L17N5555hlrT4sgCAO4cuUKJk6cqOgW0rRpUzAMg5qaGqSmpqJnz54oLCzE33//DXd3d4wcORIxMTFgGAYnT57Epk2bcP/+fbz55pvo1asX2rVr1+g5ObPeArKN2Gnx4daehkWRV6KUv++ktSc5x+WfCd+XzvedS6QsUjLzcbKwXGH/q/ss1QX+6POZy/0JytdXB3+egKyjhLi6oSuPvve8desWnn32WRw9ehQMw+CHH37A5MmTdZ5nDLo+X3Xo+5kQBGFbOLPWyp+/yrrjbKjTS7ltz9cCiZRF0tqTKuN0wdcHKctiwZ4cAFBcn//569Jn+bxPFIghZQEBwyA21LB5qUMikSApKQlr1qwBALz++utYsmQJBAJZfVZ5IpK2uZsT0lqCaDyJiYkQiUSYNGkSSkpKkJaWpji2fft2hf+4ZcuWWLNmDfr169foezqz1toLfDvU0dGm//LjmpJavTzcOH5/CQs8FcpdM0hZqNVL+X1XJ3+H45sWA1AfH8X/fSQfylNcDwAiAjyRGB2ktw4aY98SBGEc9+7dAwC9Yx7l4+7fv9+o+zqz1haWVSP5UB6kLLBwb7bGcfTscy4oPoogrIfZE350dexhWRbXrl3DvHnzAACRkZEmuW9UVJRTdjFhGEaRyJOe3WDc8J10E7uFYvWRAnLaOTgLFizA+++/DwB47rnnsHXrVjRp0sTKsyIIwpw0bdoUjz76qEXu5Wxae69eitzSaq1jEqPbICkuDCmZ+Zi2/pTBG5SE/bFz504MHz4ctbW1aN++PdLT09G6dWtrT4sgCBPDMAy++uorjBw5EkuWLEF6erqiaAUABAQEoE+fPnjzzTfRuXNnk93X2bRWHRk5pTJdfbABJxQw8Ba5czb+VGv/EY7EuXPn0Lt3b9y8eRPe3t7Yt28fYmJirD0tgiAM5LvvvkNNTQ08PT2RkpKC4cOHg2EY7Ny5ExMmTMCKFStw8+ZNtGjRAgcOHMCTTz6pOHf69Ol466230LNnT9y6dQvJycn43//+Z7K5OaveyoNsnNle1RSYoyvgVd+AXGMDf/QNCEuKC0NqVhFySxp8Fd4iV07Cjz73FIvF6Nu3L06fPq2oNP7SSy/pNVdjMCag2NmC5AjC0XBGrXU0ndUVvKsObXqprAUSKavY2zY00UXfZF5l+PpcKK6RdQp48J6U5y1Hec/dGOrq6jB27Fhs2bIFAPDuu+/iiy++4MRRGJsobCpIawnCNDz77LO4fPkyNm/ejLS0NGRnZyuKNIaFhaFfv34YM2YMmjZtatL7OqPWEraJLns5JTOfU5zaR+QKAcOgY6CXSpeds8WVEDIMekT6o6i8BgwDFJXXcMbI9TIlMx/vfTgPtw6vAwB07NoTv//+u874KL7+hvh6qNVCTWshSpglCMvRqlUrXLlyBX///TcSEhJ0jj9z5gwAWaKtKXBGrc0trcaCPdlo6iJQORYR4IkQXw+rPPuMsU8J00DxUQRhXUyW8CMUClVeY1nWoI49DMMgMTHRVFNyUrjiJTdu1DnpyGnn2Hz88cf45JNPAADDhg3DTz/9BDc3NyvPiiAIwvHoGemv6KYnT/YxVSU+MlRtm+3bt+PFF19EfX09Hn/8cfzxxx8ICAiw9rQIM0LfSSImJgYbN24EAFRVVeH27dto3rx5ozvVEtpJzSrmfN+GRQVxKkkNiwqy1tQIM/Pvv/+iV69eEIvF8PPzQ1paGp544glrT4sgCCNIS0sDwzB47733MGLECMXrgwYNwscff4y33noLDMNg8eLFnGQfOY8//jg++ugjvPXWWzhw4IAFZ+44NGviwun+wk+qdUY0BeboCng1pDNAz0h/TmcAUyIUMEiMbsMJSh4WFQQBw+gdbFRWVoaePXvi3LlzcHV1xaZNmzBs2DC97m+sfUQBxQRBOCrKNTAdTWeN8Xlr00tlLdAnSUdf9Em2lWujPGk2t6RK8d6mxYerzLux86qvr8eIESOwY8cOALL92w8//FClaCp1CCAIx8Hd3R3jx4/H+PHjrT0VgrA4uuxl/vHyB4W9MnJK4cqzJ8XVtYqkW03I9TJ58ReKZB+Ph59GdNJnehVD1ld/Na2FyL4lCMsRFxeHDRs24MMPP0RcXJzWGMja2lp89NFHYBgGcXFxFpylY3K3XqryWmJ0kNWefdbujuqsUHwUQVgfkyX8yNvP6vs6H6FQiDFjxuDtt9821ZScClchg2ci/HClnFvxIDqYnIHOSvv27SEQCDBy5EisXbsWLi5mb+hFEAThdPSM9EfK+BhOQIspK/GRoWrbhISEQCQSoV27dti3bx98fGjd5ejQd5JQxtPTkxJ9LERuSRUnUGpq9zAIGG4H2+RDeZSM54C0bNkSfn5+cHNzQ1paGjp06GDtKREEYSR5eXkAgCFDhqgcGzJkCN566y0AwMCBAzVeY+DAgXjrrbeQn59vjik6PCJ3F3Rq24ITyGLpyvG2hrGBOboCgvidAWYPaG+2z1ld0pL8felD8+bN0bZtW1y6dAnbtm3Dc889p/e9yT4iCILgwt8SdySdNcbnrW8ArSkTXfSpsi/XyZOF5ZwuefL3xJ+PnOhgH6N8D0KhEJGRkQCAhQsX4t133zV67gRBEIRtQ0XjdOu6Jp0FgDqpfvGF4f4eDxJnGUhZ2ece9fhjOPObAB6PxMHvuXfQNUJ7R4+GQh3lDwp1ALGhvhr119qd+AiCAF555RVs2LABx44dQ0JCApYuXYpOnTqpjPv777/x5ptv4ujRo2AYBq+++qoVZuvYhPuLrGqv0DPZOlB8FEFYH5NlAKxevZrz88SJE8EwDFasWAF3d3eN57m6usLPzw+dOnWCv7+/Xvdat25do+bKZ9y4cSa9njUQMuC0PZUj1TPhinA8Ro4ciaCgIDz11FNqO3ARBGFfhIWZzlhiGEYRbKUJ0lpVuoX74ubte8gtbdgEFAoYFUetKTcoyVC1baKionDw4EGEhobCy8vL2tMhLAB9JwlTQlqrHQEDKO/vaaoEDADJh/Io2NRBadmyJdLT03H37l1ERERYezoEQTSCmpoaAECrVq1UjrVs2RCE4evrq/Ea8mPya+kD6S0XqhxvGnQF5PLthtSsIrMFfDW2mrCbmxu2bt2Ks2fPIjY21qBzyT4iCAIgrdWGRMpCImUdItBX3zWEcqBzdLA3ZvVvj6zL2hNYTJnoYoguanpP8vufKBBDykLRqU/KsliwJweAYb4HhmGwYMECPPvss+jevbtJ5k4QhO0hEAggEAhw+/ZteHh46BwvkUjg6uoKgUCA+vp6rWNJa+0HKoqgW9eVj0ukrNo4N1209REpzlu4NxsCBkj++A14+7eC2DMYXcL8da4nDC3UQf4UgrA+Xbt2xYwZM/DVV1/hr7/+QufOnREaGooOHTqgWbNmqKqqwn///YeCggLFOTNnzkSXLl10Xpu01jDa+nhY1c6lZ7J1oPgogrA+DKtvCx4DEQgEYBgGd+7c0cugNebapoBhGJ0GtK0SFBSEG5X3EPTqWo1jwv1FSJ/Rw3KTIqyGRCLBqlWrMHnyZErwIQg9CQoKAgAUFxdbeSa6EQgEOscwDKO2sx7/dYZhIJFIdN6PtJartXHh3njm4ZYc51+4vwh73uwON5eG348pqzcpBzAD5q0KTOjHxo0b8dxzz6FFixbWngphBeg7aTj2pLWWhrRWhj52LQDM6t+e09VHWV+T1p7kOHZ7PxKAlPExZp03YT7S09MRGBiI9u3bW3sqBGEX2IvW+vn5oaKiAmfOnMHjjz/OOZafn4+IiAgwDIP//vtP4/c/JycHjzzyCLy9vSEWi/W6L+mtDPnfyeUrRU5fbdgS8O0GZWzBhrh06RIKCgrQt29fvcZr8nOY0j6iStiErWMvemsNSGtlaLJtbeG5bwr0fU7bk+/MUO0xxPdQWVmJnTt3YsyYMWaZC0E4IvautYbGR8kTfmjPVn/s4W/E1vzUjdEXS2iTRMoiae1JTtJPz0h/CAUMooO9ATDIulyO6GAfACyyLlcgJsQHJwrKkXb+Oqr+/QOej/dBn0cf0vk589/PiYJypGc3/K4iAkQI8RVpfK+k1YQjYA/PUX1YuHAhPv74Y9y/fx8AOBopj4tyd3fHJ598orG7Jh/SWhma7NqmrgLcrZMqfk5o749VEwwrHGRK6JlsOSg+iiAMw9xaa7IOP3zk2bKmTvaRY6o8JTPlO9kMt2pqrT0FwgLU1dVh7Nix2LJlC44ePYpVq1bplRxAEIT98NFHH2k8tm7dOhQUFMDV1RXx8fHo0KEDPD09UVVVhfPnz+PQoUOora1FWFgYxo4dq/c9SWu5ZOZVwNXFBT4iV5RX1wEA8kqrMW39KayeKDNmTW1YmrK6IdF4FixYgPfffx9du3bF/v370axZM2tPibAw9J10fOQd9ZS74RnbZU+fjnqktbpxEzLoFuEHKSvFwr0XATRUR0yKC0NKZj4KxdWcc6iSk/2yc+dODB8+HH5+fsjMzDRpl0uCIKzLI488gr/++gsbN25USfjZtGmT4t/btm3DnDlz1F5j69atAGBwxy/SWxkVNXVIycxHUlyYUYG3tryRamtzU7YbCsU1yC2pUhyzdhec//77D71790ZFRQV2796NXr166TyHX6X6WL74QQCWD2b1j1QEXTXGPqJK2ARh35DWasbaz31ToW/3GW3d32xNrw3tqKNvFWmxWIx+/fohKysL5eXleOONN3Rem3SQIJwPuebpG1xMWmsf2FrHgcboS2O1SR/dFwoYpIyP0TEuXHEtOU8GemLzwv+hJjsT969mo1P/5Qa/n56R/pzjuSXVyC2p1vheqRMfQdgOs2bNwqRJk7B+/XpkZmbi8uXLuHPnDpo1a4aQkBDExcVhzJgx8Pf3130xJUhrNRPo7cHxb8aG+modb27bz9Bnsq3ZovYCxUcRhO1htoSf4OBgc10aUqlU9yACAODt4ab1OAma/XP//n2MHDkSO3bsACD77pkq65wgCNtBU8LPhAkTUFBQgLFjx+Lrr7+Gr6+qYSUWi/H2229jw4YNKCgowJo1a3Tej7RWPQfUtBX/t/iW4t+m3pgj56FtwLIsPvroI3z66acAgNatW8Pd3d3KsyKsAX0nHZ/CwkIA3I1W+WuGomtNTlqrH7USFhk5pThewA0aOlEgxrF8Maf6X0SAJxKjg+wyGY9scyA1NRUvvfQS6uvr4efnB09PT2tPiSAIEzJw4EAcOXIE33zzDVq1aoXRo0eDYRikpqbi888/B8MwGDVqFD7//HN06dIFvXv35px/4MABzJ8/HwzDqBzTBultA/frJFiwJxvH8sVIGR9jsM7YciCqrc1N2W7gdzqwZsDXmTNn0LdvX5SVlcHPz0+tD0kd/OBt+for7UIJZg9ob5KK1doCxAmCsG1Ia7Wj/Nx3BrtPW6Czrem1oehTCOjmzZvo06cPzp49CxcXFwQGBup1bdJBgnA+Skpkz0F9/F+ktfaDrRWNM1ZfJFIWqVncquSGapO+us/fd5NIWSQfyuN8hsrX+uPsVbAHlqAmOxMA4OLljxOF5Tiz9iSnE5D836cKyyFlgbNXKzn3FTCyToS2WKiDIAjd+Pv745133sE777xjkuuR1monyLvpg3+xGBYl24fVZt/amu1na/OxdSg+iiBsF7Ml/EilUty6dQsA4OOjuol14sQJLFiwANnZ2fD19cVLL72E6dOnU6KCiRkW3Ubr8ZWH87Bwbw4AmaBJWRbTe+iukukMTml74O7duxg2bBj27NkDAPjiiy8wa9YsK8+KIAhLsXHjRqxbtw4jRozA2rVrNY7z9fXFunXrcP/+faxfvx4JCQkGdfohtKNcn0NfxynpqP3Asizeffdd/O9//wMAvPTSS1i7di1cXMy2jCYIwoqoS7DV1mWPsBw1tRLOz4XiGuSVcjv7hPh66HTQGqPBltBtZ3c2b9iwAePHj4dUKkXnzp2xb98+tb4kgiDsl+nTp2Pp0qW4ceMGZs6ciZkzZyqOsSyLHj164KuvvsLWrVvRr18/9OjRAzExMWAYBqdOncKBAwfAsiw8PDzw8ssvW/Gd2D8ZOaVIycw3WGdsORBV09xswfa2lYCv48ePo3///rh16xYeeughpKWloUOHDnqdyw/eVsZUfwe2VgmbIAjCGAQCBj0i/RET4o0zV26pPPedwe7TpnvmWEtYUut1FQK6evUqEhISkJOTA3d3d2zbtg3PPfecXtcmHSQIx0FXvBPLsrh27RrmzZsHAIiMjLTEtAgLYWtF44zVl5TMfE4CjCHnyjFW99Wtl+TXktbdR+mO+biXnwUAaBE/AV5dh+PQxTLOeP6/1REb6muThToIgiBsBSED9IgMgIRlcZBTGJmBUMBwnp18+9bW/Mi2Nh9bhuKjCMK2Mds3cdOmTRg3bhyio6Nx4sQJzrFjx46hZ8+eqK2tVbSxO3r0KE6cOKFX1wFCPWF+Hgj1E0HCAsXlNQADACwkUlajc3Pb6WKVn/VJ+HEGp7StU1VVhUGDBiEjIwMAsHTpUrz++utWnhVBEJbkhx9+AMMwnEApbbz77rtITU3FDz/8QAk/JuRenVShtfo6TklH7QOpVIrXX38dy5fLWsFPmjQJK1euhFAotPLMCIIwF5TwY/v4ebqhY6AXp7OPHH0qJxuiwfJrpGYVKzY4zaXbzuxs/uGHHzBt2jSwLItu3bph165d8PLysva0CIIwMc2bN8fvv/+OgQMH4vr165xjHTp0wJYtW+Dv74/k5GRMnDgRBw8exMGDBxVjWJaFUCjEypUr0aaN9gJHhG6M0RlbCETVpO+a5mYLtrctBHwdPnwYzz33HKqqqtCmTRscOHAAERG69wDkKAdvS6QsZx1mqr8DW0mMIgiCaAwiNyFcBAxcBAIkj+2ssjfrDHafNt0z5VqiwV4vQm6JrBiINf3shYWFSEhIQH5+PoRu7mg76hNsLfFHn3op3FwEOs8nHSQI+0PdPhHLsgZ1rGYYBomJiaacFkFwMFZf+GuWiACRwdqkr+7z7fwTBarrpZgQH+z/5zJKtn2K+1f+BQB4956G5tHPGzQnAPBwE+LNhHac90M6TBAEoYqEBWJDfVQ6vslii8O12re24EdWxtbmY6tQfBRB2D5mS/jZt28fWJbFSy+9pHJsxowZuH//Ppo0aYJevXqhsLAQ58+fx/r16zF69Gj06dPHXNNyWFwEwPDoNpgWHy7byHyw6Sbv3iNgGA3VjfiJQPpVPXIGp7QtU1lZieeeew5HjhwBwzBITk7GlClTrD0tgiAszLlz5wAA4eH6PX/DwmTOqfPnz5ttTs5ITa0Ek9eexKrxMRyHYHSwN6QskLT2pIr+ko7aPhKJBFOnTsWPP/4IAHj11VexdOlSCAS6N2gJgiAI8zElLkxFRwGgZ6S/XpWTDdFg5Wvoe46xOKuzeenSpXjzzTcBAL169cKvv/5qUGAEQRD2RadOnZCTk4MtW7bg77//RpMmTRAVFYURI0YoNo3Gjx8PDw8PzJs3D//99x8AwNXVFT169MAHH3yAZ555xppvwWEoFNcg+VAex07VVSHfFgJgNOm7prnZcucfS5GWloZBgwbh7t27CAsLQ3p6OkJCQgy6hnLwtrrPzhTYQmIUH2f6OyEIwjTcuVePtAslSLtQAinLqhRYdFa7T87EbqE4li/G2auV6BjohYndQo2+liXtdV16cOnSJSQkJKCoqAgu7h7wHfYhals+hoycUkxbfwqrJ8bqvIcmHSQtIgjbRV7YWN/X+QiFQowZMwZvv/22KadFEByMtbP4a5bE6DYG64++PgS+ne8rclOZS+LjvvjytS9w/8q/YBgG3y1fAbRPwLbTxaioroO4ulbvebVu0UTl87BFe5QgnJ0rV64o/t22bVuV1wxFfg3CMGRJmPy1jexnbfatLfiRlbG1+dgiFB9FEPaB2RJ+zpw5A4ZhVDZhc3NzcfToUTAMg927d6NHjx6QSqUYNGgQdu/ejdWrV1PCjxHUS4FF+3JwsrAcAl6b4G2niznVjaQsIGBkIhbk3ZTTinVYVJDW+8gdi4Xias7rzuaUtjZVVVW4du0aBAIB1q5dizFjxlh7SgRBWIG7d+8CAK5evQpvb2+d469evQoAuHfvnlnn5YwczClFSma+whnIb//Nry7o7Ju79kBdXR0KCgoAADNnzsSiRYvAMLSZShAEYSl8Ra5gGAaPBXohJsQbZ67c4jhhlXW0Z6Q/UsbHcDYdNQX4GqLB6hKLdJ1jLM7qbL506RIAYMCAAdi2bRuaNm1q5RkRBGFuPD09MXnyZK1jEhMTkZiYiMrKSty9exd+fn5wcTGbG9spyS2pUtircjtVVzccWwiA0aTvmuZmy51/LEVRURHu3r2LyMhIpKenIzAwsFHXs4W/A0vhTH8nBEGYHlnVY27Cj63afZZKKll9pEDRJS4jpxSrjxQY/Vy1pL2uSw/KyspQXl6OFi1aoNWIT3DXu+HY2auVZr03QRDWY/Xq1ZyfJ06cCIZhsGLFCri7u2s8z9XVFX5+fujUqRP8/f3NPU2CMAptaxZt6wZ1xzTplnzsD5n5nNflyTsRAZ5IjA5CUlwYbly/Bml1OSc+KvlQniIOTj5eFu/GIutyBaKDfQCw2JpVjLzShnG6YuIIgrANQkNlxQEYhkF9fT3nNUNRvgZhGFKWxbCoIEXDAQAI8vaARMpq1QpdxYMsXcTAmfyZxkLxUQRhH5htp7S0VOaw41eL++OPPwAA0dHR6NGjBwBAIBBgxowZ2L17N44fP26uKTkFGTmlCPcXcV6rqK7j/JySmc+pcNAz0h9CAaOXc5lfMUnZyCIsR2BgINLT0/Hvv/9i8ODB1p4OQRBWIjw8HP/99x+Sk5Px7bff6hy/YsUKxXmE6eFXD9TWQaCxm7u2YBQ7Ok2aNMFvv/2GzZs3Y/LkyWTMEgSBW7duYevWrTh+/DiuX7+Ou3fvaq3YyDAM0tPTLThDx0L8wI49mFOK4oq7CrtTKGDU6ihfBzUF+BqiwfxrRASIkBjdxiz2r7M6m7/55hs8+uijmDhxotZgCIIgnBMvLy94eXlZexoOjbKdag+daA0tnmFo5x9HgeMzCOuO9es3oE+f3mjZsqW1p2ZXOPrfCUEQ5kbVl2hJu88Q/7GlkkpM+VxVtdfNt1+ta95PPfUUfv/9d3h5eWHp33WKpCYA6BjYuLWssZ+Zpo58tKdAEKZj/PjxnJ8nTpwIABgzZgw8PDysMSXCAbCV/V9ta5aVh/MUgd9pF0pQL5XCRSDAycJySKSsQgflOp0UF6b2PWnq1icnxNdDcX918VHqkn+ndlf9vKZ2DzdLl1qCIMyLuv1XfbvoEabjSnk1ksd2xomCcsXznV8MWf6cn7b+lFrtoiIG9gHFRxGEfWC2hJ/yctniml99MTMzEwzDYMCAAZzXIyMjAQDXr18315ScBuXqBABU2pfyfxYKGKSMj9Hr2nyjSdnIIszLtWvXcP/+fUXGemhoqNHZ6wRBOAYjR47E3LlzsXz5cvj6+mLu3Llqqx7X19fjs88+w4oVK8AwDEaNGmWF2To+0cHeSD6Up3AYRgd7awxEUucotcVNWGfj7t27OH/+PKKjowHIqo8nJSVZeVYEQdgCGzZswOuvv47bt28D0M+pTI4w08HvQqBPkJSmAF9DAqz0SSwiDINlWRw9ehRPP/00AFkBmJdfftnKsyIIgnAO3F2F6BnpzwlCVbZT7aETraHFMwzt/OMozF6xFVuuNAHDCJB2oQSzB3SlZB8jcPS/E4IgzIu1q8cb4j+2VIKjpueqMcHNjbXXDbmnunn/888/CA8Ph6enJwAoipwmd5Ri2vpTOHu1Eh0DvZA8trPec9L33vqg7vcPgPYUCMKMyCuiU7IP0RjsYf932+lizs8//lmgKN7FR77GUH5Px/LFEAoYFIqr1Z4j50JuIbq9n4umfq0xLCoIU7uHITQ0VKHh/PNzS6rw/aE8uAgYFX13xkJbBGHvZGRk6PUaYV7ySmswbf0pCHh73so2oy7tsnRBHVtJnrUHKD6KIOwPsyX8eHh44M6dO7h58yZatGiheP3QoUMAoAjukNO0aVMAsmAPwvS4ChnUSdQHpRWKq5F8KA8Tu4Vi1Z8FDww09oHRFM4RPdrksg6XL19GQkICJBIJMjMzERREbWYJggBmzJiBzZs349y5c/j000+xcuVKDBw4EI888gg8PT1RVVWFCxcuYNeuXbhx4wYA4LHHHsM777xj5Zk7Hj4iVwAMx5Cd1b89Zg9or3cgki1uwjoTVVVVGDx4MI4fP479+/errFUJgnBe9u7di/Hjx4NlWTAMg06dOiEiIkJhwxKW40RBOaQstNqsckyxkUebgaZFKpXijTfewHfffYeUlBRMnjzZ2lMiCMIGKC8vx9GjR3H9+nVUVFSAYRj4+PigdevW6Nq1K8evTDQONyEDAcOgZ6Q/BAwQG+rLsVMb24nWEphKm+3hvRpLSkoKvnx9Kpp1ehbevV8GwzCN9hk460a5I/+dEARhHpo1cUHvRwJs4plhiP/YmL1fUyXpAMYFNzd2TWDIPfnzjsRVxMUNROfOnbFr1y6Of8jNRYDVE2O13tuQz85YLeL//n/IzEcLDzeVMeTvIAjTERwcbO0pEA6ApfZ/G2fjccfdrZNqHBkT4qPynpSLkGiivrIEJzbPActK0eqlhVhYUgUBI9NqbZ2BZMlHsgLYtpowRRCEfsTHx+v1GmF+MnJK0TPSn/Oass2oS7v4hZKjg73NNFMZ9pA8awtQfBRB2CdmS/iJiIjAmTNnsGfPHkX3nj///BPXr1+HUCjEU089xRlfUiJ7wFKlOeNxFTJo1sQF5WqqJ/CTfXpG+qOo4i5yS6qQW1KNBXuycSxfzDGuFu7NgYBhOKJHm1yWJzc3F7169UJRURE8PT1x5coVSvghCAIA4O7ujgMHDiAxMRGHDh3CjRs3sGrVKpVx8i4E8fHxSE1NhZubm8oYonEwYJB1mWvIZl0uR8r4GL2NR3NvwhKaqaysxHPPPYcjR46AYRhkZ2eTQUsQhIIvvvgCLMsiMjISO3bsUNi3hOWRsiwW7m3YzFNnsxK2iUQiwdSpU/Hjjz8CAM6cOWPlGREEYU1YlsXmzZvx1VdfaX0eMAyDzp07Y+bMmRg+fLgFZ+iY3LlXj/RsmR05e0B7Ff10pkRXR32v3377Ld544w0AQG3ZFUBSB7i4Ndpn4Kwb5Y76d0IQhPkQubsgZXwMAFkgrXI3eEsnSxriPzZm79eUSTrWKG5lyD2V552WlobnBg9GTU0NCgsLIRaLDd6zNeSzM1aL+L//sqpalFXVqowhCIIgbAtL7f82xsYbFhXE8dHHhPjg0MWGOLMekf5wETCcNYXye1Im3F+EWzW1nA5BdRXXcHPTHEjulIJxa4r626Vwae6n0Gq+hitzq4ardZTcShAEYRqulNcgIkAEgMGwqCCOzahbu/h2sHntYiqerBuKjyII+8VsCT8DBgzA6dOn8cEHH8DV1RUPPfQQ5syZA4Zh0LNnTzRv3pwzXr6526ZNG3NNyeGpk7Bqk318RW6KKgYAEBHgKQtAXn8KuSVVitfPXq1UOZcverTJZVnOnz+P3r174/r162jRogX27t2LLl26WHtaBGE1nLWiqDb8/PyQkZGB7du3Y/Xq1Thy5Ahu3bqlON6iRQt069YNkyZNwgsvvGC9iToALgKgXkORIm8P10Y7Yc29CUuop7y8HP369cOpU6cgEAiwZs0ajB071trTIgjChjh9+jQYhsF3331HyT5WoGekP4QPNgiP55epHLdHR62zrWnr6uowfvx4bNq0CYCsS+WXX35p5VkRBGEtKioqMGzYMEUXeHmBCnWwLIuTJ0/ixRdfRO/evfHzzz/Dy8vLUlN1aOxRPwntLFq0CLNmzQIA9OvXH8+98z/8c73GJD4D2ignCILQj+r79UhaexIxIT6QslAEw1ojWdIQ/7Exe7+m1AZrFLcy5p67du3CsGHDcP/+fURGRmLCZ6vwcfp1xITcN8iut4Suyn/fP2TmcxJ9IgJECPEV0Z4CQZiRnJwcrF27FidPnsSNGzdQU1Oj1e5lGAZ5eXkWnCFhy1hq/7cxWjT5mVCcKBDj7NVKdAz0QlSwNyfhp0uoD6b3iFD4wE8UiGUFqctrkFtazblWWx8P5Cm9xpYXyZJ9qsohcBchYMQ8uLeW7cnItZqv4crw6mBTcitBEISJUH5Wp2TmYdvpYgyLCsLU7mE6tUtd4WTAfLYxFU/WDsVHEYR9Y7aEn7feegsrV65EaWmpoqocy7IQCASYM2eOyvidO3eCYRg888wz5pqSU9Iz0h+xoT5YuDdH8VpidJAiYEpZ4DoGeqm0TyXRsx5///03+vTpg7KyMvj5+eGPP/7Ak08+ae1pEYRVcdaKovowdOhQDB06FIAsG7+qqgqenp4UDGVCmjd1VZtYCwDDohuqWJwoKIeUZXGiQAwAem/2JcWFQcqy2Ha6GAADKSsLCFZ3LiXgmoaSkhL06dMH//77L1xcXPDTTz8hMTHR2tMiCMLGEAqFAICoqCgrz8R54AegyLXwWL5YZaw92qzOtKatra3FyJEj8csvvwAAPvjgA3zyySdgGMdNcCIIQjN1dXVISEjAP//8A5ZlIRQK0adPH3Tr1g3t2rVDixYtwLIsKisrkZOTg7/++gtpaWmQSCRIS0tDnz598Ndff8HFxWwubachOtjb2lMg1GBMUjDLsvjkk0/wySefAACGDBmCzZs3w93d3WTzoo1ygiAI/bhzrx5pF0qQdqEEEQGenGOWTpY0tf+Yr1HRwcZpgzqt0xUgpksftR3XdMzQgOpt27Zh1KhRqKurQ8eOHTH2kx/w3XFZURJD7XpL6Kr89w9A4X8AgMToNg7rfyAIW2D+/Pn4+OOPIZFItCb5KEM+MkIZS+3/GqtFEimLaetPKeLKMnJKUVRxlzMm63IFAK4PHAA83ISccREBnhAo/f3X3szHzS1zIb17G4KmzdHyxc/Qok07tG7RlNNNQlnDZb4NBqv+5Ca4+nm6YYrSGoMgCIIwjqauAgR6N0VuSUPCj7i6DuLqOizcmw0BA4VuadIuS/sVqXiyZig+iiDsH7Ptjvr6+iIjIwPTpk3DkSNHAABt27bFV199hfj4eM7Y8vJy7NixAwCQkJBgrilZhKKiIvzyyy/IyMjA33//jevXr0MoFCIoKAg9evTA66+/jscee8zs84gIECExuo1CtAQMoyJkfIGb2C0Uq/4seBBozKq04CMsx4kTJ9CvXz/cunULrVq1QlpaGh599FFrT4sgrA5VFNUPLy8vh070sZbWllfXwcNVgJo6bpufiAARpnZv+DssFFchr7QGAJCeXQopy2J6jwid1xcKGAgYRmEsKxvIhOm5du0aEhISkJ2dDTc3N2zduhXPP/+8tadFEIQNEh4ejjNnzqCiogItWrSw9nQsgrXt2mFRQWq1U8DbAHcVMkg9VQQpy2Jq93C76ZLjLGvau3fvYvjw4di9ezcAWdDD7NmzrTwrgiCsyccff4y///4bADBw4EB89913Oru9X7lyBa+++ip27dqFrKwsfPrpp4rEBnvG2loL2IdmOhuGJgWzLIv33nsPixYtAgCMHDkS69atg6urq0nnRRvlBEHYI9bXWm6gtz0kS2pLnOFr1Kz+kZg9oL3B2qBJ67QFiOnSR23HNR0zJKB648aNGD9+PCQSCaKjo7Fv3z7M+j2fM8YQu96SukoaThCWY9u2bZg7dy4AoGnTpujTpw8iIyPh4eFh5ZmZF+vrLWEMxupDSma+ShFp/ppHImWRtPYkCsU1nNdraiWcnxOjgyBlgfTsEty/loOSnz+E9H41hCJvBLz4Gdz8g/FmQjsVfVWn4QKGm+A6JS7MIf3tBOFMhIWZbt1q7930rKq1LFChoSgyIOsoCgATu4Vi9ZECtbZkYws8GAoVT1YPxUcRhGNg1nKIHTp0QGZmJu7cuQOJRKIxOIplWUUASLdu3cw5JbNSVFSE4OBgTrUOT09P1NXV4eLFi7h48SJ+/PFHfP3113j99dfNOpcQXxFHuNQJGb+CEQBM7R6G6T0aJ3iNFWJTC7k9sn79ety6dQtt2rRBeno62rVrZ+0pEYRNQBVFCWtrLT/ZRwaDlMx8SFmW01FPzrbTxXol/ADOEwBsC+zfvx/Z2dlo2rQpfv31V/Tp08faUyIIwkYZN24cTp8+jR07duDtt9+29nTMjrW1Vob6SrydQ3yQnt2wFqyTsMgtrcbCvTk4UVCu6GRr6zaks6xpz549i/T0dADAkiVL8Oabb1p5RgRBWJOamhp89913YBgGo0aNwoYNG/Q6r23btti5cydGjx6NTZs2YenSpZg9ezaaNGli5hmbD1vQ2qzL5QDI1rQ1DPUJlJWVYePGjQCACRMmICUlRdGd0pTQRjlBEPaGLWjtsKggtcUYbRltiTN8jcq6XIGU8TEGa4Mx/m9d52g73lh/u1QqxYoVKyCRSPD0009j9+7d8PLyQkzILaPtekvqKmk4QViOZcuWAQA6duyIPXv2oHXr1laekfmxBb0ljMNYfeDrKgAEtWj64F8MgrybqkkIUqVnpD+S4sKw8rAsAL/6vwxZsk8zf7Qc+RlatQnhdAFUF0MGQPFadLAPZvWPRNblCrtZdxEEoZ3CwkKdYxiGUdtRj/+6PXfTs7bW3q2X4m59rcbjZVW1WLAnG8fyxYrnf9qFEvx8qgghvh6IDfVF0oMkTGMLPBCmgeKjCMIxMGvCj5xmzZppPe7r66vS9Ucb8+bNa+yUOHz44YcmuY68NW/fvn0xfvx4JCQkoGXLlpBIJDhz5gzeeecdZGZm4o033sDDDz+Mfv36meS+6tDXsWgO0WzsNUnIZcFQ7u7ueO211xASEmLt6RCEzUDVyDRTXl6O9evXIzMzE4WFhbhz5w6aNWuGkJAQxMXFYezYsfDx0X/TibRWM2H+IlTW1OH2vTpZoHFJFRbsyUZEgEjDGfo7EJwlANgWmDBhAm7duoVOnToZtA4lCML5ePnll/Hzzz/j448/xlNPPYWuXbua5LqktZo5VShG8iEg9VQRcktlne/SLpQg/mE/RASIcO3WPZWKgMqOZMC2bUhnWdPGxsbil19+QVFREaZOnWrt6RA2ChV9cR62bduG27dvw8fHBytXrjT4/JUrV2Lfvn2oqKjAtm3bMHr0aL3Os0W9tQWttbatSd99GfzPITrY2yCfgL+/P9LT07F27Vp89tlnEAgE5p4yQRCECqS1DbgIGUQEiDAsKkjRhdaWbVM+2pJjTOW3NuY6us7Rdryx8xYIBPj999/xwQcfYMGCBfD09ATgPHY9QRD6c+bMGTAMgyVLlpg82ccWtRawDduWMA362uh8XQ33F+HgxTKlEaqB9+oQChgIBQyyLlcAALwTpoARuqJZ9EC4eLWE+EE3CU2dBlOzitFGKbko7UIJZg9oj5TxMQa/d4IgbJOPPvpI47F169ahoKAArq6uiI+PR4cOHeDp6YmqqiqcP38ehw4dQm1tLcLCwjB27Fi97kdaaxgMuE/8s1crOcfzSquRV1qN9GzZc1qbXUxFkS0DxUcRhGPAsOpSXW0cgUBg0uxbiUSie5AeVFZWIj8/H506dVJ7vLa2FjExMfj333/Rq1cvRaVbYwkKCsKNynsIenWt4jVfkRuS4sIwtbt+m6RJa09yDLKE9gGIDfVp1GYr/5q9HwkwyLBq7Pn2yrVr15yi0gtB2BJBQUEAgOLiYivPpHEsXrwYc+fOxb179wBAbbWKJk2aYP78+XpXVietlaFOa5u4MLhXr7p8ivD3RG5plcrrs/q317t7nq0EPdnKPEzN9evX0apVK7uu4kIQ9oajaG1FRQUmTZqE3bt3Y+TIkejbty9at26ts4J69+7dNR4jrZWhTmvjH/bDIc5GoWE4iw1pi1RUVKBJkyZo2rSp7sEEASD5UJ5iwx4AZg9oT5s5BmIvWjt9+nSsXLkS77zzDr788kujrjFjxgwsXrwY06ZNw4oVK/Q6xxb11tJaC8j+Tkru3EfEm+sRG+KNleNiIBQwVrP7bPG7byk7WPk+EinLqX48q397CBionYP8vON5JXjER4h3BsUYPT9HtfkJwpzYi95aA9JaGcq2bc9If6SMN/45bS1WHMzldLCf1T9S0bneUO3QNF7f6yiPiw72BsAg67L6c7Rds7ZeimnrT+Hs1Up0DPRC8tjOcHPRnSB7/fp1PPTQQ/p+dARBmAB711pPT0/cvXsXZWVl8Pb2Num1bVFrAev4kQHj/kbIBlJFm22qyUbnf44nCsSKYG5AFq8mrtbcCULOrP7tMfjhpth56S7HN6BMRIAIae/0AKAaQ6YO2hMgCN3Yu9YCsqSFdevWYezYsfj666/h6+urMkYsFuPtt9/Ghg0bMG7cOKxZs0bndUlrZajbs1XGR+SK8gdJmcr4ilwVyZp85M9niZTFysN52Ha6GADzoFBGGCepEzDOT0w6rx6KjyIIy2NurTV7h5/a2lps2rQJf/zxB3JyclBZWYn6+nqN4xmGQV5entZrtm3b1iYfRF5eXhoFFgDc3NwwZswYvPvuuzh16pTJ7+/j4YKjsxOw+kgBpq0/pZeA8SswSFm20d11GlstyRm7G8grk65evRqjRo2y9nT0ghZLBGEbzJkzB1988YUiySc4OBiPPvoomjVrhqqqKpw7dw6XL1/G3bt38c4776C0tBSfffaZzuuS1mpGXbIPAAT5NOUk/PiKXAEAW7OKAACTnwnF6iMFGp+btvRcdcRue//88w969+6NSZMm4YsvvrDJv2+CIGwXNzc3PP7449i1axc2bNiADRs26DyHYRitti9prWbOXr2tc0y4vwi3aupwt06CVs3dkV9WozjmDDakLVJSUoI+ffogMDAQv/zyC9zd3a09JcIOoOptzsPp06cBAD169DD6Gr169cLixYsV19IHW9Rba2mtVMqiplaCgxfLMG39KcSG+mLhXuvYfbb43VdnByfFhZncTudvYiuTdbkcKeNj1H4WKZn5mP/7WZT99iVqy64AKVvxfy90afQcHMXmJwjCepDWqpKRU4qktSchFDAG6Ycp/MParqH7+vx7Nfws71akr15o0hr+dSRSFsmH8lTmxD9fW9V+bXNbfaRAEcCckVMq20PX8h5YlsUnn3yCJUuW4MCBA4iKitLr/RLGYUt7IgTRWEJDQ3H+/Hncvn3b5Ak/tqi1gPX11hDIBlJFm22qyUZXp7nKCT/i6lr0jPTH8XwxauqkGu995tAevN1/Jl76vy8Q4f8kKmpqUXm3DvWcUxr+5vkxZOqgPQGCcHw2btyIdevWYcSIEVi7Vn1CCgD4+vpi3bp1uH//PtavX4+EhASdnX5Ia/XDx8Odk/AjYAApC43JPkDD8zklM59TYGLh3mwIGNN0TyWdV4XiowjCMTFrws+///6LoUOHoqCgAJoaCTEMo7YTgTYKCwtNNUWL06RJEwCmy+RVxsezCVYfKeAI2LF8sVaHMl80TxSIOceN2WxtrBA7Wxv0jRs3Yvz48ZBIJPjmm28wYsQInZXCbQFaLBGWgJz92jl58iQWLFgAAIiNjcXSpUsRGxurdtwbb7yB48ePY8GCBRg8eDBiYrRXuCGt1R8PNyG6hPrg3+JbnNflRq24ug4L92bjRIGY01oc4D43bem5aovBV43h5MmT6NevHyoqKrB27VrMmDEDAQEB1p4WQRB2wu3bt9G7d29kZWVptGuNgbRWMxV6VAFs6+OBvFKZruaX1cBX5AZvkRuGRQXptCFpjWl6rl27ht69e+PChQvIzs7GmTNn0LVrV5Pfh353joczFn1xVq5fvw4AaN++vdHXeOSRRwDInjn6Yq96a26tzcgpRVHFXc5rlrT7bPG7r84OBqDia29sxwb+fZRR/hz4mvfXxeso3f457ubLNu9/2/mb0Qk/lrb5Sb8JwrEhrVWPNj+wJoz1D2uq0M+/hq7rZ13m6oPs54bjhjzP9dUaTXNqjFYpz7NQXMM5pu06LMvivffew6JFiwAAP/zwg95dJbXNgbRPM7a0J0IQjSUxMRGffPIJ9uzZg5dfftmk17ZXrQUsv2erCVvf97SGbpwoMM42VZ7bxG6hWHk4jxPoLRQwyPqgL7otTFfbBaLqvwz8sHsxIJVi46rv0WrMIjAC1fioYVFBin/L/f2pWUXILalWvN4z0p8TD0cQhGPzww8/gGEYzJw5U6/x7777LlJTU/HDDz/oTPghrdUThrtPLuVtm/uKXMEwDLyauiLYpym6hPlx4oD5yPXYkOIS6rB1nbc0FB9FEI6L2RJ+ysvL0b9/f9y4cQPh4eEYOnQovvzySzAMg1mzZqGmpgZnz57F4cOHIZVK0aFDBwwfPtxc07EZDh48CADo2LGjya/9/BMPqQiYLoeyrgoMxmy2GlrlydTn2xOrVq3ClClTwLIsnnrqKezevdsukn0AWiwRloGc/dpZtmwZAFmyz8GDBxWGHJ+YmBgcPHgQ8fHxOHnyJL777ju92tbaK+bUWnU0dRVy2pxr4niB9ucmP+n2RIHYan/vthh8ZSx//vknnn32Wdy5cwdBQUFIT08nY5YgCINYuHChoirS6NGjMXLkSISHh6Np06ZWnpn1MLfWakur8vN0w5S4MBXdFFfXQlxdCwEDnZuhtMY0LZcvX0ZCQgLy8vLQtGlT/Prrr2ZJ9gFs83dHgVyNw9mKvgDO+zdTWVkJAI2qdNyiRQsAsmRcR8cydi1Xcc1t9yn/7UcH+2BW/0hkXa6wme++OjuYv97IyClFSma+UQHH8vfJv4+mACVlzdv/zxXU712oSPbxTpiCMeMnKcbW1ksxbf0pnL1aiY6BXkge2xluLgKD3qs5sUX9JgiCsKQPWd/9M2P33fSt0K/r+rr0QdPzXJ3eRQd7c64VHax+DahpTo3RKm2fh6brSKVSvPnmm4o9lwkTJmDZsmVGr91J+/SD9poJR2LmzJnYvHkzPvroIyQkJKBdu3bWnpJNYOk9W03Y+r6nNXRDyituFu4vQqifSKttyp/b6iMFKl0dYkJ80NRNiKi23ipdee78sx/le78FwMK9dXsEJH6skuwj9/8rz0EeQ2aOLrwEQdgP586dAwCEh+v3fAwLkz1Hzp8/b7Y52QIW1VoWcBUyqJOo38mVa0JZVS1GdG6j1d6Uv2YKbF3nLQnFRxGEY2O2hJ+lS5fixo0biIyMRFZWFjw8PPDll18CAObOnQsPDw8AwJUrV5CUlIT09HTcvXsXX3zxhbmmZHWOHz+OHTt2AAAmT56s1zlBQUEaj12/fh2MR4OD9Ncz1/BiTBuNrUyVnWRyB+WJAjGkLCBgGMSG+mBit1DF2MZstjpr8IIhLFu2DK+//joAoEePHti5cyc8PT2tPCv9ocUSYQnI2a+dw4cPg2EYfPrppxqTfeS4u7tj3rx56N+/Pw4fPmyhGVoec2utOsR6dCEAgJpabkUN/nNTwit/wf/ZkjhK4OWBAwfw/PPPo6amBqGhoUhPT0doaKi1p0UQhJ2RmpoKhmEwe/ZsfPbZZ9aejtWxhtYqMyUuTLEeVC5WIUef9SKtMU1HXl4eevXqhStXrsDT0xO///474uPjzXY/W/zdUSBX43Cmoi9ynPVvpqqqCgB02q7acHd351zLUTFGawHD9TaohTx5mdGrQ15j4f/tzx7QHinjtXcftiTq7OBj+WKVcYZoj7rvu7r7qPObyzVPer8GJVs/xv3i82AYBk+Pfw9jJ0zm/L6mrT+lKESSkVOKaetPYfVE1Q7QDXsC5egZ6Q8BA8SG+iquZS6fvi3qN0EQzo2ltFZOYVk1kg/l6XyuGrvvpm/3OF3X1+QTluvDD5n5KvedFh+uVu8A/vtkONeS34OfGCSfU2P80/zPIyJAhBBf1QBmORKJBC+//DJSUlIAAO17DUPsuPcBRmD02p20Tz9or5lwJMrKyrB69WqMHj0anTt3xhtvvIH+/fujdevWOgu+tm3b1kKztCzm8CM/9NBDRs3F1vc9raEb/CVJiK+HwkaXSFkkH8rT2C0vKS4MKw/n4dsDuZxj4f4iSFkgae1JlX3u21k7UZGWDABwb9sRAcM+hMBNtaiasv+fj737ESmOjiAax927sm7lV69e1auo1NWrVwEA9+7dM+u8rIkl9mx9PFzh4+mG3JJq5JZWazhTFbleKBeA+r9+D+OXM1dhan+0reu8paD4KIJwfMyW8LN7924wDIMZM2YoknvU0bZtW+zevRvdu3fHl19+iR49eqB///4mmUNBQQFKS0tx7949dO/e3STXNJby8nK89NJLkEql6NKlCyZOnGjye+SXVeNoXhne7ReJ7WeuIreEu/mt7CRTV90oPbvBUanNQNLHCHHW4AV9+fLLL/Huu+8CAPr164ft27dr/Z7YIrRYch6s6XggZ792bty4AQCIiorSa3x0dDTnPFPgbForYFTb0uqCAeAjcuMkBkUEeKo8N4sr7mr82dLfQ3t3mAKytejQoUNx//59PPzww0hPT9fqvCAIgtBEcXExACApKckq93c2rdVGj0h/hX7K/5+aVYTckgYHsz7rRVpjmoYLFy4gISEB169fh5eXF/bu3Wu2zj5ybPF3R4FchKE4698My7JgGNsNYrAVvbWU1vqKXHHwYpniZ3065DUWU//tm9pOVmcHC9T8zRqiPZres/w/be8hJsQH+07noST1Q9RevwRGIMCa1asxbtw4lfucvVrJ+flIbpnawHL+nsDsAe0579dcPn1b1G+CICyPs2mtMrml1Yrnq7bnqjH7bhIpqxJQq6l7nK7ra/IJa+qYI3+eq9M7PlmXywGoJgfN6t8eswe0V5mTMf5pua4WirlBaInRbTRep76+HhMmTMDGjRsBAM06D0ZN5wlYuO8iBAKB0esX0j79oL1mwpFQDqZkWRbz58/H/PnzdZ7HMAzq6+tNMgdb0VrA+n5kPra+72msbjTGLo4N9eUU1JIn6sSE+EDKsli4N0ftedHBPkhae1JRdEKZNj4eWLi3Yc3QI9IfQgb4d/d6XH6Q7NMkNAr+L7wPgWsThPuLkKcUPO4jcsXxB4U3GmPj22piDcXREUTjCA8Px3///Yfk5GR8++23OsevWLFCcZ6pcEatndAtBL/9c53zmq/IDQwDeDV15TzHlYkJ8VF57kUEeCIxuo3Jn8u2rvOWgOKjCMI5MFvCT26uLJO/W7duKsdqa2s5yQ0uLi748MMP8eyzz+L7779vVMJPSUkJPv/8c/z0008oL5c54fhG8sWLFzFz5ky4u7tj06ZNcHEx28cAQJZh/MILLyA/Px9+fn7YvHmzzioecuTBZeoICgrCjUpuFvLBi2VgGAYhvh6chB8PNyGkrMzpKxQwGqs96eOo1McI0eUAtVUDyxJcunQJc+bMAQAMHjwYW7ZsUVQotSdoseQ8WNPxQM5+7TRp0gS1tbWorq6Gr6+vzvHV1TJDq7HPHGfWWvneqa/IDQCr0qZcHSxUuwAlRgcpdE+uidd4mg5GfVAOOQB1c+/ePUybNg3379/HY489hrS0NLRs2dLa0yIIwk7x9fXFtWvX0Lx5c4vd05m1VhtF5TVYeTgPWZcrEB3sDYBBsI8Ibbw9IGCAziE+nI1JTXamoWtMZ7ZftfH222/j+vXr8PX1xf79+/VOQm8MtmgfUCCXaXGG75uz/83YUtKPreltY7QWMExvW3i4cexZSySeNfZvn/98kLJQBBKZy06ODfVRFMkCZAHUhmiPrvfMt/WP5YuRMj4GQgGDpLgw7Ej5CnuvX4JA6IKfNm7Eiy+O4Jwv/0xYlhvoXSth1QaW6/LZmysh0Rb1myAIy+DMWqsOXc9VY/bdUjLzOQG3PSP9FVpiiuvL562Mn6cbpsSFISkuTG3CkVzv1Gkg/1pZl8uRMj7GJHqz8nAeJzg5wl+ExM5ttOrOzp07Fck+Tzw/ERWPDFWsV+W6Zcz6hbRPP2ivmXAk+Gty/s/mwta0FjCvH9lRMVY3DN0/Vraro4N9MKt/JLIuV0AiZRXrCVlAtohznrxbXnSwD47nl3EKiCjG+ItwtphbjKK44i5WPN8aj06VJfsMfH4Qqrq9iqu369HGxwNtvZtyAsXLq+twIKcUBx7MxVh9sNV9dWctAkQQpmLkyJGYO3culi9fDl9fX8ydO1etptXX1+Ozzz7DihUrwDAMRo0a1aj7OpvW8u3a7w/lo6ZWwnlt0jOhcBEwOFFQjrY+Hvi3+BbH1xwRIEJSXBimrT/FOS+3pAoL9mQrfKAAHH4/yBJQfBRBOA9mU5eqKlmySatWrRSvNW3aFPfu3cPt27fRokULzvjOnTsDAE6ePGn0PbOysvD888/j5s2bWg3ohx9+GJcuXcLFixexZ88ePP/880bfUxf379/H0KFDcfjwYXh5eWHfvn0ICQkx2/0A4M9LpWjryzXAamolWLg3BwJG5jjjOyjlKDsqNQVZ6GOE8K8fHeytaPnKrwhhSwaWJWjXrh02bdqE7du3Y82aNXB1dbX2lAgHwJxBUdZ0PJCzXzthYWH4+++/sXPnTrz66qs6x+/cuVNxnrGQ1soQV9eix8N+ah2aclyFDOokDZ+R3BnKd9RqqpA4LKrBcU0OQMNo0qQJfv/9d8ycORObNm2Cn5+ftadEEIQdExcXhy1btuDcuXMWqdZEWisrWNHERYDb9+pRrxQ4lFdarWJHyukZ6Q+A0Svw19A1pq1uEFqb9evXY9SoUViyZAkee+wxi9zTFu0DYzfknSGxxRg0fd8c6fNy9uC/Dh06GJ30Y8qAKVvTW0trbRsfD05QjTkTz+Tf3xMFYvSM9IeAYRAbavjzMjWrWFHgSl4RUhllO9lUzwx131ehgNH7+rq+73xbPyOnFCmZ+ZgWHw6hgMFvP36D0ffLMGbMGAwaNEjl+nx/gpABlNwQKr4DXUHL5kpItEX9JgjC/Di71qrDHHrL1xKhgDH5OpmvD1PiwhTP9ORDeSoJR8p6x9fAxmiNLv3ddpoXuMbott1feOEFfPDBB3B3d4ffMyM5uqo8b0PX7qR9BOF8ZGRkWPyetqa1gG3orT1iqqRcXfvHfL/b7AHtkTI+Bklr+XF63LWEvFte8qE8jXvjbXw8kFvK7/rDcuKjXHq8irN5FQCgsSOEvu9F17mmupYpcfYiQATRWGbMmIHNmzfj3Llz+PTTT7Fy5UoMHDgQjzzyCDw9PVFVVYULFy5g165duHHjBgDgsccewzvvvGP0PUlroZLsA8ieqwd5dqCyXTgsKuhB99UatdfMyCnFysN5EDAM7b+aAIqPIgjnwWwJP82bN0dFRQUqKyvh7e0NAPDz80NxcTFyc3PRtm1bzvjbt28DAMRisVH3u3XrFgYOHIibN2+iXbt2mDNnDjp06IDY2Fi140eMGIFPP/0Uu3fvNpvI1tbWYvjw4di7dy88PT2xZ88ei1S8rZNqNo7khkxSXBhnkxSQdwGCogvQysP5nGApKQtM76GaLKTOCOE7QKUsOAId7u/BGX+iQOzQgs2yLOrr6xXJPcOGDcOwYcOsPCvCkTBnECI5HmyXZ599FmfOnMEHH3yAp59+Gp06ddI49p9//sGHH34IhmEwcOBAo+5HWsuluOIuZvWPxLbTxcgtUdXdYB8RcksbdHZYVBsImAYno6ZEWuUKiQC0VkkkuNTW1sLNzQ0A8MQTT+CPP/6w8owIgnAEZs6ciW3btuHTTz/F/v37zdqZgLRWRk2tRK0DWRsZOaUoqrjLec1UG3m2ukFoDZS11t/fH2lpaVaekfUxdkOeEsnUo+n75kifl7MH/12+fNnaU7A5vbWG1p4tvoX/6xeJM1cqzJ54xk9KmT2gvcGdC9QVyZD1020gJsRHKTmoSGGnyzvnCAWMwck/mr6v6jrzqLu+8vnqgpTVFeU6lntTcT9XV1f8/PPPGufHf2aG+os4/gm+70BX0LKzJyQSBGE6SGtlNHUVoGuYr8HJrprQR0vM4TfWpg/aEo7UaWhjtEa3TcDXd1W9l0hZfH8gG2euVinuP2/ePMUx/tycfe1OEIT+xMfHW/R+tqa1gHX3bJ0VQ9cBmvxu/OsMiwpS7GnHhPhg3FMhmLj6BI7kqib7CBggrp0fGCUbnWVZQCpRFLcc8sJQlPk9ia/253DOrbxbh3B/kdr4tsasaWw1voVsboJoHO7u7jhw4AASExNx6NAh3LhxA6tWrVIZJ0/MiY+PR2pqqmJPy1BIazVz7iq3o5uAYTB7QHu1ccIA4Cty5XQAAmQFG0J4DQ2cef/VGCg+iiCcD7Ml/ERGRuLYsWO4fv26Iov0ySefRHFxMX7//Xf06tWLM37Xrl0AoEgOMpTFixfj5s2beOSRR3D06FE0b94c1dWaKwLEx8fj008/bVRHIW3U1dUhMTERv//+Ozw8PLBr1y489dRTZrmXIcgNGaGAQWJ0EEdcZV2AsiF4UPGIXwlp2+liTO8RrpcRwneA8itC3Kqp5/wstUxHY6sgkUjw8ssvo6KiAps3bzZ720bCOTFnECI5HmyXt99+G8uXL8etW7fw9NNPY+rUqUhMTESHDh3QrFkzVFVV4b///sPWrVuxcuVK3Lt3Dz4+PnjzzTeNuh9pLZfc0mpsO30Vw6LaqG1fXl59Dz0i/SF8sKErZVks2KPa3Y7vdOwY6MVJCkrJzNdaJdEYHKlCupzvvvsOKSkpGDsvBefLpQ7zvgiCsD5RUVH48ccfkZSUhEGDBmHJkiUIDzePs5G0trGYJ0HWVjcILc2BAwcwadIk7Nq1C48++qi1p2P3UCKZejR93+jzsn+6d+9u1qRZQ7AlvbWW1oqr67DqQScZc9tNjf3+8s+XIwtAkhXRiA72hpQF+i05zClwJUduU2tLGDTETlbXmUfX9dUFKSfFheFYvlhxfl3Fdez66FX86vcNBg8erPbeyqgPymI0+vB0BS1TUDNBEKaCtFZGlzBfpIyPMZnOatISwDz7N3xtTB7bWeW9GGovN0ZrdK0phkUFKQpZyn/m8+3ef/DetDFoEtoJaU+PBNCg26SDBEHYE7aktYA9+pEdA0PXAZp0W1N3W7kmTlx9grNXrYyUBQ5dLIOvSBZwzEolKN/3HXxd6zDp830ANBfy8Grqykn2CfcXIcTXA7Ghvo1a09hqfAutNQii8fj5+SEjIwPbt2/H6tWrceTIEdy6dUtxvEWLFujWrRsmTZqEF154oVH3Iq3VjFdTV5RV1Sp+jg310Ron/GQbb/xddAvi6oZzwDJGFT7W5MN1xBgobcjjo9LT0+Hj45z71gThjJgt86Bnz544duwY/vvvP4W4DB06FDt37sR3332HRx55BCNHjoREIsGvv/6KuXPngmEYlUQgffntt9/AMAzmzZuH5s2b6xz/8MMPAwAKCgqMup826urqMGLECPz2229o2rQpdu7cie7du5v8PobS42E/SFmZqMaE+GBit1AAwA+Z+RwRbnCQ8rNwZD8bY4TwDUdvDzeOiAtsZMPf1NTX12PixInYsGEDAODnn3/GSy+9ZOVZEY6IOYMQyfFgu/j4+OCXX37BwIEDUVVVhWXLlmHZsmVqx7IsC09PT+zYscPoxT5prSq5JVVYuDcb7/aLxJXyGuSXNbSkLa+px8GcUviK3NA5xAeneBuS36RfgpQFJjwdgmP5Ypy9Wgmvpq4qgULaqiRqQ5tB60gV0gHgq6++wsyZMwEAeZ9+Bp9eSQ7xvgiCsA3kNqqvry92796N3bt3IywsDK1bt4ZQKNR4HsMwSE9PN+hepLX64+Phio5BXjiklHD7QqcgnCosx9mrlegY6KWwebWhjwPYVjcILcmePXswdOhQ3Lt3D6+++ioyMjJsJnDfXqFEMvVo+r7R52X/HDx40NpTUGAremttrS2vqVPYhea0m/T9/mrSZP75EQEiJEa34QQgJR/K09AFSBV+cHBDV6BiRbKQLntSXWceTddXfl3duJTxMUjJzMcff51G2qq5qCy7ialTp6J3794QiUQq11FGV1AWQRCEtSCtlXEwpxRJa0+aLOlHk5aYa/+G70OWsqxKYqkl7WVda4qp3cM43Qj4c6moqMD818fgfvF/uH/1AjzaPYWThQGkmwRBmJxffvkFGzduxMmTJ1FaWora2lrU1zcUxL1y5Qq2bt0KNzc3vPbaa0bdw1a0FrC+3jozmuI4NNnXmnRbVzzIWV4nBwaq0WTi6lqwUgmq9i9F1b/pqAKwbWsqXnrpJZU1jJuQQbcIWVcg5YSfUD8RUsbHGP5B8KD4FoJwfIYOHYqhQ4cCACorK1FVVQVPT094eXmZ7B6ktYAQgETN69du3eUUP57YLRTJh/IU+hIdzLXdYkN90DnEh1Ogobz6PnJLGwo39dCz8LGmWCdHi4HShnJ81Oeff46vvvrKyjMiCMJSmC3hZ/DgwZg/fz527dqFpKQkAMCYMWOwdOlSnDlzBi+//DJefvllxXiWZeHh4YE5c+YYdb/8/HwAwDPPPKPXeLkQ37lzx6j7aaK+vh6jRo3Cjh074O7ujh07dhidxNQYwvxFuFF5DzW1DbJbfOsuDu5VL2zKG6JyB6msElJDS1V1lZD0hW84SllwRDw21PECNWprazF69Ghs3boVAPD+++9j1KhRVp4V4ahQEKLz0r17d/z999+YMWMGdu7cCalUqjJGKBRi0KBB+PLLLxEWZvzfBmmtZpZl5HI0VxlxdS0W7s1GuD83UEfeWe9EQUM1X+UEXABIzSqCzG3agL7BjdoMWkepkM6yLD777DN8+OGHAIC2nboD3ccrjtvr+yIIwrY4ePAgGIZRtIAHgLy8POTl5Wk9z5hkCNJa/SmvqUPXMD88He6nZGeyCk3NyCnF6iMFSIoLw8rD+Q862LIYFhWEqd3DDUqCdfYNwl9++QUvvvgi6urq8Oijj2Lz5s2U7GMCyIZTj6bvG31ehCmxBb21Ja01t92k7/dXkyZrSmpR5kSB+i5A8grDyoWn+Da1pkrD2j4X+ZxOFIhRKK7hBChpstk1BSkLBQye8q7GB4tfRWVZKXx8fLB7926dyT7yc825RnG2ypgEQZgO0toGMnJKkfKgq15j0ZbwYswzW9c5fB/yttPFyC2RaZ6yVlvKXta1ptCmi6Wlpejbty9K8/8DGAH8np8JN/9ggz9D+ZgTBeUPEqCg6EJAGkkQRHl5OUaMGIGMjAyOL5nvR2vZsiW++OILiMVixMTEoEuXLgbfyxa0FrAdvSW48O3rY/liRUGNpLgwtUUwZN1zfQCwyLpcwdHCjoFenA4/Yf4ijh0MAKykDmU7/4eanCMAuPFR/DXMjL6RiuIdB3IaCnpRgR+CILQh15fZs2ejT58+ite9vLxMmugjh7RWfbIPANytk+JgTilmD2iPpLgwTF5zAgcfFGhMu1CCMD8P9Iz0h+BBQpDcdhMwUBRdKq+p41yzuKJGL5tKU6yTo8RAaYMfHzVo0CDMnz/fyrMiCMKSmC3hJyYmBjt27ICLS8MthEIh9u7di3HjxmHfvn2c8WFhYVi9ejU6dOhg1P3q6mQi4Obmptd4ubjqs3GmLxKJBGPGjMG2bdvg7u6OX375BX379jXZ9Q2hqLwGdRJ+TQWuKMqFTZODdGr3cJVKTcbCd7JKpKzWKkv2zr179zBixAjs3LkTAPDZZ58ZncxGEPrAr8gCgDYYnIiwsDD88ssvKC0txV9//YXLly/jzp07aNasGUJCQvD000/Dz8+v0fchrdWMpmQfZfiOTzn8qkjKyDdQAW4VY33QZtA6QoV0lmUxZ84cLFiwAACQmJiIHtPmYdEfDQH49vi+rAUFchGEZsaNG2ex5AbSWsM4VViOVRNiNLaIl2uhcrGJhXtzIGAYh0uCNRebN2/GmDFjIJFI0KlTJ+zfv98k60qCEskMxZ4/L1pn2R7W1ltb01pT2U2a/tb1/f5q0mR9zpeyXD+4r8gVjwe14AQkabKp+feVo+1zUe6ik57dcI+eWipRTuwWqujuq9yJ8NSpU+jXrx/Ky8sREBCAtLQ0dOzYUeO9LYkzVcYkCMK0kNZyMZWdqS3hxZhntq4OPtHB3ryOdqr7zElxYTqDhE21HjbWJrh+/Tp69+6N8+fPA0IX+A9+Dx7tuqrotj6fobpEYflawJoaSTYHQVgfiUSC5557DsePH4e7uztGjRqFxx9/HO+8847KWHd3dyQmJmLFihX49ddfjUr4sbbWArant46AqZ7nfDtXbhvLk3+Sx3bG6iMFOFlYDomU5RyXk3ahBKlZxUiMDsLy0dF4ZWMW/i2+hRYebmjr44Egbw+cLb6FO/frUXv/Pkp//QJ3c08AAB5KmIB9TXvg2pqTSB7bmbOGiQ72hpSV+fKjg30wq38kZ+1gLKSFBOH4ZGZmQiqVYvXq1Ra5H2mtblKzijhd0+Xkl9Ugv6wGswe059hJ8sQc/ngZ+hWsKBRz467kPlxHiIHShrr4qI0bN8LV1dXKMyMIwpKYLeEHkGUR8vH398eePXtQUFCAv//+G/fv30dYWBg6d+4MgUBg9L1atWqFy5cvIy8vD507d9Y5/vTp0wCAtm3bGn1PPkeOHMGWLVsAyB6yEydO1Dr+5MmTaNOmjcnurww/2cdX5AbwNj4LxdVIPpSn0nIdaEgUMKT9qyHYc6CGLmpqajBkyBD88ccfAGRt9NQ5cgjC1NAmPOHv74/Bgweb7fqktQ24CIB61WZKRsGvitQz0h9CAYNCcQ3H0A3xFRn0ndZm0Np7hXSWZfH222/jm2++ASALxl+1ahUYgRBCF1e7fV/WhDSEIDSzZs0ai92LtNYw6qVSXot4bxXtUxfEa4kkWHvbYFQ333Vr12Dy5MlgWRZdunTBnj174O3tbe2pEoTdQeusBu7fv4+bN28CAHx8fODp6anXeVVVVSgvlz3PW7Vqpfcmqyasrbe2orVhvk0R7Oep6I7TWK1q7N+6Lk3WpK0SKYsr5dyN3ifbtFBJ2NZkU/PvC2hP3FGGv86QJzipY/WRApVOhB1db2LAgAG4ffs2AgMDkZ6ejsjISJ33tRSUGE0QhLGQ1nIxlZ2pbW/VmGe2rg4+s/q3x+wB7RXaK5GyWLQvRzE+OthHrf7L/30sX4yU8TFWXQ8XFRUhISEBly5dgsDFDX4vzEHTsGgAqrqtz2eoKVHY2hpJNgdBWJ8ff/wRx48fR4sWLZCRkYEnnngC1dXVGuNE+vbtixUrVuDIkSNG3c/aWgvYnt46Ao19nmsKhlYmI6cU09af4uxNayK3pEoxn5TxMUhaexIZOaWcIpfSunso3f457hWeAQB495wMt84voKyqVnGv1RNjFTZ2alYRZ70xe0B7pIyP0fs9aoK0kCAcn5YtW+L69eto1qyZRe5HWqsb5QLG6lBnJ6nzxQLAsKggrdfiF1+ICPBEYnSQQl/sPQZKG5rio5QbcRAE4RxY7VsfGhqK0NBQk12vW7duuHz5MjZt2qSXyCYnJ4NhGMTHx5tsDlJpQ+RvbW2tYgNbExKJ7m4ApkJcXQtxdS0AWfKPuLoWuSXVHCHUZvwob6iqq/BAhlIDb775piLZZ/ny5Zg+fbqVZ0Q4C7QJT5gb0toGvJq6KXRVH8L8RchXcn76ilzhLXLHsKggTH4mVFFFSTloKflQHkenDd0Y1mbQ2nvibXJyssKYnTZtGpYvX65IHLfn92VNSEMIwjYgrTWME/nlOKTUIj7cX32LeL7jWFPxC1M6gO1tg5E/34Lzf2PhK5MAAN27d8fvv/9usU0UgnA0aJ3VwNtvv43k5GQEBAQgKytL74Sf27dvIzY2FqWlpXjrrbfw1VdfNWoe1tZbW9BaX5Eb2vp5Kny86dmN1ypNf+v6JsHq0mRN2pqSmY+80hrO2NhQX844QLNNnRQXxgk4ArQn7ijD36CWSFkkrT2p9n3yP58/zxXg3feH4fbt2wgJCUF6ejrCwrjv2doJxI5eGZMgCPNBWtuAvkmkxtIQ2MvVQn2e2aqBVlyN2Xa6GPve6q5YH6w4mMu7AouThRUar5+RU6rQMWVSs4otom0sy2LIkCG4dOkS3Jt4oMWQuWgS/LjiOP8z0kf3NAWnWVsjyeYgCOvz008/gWEYzJs3D0888YTO8Y899hgA4OLFi0bdz9paC9iW3joKjX2eqwuGbuPdVCW55+zVSqPmpS5JqCJtpSLZx6fvK2jW6Vm191LXJU9+bXXv0VB7mLSQIByfzp07Y+fOnbhw4QK6detm9vuR1jYedXaS3D4+UVD+oMuszJery27mP+dDfD04z3l7j4HShrb4KIIgnAuHSfNLSkrCxo0bsWzZMvTv3x99+vTROHbhwoXYvXs3GIbB1KlTTTaHHj16gOV10bE2rkKZwaPc8YdX3BArD+fhiaAWnNf4xo8m40vdWGfnk08+weHDhzF79mxMmDDB2tMhnAjahCfMDWltA48FeuHQxQanpouAQb1U87xCfUV4sXMbjU5JdYZnYwOQHdmgnTBhArZt24ZHH30UixcvVqncTBgOaQhB2AaktYZxl9duL6+0BnmlNYgI8ERsqOw5lhQXBikrC1SqeFAIQ7n4xbT4cI0db5XRtsGo7pgpNhgtGeTLn29p0zZ466238N9//2HHjh3w8PAwy33VYe3gZoIwNbTOknHt2jWkpKQAAL7//nu0bt1a73Nbt26NlJQUDBo0CMuXL8esWbMQEBBg9FysrbfW0tpmTVwQESBCbkk1xNW1OMgL1Gmsj1fT37q+SbC6bFh1wcLqNDciwJNjP+uyqYUCBonRbYwquKG8htBVJIv/+TzzWCj6fPst5s+fjz/++ENt9U1rJxA7cmVMgiDMi7NqrTp0JZE21v5RDewVITG6jV7PbP5zXsoCC/c2XCu3pAopmfkK7cm6zE3uybpcoTEBRo782spjckuqkFtSZXZtYxgGK1aswLBhw/Dk+I9xtr6V4lhEgEjlM9JH94wNTjM3ZHMQhPU5e/YsAGDgwIF6jff1lRUpqKjQnDipDWtrLWA9vXVkZdGqLwABAABJREFU32FjnucSKYvUrCLOa8E+TdE5xBf/FldyClk2b+qKsqqGn8P8RQjzEyE62AcAy+n6BwBnrlSgsEx9FwevZ0bjXvF/8OqaCM+OvVWOdwz0AqC5S56m92ioPUxaSBCOz/Tp0/Hbb79h0aJF+PXXX81+P2fWWmNwFTKc+OQIfxGkrGpxJGPjmJz5OU/xUQRByHGYhJ/4+HiMGTMGGzZswLPPPouXXnoJvXr1UhzfvXs3cnNzsXnzZhw/fhwMw+C1115TVM5wVJSFVE7HQC9O5QVxdR0ul2uv/KTJ+FI31tlp3bo1/v33X7i7u1t7KoSTQZvwzsekSZMMPsfFxQXNmzdHUFAQoqOj8cwzz+htDJDWNuAikFVnlOuptmQfAIgN9THYaHXEhB1TOeGbNGmC33//HW5ubmTMmgjSEIKwDUhrTUNuSRUW7MnGsXwxUsbHYHqPcEzvEY6ktSc5zmB5YLM+m4faxqg7ZgrHsyWDfPnzjQ31xdTxX6Ourg5ubm5muacmrB3cTFgfcwVuWCsghNZZMn766SfU19ejS5cuGDx4sMHnDxw4EE8//TSOHj2Kn376CW+99ZbRc3FWva26X4/LvOr/yjTWx6vpb91UVXbVBQunZOarvJ4YHaT4bquzqdU9C7R9T7U9O5Tt9qS1J7W+T3X3EMaH48UXX9ToR7ZGhWJ175d0mCAIQ3FWrVWHLHBWM421f1SrHIv0Pp/vf5ZIWWw7ze16p6w96uzcpLgwHMsXq634rzxGfq1CcbXG65uD2NhY5ObmYs2xYpxVSoxKjG6jYgvo44+3VZ892RwEYX3u3LkDAPD29tZrfF1dHQDZvq0xOLPWOrLvUPl5Hh3sDSkLjV1k+aRk5nM0FoBKMm9EgCcSo4Pw88krnHECACnjYxQ/T+0us3Hl+i6uroO4uk7lni4CAM180XriMjAurorXm7oKZPu3LHBFXIMVB/MQHezNWUfI56JJswy1h0kLCcLx6devH+bMmYPPP/8co0ePxldffYVWrVrpPtFInFlrDSEiwBPDooJwPL8MBy+WKV5nASzcmwNAvV4bul/jzM95io8iCEKOwyT8AEBKSgpqa2vx888/Y8OGDdiwYYPiIff8888DgCLrdcyYMfj666+tNldr4OfphsnPhEIiBQ7mlEI5NLnybh1mD2j/wHD0Ucmw5Ttxw/1FCPH1sImqRdbmxo0bGDduHJYvX46IiAgAoGQfwirY6kYDYT7WrFnT6MV8YGAg5s2bp3dHMtJaGVIWYMBN8nEVMnAVMKip43Yb8BW5QcrKDFZHqTBlLMY64e/du4dx48ZhypQpisoppLWmhTTEeXHkanj2Cmmt6cjIKUXs53/AW+SOYVFBKpuK8sBmfTYPtY1Rdyx5bGfFvzU5nnV9/ywZ5Dv5mVDsSPkKFZImmDjtVSTFhYFhGIsn+wDWCW4mbAtzBW5YKyCE1lkyMjIywDAMRo8ebfQ1XnrpJfz1119IS0trVMIP4Jx6y7KqxaF6RvpDKGBMskmqLmg4+VAeCsXcgCNjE4smdgvFysN5nCAjfTVXGU3PAk3fU32fHbqSfYUCBi1unAIy/8CUsSsheKD52mxb/jUlUtWqmKbGkYPnCIKwLM6oterRXiiqsfZPdDBXK3QlGGlDV9c7tcmrAgYp42MUtq28M4C8+8/EbqEcuzc62FsR9MW/vik4ffo05s6di82bN6N58+YAZFrb2OAwW/efkc1BENbH19cXN2/exPXr1+Hl5aVzfE6O7FnYsmVLo+/prFprbd+hOTVB+XmefCjPINtMXfdbAS+OIMTXA9Piw1U6AUFpmPz9nb1aqXIPVwGDe7fLUbbra/j0nQ54y7o3Kyf7AMBDXk2QXyYrOJJXVo2Fe7MR4e+JnpH+YCBbHen6yAwtqEVaSBCOj7wgcmBgIDZv3ozU1FQ8+eSTCAsLg4eHh8bzGIbBqlWrjLqns2qtIYT4yj575WQfAMgr5fqE+XptqA9S+Tlv6/ZZY6H4KIIgNOFQCT9ubm7YvHkzRo4ciSVLluDo0aOKyhgAIBAI0LVrV8yYMQNDhw614kytw2OBXjhRUK62ylLHQC+e4cjNsOVXaMorrcaIzm0MNpYcTXCLioqQkJCAS5cuYeDAgTh37pzRVVgIwlzU1ksxbf0pnL1aiY6BXkge2xluLgJrT4swAd27dwfDMMjLy0NxcTEAQCQSoV27dvD09ERVVRVyc3NRVVUFAGjTpg1CQ0NRVVWFvLw8VFZWori4GJMnT0ZOTg4WLFig856ktTIyckrh4SrkvFYnYdV21hNX12Lh3mwIGAqSMcYJX1NTgyFDhuCPP/7Arl27cPHiRQQGBppzmgThVFBAn+1BWqsfAgBSnaMaqg8u3JuNWf3bKwpdyO1RiZSFhNepT93mobYNRnXH9Nlg1PX9s1R7epZl8X8zZ2DvhhUAgM8nPwehwHrPAUu9b8J2MVfghrUDQpyds2fPApBVRjSWuLg4zrUag7PrrauQwcy+kWb1zSrrHKBavddQP/HqIwUqFYXlGq5Nc+X3OVEghpSFStCSrmeBvs8OXYHEa9asweTJkyGVShEZGYn/+7//03hPddeUSFmFb96c63Z6VhIEYSqcXWvlZF2uAKBZ9xpv//D90ar+aUM0V649ct08UVCueF2TnavN/uUHK6uzy03F0aNHMWDAAFRWVmLKlCnYsmWLXnPUB/KfEQShi6ioKOzZswf79u1D+/btdY7fvHkzAOCpp54y+p7OqrXW9h2aUhM0abREyqok5eiyzdR1vwWA9GzVz2pYVBAnAXdYVJDa98fn7q0S3Nn+Ie7dLMKtXz6F34RlYASy/XIPNyFqaiUAoEj2USa3tAq5pVXoGemvsG3Ts2X/12RjS1kW204XA2CouCZBEJyCyCzLor6+HqdOnUJWVpbGc1iWbVTCj7NqrSFEB/vgh8N5Osfx9VqdDzIpLkwv29WR7TOKjyIIQhsOmZkwZMgQDBkyBHfv3kV+fj4qKyvh6emJkJAQRTUfZ+Tfolsor1Fts+rhKlRUQgQ0b+rxqz+cKDB8s08fwbWXpKD8/HwkJCSgsLAQIpEIy5cvp2QfwiaZtv6UwmmSkVOKaetPYfXEWCvPijAFBw8exOrVq/HKK6+gU6dO+OKLL9C7d29O1x+WZZGWlobZs2fjv//+w8cff4yJEycqzp8xYwbOnDmDRYsWYciQIejSpYte9yatBWrqJAaNN0Y3HQ1DnfB37tzBwIEDcfjwYQDAokWLyJglCBNDAX22C2mtdvRJ9uFzqrAcsaFc7UnJzOcUxegZ6a824EhdEG9DAHE5ekb6Q8DAoC64ur5/lmhPL5VK8corryA5ORkAMGXKFPTs2dPk9zEES7xvwrYxV+CGtQNCnB2xWAwAaN26tdHXeOihhwAAZWVlOkbqjzPrrVyHzOV75eucvJKwHEM3ZvnXA2R+rpTMfL0TbNWh61mg77NDWyDxihUr8MorrwCQJa5NmzaNc1yTP1z5mklrT3LOMde6nZ6VBEGYGmfWWqDhOapJ9xpr/8gTijT9rO3e6pBrDwDFOfJAYWN0h6/fWZfLkTI+hnMtU+wLHzx4EAMHDkR1dTU8fR9Cx8HTFInBxlybP6fj+WLO8RMFYpPosL3siRMEoZsRI0Zg9+7dmD9/PoYNG4agoCCNYw8ePIiVK1c2uguuHGfTWmv7Dk25p6JJo1My85FbYli3XG2fC/+1qd1l8V+axqqj7tYN3Nw8B5LKm3Bv4oHIYW+jXNBQHLN1iyYqc1aHvkU4hAIGAoZRXJOKaxIEIS+IbA2cTWv1JdxfhK1ZV9TGIwOACwN0f9gfXcJU907V+SD1tV35WpWaVaTTpmqs7WUJ243iowiC0IVDZyc0bdoUjz76qLWnYTNoEtfW3k043T40bepJWW5VKP7PymgSOX2MX3XirW8Gr6XIyclBQkICrl69iubNm2P37t3o1q2b1eZDENrgO03UtV8m7JMTJ07g5ZdfRlRUFDIyMtCkSROVMQzDoE+fPujevTt69OiBadOmoUOHDujSpQt69OiBw4cPIzY2FtnZ2fj+++/1TviRQ1qrPxKWRfKhPJvRMmtgiBP+1q1b6N+/P44fPw6GYZCSkqJo00wQhOmggD7bh7TWdEhYlmNrSllZsJEy8uBaPuqCeJUrFQPA7AHtDdpw1PX9a2wFYl3U19dj8uTJWLduHQDgjTfewJIlS6y2YSLH3O+bsH3MFbhh7YAQQkZ9fb3R50oksqIL5nhOOZve1klYpF0oUejh9B6WTxwxNEiKfz19z9MUrOTn6YYpcWE6nwWNfXYsXrwY77zzDgCgT58++OWXXyASiThj9NnMttS6nZ6VBEGYC2fT2gh/ERI7t+E8V5WR65cu+0dXUBFfH6KDfTg+6IndQpGaVaz23truwZ/vD5n5AAxPFNZHvxpbnXnfvn0YMmQI7t27Bxfvh+A14nOk/HsX/oGyORtzbf6cwv252i3VvEVuEI5cmZognI0xY8bgm2++wZkzZ9C1a1d8+umnnA63NTU1yM3NxebNm7FkyRJIJBLEx8djwIABJpuDs2ittX2HprTNNHU34Gt3RICnVtuM39lW3qFvYrdQxRgpC6w8nIesyxUKvVf3GaqzvevExbJknyoxhE1EaDHsI5R7PfxgbiIkRreBlGU5XYN6POyHs1dvQ1xdy7lWx0AvTgEubZ8fFawjCEKZgwcPWnsKTqO12hAwQBNXIVo1d0deqfZEz3pW1oNW327p09af4ozR9Nzna1VuSTVyS6q12lT62l6abHBz224UH0UQhD44dMIPoR9BLZoiae1JhUhp2tTj+2+Vf+aLnbIxpyxy+hi/6ow2wDiHrDk4e/YsevfujZKSEnh7e2P//v3o3Lmz7hMJwkrwnSYdA72sOBvClPzvf/9DfX09PvvsM7XJPsq4u7vj008/Rd++ffHVV1/h559/BgCIRCLMnj0b48aNQ2ZmpiWm7bD4iFzRMdALhy6qrzZdVF6Dgw++i6bUMnuqAqivE76srAx9+/bFmTNnIBQKsX79eowaNcpCsyQI54IC+ghnwFfkhqS4MGzNKuK8vu10MRKjg4zeoG3shqM1v391dXUYM2aMYk343nvvYf78+VZP9iEIwHyBG9YOCHF2/P39UVRUhOLiYrRs2dKoaxQXywJefH19TTk1p2fb6WKzJPxo6453srBcUXVfjkTKQiJlNdqz8uulZhUjt6RK8bpcuzXZxpoShaZoCG7i05hnx+eff465c+cCAAYOHIjU1FS1/ht91hSWWjfQs5IgCMI0DIsO4jxLjQ0O1hVUxNcHKctiwZ6G/dlj+WKObqq7N/8ex/LFKKqo4Ywpq6rFgj3ZOJYvVuirPn5offSrMbb1r7/+ihEjRqC2thYtWodC9MIncPH0UXtdQ67NP7fyLreopsBEtjMFMhOE4yAQCPDbb7+hZ8+eyM3NRVJSEoCGghXNmjVTjGVZFo888gi2bNlilbkSjUOXrWvIXq2m7gZ87U6MDtJ6PXWdbdOzZZqewdublv87NUvmG5/YLRSr/izAttPFAFi80CkQs/q3R9blcuSXVSP7/H+4uXkupDW3IGjiiTajPwfr16BVIb4iTIsPh0TKqnQNmrb+FOe+EQEiJI/tjNVHCvSybalgHUEQhO0hZYGaWgnyy2p0D4bmouTqfJD8574mf7GyFheKazi6qcmm0tf24tvHcr08USDW63xjoPgogiD0hRJ+nBgfkSseD/TCwQeBycrddNQRG+qL9OxSzs9y+GIXEcCtdCQXOX0cu+qMNltxeF6+fBk9evRAeXk5/P39kZaWhscff9zi8yAIQ0ge2xnT1p/C2auV6BjoheSxlKDmKBw5cgQA8OSTT+o1vlOnTpzz5MTFxQEAbty4YbrJOSHl1XXoGuYHAcNwkuzkXK+8x/lZUyVFefUlAcMgNlS3U9jRqgDW1taiV69eOHv2LFxdXbFlyxa88MIL1p4WQTgsFNBH2CsMZFWh1OEiYMAwgKebAI8FeuH89Tv49sAlsLwutRXV93GiQIyekf4c3dWXxm44WvP7N3HiREWyz7x58zB37lxK9iEIwqy0b98eRUVFOHDgAKKjo426RlpamuJahOm4VnEXtfVSTgd4U6BPd7xwf5GiEmRGTilSMvM16qL8euo6wQOabWP5cXW2tjKmLqaxePFiRbLP8OHDsW79Bqw9Xqz2+vqsKWjdThAEYV9kXa7g/Gxs4qau/VG+PiStPckZzw+uiggQYWK3UE4XIH7gkrJv28NNiJpaicqxtAslqJdK4SIQ4GRhOaKDfQCwnK4B8g66uvRLXVCXcqFKTXqcnp6O4cOHo76+Hk8++SRGf5yMZUdVOwYYY7fz58QvrBcbapqAYwpkJgjHIjAwEFlZWfjggw+watUqVFerVr13d3fHlClT8Pnnn3OSgAj7QZeta8herbr1wdR1XC0P9/fQuG6Q27HyTnx8NAVZA0BuSRUW7MnGysP5nC48X+67iNkD2iNlfAxe/N+vOPTTbEjv3YHAwwstX/wMrF8o5zrKwdi6ArcTo9vAzUWgt21LBesIgiDsC3V7t4+1bq4yTpMfNikujJOsqslfrKw5fH+zJptKX9uLb4PL9bJnpL/K9UwBxUcRBGEIdpnwExZmukU8wzDIy8sz2fXsifLqOhTfust5TVs3HW3GlGqVJK7zVS5y+jh2Nd3HFhyebdu2xfDhw/H7778jPT2dAgwIu8DNRYDVE2OtPQ3CDIjFso246upqvSoc19TIKiyUl3Of2S1atFAZS1prHCmZ+fhzVi88s/CASoty5Y1RQHslRTnp2bqdwraSFGsq3Nzc8Morr+Dtt9/G9u3bMWDAAGtPiSAIwiyQ1jYOucOYH3wEAPUPOgZU3JUgM1e1oq8ccXWdoqjF7AHtDdZPe95wnDp1Kn755Rd88sknmDlzprWnQxCEE9CnTx/s378fy5YtwxtvvAF3d3eDzr9//z6WL18OhmHQt29fvc8jvdVNTZ0E09afsojvSFe1fH3sWfnmr/L1kuLCNNrG+ibKmLqYxtChQ7F48WLEx8dj9erVWHXkssr15clLJwrKHyQgy4ps2dOagiAIgrRWPfxKxPzE1WnrT+mVYGpoQgh/vFdTV5RVKfupGUxbf4qTuMMPXFKmdYsmyC1RDVgHgK/3X4TkgXHO7xoA6K+jyroukbIqnQg0Xadr167o2rUramtrsXfvXjT3aoFmLVSTguXXjgnxUUl20vT58+39id1C9e5GYAj27FcgCEI9zZo1w5IlSzB//nwcPXoU2dnZqKyshKenJ8LCwhAfH29wog9pre2iKeFG371adfYqrxku2vqINK4V1O0tK8MvgKUO/n420DD/np0fxe7IbribdwItR34ON982KoHcGTmlWHk4D9N7RKhcp7E6R4UvCIKwFKS1pkGd6ig3FJCjyQ8rLxqhjC5NbSj2VA4pyyoKWvBtPX01SVO3eAHDYPaA9ia33Sg+iiAIQ7DLhJ/CwkKdYxiGUWu88F93lgq2bkIGtRJ1ssp9/4XiGhSWcR23Jwp0b47yxW5YVBAEjHGGm7r7aBJdU1de1AXDMFixYgU++ugjtG7d2mz3IQiC0IdWrVqhqKgIv/76K15//XWd43fs2AEAaNmyJef1sjJZpzflpCHSWuMQV9fimYUHUHO/TuMYP083TIkLU9FG1eTZhtcNqYDoCFUAX375ZTz//PMIDAy09lQIwumw9PramSGtNQ38ZB9deLgJ8VSYD/4uquRsZhqTMKtvwJYtfq+6d++OixcvktYSBGExxo4di48++gjFxcWYMmUK1q1bZ9D5U6ZMQVFRETw8PDB27Fi9zyO91Q9tVX9Nia5q+Xx7VpOGqtsUbqxtbOpiGsHBwTh+/DhatmwJwYPuB+rupxycZUwCMkEQhLUhrVWPpkrEfA1LzSpGYnSQRjtxYrdQHMsX4+zVSnQM9MLEbqEqY5TRlDwjJ7ekCrklVZzXlAOX+OcEtWiKYVFtcKqwHEfzxKipa7DB1W47P0BTd3t1drHyvjC/Q5E2PRaJRNi1axcAoHlzWdVodfvYyq/p24FB3V61OQKOKZCZIBwXDw8PJCQkICEhodHXIq21XTQl3DRmr1bA+x3xf1aGb2cKGSDY1wMMGOSVVUNcLduvVlc0Sxvy+U/pHg528VIs330Kd4TN1QZyA8C208WY2j1crdaTzhEEYQ6qq6tx9OhR5OTkoLKyEvX19VrHf/jhh1qPk9aaj9NXKlRe0+aHNdTHK9caoMHPKi/2qK07ribkNnVqVhGn+EVsqI/ZNI3iowiC0Be7TPj56KOPNB5bt24dCgoK4Orqivj4eHTo0AGenp6oqqrC+fPncejQIdTW1iIsLMygzWF756lwXxy6WKbyujwxRy5SfCcvAEj1qPqgLiFHWVAbiybRNXXlRXXs27cPxcXFmDx5MgBAIBBQsg9BEDbBgAEDkJycjA8++ABRUVHo1q2bxrFHjx7FBx98AIZhVCoCHD9+HADQpk0bxWuktcajrhKSMi083BT/Vt7olPBLNj1AlwFraHUkWww6vnjxItavX4958+YpHCBkzBKEdbDE+pqQQVprHbqE+iAq2Efh7JXTmE1YXd8bY79XptTsW7du4aOPPsKCBQvg4eEBgLSWsCy2uAYlLEtAQABmzpyJefPmYePGjbh58yZWrFihs3pifn4+pk+fjrS0NDAMg3feeQcBAQF635f0Vj0MuBUXH2vd3CL3NbRa/srDeVi4NweATEOlLIvpPSLUbgonj+3MubahVRYbmzBUX1+POXPm4NVXX0Xbtm0BAA899JDW6ztax16CIJwT0lrNqHuu85/9uSVVCntRnQasPlKgSMDJyCnF6iMFWrVCW/KMJpQDlyRSFpPXnMDBB3vKBy+WoUuYL2JDfRTd6PVBW3d7bXaxLj1eunQpOnXqhLi4OAANiT76wv/85dWfyU6xPGQjEoT+kNbaLnxd0VT00RD4mitlWSStPanWho4O5uqmhAXyy2oQESDiXJOf7CM7znDiw3xErmAASIv+Rubv/yIp7j0IBQym92yHVUcKcadK2/63+sIcZN8SBGFq6uvr8dFHH2HZsmWoqlKNcdWEroQf0lrzUSiuRvKhPM56X5vdpy7+SR/bwVR+Vn6xR3N0Y6X4KIIgjMWhEn4mTJiAgoICjB07Fl9//TWnU4EcsViMt99+Gxs2bEBBQQHWrFlj5tnaBv8W31J5LSJAhKndZQJ4srBcY0t2fXxr1qrMYO5N0d9++w2JiYmoq6uDt7c3hg4darJrEwRBNJY5c+Zg06ZNuHPnDnr06IHExEQMGjQIkZGR8PT0RHV1NXJycvDrr78iNTUVEokEzZo1w5w5czjXWbt2LQCgd+/eitdIa82HfBP3WL4YRRU1HP3tGekPASNr1y5gGMSG6jYcDdVgW3O4njt3Dr1798bNmzcBAJ9++qnV5kIQhPnX10QDpLWmx8fDBeU1qlW8PFyFACPbLE0e2xlPLUhXOd4YR62u742x3ytTaXZZWRn69u2LM2fO4NKlS9i1axdVGCMsjq2tQQnr8NFHHyErKwu7du1CWloaHn74YfTo0QPdu3fHI488ghYtWgCQJSlmZ2fj0KFDOHjwIFiWBcuyePbZZ/HJJ58YfE91OLve8stNxIaqvn9zYGi1/G2ni1V+nt4jQu2mcGP904YW01Cmrq4OY8aMwc8//4zt27cjKytLJQBZ0/UdrWMvQRDOB2mtZtQ91/kaJkeTndgYP42mewEyP7RQwCA62IcTSJwUF4biW3c5Y7dlFQNabMiIABGGRbUBwCLrcoWKjkqkLFKzivR6H+qSg5MP5eFkYTlKMzfjt1Vfo1mzZjh69CgeffRRvT4HZfifiZSFXnYKJaeYHrIRCUJ/SGttF76uTIkLa/SzTFO3vrQLJTiWL+b8PKt/JCICPFUKO1c86OyjicToNpjYLRTT1p9SdBHsHOKDT5atQ+mvC/C3RALP5l74/oNXAKh25w3zFyG/tGF/e1hUECXVEgRhEYYPH46dO3eCZVn4+/ujtLQUDMMgMDAQt2/fxu3btwHIuux4e3ujWbNmel2XtNb0uAoZ1ElY5JZUK+KjhAJGYecB6v2w6ny8+nRqjQ725mhydLB3o+Zvrlhoio8iCKIx2GXCjzo2btyIdevWYcSIEYrAZXX4+vpi3bp1uH//PtavX4+EhASnyK6tUBP0lBjdBkIBA4mU1dhVANC84WsLzs3GVl5Uhv9+vK6fwtixY1BfX48nnnhCUS2KIAjCVggKCsKuXbswePBglJeXY8uWLdiyZYvasSzLwtvbGzt27EBQUJDi9YqKCkRGRuLhhx/GyJEjtd6PtNa0KDtG5QgFDFLGx+h1vrE6bEvB/KdPn0bfvn0hFovh7++PxMREq8yDIIgGTLm+JgyHtLZxqLN7AeCpcB/EhvriZGE5Vh8pwN06bkXDOqlU4zV16a06e5r/vTH2e2UKzb5x4wb69OmDc+fOwdXVFVOmTKFkH8IqmOLv2Rb8UETjYBgG27Ztw1tvvYXvv/8eLMsiIyMDGRkZGs9hH3Qenzp1Kr755huTPMNIb1U5faXC2lPgIP++X7t1j3dE9vs3JjlH1zPE2E3c+/fvY8SIEfjtt98AAOPGjVMbTKDu+o1JMiIIgrBlSGuBpi4Ctc91+WupWUWcQlCa7ERt9qQubUuKC8PRfDEOKvmhI/w9kdg5SDFWFjjV0E0PAMBy11sVNXUau9qH+4uw7614revylMx8laKTmt4vXy+TD+Vh/u4LuJW5HreP/gwA6Nu3L9q1a6dyrj72Al97TxRw7ZQfMvMV45TPNXVyCtk2trVPQRD2CGmtbWAOm05bt77jPN06WSBW+E2UEVfXqiTlyIkIECm6Fih3ETy09zeU7vgCkErgGhCK214RiqTbziHeYAGcu1qJxwK9EB3cAr/+fQ1ggSAfD5wqFIMfcqZvUq0tQfpMELZNamoqfvvtN3h4eGDbtm3o168fBAIBACA7OxseHh4oKirCunXrsHDhQri7u2Pjxo14+umnjbofaW3jCPYVcRJSlRNWAe1FoPjoZzvwn9e29/ym+CiCIBqLwyT8/PDDD2AYBjNnztRr/LvvvovU1FT88MMPTimyYX4eimxZZUNOfmx4dBucvqJahUkZbc5NSxlCpjSgld/PjtRNKN/zDVipFLGxsdi7dy+8vRuX+UsQBGEOunXrhvPnz2PevHnYuHEjKisrVcY0b94co0ePxocffoiWLVtyjnl7e2Pp0qV63Yu01jCaugpwt05zALE6DAmsN3aT0VaC+Y8dO4b+/fujsrISrVu3Rnp6Otq3b2+VuRgLOX4dG2f9/VLQoXUhrdUfFwEQ4itCrtKmpaYyFn8XVSI9u8GRzN/srJPIvu/q7Fl+FUWAq7d8e7pnpL/K90af75W6Z05jNbu4uBgJCQm4ePEimjRpgu3bt2PAgAEGXYMgTIUp1qBUAdoxcHNzw/LlyzFq1CgsWrQI+/btQ329+oRNFxcX9O3bF7NmzTJpIR7SW1WsYRdqW28rf9+VGRYlK2BiTHKOrmeIMev/mpoavPDCC9i/fz8AYNGiRfi///s/vedkrkqRhuCsdg9BEOaFtBaK2CJ5oCzfFgz2EaGNtwcEjKzwoib/izp7Uv7sVk4aSrtQgtSsYiRGNyTzCAUMuoT6cBJ+hkUHYlp8OCRSFsmH8hQJLnJSs4pRXnOf8xqr0doG2vp46NQNfnBWRICn3v6mEwViVBxIwZ1TvwIAwp/qj82bN8PFRTXEQR97QZ32pmc32CllVbWKa5iicy+gXmvNbdvYg77byj4FQdgrpLW2galsOv5ze2K3UKw+UoBCMTdhp6aWW8jqcvld5KlJ6gGAG5X8AhoyKqrrsPJwPk4VihWvVZ1Lh3j3NwArhdtDDyNgxDy4N2vB0arZA9pjzcRYTpcFABz/vLyDYEyIj0py0okC0yV2mkvnyPdIELbNunXrwDAMXn75ZfTr10/tmDZt2mDOnDkYPHgw4uPjMXjwYJw5c4ZTFFlfSGuNJ9xfhBc6BeLLfTlqj2uypzQ93/WxHbIul6v52Xae4Y4QH0UQhPVxmISfc+fOAQDCw/V7UIeFyRyJ58+fN9ucbJn8shqsPlKAafHhKk7K/LIabD9zleMU5lNbL8XKw3mc15TF2FKGkCk3ReWfw52/96B833cAgGeeeQa7du1C8+bNG319giAIcxEQEIBly5bhm2++wblz51BYWIiqqip4enoiODgYHTt2hFAobPR9SGsN4yEvdwT7eqrt5MMnIsBTobv6om6TUb5ZaEgVQ2sE8x86dAgDBw5EVVUV2rZtiwMHDuj9d2VLkOPXsbH079dWNuJtIejQmSGt1R8pK6syrA/8SsTB3k1RyatQrMme5cN3QvP1WP695Qd16fpeqXvmNEazCwoKkJCQgIKCAnh4eGDnzp3o1auX3ucThKkxxRqUKkA7FnFxcYiLi0NNTQ2OHj2KgoIClJfLfsc+Pj4IDQ1F165dIRKJTH5v0tsGjLFF9aW2Xopp60/h7NVKdAz0QvLYznBzESiOa1tv87/vfp5umBIXpnOe2tbUmp4hDUHTxYqqk/qs/+/cuYNBgwbh4MGDAIBvv/0Wr732mtb52SJk1xIEYQ5Ia4H7D/ZRF+5t6J4jZVkIGIZjb84e0F6rT1edn4YfaCsnt6RKJVkl6zK3i5/8Z012r3IFZjneHq4or1Zvf+vjuuIHZyVGB+nl85JKpcj95RtFso/n430x47Nv1Cb7AMbZC/K1xQ+Z+SirUu8jUPceGlu4y9y2jT3ouy3sUxCEPUNaa/9oKzp1LF+scX85IsATIb4eiAnxUUncVYafHCRHXF2LhXuzEe4v87cox0e5B3VAwPCP4e/jBYaX8CvXqv9n79zjm6rv//86J01pk0LbpKkXWnrVsiluUFov3Io3QN28IJu6IaIVpru6y9exedmm0/F1Fzcv+3JRRObmhoiXTVG5tjKgF7z/oNimrS0oTZO20KSlac75/RFOeq7JyaVt0r6fj4ePh0nONaGf13nfa5qdimMKGFgG65eVAQD226Xb+Xhe4TOPNAY2XDpHvkeCiG/q6+sBAN/85jcVn3GctBHv+eefjwceeAD33HMP/vCHP+BPf/pT2OcjrY2cJocbLMOgyGZCk8Oj+Nze6cZTuz7Fe591SzRBa33XYzvEc0OBsZIfRRDE6DNmCn76+voAAEePHtU1ieXo0aMAgP5+9a4G4wHBOJELHqDuFBazYlMdnDLnrlgoE9EQKsu34OW/PYOu7WsAAFNnXIJt27YNS2IBQRDEcGAwGPCVr3wFX/nKV4bl+KS14fFFzwAYxhN00k8SCyQnGZCTmYrlswpUHZvhdLGItIvhSLJz505cc8016OvrQ1FREXbu3IkpU6aMyrVESyI+7xD6GenfNxEC8cTwQ1qrH45XFvLohmGwYm6hJLkpmD0rRu4gjlSP5WitOZFodlNTEyoqKtDe3o6JEyfizTffxKxZs8I6BkHEmlg8g8ZzwIaIHJPJhMsuu2xEz0l6C1jMRtw5pxAr5hbFtMhcbL82d7oDnYZ3NTiwclMdNiwvD2wb7Hlb/vd+5+ni2VDINXi/3Yn1y8o0bWgfx6NyY61qIlWw5//e3l4sWLAA+/btA8MwWLduHe64446Q1xePkF2rTrw0ZCCIRIW0FkhJYrHlYLvkvSd2NuLsjFTJe8I6LNavzXVtyLVIp/8Ia5CP47G5vi3oucVFrT5OmqwrvKdWXJthMgYmBom5sTQXLMOgptmFFqdbMkmgvMAa9FqA4FOKtNZZnudxxx13oOq1vwMAcmbfgHOv/S7AsvBxvOqaHIm9INgpADR9BFr3oIX83mpkEw7WVdsxbXK64tpjSSLo+2jHKQgi0SGtTXzWVtmxept606mPjvZo7rekNCewdu6zOyWT/CxmI072D8Lr057OJ9DkcMP38VCxT0reV2C74X6wySlwur2QH6I0z//vjAtyaLGeyaW6zeUJXGu0MbDh0jnyPRJEfON0+gsJc3NzA+8lJSXB5/PB4/EgLS1Nsv21116Le+65B2+88UZEBT+ktdHxxM5PoTUs1u5w47G3jgCQaoLW+q7Hdghls42Wr28s5UcRBDH6jJmCn6KiInzyySdYs2YNnnjiiZDb//Wvfw3sN14RApqleZmoKLFJDEEBrbGqcuck4Dcml88qQHISG1eGkF7BrpxTiE+uvBBP7X4W582cjX07/gOzKVXliJEdnyAIItEhrQ0Pj9enOUZdYJADBgd82K2S/CQQTheLlZvqJPvGYyAvJycHkyZNQn5+PrZv346zzz57tC8pYuLpeYeIPSP9+yZCIJ4YfkhrY0OqkQXDMDgzPQV2FS1mGSao41f+9z+/xBZIFJY7iGOlx7Fac3wcj9cPn0R/UhpME9Px9jtv46ILlc8XBJGIUAdoIlaQ3gIutxcsw8TchxlsSp48YSmY9gl/3zXNTnD8kC9a7ndVJtRKOwjvanBgfbVd04ZeX23X7JocTItTU1NRUFCAmpoaPP/887jllls0t413yK5VhxoyEER0kNYCM/MycKznlOQ9z4BPMUGnudOtSJxtdLjReNqW3XHYr1PCGrS2qklRlGM1J0saYpTm+ddyNZ0TtFGtuBaQFr2IJwEK+rvjsNRW1vNMHmpKkdo6yzAMzjvvPADAlTevwOHcr6Gpsw+rtzWAZRjVNTkaeyHUvuEUp8g1ZH6JTfJ5Z+8AdjU4UGQzI99qChR1xRLSd4IY+5DWxjd6cojkhcFiJqYkSSbPVZTYkHTaP718VkFgUg7PSx8iXG4vWPnBgpB1dh7aDUlIzZ8O23WrwCQlBz77WFF05L9+lpHeR7EtDflZJoV+lhdYA88xAPB5jzQBPpoY2HDpHPkeCSK+MRqNGBwcBMsOrXSTJk1CV1cXjh49iuzsbMn2QoN3oRAnXEhro0Nr0pwaWkMLhIYVenzYoWy20fL1jaX8KIIYiyRa7v+YKfi56aabcN999+Hpp5+G1WrFfffdpzrSe3BwEA8//DD++te/gmEY3HzzzaNwtaNPYZZJMhL2fxaUgAEUjl+OVy+1TTUaFMIsTlaOJ0NIr2AbWAZ/+dly3Dx7KkpLS5GcnKzYJprjEwRBJDqktcPLgWaXqrEaTheLRAjknXvuudi1axesVqvC6ZJoxNPzDhF7Rvr3TYS/X2L4Ia2NDcJkPbvDjYpzs9De3SdJiGp1ebC+2u4vztGZJBTOFL7SvEzJ37PQ/TAYsVpz1lfb8Xj1UaR8/QEYe134oN+KiyI6EkHEH9QBmogVpLd+hqPAPNiUvPNlney1tE/QV/8UAU+gkYaQYCy+Zrlf1mpW+nNrW1yB4h65Zqtdb3G2GYtn5ILj/c2y1J4FDAYDNm7ciLvvvjvhp+iRXasONWQgiOggrQWqGl2Yd64tULgjxsgy8J6u8mlyuNHlCT69VrwGbamXJgdbzcm4fXZ+oDsyAHA8hzV7mrCu2q55vDVLZwb+X77+a9nC8rXRwEZePKxnnf3pT3+K8vJybLSnoEGUMCyeYCTXd6HId22VHQserwLAY/GMnJBTDWNhawjXI//eWQZYtWgq1lXbJcnbTQ43vjEzd1j0hfSdIMY+pLXxjb4cIu1ROc2dHsnrsnwLkk7bsPvtTs3GFQDAhXGdd958Pf5js+Bz41no9zHweIf2PtE/KNm2vtUFoAjlBRZJAfDi0slgmSH7Wnh+ELRnc307Gjt6Ffll0cTAhkvnyPdIEPHNlClT0NDQgM8//zyQZ1JSUoL9+/dj3759mD59umT7Dz74AAAkBULhQFobGQyCKZw6wiR2judhNRvhdHsBSJs5Rcto+frGUn4UQYxFEi33n+HlJf8JyqlTp1BWVoaPP/4YDMPgjDPOwDXXXIMvfelLSEtLQ29vLw4dOoT//Oc/+OKLL8DzPKZNm4ba2lrdhR3xRk5ODr7o6UfOdzfq3sfAALPPsaHN5YZdZCRazclgGEgcjQBw2VQbnrlN2Q34iZ2f4g9vH1G8n5WWjLr7rgjjLoafyo21kmSry7+UjfXLygD4R8I/88wzWLp0KSZMmBDz4xPxRaJVZBLDT05ODgCgvV27g068cPvttwPwd7Z75plnJO+Fi/gY4UBaO/ysWjRV8eAo7naotY1AvK5zW7ZswaxZs3DmmWeO9qUQRNwSr3+/0ZJIWhsPkNbqJ4kBBnV4M4qzzXjrR/OwvtqOzfVtksKf+SU2rF9WFvHfmpZG/3V3E1ZvG3q/osSGZ6I4jx7279+PpKQk/N8nPNmnBDHOIK0NH9JbP9HqoBxhmrxW4lFRlgnfKJui+pwrLfJxa07LzUpLxp1zCocSiGR+WTXuXTgVLANVzZZrufCdyCcVrVo0FVcXTUBdXR2uu+66UF+FLuL5+T+er22kCMcXQ4wPSG/Dg7TWT5HNHHICvMD8EhvauvoUE4AA6RpU+tA7kmk+VnMypk/JkOhhcbZZMQVIjNrkHj1rfyzXRrVjLS07Cy+++CKWL18ORjQ9QOu8et+P9lr1onbeUNclt9lJg4nxDGlteIxXrQVG799IOGu0Wg7RmqUzJfsPcpykYDcYhVmpsHf2RX0PPM+j98O3kXbepSg6KwO5manYc6RT177F2WYsKc3F8lkF2LC3OXAfHA+JL1yuufLvQm7XxwLST4LQx2ivo9GyfPlyPP/883j55Zdx7bXXAgAeeOABPPzwwygoKMCBAweQlZUFAOjq6sKVV16JgwcP4qKLLsLevXvDPt941dpo86PU7GBTsgHfnV+M2hYXPj7ag/Mnp6MsPxPvfdZ9ekItj5fq29Dk8CiOFyrOqVcDRtLXJ+RH2bLPIH0iiDgn1rn/w621Y2bCz4QJE7Bz504sWbIEe/bswRdffKGazCzUN82bNw+bN29OWIGNFB8P7DnigNEgFQ+xc1hMeYHVv59MHA2MuvhMk3VqjAe0uqVzHIfvf//7ePrpp/Hvf/8bmzdvhtFojNnxifgj0SoyCULMc889FwhyCfomfk8vPM9HXPBDWhs7TEYDPF7lCNuaZic4Xhjj7u8+eMds/V2K4rHz0Nq1a/Gd73wHX/7yl7F79+6Ak4UgxhKxCCbE498vMfKQ1upHrdjHwPhtXilM4O+rtsUlSXra1eDA2io77qoY+rsL5+9Z3g2qptn/+pl3pd18d8ewA5UaVVVVuPrqq2E0GvHd32+SfEb2KUEQhBLSWz96OySKi3E4ngfL+H3Gco1cX22XFPtYzckSn3NTpyfgl5OfU15go0Vn74DkGHK/rBoH7J34+NgJyXtCB0etqX5yjd9Z+zFWL78Hra2fYf7dj2DJjYujDtDK/ZT77U4YWGbUgr/iZyAfxwd+y/HqQ6XJCAQRHaS1fsJx3bd1efDGD+Ziw95mHGh2odXpRrdnABfkZGD5rILAdpkmo0RfM01GFT2UnjgrLRnTJqfjM5cbTQ4PGjt68eibh7G5rg25FhNYhgHHK9d++YQ84TpisTbK19mbpmfj6quvxu7du9Ha2opf//rXmtuKX4sR9F1tgl+wiX+xQn5ecUKzcB/yqQxym53imARB6IW0duQJZ41WyyGS73/vwqmYX2ILOq1H4Ise7WmAhVkmSbNnLXieg+udNeh97z/oa6oFrv057LKEbLl/vTjbDIBBY0cvGjvcEntcuPfKjbWSY8gnJsi/iztPT+SLJaSfBDE+uOaaa7Bx40bs2rUrUPCzcuVKPP7442hpacE555yDSy+9FD6fD3v37oXL5X8+/853vhPR+Uhrw6cwy4R/f38Orv5LlUSbUpJYbNjbjGmT0/Hfn1+G5KShqUv+QpwGzWMKTaa0bLhQGjDk23ZifokNLMOgvGD4fH3i/Kjlv92AJ/Z1aF4bMfJQkTAhJ9Fy/8dMwQ8AZGVlYdeuXXj55ZexYcMG7N27F93d3YHPMzIyMGvWLNx+++24/vrrR+9C4wCvMgsqAAMgxcjiwgILbr04H2v2NAXGrAJ+ASrOTpPsk2xgMKs4KzACfrQRL86leZm4d+FU1LcOLdQ+nw933nknNmzYAADIzc2FwWCI6FwU/Isdwy2qozWekSBiwdy5cxXFPWrvDTektfpJYoH01KFxswKFNjO2/dAfvJVPGpB3QVq9rQEsE/sigJEyYv785z/jRz/6EQDgjDPOQGpqaszPQRDxAAUTiFhCWhs5Pt7fJcozMFRUm5OZGnAEl+Ypk4K3HGyXFPxEE7jleF4zWVmP7RGJPr/zzju49tpr0dfXh8LCQiyffx5yinxkn8YZ5EAmiPiD9NZPTbNfn7TWKa2pPTsO+1+Lta2m2SnZRp6QLCDXRB/HY3N9eN3ONte3oVKUQCv2XcvZrdKteJDj4eN4zaJ7scZ7XUfxzrMPosvxBZjkVHzs4tGoUbgUDnI/ZTQFNrHQmWBFV+PRh0oNGQgiesa71qYkMZickRp00o6Yxg43NuxtDqw7Ow/79WBXg0Py/uLSHKzeNpQMtfj0pB6O57Glvh1dHi+OdUmTfoXE2sqNtZKOyY0ONxo1JhAJOqVmH8di+rx4ne3p6cGihQuwb98+MAyD3Nzc4F/WabSSMtQKgkvzlInWwv1oEa6+ys87bXJ64HsU9hUmCWrZ7KHimGRbEgQhZjxqrfvUYNCk3+FEvkZvrm9TrOdDOUoW3LuwBPWtXYHPV26qk+xf3+oK6ILQEJJhGLQ43YpCHKjcpoEB8qwmANq5XwI854Nz2xNwf7QdAJA0MQtgWcV28jSyJaW5p5toDdnboQp65EmSI5FPRXlABDE+WLBgAa699lqcOnUq8N7kyZOxadMm3Hzzzejp6cHWrVvBMEygCOe73/0uli5dGvE5x6PWRkOr04O7/laHPKtZUvDj8vhzpnY1OLDoz1V4+555AQ1Xa9gAAEYDg0kpSRKfqVrDpFAaoDbJfbg0Qp4f9eFx6XQ+0qfRh/J6CDmJlvs/pgp+BG644QbccMMNAICenh709vYiLS0N6enxN30mHkg1sujzcoHXPIA+L4fdRzpx9wv1Gh0lpJbeT64siavFT744r1o0NTBqy+v14tZbb8WLL74IAPjZz36G1atXR5w0T8G/2DHcoppoFZkEIWb37t263hspSGtDM8hBUewDAK2dbqzcVAeWARbPyAHAoK7F3yn5o6M9iu3XVfunBMTScR3Neqs3qPjoo4/iF7/4BQDgqquuwksvvUQFP8SYhYIJxHBAWhsZQrFPsc2MHIsJu0WO4HnnZsFoYGQNMKS2bTh/z3IHkDzRWYwe2yNcfX799ddx4403YmBgAFOnTsX27dsxefJkrCwg52S8QQ5kgohfxrvecqeD31rrlHxqjxi5RnKyxKCczFQsmZmrKMZR62SvVaxjMhpwVsYE5GaaJIU7jR3uwHQiYVLPgserNI8jJ9T0PUHj395bi+3r7sMJlwPJpomwLP4VJpxdonr/4RJsOlG4yb1rq+yB5iHbD3WA4yEpaNaDVnBduFaCIIhIGa9a2z/IY49K0SmA012FgffbeiTFsVoTasS6sGJuETjeP1n2ZP8g1lfbwfEAy0C1eGd+iS2ga3om4wmU5VvC9nfJ9WiQ45F0OgFLy4/sdDqxYMEC1NfXw2AwYOPGjfjWt74l2UbrOUUrKaNyTiH22Z0Bf4AfHrUtXWHdT7h2nPh6tKblhYoph4pjkm1JEIQa40lrT/YPYvuhjlFZA+VrdGOHG40dbsl7WjlKavuX5VsUurBmT1Og6FdMeYFFpmv+4hxdk318g+j8zx/hOVQFAJhUfgMyKpaHzI8SP0OEKujheH9jLYAHxw812ACk+VTDVbhKeUAEMT5IS0vD1q1bFe9fe+21OHToEJ599lm8//77OHXqFAoLC/HNb34T8+bNi8m5x5PWRoOP9zdfSk3SXtubHG7c8VwNnrmt3L+P3Kl8Gq+PV+RbqdlYoTRgpPI41PKjnq85hj32oWKjWOtTuLpKDSQor4dQkmi5/2Oy4EdMeno6iWsogjR8ONCsHuhbPCMHLMPEbWWb1uJ86tQp3HTTTXjllVcAAA8++CAefPDBEZ+QQagz3KKaaBWZBJEokNaGh48fMkR3HHbg3oUl8HGcatdhAOjsHZCMSI8F0ay3oYKKPM/jgQcewMMPPwzA7/z4xz/+Ma5HFxNjHwomEMMNaW34tLo8aHVJA56qyVa8P5AqODWD/T2rOULlDiBh4gHgD4qKO02FIhx93rx5M2655RYMDg7iggsuwDvvvIPs7OyQ5yBGB3IgE0RiMB71Vojnaa1ToYpABG2saXbiw3ZpAwsDywaScbU62fun+7RJ9rOak5FhMqLJ4YbH60OTw4Nci1kxxU+8lhpYBjdMn4z/fWto4kGRzYQpFrNmwZJQqKsW4DSwDMonncB9f/o+Trg6kZWVhZWPPou/NQ51QBaeESIJlPo4HhwPFGenAeCRk2mSJG+991k3/rq7CSvmnp4GEMIO9ydWQfJaXPCj5xrlz0DhPscQBEHoYTxqrZzCLBPWLJ2J5CQWa/Y0SToNa02oEdulBtbfPMp1OvHJ6fbisbcaUGwzq57PwDKBNX/5rALstztxoNkl0VSBIpsZBVlmlOZlguOBFqe0gCiUv0uuR8++2xwoaNp+qAOb69uwpDQ3oEPHjx/HFVdcgY8++ghJSUl48cUXsXjxYsVxtZ5TgiVltMumHNW3dimm/pbmBb8f5SSH9qBaKr6eyo21qtccilBxTLItCYIIxXjSWmHyqx77KxbJreI1usXpUUy9Ubs+8Tn15KqoHacwywTwykbOeuAHvXC8thp9n+4HAKTPuhnps27RlR8lPEOEum7huxS+D/8kQka1CcVwFa5SHhBBEHl5efj1r389IucaT1orh4GeuXJA32DwrXYf6cT6042Pxb7bIpsZ+VYzWl3ukNNyBZ1Vm6onRm5fl+ZlYs2eppgVvQTLjxpufQpXV/VsP9aLgiivh0h0xnzBDxGavkFto1Du8C3ONkucsZEaX8MtDmqLc19fH2644QZs27YNALB69Wr8z//8T8zOSUTPcItqolVkEgQxPthysF1hrBoNDIwGVjOZKVqiWW/VRtYLOs7zPH72s5/hD3/4AwDglltuwcaNG5GURI+cxNhA6xmWggkEEX9Ip/goEZKGGx1uSWFtsL9nuSNUPjpebd9w7Fy9+rxp0ybcdttt4DgOM2fOxFtvvQWLhRyS8Qw5kAmCiFfKC6wAtNcp+fv+oKsJ5QXWQCGPOFFZemx/QdDaqqbTCcCMIrF2bVWTwh5eMbcQtS0uNImmFMg7GQP+Z3Nx12D5pL3cTBPWLyvD+mq7YsoQ4J9IpBXgPHDgABYuXIju7m6cddZZ2L59O0qmfgm5KoVL4QRWBXtCfj2LZ+SAwVCA2+kewOpth8EyUC28khcrgZc/90hfh3qG0bJpxlIwlyAIIl6wd3qwYW9zyAk1au8LqE2Jb+5UT4bycTwqN9aiLN8CjpcmU7GMdEJfvtWvnfJCJFOyARcWWLB8VkGIu5PqT59XGmNu7Biyv68pTsFll12GhoYGTJgwAVu2bMHVV1+tetRw7Sn/9EDp91Gal6m4vlCpaspJDr1o7OjVlUgVqQ0Y7QQggiCI8YR48mswYlVkIl6jtYp2g00AEvYNRz8A/7ODnkk+AJBpMqLL4y8K5ryn4Hr1EfQ11QMAMipuQ/qFN2rum8QyGBQ9GIifIYTGV1qEakIhMFyFq5QHRBAEMTLoKfbRS02zS9G4kWGAtbfOVPici7PTkJuZKrFnxTorn6onRm5fB/MJh0uo/Kjh1qdwdVXP9mN9qizl9RCJzpjMvnS5XNi0aROqq6vR0tKCkydPYuLEicjPz8ecOXOwdOlSSobRwGo2os/LSRKMreZkTJ+SgbJ8vzN3w95mrNxUF1XgTy4Om+vbsaQ0J2aBRLXFuae7C+3tfkPziSeewPe+972oz0PEFhJVgkgcSGtDM6fYgv/3+UnFmFk5XSqfT0oxYsXcQlVncSyIZr1VG1kvONR9Ph/sdn8njttvvx1r166FwWCI2XUPN2O9W4XAeLnP4UDLwUHBBGI4IK0dXrw+aeMLeadetb9nuSNUbXR8NGuBXn1ubm4Gx3GYNWsW/vOf/4zbTmKJBNm6BBG/jFe9LcoyIddqxr/qPsPm+nZcP30y7l04FfWtwROOBd+woInyIpvA8W0mcDyw4PEqSWGLuIgFUCYFWc1DnQ/lSU5ydjU4sLbKHkgi+vjYCcnnHx87EXhOr5xTKCk8WjwjB3VBApxffPEFTp48iSlTpmDHjh0oLi4GAFWdDyewqlUgVd/apWqTCceS2+HywHTFuVloFBVILZ6RE/Qa1Z5hQtk0ZEcSBBEp41Vrg7HudCdjeeMI4T35muzjeEkH4kkpSejsHZAcU97zwmhgcEmRVbLmy6cAcbJ9ON5/LrlueAZ82NXgCBQqabF4Rs7prv5+zpw0QTVBeXN9G97e+wXaP++AyWTCiof+D1s7s9Eumr4r1p1QHZvlqE8oZFDfKn2/vrUr6HEq5xRiv92pOi0wVCLVcNmAZFsSBKHGeNbazfXtIW2T4SgyCbYea00A0mNrTZ+SAas5OTAhL1y6PF6kGlkMcjzYQR8GevwT7zMvX4lJpV9T3YcBUGgz49XvzsYLB1pR2+KCj+NV7UZt9BXVUuEqQRDRUF1djYqKCuTl5aGxsREsy2pu6/P5cM455+Czzz5DdXU1Lr744qjOPZ61drjgeF7RoEnIPdLSWUEvw9FZuX0d6TRWNUY7PypcXdWz/VifKkt5PUSiM+YKfv70pz/hvvvuQ39/PwB/JaXAe++9h61bt+IXv/gFHnnkEfzwhz8crcuMWy7IyUBbl0fS+SjTbAxUwYo7VURTxSkXh8aOXklX5WhRW5wtFgu2b9+OXbt24aabbor6HETsIVElCG2qqqpiery5c+dGvC9prToGBsizmpBnMYEH8OHRHrhCFPuYkg2qTttMkxHLZxVgX1Mnalq6kGo0KLoXhyJYMo54vQ03aadyTqGiG7Fg5CUlJeHFF1/Es88+ixUrVgR1ssQjY71bhcB4uc/hYKw7OIj4gbR2+JFPANLTqVetw6JAqPVAj94Gs4fE+8+cewvWrj0bN998E9LS0kLeKzH6kK1LEPHJeNbb7r5BNImSVx97q0G1E6J8/ZL7hueX2FSPP8Vixupt6pN//lXbBo7nUdfiwrGufslnmebkwLQZtak8cl6qbwPL+HU4PdUoSX6eNnmoINbAMrirohh3VRQH3luzB9hxeEjXW5xurDmdaHzttdfipZdewvTp05GXlxf0GvQESgUdFxK81Y4BKIuchPflQe6aZqldYmAZrFo0VTP5N9gzjHhqbzD02JFUFEQQhJzxrLXB6OwdkBSAyqewrV9WJlk/5WtwZmro8L7Xx6O9q0/ynssTPHl4V4MDlRtr0SbbTyCU3btibhFYhlEkCsvxx6AnYuL1D2L6WSnY2mEBOjok+iK/Z/FzirwASq43aronLzIWtguGgWU0dUy8r5b+DYcNSLYlQRByxpvWJhmk63JjR2/IKT/DUWSitR5rTQASYrwAFJqhZmtFQ5/3dKOrJBPOuOlh9Ld+CPOX52lu//NFUwPXoWVzhnoGkBf9Ck0o5BopTAukwlWCICLhX//6F3iex2233RYyD8VgMOD222/HAw88gH/+859RFfyMN60dDowskGs14/PuPjBgcGZ6Mj5zqU+vE4p5w9HZcLQ9ls8Fo50fFW5DCD3bU3EuQcQ3Y6rg55e//CV+97vfBYQ1Ly8P5513HiZOnIje3l58/PHHaG1tRV9fH3784x/D4XDg4YcfHuWrji8ONPsXdHHBz/XTJwccpy1O6Qj0SJMctYKMsU6adHQ68dQbdWjnLQGhomIfgiASkYqKCjBMbJIkGIbB4OBgRPuS1mozu9iKJAOL5k53yNHqLOPvmCieqCdmcWkONuxtxu4j/s5LngEf/vethoADWQ96izrCLf4wsAyWlOYE9uF9g8jyfAbAH3BNTk7Gd77zHV3XGG9EUsyRiAlNVLQSOeTgIEYC0tqRR941UWtdFByfNc1OtDg9aBJ10g+1HkRbbPnzpzdjc7s5sP+qRfOp2IcgCCIKxrvenuhXNqfQYxfIbQmW8Rea1DQ7wfH+1+UFyuQgMU2dbkkikJiczFRUbqxFWb4Fi2dMlmxnNSeDBy9prNHtkSZMF9nM6OnzYtrkdKxZOjPovQi6LhQWffL+QXz6eQ8Av0Zfd911QfeXHydYoFRrsk9xthlLSnMD+3A8j5fq29DtGUSmKTkwaUEtmUxcrFReYA2a/Cu+RnkCtnhqbzD02JHUXIIgCDHjXWvlFNnMEhsSUJ9EIxTdGFgmoCvy7br69Pn2P++RFtae0LGfWCOsZqNken1pniVooY1Yr+Rdk4tsZng6PoPTl4KBJL9tO+HMYnSYk4FepT0eTHdC6Y3aZB5xEXGRzYQpFnPgeSWYP1fuC5Rrt9r17Lc7Jb9fvPuKCYJIXMaj1malTUBxtlmSzzRaU9cA7RihXIt2NTiw/nQDCLlm6JEJlgEMDINBjteYnXP6evpOgnN3w5iVCwAwmDODFvtYzP4GlHItkzf3COX7Fhf9yicxqGk22YkEQUTCu+++C4ZhcMUVV+ja/oorrsADDzyA6urqiM85HrV2OJhildrD9k71JhOA33ZbW9UkadwkR6ztpXkWcDwf8Cmr2WDyCbI/W1CCre8dBcCD48NrvOz1enHw4EFceOGFAEY3PyrchhB6to/2uSkR86cIIpEYMwU/tbW1ePTRRwEA5eXl+Mtf/oLy8nLV7X7wgx/gwIEDePTRR3HttdeirKxMsd14xTPgw54jDswvsQWckRwP1YAkEHmS41BAtU1ijMcyabKjowMzLpmHzz//Amfe8jtsP+TvIkHGI0EQiYq4U8RoQFobnD2fOnVvy2n8lOKA4cpNdYrP9YymF9Bb1KF3O6kRnIl7F05FTdNxvL/x1/jjn3dhXv6rWLRoUcjrimciKeZIxIQm+X36uOAOEGKI4QwMEQRAWjta9HmlBbha67+48HbH4aHEofkltpDrQaTFljzP41e/+hV+/5vfIKNiOdIvXBzW/gRBEIQS0lvlpDvAr3+hAnJyW2Lmac1kGAYXFQw9H++367ePBUzJBuw+nQy1/VAHfragRJIc7XQPoKLEFtgGAPoGOMkxCrLMiilFWgi6XtviwgfVb6HztcdgKpmFA1/+XVgaqydQqpbQPb/EppjgcFdFMViGwaNvHobTPYDV2w6DZZQ2Zrh2iXCNlXMKsbbKjgPNLkkDEj3PFXrsZWouQRCEAGmtlOLsNORZTIqCn9I8C2qalZq5S6SHm+vbkJtpCnp8U7IBZ2ekIiczVaKT/TJbd1DLKa0BzwP3LpyKuhYXfDyPdVVNcHm8gWsDtP2gct0w97Zj9xPfR9IkG8646bdgU/wNLKZNTpcU5gj6Ekx3QumNgWWwflkZ1lbZseVgO45190l0j2GYwDmFAlo9RbNavlP59Yh/v2DHJgiCiIbxrLVLSnPD6uo/nNPRtGKEalPitAp9tSbniuF4f4OIYPjc3Tj+z/vgc3fjzFt+B6M1R/K5kWXglT0LuNxerNxUpyg6YhkEnSIrR+s7JhuRIIhY0t7un9p5zjnn6Nq+qMi/3hw9ejSi841nrY0FLACDgcGkFCO6QkyblbPlYHvQgh+x7vin/fibRmnZYGqFrUJTiNXbGsAy+hovDwwM4KabbsK///1vvPpq4udHqRHtc1Mi5k8RRCIxZgp+nnzySQB+gd29ezdSUlJUtysrK8Pu3bsxb9481NbW4qmnnsJzzz03glcafzCAogtEW1cf8q1+B3Jdi9TZXJydhnyrKaokR3GQUR5EjgVHjx7F5ZdfjqNNhwFDEga7v4DRmpPQBiRVwBL0b2B809zcPNqXQFo7jIgLfYS/a7VpeHpG0wvoLV7Ru53cMLtnfj7q1v8SH+/fDQB48Lk38Znp3IRemyIp5khEZ7VWd2cyuEOj5eAgjSZiBWnt8JOaxKJvUJocLCT+FGenYUlpTmCdlBe7AgzqW5WTbz862oP11XbJ376P4wPJRQCPnIxUyT56ikp5nse9996Lxx57DAAwcLwJPM+DYRiaMJZgkE5Ioe+DGG1Ib/0U2UynJwkzWDwjJ+CnlXcbXrN0JjbsbUZNsxM+jkexzQww/n0AXhFQBaTTAfQin4D71K5GxXt7GhwSX7ZHZ9FuMLgjVeh8dTXAcxjsOooLstX/PUSDmn3f1uXByk11inVQj40pdIwWb69nLV1fbcfqbcrGXnq+Nz32Mk1EJQhCgLRWiTxJ15/cy4fUzMYONxo73IppO2LK8zNxcVEWappdmF9iw2cu/0RatfqezNQkzQlBqUYWfd4he9nl8WLLQX/B0W6V69xcp92cSqwbWX1t+NNP7gbXdxI+1gCfuxvZWRZMm5wO5vR3IUwJFPYLpjt69MbAMmAZ6WSfIZQJ2CvnFWnaKaGSndR0XqCmOf59xQRBJCbjWWvjqTFbMPtNS6/kmvFhezeMBka1MYdeBk924viL92HQ1S7JjxIjL/YRUCs6mhkjW45sRIIgYklvr//Z3u/PDI2wXXd3d0TnG89aGws4AJyPh9MdXrEPAGUiswyx7SaP2W6ub1PYdHK9/uhoj+S1nhyfvr4+3HjjjXjjjTcAAB9//PGoFvwEi7ONZgwuHvOnKCZJjCXGTMFPVVUVGIbBQw89pCmwAhMmTMBvfvMbLFy4EFVVVSN0hfGLmkY2dvSisaMX2w91oEJm3C2ekYO7KrQdn+EwHN00Wltbcemll8Jut8M4IQWZ1/4SqQXTASS2AUkVsAT9Gxjf5OXljfYlkNYOA2qFPgKVcwrB8Tye2NkYduddYX9h+7J8C5bPKsCaPU2qY+XF22k5xsWGGTfQj19/71Z0NR4EAGRedic6ChYE1qhEXZsieS5JRGe1+D4rN9ZKPosHgzsRIY0mYgVp7fAjL/YRw/McOB5YuakOpXmZONDskkwa0KKzd0ChgfKE2sYOt2SSbqhANMdx+MEPfoCnnnoKAHDbbbehfOkq1Lf1jHogmwgf0gkp9H0Qow3prZ9vzJwS+NsT/Lzrqu2SbXY1OLByU51qMjLLALUtXZL3altcCNZ02GpOxu2zC8Ay/rVAK3EZUBYAAcFjvcXZ5rD1cf369Xju0Z8BPI/s4guw6vGN+P6ir4R1DD1UzinEfrtT8j0KCdzydTDSphziY2ghD7hmpSXjzjmFur43PfZyPCXeEQQxupDWShFirkU2M/KtJpQXWFE5pxArnq9VbGsyGhQFrQCQYUrW1M0Pj/Zg95HOwOvibLPmtcin/oS+dr9eqX7m6EXlxlrFxDpgSDe+zB7D1VffhgHPSRgm2nDGTQ/DaJmsmOyzatFUxZQeLd3Rqzfy6UlWsxEr5haB4yGx1wc5Hpf/cQ+63KcC33E4dor4epo73ZJJTmrTGCjZiCCIWDCetXY4J/boQbyO+2RFNGL7TUuv5LZhMLs4FAVZZli4bry25ucY7P4CTNIE2G64D6kF05HEMrqn+310tOd0AS5QXmAFxyNse1NN38hGJAgilmRlZeHYsWP49NNPYbVaQ27/6aefAgAslsjyN8az1g43VrMRGaZkxRRcgRxL8Cm3Yr+oHDWfq9zfqjVpVgu3242vf/3r2LlzJwDg8ccfxw9/+MOg+ww3wXzDIxWDU9P+eMyfopgkMZYYMwU/X3zxBQBgxowZurYvLS2V7Ef4YRkoOj61uzyyrfwbxONi2NjYiEsvvRRtbW1IS0vDa6//G0eYnDFhQMZjBSwxssj/DWyub6eAADGikNbGDlOyAWenpyAnIxU1zc7T2sujvrVL8jd9V0UxWIbRNZpezZgSO7z9o2yHdJvjebAME9h+zdKZinVEzWnNnfKgY/OvcOro/wPAwLLgu5j41YWBfcabPiW6szoeDe5EhJ7TiFhBWju6NDk8gaSfYAU+AFBsS8Oxnj7Nolz5ugD4g9Hrl5Up3pdr+PJL8nD3Xd/BM888AwC4++678cQTT4Bl2YjvjRhdSCek0PdBjDakt0ChzYwDzS7stzsD/mCtCQPyjocCm+vbsHhGjkQzS/MseKm+TfO8mWYj3vtsyO5dva0huhsRsaQ0V9U3ppVU+8QTT+AHP/gBAGD+/Pl47bXXkJaWFrPrESM8Awx1nvRIJg6I18FImnLIj6FFaZ7U/rtjdmFM19/RTrwjCCJ+IK1Vp8nhxhSLKaBFajm4QrGPKdkgsTdzLSbNZCiXLFH4WHe/5jX0DWon/vZ5OcWUn1DsanBoFv1s374d1157LTweDwoKCnDHIxvQfMqEsnyLohgnHJtAr97Iv98LcjICk3z8hcvS6edy9F6T+HrueK5G8juphe3iMb5OEETiQVqrzXAXVsqTjItsZvT0eTFtcjqWzypQnF8ef12/rAyVG2sjmowr58inn6LvlQf9xT7Jqci+8UGk5J4PAEGLfYwsI5n409k7gF0NDlScm+WPL2+qk2yvRxO19I1sRIIgYkVZWRleffVVbNq0CRdddFHI7Tdt2gRAv1bKIa0dPialJEnsJgMDiAfdseCD6rncL1qcnYZ8q0nT56rWLFmYaM/x/smsAALvi8/p7j2Jq666Cnv37gXDMPi///s/rFixYri+Gt0E8w2PVAxOTfvjMX+KYpLEWGLMFPykpKRgYGAAbrdbVxWv2+0XjQkTJgz3pSUUajafyyMdrVd3ehGUO2Nrmp26F0O9RnY4xvj/+3//D5dffjk+//xzZGRkYNu2bbjwwgsxH2PDUUoJuYT834B4EhcwNv6dE5HjcrmwadMmVFdXo6WlBSdPnsTEiRORn5+POXPmYOnSpRF3rhAgrQ2PJBbQGiLgGfCh0eFG42kjdsfhIaeu/G9ayyCSayTH84Fkqe2HOrC5vh1LSnM0x9RuOdge6M6otY7IndaXTE7GO3/6H5w6+v/AsCwsi36EtPMvlewz3vQp0ROa4tHgTkToOY2IFaS1w0NhlgndngG4PIOB96zmZFTOKcRL9W2aSVPByMlMQaOjV/Ke+G9fvi4A/iRbtWl7Yr1955PP8czDP0bt9tcAAD/5yU/w2GOPgWGouD+RIZ2QQt8HMdqQ3gJ2hxt2nfon73go4LcnGaxaNFVilwbTVXGHxYoSG3624Fxsfe8oAAZd7gE43QOKfYqz08CHOG6qkcXexk78q64N3R4vMkxJuLE0FyvmFqkGHnsObMG9994LAFi4cCFefvllpKamhvRF6/FVa20jth3FDTkA6Tqo18bUu5aKr0eZ8KWv2zNBEES4kNZqs6vBgfXVdqycVwQ2iJ3nGfBJpsQKCUhyrOZkhX6qTcnTS7BiH61iIKHoRzzRdtubb2Dx4sU4deoUzsgtxIU/+AuyzsrBz0XaKfaJR2MTaGmv/PsVXgebfi4mkmsqL7BK7qu8QPnvn5KNCIKIBaS12oRTWBlJcZB8HRds1V0NDmzY2wxAOR2nck4h1lY1YcvBdoBn0OU5FeHdDTHQ+Rk6/nkffL0usClpyF7ya0w4uyTkfkU2M1797mx8/x8HUXXEIUmw3n2kE5Uba9HW1SfZR48mkr4RBDHcLFmyBK+88grWrl2LSy+9FIsXL9bcduvWrVi7di0YhsE3v/nNiM5HWjt8NDulOuOTuSg/aD8hKY4NNSF9SWlOUJ+rmr915bwiSY7VjsMd2Gd3YrfonO4T3XjxobtQW1sLlmXx3HPPYenSpbH4CqImmG94pGJwWtofb/lTFJMkxhJjpuCnsLAQ77//Pl5//XV897vfDbn966+/HtiP0KbIZlYEUwWRlYut/HUwQhnZgmG9ub49UHkbyhjfvHkzPv/8c1itVrzzzjuYPn26/gtKACghlxD/G2hxugOJ+sJ78fSwRIwsf/rTn3Dfffehv9/fuY/nhxbk9957D1u3bsUvfvELPPLII1GNFSWtDQ+tYh89rKu2Y7/dCQb+9BuWYVBeMORo9nG8wsC1mpMlx2js6A1ordqYWkDqsFZbR+QGWmfTR3C2HEJSUhL+9rcX0H1maaDrhfgaicQh0QuW4gV6TiNiBWnt8GDv9MBiMkreS09Nwl0VRWAZaI59F2MxGeHyDHVNbpcFPYuzzZK//co5heB4f4EtwGPxjBwAPB59c6g4F/BrtFhvB7s+x/t7dwAA7r//fvz6178GxwPrq5SFQkTiQDohhb4PYrQhvQ2NOMFY6Gx4wN6J/c1dkiTi+lYX1iydCWDIXyVn3rk2HO3uk3RXBIDdDQ4wAN760TwYWAaX/3G3ImG5osSGCwssqG12guM4tJ+eWJCTkYqefm9gokGfl0PVp52B/ZzuAaze1hCYaivmH/89gmMb1gMArrvuOrz44ouBQHwon7WexDE924SzDmoloOk9hryRh5j61i7N8xIEQUQDaW1wBD9seYEFOw5rT5c1sAye/lYpVm6qwwG7tAljsS0NS2bmSBKUglFsMyPXYopqmkCwRhRiP/W/6trQ+fIfcerUKUwuLAFz1X3Y9wWwT+SrjqVNINfe/XYn1i8rw8x86fc7UyWpR61ZR3F2WqCRVrjouS9KNiIIIhaQ1moTTuFJJFPX1LRDYF21HRkyP3Rtiwscj5hOuAUAz+F3/cU+qZNwxjcfQvIZ+uJ8TQ43vv+Pg5rPBOL3i7PNWFKaq0sT5d+LEMsmfzZBELHipptuwh/+8AccPHgQ3/jGN/Ctb30Ly5Ytw1e/+lVMnDgRJ0+exPvvv4+NGzfi73//OziOw/Tp0/Htb387ovOR1o4eTveAQqf0TEgP1870x3CHkDfaeHPHHtTV1YExGHDH/X/CLd/y/1sa7mmCegh2ryMVg0sU25ZiksRYYswU/Fx11VV47733cP/99+OSSy4JWuzxwQcf4IEHHgDDMLjmmmtG8CrjGyGpWKA424w8lRHxH7X3wMfxaHdJE5xqml1Ys6dJdbydXNRCGdlagchgxvgDDzyAwcFB3HTTTTjvvPN03HFiQQm5hN5OoMT44pe//CV+97vfBYp88vLycN5552HixIno7e3Fxx9/jNbWVvT19eHHP/4xHA4HHn744YjORVo7cgij08XsODwUrFxfbVd8rtYJGdAeU+t3LgdfR+QG2uLrr8VNF2TAZrPha1/7GgCaLkYQAD2nEbGDtHb4OHlqUPK6zdWHgUEOlXMKsd/ulOiqyWiAxyvtiNwvr+SVJTotKc2V2L0GlsGKuYVgGb/2sgyjmJIraLRYb43WHPzosQ3I7mvFT3/6UwD+Yp9wA89EfEE6IYW+D2K0Ib1Vp9Bmhl/KGJQXWLFi7pBPV/h73dnQKdlnkOOxtsousS3lMOCRbzUpCn4A6ZSDxTNyFAlQ7V192N0w9J442WjlpjrNJCsBwf4Vb9fSw2Hlb5+BY99WrF69GkajUbK9fH/h3n0cj8317Zqf6zmGQDjroFYCmrzoB4AuP7wY8icSBDFckNYGpyzfAh/Hg+OVzSXElOZlYuWmOtWE3BxLKg40+4ttU40sUpMN8JwaRP+gsjtjxblZeOa2cgDA2qomrK+2w+lWP6cY+fQgvZODmhxucBfdjYozJmPyZUvxbtvQMWpbXKicUyhJjlo+qyDiCXrCMcXsanBgweN7kJORKruyoe9GOF5NsxMVJTa0uTyBKYGLZ0yOOGFLj8ZTshFBELGAtFabcJJPI5lKI2701OU+JdHUzt4BdPZK47WleRasr7aHcwu6SJ91M8D5YPrSXCTb8sLa96OjPbq2y7eadfvvxPrm43jNqQwEQRCRwjAMXnnlFVRUVMBut+OFF17ACy+8oLotz/MoKirCq6++GrRxQTBIa+MLwY4OTMwDg8UzciS2W/ixJ+m/jVSjQWL35nxlNiwLvw/WlI63+woDfuxICoZjhdw2XrN0psJ2HakYXKLYthSTJMYSY6bg55577sHTTz+N7u5uXHLJJVixYgWWLFmCL3/5y4Gk508++QQvvfQS1q5di/7+flgslqgmHYw15C7gxTNycUCWkAT4E4rXV9sBRrqHZ8CHR988LEmWkouaIDrybo9yI1srECnvAnH8i89x9tlnA/A/2D300EP6bpYgEpxEeWgihpfa2lo8+uijAIDy8nL85S9/QXl5uep2P/jBD3DgwAE8+uijuPbaa1FWVhb2+Uhrh4ckVv80ICEZSksni7PNACCZACbXTrEmC0nIWutI5ZxCnHB14lA3jwsLbX5jmYwggiCIYYO0dvgwsiy8viEnrZfjsejPVXj7nnkKR+ggpxRmeWITx/MotpkBBgGHshy5w3d+iU3yuWAH3zzjDPT1nsTHnV7V5KZIAs8EQRCENqS3gNVsBMMwkkQkBkO25Opth3HA3okkAxvQJjU7dHeDAx+1B08U+vjYCdwxu1CzOEfQtRVzi3DA7sTuI0NFRa7efsm2jR3ugLYG66osIFz7v2o/Q0NLO5LS/Nrb5EnB+j/+UXV7rcSw9dV2RdGSnsYZ0RbVBHsO0BNcll+PeHrTaPkT46ELJkEQwwtprR+WATJN0qKZYps5UPASrGAWADbXt6Pd5VH9bLesCKjPq+1gbu/uC6yzd1UUA9A3ZaByTiGe2Pmp7kKfwZNOJE20AgBYYwqKrv0+yvIteLdN2nRKbSJPqKTgYJqn9kzQ2OGW+MgB6WQ7eePJ+SU2NDkckimB8muIlX5RshFBELGAtFabcPIoIrHfDCwDloHEPjQwgE+WcCVMjON4XrNpY7iItZZhGGTMXRrRcaZNTlcUFFvNyZg2eZLEJg/HnhXrW+XGWsln5M8mCCJW5OTkoL6+HqtWrcKGDRtw6tQpxTYpKSm444478Nvf/haTJk2K+FyktcOHxWTEyf5BeLkh8bSYjej3chL702pOxvQpGSjNs4DjeSx4vEqiv6u3HcaWg21YPCMHAIP61vBsNX8DqiG78PbZBfB0OwP5UTXNTqRdcGXgc0HPRjNuO5rFRnLItiWIkWfMFPxYLBZs3boV11xzDXp7e/Hkk0/iySefVN2W53mkpaXhlVdegcVCXezEWMxGfDU3EzzP46X6z9DkUHck1zQ7kZORqnCWAspuEOJuTZvr2yXCqzUWXW5YF2ebkZtpkjh8D9f/F2vv/w4ee+wx3H333RHf81iBAqXjC3poIgAEdK68vBy7d+9GSkqK6nZlZWXYvXs35s2bh9raWjz11FN47rnnwj4fae3wkGc1K6bpBUOtQ7FAbqYJa5bODEzaC9ZBSc860mxvwpM/+gauvPJK3LlsDVgNXRlPGjSe7pUgiJGHtDb2mJIN+P6lxdhc9xnsndIptU0ONyo31qKtS/p+koHFgKg4yMgyEqczANhF2s0yjKoWyB2+LMNg1aKpEg05ceIErrrqKvA8j7feegtpaWmK4yTKSPR4hvR77EK/LREJpLfAif5BTEoxSt77TJZQLCT5CBpUmqduh4ZKXJo2OR3KVlNDtDjd+OvuRgAM2mWa7PIMqu6zub4Nb/xgLjgep6cUKK+hyGby+5x5Dqd2/RVfVO/CGbf8DsaMM+HjePg4XrFeqCWGCevMOllH5sKsVHA8JA025FN3Ii2qEXer7JJNYBA/B+gJLqtdT7B1ciTW1XgKTBMEMTyQ1vrheKVO8lCfGqeGPQyfcXCk67i48EULq9kIgEdZXgb2fKpsDinnRP3r6N79HLKXPIiUKRcAgEQHxbqyclOdZF+1uLKeCXpC7Lmm2YkiW2gfezAN1XMNpF8EQcQTpLXahJNHIdep5bMKsGZPk6o9NDQdzoX326RaKi/2AYB8q98mvfJPe3Rdt9HAwOfjoVXC29/yPjpefgiZFbdj4oyrdR1TjfklNqxZOhNX/aVKlu/F46/fnonn97VE3XSW/NkEQQwn6enpePrpp7F69Wq8++67aGpqwokTJzBp0iQUFxdj9uzZqrG2cCGtjS1WczIyzUY0drhVp9y63F5UlNgkzS0q5xTirooi/HV3k2bTisYOt+QzPbaaoOl1LU7ML7GBZRiUF1hQcRaHK28byo8CgB2Hh65H0LPR1DlqEkkQ45sxU/ADAHPnzsX777+Pn/zkJ3j99dfBqXTlNRgM+PrXv47HHnsMhYU0EUOOy+3FvqbOoJ2gAL/BKu7sICY91SjpDtni9CdRqY2cz7eaVEUnlAO4r6kWT/zhUfgGB/DEE0/g9ttv10x0Hy+Qo5kgxh9VVVWB6Wah1sAJEybgN7/5DRYuXIiqqqqIz0laGxtSjSwuKbKivMCKA82usAp+BIfzPrsTNc0ueAe5QBLyrgYHVm6qC3TrrWmWGntCgpKehJ1Dhw7hsssuw+eff47Nmzdj1apVmr/neNKg8XSvBEGMDqS1seX7lxbjropiHLA7FQU/ACR2qtVshNPtDXSPSjWyODsjFZ/39MMbpKOxljNV7vAtL7BIgs0ulwsLFy5EbW0tWJbFu+++i4ULFyqOQ9M9o4f0e+xCvy0RKeNdb72+oQ7DVrN/8oBXLUPpNH94uwE5malhnyeJZfD0t0rx/X8clLyfksRgwMeD45VBWT00drixrtqOJJbBV3PTwfFAm8uDRpFtPcViRuWG/aha/2vY978FAPAcqkL6xd8ITM+VrxdqiWFr9jRJJgAIMAwb6AApXn+EYwhJyCs31akWzgQrrPFPfZB+J8XZZiwpzZU8B8ifNeQTfg0soyvZTXwtwRqHxAoKTBPE+GC8a60WTQ43Vm6qU0yNG0783Y6HKM3LDDklz+n2YvW2Bsw71xZ0OwDoObAF3bs3AABO1v8bKVMuQHG2WdN2leuXfMpAi9ODNXuaJNqollClNqWnratP8t1qTbYLdQ1qCVukXwRBxBuktdEjt5fE9p/cHpLrTiiaO91Y8Kcq3TFgr49HqpFVzdXqa6qFY+sj4H1enDz4OtIuuAJMUrLmsbSOAwBtXR7c/UI9cjNNkoIfp9uLu1+ox4bl5VHrG/mzCYIYCSZOnIhFixYN6zlIa2NHptmIfKtZdbiAAAu/DffR0R5Mm5yOO2YXAAC21LeFda5Qtppc01ctmoq52QOYXyHNj9LSs9HUOSqqJYjxzZgq+AGAwsJCbN26FQ6HA//973/R2tqKkydPYuLEicjPz8cll1yCrKys0b7MuCZYsY/RwGBSihEftndL3hdG6IkDggJqY9MFwhEdQbA8Df+F47X/BbhBTJs2De+8807ExT5jqRMsOZoJYvzxxRdfAABmzJiha/vS0lLJfpFCWhs9fV4OM/P9I9c/c+kv9qkosQWShnarFNICkCTmzC+RBmQ7ewcChmswjfjggw9wxRVXwOFwwGq14p133gnqnBhPGjSe7pUgiNGDtDZ2HGh2YcVcHgaWDbktw0htQVNykq6AbGlepur7wRy+HR0duOKKK/Dhhx8iKSkJf//731WLfYDxPd0zVjY76ffYhX5bIhpIb/30ebWLWgUGfDzsndIJQKZkA2bmZaLqU/WmUAAwyPG4+4V6xTS9/kHt4iK9PPtus2Rqwrxzs9Dl8aLP68OZ6SnY+f+OofO1x+A58l8AQPrF38Ski5YEttdaL+TaI2+kIdDTJ+1EKT9eqILEYJ/L1zYAyLeag07vkRfq7Lc7JQnOwfQzWOLacKyrFJgmiPEDaa06exul2mkxG9Hv5dA34AsyE08/FpMR/YMcUo0szj97EupanFizZ6gJFBfGST451qP5Gc/z6Nn7D/Ts/TsAIPWci5D1tZ8BAJaU5sLAMqqJ02rTFDbsbcbm+rbTMeVehQ9b76SgO2YXAuBR39oVVAMr5xSC43lsOdgOwK+X5QUWyX5ySL8IgohHSGtjSzA/k5qdJkc8cS6cZo8Carla4vwooy0fZ3zzoaDFPgBwKkjOlzh/y2hgJM0/5BPvIvXNjmd/NkEQYw/S2thw/fQcJLFM0OYTH7T3BPy9uxoc2LC3GSvnFaHLI52cyzJApilZc/J8KFtNrunbqvbj/ifuUc2PkuuZmjYC0JwQGGu0Ys9jKQeaIAhtxlzBj4DNZsO111472pcx5hB3fxSTaTZizdKZWPC4vnG0ah0JxcgDnvvtTqxZOhM121/DhtdWA5wPM2bMwNtvvw2r1Rrx/YylTrDkaCaI8UdKSgoGBgbgdrt1rYVut995N2HChJicn7Q2OrYcbA/ZydGUbAhMGQCACwusMLCMLqcy4O9wXJydhmPdfZLjBEvYqa2txYIFC9DV1YUzzjgDO3bswHnnnRf0PONJg8bTvRIEMfqQ1kbP7gYHyn77DvplQc7ibDNyM02ShhXyjr5ajmIl6g5TrcDmsWPHcPnll+PQoUNITk7G5s2b8fWvf13nucYXsbLZx5J+k9Neylj6bYnRY7zrrdcn18g0LJ6Rg811nymKfMT88LJzsK9Ju9hHQKytchs3GuQ6vUc0jb7p8y44tj6CPrs/EThj7q1Iv/gbku3L8i2BNbWm2QWO58EyAMcHb6QhIJ9yL19/QhUkBvtcvrapHR+QPmtUbqyVfBbOlJ5gPobhWFep2zNBjD/Gu9bKGVCZqheJPiaxDAZVqndcHm/gmHs+dQIAdhz268LKeUXY+l677nPI7WQBnufRvWcjThx4CQBgmjoHWdf8BMnJRswuzsLyWf5OzFp6J7eVV84rQm2LS9JAUqyNava1XC87ewewetthrFo0FeuXlQW9LwPLgGWYwPn+960G1f3E9ldpngX3LiwJWhREEAQxWowXrXWfGlRMNY0lwfxManYaMDRNrjQvE1vqj8b0etz/bzc6//1HgOeQfEYRsr/5EAypk0Lup1buk5WWjAxTsiQ2PSklCU73UDOLaZPTJfuMpXwqgiCIaBkvWjtcPLWrEWdOmoDUJAZ9Gs2g5P7eA3a/v1deEFuQZcbb98wT2WqZABjUt+rzNYo1/dTnR/D21l/Dc7JHV36UmjYCGDG91Io9k2YTw4FWTDqRY9WJfO3AGC74IUaWxTNysL7aHnTsnsD8EhvWLysL+ocidwDvanCg8pf/i7899gvwPI+LL74Yb7zxBjIyMqK67rHUCZYCpQQx/igsLMT777+P119/Hd/97ndDbv/6668H9iNGny73Kc3PGAA8lIHe+lYXgCJNp7KcRo3uUYKDWv4gO5U5hq9dczVOnjyJnJwc7NixA+eee27I84wnDRpP90oQBDFWcLmlHfjnnpOFiwqtePngUVjNycgwJeHG0lzcMbsQz7zbjC0H2xXFssEQ9FkPra2tuOyyy9DU1ITU1FS88soruPLKK8O9pXFDrGz24eh4NdwOQa3jk9NeCj2bEUT0iLv5WsxGvPGDOUhOYvHMu3bFtqZkA87OSMHiGTmonFOIddXKbYJxdkZqyMYXwJBNHAncQD8cLz+E/tYPAACZl96JSWX+YLzJaMDFRVaUF/jXi7VVdqzepj7ZRoBlgFWLpmoWBQGA1ZwMjvev3YIWhCpILM2Tfl6aN/S5fOrA4hk5WD6rIGinyGB+glD6Kd9XSFgbrnWVuj0TBEH4STYwSEtJUtiselEr9glGTbOgB/rsliKbGWuWzsTKTXUS3Zt3jhU1//gTPjtd7GM+/3JYF30fDGuA18cHOjFXzilUXKNY7+QE00Y1BI1aV22XFOHqtRvl9qbwXCPWWLn9paeYiCAIghg+TvYPYvuhjmHziQXzMwn/f6DZhVanG92eAVyQk4Gnv1WK5/e1+CfVRTDVR4veD9+G880nAPCYcPZUZC/5FdiUNM3tTUYDPKIJvkaWgVekw3eevn7xdNfzJ6eDAfDxsROYNjkda5bOBDDkl5Tb/ImcT0UQBEGMLp4BX9DmUmrss7uws0HZcOrG0xNllf7F0NPcK+cUBjT99bd3YsdL96Pf49adH6UWt1TbZqT1cizlQBPxg1ZMOpFj1Yl87QAV/BBRkpWWjDtPC6F8dHqxLQ2LSyfjmXebJY7Wtq4+rNxUFzQhRh5k9Lm78I91vwHP86ioqMDrr7+OtDRtY1Yv4TqPY0msk4PGYqA00SsqCWK4ueqqq/Dee+/h/vvvxyWXXILp06drbvvBBx/ggQceAMMwuOaaa0bwKgk1klhIOibJ0QrVCglC/uQf/5Qg8Dy6PF5JtwtTsgGAtGCoONuMfKtZ4qAWP8i+88nn8G7+CU6ePImCggLs2LEDBQUFutbisahBWoyneyUIghirVH/aiapPhxzETvcAappdWDG3CCwDzURkiykJLMsiPdWIJlHwNpzO97/85S/R1NQEs9mM//znP5g3b17kNzIOiNX0luHoeDXcDkGt45PTXgo9mxFEbHG5vbjz+Voc7e5Dj0dps3oGfGjscOOJHZ/ipfp2cHx4icaLZ+SAZYD9TZ344GgPejxeqAw5ULWJTUYDvBwH8JAkLcnp/WBboNjnzKu+jwnTFgxdv9cHjucDdq2/oCY45QVWxTojn6bjdPsnCrDMkBbIi3bkBUHyu3ypvg0sM5RkfFdFMe6qKA58vmZPU1DdESem+ThekpgdSj/VktrIB0sQBDH8DPj4iIt9IsHHcfjr7sagjagECqypePOHc2FgGbR19Uk+62z6AId3/AsAkFF6NSZdthIMw0q2EeyW3bLpQC/Vt6Gm2QmW8WusVHOk2sjxfNBiV8EWAKTJy3rtRrUJQcJxhOOS/UUQBBG/RLomDwxyWLmpDh8d7QkUuSQn+XUsmJ9JrDs7D/v1Y1eDA3e/UK86DS8afO4uuLavBcBjwpRpyF78ANjkVM3ti7PNAM+g0THk186zmrBkZq6ieGm/3Rm43j1HOrFq0VQ8d/uFkuOJ/ZJiaLI2QRAEMZLIJ/uI85T1ohVrq5ydj7/c/Tj6PW5JflQotOKWsYhlRkOoeOpYysEdS/cS72j5RBLZV5LI1w4kaMHP888/H9Pj3XrrrTE93njijtl+oVu5qQ6DPqnILi6djBVzi3Cg2SVx6DZ29KKxozdoQkzlnEKJoWkwZ+J7j6xBc9UW/P3vf4fJZIrRHciDw5H2jgyfWCQHjXUBS/SKynhmrP/bGS/cc889ePrpp9Hd3Y1LLrkEK1aswJIlS/DlL38ZEydORG9vLz755BO89NJLWLt2Lfr7+2GxWPDDH/4w5LFJa4eXZAOLQW5IN4ttZtwwIwdP7PxUYbQCfkftktLcgOHqT/4pwl0VRZKkHwG1iQRLSnMVa6j4QZZhDZh99/+i880n8PzzzyMnJwcArcUEQRDDBWnt6KFm9e1qcKD8t9uRYTJq7ufyDALwJwFF2vn+r3/9K5xOJx544AFcfPHF4V56zIl3u2C4p7dE49Qbboeg1vFjVQRFEOMF0tvw2XNE2TVRjsfLSYpf9VBkM+OO2QUBnTEY2NPNl3hsOXg06OQfo4GRdCoOxsSZX4e38zNMyDkPE6Zdpvh8V4MD66vtp9dsdV9wKJ3XmqYj1gIDy4BlGDR2+L8neUFQfWuXZN8mh1uRZCw/tta5hPMJiWlq+h4MKp4kCCIaSGsThw/bu7Fbh84DQF5WGgwsg7VVTQqN/vqCy7HgzD/jtXffx6cFN4Bh1Bs7qnU6bnK4A88QOw47sLm+HUtK/ZMD5dq49b2h54NQMWUAqGl2guP9k4yE9w0so2n36pkQFK39Fc9TZQmCSBxIa9WJ1Ccmnly3q8GBlZvqsGF5ue795fr2bqM+bQ0HgzkTthvuw8mD/0bW134K1pgSdPtj3f1IMUqLbxefjgvLtVOuJZvr2xUaI7/HSBKsCYIgEgnS2sTAM+BDmINuNX2qBoMBr732Gu68805JflQoOyxY3HK4Ypl6CBVPled9+SfZMwlpb1IO28ih5RMpzcuUDdnIHPFri5REj7MnZMHPbbfdpuo8jASGYUhkdZBqZDGrOOv0HyeD+lb/Ys/xUO3s4IfB+mq7onuTGK2EGAPLYN2tM7FmVwMOtveC43mcYGxYdM8fMCFFu3NFuMidx/LXw0kskoPGuoAlekVlPDPW/+2MFywWC7Zu3YprrrkGvb29ePLJJ/Hkk0+qbsvzPNLS0vDKK6/AYgn9sEJaO7x4ZS2MeQA8eEWxj9WcjBVzC1WNK61x6kYDIzm+KdmAH152jmaS0jsfHwVj8Cc3m7Mm4x/bdwR16grnSySDLxZQgJUgiFhDWht/ON0Dkol5wWAZBuuXlenadmBgAMnJyQCAiRMn4s0334z4GmNNvNsFw52AHI1Tb7gTv7SOP9xFUAQx1iC9jR+aHG6sq7ajrsUVSK7afqgD80tseP17s3H3C/U4YHfCo9IEQ25DF9nM6PYM4ET/ILw+HrzPG7BrGYaFddEPgl6L4GNcPCMHq7c1SI77jZm5Ie09Ye3dXN8uSYKWa0Ew36aeoiEx4egOFfAQBDGSkNYmDkITCz3sbnDg//Y0Yut7RwEAvG8QYA3ISkv26+S8H+DD9Fo0yrSsODstUMADQFXrxDR29AZsUrnWHeuWThYSa6RWcatwrB2Hh+xbLbtXz4SgYPaXHn9xPE+VJQgicSCtHWJiShIu/1J2VD6xj472BH0NBF/j5Xolt1cjhed5gBsM2Lap+V9Fav5Xde3rGfAFmkFmmpJwysvjj+80YF11E+6YXYjvnNY9tetv7OgVNcWA6jZ3zikkDSIIYkxDWju8WMxGdLm9Ubfh9wz4sHrbYWw52BZomKzmwxXruE9WITR9clrg/4uKirBz507J52ur7Fi9TVwYA9xVod50Scxo+2JD+YPlfuotB9sDjapiYW+OZD4V5ROPHNo+Eflvmzi5c4keZ0/Igh8Bnh+5aSzjnfICiyyZyb9IVm6s1dynvlXZuUmOVmCS53ncf98vUVtbi+v+5y/4/c5mAP5uT0DsHJqjWbEXi3OPdQFL9IrKeGas/9sZT8ydOxfvv/8+fvKTn+D1118HxykTYwwGA77+9a/jscceQ2FheA8qpLWxgYG0b7BXZlQ2Odx45t1m1X0317dhc307Fs/IwR2zC7Bhb3PAMFUbET8xJQkutzfw+uz0FM2/77T2A+j+2y8w8YZfI2lilqzTsR/5WtzZOxC06/BYhQKsBEEMF6S18Ykp2QCvj9MM2HI8r8t5WVdXhxtvvBEvvvgiLrroopG49LAY73ZBNE69aB2CoZ4ttI5PSdwEERmkt/HB4+8cUdjDuxocmLV6h8SODcUUiwlv3zMPPo7H0ie24bXf/QjZs5aAObdCdXtTskEyCbcs3xII9hZnmwEwWDwjByvmSrVcS+uFtbhyTqHi82ABZeG866vtqGl2YX6JDZ+53GhyeCTbqJ17+awCAIkbiCIIYuxDWpu4sACUUQXg2XebkWmaAH5wAI5XHkVSxpkwXrkSa6vsWDG3ULV4dUpmCvbbnVhXbcf5k9PxkyvPxavvH0OX+xScQbS+tsWFNUtnAvD7wxs73Iop9i1ON9bsaQror9ye0rJvQ9m9wWy7YPaXHn9xPE+VJQgi8SCtBQZ8fNRJpNMmp0viq9Mmpyu20VrjfRyPQY6H1ZyMPq8vqP84HHieR3fVRgx8/imyb3wQTFJyxMfqEhX4utxePPZWA5JERa6VcwoDWisQjjaGghooEgSRyJDWDg8utxepRlbSANlkZFUbP+mhsWNoUrqaf1as48DQNHc07cUT3/sJrtuxA5MnT1Y99paD7ZLXT+z8FCyT+A2Rlfa79F6itTdHMp+K8olHDi2fiLwuwP86MfwViR5nT+iCn/T0dCxZsgTf/va3NRdhIjYc7epD5cZaiUHm43hF0FKMj+PR1tWn+fn8EpuqYcjzPH70ox/hL3/5CwDAfeazQM78wOexdGiOZsVeLM491gUs0Ssq45mx/m9nvFFYWIitW7fC4XDgv//9L1pbW3Hy5ElMnDgR+fn5uOSSS5CVlRXRsUlrY4Met8DJfmWHRfGkgdXbDqOm2ala5AMMjVP/b1Mn9hwZGh+fk6k+Ge/ZZ59FZWUleJ6Hb89GZF3zEwBATbO6U3ddtR2dvUNTD8ZbgJECrES4UFCD0Atp7chjZBnkWlKRk5mKqk+dqtvIE4vksExo5+XevXtx1VVX4cSJE7jzzjvxwQcfgGXZGN1FbBjOKTWJsA6G69RTu6dInwe0ni3k51izdGbcfW8EkYiQ3qrDMkAQ927MkRf7CIRT7AMg0KzCdfwYXn6oEoNdn6PtP0/hm/MuhznDgkGOl0yd/978YhhYRhH4FU/3YRmE3ZVfTUfW7GlSDShrBZzvXTgVLKP0f2qdm+xQgiDiFdLa+EA+/V0PWilWTrcXg6f60fHSb9Df+j4AwDR1NlZvA7YcbMPiGTmYd26WxBfd4uqD3eFPIN7d4AADYPuP50kKXjmeVy14FXS1tsUlSUIWCnfFSV1q9pSWfRvK7o002UOPv3g0p8oSBDH2IK0FTnl9ePTNw9hvd0rsrFC+M7G/bWZ+JngAHx/twbTJ6YGCU/F266rtkv2FNX59tR2PvdWAWMLzPLp2rMXJ+tcBACff34ZJM78e03PUNPs1S9DLxTNyA9MLgNhpI0ANFAmCSGxIa4cPcbGPxWzEeWemobqpK/BeYZYJDMOgudOt21/9r7o2rK1qCjSX0GoGYWAZXOL7CJUP/xg8z2PVqlV4/vnnNY4qPblnwBdXDZEjjb3Kc3A5HpJngdI8C9bsaYo4pjuS+VSUTzz6kL9i9EjIgp9rr70Wb7zxBnp6evDMM89gw4YNuPTSS3HrrbfihhtuQGqqemIroQ95RS0ANDrcaHS4sf1QBzbXt2NJaQ44XjpZoOLcLFxYmIX6VuXUAZORxalBDmIfs9ANUYzP58Ndd92FdevWAQC+853v4PwlK/HY20cC25TmDS0Q0SYQjWbFXizOPdYFLNErKuOZsf5vZ7xis9lw7bXXxuRYpLUjTxLDINQQW7Wx8gJ3zPb/HX9y7ITkfYNKYvFTTz2F733vewCAM86djuQr7gp8tq/Jicv/uPt0d+OiwFoMQJKUNJoP7KORQEwGCxEuFNQgQkFaO3p4OR7dHm/QBhViTMkGnJWegibHUMJRq6sPra42yXZi5+XOnTvxta99DR6PB/n5+Xj11VfBsmxMNSwWxxrOKTVjcR2U35PgH5F/93p+G61nC/k59tudWL+sjIp+CCJCSG+1YTCyxT6x5slX30Xjc/disOs4GGMKshffD3OGf0q91jos1iE9QchIApVqAeX1y8o0P69vdWH9srKQ595c3x6R3hAEQQw3pLXhkZLEoH9w+AQ4FlMGBLhTHjS88Gucav8EAGC58m6k5JwHwN9RefW2BlSU2CT7fNHTL3m9t7EzMJVHHO9T0zABua10dkaKYgqBmj2lZd8OVzxMj794NKfKEgQxdiCtVSLkIun1Ocr9bfNLbPhqbkag4FRtOzGleZlYs6dJUQgULTzng+vtp9H7wVsAgLSvLsLE0mtieg7AP61efP/3LizBqkVTh0VjqIEiQRCJCGntyOJye/HJ572S91iGQaMoDquHJpXt1ezF3oP/wR1/+TUAoPiCMky/6ceKwQcCi2fkSJpEiY8bjZ7Fyo8baexVnoPr43hJEyr/s0JD2McViEU+ld7viPKJRx/yV4weCVnws3XrVrhcLvz973/H888/j7q6OrzzzjvYvn077rrrLixevBhLly7FpZdeOtqXmpBcVJAJDize/dQBNb9wY0cvHn3zMEzJBsn7SQYWd1UUAShC5cZayWdqI/jkC/vg4CCWL1+Ov/3tbwCAH/3oR/jjH/+I/9vTJNtz6KLGYgJROOgVMAr+EnLo4YcIBWntyNM3KNVKk9EAj1c6XSA91SiZsiPuFCw2wMSUF0j19ve//z1+9rOfAQCuuOIKnLX4PuxpPhn43OP1BQK2LCMd8Q7ExwP7aOh/PN0/kRhQUIMIBWnt6OLy6J8kcHZGCvIsZuRaTPiwrQsuzyAaO3oV2/k4HpUba5F6/COsf/C76O/vxznnnIMdO3YgNzcXQOQapmbTxUIPo7ULgq11Y3EdlN+T4B8BpN+9nt9G69lCfg5hikWif3cEMVqQ3moT77U+Qjd/NbzONhx68Zfw9brAJJtwxjd+jQmTvwR7pxuX/WE3GMYfnH36W6V45l07Fjy+BwBzurGFf72VT65X62KoFqgM5WcNFdzUG/yUb9fY0avQg/HuGycIIj4grQ2PGNbjDCu+/l50/OsBDHx+BGBYWBf9EGnTLlNs97GsQVWqkZXo94CPD7sbsrLzMC9JuNIq7tGyb4crHqbHXyw+d7ixWorjEQQhQFobnEgaM4gLhsTNduTbZaUl4845heB4qBYCRQPP+eB843G4P9kFAJg481pkXloJhpFqA4PQ9rvVnIxMsxHXfvVs1DZ3YZ/dCY7jkZ5qxB1zCnGwVd54oku18UQsoAaKBEEkIqS1I0+XLE7rcg9IXjMACmxmTLGYJJPcreZkADw8Az7FQANAaS927X8Z//y/3wEAUvKn49Tl9+LPVUcBqPtTV8wtAssw2FzfLokFR6tnsfLjxir2Krc35bne4R43FvlU5OuOP7T8GOSvGD2ULd8TBIvFgu9973uoqanBoUOH8POf/xy5ubno7e3Fxo0bccUVV2DKlCn4xS9+gUOHDo325SYUB1q6AfAhnc7ygKtY2MRTeORkpSVj1aKpkoV9YGAAN910U6DY5xe/+AX++Mc/gmEY1Ld2SfYXv1YTMUKJIIjbD3Xg0TcPY32MO48QBDE2Ia0deVKNLCxmIwAEin2SREG/JocbhTYzLptqw70LS1BeYA18VtvslByLhX/63vJZBQD8I+F/85vfBIp9vva1r+G1117DJVO1RxGLdVV4YBccwKNZODoa+h9P908kBmqJfQQhh7Q2MWjscGPH4Q7sbnDA5RmUfGY1JyMrLRkWsxG7Ghx47dVX8OSqlejv78d5552HqqqqQLEPELmGqdl0I6GHPo73d2PeWIs1e5oUydFaa52P40Num4ho3UOo30Ltt9F6tlA7B/k6CCI6SG8Tj0KbGRcVZKp+NtDRjC/+/nP4el1gUyZi8i2PYMLkLwEA7A43mhzuQBOLRX+uwuptDWjscKOxoxertx3G2iq/rkkm15fYcMDeqdDayjmFWLVoKi7/UnbAny3X5LVVdolWLp9VoNhHjNox1aicU4jibLPkvUj0hiAIYiQgrdWP1gSeeHI1+jw9OP6PX/iLfVgDsr72U9hmXAGjQXmR0yanS17fPrsA80tsSJZtW9viwsAgh+UbajDz4Xdw5Z/2aMYt5bbSirlFCu1Us6dC2a+q9xrBPlrXGcpfTLFagiCigbTWzwSjAfNl0+X0+ByDbSM021Hb7s7TE+rqW2Nra/E+LzpfXR0o9pl08TdUi30Afc06nO4BNHa4cbC1G1WfdsLr8+d7rZxXhO/OL5bEk4Hh9dPqtXkJgiDiDdLakUWub/JGjTz8RT/PLCvDqkVTA35Sp3sATrdXtdin0GbG8lkFMLAMVswtxJTWN/Hy6WKf1OJyZC++H6wxRbJPjSzPSrDz3vrR3JjqWaz8uMOVgxLtcWORT0W+7viD/BjxR8IW/IgpKSnBI488gpaWFuzYsQO33norzGYz2tvbsXr1apx//vkoKyvDk08+CafTGfqA4xzPgA97jnTq3l6tgCeY2SkYxeKF/cEHH8SWLVsAAA8//DB++9vfBozZYIIyVhMpo3Ewqx1HPlqYBJEgiHAhrR0Z+rwcXG6pITso0wC7w33aMctg9bahB+v326XdFDkAu490YsPeZgDAli1b8OCDDwIAZlQsQta1q7Bhfzt8HH+6C4aSeNXVRND/WGk5MfLE6rejoAYRLqS18UeRzYTi7LSg2zjdA+jsHYDL7YXXdRSOV34HcIOYOPkc7Ni5C2eeeaZk+0g1TM3JORJ6GMqRp7XWra+2SxKp55fYIloH401PhfuVJ2CH+i3C+W0q5xRGlLhAEIQ+SG8TAwY8eCiDgpz3FDo2PwjO0wPWnIEzbnkUhjOKNY/T5HAr3pNrFAC0udzYLfOH/3nHp1jwpz3YZ3eC54f0R67JWw62S7Ryw97moMHNcIKfuZkmyetY6k20GhtvGk0QRPxAWhsZ8bSMdr7+e3g77IAhCbbrVsH8pbn40eXn4qdXlki2m19iw5qlM7Fq0VRcNjUb80tsONjahYsKrbjnCum2pXmZWPTnKuxqcKCzd0Ch0Zvr2zEwyKlqi97inkgSUUYyeYWSlwiCiBXjWWszTUasP534K/dHqmmD8F5NsxPzS2y4bGo2imxmxXGFNVnL1xlr31z3uy/Ac+S/AICMOUuROfdW1WKfcPlINnkv1H0NB9RAkSCIscB41tp4oqXTjZWb6gAAeRalfsuxO9ya+VG2634BJkmZF6Vli8daz2IV01XT9Fj4aeMht2W08sDIz60N+THiD4YXR6vGEH19fdiyZQuef/557Ny5ExzHgWEYGI1GHD58GPn5+aN9iVGTk5ODL3r6kfPdjaN6HasWTVWM56rcWCsZ0ypQUWLDM6fH4Yrp6urCZZddhm9/+9v48Y9/LPks2Ihzrc/CHYseb6zZ0yQZCaz2HUdynGiPRxBjjZycHABAe3v7KF9JYkJaO3pc/qVstDg9khGywbZdv6wMg4ODuOWWW9B2YhDHLrgNDGvQ3MeUbMD3Ly3Girnx6YjVq/Oj+TwQKy0nRh767WILaW10kNaOLv+zoAQGllG1qQD/dB+nbMR8z75/wdN4AGcs+TXOnXIGlpTmYvmsAmzY24zaFhdK8zIBMKhvDU+b1NYmYcrAcOqc3LYXniuGaz85sVyTY/lcEOpY0Z4r0X0axMhCWhs9pLfxh4EBJiQZApNvxXgaa9Czcy2yFv8KRmtOTM6npulqrFo0FQAk2lScbUZjx1DS8uVfysaapTOjXsflGlhkM+PNH85FctJQD7do9CJajSW7iRhvkN5GB2ltYuHtbMPxzQ/AuuB7SC0shYEBZp9jw1O3zMD3/3EQHx3twbTJ6VizdGZAl+S6cO/CErAME9AojgdWb1O3rQWKbGZJIVAwbVHTodoWV9h2aKxsVz2QdhJEcEhro2O8aC2g/W9EbZ0FoHivptmJHYelTSDml9iwXiWPSbC5apqdsHe60dzpicm9+Pp70fHiL2H+cgUmlV8fk2MC/vsQN7ggrSEIQgxpbXSMF60dLruWgb6pdVpUnJulaNikhjw/KjU1FX9dsw7f/cf7ONDsgmdA6m++bGo2nrlteGxAMbGK+6kdR2hkIZCo+j9asdFobPWxHs8lP0b4DLfWJg3LUeOA1NRUfPvb38a3v/1t1NXV4eabb0ZTUxO8Xi8GBkIH7wh9FFhT4eN4VG6slSxaZfkW1YKfCwssqotaZmYm9u3bhwkTJig+Eypm1RYLrc/EQiZcRyItNvJqyM317REJgvw4WWnJuHNOIXWYJwgiJpDWDj9GAwOvT2n2luZZ8N5n3bqOIXQ9SEpKwgsvvIDv/O0gPm8Ibgj/8LJz4tqACfZsIGY0nwfUOh0k0rOIGmPdWBUYi78dkbiQ1o4uT+5qxEUFFlScm4WPjvbAKZrCN7/EBh7AbtmEgPSLv4FJZdeDSTKiscONR988jP12ZyDQuv1QB1Ytmhp2ApFgw8nXYD16GA1y215vN6VI95MTyzU5ls8Fob77aH+bkfhtCYIYgvR2ZDAwgIp5q4qPh6LYJ9XIgmEYVFx9Nb565zfw+K6WsK/BlGxQBHXnl9jQ5urTVfBT2+LCmqUzwfH+yT4Aj5yMVEnBT1m+JSaaI9fAptMdKsXHiUYvotVYspsIgggH0trEwpiVi8l3rgWTZATg1+U9RxyY/tDbAV/1rgYHFv25CvlWM8oLLDhgl/qb11fbsWJuEZ7+Vik27G3GM+8qJ+ekGln0ebnAa/nUn2DaojUFN1w7NFa2qx7U7HqCIIhYQVqrrwN5bYsL5QVWRcHPrgYH1lfbJbrj43jcsbFW4f+NBYaUNJz57d8HtDZcLGYjTvYPSmLIJiMLBjzml9jAMgzKC0hrCIIgYglpbXRkmJLQ5RmMeP+2Lg/ml9jw0dEepKcakWcxgQcUk9xL8zLx191N2HKwHVzZClw/Ixd3/+N9TT0vLxiZKTKxivup+Z1jlWccKyLNKxqt2KiWn1vPfSR6jnooyI8Rf4zZgp/+/n68/PLLeP7557Fjxw5wnN9hmJSUhORk5Xg2IjKanX3437caAPgXrbVVTaicU4jbLinAfrsTexs7MSAyMutaXFizpwnvftKC9zb+Bhv/+jhKZ0wHgECxj3ixLM2zAOBR39oVlgAkesBR7mBu7OhVOBgiOc6dcwoT4nsYLwnFBJHokNbGhmCdLJJYacGP0cDgx1eUAOBDJiPxnA/G/65FxvRbAfjXfqPRiJkFVuzQKPgxJRtwYYEFy2cVhLxuYa3eXN8WSG7afqgDHM9LujeO5ho+ms8DIxksFhhu/RzrxqrAaPx2BKEFae3o4hnwYaeKA7jIZsaapTNx19/qcKJmK3z9J5E599bA5/JA7YFmbUer3rV7tJyckTryYuUA1Lsm6/keo3HYEgQxtiG9HRn0FvvI6bPXo/fDd5D1tZ+AMRixu8GBdpdHMQVADxcWWCRBYKGL89oqe8ipA4BfhwwsA5ZBYOJuY4cbFSU2tHf1AeDB8TzqWrok+9W2uMKezKfWUCuWNm20dg/ZTQRBhANpbXzjdbbB9c4aZH3tpzCYMwAo7VoAisZUTQ43mhxu7DjcgSKbWfKZ0+1VNMCQU15gxZ4j2knUwew/H8crto3EDh3J5BVq7EAQxHBCWqtto8jfq5xTqKpP66r9xamCrba+2h6zYh9ffy86X3sMmfOWIfkMv9ZEWuwDAC5RYyoBj5fDztPxX+q+ThAEEXtIa6MjmmIfAOj2DKLJ4dflzt4BTLGYwDIMKkpsaHO5wfs4fPban/D0sZk4efZQ08U/vNOoOFaRzYyCLHNMbMCRjjHqaX4RaZ5xrEi0vCKtZ0g995HoOeqhID9G/DHmCn527dqFTZs2YcuWLejt7QXP+x1+06dPx6233opbbrkFNpttlK9y7OJ0e7F6WwMONLtUjd/327rx9sFGHP/n/fB22HHZlQtxtNUOs9kMH8djbVUT1lfbA52TxYtpMAGQi2dpXmZCBxwr5xRKEqiByAQhUassE034CWK8QVobW4LlPJ0SdTgEgCmWVLAMsK66WfJ+ahKDvsGhIxVkGNG85Y9oqtuJm/Ztw+P1fbj1qtlYMbcQ/hKjIeada8PHR3vgdA/AM+DDrgaHomuwGvKxsAJbDrZLCoCA0VvD9SQgDZcBPhoaHEv9VPtexrqxKpCoz0/E2IK0dviIdmQ84E9qWrmpDlX/WoOuXc8CAJJt+TB/aS4A5YQ++SQBsaN1bVUTVm8baqLB8TzuqiiOmyIUNUeenmvTcgCGe19612S5Bm6ub8eS0hzJ8UvzpM8FpXmWwMRi8QQmgOxPghgvkN7GP54j++B4dTXADaI7PRuZ828HADSeLvQxsgy8XGhlt5qTsWJuIW69OB93v1Af6ATJwK9Ld8wuQE2zdkKykQVmn2MLNMeQ20btXX2BAqDV2xowv0T676Ys3yIpKvJrPnBXhbbeqCWhxdLHHa3dQ3YTQRB6IK2NfwYcLTj+4n3gPN1wvPa/OPPmRyI6Tk+fMvEYAPY2ak+aT2IZzC+xSbSuyGZGvtWE8gJrUPtPXsAb6RRcSl4hCCLRIa0dIpiNIvdFqvkjO3sH8OibhwONDYUCoGjxeXoC+VEdjmacfedasMkpMTm2FmM1fkcQBDEakNbGC1IfsNgm/NnlhXj24R+jae82YN82nL38SRizcmNy1lBxTa0473ChlgMVqzzjWJFoeUVaz5B67oOaYhEjzZgo+GloaMDzzz+PF154AW1tbQFhnTx5Mm655RbceuutOO+880b5KscX1bKOTELC0/Hjx9Hx4n3wOj8D2CTMvOWnSEk1Yc2eJmyubw8ERrUQjOrlswqwYW9zYKHleF4invcunIpVi6YmbMDRwDJYUporSaSORBAS1VGtNmpwtBPdCGK8Q1o7OnCy1zwP1SIbcbHPnMJJ+GDjr9BatxMAkD7rZnzOZmH1tsNgGaC+VbrGHu32KKYF6TG45Gu1QJesq9RoGm96EpBCFckk0rjZSA1ntXtU+17Gi7GaqM9PROJDWjsyRFvsAwA8z+PldX/AiX3/AgCkTZ0N07kXBz6flJKEDJMRTQ6P5jHEnf7FbDnYjrsqiuO6CUI01xbuvnrXZLkGNnb0Bs4j7Mvx0icrjucUiWLCsYJN/hHer2l2guMBlmFQXkC2KkEkCqS3iYP7UBU6X/89wHMwZhdi0oWLFdvoK/YxYt+qy5CcxGLNnqbAut/ZO4Amhxs7Gzqxub4Ni2fkgAdUm1h5OUiaYyin70ivg2Wg8E0veLxKso1f84Nr4PplZQotihWCxgrPIys31cW9zUsQRGJAWps4nPqiER3/vB9c/0mwpgxYLrsz4mOdPzldVUMHgoz4m3k6vnvA7oLH62+U0eRw4xszc4P6Z2tkU3S1ErcJgiDGKuNZa7s8XqzZ0xR0errcRpEnbVbOKVSdqCqwpb490GQiWgZ7XZL8KMsVd0Vd7KNn2u1Yjd8RBEGMFONZa+MRU7IhMDxADj84gF9+73b0NOwH4M+PClXsI0yr1ROnDBXX3HKwXbK9EOcFYtd8WHyc0jwL7l1YgvrWLskx9eYZj0TDyUTLK9Lyc+u5D2qKRYw0CVvw43Q68Y9//APPP/886uvrAfiTbsxmM66//nrceuutuOyyy8Aw5OAbDeT+W6OBRV/XFzj+4i8x2HUMTFIybNf/EotvuEFzQoAaQlcNcXfD7Yc6YDVLx93WtbhQXhC5WMRDN+Xlswqw3+7ER0d7MG1yeqCD5HhAbdRgY0dv3CW6EcRYh7Q2/rB3aicOAwDn7cfOvzyEY58cAABkzr8dk8pvCHy+rtqO8yenS/bhVWKuegwu+VpdnJ2G3MxURdLsaBpvehKQQhXJ6E1Mjodnh0gNZ7V7VPte1iydGfh/MlYJIjaQ1iYOhVkm2Ds94HkeXTvX42TdqwAA83nzcc6N/wNX/9AUH6fbiwtyMoIW/Pg4Hgser1I4qFudHty2oQYfH+2RvB9P3Y+i6cwUy65OYu31aSR8i4+/9b2jks+2vncU+VazYp9Qo9rVfBg7DiemrRoPzy8EMRKQ3iYevR9th/PNvwA8h+SzSnDOrQ/BZ0zDIMdLpujpYcXcIhhYBn/d3YgndjaqbtPY4cbqbQ0ozk6TvJ9sYCSJyoKuyP22M/Mz8dhbRwLblRdYA7aosNYe6+6TnTX0fYxEUU08FxkTBJE4kNYmHv3th9Cx+UHwAx4Y0iw446bfwmiNvANyWX4mLi60oqbZBY7n8WF7t8TezUpLxh2zCwHwgcQkjufx2FsNimOF8s9WyCbplebFd/IQQRBELCCt9XPK65M0+dFjz6htI07ObO50SwpoYlbsc8KhyI9KLSyN+HimZAMuLLDg6W+V4vl9LQHNZRlgZr4VYo2NNn5HPkOCIMYjpLXxi2fAp/o+5+2H4+Xfor/lPQDK/CiBYFPiQ8UplU3r22T5MvJ/D0OvY+V3lR9n1aKpWL+sTLJNsMITeTxVnHMd6poieSYYK0Uweu6DmmIRI01CFvxcd9112LZtG7xeL3ieB8uyqKiowK233orFixfDbFYmbBCjy1STB6/9/efw9RwHY0yBbfH9SM37CgAetS1dmvtZzEZcMDkdu49IR75/JEt+kidJcTwflWDGQ6Bzw97mgMCKO0iOB8SC2eJ0x83IQYIYT5DWJh7cKQ86tvwGp9o+BgBYrrgLE2dcLdmms3dA0WlRbtbOL7HpMrjUjJuVm+ok2xRnm+PeeAtVJKM3MTkenh0iNZzV7lHtexmrxioFLojRgrQ28Zh3jhU71j+Ck3WvAwDSvrIQlgV3w9Xvg9WcLJmYxzIM5pfYFIWwAGAysqrvA4DXx6t2RW5xeiTdK0eTcAtMgxXm6ClO1Vqn5YU3/kYgjOR3EI7v43jFFEKAUdyL+DlI3jW6ptn/DKA15VD+jJAI+hIPzy8EMdyQ3iYeJ997A663nwYATMg9H9mLH0AvTP4xOyFINbK4sNCKsvxMvPdZd2D9XVtlD0yHD4a8KCfXYpIkfgm6IvfblhdYNafNazW7WjwjJy60IpbFuARBjE9IaxOP/s8+RMdLvwHv7YdhUra/2CfzrKiO+d5n3Vi/rCwwOa6tq08Su71TbeLCxlrVY4Xyz7a5pInYB+ydqG91oTQvEwCD+lZ9uhoPOkwQBKEH0lolgt2ix57R2kb4747nakNOzAkXb7e/GbIyPypyPAM+7Gpw4Pl9LdQYgiAIIsaQ1sYPRgOjq+ETd8qDzi2/Qb8oPyqj9GrFkAIAMCax8GoUDYWKUyqb1vvzWIX3Fs/IweptQ77fxTNyAv8frd9VsFnXVdtDHidYLk+wYQzyAia5TRzJM8FYySsaK/dBjC0SsuDntddeAwCkp6fjxhtvxLe//W3k5vq7Dh0/fjzs4xUWxnciaiJTnJ2GRcVmPHzHNX5jNtmE7CW/QkrOlwEg0GFCa1zuBTkZMKhURk+bnK6ZIFWcnQa5PzZcwYyHQGc8XMNoIRbMNXuaFCMHyQlPEMMPaW1iwfMcurb6i30YhsG6desxUDQPL9W3odszgD4vp9n1wu5wY36JDQaWCWtNVTNu5Jq+pDR32NbnWGlBqCIZvUnN8aDbkRqcavc4Vrpu6IECF8RoQVob3xgY6eRae6cHdS8+jpP1/mKfiaVfQ+ZlKwKdvDLNRkmhSXmBf+2s3FirsF3PzkjV3SlScGw3dvQG1iohgWq07KFwNULuSJY/d8iRazzHI+AsF6/Tcu0VksnUjr++2i75fQC/013tXoTvkpONQRRea/kw5M8IiaAv8fD8QhDDDeltYtH78Y5AsU9K3ldhW3wfWGOK7v37vBx2NzhwcaFV0uVwy8F2xbamZAN4nkefqJBIsJuLs83IzTRJNFxcFCpfP+tbXVi/rEx1DZVvm5WWjDvnFAb0fLS1ItJJsQRBEAKktYnFqc8/RcfmX4EfHEBS5lk446bfImlStmI7+ZS7UKhNSgX8mrqkNFfV9ivNy1TYVkU2M5bPKlAcW7wdw7CSz4WmkeJt9OhqPOgwQRCEHkhrlZTmWbBmTxNanFIfq5o9o2bzSBoU8eFNkQ2Fr+8Ejr9wL3y9TkV+VDhYzUZF42NgZPx35DMkCGK8QVobHzAALinKwp4j6jnBAjzPoeOlX+NU+ycAGFgX/QBpF1yB/CyzahFvWX4m9oiGDVSU2JAUJE4pRtq03oPGjt7AZ7UtLqxZOhMso4yZ+jg+6iaI4mk84R5HjFYjQ0BZwKS3cJogiNEhIQt+AIBhGJw4cQLPPvssnn322aiOMzg4GMMrIwCAZYAUoz9oapqUgSkXLcKJ7S8h+xsPYcJZ5wS2Ewvdumo7OnulCTi7GxyYLxvNPr/EhjVLZ2LD3mZVcVtS6q+U3XF46L1whS4eAp3xcA3xgFoCltbYZSoCIojYQlobP7AMoDFhFoA/yJnylavRf+wInt/4HL7xzZuwvtqOgiwzyvJzJYmyahhYRjHyNRJGskgkVgHZUEUyeu8pXnQ7ViN1x1O3CnJSEKMJaW38km4ywiULqKZNuwzuj3cgbfoiZMxdFij2AYDrp+egrsWFj472ID3ViAN2v+N4zdKZeObd5tOJxvzpzk6MRJctJiNcHmXwFgA42QOAsGaNZlJSuBohX2dDPXfINb44O01xvJXzijQLb9SOL5/WU2wzY8Vcv97Jk7cFHZSrp/B6aAKQExzvn+YkFHgFu289+jLSzS3i5fmFIIYb0tvEIbWgFEZrLpIyzoTtulVgkpIjOo5yzVUa1OKmGKZkg+R1vlXZtdPAMoE1OZz1U76teMKB1jS5kSRWdjw1aCKI8Q1pbeKQnJ2PlLyvYLD7OLJvehhJaeoaNjHFqGiaIGA1G3H+2ZNwtLsfYBhcP30yON4/safF6ZFsm281Y+W8Ivg4Hmv2NEl0Qi2/usnhxoa9zRI9lGtVKF+3QCgbjHyCBEEkEqS1fiYYDVi1aCo4nsejbw5NcS3OTsOS0hwsn1Wg0JvKOYXgeAT8s4Mchzs21qpOWY8FhtRJME+7DL3vvaHIjwqHTPME1YKfkWiQSz5DgiDGI6S1ow8PoLwgM2TBD8OwmDjjGpz6/Ajm3nE/zrlkIcryLbj14nxc80S1pOin0GYGr2J8rlk6M6R2yvW2NM8isUXL8i2SmGmwYh1xM6lgBJvGI24kFQ5yXRcaJ6oVMMltYvm+Po5H5cbaqJ8/yJdM0L+ByEjYgh+1hZiIHzjeHzRtcrixelsD5l73HTinVCBpYlZgG0HIBOEDoCpY++1OWExJYBgGmaYJKC+waoqlPCgZaaAymkDnSE0cGKnrGG3UksnUnPDA6Ca9EcRYhLR25GABcEE+F+f6piax6BtUbm3+0hycVfIVnDi7HGur7JJO+PcuLMGqRVNR2+JCaZ4FB+ydgc6HgNQ5rJa8qlc/RrJIJBYBWT1aqfee4mUizngeqRspFLggRhPS2vjCZGSRmmxAhilZtQNU8hlFOOuOp2BIs4JhGBRmmXCifxDTJqeD54cct529A2hyuLGzoROb69uwpDQXb/1obkBjfBwv6fbk9fH4/dsNivMBUIyeL8u3JFxSkp51VqzJ8s6c8iTtFqcba/Y0Bbo+b65vlzik1YLe8k6duRZT4PfQ0k75X6fwWq9uRqIvI91hOl6eXwhiuCG9TRwM5gyccfOjYFPMYAzGiI8jaIVg4y2ekYPV24a0NtXISib7nJ2RgsYOt2T/3EyT5JhCV0Z5sWio9VO+rTgBTa558ulyI0Gs7EGakkAQ4xvS2sSBMRhhu24VuIE+GEzpmttpFfv4P/Niz6fOwOu6Fpdq12FgSD/lOrHf7sSBZvUuw3IbV65VYptaq+MxENoGI58gQRCJBGmtn0yTESvnFaFyY63k/WPdfeB44Jl37QHbT2yXsAwC/sPH3joy7NeZMWcpJk6/SpIfFS43TJ8MA8ugptkFjvdrX3mBdUSmxZLPkCCI8Qhp7eiTaUrCe59169rW/KU5mJDzJbjPOluS6/P2PfNQubE2YCfaHW7YZTHf3Q0OrK+2h9ROud7K8644Xlr8EqxYR9xMKhjBpvGIG0mFg1YD4DV7miTXq2YTi/cV29+CXb9+WVlE+cij6UseKznViQ7FEyIjIQt+NmzYMNqXQITg1NFDOHWsAZPKrgPgX/TFxmxFiS3QcVcIcJbmZeLehVOx5aA0WafPywUCsE63F6u3HQbLDP2BawUltbr06iGaQOdITRyI5joSXbjUnPCJlvRGEPEOae3IEqzYRw5zer0ePNGJk/WvIWPeMjCsAQBwgp2ER988rOiEX9/ahfXLygLr4oq5yqloasbnjsPx+1Adi4BsLA2IeCmakevh5vq2hNP5kYYCF8RoQVobf3i8HL5/2bmnuz0CnLcf3XueR/rsW2BI8Wur2K61d/o7F+9qcOCDtm7VYzZ2uPHom4exz+7EhQUW1Ld2BdYaQTMu+8NuxX7ySQOmZAO+f+k5gTVqtJOSQtmU4s8FW7++VXudDeYEXzwjByzDBAp7hO8U8Ou22rRXucYX2aSTGsSyqGVLyqUzHCn1cTw4nkdxthmAP9Fcj76MtF0bL88vBDGckN7GNzzPo+fdv8P8pbkwZuUC8Bf9RItcK1bMLQLLMJqJwWpa09jhRpHNHCgC3tXgQOXGWhhYRqHlwZCvtfJAqphENtvIN0sQ4xfS2vin96MdYE2TYCryx2WZpGQYIpyip8ZHR3skr61mY2Aiwa7TiVRyndAq0gFC27haTSFL8zIBMEFtTzHkEyQIIlEgrVUijxF6BnxYve3waV/cEIJdUtPslB8ipsjzoxiGiarYB/BPF3/mtnJJjs/aKjsWPF6FY919km1jbX+Rz5AgiPEGae3owwDY8eP5+Mnm91U/V8uPSpqYhc7eATz65mFsrm/HktIc3bkx66rt2G93Bm2ELLdjxXlXfh+vtMg4WLGO3liu/BkHAIqzzVhSmhuxzaont1rLJhbvKy+43qWzcEqN0fQlx2uhSaLnc4cLxRMiIyELfpYtWzbal0AEof+zD9Hx0m/Ae/vBJKdi4lcWSDomAkBNswsLHq9CTmZqYFTu9kMdWLVoKt760Vysr7ZjXbUdnb3qHaQ217WD43nUtbg0pxDIF2f/dspq1VijTLRtH5UFONiiGK5wxZOgBEucGu2kN4IYS5DWjhzyzsKh8Az44O3+Asdf/CV8PcfBDfTBuuC7km263Kckr1ucHkl3YwGe57Hf7kRNswutLo/q+YI9VI+mPsQiIDsWDQi5A6Cxwx2xkT1eoMAFMVqQ1sYnf3ynAV4fD+6UBx1bfoNTbR/j1LEGnPnt/w04kNU4eWow6HF3Nzgkti/HA3dV+Nedbo/U7jUZ/cU94rHwngEfWAZhTxUYLkLZlPLPVy2aGmj6oYZck4uz05BvNUmeL2pbXJLmIOuq7QAQSLgONhWWkT2elBdYA88xLU7pM5BgS5YXWLHjsEOyj17WV9slkySE3y4U1GGaIGIP6W38wvMcXG//Fb3vv4neD9/CWcv+DENapub2qUYWAHBqkJNMwQ3G49uPgOP9TS+0ApNWczLqWrpQXmBBniVVojU9fV7JtuIOhkB4gUBBdwT9UiMcrYk3SMMIYvxCWhvfnHzvDbjefhowGHHmzY9gwuQvxfwc7n6pPZxhSg4U/AD+OKkeLKYkfCU3EzXN+hs5qvv19Okz+QQJgkgUSGuVVM4pxOb6NsmkVgCQD2fwF4NCtw0ZCWr5UbFg95FOSXzP729Ubx5B9hdBEER0kNaOPjyAS/+wE919Q80Q555jBcMw2FP3Cb74+y8086MA/yQ/IS6pVjQjp7N3IODr3XG4Q7VgqDQvU3Kc5s7eQN6VWq6R/LzzS2yS5lF6UHvGybP4J9Gv3FQX07wsLZtYKw+sNE/5vUaaYzWavuR4zROL10Kk4YLiCZGRkAU/RPzSZ6+HY+tvwQ8OICnzLKQWTFfdzjPgO90tsVfyvrCACouVVrfDRkevJHkGEMS3LVDRKl+cxZODhnNRVCba9o5Kom2wRVFNuNS6ImsVTwGjJyhaiVPxkPRGEAQRCeEU+wCA13UUx//xC/h6nWCSTTCfN1+xjdPtxfwSG9q6PKc7E/uN27VVdpw/OR0ftXfD5fGqHF1JaV6maBqfBQAfmE7A8Qg4l0d6mly0AVkfx8Mn8/APhwEx0kVRfgeAdFpiuAaqnmuOp2JggiCIaGDgdyILeH08uP5eHN/8IAaONQBgMPGrC4IW+wDApBQjnO6hwh2WCR5I3lLfHij4yZQlQ52dkYIVcwux5aDUoSus5/GQlCTvilnT7AxacBNq4pzcfs3NTA25jdC1C1Dap/JteY5Hkc2Mnj4vpk1Ox/JZBYqpQsXZZiyekYtBjsPlf9wN8P7pxAbGn4At2JhyDVw+qwAb9jZLNDFcm1uA7FqCIMYLPOeD882/wP3xDgCAqWQW2BCTfQTb2Wo24kT/ILy+0BlbfV4uMC1eWIdbnNKEMKd7ADsOd2DH4Q7ML7FJrzPIKbTsLC1bSWuaXSSB33iENIwgCCL+OFH7Krp2rgMApOScB6OtQNd+FrMRLrc+3zEA9A1K/dvyphZiP2Vxthm5mSbVCT8Wc4ok4Wq/3SnRyEh8j+FMpyUfJ0EQROJgYBksKc1V2Fi5FlNgSqsf/5rOyrsBxQi9+VF6MBoYhZ0rtjvVpgZkpSXjzjmFEvuLtI0gCIJIVMTFPgDw8bETON7WEjI/Skxtiwtrls4M/P9/m5zwDAwdVx4XFhByqsR2qDzO2+TwaBYVif2h0Wiw2jMOx0Mzb9c/AbAJWw62Q2icv2JudNqvnSes/OYizbEaTV9yvBaaxGsh0nBB8YTIoIIfImZ4Pj0Ax6uPAr5BGK1TkH3Tw0hKs6DIZpYZ1doIHTYA/x/1P2s/g71TfeKAGo0d7iDVutrGcSxRq7SN5FzRGuLBFkU14QpW1BMrQYmFc0HrWoSkNyF4H+uqZoIgiHhgwNGC4/+8D5y7G4bUNOTe/Fuw2UVQy3MysAzyrWaJHjndA9hzRBlMFTAlG3BWegpyLSYYTk/PUzMehf8vtpkl+9c0Rz5NbqRZX22XBJbnl9h0GRDhatlIfw9+B0COxAGgZqAGuw891xzvvy9BEIRe5BLq8/Sg418PYOB4E8CwyLrmJzB/eR4AoMhmwo2lU7Clvh2NjqGEpYoSG8rzLfjft4YaE4TqGtnk6MXyDTVYs3QmFpfmSroz5lhMWLmpDrmZJomOl+Vb4iZgK78/+Wu1iXOVG2uxflmZ6vUKGlzT7EKL0y2ZoCA410vzLPjZghI8tatR4pxXs0+F4wm2eZPIr7CrwREo0BGTbzWDZYDV244MXbfDjVWLpgadXrTP7pRMb9pnd+LCgvBsboF4KOYiCIIYbnjfIDr//Qd4DlcDACZdeCMy5i0DozMByxlGArLAumo79tudEhvQak6WFOsC/iSwVYumBvRD/rkYrUCgcuo8D5ZhFJN9xIlZ0Wh5vDwbkIYRBEHEFz37/oXuqucBAKlFZbBdtwpMUnLQfazmZEzLSQ/YN5EiaHWxLQ1geIldm281Y83SmZj2q7ckdh0AgJEaltFM1hMIdzqt2nniRWvj5ToIgiDihco5heB4XpLgWifz99W1uLBmDxSNH2KBVn5UpMw9JwscD4ndKrY71aYV3Hl68rkYit8RBEEQY4WO1k/xxYv+/Cg2JQ3Z33gIE846J+g+ZfkWiZ9y+YYaibYWhshjFtuhVrO6Db2u2o47ZhfgZwtKsPU9/3OIECcV5+sKr8O12+Q5v8IUXIHN9e2SJlPixvlC86totF8ePxWaQD7zbrPkfas5GctnKRuL6LFdR9OXHK+FJvFaiDRcUDwhMqjgh4gJ7kPV6Pz37wHOh+TsAuR9+xFMzLDg9tkFuHNOEa76S7Vimo8a++3OwNSAyjmFYBk2ouuRV+v6q255icAFWxS1hEevIMkrbSNZgKM1xIMtiuJkKo7nUdPsRKurT7KNOGlKLigtTndgRKHehxIfx6NyY23UDvpQ4kYODIIgxiqnvmhEx78eANd3AqwpHdnffBi8rUC12AcYWh9DjaoV4xnwocnhxhSLCWtvnemfoLaxVnP7Llm3Rh/PB6YBtTilBbvDMWUmGuRGqoFldB0/XJ0ZjS4MegzUaAt9x1t3CYIgxge+3i4c/+d98Ha2AmwSbF//H5hKLgl8nptpQn2rCzmZKZKCnwsLLLhjtn+qy0dHe8DzvCIZOdXISqb68fA7jVduqsP6ZWVgGf9a6uN4RUGquKNxvNg78q6Y8tfLZxVgbZVdkii9q8GhOf1WsF8BfydnMWIbcn6JTZEUJtia4ucFYQrs5vo21esXNFJu56oF/4UEbeHYSke39PXuBgcuLLBi1aKpEi1eualOcQ3iqT/+BigM6lspeYsgiLELP+iF47XV6Pt0PwAgffa3kH7JTbqLfSKls3dAMUlA7ZTlBRasnFeE2haXJDlZTHF2GpaU5mgGAuVT8PyFwspjqSVmRUK8PBsQBEEQ8QHP8+iu/htO7PsnAMB07iXI+vrPwBiMIfd1ugewt7EzZtfS5RlA5ZwCRWzWwDK4sMAi0eYimxmLZ+RIthUj9j2q+Y0BqPqSQ/kw9fg440Vr4+U6CIIg4gUDy+CuimLcVVEc0IZWlzQ2yfG86qTVaBHnRxmzC3DGNx+GwZSue/9551hxUZFN0gSK44E1S2cqJokL+AuccLrAicfiGVK7VPgO5M0mKH5HEARBJCLy/KgzvvkwkrODT61lAPzfnkYMcjy+c7p5/JqlM7FyUx0+OtqDaZPT8fS3SvH8vhbUNDvxfltP0IZPWp919g5g9bYGVJybFfAh+zXd3/gpWrtNnPPr43jst0v9zY0dvYF4q9oEQLn2h5t7JY+fiptEi3G6B7Bhb3NIG1poiBUvzSvitdBktAqRqLlIYkEFP0TU9H6yC87//AngOSSfdS6yv/EbDBjT4HR7wTIMNuxthrxvstVsxIn+QcVI2j1H/I7k7Yc6sK+pE60u9cCq1WzEtMnpYBmAB4PPXB5J9a3Xx2HB43sgdPIQFkC5eGihFB6AZSCZ3BNMlIcKapzg+KEEoHAWxOFMpBUnU2k5OMSFNEPdkdvR2NErmaSk95rkkxT8x2uL6eQi4X35a+EBiMSJIIhE5dTnR3D8n/eDP+WGIc2CM276LYzWXMk2VnMyMkxGdHsGcEFOBpbPKoCBZRTJtnrY1eDA2qomsAyDlk7t7hYZpmRJQnOby6PZATLc4tfhDmBG2h1BrjOhtGw0ujDoMVCDPWfouebx1l2CIIixz2CvC8f/sQqDrqOAwYjs63+B1KIyyTa7j6gnPj3zbjMO2J2anwNAanIS+rxKPf7oaE+gOAWAIiDb1uVBvnVool68FFyWF1gkhTkcLy262bC3WfX5I5RuqjmmxRyQFddYzcmaTSXWV9s1k7XFtqTYzlWjs3dAYv/KNTDVaFAUIdW3urB+WZnkt5Hv5+N4rK1qCiS0yScpiu+FIAhiLMD7vOh4+WH0N9cDADIqliP9wsWjdj3TJqdrTn0tzctUNM+wmo1YMbdIoWNyn+OgbOzdsZ5+yWvxZJ9g6E1mjvbZQH6e5bMKFAlm5EMlCIJIHLp3b8CJmpcBAOYvV8B69T1gWIPu/eVxWzksE3qirYDfJmQCzRBK8zLB8UDlxlrMzLeAB/Dx0R6cPzkdZfmZqGtxYX6JDSzDgOOlzTBK8zIDjabEjTLEeq3mSw7lw9Tj44wXOzxeroMgCCIekNsxHA9J8UxxthlLSnMVDRligVp+lCElLaxjMCyL2y7Jx0v1bYE8J2EquVZ8z1/gVIS7KtTXfnFcVQzF7wiCIIhEQ09+lBo8gC7PIB57qwFJp3NmkpNYbFheDkD6/FBeYIWPh+4Jt1azv4mGOD+q+lNpXHjLwXZJTBeI3m5Ty7cVH1dtAqC8kb889+pfdW3It5pQXmBV9f2GmjAU6v7ktuuWg+268q0jZazkBI9WIZL838fm+jYsKc1N2O9xrEMFP0TUGDPOBJOUjOQzipB944NgJ5gCn/1lx6eSLsZWczIyzUbkZKQGTYYCtJOpAL947j7SifklNqxfViZJkgGGCocA6ag6vYuiuvAoJxRpibJaQY2QDKV3UR6JRFr5fRZnm5FvNSsKaYT78Xe37JXsr3e0vVriVmOHW7PDsxahxE3re6POVwRBJDKGNAsMqRPBTzDhjJsegTHzLMU2PHhVp3DlnEKJkxsAklhgkFMcQsL6avVE3Ypzs5BkYFUd6PIOyVq6oge9AcxIjbdIuyPIdaaxw40Fj+/RNHiCnUd87SPd1T/Yc4ae7yZex9wSBEFECjvBjKSJVvhOdsJ2w/1Izf+q7n07ewcU9isDaduLbo968e20yf7Oj1oB2cYONxo73IE1O14KLsU6oJZspVW4o2UDCpoonxBYZDNLmnvIC2syzcmS5xXxGHv5NVjNyfhqbgZmnp4AvHJTHcryLcizmCR2rsVkhMWcjGM9/ZLzCc8icg0c5Hg89pa0C7Xa71I5pxD77c7Ad7WrwYG2Lo9iO/n5CIIgxgxsEoyWs9HfXA/LFd/BxBnXhH2I1CQGpglGnOj3hkxI1kJI/BIXtpTmWcDxQw2kJmekKva7ICdDdV2W+xyF4K+AXLv0TvZR82UCymTmaJ8N5OcRaxX5UAmCIBIPY9YUAEDaBVfCsuC7YRX76EGt2Edut4l5YuenODsjFYtn5AAY8iVvP9SBVYum4rnl5Vizp0liD88vsSkmHGh1FAbU7U8t+03uw9Tj44yFHR6LBKRg1zFWEpwIgiBC0eXxYs2eJnA8L2miU5wtLbjJt5qxcl4ROJ7HjsP6Enn1Eiw/Si+7Gxy464V6hX5G4w+U66HeZhMEQRAEEW+Eyo+ymo24ICdDtRBGYHN9m6Kxkfz5IdXIau5vMRvhEhX3rJirbKyvdE8zqs3/fBwfsX2mFW8V7EH/BEAeWw62o8vthdM9oGjkLz9Gk8ONJoc78Iwkf/ZQy80VN4FUuw4B4X6lSO892PNOJLYt5QRHh/zfRySDIIiRgwp+iKiZMPlLOOOW38FoyQGbnCL5TFzsA/i7OQnCEgt2NTiwvtqO+tauoNuFaxjLxfdYd5/mdgJqghNNt6VYJtJqiaH8PpeU5ga9Pj1ObbUg8fplZaoVxcL9xVIctL436nxFEEQikzQxC2fc9Ij//9OzVbcRG5vA0Dq3Ym6honA1P8scUou1pgIJ+iEUqdy7cGqgSEVsIAOhdQXQr1FagdRIjTfxNAVBI/QYi5VzCgOTAAQEg0fQPPExghWqaiVwCf9fOadQ05iNNogb7DlDT+eIeB1zSxAEESmscQJsNzwAr6sdE84sjvp4SQZGkogs920mGxhcUpyF0rxMXP7HPQqbMystGRmmZInerKu2447ZhfjZghJsfe8oeJ7DPrsTNc0ulBcotWA4E37EOlC5sVbymXA+NftP+DzYeHfAX5xTOacQd8wuwFV/qZI8t4gLiuXFx+Ix9vJrWDHXn2DtTyYbcujPL7FJrsXl8apOxBWeReQa6ON4sIy/UYh8yrD8O1N+/9q/B3XfJAhirMEwDDIvWwHT1NlIyTkvomP0DfLoGxyAVVbwKWA1J6PP65MU2ZiS5ZPYGMU6XdPslASI1Ro/sfIOF6eR+xzl/nAgsmYYar5MtW3WLJ0Z+P9YNNv46GiP4nOy+wiCIBKHtGmXIynzLEyY/GUwGtoVC0zJhkAhT22LS7PgxzPgQ2NHL1ZvO4ximzQZW9AYuRbtanDgmXftEu2tCzIN1sfxKC+wqvqSQ/kw9fg4YxGvjUUCUrDroAQngiDGC6e8Pjz65mEUZ5tln0idr6V5FqzZ04SX6ttjfg3B8qPCQa1bfovTI+nIHw5yX6jeZhMEQRAEEW+Eyo9iGAZrls7Eik112NvYqdoYqrHDjYse3R7Io9p+qAMWWaMmuR/XYjYCPI9M8wRMsfgLelkGmJlvBcfzqGvpwvwSGz462oPOXqVvOiczFTXNTklTjF0NDqytsoNlEFG8Vq7vQjMrwR70TwAsxl0VxajcWCvZNtgUIPk2aghx5ppmYRovMCPPnyv28dEeTJucjuWzCiT7yCcSzS+xobzAKonliif4yr+PSGxbygmOjpHK6SZiAxX8EBHR+9EOmM69ONCtIhYJUaGwmI3ITDWiqVPaAXdzfRuCJckA4Xc5EkRRSOiVd2Iszk7DktKckM7UaLo+xTKRVksMw3VS69lezTG/vtou+k7bJMlasU5i0vre4qUTNkEQhF76muqQZM2BMeNMANqFPlr4OB53PFcLH8fB5T4l+ez66Tl4+WC7ZiA2GOJuikKSrFDk4k94ZcIKfobSqJpmJzh+yPEt1+1ojLdIjEUDy2BJaY5qR8ldDQ7VaT/hTL+T35fW9a2tsks6YnI8cFdF7CbmEQRBjAcGHK3g+nqQMuUCAACbnBLStjUZDfB4fUG3AYDczFTYO7Wnt/zkyhIA2h2K75hdAJZhJJ939g5g9bbDmF9iCyQiNzn851CbKKulc7EuBFKztZbPKsB+uxMfHe1BeqpR8syhZovJNdHpHgDLAMlJLJaU5kq+h8UzcgLPG6V5mSi2mdEoOn6oTs7yc7GM38aXT7PVm0AtdqaHQv5d+e8FmtP+CIIgEh1f3wn0N78H85fnAfAHYyMt9hHDceparFYEdFZ6ikSHhOJQQFuH1ZiZrx6IlK/tZ6anwK6wtZmog7mCfsrfi9a2k59n2uR0SVCWfKgEQRDxDe8bRO9H25H2lSvBMP7uxJFobaqRRb+Xg975eRcWWAI+4ZoNTsXnRlkTDADokk2+9XE8KjfWqnT/lU6fV2vUIE+gKi+wYNWiqcMyjTwWftRYJCAFuw5KcCIIYvwhtau63F5UlNhgOJ2Ue8DeqZjIHg3DkR/l9SmbRTR29EbcUT2WDYUJgiAIYqQJJz9q2uR0bNjbjN1BJvwAyqbJ8tdq288vsWFXgyNgb/ptUWnjY2EbAaPB/1yidT3iBs1C83zBrxzKX6ym72rbq03WEU8BEk91V9tGDXmzRqHoR7jPXQ0ObNjbLHlmkdumBpbBirmFkoInec6Z+PuQF0QLtm2w2DblBEeHPE9egL7H+IQKfoiw4HkePdV/Q8++f6L3o3eQveRXYI2Rda0wMGqj7bQRRFc+Gl5rQoG4u1TlnMLAwi9enLSSewWnaW2LS7KQicfeysVTLlhrq+x4995LA5+NllHt43hslnUuEcRQzTkcTCD1OLXVqj6F8wlJV/LjR3JP4SaoJaqDYzi7chMEEb+4D7+Lztcfg2FiFs685XdImmQLur1cUy1mo+b4WsEIC7fYR9DAA83qhZ1auqKFsL6tO51oJSAYcMK6NzN/qNvDjsMd2FzfJimoicZ4izQQqmXwAEPTfjgeAaPVx/GB30P87BGsk4YwRUnr+vxTBIbwTzyUahzpB0EQhDYDx5tw/J/3gx88hexvPISUnC+DgbwXpJLy/AyAZVHT7ALP86pd/AEg32rCN8umKBoeAP7ikso5hVi5qU7zPAeaXTAwUO0UJe+8LyDXMS0d0VvwqtcWUbO1xB2cOnsHML/EBgPLoDTPPxGwcmNt0GRp8fXKj69WfNyoUlCktxlEeYEV5QXKaT7DURyr5ZiXnoOSsgiCGBv43F04/s/74XW0gPP2Y+JXFsTs2F19PhRYU9HsVJ/KLqbJ4YbFbJQEdNdV25FhMgbZCyjMMqGty3/8S4qs4Hng0W3qzSrEAVO7w42KEhvau/rQ5T4Fp9sbUdKWWDME/axrEbopMoHpftEi16blswqwYW9zwvlQCYIgxiP8oBeO11aj79P9GOiww3LFXRFP9bm4KAvvf+aCyzOoa3uxT5hRsaTVOixnmJKwYm6hwl8KKGO/8iLez1weyaT5mmanZPv61i6sX1YWl0UuwRKvYgUlOBEEMd64fvpk1LW4AlridA9gd4MDqxZNBYCYFfvEMj9KjqCVxTYzwEjznmJdGEoQBEEQ8Uyw/Ch5MwlTsgEtTjf2HAle7BMp7zZKnyF2NTjQ1iVt8MgywKpFUwP5Qmr2rxTp5+LcoVDFP3r1XW2yjngK0PplZajcWKvYZvmsAs1pO2pN/+XfhfyZRc02ld/DHc/VaH4fFedmST7zN0sE1lY1BYqu/A2R+UAjxkTNCY4XhN8nFjndxPBDBT+EbnieR/euZ3GidisAwDDRCsbgD4wyAFgAWn2OjSyDialGuEQO2nCKfQRcbm+gmtbAMnjvs27Vzo2Av7vUhuXlgddr9jSpdm0MZiyHM/ZWvq3TPYC7X6jHhuXlw9JFWQv5eTieVyQjC45etWsKJpBa5xDfi1pVcIvTLRk9HOxBRH5stSBzpBMZEtHBEcm9EgSR2PR+vBPONx4HeA6G1ElgdDiO5U0QT/ZrB2YNLIP61q6Qx5QHWTNMyafPpRRwuZbq0Tyx3kjvhZese8XZaZLPhYIaILJpdWIiDYSKDR65USwg7tIhR57AXNPswiDH4aOjPej3cigvsARG32pfn/R3cLoHsP1Qh2R70g+CIAh1Th09jOObHwR/yg1DmgVsil9rWB1NKT48diJkBygA+KD9BDgwmJKZCp6XFtouKc3RLHIR2C1zuIq1Rt55X0CuY1o6F6ygdGCQw8pNdfjoaA84npeMugfUtcTAMhI95nhgc12rZJvPXG4UZKWhptmpWgSrZkeKR7qX5mWi9PSY+Ban1JnMYKgwatrkdNx6cb6mc9rH8eB4nH6+4AMNQsTfhdrzRKzs+US1SwmCIMJl8GQnjr94HwZd7YDBCENa7JNO27v7dW8rt5E7ewckxbQAUHFuFtq7+wAwyMlMlWjxniOdOCo7n7ihklwTklgG2388D5UbayVaHE7Sllgz/H7tIft51aKpMdMSNW0irSIIgoh/OO8pOLY+gv7megD+7seRFvsA/hjwV3IzNZtIqVHT7Nc1HurnzUw1oKtvKHJ8w4ycoeaAG2sl2zIA/mdBCV5+7yiOdffBMyCNODc53GAZYP2yssB7Ow5rT6SLp2Z6wRKvYgUlOBEEMd6obXHhY5WmSDXNLkQhhxKC5UfFFAaKCedUuEkQBEGMF0LlR8mLaTwDPjR3SuOEsUS1eIeXPly839aDVpcHvErulICQ2yzk7qrlRgFQjZlqEczOVZusY2AZyT7lBRaUF1gljYSD5aWqx7Gl34X8mWX5rALstzsDMVsh70mMypDfAO1d8gZf/vPJGyJvOdgeyGem2GtsoO8xMaCCH0IXPM/B9c7/ofe9NwAA5mlXwLrwe2BYg/9zaBf7AICX4wPFPiajAR6v+tYmI4sJSYzEAayGUHl6+R92axb8CH5brQkCAsGM5XAcpJVzCvHnHZ9KnNBVRxy4/I+7sXhGDjgeeOyt4IU0gDTBatrkdKxZOhPJSazmeeXIhbg42yz5vDjbHJh4JE5SFgRaIZD17WAZRlKAs3JTnWS/tVV2VM4pxIq5hYHfRjxNSUjO1jOSUH794qQv4RrH02j68XSvBEEAJ9/fBtdbTwHgMWHyl5G95EGwE8wh95PbQ8E6SPg4HuUFUsMs1chKJhQUZpmQZzVjisWENpcHjQ53oCNxkc2kOKZcS/UUK8r1xpRswA8vOwc1zc4Qd+cn2LQ6PfgTfvnTOskoEn71oKZ5Al3uU5r7CV0oxB39xY783adH3wZ7Dlk8I0fTKSDXDuG9kdCPeAqmEwRBqNHf9jE6Xvo1+IE+GCbZcMZNv4Ux82wA+ppS6Cn2AYa6SgpYzEYw8BfQCtPyls8qwD67M+TIeaFTlNgme+bdZrxU34ZuzwAyTclYfHr6nRgtHQlW8Cq29eTUNDs1tURN+8U0OTxocigd8Jvr2wNaIWiq1iQfLXgMOcR3NThw9wv1iu5Y65eV+c9RbQ9MDgQAlhlK0g72PKHn2YY0kCAIws9gz3Ecf/GXGOz+AoxxAmw33I/U/K+O6jUJNrIp2SDx3RZnpyHfalI2NJIlIQNAl8wHLdZPLW2NVbd98g8SBEEQYriBPnRseQinPvsQAJB5+UpMKv1aVMf8oK0L03IyFN2Tg17H6eQmViOz+itTLBJ7l2WGYp1yjWx0uFHb4tJsoARI9S9U/DaemulpJV7FEkrMIQhivKHlT212ujElMzXq44fKj4oEuT06dC4q3CQIgiDGJ5HmR0WLmt1rMhoABqpanWNJxZKZOdhc34bGDjec7gHNfGXAr/ksA0lMV2hEyHE87BoFS+KYqY/jsbaq6XRelT+fCeAlTfwB7eIcIS9YPF13+6EOrFo0VdJIQ83vLBQC1TQ7FY2iwfNBJ9Bv2Nssidlu2NussFO1fAgAIK/crm91ASiCvNBI+ZogxgdU8EOEhOd8cL75BNwfbwcATJxxNTIvXwmG0V+EIiHIentmegqunzEZf3j708B7FrMR/V5OIqhCp19XkGTa8gIrAKlTV4zVbPR35g9iLOt1kAqJPalGVnKdPt4/iWD1tgZYzcmSfcSVpmLECVa7GhxYualOMqkoFHIhPibrPrmkNBcGlsGaPU2KRC5/krX0B3K5ByRO8X1NnYoRyE73AFZvOwyWgST5Wu6cFz9AbK5vw5LTSWlix7b8+j+SdWYRnBxawfKxlmQVq8QAgiDinxN1r6JrxzoAQEreBbDdcD/Y5Oid0oB/ZRfM1V0NDnzmcgeMsBanW2qgAbB3egJGpilZ6ryW639FiQ0c7zcWhXVXXzKSdG0+OyMVK+cVgeN5SXfG66dPRhLL4l91bZLrbO6UTo8LF3/C71DBDMsg5HG0NEZt2o8zaEK49DzKIqeh70zrOWTF3KJAQa7YSAcQ6BIi1o8W0fcl3P9waGU8BdMJgiDk9DW/B8fLD4MfPIWkjDNxxk2PICk9e0TOLRQKOd1eNDnc2HnYv0Ym6Vh/W10elBdYsWbpzICTt6bZGdBFp9urqmNa9qxaAFnQuL2yUfViguV8qRWb6qGxoxdrq5pwV0Wx4nrVkq0FirPNyLeaUZZvwQG79JrlNuSuBgfWV9sDNqr8uvXolJ79SAMJgiAAr+sojr/4S/hOdoJJTkX2jQ8iJff8YTlXEstg4oQkuDz6inEB+DOpRCyekQOW8dtk++3OQKCyNE/ZvVAcxJV35tdKzopV0hb5BwmCIAgB7pQbHf96EKeOHQbAwLLwe5j4lQVRH9flGcSeI9r2IKBsGvXR0R6s2dOEmfkW7Dg8pFPF2WYsKc1FTbPUjtpysD3QSfjWi/Oxtsou0Ve5LSdPjBbrX6j4bTwVy5KOEwRBjBx2hxt2WcwzXGKeH3WaCwssqo2Wci0mKtwkCIIgxh3DmR+lRbHNjByLSbVweJDjkGsxKXKnAIAFL8qDlX5elGVGd9+AJD/IM+DDjsMO7DjsUOQ5Fdu0C5oaO3oD8Ux5PtPqbYcVTf+1mmLI84e09gHU7VV5rvX8Ehvauvr8Tf8dbjQ63JoT6OW2eE2zExwvNITmsXhGjsKHIJ2GBEnjRsF+9jdEHnrfXwA19vKERwL6zhIbKvghgsL7BtH57z/Ac7gaADCp/AZkVCyPaix8v8Z0H8CfYLz1vWO4d2EJ6lu7JIFQrU6/ckxGA87OTAXH+xcorcQjf1LUUBelaBaztVVNml32BfoU9608to/jA92eBeQO7lDIhVhwhhdnm7F4Rm4gKbvFqawW5nilQJ48NSjZpqalS/PcoR4KxIin/gjdltX2mTY5XfIQ4uP4wLg/tWD5WEuyom4uBDE+6Nm/Gd17NgIAUgtnIuu6VWCNE2J2fHl+rtBl32pOVtEnKfIOFkJClLAuDXLSLhIcr909QqyvWgaZXB9ZhkXlnELsszslhnCTwx1Y78NZ54VJevKEZj1B32Aao9aZUejSITf6h7pQ+FEbWSsYrnom/5UXWFFeYJE8O62tkk42bBR9X4ByYoLavUfybBRPwXSCIAgxnsYDcLzyKOAbRJIlB2fc9DCSJmaN6jXVNLsUU/cE/BoCycRUAAEnr9xRG856Ky5WXV9tx8pNdUGdvwIftfco9FwgmO0nx2Rk4REliT2xsxEsw4R1zCWluYH73W+XFs6mpxrR2SvtrCV8P5E2j9CToEUaSBDEeGfA0YqOf94Hn7sL7AQzsr/xG0w4u2TYztfn5SRJx2rIuzWK9Wd+iQ0Aj0fflPp0dxzuwL0LS1Bxbpai6ZHAR0d7sL7aHtAKreQsrffDtbUS3T9IQUSCIIjY4Os7gY5/PYCBLxoBhoX16nuQdt78ETv/5IxUNIr8s529/oaB9y4skUylFa/z4iQev33bi+2HOvDfJqeiI/L5k9MliVffv7Q40PAoXP2LpyKbRNdxgiCI8UQs86MKssw4fqIfqUYWt80qAMsAH7Z3KxoGGqLIvSIIgiCIRGS486M0YaBoTCEw4OPR5HArGl0Aft/mmj1Nqrm23X3e0zYeg/pWF1qcbkl+kKKASKb7VrNR8mwgxBbVr1O6r2Dn/n/27jw+qvre//j7TPZJWJIQUFmSAAqut7JIXRBwqaK1rQvWjYsLSq3X1i63Xn91a92u9fZeaxeLYsFaqxWtS1uXFsvmCkStK8EsQAAheyALZJnz+2OYycyZM0uSmcyS1/Px4GHInHPmOxPMe77f8/18v9axV7sFh63neNj1V5c8scnvmJqmdhUXOP0W/fc8h3UcwNoXtxbw3P9quf7zrCM0b0qR7RyoHpfpNx/N077rTp1o+33rHK5gGxCgV6rNrR5qKPhBSGZPt3r2uW9sjjj5Uo04+bIBFftI9pNafVXVtWlDdaNfEYgkv5ujV/3uHdtzMxyG2rt6VFHbqvtf3ax3QwSY5A4fzzX78svME5SeKlRrkY6dE0r9t663qzTtcZkBE6tNU3p4TaUk0zuR96qTS7X8zWpt3Nqo6cX5kgxt2tqoHtNUTUObdwK377VKCnPlMBRwE9uXod6A9GxDaN3CMCfDfqthKfSHgmATyFaX1+msB9dpwfRxfjsueQL6qpNLA3Y98mz3F0mlcLJPsmI1FyD1maap7qYvJEnOI07SqK/9p4y0jEF5brttZgtyM7y7EPgyJOVkpunZshpdNH2cd5eBM/53rd9xz723Q6/ddKqkwN//noIghyFt2tpgu9WruximV9m2Ri1bL9tVNjzP4ZmwHMnkId9M8RXJTV9rxqzcVCOX2ZvP04vz/Tqv40Zma0dTR9jnsm5ZO7koV1edXKqlayv9VrpcXV6n+b9Yp9JRubZb7y5dOMM7cdtusMHuNXi+Z/ceWj8buUwz4Ca79X1OpJvpAOCrZ2+91NOtjKISjfnm3UrLHRnvJqnHNL35t7Jsh99AaWC5bm/fxu53+VuVDbpq+QbbwtBgwi1c4TD8++8NbZ1a9VmtbX/Ztx83vbhALtOl59/fqV3N+/36j86MtIDX1t7ZY1vE63/NfLlM6fn33VvXexb4SHMYATlaXODUhAJnwO531muGWzzCd1A4kglaZCCAoc7V0SLXgTY5coZrzCV3K3N0/CezFhfkqqm907bvm+YwVLbNfmGjsm1NSk8LnqeeCc5S/26G9fWmWrKPD3ITEQCiw+zsUE9bs+RI06iv/Ui5U04e1Oe/YNo4bahu0PrP6/12gC3b1qRli2aG3F32zYp6v0lTb1UGFtXOLMnXiRMLvf1Kyex3sWgiFdlYc9wzWYxCWABIPNGcH3XR9HG6Yd5kSdLStZVBFzQ+oZQxRADA0BHP+VHWhXrtdHS5lO6Qun1qfj7auVdrP7efg9zQ1qn7Xy337lIzPt8Z8nnG5Xt2MXLvduMyTT3w2hbv4+55wJLLDLxP7J5rbB7cLaf3Xql17HWSZRch3x10rH1ju3Fn6/3Oito2jc93+p23taFdr2/una/kWfD/qpNL9U5Vg7eYx+5z1PPv7/S+R77zgK3tsVtEyjruYL1nbl1EU2IxKqtUm1s91FDwg5AcmdkaveBOtW95W3nHnjFoz7u6vM67RZ3dyvYf7LDf9WZCof/WetbJwdZJzB/UNGvp2kotnj0xcBJv2Y6gv+CtW9cF48xM02Ejs3XhtHG65pSJWv5mtTZUN6jHlJ4r26Hn3qvRuJE5QVeLlDwfDHqfyxOSvpN8IzG9uCDgNVqrkl2m6Q1Ou20IJenow4br5MmjArYcnHNEkRbPnhg0bD3fD5zI5l7VyzdsrR8krD+DUEHDJCsAycYwDBWcdYMyD5msvH87S4YjbdDbUJibKcNw76r2m8un6/dvb9UvXv/cb4KuKfeE3Mq6Nt3/arkchnHwd7G1o2n6dcIWP77R79Hn3vPPAd+tXntcpnoslcEzSwLzy1e3y9QJ9/zDu+pFuMlDdjvnzZtS5O3Y2uWY5M5+axFNxcH3wvO8N589VfOmFHkz2i7f500p8hbzeJ7DumXtghnjtfzNatvPGpV1bbbbCHtW0Aj3+cSTi6G25fU8Zn3f3T+7Nr9jQt3Ij/fNdADwNWzauTIyc5QzaabScobFuzmSpA9rmiXJb7cdT3/Jri9WXd+mq1ds1Nb61oDH2jt7tLq8Tkue2KTlV50Q0fO7B4SDy3dm2k6QlgL7ZNZdg8q2NWvB9PHqdpl64LXeoqL2EDsL+l7TLo+Xra/yvi/3v7pZDsP93p1Q6p+jsyYWeheO8IwjeHaJtRu49jzXo+stu+NZBoXDTbQmAwEMddkTjlPRBbcpbVihMkdNiPr1JxblqrntgBrbu8MffNAF08bqsTeqbR+z6xt5TC/Oj2iBp/7eDBtqN9WG2usFgFhJHzFGYy65R91NXyhn0oxBfe58Z4YMw368M9h9ON/+1+E/ftnvMesYsCS9v73ZWzjknhjdO+66smyHd+HASCbmJHKxLIWwADBwhuyWSxq4aM6Pev79nfrWwd3areOOHoW5GYwhAgCGlHjNj+rLZ4duy6byLR2BCyZb+S5C6CmwsS6OP6ko128us3tBQ2v/1n3etgb/+8T5znRt2uremMB6r9Q69uo7n2jiKKdqGtslw9DxE/K1dG2l/vz+TnkKjq47dVJAH3vx7IkB83sdhuE3F8o6Z2l1eZ0WP75RJ5QW+C3qP29Kkc27ZT8P2DpHfEZJvrcYyjNf2rdwyW5HIQ/PZy+7hY6lvvfBU6loKFZzq1PpPUpkFPwggKuzQ91NXyhzjLtj6cjKHdRiHw/PxNVH1lV6J/GuLq/TdU9s0t6OwBu7k0fn6cJp4/yKY6ysK/82tHXpvlc2y2WaNtWprTr2ztc0q7QgYOu4lWU1Eb2GE0oLNKu0QGXbmuQwqr2ddd/JuJFUD1u9WRG8QCiYd6sbAnYZcGamq6OrdwLXhzua9fCaSpVta7QdbJeknc3tWjJnUsB7sLOp3T1BbVONKup6JwO7TOn6uZP8JoAtfnyj7e4KG6rtb/b2JWiYZAUgGZimSwd2lit73JGSJMORpmHHnxOVazszg+/EFsw1p5QqzWFoQ3WDvv1kmQxJY4ZnqbrefpcYqbfT5c7e3om8nh3sPKy/w3c1+2eR70SfZeurAjrCnt/jvtdwZqTp0BHZMmXa7vwTatefY8eOCMigNIfh7WjYdfak8IU0kns3olAdlsLcTC1dOMOvmGfVZ7UBW9ZedXKpvv1kWdjn8+UyAzvyk0fnqrjAeXBXJf+dlKTQ2/J6HvP/2e0POMaa24l8Mx3A0LN/x6fKGnukd/WgvGNOi0s7nBkOtVu2f5ekxvYuPbKuUg7D0IbqRrlMU01tB/yOKcztLboJVvTp66OdLQErBUvyZqJnh9iybY1qsuzoZ93RZ6QzXSOdGbbPObOkIGhRjm/GzbUdyLU3vTjf23brTnZS8MnCdn1A388U1tWhrMIt6BHppGQyEMBQdGB3hTIKx8uRkSVJyik9PibP4zDcu8KHUpib4R1D9jAMd575FrDmZDh00xlH+PWNPLu4e/pNLjP4LrO+PItW2PUDQ93gGmoLFg211wsA0dTdskcy0pQ+fJQkKaNgrDIKxg56O5rbu7TMZrJyYW5mRPfhhmWlq7G9N6dHZKfrSxPybXdmlexW6W2NeHe9RJ9kQiEsAAxcNIt9YjU/aldzR9B5MR6JllEAAMRCLOdHRdyGAZzb08eT361u1I2nHa5rTinV8jervX3TDdUNfvdc7RY+/r9/lOvZshpVWeZqNbV3e3fU8WU3r8eX73V8F2iU5J3ndf3cyX7fT3MYWjB9nN+90xNKQy/ULLnvx9Y0+bfbYUg3nz314CKU5sH5ZIbf/G7PGPuSJzb53dv9cEdzwPUlz3xkU9fPnewdj7BuXFDf2ultfzT64Km0cEes5lan0nuUyCj4gR/XgTbVrrxTnXXbNOaSe5R16OFRf478nHS17O9WkHoSrx6XaTvpZk15nUL1eSeOcgaEnofdJF/JvbLxazfN0cqyGr8CHOsqyT0uU4sf3xi0SKcgN0MjnZneG9Bryuu8N4dXfVart6sa9JEljKwKczOVn5uhcfnOoDeWO/v6SUL+N6k9K4U8V7bT74Z3Y3u3X6DOm1Kkd6sb/SaOG3Jo6drKgEm/Te1dtj+v597boevn+q/8vGzRTC1bX+VXzCXZb0cohQ6aYDsKxTowEv2GAYDEZbp61PDqr9T20SqNOu8Hyj1qblSv39diH0l6t6o+6Ba0wXgycXpxgW4+e4rKtjV5fx/6/o6cXpzv7cBV1LYGtC/UjVxPIY7n974np9u7elRZH3yyV7Ada5bMmaSlC2do/i/W+XX2PK/F3ckO7OxFamtD4Fa2vhraOvXYG1XaZLnm8+/t9BbLej53WDvC4TiMwNUrFkwfH7BTwpInNtlud2s3ASvgfQ/xswOARLN300tqev0RDT/hAo2ce5XtluGDJTszzbbgR3Lv/FoRYgJzR4gdcey4TDNk4WqoHWJLCv370k1tXX4TsjwmFeVqQ3Wj7c6z1tz82GZnPavJo3O1YPp4uczgBbZ2A9aeHLIrtOnL4K31WGvxNHkHAPY6tn6guufuUtb4ozX6gttkpGfE7LnCjSNL7l3J11n6tWXbmnTR9PF+i1QcOiJbf9q4XUvXVcqQoXxnhr5+/FiVbWvSv3Y0y2WaIT83TCrK9fYnfXeptwp1g2uoLVg01F4vAERLV+NO7Xn6VhkZmTrk0v9WWl5+3NpiSgGFtR6elYs948HTJuTr3ap6bdzWLMmdvcNz/At+jhs/0nuv0C4fgk1YimRiTqJPMqEQFgASRyznR3nmGllNLMqVIfcCFZKCLiIBAEAqiPX8qGhIdxjqjmQAOkLtnT3e3Xes9y99i3bsdqDv7DHDLv7oa2ZJga46uVTvVDXoo50tGpFjv5BjMO55vZMDvh9sPNe3rQW5GWq0jBNYF5s8obRQS+ZM0vVzJ3nHDTZUNwSMsT+yrlLvWuZq7d0fuCGEtd2ee8QuU7abRKws26ELp43tVx/cd5xja4P//K1kXrgjVgtYsrjJ4KDgB149HXtV+8zt6txdIRkOdTftiknBT5PN7jx2PgoxMcguYytqW3X/q5s1qch/om1OhkNj83N04bRxuuaUiVr+ZrV+8frn/pNWTU916njbCUaetlh3HpD8JwM1tnWpxWZClEewAp55U4pU09Suito2NbR1qqGtU6ZpvzKl9blvmDdJG7c2aYOlMMcuVD0a2rq0obpRF0wbq59Zqnd9pTkM3Xja4X6BOK4gx/Y9yndm+BUP9Qr8YXmCY0N1g98HmWDjGKGCxjpwv7KsRgumj495AY7dDYNgu0kAgIfZ0636v/2f2j9bK0nqrN2q3KPi3ChJb1T0rdindJTTb4LvLfOnatmimd7Hl66t9Psdecv8qSopdPpt+erMTNMJJfneHf0Wz54YciKvZ9vYcDy7AtntWLNkziRlpjv09+/N8f6+tu4gUOD0n6Q2vThfDsPwa1dhboY6ulwBBTAVtW2qqG2TM8MhGYZmluTrox0tfjezny2rkXWL2ibLZwffzxrWScfBtv/13b3H7iZ5uBvddud68nfj1ka/YudReZm6dvbEmE/SorgWQH+1vPOsmteukCR11m+TXD1S2sCHP+y2e8/JcKgjSDGPx74QW73vatlv+33PrkCRFPL6tsvaB9xQ3RBxsZNhGJo8Ok9NbQfUEKTYRwq+05Cn0Nc3M485bLjWbAncobbAma7jJxR48yvNYWjx4xuDts031yKZLNyXCVTWY288bbIchsGkZAAIob1ig+peuE/q6VJ3S61cB1qVlh6/SciStO7zhoDxUM/v8Q3Vjd7+k3WhqIa2Tv3871u8f19dXqdJRbkB1/cUqG6obgxYidFuzDLUDa6htivcUHu9ABANnfXbVfv0j9XT1iRHVq66WxviWvATzEinu68dbNd0Sbb9x3SHI2Q+eCYsWRcltPbt7MYPE32SCYWwAJAYBmt+lJXDMLz3S+9/tVwOw0ionAIAIFoSdX6UVTSLfXxZ+6Kh+oI//3t5xBsAzD1ilHY0d0gy5DKlx97onc9c39oZdE6Rvch3rvdt//TiAj27aXvAfWnfucOeuVsevuMGVs+9tyPgnnhXj6lJRblq6ehS+4Eetfstkunf7rJt9gs5V9S26t3qRt189lSVbet9PaHmInkeW1m2w2+Omy8W7gjE4iaDg4IfSJJ62pq150+3qqtuq+RI06jz/lO5U0+Ja5tCFbpI7gmwJ00q1NaGNr9JqNZK0Y4ulypq27ShulHXneoeOH6rskFrt/QG2tiDq/F7QuaRdVV+AXTs2BGS7Ff5Ny270vR1851Jo5w6obQwoMApkmrbw0bmyGE4/AqJJhflaXxBTtjAXl1epxNKCzRvSlHQYz2B5jCkDdWNcplmwHZ5nkm/LtP0Wy3Tw70Vn70ZJQV+BT8z+vGL3vozqaht8344iOXAiN0NAylw5WwGZwB4mD1dqnvpZ+rY8rYkacRJl2rEKZfFuVVufc2uNMvEYU/Rju9WtL7sVuVv7+zxTgD2ZEG4gpVgnSnJXTR66hFFWrpwhtIcgbvdBOtMWHfSCZzc7LvDkLtDF+4zimcXh7Vb6lWYm+n3WHN7d0CBbPCiWf8dm+ZOKdKyRTMDVs20FuhYs6fHZWplWY3f96yDC6FusFvfy2stuwPFSqKvxgkg8ZimqZY3/qiWt56SJOUccaKKzvuRjCgU+0ju1Ret/bT9YYp9JCk9zaEul/1xwQp6DnSHv65HqBh3mdKXS4Nv5e6rLys+2amub9Nblf6fAU4oLdSsiaP0y9c/9xuI/bfx+Xrsypl+x9qt4OxbZBoqq6y5eNXJpZLk9/elayttB25Dff7w9PMoOgWAXm2b31D9Xx6QXD3KKCrRmG/epbTcxJiA7HuD0XND0bNzbF80t3d6d6qVTF04bZyuO3WS9zqvb+7Nq+r6NnV2u5SZ7vC7Bje4AAD91bmnSnv+dKtcHXvlyBmuMd+8W5ljBr8oZOIoZ0ChrNVF08dL6tsu6ZJ78aJgelymljyxye/e5eTReVowfVxAcYzd+OFAMngwFiCiEBYA4m8w50dZF/YzTf9x30QrTAUAIBoSeX5UX/UWnXR75wJFIpK+qKd/+E5Vg+383cLcTF1zSqkMw72b/cySArlMac3Bxfvvf3WzJo/2X7yqpqlDJYXu+cgzSvL1/Ps7JRk6//ixereqUes+732eC44fG/Ccwebp+PZll66tVGWY8QLruHyocQPrfG+PYPeuL5w2zq//3hOiaGtNeZ1OnFgYchFrqXcuUrDCpMmjc1VSmGu7cAeLGbO4yWCh4Afq3levPU/fqu7GHVJauoq+8f/knHxCvJslScpJd6gj2EQn01S3y5TLMnEq2CrEq8vr9Nu1lUp3GAEB8lZFvabf9XeNdGbqounj9cbNp+nbT5bpo50tOnbsCC1dOEOS/QSkcKs5hzOhMNd2SzmPwtxMfWn8CLlMBQa76Q4LP4YiDoyybU1atmimN3CmF+dLMvwqWj1hLck2zDyTrx5ZV6XJRblqbO+UTKkgN0sXTh+n604N9cvb2s6+B53dz0SK/cCI3Q2DRF81DED8uLoOqP6F+9RR5d51ZuScRRrx5QVxbtUAWH5du0z/gseJo/x323t/e7OmTchXYW5m0MKWlWU73DvzBLnZaf0dax0gd5nujtqSJzYpzWFoenF+wCoNHqFWjrB67I0qSaZcprSrucPvscmjc9Xc3qX6VvvXJEn5Tv/XbP27JF04fZwchqFH11eFvFZNY3tAJzGSnHEXS/l3hPtyo9vaMQs1aTqayFUAfWGapprXLNfeDX+WJDmPnKNR534vasU+kv2gYiQ1szkZDh06IjvsRCnJvfNNY3t3n4txg6lp7JDDaHTvKtvYrgqf11CYm6Fjx45QmsOhbY3tIQtrJfsdjrztzrXfIr5sW5NOKC2wrLrkXsnSavHsiQED2tec4i7cWfLEppCZE2zw2ZMboQZu7SZbhToeAIay1k9Wq+Fv/yeZLmUeMlmjL/6p0nKGx7tZtnwXdwg2fhjMSGemrp87SdfPDfzdv3j2RD2zqcabe5V1bVryxCYtv+qEgOMkbnABAPrmwK5y1T5zu1wH2pSWm6/R37xbmUXFMX9eh+EeX/XIObiDuZ3C3AwZhqFjx47QNae48y2SrJ17xCilpzlC5mKPy9TixzcG3A8tKXRGvKOe555ufzK4rwsQMakHAJLPYM2P8tzHtC74NL4gV5V1/v1VAABSSSrNjwp2/9N6jExTje3d3u8V5mbo308s8ZtX47uYvrW/uXThDC15YpPerKj32+knPzfTrxBl49ZGbW2wtse/D1pR26qK2lat+qxWt8yfqlXfnyvJ3X/daFm82ZQC5v5Y+9m/eP1zrSzbcXBRrN7FksPxfMbx9Jut7Z7ks9BmsHlkviaPzlNJodPbTuu8r1A7G1nnGtmNJXiu+ah1LvZBC6aPDzo+wGLGLG4yWCj4GeK6W2q15+kfq7v5CxnpWSq64FbllB4f72Z5BS32kXv1/DVht53z99j6KtuCoC6XqYa2LjW0den+VzfLYSjgJq3Ue6M23IRcX3aTogpzM/Sl8fmaUZKv58p2hjy/oa1T2xrbdeG0cZpRUqDn39+hprYuNbR1+k3W6mVGfBN7ZklBkF+24QfNpd6VMpetrwooWmps75IjTPGRdTs999/79kvfuuuCR6wHRoLdtGflTgBWrs79qvvzT7V/24eSpPzTr9PwGV+Lc6sG5vzjxyrd4fD+Dny32v/3uXVCc0Nbp372WrkKczOCXrOitlXL1lfZ7k7zyLoqvb+92e/72RkO210RPB03T+d12aKZfjdepxfn67kIOp8e9a2dtjvYSe4OnWRfEOvhLuaRTydefpk5qShX15wy0bsatO+1nBkOvxVCmts7+9VJtGb45NF5fbrRbf2sMFiToFkRG0CkTNOlplVLte+9v0mSco89Q4Vn3yjDkRbnlrnt7zbVHGRhCiuHwxH+oD6oqGtVRZ27nzS5KM/vsYa2Lq3ZUq9b5k/VCaUFYYthQ9UgtQR5fS7TtO1LWldz9mS1wzA0b0qRHIZ7dyBrUfHbVQ2aVVrgXcXKM5kqXJFoX4tIKToFgED7/vWaGl/9lSRTWWOP1OgFd8qRlRv2vHipqG3z9jEXz54olyk9V1ajbY3t6gpTWVtc0LuIhd1E3pYO/9yz7twuDf4NrkSccJyIbQKARLZ/xyeqXXmnzM4OpQ0r0phL7lZGQeBqu7FgXQy3o8ulqiCTmjy7n68ur9M5D63XgunjvLusWu/V+Zo1cZRtMa2vZeurbFc1DjYuaDd+OJAM7mtfkEk9AJBcBnN+1KEjsm0nCKcZhm6ZP5XFIQAAKSlZ5kc5M9ICFiu0Y915ZvLoPDW3d/rNGy5wZnnvxXo0tHXp20+W+c1fsu7E49vfzEx3aPlVJ+jhNRV+85Mqalv1yLpKbahutO0rS5JM8+C9VUPbGtv8FgP2fY5l66u0Zku936nPv7/T24cPtmtue2ePKmpbvXOqPe2yU5ibofzcLF04rXeH3kfWVfq9Jmdmmg4dka2mtgN+504enavigly5TNO7KInva14wfZxff3uDZZ6awzC888OsC4lYxxSsr3F6cb7t4iOedi2YPt72M5tn/NtaJMR9ZcQKBT9DXEfVJndnNjNHoy+6Q9njj4l3k2Iq2O4/VsF+6Ybb7UbqDSWZpirr220nRR03bqQMwx081sC3U1HbpvtfLdfNZ7urbhc/vjFoQc+4fKeuOrk05KC65A6jvgwe2BURebbeC7blXrjwsl5za0Oblq6t7NPNX8/PxFN4NFgDI3Y3DFi5E4Cdzj0V2l/zqSRDBWfdoGFfOjveTRowh+Hw+x34VmVDmDPcgm3D6rGybIffLjLL36zWyrKagN1pCnIz1BjmWlJvDtndeLVTmJvhvWEdjrVoxl1MVCCX6fJuietZ4cL380OPy9SG6t7dCyrr2rT8zWpvlnmuNbOkQN0ulx54bYv3Oay7CkbaSbTm7YLp4wY0yWqwJkGTqwAi1dPaqLbNb0qS8o4/RwVnfkuGEd3CmYGwW8kxmBE5GREtLlHgTNc1sydp+ZvVQY+3DlZbd6rz2FDdoGnFBSrMzdTe/V1hJ0HbCXbK9sa2gJ+FZ+EI61brvoOot8yf6s7Gxzf6nbumvM678IfvZKrpxdaB2dADt+GKSCk6BQB/Zk+3Wj94RZKprAnHavSFt8uRmRPvZoW1odrdV0lzGHIYCrJ4UqCZpQXelQ19M2rVZ7V6p6pBpukffKapgDHNwS52sfZ7V5bV6MJp4yWZAYWyg4VJ0ADQN60frpLZ2aH0EWM05tJ7lT5iTLybFFZFbavue2WzVpbt0ILp41RckBP03mQki/6FWnzQTrTHD/vaFwy1KjAFrwCQeAZjflRhbqYWz56od6sbbAt+TigtiLgwlUUUAADJJl7zozzxaF3MIpj2rp6AnW7tWB++cNpYSYZlYXz7iwQuEuWf4fb9zcCcf+69HQFzpiaPdi/yWFHbqoq6NlXUtemW+VM1oyTfr7jm+AkjvePcgTsDSY2WopsN1Q165N9nSrLfEMGuz16Ym6GRzkxV1rV5N1zw3STguff8F2Ru7+yx/Yxk3UHH7nOQL5dljN7z9zSHu/An1LnWsQRrcZEkjcrL1LWzJ4b8/GXdZciD+8qIFQp+hrhhx58j14F2ZU84VlmHTYl3cxLGzJKCkJ1n3wpU34nBk4qc+vv35irNYWjx4xtV6bPDQZohjXRm6qhDhwevuA1j2foqlW1z32j25bvF3ZryOi15YpMunDYuYAcB/7A0tGx9VcSDAotnT9Q7VQ0B1a89LjOgPb6Ph7um1LviV0VtmzcE+3rzNxG2hUuENgBIPNnjj1HR138kV+d+5R1zWrybEyDdkAzDUFekPV9Jj71RpQ3VDTIMQ6Zp6o3PI8u14Pv2ufluLft2ZX3A6hIe1sKhnAyHxubneHfA81i9uVbT7vq78nOC7yzka/HsiXIYRtiiWcm/aMb6u/+GeYf7HWv9TGGNXU/BjDVHelym0h0Ob9GTdbK4J4M929KG+9wyGDe+o3nzgVwFEKn0YaM05pK71b75TY045TIZRvLe9JxQ4NRF08dr2fqqkNuXN7Z3K91h6JpTJgbstupleRvau3o0qShXX7Ts98sUlyk98Jr9bnYDVVnX2yf2XQEpzWH47Rhn5cnGcLvX9hadWj/H+P+9r1mYyEWn3OgHEA9GWrpGL/iJWt56WiPnXClHRla8mxSRD2qavIU4wRYskqS5U4r8dpDz3WHOyndsNCPNUFePqYa2zoAxzcEudrG+PvcCUr2vYTALbljZEAD6p/CsG+TIcmr4CecrfdioeDfHjyfzgvEU/sw9Ini7I5nwYu0DzptSpGWLZgbt80R7/LCvfUG7cVIKXgEgccViftTEUU61dHR55w01tHW6J7najFGHKmK1MxiZwlgjACCa4jU/qg/TnQZ8jsPwP3HsyGzbhaaOGTvCu4ihJPX0uJSR5s7YkyYWeHfK9eVeKMOfadPO8fk5AQVFnkWKfT333s6gu/cGziV2v75QGyJ4+vW+/eDrTp2kjVsb/a7lPw4c+nPFqLxMHTt2hDZUuxea9nwWCdfft35c8f17X8cKNm0NXOT62tkTtWTOJPW4TG/RlPWzknVM3rdICIgFCn6GoO59DUrLK/BOghrx5Yvi3KLoCDfYHClnZpp39aVgnedgwTahIFc9LlOPrKvU+9ub/a77w7OmyGEY+p+/Rz6RylpJ3NDW6W3LvClFSnMYml5coOfKavzOW11epxNKC/y2ArbulOAZfPd9XZLU2e3Sdb/fqA1bm5STkaarTi5VmkMq29akE0oLdEJpocq29QbYsvVVtgVMkQxWeN7HjVsb/SZWc/MXQLLraW+RI9MpI91daOI84qQ4tyg4U1J3H3uy9a2d/S5eDcaaeRu2NgU91traw0bmBKxoIbl3G2hs6wq5G9DcKUXa0dThvapnIliogp+5AxyQt9747u5xafHjGwM6h/45Gfj6VpfXadn6qqC7GFk/twzGje/AVaXdq3tygwBAtJk9XXIdaFeac4QkKbOoRJlFJfFtVBSsLq/TtsZ2HTd2mFZv8R9cTDP8d9J5dH2Vjhk7Iui1Tigt8BtIluQ32OopwLFueR4N7m3pDb88LSnM9cuiUBOvPQPGdgtP2B1Xts3/c4P1733NwkQuOmXyGIDBYpqmeloblT6sUJKU5hyhgjOWxLlVoaU7pG6flSYa2rq8vzNDFZGmOwxdP3ey9+/WHeaCse7M5zumGeudUa2Tsqy73dkZrDFXVjYEgMh1tzYqPc/9+9FIS1fB6dfGuUW9nBlpkuHuW84syffbidy6o6zHhuomTSrKVUtHl445bLhOKC3Ue9ub/MYQ+7pw0WCOKUbSF/Rt//TiAt189hS/3fSWPLHJ73jueQJAfMVyfpTdRFnJ/bv/hNICvb7Zv4/mmbwaqVj3KyXGGgEAA5dM86MG6sF/bAmoX/l4116/v08uytOCGePU7TL97tNWN/Qukrj28wYtf7M6YIdYuzFe6/yoSUW5tvdNpxfnBxQMWYt9Jo/OU3GBUy7TtNmBSHL4FCwvnj1RLtM8uEOPoQunjfObG7ShukEuU9pQ3Riw247vOLB10wKrY8eO8L6e1ze7/xvus0iPywwo2DqhtDDkOb6sn3/mTSnye9x33nOoz0rWew6eIiEgVij4GWIO7CpX7TO3a9jMb2jkyZfGuzlR5epP2a2Njs4eXbNigxwOh9/3V5btCBhgtk4+Wl1ep/m/WBfQqXeHghF0ZchgZh8+SmuD7G7g2X5u6dpK2yrhsm1NWrZopl+I2E0aXllW4/e6ljyxybujQntnj1+B0qrPanXL/Klatmim93uhKlUjHawItVMAACSb7n0N2vP0j5UxaryKvv5fMhxp8W5SSFGolY0Ka4znZKT57T5Q4MzQvgPdAcW9BbkZmlDgtB1QD6cwN0OzSgu0ptyddfe/Wq53qxv10Y7Ajq3H5NF5eizEqpJ2rFnpLjDq5cndYAPpoSameQb3gw36x2JlrlA3vgNXlbYvMAaAgXB1HVD9C/epe1+9xlx6r9Jyhse7SVFVVdemL5o7Ar5vzez61s6Agh6PiaOcevjy6fr2k2VBi2V8C3CsN58HqqQwVzNLCvz6wNZ+nt3KzWkOw28imHXbdffKVKbfZCq7a9n1KQeaiYmy2uVg3OgHANM01bxmuVo/WqUxl96bNEW13UG2ld1Q3aAZJYWaPDpPkqlx+U7/1RVdpt8iDMEyqsdl+uWq781ISZpeXOBd7c+6I3q0xzutNxpvPnuKbpk/NeSOtdOL84OuRhhNrGwIAJFp/WS1Gl55SEVf+5GcR5wY7+YE8BT0rCmv06zSQr9FBt+tqtc/ywPvX7Z39XjHaddsqdeJk0b53VOUQk+WSeTFFzys7bfeN+WeJwAkjljPj2rpsF9wcHpxvu1CRn3NhMHIFMYaAQADkWzzo8IxFFhg46vLZn5yg2UB4pJRTi2ZMynsolKeDLaO8U4uylNFXe/4blVdm99Cxqbdlj8HWx9qbpEkLZg+zu85rVymqR6X6S1Svn7uZL+FsjzsNkqwu88rSdedOlEOo7dAyDAMmaYphyHNKCnQc2U7At6XcJ9FrBsU9HUXRevnH4dh+I15hNrFx7d9fd0lGBgoCn6GkP07PlHtyjtldnao9V+vafj08+TIzot3s6ImWhOWTbkHodMtNzsraltVUdvqN/hstwKH3YTjNIehTSFWL7ZyZqbp0BFZ2t7YHvSYmSUF6nGZWmnZ3cf38WDf9w12924/bd7v2VXv+rKGaiSVquEmRg12+CXKRC0Aqae7pVZ7nv6xupu/UM/eOnXVbVXmGAZFPay7+ARbCVKSrjmlVGkOw/u72mXKdtWHULv3hHPsuJEBOwAEmzjta9n6qgEVtsoIfp5d5zXU7gaevA826D/YK3MFG0DgBgGAaHF17lfdn3+q/ds+lCTtr35PuUfNjW+jYqCjK8iM5QhV1bfr20+WyWEYKszNVENbZ8Ax729v0sNrKnTNKRPlMnVwhSZT5x8/Vg7DobJtjQETmyPl26/zrOxk3Yrd2g/07ErrGTiNdMt2z7G+17LrUw40ExNltUsmjwGINdN0qWnVUu1772+SpNaPVqngtMWD2oYCZ4ZGOjNUVR98bDQUZ6b/AhI9LtOvP2ma7puADsOQy+zNOs/v12C7C1jHFH2zy91vNXXfK72LJwW7yRkN1huNnsWffFeFtBbKukwNSpaxsiEAhLfvX39X46u/lGRqb9lLyjn8y96dBwZDqHFZO7/85+e68bTD9ZvLp+vR9VV6pyr47uy+7BYl2trQZntMovO8hkfXV/l939p+JvwAQGIYjPlR1kUgehkBCxn1JxMGI1MYawQA9Fcqzo+KxvTj7h6XHl5Tqa1hxrZnlhQEjPE+994OjStw+hX8SNJHO1ps7/X6KtvWqKULZ3jv+Ta1dfqd4ymKse5K61vktLq8TsvWV0XUR7e23fPZx25ebrD7vHYbHXjmRC9bX+W9x+ww3Dv4eMbp7Z471Pwta5usOymdUFoQtI2hPislw6IlSC0U/AwRHVs/UN2f75LZdUDpI8ZozKX3plSxz0DZDWx3h9gx6JlNNQcLbQyNy88Je/2ZJQV6p6oh4va0d/aosi4w9CcV5ap0VK43DJetr/LbrccjVNWq76DA1oZ2vxUfN25tDDEo0ftagl2vvxOrBjv8EmWiFoDU0tW0S3ue/rF69tbJyMzR6AtvT/rObLSNdGZqf2ePTJmaNbFQM0sK9MBr5X7HjMrL1LFjR6hsW6NOKC3U0oUzJElnPbgu6HUdhjR5dK5tJoby0Y5mHTduZETHFuZmqKGtq1871lizMljxkuTuaHpWzPDwvSlg16G1ew7fv/uK9Q10z/OuLKvx+3lwgwBANLgOtKt25Z06sPNTSYYKzrohJYt9oiVcoU5DW5fuf7VcG6ob5TAMjc/PkcOQHIZDnqHdE0oLdUJpgcq2NXknDT/2RrXqW4MPKk8enesdcLWu8GS3FbtpmnqnqkErN+3wDmCv+qxW71Q1aFmEu+pF0qccaCYmymqXTB4DEEumq0eNr/1arR/+XZKUd/w5yp939aC3o7G9K9Q6CWGdUFrgt5iDdZfVyro2Vda1ac4Ro/RWpf+Y7cqympA3IX1Z88e6aqOnLxcLwW40hspEa/tilWVkFQCEtrfsL2patVSSlDXhWI2+8PZBLfaRpENHZss0TdviWrtFI9o7e3T/q5u1oTpwUaKC3AwVOLMCJiRJ7t3vHl5TqWXrq4JOTkqWcUPfe4u+rO0fzHueLHAIAPZiPT/KmZmm755+uHcRiEfXV/mNl5Zta5Q0acCZMBiZQv8NANAfQ2l+VEFuRtiFiDPSDHUd3K1gzZZ6rdkSuCOur1MPH6Vul0vvb/dfTKOitk2NrQcCjrf2pyePzpNM069YZmt9m5YdXKDCdz7w5NG5WjB9vLe/OL04329c2TpDemXZjrB9S8+8Jl/9WZTYeu918ug875xoa//b9x5zXwuWrW26+ewpATv6BMNnJSQSCn6GgI7Kjap9/l6pp0vpBeM05pK7lT5sVLyblVCyMwy192GDAN9dfCpqW5XhMPy27HNvo9cuydCF08Zp8eyJ3tWMB8IasNbQ8whVteo7KLB0baVfOHpWprzu9xu1Yav7A4Xvaph2hUSDMbEq2hKtPQCSX1d9jfb86cfqaW2UkZWrMQt+oqyxU+PdrITT6NMJ3VDt/l0854gird3Se5PYt/DU02FzmaZfh9TKPRm5MOi2s8E0tHVpdXmd0h1Sd5CNFCaPztOC6eO0obpRr2/u7TDadXKD3WC1ZmWPy12w41lx+d2q3g5/sBUzwuVtsMcHe2UuTzt8V5Wm0wsgGno69ql25e3q/OJzyXCo8JzvKu+Y0+PdrKjITje0vztKW9aGkJFmKMPhCFjswjpZy5O/knvQ85b5U/0mKzsMI2TmLpg+3m8XhGCrHwebLOXbrkhXkZL8c9i6o8Hi2RMHnImJstolq0UBiBXT1aP6v/2v2j9dK0kaNvMbyp93zaBPQPZosNxENeTeLDXEGk1KM6Tvf2WKHnvDP3uCVQ+ttbnxWlHbFjR/wt2sHMys6M+NxsFqH1kFAMG1vPucmtcslyRll05X0fn/T46MrEFvR2Vd8IWTjh03QjWN7dre2O6dsORht7CEwzD02vdOPbhYUYNcpvt7J5S6d7+7/9XygHMmj85TSaHTe19y6drKQS1a6U+hjPXe4qi8TF07e2Jcxz1Z4BAAAkV7fpQzw6GOLpftiv+eXczfqfIviE2WYlaJ/hsAoO+G2vyoL40boVkTR+ndqgat2VJnOz5t7TvbmVyUq5KDC/0H6ytLUmN7d9hrLZg+LuAaFXVtuu+Vze5iIB/FBbmSpCVPbNLMkgKZYZpaUduqR9ZVymEYQfvMy9ZX+X328Z1T3Jd5udbx6gXTx9nu4GO9VrCx8WB9fev1yrY1admimRF9/uGzEhIJBT8prq38TdW/9IDk6lZGUYnGfPMupeXmx7tZCSeSoJxUlBt0ANxT7GOtiPV1Qmmh38QpO77Vvnaq6tpUVdemVZ/VamVZjcbnO22Pi3QAwS780hyGVlw9S1L0VoZKlIlRvs+fSO0BkNw6a6u050+3ydXeIkfOcI2++KfKOmRyvJsVNb7bt0ZTe2eP1pTXae4Ro/xWTrAWyLp3pLPPX9/clQJ3lQnWfmuBj7XYJyPNUHGhUxdOG6frTp3kzT7fgp+K2taASWCR3mC1dgjdK335v+ZodRbjtdoEnV4A0dTT1qw9f7pVXXVbJUeaRp33n8qdekq8mxU10Sz2SXcYMgz7geWuHlNdPT02Z4VmzaXFsyeG3Q5eCr/6cbDB2lDP7cvaX/XdQc+3v+f5eqCZyApOAFKZ2dOl+pceUPuWtyRJI078pkbMviJuxT52TCnszcisdIeeK6sJWHFxXH6OLpw2Vs+9tyOinWEfXV8ll2lKMlS2rff3vjW7Vpbt8MuFSLIiWuOt/elzkWUAED+maarlzafU8uYfJUk5h39ZRV+7WUZ6Rpxb5q8wN8Nvl7xIHDt2hG0u9bjMoLu2N7W5VyyeXlygx96o8k5S8u2/xXLnmv4UyljvLV57cEfAwWD3+UFyj4f7YoFDAENdLOZHtXcFrhjY3tnjN+4ZbMIrAACpJtXnR9kx1dvfvXL5hj73mT3GFzi9Cyxad2K3chftmAFj2b4LTyx5YlPQFvtymaZf/9daEGTnl/+s8G4SYNdnto6T+25OYDcvN9iijdOL83Xz2VP9xuDtruF7Lc/z2Y2NB+vrM1cYqYKCnxTm6uxQ499/I7m6lTlmkkZ/8y6l5QyPd7MSljMjzW+l45wMhw4dkS2Hw71Lz6atTSFXvJIkmYZWlu3QM5u2a3xBrtIOrmDle8P3mU3bVVnXbnv6hPwcVdb3PpbuMNQdZNnKito2VdS2ad6UIjkOrm7p8Hm+SES6W4BnYN1T6RtsYD3YDetEu5k8kPZE66Y8gNRgmqYa/7HU3ZnNHakx37xbmUUl8W5WVMV6v4GPd+3ViqtnacmcSepxmXqnyr/g5/3tzdq7334bvpLCXL8MWzB9fMDEYrv2j8jJCFgt2tf3zzxC18/1H5RYPHtiQEGR9QZqf3eQG0jnMlwuUXgDIBW0vLPSXeyTlq6ir98i5+Gz4t2khFSYm6GrTi7V//x9S1Svu7WhXUvXVvrtXOcwQm8H3+MytbJsh991rKsfBxus9RUqE62DtqEGqD2ZPJBMJFMBpLK2zW94i31GnvrvGnHixXFtT0FuRkDRji9DUr4zQ0cfNkzrK3r7Ye1dLlXVB467rimv04aqRl0/b5LSHQ5tOriwRLCx3vrWTr/VET15Zc2uitpWVdS2+t08DJcVkUwwjtX4Y6yyjPFSAAivq36bWt56WpLknDpbo776AxlpiXebPtSYqVWaIZ1yeJFmlORr8eMbNb04X77FsqF2bW9o61JDW5fuf3WzJo/O9XtsZVlNwIIO71Q1aNmimVHJF7v+qt04rjXfrjq51HvsYN/rtPv8IClg8heTlgAMZbGeHzV5dK6a27tU39q7+JHdgjNvDZsAAQAASURBVEa+E14BAEglQ2F+lJ2Pdrbo4TWVkkzVBFms2NfcI0bphNJC/Wp1b9GMJNU0dqjHZSrNYYS9R7pg+jj3fOPHN/oVFh87doT388fxE/Jtr3HhtHF+u/NYF122zqKaPDpX4/Odfs/j224psM8cao6T3bzcYH3aVZ/V6pb5U72FUNZrbKhulMs05TDcmy2E64cHm7OVaHOXgf5KvJHEFNHQ0KCf/exnevHFF7V9+3ZlZ2fr2GOP1eLFi7Vw4cJBaYMjM0ejL7pTzev/oKKv/acc2eGrM4eyw0bmqKKud+A5Nytd35w5wXuDcunaSr+V/e34nu8p6nl9c+3BYhx3aIwvyA1a8DO+MNev4OfkyYVau6U+5HOmOQwtXTjDO+gcC5GudBXsuESbGDWQ9vRn1S8AsZEIWWsYhkZ9/WY1/PV/VPCVG5RRMHZQnjeVHDt2hPdr67avkvx2DbDq7nF5O8RSb6fv0fVVfgPuzsw0vw5pvjMr6M3ruVOKdN2p9rvyWAuKrDdQ+1K4Y13B4uazp6hsW1NEnUvfc3tcpvc9I5cARFsiZK0k5c+5Ut17azXsS/OVUzpt0J432TS3d+n//tG/Yh9rVvqqqG315p+nQNe6irC1CHfZ+qqAiV3W1Y97B2sb5DLdA+a++T15dF7ITAzs/wYvU+5xmVr8+EYmIQNIOImStblHzVVXbbXS8go1fObXB+15g2ls61K6w1BGmqEOm9WUTUmN7V1KS0vT5NF5QScT+2rv6tHP/75Fk4pydcG0sX7jvBlphsYXONXS3hm0r7iybIeKC3I0b0qRahrbVWEpFvK96RmqACaShSKSbfwx2doLYGhJlKzNLCpR4bnf0/5tH6rw7P+Q4UgbtOe2k5+Trn0HugN2Pu+LH509VZKCTtopzI1s96Jdzfv9/l5R26bn3vMvyFldXhew23p/2fVX7cZxg+VbPDLO7vODVbg+NADEUiLkbbTnR2WkGX67qC+YPl6SbO8TsmI8ACDWEiFrh+r8KPfiUJvDHygp35muh6+YoW8/WRZw37WirlW/XVupG+ZN1uLZE+UyTT1bVqPm9m6NdGZoXH6OdjZ1SIYhl+keYz6htFA1TR0yTZdMU35zg+ZOKfK7/uSiPC2YMc47Fu3pu7pMU69v7p2Hdf7xY5XucAQUvyxbXxUw18rD+vkmVAGN3QYDWxvs50p7rmHtZ/d3bm+wOVuJNncZ6C8KfmJg8+bNmjdvnnbv3i1JysvL0759+7Ru3TqtW7dOf/3rX/XUU0/J4XDE5PnNni4Zae5B1KxDD9eYi38Sk+dJJTkZDo0r8C/4qW/t9JvUtHj2RHW7TP3ujWp1dPVozPAsGZJ27z2g/V09CrIRjyTpufd2hL3x7N6px3/SUbrDoVvmTz04Gdi9Mpb1WjNLCkLeVI3GCouR7ljQ350NkslQeI1AMkikrE3PK9CYS+6NyfOkuoLcDBmG4d01IFzhqnUy8pot9Vr8+EbvChiLfSYS+w64zyot8CskunD6OEmm3za0vscGy8lwqz70ZVUIa3bbrVoRyblW5BKAaEmkrDXSMzT6/B/H5HlSSU+QPmlOhkOGYSgnI03HjB2uj3e2BExmbu/s0dwpRdpQ3Ri08MeTMe7JUf6TnK0FNdZMnzw6V4tnT7Ttn3pya+naSr98WzB9XMi+q3XQ1nfFKt/t4CmOBZCoEiprDUP5866OyfP0V7fLDLrzucf6LXU65fCiiAp+PCrr2vS7N6r9vjciJ0PfnDFeb1c1aI1lEQoPz24+kmx3letxmd4FKUKN1Vrza2tDm99OelLijj8GG2dO1PYCQNyz1uXuW3mKe/KOnqe8o+fF5Ln6qqmju9/nZjgM3XTmEVo8e6Ku+/3GoMdFuluQfR808DPAyrKaft3jtOaXdXVjT3/VKpHyLdikJd/vhetDA0CsxD1vYzQ/6vtnTvEu6mu95xfp9wAAiIZEytqhMD8q3eHezfZjy0KFkWhq79b1f9ikNUEW+P/dG9W6Yd5kpTkMXT93shyGofte2ayGtk6/3ejvf3WzNlQ3BCyY7GtDtX+ftWSU03bn2nctxzkMh23xi91cq8mj87y7DfmKpIAm1LwmX9EslrbO2brq5FItXVvJzvRIGbH5LT+EdXZ26rzzztPu3bs1depUbdy4Ufv27VNbW5t+9atfKSMjQ88884zuueeemDx/y7t/1u4nfyTXgfDbx6FXR5dLa8rrNHdKkZyZ/qtq/eL1zw9uyyd9a84kXXfqRB02MkfV9e2qqm9Xe2foYh+3sAcozWHohFL/ADuhtMA9oWrRTF0/d7KunztJr910qm6ZP1VnHDlat8yfajuZyvfvnvBc9Vmt7ntls5atrwrbFskd+EvXVmrx4xvVY3mBwYLWbqeDVDMUXiOQ6OKdtR2VG7Xr0W+pq3FnTK6fLCLpAmWE6CgV5Gaosa1L/9zszqev/N9afWl8fsjrHTYyO+B7q8vrAjJu8eyJflm5dOEMv79fd+pEOQzD9mZy2bamoM/v6bQuWzTTu4NdXx73FcmqjJGe64tcAhAN8c7aroYa7Xr0W+qoKovJ9Yeaji6X2jt71NDWqZMmjdKx40baHpdmGJpVGjxHPBljzaHC3MyAPLbm0YLp4/0mQNv1T635He7muPX4606d5NN/nqTr507WskUzA/I4VjvjDjbfPvvStZUB/XYAiS3eWduzv1W7n7xZezc8H5PrD5YeU1q7JfhNz5wM+9sfe/f7T3L2LPz0YU2z3/cnFeXqjCNHa/LoXMsVAn/nenYekOz7e57f2xuqGzVvSpEmFbmvWVHbpvte2ayzHlzn/X2eqOOPwXI8UdsLYGiLd9aarh41vPygGl5+0Fv4k6xG5qSpIDdDzsw0zZ1SpE9+erZ3glKwhSfsTC7K1cQia6bau3DaOM2zrFZcUdsW8T1OX9b8snadPP1Vq3jkW7B+nl1/ua99aACIhXjnbazmR82bUqTrTp0YcM/P7j5gX+4NAgDQV/HO2qE4P6rbJa0pr1NLe2SLWFht2Bp8ztHe/V1+/b1Q9yw/2tkS8nms852C7VxrXeCqbFvw5/T0M0+fWqR5U4pUXOAM2YZQ7BaHtC6kNW9KUUBfdiD3P62fy5a/Wd2vedPJgnvFQw87/ETZo48+qoqKCuXk5Ojll19WaWmpJCkzM1M33HCD9u7dq//3//6f7r//fn37299WYWFhVJ7XNE21vPW0Wt54UpK0990/a+Spg7cVfTLIyXCoo6t3b/qMNEMZDkPtPt9bv6UuYHC6vbPn4LZ8pjZUN4asnPUocKbruPH5SjPcRTwu09T9r5aHPMd3tY8N1Q1ymb2VuL7VpXYVssFWdpL6vwKVtcp27pQi7WjqkGTKZfauWOmrLzsbJKuh8BqBRBevrJWk9i1vqe7Fn0mubjW+/ojGLEjtXfScGWmSYb/KYiTdhC6XqYLcDDkMQ6Zp+q3q2GhZ4bGyrk2PvVEZ9FqTi3J14bRxIfM0WMbZZWewjnMsb9z6riZp7Wh197j08JpKlW0Lv7KENffnTSny2+UIAAYqnlnbWVutPX+6Va72FjW8+iuNve4RGekZUbt+IppYlKuqusFZtGNDdYNqGu23Tf+gpln5Tv/3enJRrkpG5fqtgrS1wb+t+bkZamjrXeFq49ZGLV04w/u1bz6F6p/2dTv1SI+35qZnNyLfXYCScVWnULtHDEQ0dgkGEF48s7anvUV7/nSbumqr1LmnUjlHnKiMkYdE7fqJJNgOQcOz03XdqZP06Poqv1UaGy03cScUOLV04Qwtfnyj3+52nl3lrOd7dh6wG6u1jrVai4gqalt13yub9XZVg2oa2uTMcEgHx5avOrm0f29AlAXLcd+cn17sHgv33fmPHAEQD/HMWrOnS/UvPaD2LW9JkpxHnCTnESdG7fqD7fgJBVq2aKaWra/ShupGLXlikxyGdEJpYdD+pZ2SUbkyTYXt/04enavrTp2k606dpLMeXOe3k59nd55I+ys9LlMry3b4fa+msV0//MoULX+zWh1dPXq7sl5XnVyqzHT/QuF43A8M1s8L1v/tSx8aAGIhVeZHFeZm+o1vegp5AACIN+ZHxU9Xf4snQpzW1WNq1We13v6edRzZ17FjRwSdp5yRZqjLZ5Lz5NF5Ee1c63nOYDx9T6l3p5/XN/fvHqT1tS2YPl4btzb69fE/2tmiZeur/Pr21n6xyzTlMIx+3bdMpJ17YyFW94qTzVC6t03BT5Q98cQTkqRLLrnEG7C+brzxRt17771qbW3V888/r8WLFw/4OU3TVPO6x7X3nWclSc6pszXi5EsHfN1Uc+LEArlkaEN1o7LTDR07bqR2Nu1XRV1viIRaierZshpV1oUfuJ43pUjLFs2UJO8vkunF+br57Kl67A3/m8CTinJVenDylOcXTX9CM9Sgc6hioFCsgVfT2O7dOtAz2fr6uZP9junrBK1kNJDXOJTCBYileGStJLV9ulb1f/25ZLqUOWaSRp37/ahcN1HlOzP07v87Q9c9sSlgxYdgDAX2Xa2FPaE0tXcHfWzBjPEH883Qc+/tUFPbAb8CIsmdcZ5JvJ6O76rPavVOVYNfQYzna998nDw6Vwumj4/pjVvrBC/PLkeStGZLvXdb31Wf1WplWY23PZEU2JInAKIpXll74IvPVfvM7XLt3ydH7kiNXnBHShf7pBnSiJyMiHbNC2bulCLNLMnXY29UR5S5PS5TXzTvt32soa3T78a25M5fT99n6dpK223crQtczCwpCNpv6m//1NP2R9ZV6bn3dkgydeG0cbru1PCrZvrmZo/L9PuM4JGMg6CxGqRmcBgYHPHK2u7WRtU+fau6GrZLaekq+votKVvsI8nvpqevq08p1VUnl+qZTTV+Y7VWNY3temRdpd+NVfdKz73545uNnp0H7PpsS57YZLm6fX5Z+99ryuu05IlNAePN8egLBstx39x3f15wfy4gRwDEU7yy1uzuVN0L96mjcqMkaeTshUlT7GNdONFje2O7rl6xQWsPjl16vL65ToW5mRFf35Mbnvuekvs+qSGpwqcIaHy+ewXhNIehBdPH+WWty1Sf+ivL1lf5TSbSwed6/v0d3v7vmi31mv+Ldfr79+b45Wo87nmm+mQkAKknVeZH5VsKfti1FACQKJgflXzauyLb5feZTdslU8pJd6ij278v7sxM09KFM/TYG9V67r0damzdr0af+VTj83NUVd87j7mp7YDOenCdLjh+rEyZev79nTLNwPlbhbmZcpmyXejfl7VvurJsR5/HooPNZ/YdX65v7fT28T19X+tzP/feDu9iXH0dbx7IfenBMpD5xIwhuA2le9sU/ERRa2urNmzYIEmaP3++7TF5eXmaPXu2XnnlFa1atSoqIdv0+iPaV/YXSVLuMaercP53ZDjSBnzdVLN2S723oKe9U34D0+kO93Z8oWxv6Aj43uSiXI3Nz9HHO1vU0eXSCaUFWrpwhtIcht9kqFWf1WriKKfa9vtPZJ5Q4PTerPXV11/G1kFnz3ZtvsVGvjsGBBNq54Evmv1f/3Pv7Qgo+OmroVYAMxjhMtTeUww98cra1g//roZXfinJVOZhUzRmwU/kyM4Le14yaz3QrXMeWq9Kyw3RUGK1OejEUU49s6lGz2zarvEFuSoucKrJMiHZmZmmHpcZsOqjJL+JvS7T1PVzJ0e9aCaSScjWfA81Mbuitk33vbJZ71Q1aNmimf2+2ZxquWD3eiTfIuvB2a0h1d5XwFe8snb/jk9Vu/JOmZ3tShs2SmMuuUcZBWMHfN1E1mO6dxKw7iYQqZwMhx5ZOEOZ6Q69t61Jr28OX6D70c6WsAPNk0fnqaTQGdB/tObYtoY2rdxUowumjdPNZ0/x+90bzEBWSF62vurg7rtu979aLodhhM1D39xc/PjGoMcl2yBorAapIxmPIAd5DzAw8cpa03Rpzx9vVnfTFzLSs1R0wY+VUzptwNdNJtlphhxpDm2obtRzZTv8bor6LsjgUVHXdrCP16umsUOPrKtU2bYmTS8u0KSiXO8iSVLv781wO7S7dwly7wrku3uQndXldVq2vsqvyNa3fztYIslxbjJGhhwJj/cIAxGvrJVM1T77U+3f9oEkKf+0xRo+8xtRuO7gsCv2kdw7s1cG2ZWn7UBk/VlnZppcpnTlSSV6ZlON93qVdW26+eyp2lDd4B3DXV1ep8WPb1Saw9D04gLdfPYUbdraKJfp7tP6+sXrn8tlmkEXgwi2y7t1Z6LKOnfRbrwzK9J+Hr8jEQ38O8JApdL8KE//bDB3dQMAIBzmRw2uwtxMNbZ1xmzek1WojQdmlboXV9xQ3RAw/0mSqurbNfeIUfpoZ4sa2rq8f372WrnN1Xo1tHXq/lc3y2GEnrNq7ZtW1LZ6++nh+g7WfoZnLrUkXXVyqd6patCbFfXq9Fmsy7egKGDXI9P/eZ7ZVKOVZTWSjIPzsnrbYn3uq052F8kl8me8vs4nDjW/O1YFTYnedxxK9yQo+ImizZs3yzTd/xMdc8wxQY875phj9Morr+iTTz4Z8HO6DrR6O7N5X5qvgq9cL8NwhDlraAq1e0+4Yh/Jfpu+sflOzSgZ6S0eWlNep0fXV8phOPTLf37ud6zvDWSPD3c0a9HvNujjnc3au79bw7LSdfRhw1W2rcm/7Qd3K7Cb2OoJp+VvVuvd6kZta2jTF80daj84ML/qs1rdfPZULVs0038ysmlqXH6OHIZkypDDMOQyTb8VK31vWLdbBvp3NnXo9J+vUUtHl44dO0JLD0408/0FP704Xy5Tev79HbIL2VBb8E2bkK8N1Q36eNdev+v3VbDACRVE0Qwp32ttbfC/IdLXcPFca0N1g1ym5DAMnVDq377+FBVFO5QH673F0BSXrO3ar4ZXHpIkZY0/RqMvvF2OLOeAr5vounpM245jPPhmaLBOb3tnT9jOqyT9/O9b9Oi6Ku3d36XMtDQdMiJLXT2mVm7aIRm9hTpS4MrJknt3g8feqFZ7Z7dyMtLkMAwdO26EZpYU+D3//a+Wa9n6Kh07doQ3a2saA4uHw1ldXqdj73xNsw4WFVuz1vdzgN3v1lBZO724QC7TvbpHX3ZKsIp21oZ6zC7npMCVPX2/jnbWBmvHYOZtuGuRtxiIeGSt2dOl2mduk9l1QGkjxriLfVJ4t4Fo6ehy6YhbX9HInHQ1dwTfJc+XdXc8KXAV551N7Rqfn6Nul6lrHt+oj3e2aHhOhhr3+edYV4+piro2/ey1ck0clauWji69VVGvpzdsk2EYam7vlGEYGpmTqXEFTu1s6vBm7dKFMyTZZ+0j66r0XNkONbV3yjRN5edm6KLp47Vpq38/WXJP7nq7sl5pDkMnlBba9pd9v2ftk/mqrm9TZ7fLtl8rGdq0NXQ2ePifG52stV7Xk/8uU95iY5dp+q3I1d+sCDbBzDqAbN0pqa95+8i6yoNtDxwriKT9kbxHsczaobRaFKIvHlkrSa72FrnammRkZGv0RXcoe8KxUbluMtnfY0o9PQE7FEjBF2SwFuNU1LX6Fd1Yrd5cq8m3/E09pmQY0oicdC2ePUlXn1yqt6saDu4+73DvRGtIh41walfzfnV0ugtygw1h/8/fywN2LPr5a+X65T8rlJORpqtOLlGawxGw6FNfstY3o7zjyAePG+lM9x6zePZE96ToJzaFzdr3tzfpl//corJtzfp4Z4vtOG+k/TDr8Z7X9e8nlujbT5bpwx3NGunMVHFhrmaFOD+YcAtM9HWcs699W8/7Go3FLMhaDHXxytqejr3qOVjsU/CVb2vY8edE5bqJbH93ZFOh2jt7dP+rm/0WcfC4/9XNsk7TtvY30h2Gum3u07qvW65H17l32PNdyXh8gVM1QfqAnTY3jR9ZV6mlayvV2N4lQ+4Mz3dmas++A0Gz1m4M1Pee7daD92ydmWm6+pRSfWvO5JBZ615QY+rBAidTG6obJCnguZ7ZtN07Tr7qs1o9vWGrHI60gHu2viLJBuvxntfluWf70c6WpMnaWC8clQxZG+56ZC0GKpnmR+VkOOTMTNOx40ZqZkm+3tvWZNv34P8BAEAiYX7U4GqwLDocLwXODG1taNMRP3455HznNTZj3JH6xeufa2VZTdD5UVedXOrX75SsCy1L18+dZDsXeUdTh3cX31Wf1brHwQ/ef3SZ8pub7FFR26ozfv5Pb792UlGuigtyNGviKL1d1aCKut65a74LkliLl4L1cZbMsbQ1geZHhSpWsTvP9zVK0rwpRX6FWP15Lck+PyoZdnKKFsP0pAIG7KWXXtLXv/51SVJLS4uGDx9ue9wvfvEL3XTTTSosLFR9fehfvOPGjQv62M6dO71fGxnZcmTl9qPVGCjDkHz/L3I4DLlsBp37y3q9YdnuOr19PrsFZWU4dCDIyluSlJ5maFReltoOdPud19fnDiUrw6F8Z2bY5xiWna7cLPdraGrv0gGf1aXT0wx1B/mk4rl+X1nb43n+YN8PdU5/hHo/+nrdYNcK9Z5mZaQp35nRp+sO5PWGu160n2ugvvjiC6WlpamzMzE+tCO8eGat0jKUljNMEpPmU51d1tp9z1d/8z9U9lkFy1rr54D+Zq313EhFO2tDPWaXc5L8vucrkhwM9Vrs2hCsHYOZt+GulUh5S9Ymn7hmreGQwzmCRSziwNqvjbX+ZG24fm9/r2t9jkj6tZ7r2v1u7UufuC/sfrdLga810r5XuCzu7DGVmWYEvZ6vaORtNLJsMLO2P58FYoWsTT6xyFop0rw15HAOl+FgHbDBFkmORVN/MjFUjgzkulbWcd5I+2HBjg/Wzx3o2Gsss9YuRzLTjJiOI5O1A0PeJpf4Zq3kyM6TkZ7Vx1Yj2QQbAw2V+QPJWrvnCsbunmok2RDu+FCvJxKD3a/t7+eg/ryWvrYv0uvG8p4tWYuBSpb5Uf2dZwIAqYasTT7MjxqaEuWebWePK2jftr9zkfszP6qpvTPkuLpvPyZUH6evfeJgBnN+lN1jnT1m1PpxqTI/ynOM9d52PMQ6a5lFE0Wtrb2VhE5n8MpWz2P79u2LyvOm5RVS7BNH1gjqSw1dT1uTetoCVykOdb3OHjNg5Sn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VBw8eDPn1ySefxLuZgMfQoUN1+eWX62tf+1rc2nDrrbfq0KFDmj17tgYMGCBJGjJkSND/h1599VXPuX/961+DHjd+/HjL5/I+5vPPP9fChQs1aNAgz/Ueeuihdr+GhQsX6qqrrlJLS4uGDBmi1atX6+ijj/Y55t5779XUqVNVXl6u7Oxs3XTTTVq+fLl27typqqoqbdmyRU899ZSmTJkiwzD0xhtvaNWqVT7XOPHEE3XhhRdq3759uv/++9vdTiAZkLWwG7KWrAXshqyF3ZC1ZC1gN2Qt7IasJWsBOyJvYTfkLXkL2A1ZC7sha8lawG7IWtgNWUvWxl28K46Q2LyrQztShYnE0h2qanfv3m0uXbrUrKqq8vxs+vTpcamq3b59u2kYhinJ3LFjR0TneP+O58+fH/b4SKpit23bZqanp5uSzPz8fLOpqSni8+fNm+d5DcOHDzd37twZcMxzzz3nucaoUaPMXbt2hWzzBx98YI4dO9by9a1cudKUZGZnZydNZS0QDlmbXMhaspasBRIPWZtcyFqylqwFEg9Zm1zIWrKWrAUSE3mbXMhb8pa8BRIPWZtcyFqylqwFEg9Zm1zIWrKWrO1a7PADIKkMGDBA3/zmN1VQUBDvpuixxx6TaZo644wzNHTo0Li14+ijj9YVV1whSaqpqdHGjRsjOu9Pf/qTfvCDH8g0TY0cOVKrVq3SkUce6XPMwYMHNXv2bElSv3799Oabb3qqeIMZNWqU1qxZY1kZfNZZZ2nw4MFqaGjQU089FVE7AQBdi6wNRNYCAKKJrA1E1gIAoomsDUTWAgCijbwNRN4CAKKJrA1E1gIAoomsDUTWxg8FP4i5iRMnyjAMzZgxQ5K0YsUKfec731H//v2VnZ2t4447Tg8++KAOHz7sOae2tlb33nuvTjjhBOXm5io/P1/f+ta39O6770b8PP/5z3/0zW9+U/369VNWVpaOPvpo/eIXv1BNTU3E1/B21113yTAMFRUVSZK2bNmi6667TkOHDlVmZqYMw/Acu2DBAhmG4fMzK2VlZbr55pt10kknKT8/X5mZmRo6dKjOO+88Pfroo9q3b1/AOR9++KHuu+8+TZw4Uf369VNaWpry8vI0atQo/exnP9OuXbtCPmdXWLt2raZNm6aioiJlZmaqV69eGj16tO68805VV1eHPf+TTz7RtGnTNGDAAGVmZurII4/U1VdfrS1btkiSioqKZBiG7rrrrpi03//6Tz31lCZMmKDCwkJlZ2frhBNO0L333qvGxsag12hqatL8+fMlSdOmTYtJO9vjuOOO83xfXl4e9viHHnpIP/rRj2Sapk444QStWrXKsw2gt8cee0z79++XJN1zzz3q3bt3RO3JzMzUsGHDAn5uGIbnA8Hjjz8e0bUAkLWhkLXWyNroI2uB5EbWBkfWWiNro4+sBZIbWRscWWuNrI0+shZIfuRtcOStNfI2+shbILmRtcGRtdbI2ugja4HkRtYGR9ZaI2ujj6yNky7dTwi2E41t9CZMmGBKMqdPn27+5je/8WzJ5f/1rW99y2xubjbLysrMkSNHWh6Tnp5uLl++POzz3HHHHZbnSzIHDhxobtu2Lew1/N15552mJHPIkCHmv/71LzMrKyvg2la/t2Dmzp1rpqWlBW2nLLY1e//990MeL8ns0aOH+frrr1s+Z6y30XM6nebNN98csn2FhYXm22+/HfQ5/vnPf3q2fPP/ys7ONpcuXWoOGTIk5LZx/tq7jZ739b3P9f867rjjzL1791pe4/XXX/ccV1ZWFtHzmmZsttEzTdP83e9+5znuueeeC3n+3Xff7fnZmDFjfLYk9HfaaaeZksycnByzvr4+0pcZkvfv4NNPP43KNYFERta2IWvJWrI2EFkLdB5Z24asJWvJ2kBkLdB5ZG0bspasJWsDkbVAdJC3bchb8pa8DUTeAp1H1rYha8lasjYQWQt0Hlnbhqwla8naQGRtaOzwgy6zatUq3Xrrrbr44ou1du1aVVVVacuWLbr66qslSa+++qqefPJJXXrppaqpqdG8efO0c+dOVVZWasmSJerfv7+ampp0zTXXqKWlJeTz/PrXv9Y555yjVatWad++fdq6datuu+02paamavfu3brgggtCVkSGUlNToyuvvFJFRUVavHixvvjiC33xxRf65z//GfE1/vSnP+mmm25Sc3OzjjrqKD355JMqKSlRdXW1PvvsM/3jH//QhRdeqJSUFJ/zDMPQ2Wefrblz52rVqlX69NNPVVVVpa1bt+of//iHxo4dq4MHD+qyyy7Tnj17OvT6OuPBBx/Ub3/7W0nSqaeeqmXLlqmiokKlpaWaO3eu8vLyVFVVpcmTJ6u0tDTg/I8//lhXXHGFmpqa1K9fPz355JMqLy/Xnj17tGTJEg0aNEhXXnmlamtru+T1/P3vf9fChQs1depUrV+/Xvv27dMHH3yg66+/3tPeSy65RKZpBpy7atUqSa6t5YYMGdIl7Q3lk08+8Xw/cODAoMfdfvvtmjNnjiTptNNO05tvvhl0S8LGxkZt2rRJkjR27FhlZ2dHpa0nn3yy59+++/cIIDJkbRuylqztamQt0D2QtW3IWrK2q5G1QPdA1rYha8narkbWAt0HeduGvCVvuxp5C3QPZG0bspas7WpkLdA9kLVtyFqytquRtXESx2Ij2IB3deirr75qHjx4MOTX4cOHA67hrlSVZF577bWWzzNu3DhTkpmammr26NHDspJu2bJlnussW7Ys5POce+65ZnNzc8AxTzzxhOeYBx98MOg1QlXVSjKPPvpoc//+/ZavxTRDV9Xu2rXLUzV66qmnmgcOHAh6HavXEEpzc7Pnd3nHHXcEPB7Lqtq9e/eaGRkZpiTz9NNPNxsbGwPOX7dunaeS+OKLLw54/Pzzz/dUaG7dujXg8crKSnPQoEERVZF662hVrSRz5syZlsfcfvvtnmOef/75gMfHjx9vSjIvuOCCiJ7TLRZVtSUlJWZmZqan6vrQoUOW5w8YMMDz/VlnnRXy36Zpmuann37qOf4HP/hBe15mWKNGjTIlmVdddVVUrwskIrK2DVlL1noja8laIFrI2jZkLVnrjawla4FoIWvbkLVkrTeylqwFoom8bUPekrfeyFvyFogWsrYNWUvWeiNryVogWsjaNmQtWeuNrCVrI8EOP4jYt771LfXo0SPk13333Rf0/OzsbD344IOWj11++eWSpJaWFv3oRz/SUUcdFXDMueeeq8LCQknSu+++G7Ktc+fOVWpqasDPr7nmGo0ePVqS9OSTT4a8Rii//vWv1bNnzw6d+5e//EVNTU0yDEMLFy5Ujx49gh5r9RpCSU1N1RVXXCFJeuONNzrUvo5atGiRDh8+LEl6+OGHlZmZGXDMqaeeqmuuuUaS9NJLL6mystLz2N69e7V06VJJ0g033KARI0YEnN+7d29PxWdXyMzM9FQJ+5szZ4769+8vyfrfkruKddiwYbFrYBi1tbV68cUXde655+rQoUOSpJ/+9KfKyMiwPP6LL77wfH///feH/LcpSdXV1Z7ve/Xq1fkGe3H/3j766KOoXhdIdGRtG7I2EFnri6ztHLIW3RVZ24asDUTW+iJrO4esRXdF1rYhawORtb7I2s4ha9GdkbdtyNtA5K0v8rZzyFt0V2RtG7I2EFnri6ztHLIW3RVZ24asDUTW+iJrOydZspaCH3SZ0047Lej/iN5vROedd57lMYZheI778ssvgz7PiBEjdOyxxwZ9/OKLL5Ykbd26VVVVVeGabdmOyZMnt/s8t+XLl0uSTj/9dMsgicSLL76oqVOnatiwYcrJyZFhGJ6vG264QZK0bdu2DrexI9asWSNJKi4u1tixY4Med9lll0mSWltbtXbtWs/P165dK6fTKUn67ne/G/T8UI9F24QJE4JuIZeenq5vf/vbklxtN7220mttbfUEULDzY+FXv/qVz7+FXr166aKLLtKOHTskuT7MhvqQMmrUKBmGIUm68MILfbbe62ruD9TeH8QAhEfWupC1ZG2skLUAyFoXspasjRWyFgBZ60LWkrWxQtYCkMhbN/KWvI0V8hYAWetC1pK1sULWAiBrXchasjZWyNrE076SPXRrK1as0MSJEzt8/oABA4I+lpWV1a7jGhsbgx5zzDHHhGyHdwDv3LnT8z9zpHr37q28vLx2nePN/YZ34okntvvchoYGXXTRRVq2bFnYY2tra9t9/c7YuXOnJIX8gCNJxx13nOf7srIyy+9Dffjo16+fevXqpf3793eone0R6b+l2tpa1dTUeAK1qqrKE7pdGbL+DMNQ3759ddppp+maa67xfCgIZsqUKZo9e7ZuuOEGVVZW6pxzztHq1astq9wl39cW7b9HsoQs0F5krQtZa42sJWujiaxFd0XWupC11shasjaayFp0V2StC1lrjawla6OJrEV3Rt66kLfWyFvyNprIW3RXZK0LWWuNrCVro4msRXdF1rqQtdbIWrI2mpIla9nhB10mJSUlasd5VzD6y83NDXmu9+MHDx6MqE3esrOz232OtwMHDkhS2C3KrPzsZz/zBOz06dP173//W9u3b9e+fft08OBBHTx4UH/5y18kuSo7u5L7dxnudXk/7v37r6ur83zfnr9hLHX035K7MlUK/W812m699VbPv4O6ujq1tLRoz549eumll8IGrNsPf/hD/f73v5ck7dmzR2effbZKS0stjx04cKDS0tIkuarUo8ldYe39uwQQHlnrQtaStbFC1gIga13IWrI2VshaAGStC1lL1sYKWQtAIm/dyFvyNlbIWwBkrQtZS9bGClkLgKx1IWvJ2lghaxMPBT9IOt5v1uEe70jQdZa7Ire9Ad/Q0KAFCxZIkm655RYtWLBA559/voYNG6bCwkLl5uYqNzdXhw4dinaTI+L+XXb09+8dWPX19RFfI5Y6+loKCwvlcLjeXt3b6XWF9PR0z7+DnJwcTxva6yc/+Ynuu+8+SVJ5ebnOPvts7dq1K+C47OxsjRk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",
       "text/plain": [
        "<Figure size 1680x1680 with 49 Axes>"
       ]
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
index 2d34463e..39354afb 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/06_analyze_metadata_predictions_new_style_no_compute.ipynb
@@ -83,13 +83,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b921408d0d154ef2a87369a94e8efa90",
+       "model_id": "0dc4e67dbc484a6c98c51dc3b5e3939c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -103,7 +103,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae15d3aa0768485dba47ee1827809daa",
+       "model_id": "341183d43c9e4ddba5d70785bb22a6b7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -117,7 +117,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d4a80774dfe475ab2b5fe1a50173da1",
+       "model_id": "ea64a20512b341d291fea4cf9b551a6f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -131,7 +131,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9b9213c24ec403bb30746e8b2e5f016",
+       "model_id": "53e06fcf77d64b3f87a65e794c79f1b3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -145,7 +145,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf3581cc4ca546a48bfd60143d84f3e8",
+       "model_id": "3b84a4cbcf034ddbb7017558fe068f47",
        "version_major": 2,
        "version_minor": 0
       },
@@ -235,34 +235,432 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "All arrays must be of the same length",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/frame.py:709\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    703\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[1;32m    704\u001b[0m         data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[1;32m    705\u001b[0m     )\n\u001b[1;32m    707\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[0;32m--> 709\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    710\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[1;32m    711\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:481\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m    477\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    478\u001b[0m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[1;32m    479\u001b[0m         arrays \u001b[38;5;241m=\u001b[39m [x\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[0;32m--> 481\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:115\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[1;32m    113\u001b[0m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 115\u001b[0m         index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m         index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/pandas/core/internals/construction.py:655\u001b[0m, in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    653\u001b[0m lengths \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[1;32m    654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 655\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll arrays must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[1;32m    658\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    659\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    660\u001b[0m     )\n",
-      "\u001b[0;31mValueError\u001b[0m: All arrays must be of the same length"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df = pd.DataFrame(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>cell_type_top_1_accuracy</th>\n",
+       "      <th>cell_type_top_3_accuracy</th>\n",
+       "      <th>cell_type_top_5_accuracy</th>\n",
+       "      <th>cell_type_top_10_accuracy</th>\n",
+       "      <th>cell_type_loss</th>\n",
+       "      <th>disease_top_1_accuracy</th>\n",
+       "      <th>disease_top_3_accuracy</th>\n",
+       "      <th>disease_top_5_accuracy</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tissue_top_1_accuracy</th>\n",
+       "      <th>tissue_top_3_accuracy</th>\n",
+       "      <th>tissue_top_5_accuracy</th>\n",
+       "      <th>tissue_top_10_accuracy</th>\n",
+       "      <th>tissue_loss</th>\n",
+       "      <th>sex_top_1_accuracy</th>\n",
+       "      <th>sex_top_3_accuracy</th>\n",
+       "      <th>sex_top_5_accuracy</th>\n",
+       "      <th>sex_top_10_accuracy</th>\n",
+       "      <th>sex_loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.572816</td>\n",
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+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0.0000</td>\n",
+       "      <td>0.00400</td>\n",
+       "      <td>12.074639</td>\n",
+       "      <td>0.0155</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.470647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.445500</td>\n",
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+       "      <td>1.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.133987</td>\n",
+       "      <td>0.5210</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.831287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.402500</td>\n",
+       "      <td>0.735500</td>\n",
+       "      <td>0.846500</td>\n",
+       "      <td>0.943500</td>\n",
+       "      <td>2.351372</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.005723</td>\n",
+       "      <td>0.8125</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>0.375574</td>\n",
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+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.284309</td>\n",
+       "      <td>0.560331</td>\n",
+       "      <td>0.673744</td>\n",
+       "      <td>0.872087</td>\n",
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+       "      <td>0.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>15.572955</td>\n",
+       "      <td>0.4915</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.979578</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
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+       "      <td>0.446500</td>\n",
+       "      <td>0.704500</td>\n",
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+       "      <td>0.0000</td>\n",
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+       "      <td>0.999</td>\n",
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+       "      <td>1.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.008097</td>\n",
+       "      <td>0.8940</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.250716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0.540000</td>\n",
+       "      <td>0.601000</td>\n",
+       "      <td>0.629000</td>\n",
+       "      <td>0.820500</td>\n",
+       "      <td>2.369624</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.6285</td>\n",
+       "      <td>0.9360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>7.946870</td>\n",
+       "      <td>0.1955</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.420597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>107</td>\n",
+       "      <td>0.521739</td>\n",
+       "      <td>0.751976</td>\n",
+       "      <td>0.892292</td>\n",
+       "      <td>0.976285</td>\n",
+       "      <td>0.968736</td>\n",
+       "      <td>0.5590</td>\n",
+       "      <td>0.9470</td>\n",
+       "      <td>0.9960</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.236766</td>\n",
+       "      <td>0.8265</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.414492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0.430082</td>\n",
+       "      <td>0.679201</td>\n",
+       "      <td>0.809636</td>\n",
+       "      <td>0.968273</td>\n",
+       "      <td>0.964056</td>\n",
+       "      <td>0.7205</td>\n",
+       "      <td>0.9600</td>\n",
+       "      <td>0.9960</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.977</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.239326</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.931553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>109</td>\n",
+       "      <td>0.004096</td>\n",
+       "      <td>0.267408</td>\n",
+       "      <td>0.393212</td>\n",
+       "      <td>0.558806</td>\n",
+       "      <td>4.707023</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00117</td>\n",
+       "      <td>4.996072</td>\n",
+       "      <td>0.9567</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.092742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.060500</td>\n",
+       "      <td>0.115000</td>\n",
+       "      <td>0.203000</td>\n",
+       "      <td>0.332000</td>\n",
+       "      <td>4.536273</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.9615</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>6.168434</td>\n",
+       "      <td>0.4625</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.795020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>440 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             prefix  val_adata_id  cell_type_top_1_accuracy  \\\n",
+       "0    10M_001_bs1536             1                  0.572816   \n",
+       "1    10M_001_bs1536             2                  0.445500   \n",
+       "2    10M_001_bs1536             3                  0.402500   \n",
+       "3    10M_001_bs1536             4                  0.284309   \n",
+       "4    10M_001_bs1536             5                  0.446500   \n",
+       "..              ...           ...                       ...   \n",
+       "435  59M_001_bs3072           106                  0.540000   \n",
+       "436  59M_001_bs3072           107                  0.521739   \n",
+       "437  59M_001_bs3072           108                  0.430082   \n",
+       "438  59M_001_bs3072           109                  0.004096   \n",
+       "439  59M_001_bs3072           110                  0.060500   \n",
+       "\n",
+       "     cell_type_top_3_accuracy  cell_type_top_5_accuracy  \\\n",
+       "0                    0.601942                  0.601942   \n",
+       "1                    0.648500                  0.779000   \n",
+       "2                    0.735500                  0.846500   \n",
+       "3                    0.560331                  0.673744   \n",
+       "4                    0.704500                  0.827500   \n",
+       "..                        ...                       ...   \n",
+       "435                  0.601000                  0.629000   \n",
+       "436                  0.751976                  0.892292   \n",
+       "437                  0.679201                  0.809636   \n",
+       "438                  0.267408                  0.393212   \n",
+       "439                  0.115000                  0.203000   \n",
+       "\n",
+       "     cell_type_top_10_accuracy  cell_type_loss  disease_top_1_accuracy  \\\n",
+       "0                     0.604099        4.515924                  1.0000   \n",
+       "1                     0.927000        2.346420                  0.0000   \n",
+       "2                     0.943500        2.351372                  0.0000   \n",
+       "3                     0.872087        3.844428                  0.0000   \n",
+       "4                     0.914500        2.332905                  0.0000   \n",
+       "..                         ...             ...                     ...   \n",
+       "435                   0.820500        2.369624                  0.1860   \n",
+       "436                   0.976285        0.968736                  0.5590   \n",
+       "437                   0.968273        0.964056                  0.7205   \n",
+       "438                   0.558806        4.707023                  1.0000   \n",
+       "439                   0.332000        4.536273                  0.0000   \n",
+       "\n",
+       "     disease_top_3_accuracy  disease_top_5_accuracy  ...  \\\n",
+       "0                    1.0000                  1.0000  ...   \n",
+       "1                    0.0000                  0.0000  ...   \n",
+       "2                    0.0000                  0.0000  ...   \n",
+       "3                    0.0000                  0.0000  ...   \n",
+       "4                    0.0000                  0.0000  ...   \n",
+       "..                      ...                     ...  ...   \n",
+       "435                  0.6285                  0.9360  ...   \n",
+       "436                  0.9470                  0.9960  ...   \n",
+       "437                  0.9600                  0.9960  ...   \n",
+       "438                  1.0000                  1.0000  ...   \n",
+       "439                  0.8510                  0.9615  ...   \n",
+       "\n",
+       "     tissue_top_1_accuracy  tissue_top_3_accuracy  tissue_top_5_accuracy  \\\n",
+       "0                    0.000                    0.0                 0.0000   \n",
+       "1                    0.948                    1.0                 1.0000   \n",
+       "2                    1.000                    1.0                 1.0000   \n",
+       "3                    0.000                    0.0                 0.0005   \n",
+       "4                    0.999                    1.0                 1.0000   \n",
+       "..                     ...                    ...                    ...   \n",
+       "435                  0.000                    0.0                 0.0000   \n",
+       "436                  0.987                    1.0                 1.0000   \n",
+       "437                  0.977                    1.0                 1.0000   \n",
+       "438                  0.000                    0.0                 0.0000   \n",
+       "439                  0.000                    0.0                 0.0000   \n",
+       "\n",
+       "     tissue_top_10_accuracy  tissue_loss  sex_top_1_accuracy  \\\n",
+       "0                   0.00400    12.074639              0.0155   \n",
+       "1                   1.00000     0.133987              0.5210   \n",
+       "2                   1.00000     0.005723              0.8125   \n",
+       "3                   0.02400    15.572955              0.4915   \n",
+       "4                   1.00000     0.008097              0.8940   \n",
+       "..                      ...          ...                 ...   \n",
+       "435                 0.00000     7.946870              0.1955   \n",
+       "436                 1.00000     0.236766              0.8265   \n",
+       "437                 1.00000     0.239326              0.2675   \n",
+       "438                 0.00117     4.996072              0.9567   \n",
+       "439                 0.00000     6.168434              0.4625   \n",
+       "\n",
+       "     sex_top_3_accuracy  sex_top_5_accuracy  sex_top_10_accuracy  sex_loss  \n",
+       "0                   1.0                 1.0                  1.0  2.470647  \n",
+       "1                   1.0                 1.0                  1.0  0.831287  \n",
+       "2                   1.0                 1.0                  1.0  0.375574  \n",
+       "3                   1.0                 1.0                  1.0  0.979578  \n",
+       "4                   1.0                 1.0                  1.0  0.250716  \n",
+       "..                  ...                 ...                  ...       ...  \n",
+       "435                 1.0                 1.0                  1.0  1.420597  \n",
+       "436                 1.0                 1.0                  1.0  0.414492  \n",
+       "437                 1.0                 1.0                  1.0  0.931553  \n",
+       "438                 1.0                 1.0                  1.0  0.092742  \n",
+       "439                 1.0                 1.0                  1.0  0.795020  \n",
+       "\n",
+       "[440 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df"
    ]
@@ -310,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,11 +718,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "key = 'cell_type'\n",
+    "key = 'disease'\n",
     "\n",
     "metrics = [\"top_1_accuracy\", \"top_3_accuracy\", \"top_5_accuracy\", \"top_10_accuracy\"]\n",
     "n_metrics = len(metrics)\n",
@@ -368,9 +777,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "metrics = [\"cell_type_top_10_accuracy\", \"tissue_top_10_accuracy\", \"disease_top_10_accuracy\", \"sex_top_1_accuracy\"]\n",
     "n_metrics = len(metrics)\n",
@@ -414,9 +834,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "metrics = [\"cell_type_top_10_accuracy\", \"tissue_top_10_accuracy\", \"disease_top_10_accuracy\", \"sex_top_1_accuracy\"]\n",
     "n_metrics = len(metrics)\n",
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
index fa2edb4a..f75af556 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/07_test_new_metadata_prediction_implementation.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
     "\n",
     "rng_seed = 42\n",
     "n_cells = 1000\n",
-    "n_genes = 4_091\n",
+    "n_genes = 7000\n",
     "gene_selection_method = \"highly_expressed\"  # \"random\" or \"highly_expressed\"\n",
     "chunk_size = 16\n",
     "\n",
@@ -69,14 +69,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-03-02 22:37:14,887 - __main__ - INFO - Loading ontology resources ...\n"
+      "2025-03-02 23:14:20,178 - __main__ - INFO - Loading ontology resources ...\n"
      ]
     }
    ],
@@ -121,14 +121,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-03-02 22:37:16,275 - __main__ - INFO - Loading the model checkpoint from /home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt ...\n"
+      "2025-03-02 23:14:21,421 - __main__ - INFO - Loading the model checkpoint from /home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt ...\n"
      ]
     }
    ],
@@ -149,14 +149,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-03-02 22:37:20,519 - __main__ - INFO - Loading the validation AnnData object from /home/mehrtash/data/data/cellariumgpt_validation/extract_1.h5ad ...\n"
+      "2025-03-02 23:14:25,344 - __main__ - INFO - Loading the validation AnnData object from /home/mehrtash/data/data/cellariumgpt_validation/extract_1.h5ad ...\n"
      ]
     }
    ],
@@ -167,17 +167,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-03-02 22:37:23,433 - __main__ - INFO - Starting with 0 fixed genes.\n",
-      "2025-03-02 22:37:23,433 - __main__ - INFO - In addition, using up to 4091 highly expressed genes.\n",
-      "2025-03-02 22:37:23,475 - __main__ - INFO - Selected 4091 genes.\n",
-      "2025-03-02 22:37:23,476 - __main__ - INFO - Using 1000 random cells (seed = 42).\n"
+      "2025-03-02 23:14:27,330 - __main__ - INFO - Starting with 0 fixed genes.\n",
+      "2025-03-02 23:14:27,331 - __main__ - INFO - In addition, using up to 7000 highly expressed genes.\n",
+      "2025-03-02 23:14:27,376 - __main__ - INFO - Selected 7000 genes.\n",
+      "2025-03-02 23:14:27,378 - __main__ - INFO - Using 1000 random cells (seed = 42).\n"
      ]
     }
    ],
@@ -231,20 +231,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-03-02 22:37:25,800 - __main__ - INFO - Predicting metadata for 1000 cells ...\n"
+      "2025-03-02 23:14:27,480 - __main__ - INFO - Predicting metadata for 1000 cells ...\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8539546975c146a2ae0b5ae928853a05",
+       "model_id": "0115a1123ece426f8a9a70f53e2e7096",
        "version_major": 2,
        "version_minor": 0
       },
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +344,7 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31594264</th>\n",
@@ -362,7 +362,7 @@
        "    <tr>\n",
        "      <th>31601455</th>\n",
        "      <td>unknown</td>\n",
-       "      <td>cerebellar granule cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -383,7 +383,7 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31593942</th>\n",
@@ -474,12 +474,12 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31600383</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -494,7 +494,7 @@
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
@@ -552,14 +552,14 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>male</td>\n",
+       "      <td>female</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31592421</th>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
@@ -570,7 +570,7 @@
        "    <tr>\n",
        "      <th>31592116</th>\n",
        "      <td>unknown</td>\n",
-       "      <td>GABAergic neuron</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -622,7 +622,7 @@
        "    <tr>\n",
        "      <th>31600178</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>epithelial cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -630,12 +630,12 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31599815</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -648,11 +648,11 @@
        "    <tr>\n",
        "      <th>31598814</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
-       "      <td>12th week post-fertilization stage</td>\n",
+       "      <td>17th week post-fertilization stage</td>\n",
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
@@ -663,7 +663,7 @@
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
@@ -674,11 +674,11 @@
        "    <tr>\n",
        "      <th>31600997</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>GABAergic neuron</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
-       "      <td>51-year-old stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
@@ -687,11 +687,11 @@
        "    <tr>\n",
        "      <th>31598473</th>\n",
        "      <td>interneuron</td>\n",
-       "      <td>endothelial cell</td>\n",
+       "      <td>epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
-       "      <td>51-year-old stage</td>\n",
+       "      <td>12th week post-fertilization stage</td>\n",
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
@@ -713,7 +713,7 @@
        "    <tr>\n",
        "      <th>31599509</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>kidney loop of Henle epithelial cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -726,7 +726,7 @@
        "    <tr>\n",
        "      <th>31598433</th>\n",
        "      <td>interneuron</td>\n",
-       "      <td>neuron</td>\n",
+       "      <td>endothelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -765,7 +765,7 @@
        "    <tr>\n",
        "      <th>31600830</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>GABAergic neuron</td>\n",
+       "      <td>monocyte-derived dendritic cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -786,12 +786,12 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>male</td>\n",
+       "      <td>female</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31600725</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>kidney loop of Henle epithelial cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -799,7 +799,7 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31588821</th>\n",
@@ -812,20 +812,20 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>male</td>\n",
+       "      <td>female</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31591031</th>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31599410</th>\n",
@@ -838,14 +838,14 @@
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
-       "      <td>female</td>\n",
+       "      <td>male</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31591972</th>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
@@ -882,7 +882,7 @@
        "    <tr>\n",
        "      <th>31601214</th>\n",
        "      <td>cerebellar granule cell</td>\n",
-       "      <td>GABAergic neuron</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -895,7 +895,7 @@
        "    <tr>\n",
        "      <th>31589523</th>\n",
        "      <td>unknown</td>\n",
-       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>stratified epithelial cell</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -908,7 +908,7 @@
        "    <tr>\n",
        "      <th>31589177</th>\n",
        "      <td>unknown</td>\n",
-       "      <td>monocyte-derived dendritic cell</td>\n",
+       "      <td>neuron</td>\n",
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
@@ -925,7 +925,7 @@
        "      <td>dentate nucleus</td>\n",
        "      <td>cerebellum</td>\n",
        "      <td>52-year-old human stage</td>\n",
-       "      <td>51-year-old stage</td>\n",
+       "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
        "      <td>normal</td>\n",
        "      <td>male</td>\n",
@@ -936,7 +936,7 @@
        "      <td>oligodendrocyte</td>\n",
        "      <td>oligodendrocyte</td>\n",
        "      <td>dentate nucleus</td>\n",
-       "      <td>cerebellum</td>\n",
+       "      <td>temporal lobe</td>\n",
        "      <td>52-year-old human stage</td>\n",
        "      <td>61-year-old stage</td>\n",
        "      <td>normal</td>\n",
@@ -1040,216 +1040,164 @@
        "31593664                                    oligodendrocyte   \n",
        "31590994                                    oligodendrocyte   \n",
        "\n",
-       "                                cell_type_pred           tissue  \\\n",
-       "31589848                       oligodendrocyte  dentate nucleus   \n",
-       "31594264                       oligodendrocyte  dentate nucleus   \n",
-       "31601455               cerebellar granule cell  dentate nucleus   \n",
-       "31589769                       oligodendrocyte  dentate nucleus   \n",
-       "31593942                                neuron  dentate nucleus   \n",
-       "31598517                       epithelial cell  dentate nucleus   \n",
-       "31599161            stratified epithelial cell  dentate nucleus   \n",
-       "31601198                                neuron  dentate nucleus   \n",
-       "31599402            stratified epithelial cell  dentate nucleus   \n",
-       "31594186                            macrophage  dentate nucleus   \n",
-       "31590646                       oligodendrocyte  dentate nucleus   \n",
-       "31600383       monocyte-derived dendritic cell  dentate nucleus   \n",
-       "31592385                       oligodendrocyte  dentate nucleus   \n",
-       "31588945                       oligodendrocyte  dentate nucleus   \n",
-       "31590959                       oligodendrocyte  dentate nucleus   \n",
-       "31592015                       oligodendrocyte  dentate nucleus   \n",
-       "31588921                       oligodendrocyte  dentate nucleus   \n",
-       "31592421                       oligodendrocyte  dentate nucleus   \n",
-       "31592116                      GABAergic neuron  dentate nucleus   \n",
-       "31591598                       oligodendrocyte  dentate nucleus   \n",
-       "31588345                       oligodendrocyte  dentate nucleus   \n",
-       "31593570                             astrocyte  dentate nucleus   \n",
-       "31600178                       epithelial cell  dentate nucleus   \n",
-       "31599815       monocyte-derived dendritic cell  dentate nucleus   \n",
-       "31598814       monocyte-derived dendritic cell  dentate nucleus   \n",
-       "31593104                       oligodendrocyte  dentate nucleus   \n",
-       "31600997                      GABAergic neuron  dentate nucleus   \n",
-       "31598473                      endothelial cell  dentate nucleus   \n",
-       "31600098        oligodendrocyte precursor cell  dentate nucleus   \n",
-       "31599509  kidney loop of Henle epithelial cell  dentate nucleus   \n",
-       "31598433                                neuron  dentate nucleus   \n",
-       "31599851            stratified epithelial cell  dentate nucleus   \n",
-       "31593301                       oligodendrocyte  dentate nucleus   \n",
-       "31600830                      GABAergic neuron  dentate nucleus   \n",
-       "31589187                       oligodendrocyte  dentate nucleus   \n",
-       "31600725  kidney loop of Henle epithelial cell  dentate nucleus   \n",
-       "31588821                       oligodendrocyte  dentate nucleus   \n",
-       "31591031                       oligodendrocyte  dentate nucleus   \n",
-       "31599410            stratified epithelial cell  dentate nucleus   \n",
-       "31591972                       oligodendrocyte  dentate nucleus   \n",
-       "31593634        oligodendrocyte precursor cell  dentate nucleus   \n",
-       "31601467                             astrocyte  dentate nucleus   \n",
-       "31601214                      GABAergic neuron  dentate nucleus   \n",
-       "31589523       monocyte-derived dendritic cell  dentate nucleus   \n",
-       "31589177       monocyte-derived dendritic cell  dentate nucleus   \n",
-       "31601204                      GABAergic neuron  dentate nucleus   \n",
-       "31590685                       oligodendrocyte  dentate nucleus   \n",
-       "31589072                       oligodendrocyte  dentate nucleus   \n",
-       "31593664                       oligodendrocyte  dentate nucleus   \n",
-       "31590994                       oligodendrocyte  dentate nucleus   \n",
-       "\n",
-       "            tissue_pred        development_stage  \\\n",
-       "31589848  temporal lobe  52-year-old human stage   \n",
-       "31594264     cerebellum  52-year-old human stage   \n",
-       "31601455     cerebellum  52-year-old human stage   \n",
-       "31589769  temporal lobe  52-year-old human stage   \n",
-       "31593942     cerebellum  52-year-old human stage   \n",
-       "31598517     cerebellum  52-year-old human stage   \n",
-       "31599161     cerebellum  52-year-old human stage   \n",
-       "31601198     cerebellum  52-year-old human stage   \n",
-       "31599402     cerebellum  52-year-old human stage   \n",
-       "31594186  temporal lobe  52-year-old human stage   \n",
-       "31590646  temporal lobe  52-year-old human stage   \n",
-       "31600383     cerebellum  52-year-old human stage   \n",
-       "31592385     cerebellum  52-year-old human stage   \n",
-       "31588945  temporal lobe  52-year-old human stage   \n",
-       "31590959  temporal lobe  52-year-old human stage   \n",
-       "31592015  temporal lobe  52-year-old human stage   \n",
-       "31588921  temporal lobe  52-year-old human stage   \n",
-       "31592421     cerebellum  52-year-old human stage   \n",
-       "31592116     cerebellum  52-year-old human stage   \n",
-       "31591598  temporal lobe  52-year-old human stage   \n",
-       "31588345  temporal lobe  52-year-old human stage   \n",
-       "31593570     cerebellum  52-year-old human stage   \n",
-       "31600178     cerebellum  52-year-old human stage   \n",
-       "31599815     cerebellum  52-year-old human stage   \n",
-       "31598814     cerebellum  52-year-old human stage   \n",
-       "31593104     cerebellum  52-year-old human stage   \n",
-       "31600997     cerebellum  52-year-old human stage   \n",
-       "31598473     cerebellum  52-year-old human stage   \n",
-       "31600098     cerebellum  52-year-old human stage   \n",
-       "31599509     cerebellum  52-year-old human stage   \n",
-       "31598433     cerebellum  52-year-old human stage   \n",
-       "31599851     cerebellum  52-year-old human stage   \n",
-       "31593301     cerebellum  52-year-old human stage   \n",
-       "31600830     cerebellum  52-year-old human stage   \n",
-       "31589187  temporal lobe  52-year-old human stage   \n",
-       "31600725     cerebellum  52-year-old human stage   \n",
-       "31588821  temporal lobe  52-year-old human stage   \n",
-       "31591031     cerebellum  52-year-old human stage   \n",
-       "31599410     cerebellum  52-year-old human stage   \n",
-       "31591972     cerebellum  52-year-old human stage   \n",
-       "31593634     cerebellum  52-year-old human stage   \n",
-       "31601467     cerebellum  52-year-old human stage   \n",
-       "31601214     cerebellum  52-year-old human stage   \n",
-       "31589523     cerebellum  52-year-old human stage   \n",
-       "31589177     cerebellum  52-year-old human stage   \n",
-       "31601204     cerebellum  52-year-old human stage   \n",
-       "31590685     cerebellum  52-year-old human stage   \n",
-       "31589072  temporal lobe  52-year-old human stage   \n",
-       "31593664     cerebellum  52-year-old human stage   \n",
-       "31590994  temporal lobe  52-year-old human stage   \n",
+       "                           cell_type_pred           tissue    tissue_pred  \\\n",
+       "31589848                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31594264                  oligodendrocyte  dentate nucleus     cerebellum   \n",
+       "31601455       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31589769                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31593942                           neuron  dentate nucleus     cerebellum   \n",
+       "31598517                  epithelial cell  dentate nucleus     cerebellum   \n",
+       "31599161       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31601198                           neuron  dentate nucleus     cerebellum   \n",
+       "31599402       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31594186                       macrophage  dentate nucleus  temporal lobe   \n",
+       "31590646                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31600383       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31592385                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31588945                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31590959                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31592015                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31588921                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31592421                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31592116       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31591598                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31588345                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31593570                        astrocyte  dentate nucleus     cerebellum   \n",
+       "31600178       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31599815       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31598814       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31593104                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31600997       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31598473                  epithelial cell  dentate nucleus     cerebellum   \n",
+       "31600098   oligodendrocyte precursor cell  dentate nucleus     cerebellum   \n",
+       "31599509       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31598433                 endothelial cell  dentate nucleus     cerebellum   \n",
+       "31599851       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31593301                  oligodendrocyte  dentate nucleus     cerebellum   \n",
+       "31600830  monocyte-derived dendritic cell  dentate nucleus     cerebellum   \n",
+       "31589187                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31600725       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31588821                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31591031                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31599410       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31591972                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31593634   oligodendrocyte precursor cell  dentate nucleus     cerebellum   \n",
+       "31601467                        astrocyte  dentate nucleus     cerebellum   \n",
+       "31601214       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31589523       stratified epithelial cell  dentate nucleus     cerebellum   \n",
+       "31589177                           neuron  dentate nucleus     cerebellum   \n",
+       "31601204                 GABAergic neuron  dentate nucleus     cerebellum   \n",
+       "31590685                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31589072                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
+       "31593664                  oligodendrocyte  dentate nucleus     cerebellum   \n",
+       "31590994                  oligodendrocyte  dentate nucleus  temporal lobe   \n",
        "\n",
-       "                      development_stage_pred disease disease_pred   sex  \\\n",
-       "31589848                   61-year-old stage  normal       normal  male   \n",
-       "31594264                   61-year-old stage  normal       normal  male   \n",
-       "31601455                   61-year-old stage  normal       normal  male   \n",
-       "31589769                   61-year-old stage  normal       normal  male   \n",
-       "31593942                   23-year-old stage  normal       normal  male   \n",
-       "31598517                   61-year-old stage  normal       normal  male   \n",
-       "31599161                   61-year-old stage  normal       normal  male   \n",
-       "31601198                   61-year-old stage  normal       normal  male   \n",
-       "31599402                   61-year-old stage  normal       normal  male   \n",
-       "31594186                   61-year-old stage  normal       normal  male   \n",
-       "31590646                   61-year-old stage  normal       normal  male   \n",
-       "31600383                   61-year-old stage  normal       normal  male   \n",
-       "31592385                   61-year-old stage  normal       normal  male   \n",
-       "31588945                   61-year-old stage  normal       normal  male   \n",
-       "31590959                   61-year-old stage  normal       normal  male   \n",
-       "31592015                   61-year-old stage  normal       normal  male   \n",
-       "31588921                   61-year-old stage  normal       normal  male   \n",
-       "31592421                   61-year-old stage  normal       normal  male   \n",
-       "31592116  17th week post-fertilization stage  normal       normal  male   \n",
-       "31591598                   61-year-old stage  normal       normal  male   \n",
-       "31588345                   61-year-old stage  normal       normal  male   \n",
-       "31593570                   61-year-old stage  normal       normal  male   \n",
-       "31600178                   61-year-old stage  normal       normal  male   \n",
-       "31599815                   61-year-old stage  normal       normal  male   \n",
-       "31598814  12th week post-fertilization stage  normal       normal  male   \n",
-       "31593104                   61-year-old stage  normal       normal  male   \n",
-       "31600997                   51-year-old stage  normal       normal  male   \n",
-       "31598473                   51-year-old stage  normal       normal  male   \n",
-       "31600098  12th week post-fertilization stage  normal       normal  male   \n",
-       "31599509                   61-year-old stage  normal       normal  male   \n",
-       "31598433  12th week post-fertilization stage  normal       normal  male   \n",
-       "31599851                   61-year-old stage  normal       normal  male   \n",
-       "31593301                   61-year-old stage  normal       normal  male   \n",
-       "31600830                   61-year-old stage  normal       normal  male   \n",
-       "31589187                   61-year-old stage  normal       normal  male   \n",
-       "31600725                   61-year-old stage  normal       normal  male   \n",
-       "31588821                   61-year-old stage  normal       normal  male   \n",
-       "31591031                   61-year-old stage  normal       normal  male   \n",
-       "31599410                   61-year-old stage  normal       normal  male   \n",
-       "31591972                   61-year-old stage  normal       normal  male   \n",
-       "31593634                   61-year-old stage  normal       normal  male   \n",
-       "31601467                   61-year-old stage  normal       normal  male   \n",
-       "31601214                   61-year-old stage  normal       normal  male   \n",
-       "31589523                   61-year-old stage  normal       normal  male   \n",
-       "31589177                   61-year-old stage  normal       normal  male   \n",
-       "31601204                   51-year-old stage  normal       normal  male   \n",
-       "31590685                   61-year-old stage  normal       normal  male   \n",
-       "31589072                   61-year-old stage  normal       normal  male   \n",
-       "31593664                   61-year-old stage  normal       normal  male   \n",
-       "31590994                   61-year-old stage  normal       normal  male   \n",
+       "                development_stage              development_stage_pred disease  \\\n",
+       "31589848  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31594264  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31601455  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31589769  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31593942  52-year-old human stage                   23-year-old stage  normal   \n",
+       "31598517  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31599161  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31601198  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31599402  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31594186  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31590646  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31600383  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31592385  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31588945  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31590959  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31592015  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31588921  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31592421  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31592116  52-year-old human stage  17th week post-fertilization stage  normal   \n",
+       "31591598  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31588345  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31593570  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31600178  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31599815  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31598814  52-year-old human stage  17th week post-fertilization stage  normal   \n",
+       "31593104  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31600997  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31598473  52-year-old human stage  12th week post-fertilization stage  normal   \n",
+       "31600098  52-year-old human stage  12th week post-fertilization stage  normal   \n",
+       "31599509  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31598433  52-year-old human stage  12th week post-fertilization stage  normal   \n",
+       "31599851  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31593301  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31600830  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31589187  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31600725  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31588821  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31591031  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31599410  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31591972  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31593634  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31601467  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31601214  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31589523  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31589177  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31601204  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31590685  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31589072  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31593664  52-year-old human stage                   61-year-old stage  normal   \n",
+       "31590994  52-year-old human stage                   61-year-old stage  normal   \n",
        "\n",
-       "         sex_pred  \n",
-       "31589848   female  \n",
-       "31594264   female  \n",
-       "31601455   female  \n",
-       "31589769   female  \n",
-       "31593942   female  \n",
-       "31598517     male  \n",
-       "31599161   female  \n",
-       "31601198   female  \n",
-       "31599402     male  \n",
-       "31594186     male  \n",
-       "31590646   female  \n",
-       "31600383     male  \n",
-       "31592385   female  \n",
-       "31588945     male  \n",
-       "31590959   female  \n",
-       "31592015   female  \n",
-       "31588921     male  \n",
-       "31592421   female  \n",
-       "31592116     male  \n",
-       "31591598   female  \n",
-       "31588345     male  \n",
-       "31593570     male  \n",
-       "31600178   female  \n",
-       "31599815   female  \n",
-       "31598814     male  \n",
-       "31593104   female  \n",
-       "31600997     male  \n",
-       "31598473     male  \n",
-       "31600098     male  \n",
-       "31599509     male  \n",
-       "31598433     male  \n",
-       "31599851     male  \n",
-       "31593301   female  \n",
-       "31600830     male  \n",
-       "31589187     male  \n",
-       "31600725   female  \n",
-       "31588821     male  \n",
-       "31591031   female  \n",
-       "31599410   female  \n",
-       "31591972   female  \n",
-       "31593634     male  \n",
-       "31601467     male  \n",
-       "31601214     male  \n",
-       "31589523     male  \n",
-       "31589177     male  \n",
-       "31601204     male  \n",
-       "31590685   female  \n",
-       "31589072   female  \n",
-       "31593664   female  \n",
-       "31590994   female  "
+       "         disease_pred   sex sex_pred  \n",
+       "31589848       normal  male     male  \n",
+       "31594264       normal  male   female  \n",
+       "31601455       normal  male   female  \n",
+       "31589769       normal  male     male  \n",
+       "31593942       normal  male   female  \n",
+       "31598517       normal  male     male  \n",
+       "31599161       normal  male   female  \n",
+       "31601198       normal  male   female  \n",
+       "31599402       normal  male     male  \n",
+       "31594186       normal  male     male  \n",
+       "31590646       normal  male     male  \n",
+       "31600383       normal  male     male  \n",
+       "31592385       normal  male   female  \n",
+       "31588945       normal  male     male  \n",
+       "31590959       normal  male   female  \n",
+       "31592015       normal  male   female  \n",
+       "31588921       normal  male   female  \n",
+       "31592421       normal  male   female  \n",
+       "31592116       normal  male     male  \n",
+       "31591598       normal  male   female  \n",
+       "31588345       normal  male     male  \n",
+       "31593570       normal  male     male  \n",
+       "31600178       normal  male     male  \n",
+       "31599815       normal  male   female  \n",
+       "31598814       normal  male     male  \n",
+       "31593104       normal  male   female  \n",
+       "31600997       normal  male     male  \n",
+       "31598473       normal  male     male  \n",
+       "31600098       normal  male     male  \n",
+       "31599509       normal  male     male  \n",
+       "31598433       normal  male     male  \n",
+       "31599851       normal  male     male  \n",
+       "31593301       normal  male   female  \n",
+       "31600830       normal  male     male  \n",
+       "31589187       normal  male   female  \n",
+       "31600725       normal  male     male  \n",
+       "31588821       normal  male   female  \n",
+       "31591031       normal  male     male  \n",
+       "31599410       normal  male     male  \n",
+       "31591972       normal  male   female  \n",
+       "31593634       normal  male     male  \n",
+       "31601467       normal  male     male  \n",
+       "31601214       normal  male     male  \n",
+       "31589523       normal  male     male  \n",
+       "31589177       normal  male     male  \n",
+       "31601204       normal  male     male  \n",
+       "31590685       normal  male   female  \n",
+       "31589072       normal  male   female  \n",
+       "31593664       normal  male   female  \n",
+       "31590994       normal  male   female  "
       ]
      },
-     "execution_count": 63,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/08_analyze_metadata_predictions_final.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/08_analyze_metadata_predictions_final.ipynb
new file mode 100644
index 00000000..8178a656
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/08_analyze_metadata_predictions_final.ipynb
@@ -0,0 +1,13071 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analyze metadata predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "logger.setLevel(logging.DEBUG)  # Set the logging level\n",
+    "\n",
+    "# Create a handler\n",
+    "handler = logging.StreamHandler()\n",
+    "\n",
+    "# Create and set a formatter\n",
+    "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
+    "handler.setFormatter(formatter)\n",
+    "\n",
+    "# Add the handler to the logger\n",
+    "logger.addHandler(handler)\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "from cellarium.ml.utilities.inference.metadata_benchmarking.calculate_metrics import \\\n",
+    "    calculate_metrics_for_prediction_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_path = \"/home/mehrtash/data\"\n",
+    "\n",
+    "metadata_predictions_rand_w_X_root_path = os.path.join(\n",
+    "    root_path, \"data\", \"metadata_predictions_rand__4096_with_X\")\n",
+    "\n",
+    "metadata_predictions_rand_w_XY_root_path = os.path.join(\n",
+    "    root_path, \"data\", \"metadata_predictions_rand__4096_with_XY\")\n",
+    "\n",
+    "metadata_predictions_rand_wo_XY_root_path = os.path.join(\n",
+    "    root_path, \"data\", \"metadata_predictions_rand__4096_without_XY\")\n",
+    "\n",
+    "metadata_predictions_high_w_XY_root_path = os.path.join(\n",
+    "    root_path, \"data\", \"metadata_predictions_high__4096_with_XY\")\n",
+    "\n",
+    "metadata_predictions_high_wo_XY_root_path = os.path.join(\n",
+    "    root_path, \"data\", \"metadata_predictions_high__4096_without_XY\")\n",
+    "\n",
+    "prefix_list = [\n",
+    "    \"10M_001_bs1536\",\n",
+    "    \"19M_001_bs2048\",\n",
+    "    \"30M_001_bs2560\",\n",
+    "    \"59M_001_bs3072\"\n",
+    "]\n",
+    "\n",
+    "val_adata_idx_range = np.arange(1, 111)\n",
+    "\n",
+    "n_hops_dict = {\n",
+    "    'cell_type': 3,\n",
+    "    'development_stage': 3,\n",
+    "    'disease': 3,\n",
+    "    'tissue': 3,\n",
+    "    'sex': 0,\n",
+    "}\n",
+    "\n",
+    "def load_predictions_anndata(val_adata_idx: int, prefix_idx: int, metadata_predictions_root_path: str) -> sc.AnnData:\n",
+    "    path = os.path.join(\n",
+    "        metadata_predictions_root_path,\n",
+    "        prefix_list[prefix_idx],\n",
+    "        f\"extract_{val_adata_idx_range[val_adata_idx]}_metadata_prediction_scores.h5ad\")\n",
+    "    return sc.read_h5ad(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### General"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "\n",
+    "class OntologyCoarsener:\n",
+    "    def __init__(\n",
+    "            self,\n",
+    "            coarse_labels: list[str],\n",
+    "            ontology_propagation_resource_dict: dict,\n",
+    "            ontology_benchmarking_resource_dict: dict,\n",
+    "            include_other_node: bool = True,\n",
+    "            check_mutual_exclusivity: bool = True,\n",
+    "            verbose: bool = True,\n",
+    "    ):\n",
+    "\n",
+    "        # some labels might map to multiple terms, we catch all of them\n",
+    "        coarse_label_to_term_ids_map = defaultdict(list)\n",
+    "        for coarse_label in coarse_labels:\n",
+    "            for term_id, label in ontology_propagation_resource_dict['ontology_term_id_to_label'].items():\n",
+    "                if label == coarse_label:\n",
+    "                    coarse_label_to_term_ids_map[coarse_label].append(term_id)\n",
+    "\n",
+    "        # obtain all descendants of each coarse term\n",
+    "        coarse_label_to_descendants_term_ids_map = defaultdict(set)\n",
+    "        implicated_node_set = set()\n",
+    "        for coarse_label, term_ids in coarse_label_to_term_ids_map.items():\n",
+    "            for term_id in term_ids:\n",
+    "                coarse_label_to_descendants_term_ids_map[coarse_label].add(term_id)\n",
+    "                implicated_node_set.add(term_id)\n",
+    "                if term_id in ontology_benchmarking_resource_dict:\n",
+    "                    coarse_label_to_descendants_term_ids_map[coarse_label].update(\n",
+    "                        ontology_benchmarking_resource_dict[term_id]['all_descendants'])\n",
+    "                    implicated_node_set.update(\n",
+    "                        ontology_benchmarking_resource_dict[term_id]['all_descendants'])\n",
+    "                    \n",
+    "        # obtain all ancestors of each coarse term\n",
+    "        coarse_label_to_ancestors_term_ids_map = defaultdict(set)\n",
+    "        for coarse_label, term_ids in coarse_label_to_term_ids_map.items():\n",
+    "            for term_id in term_ids:\n",
+    "                coarse_label_to_ancestors_term_ids_map[coarse_label].add(term_id)\n",
+    "                implicated_node_set.add(term_id)\n",
+    "                if term_id in ontology_benchmarking_resource_dict:\n",
+    "                    coarse_label_to_ancestors_term_ids_map[coarse_label].update(\n",
+    "                        ontology_benchmarking_resource_dict[term_id]['all_ancestors'])\n",
+    "                    implicated_node_set.update(\n",
+    "                        ontology_benchmarking_resource_dict[term_id]['all_ancestors'])\n",
+    "\n",
+    "        # generate an \"other\" node\n",
+    "        if include_other_node:\n",
+    "            other_nodes = set()\n",
+    "            for term_id in ontology_benchmarking_resource_dict:\n",
+    "                if term_id not in implicated_node_set:\n",
+    "                    other_nodes.add(term_id)\n",
+    "            coarse_label_to_descendants_term_ids_map[\"other\"] = other_nodes\n",
+    "\n",
+    "        if verbose:\n",
+    "            print(\"Number of terms under each coarse label:\")\n",
+    "            for coarse_label, descendants_term_ids in coarse_label_to_descendants_term_ids_map.items():\n",
+    "                print(f\"- {coarse_label}: {len(descendants_term_ids)}\")\n",
+    "\n",
+    "        if check_mutual_exclusivity:\n",
+    "            for label1, term_ids1 in coarse_label_to_descendants_term_ids_map.items():\n",
+    "                for label2, term_ids2 in coarse_label_to_descendants_term_ids_map.items():\n",
+    "                    if label1 != label2:\n",
+    "                        intersection_terms = list(term_ids1.intersection(term_ids2))\n",
+    "                        intersection_labels = list(\n",
+    "                            map(ontology_propagation_resource_dict['ontology_term_id_to_label'].get, intersection_terms))\n",
+    "                        assert len(intersection_terms) == 0, \\\n",
+    "                            f\"{label1} and {label2} are not mutually exclusive. They share: {intersection_labels}\"\n",
+    "\n",
+    "        # inverse\n",
+    "        fine_term_id_to_coarse_label_map = dict()\n",
+    "        for coarse_label, term_ids in coarse_label_to_descendants_term_ids_map.items():\n",
+    "            for term_id in term_ids:\n",
+    "                fine_term_id_to_coarse_label_map[term_id] = coarse_label\n",
+    "\n",
+    "        # all coarse labels\n",
+    "        all_coarse_labels = np.asarray(list(coarse_label_to_descendants_term_ids_map.keys()))\n",
+    "\n",
+    "        # bookkeeping\n",
+    "        self.all_coarse_labels = all_coarse_labels\n",
+    "        self.fine_term_id_to_coarse_label_map = fine_term_id_to_coarse_label_map\n",
+    "        self.coarse_label_to_descendants_term_ids_map = coarse_label_to_descendants_term_ids_map\n",
+    "        self.coarse_label_to_ancestors_term_ids_map = coarse_label_to_ancestors_term_ids_map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cell type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Parse data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>526</td>\n",
+       "      <td>2987969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1695623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>89</td>\n",
+       "      <td>1604668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>904</td>\n",
+       "      <td>1394984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>2598</td>\n",
+       "      <td>1368136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>serous cell of epithelium of bronchus</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>sperm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>myelinating Schwann cell</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>type N enteroendocrine cell</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>pancreatic endocrine cell</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>677 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type  \\\n",
+       "0                                               neuron   \n",
+       "1    L2/3-6 intratelencephalic projecting glutamate...   \n",
+       "2                                 glutamatergic neuron   \n",
+       "3                                      oligodendrocyte   \n",
+       "4                      CD4-positive, alpha-beta T cell   \n",
+       "..                                                 ...   \n",
+       "673              serous cell of epithelium of bronchus   \n",
+       "674                                              sperm   \n",
+       "675                           myelinating Schwann cell   \n",
+       "676                        type N enteroendocrine cell   \n",
+       "677                          pancreatic endocrine cell   \n",
+       "\n",
+       "     train_donor_dataset_count  train_cell_count  \n",
+       "0                          526           2987969  \n",
+       "1                          200           1695623  \n",
+       "2                           89           1604668  \n",
+       "3                          904           1394984  \n",
+       "4                         2598           1368136  \n",
+       "..                         ...               ...  \n",
+       "673                          2                15  \n",
+       "674                          1                11  \n",
+       "675                          5                11  \n",
+       "676                          7                10  \n",
+       "677                          4                 8  \n",
+       "\n",
+       "[677 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bq_cell_type_stats_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "      <th>train_cell_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>526</td>\n",
+       "      <td>2987969</td>\n",
+       "      <td>0.069352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1695623</td>\n",
+       "      <td>0.039356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>89</td>\n",
+       "      <td>1604668</td>\n",
+       "      <td>0.037245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>904</td>\n",
+       "      <td>1394984</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>2598</td>\n",
+       "      <td>1368136</td>\n",
+       "      <td>0.031755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>2415</td>\n",
+       "      <td>1284243</td>\n",
+       "      <td>0.029808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>1472</td>\n",
+       "      <td>1059249</td>\n",
+       "      <td>0.024586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>B cell</td>\n",
+       "      <td>2802</td>\n",
+       "      <td>1007780</td>\n",
+       "      <td>0.023391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>3868</td>\n",
+       "      <td>930635</td>\n",
+       "      <td>0.021600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>1088</td>\n",
+       "      <td>858760</td>\n",
+       "      <td>0.019932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>1678</td>\n",
+       "      <td>840392</td>\n",
+       "      <td>0.019506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>720928</td>\n",
+       "      <td>0.016733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>628</td>\n",
+       "      <td>711425</td>\n",
+       "      <td>0.016512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>central memory CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>1668</td>\n",
+       "      <td>701971</td>\n",
+       "      <td>0.016293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>retinal rod cell</td>\n",
+       "      <td>60</td>\n",
+       "      <td>688136</td>\n",
+       "      <td>0.015972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>CD14-positive monocyte</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>580910</td>\n",
+       "      <td>0.013483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>neural cell</td>\n",
+       "      <td>237</td>\n",
+       "      <td>554286</td>\n",
+       "      <td>0.012865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>stromal cell</td>\n",
+       "      <td>293</td>\n",
+       "      <td>446031</td>\n",
+       "      <td>0.010353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>1339</td>\n",
+       "      <td>435022</td>\n",
+       "      <td>0.010097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type  \\\n",
+       "0                                              neuron   \n",
+       "1   L2/3-6 intratelencephalic projecting glutamate...   \n",
+       "2                                glutamatergic neuron   \n",
+       "3                                     oligodendrocyte   \n",
+       "4                     CD4-positive, alpha-beta T cell   \n",
+       "6                     CD8-positive, alpha-beta T cell   \n",
+       "7                                  classical monocyte   \n",
+       "8                                              B cell   \n",
+       "9                                 natural killer cell   \n",
+       "10                                         fibroblast   \n",
+       "11                                         macrophage   \n",
+       "12  naive thymus-derived CD4-positive, alpha-beta ...   \n",
+       "13                                          astrocyte   \n",
+       "14     central memory CD4-positive, alpha-beta T cell   \n",
+       "15                                   retinal rod cell   \n",
+       "16                                     malignant cell   \n",
+       "17                             CD14-positive monocyte   \n",
+       "18                                        neural cell   \n",
+       "19                                       stromal cell   \n",
+       "20                                   endothelial cell   \n",
+       "\n",
+       "    train_donor_dataset_count  train_cell_count  train_cell_fraction  \n",
+       "0                         526           2987969             0.069352  \n",
+       "1                         200           1695623             0.039356  \n",
+       "2                          89           1604668             0.037245  \n",
+       "3                         904           1394984             0.032378  \n",
+       "4                        2598           1368136             0.031755  \n",
+       "6                        2415           1284243             0.029808  \n",
+       "7                        1472           1059249             0.024586  \n",
+       "8                        2802           1007780             0.023391  \n",
+       "9                        3868            930635             0.021600  \n",
+       "10                       1088            858760             0.019932  \n",
+       "11                       1678            840392             0.019506  \n",
+       "12                       2012            720928             0.016733  \n",
+       "13                        628            711425             0.016512  \n",
+       "14                       1668            701971             0.016293  \n",
+       "15                         60            688136             0.015972  \n",
+       "16                        433            604373             0.014028  \n",
+       "17                       1686            580910             0.013483  \n",
+       "18                        237            554286             0.012865  \n",
+       "19                        293            446031             0.010353  \n",
+       "20                       1339            435022             0.010097  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bq_cell_type_stats_df[bq_cell_type_stats_df[\"train_cell_fraction\"] > 0.01]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "94ac1179abae4194b3d9a509564606a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a75a6aab8fd04c73aea7d41e68803646",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5fb4c5a069324e4fb2a9a0aeadc4d833",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "254f0b9603f2465ab4cc97752687f109",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7bc8e2c61754495a8c30125d4ced704",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "\n",
+    "key = \"cell_type\"\n",
+    "top_k_list = [1, 3, 5, 10]\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_rand_wo_XY_root_path,\n",
+    "    # metadata_predictions_rand_w_XY_root_path,\n",
+    "    # metadata_predictions_high_wo_XY_root_path,\n",
+    "    # metadata_predictions_high_w_XY_root_path,\n",
+    "]\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    # 'rand_w_XY',\n",
+    "    # 'high_wo_XY',\n",
+    "    # 'high_w_XY',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            labels = meta_adata.uns[f\"{key}_labels\"]\n",
+    "\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "            names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
+    "            \n",
+    "            labels_codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "\n",
+    "            pred_label_codes_n = np.argmax(logits_nk, axis=1)\n",
+    "            pred_names_n = np.asarray([terms[code] for code in pred_label_codes_n])\n",
+    "            pred_labels_n = np.asarray([labels[code] for code in pred_label_codes_n])\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = ~np.isnan(labels_codes_n)\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            logits_nk = logits_nk[valid_indices]\n",
+    "            probs_nk = probs_nk[valid_indices]\n",
+    "            \n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "\n",
+    "            pred_labels_n = pred_labels_n[valid_indices]\n",
+    "            pred_names_n = pred_names_n[valid_indices]\n",
+    "\n",
+    "            labels_codes_n = labels_codes_n[valid_indices].astype(int)\n",
+    "            \n",
+    "            loss_n = logits_nk[np.arange(len(labels_codes_n)), labels_codes_n]\n",
+    "\n",
+    "\n",
+    "            n_rows = len(labels_n)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"pred_labels\"] += pred_labels_n.tolist()\n",
+    "            results[\"pred_names\"] += pred_names_n.tolist()\n",
+    "            results[\"loss\"] += loss_n.tolist()\n",
+    "\n",
+    "            # get top k predictions\n",
+    "            for top_k in top_k_list:\n",
+    "                top_k_indices = np.argsort(probs_nk, axis=1)[:, -top_k:]\n",
+    "                top_k_probs = np.take_along_axis(probs_nk, top_k_indices, axis=1)\n",
+    "                top_k_total_prob_n = top_k_probs.sum(axis=1)\n",
+    "                top_k_total_prob_compl_n = 1 - top_k_total_prob_n\n",
+    "\n",
+    "                # calculate top k accuracy and score\n",
+    "                pred_code_n = np.any(top_k_indices == labels_codes_n[:, None], axis=1).astype(int)\n",
+    "                pred_score_n = np.where(pred_code_n == 1, top_k_total_prob_n, top_k_total_prob_compl_n)\n",
+    "\n",
+    "                results[f\"top_{top_k}_call\"] += pred_code_n.tolist()\n",
+    "                results[f\"top_{top_k}_score\"] += pred_score_n.tolist()\n",
+    "\n",
+    "            for i_hop in range(n_hops_dict[key] + 1):\n",
+    "                results[f\"hop_{i_hop}_call\"] += meta_adata[valid_indices].obs[f\"{key}_hop_{i_hop}_call\"].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>pred_labels</th>\n",
+       "      <th>pred_names</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>top_1_call</th>\n",
+       "      <th>top_1_score</th>\n",
+       "      <th>top_3_call</th>\n",
+       "      <th>top_3_score</th>\n",
+       "      <th>top_5_call</th>\n",
+       "      <th>top_5_score</th>\n",
+       "      <th>top_10_call</th>\n",
+       "      <th>top_10_score</th>\n",
+       "      <th>hop_0_call</th>\n",
+       "      <th>hop_1_call</th>\n",
+       "      <th>hop_2_call</th>\n",
+       "      <th>hop_3_call</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>CL:0001031</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-14.139661</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.016570</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.008422</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.002656</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001219</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>CL:0001031</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-13.637383</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.003287</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001867</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000937</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000407</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774127</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>CL:0000066</td>\n",
+       "      <td>-4.945252</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.905921</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.864416</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.827490</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.757097</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774128</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-4.796081</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.850299</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.718877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.625284</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.500179</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774129</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-4.748775</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.917811</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.807339</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.749288</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.637268</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774130</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-5.884660</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.743033</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.550749</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.488111</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.401230</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774131</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>CL:0000066</td>\n",
+       "      <td>-4.662181</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.909212</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.787991</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.719045</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.608887</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774132 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prefix  val_adata_id metadata_predictions_type  \\\n",
+       "0       10M_001_bs1536             1                rand_wo_XY   \n",
+       "1       10M_001_bs1536             1                rand_wo_XY   \n",
+       "2       10M_001_bs1536             1                rand_wo_XY   \n",
+       "3       10M_001_bs1536             1                rand_wo_XY   \n",
+       "4       10M_001_bs1536             1                rand_wo_XY   \n",
+       "...                ...           ...                       ...   \n",
+       "774127  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774128  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774129  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774130  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774131  59M_001_bs3072           110                rand_wo_XY   \n",
+       "\n",
+       "                         labels       names      pred_labels  pred_names  \\\n",
+       "0               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "1               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "2               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "3       cerebellar granule cell  CL:0001031           neuron  CL:0000540   \n",
+       "4       cerebellar granule cell  CL:0001031           neuron  CL:0000540   \n",
+       "...                         ...         ...              ...         ...   \n",
+       "774127           malignant cell  CL:0001064  epithelial cell  CL:0000066   \n",
+       "774128           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774129           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774130           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774131           malignant cell  CL:0001064  epithelial cell  CL:0000066   \n",
+       "\n",
+       "             loss  top_1_call  top_1_score  top_3_call  top_3_score  \\\n",
+       "0       -0.000004           1     0.999996           1     1.000000   \n",
+       "1        0.000000           1     1.000000           1     1.000000   \n",
+       "2       -0.000004           1     0.999996           1     1.000001   \n",
+       "3      -14.139661           0     0.016570           0     0.008422   \n",
+       "4      -13.637383           0     0.003287           0     0.001867   \n",
+       "...           ...         ...          ...         ...          ...   \n",
+       "774127  -4.945252           0     0.905921           0     0.864416   \n",
+       "774128  -4.796081           0     0.850299           0     0.718877   \n",
+       "774129  -4.748775           0     0.917811           0     0.807339   \n",
+       "774130  -5.884660           0     0.743033           0     0.550749   \n",
+       "774131  -4.662181           0     0.909212           0     0.787991   \n",
+       "\n",
+       "        top_5_call  top_5_score  top_10_call  top_10_score  hop_0_call  \\\n",
+       "0                1     1.000001            1      1.000001         1.0   \n",
+       "1                1     1.000000            1      1.000000         1.0   \n",
+       "2                1     1.000001            1      1.000001         1.0   \n",
+       "3                0     0.002656            0      0.001219         0.0   \n",
+       "4                0     0.000937            0      0.000407         0.0   \n",
+       "...            ...          ...          ...           ...         ...   \n",
+       "774127           0     0.827490            0      0.757097         0.0   \n",
+       "774128           0     0.625284            0      0.500179         0.0   \n",
+       "774129           0     0.749288            0      0.637268         0.0   \n",
+       "774130           0     0.488111            0      0.401230         0.0   \n",
+       "774131           0     0.719045            0      0.608887         0.0   \n",
+       "\n",
+       "        hop_1_call  hop_2_call  hop_3_call  \n",
+       "0              1.0         1.0         1.0  \n",
+       "1              1.0         1.0         1.0  \n",
+       "2              1.0         1.0         1.0  \n",
+       "3              0.0         0.0         1.0  \n",
+       "4              0.0         0.0         1.0  \n",
+       "...            ...         ...         ...  \n",
+       "774127         0.0         0.0         1.0  \n",
+       "774128         0.0         0.0         1.0  \n",
+       "774129         0.0         0.0         1.0  \n",
+       "774130         0.0         0.0         1.0  \n",
+       "774131         0.0         0.0         1.0  \n",
+       "\n",
+       "[774132 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"labels\", right_on=\"cell_type\", how=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>pred_labels</th>\n",
+       "      <th>pred_names</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>top_1_call</th>\n",
+       "      <th>top_1_score</th>\n",
+       "      <th>...</th>\n",
+       "      <th>top_10_call</th>\n",
+       "      <th>top_10_score</th>\n",
+       "      <th>hop_0_call</th>\n",
+       "      <th>hop_1_call</th>\n",
+       "      <th>hop_2_call</th>\n",
+       "      <th>hop_3_call</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "      <th>train_cell_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>904</td>\n",
+       "      <td>1394984</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>904</td>\n",
+       "      <td>1394984</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>-0.000004</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.999996</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000001</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>904</td>\n",
+       "      <td>1394984</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>CL:0001031</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-14.139661</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.016570</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001219</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>42</td>\n",
+       "      <td>71069</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>CL:0001031</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-13.637383</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.003287</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000407</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>42</td>\n",
+       "      <td>71069</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774127</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>CL:0000066</td>\n",
+       "      <td>-4.945252</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.905921</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.757097</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774128</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-4.796081</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.850299</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.500179</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774129</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-4.748775</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.917811</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.637268</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774130</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>CL:0000057</td>\n",
+       "      <td>-5.884660</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.743033</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.401230</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>774131</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>CL:0001064</td>\n",
+       "      <td>epithelial cell</td>\n",
+       "      <td>CL:0000066</td>\n",
+       "      <td>-4.662181</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.909212</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.608887</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>433</td>\n",
+       "      <td>604373</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>774132 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prefix  val_adata_id metadata_predictions_type  \\\n",
+       "0       10M_001_bs1536             1                rand_wo_XY   \n",
+       "1       10M_001_bs1536             1                rand_wo_XY   \n",
+       "2       10M_001_bs1536             1                rand_wo_XY   \n",
+       "3       10M_001_bs1536             1                rand_wo_XY   \n",
+       "4       10M_001_bs1536             1                rand_wo_XY   \n",
+       "...                ...           ...                       ...   \n",
+       "774127  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774128  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774129  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774130  59M_001_bs3072           110                rand_wo_XY   \n",
+       "774131  59M_001_bs3072           110                rand_wo_XY   \n",
+       "\n",
+       "                         labels       names      pred_labels  pred_names  \\\n",
+       "0               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "1               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "2               oligodendrocyte  CL:0000128  oligodendrocyte  CL:0000128   \n",
+       "3       cerebellar granule cell  CL:0001031           neuron  CL:0000540   \n",
+       "4       cerebellar granule cell  CL:0001031           neuron  CL:0000540   \n",
+       "...                         ...         ...              ...         ...   \n",
+       "774127           malignant cell  CL:0001064  epithelial cell  CL:0000066   \n",
+       "774128           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774129           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774130           malignant cell  CL:0001064       fibroblast  CL:0000057   \n",
+       "774131           malignant cell  CL:0001064  epithelial cell  CL:0000066   \n",
+       "\n",
+       "             loss  top_1_call  top_1_score  ...  top_10_call  top_10_score  \\\n",
+       "0       -0.000004           1     0.999996  ...            1      1.000001   \n",
+       "1        0.000000           1     1.000000  ...            1      1.000000   \n",
+       "2       -0.000004           1     0.999996  ...            1      1.000001   \n",
+       "3      -14.139661           0     0.016570  ...            0      0.001219   \n",
+       "4      -13.637383           0     0.003287  ...            0      0.000407   \n",
+       "...           ...         ...          ...  ...          ...           ...   \n",
+       "774127  -4.945252           0     0.905921  ...            0      0.757097   \n",
+       "774128  -4.796081           0     0.850299  ...            0      0.500179   \n",
+       "774129  -4.748775           0     0.917811  ...            0      0.637268   \n",
+       "774130  -5.884660           0     0.743033  ...            0      0.401230   \n",
+       "774131  -4.662181           0     0.909212  ...            0      0.608887   \n",
+       "\n",
+       "        hop_0_call  hop_1_call  hop_2_call  hop_3_call  \\\n",
+       "0              1.0         1.0         1.0         1.0   \n",
+       "1              1.0         1.0         1.0         1.0   \n",
+       "2              1.0         1.0         1.0         1.0   \n",
+       "3              0.0         0.0         0.0         1.0   \n",
+       "4              0.0         0.0         0.0         1.0   \n",
+       "...            ...         ...         ...         ...   \n",
+       "774127         0.0         0.0         0.0         1.0   \n",
+       "774128         0.0         0.0         0.0         1.0   \n",
+       "774129         0.0         0.0         0.0         1.0   \n",
+       "774130         0.0         0.0         0.0         1.0   \n",
+       "774131         0.0         0.0         0.0         1.0   \n",
+       "\n",
+       "                      cell_type  train_donor_dataset_count  train_cell_count  \\\n",
+       "0               oligodendrocyte                        904           1394984   \n",
+       "1               oligodendrocyte                        904           1394984   \n",
+       "2               oligodendrocyte                        904           1394984   \n",
+       "3       cerebellar granule cell                         42             71069   \n",
+       "4       cerebellar granule cell                         42             71069   \n",
+       "...                         ...                        ...               ...   \n",
+       "774127           malignant cell                        433            604373   \n",
+       "774128           malignant cell                        433            604373   \n",
+       "774129           malignant cell                        433            604373   \n",
+       "774130           malignant cell                        433            604373   \n",
+       "774131           malignant cell                        433            604373   \n",
+       "\n",
+       "        train_cell_fraction  \n",
+       "0                  0.032378  \n",
+       "1                  0.032378  \n",
+       "2                  0.032378  \n",
+       "3                  0.001650  \n",
+       "4                  0.001650  \n",
+       "...                     ...  \n",
+       "774127             0.014028  \n",
+       "774128             0.014028  \n",
+       "774129             0.014028  \n",
+       "774130             0.014028  \n",
+       "774131             0.014028  \n",
+       "\n",
+       "[774132 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__cell_type__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__cell_type__03_03_2025.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1934d4908b0419dbb3d0092063b581f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metadata_predictions_type = \"rand_wo_XY\"\n",
+    "train_cell_fraction_thresholds = [0, 0.002, 0.005, 1.]\n",
+    "top_k = 10\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "def compute_auc(group):\n",
+    "    # Optionally, skip groups with fewer than 10 samples\n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "    # A valid ROC AUC requires both classes to be present in the \"loss\" column.\n",
+    "    if group[f\"top_{top_k}_call\"].nunique() < 2:\n",
+    "        return np.nan\n",
+    "    return roc_auc_score(group[f\"top_{top_k}_call\"], group[\"loss\"])\n",
+    "\n",
+    "\n",
+    "points_for_violin = dict()\n",
+    "per_cell_type_stats_dict = dict()\n",
+    "\n",
+    "for prefix in tqdm(prefix_list):\n",
+    "    for i in range(len(train_cell_fraction_thresholds) - 1):\n",
+    "        _sub_df = df[\n",
+    "            (df[\"prefix\"] == prefix) &\n",
+    "            (df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "            (df[\"train_cell_fraction\"] >= train_cell_fraction_thresholds[i]) &\n",
+    "            (df[\"train_cell_fraction\"] < train_cell_fraction_thresholds[i + 1])\n",
+    "        ]\n",
+    "\n",
+    "        # auc = roc_auc_score(sub_df[f\"top_{top_k}_call\"], sub_df[f\"top_{top_k}_score\"])\n",
+    "        \n",
+    "        _sub_df = _sub_df.groupby([\"val_adata_id\", \"cell_type\"]).filter(lambda x: len(x) >= 10)\n",
+    "        \n",
+    "        n_cell_types = len(_sub_df[\"cell_type\"].unique())\n",
+    "\n",
+    "        # calculate mean accuracy\n",
+    "        per_dataset_cell_type_stats = (\n",
+    "            _sub_df.groupby([\"val_adata_id\", \"cell_type\"])\n",
+    "            .agg(accuracy=(f\"top_{top_k}_call\", \"mean\"))\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "        per_cell_type_stats = (\n",
+    "            per_dataset_cell_type_stats.groupby(\"cell_type\")\n",
+    "            .agg(accuracy=(\"accuracy\", \"mean\"))\n",
+    "            .reset_index()\n",
+    "        )\n",
+    "\n",
+    "        points_for_violin[(prefix, i)] = per_cell_type_stats[\"accuracy\"].values\n",
+    "        per_cell_type_stats_dict[(prefix, i)] = per_cell_type_stats\n",
+    "\n",
+    "        # mean_accuracy = per_cell_type_stats[\"accuracy\"].mean()\n",
+    "        quants = np.quantile(per_cell_type_stats[\"accuracy\"], [0.25, 0.5, 0.75])\n",
+    "        # std_accuracy = per_cell_type_stats[\"accuracy\"].std() / np.sqrt(len(per_cell_type_stats))\n",
+    "        \n",
+    "        # # calculate AUC\n",
+    "        # per_dataset_cell_type_stats = (\n",
+    "        #     sub_df.groupby([\"val_adata_id\", \"cell_type\"])\n",
+    "        #     .apply(compute_auc)\n",
+    "        #     .reset_index(name=\"auc\").dropna()\n",
+    "        # )\n",
+    "        # per_dataset_stats = (\n",
+    "        #     per_dataset_cell_type_stats.groupby(\"val_adata_id\")\n",
+    "        #     .agg(mean_of_aucs=(\"auc\", \"mean\"), std_of_aucs=(\"auc\", \"std\"))\n",
+    "        #     .reset_index()\n",
+    "        # )\n",
+    "        # mean_auc = per_dataset_stats[\"mean_of_aucs\"].mean()\n",
+    "        # std_auc = per_dataset_stats[\"mean_of_aucs\"].std() / np.sqrt(len(per_dataset_stats))\n",
+    "\n",
+    "        stats[\"prefix\"].append(prefix)\n",
+    "        stats[\"i_threshold\"].append(i)\n",
+    "        stats[\"n_samples\"].append(len(_sub_df))\n",
+    "        stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "        stats[\"accuracy_25\"].append(quants[0])\n",
+    "        stats[\"accuracy_50\"].append(quants[1])\n",
+    "        stats[\"accuracy_75\"].append(quants[2])\n",
+    "        stats[\"accuracy_mean\"].append(per_cell_type_stats[\"accuracy\"].mean())\n",
+    "        # stats[\"std_accuracy\"].append(std_accuracy)\n",
+    "        # stats[\"mean_auc\"].append(mean_auc)\n",
+    "        # stats[\"std_auc\"].append(std_auc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_df = pd.DataFrame(stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>i_threshold</th>\n",
+       "      <th>n_samples</th>\n",
+       "      <th>n_cell_types</th>\n",
+       "      <th>accuracy_25</th>\n",
+       "      <th>accuracy_50</th>\n",
+       "      <th>accuracy_75</th>\n",
+       "      <th>accuracy_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42842</td>\n",
+       "      <td>153</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.017514</td>\n",
+       "      <td>0.544892</td>\n",
+       "      <td>0.265912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29353</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.476561</td>\n",
+       "      <td>0.728350</td>\n",
+       "      <td>0.896799</td>\n",
+       "      <td>0.638329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>2</td>\n",
+       "      <td>119213</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.752394</td>\n",
+       "      <td>0.906061</td>\n",
+       "      <td>0.958480</td>\n",
+       "      <td>0.761904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42842</td>\n",
+       "      <td>153</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.313966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29353</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.504964</td>\n",
+       "      <td>0.766840</td>\n",
+       "      <td>0.889086</td>\n",
+       "      <td>0.650790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>2</td>\n",
+       "      <td>119213</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.683428</td>\n",
+       "      <td>0.920472</td>\n",
+       "      <td>0.976026</td>\n",
+       "      <td>0.766641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42842</td>\n",
+       "      <td>153</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.108387</td>\n",
+       "      <td>0.629108</td>\n",
+       "      <td>0.313386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29353</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.359055</td>\n",
+       "      <td>0.757175</td>\n",
+       "      <td>0.907105</td>\n",
+       "      <td>0.636023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>2</td>\n",
+       "      <td>119213</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.724852</td>\n",
+       "      <td>0.943387</td>\n",
+       "      <td>0.970939</td>\n",
+       "      <td>0.779285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42842</td>\n",
+       "      <td>153</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.202203</td>\n",
+       "      <td>0.684558</td>\n",
+       "      <td>0.346133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29353</td>\n",
+       "      <td>42</td>\n",
+       "      <td>0.494333</td>\n",
+       "      <td>0.778615</td>\n",
+       "      <td>0.899792</td>\n",
+       "      <td>0.673899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>2</td>\n",
+       "      <td>119213</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.785145</td>\n",
+       "      <td>0.933298</td>\n",
+       "      <td>0.981624</td>\n",
+       "      <td>0.787782</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            prefix  i_threshold  n_samples  n_cell_types  accuracy_25  \\\n",
+       "0   10M_001_bs1536            0      42842           153     0.000000   \n",
+       "1   10M_001_bs1536            1      29353            42     0.476561   \n",
+       "2   10M_001_bs1536            2     119213            39     0.752394   \n",
+       "3   19M_001_bs2048            0      42842           153     0.000000   \n",
+       "4   19M_001_bs2048            1      29353            42     0.504964   \n",
+       "5   19M_001_bs2048            2     119213            39     0.683428   \n",
+       "6   30M_001_bs2560            0      42842           153     0.000000   \n",
+       "7   30M_001_bs2560            1      29353            42     0.359055   \n",
+       "8   30M_001_bs2560            2     119213            39     0.724852   \n",
+       "9   59M_001_bs3072            0      42842           153     0.000000   \n",
+       "10  59M_001_bs3072            1      29353            42     0.494333   \n",
+       "11  59M_001_bs3072            2     119213            39     0.785145   \n",
+       "\n",
+       "    accuracy_50  accuracy_75  accuracy_mean  \n",
+       "0      0.017514     0.544892       0.265912  \n",
+       "1      0.728350     0.896799       0.638329  \n",
+       "2      0.906061     0.958480       0.761904  \n",
+       "3      0.111111     0.625000       0.313966  \n",
+       "4      0.766840     0.889086       0.650790  \n",
+       "5      0.920472     0.976026       0.766641  \n",
+       "6      0.108387     0.629108       0.313386  \n",
+       "7      0.757175     0.907105       0.636023  \n",
+       "8      0.943387     0.970939       0.779285  \n",
+       "9      0.202203     0.684558       0.346133  \n",
+       "10     0.778615     0.899792       0.673899  \n",
+       "11     0.933298     0.981624       0.787782  "
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_props_list = [\n",
+    "    {\n",
+    "        'metric_to_plot': 'accuracy',\n",
+    "        'label': 'Accuracy',\n",
+    "        'ylim': (0, 1),\n",
+    "        'show_legend': True,\n",
+    "        'show_violin': True,\n",
+    "    },\n",
+    "    # {\n",
+    "    #     'metric_to_plot': 'auc',\n",
+    "    #     'label': 'AUC',\n",
+    "    #     'ylim': (0.75, 1),\n",
+    "    #     'show_legend': True\n",
+    "    # },\n",
+    "]\n",
+    "\n",
+    "plot_labels = [\n",
+    "    f'< {100 * train_cell_fraction_thresholds[1]:.1f}%',\n",
+    "    f'{100 * train_cell_fraction_thresholds[1]:.1f}% - {100 * train_cell_fraction_thresholds[2]:.1f}%',\n",
+    "    f'> {100 * train_cell_fraction_thresholds[2]:.1f}%',\n",
+    "]\n",
+    "\n",
+    "offsets = [-0.2, 0, 0.2]\n",
+    "colors = [(0.6, 0.8, 0.6), (0.4, 0.6, 0.4), (0.2, 0.4, 0.2)]\n",
+    "\n",
+    "for plot_props in plot_props_list:\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "    for i in range(len(train_cell_fraction_thresholds) - 1):\n",
+    "\n",
+    "        sub_stats_df = stats_df[stats_df[\"i_threshold\"] == i]\n",
+    "\n",
+    "        y = sub_stats_df[f\"{plot_props['metric_to_plot']}_mean\"].values\n",
+    "        y_med = sub_stats_df[f\"{plot_props['metric_to_plot']}_50\"].values\n",
+    "        y_hi = sub_stats_df[f\"{plot_props['metric_to_plot']}_75\"].values\n",
+    "        y_lo = sub_stats_df[f\"{plot_props['metric_to_plot']}_25\"].values\n",
+    "        y_err = np.vstack([y_med - y_lo, y_hi - y_med])\n",
+    "        \n",
+    "        if plot_props['show_violin']:\n",
+    "            points = []\n",
+    "            for prefix in prefix_list:\n",
+    "                points.append(points_for_violin[(prefix, i)])\n",
+    "\n",
+    "            violin_parts = ax.violinplot(\n",
+    "                points,\n",
+    "                np.arange(len(prefix_list)) + offsets[i],\n",
+    "                side='both',\n",
+    "                showextrema=False,\n",
+    "                bw_method=0.1,\n",
+    "                widths=0.15,\n",
+    "            )\n",
+    "\n",
+    "            for pc in violin_parts['bodies']:\n",
+    "                pc.set_facecolor(colors[i])  # Set face color\n",
+    "                pc.set_edgecolor(None)    # Set edge color\n",
+    "                pc.set_alpha(0.5)            # Set transparency\n",
+    "\n",
+    "        ax.errorbar(\n",
+    "            np.arange(len(prefix_list)) + offsets[i],\n",
+    "            y_med,\n",
+    "            y_err,\n",
+    "            fmt='s',\n",
+    "            label=plot_labels[i],\n",
+    "            color=colors[i],\n",
+    "            lw=0.5,\n",
+    "            capsize=2,\n",
+    "            markersize=4,\n",
+    "            linestyle='-'\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "        ax.set_xticks(np.arange(len(prefix_list)))\n",
+    "        ax.set_xticklabels([x[:3] for x in prefix_list])\n",
+    "        if plot_props['show_legend']:\n",
+    "            # move the legend outside the plot\n",
+    "            ax.legend(loc='lower right', fontsize=8, bbox_to_anchor=(1.45, 0))\n",
+    "        ax.set_ylim(plot_props['ylim'])\n",
+    "        ax.set_ylabel(plot_props['label'])\n",
+    "\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['top'].set_visible(False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>47</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>42</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>50</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>98</td>\n",
+       "      <td>0.300050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.191801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>110</td>\n",
+       "      <td>0.152578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>109</td>\n",
+       "      <td>0.044177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.004642</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>102 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     val_adata_id  accuracy\n",
+       "46             48  1.000000\n",
+       "45             47  1.000000\n",
+       "40             42  1.000000\n",
+       "88             97  1.000000\n",
+       "48             50  1.000000\n",
+       "..            ...       ...\n",
+       "89             98  0.300050\n",
+       "67             69  0.191801\n",
+       "101           110  0.152578\n",
+       "100           109  0.044177\n",
+       "33             35  0.004642\n",
+       "\n",
+       "[102 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "per_cell_type_stats_dict[(prefix_list[-1], 2)].sort_values(\"accuracy\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>pred_labels</th>\n",
+       "      <th>pred_names</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>top_1_call</th>\n",
+       "      <th>top_1_score</th>\n",
+       "      <th>...</th>\n",
+       "      <th>top_10_call</th>\n",
+       "      <th>top_10_score</th>\n",
+       "      <th>hop_0_call</th>\n",
+       "      <th>hop_1_call</th>\n",
+       "      <th>hop_2_call</th>\n",
+       "      <th>hop_3_call</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "      <th>train_cell_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>32578</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-25.930508</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000938</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.668930e-06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33007</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-22.916759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000174</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.556511e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33267</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-11.456532</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.380691</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.178980e-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33349</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-20.421757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.011641</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.728535e-06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33350</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-19.790600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000942</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-8.344650e-07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33529</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-16.851460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.006490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.980232e-06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33555</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>CL:0000127</td>\n",
+       "      <td>-11.417817</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.501340</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.779531e-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33686</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-9.786108</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.489706</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.999686e-01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33868</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-13.541454</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.275758</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.382828e-05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34372</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>23</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>CL:4023016</td>\n",
+       "      <td>neuron</td>\n",
+       "      <td>CL:0000540</td>\n",
+       "      <td>-20.893860</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.006530</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.920628e-06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>232</td>\n",
+       "      <td>257900</td>\n",
+       "      <td>0.005986</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               prefix  val_adata_id metadata_predictions_type  \\\n",
+       "32578  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33007  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33267  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33349  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33350  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33529  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33555  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33686  10M_001_bs1536            23                rand_wo_XY   \n",
+       "33868  10M_001_bs1536            23                rand_wo_XY   \n",
+       "34372  10M_001_bs1536            23                rand_wo_XY   \n",
+       "\n",
+       "                                   labels       names pred_labels  pred_names  \\\n",
+       "32578  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33007  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33267  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33349  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33350  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33529  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33555  vip GABAergic cortical interneuron  CL:4023016   astrocyte  CL:0000127   \n",
+       "33686  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "33868  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "34372  vip GABAergic cortical interneuron  CL:4023016      neuron  CL:0000540   \n",
+       "\n",
+       "            loss  top_1_call  top_1_score  ...  top_10_call  top_10_score  \\\n",
+       "32578 -25.930508           0     0.000938  ...            0  1.668930e-06   \n",
+       "33007 -22.916759           0     0.000174  ...            0  6.556511e-07   \n",
+       "33267 -11.456532           0     0.380691  ...            0  1.178980e-04   \n",
+       "33349 -20.421757           0     0.011641  ...            0  1.728535e-06   \n",
+       "33350 -19.790600           0     0.000942  ...            0 -8.344650e-07   \n",
+       "33529 -16.851460           0     0.006490  ...            0  2.980232e-06   \n",
+       "33555 -11.417817           0     0.501340  ...            0  3.779531e-04   \n",
+       "33686  -9.786108           0     0.489706  ...            1  9.999686e-01   \n",
+       "33868 -13.541454           0     0.275758  ...            0  1.382828e-05   \n",
+       "34372 -20.893860           0     0.006530  ...            0  2.920628e-06   \n",
+       "\n",
+       "       hop_0_call  hop_1_call  hop_2_call  hop_3_call  \\\n",
+       "32578         0.0         0.0         0.0         0.0   \n",
+       "33007         0.0         0.0         0.0         0.0   \n",
+       "33267         0.0         0.0         0.0         0.0   \n",
+       "33349         0.0         0.0         0.0         0.0   \n",
+       "33350         0.0         0.0         0.0         0.0   \n",
+       "33529         0.0         0.0         0.0         0.0   \n",
+       "33555         0.0         0.0         0.0         0.0   \n",
+       "33686         0.0         0.0         0.0         0.0   \n",
+       "33868         0.0         0.0         0.0         0.0   \n",
+       "34372         0.0         0.0         0.0         0.0   \n",
+       "\n",
+       "                                cell_type  train_donor_dataset_count  \\\n",
+       "32578  vip GABAergic cortical interneuron                        232   \n",
+       "33007  vip GABAergic cortical interneuron                        232   \n",
+       "33267  vip GABAergic cortical interneuron                        232   \n",
+       "33349  vip GABAergic cortical interneuron                        232   \n",
+       "33350  vip GABAergic cortical interneuron                        232   \n",
+       "33529  vip GABAergic cortical interneuron                        232   \n",
+       "33555  vip GABAergic cortical interneuron                        232   \n",
+       "33686  vip GABAergic cortical interneuron                        232   \n",
+       "33868  vip GABAergic cortical interneuron                        232   \n",
+       "34372  vip GABAergic cortical interneuron                        232   \n",
+       "\n",
+       "       train_cell_count  train_cell_fraction  \n",
+       "32578            257900             0.005986  \n",
+       "33007            257900             0.005986  \n",
+       "33267            257900             0.005986  \n",
+       "33349            257900             0.005986  \n",
+       "33350            257900             0.005986  \n",
+       "33529            257900             0.005986  \n",
+       "33555            257900             0.005986  \n",
+       "33686            257900             0.005986  \n",
+       "33868            257900             0.005986  \n",
+       "34372            257900             0.005986  \n",
+       "\n",
+       "[10 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['labels'] == 'vip GABAergic cortical interneuron'].head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Parse data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcb5306379e4404fb633ca9e7442fb34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38baaf8095284606a4550e1958714e34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33441f5e5c32461293481ae3865d4ee0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed42e9739f884af7b87a8000a898d805",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b4e2a94ba9144c3822beb50187dfcf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "\n",
+    "key = \"sex\"\n",
+    "\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_rand_wo_XY_root_path,\n",
+    "    metadata_predictions_rand_w_XY_root_path,\n",
+    "    metadata_predictions_rand_w_X_root_path,\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    'rand_w_XY',\n",
+    "    'rand_w_X',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "            names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
+    "            \n",
+    "            labels_codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "\n",
+    "            # record cell types\n",
+    "            cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = ~np.isnan(labels_codes_n)\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            logits_nk = logits_nk[valid_indices]\n",
+    "            probs_nk = probs_nk[valid_indices]\n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "            cell_type_n = cell_type_n[valid_indices]\n",
+    "            labels_codes_n = labels_codes_n[valid_indices].astype(int)\n",
+    "            scores_n = logits_nk[:, 1]\n",
+    "\n",
+    "            n_rows = len(labels_n)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "\n",
+    "            results[f\"label_codes\"] += labels_codes_n.tolist()\n",
+    "            results[f\"scores\"] += scores_n.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>label_codes</th>\n",
+       "      <th>scores</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.090517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.160276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.226952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.069796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.051056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364607</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.816644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364608</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.440763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364609</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.397794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364610</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.238744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364611</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.329061</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2364612 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 prefix  val_adata_id metadata_predictions_type labels  \\\n",
+       "0        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "1        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "2        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "3        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "4        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "...                 ...           ...                       ...    ...   \n",
+       "2364607  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364608  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364609  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364610  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364611  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "\n",
+       "                names        cell_type  label_codes    scores  \n",
+       "0        PATO:0000384  oligodendrocyte            0 -0.090517  \n",
+       "1        PATO:0000384  oligodendrocyte            0 -0.160276  \n",
+       "2        PATO:0000384          unknown            0 -0.226952  \n",
+       "3        PATO:0000384  oligodendrocyte            0 -0.069796  \n",
+       "4        PATO:0000384          unknown            0 -0.051056  \n",
+       "...               ...              ...          ...       ...  \n",
+       "2364607  PATO:0000384   malignant cell            0 -0.816644  \n",
+       "2364608  PATO:0000384   malignant cell            0 -0.440763  \n",
+       "2364609  PATO:0000384   malignant cell            0 -0.397794  \n",
+       "2364610  PATO:0000384   malignant cell            0 -0.238744  \n",
+       "2364611  PATO:0000384   malignant cell            0 -0.329061  \n",
+       "\n",
+       "[2364612 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})\n",
+    "bq_cell_type_stats_df\n",
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = \\\n",
+    "    bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"cell_type\", right_on=\"cell_type\", how=\"left\").dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>label_codes</th>\n",
+       "      <th>scores</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "      <th>train_cell_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.090517</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.160276</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.069796</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.104820</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>71069.0</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.154926</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>71069.0</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364607</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.816644</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>604373.0</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364608</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.440763</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>604373.0</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364609</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.397794</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>604373.0</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364610</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.238744</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>604373.0</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2364611</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>110</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>male</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>malignant cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.329061</td>\n",
+       "      <td>433.0</td>\n",
+       "      <td>604373.0</td>\n",
+       "      <td>0.014028</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2322396 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 prefix  val_adata_id metadata_predictions_type labels  \\\n",
+       "0        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "1        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "3        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "5        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "6        10M_001_bs1536             1                rand_wo_XY   male   \n",
+       "...                 ...           ...                       ...    ...   \n",
+       "2364607  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364608  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364609  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364610  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "2364611  59M_001_bs3072           110                  rand_w_X   male   \n",
+       "\n",
+       "                names                cell_type  label_codes    scores  \\\n",
+       "0        PATO:0000384          oligodendrocyte            0 -0.090517   \n",
+       "1        PATO:0000384          oligodendrocyte            0 -0.160276   \n",
+       "3        PATO:0000384          oligodendrocyte            0 -0.069796   \n",
+       "5        PATO:0000384  cerebellar granule cell            0 -0.104820   \n",
+       "6        PATO:0000384  cerebellar granule cell            0 -0.154926   \n",
+       "...               ...                      ...          ...       ...   \n",
+       "2364607  PATO:0000384           malignant cell            0 -0.816644   \n",
+       "2364608  PATO:0000384           malignant cell            0 -0.440763   \n",
+       "2364609  PATO:0000384           malignant cell            0 -0.397794   \n",
+       "2364610  PATO:0000384           malignant cell            0 -0.238744   \n",
+       "2364611  PATO:0000384           malignant cell            0 -0.329061   \n",
+       "\n",
+       "         train_donor_dataset_count  train_cell_count  train_cell_fraction  \n",
+       "0                            904.0         1394984.0             0.032378  \n",
+       "1                            904.0         1394984.0             0.032378  \n",
+       "3                            904.0         1394984.0             0.032378  \n",
+       "5                             42.0           71069.0             0.001650  \n",
+       "6                             42.0           71069.0             0.001650  \n",
+       "...                            ...               ...                  ...  \n",
+       "2364607                      433.0          604373.0             0.014028  \n",
+       "2364608                      433.0          604373.0             0.014028  \n",
+       "2364609                      433.0          604373.0             0.014028  \n",
+       "2364610                      433.0          604373.0             0.014028  \n",
+       "2364611                      433.0          604373.0             0.014028  \n",
+       "\n",
+       "[2322396 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__sex__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__sex__03_03_2025.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f08bb9a32e04d728dfcbf2697513f09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    'rand_w_XY',\n",
+    "    'rand_w_X'\n",
+    "]\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "min_train_cell_fraction = 0.\n",
+    "\n",
+    "def compute_auc(group):\n",
+    "    # Optionally, skip groups with fewer than 10 samples\n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "    # A valid ROC AUC requires both classes to be present in the \"loss\" column.\n",
+    "    if group[f\"label_codes\"].nunique() < 2:\n",
+    "        return np.nan\n",
+    "    return roc_auc_score(group[f\"label_codes\"], group[\"scores\"])\n",
+    "\n",
+    "\n",
+    "points_for_violin = dict()\n",
+    "per_cell_type_stats_dict = dict()\n",
+    "\n",
+    "for prefix in tqdm(prefix_list):\n",
+    "    for metadata_predictions_type in metadata_predictions_types:\n",
+    "\n",
+    "        _sub_df = df[\n",
+    "            (df[\"prefix\"] == prefix) &\n",
+    "            (df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "            (df[\"train_cell_fraction\"] >= min_train_cell_fraction)\n",
+    "        ]\n",
+    "\n",
+    "        _sub_df = _sub_df.groupby([\"cell_type\"]).filter(lambda x: len(x) >= 10)\n",
+    "        n_cell_types = len(_sub_df[\"cell_type\"].unique())\n",
+    "                \n",
+    "        # calculate AUC\n",
+    "        per_cell_type_stats = (\n",
+    "            _sub_df.groupby(\"cell_type\")\n",
+    "            .apply(compute_auc)\n",
+    "            .dropna()\n",
+    "            .reset_index(name=\"auc\")\n",
+    "        )\n",
+    "\n",
+    "        points_for_violin[(prefix, metadata_predictions_type)] = per_cell_type_stats[\"auc\"].values\n",
+    "        per_cell_type_stats_dict[(prefix, metadata_predictions_type)] = per_cell_type_stats\n",
+    "        \n",
+    "        quants = np.quantile(per_cell_type_stats[\"auc\"], [0.25, 0.5, 0.75])\n",
+    "\n",
+    "        stats[\"prefix\"].append(prefix)\n",
+    "        stats[\"metadata_predictions_type\"].append(metadata_predictions_type)\n",
+    "        stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "        stats[\"auc_25\"].append(quants[0])\n",
+    "        stats[\"auc_50\"].append(quants[1])\n",
+    "        stats[\"auc_75\"].append(quants[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_df = pd.DataFrame(stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>n_cell_types</th>\n",
+       "      <th>auc_25</th>\n",
+       "      <th>auc_50</th>\n",
+       "      <th>auc_75</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.255901</td>\n",
+       "      <td>0.417649</td>\n",
+       "      <td>0.578366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.523314</td>\n",
+       "      <td>0.747785</td>\n",
+       "      <td>0.927046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.480549</td>\n",
+       "      <td>0.667245</td>\n",
+       "      <td>0.884572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.202764</td>\n",
+       "      <td>0.433269</td>\n",
+       "      <td>0.620811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.817523</td>\n",
+       "      <td>0.918605</td>\n",
+       "      <td>0.981203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.400776</td>\n",
+       "      <td>0.658629</td>\n",
+       "      <td>0.875414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.302126</td>\n",
+       "      <td>0.461800</td>\n",
+       "      <td>0.636455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.869489</td>\n",
+       "      <td>0.959909</td>\n",
+       "      <td>0.996988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.555919</td>\n",
+       "      <td>0.778308</td>\n",
+       "      <td>0.931818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_wo_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.301981</td>\n",
+       "      <td>0.438782</td>\n",
+       "      <td>0.617952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.862533</td>\n",
+       "      <td>0.955231</td>\n",
+       "      <td>0.995524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_X</td>\n",
+       "      <td>243</td>\n",
+       "      <td>0.536373</td>\n",
+       "      <td>0.791613</td>\n",
+       "      <td>0.964531</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            prefix metadata_predictions_type  n_cell_types    auc_25  \\\n",
+       "0   10M_001_bs1536                rand_wo_XY           243  0.255901   \n",
+       "1   10M_001_bs1536                 rand_w_XY           243  0.523314   \n",
+       "2   10M_001_bs1536                  rand_w_X           243  0.480549   \n",
+       "3   19M_001_bs2048                rand_wo_XY           243  0.202764   \n",
+       "4   19M_001_bs2048                 rand_w_XY           243  0.817523   \n",
+       "5   19M_001_bs2048                  rand_w_X           243  0.400776   \n",
+       "6   30M_001_bs2560                rand_wo_XY           243  0.302126   \n",
+       "7   30M_001_bs2560                 rand_w_XY           243  0.869489   \n",
+       "8   30M_001_bs2560                  rand_w_X           243  0.555919   \n",
+       "9   59M_001_bs3072                rand_wo_XY           243  0.301981   \n",
+       "10  59M_001_bs3072                 rand_w_XY           243  0.862533   \n",
+       "11  59M_001_bs3072                  rand_w_X           243  0.536373   \n",
+       "\n",
+       "      auc_50    auc_75  \n",
+       "0   0.417649  0.578366  \n",
+       "1   0.747785  0.927046  \n",
+       "2   0.667245  0.884572  \n",
+       "3   0.433269  0.620811  \n",
+       "4   0.918605  0.981203  \n",
+       "5   0.658629  0.875414  \n",
+       "6   0.461800  0.636455  \n",
+       "7   0.959909  0.996988  \n",
+       "8   0.778308  0.931818  \n",
+       "9   0.438782  0.617952  \n",
+       "10  0.955231  0.995524  \n",
+       "11  0.791613  0.964531  "
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_props_list = [\n",
+    "    {\n",
+    "        'metric_to_plot': 'auc',\n",
+    "        'label': 'AUC',\n",
+    "        'ylim': (0, 1),\n",
+    "        'show_legend': True,\n",
+    "        'show_violin': True\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    'rand_w_X',\n",
+    "    'rand_w_XY'\n",
+    "]\n",
+    "\n",
+    "plot_labels = [\n",
+    "    'Autosome',\n",
+    "    'Autosome, X',\n",
+    "    'Autosome, X, Y'\n",
+    "]\n",
+    "\n",
+    "offsets = [-0.2, 0, 0.2]\n",
+    "\n",
+    "for plot_props in plot_props_list:\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "    colors = [(0.6, 0.6, 0.8), (0.4, 0.4, 0.6), (0.2, 0.2, 0.4)]\n",
+    "\n",
+    "    for i, metadata_predictions_type in enumerate(metadata_predictions_types):\n",
+    "        \n",
+    "        sub_stats_df = stats_df[stats_df[\"metadata_predictions_type\"] == metadata_predictions_type]\n",
+    "        \n",
+    "        y = sub_stats_df[f\"{plot_props['metric_to_plot']}_50\"].values\n",
+    "        y_hi = sub_stats_df[f\"{plot_props['metric_to_plot']}_75\"].values\n",
+    "        y_lo = sub_stats_df[f\"{plot_props['metric_to_plot']}_25\"].values\n",
+    "        y_err = np.vstack([y - y_lo, y_hi - y])\n",
+    "        \n",
+    "        if plot_props['show_violin']:\n",
+    "            points = []\n",
+    "            for prefix in prefix_list:\n",
+    "                points.append(points_for_violin[(prefix, metadata_predictions_type)])\n",
+    "\n",
+    "            violin_parts = ax.violinplot(\n",
+    "                points,\n",
+    "                np.arange(len(prefix_list)) + offsets[i],\n",
+    "                side='both',\n",
+    "                showextrema=False,\n",
+    "                bw_method=0.1,\n",
+    "                widths=0.15,\n",
+    "            )\n",
+    "\n",
+    "            for pc in violin_parts['bodies']:\n",
+    "                pc.set_facecolor(colors[i])  # Set face color\n",
+    "                pc.set_edgecolor(None)    # Set edge color\n",
+    "                pc.set_alpha(0.5)            # Set transparency\n",
+    "\n",
+    "        ax.errorbar(\n",
+    "            np.arange(len(prefix_list)) + offsets[i],\n",
+    "            y,\n",
+    "            y_err,\n",
+    "            fmt='o',\n",
+    "            label=plot_labels[i],\n",
+    "            color=colors[i],\n",
+    "            lw=0.5,\n",
+    "            capsize=2,\n",
+    "            markersize=4,\n",
+    "            linestyle='-'\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "        ax.set_xticks(np.arange(len(prefix_list)))\n",
+    "        ax.set_xticklabels([x[:3] for x in prefix_list])\n",
+    "        if plot_props['show_legend']:\n",
+    "            # move the legend outside the plot\n",
+    "            ax.legend(loc='lower right', fontsize=8, bbox_to_anchor=(1.45, 0))\n",
+    "        ax.set_ylim(plot_props['ylim'])\n",
+    "        ax.set_ylabel(plot_props['label'])\n",
+    "\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.set_title(f'Sex prediction')\n",
+    "    # fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>auc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>0.999280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>neural cell</td>\n",
+       "      <td>0.986866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>mesenchymal cell</td>\n",
+       "      <td>0.947691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>0.941811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>alternatively activated macrophage</td>\n",
+       "      <td>0.933333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Schwann cell</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>vein endothelial cell</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>133 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              cell_type       auc\n",
+       "69                          interneuron  0.999280\n",
+       "103                         neural cell  0.986866\n",
+       "89                     mesenchymal cell  0.947691\n",
+       "53                           enterocyte  0.941811\n",
+       "22   alternatively activated macrophage  0.933333\n",
+       "..                                  ...       ...\n",
+       "106                          neutrophil  0.000000\n",
+       "28           capillary endothelial cell  0.000000\n",
+       "17                         Schwann cell  0.000000\n",
+       "116   pulmonary artery endothelial cell  0.000000\n",
+       "131               vein endothelial cell  0.000000\n",
+       "\n",
+       "[133 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "per_cell_type_stats_dict[(prefix_list[-1], \"rand_wo_XY\")].sort_values(\"auc\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>auc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>adipocyte of epicardial fat of left ventricle</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>M cell of gut</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>chandelier pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>near-projecting glutamatergic cortical neuron</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>0.043528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>vein endothelial cell</td>\n",
+       "      <td>0.002664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>0.000138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>neutrophil</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>133 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           cell_type       auc\n",
+       "20     adipocyte of epicardial fat of left ventricle  1.000000\n",
+       "16                                     M cell of gut  1.000000\n",
+       "54                              enteroendocrine cell  1.000000\n",
+       "37   chandelier pvalb GABAergic cortical interneuron  1.000000\n",
+       "102    near-projecting glutamatergic cortical neuron  1.000000\n",
+       "..                                               ...       ...\n",
+       "57                           epithelial cell of lung  0.043528\n",
+       "131                            vein endothelial cell  0.002664\n",
+       "28                        capillary endothelial cell  0.000138\n",
+       "106                                       neutrophil  0.000000\n",
+       "116                pulmonary artery endothelial cell  0.000000\n",
+       "\n",
+       "[133 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "per_cell_type_stats_dict[(prefix_list[-1], \"rand_w_X\")].sort_values(\"auc\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>auc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>intestine goblet cell</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>L5 extratelencephalic projecting glutamatergic...</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>L6b glutamatergic cortical neuron</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>M cell of gut</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>vein endothelial cell</td>\n",
+       "      <td>0.205567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>smooth muscle cell</td>\n",
+       "      <td>0.183007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>bronchus fibroblast of lung</td>\n",
+       "      <td>0.173010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>0.128099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>0.078947</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>133 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type       auc\n",
+       "69                                         interneuron  1.000000\n",
+       "71                               intestine goblet cell  1.000000\n",
+       "14   L5 extratelencephalic projecting glutamatergic...  1.000000\n",
+       "15                   L6b glutamatergic cortical neuron  1.000000\n",
+       "16                                       M cell of gut  1.000000\n",
+       "..                                                 ...       ...\n",
+       "131                              vein endothelial cell  0.205567\n",
+       "121                                 smooth muscle cell  0.183007\n",
+       "27                         bronchus fibroblast of lung  0.173010\n",
+       "28                          capillary endothelial cell  0.128099\n",
+       "116                  pulmonary artery endothelial cell  0.078947\n",
+       "\n",
+       "[133 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "per_cell_type_stats_dict[(prefix_list[-1], \"rand_w_XY\")].sort_values(\"auc\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Development stage\n",
+    "\n",
+    "We will coarsen into 6 classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Parse data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_meta_df = pd.read_csv(\n",
+    "    os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['dataset_id', 'donor_id', 'n_unique_cell_types',\n",
+       "       'cell_type_unknown_fraction', 'known_cell_type_mean_dist_from_root',\n",
+       "       'n_cells', 'tissue_name_ont_coarsename_coarseont', 'n_tissues',\n",
+       "       'development_stage', 'development_stage_ontology_term_id',\n",
+       "       'coarse_development_stage', 'coarse_development_stage_ontology_id',\n",
+       "       'disease', 'disease_ontology_term_id', 'sex', 'coarse_tissue',\n",
+       "       'rare_triple'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_meta_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coarse_development_stage_labels = val_meta_df['coarse_development_stage'].dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60-79 year-old stage\n",
+      "80 year-old and over stage\n",
+      "fetal stage\n",
+      "middle aged stage\n",
+      "pediatric stage\n",
+      "young adult stage\n"
+     ]
+    }
+   ],
+   "source": [
+    "for label in coarse_development_stage_labels:\n",
+    "    print(label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load ontology resources\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"hsapdv_benchmarking_resource.pkl\"), \"rb\") as f:\n",
+    "    hsapdv_benchmarking_resource_dict = pickle.load(f)\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"hsapdv_propagation_resource.pkl\"), \"rb\") as f:\n",
+    "    hsapdv_propagation_resource_dict = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60-79 year-old stage 23\n",
+      "80 year-old and over stage 22\n",
+      "fetal stage 26\n",
+      "middle aged stage 24\n",
+      "pediatric stage 33\n",
+      "young adult stage 30\n"
+     ]
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "\n",
+    "# some labels might map to multiple terms, we catch all of them\n",
+    "coarse_label_to_term_ids_map = defaultdict(list)\n",
+    "for coarse_label in coarse_development_stage_labels:\n",
+    "    for term_id, label in hsapdv_propagation_resource_dict['ontology_term_id_to_label'].items():\n",
+    "        if label == coarse_label:\n",
+    "            coarse_label_to_term_ids_map[coarse_label].append(term_id)\n",
+    "\n",
+    "# obtain all descendants of each coarse term\n",
+    "coarse_label_to_descendants_term_ids_map = defaultdict(set)\n",
+    "for coarse_label, term_ids in coarse_label_to_term_ids_map.items():\n",
+    "    for term_id in term_ids:\n",
+    "        coarse_label_to_descendants_term_ids_map[coarse_label].add(term_id)\n",
+    "        if term_id in hsapdv_benchmarking_resource_dict:\n",
+    "            coarse_label_to_descendants_term_ids_map[coarse_label].update(\n",
+    "                hsapdv_benchmarking_resource_dict[term_id]['all_descendants'])\n",
+    "            \n",
+    "for coarse_label, descendants_term_ids in coarse_label_to_descendants_term_ids_map.items():\n",
+    "    print(coarse_label, len(descendants_term_ids))\n",
+    "\n",
+    "# are these mutually exclusive? they'd better be!\n",
+    "for label1, term_ids1 in coarse_label_to_descendants_term_ids_map.items():\n",
+    "    for label2, term_ids2 in coarse_label_to_descendants_term_ids_map.items():\n",
+    "        if label1 != label2:\n",
+    "            assert len(term_ids1.intersection(term_ids2)) == 0\n",
+    "\n",
+    "all_coarse_labels = np.asarray(list(coarse_label_to_descendants_term_ids_map.keys()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from scipy.special import logsumexp\n",
+    "\n",
+    "key = \"development_stage\"\n",
+    "\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_rand_wo_XY_root_path,\n",
+    "    metadata_predictions_rand_w_XY_root_path,\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    'rand_w_XY',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
+    "\n",
+    "            # get coarsening indices (we use `j` for coarse label index, in the other appears in all_coarse_labels)\n",
+    "            coarse_label_to_idx_map = defaultdict(list)\n",
+    "            idx_to_j_map = dict()\n",
+    "\n",
+    "            for j, coarse_label in enumerate(all_coarse_labels):\n",
+    "                descendants_term_ids = coarse_label_to_descendants_term_ids_map[coarse_label]\n",
+    "                for descendants_term_id in descendants_term_ids:\n",
+    "                    if descendants_term_id in mapper:\n",
+    "                        coarse_label_to_idx_map[coarse_label].append(mapper[descendants_term_id])\n",
+    "                        idx_to_j_map[mapper[descendants_term_id]] = j\n",
+    "\n",
+    "            # get coarsened logits\n",
+    "            logits_coarse_nj = np.zeros((logits_nk.shape[0], len(all_coarse_labels)))\n",
+    "            for j, coarse_label in enumerate(all_coarse_labels):\n",
+    "                idx = coarse_label_to_idx_map[coarse_label]\n",
+    "                logits_coarse_nj[:, j] = logsumexp(logits_nk[:, idx], -1)\n",
+    "            \n",
+    "            # renormalize\n",
+    "            logits_coarse_nj = logits_coarse_nj - logsumexp(logits_coarse_nj, -1)[:, None]\n",
+    "\n",
+    "            # the coarse ground truth label\n",
+    "            labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "            names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "            codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "            coarse_codes_n = np.asarray([idx_to_j_map[code] if code in idx_to_j_map else np.nan for code in codes_n])\n",
+    "\n",
+    "            # record cell types\n",
+    "            cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = (~np.isnan(codes_n)) & (~np.isnan(coarse_codes_n))\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "            cell_type_n = cell_type_n[valid_indices]\n",
+    "            coarse_codes_n = coarse_codes_n[valid_indices].astype(int)\n",
+    "\n",
+    "            n_rows = len(labels_n)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "            results[f\"coarse_codes\"] += coarse_codes_n.tolist()\n",
+    "\n",
+    "            for j in range(len(all_coarse_labels)):\n",
+    "                results[f\"logits_coarse_{j}\"] += logits_coarse_nj[valid_indices, j].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})\n",
+    "bq_cell_type_stats_df\n",
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"cell_type\", right_on=\"cell_type\", how=\"left\").dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0e97b906d3540f0bfcebc70f7634ff6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "validation_meta_df = pd.read_csv(\n",
+    "    os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))\n",
+    "\n",
+    "# Stephen's validatdion table\n",
+    "# what we have called intestine is actually \"small intestine\"\n",
+    "tissue_ontology_term_id_to_coarse_name_map = dict()\n",
+    "for row in validation_meta_df['tissue_name_ont_coarsename_coarseont'].values:\n",
+    "    split_row = row.strip(\"()\").split(', ')\n",
+    "    coarse_name = split_row[2].strip(\"'\")\n",
+    "    if coarse_name == \"intestine\":\n",
+    "        coarse_name = \"small intestine\"\n",
+    "    tissue_ontology_term_id_to_coarse_name_map[split_row[1].strip(\"'\")] = coarse_name\n",
+    "\n",
+    "val_adata_id_to_coarse_tissue_map = dict()\n",
+    "for val_idx in tqdm(range(1, 111)):\n",
+    "    \n",
+    "    val_adata_path = os.path.join(root_path, \"data\", \"cellariumgpt_validation\", f\"extract_{val_idx}.h5ad\")\n",
+    "    val_adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    obs_df = val_adata.obs\n",
+    "    val_adata_id_to_coarse_tissue_map[val_idx] = obs_df['tissue_ontology_term_id'].map(\n",
+    "        tissue_ontology_term_id_to_coarse_name_map).iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"coarse_tissue\"] = df[\"val_adata_id\"].map(val_adata_id_to_coarse_tissue_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__development_stage__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__development_stage__03_03_2025.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0614436ea2948589e8017353b233c0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_ranking.py:375: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    # 'rand_wo_XY',\n",
+    "    'rand_w_XY'\n",
+    "]\n",
+    "\n",
+    "coarse_tissues = [\n",
+    "    \"brain\",\n",
+    "    \"heart\",\n",
+    "    \"kidney\",\n",
+    "    \"liver\",\n",
+    "    \"blood\",\n",
+    "    \"lung\",\n",
+    "    \"small intestine\",\n",
+    "]\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "min_train_cell_fraction = 0.0001\n",
+    "\n",
+    "def compute_auc(group):\n",
+    "    \n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "    \n",
+    "    if group[f\"coarse_codes\"].nunique() < 2:\n",
+    "        return np.nan\n",
+    "\n",
+    "    one_hot_codes_nj = label_binarize(group[f\"coarse_codes\"].values, classes=np.arange(len(all_coarse_labels)))\n",
+    "    \n",
+    "    probs_nj = np.zeros(one_hot_codes_nj.shape)\n",
+    "    for j in range(len(all_coarse_labels)):\n",
+    "        probs_nj[:, j] = np.exp(group[f\"logits_coarse_{j}\"].values)\n",
+    "    assert np.allclose(probs_nj.sum(axis=1), 1)\n",
+    "    \n",
+    "    raw_auc = roc_auc_score(one_hot_codes_nj, probs_nj, average=None, multi_class='ovr')\n",
+    "    auc = np.nanmean(raw_auc)\n",
+    "\n",
+    "    return auc\n",
+    "\n",
+    "\n",
+    "points_for_violin = dict()\n",
+    "per_cell_type_stats_dict = dict()\n",
+    "\n",
+    "for prefix in tqdm(prefix_list):\n",
+    "    for metadata_predictions_type in metadata_predictions_types:\n",
+    "        for coarse_tissue in coarse_tissues:\n",
+    "            _sub_df = df[\n",
+    "                (df[\"prefix\"] == prefix) &\n",
+    "                (df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "                (df[\"train_cell_fraction\"] >= min_train_cell_fraction) &\n",
+    "                (df[\"coarse_tissue\"] == coarse_tissue)\n",
+    "            ]\n",
+    "\n",
+    "            _sub_df = _sub_df.groupby([\"cell_type\"]).filter(lambda x: len(x) >= 10)\n",
+    "            n_cell_types = len(_sub_df[\"cell_type\"].unique())\n",
+    "\n",
+    "\n",
+    "            # group = sub_df\n",
+    "            # one_hot_codes_nj = label_binarize(group[f\"coarse_codes\"], classes=np.arange(len(all_coarse_labels)))\n",
+    "            \n",
+    "            # probs_nj = np.zeros(one_hot_codes_nj.shape)\n",
+    "            # for j in range(len(all_coarse_labels)):\n",
+    "            #     probs_nj[:, j] = np.exp(group[f\"logits_coarse_{j}\"])\n",
+    "            # assert np.allclose(probs_nj.sum(axis=1), 1)\n",
+    "\n",
+    "            # auc = roc_auc_score(one_hot_codes_nj, probs_nj, average='macro')\n",
+    "\n",
+    "            # stats[\"prefix\"].append(prefix)\n",
+    "            # stats[\"metadata_predictions_type\"].append(metadata_predictions_type)\n",
+    "            # stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "            # stats[\"auc\"].append(auc)\n",
+    "\n",
+    "\n",
+    "            # calculate AUC\n",
+    "            per_cell_type_stats = (\n",
+    "                _sub_df.groupby(\"cell_type\")\n",
+    "                .apply(compute_auc)\n",
+    "                .dropna()\n",
+    "                .reset_index(name=\"auc\")\n",
+    "            )\n",
+    "\n",
+    "            points_for_violin[(prefix, metadata_predictions_type, coarse_tissue)] = per_cell_type_stats[\"auc\"].values\n",
+    "            per_cell_type_stats_dict[(prefix, metadata_predictions_type, coarse_tissue)] = per_cell_type_stats\n",
+    "            \n",
+    "            if len(per_cell_type_stats) == 0:\n",
+    "                continue\n",
+    "            \n",
+    "            quants = np.quantile(per_cell_type_stats[\"auc\"], [0.25, 0.5, 0.75])\n",
+    "            auc_mean = np.nanmean(per_cell_type_stats[\"auc\"])\n",
+    "\n",
+    "            stats[\"prefix\"].append(prefix)\n",
+    "            stats[\"metadata_predictions_type\"].append(metadata_predictions_type)\n",
+    "            stats[\"coarse_tissue\"].append(coarse_tissue)\n",
+    "\n",
+    "            stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "            stats[\"auc_25\"].append(quants[0])\n",
+    "            stats[\"auc_50\"].append(quants[1])\n",
+    "            stats[\"auc_75\"].append(quants[2])\n",
+    "            stats[\"auc_mean\"].append(auc_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>coarse_tissue</th>\n",
+       "      <th>n_cell_types</th>\n",
+       "      <th>auc_25</th>\n",
+       "      <th>auc_50</th>\n",
+       "      <th>auc_75</th>\n",
+       "      <th>auc_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.479248</td>\n",
+       "      <td>0.619689</td>\n",
+       "      <td>0.724061</td>\n",
+       "      <td>0.625101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.539679</td>\n",
+       "      <td>0.614594</td>\n",
+       "      <td>0.667421</td>\n",
+       "      <td>0.553523</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.239645</td>\n",
+       "      <td>0.472204</td>\n",
+       "      <td>0.578763</td>\n",
+       "      <td>0.462243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.582606</td>\n",
+       "      <td>0.752851</td>\n",
+       "      <td>0.817974</td>\n",
+       "      <td>0.702261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.457357</td>\n",
+       "      <td>0.577314</td>\n",
+       "      <td>0.696895</td>\n",
+       "      <td>0.544657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.381441</td>\n",
+       "      <td>0.521717</td>\n",
+       "      <td>0.527962</td>\n",
+       "      <td>0.465526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.551584</td>\n",
+       "      <td>0.609105</td>\n",
+       "      <td>0.791977</td>\n",
+       "      <td>0.658604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.493673</td>\n",
+       "      <td>0.517270</td>\n",
+       "      <td>0.557064</td>\n",
+       "      <td>0.521445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.688776</td>\n",
+       "      <td>0.763021</td>\n",
+       "      <td>0.890450</td>\n",
+       "      <td>0.789002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.601608</td>\n",
+       "      <td>0.696441</td>\n",
+       "      <td>0.802608</td>\n",
+       "      <td>0.693085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.323605</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.654947</td>\n",
+       "      <td>0.469461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.559091</td>\n",
+       "      <td>0.622530</td>\n",
+       "      <td>0.722120</td>\n",
+       "      <td>0.623570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.625016</td>\n",
+       "      <td>0.685279</td>\n",
+       "      <td>0.817331</td>\n",
+       "      <td>0.698927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.397214</td>\n",
+       "      <td>0.433536</td>\n",
+       "      <td>0.525699</td>\n",
+       "      <td>0.465946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.361523</td>\n",
+       "      <td>0.589410</td>\n",
+       "      <td>0.756098</td>\n",
+       "      <td>0.525388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.611582</td>\n",
+       "      <td>0.678564</td>\n",
+       "      <td>0.800239</td>\n",
+       "      <td>0.701570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.349583</td>\n",
+       "      <td>0.577220</td>\n",
+       "      <td>0.716111</td>\n",
+       "      <td>0.520288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.361742</td>\n",
+       "      <td>0.535714</td>\n",
+       "      <td>0.619444</td>\n",
+       "      <td>0.494956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.653625</td>\n",
+       "      <td>0.764461</td>\n",
+       "      <td>0.837112</td>\n",
+       "      <td>0.739307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.642570</td>\n",
+       "      <td>0.745888</td>\n",
+       "      <td>0.781357</td>\n",
+       "      <td>0.699856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.604477</td>\n",
+       "      <td>0.627604</td>\n",
+       "      <td>0.705525</td>\n",
+       "      <td>0.691388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>58</td>\n",
+       "      <td>0.637326</td>\n",
+       "      <td>0.780729</td>\n",
+       "      <td>0.894595</td>\n",
+       "      <td>0.751499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.284054</td>\n",
+       "      <td>0.572086</td>\n",
+       "      <td>0.671979</td>\n",
+       "      <td>0.481074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>rand_w_XY</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0.433333</td>\n",
+       "      <td>0.503319</td>\n",
+       "      <td>0.565447</td>\n",
+       "      <td>0.470335</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            prefix metadata_predictions_type    coarse_tissue  n_cell_types  \\\n",
+       "0   10M_001_bs1536                 rand_w_XY            brain            47   \n",
+       "1   10M_001_bs1536                 rand_w_XY            heart            20   \n",
+       "2   10M_001_bs1536                 rand_w_XY            liver             9   \n",
+       "3   10M_001_bs1536                 rand_w_XY            blood            58   \n",
+       "4   10M_001_bs1536                 rand_w_XY             lung            52   \n",
+       "5   10M_001_bs1536                 rand_w_XY  small intestine            45   \n",
+       "6   19M_001_bs2048                 rand_w_XY            brain            47   \n",
+       "7   19M_001_bs2048                 rand_w_XY            heart            20   \n",
+       "8   19M_001_bs2048                 rand_w_XY            liver             9   \n",
+       "9   19M_001_bs2048                 rand_w_XY            blood            58   \n",
+       "10  19M_001_bs2048                 rand_w_XY             lung            52   \n",
+       "11  19M_001_bs2048                 rand_w_XY  small intestine            45   \n",
+       "12  30M_001_bs2560                 rand_w_XY            brain            47   \n",
+       "13  30M_001_bs2560                 rand_w_XY            heart            20   \n",
+       "14  30M_001_bs2560                 rand_w_XY            liver             9   \n",
+       "15  30M_001_bs2560                 rand_w_XY            blood            58   \n",
+       "16  30M_001_bs2560                 rand_w_XY             lung            52   \n",
+       "17  30M_001_bs2560                 rand_w_XY  small intestine            45   \n",
+       "18  59M_001_bs3072                 rand_w_XY            brain            47   \n",
+       "19  59M_001_bs3072                 rand_w_XY            heart            20   \n",
+       "20  59M_001_bs3072                 rand_w_XY            liver             9   \n",
+       "21  59M_001_bs3072                 rand_w_XY            blood            58   \n",
+       "22  59M_001_bs3072                 rand_w_XY             lung            52   \n",
+       "23  59M_001_bs3072                 rand_w_XY  small intestine            45   \n",
+       "\n",
+       "      auc_25    auc_50    auc_75  auc_mean  \n",
+       "0   0.479248  0.619689  0.724061  0.625101  \n",
+       "1   0.539679  0.614594  0.667421  0.553523  \n",
+       "2   0.239645  0.472204  0.578763  0.462243  \n",
+       "3   0.582606  0.752851  0.817974  0.702261  \n",
+       "4   0.457357  0.577314  0.696895  0.544657  \n",
+       "5   0.381441  0.521717  0.527962  0.465526  \n",
+       "6   0.551584  0.609105  0.791977  0.658604  \n",
+       "7   0.493673  0.517270  0.557064  0.521445  \n",
+       "8   0.688776  0.763021  0.890450  0.789002  \n",
+       "9   0.601608  0.696441  0.802608  0.693085  \n",
+       "10  0.323605  0.533333  0.654947  0.469461  \n",
+       "11  0.559091  0.622530  0.722120  0.623570  \n",
+       "12  0.625016  0.685279  0.817331  0.698927  \n",
+       "13  0.397214  0.433536  0.525699  0.465946  \n",
+       "14  0.361523  0.589410  0.756098  0.525388  \n",
+       "15  0.611582  0.678564  0.800239  0.701570  \n",
+       "16  0.349583  0.577220  0.716111  0.520288  \n",
+       "17  0.361742  0.535714  0.619444  0.494956  \n",
+       "18  0.653625  0.764461  0.837112  0.739307  \n",
+       "19  0.642570  0.745888  0.781357  0.699856  \n",
+       "20  0.604477  0.627604  0.705525  0.691388  \n",
+       "21  0.637326  0.780729  0.894595  0.751499  \n",
+       "22  0.284054  0.572086  0.671979  0.481074  \n",
+       "23  0.433333  0.503319  0.565447  0.470335  "
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_df = pd.DataFrame(stats)\n",
+    "stats_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import colorcet as cc\n",
+    "\n",
+    "metadata_predictions_type = \"rand_w_XY\"\n",
+    "\n",
+    "plot_props_list = [\n",
+    "    {\n",
+    "        'metric_to_plot': 'auc',\n",
+    "        'label': 'AUC',\n",
+    "        'ylim': (0.2, 1),\n",
+    "        'show_legend': True,\n",
+    "        'show_violin': True,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "colors = cc.glasbey_light[3:]\n",
+    "\n",
+    "plot_coarse_tissues = [\n",
+    "    \"brain\",\n",
+    "    # \"blood\",\n",
+    "    # \"heart\",\n",
+    "    # \"lung\",\n",
+    "    # \"small intestine\",\n",
+    "]\n",
+    "\n",
+    "plot_alpha = [\n",
+    "    1.0,\n",
+    "    # 1.0,\n",
+    "    # 0.2,\n",
+    "    # 0.2,\n",
+    "    # 0.2,\n",
+    "]\n",
+    "\n",
+    "\n",
+    "plot_labels = plot_coarse_tissues\n",
+    "offsets = [0]\n",
+    "\n",
+    "for plot_props in plot_props_list:\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "\n",
+    "    for i, coarse_tissue in enumerate(plot_coarse_tissues):\n",
+    "        \n",
+    "        sub_stats_df = stats_df[\n",
+    "            (stats_df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "            (stats_df[\"coarse_tissue\"] == coarse_tissue)\n",
+    "        ]\n",
+    "\n",
+    "        if len(sub_stats_df) == 0:\n",
+    "            continue\n",
+    "\n",
+    "        y = sub_stats_df[f\"{plot_props['metric_to_plot']}_mean\"].values\n",
+    "        y_hi = sub_stats_df[f\"{plot_props['metric_to_plot']}_75\"].values\n",
+    "        y_lo = sub_stats_df[f\"{plot_props['metric_to_plot']}_25\"].values\n",
+    "        y_err = np.vstack([y - y_lo, y_hi - y])\n",
+    "\n",
+    "        if plot_props['show_violin']:\n",
+    "            points = []\n",
+    "            for prefix in prefix_list:\n",
+    "                points.append(points_for_violin[(prefix, metadata_predictions_type, coarse_tissue)])\n",
+    "\n",
+    "            violin_parts = ax.violinplot(\n",
+    "                points,\n",
+    "                np.arange(len(prefix_list)) + offsets[i],\n",
+    "                side='both',\n",
+    "                showextrema=False,\n",
+    "                bw_method=0.1,\n",
+    "                widths=0.15,\n",
+    "            )\n",
+    "\n",
+    "            for pc in violin_parts['bodies']:\n",
+    "                pc.set_facecolor(colors[i])  # Set face color\n",
+    "                pc.set_edgecolor(None)    # Set edge color\n",
+    "                pc.set_alpha(0.5)            # Set transparency\n",
+    "\n",
+    "        ax.errorbar(\n",
+    "            np.arange(len(prefix_list)) + offsets[i],\n",
+    "            y,\n",
+    "            y_err,\n",
+    "            fmt='o',\n",
+    "            label=plot_labels[i],\n",
+    "            color=colors[i],\n",
+    "            lw=0.5,\n",
+    "            capsize=2,\n",
+    "            markersize=4,\n",
+    "            linestyle='-'\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "        ax.set_xticks(np.arange(len(prefix_list)))\n",
+    "        ax.set_xticklabels([x[:3] for x in prefix_list])\n",
+    "        if plot_props['show_legend']:\n",
+    "            # move the legend outside the plot\n",
+    "            ax.legend(loc='lower right', fontsize=8, bbox_to_anchor=(1.45, 0))\n",
+    "        ax.set_ylim(plot_props['ylim'])\n",
+    "        ax.set_ylabel(plot_props['label'])\n",
+    "\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.set_title(f'Development stage prediction')\n",
+    "    # fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain: 6 unique development stages, 389520 cells\n",
+      "heart: 4 unique development stages, 124496 cells\n",
+      "kidney: 1 unique development stages, 70400 cells\n",
+      "liver: 2 unique development stages, 18296 cells\n",
+      "blood: 5 unique development stages, 399344 cells\n",
+      "lung: 5 unique development stages, 133128 cells\n",
+      "small intestine: 4 unique development stages, 89032 cells\n"
+     ]
+    }
+   ],
+   "source": [
+    "# how many unique development stages do we have for each tissue\n",
+    "for coarse_tissue in coarse_tissues:\n",
+    "    _sub_df = df[df[\"coarse_tissue\"] == coarse_tissue]\n",
+    "    n_unique_development_stages = _sub_df[\"coarse_codes\"].nunique()\n",
+    "    n_cells = len(_sub_df)\n",
+    "    print(f\"{coarse_tissue}: {n_unique_development_stages} unique development stages, {n_cells} cells\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>auc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bergmann glial cell</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>interneuron</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>glial cell</td>\n",
+       "      <td>0.999755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>L6b glutamatergic cortical neuron</td>\n",
+       "      <td>0.902277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>mature astrocyte</td>\n",
+       "      <td>0.889546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>L2/3-6 intratelencephalic projecting glutamate...</td>\n",
+       "      <td>0.867692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>ependymal cell</td>\n",
+       "      <td>0.852134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>near-projecting glutamatergic cortical neuron</td>\n",
+       "      <td>0.832105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>astrocyte of the cerebral cortex</td>\n",
+       "      <td>0.828838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>caudal ganglionic eminence derived GABAergic c...</td>\n",
+       "      <td>0.811107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>corticothalamic-projecting glutamatergic corti...</td>\n",
+       "      <td>0.808834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>L5 extratelencephalic projecting glutamatergic...</td>\n",
+       "      <td>0.799732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>mature microglial cell</td>\n",
+       "      <td>0.794965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>0.777512</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "      <td>0.772181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0.756742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>sncg GABAergic cortical interneuron</td>\n",
+       "      <td>0.750407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>0.745163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>0.708835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>chandelier pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>0.697292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>0.687426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>0.675221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>sst GABAergic cortical interneuron</td>\n",
+       "      <td>0.661553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>neuron</td>\n",
+       "      <td>0.629842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>pvalb GABAergic cortical interneuron</td>\n",
+       "      <td>0.623205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>0.612766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>vascular leptomeningeal cell</td>\n",
+       "      <td>0.583333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>0.578296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>cerebral cortex endothelial cell</td>\n",
+       "      <td>0.497608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>neural cell</td>\n",
+       "      <td>0.490929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>macrophage</td>\n",
+       "      <td>0.022541</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type       auc\n",
+       "0                                 Bergmann glial cell  1.000000\n",
+       "15                                        interneuron  1.000000\n",
+       "26                                           pericyte  1.000000\n",
+       "13                                         glial cell  0.999755\n",
+       "4                   L6b glutamatergic cortical neuron  0.902277\n",
+       "18                                   mature astrocyte  0.889546\n",
+       "2   L2/3-6 intratelencephalic projecting glutamate...  0.867692\n",
+       "12                                     ependymal cell  0.852134\n",
+       "21      near-projecting glutamatergic cortical neuron  0.832105\n",
+       "6                    astrocyte of the cerebral cortex  0.828838\n",
+       "7   caudal ganglionic eminence derived GABAergic c...  0.811107\n",
+       "10  corticothalamic-projecting glutamatergic corti...  0.808834\n",
+       "3   L5 extratelencephalic projecting glutamatergic...  0.799732\n",
+       "19                             mature microglial cell  0.794965\n",
+       "25                     oligodendrocyte precursor cell  0.777512\n",
+       "16               lamp5 GABAergic cortical interneuron  0.772181\n",
+       "24                                    oligodendrocyte  0.756742\n",
+       "28                sncg GABAergic cortical interneuron  0.750407\n",
+       "1                                    GABAergic neuron  0.745163\n",
+       "31                 vip GABAergic cortical interneuron  0.708835\n",
+       "9     chandelier pvalb GABAergic cortical interneuron  0.697292\n",
+       "14                               glutamatergic neuron  0.687426\n",
+       "5                                           astrocyte  0.675221\n",
+       "29                 sst GABAergic cortical interneuron  0.661553\n",
+       "23                                             neuron  0.629842\n",
+       "27               pvalb GABAergic cortical interneuron  0.623205\n",
+       "11                                   endothelial cell  0.612766\n",
+       "30                       vascular leptomeningeal cell  0.583333\n",
+       "20                                    microglial cell  0.578296\n",
+       "8                    cerebral cortex endothelial cell  0.497608\n",
+       "22                                        neural cell  0.490929\n",
+       "17                                         macrophage  0.022541"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "per_cell_type_stats_dict[(prefix_list[-1], \"rand_w_XY\", \"brain\")].sort_values(\"auc\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tissue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Parse data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_meta_df = pd.read_csv(\n",
+    "    os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['dataset_id', 'donor_id', 'n_unique_cell_types',\n",
+       "       'cell_type_unknown_fraction', 'known_cell_type_mean_dist_from_root',\n",
+       "       'n_cells', 'tissue_name_ont_coarsename_coarseont', 'n_tissues',\n",
+       "       'development_stage', 'development_stage_ontology_term_id',\n",
+       "       'coarse_development_stage', 'coarse_development_stage_ontology_id',\n",
+       "       'disease', 'disease_ontology_term_id', 'sex', 'coarse_tissue',\n",
+       "       'rare_triple'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val_meta_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace \"intestine\" with \"small intestine\"\n",
+    "val_meta_df['coarse_tissue'] = val_meta_df['coarse_tissue'].apply(lambda x: \"small intestine\" if x == \"intestine\" else x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coarse_tissue_labels = val_meta_df['coarse_tissue'].dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain\n",
+      "blood\n",
+      "small intestine\n",
+      "eye\n",
+      "thymus\n",
+      "nose\n",
+      "heart\n",
+      "lung\n",
+      "liver\n",
+      "kidney\n"
+     ]
+    }
+   ],
+   "source": [
+    "for label in coarse_tissue_labels:\n",
+    "    print(label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# censor\n",
+    "coarse_tissue_labels = [\n",
+    "    \"brain\",\n",
+    "    \"heart\",\n",
+    "    \"kidney\",\n",
+    "    \"liver\",\n",
+    "    \"blood\",\n",
+    "    \"lung\",\n",
+    "    \"small intestine\",\n",
+    "]\n",
+    "\n",
+    "# # censor\n",
+    "# coarse_tissue_labels = [\n",
+    "#     \"brain\", #\"brain\",\n",
+    "#     \"heart plus pericardium\", # \"heart\",\n",
+    "#     \"renal system\", # \"kidney\",\n",
+    "#     \"hepatobiliary system\", # \"liver\",\n",
+    "#     \"hematopoietic system\", # \"blood\",\n",
+    "#     \"respiratory system\", # \"lung\",\n",
+    "#     \"alimentary part of gastrointestinal system\", # \"small intestine\",\n",
+    "# ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load ontology resources\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"uberon_benchmarking_resource.pkl\"), \"rb\") as f:\n",
+    "    uberon_benchmarking_resource_dict = pickle.load(f)\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"uberon_propagation_resource.pkl\"), \"rb\") as f:\n",
+    "    uberon_propagation_resource_dict = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of terms under each coarse label:\n",
+      "- brain: 134\n",
+      "- heart: 34\n",
+      "- kidney: 16\n",
+      "- liver: 5\n",
+      "- blood: 4\n",
+      "- lung: 22\n",
+      "- small intestine: 18\n",
+      "- other: 585\n"
+     ]
+    }
+   ],
+   "source": [
+    "tissue_coarsener = OntologyCoarsener(\n",
+    "    coarse_labels=coarse_tissue_labels,\n",
+    "    ontology_propagation_resource_dict=uberon_propagation_resource_dict,\n",
+    "    ontology_benchmarking_resource_dict=uberon_benchmarking_resource_dict,\n",
+    "    include_other_node=True,\n",
+    "    check_mutual_exclusivity=True,\n",
+    "    verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for coarse_label in tissue_coarsener.all_coarse_labels:\n",
+    "#     desc_terms = tissue_coarsener.coarse_label_to_descendants_term_ids_map[coarse_label]\n",
+    "#     print(coarse_label)\n",
+    "#     for desc_term in desc_terms:\n",
+    "#         desc_label = uberon_propagation_resource_dict['ontology_term_id_to_label'][desc_term]\n",
+    "#         print(f\"    {desc_label}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f827ab666a974d0ea0ab6bbf465135de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# how many validation datasets with each coarse tissue do we have?\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "    meta_adata = load_predictions_anndata(val_adata_idx, 0, metadata_predictions_rand_wo_XY_root_path)\n",
+    "    val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "    coarse_tissue_n = meta_adata.obs['tissue_ontology_term_id'].map(tissue_coarsener.fine_term_id_to_coarse_label_map)\n",
+    "    assert len(np.unique(coarse_tissue_n)) == 1\n",
+    "    results['val_adata_id'].append(val_adata_id)\n",
+    "    results['coarse_tissue'].append(coarse_tissue_n.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_adata_corase_tissue_df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain: 28 validation datasets\n",
+      "heart: 9 validation datasets\n",
+      "kidney: 5 validation datasets\n",
+      "liver: 6 validation datasets\n",
+      "blood: 34 validation datasets\n",
+      "lung: 15 validation datasets\n",
+      "small intestine: 6 validation datasets\n"
+     ]
+    }
+   ],
+   "source": [
+    "for coarse_tissue in coarse_tissue_labels:\n",
+    "    n_datasets = np.sum(val_adata_corase_tissue_df['coarse_tissue'] == coarse_tissue)\n",
+    "    print(f\"{coarse_tissue}: {n_datasets} validation datasets\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Compute AUC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 678,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16780764bffc4d6ea5e3188571f9d689",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34bc709bbb1444b6882d451222908f69",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c687cd2bb59640c89b47a75225a7c870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d55d77e932b44c595e148338302efe9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3fd505802b844969bd63985be38f87cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from scipy.special import logsumexp\n",
+    "\n",
+    "key = \"tissue\"\n",
+    "\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_high_w_XY_root_path,\n",
+    "    metadata_predictions_rand_w_XY_root_path,\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'high_w_XY',\n",
+    "    'rand_w_XY',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"{key}_class_probs\"]\n",
+    "\n",
+    "            # get coarsening indices (we use `j` for coarse label index, in the other appears in all_coarse_labels)\n",
+    "            coarse_label_to_idx_map = defaultdict(list)\n",
+    "            idx_to_j_map = dict()\n",
+    "\n",
+    "            for j, coarse_label in enumerate(tissue_coarsener.all_coarse_labels):\n",
+    "                descendants_term_ids = tissue_coarsener.coarse_label_to_descendants_term_ids_map[coarse_label]\n",
+    "                for descendants_term_id in descendants_term_ids:\n",
+    "                    if descendants_term_id in mapper:\n",
+    "                        coarse_label_to_idx_map[coarse_label].append(mapper[descendants_term_id])\n",
+    "                        idx_to_j_map[mapper[descendants_term_id]] = j\n",
+    "\n",
+    "            # get coarsened logits\n",
+    "            logits_coarse_nj = np.zeros((logits_nk.shape[0], len(tissue_coarsener.all_coarse_labels)))\n",
+    "            for j, coarse_label in enumerate(tissue_coarsener.all_coarse_labels):\n",
+    "                idx = coarse_label_to_idx_map[coarse_label]\n",
+    "                logits_coarse_nj[:, j] = logsumexp(logits_nk[:, idx], -1)\n",
+    "            \n",
+    "            # renormalize\n",
+    "            logits_coarse_nj = logits_coarse_nj - logsumexp(logits_coarse_nj, -1)[:, None]\n",
+    "\n",
+    "            # the coarse ground truth label\n",
+    "            labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "            names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "            codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "            coarse_codes_n = np.asarray([idx_to_j_map[code] if code in idx_to_j_map else np.nan for code in codes_n])\n",
+    "\n",
+    "            # record cell types\n",
+    "            cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = (~np.isnan(codes_n)) & (~np.isnan(coarse_codes_n))\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "            cell_type_n = cell_type_n[valid_indices]\n",
+    "            coarse_codes_n = coarse_codes_n[valid_indices].astype(int)\n",
+    "\n",
+    "            n_rows = len(labels_n)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "            results[f\"coarse_codes\"] += coarse_codes_n.tolist()\n",
+    "\n",
+    "            for j in range(len(tissue_coarsener.all_coarse_labels)):\n",
+    "                results[f\"logits_coarse_{j}\"] += logits_coarse_nj[valid_indices, j].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 679,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 680,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})\n",
+    "bq_cell_type_stats_df\n",
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 681,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"cell_type\", right_on=\"cell_type\", how=\"left\").dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 682,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__tissue__auc__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 683,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>names</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>coarse_codes</th>\n",
+       "      <th>logits_coarse_0</th>\n",
+       "      <th>logits_coarse_1</th>\n",
+       "      <th>logits_coarse_2</th>\n",
+       "      <th>logits_coarse_3</th>\n",
+       "      <th>logits_coarse_4</th>\n",
+       "      <th>logits_coarse_5</th>\n",
+       "      <th>logits_coarse_6</th>\n",
+       "      <th>logits_coarse_7</th>\n",
+       "      <th>train_donor_dataset_count</th>\n",
+       "      <th>train_cell_count</th>\n",
+       "      <th>train_cell_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>UBERON:0002132</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.006080</td>\n",
+       "      <td>-11.639028</td>\n",
+       "      <td>-14.243156</td>\n",
+       "      <td>-8.234098</td>\n",
+       "      <td>-16.060185</td>\n",
+       "      <td>-9.945067</td>\n",
+       "      <td>-13.658438</td>\n",
+       "      <td>-5.160767</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>UBERON:0002132</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.000114</td>\n",
+       "      <td>-10.855491</td>\n",
+       "      <td>-14.818631</td>\n",
+       "      <td>-15.753994</td>\n",
+       "      <td>-16.163912</td>\n",
+       "      <td>-10.673646</td>\n",
+       "      <td>-16.669695</td>\n",
+       "      <td>-9.552891</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>UBERON:0002132</td>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.018111</td>\n",
+       "      <td>-10.621233</td>\n",
+       "      <td>-13.099631</td>\n",
+       "      <td>-6.336529</td>\n",
+       "      <td>-15.696574</td>\n",
+       "      <td>-9.295384</td>\n",
+       "      <td>-14.406444</td>\n",
+       "      <td>-4.131512</td>\n",
+       "      <td>904.0</td>\n",
+       "      <td>1394984.0</td>\n",
+       "      <td>0.032378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>UBERON:0002132</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.007204</td>\n",
+       "      <td>-8.337449</td>\n",
+       "      <td>-11.130719</td>\n",
+       "      <td>-10.076913</td>\n",
+       "      <td>-13.889114</td>\n",
+       "      <td>-11.213383</td>\n",
+       "      <td>-7.973660</td>\n",
+       "      <td>-5.032390</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>71069.0</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>1</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>dentate nucleus</td>\n",
+       "      <td>UBERON:0002132</td>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.001497</td>\n",
+       "      <td>-9.967198</td>\n",
+       "      <td>-13.935783</td>\n",
+       "      <td>-14.410442</td>\n",
+       "      <td>-15.539149</td>\n",
+       "      <td>-13.466232</td>\n",
+       "      <td>-9.104946</td>\n",
+       "      <td>-6.618959</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>71069.0</td>\n",
+       "      <td>0.001650</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           prefix  val_adata_id metadata_predictions_type           labels  \\\n",
+       "0  10M_001_bs1536             1                 high_w_XY  dentate nucleus   \n",
+       "1  10M_001_bs1536             1                 high_w_XY  dentate nucleus   \n",
+       "3  10M_001_bs1536             1                 high_w_XY  dentate nucleus   \n",
+       "5  10M_001_bs1536             1                 high_w_XY  dentate nucleus   \n",
+       "6  10M_001_bs1536             1                 high_w_XY  dentate nucleus   \n",
+       "\n",
+       "            names                cell_type  coarse_codes  logits_coarse_0  \\\n",
+       "0  UBERON:0002132          oligodendrocyte             0        -0.006080   \n",
+       "1  UBERON:0002132          oligodendrocyte             0        -0.000114   \n",
+       "3  UBERON:0002132          oligodendrocyte             0        -0.018111   \n",
+       "5  UBERON:0002132  cerebellar granule cell             0        -0.007204   \n",
+       "6  UBERON:0002132  cerebellar granule cell             0        -0.001497   \n",
+       "\n",
+       "   logits_coarse_1  logits_coarse_2  logits_coarse_3  logits_coarse_4  \\\n",
+       "0       -11.639028       -14.243156        -8.234098       -16.060185   \n",
+       "1       -10.855491       -14.818631       -15.753994       -16.163912   \n",
+       "3       -10.621233       -13.099631        -6.336529       -15.696574   \n",
+       "5        -8.337449       -11.130719       -10.076913       -13.889114   \n",
+       "6        -9.967198       -13.935783       -14.410442       -15.539149   \n",
+       "\n",
+       "   logits_coarse_5  logits_coarse_6  logits_coarse_7  \\\n",
+       "0        -9.945067       -13.658438        -5.160767   \n",
+       "1       -10.673646       -16.669695        -9.552891   \n",
+       "3        -9.295384       -14.406444        -4.131512   \n",
+       "5       -11.213383        -7.973660        -5.032390   \n",
+       "6       -13.466232        -9.104946        -6.618959   \n",
+       "\n",
+       "   train_donor_dataset_count  train_cell_count  train_cell_fraction  \n",
+       "0                      904.0         1394984.0             0.032378  \n",
+       "1                      904.0         1394984.0             0.032378  \n",
+       "3                      904.0         1394984.0             0.032378  \n",
+       "5                       42.0           71069.0             0.001650  \n",
+       "6                       42.0           71069.0             0.001650  "
+      ]
+     },
+     "execution_count": 683,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 684,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70868877826c494191e907b455618f6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'high_w_XY'\n",
+    "]\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "min_train_cell_fraction = 0.0001\n",
+    "\n",
+    "def compute_auc(group):\n",
+    "    \n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "    \n",
+    "    if group[f\"coarse_codes\"].nunique() < 2:\n",
+    "        return np.nan\n",
+    "\n",
+    "    one_hot_codes_nj = label_binarize(group[f\"coarse_codes\"].values, classes=np.arange(len(tissue_coarsener.all_coarse_labels)))\n",
+    "    \n",
+    "    probs_nj = np.zeros(one_hot_codes_nj.shape)\n",
+    "    for j in range(len(tissue_coarsener.all_coarse_labels)):\n",
+    "        probs_nj[:, j] = np.exp(group[f\"logits_coarse_{j}\"].values)\n",
+    "    assert np.allclose(probs_nj.sum(axis=1), 1)\n",
+    "    \n",
+    "    raw_auc = roc_auc_score(one_hot_codes_nj, probs_nj, average=None, multi_class='ovr')\n",
+    "    auc = np.nanmean(raw_auc)\n",
+    "\n",
+    "    return auc\n",
+    "\n",
+    "\n",
+    "def compute_accuracy(group):\n",
+    "    \n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "\n",
+    "    target_labels_n = group[f\"coarse_codes\"].values\n",
+    "    logits_nj = np.zeros((len(target_labels_n), len(tissue_coarsener.all_coarse_labels)))\n",
+    "    for j in range(len(tissue_coarsener.all_coarse_labels)):\n",
+    "        logits_nj[:, j] = group[f\"logits_coarse_{j}\"].values\n",
+    "    pred_labels_n = np.argmax(logits_nj, axis=1)\n",
+    "\n",
+    "    accuracy = np.mean(target_labels_n == pred_labels_n)\n",
+    "\n",
+    "    return accuracy\n",
+    "\n",
+    "\n",
+    "\n",
+    "points_for_violin = dict()\n",
+    "per_cell_type_stats_dict = dict()\n",
+    "\n",
+    "for prefix in tqdm(prefix_list):\n",
+    "    for metadata_predictions_type in metadata_predictions_types:\n",
+    "        _sub_df = df[\n",
+    "            (df[\"prefix\"] == prefix) &\n",
+    "            (df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "            (df[\"train_cell_fraction\"] >= min_train_cell_fraction)\n",
+    "        ]\n",
+    "\n",
+    "        _sub_df = _sub_df.groupby([\"cell_type\"]).filter(lambda x: len(x) >= 10)\n",
+    "        n_cell_types = len(_sub_df[\"cell_type\"].unique())\n",
+    "\n",
+    "        # calculate accuracy\n",
+    "        per_cell_type_stats = (\n",
+    "            _sub_df.groupby(\"cell_type\")\n",
+    "            .apply(compute_accuracy)\n",
+    "            .dropna()\n",
+    "            .reset_index(name=\"auc\")\n",
+    "        )\n",
+    "\n",
+    "        points_for_violin[(prefix, metadata_predictions_type)] = per_cell_type_stats[\"auc\"].values\n",
+    "        per_cell_type_stats_dict[(prefix, metadata_predictions_type)] = per_cell_type_stats\n",
+    "        \n",
+    "        if len(per_cell_type_stats) == 0:\n",
+    "            continue\n",
+    "        \n",
+    "        quants = np.quantile(per_cell_type_stats[\"auc\"], [0.25, 0.5, 0.75])\n",
+    "        auc_mean = np.nanmean(per_cell_type_stats[\"auc\"])\n",
+    "\n",
+    "        stats[\"prefix\"].append(prefix)\n",
+    "        stats[\"metadata_predictions_type\"].append(metadata_predictions_type)\n",
+    "\n",
+    "        stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "        stats[\"auc_25\"].append(quants[0])\n",
+    "        stats[\"auc_50\"].append(quants[1])\n",
+    "        stats[\"auc_75\"].append(quants[2])\n",
+    "        stats[\"auc_mean\"].append(auc_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 685,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>n_cell_types</th>\n",
+       "      <th>auc_25</th>\n",
+       "      <th>auc_50</th>\n",
+       "      <th>auc_75</th>\n",
+       "      <th>auc_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>212</td>\n",
+       "      <td>0.039974</td>\n",
+       "      <td>0.490707</td>\n",
+       "      <td>0.951585</td>\n",
+       "      <td>0.489814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>212</td>\n",
+       "      <td>0.061122</td>\n",
+       "      <td>0.455472</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.461810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>212</td>\n",
+       "      <td>0.048542</td>\n",
+       "      <td>0.371165</td>\n",
+       "      <td>0.706816</td>\n",
+       "      <td>0.403978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>212</td>\n",
+       "      <td>0.012216</td>\n",
+       "      <td>0.163286</td>\n",
+       "      <td>0.716687</td>\n",
+       "      <td>0.360065</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           prefix metadata_predictions_type  n_cell_types    auc_25    auc_50  \\\n",
+       "0  10M_001_bs1536                 high_w_XY           212  0.039974  0.490707   \n",
+       "1  19M_001_bs2048                 high_w_XY           212  0.061122  0.455472   \n",
+       "2  30M_001_bs2560                 high_w_XY           212  0.048542  0.371165   \n",
+       "3  59M_001_bs3072                 high_w_XY           212  0.012216  0.163286   \n",
+       "\n",
+       "     auc_75  auc_mean  \n",
+       "0  0.951585  0.489814  \n",
+       "1  0.833333  0.461810  \n",
+       "2  0.706816  0.403978  \n",
+       "3  0.716687  0.360065  "
+      ]
+     },
+     "execution_count": 685,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_df = pd.DataFrame(stats)\n",
+    "stats_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 686,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Tissue prediction')"
+      ]
+     },
+     "execution_count": 686,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9VG0HBf0uMpg7H8S9TL7UEdYNHdWTqXSmHL2XY4CnoK8sp0Ff5ZMMKeh3kaELmT5l+d3TUVCnTnLleMkEeLWDkbuT+zUFUNDv8Uw+gV1YN4hfQzxAR5mmXHZKLoxepsSgdtnB3XkZfeWXm+SX80JBv4tMvr27sG4wx5a4v44yIGeZE+k6uQBPQV9ZBpNfl5bzQkG/i0w+nQ/k5i4cIIijjiZUo/KDcgwyWaizzJRcGGfB3YuCfs/WlUBOmX73dDTclWbZVA5l+vzJBn1BkD3g8kRBv4u8TL6dHtVg9gvl3Br31lHQoaCkHLm6MmX6ytIbTNC3GatvMPmpfi4EBf0uEgShU8Fcb/SmoNRNXgYK+mqhTF8dbYd8d/W8HyXQIPIL4N0rFI3VR3D2TD3OWhoc7jeY/OAXHNOJuWNIezrqqKWgpBy5AyztX+UZzP4401Bl97f6w7op6F8As28IAKCyZCvK9n3rcH/EwHGIHDxO7Wa5HZ1OD73BDOvZM7L3U1BSjnx5h/av0tpm9l0570cpFPQvgMmvJeiHxiQiMHwIAODob/noO/w6AC2ZPtXzleFl8JYN+novE829oyDZk7Mo6CuubWZPmb6LMJ8L+gZzL/FN0xvM8AmIdFiHdI/e6A2cqpZfThSj0xsg6L3Azl24RtDpaFplDnpCTZ86ci+AyTuww9k2Kegrw1lnLo3RV579PtUbvKlPigPH8o76mT4F/Qsg6HTtjtcXdHoYzQEqtsh9ORurT2P0lWcf9OmgyodDeUeD+bko6F+g9k68MvkE0TzkCnE2vw7NAKk8+/ne244nJ8poe4KW2lMwABT0L1h70zHQmbjKcRbc5ToeSffYz1pK1yrgw/6EN+Hc6DS1UdC/QO3NoNmV+XlI+5x9KWiGTeVRps+fTu8lDkIwmPw06TehoH+BOirvEGU4DfqU6SvOPtDTBWr4aZ1rR+0plVtR0L9A7Z1UYfSmTlylyF27FaDyAw/2QzRp//LTev6D2hOttaKgf4HaG1+rxVl27spZ8HF2MCAXThL0aYw+N61BX6sRaBT0L5DeYHJaYtDihAt35azMQJmo8ijoq6M16Gt1xjMF/W6QC+6CTq9Zrc4dObsOLtWclafTecn+myirNcPX6lwICvrdIDtJlcmXzmRUkLPgThdFV56gp6CvhtYOc60GI1DQ7wa5Eyu06pxxV87KDFR+UJ5OOD+1SEfTjJAL1xrstRoWq3nQX7x4MeLi4mA2m5GUlITNmzc7Xffs2bNYuHAhBgwYALPZjPj4eOTn56vYWim5KwvR1YaURUFfPfaZvv2/ibI8OtNftWoVMjMzkZ2djW3btiE+Ph6pqamoqKiQXX/OnDl466238Nprr+G3337DAw88gJtvvhnbt29XueUt6GpD/DkrMwhUflCcYJfp22f9RFmtQV+rwQiaBv3c3FzMnDkT6enpGD58OPLy8uDj44Nly5bJrv/+++/jmWeewcSJE9G/f3/MmjULEydOxMsvv6xyy1vIdcR0dDFv0jWU6avHvqRD5R1+Wj+7Wg1G0CzoNzU1YevWrUhJSTnfGJ0OKSkpKCoqkt3GYrHAbJb+JPL29sbGjRu5ttUZuZ9nNPujsgSdTnbyOupoVJ4g2O1nQfPKr9s6H/S1SVw0e2erqqpgtVoRHh4uWR4eHo6ysjLZbVJTU5Gbm4u9e/fCZrNhw4YNWLNmDUpLS50+j8ViQV1dneSmFLkAT1PSKk+ulKOjmrPi7LN7uioZPx4b9C/Eq6++ikGDBmHo0KEwGo3IyMhAeno6dO1MY5yTk4OAgADxFh0drVh7ZDN9mqhKcXJfDqrpK08S6CnT50YM+hp9hjV7Z0NCQqDX61FeXi5ZXl5ejoiICNltQkND8emnn6KxsRGHDx/G7t274efnh/79+zt9nqysLNTW1oq3I0eOKPYa5AK8joK+4tp+OQS9F50LwYNdoKdMn5/WX1Ra/VrVLOgbjUYkJiaioKBAXGaz2VBQUIDk5OR2tzWbzYiKikJzczM+/vhj3HTTTU7XNZlM8Pf3l9yUIhf09QYK+kprm9VTPZ8PaU2fDqq8tH6eBY1GSGn67cnMzMS0adMwZswYjB07FosWLUJjYyPS09MBAGlpaYiKikJOTg4AYNOmTTh27BgSEhJw7NgxzJ8/HzabDU8++aQm7ZcN+pTpK65tRkRBnw/7oC9QeYeb1l9RWo2Q0vTbM3XqVFRWVmLevHkoKytDQkIC8vPzxc7dkpISSb3+zJkzmDNnDg4cOAA/Pz9MnDgR77//PgIDAzVpv1ytmeaEUZ5ceYdwYJfdU9DnpzXD98igDwAZGRnIyMiQva+wsFDy97hx4/Dbb7+p0KrOEXQ6CHovMGuzuIzGjyuvbZCnTJ8PaaCn8g4vgk4HCIJm/VJ0OO+mtvO600RgynPI9Cnoc2EfhOTOjSAKEXSa/pKid7ab2mb2lOkrz6Ejl8o7nAhO/k2UJFDQd22SIC8IlIVyQB25KpHU9Cno8yIIgqbTXFDQ7ybJ1YZ0NH6ch7YHUjqw8iH97NLnmCfK9F0YXWKOv7aZPZV3iKujoO/C7LNOGkrIR9sgTzNA8mKX3dMvVr407CinoN9N0vIOZfo8tD1zkco7xNVRpu/C7LNQKjvwQeP0NcCY1i1wa4KGfSYU9LvJPuukYMRH26s4UXmHuDwNy2cU9LtJRzV97ijTVwtl9+qhoO+yJOUdCkZctM3sKdNXAx0AeNJyaDcF/W6y72SkDkY+HIZs0n7mgtls5/9NNX2+KOi7LoE6crmjTF8tzMm/iTuhoN9Nkpq+RhdFcHeOQzZpP/Ngn93bZ/1EeTR6x4VJLiZNmT4XNPeOOhizD/SU6XNF5R3XZR/0KdPng8o7KmH2NX3K9N0VBf1ukpR3KBhxQfPpq4NR0FcRZfouS5LpU9DngjJ9dUiCPtX03RalTN0kHbJJwYiHMw0ncKq2VPy7tmIPbM0W+AREwicgUsOWuRfpkE0K+jxpOU6fgn43STpyqabPxf4tH2D3D2+Lf7f+++KJ2Ui8Yb5GrXI/0kzfqmFLCE8U9LvJfrY8yvT5GHRJOs6eaQAAHP0tH5fesRi+AX0oy1eYfaCnTJ8zyvRdl3T0DnWR8OAbeD7A6w1m9I6Kh19wtMatcj+U6XsGilLdRJk+f3TlLJXYZ/eU6XNFJ2e5MMr0+aPRO+qg8o5noCjVTZTp80fj9NVB5R3PQEG/myTZPV1XlAvH+fTp4MoDZfqegYJ+Nwl2FzimaRj4aFs2o0yfDzojV0U0947rkpZ3aHfyIAgCnfmsAgr6nkHzKLV48WLExcXBbDYjKSkJmzdvbnf9RYsWYciQIfD29kZ0dDQeffRRnDlzRqXWyrAP+tSRy42OrkXMnaS8QzV9t6VplFq1ahUyMzORnZ2Nbdu2IT4+HqmpqaioqJBd/4MPPsDTTz+N7Oxs7Nq1C0uXLsWqVavwzDPPqNzy86SBnmr6vNAoKf4o01eRhlcm0/Tbk5ubi5kzZyI9PR3Dhw9HXl4efHx8sGzZMtn1f/zxR1x22WW46667EBcXhwkTJuDOO+/s8NcBT/ZzaFB5hx8qo6nAPhDR5RLdlmbfnqamJmzduhUpKSnnG6PTISUlBUVFRbLbXHrppdi6dasY5A8cOIB169Zh4sSJTp/HYrGgrq5OclMUZfqqEDN9GiHFDWX6nkGz4mhVVRWsVivCw8Mly8PDw7F7927Zbe666y5UVVXh8ssvB2MMzc3NeOCBB9ot7+Tk5GDBggWKtt2eJNOnsgM3rUFfyzMZ3R0Ffc/gUlGqsLAQf/3rX/HGG29g27ZtWLNmDdauXYvnnnvO6TZZWVmora0Vb0eOHFG0TZKyA2Wh3Ij7mfYxR3bXyKWgzxXT8HKUmmX6ISEh0Ov1KC8vlywvLy9HRESE7DZz587FPffcgxkzZgAARo4cicbGRvz5z3/Gs88+C51MrddkMsFkMin/AkR2QYgyfW7oVxR/jGr66vHEjlyj0YjExEQUFBSIy2w2GwoKCpCcnCy7zalTpxwCu17f8rOfabQTKdNXibhvaR/zQhdGV49W8QrQeGrlzMxMTJs2DWPGjMHYsWOxaNEiNDY2Ij09HQCQlpaGqKgo5OTkAAAmT56M3NxcjB49GklJSdi3bx/mzp2LyZMni8FfdZJATwGJl/PlHW3b4daYfXmHgj5fHhr0p06disrKSsybNw9lZWVISEhAfn6+2LlbUlIiyeznzJkDQRAwZ84cHDt2DKGhoZg8eTL+8pe/aPUS2nTkUkQihHSCp2b6AJCRkYGMjAzZ+woLCyV/e3l5ITs7G9nZ2Sq0rLMo0yeEdI2WHeXUO9Zd9tk9ZfqEkE7QsnxGQb+b7MeNU3mHuA2q6fNFmb67oKDPi5gZUSxSByUwXFF5x5VRR646xC8JRX1u6LOsGgr6Loy+HOqw0VS/3NEUF+rRcupqCvrdRqN3VHEuM6Lx4xzROSeqoUzfXVDWz42NtWZGFPR5ockD1cEYo6Dv0ijQq4JZm8/9g1G2zwtds0AdFPRdGw3ZVAddyo8/SXZPmT43jNk8c8I1t0F1UFXYbM3n/209q2FL3Jdk8kD6LHNEmT4h7WI2myS7tz8AEOVIrkOs02gCQw/AbDZNy5QU9LuJJlzjr21mT5k+H9LrEFPQ50W8gAoFfXdAQZ8HCvrqoExfJa3DjzUaiUZBXwmU4XNls1HQV4Mk6FNHLjfMZmv9hybPT++skij4c0GZvjp0wvmgr9NpPuu622rN8MXgrzIK+gqgWj5fFPTVQeUdlYhnl1PQJ0SWtblJ8rfN2uRkTdIdFPTVwTSePJCCvoIo4+eDMn11UE1fHa1DNam849Io2PNEQV8dgmCf6VNNnxsq7xDSPkZBXxX2mb6OyjvcnD8pi8o7boAyfh6sbWr4tmaq6fOgo5q+Khhl+oS0T5xh8xzK9PmQ1vQp6HPTGvSppu/CqAOXq7Zz7TBGs2zyIK3pU9Dnhco7hHSgbdC3WWnCNR7s59Cn0Ts8nRu9o9HcO9RFT3o8h/IOzbLJBWX6fJ2qLW251ZXhVG0pThz7GWafYPgERMInIFK1dlDQVwJdyYmrtuUcRkGfCxqnz9eu79/CtnULxL93//A2AODiidlIvGG+au2goE96vLYdXnS5RD5oamW+hl1xP2JH3YjG2lL8uPJBXPGnJTD7haia5QM9pKa/ePFixMXFwWw2IykpCZs3b3a67vjx4yEIgsNt0qRJKrbYGQpGPDgMbdPwqkPuTBL0KdNXnE9AJEJiLkZwnxHQG8wIjhqJkJiLPS/or1q1CpmZmcjOzsa2bdsQHx+P1NRUVFRUyK6/Zs0alJaWiredO3dCr9fjj3/8o8otJ2ppG/S1vNScu2qtMbfWnU8e+wVVJdtwqrZU66a5HXEaBk/tyM3NzcXMmTORnp4OAMjLy8PatWuxbNkyPP300w7rBwcHS/5euXIlfHx8NA76lOFz1ebLodX4ZnfWU+rNnsUDg35TUxO2bt2KrKwscZlOp0NKSgqKioo69RhLly7FHXfcAV9fX17N7DSqNauDJrZT3rAr7kff4dfh96J3cfS3fFw1/d/wMphVLz14Bm3jhKZBv6qqClarFeHh4ZLl4eHh2L17d4fbb968GTt37sTSpUudrmOxWGCxWMS/6+rqLrzBTojBnoI+cVE+AZEw+4Xg6G/50BvMCI1JhN5g0rpZbo0ujH4Bli5dipEjR2Ls2LFO18nJyUFAQIB4i46OVr4hFOyJW7D7BUW/ptyWpkE/JCQEer0e5eXlkuXl5eWIiIhod9vGxkasXLkS9913X7vrZWVloba2VrwdOXKk2+12joI/F20DEAUkLmj0jmfQ9J01Go1ITExEQUGBuMxms6GgoADJycntbrt69WpYLBbcfffd7a5nMpng7+8vuSmJ6vj8tQ1AFJA4ESjT9wSaj97JzMzEtGnTMGbMGIwdOxaLFi1CY2OjOJonLS0NUVFRyMnJkWy3dOlSTJkyBb1799ai2efZBX06APDhEOQp6HMn0DThbkvzoD916lRUVlZi3rx5KCsrQ0JCAvLz88XO3ZKSEuh00i/5nj17sHHjRqxfv16LJreDgj4Pgo4yfTUIgkAZvgfQPOgDQEZGBjIyMmTvKywsdFg2ZMiQHpNVM1Cmz1vbud1pigB+xOGwFPzdFqVM3WUf6CnocyHopbmJjq7fylFLsKdzIXjSdh9T0O825uTfRCk6yvTVQ8FeRRT0XRKTdOTS9AA8UKavHgFU1+dN619RFPS7yT7QU02fj7ZBvu1BgCiIAr4KqLzj2qimzx1l+uqhoZr8nQ/2FPRdFNX0edPpDNK/9QYnaxJlUOBXBWX6rsl+ml+q6fOho0xfPVTeUQHV9F0ajdPnT6CavnoEKvDw1nqyoVYnGVLQ7y5G5R3eKNNXD4V8NVBHrkuTlHQo0+fCYfQOBX1+aCoG7qgj1+XROH3e2gb5tpk/Ia5F26kuKOh3k7QjlzJ9HtoGecr0+aHyDn9U03d59uP0KdPnoe2EazqahoEfKu2ogGr6Lo3RfPrctZ1rh+beIa5MzPAp03dN0jo+BX0eHDpyBQr63FCmz9+5faxVKY2CfndRps+dw0VUKNPnjAI/T1pfs4CCfrdRTZ87ukYucSOtn1/qyHVRVNPnz+HC6Dr62PJCo3fU0NqRS0HfRdEZubw5fjkoMBHXdb4jl8o7Lomye/VpfREKQrpFcJFM//jx43j88cdRV1fncF9tbS2eeOIJlJeXK9o4l0MHAD4cgjwFfV4Y/VrlzmUy/dzcXNTV1cHf39/hvoCAANTX1yM3N1fRxrkE+5o+fWG4cKgzU6ZPXJmrDNnMz89HWlqa0/vT0tLw3//+V5FGuRYK9MTd0GeaJ5fJ9A8ePIiYmBin9/ft2xeHDh1Sok2EEK1QiZI7QXCRaRi8vb3bDeqHDh2Ct7e3Em1yXfSFIa6OPsMq0Hb66k4H/aSkJLz//vtO73/vvfcwduxYRRpFCNEG9Uvxp/XJhZ2eo/bxxx/Htddei4CAADzxxBMIDw8HAJSXl+PFF1/E8uXLsX79em4NJZ7LIRBRNsoNYzbav7wJArQcgdbpoH/VVVdh8eLFmD17Nl555RX4+/tDEATU1tbCYDDgtddew9VXX82zrcRTOQQhCkrc2Gy0dzkTNL4OcZd+Z9x///3Yv38/XnrpJdx1112444478PLLL2Pfvn2YNWvWBTVg8eLFiIuLg9lsRlJSEjZv3tzu+jU1NXjooYcQGRkJk8mEwYMHY926dRf03Mqwe/toKKEq6IQ4flp+VdH+5U7DWNHlSxBFRUXh0UcfVeTJV61ahczMTOTl5SEpKQmLFi1Camoq9uzZg7CwMIf1m5qacO211yIsLAwfffQRoqKicPjwYQQGBirSHtJTUaavFirvuL9OB/1//OMfsssDAgIwePBgJCcnd/nJc3NzMXPmTKSnpwMA8vLysHbtWixbtgxPP/20w/rLli3DyZMn8eOPP8JgMAAA4uLiuvy8irI7YtNkVXxQZq+iczPFMsZougueXCHTf+WVV2SX19TUoLa2Fpdeeik+//xzBAcHd+rxmpqasHXrVmRlZYnLdDodUlJSUFRUJLvN559/juTkZDz00EP47LPPEBoairvuugtPPfUU9Hr5OdYtFgssFov4t9w0Et0h+WLQl4SPNkGfDgJ8MMbEaz4zZqOL1XCkZYLYpZOz5G7V1dXYt28fbDYb5syZ0+knrqqqgtVqFUcBtQoPD0dZWZnsNgcOHMBHH30Eq9WKdevWYe7cuXj55Zfx/PPPO32enJwcBAQEiLfo6OhOt7FzBCf/Jkphba9TQNct4MJ+PzObVcOWEJ4UGTDav39/vPDCC9yHbNpsNoSFheHtt99GYmIipk6dimeffRZ5eXlOt8nKykJtba14O3LkiKJtss/06ecwL5Tpq8E+0FPQ580FyjsdiYmJcZqhywkJCYFer3eYmbO8vBwRERGy20RGRsJgMEhKOcOGDUNZWRmamppgNBodtjGZTDCZTJ1uV9dReYc3xyBPQZ8HSdCnX1N8aRgqFDs1bMeOHYiNje30+kajEYmJiSgoKBCX2Ww2FBQUOO0Uvuyyy8RSUqvff/8dkZGRsgFfDfZXcdL6TDu3RTV9VVCm7xk6HaXq6upkb0eOHMGnn36KRx55BFOnTu3Sk2dmZmLJkiVYsWIFdu3ahVmzZqGxsVEczZOWlibp6J01axZOnjyJ2bNn4/fff8fatWvx17/+FQ899FCXnldZ9uUdCvo80Bm56rDZmmX/TXhwgfJOYGCg05q1IAiYMWOG7DDL9kydOhWVlZWYN28eysrKkJCQgPz8fLFzt6SkBDq7TDo6OhpffvklHn30UYwaNQpRUVGYPXs2nnrqqS49r5IkgZ7KO3xQeUcVlOl7hk4H/W+++UZ2ub+/PwYNGgQ/Pz/s3LkTF110UZcakJGRgYyMDNn7CgsLHZYlJyfjp59+6tJz8CQt79AQNz6ovKMG++yeUabvtjod9MeNGye7vL6+Hh988AGWLl2KLVu2wGr1rAzBPtO3PwAQ4mrss3sbZfpu64Kj1HfffYdp06YhMjISL730Eq666qoelYGrRqCOXN5o9I46GGX6HqFLQzbLysqwfPlyLF26FHV1dbj99tthsVjw6aefYvjw4bza2KNJAz3V9Pmg8o4aJB251rMatoTw1OnUdPLkyRgyZAh++eUXLFq0CMePH8drr73Gs20uQVreoZo+cV02q32mT+UdvrRLXDqd6X/xxRf4v//7P8yaNQuDBg3i2SaXYh/oqbxDXBmjIZvq0fDHaqej1MaNG1FfX4/ExEQkJSXh9ddfR1VVFc+2uQSBavrcOZRzqLzDhSTTt1LQ50u7z3Cno9Qll1yCJUuWoLS0FPfffz9WrlyJPn36wGazYcOGDaivr+fZzh6LRu8Qd2Gzna/jU03ffXU5Svn6+mL69OnYuHEjduzYgcceewwvvPACwsLCcOONN/JoY88mmXCNavrEddln91Te4UvLwQjdSk2HDBmCF198EUePHsW///1vpdrkUgRBEDN8yvSJK6NpGNTkokG/lV6vx5QpU/D5558r8XAupzXDp5o+cWWMavrqcdVMn7QQgz0FfeLCKNNXB2MOUwiqiqKUEs6VdXQ0Tp+4MJp7Rx0t50BQpu/SdK0duJTpExcm6cil8g43jNkAxjTrzKUopYTWjlwK+sSFUXlHHeJVySjou67WYE/XyCWuTDqfPgV9bs4Ffca0meqCgr4Czgd9qunz4HAwpYMrF9KaPs29w0vrtNXMps11iCnoK+JcEKJgRFwYXTlLHa37ljJ9FybodIAgUHmHuDSacE0drUFfqwvVUNBXAHXgEndAmb46WjN8rfYxRStFCKALqPBE+1YNNgr6qmgdDktB34XRnDvEHVB5Rx1U03cDAqieT1wflXfU0RrstToBjoK+Eijgc0YXTVEDlXfUQeUdN0AducQd2JcbbBqVHjyBmOlrVEKjaKUEyvSJG6Dyjjoo03cDAo3e4YqukasOSdCnCde4ETtyKdN3YYJAMZ+4PEnQZ9pMEeAJzpd3PDjTX7x4MeLi4mA2m5GUlITNmzc7XXf58uUtlyi0u5nNZhVbK0c4l+0TLhwye8r0ebCv42s1nNATnM/0PTTor1q1CpmZmcjOzsa2bdsQHx+P1NRUVFRUON3G398fpaWl4u3w4cMqttgRDdfkTRrktbyotDuT1vRtmk0I5u48fpx+bm4uZs6cifT0dAwfPhx5eXnw8fHBsmXLnG4jCAIiIiLEW3h4uIotlm2Rxs/vaSjoK43ZbA6/qCjb56N11I5HZvpNTU3YunUrUlJSxGU6nQ4pKSkoKipyul1DQwNiY2MRHR2Nm266Cb/++qsazXWqJdOnwM8LZfb8yQV4rWrO7q61v8Qjg35VVRWsVqtDph4eHo6ysjLZbYYMGYJly5bhs88+wz//+U/YbDZceumlOHr0qOz6FosFdXV1kpviqLzDGZV3eJM7O5SGbfJxvrxD8+l3SnJyMtLS0pCQkIBx48ZhzZo1CA0NxVtvvSW7fk5ODgICAsRbdHQ0h1ZR0OfJMchT0FeaXICnoM+HR2f6ISEh0Ov1KC8vlywvLy9HREREpx7DYDBg9OjR2Ldvn+z9WVlZqK2tFW9HjhzpdrvtHdy+Bps/fRrVpb/i47/E4+D2NYo+PpFBmb7i5Ms7NFafC/FyiR6Y6RuNRiQmJqKgoEBcZrPZUFBQgOTk5E49htVqxY4dOxAZGSl7v8lkgr+/v+SmlIPb1+CrJbeisfoIAIaTx3fgqyW3UuBXGgV57qi8ox5xVJSnXhg9MzMTS5YswYoVK7Br1y7MmjULjY2NSE9PBwCkpaUhKytLXH/hwoVYv349Dhw4gG3btuHuu+/G4cOHMWPGDNXbvm3dgnP1/HNvHmMABGz65EnUnzis2Sx67odq+rzJZfoU9Plg5z7PWmX6Xpo8q52pU6eisrIS8+bNQ1lZGRISEpCfny927paUlEBnN199dXU1Zs6cibKyMgQFBSExMRE//vgjhg8frnrba8t/lz1xqOHkYfyy4UU01hxr+YnMGEw+QfALjoVf71j4BceiV3As/IJj4GX0Ub3droZq+vzJJShU3uHk3OeZafQ51jzoA0BGRgYyMjJk7yssLJT8/corr+CVV15RoVUdCwgfjJPHd0gDvyAgKHIELrtjsbiIMYam0zVoOHEYDdUlqC3/Hcd2bUBDdQmam04BALwMZvgGxZw7GJw7OATFwOQbTCd/UabPHXXkqsm+MqC+HhH0XdXFE7Px1ZJb0TJ6h7WUehjDxZOyJesJggCTTxBMPkHoHZ0g+1jNTafRUH0EDScPo+HkYVQd2Yr6E4dhOXUSAKDT6eET0KflgBAcc+7/sfAJiIROp+f7Qonbk8vqtZoQzN1pnbRQ0O+GfqNvQcrMj/HTx5loOFmC4D6jcPGkbPRLuLnLj+Vl9EZg+GAEhg+Wvd9ms+JUzXHxoFC691s0nDyMU7WlAGNgYDD7hcAvqOWA0OtcGck3KBpeBq3nJiI9nVxWTydnuScK+t3Ub/QtgCBg29r5uPXZYm7Po9Pp4RccDb/gaACXO9zPGIOl8QTqTx5Gw4nDOHlsB0p2rkVj9RFYz1rAwOBl9DlfPjp369U7FkbvAG7tJq5BLqunTN89UdBXQE+YT18QBJj9QmD2C0FoTKLsOmfPNKChugQNJ0vQcPIwKg4Wof7kYTSdrgUA6HRe8A3qe/6AcO7/3r3CetjF36mmrzS5rJ5q+rxRTd91uUhHq8Hsh6DI4QiKlB/pZLOeRWPNMTScOIz6k4dxdNd6NJw8jNN15WBgECDA3CtMLB219i/4BvaF3suo4itxjf3tSqgjVz3iwAyNLrNKQV8B7jK6Rqc3oFfvOPTqHQe5U90YYzhdX3G+s/nwFhzc/jEaa46KV1oymHtJDgitvxgMZr9uta26bBdK936LM/UVWJ93I/5w419bSmtEEXKlHBqyyYtw7r/axA0K+ooQXCbb7w5BEODjHw4f/3CExY2VXafpdB0aTrb8Uqg/cVDscG62NAIAdF5G+AVFwzc4RtK/YPYLcXrwPLJzHQ5u+1D8u7b8d3y15FakzPyYAr9CKNNXkZjpU9B3XQJdN6uV0dsfwVEjERw1UvZ+61nL+aGp1SUo2fFfNJw8jDMNVS0rCAJ8/CPO/1roHYufN7zY5lFahsduW7eQgr5CbHRGrmoEjco6rSjoE1XpDSYEhA1EQNhA2fuZzYZTdWViCan8wI+orzogsyJDbfkezq31HLJDNukiKly0/qIVNDq/hoK+AijPV46g08E3sA98A/sgvH/LpHt7f1qBmrJdDuvq9AZ88+7diBw0DhGDxiEgbJDb9K+oTXYeGLpcIh/nMn2tMn4K+qTHG3nNY/j+X3YT6p0783nctBWIHHgFSvd+h9++fR21FXth8g1G5MArETl4PALCBtNBoJMo01dPa7CnjlxXRoGFq5iRN6DfxbejbO+3OF1fgYCwIfjDTX8Vz3zuN/oWsbZ/pqEKpfu+x2/fvoHait9h8glCxMAr0WfweASED6GDgBNymT7V9PkQM3yNzn2hoK8YCia8CIKAoIhhCIoYhv1b/o3rHloH/5B+suua/ULQL+Fm8YBwpuEEyvZ9j13f56GmfA9MPoGIGHglIgeNR2DEUDoItJIr79DEdly0Bn2dQDV9QjqlK4Ha7NcbcQlTEJcwBQBwpvEkyvZ9j90/LEFN2S4YvQPEclBgxDCPPQgwmfq9VvO9uzvK9AnpUNtAfOGB2ewbjLj4mxAXfxMAwHKqGmX7vseeH5eiuvQ3GM3+iBh0JSIHjUNQxPAeNv0EP7LlHQr6XLSO2qGOXJdHP4V54Zl9m3yCEDvqRsSOuhEAYDlVg7L9G/F70XJUl/4Ko9kf4QMuR5/B4xEUOcKNDwKOn18K+nwINHrHDVDtkzNp0Of5ZTH5BCJ25A2IHXkDAKDpdC3K9m3E3k3vobr0V3iZ/BAx4HJEDhqP4D4Xuc1BQHaOd/pc86GjoE9IuxwCq4p1d6N3AGJGTkLMyEkAzh0E9v+AfZv/ierSnfAy+rYcBAaPR3Cfka57EKAAr5rWDlw6OcuFaXWtS8/RNtPXrrPV6B2AmIsmIuaiiQBa5hoqP/AD9v3vA1Qf3wEvo09LOWjQeARHjXLdgwAAKllyQuUdN0BZElc9eUSN0dsf0SOuR/SI6wEATWfqUb7/B+zfugr/+/wZ6A3e538JRI2iS1uS84kABX1XRrk+X+rV9LvLaO6F6BHXIXrEdQBaLlxTduAHHNy2Gls+fxZ6gzfCB1yGyEHj0LtvAh0EPBB15LoBxhhl+6rquZl/WwazH6KHpyJ6eCoA4KylEeUHfsSh7Wuw9T/zoPcyIbz/pYgcPL7lIKDX6Csp+2vKdfazKxGDPo3Td2EU8PnqweWdrjKYfNF32LXoO+xaAC0HgYqDRTj086fY+t9s6LyMCO9/KfoMGo/e0aO1OwgAbrXfexTK9F1fy3hmCvy8OExM5UbByGDyRdTQFEQNTQEANDedQvmBIhze8Tm2rlsAnd6A8H7JiBw8HiHRF3M7CMj1m9DssXxQecctUMDnSug5o3d48zL6IGroNYgaeg2AcweBgz+hZMd/sW3dQuh0Xgjrn4w+g8YjJOZi6PQGZZ5YLgC58X7W0vnPL82y6bIYs8mf3EJIN3kZfRA15GpEDbkaANDcdBoVB3/CkV+/wLYvnms5CPS7BJGDxiE0dswFHwTkss6e3GHu0qim7/paJquioM+LY2bvuRmol9EbfYZchT5DrgIANJ89g8qDm3B013oU5/8Vgk6HsLiWg0BI7BjovYydelzZ8g4FfS4o03cLFPD58pzyTld5GcyIHDwOkYPHATh3EDi0Gcf2fIXi9S9AEASExo5F5ODxCI39g9ODgCAzza9rn1jWk527XKJGB9Ue8a4uXrwYcXFxMJvNSEpKwubNmzu13cqVKyEIAqZMmcK3gR1gzEZxn/QIXgYzIgddiYsnzkPqrM9xzX0fImLQlTi+52t8teQ2fPnmjdj2xfMo27cR1rMWcTtB0KG6bBd++z4PtRV78dv3eSjb/4OGr8R9aZ20aJ7pr1q1CpmZmcjLy0NSUhIWLVqE1NRU7NmzB2FhYU63O3ToEB5//HFcccUVKrZWXssVhijqk55HbzAhcuAViBzY8j2xnrWg8vD/cHxvIX756u9gjCE09g9oOFmCg9s+FLc7U1+ObWvnI7jPSPGqZEQp54K+RsFf80w/NzcXM2fORHp6OoYPH468vDz4+Phg2bJlTrexWq3405/+hAULFqB///4qtlYedeRyRjV9xegNJkQMvBwXXz8HEx74DCkzP0KfIVfjUPEambUFbFu3QPU2uj2htbzjgUG/qakJW7duRUpKirhMp9MhJSUFRUVFTrdbuHAhwsLCcN9993X4HBaLBXV1dZKb0mjeceKq9F5GRAy4DGctDTL3Mpw8vhP5iyfhh1UPY8+Py1BVsk1SFiLd4YEduVVVVbBarQgPD5csDw8Px+7du2W32bhxI5YuXYri4uJOPUdOTg4WLOCcrTAGKu+oiDpyFecXHIu6yr1tlgoI7jMSqbP+g/oTB3HiaDEOFq/Bti+eg625CWa/EPSOHo3efRPQu288TD5BmrSddI3mNf2uqK+vxz333IMlS5YgJCSkU9tkZWUhMzNT/Luurg7R0dGKtosyfb7ozFD+hl1xPzatebzNUoaLJ2VD0OngHzoA/qED0G/0reK9p+srcOLoz6g8tBm7N76NptO18DJ6I6jPSIScOxj4BkVr3nFJpDQN+iEhIdDr9SgvL5csLy8vR0REhMP6+/fvx6FDhzB58mRxme3cBZ29vLywZ88eDBgwQLKNyWSCyWTi0PrzqKavLgoiyus7bAIqLr4dZXu/xen6Cnj3CsOoa59Ev4SbnW7j3StMMo8Q0HIG8cljO3DiaDFKdq5DY/URCIIOAeGD0bvvaPSOTkBg+FBt5xTycJrueaPRiMTERBQUFIjDLm02GwoKCpCRkeGw/tChQ7Fjxw7Jsjlz5qC+vh6vvvqq4hl8Z9HJWZxRkOdO0OkRFDEMQRHDsH/LvzFgzJ3oO3xClx/Hy+iDsH5JCOuXJC6z2ayoLf8dJ44WY+9P76GmfDeYrRk+gVEI6dvyiyA4ahQMZj8lX5IL0CZmaH64zczMxLRp0zBmzBiMHTsWixYtQmNjI9LT0wEAaWlpiIqKQk5ODsxmMy666CLJ9oGBgQDgsFxVVN4hLk7QOYYCuRO2LoROp0dQ5DAERQ7DwD/cCaBlOvJTtcdx4kgxjv3+NXZ8swjNlkYYzL3O9RG03HwCHH/xuzyNqwKaB/2pU6eisrIS8+bNQ1lZGRISEpCfny927paUlEDXw88MZKD59NVFmb/S5C7mwrMEIwgCfAOj4BsYJV5/GGi5BvGJY7/gxJFiHNj2IU7XlUOn90JgxHCx0zggdKCLny3cEiu0KglrHvQBICMjQ7acAwCFhYXtbrt8+XLlG9RF7Fy/AmOM6s0c0D7lTzbT1+CqXkbvAMnJZABgbW5CTdkunDhajF3fvYHayn0AY+jVO048EAT1uQheBrPq7e0WTw76ru/cm8cY1Z+JS5IL8DqZA4EW9F5G9O4bj95944FLpgFoSbDqTxzCiaPFOPzL5yjO/yuszRaYfINbSkPnDgZm32CNW+/ofIZPQd9ltQ7ZZMwGQfuTnN0QTbjGm1yA1yLT7yxBEOAf0g/+If0kI4zONFThxNGfceLIdvxe9C4sjSeh9zIhOGqkOHrILzhW08/Q+XhBQd91aXzkJqS75Or3ciWfns7sFyK5CA3Qcg2C6uM7ceJoMX5e/yIaTh4GBAEBYYNaDgR9ExAUOUy5C9J0iDJ9l8c07pghpLt6cnmnu7yM3giN+wNC4/4gLrPZrKir3IcTR4qxf8sHqCnbBZv1LLz9I9G7bwJCokcjOGoUjN7+yjfoXJxo7QtUm3u8q1pjTPp/oiyacI27ntKRqxadTo/A8CEIDB+CAWOmistP1ZbixNFilO37Hr9++zrOnqmHl8kXvaPixX4Cn4DIbpWH7MvBWqCgrwDxTaTyDnFRskM23STT7wqfgEj4BEQiesT14rKmM/U4eewXnDhajEPFa3CqthSCTo/AiGHidBP+YYNk96Gc88GeyjuuizJ9rhzm3qGOXMV5WqbfFUZzL0QMuAwRAy4Tl9msZ1FTtrtlGOnGt1FXsReM2eAXHCOOHgrucxG8jD6Sxzq4fQ3+99kzqK3Yiy9evx5/uPEvql+vgIK+As7X9OnMXOKa5AI8BX3ndHoDgqNGIjhqJAYl3QOgpU+v4WQJThwtxpGd6/Dz+r/BevYMjD6B6N03Ac2WBmxbtxAt5UmG2vI9+GrJrUiZ+bGqgZ+CvhIowycuTu56rZ5Y3ukOQRDQq3csevWORVz8TeLyM40ncfLoz/hm+Z/OLbEbvSMI2LZuoapBnwaVK4HKO8TFCYIAwX7YpiC4+FQHPYfZNxh9hlwFS2O1452sJeNXE72rCqKOXOLKdHYTrFFpR3kB4YMd+6MEAQHhQ1RtBwV9BTBQpk9cn32g1yk0wyY57+KJ2ediRGvgFwDWcqEaNVHQVwKdkcsXjdZRhX1dnzJ95fUbfQtSZn4M/7CBAFrOCE7585p2L1TDA/XUKIDKOsQd2Ad6uY5d0n39Rt8Cv6BofLP8blwz48OWSeRURu+sgmgaBuLKKNNXh9Zn5FLQVwKVd/iig6kqKNNXhxjsNfpc0zurCApKxPVJAj0FfX6Ytidz0jurICrv8EF9Jiqx6zCnMfr8aD3aj95ZBTA6OUtdtJ+5kNT0KdPnRusJGumdJYQAoKCvOsr0XRhlnnw57F/a31zYnw9B50bwo3G8oKCvKApGxJXZ1fTpQjXcUXmHEKekXw7qMOeDLjjvGSjoE0Ic0QHAbVHQJz0eZfbEHWlVQqOgT1wQHQSIC9P4VxQFfdLzUaZP3JFGwb9HBP3FixcjLi4OZrMZSUlJ2Lx5s9N116xZgzFjxiAwMBC+vr5ISEjA+++/r2Jridao3KMC2sfctJZ1PLa8s2rVKmRmZiI7Oxvbtm1DfHw8UlNTUVFRIbt+cHAwnn32WRQVFeGXX35Beno60tPT8eWXX6rccqIWmoZBHXQwVYd44punZvq5ubmYOXMm0tPTMXz4cOTl5cHHxwfLli2TXX/8+PG4+eabMWzYMAwYMACzZ8/GqFGjsHHjRpVbLodGPKiCghMnzO5ftI+5aQ32nhj0m5qasHXrVqSkpIjLdDodUlJSUFRU1OH2jDEUFBRgz549uPLKK3k2lWiJzshVh/1+pgMrP4K25R1Nr5xVVVUFq9WK8PBwyfLw8HDs3r3b6Xa1tbWIioqCxWKBXq/HG2+8gWuvvVZ2XYvFAovFIv5dV1enTOPt0Zhm4gbss3sq9fDTWt7Ran4jl7xcYq9evVBcXIyGhgYUFBQgMzMT/fv3x/jx4x3WzcnJwYIFC7i2R9D455r7ozNy1cBsNvs/tGuImztf0/fAoB8SEgK9Xo/y8nLJ8vLyckRERDjdTqfTYeDAgQCAhIQE7Nq1Czk5ObJBPysrC5mZmeLfdXV1iI6OVuYFiM79XKOgz4VjkKegzwfV9NUgZvoaXbNA05q+0WhEYmIiCgoKxGU2mw0FBQVITk7u9OPYbDZJCceeyWSCv7+/5KY4CvbEHdgdXCVZP1GUx5d3MjMzMW3aNIwZMwZjx47FokWL0NjYiPT0dABAWloaoqKikJOTA6ClXDNmzBgMGDAAFosF69atw/vvv48333xTs9cgjrulOcj5YFTeUYP08n20j7nx9KA/depUVFZWYt68eSgrK0NCQgLy8/PFzt2SkhLo7H4GNTY24sEHH8TRo0fh7e2NoUOH4p///CemTp2q1Uuwy/Qp4+fBodRAQZ8L+4OpVtdv9QRiWcdTgz4AZGRkICMjQ/a+wsJCyd/PP/88nn/+eRVa1Xni0Csq8xCXRkM21aB1eYfqEUqgYM8XdeSqwj67p45cfjy6I9dtCFTT54tq+qqgk7PUQZm+6xNoyCZXNGRTHYzKO6qg8o47oI5czijTVwWjcfpqOH8yJwV9l0UduXw5BHkK+nxQeUcdYqbvgROuuQ2q6fNFAYi4ESrvuAOBavp8UU2fuA8q77iB81OkUtBXA9X0iUuj8o4boAyfq7ZBns4W5Y86cvkRNB74QUFfATT3Dm8UgFRhl7zQZ5knbcvB9M4qgebT54rG6avEPuhTqVIFFPRdGH1BuKIavioo0KtD6wEfFPQVpPWb6THoIMCH/eeXPsv8UXnHdVGwJ+7Avo5PNX0VaJS80DtLXABNw6AGCvrq0PrzS++sEijTJ25AEugp6KuAMn1CiJbsM32N5nr3DC3BXquMn95ZJVC5gbgB+0BP5R1+zgd7CvqEOCEtn1HHOR+Smr5Or2FL3Ny5M8qZTZszyynoE0IASAM9Zfr8UKbvBlrfRK175d1W28yeMn0uaPSOOlrnjtJqDil6ZxVBwZ6ntgGIAhIfVN5RB7NZJf9XG317lESZPheONXzK9Hmg8o46GGsJ9jYK+i6stbxDGT8n1JGrBirvqEPM8Km847rE2hxl+lw4jBmngMQHnZylitagT5m+C2PiyRZ0cQ8eqKavDoHm01fF+Zp+sybPT++sEmza9sa7PQr6qpBOw0AlNF5s54I9deS6MCrv8KVrM5KEpgjghGr6qjhf3vHgTH/x4sWIi4uD2WxGUlISNm/e7HTdJUuW4IorrkBQUBCCgoKQkpLS7vpqoPIOZw6ZPg0n5MH+IirUWc6PmOlbPTTor1q1CpmZmcjOzsa2bdsQHx+P1NRUVFRUyK5fWFiIO++8E9988w2KiooQHR2NCRMm4NixYyq33A6Vd7hyzPQp6PNHQZ+X1mDvsZl+bm4uZs6cifT0dAwfPhx5eXnw8fHBsmXLZNf/17/+hQcffBAJCQkYOnQo3nnnHdhsNhQUFKjc8vPOl3co6PPQNrOnoM8JXTlLFa3BXqug76XJs57T1NSErVu3IisrS1ym0+mQkpKCoqKiTj3GqVOncPbsWQQHB8veb7FYYLFYxL9ra2sBAHV1dd1ouVR94ymcslhRW1sD01n6svDQcNoCMOCUxYqGhkZ4NVH/idIaGk+j4ZQFpyxW1DecUvQ7Qs6rravFKYsVdbW18FV4H/fq1avj0hzT0LFjxxgA9uOPP0qWP/HEE2zs2LGdeoxZs2ax/v37s9OnT8ven52dzdAyTwLd6EY3urn1rba2tsOYqWmm310vvPACVq5cicLCQpjNZtl1srKykJmZKf5ts9lw8uRJ9O7dW7HOqrq6OkRHR+PIkSPw9/dX5DGJI9rP/NE+5o/nPu7Vq1eH62ga9ENCQqDX61FeXi5ZXl5ejoiIiHa3femll/DCCy/gq6++wqhRo5yuZzKZYDKZJMsCAwMvuM3t8ff3py+KCmg/80f7mD+t9rGmHblGoxGJiYmSTtjWTtnk5GSn27344ot47rnnkJ+fjzFjxqjRVEIIcQual3cyMzMxbdo0jBkzBmPHjsWiRYvQ2NiI9PR0AEBaWhqioqKQk5MDAPjb3/6GefPm4YMPPkBcXBzKysoAAH5+fvDz89PsdRBCiCvQPOhPnToVlZWVmDdvHsrKypCQkID8/HyEh4cDAEpKSqCzOwPzzTffRFNTE2677TbJ42RnZ2P+/PlqNl1kMpmQnZ3tUEYiyqL9zB/tY/603scCYzR3ACGEeArNT84ihBCiHgr6hBDiQSjoE0KIB6GgTwghHoSCvozvvvsOkydPRp8+fSAIAj799FPJ/YwxzJs3D5GRkfD29kZKSgr27t0rWUcQBAiCgJ9++kmy3GKxiGcDFxYWcn4lPVdH+7i8vBz33nsv+vTpAx8fH1x33XUO+zguLg6CIGDlypUOjz9ixAgIgoDly5dzfBU925tvvolRo0aJJwElJyfjiy++EO8/c+YMHnroIfTu3Rt+fn649dZbJSdKHjp0CIIgQK/XO8xiW1paCi8vLwiCgEOHDqn1knqk+fPni9/31tvQoUPF+/fv34+bb74ZoaGh8Pf3x+233+5wQqqa8YKCvozGxkbEx8dj8eLFsve/+OKL+Mc//oG8vDxs2rQJvr6+SE1NxZkzZyTrRUdH491335Us++STT+h8ArS/jxljmDJlCg4cOIDPPvsM27dvR2xsLFJSUtDY2ChZV24f//TTTygrK4Ovry/X19DT9e3bFy+88AK2bt2KLVu24Oqrr8ZNN92EX3/9FQDw6KOP4j//+Q9Wr16Nb7/9FsePH8ctt9zi8DhRUVF47733JMtWrFiBqKgoVV6HKxgxYgRKS0vF28aNGwG0fM4nTJgAQRDw9ddf44cffkBTUxMmT54Mm006K69q8aJTs5p5MADsk08+Ef+22WwsIiKC/f3vfxeX1dTUMJPJxP79739LtpszZw7z9/dnp06dEpdfe+21bO7cuQwA++abb9R4CT1e2328Z88eBoDt3LlTXGa1WlloaChbsmSJuCw2NpY9/fTTzGQysZKSEnH5zJkz2cMPP8wCAgLYu+++q8ZLcBlBQUHsnXfeYTU1NcxgMLDVq1eL9+3atYsBYEVFRYwxxg4ePCh+jgcNGiR5nMGDB4uf44MHD6r5Enqc7OxsFh8fL3vfl19+yXQ6nWQitJqaGiYIAtuwYYO4TM14QZl+Fx08eBBlZWVISUkRlwUEBCApKclhOujExETExcXh448/BtByotl3332He+65R9U2u5rWqbDtJ9HT6XQwmUxiBtUqPDwcqampWLFiBYCWqbZXrVqF6dOnq9dgF2C1WrFy5Uo0NjYiOTkZW7duxdmzZyWf46FDhyImJsbhc3zjjTeiurpa3PcbN25EdXU1Jk+erOpr6Mn27t2LPn36oH///vjTn/6EkpISAC2fZUEQJCdimc1m6HQ6h8+yWvGCgn4XtU770HrGcKvw8HDxPnvTp08XLwizfPlyTJw4EaGhofwb6sJag09WVhaqq6vR1NSEv/3tbzh69ChKS0sd1p8+fTqWL18Oxhg++ugjDBgwAAkJCeo3vAfasWMH/Pz8YDKZ8MADD+CTTz7B8OHDUVZWBqPR6DD5oNzn2GAw4O677xY/x8uWLcPdd98Ng8Gg1svo0ZKSkrB8+XLk5+fjzTffxMGDB3HFFVegvr4el1xyCXx9ffHUU0/h1KlTaGxsxOOPPw6r1er0s8w7XlDQ5+zuu+9GUVERDhw4gOXLl1MG2gkGgwFr1qzB77//juDgYPj4+OCbb77B9ddfL5mSo9WkSZPQ0NCA7777DsuWLaN9bGfIkCEoLi7Gpk2bMGvWLEybNg2//fZblx9n+vTpWL16NcrKyrB69Wrax3auv/56/PGPf8SoUaOQmpqKdevWoaamBh9++CFCQ0OxevVq/Oc//4Gfnx8CAgJQU1ODiy++WPazrEa80HzuHVfTOuVzeXk5IiMjxeXl5eWy2WXv3r1xww034L777sOZM2dw/fXXo76+Xq3muqzExEQUFxejtrYWTU1NCA0NRVJSkuysql5eXrjnnnuQnZ2NTZs24ZNPPtGgxT2T0WjEwIEDAbTs0//973949dVXMXXqVDQ1NaGmpkaS7Tub1nzkyJEYOnQo7rzzTgwbNgwXXXQRiouLVXoVriUwMBCDBw/Gvn37AAATJkzA/v37UVVVBS8vLwQGBiIiIgL9+/d32FaNeEGZfhf169cPERERkumg6+rqsGnTJqfTQU+fPh2FhYVIS0uDXk/Xd+2KgIAAhIaGYu/evdiyZQtuuukm2fWmT5+Ob7/9FjfddBOCgoJUbqXrsNlssFgsSExMhMFgkHyO9+zZg5KSkg4/x5Tlt6+hoQH79++XJIVAy/VDAgMD8fXXX6OiogI33nij7Pa84wVl+jIaGhrEozTQ0nlbXFyM4OBgxMTE4JFHHsHzzz+PQYMGoV+/fpg7dy769OmDKVOmyD7eddddh8rKSroohZ2O9vHq1asRGhqKmJgY7NixA7Nnz8aUKVMwYcIE2ccbNmwYqqqq4OPjo9ZL6PGysrJw/fXXIyYmBvX19fjggw9QWFiIL7/8EgEBAbjvvvuQmZmJ4OBg+Pv74+GHH0ZycjIuueQS2cebOXMm/vjHP3K7CJGrevzxxzF58mTExsbi+PHjyM7Ohl6vx5133gkAePfddzFs2DCEhoaiqKgIs2fPxqOPPoohQ4bIPh73eKHIGCA3880338hef3LatGmMsZZhm3PnzmXh4eHMZDKxa665hu3Zs0fyGGgzDNFedXW1xw/Z7Ggfv/rqq6xv377MYDCwmJgYNmfOHGaxWCSPERsby1555RWnz+HpQzanT5/OYmNjmdFoZKGhoeyaa65h69evF+8/ffo0e/DBB1lQUBDz8fFhN998MystLRXvbx2yuX37dtnH3759Ow3ZZIxNnTqVRUZGMqPRyKKiotjUqVPZvn37xPufeuopFh4ezgwGAxs0aBB7+eWXmc1mkzyGmvGCplYmhBAPQjV9QgjxIBT0CSHEg1DQJ4QQD0JBnxBCPAgFfUII8SAU9AkhxINQ0CeEEA9CQZ8QQjwIBX1CCPEgFPQJIcSDUNAnhBAPQkGfEEI8yP8DHiwt45H76lkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import colorcet as cc\n",
+    "\n",
+    "metadata_predictions_type = \"high_w_XY\"\n",
+    "\n",
+    "plot_props_list = [\n",
+    "    {\n",
+    "        'metric_to_plot': 'auc',\n",
+    "        'label': 'AUC',\n",
+    "        'ylim': (0.2, 1),\n",
+    "        'show_legend': True,\n",
+    "        'show_violin': True,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "color = cc.glasbey_light[11]\n",
+    "\n",
+    "for plot_props in plot_props_list:\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "        \n",
+    "    sub_stats_df = stats_df[\n",
+    "        (stats_df[\"metadata_predictions_type\"] == metadata_predictions_type)\n",
+    "    ]\n",
+    "\n",
+    "    if len(sub_stats_df) == 0:\n",
+    "        continue\n",
+    "\n",
+    "    y = sub_stats_df[f\"{plot_props['metric_to_plot']}_mean\"].values\n",
+    "    y_hi = sub_stats_df[f\"{plot_props['metric_to_plot']}_75\"].values\n",
+    "    y_lo = sub_stats_df[f\"{plot_props['metric_to_plot']}_25\"].values\n",
+    "    y_err = np.vstack([y - y_lo, y_hi - y])\n",
+    "\n",
+    "    if plot_props['show_violin']:\n",
+    "        points = []\n",
+    "        for prefix in prefix_list:\n",
+    "            points.append(points_for_violin[(prefix, metadata_predictions_type)])\n",
+    "\n",
+    "        violin_parts = ax.violinplot(\n",
+    "            points,\n",
+    "            np.arange(len(prefix_list)),\n",
+    "            side='both',\n",
+    "            showextrema=False,\n",
+    "            bw_method=0.1,\n",
+    "            widths=0.15,\n",
+    "        )\n",
+    "\n",
+    "        for pc in violin_parts['bodies']:\n",
+    "            pc.set_facecolor(color)  # Set face color\n",
+    "            pc.set_edgecolor(None)    # Set edge color\n",
+    "            pc.set_alpha(0.5)            # Set transparency\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        np.arange(len(prefix_list)),\n",
+    "        y,\n",
+    "        y_err,\n",
+    "        fmt='o',\n",
+    "        lw=0.5,\n",
+    "        color=color,\n",
+    "        capsize=2,\n",
+    "        markersize=4,\n",
+    "        linestyle='-'\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    ax.set_xticks(np.arange(len(prefix_list)))\n",
+    "    ax.set_xticklabels([x[:3] for x in prefix_list])\n",
+    "    ax.set_ylim(plot_props['ylim'])\n",
+    "    ax.set_ylabel(plot_props['label'])\n",
+    "\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.set_title(f'Tissue prediction')\n",
+    "# fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 687,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>auc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>granule cell</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>gut endothelial cell</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>L2/3 intratelencephalic projecting glutamaterg...</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>inhibitory interneuron</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>lamp5 GABAergic cortical interneuron</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>transitional stage B cell</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>transit amplifying cell</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>intermediate monocyte</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>212 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type  auc\n",
+       "110                                       granule cell  1.0\n",
+       "111                               gut endothelial cell  1.0\n",
+       "21   L2/3 intratelencephalic projecting glutamaterg...  1.0\n",
+       "120                             inhibitory interneuron  1.0\n",
+       "136               lamp5 GABAergic cortical interneuron  1.0\n",
+       "..                                                 ...  ...\n",
+       "116                                         hepatocyte  0.0\n",
+       "204                          transitional stage B cell  0.0\n",
+       "202                            transit amplifying cell  0.0\n",
+       "191                        renal interstitial pericyte  0.0\n",
+       "122                              intermediate monocyte  0.0\n",
+       "\n",
+       "[212 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "# show all rows\n",
+    "with pd.option_context('display.max_rows', 50):\n",
+    "    display(per_cell_type_stats_dict[(prefix_list[-1], \"high_w_XY\")].sort_values(\"auc\", ascending=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Compute accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 688,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0734f38843b443659ccbd21b450f2b3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21158d29c92d4effbed82f048ad2b802",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d5a1a8b6b5a4b0cb6a56e172abc5905",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b9f8f551b7c4bf7a825a24fda5537e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f6188db5a3e4f59ae62e839a8097a7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from scipy.special import logsumexp\n",
+    "\n",
+    "key = \"tissue\"\n",
+    "\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_rand_wo_XY_root_path,\n",
+    "    metadata_predictions_high_w_XY_root_path,\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'rand_wo_XY',\n",
+    "    'high_w_XY',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            # get coarsening indices (we use `j` for coarse label index, in the other appears in all_coarse_labels)\n",
+    "            idx_to_j_map = dict()\n",
+    "            j_to_idx_map = defaultdict(list)\n",
+    "            for j, coarse_label in enumerate(tissue_coarsener.all_coarse_labels):\n",
+    "                for descendants_term_id in tissue_coarsener.coarse_label_to_descendants_term_ids_map[coarse_label]:\n",
+    "                    if descendants_term_id in mapper:\n",
+    "                        idx_to_j_map[mapper[descendants_term_id]] = j\n",
+    "                        j_to_idx_map[j].append(mapper[descendants_term_id])\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "\n",
+    "            # drop \"other\"\n",
+    "            logits_nk[:, j_to_idx_map[len(tissue_coarsener.all_coarse_labels) - 1]] = -np.inf\n",
+    "\n",
+    "            pred_code_n = np.argmax(logits_nk, axis=1)\n",
+    "            pred_coarse_code_n = np.asarray(\n",
+    "                [idx_to_j_map[idx] if idx in idx_to_j_map else np.nan for idx in pred_code_n])\n",
+    "\n",
+    "            # the coarse ground truth label\n",
+    "            labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "            names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "            codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "            coarse_codes_n = np.asarray([idx_to_j_map[code] if code in idx_to_j_map else np.nan for code in codes_n])\n",
+    "\n",
+    "            # did we make the right call?\n",
+    "            call_n = (pred_coarse_code_n == coarse_codes_n).astype(int)\n",
+    "\n",
+    "            # record cell types\n",
+    "            cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = (~np.isnan(codes_n)) & (~np.isnan(coarse_codes_n)) & (~np.isnan(pred_coarse_code_n)) & (~np.isnan(call_n))\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "            cell_type_n = cell_type_n[valid_indices]\n",
+    "            coarse_codes_n = coarse_codes_n[valid_indices].astype(int)\n",
+    "            pred_coarse_code_n = pred_coarse_code_n[valid_indices].astype(int)\n",
+    "            call_n = call_n[valid_indices]\n",
+    "\n",
+    "            n_rows = len(labels_n)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "            results[\"coarse_codes\"] += coarse_codes_n.tolist()\n",
+    "            results[\"pred_coarse_codes\"] += pred_coarse_code_n.tolist()\n",
+    "            results[\"call\"] += call_n.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 689,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 690,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})\n",
+    "bq_cell_type_stats_df\n",
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 691,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"cell_type\", right_on=\"cell_type\", how=\"left\").dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 692,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__tissue__accuracy__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Sneak preview (confusion matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 693,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__tissue__accuracy__03_03_2025.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 694,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "y_true = df[(df[\"prefix\"] == prefix_list[-1]) & (df[\"metadata_predictions_type\"] == \"rand_wo_XY\")][\"coarse_codes\"].values\n",
+    "y_pred = df[(df[\"prefix\"] == prefix_list[-1]) & (df[\"metadata_predictions_type\"] == \"rand_wo_XY\")][\"pred_coarse_codes\"].values\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred, labels=np.arange(len(tissue_coarsener.all_coarse_labels[:-1])), normalize='true')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 695,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'True label')"
+      ]
+     },
+     "execution_count": 695,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the confision matrix using matplotlib\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "cax = ax.matshow(np.log1p(cm), cmap='Blues')\n",
+    "ax.set_xticks(np.arange(len(tissue_coarsener.all_coarse_labels[:-1])))\n",
+    "ax.set_yticks(np.arange(len(tissue_coarsener.all_coarse_labels[:-1])))\n",
+    "ax.set_xticklabels(tissue_coarsener.all_coarse_labels[:-1], rotation=90)\n",
+    "ax.set_yticklabels(tissue_coarsener.all_coarse_labels[:-1])\n",
+    "ax.set_xlabel('Predicted label')\n",
+    "ax.set_ylabel('True label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 696,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2a5e370555d4d9db4d8180b5b6eaf4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:2524: UserWarning: y_pred contains classes not in y_true\n",
+      "  warnings.warn(\"y_pred contains classes not in y_true\")\n",
+      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:407: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import balanced_accuracy_score\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    # 'rand_wo_XY',\n",
+    "    'high_w_XY'\n",
+    "]\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "min_train_cell_fraction = 0.0001\n",
+    "\n",
+    "def compute_accuracy(group):\n",
+    "    if len(group) < 10:\n",
+    "        return np.nan\n",
+    "    accuracy = balanced_accuracy_score(group[\"coarse_codes\"], group[\"pred_coarse_codes\"])\n",
+    "    return accuracy\n",
+    "\n",
+    "points_for_violin = dict()\n",
+    "per_cell_type_stats_dict = dict()\n",
+    "\n",
+    "for prefix in tqdm(prefix_list):\n",
+    "    for metadata_predictions_type in metadata_predictions_types:\n",
+    "        _sub_df = df[\n",
+    "            (df[\"prefix\"] == prefix) &\n",
+    "            (df[\"metadata_predictions_type\"] == metadata_predictions_type) &\n",
+    "            (df[\"train_cell_fraction\"] >= min_train_cell_fraction)\n",
+    "        ]\n",
+    "\n",
+    "        _sub_df = _sub_df.groupby([\"cell_type\"]).filter(lambda x: len(x) >= 0)\n",
+    "        n_cell_types = len(_sub_df[\"cell_type\"].unique())\n",
+    "\n",
+    "        # calculate accuracy\n",
+    "        per_cell_type_stats = (\n",
+    "            _sub_df.groupby(\"cell_type\")\n",
+    "            .apply(compute_accuracy)\n",
+    "            .dropna()\n",
+    "            .reset_index(name=\"accuracy\")\n",
+    "        )\n",
+    "\n",
+    "        points_for_violin[(prefix, metadata_predictions_type)] = per_cell_type_stats[\"accuracy\"].values\n",
+    "        per_cell_type_stats_dict[(prefix, metadata_predictions_type)] = per_cell_type_stats\n",
+    "        \n",
+    "        if len(per_cell_type_stats) == 0:\n",
+    "            continue\n",
+    "        \n",
+    "        quants = np.quantile(per_cell_type_stats[\"accuracy\"], [0.25, 0.5, 0.75])\n",
+    "        accuracy_mean = np.nanmean(per_cell_type_stats[\"accuracy\"])\n",
+    "\n",
+    "        stats[\"prefix\"].append(prefix)\n",
+    "        stats[\"metadata_predictions_type\"].append(metadata_predictions_type)\n",
+    "\n",
+    "        stats[\"n_cell_types\"].append(n_cell_types)\n",
+    "        stats[\"accuracy_25\"].append(quants[0])\n",
+    "        stats[\"accuracy_50\"].append(quants[1])\n",
+    "        stats[\"accuracy_75\"].append(quants[2])\n",
+    "        stats[\"accuracy_mean\"].append(accuracy_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 697,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>prefix</th>\n",
+       "      <th>metadata_predictions_type</th>\n",
+       "      <th>n_cell_types</th>\n",
+       "      <th>accuracy_25</th>\n",
+       "      <th>accuracy_50</th>\n",
+       "      <th>accuracy_75</th>\n",
+       "      <th>accuracy_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10M_001_bs1536</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>232</td>\n",
+       "      <td>0.096354</td>\n",
+       "      <td>0.404981</td>\n",
+       "      <td>0.913234</td>\n",
+       "      <td>0.467374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19M_001_bs2048</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>232</td>\n",
+       "      <td>0.126488</td>\n",
+       "      <td>0.446124</td>\n",
+       "      <td>0.925337</td>\n",
+       "      <td>0.490484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>30M_001_bs2560</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>232</td>\n",
+       "      <td>0.041151</td>\n",
+       "      <td>0.325316</td>\n",
+       "      <td>0.797334</td>\n",
+       "      <td>0.414464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>59M_001_bs3072</td>\n",
+       "      <td>high_w_XY</td>\n",
+       "      <td>232</td>\n",
+       "      <td>0.008989</td>\n",
+       "      <td>0.321792</td>\n",
+       "      <td>0.885579</td>\n",
+       "      <td>0.427038</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           prefix metadata_predictions_type  n_cell_types  accuracy_25  \\\n",
+       "0  10M_001_bs1536                 high_w_XY           232     0.096354   \n",
+       "1  19M_001_bs2048                 high_w_XY           232     0.126488   \n",
+       "2  30M_001_bs2560                 high_w_XY           232     0.041151   \n",
+       "3  59M_001_bs3072                 high_w_XY           232     0.008989   \n",
+       "\n",
+       "   accuracy_50  accuracy_75  accuracy_mean  \n",
+       "0     0.404981     0.913234       0.467374  \n",
+       "1     0.446124     0.925337       0.490484  \n",
+       "2     0.325316     0.797334       0.414464  \n",
+       "3     0.321792     0.885579       0.427038  "
+      ]
+     },
+     "execution_count": 697,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats_df = pd.DataFrame(stats)\n",
+    "stats_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 698,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Tissue prediction')"
+      ]
+     },
+     "execution_count": 698,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import colorcet as cc\n",
+    "\n",
+    "metadata_predictions_type = \"high_w_XY\"\n",
+    "\n",
+    "plot_props_list = [\n",
+    "    {\n",
+    "        'metric_to_plot': 'accuracy',\n",
+    "        'label': 'AUC',\n",
+    "        'ylim': (0.2, 1),\n",
+    "        'show_legend': True,\n",
+    "        'show_violin': True,\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "color = cc.glasbey_light[11]\n",
+    "\n",
+    "for plot_props in plot_props_list:\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "        \n",
+    "    sub_stats_df = stats_df[\n",
+    "        (stats_df[\"metadata_predictions_type\"] == metadata_predictions_type)\n",
+    "    ]\n",
+    "\n",
+    "    if len(sub_stats_df) == 0:\n",
+    "        continue\n",
+    "\n",
+    "    y = sub_stats_df[f\"{plot_props['metric_to_plot']}_mean\"].values\n",
+    "    y_hi = sub_stats_df[f\"{plot_props['metric_to_plot']}_75\"].values\n",
+    "    y_lo = sub_stats_df[f\"{plot_props['metric_to_plot']}_25\"].values\n",
+    "    y_err = np.vstack([y - y_lo, y_hi - y])\n",
+    "\n",
+    "    if plot_props['show_violin']:\n",
+    "        points = []\n",
+    "        for prefix in prefix_list:\n",
+    "            points.append(points_for_violin[(prefix, metadata_predictions_type)])\n",
+    "\n",
+    "        violin_parts = ax.violinplot(\n",
+    "            points,\n",
+    "            np.arange(len(prefix_list)),\n",
+    "            side='both',\n",
+    "            showextrema=False,\n",
+    "            bw_method=0.1,\n",
+    "            widths=0.15,\n",
+    "        )\n",
+    "\n",
+    "        for pc in violin_parts['bodies']:\n",
+    "            pc.set_facecolor(color)  # Set face color\n",
+    "            pc.set_edgecolor(None)    # Set edge color\n",
+    "            pc.set_alpha(0.5)            # Set transparency\n",
+    "\n",
+    "    ax.errorbar(\n",
+    "        np.arange(len(prefix_list)),\n",
+    "        y,\n",
+    "        y_err,\n",
+    "        fmt='o',\n",
+    "        lw=0.5,\n",
+    "        color=color,\n",
+    "        capsize=2,\n",
+    "        markersize=4,\n",
+    "        linestyle='-'\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    ax.set_xticks(np.arange(len(prefix_list)))\n",
+    "    ax.set_xticklabels([x[:3] for x in prefix_list])\n",
+    "    ax.set_ylim(plot_props['ylim'])\n",
+    "    ax.set_ylabel(plot_props['label'])\n",
+    "\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.set_title(f'Tissue prediction')\n",
+    "# fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 701,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CD14-positive, CD16-positive monocyte</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>vip GABAergic cortical interneuron</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>forebrain neuroblast</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>L2/3 intratelencephalic projecting glutamaterg...</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>retina horizontal cell</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD8-alpha-alpha-positive, alpha-beta intraepit...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>tracheal goblet cell</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>transit amplifying cell of colon</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>transit amplifying cell</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>212 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             cell_type  accuracy\n",
+       "5                CD14-positive, CD16-positive monocyte       1.0\n",
+       "211                 vip GABAergic cortical interneuron       1.0\n",
+       "105                               forebrain neuroblast       1.0\n",
+       "96          epithelial cell of lower respiratory tract       1.0\n",
+       "21   L2/3 intratelencephalic projecting glutamaterg...       1.0\n",
+       "..                                                 ...       ...\n",
+       "193                             retina horizontal cell       0.0\n",
+       "13   CD8-alpha-alpha-positive, alpha-beta intraepit...       0.0\n",
+       "200                               tracheal goblet cell       0.0\n",
+       "203                   transit amplifying cell of colon       0.0\n",
+       "202                            transit amplifying cell       0.0\n",
+       "\n",
+       "[212 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The sex of which cell types are most predictable and least predictable?\n",
+    "# show all rows\n",
+    "with pd.option_context('display.max_rows', 50):\n",
+    "    display(per_cell_type_stats_dict[(prefix_list[-1], \"high_w_XY\")].sort_values(\"accuracy\", ascending=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sanity check (run on training chunk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = defaultdict(list)\n",
+    "\n",
+    "meta_adata = sc.read_h5ad(\n",
+    "    \"/home/mehrtash/data/data/metadata_predictions_rand__8192_without_XY_training_100M_old/extract_0_metadata_prediction_scores.h5ad\"\n",
+    ")\n",
+    "\n",
+    "key = \"tissue\"\n",
+    "prefix = \"N/A\"\n",
+    "metadata_predictions_type = \"N/A\"\n",
+    "\n",
+    "terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "# get coarsening indices (we use `j` for coarse label index, in the other appears in all_coarse_labels)\n",
+    "idx_to_j_map = dict()\n",
+    "j_to_idx_map = defaultdict(list)\n",
+    "for j, coarse_label in enumerate(tissue_coarsener.all_coarse_labels):\n",
+    "    for descendants_term_id in tissue_coarsener.coarse_label_to_descendants_term_ids_map[coarse_label]:\n",
+    "        if descendants_term_id in mapper:\n",
+    "            idx_to_j_map[mapper[descendants_term_id]] = j\n",
+    "            j_to_idx_map[j].append(mapper[descendants_term_id])\n",
+    "\n",
+    "logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "\n",
+    "# drop \"other\"\n",
+    "logits_nk[:, j_to_idx_map[len(tissue_coarsener.all_coarse_labels) - 1]] = -np.inf\n",
+    "\n",
+    "pred_code_n = np.argmax(logits_nk, axis=1)\n",
+    "pred_coarse_code_n = np.asarray(\n",
+    "    [idx_to_j_map[idx] if idx in idx_to_j_map else np.nan for idx in pred_code_n])\n",
+    "\n",
+    "# the coarse ground truth label\n",
+    "labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "coarse_codes_n = np.asarray([idx_to_j_map[code] if code in idx_to_j_map else np.nan for code in codes_n])\n",
+    "\n",
+    "# did we make the right call?\n",
+    "call_n = (pred_coarse_code_n == coarse_codes_n).astype(int)\n",
+    "\n",
+    "# record cell types\n",
+    "cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "# drop nans\n",
+    "valid_indices = (~np.isnan(codes_n)) & (~np.isnan(coarse_codes_n)) & (~np.isnan(pred_coarse_code_n)) & (~np.isnan(call_n))\n",
+    "\n",
+    "assert np.sum(valid_indices) > 0\n",
+    "\n",
+    "labels_n = labels_n[valid_indices]\n",
+    "names_n = names_n[valid_indices]\n",
+    "cell_type_n = cell_type_n[valid_indices]\n",
+    "coarse_codes_n = coarse_codes_n[valid_indices].astype(int)\n",
+    "pred_coarse_code_n = pred_coarse_code_n[valid_indices].astype(int)\n",
+    "call_n = call_n[valid_indices]\n",
+    "\n",
+    "n_rows = len(labels_n)\n",
+    "\n",
+    "results[\"prefix\"] += [prefix] * n_rows\n",
+    "results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "\n",
+    "results[\"labels\"] += labels_n.tolist()\n",
+    "results[\"names\"] += names_n.tolist()\n",
+    "results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "results[\"coarse_codes\"] += coarse_codes_n.tolist()\n",
+    "results[\"pred_coarse_codes\"] += pred_coarse_code_n.tolist()\n",
+    "results[\"call\"] += call_n.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'True label')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(results)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "y_true = df[\"coarse_codes\"].values\n",
+    "y_pred = df[\"pred_coarse_codes\"].values\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred, labels=np.arange(len(tissue_coarsener.all_coarse_labels[:-1])), normalize='true')\n",
+    "\n",
+    "# plot the confision matrix using matplotlib\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "cax = ax.matshow(np.log1p(cm), cmap='Blues')\n",
+    "ax.set_xticks(np.arange(len(tissue_coarsener.all_coarse_labels[:-1])))\n",
+    "ax.set_yticks(np.arange(len(tissue_coarsener.all_coarse_labels[:-1])))\n",
+    "ax.set_xticklabels(tissue_coarsener.all_coarse_labels[:-1], rotation=90)\n",
+    "ax.set_yticklabels(tissue_coarsener.all_coarse_labels[:-1])\n",
+    "ax.set_xlabel('Predicted label')\n",
+    "ax.set_ylabel('True label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = defaultdict(list)\n",
+    "\n",
+    "key = \"tissue\"\n",
+    "\n",
+    "meta_adata = sc.read_h5ad(\n",
+    "    \"/home/mehrtash/data/data/metadata_predictions_rand__8192_without_XY_training_100M_old/extract_0_metadata_prediction_scores.h5ad\"\n",
+    ")\n",
+    "\n",
+    "terms = meta_adata.uns[f\"{key}_ontology_term_ids\"]\n",
+    "mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "logits_nk = meta_adata.obsm[f\"{key}_class_logits\"]\n",
+    "\n",
+    "pred_codes_n = np.argmax(logits_nk, axis=1)\n",
+    "pred_names_n = np.asarray([terms[code] for code in pred_code_n])\n",
+    "pred_labels_n = np.asarray([uberon_propagation_resource_dict[\"ontology_term_id_to_label\"][term] for term in pred_names_n])\n",
+    "\n",
+    "# the coarse ground truth label\n",
+    "labels_n = meta_adata.obs[f\"{key}\"].values\n",
+    "names_n = meta_adata.obs[f\"{key}_ontology_term_id\"].values\n",
+    "codes_n = np.asarray(meta_adata.obs[f\"{key}_ontology_term_id\"].map(mapper).values)\n",
+    "\n",
+    "# record cell types\n",
+    "cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "# drop nans\n",
+    "valid_indices = ~np.isnan(codes_n)\n",
+    "\n",
+    "assert np.sum(valid_indices) > 0\n",
+    "\n",
+    "codes_n = codes_n[valid_indices]\n",
+    "labels_n = labels_n[valid_indices]\n",
+    "names_n = names_n[valid_indices]\n",
+    "\n",
+    "pred_codes_n = pred_codes_n[valid_indices]\n",
+    "pred_labels_n = pred_labels_n[valid_indices]\n",
+    "pred_names_n = pred_names_n[valid_indices]\n",
+    "\n",
+    "cell_type_n = cell_type_n[valid_indices]\n",
+    "\n",
+    "n_rows = len(labels_n)\n",
+    "\n",
+    "results[\"labels\"] += labels_n.tolist()\n",
+    "results[\"names\"] += names_n.tolist()\n",
+    "results[\"codes\"] += codes_n.tolist()\n",
+    "\n",
+    "results[\"pred_labels\"] += pred_labels_n.tolist()\n",
+    "results[\"pred_names\"] += pred_names_n.tolist()\n",
+    "results[\"pred_codes\"] += pred_codes_n.tolist()\n",
+    "\n",
+    "results[\"cell_type\"] += cell_type_n.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'True label')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "top_n_expected = 10\n",
+    "top_n_pred = 10\n",
+    "expected_codes_set = set(df[\"codes\"].value_counts().head(top_n_expected).index)\n",
+    "predicted_codes_set = set(df[\"pred_codes\"].value_counts().head(top_n_pred).index)\n",
+    "all_codes = sorted(expected_codes_set.union(predicted_codes_set))\n",
+    "other_code = np.max(all_codes) + 1\n",
+    "\n",
+    "_df = df.copy()\n",
+    "_df[\"codes\"] = _df[\"codes\"].apply(lambda x: x if x in all_codes else other_code)\n",
+    "_df[\"pred_codes\"] = _df[\"pred_codes\"].apply(lambda x: x if x in all_codes else other_code)\n",
+    "\n",
+    "y_true = _df[\"codes\"].values\n",
+    "y_pred = _df[\"pred_codes\"].values\n",
+    "labels = meta_adata.uns[\"tissue_labels\"][all_codes]\n",
+    "labels = list(labels) + [\"other\"]\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred, labels=all_codes + [other_code], normalize='true')\n",
+    "\n",
+    "# plot the confision matrix using matplotlib\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "cax = ax.matshow(cm, cmap='Blues')\n",
+    "ax.set_xticks(np.arange(len(labels)))\n",
+    "ax.set_yticks(np.arange(len(labels)))\n",
+    "ax.set_xticklabels(labels, rotation=90)\n",
+    "ax.set_yticklabels(labels)\n",
+    "ax.set_xlabel('Predicted label')\n",
+    "ax.set_ylabel('True label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Disease"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Parse data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 733,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain\n",
+      "blood\n",
+      "small intestine\n",
+      "eye\n",
+      "thymus\n",
+      "nose\n",
+      "heart\n",
+      "lung\n",
+      "liver\n",
+      "kidney\n"
+     ]
+    }
+   ],
+   "source": [
+    "val_meta_df = pd.read_csv(\n",
+    "    os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"cellxgene_validation_donors__third_draft.csv\"))\n",
+    "\n",
+    "# replace \"intestine\" with \"small intestine\"\n",
+    "val_meta_df['coarse_tissue'] = val_meta_df['coarse_tissue'].apply(\n",
+    "    lambda x: \"small intestine\" if x == \"intestine\" else x)\n",
+    "\n",
+    "coarse_tissue_labels = val_meta_df['coarse_tissue'].dropna().unique()\n",
+    "\n",
+    "for label in coarse_tissue_labels:\n",
+    "    print(label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 734,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of terms under each coarse label:\n",
+      "- brain: 134\n",
+      "- blood: 4\n",
+      "- small intestine: 18\n",
+      "- eye: 35\n",
+      "- thymus: 1\n",
+      "- nose: 5\n",
+      "- heart: 34\n",
+      "- lung: 22\n",
+      "- liver: 5\n",
+      "- kidney: 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ontology resources\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"uberon_benchmarking_resource.pkl\"), \"rb\") as f:\n",
+    "    uberon_benchmarking_resource_dict = pickle.load(f)\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"uberon_propagation_resource.pkl\"), \"rb\") as f:\n",
+    "    uberon_propagation_resource_dict = pickle.load(f)\n",
+    "\n",
+    "tissue_coarsener = OntologyCoarsener(\n",
+    "    coarse_labels=coarse_tissue_labels,\n",
+    "    ontology_propagation_resource_dict=uberon_propagation_resource_dict,\n",
+    "    ontology_benchmarking_resource_dict=uberon_benchmarking_resource_dict,\n",
+    "    include_other_node=False,\n",
+    "    verbose=True\n",
+    ")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 735,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7c25af74a914b5eb13dfa3bd28d3e50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# how many validation datasets with each coarse tissue do we have?\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "    meta_adata = load_predictions_anndata(val_adata_idx, 0, metadata_predictions_rand_wo_XY_root_path)\n",
+    "    val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "    coarse_tissue_n = meta_adata.obs['tissue_ontology_term_id'].map(tissue_coarsener.fine_term_id_to_coarse_label_map)\n",
+    "    disease_n = meta_adata.obs['disease']\n",
+    "    assert len(np.unique(coarse_tissue_n)) == 1\n",
+    "    assert len(np.unique(disease_n)) == 1\n",
+    "    results['val_adata_id'].append(val_adata_id)\n",
+    "    results['coarse_tissue'].append(coarse_tissue_n.iloc[0])\n",
+    "    results['disease'].append(disease_n.iloc[0])\n",
+    "    \n",
+    "val_adata_meta_df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 736,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brain\n",
+      "disease\n",
+      "normal                           10\n",
+      "Alzheimer disease                 3\n",
+      "COVID-19                          3\n",
+      "amyotrophic lateral sclerosis     3\n",
+      "dementia                          3\n",
+      "frontotemporal dementia           3\n",
+      "glioblastoma                      3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "blood\n",
+      "disease\n",
+      "normal                                 19\n",
+      "systemic lupus erythematosus            3\n",
+      "common variable immunodeficiency        3\n",
+      "B-cell acute lymphoblastic leukemia     3\n",
+      "COVID-19                                3\n",
+      "post-COVID-19 disorder                  3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "small intestine\n",
+      "disease\n",
+      "Crohn disease    3\n",
+      "normal           3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "eye\n",
+      "disease\n",
+      "normal    1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "thymus\n",
+      "disease\n",
+      "normal    2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "nose\n",
+      "disease\n",
+      "normal    2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "heart\n",
+      "disease\n",
+      "normal                    3\n",
+      "dilated cardiomyopathy    3\n",
+      "myocardial infarction     3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "lung\n",
+      "disease\n",
+      "non-small cell lung carcinoma    3\n",
+      "squamous cell lung carcinoma     3\n",
+      "normal                           3\n",
+      "lung adenocarcinoma              3\n",
+      "COVID-19                         3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "liver\n",
+      "disease\n",
+      "normal                            3\n",
+      "primary sclerosing cholangitis    3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "kidney\n",
+      "disease\n",
+      "clear cell renal carcinoma    3\n",
+      "normal                        2\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# what diseases do we have for each coarse tissue?\n",
+    "for coarse_tissue in coarse_tissue_labels:\n",
+    "    _sub_df = val_adata_meta_df[val_adata_meta_df[\"coarse_tissue\"] == coarse_tissue]\n",
+    "    print(coarse_tissue)\n",
+    "    print(_sub_df['disease'].value_counts())\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 737,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coarse_disease_labels = val_adata_meta_df['disease'].dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 738,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "normal\n",
+      "systemic lupus erythematosus\n",
+      "Crohn disease\n",
+      "Alzheimer disease\n",
+      "COVID-19\n",
+      "amyotrophic lateral sclerosis\n",
+      "common variable immunodeficiency\n",
+      "non-small cell lung carcinoma\n",
+      "squamous cell lung carcinoma\n",
+      "dementia\n",
+      "dilated cardiomyopathy\n",
+      "B-cell acute lymphoblastic leukemia\n",
+      "lung adenocarcinoma\n",
+      "myocardial infarction\n",
+      "post-COVID-19 disorder\n",
+      "primary sclerosing cholangitis\n",
+      "frontotemporal dementia\n",
+      "clear cell renal carcinoma\n",
+      "glioblastoma\n"
+     ]
+    }
+   ],
+   "source": [
+    "for label in coarse_disease_labels:\n",
+    "    print(label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 850,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# coarse_disease_labels = [\n",
+    "#     \"normal\",\n",
+    "#     \"systemic lupus erythematosus\",\n",
+    "#     \"Crohn disease\",\n",
+    "#     # \"Alzheimer disease\",\n",
+    "#     \"COVID-19\",\n",
+    "#     \"amyotrophic lateral sclerosis\",\n",
+    "#     \"common variable immunodeficiency\",\n",
+    "#     \"non-small cell lung carcinoma\",\n",
+    "#     \"squamous cell lung carcinoma\",\n",
+    "#     \"dementia\",\n",
+    "#     \"dilated cardiomyopathy\",\n",
+    "#     \"B-cell acute lymphoblastic leukemia\",\n",
+    "#     # \"lung adenocarcinoma\",\n",
+    "#     \"myocardial infarction\",\n",
+    "#     \"post-COVID-19 disorder\",\n",
+    "#     \"primary sclerosing cholangitis\",\n",
+    "#     # \"frontotemporal dementia\",\n",
+    "#     \"clear cell renal carcinoma\",\n",
+    "#     \"glioblastoma\",\n",
+    "# ]\n",
+    "\n",
+    "coarse_disease_labels = [\n",
+    "    \"normal\",\n",
+    "    \"systemic lupus erythematosus\",\n",
+    "    \"Crohn disease\",\n",
+    "    # \"Alzheimer disease\",\n",
+    "    \"COVID-19\",\n",
+    "    \"amyotrophic lateral sclerosis\",\n",
+    "    \"common variable immunodeficiency\",\n",
+    "    \"non-small cell lung carcinoma\",\n",
+    "    \"squamous cell lung carcinoma\",\n",
+    "    \"dementia\",\n",
+    "    \"dilated cardiomyopathy\",\n",
+    "    \"B-cell acute lymphoblastic leukemia\",\n",
+    "    # \"lung adenocarcinoma\",\n",
+    "    \"myocardial infarction\",\n",
+    "    \"post-COVID-19 disorder\",\n",
+    "    \"primary sclerosing cholangitis\",\n",
+    "    # \"frontotemporal dementia\",\n",
+    "    \"clear cell renal carcinoma\",\n",
+    "    \"glioblastoma\",\n",
+    "    # \"cancer\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 851,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of terms under each coarse label:\n",
+      "- normal: 1\n",
+      "- systemic lupus erythematosus: 1\n",
+      "- Crohn disease: 3\n",
+      "- COVID-19: 1\n",
+      "- amyotrophic lateral sclerosis: 3\n",
+      "- common variable immunodeficiency: 1\n",
+      "- non-small cell lung carcinoma: 3\n",
+      "- squamous cell lung carcinoma: 1\n",
+      "- dementia: 5\n",
+      "- dilated cardiomyopathy: 1\n",
+      "- B-cell acute lymphoblastic leukemia: 1\n",
+      "- myocardial infarction: 2\n",
+      "- post-COVID-19 disorder: 2\n",
+      "- primary sclerosing cholangitis: 1\n",
+      "- clear cell renal carcinoma: 1\n",
+      "- glioblastoma: 1\n",
+      "- other: 259\n"
+     ]
+    }
+   ],
+   "source": [
+    "# coarsen disease\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"mondo_benchmarking_resource.pkl\"), \"rb\") as f:\n",
+    "    mondo_benchmarking_resource_dict = pickle.load(f)\n",
+    "with open(os.path.join(root_path, \"data\", \"cellariumgpt_artifacts\", \"ontology\", \"mondo_propagation_resource.pkl\"), \"rb\") as f:\n",
+    "    mondo_propagation_resource_dict = pickle.load(f)\n",
+    "\n",
+    "disease_coarsener = OntologyCoarsener(\n",
+    "    coarse_labels=coarse_disease_labels,\n",
+    "    ontology_propagation_resource_dict=mondo_propagation_resource_dict,\n",
+    "    ontology_benchmarking_resource_dict=mondo_benchmarking_resource_dict,\n",
+    "    include_other_node=True,\n",
+    "    verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compute balanced accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 853,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1d66e518bd2443a8de985aa61ad3f70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4501533d7ee74d0a8939396c75852765",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a50a0e93a3f9454cb0f474e71ca821f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e21981f9548c4e3e99ef3f5e970015f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c78db71f62db4439a4ac0b6c410f601a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from scipy.special import logsumexp\n",
+    "\n",
+    "metadata_predictions_root_paths = [\n",
+    "    metadata_predictions_high_w_XY_root_path,\n",
+    "    # metadata_predictions_rand_w_XY_root_path,\n",
+    "]\n",
+    "\n",
+    "metadata_predictions_types = [\n",
+    "    'high_w_XY',\n",
+    "    # 'rand_w_XY',\n",
+    "]\n",
+    "\n",
+    "results = defaultdict(list)\n",
+    "\n",
+    "\n",
+    "for prefix_idx in tqdm(range(len(prefix_list))):\n",
+    "    prefix = prefix_list[prefix_idx]\n",
+    "    \n",
+    "    for val_adata_idx in tqdm(range(len(val_adata_idx_range))):\n",
+    "        val_adata_id = val_adata_idx_range[val_adata_idx]\n",
+    "\n",
+    "        for metadata_predictions_root_path, metadata_predictions_type in zip(\n",
+    "            metadata_predictions_root_paths, metadata_predictions_types):\n",
+    "\n",
+    "            meta_adata = load_predictions_anndata(val_adata_idx, prefix_idx, metadata_predictions_root_path)\n",
+    "            \n",
+    "            terms = meta_adata.uns[f\"disease_ontology_term_ids\"]\n",
+    "            mapper = {term: idx for idx, term in enumerate(terms)}\n",
+    "\n",
+    "            logits_nk = meta_adata.obsm[f\"disease_class_logits\"]\n",
+    "            probs_nk = meta_adata.obsm[f\"disease_class_probs\"]\n",
+    "\n",
+    "            # get coarsening indices (we use `j` for coarse label index, in the other appears in all_coarse_labels)\n",
+    "            coarse_label_to_descendant_indices_map = defaultdict(list)\n",
+    "            idx_to_j_map = dict()\n",
+    "\n",
+    "            for j, coarse_label in enumerate(disease_coarsener.all_coarse_labels):\n",
+    "                descendants_term_ids = disease_coarsener.coarse_label_to_descendants_term_ids_map[coarse_label]\n",
+    "                for descendants_term_id in descendants_term_ids:\n",
+    "                    if descendants_term_id in mapper:\n",
+    "                        coarse_label_to_descendant_indices_map[coarse_label].append(mapper[descendants_term_id])\n",
+    "                        idx_to_j_map[mapper[descendants_term_id]] = j\n",
+    "\n",
+    "            # get coarsened logits\n",
+    "            logits_coarse_nj = np.zeros((logits_nk.shape[0], len(disease_coarsener.all_coarse_labels)))\n",
+    "            for j, coarse_label in enumerate(disease_coarsener.all_coarse_labels):\n",
+    "                idx = coarse_label_to_descendant_indices_map[coarse_label]\n",
+    "                logits_coarse_nj[:, j] = logsumexp(logits_nk[:, idx], -1)\n",
+    "\n",
+    "            # renormalize\n",
+    "            logits_coarse_nj = logits_coarse_nj - logsumexp(logits_coarse_nj, -1)[:, None]\n",
+    "            \n",
+    "            # the coarse ground truth label\n",
+    "            labels_n = meta_adata.obs[f\"disease\"].values\n",
+    "            names_n = meta_adata.obs[f\"disease_ontology_term_id\"].values\n",
+    "            codes_n = np.asarray(meta_adata.obs[f\"disease_ontology_term_id\"].map(mapper).values)\n",
+    "            coarse_labels_n = np.asarray(meta_adata.obs[f\"disease_ontology_term_id\"].map(\n",
+    "                disease_coarsener.fine_term_id_to_coarse_label_map).values)\n",
+    "            coarse_codes_n = np.asarray([idx_to_j_map[code] if code in idx_to_j_map else np.nan for code in codes_n])\n",
+    "\n",
+    "            # record cell types\n",
+    "            cell_type_n = meta_adata.obs[f\"cell_type\"].values\n",
+    "\n",
+    "            # record coarse tissue type\n",
+    "            coarse_tissue_label_n = np.asarray(list(map(\n",
+    "                tissue_coarsener.fine_term_id_to_coarse_label_map.get,\n",
+    "                meta_adata.obs['tissue_ontology_term_id'].values)))\n",
+    "\n",
+    "            # drop nans\n",
+    "            valid_indices = (\n",
+    "                (~np.isnan(codes_n)) &\n",
+    "                (~np.isnan(coarse_codes_n))\n",
+    "            )\n",
+    "\n",
+    "            if np.sum(valid_indices) == 0:\n",
+    "                continue\n",
+    "\n",
+    "            labels_n = labels_n[valid_indices]\n",
+    "            names_n = names_n[valid_indices]\n",
+    "            cell_type_n = cell_type_n[valid_indices]\n",
+    "            coarse_codes_n = coarse_codes_n[valid_indices].astype(int)\n",
+    "            coarse_labels_n = coarse_labels_n[valid_indices]\n",
+    "            coarse_tissue_label_n = coarse_tissue_label_n[valid_indices]\n",
+    "            total_mrna_umis_n = meta_adata.obs['total_mrna_umis'].values[valid_indices]\n",
+    "            assay_n = meta_adata.obs['assay'].values[valid_indices]\n",
+    "            suspension_type_n = meta_adata.obs['suspension_type'].values[valid_indices]\n",
+    "\n",
+    "            n_rows = len(valid_indices)\n",
+    "\n",
+    "            results[\"prefix\"] += [prefix] * n_rows\n",
+    "            results[\"val_adata_id\"] += [val_adata_id] * n_rows\n",
+    "            results[\"metadata_predictions_type\"] += [metadata_predictions_type] * n_rows\n",
+    "\n",
+    "            results[\"labels\"] += labels_n.tolist()\n",
+    "            results[\"names\"] += names_n.tolist()\n",
+    "            results[\"cell_type\"] += cell_type_n.tolist()\n",
+    "            results[\"coarse_codes\"] += coarse_codes_n.tolist()\n",
+    "            results[\"coarse_labels\"] += coarse_labels_n.tolist()\n",
+    "            results[\"coarse_tissue_label\"] += coarse_tissue_label_n.tolist()\n",
+    "            results[\"total_mrna_umis\"] += total_mrna_umis_n.tolist()\n",
+    "            results[\"assay\"] += assay_n.tolist()\n",
+    "            results[\"suspension_type\"] += suspension_type_n.tolist()\n",
+    "\n",
+    "            for j in range(len(disease_coarsener.all_coarse_labels)):\n",
+    "                results[f\"logits_coarse_{j}\"] += logits_coarse_nj[valid_indices, j].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 854,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 855,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bq_cell_type_stats_csv_path = os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"bquxjob_cc87f8c_1955cb8b936.csv\")\n",
+    "bq_cell_type_stats_df = pd.read_csv(bq_cell_type_stats_csv_path)\n",
+    "\n",
+    "# delete the row \"cell_type\" == \"unknown\"\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df[bq_cell_type_stats_df[\"cell_type\"] != \"unknown\"]\n",
+    "\n",
+    "# rename columns\n",
+    "bq_cell_type_stats_df = bq_cell_type_stats_df.rename(columns={\n",
+    "    \"cell_type\": \"cell_type\",\n",
+    "    \"donor_dataset_count\": \"train_donor_dataset_count\",\n",
+    "    \"cell_count\": \"train_cell_count\",\n",
+    "})\n",
+    "bq_cell_type_stats_df\n",
+    "bq_cell_type_stats_df[\"train_cell_fraction\"] = bq_cell_type_stats_df[\"train_cell_count\"] / bq_cell_type_stats_df[\"train_cell_count\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 856,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, bq_cell_type_stats_df, left_on=\"cell_type\", right_on=\"cell_type\", how=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 857,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__disease__accuracy__03_03_2025.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(\n",
+    "    root_path, \"data\", \"cellariumgpt_artifacts\", \"metadata_predictions_scores__disease__accuracy__03_03_2025.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 862,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f1d88936a9e4443a8f73454151f4b94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/110 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "all_val_adata_ids = df[\"val_adata_id\"].unique()\n",
+    "\n",
+    "stats = defaultdict(list)\n",
+    "\n",
+    "for i in tqdm(range(len(all_val_adata_ids))):\n",
+    "    for j in range(i + 1, len(all_val_adata_ids)):\n",
+    "\n",
+    "        id_i = all_val_adata_ids[i]\n",
+    "        id_j = all_val_adata_ids[j]\n",
+    "        sub_df_i = df[df[\"val_adata_id\"] == id_i]\n",
+    "        sub_df_j = df[df[\"val_adata_id\"] == id_j]\n",
+    "        assay_i = sub_df_i[\"assay\"].values[0]\n",
+    "        assay_j = sub_df_j[\"assay\"].values[0]\n",
+    "        suspension_type_i = sub_df_i[\"suspension_type\"].values[0]\n",
+    "        suspension_type_j = sub_df_j[\"suspension_type\"].values[0]\n",
+    "        disease_i = sub_df_i[\"coarse_labels\"].values[0]\n",
+    "        disease_j = sub_df_j[\"coarse_labels\"].values[0]\n",
+    "        tissue_i = sub_df_i[\"coarse_tissue_label\"].values[0]\n",
+    "        tissue_j = sub_df_j[\"coarse_tissue_label\"].values[0]\n",
+    "        \n",
+    "        if assay_i != assay_j:\n",
+    "            continue\n",
+    "        if suspension_type_i != suspension_type_j:\n",
+    "            continue\n",
+    "        if tissue_i != tissue_j:\n",
+    "            continue\n",
+    "        if (disease_i != \"normal\") and (disease_j != \"normal\"):\n",
+    "            continue\n",
+    "        if (disease_i == \"normal\") and (disease_j == \"normal\"):\n",
+    "            continue\n",
+    "        \n",
+    "        if disease_i == \"normal\":\n",
+    "            normal_id = id_i\n",
+    "            disease_id = id_j\n",
+    "            normal_df = sub_df_i\n",
+    "            disease_df = sub_df_j\n",
+    "            normal_label = \"normal\"\n",
+    "            disease_label = disease_j\n",
+    "        else:\n",
+    "            normal_id = id_j\n",
+    "            disease_id = id_i\n",
+    "            normal_df = sub_df_j\n",
+    "            disease_df = sub_df_i\n",
+    "            normal_label = \"normal\"\n",
+    "            disease_label = disease_i\n",
+    "\n",
+    "        for prefix in prefix_list:\n",
+    "            _normal_df = normal_df[normal_df[\"prefix\"] == prefix]\n",
+    "            _disease_df = disease_df[disease_df[\"prefix\"] == prefix]\n",
+    "            # calculate auc\n",
+    "            normal_index = 0\n",
+    "            disease_index = disease_coarsener.all_coarse_labels.tolist().index(disease_label)\n",
+    "            normal_df_logits_n2 = np.vstack(\n",
+    "                [_normal_df[f\"logits_coarse_{normal_index}\"].values,\n",
+    "                 _normal_df[f\"logits_coarse_{disease_index}\"].values]).T\n",
+    "            normal_df_logits_n2 = normal_df_logits_n2 - logsumexp(normal_df_logits_n2, -1)[:, None]\n",
+    "            disease_df_logits_n2 = np.vstack(\n",
+    "                [_disease_df[f\"logits_coarse_{normal_index}\"].values,\n",
+    "                 _disease_df[f\"logits_coarse_{disease_index}\"].values]).T\n",
+    "            disease_df_logits_n2 = disease_df_logits_n2 - logsumexp(disease_df_logits_n2, -1)[:, None]\n",
+    "            stacked_logits_n2 = np.vstack([normal_df_logits_n2, disease_df_logits_n2])\n",
+    "            stacked_disease_labels_n = np.hstack([np.zeros(len(_normal_df)), np.ones(len(_disease_df))])\n",
+    "            auc = roc_auc_score(stacked_disease_labels_n, stacked_logits_n2[:, 1])\n",
+    "\n",
+    "            # calculate accuracy\n",
+    "            pred_label_n = np.argmax(stacked_logits_n2, axis=1)\n",
+    "            expected_label_n = stacked_disease_labels_n\n",
+    "            balanced_accuracy = balanced_accuracy_score(expected_label_n, pred_label_n)\n",
+    "\n",
+    "            stats[\"prefix\"].append(prefix)\n",
+    "            stats[\"normal_val_adata_id\"].append(normal_id)\n",
+    "            stats[\"disease_val_adata_id\"].append(disease_id)\n",
+    "            stats[\"coarse_tissue\"].append(tissue_i)\n",
+    "            stats[\"disease_label\"].append(disease_label)\n",
+    "            stats[\"assay\"].append(assay_i)\n",
+    "            stats[\"suspension_type\"].append(suspension_type_i)\n",
+    "            stats[\"auc\"].append(auc)\n",
+    "            stats[\"balanced_accuracy\"].append(balanced_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 863,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stats_df = pd.DataFrame(stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 897,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefix_idx = 3\n",
+    "good_index = stats_df[stats_df[\"prefix\"] == prefix_list[prefix_idx]][\"auc\"] > 0.5\n",
+    "good_coarse_tissues = stats_df[stats_df[\"prefix\"] == prefix_list[prefix_idx]][\"coarse_tissue\"][good_index].values\n",
+    "good_disease_labels = stats_df[stats_df[\"prefix\"] == prefix_list[prefix_idx]][\"disease_label\"][good_index].values\n",
+    "good_tissue_disease_pair_set = set(zip(good_coarse_tissues, good_disease_labels))\n",
+    "\n",
+    "# filter\n",
+    "filtered_stats_df = stats_df[\n",
+    "    stats_df.apply(lambda x: (x[\"coarse_tissue\"], x[\"disease_label\"]) in good_tissue_disease_pair_set, axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 905,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>auc_mean</th>\n",
+       "      <th>balanced_accuracy_mean</th>\n",
+       "      <th>count</th>\n",
+       "      <th>assay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prefix</th>\n",
+       "      <th>coarse_tissue</th>\n",
+       "      <th>disease_label</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">10M_001_bs1536</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">blood</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.529299</td>\n",
+       "      <td>0.505450</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>common variable immunodeficiency</th>\n",
+       "      <td>0.750724</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>post-COVID-19 disorder</th>\n",
+       "      <td>0.605068</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>systemic lupus erythematosus</th>\n",
+       "      <td>0.682581</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">brain</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.734318</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amyotrophic lateral sclerosis</th>\n",
+       "      <td>0.433463</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dementia</th>\n",
+       "      <td>0.599291</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">lung</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.857496</td>\n",
+       "      <td>0.640500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>non-small cell lung carcinoma</th>\n",
+       "      <td>0.160025</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>squamous cell lung carcinoma</th>\n",
+       "      <td>0.396904</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>small intestine</th>\n",
+       "      <th>Crohn disease</th>\n",
+       "      <td>0.707470</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">19M_001_bs2048</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">blood</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.553816</td>\n",
+       "      <td>0.511700</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>common variable immunodeficiency</th>\n",
+       "      <td>0.928521</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>post-COVID-19 disorder</th>\n",
+       "      <td>0.726175</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>systemic lupus erythematosus</th>\n",
+       "      <td>0.367019</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">brain</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.644483</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amyotrophic lateral sclerosis</th>\n",
+       "      <td>0.408419</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dementia</th>\n",
+       "      <td>0.511539</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">lung</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.833453</td>\n",
+       "      <td>0.745750</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>non-small cell lung carcinoma</th>\n",
+       "      <td>0.297680</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>squamous cell lung carcinoma</th>\n",
+       "      <td>0.532986</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>small intestine</th>\n",
+       "      <th>Crohn disease</th>\n",
+       "      <td>0.431057</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">30M_001_bs2560</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">blood</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.471444</td>\n",
+       "      <td>0.529175</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>common variable immunodeficiency</th>\n",
+       "      <td>0.880490</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>post-COVID-19 disorder</th>\n",
+       "      <td>0.901318</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>systemic lupus erythematosus</th>\n",
+       "      <td>0.792833</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">brain</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.786772</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amyotrophic lateral sclerosis</th>\n",
+       "      <td>0.614216</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dementia</th>\n",
+       "      <td>0.607612</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">lung</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.732230</td>\n",
+       "      <td>0.651875</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>non-small cell lung carcinoma</th>\n",
+       "      <td>0.924707</td>\n",
+       "      <td>0.511667</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>squamous cell lung carcinoma</th>\n",
+       "      <td>0.803926</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>small intestine</th>\n",
+       "      <th>Crohn disease</th>\n",
+       "      <td>0.548762</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">59M_001_bs3072</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">blood</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.667377</td>\n",
+       "      <td>0.521850</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>common variable immunodeficiency</th>\n",
+       "      <td>0.859101</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>post-COVID-19 disorder</th>\n",
+       "      <td>0.881168</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>systemic lupus erythematosus</th>\n",
+       "      <td>0.492928</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">brain</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.661897</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>amyotrophic lateral sclerosis</th>\n",
+       "      <td>0.590576</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>24</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dementia</th>\n",
+       "      <td>0.649248</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">lung</th>\n",
+       "      <th>COVID-19</th>\n",
+       "      <td>0.749953</td>\n",
+       "      <td>0.663875</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>non-small cell lung carcinoma</th>\n",
+       "      <td>0.724588</td>\n",
+       "      <td>0.561161</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>squamous cell lung carcinoma</th>\n",
+       "      <td>0.601388</td>\n",
+       "      <td>0.501167</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>small intestine</th>\n",
+       "      <th>Crohn disease</th>\n",
+       "      <td>0.644288</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                 auc_mean  \\\n",
+       "prefix         coarse_tissue   disease_label                                \n",
+       "10M_001_bs1536 blood           COVID-19                          0.529299   \n",
+       "                               common variable immunodeficiency  0.750724   \n",
+       "                               post-COVID-19 disorder            0.605068   \n",
+       "                               systemic lupus erythematosus      0.682581   \n",
+       "               brain           COVID-19                          0.734318   \n",
+       "                               amyotrophic lateral sclerosis     0.433463   \n",
+       "                               dementia                          0.599291   \n",
+       "               lung            COVID-19                          0.857496   \n",
+       "                               non-small cell lung carcinoma     0.160025   \n",
+       "                               squamous cell lung carcinoma      0.396904   \n",
+       "               small intestine Crohn disease                     0.707470   \n",
+       "19M_001_bs2048 blood           COVID-19                          0.553816   \n",
+       "                               common variable immunodeficiency  0.928521   \n",
+       "                               post-COVID-19 disorder            0.726175   \n",
+       "                               systemic lupus erythematosus      0.367019   \n",
+       "               brain           COVID-19                          0.644483   \n",
+       "                               amyotrophic lateral sclerosis     0.408419   \n",
+       "                               dementia                          0.511539   \n",
+       "               lung            COVID-19                          0.833453   \n",
+       "                               non-small cell lung carcinoma     0.297680   \n",
+       "                               squamous cell lung carcinoma      0.532986   \n",
+       "               small intestine Crohn disease                     0.431057   \n",
+       "30M_001_bs2560 blood           COVID-19                          0.471444   \n",
+       "                               common variable immunodeficiency  0.880490   \n",
+       "                               post-COVID-19 disorder            0.901318   \n",
+       "                               systemic lupus erythematosus      0.792833   \n",
+       "               brain           COVID-19                          0.786772   \n",
+       "                               amyotrophic lateral sclerosis     0.614216   \n",
+       "                               dementia                          0.607612   \n",
+       "               lung            COVID-19                          0.732230   \n",
+       "                               non-small cell lung carcinoma     0.924707   \n",
+       "                               squamous cell lung carcinoma      0.803926   \n",
+       "               small intestine Crohn disease                     0.548762   \n",
+       "59M_001_bs3072 blood           COVID-19                          0.667377   \n",
+       "                               common variable immunodeficiency  0.859101   \n",
+       "                               post-COVID-19 disorder            0.881168   \n",
+       "                               systemic lupus erythematosus      0.492928   \n",
+       "               brain           COVID-19                          0.661897   \n",
+       "                               amyotrophic lateral sclerosis     0.590576   \n",
+       "                               dementia                          0.649248   \n",
+       "               lung            COVID-19                          0.749953   \n",
+       "                               non-small cell lung carcinoma     0.724588   \n",
+       "                               squamous cell lung carcinoma      0.601388   \n",
+       "               small intestine Crohn disease                     0.644288   \n",
+       "\n",
+       "                                                                 balanced_accuracy_mean  \\\n",
+       "prefix         coarse_tissue   disease_label                                              \n",
+       "10M_001_bs1536 blood           COVID-19                                        0.505450   \n",
+       "                               common variable immunodeficiency                0.500000   \n",
+       "                               post-COVID-19 disorder                          0.500000   \n",
+       "                               systemic lupus erythematosus                    0.500000   \n",
+       "               brain           COVID-19                                        0.500000   \n",
+       "                               amyotrophic lateral sclerosis                   0.500000   \n",
+       "                               dementia                                        0.500000   \n",
+       "               lung            COVID-19                                        0.640500   \n",
+       "                               non-small cell lung carcinoma                   0.500000   \n",
+       "                               squamous cell lung carcinoma                    0.500000   \n",
+       "               small intestine Crohn disease                                   0.500000   \n",
+       "19M_001_bs2048 blood           COVID-19                                        0.511700   \n",
+       "                               common variable immunodeficiency                0.500000   \n",
+       "                               post-COVID-19 disorder                          0.500000   \n",
+       "                               systemic lupus erythematosus                    0.500000   \n",
+       "               brain           COVID-19                                        0.500000   \n",
+       "                               amyotrophic lateral sclerosis                   0.500000   \n",
+       "                               dementia                                        0.500000   \n",
+       "               lung            COVID-19                                        0.745750   \n",
+       "                               non-small cell lung carcinoma                   0.500000   \n",
+       "                               squamous cell lung carcinoma                    0.500000   \n",
+       "               small intestine Crohn disease                                   0.500000   \n",
+       "30M_001_bs2560 blood           COVID-19                                        0.529175   \n",
+       "                               common variable immunodeficiency                0.500000   \n",
+       "                               post-COVID-19 disorder                          0.500000   \n",
+       "                               systemic lupus erythematosus                    0.500000   \n",
+       "               brain           COVID-19                                        0.500000   \n",
+       "                               amyotrophic lateral sclerosis                   0.500000   \n",
+       "                               dementia                                        0.500000   \n",
+       "               lung            COVID-19                                        0.651875   \n",
+       "                               non-small cell lung carcinoma                   0.511667   \n",
+       "                               squamous cell lung carcinoma                    0.500000   \n",
+       "               small intestine Crohn disease                                   0.500000   \n",
+       "59M_001_bs3072 blood           COVID-19                                        0.521850   \n",
+       "                               common variable immunodeficiency                0.500000   \n",
+       "                               post-COVID-19 disorder                          0.500000   \n",
+       "                               systemic lupus erythematosus                    0.500000   \n",
+       "               brain           COVID-19                                        0.500000   \n",
+       "                               amyotrophic lateral sclerosis                   0.500000   \n",
+       "                               dementia                                        0.500000   \n",
+       "               lung            COVID-19                                        0.663875   \n",
+       "                               non-small cell lung carcinoma                   0.561161   \n",
+       "                               squamous cell lung carcinoma                    0.501167   \n",
+       "               small intestine Crohn disease                                   0.500000   \n",
+       "\n",
+       "                                                                 count  \\\n",
+       "prefix         coarse_tissue   disease_label                             \n",
+       "10M_001_bs1536 blood           COVID-19                             10   \n",
+       "                               common variable immunodeficiency      6   \n",
+       "                               post-COVID-19 disorder                6   \n",
+       "                               systemic lupus erythematosus         27   \n",
+       "               brain           COVID-19                             24   \n",
+       "                               amyotrophic lateral sclerosis        24   \n",
+       "                               dementia                             48   \n",
+       "               lung            COVID-19                              4   \n",
+       "                               non-small cell lung carcinoma         3   \n",
+       "                               squamous cell lung carcinoma          3   \n",
+       "               small intestine Crohn disease                         5   \n",
+       "19M_001_bs2048 blood           COVID-19                             10   \n",
+       "                               common variable immunodeficiency      6   \n",
+       "                               post-COVID-19 disorder                6   \n",
+       "                               systemic lupus erythematosus         27   \n",
+       "               brain           COVID-19                             24   \n",
+       "                               amyotrophic lateral sclerosis        24   \n",
+       "                               dementia                             48   \n",
+       "               lung            COVID-19                              4   \n",
+       "                               non-small cell lung carcinoma         3   \n",
+       "                               squamous cell lung carcinoma          3   \n",
+       "               small intestine Crohn disease                         5   \n",
+       "30M_001_bs2560 blood           COVID-19                             10   \n",
+       "                               common variable immunodeficiency      6   \n",
+       "                               post-COVID-19 disorder                6   \n",
+       "                               systemic lupus erythematosus         27   \n",
+       "               brain           COVID-19                             24   \n",
+       "                               amyotrophic lateral sclerosis        24   \n",
+       "                               dementia                             48   \n",
+       "               lung            COVID-19                              4   \n",
+       "                               non-small cell lung carcinoma         3   \n",
+       "                               squamous cell lung carcinoma          3   \n",
+       "               small intestine Crohn disease                         5   \n",
+       "59M_001_bs3072 blood           COVID-19                             10   \n",
+       "                               common variable immunodeficiency      6   \n",
+       "                               post-COVID-19 disorder                6   \n",
+       "                               systemic lupus erythematosus         27   \n",
+       "               brain           COVID-19                             24   \n",
+       "                               amyotrophic lateral sclerosis        24   \n",
+       "                               dementia                             48   \n",
+       "               lung            COVID-19                              4   \n",
+       "                               non-small cell lung carcinoma         3   \n",
+       "                               squamous cell lung carcinoma          3   \n",
+       "               small intestine Crohn disease                         5   \n",
+       "\n",
+       "                                                                     assay  \n",
+       "prefix         coarse_tissue   disease_label                                \n",
+       "10M_001_bs1536 blood           COVID-19                          10x 5' v1  \n",
+       "                               common variable immunodeficiency  10x 5' v1  \n",
+       "                               post-COVID-19 disorder            10x 5' v1  \n",
+       "                               systemic lupus erythematosus      10x 3' v2  \n",
+       "               brain           COVID-19                          10x 3' v3  \n",
+       "                               amyotrophic lateral sclerosis     10x 3' v3  \n",
+       "                               dementia                          10x 3' v3  \n",
+       "               lung            COVID-19                          10x 5' v1  \n",
+       "                               non-small cell lung carcinoma     10x 3' v2  \n",
+       "                               squamous cell lung carcinoma      10x 3' v2  \n",
+       "               small intestine Crohn disease                     10x 3' v2  \n",
+       "19M_001_bs2048 blood           COVID-19                          10x 5' v1  \n",
+       "                               common variable immunodeficiency  10x 5' v1  \n",
+       "                               post-COVID-19 disorder            10x 5' v1  \n",
+       "                               systemic lupus erythematosus      10x 3' v2  \n",
+       "               brain           COVID-19                          10x 3' v3  \n",
+       "                               amyotrophic lateral sclerosis     10x 3' v3  \n",
+       "                               dementia                          10x 3' v3  \n",
+       "               lung            COVID-19                          10x 5' v1  \n",
+       "                               non-small cell lung carcinoma     10x 3' v2  \n",
+       "                               squamous cell lung carcinoma      10x 3' v2  \n",
+       "               small intestine Crohn disease                     10x 3' v2  \n",
+       "30M_001_bs2560 blood           COVID-19                          10x 5' v1  \n",
+       "                               common variable immunodeficiency  10x 5' v1  \n",
+       "                               post-COVID-19 disorder            10x 5' v1  \n",
+       "                               systemic lupus erythematosus      10x 3' v2  \n",
+       "               brain           COVID-19                          10x 3' v3  \n",
+       "                               amyotrophic lateral sclerosis     10x 3' v3  \n",
+       "                               dementia                          10x 3' v3  \n",
+       "               lung            COVID-19                          10x 5' v1  \n",
+       "                               non-small cell lung carcinoma     10x 3' v2  \n",
+       "                               squamous cell lung carcinoma      10x 3' v2  \n",
+       "               small intestine Crohn disease                     10x 3' v2  \n",
+       "59M_001_bs3072 blood           COVID-19                          10x 5' v1  \n",
+       "                               common variable immunodeficiency  10x 5' v1  \n",
+       "                               post-COVID-19 disorder            10x 5' v1  \n",
+       "                               systemic lupus erythematosus      10x 3' v2  \n",
+       "               brain           COVID-19                          10x 3' v3  \n",
+       "                               amyotrophic lateral sclerosis     10x 3' v3  \n",
+       "                               dementia                          10x 3' v3  \n",
+       "               lung            COVID-19                          10x 5' v1  \n",
+       "                               non-small cell lung carcinoma     10x 3' v2  \n",
+       "                               squamous cell lung carcinoma      10x 3' v2  \n",
+       "               small intestine Crohn disease                     10x 3' v2  "
+      ]
+     },
+     "execution_count": 905,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_stats_df.groupby([\"prefix\", \"coarse_tissue\", \"disease_label\"]).agg(\n",
+    "    auc_mean=(\"auc\", \"mean\"),\n",
+    "    balanced_accuracy_mean=(\"balanced_accuracy\", \"mean\"),\n",
+    "    count=(\"auc\", \"count\"),\n",
+    "    assay=(\"assay\", \"first\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 907,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>val_adata_id</th>\n",
+       "      <th>coarse_tissue</th>\n",
+       "      <th>disease</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>59</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>67</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>71</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>75</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>81</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>82</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>83</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>94</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>95</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>103</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>107</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>108</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     val_adata_id    coarse_tissue                           disease\n",
+       "3               4  small intestine                     Crohn disease\n",
+       "6               7  small intestine                     Crohn disease\n",
+       "21             22            brain                          COVID-19\n",
+       "22             23            brain                          COVID-19\n",
+       "23             24            brain                          COVID-19\n",
+       "28             29            brain     amyotrophic lateral sclerosis\n",
+       "29             30            blood  common variable immunodeficiency\n",
+       "30             31            blood  common variable immunodeficiency\n",
+       "31             32            blood  common variable immunodeficiency\n",
+       "58             59            brain     amyotrophic lateral sclerosis\n",
+       "66             67             lung                          COVID-19\n",
+       "70             71            brain     amyotrophic lateral sclerosis\n",
+       "74             75            blood                          COVID-19\n",
+       "80             81            blood            post-COVID-19 disorder\n",
+       "81             82            blood            post-COVID-19 disorder\n",
+       "82             83            blood            post-COVID-19 disorder\n",
+       "93             94            blood                          COVID-19\n",
+       "94             95            blood                          COVID-19\n",
+       "102           103  small intestine                     Crohn disease\n",
+       "106           107             lung                          COVID-19\n",
+       "107           108             lung                          COVID-19"
+      ]
+     },
+     "execution_count": 907,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "study_pairs = [\n",
+    "    (\"blood\", \"COVID-19\"),\n",
+    "    (\"blood\", \"common variable immunodeficiency\"),\n",
+    "    (\"blood\", \"post-COVID-19 disorder\"),\n",
+    "    (\"brain\", \"COVID-19\"),\n",
+    "    (\"brain\", \"amyotrophic lateral sclerosis\"),\n",
+    "    (\"lung\", \"COVID-19\"),\n",
+    "    (\"small intestine\", \"Crohn disease\")\n",
+    "]\n",
+    "\n",
+    "val_adata_meta_df[val_adata_meta_df.apply(\n",
+    "    lambda x: (x[\"coarse_tissue\"], x[\"disease\"]) in study_pairs, axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Playground"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_adata_id = 67\n",
+    "meta_adata = load_predictions_anndata(val_adata_id - 1, 3, metadata_predictions_high_w_XY_root_path)\n",
+    "adata = sc.read_h5ad(f\"/home/mehrtash/data/data/cellariumgpt_validation/extract_{val_adata_id}__processed.h5ad\")\n",
+    "adata = adata[meta_adata.obs.index].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 1550 × 5000\n",
+       "    obs: 'original_cell_id', 'cell_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'donor_id', 'is_primary_data', 'organism_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'suspension_type', 'tissue_ontology_term_id', 'assay', 'development_stage', 'disease', 'organism', 'self_reported_ethnicity', 'sex', 'tissue', 'total_mrna_umis', 'cell_type_distance_from_root', 'bq_row_index', 'donor_dataset_concat', 'farm_finger', 'dataset_id', 'dataset_version_id', 'cas_ingest_id'\n",
+       "    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'\n",
+       "    uns: 'hvg', 'log1p', 'neighbors', 'pca', 'umap'\n",
+       "    obsm: 'X_pca', 'X_umap'\n",
+       "    varm: 'PCs'\n",
+       "    layers: 'counts', 'measured_genes_mask'\n",
+       "    obsp: 'connectivities', 'distances'"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 1550 × 0\n",
+       "    obs: 'original_cell_id', 'cell_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'donor_id', 'is_primary_data', 'organism_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'sex_ontology_term_id', 'suspension_type', 'tissue_ontology_term_id', 'assay', 'development_stage', 'disease', 'organism', 'self_reported_ethnicity', 'sex', 'tissue', 'total_mrna_umis', 'cell_type_distance_from_root', 'bq_row_index', 'donor_dataset_concat', 'farm_finger', 'dataset_id', 'dataset_version_id', 'cas_ingest_id', 'cell_type_detail', 'cell_type_hop_0_call', 'cell_type_hop_1_call', 'cell_type_hop_2_call', 'cell_type_hop_3_call', 'development_stage_detail', 'development_stage_hop_0_call', 'development_stage_hop_1_call', 'development_stage_hop_2_call', 'development_stage_hop_3_call', 'disease_detail', 'disease_hop_0_call', 'disease_hop_1_call', 'disease_hop_2_call', 'disease_hop_3_call', 'tissue_detail', 'tissue_hop_0_call', 'tissue_hop_1_call', 'tissue_hop_2_call', 'tissue_hop_3_call', 'sex_detail', 'sex_hop_0_call'\n",
+       "    uns: 'cell_type_labels', 'cell_type_ontology_term_ids', 'cell_type_propagated_labels', 'cell_type_propagated_ontology_term_ids', 'development_stage_labels', 'development_stage_ontology_term_ids', 'development_stage_propagated_labels', 'development_stage_propagated_ontology_term_ids', 'disease_labels', 'disease_ontology_term_ids', 'disease_propagated_labels', 'disease_propagated_ontology_term_ids', 'sex_labels', 'sex_ontology_term_ids', 'sex_propagated_labels', 'sex_propagated_ontology_term_ids', 'tissue_labels', 'tissue_ontology_term_ids', 'tissue_propagated_labels', 'tissue_propagated_ontology_term_ids'\n",
+       "    obsm: 'cell_type_class_logits', 'cell_type_class_probs', 'cell_type_propagated_class_probs', 'development_stage_class_logits', 'development_stage_class_probs', 'development_stage_propagated_class_probs', 'disease_class_logits', 'disease_class_probs', 'disease_propagated_class_probs', 'sex_class_logits', 'sex_class_probs', 'sex_propagated_class_probs', 'tissue_class_logits', 'tissue_class_probs', 'tissue_propagated_class_probs'"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "meta_adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for key in meta_adata.obsm.keys():\n",
+    "    adata.obsm[key] = meta_adata.obsm[key]\n",
+    "\n",
+    "for key in meta_adata.uns.keys():\n",
+    "    adata.uns[key] = meta_adata.uns[key]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make hard metadata calls\n",
+    "keys = [\"cell_type\", \"disease\", \"tissue\", \"development_stage\", \"sex\"]\n",
+    "for key in keys:\n",
+    "    logits_nk = adata.obsm[f\"{key}_class_logits\"]\n",
+    "    pred_code_n = np.argmax(logits_nk, axis=1)\n",
+    "    mapper = {term: idx for idx, term in enumerate(meta_adata.uns[f\"{key}_labels\"])}\n",
+    "    pred_label_n = np.asarray([meta_adata.uns[f\"{key}_labels\"][code] for code in pred_code_n])\n",
+    "    adata.obs[f\"{key}_hard_pred\"] = pd.Categorical(pred_label_n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+Wp7ebgAI8I8xcWjVKofv6zSO3wOorjc5MWIhhBBnEgkChThN7T5cQUWd8uxd6uFKAP7and/ufbr5uXHH2Dj+vHcUBp39X3mdRkVSqONZvkg/V1IkSUQIIc46EgQKcZoK83bF0eRdclhTVl5xVUOb90gM9eLda/vx7O+7OffNFdQ1mu3O0arVTEkOVbw+IciDD64biLqNWUQhhBBnJgkChTiFGk1mDhZXU9dov9wa4m3gykHd7I43h2NZJdXkltU6vPef943iz3tH8ceOPD5dnYnRpJw+Utto4tL+EVzYNwzVkZv7uev53+V9WHj/GJLCpAyEEEKcjSQxRIhT5NdtuTz7226KqurxMmh5YFIPZgyPtjnn+YuS6RPhzWsL95Ff2bT3zwJ8tuYgeRV1bWYFG7Qa5m4+xHtLD7Q7lg2ZpbxxVT8entKTwsp6eoV5odPI74hCCHE2kyBQiJOopLqBNQeKMVvM/PPbbdakj4o6I0/9uouymgb+3JnH4fI6RiUE8Nh5iVwxMJLnft9td69lewsxaNXUGe2XeNUq+HtXHs//ucepceWU1QAQ7uNKuI/rcXyFQgghzhQSBApxkvy8JYeH526nQSFoa/Zai/Zvv28/TOrhCv68ZxT1Ctc0mMy467WKQaDZgtMBIMDI+ECnzxVCCHF2kPUeIU6C8tpGHv1xR5sBoJIDhdWszShmUi/7Xr5mC1QeY+2/ZmoV3DMuXvb9CSFEFyQzgUKcBFuySqlVSP5wxo+bc/j3RSlU1hlZsb/ouMcS7e/GQ5N7YLJA3wgfuvm7Hfc9hRBCnHlkJlCIk8DRPrsADz1eBi0Do3yJ9XdXPGfBrjy8DFq+uGkId4+L75Tx/G/hPl74I5XP1mRS2yCFoIUQoiuSIFCIkyAh2JPJrZZ09Vo1s2cMYvvTk/lw+kAyiqsVr61tNLMyrWkGUN8JGbuZxTWkF1ZzuLyO2SszeHju9uO+pxBCiDOPLAcLcZK8dXV/vlh7kKV7CwjyNDBzRDTJRzpxbMgsabPcS9WRziH9o3xRq5r2AzoS6m3gcHmd0+Oatz2X/5uayM6ccoxmC6MTAnHVa5y+XgghxJlJZbFY2vrsOSEqKirw9vamvLwcLy/ZkC66roW785m3PZfaRhMLdjluAXd+7xDc9Tq+35TdZgA4fVgUaflVrE4v7tA4gjxdKDhSh9DPXc8nNwyiT6RPh+4hhDj7yef32UWCQCFOkTf/3serLUrCHK8B3XyZe8dwtmSVcvG7q52+zsNFQ1W97b7AXmFezLtnVKeNTQhxdpDP77OL7AkU4hTILavhtUX2AaBGdew9ejdllbL5YClxQR68f11/gjxdFM/zddNZ28Mlh3tRo5AYsiu3gk9XZTD1zRWMeXkJL/yZKgkkQghxlpGZQCFOgcd+3MHX67NOyL01ahVTeoXQPdjDpvh0s1tHx3LTyBjqGk1E+bsz4sXF5LTqQeyq09iVtJmaEso71/Y/IWMWQpwZ5PP77CIzgUKcAnvzKzt0vo+rFrWTk4Qms4V5Ow6zKLVA8X2tWkWwl4GoIyVp7hlvX3bGw8U+Z+yPnYfJr3A+4UQIIcTpTbKDxRmp3mhi/s48tmWXsSW7jP35lViAYbH+PHF+kjXAOV3FBriz6WCpU+e66tR8PHMwh0pquGfOVqefsSevwu6YCrioX7jNsSsHdSPI08B3G7Mxmi0MiPLl3SVpdtdaLE39in/ZlkNuWR3D4vy5f2J3AjyUl52FEEKc3mQ5WJxRluwt4H9/7SU1twKTg59cf3c9ix8ci7erzuZ4SXUDn67KYFduBUlhXtwwPBr/UxTApBVUcvG7q6msa7vtW68wL764aQh+7noAftx0iAd/2GaTIdwv0ocdOWW07kin06hoVPgmzbl1KENj/RWf99L8Pby39IDiexG+rhwur8PU4uGJoV7Mu3skamenKYUQZzT5/D67yEygOGNsP1TGLZ9txNhWjRSguLqBB7/fRqi3gTHdAxmfGEx1vZFL31tNRlFTQeZFewr4bVsuv98zSnHpszNV1xv5O7Wp/Mu4nkF4GnTEB3nyxz2j+HLtQfIq6qisM7J4j/3y7UV9w60BIMAlAyKIDfJg9soM8ivqGBkfQEyAO5+symBzVpnNtWHerhwsqbG758Hiapsg0GS2MGdDFr9ty2Vteoni1xAb4E6UvxuHSm33DqYermBtRjHD4wKc/n4IIYQ4PUgQKM4Y36zPbjcAbLZwd1PQ9fmag0T4ulLXaKKoqsHmnMziGn7eksN1Q6M6fazNduaUc/3sdZTWNALg7arjk5mD6N/Nl0g/Nx49LxFoKhZ9oLCKg8VHg7beEd5cPaSb3T37Rvrw1tX9KKis46oP15Je2BTYqgAXnRpXnYbJySGU1zTaBYEqFQyOsZ0FfPiH7czdfMjh1zA01o9vbhnKDZ9sUHy/7MjXJoQQ4swiQaA4Y9Q0tL106kjr2auWshVmypo1msz8d/4e5mzIptFk5oI+YTw5rVeHZg6f+GWnNQAEKK9t5ImfdzLvnlHUNpjILa8l9XAF93yzxWaJd2pKCK9f1Q9dG23i3lqUZg0AASw0lZi5eVQsr/y1F6WNHiPjA3Bv0Q0ku6SGH7c4DgAB+kb6snB3PlX19t9/V52GEfEyCyiEEGciCQLFGeO8lFB+2ZrbqfccGqe8Nw7glQV7+WhFhvX1dxsPUdNg4u1rnCuT0mgys6XVEi001eD7cPkB3lqcRmWdEY1aZdcFZOneQhpN5jaDwA2Z9ku31Q0mXv1rn2IACLBifxHj/reML28eQt9IH3LLah2eC02dRMpqGrj1i0127/m563np0t52ey+FEEKcGaREjDhjTO4Vwj8ndMftyEyWXnN8yQiXD4hgbPdAoGmG7oU/Upn21kr+8cUmtmSV8u3GbLtr/tyZR0Wdc8ufZTWN1rG25OOm4z9/7LEmhZgUlrirG0wUt1q+bi3K383umAowtZPrVVVv5L/z9wCQEuGNp8LMZlygO/eOT+CTmYP4fpP9TOHwOH/WPDqOiUnBbT5LCCHE6UuCQHFGuXdCAusfn8DiB8bw+90j0SlkpbaXqKrXqujfzYdxPYNQqVRYLBamf7yeD5ansyOnnPm78rjyw7U0NJrtrjVbLFjsD9uxWCxcP3udYjeOpND2M+rc9Roe/2kHs1dmUG9U7tRx5znxGHS2f4WnpIS0Pzia9ioCuOm1/OeSFPTao/dJCvXi+38M558Tu1NZZ1QMUouq6nHR2ge4QgghzhyyHCzOOB4uWjwCPQD4ZOZgXvgzlT15lXi6aBnbM5B7xyewIbOU/PJa9hVU8fu2w7QMYxqMFjZnlXH7V5t5aHIPBkT5si27zOYZDUYzEYHuNnvuAM7pEYS3W/vLn+syStiTZ18QelC0LwOjfFl9oLjN66sbTCzfX8Ty/UWsTS/mo+kD7c7pHeHD73eP5Mu1WRRV1TMhMZgL+4Zx59eb+WNHXpv3T2wRiE7rE8bwOH9WphXh7+7C8Dh/a8mXxFAvDDo1da0C4v7dfNu8vxBCiNOfBIHijDYyIYB5CaPsjueU1rF8XyH1jUbaWhx9f9kBnrmgl+J7Yd6u9I304bdtuZjMFsb1DOKlS3s7Na6KWuUlY51GzWUDInl/+QEajEdH5umi4akLevH1uiy7Ui8Ld+ezJ6+CniH2M4jxQZ483Wr8b17Vj9EJh1ixv4hQbwM+bjr+t/DoPkFXnYaHJvewucbfw4UL+9oWkYambObHz0vkyV93Wa8P93HlnvEJABRX1fPKX/tYsb+QEC8Dt42Jsy4R78mroLiqgQFRvhh0MmsohBCnGwkCxVnn5y053PftVqfOrawzkhTqhV6rpqFVteUJiUHcMCKGf1+UjMlswdOgo6Cijtu/3MSi1AJ83HTcNDKG28bE2d13RHwAngatXTHoPXmVfLE2k8YWAaCfu57ZMwbSr5sv36y334cIkFNaqxgEKtFq1Fw1uBtXDT5aXmZM9yDm7TiMm17DJf3DifC1308IsCg1n5cX7OVAYRX9In15fGoi1w+LZnh8AEv2FODvoWdKr1Bc9RosFgs3fLKBHUeWlg+V1rLp8418NGMgn63OZMX+IqApkHzjqr4Mi/NnX14VoT4G6TIihBCnAekYIk5rBZV1fLchm8PldYxKCGRyr2BUqrY3/Y17ZSnpRdVtntMsPsiDv+8fw89bcnjspx3WPXxTe4fy+pV9rdm569KLSSus4p0laeSW2fbPff7iZK4dYl9r8JetOdzrZJu3/1ycwjVDuvG/v/by1mLblm0uWjXrHhuPj5vewdWdY3duBRe8vdKmFqOXQcvyh89RfPamgyVc+t4au+Oh3gYOl9t+j9z0GrRqFRV1RlTAiHh/PrlhEDrZVyjEGUU+v88uMhMoTltZxTVc/O4qiqubsmS/WpfF1YMjeeGStpdk26oL2JKXQcsLl6QATf10xycGsT6jhN+3H2bBrjwSn5iPTqNqmgGsrHd4n2/XZ9sFgeW1jQ7brylZk17MNUO6cevoWFYfKLb2FdZr1Pz7omSnA8BGk5lt2WX4uOmJD/Jw+vkAP2w6ZFeMu6LOyLwdhxWD3EwHgXbrABCwSZCxACvTipn61kr++ueYDo1RCCFE55EgUJxWjCYzJosFF62G95cfsAaAzb5Zn80to2KJDXQc4AyL82fZvkKH70f4uvLouYmM6RFoU/jZ06Djr135/LQl5+h4zBZqGx0HgAClNUfHWFhZz6q0Ir5Zd1AxMcSRSF9X6xjm3j6c9Rkl5FfUMSzO3+ml0w2ZJdz51WZrwDoqIYD3rhvgdHHrRpNy2nNj66bER3gdZ33AfflVLNydx8Qk5zKahRBCdC4JAsVpodFk5vl5qXy7IZsGk5kJiUEUVCgHX/sLqtoMAp+clsT1s9aRe2RGyqBT4+mio6KukXE9g3hqWi9CvA1219U1mmwCQGf1j2rKlP15Sw4P/7CdBgfBVLNADz2FLWoA+rvruX6Y7UzbgChfZq1I57pZ69CoVVwxMJLpw6IcLoUbTWbu/nqLzYzliv1FvPH3Pib1CmH+zjywQIi3gaQwL4bH+dvd64K+YXyx9qDNMb1WzZTkUMVn9o30RaOGdr7cNj3w3TY2/l+QTYkaIYQQJ4cEgeKk2XSwlEOlNQyK9iPMx9Xmvdf/3senqzOtrxfsyifcxz5Q06hVpIR7t/mcuEAPlj50Dsv3FdJgMjOmeyDuTsyGWSxgNHcsotGommbKrnh/DVuzy9oNAAEaTGbO6RGI5chYZ46IJtTb9vvx0vw9fLg83fr6qV93UVnXyF3jEhTvuSu3grwK+2XYuZtzbLqeNOsT6cMXNw3Gy3B0Nm9QtB//uTiFVxfupaiqgUg/V565QDlgBgj0dOH+iT14ecHedr9mtQq7rijQtNy8eE8BU5JlNlAIIU42CQLFCVfXaOKWzzdas0U1ahWPnZfITSNjrOfM3WQ/A5dTVkekryvZLfb43T0u3i6AVKLXqpnQwW4WrnoNE5OCWbAr36nzdeqm/W1/7Gy7Jl9r5bVGluwt5MqBkTxxfpLd+/VGE1+2mpED+HhVpsMg0NGSb2m1cteRbdllfLQ8nQcm2ZaKuWZINy4fGEFpdQMBHi7WeoGO3HlOPKMSAliyp5BDpTWK3UV6hnhyxcAInv09VfEeznZgEUII0bkkCBQn3JdrD1oDQGhqk/afP1IZHufHxoNlVDnoSgHw1S1D2HSwlMPldYxOCCS5nVlAaErKWJ1WhI+bnqGxfu1mEwOU1TSwJbuMCYnB7Mgpt8sAVuLlqrfbs9gRP2w+xENTetjt+WswmhU7jZTXNmKxWKg3mlmTXoyLRs2QWH80ahXzdykHom2l/q9xULBap1ET5KU8+6ekd4QPvSN8qG0wsfpAMTllR4N2g1bNB9cPIMrfnb35lXy7wTZI1GvVnNMjyOlnCSGE6DwSBIoTTqk7hsls4ZJ3V1Or0JqtWbiPKwcKqjm/d5i1VEt7Pl+dyX/+TLV2uEgK9WJq71CW7i0gwteVf52bSLCXgb925fHj5hxUqqZlze82Ztt1xQAYHO3H+swSxWeV1Rx7AAhN34OSIzNuLXkadAyN9WNtuu1zx/cMYtuhcm7+bANFR/YUxgS488ZVfXlr8f4OP7+bn3KtwGPlqtfw7W1DeePv/WzOKiU+yIO7xyUQ5e8OwEuX9sHbVc+nqzJpMJnxc9fz/EXJBHpKzUAhhDgVpE6g6BR1jSYWpRZQ22hifM8gfN2PljR54ueddgkHSly0aixgV7TZy9DU3/b83mEAbMkqZU16MZG+bkzuFYJeq+ZweS23fL6RnTkVbT5Do1Zx88hoPlhuv09OiUGrxsugo6Cq7QxhJSFeLpgsFgorlYNFf3c9Gx6foLjkmlVcw61fbLRmGPeJ9OHD6wdw/ex17Muvsjk32t+NzOKaDo3NTa9h7u3DbdrHnSwl1Q3kltWSEOwh/YeFOMPI5/fZRWYCxXE7UFjFNR+tJf9INq+rTsMH1w9gdPdAAGaOiObnLTlU1hvbug31RjNhPq7kltnW+auoM3LfnK30ifDhi7UHbRImeoZ48u1tw3j0xx3tBoDQNPv2oZMBIECd0QzHuGctz0F2c7MofzeHe+66+bsx/77R7M6tQKtR0T3Yk/yKOrsAECCrxLkAMDHUkyBPA5F+rswYFk1CsKdT13U2P3c9fu4ntvC1EEKI9kkQKI7bf+alWgNAgNpGEw9+v42Ppg+kT6QPsYEe/HLXCGavzGDOhmyH+/80KuwCwGZGs4Wv12fZBIDQ1IZt9sr0NusCttaRqW93Fw3V9fb78zqDTtPUqu7v1Hxyy2oZmRBg1xouKezoay+DDje9xm6/oLuLfXu61lx1Gj65YbBNpu+SvQW89Oce9uVXkhLhw/9NTWRQtF8nfGVCCCHOBFKcSxy3dRn2e+YKKuu58J1VXPjOKoqr6okN9OD5i1PwbaPzhamd6OxAgf0sGMD2Q+W4nIA6c1o19Inw6fT7NhvbI5Bpb63kjq828+95qUx5fQVvLnK8t89Vr2HmiGi74xaLBW2rGcWR8QHWjiGBHi5M6x1qUww6raCSWz9vWm42W5qyhWd8vJ58hTIzQgghzk4SBIrjFuXvOMFgW3YZL/65x/r60v7hx/QMb1cdaw4UKb4X4eNqt4/weKkAo1k5qaUzdA/2oKrOyN58264ir/+9zya7trUHJ/Xgv5f1ZnicvzXwrao3YTRb0KhUnJcSwvvX9eeLmwZzx9g4VEBhVT3fbTrExNeWsS696ev5cXMOja2i7poGE79ty+3cL1QIIcRpS4JAcdzuHZ9AW+XklrZYqvX36NheMBUwPM6fa4d2o1JhWdZdryEmwEOxEHFLMQHudO9AL93Wt2unXF6HZRRVsyGz1O642QJbs8owmy0cKq2hrvHo17w+o4RL3lvN4z/tILu0hvpWga/JYiHI08CU5FCMZgv/+WOPzddR12jmpflNAbmjJfnWvYOFEEKcvSQIFMdtUq8Qvv/HcC7oo9xerK7RxH1ztrDmQDEZhcpLuo5YgF5hXrjrlbevDo31Jyaw/VInJdX1jO0RhOYYgzk3vZZPbhiEm65z/so0mixE+ikXvS6vbWD0y0sY+dISBv37b95ZkkZuWS0zPl7PlqwyGk0WskuUZwu3HyoDmnoYFylkNO8+3JQ8M61PmF1gq9OomJqi/GcohBDi7CNBoDgmOWW1zNt+mD15TUHFgChfeoUpF3KurDPy89Zcrv5oLd9ssO8o0Z71maX4uOoU32swmRmdEEhCO7N85bVGPlyRToBCTTqdRoW7vu1SJfVGE0v3FvDRjEGMiPdH2wkzgykR3nZZsucmh/Dc76kcOtIlpbLeyMsL9vLKX3upbWw/QaX5uiBPF8X6e81/Rsnh3rxyeR+CvZrOifRz5Z1r+hPZybUDhRBCnL4kO1h02NuL9/Pa3/utS4oX9AnjtSv7drh9WrNwHwOzZwxi5qcbOFxun5gQ4++Gp4Mg0FWnQatR882tQ3lz0X7WphfjqtPQYDSTmldpd35doxmNWmWzHHrjyBgu6hvO24vT0GlV1Dea+bPV19JosvDZmqbOJ/PvG02d0UTvp/86pq+32dO/7uaFi5M5XF5HbnkdI+MDqDea7J4NsEEh+UZJcws2rUbN4+clcv93W61L5a46Df86t6f13Ev6R3Bh33DKahrwddO32yJOCCHE2UWCQNEh+/IreeWvfTbHft2Wyzk9A/FwObbCv/26+XKgsFoxANRr1fxjbByh3q646zVUtyqPMrV3KBaLhW3ZZeg0aq4e3I1L+kdgNJkZ8O+/7e7XYDTz3W3D+GrdQarrjZyXEoqXQccFb62ksUVg2CfCm8ziGsprbWsEphdV83dqPuE+BlR0rNyMkp+35vLtbcOsrx0lZrTsn9wWrVrN79tzOb93GBf1CycpzIt52w/jolNzUd9wu77LGrUKfw/p2CGEEF2RBIGiQ1buV87QffiH7Rxr85mL+oWzPbtM8b3zU0KttfPeu24A/5q7ndzyOgw6NTNHxHBh33Ae+WE7327Mtl4za0UGH80YoHg/nUbFgChfBkT5Wo+N+99SmwAQYNuhcnqFedkFgdC03+77jdmKAeCgaF+7hI+EIA9uGhnDv37cYXf+3rxKXlmwh/WZpbjrNXQP9lSsBeisqnojd3+zBT93PcPjAuge7En3iaemKLQQQojTmwSBol3vLzvAJ6syKKtpJCVced9f63Ij0NQ2ray20dqT189Nh9FioaL2aGHjEXH+TEgMxuyogHSLTI7R3QNZ8cg4MourCfR0wcugY09ehU0ACE37Fb9co9ymzlVnO1tZXW8kvbBa8dxdufYdSFQ0JZlsdRC0BnjorUGcXqPmsv7hPHVhLzQqFa//vZ+8VnX4ymobeXvJAevrJXuPZlKrVRDt7056kfL4HLFYYM76bIbHBXToOiGEEF2LBIGiTV+sPWhT52/jwVLUKtotyQL2bdOq6o18cdMQ0ouq2XGonAv7hjEk1h/AYRuxAwW2AZBGrSIu8GgSSOph5VZxB4trCPR0obDSdgxBXrZLn256DSHeLuSVO9cb2AK8sSjN4ft/7sy3/n+DyczcLTncN7E73m46Qr0NdkFgW8wWuG9Cd56bt9vu62jP2vRi6hpNGHTSm1cIIYQyCQJFm77dkGV3zGwBnRoaO1ifucFk4Z2lB/j8xsFcPdj2PUdJCe3lKiQ7yEhek16sGKimFVRR12iioKKeD5YfYH9BFb3DvckrL3DmS+iweqOZ+Tvz2HaojC0OZg/b8su2HJ48P4n7vt3qsLafkoLKej5Yls69ExI6/EwhhBBdgwSBok2NRuXAo6MBYLN0B3UC+0X6EBfozoFWS7OXDYho834JwZ5MHxbF562Wfx3FS7WNZjKKqrl21jpKqhusx0O9DdQ3miipsd8DeLxeXbiXstq2e/s6sii1gMWpBW0moAR46CmqarA7vmhPPneNi2f5vkLyK+oYmRBAeW0jy/cVEeLtwrnJoTJTKIQQXZgEgaJNF/QN4+UFezvtfoOi/RSPq1QqPrlhMI//vIOVaUX4uOq4eVQsVw3u1u49n70wmSnJIaw5UMxXaw+2GcilhHuzYFeeTQAIcLi8Dje9hoFRPmw8WNahr6ktahXHHAA2aysA9HDR8vCUHjz8g33SiaeLlmlvrbQWiG7tjb/3890/hhHkabA5Xm80sSqtCJVKxYi4APQnoC+zEEKIU0+CQNGm20bHsnp/EavSj7+HbqiXC/dP7O7w/W7+bnxx0xAajGZ0GhUqlXN165bsLeCHjYewYMFFpwGUg8BwH1devrw3n6zMVHy/psHExoNleBq0VNYdX+AG0CPYg735HeuQ0hF+bjo+u2kQv2w5jJdBS0WLMatUTT2D97Xx/MziGj5cls5tY+Jw0anxMujYlVvOzE82UHBkD2Kot4HPbxxMQrBkGAshxNlGgkDRJq1GzeBY/04JAu+Z0L3djhQLd+fz1uL9ZJfUMDTWn0fPTaSbv+Nrvl6XxWM/2c+CtdS/mw+PTOnJwGg/NGoVo7oH2GUUt1RZZ2RYrD9rM4qxWMDHTcdlA8KZtSJT8fzxiUHkldfZZBPfNDKGrJKaDgWB/bv5sCev0unyMCU1jVz+3lrqWvQQ1qibSuDUN5rYdqi83XvM2ZDFrJUZaNUqLugbRlpBlTUAhKYZ0id+2cmcW4e1cRchhBBnIgkCRbsOl7ddqNjZunZr04u5uo3l3Q2ZJdz2xUbrfr4/d+axI6ecxQ+Mdbgk+dbi/Yrj8XHTUVVn5NzkUB6bmoh3i44j5yWHMjg6k/Wt6vm1dNPIGP57WW/yKupICffGoNOwKdM+uWNKr2Bev6pf0/sHSzlQUEXfbj7sy69k9sqMNr4b9gw6DbNnDOKJX3aSVlBl19lEScsAEMBktpAU6smnq5VL5LRWVd/052Y0W/hxc47iOWvTS9iXX0FVvYmeIZ64OejjLIQQ4swi/5qLdg2PD2DOBsczZ45q/LUW6u3a5vvfrMuyS+g4VFrLsn2FTEwKtjm+L7+S7zZmK3YZqWkwsf7xCXi4KP94q9UqMtqovefjpmNEfACueo3NzGVNg/0ScVphtTW5omUR6i/XOheEtZQc7sWwOH/+vn8MZTUNrD1QzN1ztijWYGzLd23McrZHpWqqM9japNdWAE17EJ+5oBeXtpOwI4QQ4vQnO75Fu85PCeXifuEO3w/zcaWHE3vGdhwqI7+NOnmOZhOzSmwDtqV7C5j65gpmrVCeaesd4e0wAGxWptAJBJqKQX94/UBc9bZZsw1Gs+LSblpBFdX19sFht3aWvZWsTivmsvdW8836LLxddfz3r70dDgABahqOMXUb0LVTk6eq3shDP2wjp8y5NnZCCCFOXxIEinap1Speu7Ivj5+XqPj+FYMi+f2ekbx/XX+GxSpn/wKsOlDM7V9ucvj+eb1DFY9/t/GQzeuXFzgOjvzc9fz7omTF94qr6nllwV5u+nQDXgad4jlTegUzOMb+a9Br1UQr7E2M8HXFTW9fZmVMj0CcS2s5amduBRsPlvLojzv497xUh51MmnX0/i35ues5p0eg3fEGJ4JOswUW7c5v9zwhhBCnN1kOFk5LzVMuNeKm16DTqJmSHMrAKF+GvrAYo4Ml4s1ZZWQUVRMT4G73Xt8IH8VuJHvzKkkrqMRFq6Gyzsiew5WK937h4mQu6hdhN4sHUFnXyLS3VpKrsHzczNOg5dmLUhy+/69ze3Ln11us+/TUKnhkSk9rFnNdo4l/fLmJ5fsKneqo0pYvHLS9a+l4HvHCRckMSwjg+d9Tmbcjl+p6U4fuV3YC6ikKIYQ4uSQIFE7zdVNu7dbyeICngfev689dX2+xS1pozyNztzsMnp78ZSerD5S0MTYdVw7q5rDzyN1fb1EMAPtEeFNe28iUXqE8cm6PNsvSTEkO5be73Pl5aw5ms4WL+oWT3KKX8j++3MTSFr1/j0eDyfH3TqdRHdMycUv//mM3X98yjKcv6MXNo2KY+NryDl3/0Yp0CqvqifB1JTrAnfE9g9BqZGFBCCHOJBIECqddM6QbX6/Lorbx6N69SD9Xu6SNCUkhbHlyEo//tIMft9hmnPbv5qM4C1jTYGSNgzI0Id6GNgNAaCoC7SgAPFRSw9J9ysFZcxmV95cfoNFs5onzk6zvVdQ18u/fd/PHjjzc9BquHxrFnefE85jCsrjJbGG5g2e0FB/kQXZJNfUOOrG05+5x8Xy8KoNGk/L+SQ3g5kSdw+zSOkb9dwkuWjXXDe3mVCZyS5X1Rr5okfzSJ9KHr28egns7ezGFEEKcPuRXd+G0uEAPvrl1KBMSg4gNdOeKgRF8e+swxdZjrnoNL1/eh9vGxOLhokWtgnE9g3jvugGK99Zr1HgqBBB6jcqpH9Khcf7UNpgoV1im/Gqdc5m6s1dmsK1FCZj7v93KdxsPUVVvpKCynv8t3Mf7yw8oXms0mdtcAtZpVNw9Lh6DTk290YKHi5ZgLxenxtXS+MRgGtuYYTUBA7r58MplvblpZAwPT+7R5v3qjWZmr8zEopQSDEzoGYSnof3Ablt2Gd+st+8zLYQQ4vQlv7aLDukb6cOsGYOcOlejVvHouYk8PLknjSZzm31qtRo1M4ZH8/aSNJvjd49LYP6uvDb38gV56sksqqbvs39RbzQzJMaPVy7vYy3vouvAMuW6jGL6RPpQUFHHoj0Fdu/PWZ/NHWPjbY7VNZq45qO1bd7XTa/li7UHrXvpquqNVClkFbclyNOFMB9Du8kb2aW1XDYw0vp6V24F83YcbvMapQC2e7AH9Saz091TtmSVOXWeEEKI04MEgeKE06hVaNSOA8BmD0zqToCHnp+25KBSqbh0QATXD40ixNvAQz9sV7wm2t+NCYnBzGpRmHldRgl3fLWZ3+4eCcAFfcN5a0maYv271qL8m5aqG0xmxfNzy2q555vNlNca2ZlTTnyQB30ifNjcTgAU6OlCWsGxt5DzddMxa8ZAgjwNdA/2aLMdXKCnCxaLBZVKxbbsMowmM+G+BnJKHQfSLWnUKs5NDuH2sXFc+PYqp8cYF+Th9LlCCCFOPQkCxWlDpVJxw4gYbhgRY3P88oGRqFUqXl+0j+ySo/XpAjz0zJoxkFs/ty87syOnnAOFVUT7u7NgVx4B7i4UV9VjBgw6NTeNjOHHzTk2xaZ7R3gzvmcQABG+bvTr5mM3u2U0W/h129FZteKMEtZltL1f0aBTM61PKK8ttO9u0p7ze4dy2+g4eoV5Wfc8vnBJb278dAPlDmodrk0v4ZW/9hLkZeCpX3Z1+Jkp4d5cM6Qbl723xmGWd2vhPq5cPzSqw88SQghx6kgQKM4Ilw6I4NIBEWSX1PDX7nw8XbScmxKCp0GHxkFCiFat4r/z9/DB8nSb48Pj/Hlock9mDIvmrq83szmrDKO5aeYsvaia7kcKXw+K8j2uJU6NCq4cFMk/xsQT4Knn89UHKa5usDmnb6QPO3PKUatUeLpqKa46+r5a1dS+LiXiaAbykr0FvLJgLzUNRsVyOs0+WZWpmOihUamICXAjrY0ahFOSQ3jip502CUBtcddr+OKmwQR6dnyPoxBCiFNHEkPEGSXSz42bRsZwxaBIPI8UfL5yUKTdeUNi/IjwdVNs37Z4TyFfrj3I36n5rM8stc52bcsuY+YnGzAeKc/iKKPYGf4eehY9MJb/XNKbbv5uuOm1fHbjYJLDvGzO236oDE+DlugAN7vae2YL/NQiu3pPXgW3fLaRXbkVNJosbSai1DSYqFdIIDFZLG22zLtsQARXD47kQBvntFbdYGKxwv5JIYQQpzeZCRRnvJtGxlDbYOKzNZlU1BmZ3CuEp6clYTSbHbai+7+fd6JVmEHMKatlfUYJw+MDOpRQ0tpnMwcT3aoUTnK4N7GBHuzMPVp022yB0ppGSh0UX166t5APlh1gYLQff+447PTybHygBwcKq+wKQKtU4CivJDHUk1cu78PaA0VOPaOlwsr6Dl8jhBDi1JIgUJzxVCoVd49P4O7xCXbvJYV5sStXudOJo4BKo1ZhsVgI9TY4vLaZi1Ztl0QypnugTRHpljYdLG3zfq1lldTwwp97AEhwMvEiwMOFV6/sw/vLDvDHjjyb95LDvNmRU6543Z7DlYx8aTE5pR3vCzxGoQWdEEKI05sEgeKs9tbV/Zj46jKHs1+txQS4Myjaj89WZ/J3attLnCpg6YNjySiuZvaKDAqr6hnbI4jbx8Q5vCY6wI2cso4HWQD728kunj4sivGJwXTzc2Xh7nz6hPsQ7uPKnzvz0KpV3DgyhmGx/pz7xgrFANgCHOpgAKhRq7hlVCzD4wI6dJ0QQohTT4JAcVaLDfTgq1uG8uD32xwGON6uTR02RiYE8tyFvVCrVXy78VC797YA+ZX1DI8LUAyCNh0s5dWFe9mbV0XvCG8emtyDe8YlsCGzlAYHBZ8n9wrGy6BjS3aZYkmZiUnBLNlTYBfEues1PHpuIusyipn8+grr/Q06NR/PGMTw+KPje+fa/tz/7RaqGzrW1g/AoFUzrmcQNwyPobrRSM8QT0K9XTt8HyGEEKeeBIHirDc01p8VD5/Doj0F3Pb5RptZwbhAd/765xhUYNN2zuzE3js3vYa4QPsWeADZJTVcN2udNcN28Z4CNmeVsuSBsfx210i+WZ/FweJqduVWUFBZj0oF5yaH8OoVfTHoNDz0/TbFINDbVYenQWu3h7C6wUROWS3P/r7bJsCsazTz7O+7mX/faOuxyb1CGBLrz+I9zie+XNAnlKsHRzEszt/pa4QQQpzeJAgUXYJKpWJCYjCf3zSEtxenkV1aw/A4fx6Y1EOxxMxF/cJ5af4em2Mt++uqVPDIlJ54GnTszq1g3o5c9BoNF/cLp5u/G99vzLYrsVJW08hv23OZPiyapy/oBYDFYmF/QRWeBq3NjJqjwst1jSb6RPqwdK9tAOfjpuNAQSXpCqVf9uRV0mgy2yS6OLvs62XQctuYOO48J779k4UQQpxRJAgUXcqI+ABGxLe/f+3W0bEUVtbzzfosahtNDI3144mpSWzKKqWsppGJScEkhnoxd9MhHvphm7Vcy7tL0/h05mCq6pWzklu3ilOpVNa6hC0NdzDjlhTqRaCnC+szSqyZzyoVVNYZue3LzYrXxAa422U6D4r2U+w60lx7MCnUi+cvTiYl3BvtcWRJCyGEOH2pLI46x59AFRUVeHt7U15ejpeXV/sXCHGK1Bub6u15HalJ2FKjycywFxZTVGVbHqVvpA+PT03k8vfX2BxXq+Cvf44h3sks33u+2WzTnQSaZuYqjvTyVQF9In3Yml3m8B5atYp3r+3PpF4hNscLK+uZ+Noym9qE8UEezP3HcCrrG4nwdXNqjEKIrkU+v88uMhMoRBtctBpctMp9j4uq6u0CQIDUwxUMivbj/6Ym8vrf+6mqN+LjpuP/piY5HQACioFYcwAITYkpjgLAvpHeDI0N4OJ+4fQIsZ9pDPR0YckDY/ly7UF25pYzpnsQVwyMQKtR4+1mH/AKIYQ4+0gQKMQxCvRwIdjLhfwK20CwuUbgzaNiuXpwN3LLaon0c8Ogsw0mG01mPl2VyV+78/B21XPD8GhGJhxdqs4sdr5rR2tTU8K4ZXRsm+f4uusVaysKIYToGmSzjxDHSKtR8/jUJJvEEje9hn+d29P62t1FS0Kwp10ACPDwD9t5/o9UNmSW8ndqPtd/vI7Fe/Kt7w+K9nNqHF4G29/l/Nz1XNI/vKNfjhBCiC5GZgKFOA4X9AkjKdSLP3YcRq9Vc2HfMKfq5uWW1fLz1hybYxYLfLAsnXE9gwG4enA3/tqVz5r0Yus5Llq1TU/gwdF+vHhpCm8vSWNXTgVJYV7cPS4efw+XTvoKhRBCnK0kCBTiOMUHeXBPB5dVCyvrUUrJKmjRg9eg0/DJzIH8e14qa9NLiAtw54qBkaxIK+JgcTVDY/25flgUbnotr17R9zi/CiGEEF2NBIFCnAJJYU2lXgorbfcTjk6wLV9z9zdbWbi7aYk4raCKpfsKmXv7cIe9iYUQQghnyZ5AIU4BnUbNa1f0tdnP16+bD/+c2N36ek9ehTUAbFZvNPPh8vSTNk4hhBBnL5kJFOIUGZkQwLrHJrAuoxgfNz19I31s3s8tU+7qkePguBBCCNEREgQKcQq56jWM7RGk+F5SqBcqmuoBtjQo2veEj0sIIcTZT5aDhThNrdhfZBcAAgRI5q8QQohOIEGgEKcpR8Wi88rrTvJIhBBCnI0kCBTiNDXQQbHoQTHOFZEWQggh2iJBoBCnqbHdA7m4n23nj6kpoUxMDD5FIxJCCHE2kcQQIU5TKpWK167sy/XDotiVU05iqJfD2UEhhBCioyQIFOI017+bL/27SUawEEKIziXLwUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIIYQQXZAEgUIIcQaqrGuk0WQ+1cMQQpzBtKd6AEIIIZx3oLCKR37YzsaDpXi6aLlhRDT3T+yOSqUCoNFk5pv1WSzdW0ighwszhkeTFOZ1ikcthDgdSRAohBBnCLPZwk2fbiCzuAaAynojby1OI8TbwBUDI5m/M483F+1nf0GV9Zqft+bw3W3D6BPpc4pGLYQ4XUkQKIQQZ4gt2aXWALCluZsO8fOWHDZkltq9V280M/3j9VwxMII7xsbj664/GUMVQpwBZE+gEEKcIdRHlnxbyy6pVQwAm5XXNvLRigyunbUOs9lyooYnhDjDSBAohBBniL6RPkT7u9kdL6yqd+r63YcrWLy3AGOLhJL0wio+W53JHzsO02CURBMhuhJZDhZCiNPAgp2H+XhVJtklNUT5u3PHOXGMSgjk1225vLloP4dKaxgW64+Pq+64nnPzZxvRa9WMjA8gIciDD1ekYzkyORgf5MG3tw7F38OlE74iIcTpTmWxWE762kBFRQXe3t6Ul5fj5SVZa0KIrqGgoo7D5XX0DPXERasBoKbByPTZ69h4sMzmXI1KxRPnJ/LM77tp+a+0RgUmJ/7V1qhUmI7hn/ebR8bwf+cndfg60TXI5/fZRWYChRCik+VX1PHl2oMcLK5hcIwfl/YP57l5qXy7IRuT2YKfu54XL0lhUq8QPlmVaRcAApgsFj5amUHrOM6ZAPCKgREs3VNAQVVDh8e+8eDRvYUFlXXoNWp83CSZRIizkQSBQgjRifLK65j29koKK5v26f26LZfPVmfalG0pqW7gnjlbuH1MHLNWZji8V22D8ZjGMDjGH41azTfrszp8bWygO7lltdw3ZyvrM0vQqFVMTQnlxUtTcNPLR4YQZxNJDBFCiE706epMawDYrGUA2Kyu0cxrf++nss5xoDcqIbDDz/d105EQ5M66jOIOX+tp0HL7mDjunbOF9ZklAJjMFn7dlst/5+/t8P2EEKc3CQKFEKITZRTZB3zHIsjThdeu6Mv9E7vjrtc4dU20vxtf3jyEe+dsJb2wukPPOy8lhD/uGYWnQadYbub37bkdup8Q4vQnQaAQQnSiQdF+x32PoTF+/H73SNRqFfeMT+D5i1Ocus7HTc+/ftiuWFC6LS5aFS9c3JtIPzf0WjVqhXKEzYkszcpqGvhsdSav/rWXLVmOaxQKIU5fEgQKIUQnunZIFG5tzNz5ueu5alCkw1Iveq2aly/vQ5CXwXrM3cW5vXhbs8vYkVvR7nkGre0//ZcPjMTbTWcd35TkELtrrh4caf3/rOIaJr62nKd+3cWbi9O4+N3VvL14v1NjFEKcPiQIFEKITuSq15AU6unw/bvHxTNjeDT/Oq+n4oxbg9HMK3/Z7r8b0z0QjdLJx0AF1LUqCv3j5hwq6xoBMJrM9I7wIdjLBZ1GhZ+7ngcmdueOsfHW899avN9u3+Obi9IocrJotRDi9CCpXkII0UkaTWYW7s4n0NPg8Jz//JFK45E6L5F+rmSX1Nqdsz6jhA2ZJfi764kN9ECvVZMS7sXW7PLjHqNShZmaBhOr0oqYkhzKw3O38+PmHOt75bWN9In0Qa1WUW80oVap2JFjP44Gk5l9eZUExEuhaSHOFBIECiFEJ6hpMHL1h2vZduhogKRVqzC26tXb2KLQX3ZJLSoVdrUAi6rqufz9NQCM7xnE29f0594J3bnp0w20vF2Yj4HcsrpOGf/HKzPpE+nDz1tybI6bzBbeWZLGtxuymb8rD61aRai3fZCr06iID/bolLEIIU4OCQKFEKIDfth0iG/WZ1FvNDGtdxg3j4pFo1bxzfpsmwAQsAsAlSg19WgZKC7aU8DdX2/Gx11PsKcLhyuallxDvFx49fI+3DNnKwVHlmZddRq6B7mzLaf9fYGtrc8sYfm+QpSGvCOnnJoGE9AUFColntw+Jo6gNmZAhRCnHwkChRDCCRV1jXy3IZt/z0u1HtuZU0FOWS3PXpjMtuyy436Gr5uO0ppGu+N/7ymwO5ZXUc8dX2+hpPpoV5DaRhODY/2ZdcNgLFi486vNiuVeHHlt4X783fUUV9t2GmkOAB3pHuzB/ZN6OP0cIcTpQRJDhBCihZ055Xy4/AB/7DhMo8lMblkt181aR++n/+L5FgFgszkbstmZU46Hi3JG8PA4f6efrRQAtqWk2r4t3Jdrs/A0aCmuaiAusGPLs3kVdfxrSk88WmQj9wrzor2UlH35VdQ1th0oCiFOPzITKIQQR/x3/h7eXXrA+jox1Au9RmVd5lVa3G0wmjn/rZUAaNQqTC3WU8/pEcgnMwez6WAJO3Mq2JpVyi/bchWXXJt5u+oor+1YMNhSbaOJp37ZybcbD1mPues1qNXg5+5i7VCiFEAC9IvyZe1j41mdVoSvu56BUb5c9v4aNh10PKMY6OmCi1bmFIQ400gQKIQQQHphFe+1CAABUg93bG+dyWzBy6DlvN6hDInx4/zeYQAMiPKjtsHMU7/uavce5/QIJNzXlUWpBWjUsCu3skNj6BHsaRMAAlQ3mHjz6n5c0KdpPIfLaxn2wmK7a/3c9cQHNc0eTup1tFZge7N8d4+LR6XqnBI2QoiTR351E0II4P1lBxRn+jqqos7IvrxKvAw6dJqj/8Qu2pPf7rWuOg23jYnjock9mX/faH6/exTaDtQHjA10Z1iccseSb9ZlWf8/1NuVu8fF27yv16qZNX2A4rX785Vb4Y3tHsis6QOZPiza6TEKIU4fMhMohOjy6hpNzNt+WPG9MG8DueUdK8OyOauMmz7byLMX9GL68GgA/Nz0bV4zvmcQD0/pSY+Qo4WmVSoVl/QP57tWM3utTUoK5q5x8aSEe9sVmm52uNy2HuEDk3owMSmYv3bl4euu5+rB3XDT234kNBjNaNQqksO92JxVZvNeiJeB2TcM6rQi1kKIk0+CQCFEl5dZXE21QgasWgWzZgzkvWXpzNve9l4+JU/+uotGs4WbRsZw+cBIPlyRbt2T15JWreL96wdgMlswmsxoW8wg/ufiFFbsL+Jwq0DUTa9mVEIg4xODuax/BOojwdjgGD/eWWK7rA0Q7GVfvqV3hA+9I3zsjhdU1vH4TztZlJqPm17L+J5BpB6uoLbRbB3v/52f2GYAWFVvpKymgQhfN4fnCCFOLQkChRBdXqSvG+56jV0gODjGn6Qwb966uh9FlXWsSS+xu1YFuClc2+w/81KZ1juUEG8Dr1zeh9u+2GR3jptew42fbmBlWhEeei3XD4viwUk9KKtt5FBpDX/cM4pJry2lsOpowkhNg5n9+VVcNzTKeiy7pIZQb1fCfVzJKbOd+bt6cDeb18VV9TSYzIR6u9qN566vtrA+s+lrrao38su2XG4dHUuAh54Go5nzUkKJdZB5bLFYeOHPPXy2OpN6o5m4QHdeubwP/br5Kp4vhDh1VBaLUqnSE6uiogJvb2/Ky8vx8vI62Y8XQggAftpyiI9XZlJa00Cot8Gmpp6bXsPHNwzk9+2H+WVLDpX1x14C5eHJPbhsQAT3f7eNlWlFTl0zPM6fjZmlNJjMeLhoqaq3n0Fs1ivMC29XHasPFANNewO9DTq2ZJcR4KHnttFx3DI6FoDqeiMP/bCN+TvzMFtgQJQvb1zV1zpjl11Sw6j/LrF7Rqi3gTWPjmd3bgX7CypJCfe2CQRrG0x8uyGLuZtz7NrKBXjoWfWvcbholcvoiDOHfH6fXWQmUAjRJf26LZd/frvN+vpQaS19I70ZGOWHh0HLZQMieHnBXn7Zmnvcz9qUVcqqA0WsSit2+prmgA5oMwAE2JVrm8WcXlhNv24+7P33FPQatU3m7ot/7uGPHXlHx3awlPvmbOWH24e3+Yy88jpu+2IjC3Y1JbioVHDzyBgen5qEyWzh+tnr2OigjExRVQNrDhQztkdQm88QQpxckh0shOiSPludaXdsa3Y5kX5u3DehO96uOofJIh1lNFk6FAB2hi1ZZUd6EzcFgDUNRtZnlPDbNvugduPBUvIrmvYcRvq5Ee1vv4/PAtYAEJra3X20IoNNB0tZurfAYQDYzFUns4BCnG5kJlAI0SVV1ikXZH7mt10MiPIl3Me1zd6/ahVOJ4p4G3QdOr+zXPPRWv53RR9e+HMPu3Md1zzUqFXoWySjnN87jLeXpDn1jNVpRbjq2w7wEoI8GBStXLpGCHHqyEygEKJLmtyiGHJLZgs8/MN2tmSXEhtgPyOm16jRqVUdCuj2FlQ6fJ6zwnwMzLt7BDcMj6abn+24WgZwLRVU1nPjpxvaDAABJiYG42lomhOwWCwMinY+iSPc15WBDgI8F62aqSmhfHbjYGv2shDi9CGJIUKILmf5vkJe+DOV1MNtd+NQodwqrqOCPF2obzRR3qI8jK+brkO9gl+9og+X9I8AoNFk5tsN2azYX0iotyvTh0Uxe2UGX7UoCN0eg06NQatBq1FRVNWAr5uO0d0D2ZBR4nRdxNgAN/64dzQGnYanf93Fpy2W2EfGBzD7hoGSDHKWkc/vs4sEgUKILuVAYRXnvr6CBpP5pD1Tq1bZLS0HeeopqW5sc8m5pTDvpuLMiaFH/83cml1GTb2RgdF+FFfXM/KlJTa9i9syJNqPTVmlTj9fyZc3DWZkQqD19c6ccjZnlRIX6MHwOH9pJXcWks/vs4vsCRRCdCk/bc45qQGgUv1BgILKBsXzu/m5kVVSY3c8t7yOW7/YyLIHz6G4uoEbP91gLcUS5OnCh9MHcsuoWN5fdrRQtKN9iCpgXaZ9zcOOUKsgLuhoiZiKukYCPFykhZwQZxDZEyiE6FIazcoBYKSPfdHkzuCoiLQjvcK8uHJghOJ72SW1bD1Uxot/7rGpxVdQWc/9327lhuFR1r194DgRJdz3+L9WHzcdeo2aRpOZR3/czoDnFjL0hUVc8PZK0gqUew0LIU4vEgQKIbqUab3DFI8X1zQQ0So4umVUDP83NZFgTxfrMR9XHWO6B+LpcmIWUv7cmYdaraZPhLfi+wathqV7C+yOpxdV8/rf+xXb0rX+hz6ntNbuHEfUwMh4f9xbZQCXVDfywp97eHfJAb5Zn02jqSni3H6onH98uYlTsNNICNFBshwshOhSksO9FZMyahpMfH5jXw4UVpFbVseohABr1uvNo2LJLqmhoLKelHBv9Fo17yxJ4+UFe49pDJ4GrWKw1uyb9Vk8OLk72w7Zdt7o182HpDAvAj1dKK62XU7WqmH+rjyU+LjpKGnx9SqFZ46SYOKCPFjpoMbh4j0F+Lvr7Y6nFVSReriSpDDZMybE6UyCQCFEl9Mn0oelewttjrnqNHQP8XRY7iTSz41ATxfmbj7EnsMVxAR6EOzlQn5FfYef32Bsf0/ie0sOMDUllEV78qlrbDrfoNVQWt3ABX3C2JNnG4C66bWUOcg2LmknCzncx5WqeiPltfbn7W9jaTfAQ++wPE1FbQOr0oroFeaFj5t9oCiEOPUkCBRCdDn/nNCd9Rkl1LTYr3fXuHi8DDqH19Q1mrj8/TU2e/H6RvhgMlsoqlJO8nCk3okgsLrBxLwdth1L1qQX8+Svu/B2tf+nu6KNmUVHrhwYwbVDo+gV5s2F76ykPMf5kjUAN46MYeGufHa2qkPo7qLhmlnrMFuaagU+PjVREkaEOA1JiRghRJeUXVLD9xuzqagzMrlXCMPi/Ns8f876LP71446TNDrHdBoVY7sHsTA1v/2T26DXqvnjnpHEB3kCMG/7Ye78erNz12pUNJgs+Lnrqa43thvUqlSw6P4xxAZ6tHmeOP3J5/fZRWYChRBdUqSfG/dP6uH0+XvylAtL+7jpHC7DqgCPdvb/dZRBq2FMjwC7ILAjha1Twr2ZmhLCV+uyCPEycPnASKb2DiW/Monn56W2W2uw4UgSSEm1czOgFgss2VsoQaAQpxnJDhZCiDZsySrlP3+k8svWHMX3R8T5o3FQFNnPQ8+Gxyfw3IW90Gk6p3DyNUO6MTUllPgWAZVeo2ZAVPut3nQaFS9ckky/bj68OH8vn6zK5IU/9zDl9eUcKq3hxhExrHzkHG4aGdMpY20pwEP2BQpxuunwTGBtbS2bNm3Cz8+PpKQkm/fq6ur47rvvmD59eqcNUAghTiaT2cKcDVnM35nHnrxKCivbTvyYtyMPP3cd5bWNtK5BXVlnxGS2cP2waBJDvXhzcRoZRVVE+rqxLbvMrobghMQgKmobWZ9ZqvisS/qH89DkHlz14VrSCo8mbFgsFib1CmbjQeXrmjWaLGzILOWnLbYBbUFlPdd+tI4ADxcOllTTK8wLF63aqb2LAG56jXV/ZZS/G4WV9Tb7LSN8XYkN8KDeaDrmNnL78yvZdLCUmAB3hsS2vXQvhHBOh/YE7tu3j0mTJpGVlYVKpWLkyJHMmTOH0NBQAPLz8wkLC8Nkars4quwpEEKcrh79cTvfrM/utPstuG80PUI8ra9/25bL3d9ssTtvVEIAb1/TH29XHf/8dqtdoAZNXTpCvQ3klNn39r2kXzg+bno+W5PZ5nJuoIeeQicSWVSqpmVcZ7x/XX+CvQwA9I30YU9eJe8sSeNAYTU6jYp9eZXUGc34u+t5+oJeTOujXKvRkZfm7+G9pUc7oYzuHsis6QPRa8/Mxaw/dxzmk9WZVNQ2MqlXCHeMjcOgOzN6LMvn99mlQ3+DHnnkEZKTkykoKGDv3r14enoyYsQIsrKcb1ouhBCnq/yKOr7beKjT7uftqiPK343ymkY2Z5VSXttoE8w002lU1gAQ4D8Xp3BJ/3C788wWFANAaJrN+8eYWC7oE0aEryuO4qMwH1fUTqxMOxMAuuk1PD0tiSnJofTr5ku/br6oVCoSQ714+5r+PDylB9sPlVN3ZEaxuLqBf367ldwy54tV786tsPueLd9XyA+bOu/P6WT6fXsut3+1mfUZJezJq+TNRft54LttTl27IbOEP3YcZn9+Jcv3FVBd33l7TUXX1KHl4NWrV/P3338TEBBAQEAAv/32G3fccQejRo1iyZIluLu7n6hxCiHECZdXXtduUkRHxAW688HyA7y1KA2j2YJOo0Krto/OGk0WSqobrEGgq17D5QMi+XGz8j5EJaMSArjqw7WkF1W3ed7Vg7sxoqSGdxWC0Y7QquGecfFMHxZNRV0ju3MriPJ3I9T7aNeV+Tvsi1cbzRYW7s5nxvDodp9hsVh4deEexffWZRRzzZBuxzz+Y5VfUcfC3fm46TVM7hWCewc7x8xakWF37I+dh8ktqyXMQevCyrpGZn6ywW65X6WCGcOiefqCXh0agxDNOvTTW1tbi1Z79BKVSsV7773HXXfdxZgxY/j66687fYBCiM6xM6ecXbnl9ArzJjlcuSVZV9cz1BN/d71dN46WQr1cqGk0KxZWbm1zVhmbs8qsrxtNFhoVtssEeboQ7e9mfV1S3cA7S9KcHneAhx4/D327AWDTGMw8PKUnfu56/j0v1elntGY0w4vz97JoTyE7c8qpbTShUau4bkg3nr6gFyqVymGA5NFO4NRoMvPin3v4el0WtY3K24ui/Dtn0sFisbA+o4Ti6gZGxAXg7ea4VuRfu/K46+stNBzZ/BngsYdvbhlCQnDTcv+BwirWZ5TQzc+N4XH+qBQShkpr7H+2LBZYfaCYywYo94x+f9kBxf2eFgt8ujoTb4OWYG9X0goq2ZtfSV55HYOi/bhvQndCvA1OfR9E19ShILBnz55s3LiRxMREm+Nvv/02ABdccEHnjUwI0SksFgsP/bDdZvnssgERvHxZb8UPqa7MRavhv5f15u5vtlgTG7xddXi4aJtmCS0WSmoauW9CAn0jfXjt732sz2g7GUNJ63IupTUNpBVUWYOJx37cwcq0IqfvV1TVwM9Ozhr6uTf1QW5uhff52oNOLf1qVODmYl/uZkNmifX/TWYLn605SHK4N5cPjOSqwZF8ue6gTYeUQE8XpiSHtPmsVxfuY/ZK+xmzZi5aNdd1wixgWU0DMz5eb23PZ9Cpee2KvpybEmp37qGSGh77aYc1AAQoqqrnxT/3MPuGQbzx935eX7TP+r0cHOPHZzMH46rXsOlgKV+syaSstpFwH1cOFtfY3f/dJfvtgsBGk5nXFu7jw+XpbX4dbyy2/4XhQGE1a9OL+eufY87YvZPixOvQT8bFF1/MN998o/je22+/zdVXXy1Nw4U4zSxV2D/1w6ZDLN1X6OCKrm18YjBrHh3Pu9f256ubh/Dedf3JKavFdOTftnqjmZfm78XDRcekpLaDGUda/yvZaLLwwp9Ny57ZJdUOewC3Ja2wivZi+ih/NyYkBVlfP3NhMosfGMv71w1g/r2j0LaxWdBkwel6h28s2g9A92BPPr9xMENj/ZqCv14hzLl1aLtLqN+3sy/z8oERBHkd2wzX9kNl/Lo1l72HK3hzUZpNf+a6RjOPzN1ObYvM5pLqBq6fvY6R/12i2Blm48FS0gurbAJAgPUZJXy17iCr0oq48oM1/Lw1l6V7C1l9QLkPc3pRDTmltsHhf+fv4d2lBzAe4xaFzOIaFu85vqLi4uzWoZnARx99lEcffdTh+++++y7vvvvucQ9KCNF51qWXKB5fm17MOT2CFN/r6rxddZx3ZDbo+Xm7Fc/5OzWPXAdJGtC0X0unUTvVJxhg8Z4C/vntFv5OLej4gAGNSsXohACW7bOfQVQBE5OCefbCZLsSLTEB7kT7u6FSqTi/dyg/b809pue3lFtWS12jCYNOw9BYf+bcOqyDd3Ac9HgZtNw2Oq7DYzpcXsuM2evZ16IXslLf44o6I+8uTWNDZgn1RjNVdcY2+yfHBrqzMbNUcTZ1XUYJS/YWOB3E3fftVsb3DKagoo6USO9OyVL/Y0ceU5LtZzaFgGOoE5iZmcnChQtpaGhgzJgxJCcnn4hxCSE6STc/tw4dF7Z83ZWLHH++5iAX9LHP4AW4cUQ01w+LYvz/lnXoWT9taT8AU6uasoRb69vNl/sndmfV68toHXdagNpGk93+sNyyWp78ZSdL9hbi565n+rAorh8axa/bcmk0mW1q/SkxaFXUGe0HY7bAdxuzuX5oVJtbDtILq3h/2QH2F1TRv5sv/xgTR6CnCxf3C+ejVgkU3q46RiUE4Oem57PVmZybEsKAKL82x9fSU7/ssgkAAZul3WYq4C2F5VUlOo2Kf07o7nC5NdrfrUNB/YbMUjY4qBF5rJbvL8RstqB2JiVcdDkdWg5esmQJvXr14rbbbuPuu++mf//+fPnllydqbEKITnBh3zCiWgV8sQHuXNRXOYARti7rH4GPQrJAaU0j323MIqVVks20PmE8Oa0XMQEejO3EmdZ+kd5cPiCC16/sa/eeCrh/Ynfigzx45XL79wF25JTbHbvps438nVqAyWyhsLKe//21j5Rwb7Y9NYnbx7Y/23bFwG5c2Fe55t+Tv+ziqV93Obw2t6yWS95bzXcbD7Elq4zZKzO44oM11DWaeHByD64Z0g0XrRq1CiYlBfPGVX1ZvKeAz9ceZNbKDC59b02b+wZbc3ZZ1FXffr0+FXD90Ch+uXMko7sHMjTWnxHxtgWs/dz1XD8sikg/5Yzfk6WsppE6Y9vBvOi6OhQEPvHEE0ycOJGcnByKi4u55ZZbePjhh0/U2IQQx6mu0cSjP+4g+8heIy+DluuGdOOifmHc880WnvplJ5lOZJR2ZUFeBr6/bRjhPvZ70GobzVw3pBtvX9OPmcOjGRbrx9asUm78dAPbsst4fGrPNvfZdcSW7HK+33SIL9dl8eCk7rgeKS7sadDy4qUpxAc1tZGbkhxiLTXTUoiXC/fN2cJVH67h3aVpbMwsIfVwhd15byzaT+rhCkKc2HP31+58XrykN1cPjkTpq/xizUHyypVrAs5Zn2XXczmjqJoFu/LIKa2lqLIedxctfSJ8uH5YFLNWZNjNTL62cJ+1Vl5pdQMPfLeVlKcXMOyFRXy+JtPm3AAP5a9Ho4Ix3QMZEefPNUMiMSrMDrYW6mPguYuSSQo7Wix59oxBPD0tiXOTQ7htdCy/3T2SF/7Yw3KF5fmTKdLXFTd9hxf9RBfRoY4hPj4+rF692tourqamBi8vL/Lz8/H3d76Nj1QcF+LEaDSZ+WRVBvN3NiUWVNUb2ZdvuwTm7qKhuv7oh6mnQctvd40kOkDqfLblkR+28+1G+z1avm46Hp+ayMsL9pJfcbTFnKtOw2Pn9eSJXxzPhh2rD64fwLA4fw6V1BIT4G43e/XVuoP8308729hZB/26+bClRfma1h6c1J0v1h60+ZpaC/J04be7RxLsZSD5qQVUKRQvvm9CAvdN6E5hZT1/7jwMNAWq/1uwT/H7+fDkHny2JtPmuTqNCo1KZS063dI3twxhWFwA572xgt2tgtpRCQE8e2EyMQHuzF6ZwXO/K+/vhKbM4LpG5/Zv9o3w5ue7RrZ5zuoDRVzz0bo2z+kR7Mne/EqnntlMo1Y5XctSBcy5dWinttmTz++zS4dmAisqKggICLC+dnNzw9XVlfJy+2UGIcTJ96+5O/jPH3us9elaB4CATQAITRmfLy/Yy2erM1m5v0gy/B24YlCkYqeN0ppGHvp+u12wVNtoYlkHMrD9FJacB0b5Kp67P78SL4OOpDAvxeXLa4dEkRjqqXDlUVuyyhRn75q9uTiNt67qy6AoX3QOZjMLKusZ8eJivlh7kHBf5WXPkqp61qYXM+blJTz5yy6e/GUXY/67lBBvF8Xz1SqV3fey0WRRDAABbv58Iz9tOWQXAAKs2F/E+P8t5cfNh7hpZAz/vTSFSF/ljinOBoAAO3IreH7ebmoaHGdL7861H0+zQE8XHj8vkQX/HM1d58Sj6cBssb+b8h5VJY9M6Sl9lkWbOjxHvGDBAry9j+6BMZvNLFq0iJ07d1qPSb1AIU6+gso6ftpybK205u04zLwdTbM0I+MDmH3DQLss0q5uQJQv717bnyd/2UVBpW2Q4ihsrm8029UEdEQpgWJPnvIs0cLd+VzYN5zII3s916UX8/aSNA4UVDEg2o8HJ3XnQGH7y/xtjavBaGb6JxvaDY6MZgtP/7qLu8+JY6/CePt28+XpX3fZLOXWNpr4c0ceVw/uxpwNWVgsTbN990/swcaDytnsjlTXm3jj7/0O3zdb4N/zUjk3OYRQH1ceObcnj/+0g/LatsvdBHm68NKlvbn9q0123wOT2cJHKzI4VFrLe9cNULw+KVR5luz/piZy86hY6+sHJ/fg6iHdSM2twNOg5Znfd7cZQN42Npbnfm+/yLeLVs1VgyPbPU+ceaKjo7nvvvu47777jvteHVoOViu0O7K7oUqFSaEifksynSxE50s9XMG5b6zolHu9cEkKVw8++S25zgSzVqQ73WnDTa+x28eWHOZFal6l00t6Hi5axWXWuEB3Zs8YxIPfb7PrJuHjqsNisVDuZF2/ztI9yMMmA9fTRUuEryupDoLZ1GenkF9Rx4HCKlLCvflweTqzOpDs0RHR/m5kHinS7CjDuqVB0b58/4/hvLVoP/9buE/xHJUK1vxrvMOuHLd+vpG/dh9NSOkZ4skPtw9vt2NKXaOJkqp6vlqfxaLUAkprGgj2NHDdsCiuGBjJJe+usulEYx0PTYF9gIeely/rwzk9O78ElHx+n3qdGQR2aCbQbHZ+ulwIcXJ1D/Yk3MeVnDLljfgd8e2GLAkCHTi/dxiv/LVXcYZMo1JhsljQa9VMTgrmt+2H7c7Zk1fJB9f15+bPN7X7LHe9RjEAhKaOENfOWqf4513mREu7E2F/QRWvXtGHedsPs2hPAZX1RocBYISvKwadmugAd6ID3Gkwmvl0dabiuVcNjkSFim/WZx3TuNQqrAEgtB8AAvxjTFN29N3jExgQ5cstn2+kulVAb7Hg8M8H4L3rBrBwdz6bs0qJC3Tngj7hTmUfG3QawnzdeGhyTx6a3NPu/dk3DOLc11eQV3G0TuWQGD/eubY/pdUNxAS4o1WogXimMJmbWvkVVNYR5GlgcIxfh5bMT7WGhgb0eueX7U+lTv0pMZvN/P777515SyGEkzRqFa9d2ReDrv2/1jpN2/+gbs0u55xXlrBw9+nbbaC2wcSTv+wk5ekF9H32L56ft9vpwszHI8TbwMc3DKJ7sP2eO5PFwrVDurHu0fFMHx6teL3RbKG2way4L621m0bGtPm+swF/pK8rH00f6NS5zjA4qItnoenr21fQdrKDiqavreUSeOrhCodFlS/rH8F/Lk7mvWv74+7S8W0KjrY2RPgqz+BdPTiS8YnB1tfD4wO4cpD9L0XxQR7WrGwlGrWKKckhPHZeIlcO6uZUAOgMXzc9ax4dx+zpA7lvQgIfXD+Ar28ZSoCHCwnBnmd0ADh/52FGvrSYqz9ay71ztnL1R2sZ+dJi5u+0/4Wqs4wdO5Z77rmHhx9+GD8/P0JCQnj66aet72dlZXHhhRfi4eGBl5cXV1xxBfn5R/9tfPrpp+nbty+zZs0iJiYGg6Hp50qlUvHBBx9w/vnn4+bmRmJiImvWrCEtLY2xY8fi7u7O8OHDOXDggPVeBw4c4MILLyQ4OBgPDw8GDRrE33//fcK+9k75SUlLS+Oxxx4jIiKCiy++uDNuKUSXtfpAEZ+vyWTHoY4nXNU1mpwKhBpN7U+FZBTVcOvnG216w55OnvxlJ5+vOUhlnZGymkY+WpHBS/P3nJRnD48L4IFJ3RXfSyuowtddT98Ib8X3oalXsFK3lvhAD24bE8v0YVHMuXUo90/qgYuDgMvb4PxCTqPZzITEIKL9nSsQrteoUPo9IcBDzwuXpPDb3SPpHqwc/ET6ulFarTwT2VzWxgI8Py+VX7Ye7Xcc5OWimKhi0Krp382XeqMZb1cdJidKuLSkUanoGaKcJHOoVLnji1I7wAcmdWdiUrC1NV9coDvvXNO/Q2PpTCqVivFJwdw3oTuTe4WcUTNljszfeZjbv9zM4XLbP5e88jpu/3LzCQ0EP/vsM9zd3Vm3bh3//e9/efbZZ1m4cCFms5kLL7yQkpISli1bxsKFC0lPT+fKK6+0uT4tLY25c+fy448/snXrVuvx5557junTp7N161Z69uzJNddcw2233cajjz7Kxo0bsVgs3HXXXdbzq6qqOO+881i0aBFbtmxhypQpTJs2jaysY5sFb88xFw+qra3l+++/Z9asWaxatYpRo0bx5JNPShAoxDFqNJm55fONLN17NKP0miHd+M/FKU5db7FYePKXnU4tdTnLAny9LotB0c53ZjgZ6hpN/KLQ3uzbDdk8cX7SSRlDmLdyNqybXsP8nYc5WFyj+D5A0ZGM2dbSCqu4wTea7sGedA/24LWF+3DRqqlvFdirgPevH8CjP+6wWeZ0JMTLgEql4n9X9OWWzzZQUtP2cnHDkV8SXLRqdBo1Q2L8uHtcPL0jfKydJz6aPpBpb62kosW+w8ExfgyN9WNczyB+3Wb75+PtqrVJxjCaLTz16y4m9wrBoNMQ6u3KJf3Dmbs5x+a6Jy9IYmVaEfd8s+WYlrmfubAXa9OL2ZJd5tT5iaGeDImx/3l3d9Hy0fSB5JbVUlVvVJwJFsfOZLbwzG+7FZOVLDT9zD/z224mJp2YgLd379489dRTACQkJPD222+zaNEiAHbs2EFGRgaRkU2JNp9//jm9evViw4YNDBo0CGhaAv78888JDAy0ue/MmTO54oorAHjkkUcYNmwYTzzxBJMnTwbg3nvvZebMmdbz+/TpQ58+fayvn3vuOX766Sd+/fVXm2Cxs3Q4CNywYQOzZs1izpw5xMXFce2117J69Wreffdda/1AIUTH/bwlxyYAhKYA7JJ+4Qx0IggrqmpwKiDoqLb2PJ0qZosFo8Ie5QaTGYvF0marss6SEuHNqIQAVuw/WgxYpYIlewtZsreQtobwZhttyf7v56ZKC0rBX7PbxsQxLC6A2TcM4tEfd7A+owQPFw09QjzZdLDM7vxbj/TaHRDly5tX9+O1hfvZlGXfnqx1Dbp6o5meIZ7MvmGQ3blR/u78etdIPl6VQVZJDUNj/Zk+rKlN3BPnJ5FVUsPWI4FXtL/bkQQZ25+lsppG0gqqSD7SdeWlS3vTK8ybv3bn4eGi5aaRsfSP8mHAc387/DlU0dQC8WCJ8s/+iPgA/uvEDLGPq5ayWiOphysZ8dJi3r9ugGJ5lTCfU9sB5Gy1PqPEbgawJQtwuLyO9RklDIvr/LI3vXv3tnkdGhpKQUEBqampREZGWgNAgKSkJHx8fEhNTbUGgVFRUXYBYOv7Bgc3bTFISUmxOVZXV0dFRQVeXl5UVVXx9NNPM2/ePA4fPozRaKS2tvb0mAns3bs3FRUVXHPNNaxevZpevXoB8K9//euEDE6IrmTTQfsPZWjqJ+pMEOjjpsPfXU9xdYPNcaUMVQCtWuVUY/vzUuyXxk41N72WCYnBNpmXABf0CTspAWCzD68fyMerMlixv5C6RrM16IGmpIHjoRQABnq48OyFvTg3JRSAuEAPvrttGNX1Rly0apbvL+SmzzbaPHt4nD/npYSSV17HTZ9uYJdCPb2jY7Yf9LZD5RRW1hPoaV/XLzrAnWcvPNo/vq7RxPtL01iXUUqfCG9uGRVDhK8bKeHeTP94vV1pHb1WTcSR+oL5FXV8vCqDtPwqhscFMGN4NN6uOn7fltvmLyLBXgaHAaC/u55QL0O7PZABylrMUpbWNHL/d9tY8fA50nP3JCmodBwAHst5HaXT2dbpVKlUHUqGdXdXLrbf8r7N/zYpHWt+1oMPPsjChQt55ZVXiI+Px9XVlcsuu4yGBtt/1ztLh4LAvXv3cuWVV3LOOefIrJ8QncxRx46YgLb3ce3MKeeHTYfYdLDU7gNLo1Zx5zlxvLzAvsTFxzMH8f2GbMUMVmhKHrlxZAwX94tw8is4uV68tDdmyzYW7SlArVIxpVcIT007uf8uueo13HlOPHeeE8/tX25iq30DjE5VVW+0BoAtuR8pOfL8vFS74LOmwUhaQRUXvbOSqvqO95B10apxd9Gw+kAR/52/lz15FfQO9+Ff5/Wkf7ejxaxTD1dw+ftrbAK2L9Ye5N1r+9Mn0od7JySwIbPEJri9dVQsPm56iqvqufDtVdZs10V7CvhzZx6/3DmC7Qo9j1tqmSHbmk6jYtPBEsJ8XMlyECiCcsmYnLJa0gqrZNn3JAnybL9NYUfO6yyJiYlkZ2eTnZ1tnQ3cvXs3ZWVlJyQOWrVqFTfccIN1a11VVRWZmZmd/pxmHQoC09PT+fTTT7n99tupra3l6quv5tprrz2pv3kLcba6alAkX607SHbJ0YzPvpE+TGiRpdjaoz/uUCybEeih59yUUC4bEEHvCB+MJgtvLk7DZLagUTUtJ45OCGRrVhlgHwReOSiSx85LVOxBe7rwc9cza8YgymsaUanBy3Bqx+qoVlxiiCcHS6ppMFowWyzHtWezrtHE+owSBivsWSuraVAsEL01u5x3lux3KgBUGtvVg7uRWVTN9bPXW5eK12eWcP2sdSx5cCxBR3oMP/HzDrsZO7MF/jt/L1OSQxkU7cfvd4/km/XZVNQ1MqVXCBOSmn62v92YbRfMpR6u4K/deQR6KHcWcUZeRT3Xzl7f5jkqmn6Wiqoa7I7r28miF51ncIwfod4G8srrFPcFqmj6O6b0s38iTZgwgZSUFK699lpef/11jEYjd9xxB2PGjGHgwM7LuG+WkJDAjz/+yLRp05q2VjzxxAktz9eh7ODw8HAef/xx0tLS+OKLL8jLy2PEiBEYjUY+/fRT9u1TLqgphGifj5ueX+4cyf0Tu3N+71CeOD+Jr28Z4rDcw+oDRQ7rphVWNVgDQIvFwrqMEusHuMnSlECRU1bLeSkhdqVKVCq4fmjUaR0AtuTtpjvlASDAjGHReLYqAtw7wpsf/jGcMB83jObjCwChaV/UNR+tVdw64GnQ4e9hX5ssNtCdtIL2u4dA09Lxy5f1ZkCULynh3jx+XiKPnZfIDZ9ssCtuXd1gsiZ/GE1mNirsRQRIL6rGeCSjNyHYkyenJfHK5X2sASBg84tPS1klNUzrE+awJE1nsIBdANh8/Pk/Tk62uWhatWieyW8deje/fmpa0knPglapVPzyyy/4+voyevRoJkyYQGxsLN9+++0Jed6rr76Kr68vw4cPZ9q0aUyePJn+/U9cFnqHOoYoKS8v56uvvuLjjz9m8+bNJCcns3379javkYrjQjiWXljFb9sOo1HDBX3C6eagrMcbf+/ntb8d/+L16cxBjO0R5LCR/W2jY3n0vER+2nKI5+elUlTVgK+bjkem9OQqKRR9TPbnV/LRinQOFtcwJNafm0fF8OeOwzwyd0enPmdq71BuHxPHm4v2s7+gij4R3iSGevHfBXttgjUV8M61/Vl9oIgv17a9sVyvVTP/3lHEBtqWflmUms9Nn21UvOahyT24aWQMZouFCf9bRq7Cxv5eYV7Mu2eUw+fWNBj5aEU6ry20b/320x3D6dfNl7XpxTw/L5UdOeV0D/ag0Wgm4wQkQbWmUsGm/5uIn/uZUfj3ZDjRn9/zdx7mmd922ySJhHobeGpaElOS7bdCiONzzCVimnl7e3PHHXdwxx13sHXrVj7++OPOGJcQXdKCXXnc+dVma8LGm4vTmD1jIKMS7LPOotqo+aZRwduL0/hzx2F0GuUCtZnFTbNDF/eLYGpKGPkVdQR5uUjP4OOQEOzJfy/rY3OsrVIxx+pQSQ2Xvrfaur8uo6ianxVK5ngZtPSO8OaNRY576zaL9nOzCwABch0UpFYBW7NLSX5qHxaaSqu0DgJVKjinRyBGk1lxRvv7jdk8+9tuKuuNdvvybh7ZlFTy3YZsPA1afrh9GHqNGpVKxTO/7SJjVWa7X5MjzrSNg6bkHmfb+4nOMSU5lIlJIWd0x5AzyXHPBB4LmQkUwp7FYmHMy0vtNrD3DPFk/n2j7c6vN5q48O1V7HHQlqs9T5yf1G5HCnH8lu0rZMbHbe9L6yhfNy2lNc6V7unXzYctCn1mW7tyYCQvXdbb7nhaQRUTXl1mdzzIQ09Bq2XUAVG+eLhoWX2gyKYg+YTEIGbNsC0zk11Sw9hXltoFWdcMieTGETGkFVRzz5wt1uLnEb6ufHPLUCL93CisrOfKD9aQXtT+MrerTo3RbLGO56K+YahU8NMW+6C5tZHxAXx585B2z+tK5PP77NKhmcBx48a1e45KpbIWWBRCOK+i1qiYwbgnrxKz2WKX+eui1fDdP4Yxe0UGHy5Pp7bR+czPITF+XDukacm3pLqBbdllRAe4E+Zj4Ku1WSzbV0iIl4EbRkSTGGr/D31lXSNfrs1i+6EyeoR4Mn1YtCyZOTA6IYArBkbw3cZD1mP+7nrCfQwUVjW0WRvNEWcDQOBI8k/bwrwN3D0+XvG9+CAPHpzUnVcX7rPOng2M8mWjwr7ELVml3Do6jmX7bOtd/p1aYJfQsig1X3GW7bsNh5iaEsbjP+2w6X5zqLSWV/7ayxtX9SPQ04U/7xvFwt355FfUkxTqxZK9BRwoqKJ/lC/nJoew6kAxBq2aKckhNJosbM0upZufO/FBHuRX1LEjp4K0giqH3xPtkTaMQpzNOhQELl26lKioKKZOnWpXU0cIcWwKKupYtq8QXzcdEb6uHCq1XX7rGeLpsFaZl0FHmI/BqQDw8amJuOu1RPm7MTzOH5VKxRdrMnluXqr1wzbU22ATlPyyLYcf/jHcWswXmmYgr/hgLalH6s39uTOPHzfn8NvdI8+YZJKTSaVS8d/L+jB9WDQ7c8pJCvMiwteNe77Zwvacpu+hRg0d7IbmtPaWenqGePLpzEGEOOiAAnDXuAQu7BvO5qxSgr0MzFqZ7vDc9ELlwGp/QaVNEOjjpvxLg9Fs4fGfdtjVu4SmgsLNXLQazu8dZn3duoBw66XtcT2PJqIEexlYcN9oVh8oory2kf/MS7Vbyp6YFKxYG1GIs0mHgsCXXnqJTz75hO+//55rr72WG2+8keTk5PYvFELYMJstrEwr4tdtufy85RDNEx7urRrMa1Vw/0TlHrWHSmvwctWRV16v+H5LKhVMTAy2qUWYVVzDU7/ustkb1XpWqq7RzEcr0nnjqn7WY3/uyLMGgNZ7ldQwd9MhblRYXk4rqGRffiUJQZ4kdOGaa8nh3tZg+p5vtrAy7WinEZO5qUxJaU1T4HMyN+nsyavkiV928dH0tstdRPq5sWJ/ETd+usFh8eXoAHcMOuU9pX0jfQAor2nE3UXD5F4hhHq5cLjC/uc3s7gGnUZl1+O6rX2wHaVRq6x7bX3d9Nz2xSZriZtIP1ceOy+x054lxOmqQ0HgQw89xEMPPcSaNWv4+OOPGTFiBD169ODGG2/kmmuukf0BQrRQXW8ku7SGKD93XFsEdzlltVzw1krFmY7qVh+uao2KW7/YhFoF4b5uPDipO92DPfnnt1vZk1eJXqPmnJ72SSOtXTkw0q7N2oq0Qqc2x7eemXSU6NCcaLIhs4Qdh8rRqFW8syTNpkvEJf3CeeXyPl2+C8P8nXl2x0prGljx8Dm46jRcO2vdMe/1dMRdr7H7+Wr2d2q+w64gzTKLqvm/n3e0+TOTXlhNemE1KmxnIIO9XLjonVXWoM6gVTMszp+UCB8K9+TTujmKu17DtUOj+HD50RlHnUbF3eMS2vsyj8mI+ADWPDqOZfsKcdVpGN09EJ2D0kxCnE2OKTt42LBhDBs2jDfeeIPvv/+ed955hwcffJDc3FwJBIUAZq/M4LWF+6iqN+LhouGqQd24dUwsQZ4Grv5wrWIAqKTB2PShabY0baS/d85WNKqmWn/Q1Ct3wa58hsf529QCHJ0QwJgeQRRU1LF8XyFzNmQzZ0M2yeFevHftACL93JwuwpscZvt3elCMr+J5KRHeXPruasWetM1+3JLDmB6BXNg33Klnn63cXDQ01NhGPnqNmgAPFww6DXNuHcol760mXaH4c0tKs2VKVNj/gtGSM1mwi/cUOAwAAzxsiy1baAr0+kf5svpAMfmtZvvqjGaWtOqTbfN+o5mbR8XQJ8KH+bvy8DRouWZwN5ttCZ3N06CzWV4Wois4rhIxmzdvZtmyZaSmppKcnCz7BIUANh0s4bnfd1tfV9WbmLUyg49XZTIszq/N9lXOUPrMr6wzsvzhc9iVU073YE/rsu+dX20mtcWM0s6cCh7+YTvf3DqUcT2D6BHsyd78tmecPltzkAW78vjy5iHEB3kyPC6AqwdH8s36oz3SVCp46Pu264M2W76vqMsHgdcPjeKtxWk2xy4fGGFdSvVx0/P+dQO44oM1lNU0Ws+5f2ICJdWN1oQcDxctH63IsLt/677QapUKUxtrzINj/Bx2PGnmKPHn1tGxLN9XaFdwuc5oZmOm418I2mKyWNh8sIypvUOZ2ltqwwlxonQ4CMzNzeXTTz/l008/paKiguuuu45169ZJL2EhjlBa6gMwWyysSis+Ic901WkI93El3Md2c/9fu+3Hsia9mIq6RrwMOr65dSjvLzvAnPVZVNQ5zjjNq6hn4qvLuaBPGHkVdRwsrmFEvD+bDpZS12ju0B620HaCja7gnxO646rX8N2GbIxmCxf3C7db6uwe7Mmi+8fw89ZcKmobmZgUbDcTVlbTwMLd+WS2WKIf2yOQR8/tydfrsiitaaRvpA/PtvilpLUR8f787/K+7Y55SnII4QtcyWlRN9BNr+HaId1IL6xSXL5uOI5slxgHvbSFEJ2nQ0Hgeeedx5IlS5g0aRIvv/wyU6dORas97nrTQpxVPFxO/oz4NUOUO3y4aDU0moytjqnRH9nv5Oeu57HzEqltMPHF2oNtPsMC/LLtaG211r1enTUk9uT2/jwdqdUq7hgbzx1jlcuyNPP3cGmzlqOPm55f7hrJ9xuzSS+qZmCUL9P6hKHTqHnmwqakPbPZwqerM+1moM9LCeH5i1LwdbK0j0HXVJLo9YX72JxVSlygB3ePSyDK353bx8axfF+RTdB3Qe9QFu8ttOsn7AyVCsk0F+Ik6FCxaLVaTWhoKEFBQahUjjd2b968uc37SLFJcTY7VFrD5NeWt7kHq7OogBtGRPPUtF527xlNZvo+u9DuQzjcx5VV/7Kt+ZlWUMWFb688KWOe1ieMt67u1/6JotOszyjh1i82WpeW44M8+OKmwYS2URamIzKKqnnpzz1sPFiCu17LtUO7cdPIWL7dkM1jP9m3zGudOKLk3xclc93QqE4Zn+g88vl9dunQNN6TTz7ZZvAnhIAIXze+umUoL83fw5oDnbf866pTU28022zOtwB/7crn/6baN1bPKatVnIUpr7VPSokP8uCnO0fw0fJ0dueWsye/6oS1y2qrQK84MQbH+LHmX+NZlVaEm17D0Fj/TsvQzi2r5eJ3V1kDzCIamLsph5kjYrhmSDcGx/iyYFc+Oo2K7sEeDOzmh0Gv4eUFe/lgueN6g14yEyjECdehIPDpp58+QcMQ4uzSN9KHb24ZypO/7OTzNW0vszrr7vEJfLg83SZRAJqCvYPF1XbFcYM8DXi6aKlsFQjGBdnX6qtrNFFUVU91g5Fdhzu3NElrzfXixMnlqtcwISm4/RM76Ot1WXY/k3vzK1mUms+U5FDigzyJV/iZu2tcPAcKq1iUWmA3KxjmbWDSCRirEMJWhwoh+fr64ufnZ/dfTEwMkydPZuHChSdqnEKckZIUWq4106ia2m95GbS0NynTN8Kb20bH0c3Pvliui1ZNgEJ9N1e9hrvG2e4502lUdsWnV6cVMfzFxVzz0Tr+2GGfSJIS7o22jQFq1U3Le86I8HXlHgftycSZKbe8Vvl4meM9oxaLhekfr+fvVgGgh4uW/t18+OSGwQ6LTgshOk+HZgJff/11xeNlZWVs2rSJ888/nx9++IFp06Z1xtiEOOMFO8iE9XDR8u2tQ+l1JNuzpsHIZ6sPWjf4t9YvyheNWsVd58Tzjy832SwJzxwRg5dBeenstjFxJIZ68fv2XNz0Wi4fGEGvsKMZpg1GM/fM2UpJG3UL88prbcqNtNZc6FetampjV1bbaHeOVg33ju/OzaNibQpnizPf6IRAftycY3+8e4DDa9aml7BFoadxVb2RzVllXPfxOr65ZYjiDKIQovN0KDGkPa+++io//PADq1evbvM82VgqugqT2cL5b620abPmolXz290j6a7QQi2zqJrxry6z24/3wz+GMTC6Kat2fUYJX687SHWDifNSQri4X8Qxj29zVimXvNv239eOaF2frtlrV/Y5rnGK05fJbOHB77fx05amQFCtako+CvNx5dL+EVwxKNLump+35HDft1vbvO/I+AC+vHnIiRiyOA7y+X126dQgcN++fQwdOpSSkpI2z5MfItEZGk3mM6K1U2l1A+8uTWNtegnd/N24fUxcm50Pftmaw3O/76aoqgEvg5b7J3bnhhGOy4Qcj6ziGka/vMTh+xG+rhRW1lFvPPZ/JnQaFav/Nb7NlmTizLc/v5J3lqTx89Zcm+MPTe7BnefYbgEoqKxjxIuL2+124uum442r+jK6e1Cnj1ccG/n8Prt0ahC4Y8cOJk6cSF6ecrHcZvJDJI6FyWzhrcX7+XJNJmW1RoxmC4GeLtw9Lp7pw6JP9fA6VYPRTG5ZLSHehhOyN6q0uoG16cUEeRmYtSKdP1sVuO7fzYeR8QFMHx7Ne0sPMHulfVcKJR4uWlSqpg4mze4YG8fDU3p26vjF6cdstjDg3wspbZUk4uumY9P/TbTLRv52QxZP/rKL+taNg1vRqFUsfmAMUf5SPPp0IJ/fZ5dOrfQ8e/Zs+vbt25m3FMLqf3/t5d2lB2yOFVbW8+Qvu5i34zA9gj25alA3ksJO3T9Mm7NKySisZmC073F9aOm1amvrt87285YcHpm73frhOzDal7vOiWf5/kJ83fTMHBHN2B5HZ14eOy+REC8DP2/NQadRc6ikhiIHewgfmtyDiUnBfL/xECXV9YxPDGZ098AT8nWI00uDyWwXAAKU1jTSYDJjUNv+MnPloG5MSgphc1YpK9OK+GRVpuJ9TWYLP23J4b4J3RXfF0Icuw4Fgffff7/i8fLycjZv3sy+fftYvnx5pwxMiNba6mixLr2EdeklfLM+i09uGMzIBMeb0k+EBqOZ27/cxKI9BUBTx4N7xiXwz4mn1wdXeW0jj/64w2b2ZWNmKcNj/fn1rpGK12jUKm4ZHcsto2Mxmy3EPvaH4nkfzxjIuMSmsh73TkhQPEecvQw6DcNi/VmTblsbc1isv8PZbF93PeMTgxmfGIzRZHH4d7yhndlCIcSx6dCGqi1btij+V1RUxMSJE9m5cycDBgw4UWMVXVytE90sGk0W3ly0n3qjiQW78vhtW+4xta3qqLmbD1kDQACLBd5YtJ/9+Se25t6Bwip+25bLgULnCjBvPlhKbaP993FlWpFT16vVKsUyNQEeLozpIfu2urr/XJJi0/M32t+N/1ySYn1tMlvYnFXK3lZ9hn/acshhAKgCzu8ddkLGK0RX16GZwCVLHG8gF+JEWn2gqdNBRV37Ad2+gkqGv7CY4iNLlh4uGj6+YTCDYzq3Z21pdQPrMooJ9jI47AyyJr2YBIUs4M7QuhD19GFRPHukX6wjYT7KbcJCHRxX8sCk7tz37VZa7ia+d0KCXccS0fXEBLiz6P4xbMoqBWBAN1/rXsCdOeXc9sUmcsqa6goOjvbjw+kD8HHT89biNMX7ebpoeObC5FO6xUOIs1mn7gkU4kTYdLCU6bPXt1mrrqW6BhN1LZaPqupN3P/dVlY+Mq6Nqzqm9b66cB/leoCRvvazZp1hxf5Cu04kn685yKSkkDaXwnuEeDIhMYi/U4/OWuq1am4dFev0sy/sG06Yjys/bDyE0Wzh4n7hJ335XZy+1GoVg6Ltf+G6d84WawAIsD6zhBf/3MOLl/bmUKlywekF940m7AT9HRJCSBAozgCfrc50OgAEbALAZodKa8mvqCPYSzlY6wilfXU5ZXW46tTUNh491ifShzEnKCliVZryzOPKtKJ2A7J3ru3PZ6szWbavkCBPAzeOiCElwnHJGiWDov0UP+iFUJJZVM2BQvsi6M2/jAyP82fp3kKb9+KDPJwKADdnlbI6rYgwH1fOSwmVTiNCdIAEgeK0V1rjuJuFs1Qq8O6khvSbDpYo7quLD/JgcIw/6YVVDIrxY/qwaLuyGJ3F0cyjo+MtuWg13Do6jltHx3X2sIRQ5OWqUywkXm80MeLFxYR6Gwjw0FNU1fR33cdNx0uXpijdyspisXDTZxtYvOdo8Pj2kjS+v20Y/h5Sk1IIZ0gQKE5743sGsWK/c4kLjsQFenTaDEGIl/L+uSh/d544P6lTntGeC/uF88HydJtltAhfVy7qF35Sni9ER/i56wnwcCGvwrafcGWdkco6Izllteg1Kp6/OBl/dz2juwdSVtPIzE/Wsyu3gnAfV56clkS/br7Wax/+YbtNAAiQXljNrJUZPCJ1KYVwyunfbkF0edcNjeKSfuGojkyq+bjpuHd8gk0WYltcdRrevKpfp40nKcyLcT1tM2H1WjW3dGBf3fHyMuj48fbh3Do6lhHx/tw2OpYf7xiOp4MewkKcSqmHy+0CwNYaTBbWpZcwJTmU6noT4/63lCV7CymorGdLdhmXvLeaTZlN3ahKqhsU+xUDbD9U1tnDF+KsJTOB4rSn1ah59cq+3D+pO3nldSSHe2PQabhvQgIl1Q18tzGbl+bvtbvOy1XLraNiuWxAJCHex78XsKV3r+3Pp6szWb6vkGCvY9tXd7yCvAw8dl7iSX2mEMdi3o62u0g1+3VbLiXVDcQGuFHXaLu312KBZ+el8sudI8grr8PkoNlVQtCJycYX4mwkQaA4Y0T4uhHRYqO4SqXC38OFm0bGMntlJkVV9TbnX9IvgrvGnZiixQadhn+MieMfY2RfnRDtcdU5v+i0Mq2ItenKe2kPH8kujg/yIMDDxe7vvEGr5pbRJ29GXogznSwHizOeXqvm1Sv64GU4+jtN30gf7pOuFUKcFs7pEdyh8x1VAwjydGFDZgl6rZoXL0nBRWsbLIb5ulLqoKWhEMKeymJxMKd+AkkDanEi1DQYWZdego+bzmYDuRDi1Hr0xx18sz6r0+43pVcI71zbnzkbsnj8p5027wV46HlqWhKRfu70jfTptGeKJvL5fXaR5WBx1nDTazmnp7QuE+J0snxfYacGgADzd+Uxf2cef+3Kt3uvqKqBu7/ZCsCIeH9mTR+Eq15qBwqhRJaDhRBCnDBr05ULmx+vF/5IxdROEflVacV8vCrjhDxfiLOBBIFCCCFOmEi/E9P27VBZLbllNe2et2J/YbvnCNFVSRAohBDihLmwbxixTtb07Kj0ohp0GhXuLhocNecJ81Yu7i6EkCBQCCHECeSm1/LD7cO5Z3wC43sGMX1oVKfev9FkobrexC2jYxnVqm+2i1bNjSNjOvV5QpxNJDFECCHECeXnruf+id0BqG0wMWdDFg2mzi1MMXfTIVY+Mo6PV2WwYl8RoT4GbhoZQ6+wk1vEXYgziQSBQgghThpXvYbbx8TxxuK0Tr+3QafhjrHx3DE2vtPvLcTZSJaDhRBCnFT/nNSDu8fFo3Gwj68tPq46RsYH2B2/pH9EJ4xMiK5FgkAhhBAn3QOTerDzmSlE+dtnD2scZHl083Plw+kDefPqfpybHIJGrcKgU3P90CgemNT9RA9ZiLOOdAwRQghxyuSW1fLYTztYtq8QTxctNwyP5qJ+Yfzjy83sy68CINzHlSfOT2RyrxBUqqMBYr3RhFqlQqeR+YyTRT6/zy4SBAohhDjl6o0mdGo16hazgHnldZTVNtA9yNPmuDh15PP77CKJIUIIIU45F619a7cQbwMh3oZTMBohugaZQxdCCCGE6IIkCBRCCCGE6IIkCBRCCCGE6IIkCBRCCCGE6IIkCBRCCCGE6IIkCBRCCCGE6IIkCBRnrZyyWjZkllDbYDrVQxFCCCFOO1InUJx1TGYL/5q7nbmbD2G2gJdBy4uX9ua8lNBTPTQhhBDitCEzgeKsM2dDFt9vagoAASrqjPzz262UVjec2oEJIYQQpxEJAkWHfbH2INPeWsm5b6zg/WUHMJnb7zxoMltoMJpPwuhgUWqB3bF6o5mft+aclOcLIYQQZwJZDhYd8t7SA7w0f4/1derhCrJKagjzNpBbXseIuADOTQ6x9vk0msy8+OcevlmfRZ3RzITEIJ6/OIUAD5cTNkZfN73i8df/3s+FfcPxc1d+XwghhOhKZCZQdMjslRl2x75el8Urf+3j63VZ3Pn1Zh6eu9363luL05i1MoPqBhMms4UFu/K5++stJ3SMM4ZHoVHZN5svr21k7qZDJ/TZQgghxJlCgkBhp6ymgVkr0nn6110s2JWHxXJ0ube8tv19dT9sOsS+/EoA5m62D7rWpBdzuLwWALPZwjtL0hj138UMfv5vnv1t93Fn8/aO8OGifmGK7+VV1B3XvYUQQoizhSwHCxsFFXVc/O5qcsqagrRPV2dySf9wXr2iLwDjewYzf1deu/fZcaic7sGeWBxsF6ypN7Elq5S/duXx3rJ06/GPV2VQUFnHxKRgVCoV43sG4e7S8R/Ta4Z0Y+5m+z2AY7oHdvheJ4LJbKGkugF/d7116VwIIYQ4mSQIPA3VNBipbzTj28l714wmM28uTrMuiV7aP5y7xyeg0xydEJ69MsMaADb7cXMON4+MJSnMi2cv7MXhijq2ZZcBTeVXKuqMds/aX9A0E3jpgAjeXLTf5r24QHcuemcVlfX21wH8vv0wv28/DICvm44vbhpCcri33XkNRjOL9+RTWtPIOT2CCPE2WN8bEOXHnefE8f6ydExmC2oV3DA8htGnQRD44+ZDvDR/D/kV9YT7uPLktCTG9ghEq1azL7+SuZuyqW00M7V3KMPjAqzXNZrM/LHjMDtzykkM9eL83mHotTKZL4QQ4tioLBZHczUnTkVFBd7e3pSXl+Pl5XWyH3/aajSZeea3XXy38RANRjODo/145fI+dPN365T7//v33cxqtafvxhExPDktyfp6xsfrWbav0O7a167sw8X9Iqyv9+ZVYjSbWZtezHO/p9qd7+Gi4e5xCcwcHs1LC/Y2JYY0mhgeF8CaA8WYOvBj17+bDz/eMcL6evm+Ql75ay+7ciqs99FpVLx8WR8u6hduc21OWS2puRX0CPEk0q9zvo8dVVHXiNFkwc9dz86ccqa9vVJxhlSjAlOr4/83NZGbR8XSaDJz/ex1rE0vsb43MMqXr28ZKoGgEOKkkc/vs4vMBJ5G3l1ygC/XZllfr88s4R9fbuKPe0cd971NZgtfrcuyOz5nQxaPndcT7ZHZwD4R3opBYEq4j/X/6xpNBHjo8f//9u47PKoye+D4d2p6772HUEPvHURE0MVesLHq2nHV1VV3bT91d3XVtVfEhgoqWABBpXcIvUMI6b33TDIzvz+GDJnMnWQCKErO53l8Hube987cGSJz8r7vOcfTxeH+vdomI//68TCrjxRzUZ8w1jw0Hh93HV9szWZDemmX7n1ndiWGFhN6rZo9OZXM/mg7Le3K0jQbzfzzu/1c2DsUN73GejzC140IX7cuvd7Z0GI0se5YCR+sP8GWjDJMZhidGEhsoLvDJfL2ASDAqyuPMWt4DD8fLLIJAAHSsipYui/fJjgXQgghnCVB4O/Id3vs97AdLKjmWFENSSFeHCqoJr24lv5RvjazWnVNLXyVlsPhwhr6RPgwtXcoG4+XolapmNQzGHe9luYWEw3N9gFbg8GI0Wy2/iDcMCKGuSezeVsNjvEjMdgTk8nMf1Yc5pNNWTQ0G+kX6cN/r0zlgl4h/HywSPE9bckoZ0tGOS/9dITPbxuOr4PyLR0J93G1znZ9vjXbLgBsVdPYwqHCagZG+3X5Nc6m9OJabp63jdwK22X1DemlZJZ1LSCtaWyhqLqRfXlViucP5lczc8Bp36oQQohuTILAX0l2WR0/HiiktKaJUYmBjO8R3Ok1OrXysp5areK+L3bx/Z58AFRAQrAHWWX1+LjpUKtUFNc0WQZvz+HJ7w5Yl0kDPPR8PHuow9knd70GF+2pmbOVh4ptAkCwzDidKK1j3dES3m2TxLE3t4o/f7ydx6b2ZHd2BSW1jjOHqxtbeGHFEf42pQdqFThRX9pqzuQk659rDcr7CFv9sDv/rAeBZbVNLNlbQEOzkYv6hBLl506LyexwGfbxxfvsAsBWuRUNaNXgbN3sAA89//x2P+uOKc+eduVzFEIIIdqSPYFn2a7sCu74dAdFrUHZSbOGR/Psn/p2eO2HG07wzJKDNseGxflzzdAo/rpgz2nf05BYP168IpXx/11jd65PhDdL7j213Dzrg62Ky7W3jIxlb14VO7Iq7M4p7WVT0pXgLzbAnQHRfoxNDmTDsTJ2ZVeQEOzJgChfXlhxxOF1rjo12x6fjLerjtqmFhbtzCWztJ6hcf5M6RXS5UzcfblVXPfBFmpOJr+oVeCi1dDYYmRsUhDPX9bXZrm5sdlIyj+Xd/icXfkcgjz1HQbXAR46Nj86WfYFCiF+E+fz93d3JDOBZ1FJTRNXvrNZcbnysy3Z3DIqjoQgT4fX3zIqloZmIx9tyqS6oZkLe4fy5Ixe/OvHww6vccb2zAqi/N0ZmxzEunb7/W4dHW/zuKFZeabNMoOlHEA5EwCC84GPVq3ijesGEh/kweSX1pJfZantl1Fax7oj9i3h2mpsNlFc3YTRaObytzeRUVoHWErPTO8XxhvXDXTuJk7614+HrAFg63toXVZfe7SE2z5Os9mzqdeo8ffQU95Bn+KuzN51FAAClNU1c7SoRjF7WgghhOiIBIFOKK5u5IMNJzhUUE2fCB/+PDpOse3ZO2vTHe5XA0uLtY6CQJVKxd0TErl7QqLN8fAzTGwI93VFo1bx5nUDeOmno/y4vwAfNx23j02wZtM2NhtpajYxLjmYHVmVds+RGu1DhK8bW0/YJicEeOgp6yDgcYZeq2ZAlC/HS+qIC3TnvklJ9InwYdHOXGsA2Kqpk4gz1NuVuEAP3lydbg0AWy3ZW8Ds0RVOLReX1Tbh7aZjb67yXrxWBwuqOZhfTa9wy2/EarWKv4yNP+PA3Vk6jYqwNqVxhBBCCGdJENiJqoZmm+LJ64+V8uO+ApbeN8auiHFRdZPSUwCgUkHvcPvZGpPJTH5VAwEeLjZZrW1dNzSad9cep8nZjWTtuOo0mM1m5m08wcK0HOoNRoqrm1i+v5BJKUH875djfLk9h6YWE4Nj/PBz01HR0Gy93tddx9WDo8ksq+PKQZGsP1ZKeb2BCT2CuHFELDd+uA1jm+BXBTgz2eXjpuOWUbHMHBBBTICH3fmK+maFqxxz02n41+V90ahVHCqoVhxzqKDjxJFtJ8p5bPE+0otr8XPX4eNmWVbuiLndu71hRAxF1Y1sPVGOq06tGFQ74qjuoiPXD4sh4FfswyyEEOL8JUFgJ77ZkWtXPDmzrJ4f9uRzzdBoAI4V1eCq0zCpZ7C1yHF7qZE+VNQbiMMS7OzPq+LFFUfYdqKchmYjHnoNd4xL4N5JSXbXhvq4MmdSUod74eZMTMRFr+GrtFxOtJsByyip49I3NrK3TYapGfjlUBGXvFFNTpskhrSsCpKCPZnaN5S9uVWkhHpz08gYbv80jV3ZlQC4aNW8dGUqM1ItrdnmTErkw42ZVNY3o1GrbALCVq5aNY3tgtg5k5KYPTrO5lhuRT2FVY30ifBhYkowzy092OnyqY+blmh/d+4YG8/RwhreXn2cinrl2cm+HSybVjc28+ePtluLWFfUN1NR34xWrXI4w5sS6mUT3KdllnPrJ2lUngxgI/3cGJsU6DCxo62JKUGMSgjk/5ba111sb1RCAJcNjOSygRGdjhVCCCGUSBDYifYBYNvjGSW13DV/J4cLLd0xRicGMC4pkLUKX/i7c6q47K1NPHBBMiMSArju/S00t1narDMYeennoySFeDG1T6jNtS1GE18r9OD1ddOh06ipqDfw+fYcbhkVi8HBbOFeByVGchSyWI8V1/LW9QNJCvECLPviWgNAgKYWEw99tYfh8f48+f0Blu071UaufQDoplPTP9qPOZMSWXW4hKV7C3DXa5g1PIabRsbavMeHv9nL4l15mM2WWcL/XN6PZ//Ul+eWHrTLWG6rqqGFfXnV3P3FbodjAK4YFEm/SF+H51cdKlbsYjI2OZCUUG8am41UNzaz4kARtU0tjE4M5PmZp5J9zGYzD3+91xoAgmUvpdls5pohUSzYntPhDOmaIyXcPjYBvUaFoZNl79qmFi7obWmtJ4QQQpwOCQI7Ee6jvB9vREIA9y/YbQ0AATakl3H14Cjm/3kYH23K5OdD9rXz/vfLUbZmBNgEgG39sDefqX1CKattYvmBQuqbjKjVKjJK6uzGqtUqSmotS9AlNU28sPwIwV5nZ2nwg/UZXDk4isGx/mxKL7M739RiYsS/VnW4BxLAw0XL5uNlbD9RzswBEax+aLxiJuvn27JZ1KbXb1VDM39dsJstj05iRmoYR4tqSC+u49FFe7uUWJEc4smUXqEMi/dndGJg5xcoKKkx8OHNKdbH/7ncTLPRhKtOg8lkZvXhYjLL6ogL9LDbhwiQV9lIs9HU6RK5yQzf78nni9uHc/+Xu8mtaECvVRPt58axdn//e3Kr+GBdBg9M6XFa70kIIYSQILATn2w+oXj86R8OcqRNANhq0a5cvt6R4zBj1mSG4woBXStXrYY1R4q5/ZMdGIwd7wFUykCt7OI+OkcWpOWyIC2Xv1+UQpiPq2Kx4s4CQIDSk9mtLSYzX+3IJcLPjfsnJ9uN++WQfdZvQ7ORjcdLmdY3jEEx/gyK8adHqBdfpeWcDJKVC1S3ZTbDQxc6Fyj5uusUj7cvsq1Rq9CoNTQYjNwwdytpbcrmOFo6dlRMu73i6iYGxfiz/pGJ1mPL9xdyx2c77MZuaZekI4QQQnSFBIEd+HZXHlnlysvBSgEg4HCGr634IA8KqxsVz9U1WfalOVt2pb3OAsdWHe1za+vln44wPD7g9G5GwZK9BYpBoL+DAMyvXYeR/lG+9I/yxWgyM/aF1Q6X61uNSw5y+t78PZS7mYR4u7Aru4LXV6VzorSOgdF+PDAlmR/3FdgEgOA4MHa2jt+4Hvb3G+Ogd3TMOeqFLIQQ4vwgFWY78MPJDh1n27VDIulxcr9dK83JrV3LDxSddgDojAg/N764bRjpz09j1vBom3NKu8sMRrNTSQ3OcneQAX3TyFi7OoR9IrwZHu+vOF6jVvHilf3wdj31e0yErxseLqeef3CMH/dOSmL5/kImv7yW+EeXcsXbm9iTU6n4nP0ifekbYV/8dHxyENe+v4VVh4s5UVrHNztzufrdzWzPVJ6JC/G2X5K/YXgsySH25YHavuXp/cK4dkiU3ZieYd5M7W27T9TTRcvtY+PtxgohhBDOko4hHbj14+2Ky5RnysdNR+8wLzZl/LbLeRf0Cublq/rj5WqZdcssrePC/6077dIzABN6BFJaa2Bfnn1JFqXOGE/N6MUFvUNtumy02ny8jHfWHie/soFRiYHMmZSEn4ee4upGvtyeQ0GV5fi0PmHWzh/1hhY2pZfh4aIlOcSTuRtOsCWjjJRQLx6Z2pOs8jpmvrXJJmHF21XL+ocn4qMw+/jAwt02exMBvFy1NgWjW03pFcxPB+1/Pt6/YRBL9hXw475CXPVqJqWE8Pi0nui0auauz+DnQ0XoNWquHhLFhb1D2ZtXRZSfO4nBjmtINhtNLEzLYf3RUkJ9XLlxRAzxHdScFEKIX8Mf5ftbOEeCQAX786p45Ju9HMhXrjX3R9UzzIuXruxvLWz8n+WHeXvN8S4/jwpLe7YIX3dOlNZZ+xS3Fx/oweBYP34+WIS3qxYvNx0H86sxmSEx2JM3rxtIj1AvxWtb5ZTXM/Otjda9hQCXD4zkpatSbcbVNrUw4/UNNuVxkoI9GRrrx/xtOXbPe2HvEPbnVdPQbGR6vzAemZqCTqOm71MrnA6KZ/YPZ1NGmU19yDFJgXz652GA5fN9f12Gtc/w36b04DaZvRNC/IH93r+/RdfInsCTzGYz+/OqaTYZ+cunOympUS787EwhZK3aMgPWlSzW38Khghqmv76eV68ZwIzUcKoaOk4iUaksiRXtRfu7kV/VSHpJbYfX3z423lpL8aGFe2zK3KQX13Ljh1vZ/PdJHfbzfW9dhk0ACPDNzlzcdGp83fVc0j+c5BAvFu+0r494rLgWfw/lvYYr2iSVfLI5i7I6Ay9dmerUPslWi3fn0y/Sh8sGRlBQ2cjQuAAuH2Sp27fuaIlNgG1oMfHcskOMSgy0BuFCCCHEuSR7ArHMNk3933pmvLGBy97arBgAjogPYNPfJ5Lxr2n87cIeDve2AbSYOg8AO4h7APBz1/Gn/uHcNiZO8by3q5b+Ub7MSA2zOe7pouWd6wcS5KCLhMkMj3yzl/TiGi7oFdLhPTiaI84qb+gwASbKz41Xrk61BoBgKX3SXlF1E+uOldgdbyu9WDnQ/GxrNm+sTueiV9ezdG8BWWX1iuOqG+yXcZU++h/3FdBgMDKlk8+kvb25VWjVav53zQCuGxaNi9byc7HqsPI2glWHncsSFkIIIX5tEgQCjy7ax5Ei5WzfVh4uGsJ93az9fQ8+M1VxX5szdBqVwyBRq1bRJ8KbP4+O46lLevO3C1OID7JtqebpomXJvWP49u5RzB4Vh15zKqypbWrhpZ+PWusHKqk3GJn55iYSgzy5b2KiXUJGK5UK5kxOQqfpWkHiAdG+zBwQaX3cYDA6zFq+ed52Ln5tPQcVlt735VaRW6kc3LUymsw8v+wQsYH2befAUo7nznEJBHpaMn/jAj3wdLGfADeZLUWxt2eWd/n9bki3T5xxlGksLd6EEEL8XnT7ILDxZC26zlw12D5rs6PZwI50NIvWYrIsS//3p6Nc/NoGqhqaWXD7CG4ZFUvfCB8u7R/O13eOIPpk2ZA3VqXbdZc45mD2rK2aphY+3ZLFA1N6MDROOQPXx1XHq78cc6rsTVu1TUZ2ZFVYu5fUGzruhXsgv5rbPkmjpU2gWFDVwLXvbyHHQYmetvIqG0iNVG4HZzCa+HhzJgv/MoId/5jMqgfHMVOh1Zqfu46FabmU1hqs77c1Ng7xcmGYg88IIFzhl4GrBkfh42a7FB3q7cr0fmF2Y4UQQohzodvvCdRp1Hi5aKlul/2pPdkD19tVh6tezQvLj7Aru5KC6gY2pZcR5uvGoBg/pwKu05VX2cAnmzN5cEoPnpzRW3HMiTLHhac78+2uPD7elOnwfGUnewYdWXW4mFWHiwn01PPK1f0JcqKLSV5lA2lZFQyPD6CgqoGbPtxGrUILNyXR/u70ifBhcIyfXd0+sMx8LtqZx0V9Q3l15TEq6w0MjvFjZ3YFJrOl9qBS2ZhIP3fm3jSYrSfK+ce3+xVfW69Vc/sY+2SPUB9Xvr5jBK+vSudoUQ39In24d2KSNTNbCCGEONe6fRCoUau4eVQcr608ZnP8nomJqIBXfjlGVSMU0UT62lMBX3FNE3tzK7lheDTrj5VS09iCu16j2Iv3TOxv16nD0GLip4OFZJXVMyzOn8ExfnYt5Vw0apqcKBpd7CD5BZTLu3RVaa2Bez7fxcZHJhLm40pBlXKB7FZ6rRqz2cwt87ZztMi54FqnUfH4xT1RqVS8df1ArvtgC+nF9oHx4cJq3l133GZW846xCdwy2tJvecwLq+2uaWw2khTixf8tPaT42iPiA3j84p70iVCehUwK8eK1awc49T6EEEKI31q3DwIB/jo5iUBPPd/syAWViisGRjBreAy9nlje4XVmM5TVGVjztwkA1DW18J/lh/lhTz4VXWjf5qpTE+7jpth3ds3REh76ag/Pz+xLU4uRa97bYlO65opBkUT7u5Ndbtk7p1GreO6yvjQ2G/l0SxYZJbVdXs4Fy9JlfidBmzOqGprZmV3BS1emcsdnO+xmXFulhHoxIMqXndmVNv2YlQR46pk9KhaTCaanhhN3cj9gsLcr794wmAteXmsXwGaV1dt9Dh9vzuTuiQmEeLvSN8LHrjXetL6WpVsvhT2EAPdOTHQYAAohhBC/dxIEAiqVihtHxHLjiFjrsR1ZFTQ0dz6b9vPBIpIeX8boxEDiAz2obWzh8kGRfLBeuedwe5/OHsqY5CBu/Xi7YhBoNsPXO3I5XlxLSqiXXe3Cr3fkctXgSJqNJnQaNbNHx3LFoMiT15r553cHnLqP9gI8Xc5KEAjwVVoOj1yUwpbHJrEpvQwvVy3Himt4b90JCqsaGZUYwPOX9UWlUln3EXbkwQt6cN2waMVzCUGePD+zL88tPURNUwsuWjV3T0jkk82ZdmMbmo0UVTeiUasI93Vlf16VtfzPBb2CeXiqpefw9cOjWba/wCZbOjnE86y20xNCCCF+axIEKjCZzNy/YJdTY1tnl1YfKWH1kY7LnbSn16gZEOMHQJiPa4djd+VUsstBu7OFaafq7z31/UG8XXVcNjDSYXkVZ5TVNvHW9QP5aFMmVfUGBsX4c0GvYO6av4uGZqN13ISUIPblVlPaQTbyD3sL2JxRzrL7RjP5ZAkWzck9lwajibVHS3hxxRH+c3k/hsT6Ee5jPwuZGulDUogXlw2IYGRiYIf3fs3QaC7pH86RwhqKqppQqS3Xrzxs+/cT5OVCTIAHT3y336ZuIIBeo8Fdb/nfY2RCIO/OGsRbayzdTEYnBfLI1JQO6xsKIYQQv3cSBCpIL6l1Kiu1K9z1GuoNRptjVwyOpKy2iQ3HStmTW+Xgyq77cOMJLhsYSf9oXz7enHVaz9HQbGRa3zDrkmirb+8exbyNJyisbmRsUhCzhseQXV7Hv388TFpWBQEeehqbTeRV2n5+pbVNfLEthzmTkzC0mLjjsx3WItAmMyzamUdyiBd3jEvgg5uG8OBXezhUUI2bTsMNI2J49KIUVCrng67K+mYe/GqPdb+kp4sWP3eddZler1Hzf5f2QatWsXhXnt31yw8UUm9osQaCU3qHMqVd/14hhBDij0yCQAV+7nrrTNWZmjkggp5hXlw+MJIf9xfyxbZsmlpMXJIaTmlNE+P/u8ZhUebTVVFnCXSm9wtn0c481h/rvAROex4O9sH1CPXi35f3szmWGOzFBzcNsT5+f10Gzy2zT6bIO1nzLy2r3K4LCMDy/YXcMS6BXuHe/DhnDEXVjXi5aq2BmDMaDEY0ahXPLztkkzBT29RCpJ8bT8zoRYPBxOSewQR7u2I2m1ErBJdqFYrHhRBCiPOFBIEKgrxc+FP/CL5p0+bsdM0aHs2gGP+Tf45h1vAYwNJW7MYPt53x8+s10G6CET93He+sPU5sgAdzbxzMmqMlPLPkILldyFyuUei00dRi5EhhDX7uetYeLWFfbhUpYV5cOTjKpgDziATlvXKjEgOprDfgolUuT+nlavvjGOLd8RJ5W8XVjfx90T5WHynGRatWDKxzKxroH+VnTSQBy37QQTF+doHy9H7huOpOrw6kEEII8UcgQaAD/768LwnBHqzYX4iXq460rHIanUgUaSvK340BUX6K59Z30i7NGW46DR/cNIjZH6XR1CahYn9+NftPJpD0jfDB0GLqUgAIUN3YTHF1I8EnA7Hl+wt5bPE+yuvsZ/AWbM9h0V0jrTN2fSJ8uH1sPO+vy7AmWoxPDuKjTZnM+XI37noNId4uFFXb7iO8qU1iTiujycyrvxzli+05NBstM6h/vygFvUbNL4eKOF5Sx8BoP1755SjbTpQDOPx7UqvgYH6VTRD42ZYsuwCwR4gXz/6pT4efT4PByLpjJei1akYnBqLTOK67/t3uPN5ec5zC6kbGJAXx+LSehDrYA1pc3chba46zK7uChGBP7hqfQGKwV4f3IoQQQpwOCQId0GnU3DU+kbvGJ1JS08SQ537p0vXB3i58cOMQh8kDXZnlAogP9KCwutFmX2FDs5H5W7NtAsD22pc9cZYZyCyrJ9jblfI6A3O+3OXwdQ4X1rBoZy4+bnpWHy6mqcXI6iMl1gAwwENHekmtNRCtNxipNxgZFONHYVUjPu46Bsf40Ww02ezDA/jfL0d5fVW69fEnm7OoqDOQX9XIDoXC0B0xmeHuz3fxwvIjvHfjYHqEevHeugy7cdnl9R0uBe/IKueWj7Zb+xJH+rkx/9ZhxATYt65bdbiIOV/utj7+YU8+RwqrWXH/WLs9jo3NRq58d7O1D/Ke3Cq+253Pgxckc8e4BElEEUIIcVZ1+7ZxzjA72LSn06gcLm2OiA8g2t+d9cdK7Ao+55TXszunEk0X9pw1NhvtEksAVh4qdvo5usJNpyElzDIDte5oSYeBJsA7azK494tdLNqVx9J9hTb3WlbXrDgT2WIyc+3QKA7lV/PJ5izunL+T1Kd/YlGbZfgvtmXbXbd0X0GXA8C2ssrruenDrTQbTRTX2JfBaWg2UtPouM7jnZ/ttAaAYFlmfnyxckeR+Vvs7/9oUa111rKt5fsLrQFgK6PJzAsrjvDMkoMO70cIIYQ4HRIEOiHY25VRifb73G4cEcMrV6cqXuPlqmP4v1Zyw9xtTH99A9e+t4WaxmYaDJaCz0v2FmA0n+pR2zfcu8N7cFSzr7PgzBmRvq6EtpmZVKngsYt74n2yxdmSvfmdPkduZdezqQ0tRv7701HahtjNRjMPfbWHgirL8zUpLO2ehXwdCqub2Hy8jAk9gu3O9Q73ti6Dt5dVVqfYaWVzRhlGk5n9eVU2mdFty+m0Va9wvKSDDi6fb82mqk0B8qYWIz/syWfuhhNnVApICCFE9yXLwU7639UD+Ps3e1l9pBidRs0VgyJ5eGoKLloN1w0r4/Otp2Z8BkT7smRPHlVtZos2Z5Txxup0eoZ625VPMZlheEIAt41L4Iut2WzPLKdFIdIZHu/Plgz7GaQzcevoOB66sAdmM/y4v4DyOgMTU4KJD/IEILeinlWHz3y20UWrtgtYYwM8OFRg3x3EZLb0H75+WAzTU8MVZwOVDIn1Y1d2Ja46DUazmQaFmdO2VCp4YkYvTpTWWbuUhPu48uIVyoE9QJGjAtpmM2P+s4r8qkZUKku3kZevSuXifmFsOl5mMzTAQ88IhULT43oEKWZVAxiMJkrrmvBx11Fc08jV727hxMni4v8H/OPintx6sodxYVUjZsyE+bh1+P6FEEJ0bxIEKtiRVcGSvfm46jRcPjCSxGBPgrxcmHvzEOqaWtCoVTaZo8/P7MvlAyNIy6wgPsgTL1ct17y3xe55v92Zx7Fw5ZZoxTVNrDhQyLbMcsXlZ51GxePTerFsfwGrDhVT1dBMYfWZdfQYmxTIY9N6WveaXTYw0nquoKqBH/bkk15ce8Yzb63B1rqjJaw6XIy/h57bxsTj567nx/2Fitf4uesBePzinlQ3NLNsXwGd3YaXq44DT1+IXqvmQH41jy3ex14H9RcjfN0YER+AVqPmxzlj2JVTiaHFxOAYP7QdJHkkBHsq91VWqayztWYzLN1bQHKwF/dOTCSjpI5Pt2RhaDERG+DOS1elKmYeJ4d48di0FP7942G754/ydyPu5J7Dt1YftwaArV5YfoTxPYJ4+oeD1kSX4fH+vHbtAIK9urb/VAghRPegMjva8PYrqq6uxsfHh6qqKry9O14G/a19uiWLf357an+XXqtm3s1DGNVBl4pDBdU89f0BtmWWE+Xnzqzh0Ty/7HCXXndgtKVvbkem9ArhvRsHA/DU9wf4aFNml16jrQFRviy6a6RiAeYtGWXcMm+7w6VMZ2lUMCjGn4cu7MHQOH+78w0GIxP+u8YumNWoVWx5dBJBXi4AVNYbeH7ZIZvOKI68cd0ApvcLtz6uamjGZDLx1A8HWXmomNqmFobF+fPczD4Os26bjSZWHiomv7KBUYmB9Ai1HffskoN8sOFUW0CNCpTaM/cO92bpfWMAS7b1gbwqVhwoIru8nqFx/tw4IsaaBJNTXs/aoyUEebkQ7uPKnZ/ttC6x+7rreHfWIIadnD285I0NisHt0Fg/tmXa7pUcER9Aj1Av8iobGJUQwLXDonHRSukbIcTp+T1/f4uukyCwjaYWI8OeX0llvW1SQP8oX769e5TiNQfzq7n+gy3WThSttGpwZrueTq3ixpGxfLQps9Pi1O46NYNi/IkJcGdkYiD3fL7T6Vm6uEAPmpqN1Da1MK1vGH8ZF8+GY6VUNTQzPD6AwbGngrQZr2847azi9lx1arY+Ohkfd53i+U82ZfLE9/b9jf85vRd/Hh1HU4uRGa9v4GiRc/vebh4Zy1OX9FY8ZzabaTaa0TtI5gGoqm/m6vc2W5eHAR68IJl7JyXZjPthTz4/HSzCx03L6MRA7vhsp91zjUwI4PPbhgOWIG/GGxtsfraGxvqz4C/D+WxrNk9+t9/6d5kY7Mnntw0jo6SOekMLI+IDcdOfCtz+umC3YpcTZwqcT0wJ5sObh3Q4RgghHPm9fn+L03PeLwc3G00s2ZtPWmYFCUGeXDk4Ei9X5YCkpKbJLgAEOFZkv4SbXlzD3fN3cUThHDgXAAK8ft0AxiQF8bETs3r1zSbWp5eyPh2+SstlaKw/WxSyTJXcPymRSwdYlnu3nSjn4tc22GTwRvm5Me+WISQEeToMAO+fnMSJklq+21Pg1GuCpWbf8gMFBHu7khjkSZS/u8359sFzq6yyOoqqG3ny+wNOB4BgWa51RKVSodd2nJE9d0OGTQAI8MovR5k5MIJIv1P3PiM1nBmpp2YcRyYE2O39u3lkrPXPH2/KtPvZ2pZZzooDhTy75KBNMJ9eXMvba47z5AzlYPau8Qn8cqiImsZTe06vHRrFD3sKqG2yL/Ld1qrDxezNraRfpG+H44QQQpz/zusg0Gw2c+vHaaw9eqow82dbslh81yhrZq6/h956LtTbVbGIsdIXZkcBYFe46bV4uGi5qG8YP+zpPAu3VZPRxMGCaqfHz1mwh3mbsnjl6v48s+SAXbmZnIoG7vl8F1/fOZJwH1e7bORwH1fum5hESW2jYhDo6aJhYIwf647at6h7bPE+jCbL3sDrh0Xzf5f2sS5DD4lTLqYdF+jBRa+uVyxO7UhisCeXDYhwerySHdn2pWdMZtiVXWkTBLb37g2DuGv+TradKEerVjG9XzgX9Aqxns+pqFe8btuJcsUM77RMxyVwkkK8mHfzEN5YnU5zi4mrh0QzIzUMb1cd7yrUPWwvu7xegkAhhBDndxC4Ib3UJgAEyCit47K3N5JRUocZCPd15Z4JiVw9JBqtRs2cyUk8vmi/NQlBr1XzyNQets9RUntWAsBgLxdKqhuZ+N815JTXE+LtQnmdAa1azZWDIxmdEMiX27NtCi+3ZTB2rTzM7pxKLn1jA9WNyrNFhwtrGPGvlTYzTGApYfPIRSmo1SpCvN24Z0Iib6xOtzn/6jUDCPB0YVN6qd0saOttms3w2ZZs1h0tpaKuidhAD3qGejMszp+tbWY0L+oTypHCGqcCwKGx/kT6uZES5sU1Q6Md9jx2VnygJxvTy+yOJwQ5nmEES9eU1oSMJmBBWg7+nnoemZoCWPbmrThQZHONSgUX9Axl3qZMuzZ3tY3N3PP5TiL93LlhRAwRvqcyfedvtexbbZ09LK01MDopkL9d2ANXnYYP1mdQ5yAzWqtWMSTWfn+mEEKI7ue8rhPoaBnx+MkAECC/spHHFu/nvi93AbB4Z55NwGVoMbG5XVmWjgINLyeDkIQgD64aHMVDX+8lo7SOZpOZouomAj307PrnBfQO9+Yf3+1nlYMAECDKzx2/dnvtPDt5fUcBYKv2AaCLVo2/h54nvz/A44v3UdfUwkMX9uCn+8dyy6hY7p6QwM8PjGNSzxAq6g1O7VHMLq+npsnIvrxqFu7IZeuJcpKCPXn6kt58c+dI3p41iIySus6fCBga58/LV/fn9rEJ1rqGZ+K2MfE2s8MA0/uF0auTOo5KnUc+2ZRJU4slGLt2WDRjkk4lF6lV8NCUHmi1KoYqBGUnyupZsreAd9YeZ/pr68kpt8wkVtYbeOYH2+XjI0U1vLU6Ha1GzV8vSOa1awc4vM+JKcHSE1kIIQRwns8Epkb6OD126d4CZvQrYLvCMtzXO3K4c3yC9XGItyvxgR5ktCvToVLB27MG8tji/WSXKy//tYoJ8LCZTWtVUN3EY4v3smhX50vDN4+MZVLPEL7Ylk1hVSNuejUfbcrq9LquaGox0VRrmZGbvzWbyvpm3rx+IC0mM1F+7kT7uxMb4MH8rVn8Y/H+Tsu4OHKsuBYXrZpBMZbl4f7RvmzL7Hi/o5tOw5WDIzsc0yq7rJ5l+wvQadTMSA1zWDYlOsCdpfeN5vOt2eRVNjA6MZBL+3e+xFxWa1/ouc5gpMFgxEWrwUWr4dM/DyMts5yssnr6Rfrwz+/28+KKI50+d0V9M3M3nOCpS3qz6XiZ4vJx289qUs8QbhsTx4cbLclGKrD+vfx0sIhDr69n8V2jCPR06fS1hRBCnL/O6yBwcKw/lw2IYFGbTErFGm8nZZYqB25K+dMBnnq7INBstmRultR2vIzprlN3WIDZmQBQfbIgsZ+Hntmj43jppyMs3un8nsLT9eP+Ap7+/gDz2iSyxAV62NWtOx2H2uxxvH1sPL8cLLL5jG8YEUN+RQN7cqtIDvHkwSnJiv1621u+v5B7v9hJ88k6Li//dIRP/jyUQTHKy6JhPm48OKWH4rn2DC0m7vl8p2KJmIHRvvi6284qDo71Z3CsP2+tSe9S4e8TpXXszK7gb1/vUTzf/nN4/OJe3DomnhX7C+2yr3PKG/ho4wmuGhxNoJfeplezEEKI7uO8/9f/5av7c8WgSHZkVZAQ7Mnm42V8ukV5tmxyrxBWHCxkV7t6fZcNtJ8JSgrxUpw17CwABOgZ7nNGvW/BEsjeOX8Hz83sy+OL9531TiIdve68dpnMZyMABOgdYZm5PV5Syys/H6XZaKJPuDdD4vy5clBUp0uySkwmM/+35KA1AATLDN1zSw+x6C7lsj8VdQaOFdeSGOxptzTc3oK0HH46WGR3PMLXlReu6GdzrLimkae/P8jPB4swd3HOVKtW8cCC3dQ12e/1c9druGNcvN3xEG9Xh+Vw3lmbwRurj6PTqLhjXIJN0NtiNLEhvRRDi4kxSUE25WmEEEKcP877IBBgZGIgIxMDySqrI8TbBVSWXqxta6rdNiaOxGBP3r5+EH9ftJc1R0qsy2hL9xUwKMafEQmnWn2NTAjgy23ZNrOKoxMD2ZBunx3bXmkHPWK7YktGOde9v8Uum7ktV52aRoX+u6crwFNPmROBbkd0ahXN7aZjB8X4cUlqOJX1Bq56ZzNlJ5NCcioaOFxYw7VDo0/rtUprm+za9AEOy+C8tSad//1yDEOLCb1WzZxJSdw9IdHh8284VqJ4/N6JSXbFqP/y6Q67XzCctbKDmeMvbh9O73DlrQ99HWyJaG1L2Gw08/oqSzvDaf3CyCqrY9bcreSUWz4zfw89H9w0mIHRylncQggh/ri6RRBY19TC3Z/vZM0Ryxd2hK8bC24fTkFVIzkV9YxMCKR/lC8AoT6uPDSlB+uPlVqDxEMFNcz+aDtrHx5PsJcrhhYTT31/0G5ZeUxSACU1TZ1mDtcaWojwdVMMTrqqowAQOKsBIEBKiBcba+2zZ7vi27tH4abXcKSwhn15VaSEeXNRn1B0GjVfpeVYA8BWLSYzf3pzI3GBHkzoEcxdExKcXsL099AT6OlCabs9e54uWkwms7VlHljaBb6w/NQePUOLiRdXHGF4vH+HS8dKIvxsjx8rqulSAKjXqpgzKYmXfjraYbJNqLcrsf7uvLbyGNszy4kJcOfW0fHEBlqWh3uH+3D9sGjmb+24//L76zOY1i+Mp384aA0AAcrrDPz9m7389NdxTt+7EEKIP4bzOju41Ss/H7UGgAB5lQ38deFuLu4bxl3jE60BYKvFu/LsOi80NBtZttdSHy8ts9wuqABYcaCIuTcPZlJKMBq1iiBPF9wVltKGxwfw7d2jGBEfQMeliy3Uzgz6DWjUKm4dG3dGz/Hn0XH0jvAhPsiTi/qG8fDUFC5JDUd3sl+vUrFugHqDkQP51byxOp0723XnyCqr474vdjH6P6u4ed42drWp9afVqBWXSivqm232igKsOmy/rAvwyyHHs3A3jYy1y8hOjfJlVIJtm8GWLjZgHhDlR3ygZ6fZ1lcOiuTGedt5+eejrD9Wymdbsrn0zY3WbGKA52b2JTmk4xI3rckmGxVmso8W1VJ8hn2qhRBC/P50i5nAnw/Zf7nnlDdwqLBacRnNUSO91sOOSsR4uGiJ9HPntWsHsDe3knBfNw4X1nDvF7swnPySDfdx5cHJyfzv56NsznBuRs1ktiRf9I/yYbETSSO/Fo0KXl+Z7nRLPCUeCkHx6sPFrDhQiI+bjsGxnS87rj1awvqjxYxJDqauqYWr391i7T+cW9HA1oxyls0ZQ9zJ2TBHpWNWHS7iikGnsov93JX3/wV0sC8wLtCDb+8eyfvrTpBVXsfw+AD+PDrOZoaxst7A1owy/N31lNd3vpTu5aLlsWk9cdF1/jvaO+uO2+x3BEu/5E+3ZPHYtJ6YTGZeW3ms064rlw+y7HsN83Els8w+QWr1kWKuHmK7JL8lo4xFOy39nBODPYkJ8GBMUqAkmgghxB9Et/jX2tddT1a7LzaVCrvMzVYzB0Tw0aYTNrMwLlo1mzPKeHdtBtH+7sQHupPRLpv4huExLNmbz6Pf7KPmZPuuS1LDWfu38aw7WoKniw53vYYr3t3cpU4YYEm+0KhUXd6TF+bjQkGV/aylRq3CZFJOT9BrVPi662gxYXOfBqOZnae5p63Va6vS0ahVzJmcDFgydV9bdapUzvvrnXuemz9K4+NbhlJY3WgNAFs1NBtZsD2Hv19kKdTsKLmj/fGZAyJ4e81xm+Vofw89MzvpQpIY7MV/2iWBtMopr+eytzdR0sk+UI0KbhwRQ2ygJ73DvViYlkNhVSPBXi4Ud3Bt+wCwVV6FZUn3rTXp/G/lsQ5fe3icP7NHxWEymQnyclEMAl/+6ahNELgwLYeHv95rN87HTcd7NwxiWHyA3TkhhBC/L91iOXj2qFi7Yxf2CrXpwnC4sJr7vtjFtFfXsyAtmydn9LKeTwz2xMdNx08HiiisbmRbZjlZ5Q2MTAjATafB00XL9H5h9A735sGFe6wBIMD3e/L56UARVw+JZlLPYP66cHeXA8BW6SW1GLq4x89Dr1MsIG10EACCJdh7ckbv077PzrzyyzG2nSinst7AO+2KLDu7amo0mbl/wS6KHCxTphfXYD45pTu+RxBJ7XoKu2jV3DA81uZYgKcLC+8YwaX9w0kM9uSS1HAW/mUEAWdQT++NVemdBoAAs0bE8OQlfRgW78+NH25n/tZsVh4u7jAA7MioRMty9BfbchyOmZEazqK7RvLlX0bw8eZMBj/7s2LGO0BRTRNpWZYMdLPZzMs/HVUcV9XQzJ3zd1DlxIynEEKIc0tlNjta/Pz1VFdX4+PjQ1VVFd7eXS/70ZE9OZW8tz6D/EpLkHbHuAS8XHXcNX8Hy/cXYjKDv4eON64byMiT+7ZyyuuZ9up6m+At2t+dn+4fg9EMm46XcdsnaXavpdeobVq3Rfm5kVNhn+wR7OVCUrAneq2a1UeUs0l/Lckhnnx/9yjumL+THZkV6DQqKuqbOy1QolZZZgsdzTSdKQ+9hlGJgYrlVboixt+NrHLlBBtfdx2X9o/g5pP79l5fdYytGZbkibsm2O8FNZvNbEwvI724hv7RfnbnO1LX1IIZ+44t019fz/68jns8e7po+fmBsZb6hAv38M3JJVZnRfpZkoxa/0+e3DOEt2cNRKdRM/jZXxT3r752bX8uSY2gwWDkgYW7+HF/538Pk1KCmXvzEOoNLfR6YkWHY/099Cy+a6RTdRyFEH8cv+b3t/jtnVfLwfvzqrjy3c3W/Xe7sivZfLyc64dFs2xfoXVceV0zd8/fyZqHJvDdnjw+3HDCJgAES2uzFQeLGJsUxA97lPfhte/dqxQAAhTXNJ32jI4ST72GWge9YdsbmeDPrLnbSDtZl9Bdr3GqQp3JDKZfKQAES62+nw4WdVi8G0CFJchx9Nk6CgDBkmTy8aZMvtiWzbybh/DMpX3sxmSU1JJX2UDvMG/uX7iHdW16TV87NIp/XWa7zLs9s5yPN2VS1dDMlF4hxAS489cFe6xLyInBnrx7wyBrr+FeYd52QaBGpSI51JOaxhYGxfjx94tS0KhUPLpoL0v3OVcovO1nlnvys0kO8eSFy1PpH+1rPTcjNYx5GzNtrh8U48slqRH8fLCI+7/c5bDPcHultU1klNayM6uSMB9XCqocJ4uU1xl4fVU6j0xN4eeDRbho1VzYJ7TTtoZCCCF+O+fVTGBXZ1FSI33Yk6tcLw7gvomJLEjL6bQMy29Jo4KrBkfxxXbHy3ytVMDVQ6L40omxHdGpwYyqyxmuzmrb1qy9aH93vrx9OHO+3OVwqdIZqVG+fHf3qeLQzUYTDyzcYw3w9RoVBoWg96s7RjDkZG/fdUdLuOWj7TaZ4yqVfSJRuK8rGx6eiFqtIrusnsvfsd0TeM+ERB668FRx5majiQtfWWfXgUaJv4ee+bcOY97GEyxMs/9Zf++GQUzpHWp93GAw8uiivXy3O9/6Gfu66Xj60t48vng/tU0d95I+E5G+bpTUNlkzj1vvvWeYzB4I8UclM4Hnl/NqT2BxTdfKWHQUAKpUlhkWpQBQq4bYAPcu39/ZYDJDi9nMpJSgTseasWzgP1PNpq6XOOmKl65MZYiDrGA3nZpwXze+umMkDznZyk3J4QLb2bj5W7JsZniVAkCAtDaB59trjtuVDlL6FSq/spFdOZbrogPc+eHeUQyP88dVp8bHTUuLyUxzm1nk1YeLHQaAGpWlkHbPUC8u7R/O13eMoGeYt8NOIIcLa2hqMbJgezaPLtrHl9uzGRTjZxNkVzY087ev9p6VANDHTctFbYLOtsrrDTZ9jsvrDDy/7NAZv6YQQoiz47xamxkU7cf6Y5137AA6LNehUkGfcG/Si5XLagR5KZfR6IqOZr86Yga+SstlZIJz2ZdnM3Zz1appPPmlrlZBkKcerUZNzzBvYgM8mL81i4YuJq4EeOgZkxxIRYNBcabPrU25kTvGxbM/r4rlBwrtxnWmX7vOGe+tz3Aw0lZrmRmAgirni3urVadKxLy+Mp0tJyxJFY3NJt5ZexxDi4knZvQCHNdGTI3y4ZWr+vP6qnSW7M0nvaQWFfDszL70jVDuBNIrzIsbPtjGtsxTbQQ9XOzL8rTfynC6qhpaWK/QNcVdr6FeYZl5e5v7EkIIcW6dNzOBjc1GFu/O63ScTqNiQo8g3r9pMDqNchVmsxn25VVzoEB5Q39He6Gcdaax2Zn2HnYkLtADvUb5x8LDRcuSe0fz9vUDWXLvaDQaNXmVjfxyqJgPNpxApeq8qnW0n5u1QHaItwthvq4MeW4lLyw/olg4e2KPUzOeWo2ad24YxHd3j1Qswg1w9ZBIEoM9bZ7L00XL3y/qaX1cXNNIQWXnf4cDon2Z3DPY+rg147Yz0f7uuGjVHCmswdBi4usd9su2H2/OpMFgmYlLclDI+eaRsTy/9BCLd+XRbDTTbDTz7e58nvhuP5f2j2BwjO3s6dBYP0pqDTYBIKDYb9iRSD9Xp8eCJctaaX9qS4vya8YFdly0WgghxG/nvJkJXL6/0K4WoJKv7xhJ6smsz39O78XTPxy0W+JrZTSZcderqTecmjUZEOXLrpzKDl9DaZYv2t8NjVpFQVUjRpP5jLNu3fQam6W2syXC141Hp/XgsUX7KW1Xj3BGqqV0Sn5lA6+uPEZ+u0BKaeanvew2CR56jdqaNOHovaxPL+W+kzUFW5XUGBy+1p3jEokN9CCztI5l+wvQa9RckhpOsPep4Ka4uslhEN43wodwXzeGx/tzzZBotG0C4gcuSObbXXl2iRRtZ0iTgi1dPqa9tgGAPhHe1kSltowmMx9sOMG9E5P4bIt9SzedRkVdUwu/KPQMXrKngP9c3o8vbh/OigOF/LivgLVHS9mWWcF2B78cdDbzHOHrxn+vTGV4vD/PLj3ER5syHf5/0VZcgDuHFQpRGxT+OlUqy2cohBDi9+G8CQId1Ytr76nvD/Dilf1IDPbixhGxTO4ZwqbjZZworeXN1cftxpvM8OyfelNQ2UhpnYEtGZ0vN8cHeXC8xHaPV3YHWaydaV96xkOvYVicPysOnFl5FSUb0kvZ0K51mFoF0/uFc1GfEIY9v5KqBuXly65ylPHb1vbMCtIyyxkce6p3b/v7a9U/ytfaMzc20IO7xicqjksO8SLQU28X5ALsy6uixWTmnVkD7WY2jWazYiatp6uOtL+Nx1Wr5qZ529iYfqoTzP68anzcdIqf2cpDxdw7McmmzV2rZqOZ/65QrsXXOs2p06gZER/AAwv2WJd3HaV53TbWspR+tKhG8X2H+rgyIiGABoORvTmVTgWAgGIA6MjlAyK4oFeI0+OFEEL8us6b5eCxyc4t1e3KqeSSNzby04FCHl20j4e/3ktBZQNXDopS7NHb2GzCx03PibI6FmzPIaus88Alo6TzLE9nebtqabtqHeip596JSb9KAOjI5J4hXDk4kmve39ppAOhghf2MtO/d27bId1vXDY1WPF5c3chji/cx5ZW13PZJGocKqnnhin4Ol5QPFVSz9YT93jW1SoXSirdWrcLTRYvBaLIJAFtVNyp/Zq0dSxKC7ZdINSpLAoeSP/W39FourGrkolfXd7q/b3i8Pw9OSebz24bz3T2jFX/O+0b40Nhs5Ip3NtnNJqqg097DrfQaS31JJbNGxDr1HEIIIX4b581MYFmt87NT9QYjd83fac143ZBeyuojxfSP8lVsi5ZRUsuP+51PRjibebTVjS1UN57K4iytNfDplqwOr/Fx01LVcPZKf5woreXmD7c5nGVq695JSfi46SiuaWJgtC9ZZfUUVTey6nCx3eyos1LbJXVcMSiSuRtO2LSLiw1wZ3pqmN21hhYT17y3xZp9e7SolvXHSlhy72i2PDaJq97ZzOHCGrvrqhUCsEBPFyalBNsFpVcOtvQf1mnUeLpo7bJufdx0pIR6sSXDNrBUAbkV9dw3MYkNx0pskmqMZst+u/bL5L5uOp66pDcA/1l+2GH9ydGJgYxICCA5xIuJKcHWwCzC1407xyfYzHpH+Lpxx7gEXvnlKAfy7ffBmsGprRYAlolS+x8UjVrVpeLbQgghfn3nTRDY2tKqPY3K8oXaXvuSJzuzK7l3YqJdEKjCUvD3TKspKtWTa0urVhHkpVfs89tecSdL31cMimJEfAAfbsggs7yemsYWatoEklo1dGU74fGSOodZxq17zdQqS3B294REdAqJJRf1DeOmD7fZ3IczgrxcuKBXCCU1TQR46FGrVfh56Fl010g+WH+Cw4XV9I304dbR8bjr7X+cVymUX2lsNvHZlmyeuqQ31w+L5p/fHbA57+WidZgE8tJV/Xn6+wMs2VeAi1bNdcOimTMpCbAEgWOSAu1+YbhlZByzR8fy35+OMn9rFi0nfyBXHi5mX14Vy+8fy7S+YXyz0zaxSWmfZFKIp/V9bjquvCyuUcGDU5IZEK1cdudvF6YwMSWYtUdLCfV2ZUZqGF6uOpbsKVAc7+heHPH30Nu1HJzW1z5AF0IIcW6dN0Fga4eG9rqSf/H6qnS7Y16uWl5bdUxx/HVDo2lobmHxrs67PHQWRLaYzE4FgAAuOjXNDjI+/dx1/GVcPMFerkzuFcK0V9fbJXA8Nq0XPcO8OZhfzdpjxezJqepwmbej7WFDYv24Y3wiC9Oy2ZNTxd+/2cecSUlEt6ujODDaj/UPT+DrtFyedbJW3JikQCalBDPy36sormkiwteNJ2b04sLeoYSf/HNnHPU/bj1+3bAYDhbUsDAtB6PJTLCXC/+9MhUPB50tfNx0vHx1f166KtVuz2BOeT2/tGuDp1LBBb2C8XK1zAa2tPuBLK5p4usdOU6XHGo7axnh62ZXx1IFvHHdQIcBYKtBMf4MivG3OeaqU94dkqCwx7UjLlrLrN/ukwlUoxIDePrk7KUQQojfj/MmCFSpLIWFnalTF+Klp6jGuQb31Y0t7FPo/ToyIYBHp6WgUas4XlLH3g4KT1tf19vlrHQfqW0yolVb9qc1G834uOkI9NQzINqPa4ZEsSm9jBBvV7xdtRxUKHPz9Y5c/ja1B8mhnny/N98aALbvheyMqX1CeXDhbipO1ro7UlTDumMl/PLXcfi462zG+rrruXVsPCsPF7M5w37vXFuRfm48fGEKl7y5wRpA51U2cM/nO1n14Hii/J0r1j2+RxAatcou0WHSydIvGrWKf13WlwcuSKa4ppEeIV42GcGOKJXDWbqvgGaFYtLf7ymgV7gPhQ5KCxVUNZIc4ulU2Z/oNu/7nomJ3Ppxmk2QfvOoWC46zVm364bF8H9LDtocSwzy5Os7RzDllXVOtz6M8HXn6ztHklVWh0atItLv3BRWF0II0bHzIgj8ZkcuD361x+nxWo2aUYkBipv4nTExJZgPbx5iffy/q/tz3ftbrXvUlPZyAZQpZGUqcXR9W63L2dH+7qy4fyxueg0Ltmdz7ftbrOVnUkK9FK89VFDNLfO22x03GE0Ee7k4/WU/NM6POoPRGgC2Kqlp4pr3N5MU5MmVQ6IYk2Tb3eT//tSbS9/caK1fp1aBm05jzbz1c9Px/o2DWbwrz24GtdloZum+Am4aEcu/fzzEt7vz0WlUXD0kir9OTrYL4MJ93fj3ZX156vsD1BmMqFVw/bAYLkkNtxkX5OVCkJeLU+/bEQf5ENbjY5MDeXWl/azy2OQgEgI9+flgkU3mbr9IH5tfLtQquHdikvXxxJQQFvxlBJ9tyaK2sYWpfUK5YlDkad//7FGxGE0mPtmcRW1TCxf1CeXRaT3xdtUx/9ZhPLPkIFszyokOcGd4nD/f7s5X7Dpy48hYAGICPOzOCSGE+P04L3oHX/DyWo456O7hyNTeoafVeQIgzMeV9Q9PsAYcsz7Y6rBsyW/hykGRPDatJ8P/tdIueAzw0FPmYEn0TPh76Nn++GT+tewQH2w40eHYJ6b3YvboOOvjez/fyQ97bfefBXm68Pq1/TGZYWicP1qNmheWH+atNfZle/45vRcH8qpYtMt2D92d4xN4ZGqK4j3UNrVwIK+K6AB3wnyUs4vPVH5lAxNfWkNjm9lonUbFsvvGkBRiCchfWH6Yd9Yex2S2zF7fMDyGZy7tA1iC54VpORRVNzIuOYhJPUNYsjefpXsLcNNpuHZYtLWP8e9Bi9HETweLWLA9mwP51QR6ujB7dBxXDY4617cmhPiVSO/g88t5EQT2f+Ynh623HAn1dqHwDJZmX7yiH1cOjqKpxUjKP5efceIIoLhs6SxHQa2fu46kEE+2Z1bgqtXQ0NxxQWdn29k9NCWZeyYmsel4Kde9v7XDse56DbueuAAXraUkS89/Lle8j8V3jmRAmy4Y6cW1TGtXAsVDr2HpfaOZ/PI6u+QeX3cdu5+Y4sTd/3o2HS/l2SWHOFhQTWKwJ3+fmsLkdrXxcivqOZBfTY8QL2tdQyGE+COQIPD8cl7UCRyXHNT5oHbK686s4HHrzJ9OrcbbVdfJaOecbgAI8Msh5bqBFfXNFFc3sffJKbx/46BOn6c107e1pIhaBdo2y5xqFVwzJIo7TxZiHpkQyH0TEx224ANLSZ62QbqjZdP2pVoSgz15/6bB9ArzRqNWMSDal49nD8XPwwWjQtSt1JnjtzYyIZBlc8aQ/txF/PLAOLsAECDSz50Le4dKACiEEOKcOi+CwMen9aRXmP1vJB4uWl6+KlWxeG1XEyDaay1YrFarmD0qrpPRpwyO8SPAQ+8wE9OvXTKFo3HttZjM6LXKYzPL6tl0vIz1x5xbsjaZ4fqhUaRG+mAyQ0ubeOvDm4bw78v72XymD0zpwaa/T+L+SUkKz2bZ4xjcZr9d30hfxXHhfq4s3VvAop25VJ0MGsclB7FszhiOPz+NxXeNYnCsPz5uOsXAf0a/cLtj54ozySVCCCHEuXReJIYEe7uybM4YdmZX0Ggw0jvCm7JaA1H+7ug0aj7alOlU9q6z/Nx1zBoeY308Z3ISgV563luX0WFR3UtSw3nl6v5o1Cqe/uEA8zZm2pzXaVR8ePMQPt6Uyc7sShKDPblvUhI3fLCVGoUN+G35uOqoctCZAqCy3sCy/Y7rwLWXllnBQYUiyh9vzmR8SrDd8SAvFy7oHcL/FBIfAjz1Ntm0z1zam+mvbbAJxN10au78dAf1J/fTeeg1vH/jYEY6qNf34hWpPLBwNxvSS1GrVEztHco/nSgZI4QQQgiL8yIIbDWwTW00Hze99c8PXJDMrR+n2e0hOx3hvq58cdtwwtu1Lrt+WAxjEoMY/9/VdnX1/D30vH/DIAa12dR/w/AYPt+abZPIcWn/CAZE+9nUeNudU9lpAKhVq7hrQjz/+vGI4nmdRsWEHsF2QWdHHL1meRf3XgJ2y+XJIV58e/coXv75CKsOF2MyY1fap85g5LHF+1j90HjFcixBXi58+udhlNcZ0KhUduVohBBCCNGx8yoIdGR8j2C+u2cUX6Xl0thsZHq/cN5Ze/y0MnqvHhztsPRFdIA7D13Yg/+uOGINBPtG+LDwLyNwa9endsH2HLtM3l8OFdHYbMRVd2psboXyzGJsgDujEgMJ8XblllGxuOk0fLY1m5xy297Gbjo1/768H8HertwwIobHF+936n2G+rhSUd9sVwLkgp7BfL0jh9dWplNY3Yivm47Zo+P4y9h4eoV5kxTsaZepPSTWjy+3ZrP2WAnpxbWkRvkyZ1ISEb5uHRaiziyrp7C6scNs3tb+u0IIIYTomvMiO/h01BtaeGfNcdYcLSHYy5WL+4Xy1urjNgFM+2zdlFAvFtw+otNZp+yyeracKCPa353h8QGKY6b+b51iz9rLBkZgNsOIhAAuGxBBeZ2Bkf9eZTeLeff4BIbE+ZMQ5GktnJxRUss/v9vPpuNlBHjoubR/BA9N6WETgH66JYtPNmWSU15PoxOJFGrVqY4hF/UJJdrfnXfXZdiNe2xaCrePTeBEaR0PLtzNzuxK9BoVXq46xRI1od6u9AzzYvWREoev7eWqZfvjk22CYiGEEOfO7+H7W5w93TYIdORIYQ25FfWoVDAo2p8TZXVsOl5K1MmMTkfJF11104fbWHvUcQAElrIv79wwiI83ZfLMkoPWgDTK343CykaaTWZUQKCnCyazmWHx/jwyNYUIXze7xIQ9OZW8veY42eX1DIv3Z3CMH3d/vsupe43xd+fTPw/Dx13HkGd/xqDQiy/QU0+gpwsFVY2MSQrkznEJvLc+g+92O26pd0lqON/vcXz+kakp3Dk+wal7FEII8ev7PX9/i66TIPAc2Zheyo0fbuu0LMz394yiX6QvBVUNbMkow2w288DCvQ7HR/i6sfLBcTazZ0cKa7jkjQ02y88poV6M7xHEO2vtZ/WULJ8zhvlbs/h0S7ZT45OCPSmrbepwD+GDFySzJ7fKWt5Go7Ysn0f5e3BJajgXKJRXEUIIce7I9/f5pVvsCfw9GpUYyPxbhzFv4wnK6wyU1ho4UVpnNy6jpI5+kb6E+bgxc0AkH6zvOGjLq2xg1eFiprXpH/vx5ky7/YeHC2u4YmCEU/fqolHx50+2k1eh3PtWybHiWvSdlEm5sE8o905KYn9eFdnl9QyO8SPY29Xp1xBCCCHE6ZMg8BwaHh9g3TP42spjvPzzUZvzKpVtxjPgVH/b2kbbZI6yWuXOKD7uetx1Guo76SLSYqbDANDfQ6dYfNtRLUZXnZpHpqaQfLKVWp8IH/pE+HR4D0IIIYQ4u6Si7e/EzaNi6RNhO7V+78QkogPcbY5d2DuU2HbH2tJr1UzsaVvHb6JCXT+dRsXwBH/igjrvWuFoyXpKr2C2PjaJv12o3K+3LbUK7p2YyJe3DWfro5O5ZVQcNY3NXeryUdXQzAfrM/jHt/v4dlceLWdY8FsIIYTozmRP4O9Ii9HE6iMl5FbUMyIhgJRQ5c+muLqRt9YcZ2d2BU0tJk6U1mJoMRPk5cJzf+rDlN6hNuONJjMPf72XRbtyMZstvXwfmZrC/K1ZHC2qVXwNZyy4fTjD4gMwm808v+wQn2zOslt2bmv3Exfg667nRGkdj3y9l22Z5Xi6aLlpZAwPTemhWA+wVUWdgT+9tdGmGPfknsF8cNOQ075/IYQQXSPf3+cXCQLPA9WNzRRVNRIb6IGug314OeX15JTX0zfSh3fWHufN1cedfg0XrdomwLtuWDTPz+xrM6amsZmKumaeW3aQFQdsexkPjPZl0V2jMJvNTHppLRnt9j8+fUlvbhoZ6/D1X195jJfaLZcDfHHbcEYkKJfhEUIIcXbJ9/f5RfYEnge8XXV2XTmURPm7W2sK7squ7NJrTEwJ5pZRcRwtqqF/lK/iHj4vVx1erjqevqQPuRUNHMivBiA+0IMXr0wFLB1Q2geAAC/9dIRRiQEkBnspvv7RYuUZy2PFNRIECiGEEKdBgsBuKjHYk03Hy5wePyM1nKFx/gyN8+90bKiPK0vvG8P+vCpaTGZSI32sS70atfKSb3VjC1e/t4W7xidQVmtgTFKQTXCXGunDDwo1BTcdL+O73fn0jfDhjnEJhPpIdrEQQgjhDFkO7qZyyuv505sbbbp5xPi7M65HEIcKqtmeWWE9Pi45iLk3DbYrQH26HHVLae/W0XH8Y3ovAOqaWrjmvS3sy6uynnfVqWls03M4wteN5fePwcuJWVEhhBBdJ9/f5xcJArux4upGPt+WTUFlI6OSApneNwy1WoXJZGbN0WIO5FXTK9ybCT2CUTuYwTsdeZUNXP7WRgqrlUvXtFKpYO1DE6wZ0oYWEysOFJJZWkdRdROfbc2yu+bZP/Vh1vCYs3avQgghTpHv7/OLLAd3Y8Hertw/OdnuuFqtYmJKCBNTfp2OHRG+bjx/WV9mf5TW4TizGV5deZRxyUFc1DcMvVbNjNRwAJ5dclDxmqJq5wtaCyGEEN2ZBIHinJiYEsLDU3vw3xVH6Khz3jc78/hmZx6aBXsI8XHBRavhoj6hDE8I4IMNJ+zGq1SWpWONWmXTOk8IIYQQtmQ5WJxTd3y6g+UHCrt83aX9w/H30PPRpkza/wRr1CqMJjMRvm48dGEyMwdEnqW7FUKI7k2+v88v0jFEnFO3jY1H226/obu+8xm8H/bkc/eERNY+NIEgL73NudYOJ3mVDTywcA87ssrP3g0LIYQQ5wkJAsU5NSjGjy9uH86FvUMI9LQEc/WGjnsZA5jMcPFr67nm/c2U1BgcjjObLUvKQgghhLAlewLFOTck1h+TyWzXZaQzRZ1kFwshhBDCMZkJFL8LmzOcL1zdFSoVXDYg4ld5biGEEOKPTGYCxe9CpJ/7WX/OcB9XHpzSg8GxnXc5EUIIIbobCQLF78LFfcN4a006GSX2fYW7qkeIF9/ePQpXndrark4IIYQQtmQ5WPwuuOk1fH3HSO6dkIDbGdT302lUPDmjF256jQSAQgghRAekTqD43ckqq+OxxfvYmF6Gp4uG2ib7bGG9RsWeJy9kW2Y56cU1ZJbW467XcOXgSBKDvc7BXQshxPlPvr/PLxIEit+tphYjJpOZMS+sprTWtgzMP6b35NbR8efozoQQonuS7+/ziywHi98tF60GN72WeTcPpWeY5R8bDxcN901MlABQCCGEOEOSGCJ+9/pG+vDjnDGU1jbh6aI9r3oCHyms4WBBFb3DfUgOkWVsIYQQvx0JAsUfRqCny7m+hbNmV3YFr61MZ/WRYuux64dF89zMvufwroQQQnQnEgQK8RsqrzNwy0fb2ZNTaXdu/tZsEoM9mZgSTEyAx29/c0IIIboV2RMoxG/oxRWHFQPAVk//cJBxL67h1o/TaHCih7IQQghxuiQIFOI3tOZIiVPjfjlUxJur03/luxFCCNGdSRAoxG8o2Mv5fY0fb87kk82ZVNYbOh8shBBCdJEEgUL8hv4yLsHpsTWNLTzx3QEGPPMzDyzcjdH0m5f0FEIIcR6TIFCI39C0vmHMu3kIE3oE4eeuc+oaM7BoZx7Dnv+FstqmX/cGhRBCdBsSBArxG5uQEsy8W4Yyvkdwl64rrTXw6spjv9JdCSGE6G4kCBTiHLlqcBQqVdeu2XK87Ne5GSGEEN2OBIFCnCMjEgL439X9iQ/yQKdRMTIhgKcv6YW3q+PynYkhnr/hHQohhDifSbFoIc6hS/tHcGn/CJtjE1NC+MunaRwsqLE5rteouHNc4m95e0IIIc5jEgQK8TsT5e/OsjljaTQY+c/yw+zOqaRHqBezR8dJf2EhhBBnjQSBQvxOueo1PHlJ73N9G0IIIc5TsidQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbkiBQCCGEEKIbOie9g81mMwDV1dXn4uWFEEIIcRpav7dbv8fFH9s5CQJramoAiIqKOhcvL4QQQogzUFNTg4+Pz7m+DXGGVOZzEM6bTCby8/Px8vJCpVL91i8vhBBCiNNgNpupqakhPDwctVp2lP3RnZMgUAghhBBCnFsSxgshhBBCdEMSBAohhBBCdEMSBAohhBBCdEMSBAohhBBCdEMSBArRDY0fP57777/f7vhHH32Er68vAE899RQqlYqpU6fajXvxxRdRqVSMHz/e7lxubi56vZ4+ffoovrZKpbL+5+Pjw6hRo1i1apX1/Lp165gxYwbh4eGoVCq+/fbb03mLQgghOiFBoBDCobCwMFavXk1ubq7N8Q8//JDo6GjFaz766COuuuoqqqur2bp1q+KYefPmUVBQwMaNGwkMDGT69OlkZGQAUFdXR2pqKm+++ebZfTNCCCFsSBAohHAoODiYKVOm8PHHH1uPbdq0idLSUi6++GK78WazmXnz5nHDDTdw3XXXMXfuXMXn9fX1JTQ0lD59+vD222/T0NDAzz//DMBFF13Es88+y8yZM3+dNyWEEAKQIFAI0YnZs2fz0UcfWR9/+OGHXH/99ej1eruxq1evpr6+nsmTJzNr1iy+/PJL6urqOnx+Nzc3AAwGw1m9byGEEB2TIFAI0aHp06dTXV3NunXrqKurY+HChcyePVtx7Ny5c7nmmmvQaDT06dOH+Ph4vvrqK4fPXV9fzz/+8Q80Gg3jxo37td6CEEIIBeekd7AQ4o9Dp9Mxa9Ys5s2bR0ZGBsnJyfTr189uXGVlJYsWLWLDhg3WY7NmzWLu3LncfPPNNmOvvfZaNBoNDQ0NBAUFMXfuXMXnFEII8euRIFCIbsjb25uqqiq745WVlYpN4WfPns2wYcPYv3+/w1nAzz//nMbGRoYNG2Y9ZjabMZlMHD16lOTkZOvxV155hcmTJ+Pj40NQUNBZeEdCCCG6SpaDheiGevTowc6dO+2O79y50yZYa9W7d2969+7N/v37ue666xSfc+7cuTz44IPs3r3b+t+ePXsYM2YMH374oc3Y0NBQEhMTJQAUQohzSGYCheiG7rzzTt544w3uu+8+br31VlxcXFi6dClffPEFP/zwg+I1q1atorm52VpHsK3du3ezc+dO5s+fT0pKis25a6+9lmeeeYZnn30Wrbbzf3Jqa2tJT0+3Pj5x4gS7d+/G39/fYVkaIYQQXSczgUJ0Q/Hx8axbt47Dhw8zefJkhg0bxsKFC/nqq68Ui0MDeHh4KAaAYJkF7NWrl10ACDBz5kyKi4tZtmyZU/eWlpbGgAEDGDBgAAAPPPAAAwYM4IknnnDuzQkhhHCKymw2m8/1TQghhBBCiN+WzAQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRDEgQKIYQQQnRD/w/4WFIu/mNwPAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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UIiYmBps3b8b8+fPRrVu3UvVZq1Yt8Pl8zJkzB1lZWRCLxWjVqpXNPOgncnJy4Ovri27duqFmzZqQyWQ4fPgwIiMjzWZX69ati40bN2L8+PGoX78+ZDIZOnfujE6dOmHbtm3o0qULOnbsiNjYWCxZsgRVq1Y1W6AZFhaGBg0a4PPPP8ft27dRuXJl7Nq1C+np6QDMZ6x/++03NG3aFDVq1MCQIUMQHByMpKQknDlzBg8ePEB0dHSp3puS6NSpE1avXg0HBwdUrVoVZ86cweHDh+Hi4lKi9kKhEDNnzsSwYcPQqlUr9OzZE7GxsVixYsUz5yg/z/diaX5mfvjhB3Ts2BFNmzbFwIEDkZ6ebvqZLvg1JISQt8ar2GqDvDtOnDjB6taty0QiEQsODmZLliyxun/x1q1bWdOmTZm9vT2zt7dnlStXZiNGjGA3b9606HPKlCkMACtfvrzN+x47doyFhoYyBwcHJpFIWLly5Vh4eDiLiooy1bE2Dq1Wy6ZPn86CgoKYUChkfn5+bPLkyWbbjzFm3JKrY8eO7MCBAywkJISJxWJWuXJltnnzZrN6T7bkioyMNCt/cu+UlBSz8v79+zN7e3uL51m2bBmrW7cuk0qlTC6Xsxo1arAvv/ySPXr0yGJMhTVv3txiy7Hly5ez4OBg01Z9JdkqLj8/n02YMIHVrFmTyeVyZm9vz2rWrMkWLVpkVk+pVLLevXszR0dHs23DDAYD+/7771lAQAATi8Wsdu3abPfu3ax///4WW4ulpKSw3r17M7lczhwcHFh4eDg7ffo0A8A2bNhgVvfOnTusX79+zNPTkwmFQubj48M6derEtmzZUuwzFQTA6tZ3AQEBrH///qbXGRkZbMCAAczV1ZXJZDIWGhrKbty4YVHP1tf+iUWLFrGgoCAmFotZvXr12MmTJ61+rYrzvN+LT5TkZ4Yx489qlSpVmFgsZlWrVmXbtm2z+jUkhJC3AcdYGa3yIeQdEhgYiOrVq2P37t2veijvjB07dqBLly44deoUmjRp8qqH89qg70VCCHlxKEeZEPLaUavVZq/1ej0WLFgAhUKBOnXqvKJREUIIeddQjjIhBEqlstgcUzc3N5vbp5W1UaNGQa1Wo1GjRsjPz8e2bdvwzz//4Pvvvy/17h+FD9woTCqVPvNBHy9SSkpKkQsERSIRnJ2dX+KICCHk3UOBMiEEP/30E6ZPn15kndjY2DLb9q44rVq1ws8//4zdu3cjLy8P5cuXx4IFCzBy5MhS9+Xl5VXk9f79+yMiIuIZR/ri1K9f3+KQm4KaN2+O48ePv7wBEULIO4hylAkhuHv3brHHXDdt2hQSieQljajsHD58uMjr3t7er+URzKdPn7ZIQSnIyckJdevWfYkjIoSQdw8FyoQQQgghhFhBi/kIIYQQQgix4pXkKBsMBjx69Ahyufy5j9MlhBBCyMvBGENOTg68vb3B49FcG3n7vZJA+dGjR/Dz83sVtyaEEELIc7p//z58fX1f9TAIeeFeSaAsl8sBGH/QFArFqxgCIYQQQkopOzsbfn5+ps9xQt52ryRQfpJuoVAoKFAmhBBC3jCUNkneFZRgRAghhBBCiBUUKBNCCCGEEGIFBcqEEEIIIYRYQYEyIYQQQgghVlCgTAghhBBCiBUUKBNCCCGEEGIFBcqEEEIIIYRYQYEyIYQQQgghVlCgTAghhBBCiBUUKBNCCCGEEGLFKznCmhDyauRGRSH/7l3Y1a4NcYUKr3o4hBBCyGuNAmVC3iK5UVHI3rsXnEgM+QcfQODoAKG3N2Aw4P6oUVCdOGmq6zxwIDy+nPAKR0sIIYS83ihQJuQtoL5yFSmLfoPq2HFTWXpEBABA4OEBeZvWZkEyAKT/+ScUoe0grVnzJY6UEEIIeXNQjjIhb7iUhb8hrnt3syC5IF1SEjLWb7B67eGEL6H+7z/cGzgIN2rXwd2PPkLOsWMvcLSEEELIm4NjjLGXfdPs7Gw4ODggKysLCoXiZd+ekLeGNjERt1u3AfT6Z+6DZ28Pg0r1tEAgQODGDZBWq1YGIySEvE3o85u8ayj1gpA3WN61688VJAMwD5IBQKfDvQEDwQmFkLVsAY8vvgDf0fG57kEIIYS8iShQJuQ1p8vIQPqKCGji4iCpXQuOYWEQODkBAMTlywEcB5TxH4YM2dkAgKwtW6GNv4eA1avKtH9CCCHkTUA5yoS8xrIPHkRM02ZIW7YMOQcPImXOj7jbqTO0Dx8CAASenhB4eBTbDyeTPfMYciMjkX/nzjO3J4QQQt5UFCgT8poy5OUhYcrXFqkV+rQ0pP7+OwDjzhW6xMRi+3IdPBgQFPEHJKGw6LHk5hY/YEIIIeQtQ4EyIa+p/Bs3YMjJsXHtJgAg58jRYvsR+vnBZegQBO/cAU4itlJBCHHlyrbb+/pCQgv7CCGEvIMoUCbkNSXw8gJ41n9EJVWrAgB0aWlF9iGuXBk+8+Yh6fsfcPfDMLC8fIs6nEAARbu2VtuLypWD78IF4GyMgxBCCHmb0acfIa8Q02qhuXcPhrw8i2tCDw84dutm2YjjwABo7t+HLiHBZt9BO3cgeMd25Bw4gIw1awCdzvoY1Go4dukCRadOxoWBAHjOzvCa/QPK7dkNSRGzzYQQQsjbjHa9IOQVydqzB0k/zIY+NRU8hQJuo0fD+ZM+ZnU8p02FpEZ1pCxYCH1ysrGQMWSuXQtdUlKRu13wxGJk7tyJtOXLix1LblQUfH6aC/fx46BLTYWkShVwxeQtE0IIIW87CpQJeYl0GRnIPXsWzGDAoy8nmhbqGbKzkTRzJvRZmcg5eAjaxETImjSG+4QJcOzaFck/zLboS3nqFCARA1bSKcDjIfvYMaTM+bFE49I+fAQAEHp7Q+jt/ewPSAghhLxF6GQ+Ql6SrL/+QsKUr8E0mhK3EQUHI2jHdtysU9cydYLHA8/ODgal8rnHFrRjO6VYEEKKRZ/f5F1DOcqEvAT67GwkfDu1VEEyAGju3kXuuXOQt25tedFgeP4gmceD62efUpBMCCGEWEGpF4S8BOroaDC1+pnaZu7cCc9pU2FQKqE6ffq5xyIMCID7uLFgej2kISEQ+fk9d5+EEELI24hmlAl5CYReXlbL+S4u4CkUkNapA2FgoNU6ykOHwZfL4f/H73D59NMyGU/K/F+RPPcnZKxZC8MzBvCEEELI244CZUJeAnH58pC3bWNWxolE8FuyGJXOnYXvbwuhjY+32pbl5UF15gwAgCd6/p0otPHx0MTGQpeYiPSVK42n/xFCCCHEAgXKhLwkPj//DI/Jk2DfpAkcwsIQuGE9pDVqAADU588XudWb/nEusrR2bZuHkDwh8PQs1biy9++HNjkZOceOIefwYZphJoQQQh6jXS8IeYVyjh5F9r79MKjVUB4+bLOe/IMO4NnZIWvbdsBgsFnPqXdv5N+5g9yzZ0s1DoGbG3QpKQAAvpMT/JYtNQXxhBDyBH1+k3cNBcqEvCIpixYh9dcFZdaftHZtBK5fh9zoaMT3/LjE7Xj29jCoVGZl4qpVELxtW5mNjRDydqDPb/KuodQLQl4B7aNHSF2w0PICn//MfaovXkTuxUsQBwfD59f54Lu7W63Hc3Q0HVUtqVrVaqpF/rXrSF+9Gnc/+gi324Ui+aefKCWDEELIO4dmlAl5BRKmTkPmxo0vpnM+H/K2bSGuUN5qMO48aBCc+/UDy8+DyN8ft1u1hvbRI7M6nFRqsZ2dvH17+M7734sZMyHkjUCf3+RdQzPKhLwC+bdulao+z8Gh2EV8Jno9cvbvh/LYcauXOT4fQg93iPz9AQCun1luOcezt7coyzlwANqk5BKPmRBCCHnT0YEj5I1k0GiQc/AQ1JcvQ305GpqY2wAAaYP68Jw82RQEvq5EQUFQX7xYorqcRAL/pUugefAAj76YUOJ75N+8aaUzDg6dO5kVOXbrBoGbGzK3bgPT62FXuzZSly2zbMsYVKf+Rtbu3dA9SoBdw4ZwGz0KAheXEo+JEEIIeZNQ6gV5oyhPnkTyr78i/9p1m7s/8FycUX7fPvALfW/pMjKQsXo18q5dh7hKZTj37QuBs/PLGLaF/Dt3EPdxLxhycoqsJ65aBf5//AGBkxMA4yl9CZO/Mnt2Sc2ayPvvP0CnM2vLCYVgWq1Fn/6rVsK+QQOr90v++RekLV9u9ZrQxwfaxERAr386vsqVEbRtK7iSznYTQt5o9PlN3jU0o0zeGOorV3H/sxEWAWFhhrR0PJo0GUJPT9g3awp5y5YwqFSI/7gXNI8P9VAeP47svXsRtHUb+DLLNIOyZFCpkHPsOMAYZC1bgC+TQVyuHIK2b0PG+vXQJSXDoFJaTZVw6NTZFCQDgOOHH0IcFIT0iJXQJSfDrnEjiAMDkbZqNfIuXTJrK/DygvbePYs+tffuAQUCZabXI3PzFmTv3YPcc5FWn0EUGAihvz+0Dx+aleffuIHcc5Gwb/heid8PQggh5E1BgTJ5Y2Ru3lxskPyE8uhRAEDGunUQ+vjAkJcHfVqaWR1t/D1k7/4LTh+XfCu10lL/9x/uDxoMfWYmAGOusf/SJZDWqgWRry88JhhTKXLPn0f+nbtmga2kenU49uhh0ac0JAQ+v/wMXUoK4vv1hyY21niB48CJxeBJJJC3awt9ZpZloMxxsKtXz6woYcrXyNqxw+YzSN97DwERK3B/6DCr1588GyGEEPK2ob+XkjeGITf3mdppHz60CJKf0Ny/b7Md02qRNOdH3KzfADdq1cajKVOgV6ps1rcm6bsZZoGkISsLCd99Z/y3Wo38u7HI2rcP8X37mQW18vbtEbh+XZGz3amLFz8NkgGAMXA8HpwHDEDm5i3IOXjQoo1d40bg7OxMrzUPHiJr584in8EupAaUR4/CoFJaXOOkUtg3blRke0IIIeRNRTPK5I2haB+K7N27y7RP+/dspwwkz5uH9BUrTK+ztm4DU6vh88svJeqbabVQR0dblOdfu47UP/5E2pIlxhxlPt8i31p18iSYVgtOKLTZf27UeYsyQ24uUn791eZx2Lmn/8HdDh/Af8WfkIaEQJfwqMijswVubtBlZuHBiJEW1/hOTvCaNdMiF5wQQgh5W9CMMnljyNu0geuokU9nRIsIIkvC4aOPYN+sGQBAn52NpLlzEdu1Gx6MGg11dDSytmy1aJN94CD0xSzAe0KflQVOKrUo5zs6ImXu3KcL+QosjnvCkJsLXXp6kf2LAqzs7MFxVvsz61ulQvIvxv2QJdWqgSeTWfYdFATXESPgt2wpsqyc0GfXsCHKnzgOeatWRd6LEEIIeZNRoEzeKG4jRqDCyZMI3rcXQdu2AgIrfxQpZgcGTiSCpFZNyJo3B8dxYIzh3uAhSP/jT+T99x9yDh1CfN9+MGg0lo0NBpu7bRTEGMO9QYMtDu0AAHGlSsW25+zskDhtGtJXrrQ+DgAuQ4eBk0jMyuTt2hXbNwDkXbsGAODZ2cHru+ngRKKn46tSBQHr1sJt1EjjLwVWAm99Wip4BdoQQgghbyNKvSBvHL7MHnxZEADAf9lSJM39Cfm3boEnk0H2/vtwHfEZ1OcvQJuchPyY28jZt88svYBpNMi7FI2HY8ZAM24cpLVrIe/yZbN7MI0GoqAg8xxgALL33wffwaHYMeZGRlrdx1haty7s6tZB7tmzRbZnublQnTpt/N+5SPj9ZnnCnrRGdQRt3YKM9RugT0+DrGVLKDp1wsOx45Bz4ECR/UsKBOuKDz6AXcOGUP1zBgJnJ9g1bGja7k1SuTI4iQQsL8/83rVqFdk/IYQQ8jagQJm80ewbN0bwdsvUAN2jR1CdPgVDfn6RObhpv/8Oz2++tnpN6OUFaUgIsvbuBfR6yJo3h9esmSUal639kTmBAA5duiD19z+AAjPFnFwOz6++QubGjVAX2uZNeeQI8m7egqRSRYv+xOXKwfPrKWZlPj//hMymTaA6/Q+Enp7gOTogdf7TvGVOKoXbuLFmbQTOznDo1NGif75CAfcvJyBpxkxTe6G3N1w/+wwAoEtPR8q8+VCdPg2BhwdcBg8ypWPk3bwFfXoapLVrg1do5psQQgh5E1CgTN46WX/9hUcTvixRXUNODsSVK4MTicAKpTjIWraEc99P4Dn1WzCDAXyZDNrkZDwYPQbKY8fAd3SEc3h/uAwaZNGvfcOG4MnlFgFzfkwMMtatBwocBMJ3coLfksWQ1qyJzE2brI5T++ih1UDZGk4ggFP37nDq3v3pszRthpwD+8FJpXD88EMIfXysts05dgwp8+Yj/+5dSGuGwGPiRDj37g37ho2gPHkCAmdnyNu2BU8qBWMM94cMNR52AuPuIg9GXITvot+QsWYtVKdPAzBuiefz01zYvfce8m/FQOjlSaf5EUIIeSPQyXzktaZLSUHm1q3QJibCvkkTyNu0AcdxRba5074DNHFxJepfVK4cyu3Zjay//kLC1Glgj7egk3doD58ffzTtOpEbGYn8O3eRunQpdAkJZn14TpsGp497WvSd+ddfSChhwO45fTqcevZA8vz5SFu8xOwaJxajwonj4Ds6lqivZ5V34wZiu3U326uap1Cg/MEDVu+de+Ei4nv3tijne3pCn5hoVsazswMEAhiys417OTdqCL+lS8F7zgWZhJCXiz6/ybuGZpTJa0tz/z7ien4M/ePdHzI3bIRj9+7wmvFdke0Knx5nC0+hgNd30wEADp07Q9ayJXIjo5C9dy9yjhzBjdp1AKEQfJkM+pQUm/1kbtliESjrs7OR/vsfJRoHAOSePQunnj3gMmgQcv89C/XFiwCMx1B7Tp1a4iCZabVQX7kCvqMjxMHBJb4/AGRt325xoIshOxvZ+w9Y/UVAcy/eaj+Fg2Sg0B7YjCH3nzOI7fIRyu3+q1RjJIQQQl4mCpTJa4XpdGAGA3giEdJ+/8MUJD+RuXkznAcOgDgoyGYfdg0bQvX33zavC3x84DHhC9g3bWZ2oAdfJkPOkcPI/qtA8KbTQW9l54qCCh4ooktNherMGWRs2mR1MZ8tQl9f0xgC169DblSU8Yjq994rcZpC7vnzeDh2HHSPg3r7Jk3gM39+iY/oZgXSQUpSzpM/32yS5vZtZB89BkWrls/VDyGEEPKiUKBMXgtMq0XSj3ORuWULmEYDeauW0CYnW62ruXOnyEDZY/Jk3Lt925QiwUkk4MlkMOTkQNa8OTymfAWhh4dFO0N+PrJ37ir12KW1agIw5kYnfDXFZmD5BN/VFfrU1KevnZ3h1LuXeZ+1ayM9IgL3Bg4C+Dw4ftQVTn1620w7YTodHo7/3BQkA4Dq9Gmk/vYb5G1aI+fgITAAQg8PSKpUNu5sUagvRceOxvzpAjiRCPJ2ba3e065miPGwlGL2bS5KwqSJkJ86ZbY9HSGEEPK6oECZvDS5Fy9C++Ah7OrVhdDLy+xaysLfkLF6tel1zqHDEHh7W3bC50NSrVqR9xEHB6H8gf1QnjoNptFA1qwpePYlmFU1GMBKG/Tx+TBodYj7pC/yoqOLDZIB49Zz9s3fBxggDgqCc7++EHp6mtVJ/vkXpP/5p+l10rWZMChz4Dp8uNU+865fhy4pyaI8a+dOs9MFn5CEhMD/j9/Bl8tNZXZ168Jz+nSk/Por9GlpEPr6wuPrKVZ/qQAAgasr3EaNQsq8ecU+M3g8q/tPG7JzkHPiBBRtrQfjhBBCyKtEi/nIC2fIz8eDz0aYdkEAnw+PLyfAuX9/U52Y5i2sBnpCHx+znGPXkSPhNnLECxvrg1GjkHPocMkqC4XG4O8ZZ1QdunWF90zL7eYMGg1uNWxkWlj4BN/JCRXP/GO1r/y7sbj7wQeWFzjO5vZ4Lp8Oh/uYMRblTKuFLiMDAldX037KRVFfuQrlyRPQPnyIrG3bLa6LK1aEQ7euSP7+B6vtvWbNgmPXj4q9DyHk1aPPb/KuoRll8sJlrFv/NEgGAL0eST8atwtTX7wIvVJpcybXP2IF1BcvQpuYBPsmjSEtZjYZMC6kU535F3xHR9g1qF/sLhmAMc9Yffky7Fu2gvrqfxY7W1jDVyigT0srtp4tWdt3wH3cOIscZKbRWATJgPG5GGNgGg1yz54FJxLBrn59cHw+cg4dsn6TIn4Pzj17zmo5JxRC6O5e4ueQ1qgOaY3qMKjVUP37L3SPCrx3Egl8Fy6AyN8f+bduWRwLzolEkDV/v8T3IoQQQl4mCpTJC5f777+WhXo94j7uZXHiW0ECb29o7t6FokMH0zZtxUlfuw7Jc+ea+hVXqQJFaCiUf/8NoY8P3Cd8AaGbG3KOHEHWjp0Ax4Hv5oqsrdusjkVarx7UUVFW71VwEd8z0euhT0+3CJT5MhnsGjRA7jnzQFbWsgXyrlzB/U8/MwXoooAAeP/8E1IXLy717UV+vs88dGt4UikCV69Gym+LoL50CeJywXAZPhwif38AgPfMmeArHJCxejWYVgu+kxM8p02DwNW1TMdBCCGElBVKvSBlwpCXB+Xx4zCo8yBr0RwCJyfTtcTvvrNYJGYNJxKBAWYn1gEATyGH1/TpUHToAABQR0dDdfYcRL4+xn2VRSJoExNx/7MRyL92reib8PlwDg9H+h8l3LpNIgFfLi9yezhbBB4eYAaDzbZ8Z2dUOPW31fQGzf37eDBiJPJv3TIOIyQEvgsW4P7gQciPuW1WV+jvD+29e6UaG2dnh8D168yOsn5ZdBkZ0CUkQFS+PHi0iI+QNwp9fpN3DQXK5Lnl343FvfBw6B7vUsFJpfBdsACypk2M12NjEde9BwxKZbF9Cby8rKc9CAQot38/MtatM1vkJq5YEQFrVuPhF19AddL2lnBmisjbtUoiAYqY+X5W0lq1ELih6F8g8m7cAMfnQ1yhArRJybjdvLllJRsL5QoTV6oEgbsbhL6+cO7TB+Ly5Z916ISQdxR9fpN3TfErdQgpRvKPP5qCZABgajUeTZ6M3MtXABh3dgjcvAmOH/c0bidmC49nOzdYp0PGxo1mQTIA5N+6hbSVK6H6+1TJB1yKIJlnb/9CgmTAmAvMNBpk7z+AtIgI5N28ZVFHUrkyxBUqAAD4Cjk4OzvrYyzuXlIp/JYthf/y5fCaOhXi8uWhPHkSdz8Mw/Wq1RDboydyz59//ocihBBC3iIUKJPnlhsZaVGmT0lBfI8eiO3RE7r0dIiDguA1bRr4BVIyLBQzK6q5e9dqed6VK+DE4lKNuUQEAkhq1Cj7fh+zf/99xHbthodjxyJ59hzEfvghUhYtslmfJ5XCuW9fi3JmMAAC8+UGdo0bQVSuHADjvs2KDzqAaZ+eupd/5w7ujxhpPBTFYEDe5cu4N2QotEnW964mhBBC3kUUKJPnJgzwt3kt7/JlJP/0s+m1Q9iHz3QPnkIB1dmz1u/v4wNWKK/5uXEcoNNZX4hYBkTly8OgzEF+TIxZeerC36B99MhmO7exY+A1a5bxwJDHvxwwlcp49DSfB3loKHx+nQ//P/6A69AhAMdBn5qKrK3bcLdTJ9MvNVk7dwGF9nxmubnI3ru3jJ+UEEIIeXNRoEyem9uIEcY8WRuUf580/VvgXLLjmE04DnYNG8Kp18dgVnKcOTs7iAIDi52NFgYGQlSanNzC6Rkl2E+4NDTx8cg9f8HygsEA9eXLYAYDtA8fwlAg7SM3KgrxH/dCwrRp0D54AJafb95Wb4DAzQ2Kdu0AnQ5Jc38yew6Wl4fkn38x/luvg1W2ygkhhJB3EAXK5LnJW7dGwNo1UHTsaPU6y8vHwwlfQnX2HPJjY0vXOWOQVKkCnpXcXACwa9AAooCAYrvRp6dD9n6zonOki8CTSuG7dCkglT5TewtaLYS+PlYv6bKycadtO9xu3QYxTZshdekyaBMScG/IUKijowGtFtoHD6y2zbt61dhHaqrZMdmm6zduAAAUH3xgGfwLhZC3b/8cD0UIIYS8XShQJs9E++gRsvfvNy1As6tdG5KqVazWNeTkIPuvv3Cvf39kbd5c6nvlnj8PnoOD1WtMq4GsaVOIypcrsg9DdjbS/1wBvouVGW2BwOoiObP2Gg1UJ0/Cb/Ei2DVq+MwBd0HSatUtcrbl7dohefZs02mEBqUSKf/7H5LnzwdTq4vt80k7gZsb+G6W+xNLqlR5fO9q8P7hewgeHywi9PWF77z/QeRbtnsrE0IIIW8yOnCElFrqkiVIWbDQdHSzomNHeP84B9kHDj5TfwIfb/gtWoR7w4ZDn5hocV0UEAC+XG61LU8qBScQIGDlSqT+tgi5kefASaQwaDTQ3LxpUZ/l5RmD3AInAbqE94e8U2ekLV0KTiAAy89HzsFCz6LVImPtWqhOn0bwrp0w5OfjVv0Gz/S8TyTNmgXP6dOhS0qENiER9o0bwZCvsbw3AHVUyXak0OfkAAA4gQAeX36JRxMnmdJSOKkU7l98bqrr8OGHUHTqBH1WFviOjiU6rpoQQgh5l1CgTEolPyYGKfPmm5Vl79kDWfP3wbMvelbWFmlITWhiY60GyZxIBJfBgyH08gTPzg6GQkc7K0LbgzEG9eXL4IRCOHbvAYewD8F0OsQ0bmLRH9NqEbBmNTI3bIQhVwV5u1DwFXLEdetmXBD3mKRGDWjuxcOQlW3WXhMXh5yjxyDw8S79fsxWZO/ejYDVq56+trGYzlaqRWEcn4/sffug6NABDp07Q1K5MrL3HwAnFsOhcycIvbws6gucnZ/9AQghhJC3GAXKpFRUZ85YLX805etnDhod/q8z8q5csXpN3qEDJJUqAgB8fv0VCd98A11CAjiJBM59+8Khcyc8+vprZG3ZamqTHhEB30W/We2PEwphV7s27GrXNpXd6fCBWZAMGLecE1epgvxCgTJgzP/N3L7N6vNK69aFutB+xMLy5eDSPxyJ33xjUT8vJgbJ8+ZDHRUFzs4O4goVwNnZgRX6haCkDCoVHo7/HHwnZ9g3fA/iChXg9ngfZkIIIYSUDgXKpFhpv/+O9FWroc/KgqRaVeuVCm01BgB8Dw8YsrKM6Q4AeE5OgF4HQ3aOqY5do0aQt2wJpre+awVXIBdY1rQJyh8+BE38PQjcXMGXy5F385ZZkAwY86cz1ls/8Y6TSMxeG1QqaGwsMMy/ft1KBxy06enIi75stQ3f1cU0882JRFCEhcHz6yngeDykLlwIXVKS+f0zM5G2ZInpterk0x1CwONBGBAA7TMsgMzcvBn2Dd8rXTtCCCGEmKGkRFKkjPXrkfzTz9AlJ4Pl50N94WKJt0rTJyWZgmTAGJT6LloEz++mw7FnT/ivXoWAFcaT9gTO1g8i0cSaHzLC8fkQBweZcpbzb96w3u7effBdLRezCd3dzPuzswPfw6NEzwMAYAzpixZBn5lp9bLywEFTegjTaJC9cyf0mZlgBgMEnp4lvw8AGAxwGznC6qK84qjOnYOh8PZxhBBCCCkVmlEmRcrcvMWy8MlJcLpS7rmr0SBtyVL4/77c4pLNhWRc0UG5pKr1Ge7cs2et7q2cd+cuDHl50KWkIO2PP6C5fQfS6tWgLDTTW1ZYfj5yDh2C+vIV5EVHl7p99u498Jg0GY++/NJsAWJx9CkpSPv9d+Me14QQQgh5JhQokyIxKykVAEofJD9mK81BUrMmRMHBFsdUO3QJK7I/cfnycOrdGxnr1plfsHUAiVoNTVw87g0YAH1GhqlY4OkJgyYfhvQM6+2eQ8qvC2DIynqmtspjx6A8frzI/G++iwv0aWlW2h6H6/DhUJ06BW1yMmSNG0OfnQ3lqVMQenhA3q4deIVSUQghhBDyFAXKpEiKTp2Q8r//lVl/0rp1rJZzHAe/pUuQOHUaVGfOgO/gAOfwcDh1715sn57ffgN5u3ZQnf0XmRs2mgXAhUmqVUPO4cMWdXSJieCkUkhq10bexYule6ii8HjPHCSbFBEk8+zt4TZ+PBKnTLG8JpMhtlt367nWAIS//YbANWsgcDNPRzFoNFD98w84Hg/2DRuCE4meb/yEEELIG4oCZVIkl0EDoTxzBup//33uvviennAbPdrmdZGfH/z//ANMowGEQnAcV6J+lSdPInPbdoAxcGKxzXpCb294ff890gtsx1YQU6uRd/EieHI5DDk5VuuUhrhiReTfuvXc/djCd3KC3/JlyN69x3LMHAddWio0MbdtttfG30PaH3/CZfAgcGKxcXHk9eu4P3QYdCkpAIwz7f6/L4e4NMd/E0IIIW8JCpRJkTiBAPb165VJoOz22afFnvyWc/QoUhcthvbBA9g1aAD3CV9A5Odns37Gxk1InDq1yD6ltWrB/fPxkNapA47Ph6xJE4udMgoy5OTArkED5EZGAoyB7+AAxUddkLEiwmp9+5YtoEtMMpu5derfH9oHD0oVKEtr10bezZsl3hpOn5GB+D6fgBVctMfnQ1q7Nlhenuk466Jkbt6M9IgIQCCAQ8eOyL9zxxQkA8aZ9sTvZiBg1coSPwchhBDytqBAmRRLZ+UgkIJKuu9v7rlIOPXoYfv6+fN4MHKUKb845+BB5P33H8rt22vzz/+pixdbHQ/fwQEGlQrydm3h8eWX4CsUpuvy0FCr+x0X5BweDq/vZ0GXlARJ9ergicVQX7hosSBP1rYtfOb+CJ5EgtyLF6G5exfSkBDk376NhytLF1xyYhH8Fi9G4nffQXPnjsUJgtawwjtb6PWQVK6MjDVrSnRPg0pl/IdOh6ydO63WyT13Dnkxt8FUSogrVgSvmOO+CSGEkLcFBcqkWHYNG1rf/eKJEu7GIPQqenu0zE2bLBbhaR8+hPLUKchbtTIrz4+JQea2bVaDeJabi+CTJ8GX2Vu9D8fjQRMfb3McfAcH2DdqCJ5UajYDblCrLepq7t41LYgreJBJxvoNNvu3RVK1Guzfa4Bye3ZDn5kJ5blIPPr8c6t7VBclc9u2Ut/bxMZpg7GdOwMw5j17fD0FjmFhz34PQggh5A1B+yiTYik6dIDi/zrbvC7w9oK4BKe/qa9chTYp2eZ1Q65lIAoAmvvmxzcr//4bdz/qinQbqRCS6tVtBslP6G0tsOM4+P62EDyp1KyYaTTQWEmj0Ny583RWtgChv+10EVty//0Xcb37IGPTJvAcHJD6v/+VOkgG8Myn+gEAhMIiLxuUSiR8NQXaR4+e/R6EEELIG4ICZVIsjseDz48/wv3LL61ed+zaDUHbtsLn1/mwe6+BzX5y//0XD8eMsXld3j7UannWVvN84uQiAki+kxM8beQs69LTkTxvHu4P/9R0YElh9m3bwq5ePYtyTiSCMMDfolzo4wPOSiqCrFkz4+xsKeRduwb1hQtI/HYqkmfPsbmV3tNBla7/gvjOTrB//33LCxpN8Y0NBuQcO/bM9yaEEELeFJR6QUos/+ZNq+U8qQScUAhFu3aQ1q2L2+83t5mOob50CZq4OIgCAy2uSUNCjKf+FUq/yL91C/l37oATS2BQ5iD/pvUFcp7ffQeHzp0sZoMBQK9UIrZrN+gSEmw+H08uh/e339i87v7553g4bvzTZ+Px4P75eNPuHIb8fDwYOQqq06dt7+NcQraO4DZTxLZxxfGc/h3sG76HpDlzkL3/AJhKVar+bM7IE0IIIW8RCpRJifEdHa2WC5yeHj8tdHGB78IFeDh2nOVCs2IkfP2NzQAzccZM5Bax8wbfyQmO3braPOHv4bjxVoNkcY0aMGRnQ962Ddw//7zILekU7dpBtHULsnb9BRgMcPi/zmYnAz4YNQqqv/+22b40WFEzu89yKmIhSbNnI2BlBDynTIFLeDjudrKdWmNN+p8roE9Ng9DHG6KAAMhatAAnoP+cEEIIebtwjD3HtNQzys7OhoODA7KysqAosBsBeb3l341FbNeuYAUWtQl9fRG8Zzd4hfYvNqjVSJg6Ddm7dpmVS2vVQuAGy9lSQ24ubtapa/W+Ak8P6BKLPmLavmlTq0djA4DmwQPcadO2yPYA4Ny/PzwmTzK91ufkIGn2bOTsPwCenR2ceveCy7BhVoNxptfjRo2QYmeSReXLQ3P/PlDKXyKecPn0U2SsWmU1LxoAwOOBZ28HQ46yRP1xYjGcen2M9NVrSnVEdmGSkBAErPgTPPuic8MJIW82+vwm7xrKUSYlJg4OQsDKCMhatoQoKAgOXT9CwOpVFkEyAPCkUnj/8D1cBg8yBk88HmTNm8Nn/nyrfXMiEXgymWW5UAhwxX+b2jVoAINabTUlIGPjphI8HZC+ciXUV66YXj/6ciKytm6DQaWCLiUFKfN/Rdrvf1hty3S6ooNkoRAunw4HTyQC8vPBk8kg8HAv0bgKkrdsAUNRC/wMBkhq14bXD9/DOTwcruPHFdkfy89HesRKm2kXslYtwZNbfl0Ky7t8GRmbNhdbjxBCCHmT0N9KSalIQ0Lgt3hRiepyfD7cv/gCbuPGgel0VgNqU12BAE6f9EHakqVm5a6ffYrsQ4eKzC3mu7lBEx+HWw0bgeXnw65+fXj98ANEvj4AAJ6o6J0cCso9FwlpjRrQJidDefy4xfXMzZvhOnSIWZkhPx/3wsOL7JcnlSJz3XpTIG9QKmFQlmzW9wmBmxsEXl7FLrjTPXgIxy5dTK/zr11Hzv79RXduJcgXVygPQ76mxLPT6kJ7TBNCCCFvOgqUyQvH8fng+Pxi67mNGQOBiyuydu0CeBwcw8Lg1KsXBB6eSPjqK6tthP7+kLduZbZVXG5kJB6OGYOgrca9nxUdOyJ10eISLVYTPdnZQqu1Wl+bkICHn38OfXY28v67BnG5cpDUqA71xUtF9itwczMeIvKM+E5O8F20CEI3N4grlEd+EUdTC1xdwRgDx3G4ei8dRwKbIK2VFypePY26ySU4KZDPhyK0HZwHD0Zcj54lHqM4OLjEdQkhhJA3AeUokzdC5o4dSF34G7QPnu6pzHdxQcCqlXgwYiQ0cXEWbYL37oUowB9py39H+po10KelAYyBk0jg3L8fsnbuMjuwRFK9OgI3rDctSovr+XGZzJJyEglchgxB6oIFpW4r/6ADXAYOgqRqFVNudO7Fi7g//FMYith5wmXYMNys0gD6rybAIzcdAHDNOQC3HXzwf7H/FHlPSc0QuI8bj/uffmqWj14UgbcXgjZtgsDVtYRPRgh5E9HnN3nX0IwyeSM4hoXBMSwMmgcPoTxyGDyZDPLQUPBlMkBgfbaaE/CR/MsvSP/jT7Nyu4bvwX3cODj16YOH4z+H+tIl4y4SPB40sbGmw1Okdes8X6DM48Gxeze4DB4MgYsLMtauhT493ayKpGYI8v67Bo7jwFMojMF8gfbO/fpDWr2aqUh58iSS582DITfX6lZ6T6SvXg2d5C945j69X9X0eCTL3cAqVgZ364bNYSvatkXi9OklDpI5ezv4//EHBcmEEELeOrSYj7xRRL4+cO7fH45duxqDZBgPPCnMrn59CH18rB4lrTp+AhkbNkB57DjUUVGmrdbyLl/GvWHDjAvzgOfa6o3v4oLgfXvhNX06RH5+4NnZwf/35RAX2E4OAPKuXAVfLocwMBD6zEzzTgwGs11D8m7ewv3PRiD/2nVjakgRiwdZbi480y1Pz6udcA1JCWlWWhg5dOkCx+7diz/spOC9VLlQnjhR4vqEEELIm4JmlMkbzzm8P1ieGulr1sKQkwN5mzbw+HoKmF5vc1Y0cdp0437EhegeJSA3Kgr2DRsWe5xzUfyXL4M4IMCsTFK1KsRBQci/du1pocEAfUYG9BkZVvtR/v030v74A9I6dZBz4GCJ908WBQdDHRcPvsF8yzeV2B4eOalW24grV4b3D99Dee5cie5RkC4lpdRtCCGEkNcdBcrkjcdxHFw//RSun35qcU1cuTLyr1+33tBG0Mnx+WCMQejhaZy9LYpYbNyFokCqv32zZmYHkRSUe/FC0f0Vor1/H8lzfwJg3IO5JPiurvCeMwfxq9aB/bXd/GKNmuD/c9hqu/ybNxHTpi10Dx+WaowAIGtm5ThsQggh5A1HgTJ5q/n88jPuduxU4iOlRQEBkNati4w1a6E8dqzoyhyH8gf2QxMXj/SICOhSUyF7/324DBlcZP+6R7a3uitIz/Fw0qcm/nMOgo8qBaGxZ2FXRH3HPn0gb9ECQn8/KA8fgWulcsjzGILkw8ehE4nh1bc3mtaugbthx63/ksAYdAUWS5YInw+XgQNg3/C90rUjhBBC3gC06wV566nOnUPC5K+gtTFTylMoYFAqYd+4MTy//QYif3/cDeuC/Bu2F7w9EbhpI6QhIVav5V68iJRff0V+zG1Iq1WD2/hxMGRn496gwTaPqJa1aQO+QgF1dDSmuDbDv17VTdc8ValYxbsM/RHLFAzOzg4VT59CblQUHowYaeqfk0jgt2SxMZXksZzDh/Fg4iTA1ul+RRGLIWvRAs59PwHLzYW4YkUIPT1L3w8h5I1En9/kXUMzyuStZ9+gAcodPgTl8eN4MHKU2VHNouBgBP+1C+A486OpS3CcM2dnB5GNvYM1Dx7g3sBBphxp5YkTUF+6hOD9+xC4ZTMyN22G9v595F27Zszv5TjI27WD95zZ4EkkOPDz7/g3xcusz0R7V+yq3BGdz/4DQ6GFfyw3F9qEBCR9/4NZEM7y8pD0w2wE79xhKpO3aQP7evWgKsUCPEXHjnDs0QP27zUocRtCCCHkTUeBMnkncBwHecuW8P99OVKXLIX2wQPYNXwPbqPHWD0MRfF/nZHy8y/mhXz+0wCa4+A+fjz4MhnybtxA9r794ERCOPzf/0Hk54esbdssFhLqs7KQvXcvnPv0gefXUwAAjDFobt8GTy43m5lNrNUYOGS580ScyBHSkBpQnTTfkYPn4IC8u3et7laRf/MmmFZrPA78Md2jkuUhcwoFXAcOhOvwYSWqTwghhLxNKFAm7xT7Ro1g36hRsfVcBg6EPjUVGZs2g6nVsGvQAO6TJkJ98SL0WVmQt24NSaVKyNyxAwlfTTHlQKctWw6/ZctgsJHWYFDlmr3mOM60b3NBtSt5Ww2Uq3opoOjQAbmRUU8DcY6DQanEo5GjrN5TFBhoFiQDgLROXeun+z3em1lcpQo8p02FtFo10wEshBBCyLuGcpQJKYJBowHLzwdfLre4xrRaxLRsBX2q+XZrkpoh8PjyS8T3+cS8AY+H4N1/lfio59HrL2BXtPnCv9qaZMza+yM4AOA4SEJqIC/6su1OBAL4zp8HeevWZsW61FTc7dgJ+gKn+4nKlUPgurUwKJUQ+viUaIyEkHcLfX6Tdw1NFRFSBJ5IBIhEVq/p0tIsgmQAyL9xE3Z168J90kSkLlgIg0oFvoMD3CdNKnGQDAC+TpZ7XFwUueOSa3nUTr0NMGYzSJaE1ID9e+9B0bkzJBUrWlwXuLoieP8+ZKzfgPzr12DfrBkcP/oInEAAvoNDicdICCGEvM0oUCbkGQlcXSFwd4cuOdms/Mkeyi7h4XDq3h3ahAQI/fzAE4vN6jGtFumr1yDnyBHwHRzg/Ekf2DdubLoel2Y9feORzM0YKBdB0b4DXAYOKHr8Tk5w+8xy72lCCCGEGNER1oQ8I04ggPvEL42L/J6U2dnB/YvPTa959vYQly9vESQDwKMpU5D8449Qnz8P5dGjuDdoMHKOHzddrx/obPW+1dPumr3mFfrzJ9/JCQ5hHz7LIxFCCCGkAJpRJuQ5OHTsCEmVKsjevx88kQiKTp1KtK+wNiEB2X/tNi9kDOl//Al5ixYAgF4N/HHwvyScuZtmqvJx3CkE5CSZXkvr1YXXjBlIW7IUedeuQVylMlw//RQCZ+tBNiGEEEJKjhbzEfIKqK9cQVz3HhblooAAlDuw3/Ran5+Pg39sxe1rsagjZwhp1wSq0/9Ac/8e7Bs0gFOvXuDZFXVeHyGElB36/CbvGppRJuQVkFSuDL6bK/Qp5osB7Zs2NXv9aPznCDxyBIGPXz/cuwUB69ZCWq3ayxkoIYQQ8g6jHGVCXgFOKITPnDlm+cXSmjXhNmqk6XXezVtQHjli1o7l5yP9zxUvbZyEEELIu4xmlAl5RewbN0aFE8eRGxUFvoMDpCEhZte1CY+sttM+sl5OCCGEkLJFgTIhrxBPKoWsWTOr1yRVqgAcBxRaRmBXt+7LGBohhBDyzqPUC0JeU6rT/1gEyQDAd3V9BaMhhBBC3j0UKBPymtLEx1st1yUmvuSREEIIIe8mCpQJeU3Z1a1jvbwepV4QQgghLwMFyoS8puybNYPi/zqblcnbt4esVatXNCJCCCHk3UIHjhDymlNfugT1tWuQVK4MuzrWZ5kJIeRloM9v8q6hXS8Iec1Ja9WCtFatVz0MQggh5J1DqReEEEIIIYRYQYEyIYQQQgghVlCgTAghhBBCiBUUKBNCCCGEEGIFBcqEEEIIIe+48PBwhIWFFVmnRYsWGDt27EsZz+uCAmVCCCGEvLN++OEH1K9fH3K5HO7u7ggLC8PNmzfN6ixbtgwtWrSAQqEAx3HIzMx8NYN9g0VERMDR0fFVD6PUKFAmhBBCyGtDb2A4cycNOy89xJk7adAbXuxxDydOnMCIESPw77//4tChQ9BqtWjXrh1UKpWpTm5uLtq3b4+vvvrqhY6lLGi12lc9hLcKBcqEEEIIeS3sv5qApnOOotfyfzFmwyX0Wv4vms45iv1XE17cPffvR3h4OKpVq4aaNWsiIiIC9+7dw/nz5011xo4di0mTJqFhw4Yl6jMuLg48Hg9RUVFm5fPmzUNAQAAMBgMA4OrVq+jQoQNkMhk8PDzQt29fpKammo2tadOmcHR0hIuLCzp16oQ7d+6Y3YfjOGzcuBHNmzeHRCLB2rVrrY7pypUraNWqFaRSKVxcXDB06FAolUqbz6BSqdCvXz/IZDJ4eXnh559/Lva5o6Oj0bJlS8jlcigUCtStWxdRUVE4fvw4BgwYgKysLHAcB47jMG3aNADA6tWrUa9ePcjlcnh6eqJ3795ITk4263fXrl2oUKECJBIJWrZsiZUrV1rM7J86dQrNmjWDVCqFn58fRo8ebfbLzrOiQJkQQgghr9z+qwn4dM0FJGTlmZUnZuXh0zUXXmiwXFBWVhYAwNnZ+Zn7CAwMRJs2bbBixQqz8hUrViA8PBw8Hg+ZmZlo1aoVateujaioKOzfvx9JSUno0aOHqb5KpcL48eMRFRWFI0eOgMfjoUuXLqZA+4lJkyZhzJgxuH79OkJDQy3Go1KpEBoaCicnJ0RGRmLz5s04fPgwRo4cafMZJkyYgBMnTmDnzp04ePAgjh8/jgsXLhT53H369IGvry8iIyNx/vx5TJo0CUKhEI0bN8a8efOgUCiQkJCAhIQEfPHFFwCMM+AzZsxAdHQ0duzYgbi4OISHh5v6jI2NRbdu3RAWFobo6GgMGzYMU6ZMMbvvnTt30L59e3Tt2hWXL1/Gxo0bcerUqSKfr8TYK5CVlcUAsKysrFdxe0IIIYQ8gxf1+a3TG1jD7w+zgIm7rf4vcOJu1vD7w0ynN5TpfQvT6/WsY8eOrEmTJlavHzt2jAFgGRkZxfa1ceNG5uTkxPLy8hhjjJ0/f55xHMdiY2MZY4zNmDGDtWvXzqzN/fv3GQB28+ZNq32mpKQwAOzKlSuMMcZiY2MZADZv3rwix7Js2TLm5OTElEqlqWzPnj2Mx+OxxMRExhhj/fv3Zx9++CFjjLGcnBwmEonYpk2bTPXT0tKYVCplY8aMsXkfuVzOIiIirF5bsWIFc3BwKHKcjDEWGRnJALCcnBzGGGMTJ05k1atXN6szZcoUs6/DoEGD2NChQ83q/P3334zH4zG1Wl3sPYtCM8qEEEIIeaXOxaZbzCQXxAAkZOXhXGz6Cx3HiBEjcPXqVWzYsKFU7Z6kT8hkMlSrVg0AEBYWBj6fj+3btwMwLmZr2bIlAgMDARjTFI4dO2ZqJ5PJULlyZQAwpVfExMSgV69eCA4OhkKhMLW9d++e2f3r1atn+ne1atVM/XXo0AEAcP36ddSsWRP29vamek2aNIHBYLBYuPjk/hqNBu+9956pzNnZGZUqVSryfRg/fjwGDx6MNm3aYPbs2WZpIracP38enTt3hr+/P+RyOZo3b272jDdv3kT9+vXN2jRo0MDsdXR0NCIiIszey9DQUBgMBsTGxhY7hqIInqs1IYQQQshzSs6xHSQ/S71nMXLkSOzevRsnT56Er69vqdr+/vvvUKvVAAChUAgAEIlE6NevH1asWIGPPvoI69atw/z5801tlEolOnfujDlz5lj05+XlBQDo3LkzAgICsHz5cnh7e8NgMKB69erQaDRm9QsGwHv37jUt6JNKpaV6juc1bdo09O7dG3v27MG+ffswdepUbNiwAV26dLFa/0lKSGhoKNauXQs3Nzfcu3cPoaGhFs9YFKVSiWHDhmH06NEW1/z9/Z/5eQAKlAkh5I2kVyrBE4vBPf5QJuRN5i6XlGm90mCMYdSoUdi+fTuOHz+OoKCgUvfh4+NjtXzw4MGoXr06Fi1aBJ1Oh48++sh0rU6dOti6dSsCAwMhEFiGY2lpabh58yaWL1+OZs2aATAuWCtOQECARVmVKlUQEREBlUplCqpPnz4NHo9ndZa4XLlyEAqFOHv2rCnQzMjIwK1bt0wzvrZUrFgRFStWxLhx49CrVy+sWLECXbp0gUgkgl6vN6t748YNpKWlYfbs2fDz8wMAiwWQlSpVwt69e83KIiMjzV7XqVMH165dQ/ny5Ysc27Og1AtCCHmD5N+NRVzvPrhVrz5uNWqM5PnzwdjT7bOYVov0tWtxf9hwPPr6a+TduPEKR0tIyTQIcoaXgwScjescAC8HCRoEPfsCO1tGjBiBNWvWYN26dZDL5UhMTERiYqJphhgAEhMTcenSJdy+fRuAcQeJS5cuIT296FSQKlWqoGHDhpg4cSJ69eplNsM7YsQIpKeno1evXoiMjMSdO3dw4MABDBgwAHq9Hk5OTnBxccGyZctw+/ZtHD16FOPHj3+mZ+zTpw8kEgn69++Pq1ev4tixYxg1ahT69u0LDw8Pi/oymQyDBg3ChAkTcPToUVy9etW0CNEWtVqNkSNH4vjx44iPj8fp06cRGRmJKlWqADAucFQqlThy5AhSU1ORm5sLf39/iEQiLFiwAHfv3sWuXbswY8YMs36HDRuGGzduYOLEibh16xY2bdqEiIgIAADHGb9jJk6ciH/++QcjR47EpUuXEBMTg507d9JiPkIIeZcY9HoW064du1apstn/0tdvYAaNhmXt2cNuf9DR7Nr1kJos9/LlVz108pZ4kZ/f+648YoGPF+4VXsgXOHE323flUZnfkzHGYEyBtvjfihUrTHWmTp1abB1b/vjjDwaAnTt3zuLarVu3WJcuXZijoyOTSqWscuXKbOzYscxgMC5aPHToEKtSpQoTi8UsJCSEHT9+nAFg27dvZ4w9Xcx38eLFYsdx+fJl1rJlSyaRSJizszMbMmSIacEcY+aL+RgzLuj75JNPmJ2dHfPw8GA//vgja968uc3FfPn5+ezjjz9mfn5+TCQSMW9vbzZy5EizxXTDhw9nLi4uDACbOnUqY4yxdevWscDAQCYWi1mjRo3Yrl27LJ5p586drHz58kwsFrMWLVqwxYsXMwBmfZ87d461bduWyWQyZm9vz0JCQtisWbOKfV+KwzFWYCriJcnOzoaDgwOysrKgUChe9u0JIeSNlHvhIuJ797Yol9auBfD4UBfY97UgnkIBx65d4TJ0CAROTi94lORt9qI/v/dfTcD0v66ZLezzcpBgaueqaF/dq8zv9zLMmDEDmzdvxuXLl1/1UN4as2bNwpIlS3D//v0Xfi/KUSaEkDcEx7f+Z0/N/QfQFzikoDBDdjbSV6yA6t9/EbR1C7gi/nxKyKvUvroX2lb1xLnYdCTn5MFdbky34PNsJWW8vpRKJeLi4rBw4ULMnDnzVQ/njbZo0SLUr18fLi4uOH36NObOnVs2aRUlQIEyIYS8ISQhIRAGBEAbH29WXlSQXFD+9etQHj8B2fvNwD1ePJQfGwvV6X8gcHWFvFVLcCJRmY+bkNLg8zg0Kufyqofx3EaOHIn169cjLCwMAwcOfNXDeaPFxMRg5syZSE9Ph7+/Pz7//HNMnjz5pdybUi8IIeQ1kHPoENJXrYbmwQOI/P3hMnQIZE2aIGvPHqQuWgztw4ewe68BdBmZyH/eP+GKRLBv0hji4HJI//NP4PHHgKhcOQSsXgXBc5xIRt5u9PlN3jUUKBNCyEuiTU6GLikJ4kqVwHs8c2vIzUX84CHIK3w0LJ8Pj0kTkfT9D6ZAFgDA4wGFjq+1is8HCm3FVBLO4eHwmDSx1O3Iu4E+v8m7hlIvCCGkjGmTkpGxYT209+7Drn49KMLCkPzDbGRu2QLo9eA7OcFr5gzIW7dG+qrVlkEyAOj1SF8RYR4kAyUKkh26doXy5EnoU1JKPfbci0/HoktJAScUgu/oWOp+CCHkbUCBMiGElCFtUhJiu3WDPsWYN5y9Zw/S16yB5vbTo1z1GRl4+PkXcBk8GOkrV9rsS5+ntnmtKHb16oHj85G5aVOp24oDg6BNSMDDCROgjjoP8PlQtG8PrxnfgWdn90zjIYSQNxUtfSaEkDKUsWaNKUh+omCQ/ATLy0PqwoUw5OTY7EvWuEmp7893dIS4fDnkFjq5qiR4cjlchgzGwy8eB8kAoNcje88eJP/yv1L3RwghbzoKlAkhpAxp4uLKpB+Buzu8f5wD19GjwJVwJlcYEAD/FX/i4RcToImNLdX95O1DEbR9G3hyudX9mLP37StVf4QQ8jagQJkQQsqQtE7d5++jQX0EbtkMjseD22efwWvatBK14zs64OHX31hsH1cssRhe06dD5Otr3B7Oyj7LnNh82zh9ZibS16xFyq+/Qh0dXbr7EULIG4ICZUIIKUNOH/cscgaY7+QEx+7dwXNwsHqdE4ng/f0PELq7m8p4MvsS3Tsv+jI0//1XbD1OLDZ77fhRF/Afj0fg5AR527YWbZx69DD9W3P/Pu507oykmTORumgx4np+jNQlS0o0RkLI6yk8PBxhYWFF1mnRogXGjh37UsbzuqBAmRBCyhBPKoW4cmWb110//RROn/SB2xefW525ZRoNUubNMyuTNW1q3O6tLHAcWH6+WVHWzl3QK5XG++t0kNSoAYG7u3HHC2dnuI0ZDZehQ031UxcvscjDTv1tEXRpaWUzRkJeosWLFyMkJAQKhQIKhQKNGjXCPiupRmfOnEGrVq1gb28PhUKB999/H2r1sy24fRdFRETA8Q3cQYd2vSCEkDLCtFrkHDkKoasr8mzUSZo7F9BqAQBCX19oHzywqJMbFYXc8+fBd3aGOCgInEgESbVqyHveg0YAy+3mALDcXKjOnIGibVskTPkaWTt3mq7ps7IgqRECjseDQaMBx3HIszJrzbRa5MfEQODy5p+oRl4xgx6I/wdQJgEyDyCgMcAro18UrfD19cXs2bNRoUIFMMawcuVKfPjhh7h48SKqVasGwBgkt2/fHpMnT8aCBQsgEAgQHR0N3mt4HLxWq4VQKHzVw3hrvH5fYUIIeQMZcnMR17sPHo4di5yDB42FAitzEY+DZADGIJnjLKroUlMR3+cT3O3wAe4P/xQGtRpuI0dYzEALvLzKbPzpq1ZBm5iIrL/+Mr+g1yNtyRI8GDsON2vXwc169WGwNosmFEJcrlyZjYe8o67tAuZVB1Z2ArYOMv7/vOrG8hekc+fO+OCDD1ChQgVUrFgRs2bNgkwmw7///muqM27cOIwePRqTJk1CtWrVUKlSJfTo0QPiQmlMT8TFxYHH4yEqKsqsfN68eQgICIDh8X7oV69eRYcOHSCTyeDh4YG+ffsitcCR9Pv370fTpk3h6OgIFxcXdOrUCXfu3DG7D8dx2LhxI5o3bw6JRIK1a9daHdOVK1fQqlUrSKVSuLi4YOjQoVA+/kuSNSqVCv369YNMJoOXlxd+/vnnYt/L6OhotGzZEnK5HAqFAnXr1kVUVBSOHz+OAQMGICsrCxzHgeM4THu89mL16tWoV68e5HI5PD090bt3byQnJ5v1u2vXLlSoUAESiQQtW7bEypUrwXEcMjMzTXVOnTqFZs2aQSqVws/PD6NHj4ZKpSp2zMWhQJkQQkohc/sOxPXqjdiPuiLtjz/AHp9+l7l5M/KuXDGvrNMV36G1w1ELtFMeP46H4z9H9r794Lu5mcoF7u7wnjPbrIyTSCCqUaN0D/SYOjIKylOnrB5oor52DTn79wN6PVh+PrT37lnUcR0yGIICYyGk1K7tAjb1A7IfmZdnJxjLX2Cw/IRer8eGDRugUqnQqFEjAEBycjLOnj0Ld3d3NG7cGB4eHmjevDlOnTpls5/AwEC0adMGK1asMCtfsWIFwsPDwePxkJmZiVatWqF27dqIiorC/v37kZSUhB4F1gOoVCqMHz8eUVFROHLkCHg8Hrp06WIKtJ+YNGkSxowZg+vXryM0NNRiPCqVCqGhoXByckJkZCQ2b96Mw4cPY+TIkTafYcKECThx4gR27tyJgwcP4vjx47hg7XCkAvr06QNfX19ERkbi/PnzmDRpEoRCIRo3box58+ZBoVAgISEBCQkJ+OKLLwAYZ8BnzJiB6Oho7NixA3FxcQgPDzf1GRsbi27duiEsLAzR0dEYNmwYpkyZYnbfO3fuoH379ujatSsuX76MjRs34tSpU0U+X0nREdaEEFIC+pwcZG7ZiuQ5c8zKnXr3hue33+Dh518ge8+e57oHz9ERhgIzJMXhOzlBn5FhVuY8YABcBg0EGMODseOsbvVmi8DDA0yrhT49vcRtAEBUvjzK7f6r+IrkjffCPr8NeuPMceEg2YQDFN7A2CsvJA3jypUraNSoEfLy8iCTybBu3Tp88MEHAIB///0XjRo1grOzM3766SfUqlULq1atwqJFi3D16lVUqFDBap+bNm3C8OHDkZCQALFYjAsXLqBevXq4e/cuAgMDMXPmTPz99984cOCAqc2DBw/g5+eHmzdvomLFihZ9pqamws3NDVeuXEH16tURFxeHoKAgzJs3D2PGjLH5fMuXL8fEiRNx//592NsbFwfv3bsXnTt3xqNHj+Dh4YHw8HBkZmZix44dUCqVcHFxwZo1a9C9e3cAQHp6Onx9fTF06FDMK7SO4gmFQoEFCxagf//+FtciIiIwduxYs1lga6KiolC/fn3k5ORAJpNh0qRJ2LNnD64UmIj4+uuvMWvWLGRkZMDR0RGDBw8Gn8/H0qVLTXVOnTqF5s2bQ6VSQSKRFHnPotCMMiGEFKD+7z+k/fEnsvcfANNqoU1IwL2BA3GrfgOLIBkAMrdsgfq/azZ3prBr2LDE9y5NkAzAIkgGgIwNG8CTy6FLT4coOLhU/emSkuD++Xjw7J8+i7hKFavpIQVpbt+GodACQUJKJf6fIoJkAGBA9kNjvRegUqVKuHTpEs6ePYtPP/0U/fv3x7Vr1wDANHs7bNgwDBgwALVr18b//vc/VKpUCX/++ScAmNInZDKZKa85LCwMfD4f27dvB2AMFFu2bInAwEAAxjSFY8eOmdrJZDJUfrwQ+El6RUxMDHr16oXg4GAoFApT23uF/qpTr14907+rVatm6q9Dhw4AgOvXr6NmzZqmIBkAmjRpAoPBgJs3b1q8H3fu3IFGo8F7771nKnN2dkalSpWKfB/Hjx+PwYMHo02bNpg9e7ZZmogt58+fR+fOneHv7w+5XI7mzZubPePNmzdRv359szYNGjQwex0dHY2IiAiz9zI0NBQGgwGxpdxTvjBazEcIIY8l//I/pC1bZnotrlwZnFBomVJRANNoENe1q/EFnw88TsUAAPvm78N/6VLkXriIvGvXoL4cjezde6ymNzzBc3CAISvrmZ+BqdVI/G4GsrZuNZVx9vYAjweBk5PxJECOszlrLK1dG+VPnEDu2X/Bd3SEtE4dxPfuA/XFizbvyXdzNe6/TMizUiaVbb1SEolEKF++PACgbt26iIyMxPz587F06VJ4PV4LULVqVbM2VapUMQVzv//+u2kHjCcL6UQiEfr164cVK1bgo48+wrp16zB//vynj6JUonPnzphj5RfwJ/fs3LkzAgICsHz5cnh7e8NgMKB69erQaDRm9QsGwHv37oX28VoIqVT67G/KM5g2bRp69+6NPXv2YN++fZg6dSo2bNiALl26WK3/JCUkNDQUa9euhZubG+7du4fQ0FCLZyyKUqnEsGHDMHr0aItr/v7+z/w8AAXKhBACAMiPjUXa8uXmZTdulK4TvR48uRzy9u1hX78eFI9nc+zq1AbLUyNp5sxiu5C93wxCbx8ojx8HeBzyr5duDOIKFcyCZABgKhW8f/4JDh07AgC0iYm43aKlRVu+szPEj2eh5a1bm8qLmy12HT4cXDGzzoQUSeZRtvWek8FgQP7j7/vAwEB4e3tbzLzeunXLNGPr4+NjtZ/BgwejevXqWLRoEXQ6HT766CPTtTp16mDr1q0IDAyEwMrC37S0NNy8eRPLly9Hs2bNAKDIvOgnAgICLMqqVKmCiIgIqFQqU1B9+vRp8Hg8q7PE5cqVg1AoxNmzZ02BZkZGBm7dumWa8bWlYsWKqFixIsaNG4devXphxYoV6NKlC0QiEfQFJhIA4MaNG0hLS8Ps2bPh5+cHABYLICtVqoS9e/ealUVGRpq9rlOnDq5du2b6ZacsUeoFIYQASPv9d+sL60rJkJOD/JgY8ORycAW2aMo5frzYtpxUCpfBg+E+biyCd+5A0LZt1nfOsEEUFARpw/esXsvctNn0b6GnJ1w+/dT83iIRfH9baLWtJibGarl9s2bwXbQIzn36lHiMhFgV0NiYgwxbv3BxgMLHWK+MTZ48GSdPnkRcXByuXLmCyZMn4/jx4+jz+Pua4zhMmDABv/76K7Zs2YLbt2/jm2++wY0bNzBo0KAi+65SpQoaNmyIiRMnolevXmYzvCNGjEB6ejp69eqFyMhI3LlzBwcOHMCAAQOg1+vh5OQEFxcXLFu2DLdv38bRo0cxfvz4Z3rGPn36QCKRoH///rh69SqOHTuGUaNGoW/fvvDwsPzlQyaTYdCgQZgwYQKOHj2Kq1evmhYh2qJWqzFy5EgcP34c8fHxOH36NCIjI1GlShUAxl84lEoljhw5gtTUVOTm5sLf3x8ikQgLFizA3bt3sWvXLsyYMcOs32HDhuHGjRuYOHEibt26hU2bNiEiIgIATL+gT5w4Ef/88w9GjhyJS5cuISYmBjt37iyTxXwUKBNC3nmGvDxk7z9g9dqzbMGWd+kSHnz6GdLXrnvaj5NTkW3sW7RA4MYNkBSY3eE4Dg4ffljs/WRtWiNw82YE790Dvp31XGltYqLZa/cxoxG4eTNchg+D++TJqPjvGdjVrm1Wh2k0YHo9JI9zLgsSeHjAb8liyFtZzkwTUmo8PtD+SQpC4WD58ev2s1/IQr7k5GT069cPlSpVQuvWrREZGYkDBw6gbYETKseOHYvJkydj3LhxqFmzJo4cOYJDhw6hXAm2RBw0aBA0Gg0GDhxoVu7t7Y3Tp09Dr9ejXbt2qFGjBsaOHQtHR0fweDzweDxs2LAB58+fR/Xq1TFu3DjMnTv3mZ7Rzs4OBw4cQHp6OurXr49u3bqhdevWWLjQ+i/HADB37lw0a9YMnTt3Rps2bdC0aVPUrVvXZn0+n4+0tDT069cPFStWRI8ePdChQwdMnz4dANC4cWMMHz4cPXv2hJubG3788Ue4ubkhIiICmzdvRtWqVTF79mz89NNPZv0GBQVhy5Yt2LZtG0JCQrB48WLTrhdPtucLCQnBiRMncOvWLTRr1gy1a9fGt99+C29v72d6vwqiXS8IIe+8vJu3EGstIOXxELRtK9KWLUf2/v1F5hbb4jF5Epz794c2KQl3OnUGy8mxrCQQoPLFC2B6PTiBAFyBWWSm0+F2m7bQFQp0OakU9k2aQN6yJRy6hIF7PNOjPHUK9wcPsbiFXf36CFi9qkRj1qWkIGHadCiPHQNPKoWsZQtkHzkKPNk/WSCAz9wfTakl1uiVKhiyMiG08Sdp8mZ64Z/f13YB+yeaL+xT+BiD5Kr/V/b3ewlmzJiBzZs343JZHBhEAACzZs3CkiVLcP/+/Rd+L8pRJoS880S+PuDZ2cGQm2tWLq1XD5LKleHzy8/QpqVCffacZWOOAyeVghVq+0TSj3Mh79ABQg8P+Hw/Cw9GWS424aRS3B82HKozZ8Czt4dT795wGzsG+qwsaB88RND2bbjzfx/CkJJiasPUauTfvg2nXh+byjQPHkLg4Qmhtze0j8x3EHDs0d3stS49HUyjgdDT02I8D8aNgzrKuK2cQaVC9u49cB40EAJnFzCtBvLQUIiDgqw+L2MMyXN/QsbatWD5+RAFB8P7h+8hrVnTan1CzFT9P6Byx5d6Mt+LolQqERcXh4ULF2JmCdYnENsWLVqE+vXrw8XFBadPn8bcuXPLJK2iJGhGmRDyzsratQvpK1dBn5kJgaen2Z7DnJ0d/JYsRva+fcjavcf6THAJuY0fB4ewMDyaNBm5/5Rseyu7hg2hPn8eTKsFz94ehiJOmBJXrQq+Qo7cf88CAITBweDL5ciLjgbfxQUugwbBZeAAAMbA99FXU5Bz6BBgMEBauzZ8fpprmvnVPHiAO23aWtxD4OmJCsePIe/GDeTH3IakejWzYNmgViNzy1Zk7dyJvKtXzdryXVxQ/thR8GhnjDcefX6XXHh4ONavX4+wsDCsW7cOfP6bF+y/LsaNG4eNGzciPT0d/v7+6Nu3LyZPnmx1EWRZo0CZEPJOytqzB48+/8KsTBISArs6dcCTyeDYJQzJ/5uH7N27n/tespYtYchTI/fMv8VXLiPSmjXhv3oVOKHQbEeKxO++Q8a69eZ169RB4Drjsbe2AmVwHGStW0N5+LDptXN4ODwmfgmm1yO+bz+oizi1y2/5Msger9wnby76/CbvGlrMRwh5J2WsWWtRlnf5MoS+vnAbOQI8BwdjXnIZMOh1LzVIBgB1dDS0Dx6YgmRDbi5yo6KQtWevZd0LF6BNSgYAiHx9IQywsu8oY0+D5Mev01esQO7Fi1CePFlkkAwAvOc4GYsQQl4VCpQJIe8kg9J6KkXS998j79o1MK0W0Olsd1CKfYP5cjlQxLZKL0p8/3CoTv+Du10+ws06dRH/SV/rh5nw+eBET7eyK2qRXmG5//4LTXx8kXVE5ctBWsRqeUIIeV1RoEwIeSfJ2rSxfsFgwKMpU6COjobQ2oI1kci4t3EpstY0Mbcht3W/wnV51nPuBF5eCNi+DU6ffALh4435nyi4X3NB+pQU3B8+HPnXrxd5T3mrVsZgHsbFeHYFjsMtjtDbG3Z16li9xonFkLdvD//ly027chBCyJuEcpQJIe8c5anTSPppLjQ3bhZdkePK5BASvpsbWH4+DNnZT8ucnKDPyDCrl80T47xnZbR8FG3Rh9ec2XB8vIUd02qRuXUrVKdPQ+DpBafevZC+chUyN2wo8Zg4iQScWAxOIIA+LQ18R0fYN22C3PMXoEtIKFEfwqAgBG/fBp5EgsSZs5CxZo3pmn3jRvBdsoQW8L1l6PObvGsoUCaEvFPy78Yi9sMPjakVL4tAYJHGwXNzgzpbCXG+2lT20N4VD2UuaJBkGcCvaj0I/b4eiipeT/+bqb58GYbcXNjVqQNdejput24DFDoi1pbsjsMQk+IILRPBLTUagfH7wDeU7j3x+/MPyBo/PSkt79o15F68CHFwMOwaNqRjrd9C9PlN3jX0tzBCyDsla9fOlxokc3Z2VnOdDSkpZkEyAPioUuHgZXmcbD5PgD0ifwxdHQWDgUGXmorYrt0Q16Mn7oUPwO3WbaBLTjZtAWdiI90h0aM+olQhyLLzR669J+IDQnGtSnjpHozHgzg42PRSn5MDvosLnPv0gX2jRhQkE0LeChQoE0LeLTYW6Al8fV/I7WwdRGJLXXcJHLp1heFxoJkjlOKnur2QLbbH/XQ1Lj3IRPJPPyPvv/9MbXQpKXj05UQ49vkEPLnsaWc2ThJMatDHoizFNQR54qKP2S6I7+AATiQC02qR8M23uNW4CW43b4HYbt2Rf/duifshhLwewsPDERYWVmSdFi1aYOzYsS9lPK8LCpQJIe8UxQcfWC3Xp6dDUOi4ZeeBA+A+aSIE7u6mMp6jI+ybNQNPJivcRZlQHjwIjsfHLwN/xPhmI/FJ+29xyufpqXYSAR/Kkyct2mni4pC6cCEMOUrLTgvNLOemZFrW4XjQCu2ttOVg16iRcWa8AH1GBpLn/oTUZcuQuXkz8HiWPu/qVTwYNRqvIKuPkOc2e/ZscBxnEQwuW7YMLVq0gEKhAMdxyMzMfCXje5NFRETA0dHxVQ+j1OgIa0LIO0VStSr4jo7QF/qgY7m58Fm+DJrYWGgTEmHfpLFpNweX8HBoHjyELiUZ0mrVwIlESF2yFCnz5j3TGHhyOQxFnPSXuWkTuvzYFiPTzNMXavs7oqq3Anfd3KBPTzdvJBAg5/Ahq/3xHRzMFg66plzGfb/WZnUkeWmQKR9atBUFByP3zBmr/SqPHwffxdmiXHPnDvJv3oSkcmWr7Qgpit6gx4XkC0jJTYGbnRvquNcB/yUcYR0ZGYmlS5ciJCTE4lpubi7at2+P9u3bY/LkyS98LM9Dq9VCaGMnHFJ6NKNMCHnnSEJqWJRxUinEFSvCsVs3uI0aabHlmcjXB5IqVZC5YwcSZ8wEZ29vNtNcGkyTX2ydCtPG4CsvFXx1OZBpctHq/nlMu7QeuowMKDpazorzpFIYMq3skQxY7K4RFLcHLmlPj5mWO4sR8nALOFjOAmtu37E5RoGrCziR2Po9s7OhOnPG4hcSQopyOP4wQreGYuCBgZj490QMPDAQoVtDcTj+cPGNn4NSqUSfPn2wfPlyODlZpiCNHTsWkyZNQsOGDUvUX1xcHHg8HqKioszK582bh4CAABgep0VdvXoVHTp0gEwmg4eHB/r27YvU1FRT/f3796Np06ZwdHSEi4sLOnXqhDt37pjdh+M4bNy4Ec2bN4dEIsHatZaHKQHAlStX0KpVK0ilUri4uGDo0KFQKq38BeoxlUqFfv36QSaTwcvLCz///HOxzx0dHY2WLVtCLpdDoVCgbt26iIqKwvHjxzFgwABkZWWB4zhwHIdp06YBAFavXo169epBLpfD09MTvXv3RnJyslm/u3btQoUKFSCRSNCyZUusXLnSYmb/1KlTaNasGaRSKfz8/DB69GioVKpix1wcCpQJIe8ct1GjLFIJXIcPN+0lbI0hLw/xfT5B4rdTkbF2LZJnzYLA0wN8F5dS35/la4qtY8jNRbPFU7F893Rs3vstJpxfD9HJI0iaMRNaK9u3FTVDXZhAn4+aVxajve8VdJ9cD5/MbAxXeRGHq9jg1K8/hB6Wvyzw7O1xL3wA7g0YiJjmLZBu44ObkIIOxx/G+OPjkZSbZFaenJuM8cfHv9BgecSIEejYsSPalHC/8+IEBgaiTZs2WLFihVn5ihUrEB4eDh6Ph8zMTLRq1Qq1a9dGVFQU9u/fj6SkJPTo0cNUX6VSYfz48YiKisKRI0fA4/HQpUsXU6D9xKRJkzBmzBhcv34doaGhFuNRqVQIDQ2Fk5MTIiMjsXnzZhw+fBgjR460+QwTJkzAiRMnsHPnThw8eBDHjx/HhWJO4OzTpw98fX0RGRmJ8+fPY9KkSRAKhWjcuDHmzZsHhUKBhIQEJCQk4IsvvgBgnAGfMWMGoqOjsWPHDsTFxSE8PNzUZ2xsLLp164awsDBER0dj2LBhmDJlitl979y5g/bt26Nr1664fPkyNm7ciFOnThX5fCVF28MRQt5JmgcPkLVtG/TZOZC3bQv79xoUWT9j82YkfvPtSxpdEYRCyJo1g/Lo0efqhhOJELR9G8TlygEAsvfvx8Ox40rclmk04Ds7w6BSgeUXM0POcQjeuwdiawe4kDfKi/r81hv0CN0aahEkP8GBg4edB/Z33V/maRgbNmzArFmzEBkZCYlEghYtWqBWrVqYZyW16vjx42jZsiUyMjKKzbfdtGkThg8fjoSEBIjFYly4cAH16tXD3bt3ERgYiJkzZ+Lvv//GgQMHTG0ePHgAPz8/3Lx5ExUrVrToMzU1FW5ubrhy5QqqV6+OuLg4BAUFYd68eRgzZozNsSxfvhwTJ07E/fv3YW9vXIuwd+9edO7cGY8ePYKHhwfCw8ORmZmJHTt2QKlUwsXFBWvWrEH37t0BAOnp6fD19cXQoUOtvjcAoFAosGDBAvTv39/iWkREBMaOHVtsfndUVBTq16+PnJwcyGQyTJo0CXv27MGVK1dMdb7++mvMmjXL9HUYPHgw+Hw+li5daqpz6tQpNG/eHCqVChKJpMh7FoVmlAkh7ySRry/cRo+G59dTig2SASD/VozVcr6jg+1GHFfmi/54YjHsmzW1eq+SklSvDrdRo5CxcSPS/vjDmM7Rvj3cv/oK4BcfhDCNcUZcn55efJAMAIxBZWUBIiFPXEi+YDNIBgAGhsTcRFxILnpGs7Tu37+PMWPGYO3atc8VTD1Jn5DJZKhWrRoAICwsDHw+H9u3bwdgDBRbtmyJwMBAAMY0hWPHjpnayWQyVH6c1/8kvSImJga9evVCcHAwFAqFqe29e/fM7l+vwGma1apVM/XX4fFx9NevX0fNmjVNQTIANGnSBAaDATdvWu7bfufOHWg0Grz33numMmdnZ1SqVKnI92H8+PEYPHgw2rRpg9mzZ5ulidhy/vx5dO7cGf7+/pDL5WjevLnZM968eRP169c3a9Oggfl/s6OjoxEREWH2XoaGhsJgMCA2NrbYMRSFFvMRQkgR1NHRyD5wEFm7d1u9Lm3YEMpDh60e9MF3dkb5o0eQuXUrkn6YbdoZ4nk49uwBRYcOyFi9BprH27BxQiEkITWgPl9MECEUwvPbb5F//TqSC+QbpkesROCG9XDp1xeKdm2RHhGB9IiVzz3WgvgurmXaH3m7pOSmlGm9kjp//jySk5NRp8CaBL1ej5MnT2LhwoXIz88HvwS/PP7+++9Qq437oj9ZSCcSidCvXz+sWLECH330EdatW4f58+eb2iiVSnTu3Blz5syx6M/LywsA0LlzZwQEBGD58uXw9vaGwWBA9erVodGYp28VDID37t0L7eP/1kil0pK+FWVi2rRp6N27N/bs2YN9+/Zh6tSp2LBhA7p06WK1/pOUkNDQUKxduxZubm64d+8eQkNDLZ6xKEqlEsOGDcPo0aMtrvn7+z/z8wDPECir1WqcP38ezs7OqFq1qtm1vLw8bNq0Cf369XuuQRFCyKvC9Hpkbt6CnEMHkXfzFvQFFtZYo9x/wHgcdXa2RbBsyMkBdDo49+4NSeXKSF20GJq4OAh9faG+cgWs0EITWcuW0OfkQF1oARAAKIVSpPX7FJXHDUB8v/6mIBkAGGOQtWpdfKCs1SL3fBSyd+4yK9alpCB+wEAIXFyguXcPkipVTOkVJcHZ2Zn2ixb6+0OXmmq2f7TQxwfCwAAYNJpnPtI6//Zt46l/gYGwKzS7RN58bnZuZVqvpFq3bm32J30AGDBgACpXroyJEyeWKEgGAJ9CW0s+MXjwYFSvXh2LFi2CTqfDRx99ZLpWp04dbN26FYGBgRAILMOxtLQ03Lx5E8uXL0ezZs0AGNMJihMQEGBRVqVKFUREREClUpmC6tOnT4PH41mdJS5XrhyEQiHOnj1rCjQzMjJw69Yt04yvLRUrVkTFihUxbtw49OrVCytWrECXLl0gEomgL/TfyBs3biAtLQ2zZ8+Gn58fAFgsgKxUqRL27t1rVhYZGWn2uk6dOrh27RrKly9f5NieRalSL27duoUqVarg/fffR40aNdC8eXMkFFhUkpWVhQEDBhTRAyGEvN4Sp01H4rRpUJ3+p9gg+Ql9RobVGWWm0UDz0Ljlml2dOvD/fTncx49D7r//WgTJ9o0bw3vObASuWQ3F/3W26GtHcFOMSPfG1j6fQV14QY1OB82tm3Du36/Y1AnVqdOAlaUp2nv3oL54Efq0NKhOnSrV6YXes2cjcOMGBG7cgHIH9iNw/TooPugAceXKkNSoDm1aGuK7dcftFi2RXegDrySSf/4Fdzt1RuI33yK+bz/cGzykxEH86yj7wEHEf9IXdz8MQ8qvC2AoSfrKW66Oex142HmAg/UUIg4cPO08Uce9jtXrz0oul6N69epm/7O3t4eLiwuqV69uqpeYmIhLly7h9u3bAIw7SFy6dAnphbdpLKRKlSpo2LAhJk6ciF69epnN8I4YMQLp6eno1asXIiMjcefOHRw4cAADBgyAXq+Hk5MTXFxcsGzZMty+fRtHjx7F+PHjn+k5+/TpA4lEgv79++Pq1as4duwYRo0ahb59+8LDw/I0UJlMhkGDBmHChAk4evQorl69alqEaItarcbIkSNx/PhxxMfH4/Tp04iMjESVKlUAGBc4KpVKHDlyBKmpqcjNzYW/vz9EIhEWLFiAu3fvYteuXZgxY4ZZv8OGDcONGzcwceJE3Lp1C5s2bUJERAQAmE4AnThxIv755x+MHDkSly5dQkxMDHbu3Fkmi/lKFShPnDgR1atXR3JyMm7evAm5XI4mTZpY5MoQQsibSJuUjMxt28qsP55CAZG/P/RZWVBfugR9djZSl/9uWVEohM+8/4H/eHGU1/Tp4HX/GBliGZKljlhduR3WV24LPQM2Si0X+ADGWWHnQYOg6PgBhD4+gJUZKgAQeHnZPNraTAnWeXN2dvCYMgWKdm0hrVkT0po1wXEcJJUqweeXX+A+fhzyrlwF8vIAGHOaH3450equHbbk3biBtOXLzcpUp04hc/uOEvfxOsnetw8Px4xBblQU8m/eROqiRXg0cVKJ2uaeP4/s/QeQE5+AxEtx0KnfngCbz+NjUgPj+1A4WH7yemKDiS9lP2VrlixZgtq1a2PIkCEAgPfffx+1a9fGrl27imkJDBo0CBqNBgMHDjQr9/b2xunTp6HX69GuXTvUqFEDY8eOhaOjI3g8Hng8HjZs2IDz58+jevXqGDduHObOnftM47ezs8OBAweQnp6O+vXro1u3bmjdujUWLlxos83cuXPRrFkzdO7cGW3atEHTpk1Rt25dm/X5fD7S0tLQr18/VKxYET169ECHDh0wffp0AEDjxo0xfPhw9OzZE25ubvjxxx/h5uaGiIgIbN68GVWrVsXs2bPx008/mfUbFBSELVu2YNu2bQgJCcHixYtNu16IxcbtKUNCQnDixAncunULzZo1Q+3atfHtt9/C29v7md6vgkq164WHhwcOHz6MGjWMe5AyxvDZZ59h7969OHbsGOzt7eHt7W0xtV4Y7XpBCHkdqa9cQVz3HsVXLCFprZqwf/99pC5abDw6WyAAJxSCPc5lLKjc/n0QPV6oAwBn7qRh9Py96BFzDB6qdBzyr4/bjr7QcxwiDv1gMe/m9sUXyNqyBZq4uCLH5DlzBrT37iNt2bLneziBAK5jRsN10CAYVCrkXb8Okb8/hJ6epiqPvv4aWVu2WjT1+PprOH9ieYx2YYwx3B8xEiorO3woOnWCz0/PFjQ8D21SMpTHjoInlULepg149lZOMyxCbM+eyIu+bF7IcSh/9AiEj/NSC9Mrlbg/dBjUFy7gdnAY7vu2AOMJIdIp0bAeDzWGd3rWxym1F/35fTj+MGafm222sM/TzhMTG0xEm4Cy2brtZZsxYwY2b96My5cvF1+ZlMisWbOwZMkS3L9//4Xfq1Q5ymq12iyPhuM4LF68GCNHjkTz5s2xbt26Mh8gIaRsqP/7D/nXr0NcpQqkj1dlE3PiSpXAd3a2PPWuAL6nB1iuGobs7GL7U1+KhvpS9NMCnQ5MZ7lfMd/NDcICeYW6jAx4fT8Rq06fxlWXIExuPAw6/tP/9i6t8X8YfuXpTBbf1cWYX1xMkAwY00Hcx48D39kJybMtFxGVmE6H1J9/ger4CeRdu2YM/vl8OH38MTy+ngKO48C3EUTyZEUHl0yrRfJPPyNj0yarv1QAgOg5F+iY7sUY1FFR0KWlw75RQ/AdbO9iknPkCB6OHWdKS+G7uiIgYgXEj/Mi8+/GIjcqEiI/P9g1bGj6s3BB+oxMa4OA6t9/4WhjwVPa8t+hvnABSe51cc+/ralcI5Dh1HkNnBavgL2zPTR37iAvJga6pCTY1a0D15EjIbTyZ/XXWZuANmjp1/KVnMxX1pRKJeLi4rBw4ULMnDnzVQ/njbZo0SLUr18fLi4uOH36NObOnVsmaRUlUapAuXLlyoiKijLlmzzxZOr+//7v/8puZISQMsEYQ8JXU5D1eIsiAHDo0gVe38+y+kH+LuOJRPCaNRMPP//CtBiN56AAz94euqRkQK+HISMTriNHQBoSgpQFC6COOl/6G3GcWWqDPjMTmjt3TAFX4rdToT59GgCwvmIbsyAZAHYHNUHPW0fhlG88VUufmoasXTtLdGuBs/HIaZfwcGjvP0DGunUlSrMAn288/a/QSV7q8wWeX69Hxtq1kFSvBscuXeDYrRsy1m8wyyfmu7lC0bYtipLy6wKkr7S96wYnFsPx457Fj7kY+sxM3BsyFHmPF3NxEgm858yBIrSdRV3Nw4dI+HaqWe62PjUVyT/9DL8li5Hy229IXfib6b20q1cPfsuXgSeVIvfiRWSsWw99ViaE3l7QWklXTF223CJQ1uoN+N+hW6iy9QDKA0hxrWXRzsAX4daGE/BKOms+3rt3oTp3DuX++gvcMy6gfFX4PD7qe775CzZHjhyJ9evXIywszCLtgpROTEwMZs6cifT0dPj7++Pzzz9/aUeJlyr14ocffsDff/9tsfrwic8++wxLliyxODGmMEq9IOTlUZ48iftDh1mU+y1bCtn777+CEb3+9NnZUP1zBnwHBRjH4X645SLlwC1bkBsV+XyzsgXYN28O/6VLkP/gIe4WOB1sYJtJSJBZbq027/h8VMp8+mdHvrs79CkpRQa9Qn9/BO/+y2znCU1cHPJiYiDy90fsR12tLkosLaGPD8ofMZ6ipjp3DqkLf0N+7F3Y1aoNt3HjIA4u+uCRW02bFbmQ0rHXx/CaOvWZxqa+chWae/EQV6iArK1bkb5yldl1nkKBCieOg/d40ZUuIwOPPv8Cqn/+sdofz8EBgRvW4+4HHS3ee/eJEyGpXAn3hgw1pt4Uo9zRIxAVyKmctecalv8di6/OrUKzR5dxvWJvJHg3sWhX4+pSuKVa/7O+z6/zoWhnGfg/K/r8Ju+aUs0oT548ucgIftGiRVi0aNFzD4oQUnZyC22jYyo/d44CZRv4CgUU7Y3HwCbN+dFqnZxjR6FLSLTdCccZ85FLuDuD6sQJPPxyosWJe9XTYi0CZZkmF4HZ5gviOD4f9k2aQGVt+yiOg6x1a3h+87XF9myiwEAIAwLAcRwUH3yA7L/+KtF4i6JNSIAhP994OEqDBrBfVfyBLmaKCPZ5CgVcBg0u/ZgSE3FvyFBoYp4eHGNtptWQnY3UZcuhPn8eLD8feqUSmiIOTRAHBhp3IbEy5tyoKChPnihRkAwAjyZMgKxlS+iSUyCuXh0bzhvHt6VCC7yXeA0+j/5GomdDsAJpCBJ1ClzS/rPZZ86Bg2UaKBPyrin1yXxxcXFYvnw5fvvtN1y9evVFjIkQUoaEvn6lKifm+E5OVssz1q0Hz876Zv5O/fsjeN9eq/nIRcnetcsitaHv9f3wVj49ZEEIA0ZGb4XYYN63tGYIPL6abH23C8bAcnMt8lW1CQm4/+lnuFG9Bm41awZxcDCcevcCz8EBnJ1d8QN+vOLcgsGAzK1bUdwfLPNjY/FoyhTE9fwYST/Mhu7xLLKDlTQ+nkIBeYcOUHTqhIw1a5B74WLx4ysgceZMsyAZgM1fYtIWL0buuXNQR0cXGSRDKITrqFEQ+ln/WRL5+0P3qOQ7fKjPX0DKTz8jY9UqPPpyIvLUeQBj0HN8zK7XB+ec3OF8dyMcM25BnJcBj6RzqH3pV/CY7b8CqE6fAivmr7yEENtKlXpx7NgxdOrUyXT6jEAgwJ9//olPPvmkVDelP90Q8vIYVCrc7fKRWV6kKDAQQVu3lHrF/rtIm5yM2M7/B31WluVFqRSS4GDk/fd0Rk/xwQfw+cV46t39YcOhPHHiuceg43i4HPoxtJVroEV5F6hHDjWvwHEI3rMb4uBgZP31Fx5N+NKiD76DAyqe/des7G5YF+TfuGFW5jVrJhy7dkXq4sVImf9rkeNy7N0bhpwcm7PQjr17w+vbb6xe0yYkIDasi9n7KgoIQNCunQDHIWnW98jasQNMq4WsVUs49eyJB2PGmh1k4jF5Epz79y9yjE9cr16jRDO7BQ9PsV2Jg+PHH8OpZw9IHh85HD9gAHLPPH1/+U5OCNy0EYnTv7M+y18Cf1T9AKHx5+CrMv4Cccm1PJLsHBF6z/JAmqJUunAevJL84lMC9PlN3jWlCpSbNm0KV1dXLF68GBKJBF9//TW2b9+OR48eleqm9INGyMthyMtDwpSvkb1vH2AwgKdQQPHBBxC4uyPv8mUIfXzg3K8vRFZOciJP5d++jXtDh0Fn5b91njNngG9vj9wLF5F/6xY0Dx9CUq4cXEeOAE8mw93O/1fiP72XhF29erBr2hRpS5eCqdXgyeXwmPglHLt1AwAY8vMR835zGAoF9uKKFSGuVAm6pCTYN20Kad26uNfHcos2gZcX/JYsRt5/15Dw1VdFjkXg4YFy+/ch6fsfkLlli2X6Aceh/PFjVndeSPn1V+O2eYV4//QTJNWqIvnnn6E+fwFCf3+4jR6F9D/+QFJ0PNKcq0KcnwW31IsQSsWocPKEcbFlRgaS58xBzpGj4NnbwWXIUDj36W3qN6Z5C+iSkizuBz4f9o0bgWl1EAX4G/dnLiZdRuDlhQrHzFNkDPn5yNy0GbmRkRD6+cK5Tx8kzZ6DnIMHi+yrKHpw4MP8PTWAAw8l/tiG0NcX5Q8feuYxFEaf3+RdU6pA2dHREf/884/p6Orc3FwoFAokJSXBxcWlxDelHzRCXgym1SJ91WrkHDoELZ8HdX4ehFevmdXh7O3NToXjyeUI2rKZguVi2NoTmO/kBPcvJyDlf/OgS042lXNSKdwnfIGk72ZYtHlevgsXwO6996B9+BCigADTwrMnMjZsROL06UXm+kpr1TTfuq4QtzFjkLF+vdkzFcZ3dUXQ1q0QerjjZr36FmkjAOA6ciTcRo6ALjUV2QcOAAAU7dohef58q++n27hxyFi71vy+QiESfJrgemB349hzk+Dz6G94P/oHwSuWwv6993C3y0fIv37drK+4/+uDyl+OQ5CrPdJXrkTSD7NtPgsnkYA9PhilOJKQEARt2lhkHdW/Z3EvPLzIOqIKFSzSQYrF55d8wSXHwX/VStiX4ZHf9PlN3jWlylHOzs6Gq+vTRSV2dnaQSqXIsvYnSULIS5fwzbdInjsX6kuXoDt/wSJIBmBxdLIhJwfJ/5uH9DVrofrnn2LzSt9Vjl27Wj3RTp+RgYSvplgElEythurvkv/JnefkaFEmrWP9uN7827fBl8shqVzZIkgGAKePe0JcqVKR91NfijZuU2dD6uLF8P75J+MYhEKrdfSpqbjdujUy1q+H0MYJWLr0dKjOncPtdqFImjETSTNm4na7UAg9PK3WB49nGZxrtdBrjMGhe/J5vHduJvwfHIPAkI8Hn41A1l9/WQTJAOCxZzNa/3wc2y48gHP//vCcNQtCX1+rX8eSBskAkPfff0ia8yMMRaRo5FkZzxN8N1e4f/klyv21Cy7DhxV77LhZW2frOfPWuI4fX6ZBMiHvolIv5jtw4AB27dpl+p/BYMCRI0fMygghL58uJQVZz/jzl7N/P5JmzsS9gYNwf9AgGEq4U8O7xK52beMx025ulhdt/HLBNJoig9GCOM7yP8d5t25ZrZt9+Ag0Dx6aXqvOReLewEGIadkKD8d/Ds29e9DExhZ/0yJ+KWIaDe4PGWrc0aHA3sEWdDokzpwFWTvreyPb1QxB0qzvzfJ+mVqN7AMH4Nijx9P3RyiE2+fj8c+DHKv98PUacAY9KtzeCh6eLk4zqFRI+c36bktSvQZuyjTM3HMdeRothJ4ecP98PHgyme3neUzg5ga/pUvASSSWF/V6pK9YgUeTbO8CJalS2Wq5+8SJqPj333AZaNxy0H3sWJQ/dBC+ixbBf/UqiAudU1CYy+CS7fjBicVw7t6tRHUJAYDw8HCEhYUVWadFixYYO3bsSxnP66JU28MBQH8rCyeGDXu6RyvHccUeYU0IKXu69HSgDFa3q/45g6wdO+DUo+yOcn5bKNq1g/bhIyTPKdneybkXL1oEo+KqVZF/86bFn8+tnQbIlErwZDKLlIb8q1dxf+hQ+C5ehITJXxmD2ceyExKgPH3auCNFfn5JH82qEs+y6vVIW/gbRBXKQxNz21TMk8mQ+ucKaG7etGiiuX0bQZs3wWXQQOTfvQtJtWo4vPs0vLf8ZvUWye61IdJkQ6yx/AumtogTCaeeXYGvGw1G7IdhQOzdxwMrfo5I6O8PWfPmcB021OaixpxDh6BNSrKag23fsCFkbVpDefiIqUxcqRIcu3e3vJe3t2lGPnj7Nhjy86FPT0fGxk1QHjsGfUYGBO7ucOrVC45dP0LOvv1QX7pkOaDHB9nwXVzg/f0s8B0di31OYm727NmYPHkyxowZg3nz5pldO3PmDKZMmYKzZ8+Cz+ejVq1aOHDgAKRW/qpDLEVERGDs2LHIzMx81UMplVIFysUdJEIIeXXE5ctD4O1V4u2o/vGshgZJ1yFg5j/XDEDmlq0UKNug+KADUubPtx5EPs4f5UQiyNu0RvbefRZV8m/dgu/CBXjw6WfF3ouzs7Oa9wsYT167N2Cg1QWGhRfyvSya23fgNWc2cvbth/L4cRiUSqtBMmA8lISTSCAKCIAoIABMo4Hrwtmw15sH9zqOB9Z3EDwCQpF+4j60fCmEeuvHWlsTlJ2ILy9tApLuPi0swWeZy+BBAADXTz+FtHZtPPhshGWqBWMwFEplKsh3/nzkHD0K9cVLEAcHQdGxo9VUmcJ4YjF4Xl5wHzsG7mPHWPa7ZDFiPwwzW5xoV78+fOb9D/qMDIgCA8FZ2ybwDcH0euRGnYcuJQUCNzfY1asLrhTpKc8qMjISS5cuRUhIiMW1M2fOoH379pg8eTIWLFgAgUCA6Oho8ErwS9fLptVqIbSRLkVKr0y/wgaDAbt37y7LLgkhJcTx+fh/9s47Ooqyi8PPbE+ym947oYTeexGpwYKIIFWQpqAoVZr6CYgiNlREBEFp0sQCqIAiGqRIl95DQkJ6TzZt23x/JCzZ7CYEAes85+Sc7Ntntsxv7tz33qC33nL8qLgCRo0zX9buYieSAQRAf+Y0R/r1Jb9C8ou/E5aiIlJencfFVq251KYtqW++Ve3kHneC0s+PkKVLUdWubV9pNuM+aBC19kTj4SCiBAAmE+aiompZNT1HVB36zJFIdoQiOJigjxxbav8IQmXxk0URTGZKrlxxXG8dQMBjxAibFOrFFy+iLcyza5rm5I5hyAjuHxJJ1LgmCLrb30BWX+84MYwiKMhhufuAx9F16WJ97dK2Le4O3BhUNWuijoiodF5BLse1Rw/8pk/DvX//aonk6qBwd6dW9C8Ef7wE7+eeI3jxh4SuWonCywt1rVr/aJGc9+OPXOnWnfgnnyTphReIf/JJrnTrTt4dRA+pDnq9nqFDh7J8+XI8HMROnzx5MhMmTGDmzJk0aNCAyMhIBgwYgLqS70JcXBwymYyjR21D+b3//vuEhYVZDY9nzpzhgQceQKvV4ufnx7Bhw8gol5Vy586ddOzYEXd3d7y8vHj44YeJKRfbOy4uDkEQ2LRpE507d0aj0bBu3TqHazp9+jRdu3bFyckJLy8vnn76afSV3IgDFBQUMHz4cLRaLQEBAbz77ruVn8AyTp48SZcuXdDpdLi6utKiRQuOHj1KdHQ0I0eOJDc3F0EQEASBOXPmALB27VpatmyJTqfD39+fIUOGkFZhn8K2bduoXbs2Go2GLl26sHr1agRBsLFO79u3j06dOuHk5ERISAgTJkygoIob2epyV4TylStXePHFFwkODqZvhVz1EhISt0fBwUNkrVtH0ZnKs21VhqW4pFpiMR85CTpfSmSOL6gKi8ip4jxiJ0yg8Nix217Hn0HKvNfIXr8eS34+5txcslauJO3dhX/K3C5t2+A7cYLDOkNMDAoPDzQOrFI3sGRnO8yKqKoZgdeY0XgMGULomtX4TpjgMHsclKZOrjZGI7quXVCGhlavvUrlcIOZ3Nsb/1fnUuOrL1HVruWwqzI4GHN2tsM64YZQFEXS3nyT3G9vGlYUPj6IDvy08928aB7qgVhSgr/hKoqinOodQxmiXI6uVrjDOlNiosNyXbdudmU+Eyag7dbN6lOtiogg6L0/5/PmCEEQ0HXpgs9z49F17/6nWFzvNXk//kjixEmYUmxvbEypqSROnHRPxfL48eN56KGH6F4uffwN0tLSOHToEL6+vrRv3x4/Pz86d+7MviriY4eHh9O9e3dWrlxpU75y5UpGjBiBTCYjJyeHrl270qxZM44ePcrOnTtJTU1lQLmneQUFBUyZMoWjR4+ye/duZDIZffv2tXvCP3PmTCZOnMj58+eJioqyW09BQQFRUVF4eHhw5MgRNm/ezE8//cRzzz1X6TFMmzaNPXv2sHXrVn788Ueio6M5Xs7NyxFDhw4lODiYI0eOcOzYMWbOnIlSqaR9+/a8//77uLq6kpycTHJyMi+88AJQagGfN28eJ0+eZMuWLcTFxTGiXMSY2NhY+vfvz6OPPsrJkycZO3YsL730ks28MTEx9OrVi379+nHq1Ck2bdrEvn37qjy+6vKHbzuLiorYvHkzK1asYP/+/XTq1IlXXnlFEsoSEn8Q0WgkYfx4Cn7day1zHziQgLlzqtdfFEmZ92q1HivvCGuLXuXM2nq9GHPW/imQSSagd1KTrnXCa9MmnFu0qPZx/BlYiosdJrnI+fJL/GbN/FPWoPAPcFguODuT9+OPGOITKu1rysik4PBhu3JDzFUUTwxDU7sWqtq1Sf9wMYJabX/zIwgELVpEyiv/w3gt3m4cu7X6+SEIAoFvLiDh2fFYKhGyNxdSOp+gUoFKhUvLlng9+wxODRsilFnCQz76iNj+j2PJu2kFdm7ZEufWrdDefz95339vM6TM1dWmLSYTqa+9hq5Hd2QaDUp/f9z6PELeli3WJhZBoMGU8RQeOEDi1Kl/yKXE/+WXKDxylOKTp6rVXl23Lk4OIkXIXFwI+WhxaXpuvR61oycKEn8Y0Wwmdf4bjjeYimJpEpr5b6Dr1u2u3xRs3LiR48ePc+TIEYf1V6+Wuu3MmTOHd955h6ZNm7JmzRq6devGmTNnqF3JZ2HMmDGMGzeOhQsXolarOX78OKdPn2br1q0ALF68mGbNmjF//nxrn88++4yQkBAuXbpEnTp16Nevn82Yn332GT4+Ppw7d46GDRtayydNmsRjjz1W6TGuX7+e4uJi1qxZg0tZoqnFixfTu3dv3nzzTfwq+Nnr9Xo+/fRTPv/8c7qV3TiuXr2a4ODgSucAiI+PZ9q0adQtS8RT/ty4ubkhCAL+/rYRb0aNGmX9PyIigkWLFtGqVSv0ej1arZZly5YRGRnJ22+/DUBkZCRnzpzh9ddft/Z74403GDp0qHWjYe3atVm0aBGdO3e25v74o9y2RfnIkSOMHTsWf39/3n//ffr06YMgCCxZsoRx48bZnWwJCYnqkfvtdzYiGSBn0yYKb3EHfwNzZmaloinZ2ZNclTMZGlc+j+zB+rqlEQq+qn0/n0d2p6K0vuzniUkuQ2YRsRTcIkvZX4HFguhg07BoNP5p4e2cGjbApUMH20JBoGDPHhInTCS9iseUmR9/XGn2t9S5c7n2xDCu3NeZjI8+wpJvHwXCa8xotG1aE/LxUpxbtiydWqvFqVkzh2N6jS69EDk3a0bQwndxau64XUUrsmgwoK5Rg5ClH+PcuLFVJENpeuYam7/AY8gQXO7rhO8LUwn5ZBmCIOA3ayaaJjct6sqwUGQOHlGbc3MxXL3pOxz42jz8XpyFU+vWOHXrTviqVdSM6kLipEmVi2RBQBlaeTp2bbt21cqMJ5RZ6EsuXCCmazcKKxFNyoAASSTfAwqPHrOzJNsgiphSUig8enefcCUkJDBx4kTWrVtXqZi6Yb0dO3YsI0eOpFmzZrz33ntERkby2WefAVjdJ7RaLQ0aNADg0UcfRS6X88033wClm9m6dOlCeHg4UOqm8Msvv1j7abVaq8C84V5x+fJlBg8eTEREBK6urta+8fG2v/Uty34HABo0aGAd74EHHgDg/PnzNGnSxCqSATp06IDFYuGig30EMTExGAwG2rRpYy3z9PQk8hZhJ6dMmcKYMWPo3r07CxYssHETqYxjx47Ru3dvQkND0el0dO7c2eYYL168SKsKN6+tW7e2eX3y5ElWrVplcy6joqKwWCzEVicCUBXclkW5cePG5OXlMWTIEA4cOGD9MMyc+edYcCQk/s0U/e5YEBceO4ZzJfF0yyN3c0Pu6WkXPaHI1ZOn7p+BWWYrghQyAV1hLo/F7CXXSU26qzMKi4VUVxeytU5oDCZ88gtxjer5xw/qHiFzdkbbtYtNRAEA14cesvF7vdcEL/6QrDVrKdi/H0txMcWnylkt71Cwiw4iVsi9vfH/3/+s74k6ogZhn6/FUlCAoFaj37evdJNgubmd27XFNSoKY2oqCeOecRhvuKo1F58+jSkjA0W5GPo3UIWF4V8uRbWluJiM5SsoPHoEp4aN8BwxElVwEJoGDUgYMwZTerpNf0GlskZ7MKamkb12DSVXYnBp2wbPJ55A7upK7o6dlW5oBFD4+mKsxHov9/RE4e9fZbxj66GXE+LmnBySZsyk5k+7bG4OJO4dFT8bd9quuhw7doy0tDSal/uNNZvN/PrrryxevJiSkhICAkqfHt1ItnaDevXqWcXcihUrKCoq3WR6YyOdSqVi+PDhrFy5kscee4z169fzwQcfWPvr9XqrRbciN+bs3bs3YWFhLF++nMDAQCwWCw0bNsRQ4SlTeQG8fft2jGUhHf/siBxz5sxhyJAhfP/99+zYsYPZs2ezcePGSr0NbriEREVFsW7dOnx8fIiPjycqKsruGKtCr9czduxYJkywd4kLra7LWSXcllC+ePEiAwcOpEuXLnYfGAkJiTujssx4t8qYV3zuHDnfbCkV2hUv6nI5wsQXMJ+2f1T52chWmDd8jrOpBGcTFKkVXPHzpFClwCevkPoZefiOHoXbI4/84WO6lwTMm0ey2YI+OhpkMnQ9euD3UtUpl+82MicnvMc+jffYp7k+YaKtUL4HWPR6hzcusrKLZNpbb9uJXUtBISVXrxL3+IAqIzRUhqBWI3N2puDgIdLeW0jJxUtoGjbA74UXcGra1Nqu+OJF4oY+gVgmaguA7A0bCHr/PZwaNcJ7/HgKjx23uQHwHDUSubs7pqws4gYMsEZx0EdHk//jLmp8sYni01WfU4dpqW+gUFB44gTKgACMCZW7wiCT2bksGZOSMMTESNbjPwmFo/jkd9CuunTr1o3Tp0/blI0cOZK6desyY8YM5HI54eHhBAYG2lleL126ZLXYBlWyMXTMmDE0bNiQJUuWYDKZbNwjmjdvzldffUV4eDgKBxswMzMzuXjxIsuXL6dTp04AVfpF3yDMwTWjXr16rFq1ioKCAquo3r9/PzKZzKGVuGbNmiiVSg4dOmQVmtnZ2Vy6dMlq8a2MOnXqUKdOHSZPnszgwYNZuXIlffv2RaVS2YUPvnDhApmZmSxYsICQkNInQxU3QEZGRrJ9+3absopuMs2bN+fcuXPUquV478SdcFtC+erVq6xatYpnnnmGoqIiBg8ezNChQ/9UC46ExL8V9/79yd64CeP169YyTZPG6Lp2rbRP8iuzyfniC7tyubc3rj174ta3L06NGjLjx3Nc/nQt9dJjSNd6EzByOPfV9iHdW82N/dWBOQUE5pQKKbfH++M3bRryv3GKWoWHByEfL8GcmwsyGXKd7q9dj79jtzN13bqUxMeX+v1aLHcU61osKaHw6FGru0V5zDk5Nm4MNyg+dYqMpcuqJ5IdrM19wAAM164RP3q0NfZz0dFjxI8aTcTOHSh9fQFInvuqVSSXHy994Xu49uyJc4sW1PjqS3I2b8acl4+uRw90XUsjS+R8+ZWd4C25cIH83btReP9xYWROSyNhxMiqGwkCMk9PLOUiDdwop5KNlBJ3H+eWLVD4+5d+Dhw9jREEFH5+OLe8u/sldDqdja8vlFpnvby8rOWCIDBt2jRmz55NkyZNaNq0KatXr+bChQt8+eWXVY5fr1492rZty4wZMxg1apSNhXf8+PEsX76cwYMHM336dDw9Pbly5QobN25kxYoVeHh44OXlxSeffEJAQADx8fF/+An+0KFDmT17Nk8++SRz5swhPT2d559/nmHDhjl0mdVqtYwePZpp06bh5eWFr68vL730UpXh8IqKipg2bRr9+/enRo0aXL9+nSNHjlj9rMPDw9Hr9ezevZsmTZrg7OxMaGgoKpWKDz/8kHHjxnHmzBnmzZtnM+7YsWNZuHAhM2bMYPTo0Zw4cYJVq1YBWPXnjBkzaNu2Lc899xxjxozBxcWFc+fOsWvXLhYvXvyHztkNbuuZUlBQEC+99BJXrlxh7dq1pKSk0KFDB0wmE6tWreJSJVmkJCQkbo3c3Z3wzV/gPeF5XB98AL9ZMwlbubLSUE8FBw85FMlQmlr4hkgWRZEH17/NUye30DHpNH0v/UKHt6diTErCNSrK3gotCHgMGvS3Fsnlkbu5/eUiGcBz6FC7jG+ahg0JW/c5qoAAMJnuPCGMKHJtxMjSRCYVkOl0yLw87cpVNWpgqIafIIBz27YEzJ+PU7NmaBo0wHf6dHynvUDC02PtEqRYCgvJ+77UyiOaTBRX4ktviItDNJmA0ljffrNmEfjGfKtIBmxuDm36JlzH9aEH4Q424twSUbQXyWXlaW+9fe/mlbBBkMvxe7Es02FF41vZa78XZ/1l0T0mTZrErFmzmDx5Mk2aNGH37t3s2rWLmjVr3rLv6NGjMRgMNpvWAAIDA9m/fz9ms5mePXvSqFEjJk2ahLu7OzKZDJlMxsaNGzl27BgNGzZk8uTJ1g1tt4uzszM//PADWVlZtGrViv79+9OtW7cqReTbb79Np06d6N27N927d6djx460qGJjt1wuJzMzk+HDh1OnTh0GDBjAAw88wNy5cwFo374948aNY+DAgfj4+PDWW2/h4+PDqlWr2Lx5M/Xr12fBggW88847NuPWqFGDL7/8kq+//prGjRvz8ccfW6Ne3AjP17hxY/bs2cOlS5fo1KkTzZo145VXXiGwzLXrThDEO9z5kpuby7p16/jss884fvw4DRs25NQtHj/m5eXh5uZGbm4urv+Qi7GExJ9FSWwsedu3l8ZgfeghVCGONyqlf/QRGR9W/iMXsvwTtJ06UXDwEPHlQu3cwHP0KPymTSN32zZS33wLc2Ymcnd3fKZOwcNB9jCJW1Ny5QqZK1dijE/AuVUrPEeOIP+HH0h++X+37nwb6B7ohfdTT5G+ZAmGy1fQNG6MOjKS9PfesxW0gkDQe+9RcOggORs2VjmmoFJRY+sW1DVq2JTn//JLpclRfCZNKo31bLEQ89DDmJLtk92o69Uj4puvK53XUlhI5spVZHz4oV1d+KaNODVpQsHhw6S9+RbFZ8+irl0Li8GI8dq1Ko/nriAI1D6wH4WDuLr/Ve719Tvvxx9Jnf+GzcY+hb8/fi/OwrXn32+/RHWYN28emzdvvqU2kqg+r7/+OkuXLiWhKpequ8QdRyV3c3Pj2Wef5dlnn+XEiRPWHaASEhK3T/5PP3F90uRS6yOQseRjgj9egrZidAVKow5UpECl5KqvO3oXDde2bqbuTz+hruRRmbFsE4rbI4/g2qsXxrQ0FL6+yKTHzX8Yda1aBJYLWQRUGSbuj2K4nkjcoMHWsHGGa9fAQbg8mU6HplFDMpbcOtmIMjTUTiQDGB2IXwAEgcLTp0lv2QpEEU3dSHuhLAhoO9+HaDI5fDKS8/U3pM6fX7pZryz98g08R4xAGRREzldfIdPqCNuwHkGpRBAEUubPJ3vN2lseU6U48El2iCjaWdIl7i2uPXui69btL8nMd7fR6/XExcWxePFiXnvttb96Of9olixZQqtWrfDy8mL//v28/fbbdyVGcnW4Y4vyH0GyKEtI2COKIjE9o+w2HakjI4nYusWuvcVgIO7xAZSUbTApVsjZGxmCUXHzguJSbKDjpQTkDr7lfrNm4vlk1ZnfJO4c/d59JDz11F0dU+bujqVcRqqq0DRpQvHJk7ds59a/H4EOLuYlV69y9cGH7Mrl3t6YK7gsODVrhuDiQuHBg9abPQBtly6EfLzEpq3h+nVionrZCVG3AQPwenI4JVevkjT1BevNgDIoiNDVq1EFB2HKyODaE8MwxMXd8rgEJycwmRDLogC49u4NgkDetm237OvSvh2hkvHHBun6XX1GjBjBhg0bePTRR1m/fj3yf6DY/7swefJkNm3aRFZWFqGhoQwbNoxZs2Y53AR5t7ktody1ik1F1gEFgd27d1fZRvqiSUjYY87N5VKbtg7r6p476zBMlTk/n6xVq8n87DMuumq44m/vo9r0WiqBObabrJxbtSJk+SfINBpM2dkUnzqFKiwMRWAgORs3ot+7D4WfL57DhqFxsCParNeTvWEDxafPoK5TB4+hQ6TH05UgiiLJL79M7lc33Q/knp4oAgMxZGUjJjnODnfXqGCpdYQiIIDwz9eirGTnfsbSpaQv+tBqhXVq3pwiRz7JMhmeo0eTtXy5XVXY52ttNiFmrf2c1ArW99LFKAhdvpzEqVPtQh26PvwwQe+U+mhaDAb0u3djSktDXa8e+ug9GK5exalpU3RRPSk8eBBBrUHXswei0UjRyZOoQkNRR0RgTE0jftSoqn23FQpqR//iMCzefxnp+i3xX+O2pHh0dDRhYWE89NBD1jiBEhISd4YxLY2CffuRubuhDArCWCGtrrpOnUpjucp1OpQB/ohFRRR7aR22KVaWWjF8Z0xH5uyCKjQE57ZtEQSBrPXrSVvwptVqp/D3t/ENzPvue8LXr0NTLhykxWDg2hPDKLlwAYD8H38kd+tWanz15T9mA+CfiSAIBL7+Oh5DhlB87hyauvUoiqjDhA2/s+9KBvcnHOO5M9twKbn90G3V4hYiWVWnDqHLP0FZRbIo73HjcH24N0UnTqDw8yVr5apK2zqKvAFQciXGRijL3StJwW0ykTxntp1IBigsFzZKplLhWhaaC8ClQgKCim4kuvvvt/6v9PMlYttWCg4exJybR9rbb9u5jei6dpVEsoSExO0J5TfffJOVK1eyefNmhg4dyqhRo+zCqkhISNwa0WKh4MBv5H3/Pbnffmt9TC04O9s2lMvxmTTR4RjGxERkrq4Yy8Jq+eQXct3LXqh65xeBIKDr2tUmJrMhIYHU11638dWsmBlLLC4m87OVViseQP4PP1hFsnUtCQnkbtmC5/DhdvOXXL1K8aVLaGrVQn0PYlz+U3Bq0ACnsiRNM8pEMkB0SAuO+dXltYvfUOdqmYvEn+gRZ7h0iZRX5xHyUdUhlFTBQRTs30/C2HGVZhVUhYUhqyRChVNZpj5zbi4yFxd03bsj9/fH7CAbm/FaPCiVUOYuYR3/DhMHlEeQy62+/woPd64/97w1hJ4yOBjf6dPu2lwSEhL/XG5LKE+bNo1p06bx22+/8dlnn9GhQwciIyMZNWoUQ4YMkR7DSEiUw1JQgOF6IqrQEGTlYmcakpOJ698fc6a9xayiACl01rDlrXlkLluIq7cv7QYPp4ZPAEnTplNy6RKCUom2LPi7f24BwZl5VrEsiCK1U7JwLTbg1r+fNUTXDQr2H6jWhqaKFm5DhdSp1vK40igEhceOUXz2LMgVZC5bhiktzdrGrc8jBLzxxn8+29nOM7biMF/lwqTGT/D7ysVo5RA/arTV9/xuITg7Vypw9T//XGn2vRsYrl0jZe7cKj8zhthYDLGxdu4eCj8/YgcMvCl8NRpcWrfGqWFD9BkZNv7MAIKLCx6DBpL1aTn/YKUS72fGVeNIbx+Xdu2otSeagr17ETQatB07IkhPTSUkJLjDzXyFhYVs3ryZjz76iHPnzpGUlFQtsSz5OEn828lavZr0RR+WphbWanF/vD/eo0ah8PHhSvcelcaNLY8F2FM3lCK17QW7dVwq3rkVfI7btqHwyFEwm9FrVNDlfgJbtkaRnEzB3n1W0aWpX5+gRYtQBQeRv3s318ffetewxxND8X/5ZevrysLNeb+5gCs7v6f4+HF88gqRV/LTEvj227j1fviW8/6bafrqj+QU2lpL1QoZJ2f3RKOUY87JIW7wkFLRWRUKhZ3IdEg1/JRr7Ymu0v0ia80aUue/4bBO5uWFJTPTdkqNBqemTSg8eOjW67MbUEbtPdEUHjtG/o+7kOl0eAwcYOMCJPHXIF2/Jf5r3JFZ5/jx4+zZs4fz58/TsGFDyW9ZQgIoPP47qW8ssD7GFfV6sleu4nLn+7k2clSlIrmijMnQOduJZIAEdxe7Mku+nlq7fiT4o8U02bqNVu99QNDQoRivJ9pYJovPnSO5LFC7tnPnaqXnzf58HZc6309J2cYnl7ZtcK8QZ1kUBBJffonU82c5HuZHdL1Q8h2sHaBg//5bzvlvZ1hb+xSzj7cMRlPmTy53dyd40QfI3Wz9eL0nTMDjiSdwatoU98cfx3PYMMcTVNwJfovsqc4tW1YpkgHklWzW9Bw9CqWXl125WFxM0XH7xCjVwmKh8MQJXHv1ImjhuwTMnSOJZAkJib+E246rkZSUxKpVq1i1ahV5eXk88cQTHDp0iPrSj5iEBAD5u3Y5rrBYKPztt0r76dVKdCU3rYwWmWNxY3YgegQnDcrAQJQVshDlO4hAU3joEOb8fOQ6HaFrVpO5fAU5mzdjyc+vdG3m1FSuPtwb14cexJSahiE+Hud27Sj6/XfE4mIEUURtNBOZkoVZJhDn4875IG9aX7WPwVtZquf/EpO718FJJeeLIwmYLCJ9mwXxfFfbmxZ17dpE7NhO3rfflqZ87tbVTiyac3LI/3l3qU9vGS73dcL3hRfI2fQF5pwcnBo3JvUNx5ZgAOd2bQlcsOCWa9b16IEyMBBjUpK1THB2xmPgQAxXYylxkJn1xibRP0J5f3oJCQmJv4rbEsoPPvggv/zyCz179uTtt9/moYce+lNi2ElI/JOQae0tvtXB2WAiw0WDd0ExAN75hShNZpu4yAABFUK9AXgMHOh4LWo1lgobogSVCqEsqYjCwwO/6dMQi4vIXr+h6gWKInnffW99aSrbRFiRkMw84nzcyXRxcljvXCE6wX8RmUzg2ftr8ez9VW9uVHh6VhnrWu7uTo3Nm8n5+msMsXE4N2+G64MPIiiV+P+v1F1GtFjI+vxzu/jcuqgo/OfMrnZYP5lGQ9jna0lf/BFFJ06giqiB97hnUIWG4vXUUxTs22eNVQzg+tCD6KP3WJ+s3BaCYGdNl5CQuLeMGDGCnJwctmzZUmmb+++/n6ZNm/L+++//aev6q7kt14udO3fi6elJfHw8c+fOpXXr1jRv3tzuT0Liv4z7o48iqxi9ohrIRZEEL1fSygSmwiLSIi4Fl+JSq5zcbKFWShZB5YRykUqJ8unRuPXubTeeaDI59EuVe3sjU6ttyjyeeOIPrdkRCkvpnM4Go8P63C+/uivzSJQid3XFa8QIAubOwa1PH7tNaIJMRuAb822Ep6pmTfxmzbzt2NfKwEAC579Oze3fE7J4MU4NS6N4KDw9cLn/fuTe3ihDQvCdPo3At97Cd/p0xwPdwhUEUUT/yy+3tTYJibvBggULEASBSZMm2ZTHxMTQt29ffHx8cHV1ZcCAAaRWYiyQcMyqVatwd3f/q5dx29yWOfiVV15BuNUPnITEfxxlUBChq1aS9u5CCg/d3kYmg0KOXqPGt6AIAM+CYjpfTKBIKUep0iAvKi03C3DFz5MYPw90MWeJsJiRyWwtz8akJIfWPHNenl2ZOiKC8E0byVy5iuLz50sfo//B1L3Jbi4gitRKzXZYX1JJnF2Je4dzy5bUiv6Fgt8OInN2wrl167sWecSYnEzcwEGYc3MBMAO532zBc9gwPAYOwLllC/J/2o2gUKCqXQvn5s2RaTSkvfc+WZ9+Wum4Mp3urqxP4p+HxSKSfDmHgrwSXFzVBNR2R1aJK9rd5MiRIyxbtozGjRvblBcUFNCzZ0+aNGnCzz//DMD//vc/evfuzcGDB5H9zaL4GI1Gac/YXeS23t05c+Ywe/bsW/5JSPzXcWrcmLDVq3B/Ymi1++RpVGRqnVBY7KMYOBnNBIwdy/6W9TlYM5Bf6ocT41dqDczPSCfHQSxahY8PMq19EhJ1RIRdmaWkBFNmFpbCAkrOn//DIrlEIUOvVnDfhXgby7fNsVS4CEn8OcicnNB17YJL27Z3NTxf9qZNVpF8g5LLl8kvswira9bEe+zTeI0ehe6++5BrtQgKBd7PjEPbpYtD67IiIABdt253bY0S/xxifk9jzYsH2PLe7+z69Bxb3vudNS8eIOb3tFt3vgP0ej1Dhw5l+fLleFR40rJ//37i4uJYtWoVjRo1olGjRqxevZqjR49ahXNF4uLikMlkHC2XJAfg/fffJywsDEtZmMUzZ87wwAMPoNVq8fPzY9iwYWSUSw2/c+dOOnbsiLu7O15eXjz88MPElMsoGRcXhyAIbNq0ic6dO6PRaFi3bp3DNZ0+fZquXbvi5OSEl5cXTz/9NHq9499pKL1BGD58OFqtloCAAN59992qTyJw8uRJunTpgk6nw9XVlRYtWnD06FGio6MZOXIkubm5CIKAIAjMmTMHgLVr19KyZUt0Oh3+/v4MGTKEtDTb93vbtm3Url0bjUZDly5dWL16NYIgkJOTY22zb98+OnXqhJOTEyEhIUyYMIGCP+L6VYHb+rX08PDA09PT7q9GjRpERUWxq7JNTBIS/1EcpX+2IpejadECk6cH173cOBvkTf3EDEKz7X+41E2a4DV6FE5BwWRpnTCU81tWKFW4OHicJXNyso87q1Ti8/zzNkUFBw9y5f4uxI8YQf7OH+znbtDAPopCeRQKq9hRmyw0TsxEa3AcskwZFIT3s89UPpbEPw5Tsv1NGtgnrymPKIokjB5T6l5Rzj1I0GpxatqUkGVLK01cIvHvJeb3NHYuO0NBTolNeUFOCTuXnbmnYnn8+PE89NBDdO/e3a6upKQEQRBQl3NZ02g0yGQy9u3b53C88PBwunfvzsqVK23KV65cyYgRI5DJZOTk5NC1a1eaNWvG0aNH2blzJ6mpqQwYMMDavqCggClTpnD06FF2796NTCajb9++VqF9g5kzZzJx4kTOnz9PVFSU3XoKCgqIiorCw8ODI0eOsHnzZn766Seee67yEKHTpk1jz549bN26lR9//JHo6GiOO0pdX46hQ4cSHBzMkSNHOHbsGDNnzkSpVNK+fXvef/99XF1dSU5OJjk5mRdeeAEotYDPmzePkydPsmXLFuLi4hhRLgRpbGws/fv359FHH+XkyZOMHTuWl8qiN90gJiaGXr160a9fP06dOsWmTZvYt29flcdXXW7L9aIy5+2cnByOHTvGww8/zJdffklvB/6SEhL/RSoLuSXTaglduwanevUAaFBYSNa6deR8/Q3GTHvXCOcmjRHkcto+NpBt785HFG/+SDZ78BHUzo43EHqNHo06si55O7Yjc3LGvd9jaMrmhNKoBIkvTMOc7dhNAso27VUVq/dGnUyGTKfDUsG6CIBCgff4Z/EaMcIm+YrEPx+Xjh3I3brVvrws650jCg8foejkSbtyUa+n6MQJ4kePJmzVKtQ1a97VtUr8fbFYRPZuulxlm31fXKZGE5+77oaxceNGjh8/zpEjRxzWt23bFhcXF2bMmMH8+fMRRZGZM2diNptJTraP7HODMWPGMG7cOBYuXIhareb48eOcPn2arWXfl8WLF9OsWTPmz59v7fPZZ58REhLCpUuXqFOnDv369bMZ87PPPsPHx4dz587ZZEaeNGkSjz32WKVrWb9+PcXFxaxZswYXFxfr/L179+bNN9/Er8K1Sq/X8+mnn/L555/TrezpzurVqwkODq50DoD4+HimTZtG3bp1AahdLgSpm5sbgiDg7+9v02fUqFHW/yMiIli0aBGtWrVCr9ej1WpZtmwZkZGRvP12aYbYyMhIzpw5w+uvv27t98YbbzB06FCrb3nt2rVZtGgRnTt35uOPP0ZzBzfetyWUn6xi9zVA06ZNeeONNyShLCFRhkuHDqjr1rVJ+Syo1YRvWG8Tw1jm7Iz3U0/h2rMnMQ8+ZOf64NqrFwC1WrVlwOz5nPppJ4biYuq07UD9Tl2qXIO2Ywe0HR2LluJz5zCXe8zniFvVW7FYKo1wEDB/Pu6PSL8L/0ZcH3wQ/d695G37trRAJkMZGEjKnLm4PdoH9woXegBTWtWboMzpGaS8Pp+wzyr3YZb4d5F8OcfOklwRfXYJyZdzCIq8vU2oVZGQkMDEiRPZtWtXpWLKx8eHzZs388wzz7Bo0SJkMhmDBw+mefPmVv/kBx54gL179wIQFhbG2bNnefTRRxk/fjzffPMNgwYNYtWqVXTp0oXw8HCg1E3hl19+QevARS4mJoY6depw+fJlXnnlFQ4dOkRGRobVkhwfH28jlFu2bGn9v0GDBly7VpoptVOnTuzYsYPz58/TpEkTq0gG6NChAxaLhYsXL9oJ5ZiYGAwGA23atLGWeXp6ElnVU1JgypQpjBkzhrVr19K9e3cef/xxat7ihvfYsWPMmTOHkydPkp2dbXOM9evX5+LFi7Rq1cqmT+sK0ZNOnjzJqVOnbNxORFHEYrEQGxtLvXIGotvlrsZ2e/jhh3nttdfu5pASEpUiGo1/+zSzglxO6MrPyPxkOYWHD6MMDcFrzJhKE32owsIIXLCA1AULMGdmInN1xef553EuF00muF5Dgus1dNj/dpE7SBRRHmVQEMaMDCip+gJmxZHlWalE277dH1idxD8BQS4n6K238H76aTKWLSPv2+8wXr+O8fp1Co8cwZSRiffYp236uLRtC0rlzZTWDig8cIBL7doT+PZbaDt2vNeHIfEXU5BXvd+Y6rarLseOHSMtLc0mYpfZbObXX39l8eLFlJSUIJfL6dmzJzExMWRkZKBQKHB3d8ff35+Isj0fK1asoKhss/WNjXQqlYrhw4ezcuVKHnvsMdavX88HH3xgnUev11stuhUJCAgAoHfv3oSFhbF8+XICAwOxWCw0bNgQQ4UY5eUF8Pbt2zGWfbec/uQneHPmzGHIkCF8//337Nixg9mzZ7Nx40b69u3rsP0Nl5CoqCjWrVuHj48P8fHxREVF2R1jVej1esaOHcuECRPs6kJDQ//w8cBdFsolJSWoyuKzSkjcbUSzmYyPl5K1cSOWnBwwmZD7+OA9biyeQ6u/ae7PRuHhgd+MSsJkOcCt98O4RvXEmJyMws/vnvhqmrKzKTx8BIWvD7qePcn/8UebeqemTXFp3w6PoUPJ/GQ5WatXV2tcQatFEEozBd7Aa+RIFN7ed3X9En8/VBERFOy199fMWrkSr6fG2GwgVPj4EDD7FVLmvYZYxU2YOTubhLHjqLljO6o7vNhJ/L1xcVXfutFttKsu3bp14/Tp0zZlI0eOpG7dusyYMQO53DaakHfZb9nPP/9MWloajzzyCABBQUEOxx8zZgwNGzZkyZIlmEwmG/eI5s2b89VXXxEeHu4wJ0VmZiYXL15k+fLldOrUCaBSn+jyhDlI1lOvXj1WrVpFQUGBVVTv378fmUzm0Epcs2ZNlEolhw4dsgrN7OxsLl26ROfOnaucv06dOtSpU4fJkyczePBgVq5cSd++fVGpVJgrPC29cOECmZmZLFiwgJCQEAC7DZCRkZFs377dpqyim0zz5s05d+4ctWpVHZv+j3BXhfKnn35K06ZN7+aQEhJW0j9YROYnn9iUmdPTSZ33Gvk7f0BduzbuAx5HU+Yb9VdQdOIEJXFxODdvfkcXdkGlumeZyXK//Zbkl/9nFShOLVrgOW4shfv2I/fwwHP4MLRlP8oAvtOnofDzI/e7bxGUSgwJ17FkZTkc23fSJHTdupLz9deYs7LRdulSqduHxL8L0WjEXG4H+g3MOTmlT38qxO52798fbbduFJ04QcGB38heu9bxwGYzudu+xee58fdg1RJ/FwJqu+Pirq7S/ULrURoq7m6i0+lsXBig1Drr5eVlU75y5Urq1auHj48Pv/32GxMnTmTy5Mm3dEWoV68ebdu2ZcaMGYwaNcrGwjt+/HiWL1/O4MGDmT59Op6enly5coWNGzeyYsUKPDw88PLy4pNPPiEgIID4+Hhmzpz5h45z6NChzJ49myeffJI5c+aQnp7O888/z7Bhw+zcLgC0Wi2jR49m2rRpeHl54evry0svvVRlKLyioiKmTZtG//79qVGjBtevX+fIkSNWP+vw8HD0ej27d++mSZMmODs7Exoaikql4sMPP2TcuHGcOXOGefPm2Yw7duxYFi5cyIwZMxg9ejQnTpxg1apVANaQxTNmzKBt27Y899xzjBkzBhcXF86dO8euXbtYvHjxHzpnN7gtoTxlyhSH5bm5uRw/fpxLly7x66+/3tGCJCQqI2tD5ZnjCo8cofDIEbI3byZ02VJc2rf/E1dWuinu+oSJ6KOjSwsEAe9nn8Xn+TvfcXs3MeflkfzKbBsrXtGxY7i0aU2NLzc77CPI5XiNGonXqJGIFgsX6jdw2C546cfo7r8fAJ/xkqj5ryFTq3Fu08YudrhzmzZ2CW5uoPDwQNelC7ouXRDNZnLWr3fY7k5SYUv8M5DJBDoNrM3OZWcqbdNxQO0/JZ6yIy5evMisWbPIysoiPDycl156icmTJ1er7+jRozlw4IDNpjWAwMBA9u/fz4wZM+jZsyclJSWEhYXRq1cvZDIZgiCwceNGJkyYQMOGDYmMjGTRokXcX/Y7ezs4Ozvzww8/MHHiRFq1aoWzszP9+vVj4cKFlfZ5++23re4hOp2OqVOnkutos3YZcrmczMxMhg8fTmpqKt7e3jz22GPMnTsXgPbt2zNu3DgGDhxIZmYms2fPZs6cOaxatYoXX3yRRYsW0bx5c9555x2rpR6gRo0afPnll0ydOpUPPviAdu3a8dJLL/HMM89YI5E0btyYPXv28NJLL9GpUydEUaRmzZoMrCRr7e0giKKD1F2V0KWL401Drq6uREZG8swzz1CjRo1bjpOXl4ebmxu5ubm4urpWf7US/2nON2xUdfSFMpxatiD0s88o+PVXRIMBl/s6I/+DaaWrS/YXX5Dyin0M8YjvvkV9Dx4F3aDkaiwlF86jrlsPdcStv3v6PXtIGDvOrtypaVPCN94ihXUZV3r0tEuHLPf2pvaeaIQKjykl/lsY4uJIGDsOQ9lGImVYKKHLlqEq27wkms0Unz6N4OyMpk4da7/cbdtImj7D8aCCQI1vvv5LnxRJ3OReX79jfk9j76bLNpZlrYeajgNqU7OZ712f789g3rx5bN68mVOnTv3VS/nX8Prrr7N06VISKlyL7gW3ZVH+RUopKvEXUXDwEDJnZywOsspVxHAlhiv3d8Fc5h4gaLWELv0Y53K7gu8GpuxsCo8cQennR+Ghw47XfejQPRPKKa/OI7ucBc5jyBD8X/lflX0UZRtE7Mv9HZY7wmfCBJKmT7eJf+sz/llJJEugCg8nYsd2in7/HQCnZs2svsnF585x/bnnMSYllda1bEHI4sXI3d3J+Hipw/EErZaA/70sieT/EDWb+VKjic9fkpnvbqPX64mLi2Px4sVSoIM7ZMmSJbRq1QovLy/279/P22+/fVdiJFeHu+qjLCFxLyj8/Xfix4ypljUZwFxUZBOlQdTrSZoxk1q7f7pra6ro56sIDHTYTnWLmJN/FP3+/TYiGSB7/Xp03btV6XaiqVMHbZcupYkeyhBUKrwqPBKsCrfeD6MMDCDnm2/AZMbtkd5/uquLxN8XQSbDuUULu/LEF6ZZRTJA0dFjpL37LgHz5mFMTHQ4Vs1vt6Gs5OZO4t+LTCbc1RBwfxXPPfccGzZs4NFHH7Vzu5C4PS5fvsxrr71GVlYWoaGhTJ06lVmzZv0pc9+W68XdQnK9kLgdEqe+QN7339/xOLX27EHpd+eP7sx5eVzufD9iWSigGwgaDWJxsfW1pnFjwjduuKvpgm+Q9u67ZC5fYVfu9dQYfKdOrbKvxWAge+3n6PftReHjg+fwJ3Fq6NjvWELibmC4do2YqF525XJvb+rs20v8009T8OtemzpVzZrU/P67W45ddOIEBQcPogwIQBcVJWX0u8dI12+J/xqSRVnib09VWeOqjSAgd3e783GAwuPH7UQygLpmTZxbtqQkLhbnFi3xGDLknohkqMqF4tbWN5lKhdfoUXiNliwcEn8OMlfX0lTnFZ4KicXFXO7aFaV/AHJvb2tyG7mbGwEVdr5XRBRFrj/zDProPdYy1dJlhK37HIWn590/CAkJif8kklCW+Nuj7dKFggMH7mgMVUREpTvvbxelv2N/XlVYKH6z/ljontvFrXdvsj79zOaRtTIoCLdyO4UlJP4uKDw8UHh5laZDL4dFr8ei12NKSgaVCv85c5B7eqDt2BFTbi7xTz9NyfkLKAID8XtxFs5Nmlj7Jr/4ko1IBjDExpK1chW+Ux1HaJKQkJC4Xe6NuUtC4i7iMXgQbn0egbJ4iXI3N7zGj692nGHByYmgd9+5a+vR1K2LtkLAdUGlwnPkyLs2x62Q63SEbViP56hROLdri+foUYRv3IDcQSpUCYm/muKLl+xEsh0GA4VHjuDasyeWwkKuPvAgBb/uxZSeTvHJk1wbNJjCsk2CpuxscrdtczhM0ZnTDsslJCQk/giSj7LEPwZjYiLG1FQ09esj02gQRRFzdjY5X35FuoNYkDJXV7xGjcStb1+UDgKq3wmWkhKy165Fv28/Cl/Jz1dCoirSPviAzEoiW1TEpX07FOE1yHUQU1nTuDE1vthE8YULxD7qOCWux7Bh+L/04h2tV6JypOu3xH8NyfVC4h+DMigIZbk0oYIgoPD0xGvEk2StWWP1b7yB2yOP4D3OPmbw3UCmVuM1ZgxeY8bck/ElJP5NyNTV32BXcOA3OHzEYZ0xORkAdUSEjU/zDQS1Gq+RI/7wOiUkJCQqIrleSPzjEVQqAhcsKN0wVIamSWMp5a2ExN8E7f2db92oPJWEglT4+FB47BiCSkXAq69ChX0HysBATHdj86+EhIREGZJQlvhXoO3YgdrRvxCybCnhmzZSY9Mm5O7uf/WyJCQkgOz11cv6eCtKzp3j2tAnuP78BLSd78O/QhxVQ2wsCU+PJff77RRJWdAkJG6L+++/n0mTJlXZJjw8nPfff/9PWc/fBUkoS/xrkDk7o+3cGadyO+MlJCT+WvT79pPzxRd3dcz8XbtK/3bvtqszZ2aSNHUqcQMGcm3kSCwOQjlKSJRnzpw5CIJg81e3XDbIrKwsnn/+eSIjI3FyciI0NJQJEyaQm5v7F676n8ecOXNo2rTpX72M20byUZaQkJCQuGcUHnac3v1OSXv7HZShoVXP/dtBslavwXvc2HuyBol7g8ViJvH8WfQ52WjdPQiq1wCZTH5P52zQoAE//XQze6tCcVMeJSUlkZSUxDvvvEP9+vW5du0a48aNIykpiS+//PKerut2EUURs9lss36JO0OyKEtISEhI3DOUwUG3bvQHMCYmYkxJuWW7O43BLvHncvnQAZaPH80Xr77I9kVv88WrL7J8/GguH7q376NCocDf39/65+3tba1r2LAhX331Fb1796ZmzZp07dqV119/nW+//RZTJf70v/76K0qlkpQKn9FJkybRqVMn6+t9+/bRqVMnnJycCAkJYcKECRQUFFjr165dS8uWLdHpdPj7+zNkyBDS0tKs9dHR0QiCwI4dO2jRogVqtZp9+/Y5XNOePXto3bo1arWagIAAZs6cWen6AdLS0ujduzdOTk7UqFGDdevWVX0Sy9bTunVrXFxccHd3p0OHDly7do1Vq1Yxd+5cTp48abXar1q1CoCFCxfSqFEjXFxcCAkJ4dlnn0Wv19uMu3z5ckJCQnB2dqZv374sXLgQ9wrulVu3bqV58+ZoNBoiIiKYO3dulcdXXSShLCEhISFxz3B7+GFU4eH3ZGxjbCwoFMhcXKCSLJjKAMcJgiT+flw+dIBtC+ejz7KNZqLPymDbwvn3VCxfvnyZwMBAIiIiGDp0KPHx8VW2vxEerzLL7X333UdERARr1661lhmNRtatW8eoUaVZUWNiYujVqxf9+vXj1KlTbNq0iX379vHcc8/Z9Jk3bx4nT55ky5YtxMXFMWLECLv5Zs6cyYIFCzh//jyNGze2q09MTOTBBx+kVatWnDx5ko8//phPP/2U1157rdJjHDFiBAkJCfzyyy98+eWXLFmyxEakV8RkMvHoo4/SuXNnTp06xW+//cbTTz+NIAgMHDiQqVOn0qBBA5KTk0lOTmbgwIEAyGQyFi1axNmzZ1m9ejU///wz06dPt467f/9+xo0bx8SJEzlx4gQ9evTg9ddft5l77969DB8+nIkTJ3Lu3DmWLVvGqlWr7Nr9EaQ4yhISEhIS9xRTdjbZaz+n+Nw5FIGB5DiIkXyneI0ZTfG58zYWZEGtJnzDejT169/1+f6r3Kvrt8ViZvn40XYiuTw6L2/GLP70rrth7NixA71eT2RkJMnJycydO5fExETOnDmDTqeza5+RkUGLFi144oknqhRib731FqtWreLcuXMAfP311zz55JOkpKTg4uLCmDFjkMvlLFu2zNpn3759dO7cmYKCAjQa+7CKR48epVWrVuTn56PVaomOjqZLly5s2bKFPn36VLqWl156ia+++orz588jlCXvWrJkCTNmzCA3NxeZTMb9999P06ZNef/997l06RKRkZEcPnyYVq1aAXDhwgXq1avHe++953DTX1ZWFl5eXkRHR9O5s32kmzlz5rBlyxZOnDhR6ToBvvzyS8aNG0dGWfjHQYMGodfr+e6776xtnnjiCb777jtycnIA6N69O926dWNWuQ2+n3/+OdOnTycpKanK+W6FZFGWkJCQkLinKDw88JnwPCFLP8Zv2gugVN71OXK+2ULwko/wmTIF5zZtcOvTh/CNGySR/A8h8fzZKkUyQH5mBonnz971uR944AEef/xxGjduTFRUFNu3bycnJ4cvHGxCzcvL46GHHqJ+/frMmTPHWt6gQQO0Wi1arZYHHngAKLXIXrlyhYMHDwKwatUqBgwYgIuLCwAnT55k1apV1n5arZaoqCgsFguxsbEAHDt2jN69exMaGopOp7MK0IoW75YtW1r/Lz/euLJcAufPn6ddu3ZWkQzQoUMH9Ho9169ftzvO8+fPo1AoaNGihbWsbt26du4O5fH09GTEiBFERUXRu3dvPvjgA5LLYp9XxU8//US3bt0ICgpCp9MxbNgwMjMzKSwsBODixYu0bt3apk/F1ydPnuTVV1+1OfannnqK5ORk6zh/FMnbW0JCQkLiT0Pm5IT300+R8dGSuz+2RoP300/h/fRTd31siXuLPqd68a+r2+5OcHd3p06dOly5csWmPD8/n169eqHT6fjmm29Qlrvh2759O0ajEQAnJycAfH196d27NytXrqRGjRrs2LGD6Ojom8ei1zN27FgmTJhgt4bQ0FAKCgqIiooiKiqKdevW4ePjQ3x8PFFRURgMBpv2N8Q3YGOx/bOf2q9cuZIJEyawc+dONm3axMsvv8yuXbto27atw/ZxcXE8/PDDPPPMM7z++ut4enqyb98+Ro8ejcFgwNnZuVrz6vV65s6dy2OPPWZX58gyfztIQllCQkJC4k/F5/nnES0WMj9ZDmbzbfWVubmhadCAwgqb9NyqeOws8fdH6+5xV9vdCXq9npiYGIYNG2Yty8vLIyoqCrVazbZt2+zEV1hYmMOxxowZw+DBgwkODqZmzZp06NDBWte8eXPOnTtHrVq1HPY9ffo0mZmZLFiwgJCQEKDU9eJWOBqvXr16fPXVV4iiaLUq79+/H51OR3BwsF37unXrYjKZOHbsmNX14uLFi1ZXh6po1qwZzZo1Y9asWbRr147169fTtm1bVCoV5grf92PHjmGxWHj33XeRle0zqGjJj4yM5MgR22ydFV83b96cixcvVnou7wTJ9UJCQkJC4k/Hd+JEIo8ecRziTe7YB1UREkLIR4sJevcddD17glyOoNHgMWQwPpMm3uMVS9xLguo1QOvpXWUbnZc3QfUa3PW5X3jhBfbs2UNcXBwHDhygb9++yOVyBg8eDJSK5J49e1JQUMCnn35KXl4eKSkppKSk2Am/ikRFReHq6sprr73GyJEjbepmzJjBgQMHeO655zhx4gSXL19m69at1s18oaGhqFQqPvzwQ65evcq2bduYN2/eHzrGZ599loSEBJ5//nkuXLjA1q1bmT17NlOmTLEK1PJERkbSq1cvxo4dy6FDhzh27BhjxoyxWssdERsby6xZs/jtt9+4du0aP/74I5cvX6ZevXpAabKS2NhYTpw4QUZGBiUlJdSqVQuj0Wg9xrVr17J06VKbcZ9//nm2b9/OwoULuXz5MsuWLWPHjh02biSvvPIKa9asYe7cuZw9e5bz58+zceNGXn755T90vmwQ/wJyc3NFQMzNzf0rppeQkJCQ+JtgSEoSrz31lHiubj3xQqvWYtoHH4hFsbHilYceFs9F1hXPRdYVL3fpKub+8INosVhs+ppLSkSLwfAXrfy/yb28fl86uF98Z8BDlf5dOrj/rs8piqI4cOBAMSAgQFSpVGJQUJA4cOBA8cqVK9b6X375RQQc/sXGxt5y/P/973+iXC4Xk5KS7OoOHz4s9ujRQ9RqtaKLi4vYuHFj8fXXX7fWr1+/XgwPDxfVarXYrl07cdu2bSIg/v777zZry87OvuU6oqOjxVatWokqlUr09/cXZ8yYIRqNRmt9586dxYkTJ1pfJycniw899JCoVqvF0NBQcc2aNWJYWJj43nvvORw/JSVFfPTRR63nMiwsTHzllVdEs9ksiqIoFhcXi/369RPd3d1FQFy5cqUoiqK4cOFCMSAgQHRychKjoqLENWvW2B3TJ598IgYFBYlOTk7io48+Kr722muiv7+/zfw7d+4U27dvLzo5OYmurq5i69atxU8++eSW5+VWSFEvJCQkJCT+ciwGA4JCgVDOumVMTcWck4u6di2bcom/jnt9/b586AA/r/rEZmOfzsubLk8+Te027e/6fH8Go0ePJj09nW3btv3VS/nX8NRTT3HhwgX27t17z+eSfJQlJCQkJP5yZCqVXZnSzw+ln99fsBqJv4rabdpTs1WbPz0z370gNzeX06dPs379ekkk3yHvvPMOPXr0wMXFhR07drB69WqWLLn7G4IdIQllCQkJCQkJib8NMpmckAb2STP+afTp04fDhw8zbtw4evTo8Vcv5x/N4cOHeeutt8jPzyciIoJFixYxZsyYP2VuyfVCQkJCQkJColpI12+J/xqS05eEhISEhISEhISEAyShLCEhISEhISEhIeEASShLSEhISEhISEhIOEASyhISEhISEhISEhIOkISyxL+WZH0yx1OPU2Qq+quXIiEhISEhIfEPRAoPJ/Gvw2wxM+e3OWyL2YZFtKBT6ZjTbg49w3v+1UuTkJCQkJCQ+AchWZQl/nV8dfkrtlzZgkW0AJBvyOfFfS+SU5zz1y5MQkJCQkLib8qcOXNo2rRplW1GjBjBo48++qes5++CJJQlbpvsDRuI7defq4/2JXPFCkSz+ZZ9RLMZ0WD4E1YHe67vsSsrMZfwfez3f8r8EhISEhL/LBITE3niiSfw8vLCycmJRo0acfToUWv9119/Tc+ePfHy8kIQBE6cOPHXLfYfSnR0NIIgkJOT81cv5baQXC8kbouM5ctJf3eh9XXahQsUJSZyvVFd8jPTCW3YlDpt2iPISu/BRJOJtHfeJeeLL7CUlKDr2gX/OXNQeHndszW6q90dli85sYQHazyIh8bjns0tISEhIXFniBaRkthcLPkGZDoV6hpuCDLhns2XnZ1Nhw4d6NKlCzt27MDHx4fLly/j4XHzWlFQUEDHjh0ZMGAATz311D1by93AaDSiVCr/6mX8a5AsyhK3RdbqNXZlOZu+4LcNqzn1006+e38BPyxdZK3L+HgpWatWYSksBLOZ/F0/kThl6j1d45C6Q5AJ9h/tPEMe22K23dO5JSQkJCT+OEVnMkh58zAZy0+TtfEiGctPk/LmYYrOZNyzOd98801CQkJYuXIlrVu3pkaNGvTs2ZOaNWta2wwbNoxXXnmF7t27V2vMgoICXF1d+fLLL23Kt2zZgouLC/n5+QAkJCQwYMAA3N3d8fT0pE+fPsTFxVnbHzlyhB49euDt7Y2bmxudO3fm+PHjNmMKgsDHH3/MI488gouLC6+//rrDNcXHx9OnTx+0Wi2urq4MGDCA1NTUSo/BbDYzZcoU3N3d8fLyYvr06dwqmfO1a9fo3bs3Hh4euLi40KBBA7Zv305cXBxdunQBwMPDA0EQGDFiBAA7d+6kY8eO1nkefvhhYmJibMY9cOAATZs2RaPR0LJlS7Zs2WJn2T9z5gwPPPAAWq0WPz8/hg0bRkbGnX9uJKEsYUeRPp+j333DzyuXcfnIbzZfDHNurl17ucWCh76IiLRs/HP0nIveRUbCNQByt2yxa1946BDGlBQARIuFjGWfcKV7Dy53uo/UN97AUnRnUSoaeDfg4YiHHdalFlb+oyAhISEh8ddRdCaDzM/PY861ddMz5xrI/Pz8PRPL27Zto2XLljz++OP4+vrSrFkzli9ffkdjuri4MGjQIFauXGlTvnLlSvr3749Op8NoNBIVFYVOp2Pv3r3s378frVZLr169MJS5Kubn5/Pkk0+yb98+Dh48SO3atXnwwQetQvsGc+bMoW/fvpw+fZpRo0bZrcdisdCnTx+ysrLYs2cPu3bt4urVqwwcOLDSY3j33XdZtWoVn332Gfv27SMrK4tvvvmmyuMeP348JSUl/Prrr5w+fZo333wTrVZLSEgIX331FQAXL14kOTmZDz74ACi9qZgyZQpHjx5l9+7dyGQy+vbti8VSus8oLy+P3r1706hRI44fP868efOYMWOGzbw5OTl07dqVZs2acfToUXbu3ElqaioDBgyocr3VQXK9kLBBn53F+penkp+RDsDvO7+l/n1deWD8FAB0999P/q5dNn2KFXLaxKZYX+c6qci4fBnvkDCo5O6zyFjI+fRTuG/8iYKPbv4gZa1egzE9HV3XbiCUzidzcbnt43i8zuMOrccdAzve9lj3AtFsxpydjdzT0+qmIiEhIfFfRbSI5HwbU2WbnG+voqnvddfdMK5evcrHH3/MlClTePHFFzly5AgTJkxApVLx5JNP/uFxx4wZQ/v27UlOTiYgIIC0tDS2b9/OTz/9BMCmTZuwWCysWLECQSg9ppUrV+Lu7k50dDQ9e/aka9euNmN+8sknuLu7s2fPHh5++KZBaMiQIYwcObLStezevZvTp08TGxtLSEgIAGvWrKFBgwYcOXKEVq1a2fV5//33mTVrFo899hgAS5cu5YcffqjymOPj4+nXrx+NGjUCICIiwlrn6ekJgK+vL+7u7tbyfv362Yzx2Wef4ePjw7lz52jYsCHr169HEASWL1+ORqOhfv36JCYm2rjALF68mGbNmjF//nybcUJCQrh06RJ16tSpct1VIV2h/4ZYCgsxZWff9XFFk4n0RR9ypWs3LnftSvqiRYhGo02bY99vsYrkG5z79WfS4q4C4Pe/l9E0bmytM3m4ozHZbuZzKzKgPXm69H8Hu2N/61OLqL1DGbp9KPFr7O/a87fvIOmFF0ia+gJXunWn6OxZx8djMJD3449kf/EFxgqPj5r6NuWpRk8hF+QAyAQZT9R7gvZB7R2O9WeSu3UrV7p05XLHTsR070H+Tz9hMRgQzWaKL1wgZcGbJM+ZQ8HBQzb9RKOR3G+/I/XNt8jduvVP2xwpISEhca8pic21syRXxJxbQkms/VPNO8VisdC8eXPmz59Ps2bNePrpp3nqqadYunRptccYN24cWq3W+gfQunVrGjRowOrVqwH4/PPPCQsL47777gPg5MmTXLlyBZ1OZ+3n6elJcXGx1fUgNTWVp556itq1a+Pm5oarqyt6vZ74+Hib+Vu2bGn9/4b7gVarpUGDBgCcP3+ekJAQq0gGqF+/Pu7u7pw/f97ueHJzc0lOTqZNmzbWMoVCYTOPIyZMmMBrr71Ghw4dmD17NqdOnbrlubt8+TKDBw8mIiICV1dXwsPDAazHePHiRRo3boxGo7H2ad26tc0YJ0+e5JdffrF5D+rWrQtg58Zxu0gW5b8RotFIyvz55H71NaLBgFPLFgS+8Qaqch/sOyHtnXfJWrXK+jpjycdYCgrwmzXrZlmZy0RFMhKu4RsegdLXlxpfbKL40iUwm8k9coSs+W/YtS/4fB2Zrq54jRuLpaCA7M2bEYuL0Ue1ZVGDo5iNpY9U1Ea7rjaYc3JInfca4Rs3WMviTh7nwIY1ZMVcxjNXT2RyFs7zXiNw/uu49e5tbTeh+QQer/M4F7MvUsu9FsG64OqcpruOOT8f0WRC4eFB0dmzJM2cZbW0G5OSuP7c86UN5XIoF0EkZ+MmfGfOwGvECESjkfjRYyg8fNhan73pC8JWrURQqf7U45GQkJC421jyq3fjX912t0NAQAD169e3KatXr57VVaA6vPrqq7zwwgt25WPGjOGjjz5i5syZrFy5kpEjR1qtx3q9nhYtWrBu3Tq7fj4+PgA8+eSTZGZm8sEHHxAWFoZaraZdu3ZW14wbuJR78rpixQqKylwY/+xNfWPGjCEqKorvv/+eH3/8kTfeeIN3332X559/vtI+vXv3JiwsjOXLlxMYGIjFYqFhw4Z2x1gVer2e3r178+abb9rVBQQE/KFjuYFkUf4bkfHJJ+Rs2Gi1FBYdPcb1CRPvytii2UzWpo125dlfbEY0mayv/WvWdtjfL6KW9X9LcTEKb2809eqha9jQYXtLQQEnV7zDhyvGsvvRUHx/3Unkid+59HRXzKIFV72Cxlfc2B/pTpFSXuXai06csJ6TlCuX+ObNuSTHXqFEJpDsoeNQzUAsJiMpr86z828O0AZwf8j9f7pIFk0m8qOjuTZiJJfatOVyu/bEjxpFzpdfVuqOgoMwexkfLcFSXEz+rl02Ihmg6Phx8nbuvBfLl5CQkPhTkemqd8Nf3Xa3Q4cOHbh48aJN2aVLlwgLC6v2GL6+vtSqVcv6d4MnnniCa9eusWjRIs6dO2fjytG8eXMuX75s17dWrVq4ubkBsH//fiZMmMCDDz5IgwYNUKvVt9ygFhQUZB3nxjHUq1ePhIQEEhISrO3OnTtHTk6O3U0CgJubGwEBARw6dPPJpslk4tixY7c8FyEhIYwbN46vv/6aqVOnWv29VWVGHXO5a11mZiYXL17k5Zdfplu3btSrV4/sCk/UIyMjOX36NCUlJdayI0eO2LRp3rw5Z8+eJTw83O5cuvwB983ySEL5b0Ted/ZxfkvOn6fkyhUALmZdZGfsThL1iTZtLAUFZK39nOT//Y/sjRsxZmSQ++135G3fXhptArCYjJhLiu3GtxQXI5Y5zAM06/EgOrntHWi90Jp4BYUgWiykvfMOl9q153L7DsT2fxy5Toe2Wze7cQ/VgclPy1nhcpQ3Dr/Bw9/24WJ+DG5qN8KTnXl0byDNL7ljxotfI0PJctHYjXEDRUCA1Wp68qedWCoIyiK1kjSdC5b8fEoq/Nj9FZRcvUpMVC+uj3uGwoMHoez8Fhz4Df2vv97WWJb8fExpaZW6nxSfv3DH65WQkJD4q1HXcEPuVrUIlrupUddwu+tzT548mYMHDzJ//nyuXLnC+vXr+eSTTxg/fry1TVZWFidOnODcuXNAqTvAiRMnSElJqWxYoDTCw2OPPca0adPo2bMnwcE3jTZDhw7F29ubPn36sHfvXmJjY4mOjmbChAlcv34dgNq1a7N27VrOnz/PoUOHGDp0KE5OTrd9jN27d6dRo0YMHTqU48ePc/jwYYYPH07nzp0rdaeYOHEiCxYsYMuWLVy4cIFnn332ljGQJ02axA8//EBsbCzHjx/nl19+oV69egCEhYUhCALfffcd6enp6PV6PDw88PLy4pNPPuHKlSv8/PPPTJkyxWbMIUOGYLFYePrppzl//jw//PAD77zzDoDVOj9+/HiysrIYPHgwR44cISYmhh9++IGRI0faCPM/giSU7xGG+HgyP/2U1DffQr93b7X6CArHnjAWuZzpv06n/7f9mfbrNAaveYBdox9m0yNN+OGxTlzp9QCpr79OzuYvSZkzlyud7ydp2jQSp0zlSvceFJ87xwV9DEdr2W+AOFfPGVm5R/clP+2m/e8XaRyfRu2ULNpeSaTGtz9iiIsje/0GMld8ilhmtS0+c4aEZ54lZkofNvXz5Yad1CTA8l5yLOU2XOQb8vng+Ac0k0fS9rw3MvFmnVku40JA5XGVfcY/e/O8FjuOiGGSl46X+/32Ssf5o5iyssj6fB2ZK1ZgiI9HtFiq9A9OmT0HY2KiwzpTYhJU8j47Qu7pScqrr5L16WeOG4gWx+USEhIS/yAEmYB775pVtnHvHXFP4im3atWKb775hg0bNtCwYUPmzZvH+++/z9ChQ61ttm3bRrNmzXjooYcAGDRoEM2aNauWH/Po0aMxGAx20SicnZ359ddfCQ0N5bHHHqNevXqMHj2a4uJiXF1dAfj000/Jzs6mefPmDBs2jAkTJuDr63vbxygIAlu3bsXDw4P77ruP7t27ExERwaZNmyrtM3XqVIYNG8aTTz5Ju3bt0Ol09O3bt8p5zGYz48ePp169evTq1Ys6deqwZMkSoNTSPXfuXGbOnImfnx/PPfccMpmMjRs3cuzYMRo2bMjkyZN5++23bcZ0dXXl22+/5cSJEzRt2pSXXnqJV155BcDqtxwYGMj+/fsxm8307NmTRo0aMWnSJNzd3ZHd4YZ5QbxVULx7QF5eHm5ubuTm5lo/DP8Wik6eJOH5CZjT0mzK3QcPImD27Cr7Zq1ZQ2oFf1/nVq04NedxXtz3IkEZIo/+ZsGlCJb0lqF3Kv3BqH9NZPpXZpxLHI0KTi1aIPt4PoM/f5AJ2yw0jit9y0+HCfw0qiGfDPzC2vbaqNEUHjhgN4bHsGEUnzlD0e+/29XNHabibLCFmV+YaR4jkuwBE8fZikFXvYKuJ/xwz3MsEmUWkV6nr1pfK8PCcGrSGHNUJ5apD3Iq/RQ13GrwiKktJz9db9e/ybVUgnL0CBoNtff+ilynw1BUyNlffyYnJZngeg2o1bLtbUeYKDpzlviRI7HcCMUjkyGo1YjFxbh07EjA3DkoAwOt7S0lJVxs0tThWIrQ9ihD2oAAxti9mBKPOmxnc168vbFU8ZhN5ulJnehfJD9lCQmJP4V7ff0uOpNBzrcxNhv75G5q3HtH4NTQ+67P92ewdu1aJk+eTFJSktX9QOLOWLduHSNHjiQ3N/cPWdhvB2kz313ElJFB3NAnoJzP7w1yNmzEc9hw1BE1Ku3vMWwYlqJisj5fiyUvH1337vi99CLLzy3EK09k3lozGgM885zcKpIBzoUJfNlBxvCfHVsXi44do65LEI1qd+S1wfvxyBcRRMhyFXij1XCbtmIlMYyNSYmVWryLZWZA4JNeMqZ+bSYsHVyKRArKrfG+E96VimQAXXE5la9QELTwXcQ6NeiztQ8pBaWPtuLy4riWeZT78LTrn+ShIyhHj1hcjCk9HQMiG/43jezkUsvu8e1biWzXiYcnzbDrWxVp77xzUyQDWCzWc1Swdy8J458j4puvrdWCUoncwwNzBR8rVd3eqOve3Gio8K5HsUqHMfaXKuevSiQDWLKyKLlyBY0DHzMJCQmJfxpODb3R1Pf6UzPz3SsKCwtJTk5mwYIFjB07VhLJd8CaNWuIiIggKCiIkydPMmPGDAYMGHDPRTJIrhfVwpiWRupbbxM/ajRp7y7ElJnpsF3m8hUORfINSi5W7U8qCALeY5+mzt691D15gqB330Hh6UmANoBuJyxoi+GqP+S62P9g/F6z8h8RRUAAglzOO53fYWi9oQRo/KjpWZvXOrxmTcxhKS7GnJuLy32dHI6hadQY98f725Xnh3oRE1g6d5arwEsjFEwfJafJ1ZuiXVsoxztPXen65AolzYJrIvf2xqlFC0KWLcWpQQN2x++2imSA8BSRZ7+097MGMJX9iCr8/FCFhXHix++tIvkGF3/bS9Kl6vn0mrKyEI1Gik+frrJdyfnzFF+4OaYgk+E1ZrRtI0GOqubNbE6iaMGUchqZ1henTjNwvm8G8sAWuDyyBMHtNiOcKJUo/P1vr4+EhITE3xhBJqCp6Y5zU180Nd3/kSIZ4K233qJu3br4+/szq1x0KYnbJyUlhSeeeIJ69eoxefJkHn/8cT755JM/ZW7JonwLzHl5xA0ahCkpGYCCAwfI+/EHIr7+2i4RhjGtiqxvgoCmzKG9PKLFgik5GbmnJ7JK7oz61+7P1uIVQDHuBSCIIqJg+8Phoa/cg0am0SCKIkXLV/Pop5vpU1gIQjLaLj9imt+ZjA8Xk/Pll4glJTg1b47M3R1LOYd9mbs7Hv37kZKRStHI4ah/3IU8PRNt5/tQPDcU+aFxmMVSZ3lBFCnSyBgSbeaBYyZ+qyvDqYTSSA8V1qxx0dLsgUeo36kL7v724VtySnJsXj+520xQdhGX/E0YlLYfXf/cAgQnJwLmvYogl5N+LdbhuUi/FktgnbqVnqvCo0dJnj0HQ0wMcnd35G5uWAoKKm0P2EWx8Bg8GGNqGoVHjiBoNBSfvYSgLH1vRWMRhfvfw5ITV9ZaQNWgL06tnsKUdBwstsd8KzwGDULhaW9hl5CQkJD4a5kzZw5z5sz5q5fxr2D69OlMnz79L5lbEsq3IHfLFqtIvoHxWjy527fj8fjjAJRcuYKg0aDr0oX8HY7DdWkaNcJcTnymXr3CkbWfkXviBN6pGYSVWPAeMxrvZ56x6+vn4kfH3s9zMm01mVoNj/9sZl/jXJJ8Sq2rgggjWj2DT4CanK+/xlguTzyAITaWuMcHUHzmzM1CUUT/88/E9e+P8fpNy2vR8eOoatbEuUcPis6eQVMnEu2Tw/ji/TdIvlwaUUIR4E7UvDkEty8Nmr7o0lj2RX+OMSebHmfkeOSWBkf2zYPIRAtg4WQdE4lOttE02vUfTPMH+9ie28REjKmpaOrX577g+3jn6DtYyjasRV4HuQgt4lI4GepHoVqJYBEJMwvU9Q/Gc8wYzuekEzN7BsV62/Se1nNZLsxdRcz5+SSMewaLXl/6Oien9D1TKCp9UqCOjLS5ASo8fpzrzzxrTfWtCArCpXUzzNmxyD1qYIj5qZxIBhAxnP0ac8YV3PoOx7XbeNIWLHC8QJkKmVsooqkIp4ahuPV5BLc+fRy3lZCQkJCQkLhjJKF8C4yJSY7Lk5IoiY0lceIkSi5dAsC5XTtcOnakYN8+u/bFp04RN2gw3hOep6RrZ76c9xJmkwlUMlJCfMnJyqPxB4tQ1aqFa48eNn0tZjMHDx0i21NXOk+Jkh5H1MQ2LiLqjIlaZ7JRuG+CYcOhkmgMNiK5/HFct4/OYIiJIfiD91GXxYL8dd1Kq0gGMBkN/LDkfULqNyR79qv4/PADN/fB2gpKQaOBVi3o8ex4zpz9nUsH96FUa2jS4wGa9brpsyuaTCS/9DK527aBKCJzcyPgtXm83PZl3jnyDoWmQlI8BYIzRDwKS+h8IZ4ClRKV2YzKbKEE2P7eApLdtQ6PE6BB5+6VxokG0EdHW0VyeVw6dEATGVnqnpKfh/6n3Vj0elzatcP/1VdvHoMokvziS1aRDGBKTCRfrSSwaxEliTmYMy87nNuceoqsFS8SunIFKJVQIWOizCMCS3YsluzSUIEWvSe6bt2soXEkJCQkJCQk7j6SUL4Fikoyuri0aUvStOlWkQxQ+NtvuPXvR8jKz8j+/HP0u3+265ex+CPOZVwvFcnluO6ho3ZKNnnbt+PaowemrCzyf9yFpaiIdDcXspNsBa2AwP2n3Kh9stQ/1pyeQfrChSjKsvncKUe+30KNqAcJqluf+DMn7epNRgOfjX2SNhfj0VUyRr5ayZlmkWTnpSB7ew71OnVh1PvLkCvsMwVlb9pE7tat1teW3FySps/gsehfeODxB7iScwUfrxjypr8CFgsCoDXcFJOFKgXJbvZBxb2CQ6nVqh3B9RsS1qhplccs4lh05ujzaDxl8s12r5kRjcZSlxaLBf2ePRiuxaMKD8dQwZoPoIy7xgnnQkryD9PAxQ9zeiV+0iYDed99T9ia1SROm4YpMQlBpULdsD3Fx/cAN108ik+fJmvlSnwmTKjymCQkJCQkJCT+OJJQvgVZ6+1DkQGkzJ+PoZxIvkHu1m3kfrPFYZY1ACwWCq85SBMtCBSpFMjUGvR793J9/HPWWL1J7loI87PrUpybY1dmKmfN/KNkuWg4dCCaQwei6TRkBDovb1KvXrFrZ0DkbJA3bWPsre4icDzcn4LCUgutxWzmbPRPuHr70v7xIXbt9b9E249RVETBbwdx7RVFU9+m8HBTPENrk/P115gzs8jftcvatlihsPOBvkHHQcOqddx5QX6UKOSoTTffOxGI06poXK6dIJcjyOVYioqIHz2GouPHb1Y6cNMoUiq4cuQgjZw6oqrVFGPiETAW3hzPxQ+xoNS/3ZSejnOzZtT+6aeb69q1i8Rj9tExCg8fsSuTkJCQkJCQuHtIUS+qIPfbbzHFxzuscySSgdJH5rfIAhPubh8LUmEy4VZkwFxYQMLYcTYJLbzzC5Gb7UO/+ec62GRW3dzolYR6y9OoOF5OlJ9b9SmhCamV2FpLRbWjbYT5GhUFGvtQOBd/c5x8Re7hUa1yp8aNCZgzh6D330MReNPa71ZUgtJkf97DmzSvZOX2qNzcORwRQIa29JgKVEpOhPpSEhxUGh977DhionqRNHMWxqQksjdtshXJ4NCXOcbPA7lSSbYhFZnWF5f7X0ZZqyeKoJaomw5DcLn5edB26mi/rtBQh+tVVlIuISEhISEhcXeQhHIV5N2DLG8A9Xo8gHfIzRzynvlFdL2UiNxiQf/jLmvK4xuozBaaXktFZSwVYYJFJDw9h6BsxxvWqkIRFETo6lXUO3Ma98GDbOqMCjmX/DywlFlmffMKaHU+Ds0PP9H20nVkFnux7mQyOxTRMovjKBxKteNU1Z5PDLUT75r69XFu3cphe0EuJ3D+G8jKAt7LRZGWJgUqzc3IIYGR9WnbbxB5u3YR89DDnK/fgLghQymqJOSbf83aaOvVI1+jxiiXoTCbcSsqoVG9xlx7ckSZi8U1crds4dqw4RQedZzzPrtmOKmuziS7uXA4IoAkDx1Nox5C75FPVkkyMhdvNA3749TqaWTOnljSSlOiuj74AO5lG0RtzkNkJLoKfusyFxe8Ro10OL+EhISEhITE3UHKzFcFCc88i/6XqhNC/BFkbm6cfrAL186dpkZaDvWSHcdlrogF0GtUqI0m1A4szLdC260bgW8uQK4t3fBmuHaNq4/0QSyxTelnBuK93fDQF+FefNNCfdXbjQtBttbwZnUaUpibQ96F8/jlFRCQU2AVzodqBZHpYiuMew4bQ93mrW2y2d2g4NBhMleswJichEu79ng/+wwKDw/02Vmc/vkH8jMzCGvUlDptOlgz7FkKCyk4eAiZiwvODSIw/raM62dP4hQYSUDvKRRdSSRu0CAbK7/M1ZVau35E7uZmt4aEWbPQf7PFpkzuF4Y51d5dRtutG/rdu+3K/ZYu4bdTR7l8aD9KlZr6jZrT4ckxWDRqft/+LcbzeXg4+xPUoRGudT0pPn0KZXAw6ogIu7FuIBqN5Hz1NQX796Pw98djyGDUNSpPXiMhISFxL/inXL8lJO4WklB2QNHZsyT/73+UnDt/V8bLcVJjUMjxLChCYRHJ06jYFxmC3Gyh27k4FJVYX+826rp1CVzwBpq6pXGE095dSOby5ZW2NwPyCmWprs4keroi06jxdvPibEke5nIfofD0HOonlQn/WjWJf7gHV44ewsnJiZaZhShPnAKLBVXNmgS9txBNnTpVrjk3LYX1L79AYTl/7Aadu9Hr2cm2DUvyYVlnyIq5WeZTl5TMXmSv/4KKaLt3p/jcWcSiYlwfeADfqVMQlEoutmptd+MAAjhwMHF9pDeFBw9hKpeu3KVDB0I/XQGUnd+VK8FkQlCp8Jk0SbICS0hI/KP5u1+/JSTuNtJmvjJEUaT47DlEk4nrzz+HOb2S1MGCYJdgwg6FHCwiRkSO1gggW1vqDqAwW2gSfzMpibPB+KeJZICSCxeIfawfQe+8jeuDD2LOy6uyvdzBsfrlFRLs7o3xSiLJLhkoA70xq25+jK55u1EzLQe1yYz/k09S7/HHiQKSZs4id9cWaztDTAwJY56iVvQvVuuwI458+42NSAaI/XEH17IKcNJqcXvoIdS1a8PJjbYiGSD9AhpLsMNx9eU2y2WvX48pO4vABQsQHcZLdvwe5W37Fk3Dhrg++iim5GScW7bEre+jpePv229zEyIaDKS99RYu7dtZb1QkJCQkJCQk/t5IPsqA4fp1Yh/pQ1z//lwbNMihSHZu04Zav/xM3XNn8XxhMgVezhgqmltvYDKDxUKMn4dVJAOY5DJOhvjiWlBM7bRcwjJyramXKyL3cMe1d288Rzq2QMp0OjRNGuP60IO25S4uBC76ALm3/YZBACwWkl7+H8VXr6Lr1rWSAyijkhsCY3w8mEwE5BbQ4fJ11Mab4lIUBIwR4QS+9aY1IQtA3nZ7f29TWhoF+/dXuYSsxASb1955hXS6mEDh5+vIXLqMq4/2JW/nTsiOc9hfTk6V498g/4cfsRQWouvWza5OcK7kXFIan1qQywh6+y08Bg5ApirdwKiPjnbYvrJyCQkJCQkJib8fklAGUl55hZLLjhNB3EDm7IwyIABBEPAb8zQt9x/Dyd/ez7Y8GTpnuzKTQk6Rq5bayRmEZuWjsIi29kqFAnWDBngOfxL/l17Ed/IkVBV8UWUuLtT4+itqbNqE5/DhoLoZXcJSUEDGog8xZ1RiEQfEwkKuDRiIKiIC72efqTQCBoKA93PPlSbAqAS1yUxYxs2QdAqzGb/Ierg98sjNNRUV2UTxKE/CU09z9bHHKL5gH1u46MxZIg+doOvZODpcTKBmShZ1kzKQlz9hZjNpb72N0clxIpHDcYU4j38GuZcXAKqwMGRaB0lJLBbS3nmXwmPHkLn6UupuIaDwb4Km+QiQV34OCg/8Zlcm96wkioeUblpCQkJCQuIfw39eKFuKiyn47eAt27n372dXJndyctDyJuUtreVRFRTZvBaAWG83zgZ5k6ZRUnL2LOkffEDsY/0w5+URtnYNHsOHoWnQANeHHyZs/XpUISEAZHy81C4knOGKfczjilj0erLXb8BnwgScW7Rw2Ebm6krG4sV2WeIq4lJSWi+IIg2uZyDk6yn8/XerOLYUFVXVnZJz50l49lkbtwdjSgrxTz6JKiYWjcmMW7GByNRsXEvs12JMSsLk1ZLcONv340yOHydKPNl65Fd8vthE7QP7idi5w0bE30Du7k7uV19hzszEkpeGqm5vtA+9i1Pb8ahq1MelfedK118+TN0N3Pv1Q1Zhs6DCzw/XBx+0ayshISEhISHx9+Q/76MsKJXIdDosFf11FQowm5HpdMg0GtLeXUjh7ycwpaZSePAgioAAnJs1w3AlxvHAQERaDhk6Z8RyiTD8cvQ2GeVukKXVkOqmxa9cbGRjUhJZ69bhO3Ei/i++6HAOg6PkJdUk77tvyV63DkQRk0xAbrHNTWepkLykSCnHyWgfqzjHSU3DhDR88wrRmMzoo6PRR0cj9/Ii8K03UXjfOlugKSmZwuPHcWndulQkj3kKS4GDONEOUIaEoKnfgFRDN84fPEVemIrUYi1pxaU5A40lxZzd+zMtm7QkY/FHmHNzcWrenKITJ8BiQdO4McUVQsYZLmwDw0VCV65D/8v3pKz9ycHMIKhUeI0aZb8mPz/C131OxsdLKbl8GU2jhng/86w14oiEhISEhITE3x9JKMvleD7xBBlLltiUe48bB4JAxocflorotDSyrl611pvS0yk+fRr3wYMpOLAfS74embMzxuvXrW28Corxz8kn2cMVwSJSIz2H2qlZdmuwALlOalyKDXjpba2vxefO2bwWDQbyd+/GEJ+Ac6uWODVvhiE21vaYVKpKXR1uYBaAtHQyXTScC/Ih30mNxmAkMiWLoGw9yGR28ZxlIlzxdadWWo61LNNFQ4bOiXqX7Y/LnJlJ4pSp1Nr9Ewp/f0wpKVWuSaZSIYoiCU+PrZZVHAClEr8Z0xEEgaD33+Py9Cmczsmxa6aPuUzs62/bWMc9x4zGc9hwRKOBmO497PpY8rNQ+rmQ/9MuuzoA5zat8ZsxA039+g7r1bVqEfTuO9U7DgkJCQkJCYm/Hf95oQzg/fxzyL08yd2yFQQBt0f74DF4MBea3SKrmyhizsqi1g8/AKX+wWnvLiR3x3b0BQXEe+lIdi+1aooyAYNCbutfW8a1EH/8fAOIOHDULnlHwa97SZr1IgFz52AxGLg2fLhN2Dq3vn1RhoRgTCjb9CaX4z93LmJJMdnrN2CIjUWs4DpxqA60uQQlchlHawRglpd64BSrlJwM8cW5xIiPmyem5GSbfmqTmXyNil8jQ/AoKKJQpSRT60T9pMr9oS25uRSdOEnggje4PmGiveX+xth16qBp0oSiEycoqSzrYRlyLy88hg9HsJhxfeABVOHhACh9fWkxfwGnpzyLKNqKfI8zF+xcSLLXrcd73DjkWl80DRpQfPasTb0uKgrAsU8z4P3MM5WKZAkJCQkJCYl/PpJQBgRBwHPoUDyHDrWWFf7+OxQX37Jv/s8/c75RY1zat0ddIxxLgR7TE0OI/vUHu7Z5Tmq7suAVy6nXsWNpchMHbg2IIrnffEPJ1aso6tYh79IljCql1X0j95tvcOvfjwKjEUGpxPPJ4biXhSgTLRZSX51nM1yKB+xpJKPNJQsp7lqrSC53Mkj00BHgbi+UARonpHPZ34NkNy1yi0j9xAzCM6sOM5fz9df4TZ1C7ehfKDh4CLlOS/GVK2R99hnG1DSc2rcnaM5sBEFAdOCWUhGfCRPwGDjAYZ1nYDDdnxrPnrWfYigqRKFU0brv43gs/IiKZ1csKsKUmoogk6EICIBz56yRPrRdu+I7pTRWs8egQeT/8INNFBB17Vo4t259y7VKSEhISEhI/HORhLIDRIuFpGnTq9e4zEpZsGcPBXv2AGBQyBHqh9n4JgPoim0TWQhKJc5NmwGg8PevcprikycRT57kfIgPSZ6uaIsNNL2WimuxgfRvvkJVpgJTX3sduU6HW58+GGKu2o1zKlzgZA2BPCcQqgjhbMrKIuj998n+/HNMubk4N2uGtltXEidNpm5yFnWTS10tXDp3pvjs2SqjbORv307h4cPU+PordF27AJCtc+GXyBDyPNUIOYnU+2o9PcdOwLlFcxQBAfbW7EYN0dSqjVufR3Bp27bKc9W4WxT1OnQmPeEaBVmZIBNQNG2CeffPNu3kPt6oQkNJeXWeTVxlKHVfkTmXRi1xaduG4MUfkvHJJ5iSknFp3x6fKVOqjP8sISEhISEh8c9HEsoOMMTE2Pga3y4ak5mItBxi/G6GCNOo1NRKzbFp5/bYY5izMin47YDdZjJHCEBkSjZJHjr0GhXHw/3pfCGeOD+ok3SzXdbqNbj16YNTk8alm/XK4ZkPRqXAm4/LGb1Tj9zsZWtVFkWCs/MpcXLBtVcUrr2ibPqHb9pI9tq1GFNT0XbsiMegQRgSEkh7512Kjh9H7uWFWFyMMSnJpp85I4OczZvxGT8es8nItoXzrYlERNHCuV9/xis4lNZ9+hPy8RKSZs6i5MIFBCcnPAYPxnfaCwiC45jTjijS57NzyXtkJ5W+j2onJ1oEB+B+vVSAC0ol/q+8AgoFud9+a9c/f9cuLIWFVrGs69bNYYxlCQkJCQkJiX8vklB2gNzDA+RyMDtwhagmkSlZeOcXUjJsMK41a9GgczdKtu8g+4svEEsMuD70IKbMLGKiet060185nIwm5BYRs1ygUK3kQKSa4CxbS7W5bDOb6wMPkLtlKwUHDljrmseIhKWKXA4SmDlaoOb1VFpc9MQrT0ZEeg6e+iKyXDSkRwTT0MH8mjp1CJhn686hrlmTkI9vbobM/GwlaW+9Zdf3hnhOvHDeLtsewJXDv9G6T380desSseUbjKlpyHVaq1itDpaiIgS5nF8//8wqkgFKioo4UzecxydOQSwsRNvlfpS+voiiWLpxsSIymeNyCQkJCQkJif8MklB2gMLbG7fevcndsuWOxvEqKCbsvh44Ny91r3AeNAiPQYOA0hTHCWPG3PaY+WolumIDBoWMQrWKQ01UtN1s4HyAJyluLigsFhp7BuCxYgXKsDCCl35M/r59JC1YAPEJyESYvd7Mt61lnA8V8M414FxiouOlVJRl6bR1JUaSdJl2c1sMBkouXkLu6UHB3r0UnTmDpk4kbo89hlzrYm3n0raNw7W7tG1HkT4fhcpx8g5VBUGs9POt9nkxpqWR8r9X0P/6K4JajdpDi+DjauP+kpeehtiyGR4BQdYyQRBwbtbMLkOg6wO9kGk01Z5fQkJCQkJC4t+HJJQrIWDeq6giIsjftQu5Tkvh8d8Rq7G5rzzK4GCcmjZxWHer1M2OMAsCaqOZ9lcSAUjzd2fktA/5/tQc0tSl+bQbXE/H7dJ10g4eAUDdsCEHQn3wLMzjRn4/bTEM/rU0KkSJXEa2S55VJN8gID4JY1oaSt9SsZq3axcpr8zGnJ1t0y4XyPnyS8I3brBafjX16+P11EgyV6y6aS3v0Y3vD/1C8udLUao1uHh4UpBtG1KuWa+H7Y5ZNJvJ+OgjsjdvBoMR14cewveFqQgqFfk//4zhaixOzZqR8eGHFB49WtqnuJjg5GKKLGYu+9/MhCcIAhmxV22EcvbGjXbvhap2bQJmz67sbQCgyFTEgaQDqGQq2ga2RSmrPHNf7nffk/nJJ5hSU3Hp0AHfGdNR+vk5bGtMSyPzk+UUnTyJOiICr6efQl2zZpVrkZCQkJCQkLg3CKJ4G8/97xJ5eXm4ubmRm5uLq6vrnz39bWPKyOByx0631Ufh40PIpyvQ1KnjsD5z1SrSFrxZ7fGUNWpgSEtFKCi0KRf69Ob7uNJYy66FJXS8bO9bfS7Qi3SdMx0vXUfu4O0uVsjRmOzdTMI+X4tzy5aYsrO5cn8XxJISuzY38Js9G4WbK/o9e5DLi/CwfAOmAooyVaj8Pdigb0t2WrpNn8DIeuRnZqDR6qgREUlk7Xp4t21r42qR9sEHZH681Kaf7sEHMSUnU/T775WuB6BQqSC6fphdeXPBiXavvo6mTh2u9Oh5M7ReGYKTE3UO7EdWSebFE2kneO6nZ8k15gMQ5BLE8p7LCXENsWubHx3N9XHP2JSpa9eixrZtdj7XluJirj7SB2N8/M1CuRyfCRPwemqMtHlQQkLiL+efdv2WkLhTpCtvNRArJN6wolQiqO1DvgE4t2mDKiQE/f79dklDctNSOZSdQnSDGhyp4U+2k+qWa0hRy+1EMoD+l1+Qla3Po9CxxdujoJiaadkIZSK5olRWOxDJgpMT6shIAAr27atUJMudTLhHFJC/YRmJU6aSu3UbWV/vIvY7ZyxGAbewInIs+XYiGcBiNtO4Wy98fjuC53uLyXx2POfbtSNn61Zrm5wvNtv1y9+585Yi+cZxyRy8d+dK8ol/eiyi0Ygp3X5dYlER5vz8Ssd96ccpVpEMkFiQyIJ9rzpsm7Nxk11ZyeUrFJVZv8uTv2uXrUgGMJtJf+89Ut9YUOl6JCQkJCQkJO4NklCuBkpfX5zb2Yck8xw6hIA3HVuFZTodl+/vQsLoMcQ+1o9rT47ArNdjLClm09yZnD+4j0KFjHRXFw7XCUFo0rjS+dN1Tpw1OxbBmvwCep2OpePFBKsQrojaaCI4W299swVsxbIyKBBFeVcAQcBv+jTkutJkKXk7dlS6NnORgvxEDYWXbMO5WUwyMs6V9pfZSfOyvgYjMcs+Jjwtx7o2WYmB5BdfwliWxc+hQK/sxqUCAiIWB1bYYpWCkrRUCg4dRnvffXb16vr1rC4nFUnOiifebC+uD6UeQTSbKTp71ibih6USdx1LUZFdmSm98hB7ORs3Yi6XUtxiMJC3fTtZq1dTctU+DKCEhISEhITEnSMJ5WoS9PbbaO+/H2QyBLUa90ED8ZkyBbdeUbgPHGjTVtO0CXnbt2MpJ2wKDx0ic+lSrhz+jfwMW6FlBlIe7kngu+/g3KY1eToXzgV48XuoLwdqBXEkIhC9sxqNg01yN1wpXIsN1EnJIsPFdgNaoUqBk8Fk1+/GQ3/PkSOouX07NXfuIPDNBfjOmEHE9u/xGDwYAGNiIvroPVWeG3OJvNyINzHkl7rA+znp8dHar8E9IAC/3AIHA5qtc7o++GCVc5fHqUULUCiQabUITk7IRHBzYGV3LSxBYRFBAL8XZ6Eu5x6jCAggcP78SufQJKShKbEX/q0uWbjSvQdx/fpzpVt3rk+ejMVgwLVXL7u2ck9PnNvYv5faTh0rnVc0GjFllvp0m9LTie39CIlTppL6xgKuPvgQmatWWdsaU1OtNxoSEhISEhISfxxpM58DCn//nbwdO5CpNbj1fRR1RAQKb29Cln6MpaCgVIyVc7kImDsHtz59KPr9OKoaNZDpdMQPG243bu62bzEmXUdXVEJ+hSx9RVlZ5B8/Q+HRY+gAV4WMM97eVouoT7ER/1emkb/zB/TR0Zjz8jClptqMobCIeBaUCsNiuZwkTy0xvh60v5wIdnnpwLlTR3ynTbP6vrr16WOtM6akkPf9dkquxlTbglsRtdeNLHsCfccM4+f9cVw9fgRnV1daPNwXJ50r8V9vc9hX7u4OgO/06Zjz8uwy4zlCptMReewogkpFyfnzJL8ymwYxlzlSMxBjWaxohdlMg8R0FIEBuLRpg6BQUGPrFopPnsRiMODcvDmCovKvhS4kgoeXCHzZvtxazSLjtouYSsqs6qJI/o6dZNaqhfczz2CIjSV7wwZEgwFlWCiBbyyw+fxYz1ft2vhOm0baO+/YHasyOBhVeKm/dcYnyzFcu2ZTn/7uQrT33Ufqa69bwwE6t25N0LvvoPDxqfK8SUhISEhISDhG2sxXgewNG0iZe9PfVFCpCFm2FJd27SrtU3zxIqmvvU7h0aMog4PxGDyItLfetmsX76njqq8HxUo5aqOZIvXNSAkd3fxx/XW/XfuzwT745RbQ8Ho6nl26ELJ4MQApr71O9uefV3kserWSXyNDqJGeS71k23BvmiZNCN+4wWESj4LDh0kYOw7RgXtAdSlQKTlWN4j7O/gQ+eg4CGtv18ZYUswX40bR5OAJm02GokJO7eholN7eQGlc6NS33yH3q69uOW/QewtxfeAB62tzXh5G4PTi98n7JRqfxFTcWrTAf87sSqNJmE0mrh4/TH5GOqENm+AdGm5Tn7rgTbYdXs2++gIqEzxyRKBWov2NiLp+PSK+/rp0zPx8is+dI/+nnzDGJ+DcqiUeQ4ZYNy7mpqUQd/I4zu4eBDq7kjxhAsbEUhcOuZsbwYs/xLlVKwBi+z9O8ZkzdvM5tWxp5/vs3KY16tp1MCYn49K2Le4DByBT3donXkJCQsIRf+frt4TEvUASyuWwGAxc6XSfjS8ogKZJY2psst+UBZAZG0Pi8BEoK/qXVkhYkuLqwvEaFdJUiyIyhYLmPR/G9+0P7BKciIAogOzGO+TkhHPz5qhCQnBp15bEyVNuae3dVzuYPCcVLRQuBCVnYCkowDWqJ55jxlCwfz/mvDycW7XCpUULa5/Yfv0pPnu2ynErwywInA72JsVdh0UmoFCpGfvxajRarcP2yV9s4vdFC3EvLMHZYEJmsaC0iPjNmonnk09iMRiI69ePkstXqjW/x7Bh+L/0osM6URTBaESoQigW6/V8MXcm6fFx1rIOA56gbb9BNu3ytm8n/6fdyNxccWnfgcTnn7cby7ltW8JWrQTAcP06cf3623y2nFq2IGztWk7u2sHPny1FFEvfS8+gEAb873W4fAVLYSEubdrYROBInD6dvG322QQdJckREbhY63HCE3ahKclG27kzIcuW2veVkJCQqAZ/1+u3hMS94l/veiEajeTt2EHh8eOoIyLKkmM4Fm3m9HQ7kQxgcCDSMq8n8N37C8hIuAYBrvg5y2mSkFbq+wp2giXBS2c/oSDw8KQZ1GzQhIvvLLKvBoTytzFFRRTu308hkPv11+R3uQ/53n04G0yYBcFh6DexzGDsP/YZanfuAkDh0aPE9n0MsfBmFA1lcDAhy5aiioioVD9N8JcAAEHzSURBVCR7P/cchrg48r77zmE9wIUAT5I8b/54mgwlxP62lxC1C6qICFTBwTbtFekZ1MjIsxvHcC0eY2oaqa+/Xm2RDKCOqFFpnSAIcAtr6rHtW2xEMsCBzeupf19XXH1ubvBzffBBG//p7LZtKTx40Kaf57AnbtZ/vs7us1V09BjZP//MnjUrrCIZICsxgcNbv6TLiKcdrtH76afR/xKNpVxkDrcBA8j//vtS16ByWGQKkgM7kOndiLaH56Hfs4ei02dwauQo76KEhISEhIREef7Vm/lEUSTh2fEkTZ9BzsZNpM5/g7j+j2POzcWUnY2pQvIMhZ8fCgfRDjSNGtmVWUUygCCQ6q7lUrnkFhWxOHBxAFCq1MhcXHDt2bPSvvkqBeYK/UWDAfHsWaLrhbGzUQ3OBXnb9ctxUuOdX0i3s3GIY58lbuBADHFxpM5/w0YkAxivXydxylSMxUUQHGQ3liIgAO9nn8F3+jSHa5RptVzv/wjXfNzt6rJeeoWEp8cS06MnyXPnUv4hhnOLlg7HU9UIJ/bRR8n/8UfH83nWROZmG7dYVbMmbo884rB9dUm6eN6uTBQtJF2+UGW/4MUfInbrQonWBb2XB4aRw9B27WqtNybax7cGyDp5ApPRYFeeePGcg9alqGvVKnUHuu8+nNu1I/DddwiYOwf3QQPt2ib7t0OUKSjReJLmU5oh0ng9wa6dhISEhISEhD3/aotywYEDFOzda1NmiIsjbvAQDLGxIIqlAnDcONz790NQKPB+bjwps+dYN1MJKhU+U6fYjJGVlEhGwjUEUcQvtwCN0USGzpkUNxfqJ9mnfgYIzNaTqbNN0ezi4UlBTjafTRpLQUoyLWuG4x6fiKBQ4N63L87t2xP/3VZiz56kcUKa3ZgueQXg54pFJiPByxWF2UKNtGzUZgsmmQwQqZd8M/td0clTXB0wEDHP3oILUHLxIp+PfgKF0kyL8i4fMhm+U6ciyGQofX3xGjeWzKXLbnaUyQh8+y1cQwK5OGsqtRPT8MkvpFipINXVBc88fWk7USRnw0YK9u3DlJOLKiwUTZ1InFq1oujIEetwup49Kbl02S4LYHlUNbuh8GuITHYaQUhFXScS9wGPI3NxqbRPdfAIDCb+zEm7cs/AYAetb3Jiz09EZ8RDzTL3muMHyNuwmk5DRgDg3LoN+bt+su0kCLjffz8cjrbZvGcWRM57pDBtzzQCtYEMihxEgDbAWp+9cRMpr75qdbsxZ2Tg0r49vpMnI9M4kfr5FxiLTaT4tSE2/KbV26DSgUKBU/ObbjYSEhISEhISlfOvFsqGK44f2RvKxZ01JSeTMns2BQd/I/i998jdus1GtIgGA4WHDuHc+GacY5VGg9Jsoe2VRHTFN6yBmcR6uSHTarHo9XZzhmTnU6RSEOfjjkkuwzsgiEbN2nBq/jyMTmoMGhUHtHJ0HZsz6oNl6Ld9S+rcuZx2EhArsUargwLR6HQU55cK32vertQqNCLk5qG0WHAvsrdUViaSodQnurhQT3h+EQa5HEG0IGqccNE4kTpvHoVHj+A3bRq+kybh+tBD5GzejMzJGbc+fVBH1ED49Vc6n7+GwlQaCs7JaMa90D4OsjGh1LpacuYsJWdK3TxUtWrhMWgQmgb1cW7WjLgnnrDrdwNFcGsUgc0RBBnaLo/hFhVeadvbpeXDfbn0216K8m+ep8h2nfANj6iy35Fvv7Yr+/2H72n3+FAUSiXuAwegj46+mS5bJsNn4kScRWjtH8bJ+CuUKEu/jnuapRHvFw9xpefm68tfs+GhDQTrgks3Nr7xho1vesnly2Qu+wS/mTPwef45CjoP4vslp24uRLTgm/47gSkH0N7fGZmTbQhBCQkJCQkJCcf8q4WyI5eJysjfsZO8Bx6k6Ngxu7rcb7bg/dRT1tdaTy9aKFzKieRSwrPzCVqxnJTZc+zSIgPUSc2mZloOJrkML48Qche8g9bHjWKlArfCYnKdNRSlp3H1tXmYvyiN8FBYK4g8JxUlCrlNBj0R8Bo6lOE9u3N69w/kZ2YSVmREPL2i2sfsiGbXUm0FdkEh5rKMgDkbN2HOySX4/ffAbEYVHIwyOARVWGiplXPuXBQV/KQFoFgh47K/JzVTc3A22sdThtKbGkGtwrlZqXuAU+MmFB2t+F4IaNo+j9K/1L9WUMpwaelHdTAkJJD/ww8ISiWuDz5Yacg0dz9/hr25iFM/7SAvI52wRk2p27HzLcdXpKTiX1RClosGQ5ngNRYXYSwpRqFUIlOpCP10BYXHj2OIj0fTsCGpr84j/b338Aa6Atc9tJwPkRPvbxv7Oackh7Xn1jKrzSwKDh5ymISlsFy0i/DG3jTtHsLJn68jWkQanf8Mn7TSTIb6n3YTe6Ef4Zs2ovDyqta5k5CQkJCQ+K/yrxbKzs2b49bnkVIr8Q1kAlgcB/owxF9zWO4oskSgXE3FVBaCxULS9BmYMyrPsCYXRRQqNfroaI5FBJBRzh1DEEVaXE3CfCbWWualLyLHRcOhiADqJ2XiUVBEoUqJk0XENSoKhYcHbaJ6k/7+B+R+910lOfCqhwC4FxkoUcgxywScHSQqyf/xR1Lmv0H2mjXWMmV4OMa4uErH1ZgsyC0iuc5qnHMdC2WAkgsXrf97jR6F/uefMZQb13PUWOS+92G4no/SzxnXnuEovJwcjGRL3q5dJE6ZCsbSuM7pHywiZMUKnJs3c9he5+VNh4HDbjkulD5xuD5lCh0ulKaetghwPsCLaz7uBNSpi5PWdhOnc/PmODdvTsYnyyk8fNhaLgAh2XoSfcDR1/Ja/jWKTpwg+aWXHK5DFRpq87pD/9o07R5Kxq+HKYy2TfdtvH6drLVrce/XD4WXlzVEnYSEhISEhIQt/2qhDBD45pu49e1L0e+/o6oRQcGhQ+Rs2OCwra5rV/S7fqLopK2PqtujfezaamrVotiB9bkqkWztW7cuKefP2IhkAFEQSPB2wyMhjSKlAo3RTERaDhk6J3KdNRyuGYggijS8no4uK5/EiZPwnzOHlDlzbETXnfJzvVBEmYzuZ2JRmSvcJFgsNiIZqFIk36BWajZng7wJcJSJrwxN/foAlFyNJWPxh1hMJtT16+PcsgXujz2Gpm7d2z4W0WIpdVUwGq1llsJC0t56i/CNjj8HpuxsDDExqGrWROHhUeX4OV99hf6n3dbXMhHqJ2VSXLMGUWMn2o6bnk7K6/PR795d6Q1NvQQQLCKizNbdRiEouDR7JqoC+/MnODvj9dQYu3IXdzWGkhQK7Wogc8WnZC5dhqBU4jlmDL4TJ/y/vfsOk6q+Gjj+vXd62dle2QIsS5FuARQRRIINjVhRsWGsyStEY40tibEkb8xrAxuCiQV774iKioCKiEgvy7KF7XV6ue8fszu7s3MX0IAQOJ/n8XmYW393IJkzvzm/c2L7tFAI91dfoQUCOI46Kq40nRBCCHEwOeADZQDHmDE4xowhUFYWrWqhKDS98EJcCbe0Sy7B0rcvvR58gKrbbsO9+HNQFNA0Wt57H9vIQ3GMHhU73j5mNE0vvRQ322w/6ig87V3RdiZUV4vPpP/WN9ssLBrUm4DJgCkUZkBVA0dtrKDeacNvMpDe6sXanoLhWb6csksuSejQ15VitaL5Ets4hxUw6ERrzTYzWnunvsoUJ73r43OaDenphOv1Fyx2tz4nldwmNy5fAHM4QoPTxsbsVPo1tqEEgnHH2kaOxDXlZMJNTWybPp1wQ3u7ZsC/YQMpZ521W/fsLlRXR6iyKmG7t4cSeHWPP0Hdww+jBQIoZjMZV19NxpVX9Hh9t87ftwKcPPnXpObHV+Uo/93/JHwJ6y7ZC2d/HuGF8Ya47Z+Xfcpv1yc2NQEoevpprIMG6e6zDemhDFx7HrkWDFI/Zw7WAf1xnXACgbIyymZcSrA8mkduSE2lYM5sbCNG7HTcQgghxIHooAiUI2435b//fTT4BUx5eRT9+1+EduwgUF6BY8xobO2L9UzZ2WTNmsXWL5fEAmn/+vVsv/JK+n34AcbMTLRAgOq/3p2QkmE/6ijCtbX4N27c6XjCbjdZKemokUisRXUHt8UUDdCBoNHA6vwMXF4/GW36XfJ81dU7/UvUC5IB1uek06+6EXOXZwgpCmvzOsvMrc9NxxyOkNvURsf8prl/f7xffbXT5wMIGlRKM1IoT3Nx7NpteE1G/EYDG3PSOOqpf5GkGvBv3Ihv9Y9YBvTHNXkyislEwyuvxILkzoGFKD37HMx9euM85hgyLr98t9MFjGlpGDIyEmb6VYcDLRKJte+GaOvy2vvvj73WAgFq/+//sI8a1WOahjE7R3e7KS837rV/06ZdBskdzliiceRWA1se+C0Pf/cwESJoCrgt4OiWnmzMzsZUVEjt7Nl4v/kWU1Eh6RdfjLko2u7aOmgQKdPOoWmBfsOcDvXz5uM64QSq/3p3LEgGCDc2UnXbbfR9S6fBiRBCCHGAO6DrKHeofejhWJAMEKyspPKGG0k64QQyLr8sFiR3aH7jzcQOZ14vLe9/AIBnxQrdFAv3woUUPDoH54QJYDBgyMhA0QnoHKNG0f+55xjdq29nCTbA5g/EguQYRWG7Tm3iDhFVIWD46X+NGW1eyro0QWm2mfm8fz4Nzs6f2cMGlZWFWQQ70gAMBjIuubjHa3Yc12I183WfXMIGFb/JSK3ThttiIrPVw2Enn0ZGcT8sffrgmjyZrGt/T/LJJ6OYou28IzoNXwDCXi91WzZT9cQTlM+cFbcvUFZGxXV/YNNxkyi7/PK4gFQxGkm/9NKE60WamuJz14G2Tz/TvXfbJ5/0+Mxp089PKElnHTYsoeW5FtafDe5Jv/xh9E7uTYToF5mJ32sJQTJA8tSpbP/NZdQ9+BDuJUtoen4BW88+h0CXYDf3zjsxl/Tb6f06Fgi6uzVNAfBv3ESwJrE8oRBCCHGgOyhmlFsXLUrYFiwvx79+fQ8/WfeQQdpe0aGnWr2qw4GpVy96/eN/8axejTk3F/+GDVRcex1aIFpJwpibS+Y111D70MOkvv8xEw0qDQ4r1mCYRoeVtTqNQ7anOgmWFHNUcha+V1+P22eOaARU8JiMPVaU0JPZ6sEYtsReJ3sDjCyrYVN2KjUueyxgz21qw9yx+NFgoG72HDAaYz/dd/V9YRaNDhtBY3zagEGDFI8PSyhMYUviDHfbZ5/RunAhqsuF7bDEGr81SXZ+KMhEjWj0amylaeW3pH75JUljxxJxu9l2wYWx9JNgRQWer7+h72uvYu7dO3p/l05XRKDt009JmXpa5zhTU3SPM6T13EjG3Ls3vV98gfp58wiWbcc+ahRpF18UN1MdbmrCs/xrDKmpO60N3UF1Osm64Xr8rujfj9WvMmKjGZ/RH0u76dAwdy5aMD6NJdLcTONzz5N9w/VokQhLX15Ayi66Gya3vw/G7GyCZWUJ+92ff07KGWfEb1u+nOY33oi+D8X9sBQWRHOaZXGgEEKIA8RBESgbUlISP/wVBUNysu7xrlNOpeHfz8SlVigWC+7ly6mfOxdTQb5upYfU886l5b33qLrt9lgtZdfJJ1P84Qe4v/gC1elEsdvZdt75sYDJHI6Q0xJdbmULhtiYk0rI0CXQ1DQGVtaRsqmCygH9yUhPI1Ifn5pgjmisyUthRHniLLchN4dw1Y7EhzQa2JiXwYitVVjaF+yleP0cXrqDurRk1gwtIbuumX7bazvPCQTwrlwJgN9oYEVRNvZAkOKaJpz+IPmNbdQkx7cHt/uDtFlMrOidQ8igsvSzdxmTlcaYs88DoPbBB6PBd4d58+POD6oq3xVlk+72MrJ0RyyvevsVV1D0+OMEq2sScrQ1r5emV14h67rrou9Bqn6ga0iLX6iXfOqp1D/+RFzqhyE1leRf77zbn6W4mLy77tLdFygvp/TccwnX7mKRp8FA6nnnYS4qwnrIIJpeeQXbjmpOy+xH0sYA9Q6FlUUexmyOz7fuHiR3CFZWArDs9ZdY8spzjDcbcehUMQGwjRpF2oUXokUiGDMzdQPlmgcejAuUm155VbcCh5qcTMHDD2E/4oidP68QQgjxX+CgSL1IuyCx1FfSpEmY8vJir2vLSnnnwb/zrxuv4culn5F85+2x/ebiYlSXi7aFCwlVV+P95luC27djO3IMis2K6nSQdNJJWAYOpPKmm+MajrS88w6tCz8m5cwzcU6YQNX1NxBubCSkKpRmuPCaOoNiSyjMqM1VpLd6UDQNNRLhiC2V9K1rIc3tI23FKvAnNhEBSNVp7AFgsNlRnIkz4NtddohEYkFyVxkNzUw77XwG/LgRg6Y/u24JhbEGQ1SkuVhS0guP2UhOs5vB5bVYAyHQNDJaPQyqqGNNrwxC7ekhYVXly1eeo3ztasJNTdQ/OTf+wt3yvmtdNsKqwqCK+rjFh0ooTMX1NxCq0V/I6N+0OdYq23nMOMz9iuP2KxYLqeeeG7fNmJZG0TPP4JoyBXNxMa6TT6bo2Wcw7mRGeVfqHn1010EykDptGjl/vAX7EUdQ9pvLaFrwAuXLviJlQxCDFp3dtwZ3P32jI/Xjh0XRdKGNOWkJv5O4Tj6J3guep/e/nqbxmWfZOPZo3TriAOGaGjwrVgDR1vC1Dz6oe1ykuZnt18wk1EMKjRBCCPHf5ICbUfb+8AP1Tz1FqLIK+5gxpF/2G5JPmULrxwtp/fAjiEQwpKaSMv382DnNNdUsuP0GAt7ozG5t6RZKs3O46L13MYRCuJcto/zq38bfKBzG9+0KtEAADWh99108P6zSbQZR99hjNC9aRGuyE1NzMyFVYUm/XrTZLBjDEfIbOwPrFK+fUVuq+HRgAX1rm8lsi09V0Ov6t6NLqkR3iqrS/4svKL9mJt4VK9DMJjYbNTZkpTC4oufqFZXXXQcmU1xZte5s7akeIYOBsnQXA6saKKpvoai+BQ1QVQ3vUIWJ2Zup8TlZ25JFWIsGzF9ffx2HF5XEUlJ6YgxrGCMRHIHEcYTr62l89TXd89o++YQNRx5J8pRTSJt+PkXz51M3ew6er7/GVFRIxuWXY+3fP+4cTdMI7ajCNnw4aRdekJC7vjNhvwcDYbDEp3n416zd5bmK0xkr79Ywbx6aJ/rvsMYVn8LQ4LARIfHbrbFXL0KVlbHUIOfEiaScPhWAUPv7W5mahNdkpFdjK6oGI+78M5nHn0DE62X7zFm0ffDBLsdZ/8ST2OfMRvN6d1ppJdLYyJYTo0F49/rOQgghxH+TAypQ9q1Zw7bzp8eCL+/33+NevpzkaefQ+n5nIBBubKRy5iyKP/yA5rfeouzFBZTU1VCakYzbagaguXoHm77+ipLBw2l+513d+3UP8sLby9mcmUzvupa4mVhffR0fZdix1IYYC5SnJtFmi+afbs5OJbPFEzezW57qxOX1Y+0h51hxOAi73fhMRipTnZRmuOhf1aB7rG3MaMpmXIq3fTYQu536bBcDdjRS0Nja85sZieg2WumqztkZyHUvd6eqGoUT67BndAa4Q1N28GLZMMKaiqGllbaPPwZV3el9Mtw+nDl5eDaU6zZACW3roUkMEGlqpvGZZ2h68UUKHnuUnNtvSzimobKClroacor6UnPdH3B/8UVsX8pZZ5H7lz/HHe/59lsan32WcFMzzknH0Tp4IE0vXMNAwxoMhjDe1MHYzn8aMkoAsAwaiG/NmvibqiqW/v0Jt7ZiHzmSrD9cB6pK1W230/L++7HDLN3ykX1mI2vzMjhkRwNK12olFRUAmEtKyL3rLuzDOwP8gUcdw4r3oosW3RYT29NdJA0fTubxJ9C6aBEV19+AplObWU+org5/aSne71ZizMkhtEMnpadduKGBujmPknXdtbR+vAjFYiZp0q8w6Py6IYQQQuyvFE3r4bf1vailpYXk5GSam5txuVx77LqVN91M8+uvJ2xvsFtJ8yQuIrMOHYrvhx9ir0OqwpKSfNrag+Vxp51N6py5hH7Civ8f8jMwhyIM2NEZuK7qlUF5RjQfeszGCipTnZRldOZHm4Mh8utbyGnxYA8EiSgKVSlOTMEg+c3R2cWAQaXFZsEejpA35dRoDedd0BSF5DPPpKXbsQGjAUMojAFosFlwBIK6KRgxJlN0trKj9i6wOSuFDbmdLZCHl1XTq8vMuKvIQ68jmxIu9W7FADY2ZjBufRm2jlSC9nrVurcuKCDr+WdZ8Zc7yfxg0c/OFbIOG0afFztLpIVDId575H7WL1kMQFGLl8FbKxPOK3r2GeztCwzbvviS7VdcEauI4jUZaBxr4bic+IVyYVchhlnfg6oS2L6d0vPOi0u/SL/yCrJmzYq91oJBtpxyalwXQojmZy8eWIC/y5cQW5KL6Tf9ibZ5T9P8yisJ481/5GGSjjuu8xp+Hx89/jDqiy9TUNuMCqgpyeTcehs77riDyG4GyT+HKS+PUH197FcWQ2oqhfPnYR0wYK/dUwixd+2tz28h9lcHVI5yqLZWd7vSQxDWNUgGMEY0+tQ0tZ+kkLJ2o36QbDBgaq9T25UGNDht7EjunDWLQCxIBvimbw7BbuXcAiYj5kiEFK8fcziCNRSmT10zSYEQEWBbuotFhxSxvDiPT/vn80NuGo5jJ+g+U/yANGrfeD1hszkUJmQ08G1RNkv75/PJIUWsys+kMtmBbrgcDMZVuShPTWJjdmfebq/GFoYXVaAYOs+2puinbBQqTYzeXNEZJAO5996D7fDEahcAqtWKKyOTCQ88Ehdc/lT+9evjXq9a+F4sSAZIatafXe/IywWof+KJuLKBVSlOBroS/30YWsqg/GsAzAUF9HnlFexHHIFitaK6XBAOxy3Ca1u8OCFIBjBFIhy5tZr+Rf3ILOzDoLETmPbnv+PqV4JiNumO17d+PZFAgKaXX6bq9jtofeElxmTkU9QeJEN0pr3q5pv3SJCsulw4J0/W3RdqbIxLRQo3NlJz39/+43sKIYQQv5QDKvXCOnIE7i+/jNsWBprsFlK98bnDPZXqsgeCqMCI5CxMa9aht3zKmJlJcNs2wooSS7HQgA05abgtZlxdFtapgCUYis0KhgwGqlK7lSvTNAq6dcADcPqCLBpUSMBkjMtB/uGzhRSk5CUc350CWLrl9oYVhR8KMqlMccauGVFVytNdlKe7GLq9hoIG/aBRsVjQ/H4KGlvJcvtoLeiFM6KR2X8I9lHH0bjpNcJt0Wf3NpiBxEAsZWsAg7dzAaMhLQ3zMcdgb2nG+03iQjKlS/vkjN9civ/HH2n96KMen9lnULHqzI5bu3Wo+/rNV+Neuy36gWdHiTmA4I7EDn89/hyjdj5j3ezZeL6OBs6az0f9E0+iBQJk33wzEC0fp8c6bCh97/sbOXNmx1IyvNtrCN9xB7bBg9E7yzpwIGUzZsS9l3rlDHuqlvFTRVpa9LsT2mxo3sQmOV2/eAghhBD7uwNmRjni89HyZnz3MA0wAH3aF5cFDCqYTDjGH0P+7EeiKQXdWIMhJq7eSs5nS/Ct1V+I1ZGbadA0fsxLZ2VBFp8OKmRzdrTcWGF9/Ir/kaU7UHXycFNbPbHthkhiyKVqGiGjQXehXujrb2J/3pHs4LuibL4vyKLBYdUdc4fN2SlUpib1uPjPM3ggitmsu091OOjz6iv0euABBr7wAjkeP9at22hd+Alldy8gEukMDlvLrbTtsMSd37bDQrV9KFpH58H8XqwYeyhzrryA5z54jdX5mQkz2s7x42N/VoxG8h96kKKXXsSdmljar9VqgosvwNy3b9zzqQ5HNA+4nbupkdb6+F8fytNcuM3x3xttw4eTdOyxsdfdm4jkNrn5sTE7YRyhlL742pz4NmxACwRofu31hGMannmWcHvXRHOxfjOQtOnTqfnb36L/roNBCAZpeestqv/yF1ynnILt0EPjx3vYYQTr6xO+cPyUmWNTr167fSy0f3nSWWCq6dTZhvgvHkIIIcT+7oCZUW796KOE+q9Ktz+bwxF6L1iAbehQALJvupHqu++J+zk9rtZsOAw2G3SZGbMOH4bv+1Wx1/2rGqh12SmqbyasqjRbzVjDEbQu90/z+JncplGfYiNl41YIhag3G8hp8dBmMbE+J41al53s9nrKHWpc9oQW1zF2O3i8bOqWK1yR6uTQbdXkNOsHR1Xd6hx3lz84ibxp91N1+x1E6uOrYrhOOglz374Eq6qofWQ2oapuNX09XcavKWz/LA1nng9rahBfo4m2SitritpoGlSIORxBLSigpWwrAOFgkLJ0F+ZgiP7VnTP97iVfkvnbq+PuE66tw9GYWH5sfU46Uy64kNTrbyKwbRstH3yIYjbhOukkTFlZsePaGuKfK7PFTXFNE6qmYRk8GHNeHvYjjiDlrDNRjJ3/E8m85hqa33o7tvjNFgxhW2limbWEIcml2A1B3MnDaFjiwP1otOqE9ZBD9Ct7hMM0PDWPzKuvomnBgsT9RiMhj0e3K2DLu++Se9dfKHp6Pq0LF9LywYe4P/8c77ffdi7a7G4neeAQbYSTd++92EcdQc2999HwzDMJ3Sn1mHr3JtAtrQXQr5aiKGRe8z+7vKYQQgixvzhgAuXdXXBXfddfyb37r1iKi0k7/3ySJk7EvXQZgdJS6h97LOF4RdPIvuN2gjuqCdXX4Vm2PG6/SdPI6xKUZrR5UfoVozVvjjtO3VpKZpfXOe1rC53+IIdt6yy11RFg+4wGGm1W1EgEZ3YuLbWdx5isNpyHFeP+8CO2ZMU3zUBR2Jid2mOgbOyhwkSy28ew8hoc32+i0rAQLdwlQFdVXCeeSNLxk9kwfgLa7tbI1RTaKmy0Vdhiz9ZqNRMwGQmYgNrEEmPlaUlxgbL32xV4VqzA3mX21P3VV7q3G5jfm9Sc9trXRUVkXH6Z7nEZhUXYk1PwNDeR1ubl8K07Yl9q/D/+COEwvR58AKX7rHs4nFAhIrPVi+G7DEwflYHZTP1vLsOzqrMNtG/NGlSXi0hLYmpN22efknn1VbEmLnFCIer++X+64++YLVdMJuyjRlFx403QEYz3EAynXXIxvjVr8W/cSLg+sSygKScHx+hRRLxevKtX71aQDOgHyT1IPu00kiZO3O3jhRBCiH3tgEm9sB999G4d5/3+e7aedTZVXyzmo8cf5vW5j7Au4sN51hnRUmXdaD4fhuRkAqWlNL/8CsHt23d5D23L5l0e05OAob25RCjMwOoGjtnRgqFLpz57cgqn/upUfB98SMBoiDXy6MrTw0IvgKK6xIDNGghy5KYKknxBQIkPkgHnxGMJXnQ+P15x2a6D5K5dBbspzUjG20MucAef2cQXJb3wd2mD3frJp3HHmHJzdc8tOXGK7vZgTQ1Vd9zJllNOYftvf0dg7TqOv3ImJouVorpmuieh+Neti+UUx1FV3ZQVxWDAbLOj+QN4vlqasD/Sqp/zbUyJfskxFxcn7jQYiPTwXrtOmYJiMhGsrmbr1KmdQXIP7KNGkTlrFkXz59HnpRd1/51bBw8m4vdTev75iU1HFAVziX56SAKzucd/A6nnTtu9awghhBD7iQNmRrl7mkDnMqvEwKYtFODDh/+XoBadXS37YSVbVn7DkcOG4dOZ3fNv2Urrhx8mbPeYDNh1u6V1TbzoGIYGmn5ecIeQqmAJx88I2mvrOeKjL/CZjGzJTKEcqH/+eexE86nt/iCebsFnpkE/xxggv7GViAKlmSkEDSrZzW4GVDXs9BuTf1sp7991G8f10P2vq4yrrsTgSiZUW4t1xAiC28sIVdewfONqNrTq13rursVuZW1uOiO2R38lsHVbiJc89TQann46rumFqagQ14knJlxLCwQou/CiWFUJ/8ZNuL/8kj6vvMwVjz7NlgsuhOY1CefpBbfG9HScEyYkpEMktzf3UEwmVIcjISfY4HJhGTAAz/L4XyNQFYIVFWRcdRVtS5bEpfgQDkeDzm5BsJqSTE576+iaf/yDUI1+pRf7UUfiGD0GS0k/nOPHo7QHr6a8PNIvuyzu1xNjXi7pl/2Guoce0m+QomkEy3b9BRHoOWg3GH5SAxchhBBif3DABMqeFd8BYM/xktrPgzU5hKJq1K1LoXmLOW6WdFtGcixI7lC1YR2+yb+G7oGyomAZNFD3J21NVQmqGqZu6QxJuT7CARVPbXQxm6JqpA3xUL8qsfpArdPGtoxkQlYLORY7hd+vTey8FtFw+oMMK68loip4Wn3YiYbiQ8pr+bZ3DuH2mWVrIMSYYyaSdcVMGp5+mkBZGeG2NrQugV9hi5fChlZ0A/outqUlsaZXJiYtQq+aprgqHx3CqoohEgFVJXnqaWRccQWKziLJwzasY9s9d+D37N7Cstr2rnSGzEySjptIqK4OQ1oaiqpiTE2l94LnaZg/H9/6DdiGDCbt4otR7faE67R+9llC6TXN56NxwQvk/PEWss84g+of4wNl1enEMWaM7rjy7ruX6r/+lZb33o+2wT7nbDJ/G+3aqJhMOI4eS+sH8V+qUi+8gLSLLqL2/x6gccGCWKm9tk8+pXT1j/R58w2Sjz8+sQa4TtBpKe4Xe07P0mX6b57BQNbMmdiGD9fdnfX7WTjHj8f9xRcYs7NxnXwSBqeTlnff078e6Hac7IleRRnX8cfv9vlCCCHE/uKACZQtffuAopE3qhl/s4mWMhvW1CCugja8dcn4GzvDT69J/7GrnnmMXIW4ml+q00n97Dm6x+dMPZ1gwI/v1ddj20JGhcxDm7E4InhqzYS8KvasABXN0SoNXUPTapedb3vnxH7ObyBAS34mw8r1ZwkBiuqaqcnPIaMxmkKR0eZl4ppt1LjsGDSNHNVM0fQLMGZmkjTxWLZMPT1h0V3WDddjHTAQ39q1eJcuJrD+e/w73HHPHQG2ZaagqQpKMEyax4+q82Wh+bjx9DvvfFYs/pia2mqyn3iYMWecS0p2Ttxxef0HculDT/Ljpwv57N9ze3y+DnZjEOexR+E46lg2HTuRUG0tprw8sm+5maRJkzDl5sbKq+1MuCGxBGB0e3R2O/Wcc/CvW0/TK69AOIwxM5Pce+7RLakG0dnhvPvuI/feexNymAPlFbR+vCj+BEXBedxxGJxOLP1L4upRQ7T2d/OrrxHYSYfBrvwbNsT+bMrLS8zNVxR6/fP+HoPkDvZDR2I/dGT8qVb9iinmPn0IbN26W+MDUCzmuEWv9iPHkH3brbt9vhBCCLG/OGACZRQF1Wljy/uZRAJdcyQTZ02zHC6quxci0zTSarwJhXEjra34fvwx4Xb2MWPI/cMfUFSV0o2b8bc3L/GpKqu8uRzhqMCe2d5KO2RkdXk2WUXJKFVVscoaW7JSEnJeK9Jd9KtuJKwqJPkTKwdYwyGGp6/D0s9Aa7mdiC+EJSmJPqlp2EaMIPnMM3EvXYoxKxvVlYRfp8Rd86uvYbn291hK+tHy7rv4q6KzvIrZjBYI4DUZWZebHutQOHJbNWnu+M6GrRYT6/LSGXzi8bw89xF8rdHAvW77Nkq/X8HF/5iD1RlfYcPmTOLwKVPZsuJrtv+4ip0ZPQgyzryW0rPOis3mBysrKf/9tRS/9y7m/Pydnt/Becy4aM5st8Vpzvayb4rBQO6f/0TmNf9DqLYWS0lJXKWLniQs9ANa338vIRBG02h95x1sAwcSqtZfcBqs3oGlXz+83323y/uaCjqfO/3KKyi/+rdxLcBTL5iOq4cGILuSes7ZVN9zb9w2c9++FD33LFtPObXHhj4JY8zrRe/nniVQVoZiMPzkknNCCCHE/uKACJSbXn+dqps6Zhe7LyRKDGh6N3toHT+K0vZgTdE0BlTW99jG2W02Yg2GY2kHjvHjKXzs0dj+Xn+7j7KLLyFUXY0zEMa31MFbgwbSK7UFd8jE2oYshm6vZXn/TPq4HfSpiy7S8unMbGvAF8P7EQoEmLC2DHswPvCqTnfgSPXSp6iOyEiIOIsx/n4RmO00vfwyZRdfHCvNZenfX/d5/OvXs/3yKxLvHQiwduQhbA37YgG8zR9MCJIBVA0so0YR9PliQXIHc3klG2dcQmpBESmnT8U5dmzc/uMuvYpnb7mWoC+ak6sqCkOzmqlpBpMaYVhegAFXPkX1v95NTHkJBml9/31Szz+fmv/9B81vv41iNJJy5plk/s/vEoJcU24uuX/5C9V33UXE4wFVJXXaObhOPinuOGNGBsaMDN33a7cpPWR6t293jB1L3SOPJOx2Hn005j59aF20KK4ihXXIEHyrV3ceqKpkXHVV7GXShAkU/ftfND6/gEhbG0mTJ5M89bSfPfzUCy9EC4VpfO652PWybrgeQ1IShfOeovqee/EsX46psBD7EUfQ8vbbRHRqKKeefx4A5sLCnz0WIYQQYn+gaNpOiqvuJXu6V/zmKVMIbPpplSacv5pE+Ref47EYSWvzYQ31XA5rVX4mdUk2xmyqxB4M0Vrcm8PffBu1fYFU2YwZuJfElyzzmoyszs/EGgzRu66ZoEFlab9emEJhjthSRYrXzw+9Mtjepb11d0lePyO2VZPkDxJRoCI1idX5maDA9D7fkWWNzgS78y7BctYdbJpwbEIuqSE9XbccWE++7pMbyw+GaKB87LqyhOO0jHQGLv6Mxc/M49t3Xo9tH1hRR59ulSSyb7mZtAsvjL1+5//uY91Xn8ddL9np4tJbr0bRwlA0FgxGau7/J/WPP55w7+ybb8K3Zg3Nb7wZtz39ssvIuu5a3ecKt7nxr12DqaAAU06O7jH/qWBVFZtPPAnN1+WLhclE39dexdIvWjWi5v5/Uv/kk9FZYEUh9dxzybn9NgBCdXU0vfIqoepqHOOOJunYY2l57z1a3nsf1WYl5eyzsR+m3+p7X9BCIVo//piml17Gu24tprR00i66iJQzTt/XQxNC7CV7+vNbiP3dAREobxg9hvDu1vZtZ8zOjqua0JM6p42v++SiqQq9GloZvr2G5X1yOezGWxgyYRKRQID1w0fstJkDQE2SjW/6dradTm3zYoxEaLRbo933AIOiENa5js0fJGRQCXYpmXZ4Wjnjs6N5o7U/JOHLm0rbRwsTzjWkpGAuLsa7YgWK1arbVrjJbmFtbjrNdguWcCQhh3vMxgrSPPGzypmzZpJx5ZWUrV7FS3+5BYi26p64ZlvCHL5qt1Oy9CvU9o5/D08/HX8wcaHaRXfcS8YhnRUu/Fu2sPXXp8W1W1btdnq/+gpbppySkOZgSE6m/7LE8my/JPfSZVTfdx/+tWsxFxeTdd11JE08Nu6YYEUFvrVrsZSUYC4q2kcjFUKIn04CZXGwOSBSLxzjxtHy9ts/6ZxQYyNBVcHUrXV0m9nItqxUlEiENquZOqetc7Gdw8rqXhnUuexsW/UdQyZMQjEaow0ldhGom7uVkWt0RptwjCzdgTUYImRQ8ZpMrC7ITDhXv/Zw57jdNWa86z/VvW+4qYlQXS39ly/D88NqymfMiNsfMKgs75uHBqS5ffhMRhSzAoqCFolg0iBkNaN5fNEAWFVJPuMM0i+LNvMoHDKMMWdMY/nrL5NV36JbQyPi8RBubELNjnbHs4QjdK+hoEYiaGvXQZdA2dK3L/mzZ1Nz//34N2zANmQIWTfegDEtTbchRkSvG9wvzDFmNH1fexUtFOox19nUq5fk7QohhBD/BQ6IQDnrhuvxb96csHBNcTjIuf02qm75Y2JgFQjwQ1EWJdWNJPmChBUIGFW+GFDQY9tor9lIWXuqhCszGvQpqkrahRdQ99DDOx2jpf3+eQMG0VhViamlhd5l1eR26aAXUn1sHdgHt7sz79NoNoPfT35dMxmtXrxmI9syXBySXIOmQcN6B95aCxCKLcbrLritDM+yZXh6WCyW3uZlWFlNrMxdTZKd8BWXkvHZEgxffxtXri7/0UdJOmZc3Pljz57OiMknU/PyS/j+958J11csFoxZnV8ABmfl89WO0rhjCutbsGXl0PL++2h+P84JEzAkJ+McdzTOcYnNZBzjjsa9OD59w3VSYh3lfWV3FgQKIYQQYv92QKRedPCuXEnE58M6aBChhgbM+fkoJhNbzzwrflEUEFRVPh5cRERVsfqDjF9XxuqCTCrSdj0ea5KLC+59AFdGZ/DXuOAF6p96imBZYj4vQFmai9C0Mznxd9eiqgZ23H03jf/6N9a0AAZLtJScpljIevF5li18j8qN60jvVcCYM6ZRN/0ijE2dM9YRBZJyvATbTARaowFZT22SO+Te9RfqHn2MYHl5wr6QqmDsNrPuOWwE9m9XJhzrGH8MhTqtvgG8a9dSOjUxP9WYm0vJJ51l0/wbN/LVJRdRlmQlpCrkNrWRFY52I1Tbol8cVLud/NmP9FjPOFRXR+WNN+FesgRUlaRf/Yrcu+7C4NQv6yaEEOI/J6kX4mBzQE172UaMiP3ZkNy5SC5z5jVsv+rquJzWTdmpsZnjiKpgAPw91FfuKikjk7NvuzsuSAZInXYOjrFHsfn4E+LKdQGQns6hjz9K5uDOtIKMC6eRXDcbmyvaCCQcUHDnzsA1aDAnDRocO867ahVNTfFpHaoG7ipb5wajkfQrLqf27/+rP2iTCccxx9Dw72d0d3cPkgGctfXdC+hFx9nYpH8Pem5dYkhKinttKSlhzJNPUfTwI+xYtoRauwWP34u9rTMPOuLxUHXHHRS//75uKTZjRgaFc58k1NiIoqpxf99CCCGEEHvCzjoXHzCc48bR56UXSZ0+nZSzzqTwqbmEj5sQ2x8wGWm0W8lo8ezyWkOPnUxKTq7uPnNBAZmzZkGX1A3r4MEMWPhRXJAMYFwzLxYkAxjMGi7PqxCMXzQXrKjQvZepsJCUc84hY+Y19F/6FekXXYRJp7awYrORd889mLKySD333F0+Xwc1rw8kJc4WJE08lqbXXmPTryazbthwNow7hvonn0TTNCwDB2LuV5xwjn/cWErffpPtM2eyecoUKm++BdXpZMuhg1nSN5eNOWmkuhM7vwW3le1ywaUxNVWCZCGEEELsFQdU6sVPEfT5WP7my2z97lucaWmMHDmawAMPsTTYRk1y9Od7RVXRuswOZxT25pw77k1opNFdYPt2PMu/xlSQj2PUKP2DZh8FNYmNTBqdM/DUmHCMHkPyr08l1NDAponHJVR4SL/icuyHH465T59Y8w3/1q3s+POf8SxdhiEtjeRTppA5cyaqrXP2ufH552l49lmC28vR/H6MziBaUCXs76yooSkGVgy/BjUSYsj6+Zj80YA+afJkTAUFNMxN7KyXdf31pF86g0BpKZU33Yx35UoCdhsrhpbgaWli3PryuFbfxuxs1kwez6ZV3wIwbv12knzx+dVqUhIlX3yOarHs7O0WQgjxC9kfPr+F+CUdtIFyT3wbNtBQX4vPYiav/yAad1RQtnoVyVnZlIw6EoNRrwLFz/DMGbApsZzbprezCLZFU0CSfvUr8h96kIZnnqX6nntiCxJN+fkEd+yIBs+KgiE9HU3TcBx+OFnXXYspLy9hMZn3hx+of/wJAuXl2I84HNvIQ6n8/SySCry0bu+sm9ycVMTm4qk0pZQAoERC5FvrOOH6cRiSk9lw9DjQWTBoSE/HmJ5OsLoa59ijSLvsMj756G3WfvkZJTsaKKlObCUdnHYmH62NLjDMaWpj5LbquPSNzOuuJaO9uoYQQoh9b3/+/BZib5BAeV/Z8in8+3TQOqtxtJRZqViSFndY75dewjZ0CMEdO/AsX46maVTdeFOPlzXm5VL83ntxs7C+DRsoPevsuGYklv79yTj9SCr/Ph8t3BmefjbufsKGxBnc6TcNxvP0YzQ9//xuPZ65XzEf5LjwtrYwuLyWovrEhYYZs2byZWstm79ZBkCaN8DhKdmkZWThmnIySRMn7ta9hBBC/DLk81scbA6oxXz/VfpOgIvehKVzwFNP4w9tVC+tSzgsULoV29AhmHJySD71VOrnz9/pZUOVVbR98imuE46PbWt89rmEjn3+DRtQnFMS+qSYAy14bZlYfI1k1n6HphqpzzuMqssuQdu+dbcfL7BpM6N3OAiEQzTb9FMnXMcdx2klJVRv2URzzQ7yBhyCMzVN91ghhBBCiF+aBMr7Uu+jo/8Bodmz0d57KH6/osRV8gAwZiQ2JOku0qUOM0C4Qb+FdVjNwJZrwlvRmf9cWPYhdenDGPrjk6hadHuk9A20bosMuzKkphJuTEytsLe5sQMpXj9Ndgsurx9VA8VqJevaa7GURNM7svv2I7tvv10+lxBCCCHEL+mgqHrx3yDtgguwHnJI3LaMq67CXFAQty3pV5MwFRX2eB3FbMY5YULcNuf48YkHmkzYR4+CtPgAtVfVEob8ODcWJAOoPQTJzuOOo2TxZ2T+flaP44mN2x/Cd8uNFPzraUo++5S0Cy8g3Nam2yClJ+GWFurnz6fqT3+i+a230LotcBRCCCGE2JMkR3k/ooVCtC1eTLC8Avvo0VgH9Nc9LlhTQ/3jT0QbrAQCBEpLIRDAkJlB7p13knTccfHXDYep+uOtNL/xBmgait1O1rXX0vTCAnwbN/VY/3hXiv79L+xHHIGmadT87e80PpeY4tFV/6VfYUhJiVbGuPVWvN98i+pwkDp9OpmzZurWS+4Qamyk9JxpcQ1dnMceS8Gc2T9z9EIIIX4q+fwWBxsJlA8A4dZWQtXVmIuKUEw9V+UIlJcTLC/HOmQI9U88Sf1jj+ExGbAFw7sMlhWLJS4ITjnnHHL/dGf8ONraCDc2Un3f32hbGF/RwzZiBL0XPI+maWw58aRocN9F9q23kjb9/B7vXzdnDrUPPJiwvfDpp3GM7qEEnxBCiD1KPr/FwUZylA8AhqSkhO53esz5+bGay97vvwfAFNb4viCL4tom7P4gfqMBezAxpcExfjzpF16Af9MmrEOHYhs8OOEYg9OJwekk57Zb2V5ZgX/N2uh9e/cm9+67AfCtWpUQJAPUPvAAjiPHYClObFgC4N+4SX/7po0SKAshhBBir5BA+SBl6dsXz9KlmCIRHIEgnw+I5kLnNLVx6LbEbnjJJ52E/fDDsR9++C6vbcrOpu+rr+L98UcIh7EOHdqZVqEadM+JtLZSeuGFZFx2GeGGRhxjx8YFwNahQ2l5992E8zzLltHy9jtYBw8m/bLfYMrO3p3HF0IIIYTYJUm9OEgFysspPfscwg0NAFQn2WkYNoiUsWMp/GQJwa+/iR3rGDeOgjmzE5qY/Fxbfn0a/vXrd3lc2sUXk33TjQBE3G62XXgRvh87uxkqViuar3OhoTEvl75vvolhF50ThRBC/Dzy+S0ONhIoH8SCNTU0vfgSwR1VOI48EteJJ8badrctXox/7VosAwfiHD8eRd1zBVKClZWUnnseoerEmes4ikLxhx/EKn9ogQCtCxcS2LaNYE2tbvOTnDvvIHXatD02ViGEEJ3k81scbCRQFvtE66efUn7lVbs8znXaaTiPPhrX8ZPjFipW33sfDTrNV9KvupKsmTP35FCFEEK0k89vcbCROspin0iaMIHMa6+FXcxUt7z+OpV/+APrRoxk48SJbD7+BGru/2e0BrSOepeDgM9LZCdl6oQQQgghdofMKIt9avv/XEPbRx/95PNcU6ZgSEul8d/PgKahAZuzUqh12RlS1UiS24spL4/MWTNJPvXUPT9wIYQ4CMnntzjYyIyy2KfSZ1wC3RYJKnb7Ls9refddMq64guIPP2Dd8IF8MqiQrZkpHL6liiS3F4jmQlfeeBOeFd/tlbELIYQQ4sAm5eHEPmUfOZKip+fTMH8+nu++I1xXj+bx7PrESIQtU09HMxrJaG7AYTaiaGCKdPuBRNNofuMN7IeO3DsPIIQQQogDlgTKYp+zH3YYRCK0frRw1wd3Ea6pASADwL3nxyWEEEKIg5ukXoj9gnvZ8r1zYUUh+deSoyyEEEKIn05mlMV+wdSr1x67lgYogDE3l8yZ12A/9NA9dm0hhBBCHDwkUBb7BdcJx1P/+OMEtm79j69lLSmh94svoFitna2zhRBCCCF+Ikm9EPsF1Waj6LlnSb/qShSb7WdfRzGZyP7jLag2mwTJQgghhPiPSB1lsd8JlJVRdccdeL5aiupwEHEnrtRTTCb6L1+O55tv8G/eTGDbNlS7jZTTT8dSXLwPRi2EEAc++fwWBxsJlMV+KxIIoEUibJ40iXBdfdy+rJtvIv2ii/bRyIQQ4uAkn9/iYCOpF2K/pZrNGKxWCh59DMvAgdFtDgcZV18lQbIQQggh9jpZzCf2e7Yhg+n7+muE6utRnU5Ui2VfD2mP8W3YgH/dOqyDBmEpKdnXwxFCCCFEFxIoi/8axvT0fT2EPcbz/ffUz55D22efxbalTDuH3Dvv3HeDEkIIIUQcCZSF+AWFGhvZfsWV+FatStjXtOAFzMXFJI0fj7mwcB+MTgghhBBdSY6yEL+g2vv/qRskd6j5691snnw826/+LRGv9xccmRBCCCG6k0BZiF9Q2+ef795xixZR99hje3k0QgghhNgZCZSF+AUZMzN3+9jGZ5+j4bnnCDc17b0BCSGEEKJHEigL8QtKv/TS3T420tpK9Z//woYjj6LixpvQwuG9ODIhhBBCdCeBshC/INcJx1Pw2KM4xh+DISVl907SNFreeIONx4wn1NCwV8cnhBBCiE4SKAvxC3OOH0/hY4/hOGbcTzovXF9P7SOP7KVRCSGEEKI7CZSF2EdSzjgTFOUnneNZ/vVeGo0QQgghupNAWYh9xDF6FHl//zvmPn3AZMI+ejTZt92K6nL1eI6lb99fcIRCCCHEwU0ajgixDyVPOZnkKSfHbXOOn0D5736Hf926+IPNZtIvu+wXHJ0QQghxcJNAWYj9jDm/F31ff42Iz0fNP+7Hu2oV1gH9SbvgAiwlJft6eEIIIcRBQwJlIfZTqtVKzh9v2dfDEEIIIQ5akqMshBBCCCGEDgmUhRBCCCGE0CGBshBCCCGEEDokUBZCCCGEEEKHBMpCCCGEEELokEBZCCGEEEIIHRIoCyGEEEIIoUMCZSGEEEIIIXRIoCyEEEIIIYQOCZSFEEIIIYTQIYGyEEIIIYQQOoz74qaapgHQ0tKyL24vhBBCiJ+h43O743NciAPdPgmUW1tbASgoKNgXtxdCCCHEf6C1tZXk5OR9PQwh9jpF2wdfCyORCJWVlSQlJaEoyi99eyGEEEL8DJqm0draSl5eHqoq2ZviwLdPAmUhhBBCCCH2d/J1UAghhBBCCB0SKAshhBBCCKFDAmUhhBBCCCF0SKAshBBCCCGEDgmUhTgITZgwgVmzZiVsnz9/PikpKQDceeedKIrCCSeckHDc3//+dxRFYcKECQn7ysvLMZvNDBkyRPfeiqLE/ktOTmbs2LEsWrQotn/x4sWccsop5OXloSgKr7/++s95RCGEEOI/JoGyEKJHubm5fPLJJ5SXl8dtf+qppygsLNQ9Z/78+Zx99tm0tLSwbNky3WPmzZtHVVUVX375JRkZGUyZMoUtW7YA4Ha7GT58OI888siefRghhBDiJ5JAWQjRo6ysLCZPnszTTz8d27ZkyRLq6uo4+eSTE47XNI158+ZxwQUXcN555zF37lzd66akpJCTk8OQIUOYM2cOXq+Xjz76CIATTzyRu+66i6lTp+6dhxJCCCF2kwTKQoidmjFjBvPnz4+9fuqppzj//PMxm80Jx37yySd4PB4mTZrE9OnTWbBgAW63e6fXt9lsAAQCgT06biGEEOI/JYGyEGKnpkyZQktLC4sXL8btdvPiiy8yY8YM3WPnzp3LtGnTMBgMDBkyhL59+/LSSy/1eG2Px8Ott96KwWBg/Pjxe+sRhBBCiJ/FuK8HIITYv5lMJqZPn868efPYsmUL/fv3Z9iwYQnHNTU18eqrr/LFF1/Etk2fPp25c+dy8cUXxx177rnnYjAY8Hq9ZGZmMnfuXN1rCiGEEPuSBMpCHIRcLhfNzc0J25uamkhOTk7YPmPGDEaPHs3q1at7nE1+7rnn8Pl8jB49OrZN0zQikQgbNmygf//+se3//Oc/mTRpEsnJyWRmZu6BJxJCCCH2PEm9EOIgNGDAAFasWJGwfcWKFXEBbYfBgwczePBgVq9ezXnnnad7zblz53LdddexcuXK2H/ff/8948aN46mnnoo7Nicnh379+kmQLIQQYr8mM8pCHISuuuoqHn74Ya655hp+85vfYLFYeOedd3j++ed56623dM9ZtGgRwWAwVme5q5UrV7JixQqeffZZBg4cGLfv3HPP5c9//jN33XUXRuOu/y+nra2NTZs2xV5v3bqVlStXkpaW1mNJOiGEEGJvkBllIQ5Cffv2ZfHixaxbt45JkyYxevRoXnzxRV566SXdBiMADodDN0iG6GzyIYcckhAkA0ydOpWamhrefffd3RrbN998w8iRIxk5ciQA1157LSNHjuT222/fvYcTQggh9hBF0zRtXw9CCCGEEEKI/Y3MKAshhBBCCKFDAmUhhBBCCCF0SKAshBBCCCGEDgmUhRBCCCGE0CGBshBCCCGEEDokUBZCCCGEEEKHBMpCCCGEEELokEBZCCGEEEIIHRIoCyGEEEIIoUMCZSGEEEIIIXRIoCyEEEIIIYQOCZSFEEIIIYTQ8f8mBX8UlkXcFQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for key in [\"cell_type_hard_pred\", \"disease_hard_pred\", \"tissue_hard_pred\", \"development_stage_hard_pred\", \"sex_hard_pred\"]:\n",
+    "    sc.pl.umap(adata, color=key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lung parenchyma\n",
+      "middle lobe of lung\n",
+      "pulmonary lobule\n",
+      "alveolar system\n",
+      "upper lobe of left lung\n",
+      "lung\n",
+      "bronchopulmonary segment\n",
+      "lobe of lung\n",
+      "right lung lobe\n",
+      "lower lobe of right lung\n",
+      "upper lobe of right lung\n",
+      "left lung lobe\n",
+      "lower lobe of left lung\n",
+      "middle lobe of right lung\n",
+      "alveolus of lung\n",
+      "lingula of left lung\n",
+      "right lung\n",
+      "lower lobe of lung\n",
+      "pulmonary acinus\n",
+      "upper lobe of lung\n",
+      "left lung\n",
+      "secondary pulmonary lobule\n"
+     ]
+    }
+   ],
+   "source": [
+    "for term_id in tissue_coarsener.coarse_label_to_descendants_term_ids_map['lung']:\n",
+    "    print(uberon_propagation_resource_dict['ontology_term_id_to_label'][term_id])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/09_disease_case_studies.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/09_disease_case_studies.ipynb
new file mode 100644
index 00000000..f590208e
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/09_disease_case_studies.ipynb
@@ -0,0 +1,12184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore disease validation datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import typing as t\n",
+    "import pickle\n",
+    "import logging\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "# To suppress the stupid AnnData warning ...\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"Transforming to str index.\")\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_path = \"/home/mehrtash/data\"\n",
+    "val_adata_root_path = os.path.join(root_path, \"data\", \"cellariumgpt_validation\")\n",
+    "metadata_prediction_root_path = os.path.join(root_path, \"data\", \"metadata_prediction_high__8192_with_XY_deep\")\n",
+    "\n",
+    "# these are the validation datasets we have precomputed metadata for\n",
+    "manifest = [\n",
+    "    (\"4\", \"small intestine\", \"Crohn disease\"),\n",
+    "    (\"7\", \"small intestine\", \"Crohn disease\"),\n",
+    "    (\"22\", \"brain\", \"COVID-19\"),\n",
+    "    (\"23\", \"brain\", \"COVID-19\"),\n",
+    "    (\"24\", \"brain\", \"COVID-19\"),\n",
+    "    (\"29\", \"brain\", \"amyotrophic lateral sclerosis\"),\n",
+    "    (\"30\", \"blood\", \"common variable immunodeficiency\"),\n",
+    "    (\"31\", \"blood\", \"common variable immunodeficiency\"),\n",
+    "    (\"32\", \"blood\", \"common variable immunodeficiency\"),\n",
+    "    (\"59\", \"brain\", \"amyotrophic lateral sclerosis\"),\n",
+    "    (\"67\", \"lung\", \"COVID-19\"),\n",
+    "    (\"71\", \"brain\", \"amyotrophic lateral sclerosis\"),\n",
+    "    (\"75\", \"blood\", \"COVID-19\"),\n",
+    "    (\"81\", \"blood\", \"post-COVID-19 disorder\"),\n",
+    "    (\"82\", \"blood\", \"post-COVID-19 disorder\"),\n",
+    "    (\"83\", \"blood\", \"post-COVID-19 disorder\"),\n",
+    "    (\"94\", \"blood\", \"COVID-19\"),\n",
+    "    (\"95\", \"blood\", \"COVID-19\"),\n",
+    "    (\"103\", \"small intestine\", \"Crohn disease\"),\n",
+    "    (\"107\", \"lung\", \"COVID-19\"),\n",
+    "    (\"108\", \"lung\", \"COVID-19\"),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>condition</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>29</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>59</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>67</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>71</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>75</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>81</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>82</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>83</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>94</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>95</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>103</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>107</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>108</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dataset_id           tissue                         condition\n",
+       "0           4  small intestine                     Crohn disease\n",
+       "1           7  small intestine                     Crohn disease\n",
+       "2          22            brain                          COVID-19\n",
+       "3          23            brain                          COVID-19\n",
+       "4          24            brain                          COVID-19\n",
+       "5          29            brain     amyotrophic lateral sclerosis\n",
+       "6          30            blood  common variable immunodeficiency\n",
+       "7          31            blood  common variable immunodeficiency\n",
+       "8          32            blood  common variable immunodeficiency\n",
+       "9          59            brain     amyotrophic lateral sclerosis\n",
+       "10         67             lung                          COVID-19\n",
+       "11         71            brain     amyotrophic lateral sclerosis\n",
+       "12         75            blood                          COVID-19\n",
+       "13         81            blood            post-COVID-19 disorder\n",
+       "14         82            blood            post-COVID-19 disorder\n",
+       "15         83            blood            post-COVID-19 disorder\n",
+       "16         94            blood                          COVID-19\n",
+       "17         95            blood                          COVID-19\n",
+       "18        103  small intestine                     Crohn disease\n",
+       "19        107             lung                          COVID-19\n",
+       "20        108             lung                          COVID-19"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "manifest_df = pd.DataFrame(manifest, columns=[\"dataset_id\", \"tissue\", \"condition\"])\n",
+    "display(manifest_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Run standard scanpy preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d57c466d1f645fcb7208981a157aa0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/21 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n",
+      "/opt/conda/lib/python3.10/site-packages/scanpy/preprocessing/_scale.py:318: UserWarning: Received a view of an AnnData. Making a copy.\n",
+      "  view_to_actual(adata)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "for row in tqdm(manifest):\n",
+    "\n",
+    "    val_adata_id = row[0]\n",
+    "    val_adata_path = os.path.join(val_adata_root_path, f\"extract_{val_adata_id}.h5ad\")\n",
+    "    adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "    adata.layers['counts'] = adata.X.copy()\n",
+    "\n",
+    "    sc.pp.normalize_total(adata, target_sum=1e4)\n",
+    "    sc.pp.log1p(adata)\n",
+    "    sc.pp.highly_variable_genes(adata)\n",
+    "\n",
+    "    adata = adata[:, adata.var['highly_variable']]\n",
+    "\n",
+    "    sc.pp.scale(adata, max_value=10)\n",
+    "    sc.pp.pca(adata, n_comps=50)\n",
+    "    sc.pp.neighbors(adata, n_pcs=50, n_neighbors=30)\n",
+    "    sc.tl.umap(adata)\n",
+    "\n",
+    "    adata.write_h5ad(os.path.join(val_adata_root_path, f\"extract_{val_adata_id}_pp.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>condition</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>29</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>common variable immunodeficiency</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>59</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>67</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>71</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>amyotrophic lateral sclerosis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>75</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>81</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>82</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>83</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>post-COVID-19 disorder</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>94</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>95</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>103</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>Crohn disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>107</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>108</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>COVID-19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dataset_id           tissue                         condition\n",
+       "0           4  small intestine                     Crohn disease\n",
+       "1           7  small intestine                     Crohn disease\n",
+       "2          22            brain                          COVID-19\n",
+       "3          23            brain                          COVID-19\n",
+       "4          24            brain                          COVID-19\n",
+       "5          29            brain     amyotrophic lateral sclerosis\n",
+       "6          30            blood  common variable immunodeficiency\n",
+       "7          31            blood  common variable immunodeficiency\n",
+       "8          32            blood  common variable immunodeficiency\n",
+       "9          59            brain     amyotrophic lateral sclerosis\n",
+       "10         67             lung                          COVID-19\n",
+       "11         71            brain     amyotrophic lateral sclerosis\n",
+       "12         75            blood                          COVID-19\n",
+       "13         81            blood            post-COVID-19 disorder\n",
+       "14         82            blood            post-COVID-19 disorder\n",
+       "15         83            blood            post-COVID-19 disorder\n",
+       "16         94            blood                          COVID-19\n",
+       "17         95            blood                          COVID-19\n",
+       "18        103  small intestine                     Crohn disease\n",
+       "19        107             lung                          COVID-19\n",
+       "20        108             lung                          COVID-19"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "manifest_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "manifest_idx = 15\n",
+    "\n",
+    "val_adata_id = manifest[manifest_idx][0]\n",
+    "\n",
+    "# load the preprocessed adata\n",
+    "val_adata_path = os.path.join(val_adata_root_path, f\"extract_{val_adata_id}_pp.h5ad\")\n",
+    "adata = sc.read_h5ad(val_adata_path)\n",
+    "\n",
+    "# load the metadata prediction\n",
+    "meta_adata_path = os.path.join(\n",
+    "    metadata_prediction_root_path, f\"extract_{val_adata_id}_metadata_prediction_scores.h5ad\")\n",
+    "meta_adata = sc.read_h5ad(meta_adata_path)\n",
+    "\n",
+    "assert adata.n_obs == meta_adata.n_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# align and transfer the metadata\n",
+    "adata = adata[meta_adata.obs.index].copy()\n",
+    "for key in meta_adata.obsm.keys():\n",
+    "    adata.obsm[key] = meta_adata.obsm[key]\n",
+    "for key in meta_adata.uns.keys():\n",
+    "    adata.uns[key] = meta_adata.uns[key]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# top_k predictions\n",
+    "top_k = 5\n",
+    "\n",
+    "for metadata_key in [\"disease\", \"tissue\", \"cell_type\", \"sex\"]:\n",
+    "    logits_nk = adata.obsm[f\"{metadata_key}_class_logits\"]\n",
+    "    top_k_indices_nk = np.argsort(logits_nk, axis=1)[:, -top_k:][:, ::-1]\n",
+    "    for k in range(top_k_indices_nk.shape[-1]):\n",
+    "        adata.obs[f\"{metadata_key}_top_{k+1}_label\"] = adata.uns[f\"{metadata_key}_labels\"][top_k_indices_nk[:, k]]\n",
+    "        adata.obs[f\"{metadata_key}_top_{k+1}_score\"] = logits_nk[np.arange(logits_nk.shape[0]), top_k_indices_nk[:, k]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# disease score\n",
+    "from scipy.special import logsumexp\n",
+    "\n",
+    "disease_logits_nk = adata.obsm[\"disease_class_logits\"]\n",
+    "disease = adata.obs[\"disease\"].values[0]\n",
+    "disease_label_to_code_map = {label: code for code, label in enumerate(adata.uns[\"disease_labels\"])}\n",
+    "assert disease in disease_label_to_code_map\n",
+    "disease_code = disease_label_to_code_map[disease]\n",
+    "normal_code = disease_label_to_code_map[\"normal\"]\n",
+    "assert normal_code == 0  # sanity check\n",
+    "\n",
+    "disease_logits_n2 = np.zeros((disease_logits_nk.shape[0], 2))\n",
+    "disease_logits_n2[:, 0] = disease_logits_nk[:, normal_code]\n",
+    "disease_logits_n2[:, 1] = disease_logits_nk[:, disease_code]\n",
+    "disease_logits_n2 = disease_logits_n2 - logsumexp(disease_logits_n2, axis=1)[:, None]\n",
+    "\n",
+    "adata.obs[\"disease_score\"] = disease_logits_n2[:, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 296,
+       "width": 1273
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.umap(adata, color='cell_type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 337,
+       "width": 1133
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.umap(adata, color='cell_type_top_1_label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 296,
+       "width": 303
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sc.pl.umap(adata, color='disease_score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import colorcet as cc\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def generate_interactive_plotly_for_metadata(\n",
+    "        adata: sc.AnnData,\n",
+    "        top_k: int,\n",
+    "        metadata_key: str,\n",
+    "        use_continuous: bool = False,\n",
+    "        value_key: str = None,\n",
+    "        vmin: float = None,\n",
+    "        vmax: float = None,\n",
+    "        width: int = 800,\n",
+    "        height: int = 600,\n",
+    "        markersize: int = 4,\n",
+    "    ):\n",
+    "    \n",
+    "    # Prepare data for plotting\n",
+    "    umap_df = pd.DataFrame({\n",
+    "        'UMAP_1': adata.obsm['X_umap'][:, 0],\n",
+    "        'UMAP_2': adata.obsm['X_umap'][:, 1]\n",
+    "    })\n",
+    "    \n",
+    "    if use_continuous and value_key:\n",
+    "        umap_df['value'] = adata.obs[value_key].values\n",
+    "        color = 'value'\n",
+    "        color_continuous_scale = 'RdBu_r'\n",
+    "    else:\n",
+    "        # Extract unique labels\n",
+    "        labels = adata.obs[f\"{metadata_key}_top_1_label\"].unique()\n",
+    "\n",
+    "        # Assign colors to labels\n",
+    "        colormap = {label: cc.glasbey[i % len(cc.glasbey)] for i, label in enumerate(labels)}\n",
+    "        \n",
+    "        umap_df['label'] = adata.obs[f\"{metadata_key}_top_1_label\"].values\n",
+    "        color = 'label'\n",
+    "        color_discrete_map = colormap\n",
+    "    \n",
+    "    # Add hover text\n",
+    "    hover_texts = []\n",
+    "    for i in range(len(umap_df)):\n",
+    "        hover_text = []\n",
+    "        if use_continuous and value_key:\n",
+    "            hover_text.append(f\"value: {adata.obs[value_key].iloc[i]:.3f}\")\n",
+    "        for k in range(top_k):\n",
+    "            label_key = f\"{metadata_key}_top_{k + 1}_label\"\n",
+    "            prob_key = f\"{metadata_key}_top_{k + 1}_score\"\n",
+    "            hover_text.append(f\"{adata.obs[label_key].iloc[i]}: {adata.obs[prob_key].iloc[i]:.3f}\")\n",
+    "        hover_texts.append(\"<br>\".join(hover_text))\n",
+    "    umap_df['hover_text'] = hover_texts\n",
+    "    \n",
+    "    # Create scatter plot\n",
+    "    if use_continuous and value_key:\n",
+    "        fig = px.scatter(\n",
+    "            umap_df,\n",
+    "            x='UMAP_1',\n",
+    "            y='UMAP_2',\n",
+    "            color=color,\n",
+    "            color_continuous_scale=color_continuous_scale,\n",
+    "            hover_name='hover_text',\n",
+    "            title='UMAP Scatter Plot',\n",
+    "            range_color=[vmin, vmax]\n",
+    "        )\n",
+    "    else:\n",
+    "        fig = px.scatter(\n",
+    "            umap_df,\n",
+    "            x='UMAP_1',\n",
+    "            y='UMAP_2',\n",
+    "            color=color,\n",
+    "            color_discrete_map=color_discrete_map,\n",
+    "            hover_name='hover_text',\n",
+    "        )\n",
+    "    \n",
+    "    # Update layout\n",
+    "    fig.update_layout(\n",
+    "        plot_bgcolor='white',\n",
+    "        xaxis=dict(title='UMAP_1', showgrid=False),\n",
+    "        yaxis=dict(title='UMAP_2', showgrid=False),\n",
+    "        width=width,\n",
+    "        height=height\n",
+    "    )\n",
+    "    \n",
+    "    # Update marker size\n",
+    "    fig.update_traces(marker=dict(size=markersize))\n",
+    "    \n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br><br>label=T follicular helper cell<br>UMAP_1=%{x}<br>UMAP_2=%{y}<extra></extra>",
+         "hovertext": [
+          "T follicular helper cell: -2.174<br>effector memory CD4-positive, alpha-beta T cell: -2.489<br>central memory CD4-positive, alpha-beta T cell: -2.944<br>T-helper 22 cell: -3.309<br>mature NK T cell: -3.847",
+          "T follicular helper cell: -2.301<br>central memory CD4-positive, alpha-beta T cell: -2.452<br>effector memory CD4-positive, alpha-beta T cell: -2.525<br>T-helper 22 cell: -3.223<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.959",
+          "T follicular helper cell: -1.685<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.922<br>central memory CD4-positive, alpha-beta T cell: -1.985<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.644<br>effector memory CD4-positive, alpha-beta T cell: -3.067",
+          "T follicular helper cell: -1.699<br>central memory CD4-positive, alpha-beta T cell: -2.097<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.247<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.643<br>effector memory CD4-positive, alpha-beta T cell: -2.958",
+          "T follicular helper cell: -1.995<br>effector memory CD4-positive, alpha-beta T cell: -2.036<br>central memory CD4-positive, alpha-beta T cell: -2.557<br>T-helper 22 cell: -3.130<br>effector memory CD8-positive, alpha-beta T cell: -3.592",
+          "T follicular helper cell: -2.424<br>regulatory T cell: -2.845<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.851<br>effector memory CD4-positive, alpha-beta T cell: -2.892<br>T cell: -2.911",
+          "T follicular helper cell: -2.346<br>effector memory CD4-positive, alpha-beta T cell: -2.493<br>effector memory CD8-positive, alpha-beta T cell: -2.580<br>T-helper 22 cell: -2.698<br>central memory CD4-positive, alpha-beta T cell: -2.812",
+          "T follicular helper cell: -1.528<br>central memory CD4-positive, alpha-beta T cell: -1.910<br>effector memory CD4-positive, alpha-beta T cell: -2.658<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.725<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.858",
+          "T follicular helper cell: -2.475<br>effector memory CD4-positive, alpha-beta T cell: -2.656<br>central memory CD4-positive, alpha-beta T cell: -2.856<br>T-helper 22 cell: -2.977<br>effector memory CD8-positive, alpha-beta T cell: -3.455",
+          "T follicular helper cell: -1.975<br>central memory CD4-positive, alpha-beta T cell: -2.041<br>effector memory CD4-positive, alpha-beta T cell: -2.555<br>T-helper 22 cell: -2.591<br>effector memory CD8-positive, alpha-beta T cell: -3.526",
+          "T follicular helper cell: -2.009<br>effector memory CD4-positive, alpha-beta T cell: -2.386<br>T-helper 22 cell: -3.303<br>central memory CD4-positive, alpha-beta T cell: -3.454<br>mature NK T cell: -3.503",
+          "T follicular helper cell: -1.811<br>effector memory CD4-positive, alpha-beta T cell: -2.176<br>central memory CD4-positive, alpha-beta T cell: -2.252<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.500<br>T-helper 22 cell: -3.255",
+          "T follicular helper cell: -1.803<br>central memory CD4-positive, alpha-beta T cell: -2.257<br>effector memory CD4-positive, alpha-beta T cell: -2.586<br>T-helper 22 cell: -3.217<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.484",
+          "T follicular helper cell: -1.765<br>central memory CD4-positive, alpha-beta T cell: -1.823<br>effector memory CD4-positive, alpha-beta T cell: -2.070<br>T-helper 22 cell: -2.397<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.623",
+          "T follicular helper cell: -2.086<br>effector memory CD4-positive, alpha-beta T cell: -2.120<br>central memory CD4-positive, alpha-beta T cell: -2.417<br>T-helper 22 cell: -2.576<br>effector memory CD8-positive, alpha-beta T cell: -3.152",
+          "T follicular helper cell: -1.310<br>central memory CD4-positive, alpha-beta T cell: -2.231<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.685<br>effector memory CD4-positive, alpha-beta T cell: -2.960<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.566",
+          "T follicular helper cell: -1.419<br>central memory CD4-positive, alpha-beta T cell: -2.083<br>effector memory CD4-positive, alpha-beta T cell: -2.448<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.865<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.983",
+          "T follicular helper cell: -1.663<br>central memory CD4-positive, alpha-beta T cell: -2.169<br>effector memory CD4-positive, alpha-beta T cell: -2.233<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.616<br>T-helper 22 cell: -3.091",
+          "T follicular helper cell: -1.807<br>central memory CD4-positive, alpha-beta T cell: -1.851<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.230<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.537<br>effector memory CD4-positive, alpha-beta T cell: -3.299",
+          "T follicular helper cell: -1.913<br>central memory CD4-positive, alpha-beta T cell: -2.482<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.513<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.721<br>effector memory CD4-positive, alpha-beta T cell: -2.856",
+          "T follicular helper cell: -1.824<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.150<br>central memory CD4-positive, alpha-beta T cell: -2.257<br>effector memory CD4-positive, alpha-beta T cell: -2.705<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.945",
+          "T follicular helper cell: -1.710<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.845<br>central memory CD4-positive, alpha-beta T cell: -1.951<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.507<br>effector memory CD4-positive, alpha-beta T cell: -2.807",
+          "T follicular helper cell: -1.834<br>central memory CD4-positive, alpha-beta T cell: -1.894<br>effector memory CD4-positive, alpha-beta T cell: -2.392<br>T-helper 22 cell: -3.054<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.124",
+          "T follicular helper cell: -1.724<br>central memory CD4-positive, alpha-beta T cell: -1.747<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.246<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.666<br>effector memory CD4-positive, alpha-beta T cell: -2.771",
+          "T follicular helper cell: -1.897<br>effector memory CD4-positive, alpha-beta T cell: -2.224<br>T-helper 22 cell: -2.904<br>central memory CD4-positive, alpha-beta T cell: -2.905<br>mature NK T cell: -3.580",
+          "T follicular helper cell: -1.509<br>central memory CD4-positive, alpha-beta T cell: -2.013<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.587<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.824<br>effector memory CD4-positive, alpha-beta T cell: -3.200",
+          "T follicular helper cell: -1.995<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.758<br>effector memory CD4-positive, alpha-beta T cell: -2.804<br>central memory CD4-positive, alpha-beta T cell: -2.869<br>T cell: -3.370",
+          "T follicular helper cell: -1.696<br>central memory CD4-positive, alpha-beta T cell: -1.866<br>effector memory CD4-positive, alpha-beta T cell: -2.364<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.389<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.637",
+          "T follicular helper cell: -2.168<br>central memory CD4-positive, alpha-beta T cell: -2.785<br>effector memory CD4-positive, alpha-beta T cell: -2.892<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.605<br>T-helper 22 cell: -3.813"
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+         "hovertemplate": "<b>%{hovertext}</b><br><br>label=CD16-positive, CD56-dim natural killer cell, human<br>UMAP_1=%{x}<br>UMAP_2=%{y}<extra></extra>",
+         "hovertext": [
+          "CD16-positive, CD56-dim natural killer cell, human: -0.997<br>natural killer cell: -1.539<br>CD16-negative, CD56-bright natural killer cell, human: -2.161<br>activated type II NK T cell: -4.115<br>mature NK T cell: -4.381",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.680<br>natural killer cell: -1.873<br>mature NK T cell: -3.364<br>CD16-negative, CD56-bright natural killer cell, human: -3.673<br>gamma-delta T cell: -4.411",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.741<br>natural killer cell: -1.358<br>CD16-negative, CD56-bright natural killer cell, human: -3.296<br>mature NK T cell: -4.232<br>activated type II NK T cell: -4.598",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.484<br>natural killer cell: -2.135<br>CD16-negative, CD56-bright natural killer cell, human: -2.447<br>mature NK T cell: -4.345<br>activated type II NK T cell: -4.521",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.504<br>natural killer cell: -1.691<br>CD16-negative, CD56-bright natural killer cell, human: -3.602<br>mature NK T cell: -4.274<br>activated type II NK T cell: -4.822",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.962<br>natural killer cell: -1.133<br>mature NK T cell: -3.452<br>gamma-delta T cell: -3.971<br>CD16-negative, CD56-bright natural killer cell, human: -4.582",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.811<br>CD16-negative, CD56-bright natural killer cell, human: -1.916<br>natural killer cell: -2.506<br>gamma-delta T cell: -3.548<br>mature NK T cell: -3.576",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.607<br>natural killer cell: -1.959<br>CD16-negative, CD56-bright natural killer cell, human: -3.204<br>mature NK T cell: -4.145<br>activated type II NK T cell: -4.480",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.369<br>natural killer cell: -1.978<br>CD16-negative, CD56-bright natural killer cell, human: -3.798<br>mature NK T cell: -4.179<br>astrocyte of the cerebral cortex: -4.999",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.558<br>natural killer cell: -1.764<br>CD16-negative, CD56-bright natural killer cell, human: -3.394<br>mature NK T cell: -3.456<br>astrocyte of the cerebral cortex: -3.963",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.485<br>natural killer cell: -1.676<br>mature NK T cell: -3.824<br>CD16-negative, CD56-bright natural killer cell, human: -4.362<br>astrocyte of the cerebral cortex: -4.685",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.285<br>natural killer cell: -2.481<br>CD16-negative, CD56-bright natural killer cell, human: -3.339<br>mature NK T cell: -4.366<br>gamma-delta T cell: -5.053",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.463<br>natural killer cell: -1.655<br>mature NK T cell: -3.926<br>CD16-negative, CD56-bright natural killer cell, human: -4.133<br>activated type II NK T cell: -5.171",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.371<br>natural killer cell: -1.695<br>mature NK T cell: -4.130<br>astrocyte of the cerebral cortex: -5.243<br>CD16-negative, CD56-bright natural killer cell, human: -5.275",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.721<br>CD16-negative, CD56-bright natural killer cell, human: -1.687<br>natural killer cell: -2.525<br>gamma-delta T cell: -4.131<br>mature NK T cell: -4.408",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -1.952<br>CD16-negative, CD56-bright natural killer cell, human: -3.146<br>mature NK T cell: -4.305<br>activated type II NK T cell: -4.803",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.256<br>CD16-negative, CD56-bright natural killer cell, human: -1.616<br>natural killer cell: -2.142<br>gamma-delta T cell: -3.362<br>mature NK T cell: -3.632",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.439<br>natural killer cell: -1.860<br>CD16-negative, CD56-bright natural killer cell, human: -3.625<br>mature NK T cell: -3.883<br>activated type II NK T cell: -5.008",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.624<br>natural killer cell: -1.904<br>CD16-negative, CD56-bright natural killer cell, human: -2.711<br>mature NK T cell: -3.862<br>astrocyte of the cerebral cortex: -4.787",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -1.696<br>CD16-negative, CD56-bright natural killer cell, human: -2.993<br>mature NK T cell: -3.497<br>astrocyte of the cerebral cortex: -4.620",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.657<br>natural killer cell: -1.585<br>CD16-negative, CD56-bright natural killer cell, human: -3.792<br>mature NK T cell: -4.221<br>activated type II NK T cell: -4.603",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.896<br>natural killer cell: -1.955<br>CD16-negative, CD56-bright natural killer cell, human: -3.121<br>mature NK T cell: -3.370<br>gamma-delta T cell: -3.522",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.156<br>natural killer cell: -2.625<br>gamma-delta T cell: -2.883<br>mature NK T cell: -3.154<br>effector CD8-positive, alpha-beta T cell: -3.291",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.732<br>natural killer cell: -1.694<br>CD16-negative, CD56-bright natural killer cell, human: -2.375<br>mature NK T cell: -3.892<br>gamma-delta T cell: -3.921",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.607<br>CD16-negative, CD56-bright natural killer cell, human: -3.290<br>mature NK T cell: -4.301<br>activated type II NK T cell: -4.878",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.835<br>natural killer cell: -1.105<br>mature NK T cell: -3.454<br>astrocyte of the cerebral cortex: -4.592<br>CD16-negative, CD56-bright natural killer cell, human: -4.658",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -1.335<br>CD16-negative, CD56-bright natural killer cell, human: -2.671<br>mature NK T cell: -4.196<br>gamma-delta T cell: -4.595",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.685<br>CD16-negative, CD56-bright natural killer cell, human: -2.164<br>gamma-delta T cell: -2.336<br>natural killer cell: -2.628<br>effector memory CD8-positive, alpha-beta T cell: -2.697",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.498<br>natural killer cell: -1.575<br>CD16-negative, CD56-bright natural killer cell, human: -4.034<br>mature NK T cell: -4.078<br>activated type II NK T cell: -4.936",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.692<br>natural killer cell: -2.326<br>CD16-negative, CD56-bright natural killer cell, human: -3.250<br>mature NK T cell: -3.625<br>gamma-delta T cell: -3.863",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.848<br>natural killer cell: -1.355<br>CD16-negative, CD56-bright natural killer cell, human: -3.086<br>mature NK T cell: -3.655<br>gamma-delta T cell: -3.941",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.894<br>gamma-delta T cell: -2.450<br>CD16-negative, CD56-bright natural killer cell, human: -2.557<br>mature NK T cell: -2.678<br>natural killer cell: -2.704",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.387<br>gamma-delta T cell: -2.375<br>CD16-negative, CD56-bright natural killer cell, human: -2.443<br>natural killer cell: -2.656<br>mature NK T cell: -3.011",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.331<br>natural killer cell: -2.563<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.275<br>gamma-delta T cell: -4.350",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.673<br>natural killer cell: -1.415<br>CD16-negative, CD56-bright natural killer cell, human: -3.306<br>activated type II NK T cell: -4.328<br>mature NK T cell: -4.423",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.699<br>natural killer cell: -1.197<br>mature NK T cell: -4.045<br>CD16-negative, CD56-bright natural killer cell, human: -4.205<br>activated type II NK T cell: -4.823",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.489<br>CD16-negative, CD56-bright natural killer cell, human: -1.568<br>natural killer cell: -2.539<br>gamma-delta T cell: -2.856<br>mature NK T cell: -3.432",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.005<br>CD16-negative, CD56-bright natural killer cell, human: -2.217<br>natural killer cell: -2.697<br>gamma-delta T cell: -4.060<br>mature NK T cell: -4.075",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.371<br>natural killer cell: -1.934<br>mature NK T cell: -2.639<br>CD16-negative, CD56-bright natural killer cell, human: -2.898<br>gamma-delta T cell: -3.103",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.822<br>natural killer cell: -2.544<br>CD16-negative, CD56-bright natural killer cell, human: -2.656<br>mature NK T cell: -3.719<br>gamma-delta T cell: -3.801",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.411<br>natural killer cell: -1.959<br>mature NK T cell: -3.520<br>CD16-negative, CD56-bright natural killer cell, human: -4.591<br>gamma-delta T cell: -4.629",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.723<br>CD16-negative, CD56-bright natural killer cell, human: -2.075<br>natural killer cell: -2.291<br>mature NK T cell: -3.955<br>gamma-delta T cell: -4.228",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.877<br>natural killer cell: -1.048<br>CD16-negative, CD56-bright natural killer cell, human: -3.302<br>mature NK T cell: -3.863<br>activated type II NK T cell: -4.798",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.326<br>natural killer cell: -2.095<br>CD16-negative, CD56-bright natural killer cell, human: -3.549<br>mature NK T cell: -4.346<br>astrocyte of the cerebral cortex: -5.129",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.489<br>natural killer cell: -2.124<br>CD16-negative, CD56-bright natural killer cell, human: -3.264<br>mature NK T cell: -3.865<br>gamma-delta T cell: -4.902",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.438<br>natural killer cell: -1.548<br>mature NK T cell: -3.989<br>astrocyte of the cerebral cortex: -4.831<br>CD16-negative, CD56-bright natural killer cell, human: -5.097",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.453<br>natural killer cell: -2.089<br>CD16-negative, CD56-bright natural killer cell, human: -3.782<br>gamma-delta T cell: -4.022<br>mature NK T cell: -4.037",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.664<br>natural killer cell: -1.881<br>CD16-negative, CD56-bright natural killer cell, human: -3.064<br>mature NK T cell: -3.803<br>immature NK T cell: -4.513",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.095<br>natural killer cell: -1.900<br>CD16-negative, CD56-bright natural killer cell, human: -2.137<br>gamma-delta T cell: -3.411<br>mature NK T cell: -4.067",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -2.148<br>CD16-negative, CD56-bright natural killer cell, human: -2.344<br>mature NK T cell: -4.538<br>activated type II NK T cell: -4.693",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.373<br>CD16-negative, CD56-bright natural killer cell, human: -3.507<br>mature NK T cell: -4.547<br>activated type II NK T cell: -4.629",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.711<br>natural killer cell: -1.797<br>mature NK T cell: -3.004<br>CD8-positive, alpha-beta T cell: -3.106<br>gamma-delta T cell: -3.153",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.626<br>natural killer cell: -1.353<br>mature NK T cell: -3.382<br>astrocyte of the cerebral cortex: -4.712<br>CD16-negative, CD56-bright natural killer cell, human: -4.720",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.222<br>natural killer cell: -1.485<br>mature NK T cell: -2.608<br>CD8-positive, alpha-beta T cell: -2.713<br>gamma-delta T cell: -2.980",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.932<br>natural killer cell: -1.327<br>mature NK T cell: -2.961<br>gamma-delta T cell: -3.675<br>CD16-negative, CD56-bright natural killer cell, human: -4.246",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.932<br>CD16-negative, CD56-bright natural killer cell, human: -2.373<br>natural killer cell: -2.586<br>mature NK T cell: -3.396<br>gamma-delta T cell: -3.617",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.795<br>natural killer cell: -1.978<br>CD16-negative, CD56-bright natural killer cell, human: -2.410<br>mature NK T cell: -3.496<br>gamma-delta T cell: -3.708",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.273<br>CD16-negative, CD56-bright natural killer cell, human: -1.594<br>natural killer cell: -2.069<br>mature NK T cell: -3.726<br>gamma-delta T cell: -3.854",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.759<br>CD16-negative, CD56-bright natural killer cell, human: -2.105<br>natural killer cell: -2.202<br>activated type II NK T cell: -4.114<br>mature NK T cell: -4.130",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.619<br>natural killer cell: -1.202<br>mature NK T cell: -4.158<br>astrocyte of the cerebral cortex: -4.772<br>CD16-negative, CD56-bright natural killer cell, human: -5.095",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.085<br>CD16-negative, CD56-bright natural killer cell, human: -1.347<br>natural killer cell: -2.116<br>gamma-delta T cell: -3.772<br>mature NK T cell: -4.163",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.432<br>natural killer cell: -1.950<br>CD16-negative, CD56-bright natural killer cell, human: -3.174<br>mature NK T cell: -4.752<br>activated type II NK T cell: -4.861",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.937<br>CD16-negative, CD56-bright natural killer cell, human: -1.607<br>natural killer cell: -2.486<br>gamma-delta T cell: -3.120<br>mature NK T cell: -4.301",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.635<br>natural killer cell: -1.549<br>CD16-negative, CD56-bright natural killer cell, human: -3.596<br>mature NK T cell: -4.204<br>gamma-delta T cell: -4.517",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.655<br>CD16-negative, CD56-bright natural killer cell, human: -1.753<br>natural killer cell: -2.203<br>gamma-delta T cell: -2.658<br>mature NK T cell: -3.103",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.408<br>CD16-negative, CD56-bright natural killer cell, human: -1.696<br>natural killer cell: -1.838<br>mature NK T cell: -3.369<br>gamma-delta T cell: -3.620",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.937<br>natural killer cell: -1.630<br>CD16-negative, CD56-bright natural killer cell, human: -2.254<br>activated type II NK T cell: -4.279<br>mature NK T cell: -4.318",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.590<br>natural killer cell: -1.826<br>CD16-negative, CD56-bright natural killer cell, human: -3.421<br>mature NK T cell: -4.252<br>activated type II NK T cell: -4.714",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.124<br>CD16-negative, CD56-bright natural killer cell, human: -2.114<br>natural killer cell: -2.208<br>gamma-delta T cell: -3.521<br>mature NK T cell: -3.676",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.918<br>natural killer cell: -2.152<br>CD16-negative, CD56-bright natural killer cell, human: -2.157<br>gamma-delta T cell: -3.751<br>mature NK T cell: -4.068",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.473<br>CD16-negative, CD56-bright natural killer cell, human: -3.042<br>mature NK T cell: -4.489<br>gamma-delta T cell: -4.564",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.725<br>natural killer cell: -1.401<br>CD16-negative, CD56-bright natural killer cell, human: -3.390<br>mature NK T cell: -4.229<br>activated type II NK T cell: -4.506",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.572<br>natural killer cell: -1.477<br>CD16-negative, CD56-bright natural killer cell, human: -3.785<br>mature NK T cell: -4.370<br>activated type II NK T cell: -4.851",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.850<br>natural killer cell: -1.906<br>CD16-negative, CD56-bright natural killer cell, human: -2.178<br>mature NK T cell: -3.909<br>gamma-delta T cell: -4.326",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.806<br>natural killer cell: -1.678<br>CD16-negative, CD56-bright natural killer cell, human: -2.358<br>gamma-delta T cell: -3.916<br>mature NK T cell: -4.096",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.771<br>natural killer cell: -1.610<br>CD16-negative, CD56-bright natural killer cell, human: -2.621<br>mature NK T cell: -4.032<br>activated type II NK T cell: -4.470",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.360<br>natural killer cell: -1.884<br>mature NK T cell: -3.965<br>CD16-negative, CD56-bright natural killer cell, human: -4.683<br>gamma-delta T cell: -4.881",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.650<br>natural killer cell: -1.380<br>mature NK T cell: -3.617<br>CD16-negative, CD56-bright natural killer cell, human: -4.315<br>astrocyte of the cerebral cortex: -4.787",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.560<br>CD16-negative, CD56-bright natural killer cell, human: -2.801<br>mature NK T cell: -4.287<br>activated type II NK T cell: -4.461",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.433<br>natural killer cell: -1.955<br>CD16-negative, CD56-bright natural killer cell, human: -3.640<br>mature NK T cell: -4.015<br>astrocyte of the cerebral cortex: -4.767",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.920<br>CD16-negative, CD56-bright natural killer cell, human: -1.756<br>natural killer cell: -1.970<br>activated type II NK T cell: -3.975<br>mature NK T cell: -4.214",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.355<br>natural killer cell: -1.679<br>CD16-negative, CD56-bright natural killer cell, human: -2.341<br>activated type II NK T cell: -3.859<br>mature NK T cell: -4.154",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.036<br>CD16-negative, CD56-bright natural killer cell, human: -1.656<br>natural killer cell: -2.269<br>mature NK T cell: -3.688<br>gamma-delta T cell: -3.766",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.581<br>CD16-negative, CD56-bright natural killer cell, human: -3.539<br>mature NK T cell: -4.365<br>activated type II NK T cell: -4.760",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.797<br>natural killer cell: -1.186<br>CD16-negative, CD56-bright natural killer cell, human: -3.458<br>mature NK T cell: -3.905<br>gamma-delta T cell: -4.676",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.730<br>natural killer cell: -1.870<br>CD16-negative, CD56-bright natural killer cell, human: -2.875<br>gamma-delta T cell: -3.545<br>mature NK T cell: -3.646",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.895<br>natural killer cell: -1.794<br>CD16-negative, CD56-bright natural killer cell, human: -2.629<br>mature NK T cell: -3.415<br>gamma-delta T cell: -4.492",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.021<br>CD16-negative, CD56-bright natural killer cell, human: -2.217<br>gamma-delta T cell: -2.666<br>natural killer cell: -2.674<br>mature NK T cell: -3.132",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.489<br>natural killer cell: -1.666<br>CD16-negative, CD56-bright natural killer cell, human: -3.662<br>mature NK T cell: -4.530<br>activated type II NK T cell: -4.846",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.618<br>natural killer cell: -2.016<br>CD16-negative, CD56-bright natural killer cell, human: -2.453<br>mature NK T cell: -3.514<br>gamma-delta T cell: -4.637",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.560<br>natural killer cell: -1.680<br>CD16-negative, CD56-bright natural killer cell, human: -3.533<br>mature NK T cell: -3.888<br>astrocyte of the cerebral cortex: -4.686",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.755<br>natural killer cell: -1.693<br>CD16-negative, CD56-bright natural killer cell, human: -3.104<br>mature NK T cell: -3.491<br>lymphocyte: -4.710",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.472<br>natural killer cell: -2.149<br>CD16-negative, CD56-bright natural killer cell, human: -3.074<br>mature NK T cell: -3.820<br>gamma-delta T cell: -4.755",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.125<br>natural killer cell: -1.659<br>mature NK T cell: -2.510<br>CD16-negative, CD56-bright natural killer cell, human: -2.804<br>gamma-delta T cell: -2.856",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.806<br>CD16-negative, CD56-bright natural killer cell, human: -1.872<br>natural killer cell: -2.107<br>mature NK T cell: -4.074<br>activated type II NK T cell: -4.148",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.318<br>natural killer cell: -2.026<br>CD16-negative, CD56-bright natural killer cell, human: -3.616<br>mature NK T cell: -4.497<br>activated type II NK T cell: -5.366",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -2.169<br>CD16-negative, CD56-bright natural killer cell, human: -2.407<br>mature NK T cell: -4.221<br>gamma-delta T cell: -4.417",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.715<br>natural killer cell: -1.237<br>mature NK T cell: -3.921<br>CD16-negative, CD56-bright natural killer cell, human: -4.625<br>astrocyte of the cerebral cortex: -4.725",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.200<br>natural killer cell: -1.538<br>CD16-negative, CD56-bright natural killer cell, human: -1.929<br>mature NK T cell: -3.633<br>gamma-delta T cell: -3.989",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.022<br>natural killer cell: -1.733<br>CD16-negative, CD56-bright natural killer cell, human: -2.219<br>mature NK T cell: -4.100<br>gamma-delta T cell: -4.231",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.817<br>CD16-negative, CD56-bright natural killer cell, human: -1.850<br>natural killer cell: -2.369<br>mature NK T cell: -3.658<br>gamma-delta T cell: -4.084",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.423<br>CD16-negative, CD56-bright natural killer cell, human: -2.088<br>natural killer cell: -2.355<br>gamma-delta T cell: -3.154<br>mature NK T cell: -3.216",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.718<br>CD16-negative, CD56-bright natural killer cell, human: -3.542<br>mature NK T cell: -4.361<br>activated type II NK T cell: -4.992",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.398<br>natural killer cell: -2.314<br>mature NK T cell: -3.699<br>CD16-negative, CD56-bright natural killer cell, human: -3.726<br>gamma-delta T cell: -4.778",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.603<br>natural killer cell: -2.240<br>CD16-negative, CD56-bright natural killer cell, human: -2.263<br>mature NK T cell: -3.830<br>gamma-delta T cell: -3.988",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.101<br>natural killer cell: -2.076<br>mature NK T cell: -2.765<br>gamma-delta T cell: -3.119<br>CD8-positive, alpha-beta T cell: -3.407",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.665<br>natural killer cell: -2.442<br>CD16-negative, CD56-bright natural killer cell, human: -2.624<br>mature NK T cell: -3.725<br>gamma-delta T cell: -4.378",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.554<br>natural killer cell: -1.782<br>CD16-negative, CD56-bright natural killer cell, human: -2.954<br>mature NK T cell: -4.484<br>activated type II NK T cell: -4.693",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.842<br>natural killer cell: -1.221<br>mature NK T cell: -3.671<br>CD16-negative, CD56-bright natural killer cell, human: -4.111<br>gamma-delta T cell: -4.557",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.829<br>natural killer cell: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -2.874<br>mature NK T cell: -3.298<br>gamma-delta T cell: -3.959",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.471<br>natural killer cell: -1.815<br>CD16-negative, CD56-bright natural killer cell, human: -3.699<br>mature NK T cell: -4.067<br>gamma-delta T cell: -4.867",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.774<br>natural killer cell: -1.981<br>CD16-negative, CD56-bright natural killer cell, human: -2.096<br>mature NK T cell: -3.727<br>gamma-delta T cell: -3.985",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.007<br>CD16-negative, CD56-bright natural killer cell, human: -1.847<br>natural killer cell: -1.992<br>mature NK T cell: -3.135<br>gamma-delta T cell: -3.393",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.419<br>natural killer cell: -1.844<br>CD16-negative, CD56-bright natural killer cell, human: -3.280<br>mature NK T cell: -4.214<br>gamma-delta T cell: -5.048",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.446<br>natural killer cell: -1.557<br>mature NK T cell: -3.901<br>astrocyte of the cerebral cortex: -4.964<br>CD16-negative, CD56-bright natural killer cell, human: -5.038",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.175<br>CD16-negative, CD56-bright natural killer cell, human: -1.532<br>natural killer cell: -1.629<br>activated type II NK T cell: -4.065<br>gamma-delta T cell: -4.421",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.661<br>natural killer cell: -1.311<br>mature NK T cell: -3.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.743<br>activated type II NK T cell: -4.751",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.519<br>natural killer cell: -1.482<br>mature NK T cell: -3.954<br>CD16-negative, CD56-bright natural killer cell, human: -3.991<br>astrocyte of the cerebral cortex: -4.845",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.588<br>natural killer cell: -1.484<br>CD16-negative, CD56-bright natural killer cell, human: -3.859<br>mature NK T cell: -3.915<br>activated type II NK T cell: -5.092",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.380<br>natural killer cell: -2.146<br>CD16-negative, CD56-bright natural killer cell, human: -3.277<br>mature NK T cell: -4.149<br>astrocyte of the cerebral cortex: -5.089",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.997<br>CD16-negative, CD56-bright natural killer cell, human: -1.701<br>natural killer cell: -2.122<br>activated type II NK T cell: -4.030<br>mature NK T cell: -4.222",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.372<br>natural killer cell: -2.006<br>mature NK T cell: -3.929<br>CD16-negative, CD56-bright natural killer cell, human: -4.059<br>astrocyte of the cerebral cortex: -4.899",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.459<br>natural killer cell: -1.582<br>mature NK T cell: -4.109<br>CD16-negative, CD56-bright natural killer cell, human: -4.348<br>astrocyte of the cerebral cortex: -4.973",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.345<br>natural killer cell: -2.300<br>CD16-negative, CD56-bright natural killer cell, human: -3.363<br>mature NK T cell: -4.426<br>activated type II NK T cell: -4.922",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.957<br>natural killer cell: -1.288<br>CD16-negative, CD56-bright natural killer cell, human: -2.945<br>mature NK T cell: -4.110<br>activated type II NK T cell: -4.206",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.758<br>natural killer cell: -1.875<br>CD16-negative, CD56-bright natural killer cell, human: -3.214<br>mature NK T cell: -3.534<br>gamma-delta T cell: -4.444",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.438<br>natural killer cell: -1.757<br>CD16-negative, CD56-bright natural killer cell, human: -3.981<br>mature NK T cell: -4.070<br>gamma-delta T cell: -5.021",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.978<br>natural killer cell: -1.119<br>CD16-negative, CD56-bright natural killer cell, human: -3.247<br>mature NK T cell: -4.144<br>activated type II NK T cell: -4.213",
+          "CD16-positive, CD56-dim natural killer cell, human: -2.003<br>gamma-delta T cell: -2.463<br>natural killer cell: -2.530<br>CD16-negative, CD56-bright natural killer cell, human: -2.556<br>mature NK T cell: -2.712",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.933<br>natural killer cell: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -2.840<br>mature NK T cell: -3.207<br>gamma-delta T cell: -3.778",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.203<br>CD16-negative, CD56-bright natural killer cell, human: -2.390<br>natural killer cell: -2.481<br>gamma-delta T cell: -3.085<br>mature NK T cell: -3.471",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.579<br>natural killer cell: -1.571<br>CD16-negative, CD56-bright natural killer cell, human: -3.617<br>mature NK T cell: -4.190<br>activated type II NK T cell: -4.608",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.736<br>natural killer cell: -2.353<br>CD16-negative, CD56-bright natural killer cell, human: -2.473<br>gamma-delta T cell: -3.536<br>mature NK T cell: -3.675",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.089<br>natural killer cell: -1.501<br>CD16-negative, CD56-bright natural killer cell, human: -2.457<br>gamma-delta T cell: -3.256<br>mature NK T cell: -3.839",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.023<br>natural killer cell: -1.055<br>CD16-negative, CD56-bright natural killer cell, human: -3.012<br>mature NK T cell: -3.911<br>gamma-delta T cell: -4.397",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.413<br>natural killer cell: -1.841<br>mature NK T cell: -4.099<br>astrocyte of the cerebral cortex: -4.580<br>CD16-negative, CD56-bright natural killer cell, human: -4.768",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.412<br>natural killer cell: -1.864<br>mature NK T cell: -3.962<br>CD16-negative, CD56-bright natural killer cell, human: -4.092<br>astrocyte of the cerebral cortex: -5.094",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.649<br>natural killer cell: -1.786<br>CD16-negative, CD56-bright natural killer cell, human: -2.490<br>activated type II NK T cell: -4.384<br>mature NK T cell: -4.464",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.518<br>natural killer cell: -2.013<br>CD16-negative, CD56-bright natural killer cell, human: -2.555<br>mature NK T cell: -4.388<br>activated type II NK T cell: -4.546",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.945<br>natural killer cell: -1.218<br>CD16-negative, CD56-bright natural killer cell, human: -3.626<br>mature NK T cell: -3.795<br>gamma-delta T cell: -4.598",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.793<br>natural killer cell: -2.396<br>gamma-delta T cell: -2.626<br>mature NK T cell: -3.145<br>effector CD8-positive, alpha-beta T cell: -3.474",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.542<br>natural killer cell: -1.781<br>CD16-negative, CD56-bright natural killer cell, human: -3.385<br>mature NK T cell: -4.288<br>activated type II NK T cell: -4.833",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.488<br>natural killer cell: -1.572<br>mature NK T cell: -4.004<br>CD16-negative, CD56-bright natural killer cell, human: -4.699<br>astrocyte of the cerebral cortex: -4.870",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.542<br>natural killer cell: -1.437<br>mature NK T cell: -3.795<br>CD16-negative, CD56-bright natural killer cell, human: -3.927<br>astrocyte of the cerebral cortex: -5.096",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -2.318<br>CD16-negative, CD56-bright natural killer cell, human: -2.725<br>mature NK T cell: -3.352<br>gamma-delta T cell: -4.366",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.669<br>natural killer cell: -1.289<br>CD16-negative, CD56-bright natural killer cell, human: -3.902<br>mature NK T cell: -4.150<br>activated type II NK T cell: -4.849",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.032<br>natural killer cell: -2.019<br>CD16-negative, CD56-bright natural killer cell, human: -2.043<br>gamma-delta T cell: -3.703<br>activated type II NK T cell: -4.203",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.654<br>natural killer cell: -2.051<br>CD16-negative, CD56-bright natural killer cell, human: -3.256<br>mature NK T cell: -3.784<br>gamma-delta T cell: -4.207",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.682<br>natural killer cell: -1.841<br>CD16-negative, CD56-bright natural killer cell, human: -2.618<br>mature NK T cell: -3.964<br>activated type II NK T cell: -4.317",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.516<br>natural killer cell: -2.128<br>CD16-negative, CD56-bright natural killer cell, human: -2.893<br>mature NK T cell: -4.329<br>activated type II NK T cell: -4.664",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.057<br>natural killer cell: -1.567<br>CD16-negative, CD56-bright natural killer cell, human: -2.420<br>mature NK T cell: -3.933<br>gamma-delta T cell: -4.262",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.492<br>natural killer cell: -1.543<br>CD16-negative, CD56-bright natural killer cell, human: -4.158<br>mature NK T cell: -4.231<br>astrocyte of the cerebral cortex: -4.820",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.757<br>natural killer cell: -1.564<br>CD16-negative, CD56-bright natural killer cell, human: -3.300<br>mature NK T cell: -3.774<br>activated type II NK T cell: -4.615",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.960<br>natural killer cell: -1.378<br>CD16-negative, CD56-bright natural killer cell, human: -2.925<br>mature NK T cell: -4.064<br>gamma-delta T cell: -4.093",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.561<br>natural killer cell: -2.210<br>CD16-negative, CD56-bright natural killer cell, human: -3.014<br>gamma-delta T cell: -3.538<br>mature NK T cell: -4.001",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.962<br>natural killer cell: -1.484<br>CD16-negative, CD56-bright natural killer cell, human: -2.849<br>mature NK T cell: -4.013<br>activated type II NK T cell: -4.470",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.583<br>natural killer cell: -1.857<br>CD16-negative, CD56-bright natural killer cell, human: -3.158<br>mature NK T cell: -3.631<br>gamma-delta T cell: -4.241",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.400<br>CD16-negative, CD56-bright natural killer cell, human: -1.650<br>natural killer cell: -2.044<br>mature NK T cell: -3.804<br>gamma-delta T cell: -3.874",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.385<br>natural killer cell: -1.998<br>mature NK T cell: -3.886<br>CD16-negative, CD56-bright natural killer cell, human: -4.216<br>gamma-delta T cell: -4.709",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.668<br>natural killer cell: -1.434<br>mature NK T cell: -3.773<br>CD16-negative, CD56-bright natural killer cell, human: -4.287<br>astrocyte of the cerebral cortex: -4.702",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.734<br>natural killer cell: -1.693<br>CD16-negative, CD56-bright natural killer cell, human: -3.725<br>mature NK T cell: -3.726<br>astrocyte of the cerebral cortex: -4.606",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.833<br>natural killer cell: -1.999<br>CD16-negative, CD56-bright natural killer cell, human: -2.033<br>gamma-delta T cell: -4.267<br>mature NK T cell: -4.287",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.852<br>natural killer cell: -1.611<br>CD16-negative, CD56-bright natural killer cell, human: -2.325<br>mature NK T cell: -4.086<br>gamma-delta T cell: -4.386",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.552<br>natural killer cell: -2.222<br>CD16-negative, CD56-bright natural killer cell, human: -2.473<br>activated type II NK T cell: -4.379<br>mature NK T cell: -4.659",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.187<br>CD16-negative, CD56-bright natural killer cell, human: -1.765<br>natural killer cell: -2.199<br>gamma-delta T cell: -3.295<br>mature NK T cell: -3.770",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.753<br>natural killer cell: -2.405<br>CD16-negative, CD56-bright natural killer cell, human: -2.579<br>mature NK T cell: -3.508<br>gamma-delta T cell: -3.829",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.637<br>natural killer cell: -2.004<br>CD16-negative, CD56-bright natural killer cell, human: -2.429<br>mature NK T cell: -3.731<br>gamma-delta T cell: -4.000",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.667<br>natural killer cell: -1.171<br>mature NK T cell: -3.843<br>CD16-negative, CD56-bright natural killer cell, human: -4.591<br>astrocyte of the cerebral cortex: -5.105",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -2.007<br>CD16-negative, CD56-bright natural killer cell, human: -3.461<br>mature NK T cell: -4.172<br>activated type II NK T cell: -4.710",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.192<br>natural killer cell: -1.785<br>CD16-negative, CD56-bright natural killer cell, human: -2.735<br>mature NK T cell: -3.038<br>gamma-delta T cell: -3.190",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -2.213<br>CD16-negative, CD56-bright natural killer cell, human: -3.093<br>mature NK T cell: -4.431<br>activated type II NK T cell: -4.596",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.703<br>CD16-negative, CD56-bright natural killer cell, human: -1.975<br>natural killer cell: -2.464<br>gamma-delta T cell: -3.949<br>mature NK T cell: -4.036",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.511<br>CD16-negative, CD56-bright natural killer cell, human: -2.226<br>natural killer cell: -2.290<br>mature NK T cell: -4.382<br>activated type II NK T cell: -4.483",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.465<br>natural killer cell: -2.018<br>CD16-negative, CD56-bright natural killer cell, human: -2.664<br>mature NK T cell: -4.602<br>activated type II NK T cell: -4.758",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.584<br>natural killer cell: -1.419<br>mature NK T cell: -4.099<br>CD16-negative, CD56-bright natural killer cell, human: -4.740<br>astrocyte of the cerebral cortex: -4.815",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.030<br>natural killer cell: -1.685<br>CD16-negative, CD56-bright natural killer cell, human: -2.394<br>mature NK T cell: -3.244<br>gamma-delta T cell: -3.431",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.704<br>natural killer cell: -1.800<br>CD16-negative, CD56-bright natural killer cell, human: -2.666<br>activated type II NK T cell: -4.284<br>mature NK T cell: -4.300",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.507<br>natural killer cell: -1.717<br>mature NK T cell: -3.737<br>CD16-negative, CD56-bright natural killer cell, human: -4.273<br>astrocyte of the cerebral cortex: -4.847",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.532<br>CD16-negative, CD56-bright natural killer cell, human: -1.839<br>natural killer cell: -2.373<br>activated type II NK T cell: -3.532<br>mature NK T cell: -3.962",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.653<br>CD16-negative, CD56-bright natural killer cell, human: -2.144<br>natural killer cell: -2.315<br>mature NK T cell: -4.039<br>gamma-delta T cell: -4.638",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.569<br>natural killer cell: -1.561<br>CD16-negative, CD56-bright natural killer cell, human: -3.790<br>gamma-delta T cell: -4.062<br>mature NK T cell: -4.070",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.072<br>natural killer cell: -1.615<br>CD16-negative, CD56-bright natural killer cell, human: -2.563<br>mature NK T cell: -3.263<br>gamma-delta T cell: -3.849",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.009<br>CD16-negative, CD56-bright natural killer cell, human: -1.790<br>natural killer cell: -1.822<br>mature NK T cell: -3.628<br>activated type II NK T cell: -4.363",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.174<br>CD16-negative, CD56-bright natural killer cell, human: -1.870<br>natural killer cell: -2.137<br>mature NK T cell: -3.127<br>gamma-delta T cell: -3.183",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.725<br>natural killer cell: -1.293<br>mature NK T cell: -3.657<br>CD16-negative, CD56-bright natural killer cell, human: -4.067<br>gamma-delta T cell: -4.817",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.017<br>CD16-negative, CD56-bright natural killer cell, human: -1.502<br>natural killer cell: -2.453<br>gamma-delta T cell: -3.768<br>mature NK T cell: -4.046",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.981<br>natural killer cell: -1.140<br>mature NK T cell: -3.336<br>CD16-negative, CD56-bright natural killer cell, human: -3.487<br>gamma-delta T cell: -4.261",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.720<br>CD16-negative, CD56-bright natural killer cell, human: -1.981<br>natural killer cell: -2.517<br>mature NK T cell: -3.956<br>gamma-delta T cell: -4.248",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.779<br>CD16-negative, CD56-bright natural killer cell, human: -2.233<br>natural killer cell: -2.347<br>gamma-delta T cell: -2.806<br>mature NK T cell: -3.367",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -1.475<br>mature NK T cell: -4.096<br>CD16-negative, CD56-bright natural killer cell, human: -4.519<br>astrocyte of the cerebral cortex: -4.737",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.312<br>natural killer cell: -2.327<br>CD16-negative, CD56-bright natural killer cell, human: -3.058<br>mature NK T cell: -4.548<br>activated type II NK T cell: -4.834",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.800<br>natural killer cell: -1.488<br>mature NK T cell: -3.491<br>CD16-negative, CD56-bright natural killer cell, human: -3.947<br>gamma-delta T cell: -4.526",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -2.108<br>CD16-negative, CD56-bright natural killer cell, human: -2.272<br>gamma-delta T cell: -3.899<br>mature NK T cell: -3.973",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.095<br>natural killer cell: -1.592<br>CD16-negative, CD56-bright natural killer cell, human: -1.875<br>activated type II NK T cell: -4.033<br>gamma-delta T cell: -4.290",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.655<br>natural killer cell: -1.806<br>gamma-delta T cell: -2.828<br>CD16-negative, CD56-bright natural killer cell, human: -2.923<br>mature NK T cell: -3.212",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.575<br>natural killer cell: -2.113<br>CD16-negative, CD56-bright natural killer cell, human: -2.132<br>activated type II NK T cell: -4.407<br>mature NK T cell: -4.619",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.574<br>natural killer cell: -1.804<br>CD16-negative, CD56-bright natural killer cell, human: -3.528<br>mature NK T cell: -3.633<br>gamma-delta T cell: -4.125",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.585<br>mature NK T cell: -3.995<br>CD16-negative, CD56-bright natural killer cell, human: -4.527<br>astrocyte of the cerebral cortex: -4.646",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.025<br>CD16-negative, CD56-bright natural killer cell, human: -1.842<br>natural killer cell: -1.863<br>mature NK T cell: -3.952<br>activated type II NK T cell: -3.985",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.798<br>natural killer cell: -1.775<br>CD16-negative, CD56-bright natural killer cell, human: -3.003<br>mature NK T cell: -3.688<br>gamma-delta T cell: -4.274",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.887<br>natural killer cell: -1.640<br>CD16-negative, CD56-bright natural killer cell, human: -2.058<br>activated type II NK T cell: -4.060<br>mature NK T cell: -4.218",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.987<br>natural killer cell: -1.205<br>CD16-negative, CD56-bright natural killer cell, human: -3.264<br>mature NK T cell: -3.632<br>activated type II NK T cell: -4.559",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.436<br>natural killer cell: -2.188<br>CD16-negative, CD56-bright natural killer cell, human: -3.151<br>mature NK T cell: -4.631<br>activated type II NK T cell: -4.665",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.372<br>natural killer cell: -1.446<br>CD16-negative, CD56-bright natural killer cell, human: -1.788<br>gamma-delta T cell: -3.878<br>mature NK T cell: -4.078",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.460<br>natural killer cell: -2.149<br>CD16-negative, CD56-bright natural killer cell, human: -3.250<br>gamma-delta T cell: -3.946<br>mature NK T cell: -3.967",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.688<br>CD16-negative, CD56-bright natural killer cell, human: -1.978<br>natural killer cell: -2.557<br>mature NK T cell: -4.234<br>gamma-delta T cell: -4.271",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.702<br>natural killer cell: -1.582<br>CD16-negative, CD56-bright natural killer cell, human: -3.084<br>mature NK T cell: -3.793<br>gamma-delta T cell: -4.453",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -1.454<br>mature NK T cell: -4.114<br>CD16-negative, CD56-bright natural killer cell, human: -4.546<br>astrocyte of the cerebral cortex: -4.850",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.526<br>CD16-negative, CD56-bright natural killer cell, human: -3.310<br>mature NK T cell: -3.870<br>activated type II NK T cell: -4.870",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.297<br>natural killer cell: -2.330<br>CD16-negative, CD56-bright natural killer cell, human: -4.087<br>mature NK T cell: -4.450<br>gamma-delta T cell: -4.945",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.897<br>CD16-negative, CD56-bright natural killer cell, human: -2.417<br>mature NK T cell: -3.895<br>activated type II NK T cell: -4.357",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.806<br>CD16-negative, CD56-bright natural killer cell, human: -1.873<br>natural killer cell: -2.047<br>activated type II NK T cell: -4.116<br>mature NK T cell: -4.176",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.826<br>CD16-negative, CD56-bright natural killer cell, human: -2.924<br>mature NK T cell: -4.751<br>activated type II NK T cell: -4.761",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.268<br>CD16-negative, CD56-bright natural killer cell, human: -1.507<br>natural killer cell: -1.721<br>activated type II NK T cell: -3.867<br>gamma-delta T cell: -4.343",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.537<br>natural killer cell: -2.202<br>CD16-negative, CD56-bright natural killer cell, human: -2.996<br>mature NK T cell: -3.828<br>gamma-delta T cell: -4.337",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.451<br>natural killer cell: -1.691<br>mature NK T cell: -3.910<br>CD16-negative, CD56-bright natural killer cell, human: -4.444<br>astrocyte of the cerebral cortex: -5.012",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.594<br>natural killer cell: -1.255<br>CD16-negative, CD56-bright natural killer cell, human: -4.050<br>mature NK T cell: -4.180<br>activated type II NK T cell: -4.918",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.758<br>natural killer cell: -2.127<br>CD16-negative, CD56-bright natural killer cell, human: -2.826<br>mature NK T cell: -3.286<br>gamma-delta T cell: -4.097",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.960<br>natural killer cell: -1.619<br>CD16-negative, CD56-bright natural killer cell, human: -3.215<br>mature NK T cell: -3.320<br>gamma-delta T cell: -4.224",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.420<br>natural killer cell: -1.512<br>gamma-delta T cell: -2.730<br>CD16-negative, CD56-bright natural killer cell, human: -2.881<br>mature NK T cell: -3.112",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.935<br>natural killer cell: -1.920<br>CD16-negative, CD56-bright natural killer cell, human: -2.972<br>mature NK T cell: -3.349<br>gamma-delta T cell: -3.846",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.607<br>natural killer cell: -1.305<br>CD16-negative, CD56-bright natural killer cell, human: -3.861<br>mature NK T cell: -4.336<br>activated type II NK T cell: -4.906",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.754<br>natural killer cell: -1.854<br>CD16-negative, CD56-bright natural killer cell, human: -2.390<br>mature NK T cell: -3.943<br>activated type II NK T cell: -4.222",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.789<br>natural killer cell: -2.155<br>CD16-negative, CD56-bright natural killer cell, human: -2.690<br>gamma-delta T cell: -3.528<br>mature NK T cell: -3.673",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.772<br>natural killer cell: -1.763<br>CD16-negative, CD56-bright natural killer cell, human: -2.253<br>activated type II NK T cell: -4.154<br>mature NK T cell: -4.459",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.598<br>CD16-negative, CD56-bright natural killer cell, human: -2.908<br>mature NK T cell: -4.300<br>activated type II NK T cell: -4.592",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.319<br>natural killer cell: -2.379<br>mature NK T cell: -3.831<br>CD16-negative, CD56-bright natural killer cell, human: -4.158<br>gamma-delta T cell: -4.554",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.873<br>CD16-negative, CD56-bright natural killer cell, human: -3.832<br>mature NK T cell: -4.241<br>astrocyte of the cerebral cortex: -4.800",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.803<br>CD16-negative, CD56-bright natural killer cell, human: -2.871<br>mature NK T cell: -4.047<br>gamma-delta T cell: -4.659",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.891<br>natural killer cell: -1.527<br>CD16-negative, CD56-bright natural killer cell, human: -2.630<br>mature NK T cell: -4.054<br>activated type II NK T cell: -4.144",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.830<br>natural killer cell: -1.681<br>CD16-negative, CD56-bright natural killer cell, human: -2.850<br>mature NK T cell: -3.462<br>gamma-delta T cell: -4.020",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.640<br>natural killer cell: -1.778<br>mature NK T cell: -3.326<br>CD16-negative, CD56-bright natural killer cell, human: -3.474<br>gamma-delta T cell: -4.414",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.645<br>natural killer cell: -1.689<br>CD16-negative, CD56-bright natural killer cell, human: -2.566<br>mature NK T cell: -3.694<br>activated type II NK T cell: -4.629",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.796<br>natural killer cell: -1.617<br>CD16-negative, CD56-bright natural killer cell, human: -2.796<br>mature NK T cell: -3.777<br>activated type II NK T cell: -4.643",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -2.189<br>CD16-negative, CD56-bright natural killer cell, human: -2.669<br>mature NK T cell: -4.130<br>gamma-delta T cell: -4.428",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.645<br>natural killer cell: -1.442<br>mature NK T cell: -3.504<br>gamma-delta T cell: -4.736<br>CD16-negative, CD56-bright natural killer cell, human: -4.788",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.821<br>natural killer cell: -2.295<br>CD16-negative, CD56-bright natural killer cell, human: -2.648<br>mature NK T cell: -3.608<br>gamma-delta T cell: -3.895",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.570<br>natural killer cell: -1.679<br>mature NK T cell: -3.620<br>CD16-negative, CD56-bright natural killer cell, human: -4.343<br>gamma-delta T cell: -4.569",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.688<br>natural killer cell: -1.590<br>CD16-negative, CD56-bright natural killer cell, human: -4.073<br>mature NK T cell: -4.097<br>astrocyte of the cerebral cortex: -4.654",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.028<br>natural killer cell: -2.228<br>CD16-negative, CD56-bright natural killer cell, human: -2.321<br>mature NK T cell: -3.433<br>gamma-delta T cell: -3.479",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.502<br>natural killer cell: -1.740<br>CD16-negative, CD56-bright natural killer cell, human: -3.362<br>mature NK T cell: -4.130<br>activated type II NK T cell: -4.826",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.540<br>natural killer cell: -1.981<br>CD16-negative, CD56-bright natural killer cell, human: -2.388<br>mature NK T cell: -4.329<br>activated type II NK T cell: -4.559",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.731<br>natural killer cell: -1.811<br>mature NK T cell: -2.693<br>gamma-delta T cell: -3.002<br>effector memory CD8-positive, alpha-beta T cell, terminally differentiated: -3.128",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.749<br>CD16-negative, CD56-bright natural killer cell, human: -1.881<br>natural killer cell: -2.390<br>mature NK T cell: -4.027<br>gamma-delta T cell: -4.369",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.682<br>natural killer cell: -1.770<br>CD16-negative, CD56-bright natural killer cell, human: -2.827<br>mature NK T cell: -3.718<br>activated type II NK T cell: -4.761",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.860<br>natural killer cell: -1.622<br>CD16-negative, CD56-bright natural killer cell, human: -2.280<br>activated type II NK T cell: -4.086<br>mature NK T cell: -4.403",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.172<br>CD16-negative, CD56-bright natural killer cell, human: -2.009<br>natural killer cell: -2.016<br>gamma-delta T cell: -3.578<br>mature NK T cell: -3.878",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.109<br>CD16-negative, CD56-bright natural killer cell, human: -1.364<br>natural killer cell: -2.300<br>gamma-delta T cell: -3.568<br>mature NK T cell: -3.902",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.734<br>natural killer cell: -2.118<br>CD16-negative, CD56-bright natural killer cell, human: -2.260<br>mature NK T cell: -3.976<br>gamma-delta T cell: -4.398",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.385<br>natural killer cell: -1.878<br>mature NK T cell: -4.151<br>CD16-negative, CD56-bright natural killer cell, human: -4.275<br>astrocyte of the cerebral cortex: -4.988",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.411<br>natural killer cell: -1.924<br>CD16-negative, CD56-bright natural killer cell, human: -2.427<br>mature NK T cell: -2.529<br>gamma-delta T cell: -3.057",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.194<br>natural killer cell: -1.489<br>CD16-negative, CD56-bright natural killer cell, human: -2.511<br>gamma-delta T cell: -3.002<br>mature NK T cell: -3.360",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.745<br>natural killer cell: -2.318<br>CD16-negative, CD56-bright natural killer cell, human: -3.236<br>mature NK T cell: -3.266<br>gamma-delta T cell: -3.743",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.450<br>natural killer cell: -1.961<br>CD16-negative, CD56-bright natural killer cell, human: -3.170<br>mature NK T cell: -4.361<br>activated type II NK T cell: -4.852",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.421<br>natural killer cell: -1.902<br>CD16-negative, CD56-bright natural killer cell, human: -3.543<br>mature NK T cell: -4.478<br>activated type II NK T cell: -4.809",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.623<br>natural killer cell: -1.412<br>mature NK T cell: -3.859<br>CD16-negative, CD56-bright natural killer cell, human: -3.871<br>gamma-delta T cell: -4.694",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.431<br>natural killer cell: -2.104<br>CD16-negative, CD56-bright natural killer cell, human: -3.179<br>mature NK T cell: -4.320<br>activated type II NK T cell: -4.762",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.555<br>natural killer cell: -2.265<br>CD16-negative, CD56-bright natural killer cell, human: -3.590<br>mature NK T cell: -3.731<br>gamma-delta T cell: -3.999",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.563<br>natural killer cell: -2.037<br>CD16-negative, CD56-bright natural killer cell, human: -2.748<br>mature NK T cell: -4.030<br>gamma-delta T cell: -4.596",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.615<br>natural killer cell: -1.580<br>CD16-negative, CD56-bright natural killer cell, human: -3.577<br>gamma-delta T cell: -4.140<br>mature NK T cell: -4.298",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.606<br>natural killer cell: -1.716<br>CD16-negative, CD56-bright natural killer cell, human: -2.793<br>mature NK T cell: -4.436<br>activated type II NK T cell: -4.598",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.535<br>natural killer cell: -1.719<br>CD16-negative, CD56-bright natural killer cell, human: -3.013<br>mature NK T cell: -4.609<br>activated type II NK T cell: -4.619",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.477<br>mature NK T cell: -3.960<br>CD16-negative, CD56-bright natural killer cell, human: -4.432<br>astrocyte of the cerebral cortex: -4.802",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.641<br>natural killer cell: -1.652<br>CD16-negative, CD56-bright natural killer cell, human: -2.797<br>mature NK T cell: -3.510<br>activated type II NK T cell: -4.892",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.877<br>natural killer cell: -1.490<br>mature NK T cell: -3.159<br>gamma-delta T cell: -3.683<br>CD16-negative, CD56-bright natural killer cell, human: -3.823",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.520<br>natural killer cell: -1.660<br>CD16-negative, CD56-bright natural killer cell, human: -3.396<br>mature NK T cell: -4.077<br>astrocyte of the cerebral cortex: -4.908",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.609<br>natural killer cell: -1.432<br>mature NK T cell: -3.758<br>CD16-negative, CD56-bright natural killer cell, human: -4.266<br>astrocyte of the cerebral cortex: -4.832",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -1.354<br>mature NK T cell: -3.570<br>CD16-negative, CD56-bright natural killer cell, human: -4.344<br>gamma-delta T cell: -4.716",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.483<br>natural killer cell: -1.466<br>mature NK T cell: -4.246<br>CD16-negative, CD56-bright natural killer cell, human: -4.501<br>astrocyte of the cerebral cortex: -5.016",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.836<br>natural killer cell: -1.742<br>CD16-negative, CD56-bright natural killer cell, human: -2.868<br>mature NK T cell: -3.406<br>gamma-delta T cell: -4.061",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.721<br>CD16-negative, CD56-bright natural killer cell, human: -2.050<br>natural killer cell: -2.191<br>gamma-delta T cell: -4.185<br>activated type II NK T cell: -4.364",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.749<br>natural killer cell: -1.301<br>CD16-negative, CD56-bright natural killer cell, human: -3.563<br>mature NK T cell: -3.836<br>astrocyte of the cerebral cortex: -4.679",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.202<br>natural killer cell: -1.913<br>CD16-negative, CD56-bright natural killer cell, human: -2.072<br>mature NK T cell: -3.165<br>gamma-delta T cell: -3.312",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -1.784<br>CD16-negative, CD56-bright natural killer cell, human: -2.706<br>mature NK T cell: -4.427<br>activated type II NK T cell: -4.487",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.480<br>natural killer cell: -1.779<br>CD16-negative, CD56-bright natural killer cell, human: -3.524<br>mature NK T cell: -4.264<br>activated type II NK T cell: -4.793",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.639<br>CD16-negative, CD56-bright natural killer cell, human: -2.319<br>natural killer cell: -2.475<br>gamma-delta T cell: -3.979<br>mature NK T cell: -3.989",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.164<br>natural killer cell: -1.667<br>CD16-negative, CD56-bright natural killer cell, human: -1.782<br>activated type II NK T cell: -3.786<br>mature NK T cell: -4.745",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.330<br>natural killer cell: -2.026<br>CD16-negative, CD56-bright natural killer cell, human: -4.037<br>mature NK T cell: -4.177<br>astrocyte of the cerebral cortex: -5.122",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.880<br>natural killer cell: -2.245<br>CD16-negative, CD56-bright natural killer cell, human: -2.432<br>gamma-delta T cell: -3.142<br>mature NK T cell: -3.835",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.435<br>natural killer cell: -2.242<br>CD16-negative, CD56-bright natural killer cell, human: -3.105<br>gamma-delta T cell: -4.002<br>mature NK T cell: -4.088",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.718<br>natural killer cell: -1.340<br>CD16-negative, CD56-bright natural killer cell, human: -3.091<br>mature NK T cell: -4.017<br>activated type II NK T cell: -4.745",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.915<br>natural killer cell: -1.560<br>CD16-negative, CD56-bright natural killer cell, human: -2.563<br>mature NK T cell: -3.418<br>gamma-delta T cell: -3.830",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.513<br>natural killer cell: -2.188<br>CD16-negative, CD56-bright natural killer cell, human: -2.369<br>mature NK T cell: -4.376<br>activated type II NK T cell: -4.440",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -1.709<br>CD16-negative, CD56-bright natural killer cell, human: -3.589<br>mature NK T cell: -4.533<br>activated type II NK T cell: -5.043",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.659<br>natural killer cell: -1.562<br>mature NK T cell: -3.295<br>gamma-delta T cell: -3.935<br>CD16-negative, CD56-bright natural killer cell, human: -4.256",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.848<br>CD16-negative, CD56-bright natural killer cell, human: -2.086<br>natural killer cell: -2.439<br>mature NK T cell: -3.831<br>gamma-delta T cell: -3.875",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.512<br>CD16-negative, CD56-bright natural killer cell, human: -2.384<br>natural killer cell: -2.729<br>mature NK T cell: -4.367<br>activated type II NK T cell: -4.506",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.294<br>mature NK T cell: -3.590<br>CD16-negative, CD56-bright natural killer cell, human: -4.348<br>gamma-delta T cell: -4.620",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.470<br>natural killer cell: -1.824<br>CD16-negative, CD56-bright natural killer cell, human: -2.891<br>mature NK T cell: -4.075<br>activated type II NK T cell: -4.964",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.981<br>CD16-negative, CD56-bright natural killer cell, human: -1.922<br>natural killer cell: -2.409<br>gamma-delta T cell: -3.721<br>activated type II NK T cell: -4.025",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.757<br>natural killer cell: -1.496<br>mature NK T cell: -3.242<br>gamma-delta T cell: -4.180<br>astrocyte of the cerebral cortex: -4.521",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.367<br>natural killer cell: -2.012<br>mature NK T cell: -3.728<br>CD16-negative, CD56-bright natural killer cell, human: -4.095<br>astrocyte of the cerebral cortex: -4.818",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.678<br>natural killer cell: -2.280<br>CD16-negative, CD56-bright natural killer cell, human: -3.144<br>gamma-delta T cell: -3.195<br>mature NK T cell: -3.341",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.164<br>mature NK T cell: -4.163<br>CD16-negative, CD56-bright natural killer cell, human: -4.317<br>astrocyte of the cerebral cortex: -4.764",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.546<br>natural killer cell: -2.436<br>CD16-negative, CD56-bright natural killer cell, human: -2.854<br>mature NK T cell: -3.844<br>gamma-delta T cell: -4.095",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.637<br>natural killer cell: -1.345<br>mature NK T cell: -3.889<br>CD16-negative, CD56-bright natural killer cell, human: -3.903<br>gamma-delta T cell: -4.802",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.409<br>CD16-negative, CD56-bright natural killer cell, human: -1.426<br>natural killer cell: -2.011<br>gamma-delta T cell: -3.543<br>mature NK T cell: -4.114",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.543<br>CD16-negative, CD56-bright natural killer cell, human: -1.980<br>natural killer cell: -2.178<br>activated type II NK T cell: -4.413<br>mature NK T cell: -4.637",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.909<br>natural killer cell: -1.901<br>CD16-negative, CD56-bright natural killer cell, human: -2.409<br>mature NK T cell: -3.721<br>gamma-delta T cell: -4.552",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.599<br>natural killer cell: -1.580<br>CD16-negative, CD56-bright natural killer cell, human: -3.517<br>mature NK T cell: -3.830<br>gamma-delta T cell: -3.993",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.930<br>natural killer cell: -1.590<br>mature NK T cell: -3.384<br>CD16-negative, CD56-bright natural killer cell, human: -3.541<br>gamma-delta T cell: -3.593",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.666<br>natural killer cell: -1.702<br>CD16-negative, CD56-bright natural killer cell, human: -3.443<br>mature NK T cell: -3.984<br>activated type II NK T cell: -4.438",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.470<br>natural killer cell: -2.025<br>CD16-negative, CD56-bright natural killer cell, human: -2.832<br>mature NK T cell: -3.828<br>gamma-delta T cell: -4.854",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.286<br>natural killer cell: -1.537<br>mature NK T cell: -3.118<br>CD16-negative, CD56-bright natural killer cell, human: -3.374<br>gamma-delta T cell: -3.577",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.559<br>natural killer cell: -1.837<br>CD16-negative, CD56-bright natural killer cell, human: -2.540<br>mature NK T cell: -4.036<br>activated type II NK T cell: -4.735",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.714<br>natural killer cell: -1.265<br>CD16-negative, CD56-bright natural killer cell, human: -3.711<br>mature NK T cell: -4.214<br>activated type II NK T cell: -4.799",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.759<br>natural killer cell: -2.273<br>CD16-negative, CD56-bright natural killer cell, human: -3.234<br>mature NK T cell: -3.432<br>gamma-delta T cell: -3.632",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.615<br>natural killer cell: -2.172<br>CD16-negative, CD56-bright natural killer cell, human: -2.634<br>mature NK T cell: -4.021<br>activated type II NK T cell: -4.421",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.675<br>CD16-negative, CD56-bright natural killer cell, human: -3.387<br>mature NK T cell: -4.049<br>activated type II NK T cell: -4.578",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.746<br>natural killer cell: -2.089<br>CD16-negative, CD56-bright natural killer cell, human: -2.449<br>mature NK T cell: -3.547<br>gamma-delta T cell: -4.019",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.997<br>CD16-negative, CD56-bright natural killer cell, human: -1.904<br>natural killer cell: -2.109<br>gamma-delta T cell: -3.890<br>activated type II NK T cell: -4.085",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.690<br>natural killer cell: -2.106<br>CD16-negative, CD56-bright natural killer cell, human: -2.404<br>gamma-delta T cell: -3.757<br>mature NK T cell: -4.023",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.329<br>natural killer cell: -2.012<br>CD16-negative, CD56-bright natural killer cell, human: -4.120<br>mature NK T cell: -4.364<br>astrocyte of the cerebral cortex: -5.045",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.695<br>natural killer cell: -1.415<br>CD16-negative, CD56-bright natural killer cell, human: -3.127<br>mature NK T cell: -4.177<br>activated type II NK T cell: -4.666",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.605<br>natural killer cell: -1.431<br>CD16-negative, CD56-bright natural killer cell, human: -3.894<br>mature NK T cell: -3.915<br>activated type II NK T cell: -4.842",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.627<br>natural killer cell: -2.145<br>CD16-negative, CD56-bright natural killer cell, human: -2.746<br>mature NK T cell: -4.046<br>astrocyte of the cerebral cortex: -4.374",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.276<br>natural killer cell: -1.754<br>CD16-negative, CD56-bright natural killer cell, human: -1.771<br>gamma-delta T cell: -3.216<br>mature NK T cell: -3.416",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.372<br>natural killer cell: -1.831<br>CD16-negative, CD56-bright natural killer cell, human: -3.883<br>mature NK T cell: -4.259<br>activated type II NK T cell: -5.147",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.388<br>natural killer cell: -1.992<br>CD16-negative, CD56-bright natural killer cell, human: -3.766<br>mature NK T cell: -4.178<br>activated type II NK T cell: -5.048",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.160<br>natural killer cell: -1.492<br>CD16-negative, CD56-bright natural killer cell, human: -2.797<br>gamma-delta T cell: -3.325<br>mature NK T cell: -3.457",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.552<br>CD16-negative, CD56-bright natural killer cell, human: -3.326<br>mature NK T cell: -3.827<br>activated type II NK T cell: -4.749",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.601<br>CD16-negative, CD56-bright natural killer cell, human: -1.954<br>natural killer cell: -2.220<br>activated type II NK T cell: -4.461<br>mature NK T cell: -4.588",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.878<br>natural killer cell: -1.811<br>CD16-negative, CD56-bright natural killer cell, human: -2.003<br>activated type II NK T cell: -4.026<br>mature NK T cell: -4.231",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.554<br>natural killer cell: -1.692<br>CD16-negative, CD56-bright natural killer cell, human: -3.568<br>mature NK T cell: -3.929<br>gamma-delta T cell: -4.686",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.500<br>mature NK T cell: -4.075<br>activated type II NK T cell: -4.478",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.411<br>natural killer cell: -2.121<br>CD16-negative, CD56-bright natural killer cell, human: -3.292<br>mature NK T cell: -4.299<br>gamma-delta T cell: -4.658",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.831<br>natural killer cell: -1.593<br>CD16-negative, CD56-bright natural killer cell, human: -3.556<br>mature NK T cell: -3.665<br>gamma-delta T cell: -4.281",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.864<br>natural killer cell: -2.006<br>CD16-negative, CD56-bright natural killer cell, human: -2.550<br>gamma-delta T cell: -3.881<br>activated type II NK T cell: -4.369",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.016<br>natural killer cell: -1.645<br>CD16-negative, CD56-bright natural killer cell, human: -2.818<br>mature NK T cell: -3.298<br>gamma-delta T cell: -3.769",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.314<br>natural killer cell: -1.853<br>mature NK T cell: -4.542<br>CD16-negative, CD56-bright natural killer cell, human: -4.748<br>astrocyte of the cerebral cortex: -5.051",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.651<br>natural killer cell: -1.410<br>CD16-negative, CD56-bright natural killer cell, human: -3.942<br>mature NK T cell: -4.012<br>gamma-delta T cell: -4.812",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.900<br>natural killer cell: -1.874<br>CD16-negative, CD56-bright natural killer cell, human: -2.243<br>mature NK T cell: -3.886<br>gamma-delta T cell: -4.081",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.103<br>natural killer cell: -1.530<br>mature NK T cell: -2.907<br>gamma-delta T cell: -3.573<br>CD16-negative, CD56-bright natural killer cell, human: -3.802",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.016<br>natural killer cell: -1.598<br>CD16-negative, CD56-bright natural killer cell, human: -1.920<br>mature NK T cell: -3.889<br>gamma-delta T cell: -4.138",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.493<br>natural killer cell: -1.758<br>CD16-negative, CD56-bright natural killer cell, human: -3.088<br>mature NK T cell: -3.991<br>activated type II NK T cell: -4.980",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.671<br>natural killer cell: -1.977<br>CD16-negative, CD56-bright natural killer cell, human: -2.588<br>activated type II NK T cell: -4.103<br>mature NK T cell: -4.615",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.541<br>natural killer cell: -2.008<br>CD16-negative, CD56-bright natural killer cell, human: -2.780<br>mature NK T cell: -4.052<br>gamma-delta T cell: -4.616",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.748<br>CD16-negative, CD56-bright natural killer cell, human: -1.909<br>natural killer cell: -2.206<br>mature NK T cell: -2.517<br>gamma-delta T cell: -2.630",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.682<br>CD16-negative, CD56-bright natural killer cell, human: -2.157<br>natural killer cell: -2.342<br>mature NK T cell: -4.257<br>activated type II NK T cell: -4.340",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.952<br>natural killer cell: -2.258<br>CD16-negative, CD56-bright natural killer cell, human: -2.617<br>gamma-delta T cell: -3.460<br>mature NK T cell: -3.786",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.434<br>natural killer cell: -1.662<br>CD16-negative, CD56-bright natural killer cell, human: -3.910<br>mature NK T cell: -4.353<br>astrocyte of the cerebral cortex: -4.870",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.718<br>natural killer cell: -1.178<br>mature NK T cell: -3.749<br>CD16-negative, CD56-bright natural killer cell, human: -4.491<br>astrocyte of the cerebral cortex: -4.583",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.004<br>CD16-negative, CD56-bright natural killer cell, human: -1.516<br>natural killer cell: -2.579<br>gamma-delta T cell: -3.527<br>activated type II NK T cell: -4.162",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.938<br>natural killer cell: -1.249<br>mature NK T cell: -3.524<br>CD16-negative, CD56-bright natural killer cell, human: -4.185<br>activated type II NK T cell: -4.777",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.796<br>CD16-negative, CD56-bright natural killer cell, human: -1.366<br>natural killer cell: -2.107<br>activated type II NK T cell: -4.216<br>mature NK T cell: -4.856",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.892<br>natural killer cell: -1.281<br>mature NK T cell: -3.678<br>CD16-negative, CD56-bright natural killer cell, human: -4.105<br>gamma-delta T cell: -4.272",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.594<br>natural killer cell: -2.034<br>CD16-negative, CD56-bright natural killer cell, human: -2.697<br>gamma-delta T cell: -3.310<br>mature NK T cell: -4.174",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.385<br>CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -1.684<br>gamma-delta T cell: -3.850<br>activated type II NK T cell: -3.872",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.773<br>CD16-negative, CD56-bright natural killer cell, human: -3.602<br>mature NK T cell: -3.993<br>activated type II NK T cell: -4.792",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.953<br>CD16-negative, CD56-bright natural killer cell, human: -3.227<br>mature NK T cell: -3.329<br>gamma-delta T cell: -4.604",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.653<br>natural killer cell: -1.643<br>CD16-negative, CD56-bright natural killer cell, human: -3.670<br>mature NK T cell: -3.855<br>activated type II NK T cell: -4.712",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.247<br>natural killer cell: -1.358<br>CD16-negative, CD56-bright natural killer cell, human: -1.870<br>mature NK T cell: -3.452<br>gamma-delta T cell: -3.882",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.810<br>natural killer cell: -1.811<br>gamma-delta T cell: -3.262<br>mature NK T cell: -3.462<br>CD8-positive, alpha-beta T cell: -3.539",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.478<br>natural killer cell: -1.817<br>CD16-negative, CD56-bright natural killer cell, human: -3.562<br>mature NK T cell: -3.919<br>astrocyte of the cerebral cortex: -4.828",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.435<br>natural killer cell: -1.907<br>CD16-negative, CD56-bright natural killer cell, human: -3.081<br>mature NK T cell: -4.065<br>activated type II NK T cell: -4.938",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.498<br>natural killer cell: -1.888<br>CD16-negative, CD56-bright natural killer cell, human: -3.474<br>mature NK T cell: -4.257<br>gamma-delta T cell: -4.676",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.660<br>natural killer cell: -1.435<br>CD16-negative, CD56-bright natural killer cell, human: -3.008<br>mature NK T cell: -4.108<br>activated type II NK T cell: -4.856",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.539<br>natural killer cell: -1.505<br>mature NK T cell: -3.883<br>CD16-negative, CD56-bright natural killer cell, human: -4.730<br>astrocyte of the cerebral cortex: -4.875",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.679<br>natural killer cell: -1.394<br>CD16-negative, CD56-bright natural killer cell, human: -3.518<br>mature NK T cell: -4.198<br>activated type II NK T cell: -4.436",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.565<br>natural killer cell: -1.815<br>mature NK T cell: -3.367<br>gamma-delta T cell: -4.501<br>astrocyte of the cerebral cortex: -4.627",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.570<br>natural killer cell: -1.521<br>mature NK T cell: -3.958<br>CD16-negative, CD56-bright natural killer cell, human: -4.054<br>astrocyte of the cerebral cortex: -4.638",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.163<br>natural killer cell: -1.812<br>CD16-negative, CD56-bright natural killer cell, human: -2.516<br>mature NK T cell: -2.695<br>gamma-delta T cell: -2.916",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.551<br>natural killer cell: -1.474<br>CD16-negative, CD56-bright natural killer cell, human: -4.086<br>mature NK T cell: -4.151<br>astrocyte of the cerebral cortex: -4.917",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.996<br>natural killer cell: -1.204<br>CD16-negative, CD56-bright natural killer cell, human: -3.152<br>gamma-delta T cell: -3.621<br>mature NK T cell: -3.653",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.595<br>natural killer cell: -1.610<br>CD16-negative, CD56-bright natural killer cell, human: -3.771<br>mature NK T cell: -3.863<br>gamma-delta T cell: -4.919",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.687<br>natural killer cell: -1.390<br>CD16-negative, CD56-bright natural killer cell, human: -2.945<br>mature NK T cell: -4.336<br>activated type II NK T cell: -4.523",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.861<br>CD16-negative, CD56-bright natural killer cell, human: -2.192<br>natural killer cell: -2.212<br>mature NK T cell: -3.263<br>gamma-delta T cell: -3.812",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.780<br>natural killer cell: -1.119<br>mature NK T cell: -3.701<br>CD16-negative, CD56-bright natural killer cell, human: -4.006<br>astrocyte of the cerebral cortex: -4.889",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.654<br>natural killer cell: -1.925<br>CD16-negative, CD56-bright natural killer cell, human: -2.372<br>activated type II NK T cell: -4.280<br>mature NK T cell: -4.684",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.801<br>CD16-negative, CD56-bright natural killer cell, human: -1.758<br>natural killer cell: -2.363<br>gamma-delta T cell: -3.877<br>mature NK T cell: -4.341",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.764<br>natural killer cell: -1.323<br>CD16-negative, CD56-bright natural killer cell, human: -3.559<br>mature NK T cell: -3.620<br>gamma-delta T cell: -4.568",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.407<br>natural killer cell: -2.004<br>CD16-negative, CD56-bright natural killer cell, human: -3.257<br>mature NK T cell: -4.129<br>activated type II NK T cell: -4.788",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.528<br>natural killer cell: -1.915<br>CD16-negative, CD56-bright natural killer cell, human: -3.562<br>mature NK T cell: -4.038<br>gamma-delta T cell: -4.590",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.376<br>CD16-negative, CD56-bright natural killer cell, human: -2.197<br>natural killer cell: -2.682<br>gamma-delta T cell: -3.105<br>mature NK T cell: -3.129",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.404<br>CD16-negative, CD56-bright natural killer cell, human: -2.056<br>natural killer cell: -2.294<br>gamma-delta T cell: -2.456<br>mature NK T cell: -3.506",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.516<br>natural killer cell: -1.703<br>CD16-negative, CD56-bright natural killer cell, human: -3.793<br>mature NK T cell: -4.429<br>activated type II NK T cell: -4.820",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.448<br>natural killer cell: -2.019<br>mature NK T cell: -3.739<br>CD16-negative, CD56-bright natural killer cell, human: -3.960<br>gamma-delta T cell: -4.722",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.009<br>natural killer cell: -2.003<br>gamma-delta T cell: -2.690<br>CD16-negative, CD56-bright natural killer cell, human: -3.102<br>mature NK T cell: -3.157",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.386<br>natural killer cell: -1.959<br>CD16-negative, CD56-bright natural killer cell, human: -3.579<br>mature NK T cell: -4.297<br>activated type II NK T cell: -5.035",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.625<br>natural killer cell: -1.408<br>CD16-negative, CD56-bright natural killer cell, human: -3.864<br>mature NK T cell: -3.941<br>activated type II NK T cell: -4.937",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.499<br>natural killer cell: -1.669<br>CD16-negative, CD56-bright natural killer cell, human: -3.520<br>mature NK T cell: -4.129<br>activated type II NK T cell: -4.678",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.692<br>natural killer cell: -1.719<br>CD16-negative, CD56-bright natural killer cell, human: -2.814<br>mature NK T cell: -4.079<br>activated type II NK T cell: -4.241",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.298<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -3.800<br>mature NK T cell: -4.511<br>astrocyte of the cerebral cortex: -5.118",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.368<br>natural killer cell: -2.115<br>CD16-negative, CD56-bright natural killer cell, human: -3.533<br>mature NK T cell: -4.403<br>activated type II NK T cell: -5.027",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.431<br>natural killer cell: -1.789<br>CD16-negative, CD56-bright natural killer cell, human: -3.341<br>mature NK T cell: -4.264<br>activated type II NK T cell: -5.052",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.190<br>natural killer cell: -1.365<br>CD16-negative, CD56-bright natural killer cell, human: -2.920<br>mature NK T cell: -3.436<br>gamma-delta T cell: -3.848",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.544<br>natural killer cell: -1.399<br>mature NK T cell: -4.069<br>CD16-negative, CD56-bright natural killer cell, human: -4.420<br>activated type II NK T cell: -4.895",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.031<br>natural killer cell: -1.833<br>CD16-negative, CD56-bright natural killer cell, human: -2.798<br>mature NK T cell: -3.687<br>gamma-delta T cell: -3.934",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.529<br>natural killer cell: -1.459<br>mature NK T cell: -4.095<br>CD16-negative, CD56-bright natural killer cell, human: -4.703<br>astrocyte of the cerebral cortex: -4.803",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.218<br>CD16-negative, CD56-bright natural killer cell, human: -3.848<br>mature NK T cell: -4.250<br>activated type II NK T cell: -4.911",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.890<br>natural killer cell: -1.127<br>CD16-negative, CD56-bright natural killer cell, human: -3.483<br>mature NK T cell: -4.185<br>activated type II NK T cell: -4.568",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.509<br>natural killer cell: -1.714<br>CD16-negative, CD56-bright natural killer cell, human: -3.373<br>mature NK T cell: -4.167<br>activated type II NK T cell: -4.898",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.248<br>natural killer cell: -1.424<br>CD16-negative, CD56-bright natural killer cell, human: -2.911<br>mature NK T cell: -3.188<br>gamma-delta T cell: -3.540",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.389<br>natural killer cell: -1.891<br>CD16-negative, CD56-bright natural killer cell, human: -3.624<br>mature NK T cell: -4.833<br>activated type II NK T cell: -4.968",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.759<br>natural killer cell: -1.500<br>gamma-delta T cell: -3.484<br>mature NK T cell: -3.589<br>CD8-positive, alpha-beta T cell: -3.873",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.452<br>gamma-delta T cell: -2.466<br>natural killer cell: -2.614<br>CD16-negative, CD56-bright natural killer cell, human: -2.663<br>mature NK T cell: -2.992",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.578<br>natural killer cell: -1.494<br>CD16-negative, CD56-bright natural killer cell, human: -3.593<br>mature NK T cell: -4.131<br>astrocyte of the cerebral cortex: -4.862",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.445<br>natural killer cell: -2.257<br>CD16-negative, CD56-bright natural killer cell, human: -2.695<br>mature NK T cell: -4.536<br>activated type II NK T cell: -4.624",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.331<br>CD16-negative, CD56-bright natural killer cell, human: -1.444<br>natural killer cell: -1.995<br>mature NK T cell: -4.045<br>activated type II NK T cell: -4.058",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.612<br>natural killer cell: -1.956<br>CD16-negative, CD56-bright natural killer cell, human: -3.289<br>mature NK T cell: -3.745<br>gamma-delta T cell: -4.688",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.722<br>CD16-negative, CD56-bright natural killer cell, human: -1.848<br>natural killer cell: -2.077<br>mature NK T cell: -3.894<br>activated type II NK T cell: -4.512",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.543<br>natural killer cell: -1.639<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.091<br>activated type II NK T cell: -4.657",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.977<br>CD16-negative, CD56-bright natural killer cell, human: -1.603<br>natural killer cell: -2.379<br>mature NK T cell: -3.960<br>gamma-delta T cell: -4.178",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.567<br>natural killer cell: -1.753<br>CD16-negative, CD56-bright natural killer cell, human: -3.109<br>mature NK T cell: -4.195<br>activated type II NK T cell: -4.754",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.817<br>CD16-negative, CD56-bright natural killer cell, human: -2.455<br>natural killer cell: -2.497<br>gamma-delta T cell: -3.111<br>mature NK T cell: -3.856",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.822<br>natural killer cell: -1.651<br>CD16-negative, CD56-bright natural killer cell, human: -2.918<br>mature NK T cell: -3.801<br>gamma-delta T cell: -4.005",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.557<br>natural killer cell: -1.797<br>CD16-negative, CD56-bright natural killer cell, human: -2.880<br>mature NK T cell: -4.000<br>activated type II NK T cell: -4.580",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.487<br>natural killer cell: -1.802<br>CD16-negative, CD56-bright natural killer cell, human: -2.723<br>activated type II NK T cell: -4.518<br>mature NK T cell: -4.705",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.782<br>natural killer cell: -1.211<br>CD16-negative, CD56-bright natural killer cell, human: -3.754<br>mature NK T cell: -4.030<br>activated type II NK T cell: -4.619",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.154<br>CD16-negative, CD56-bright natural killer cell, human: -1.878<br>natural killer cell: -2.675<br>gamma-delta T cell: -2.696<br>mature NK T cell: -3.555",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.297<br>natural killer cell: -2.209<br>CD16-negative, CD56-bright natural killer cell, human: -4.191<br>mature NK T cell: -4.297<br>gamma-delta T cell: -5.226",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.436<br>natural killer cell: -1.803<br>CD16-negative, CD56-bright natural killer cell, human: -3.971<br>mature NK T cell: -4.313<br>activated type II NK T cell: -4.856",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.481<br>natural killer cell: -1.596<br>CD16-negative, CD56-bright natural killer cell, human: -3.700<br>mature NK T cell: -4.249<br>activated type II NK T cell: -4.965",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.422<br>natural killer cell: -2.007<br>CD16-negative, CD56-bright natural killer cell, human: -3.209<br>mature NK T cell: -4.727<br>activated type II NK T cell: -4.741",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.493<br>natural killer cell: -1.543<br>CD16-negative, CD56-bright natural killer cell, human: -3.859<br>mature NK T cell: -4.163<br>astrocyte of the cerebral cortex: -4.926",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.781<br>CD16-negative, CD56-bright natural killer cell, human: -1.985<br>natural killer cell: -2.078<br>activated type II NK T cell: -4.213<br>mature NK T cell: -4.272",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.582<br>natural killer cell: -1.661<br>CD16-negative, CD56-bright natural killer cell, human: -3.573<br>mature NK T cell: -3.747<br>gamma-delta T cell: -4.858",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.689<br>natural killer cell: -1.504<br>CD16-negative, CD56-bright natural killer cell, human: -3.548<br>mature NK T cell: -3.907<br>gamma-delta T cell: -4.337",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.705<br>natural killer cell: -1.214<br>mature NK T cell: -4.027<br>CD16-negative, CD56-bright natural killer cell, human: -4.146<br>gamma-delta T cell: -4.974",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.645<br>CD16-negative, CD56-bright natural killer cell, human: -1.840<br>natural killer cell: -2.626<br>gamma-delta T cell: -4.037<br>mature NK T cell: -4.130",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.504<br>natural killer cell: -1.859<br>mature NK T cell: -3.736<br>CD16-negative, CD56-bright natural killer cell, human: -3.860<br>gamma-delta T cell: -4.450",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.730<br>natural killer cell: -1.617<br>CD16-negative, CD56-bright natural killer cell, human: -2.760<br>mature NK T cell: -3.766<br>gamma-delta T cell: -4.067",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.455<br>natural killer cell: -1.808<br>CD16-negative, CD56-bright natural killer cell, human: -2.919<br>mature NK T cell: -4.421<br>activated type II NK T cell: -4.875",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.433<br>natural killer cell: -2.164<br>CD16-negative, CD56-bright natural killer cell, human: -3.062<br>mature NK T cell: -4.370<br>gamma-delta T cell: -4.685",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.720<br>natural killer cell: -1.698<br>CD16-negative, CD56-bright natural killer cell, human: -2.551<br>mature NK T cell: -3.531<br>gamma-delta T cell: -4.535",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.623<br>natural killer cell: -2.231<br>CD16-negative, CD56-bright natural killer cell, human: -2.343<br>mature NK T cell: -3.984<br>activated type II NK T cell: -4.452",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.787<br>natural killer cell: -1.652<br>CD16-negative, CD56-bright natural killer cell, human: -2.307<br>mature NK T cell: -3.950<br>gamma-delta T cell: -4.503",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.272<br>CD16-negative, CD56-bright natural killer cell, human: -1.815<br>natural killer cell: -2.077<br>gamma-delta T cell: -3.363<br>mature NK T cell: -3.751",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.640<br>natural killer cell: -1.365<br>CD16-negative, CD56-bright natural killer cell, human: -3.166<br>mature NK T cell: -3.990<br>activated type II NK T cell: -4.459",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.258<br>CD16-negative, CD56-bright natural killer cell, human: -1.600<br>natural killer cell: -2.044<br>mature NK T cell: -3.209<br>gamma-delta T cell: -3.663",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -1.691<br>natural killer cell: -1.804<br>gamma-delta T cell: -2.955<br>mature NK T cell: -3.267",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.410<br>natural killer cell: -2.190<br>CD16-negative, CD56-bright natural killer cell, human: -3.640<br>gamma-delta T cell: -4.258<br>mature NK T cell: -4.423",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.026<br>CD16-negative, CD56-bright natural killer cell, human: -1.775<br>natural killer cell: -2.172<br>gamma-delta T cell: -3.475<br>mature NK T cell: -4.026",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.734<br>CD16-negative, CD56-bright natural killer cell, human: -1.991<br>natural killer cell: -2.321<br>gamma-delta T cell: -3.541<br>mature NK T cell: -3.798",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.448<br>natural killer cell: -1.850<br>CD16-negative, CD56-bright natural killer cell, human: -3.779<br>mature NK T cell: -4.109<br>astrocyte of the cerebral cortex: -4.927",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.662<br>natural killer cell: -1.352<br>CD16-negative, CD56-bright natural killer cell, human: -4.017<br>mature NK T cell: -4.242<br>CD8-positive, alpha-beta T cell: -4.727",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.556<br>mature NK T cell: -3.605<br>CD16-negative, CD56-bright natural killer cell, human: -4.498<br>gamma-delta T cell: -4.844",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.649<br>natural killer cell: -1.274<br>mature NK T cell: -3.796<br>CD16-negative, CD56-bright natural killer cell, human: -4.333<br>astrocyte of the cerebral cortex: -4.580",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.521<br>CD16-negative, CD56-bright natural killer cell, human: -3.437<br>mature NK T cell: -4.107<br>activated type II NK T cell: -4.760",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.562<br>natural killer cell: -1.475<br>CD16-negative, CD56-bright natural killer cell, human: -3.555<br>mature NK T cell: -4.057<br>activated type II NK T cell: -4.890",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.546<br>natural killer cell: -1.956<br>CD16-negative, CD56-bright natural killer cell, human: -2.845<br>mature NK T cell: -4.144<br>activated type II NK T cell: -4.606",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.590<br>natural killer cell: -1.646<br>mature NK T cell: -3.692<br>gamma-delta T cell: -3.972<br>CD16-negative, CD56-bright natural killer cell, human: -4.032",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.213<br>natural killer cell: -1.461<br>CD16-negative, CD56-bright natural killer cell, human: -1.855<br>mature NK T cell: -3.661<br>activated type II NK T cell: -4.224",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -1.551<br>mature NK T cell: -3.645<br>CD16-negative, CD56-bright natural killer cell, human: -3.969<br>gamma-delta T cell: -4.576",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.537<br>natural killer cell: -1.785<br>mature NK T cell: -3.917<br>CD16-negative, CD56-bright natural killer cell, human: -4.093<br>gamma-delta T cell: -4.734",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.482<br>natural killer cell: -1.546<br>mature NK T cell: -4.012<br>astrocyte of the cerebral cortex: -4.710<br>gamma-delta T cell: -4.817",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.918<br>natural killer cell: -1.858<br>CD16-negative, CD56-bright natural killer cell, human: -2.181<br>mature NK T cell: -3.711<br>gamma-delta T cell: -4.275",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.855<br>natural killer cell: -1.842<br>CD16-negative, CD56-bright natural killer cell, human: -2.624<br>mature NK T cell: -3.712<br>activated type II NK T cell: -4.375",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.899<br>CD16-negative, CD56-bright natural killer cell, human: -3.246<br>mature NK T cell: -3.876<br>gamma-delta T cell: -4.936",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.137<br>CD16-negative, CD56-bright natural killer cell, human: -1.482<br>natural killer cell: -2.663<br>gamma-delta T cell: -3.683<br>activated type II NK T cell: -4.227",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.471<br>natural killer cell: -1.780<br>CD16-negative, CD56-bright natural killer cell, human: -3.652<br>mature NK T cell: -4.458<br>activated type II NK T cell: -4.834",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.564<br>natural killer cell: -1.660<br>CD16-negative, CD56-bright natural killer cell, human: -3.630<br>mature NK T cell: -4.085<br>activated type II NK T cell: -4.954",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.576<br>natural killer cell: -1.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.199<br>gamma-delta T cell: -4.636",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.466<br>natural killer cell: -1.886<br>CD16-negative, CD56-bright natural killer cell, human: -3.238<br>mature NK T cell: -4.160<br>activated type II NK T cell: -4.993",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.501<br>natural killer cell: -1.543<br>mature NK T cell: -3.713<br>CD16-negative, CD56-bright natural killer cell, human: -4.688<br>gamma-delta T cell: -5.044",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.847<br>CD16-negative, CD56-bright natural killer cell, human: -1.806<br>natural killer cell: -2.209<br>gamma-delta T cell: -3.754<br>mature NK T cell: -3.846",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.429<br>natural killer cell: -1.850<br>CD16-negative, CD56-bright natural killer cell, human: -3.253<br>mature NK T cell: -4.571<br>activated type II NK T cell: -4.819",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.659<br>natural killer cell: -1.793<br>CD16-negative, CD56-bright natural killer cell, human: -3.616<br>mature NK T cell: -3.767<br>gamma-delta T cell: -4.293",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.574<br>natural killer cell: -1.468<br>mature NK T cell: -3.926<br>CD16-negative, CD56-bright natural killer cell, human: -4.820<br>astrocyte of the cerebral cortex: -5.061",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.409<br>natural killer cell: -1.695<br>CD16-negative, CD56-bright natural killer cell, human: -3.956<br>mature NK T cell: -4.457<br>activated type II NK T cell: -5.131",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.481<br>natural killer cell: -1.849<br>CD16-negative, CD56-bright natural killer cell, human: -3.053<br>mature NK T cell: -4.130<br>activated type II NK T cell: -4.680",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.392<br>natural killer cell: -2.783<br>mature NK T cell: -2.913<br>CD16-negative, CD56-bright natural killer cell, human: -3.006<br>gamma-delta T cell: -3.290",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.789<br>CD16-negative, CD56-bright natural killer cell, human: -1.917<br>natural killer cell: -2.300<br>gamma-delta T cell: -2.620<br>mature NK T cell: -3.070",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.259<br>CD16-negative, CD56-bright natural killer cell, human: -1.659<br>natural killer cell: -2.152<br>gamma-delta T cell: -3.717<br>activated type II NK T cell: -4.018",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.781<br>CD16-negative, CD56-bright natural killer cell, human: -3.543<br>mature NK T cell: -3.886<br>astrocyte of the cerebral cortex: -4.808",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.427<br>mature NK T cell: -3.981<br>CD16-negative, CD56-bright natural killer cell, human: -4.585<br>astrocyte of the cerebral cortex: -4.792",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.619<br>mature NK T cell: -3.663<br>CD16-negative, CD56-bright natural killer cell, human: -4.246<br>astrocyte of the cerebral cortex: -5.083",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.611<br>natural killer cell: -1.982<br>CD16-negative, CD56-bright natural killer cell, human: -2.206<br>mature NK T cell: -4.022<br>gamma-delta T cell: -4.344",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.769<br>natural killer cell: -1.105<br>CD16-negative, CD56-bright natural killer cell, human: -3.875<br>mature NK T cell: -4.223<br>activated type II NK T cell: -4.804",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.630<br>natural killer cell: -1.600<br>CD16-negative, CD56-bright natural killer cell, human: -3.083<br>mature NK T cell: -3.568<br>gamma-delta T cell: -4.356",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.191<br>CD16-negative, CD56-bright natural killer cell, human: -1.373<br>natural killer cell: -2.416<br>gamma-delta T cell: -3.292<br>mature NK T cell: -3.979",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.653<br>natural killer cell: -1.392<br>mature NK T cell: -3.644<br>CD16-negative, CD56-bright natural killer cell, human: -4.179<br>astrocyte of the cerebral cortex: -4.565",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.373<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -2.496<br>gamma-delta T cell: -2.602<br>mature NK T cell: -3.305",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.381<br>natural killer cell: -1.751<br>mature NK T cell: -4.391<br>CD16-negative, CD56-bright natural killer cell, human: -4.407<br>astrocyte of the cerebral cortex: -4.985",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.383<br>gamma-delta T cell: -2.124<br>CD16-negative, CD56-bright natural killer cell, human: -2.611<br>natural killer cell: -2.794<br>mature NK T cell: -2.807",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.604<br>natural killer cell: -1.753<br>CD16-negative, CD56-bright natural killer cell, human: -2.559<br>mature NK T cell: -4.321<br>activated type II NK T cell: -4.330",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.835<br>CD16-negative, CD56-bright natural killer cell, human: -1.637<br>natural killer cell: -2.054<br>mature NK T cell: -4.223<br>activated type II NK T cell: -4.501",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.708<br>natural killer cell: -1.480<br>CD16-negative, CD56-bright natural killer cell, human: -3.574<br>mature NK T cell: -4.219<br>activated type II NK T cell: -4.509",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.364<br>natural killer cell: -2.038<br>CD16-negative, CD56-bright natural killer cell, human: -3.222<br>mature NK T cell: -4.401<br>activated type II NK T cell: -4.886",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.596<br>natural killer cell: -1.482<br>CD16-negative, CD56-bright natural killer cell, human: -3.571<br>mature NK T cell: -3.843<br>gamma-delta T cell: -4.881",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.396<br>natural killer cell: -2.307<br>CD16-negative, CD56-bright natural killer cell, human: -3.063<br>gamma-delta T cell: -4.378<br>mature NK T cell: -4.548",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.519<br>natural killer cell: -2.279<br>CD16-negative, CD56-bright natural killer cell, human: -2.497<br>mature NK T cell: -4.174<br>activated type II NK T cell: -4.706",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.388<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -3.129<br>mature NK T cell: -4.036<br>gamma-delta T cell: -4.951",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.643<br>natural killer cell: -1.804<br>CD16-negative, CD56-bright natural killer cell, human: -2.814<br>mature NK T cell: -4.074<br>gamma-delta T cell: -4.517",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.591<br>natural killer cell: -1.435<br>CD16-negative, CD56-bright natural killer cell, human: -3.769<br>mature NK T cell: -4.022<br>activated type II NK T cell: -4.882",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.828<br>natural killer cell: -1.094<br>mature NK T cell: -3.668<br>CD16-negative, CD56-bright natural killer cell, human: -4.577<br>astrocyte of the cerebral cortex: -4.751",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.821<br>natural killer cell: -2.079<br>CD16-negative, CD56-bright natural killer cell, human: -2.524<br>gamma-delta T cell: -3.141<br>mature NK T cell: -3.431",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.660<br>natural killer cell: -1.622<br>CD16-negative, CD56-bright natural killer cell, human: -2.849<br>mature NK T cell: -3.965<br>lymphocyte: -4.606",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.107<br>natural killer cell: -1.664<br>CD16-negative, CD56-bright natural killer cell, human: -1.683<br>activated type II NK T cell: -3.851<br>mature NK T cell: -4.635",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.664<br>natural killer cell: -1.527<br>mature NK T cell: -3.428<br>CD16-negative, CD56-bright natural killer cell, human: -3.892<br>gamma-delta T cell: -4.535",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.621<br>natural killer cell: -1.835<br>CD16-negative, CD56-bright natural killer cell, human: -2.594<br>mature NK T cell: -4.491<br>gamma-delta T cell: -4.621",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.414<br>natural killer cell: -1.689<br>mature NK T cell: -4.285<br>CD16-negative, CD56-bright natural killer cell, human: -4.288<br>astrocyte of the cerebral cortex: -5.020",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.677<br>CD16-negative, CD56-bright natural killer cell, human: -3.944<br>mature NK T cell: -4.148<br>gamma-delta T cell: -4.675",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.138<br>CD16-negative, CD56-bright natural killer cell, human: -1.678<br>natural killer cell: -1.679<br>activated type II NK T cell: -4.004<br>mature NK T cell: -4.247",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.491<br>natural killer cell: -1.845<br>CD16-negative, CD56-bright natural killer cell, human: -3.092<br>mature NK T cell: -4.428<br>activated type II NK T cell: -4.688",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.112<br>natural killer cell: -1.640<br>CD16-negative, CD56-bright natural killer cell, human: -2.274<br>gamma-delta T cell: -3.350<br>mature NK T cell: -3.676",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.200<br>natural killer cell: -1.440<br>CD16-negative, CD56-bright natural killer cell, human: -1.527<br>activated type II NK T cell: -4.328<br>mature NK T cell: -4.541",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.600<br>natural killer cell: -1.689<br>CD16-negative, CD56-bright natural killer cell, human: -3.085<br>mature NK T cell: -4.055<br>activated type II NK T cell: -4.633",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -2.138<br>CD16-negative, CD56-bright natural killer cell, human: -2.655<br>mature NK T cell: -4.088<br>gamma-delta T cell: -4.570",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.814<br>CD16-negative, CD56-bright natural killer cell, human: -1.725<br>natural killer cell: -1.885<br>activated type II NK T cell: -3.990<br>mature NK T cell: -4.699",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.466<br>natural killer cell: -1.501<br>mature NK T cell: -4.211<br>CD16-negative, CD56-bright natural killer cell, human: -4.583<br>astrocyte of the cerebral cortex: -4.890",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.917<br>natural killer cell: -2.111<br>CD16-negative, CD56-bright natural killer cell, human: -2.697<br>gamma-delta T cell: -2.740<br>CD8-positive, alpha-beta T cell: -3.396",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.618<br>natural killer cell: -1.648<br>CD16-negative, CD56-bright natural killer cell, human: -2.939<br>mature NK T cell: -4.077<br>activated type II NK T cell: -4.365",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.626<br>natural killer cell: -2.208<br>CD16-negative, CD56-bright natural killer cell, human: -2.431<br>mature NK T cell: -3.733<br>gamma-delta T cell: -4.028",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.775<br>natural killer cell: -2.092<br>CD16-negative, CD56-bright natural killer cell, human: -2.494<br>mature NK T cell: -3.583<br>immature NK T cell: -4.534",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.409<br>natural killer cell: -1.559<br>CD16-negative, CD56-bright natural killer cell, human: -1.825<br>activated type II NK T cell: -3.807<br>mature NK T cell: -4.023",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.629<br>natural killer cell: -1.578<br>mature NK T cell: -3.707<br>CD16-negative, CD56-bright natural killer cell, human: -4.037<br>astrocyte of the cerebral cortex: -4.846",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.109<br>CD16-negative, CD56-bright natural killer cell, human: -1.279<br>natural killer cell: -2.054<br>gamma-delta T cell: -4.093<br>mature NK T cell: -4.178",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.899<br>natural killer cell: -1.458<br>CD16-negative, CD56-bright natural killer cell, human: -2.688<br>mature NK T cell: -4.173<br>activated type II NK T cell: -4.405",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.565<br>mature NK T cell: -3.643<br>CD16-negative, CD56-bright natural killer cell, human: -3.945<br>astrocyte of the cerebral cortex: -4.818",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.826<br>natural killer cell: -2.096<br>CD16-negative, CD56-bright natural killer cell, human: -2.576<br>gamma-delta T cell: -3.589<br>mature NK T cell: -3.697",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.015<br>natural killer cell: -1.912<br>CD16-negative, CD56-bright natural killer cell, human: -2.605<br>gamma-delta T cell: -3.130<br>mature NK T cell: -3.194",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.974<br>natural killer cell: -1.101<br>CD16-negative, CD56-bright natural killer cell, human: -3.582<br>mature NK T cell: -4.052<br>gamma-delta T cell: -4.304",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.707<br>natural killer cell: -2.132<br>CD16-negative, CD56-bright natural killer cell, human: -2.495<br>mature NK T cell: -3.480<br>gamma-delta T cell: -3.595",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.444<br>natural killer cell: -1.828<br>CD16-negative, CD56-bright natural killer cell, human: -3.193<br>mature NK T cell: -4.624<br>activated type II NK T cell: -4.861",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.533<br>natural killer cell: -2.288<br>CD16-negative, CD56-bright natural killer cell, human: -2.834<br>mature NK T cell: -3.843<br>gamma-delta T cell: -4.060",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.198<br>CD16-negative, CD56-bright natural killer cell, human: -1.615<br>natural killer cell: -1.667<br>mature NK T cell: -4.031<br>gamma-delta T cell: -4.116",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.314<br>CD16-negative, CD56-bright natural killer cell, human: -1.729<br>natural killer cell: -2.498<br>gamma-delta T cell: -2.721<br>mature NK T cell: -3.578",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.596<br>natural killer cell: -1.539<br>CD16-negative, CD56-bright natural killer cell, human: -3.346<br>mature NK T cell: -3.980<br>activated type II NK T cell: -4.639",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.551<br>natural killer cell: -1.662<br>CD16-negative, CD56-bright natural killer cell, human: -2.677<br>mature NK T cell: -4.385<br>activated type II NK T cell: -4.654",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.151<br>natural killer cell: -1.294<br>CD16-negative, CD56-bright natural killer cell, human: -2.450<br>mature NK T cell: -3.621<br>activated type II NK T cell: -4.289",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.328<br>CD16-negative, CD56-bright natural killer cell, human: -1.493<br>natural killer cell: -2.429<br>gamma-delta T cell: -3.118<br>mature NK T cell: -3.513",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.484<br>natural killer cell: -2.041<br>CD16-negative, CD56-bright natural killer cell, human: -4.028<br>gamma-delta T cell: -4.078<br>mature NK T cell: -4.173",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.374<br>natural killer cell: -2.095<br>mature NK T cell: -3.514<br>CD16-negative, CD56-bright natural killer cell, human: -4.711<br>astrocyte of the cerebral cortex: -4.990",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.638<br>natural killer cell: -1.288<br>CD16-negative, CD56-bright natural killer cell, human: -4.036<br>mature NK T cell: -4.111<br>activated type II NK T cell: -5.103",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.037<br>natural killer cell: -1.099<br>CD16-negative, CD56-bright natural killer cell, human: -2.671<br>mature NK T cell: -4.372<br>activated type II NK T cell: -4.797",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.369<br>natural killer cell: -2.108<br>mature NK T cell: -4.089<br>CD16-negative, CD56-bright natural killer cell, human: -4.263<br>astrocyte of the cerebral cortex: -5.010",
+          "CD16-positive, CD56-dim natural killer cell, human: -1.224<br>natural killer cell: -1.352<br>gamma-delta T cell: -2.917<br>mature NK T cell: -3.157<br>CD16-negative, CD56-bright natural killer cell, human: -3.831",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.839<br>natural killer cell: -1.518<br>CD16-negative, CD56-bright natural killer cell, human: -2.977<br>mature NK T cell: -3.875<br>activated type II NK T cell: -4.484",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.578<br>natural killer cell: -2.209<br>CD16-negative, CD56-bright natural killer cell, human: -2.401<br>mature NK T cell: -4.269<br>activated type II NK T cell: -4.788",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.526<br>natural killer cell: -2.232<br>mature NK T cell: -3.549<br>CD16-negative, CD56-bright natural killer cell, human: -3.741<br>gamma-delta T cell: -4.025",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.582<br>natural killer cell: -1.867<br>CD16-negative, CD56-bright natural killer cell, human: -3.369<br>mature NK T cell: -3.711<br>gamma-delta T cell: -4.356",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.392<br>natural killer cell: -2.268<br>CD16-negative, CD56-bright natural killer cell, human: -3.005<br>mature NK T cell: -4.756<br>activated type II NK T cell: -4.768",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.710<br>natural killer cell: -1.348<br>mature NK T cell: -3.609<br>CD16-negative, CD56-bright natural killer cell, human: -4.349<br>gamma-delta T cell: -4.488",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.589<br>natural killer cell: -2.469<br>CD16-negative, CD56-bright natural killer cell, human: -2.510<br>mature NK T cell: -3.860<br>activated type II NK T cell: -4.319",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.605<br>natural killer cell: -1.607<br>CD16-negative, CD56-bright natural killer cell, human: -3.036<br>mature NK T cell: -4.282<br>activated type II NK T cell: -4.633",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.847<br>natural killer cell: -2.615<br>CD16-negative, CD56-bright natural killer cell, human: -2.743<br>gamma-delta T cell: -3.021<br>mature NK T cell: -3.239",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.668<br>natural killer cell: -2.633<br>CD16-negative, CD56-bright natural killer cell, human: -2.864<br>mature NK T cell: -3.277<br>gamma-delta T cell: -3.564",
+          "CD16-positive, CD56-dim natural killer cell, human: -0.524<br>natural killer cell: -1.633<br>mature NK T cell: -3.704<br>CD16-negative, CD56-bright natural killer cell, human: -3.844<br>astrocyte of the cerebral cortex: -4.811"
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+         "hovertext": [
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+          "CD16-negative, CD56-bright natural killer cell, human: -1.160<br>gamma-delta T cell: -2.823<br>CD16-positive, CD56-dim natural killer cell, human: -3.236<br>mucosal invariant T cell: -3.678<br>innate lymphoid cell: -3.688",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.294<br>natural killer cell: -2.241<br>CD16-positive, CD56-dim natural killer cell, human: -2.504<br>gamma-delta T cell: -2.676<br>mature NK T cell: -3.286",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.958<br>gamma-delta T cell: -2.417<br>CD16-positive, CD56-dim natural killer cell, human: -2.824<br>natural killer cell: -2.876<br>mature NK T cell: -3.277",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.404<br>natural killer cell: -2.350<br>gamma-delta T cell: -2.548<br>CD16-positive, CD56-dim natural killer cell, human: -3.085<br>mature NK T cell: -3.486",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.740<br>natural killer cell: -2.566<br>CD16-positive, CD56-dim natural killer cell, human: -2.926<br>gamma-delta T cell: -3.366<br>mature NK T cell: -4.307",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.168<br>natural killer cell: -2.865<br>gamma-delta T cell: -3.099<br>mature NK T cell: -3.812<br>effector memory CD8-positive, alpha-beta T cell: -3.843",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.497<br>CD16-positive, CD56-dim natural killer cell, human: -1.933<br>natural killer cell: -2.438<br>gamma-delta T cell: -2.609<br>mature NK T cell: -3.407",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.852<br>natural killer cell: -2.754<br>CD16-positive, CD56-dim natural killer cell, human: -3.500<br>activated type II NK T cell: -4.087<br>gamma-delta T cell: -4.230",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.949<br>CD16-positive, CD56-dim natural killer cell, human: -2.498<br>natural killer cell: -2.751<br>gamma-delta T cell: -3.198<br>mature NK T cell: -3.843",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.534<br>CD16-positive, CD56-dim natural killer cell, human: -2.056<br>natural killer cell: -2.392<br>gamma-delta T cell: -2.663<br>mature NK T cell: -3.687",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.145<br>gamma-delta T cell: -2.205<br>CD16-positive, CD56-dim natural killer cell, human: -2.834<br>natural killer cell: -3.199<br>effector memory CD8-positive, alpha-beta T cell: -3.371",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.437<br>natural killer cell: -3.333<br>gamma-delta T cell: -3.548<br>effector memory CD8-positive, alpha-beta T cell: -3.673<br>mature NK T cell: -3.775",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.555<br>gamma-delta T cell: -2.517<br>natural killer cell: -2.873<br>mature NK T cell: -3.042<br>CD16-positive, CD56-dim natural killer cell, human: -3.314",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.043<br>CD16-positive, CD56-dim natural killer cell, human: -2.157<br>gamma-delta T cell: -2.922<br>natural killer cell: -2.954<br>mature NK T cell: -3.636",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.364<br>CD16-positive, CD56-dim natural killer cell, human: -2.361<br>gamma-delta T cell: -2.525<br>natural killer cell: -2.598<br>mature NK T cell: -3.294",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.145<br>natural killer cell: -2.365<br>CD16-positive, CD56-dim natural killer cell, human: -2.799<br>mature NK T cell: -3.821<br>activated type II NK T cell: -4.101",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.926<br>natural killer cell: -3.067<br>gamma-delta T cell: -3.598<br>CD16-positive, CD56-dim natural killer cell, human: -3.667<br>innate lymphoid cell: -3.977",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.828<br>natural killer cell: -2.522<br>gamma-delta T cell: -3.067<br>CD16-positive, CD56-dim natural killer cell, human: -3.138<br>innate lymphoid cell: -4.063",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.043<br>effector memory CD8-positive, alpha-beta T cell: -3.270<br>gamma-delta T cell: -3.336<br>natural killer cell: -3.380<br>mature NK T cell: -3.741",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.016<br>natural killer cell: -2.409<br>CD16-positive, CD56-dim natural killer cell, human: -2.670<br>gamma-delta T cell: -3.153<br>mature NK T cell: -3.715",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.879<br>gamma-delta T cell: -3.002<br>CD16-positive, CD56-dim natural killer cell, human: -3.387<br>natural killer cell: -3.433<br>activated type II NK T cell: -4.034",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.143<br>CD16-positive, CD56-dim natural killer cell, human: -1.622<br>natural killer cell: -2.118<br>mature NK T cell: -3.496<br>gamma-delta T cell: -3.778",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.037<br>natural killer cell: -1.966<br>CD16-positive, CD56-dim natural killer cell, human: -2.800<br>gamma-delta T cell: -3.441<br>mature NK T cell: -3.957",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.254<br>CD16-positive, CD56-dim natural killer cell, human: -2.005<br>natural killer cell: -2.607<br>gamma-delta T cell: -2.762<br>mature NK T cell: -3.455",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.683<br>natural killer cell: -2.680<br>activated type II NK T cell: -3.935<br>group 3 innate lymphoid cell: -4.039<br>CD16-positive, CD56-dim natural killer cell, human: -4.093",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.896<br>gamma-delta T cell: -2.830<br>natural killer cell: -3.456<br>effector memory CD8-positive, alpha-beta T cell: -3.721<br>mucosal invariant T cell: -3.851",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.911<br>CD16-positive, CD56-dim natural killer cell, human: -2.580<br>gamma-delta T cell: -2.735<br>natural killer cell: -2.970<br>mature NK T cell: -3.869",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.079<br>natural killer cell: -2.452<br>CD16-positive, CD56-dim natural killer cell, human: -2.701<br>gamma-delta T cell: -3.125<br>mature NK T cell: -3.767",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.441<br>gamma-delta T cell: -2.064<br>CD16-positive, CD56-dim natural killer cell, human: -3.039<br>effector memory CD8-positive, alpha-beta T cell: -3.323<br>mucosal invariant T cell: -3.559",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.399<br>gamma-delta T cell: -2.868<br>effector memory CD8-positive, alpha-beta T cell: -3.647<br>CD16-positive, CD56-dim natural killer cell, human: -3.685<br>natural killer cell: -3.735",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.043<br>natural killer cell: -2.398<br>gamma-delta T cell: -3.436<br>innate lymphoid cell: -4.113<br>CD16-positive, CD56-dim natural killer cell, human: -4.213",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.255<br>CD16-positive, CD56-dim natural killer cell, human: -1.413<br>natural killer cell: -1.912<br>gamma-delta T cell: -3.501<br>mature NK T cell: -3.645",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.785<br>natural killer cell: -2.113<br>CD16-positive, CD56-dim natural killer cell, human: -2.570<br>gamma-delta T cell: -3.659<br>mature NK T cell: -4.113",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.135<br>CD16-positive, CD56-dim natural killer cell, human: -2.036<br>natural killer cell: -2.387<br>gamma-delta T cell: -3.394<br>mature NK T cell: -3.614",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.867<br>gamma-delta T cell: -2.976<br>natural killer cell: -3.150<br>effector memory CD8-positive, alpha-beta T cell: -3.820<br>CD16-positive, CD56-dim natural killer cell, human: -3.988",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.654<br>natural killer cell: -2.880<br>CD16-positive, CD56-dim natural killer cell, human: -3.136<br>gamma-delta T cell: -3.202<br>mature NK T cell: -3.615",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.345<br>CD16-positive, CD56-dim natural killer cell, human: -1.595<br>natural killer cell: -1.603<br>gamma-delta T cell: -3.782<br>mature NK T cell: -4.013",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.226<br>natural killer cell: -2.197<br>CD16-positive, CD56-dim natural killer cell, human: -3.167<br>activated type II NK T cell: -3.866<br>mature NK T cell: -4.028",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.336<br>effector memory CD8-positive, alpha-beta T cell: -3.528<br>natural killer cell: -3.540<br>gamma-delta T cell: -3.561<br>innate lymphoid cell: -3.718",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.926<br>natural killer cell: -2.437<br>gamma-delta T cell: -2.906<br>CD16-positive, CD56-dim natural killer cell, human: -3.018<br>mature NK T cell: -3.580",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.982<br>gamma-delta T cell: -2.952<br>CD16-positive, CD56-dim natural killer cell, human: -3.156<br>natural killer cell: -3.197<br>mature NK T cell: -3.508",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.949<br>CD16-positive, CD56-dim natural killer cell, human: -2.995<br>gamma-delta T cell: -3.097<br>natural killer cell: -3.215<br>mature NK T cell: -3.990",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.351<br>CD16-positive, CD56-dim natural killer cell, human: -1.688<br>natural killer cell: -2.290<br>gamma-delta T cell: -2.887<br>mature NK T cell: -3.630",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.778<br>gamma-delta T cell: -2.762<br>CD16-positive, CD56-dim natural killer cell, human: -3.091<br>natural killer cell: -3.094<br>mature NK T cell: -3.749",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.194<br>natural killer cell: -1.837<br>CD16-positive, CD56-dim natural killer cell, human: -2.183<br>gamma-delta T cell: -2.836<br>mature NK T cell: -3.430",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.700<br>CD16-positive, CD56-dim natural killer cell, human: -2.677<br>natural killer cell: -2.827<br>gamma-delta T cell: -3.518<br>activated type II NK T cell: -4.201",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.829<br>natural killer cell: -2.575<br>gamma-delta T cell: -3.359<br>CD16-positive, CD56-dim natural killer cell, human: -3.966<br>effector memory CD8-positive, alpha-beta T cell: -4.187",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.202<br>gamma-delta T cell: -3.341<br>natural killer cell: -3.428<br>innate lymphoid cell: -3.627<br>CD16-positive, CD56-dim natural killer cell, human: -3.718",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.165<br>natural killer cell: -2.110<br>CD16-positive, CD56-dim natural killer cell, human: -2.737<br>mast cell: -3.610<br>innate lymphoid cell: -3.646",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -2.007<br>CD16-positive, CD56-dim natural killer cell, human: -3.375<br>mature NK T cell: -3.544<br>gamma-delta T cell: -3.729",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.899<br>gamma-delta T cell: -2.760<br>natural killer cell: -3.240<br>effector memory CD8-positive, alpha-beta T cell: -3.516<br>mucosal invariant T cell: -3.906",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.390<br>CD16-positive, CD56-dim natural killer cell, human: -2.140<br>natural killer cell: -2.592<br>gamma-delta T cell: -2.799<br>mature NK T cell: -3.968",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.534<br>natural killer cell: -2.378<br>gamma-delta T cell: -2.810<br>effector memory CD8-positive, alpha-beta T cell: -3.034<br>CD16-positive, CD56-dim natural killer cell, human: -3.384",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.465<br>CD16-positive, CD56-dim natural killer cell, human: -1.494<br>natural killer cell: -2.244<br>gamma-delta T cell: -3.584<br>activated type II NK T cell: -3.973",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.795<br>CD16-positive, CD56-dim natural killer cell, human: -2.530<br>mature NK T cell: -2.631<br>natural killer cell: -2.658<br>gamma-delta T cell: -2.827",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.729<br>gamma-delta T cell: -2.424<br>CD16-positive, CD56-dim natural killer cell, human: -2.903<br>natural killer cell: -2.922<br>effector memory CD8-positive, alpha-beta T cell: -2.993",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.257<br>gamma-delta T cell: -2.661<br>CD16-positive, CD56-dim natural killer cell, human: -3.112<br>natural killer cell: -3.152<br>mature NK T cell: -3.250",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.924<br>CD16-positive, CD56-dim natural killer cell, human: -1.808<br>natural killer cell: -2.315<br>mature NK T cell: -3.718<br>gamma-delta T cell: -3.818",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.196<br>gamma-delta T cell: -3.106<br>natural killer cell: -3.379<br>mature NK T cell: -3.734<br>innate lymphoid cell: -3.867",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.672<br>natural killer cell: -1.741<br>CD16-positive, CD56-dim natural killer cell, human: -3.010<br>mature NK T cell: -3.658<br>gamma-delta T cell: -3.931",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.230<br>CD16-positive, CD56-dim natural killer cell, human: -1.459<br>natural killer cell: -2.547<br>gamma-delta T cell: -3.285<br>mature NK T cell: -3.785",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.560<br>gamma-delta T cell: -3.003<br>effector memory CD8-positive, alpha-beta T cell: -3.611<br>innate lymphoid cell: -3.743<br>mature NK T cell: -3.752",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.463<br>CD16-positive, CD56-dim natural killer cell, human: -1.855<br>natural killer cell: -2.167<br>gamma-delta T cell: -3.332<br>activated type II NK T cell: -3.797",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.924<br>gamma-delta T cell: -2.488<br>natural killer cell: -2.659<br>CD16-positive, CD56-dim natural killer cell, human: -2.804<br>mature NK T cell: -2.859",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.284<br>natural killer cell: -2.003<br>innate lymphoid cell: -3.464<br>lymphocyte: -3.669<br>group 3 innate lymphoid cell: -3.921",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.170<br>gamma-delta T cell: -3.259<br>natural killer cell: -3.679<br>innate lymphoid cell: -4.083<br>CD16-positive, CD56-dim natural killer cell, human: -4.128",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.800<br>natural killer cell: -2.593<br>CD16-positive, CD56-dim natural killer cell, human: -3.478<br>gamma-delta T cell: -3.574<br>innate lymphoid cell: -3.692",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.974<br>natural killer cell: -2.490<br>gamma-delta T cell: -2.855<br>CD16-positive, CD56-dim natural killer cell, human: -2.950<br>mature NK T cell: -3.689",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.157<br>gamma-delta T cell: -2.574<br>CD16-positive, CD56-dim natural killer cell, human: -2.578<br>natural killer cell: -3.079<br>mature NK T cell: -3.663",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.012<br>natural killer cell: -2.317<br>CD16-positive, CD56-dim natural killer cell, human: -2.959<br>gamma-delta T cell: -3.564<br>activated type II NK T cell: -3.924",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.022<br>natural killer cell: -2.289<br>CD16-positive, CD56-dim natural killer cell, human: -3.283<br>innate lymphoid cell: -3.443<br>lymphocyte: -4.144",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.850<br>natural killer cell: -2.701<br>innate lymphoid cell: -3.622<br>group 3 innate lymphoid cell: -4.261<br>CD16-positive, CD56-dim natural killer cell, human: -4.292",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.167<br>CD16-positive, CD56-dim natural killer cell, human: -2.820<br>natural killer cell: -2.864<br>gamma-delta T cell: -3.026<br>activated type II NK T cell: -4.013",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.784<br>natural killer cell: -2.889<br>CD16-positive, CD56-dim natural killer cell, human: -3.352<br>activated type II NK T cell: -4.004<br>hepatic pit cell: -4.372",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.612<br>gamma-delta T cell: -2.854<br>CD16-positive, CD56-dim natural killer cell, human: -3.244<br>natural killer cell: -3.548<br>mature NK T cell: -3.950",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.177<br>natural killer cell: -2.917<br>CD16-positive, CD56-dim natural killer cell, human: -2.993<br>gamma-delta T cell: -3.423<br>mature NK T cell: -3.687",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.803<br>gamma-delta T cell: -3.533<br>CD16-positive, CD56-dim natural killer cell, human: -3.642<br>natural killer cell: -3.680<br>innate lymphoid cell: -3.807",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.869<br>gamma-delta T cell: -2.867<br>CD16-positive, CD56-dim natural killer cell, human: -3.143<br>natural killer cell: -3.283<br>mucosal invariant T cell: -3.971",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.078<br>natural killer cell: -2.861<br>gamma-delta T cell: -3.065<br>CD16-positive, CD56-dim natural killer cell, human: -3.181<br>mature NK T cell: -3.698",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.583<br>CD16-positive, CD56-dim natural killer cell, human: -1.669<br>natural killer cell: -2.586<br>gamma-delta T cell: -2.620<br>mature NK T cell: -3.451",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.835<br>natural killer cell: -2.420<br>CD16-positive, CD56-dim natural killer cell, human: -2.755<br>gamma-delta T cell: -3.826<br>activated type II NK T cell: -4.251",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.955<br>natural killer cell: -2.617<br>gamma-delta T cell: -3.033<br>CD16-positive, CD56-dim natural killer cell, human: -3.566<br>mature NK T cell: -3.676",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.181<br>natural killer cell: -2.186<br>CD16-positive, CD56-dim natural killer cell, human: -2.576<br>gamma-delta T cell: -2.852<br>mature NK T cell: -3.494",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.220<br>gamma-delta T cell: -2.585<br>effector memory CD8-positive, alpha-beta T cell: -2.886<br>CD16-positive, CD56-dim natural killer cell, human: -3.645<br>mature NK T cell: -3.666",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.757<br>gamma-delta T cell: -3.216<br>T cell: -3.417<br>effector memory CD8-positive, alpha-beta T cell: -3.441<br>effector memory CD4-positive, alpha-beta T cell: -3.631",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.664<br>CD16-positive, CD56-dim natural killer cell, human: -2.721<br>natural killer cell: -2.782<br>gamma-delta T cell: -3.842<br>innate lymphoid cell: -4.169",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.090<br>natural killer cell: -2.736<br>gamma-delta T cell: -2.907<br>CD16-positive, CD56-dim natural killer cell, human: -3.113<br>mature NK T cell: -3.764",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.887<br>gamma-delta T cell: -2.940<br>effector memory CD8-positive, alpha-beta T cell: -3.402<br>natural killer cell: -3.415<br>mature NK T cell: -3.669",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.656<br>gamma-delta T cell: -3.199<br>natural killer cell: -3.531<br>CD16-positive, CD56-dim natural killer cell, human: -3.698<br>mature NK T cell: -3.795",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.181<br>CD16-positive, CD56-dim natural killer cell, human: -2.362<br>natural killer cell: -2.485<br>gamma-delta T cell: -2.595<br>mature NK T cell: -3.283",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.554<br>natural killer cell: -3.282<br>gamma-delta T cell: -3.505<br>innate lymphoid cell: -3.778<br>effector memory CD8-positive, alpha-beta T cell: -3.836",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.184<br>CD16-positive, CD56-dim natural killer cell, human: -1.639<br>natural killer cell: -2.646<br>gamma-delta T cell: -3.079<br>mature NK T cell: -3.478",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.674<br>gamma-delta T cell: -2.332<br>effector memory CD8-positive, alpha-beta T cell: -2.991<br>mucosal invariant T cell: -3.355<br>effector CD8-positive, alpha-beta T cell: -3.849",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.979<br>natural killer cell: -2.617<br>CD16-positive, CD56-dim natural killer cell, human: -2.760<br>gamma-delta T cell: -2.923<br>mature NK T cell: -4.232",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.703<br>gamma-delta T cell: -2.192<br>CD16-positive, CD56-dim natural killer cell, human: -2.609<br>natural killer cell: -2.933<br>mature NK T cell: -3.196",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.457<br>CD16-positive, CD56-dim natural killer cell, human: -1.901<br>gamma-delta T cell: -2.372<br>natural killer cell: -2.731<br>effector memory CD8-positive, alpha-beta T cell: -3.514",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.648<br>natural killer cell: -2.996<br>CD16-positive, CD56-dim natural killer cell, human: -3.184<br>gamma-delta T cell: -3.472<br>mature NK T cell: -4.009",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.869<br>natural killer cell: -2.737<br>CD16-positive, CD56-dim natural killer cell, human: -3.081<br>gamma-delta T cell: -3.215<br>innate lymphoid cell: -3.954",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.944<br>CD16-positive, CD56-dim natural killer cell, human: -2.291<br>gamma-delta T cell: -2.668<br>natural killer cell: -2.817<br>mature NK T cell: -3.503",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.959<br>gamma-delta T cell: -2.114<br>effector memory CD8-positive, alpha-beta T cell: -3.329<br>mucosal invariant T cell: -3.398<br>mature NK T cell: -3.486",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.185<br>natural killer cell: -2.291<br>CD16-positive, CD56-dim natural killer cell, human: -3.415<br>gamma-delta T cell: -3.967<br>innate lymphoid cell: -3.968",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.165<br>CD16-positive, CD56-dim natural killer cell, human: -1.517<br>natural killer cell: -2.797<br>activated type II NK T cell: -3.791<br>gamma-delta T cell: -3.874",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.673<br>natural killer cell: -2.775<br>CD16-positive, CD56-dim natural killer cell, human: -3.041<br>gamma-delta T cell: -3.889<br>innate lymphoid cell: -4.138",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.642<br>gamma-delta T cell: -2.669<br>natural killer cell: -3.061<br>CD16-positive, CD56-dim natural killer cell, human: -3.352<br>effector memory CD8-positive, alpha-beta T cell: -3.382",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.713<br>natural killer cell: -2.410<br>CD16-positive, CD56-dim natural killer cell, human: -3.419<br>innate lymphoid cell: -3.774<br>gamma-delta T cell: -3.905",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.834<br>CD16-positive, CD56-dim natural killer cell, human: -2.809<br>natural killer cell: -3.097<br>gamma-delta T cell: -3.377<br>mature NK T cell: -4.136",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.924<br>natural killer cell: -3.272<br>gamma-delta T cell: -3.449<br>innate lymphoid cell: -3.812<br>mature NK T cell: -4.016",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.032<br>CD16-positive, CD56-dim natural killer cell, human: -2.210<br>natural killer cell: -2.382<br>gamma-delta T cell: -2.769<br>mature NK T cell: -3.270",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.256<br>gamma-delta T cell: -3.350<br>effector memory CD8-positive, alpha-beta T cell: -3.765<br>mature NK T cell: -3.861<br>natural killer cell: -3.869",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.172<br>gamma-delta T cell: -2.565<br>effector memory CD8-positive, alpha-beta T cell: -3.497<br>mature NK T cell: -3.555<br>natural killer cell: -3.660",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.658<br>natural killer cell: -2.584<br>CD16-positive, CD56-dim natural killer cell, human: -3.209<br>gamma-delta T cell: -4.080<br>activated type II NK T cell: -4.254",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.027<br>natural killer cell: -2.553<br>gamma-delta T cell: -2.746<br>CD16-positive, CD56-dim natural killer cell, human: -3.439<br>mature NK T cell: -3.475",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.696<br>natural killer cell: -3.045<br>gamma-delta T cell: -3.518<br>CD16-positive, CD56-dim natural killer cell, human: -3.819<br>mature NK T cell: -4.146",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.006<br>natural killer cell: -1.944<br>CD16-positive, CD56-dim natural killer cell, human: -1.995<br>gamma-delta T cell: -4.104<br>activated type II NK T cell: -4.330",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.074<br>T cell: -3.324<br>mast cell: -3.603<br>mature NK T cell: -3.603<br>effector memory CD4-positive, alpha-beta T cell: -3.668",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.175<br>natural killer cell: -3.306<br>gamma-delta T cell: -3.354<br>CD16-positive, CD56-dim natural killer cell, human: -3.495<br>innate lymphoid cell: -3.952",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.571<br>gamma-delta T cell: -3.573<br>natural killer cell: -3.615<br>CD16-positive, CD56-dim natural killer cell, human: -3.753<br>innate lymphoid cell: -4.262",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.952<br>natural killer cell: -2.807<br>CD16-positive, CD56-dim natural killer cell, human: -2.916<br>gamma-delta T cell: -2.968<br>mature NK T cell: -3.994",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.997<br>CD16-positive, CD56-dim natural killer cell, human: -2.743<br>gamma-delta T cell: -2.905<br>natural killer cell: -3.010<br>effector memory CD8-positive, alpha-beta T cell: -3.666",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.276<br>CD16-positive, CD56-dim natural killer cell, human: -1.541<br>natural killer cell: -2.823<br>gamma-delta T cell: -3.412<br>mature NK T cell: -3.766",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.234<br>CD16-positive, CD56-dim natural killer cell, human: -1.256<br>natural killer cell: -2.391<br>gamma-delta T cell: -4.006<br>activated type II NK T cell: -4.088",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -3.063<br>gamma-delta T cell: -3.139<br>CD16-positive, CD56-dim natural killer cell, human: -3.617<br>mature NK T cell: -3.644",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.565<br>natural killer cell: -2.688<br>gamma-delta T cell: -3.653<br>CD16-positive, CD56-dim natural killer cell, human: -3.968<br>innate lymphoid cell: -4.246",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.266<br>CD16-positive, CD56-dim natural killer cell, human: -1.383<br>natural killer cell: -2.299<br>mature NK T cell: -3.343<br>gamma-delta T cell: -3.616",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.744<br>natural killer cell: -2.740<br>CD16-positive, CD56-dim natural killer cell, human: -3.468<br>gamma-delta T cell: -3.661<br>innate lymphoid cell: -4.038",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.305<br>CD16-positive, CD56-dim natural killer cell, human: -1.575<br>natural killer cell: -2.073<br>mature NK T cell: -3.957<br>activated type II NK T cell: -4.035",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.813<br>gamma-delta T cell: -3.197<br>natural killer cell: -3.459<br>CD16-positive, CD56-dim natural killer cell, human: -3.551<br>innate lymphoid cell: -3.987",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.820<br>gamma-delta T cell: -3.120<br>natural killer cell: -3.715<br>effector memory CD8-positive, alpha-beta T cell: -3.730<br>innate lymphoid cell: -3.861",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.258<br>natural killer cell: -3.552<br>lymphocyte: -3.808<br>progenitor cell: -4.126<br>innate lymphoid cell: -4.154",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.908<br>CD16-positive, CD56-dim natural killer cell, human: -2.274<br>gamma-delta T cell: -2.507<br>natural killer cell: -2.540<br>mature NK T cell: -3.656",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.760<br>natural killer cell: -2.478<br>CD16-positive, CD56-dim natural killer cell, human: -2.923<br>gamma-delta T cell: -3.516<br>mature NK T cell: -4.061",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.562<br>group 3 innate lymphoid cell: -2.882<br>innate lymphoid cell: -3.454<br>natural killer cell: -3.735<br>hematopoietic precursor cell: -4.125",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.046<br>gamma-delta T cell: -3.416<br>natural killer cell: -3.512<br>innate lymphoid cell: -3.569<br>group 3 innate lymphoid cell: -3.853",
+          "CD16-negative, CD56-bright natural killer cell, human: -2.528<br>effector memory CD4-positive, alpha-beta T cell: -3.005<br>gamma-delta T cell: -3.256<br>T follicular helper cell: -3.420<br>mucosal invariant T cell: -3.682",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.960<br>CD16-positive, CD56-dim natural killer cell, human: -2.465<br>gamma-delta T cell: -2.639<br>natural killer cell: -2.934<br>effector memory CD8-positive, alpha-beta T cell: -3.604",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.291<br>natural killer cell: -1.604<br>CD16-positive, CD56-dim natural killer cell, human: -2.523<br>gamma-delta T cell: -4.019<br>mature NK T cell: -4.191",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.056<br>natural killer cell: -1.883<br>CD16-positive, CD56-dim natural killer cell, human: -2.396<br>mature NK T cell: -3.262<br>gamma-delta T cell: -3.385",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.652<br>CD16-positive, CD56-dim natural killer cell, human: -2.506<br>gamma-delta T cell: -2.662<br>natural killer cell: -2.996<br>mature NK T cell: -3.602",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.008<br>natural killer cell: -2.885<br>gamma-delta T cell: -3.341<br>mature NK T cell: -3.850<br>innate lymphoid cell: -4.024",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.532<br>natural killer cell: -3.109<br>gamma-delta T cell: -3.331<br>innate lymphoid cell: -3.704<br>mature NK T cell: -3.744",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.098<br>gamma-delta T cell: -2.727<br>effector memory CD8-positive, alpha-beta T cell: -3.184<br>mature NK T cell: -3.563<br>mucosal invariant T cell: -3.978",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.856<br>CD16-positive, CD56-dim natural killer cell, human: -1.564<br>natural killer cell: -2.401<br>gamma-delta T cell: -4.139<br>mature NK T cell: -4.167",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.888<br>gamma-delta T cell: -2.407<br>CD16-positive, CD56-dim natural killer cell, human: -2.709<br>natural killer cell: -2.941<br>mature NK T cell: -3.924",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.796<br>natural killer cell: -2.420<br>CD16-positive, CD56-dim natural killer cell, human: -2.847<br>gamma-delta T cell: -2.888<br>mature NK T cell: -4.199",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.291<br>gamma-delta T cell: -2.928<br>natural killer cell: -2.962<br>CD16-positive, CD56-dim natural killer cell, human: -3.794<br>innate lymphoid cell: -3.983",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.987<br>natural killer cell: -3.156<br>CD16-positive, CD56-dim natural killer cell, human: -3.183<br>gamma-delta T cell: -3.503<br>activated type II NK T cell: -4.118",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.108<br>gamma-delta T cell: -2.734<br>natural killer cell: -2.786<br>effector memory CD8-positive, alpha-beta T cell: -3.208<br>mature NK T cell: -3.472",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.443<br>natural killer cell: -1.874<br>CD16-positive, CD56-dim natural killer cell, human: -2.699<br>gamma-delta T cell: -3.533<br>mature NK T cell: -3.926",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.217<br>gamma-delta T cell: -3.222<br>natural killer cell: -3.324<br>effector memory CD8-positive, alpha-beta T cell: -3.716<br>innate lymphoid cell: -3.772",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.979<br>gamma-delta T cell: -2.083<br>effector memory CD8-positive, alpha-beta T cell: -2.477<br>effector CD8-positive, alpha-beta T cell: -3.231<br>mature NK T cell: -3.292",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.895<br>CD16-positive, CD56-dim natural killer cell, human: -2.237<br>natural killer cell: -2.663<br>activated type II NK T cell: -3.709<br>gamma-delta T cell: -3.981",
+          "CD16-negative, CD56-bright natural killer cell, human: -0.766<br>natural killer cell: -2.628<br>CD16-positive, CD56-dim natural killer cell, human: -3.397<br>gamma-delta T cell: -3.499<br>mature NK T cell: -3.729",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.004<br>gamma-delta T cell: -2.754<br>natural killer cell: -3.092<br>CD16-positive, CD56-dim natural killer cell, human: -3.329<br>effector memory CD8-positive, alpha-beta T cell: -3.493",
+          "CD16-negative, CD56-bright natural killer cell, human: -1.158<br>natural killer cell: -2.958<br>gamma-delta T cell: -3.043<br>mature NK T cell: -3.703<br>effector memory CD8-positive, alpha-beta T cell: -3.799"
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+         "yaxis": "y"
+        },
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br><br>label=plasmacytoid dendritic cell<br>UMAP_1=%{x}<br>UMAP_2=%{y}<extra></extra>",
+         "hovertext": [
+          "plasmacytoid dendritic cell: -0.189<br>plasmacytoid dendritic cell, human: -3.037<br>dendritic cell: -3.171<br>myeloid dendritic cell: -5.711<br>CD1c-positive myeloid dendritic cell: -5.829",
+          "plasmacytoid dendritic cell: -0.394<br>plasmacytoid dendritic cell, human: -2.133<br>dendritic cell: -3.046<br>conventional dendritic cell: -4.507<br>dendritic cell, human: -5.009",
+          "plasmacytoid dendritic cell: -0.321<br>plasmacytoid dendritic cell, human: -2.877<br>dendritic cell: -3.356<br>B cell: -5.030<br>myeloid dendritic cell: -5.201",
+          "plasmacytoid dendritic cell: -0.415<br>plasmacytoid dendritic cell, human: -2.322<br>dendritic cell: -2.532<br>dendritic cell, human: -4.412<br>myeloid dendritic cell: -4.733",
+          "plasmacytoid dendritic cell: -0.280<br>plasmacytoid dendritic cell, human: -2.659<br>dendritic cell: -2.767<br>myeloid dendritic cell: -5.136<br>dendritic cell, human: -5.249",
+          "plasmacytoid dendritic cell: -0.886<br>B cell: -2.165<br>dendritic cell: -2.354<br>plasmacytoid dendritic cell, human: -3.479<br>plasma cell: -3.947",
+          "plasmacytoid dendritic cell: -0.266<br>plasmacytoid dendritic cell, human: -2.656<br>dendritic cell: -3.083<br>myeloid dendritic cell: -5.225<br>dendritic cell, human: -5.404",
+          "plasmacytoid dendritic cell: -0.187<br>plasmacytoid dendritic cell, human: -2.909<br>dendritic cell: -3.326<br>myeloid dendritic cell: -5.717<br>B cell: -5.956",
+          "plasmacytoid dendritic cell: -0.297<br>dendritic cell: -2.743<br>plasmacytoid dendritic cell, human: -2.944<br>dendritic cell, human: -4.654<br>myeloid dendritic cell: -4.667",
+          "plasmacytoid dendritic cell: -0.477<br>plasmacytoid dendritic cell, human: -2.089<br>dendritic cell: -2.632<br>myeloid dendritic cell: -4.838<br>dendritic cell, human: -4.989",
+          "plasmacytoid dendritic cell: -0.435<br>plasmacytoid dendritic cell, human: -2.474<br>dendritic cell: -3.143<br>B cell: -4.977<br>dendritic cell, human: -4.988",
+          "plasmacytoid dendritic cell: -0.313<br>dendritic cell: -2.804<br>plasmacytoid dendritic cell, human: -2.884<br>plasma cell: -4.125<br>conventional dendritic cell: -4.922",
+          "plasmacytoid dendritic cell: -0.481<br>plasmacytoid dendritic cell, human: -2.478<br>dendritic cell: -2.721<br>plasma cell: -3.470<br>conventional dendritic cell: -4.810",
+          "plasmacytoid dendritic cell: -0.383<br>dendritic cell: -2.676<br>plasmacytoid dendritic cell, human: -2.693<br>conventional dendritic cell: -4.542<br>dendritic cell, human: -4.722",
+          "plasmacytoid dendritic cell: -0.214<br>plasmacytoid dendritic cell, human: -2.984<br>dendritic cell: -3.361<br>dendritic cell, human: -5.709<br>B cell: -5.733",
+          "plasmacytoid dendritic cell: -0.357<br>plasmacytoid dendritic cell, human: -2.536<br>dendritic cell: -2.916<br>B cell: -4.835<br>conventional dendritic cell: -5.118",
+          "plasmacytoid dendritic cell: -0.238<br>dendritic cell: -3.127<br>plasmacytoid dendritic cell, human: -3.254<br>B cell: -5.226<br>myeloid dendritic cell: -5.584",
+          "plasmacytoid dendritic cell: -0.707<br>dendritic cell: -2.344<br>plasmacytoid dendritic cell, human: -2.479<br>plasma cell: -3.900<br>dendritic cell, human: -4.766",
+          "plasmacytoid dendritic cell: -0.385<br>plasmacytoid dendritic cell, human: -2.806<br>dendritic cell: -2.807<br>CD1c-positive myeloid dendritic cell: -4.916<br>conventional dendritic cell: -4.946",
+          "plasmacytoid dendritic cell: -0.288<br>plasmacytoid dendritic cell, human: -2.713<br>dendritic cell: -3.168<br>B cell: -5.217<br>conventional dendritic cell: -5.244",
+          "plasmacytoid dendritic cell: -0.251<br>plasmacytoid dendritic cell, human: -2.733<br>dendritic cell: -2.982<br>myeloid dendritic cell: -5.299<br>dendritic cell, human: -5.399",
+          "plasmacytoid dendritic cell: -0.390<br>plasmacytoid dendritic cell, human: -2.488<br>dendritic cell: -2.849<br>conventional dendritic cell: -4.695<br>dendritic cell, human: -4.720",
+          "plasmacytoid dendritic cell: -0.329<br>plasmacytoid dendritic cell, human: -2.773<br>dendritic cell: -2.978<br>B cell: -5.236<br>myeloid dendritic cell: -5.244",
+          "plasmacytoid dendritic cell: -0.360<br>dendritic cell: -2.381<br>plasmacytoid dendritic cell, human: -2.749<br>conventional dendritic cell: -4.607<br>myeloid dendritic cell: -4.691",
+          "plasmacytoid dendritic cell: -0.437<br>plasmacytoid dendritic cell, human: -2.354<br>dendritic cell: -3.553<br>mast cell: -4.687<br>hematopoietic precursor cell: -5.041",
+          "plasmacytoid dendritic cell: -0.220<br>plasmacytoid dendritic cell, human: -2.963<br>dendritic cell: -3.280<br>myeloid dendritic cell: -5.582<br>CD1c-positive myeloid dendritic cell: -5.891",
+          "plasmacytoid dendritic cell: -0.211<br>plasmacytoid dendritic cell, human: -2.894<br>dendritic cell: -3.190<br>B cell: -5.613<br>myeloid dendritic cell: -5.942",
+          "plasmacytoid dendritic cell: -0.390<br>plasmacytoid dendritic cell, human: -2.705<br>dendritic cell: -2.955<br>myeloid dendritic cell: -5.020<br>B cell: -5.106",
+          "plasmacytoid dendritic cell: -0.223<br>plasmacytoid dendritic cell, human: -2.829<br>dendritic cell: -3.137<br>B cell: -5.483<br>plasma cell: -5.542",
+          "plasmacytoid dendritic cell: -0.207<br>plasmacytoid dendritic cell, human: -2.940<br>dendritic cell: -3.225<br>dendritic cell, human: -5.549<br>myeloid dendritic cell: -5.662",
+          "plasmacytoid dendritic cell: -0.243<br>plasmacytoid dendritic cell, human: -2.562<br>dendritic cell: -3.444<br>myeloid dendritic cell: -5.808<br>dendritic cell, human: -5.820",
+          "plasmacytoid dendritic cell: -0.383<br>plasmacytoid dendritic cell, human: -2.681<br>dendritic cell: -2.767<br>myeloid dendritic cell: -4.783<br>B cell: -5.032",
+          "plasmacytoid dendritic cell: -0.351<br>plasmacytoid dendritic cell, human: -2.711<br>dendritic cell: -2.785<br>dendritic cell, human: -4.780<br>myeloid dendritic cell: -4.996",
+          "plasmacytoid dendritic cell: -0.256<br>plasmacytoid dendritic cell, human: -2.611<br>dendritic cell: -2.967<br>myeloid dendritic cell: -5.365<br>conventional dendritic cell: -5.533",
+          "plasmacytoid dendritic cell: -0.203<br>dendritic cell: -2.975<br>plasmacytoid dendritic cell, human: -3.032<br>myeloid dendritic cell: -5.420<br>dendritic cell, human: -5.696",
+          "plasmacytoid dendritic cell: -0.179<br>plasmacytoid dendritic cell, human: -2.874<br>dendritic cell: -3.942<br>myeloid dendritic cell: -5.934<br>hematopoietic precursor cell: -6.076",
+          "plasmacytoid dendritic cell: -0.300<br>dendritic cell: -2.723<br>plasmacytoid dendritic cell, human: -2.796<br>plasma cell: -4.646<br>B cell: -4.866",
+          "plasmacytoid dendritic cell: -0.234<br>plasmacytoid dendritic cell, human: -2.860<br>dendritic cell: -2.940<br>myeloid dendritic cell: -5.401<br>dendritic cell, human: -5.518",
+          "plasmacytoid dendritic cell: -0.660<br>plasmacytoid dendritic cell, human: -2.426<br>dendritic cell: -2.435<br>dendritic cell, human: -4.569<br>conventional dendritic cell: -4.935",
+          "plasmacytoid dendritic cell: -0.208<br>plasmacytoid dendritic cell, human: -3.087<br>dendritic cell: -3.156<br>myeloid dendritic cell: -5.339<br>dendritic cell, human: -5.496",
+          "plasmacytoid dendritic cell: -0.250<br>plasmacytoid dendritic cell, human: -2.695<br>dendritic cell: -3.062<br>dendritic cell, human: -5.432<br>myeloid dendritic cell: -5.552",
+          "plasmacytoid dendritic cell: -0.163<br>plasmacytoid dendritic cell, human: -3.228<br>dendritic cell: -3.342<br>conventional dendritic cell: -5.501<br>plasma cell: -5.955",
+          "plasmacytoid dendritic cell: -0.268<br>plasmacytoid dendritic cell, human: -2.673<br>dendritic cell: -3.437<br>conventional dendritic cell: -5.254<br>myeloid dendritic cell: -5.642",
+          "plasmacytoid dendritic cell: -0.238<br>plasmacytoid dendritic cell, human: -2.849<br>dendritic cell: -3.006<br>dendritic cell, human: -5.127<br>myeloid dendritic cell: -5.169",
+          "plasmacytoid dendritic cell: -0.206<br>plasmacytoid dendritic cell, human: -2.952<br>dendritic cell: -3.292<br>dendritic cell, human: -5.473<br>myeloid dendritic cell: -5.752",
+          "plasmacytoid dendritic cell: -0.208<br>dendritic cell: -2.930<br>plasmacytoid dendritic cell, human: -3.140<br>myeloid dendritic cell: -5.163<br>dendritic cell, human: -5.735",
+          "plasmacytoid dendritic cell: -0.368<br>plasmacytoid dendritic cell, human: -2.662<br>dendritic cell: -2.868<br>myeloid dendritic cell: -4.662<br>dendritic cell, human: -5.043",
+          "plasmacytoid dendritic cell: -0.476<br>dendritic cell: -2.101<br>plasmacytoid dendritic cell, human: -2.984<br>B cell: -3.757<br>plasma cell: -3.832",
+          "plasmacytoid dendritic cell: -0.212<br>plasmacytoid dendritic cell, human: -2.769<br>dendritic cell: -3.396<br>myeloid dendritic cell: -5.676<br>dendritic cell, human: -5.786",
+          "plasmacytoid dendritic cell: -0.218<br>plasmacytoid dendritic cell, human: -2.815<br>dendritic cell: -3.313<br>dendritic cell, human: -5.441<br>myeloid dendritic cell: -5.502",
+          "plasmacytoid dendritic cell: -0.281<br>plasmacytoid dendritic cell, human: -2.624<br>dendritic cell: -3.046<br>myeloid dendritic cell: -4.863<br>dendritic cell, human: -4.983",
+          "plasmacytoid dendritic cell: -0.367<br>dendritic cell: -2.705<br>plasmacytoid dendritic cell, human: -2.708<br>myeloid dendritic cell: -4.921<br>dendritic cell, human: -4.969",
+          "plasmacytoid dendritic cell: -0.229<br>dendritic cell: -2.847<br>plasmacytoid dendritic cell, human: -2.922<br>myeloid dendritic cell: -5.164<br>conventional dendritic cell: -5.492",
+          "plasmacytoid dendritic cell: -0.323<br>dendritic cell: -2.524<br>plasmacytoid dendritic cell, human: -2.801<br>myeloid dendritic cell: -4.884<br>conventional dendritic cell: -4.898",
+          "plasmacytoid dendritic cell: -0.327<br>plasmacytoid dendritic cell, human: -2.508<br>dendritic cell: -2.740<br>myeloid dendritic cell: -5.244<br>dendritic cell, human: -5.278",
+          "plasmacytoid dendritic cell: -0.401<br>plasmacytoid dendritic cell, human: -2.354<br>dendritic cell: -2.972<br>dendritic cell, human: -4.869<br>conventional dendritic cell: -5.090",
+          "plasmacytoid dendritic cell: -0.237<br>plasmacytoid dendritic cell, human: -2.840<br>dendritic cell: -3.126<br>dendritic cell, human: -5.680<br>myeloid dendritic cell: -5.757",
+          "plasmacytoid dendritic cell: -0.330<br>plasmacytoid dendritic cell, human: -2.358<br>dendritic cell: -3.110<br>dendritic cell, human: -5.422<br>myeloid dendritic cell: -5.470",
+          "plasmacytoid dendritic cell: -0.161<br>plasmacytoid dendritic cell, human: -3.185<br>dendritic cell: -3.511<br>myeloid dendritic cell: -5.856<br>dendritic cell, human: -6.104",
+          "plasmacytoid dendritic cell: -0.266<br>plasmacytoid dendritic cell, human: -2.727<br>dendritic cell: -2.977<br>dendritic cell, human: -5.224<br>myeloid dendritic cell: -5.230",
+          "plasmacytoid dendritic cell: -0.233<br>plasmacytoid dendritic cell, human: -2.847<br>dendritic cell: -2.923<br>myeloid dendritic cell: -5.406<br>dendritic cell, human: -5.535",
+          "plasmacytoid dendritic cell: -0.191<br>plasmacytoid dendritic cell, human: -2.741<br>dendritic cell: -3.658<br>dendritic cell, human: -5.882<br>myeloid dendritic cell: -6.016",
+          "plasmacytoid dendritic cell: -2.626<br>mast cell: -3.074<br>dendritic cell: -3.121<br>plasmacytoid dendritic cell, human: -3.164<br>CD16-negative, CD56-bright natural killer cell, human: -3.385",
+          "plasmacytoid dendritic cell: -0.171<br>plasmacytoid dendritic cell, human: -2.887<br>dendritic cell: -3.676<br>myeloid dendritic cell: -6.036<br>dendritic cell, human: -6.117",
+          "plasmacytoid dendritic cell: -0.638<br>dendritic cell: -2.145<br>plasmacytoid dendritic cell, human: -2.242<br>conventional dendritic cell: -3.861<br>dendritic cell, human: -4.276",
+          "plasmacytoid dendritic cell: -0.510<br>dendritic cell: -2.330<br>plasmacytoid dendritic cell, human: -2.557<br>dendritic cell, human: -4.748<br>myeloid dendritic cell: -4.855",
+          "plasmacytoid dendritic cell: -0.309<br>plasmacytoid dendritic cell, human: -2.667<br>dendritic cell: -2.946<br>conventional dendritic cell: -4.285<br>myeloid dendritic cell: -4.849",
+          "plasmacytoid dendritic cell: -0.247<br>plasmacytoid dendritic cell, human: -2.701<br>dendritic cell: -3.328<br>myeloid dendritic cell: -5.094<br>dendritic cell, human: -5.301",
+          "plasmacytoid dendritic cell: -0.445<br>dendritic cell: -2.821<br>plasmacytoid dendritic cell, human: -3.110<br>plasma cell: -3.686<br>B cell: -4.213",
+          "plasmacytoid dendritic cell: -0.332<br>dendritic cell: -2.432<br>plasmacytoid dendritic cell, human: -2.821<br>myeloid dendritic cell: -4.616<br>conventional dendritic cell: -4.695",
+          "plasmacytoid dendritic cell: -1.337<br>mast cell: -2.344<br>plasmacytoid dendritic cell, human: -2.931<br>dendritic cell: -3.380<br>hematopoietic precursor cell: -3.580",
+          "plasmacytoid dendritic cell: -0.371<br>dendritic cell: -2.472<br>plasmacytoid dendritic cell, human: -2.607<br>myeloid dendritic cell: -4.795<br>conventional dendritic cell: -4.868",
+          "plasmacytoid dendritic cell: -0.374<br>dendritic cell: -2.278<br>plasmacytoid dendritic cell, human: -2.897<br>myeloid dendritic cell: -4.314<br>dendritic cell, human: -4.840",
+          "plasmacytoid dendritic cell: -0.172<br>plasmacytoid dendritic cell, human: -2.918<br>dendritic cell: -3.667<br>B cell: -5.801<br>plasma cell: -6.013",
+          "plasmacytoid dendritic cell: -0.367<br>plasmacytoid dendritic cell, human: -2.483<br>dendritic cell: -3.067<br>dendritic cell, human: -4.865<br>conventional dendritic cell: -5.022",
+          "plasmacytoid dendritic cell: -0.377<br>plasmacytoid dendritic cell, human: -2.455<br>dendritic cell: -3.249<br>conventional dendritic cell: -4.739<br>hematopoietic precursor cell: -4.916",
+          "plasmacytoid dendritic cell: -0.304<br>dendritic cell: -2.591<br>plasmacytoid dendritic cell, human: -2.861<br>conventional dendritic cell: -4.751<br>myeloid dendritic cell: -4.854",
+          "plasmacytoid dendritic cell: -0.217<br>plasmacytoid dendritic cell, human: -2.750<br>dendritic cell: -3.267<br>myeloid dendritic cell: -5.930<br>hematopoietic precursor cell: -6.075",
+          "plasmacytoid dendritic cell: -0.226<br>plasmacytoid dendritic cell, human: -2.913<br>dendritic cell: -3.105<br>dendritic cell, human: -5.293<br>myeloid dendritic cell: -5.448",
+          "plasmacytoid dendritic cell: -0.200<br>plasmacytoid dendritic cell, human: -2.889<br>dendritic cell: -3.683<br>mast cell: -5.459<br>B cell: -5.963",
+          "plasmacytoid dendritic cell: -0.208<br>plasmacytoid dendritic cell, human: -2.940<br>dendritic cell: -3.417<br>dendritic cell, human: -5.861<br>myeloid dendritic cell: -5.876",
+          "plasmacytoid dendritic cell: -0.935<br>dendritic cell: -2.613<br>B cell: -3.401<br>plasma cell: -3.437<br>plasmacytoid dendritic cell, human: -3.452",
+          "plasmacytoid dendritic cell: -0.342<br>dendritic cell: -2.551<br>plasmacytoid dendritic cell, human: -2.782<br>myeloid dendritic cell: -4.820<br>dendritic cell, human: -4.831",
+          "plasmacytoid dendritic cell: -0.324<br>plasmacytoid dendritic cell, human: -2.647<br>dendritic cell: -3.113<br>myeloid dendritic cell: -5.368<br>dendritic cell, human: -5.479",
+          "plasmacytoid dendritic cell: -0.210<br>plasmacytoid dendritic cell, human: -2.927<br>dendritic cell: -2.997<br>myeloid dendritic cell: -5.449<br>conventional dendritic cell: -5.538",
+          "plasmacytoid dendritic cell: -0.403<br>dendritic cell: -2.290<br>plasmacytoid dendritic cell, human: -2.630<br>plasma cell: -4.264<br>conventional dendritic cell: -4.606",
+          "plasmacytoid dendritic cell: -0.438<br>dendritic cell: -2.368<br>plasmacytoid dendritic cell, human: -2.539<br>dendritic cell, human: -4.768<br>myeloid dendritic cell: -4.867",
+          "plasmacytoid dendritic cell: -0.406<br>plasmacytoid dendritic cell, human: -2.624<br>dendritic cell: -2.984<br>conventional dendritic cell: -4.887<br>hematopoietic precursor cell: -4.923",
+          "plasmacytoid dendritic cell: -0.389<br>dendritic cell: -2.476<br>plasmacytoid dendritic cell, human: -2.919<br>myeloid dendritic cell: -4.678<br>B cell: -4.779",
+          "plasmacytoid dendritic cell: -0.354<br>dendritic cell: -2.520<br>plasmacytoid dendritic cell, human: -2.564<br>conventional dendritic cell: -5.115<br>myeloid dendritic cell: -5.294",
+          "plasmacytoid dendritic cell: -0.187<br>plasmacytoid dendritic cell, human: -3.083<br>dendritic cell: -3.182<br>myeloid dendritic cell: -5.648<br>dendritic cell, human: -5.957",
+          "plasmacytoid dendritic cell: -0.226<br>plasmacytoid dendritic cell, human: -2.837<br>dendritic cell: -3.208<br>plasma cell: -5.566<br>myeloid dendritic cell: -5.621"
+         ],
+         "legendgroup": "plasmacytoid dendritic cell",
+         "marker": {
+          "color": "#00acc6",
+          "size": 4,
+          "symbol": "circle"
+         },
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.796<br>central memory CD4-positive, alpha-beta T cell: -1.850<br>effector memory CD4-positive, alpha-beta T cell: -2.637<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.016<br>T follicular helper cell: -3.131",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.294<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.700<br>central memory CD4-positive, alpha-beta T cell: -2.004<br>T follicular helper cell: -3.189<br>effector memory CD4-positive, alpha-beta T cell: -3.270",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.898<br>central memory CD4-positive, alpha-beta T cell: -1.662<br>T follicular helper cell: -2.286<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.433<br>effector memory CD4-positive, alpha-beta T cell: -4.174",
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.559<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.848<br>central memory CD4-positive, alpha-beta T cell: -2.761<br>T cell: -4.075<br>T follicular helper cell: -4.563",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.302<br>central memory CD4-positive, alpha-beta T cell: -1.670<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.683<br>T follicular helper cell: -2.490<br>effector memory CD4-positive, alpha-beta T cell: -4.090",
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.126<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.355<br>central memory CD4-positive, alpha-beta T cell: -2.745<br>T cell: -3.268<br>T follicular helper cell: -4.256",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.355<br>central memory CD4-positive, alpha-beta T cell: -1.857<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.376<br>effector memory CD4-positive, alpha-beta T cell: -2.757<br>T-helper 22 cell: -2.907",
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.593<br>central memory CD4-positive, alpha-beta T cell: -1.639<br>effector memory CD4-positive, alpha-beta T cell: -2.374<br>T-helper 22 cell: -2.601<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.638",
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.617<br>central memory CD4-positive, alpha-beta T cell: -1.954<br>effector memory CD4-positive, alpha-beta T cell: -2.600<br>T-helper 22 cell: -2.888<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.038",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.706<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.803<br>central memory CD4-positive, alpha-beta T cell: -2.260<br>T follicular helper cell: -3.571<br>T cell: -4.039",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.528<br>central memory CD4-positive, alpha-beta T cell: -1.609<br>T follicular helper cell: -1.737<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.750<br>effector memory CD4-positive, alpha-beta T cell: -3.184",
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+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.090<br>central memory CD4-positive, alpha-beta T cell: -1.803<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.961<br>effector memory CD4-positive, alpha-beta T cell: -3.418<br>T follicular helper cell: -3.493",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.734<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.661<br>central memory CD4-positive, alpha-beta T cell: -2.422<br>T follicular helper cell: -4.076<br>T cell: -4.206",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.174<br>central memory CD4-positive, alpha-beta T cell: -1.690<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.393<br>T follicular helper cell: -2.463<br>effector memory CD4-positive, alpha-beta T cell: -2.989",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.117<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.364<br>central memory CD4-positive, alpha-beta T cell: -2.289<br>T follicular helper cell: -3.297<br>effector memory CD8-positive, alpha-beta T cell: -3.731",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.265<br>central memory CD4-positive, alpha-beta T cell: -2.018<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.230<br>T follicular helper cell: -3.229<br>effector memory CD4-positive, alpha-beta T cell: -3.265",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.551<br>central memory CD4-positive, alpha-beta T cell: -2.015<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.686<br>T follicular helper cell: -3.237<br>effector memory CD4-positive, alpha-beta T cell: -3.875",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.651<br>central memory CD4-positive, alpha-beta T cell: -1.808<br>effector memory CD4-positive, alpha-beta T cell: -2.508<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.605<br>T follicular helper cell: -3.104",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.862<br>central memory CD4-positive, alpha-beta T cell: -1.806<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.877<br>T follicular helper cell: -3.551<br>effector memory CD4-positive, alpha-beta T cell: -3.976",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.682<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.535<br>central memory CD4-positive, alpha-beta T cell: -2.220<br>T follicular helper cell: -3.939<br>effector memory CD4-positive, alpha-beta T cell: -4.306",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.986<br>central memory CD4-positive, alpha-beta T cell: -1.887<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.188<br>T follicular helper cell: -3.307<br>effector memory CD4-positive, alpha-beta T cell: -3.488",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.397<br>central memory CD4-positive, alpha-beta T cell: -1.841<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.915<br>T follicular helper cell: -2.502<br>effector memory CD4-positive, alpha-beta T cell: -3.231",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.079<br>central memory CD4-positive, alpha-beta T cell: -1.790<br>T follicular helper cell: -2.703<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.028<br>effector memory CD4-positive, alpha-beta T cell: -3.430",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.316<br>central memory CD4-positive, alpha-beta T cell: -2.136<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.528<br>effector memory CD4-positive, alpha-beta T cell: -2.781<br>T-helper 22 cell: -3.104",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.494<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.003<br>effector memory CD4-positive, alpha-beta T cell: -2.137<br>central memory CD4-positive, alpha-beta T cell: -2.279<br>effector memory CD8-positive, alpha-beta T cell: -3.011",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.695<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.950<br>central memory CD4-positive, alpha-beta T cell: -1.990<br>T follicular helper cell: -2.444<br>effector memory CD4-positive, alpha-beta T cell: -2.771",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.409<br>central memory CD4-positive, alpha-beta T cell: -1.756<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.465<br>effector memory CD4-positive, alpha-beta T cell: -2.638<br>T-helper 22 cell: -3.424",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.549<br>central memory CD4-positive, alpha-beta T cell: -1.777<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.095<br>effector memory CD4-positive, alpha-beta T cell: -2.271<br>T-helper 22 cell: -3.237",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.944<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.829<br>central memory CD4-positive, alpha-beta T cell: -2.002<br>effector memory CD4-positive, alpha-beta T cell: -3.173<br>T follicular helper cell: -3.822",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.968<br>central memory CD4-positive, alpha-beta T cell: -1.647<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.785<br>T follicular helper cell: -2.901<br>effector memory CD4-positive, alpha-beta T cell: -3.185",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.044<br>central memory CD4-positive, alpha-beta T cell: -1.990<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.403<br>T-helper 22 cell: -3.548<br>effector memory CD4-positive, alpha-beta T cell: -3.562",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.726<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.003<br>effector memory CD8-positive, alpha-beta T cell: -2.102<br>effector memory CD4-positive, alpha-beta T cell: -2.463<br>central memory CD4-positive, alpha-beta T cell: -2.468",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.446<br>central memory CD4-positive, alpha-beta T cell: -1.566<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.153<br>T follicular helper cell: -2.764<br>effector memory CD4-positive, alpha-beta T cell: -3.763",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.263<br>central memory CD4-positive, alpha-beta T cell: -1.880<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.940<br>T-helper 22 cell: -3.216<br>effector memory CD4-positive, alpha-beta T cell: -3.219",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.439<br>effector memory CD4-positive, alpha-beta T cell: -2.327<br>T follicular helper cell: -2.451<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.675<br>central memory CD4-positive, alpha-beta T cell: -2.729",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.469<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.010<br>central memory CD4-positive, alpha-beta T cell: -2.204<br>effector memory CD4-positive, alpha-beta T cell: -3.048<br>T follicular helper cell: -3.156",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.248<br>central memory CD4-positive, alpha-beta T cell: -1.771<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.597<br>T follicular helper cell: -2.778<br>T cell: -3.386",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.381<br>central memory CD4-positive, alpha-beta T cell: -1.873<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.955<br>T follicular helper cell: -2.848<br>effector memory CD4-positive, alpha-beta T cell: -2.925",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.236<br>central memory CD4-positive, alpha-beta T cell: -1.941<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.980<br>T follicular helper cell: -3.383<br>effector memory CD4-positive, alpha-beta T cell: -3.387",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.749<br>central memory CD4-positive, alpha-beta T cell: -1.943<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.116<br>T follicular helper cell: -3.350<br>T cell: -4.211",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.569<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.563<br>central memory CD4-positive, alpha-beta T cell: -2.530<br>T cell: -4.413<br>T follicular helper cell: -4.551",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -2.531<br>CD4-positive, alpha-beta T cell: -3.245<br>T cell: -3.452<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.881<br>erythrocyte: -3.967",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.215<br>central memory CD4-positive, alpha-beta T cell: -2.108<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.370<br>T follicular helper cell: -2.464<br>effector memory CD4-positive, alpha-beta T cell: -2.849",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.612<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.641<br>central memory CD4-positive, alpha-beta T cell: -2.299<br>T follicular helper cell: -4.095<br>effector memory CD4-positive, alpha-beta T cell: -4.478",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.922<br>central memory CD4-positive, alpha-beta T cell: -2.120<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.144<br>effector memory CD4-positive, alpha-beta T cell: -2.482<br>T follicular helper cell: -3.078",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.751<br>central memory CD4-positive, alpha-beta T cell: -1.854<br>effector memory CD4-positive, alpha-beta T cell: -2.196<br>T-helper 22 cell: -2.339<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.038",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.295<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.506<br>central memory CD4-positive, alpha-beta T cell: -2.193<br>T follicular helper cell: -3.333<br>effector memory CD4-positive, alpha-beta T cell: -3.688",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.540<br>central memory CD4-positive, alpha-beta T cell: -1.805<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.217<br>effector memory CD4-positive, alpha-beta T cell: -2.357<br>T follicular helper cell: -3.048",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.896<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.994<br>central memory CD4-positive, alpha-beta T cell: -2.175<br>effector memory CD4-positive, alpha-beta T cell: -3.112<br>T follicular helper cell: -3.593",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.450<br>central memory CD4-positive, alpha-beta T cell: -2.068<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.677<br>T follicular helper cell: -3.471<br>effector memory CD4-positive, alpha-beta T cell: -4.084",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.625<br>central memory CD4-positive, alpha-beta T cell: -1.910<br>T follicular helper cell: -2.517<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.562<br>effector memory CD4-positive, alpha-beta T cell: -2.671",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.847<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.165<br>central memory CD4-positive, alpha-beta T cell: -2.903<br>T follicular helper cell: -3.981<br>T cell: -4.045",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.985<br>central memory CD4-positive, alpha-beta T cell: -2.041<br>effector memory CD4-positive, alpha-beta T cell: -2.064<br>T-helper 22 cell: -2.581<br>T follicular helper cell: -3.173",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -1.027<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.034<br>central memory CD4-positive, alpha-beta T cell: -2.942<br>T cell: -4.058<br>granule cell: -4.734",
+          "naive thymus-derived CD4-positive, alpha-beta T cell: -0.671<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.994<br>central memory CD4-positive, alpha-beta T cell: -2.112<br>T follicular helper cell: -3.401<br>effector memory CD4-positive, alpha-beta T cell: -3.981"
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+         "hovertext": [
+          "classical monocyte: -1.212<br>CD14-positive monocyte: -1.706<br>pvalb GABAergic cortical interneuron: -2.739<br>monocyte: -3.786<br>macrophage: -3.796",
+          "classical monocyte: -1.909<br>CD14-positive monocyte: -2.127<br>dendritic cell, human: -2.771<br>pvalb GABAergic cortical interneuron: -2.946<br>dendritic cell: -3.312",
+          "classical monocyte: -1.098<br>CD14-positive monocyte: -1.792<br>pvalb GABAergic cortical interneuron: -2.618<br>macrophage: -3.831<br>monocyte: -3.974",
+          "classical monocyte: -0.993<br>CD14-positive monocyte: -1.768<br>macrophage: -3.446<br>pvalb GABAergic cortical interneuron: -3.480<br>monocyte: -3.835",
+          "classical monocyte: -1.060<br>CD14-positive monocyte: -1.607<br>pvalb GABAergic cortical interneuron: -2.897<br>monocyte: -3.590<br>macrophage: -4.418",
+          "classical monocyte: -1.143<br>CD14-positive monocyte: -1.429<br>pvalb GABAergic cortical interneuron: -2.813<br>monocyte: -3.704<br>macrophage: -4.217",
+          "classical monocyte: -1.959<br>CD14-positive monocyte: -2.129<br>dendritic cell, human: -2.449<br>CD1c-positive myeloid dendritic cell: -2.759<br>macrophage: -3.045",
+          "classical monocyte: -1.122<br>CD14-positive monocyte: -2.174<br>macrophage: -3.035<br>monocyte: -3.159<br>pvalb GABAergic cortical interneuron: -3.647",
+          "classical monocyte: -1.542<br>CD14-positive monocyte: -2.619<br>dendritic cell, human: -2.636<br>CD1c-positive myeloid dendritic cell: -3.135<br>conventional dendritic cell: -3.367",
+          "classical monocyte: -1.289<br>CD14-positive monocyte: -1.515<br>pvalb GABAergic cortical interneuron: -3.026<br>dendritic cell, human: -4.126<br>monocyte: -4.140",
+          "classical monocyte: -1.174<br>CD14-positive monocyte: -1.560<br>pvalb GABAergic cortical interneuron: -2.863<br>monocyte: -3.588<br>macrophage: -4.238",
+          "classical monocyte: -1.543<br>CD14-positive monocyte: -1.703<br>dendritic cell, human: -2.988<br>macrophage: -3.258<br>CD1c-positive myeloid dendritic cell: -3.354",
+          "classical monocyte: -1.125<br>CD14-positive monocyte: -1.410<br>pvalb GABAergic cortical interneuron: -3.069<br>macrophage: -3.927<br>monocyte: -4.039",
+          "classical monocyte: -1.234<br>CD14-positive monocyte: -1.689<br>pvalb GABAergic cortical interneuron: -2.927<br>macrophage: -3.467<br>monocyte: -3.827",
+          "classical monocyte: -1.121<br>CD14-positive monocyte: -1.900<br>pvalb GABAergic cortical interneuron: -3.504<br>CD1c-positive myeloid dendritic cell: -3.569<br>monocyte: -3.656",
+          "classical monocyte: -1.255<br>CD14-positive monocyte: -1.511<br>pvalb GABAergic cortical interneuron: -3.049<br>dendritic cell, human: -3.660<br>macrophage: -3.667",
+          "classical monocyte: -1.288<br>macrophage: -2.300<br>CD14-positive monocyte: -2.608<br>alveolar macrophage: -3.103<br>monocyte: -3.515",
+          "classical monocyte: -1.515<br>CD14-positive monocyte: -1.861<br>pvalb GABAergic cortical interneuron: -2.758<br>monocyte: -3.630<br>macrophage: -3.960",
+          "classical monocyte: -2.051<br>CD14-positive monocyte: -2.120<br>non-classical monocyte: -2.850<br>CD1c-positive myeloid dendritic cell: -3.433<br>mature NK T cell: -3.821",
+          "classical monocyte: -1.292<br>CD14-positive monocyte: -1.487<br>pvalb GABAergic cortical interneuron: -3.246<br>CD1c-positive myeloid dendritic cell: -3.379<br>dendritic cell, human: -3.775",
+          "classical monocyte: -1.156<br>CD14-positive monocyte: -1.775<br>pvalb GABAergic cortical interneuron: -2.858<br>monocyte: -3.816<br>dendritic cell, human: -4.010",
+          "classical monocyte: -2.102<br>CD14-positive monocyte: -2.390<br>CD1c-positive myeloid dendritic cell: -2.526<br>dendritic cell, human: -2.725<br>non-classical monocyte: -2.869",
+          "classical monocyte: -1.408<br>CD14-positive monocyte: -1.637<br>pvalb GABAergic cortical interneuron: -3.429<br>macrophage: -3.677<br>monocyte: -3.921",
+          "classical monocyte: -1.551<br>CD14-positive monocyte: -1.694<br>pvalb GABAergic cortical interneuron: -2.756<br>monocyte: -3.975<br>dendritic cell, human: -4.325",
+          "classical monocyte: -1.055<br>CD14-positive monocyte: -1.525<br>pvalb GABAergic cortical interneuron: -3.558<br>macrophage: -3.751<br>CD1c-positive myeloid dendritic cell: -3.889",
+          "classical monocyte: -2.037<br>CD14-positive monocyte: -2.286<br>dendritic cell, human: -2.388<br>CD1c-positive myeloid dendritic cell: -2.865<br>conventional dendritic cell: -3.027",
+          "classical monocyte: -1.435<br>CD14-positive monocyte: -2.293<br>pvalb GABAergic cortical interneuron: -2.761<br>monocyte: -3.487<br>macrophage: -3.768",
+          "classical monocyte: -1.016<br>CD14-positive monocyte: -1.345<br>pvalb GABAergic cortical interneuron: -3.491<br>macrophage: -3.821<br>monocyte: -4.327",
+          "classical monocyte: -1.768<br>CD14-positive monocyte: -1.969<br>pvalb GABAergic cortical interneuron: -3.007<br>dendritic cell: -3.106<br>CD1c-positive myeloid dendritic cell: -3.126",
+          "classical monocyte: -0.891<br>CD14-positive monocyte: -1.791<br>pvalb GABAergic cortical interneuron: -3.143<br>macrophage: -3.673<br>monocyte: -4.310",
+          "classical monocyte: -1.664<br>macrophage: -1.690<br>CD14-positive monocyte: -2.011<br>monocyte: -3.510<br>alveolar macrophage: -3.748",
+          "classical monocyte: -0.895<br>CD14-positive monocyte: -2.118<br>pvalb GABAergic cortical interneuron: -3.328<br>macrophage: -3.438<br>monocyte: -4.113",
+          "classical monocyte: -1.213<br>CD14-positive monocyte: -2.153<br>macrophage: -3.334<br>pvalb GABAergic cortical interneuron: -3.337<br>monocyte: -3.484",
+          "classical monocyte: -1.718<br>CD14-positive monocyte: -1.725<br>CD1c-positive myeloid dendritic cell: -2.945<br>pvalb GABAergic cortical interneuron: -3.277<br>monocyte: -3.572",
+          "classical monocyte: -1.336<br>CD14-positive monocyte: -1.864<br>dendritic cell, human: -3.104<br>CD1c-positive myeloid dendritic cell: -3.266<br>pvalb GABAergic cortical interneuron: -3.314",
+          "classical monocyte: -1.404<br>CD14-positive monocyte: -1.924<br>pvalb GABAergic cortical interneuron: -2.616<br>monocyte: -3.123<br>macrophage: -3.896",
+          "classical monocyte: -1.271<br>CD14-positive monocyte: -1.608<br>pvalb GABAergic cortical interneuron: -3.114<br>macrophage: -3.253<br>monocyte: -3.260",
+          "classical monocyte: -1.625<br>CD14-positive monocyte: -2.123<br>CD1c-positive myeloid dendritic cell: -2.936<br>conventional dendritic cell: -3.188<br>pvalb GABAergic cortical interneuron: -3.220",
+          "classical monocyte: -1.278<br>CD14-positive monocyte: -1.831<br>pvalb GABAergic cortical interneuron: -2.921<br>monocyte: -3.775<br>macrophage: -3.888",
+          "classical monocyte: -1.202<br>CD14-positive monocyte: -1.935<br>pvalb GABAergic cortical interneuron: -3.291<br>dendritic cell, human: -3.903<br>CD1c-positive myeloid dendritic cell: -3.910",
+          "classical monocyte: -1.428<br>CD14-positive monocyte: -1.650<br>pvalb GABAergic cortical interneuron: -3.283<br>macrophage: -3.356<br>CD1c-positive myeloid dendritic cell: -3.890",
+          "classical monocyte: -1.253<br>CD14-positive monocyte: -1.329<br>pvalb GABAergic cortical interneuron: -2.815<br>monocyte: -3.608<br>macrophage: -4.101",
+          "classical monocyte: -1.263<br>CD14-positive monocyte: -1.325<br>pvalb GABAergic cortical interneuron: -2.785<br>monocyte: -3.580<br>macrophage: -3.953",
+          "classical monocyte: -1.174<br>CD14-positive monocyte: -2.170<br>dendritic cell, human: -3.187<br>non-classical monocyte: -3.208<br>macrophage: -3.208",
+          "classical monocyte: -1.292<br>CD14-positive monocyte: -1.399<br>pvalb GABAergic cortical interneuron: -3.179<br>macrophage: -3.693<br>CD1c-positive myeloid dendritic cell: -3.935",
+          "classical monocyte: -1.120<br>CD14-positive monocyte: -1.819<br>pvalb GABAergic cortical interneuron: -2.680<br>monocyte: -3.726<br>macrophage: -3.980",
+          "classical monocyte: -1.246<br>CD14-positive monocyte: -2.262<br>pvalb GABAergic cortical interneuron: -2.965<br>macrophage: -3.612<br>monocyte: -3.942",
+          "classical monocyte: -0.838<br>CD14-positive monocyte: -1.560<br>pvalb GABAergic cortical interneuron: -3.276<br>monocyte: -4.038<br>macrophage: -4.428",
+          "classical monocyte: -1.184<br>CD14-positive monocyte: -1.567<br>pvalb GABAergic cortical interneuron: -2.709<br>monocyte: -3.736<br>macrophage: -3.990",
+          "classical monocyte: -1.483<br>CD14-positive monocyte: -1.535<br>monocyte: -2.986<br>pvalb GABAergic cortical interneuron: -3.006<br>macrophage: -3.873",
+          "classical monocyte: -1.259<br>CD14-positive monocyte: -1.412<br>pvalb GABAergic cortical interneuron: -3.421<br>monocyte: -3.529<br>CD1c-positive myeloid dendritic cell: -3.865",
+          "classical monocyte: -1.394<br>CD14-positive monocyte: -2.017<br>pvalb GABAergic cortical interneuron: -2.683<br>monocyte: -3.789<br>myeloid leukocyte: -4.092",
+          "classical monocyte: -1.130<br>CD14-positive monocyte: -1.411<br>pvalb GABAergic cortical interneuron: -3.023<br>monocyte: -3.533<br>macrophage: -4.270",
+          "classical monocyte: -1.380<br>CD14-positive monocyte: -1.995<br>pvalb GABAergic cortical interneuron: -2.741<br>monocyte: -3.354<br>dendritic cell, human: -3.783",
+          "classical monocyte: -2.083<br>CD14-positive monocyte: -2.249<br>CD1c-positive myeloid dendritic cell: -2.868<br>dendritic cell, human: -3.231<br>conventional dendritic cell: -3.469",
+          "classical monocyte: -1.140<br>CD14-positive monocyte: -1.604<br>pvalb GABAergic cortical interneuron: -3.359<br>dendritic cell, human: -3.502<br>CD1c-positive myeloid dendritic cell: -3.587",
+          "classical monocyte: -2.399<br>dendritic cell, human: -2.584<br>dendritic cell: -2.990<br>CD14-positive monocyte: -3.161<br>CD1c-positive myeloid dendritic cell: -3.261",
+          "classical monocyte: -1.103<br>CD14-positive monocyte: -1.654<br>pvalb GABAergic cortical interneuron: -2.929<br>monocyte: -3.879<br>macrophage: -4.135",
+          "classical monocyte: -1.564<br>CD14-positive monocyte: -1.636<br>pvalb GABAergic cortical interneuron: -3.207<br>CD1c-positive myeloid dendritic cell: -3.276<br>dendritic cell, human: -3.492",
+          "classical monocyte: -1.175<br>CD14-positive monocyte: -1.722<br>pvalb GABAergic cortical interneuron: -2.870<br>macrophage: -3.671<br>monocyte: -4.156",
+          "classical monocyte: -1.046<br>CD14-positive monocyte: -1.519<br>pvalb GABAergic cortical interneuron: -2.903<br>monocyte: -4.004<br>macrophage: -4.173",
+          "classical monocyte: -1.240<br>CD14-positive monocyte: -1.246<br>pvalb GABAergic cortical interneuron: -3.107<br>monocyte: -3.447<br>macrophage: -4.109",
+          "classical monocyte: -1.438<br>CD14-positive monocyte: -1.728<br>pvalb GABAergic cortical interneuron: -2.985<br>dendritic cell, human: -3.638<br>macrophage: -3.677",
+          "classical monocyte: -1.074<br>CD14-positive monocyte: -1.557<br>pvalb GABAergic cortical interneuron: -3.119<br>dendritic cell, human: -3.584<br>macrophage: -3.737",
+          "classical monocyte: -0.966<br>CD14-positive monocyte: -1.115<br>pvalb GABAergic cortical interneuron: -3.500<br>monocyte: -3.830<br>neutrophil: -4.261",
+          "classical monocyte: -1.843<br>CD14-positive monocyte: -2.152<br>CD1c-positive myeloid dendritic cell: -2.602<br>dendritic cell, human: -2.665<br>conventional dendritic cell: -3.183",
+          "classical monocyte: -1.001<br>CD14-positive monocyte: -1.532<br>pvalb GABAergic cortical interneuron: -3.228<br>monocyte: -3.695<br>macrophage: -3.926",
+          "classical monocyte: -1.244<br>macrophage: -2.122<br>alveolar macrophage: -2.983<br>CD14-positive monocyte: -3.298<br>non-classical monocyte: -3.550",
+          "classical monocyte: -1.312<br>CD14-positive monocyte: -1.677<br>pvalb GABAergic cortical interneuron: -3.174<br>dendritic cell, human: -3.475<br>CD1c-positive myeloid dendritic cell: -3.585",
+          "classical monocyte: -2.090<br>CD14-positive monocyte: -2.153<br>CD1c-positive myeloid dendritic cell: -2.541<br>dendritic cell, human: -2.688<br>conventional dendritic cell: -3.141",
+          "classical monocyte: -0.967<br>CD14-positive monocyte: -1.260<br>pvalb GABAergic cortical interneuron: -3.248<br>monocyte: -3.855<br>macrophage: -4.528",
+          "classical monocyte: -1.213<br>CD14-positive monocyte: -1.432<br>pvalb GABAergic cortical interneuron: -3.368<br>dendritic cell, human: -3.617<br>dendritic cell: -3.788",
+          "classical monocyte: -1.161<br>CD14-positive monocyte: -1.703<br>pvalb GABAergic cortical interneuron: -3.089<br>monocyte: -3.677<br>CD1c-positive myeloid dendritic cell: -3.845",
+          "classical monocyte: -1.143<br>CD14-positive monocyte: -1.153<br>pvalb GABAergic cortical interneuron: -3.086<br>monocyte: -3.990<br>macrophage: -4.008",
+          "classical monocyte: -1.165<br>CD14-positive monocyte: -1.718<br>macrophage: -2.932<br>monocyte: -3.422<br>neutrophil: -3.794",
+          "classical monocyte: -1.626<br>CD14-positive monocyte: -1.711<br>pvalb GABAergic cortical interneuron: -2.986<br>dendritic cell, human: -3.299<br>macrophage: -3.703",
+          "classical monocyte: -0.973<br>macrophage: -2.498<br>CD14-positive monocyte: -2.795<br>monocyte: -3.040<br>alveolar macrophage: -3.332",
+          "classical monocyte: -1.868<br>CD14-positive monocyte: -2.599<br>CD1c-positive myeloid dendritic cell: -2.614<br>dendritic cell, human: -2.666<br>conventional dendritic cell: -2.955",
+          "classical monocyte: -2.061<br>CD14-positive monocyte: -2.266<br>CD1c-positive myeloid dendritic cell: -2.431<br>dendritic cell, human: -2.641<br>dendritic cell: -3.007",
+          "classical monocyte: -0.850<br>CD14-positive monocyte: -1.974<br>pvalb GABAergic cortical interneuron: -3.111<br>macrophage: -3.515<br>monocyte: -3.616",
+          "classical monocyte: -1.672<br>CD14-positive monocyte: -1.917<br>dendritic cell, human: -2.935<br>CD1c-positive myeloid dendritic cell: -3.112<br>macrophage: -3.482",
+          "classical monocyte: -0.881<br>CD14-positive monocyte: -2.022<br>pvalb GABAergic cortical interneuron: -3.352<br>macrophage: -3.920<br>monocyte: -4.005",
+          "classical monocyte: -1.228<br>CD14-positive monocyte: -1.363<br>pvalb GABAergic cortical interneuron: -3.029<br>monocyte: -3.201<br>macrophage: -3.962",
+          "classical monocyte: -1.078<br>CD14-positive monocyte: -1.656<br>pvalb GABAergic cortical interneuron: -2.768<br>monocyte: -3.917<br>macrophage: -3.976",
+          "classical monocyte: -1.376<br>CD14-positive monocyte: -1.840<br>pvalb GABAergic cortical interneuron: -2.967<br>macrophage: -3.292<br>CD1c-positive myeloid dendritic cell: -3.744",
+          "classical monocyte: -1.352<br>CD14-positive monocyte: -1.462<br>pvalb GABAergic cortical interneuron: -2.909<br>monocyte: -3.615<br>dendritic cell, human: -4.076",
+          "classical monocyte: -1.135<br>CD14-positive monocyte: -1.142<br>pvalb GABAergic cortical interneuron: -3.260<br>monocyte: -3.693<br>macrophage: -4.162",
+          "classical monocyte: -1.314<br>CD14-positive monocyte: -2.076<br>pvalb GABAergic cortical interneuron: -3.117<br>dendritic cell, human: -3.264<br>CD1c-positive myeloid dendritic cell: -3.332",
+          "classical monocyte: -0.975<br>CD14-positive monocyte: -1.744<br>pvalb GABAergic cortical interneuron: -2.518<br>monocyte: -3.665<br>macrophage: -4.412",
+          "classical monocyte: -1.112<br>CD14-positive monocyte: -1.277<br>pvalb GABAergic cortical interneuron: -3.221<br>monocyte: -3.900<br>macrophage: -4.120",
+          "classical monocyte: -1.349<br>monocyte: -2.523<br>CD14-positive monocyte: -2.712<br>macrophage: -2.715<br>dendritic cell: -3.300",
+          "classical monocyte: -1.268<br>CD14-positive monocyte: -2.058<br>pvalb GABAergic cortical interneuron: -3.242<br>macrophage: -3.424<br>dendritic cell, human: -3.457",
+          "classical monocyte: -1.275<br>CD14-positive monocyte: -1.852<br>pvalb GABAergic cortical interneuron: -2.819<br>monocyte: -3.272<br>macrophage: -3.941",
+          "classical monocyte: -1.300<br>CD14-positive monocyte: -1.626<br>pvalb GABAergic cortical interneuron: -3.066<br>macrophage: -3.148<br>CD1c-positive myeloid dendritic cell: -3.464",
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+          "classical monocyte: -1.422<br>CD14-positive monocyte: -2.533<br>pvalb GABAergic cortical interneuron: -2.959<br>monocyte: -3.934<br>macrophage: -3.983",
+          "classical monocyte: -1.253<br>CD14-positive monocyte: -1.834<br>pvalb GABAergic cortical interneuron: -2.866<br>dendritic cell, human: -3.772<br>macrophage: -3.877",
+          "classical monocyte: -2.038<br>CD14-positive monocyte: -2.087<br>dendritic cell, human: -2.422<br>CD1c-positive myeloid dendritic cell: -2.820<br>conventional dendritic cell: -3.061",
+          "classical monocyte: -1.263<br>CD14-positive monocyte: -1.779<br>macrophage: -3.264<br>pvalb GABAergic cortical interneuron: -3.298<br>dendritic cell, human: -3.631",
+          "classical monocyte: -1.243<br>CD14-positive monocyte: -1.549<br>pvalb GABAergic cortical interneuron: -3.103<br>monocyte: -3.504<br>alveolar macrophage: -3.988",
+          "classical monocyte: -1.289<br>CD14-positive monocyte: -1.904<br>macrophage: -2.821<br>monocyte: -2.886<br>alveolar macrophage: -2.992",
+          "classical monocyte: -1.556<br>CD14-positive monocyte: -1.646<br>CD1c-positive myeloid dendritic cell: -2.949<br>dendritic cell, human: -3.411<br>pvalb GABAergic cortical interneuron: -3.442",
+          "classical monocyte: -1.303<br>CD14-positive monocyte: -1.427<br>pvalb GABAergic cortical interneuron: -3.026<br>monocyte: -3.778<br>promonocyte: -4.267",
+          "classical monocyte: -1.244<br>CD14-positive monocyte: -2.245<br>macrophage: -3.013<br>monocyte: -3.677<br>non-classical monocyte: -4.016",
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+          "classical monocyte: -1.334<br>CD14-positive monocyte: -2.265<br>monocyte: -2.777<br>macrophage: -2.892<br>pvalb GABAergic cortical interneuron: -3.464",
+          "classical monocyte: -2.199<br>CD14-positive monocyte: -2.282<br>CD1c-positive myeloid dendritic cell: -2.581<br>dendritic cell, human: -2.976<br>conventional dendritic cell: -2.988",
+          "classical monocyte: -1.375<br>CD14-positive monocyte: -1.645<br>pvalb GABAergic cortical interneuron: -3.279<br>dendritic cell, human: -3.354<br>macrophage: -3.504",
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+          "classical monocyte: -0.957<br>CD14-positive monocyte: -1.780<br>pvalb GABAergic cortical interneuron: -2.776<br>monocyte: -4.060<br>macrophage: -4.349"
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+          "natural killer cell: -1.363<br>CD16-negative, CD56-bright natural killer cell, human: -1.404<br>CD16-positive, CD56-dim natural killer cell, human: -1.631<br>mature NK T cell: -3.972<br>gamma-delta T cell: -4.159",
+          "natural killer cell: -0.899<br>CD16-positive, CD56-dim natural killer cell, human: -0.909<br>mature NK T cell: -4.228<br>CD8-positive, alpha-beta T cell: -4.449<br>CD16-negative, CD56-bright natural killer cell, human: -4.451",
+          "natural killer cell: -0.785<br>CD16-positive, CD56-dim natural killer cell, human: -1.117<br>CD16-negative, CD56-bright natural killer cell, human: -3.936<br>mature NK T cell: -4.167<br>lymphocyte: -4.762",
+          "natural killer cell: -0.937<br>CD16-positive, CD56-dim natural killer cell, human: -1.098<br>mature NK T cell: -3.853<br>CD16-negative, CD56-bright natural killer cell, human: -4.369<br>astrocyte of the cerebral cortex: -4.735",
+          "natural killer cell: -0.555<br>CD16-negative, CD56-bright natural killer cell, human: -2.155<br>CD16-positive, CD56-dim natural killer cell, human: -2.513<br>lymphocyte: -4.323<br>mature NK T cell: -4.510",
+          "natural killer cell: -0.632<br>CD16-positive, CD56-dim natural killer cell, human: -2.112<br>CD16-negative, CD56-bright natural killer cell, human: -2.699<br>lymphocyte: -3.367<br>mature NK T cell: -4.570",
+          "natural killer cell: -0.811<br>CD16-positive, CD56-dim natural killer cell, human: -1.339<br>CD16-negative, CD56-bright natural killer cell, human: -2.863<br>mature NK T cell: -4.167<br>activated type II NK T cell: -4.542",
+          "natural killer cell: -0.804<br>lymphocyte: -3.317<br>CD16-positive, CD56-dim natural killer cell, human: -3.433<br>T cell: -3.837<br>CD8-positive, alpha-beta T cell: -3.954",
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+          "natural killer cell: -0.501<br>lymphocyte: -2.693<br>CD16-positive, CD56-dim natural killer cell, human: -3.579<br>CD8-positive, alpha-beta T cell: -3.729<br>T cell: -3.935",
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+          "natural killer cell: -1.598<br>CD16-positive, CD56-dim natural killer cell, human: -2.234<br>CD8-positive, alpha-beta T cell: -2.840<br>gamma-delta T cell: -2.919<br>mature NK T cell: -3.248",
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+          "natural killer cell: -0.647<br>CD16-positive, CD56-dim natural killer cell, human: -2.113<br>CD8-positive, alpha-beta T cell: -2.733<br>gamma-delta T cell: -3.856<br>mature NK T cell: -3.982",
+          "natural killer cell: -1.109<br>CD16-positive, CD56-dim natural killer cell, human: -1.380<br>CD16-negative, CD56-bright natural killer cell, human: -2.751<br>mature NK T cell: -3.774<br>gamma-delta T cell: -4.195",
+          "natural killer cell: -0.705<br>CD16-positive, CD56-dim natural killer cell, human: -1.382<br>lymphocyte: -3.979<br>mature NK T cell: -4.218<br>CD16-negative, CD56-bright natural killer cell, human: -4.429",
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+          "natural killer cell: -1.311<br>CD16-positive, CD56-dim natural killer cell, human: -1.413<br>mature NK T cell: -3.249<br>CD16-negative, CD56-bright natural killer cell, human: -3.515<br>gamma-delta T cell: -3.694",
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+          "natural killer cell: -0.747<br>CD16-positive, CD56-dim natural killer cell, human: -1.162<br>CD16-negative, CD56-bright natural killer cell, human: -3.760<br>mature NK T cell: -4.092<br>activated type II NK T cell: -4.741",
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+          "gamma-delta T cell: -1.868<br>mature NK T cell: -1.996<br>CD16-positive, CD56-dim natural killer cell, human: -2.127<br>natural killer cell: -2.364<br>CD8-positive, alpha-beta T cell: -2.554",
+          "gamma-delta T cell: -2.388<br>mature NK T cell: -2.577<br>natural killer cell: -2.618<br>CD16-positive, CD56-dim natural killer cell, human: -2.757<br>CD8-positive, alpha-beta T cell: -2.855",
+          "gamma-delta T cell: -2.684<br>effector memory CD8-positive, alpha-beta T cell: -2.742<br>mature NK T cell: -2.839<br>CD16-negative, CD56-bright natural killer cell, human: -2.962<br>effector CD8-positive, alpha-beta T cell: -3.164",
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+          "CD14-positive monocyte: -1.215<br>classical monocyte: -2.266<br>monocyte: -2.643<br>pvalb GABAergic cortical interneuron: -2.932<br>promonocyte: -3.974",
+          "CD14-positive monocyte: -0.999<br>classical monocyte: -1.715<br>pvalb GABAergic cortical interneuron: -2.946<br>monocyte: -2.972<br>promonocyte: -4.388",
+          "CD14-positive monocyte: -1.345<br>classical monocyte: -1.530<br>pvalb GABAergic cortical interneuron: -3.296<br>CD1c-positive myeloid dendritic cell: -3.538<br>macrophage: -3.650",
+          "CD14-positive monocyte: -1.048<br>classical monocyte: -1.238<br>pvalb GABAergic cortical interneuron: -2.922<br>monocyte: -3.664<br>macrophage: -4.626",
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+          "CD14-positive monocyte: -1.864<br>classical monocyte: -2.046<br>macrophage: -2.904<br>CD1c-positive myeloid dendritic cell: -3.051<br>dendritic cell, human: -3.091",
+          "CD14-positive monocyte: -1.439<br>classical monocyte: -1.821<br>macrophage: -2.083<br>CD1c-positive myeloid dendritic cell: -3.276<br>alveolar macrophage: -3.489",
+          "CD14-positive monocyte: -1.073<br>classical monocyte: -1.516<br>non-classical monocyte: -3.564<br>pvalb GABAergic cortical interneuron: -3.721<br>macrophage: -3.728",
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+          "CD14-positive monocyte: -1.844<br>classical monocyte: -2.143<br>dendritic cell, human: -2.676<br>CD1c-positive myeloid dendritic cell: -3.322<br>pvalb GABAergic cortical interneuron: -3.755",
+          "CD14-positive monocyte: -1.115<br>classical monocyte: -1.509<br>pvalb GABAergic cortical interneuron: -2.932<br>monocyte: -3.151<br>promonocyte: -4.281",
+          "CD14-positive monocyte: -0.787<br>classical monocyte: -1.553<br>pvalb GABAergic cortical interneuron: -3.378<br>monocyte: -3.658<br>macrophage: -4.417",
+          "CD14-positive monocyte: -0.707<br>classical monocyte: -1.645<br>pvalb GABAergic cortical interneuron: -3.311<br>monocyte: -3.390<br>macrophage: -4.419",
+          "CD14-positive monocyte: -1.768<br>classical monocyte: -1.782<br>dendritic cell, human: -3.108<br>CD1c-positive myeloid dendritic cell: -3.112<br>macrophage: -3.303",
+          "CD14-positive monocyte: -0.740<br>classical monocyte: -2.064<br>pvalb GABAergic cortical interneuron: -2.975<br>monocyte: -3.162<br>CD1c-positive myeloid dendritic cell: -4.521",
+          "CD14-positive monocyte: -2.069<br>classical monocyte: -2.174<br>conventional dendritic cell: -2.727<br>CD1c-positive myeloid dendritic cell: -2.790<br>dendritic cell: -3.068",
+          "CD14-positive monocyte: -1.330<br>classical monocyte: -1.407<br>pvalb GABAergic cortical interneuron: -3.060<br>dendritic cell, human: -3.761<br>macrophage: -3.866",
+          "CD14-positive monocyte: -2.290<br>classical monocyte: -2.358<br>dendritic cell, human: -2.519<br>CD1c-positive myeloid dendritic cell: -2.535<br>conventional dendritic cell: -2.921",
+          "CD14-positive monocyte: -0.742<br>classical monocyte: -2.242<br>monocyte: -2.963<br>pvalb GABAergic cortical interneuron: -3.272<br>promonocyte: -4.346",
+          "CD14-positive monocyte: -0.860<br>classical monocyte: -1.377<br>pvalb GABAergic cortical interneuron: -3.681<br>monocyte: -4.225<br>macrophage: -4.285",
+          "CD14-positive monocyte: -1.103<br>classical monocyte: -1.355<br>pvalb GABAergic cortical interneuron: -3.061<br>monocyte: -3.740<br>macrophage: -4.170",
+          "CD14-positive monocyte: -1.252<br>classical monocyte: -1.336<br>pvalb GABAergic cortical interneuron: -3.327<br>CD1c-positive myeloid dendritic cell: -3.564<br>monocyte: -3.843",
+          "CD14-positive monocyte: -1.379<br>classical monocyte: -1.538<br>pvalb GABAergic cortical interneuron: -2.772<br>monocyte: -3.383<br>dendritic cell, human: -3.545",
+          "CD14-positive monocyte: -1.063<br>classical monocyte: -1.179<br>pvalb GABAergic cortical interneuron: -3.149<br>monocyte: -4.302<br>macrophage: -4.380",
+          "CD14-positive monocyte: -1.463<br>classical monocyte: -1.610<br>pvalb GABAergic cortical interneuron: -2.723<br>monocyte: -3.472<br>dendritic cell, human: -3.607",
+          "CD14-positive monocyte: -1.068<br>classical monocyte: -1.213<br>pvalb GABAergic cortical interneuron: -3.182<br>monocyte: -4.005<br>macrophage: -4.101",
+          "CD14-positive monocyte: -1.424<br>classical monocyte: -1.488<br>pvalb GABAergic cortical interneuron: -3.273<br>CD1c-positive myeloid dendritic cell: -3.532<br>dendritic cell, human: -3.824",
+          "CD14-positive monocyte: -1.627<br>classical monocyte: -1.793<br>dendritic cell, human: -3.051<br>CD1c-positive myeloid dendritic cell: -3.279<br>dendritic cell: -3.587",
+          "CD14-positive monocyte: -1.607<br>classical monocyte: -1.991<br>CD1c-positive myeloid dendritic cell: -2.785<br>dendritic cell, human: -2.988<br>conventional dendritic cell: -3.023",
+          "CD14-positive monocyte: -1.364<br>classical monocyte: -1.604<br>pvalb GABAergic cortical interneuron: -3.236<br>monocyte: -3.405<br>macrophage: -3.938",
+          "CD14-positive monocyte: -1.496<br>classical monocyte: -1.719<br>monocyte: -2.433<br>pvalb GABAergic cortical interneuron: -3.294<br>promonocyte: -4.017",
+          "CD14-positive monocyte: -1.355<br>classical monocyte: -1.653<br>pvalb GABAergic cortical interneuron: -2.855<br>monocyte: -3.679<br>macrophage: -4.037",
+          "CD14-positive monocyte: -1.241<br>classical monocyte: -1.335<br>pvalb GABAergic cortical interneuron: -2.892<br>monocyte: -3.541<br>macrophage: -4.069",
+          "CD14-positive monocyte: -1.137<br>classical monocyte: -1.496<br>pvalb GABAergic cortical interneuron: -3.179<br>macrophage: -3.740<br>dendritic cell, human: -4.073",
+          "CD14-positive monocyte: -1.066<br>classical monocyte: -1.130<br>pvalb GABAergic cortical interneuron: -3.265<br>monocyte: -3.836<br>macrophage: -4.274",
+          "CD14-positive monocyte: -1.183<br>classical monocyte: -1.438<br>pvalb GABAergic cortical interneuron: -3.110<br>monocyte: -3.532<br>dendritic cell, human: -3.801",
+          "CD14-positive monocyte: -1.102<br>classical monocyte: -1.608<br>monocyte: -3.069<br>pvalb GABAergic cortical interneuron: -3.159<br>promonocyte: -4.018",
+          "CD14-positive monocyte: -0.927<br>classical monocyte: -1.240<br>pvalb GABAergic cortical interneuron: -3.192<br>monocyte: -3.660<br>neutrophil: -4.345",
+          "CD14-positive monocyte: -1.024<br>classical monocyte: -1.989<br>pvalb GABAergic cortical interneuron: -2.772<br>monocyte: -3.049<br>promonocyte: -4.124",
+          "CD14-positive monocyte: -1.245<br>classical monocyte: -1.419<br>pvalb GABAergic cortical interneuron: -3.297<br>macrophage: -3.909<br>dendritic cell, human: -4.084",
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+          "CD14-positive monocyte: -1.135<br>classical monocyte: -1.293<br>pvalb GABAergic cortical interneuron: -3.007<br>monocyte: -3.811<br>macrophage: -4.356",
+          "CD14-positive monocyte: -1.118<br>classical monocyte: -1.483<br>pvalb GABAergic cortical interneuron: -2.689<br>monocyte: -3.065<br>macrophage: -4.484",
+          "CD14-positive monocyte: -1.058<br>classical monocyte: -1.218<br>pvalb GABAergic cortical interneuron: -3.335<br>macrophage: -4.106<br>monocyte: -4.227",
+          "CD14-positive monocyte: -1.214<br>classical monocyte: -1.406<br>CD1c-positive myeloid dendritic cell: -3.330<br>pvalb GABAergic cortical interneuron: -3.419<br>macrophage: -3.810",
+          "CD14-positive monocyte: -0.986<br>classical monocyte: -1.299<br>pvalb GABAergic cortical interneuron: -3.484<br>neutrophil: -3.896<br>macrophage: -4.347",
+          "CD14-positive monocyte: -1.055<br>classical monocyte: -1.069<br>pvalb GABAergic cortical interneuron: -3.291<br>monocyte: -3.732<br>macrophage: -4.260",
+          "CD14-positive monocyte: -0.960<br>classical monocyte: -1.434<br>pvalb GABAergic cortical interneuron: -3.094<br>monocyte: -3.484<br>macrophage: -4.402",
+          "CD14-positive monocyte: -1.155<br>classical monocyte: -1.550<br>pvalb GABAergic cortical interneuron: -2.785<br>monocyte: -3.268<br>macrophage: -4.483",
+          "CD14-positive monocyte: -1.791<br>classical monocyte: -1.948<br>pvalb GABAergic cortical interneuron: -3.138<br>dendritic cell, human: -3.299<br>CD1c-positive myeloid dendritic cell: -3.315",
+          "CD14-positive monocyte: -1.449<br>classical monocyte: -1.816<br>monocyte: -2.538<br>pvalb GABAergic cortical interneuron: -2.981<br>macrophage: -3.806",
+          "CD14-positive monocyte: -0.876<br>classical monocyte: -1.519<br>pvalb GABAergic cortical interneuron: -2.966<br>monocyte: -4.050<br>macrophage: -4.203",
+          "CD14-positive monocyte: -1.143<br>classical monocyte: -1.467<br>pvalb GABAergic cortical interneuron: -2.929<br>monocyte: -3.001<br>promonocyte: -4.337",
+          "CD14-positive monocyte: -0.874<br>classical monocyte: -1.304<br>pvalb GABAergic cortical interneuron: -3.295<br>monocyte: -3.902<br>neutrophil: -4.553",
+          "CD14-positive monocyte: -1.463<br>classical monocyte: -2.271<br>monocyte: -2.289<br>macrophage: -2.670<br>dendritic cell: -3.516"
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alpha-beta T cell: -3.454\\u003cbr\\u003emature NK T cell: -3.503\",\"T follicular helper cell: -1.811\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.176\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.252\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.500\\u003cbr\\u003eT-helper 22 cell: -3.255\",\"T follicular helper cell: -1.803\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.257\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.586\\u003cbr\\u003eT-helper 22 cell: -3.217\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.484\",\"T follicular helper cell: -1.765\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.823\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.070\\u003cbr\\u003eT-helper 22 cell: -2.397\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.623\",\"T follicular helper cell: 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alpha-beta T cell: -2.169\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.233\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.616\\u003cbr\\u003eT-helper 22 cell: -3.091\",\"T follicular helper cell: -1.807\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.851\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.230\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.537\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.299\",\"T follicular helper cell: -1.913\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.482\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.513\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.721\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.856\",\"T follicular helper cell: -1.824\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: 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CD8-positive, alpha-beta T cell: -2.246\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.666\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.771\",\"T follicular helper cell: -1.897\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.224\\u003cbr\\u003eT-helper 22 cell: -2.904\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.905\\u003cbr\\u003emature NK T cell: -3.580\",\"T follicular helper cell: -1.509\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.013\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.587\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.824\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.200\",\"T follicular helper cell: -1.995\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.758\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.804\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.869\\u003cbr\\u003eT cell: -3.370\",\"T follicular helper cell: -1.696\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.866\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.364\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.389\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.637\",\"T follicular helper cell: -2.168\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.785\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.892\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.605\\u003cbr\\u003eT-helper 22 cell: -3.813\"],\"legendgroup\":\"T follicular helper cell\",\"marker\":{\"color\":\"#d60000\",\"symbol\":\"circle\",\"size\":4},\"mode\":\"markers\",\"name\":\"T follicular helper 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killer cell, human: -0.484\\u003cbr\\u003enatural killer cell: -2.135\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.447\\u003cbr\\u003emature NK T cell: -4.345\\u003cbr\\u003eactivated type II NK T cell: -4.521\",\"CD16-positive, CD56-dim natural killer cell, human: -0.504\\u003cbr\\u003enatural killer cell: -1.691\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.602\\u003cbr\\u003emature NK T cell: -4.274\\u003cbr\\u003eactivated type II NK T cell: -4.822\",\"CD16-positive, CD56-dim natural killer cell, human: -0.962\\u003cbr\\u003enatural killer cell: -1.133\\u003cbr\\u003emature NK T cell: -3.452\\u003cbr\\u003egamma-delta T cell: -3.971\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.582\",\"CD16-positive, CD56-dim natural killer cell, human: -0.811\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.916\\u003cbr\\u003enatural killer cell: -2.506\\u003cbr\\u003egamma-delta T cell: 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-1.676\\u003cbr\\u003emature NK T cell: -3.824\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.362\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.685\",\"CD16-positive, CD56-dim natural killer cell, human: -0.285\\u003cbr\\u003enatural killer cell: -2.481\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.339\\u003cbr\\u003emature NK T cell: -4.366\\u003cbr\\u003egamma-delta T cell: -5.053\",\"CD16-positive, CD56-dim natural killer cell, human: -0.463\\u003cbr\\u003enatural killer cell: -1.655\\u003cbr\\u003emature NK T cell: -3.926\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.133\\u003cbr\\u003eactivated type II NK T cell: -5.171\",\"CD16-positive, CD56-dim natural killer cell, human: -0.371\\u003cbr\\u003enatural killer cell: -1.695\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.243\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.275\",\"CD16-positive, CD56-dim natural killer cell, human: -0.721\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.687\\u003cbr\\u003enatural killer cell: -2.525\\u003cbr\\u003egamma-delta T cell: -4.131\\u003cbr\\u003emature NK T cell: -4.408\",\"CD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -1.952\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.146\\u003cbr\\u003emature NK T cell: -4.305\\u003cbr\\u003eactivated type II NK T cell: -4.803\",\"CD16-positive, CD56-dim natural killer cell, human: -1.256\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.616\\u003cbr\\u003enatural killer cell: -2.142\\u003cbr\\u003egamma-delta T cell: -3.362\\u003cbr\\u003emature NK T cell: -3.632\",\"CD16-positive, CD56-dim natural killer cell, human: -0.439\\u003cbr\\u003enatural killer cell: -1.860\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: 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-4.878\",\"CD16-positive, CD56-dim natural killer cell, human: -0.835\\u003cbr\\u003enatural killer cell: -1.105\\u003cbr\\u003emature NK T cell: -3.454\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.592\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.658\",\"CD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003enatural killer cell: -1.335\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.671\\u003cbr\\u003emature NK T cell: -4.196\\u003cbr\\u003egamma-delta T cell: -4.595\",\"CD16-positive, CD56-dim natural killer cell, human: -1.685\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.164\\u003cbr\\u003egamma-delta T cell: -2.336\\u003cbr\\u003enatural killer cell: -2.628\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.697\",\"CD16-positive, CD56-dim natural killer cell, human: -0.498\\u003cbr\\u003enatural killer cell: -1.575\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.034\\u003cbr\\u003emature NK T cell: -4.078\\u003cbr\\u003eactivated type II NK T cell: -4.936\",\"CD16-positive, CD56-dim natural killer cell, human: -0.692\\u003cbr\\u003enatural killer cell: -2.326\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.250\\u003cbr\\u003emature NK T cell: -3.625\\u003cbr\\u003egamma-delta T cell: -3.863\",\"CD16-positive, CD56-dim natural killer cell, human: -0.848\\u003cbr\\u003enatural killer cell: -1.355\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.086\\u003cbr\\u003emature NK T cell: -3.655\\u003cbr\\u003egamma-delta T cell: -3.941\",\"CD16-positive, CD56-dim natural killer cell, human: -1.894\\u003cbr\\u003egamma-delta T cell: -2.450\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.557\\u003cbr\\u003emature NK T cell: -2.678\\u003cbr\\u003enatural killer cell: -2.704\",\"CD16-positive, CD56-dim natural killer cell, human: -1.387\\u003cbr\\u003egamma-delta T cell: -2.375\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.443\\u003cbr\\u003enatural killer cell: -2.656\\u003cbr\\u003emature NK T cell: -3.011\",\"CD16-positive, CD56-dim natural killer cell, human: -0.331\\u003cbr\\u003enatural killer cell: -2.563\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.275\\u003cbr\\u003egamma-delta T cell: -4.350\",\"CD16-positive, CD56-dim natural killer cell, human: -0.673\\u003cbr\\u003enatural killer cell: -1.415\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.306\\u003cbr\\u003eactivated type II NK T cell: -4.328\\u003cbr\\u003emature NK T cell: -4.423\",\"CD16-positive, CD56-dim natural killer cell, human: -0.699\\u003cbr\\u003enatural killer cell: -1.197\\u003cbr\\u003emature NK T cell: -4.045\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.205\\u003cbr\\u003eactivated type II NK T cell: -4.823\",\"CD16-positive, CD56-dim natural killer cell, human: -1.489\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.568\\u003cbr\\u003enatural killer cell: -2.539\\u003cbr\\u003egamma-delta T cell: -2.856\\u003cbr\\u003emature NK T cell: -3.432\",\"CD16-positive, CD56-dim natural killer cell, human: -1.005\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.217\\u003cbr\\u003enatural killer cell: -2.697\\u003cbr\\u003egamma-delta T cell: -4.060\\u003cbr\\u003emature NK T cell: -4.075\",\"CD16-positive, CD56-dim natural killer cell, human: -1.371\\u003cbr\\u003enatural killer cell: -1.934\\u003cbr\\u003emature NK T cell: -2.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.898\\u003cbr\\u003egamma-delta T cell: -3.103\",\"CD16-positive, CD56-dim natural killer cell, human: -0.822\\u003cbr\\u003enatural killer cell: -2.544\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.656\\u003cbr\\u003emature NK T cell: -3.719\\u003cbr\\u003egamma-delta T cell: -3.801\",\"CD16-positive, CD56-dim natural killer cell, human: -0.411\\u003cbr\\u003enatural killer cell: -1.959\\u003cbr\\u003emature NK T cell: -3.520\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.591\\u003cbr\\u003egamma-delta T cell: -4.629\",\"CD16-positive, CD56-dim natural killer cell, human: -0.723\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.075\\u003cbr\\u003enatural killer cell: -2.291\\u003cbr\\u003emature NK T cell: -3.955\\u003cbr\\u003egamma-delta T cell: -4.228\",\"CD16-positive, CD56-dim natural killer cell, human: -0.877\\u003cbr\\u003enatural killer cell: -1.048\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.302\\u003cbr\\u003emature NK T cell: -3.863\\u003cbr\\u003eactivated type II NK T cell: -4.798\",\"CD16-positive, CD56-dim natural killer cell, human: -0.326\\u003cbr\\u003enatural killer cell: -2.095\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.549\\u003cbr\\u003emature NK T cell: -4.346\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.129\",\"CD16-positive, CD56-dim natural killer cell, human: -0.489\\u003cbr\\u003enatural killer cell: -2.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.264\\u003cbr\\u003emature NK T cell: -3.865\\u003cbr\\u003egamma-delta T cell: -4.902\",\"CD16-positive, CD56-dim natural killer cell, human: -0.438\\u003cbr\\u003enatural killer cell: -1.548\\u003cbr\\u003emature NK T cell: -3.989\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.097\",\"CD16-positive, CD56-dim natural killer cell, human: -0.453\\u003cbr\\u003enatural killer cell: -2.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.782\\u003cbr\\u003egamma-delta T cell: -4.022\\u003cbr\\u003emature NK T cell: -4.037\",\"CD16-positive, CD56-dim natural killer cell, human: -0.664\\u003cbr\\u003enatural killer cell: -1.881\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.064\\u003cbr\\u003emature NK T cell: -3.803\\u003cbr\\u003eimmature NK T cell: -4.513\",\"CD16-positive, CD56-dim natural killer cell, human: -1.095\\u003cbr\\u003enatural killer cell: -1.900\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.137\\u003cbr\\u003egamma-delta T cell: -3.411\\u003cbr\\u003emature NK T cell: -4.067\",\"CD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -2.148\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.344\\u003cbr\\u003emature NK T cell: -4.538\\u003cbr\\u003eactivated type II NK T cell: -4.693\",\"CD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.373\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.507\\u003cbr\\u003emature NK T cell: -4.547\\u003cbr\\u003eactivated type II NK T cell: -4.629\",\"CD16-positive, CD56-dim natural killer cell, human: -0.711\\u003cbr\\u003enatural killer cell: -1.797\\u003cbr\\u003emature NK T cell: -3.004\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.106\\u003cbr\\u003egamma-delta T cell: -3.153\",\"CD16-positive, CD56-dim natural killer cell, human: -0.626\\u003cbr\\u003enatural killer cell: -1.353\\u003cbr\\u003emature NK T cell: -3.382\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.720\",\"CD16-positive, CD56-dim natural killer cell, human: -1.222\\u003cbr\\u003enatural killer cell: -1.485\\u003cbr\\u003emature NK T cell: -2.608\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.713\\u003cbr\\u003egamma-delta T cell: -2.980\",\"CD16-positive, CD56-dim natural killer cell, human: -0.932\\u003cbr\\u003enatural killer cell: -1.327\\u003cbr\\u003emature NK T cell: -2.961\\u003cbr\\u003egamma-delta T cell: -3.675\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.246\",\"CD16-positive, CD56-dim natural killer cell, human: -0.932\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.373\\u003cbr\\u003enatural killer cell: -2.586\\u003cbr\\u003emature NK T cell: -3.396\\u003cbr\\u003egamma-delta T cell: -3.617\",\"CD16-positive, CD56-dim natural killer cell, human: -0.795\\u003cbr\\u003enatural killer cell: -1.978\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.410\\u003cbr\\u003emature NK T cell: -3.496\\u003cbr\\u003egamma-delta T cell: -3.708\",\"CD16-positive, CD56-dim natural killer cell, human: -1.273\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.594\\u003cbr\\u003enatural killer cell: -2.069\\u003cbr\\u003emature NK T cell: -3.726\\u003cbr\\u003egamma-delta T cell: -3.854\",\"CD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.105\\u003cbr\\u003enatural killer cell: -2.202\\u003cbr\\u003eactivated type II NK T cell: -4.114\\u003cbr\\u003emature NK T cell: -4.130\",\"CD16-positive, CD56-dim natural killer cell, human: -0.619\\u003cbr\\u003enatural killer cell: -1.202\\u003cbr\\u003emature NK T cell: -4.158\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.772\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.095\",\"CD16-positive, CD56-dim natural killer cell, human: -1.085\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.347\\u003cbr\\u003enatural killer cell: -2.116\\u003cbr\\u003egamma-delta T cell: -3.772\\u003cbr\\u003emature NK T cell: -4.163\",\"CD16-positive, CD56-dim natural killer cell, human: -0.432\\u003cbr\\u003enatural killer cell: -1.950\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.174\\u003cbr\\u003emature NK T cell: -4.752\\u003cbr\\u003eactivated type II NK T cell: -4.861\",\"CD16-positive, CD56-dim natural killer cell, human: -0.937\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.607\\u003cbr\\u003enatural killer cell: -2.486\\u003cbr\\u003egamma-delta T cell: -3.120\\u003cbr\\u003emature NK T cell: -4.301\",\"CD16-positive, CD56-dim natural killer cell, human: -0.635\\u003cbr\\u003enatural killer cell: -1.549\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.596\\u003cbr\\u003emature NK T cell: -4.204\\u003cbr\\u003egamma-delta T cell: -4.517\",\"CD16-positive, CD56-dim natural killer cell, human: -1.655\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.753\\u003cbr\\u003enatural killer cell: -2.203\\u003cbr\\u003egamma-delta T cell: -2.658\\u003cbr\\u003emature NK T cell: -3.103\",\"CD16-positive, CD56-dim natural killer cell, human: -1.408\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.696\\u003cbr\\u003enatural killer cell: -1.838\\u003cbr\\u003emature NK T cell: -3.369\\u003cbr\\u003egamma-delta T cell: -3.620\",\"CD16-positive, CD56-dim natural killer cell, human: -0.937\\u003cbr\\u003enatural killer cell: -1.630\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.254\\u003cbr\\u003eactivated type II NK T cell: -4.279\\u003cbr\\u003emature NK T cell: -4.318\",\"CD16-positive, CD56-dim natural killer cell, human: -0.590\\u003cbr\\u003enatural killer cell: -1.826\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.421\\u003cbr\\u003emature NK T cell: -4.252\\u003cbr\\u003eactivated type II NK T cell: -4.714\",\"CD16-positive, CD56-dim natural killer cell, human: -1.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.114\\u003cbr\\u003enatural killer cell: -2.208\\u003cbr\\u003egamma-delta T cell: -3.521\\u003cbr\\u003emature NK T cell: -3.676\",\"CD16-positive, CD56-dim natural killer cell, human: -0.918\\u003cbr\\u003enatural killer cell: -2.152\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.157\\u003cbr\\u003egamma-delta T cell: -3.751\\u003cbr\\u003emature NK T cell: -4.068\",\"CD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.473\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.042\\u003cbr\\u003emature NK T cell: -4.489\\u003cbr\\u003egamma-delta T cell: -4.564\",\"CD16-positive, CD56-dim natural killer cell, human: -0.725\\u003cbr\\u003enatural killer cell: -1.401\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.390\\u003cbr\\u003emature NK T cell: -4.229\\u003cbr\\u003eactivated type II NK T cell: -4.506\",\"CD16-positive, CD56-dim natural killer cell, human: -0.572\\u003cbr\\u003enatural killer cell: -1.477\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.785\\u003cbr\\u003emature NK T cell: -4.370\\u003cbr\\u003eactivated type II NK T cell: -4.851\",\"CD16-positive, CD56-dim natural killer cell, human: -0.850\\u003cbr\\u003enatural killer cell: -1.906\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.178\\u003cbr\\u003emature NK T cell: -3.909\\u003cbr\\u003egamma-delta T cell: -4.326\",\"CD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003enatural killer cell: -1.678\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.358\\u003cbr\\u003egamma-delta T cell: -3.916\\u003cbr\\u003emature NK T cell: -4.096\",\"CD16-positive, CD56-dim natural killer cell, human: -0.771\\u003cbr\\u003enatural killer cell: -1.610\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.621\\u003cbr\\u003emature NK T cell: -4.032\\u003cbr\\u003eactivated type II NK T cell: -4.470\",\"CD16-positive, CD56-dim natural killer cell, human: -0.360\\u003cbr\\u003enatural killer cell: -1.884\\u003cbr\\u003emature NK T cell: -3.965\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.683\\u003cbr\\u003egamma-delta T cell: -4.881\",\"CD16-positive, CD56-dim natural killer cell, human: -0.650\\u003cbr\\u003enatural killer cell: -1.380\\u003cbr\\u003emature NK T cell: -3.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.315\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.787\",\"CD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.560\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.801\\u003cbr\\u003emature NK T cell: -4.287\\u003cbr\\u003eactivated type II NK T cell: -4.461\",\"CD16-positive, CD56-dim natural killer cell, human: -0.433\\u003cbr\\u003enatural killer cell: -1.955\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.640\\u003cbr\\u003emature NK T cell: -4.015\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.767\",\"CD16-positive, CD56-dim natural killer cell, human: -0.920\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.756\\u003cbr\\u003enatural killer cell: -1.970\\u003cbr\\u003eactivated type II NK T cell: -3.975\\u003cbr\\u003emature NK T cell: -4.214\",\"CD16-positive, CD56-dim natural killer cell, human: -1.355\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.341\\u003cbr\\u003eactivated type II NK T cell: -3.859\\u003cbr\\u003emature NK T cell: -4.154\",\"CD16-positive, CD56-dim natural killer cell, human: -1.036\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.656\\u003cbr\\u003enatural killer cell: -2.269\\u003cbr\\u003emature NK T cell: -3.688\\u003cbr\\u003egamma-delta T cell: -3.766\",\"CD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.581\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.539\\u003cbr\\u003emature NK T cell: -4.365\\u003cbr\\u003eactivated type II NK T cell: -4.760\",\"CD16-positive, CD56-dim natural killer cell, human: -0.797\\u003cbr\\u003enatural killer cell: -1.186\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.458\\u003cbr\\u003emature NK T cell: -3.905\\u003cbr\\u003egamma-delta T cell: -4.676\",\"CD16-positive, CD56-dim natural killer cell, human: -0.730\\u003cbr\\u003enatural killer cell: -1.870\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.875\\u003cbr\\u003egamma-delta T cell: -3.545\\u003cbr\\u003emature NK T cell: -3.646\",\"CD16-positive, CD56-dim natural killer cell, human: -0.895\\u003cbr\\u003enatural killer cell: -1.794\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.629\\u003cbr\\u003emature NK T cell: -3.415\\u003cbr\\u003egamma-delta T cell: -4.492\",\"CD16-positive, CD56-dim natural killer cell, human: -1.021\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.217\\u003cbr\\u003egamma-delta T cell: -2.666\\u003cbr\\u003enatural killer cell: -2.674\\u003cbr\\u003emature NK T cell: -3.132\",\"CD16-positive, CD56-dim natural killer cell, human: -0.489\\u003cbr\\u003enatural killer cell: -1.666\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.662\\u003cbr\\u003emature NK T cell: -4.530\\u003cbr\\u003eactivated type II NK T cell: -4.846\",\"CD16-positive, CD56-dim natural killer cell, human: -0.618\\u003cbr\\u003enatural killer cell: -2.016\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.453\\u003cbr\\u003emature NK T cell: -3.514\\u003cbr\\u003egamma-delta T cell: -4.637\",\"CD16-positive, CD56-dim natural killer cell, human: -0.560\\u003cbr\\u003enatural killer cell: -1.680\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.533\\u003cbr\\u003emature NK T cell: -3.888\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.686\",\"CD16-positive, CD56-dim natural killer cell, human: -0.755\\u003cbr\\u003enatural killer cell: -1.693\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.104\\u003cbr\\u003emature NK T cell: -3.491\\u003cbr\\u003elymphocyte: -4.710\",\"CD16-positive, CD56-dim natural killer cell, human: -0.472\\u003cbr\\u003enatural killer cell: -2.149\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.074\\u003cbr\\u003emature NK T cell: -3.820\\u003cbr\\u003egamma-delta T cell: -4.755\",\"CD16-positive, CD56-dim natural killer cell, human: -1.125\\u003cbr\\u003enatural killer cell: -1.659\\u003cbr\\u003emature NK T cell: -2.510\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.804\\u003cbr\\u003egamma-delta T cell: -2.856\",\"CD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.872\\u003cbr\\u003enatural killer cell: -2.107\\u003cbr\\u003emature NK T cell: -4.074\\u003cbr\\u003eactivated type II NK T cell: -4.148\",\"CD16-positive, CD56-dim natural killer cell, human: -0.318\\u003cbr\\u003enatural killer cell: -2.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.616\\u003cbr\\u003emature NK T cell: -4.497\\u003cbr\\u003eactivated type II NK T cell: -5.366\",\"CD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -2.169\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.407\\u003cbr\\u003emature NK T cell: -4.221\\u003cbr\\u003egamma-delta T cell: -4.417\",\"CD16-positive, CD56-dim natural killer cell, human: -0.715\\u003cbr\\u003enatural killer cell: -1.237\\u003cbr\\u003emature NK T cell: -3.921\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.625\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.725\",\"CD16-positive, CD56-dim natural killer cell, human: -1.200\\u003cbr\\u003enatural killer cell: -1.538\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.929\\u003cbr\\u003emature NK T cell: -3.633\\u003cbr\\u003egamma-delta T cell: -3.989\",\"CD16-positive, CD56-dim natural killer cell, human: -1.022\\u003cbr\\u003enatural killer cell: -1.733\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.219\\u003cbr\\u003emature NK T cell: -4.100\\u003cbr\\u003egamma-delta T cell: -4.231\",\"CD16-positive, CD56-dim natural killer cell, human: -0.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.850\\u003cbr\\u003enatural killer cell: -2.369\\u003cbr\\u003emature NK T cell: -3.658\\u003cbr\\u003egamma-delta T cell: -4.084\",\"CD16-positive, CD56-dim natural killer cell, human: -1.423\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.088\\u003cbr\\u003enatural killer cell: -2.355\\u003cbr\\u003egamma-delta T cell: -3.154\\u003cbr\\u003emature NK T cell: -3.216\",\"CD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.718\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.542\\u003cbr\\u003emature NK T cell: -4.361\\u003cbr\\u003eactivated type II NK T cell: -4.992\",\"CD16-positive, CD56-dim natural killer cell, human: -0.398\\u003cbr\\u003enatural killer cell: -2.314\\u003cbr\\u003emature NK T cell: -3.699\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.726\\u003cbr\\u003egamma-delta T cell: -4.778\",\"CD16-positive, CD56-dim natural killer cell, human: -0.603\\u003cbr\\u003enatural killer cell: -2.240\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.263\\u003cbr\\u003emature NK T cell: -3.830\\u003cbr\\u003egamma-delta T cell: -3.988\",\"CD16-positive, CD56-dim natural killer cell, human: -1.101\\u003cbr\\u003enatural killer cell: -2.076\\u003cbr\\u003emature NK T cell: -2.765\\u003cbr\\u003egamma-delta T cell: -3.119\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.407\",\"CD16-positive, CD56-dim natural killer cell, human: -0.665\\u003cbr\\u003enatural killer cell: -2.442\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.624\\u003cbr\\u003emature NK T cell: -3.725\\u003cbr\\u003egamma-delta T cell: -4.378\",\"CD16-positive, CD56-dim natural killer cell, human: -0.554\\u003cbr\\u003enatural killer cell: -1.782\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.954\\u003cbr\\u003emature NK T cell: -4.484\\u003cbr\\u003eactivated type II NK T cell: -4.693\",\"CD16-positive, CD56-dim natural killer cell, human: -0.842\\u003cbr\\u003enatural killer cell: -1.221\\u003cbr\\u003emature NK T cell: -3.671\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.111\\u003cbr\\u003egamma-delta T cell: -4.557\",\"CD16-positive, CD56-dim natural killer cell, human: -0.829\\u003cbr\\u003enatural killer cell: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.874\\u003cbr\\u003emature NK T cell: -3.298\\u003cbr\\u003egamma-delta T cell: -3.959\",\"CD16-positive, CD56-dim natural killer cell, human: -0.471\\u003cbr\\u003enatural killer cell: -1.815\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.699\\u003cbr\\u003emature NK T cell: -4.067\\u003cbr\\u003egamma-delta T cell: -4.867\",\"CD16-positive, CD56-dim natural killer cell, human: -0.774\\u003cbr\\u003enatural killer cell: -1.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.096\\u003cbr\\u003emature NK T cell: -3.727\\u003cbr\\u003egamma-delta T cell: -3.985\",\"CD16-positive, CD56-dim natural killer cell, human: -1.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.847\\u003cbr\\u003enatural killer cell: -1.992\\u003cbr\\u003emature NK T cell: -3.135\\u003cbr\\u003egamma-delta T cell: -3.393\",\"CD16-positive, CD56-dim natural killer cell, human: -0.419\\u003cbr\\u003enatural killer cell: -1.844\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.280\\u003cbr\\u003emature NK T cell: -4.214\\u003cbr\\u003egamma-delta T cell: -5.048\",\"CD16-positive, CD56-dim natural killer cell, human: -0.446\\u003cbr\\u003enatural killer cell: -1.557\\u003cbr\\u003emature NK T cell: -3.901\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.964\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.038\",\"CD16-positive, CD56-dim natural killer cell, human: -1.175\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.532\\u003cbr\\u003enatural killer cell: -1.629\\u003cbr\\u003eactivated type II NK T cell: -4.065\\u003cbr\\u003egamma-delta T cell: -4.421\",\"CD16-positive, CD56-dim natural killer cell, human: -0.661\\u003cbr\\u003enatural killer cell: -1.311\\u003cbr\\u003emature NK T cell: -3.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.743\\u003cbr\\u003eactivated type II NK T cell: -4.751\",\"CD16-positive, CD56-dim natural killer cell, human: -0.519\\u003cbr\\u003enatural killer cell: -1.482\\u003cbr\\u003emature NK T cell: -3.954\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.991\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.845\",\"CD16-positive, CD56-dim natural killer cell, human: -0.588\\u003cbr\\u003enatural killer cell: -1.484\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.859\\u003cbr\\u003emature NK T cell: -3.915\\u003cbr\\u003eactivated type II NK T cell: -5.092\",\"CD16-positive, CD56-dim natural killer cell, human: -0.380\\u003cbr\\u003enatural killer cell: -2.146\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.277\\u003cbr\\u003emature NK T cell: -4.149\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.089\",\"CD16-positive, CD56-dim natural killer cell, human: -0.997\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.701\\u003cbr\\u003enatural killer cell: -2.122\\u003cbr\\u003eactivated type II NK T cell: -4.030\\u003cbr\\u003emature NK T cell: -4.222\",\"CD16-positive, CD56-dim natural killer cell, human: -0.372\\u003cbr\\u003enatural killer cell: -2.006\\u003cbr\\u003emature NK T cell: -3.929\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.059\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.899\",\"CD16-positive, CD56-dim natural killer cell, human: -0.459\\u003cbr\\u003enatural killer cell: -1.582\\u003cbr\\u003emature NK T cell: -4.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.348\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.973\",\"CD16-positive, CD56-dim natural killer cell, human: -0.345\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.363\\u003cbr\\u003emature NK T cell: -4.426\\u003cbr\\u003eactivated type II NK T cell: -4.922\",\"CD16-positive, CD56-dim natural killer cell, human: -0.957\\u003cbr\\u003enatural killer cell: -1.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.945\\u003cbr\\u003emature NK T cell: -4.110\\u003cbr\\u003eactivated type II NK T cell: -4.206\",\"CD16-positive, CD56-dim natural killer cell, human: -0.758\\u003cbr\\u003enatural killer cell: -1.875\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.214\\u003cbr\\u003emature NK T cell: -3.534\\u003cbr\\u003egamma-delta T cell: -4.444\",\"CD16-positive, CD56-dim natural killer cell, human: -0.438\\u003cbr\\u003enatural killer cell: -1.757\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.981\\u003cbr\\u003emature NK T cell: -4.070\\u003cbr\\u003egamma-delta T cell: -5.021\",\"CD16-positive, CD56-dim natural killer cell, human: -0.978\\u003cbr\\u003enatural killer cell: -1.119\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.247\\u003cbr\\u003emature NK T cell: -4.144\\u003cbr\\u003eactivated type II NK T cell: -4.213\",\"CD16-positive, CD56-dim natural killer cell, human: -2.003\\u003cbr\\u003egamma-delta T cell: -2.463\\u003cbr\\u003enatural killer cell: -2.530\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.556\\u003cbr\\u003emature NK T cell: -2.712\",\"CD16-positive, CD56-dim natural killer cell, human: -0.933\\u003cbr\\u003enatural killer cell: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.840\\u003cbr\\u003emature NK T cell: -3.207\\u003cbr\\u003egamma-delta T cell: -3.778\",\"CD16-positive, CD56-dim natural killer cell, human: -1.203\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.390\\u003cbr\\u003enatural killer cell: -2.481\\u003cbr\\u003egamma-delta T cell: -3.085\\u003cbr\\u003emature NK T cell: -3.471\",\"CD16-positive, CD56-dim natural killer cell, human: -0.579\\u003cbr\\u003enatural killer cell: -1.571\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.617\\u003cbr\\u003emature NK T cell: -4.190\\u003cbr\\u003eactivated type II NK T cell: -4.608\",\"CD16-positive, CD56-dim natural killer cell, human: -0.736\\u003cbr\\u003enatural killer cell: -2.353\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.473\\u003cbr\\u003egamma-delta T cell: -3.536\\u003cbr\\u003emature NK T cell: -3.675\",\"CD16-positive, CD56-dim natural killer cell, human: -1.089\\u003cbr\\u003enatural killer cell: -1.501\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.457\\u003cbr\\u003egamma-delta T cell: -3.256\\u003cbr\\u003emature NK T cell: -3.839\",\"CD16-positive, CD56-dim natural killer cell, human: -1.023\\u003cbr\\u003enatural killer cell: -1.055\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.012\\u003cbr\\u003emature NK T cell: -3.911\\u003cbr\\u003egamma-delta T cell: -4.397\",\"CD16-positive, CD56-dim natural killer cell, human: -0.413\\u003cbr\\u003enatural killer cell: -1.841\\u003cbr\\u003emature NK T cell: -4.099\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.768\",\"CD16-positive, CD56-dim natural killer cell, human: -0.412\\u003cbr\\u003enatural killer cell: -1.864\\u003cbr\\u003emature NK T cell: -3.962\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.092\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.094\",\"CD16-positive, CD56-dim natural killer cell, human: -0.649\\u003cbr\\u003enatural killer cell: -1.786\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.490\\u003cbr\\u003eactivated type II NK T cell: -4.384\\u003cbr\\u003emature NK T cell: -4.464\",\"CD16-positive, CD56-dim natural killer cell, human: -0.518\\u003cbr\\u003enatural killer cell: -2.013\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.555\\u003cbr\\u003emature NK T cell: -4.388\\u003cbr\\u003eactivated type II NK T cell: -4.546\",\"CD16-positive, CD56-dim natural killer cell, human: -0.945\\u003cbr\\u003enatural killer cell: -1.218\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.626\\u003cbr\\u003emature NK T cell: -3.795\\u003cbr\\u003egamma-delta T cell: -4.598\",\"CD16-positive, CD56-dim natural killer cell, human: -0.793\\u003cbr\\u003enatural killer cell: -2.396\\u003cbr\\u003egamma-delta T cell: -2.626\\u003cbr\\u003emature NK T cell: -3.145\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.474\",\"CD16-positive, CD56-dim natural killer cell, human: -0.542\\u003cbr\\u003enatural killer cell: -1.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.385\\u003cbr\\u003emature NK T cell: -4.288\\u003cbr\\u003eactivated type II NK T cell: -4.833\",\"CD16-positive, CD56-dim natural killer cell, human: -0.488\\u003cbr\\u003enatural killer cell: -1.572\\u003cbr\\u003emature NK T cell: -4.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.699\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.870\",\"CD16-positive, CD56-dim natural killer cell, human: -0.542\\u003cbr\\u003enatural killer cell: -1.437\\u003cbr\\u003emature NK T cell: -3.795\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.927\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.096\",\"CD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -2.318\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.725\\u003cbr\\u003emature NK T cell: -3.352\\u003cbr\\u003egamma-delta T cell: -4.366\",\"CD16-positive, CD56-dim natural killer cell, human: -0.669\\u003cbr\\u003enatural killer cell: -1.289\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.902\\u003cbr\\u003emature NK T cell: -4.150\\u003cbr\\u003eactivated type II NK T cell: -4.849\",\"CD16-positive, CD56-dim natural killer cell, human: -1.032\\u003cbr\\u003enatural killer cell: -2.019\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.043\\u003cbr\\u003egamma-delta T cell: -3.703\\u003cbr\\u003eactivated type II NK T cell: -4.203\",\"CD16-positive, CD56-dim natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -2.051\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.256\\u003cbr\\u003emature NK T cell: -3.784\\u003cbr\\u003egamma-delta T cell: -4.207\",\"CD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003enatural killer cell: -1.841\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.618\\u003cbr\\u003emature NK T cell: -3.964\\u003cbr\\u003eactivated type II NK T cell: -4.317\",\"CD16-positive, CD56-dim natural killer cell, human: -0.516\\u003cbr\\u003enatural killer cell: -2.128\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.893\\u003cbr\\u003emature NK T cell: -4.329\\u003cbr\\u003eactivated type II NK T cell: -4.664\",\"CD16-positive, CD56-dim natural killer cell, human: -1.057\\u003cbr\\u003enatural killer cell: -1.567\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.420\\u003cbr\\u003emature NK T cell: -3.933\\u003cbr\\u003egamma-delta T cell: -4.262\",\"CD16-positive, CD56-dim natural killer cell, human: -0.492\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.158\\u003cbr\\u003emature NK T cell: -4.231\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.820\",\"CD16-positive, CD56-dim natural killer cell, human: -0.757\\u003cbr\\u003enatural killer cell: -1.564\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.300\\u003cbr\\u003emature NK T cell: -3.774\\u003cbr\\u003eactivated type II NK T cell: -4.615\",\"CD16-positive, CD56-dim natural killer cell, human: -0.960\\u003cbr\\u003enatural killer cell: -1.378\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.925\\u003cbr\\u003emature NK T cell: -4.064\\u003cbr\\u003egamma-delta T cell: -4.093\",\"CD16-positive, CD56-dim natural killer cell, human: -0.561\\u003cbr\\u003enatural killer cell: -2.210\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.014\\u003cbr\\u003egamma-delta T cell: -3.538\\u003cbr\\u003emature NK T cell: -4.001\",\"CD16-positive, CD56-dim natural killer cell, human: -0.962\\u003cbr\\u003enatural killer cell: -1.484\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.849\\u003cbr\\u003emature NK T cell: -4.013\\u003cbr\\u003eactivated type II NK T cell: -4.470\",\"CD16-positive, CD56-dim natural killer cell, human: -0.583\\u003cbr\\u003enatural killer cell: -1.857\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.158\\u003cbr\\u003emature NK T cell: -3.631\\u003cbr\\u003egamma-delta T cell: -4.241\",\"CD16-positive, CD56-dim natural killer cell, human: -1.400\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.650\\u003cbr\\u003enatural killer cell: -2.044\\u003cbr\\u003emature NK T cell: -3.804\\u003cbr\\u003egamma-delta T cell: -3.874\",\"CD16-positive, CD56-dim natural killer cell, human: -0.385\\u003cbr\\u003enatural killer cell: -1.998\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.216\\u003cbr\\u003egamma-delta T cell: -4.709\",\"CD16-positive, CD56-dim natural killer cell, human: -0.668\\u003cbr\\u003enatural killer cell: -1.434\\u003cbr\\u003emature NK T cell: -3.773\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.287\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.702\",\"CD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003enatural killer cell: -1.693\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.725\\u003cbr\\u003emature NK T cell: -3.726\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.606\",\"CD16-positive, CD56-dim natural killer cell, human: -0.833\\u003cbr\\u003enatural killer cell: -1.999\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.033\\u003cbr\\u003egamma-delta T cell: -4.267\\u003cbr\\u003emature NK T cell: -4.287\",\"CD16-positive, CD56-dim natural killer cell, human: -0.852\\u003cbr\\u003enatural killer cell: -1.611\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.325\\u003cbr\\u003emature NK T cell: -4.086\\u003cbr\\u003egamma-delta T cell: -4.386\",\"CD16-positive, CD56-dim natural killer cell, human: -0.552\\u003cbr\\u003enatural killer cell: -2.222\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.473\\u003cbr\\u003eactivated type II NK T cell: -4.379\\u003cbr\\u003emature NK T cell: -4.659\",\"CD16-positive, CD56-dim natural killer cell, human: -1.187\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.765\\u003cbr\\u003enatural killer cell: -2.199\\u003cbr\\u003egamma-delta T cell: -3.295\\u003cbr\\u003emature NK T cell: -3.770\",\"CD16-positive, CD56-dim natural killer cell, human: -0.753\\u003cbr\\u003enatural killer cell: -2.405\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.579\\u003cbr\\u003emature NK T cell: -3.508\\u003cbr\\u003egamma-delta T cell: -3.829\",\"CD16-positive, CD56-dim natural killer cell, human: -0.637\\u003cbr\\u003enatural killer cell: -2.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.429\\u003cbr\\u003emature NK T cell: -3.731\\u003cbr\\u003egamma-delta T cell: -4.000\",\"CD16-positive, CD56-dim natural killer cell, human: -0.667\\u003cbr\\u003enatural killer cell: -1.171\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.591\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.105\",\"CD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.461\\u003cbr\\u003emature NK T cell: -4.172\\u003cbr\\u003eactivated type II NK T cell: -4.710\",\"CD16-positive, CD56-dim natural killer cell, human: -1.192\\u003cbr\\u003enatural killer cell: -1.785\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.735\\u003cbr\\u003emature NK T cell: -3.038\\u003cbr\\u003egamma-delta T cell: -3.190\",\"CD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -2.213\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.093\\u003cbr\\u003emature NK T cell: -4.431\\u003cbr\\u003eactivated type II NK T cell: -4.596\",\"CD16-positive, CD56-dim natural killer cell, human: -0.703\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.975\\u003cbr\\u003enatural killer cell: -2.464\\u003cbr\\u003egamma-delta T cell: -3.949\\u003cbr\\u003emature NK T cell: -4.036\",\"CD16-positive, CD56-dim natural killer cell, human: -0.511\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.226\\u003cbr\\u003enatural killer cell: -2.290\\u003cbr\\u003emature NK T cell: -4.382\\u003cbr\\u003eactivated type II NK T cell: -4.483\",\"CD16-positive, CD56-dim natural killer cell, human: -0.465\\u003cbr\\u003enatural killer cell: -2.018\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.664\\u003cbr\\u003emature NK T cell: -4.602\\u003cbr\\u003eactivated type II NK T cell: -4.758\",\"CD16-positive, CD56-dim natural killer cell, human: -0.584\\u003cbr\\u003enatural killer cell: -1.419\\u003cbr\\u003emature NK T cell: -4.099\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.740\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.815\",\"CD16-positive, CD56-dim natural killer cell, human: -1.030\\u003cbr\\u003enatural killer cell: -1.685\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.394\\u003cbr\\u003emature NK T cell: -3.244\\u003cbr\\u003egamma-delta T cell: -3.431\",\"CD16-positive, CD56-dim natural killer cell, human: -0.704\\u003cbr\\u003enatural killer cell: -1.800\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.666\\u003cbr\\u003eactivated type II NK T cell: -4.284\\u003cbr\\u003emature NK T cell: -4.300\",\"CD16-positive, CD56-dim natural killer cell, human: -0.507\\u003cbr\\u003enatural killer cell: -1.717\\u003cbr\\u003emature NK T cell: -3.737\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.273\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.847\",\"CD16-positive, CD56-dim natural killer cell, human: -1.532\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.839\\u003cbr\\u003enatural killer cell: -2.373\\u003cbr\\u003eactivated type II NK T cell: -3.532\\u003cbr\\u003emature NK T cell: -3.962\",\"CD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.144\\u003cbr\\u003enatural killer cell: -2.315\\u003cbr\\u003emature NK T cell: -4.039\\u003cbr\\u003egamma-delta T cell: -4.638\",\"CD16-positive, CD56-dim natural killer cell, human: -0.569\\u003cbr\\u003enatural killer cell: -1.561\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.790\\u003cbr\\u003egamma-delta T cell: -4.062\\u003cbr\\u003emature NK T cell: -4.070\",\"CD16-positive, CD56-dim natural killer cell, human: -1.072\\u003cbr\\u003enatural killer cell: -1.615\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.563\\u003cbr\\u003emature NK T cell: -3.263\\u003cbr\\u003egamma-delta T cell: -3.849\",\"CD16-positive, CD56-dim natural killer cell, human: -1.009\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.790\\u003cbr\\u003enatural killer cell: -1.822\\u003cbr\\u003emature NK T cell: -3.628\\u003cbr\\u003eactivated type II NK T cell: -4.363\",\"CD16-positive, CD56-dim natural killer cell, human: -1.174\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.870\\u003cbr\\u003enatural killer cell: -2.137\\u003cbr\\u003emature NK T cell: -3.127\\u003cbr\\u003egamma-delta T cell: -3.183\",\"CD16-positive, CD56-dim natural killer cell, human: -0.725\\u003cbr\\u003enatural killer cell: -1.293\\u003cbr\\u003emature NK T cell: -3.657\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.067\\u003cbr\\u003egamma-delta T cell: -4.817\",\"CD16-positive, CD56-dim natural killer cell, human: -1.017\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.502\\u003cbr\\u003enatural killer cell: -2.453\\u003cbr\\u003egamma-delta T cell: -3.768\\u003cbr\\u003emature NK T cell: -4.046\",\"CD16-positive, CD56-dim natural killer cell, human: -0.981\\u003cbr\\u003enatural killer cell: -1.140\\u003cbr\\u003emature NK T cell: -3.336\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.487\\u003cbr\\u003egamma-delta T cell: -4.261\",\"CD16-positive, CD56-dim natural killer cell, human: -0.720\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.981\\u003cbr\\u003enatural killer cell: -2.517\\u003cbr\\u003emature NK T cell: -3.956\\u003cbr\\u003egamma-delta T cell: -4.248\",\"CD16-positive, CD56-dim natural killer cell, human: -1.779\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.233\\u003cbr\\u003enatural killer cell: -2.347\\u003cbr\\u003egamma-delta T cell: -2.806\\u003cbr\\u003emature NK T cell: -3.367\",\"CD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -1.475\\u003cbr\\u003emature NK T cell: -4.096\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.519\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.737\",\"CD16-positive, CD56-dim natural killer cell, human: -0.312\\u003cbr\\u003enatural killer cell: -2.327\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.058\\u003cbr\\u003emature NK T cell: -4.548\\u003cbr\\u003eactivated type II NK T cell: -4.834\",\"CD16-positive, CD56-dim natural killer cell, human: -0.800\\u003cbr\\u003enatural killer cell: -1.488\\u003cbr\\u003emature NK T cell: -3.491\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.947\\u003cbr\\u003egamma-delta T cell: -4.526\",\"CD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003enatural killer cell: -2.108\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.272\\u003cbr\\u003egamma-delta T cell: -3.899\\u003cbr\\u003emature NK T cell: -3.973\",\"CD16-positive, CD56-dim natural killer cell, human: -1.095\\u003cbr\\u003enatural killer cell: -1.592\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.875\\u003cbr\\u003eactivated type II NK T cell: -4.033\\u003cbr\\u003egamma-delta T cell: -4.290\",\"CD16-positive, CD56-dim natural killer cell, human: -1.655\\u003cbr\\u003enatural killer cell: -1.806\\u003cbr\\u003egamma-delta T cell: -2.828\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.923\\u003cbr\\u003emature NK T cell: -3.212\",\"CD16-positive, CD56-dim natural killer cell, human: -0.575\\u003cbr\\u003enatural killer cell: -2.113\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.132\\u003cbr\\u003eactivated type II NK T cell: -4.407\\u003cbr\\u003emature NK T cell: -4.619\",\"CD16-positive, CD56-dim natural killer cell, human: -0.574\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.528\\u003cbr\\u003emature NK T cell: -3.633\\u003cbr\\u003egamma-delta T cell: -4.125\",\"CD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.585\\u003cbr\\u003emature NK T cell: -3.995\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.527\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.646\",\"CD16-positive, CD56-dim natural killer cell, human: -1.025\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.842\\u003cbr\\u003enatural killer cell: -1.863\\u003cbr\\u003emature NK T cell: -3.952\\u003cbr\\u003eactivated type II NK T cell: -3.985\",\"CD16-positive, CD56-dim natural killer cell, human: -0.798\\u003cbr\\u003enatural killer cell: -1.775\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.003\\u003cbr\\u003emature NK T cell: -3.688\\u003cbr\\u003egamma-delta T cell: -4.274\",\"CD16-positive, CD56-dim natural killer cell, human: -0.887\\u003cbr\\u003enatural killer cell: -1.640\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.058\\u003cbr\\u003eactivated type II NK T cell: -4.060\\u003cbr\\u003emature NK T cell: -4.218\",\"CD16-positive, CD56-dim natural killer cell, human: -0.987\\u003cbr\\u003enatural killer cell: -1.205\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.264\\u003cbr\\u003emature NK T cell: -3.632\\u003cbr\\u003eactivated type II NK T cell: -4.559\",\"CD16-positive, CD56-dim natural killer cell, human: -0.436\\u003cbr\\u003enatural killer cell: -2.188\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.151\\u003cbr\\u003emature NK T cell: -4.631\\u003cbr\\u003eactivated type II NK T cell: -4.665\",\"CD16-positive, CD56-dim natural killer cell, human: -1.372\\u003cbr\\u003enatural killer cell: -1.446\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.788\\u003cbr\\u003egamma-delta T cell: -3.878\\u003cbr\\u003emature NK T cell: -4.078\",\"CD16-positive, CD56-dim natural killer cell, human: -0.460\\u003cbr\\u003enatural killer cell: -2.149\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.250\\u003cbr\\u003egamma-delta T cell: -3.946\\u003cbr\\u003emature NK T cell: -3.967\",\"CD16-positive, CD56-dim natural killer cell, human: -0.688\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.978\\u003cbr\\u003enatural killer cell: -2.557\\u003cbr\\u003emature NK T cell: -4.234\\u003cbr\\u003egamma-delta T cell: -4.271\",\"CD16-positive, CD56-dim natural killer cell, human: -0.702\\u003cbr\\u003enatural killer cell: -1.582\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.084\\u003cbr\\u003emature NK T cell: -3.793\\u003cbr\\u003egamma-delta T cell: -4.453\",\"CD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -1.454\\u003cbr\\u003emature NK T cell: -4.114\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.546\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.850\",\"CD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.526\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.310\\u003cbr\\u003emature NK T cell: -3.870\\u003cbr\\u003eactivated type II NK T cell: -4.870\",\"CD16-positive, CD56-dim natural killer cell, human: -0.297\\u003cbr\\u003enatural killer cell: -2.330\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.087\\u003cbr\\u003emature NK T cell: -4.450\\u003cbr\\u003egamma-delta T cell: -4.945\",\"CD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.897\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.417\\u003cbr\\u003emature NK T cell: -3.895\\u003cbr\\u003eactivated type II NK T cell: -4.357\",\"CD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.873\\u003cbr\\u003enatural killer cell: -2.047\\u003cbr\\u003eactivated type II NK T cell: -4.116\\u003cbr\\u003emature NK T cell: -4.176\",\"CD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.826\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.924\\u003cbr\\u003emature NK T cell: -4.751\\u003cbr\\u003eactivated type II NK T cell: -4.761\",\"CD16-positive, CD56-dim natural killer cell, human: -1.268\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.507\\u003cbr\\u003enatural killer cell: -1.721\\u003cbr\\u003eactivated type II NK T cell: -3.867\\u003cbr\\u003egamma-delta T cell: -4.343\",\"CD16-positive, CD56-dim natural killer cell, human: -0.537\\u003cbr\\u003enatural killer cell: -2.202\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.996\\u003cbr\\u003emature NK T cell: -3.828\\u003cbr\\u003egamma-delta T cell: -4.337\",\"CD16-positive, CD56-dim natural killer cell, human: -0.451\\u003cbr\\u003enatural killer cell: -1.691\\u003cbr\\u003emature NK T cell: -3.910\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.444\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.012\",\"CD16-positive, CD56-dim natural killer cell, human: -0.594\\u003cbr\\u003enatural killer cell: -1.255\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.050\\u003cbr\\u003emature NK T cell: -4.180\\u003cbr\\u003eactivated type II NK T cell: -4.918\",\"CD16-positive, CD56-dim natural killer cell, human: -0.758\\u003cbr\\u003enatural killer cell: -2.127\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.826\\u003cbr\\u003emature NK T cell: -3.286\\u003cbr\\u003egamma-delta T cell: -4.097\",\"CD16-positive, CD56-dim natural killer cell, human: -0.960\\u003cbr\\u003enatural killer cell: -1.619\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.215\\u003cbr\\u003emature NK T cell: -3.320\\u003cbr\\u003egamma-delta T cell: -4.224\",\"CD16-positive, CD56-dim natural killer cell, human: -1.420\\u003cbr\\u003enatural killer cell: -1.512\\u003cbr\\u003egamma-delta T cell: -2.730\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.881\\u003cbr\\u003emature NK T cell: -3.112\",\"CD16-positive, CD56-dim natural killer cell, human: -0.935\\u003cbr\\u003enatural killer cell: -1.920\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.972\\u003cbr\\u003emature NK T cell: -3.349\\u003cbr\\u003egamma-delta T cell: -3.846\",\"CD16-positive, CD56-dim natural killer cell, human: -0.607\\u003cbr\\u003enatural killer cell: -1.305\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.861\\u003cbr\\u003emature NK T cell: -4.336\\u003cbr\\u003eactivated type II NK T cell: -4.906\",\"CD16-positive, CD56-dim natural killer cell, human: -0.754\\u003cbr\\u003enatural killer cell: -1.854\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.390\\u003cbr\\u003emature NK T cell: -3.943\\u003cbr\\u003eactivated type II NK T cell: -4.222\",\"CD16-positive, CD56-dim natural killer cell, human: -0.789\\u003cbr\\u003enatural killer cell: -2.155\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.690\\u003cbr\\u003egamma-delta T cell: -3.528\\u003cbr\\u003emature NK T cell: -3.673\",\"CD16-positive, CD56-dim natural killer cell, human: -0.772\\u003cbr\\u003enatural killer cell: -1.763\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.253\\u003cbr\\u003eactivated type II NK T cell: -4.154\\u003cbr\\u003emature NK T cell: -4.459\",\"CD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.598\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.908\\u003cbr\\u003emature NK T cell: -4.300\\u003cbr\\u003eactivated type II NK T cell: -4.592\",\"CD16-positive, CD56-dim natural killer cell, human: -0.319\\u003cbr\\u003enatural killer cell: -2.379\\u003cbr\\u003emature NK T cell: -3.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.158\\u003cbr\\u003egamma-delta T cell: -4.554\",\"CD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.873\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.832\\u003cbr\\u003emature NK T cell: -4.241\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.800\",\"CD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.803\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.871\\u003cbr\\u003emature NK T cell: -4.047\\u003cbr\\u003egamma-delta T cell: -4.659\",\"CD16-positive, CD56-dim natural killer cell, human: -0.891\\u003cbr\\u003enatural killer cell: -1.527\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.630\\u003cbr\\u003emature NK T cell: -4.054\\u003cbr\\u003eactivated type II NK T cell: -4.144\",\"CD16-positive, CD56-dim natural killer cell, human: -0.830\\u003cbr\\u003enatural killer cell: -1.681\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.850\\u003cbr\\u003emature NK T cell: -3.462\\u003cbr\\u003egamma-delta T cell: -4.020\",\"CD16-positive, CD56-dim natural killer cell, human: -0.640\\u003cbr\\u003enatural killer cell: -1.778\\u003cbr\\u003emature NK T cell: -3.326\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.474\\u003cbr\\u003egamma-delta T cell: -4.414\",\"CD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.566\\u003cbr\\u003emature NK T cell: -3.694\\u003cbr\\u003eactivated type II NK T cell: -4.629\",\"CD16-positive, CD56-dim natural killer cell, human: -0.796\\u003cbr\\u003enatural killer cell: -1.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.796\\u003cbr\\u003emature NK T cell: -3.777\\u003cbr\\u003eactivated type II NK T cell: -4.643\",\"CD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -2.189\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.669\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003egamma-delta T cell: -4.428\",\"CD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003enatural killer cell: -1.442\\u003cbr\\u003emature NK T cell: -3.504\\u003cbr\\u003egamma-delta T cell: -4.736\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.788\",\"CD16-positive, CD56-dim natural killer cell, human: -0.821\\u003cbr\\u003enatural killer cell: -2.295\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.648\\u003cbr\\u003emature NK T cell: -3.608\\u003cbr\\u003egamma-delta T cell: -3.895\",\"CD16-positive, CD56-dim natural killer cell, human: -0.570\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003emature NK T cell: -3.620\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.343\\u003cbr\\u003egamma-delta T cell: -4.569\",\"CD16-positive, CD56-dim natural killer cell, human: -0.688\\u003cbr\\u003enatural killer cell: -1.590\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.073\\u003cbr\\u003emature NK T cell: -4.097\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.654\",\"CD16-positive, CD56-dim natural killer cell, human: -1.028\\u003cbr\\u003enatural killer cell: -2.228\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.321\\u003cbr\\u003emature NK T cell: -3.433\\u003cbr\\u003egamma-delta T cell: -3.479\",\"CD16-positive, CD56-dim natural killer cell, human: -0.502\\u003cbr\\u003enatural killer cell: -1.740\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.362\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eactivated type II NK T cell: -4.826\",\"CD16-positive, CD56-dim natural killer cell, human: -0.540\\u003cbr\\u003enatural killer cell: -1.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.388\\u003cbr\\u003emature NK T cell: -4.329\\u003cbr\\u003eactivated type II NK T cell: -4.559\",\"CD16-positive, CD56-dim natural killer cell, human: -1.731\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003emature NK T cell: -2.693\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell, terminally differentiated: -3.128\",\"CD16-positive, CD56-dim natural killer cell, human: -0.749\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.881\\u003cbr\\u003enatural killer cell: -2.390\\u003cbr\\u003emature NK T cell: -4.027\\u003cbr\\u003egamma-delta T cell: -4.369\",\"CD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003enatural killer cell: -1.770\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.827\\u003cbr\\u003emature NK T cell: -3.718\\u003cbr\\u003eactivated type II NK T cell: -4.761\",\"CD16-positive, CD56-dim natural killer cell, human: -0.860\\u003cbr\\u003enatural killer cell: -1.622\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.280\\u003cbr\\u003eactivated type II NK T cell: -4.086\\u003cbr\\u003emature NK T cell: -4.403\",\"CD16-positive, CD56-dim natural killer cell, human: -1.172\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.009\\u003cbr\\u003enatural killer cell: -2.016\\u003cbr\\u003egamma-delta T cell: -3.578\\u003cbr\\u003emature NK T cell: -3.878\",\"CD16-positive, CD56-dim natural killer cell, human: -1.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.364\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003egamma-delta T cell: -3.568\\u003cbr\\u003emature NK T cell: -3.902\",\"CD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003enatural killer cell: -2.118\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.260\\u003cbr\\u003emature NK T cell: -3.976\\u003cbr\\u003egamma-delta T cell: -4.398\",\"CD16-positive, CD56-dim natural killer cell, human: -0.385\\u003cbr\\u003enatural killer cell: -1.878\\u003cbr\\u003emature NK T cell: -4.151\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.275\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.988\",\"CD16-positive, CD56-dim natural killer cell, human: -1.411\\u003cbr\\u003enatural killer cell: -1.924\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.427\\u003cbr\\u003emature NK T cell: -2.529\\u003cbr\\u003egamma-delta T cell: -3.057\",\"CD16-positive, CD56-dim natural killer cell, human: -1.194\\u003cbr\\u003enatural killer cell: -1.489\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.511\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003emature NK T cell: -3.360\",\"CD16-positive, CD56-dim natural killer cell, human: -0.745\\u003cbr\\u003enatural killer cell: -2.318\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.236\\u003cbr\\u003emature NK T cell: -3.266\\u003cbr\\u003egamma-delta T cell: -3.743\",\"CD16-positive, CD56-dim natural killer cell, human: -0.450\\u003cbr\\u003enatural killer cell: -1.961\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.170\\u003cbr\\u003emature NK T cell: -4.361\\u003cbr\\u003eactivated type II NK T cell: -4.852\",\"CD16-positive, CD56-dim natural killer cell, human: -0.421\\u003cbr\\u003enatural killer cell: -1.902\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.543\\u003cbr\\u003emature NK T cell: -4.478\\u003cbr\\u003eactivated type II NK T cell: -4.809\",\"CD16-positive, CD56-dim natural killer cell, human: -0.623\\u003cbr\\u003enatural killer cell: -1.412\\u003cbr\\u003emature NK T cell: -3.859\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.871\\u003cbr\\u003egamma-delta T cell: -4.694\",\"CD16-positive, CD56-dim natural killer cell, human: -0.431\\u003cbr\\u003enatural killer cell: -2.104\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.179\\u003cbr\\u003emature NK T cell: -4.320\\u003cbr\\u003eactivated type II NK T cell: -4.762\",\"CD16-positive, CD56-dim natural killer cell, human: -0.555\\u003cbr\\u003enatural killer cell: -2.265\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.590\\u003cbr\\u003emature NK T cell: -3.731\\u003cbr\\u003egamma-delta T cell: -3.999\",\"CD16-positive, CD56-dim natural killer cell, human: -0.563\\u003cbr\\u003enatural killer cell: -2.037\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.748\\u003cbr\\u003emature NK T cell: -4.030\\u003cbr\\u003egamma-delta T cell: -4.596\",\"CD16-positive, CD56-dim natural killer cell, human: -0.615\\u003cbr\\u003enatural killer cell: -1.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.577\\u003cbr\\u003egamma-delta T cell: -4.140\\u003cbr\\u003emature NK T cell: -4.298\",\"CD16-positive, CD56-dim natural killer cell, human: -0.606\\u003cbr\\u003enatural killer cell: -1.716\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.793\\u003cbr\\u003emature NK T cell: -4.436\\u003cbr\\u003eactivated type II NK T cell: -4.598\",\"CD16-positive, CD56-dim natural killer cell, human: -0.535\\u003cbr\\u003enatural killer cell: -1.719\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.013\\u003cbr\\u003emature NK T cell: -4.609\\u003cbr\\u003eactivated type II NK T cell: -4.619\",\"CD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.477\\u003cbr\\u003emature NK T cell: -3.960\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.432\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.802\",\"CD16-positive, CD56-dim natural killer cell, human: -0.641\\u003cbr\\u003enatural killer cell: -1.652\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.797\\u003cbr\\u003emature NK T cell: -3.510\\u003cbr\\u003eactivated type II NK T cell: -4.892\",\"CD16-positive, CD56-dim natural killer cell, human: -0.877\\u003cbr\\u003enatural killer cell: -1.490\\u003cbr\\u003emature NK T cell: -3.159\\u003cbr\\u003egamma-delta T cell: -3.683\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.823\",\"CD16-positive, CD56-dim natural killer cell, human: -0.520\\u003cbr\\u003enatural killer cell: -1.660\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.396\\u003cbr\\u003emature NK T cell: -4.077\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.908\",\"CD16-positive, CD56-dim natural killer cell, human: -0.609\\u003cbr\\u003enatural killer cell: -1.432\\u003cbr\\u003emature NK T cell: -3.758\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.266\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.832\",\"CD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -1.354\\u003cbr\\u003emature NK T cell: -3.570\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.344\\u003cbr\\u003egamma-delta T cell: -4.716\",\"CD16-positive, CD56-dim natural killer cell, human: -0.483\\u003cbr\\u003enatural killer cell: -1.466\\u003cbr\\u003emature NK T cell: -4.246\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.501\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.016\",\"CD16-positive, CD56-dim natural killer cell, human: -0.836\\u003cbr\\u003enatural killer cell: -1.742\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.868\\u003cbr\\u003emature NK T cell: -3.406\\u003cbr\\u003egamma-delta T cell: -4.061\",\"CD16-positive, CD56-dim natural killer cell, human: -0.721\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.050\\u003cbr\\u003enatural killer cell: -2.191\\u003cbr\\u003egamma-delta T cell: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.364\",\"CD16-positive, CD56-dim natural killer cell, human: -0.749\\u003cbr\\u003enatural killer cell: -1.301\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.563\\u003cbr\\u003emature NK T cell: -3.836\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.679\",\"CD16-positive, CD56-dim natural killer cell, human: -1.202\\u003cbr\\u003enatural killer cell: -1.913\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.072\\u003cbr\\u003emature NK T cell: -3.165\\u003cbr\\u003egamma-delta T cell: -3.312\",\"CD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -1.784\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.706\\u003cbr\\u003emature NK T cell: -4.427\\u003cbr\\u003eactivated type II NK T cell: -4.487\",\"CD16-positive, CD56-dim natural killer cell, human: -0.480\\u003cbr\\u003enatural killer cell: -1.779\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.524\\u003cbr\\u003emature NK T cell: -4.264\\u003cbr\\u003eactivated type II NK T cell: -4.793\",\"CD16-positive, CD56-dim natural killer cell, human: -0.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.319\\u003cbr\\u003enatural killer cell: -2.475\\u003cbr\\u003egamma-delta T cell: -3.979\\u003cbr\\u003emature NK T cell: -3.989\",\"CD16-positive, CD56-dim natural killer cell, human: -1.164\\u003cbr\\u003enatural killer cell: -1.667\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.782\\u003cbr\\u003eactivated type II NK T cell: -3.786\\u003cbr\\u003emature NK T cell: -4.745\",\"CD16-positive, CD56-dim natural killer cell, human: -0.330\\u003cbr\\u003enatural killer cell: -2.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.037\\u003cbr\\u003emature NK T cell: -4.177\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.122\",\"CD16-positive, CD56-dim natural killer cell, human: -0.880\\u003cbr\\u003enatural killer cell: -2.245\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.432\\u003cbr\\u003egamma-delta T cell: -3.142\\u003cbr\\u003emature NK T cell: -3.835\",\"CD16-positive, CD56-dim natural killer cell, human: -0.435\\u003cbr\\u003enatural killer cell: -2.242\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.105\\u003cbr\\u003egamma-delta T cell: -4.002\\u003cbr\\u003emature NK T cell: -4.088\",\"CD16-positive, CD56-dim natural killer cell, human: -0.718\\u003cbr\\u003enatural killer cell: -1.340\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.091\\u003cbr\\u003emature NK T cell: -4.017\\u003cbr\\u003eactivated type II NK T cell: -4.745\",\"CD16-positive, CD56-dim natural killer cell, human: -0.915\\u003cbr\\u003enatural killer cell: -1.560\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.563\\u003cbr\\u003emature NK T cell: -3.418\\u003cbr\\u003egamma-delta T cell: -3.830\",\"CD16-positive, CD56-dim natural killer cell, human: -0.513\\u003cbr\\u003enatural killer cell: -2.188\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.369\\u003cbr\\u003emature NK T cell: -4.376\\u003cbr\\u003eactivated type II NK T cell: -4.440\",\"CD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -1.709\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.589\\u003cbr\\u003emature NK T cell: -4.533\\u003cbr\\u003eactivated type II NK T cell: -5.043\",\"CD16-positive, CD56-dim natural killer cell, human: -0.659\\u003cbr\\u003enatural killer cell: -1.562\\u003cbr\\u003emature NK T cell: -3.295\\u003cbr\\u003egamma-delta T cell: -3.935\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.256\",\"CD16-positive, CD56-dim natural killer cell, human: -0.848\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.086\\u003cbr\\u003enatural killer cell: -2.439\\u003cbr\\u003emature NK T cell: -3.831\\u003cbr\\u003egamma-delta T cell: -3.875\",\"CD16-positive, CD56-dim natural killer cell, human: -0.512\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.384\\u003cbr\\u003enatural killer cell: -2.729\\u003cbr\\u003emature NK T cell: -4.367\\u003cbr\\u003eactivated type II NK T cell: -4.506\",\"CD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.294\\u003cbr\\u003emature NK T cell: -3.590\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.348\\u003cbr\\u003egamma-delta T cell: -4.620\",\"CD16-positive, CD56-dim natural killer cell, human: -0.470\\u003cbr\\u003enatural killer cell: -1.824\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.891\\u003cbr\\u003emature NK T cell: -4.075\\u003cbr\\u003eactivated type II NK T cell: -4.964\",\"CD16-positive, CD56-dim natural killer cell, human: -0.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.922\\u003cbr\\u003enatural killer cell: -2.409\\u003cbr\\u003egamma-delta T cell: -3.721\\u003cbr\\u003eactivated type II NK T cell: -4.025\",\"CD16-positive, CD56-dim natural killer cell, human: -0.757\\u003cbr\\u003enatural killer cell: -1.496\\u003cbr\\u003emature NK T cell: -3.242\\u003cbr\\u003egamma-delta T cell: -4.180\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.521\",\"CD16-positive, CD56-dim natural killer cell, human: -0.367\\u003cbr\\u003enatural killer cell: -2.012\\u003cbr\\u003emature NK T cell: -3.728\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.095\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.818\",\"CD16-positive, CD56-dim natural killer cell, human: -0.678\\u003cbr\\u003enatural killer cell: -2.280\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.144\\u003cbr\\u003egamma-delta T cell: -3.195\\u003cbr\\u003emature NK T cell: -3.341\",\"CD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.164\\u003cbr\\u003emature NK T cell: -4.163\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.317\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.764\",\"CD16-positive, CD56-dim natural killer cell, human: -0.546\\u003cbr\\u003enatural killer cell: -2.436\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.854\\u003cbr\\u003emature NK T cell: -3.844\\u003cbr\\u003egamma-delta T cell: -4.095\",\"CD16-positive, CD56-dim natural killer cell, human: -0.637\\u003cbr\\u003enatural killer cell: -1.345\\u003cbr\\u003emature NK T cell: -3.889\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.903\\u003cbr\\u003egamma-delta T cell: -4.802\",\"CD16-positive, CD56-dim natural killer cell, human: -1.409\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.426\\u003cbr\\u003enatural killer cell: -2.011\\u003cbr\\u003egamma-delta T cell: -3.543\\u003cbr\\u003emature NK T cell: -4.114\",\"CD16-positive, CD56-dim natural killer cell, human: -0.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.980\\u003cbr\\u003enatural killer cell: -2.178\\u003cbr\\u003eactivated type II NK T cell: -4.413\\u003cbr\\u003emature NK T cell: -4.637\",\"CD16-positive, CD56-dim natural killer cell, human: -0.909\\u003cbr\\u003enatural killer cell: -1.901\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.409\\u003cbr\\u003emature NK T cell: -3.721\\u003cbr\\u003egamma-delta T cell: -4.552\",\"CD16-positive, CD56-dim natural killer cell, human: -0.599\\u003cbr\\u003enatural killer cell: -1.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.517\\u003cbr\\u003emature NK T cell: -3.830\\u003cbr\\u003egamma-delta T cell: -3.993\",\"CD16-positive, CD56-dim natural killer cell, human: -0.930\\u003cbr\\u003enatural killer cell: -1.590\\u003cbr\\u003emature NK T cell: -3.384\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.541\\u003cbr\\u003egamma-delta T cell: -3.593\",\"CD16-positive, CD56-dim natural killer cell, human: -0.666\\u003cbr\\u003enatural killer cell: -1.702\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.443\\u003cbr\\u003emature NK T cell: -3.984\\u003cbr\\u003eactivated type II NK T cell: -4.438\",\"CD16-positive, CD56-dim natural killer cell, human: -0.470\\u003cbr\\u003enatural killer cell: -2.025\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.832\\u003cbr\\u003emature NK T cell: -3.828\\u003cbr\\u003egamma-delta T cell: -4.854\",\"CD16-positive, CD56-dim natural killer cell, human: -1.286\\u003cbr\\u003enatural killer cell: -1.537\\u003cbr\\u003emature NK T cell: -3.118\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.374\\u003cbr\\u003egamma-delta T cell: -3.577\",\"CD16-positive, CD56-dim natural killer cell, human: -0.559\\u003cbr\\u003enatural killer cell: -1.837\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.540\\u003cbr\\u003emature NK T cell: -4.036\\u003cbr\\u003eactivated type II NK T cell: -4.735\",\"CD16-positive, CD56-dim natural killer cell, human: -0.714\\u003cbr\\u003enatural killer cell: -1.265\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.711\\u003cbr\\u003emature NK T cell: -4.214\\u003cbr\\u003eactivated type II NK T cell: -4.799\",\"CD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003enatural killer cell: -2.273\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.234\\u003cbr\\u003emature NK T cell: -3.432\\u003cbr\\u003egamma-delta T cell: -3.632\",\"CD16-positive, CD56-dim natural killer cell, human: -0.615\\u003cbr\\u003enatural killer cell: -2.172\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.634\\u003cbr\\u003emature NK T cell: -4.021\\u003cbr\\u003eactivated type II NK T cell: -4.421\",\"CD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.675\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.387\\u003cbr\\u003emature NK T cell: -4.049\\u003cbr\\u003eactivated type II NK T cell: -4.578\",\"CD16-positive, CD56-dim natural killer cell, human: -0.746\\u003cbr\\u003enatural killer cell: -2.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.449\\u003cbr\\u003emature NK T cell: -3.547\\u003cbr\\u003egamma-delta T cell: -4.019\",\"CD16-positive, CD56-dim natural killer cell, human: -0.997\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.904\\u003cbr\\u003enatural killer cell: -2.109\\u003cbr\\u003egamma-delta T cell: -3.890\\u003cbr\\u003eactivated type II NK T cell: -4.085\",\"CD16-positive, CD56-dim natural killer cell, human: -0.690\\u003cbr\\u003enatural killer cell: -2.106\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.404\\u003cbr\\u003egamma-delta T cell: -3.757\\u003cbr\\u003emature NK T cell: -4.023\",\"CD16-positive, CD56-dim natural killer cell, human: -0.329\\u003cbr\\u003enatural killer cell: -2.012\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.120\\u003cbr\\u003emature NK T cell: -4.364\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.045\",\"CD16-positive, CD56-dim natural killer cell, human: -0.695\\u003cbr\\u003enatural killer cell: -1.415\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.127\\u003cbr\\u003emature NK T cell: -4.177\\u003cbr\\u003eactivated type II NK T cell: -4.666\",\"CD16-positive, CD56-dim natural killer cell, human: -0.605\\u003cbr\\u003enatural killer cell: -1.431\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.894\\u003cbr\\u003emature NK T cell: -3.915\\u003cbr\\u003eactivated type II NK T cell: -4.842\",\"CD16-positive, CD56-dim natural killer cell, human: -0.627\\u003cbr\\u003enatural killer cell: -2.145\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.746\\u003cbr\\u003emature NK T cell: -4.046\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.374\",\"CD16-positive, CD56-dim natural killer cell, human: -1.276\\u003cbr\\u003enatural killer cell: -1.754\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.771\\u003cbr\\u003egamma-delta T cell: -3.216\\u003cbr\\u003emature NK T cell: -3.416\",\"CD16-positive, CD56-dim natural killer cell, human: -0.372\\u003cbr\\u003enatural killer cell: -1.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.883\\u003cbr\\u003emature NK T cell: -4.259\\u003cbr\\u003eactivated type II NK T cell: -5.147\",\"CD16-positive, CD56-dim natural killer cell, human: -0.388\\u003cbr\\u003enatural killer cell: -1.992\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.766\\u003cbr\\u003emature NK T cell: -4.178\\u003cbr\\u003eactivated type II NK T cell: -5.048\",\"CD16-positive, CD56-dim natural killer cell, human: -1.160\\u003cbr\\u003enatural killer cell: -1.492\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.797\\u003cbr\\u003egamma-delta T cell: -3.325\\u003cbr\\u003emature NK T cell: -3.457\",\"CD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.552\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.326\\u003cbr\\u003emature NK T cell: -3.827\\u003cbr\\u003eactivated type II NK T cell: -4.749\",\"CD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.954\\u003cbr\\u003enatural killer cell: -2.220\\u003cbr\\u003eactivated type II NK T cell: -4.461\\u003cbr\\u003emature NK T cell: -4.588\",\"CD16-positive, CD56-dim natural killer cell, human: -0.878\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.003\\u003cbr\\u003eactivated type II NK T cell: -4.026\\u003cbr\\u003emature NK T cell: -4.231\",\"CD16-positive, CD56-dim natural killer cell, human: -0.554\\u003cbr\\u003enatural killer cell: -1.692\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.568\\u003cbr\\u003emature NK T cell: -3.929\\u003cbr\\u003egamma-delta T cell: -4.686\",\"CD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.500\\u003cbr\\u003emature NK T cell: -4.075\\u003cbr\\u003eactivated type II NK T cell: -4.478\",\"CD16-positive, CD56-dim natural killer cell, human: -0.411\\u003cbr\\u003enatural killer cell: -2.121\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.292\\u003cbr\\u003emature NK T cell: -4.299\\u003cbr\\u003egamma-delta T cell: -4.658\",\"CD16-positive, CD56-dim natural killer cell, human: -0.831\\u003cbr\\u003enatural killer cell: -1.593\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.556\\u003cbr\\u003emature NK T cell: -3.665\\u003cbr\\u003egamma-delta T cell: -4.281\",\"CD16-positive, CD56-dim natural killer cell, human: -0.864\\u003cbr\\u003enatural killer cell: -2.006\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.550\\u003cbr\\u003egamma-delta T cell: -3.881\\u003cbr\\u003eactivated type II NK T cell: -4.369\",\"CD16-positive, CD56-dim natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -1.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.818\\u003cbr\\u003emature NK T cell: -3.298\\u003cbr\\u003egamma-delta T cell: -3.769\",\"CD16-positive, CD56-dim natural killer cell, human: -0.314\\u003cbr\\u003enatural killer cell: -1.853\\u003cbr\\u003emature NK T cell: -4.542\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.748\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.051\",\"CD16-positive, CD56-dim natural killer cell, human: -0.651\\u003cbr\\u003enatural killer cell: -1.410\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.942\\u003cbr\\u003emature NK T cell: -4.012\\u003cbr\\u003egamma-delta T cell: -4.812\",\"CD16-positive, CD56-dim natural killer cell, human: -0.900\\u003cbr\\u003enatural killer cell: -1.874\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.243\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003egamma-delta T cell: -4.081\",\"CD16-positive, CD56-dim natural killer cell, human: -1.103\\u003cbr\\u003enatural killer cell: -1.530\\u003cbr\\u003emature NK T cell: -2.907\\u003cbr\\u003egamma-delta T cell: -3.573\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.802\",\"CD16-positive, CD56-dim natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -1.598\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.920\\u003cbr\\u003emature NK T cell: -3.889\\u003cbr\\u003egamma-delta T cell: -4.138\",\"CD16-positive, CD56-dim natural killer cell, human: -0.493\\u003cbr\\u003enatural killer cell: -1.758\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.088\\u003cbr\\u003emature NK T cell: -3.991\\u003cbr\\u003eactivated type II NK T cell: -4.980\",\"CD16-positive, CD56-dim natural killer cell, human: -0.671\\u003cbr\\u003enatural killer cell: -1.977\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.588\\u003cbr\\u003eactivated type II NK T cell: -4.103\\u003cbr\\u003emature NK T cell: -4.615\",\"CD16-positive, CD56-dim natural killer cell, human: -0.541\\u003cbr\\u003enatural killer cell: -2.008\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.780\\u003cbr\\u003emature NK T cell: -4.052\\u003cbr\\u003egamma-delta T cell: -4.616\",\"CD16-positive, CD56-dim natural killer cell, human: -1.748\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.909\\u003cbr\\u003enatural killer cell: -2.206\\u003cbr\\u003emature NK T cell: -2.517\\u003cbr\\u003egamma-delta T cell: -2.630\",\"CD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.157\\u003cbr\\u003enatural killer cell: -2.342\\u003cbr\\u003emature NK T cell: -4.257\\u003cbr\\u003eactivated type II NK T cell: -4.340\",\"CD16-positive, CD56-dim natural killer cell, human: -0.952\\u003cbr\\u003enatural killer cell: -2.258\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.617\\u003cbr\\u003egamma-delta T cell: -3.460\\u003cbr\\u003emature NK T cell: -3.786\",\"CD16-positive, CD56-dim natural killer cell, human: -0.434\\u003cbr\\u003enatural killer cell: -1.662\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.910\\u003cbr\\u003emature NK T cell: -4.353\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.870\",\"CD16-positive, CD56-dim natural killer cell, human: -0.718\\u003cbr\\u003enatural killer cell: -1.178\\u003cbr\\u003emature NK T cell: -3.749\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.491\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.583\",\"CD16-positive, CD56-dim natural killer cell, human: -1.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.516\\u003cbr\\u003enatural killer cell: -2.579\\u003cbr\\u003egamma-delta T cell: -3.527\\u003cbr\\u003eactivated type II NK T cell: -4.162\",\"CD16-positive, CD56-dim natural killer cell, human: -0.938\\u003cbr\\u003enatural killer cell: -1.249\\u003cbr\\u003emature NK T cell: -3.524\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.777\",\"CD16-positive, CD56-dim natural killer cell, human: -0.796\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.366\\u003cbr\\u003enatural killer cell: -2.107\\u003cbr\\u003eactivated type II NK T cell: -4.216\\u003cbr\\u003emature NK T cell: -4.856\",\"CD16-positive, CD56-dim natural killer cell, human: -0.892\\u003cbr\\u003enatural killer cell: -1.281\\u003cbr\\u003emature NK T cell: -3.678\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.105\\u003cbr\\u003egamma-delta T cell: -4.272\",\"CD16-positive, CD56-dim natural killer cell, human: -0.594\\u003cbr\\u003enatural killer cell: -2.034\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.697\\u003cbr\\u003egamma-delta T cell: -3.310\\u003cbr\\u003emature NK T cell: -4.174\",\"CD16-positive, CD56-dim natural killer cell, human: -1.385\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -1.684\\u003cbr\\u003egamma-delta T cell: -3.850\\u003cbr\\u003eactivated type II NK T cell: -3.872\",\"CD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.773\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.602\\u003cbr\\u003emature NK T cell: -3.993\\u003cbr\\u003eactivated type II NK T cell: -4.792\",\"CD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.953\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.227\\u003cbr\\u003emature NK T cell: -3.329\\u003cbr\\u003egamma-delta T cell: -4.604\",\"CD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003enatural killer cell: -1.643\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.670\\u003cbr\\u003emature NK T cell: -3.855\\u003cbr\\u003eactivated type II NK T cell: -4.712\",\"CD16-positive, CD56-dim natural killer cell, human: -1.247\\u003cbr\\u003enatural killer cell: -1.358\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.870\\u003cbr\\u003emature NK T cell: -3.452\\u003cbr\\u003egamma-delta T cell: -3.882\",\"CD16-positive, CD56-dim natural killer cell, human: -0.810\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003egamma-delta T cell: -3.262\\u003cbr\\u003emature NK T cell: -3.462\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.539\",\"CD16-positive, CD56-dim natural killer cell, human: -0.478\\u003cbr\\u003enatural killer cell: -1.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.562\\u003cbr\\u003emature NK T cell: -3.919\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.828\",\"CD16-positive, CD56-dim natural killer cell, human: -0.435\\u003cbr\\u003enatural killer cell: -1.907\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.081\\u003cbr\\u003emature NK T cell: -4.065\\u003cbr\\u003eactivated type II NK T cell: -4.938\",\"CD16-positive, CD56-dim natural killer cell, human: -0.498\\u003cbr\\u003enatural killer cell: -1.888\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.474\\u003cbr\\u003emature NK T cell: -4.257\\u003cbr\\u003egamma-delta T cell: -4.676\",\"CD16-positive, CD56-dim natural killer cell, human: -0.660\\u003cbr\\u003enatural killer cell: -1.435\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.008\\u003cbr\\u003emature NK T cell: -4.108\\u003cbr\\u003eactivated type II NK T cell: -4.856\",\"CD16-positive, CD56-dim natural killer cell, human: -0.539\\u003cbr\\u003enatural killer cell: -1.505\\u003cbr\\u003emature NK T cell: -3.883\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.730\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.875\",\"CD16-positive, CD56-dim natural killer cell, human: -0.679\\u003cbr\\u003enatural killer cell: -1.394\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.518\\u003cbr\\u003emature NK T cell: -4.198\\u003cbr\\u003eactivated type II NK T cell: -4.436\",\"CD16-positive, CD56-dim natural killer cell, human: -0.565\\u003cbr\\u003enatural killer cell: -1.815\\u003cbr\\u003emature NK T cell: -3.367\\u003cbr\\u003egamma-delta T cell: -4.501\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.627\",\"CD16-positive, CD56-dim natural killer cell, human: -0.570\\u003cbr\\u003enatural killer cell: -1.521\\u003cbr\\u003emature NK T cell: -3.958\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.054\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.638\",\"CD16-positive, CD56-dim natural killer cell, human: -1.163\\u003cbr\\u003enatural killer cell: -1.812\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.516\\u003cbr\\u003emature NK T cell: -2.695\\u003cbr\\u003egamma-delta T cell: -2.916\",\"CD16-positive, CD56-dim natural killer cell, human: -0.551\\u003cbr\\u003enatural killer cell: -1.474\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.086\\u003cbr\\u003emature NK T cell: -4.151\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.917\",\"CD16-positive, CD56-dim natural killer cell, human: -0.996\\u003cbr\\u003enatural killer cell: -1.204\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.152\\u003cbr\\u003egamma-delta T cell: -3.621\\u003cbr\\u003emature NK T cell: -3.653\",\"CD16-positive, CD56-dim natural killer cell, human: -0.595\\u003cbr\\u003enatural killer cell: -1.610\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.771\\u003cbr\\u003emature NK T cell: -3.863\\u003cbr\\u003egamma-delta T cell: -4.919\",\"CD16-positive, CD56-dim natural killer cell, human: -0.687\\u003cbr\\u003enatural killer cell: -1.390\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.945\\u003cbr\\u003emature NK T cell: -4.336\\u003cbr\\u003eactivated type II NK T cell: -4.523\",\"CD16-positive, CD56-dim natural killer cell, human: -0.861\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.192\\u003cbr\\u003enatural killer cell: -2.212\\u003cbr\\u003emature NK T cell: -3.263\\u003cbr\\u003egamma-delta T cell: -3.812\",\"CD16-positive, CD56-dim natural killer cell, human: -0.780\\u003cbr\\u003enatural killer cell: -1.119\\u003cbr\\u003emature NK T cell: -3.701\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.006\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.889\",\"CD16-positive, CD56-dim natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -1.925\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.372\\u003cbr\\u003eactivated type II NK T cell: -4.280\\u003cbr\\u003emature NK T cell: -4.684\",\"CD16-positive, CD56-dim natural killer cell, human: -0.801\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.758\\u003cbr\\u003enatural killer cell: -2.363\\u003cbr\\u003egamma-delta T cell: -3.877\\u003cbr\\u003emature NK T cell: -4.341\",\"CD16-positive, CD56-dim natural killer cell, human: -0.764\\u003cbr\\u003enatural killer cell: -1.323\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.559\\u003cbr\\u003emature NK T cell: -3.620\\u003cbr\\u003egamma-delta T cell: -4.568\",\"CD16-positive, CD56-dim natural killer cell, human: -0.407\\u003cbr\\u003enatural killer cell: -2.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.257\\u003cbr\\u003emature NK T cell: -4.129\\u003cbr\\u003eactivated type II NK T cell: -4.788\",\"CD16-positive, CD56-dim natural killer cell, human: -0.528\\u003cbr\\u003enatural killer cell: -1.915\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.562\\u003cbr\\u003emature NK T cell: -4.038\\u003cbr\\u003egamma-delta T cell: -4.590\",\"CD16-positive, CD56-dim natural killer cell, human: -1.376\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.197\\u003cbr\\u003enatural killer cell: -2.682\\u003cbr\\u003egamma-delta T cell: -3.105\\u003cbr\\u003emature NK T cell: -3.129\",\"CD16-positive, CD56-dim natural killer cell, human: -1.404\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.056\\u003cbr\\u003enatural killer cell: -2.294\\u003cbr\\u003egamma-delta T cell: -2.456\\u003cbr\\u003emature NK T cell: -3.506\",\"CD16-positive, CD56-dim natural killer cell, human: -0.516\\u003cbr\\u003enatural killer cell: -1.703\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.793\\u003cbr\\u003emature NK T cell: -4.429\\u003cbr\\u003eactivated type II NK T cell: -4.820\",\"CD16-positive, CD56-dim natural killer cell, human: -0.448\\u003cbr\\u003enatural killer cell: -2.019\\u003cbr\\u003emature NK T cell: -3.739\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.960\\u003cbr\\u003egamma-delta T cell: -4.722\",\"CD16-positive, CD56-dim natural killer cell, human: -1.009\\u003cbr\\u003enatural killer cell: -2.003\\u003cbr\\u003egamma-delta T cell: -2.690\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.102\\u003cbr\\u003emature NK T cell: -3.157\",\"CD16-positive, CD56-dim natural killer cell, human: -0.386\\u003cbr\\u003enatural killer cell: -1.959\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.579\\u003cbr\\u003emature NK T cell: -4.297\\u003cbr\\u003eactivated type II NK T cell: -5.035\",\"CD16-positive, CD56-dim natural killer cell, human: -0.625\\u003cbr\\u003enatural killer cell: -1.408\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.864\\u003cbr\\u003emature NK T cell: -3.941\\u003cbr\\u003eactivated type II NK T cell: -4.937\",\"CD16-positive, CD56-dim natural killer cell, human: -0.499\\u003cbr\\u003enatural killer cell: -1.669\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.520\\u003cbr\\u003emature NK T cell: -4.129\\u003cbr\\u003eactivated type II NK T cell: -4.678\",\"CD16-positive, CD56-dim natural killer cell, human: -0.692\\u003cbr\\u003enatural killer cell: -1.719\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.814\\u003cbr\\u003emature NK T cell: -4.079\\u003cbr\\u003eactivated type II NK T cell: -4.241\",\"CD16-positive, CD56-dim natural killer cell, human: -0.298\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.800\\u003cbr\\u003emature NK T cell: -4.511\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.118\",\"CD16-positive, CD56-dim natural killer cell, human: -0.368\\u003cbr\\u003enatural killer cell: -2.115\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.533\\u003cbr\\u003emature NK T cell: -4.403\\u003cbr\\u003eactivated type II NK T cell: -5.027\",\"CD16-positive, CD56-dim natural killer cell, human: -0.431\\u003cbr\\u003enatural killer cell: -1.789\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.341\\u003cbr\\u003emature NK T cell: -4.264\\u003cbr\\u003eactivated type II NK T cell: -5.052\",\"CD16-positive, CD56-dim natural killer cell, human: -1.190\\u003cbr\\u003enatural killer cell: -1.365\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.920\\u003cbr\\u003emature NK T cell: -3.436\\u003cbr\\u003egamma-delta T cell: -3.848\",\"CD16-positive, CD56-dim natural killer cell, human: -0.544\\u003cbr\\u003enatural killer cell: -1.399\\u003cbr\\u003emature NK T cell: -4.069\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.420\\u003cbr\\u003eactivated type II NK T cell: -4.895\",\"CD16-positive, CD56-dim natural killer cell, human: -1.031\\u003cbr\\u003enatural killer cell: -1.833\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.798\\u003cbr\\u003emature NK T cell: -3.687\\u003cbr\\u003egamma-delta T cell: -3.934\",\"CD16-positive, CD56-dim natural killer cell, human: -0.529\\u003cbr\\u003enatural killer cell: -1.459\\u003cbr\\u003emature NK T cell: -4.095\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.703\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.803\",\"CD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.218\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.848\\u003cbr\\u003emature NK T cell: -4.250\\u003cbr\\u003eactivated type II NK T cell: -4.911\",\"CD16-positive, CD56-dim natural killer cell, human: -0.890\\u003cbr\\u003enatural killer cell: -1.127\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.483\\u003cbr\\u003emature NK T cell: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.568\",\"CD16-positive, CD56-dim natural killer cell, human: -0.509\\u003cbr\\u003enatural killer cell: -1.714\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.373\\u003cbr\\u003emature NK T cell: -4.167\\u003cbr\\u003eactivated type II NK T cell: -4.898\",\"CD16-positive, CD56-dim natural killer cell, human: -1.248\\u003cbr\\u003enatural killer cell: -1.424\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.911\\u003cbr\\u003emature NK T cell: -3.188\\u003cbr\\u003egamma-delta T cell: -3.540\",\"CD16-positive, CD56-dim natural killer cell, human: -0.389\\u003cbr\\u003enatural killer cell: -1.891\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.624\\u003cbr\\u003emature NK T cell: -4.833\\u003cbr\\u003eactivated type II NK T cell: -4.968\",\"CD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003enatural killer cell: -1.500\\u003cbr\\u003egamma-delta T cell: -3.484\\u003cbr\\u003emature NK T cell: -3.589\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.873\",\"CD16-positive, CD56-dim natural killer cell, human: -1.452\\u003cbr\\u003egamma-delta T cell: -2.466\\u003cbr\\u003enatural killer cell: -2.614\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.663\\u003cbr\\u003emature NK T cell: -2.992\",\"CD16-positive, CD56-dim natural killer cell, human: -0.578\\u003cbr\\u003enatural killer cell: -1.494\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.593\\u003cbr\\u003emature NK T cell: -4.131\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.862\",\"CD16-positive, CD56-dim natural killer cell, human: -0.445\\u003cbr\\u003enatural killer cell: -2.257\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.695\\u003cbr\\u003emature NK T cell: -4.536\\u003cbr\\u003eactivated type II NK T cell: -4.624\",\"CD16-positive, CD56-dim natural killer cell, human: -1.331\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.444\\u003cbr\\u003enatural killer cell: -1.995\\u003cbr\\u003emature NK T cell: -4.045\\u003cbr\\u003eactivated type II NK T cell: -4.058\",\"CD16-positive, CD56-dim natural killer cell, human: -0.612\\u003cbr\\u003enatural killer cell: -1.956\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.289\\u003cbr\\u003emature NK T cell: -3.745\\u003cbr\\u003egamma-delta T cell: -4.688\",\"CD16-positive, CD56-dim natural killer cell, human: -0.722\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.848\\u003cbr\\u003enatural killer cell: -2.077\\u003cbr\\u003emature NK T cell: -3.894\\u003cbr\\u003eactivated type II NK T cell: -4.512\",\"CD16-positive, CD56-dim natural killer cell, human: -0.543\\u003cbr\\u003enatural killer cell: -1.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.091\\u003cbr\\u003eactivated type II NK T cell: -4.657\",\"CD16-positive, CD56-dim natural killer cell, human: -0.977\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.603\\u003cbr\\u003enatural killer cell: -2.379\\u003cbr\\u003emature NK T cell: -3.960\\u003cbr\\u003egamma-delta T cell: -4.178\",\"CD16-positive, CD56-dim natural killer cell, human: -0.567\\u003cbr\\u003enatural killer cell: -1.753\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.109\\u003cbr\\u003emature NK T cell: -4.195\\u003cbr\\u003eactivated type II NK T cell: -4.754\",\"CD16-positive, CD56-dim natural killer cell, human: -0.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.455\\u003cbr\\u003enatural killer cell: -2.497\\u003cbr\\u003egamma-delta T cell: -3.111\\u003cbr\\u003emature NK T cell: -3.856\",\"CD16-positive, CD56-dim natural killer cell, human: -0.822\\u003cbr\\u003enatural killer cell: -1.651\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.918\\u003cbr\\u003emature NK T cell: -3.801\\u003cbr\\u003egamma-delta T cell: -4.005\",\"CD16-positive, CD56-dim natural killer cell, human: -0.557\\u003cbr\\u003enatural killer cell: -1.797\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.880\\u003cbr\\u003emature NK T cell: -4.000\\u003cbr\\u003eactivated type II NK T cell: -4.580\",\"CD16-positive, CD56-dim natural killer cell, human: -0.487\\u003cbr\\u003enatural killer cell: -1.802\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.723\\u003cbr\\u003eactivated type II NK T cell: -4.518\\u003cbr\\u003emature NK T cell: -4.705\",\"CD16-positive, CD56-dim natural killer cell, human: -0.782\\u003cbr\\u003enatural killer cell: -1.211\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.754\\u003cbr\\u003emature NK T cell: -4.030\\u003cbr\\u003eactivated type II NK T cell: -4.619\",\"CD16-positive, CD56-dim natural killer cell, human: -1.154\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.878\\u003cbr\\u003enatural killer cell: -2.675\\u003cbr\\u003egamma-delta T cell: -2.696\\u003cbr\\u003emature NK T cell: -3.555\",\"CD16-positive, CD56-dim natural killer cell, human: -0.297\\u003cbr\\u003enatural killer cell: -2.209\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.191\\u003cbr\\u003emature NK T cell: -4.297\\u003cbr\\u003egamma-delta T cell: -5.226\",\"CD16-positive, CD56-dim natural killer cell, human: -0.436\\u003cbr\\u003enatural killer cell: -1.803\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.971\\u003cbr\\u003emature NK T cell: -4.313\\u003cbr\\u003eactivated type II NK T cell: -4.856\",\"CD16-positive, CD56-dim natural killer cell, human: -0.481\\u003cbr\\u003enatural killer cell: -1.596\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.700\\u003cbr\\u003emature NK T cell: -4.249\\u003cbr\\u003eactivated type II NK T cell: -4.965\",\"CD16-positive, CD56-dim natural killer cell, human: -0.422\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.209\\u003cbr\\u003emature NK T cell: -4.727\\u003cbr\\u003eactivated type II NK T cell: -4.741\",\"CD16-positive, CD56-dim natural killer cell, human: -0.493\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.859\\u003cbr\\u003emature NK T cell: -4.163\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.926\",\"CD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.985\\u003cbr\\u003enatural killer cell: -2.078\\u003cbr\\u003eactivated type II NK T cell: -4.213\\u003cbr\\u003emature NK T cell: -4.272\",\"CD16-positive, CD56-dim natural killer cell, human: -0.582\\u003cbr\\u003enatural killer cell: -1.661\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.573\\u003cbr\\u003emature NK T cell: -3.747\\u003cbr\\u003egamma-delta T cell: -4.858\",\"CD16-positive, CD56-dim natural killer cell, human: -0.689\\u003cbr\\u003enatural killer cell: -1.504\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.548\\u003cbr\\u003emature NK T cell: -3.907\\u003cbr\\u003egamma-delta T cell: -4.337\",\"CD16-positive, CD56-dim natural killer cell, human: -0.705\\u003cbr\\u003enatural killer cell: -1.214\\u003cbr\\u003emature NK T cell: -4.027\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.146\\u003cbr\\u003egamma-delta T cell: -4.974\",\"CD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.840\\u003cbr\\u003enatural killer cell: -2.626\\u003cbr\\u003egamma-delta T cell: -4.037\\u003cbr\\u003emature NK T cell: -4.130\",\"CD16-positive, CD56-dim natural killer cell, human: -0.504\\u003cbr\\u003enatural killer cell: -1.859\\u003cbr\\u003emature NK T cell: -3.736\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.860\\u003cbr\\u003egamma-delta T cell: -4.450\",\"CD16-positive, CD56-dim natural killer cell, human: -0.730\\u003cbr\\u003enatural killer cell: -1.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.760\\u003cbr\\u003emature NK T cell: -3.766\\u003cbr\\u003egamma-delta T cell: -4.067\",\"CD16-positive, CD56-dim natural killer cell, human: -0.455\\u003cbr\\u003enatural killer cell: -1.808\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.919\\u003cbr\\u003emature NK T cell: -4.421\\u003cbr\\u003eactivated type II NK T cell: -4.875\",\"CD16-positive, CD56-dim natural killer cell, human: -0.433\\u003cbr\\u003enatural killer cell: -2.164\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.062\\u003cbr\\u003emature NK T cell: -4.370\\u003cbr\\u003egamma-delta T cell: -4.685\",\"CD16-positive, CD56-dim natural killer cell, human: -0.720\\u003cbr\\u003enatural killer cell: -1.698\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.551\\u003cbr\\u003emature NK T cell: -3.531\\u003cbr\\u003egamma-delta T cell: -4.535\",\"CD16-positive, CD56-dim natural killer cell, human: -0.623\\u003cbr\\u003enatural killer cell: -2.231\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.343\\u003cbr\\u003emature NK T cell: -3.984\\u003cbr\\u003eactivated type II NK T cell: -4.452\",\"CD16-positive, CD56-dim natural killer cell, human: -0.787\\u003cbr\\u003enatural killer cell: -1.652\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.307\\u003cbr\\u003emature NK T cell: -3.950\\u003cbr\\u003egamma-delta T cell: -4.503\",\"CD16-positive, CD56-dim natural killer cell, human: -1.272\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.815\\u003cbr\\u003enatural killer cell: -2.077\\u003cbr\\u003egamma-delta T cell: -3.363\\u003cbr\\u003emature NK T cell: -3.751\",\"CD16-positive, CD56-dim natural killer cell, human: -0.640\\u003cbr\\u003enatural killer cell: -1.365\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.166\\u003cbr\\u003emature NK T cell: -3.990\\u003cbr\\u003eactivated type II NK T cell: -4.459\",\"CD16-positive, CD56-dim natural killer cell, human: -1.258\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.600\\u003cbr\\u003enatural killer cell: -2.044\\u003cbr\\u003emature NK T cell: -3.209\\u003cbr\\u003egamma-delta T cell: -3.663\",\"CD16-positive, CD56-dim natural killer cell, human: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.691\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003egamma-delta T cell: -2.955\\u003cbr\\u003emature NK T cell: -3.267\",\"CD16-positive, CD56-dim natural killer cell, human: -0.410\\u003cbr\\u003enatural killer cell: -2.190\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.640\\u003cbr\\u003egamma-delta T cell: -4.258\\u003cbr\\u003emature NK T cell: -4.423\",\"CD16-positive, CD56-dim natural killer cell, human: -1.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.775\\u003cbr\\u003enatural killer cell: -2.172\\u003cbr\\u003egamma-delta T cell: -3.475\\u003cbr\\u003emature NK T cell: -4.026\",\"CD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.991\\u003cbr\\u003enatural killer cell: -2.321\\u003cbr\\u003egamma-delta T cell: -3.541\\u003cbr\\u003emature NK T cell: -3.798\",\"CD16-positive, CD56-dim natural killer cell, human: -0.448\\u003cbr\\u003enatural killer cell: -1.850\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.779\\u003cbr\\u003emature NK T cell: -4.109\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.927\",\"CD16-positive, CD56-dim natural killer cell, human: -0.662\\u003cbr\\u003enatural killer cell: -1.352\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.017\\u003cbr\\u003emature NK T cell: -4.242\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -4.727\",\"CD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.556\\u003cbr\\u003emature NK T cell: -3.605\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.498\\u003cbr\\u003egamma-delta T cell: -4.844\",\"CD16-positive, CD56-dim natural killer cell, human: -0.649\\u003cbr\\u003enatural killer cell: -1.274\\u003cbr\\u003emature NK T cell: -3.796\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.333\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.580\",\"CD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.521\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.437\\u003cbr\\u003emature NK T cell: -4.107\\u003cbr\\u003eactivated type II NK T cell: -4.760\",\"CD16-positive, CD56-dim natural killer cell, human: -0.562\\u003cbr\\u003enatural killer cell: -1.475\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.555\\u003cbr\\u003emature NK T cell: -4.057\\u003cbr\\u003eactivated type II NK T cell: -4.890\",\"CD16-positive, CD56-dim natural killer cell, human: -0.546\\u003cbr\\u003enatural killer cell: -1.956\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.845\\u003cbr\\u003emature NK T cell: -4.144\\u003cbr\\u003eactivated type II NK T cell: -4.606\",\"CD16-positive, CD56-dim natural killer cell, human: -0.590\\u003cbr\\u003enatural killer cell: -1.646\\u003cbr\\u003emature NK T cell: -3.692\\u003cbr\\u003egamma-delta T cell: -3.972\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.032\",\"CD16-positive, CD56-dim natural killer cell, human: -1.213\\u003cbr\\u003enatural killer cell: -1.461\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.855\\u003cbr\\u003emature NK T cell: -3.661\\u003cbr\\u003eactivated type II NK T cell: -4.224\",\"CD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -1.551\\u003cbr\\u003emature NK T cell: -3.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.969\\u003cbr\\u003egamma-delta T cell: -4.576\",\"CD16-positive, CD56-dim natural killer cell, human: -0.537\\u003cbr\\u003enatural killer cell: -1.785\\u003cbr\\u003emature NK T cell: -3.917\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.093\\u003cbr\\u003egamma-delta T cell: -4.734\",\"CD16-positive, CD56-dim natural killer cell, human: -0.482\\u003cbr\\u003enatural killer cell: -1.546\\u003cbr\\u003emature NK T cell: -4.012\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.710\\u003cbr\\u003egamma-delta T cell: -4.817\",\"CD16-positive, CD56-dim natural killer cell, human: -0.918\\u003cbr\\u003enatural killer cell: -1.858\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.181\\u003cbr\\u003emature NK T cell: -3.711\\u003cbr\\u003egamma-delta T cell: -4.275\",\"CD16-positive, CD56-dim natural killer cell, human: -0.855\\u003cbr\\u003enatural killer cell: -1.842\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.624\\u003cbr\\u003emature NK T cell: -3.712\\u003cbr\\u003eactivated type II NK T cell: -4.375\",\"CD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.899\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.246\\u003cbr\\u003emature NK T cell: -3.876\\u003cbr\\u003egamma-delta T cell: -4.936\",\"CD16-positive, CD56-dim natural killer cell, human: -1.137\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.482\\u003cbr\\u003enatural killer cell: -2.663\\u003cbr\\u003egamma-delta T cell: -3.683\\u003cbr\\u003eactivated type II NK T cell: -4.227\",\"CD16-positive, CD56-dim natural killer cell, human: -0.471\\u003cbr\\u003enatural killer cell: -1.780\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.652\\u003cbr\\u003emature NK T cell: -4.458\\u003cbr\\u003eactivated type II NK T cell: -4.834\",\"CD16-positive, CD56-dim natural killer cell, human: -0.564\\u003cbr\\u003enatural killer cell: -1.660\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.630\\u003cbr\\u003emature NK T cell: -4.085\\u003cbr\\u003eactivated type II NK T cell: -4.954\",\"CD16-positive, CD56-dim natural killer cell, human: -0.576\\u003cbr\\u003enatural killer cell: -1.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.199\\u003cbr\\u003egamma-delta T cell: -4.636\",\"CD16-positive, CD56-dim natural killer cell, human: -0.466\\u003cbr\\u003enatural killer cell: -1.886\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.238\\u003cbr\\u003emature NK T cell: -4.160\\u003cbr\\u003eactivated type II NK T cell: -4.993\",\"CD16-positive, CD56-dim natural killer cell, human: -0.501\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003emature NK T cell: -3.713\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.688\\u003cbr\\u003egamma-delta T cell: -5.044\",\"CD16-positive, CD56-dim natural killer cell, human: -0.847\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.806\\u003cbr\\u003enatural killer cell: -2.209\\u003cbr\\u003egamma-delta T cell: -3.754\\u003cbr\\u003emature NK T cell: -3.846\",\"CD16-positive, CD56-dim natural killer cell, human: -0.429\\u003cbr\\u003enatural killer cell: -1.850\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.253\\u003cbr\\u003emature NK T cell: -4.571\\u003cbr\\u003eactivated type II NK T cell: -4.819\",\"CD16-positive, CD56-dim natural killer cell, human: -0.659\\u003cbr\\u003enatural killer cell: -1.793\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.616\\u003cbr\\u003emature NK T cell: -3.767\\u003cbr\\u003egamma-delta T cell: -4.293\",\"CD16-positive, CD56-dim natural killer cell, human: -0.574\\u003cbr\\u003enatural killer cell: -1.468\\u003cbr\\u003emature NK T cell: -3.926\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.820\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.061\",\"CD16-positive, CD56-dim natural killer cell, human: -0.409\\u003cbr\\u003enatural killer cell: -1.695\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.956\\u003cbr\\u003emature NK T cell: -4.457\\u003cbr\\u003eactivated type II NK T cell: -5.131\",\"CD16-positive, CD56-dim natural killer cell, human: -0.481\\u003cbr\\u003enatural killer cell: -1.849\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.053\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eactivated type II NK T cell: -4.680\",\"CD16-positive, CD56-dim natural killer cell, human: -1.392\\u003cbr\\u003enatural killer cell: -2.783\\u003cbr\\u003emature NK T cell: -2.913\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.006\\u003cbr\\u003egamma-delta T cell: -3.290\",\"CD16-positive, CD56-dim natural killer cell, human: -1.789\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.917\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003egamma-delta T cell: -2.620\\u003cbr\\u003emature NK T cell: -3.070\",\"CD16-positive, CD56-dim natural killer cell, human: -1.259\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.659\\u003cbr\\u003enatural killer cell: -2.152\\u003cbr\\u003egamma-delta T cell: -3.717\\u003cbr\\u003eactivated type II NK T cell: -4.018\",\"CD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.543\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.808\",\"CD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.427\\u003cbr\\u003emature NK T cell: -3.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.585\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.792\",\"CD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.619\\u003cbr\\u003emature NK T cell: -3.663\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.246\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.083\",\"CD16-positive, CD56-dim natural killer cell, human: -0.611\\u003cbr\\u003enatural killer cell: -1.982\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.206\\u003cbr\\u003emature NK T cell: -4.022\\u003cbr\\u003egamma-delta T cell: -4.344\",\"CD16-positive, CD56-dim natural killer cell, human: -0.769\\u003cbr\\u003enatural killer cell: -1.105\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.875\\u003cbr\\u003emature NK T cell: -4.223\\u003cbr\\u003eactivated type II NK T cell: -4.804\",\"CD16-positive, CD56-dim natural killer cell, human: -0.630\\u003cbr\\u003enatural killer cell: -1.600\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.083\\u003cbr\\u003emature NK T cell: -3.568\\u003cbr\\u003egamma-delta T cell: -4.356\",\"CD16-positive, CD56-dim natural killer cell, human: -1.191\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.373\\u003cbr\\u003enatural killer cell: -2.416\\u003cbr\\u003egamma-delta T cell: -3.292\\u003cbr\\u003emature NK T cell: -3.979\",\"CD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003enatural killer cell: -1.392\\u003cbr\\u003emature NK T cell: -3.644\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.179\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.565\",\"CD16-positive, CD56-dim natural killer cell, human: -1.373\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.496\\u003cbr\\u003egamma-delta T cell: -2.602\\u003cbr\\u003emature NK T cell: -3.305\",\"CD16-positive, CD56-dim natural killer cell, human: -0.381\\u003cbr\\u003enatural killer cell: -1.751\\u003cbr\\u003emature NK T cell: -4.391\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.407\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.985\",\"CD16-positive, CD56-dim natural killer cell, human: -1.383\\u003cbr\\u003egamma-delta T cell: -2.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.611\\u003cbr\\u003enatural killer cell: -2.794\\u003cbr\\u003emature NK T cell: -2.807\",\"CD16-positive, CD56-dim natural killer cell, human: -0.604\\u003cbr\\u003enatural killer cell: -1.753\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.559\\u003cbr\\u003emature NK T cell: -4.321\\u003cbr\\u003eactivated type II NK T cell: -4.330\",\"CD16-positive, CD56-dim natural killer cell, human: -0.835\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.637\\u003cbr\\u003enatural killer cell: -2.054\\u003cbr\\u003emature NK T cell: -4.223\\u003cbr\\u003eactivated type II NK T cell: -4.501\",\"CD16-positive, CD56-dim natural killer cell, human: -0.708\\u003cbr\\u003enatural killer cell: -1.480\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.574\\u003cbr\\u003emature NK T cell: -4.219\\u003cbr\\u003eactivated type II NK T cell: -4.509\",\"CD16-positive, CD56-dim natural killer cell, human: -0.364\\u003cbr\\u003enatural killer cell: -2.038\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.222\\u003cbr\\u003emature NK T cell: -4.401\\u003cbr\\u003eactivated type II NK T cell: -4.886\",\"CD16-positive, CD56-dim natural killer cell, human: -0.596\\u003cbr\\u003enatural killer cell: -1.482\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.571\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003egamma-delta T cell: -4.881\",\"CD16-positive, CD56-dim natural killer cell, human: -0.396\\u003cbr\\u003enatural killer cell: -2.307\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.063\\u003cbr\\u003egamma-delta T cell: -4.378\\u003cbr\\u003emature NK T cell: -4.548\",\"CD16-positive, CD56-dim natural killer cell, human: -0.519\\u003cbr\\u003enatural killer cell: -2.279\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.497\\u003cbr\\u003emature NK T cell: -4.174\\u003cbr\\u003eactivated type II NK T cell: -4.706\",\"CD16-positive, CD56-dim natural killer cell, human: -0.388\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.129\\u003cbr\\u003emature NK T cell: -4.036\\u003cbr\\u003egamma-delta T cell: -4.951\",\"CD16-positive, CD56-dim natural killer cell, human: -0.643\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.814\\u003cbr\\u003emature NK T cell: -4.074\\u003cbr\\u003egamma-delta T cell: -4.517\",\"CD16-positive, CD56-dim natural killer cell, human: -0.591\\u003cbr\\u003enatural killer cell: -1.435\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.769\\u003cbr\\u003emature NK T cell: -4.022\\u003cbr\\u003eactivated type II NK T cell: -4.882\",\"CD16-positive, CD56-dim natural killer cell, human: -0.828\\u003cbr\\u003enatural killer cell: -1.094\\u003cbr\\u003emature NK T cell: -3.668\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.577\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.751\",\"CD16-positive, CD56-dim natural killer cell, human: -0.821\\u003cbr\\u003enatural killer cell: -2.079\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.524\\u003cbr\\u003egamma-delta T cell: -3.141\\u003cbr\\u003emature NK T cell: -3.431\",\"CD16-positive, CD56-dim natural killer cell, human: -0.660\\u003cbr\\u003enatural killer cell: -1.622\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.849\\u003cbr\\u003emature NK T cell: -3.965\\u003cbr\\u003elymphocyte: -4.606\",\"CD16-positive, CD56-dim natural killer cell, human: -1.107\\u003cbr\\u003enatural killer cell: -1.664\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.683\\u003cbr\\u003eactivated type II NK T cell: -3.851\\u003cbr\\u003emature NK T cell: -4.635\",\"CD16-positive, CD56-dim natural killer cell, human: -0.664\\u003cbr\\u003enatural killer cell: -1.527\\u003cbr\\u003emature NK T cell: -3.428\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.892\\u003cbr\\u003egamma-delta T cell: -4.535\",\"CD16-positive, CD56-dim natural killer cell, human: -0.621\\u003cbr\\u003enatural killer cell: -1.835\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.594\\u003cbr\\u003emature NK T cell: -4.491\\u003cbr\\u003egamma-delta T cell: -4.621\",\"CD16-positive, CD56-dim natural killer cell, human: -0.414\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003emature NK T cell: -4.285\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.288\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.020\",\"CD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.677\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.944\\u003cbr\\u003emature NK T cell: -4.148\\u003cbr\\u003egamma-delta T cell: -4.675\",\"CD16-positive, CD56-dim natural killer cell, human: -1.138\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.678\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003eactivated type II NK T cell: -4.004\\u003cbr\\u003emature NK T cell: -4.247\",\"CD16-positive, CD56-dim natural killer cell, human: -0.491\\u003cbr\\u003enatural killer cell: -1.845\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.092\\u003cbr\\u003emature NK T cell: -4.428\\u003cbr\\u003eactivated type II NK T cell: -4.688\",\"CD16-positive, CD56-dim natural killer cell, human: -1.112\\u003cbr\\u003enatural killer cell: -1.640\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.274\\u003cbr\\u003egamma-delta T cell: -3.350\\u003cbr\\u003emature NK T cell: -3.676\",\"CD16-positive, CD56-dim natural killer cell, human: -1.200\\u003cbr\\u003enatural killer cell: -1.440\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.527\\u003cbr\\u003eactivated type II NK T cell: -4.328\\u003cbr\\u003emature NK T cell: -4.541\",\"CD16-positive, CD56-dim natural killer cell, human: -0.600\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.085\\u003cbr\\u003emature NK T cell: -4.055\\u003cbr\\u003eactivated type II NK T cell: -4.633\",\"CD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -2.138\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.655\\u003cbr\\u003emature NK T cell: -4.088\\u003cbr\\u003egamma-delta T cell: -4.570\",\"CD16-positive, CD56-dim natural killer cell, human: -0.814\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.725\\u003cbr\\u003enatural killer cell: -1.885\\u003cbr\\u003eactivated type II NK T cell: -3.990\\u003cbr\\u003emature NK T cell: -4.699\",\"CD16-positive, CD56-dim natural killer cell, human: -0.466\\u003cbr\\u003enatural killer cell: -1.501\\u003cbr\\u003emature NK T cell: -4.211\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.583\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.890\",\"CD16-positive, CD56-dim natural killer cell, human: -0.917\\u003cbr\\u003enatural killer cell: -2.111\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.697\\u003cbr\\u003egamma-delta T cell: -2.740\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.396\",\"CD16-positive, CD56-dim natural killer cell, human: -0.618\\u003cbr\\u003enatural killer cell: -1.648\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.939\\u003cbr\\u003emature NK T cell: -4.077\\u003cbr\\u003eactivated type II NK T cell: -4.365\",\"CD16-positive, CD56-dim natural killer cell, human: -0.626\\u003cbr\\u003enatural killer cell: -2.208\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.431\\u003cbr\\u003emature NK T cell: -3.733\\u003cbr\\u003egamma-delta T cell: -4.028\",\"CD16-positive, CD56-dim natural killer cell, human: -0.775\\u003cbr\\u003enatural killer cell: -2.092\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.494\\u003cbr\\u003emature NK T cell: -3.583\\u003cbr\\u003eimmature NK T cell: -4.534\",\"CD16-positive, CD56-dim natural killer cell, human: -1.409\\u003cbr\\u003enatural killer cell: -1.559\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.825\\u003cbr\\u003eactivated type II NK T cell: -3.807\\u003cbr\\u003emature NK T cell: -4.023\",\"CD16-positive, CD56-dim natural killer cell, human: -0.629\\u003cbr\\u003enatural killer cell: -1.578\\u003cbr\\u003emature NK T cell: -3.707\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.037\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.846\",\"CD16-positive, CD56-dim natural killer cell, human: -1.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.279\\u003cbr\\u003enatural killer cell: -2.054\\u003cbr\\u003egamma-delta T cell: -4.093\\u003cbr\\u003emature NK T cell: -4.178\",\"CD16-positive, CD56-dim natural killer cell, human: -0.899\\u003cbr\\u003enatural killer cell: -1.458\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.688\\u003cbr\\u003emature NK T cell: -4.173\\u003cbr\\u003eactivated type II NK T cell: -4.405\",\"CD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.565\\u003cbr\\u003emature NK T cell: -3.643\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.945\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.818\",\"CD16-positive, CD56-dim natural killer cell, human: -0.826\\u003cbr\\u003enatural killer cell: -2.096\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.576\\u003cbr\\u003egamma-delta T cell: -3.589\\u003cbr\\u003emature NK T cell: -3.697\",\"CD16-positive, CD56-dim natural killer cell, human: -1.015\\u003cbr\\u003enatural killer cell: -1.912\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.605\\u003cbr\\u003egamma-delta T cell: -3.130\\u003cbr\\u003emature NK T cell: -3.194\",\"CD16-positive, CD56-dim natural killer cell, human: -0.974\\u003cbr\\u003enatural killer cell: -1.101\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.582\\u003cbr\\u003emature NK T cell: -4.052\\u003cbr\\u003egamma-delta T cell: -4.304\",\"CD16-positive, CD56-dim natural killer cell, human: -0.707\\u003cbr\\u003enatural killer cell: -2.132\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.495\\u003cbr\\u003emature NK T cell: -3.480\\u003cbr\\u003egamma-delta T cell: -3.595\",\"CD16-positive, CD56-dim natural killer cell, human: -0.444\\u003cbr\\u003enatural killer cell: -1.828\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.193\\u003cbr\\u003emature NK T cell: -4.624\\u003cbr\\u003eactivated type II NK T cell: -4.861\",\"CD16-positive, CD56-dim natural killer cell, human: -0.533\\u003cbr\\u003enatural killer cell: -2.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.834\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003egamma-delta T cell: -4.060\",\"CD16-positive, CD56-dim natural killer cell, human: -1.198\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.615\\u003cbr\\u003enatural killer cell: -1.667\\u003cbr\\u003emature NK T cell: -4.031\\u003cbr\\u003egamma-delta T cell: -4.116\",\"CD16-positive, CD56-dim natural killer cell, human: -1.314\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.729\\u003cbr\\u003enatural killer cell: -2.498\\u003cbr\\u003egamma-delta T cell: -2.721\\u003cbr\\u003emature NK T cell: -3.578\",\"CD16-positive, CD56-dim natural killer cell, human: -0.596\\u003cbr\\u003enatural killer cell: -1.539\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.346\\u003cbr\\u003emature NK T cell: -3.980\\u003cbr\\u003eactivated type II NK T cell: -4.639\",\"CD16-positive, CD56-dim natural killer cell, human: -0.551\\u003cbr\\u003enatural killer cell: -1.662\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.677\\u003cbr\\u003emature NK T cell: -4.385\\u003cbr\\u003eactivated type II NK T cell: -4.654\",\"CD16-positive, CD56-dim natural killer cell, human: -1.151\\u003cbr\\u003enatural killer cell: -1.294\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.450\\u003cbr\\u003emature NK T cell: -3.621\\u003cbr\\u003eactivated type II NK T cell: -4.289\",\"CD16-positive, CD56-dim natural killer cell, human: -1.328\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.493\\u003cbr\\u003enatural killer cell: -2.429\\u003cbr\\u003egamma-delta T cell: -3.118\\u003cbr\\u003emature NK T cell: -3.513\",\"CD16-positive, CD56-dim natural killer cell, human: -0.484\\u003cbr\\u003enatural killer cell: -2.041\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.028\\u003cbr\\u003egamma-delta T cell: -4.078\\u003cbr\\u003emature NK T cell: -4.173\",\"CD16-positive, CD56-dim natural killer cell, human: -0.374\\u003cbr\\u003enatural killer cell: -2.095\\u003cbr\\u003emature NK T cell: -3.514\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.711\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.990\",\"CD16-positive, CD56-dim natural killer cell, human: -0.638\\u003cbr\\u003enatural killer cell: -1.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.036\\u003cbr\\u003emature NK T cell: -4.111\\u003cbr\\u003eactivated type II NK T cell: -5.103\",\"CD16-positive, CD56-dim natural killer cell, human: -1.037\\u003cbr\\u003enatural killer cell: -1.099\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.671\\u003cbr\\u003emature NK T cell: -4.372\\u003cbr\\u003eactivated type II NK T cell: -4.797\",\"CD16-positive, CD56-dim natural killer cell, human: -0.369\\u003cbr\\u003enatural killer cell: -2.108\\u003cbr\\u003emature NK T cell: -4.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.263\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.010\",\"CD16-positive, CD56-dim natural killer cell, human: -1.224\\u003cbr\\u003enatural killer cell: -1.352\\u003cbr\\u003egamma-delta T cell: -2.917\\u003cbr\\u003emature NK T cell: -3.157\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.831\",\"CD16-positive, CD56-dim natural killer cell, human: -0.839\\u003cbr\\u003enatural killer cell: 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CD56-bright natural killer cell, human\\u003cbr\\u003eUMAP_1=%{x}\\u003cbr\\u003eUMAP_2=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"hovertext\":[\"CD16-negative, CD56-bright natural killer cell, human: -0.977\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.651\\u003cbr\\u003enatural killer cell: -2.298\\u003cbr\\u003egamma-delta T cell: -3.829\\u003cbr\\u003eactivated type II NK T cell: -3.949\",\"CD16-negative, CD56-bright natural killer cell, human: -0.777\\u003cbr\\u003enatural killer cell: -3.172\\u003cbr\\u003egamma-delta T cell: -3.315\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.819\\u003cbr\\u003einnate lymphoid cell: -3.915\",\"CD16-negative, CD56-bright natural killer cell, human: -0.993\\u003cbr\\u003enatural killer cell: -2.736\\u003cbr\\u003egamma-delta T cell: -2.885\\u003cbr\\u003emucosal invariant T cell: -3.614\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.648\",\"CD16-negative, CD56-bright natural killer cell, human: -0.688\\u003cbr\\u003enatural killer cell: -2.660\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.272\\u003cbr\\u003egamma-delta T cell: -3.384\\u003cbr\\u003einnate lymphoid cell: -3.960\",\"CD16-negative, CD56-bright natural killer cell, human: -0.814\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.751\\u003cbr\\u003enatural killer cell: -3.045\\u003cbr\\u003egamma-delta T cell: -3.246\\u003cbr\\u003emature NK T cell: -3.701\",\"CD16-negative, CD56-bright natural killer cell, human: -1.160\\u003cbr\\u003egamma-delta T cell: -2.823\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.236\\u003cbr\\u003emucosal invariant T cell: -3.678\\u003cbr\\u003einnate lymphoid cell: -3.688\",\"CD16-negative, CD56-bright natural killer cell, human: -1.294\\u003cbr\\u003enatural killer cell: -2.241\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.504\\u003cbr\\u003egamma-delta T cell: -2.676\\u003cbr\\u003emature NK T cell: -3.286\",\"CD16-negative, CD56-bright natural killer cell, human: -0.958\\u003cbr\\u003egamma-delta T cell: -2.417\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.824\\u003cbr\\u003enatural killer cell: -2.876\\u003cbr\\u003emature NK T cell: -3.277\",\"CD16-negative, CD56-bright natural killer cell, human: -1.404\\u003cbr\\u003enatural killer cell: -2.350\\u003cbr\\u003egamma-delta T cell: -2.548\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.085\\u003cbr\\u003emature NK T cell: -3.486\",\"CD16-negative, CD56-bright natural killer cell, human: -0.740\\u003cbr\\u003enatural killer cell: -2.566\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.926\\u003cbr\\u003egamma-delta T cell: -3.366\\u003cbr\\u003emature NK T cell: -4.307\",\"CD16-negative, CD56-bright natural killer cell, human: -1.168\\u003cbr\\u003enatural killer cell: -2.865\\u003cbr\\u003egamma-delta T cell: -3.099\\u003cbr\\u003emature NK T cell: -3.812\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.843\",\"CD16-negative, CD56-bright natural killer cell, human: -1.497\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.933\\u003cbr\\u003enatural killer cell: -2.438\\u003cbr\\u003egamma-delta T cell: -2.609\\u003cbr\\u003emature NK T cell: -3.407\",\"CD16-negative, CD56-bright natural killer cell, human: -0.852\\u003cbr\\u003enatural killer cell: -2.754\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.500\\u003cbr\\u003eactivated type II NK T cell: -4.087\\u003cbr\\u003egamma-delta T cell: -4.230\",\"CD16-negative, CD56-bright natural killer cell, human: -0.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.498\\u003cbr\\u003enatural killer cell: -2.751\\u003cbr\\u003egamma-delta T cell: -3.198\\u003cbr\\u003emature NK T cell: -3.843\",\"CD16-negative, CD56-bright natural killer cell, human: -1.534\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.056\\u003cbr\\u003enatural killer cell: -2.392\\u003cbr\\u003egamma-delta T cell: -2.663\\u003cbr\\u003emature NK T cell: -3.687\",\"CD16-negative, CD56-bright natural killer cell, human: -1.145\\u003cbr\\u003egamma-delta T cell: -2.205\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.834\\u003cbr\\u003enatural killer cell: -3.199\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.371\",\"CD16-negative, CD56-bright natural killer cell, human: -1.437\\u003cbr\\u003enatural killer cell: -3.333\\u003cbr\\u003egamma-delta T cell: -3.548\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.673\\u003cbr\\u003emature NK T cell: -3.775\",\"CD16-negative, CD56-bright natural killer cell, human: -1.555\\u003cbr\\u003egamma-delta T cell: -2.517\\u003cbr\\u003enatural killer cell: -2.873\\u003cbr\\u003emature NK T cell: -3.042\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.314\",\"CD16-negative, CD56-bright natural killer cell, human: -1.043\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.157\\u003cbr\\u003egamma-delta T cell: -2.922\\u003cbr\\u003enatural killer cell: -2.954\\u003cbr\\u003emature NK T cell: -3.636\",\"CD16-negative, CD56-bright natural killer cell, human: -1.364\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.361\\u003cbr\\u003egamma-delta T cell: -2.525\\u003cbr\\u003enatural killer cell: -2.598\\u003cbr\\u003emature NK T cell: -3.294\",\"CD16-negative, CD56-bright natural killer cell, human: -1.145\\u003cbr\\u003enatural killer cell: -2.365\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.799\\u003cbr\\u003emature NK T cell: -3.821\\u003cbr\\u003eactivated type II NK T cell: -4.101\",\"CD16-negative, CD56-bright natural killer cell, human: -0.926\\u003cbr\\u003enatural killer cell: -3.067\\u003cbr\\u003egamma-delta T cell: -3.598\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.667\\u003cbr\\u003einnate lymphoid cell: -3.977\",\"CD16-negative, CD56-bright natural killer cell, human: -0.828\\u003cbr\\u003enatural killer cell: -2.522\\u003cbr\\u003egamma-delta T cell: -3.067\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.138\\u003cbr\\u003einnate lymphoid cell: -4.063\",\"CD16-negative, CD56-bright natural killer cell, human: -2.043\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.270\\u003cbr\\u003egamma-delta T cell: -3.336\\u003cbr\\u003enatural killer cell: -3.380\\u003cbr\\u003emature NK T cell: -3.741\",\"CD16-negative, CD56-bright natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -2.409\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.670\\u003cbr\\u003egamma-delta T cell: -3.153\\u003cbr\\u003emature NK T cell: -3.715\",\"CD16-negative, CD56-bright natural killer cell, human: -0.879\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.387\\u003cbr\\u003enatural killer cell: -3.433\\u003cbr\\u003eactivated type II NK T cell: -4.034\",\"CD16-negative, CD56-bright natural killer cell, human: -1.143\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.622\\u003cbr\\u003enatural killer cell: -2.118\\u003cbr\\u003emature NK T cell: -3.496\\u003cbr\\u003egamma-delta T cell: -3.778\",\"CD16-negative, CD56-bright natural killer cell, human: -1.037\\u003cbr\\u003enatural killer cell: -1.966\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.800\\u003cbr\\u003egamma-delta T cell: -3.441\\u003cbr\\u003emature NK T cell: -3.957\",\"CD16-negative, CD56-bright natural killer cell, human: -1.254\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.005\\u003cbr\\u003enatural killer cell: -2.607\\u003cbr\\u003egamma-delta T cell: -2.762\\u003cbr\\u003emature NK T cell: -3.455\",\"CD16-negative, CD56-bright natural killer cell, human: -0.683\\u003cbr\\u003enatural killer cell: -2.680\\u003cbr\\u003eactivated type II NK T cell: -3.935\\u003cbr\\u003egroup 3 innate lymphoid cell: -4.039\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.093\",\"CD16-negative, CD56-bright natural killer cell, human: -0.896\\u003cbr\\u003egamma-delta T cell: -2.830\\u003cbr\\u003enatural killer cell: -3.456\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.721\\u003cbr\\u003emucosal invariant T cell: -3.851\",\"CD16-negative, CD56-bright natural killer cell, human: -0.911\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.580\\u003cbr\\u003egamma-delta T cell: -2.735\\u003cbr\\u003enatural killer cell: -2.970\\u003cbr\\u003emature NK T cell: -3.869\",\"CD16-negative, CD56-bright natural killer cell, human: -1.079\\u003cbr\\u003enatural killer cell: -2.452\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.701\\u003cbr\\u003egamma-delta T cell: -3.125\\u003cbr\\u003emature NK T cell: -3.767\",\"CD16-negative, CD56-bright natural killer cell, human: -1.441\\u003cbr\\u003egamma-delta T cell: -2.064\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.039\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.323\\u003cbr\\u003emucosal invariant T cell: -3.559\",\"CD16-negative, CD56-bright natural killer cell, human: -1.399\\u003cbr\\u003egamma-delta T cell: -2.868\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.647\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.685\\u003cbr\\u003enatural killer cell: -3.735\",\"CD16-negative, CD56-bright natural killer cell, human: -1.043\\u003cbr\\u003enatural killer cell: -2.398\\u003cbr\\u003egamma-delta T cell: -3.436\\u003cbr\\u003einnate lymphoid cell: -4.113\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.213\",\"CD16-negative, CD56-bright natural killer cell, human: -1.255\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.413\\u003cbr\\u003enatural killer cell: -1.912\\u003cbr\\u003egamma-delta T cell: -3.501\\u003cbr\\u003emature NK T cell: -3.645\",\"CD16-negative, CD56-bright natural killer cell, human: -0.785\\u003cbr\\u003enatural killer cell: -2.113\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.570\\u003cbr\\u003egamma-delta T cell: -3.659\\u003cbr\\u003emature NK T cell: -4.113\",\"CD16-negative, CD56-bright natural killer cell, human: -1.135\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.036\\u003cbr\\u003enatural killer cell: -2.387\\u003cbr\\u003egamma-delta T cell: -3.394\\u003cbr\\u003emature NK T cell: -3.614\",\"CD16-negative, CD56-bright natural killer cell, human: -0.867\\u003cbr\\u003egamma-delta T cell: -2.976\\u003cbr\\u003enatural killer cell: -3.150\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.820\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.988\",\"CD16-negative, CD56-bright natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -2.880\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.136\\u003cbr\\u003egamma-delta T cell: -3.202\\u003cbr\\u003emature NK T cell: -3.615\",\"CD16-negative, CD56-bright natural killer cell, human: -1.345\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.595\\u003cbr\\u003enatural killer cell: -1.603\\u003cbr\\u003egamma-delta T cell: -3.782\\u003cbr\\u003emature NK T cell: -4.013\",\"CD16-negative, CD56-bright natural killer cell, human: -1.226\\u003cbr\\u003enatural killer cell: -2.197\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.167\\u003cbr\\u003eactivated type II NK T cell: -3.866\\u003cbr\\u003emature NK T cell: -4.028\",\"CD16-negative, CD56-bright natural killer cell, human: -2.336\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.528\\u003cbr\\u003enatural killer cell: -3.540\\u003cbr\\u003egamma-delta T cell: -3.561\\u003cbr\\u003einnate lymphoid cell: -3.718\",\"CD16-negative, CD56-bright natural killer cell, human: -0.926\\u003cbr\\u003enatural killer cell: -2.437\\u003cbr\\u003egamma-delta T cell: -2.906\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.018\\u003cbr\\u003emature NK T cell: -3.580\",\"CD16-negative, CD56-bright natural killer cell, human: -0.982\\u003cbr\\u003egamma-delta T cell: -2.952\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.156\\u003cbr\\u003enatural killer cell: -3.197\\u003cbr\\u003emature NK T cell: -3.508\",\"CD16-negative, CD56-bright natural killer cell, human: -0.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.995\\u003cbr\\u003egamma-delta T cell: -3.097\\u003cbr\\u003enatural killer cell: -3.215\\u003cbr\\u003emature NK T cell: -3.990\",\"CD16-negative, CD56-bright natural killer cell, human: -1.351\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.688\\u003cbr\\u003enatural killer cell: -2.290\\u003cbr\\u003egamma-delta T cell: -2.887\\u003cbr\\u003emature NK T cell: -3.630\",\"CD16-negative, CD56-bright natural killer cell, human: -0.778\\u003cbr\\u003egamma-delta T cell: -2.762\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.091\\u003cbr\\u003enatural killer cell: -3.094\\u003cbr\\u003emature NK T cell: -3.749\",\"CD16-negative, CD56-bright natural killer cell, human: -1.194\\u003cbr\\u003enatural killer cell: -1.837\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.183\\u003cbr\\u003egamma-delta T cell: -2.836\\u003cbr\\u003emature NK T cell: -3.430\",\"CD16-negative, CD56-bright natural killer cell, human: -0.700\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.677\\u003cbr\\u003enatural killer cell: -2.827\\u003cbr\\u003egamma-delta T cell: -3.518\\u003cbr\\u003eactivated type II NK T cell: -4.201\",\"CD16-negative, CD56-bright natural killer cell, human: -0.829\\u003cbr\\u003enatural killer cell: -2.575\\u003cbr\\u003egamma-delta T cell: -3.359\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.966\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -4.187\",\"CD16-negative, CD56-bright natural killer cell, human: -1.202\\u003cbr\\u003egamma-delta T cell: -3.341\\u003cbr\\u003enatural killer cell: -3.428\\u003cbr\\u003einnate lymphoid cell: -3.627\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.718\",\"CD16-negative, CD56-bright natural killer cell, human: -1.165\\u003cbr\\u003enatural killer cell: -2.110\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.737\\u003cbr\\u003emast cell: -3.610\\u003cbr\\u003einnate lymphoid cell: -3.646\",\"CD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.375\\u003cbr\\u003emature NK T cell: -3.544\\u003cbr\\u003egamma-delta T cell: -3.729\",\"CD16-negative, CD56-bright natural killer cell, human: -0.899\\u003cbr\\u003egamma-delta T cell: -2.760\\u003cbr\\u003enatural killer cell: -3.240\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.516\\u003cbr\\u003emucosal invariant T cell: -3.906\",\"CD16-negative, CD56-bright natural killer cell, human: -1.390\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.140\\u003cbr\\u003enatural killer cell: -2.592\\u003cbr\\u003egamma-delta T cell: -2.799\\u003cbr\\u003emature NK T cell: -3.968\",\"CD16-negative, CD56-bright natural killer cell, human: -1.534\\u003cbr\\u003enatural killer cell: -2.378\\u003cbr\\u003egamma-delta T cell: -2.810\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.034\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.384\",\"CD16-negative, CD56-bright natural killer cell, human: -1.465\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.494\\u003cbr\\u003enatural killer cell: -2.244\\u003cbr\\u003egamma-delta T cell: -3.584\\u003cbr\\u003eactivated type II NK T cell: -3.973\",\"CD16-negative, CD56-bright natural killer cell, human: -1.795\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.530\\u003cbr\\u003emature NK T cell: -2.631\\u003cbr\\u003enatural killer cell: -2.658\\u003cbr\\u003egamma-delta T cell: -2.827\",\"CD16-negative, CD56-bright natural killer cell, human: -1.729\\u003cbr\\u003egamma-delta T cell: -2.424\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.903\\u003cbr\\u003enatural killer cell: -2.922\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.993\",\"CD16-negative, CD56-bright natural killer cell, human: -1.257\\u003cbr\\u003egamma-delta T cell: -2.661\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.112\\u003cbr\\u003enatural killer cell: -3.152\\u003cbr\\u003emature NK T cell: -3.250\",\"CD16-negative, CD56-bright natural killer cell, human: -0.924\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.808\\u003cbr\\u003enatural killer cell: -2.315\\u003cbr\\u003emature NK T cell: -3.718\\u003cbr\\u003egamma-delta T cell: -3.818\",\"CD16-negative, CD56-bright natural killer cell, human: -1.196\\u003cbr\\u003egamma-delta T cell: -3.106\\u003cbr\\u003enatural killer cell: -3.379\\u003cbr\\u003emature NK T cell: -3.734\\u003cbr\\u003einnate lymphoid cell: -3.867\",\"CD16-negative, CD56-bright natural killer cell, human: -1.672\\u003cbr\\u003enatural killer cell: -1.741\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.010\\u003cbr\\u003emature NK T cell: -3.658\\u003cbr\\u003egamma-delta T cell: -3.931\",\"CD16-negative, CD56-bright natural killer cell, human: -1.230\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.459\\u003cbr\\u003enatural killer cell: -2.547\\u003cbr\\u003egamma-delta T cell: -3.285\\u003cbr\\u003emature NK T cell: -3.785\",\"CD16-negative, CD56-bright natural killer cell, human: -1.560\\u003cbr\\u003egamma-delta T cell: -3.003\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.611\\u003cbr\\u003einnate lymphoid cell: -3.743\\u003cbr\\u003emature NK T cell: -3.752\",\"CD16-negative, CD56-bright natural killer cell, human: -1.463\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.855\\u003cbr\\u003enatural killer cell: -2.167\\u003cbr\\u003egamma-delta T cell: -3.332\\u003cbr\\u003eactivated type II NK T cell: -3.797\",\"CD16-negative, CD56-bright natural killer cell, human: -1.924\\u003cbr\\u003egamma-delta T cell: -2.488\\u003cbr\\u003enatural killer cell: -2.659\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.804\\u003cbr\\u003emature NK T cell: -2.859\",\"CD16-negative, CD56-bright natural killer cell, human: -1.284\\u003cbr\\u003enatural killer cell: -2.003\\u003cbr\\u003einnate lymphoid cell: -3.464\\u003cbr\\u003elymphocyte: -3.669\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.921\",\"CD16-negative, CD56-bright natural killer cell, human: -1.170\\u003cbr\\u003egamma-delta T cell: -3.259\\u003cbr\\u003enatural killer cell: -3.679\\u003cbr\\u003einnate lymphoid cell: -4.083\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.128\",\"CD16-negative, CD56-bright natural killer cell, human: -0.800\\u003cbr\\u003enatural killer cell: -2.593\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.478\\u003cbr\\u003egamma-delta T cell: -3.574\\u003cbr\\u003einnate lymphoid cell: -3.692\",\"CD16-negative, CD56-bright natural killer cell, human: -0.974\\u003cbr\\u003enatural killer cell: -2.490\\u003cbr\\u003egamma-delta T cell: -2.855\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.950\\u003cbr\\u003emature NK T cell: -3.689\",\"CD16-negative, CD56-bright natural killer cell, human: -1.157\\u003cbr\\u003egamma-delta T cell: -2.574\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.578\\u003cbr\\u003enatural killer cell: -3.079\\u003cbr\\u003emature NK T cell: -3.663\",\"CD16-negative, CD56-bright natural killer cell, human: -1.012\\u003cbr\\u003enatural killer cell: -2.317\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.959\\u003cbr\\u003egamma-delta T cell: -3.564\\u003cbr\\u003eactivated type II NK T cell: -3.924\",\"CD16-negative, CD56-bright natural killer cell, human: -1.022\\u003cbr\\u003enatural killer cell: -2.289\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.283\\u003cbr\\u003einnate lymphoid cell: -3.443\\u003cbr\\u003elymphocyte: -4.144\",\"CD16-negative, CD56-bright natural killer cell, human: -0.850\\u003cbr\\u003enatural killer cell: -2.701\\u003cbr\\u003einnate lymphoid cell: -3.622\\u003cbr\\u003egroup 3 innate lymphoid cell: -4.261\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.292\",\"CD16-negative, CD56-bright natural killer cell, human: -1.167\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.820\\u003cbr\\u003enatural killer cell: -2.864\\u003cbr\\u003egamma-delta T cell: -3.026\\u003cbr\\u003eactivated type II NK T cell: -4.013\",\"CD16-negative, CD56-bright natural killer cell, human: -0.784\\u003cbr\\u003enatural killer cell: -2.889\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.352\\u003cbr\\u003eactivated type II NK T cell: -4.004\\u003cbr\\u003ehepatic pit cell: -4.372\",\"CD16-negative, CD56-bright natural killer cell, human: -0.612\\u003cbr\\u003egamma-delta T cell: -2.854\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.244\\u003cbr\\u003enatural killer cell: -3.548\\u003cbr\\u003emature NK T cell: -3.950\",\"CD16-negative, CD56-bright natural killer cell, human: -1.177\\u003cbr\\u003enatural killer cell: -2.917\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.993\\u003cbr\\u003egamma-delta T cell: -3.423\\u003cbr\\u003emature NK T cell: -3.687\",\"CD16-negative, CD56-bright natural killer cell, human: -0.803\\u003cbr\\u003egamma-delta T cell: -3.533\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.642\\u003cbr\\u003enatural killer cell: -3.680\\u003cbr\\u003einnate lymphoid cell: -3.807\",\"CD16-negative, CD56-bright natural killer cell, human: -0.869\\u003cbr\\u003egamma-delta T cell: -2.867\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.143\\u003cbr\\u003enatural killer cell: -3.283\\u003cbr\\u003emucosal invariant T cell: -3.971\",\"CD16-negative, CD56-bright natural killer cell, human: -1.078\\u003cbr\\u003enatural killer cell: -2.861\\u003cbr\\u003egamma-delta T cell: -3.065\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.181\\u003cbr\\u003emature NK T cell: -3.698\",\"CD16-negative, CD56-bright natural killer cell, human: -1.583\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.669\\u003cbr\\u003enatural killer cell: -2.586\\u003cbr\\u003egamma-delta T cell: -2.620\\u003cbr\\u003emature NK T cell: -3.451\",\"CD16-negative, CD56-bright natural killer cell, human: -0.835\\u003cbr\\u003enatural killer cell: -2.420\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.755\\u003cbr\\u003egamma-delta T cell: -3.826\\u003cbr\\u003eactivated type II NK T cell: -4.251\",\"CD16-negative, CD56-bright natural killer cell, human: -0.955\\u003cbr\\u003enatural killer cell: -2.617\\u003cbr\\u003egamma-delta T cell: -3.033\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.566\\u003cbr\\u003emature NK T cell: -3.676\",\"CD16-negative, CD56-bright natural killer cell, human: -1.181\\u003cbr\\u003enatural killer cell: -2.186\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.576\\u003cbr\\u003egamma-delta T cell: -2.852\\u003cbr\\u003emature NK T cell: -3.494\",\"CD16-negative, CD56-bright natural killer cell, human: -1.220\\u003cbr\\u003egamma-delta T cell: -2.585\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.886\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.645\\u003cbr\\u003emature NK T cell: -3.666\",\"CD16-negative, CD56-bright natural killer cell, human: -2.757\\u003cbr\\u003egamma-delta T cell: -3.216\\u003cbr\\u003eT cell: -3.417\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.441\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.631\",\"CD16-negative, CD56-bright natural killer cell, human: -0.664\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.721\\u003cbr\\u003enatural killer cell: -2.782\\u003cbr\\u003egamma-delta T cell: -3.842\\u003cbr\\u003einnate lymphoid cell: -4.169\",\"CD16-negative, CD56-bright natural killer cell, human: -1.090\\u003cbr\\u003enatural killer cell: -2.736\\u003cbr\\u003egamma-delta T cell: -2.907\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.113\\u003cbr\\u003emature NK T cell: -3.764\",\"CD16-negative, CD56-bright natural killer cell, human: -0.887\\u003cbr\\u003egamma-delta T cell: -2.940\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.402\\u003cbr\\u003enatural killer cell: -3.415\\u003cbr\\u003emature NK T cell: -3.669\",\"CD16-negative, CD56-bright natural killer cell, human: -0.656\\u003cbr\\u003egamma-delta T cell: -3.199\\u003cbr\\u003enatural killer cell: -3.531\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.698\\u003cbr\\u003emature NK T cell: -3.795\",\"CD16-negative, CD56-bright natural killer cell, human: -1.181\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.362\\u003cbr\\u003enatural killer cell: -2.485\\u003cbr\\u003egamma-delta T cell: -2.595\\u003cbr\\u003emature NK T cell: -3.283\",\"CD16-negative, CD56-bright natural killer cell, human: -1.554\\u003cbr\\u003enatural killer cell: -3.282\\u003cbr\\u003egamma-delta T cell: -3.505\\u003cbr\\u003einnate lymphoid cell: -3.778\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.836\",\"CD16-negative, CD56-bright natural killer cell, human: -1.184\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.639\\u003cbr\\u003enatural killer cell: -2.646\\u003cbr\\u003egamma-delta T cell: -3.079\\u003cbr\\u003emature NK T cell: -3.478\",\"CD16-negative, CD56-bright natural killer cell, human: -1.674\\u003cbr\\u003egamma-delta T cell: -2.332\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.991\\u003cbr\\u003emucosal invariant T cell: -3.355\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.849\",\"CD16-negative, CD56-bright natural killer cell, human: -0.979\\u003cbr\\u003enatural killer cell: -2.617\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.760\\u003cbr\\u003egamma-delta T cell: -2.923\\u003cbr\\u003emature NK T cell: -4.232\",\"CD16-negative, CD56-bright natural killer cell, human: -1.703\\u003cbr\\u003egamma-delta T cell: -2.192\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.609\\u003cbr\\u003enatural killer cell: -2.933\\u003cbr\\u003emature NK T cell: -3.196\",\"CD16-negative, CD56-bright natural killer cell, human: -1.457\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.901\\u003cbr\\u003egamma-delta T cell: -2.372\\u003cbr\\u003enatural killer cell: -2.731\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.514\",\"CD16-negative, CD56-bright natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -2.996\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.184\\u003cbr\\u003egamma-delta T cell: -3.472\\u003cbr\\u003emature NK T cell: -4.009\",\"CD16-negative, CD56-bright natural killer cell, human: -0.869\\u003cbr\\u003enatural killer cell: -2.737\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.081\\u003cbr\\u003egamma-delta T cell: -3.215\\u003cbr\\u003einnate lymphoid cell: -3.954\",\"CD16-negative, CD56-bright natural killer cell, human: -0.944\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.291\\u003cbr\\u003egamma-delta T cell: -2.668\\u003cbr\\u003enatural killer cell: -2.817\\u003cbr\\u003emature NK T cell: -3.503\",\"CD16-negative, CD56-bright natural killer cell, human: -1.959\\u003cbr\\u003egamma-delta T cell: -2.114\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.329\\u003cbr\\u003emucosal invariant T cell: -3.398\\u003cbr\\u003emature NK T cell: -3.486\",\"CD16-negative, CD56-bright natural killer cell, human: -1.185\\u003cbr\\u003enatural killer cell: -2.291\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.415\\u003cbr\\u003egamma-delta T cell: -3.967\\u003cbr\\u003einnate lymphoid cell: -3.968\",\"CD16-negative, CD56-bright natural killer cell, human: -1.165\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.517\\u003cbr\\u003enatural killer cell: -2.797\\u003cbr\\u003eactivated type II NK T cell: -3.791\\u003cbr\\u003egamma-delta T cell: -3.874\",\"CD16-negative, CD56-bright natural killer cell, human: -0.673\\u003cbr\\u003enatural killer cell: -2.775\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.041\\u003cbr\\u003egamma-delta T cell: -3.889\\u003cbr\\u003einnate lymphoid cell: -4.138\",\"CD16-negative, CD56-bright natural killer cell, human: -1.642\\u003cbr\\u003egamma-delta T cell: -2.669\\u003cbr\\u003enatural killer cell: -3.061\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.352\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.382\",\"CD16-negative, CD56-bright natural killer cell, human: -0.713\\u003cbr\\u003enatural killer cell: -2.410\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.419\\u003cbr\\u003einnate lymphoid cell: -3.774\\u003cbr\\u003egamma-delta T cell: -3.905\",\"CD16-negative, CD56-bright natural killer cell, human: -0.834\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.809\\u003cbr\\u003enatural killer cell: -3.097\\u003cbr\\u003egamma-delta T cell: -3.377\\u003cbr\\u003emature NK T cell: -4.136\",\"CD16-negative, CD56-bright natural killer cell, human: -0.924\\u003cbr\\u003enatural killer cell: -3.272\\u003cbr\\u003egamma-delta T cell: -3.449\\u003cbr\\u003einnate lymphoid cell: -3.812\\u003cbr\\u003emature NK T cell: -4.016\",\"CD16-negative, CD56-bright natural killer cell, human: -2.032\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.210\\u003cbr\\u003enatural killer cell: -2.382\\u003cbr\\u003egamma-delta T cell: -2.769\\u003cbr\\u003emature NK T cell: -3.270\",\"CD16-negative, CD56-bright natural killer cell, human: -2.256\\u003cbr\\u003egamma-delta T cell: -3.350\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.765\\u003cbr\\u003emature NK T cell: -3.861\\u003cbr\\u003enatural killer cell: -3.869\",\"CD16-negative, CD56-bright natural killer cell, human: -1.172\\u003cbr\\u003egamma-delta T cell: -2.565\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.497\\u003cbr\\u003emature NK T cell: -3.555\\u003cbr\\u003enatural killer cell: -3.660\",\"CD16-negative, CD56-bright natural killer cell, human: -0.658\\u003cbr\\u003enatural killer cell: -2.584\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.209\\u003cbr\\u003egamma-delta T cell: -4.080\\u003cbr\\u003eactivated type II NK T cell: -4.254\",\"CD16-negative, CD56-bright natural killer cell, human: -1.027\\u003cbr\\u003enatural killer cell: -2.553\\u003cbr\\u003egamma-delta T cell: -2.746\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.439\\u003cbr\\u003emature NK T cell: -3.475\",\"CD16-negative, CD56-bright natural killer cell, human: -0.696\\u003cbr\\u003enatural killer cell: -3.045\\u003cbr\\u003egamma-delta T cell: -3.518\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.819\\u003cbr\\u003emature NK T cell: -4.146\",\"CD16-negative, CD56-bright natural killer cell, human: -1.006\\u003cbr\\u003enatural killer cell: -1.944\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.995\\u003cbr\\u003egamma-delta T cell: -4.104\\u003cbr\\u003eactivated type II NK T cell: -4.330\",\"CD16-negative, CD56-bright natural killer cell, human: -2.074\\u003cbr\\u003eT cell: -3.324\\u003cbr\\u003emast cell: -3.603\\u003cbr\\u003emature NK T cell: -3.603\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.668\",\"CD16-negative, CD56-bright natural killer cell, human: -1.175\\u003cbr\\u003enatural killer cell: -3.306\\u003cbr\\u003egamma-delta T cell: -3.354\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.495\\u003cbr\\u003einnate lymphoid cell: 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killer cell: -2.823\\u003cbr\\u003egamma-delta T cell: -3.412\\u003cbr\\u003emature NK T cell: -3.766\",\"CD16-negative, CD56-bright natural killer cell, human: -1.234\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.256\\u003cbr\\u003enatural killer cell: -2.391\\u003cbr\\u003egamma-delta T cell: -4.006\\u003cbr\\u003eactivated type II NK T cell: -4.088\",\"CD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -3.063\\u003cbr\\u003egamma-delta T cell: -3.139\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.617\\u003cbr\\u003emature NK T cell: -3.644\",\"CD16-negative, CD56-bright natural killer cell, human: -0.565\\u003cbr\\u003enatural killer cell: -2.688\\u003cbr\\u003egamma-delta T cell: -3.653\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.968\\u003cbr\\u003einnate lymphoid cell: -4.246\",\"CD16-negative, CD56-bright natural killer cell, human: -1.266\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.383\\u003cbr\\u003enatural killer cell: -2.299\\u003cbr\\u003emature NK T cell: -3.343\\u003cbr\\u003egamma-delta T cell: -3.616\",\"CD16-negative, CD56-bright natural killer cell, human: -0.744\\u003cbr\\u003enatural killer cell: -2.740\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.468\\u003cbr\\u003egamma-delta T cell: -3.661\\u003cbr\\u003einnate lymphoid cell: -4.038\",\"CD16-negative, CD56-bright natural killer cell, human: -1.305\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.575\\u003cbr\\u003enatural killer cell: -2.073\\u003cbr\\u003emature NK T cell: -3.957\\u003cbr\\u003eactivated type II NK T cell: -4.035\",\"CD16-negative, CD56-bright natural killer cell, human: -0.813\\u003cbr\\u003egamma-delta T cell: -3.197\\u003cbr\\u003enatural killer cell: -3.459\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.551\\u003cbr\\u003einnate lymphoid cell: -3.987\",\"CD16-negative, CD56-bright natural killer cell, human: -0.820\\u003cbr\\u003egamma-delta T cell: -3.120\\u003cbr\\u003enatural killer cell: -3.715\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.730\\u003cbr\\u003einnate lymphoid cell: -3.861\",\"CD16-negative, CD56-bright natural killer cell, human: -1.258\\u003cbr\\u003enatural killer cell: -3.552\\u003cbr\\u003elymphocyte: -3.808\\u003cbr\\u003eprogenitor cell: -4.126\\u003cbr\\u003einnate lymphoid cell: -4.154\",\"CD16-negative, CD56-bright natural killer cell, human: -1.908\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.274\\u003cbr\\u003egamma-delta T cell: -2.507\\u003cbr\\u003enatural killer cell: -2.540\\u003cbr\\u003emature NK T cell: -3.656\",\"CD16-negative, CD56-bright natural killer cell, human: -0.760\\u003cbr\\u003enatural killer cell: -2.478\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.923\\u003cbr\\u003egamma-delta T cell: 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-2.639\\u003cbr\\u003enatural killer cell: -2.934\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.604\",\"CD16-negative, CD56-bright natural killer cell, human: -1.291\\u003cbr\\u003enatural killer cell: -1.604\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.523\\u003cbr\\u003egamma-delta T cell: -4.019\\u003cbr\\u003emature NK T cell: -4.191\",\"CD16-negative, CD56-bright natural killer cell, human: -1.056\\u003cbr\\u003enatural killer cell: -1.883\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.396\\u003cbr\\u003emature NK T cell: -3.262\\u003cbr\\u003egamma-delta T cell: -3.385\",\"CD16-negative, CD56-bright natural killer cell, human: -1.652\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.506\\u003cbr\\u003egamma-delta T cell: -2.662\\u003cbr\\u003enatural killer cell: -2.996\\u003cbr\\u003emature NK T cell: -3.602\",\"CD16-negative, CD56-bright natural killer cell, human: -1.008\\u003cbr\\u003enatural killer cell: -2.885\\u003cbr\\u003egamma-delta T cell: -3.341\\u003cbr\\u003emature NK T cell: -3.850\\u003cbr\\u003einnate lymphoid cell: -4.024\",\"CD16-negative, CD56-bright natural killer cell, human: -1.532\\u003cbr\\u003enatural killer cell: -3.109\\u003cbr\\u003egamma-delta T cell: -3.331\\u003cbr\\u003einnate lymphoid cell: -3.704\\u003cbr\\u003emature NK T cell: -3.744\",\"CD16-negative, CD56-bright natural killer cell, human: -1.098\\u003cbr\\u003egamma-delta T cell: -2.727\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.184\\u003cbr\\u003emature NK T cell: -3.563\\u003cbr\\u003emucosal invariant T cell: -3.978\",\"CD16-negative, CD56-bright natural killer cell, human: -0.856\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.564\\u003cbr\\u003enatural killer cell: -2.401\\u003cbr\\u003egamma-delta T cell: -4.139\\u003cbr\\u003emature NK T cell: -4.167\",\"CD16-negative, CD56-bright natural killer cell, human: -0.888\\u003cbr\\u003egamma-delta T cell: -2.407\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.709\\u003cbr\\u003enatural killer cell: -2.941\\u003cbr\\u003emature NK T cell: -3.924\",\"CD16-negative, CD56-bright natural killer cell, human: -0.796\\u003cbr\\u003enatural killer cell: -2.420\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.847\\u003cbr\\u003egamma-delta T cell: -2.888\\u003cbr\\u003emature NK T cell: -4.199\",\"CD16-negative, CD56-bright natural killer cell, human: -1.291\\u003cbr\\u003egamma-delta T cell: -2.928\\u003cbr\\u003enatural killer cell: -2.962\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.794\\u003cbr\\u003einnate lymphoid cell: -3.983\",\"CD16-negative, CD56-bright natural killer cell, human: -0.987\\u003cbr\\u003enatural killer cell: -3.156\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.183\\u003cbr\\u003egamma-delta T cell: -3.503\\u003cbr\\u003eactivated type II NK T cell: -4.118\",\"CD16-negative, CD56-bright natural killer cell, human: -1.108\\u003cbr\\u003egamma-delta T cell: -2.734\\u003cbr\\u003enatural killer cell: -2.786\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.208\\u003cbr\\u003emature NK T cell: -3.472\",\"CD16-negative, CD56-bright natural killer cell, human: -1.443\\u003cbr\\u003enatural killer cell: -1.874\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.699\\u003cbr\\u003egamma-delta T cell: -3.533\\u003cbr\\u003emature NK T cell: -3.926\",\"CD16-negative, CD56-bright natural killer cell, human: -1.217\\u003cbr\\u003egamma-delta T cell: -3.222\\u003cbr\\u003enatural killer cell: -3.324\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.716\\u003cbr\\u003einnate lymphoid cell: -3.772\",\"CD16-negative, CD56-bright natural killer cell, human: -1.979\\u003cbr\\u003egamma-delta T cell: -2.083\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: 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-2.498\\u003cbr\\u003eT follicular helper cell: -3.139\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.230\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.609\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.963\\u003cbr\\u003eT follicular helper cell: -2.919\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.368\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.126\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.355\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.745\\u003cbr\\u003eT cell: -3.268\\u003cbr\\u003eT follicular helper cell: -4.256\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.355\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.857\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.376\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.757\\u003cbr\\u003eT-helper 22 cell: -2.907\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.224\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.895\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.515\\u003cbr\\u003eT follicular helper cell: -3.084\\u003cbr\\u003eT cell: -3.342\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.593\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.639\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.374\\u003cbr\\u003eT-helper 22 cell: -2.601\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.638\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.329\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.842\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.439\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.745\\u003cbr\\u003eT follicular helper cell: -2.787\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.617\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.954\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.600\\u003cbr\\u003eT-helper 22 cell: -2.888\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.038\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.706\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.803\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.260\\u003cbr\\u003eT follicular helper cell: -3.571\\u003cbr\\u003eT cell: -4.039\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.528\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.609\\u003cbr\\u003eT follicular helper cell: -1.737\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.750\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.184\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.941\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.333\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.676\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.721\\u003cbr\\u003eT cell: -3.095\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.181\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.480\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.886\\u003cbr\\u003eT follicular helper cell: -2.896\\u003cbr\\u003eT cell: -4.355\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.090\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.803\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.961\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.418\\u003cbr\\u003eT follicular helper cell: -3.493\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.734\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.661\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.422\\u003cbr\\u003eT follicular helper cell: -4.076\\u003cbr\\u003eT cell: -4.206\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.174\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.690\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.393\\u003cbr\\u003eT follicular helper cell: -2.463\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.989\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.117\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.364\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.289\\u003cbr\\u003eT follicular helper cell: -3.297\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.731\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.265\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.018\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.230\\u003cbr\\u003eT follicular helper cell: -3.229\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.265\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.551\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.015\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.686\\u003cbr\\u003eT follicular helper cell: -3.237\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.875\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.651\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.808\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.508\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.605\\u003cbr\\u003eT follicular helper cell: -3.104\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.862\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.806\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.877\\u003cbr\\u003eT follicular helper cell: -3.551\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.976\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.682\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.535\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.220\\u003cbr\\u003eT follicular helper cell: -3.939\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.306\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.986\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.887\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.188\\u003cbr\\u003eT follicular helper cell: -3.307\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.488\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.397\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.841\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.915\\u003cbr\\u003eT follicular helper cell: -2.502\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.231\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.079\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.790\\u003cbr\\u003eT follicular helper cell: -2.703\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.028\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.430\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.316\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.136\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.528\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.781\\u003cbr\\u003eT-helper 22 cell: -3.104\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.494\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.137\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.279\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.011\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.695\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.950\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.990\\u003cbr\\u003eT follicular helper cell: -2.444\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.771\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.409\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.756\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.465\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.638\\u003cbr\\u003eT-helper 22 cell: -3.424\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.549\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.777\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.095\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.271\\u003cbr\\u003eT-helper 22 cell: -3.237\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.944\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.829\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.002\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.173\\u003cbr\\u003eT follicular helper cell: -3.822\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.968\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.647\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.785\\u003cbr\\u003eT follicular helper cell: -2.901\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.185\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.044\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.990\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.403\\u003cbr\\u003eT-helper 22 cell: -3.548\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.562\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.726\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.102\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.463\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.468\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.446\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.566\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.153\\u003cbr\\u003eT follicular helper cell: -2.764\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.763\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.263\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.880\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.940\\u003cbr\\u003eT-helper 22 cell: -3.216\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.219\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.439\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.327\\u003cbr\\u003eT follicular helper cell: -2.451\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.675\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.729\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.469\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.010\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.204\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.048\\u003cbr\\u003eT follicular helper cell: -3.156\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.248\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.771\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.597\\u003cbr\\u003eT follicular helper cell: -2.778\\u003cbr\\u003eT cell: -3.386\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.381\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.873\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.955\\u003cbr\\u003eT follicular helper cell: -2.848\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.925\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.236\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.941\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.980\\u003cbr\\u003eT follicular helper cell: -3.383\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.387\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.749\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.943\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.116\\u003cbr\\u003eT follicular helper cell: -3.350\\u003cbr\\u003eT cell: -4.211\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.569\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.563\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.530\\u003cbr\\u003eT cell: -4.413\\u003cbr\\u003eT follicular helper cell: -4.551\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -2.531\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -3.245\\u003cbr\\u003eT cell: -3.452\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.881\\u003cbr\\u003eerythrocyte: -3.967\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.215\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.108\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.370\\u003cbr\\u003eT follicular helper cell: -2.464\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.849\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.612\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.641\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.299\\u003cbr\\u003eT follicular helper cell: -4.095\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.478\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.922\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.120\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.144\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.482\\u003cbr\\u003eT follicular helper cell: -3.078\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.751\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.854\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.196\\u003cbr\\u003eT-helper 22 cell: -2.339\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.038\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.295\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.506\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.193\\u003cbr\\u003eT follicular helper cell: -3.333\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.688\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.540\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.805\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.217\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.357\\u003cbr\\u003eT follicular helper cell: -3.048\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.896\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.994\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.175\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.112\\u003cbr\\u003eT follicular helper cell: -3.593\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.450\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.068\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.677\\u003cbr\\u003eT follicular helper cell: -3.471\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.084\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.625\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.910\\u003cbr\\u003eT follicular helper cell: -2.517\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.562\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.671\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -0.847\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.165\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.903\\u003cbr\\u003eT follicular helper cell: -3.981\\u003cbr\\u003eT cell: -4.045\",\"naive thymus-derived CD4-positive, alpha-beta T cell: -1.985\\u003cbr\\u003ecentral memory CD4-positive, 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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('9748422d-1def-467c-8f85-0c85775b006f');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generate_interactive_plotly_for_metadata(adata, 5, \"cell_type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br><br>UMAP_1=%{x}<br>UMAP_2=%{y}<br>value=%{marker.color}<extra></extra>",
+         "hovertext": [
+          "value: -4.000<br>T follicular helper cell: -2.174<br>effector memory CD4-positive, alpha-beta T cell: -2.489<br>central memory CD4-positive, alpha-beta T cell: -2.944<br>T-helper 22 cell: -3.309<br>mature NK T cell: -3.847",
+          "value: -5.852<br>CD16-positive, CD56-dim natural killer cell, human: -0.997<br>natural killer cell: -1.539<br>CD16-negative, CD56-bright natural killer cell, human: -2.161<br>activated type II NK T cell: -4.115<br>mature NK T cell: -4.381",
+          "value: -5.866<br>CD16-positive, CD56-dim natural killer cell, human: -0.680<br>natural killer cell: -1.873<br>mature NK T cell: -3.364<br>CD16-negative, CD56-bright natural killer cell, human: -3.673<br>gamma-delta T cell: -4.411",
+          "value: -6.784<br>CD16-negative, CD56-bright natural killer cell, human: -0.977<br>CD16-positive, CD56-dim natural killer cell, human: -1.651<br>natural killer cell: -2.298<br>gamma-delta T cell: -3.829<br>activated type II NK T cell: -3.949",
+          "value: -5.707<br>plasmacytoid dendritic cell: -0.189<br>plasmacytoid dendritic cell, human: -3.037<br>dendritic cell: -3.171<br>myeloid dendritic cell: -5.711<br>CD1c-positive myeloid dendritic cell: -5.829",
+          "value: -6.231<br>CD16-negative, CD56-bright natural killer cell, human: -0.777<br>natural killer cell: -3.172<br>gamma-delta T cell: -3.315<br>CD16-positive, CD56-dim natural killer cell, human: -3.819<br>innate lymphoid cell: -3.915",
+          "value: -6.036<br>T cell: -2.663<br>CD16-negative, CD56-bright natural killer cell, human: -2.953<br>gamma-delta T cell: -3.444<br>mast cell: -3.554<br>mature NK T cell: -3.582",
+          "value: -5.603<br>CD16-negative, CD56-bright natural killer cell, human: -0.993<br>natural killer cell: -2.736<br>gamma-delta T cell: -2.885<br>mucosal invariant T cell: -3.614<br>effector memory CD8-positive, alpha-beta T cell: -3.648",
+          "value: -5.188<br>CD16-positive, CD56-dim natural killer cell, human: -0.741<br>natural killer cell: -1.358<br>CD16-negative, CD56-bright natural killer cell, human: -3.296<br>mature NK T cell: -4.232<br>activated type II NK T cell: -4.598",
+          "value: -6.896<br>CD16-positive, CD56-dim natural killer cell, human: -0.484<br>natural killer cell: -2.135<br>CD16-negative, CD56-bright natural killer cell, human: -2.447<br>mature NK T cell: -4.345<br>activated type II NK T cell: -4.521",
+          "value: -5.539<br>CD16-negative, CD56-bright natural killer cell, human: -0.688<br>natural killer cell: -2.660<br>CD16-positive, CD56-dim natural killer cell, human: -3.272<br>gamma-delta T cell: -3.384<br>innate lymphoid cell: -3.960",
+          "value: -4.759<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.174<br>central memory CD4-positive, alpha-beta T cell: -1.579<br>T follicular helper cell: -2.935<br>T cell: -3.250<br>effector memory CD4-positive, alpha-beta T cell: -3.258",
+          "value: -7.131<br>CD16-negative, CD56-bright natural killer cell, human: -0.814<br>CD16-positive, CD56-dim natural killer cell, human: -2.751<br>natural killer cell: -3.045<br>gamma-delta T cell: -3.246<br>mature NK T cell: -3.701",
+          "value: -5.278<br>plasmacytoid dendritic cell: -0.394<br>plasmacytoid dendritic cell, human: -2.133<br>dendritic cell: -3.046<br>conventional dendritic cell: -4.507<br>dendritic cell, human: -5.009",
+          "value: -6.335<br>CD16-positive, CD56-dim natural killer cell, human: -0.504<br>natural killer cell: -1.691<br>CD16-negative, CD56-bright natural killer cell, human: -3.602<br>mature NK T cell: -4.274<br>activated type II NK T cell: -4.822",
+          "value: -6.383<br>plasmacytoid dendritic cell: -0.321<br>plasmacytoid dendritic cell, human: -2.877<br>dendritic cell: -3.356<br>B cell: -5.030<br>myeloid dendritic cell: -5.201",
+          "value: -5.611<br>CD16-negative, CD56-bright natural killer cell, human: -1.160<br>gamma-delta T cell: -2.823<br>CD16-positive, CD56-dim natural killer cell, human: -3.236<br>mucosal invariant T cell: -3.678<br>innate lymphoid cell: -3.688",
+          "value: -5.934<br>plasmacytoid dendritic cell: -0.415<br>plasmacytoid dendritic cell, human: -2.322<br>dendritic cell: -2.532<br>dendritic cell, human: -4.412<br>myeloid dendritic cell: -4.733",
+          "value: -5.170<br>CD16-positive, CD56-dim natural killer cell, human: -0.962<br>natural killer cell: -1.133<br>mature NK T cell: -3.452<br>gamma-delta T cell: -3.971<br>CD16-negative, CD56-bright natural killer cell, human: -4.582",
+          "value: -6.136<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.796<br>central memory CD4-positive, alpha-beta T cell: -1.850<br>effector memory CD4-positive, alpha-beta T cell: -2.637<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.016<br>T follicular helper cell: -3.131",
+          "value: -6.785<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.294<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.700<br>central memory CD4-positive, alpha-beta T cell: -2.004<br>T follicular helper cell: -3.189<br>effector memory CD4-positive, alpha-beta T cell: -3.270",
+          "value: -7.078<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.898<br>central memory CD4-positive, alpha-beta T cell: -1.662<br>T follicular helper cell: -2.286<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.433<br>effector memory CD4-positive, alpha-beta T cell: -4.174",
+          "value: -5.915<br>plasmacytoid dendritic cell: -0.280<br>plasmacytoid dendritic cell, human: -2.659<br>dendritic cell: -2.767<br>myeloid dendritic cell: -5.136<br>dendritic cell, human: -5.249",
+          "value: -7.649<br>CD16-positive, CD56-dim natural killer cell, human: -0.811<br>CD16-negative, CD56-bright natural killer cell, human: -1.916<br>natural killer cell: -2.506<br>gamma-delta T cell: -3.548<br>mature NK T cell: -3.576",
+          "value: -6.538<br>CD16-positive, CD56-dim natural killer cell, human: -0.607<br>natural killer cell: -1.959<br>CD16-negative, CD56-bright natural killer cell, human: -3.204<br>mature NK T cell: -4.145<br>activated type II NK T cell: -4.480",
+          "value: -6.363<br>CD16-positive, CD56-dim natural killer cell, human: -0.369<br>natural killer cell: -1.978<br>CD16-negative, CD56-bright natural killer cell, human: -3.798<br>mature NK T cell: -4.179<br>astrocyte of the cerebral cortex: -4.999",
+          "value: -7.078<br>CD16-negative, CD56-bright natural killer cell, human: -1.294<br>natural killer cell: -2.241<br>CD16-positive, CD56-dim natural killer cell, human: -2.504<br>gamma-delta T cell: -2.676<br>mature NK T cell: -3.286",
+          "value: -5.412<br>CD16-positive, CD56-dim natural killer cell, human: -1.558<br>natural killer cell: -1.764<br>CD16-negative, CD56-bright natural killer cell, human: -3.394<br>mature NK T cell: -3.456<br>astrocyte of the cerebral cortex: -3.963",
+          "value: -6.504<br>classical monocyte: -1.212<br>CD14-positive monocyte: -1.706<br>pvalb GABAergic cortical interneuron: -2.739<br>monocyte: -3.786<br>macrophage: -3.796",
+          "value: -4.850<br>T follicular helper cell: -2.301<br>central memory CD4-positive, alpha-beta T cell: -2.452<br>effector memory CD4-positive, alpha-beta T cell: -2.525<br>T-helper 22 cell: -3.223<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.959",
+          "value: -5.612<br>CD16-positive, CD56-dim natural killer cell, human: -0.485<br>natural killer cell: -1.676<br>mature NK T cell: -3.824<br>CD16-negative, CD56-bright natural killer cell, human: -4.362<br>astrocyte of the cerebral cortex: -4.685",
+          "value: -6.510<br>CD16-positive, CD56-dim natural killer cell, human: -0.285<br>natural killer cell: -2.481<br>CD16-negative, CD56-bright natural killer cell, human: -3.339<br>mature NK T cell: -4.366<br>gamma-delta T cell: -5.053",
+          "value: -6.984<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.323<br>central memory CD4-positive, alpha-beta T cell: -1.631<br>T follicular helper cell: -1.950<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.867<br>effector memory CD4-positive, alpha-beta T cell: -3.314",
+          "value: -6.284<br>CD16-negative, CD56-bright natural killer cell, human: -0.958<br>gamma-delta T cell: -2.417<br>CD16-positive, CD56-dim natural killer cell, human: -2.824<br>natural killer cell: -2.876<br>mature NK T cell: -3.277",
+          "value: -5.262<br>plasmacytoid dendritic cell: -0.886<br>B cell: -2.165<br>dendritic cell: -2.354<br>plasmacytoid dendritic cell, human: -3.479<br>plasma cell: -3.947",
+          "value: -6.698<br>classical monocyte: -1.909<br>CD14-positive monocyte: -2.127<br>dendritic cell, human: -2.771<br>pvalb GABAergic cortical interneuron: -2.946<br>dendritic cell: -3.312",
+          "value: -5.866<br>natural killer cell: -1.102<br>CD16-positive, CD56-dim natural killer cell, human: -1.535<br>mature NK T cell: -2.751<br>gamma-delta T cell: -3.599<br>CD8-positive, alpha-beta T cell: -4.043",
+          "value: -6.125<br>CD16-positive, CD56-dim natural killer cell, human: -0.463<br>natural killer cell: -1.655<br>mature NK T cell: -3.926<br>CD16-negative, CD56-bright natural killer cell, human: -4.133<br>activated type II NK T cell: -5.171",
+          "value: -6.296<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.559<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.848<br>central memory CD4-positive, alpha-beta T cell: -2.761<br>T cell: -4.075<br>T follicular helper cell: -4.563",
+          "value: -2.758<br>gamma-delta T cell: -1.934<br>T cell: -2.295<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.150<br>mature NK T cell: -3.176<br>mucosal invariant T cell: -3.443",
+          "value: -6.175<br>CD14-positive monocyte: -0.998<br>classical monocyte: -1.784<br>monocyte: -2.950<br>pvalb GABAergic cortical interneuron: -3.240<br>macrophage: -3.668",
+          "value: -5.869<br>classical monocyte: -1.098<br>CD14-positive monocyte: -1.792<br>pvalb GABAergic cortical interneuron: -2.618<br>macrophage: -3.831<br>monocyte: -3.974",
+          "value: -5.508<br>plasmacytoid dendritic cell: -0.266<br>plasmacytoid dendritic cell, human: -2.656<br>dendritic cell: -3.083<br>myeloid dendritic cell: -5.225<br>dendritic cell, human: -5.404",
+          "value: -5.137<br>central memory CD4-positive, alpha-beta T cell: -2.064<br>effector memory CD4-positive, alpha-beta T cell: -2.167<br>T follicular helper cell: -2.306<br>T-helper 22 cell: -2.609<br>effector memory CD8-positive, alpha-beta T cell: -3.233",
+          "value: -4.962<br>natural killer cell: -1.018<br>CD16-positive, CD56-dim natural killer cell, human: -1.118<br>CD16-negative, CD56-bright natural killer cell, human: -3.049<br>mature NK T cell: -3.823<br>activated type II NK T cell: -4.403",
+          "value: -6.270<br>CD16-positive, CD56-dim natural killer cell, human: -0.371<br>natural killer cell: -1.695<br>mature NK T cell: -4.130<br>astrocyte of the cerebral cortex: -5.243<br>CD16-negative, CD56-bright natural killer cell, human: -5.275",
+          "value: -6.315<br>CD16-positive, CD56-dim natural killer cell, human: -0.721<br>CD16-negative, CD56-bright natural killer cell, human: -1.687<br>natural killer cell: -2.525<br>gamma-delta T cell: -4.131<br>mature NK T cell: -4.408",
+          "value: -6.437<br>CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -1.952<br>CD16-negative, CD56-bright natural killer cell, human: -3.146<br>mature NK T cell: -4.305<br>activated type II NK T cell: -4.803",
+          "value: -5.648<br>plasmacytoid dendritic cell: -0.187<br>plasmacytoid dendritic cell, human: -2.909<br>dendritic cell: -3.326<br>myeloid dendritic cell: -5.717<br>B cell: -5.956",
+          "value: -6.002<br>plasmacytoid dendritic cell: -0.297<br>dendritic cell: -2.743<br>plasmacytoid dendritic cell, human: -2.944<br>dendritic cell, human: -4.654<br>myeloid dendritic cell: -4.667",
+          "value: -6.060<br>CD16-positive, CD56-dim natural killer cell, human: -1.256<br>CD16-negative, CD56-bright natural killer cell, human: -1.616<br>natural killer cell: -2.142<br>gamma-delta T cell: -3.362<br>mature NK T cell: -3.632",
+          "value: -5.617<br>plasmacytoid dendritic cell: -0.477<br>plasmacytoid dendritic cell, human: -2.089<br>dendritic cell: -2.632<br>myeloid dendritic cell: -4.838<br>dendritic cell, human: -4.989",
+          "value: -6.794<br>T follicular helper cell: -1.685<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.922<br>central memory CD4-positive, alpha-beta T cell: -1.985<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.644<br>effector memory CD4-positive, alpha-beta T cell: -3.067",
+          "value: -5.921<br>CD16-positive, CD56-dim natural killer cell, human: -0.439<br>natural killer cell: -1.860<br>CD16-negative, CD56-bright natural killer cell, human: -3.625<br>mature NK T cell: -3.883<br>activated type II NK T cell: -5.008",
+          "value: -6.052<br>CD16-positive, CD56-dim natural killer cell, human: -0.624<br>natural killer cell: -1.904<br>CD16-negative, CD56-bright natural killer cell, human: -2.711<br>mature NK T cell: -3.862<br>astrocyte of the cerebral cortex: -4.787",
+          "value: -5.648<br>natural killer cell: -1.096<br>CD16-positive, CD56-dim natural killer cell, human: -1.157<br>mature NK T cell: -3.349<br>CD16-negative, CD56-bright natural killer cell, human: -3.423<br>gamma-delta T cell: -4.365",
+          "value: -5.696<br>plasmacytoid dendritic cell: -0.435<br>plasmacytoid dendritic cell, human: -2.474<br>dendritic cell: -3.143<br>B cell: -4.977<br>dendritic cell, human: -4.988",
+          "value: -5.790<br>CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -1.696<br>CD16-negative, CD56-bright natural killer cell, human: -2.993<br>mature NK T cell: -3.497<br>astrocyte of the cerebral cortex: -4.620",
+          "value: -6.298<br>CD16-negative, CD56-bright natural killer cell, human: -1.404<br>natural killer cell: -2.350<br>gamma-delta T cell: -2.548<br>CD16-positive, CD56-dim natural killer cell, human: -3.085<br>mature NK T cell: -3.486",
+          "value: -5.973<br>plasmacytoid dendritic cell: -0.313<br>dendritic cell: -2.804<br>plasmacytoid dendritic cell, human: -2.884<br>plasma cell: -4.125<br>conventional dendritic cell: -4.922",
+          "value: -6.318<br>CD16-positive, CD56-dim natural killer cell, human: -0.657<br>natural killer cell: -1.585<br>CD16-negative, CD56-bright natural killer cell, human: -3.792<br>mature NK T cell: -4.221<br>activated type II NK T cell: -4.603",
+          "value: -6.829<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.302<br>central memory CD4-positive, alpha-beta T cell: -1.670<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.683<br>T follicular helper cell: -2.490<br>effector memory CD4-positive, alpha-beta T cell: -4.090",
+          "value: -5.250<br>CD16-positive, CD56-dim natural killer cell, human: -0.896<br>natural killer cell: -1.955<br>CD16-negative, CD56-bright natural killer cell, human: -3.121<br>mature NK T cell: -3.370<br>gamma-delta T cell: -3.522",
+          "value: -6.264<br>classical monocyte: -0.993<br>CD14-positive monocyte: -1.768<br>macrophage: -3.446<br>pvalb GABAergic cortical interneuron: -3.480<br>monocyte: -3.835",
+          "value: -5.916<br>CD16-positive, CD56-dim natural killer cell, human: -1.156<br>natural killer cell: -2.625<br>gamma-delta T cell: -2.883<br>mature NK T cell: -3.154<br>effector CD8-positive, alpha-beta T cell: -3.291",
+          "value: -5.312<br>CD16-positive, CD56-dim natural killer cell, human: -0.732<br>natural killer cell: -1.694<br>CD16-negative, CD56-bright natural killer cell, human: -2.375<br>mature NK T cell: -3.892<br>gamma-delta T cell: -3.921",
+          "value: -6.305<br>CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.607<br>CD16-negative, CD56-bright natural killer cell, human: -3.290<br>mature NK T cell: -4.301<br>activated type II NK T cell: -4.878",
+          "value: -5.077<br>CD16-positive, CD56-dim natural killer cell, human: -0.835<br>natural killer cell: -1.105<br>mature NK T cell: -3.454<br>astrocyte of the cerebral cortex: -4.592<br>CD16-negative, CD56-bright natural killer cell, human: -4.658",
+          "value: -5.038<br>CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -1.335<br>CD16-negative, CD56-bright natural killer cell, human: -2.671<br>mature NK T cell: -4.196<br>gamma-delta T cell: -4.595",
+          "value: -5.853<br>CD16-positive, CD56-dim natural killer cell, human: -1.685<br>CD16-negative, CD56-bright natural killer cell, human: -2.164<br>gamma-delta T cell: -2.336<br>natural killer cell: -2.628<br>effector memory CD8-positive, alpha-beta T cell: -2.697",
+          "value: -6.047<br>CD16-negative, CD56-bright natural killer cell, human: -0.740<br>natural killer cell: -2.566<br>CD16-positive, CD56-dim natural killer cell, human: -2.926<br>gamma-delta T cell: -3.366<br>mature NK T cell: -4.307",
+          "value: -4.185<br>gamma-delta T cell: -2.114<br>natural killer cell: -2.524<br>CD8-positive, alpha-beta T cell: -2.580<br>CD16-negative, CD56-bright natural killer cell, human: -2.619<br>CD16-positive, CD56-dim natural killer cell, human: -2.699",
+          "value: -5.975<br>CD16-positive, CD56-dim natural killer cell, human: -0.498<br>natural killer cell: -1.575<br>CD16-negative, CD56-bright natural killer cell, human: -4.034<br>mature NK T cell: -4.078<br>activated type II NK T cell: -4.936",
+          "value: -6.193<br>CD16-negative, CD56-bright natural killer cell, human: -1.168<br>natural killer cell: -2.865<br>gamma-delta T cell: -3.099<br>mature NK T cell: -3.812<br>effector memory CD8-positive, alpha-beta T cell: -3.843",
+          "value: -5.763<br>central memory CD4-positive, alpha-beta T cell: -1.451<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.003<br>effector memory CD4-positive, alpha-beta T cell: -2.373<br>T-helper 22 cell: -2.756<br>T follicular helper cell: -3.566",
+          "value: -6.947<br>dendritic cell: -1.178<br>conventional dendritic cell: -1.930<br>dendritic cell, human: -2.260<br>myeloid dendritic cell: -2.277<br>CD1c-positive myeloid dendritic cell: -2.407",
+          "value: -5.833<br>CD16-negative, CD56-bright natural killer cell, human: -1.497<br>CD16-positive, CD56-dim natural killer cell, human: -1.933<br>natural killer cell: -2.438<br>gamma-delta T cell: -2.609<br>mature NK T cell: -3.407",
+          "value: -5.840<br>CD16-positive, CD56-dim natural killer cell, human: -0.692<br>natural killer cell: -2.326<br>CD16-negative, CD56-bright natural killer cell, human: -3.250<br>mature NK T cell: -3.625<br>gamma-delta T cell: -3.863",
+          "value: -5.260<br>CD16-positive, CD56-dim natural killer cell, human: -0.848<br>natural killer cell: -1.355<br>CD16-negative, CD56-bright natural killer cell, human: -3.086<br>mature NK T cell: -3.655<br>gamma-delta T cell: -3.941",
+          "value: -5.798<br>conventional dendritic cell: -1.728<br>dendritic cell: -2.094<br>CD1c-positive myeloid dendritic cell: -2.267<br>myeloid dendritic cell: -2.326<br>dendritic cell, human: -2.367",
+          "value: -7.210<br>CD14-positive monocyte: -1.215<br>classical monocyte: -2.266<br>monocyte: -2.643<br>pvalb GABAergic cortical interneuron: -2.932<br>promonocyte: -3.974",
+          "value: -4.969<br>CD16-negative, CD56-bright natural killer cell, human: -0.852<br>natural killer cell: -2.754<br>CD16-positive, CD56-dim natural killer cell, human: -3.500<br>activated type II NK T cell: -4.087<br>gamma-delta T cell: -4.230",
+          "value: -5.296<br>plasmacytoid dendritic cell: -0.481<br>plasmacytoid dendritic cell, human: -2.478<br>dendritic cell: -2.721<br>plasma cell: -3.470<br>conventional dendritic cell: -4.810",
+          "value: -5.421<br>natural killer cell: -1.363<br>CD16-negative, CD56-bright natural killer cell, human: -1.404<br>CD16-positive, CD56-dim natural killer cell, human: -1.631<br>mature NK T cell: -3.972<br>gamma-delta T cell: -4.159",
+          "value: -5.590<br>CD16-positive, CD56-dim natural killer cell, human: -1.894<br>gamma-delta T cell: -2.450<br>CD16-negative, CD56-bright natural killer cell, human: -2.557<br>mature NK T cell: -2.678<br>natural killer cell: -2.704",
+          "value: -5.897<br>CD16-negative, CD56-bright natural killer cell, human: -0.949<br>CD16-positive, CD56-dim natural killer cell, human: -2.498<br>natural killer cell: -2.751<br>gamma-delta T cell: -3.198<br>mature NK T cell: -3.843",
+          "value: -6.086<br>CD14-positive monocyte: -0.999<br>classical monocyte: -1.715<br>pvalb GABAergic cortical interneuron: -2.946<br>monocyte: -2.972<br>promonocyte: -4.388",
+          "value: -6.655<br>CD14-positive monocyte: -1.345<br>classical monocyte: -1.530<br>pvalb GABAergic cortical interneuron: -3.296<br>CD1c-positive myeloid dendritic cell: -3.538<br>macrophage: -3.650",
+          "value: -6.108<br>CD14-positive monocyte: -1.048<br>classical monocyte: -1.238<br>pvalb GABAergic cortical interneuron: -2.922<br>monocyte: -3.664<br>macrophage: -4.626",
+          "value: -5.918<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.610<br>central memory CD4-positive, alpha-beta T cell: -1.992<br>effector memory CD4-positive, alpha-beta T cell: -2.347<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.498<br>T follicular helper cell: -3.139",
+          "value: -5.093<br>classical monocyte: -1.060<br>CD14-positive monocyte: -1.607<br>pvalb GABAergic cortical interneuron: -2.897<br>monocyte: -3.590<br>macrophage: -4.418",
+          "value: -4.627<br>CD16-positive, CD56-dim natural killer cell, human: -1.387<br>gamma-delta T cell: -2.375<br>CD16-negative, CD56-bright natural killer cell, human: -2.443<br>natural killer cell: -2.656<br>mature NK T cell: -3.011",
+          "value: -4.813<br>effector memory CD4-positive, alpha-beta T cell: -1.974<br>central memory CD4-positive, alpha-beta T cell: -2.592<br>T-helper 22 cell: -2.729<br>mucosal invariant T cell: -2.829<br>effector memory CD8-positive, alpha-beta T cell: -3.081",
+          "value: -5.370<br>CD16-positive, CD56-dim natural killer cell, human: -0.331<br>natural killer cell: -2.563<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.275<br>gamma-delta T cell: -4.350",
+          "value: -5.905<br>classical monocyte: -1.143<br>CD14-positive monocyte: -1.429<br>pvalb GABAergic cortical interneuron: -2.813<br>monocyte: -3.704<br>macrophage: -4.217",
+          "value: -5.826<br>CD16-positive, CD56-dim natural killer cell, human: -0.673<br>natural killer cell: -1.415<br>CD16-negative, CD56-bright natural killer cell, human: -3.306<br>activated type II NK T cell: -4.328<br>mature NK T cell: -4.423",
+          "value: -4.178<br>effector memory CD4-positive, alpha-beta T cell: -1.934<br>central memory CD4-positive, alpha-beta T cell: -2.132<br>T follicular helper cell: -2.647<br>T-helper 22 cell: -2.821<br>mature NK T cell: -3.374",
+          "value: -6.914<br>CD16-positive, CD56-dim natural killer cell, human: -0.699<br>natural killer cell: -1.197<br>mature NK T cell: -4.045<br>CD16-negative, CD56-bright natural killer cell, human: -4.205<br>activated type II NK T cell: -4.823",
+          "value: -5.185<br>CD16-positive, CD56-dim natural killer cell, human: -1.489<br>CD16-negative, CD56-bright natural killer cell, human: -1.568<br>natural killer cell: -2.539<br>gamma-delta T cell: -2.856<br>mature NK T cell: -3.432",
+          "value: -6.427<br>CD16-positive, CD56-dim natural killer cell, human: -1.005<br>CD16-negative, CD56-bright natural killer cell, human: -2.217<br>natural killer cell: -2.697<br>gamma-delta T cell: -4.060<br>mature NK T cell: -4.075",
+          "value: -6.307<br>T follicular helper cell: -1.699<br>central memory CD4-positive, alpha-beta T cell: -2.097<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.247<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.643<br>effector memory CD4-positive, alpha-beta T cell: -2.958",
+          "value: -5.424<br>central memory CD4-positive, alpha-beta T cell: -1.695<br>effector memory CD4-positive, alpha-beta T cell: -1.886<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.530<br>T-helper 22 cell: -2.909<br>T follicular helper cell: -2.993",
+          "value: -5.742<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.230<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.609<br>central memory CD4-positive, alpha-beta T cell: -1.963<br>T follicular helper cell: -2.919<br>effector memory CD4-positive, alpha-beta T cell: -3.368",
+          "value: -4.612<br>CD16-positive, CD56-dim natural killer cell, human: -1.371<br>natural killer cell: -1.934<br>mature NK T cell: -2.639<br>CD16-negative, CD56-bright natural killer cell, human: -2.898<br>gamma-delta T cell: -3.103",
+          "value: -6.048<br>CD16-positive, CD56-dim natural killer cell, human: -0.822<br>natural killer cell: -2.544<br>CD16-negative, CD56-bright natural killer cell, human: -2.656<br>mature NK T cell: -3.719<br>gamma-delta T cell: -3.801",
+          "value: -5.894<br>CD16-positive, CD56-dim natural killer cell, human: -0.411<br>natural killer cell: -1.959<br>mature NK T cell: -3.520<br>CD16-negative, CD56-bright natural killer cell, human: -4.591<br>gamma-delta T cell: -4.629",
+          "value: -5.962<br>CD16-positive, CD56-dim natural killer cell, human: -0.723<br>CD16-negative, CD56-bright natural killer cell, human: -2.075<br>natural killer cell: -2.291<br>mature NK T cell: -3.955<br>gamma-delta T cell: -4.228",
+          "value: -6.445<br>CD16-negative, CD56-bright natural killer cell, human: -1.534<br>CD16-positive, CD56-dim natural killer cell, human: -2.056<br>natural killer cell: -2.392<br>gamma-delta T cell: -2.663<br>mature NK T cell: -3.687",
+          "value: -5.783<br>CD16-negative, CD56-bright natural killer cell, human: -1.145<br>gamma-delta T cell: -2.205<br>CD16-positive, CD56-dim natural killer cell, human: -2.834<br>natural killer cell: -3.199<br>effector memory CD8-positive, alpha-beta T cell: -3.371",
+          "value: -6.133<br>CD16-positive, CD56-dim natural killer cell, human: -0.877<br>natural killer cell: -1.048<br>CD16-negative, CD56-bright natural killer cell, human: -3.302<br>mature NK T cell: -3.863<br>activated type II NK T cell: -4.798",
+          "value: -6.492<br>CD16-positive, CD56-dim natural killer cell, human: -0.326<br>natural killer cell: -2.095<br>CD16-negative, CD56-bright natural killer cell, human: -3.549<br>mature NK T cell: -4.346<br>astrocyte of the cerebral cortex: -5.129",
+          "value: -5.530<br>central memory CD4-positive, alpha-beta T cell: -1.837<br>effector memory CD4-positive, alpha-beta T cell: -2.212<br>T follicular helper cell: -2.560<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.673<br>T-helper 22 cell: -3.030",
+          "value: -5.509<br>non-classical monocyte: -2.023<br>CD14-positive monocyte: -2.400<br>classical monocyte: -2.652<br>CD14-low, CD16-positive monocyte: -2.841<br>macrophage: -3.116",
+          "value: -5.116<br>CD16-negative, CD56-bright natural killer cell, human: -1.437<br>natural killer cell: -3.333<br>gamma-delta T cell: -3.548<br>effector memory CD8-positive, alpha-beta T cell: -3.673<br>mature NK T cell: -3.775",
+          "value: -5.711<br>CD16-positive, CD56-dim natural killer cell, human: -0.489<br>natural killer cell: -2.124<br>CD16-negative, CD56-bright natural killer cell, human: -3.264<br>mature NK T cell: -3.865<br>gamma-delta T cell: -4.902",
+          "value: -6.578<br>CD16-positive, CD56-dim natural killer cell, human: -0.438<br>natural killer cell: -1.548<br>mature NK T cell: -3.989<br>astrocyte of the cerebral cortex: -4.831<br>CD16-negative, CD56-bright natural killer cell, human: -5.097",
+          "value: -5.799<br>CD16-positive, CD56-dim natural killer cell, human: -0.453<br>natural killer cell: -2.089<br>CD16-negative, CD56-bright natural killer cell, human: -3.782<br>gamma-delta T cell: -4.022<br>mature NK T cell: -4.037",
+          "value: -5.918<br>CD16-positive, CD56-dim natural killer cell, human: -0.664<br>natural killer cell: -1.881<br>CD16-negative, CD56-bright natural killer cell, human: -3.064<br>mature NK T cell: -3.803<br>immature NK T cell: -4.513",
+          "value: -6.995<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.712<br>T follicular helper cell: -1.789<br>central memory CD4-positive, alpha-beta T cell: -2.068<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.797<br>effector memory CD4-positive, alpha-beta T cell: -3.460",
+          "value: -6.586<br>CD14-positive monocyte: -1.263<br>classical monocyte: -1.329<br>pvalb GABAergic cortical interneuron: -2.637<br>monocyte: -3.407<br>macrophage: -4.166",
+          "value: -4.828<br>CD16-negative, CD56-bright natural killer cell, human: -1.555<br>gamma-delta T cell: -2.517<br>natural killer cell: -2.873<br>mature NK T cell: -3.042<br>CD16-positive, CD56-dim natural killer cell, human: -3.314",
+          "value: -5.526<br>CD16-positive, CD56-dim natural killer cell, human: -1.095<br>natural killer cell: -1.900<br>CD16-negative, CD56-bright natural killer cell, human: -2.137<br>gamma-delta T cell: -3.411<br>mature NK T cell: -4.067",
+          "value: -6.704<br>CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -2.148<br>CD16-negative, CD56-bright natural killer cell, human: -2.344<br>mature NK T cell: -4.538<br>activated type II NK T cell: -4.693",
+          "value: -4.856<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.126<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.355<br>central memory CD4-positive, alpha-beta T cell: -2.745<br>T cell: -3.268<br>T follicular helper cell: -4.256",
+          "value: -5.744<br>CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.373<br>CD16-negative, CD56-bright natural killer cell, human: -3.507<br>mature NK T cell: -4.547<br>activated type II NK T cell: -4.629",
+          "value: -6.772<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.654<br>central memory CD4-positive, alpha-beta T cell: -1.826<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.022<br>T follicular helper cell: -2.047<br>effector memory CD4-positive, alpha-beta T cell: -3.142",
+          "value: -4.839<br>effector memory CD4-positive, alpha-beta T cell: -1.743<br>central memory CD4-positive, alpha-beta T cell: -2.298<br>T-helper 22 cell: -2.912<br>T follicular helper cell: -2.916<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.520",
+          "value: -5.632<br>CD16-positive, CD56-dim natural killer cell, human: -0.711<br>natural killer cell: -1.797<br>mature NK T cell: -3.004<br>CD8-positive, alpha-beta T cell: -3.106<br>gamma-delta T cell: -3.153",
+          "value: -6.322<br>CD16-positive, CD56-dim natural killer cell, human: -0.626<br>natural killer cell: -1.353<br>mature NK T cell: -3.382<br>astrocyte of the cerebral cortex: -4.712<br>CD16-negative, CD56-bright natural killer cell, human: -4.720",
+          "value: -6.806<br>classical monocyte: -1.959<br>CD14-positive monocyte: -2.129<br>dendritic cell, human: -2.449<br>CD1c-positive myeloid dendritic cell: -2.759<br>macrophage: -3.045",
+          "value: -4.789<br>CD16-positive, CD56-dim natural killer cell, human: -1.222<br>natural killer cell: -1.485<br>mature NK T cell: -2.608<br>CD8-positive, alpha-beta T cell: -2.713<br>gamma-delta T cell: -2.980",
+          "value: -6.845<br>CD16-negative, CD56-bright natural killer cell, human: -1.043<br>CD16-positive, CD56-dim natural killer cell, human: -2.157<br>gamma-delta T cell: -2.922<br>natural killer cell: -2.954<br>mature NK T cell: -3.636",
+          "value: -6.294<br>CD16-positive, CD56-dim natural killer cell, human: -0.932<br>natural killer cell: -1.327<br>mature NK T cell: -2.961<br>gamma-delta T cell: -3.675<br>CD16-negative, CD56-bright natural killer cell, human: -4.246",
+          "value: -5.988<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.355<br>central memory CD4-positive, alpha-beta T cell: -1.857<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.376<br>effector memory CD4-positive, alpha-beta T cell: -2.757<br>T-helper 22 cell: -2.907",
+          "value: -5.607<br>plasmacytoid dendritic cell: -0.383<br>dendritic cell: -2.676<br>plasmacytoid dendritic cell, human: -2.693<br>conventional dendritic cell: -4.542<br>dendritic cell, human: -4.722",
+          "value: -5.859<br>central memory CD4-positive, alpha-beta T cell: -1.498<br>T follicular helper cell: -2.002<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.291<br>effector memory CD4-positive, alpha-beta T cell: -2.380<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.453",
+          "value: -6.953<br>CD14-positive monocyte: -1.864<br>classical monocyte: -2.046<br>macrophage: -2.904<br>CD1c-positive myeloid dendritic cell: -3.051<br>dendritic cell, human: -3.091",
+          "value: -3.902<br>effector memory CD4-positive, alpha-beta T cell: -2.362<br>T cell: -2.751<br>T follicular helper cell: -2.776<br>central memory CD4-positive, alpha-beta T cell: -2.851<br>T-helper 22 cell: -2.922",
+          "value: -5.760<br>CD16-negative, CD56-bright natural killer cell, human: -1.364<br>CD16-positive, CD56-dim natural killer cell, human: -2.361<br>gamma-delta T cell: -2.525<br>natural killer cell: -2.598<br>mature NK T cell: -3.294",
+          "value: -7.357<br>CD16-positive, CD56-dim natural killer cell, human: -0.932<br>CD16-negative, CD56-bright natural killer cell, human: -2.373<br>natural killer cell: -2.586<br>mature NK T cell: -3.396<br>gamma-delta T cell: -3.617",
+          "value: -4.500<br>CD14-positive monocyte: -1.439<br>classical monocyte: -1.821<br>macrophage: -2.083<br>CD1c-positive myeloid dendritic cell: -3.276<br>alveolar macrophage: -3.489",
+          "value: -5.867<br>plasmacytoid dendritic cell: -0.214<br>plasmacytoid dendritic cell, human: -2.984<br>dendritic cell: -3.361<br>dendritic cell, human: -5.709<br>B cell: -5.733",
+          "value: -5.673<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.155<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.357<br>central memory CD4-positive, alpha-beta T cell: -2.465<br>T cell: -3.911<br>granule cell: -4.426",
+          "value: -4.475<br>T cell: -2.724<br>mast cell: -2.888<br>central memory CD4-positive, alpha-beta T cell: -2.948<br>effector memory CD4-positive, alpha-beta T cell: -3.015<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.478",
+          "value: -5.260<br>CD16-positive, CD56-dim natural killer cell, human: -0.795<br>natural killer cell: -1.978<br>CD16-negative, CD56-bright natural killer cell, human: -2.410<br>mature NK T cell: -3.496<br>gamma-delta T cell: -3.708",
+          "value: -6.310<br>CD14-positive monocyte: -1.073<br>classical monocyte: -1.516<br>non-classical monocyte: -3.564<br>pvalb GABAergic cortical interneuron: -3.721<br>macrophage: -3.728",
+          "value: -6.820<br>CD16-negative, CD56-bright natural killer cell, human: -1.145<br>natural killer cell: -2.365<br>CD16-positive, CD56-dim natural killer cell, human: -2.799<br>mature NK T cell: -3.821<br>activated type II NK T cell: -4.101",
+          "value: -4.760<br>CD16-negative, CD56-bright natural killer cell, human: -0.926<br>natural killer cell: -3.067<br>gamma-delta T cell: -3.598<br>CD16-positive, CD56-dim natural killer cell, human: -3.667<br>innate lymphoid cell: -3.977",
+          "value: -5.416<br>plasmacytoid dendritic cell: -0.357<br>plasmacytoid dendritic cell, human: -2.536<br>dendritic cell: -2.916<br>B cell: -4.835<br>conventional dendritic cell: -5.118",
+          "value: -4.345<br>classical monocyte: -1.122<br>CD14-positive monocyte: -2.174<br>macrophage: -3.035<br>monocyte: -3.159<br>pvalb GABAergic cortical interneuron: -3.647",
+          "value: -5.988<br>CD16-positive, CD56-dim natural killer cell, human: -1.273<br>CD16-negative, CD56-bright natural killer cell, human: -1.594<br>natural killer cell: -2.069<br>mature NK T cell: -3.726<br>gamma-delta T cell: -3.854",
+          "value: -5.618<br>CD16-negative, CD56-bright natural killer cell, human: -0.828<br>natural killer cell: -2.522<br>gamma-delta T cell: -3.067<br>CD16-positive, CD56-dim natural killer cell, human: -3.138<br>innate lymphoid cell: -4.063",
+          "value: -6.100<br>CD14-positive monocyte: -1.372<br>classical monocyte: -1.654<br>pvalb GABAergic cortical interneuron: -2.959<br>CD1c-positive myeloid dendritic cell: -3.182<br>monocyte: -3.297",
+          "value: -6.995<br>CD16-positive, CD56-dim natural killer cell, human: -0.759<br>CD16-negative, CD56-bright natural killer cell, human: -2.105<br>natural killer cell: -2.202<br>activated type II NK T cell: -4.114<br>mature NK T cell: -4.130",
+          "value: -6.261<br>CD16-positive, CD56-dim natural killer cell, human: -0.619<br>natural killer cell: -1.202<br>mature NK T cell: -4.158<br>astrocyte of the cerebral cortex: -4.772<br>CD16-negative, CD56-bright natural killer cell, human: -5.095",
+          "value: -4.083<br>CD16-positive, CD56-dim natural killer cell, human: -1.085<br>CD16-negative, CD56-bright natural killer cell, human: -1.347<br>natural killer cell: -2.116<br>gamma-delta T cell: -3.772<br>mature NK T cell: -4.163",
+          "value: -6.441<br>CD16-positive, CD56-dim natural killer cell, human: -0.432<br>natural killer cell: -1.950<br>CD16-negative, CD56-bright natural killer cell, human: -3.174<br>mature NK T cell: -4.752<br>activated type II NK T cell: -4.861",
+          "value: -6.232<br>CD16-positive, CD56-dim natural killer cell, human: -0.937<br>CD16-negative, CD56-bright natural killer cell, human: -1.607<br>natural killer cell: -2.486<br>gamma-delta T cell: -3.120<br>mature NK T cell: -4.301",
+          "value: -5.190<br>CD14-positive monocyte: -0.894<br>classical monocyte: -1.832<br>monocyte: -2.966<br>pvalb GABAergic cortical interneuron: -3.295<br>promonocyte: -4.022",
+          "value: -5.607<br>CD16-positive, CD56-dim natural killer cell, human: -0.635<br>natural killer cell: -1.549<br>CD16-negative, CD56-bright natural killer cell, human: -3.596<br>mature NK T cell: -4.204<br>gamma-delta T cell: -4.517",
+          "value: -4.525<br>CD16-positive, CD56-dim natural killer cell, human: -1.655<br>CD16-negative, CD56-bright natural killer cell, human: -1.753<br>natural killer cell: -2.203<br>gamma-delta T cell: -2.658<br>mature NK T cell: -3.103",
+          "value: -5.687<br>CD16-positive, CD56-dim natural killer cell, human: -1.408<br>CD16-negative, CD56-bright natural killer cell, human: -1.696<br>natural killer cell: -1.838<br>mature NK T cell: -3.369<br>gamma-delta T cell: -3.620",
+          "value: -5.534<br>CD16-negative, CD56-bright natural killer cell, human: -2.043<br>effector memory CD8-positive, alpha-beta T cell: -3.270<br>gamma-delta T cell: -3.336<br>natural killer cell: -3.380<br>mature NK T cell: -3.741",
+          "value: -6.017<br>CD16-negative, CD56-bright natural killer cell, human: -1.016<br>natural killer cell: -2.409<br>CD16-positive, CD56-dim natural killer cell, human: -2.670<br>gamma-delta T cell: -3.153<br>mature NK T cell: -3.715",
+          "value: -6.500<br>dendritic cell, human: -2.054<br>conventional dendritic cell: -2.125<br>CD1c-positive myeloid dendritic cell: -2.276<br>dendritic cell: -2.731<br>myeloid dendritic cell: -3.388",
+          "value: -5.563<br>T follicular helper cell: -1.995<br>effector memory CD4-positive, alpha-beta T cell: -2.036<br>central memory CD4-positive, alpha-beta T cell: -2.557<br>T-helper 22 cell: -3.130<br>effector memory CD8-positive, alpha-beta T cell: -3.592",
+          "value: -5.713<br>CD16-positive, CD56-dim natural killer cell, human: -0.937<br>natural killer cell: -1.630<br>CD16-negative, CD56-bright natural killer cell, human: -2.254<br>activated type II NK T cell: -4.279<br>mature NK T cell: -4.318",
+          "value: -6.354<br>dendritic cell: -1.714<br>conventional dendritic cell: -1.798<br>CD1c-positive myeloid dendritic cell: -2.193<br>dendritic cell, human: -2.237<br>myeloid dendritic cell: -2.686",
+          "value: -7.262<br>CD16-negative, CD56-bright natural killer cell, human: -0.879<br>gamma-delta T cell: -3.002<br>CD16-positive, CD56-dim natural killer cell, human: -3.387<br>natural killer cell: -3.433<br>activated type II NK T cell: -4.034",
+          "value: -5.323<br>CD16-negative, CD56-bright natural killer cell, human: -1.143<br>CD16-positive, CD56-dim natural killer cell, human: -1.622<br>natural killer cell: -2.118<br>mature NK T cell: -3.496<br>gamma-delta T cell: -3.778",
+          "value: -5.092<br>CD16-positive, CD56-dim natural killer cell, human: -0.590<br>natural killer cell: -1.826<br>CD16-negative, CD56-bright natural killer cell, human: -3.421<br>mature NK T cell: -4.252<br>activated type II NK T cell: -4.714",
+          "value: -6.523<br>CD16-positive, CD56-dim natural killer cell, human: -1.124<br>CD16-negative, CD56-bright natural killer cell, human: -2.114<br>natural killer cell: -2.208<br>gamma-delta T cell: -3.521<br>mature NK T cell: -3.676",
+          "value: -4.493<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.224<br>central memory CD4-positive, alpha-beta T cell: -1.895<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.515<br>T follicular helper cell: -3.084<br>T cell: -3.342",
+          "value: -6.561<br>CD14-positive monocyte: -1.844<br>classical monocyte: -2.143<br>dendritic cell, human: -2.676<br>CD1c-positive myeloid dendritic cell: -3.322<br>pvalb GABAergic cortical interneuron: -3.755",
+          "value: -5.606<br>T follicular helper cell: -2.424<br>regulatory T cell: -2.845<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.851<br>effector memory CD4-positive, alpha-beta T cell: -2.892<br>T cell: -2.911",
+          "value: -5.355<br>CD16-positive, CD56-dim natural killer cell, human: -0.918<br>natural killer cell: -2.152<br>CD16-negative, CD56-bright natural killer cell, human: -2.157<br>gamma-delta T cell: -3.751<br>mature NK T cell: -4.068",
+          "value: -6.765<br>CD14-positive monocyte: -1.115<br>classical monocyte: -1.509<br>pvalb GABAergic cortical interneuron: -2.932<br>monocyte: -3.151<br>promonocyte: -4.281",
+          "value: -5.574<br>CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.473<br>CD16-negative, CD56-bright natural killer cell, human: -3.042<br>mature NK T cell: -4.489<br>gamma-delta T cell: -4.564",
+          "value: -5.572<br>CD16-positive, CD56-dim natural killer cell, human: -0.725<br>natural killer cell: -1.401<br>CD16-negative, CD56-bright natural killer cell, human: -3.390<br>mature NK T cell: -4.229<br>activated type II NK T cell: -4.506",
+          "value: -4.550<br>T follicular helper cell: -2.346<br>effector memory CD4-positive, alpha-beta T cell: -2.493<br>effector memory CD8-positive, alpha-beta T cell: -2.580<br>T-helper 22 cell: -2.698<br>central memory CD4-positive, alpha-beta T cell: -2.812",
+          "value: -5.334<br>natural killer cell: -0.899<br>CD16-positive, CD56-dim natural killer cell, human: -0.909<br>mature NK T cell: -4.228<br>CD8-positive, alpha-beta T cell: -4.449<br>CD16-negative, CD56-bright natural killer cell, human: -4.451",
+          "value: -6.311<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.593<br>central memory CD4-positive, alpha-beta T cell: -1.639<br>effector memory CD4-positive, alpha-beta T cell: -2.374<br>T-helper 22 cell: -2.601<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.638",
+          "value: -6.199<br>CD16-negative, CD56-bright natural killer cell, human: -1.037<br>natural killer cell: -1.966<br>CD16-positive, CD56-dim natural killer cell, human: -2.800<br>gamma-delta T cell: -3.441<br>mature NK T cell: -3.957",
+          "value: -6.465<br>classical monocyte: -1.542<br>CD14-positive monocyte: -2.619<br>dendritic cell, human: -2.636<br>CD1c-positive myeloid dendritic cell: -3.135<br>conventional dendritic cell: -3.367",
+          "value: -5.635<br>plasmacytoid dendritic cell: -0.238<br>dendritic cell: -3.127<br>plasmacytoid dendritic cell, human: -3.254<br>B cell: -5.226<br>myeloid dendritic cell: -5.584",
+          "value: -6.181<br>CD16-positive, CD56-dim natural killer cell, human: -0.572<br>natural killer cell: -1.477<br>CD16-negative, CD56-bright natural killer cell, human: -3.785<br>mature NK T cell: -4.370<br>activated type II NK T cell: -4.851",
+          "value: -5.213<br>plasmacytoid dendritic cell: -0.707<br>dendritic cell: -2.344<br>plasmacytoid dendritic cell, human: -2.479<br>plasma cell: -3.900<br>dendritic cell, human: -4.766",
+          "value: -6.121<br>natural killer cell: -0.785<br>CD16-positive, CD56-dim natural killer cell, human: -1.117<br>CD16-negative, CD56-bright natural killer cell, human: -3.936<br>mature NK T cell: -4.167<br>lymphocyte: -4.762",
+          "value: -5.659<br>CD16-positive, CD56-dim natural killer cell, human: -0.850<br>natural killer cell: -1.906<br>CD16-negative, CD56-bright natural killer cell, human: -2.178<br>mature NK T cell: -3.909<br>gamma-delta T cell: -4.326",
+          "value: -4.554<br>CD16-positive, CD56-dim natural killer cell, human: -0.806<br>natural killer cell: -1.678<br>CD16-negative, CD56-bright natural killer cell, human: -2.358<br>gamma-delta T cell: -3.916<br>mature NK T cell: -4.096",
+          "value: -4.759<br>CD16-negative, CD56-bright natural killer cell, human: -1.254<br>CD16-positive, CD56-dim natural killer cell, human: -2.005<br>natural killer cell: -2.607<br>gamma-delta T cell: -2.762<br>mature NK T cell: -3.455",
+          "value: -5.817<br>plasmacytoid dendritic cell: -0.385<br>plasmacytoid dendritic cell, human: -2.806<br>dendritic cell: -2.807<br>CD1c-positive myeloid dendritic cell: -4.916<br>conventional dendritic cell: -4.946",
+          "value: -6.421<br>classical monocyte: -1.289<br>CD14-positive monocyte: -1.515<br>pvalb GABAergic cortical interneuron: -3.026<br>dendritic cell, human: -4.126<br>monocyte: -4.140",
+          "value: -5.637<br>conventional dendritic cell: -1.531<br>dendritic cell, human: -2.180<br>CD1c-positive myeloid dendritic cell: -2.290<br>dendritic cell: -2.355<br>myeloid dendritic cell: -2.586",
+          "value: -6.268<br>CD16-negative, CD56-bright natural killer cell, human: -0.683<br>natural killer cell: -2.680<br>activated type II NK T cell: -3.935<br>group 3 innate lymphoid cell: -4.039<br>CD16-positive, CD56-dim natural killer cell, human: -4.093",
+          "value: -5.580<br>CD16-positive, CD56-dim natural killer cell, human: -0.771<br>natural killer cell: -1.610<br>CD16-negative, CD56-bright natural killer cell, human: -2.621<br>mature NK T cell: -4.032<br>activated type II NK T cell: -4.470",
+          "value: -5.489<br>plasmacytoid dendritic cell: -0.288<br>plasmacytoid dendritic cell, human: -2.713<br>dendritic cell: -3.168<br>B cell: -5.217<br>conventional dendritic cell: -5.244",
+          "value: -6.158<br>CD16-positive, CD56-dim natural killer cell, human: -0.360<br>natural killer cell: -1.884<br>mature NK T cell: -3.965<br>CD16-negative, CD56-bright natural killer cell, human: -4.683<br>gamma-delta T cell: -4.881",
+          "value: -6.017<br>classical monocyte: -1.174<br>CD14-positive monocyte: -1.560<br>pvalb GABAergic cortical interneuron: -2.863<br>monocyte: -3.588<br>macrophage: -4.238",
+          "value: -5.826<br>CD14-positive monocyte: -0.787<br>classical monocyte: -1.553<br>pvalb GABAergic cortical interneuron: -3.378<br>monocyte: -3.658<br>macrophage: -4.417",
+          "value: -6.004<br>CD16-positive, CD56-dim natural killer cell, human: -0.650<br>natural killer cell: -1.380<br>mature NK T cell: -3.617<br>CD16-negative, CD56-bright natural killer cell, human: -4.315<br>astrocyte of the cerebral cortex: -4.787",
+          "value: -6.797<br>CD16-negative, CD56-bright natural killer cell, human: -0.896<br>gamma-delta T cell: -2.830<br>natural killer cell: -3.456<br>effector memory CD8-positive, alpha-beta T cell: -3.721<br>mucosal invariant T cell: -3.851",
+          "value: -5.694<br>plasmacytoid dendritic cell: -0.251<br>plasmacytoid dendritic cell, human: -2.733<br>dendritic cell: -2.982<br>myeloid dendritic cell: -5.299<br>dendritic cell, human: -5.399",
+          "value: -5.671<br>CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.560<br>CD16-negative, CD56-bright natural killer cell, human: -2.801<br>mature NK T cell: -4.287<br>activated type II NK T cell: -4.461",
+          "value: -6.024<br>CD16-positive, CD56-dim natural killer cell, human: -0.433<br>natural killer cell: -1.955<br>CD16-negative, CD56-bright natural killer cell, human: -3.640<br>mature NK T cell: -4.015<br>astrocyte of the cerebral cortex: -4.767",
+          "value: -6.403<br>CD16-positive, CD56-dim natural killer cell, human: -0.920<br>CD16-negative, CD56-bright natural killer cell, human: -1.756<br>natural killer cell: -1.970<br>activated type II NK T cell: -3.975<br>mature NK T cell: -4.214",
+          "value: -5.406<br>CD16-positive, CD56-dim natural killer cell, human: -1.355<br>natural killer cell: -1.679<br>CD16-negative, CD56-bright natural killer cell, human: -2.341<br>activated type II NK T cell: -3.859<br>mature NK T cell: -4.154",
+          "value: -6.640<br>CD16-positive, CD56-dim natural killer cell, human: -1.036<br>CD16-negative, CD56-bright natural killer cell, human: -1.656<br>natural killer cell: -2.269<br>mature NK T cell: -3.688<br>gamma-delta T cell: -3.766",
+          "value: -4.819<br>B cell: -0.431<br>naive B cell: -3.040<br>class switched memory B cell: -3.118<br>unswitched memory B cell: -3.774<br>memory B cell: -4.027",
+          "value: -5.831<br>plasmacytoid dendritic cell: -0.390<br>plasmacytoid dendritic cell, human: -2.488<br>dendritic cell: -2.849<br>conventional dendritic cell: -4.695<br>dendritic cell, human: -4.720",
+          "value: -5.586<br>T follicular helper cell: -1.528<br>central memory CD4-positive, alpha-beta T cell: -1.910<br>effector memory CD4-positive, alpha-beta T cell: -2.658<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.725<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.858",
+          "value: -5.773<br>CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.581<br>CD16-negative, CD56-bright natural killer cell, human: -3.539<br>mature NK T cell: -4.365<br>activated type II NK T cell: -4.760",
+          "value: -6.650<br>central memory CD4-positive, alpha-beta T cell: -1.653<br>T follicular helper cell: -1.727<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.453<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.561<br>effector memory CD4-positive, alpha-beta T cell: -2.584",
+          "value: -5.216<br>effector memory CD4-positive, alpha-beta T cell: -1.911<br>central memory CD4-positive, alpha-beta T cell: -2.463<br>T follicular helper cell: -2.749<br>T-helper 22 cell: -2.901<br>effector memory CD8-positive, alpha-beta T cell: -2.986",
+          "value: -6.135<br>CD16-positive, CD56-dim natural killer cell, human: -0.797<br>natural killer cell: -1.186<br>CD16-negative, CD56-bright natural killer cell, human: -3.458<br>mature NK T cell: -3.905<br>gamma-delta T cell: -4.676",
+          "value: -5.986<br>CD16-negative, CD56-bright natural killer cell, human: -0.911<br>CD16-positive, CD56-dim natural killer cell, human: -2.580<br>gamma-delta T cell: -2.735<br>natural killer cell: -2.970<br>mature NK T cell: -3.869",
+          "value: -6.429<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.329<br>central memory CD4-positive, alpha-beta T cell: -1.842<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.439<br>effector memory CD4-positive, alpha-beta T cell: -2.745<br>T follicular helper cell: -2.787",
+          "value: -6.399<br>CD16-negative, CD56-bright natural killer cell, human: -1.079<br>natural killer cell: -2.452<br>CD16-positive, CD56-dim natural killer cell, human: -2.701<br>gamma-delta T cell: -3.125<br>mature NK T cell: -3.767",
+          "value: -5.542<br>CD14-positive monocyte: -0.707<br>classical monocyte: -1.645<br>pvalb GABAergic cortical interneuron: -3.311<br>monocyte: -3.390<br>macrophage: -4.419",
+          "value: -5.780<br>CD16-negative, CD56-bright natural killer cell, human: -1.441<br>gamma-delta T cell: -2.064<br>CD16-positive, CD56-dim natural killer cell, human: -3.039<br>effector memory CD8-positive, alpha-beta T cell: -3.323<br>mucosal invariant T cell: -3.559",
+          "value: -4.873<br>CD16-positive, CD56-dim natural killer cell, human: -0.730<br>natural killer cell: -1.870<br>CD16-negative, CD56-bright natural killer cell, human: -2.875<br>gamma-delta T cell: -3.545<br>mature NK T cell: -3.646",
+          "value: -5.958<br>effector CD8-positive, alpha-beta T cell: -2.328<br>effector memory CD8-positive, alpha-beta T cell: -2.421<br>CD8-positive, alpha-beta T cell: -2.600<br>mature NK T cell: -2.752<br>gamma-delta T cell: -3.063",
+          "value: -6.942<br>classical monocyte: -1.543<br>CD14-positive monocyte: -1.703<br>dendritic cell, human: -2.988<br>macrophage: -3.258<br>CD1c-positive myeloid dendritic cell: -3.354",
+          "value: -5.708<br>CD16-positive, CD56-dim natural killer cell, human: -0.895<br>natural killer cell: -1.794<br>CD16-negative, CD56-bright natural killer cell, human: -2.629<br>mature NK T cell: -3.415<br>gamma-delta T cell: -4.492",
+          "value: -6.614<br>CD16-positive, CD56-dim natural killer cell, human: -1.021<br>CD16-negative, CD56-bright natural killer cell, human: -2.217<br>gamma-delta T cell: -2.666<br>natural killer cell: -2.674<br>mature NK T cell: -3.132",
+          "value: -5.779<br>CD16-negative, CD56-bright natural killer cell, human: -1.399<br>gamma-delta T cell: -2.868<br>effector memory CD8-positive, alpha-beta T cell: -3.647<br>CD16-positive, CD56-dim natural killer cell, human: -3.685<br>natural killer cell: -3.735",
+          "value: -6.284<br>CD16-positive, CD56-dim natural killer cell, human: -0.489<br>natural killer cell: -1.666<br>CD16-negative, CD56-bright natural killer cell, human: -3.662<br>mature NK T cell: -4.530<br>activated type II NK T cell: -4.846",
+          "value: -6.401<br>CD14-positive monocyte: -1.768<br>classical monocyte: -1.782<br>dendritic cell, human: -3.108<br>CD1c-positive myeloid dendritic cell: -3.112<br>macrophage: -3.303",
+          "value: -5.098<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.617<br>central memory CD4-positive, alpha-beta T cell: -1.954<br>effector memory CD4-positive, alpha-beta T cell: -2.600<br>T-helper 22 cell: -2.888<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.038",
+          "value: -5.602<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.706<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.803<br>central memory CD4-positive, alpha-beta T cell: -2.260<br>T follicular helper cell: -3.571<br>T cell: -4.039",
+          "value: -6.264<br>classical monocyte: -1.125<br>CD14-positive monocyte: -1.410<br>pvalb GABAergic cortical interneuron: -3.069<br>macrophage: -3.927<br>monocyte: -4.039",
+          "value: -5.733<br>natural killer cell: -0.937<br>CD16-positive, CD56-dim natural killer cell, human: -1.098<br>mature NK T cell: -3.853<br>CD16-negative, CD56-bright natural killer cell, human: -4.369<br>astrocyte of the cerebral cortex: -4.735",
+          "value: -7.119<br>CD16-positive, CD56-dim natural killer cell, human: -0.618<br>natural killer cell: -2.016<br>CD16-negative, CD56-bright natural killer cell, human: -2.453<br>mature NK T cell: -3.514<br>gamma-delta T cell: -4.637",
+          "value: -5.997<br>T follicular helper cell: -2.475<br>effector memory CD4-positive, alpha-beta T cell: -2.656<br>central memory CD4-positive, alpha-beta T cell: -2.856<br>T-helper 22 cell: -2.977<br>effector memory CD8-positive, alpha-beta T cell: -3.455",
+          "value: -6.494<br>T follicular helper cell: -1.975<br>central memory CD4-positive, alpha-beta T cell: -2.041<br>effector memory CD4-positive, alpha-beta T cell: -2.555<br>T-helper 22 cell: -2.591<br>effector memory CD8-positive, alpha-beta T cell: -3.526",
+          "value: -4.684<br>CD16-negative, CD56-bright natural killer cell, human: -1.043<br>natural killer cell: -2.398<br>gamma-delta T cell: -3.436<br>innate lymphoid cell: -4.113<br>CD16-positive, CD56-dim natural killer cell, human: -4.213",
+          "value: -6.133<br>CD16-negative, CD56-bright natural killer cell, human: -1.255<br>CD16-positive, CD56-dim natural killer cell, human: -1.413<br>natural killer cell: -1.912<br>gamma-delta T cell: -3.501<br>mature NK T cell: -3.645",
+          "value: -5.653<br>CD16-positive, CD56-dim natural killer cell, human: -0.560<br>natural killer cell: -1.680<br>CD16-negative, CD56-bright natural killer cell, human: -3.533<br>mature NK T cell: -3.888<br>astrocyte of the cerebral cortex: -4.686",
+          "value: -5.005<br>CD16-negative, CD56-bright natural killer cell, human: -0.785<br>natural killer cell: -2.113<br>CD16-positive, CD56-dim natural killer cell, human: -2.570<br>gamma-delta T cell: -3.659<br>mature NK T cell: -4.113",
+          "value: -5.743<br>effector memory CD4-positive, alpha-beta T cell: -2.173<br>central memory CD4-positive, alpha-beta T cell: -2.405<br>effector memory CD8-positive, alpha-beta T cell: -2.512<br>T follicular helper cell: -2.538<br>T-helper 22 cell: -2.662",
+          "value: -5.956<br>plasmacytoid dendritic cell: -0.329<br>plasmacytoid dendritic cell, human: -2.773<br>dendritic cell: -2.978<br>B cell: -5.236<br>myeloid dendritic cell: -5.244",
+          "value: -4.540<br>CD16-positive, CD56-dim natural killer cell, human: -0.755<br>natural killer cell: -1.693<br>CD16-negative, CD56-bright natural killer cell, human: -3.104<br>mature NK T cell: -3.491<br>lymphocyte: -4.710",
+          "value: -5.238<br>B cell: -0.714<br>naive B cell: -2.023<br>malignant cell: -3.131<br>class switched memory B cell: -3.550<br>unswitched memory B cell: -3.756",
+          "value: -5.985<br>CD16-negative, CD56-bright natural killer cell, human: -1.135<br>CD16-positive, CD56-dim natural killer cell, human: -2.036<br>natural killer cell: -2.387<br>gamma-delta T cell: -3.394<br>mature NK T cell: -3.614",
+          "value: -5.815<br>CD16-positive, CD56-dim natural killer cell, human: -0.472<br>natural killer cell: -2.149<br>CD16-negative, CD56-bright natural killer cell, human: -3.074<br>mature NK T cell: -3.820<br>gamma-delta T cell: -4.755",
+          "value: -6.174<br>classical monocyte: -1.234<br>CD14-positive monocyte: -1.689<br>pvalb GABAergic cortical interneuron: -2.927<br>macrophage: -3.467<br>monocyte: -3.827",
+          "value: -4.399<br>CD16-positive, CD56-dim natural killer cell, human: -1.125<br>natural killer cell: -1.659<br>mature NK T cell: -2.510<br>CD16-negative, CD56-bright natural killer cell, human: -2.804<br>gamma-delta T cell: -2.856",
+          "value: -7.097<br>CD16-positive, CD56-dim natural killer cell, human: -0.806<br>CD16-negative, CD56-bright natural killer cell, human: -1.872<br>natural killer cell: -2.107<br>mature NK T cell: -4.074<br>activated type II NK T cell: -4.148",
+          "value: -5.423<br>plasmacytoid dendritic cell: -0.360<br>dendritic cell: -2.381<br>plasmacytoid dendritic cell, human: -2.749<br>conventional dendritic cell: -4.607<br>myeloid dendritic cell: -4.691",
+          "value: -6.154<br>CD16-positive, CD56-dim natural killer cell, human: -0.318<br>natural killer cell: -2.026<br>CD16-negative, CD56-bright natural killer cell, human: -3.616<br>mature NK T cell: -4.497<br>activated type II NK T cell: -5.366",
+          "value: -6.877<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.528<br>central memory CD4-positive, alpha-beta T cell: -1.609<br>T follicular helper cell: -1.737<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.750<br>effector memory CD4-positive, alpha-beta T cell: -3.184",
+          "value: -6.495<br>CD16-negative, CD56-bright natural killer cell, human: -0.867<br>gamma-delta T cell: -2.976<br>natural killer cell: -3.150<br>effector memory CD8-positive, alpha-beta T cell: -3.820<br>CD16-positive, CD56-dim natural killer cell, human: -3.988",
+          "value: -5.547<br>central memory CD4-positive, alpha-beta T cell: -1.863<br>effector memory CD4-positive, alpha-beta T cell: -1.976<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.001<br>T-helper 22 cell: -2.792<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.948",
+          "value: -6.344<br>CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -2.169<br>CD16-negative, CD56-bright natural killer cell, human: -2.407<br>mature NK T cell: -4.221<br>gamma-delta T cell: -4.417",
+          "value: -5.943<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.941<br>effector memory CD4-positive, alpha-beta T cell: -2.333<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.676<br>central memory CD4-positive, alpha-beta T cell: -2.721<br>T cell: -3.095",
+          "value: -6.845<br>CD14-positive monocyte: -0.740<br>classical monocyte: -2.064<br>pvalb GABAergic cortical interneuron: -2.975<br>monocyte: -3.162<br>CD1c-positive myeloid dendritic cell: -4.521",
+          "value: -5.370<br>CD14-positive monocyte: -2.069<br>classical monocyte: -2.174<br>conventional dendritic cell: -2.727<br>CD1c-positive myeloid dendritic cell: -2.790<br>dendritic cell: -3.068",
+          "value: -5.505<br>CD16-negative, CD56-bright natural killer cell, human: -0.654<br>natural killer cell: -2.880<br>CD16-positive, CD56-dim natural killer cell, human: -3.136<br>gamma-delta T cell: -3.202<br>mature NK T cell: -3.615",
+          "value: -5.797<br>CD16-positive, CD56-dim natural killer cell, human: -0.715<br>natural killer cell: -1.237<br>mature NK T cell: -3.921<br>CD16-negative, CD56-bright natural killer cell, human: -4.625<br>astrocyte of the cerebral cortex: -4.725",
+          "value: -5.576<br>CD16-positive, CD56-dim natural killer cell, human: -1.200<br>natural killer cell: -1.538<br>CD16-negative, CD56-bright natural killer cell, human: -1.929<br>mature NK T cell: -3.633<br>gamma-delta T cell: -3.989",
+          "value: -5.540<br>CD16-positive, CD56-dim natural killer cell, human: -1.022<br>natural killer cell: -1.733<br>CD16-negative, CD56-bright natural killer cell, human: -2.219<br>mature NK T cell: -4.100<br>gamma-delta T cell: -4.231",
+          "value: -5.051<br>natural killer cell: -0.555<br>CD16-negative, CD56-bright natural killer cell, human: -2.155<br>CD16-positive, CD56-dim natural killer cell, human: -2.513<br>lymphocyte: -4.323<br>mature NK T cell: -4.510",
+          "value: -4.320<br>effector memory CD8-positive, alpha-beta T cell: -2.599<br>effector memory CD4-positive, alpha-beta T cell: -2.733<br>T follicular helper cell: -2.782<br>mature NK T cell: -3.055<br>T-helper 22 cell: -3.181",
+          "value: -5.965<br>CD16-positive, CD56-dim natural killer cell, human: -0.817<br>CD16-negative, CD56-bright natural killer cell, human: -1.850<br>natural killer cell: -2.369<br>mature NK T cell: -3.658<br>gamma-delta T cell: -4.084",
+          "value: -6.530<br>classical monocyte: -1.121<br>CD14-positive monocyte: -1.900<br>pvalb GABAergic cortical interneuron: -3.504<br>CD1c-positive myeloid dendritic cell: -3.569<br>monocyte: -3.656",
+          "value: -6.164<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.181<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.480<br>central memory CD4-positive, alpha-beta T cell: -1.886<br>T follicular helper cell: -2.896<br>T cell: -4.355",
+          "value: -6.665<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.090<br>central memory CD4-positive, alpha-beta T cell: -1.803<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.961<br>effector memory CD4-positive, alpha-beta T cell: -3.418<br>T follicular helper cell: -3.493",
+          "value: -6.022<br>CD16-positive, CD56-dim natural killer cell, human: -1.423<br>CD16-negative, CD56-bright natural killer cell, human: -2.088<br>natural killer cell: -2.355<br>gamma-delta T cell: -3.154<br>mature NK T cell: -3.216",
+          "value: -5.018<br>plasmacytoid dendritic cell: -0.437<br>plasmacytoid dendritic cell, human: -2.354<br>dendritic cell: -3.553<br>mast cell: -4.687<br>hematopoietic precursor cell: -5.041",
+          "value: -6.651<br>CD14-positive monocyte: -1.330<br>classical monocyte: -1.407<br>pvalb GABAergic cortical interneuron: -3.060<br>dendritic cell, human: -3.761<br>macrophage: -3.866",
+          "value: -6.490<br>classical monocyte: -1.255<br>CD14-positive monocyte: -1.511<br>pvalb GABAergic cortical interneuron: -3.049<br>dendritic cell, human: -3.660<br>macrophage: -3.667",
+          "value: -4.615<br>classical monocyte: -1.288<br>macrophage: -2.300<br>CD14-positive monocyte: -2.608<br>alveolar macrophage: -3.103<br>monocyte: -3.515",
+          "value: -6.261<br>CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.718<br>CD16-negative, CD56-bright natural killer cell, human: -3.542<br>mature NK T cell: -4.361<br>activated type II NK T cell: -4.992",
+          "value: -6.008<br>CD16-positive, CD56-dim natural killer cell, human: -0.398<br>natural killer cell: -2.314<br>mature NK T cell: -3.699<br>CD16-negative, CD56-bright natural killer cell, human: -3.726<br>gamma-delta T cell: -4.778",
+          "value: -6.250<br>CD16-positive, CD56-dim natural killer cell, human: -0.603<br>natural killer cell: -2.240<br>CD16-negative, CD56-bright natural killer cell, human: -2.263<br>mature NK T cell: -3.830<br>gamma-delta T cell: -3.988",
+          "value: -5.147<br>CD16-positive, CD56-dim natural killer cell, human: -1.101<br>natural killer cell: -2.076<br>mature NK T cell: -2.765<br>gamma-delta T cell: -3.119<br>CD8-positive, alpha-beta T cell: -3.407",
+          "value: -5.643<br>central memory CD4-positive, alpha-beta T cell: -1.856<br>effector memory CD4-positive, alpha-beta T cell: -1.907<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.370<br>T follicular helper cell: -2.737<br>T-helper 22 cell: -3.268",
+          "value: -6.743<br>CD16-positive, CD56-dim natural killer cell, human: -0.665<br>natural killer cell: -2.442<br>CD16-negative, CD56-bright natural killer cell, human: -2.624<br>mature NK T cell: -3.725<br>gamma-delta T cell: -4.378",
+          "value: -5.936<br>CD16-positive, CD56-dim natural killer cell, human: -0.554<br>natural killer cell: -1.782<br>CD16-negative, CD56-bright natural killer cell, human: -2.954<br>mature NK T cell: -4.484<br>activated type II NK T cell: -4.693",
+          "value: -6.770<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.609<br>T follicular helper cell: -1.861<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.899<br>central memory CD4-positive, alpha-beta T cell: -1.959<br>effector memory CD4-positive, alpha-beta T cell: -3.091",
+          "value: -5.614<br>plasmacytoid dendritic cell: -0.220<br>plasmacytoid dendritic cell, human: -2.963<br>dendritic cell: -3.280<br>myeloid dendritic cell: -5.582<br>CD1c-positive myeloid dendritic cell: -5.891",
+          "value: -6.184<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.734<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.661<br>central memory CD4-positive, alpha-beta T cell: -2.422<br>T follicular helper cell: -4.076<br>T cell: -4.206",
+          "value: -4.567<br>natural killer cell: -0.632<br>CD16-positive, CD56-dim natural killer cell, human: -2.112<br>CD16-negative, CD56-bright natural killer cell, human: -2.699<br>lymphocyte: -3.367<br>mature NK T cell: -4.570",
+          "value: -5.526<br>plasmacytoid dendritic cell: -0.211<br>plasmacytoid dendritic cell, human: -2.894<br>dendritic cell: -3.190<br>B cell: -5.613<br>myeloid dendritic cell: -5.942",
+          "value: -5.187<br>CD16-positive, CD56-dim natural killer cell, human: -0.842<br>natural killer cell: -1.221<br>mature NK T cell: -3.671<br>CD16-negative, CD56-bright natural killer cell, human: -4.111<br>gamma-delta T cell: -4.557",
+          "value: -5.019<br>B cell: -2.102<br>dendritic cell: -2.334<br>conventional dendritic cell: -2.440<br>plasmacytoid dendritic cell: -2.987<br>CD1c-positive myeloid dendritic cell: -3.210",
+          "value: -5.551<br>CD16-positive, CD56-dim natural killer cell, human: -0.829<br>natural killer cell: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -2.874<br>mature NK T cell: -3.298<br>gamma-delta T cell: -3.959",
+          "value: -6.693<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.174<br>central memory CD4-positive, alpha-beta T cell: -1.690<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.393<br>T follicular helper cell: -2.463<br>effector memory CD4-positive, alpha-beta T cell: -2.989",
+          "value: -5.651<br>hematopoietic precursor cell: -2.801<br>dendritic cell: -2.942<br>dendritic cell, human: -3.236<br>hematopoietic stem cell: -3.256<br>group 3 innate lymphoid cell: -3.408",
+          "value: -5.994<br>CD16-positive, CD56-dim natural killer cell, human: -0.471<br>natural killer cell: -1.815<br>CD16-negative, CD56-bright natural killer cell, human: -3.699<br>mature NK T cell: -4.067<br>gamma-delta T cell: -4.867",
+          "value: -5.656<br>CD16-negative, CD56-bright natural killer cell, human: -1.345<br>CD16-positive, CD56-dim natural killer cell, human: -1.595<br>natural killer cell: -1.603<br>gamma-delta T cell: -3.782<br>mature NK T cell: -4.013",
+          "value: -6.995<br>CD14-positive monocyte: -2.290<br>classical monocyte: -2.358<br>dendritic cell, human: -2.519<br>CD1c-positive myeloid dendritic cell: -2.535<br>conventional dendritic cell: -2.921",
+          "value: -7.148<br>CD16-negative, CD56-bright natural killer cell, human: -1.226<br>natural killer cell: -2.197<br>CD16-positive, CD56-dim natural killer cell, human: -3.167<br>activated type II NK T cell: -3.866<br>mature NK T cell: -4.028",
+          "value: -5.931<br>plasmacytoid dendritic cell: -0.390<br>plasmacytoid dendritic cell, human: -2.705<br>dendritic cell: -2.955<br>myeloid dendritic cell: -5.020<br>B cell: -5.106",
+          "value: -5.693<br>CD16-negative, CD56-bright natural killer cell, human: -2.336<br>effector memory CD8-positive, alpha-beta T cell: -3.528<br>natural killer cell: -3.540<br>gamma-delta T cell: -3.561<br>innate lymphoid cell: -3.718",
+          "value: -5.470<br>CD16-negative, CD56-bright natural killer cell, human: -0.926<br>natural killer cell: -2.437<br>gamma-delta T cell: -2.906<br>CD16-positive, CD56-dim natural killer cell, human: -3.018<br>mature NK T cell: -3.580",
+          "value: -5.587<br>CD16-positive, CD56-dim natural killer cell, human: -0.774<br>natural killer cell: -1.981<br>CD16-negative, CD56-bright natural killer cell, human: -2.096<br>mature NK T cell: -3.727<br>gamma-delta T cell: -3.985",
+          "value: -6.287<br>central memory CD4-positive, alpha-beta T cell: -1.733<br>T follicular helper cell: -1.862<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.295<br>effector memory CD4-positive, alpha-beta T cell: -2.676<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.023",
+          "value: -5.844<br>CD16-positive, CD56-dim natural killer cell, human: -1.007<br>CD16-negative, CD56-bright natural killer cell, human: -1.847<br>natural killer cell: -1.992<br>mature NK T cell: -3.135<br>gamma-delta T cell: -3.393",
+          "value: -5.276<br>T follicular helper cell: -2.009<br>effector memory CD4-positive, alpha-beta T cell: -2.386<br>T-helper 22 cell: -3.303<br>central memory CD4-positive, alpha-beta T cell: -3.454<br>mature NK T cell: -3.503",
+          "value: -5.288<br>CD16-negative, CD56-bright natural killer cell, human: -0.982<br>gamma-delta T cell: -2.952<br>CD16-positive, CD56-dim natural killer cell, human: -3.156<br>natural killer cell: -3.197<br>mature NK T cell: -3.508",
+          "value: -5.727<br>CD16-positive, CD56-dim natural killer cell, human: -0.419<br>natural killer cell: -1.844<br>CD16-negative, CD56-bright natural killer cell, human: -3.280<br>mature NK T cell: -4.214<br>gamma-delta T cell: -5.048",
+          "value: -6.512<br>CD16-positive, CD56-dim natural killer cell, human: -0.446<br>natural killer cell: -1.557<br>mature NK T cell: -3.901<br>astrocyte of the cerebral cortex: -4.964<br>CD16-negative, CD56-bright natural killer cell, human: -5.038",
+          "value: -6.395<br>CD14-positive monocyte: -0.742<br>classical monocyte: -2.242<br>monocyte: -2.963<br>pvalb GABAergic cortical interneuron: -3.272<br>promonocyte: -4.346",
+          "value: -4.993<br>classical monocyte: -1.515<br>CD14-positive monocyte: -1.861<br>pvalb GABAergic cortical interneuron: -2.758<br>monocyte: -3.630<br>macrophage: -3.960",
+          "value: -4.565<br>effector memory CD4-positive, alpha-beta T cell: -2.140<br>T follicular helper cell: -2.661<br>central memory CD4-positive, alpha-beta T cell: -2.746<br>effector memory CD8-positive, alpha-beta T cell: -2.829<br>T-helper 22 cell: -2.905",
+          "value: -6.084<br>CD16-positive, CD56-dim natural killer cell, human: -1.175<br>CD16-negative, CD56-bright natural killer cell, human: -1.532<br>natural killer cell: -1.629<br>activated type II NK T cell: -4.065<br>gamma-delta T cell: -4.421",
+          "value: -5.742<br>CD16-positive, CD56-dim natural killer cell, human: -0.661<br>natural killer cell: -1.311<br>mature NK T cell: -3.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.743<br>activated type II NK T cell: -4.751",
+          "value: -4.328<br>CD4-positive, alpha-beta T cell: -2.691<br>T cell: -2.714<br>effector memory CD4-positive, alpha-beta T cell: -3.252<br>B cell: -3.416<br>regulatory T cell: -3.546",
+          "value: -5.501<br>CD16-positive, CD56-dim natural killer cell, human: -0.519<br>natural killer cell: -1.482<br>mature NK T cell: -3.954<br>CD16-negative, CD56-bright natural killer cell, human: -3.991<br>astrocyte of the cerebral cortex: -4.845",
+          "value: -6.392<br>CD16-negative, CD56-bright natural killer cell, human: -0.949<br>CD16-positive, CD56-dim natural killer cell, human: -2.995<br>gamma-delta T cell: -3.097<br>natural killer cell: -3.215<br>mature NK T cell: -3.990",
+          "value: -5.499<br>CD16-positive, CD56-dim natural killer cell, human: -0.588<br>natural killer cell: -1.484<br>CD16-negative, CD56-bright natural killer cell, human: -3.859<br>mature NK T cell: -3.915<br>activated type II NK T cell: -5.092",
+          "value: -5.560<br>effector memory CD4-positive, alpha-beta T cell: -1.696<br>central memory CD4-positive, alpha-beta T cell: -2.002<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.519<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.944<br>T-helper 22 cell: -3.009",
+          "value: -6.558<br>CD16-negative, CD56-bright natural killer cell, human: -1.351<br>CD16-positive, CD56-dim natural killer cell, human: -1.688<br>natural killer cell: -2.290<br>gamma-delta T cell: -2.887<br>mature NK T cell: -3.630",
+          "value: -6.691<br>T follicular helper cell: -1.811<br>effector memory CD4-positive, alpha-beta T cell: -2.176<br>central memory CD4-positive, alpha-beta T cell: -2.252<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.500<br>T-helper 22 cell: -3.255",
+          "value: -5.920<br>CD16-negative, CD56-bright natural killer cell, human: -0.778<br>gamma-delta T cell: -2.762<br>CD16-positive, CD56-dim natural killer cell, human: -3.091<br>natural killer cell: -3.094<br>mature NK T cell: -3.749",
+          "value: -4.923<br>hematopoietic precursor cell: -1.245<br>hematopoietic stem cell: -2.963<br>CD34-positive, CD38-negative hematopoietic stem cell: -3.161<br>mast cell: -3.943<br>progenitor cell: -4.184",
+          "value: -6.747<br>CD16-positive, CD56-dim natural killer cell, human: -0.380<br>natural killer cell: -2.146<br>CD16-negative, CD56-bright natural killer cell, human: -3.277<br>mature NK T cell: -4.149<br>astrocyte of the cerebral cortex: -5.089",
+          "value: -6.779<br>CD16-positive, CD56-dim natural killer cell, human: -0.997<br>CD16-negative, CD56-bright natural killer cell, human: -1.701<br>natural killer cell: -2.122<br>activated type II NK T cell: -4.030<br>mature NK T cell: -4.222",
+          "value: -7.064<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.117<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.364<br>central memory CD4-positive, alpha-beta T cell: -2.289<br>T follicular helper cell: -3.297<br>effector memory CD8-positive, alpha-beta T cell: -3.731",
+          "value: -6.034<br>classical monocyte: -2.051<br>CD14-positive monocyte: -2.120<br>non-classical monocyte: -2.850<br>CD1c-positive myeloid dendritic cell: -3.433<br>mature NK T cell: -3.821",
+          "value: -6.082<br>CD16-positive, CD56-dim natural killer cell, human: -0.372<br>natural killer cell: -2.006<br>mature NK T cell: -3.929<br>CD16-negative, CD56-bright natural killer cell, human: -4.059<br>astrocyte of the cerebral cortex: -4.899",
+          "value: -5.171<br>effector memory CD8-positive, alpha-beta T cell: -1.514<br>mucosal invariant T cell: -2.057<br>gamma-delta T cell: -2.267<br>effector CD8-positive, alpha-beta T cell: -2.310<br>mature NK T cell: -3.067",
+          "value: -5.887<br>CD16-positive, CD56-dim natural killer cell, human: -0.459<br>natural killer cell: -1.582<br>mature NK T cell: -4.109<br>CD16-negative, CD56-bright natural killer cell, human: -4.348<br>astrocyte of the cerebral cortex: -4.973",
+          "value: -5.831<br>CD16-positive, CD56-dim natural killer cell, human: -0.345<br>natural killer cell: -2.300<br>CD16-negative, CD56-bright natural killer cell, human: -3.363<br>mature NK T cell: -4.426<br>activated type II NK T cell: -4.922",
+          "value: -5.696<br>CD16-positive, CD56-dim natural killer cell, human: -0.957<br>natural killer cell: -1.288<br>CD16-negative, CD56-bright natural killer cell, human: -2.945<br>mature NK T cell: -4.110<br>activated type II NK T cell: -4.206",
+          "value: -5.239<br>plasmacytoid dendritic cell: -0.223<br>plasmacytoid dendritic cell, human: -2.829<br>dendritic cell: -3.137<br>B cell: -5.483<br>plasma cell: -5.542",
+          "value: -5.545<br>CD16-positive, CD56-dim natural killer cell, human: -0.758<br>natural killer cell: -1.875<br>CD16-negative, CD56-bright natural killer cell, human: -3.214<br>mature NK T cell: -3.534<br>gamma-delta T cell: -4.444",
+          "value: -6.117<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.265<br>central memory CD4-positive, alpha-beta T cell: -2.018<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.230<br>T follicular helper cell: -3.229<br>effector memory CD4-positive, alpha-beta T cell: -3.265",
+          "value: -6.230<br>CD16-positive, CD56-dim natural killer cell, human: -0.438<br>natural killer cell: -1.757<br>CD16-negative, CD56-bright natural killer cell, human: -3.981<br>mature NK T cell: -4.070<br>gamma-delta T cell: -5.021",
+          "value: -4.219<br>effector memory CD4-positive, alpha-beta T cell: -2.023<br>T-helper 22 cell: -2.282<br>central memory CD4-positive, alpha-beta T cell: -2.689<br>T follicular helper cell: -2.881<br>effector memory CD8-positive, alpha-beta T cell: -3.258",
+          "value: -6.741<br>CD16-positive, CD56-dim natural killer cell, human: -0.978<br>natural killer cell: -1.119<br>CD16-negative, CD56-bright natural killer cell, human: -3.247<br>mature NK T cell: -4.144<br>activated type II NK T cell: -4.213",
+          "value: -5.108<br>dendritic cell: -1.602<br>dendritic cell, human: -2.508<br>plasmacytoid dendritic cell: -2.523<br>conventional dendritic cell: -2.654<br>CD1c-positive myeloid dendritic cell: -2.719",
+          "value: -6.234<br>CD16-positive, CD56-dim natural killer cell, human: -2.003<br>gamma-delta T cell: -2.463<br>natural killer cell: -2.530<br>CD16-negative, CD56-bright natural killer cell, human: -2.556<br>mature NK T cell: -2.712",
+          "value: -6.090<br>T follicular helper cell: -1.803<br>central memory CD4-positive, alpha-beta T cell: -2.257<br>effector memory CD4-positive, alpha-beta T cell: -2.586<br>T-helper 22 cell: -3.217<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.484",
+          "value: -5.150<br>CD16-positive, CD56-dim natural killer cell, human: -0.933<br>natural killer cell: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -2.840<br>mature NK T cell: -3.207<br>gamma-delta T cell: -3.778",
+          "value: -5.432<br>CD16-positive, CD56-dim natural killer cell, human: -1.203<br>CD16-negative, CD56-bright natural killer cell, human: -2.390<br>natural killer cell: -2.481<br>gamma-delta T cell: -3.085<br>mature NK T cell: -3.471",
+          "value: -6.817<br>classical monocyte: -1.292<br>CD14-positive monocyte: -1.487<br>pvalb GABAergic cortical interneuron: -3.246<br>CD1c-positive myeloid dendritic cell: -3.379<br>dendritic cell, human: -3.775",
+          "value: -5.806<br>CD16-positive, CD56-dim natural killer cell, human: -0.579<br>natural killer cell: -1.571<br>CD16-negative, CD56-bright natural killer cell, human: -3.617<br>mature NK T cell: -4.190<br>activated type II NK T cell: -4.608",
+          "value: -6.475<br>CD16-positive, CD56-dim natural killer cell, human: -0.736<br>natural killer cell: -2.353<br>CD16-negative, CD56-bright natural killer cell, human: -2.473<br>gamma-delta T cell: -3.536<br>mature NK T cell: -3.675",
+          "value: -6.679<br>CD14-positive monocyte: -0.860<br>classical monocyte: -1.377<br>pvalb GABAergic cortical interneuron: -3.681<br>monocyte: -4.225<br>macrophage: -4.285",
+          "value: -5.542<br>effector memory CD4-positive, alpha-beta T cell: -1.723<br>T follicular helper cell: -2.235<br>central memory CD4-positive, alpha-beta T cell: -2.396<br>T-helper 22 cell: -2.420<br>effector memory CD8-positive, alpha-beta T cell: -3.034",
+          "value: -5.656<br>CD16-positive, CD56-dim natural killer cell, human: -1.089<br>natural killer cell: -1.501<br>CD16-negative, CD56-bright natural killer cell, human: -2.457<br>gamma-delta T cell: -3.256<br>mature NK T cell: -3.839",
+          "value: -5.839<br>CD16-negative, CD56-bright natural killer cell, human: -1.194<br>natural killer cell: -1.837<br>CD16-positive, CD56-dim natural killer cell, human: -2.183<br>gamma-delta T cell: -2.836<br>mature NK T cell: -3.430",
+          "value: -5.401<br>CD16-negative, CD56-bright natural killer cell, human: -0.700<br>CD16-positive, CD56-dim natural killer cell, human: -2.677<br>natural killer cell: -2.827<br>gamma-delta T cell: -3.518<br>activated type II NK T cell: -4.201",
+          "value: -4.027<br>CD16-positive, CD56-dim natural killer cell, human: -1.023<br>natural killer cell: -1.055<br>CD16-negative, CD56-bright natural killer cell, human: -3.012<br>mature NK T cell: -3.911<br>gamma-delta T cell: -4.397",
+          "value: -6.528<br>CD16-positive, CD56-dim natural killer cell, human: -0.413<br>natural killer cell: -1.841<br>mature NK T cell: -4.099<br>astrocyte of the cerebral cortex: -4.580<br>CD16-negative, CD56-bright natural killer cell, human: -4.768",
+          "value: -6.568<br>classical monocyte: -1.156<br>CD14-positive monocyte: -1.775<br>pvalb GABAergic cortical interneuron: -2.858<br>monocyte: -3.816<br>dendritic cell, human: -4.010",
+          "value: -5.584<br>CD16-positive, CD56-dim natural killer cell, human: -0.412<br>natural killer cell: -1.864<br>mature NK T cell: -3.962<br>CD16-negative, CD56-bright natural killer cell, human: -4.092<br>astrocyte of the cerebral cortex: -5.094",
+          "value: -6.127<br>CD16-negative, CD56-bright natural killer cell, human: -0.829<br>natural killer cell: -2.575<br>gamma-delta T cell: -3.359<br>CD16-positive, CD56-dim natural killer cell, human: -3.966<br>effector memory CD8-positive, alpha-beta T cell: -4.187",
+          "value: -5.896<br>CD16-positive, CD56-dim natural killer cell, human: -0.649<br>natural killer cell: -1.786<br>CD16-negative, CD56-bright natural killer cell, human: -2.490<br>activated type II NK T cell: -4.384<br>mature NK T cell: -4.464",
+          "value: -5.353<br>natural killer cell: -0.811<br>CD16-positive, CD56-dim natural killer cell, human: -1.339<br>CD16-negative, CD56-bright natural killer cell, human: -2.863<br>mature NK T cell: -4.167<br>activated type II NK T cell: -4.542",
+          "value: -5.952<br>T follicular helper cell: -1.765<br>central memory CD4-positive, alpha-beta T cell: -1.823<br>effector memory CD4-positive, alpha-beta T cell: -2.070<br>T-helper 22 cell: -2.397<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.623",
+          "value: -6.214<br>CD14-positive monocyte: -1.103<br>classical monocyte: -1.355<br>pvalb GABAergic cortical interneuron: -3.061<br>monocyte: -3.740<br>macrophage: -4.170",
+          "value: -6.485<br>CD16-positive, CD56-dim natural killer cell, human: -0.518<br>natural killer cell: -2.013<br>CD16-negative, CD56-bright natural killer cell, human: -2.555<br>mature NK T cell: -4.388<br>activated type II NK T cell: -4.546",
+          "value: -6.145<br>CD16-positive, CD56-dim natural killer cell, human: -0.945<br>natural killer cell: -1.218<br>CD16-negative, CD56-bright natural killer cell, human: -3.626<br>mature NK T cell: -3.795<br>gamma-delta T cell: -4.598",
+          "value: -6.415<br>classical monocyte: -2.102<br>CD14-positive monocyte: -2.390<br>CD1c-positive myeloid dendritic cell: -2.526<br>dendritic cell, human: -2.725<br>non-classical monocyte: -2.869",
+          "value: -6.343<br>classical monocyte: -1.408<br>CD14-positive monocyte: -1.637<br>pvalb GABAergic cortical interneuron: -3.429<br>macrophage: -3.677<br>monocyte: -3.921",
+          "value: -3.768<br>effector memory CD4-positive, alpha-beta T cell: -1.551<br>central memory CD4-positive, alpha-beta T cell: -2.295<br>T-helper 22 cell: -2.866<br>mature NK T cell: -3.187<br>T follicular helper cell: -3.378",
+          "value: -3.592<br>natural killer cell: -0.804<br>lymphocyte: -3.317<br>CD16-positive, CD56-dim natural killer cell, human: -3.433<br>T cell: -3.837<br>CD8-positive, alpha-beta T cell: -3.954",
+          "value: -4.813<br>effector memory CD4-positive, alpha-beta T cell: -1.839<br>central memory CD4-positive, alpha-beta T cell: -1.857<br>effector memory CD8-positive, alpha-beta T cell: -1.944<br>T follicular helper cell: -2.810<br>T-helper 22 cell: -2.903",
+          "value: -7.674<br>CD16-negative, CD56-bright natural killer cell, human: -1.202<br>gamma-delta T cell: -3.341<br>natural killer cell: -3.428<br>innate lymphoid cell: -3.627<br>CD16-positive, CD56-dim natural killer cell, human: -3.718",
+          "value: -5.596<br>plasmacytoid dendritic cell: -0.207<br>plasmacytoid dendritic cell, human: -2.940<br>dendritic cell: -3.225<br>dendritic cell, human: -5.549<br>myeloid dendritic cell: -5.662",
+          "value: -6.192<br>CD16-positive, CD56-dim natural killer cell, human: -0.793<br>natural killer cell: -2.396<br>gamma-delta T cell: -2.626<br>mature NK T cell: -3.145<br>effector CD8-positive, alpha-beta T cell: -3.474",
+          "value: -6.092<br>classical monocyte: -1.551<br>CD14-positive monocyte: -1.694<br>pvalb GABAergic cortical interneuron: -2.756<br>monocyte: -3.975<br>dendritic cell, human: -4.325",
+          "value: -4.854<br>CD16-negative, CD56-bright natural killer cell, human: -1.165<br>natural killer cell: -2.110<br>CD16-positive, CD56-dim natural killer cell, human: -2.737<br>mast cell: -3.610<br>innate lymphoid cell: -3.646",
+          "value: -6.681<br>classical monocyte: -1.055<br>CD14-positive monocyte: -1.525<br>pvalb GABAergic cortical interneuron: -3.558<br>macrophage: -3.751<br>CD1c-positive myeloid dendritic cell: -3.889",
+          "value: -6.587<br>CD16-positive, CD56-dim natural killer cell, human: -0.542<br>natural killer cell: -1.781<br>CD16-negative, CD56-bright natural killer cell, human: -3.385<br>mature NK T cell: -4.288<br>activated type II NK T cell: -4.833",
+          "value: -6.025<br>CD16-positive, CD56-dim natural killer cell, human: -0.488<br>natural killer cell: -1.572<br>mature NK T cell: -4.004<br>CD16-negative, CD56-bright natural killer cell, human: -4.699<br>astrocyte of the cerebral cortex: -4.870",
+          "value: -7.184<br>classical monocyte: -2.037<br>CD14-positive monocyte: -2.286<br>dendritic cell, human: -2.388<br>CD1c-positive myeloid dendritic cell: -2.865<br>conventional dendritic cell: -3.027",
+          "value: -6.046<br>CD16-positive, CD56-dim natural killer cell, human: -0.542<br>natural killer cell: -1.437<br>mature NK T cell: -3.795<br>CD16-negative, CD56-bright natural killer cell, human: -3.927<br>astrocyte of the cerebral cortex: -5.096",
+          "value: -4.342<br>effector memory CD4-positive, alpha-beta T cell: -2.742<br>mucosal invariant T cell: -2.796<br>effector memory CD8-positive, alpha-beta T cell: -2.855<br>central memory CD4-positive, alpha-beta T cell: -3.108<br>gamma-delta T cell: -3.172",
+          "value: -6.135<br>CD14-positive monocyte: -1.252<br>classical monocyte: -1.336<br>pvalb GABAergic cortical interneuron: -3.327<br>CD1c-positive myeloid dendritic cell: -3.564<br>monocyte: -3.843",
+          "value: -6.863<br>CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -2.318<br>CD16-negative, CD56-bright natural killer cell, human: -2.725<br>mature NK T cell: -3.352<br>gamma-delta T cell: -4.366",
+          "value: -4.844<br>CD16-positive, CD56-dim natural killer cell, human: -0.669<br>natural killer cell: -1.289<br>CD16-negative, CD56-bright natural killer cell, human: -3.902<br>mature NK T cell: -4.150<br>activated type II NK T cell: -4.849",
+          "value: -5.119<br>conventional dendritic cell: -1.413<br>CD1c-positive myeloid dendritic cell: -2.102<br>dendritic cell: -2.113<br>dendritic cell, human: -2.831<br>myeloid dendritic cell: -2.894",
+          "value: -6.990<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.551<br>central memory CD4-positive, alpha-beta T cell: -2.015<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.686<br>T follicular helper cell: -3.237<br>effector memory CD4-positive, alpha-beta T cell: -3.875",
+          "value: -6.002<br>CD16-positive, CD56-dim natural killer cell, human: -1.032<br>natural killer cell: -2.019<br>CD16-negative, CD56-bright natural killer cell, human: -2.043<br>gamma-delta T cell: -3.703<br>activated type II NK T cell: -4.203",
+          "value: -5.210<br>classical monocyte: -1.435<br>CD14-positive monocyte: -2.293<br>pvalb GABAergic cortical interneuron: -2.761<br>monocyte: -3.487<br>macrophage: -3.768",
+          "value: -4.513<br>natural killer cell: -2.025<br>CD16-positive, CD56-dim natural killer cell, human: -2.122<br>gamma-delta T cell: -2.227<br>mature NK T cell: -2.890<br>CD16-negative, CD56-bright natural killer cell, human: -3.014",
+          "value: -5.295<br>CD16-positive, CD56-dim natural killer cell, human: -0.654<br>natural killer cell: -2.051<br>CD16-negative, CD56-bright natural killer cell, human: -3.256<br>mature NK T cell: -3.784<br>gamma-delta T cell: -4.207",
+          "value: -4.529<br>natural killer cell: -0.501<br>lymphocyte: -2.693<br>CD16-positive, CD56-dim natural killer cell, human: -3.579<br>CD8-positive, alpha-beta T cell: -3.729<br>T cell: -3.935",
+          "value: -6.976<br>CD16-positive, CD56-dim natural killer cell, human: -0.682<br>natural killer cell: -1.841<br>CD16-negative, CD56-bright natural killer cell, human: -2.618<br>mature NK T cell: -3.964<br>activated type II NK T cell: -4.317",
+          "value: -4.749<br>CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -2.007<br>CD16-positive, CD56-dim natural killer cell, human: -3.375<br>mature NK T cell: -3.544<br>gamma-delta T cell: -3.729",
+          "value: -5.478<br>gamma-delta T cell: -2.053<br>CD16-negative, CD56-bright natural killer cell, human: -2.258<br>effector memory CD8-positive, alpha-beta T cell: -2.929<br>CD16-positive, CD56-dim natural killer cell, human: -3.591<br>natural killer cell: -3.608",
+          "value: -5.133<br>plasmacytoid dendritic cell: -0.243<br>plasmacytoid dendritic cell, human: -2.562<br>dendritic cell: -3.444<br>myeloid dendritic cell: -5.808<br>dendritic cell, human: -5.820",
+          "value: -6.712<br>CD16-positive, CD56-dim natural killer cell, human: -0.516<br>natural killer cell: -2.128<br>CD16-negative, CD56-bright natural killer cell, human: -2.893<br>mature NK T cell: -4.329<br>activated type II NK T cell: -4.664",
+          "value: -5.401<br>CD16-positive, CD56-dim natural killer cell, human: -1.057<br>natural killer cell: -1.567<br>CD16-negative, CD56-bright natural killer cell, human: -2.420<br>mature NK T cell: -3.933<br>gamma-delta T cell: -4.262",
+          "value: -5.349<br>natural killer cell: -1.343<br>CD16-positive, CD56-dim natural killer cell, human: -1.550<br>CD16-negative, CD56-bright natural killer cell, human: -2.487<br>mature NK T cell: -3.252<br>gamma-delta T cell: -3.992",
+          "value: -5.902<br>CD16-positive, CD56-dim natural killer cell, human: -0.492<br>natural killer cell: -1.543<br>CD16-negative, CD56-bright natural killer cell, human: -4.158<br>mature NK T cell: -4.231<br>astrocyte of the cerebral cortex: -4.820",
+          "value: -5.911<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.651<br>central memory CD4-positive, alpha-beta T cell: -1.808<br>effector memory CD4-positive, alpha-beta T cell: -2.508<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.605<br>T follicular helper cell: -3.104",
+          "value: -7.115<br>CD16-negative, CD56-bright natural killer cell, human: -0.899<br>gamma-delta T cell: -2.760<br>natural killer cell: -3.240<br>effector memory CD8-positive, alpha-beta T cell: -3.516<br>mucosal invariant T cell: -3.906",
+          "value: -5.382<br>CD16-positive, CD56-dim natural killer cell, human: -0.757<br>natural killer cell: -1.564<br>CD16-negative, CD56-bright natural killer cell, human: -3.300<br>mature NK T cell: -3.774<br>activated type II NK T cell: -4.615",
+          "value: -5.417<br>plasmacytoid dendritic cell: -0.383<br>plasmacytoid dendritic cell, human: -2.681<br>dendritic cell: -2.767<br>myeloid dendritic cell: -4.783<br>B cell: -5.032",
+          "value: -4.912<br>CD16-positive, CD56-dim natural killer cell, human: -0.960<br>natural killer cell: -1.378<br>CD16-negative, CD56-bright natural killer cell, human: -2.925<br>mature NK T cell: -4.064<br>gamma-delta T cell: -4.093",
+          "value: -5.870<br>CD16-positive, CD56-dim natural killer cell, human: -0.561<br>natural killer cell: -2.210<br>CD16-negative, CD56-bright natural killer cell, human: -3.014<br>gamma-delta T cell: -3.538<br>mature NK T cell: -4.001",
+          "value: -6.134<br>CD16-positive, CD56-dim natural killer cell, human: -0.962<br>natural killer cell: -1.484<br>CD16-negative, CD56-bright natural killer cell, human: -2.849<br>mature NK T cell: -4.013<br>activated type II NK T cell: -4.470",
+          "value: -6.296<br>CD16-negative, CD56-bright natural killer cell, human: -1.390<br>CD16-positive, CD56-dim natural killer cell, human: -2.140<br>natural killer cell: -2.592<br>gamma-delta T cell: -2.799<br>mature NK T cell: -3.968",
+          "value: -5.495<br>CD16-positive, CD56-dim natural killer cell, human: -0.583<br>natural killer cell: -1.857<br>CD16-negative, CD56-bright natural killer cell, human: -3.158<br>mature NK T cell: -3.631<br>gamma-delta T cell: -4.241",
+          "value: -5.994<br>CD16-positive, CD56-dim natural killer cell, human: -1.400<br>CD16-negative, CD56-bright natural killer cell, human: -1.650<br>natural killer cell: -2.044<br>mature NK T cell: -3.804<br>gamma-delta T cell: -3.874",
+          "value: -6.568<br>CD16-negative, CD56-bright natural killer cell, human: -1.534<br>natural killer cell: -2.378<br>gamma-delta T cell: -2.810<br>effector memory CD8-positive, alpha-beta T cell: -3.034<br>CD16-positive, CD56-dim natural killer cell, human: -3.384",
+          "value: -5.706<br>CD16-positive, CD56-dim natural killer cell, human: -0.385<br>natural killer cell: -1.998<br>mature NK T cell: -3.886<br>CD16-negative, CD56-bright natural killer cell, human: -4.216<br>gamma-delta T cell: -4.709",
+          "value: -6.538<br>CD16-negative, CD56-bright natural killer cell, human: -1.465<br>CD16-positive, CD56-dim natural killer cell, human: -1.494<br>natural killer cell: -2.244<br>gamma-delta T cell: -3.584<br>activated type II NK T cell: -3.973",
+          "value: -5.758<br>natural killer cell: -1.598<br>CD16-positive, CD56-dim natural killer cell, human: -2.234<br>CD8-positive, alpha-beta T cell: -2.840<br>gamma-delta T cell: -2.919<br>mature NK T cell: -3.248",
+          "value: -5.749<br>CD16-positive, CD56-dim natural killer cell, human: -0.668<br>natural killer cell: -1.434<br>mature NK T cell: -3.773<br>CD16-negative, CD56-bright natural killer cell, human: -4.287<br>astrocyte of the cerebral cortex: -4.702",
+          "value: -6.590<br>classical monocyte: -1.016<br>CD14-positive monocyte: -1.345<br>pvalb GABAergic cortical interneuron: -3.491<br>macrophage: -3.821<br>monocyte: -4.327",
+          "value: -6.777<br>classical monocyte: -1.768<br>CD14-positive monocyte: -1.969<br>pvalb GABAergic cortical interneuron: -3.007<br>dendritic cell: -3.106<br>CD1c-positive myeloid dendritic cell: -3.126",
+          "value: -5.149<br>CD16-positive, CD56-dim natural killer cell, human: -0.734<br>natural killer cell: -1.693<br>CD16-negative, CD56-bright natural killer cell, human: -3.725<br>mature NK T cell: -3.726<br>astrocyte of the cerebral cortex: -4.606",
+          "value: -6.438<br>CD16-positive, CD56-dim natural killer cell, human: -0.833<br>natural killer cell: -1.999<br>CD16-negative, CD56-bright natural killer cell, human: -2.033<br>gamma-delta T cell: -4.267<br>mature NK T cell: -4.287",
+          "value: -6.015<br>CD16-positive, CD56-dim natural killer cell, human: -0.852<br>natural killer cell: -1.611<br>CD16-negative, CD56-bright natural killer cell, human: -2.325<br>mature NK T cell: -4.086<br>gamma-delta T cell: -4.386",
+          "value: -5.905<br>classical monocyte: -0.891<br>CD14-positive monocyte: -1.791<br>pvalb GABAergic cortical interneuron: -3.143<br>macrophage: -3.673<br>monocyte: -4.310",
+          "value: -5.120<br>natural killer cell: -0.757<br>CD16-positive, CD56-dim natural killer cell, human: -1.360<br>CD16-negative, CD56-bright natural killer cell, human: -3.096<br>mature NK T cell: -3.733<br>gamma-delta T cell: -4.710",
+          "value: -7.276<br>CD14-positive monocyte: -1.379<br>classical monocyte: -1.538<br>pvalb GABAergic cortical interneuron: -2.772<br>monocyte: -3.383<br>dendritic cell, human: -3.545",
+          "value: -6.676<br>CD16-positive, CD56-dim natural killer cell, human: -0.552<br>natural killer cell: -2.222<br>CD16-negative, CD56-bright natural killer cell, human: -2.473<br>activated type II NK T cell: -4.379<br>mature NK T cell: -4.659",
+          "value: -4.774<br>classical monocyte: -1.664<br>macrophage: -1.690<br>CD14-positive monocyte: -2.011<br>monocyte: -3.510<br>alveolar macrophage: -3.748",
+          "value: -5.572<br>CD16-negative, CD56-bright natural killer cell, human: -1.795<br>CD16-positive, CD56-dim natural killer cell, human: -2.530<br>mature NK T cell: -2.631<br>natural killer cell: -2.658<br>gamma-delta T cell: -2.827",
+          "value: -6.149<br>CD16-positive, CD56-dim natural killer cell, human: -1.187<br>CD16-negative, CD56-bright natural killer cell, human: -1.765<br>natural killer cell: -2.199<br>gamma-delta T cell: -3.295<br>mature NK T cell: -3.770",
+          "value: -6.330<br>central memory CD4-positive, alpha-beta T cell: -1.629<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.641<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.131<br>T follicular helper cell: -2.336<br>effector memory CD4-positive, alpha-beta T cell: -2.789",
+          "value: -6.212<br>CD16-positive, CD56-dim natural killer cell, human: -0.753<br>natural killer cell: -2.405<br>CD16-negative, CD56-bright natural killer cell, human: -2.579<br>mature NK T cell: -3.508<br>gamma-delta T cell: -3.829",
+          "value: -5.586<br>CD16-positive, CD56-dim natural killer cell, human: -0.637<br>natural killer cell: -2.004<br>CD16-negative, CD56-bright natural killer cell, human: -2.429<br>mature NK T cell: -3.731<br>gamma-delta T cell: -4.000",
+          "value: -5.182<br>classical monocyte: -0.895<br>CD14-positive monocyte: -2.118<br>pvalb GABAergic cortical interneuron: -3.328<br>macrophage: -3.438<br>monocyte: -4.113",
+          "value: -6.240<br>CD16-positive, CD56-dim natural killer cell, human: -0.667<br>natural killer cell: -1.171<br>mature NK T cell: -3.843<br>CD16-negative, CD56-bright natural killer cell, human: -4.591<br>astrocyte of the cerebral cortex: -5.105",
+          "value: -5.937<br>CD16-negative, CD56-bright natural killer cell, human: -1.729<br>gamma-delta T cell: -2.424<br>CD16-positive, CD56-dim natural killer cell, human: -2.903<br>natural killer cell: -2.922<br>effector memory CD8-positive, alpha-beta T cell: -2.993",
+          "value: -6.172<br>CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -2.007<br>CD16-negative, CD56-bright natural killer cell, human: -3.461<br>mature NK T cell: -4.172<br>activated type II NK T cell: -4.710",
+          "value: -5.050<br>CD16-positive, CD56-dim natural killer cell, human: -1.192<br>natural killer cell: -1.785<br>CD16-negative, CD56-bright natural killer cell, human: -2.735<br>mature NK T cell: -3.038<br>gamma-delta T cell: -3.190",
+          "value: -6.765<br>CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -2.213<br>CD16-negative, CD56-bright natural killer cell, human: -3.093<br>mature NK T cell: -4.431<br>activated type II NK T cell: -4.596",
+          "value: -6.416<br>CD16-positive, CD56-dim natural killer cell, human: -0.703<br>CD16-negative, CD56-bright natural killer cell, human: -1.975<br>natural killer cell: -2.464<br>gamma-delta T cell: -3.949<br>mature NK T cell: -4.036",
+          "value: -6.661<br>CD16-positive, CD56-dim natural killer cell, human: -0.511<br>CD16-negative, CD56-bright natural killer cell, human: -2.226<br>natural killer cell: -2.290<br>mature NK T cell: -4.382<br>activated type II NK T cell: -4.483",
+          "value: -6.487<br>CD14-positive monocyte: -1.063<br>classical monocyte: -1.179<br>pvalb GABAergic cortical interneuron: -3.149<br>monocyte: -4.302<br>macrophage: -4.380",
+          "value: -5.563<br>natural killer cell: -1.401<br>CD16-positive, CD56-dim natural killer cell, human: -1.471<br>CD16-negative, CD56-bright natural killer cell, human: -2.422<br>gamma-delta T cell: -3.121<br>mature NK T cell: -3.133",
+          "value: -5.566<br>CD16-positive, CD56-dim natural killer cell, human: -0.465<br>natural killer cell: -2.018<br>CD16-negative, CD56-bright natural killer cell, human: -2.664<br>mature NK T cell: -4.602<br>activated type II NK T cell: -4.758",
+          "value: -5.256<br>CD16-negative, CD56-bright natural killer cell, human: -1.257<br>gamma-delta T cell: -2.661<br>CD16-positive, CD56-dim natural killer cell, human: -3.112<br>natural killer cell: -3.152<br>mature NK T cell: -3.250",
+          "value: -6.642<br>CD16-positive, CD56-dim natural killer cell, human: -0.584<br>natural killer cell: -1.419<br>mature NK T cell: -4.099<br>CD16-negative, CD56-bright natural killer cell, human: -4.740<br>astrocyte of the cerebral cortex: -4.815",
+          "value: -5.179<br>classical monocyte: -1.213<br>CD14-positive monocyte: -2.153<br>macrophage: -3.334<br>pvalb GABAergic cortical interneuron: -3.337<br>monocyte: -3.484",
+          "value: -6.031<br>classical monocyte: -1.718<br>CD14-positive monocyte: -1.725<br>CD1c-positive myeloid dendritic cell: -2.945<br>pvalb GABAergic cortical interneuron: -3.277<br>monocyte: -3.572",
+          "value: -4.882<br>CD16-positive, CD56-dim natural killer cell, human: -1.030<br>natural killer cell: -1.685<br>CD16-negative, CD56-bright natural killer cell, human: -2.394<br>mature NK T cell: -3.244<br>gamma-delta T cell: -3.431",
+          "value: -5.725<br>CD16-positive, CD56-dim natural killer cell, human: -0.704<br>natural killer cell: -1.800<br>CD16-negative, CD56-bright natural killer cell, human: -2.666<br>activated type II NK T cell: -4.284<br>mature NK T cell: -4.300",
+          "value: -6.461<br>conventional dendritic cell: -2.012<br>dendritic cell, human: -2.172<br>CD1c-positive myeloid dendritic cell: -2.233<br>dendritic cell: -2.260<br>myeloid dendritic cell: -3.206",
+          "value: -6.705<br>CD16-positive, CD56-dim natural killer cell, human: -0.507<br>natural killer cell: -1.717<br>mature NK T cell: -3.737<br>CD16-negative, CD56-bright natural killer cell, human: -4.273<br>astrocyte of the cerebral cortex: -4.847",
+          "value: -6.330<br>CD16-positive, CD56-dim natural killer cell, human: -1.532<br>CD16-negative, CD56-bright natural killer cell, human: -1.839<br>natural killer cell: -2.373<br>activated type II NK T cell: -3.532<br>mature NK T cell: -3.962",
+          "value: -6.705<br>CD16-positive, CD56-dim natural killer cell, human: -0.653<br>CD16-negative, CD56-bright natural killer cell, human: -2.144<br>natural killer cell: -2.315<br>mature NK T cell: -4.039<br>gamma-delta T cell: -4.638",
+          "value: -6.878<br>CD16-positive, CD56-dim natural killer cell, human: -0.569<br>natural killer cell: -1.561<br>CD16-negative, CD56-bright natural killer cell, human: -3.790<br>gamma-delta T cell: -4.062<br>mature NK T cell: -4.070",
+          "value: -5.809<br>plasmacytoid dendritic cell: -0.351<br>plasmacytoid dendritic cell, human: -2.711<br>dendritic cell: -2.785<br>dendritic cell, human: -4.780<br>myeloid dendritic cell: -4.996",
+          "value: -5.587<br>CD16-positive, CD56-dim natural killer cell, human: -1.072<br>natural killer cell: -1.615<br>CD16-negative, CD56-bright natural killer cell, human: -2.563<br>mature NK T cell: -3.263<br>gamma-delta T cell: -3.849",
+          "value: -6.430<br>CD16-positive, CD56-dim natural killer cell, human: -1.009<br>CD16-negative, CD56-bright natural killer cell, human: -1.790<br>natural killer cell: -1.822<br>mature NK T cell: -3.628<br>activated type II NK T cell: -4.363",
+          "value: -4.000<br>gamma-delta T cell: -1.741<br>CD16-negative, CD56-bright natural killer cell, human: -2.491<br>CD16-positive, CD56-dim natural killer cell, human: -2.947<br>mature NK T cell: -3.067<br>CD8-positive, alpha-beta T cell: -3.309",
+          "value: -5.678<br>CD16-positive, CD56-dim natural killer cell, human: -1.174<br>CD16-negative, CD56-bright natural killer cell, human: -1.870<br>natural killer cell: -2.137<br>mature NK T cell: -3.127<br>gamma-delta T cell: -3.183",
+          "value: -6.095<br>CD16-positive, CD56-dim natural killer cell, human: -0.725<br>natural killer cell: -1.293<br>mature NK T cell: -3.657<br>CD16-negative, CD56-bright natural killer cell, human: -4.067<br>gamma-delta T cell: -4.817",
+          "value: -4.415<br>plasmacytoid dendritic cell: -0.256<br>plasmacytoid dendritic cell, human: -2.611<br>dendritic cell: -2.967<br>myeloid dendritic cell: -5.365<br>conventional dendritic cell: -5.533",
+          "value: -6.321<br>CD16-positive, CD56-dim natural killer cell, human: -1.017<br>CD16-negative, CD56-bright natural killer cell, human: -1.502<br>natural killer cell: -2.453<br>gamma-delta T cell: -3.768<br>mature NK T cell: -4.046",
+          "value: -5.126<br>CD16-positive, CD56-dim natural killer cell, human: -0.981<br>natural killer cell: -1.140<br>mature NK T cell: -3.336<br>CD16-negative, CD56-bright natural killer cell, human: -3.487<br>gamma-delta T cell: -4.261",
+          "value: -6.808<br>CD16-negative, CD56-bright natural killer cell, human: -0.924<br>CD16-positive, CD56-dim natural killer cell, human: -1.808<br>natural killer cell: -2.315<br>mature NK T cell: -3.718<br>gamma-delta T cell: -3.818",
+          "value: -3.955<br>effector memory CD4-positive, alpha-beta T cell: -2.078<br>mature NK T cell: -2.712<br>T cell: -2.796<br>central memory CD4-positive, alpha-beta T cell: -2.991<br>effector memory CD8-positive, alpha-beta T cell: -3.066",
+          "value: -4.517<br>effector memory CD4-positive, alpha-beta T cell: -2.092<br>T follicular helper cell: -2.768<br>central memory CD4-positive, alpha-beta T cell: -2.914<br>T-helper 22 cell: -2.965<br>effector memory CD8-positive, alpha-beta T cell: -3.119",
+          "value: -4.110<br>CD4-positive, alpha-beta T cell: -2.669<br>T cell: -2.851<br>mast cell: -3.053<br>effector memory CD4-positive, alpha-beta T cell: -3.404<br>central memory CD4-positive, alpha-beta T cell: -3.730",
+          "value: -6.932<br>CD16-negative, CD56-bright natural killer cell, human: -1.196<br>gamma-delta T cell: -3.106<br>natural killer cell: -3.379<br>mature NK T cell: -3.734<br>innate lymphoid cell: -3.867",
+          "value: -6.511<br>CD16-positive, CD56-dim natural killer cell, human: -0.720<br>CD16-negative, CD56-bright natural killer cell, human: -1.981<br>natural killer cell: -2.517<br>mature NK T cell: -3.956<br>gamma-delta T cell: -4.248",
+          "value: -5.266<br>CD16-positive, CD56-dim natural killer cell, human: -1.779<br>CD16-negative, CD56-bright natural killer cell, human: -2.233<br>natural killer cell: -2.347<br>gamma-delta T cell: -2.806<br>mature NK T cell: -3.367",
+          "value: -5.860<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.862<br>central memory CD4-positive, alpha-beta T cell: -1.806<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.877<br>T follicular helper cell: -3.551<br>effector memory CD4-positive, alpha-beta T cell: -3.976",
+          "value: -6.709<br>classical monocyte: -1.336<br>CD14-positive monocyte: -1.864<br>dendritic cell, human: -3.104<br>CD1c-positive myeloid dendritic cell: -3.266<br>pvalb GABAergic cortical interneuron: -3.314",
+          "value: -5.924<br>CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -1.475<br>mature NK T cell: -4.096<br>CD16-negative, CD56-bright natural killer cell, human: -4.519<br>astrocyte of the cerebral cortex: -4.737",
+          "value: -6.907<br>CD16-positive, CD56-dim natural killer cell, human: -0.312<br>natural killer cell: -2.327<br>CD16-negative, CD56-bright natural killer cell, human: -3.058<br>mature NK T cell: -4.548<br>activated type II NK T cell: -4.834",
+          "value: -6.788<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.682<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.535<br>central memory CD4-positive, alpha-beta T cell: -2.220<br>T follicular helper cell: -3.939<br>effector memory CD4-positive, alpha-beta T cell: -4.306",
+          "value: -6.389<br>CD16-positive, CD56-dim natural killer cell, human: -0.800<br>natural killer cell: -1.488<br>mature NK T cell: -3.491<br>CD16-negative, CD56-bright natural killer cell, human: -3.947<br>gamma-delta T cell: -4.526",
+          "value: -5.511<br>T follicular helper cell: -2.086<br>effector memory CD4-positive, alpha-beta T cell: -2.120<br>central memory CD4-positive, alpha-beta T cell: -2.417<br>T-helper 22 cell: -2.576<br>effector memory CD8-positive, alpha-beta T cell: -3.152",
+          "value: -5.855<br>classical monocyte: -1.404<br>CD14-positive monocyte: -1.924<br>pvalb GABAergic cortical interneuron: -2.616<br>monocyte: -3.123<br>macrophage: -3.896",
+          "value: -5.689<br>CD16-positive, CD56-dim natural killer cell, human: -0.781<br>natural killer cell: -2.108<br>CD16-negative, CD56-bright natural killer cell, human: -2.272<br>gamma-delta T cell: -3.899<br>mature NK T cell: -3.973",
+          "value: -5.789<br>plasmacytoid dendritic cell: -0.203<br>dendritic cell: -2.975<br>plasmacytoid dendritic cell, human: -3.032<br>myeloid dendritic cell: -5.420<br>dendritic cell, human: -5.696",
+          "value: -5.684<br>CD16-negative, CD56-bright natural killer cell, human: -1.672<br>natural killer cell: -1.741<br>CD16-positive, CD56-dim natural killer cell, human: -3.010<br>mature NK T cell: -3.658<br>gamma-delta T cell: -3.931",
+          "value: -5.162<br>effector memory CD4-positive, alpha-beta T cell: -1.888<br>T follicular helper cell: -2.480<br>effector memory CD8-positive, alpha-beta T cell: -2.637<br>central memory CD4-positive, alpha-beta T cell: -2.913<br>T-helper 22 cell: -2.924",
+          "value: -5.389<br>CD16-positive, CD56-dim natural killer cell, human: -1.095<br>natural killer cell: -1.592<br>CD16-negative, CD56-bright natural killer cell, human: -1.875<br>activated type II NK T cell: -4.033<br>gamma-delta T cell: -4.290",
+          "value: -6.487<br>CD16-negative, CD56-bright natural killer cell, human: -1.230<br>CD16-positive, CD56-dim natural killer cell, human: -1.459<br>natural killer cell: -2.547<br>gamma-delta T cell: -3.285<br>mature NK T cell: -3.785",
+          "value: -5.225<br>CD16-positive, CD56-dim natural killer cell, human: -1.655<br>natural killer cell: -1.806<br>gamma-delta T cell: -2.828<br>CD16-negative, CD56-bright natural killer cell, human: -2.923<br>mature NK T cell: -3.212",
+          "value: -6.232<br>CD16-positive, CD56-dim natural killer cell, human: -0.575<br>natural killer cell: -2.113<br>CD16-negative, CD56-bright natural killer cell, human: -2.132<br>activated type II NK T cell: -4.407<br>mature NK T cell: -4.619",
+          "value: -6.025<br>CD16-positive, CD56-dim natural killer cell, human: -0.574<br>natural killer cell: -1.804<br>CD16-negative, CD56-bright natural killer cell, human: -3.528<br>mature NK T cell: -3.633<br>gamma-delta T cell: -4.125",
+          "value: -6.567<br>CD16-negative, CD56-bright natural killer cell, human: -1.560<br>gamma-delta T cell: -3.003<br>effector memory CD8-positive, alpha-beta T cell: -3.611<br>innate lymphoid cell: -3.743<br>mature NK T cell: -3.752",
+          "value: -6.252<br>CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.585<br>mature NK T cell: -3.995<br>CD16-negative, CD56-bright natural killer cell, human: -4.527<br>astrocyte of the cerebral cortex: -4.646",
+          "value: -6.896<br>T follicular helper cell: -1.310<br>central memory CD4-positive, alpha-beta T cell: -2.231<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.685<br>effector memory CD4-positive, alpha-beta T cell: -2.960<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.566",
+          "value: -5.374<br>CD16-positive, CD56-dim natural killer cell, human: -1.025<br>CD16-negative, CD56-bright natural killer cell, human: -1.842<br>natural killer cell: -1.863<br>mature NK T cell: -3.952<br>activated type II NK T cell: -3.985",
+          "value: -4.185<br>classical monocyte: -1.271<br>CD14-positive monocyte: -1.608<br>pvalb GABAergic cortical interneuron: -3.114<br>macrophage: -3.253<br>monocyte: -3.260",
+          "value: -6.870<br>T follicular helper cell: -1.419<br>central memory CD4-positive, alpha-beta T cell: -2.083<br>effector memory CD4-positive, alpha-beta T cell: -2.448<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.865<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.983",
+          "value: -5.665<br>plasmacytoid dendritic cell: -0.179<br>plasmacytoid dendritic cell, human: -2.874<br>dendritic cell: -3.942<br>myeloid dendritic cell: -5.934<br>hematopoietic precursor cell: -6.076",
+          "value: -6.513<br>dendritic cell, human: -2.476<br>conventional dendritic cell: -2.492<br>CD1c-positive myeloid dendritic cell: -2.509<br>dendritic cell: -3.174<br>CD14-positive monocyte: -3.225",
+          "value: -5.732<br>natural killer cell: -0.647<br>CD16-positive, CD56-dim natural killer cell, human: -2.113<br>CD8-positive, alpha-beta T cell: -2.733<br>gamma-delta T cell: -3.856<br>mature NK T cell: -3.982",
+          "value: -6.117<br>classical monocyte: -1.625<br>CD14-positive monocyte: -2.123<br>CD1c-positive myeloid dendritic cell: -2.936<br>conventional dendritic cell: -3.188<br>pvalb GABAergic cortical interneuron: -3.220",
+          "value: -6.089<br>effector memory CD4-positive, alpha-beta T cell: -1.926<br>central memory CD4-positive, alpha-beta T cell: -2.194<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.480<br>mucosal invariant T cell: -2.569<br>effector memory CD8-positive, alpha-beta T cell: -2.851",
+          "value: -4.193<br>effector CD8-positive, alpha-beta T cell: -1.893<br>effector memory CD8-positive, alpha-beta T cell: -2.327<br>mature NK T cell: -2.597<br>CD8-positive, alpha-beta T cell: -3.019<br>gamma-delta T cell: -3.521",
+          "value: -5.850<br>CD16-positive, CD56-dim natural killer cell, human: -0.798<br>natural killer cell: -1.775<br>CD16-negative, CD56-bright natural killer cell, human: -3.003<br>mature NK T cell: -3.688<br>gamma-delta T cell: -4.274",
+          "value: -6.395<br>CD16-negative, CD56-bright natural killer cell, human: -1.463<br>CD16-positive, CD56-dim natural killer cell, human: -1.855<br>natural killer cell: -2.167<br>gamma-delta T cell: -3.332<br>activated type II NK T cell: -3.797",
+          "value: -6.615<br>CD16-positive, CD56-dim natural killer cell, human: -0.887<br>natural killer cell: -1.640<br>CD16-negative, CD56-bright natural killer cell, human: -2.058<br>activated type II NK T cell: -4.060<br>mature NK T cell: -4.218",
+          "value: -5.887<br>classical monocyte: -1.278<br>CD14-positive monocyte: -1.831<br>pvalb GABAergic cortical interneuron: -2.921<br>monocyte: -3.775<br>macrophage: -3.888",
+          "value: -5.535<br>CD16-positive, CD56-dim natural killer cell, human: -0.987<br>natural killer cell: -1.205<br>CD16-negative, CD56-bright natural killer cell, human: -3.264<br>mature NK T cell: -3.632<br>activated type II NK T cell: -4.559",
+          "value: -6.248<br>CD16-positive, CD56-dim natural killer cell, human: -0.436<br>natural killer cell: -2.188<br>CD16-negative, CD56-bright natural killer cell, human: -3.151<br>mature NK T cell: -4.631<br>activated type II NK T cell: -4.665",
+          "value: -3.966<br>CD16-negative, CD56-bright natural killer cell, human: -1.924<br>gamma-delta T cell: -2.488<br>natural killer cell: -2.659<br>CD16-positive, CD56-dim natural killer cell, human: -2.804<br>mature NK T cell: -2.859",
+          "value: -5.199<br>CD16-positive, CD56-dim natural killer cell, human: -1.372<br>natural killer cell: -1.446<br>CD16-negative, CD56-bright natural killer cell, human: -1.788<br>gamma-delta T cell: -3.878<br>mature NK T cell: -4.078",
+          "value: -5.459<br>CD16-positive, CD56-dim natural killer cell, human: -0.460<br>natural killer cell: -2.149<br>CD16-negative, CD56-bright natural killer cell, human: -3.250<br>gamma-delta T cell: -3.946<br>mature NK T cell: -3.967",
+          "value: -6.638<br>CD16-positive, CD56-dim natural killer cell, human: -0.688<br>CD16-negative, CD56-bright natural killer cell, human: -1.978<br>natural killer cell: -2.557<br>mature NK T cell: -4.234<br>gamma-delta T cell: -4.271",
+          "value: -5.649<br>B cell: -1.228<br>naive B cell: -1.308<br>malignant cell: -2.962<br>memory B cell: -3.455<br>class switched memory B cell: -3.474",
+          "value: -5.990<br>CD16-positive, CD56-dim natural killer cell, human: -0.702<br>natural killer cell: -1.582<br>CD16-negative, CD56-bright natural killer cell, human: -3.084<br>mature NK T cell: -3.793<br>gamma-delta T cell: -4.453",
+          "value: -6.559<br>CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -1.454<br>mature NK T cell: -4.114<br>CD16-negative, CD56-bright natural killer cell, human: -4.546<br>astrocyte of the cerebral cortex: -4.850",
+          "value: -7.299<br>CD1c-positive myeloid dendritic cell: -2.252<br>CD14-positive monocyte: -2.254<br>classical monocyte: -2.473<br>dendritic cell, human: -2.738<br>conventional dendritic cell: -2.765",
+          "value: -6.264<br>classical monocyte: -1.202<br>CD14-positive monocyte: -1.935<br>pvalb GABAergic cortical interneuron: -3.291<br>dendritic cell, human: -3.903<br>CD1c-positive myeloid dendritic cell: -3.910",
+          "value: -5.344<br>CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.526<br>CD16-negative, CD56-bright natural killer cell, human: -3.310<br>mature NK T cell: -3.870<br>activated type II NK T cell: -4.870",
+          "value: -5.949<br>CD16-positive, CD56-dim natural killer cell, human: -0.297<br>natural killer cell: -2.330<br>CD16-negative, CD56-bright natural killer cell, human: -4.087<br>mature NK T cell: -4.450<br>gamma-delta T cell: -4.945",
+          "value: -4.921<br>B cell: -0.811<br>naive B cell: -2.575<br>malignant cell: -2.917<br>class switched memory B cell: -2.988<br>memory B cell: -3.532",
+          "value: -6.864<br>CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.897<br>CD16-negative, CD56-bright natural killer cell, human: -2.417<br>mature NK T cell: -3.895<br>activated type II NK T cell: -4.357",
+          "value: -6.715<br>CD16-positive, CD56-dim natural killer cell, human: -0.806<br>CD16-negative, CD56-bright natural killer cell, human: -1.873<br>natural killer cell: -2.047<br>activated type II NK T cell: -4.116<br>mature NK T cell: -4.176",
+          "value: -6.561<br>effector memory CD4-positive, alpha-beta T cell: -1.937<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.348<br>central memory CD4-positive, alpha-beta T cell: -2.464<br>mucosal invariant T cell: -2.634<br>T follicular helper cell: -2.719",
+          "value: -5.844<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.986<br>central memory CD4-positive, alpha-beta T cell: -1.887<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.188<br>T follicular helper cell: -3.307<br>effector memory CD4-positive, alpha-beta T cell: -3.488",
+          "value: -5.359<br>plasmacytoid dendritic cell: -0.300<br>dendritic cell: -2.723<br>plasmacytoid dendritic cell, human: -2.796<br>plasma cell: -4.646<br>B cell: -4.866",
+          "value: -5.786<br>CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.826<br>CD16-negative, CD56-bright natural killer cell, human: -2.924<br>mature NK T cell: -4.751<br>activated type II NK T cell: -4.761",
+          "value: -6.155<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.397<br>central memory CD4-positive, alpha-beta T cell: -1.841<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.915<br>T follicular helper cell: -2.502<br>effector memory CD4-positive, alpha-beta T cell: -3.231",
+          "value: -5.788<br>CD16-positive, CD56-dim natural killer cell, human: -1.268<br>CD16-negative, CD56-bright natural killer cell, human: -1.507<br>natural killer cell: -1.721<br>activated type II NK T cell: -3.867<br>gamma-delta T cell: -4.343",
+          "value: -5.581<br>plasmacytoid dendritic cell: -0.234<br>plasmacytoid dendritic cell, human: -2.860<br>dendritic cell: -2.940<br>myeloid dendritic cell: -5.401<br>dendritic cell, human: -5.518",
+          "value: -6.642<br>classical monocyte: -1.428<br>CD14-positive monocyte: -1.650<br>pvalb GABAergic cortical interneuron: -3.283<br>macrophage: -3.356<br>CD1c-positive myeloid dendritic cell: -3.890",
+          "value: -5.884<br>CD16-positive, CD56-dim natural killer cell, human: -0.537<br>natural killer cell: -2.202<br>CD16-negative, CD56-bright natural killer cell, human: -2.996<br>mature NK T cell: -3.828<br>gamma-delta T cell: -4.337",
+          "value: -5.177<br>effector memory CD4-positive, alpha-beta T cell: -2.102<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.347<br>central memory CD4-positive, alpha-beta T cell: -2.545<br>effector memory CD8-positive, alpha-beta T cell: -2.610<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.033",
+          "value: -5.993<br>central memory CD4-positive, alpha-beta T cell: -1.959<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.965<br>T follicular helper cell: -2.425<br>effector memory CD4-positive, alpha-beta T cell: -2.555<br>effector memory CD8-positive, alpha-beta T cell: -2.751",
+          "value: -5.649<br>classical monocyte: -1.253<br>CD14-positive monocyte: -1.329<br>pvalb GABAergic cortical interneuron: -2.815<br>monocyte: -3.608<br>macrophage: -4.101",
+          "value: -5.487<br>CD16-negative, CD56-bright natural killer cell, human: -1.284<br>natural killer cell: -2.003<br>innate lymphoid cell: -3.464<br>lymphocyte: -3.669<br>group 3 innate lymphoid cell: -3.921",
+          "value: -6.958<br>CD14-positive monocyte: -1.463<br>classical monocyte: -1.610<br>pvalb GABAergic cortical interneuron: -2.723<br>monocyte: -3.472<br>dendritic cell, human: -3.607",
+          "value: -6.917<br>CD16-positive, CD56-dim natural killer cell, human: -0.451<br>natural killer cell: -1.691<br>mature NK T cell: -3.910<br>CD16-negative, CD56-bright natural killer cell, human: -4.444<br>astrocyte of the cerebral cortex: -5.012",
+          "value: -6.166<br>CD16-positive, CD56-dim natural killer cell, human: -0.594<br>natural killer cell: -1.255<br>CD16-negative, CD56-bright natural killer cell, human: -4.050<br>mature NK T cell: -4.180<br>activated type II NK T cell: -4.918",
+          "value: -6.131<br>CD16-positive, CD56-dim natural killer cell, human: -0.758<br>natural killer cell: -2.127<br>CD16-negative, CD56-bright natural killer cell, human: -2.826<br>mature NK T cell: -3.286<br>gamma-delta T cell: -4.097",
+          "value: -5.088<br>effector memory CD4-positive, alpha-beta T cell: -1.999<br>central memory CD4-positive, alpha-beta T cell: -2.204<br>T follicular helper cell: -2.335<br>T-helper 22 cell: -2.782<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.403",
+          "value: -5.770<br>plasmacytoid dendritic cell: -0.660<br>plasmacytoid dendritic cell, human: -2.426<br>dendritic cell: -2.435<br>dendritic cell, human: -4.569<br>conventional dendritic cell: -4.935",
+          "value: -5.333<br>CD16-positive, CD56-dim natural killer cell, human: -0.960<br>natural killer cell: -1.619<br>CD16-negative, CD56-bright natural killer cell, human: -3.215<br>mature NK T cell: -3.320<br>gamma-delta T cell: -4.224",
+          "value: -5.821<br>classical monocyte: -1.263<br>CD14-positive monocyte: -1.325<br>pvalb GABAergic cortical interneuron: -2.785<br>monocyte: -3.580<br>macrophage: -3.953",
+          "value: -5.902<br>CD16-positive, CD56-dim natural killer cell, human: -1.420<br>natural killer cell: -1.512<br>gamma-delta T cell: -2.730<br>CD16-negative, CD56-bright natural killer cell, human: -2.881<br>mature NK T cell: -3.112",
+          "value: -4.980<br>CD16-positive, CD56-dim natural killer cell, human: -0.935<br>natural killer cell: -1.920<br>CD16-negative, CD56-bright natural killer cell, human: -2.972<br>mature NK T cell: -3.349<br>gamma-delta T cell: -3.846",
+          "value: -6.442<br>CD16-positive, CD56-dim natural killer cell, human: -0.607<br>natural killer cell: -1.305<br>CD16-negative, CD56-bright natural killer cell, human: -3.861<br>mature NK T cell: -4.336<br>activated type II NK T cell: -4.906",
+          "value: -4.678<br>T-helper 22 cell: -2.163<br>central memory CD4-positive, alpha-beta T cell: -2.182<br>effector memory CD4-positive, alpha-beta T cell: -2.293<br>T follicular helper cell: -2.487<br>mature NK T cell: -3.434",
+          "value: -6.512<br>CD16-positive, CD56-dim natural killer cell, human: -0.754<br>natural killer cell: -1.854<br>CD16-negative, CD56-bright natural killer cell, human: -2.390<br>mature NK T cell: -3.943<br>activated type II NK T cell: -4.222",
+          "value: -5.725<br>CD16-positive, CD56-dim natural killer cell, human: -0.789<br>natural killer cell: -2.155<br>CD16-negative, CD56-bright natural killer cell, human: -2.690<br>gamma-delta T cell: -3.528<br>mature NK T cell: -3.673",
+          "value: -6.256<br>classical monocyte: -1.174<br>CD14-positive monocyte: -2.170<br>dendritic cell, human: -3.187<br>non-classical monocyte: -3.208<br>macrophage: -3.208",
+          "value: -5.359<br>CD16-positive, CD56-dim natural killer cell, human: -0.772<br>natural killer cell: -1.763<br>CD16-negative, CD56-bright natural killer cell, human: -2.253<br>activated type II NK T cell: -4.154<br>mature NK T cell: -4.459",
+          "value: -6.893<br>CD16-negative, CD56-bright natural killer cell, human: -1.170<br>gamma-delta T cell: -3.259<br>natural killer cell: -3.679<br>innate lymphoid cell: -4.083<br>CD16-positive, CD56-dim natural killer cell, human: -4.128",
+          "value: -5.969<br>plasmacytoid dendritic cell: -0.208<br>plasmacytoid dendritic cell, human: -3.087<br>dendritic cell: -3.156<br>myeloid dendritic cell: -5.339<br>dendritic cell, human: -5.496",
+          "value: -6.112<br>CD16-positive, CD56-dim natural killer cell, human: -0.628<br>natural killer cell: -1.598<br>CD16-negative, CD56-bright natural killer cell, human: -2.908<br>mature NK T cell: -4.300<br>activated type II NK T cell: -4.592",
+          "value: -5.934<br>CD16-positive, CD56-dim natural killer cell, human: -0.319<br>natural killer cell: -2.379<br>mature NK T cell: -3.831<br>CD16-negative, CD56-bright natural killer cell, human: -4.158<br>gamma-delta T cell: -4.554",
+          "value: -5.554<br>CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.873<br>CD16-negative, CD56-bright natural killer cell, human: -3.832<br>mature NK T cell: -4.241<br>astrocyte of the cerebral cortex: -4.800",
+          "value: -5.152<br>CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.803<br>CD16-negative, CD56-bright natural killer cell, human: -2.871<br>mature NK T cell: -4.047<br>gamma-delta T cell: -4.659",
+          "value: -4.817<br>CD16-negative, CD56-bright natural killer cell, human: -0.800<br>natural killer cell: -2.593<br>CD16-positive, CD56-dim natural killer cell, human: -3.478<br>gamma-delta T cell: -3.574<br>innate lymphoid cell: -3.692",
+          "value: -4.413<br>hematopoietic precursor cell: -2.168<br>hematopoietic stem cell: -2.334<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.435<br>progenitor cell: -3.569<br>common myeloid progenitor: -3.981",
+          "value: -5.708<br>CD16-negative, CD56-bright natural killer cell, human: -0.974<br>natural killer cell: -2.490<br>gamma-delta T cell: -2.855<br>CD16-positive, CD56-dim natural killer cell, human: -2.950<br>mature NK T cell: -3.689",
+          "value: -6.730<br>CD16-positive, CD56-dim natural killer cell, human: -0.891<br>natural killer cell: -1.527<br>CD16-negative, CD56-bright natural killer cell, human: -2.630<br>mature NK T cell: -4.054<br>activated type II NK T cell: -4.144",
+          "value: -5.399<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.079<br>central memory CD4-positive, alpha-beta T cell: -1.790<br>T follicular helper cell: -2.703<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.028<br>effector memory CD4-positive, alpha-beta T cell: -3.430",
+          "value: -5.252<br>CD16-positive, CD56-dim natural killer cell, human: -0.830<br>natural killer cell: -1.681<br>CD16-negative, CD56-bright natural killer cell, human: -2.850<br>mature NK T cell: -3.462<br>gamma-delta T cell: -4.020",
+          "value: -5.238<br>CD16-positive, CD56-dim natural killer cell, human: -0.640<br>natural killer cell: -1.778<br>mature NK T cell: -3.326<br>CD16-negative, CD56-bright natural killer cell, human: -3.474<br>gamma-delta T cell: -4.414",
+          "value: -4.953<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.096<br>hematopoietic precursor cell: -2.555<br>hematopoietic stem cell: -3.318<br>erythroid progenitor cell: -3.579<br>mast cell: -3.651",
+          "value: -6.191<br>CD16-positive, CD56-dim natural killer cell, human: -0.645<br>natural killer cell: -1.689<br>CD16-negative, CD56-bright natural killer cell, human: -2.566<br>mature NK T cell: -3.694<br>activated type II NK T cell: -4.629",
+          "value: -5.228<br>effector memory CD8-positive, alpha-beta T cell: -1.781<br>effector CD8-positive, alpha-beta T cell: -1.986<br>gamma-delta T cell: -2.698<br>mature NK T cell: -2.778<br>effector memory CD4-positive, alpha-beta T cell: -3.581",
+          "value: -6.458<br>effector memory CD4-positive, alpha-beta T cell: -1.985<br>effector memory CD8-positive, alpha-beta T cell: -2.486<br>gamma-delta T cell: -2.998<br>mucosal invariant T cell: -3.310<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.354",
+          "value: -5.126<br>CD16-positive, CD56-dim natural killer cell, human: -0.796<br>natural killer cell: -1.617<br>CD16-negative, CD56-bright natural killer cell, human: -2.796<br>mature NK T cell: -3.777<br>activated type II NK T cell: -4.643",
+          "value: -5.618<br>plasmacytoid dendritic cell: -0.250<br>plasmacytoid dendritic cell, human: -2.695<br>dendritic cell: -3.062<br>dendritic cell, human: -5.432<br>myeloid dendritic cell: -5.552",
+          "value: -6.476<br>CD16-positive, CD56-dim natural killer cell, human: -0.515<br>natural killer cell: -2.189<br>CD16-negative, CD56-bright natural killer cell, human: -2.669<br>mature NK T cell: -4.130<br>gamma-delta T cell: -4.428",
+          "value: -6.473<br>CD16-negative, CD56-bright natural killer cell, human: -1.157<br>gamma-delta T cell: -2.574<br>CD16-positive, CD56-dim natural killer cell, human: -2.578<br>natural killer cell: -3.079<br>mature NK T cell: -3.663",
+          "value: -5.712<br>CD16-positive, CD56-dim natural killer cell, human: -0.645<br>natural killer cell: -1.442<br>mature NK T cell: -3.504<br>gamma-delta T cell: -4.736<br>CD16-negative, CD56-bright natural killer cell, human: -4.788",
+          "value: -6.099<br>CD16-positive, CD56-dim natural killer cell, human: -0.821<br>natural killer cell: -2.295<br>CD16-negative, CD56-bright natural killer cell, human: -2.648<br>mature NK T cell: -3.608<br>gamma-delta T cell: -3.895",
+          "value: -6.498<br>CD16-positive, CD56-dim natural killer cell, human: -0.570<br>natural killer cell: -1.679<br>mature NK T cell: -3.620<br>CD16-negative, CD56-bright natural killer cell, human: -4.343<br>gamma-delta T cell: -4.569",
+          "value: -5.945<br>central memory CD4-positive, alpha-beta T cell: -1.647<br>effector memory CD4-positive, alpha-beta T cell: -2.232<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.361<br>T follicular helper cell: -2.409<br>T-helper 22 cell: -2.451",
+          "value: -6.548<br>classical monocyte: -1.292<br>CD14-positive monocyte: -1.399<br>pvalb GABAergic cortical interneuron: -3.179<br>macrophage: -3.693<br>CD1c-positive myeloid dendritic cell: -3.935",
+          "value: -6.136<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.940<br>T follicular helper cell: -1.971<br>central memory CD4-positive, alpha-beta T cell: -2.270<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.757<br>T cell: -3.864",
+          "value: -6.052<br>CD16-positive, CD56-dim natural killer cell, human: -0.688<br>natural killer cell: -1.590<br>CD16-negative, CD56-bright natural killer cell, human: -4.073<br>mature NK T cell: -4.097<br>astrocyte of the cerebral cortex: -4.654",
+          "value: -4.776<br>central memory CD4-positive, alpha-beta T cell: -1.866<br>T follicular helper cell: -2.235<br>effector memory CD4-positive, alpha-beta T cell: -2.437<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.619<br>T-helper 22 cell: -2.623",
+          "value: -5.413<br>natural killer cell: -1.109<br>CD16-positive, CD56-dim natural killer cell, human: -1.380<br>CD16-negative, CD56-bright natural killer cell, human: -2.751<br>mature NK T cell: -3.774<br>gamma-delta T cell: -4.195",
+          "value: -5.652<br>CD16-positive, CD56-dim natural killer cell, human: -1.028<br>natural killer cell: -2.228<br>CD16-negative, CD56-bright natural killer cell, human: -2.321<br>mature NK T cell: -3.433<br>gamma-delta T cell: -3.479",
+          "value: -4.903<br>plasmacytoid dendritic cell: -0.163<br>plasmacytoid dendritic cell, human: -3.228<br>dendritic cell: -3.342<br>conventional dendritic cell: -5.501<br>plasma cell: -5.955",
+          "value: -5.869<br>CD16-positive, CD56-dim natural killer cell, human: -0.502<br>natural killer cell: -1.740<br>CD16-negative, CD56-bright natural killer cell, human: -3.362<br>mature NK T cell: -4.130<br>activated type II NK T cell: -4.826",
+          "value: -6.513<br>CD16-positive, CD56-dim natural killer cell, human: -0.540<br>natural killer cell: -1.981<br>CD16-negative, CD56-bright natural killer cell, human: -2.388<br>mature NK T cell: -4.329<br>activated type II NK T cell: -4.559",
+          "value: -5.755<br>hematopoietic precursor cell: -1.621<br>hematopoietic stem cell: -3.053<br>CD34-positive, CD38-negative hematopoietic stem cell: -3.203<br>common myeloid progenitor: -3.514<br>progenitor cell: -3.545",
+          "value: -6.200<br>T follicular helper cell: -1.663<br>central memory CD4-positive, alpha-beta T cell: -2.169<br>effector memory CD4-positive, alpha-beta T cell: -2.233<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.616<br>T-helper 22 cell: -3.091",
+          "value: -5.095<br>CD16-positive, CD56-dim natural killer cell, human: -1.731<br>natural killer cell: -1.811<br>mature NK T cell: -2.693<br>gamma-delta T cell: -3.002<br>effector memory CD8-positive, alpha-beta T cell, terminally differentiated: -3.128",
+          "value: -5.466<br>classical monocyte: -1.120<br>CD14-positive monocyte: -1.819<br>pvalb GABAergic cortical interneuron: -2.680<br>monocyte: -3.726<br>macrophage: -3.980",
+          "value: -6.917<br>CD16-positive, CD56-dim natural killer cell, human: -0.749<br>CD16-negative, CD56-bright natural killer cell, human: -1.881<br>natural killer cell: -2.390<br>mature NK T cell: -4.027<br>gamma-delta T cell: -4.369",
+          "value: -6.303<br>CD16-positive, CD56-dim natural killer cell, human: -0.682<br>natural killer cell: -1.770<br>CD16-negative, CD56-bright natural killer cell, human: -2.827<br>mature NK T cell: -3.718<br>activated type II NK T cell: -4.761",
+          "value: -4.682<br>B cell: -0.837<br>naive B cell: -2.014<br>mature B cell: -3.249<br>class switched memory B cell: -3.459<br>memory B cell: -3.596",
+          "value: -6.391<br>effector memory CD4-positive, alpha-beta T cell: -1.980<br>T follicular helper cell: -2.026<br>central memory CD4-positive, alpha-beta T cell: -2.575<br>T-helper 22 cell: -2.942<br>effector memory CD8-positive, alpha-beta T cell: -3.111",
+          "value: -5.563<br>CD16-positive, CD56-dim natural killer cell, human: -0.860<br>natural killer cell: -1.622<br>CD16-negative, CD56-bright natural killer cell, human: -2.280<br>activated type II NK T cell: -4.086<br>mature NK T cell: -4.403",
+          "value: -6.054<br>CD16-positive, CD56-dim natural killer cell, human: -1.172<br>CD16-negative, CD56-bright natural killer cell, human: -2.009<br>natural killer cell: -2.016<br>gamma-delta T cell: -3.578<br>mature NK T cell: -3.878",
+          "value: -5.535<br>CD16-positive, CD56-dim natural killer cell, human: -1.109<br>CD16-negative, CD56-bright natural killer cell, human: -1.364<br>natural killer cell: -2.300<br>gamma-delta T cell: -3.568<br>mature NK T cell: -3.902",
+          "value: -5.983<br>CD16-positive, CD56-dim natural killer cell, human: -0.734<br>natural killer cell: -2.118<br>CD16-negative, CD56-bright natural killer cell, human: -2.260<br>mature NK T cell: -3.976<br>gamma-delta T cell: -4.398",
+          "value: -4.119<br>effector CD8-positive, alpha-beta T cell: -2.041<br>effector memory CD8-positive, alpha-beta T cell: -2.504<br>mature NK T cell: -2.595<br>effector memory CD8-positive, alpha-beta T cell, terminally differentiated: -2.936<br>gamma-delta T cell: -3.119",
+          "value: -6.425<br>effector memory CD4-positive, alpha-beta T cell: -1.976<br>central memory CD4-positive, alpha-beta T cell: -2.356<br>T follicular helper cell: -2.402<br>effector memory CD8-positive, alpha-beta T cell: -2.928<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.936",
+          "value: -6.280<br>CD16-positive, CD56-dim natural killer cell, human: -0.385<br>natural killer cell: -1.878<br>mature NK T cell: -4.151<br>CD16-negative, CD56-bright natural killer cell, human: -4.275<br>astrocyte of the cerebral cortex: -4.988",
+          "value: -6.023<br>T follicular helper cell: -1.807<br>central memory CD4-positive, alpha-beta T cell: -1.851<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.230<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.537<br>effector memory CD4-positive, alpha-beta T cell: -3.299",
+          "value: -5.553<br>CD16-positive, CD56-dim natural killer cell, human: -1.411<br>natural killer cell: -1.924<br>CD16-negative, CD56-bright natural killer cell, human: -2.427<br>mature NK T cell: -2.529<br>gamma-delta T cell: -3.057",
+          "value: -5.928<br>central memory CD4-positive, alpha-beta T cell: -1.782<br>T follicular helper cell: -1.790<br>effector memory CD4-positive, alpha-beta T cell: -2.413<br>T-helper 22 cell: -2.626<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.848",
+          "value: -4.169<br>CD16-positive, CD56-dim natural killer cell, human: -1.194<br>natural killer cell: -1.489<br>CD16-negative, CD56-bright natural killer cell, human: -2.511<br>gamma-delta T cell: -3.002<br>mature NK T cell: -3.360",
+          "value: -6.163<br>CD16-negative, CD56-bright natural killer cell, human: -1.012<br>natural killer cell: -2.317<br>CD16-positive, CD56-dim natural killer cell, human: -2.959<br>gamma-delta T cell: -3.564<br>activated type II NK T cell: -3.924",
+          "value: -6.088<br>classical monocyte: -1.246<br>CD14-positive monocyte: -2.262<br>pvalb GABAergic cortical interneuron: -2.965<br>macrophage: -3.612<br>monocyte: -3.942",
+          "value: -5.342<br>effector memory CD4-positive, alpha-beta T cell: -2.067<br>T follicular helper cell: -2.378<br>central memory CD4-positive, alpha-beta T cell: -2.873<br>effector memory CD8-positive, alpha-beta T cell: -3.118<br>T-helper 22 cell: -3.296",
+          "value: -5.514<br>dendritic cell: -1.571<br>conventional dendritic cell: -2.061<br>dendritic cell, human: -2.787<br>myeloid dendritic cell: -2.885<br>CD1c-positive myeloid dendritic cell: -2.968",
+          "value: -5.409<br>CD16-positive, CD56-dim natural killer cell, human: -0.745<br>natural killer cell: -2.318<br>CD16-negative, CD56-bright natural killer cell, human: -3.236<br>mature NK T cell: -3.266<br>gamma-delta T cell: -3.743",
+          "value: -6.132<br>CD16-positive, CD56-dim natural killer cell, human: -0.450<br>natural killer cell: -1.961<br>CD16-negative, CD56-bright natural killer cell, human: -3.170<br>mature NK T cell: -4.361<br>activated type II NK T cell: -4.852",
+          "value: -5.974<br>CD16-positive, CD56-dim natural killer cell, human: -0.421<br>natural killer cell: -1.902<br>CD16-negative, CD56-bright natural killer cell, human: -3.543<br>mature NK T cell: -4.478<br>activated type II NK T cell: -4.809",
+          "value: -6.129<br>CD16-positive, CD56-dim natural killer cell, human: -0.623<br>natural killer cell: -1.412<br>mature NK T cell: -3.859<br>CD16-negative, CD56-bright natural killer cell, human: -3.871<br>gamma-delta T cell: -4.694",
+          "value: -5.710<br>central memory CD4-positive, alpha-beta T cell: -2.055<br>effector memory CD4-positive, alpha-beta T cell: -2.228<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.267<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.317<br>effector memory CD8-positive, alpha-beta T cell: -2.724",
+          "value: -4.635<br>effector memory CD4-positive, alpha-beta T cell: -1.861<br>central memory CD4-positive, alpha-beta T cell: -2.381<br>effector memory CD8-positive, alpha-beta T cell: -2.796<br>mature NK T cell: -2.974<br>T-helper 22 cell: -3.005",
+          "value: -6.772<br>CD16-positive, CD56-dim natural killer cell, human: -0.431<br>natural killer cell: -2.104<br>CD16-negative, CD56-bright natural killer cell, human: -3.179<br>mature NK T cell: -4.320<br>activated type II NK T cell: -4.762",
+          "value: -5.372<br>plasmacytoid dendritic cell: -0.268<br>plasmacytoid dendritic cell, human: -2.673<br>dendritic cell: -3.437<br>conventional dendritic cell: -5.254<br>myeloid dendritic cell: -5.642",
+          "value: -4.995<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.316<br>central memory CD4-positive, alpha-beta T cell: -2.136<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.528<br>effector memory CD4-positive, alpha-beta T cell: -2.781<br>T-helper 22 cell: -3.104",
+          "value: -5.439<br>CD16-positive, CD56-dim natural killer cell, human: -0.555<br>natural killer cell: -2.265<br>CD16-negative, CD56-bright natural killer cell, human: -3.590<br>mature NK T cell: -3.731<br>gamma-delta T cell: -3.999",
+          "value: -5.848<br>plasmacytoid dendritic cell: -0.238<br>plasmacytoid dendritic cell, human: -2.849<br>dendritic cell: -3.006<br>dendritic cell, human: -5.127<br>myeloid dendritic cell: -5.169",
+          "value: -5.172<br>CD16-positive, CD56-dim natural killer cell, human: -0.563<br>natural killer cell: -2.037<br>CD16-negative, CD56-bright natural killer cell, human: -2.748<br>mature NK T cell: -4.030<br>gamma-delta T cell: -4.596",
+          "value: -4.746<br>natural killer cell: -0.705<br>CD16-positive, CD56-dim natural killer cell, human: -1.382<br>lymphocyte: -3.979<br>mature NK T cell: -4.218<br>CD16-negative, CD56-bright natural killer cell, human: -4.429",
+          "value: -5.641<br>CD16-positive, CD56-dim natural killer cell, human: -0.615<br>natural killer cell: -1.580<br>CD16-negative, CD56-bright natural killer cell, human: -3.577<br>gamma-delta T cell: -4.140<br>mature NK T cell: -4.298",
+          "value: -5.292<br>CD16-positive, CD56-dim natural killer cell, human: -0.606<br>natural killer cell: -1.716<br>CD16-negative, CD56-bright natural killer cell, human: -2.793<br>mature NK T cell: -4.436<br>activated type II NK T cell: -4.598",
+          "value: -4.472<br>CD16-negative, CD56-bright natural killer cell, human: -1.022<br>natural killer cell: -2.289<br>CD16-positive, CD56-dim natural killer cell, human: -3.283<br>innate lymphoid cell: -3.443<br>lymphocyte: -4.144",
+          "value: -5.945<br>plasmacytoid dendritic cell: -0.206<br>plasmacytoid dendritic cell, human: -2.952<br>dendritic cell: -3.292<br>dendritic cell, human: -5.473<br>myeloid dendritic cell: -5.752",
+          "value: -6.390<br>CD16-positive, CD56-dim natural killer cell, human: -0.535<br>natural killer cell: -1.719<br>CD16-negative, CD56-bright natural killer cell, human: -3.013<br>mature NK T cell: -4.609<br>activated type II NK T cell: -4.619",
+          "value: -6.170<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.494<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.003<br>effector memory CD4-positive, alpha-beta T cell: -2.137<br>central memory CD4-positive, alpha-beta T cell: -2.279<br>effector memory CD8-positive, alpha-beta T cell: -3.011",
+          "value: -6.140<br>CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.477<br>mature NK T cell: -3.960<br>CD16-negative, CD56-bright natural killer cell, human: -4.432<br>astrocyte of the cerebral cortex: -4.802",
+          "value: -4.513<br>natural killer cell: -0.433<br>CD16-positive, CD56-dim natural killer cell, human: -2.652<br>lymphocyte: -3.119<br>T cell: -4.321<br>CD16-negative, CD56-bright natural killer cell, human: -4.454",
+          "value: -5.738<br>CD16-positive, CD56-dim natural killer cell, human: -0.641<br>natural killer cell: -1.652<br>CD16-negative, CD56-bright natural killer cell, human: -2.797<br>mature NK T cell: -3.510<br>activated type II NK T cell: -4.892",
+          "value: -5.688<br>CD16-positive, CD56-dim natural killer cell, human: -0.877<br>natural killer cell: -1.490<br>mature NK T cell: -3.159<br>gamma-delta T cell: -3.683<br>CD16-negative, CD56-bright natural killer cell, human: -3.823",
+          "value: -6.111<br>CD16-positive, CD56-dim natural killer cell, human: -0.520<br>natural killer cell: -1.660<br>CD16-negative, CD56-bright natural killer cell, human: -3.396<br>mature NK T cell: -4.077<br>astrocyte of the cerebral cortex: -4.908",
+          "value: -6.357<br>CD16-positive, CD56-dim natural killer cell, human: -0.609<br>natural killer cell: -1.432<br>mature NK T cell: -3.758<br>CD16-negative, CD56-bright natural killer cell, human: -4.266<br>astrocyte of the cerebral cortex: -4.832",
+          "value: -6.717<br>central memory CD4-positive, alpha-beta T cell: -1.675<br>T follicular helper cell: -1.821<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.228<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.553<br>effector memory CD4-positive, alpha-beta T cell: -2.608",
+          "value: -5.484<br>classical monocyte: -0.838<br>CD14-positive monocyte: -1.560<br>pvalb GABAergic cortical interneuron: -3.276<br>monocyte: -4.038<br>macrophage: -4.428",
+          "value: -5.313<br>natural killer cell: -1.311<br>CD16-positive, CD56-dim natural killer cell, human: -1.413<br>mature NK T cell: -3.249<br>CD16-negative, CD56-bright natural killer cell, human: -3.515<br>gamma-delta T cell: -3.694",
+          "value: -5.908<br>CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -1.354<br>mature NK T cell: -3.570<br>CD16-negative, CD56-bright natural killer cell, human: -4.344<br>gamma-delta T cell: -4.716",
+          "value: -6.575<br>effector memory CD8-positive, alpha-beta T cell: -2.216<br>mucosal invariant T cell: -2.491<br>gamma-delta T cell: -2.497<br>effector CD8-positive, alpha-beta T cell: -2.969<br>effector memory CD4-positive, alpha-beta T cell: -3.066",
+          "value: -5.003<br>CD16-negative, CD56-bright natural killer cell, human: -0.850<br>natural killer cell: -2.701<br>innate lymphoid cell: -3.622<br>group 3 innate lymphoid cell: -4.261<br>CD16-positive, CD56-dim natural killer cell, human: -4.292",
+          "value: -5.845<br>classical monocyte: -1.184<br>CD14-positive monocyte: -1.567<br>pvalb GABAergic cortical interneuron: -2.709<br>monocyte: -3.736<br>macrophage: -3.990",
+          "value: -6.901<br>CD16-positive, CD56-dim natural killer cell, human: -0.483<br>natural killer cell: -1.466<br>mature NK T cell: -4.246<br>CD16-negative, CD56-bright natural killer cell, human: -4.501<br>astrocyte of the cerebral cortex: -5.016",
+          "value: -6.286<br>CD14-positive monocyte: -1.068<br>classical monocyte: -1.213<br>pvalb GABAergic cortical interneuron: -3.182<br>monocyte: -4.005<br>macrophage: -4.101",
+          "value: -5.569<br>CD16-positive, CD56-dim natural killer cell, human: -0.836<br>natural killer cell: -1.742<br>CD16-negative, CD56-bright natural killer cell, human: -2.868<br>mature NK T cell: -3.406<br>gamma-delta T cell: -4.061",
+          "value: -6.122<br>CD16-positive, CD56-dim natural killer cell, human: -0.721<br>CD16-negative, CD56-bright natural killer cell, human: -2.050<br>natural killer cell: -2.191<br>gamma-delta T cell: -4.185<br>activated type II NK T cell: -4.364",
+          "value: -5.113<br>CD16-positive, CD56-dim natural killer cell, human: -0.749<br>natural killer cell: -1.301<br>CD16-negative, CD56-bright natural killer cell, human: -3.563<br>mature NK T cell: -3.836<br>astrocyte of the cerebral cortex: -4.679",
+          "value: -6.396<br>CD16-negative, CD56-bright natural killer cell, human: -1.167<br>CD16-positive, CD56-dim natural killer cell, human: -2.820<br>natural killer cell: -2.864<br>gamma-delta T cell: -3.026<br>activated type II NK T cell: -4.013",
+          "value: -6.004<br>CD16-positive, CD56-dim natural killer cell, human: -1.202<br>natural killer cell: -1.913<br>CD16-negative, CD56-bright natural killer cell, human: -2.072<br>mature NK T cell: -3.165<br>gamma-delta T cell: -3.312",
+          "value: -3.840<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.505<br>hematopoietic precursor cell: -2.592<br>hematopoietic stem cell: -2.706<br>progenitor cell: -3.583<br>serous secreting cell of bronchus submucosal gland: -4.019",
+          "value: -6.438<br>classical monocyte: -1.483<br>CD14-positive monocyte: -1.535<br>monocyte: -2.986<br>pvalb GABAergic cortical interneuron: -3.006<br>macrophage: -3.873",
+          "value: -6.047<br>CD16-positive, CD56-dim natural killer cell, human: -0.523<br>natural killer cell: -1.784<br>CD16-negative, CD56-bright natural killer cell, human: -2.706<br>mature NK T cell: -4.427<br>activated type II NK T cell: -4.487",
+          "value: -4.906<br>conventional dendritic cell: -1.909<br>dendritic cell: -2.056<br>CD1c-positive myeloid dendritic cell: -2.468<br>plasmacytoid dendritic cell: -3.103<br>macrophage: -3.284",
+          "value: -4.887<br>natural killer cell: -1.027<br>CD16-positive, CD56-dim natural killer cell, human: -1.213<br>CD16-negative, CD56-bright natural killer cell, human: -2.105<br>mature NK T cell: -4.272<br>gamma-delta T cell: -4.449",
+          "value: -6.467<br>classical monocyte: -1.259<br>CD14-positive monocyte: -1.412<br>pvalb GABAergic cortical interneuron: -3.421<br>monocyte: -3.529<br>CD1c-positive myeloid dendritic cell: -3.865",
+          "value: -5.997<br>CD16-positive, CD56-dim natural killer cell, human: -0.480<br>natural killer cell: -1.779<br>CD16-negative, CD56-bright natural killer cell, human: -3.524<br>mature NK T cell: -4.264<br>activated type II NK T cell: -4.793",
+          "value: -5.186<br>plasmacytoid dendritic cell: -0.208<br>dendritic cell: -2.930<br>plasmacytoid dendritic cell, human: -3.140<br>myeloid dendritic cell: -5.163<br>dendritic cell, human: -5.735",
+          "value: -6.723<br>dendritic cell: -1.257<br>conventional dendritic cell: -2.002<br>dendritic cell, human: -2.342<br>myeloid dendritic cell: -2.444<br>CD1c-positive myeloid dendritic cell: -2.473",
+          "value: -6.687<br>CD16-positive, CD56-dim natural killer cell, human: -0.639<br>CD16-negative, CD56-bright natural killer cell, human: -2.319<br>natural killer cell: -2.475<br>gamma-delta T cell: -3.979<br>mature NK T cell: -3.989",
+          "value: -5.940<br>CD16-positive, CD56-dim natural killer cell, human: -1.164<br>natural killer cell: -1.667<br>CD16-negative, CD56-bright natural killer cell, human: -1.782<br>activated type II NK T cell: -3.786<br>mature NK T cell: -4.745",
+          "value: -5.346<br>CD16-negative, CD56-bright natural killer cell, human: -0.784<br>natural killer cell: -2.889<br>CD16-positive, CD56-dim natural killer cell, human: -3.352<br>activated type II NK T cell: -4.004<br>hepatic pit cell: -4.372",
+          "value: -5.511<br>natural killer cell: -0.633<br>CD16-positive, CD56-dim natural killer cell, human: -1.894<br>CD16-negative, CD56-bright natural killer cell, human: -2.569<br>mature NK T cell: -3.996<br>gamma-delta T cell: -4.279",
+          "value: -5.060<br>classical monocyte: -1.394<br>CD14-positive monocyte: -2.017<br>pvalb GABAergic cortical interneuron: -2.683<br>monocyte: -3.789<br>myeloid leukocyte: -4.092",
+          "value: -5.956<br>CD16-positive, CD56-dim natural killer cell, human: -0.330<br>natural killer cell: -2.026<br>CD16-negative, CD56-bright natural killer cell, human: -4.037<br>mature NK T cell: -4.177<br>astrocyte of the cerebral cortex: -5.122",
+          "value: -4.160<br>CD16-positive, CD56-dim natural killer cell, human: -0.880<br>natural killer cell: -2.245<br>CD16-negative, CD56-bright natural killer cell, human: -2.432<br>gamma-delta T cell: -3.142<br>mature NK T cell: -3.835",
+          "value: -5.603<br>T follicular helper cell: -1.913<br>central memory CD4-positive, alpha-beta T cell: -2.482<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.513<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.721<br>effector memory CD4-positive, alpha-beta T cell: -2.856",
+          "value: -4.925<br>effector memory CD4-positive, alpha-beta T cell: -1.945<br>central memory CD4-positive, alpha-beta T cell: -2.321<br>T follicular helper cell: -2.351<br>T-helper 22 cell: -2.787<br>effector memory CD8-positive, alpha-beta T cell: -3.429",
+          "value: -5.391<br>plasmacytoid dendritic cell: -0.368<br>plasmacytoid dendritic cell, human: -2.662<br>dendritic cell: -2.868<br>myeloid dendritic cell: -4.662<br>dendritic cell, human: -5.043",
+          "value: -5.392<br>CD16-positive, CD56-dim natural killer cell, human: -0.435<br>natural killer cell: -2.242<br>CD16-negative, CD56-bright natural killer cell, human: -3.105<br>gamma-delta T cell: -4.002<br>mature NK T cell: -4.088",
+          "value: -6.036<br>CD16-positive, CD56-dim natural killer cell, human: -0.718<br>natural killer cell: -1.340<br>CD16-negative, CD56-bright natural killer cell, human: -3.091<br>mature NK T cell: -4.017<br>activated type II NK T cell: -4.745",
+          "value: -5.300<br>CD16-positive, CD56-dim natural killer cell, human: -0.915<br>natural killer cell: -1.560<br>CD16-negative, CD56-bright natural killer cell, human: -2.563<br>mature NK T cell: -3.418<br>gamma-delta T cell: -3.830",
+          "value: -6.012<br>CD16-positive, CD56-dim natural killer cell, human: -0.513<br>natural killer cell: -2.188<br>CD16-negative, CD56-bright natural killer cell, human: -2.369<br>mature NK T cell: -4.376<br>activated type II NK T cell: -4.440",
+          "value: -6.143<br>classical monocyte: -1.130<br>CD14-positive monocyte: -1.411<br>pvalb GABAergic cortical interneuron: -3.023<br>monocyte: -3.533<br>macrophage: -4.270",
+          "value: -6.241<br>CD16-positive, CD56-dim natural killer cell, human: -0.423<br>natural killer cell: -1.709<br>CD16-negative, CD56-bright natural killer cell, human: -3.589<br>mature NK T cell: -4.533<br>activated type II NK T cell: -5.043",
+          "value: -6.487<br>classical monocyte: -1.380<br>CD14-positive monocyte: -1.995<br>pvalb GABAergic cortical interneuron: -2.741<br>monocyte: -3.354<br>dendritic cell, human: -3.783",
+          "value: -5.565<br>CD16-positive, CD56-dim natural killer cell, human: -0.659<br>natural killer cell: -1.562<br>mature NK T cell: -3.295<br>gamma-delta T cell: -3.935<br>CD16-negative, CD56-bright natural killer cell, human: -4.256",
+          "value: -6.633<br>CD16-negative, CD56-bright natural killer cell, human: -0.612<br>gamma-delta T cell: -2.854<br>CD16-positive, CD56-dim natural killer cell, human: -3.244<br>natural killer cell: -3.548<br>mature NK T cell: -3.950",
+          "value: -6.566<br>CD16-positive, CD56-dim natural killer cell, human: -0.848<br>CD16-negative, CD56-bright natural killer cell, human: -2.086<br>natural killer cell: -2.439<br>mature NK T cell: -3.831<br>gamma-delta T cell: -3.875",
+          "value: -7.337<br>CD16-positive, CD56-dim natural killer cell, human: -0.512<br>CD16-negative, CD56-bright natural killer cell, human: -2.384<br>natural killer cell: -2.729<br>mature NK T cell: -4.367<br>activated type II NK T cell: -4.506",
+          "value: -5.259<br>CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.294<br>mature NK T cell: -3.590<br>CD16-negative, CD56-bright natural killer cell, human: -4.348<br>gamma-delta T cell: -4.620",
+          "value: -5.491<br>natural killer cell: -0.991<br>CD16-positive, CD56-dim natural killer cell, human: -2.166<br>T cell: -3.360<br>CD16-negative, CD56-bright natural killer cell, human: -3.631<br>mature NK T cell: -3.971",
+          "value: -5.697<br>central memory CD4-positive, alpha-beta T cell: -1.763<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.902<br>effector memory CD4-positive, alpha-beta T cell: -2.329<br>T-helper 22 cell: -2.604<br>T follicular helper cell: -2.844",
+          "value: -5.529<br>CD16-positive, CD56-dim natural killer cell, human: -0.470<br>natural killer cell: -1.824<br>CD16-negative, CD56-bright natural killer cell, human: -2.891<br>mature NK T cell: -4.075<br>activated type II NK T cell: -4.964",
+          "value: -6.955<br>CD16-positive, CD56-dim natural killer cell, human: -0.981<br>CD16-negative, CD56-bright natural killer cell, human: -1.922<br>natural killer cell: -2.409<br>gamma-delta T cell: -3.721<br>activated type II NK T cell: -4.025",
+          "value: -6.522<br>CD16-positive, CD56-dim natural killer cell, human: -0.757<br>natural killer cell: -1.496<br>mature NK T cell: -3.242<br>gamma-delta T cell: -4.180<br>astrocyte of the cerebral cortex: -4.521",
+          "value: -5.698<br>CD16-positive, CD56-dim natural killer cell, human: -0.367<br>natural killer cell: -2.012<br>mature NK T cell: -3.728<br>CD16-negative, CD56-bright natural killer cell, human: -4.095<br>astrocyte of the cerebral cortex: -4.818",
+          "value: -5.856<br>CD16-positive, CD56-dim natural killer cell, human: -0.678<br>natural killer cell: -2.280<br>CD16-negative, CD56-bright natural killer cell, human: -3.144<br>gamma-delta T cell: -3.195<br>mature NK T cell: -3.341",
+          "value: -6.785<br>central memory CD4-positive, alpha-beta T cell: -1.453<br>T follicular helper cell: -2.116<br>effector memory CD4-positive, alpha-beta T cell: -2.251<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.391<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.547",
+          "value: -5.907<br>CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.164<br>mature NK T cell: -4.163<br>CD16-negative, CD56-bright natural killer cell, human: -4.317<br>astrocyte of the cerebral cortex: -4.764",
+          "value: -5.980<br>CD14-positive monocyte: -1.424<br>classical monocyte: -1.488<br>pvalb GABAergic cortical interneuron: -3.273<br>CD1c-positive myeloid dendritic cell: -3.532<br>dendritic cell, human: -3.824",
+          "value: -7.101<br>CD14-positive monocyte: -1.627<br>classical monocyte: -1.793<br>dendritic cell, human: -3.051<br>CD1c-positive myeloid dendritic cell: -3.279<br>dendritic cell: -3.587",
+          "value: -6.389<br>CD16-positive, CD56-dim natural killer cell, human: -0.546<br>natural killer cell: -2.436<br>CD16-negative, CD56-bright natural killer cell, human: -2.854<br>mature NK T cell: -3.844<br>gamma-delta T cell: -4.095",
+          "value: -6.628<br>CD16-positive, CD56-dim natural killer cell, human: -0.637<br>natural killer cell: -1.345<br>mature NK T cell: -3.889<br>CD16-negative, CD56-bright natural killer cell, human: -3.903<br>gamma-delta T cell: -4.802",
+          "value: -4.997<br>dendritic cell: -1.819<br>plasmacytoid dendritic cell: -1.861<br>conventional dendritic cell: -2.283<br>plasmacytoid dendritic cell, human: -2.922<br>CD1c-positive myeloid dendritic cell: -3.050",
+          "value: -5.388<br>plasmacytoid dendritic cell: -0.476<br>dendritic cell: -2.101<br>plasmacytoid dendritic cell, human: -2.984<br>B cell: -3.757<br>plasma cell: -3.832",
+          "value: -4.807<br>CD16-negative, CD56-bright natural killer cell, human: -1.177<br>natural killer cell: -2.917<br>CD16-positive, CD56-dim natural killer cell, human: -2.993<br>gamma-delta T cell: -3.423<br>mature NK T cell: -3.687",
+          "value: -4.964<br>effector memory CD4-positive, alpha-beta T cell: -1.768<br>T-helper 22 cell: -2.700<br>T follicular helper cell: -2.700<br>central memory CD4-positive, alpha-beta T cell: -2.866<br>effector memory CD8-positive, alpha-beta T cell: -3.272",
+          "value: -5.742<br>CD16-positive, CD56-dim natural killer cell, human: -1.409<br>CD16-negative, CD56-bright natural killer cell, human: -1.426<br>natural killer cell: -2.011<br>gamma-delta T cell: -3.543<br>mature NK T cell: -4.114",
+          "value: -6.968<br>CD14-positive monocyte: -1.607<br>classical monocyte: -1.991<br>CD1c-positive myeloid dendritic cell: -2.785<br>dendritic cell, human: -2.988<br>conventional dendritic cell: -3.023",
+          "value: -6.732<br>classical monocyte: -2.083<br>CD14-positive monocyte: -2.249<br>CD1c-positive myeloid dendritic cell: -2.868<br>dendritic cell, human: -3.231<br>conventional dendritic cell: -3.469",
+          "value: -6.597<br>CD16-positive, CD56-dim natural killer cell, human: -0.543<br>CD16-negative, CD56-bright natural killer cell, human: -1.980<br>natural killer cell: -2.178<br>activated type II NK T cell: -4.413<br>mature NK T cell: -4.637",
+          "value: -6.406<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.695<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.950<br>central memory CD4-positive, alpha-beta T cell: -1.990<br>T follicular helper cell: -2.444<br>effector memory CD4-positive, alpha-beta T cell: -2.771",
+          "value: -6.093<br>CD16-positive, CD56-dim natural killer cell, human: -0.909<br>natural killer cell: -1.901<br>CD16-negative, CD56-bright natural killer cell, human: -2.409<br>mature NK T cell: -3.721<br>gamma-delta T cell: -4.552",
+          "value: -5.969<br>CD16-positive, CD56-dim natural killer cell, human: -0.599<br>natural killer cell: -1.580<br>CD16-negative, CD56-bright natural killer cell, human: -3.517<br>mature NK T cell: -3.830<br>gamma-delta T cell: -3.993",
+          "value: -5.523<br>CD16-positive, CD56-dim natural killer cell, human: -0.930<br>natural killer cell: -1.590<br>mature NK T cell: -3.384<br>CD16-negative, CD56-bright natural killer cell, human: -3.541<br>gamma-delta T cell: -3.593",
+          "value: -6.719<br>CD16-positive, CD56-dim natural killer cell, human: -0.666<br>natural killer cell: -1.702<br>CD16-negative, CD56-bright natural killer cell, human: -3.443<br>mature NK T cell: -3.984<br>activated type II NK T cell: -4.438",
+          "value: -7.773<br>CD16-negative, CD56-bright natural killer cell, human: -0.803<br>gamma-delta T cell: -3.533<br>CD16-positive, CD56-dim natural killer cell, human: -3.642<br>natural killer cell: -3.680<br>innate lymphoid cell: -3.807",
+          "value: -6.237<br>CD16-positive, CD56-dim natural killer cell, human: -0.470<br>natural killer cell: -2.025<br>CD16-negative, CD56-bright natural killer cell, human: -2.832<br>mature NK T cell: -3.828<br>gamma-delta T cell: -4.854",
+          "value: -5.598<br>CD16-positive, CD56-dim natural killer cell, human: -1.286<br>natural killer cell: -1.537<br>mature NK T cell: -3.118<br>CD16-negative, CD56-bright natural killer cell, human: -3.374<br>gamma-delta T cell: -3.577",
+          "value: -5.715<br>CD16-positive, CD56-dim natural killer cell, human: -0.559<br>natural killer cell: -1.837<br>CD16-negative, CD56-bright natural killer cell, human: -2.540<br>mature NK T cell: -4.036<br>activated type II NK T cell: -4.735",
+          "value: -6.376<br>classical monocyte: -1.140<br>CD14-positive monocyte: -1.604<br>pvalb GABAergic cortical interneuron: -3.359<br>dendritic cell, human: -3.502<br>CD1c-positive myeloid dendritic cell: -3.587",
+          "value: -5.743<br>CD16-positive, CD56-dim natural killer cell, human: -0.714<br>natural killer cell: -1.265<br>CD16-negative, CD56-bright natural killer cell, human: -3.711<br>mature NK T cell: -4.214<br>activated type II NK T cell: -4.799",
+          "value: -5.871<br>classical monocyte: -2.399<br>dendritic cell, human: -2.584<br>dendritic cell: -2.990<br>CD14-positive monocyte: -3.161<br>CD1c-positive myeloid dendritic cell: -3.261",
+          "value: -7.066<br>CD16-negative, CD56-bright natural killer cell, human: -0.869<br>gamma-delta T cell: -2.867<br>CD16-positive, CD56-dim natural killer cell, human: -3.143<br>natural killer cell: -3.283<br>mucosal invariant T cell: -3.971",
+          "value: -6.366<br>CD16-negative, CD56-bright natural killer cell, human: -1.078<br>natural killer cell: -2.861<br>gamma-delta T cell: -3.065<br>CD16-positive, CD56-dim natural killer cell, human: -3.181<br>mature NK T cell: -3.698",
+          "value: -5.882<br>CD16-positive, CD56-dim natural killer cell, human: -0.759<br>natural killer cell: -2.273<br>CD16-negative, CD56-bright natural killer cell, human: -3.234<br>mature NK T cell: -3.432<br>gamma-delta T cell: -3.632",
+          "value: -6.543<br>CD16-positive, CD56-dim natural killer cell, human: -0.615<br>natural killer cell: -2.172<br>CD16-negative, CD56-bright natural killer cell, human: -2.634<br>mature NK T cell: -4.021<br>activated type II NK T cell: -4.421",
+          "value: -6.511<br>T follicular helper cell: -1.824<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.150<br>central memory CD4-positive, alpha-beta T cell: -2.257<br>effector memory CD4-positive, alpha-beta T cell: -2.705<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.945",
+          "value: -5.949<br>classical monocyte: -1.103<br>CD14-positive monocyte: -1.654<br>pvalb GABAergic cortical interneuron: -2.929<br>monocyte: -3.879<br>macrophage: -4.135",
+          "value: -5.780<br>CD16-positive, CD56-dim natural killer cell, human: -0.617<br>natural killer cell: -1.675<br>CD16-negative, CD56-bright natural killer cell, human: -3.387<br>mature NK T cell: -4.049<br>activated type II NK T cell: -4.578",
+          "value: -6.107<br>CD14-positive monocyte: -1.364<br>classical monocyte: -1.604<br>pvalb GABAergic cortical interneuron: -3.236<br>monocyte: -3.405<br>macrophage: -3.938",
+          "value: -5.910<br>CD16-negative, CD56-bright natural killer cell, human: -1.583<br>CD16-positive, CD56-dim natural killer cell, human: -1.669<br>natural killer cell: -2.586<br>gamma-delta T cell: -2.620<br>mature NK T cell: -3.451",
+          "value: -6.097<br>CD16-negative, CD56-bright natural killer cell, human: -0.835<br>natural killer cell: -2.420<br>CD16-positive, CD56-dim natural killer cell, human: -2.755<br>gamma-delta T cell: -3.826<br>activated type II NK T cell: -4.251",
+          "value: -5.749<br>effector memory CD4-positive, alpha-beta T cell: -1.792<br>central memory CD4-positive, alpha-beta T cell: -1.928<br>T follicular helper cell: -2.589<br>T-helper 22 cell: -2.698<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.119",
+          "value: -6.087<br>CD16-positive, CD56-dim natural killer cell, human: -0.746<br>natural killer cell: -2.089<br>CD16-negative, CD56-bright natural killer cell, human: -2.449<br>mature NK T cell: -3.547<br>gamma-delta T cell: -4.019",
+          "value: -6.461<br>CD16-positive, CD56-dim natural killer cell, human: -0.997<br>CD16-negative, CD56-bright natural killer cell, human: -1.904<br>natural killer cell: -2.109<br>gamma-delta T cell: -3.890<br>activated type II NK T cell: -4.085",
+          "value: -6.881<br>classical monocyte: -1.564<br>CD14-positive monocyte: -1.636<br>pvalb GABAergic cortical interneuron: -3.207<br>CD1c-positive myeloid dendritic cell: -3.276<br>dendritic cell, human: -3.492",
+          "value: -5.614<br>plasmacytoid dendritic cell: -0.212<br>plasmacytoid dendritic cell, human: -2.769<br>dendritic cell: -3.396<br>myeloid dendritic cell: -5.676<br>dendritic cell, human: -5.786",
+          "value: -5.518<br>plasmacytoid dendritic cell: -0.218<br>plasmacytoid dendritic cell, human: -2.815<br>dendritic cell: -3.313<br>dendritic cell, human: -5.441<br>myeloid dendritic cell: -5.502",
+          "value: -6.203<br>effector memory CD4-positive, alpha-beta T cell: -2.064<br>central memory CD4-positive, alpha-beta T cell: -2.982<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.996<br>T follicular helper cell: -3.204<br>effector memory CD8-positive, alpha-beta T cell: -3.211",
+          "value: -5.985<br>classical monocyte: -1.175<br>CD14-positive monocyte: -1.722<br>pvalb GABAergic cortical interneuron: -2.870<br>macrophage: -3.671<br>monocyte: -4.156",
+          "value: -7.054<br>T follicular helper cell: -1.710<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.845<br>central memory CD4-positive, alpha-beta T cell: -1.951<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.507<br>effector memory CD4-positive, alpha-beta T cell: -2.807",
+          "value: -5.855<br>CD16-positive, CD56-dim natural killer cell, human: -0.690<br>natural killer cell: -2.106<br>CD16-negative, CD56-bright natural killer cell, human: -2.404<br>gamma-delta T cell: -3.757<br>mature NK T cell: -4.023",
+          "value: -6.783<br>CD16-positive, CD56-dim natural killer cell, human: -0.329<br>natural killer cell: -2.012<br>CD16-negative, CD56-bright natural killer cell, human: -4.120<br>mature NK T cell: -4.364<br>astrocyte of the cerebral cortex: -5.045",
+          "value: -5.929<br>CD16-positive, CD56-dim natural killer cell, human: -0.695<br>natural killer cell: -1.415<br>CD16-negative, CD56-bright natural killer cell, human: -3.127<br>mature NK T cell: -4.177<br>activated type II NK T cell: -4.666",
+          "value: -4.183<br>effector memory CD4-positive, alpha-beta T cell: -2.360<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.675<br>central memory CD4-positive, alpha-beta T cell: -2.785<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.887<br>mature NK T cell: -3.288",
+          "value: -6.576<br>CD16-positive, CD56-dim natural killer cell, human: -0.605<br>natural killer cell: -1.431<br>CD16-negative, CD56-bright natural killer cell, human: -3.894<br>mature NK T cell: -3.915<br>activated type II NK T cell: -4.842",
+          "value: -5.609<br>CD16-positive, CD56-dim natural killer cell, human: -0.627<br>natural killer cell: -2.145<br>CD16-negative, CD56-bright natural killer cell, human: -2.746<br>mature NK T cell: -4.046<br>astrocyte of the cerebral cortex: -4.374",
+          "value: -5.681<br>CD16-positive, CD56-dim natural killer cell, human: -1.276<br>natural killer cell: -1.754<br>CD16-negative, CD56-bright natural killer cell, human: -1.771<br>gamma-delta T cell: -3.216<br>mature NK T cell: -3.416",
+          "value: -6.185<br>CD16-negative, CD56-bright natural killer cell, human: -0.955<br>natural killer cell: -2.617<br>gamma-delta T cell: -3.033<br>CD16-positive, CD56-dim natural killer cell, human: -3.566<br>mature NK T cell: -3.676",
+          "value: -5.670<br>central memory CD4-positive, alpha-beta T cell: -1.835<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.155<br>effector memory CD4-positive, alpha-beta T cell: -2.167<br>T-helper 22 cell: -2.564<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.722",
+          "value: -5.938<br>plasmacytoid dendritic cell: -0.281<br>plasmacytoid dendritic cell, human: -2.624<br>dendritic cell: -3.046<br>myeloid dendritic cell: -4.863<br>dendritic cell, human: -4.983",
+          "value: -3.629<br>gamma-delta T cell: -1.868<br>mature NK T cell: -1.996<br>CD16-positive, CD56-dim natural killer cell, human: -2.127<br>natural killer cell: -2.364<br>CD8-positive, alpha-beta T cell: -2.554",
+          "value: -5.410<br>CD16-negative, CD56-bright natural killer cell, human: -1.181<br>natural killer cell: -2.186<br>CD16-positive, CD56-dim natural killer cell, human: -2.576<br>gamma-delta T cell: -2.852<br>mature NK T cell: -3.494",
+          "value: -6.518<br>CD16-positive, CD56-dim natural killer cell, human: -0.372<br>natural killer cell: -1.831<br>CD16-negative, CD56-bright natural killer cell, human: -3.883<br>mature NK T cell: -4.259<br>activated type II NK T cell: -5.147",
+          "value: -5.365<br>CD14-positive monocyte: -1.496<br>classical monocyte: -1.719<br>monocyte: -2.433<br>pvalb GABAergic cortical interneuron: -3.294<br>promonocyte: -4.017",
+          "value: -6.243<br>CD16-positive, CD56-dim natural killer cell, human: -0.388<br>natural killer cell: -1.992<br>CD16-negative, CD56-bright natural killer cell, human: -3.766<br>mature NK T cell: -4.178<br>activated type II NK T cell: -5.048",
+          "value: -5.971<br>plasmacytoid dendritic cell: -0.367<br>dendritic cell: -2.705<br>plasmacytoid dendritic cell, human: -2.708<br>myeloid dendritic cell: -4.921<br>dendritic cell, human: -4.969",
+          "value: -6.002<br>effector memory CD4-positive, alpha-beta T cell: -1.768<br>central memory CD4-positive, alpha-beta T cell: -1.829<br>T-helper 22 cell: -2.206<br>T follicular helper cell: -2.476<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.948",
+          "value: -5.550<br>CD16-negative, CD56-bright natural killer cell, human: -1.220<br>gamma-delta T cell: -2.585<br>effector memory CD8-positive, alpha-beta T cell: -2.886<br>CD16-positive, CD56-dim natural killer cell, human: -3.645<br>mature NK T cell: -3.666",
+          "value: -4.753<br>CD16-negative, CD56-bright natural killer cell, human: -2.757<br>gamma-delta T cell: -3.216<br>T cell: -3.417<br>effector memory CD8-positive, alpha-beta T cell: -3.441<br>effector memory CD4-positive, alpha-beta T cell: -3.631",
+          "value: -4.981<br>CD16-negative, CD56-bright natural killer cell, human: -0.664<br>CD16-positive, CD56-dim natural killer cell, human: -2.721<br>natural killer cell: -2.782<br>gamma-delta T cell: -3.842<br>innate lymphoid cell: -4.169",
+          "value: -5.243<br>CD16-positive, CD56-dim natural killer cell, human: -1.160<br>natural killer cell: -1.492<br>CD16-negative, CD56-bright natural killer cell, human: -2.797<br>gamma-delta T cell: -3.325<br>mature NK T cell: -3.457",
+          "value: -4.777<br>hematopoietic precursor cell: -1.908<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.385<br>mast cell: -2.871<br>hematopoietic stem cell: -3.383<br>granulocyte: -3.655",
+          "value: -5.834<br>classical monocyte: -1.046<br>CD14-positive monocyte: -1.519<br>pvalb GABAergic cortical interneuron: -2.903<br>monocyte: -4.004<br>macrophage: -4.173",
+          "value: -5.850<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.409<br>central memory CD4-positive, alpha-beta T cell: -1.756<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.465<br>effector memory CD4-positive, alpha-beta T cell: -2.638<br>T-helper 22 cell: -3.424",
+          "value: -5.988<br>CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.552<br>CD16-negative, CD56-bright natural killer cell, human: -3.326<br>mature NK T cell: -3.827<br>activated type II NK T cell: -4.749",
+          "value: -6.125<br>CD16-positive, CD56-dim natural killer cell, human: -0.601<br>CD16-negative, CD56-bright natural killer cell, human: -1.954<br>natural killer cell: -2.220<br>activated type II NK T cell: -4.461<br>mature NK T cell: -4.588",
+          "value: -5.919<br>central memory CD4-positive, alpha-beta T cell: -1.680<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.717<br>T follicular helper cell: -1.779<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.633<br>effector memory CD4-positive, alpha-beta T cell: -3.382",
+          "value: -6.132<br>CD16-positive, CD56-dim natural killer cell, human: -0.878<br>natural killer cell: -1.811<br>CD16-negative, CD56-bright natural killer cell, human: -2.003<br>activated type II NK T cell: -4.026<br>mature NK T cell: -4.231",
+          "value: -6.159<br>CD16-positive, CD56-dim natural killer cell, human: -0.554<br>natural killer cell: -1.692<br>CD16-negative, CD56-bright natural killer cell, human: -3.568<br>mature NK T cell: -3.929<br>gamma-delta T cell: -4.686",
+          "value: -6.322<br>CD14-positive monocyte: -1.355<br>classical monocyte: -1.653<br>pvalb GABAergic cortical interneuron: -2.855<br>monocyte: -3.679<br>macrophage: -4.037",
+          "value: -5.728<br>CD16-negative, CD56-bright natural killer cell, human: -1.090<br>natural killer cell: -2.736<br>gamma-delta T cell: -2.907<br>CD16-positive, CD56-dim natural killer cell, human: -3.113<br>mature NK T cell: -3.764",
+          "value: -6.414<br>CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.500<br>mature NK T cell: -4.075<br>activated type II NK T cell: -4.478",
+          "value: -6.489<br>CD16-positive, CD56-dim natural killer cell, human: -0.411<br>natural killer cell: -2.121<br>CD16-negative, CD56-bright natural killer cell, human: -3.292<br>mature NK T cell: -4.299<br>gamma-delta T cell: -4.658",
+          "value: -5.549<br>CD16-positive, CD56-dim natural killer cell, human: -0.831<br>natural killer cell: -1.593<br>CD16-negative, CD56-bright natural killer cell, human: -3.556<br>mature NK T cell: -3.665<br>gamma-delta T cell: -4.281",
+          "value: -5.000<br>CD16-positive, CD56-dim natural killer cell, human: -0.864<br>natural killer cell: -2.006<br>CD16-negative, CD56-bright natural killer cell, human: -2.550<br>gamma-delta T cell: -3.881<br>activated type II NK T cell: -4.369",
+          "value: -6.346<br>classical monocyte: -1.240<br>CD14-positive monocyte: -1.246<br>pvalb GABAergic cortical interneuron: -3.107<br>monocyte: -3.447<br>macrophage: -4.109",
+          "value: -5.484<br>plasmacytoid dendritic cell: -0.229<br>dendritic cell: -2.847<br>plasmacytoid dendritic cell, human: -2.922<br>myeloid dendritic cell: -5.164<br>conventional dendritic cell: -5.492",
+          "value: -4.969<br>CD16-positive, CD56-dim natural killer cell, human: -1.016<br>natural killer cell: -1.645<br>CD16-negative, CD56-bright natural killer cell, human: -2.818<br>mature NK T cell: -3.298<br>gamma-delta T cell: -3.769",
+          "value: -5.774<br>naive B cell: -1.205<br>B cell: -1.324<br>malignant cell: -3.164<br>class switched memory B cell: -3.462<br>memory B cell: -3.486",
+          "value: -6.092<br>CD16-positive, CD56-dim natural killer cell, human: -0.314<br>natural killer cell: -1.853<br>mature NK T cell: -4.542<br>CD16-negative, CD56-bright natural killer cell, human: -4.748<br>astrocyte of the cerebral cortex: -5.051",
+          "value: -6.254<br>CD16-positive, CD56-dim natural killer cell, human: -0.651<br>natural killer cell: -1.410<br>CD16-negative, CD56-bright natural killer cell, human: -3.942<br>mature NK T cell: -4.012<br>gamma-delta T cell: -4.812",
+          "value: -6.429<br>CD14-positive monocyte: -1.241<br>classical monocyte: -1.335<br>pvalb GABAergic cortical interneuron: -2.892<br>monocyte: -3.541<br>macrophage: -4.069",
+          "value: -6.247<br>plasmacytoid dendritic cell: -0.323<br>dendritic cell: -2.524<br>plasmacytoid dendritic cell, human: -2.801<br>myeloid dendritic cell: -4.884<br>conventional dendritic cell: -4.898",
+          "value: -5.620<br>CD16-positive, CD56-dim natural killer cell, human: -0.900<br>natural killer cell: -1.874<br>CD16-negative, CD56-bright natural killer cell, human: -2.243<br>mature NK T cell: -3.886<br>gamma-delta T cell: -4.081",
+          "value: -5.029<br>plasmacytoid dendritic cell: -0.327<br>plasmacytoid dendritic cell, human: -2.508<br>dendritic cell: -2.740<br>myeloid dendritic cell: -5.244<br>dendritic cell, human: -5.278",
+          "value: -6.296<br>CD16-negative, CD56-bright natural killer cell, human: -0.887<br>gamma-delta T cell: -2.940<br>effector memory CD8-positive, alpha-beta T cell: -3.402<br>natural killer cell: -3.415<br>mature NK T cell: -3.669",
+          "value: -5.923<br>plasmacytoid dendritic cell: -0.401<br>plasmacytoid dendritic cell, human: -2.354<br>dendritic cell: -2.972<br>dendritic cell, human: -4.869<br>conventional dendritic cell: -5.090",
+          "value: -5.004<br>CD16-positive, CD56-dim natural killer cell, human: -1.103<br>natural killer cell: -1.530<br>mature NK T cell: -2.907<br>gamma-delta T cell: -3.573<br>CD16-negative, CD56-bright natural killer cell, human: -3.802",
+          "value: -5.306<br>plasmacytoid dendritic cell: -0.237<br>plasmacytoid dendritic cell, human: -2.840<br>dendritic cell: -3.126<br>dendritic cell, human: -5.680<br>myeloid dendritic cell: -5.757",
+          "value: -6.505<br>CD16-positive, CD56-dim natural killer cell, human: -1.016<br>natural killer cell: -1.598<br>CD16-negative, CD56-bright natural killer cell, human: -1.920<br>mature NK T cell: -3.889<br>gamma-delta T cell: -4.138",
+          "value: -5.468<br>plasmacytoid dendritic cell: -0.330<br>plasmacytoid dendritic cell, human: -2.358<br>dendritic cell: -3.110<br>dendritic cell, human: -5.422<br>myeloid dendritic cell: -5.470",
+          "value: -5.732<br>plasmacytoid dendritic cell: -0.161<br>plasmacytoid dendritic cell, human: -3.185<br>dendritic cell: -3.511<br>myeloid dendritic cell: -5.856<br>dendritic cell, human: -6.104",
+          "value: -6.693<br>CD16-positive, CD56-dim natural killer cell, human: -0.493<br>natural killer cell: -1.758<br>CD16-negative, CD56-bright natural killer cell, human: -3.088<br>mature NK T cell: -3.991<br>activated type II NK T cell: -4.980",
+          "value: -6.289<br>classical monocyte: -1.438<br>CD14-positive monocyte: -1.728<br>pvalb GABAergic cortical interneuron: -2.985<br>dendritic cell, human: -3.638<br>macrophage: -3.677",
+          "value: -5.946<br>CD16-positive, CD56-dim natural killer cell, human: -0.671<br>natural killer cell: -1.977<br>CD16-negative, CD56-bright natural killer cell, human: -2.588<br>activated type II NK T cell: -4.103<br>mature NK T cell: -4.615",
+          "value: -5.957<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.549<br>central memory CD4-positive, alpha-beta T cell: -1.777<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.095<br>effector memory CD4-positive, alpha-beta T cell: -2.271<br>T-helper 22 cell: -3.237",
+          "value: -6.327<br>natural killer cell: -0.793<br>CD16-positive, CD56-dim natural killer cell, human: -1.615<br>CD16-negative, CD56-bright natural killer cell, human: -3.378<br>mature NK T cell: -4.023<br>activated type II NK T cell: -4.598",
+          "value: -5.664<br>CD16-positive, CD56-dim natural killer cell, human: -0.541<br>natural killer cell: -2.008<br>CD16-negative, CD56-bright natural killer cell, human: -2.780<br>mature NK T cell: -4.052<br>gamma-delta T cell: -4.616",
+          "value: -5.795<br>plasmacytoid dendritic cell: -0.266<br>plasmacytoid dendritic cell, human: -2.727<br>dendritic cell: -2.977<br>dendritic cell, human: -5.224<br>myeloid dendritic cell: -5.230",
+          "value: -6.334<br>CD16-negative, CD56-bright natural killer cell, human: -0.656<br>gamma-delta T cell: -3.199<br>natural killer cell: -3.531<br>CD16-positive, CD56-dim natural killer cell, human: -3.698<br>mature NK T cell: -3.795",
+          "value: -4.588<br>CD16-positive, CD56-dim natural killer cell, human: -1.748<br>CD16-negative, CD56-bright natural killer cell, human: -1.909<br>natural killer cell: -2.206<br>mature NK T cell: -2.517<br>gamma-delta T cell: -2.630",
+          "value: -5.583<br>central memory CD4-positive, alpha-beta T cell: -1.643<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.281<br>effector memory CD4-positive, alpha-beta T cell: -2.335<br>T follicular helper cell: -2.948<br>T-helper 22 cell: -3.106",
+          "value: -4.423<br>central memory CD4-positive, alpha-beta T cell: -1.947<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.318<br>T follicular helper cell: -2.375<br>effector memory CD4-positive, alpha-beta T cell: -2.906<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.020",
+          "value: -6.448<br>CD16-positive, CD56-dim natural killer cell, human: -0.682<br>CD16-negative, CD56-bright natural killer cell, human: -2.157<br>natural killer cell: -2.342<br>mature NK T cell: -4.257<br>activated type II NK T cell: -4.340",
+          "value: -5.674<br>central memory CD4-positive, alpha-beta T cell: -1.582<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.947<br>effector memory CD4-positive, alpha-beta T cell: -2.548<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.009<br>T-helper 22 cell: -3.031",
+          "value: -5.910<br>CD16-positive, CD56-dim natural killer cell, human: -0.952<br>natural killer cell: -2.258<br>CD16-negative, CD56-bright natural killer cell, human: -2.617<br>gamma-delta T cell: -3.460<br>mature NK T cell: -3.786",
+          "value: -6.455<br>CD16-negative, CD56-bright natural killer cell, human: -1.181<br>CD16-positive, CD56-dim natural killer cell, human: -2.362<br>natural killer cell: -2.485<br>gamma-delta T cell: -2.595<br>mature NK T cell: -3.283",
+          "value: -6.644<br>classical monocyte: -1.074<br>CD14-positive monocyte: -1.557<br>pvalb GABAergic cortical interneuron: -3.119<br>dendritic cell, human: -3.584<br>macrophage: -3.737",
+          "value: -5.456<br>natural killer cell: -1.305<br>CD16-positive, CD56-dim natural killer cell, human: -1.310<br>CD16-negative, CD56-bright natural killer cell, human: -2.182<br>mature NK T cell: -3.723<br>gamma-delta T cell: -4.367",
+          "value: -5.349<br>CD16-positive, CD56-dim natural killer cell, human: -0.434<br>natural killer cell: -1.662<br>CD16-negative, CD56-bright natural killer cell, human: -3.910<br>mature NK T cell: -4.353<br>astrocyte of the cerebral cortex: -4.870",
+          "value: -6.275<br>CD16-positive, CD56-dim natural killer cell, human: -0.718<br>natural killer cell: -1.178<br>mature NK T cell: -3.749<br>CD16-negative, CD56-bright natural killer cell, human: -4.491<br>astrocyte of the cerebral cortex: -4.583",
+          "value: -6.168<br>CD16-negative, CD56-bright natural killer cell, human: -1.554<br>natural killer cell: -3.282<br>gamma-delta T cell: -3.505<br>innate lymphoid cell: -3.778<br>effector memory CD8-positive, alpha-beta T cell: -3.836",
+          "value: -5.484<br>CD16-positive, CD56-dim natural killer cell, human: -1.004<br>CD16-negative, CD56-bright natural killer cell, human: -1.516<br>natural killer cell: -2.579<br>gamma-delta T cell: -3.527<br>activated type II NK T cell: -4.162",
+          "value: -5.632<br>classical monocyte: -0.966<br>CD14-positive monocyte: -1.115<br>pvalb GABAergic cortical interneuron: -3.500<br>monocyte: -3.830<br>neutrophil: -4.261",
+          "value: -6.418<br>mature NK T cell: -1.443<br>CD14-low, CD16-positive monocyte: -1.577<br>non-classical monocyte: -1.914<br>CD14-positive monocyte: -3.926<br>retinal cone cell: -4.151",
+          "value: -7.350<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.944<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.829<br>central memory CD4-positive, alpha-beta T cell: -2.002<br>effector memory CD4-positive, alpha-beta T cell: -3.173<br>T follicular helper cell: -3.822",
+          "value: -5.533<br>CD16-positive, CD56-dim natural killer cell, human: -0.938<br>natural killer cell: -1.249<br>mature NK T cell: -3.524<br>CD16-negative, CD56-bright natural killer cell, human: -4.185<br>activated type II NK T cell: -4.777",
+          "value: -6.738<br>CD16-positive, CD56-dim natural killer cell, human: -0.796<br>CD16-negative, CD56-bright natural killer cell, human: -1.366<br>natural killer cell: -2.107<br>activated type II NK T cell: -4.216<br>mature NK T cell: -4.856",
+          "value: -5.399<br>plasmacytoid dendritic cell: -0.233<br>plasmacytoid dendritic cell, human: -2.847<br>dendritic cell: -2.923<br>myeloid dendritic cell: -5.406<br>dendritic cell, human: -5.535",
+          "value: -6.425<br>CD16-positive, CD56-dim natural killer cell, human: -0.892<br>natural killer cell: -1.281<br>mature NK T cell: -3.678<br>CD16-negative, CD56-bright natural killer cell, human: -4.105<br>gamma-delta T cell: -4.272",
+          "value: -4.902<br>CD16-positive, CD56-dim natural killer cell, human: -0.594<br>natural killer cell: -2.034<br>CD16-negative, CD56-bright natural killer cell, human: -2.697<br>gamma-delta T cell: -3.310<br>mature NK T cell: -4.174",
+          "value: -6.446<br>CD16-negative, CD56-bright natural killer cell, human: -1.184<br>CD16-positive, CD56-dim natural killer cell, human: -1.639<br>natural killer cell: -2.646<br>gamma-delta T cell: -3.079<br>mature NK T cell: -3.478",
+          "value: -7.820<br>CD16-negative, CD56-bright natural killer cell, human: -1.674<br>gamma-delta T cell: -2.332<br>effector memory CD8-positive, alpha-beta T cell: -2.991<br>mucosal invariant T cell: -3.355<br>effector CD8-positive, alpha-beta T cell: -3.849",
+          "value: -5.806<br>CD16-positive, CD56-dim natural killer cell, human: -1.385<br>CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -1.684<br>gamma-delta T cell: -3.850<br>activated type II NK T cell: -3.872",
+          "value: -5.987<br>CD16-negative, CD56-bright natural killer cell, human: -0.979<br>natural killer cell: -2.617<br>CD16-positive, CD56-dim natural killer cell, human: -2.760<br>gamma-delta T cell: -2.923<br>mature NK T cell: -4.232",
+          "value: -5.596<br>CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.773<br>CD16-negative, CD56-bright natural killer cell, human: -3.602<br>mature NK T cell: -3.993<br>activated type II NK T cell: -4.792",
+          "value: -6.596<br>CD16-negative, CD56-bright natural killer cell, human: -1.703<br>gamma-delta T cell: -2.192<br>CD16-positive, CD56-dim natural killer cell, human: -2.609<br>natural killer cell: -2.933<br>mature NK T cell: -3.196",
+          "value: -5.593<br>plasmacytoid dendritic cell: -0.191<br>plasmacytoid dendritic cell, human: -2.741<br>dendritic cell: -3.658<br>dendritic cell, human: -5.882<br>myeloid dendritic cell: -6.016",
+          "value: -5.876<br>CD16-positive, CD56-dim natural killer cell, human: -0.809<br>natural killer cell: -1.953<br>CD16-negative, CD56-bright natural killer cell, human: -3.227<br>mature NK T cell: -3.329<br>gamma-delta T cell: -4.604",
+          "value: -7.100<br>dendritic cell: -1.601<br>dendritic cell, human: -2.083<br>conventional dendritic cell: -2.182<br>CD1c-positive myeloid dendritic cell: -2.515<br>myeloid dendritic cell: -2.655",
+          "value: -5.106<br>natural killer cell: -1.602<br>CD16-positive, CD56-dim natural killer cell, human: -1.729<br>CD16-negative, CD56-bright natural killer cell, human: -2.612<br>mature NK T cell: -2.987<br>gamma-delta T cell: -3.480",
+          "value: -5.209<br>plasmacytoid dendritic cell: -2.626<br>mast cell: -3.074<br>dendritic cell: -3.121<br>plasmacytoid dendritic cell, human: -3.164<br>CD16-negative, CD56-bright natural killer cell, human: -3.385",
+          "value: -5.358<br>plasmacytoid dendritic cell: -0.171<br>plasmacytoid dendritic cell, human: -2.887<br>dendritic cell: -3.676<br>myeloid dendritic cell: -6.036<br>dendritic cell, human: -6.117",
+          "value: -6.640<br>CD14-positive monocyte: -1.137<br>classical monocyte: -1.496<br>pvalb GABAergic cortical interneuron: -3.179<br>macrophage: -3.740<br>dendritic cell, human: -4.073",
+          "value: -5.946<br>CD16-positive, CD56-dim natural killer cell, human: -0.653<br>natural killer cell: -1.643<br>CD16-negative, CD56-bright natural killer cell, human: -3.670<br>mature NK T cell: -3.855<br>activated type II NK T cell: -4.712",
+          "value: -5.142<br>CD16-positive, CD56-dim natural killer cell, human: -1.247<br>natural killer cell: -1.358<br>CD16-negative, CD56-bright natural killer cell, human: -1.870<br>mature NK T cell: -3.452<br>gamma-delta T cell: -3.882",
+          "value: -5.786<br>CD16-positive, CD56-dim natural killer cell, human: -0.810<br>natural killer cell: -1.811<br>gamma-delta T cell: -3.262<br>mature NK T cell: -3.462<br>CD8-positive, alpha-beta T cell: -3.539",
+          "value: -6.039<br>CD16-positive, CD56-dim natural killer cell, human: -0.478<br>natural killer cell: -1.817<br>CD16-negative, CD56-bright natural killer cell, human: -3.562<br>mature NK T cell: -3.919<br>astrocyte of the cerebral cortex: -4.828",
+          "value: -5.817<br>central memory CD4-positive, alpha-beta T cell: -1.878<br>T follicular helper cell: -2.070<br>effector memory CD4-positive, alpha-beta T cell: -2.154<br>T-helper 22 cell: -2.571<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.983",
+          "value: -6.674<br>classical monocyte: -1.843<br>CD14-positive monocyte: -2.152<br>CD1c-positive myeloid dendritic cell: -2.602<br>dendritic cell, human: -2.665<br>conventional dendritic cell: -3.183",
+          "value: -5.469<br>CD16-positive, CD56-dim natural killer cell, human: -0.435<br>natural killer cell: -1.907<br>CD16-negative, CD56-bright natural killer cell, human: -3.081<br>mature NK T cell: -4.065<br>activated type II NK T cell: -4.938",
+          "value: -5.647<br>dendritic cell: -1.609<br>conventional dendritic cell: -1.671<br>CD1c-positive myeloid dendritic cell: -2.548<br>dendritic cell, human: -2.595<br>myeloid dendritic cell: -2.797",
+          "value: -5.929<br>CD16-positive, CD56-dim natural killer cell, human: -0.498<br>natural killer cell: -1.888<br>CD16-negative, CD56-bright natural killer cell, human: -3.474<br>mature NK T cell: -4.257<br>gamma-delta T cell: -4.676",
+          "value: -6.625<br>CD16-negative, CD56-bright natural killer cell, human: -1.457<br>CD16-positive, CD56-dim natural killer cell, human: -1.901<br>gamma-delta T cell: -2.372<br>natural killer cell: -2.731<br>effector memory CD8-positive, alpha-beta T cell: -3.514",
+          "value: -5.428<br>plasmacytoid dendritic cell: -0.638<br>dendritic cell: -2.145<br>plasmacytoid dendritic cell, human: -2.242<br>conventional dendritic cell: -3.861<br>dendritic cell, human: -4.276",
+          "value: -6.048<br>natural killer cell: -0.747<br>CD16-positive, CD56-dim natural killer cell, human: -1.162<br>CD16-negative, CD56-bright natural killer cell, human: -3.760<br>mature NK T cell: -4.092<br>activated type II NK T cell: -4.741",
+          "value: -5.644<br>CD16-positive, CD56-dim natural killer cell, human: -0.660<br>natural killer cell: -1.435<br>CD16-negative, CD56-bright natural killer cell, human: -3.008<br>mature NK T cell: -4.108<br>activated type II NK T cell: -4.856",
+          "value: -6.202<br>central memory CD4-positive, alpha-beta T cell: -1.670<br>T follicular helper cell: -1.973<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.990<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.361<br>effector memory CD4-positive, alpha-beta T cell: -2.721",
+          "value: -6.763<br>CD16-positive, CD56-dim natural killer cell, human: -0.539<br>natural killer cell: -1.505<br>mature NK T cell: -3.883<br>CD16-negative, CD56-bright natural killer cell, human: -4.730<br>astrocyte of the cerebral cortex: -4.875",
+          "value: -5.916<br>classical monocyte: -1.001<br>CD14-positive monocyte: -1.532<br>pvalb GABAergic cortical interneuron: -3.228<br>monocyte: -3.695<br>macrophage: -3.926",
+          "value: -5.330<br>CD16-positive, CD56-dim natural killer cell, human: -0.679<br>natural killer cell: -1.394<br>CD16-negative, CD56-bright natural killer cell, human: -3.518<br>mature NK T cell: -4.198<br>activated type II NK T cell: -4.436",
+          "value: -5.709<br>CD16-negative, CD56-bright natural killer cell, human: -0.648<br>natural killer cell: -2.996<br>CD16-positive, CD56-dim natural killer cell, human: -3.184<br>gamma-delta T cell: -3.472<br>mature NK T cell: -4.009",
+          "value: -4.828<br>classical monocyte: -1.244<br>macrophage: -2.122<br>alveolar macrophage: -2.983<br>CD14-positive monocyte: -3.298<br>non-classical monocyte: -3.550",
+          "value: -6.397<br>CD16-positive, CD56-dim natural killer cell, human: -0.565<br>natural killer cell: -1.815<br>mature NK T cell: -3.367<br>gamma-delta T cell: -4.501<br>astrocyte of the cerebral cortex: -4.627",
+          "value: -5.870<br>plasmacytoid dendritic cell: -0.510<br>dendritic cell: -2.330<br>plasmacytoid dendritic cell, human: -2.557<br>dendritic cell, human: -4.748<br>myeloid dendritic cell: -4.855",
+          "value: -4.862<br>CD16-negative, CD56-bright natural killer cell, human: -0.869<br>natural killer cell: -2.737<br>CD16-positive, CD56-dim natural killer cell, human: -3.081<br>gamma-delta T cell: -3.215<br>innate lymphoid cell: -3.954",
+          "value: -6.522<br>classical monocyte: -1.312<br>CD14-positive monocyte: -1.677<br>pvalb GABAergic cortical interneuron: -3.174<br>dendritic cell, human: -3.475<br>CD1c-positive myeloid dendritic cell: -3.585",
+          "value: -5.143<br>T-helper 22 cell: -2.137<br>central memory CD4-positive, alpha-beta T cell: -2.223<br>effector memory CD4-positive, alpha-beta T cell: -2.245<br>T follicular helper cell: -2.436<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.836",
+          "value: -6.402<br>CD16-positive, CD56-dim natural killer cell, human: -0.570<br>natural killer cell: -1.521<br>mature NK T cell: -3.958<br>CD16-negative, CD56-bright natural killer cell, human: -4.054<br>astrocyte of the cerebral cortex: -4.638",
+          "value: -5.317<br>CD16-positive, CD56-dim natural killer cell, human: -1.163<br>natural killer cell: -1.812<br>CD16-negative, CD56-bright natural killer cell, human: -2.516<br>mature NK T cell: -2.695<br>gamma-delta T cell: -2.916",
+          "value: -6.618<br>CD16-positive, CD56-dim natural killer cell, human: -0.551<br>natural killer cell: -1.474<br>CD16-negative, CD56-bright natural killer cell, human: -4.086<br>mature NK T cell: -4.151<br>astrocyte of the cerebral cortex: -4.917",
+          "value: -6.844<br>natural killer cell: -1.136<br>CD16-positive, CD56-dim natural killer cell, human: -1.235<br>CD16-negative, CD56-bright natural killer cell, human: -2.963<br>mature NK T cell: -4.115<br>gamma-delta T cell: -4.250",
+          "value: -5.503<br>plasmacytoid dendritic cell: -0.309<br>plasmacytoid dendritic cell, human: -2.667<br>dendritic cell: -2.946<br>conventional dendritic cell: -4.285<br>myeloid dendritic cell: -4.849",
+          "value: -6.009<br>CD14-positive monocyte: -1.066<br>classical monocyte: -1.130<br>pvalb GABAergic cortical interneuron: -3.265<br>monocyte: -3.836<br>macrophage: -4.274",
+          "value: -6.083<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.968<br>central memory CD4-positive, alpha-beta T cell: -1.647<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.785<br>T follicular helper cell: -2.901<br>effector memory CD4-positive, alpha-beta T cell: -3.185",
+          "value: -5.027<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.044<br>central memory CD4-positive, alpha-beta T cell: -1.990<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.403<br>T-helper 22 cell: -3.548<br>effector memory CD4-positive, alpha-beta T cell: -3.562",
+          "value: -5.834<br>CD16-positive, CD56-dim natural killer cell, human: -0.996<br>natural killer cell: -1.204<br>CD16-negative, CD56-bright natural killer cell, human: -3.152<br>gamma-delta T cell: -3.621<br>mature NK T cell: -3.653",
+          "value: -6.603<br>CD16-negative, CD56-bright natural killer cell, human: -0.944<br>CD16-positive, CD56-dim natural killer cell, human: -2.291<br>gamma-delta T cell: -2.668<br>natural killer cell: -2.817<br>mature NK T cell: -3.503",
+          "value: -5.809<br>T follicular helper cell: -1.834<br>central memory CD4-positive, alpha-beta T cell: -1.894<br>effector memory CD4-positive, alpha-beta T cell: -2.392<br>T-helper 22 cell: -3.054<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.124",
+          "value: -5.451<br>CD16-positive, CD56-dim natural killer cell, human: -0.595<br>natural killer cell: -1.610<br>CD16-negative, CD56-bright natural killer cell, human: -3.771<br>mature NK T cell: -3.863<br>gamma-delta T cell: -4.919",
+          "value: -6.440<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.726<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.003<br>effector memory CD8-positive, alpha-beta T cell: -2.102<br>effector memory CD4-positive, alpha-beta T cell: -2.463<br>central memory CD4-positive, alpha-beta T cell: -2.468",
+          "value: -6.891<br>CD14-positive monocyte: -1.183<br>classical monocyte: -1.438<br>pvalb GABAergic cortical interneuron: -3.110<br>monocyte: -3.532<br>dendritic cell, human: -3.801",
+          "value: -6.176<br>CD16-positive, CD56-dim natural killer cell, human: -0.687<br>natural killer cell: -1.390<br>CD16-negative, CD56-bright natural killer cell, human: -2.945<br>mature NK T cell: -4.336<br>activated type II NK T cell: -4.523",
+          "value: -6.577<br>CD16-negative, CD56-bright natural killer cell, human: -1.959<br>gamma-delta T cell: -2.114<br>effector memory CD8-positive, alpha-beta T cell: -3.329<br>mucosal invariant T cell: -3.398<br>mature NK T cell: -3.486",
+          "value: -6.352<br>CD16-positive, CD56-dim natural killer cell, human: -0.861<br>CD16-negative, CD56-bright natural killer cell, human: -2.192<br>natural killer cell: -2.212<br>mature NK T cell: -3.263<br>gamma-delta T cell: -3.812",
+          "value: -6.209<br>CD16-positive, CD56-dim natural killer cell, human: -0.780<br>natural killer cell: -1.119<br>mature NK T cell: -3.701<br>CD16-negative, CD56-bright natural killer cell, human: -4.006<br>astrocyte of the cerebral cortex: -4.889",
+          "value: -4.699<br>hematopoietic precursor cell: -1.604<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.703<br>hematopoietic stem cell: -2.708<br>mast cell: -3.702<br>erythroid progenitor cell: -4.103",
+          "value: -4.552<br>mast cell: -2.371<br>hematopoietic precursor cell: -2.411<br>hematopoietic stem cell: -2.999<br>CD34-positive, CD38-negative hematopoietic stem cell: -3.351<br>group 3 innate lymphoid cell: -3.810",
+          "value: -5.157<br>CD16-negative, CD56-bright natural killer cell, human: -1.185<br>natural killer cell: -2.291<br>CD16-positive, CD56-dim natural killer cell, human: -3.415<br>gamma-delta T cell: -3.967<br>innate lymphoid cell: -3.968",
+          "value: -5.399<br>plasmacytoid dendritic cell: -0.247<br>plasmacytoid dendritic cell, human: -2.701<br>dendritic cell: -3.328<br>myeloid dendritic cell: -5.094<br>dendritic cell, human: -5.301",
+          "value: -6.468<br>CD16-positive, CD56-dim natural killer cell, human: -0.654<br>natural killer cell: -1.925<br>CD16-negative, CD56-bright natural killer cell, human: -2.372<br>activated type II NK T cell: -4.280<br>mature NK T cell: -4.684",
+          "value: -6.754<br>classical monocyte: -2.090<br>CD14-positive monocyte: -2.153<br>CD1c-positive myeloid dendritic cell: -2.541<br>dendritic cell, human: -2.688<br>conventional dendritic cell: -3.141",
+          "value: -4.770<br>central memory CD4-positive, alpha-beta T cell: -1.575<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.698<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.472<br>effector memory CD4-positive, alpha-beta T cell: -2.630<br>T-helper 22 cell: -2.637",
+          "value: -5.401<br>plasmacytoid dendritic cell: -0.445<br>dendritic cell: -2.821<br>plasmacytoid dendritic cell, human: -3.110<br>plasma cell: -3.686<br>B cell: -4.213",
+          "value: -4.897<br>CD16-positive, CD56-dim natural killer cell, human: -0.801<br>CD16-negative, CD56-bright natural killer cell, human: -1.758<br>natural killer cell: -2.363<br>gamma-delta T cell: -3.877<br>mature NK T cell: -4.341",
+          "value: -5.973<br>CD16-positive, CD56-dim natural killer cell, human: -0.764<br>natural killer cell: -1.323<br>CD16-negative, CD56-bright natural killer cell, human: -3.559<br>mature NK T cell: -3.620<br>gamma-delta T cell: -4.568",
+          "value: -5.737<br>classical monocyte: -0.967<br>CD14-positive monocyte: -1.260<br>pvalb GABAergic cortical interneuron: -3.248<br>monocyte: -3.855<br>macrophage: -4.528",
+          "value: -6.258<br>CD16-negative, CD56-bright natural killer cell, human: -1.165<br>CD16-positive, CD56-dim natural killer cell, human: -1.517<br>natural killer cell: -2.797<br>activated type II NK T cell: -3.791<br>gamma-delta T cell: -3.874",
+          "value: -5.364<br>plasmacytoid dendritic cell: -0.332<br>dendritic cell: -2.432<br>plasmacytoid dendritic cell, human: -2.821<br>myeloid dendritic cell: -4.616<br>conventional dendritic cell: -4.695",
+          "value: -6.973<br>classical monocyte: -1.213<br>CD14-positive monocyte: -1.432<br>pvalb GABAergic cortical interneuron: -3.368<br>dendritic cell, human: -3.617<br>dendritic cell: -3.788",
+          "value: -6.475<br>CD16-positive, CD56-dim natural killer cell, human: -0.407<br>natural killer cell: -2.004<br>CD16-negative, CD56-bright natural killer cell, human: -3.257<br>mature NK T cell: -4.129<br>activated type II NK T cell: -4.788",
+          "value: -5.568<br>CD16-positive, CD56-dim natural killer cell, human: -0.528<br>natural killer cell: -1.915<br>CD16-negative, CD56-bright natural killer cell, human: -3.562<br>mature NK T cell: -4.038<br>gamma-delta T cell: -4.590",
+          "value: -6.732<br>CD16-positive, CD56-dim natural killer cell, human: -1.376<br>CD16-negative, CD56-bright natural killer cell, human: -2.197<br>natural killer cell: -2.682<br>gamma-delta T cell: -3.105<br>mature NK T cell: -3.129",
+          "value: -6.333<br>classical monocyte: -1.161<br>CD14-positive monocyte: -1.703<br>pvalb GABAergic cortical interneuron: -3.089<br>monocyte: -3.677<br>CD1c-positive myeloid dendritic cell: -3.845",
+          "value: -4.846<br>CD16-positive, CD56-dim natural killer cell, human: -1.404<br>CD16-negative, CD56-bright natural killer cell, human: -2.056<br>natural killer cell: -2.294<br>gamma-delta T cell: -2.456<br>mature NK T cell: -3.506",
+          "value: -4.934<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.446<br>central memory CD4-positive, alpha-beta T cell: -1.566<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.153<br>T follicular helper cell: -2.764<br>effector memory CD4-positive, alpha-beta T cell: -3.763",
+          "value: -5.541<br>CD16-positive, CD56-dim natural killer cell, human: -0.516<br>natural killer cell: -1.703<br>CD16-negative, CD56-bright natural killer cell, human: -3.793<br>mature NK T cell: -4.429<br>activated type II NK T cell: -4.820",
+          "value: -5.949<br>CD16-negative, CD56-bright natural killer cell, human: -0.673<br>natural killer cell: -2.775<br>CD16-positive, CD56-dim natural killer cell, human: -3.041<br>gamma-delta T cell: -3.889<br>innate lymphoid cell: -4.138",
+          "value: -5.937<br>CD16-positive, CD56-dim natural killer cell, human: -0.448<br>natural killer cell: -2.019<br>mature NK T cell: -3.739<br>CD16-negative, CD56-bright natural killer cell, human: -3.960<br>gamma-delta T cell: -4.722",
+          "value: -4.480<br>effector memory CD4-positive, alpha-beta T cell: -2.132<br>central memory CD4-positive, alpha-beta T cell: -2.489<br>T follicular helper cell: -2.718<br>T-helper 22 cell: -2.895<br>effector memory CD8-positive, alpha-beta T cell: -3.659",
+          "value: -4.126<br>CD16-positive, CD56-dim natural killer cell, human: -1.009<br>natural killer cell: -2.003<br>gamma-delta T cell: -2.690<br>CD16-negative, CD56-bright natural killer cell, human: -3.102<br>mature NK T cell: -3.157",
+          "value: -5.374<br>plasmacytoid dendritic cell: -1.337<br>mast cell: -2.344<br>plasmacytoid dendritic cell, human: -2.931<br>dendritic cell: -3.380<br>hematopoietic precursor cell: -3.580",
+          "value: -5.866<br>CD16-negative, CD56-bright natural killer cell, human: -1.642<br>gamma-delta T cell: -2.669<br>natural killer cell: -3.061<br>CD16-positive, CD56-dim natural killer cell, human: -3.352<br>effector memory CD8-positive, alpha-beta T cell: -3.382",
+          "value: -4.754<br>CD16-negative, CD56-bright natural killer cell, human: -0.713<br>natural killer cell: -2.410<br>CD16-positive, CD56-dim natural killer cell, human: -3.419<br>innate lymphoid cell: -3.774<br>gamma-delta T cell: -3.905",
+          "value: -5.580<br>CD16-positive, CD56-dim natural killer cell, human: -0.386<br>natural killer cell: -1.959<br>CD16-negative, CD56-bright natural killer cell, human: -3.579<br>mature NK T cell: -4.297<br>activated type II NK T cell: -5.035",
+          "value: -6.046<br>CD16-positive, CD56-dim natural killer cell, human: -0.625<br>natural killer cell: -1.408<br>CD16-negative, CD56-bright natural killer cell, human: -3.864<br>mature NK T cell: -3.941<br>activated type II NK T cell: -4.937",
+          "value: -5.852<br>CD16-positive, CD56-dim natural killer cell, human: -0.499<br>natural killer cell: -1.669<br>CD16-negative, CD56-bright natural killer cell, human: -3.520<br>mature NK T cell: -4.129<br>activated type II NK T cell: -4.678",
+          "value: -7.029<br>non-classical monocyte: -1.883<br>CD14-low, CD16-positive monocyte: -2.070<br>mature NK T cell: -2.093<br>CD14-positive monocyte: -2.343<br>classical monocyte: -3.337",
+          "value: -6.146<br>mast cell: -2.266<br>plasmacytoid dendritic cell: -2.493<br>hematopoietic precursor cell: -2.817<br>CD34-positive, CD38-negative hematopoietic stem cell: -3.018<br>group 3 innate lymphoid cell: -3.486",
+          "value: -5.458<br>CD16-positive, CD56-dim natural killer cell, human: -0.692<br>natural killer cell: -1.719<br>CD16-negative, CD56-bright natural killer cell, human: -2.814<br>mature NK T cell: -4.079<br>activated type II NK T cell: -4.241",
+          "value: -5.893<br>CD16-positive, CD56-dim natural killer cell, human: -0.298<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -3.800<br>mature NK T cell: -4.511<br>astrocyte of the cerebral cortex: -5.118",
+          "value: -5.455<br>plasmacytoid dendritic cell: -0.371<br>dendritic cell: -2.472<br>plasmacytoid dendritic cell, human: -2.607<br>myeloid dendritic cell: -4.795<br>conventional dendritic cell: -4.868",
+          "value: -6.711<br>CD16-positive, CD56-dim natural killer cell, human: -0.368<br>natural killer cell: -2.115<br>CD16-negative, CD56-bright natural killer cell, human: -3.533<br>mature NK T cell: -4.403<br>activated type II NK T cell: -5.027",
+          "value: -5.602<br>CD14-positive monocyte: -1.102<br>classical monocyte: -1.608<br>monocyte: -3.069<br>pvalb GABAergic cortical interneuron: -3.159<br>promonocyte: -4.018",
+          "value: -6.272<br>CD16-positive, CD56-dim natural killer cell, human: -0.431<br>natural killer cell: -1.789<br>CD16-negative, CD56-bright natural killer cell, human: -3.341<br>mature NK T cell: -4.264<br>activated type II NK T cell: -5.052",
+          "value: -5.906<br>central memory CD4-positive, alpha-beta T cell: -1.781<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.873<br>effector memory CD4-positive, alpha-beta T cell: -2.132<br>T follicular helper cell: -2.669<br>T-helper 22 cell: -2.674",
+          "value: -5.735<br>CD14-positive monocyte: -0.927<br>classical monocyte: -1.240<br>pvalb GABAergic cortical interneuron: -3.192<br>monocyte: -3.660<br>neutrophil: -4.345",
+          "value: -6.637<br>CD16-negative, CD56-bright natural killer cell, human: -0.834<br>CD16-positive, CD56-dim natural killer cell, human: -2.809<br>natural killer cell: -3.097<br>gamma-delta T cell: -3.377<br>mature NK T cell: -4.136",
+          "value: -5.160<br>CD14-positive monocyte: -1.024<br>classical monocyte: -1.989<br>pvalb GABAergic cortical interneuron: -2.772<br>monocyte: -3.049<br>promonocyte: -4.124",
+          "value: -5.689<br>CD16-negative, CD56-bright natural killer cell, human: -0.924<br>natural killer cell: -3.272<br>gamma-delta T cell: -3.449<br>innate lymphoid cell: -3.812<br>mature NK T cell: -4.016",
+          "value: -4.576<br>CD16-positive, CD56-dim natural killer cell, human: -1.190<br>natural killer cell: -1.365<br>CD16-negative, CD56-bright natural killer cell, human: -2.920<br>mature NK T cell: -3.436<br>gamma-delta T cell: -3.848",
+          "value: -6.707<br>CD16-positive, CD56-dim natural killer cell, human: -0.544<br>natural killer cell: -1.399<br>mature NK T cell: -4.069<br>CD16-negative, CD56-bright natural killer cell, human: -4.420<br>activated type II NK T cell: -4.895",
+          "value: -6.405<br>CD14-positive monocyte: -1.245<br>classical monocyte: -1.419<br>pvalb GABAergic cortical interneuron: -3.297<br>macrophage: -3.909<br>dendritic cell, human: -4.084",
+          "value: -4.599<br>mast cell: -1.103<br>group 3 innate lymphoid cell: -3.579<br>dendritic cell: -3.890<br>hematopoietic precursor cell: -3.904<br>granulocyte: -3.918",
+          "value: -5.485<br>natural killer cell: -0.932<br>CD16-positive, CD56-dim natural killer cell, human: -2.214<br>CD16-negative, CD56-bright natural killer cell, human: -2.580<br>activated type II NK T cell: -3.817<br>mature NK T cell: -3.996",
+          "value: -6.347<br>CD16-positive, CD56-dim natural killer cell, human: -1.031<br>natural killer cell: -1.833<br>CD16-negative, CD56-bright natural killer cell, human: -2.798<br>mature NK T cell: -3.687<br>gamma-delta T cell: -3.934",
+          "value: -5.273<br>plasmacytoid dendritic cell: -0.374<br>dendritic cell: -2.278<br>plasmacytoid dendritic cell, human: -2.897<br>myeloid dendritic cell: -4.314<br>dendritic cell, human: -4.840",
+          "value: -4.661<br>effector memory CD4-positive, alpha-beta T cell: -2.478<br>T-helper 22 cell: -2.509<br>T follicular helper cell: -2.693<br>central memory CD4-positive, alpha-beta T cell: -2.872<br>mucosal invariant T cell: -2.903",
+          "value: -4.863<br>plasma cell: -1.414<br>B cell: -1.606<br>plasmablast: -2.719<br>IgA plasma cell: -3.658<br>IgM plasma cell: -3.685",
+          "value: -7.067<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.708<br>T follicular helper cell: -1.849<br>central memory CD4-positive, alpha-beta T cell: -1.966<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.280<br>effector memory CD4-positive, alpha-beta T cell: -3.044",
+          "value: -5.875<br>classical monocyte: -1.143<br>CD14-positive monocyte: -1.153<br>pvalb GABAergic cortical interneuron: -3.086<br>monocyte: -3.990<br>macrophage: -4.008",
+          "value: -4.838<br>classical monocyte: -1.165<br>CD14-positive monocyte: -1.718<br>macrophage: -2.932<br>monocyte: -3.422<br>neutrophil: -3.794",
+          "value: -6.717<br>classical monocyte: -1.626<br>CD14-positive monocyte: -1.711<br>pvalb GABAergic cortical interneuron: -2.986<br>dendritic cell, human: -3.299<br>macrophage: -3.703",
+          "value: -5.805<br>conventional dendritic cell: -1.620<br>dendritic cell: -1.782<br>dendritic cell, human: -2.247<br>CD1c-positive myeloid dendritic cell: -2.374<br>myeloid dendritic cell: -2.502",
+          "value: -6.991<br>CD16-positive, CD56-dim natural killer cell, human: -0.529<br>natural killer cell: -1.459<br>mature NK T cell: -4.095<br>CD16-negative, CD56-bright natural killer cell, human: -4.703<br>astrocyte of the cerebral cortex: -4.803",
+          "value: -5.757<br>CD16-positive, CD56-dim natural killer cell, human: -0.648<br>natural killer cell: -1.218<br>CD16-negative, CD56-bright natural killer cell, human: -3.848<br>mature NK T cell: -4.250<br>activated type II NK T cell: -4.911",
+          "value: -4.697<br>effector memory CD4-positive, alpha-beta T cell: -1.968<br>central memory CD4-positive, alpha-beta T cell: -2.315<br>T follicular helper cell: -2.611<br>T-helper 22 cell: -2.999<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.299",
+          "value: -5.833<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.263<br>central memory CD4-positive, alpha-beta T cell: -1.880<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.940<br>T-helper 22 cell: -3.216<br>effector memory CD4-positive, alpha-beta T cell: -3.219",
+          "value: -3.918<br>dendritic cell: -2.661<br>lymphocyte: -3.291<br>CD1c-positive myeloid dendritic cell: -3.604<br>classical monocyte: -3.628<br>macrophage: -3.742",
+          "value: -6.127<br>CD16-positive, CD56-dim natural killer cell, human: -0.890<br>natural killer cell: -1.127<br>CD16-negative, CD56-bright natural killer cell, human: -3.483<br>mature NK T cell: -4.185<br>activated type II NK T cell: -4.568",
+          "value: -4.594<br>CD16-negative, CD56-bright natural killer cell, human: -2.032<br>CD16-positive, CD56-dim natural killer cell, human: -2.210<br>natural killer cell: -2.382<br>gamma-delta T cell: -2.769<br>mature NK T cell: -3.270",
+          "value: -4.965<br>plasmacytoid dendritic cell: -0.172<br>plasmacytoid dendritic cell, human: -2.918<br>dendritic cell: -3.667<br>B cell: -5.801<br>plasma cell: -6.013",
+          "value: -5.144<br>classical monocyte: -0.973<br>macrophage: -2.498<br>CD14-positive monocyte: -2.795<br>monocyte: -3.040<br>alveolar macrophage: -3.332",
+          "value: -6.119<br>CD16-positive, CD56-dim natural killer cell, human: -0.509<br>natural killer cell: -1.714<br>CD16-negative, CD56-bright natural killer cell, human: -3.373<br>mature NK T cell: -4.167<br>activated type II NK T cell: -4.898",
+          "value: -4.738<br>CD16-positive, CD56-dim natural killer cell, human: -1.248<br>natural killer cell: -1.424<br>CD16-negative, CD56-bright natural killer cell, human: -2.911<br>mature NK T cell: -3.188<br>gamma-delta T cell: -3.540",
+          "value: -5.106<br>effector CD8-positive, alpha-beta T cell: -1.729<br>effector memory CD8-positive, alpha-beta T cell: -1.772<br>gamma-delta T cell: -2.495<br>mature NK T cell: -2.887<br>mucosal invariant T cell: -3.220",
+          "value: -6.219<br>CD16-positive, CD56-dim natural killer cell, human: -0.389<br>natural killer cell: -1.891<br>CD16-negative, CD56-bright natural killer cell, human: -3.624<br>mature NK T cell: -4.833<br>activated type II NK T cell: -4.968",
+          "value: -5.892<br>classical monocyte: -1.868<br>CD14-positive monocyte: -2.599<br>CD1c-positive myeloid dendritic cell: -2.614<br>dendritic cell, human: -2.666<br>conventional dendritic cell: -2.955",
+          "value: -5.101<br>CD16-positive, CD56-dim natural killer cell, human: -0.759<br>natural killer cell: -1.500<br>gamma-delta T cell: -3.484<br>mature NK T cell: -3.589<br>CD8-positive, alpha-beta T cell: -3.873",
+          "value: -5.045<br>CD16-positive, CD56-dim natural killer cell, human: -1.452<br>gamma-delta T cell: -2.466<br>natural killer cell: -2.614<br>CD16-negative, CD56-bright natural killer cell, human: -2.663<br>mature NK T cell: -2.992",
+          "value: -5.398<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.439<br>effector memory CD4-positive, alpha-beta T cell: -2.327<br>T follicular helper cell: -2.451<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.675<br>central memory CD4-positive, alpha-beta T cell: -2.729",
+          "value: -6.619<br>classical monocyte: -2.061<br>CD14-positive monocyte: -2.266<br>CD1c-positive myeloid dendritic cell: -2.431<br>dendritic cell, human: -2.641<br>dendritic cell: -3.007",
+          "value: -6.001<br>CD16-positive, CD56-dim natural killer cell, human: -0.578<br>natural killer cell: -1.494<br>CD16-negative, CD56-bright natural killer cell, human: -3.593<br>mature NK T cell: -4.131<br>astrocyte of the cerebral cortex: -4.862",
+          "value: -6.703<br>CD16-positive, CD56-dim natural killer cell, human: -0.445<br>natural killer cell: -2.257<br>CD16-negative, CD56-bright natural killer cell, human: -2.695<br>mature NK T cell: -4.536<br>activated type II NK T cell: -4.624",
+          "value: -4.948<br>classical monocyte: -0.850<br>CD14-positive monocyte: -1.974<br>pvalb GABAergic cortical interneuron: -3.111<br>macrophage: -3.515<br>monocyte: -3.616",
+          "value: -5.899<br>CD16-negative, CD56-bright natural killer cell, human: -2.256<br>gamma-delta T cell: -3.350<br>effector memory CD8-positive, alpha-beta T cell: -3.765<br>mature NK T cell: -3.861<br>natural killer cell: -3.869",
+          "value: -6.629<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.469<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.010<br>central memory CD4-positive, alpha-beta T cell: -2.204<br>effector memory CD4-positive, alpha-beta T cell: -3.048<br>T follicular helper cell: -3.156",
+          "value: -6.182<br>CD16-positive, CD56-dim natural killer cell, human: -1.331<br>CD16-negative, CD56-bright natural killer cell, human: -1.444<br>natural killer cell: -1.995<br>mature NK T cell: -4.045<br>activated type II NK T cell: -4.058",
+          "value: -6.405<br>CD16-positive, CD56-dim natural killer cell, human: -0.612<br>natural killer cell: -1.956<br>CD16-negative, CD56-bright natural killer cell, human: -3.289<br>mature NK T cell: -3.745<br>gamma-delta T cell: -4.688",
+          "value: -5.862<br>CD16-negative, CD56-bright natural killer cell, human: -1.172<br>gamma-delta T cell: -2.565<br>effector memory CD8-positive, alpha-beta T cell: -3.497<br>mature NK T cell: -3.555<br>natural killer cell: -3.660",
+          "value: -6.537<br>CD16-negative, CD56-bright natural killer cell, human: -0.658<br>natural killer cell: -2.584<br>CD16-positive, CD56-dim natural killer cell, human: -3.209<br>gamma-delta T cell: -4.080<br>activated type II NK T cell: -4.254",
+          "value: -5.874<br>CD16-negative, CD56-bright natural killer cell, human: -1.027<br>natural killer cell: -2.553<br>gamma-delta T cell: -2.746<br>CD16-positive, CD56-dim natural killer cell, human: -3.439<br>mature NK T cell: -3.475",
+          "value: -4.670<br>B cell: -0.826<br>naive B cell: -1.732<br>class switched memory B cell: -3.411<br>malignant cell: -3.438<br>mature B cell: -3.604",
+          "value: -6.934<br>CD16-positive, CD56-dim natural killer cell, human: -0.722<br>CD16-negative, CD56-bright natural killer cell, human: -1.848<br>natural killer cell: -2.077<br>mature NK T cell: -3.894<br>activated type II NK T cell: -4.512",
+          "value: -5.819<br>plasmacytoid dendritic cell: -0.367<br>plasmacytoid dendritic cell, human: -2.483<br>dendritic cell: -3.067<br>dendritic cell, human: -4.865<br>conventional dendritic cell: -5.022",
+          "value: -5.774<br>CD16-positive, CD56-dim natural killer cell, human: -0.543<br>natural killer cell: -1.639<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.091<br>activated type II NK T cell: -4.657",
+          "value: -5.954<br>CD14-positive monocyte: -1.184<br>classical monocyte: -1.294<br>pvalb GABAergic cortical interneuron: -3.451<br>macrophage: -4.004<br>CD1c-positive myeloid dendritic cell: -4.144",
+          "value: -6.578<br>CD16-positive, CD56-dim natural killer cell, human: -0.977<br>CD16-negative, CD56-bright natural killer cell, human: -1.603<br>natural killer cell: -2.379<br>mature NK T cell: -3.960<br>gamma-delta T cell: -4.178",
+          "value: -6.741<br>CD16-positive, CD56-dim natural killer cell, human: -0.567<br>natural killer cell: -1.753<br>CD16-negative, CD56-bright natural killer cell, human: -3.109<br>mature NK T cell: -4.195<br>activated type II NK T cell: -4.754",
+          "value: -5.439<br>CD16-positive, CD56-dim natural killer cell, human: -0.817<br>CD16-negative, CD56-bright natural killer cell, human: -2.455<br>natural killer cell: -2.497<br>gamma-delta T cell: -3.111<br>mature NK T cell: -3.856",
+          "value: -4.442<br>central memory CD4-positive, alpha-beta T cell: -1.689<br>effector memory CD4-positive, alpha-beta T cell: -1.952<br>T-helper 22 cell: -2.587<br>T follicular helper cell: -2.748<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.972",
+          "value: -6.796<br>classical monocyte: -1.672<br>CD14-positive monocyte: -1.917<br>dendritic cell, human: -2.935<br>CD1c-positive myeloid dendritic cell: -3.112<br>macrophage: -3.482",
+          "value: -5.098<br>classical monocyte: -0.881<br>CD14-positive monocyte: -2.022<br>pvalb GABAergic cortical interneuron: -3.352<br>macrophage: -3.920<br>monocyte: -4.005",
+          "value: -6.259<br>CD16-negative, CD56-bright natural killer cell, human: -0.696<br>natural killer cell: -3.045<br>gamma-delta T cell: -3.518<br>CD16-positive, CD56-dim natural killer cell, human: -3.819<br>mature NK T cell: -4.146",
+          "value: -5.371<br>CD16-negative, CD56-bright natural killer cell, human: -1.006<br>natural killer cell: -1.944<br>CD16-positive, CD56-dim natural killer cell, human: -1.995<br>gamma-delta T cell: -4.104<br>activated type II NK T cell: -4.330",
+          "value: -4.753<br>B cell: -0.660<br>naive B cell: -2.369<br>class switched memory B cell: -2.894<br>malignant cell: -3.204<br>memory B cell: -3.548",
+          "value: -4.979<br>classical monocyte: -1.228<br>CD14-positive monocyte: -1.363<br>pvalb GABAergic cortical interneuron: -3.029<br>monocyte: -3.201<br>macrophage: -3.962",
+          "value: -4.959<br>CD16-positive, CD56-dim natural killer cell, human: -0.822<br>natural killer cell: -1.651<br>CD16-negative, CD56-bright natural killer cell, human: -2.918<br>mature NK T cell: -3.801<br>gamma-delta T cell: -4.005",
+          "value: -5.858<br>CD14-positive monocyte: -1.135<br>classical monocyte: -1.293<br>pvalb GABAergic cortical interneuron: -3.007<br>monocyte: -3.811<br>macrophage: -4.356",
+          "value: -6.497<br>CD16-positive, CD56-dim natural killer cell, human: -0.557<br>natural killer cell: -1.797<br>CD16-negative, CD56-bright natural killer cell, human: -2.880<br>mature NK T cell: -4.000<br>activated type II NK T cell: -4.580",
+          "value: -5.971<br>CD16-negative, CD56-bright natural killer cell, human: -2.074<br>T cell: -3.324<br>mast cell: -3.603<br>mature NK T cell: -3.603<br>effector memory CD4-positive, alpha-beta T cell: -3.668",
+          "value: -4.723<br>plasmacytoid dendritic cell: -0.377<br>plasmacytoid dendritic cell, human: -2.455<br>dendritic cell: -3.249<br>conventional dendritic cell: -4.739<br>hematopoietic precursor cell: -4.916",
+          "value: -5.674<br>plasmacytoid dendritic cell: -0.304<br>dendritic cell: -2.591<br>plasmacytoid dendritic cell, human: -2.861<br>conventional dendritic cell: -4.751<br>myeloid dendritic cell: -4.854",
+          "value: -6.421<br>CD16-positive, CD56-dim natural killer cell, human: -0.487<br>natural killer cell: -1.802<br>CD16-negative, CD56-bright natural killer cell, human: -2.723<br>activated type II NK T cell: -4.518<br>mature NK T cell: -4.705",
+          "value: -5.718<br>CD16-positive, CD56-dim natural killer cell, human: -0.782<br>natural killer cell: -1.211<br>CD16-negative, CD56-bright natural killer cell, human: -3.754<br>mature NK T cell: -4.030<br>activated type II NK T cell: -4.619",
+          "value: -5.748<br>classical monocyte: -1.078<br>CD14-positive monocyte: -1.656<br>pvalb GABAergic cortical interneuron: -2.768<br>monocyte: -3.917<br>macrophage: -3.976",
+          "value: -6.638<br>dendritic cell: -1.006<br>dendritic cell, human: -1.999<br>myeloid dendritic cell: -2.557<br>conventional dendritic cell: -2.691<br>CD1c-positive myeloid dendritic cell: -3.001",
+          "value: -6.262<br>CD16-negative, CD56-bright natural killer cell, human: -1.175<br>natural killer cell: -3.306<br>gamma-delta T cell: -3.354<br>CD16-positive, CD56-dim natural killer cell, human: -3.495<br>innate lymphoid cell: -3.952",
+          "value: -6.554<br>CD16-negative, CD56-bright natural killer cell, human: -0.571<br>gamma-delta T cell: -3.573<br>natural killer cell: -3.615<br>CD16-positive, CD56-dim natural killer cell, human: -3.753<br>innate lymphoid cell: -4.262",
+          "value: -5.539<br>CD16-negative, CD56-bright natural killer cell, human: -0.952<br>natural killer cell: -2.807<br>CD16-positive, CD56-dim natural killer cell, human: -2.916<br>gamma-delta T cell: -2.968<br>mature NK T cell: -3.994",
+          "value: -5.676<br>CD16-negative, CD56-bright natural killer cell, human: -0.997<br>CD16-positive, CD56-dim natural killer cell, human: -2.743<br>gamma-delta T cell: -2.905<br>natural killer cell: -3.010<br>effector memory CD8-positive, alpha-beta T cell: -3.666",
+          "value: -5.783<br>CD16-positive, CD56-dim natural killer cell, human: -1.154<br>CD16-negative, CD56-bright natural killer cell, human: -1.878<br>natural killer cell: -2.675<br>gamma-delta T cell: -2.696<br>mature NK T cell: -3.555",
+          "value: -5.502<br>CD16-positive, CD56-dim natural killer cell, human: -0.297<br>natural killer cell: -2.209<br>CD16-negative, CD56-bright natural killer cell, human: -4.191<br>mature NK T cell: -4.297<br>gamma-delta T cell: -5.226",
+          "value: -6.864<br>CD14-positive monocyte: -1.118<br>classical monocyte: -1.483<br>pvalb GABAergic cortical interneuron: -2.689<br>monocyte: -3.065<br>macrophage: -4.484",
+          "value: -4.549<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.248<br>central memory CD4-positive, alpha-beta T cell: -1.771<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.597<br>T follicular helper cell: -2.778<br>T cell: -3.386",
+          "value: -6.857<br>natural killer cell: -0.890<br>CD16-positive, CD56-dim natural killer cell, human: -0.981<br>CD16-negative, CD56-bright natural killer cell, human: -4.001<br>mature NK T cell: -4.040<br>gamma-delta T cell: -4.741",
+          "value: -6.685<br>CD16-positive, CD56-dim natural killer cell, human: -0.436<br>natural killer cell: -1.803<br>CD16-negative, CD56-bright natural killer cell, human: -3.971<br>mature NK T cell: -4.313<br>activated type II NK T cell: -4.856",
+          "value: -4.964<br>hematopoietic precursor cell: -1.720<br>hematopoietic stem cell: -2.879<br>CD34-positive, CD38-negative hematopoietic stem cell: -3.170<br>common myeloid progenitor: -3.490<br>mast cell: -3.898",
+          "value: -6.513<br>CD14-positive monocyte: -1.058<br>classical monocyte: -1.218<br>pvalb GABAergic cortical interneuron: -3.335<br>macrophage: -4.106<br>monocyte: -4.227",
+          "value: -6.232<br>CD16-positive, CD56-dim natural killer cell, human: -0.481<br>natural killer cell: -1.596<br>CD16-negative, CD56-bright natural killer cell, human: -3.700<br>mature NK T cell: -4.249<br>activated type II NK T cell: -4.965",
+          "value: -5.450<br>natural killer cell: -1.272<br>CD16-positive, CD56-dim natural killer cell, human: -1.493<br>CD8-positive, alpha-beta T cell: -2.754<br>T cell: -3.431<br>CD4-positive, alpha-beta T cell: -3.515",
+          "value: -4.787<br>B cell: -0.952<br>naive B cell: -1.565<br>malignant cell: -3.002<br>class switched memory B cell: -3.557<br>immature B cell: -3.734",
+          "value: -5.706<br>CD16-positive, CD56-dim natural killer cell, human: -0.422<br>natural killer cell: -2.007<br>CD16-negative, CD56-bright natural killer cell, human: -3.209<br>mature NK T cell: -4.727<br>activated type II NK T cell: -4.741",
+          "value: -5.604<br>effector memory CD4-positive, alpha-beta T cell: -1.970<br>central memory CD4-positive, alpha-beta T cell: -2.019<br>T follicular helper cell: -2.137<br>T-helper 22 cell: -2.689<br>naive thymus-derived CD4-positive, alpha-beta T cell: -3.207",
+          "value: -6.219<br>CD16-positive, CD56-dim natural killer cell, human: -0.493<br>natural killer cell: -1.543<br>CD16-negative, CD56-bright natural killer cell, human: -3.859<br>mature NK T cell: -4.163<br>astrocyte of the cerebral cortex: -4.926",
+          "value: -4.469<br>mast cell: -1.717<br>plasmacytoid dendritic cell: -3.087<br>group 3 innate lymphoid cell: -3.461<br>dendritic cell: -3.529<br>hematopoietic precursor cell: -3.558",
+          "value: -5.466<br>plasmacytoid dendritic cell: -0.217<br>plasmacytoid dendritic cell, human: -2.750<br>dendritic cell: -3.267<br>myeloid dendritic cell: -5.930<br>hematopoietic precursor cell: -6.075",
+          "value: -6.458<br>CD16-positive, CD56-dim natural killer cell, human: -0.781<br>CD16-negative, CD56-bright natural killer cell, human: -1.985<br>natural killer cell: -2.078<br>activated type II NK T cell: -4.213<br>mature NK T cell: -4.272",
+          "value: -5.182<br>CD16-positive, CD56-dim natural killer cell, human: -0.582<br>natural killer cell: -1.661<br>CD16-negative, CD56-bright natural killer cell, human: -3.573<br>mature NK T cell: -3.747<br>gamma-delta T cell: -4.858",
+          "value: -5.797<br>CD16-positive, CD56-dim natural killer cell, human: -0.689<br>natural killer cell: -1.504<br>CD16-negative, CD56-bright natural killer cell, human: -3.548<br>mature NK T cell: -3.907<br>gamma-delta T cell: -4.337",
+          "value: -5.632<br>CD16-positive, CD56-dim natural killer cell, human: -0.705<br>natural killer cell: -1.214<br>mature NK T cell: -4.027<br>CD16-negative, CD56-bright natural killer cell, human: -4.146<br>gamma-delta T cell: -4.974",
+          "value: -6.252<br>classical monocyte: -1.376<br>CD14-positive monocyte: -1.840<br>pvalb GABAergic cortical interneuron: -2.967<br>macrophage: -3.292<br>CD1c-positive myeloid dendritic cell: -3.744",
+          "value: -6.728<br>CD16-positive, CD56-dim natural killer cell, human: -0.645<br>CD16-negative, CD56-bright natural killer cell, human: -1.840<br>natural killer cell: -2.626<br>gamma-delta T cell: -4.037<br>mature NK T cell: -4.130",
+          "value: -6.348<br>CD16-negative, CD56-bright natural killer cell, human: -1.276<br>CD16-positive, CD56-dim natural killer cell, human: -1.541<br>natural killer cell: -2.823<br>gamma-delta T cell: -3.412<br>mature NK T cell: -3.766",
+          "value: -4.698<br>gamma-delta T cell: -2.388<br>mature NK T cell: -2.577<br>natural killer cell: -2.618<br>CD16-positive, CD56-dim natural killer cell, human: -2.757<br>CD8-positive, alpha-beta T cell: -2.855",
+          "value: -4.727<br>natural killer cell: -1.298<br>CD16-positive, CD56-dim natural killer cell, human: -1.375<br>CD16-negative, CD56-bright natural killer cell, human: -2.026<br>mature NK T cell: -3.898<br>gamma-delta T cell: -3.985",
+          "value: -6.745<br>classical monocyte: -1.352<br>CD14-positive monocyte: -1.462<br>pvalb GABAergic cortical interneuron: -2.909<br>monocyte: -3.615<br>dendritic cell, human: -4.076",
+          "value: -5.602<br>CD16-positive, CD56-dim natural killer cell, human: -0.504<br>natural killer cell: -1.859<br>mature NK T cell: -3.736<br>CD16-negative, CD56-bright natural killer cell, human: -3.860<br>gamma-delta T cell: -4.450",
+          "value: -6.164<br>CD16-negative, CD56-bright natural killer cell, human: -1.234<br>CD16-positive, CD56-dim natural killer cell, human: -1.256<br>natural killer cell: -2.391<br>gamma-delta T cell: -4.006<br>activated type II NK T cell: -4.088",
+          "value: -5.737<br>CD16-negative, CD56-bright natural killer cell, human: -1.504<br>natural killer cell: -3.063<br>gamma-delta T cell: -3.139<br>CD16-positive, CD56-dim natural killer cell, human: -3.617<br>mature NK T cell: -3.644",
+          "value: -6.106<br>classical monocyte: -1.135<br>CD14-positive monocyte: -1.142<br>pvalb GABAergic cortical interneuron: -3.260<br>monocyte: -3.693<br>macrophage: -4.162",
+          "value: -6.560<br>T follicular helper cell: -1.724<br>central memory CD4-positive, alpha-beta T cell: -1.747<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.246<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.666<br>effector memory CD4-positive, alpha-beta T cell: -2.771",
+          "value: -5.994<br>CD16-positive, CD56-dim natural killer cell, human: -0.730<br>natural killer cell: -1.617<br>CD16-negative, CD56-bright natural killer cell, human: -2.760<br>mature NK T cell: -3.766<br>gamma-delta T cell: -4.067",
+          "value: -5.582<br>CD16-positive, CD56-dim natural killer cell, human: -0.455<br>natural killer cell: -1.808<br>CD16-negative, CD56-bright natural killer cell, human: -2.919<br>mature NK T cell: -4.421<br>activated type II NK T cell: -4.875",
+          "value: -6.271<br>CD16-positive, CD56-dim natural killer cell, human: -0.433<br>natural killer cell: -2.164<br>CD16-negative, CD56-bright natural killer cell, human: -3.062<br>mature NK T cell: -4.370<br>gamma-delta T cell: -4.685",
+          "value: -5.719<br>CD16-positive, CD56-dim natural killer cell, human: -0.720<br>natural killer cell: -1.698<br>CD16-negative, CD56-bright natural killer cell, human: -2.551<br>mature NK T cell: -3.531<br>gamma-delta T cell: -4.535",
+          "value: -6.234<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.381<br>central memory CD4-positive, alpha-beta T cell: -1.873<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.955<br>T follicular helper cell: -2.848<br>effector memory CD4-positive, alpha-beta T cell: -2.925",
+          "value: -6.959<br>CD16-positive, CD56-dim natural killer cell, human: -0.623<br>natural killer cell: -2.231<br>CD16-negative, CD56-bright natural killer cell, human: -2.343<br>mature NK T cell: -3.984<br>activated type II NK T cell: -4.452",
+          "value: -5.468<br>T follicular helper cell: -1.897<br>effector memory CD4-positive, alpha-beta T cell: -2.224<br>T-helper 22 cell: -2.904<br>central memory CD4-positive, alpha-beta T cell: -2.905<br>mature NK T cell: -3.580",
+          "value: -6.194<br>CD16-positive, CD56-dim natural killer cell, human: -0.787<br>natural killer cell: -1.652<br>CD16-negative, CD56-bright natural killer cell, human: -2.307<br>mature NK T cell: -3.950<br>gamma-delta T cell: -4.503",
+          "value: -5.123<br>CD16-positive, CD56-dim natural killer cell, human: -1.272<br>CD16-negative, CD56-bright natural killer cell, human: -1.815<br>natural killer cell: -2.077<br>gamma-delta T cell: -3.363<br>mature NK T cell: -3.751",
+          "value: -6.374<br>CD16-positive, CD56-dim natural killer cell, human: -0.640<br>natural killer cell: -1.365<br>CD16-negative, CD56-bright natural killer cell, human: -3.166<br>mature NK T cell: -3.990<br>activated type II NK T cell: -4.459",
+          "value: -6.081<br>CD16-positive, CD56-dim natural killer cell, human: -1.258<br>CD16-negative, CD56-bright natural killer cell, human: -1.600<br>natural killer cell: -2.044<br>mature NK T cell: -3.209<br>gamma-delta T cell: -3.663",
+          "value: -4.107<br>CD16-positive, CD56-dim natural killer cell, human: -1.673<br>CD16-negative, CD56-bright natural killer cell, human: -1.691<br>natural killer cell: -1.804<br>gamma-delta T cell: -2.955<br>mature NK T cell: -3.267",
+          "value: -6.681<br>CD14-positive monocyte: -1.214<br>classical monocyte: -1.406<br>CD1c-positive myeloid dendritic cell: -3.330<br>pvalb GABAergic cortical interneuron: -3.419<br>macrophage: -3.810",
+          "value: -5.691<br>CD16-positive, CD56-dim natural killer cell, human: -0.410<br>natural killer cell: -2.190<br>CD16-negative, CD56-bright natural killer cell, human: -3.640<br>gamma-delta T cell: -4.258<br>mature NK T cell: -4.423",
+          "value: -6.526<br>CD16-positive, CD56-dim natural killer cell, human: -1.026<br>CD16-negative, CD56-bright natural killer cell, human: -1.775<br>natural killer cell: -2.172<br>gamma-delta T cell: -3.475<br>mature NK T cell: -4.026",
+          "value: -6.184<br>CD16-negative, CD56-bright natural killer cell, human: -0.565<br>natural killer cell: -2.688<br>gamma-delta T cell: -3.653<br>CD16-positive, CD56-dim natural killer cell, human: -3.968<br>innate lymphoid cell: -4.246",
+          "value: -6.714<br>CD14-positive monocyte: -0.986<br>classical monocyte: -1.299<br>pvalb GABAergic cortical interneuron: -3.484<br>neutrophil: -3.896<br>macrophage: -4.347",
+          "value: -6.649<br>CD16-positive, CD56-dim natural killer cell, human: -0.734<br>CD16-negative, CD56-bright natural killer cell, human: -1.991<br>natural killer cell: -2.321<br>gamma-delta T cell: -3.541<br>mature NK T cell: -3.798",
+          "value: -6.930<br>T follicular helper cell: -1.509<br>central memory CD4-positive, alpha-beta T cell: -2.013<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.587<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.824<br>effector memory CD4-positive, alpha-beta T cell: -3.200",
+          "value: -5.816<br>CD16-positive, CD56-dim natural killer cell, human: -0.448<br>natural killer cell: -1.850<br>CD16-negative, CD56-bright natural killer cell, human: -3.779<br>mature NK T cell: -4.109<br>astrocyte of the cerebral cortex: -4.927",
+          "value: -6.464<br>CD16-positive, CD56-dim natural killer cell, human: -0.662<br>natural killer cell: -1.352<br>CD16-negative, CD56-bright natural killer cell, human: -4.017<br>mature NK T cell: -4.242<br>CD8-positive, alpha-beta T cell: -4.727",
+          "value: -5.886<br>CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -1.556<br>mature NK T cell: -3.605<br>CD16-negative, CD56-bright natural killer cell, human: -4.498<br>gamma-delta T cell: -4.844",
+          "value: -5.744<br>CD16-positive, CD56-dim natural killer cell, human: -0.649<br>natural killer cell: -1.274<br>mature NK T cell: -3.796<br>CD16-negative, CD56-bright natural killer cell, human: -4.333<br>astrocyte of the cerebral cortex: -4.580",
+          "value: -5.915<br>classical monocyte: -1.314<br>CD14-positive monocyte: -2.076<br>pvalb GABAergic cortical interneuron: -3.117<br>dendritic cell, human: -3.264<br>CD1c-positive myeloid dendritic cell: -3.332",
+          "value: -5.630<br>CD16-positive, CD56-dim natural killer cell, human: -0.601<br>natural killer cell: -1.521<br>CD16-negative, CD56-bright natural killer cell, human: -3.437<br>mature NK T cell: -4.107<br>activated type II NK T cell: -4.760",
+          "value: -5.928<br>CD16-positive, CD56-dim natural killer cell, human: -0.562<br>natural killer cell: -1.475<br>CD16-negative, CD56-bright natural killer cell, human: -3.555<br>mature NK T cell: -4.057<br>activated type II NK T cell: -4.890",
+          "value: -6.190<br>CD16-positive, CD56-dim natural killer cell, human: -0.546<br>natural killer cell: -1.956<br>CD16-negative, CD56-bright natural killer cell, human: -2.845<br>mature NK T cell: -4.144<br>activated type II NK T cell: -4.606",
+          "value: -4.565<br>classical monocyte: -0.975<br>CD14-positive monocyte: -1.744<br>pvalb GABAergic cortical interneuron: -2.518<br>monocyte: -3.665<br>macrophage: -4.412",
+          "value: -6.652<br>CD16-negative, CD56-bright natural killer cell, human: -1.266<br>CD16-positive, CD56-dim natural killer cell, human: -1.383<br>natural killer cell: -2.299<br>mature NK T cell: -3.343<br>gamma-delta T cell: -3.616",
+          "value: -6.220<br>classical monocyte: -1.112<br>CD14-positive monocyte: -1.277<br>pvalb GABAergic cortical interneuron: -3.221<br>monocyte: -3.900<br>macrophage: -4.120",
+          "value: -6.610<br>CD16-positive, CD56-dim natural killer cell, human: -0.590<br>natural killer cell: -1.646<br>mature NK T cell: -3.692<br>gamma-delta T cell: -3.972<br>CD16-negative, CD56-bright natural killer cell, human: -4.032",
+          "value: -5.954<br>CD16-positive, CD56-dim natural killer cell, human: -1.213<br>natural killer cell: -1.461<br>CD16-negative, CD56-bright natural killer cell, human: -1.855<br>mature NK T cell: -3.661<br>activated type II NK T cell: -4.224",
+          "value: -6.690<br>CD16-positive, CD56-dim natural killer cell, human: -0.634<br>natural killer cell: -1.551<br>mature NK T cell: -3.645<br>CD16-negative, CD56-bright natural killer cell, human: -3.969<br>gamma-delta T cell: -4.576",
+          "value: -5.614<br>CD16-negative, CD56-bright natural killer cell, human: -0.744<br>natural killer cell: -2.740<br>CD16-positive, CD56-dim natural killer cell, human: -3.468<br>gamma-delta T cell: -3.661<br>innate lymphoid cell: -4.038",
+          "value: -4.624<br>classical monocyte: -1.349<br>monocyte: -2.523<br>CD14-positive monocyte: -2.712<br>macrophage: -2.715<br>dendritic cell: -3.300",
+          "value: -4.695<br>natural killer cell: -2.193<br>CD16-positive, CD56-dim natural killer cell, human: -2.344<br>gamma-delta T cell: -3.057<br>CD8-positive, alpha-beta T cell: -3.063<br>CD16-negative, CD56-bright natural killer cell, human: -3.069",
+          "value: -6.767<br>classical monocyte: -1.268<br>CD14-positive monocyte: -2.058<br>pvalb GABAergic cortical interneuron: -3.242<br>macrophage: -3.424<br>dendritic cell, human: -3.457",
+          "value: -6.015<br>CD16-positive, CD56-dim natural killer cell, human: -0.537<br>natural killer cell: -1.785<br>mature NK T cell: -3.917<br>CD16-negative, CD56-bright natural killer cell, human: -4.093<br>gamma-delta T cell: -4.734",
+          "value: -6.523<br>CD16-negative, CD56-bright natural killer cell, human: -1.305<br>CD16-positive, CD56-dim natural killer cell, human: -1.575<br>natural killer cell: -2.073<br>mature NK T cell: -3.957<br>activated type II NK T cell: -4.035",
+          "value: -6.758<br>CD16-positive, CD56-dim natural killer cell, human: -0.482<br>natural killer cell: -1.546<br>mature NK T cell: -4.012<br>astrocyte of the cerebral cortex: -4.710<br>gamma-delta T cell: -4.817",
+          "value: -6.312<br>CD16-positive, CD56-dim natural killer cell, human: -0.918<br>natural killer cell: -1.858<br>CD16-negative, CD56-bright natural killer cell, human: -2.181<br>mature NK T cell: -3.711<br>gamma-delta T cell: -4.275",
+          "value: -6.170<br>CD16-positive, CD56-dim natural killer cell, human: -0.855<br>natural killer cell: -1.842<br>CD16-negative, CD56-bright natural killer cell, human: -2.624<br>mature NK T cell: -3.712<br>activated type II NK T cell: -4.375",
+          "value: -5.961<br>CD16-positive, CD56-dim natural killer cell, human: -0.486<br>natural killer cell: -1.899<br>CD16-negative, CD56-bright natural killer cell, human: -3.246<br>mature NK T cell: -3.876<br>gamma-delta T cell: -4.936",
+          "value: -5.704<br>classical monocyte: -1.275<br>CD14-positive monocyte: -1.852<br>pvalb GABAergic cortical interneuron: -2.819<br>monocyte: -3.272<br>macrophage: -3.941",
+          "value: -5.982<br>CD16-negative, CD56-bright natural killer cell, human: -0.813<br>gamma-delta T cell: -3.197<br>natural killer cell: -3.459<br>CD16-positive, CD56-dim natural killer cell, human: -3.551<br>innate lymphoid cell: -3.987",
+          "value: -5.425<br>CD16-negative, CD56-bright natural killer cell, human: -0.820<br>gamma-delta T cell: -3.120<br>natural killer cell: -3.715<br>effector memory CD8-positive, alpha-beta T cell: -3.730<br>innate lymphoid cell: -3.861",
+          "value: -6.054<br>CD16-positive, CD56-dim natural killer cell, human: -1.137<br>CD16-negative, CD56-bright natural killer cell, human: -1.482<br>natural killer cell: -2.663<br>gamma-delta T cell: -3.683<br>activated type II NK T cell: -4.227",
+          "value: -6.197<br>CD16-positive, CD56-dim natural killer cell, human: -0.471<br>natural killer cell: -1.780<br>CD16-negative, CD56-bright natural killer cell, human: -3.652<br>mature NK T cell: -4.458<br>activated type II NK T cell: -4.834",
+          "value: -4.662<br>T follicular helper cell: -1.995<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.758<br>effector memory CD4-positive, alpha-beta T cell: -2.804<br>central memory CD4-positive, alpha-beta T cell: -2.869<br>T cell: -3.370",
+          "value: -6.245<br>non-classical monocyte: -1.535<br>CD14-low, CD16-positive monocyte: -2.371<br>mature NK T cell: -2.418<br>CD14-positive monocyte: -2.788<br>classical monocyte: -3.001",
+          "value: -5.289<br>CD16-positive, CD56-dim natural killer cell, human: -0.564<br>natural killer cell: -1.660<br>CD16-negative, CD56-bright natural killer cell, human: -3.630<br>mature NK T cell: -4.085<br>activated type II NK T cell: -4.954",
+          "value: -6.277<br>central memory CD4-positive, alpha-beta T cell: -1.566<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.660<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.049<br>effector memory CD4-positive, alpha-beta T cell: -2.719<br>T follicular helper cell: -2.782",
+          "value: -5.852<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.236<br>central memory CD4-positive, alpha-beta T cell: -1.941<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.980<br>T follicular helper cell: -3.383<br>effector memory CD4-positive, alpha-beta T cell: -3.387",
+          "value: -5.081<br>effector memory CD8-positive, alpha-beta T cell: -2.256<br>effector CD8-positive, alpha-beta T cell: -2.256<br>gamma-delta T cell: -2.282<br>mature NK T cell: -2.669<br>CD8-positive, alpha-beta T cell: -3.245",
+          "value: -6.774<br>CD14-positive monocyte: -1.055<br>classical monocyte: -1.069<br>pvalb GABAergic cortical interneuron: -3.291<br>monocyte: -3.732<br>macrophage: -4.260",
+          "value: -6.163<br>CD16-positive, CD56-dim natural killer cell, human: -0.576<br>natural killer cell: -1.712<br>CD16-negative, CD56-bright natural killer cell, human: -3.173<br>mature NK T cell: -4.199<br>gamma-delta T cell: -4.636",
+          "value: -5.837<br>CD16-positive, CD56-dim natural killer cell, human: -0.466<br>natural killer cell: -1.886<br>CD16-negative, CD56-bright natural killer cell, human: -3.238<br>mature NK T cell: -4.160<br>activated type II NK T cell: -4.993",
+          "value: -6.481<br>CD14-positive monocyte: -0.960<br>classical monocyte: -1.434<br>pvalb GABAergic cortical interneuron: -3.094<br>monocyte: -3.484<br>macrophage: -4.402",
+          "value: -6.194<br>plasmacytoid dendritic cell: -0.226<br>plasmacytoid dendritic cell, human: -2.913<br>dendritic cell: -3.105<br>dendritic cell, human: -5.293<br>myeloid dendritic cell: -5.448",
+          "value: -4.667<br>effector memory CD4-positive, alpha-beta T cell: -2.093<br>T follicular helper cell: -2.961<br>T-helper 22 cell: -3.076<br>central memory CD4-positive, alpha-beta T cell: -3.279<br>effector memory CD8-positive, alpha-beta T cell: -3.298",
+          "value: -5.480<br>CD16-positive, CD56-dim natural killer cell, human: -0.501<br>natural killer cell: -1.543<br>mature NK T cell: -3.713<br>CD16-negative, CD56-bright natural killer cell, human: -4.688<br>gamma-delta T cell: -5.044",
+          "value: -6.049<br>CD16-positive, CD56-dim natural killer cell, human: -0.847<br>CD16-negative, CD56-bright natural killer cell, human: -1.806<br>natural killer cell: -2.209<br>gamma-delta T cell: -3.754<br>mature NK T cell: -3.846",
+          "value: -6.104<br>CD16-positive, CD56-dim natural killer cell, human: -0.429<br>natural killer cell: -1.850<br>CD16-negative, CD56-bright natural killer cell, human: -3.253<br>mature NK T cell: -4.571<br>activated type II NK T cell: -4.819",
+          "value: -5.298<br>CD16-positive, CD56-dim natural killer cell, human: -0.659<br>natural killer cell: -1.793<br>CD16-negative, CD56-bright natural killer cell, human: -3.616<br>mature NK T cell: -3.767<br>gamma-delta T cell: -4.293",
+          "value: -6.675<br>CD16-positive, CD56-dim natural killer cell, human: -0.574<br>natural killer cell: -1.468<br>mature NK T cell: -3.926<br>CD16-negative, CD56-bright natural killer cell, human: -4.820<br>astrocyte of the cerebral cortex: -5.061",
+          "value: -5.025<br>CD16-negative, CD56-bright natural killer cell, human: -1.258<br>natural killer cell: -3.552<br>lymphocyte: -3.808<br>progenitor cell: -4.126<br>innate lymphoid cell: -4.154",
+          "value: -6.356<br>CD16-positive, CD56-dim natural killer cell, human: -0.409<br>natural killer cell: -1.695<br>CD16-negative, CD56-bright natural killer cell, human: -3.956<br>mature NK T cell: -4.457<br>activated type II NK T cell: -5.131",
+          "value: -6.086<br>CD14-positive monocyte: -1.155<br>classical monocyte: -1.550<br>pvalb GABAergic cortical interneuron: -2.785<br>monocyte: -3.268<br>macrophage: -4.483",
+          "value: -6.175<br>central memory CD4-positive, alpha-beta T cell: -1.721<br>effector memory CD4-positive, alpha-beta T cell: -2.151<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.303<br>T follicular helper cell: -2.456<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.903",
+          "value: -5.840<br>CD16-positive, CD56-dim natural killer cell, human: -0.481<br>natural killer cell: -1.849<br>CD16-negative, CD56-bright natural killer cell, human: -3.053<br>mature NK T cell: -4.130<br>activated type II NK T cell: -4.680",
+          "value: -6.450<br>CD16-positive, CD56-dim natural killer cell, human: -1.392<br>natural killer cell: -2.783<br>mature NK T cell: -2.913<br>CD16-negative, CD56-bright natural killer cell, human: -3.006<br>gamma-delta T cell: -3.290",
+          "value: -5.967<br>CD16-positive, CD56-dim natural killer cell, human: -1.789<br>CD16-negative, CD56-bright natural killer cell, human: -1.917<br>natural killer cell: -2.300<br>gamma-delta T cell: -2.620<br>mature NK T cell: -3.070",
+          "value: -4.949<br>dendritic cell: -1.736<br>plasmacytoid dendritic cell: -1.816<br>conventional dendritic cell: -2.443<br>plasmacytoid dendritic cell, human: -3.064<br>myeloid dendritic cell: -3.284",
+          "value: -6.009<br>central memory CD4-positive, alpha-beta T cell: -1.954<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.004<br>effector memory CD4-positive, alpha-beta T cell: -2.268<br>T follicular helper cell: -2.823<br>T-helper 22 cell: -2.982",
+          "value: -6.553<br>CD16-positive, CD56-dim natural killer cell, human: -1.259<br>CD16-negative, CD56-bright natural killer cell, human: -1.659<br>natural killer cell: -2.152<br>gamma-delta T cell: -3.717<br>activated type II NK T cell: -4.018",
+          "value: -6.383<br>CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.781<br>CD16-negative, CD56-bright natural killer cell, human: -3.543<br>mature NK T cell: -3.886<br>astrocyte of the cerebral cortex: -4.808",
+          "value: -4.878<br>plasmacytoid dendritic cell: -0.200<br>plasmacytoid dendritic cell, human: -2.889<br>dendritic cell: -3.683<br>mast cell: -5.459<br>B cell: -5.963",
+          "value: -4.631<br>CD16-negative, CD56-bright natural killer cell, human: -1.908<br>CD16-positive, CD56-dim natural killer cell, human: -2.274<br>gamma-delta T cell: -2.507<br>natural killer cell: -2.540<br>mature NK T cell: -3.656",
+          "value: -6.453<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.749<br>central memory CD4-positive, alpha-beta T cell: -1.943<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.116<br>T follicular helper cell: -3.350<br>T cell: -4.211",
+          "value: -5.455<br>CD16-negative, CD56-bright natural killer cell, human: -0.760<br>natural killer cell: -2.478<br>CD16-positive, CD56-dim natural killer cell, human: -2.923<br>gamma-delta T cell: -3.516<br>mature NK T cell: -4.061",
+          "value: -4.224<br>hematopoietic precursor cell: -1.628<br>CD34-positive, CD38-negative hematopoietic stem cell: -2.770<br>hematopoietic stem cell: -2.796<br>mast cell: -3.707<br>granulocyte: -4.169",
+          "value: -6.236<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.569<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.563<br>central memory CD4-positive, alpha-beta T cell: -2.530<br>T cell: -4.413<br>T follicular helper cell: -4.551",
+          "value: -6.714<br>CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.427<br>mature NK T cell: -3.981<br>CD16-negative, CD56-bright natural killer cell, human: -4.585<br>astrocyte of the cerebral cortex: -4.792",
+          "value: -4.850<br>effector memory CD4-positive, alpha-beta T cell: -2.003<br>T follicular helper cell: -2.121<br>central memory CD4-positive, alpha-beta T cell: -2.571<br>T-helper 22 cell: -2.725<br>effector memory CD8-positive, alpha-beta T cell: -3.508",
+          "value: -6.419<br>classical monocyte: -1.300<br>CD14-positive monocyte: -1.626<br>pvalb GABAergic cortical interneuron: -3.066<br>macrophage: -3.148<br>CD1c-positive myeloid dendritic cell: -3.464",
+          "value: -5.773<br>CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.619<br>mature NK T cell: -3.663<br>CD16-negative, CD56-bright natural killer cell, human: -4.246<br>astrocyte of the cerebral cortex: -5.083",
+          "value: -5.855<br>CD16-positive, CD56-dim natural killer cell, human: -0.611<br>natural killer cell: -1.982<br>CD16-negative, CD56-bright natural killer cell, human: -2.206<br>mature NK T cell: -4.022<br>gamma-delta T cell: -4.344",
+          "value: -6.017<br>CD16-positive, CD56-dim natural killer cell, human: -0.769<br>natural killer cell: -1.105<br>CD16-negative, CD56-bright natural killer cell, human: -3.875<br>mature NK T cell: -4.223<br>activated type II NK T cell: -4.804",
+          "value: -6.126<br>classical monocyte: -1.847<br>CD14-positive monocyte: -2.110<br>non-classical monocyte: -2.825<br>macrophage: -3.420<br>CD1c-positive myeloid dendritic cell: -3.458",
+          "value: -6.179<br>CD16-positive, CD56-dim natural killer cell, human: -0.630<br>natural killer cell: -1.600<br>CD16-negative, CD56-bright natural killer cell, human: -3.083<br>mature NK T cell: -3.568<br>gamma-delta T cell: -4.356",
+          "value: -7.006<br>CD16-positive, CD56-dim natural killer cell, human: -1.191<br>CD16-negative, CD56-bright natural killer cell, human: -1.373<br>natural killer cell: -2.416<br>gamma-delta T cell: -3.292<br>mature NK T cell: -3.979",
+          "value: -5.565<br>classical monocyte: -1.422<br>CD14-positive monocyte: -2.533<br>pvalb GABAergic cortical interneuron: -2.959<br>monocyte: -3.934<br>macrophage: -3.983",
+          "value: -5.532<br>plasmacytoid dendritic cell: -0.208<br>plasmacytoid dendritic cell, human: -2.940<br>dendritic cell: -3.417<br>dendritic cell, human: -5.861<br>myeloid dendritic cell: -5.876",
+          "value: -4.382<br>plasmacytoid dendritic cell: -0.935<br>dendritic cell: -2.613<br>B cell: -3.401<br>plasma cell: -3.437<br>plasmacytoid dendritic cell, human: -3.452",
+          "value: -5.386<br>CD16-positive, CD56-dim natural killer cell, human: -0.653<br>natural killer cell: -1.392<br>mature NK T cell: -3.644<br>CD16-negative, CD56-bright natural killer cell, human: -4.179<br>astrocyte of the cerebral cortex: -4.565",
+          "value: -3.686<br>T cell: -2.240<br>mature NK T cell: -3.126<br>gamma-delta T cell: -3.262<br>CD4-positive, alpha-beta T cell: -3.301<br>T follicular helper cell: -3.336",
+          "value: -5.192<br>CD16-positive, CD56-dim natural killer cell, human: -1.373<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -2.496<br>gamma-delta T cell: -2.602<br>mature NK T cell: -3.305",
+          "value: -5.145<br>effector memory CD4-positive, alpha-beta T cell: -2.089<br>T follicular helper cell: -2.264<br>effector memory CD8-positive, alpha-beta T cell: -2.492<br>central memory CD4-positive, alpha-beta T cell: -2.528<br>T-helper 22 cell: -2.765",
+          "value: -6.529<br>classical monocyte: -1.253<br>CD14-positive monocyte: -1.834<br>pvalb GABAergic cortical interneuron: -2.866<br>dendritic cell, human: -3.772<br>macrophage: -3.877",
+          "value: -6.337<br>CD16-negative, CD56-bright natural killer cell, human: -1.562<br>group 3 innate lymphoid cell: -2.882<br>innate lymphoid cell: -3.454<br>natural killer cell: -3.735<br>hematopoietic precursor cell: -4.125",
+          "value: -5.596<br>plasmacytoid dendritic cell: -0.342<br>dendritic cell: -2.551<br>plasmacytoid dendritic cell, human: -2.782<br>myeloid dendritic cell: -4.820<br>dendritic cell, human: -4.831",
+          "value: -5.949<br>CD16-positive, CD56-dim natural killer cell, human: -0.381<br>natural killer cell: -1.751<br>mature NK T cell: -4.391<br>CD16-negative, CD56-bright natural killer cell, human: -4.407<br>astrocyte of the cerebral cortex: -4.985",
+          "value: -5.477<br>CD16-positive, CD56-dim natural killer cell, human: -1.383<br>gamma-delta T cell: -2.124<br>CD16-negative, CD56-bright natural killer cell, human: -2.611<br>natural killer cell: -2.794<br>mature NK T cell: -2.807",
+          "value: -6.147<br>plasmacytoid dendritic cell: -0.324<br>plasmacytoid dendritic cell, human: -2.647<br>dendritic cell: -3.113<br>myeloid dendritic cell: -5.368<br>dendritic cell, human: -5.479",
+          "value: -5.876<br>effector memory CD4-positive, alpha-beta T cell: -2.423<br>T follicular helper cell: -2.574<br>T-helper 22 cell: -2.856<br>central memory CD4-positive, alpha-beta T cell: -3.350<br>mucosal invariant T cell: -3.415",
+          "value: -5.485<br>CD16-negative, CD56-bright natural killer cell, human: -1.046<br>gamma-delta T cell: -3.416<br>natural killer cell: -3.512<br>innate lymphoid cell: -3.569<br>group 3 innate lymphoid cell: -3.853",
+          "value: -7.126<br>classical monocyte: -2.038<br>CD14-positive monocyte: -2.087<br>dendritic cell, human: -2.422<br>CD1c-positive myeloid dendritic cell: -2.820<br>conventional dendritic cell: -3.061",
+          "value: -6.139<br>CD16-positive, CD56-dim natural killer cell, human: -0.604<br>natural killer cell: -1.753<br>CD16-negative, CD56-bright natural killer cell, human: -2.559<br>mature NK T cell: -4.321<br>activated type II NK T cell: -4.330",
+          "value: -6.282<br>CD16-positive, CD56-dim natural killer cell, human: -0.835<br>CD16-negative, CD56-bright natural killer cell, human: -1.637<br>natural killer cell: -2.054<br>mature NK T cell: -4.223<br>activated type II NK T cell: -4.501",
+          "value: -6.257<br>CD16-positive, CD56-dim natural killer cell, human: -0.708<br>natural killer cell: -1.480<br>CD16-negative, CD56-bright natural killer cell, human: -3.574<br>mature NK T cell: -4.219<br>activated type II NK T cell: -4.509",
+          "value: -6.293<br>CD16-positive, CD56-dim natural killer cell, human: -0.364<br>natural killer cell: -2.038<br>CD16-negative, CD56-bright natural killer cell, human: -3.222<br>mature NK T cell: -4.401<br>activated type II NK T cell: -4.886",
+          "value: -5.718<br>CD16-positive, CD56-dim natural killer cell, human: -0.596<br>natural killer cell: -1.482<br>CD16-negative, CD56-bright natural killer cell, human: -3.571<br>mature NK T cell: -3.843<br>gamma-delta T cell: -4.881",
+          "value: -5.611<br>CD16-negative, CD56-bright natural killer cell, human: -2.528<br>effector memory CD4-positive, alpha-beta T cell: -3.005<br>gamma-delta T cell: -3.256<br>T follicular helper cell: -3.420<br>mucosal invariant T cell: -3.682",
+          "value: -5.543<br>CD16-negative, CD56-bright natural killer cell, human: -1.960<br>CD16-positive, CD56-dim natural killer cell, human: -2.465<br>gamma-delta T cell: -2.639<br>natural killer cell: -2.934<br>effector memory CD8-positive, alpha-beta T cell: -3.604",
+          "value: -6.064<br>classical monocyte: -1.263<br>CD14-positive monocyte: -1.779<br>macrophage: -3.264<br>pvalb GABAergic cortical interneuron: -3.298<br>dendritic cell, human: -3.631",
+          "value: -6.073<br>classical monocyte: -1.243<br>CD14-positive monocyte: -1.549<br>pvalb GABAergic cortical interneuron: -3.103<br>monocyte: -3.504<br>alveolar macrophage: -3.988",
+          "value: -5.063<br>effector memory CD8-positive, alpha-beta T cell: -2.207<br>effector memory CD4-positive, alpha-beta T cell: -2.417<br>T follicular helper cell: -2.647<br>central memory CD4-positive, alpha-beta T cell: -2.863<br>T-helper 22 cell: -2.984",
+          "value: -6.190<br>CD16-positive, CD56-dim natural killer cell, human: -0.396<br>natural killer cell: -2.307<br>CD16-negative, CD56-bright natural killer cell, human: -3.063<br>gamma-delta T cell: -4.378<br>mature NK T cell: -4.548",
+          "value: -6.533<br>CD16-positive, CD56-dim natural killer cell, human: -0.519<br>natural killer cell: -2.279<br>CD16-negative, CD56-bright natural killer cell, human: -2.497<br>mature NK T cell: -4.174<br>activated type II NK T cell: -4.706",
+          "value: -6.640<br>CD14-positive monocyte: -1.791<br>classical monocyte: -1.948<br>pvalb GABAergic cortical interneuron: -3.138<br>dendritic cell, human: -3.299<br>CD1c-positive myeloid dendritic cell: -3.315",
+          "value: -4.215<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.531<br>CD4-positive, alpha-beta T cell: -3.245<br>T cell: -3.452<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.881<br>erythrocyte: -3.967",
+          "value: -6.617<br>CD16-positive, CD56-dim natural killer cell, human: -0.388<br>natural killer cell: -2.193<br>CD16-negative, CD56-bright natural killer cell, human: -3.129<br>mature NK T cell: -4.036<br>gamma-delta T cell: -4.951",
+          "value: -4.760<br>CD16-negative, CD56-bright natural killer cell, human: -1.291<br>natural killer cell: -1.604<br>CD16-positive, CD56-dim natural killer cell, human: -2.523<br>gamma-delta T cell: -4.019<br>mature NK T cell: -4.191",
+          "value: -5.438<br>CD16-positive, CD56-dim natural killer cell, human: -0.643<br>natural killer cell: -1.804<br>CD16-negative, CD56-bright natural killer cell, human: -2.814<br>mature NK T cell: -4.074<br>gamma-delta T cell: -4.517",
+          "value: -6.851<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.215<br>central memory CD4-positive, alpha-beta T cell: -2.108<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.370<br>T follicular helper cell: -2.464<br>effector memory CD4-positive, alpha-beta T cell: -2.849",
+          "value: -5.211<br>classical monocyte: -1.289<br>CD14-positive monocyte: -1.904<br>macrophage: -2.821<br>monocyte: -2.886<br>alveolar macrophage: -2.992",
+          "value: -4.968<br>CD16-positive, CD56-dim natural killer cell, human: -0.591<br>natural killer cell: -1.435<br>CD16-negative, CD56-bright natural killer cell, human: -3.769<br>mature NK T cell: -4.022<br>activated type II NK T cell: -4.882",
+          "value: -6.816<br>classical monocyte: -1.556<br>CD14-positive monocyte: -1.646<br>CD1c-positive myeloid dendritic cell: -2.949<br>dendritic cell, human: -3.411<br>pvalb GABAergic cortical interneuron: -3.442",
+          "value: -5.699<br>CD16-positive, CD56-dim natural killer cell, human: -0.828<br>natural killer cell: -1.094<br>mature NK T cell: -3.668<br>CD16-negative, CD56-bright natural killer cell, human: -4.577<br>astrocyte of the cerebral cortex: -4.751",
+          "value: -5.883<br>CD14-positive monocyte: -1.449<br>classical monocyte: -1.816<br>monocyte: -2.538<br>pvalb GABAergic cortical interneuron: -2.981<br>macrophage: -3.806",
+          "value: -5.229<br>CD16-positive, CD56-dim natural killer cell, human: -0.821<br>natural killer cell: -2.079<br>CD16-negative, CD56-bright natural killer cell, human: -2.524<br>gamma-delta T cell: -3.141<br>mature NK T cell: -3.431",
+          "value: -5.031<br>CD16-positive, CD56-dim natural killer cell, human: -0.660<br>natural killer cell: -1.622<br>CD16-negative, CD56-bright natural killer cell, human: -2.849<br>mature NK T cell: -3.965<br>lymphocyte: -4.606",
+          "value: -5.395<br>natural killer cell: -1.235<br>CD16-positive, CD56-dim natural killer cell, human: -2.268<br>CD16-negative, CD56-bright natural killer cell, human: -2.801<br>mature NK T cell: -3.130<br>CD8-positive, alpha-beta T cell: -3.419",
+          "value: -5.879<br>CD16-negative, CD56-bright natural killer cell, human: -1.056<br>natural killer cell: -1.883<br>CD16-positive, CD56-dim natural killer cell, human: -2.396<br>mature NK T cell: -3.262<br>gamma-delta T cell: -3.385",
+          "value: -5.928<br>CD16-positive, CD56-dim natural killer cell, human: -1.107<br>natural killer cell: -1.664<br>CD16-negative, CD56-bright natural killer cell, human: -1.683<br>activated type II NK T cell: -3.851<br>mature NK T cell: -4.635",
+          "value: -5.842<br>classical monocyte: -1.303<br>CD14-positive monocyte: -1.427<br>pvalb GABAergic cortical interneuron: -3.026<br>monocyte: -3.778<br>promonocyte: -4.267",
+          "value: -7.551<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.612<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.641<br>central memory CD4-positive, alpha-beta T cell: -2.299<br>T follicular helper cell: -4.095<br>effector memory CD4-positive, alpha-beta T cell: -4.478",
+          "value: -5.449<br>natural killer cell: -1.001<br>CD16-positive, CD56-dim natural killer cell, human: -1.705<br>CD16-negative, CD56-bright natural killer cell, human: -2.087<br>activated type II NK T cell: -3.739<br>lymphocyte: -3.930",
+          "value: -7.179<br>CD16-negative, CD56-bright natural killer cell, human: -1.652<br>CD16-positive, CD56-dim natural killer cell, human: -2.506<br>gamma-delta T cell: -2.662<br>natural killer cell: -2.996<br>mature NK T cell: -3.602",
+          "value: -5.172<br>classical monocyte: -1.244<br>CD14-positive monocyte: -2.245<br>macrophage: -3.013<br>monocyte: -3.677<br>non-classical monocyte: -4.016",
+          "value: -5.720<br>CD16-positive, CD56-dim natural killer cell, human: -0.664<br>natural killer cell: -1.527<br>mature NK T cell: -3.428<br>CD16-negative, CD56-bright natural killer cell, human: -3.892<br>gamma-delta T cell: -4.535",
+          "value: -5.790<br>CD16-positive, CD56-dim natural killer cell, human: -0.621<br>natural killer cell: -1.835<br>CD16-negative, CD56-bright natural killer cell, human: -2.594<br>mature NK T cell: -4.491<br>gamma-delta T cell: -4.621",
+          "value: -6.343<br>CD16-positive, CD56-dim natural killer cell, human: -0.414<br>natural killer cell: -1.689<br>mature NK T cell: -4.285<br>CD16-negative, CD56-bright natural killer cell, human: -4.288<br>astrocyte of the cerebral cortex: -5.020",
+          "value: -6.002<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.922<br>central memory CD4-positive, alpha-beta T cell: -2.120<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.144<br>effector memory CD4-positive, alpha-beta T cell: -2.482<br>T follicular helper cell: -3.078",
+          "value: -5.093<br>classical monocyte: -1.015<br>CD14-positive monocyte: -2.188<br>macrophage: -2.669<br>pvalb GABAergic cortical interneuron: -3.034<br>monocyte: -3.391",
+          "value: -6.305<br>classical monocyte: -1.186<br>CD14-positive monocyte: -1.294<br>pvalb GABAergic cortical interneuron: -2.696<br>monocyte: -3.695<br>dendritic cell, human: -4.192",
+          "value: -5.564<br>effector memory CD4-positive, alpha-beta T cell: -2.195<br>T follicular helper cell: -2.483<br>T-helper 22 cell: -2.702<br>central memory CD4-positive, alpha-beta T cell: -2.780<br>effector memory CD8-positive, alpha-beta T cell: -3.426",
+          "value: -5.736<br>classical monocyte: -1.481<br>CD14-positive monocyte: -1.615<br>pvalb GABAergic cortical interneuron: -2.878<br>macrophage: -3.871<br>CD1c-positive myeloid dendritic cell: -3.873",
+          "value: -5.319<br>plasmacytoid dendritic cell: -0.210<br>plasmacytoid dendritic cell, human: -2.927<br>dendritic cell: -2.997<br>myeloid dendritic cell: -5.449<br>conventional dendritic cell: -5.538",
+          "value: -5.712<br>CD16-positive, CD56-dim natural killer cell, human: -0.490<br>natural killer cell: -1.677<br>CD16-negative, CD56-bright natural killer cell, human: -3.944<br>mature NK T cell: -4.148<br>gamma-delta T cell: -4.675",
+          "value: -6.225<br>effector memory CD4-positive, alpha-beta T cell: -1.773<br>central memory CD4-positive, alpha-beta T cell: -2.114<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.285<br>T-helper 22 cell: -2.527<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.920",
+          "value: -6.113<br>CD16-negative, CD56-bright natural killer cell, human: -1.008<br>natural killer cell: -2.885<br>gamma-delta T cell: -3.341<br>mature NK T cell: -3.850<br>innate lymphoid cell: -4.024",
+          "value: -5.366<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.751<br>central memory CD4-positive, alpha-beta T cell: -1.854<br>effector memory CD4-positive, alpha-beta T cell: -2.196<br>T-helper 22 cell: -2.339<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.038",
+          "value: -5.598<br>classical monocyte: -1.075<br>CD14-positive monocyte: -1.302<br>pvalb GABAergic cortical interneuron: -3.036<br>monocyte: -3.887<br>macrophage: -3.908",
+          "value: -6.462<br>CD16-positive, CD56-dim natural killer cell, human: -1.138<br>CD16-negative, CD56-bright natural killer cell, human: -1.678<br>natural killer cell: -1.679<br>activated type II NK T cell: -4.004<br>mature NK T cell: -4.247",
+          "value: -6.162<br>CD16-positive, CD56-dim natural killer cell, human: -0.491<br>natural killer cell: -1.845<br>CD16-negative, CD56-bright natural killer cell, human: -3.092<br>mature NK T cell: -4.428<br>activated type II NK T cell: -4.688",
+          "value: -6.028<br>central memory CD4-positive, alpha-beta T cell: -1.519<br>T follicular helper cell: -2.009<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.013<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.468<br>effector memory CD4-positive, alpha-beta T cell: -2.735",
+          "value: -6.195<br>CD16-positive, CD56-dim natural killer cell, human: -1.112<br>natural killer cell: -1.640<br>CD16-negative, CD56-bright natural killer cell, human: -2.274<br>gamma-delta T cell: -3.350<br>mature NK T cell: -3.676",
+          "value: -6.693<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.234<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.928<br>central memory CD4-positive, alpha-beta T cell: -2.351<br>T follicular helper cell: -2.460<br>effector memory CD4-positive, alpha-beta T cell: -3.404",
+          "value: -6.230<br>CD16-positive, CD56-dim natural killer cell, human: -1.200<br>natural killer cell: -1.440<br>CD16-negative, CD56-bright natural killer cell, human: -1.527<br>activated type II NK T cell: -4.328<br>mature NK T cell: -4.541",
+          "value: -6.238<br>CD16-positive, CD56-dim natural killer cell, human: -0.600<br>natural killer cell: -1.689<br>CD16-negative, CD56-bright natural killer cell, human: -3.085<br>mature NK T cell: -4.055<br>activated type II NK T cell: -4.633",
+          "value: -4.672<br>gamma-delta T cell: -2.684<br>effector memory CD8-positive, alpha-beta T cell: -2.742<br>mature NK T cell: -2.839<br>CD16-negative, CD56-bright natural killer cell, human: -2.962<br>effector CD8-positive, alpha-beta T cell: -3.164",
+          "value: -6.403<br>CD16-positive, CD56-dim natural killer cell, human: -0.494<br>natural killer cell: -2.138<br>CD16-negative, CD56-bright natural killer cell, human: -2.655<br>mature NK T cell: -4.088<br>gamma-delta T cell: -4.570",
+          "value: -6.144<br>CD16-positive, CD56-dim natural killer cell, human: -0.814<br>CD16-negative, CD56-bright natural killer cell, human: -1.725<br>natural killer cell: -1.885<br>activated type II NK T cell: -3.990<br>mature NK T cell: -4.699",
+          "value: -5.717<br>natural killer cell: -1.625<br>CD16-positive, CD56-dim natural killer cell, human: -1.931<br>CD16-negative, CD56-bright natural killer cell, human: -2.059<br>gamma-delta T cell: -3.488<br>mature NK T cell: -3.817",
+          "value: -6.229<br>CD16-positive, CD56-dim natural killer cell, human: -0.466<br>natural killer cell: -1.501<br>mature NK T cell: -4.211<br>CD16-negative, CD56-bright natural killer cell, human: -4.583<br>astrocyte of the cerebral cortex: -4.890",
+          "value: -6.007<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.295<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.506<br>central memory CD4-positive, alpha-beta T cell: -2.193<br>T follicular helper cell: -3.333<br>effector memory CD4-positive, alpha-beta T cell: -3.688",
+          "value: -4.252<br>CD16-negative, CD56-bright natural killer cell, human: -1.532<br>natural killer cell: -3.109<br>gamma-delta T cell: -3.331<br>innate lymphoid cell: -3.704<br>mature NK T cell: -3.744",
+          "value: -6.337<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.540<br>central memory CD4-positive, alpha-beta T cell: -1.805<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.217<br>effector memory CD4-positive, alpha-beta T cell: -2.357<br>T follicular helper cell: -3.048",
+          "value: -5.090<br>CD16-positive, CD56-dim natural killer cell, human: -0.917<br>natural killer cell: -2.111<br>CD16-negative, CD56-bright natural killer cell, human: -2.697<br>gamma-delta T cell: -2.740<br>CD8-positive, alpha-beta T cell: -3.396",
+          "value: -6.070<br>plasmacytoid dendritic cell: -0.403<br>dendritic cell: -2.290<br>plasmacytoid dendritic cell, human: -2.630<br>plasma cell: -4.264<br>conventional dendritic cell: -4.606",
+          "value: -6.668<br>CD1c-positive myeloid dendritic cell: -2.301<br>dendritic cell, human: -2.317<br>conventional dendritic cell: -2.439<br>classical monocyte: -2.451<br>CD14-positive monocyte: -2.711",
+          "value: -7.318<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.896<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.994<br>central memory CD4-positive, alpha-beta T cell: -2.175<br>effector memory CD4-positive, alpha-beta T cell: -3.112<br>T follicular helper cell: -3.593",
+          "value: -5.845<br>CD16-positive, CD56-dim natural killer cell, human: -0.618<br>natural killer cell: -1.648<br>CD16-negative, CD56-bright natural killer cell, human: -2.939<br>mature NK T cell: -4.077<br>activated type II NK T cell: -4.365",
+          "value: -4.970<br>gamma-delta T cell: -1.947<br>effector memory CD8-positive, alpha-beta T cell: -2.076<br>CD16-negative, CD56-bright natural killer cell, human: -2.512<br>effector CD8-positive, alpha-beta T cell: -2.773<br>mature NK T cell: -2.922",
+          "value: -6.512<br>CD16-negative, CD56-bright natural killer cell, human: -1.098<br>gamma-delta T cell: -2.727<br>effector memory CD8-positive, alpha-beta T cell: -3.184<br>mature NK T cell: -3.563<br>mucosal invariant T cell: -3.978",
+          "value: -5.396<br>central memory CD4-positive, alpha-beta T cell: -1.969<br>effector memory CD4-positive, alpha-beta T cell: -2.043<br>T follicular helper cell: -2.208<br>T-helper 22 cell: -2.626<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.979",
+          "value: -5.286<br>plasmacytoid dendritic cell: -0.438<br>dendritic cell: -2.368<br>plasmacytoid dendritic cell, human: -2.539<br>dendritic cell, human: -4.768<br>myeloid dendritic cell: -4.867",
+          "value: -4.521<br>classical monocyte: -1.334<br>CD14-positive monocyte: -2.265<br>monocyte: -2.777<br>macrophage: -2.892<br>pvalb GABAergic cortical interneuron: -3.464",
+          "value: -6.227<br>CD16-negative, CD56-bright natural killer cell, human: -0.856<br>CD16-positive, CD56-dim natural killer cell, human: -1.564<br>natural killer cell: -2.401<br>gamma-delta T cell: -4.139<br>mature NK T cell: -4.167",
+          "value: -5.843<br>CD16-positive, CD56-dim natural killer cell, human: -0.626<br>natural killer cell: -2.208<br>CD16-negative, CD56-bright natural killer cell, human: -2.431<br>mature NK T cell: -3.733<br>gamma-delta T cell: -4.028",
+          "value: -5.684<br>CD1c-positive myeloid dendritic cell: -2.182<br>conventional dendritic cell: -2.274<br>classical monocyte: -2.414<br>dendritic cell: -2.501<br>dendritic cell, human: -2.790",
+          "value: -6.836<br>CD14-positive monocyte: -0.876<br>classical monocyte: -1.519<br>pvalb GABAergic cortical interneuron: -2.966<br>monocyte: -4.050<br>macrophage: -4.203",
+          "value: -5.768<br>CD16-positive, CD56-dim natural killer cell, human: -0.775<br>natural killer cell: -2.092<br>CD16-negative, CD56-bright natural killer cell, human: -2.494<br>mature NK T cell: -3.583<br>immature NK T cell: -4.534",
+          "value: -4.790<br>central memory CD4-positive, alpha-beta T cell: -1.826<br>effector memory CD4-positive, alpha-beta T cell: -2.460<br>T-helper 22 cell: -2.511<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.599<br>T follicular helper cell: -2.728",
+          "value: -6.033<br>CD16-positive, CD56-dim natural killer cell, human: -1.409<br>natural killer cell: -1.559<br>CD16-negative, CD56-bright natural killer cell, human: -1.825<br>activated type II NK T cell: -3.807<br>mature NK T cell: -4.023",
+          "value: -6.131<br>CD16-negative, CD56-bright natural killer cell, human: -0.888<br>gamma-delta T cell: -2.407<br>CD16-positive, CD56-dim natural killer cell, human: -2.709<br>natural killer cell: -2.941<br>mature NK T cell: -3.924",
+          "value: -5.456<br>CD16-positive, CD56-dim natural killer cell, human: -0.629<br>natural killer cell: -1.578<br>mature NK T cell: -3.707<br>CD16-negative, CD56-bright natural killer cell, human: -4.037<br>astrocyte of the cerebral cortex: -4.846",
+          "value: -5.979<br>CD16-negative, CD56-bright natural killer cell, human: -0.796<br>natural killer cell: -2.420<br>CD16-positive, CD56-dim natural killer cell, human: -2.847<br>gamma-delta T cell: -2.888<br>mature NK T cell: -4.199",
+          "value: -4.982<br>natural killer cell: -1.949<br>CD16-negative, CD56-bright natural killer cell, human: -1.983<br>CD16-positive, CD56-dim natural killer cell, human: -2.615<br>gamma-delta T cell: -3.748<br>lymphocyte: -3.759",
+          "value: -4.534<br>plasmacytoid dendritic cell: -0.406<br>plasmacytoid dendritic cell, human: -2.624<br>dendritic cell: -2.984<br>conventional dendritic cell: -4.887<br>hematopoietic precursor cell: -4.923",
+          "value: -6.242<br>CD16-positive, CD56-dim natural killer cell, human: -1.109<br>CD16-negative, CD56-bright natural killer cell, human: -1.279<br>natural killer cell: -2.054<br>gamma-delta T cell: -4.093<br>mature NK T cell: -4.178",
+          "value: -5.892<br>gamma-delta T cell: -1.792<br>CD16-negative, CD56-bright natural killer cell, human: -2.006<br>effector memory CD8-positive, alpha-beta T cell: -2.423<br>CD16-positive, CD56-dim natural killer cell, human: -2.800<br>mature NK T cell: -3.074",
+          "value: -5.464<br>CD16-positive, CD56-dim natural killer cell, human: -0.899<br>natural killer cell: -1.458<br>CD16-negative, CD56-bright natural killer cell, human: -2.688<br>mature NK T cell: -4.173<br>activated type II NK T cell: -4.405",
+          "value: -5.901<br>CD16-positive, CD56-dim natural killer cell, human: -0.497<br>natural killer cell: -1.565<br>mature NK T cell: -3.643<br>CD16-negative, CD56-bright natural killer cell, human: -3.945<br>astrocyte of the cerebral cortex: -4.818",
+          "value: -6.282<br>CD16-positive, CD56-dim natural killer cell, human: -0.826<br>natural killer cell: -2.096<br>CD16-negative, CD56-bright natural killer cell, human: -2.576<br>gamma-delta T cell: -3.589<br>mature NK T cell: -3.697",
+          "value: -5.156<br>CD16-positive, CD56-dim natural killer cell, human: -1.015<br>natural killer cell: -1.912<br>CD16-negative, CD56-bright natural killer cell, human: -2.605<br>gamma-delta T cell: -3.130<br>mature NK T cell: -3.194",
+          "value: -6.309<br>CD16-positive, CD56-dim natural killer cell, human: -0.974<br>natural killer cell: -1.101<br>CD16-negative, CD56-bright natural killer cell, human: -3.582<br>mature NK T cell: -4.052<br>gamma-delta T cell: -4.304",
+          "value: -6.227<br>CD16-positive, CD56-dim natural killer cell, human: -0.707<br>natural killer cell: -2.132<br>CD16-negative, CD56-bright natural killer cell, human: -2.495<br>mature NK T cell: -3.480<br>gamma-delta T cell: -3.595",
+          "value: -5.801<br>CD16-positive, CD56-dim natural killer cell, human: -0.444<br>natural killer cell: -1.828<br>CD16-negative, CD56-bright natural killer cell, human: -3.193<br>mature NK T cell: -4.624<br>activated type II NK T cell: -4.861",
+          "value: -6.095<br>CD16-positive, CD56-dim natural killer cell, human: -0.533<br>natural killer cell: -2.288<br>CD16-negative, CD56-bright natural killer cell, human: -2.834<br>mature NK T cell: -3.843<br>gamma-delta T cell: -4.060",
+          "value: -5.424<br>plasmacytoid dendritic cell: -0.389<br>dendritic cell: -2.476<br>plasmacytoid dendritic cell, human: -2.919<br>myeloid dendritic cell: -4.678<br>B cell: -4.779",
+          "value: -6.554<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.450<br>central memory CD4-positive, alpha-beta T cell: -2.068<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.677<br>T follicular helper cell: -3.471<br>effector memory CD4-positive, alpha-beta T cell: -4.084",
+          "value: -5.355<br>CD16-positive, CD56-dim natural killer cell, human: -1.198<br>CD16-negative, CD56-bright natural killer cell, human: -1.615<br>natural killer cell: -1.667<br>mature NK T cell: -4.031<br>gamma-delta T cell: -4.116",
+          "value: -5.636<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.625<br>central memory CD4-positive, alpha-beta T cell: -1.910<br>T follicular helper cell: -2.517<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.562<br>effector memory CD4-positive, alpha-beta T cell: -2.671",
+          "value: -6.093<br>CD16-negative, CD56-bright natural killer cell, human: -1.291<br>gamma-delta T cell: -2.928<br>natural killer cell: -2.962<br>CD16-positive, CD56-dim natural killer cell, human: -3.794<br>innate lymphoid cell: -3.983",
+          "value: -5.929<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.847<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.165<br>central memory CD4-positive, alpha-beta T cell: -2.903<br>T follicular helper cell: -3.981<br>T cell: -4.045",
+          "value: -6.791<br>CD16-negative, CD56-bright natural killer cell, human: -0.987<br>natural killer cell: -3.156<br>CD16-positive, CD56-dim natural killer cell, human: -3.183<br>gamma-delta T cell: -3.503<br>activated type II NK T cell: -4.118",
+          "value: -6.433<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.985<br>central memory CD4-positive, alpha-beta T cell: -2.041<br>effector memory CD4-positive, alpha-beta T cell: -2.064<br>T-helper 22 cell: -2.581<br>T follicular helper cell: -3.173",
+          "value: -5.964<br>central memory CD4-positive, alpha-beta T cell: -1.459<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.732<br>effector memory CD4-positive, alpha-beta T cell: -2.213<br>T-helper 22 cell: -2.391<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.055",
+          "value: -5.973<br>T follicular helper cell: -1.696<br>central memory CD4-positive, alpha-beta T cell: -1.866<br>effector memory CD4-positive, alpha-beta T cell: -2.364<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.389<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.637",
+          "value: -5.737<br>dendritic cell: -2.687<br>mast cell: -3.000<br>hematopoietic precursor cell: -3.054<br>plasmacytoid dendritic cell: -3.073<br>dendritic cell, human: -3.344",
+          "value: -6.761<br>classical monocyte: -2.199<br>CD14-positive monocyte: -2.282<br>CD1c-positive myeloid dendritic cell: -2.581<br>dendritic cell, human: -2.976<br>conventional dendritic cell: -2.988",
+          "value: -5.603<br>CD16-negative, CD56-bright natural killer cell, human: -1.108<br>gamma-delta T cell: -2.734<br>natural killer cell: -2.786<br>effector memory CD8-positive, alpha-beta T cell: -3.208<br>mature NK T cell: -3.472",
+          "value: -6.022<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.027<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.034<br>central memory CD4-positive, alpha-beta T cell: -2.942<br>T cell: -4.058<br>granule cell: -4.734",
+          "value: -5.351<br>plasmacytoid dendritic cell: -0.354<br>dendritic cell: -2.520<br>plasmacytoid dendritic cell, human: -2.564<br>conventional dendritic cell: -5.115<br>myeloid dendritic cell: -5.294",
+          "value: -4.785<br>central memory CD4-positive, alpha-beta T cell: -2.077<br>T follicular helper cell: -2.563<br>effector memory CD4-positive, alpha-beta T cell: -2.567<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.964<br>T-helper 22 cell: -3.161",
+          "value: -6.956<br>CD16-positive, CD56-dim natural killer cell, human: -1.314<br>CD16-negative, CD56-bright natural killer cell, human: -1.729<br>natural killer cell: -2.498<br>gamma-delta T cell: -2.721<br>mature NK T cell: -3.578",
+          "value: -6.199<br>CD16-positive, CD56-dim natural killer cell, human: -0.596<br>natural killer cell: -1.539<br>CD16-negative, CD56-bright natural killer cell, human: -3.346<br>mature NK T cell: -3.980<br>activated type II NK T cell: -4.639",
+          "value: -4.939<br>CD16-negative, CD56-bright natural killer cell, human: -1.443<br>natural killer cell: -1.874<br>CD16-positive, CD56-dim natural killer cell, human: -2.699<br>gamma-delta T cell: -3.533<br>mature NK T cell: -3.926",
+          "value: -6.016<br>naive thymus-derived CD4-positive, alpha-beta T cell: -0.671<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.994<br>central memory CD4-positive, alpha-beta T cell: -2.112<br>T follicular helper cell: -3.401<br>effector memory CD4-positive, alpha-beta T cell: -3.981",
+          "value: -6.200<br>CD16-positive, CD56-dim natural killer cell, human: -0.551<br>natural killer cell: -1.662<br>CD16-negative, CD56-bright natural killer cell, human: -2.677<br>mature NK T cell: -4.385<br>activated type II NK T cell: -4.654",
+          "value: -5.516<br>CD16-positive, CD56-dim natural killer cell, human: -1.151<br>natural killer cell: -1.294<br>CD16-negative, CD56-bright natural killer cell, human: -2.450<br>mature NK T cell: -3.621<br>activated type II NK T cell: -4.289",
+          "value: -6.035<br>CD16-positive, CD56-dim natural killer cell, human: -1.328<br>CD16-negative, CD56-bright natural killer cell, human: -1.493<br>natural killer cell: -2.429<br>gamma-delta T cell: -3.118<br>mature NK T cell: -3.513",
+          "value: -6.079<br>CD14-positive monocyte: -1.143<br>classical monocyte: -1.467<br>pvalb GABAergic cortical interneuron: -2.929<br>monocyte: -3.001<br>promonocyte: -4.337",
+          "value: -5.852<br>CD16-positive, CD56-dim natural killer cell, human: -0.484<br>natural killer cell: -2.041<br>CD16-negative, CD56-bright natural killer cell, human: -4.028<br>gamma-delta T cell: -4.078<br>mature NK T cell: -4.173",
+          "value: -5.328<br>T follicular helper cell: -2.168<br>central memory CD4-positive, alpha-beta T cell: -2.785<br>effector memory CD4-positive, alpha-beta T cell: -2.892<br>naive thymus-derived CD8-positive, alpha-beta T cell: -3.605<br>T-helper 22 cell: -3.813",
+          "value: -6.403<br>CD16-negative, CD56-bright natural killer cell, human: -1.217<br>gamma-delta T cell: -3.222<br>natural killer cell: -3.324<br>effector memory CD8-positive, alpha-beta T cell: -3.716<br>innate lymphoid cell: -3.772",
+          "value: -5.427<br>plasmacytoid dendritic cell: -0.187<br>plasmacytoid dendritic cell, human: -3.083<br>dendritic cell: -3.182<br>myeloid dendritic cell: -5.648<br>dendritic cell, human: -5.957",
+          "value: -5.761<br>CD16-positive, CD56-dim natural killer cell, human: -0.374<br>natural killer cell: -2.095<br>mature NK T cell: -3.514<br>CD16-negative, CD56-bright natural killer cell, human: -4.711<br>astrocyte of the cerebral cortex: -4.990",
+          "value: -6.540<br>CD16-positive, CD56-dim natural killer cell, human: -0.638<br>natural killer cell: -1.288<br>CD16-negative, CD56-bright natural killer cell, human: -4.036<br>mature NK T cell: -4.111<br>activated type II NK T cell: -5.103",
+          "value: -4.610<br>central memory CD4-positive, alpha-beta T cell: -2.582<br>effector memory CD4-positive, alpha-beta T cell: -2.971<br>T follicular helper cell: -2.979<br>T-helper 22 cell: -3.337<br>T cell: -3.479",
+          "value: -5.689<br>central memory CD4-positive, alpha-beta T cell: -1.841<br>effector memory CD4-positive, alpha-beta T cell: -2.014<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.145<br>T follicular helper cell: -2.636<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.705",
+          "value: -5.846<br>CD16-negative, CD56-bright natural killer cell, human: -1.979<br>gamma-delta T cell: -2.083<br>effector memory CD8-positive, alpha-beta T cell: -2.477<br>effector CD8-positive, alpha-beta T cell: -3.231<br>mature NK T cell: -3.292",
+          "value: -5.160<br>CD16-positive, CD56-dim natural killer cell, human: -1.037<br>natural killer cell: -1.099<br>CD16-negative, CD56-bright natural killer cell, human: -2.671<br>mature NK T cell: -4.372<br>activated type II NK T cell: -4.797",
+          "value: -6.847<br>classical monocyte: -1.375<br>CD14-positive monocyte: -1.645<br>pvalb GABAergic cortical interneuron: -3.279<br>dendritic cell, human: -3.354<br>macrophage: -3.504",
+          "value: -5.986<br>non-classical monocyte: -1.240<br>classical monocyte: -2.492<br>CD14-low, CD16-positive monocyte: -2.531<br>mature NK T cell: -2.637<br>CD14-positive monocyte: -2.841",
+          "value: -5.756<br>CD16-positive, CD56-dim natural killer cell, human: -0.369<br>natural killer cell: -2.108<br>mature NK T cell: -4.089<br>CD16-negative, CD56-bright natural killer cell, human: -4.263<br>astrocyte of the cerebral cortex: -5.010",
+          "value: -5.904<br>CD14-positive monocyte: -0.874<br>classical monocyte: -1.304<br>pvalb GABAergic cortical interneuron: -3.295<br>monocyte: -3.902<br>neutrophil: -4.553",
+          "value: -4.495<br>plasmacytoid dendritic cell: -0.226<br>plasmacytoid dendritic cell, human: -2.837<br>dendritic cell: -3.208<br>plasma cell: -5.566<br>myeloid dendritic cell: -5.621",
+          "value: -7.401<br>CD16-negative, CD56-bright natural killer cell, human: -0.895<br>CD16-positive, CD56-dim natural killer cell, human: -2.237<br>natural killer cell: -2.663<br>activated type II NK T cell: -3.709<br>gamma-delta T cell: -3.981",
+          "value: -5.704<br>CD16-negative, CD56-bright natural killer cell, human: -0.766<br>natural killer cell: -2.628<br>CD16-positive, CD56-dim natural killer cell, human: -3.397<br>gamma-delta T cell: -3.499<br>mature NK T cell: -3.729",
+          "value: -6.064<br>CD16-positive, CD56-dim natural killer cell, human: -1.224<br>natural killer cell: -1.352<br>gamma-delta T cell: -2.917<br>mature NK T cell: -3.157<br>CD16-negative, CD56-bright natural killer cell, human: -3.831",
+          "value: -6.604<br>naive thymus-derived CD8-positive, alpha-beta T cell: -1.690<br>central memory CD4-positive, alpha-beta T cell: -1.775<br>naive thymus-derived CD4-positive, alpha-beta T cell: -1.996<br>T follicular helper cell: -1.996<br>effector memory CD4-positive, alpha-beta T cell: -2.961",
+          "value: -5.661<br>CD16-positive, CD56-dim natural killer cell, human: -0.839<br>natural killer cell: -1.518<br>CD16-negative, CD56-bright natural killer cell, human: -2.977<br>mature NK T cell: -3.875<br>activated type II NK T cell: -4.484",
+          "value: -6.683<br>CD16-positive, CD56-dim natural killer cell, human: -0.578<br>natural killer cell: -2.209<br>CD16-negative, CD56-bright natural killer cell, human: -2.401<br>mature NK T cell: -4.269<br>activated type II NK T cell: -4.788",
+          "value: -5.360<br>CD16-positive, CD56-dim natural killer cell, human: -0.526<br>natural killer cell: -2.232<br>mature NK T cell: -3.549<br>CD16-negative, CD56-bright natural killer cell, human: -3.741<br>gamma-delta T cell: -4.025",
+          "value: -5.895<br>CD16-positive, CD56-dim natural killer cell, human: -0.582<br>natural killer cell: -1.867<br>CD16-negative, CD56-bright natural killer cell, human: -3.369<br>mature NK T cell: -3.711<br>gamma-delta T cell: -4.356",
+          "value: -5.113<br>CD16-negative, CD56-bright natural killer cell, human: -1.004<br>gamma-delta T cell: -2.754<br>natural killer cell: -3.092<br>CD16-positive, CD56-dim natural killer cell, human: -3.329<br>effector memory CD8-positive, alpha-beta T cell: -3.493",
+          "value: -5.555<br>central memory CD4-positive, alpha-beta T cell: -1.778<br>effector memory CD4-positive, alpha-beta T cell: -2.230<br>naive thymus-derived CD4-positive, alpha-beta T cell: -2.433<br>T follicular helper cell: -2.516<br>naive thymus-derived CD8-positive, alpha-beta T cell: -2.672",
+          "value: -6.597<br>CD16-positive, CD56-dim natural killer cell, human: -0.392<br>natural killer cell: -2.268<br>CD16-negative, CD56-bright natural killer cell, human: -3.005<br>mature NK T cell: -4.756<br>activated type II NK T cell: -4.768",
+          "value: -5.805<br>CD16-positive, CD56-dim natural killer cell, human: -0.710<br>natural killer cell: -1.348<br>mature NK T cell: -3.609<br>CD16-negative, CD56-bright natural killer cell, human: -4.349<br>gamma-delta T cell: -4.488",
+          "value: -7.175<br>CD16-positive, CD56-dim natural killer cell, human: -0.589<br>natural killer cell: -2.469<br>CD16-negative, CD56-bright natural killer cell, human: -2.510<br>mature NK T cell: -3.860<br>activated type II NK T cell: -4.319",
+          "value: -5.336<br>CD16-negative, CD56-bright natural killer cell, human: -1.158<br>natural killer cell: -2.958<br>gamma-delta T cell: -3.043<br>mature NK T cell: -3.703<br>effector memory CD8-positive, alpha-beta T cell: -3.799",
+          "value: -2.369<br>natural killer cell: -1.689<br>CD16-positive, CD56-dim natural killer cell, human: -2.158<br>gamma-delta T cell: -2.250<br>CD16-negative, CD56-bright natural killer cell, human: -2.553<br>mature NK T cell: -2.935",
+          "value: -5.913<br>CD16-positive, CD56-dim natural killer cell, human: -0.605<br>natural killer cell: -1.607<br>CD16-negative, CD56-bright natural killer cell, human: -3.036<br>mature NK T cell: -4.282<br>activated type II NK T cell: -4.633",
+          "value: -6.613<br>CD16-positive, CD56-dim natural killer cell, human: -0.847<br>natural killer cell: -2.615<br>CD16-negative, CD56-bright natural killer cell, human: -2.743<br>gamma-delta T cell: -3.021<br>mature NK T cell: -3.239",
+          "value: -3.556<br>CD14-positive monocyte: -1.463<br>classical monocyte: -2.271<br>monocyte: -2.289<br>macrophage: -2.670<br>dendritic cell: -3.516",
+          "value: -5.288<br>CD8-positive, alpha-beta T cell: -1.624<br>mature NK T cell: -2.335<br>effector CD8-positive, alpha-beta T cell: -2.656<br>natural killer cell: -2.892<br>gamma-delta T cell: -3.198",
+          "value: -6.937<br>CD16-positive, CD56-dim natural killer cell, human: -0.668<br>natural killer cell: -2.633<br>CD16-negative, CD56-bright natural killer cell, human: -2.864<br>mature NK T cell: -3.277<br>gamma-delta T cell: -3.564",
+          "value: -5.873<br>classical monocyte: -0.808<br>CD14-positive monocyte: -2.356<br>alveolar macrophage: -3.781<br>monocyte: -3.802<br>macrophage: -3.991",
+          "value: -5.710<br>CD16-positive, CD56-dim natural killer cell, human: -0.524<br>natural killer cell: -1.633<br>mature NK T cell: -3.704<br>CD16-negative, CD56-bright natural killer cell, human: -3.844<br>astrocyte of the cerebral cortex: -4.811",
+          "value: -5.166<br>classical monocyte: -0.957<br>CD14-positive monocyte: -1.780<br>pvalb GABAergic cortical interneuron: -2.776<br>monocyte: -4.060<br>macrophage: -4.349"
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+        "xaxis": {
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-0.607\\u003cbr\\u003enatural killer cell: -1.959\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.204\\u003cbr\\u003emature NK T cell: -4.145\\u003cbr\\u003eactivated type II NK T cell: -4.480\",\"value: -6.363\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.369\\u003cbr\\u003enatural killer cell: -1.978\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.798\\u003cbr\\u003emature NK T cell: -4.179\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.999\",\"value: -7.078\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.294\\u003cbr\\u003enatural killer cell: -2.241\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.504\\u003cbr\\u003egamma-delta T cell: -2.676\\u003cbr\\u003emature NK T cell: -3.286\",\"value: -5.412\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.558\\u003cbr\\u003enatural killer cell: -1.764\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.394\\u003cbr\\u003emature NK T cell: -3.456\\u003cbr\\u003eastrocyte of the cerebral cortex: -3.963\",\"value: -6.504\\u003cbr\\u003eclassical monocyte: -1.212\\u003cbr\\u003eCD14-positive monocyte: -1.706\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.739\\u003cbr\\u003emonocyte: -3.786\\u003cbr\\u003emacrophage: -3.796\",\"value: -4.850\\u003cbr\\u003eT follicular helper cell: -2.301\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.452\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.525\\u003cbr\\u003eT-helper 22 cell: -3.223\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.959\",\"value: -5.612\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.485\\u003cbr\\u003enatural killer cell: -1.676\\u003cbr\\u003emature NK T cell: -3.824\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.362\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.685\",\"value: -6.510\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.285\\u003cbr\\u003enatural killer cell: -2.481\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.339\\u003cbr\\u003emature NK T cell: -4.366\\u003cbr\\u003egamma-delta T cell: -5.053\",\"value: -6.984\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.323\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.631\\u003cbr\\u003eT follicular helper cell: -1.950\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.867\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.314\",\"value: -6.284\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.958\\u003cbr\\u003egamma-delta T cell: -2.417\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.824\\u003cbr\\u003enatural killer cell: -2.876\\u003cbr\\u003emature NK T cell: -3.277\",\"value: -5.262\\u003cbr\\u003eplasmacytoid dendritic cell: -0.886\\u003cbr\\u003eB cell: -2.165\\u003cbr\\u003edendritic cell: -2.354\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.479\\u003cbr\\u003eplasma cell: -3.947\",\"value: -6.698\\u003cbr\\u003eclassical monocyte: -1.909\\u003cbr\\u003eCD14-positive monocyte: -2.127\\u003cbr\\u003edendritic cell, human: -2.771\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.946\\u003cbr\\u003edendritic cell: -3.312\",\"value: -5.866\\u003cbr\\u003enatural killer cell: -1.102\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.535\\u003cbr\\u003emature NK T cell: -2.751\\u003cbr\\u003egamma-delta T cell: -3.599\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -4.043\",\"value: -6.125\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.463\\u003cbr\\u003enatural killer cell: -1.655\\u003cbr\\u003emature NK T cell: -3.926\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.133\\u003cbr\\u003eactivated type II NK T cell: -5.171\",\"value: -6.296\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.559\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.848\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.761\\u003cbr\\u003eT cell: -4.075\\u003cbr\\u003eT follicular helper cell: -4.563\",\"value: -2.758\\u003cbr\\u003egamma-delta T cell: -1.934\\u003cbr\\u003eT cell: -2.295\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.150\\u003cbr\\u003emature NK T cell: -3.176\\u003cbr\\u003emucosal invariant T cell: -3.443\",\"value: -6.175\\u003cbr\\u003eCD14-positive monocyte: -0.998\\u003cbr\\u003eclassical monocyte: -1.784\\u003cbr\\u003emonocyte: -2.950\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.240\\u003cbr\\u003emacrophage: -3.668\",\"value: -5.869\\u003cbr\\u003eclassical monocyte: -1.098\\u003cbr\\u003eCD14-positive monocyte: -1.792\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.618\\u003cbr\\u003emacrophage: -3.831\\u003cbr\\u003emonocyte: -3.974\",\"value: -5.508\\u003cbr\\u003eplasmacytoid dendritic cell: -0.266\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.656\\u003cbr\\u003edendritic cell: -3.083\\u003cbr\\u003emyeloid dendritic cell: -5.225\\u003cbr\\u003edendritic cell, human: -5.404\",\"value: -5.137\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.064\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.167\\u003cbr\\u003eT follicular helper cell: -2.306\\u003cbr\\u003eT-helper 22 cell: -2.609\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.233\",\"value: -4.962\\u003cbr\\u003enatural killer cell: -1.018\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.118\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.049\\u003cbr\\u003emature NK T cell: -3.823\\u003cbr\\u003eactivated type II NK T cell: -4.403\",\"value: -6.270\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.371\\u003cbr\\u003enatural killer cell: -1.695\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.243\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.275\",\"value: -6.315\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.721\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.687\\u003cbr\\u003enatural killer cell: -2.525\\u003cbr\\u003egamma-delta T cell: -4.131\\u003cbr\\u003emature NK T cell: -4.408\",\"value: -6.437\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -1.952\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.146\\u003cbr\\u003emature NK T cell: -4.305\\u003cbr\\u003eactivated type II NK T cell: -4.803\",\"value: -5.648\\u003cbr\\u003eplasmacytoid dendritic cell: -0.187\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.909\\u003cbr\\u003edendritic cell: -3.326\\u003cbr\\u003emyeloid dendritic cell: -5.717\\u003cbr\\u003eB cell: -5.956\",\"value: -6.002\\u003cbr\\u003eplasmacytoid dendritic cell: -0.297\\u003cbr\\u003edendritic cell: -2.743\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.944\\u003cbr\\u003edendritic cell, human: -4.654\\u003cbr\\u003emyeloid dendritic cell: -4.667\",\"value: -6.060\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.256\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.616\\u003cbr\\u003enatural killer cell: -2.142\\u003cbr\\u003egamma-delta T cell: -3.362\\u003cbr\\u003emature NK T cell: -3.632\",\"value: -5.617\\u003cbr\\u003eplasmacytoid dendritic cell: -0.477\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.089\\u003cbr\\u003edendritic cell: -2.632\\u003cbr\\u003emyeloid dendritic cell: -4.838\\u003cbr\\u003edendritic cell, human: -4.989\",\"value: -6.794\\u003cbr\\u003eT follicular helper cell: -1.685\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.922\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.985\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.644\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.067\",\"value: -5.921\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.439\\u003cbr\\u003enatural killer cell: -1.860\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.625\\u003cbr\\u003emature NK T cell: -3.883\\u003cbr\\u003eactivated type II NK T cell: -5.008\",\"value: -6.052\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.624\\u003cbr\\u003enatural killer cell: -1.904\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.711\\u003cbr\\u003emature NK T cell: -3.862\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.787\",\"value: -5.648\\u003cbr\\u003enatural killer cell: -1.096\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.157\\u003cbr\\u003emature NK T cell: -3.349\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.423\\u003cbr\\u003egamma-delta T cell: -4.365\",\"value: -5.696\\u003cbr\\u003eplasmacytoid dendritic cell: -0.435\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.474\\u003cbr\\u003edendritic cell: -3.143\\u003cbr\\u003eB cell: -4.977\\u003cbr\\u003edendritic cell, human: -4.988\",\"value: -5.790\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003enatural killer cell: -1.696\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.993\\u003cbr\\u003emature NK T cell: -3.497\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.620\",\"value: -6.298\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.404\\u003cbr\\u003enatural killer cell: -2.350\\u003cbr\\u003egamma-delta T cell: -2.548\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.085\\u003cbr\\u003emature NK T cell: -3.486\",\"value: -5.973\\u003cbr\\u003eplasmacytoid dendritic cell: -0.313\\u003cbr\\u003edendritic cell: -2.804\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.884\\u003cbr\\u003eplasma cell: -4.125\\u003cbr\\u003econventional dendritic cell: -4.922\",\"value: -6.318\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.657\\u003cbr\\u003enatural killer cell: -1.585\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.792\\u003cbr\\u003emature NK T cell: -4.221\\u003cbr\\u003eactivated type II NK T cell: -4.603\",\"value: -6.829\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.302\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.670\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.683\\u003cbr\\u003eT follicular helper cell: -2.490\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.090\",\"value: -5.250\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.896\\u003cbr\\u003enatural killer cell: -1.955\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.121\\u003cbr\\u003emature NK T cell: -3.370\\u003cbr\\u003egamma-delta T cell: -3.522\",\"value: -6.264\\u003cbr\\u003eclassical monocyte: -0.993\\u003cbr\\u003eCD14-positive monocyte: -1.768\\u003cbr\\u003emacrophage: -3.446\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.480\\u003cbr\\u003emonocyte: -3.835\",\"value: -5.916\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.156\\u003cbr\\u003enatural killer cell: -2.625\\u003cbr\\u003egamma-delta T cell: -2.883\\u003cbr\\u003emature NK T cell: -3.154\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.291\",\"value: -5.312\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.732\\u003cbr\\u003enatural killer cell: -1.694\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.375\\u003cbr\\u003emature NK T cell: -3.892\\u003cbr\\u003egamma-delta T cell: -3.921\",\"value: -6.305\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.607\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.290\\u003cbr\\u003emature NK T cell: -4.301\\u003cbr\\u003eactivated type II NK T cell: -4.878\",\"value: -5.077\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.835\\u003cbr\\u003enatural killer cell: -1.105\\u003cbr\\u003emature NK T cell: -3.454\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.592\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.658\",\"value: -5.038\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003enatural killer cell: -1.335\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.671\\u003cbr\\u003emature NK T cell: -4.196\\u003cbr\\u003egamma-delta T cell: -4.595\",\"value: -5.853\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.685\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.164\\u003cbr\\u003egamma-delta T cell: -2.336\\u003cbr\\u003enatural killer cell: -2.628\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.697\",\"value: -6.047\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.740\\u003cbr\\u003enatural killer cell: -2.566\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.926\\u003cbr\\u003egamma-delta T cell: -3.366\\u003cbr\\u003emature NK T cell: -4.307\",\"value: -4.185\\u003cbr\\u003egamma-delta T cell: -2.114\\u003cbr\\u003enatural killer cell: -2.524\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.619\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.699\",\"value: -5.975\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.498\\u003cbr\\u003enatural killer cell: -1.575\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.034\\u003cbr\\u003emature NK T cell: -4.078\\u003cbr\\u003eactivated type II NK T cell: -4.936\",\"value: -6.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.168\\u003cbr\\u003enatural killer cell: -2.865\\u003cbr\\u003egamma-delta T cell: -3.099\\u003cbr\\u003emature NK T cell: -3.812\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.843\",\"value: -5.763\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.451\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.373\\u003cbr\\u003eT-helper 22 cell: -2.756\\u003cbr\\u003eT follicular helper cell: -3.566\",\"value: -6.947\\u003cbr\\u003edendritic cell: -1.178\\u003cbr\\u003econventional dendritic cell: -1.930\\u003cbr\\u003edendritic cell, human: -2.260\\u003cbr\\u003emyeloid dendritic cell: -2.277\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.407\",\"value: -5.833\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.497\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.933\\u003cbr\\u003enatural killer cell: -2.438\\u003cbr\\u003egamma-delta T cell: -2.609\\u003cbr\\u003emature NK T cell: -3.407\",\"value: -5.840\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.692\\u003cbr\\u003enatural killer cell: -2.326\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.250\\u003cbr\\u003emature NK T cell: -3.625\\u003cbr\\u003egamma-delta T cell: -3.863\",\"value: -5.260\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.848\\u003cbr\\u003enatural killer cell: -1.355\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.086\\u003cbr\\u003emature NK T cell: -3.655\\u003cbr\\u003egamma-delta T cell: -3.941\",\"value: -5.798\\u003cbr\\u003econventional dendritic cell: -1.728\\u003cbr\\u003edendritic cell: -2.094\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.267\\u003cbr\\u003emyeloid dendritic cell: -2.326\\u003cbr\\u003edendritic cell, human: -2.367\",\"value: -7.210\\u003cbr\\u003eCD14-positive monocyte: -1.215\\u003cbr\\u003eclassical monocyte: -2.266\\u003cbr\\u003emonocyte: -2.643\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.932\\u003cbr\\u003epromonocyte: -3.974\",\"value: -4.969\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.852\\u003cbr\\u003enatural killer cell: -2.754\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.500\\u003cbr\\u003eactivated type II NK T cell: -4.087\\u003cbr\\u003egamma-delta T cell: -4.230\",\"value: -5.296\\u003cbr\\u003eplasmacytoid dendritic cell: -0.481\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.478\\u003cbr\\u003edendritic cell: -2.721\\u003cbr\\u003eplasma cell: -3.470\\u003cbr\\u003econventional dendritic cell: -4.810\",\"value: -5.421\\u003cbr\\u003enatural killer cell: -1.363\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.404\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.631\\u003cbr\\u003emature NK T cell: -3.972\\u003cbr\\u003egamma-delta T cell: -4.159\",\"value: -5.590\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.894\\u003cbr\\u003egamma-delta T cell: -2.450\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.557\\u003cbr\\u003emature NK T cell: -2.678\\u003cbr\\u003enatural killer cell: -2.704\",\"value: -5.897\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.498\\u003cbr\\u003enatural killer cell: -2.751\\u003cbr\\u003egamma-delta T cell: -3.198\\u003cbr\\u003emature NK T cell: -3.843\",\"value: -6.086\\u003cbr\\u003eCD14-positive monocyte: -0.999\\u003cbr\\u003eclassical monocyte: -1.715\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.946\\u003cbr\\u003emonocyte: -2.972\\u003cbr\\u003epromonocyte: -4.388\",\"value: -6.655\\u003cbr\\u003eCD14-positive monocyte: -1.345\\u003cbr\\u003eclassical monocyte: -1.530\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.296\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.538\\u003cbr\\u003emacrophage: -3.650\",\"value: -6.108\\u003cbr\\u003eCD14-positive monocyte: -1.048\\u003cbr\\u003eclassical monocyte: -1.238\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.922\\u003cbr\\u003emonocyte: -3.664\\u003cbr\\u003emacrophage: -4.626\",\"value: -5.918\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.610\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.992\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.347\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.498\\u003cbr\\u003eT follicular helper cell: -3.139\",\"value: -5.093\\u003cbr\\u003eclassical monocyte: -1.060\\u003cbr\\u003eCD14-positive monocyte: -1.607\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.897\\u003cbr\\u003emonocyte: -3.590\\u003cbr\\u003emacrophage: -4.418\",\"value: -4.627\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.387\\u003cbr\\u003egamma-delta T cell: -2.375\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.443\\u003cbr\\u003enatural killer cell: -2.656\\u003cbr\\u003emature NK T cell: -3.011\",\"value: -4.813\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.974\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.592\\u003cbr\\u003eT-helper 22 cell: -2.729\\u003cbr\\u003emucosal invariant T cell: -2.829\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.081\",\"value: -5.370\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.331\\u003cbr\\u003enatural killer cell: -2.563\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.275\\u003cbr\\u003egamma-delta T cell: -4.350\",\"value: -5.905\\u003cbr\\u003eclassical monocyte: -1.143\\u003cbr\\u003eCD14-positive monocyte: -1.429\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.813\\u003cbr\\u003emonocyte: -3.704\\u003cbr\\u003emacrophage: -4.217\",\"value: -5.826\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.673\\u003cbr\\u003enatural killer cell: -1.415\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.306\\u003cbr\\u003eactivated type II NK T cell: -4.328\\u003cbr\\u003emature NK T cell: -4.423\",\"value: -4.178\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.934\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.132\\u003cbr\\u003eT follicular helper cell: -2.647\\u003cbr\\u003eT-helper 22 cell: -2.821\\u003cbr\\u003emature NK T cell: -3.374\",\"value: -6.914\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.699\\u003cbr\\u003enatural killer cell: -1.197\\u003cbr\\u003emature NK T cell: -4.045\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.205\\u003cbr\\u003eactivated type II NK T cell: -4.823\",\"value: -5.185\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.489\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.568\\u003cbr\\u003enatural killer cell: -2.539\\u003cbr\\u003egamma-delta T cell: -2.856\\u003cbr\\u003emature NK T cell: -3.432\",\"value: -6.427\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.005\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.217\\u003cbr\\u003enatural killer cell: -2.697\\u003cbr\\u003egamma-delta T cell: -4.060\\u003cbr\\u003emature NK T cell: -4.075\",\"value: -6.307\\u003cbr\\u003eT follicular helper cell: -1.699\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.097\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.247\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.643\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.958\",\"value: -5.424\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.695\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.886\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.530\\u003cbr\\u003eT-helper 22 cell: -2.909\\u003cbr\\u003eT follicular helper cell: -2.993\",\"value: -5.742\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.230\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.609\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.963\\u003cbr\\u003eT follicular helper cell: -2.919\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.368\",\"value: -4.612\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.371\\u003cbr\\u003enatural killer cell: -1.934\\u003cbr\\u003emature NK T cell: -2.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.898\\u003cbr\\u003egamma-delta T cell: -3.103\",\"value: -6.048\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.822\\u003cbr\\u003enatural killer cell: -2.544\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.656\\u003cbr\\u003emature NK T cell: -3.719\\u003cbr\\u003egamma-delta T cell: -3.801\",\"value: -5.894\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.411\\u003cbr\\u003enatural killer cell: -1.959\\u003cbr\\u003emature NK T cell: -3.520\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.591\\u003cbr\\u003egamma-delta T cell: -4.629\",\"value: -5.962\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.723\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.075\\u003cbr\\u003enatural killer cell: -2.291\\u003cbr\\u003emature NK T cell: -3.955\\u003cbr\\u003egamma-delta T cell: -4.228\",\"value: -6.445\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.534\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.056\\u003cbr\\u003enatural killer cell: -2.392\\u003cbr\\u003egamma-delta T cell: -2.663\\u003cbr\\u003emature NK T cell: -3.687\",\"value: -5.783\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.145\\u003cbr\\u003egamma-delta T cell: -2.205\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.834\\u003cbr\\u003enatural killer cell: -3.199\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.371\",\"value: -6.133\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.877\\u003cbr\\u003enatural killer cell: -1.048\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.302\\u003cbr\\u003emature NK T cell: -3.863\\u003cbr\\u003eactivated type II NK T cell: -4.798\",\"value: -6.492\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.326\\u003cbr\\u003enatural killer cell: -2.095\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.549\\u003cbr\\u003emature NK T cell: -4.346\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.129\",\"value: -5.530\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.837\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.212\\u003cbr\\u003eT follicular helper cell: -2.560\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.673\\u003cbr\\u003eT-helper 22 cell: -3.030\",\"value: -5.509\\u003cbr\\u003enon-classical monocyte: -2.023\\u003cbr\\u003eCD14-positive monocyte: -2.400\\u003cbr\\u003eclassical monocyte: -2.652\\u003cbr\\u003eCD14-low, CD16-positive monocyte: -2.841\\u003cbr\\u003emacrophage: -3.116\",\"value: -5.116\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.437\\u003cbr\\u003enatural killer cell: -3.333\\u003cbr\\u003egamma-delta T cell: -3.548\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.673\\u003cbr\\u003emature NK T cell: -3.775\",\"value: -5.711\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.489\\u003cbr\\u003enatural killer cell: -2.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.264\\u003cbr\\u003emature NK T cell: -3.865\\u003cbr\\u003egamma-delta T cell: -4.902\",\"value: -6.578\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.438\\u003cbr\\u003enatural killer cell: -1.548\\u003cbr\\u003emature NK T cell: -3.989\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.097\",\"value: -5.799\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.453\\u003cbr\\u003enatural killer cell: -2.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.782\\u003cbr\\u003egamma-delta T cell: -4.022\\u003cbr\\u003emature NK T cell: -4.037\",\"value: -5.918\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.664\\u003cbr\\u003enatural killer cell: -1.881\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.064\\u003cbr\\u003emature NK T cell: -3.803\\u003cbr\\u003eimmature NK T cell: -4.513\",\"value: -6.995\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.712\\u003cbr\\u003eT follicular helper cell: -1.789\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.068\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.797\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.460\",\"value: -6.586\\u003cbr\\u003eCD14-positive monocyte: -1.263\\u003cbr\\u003eclassical monocyte: -1.329\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.637\\u003cbr\\u003emonocyte: -3.407\\u003cbr\\u003emacrophage: -4.166\",\"value: -4.828\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.555\\u003cbr\\u003egamma-delta T cell: -2.517\\u003cbr\\u003enatural killer cell: -2.873\\u003cbr\\u003emature NK T cell: -3.042\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.314\",\"value: -5.526\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.095\\u003cbr\\u003enatural killer cell: -1.900\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.137\\u003cbr\\u003egamma-delta T cell: -3.411\\u003cbr\\u003emature NK T cell: -4.067\",\"value: -6.704\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -2.148\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.344\\u003cbr\\u003emature NK T cell: -4.538\\u003cbr\\u003eactivated type II NK T cell: -4.693\",\"value: -4.856\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.126\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.355\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.745\\u003cbr\\u003eT cell: -3.268\\u003cbr\\u003eT follicular helper cell: -4.256\",\"value: -5.744\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.373\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.507\\u003cbr\\u003emature NK T cell: -4.547\\u003cbr\\u003eactivated type II NK T cell: -4.629\",\"value: -6.772\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.654\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.826\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.022\\u003cbr\\u003eT follicular helper cell: -2.047\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.142\",\"value: -4.839\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.743\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.298\\u003cbr\\u003eT-helper 22 cell: -2.912\\u003cbr\\u003eT follicular helper cell: -2.916\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.520\",\"value: -5.632\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.711\\u003cbr\\u003enatural killer cell: -1.797\\u003cbr\\u003emature NK T cell: -3.004\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.106\\u003cbr\\u003egamma-delta T cell: -3.153\",\"value: -6.322\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.626\\u003cbr\\u003enatural killer cell: -1.353\\u003cbr\\u003emature NK T cell: -3.382\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.720\",\"value: -6.806\\u003cbr\\u003eclassical monocyte: -1.959\\u003cbr\\u003eCD14-positive monocyte: -2.129\\u003cbr\\u003edendritic cell, human: -2.449\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.759\\u003cbr\\u003emacrophage: -3.045\",\"value: -4.789\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.222\\u003cbr\\u003enatural killer cell: -1.485\\u003cbr\\u003emature NK T cell: -2.608\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.713\\u003cbr\\u003egamma-delta T cell: -2.980\",\"value: -6.845\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.043\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.157\\u003cbr\\u003egamma-delta T cell: -2.922\\u003cbr\\u003enatural killer cell: -2.954\\u003cbr\\u003emature NK T cell: -3.636\",\"value: -6.294\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.932\\u003cbr\\u003enatural killer cell: -1.327\\u003cbr\\u003emature NK T cell: -2.961\\u003cbr\\u003egamma-delta T cell: -3.675\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.246\",\"value: -5.988\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.355\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.857\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.376\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.757\\u003cbr\\u003eT-helper 22 cell: -2.907\",\"value: -5.607\\u003cbr\\u003eplasmacytoid dendritic cell: -0.383\\u003cbr\\u003edendritic cell: -2.676\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.693\\u003cbr\\u003econventional dendritic cell: -4.542\\u003cbr\\u003edendritic cell, human: -4.722\",\"value: -5.859\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.498\\u003cbr\\u003eT follicular helper cell: -2.002\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.291\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.380\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.453\",\"value: -6.953\\u003cbr\\u003eCD14-positive monocyte: -1.864\\u003cbr\\u003eclassical monocyte: -2.046\\u003cbr\\u003emacrophage: -2.904\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.051\\u003cbr\\u003edendritic cell, human: -3.091\",\"value: -3.902\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.362\\u003cbr\\u003eT cell: -2.751\\u003cbr\\u003eT follicular helper cell: -2.776\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.851\\u003cbr\\u003eT-helper 22 cell: -2.922\",\"value: -5.760\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.364\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.361\\u003cbr\\u003egamma-delta T cell: -2.525\\u003cbr\\u003enatural killer cell: -2.598\\u003cbr\\u003emature NK T cell: -3.294\",\"value: -7.357\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.932\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.373\\u003cbr\\u003enatural killer cell: -2.586\\u003cbr\\u003emature NK T cell: -3.396\\u003cbr\\u003egamma-delta T cell: -3.617\",\"value: -4.500\\u003cbr\\u003eCD14-positive monocyte: -1.439\\u003cbr\\u003eclassical monocyte: -1.821\\u003cbr\\u003emacrophage: -2.083\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.276\\u003cbr\\u003ealveolar macrophage: -3.489\",\"value: -5.867\\u003cbr\\u003eplasmacytoid dendritic cell: -0.214\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.984\\u003cbr\\u003edendritic cell: -3.361\\u003cbr\\u003edendritic cell, human: -5.709\\u003cbr\\u003eB cell: -5.733\",\"value: -5.673\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.155\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.357\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.465\\u003cbr\\u003eT cell: -3.911\\u003cbr\\u003egranule cell: -4.426\",\"value: -4.475\\u003cbr\\u003eT cell: -2.724\\u003cbr\\u003emast cell: -2.888\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.948\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.015\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.478\",\"value: -5.260\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.795\\u003cbr\\u003enatural killer cell: -1.978\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.410\\u003cbr\\u003emature NK T cell: -3.496\\u003cbr\\u003egamma-delta T cell: -3.708\",\"value: -6.310\\u003cbr\\u003eCD14-positive monocyte: -1.073\\u003cbr\\u003eclassical monocyte: -1.516\\u003cbr\\u003enon-classical monocyte: -3.564\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.721\\u003cbr\\u003emacrophage: -3.728\",\"value: -6.820\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.145\\u003cbr\\u003enatural killer cell: -2.365\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.799\\u003cbr\\u003emature NK T cell: -3.821\\u003cbr\\u003eactivated type II NK T cell: -4.101\",\"value: -4.760\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.926\\u003cbr\\u003enatural killer cell: -3.067\\u003cbr\\u003egamma-delta T cell: -3.598\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.667\\u003cbr\\u003einnate lymphoid cell: -3.977\",\"value: -5.416\\u003cbr\\u003eplasmacytoid dendritic cell: -0.357\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.536\\u003cbr\\u003edendritic cell: -2.916\\u003cbr\\u003eB cell: -4.835\\u003cbr\\u003econventional dendritic cell: -5.118\",\"value: -4.345\\u003cbr\\u003eclassical monocyte: -1.122\\u003cbr\\u003eCD14-positive monocyte: -2.174\\u003cbr\\u003emacrophage: -3.035\\u003cbr\\u003emonocyte: -3.159\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.647\",\"value: -5.988\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.273\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.594\\u003cbr\\u003enatural killer cell: -2.069\\u003cbr\\u003emature NK T cell: -3.726\\u003cbr\\u003egamma-delta T cell: -3.854\",\"value: -5.618\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.828\\u003cbr\\u003enatural killer cell: -2.522\\u003cbr\\u003egamma-delta T cell: -3.067\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.138\\u003cbr\\u003einnate lymphoid cell: -4.063\",\"value: -6.100\\u003cbr\\u003eCD14-positive monocyte: -1.372\\u003cbr\\u003eclassical monocyte: -1.654\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.959\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.182\\u003cbr\\u003emonocyte: -3.297\",\"value: -6.995\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.105\\u003cbr\\u003enatural killer cell: -2.202\\u003cbr\\u003eactivated type II NK T cell: -4.114\\u003cbr\\u003emature NK T cell: -4.130\",\"value: -6.261\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.619\\u003cbr\\u003enatural killer cell: -1.202\\u003cbr\\u003emature NK T cell: -4.158\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.772\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.095\",\"value: -4.083\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.085\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.347\\u003cbr\\u003enatural killer cell: -2.116\\u003cbr\\u003egamma-delta T cell: -3.772\\u003cbr\\u003emature NK T cell: -4.163\",\"value: -6.441\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.432\\u003cbr\\u003enatural killer cell: -1.950\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.174\\u003cbr\\u003emature NK T cell: -4.752\\u003cbr\\u003eactivated type II NK T cell: -4.861\",\"value: -6.232\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.937\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.607\\u003cbr\\u003enatural killer cell: -2.486\\u003cbr\\u003egamma-delta T cell: -3.120\\u003cbr\\u003emature NK T cell: -4.301\",\"value: -5.190\\u003cbr\\u003eCD14-positive monocyte: -0.894\\u003cbr\\u003eclassical monocyte: -1.832\\u003cbr\\u003emonocyte: -2.966\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.295\\u003cbr\\u003epromonocyte: -4.022\",\"value: -5.607\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.635\\u003cbr\\u003enatural killer cell: -1.549\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.596\\u003cbr\\u003emature NK T cell: -4.204\\u003cbr\\u003egamma-delta T cell: -4.517\",\"value: -4.525\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.655\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.753\\u003cbr\\u003enatural killer cell: -2.203\\u003cbr\\u003egamma-delta T cell: -2.658\\u003cbr\\u003emature NK T cell: -3.103\",\"value: -5.687\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.408\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.696\\u003cbr\\u003enatural killer cell: -1.838\\u003cbr\\u003emature NK T cell: -3.369\\u003cbr\\u003egamma-delta T cell: -3.620\",\"value: -5.534\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.043\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.270\\u003cbr\\u003egamma-delta T cell: -3.336\\u003cbr\\u003enatural killer cell: -3.380\\u003cbr\\u003emature NK T cell: -3.741\",\"value: -6.017\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -2.409\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.670\\u003cbr\\u003egamma-delta T cell: -3.153\\u003cbr\\u003emature NK T cell: -3.715\",\"value: -6.500\\u003cbr\\u003edendritic cell, human: -2.054\\u003cbr\\u003econventional dendritic cell: -2.125\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.276\\u003cbr\\u003edendritic cell: -2.731\\u003cbr\\u003emyeloid dendritic cell: -3.388\",\"value: -5.563\\u003cbr\\u003eT follicular helper cell: -1.995\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.036\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.557\\u003cbr\\u003eT-helper 22 cell: -3.130\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.592\",\"value: -5.713\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.937\\u003cbr\\u003enatural killer cell: -1.630\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.254\\u003cbr\\u003eactivated type II NK T cell: -4.279\\u003cbr\\u003emature NK T cell: -4.318\",\"value: -6.354\\u003cbr\\u003edendritic cell: -1.714\\u003cbr\\u003econventional dendritic cell: -1.798\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.193\\u003cbr\\u003edendritic cell, human: -2.237\\u003cbr\\u003emyeloid dendritic cell: -2.686\",\"value: -7.262\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.879\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.387\\u003cbr\\u003enatural killer cell: -3.433\\u003cbr\\u003eactivated type II NK T cell: -4.034\",\"value: -5.323\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.143\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.622\\u003cbr\\u003enatural killer cell: -2.118\\u003cbr\\u003emature NK T cell: -3.496\\u003cbr\\u003egamma-delta T cell: -3.778\",\"value: -5.092\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.590\\u003cbr\\u003enatural killer cell: -1.826\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.421\\u003cbr\\u003emature NK T cell: -4.252\\u003cbr\\u003eactivated type II NK T cell: -4.714\",\"value: -6.523\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.114\\u003cbr\\u003enatural killer cell: -2.208\\u003cbr\\u003egamma-delta T cell: -3.521\\u003cbr\\u003emature NK T cell: -3.676\",\"value: -4.493\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.224\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.895\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.515\\u003cbr\\u003eT follicular helper cell: -3.084\\u003cbr\\u003eT cell: -3.342\",\"value: -6.561\\u003cbr\\u003eCD14-positive monocyte: -1.844\\u003cbr\\u003eclassical monocyte: -2.143\\u003cbr\\u003edendritic cell, human: -2.676\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.322\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.755\",\"value: -5.606\\u003cbr\\u003eT follicular helper cell: -2.424\\u003cbr\\u003eregulatory T cell: -2.845\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.851\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.892\\u003cbr\\u003eT cell: -2.911\",\"value: -5.355\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.918\\u003cbr\\u003enatural killer cell: -2.152\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.157\\u003cbr\\u003egamma-delta T cell: -3.751\\u003cbr\\u003emature NK T cell: -4.068\",\"value: -6.765\\u003cbr\\u003eCD14-positive monocyte: -1.115\\u003cbr\\u003eclassical monocyte: -1.509\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.932\\u003cbr\\u003emonocyte: -3.151\\u003cbr\\u003epromonocyte: -4.281\",\"value: -5.574\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.473\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.042\\u003cbr\\u003emature NK T cell: -4.489\\u003cbr\\u003egamma-delta T cell: -4.564\",\"value: -5.572\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.725\\u003cbr\\u003enatural killer cell: -1.401\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.390\\u003cbr\\u003emature NK T cell: -4.229\\u003cbr\\u003eactivated type II NK T cell: -4.506\",\"value: -4.550\\u003cbr\\u003eT follicular helper cell: -2.346\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.493\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.580\\u003cbr\\u003eT-helper 22 cell: -2.698\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.812\",\"value: -5.334\\u003cbr\\u003enatural killer cell: -0.899\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.909\\u003cbr\\u003emature NK T cell: -4.228\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -4.449\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.451\",\"value: -6.311\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.593\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.639\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.374\\u003cbr\\u003eT-helper 22 cell: -2.601\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.638\",\"value: -6.199\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.037\\u003cbr\\u003enatural killer cell: -1.966\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.800\\u003cbr\\u003egamma-delta T cell: -3.441\\u003cbr\\u003emature NK T cell: -3.957\",\"value: -6.465\\u003cbr\\u003eclassical monocyte: -1.542\\u003cbr\\u003eCD14-positive monocyte: -2.619\\u003cbr\\u003edendritic cell, human: -2.636\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.135\\u003cbr\\u003econventional dendritic cell: -3.367\",\"value: -5.635\\u003cbr\\u003eplasmacytoid dendritic cell: -0.238\\u003cbr\\u003edendritic cell: -3.127\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.254\\u003cbr\\u003eB cell: -5.226\\u003cbr\\u003emyeloid dendritic cell: -5.584\",\"value: -6.181\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.572\\u003cbr\\u003enatural killer cell: -1.477\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.785\\u003cbr\\u003emature NK T cell: -4.370\\u003cbr\\u003eactivated type II NK T cell: -4.851\",\"value: -5.213\\u003cbr\\u003eplasmacytoid dendritic cell: -0.707\\u003cbr\\u003edendritic cell: -2.344\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.479\\u003cbr\\u003eplasma cell: -3.900\\u003cbr\\u003edendritic cell, human: -4.766\",\"value: -6.121\\u003cbr\\u003enatural killer cell: -0.785\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.117\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.936\\u003cbr\\u003emature NK T cell: -4.167\\u003cbr\\u003elymphocyte: -4.762\",\"value: -5.659\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.850\\u003cbr\\u003enatural killer cell: -1.906\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.178\\u003cbr\\u003emature NK T cell: -3.909\\u003cbr\\u003egamma-delta T cell: -4.326\",\"value: -4.554\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003enatural killer cell: -1.678\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.358\\u003cbr\\u003egamma-delta T cell: -3.916\\u003cbr\\u003emature NK T cell: -4.096\",\"value: -4.759\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.254\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.005\\u003cbr\\u003enatural killer cell: -2.607\\u003cbr\\u003egamma-delta T cell: -2.762\\u003cbr\\u003emature NK T cell: -3.455\",\"value: -5.817\\u003cbr\\u003eplasmacytoid dendritic cell: -0.385\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.806\\u003cbr\\u003edendritic cell: -2.807\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -4.916\\u003cbr\\u003econventional dendritic cell: -4.946\",\"value: -6.421\\u003cbr\\u003eclassical monocyte: -1.289\\u003cbr\\u003eCD14-positive monocyte: -1.515\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.026\\u003cbr\\u003edendritic cell, human: -4.126\\u003cbr\\u003emonocyte: -4.140\",\"value: -5.637\\u003cbr\\u003econventional dendritic cell: -1.531\\u003cbr\\u003edendritic cell, human: -2.180\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.290\\u003cbr\\u003edendritic cell: -2.355\\u003cbr\\u003emyeloid dendritic cell: -2.586\",\"value: -6.268\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.683\\u003cbr\\u003enatural killer cell: -2.680\\u003cbr\\u003eactivated type II NK T cell: -3.935\\u003cbr\\u003egroup 3 innate lymphoid cell: -4.039\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.093\",\"value: -5.580\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.771\\u003cbr\\u003enatural killer cell: -1.610\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.621\\u003cbr\\u003emature NK T cell: -4.032\\u003cbr\\u003eactivated type II NK T cell: -4.470\",\"value: -5.489\\u003cbr\\u003eplasmacytoid dendritic cell: -0.288\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.713\\u003cbr\\u003edendritic cell: -3.168\\u003cbr\\u003eB cell: -5.217\\u003cbr\\u003econventional dendritic cell: -5.244\",\"value: -6.158\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.360\\u003cbr\\u003enatural killer cell: -1.884\\u003cbr\\u003emature NK T cell: -3.965\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.683\\u003cbr\\u003egamma-delta T cell: -4.881\",\"value: -6.017\\u003cbr\\u003eclassical monocyte: -1.174\\u003cbr\\u003eCD14-positive monocyte: -1.560\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.863\\u003cbr\\u003emonocyte: -3.588\\u003cbr\\u003emacrophage: -4.238\",\"value: -5.826\\u003cbr\\u003eCD14-positive monocyte: -0.787\\u003cbr\\u003eclassical monocyte: -1.553\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.378\\u003cbr\\u003emonocyte: -3.658\\u003cbr\\u003emacrophage: -4.417\",\"value: -6.004\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.650\\u003cbr\\u003enatural killer cell: -1.380\\u003cbr\\u003emature NK T cell: -3.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.315\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.787\",\"value: -6.797\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.896\\u003cbr\\u003egamma-delta T cell: -2.830\\u003cbr\\u003enatural killer cell: -3.456\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.721\\u003cbr\\u003emucosal invariant T cell: -3.851\",\"value: -5.694\\u003cbr\\u003eplasmacytoid dendritic cell: -0.251\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.733\\u003cbr\\u003edendritic cell: -2.982\\u003cbr\\u003emyeloid dendritic cell: -5.299\\u003cbr\\u003edendritic cell, human: -5.399\",\"value: -5.671\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.560\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.801\\u003cbr\\u003emature NK T cell: -4.287\\u003cbr\\u003eactivated type II NK T cell: -4.461\",\"value: -6.024\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.433\\u003cbr\\u003enatural killer cell: -1.955\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.640\\u003cbr\\u003emature NK T cell: -4.015\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.767\",\"value: -6.403\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.920\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.756\\u003cbr\\u003enatural killer cell: -1.970\\u003cbr\\u003eactivated type II NK T cell: -3.975\\u003cbr\\u003emature NK T cell: -4.214\",\"value: -5.406\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.355\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.341\\u003cbr\\u003eactivated type II NK T cell: -3.859\\u003cbr\\u003emature NK T cell: -4.154\",\"value: -6.640\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.036\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.656\\u003cbr\\u003enatural killer cell: -2.269\\u003cbr\\u003emature NK T cell: -3.688\\u003cbr\\u003egamma-delta T cell: -3.766\",\"value: -4.819\\u003cbr\\u003eB cell: -0.431\\u003cbr\\u003enaive B cell: -3.040\\u003cbr\\u003eclass switched memory B cell: -3.118\\u003cbr\\u003eunswitched memory B cell: -3.774\\u003cbr\\u003ememory B cell: -4.027\",\"value: -5.831\\u003cbr\\u003eplasmacytoid dendritic cell: -0.390\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.488\\u003cbr\\u003edendritic cell: -2.849\\u003cbr\\u003econventional dendritic cell: -4.695\\u003cbr\\u003edendritic cell, human: -4.720\",\"value: -5.586\\u003cbr\\u003eT follicular helper cell: -1.528\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.910\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.658\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.725\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.858\",\"value: -5.773\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.581\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.539\\u003cbr\\u003emature NK T cell: -4.365\\u003cbr\\u003eactivated type II NK T cell: -4.760\",\"value: -6.650\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.653\\u003cbr\\u003eT follicular helper cell: -1.727\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.453\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.561\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.584\",\"value: -5.216\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.911\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.463\\u003cbr\\u003eT follicular helper cell: -2.749\\u003cbr\\u003eT-helper 22 cell: -2.901\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.986\",\"value: -6.135\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.797\\u003cbr\\u003enatural killer cell: -1.186\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.458\\u003cbr\\u003emature NK T cell: -3.905\\u003cbr\\u003egamma-delta T cell: -4.676\",\"value: -5.986\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.911\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.580\\u003cbr\\u003egamma-delta T cell: -2.735\\u003cbr\\u003enatural killer cell: -2.970\\u003cbr\\u003emature NK T cell: -3.869\",\"value: -6.429\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.329\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.842\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.439\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.745\\u003cbr\\u003eT follicular helper cell: -2.787\",\"value: -6.399\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.079\\u003cbr\\u003enatural killer cell: -2.452\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.701\\u003cbr\\u003egamma-delta T cell: -3.125\\u003cbr\\u003emature NK T cell: -3.767\",\"value: -5.542\\u003cbr\\u003eCD14-positive monocyte: -0.707\\u003cbr\\u003eclassical monocyte: -1.645\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.311\\u003cbr\\u003emonocyte: -3.390\\u003cbr\\u003emacrophage: -4.419\",\"value: -5.780\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.441\\u003cbr\\u003egamma-delta T cell: -2.064\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.039\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.323\\u003cbr\\u003emucosal invariant T cell: -3.559\",\"value: -4.873\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.730\\u003cbr\\u003enatural killer cell: -1.870\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.875\\u003cbr\\u003egamma-delta T cell: -3.545\\u003cbr\\u003emature NK T cell: -3.646\",\"value: -5.958\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.328\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.421\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.600\\u003cbr\\u003emature NK T cell: -2.752\\u003cbr\\u003egamma-delta T cell: -3.063\",\"value: -6.942\\u003cbr\\u003eclassical monocyte: -1.543\\u003cbr\\u003eCD14-positive monocyte: -1.703\\u003cbr\\u003edendritic cell, human: -2.988\\u003cbr\\u003emacrophage: -3.258\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.354\",\"value: -5.708\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.895\\u003cbr\\u003enatural killer cell: -1.794\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.629\\u003cbr\\u003emature NK T cell: -3.415\\u003cbr\\u003egamma-delta T cell: -4.492\",\"value: -6.614\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.021\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.217\\u003cbr\\u003egamma-delta T cell: -2.666\\u003cbr\\u003enatural killer cell: -2.674\\u003cbr\\u003emature NK T cell: -3.132\",\"value: -5.779\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.399\\u003cbr\\u003egamma-delta T cell: -2.868\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.647\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.685\\u003cbr\\u003enatural killer cell: -3.735\",\"value: -6.284\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.489\\u003cbr\\u003enatural killer cell: -1.666\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.662\\u003cbr\\u003emature NK T cell: -4.530\\u003cbr\\u003eactivated type II NK T cell: -4.846\",\"value: -6.401\\u003cbr\\u003eCD14-positive monocyte: -1.768\\u003cbr\\u003eclassical monocyte: -1.782\\u003cbr\\u003edendritic cell, human: -3.108\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.112\\u003cbr\\u003emacrophage: -3.303\",\"value: -5.098\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.617\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.954\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.600\\u003cbr\\u003eT-helper 22 cell: -2.888\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.038\",\"value: -5.602\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.706\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.803\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.260\\u003cbr\\u003eT follicular helper cell: -3.571\\u003cbr\\u003eT cell: -4.039\",\"value: -6.264\\u003cbr\\u003eclassical monocyte: -1.125\\u003cbr\\u003eCD14-positive monocyte: -1.410\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.069\\u003cbr\\u003emacrophage: -3.927\\u003cbr\\u003emonocyte: -4.039\",\"value: -5.733\\u003cbr\\u003enatural killer cell: -0.937\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.098\\u003cbr\\u003emature NK T cell: -3.853\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.369\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.735\",\"value: -7.119\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.618\\u003cbr\\u003enatural killer cell: -2.016\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.453\\u003cbr\\u003emature NK T cell: -3.514\\u003cbr\\u003egamma-delta T cell: -4.637\",\"value: -5.997\\u003cbr\\u003eT follicular helper cell: -2.475\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.656\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.856\\u003cbr\\u003eT-helper 22 cell: -2.977\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.455\",\"value: -6.494\\u003cbr\\u003eT follicular helper cell: -1.975\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.041\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.555\\u003cbr\\u003eT-helper 22 cell: -2.591\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.526\",\"value: -4.684\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.043\\u003cbr\\u003enatural killer cell: -2.398\\u003cbr\\u003egamma-delta T cell: -3.436\\u003cbr\\u003einnate lymphoid cell: -4.113\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.213\",\"value: -6.133\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.255\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.413\\u003cbr\\u003enatural killer cell: -1.912\\u003cbr\\u003egamma-delta T cell: -3.501\\u003cbr\\u003emature NK T cell: -3.645\",\"value: -5.653\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.560\\u003cbr\\u003enatural killer cell: -1.680\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.533\\u003cbr\\u003emature NK T cell: -3.888\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.686\",\"value: -5.005\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.785\\u003cbr\\u003enatural killer cell: -2.113\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.570\\u003cbr\\u003egamma-delta T cell: -3.659\\u003cbr\\u003emature NK T cell: -4.113\",\"value: -5.743\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.173\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.405\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.512\\u003cbr\\u003eT follicular helper cell: -2.538\\u003cbr\\u003eT-helper 22 cell: -2.662\",\"value: -5.956\\u003cbr\\u003eplasmacytoid dendritic cell: -0.329\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.773\\u003cbr\\u003edendritic cell: -2.978\\u003cbr\\u003eB cell: -5.236\\u003cbr\\u003emyeloid dendritic cell: -5.244\",\"value: -4.540\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.755\\u003cbr\\u003enatural killer cell: -1.693\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.104\\u003cbr\\u003emature NK T cell: -3.491\\u003cbr\\u003elymphocyte: -4.710\",\"value: -5.238\\u003cbr\\u003eB cell: -0.714\\u003cbr\\u003enaive B cell: -2.023\\u003cbr\\u003emalignant cell: -3.131\\u003cbr\\u003eclass switched memory B cell: -3.550\\u003cbr\\u003eunswitched memory B cell: -3.756\",\"value: -5.985\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.135\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.036\\u003cbr\\u003enatural killer cell: -2.387\\u003cbr\\u003egamma-delta T cell: -3.394\\u003cbr\\u003emature NK T cell: -3.614\",\"value: -5.815\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.472\\u003cbr\\u003enatural killer cell: -2.149\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.074\\u003cbr\\u003emature NK T cell: -3.820\\u003cbr\\u003egamma-delta T cell: -4.755\",\"value: -6.174\\u003cbr\\u003eclassical monocyte: -1.234\\u003cbr\\u003eCD14-positive monocyte: -1.689\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.927\\u003cbr\\u003emacrophage: -3.467\\u003cbr\\u003emonocyte: -3.827\",\"value: -4.399\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.125\\u003cbr\\u003enatural killer cell: -1.659\\u003cbr\\u003emature NK T cell: -2.510\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.804\\u003cbr\\u003egamma-delta T cell: -2.856\",\"value: -7.097\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.872\\u003cbr\\u003enatural killer cell: -2.107\\u003cbr\\u003emature NK T cell: -4.074\\u003cbr\\u003eactivated type II NK T cell: -4.148\",\"value: -5.423\\u003cbr\\u003eplasmacytoid dendritic cell: -0.360\\u003cbr\\u003edendritic cell: -2.381\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.749\\u003cbr\\u003econventional dendritic cell: -4.607\\u003cbr\\u003emyeloid dendritic cell: -4.691\",\"value: -6.154\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.318\\u003cbr\\u003enatural killer cell: -2.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.616\\u003cbr\\u003emature NK T cell: -4.497\\u003cbr\\u003eactivated type II NK T cell: -5.366\",\"value: -6.877\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.528\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.609\\u003cbr\\u003eT follicular helper cell: -1.737\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.750\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.184\",\"value: -6.495\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.867\\u003cbr\\u003egamma-delta T cell: -2.976\\u003cbr\\u003enatural killer cell: -3.150\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.820\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.988\",\"value: -5.547\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.863\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.976\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.001\\u003cbr\\u003eT-helper 22 cell: -2.792\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.948\",\"value: -6.344\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -2.169\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.407\\u003cbr\\u003emature NK T cell: -4.221\\u003cbr\\u003egamma-delta T cell: -4.417\",\"value: -5.943\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.941\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.333\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.676\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.721\\u003cbr\\u003eT cell: -3.095\",\"value: -6.845\\u003cbr\\u003eCD14-positive monocyte: -0.740\\u003cbr\\u003eclassical monocyte: -2.064\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.975\\u003cbr\\u003emonocyte: -3.162\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -4.521\",\"value: -5.370\\u003cbr\\u003eCD14-positive monocyte: -2.069\\u003cbr\\u003eclassical monocyte: -2.174\\u003cbr\\u003econventional dendritic cell: -2.727\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.790\\u003cbr\\u003edendritic cell: -3.068\",\"value: -5.505\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -2.880\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.136\\u003cbr\\u003egamma-delta T cell: -3.202\\u003cbr\\u003emature NK T cell: -3.615\",\"value: -5.797\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.715\\u003cbr\\u003enatural killer cell: -1.237\\u003cbr\\u003emature NK T cell: -3.921\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.625\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.725\",\"value: -5.576\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.200\\u003cbr\\u003enatural killer cell: -1.538\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.929\\u003cbr\\u003emature NK T cell: -3.633\\u003cbr\\u003egamma-delta T cell: -3.989\",\"value: -5.540\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.022\\u003cbr\\u003enatural killer cell: -1.733\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.219\\u003cbr\\u003emature NK T cell: -4.100\\u003cbr\\u003egamma-delta T cell: -4.231\",\"value: -5.051\\u003cbr\\u003enatural killer cell: -0.555\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.155\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.513\\u003cbr\\u003elymphocyte: -4.323\\u003cbr\\u003emature NK T cell: -4.510\",\"value: -4.320\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.599\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.733\\u003cbr\\u003eT follicular helper cell: -2.782\\u003cbr\\u003emature NK T cell: -3.055\\u003cbr\\u003eT-helper 22 cell: -3.181\",\"value: -5.965\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.850\\u003cbr\\u003enatural killer cell: -2.369\\u003cbr\\u003emature NK T cell: -3.658\\u003cbr\\u003egamma-delta T cell: -4.084\",\"value: -6.530\\u003cbr\\u003eclassical monocyte: -1.121\\u003cbr\\u003eCD14-positive monocyte: -1.900\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.504\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.569\\u003cbr\\u003emonocyte: -3.656\",\"value: -6.164\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.181\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.480\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.886\\u003cbr\\u003eT follicular helper cell: -2.896\\u003cbr\\u003eT cell: -4.355\",\"value: -6.665\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.090\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.803\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.961\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.418\\u003cbr\\u003eT follicular helper cell: -3.493\",\"value: -6.022\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.423\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.088\\u003cbr\\u003enatural killer cell: -2.355\\u003cbr\\u003egamma-delta T cell: -3.154\\u003cbr\\u003emature NK T cell: -3.216\",\"value: -5.018\\u003cbr\\u003eplasmacytoid dendritic cell: -0.437\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.354\\u003cbr\\u003edendritic cell: -3.553\\u003cbr\\u003emast cell: -4.687\\u003cbr\\u003ehematopoietic precursor cell: -5.041\",\"value: -6.651\\u003cbr\\u003eCD14-positive monocyte: -1.330\\u003cbr\\u003eclassical monocyte: -1.407\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.060\\u003cbr\\u003edendritic cell, human: -3.761\\u003cbr\\u003emacrophage: -3.866\",\"value: -6.490\\u003cbr\\u003eclassical monocyte: -1.255\\u003cbr\\u003eCD14-positive monocyte: -1.511\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.049\\u003cbr\\u003edendritic cell, human: -3.660\\u003cbr\\u003emacrophage: -3.667\",\"value: -4.615\\u003cbr\\u003eclassical monocyte: -1.288\\u003cbr\\u003emacrophage: -2.300\\u003cbr\\u003eCD14-positive monocyte: -2.608\\u003cbr\\u003ealveolar macrophage: -3.103\\u003cbr\\u003emonocyte: -3.515\",\"value: -6.261\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.718\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.542\\u003cbr\\u003emature NK T cell: -4.361\\u003cbr\\u003eactivated type II NK T cell: -4.992\",\"value: -6.008\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.398\\u003cbr\\u003enatural killer cell: -2.314\\u003cbr\\u003emature NK T cell: -3.699\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.726\\u003cbr\\u003egamma-delta T cell: -4.778\",\"value: -6.250\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.603\\u003cbr\\u003enatural killer cell: -2.240\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.263\\u003cbr\\u003emature NK T cell: -3.830\\u003cbr\\u003egamma-delta T cell: -3.988\",\"value: -5.147\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.101\\u003cbr\\u003enatural killer cell: -2.076\\u003cbr\\u003emature NK T cell: -2.765\\u003cbr\\u003egamma-delta T cell: -3.119\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.407\",\"value: -5.643\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.856\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.907\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.370\\u003cbr\\u003eT follicular helper cell: -2.737\\u003cbr\\u003eT-helper 22 cell: -3.268\",\"value: -6.743\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.665\\u003cbr\\u003enatural killer cell: -2.442\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.624\\u003cbr\\u003emature NK T cell: -3.725\\u003cbr\\u003egamma-delta T cell: -4.378\",\"value: -5.936\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.554\\u003cbr\\u003enatural killer cell: -1.782\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.954\\u003cbr\\u003emature NK T cell: -4.484\\u003cbr\\u003eactivated type II NK T cell: -4.693\",\"value: -6.770\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.609\\u003cbr\\u003eT follicular helper cell: -1.861\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.899\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.959\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.091\",\"value: -5.614\\u003cbr\\u003eplasmacytoid dendritic cell: -0.220\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.963\\u003cbr\\u003edendritic cell: -3.280\\u003cbr\\u003emyeloid dendritic cell: -5.582\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -5.891\",\"value: -6.184\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.734\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.661\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.422\\u003cbr\\u003eT follicular helper cell: -4.076\\u003cbr\\u003eT cell: -4.206\",\"value: -4.567\\u003cbr\\u003enatural killer cell: -0.632\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.112\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.699\\u003cbr\\u003elymphocyte: -3.367\\u003cbr\\u003emature NK T cell: -4.570\",\"value: -5.526\\u003cbr\\u003eplasmacytoid dendritic cell: -0.211\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.894\\u003cbr\\u003edendritic cell: -3.190\\u003cbr\\u003eB cell: -5.613\\u003cbr\\u003emyeloid dendritic cell: -5.942\",\"value: -5.187\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.842\\u003cbr\\u003enatural killer cell: -1.221\\u003cbr\\u003emature NK T cell: -3.671\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.111\\u003cbr\\u003egamma-delta T cell: -4.557\",\"value: -5.019\\u003cbr\\u003eB cell: -2.102\\u003cbr\\u003edendritic cell: -2.334\\u003cbr\\u003econventional dendritic cell: -2.440\\u003cbr\\u003eplasmacytoid dendritic cell: -2.987\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.210\",\"value: -5.551\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.829\\u003cbr\\u003enatural killer cell: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.874\\u003cbr\\u003emature NK T cell: -3.298\\u003cbr\\u003egamma-delta T cell: -3.959\",\"value: -6.693\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.174\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.690\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.393\\u003cbr\\u003eT follicular helper cell: -2.463\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.989\",\"value: -5.651\\u003cbr\\u003ehematopoietic precursor cell: -2.801\\u003cbr\\u003edendritic cell: -2.942\\u003cbr\\u003edendritic cell, human: -3.236\\u003cbr\\u003ehematopoietic stem cell: -3.256\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.408\",\"value: -5.994\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.471\\u003cbr\\u003enatural killer cell: -1.815\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.699\\u003cbr\\u003emature NK T cell: -4.067\\u003cbr\\u003egamma-delta T cell: -4.867\",\"value: -5.656\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.345\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.595\\u003cbr\\u003enatural killer cell: -1.603\\u003cbr\\u003egamma-delta T cell: -3.782\\u003cbr\\u003emature NK T cell: -4.013\",\"value: -6.995\\u003cbr\\u003eCD14-positive monocyte: -2.290\\u003cbr\\u003eclassical monocyte: -2.358\\u003cbr\\u003edendritic cell, human: -2.519\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.535\\u003cbr\\u003econventional dendritic cell: -2.921\",\"value: -7.148\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.226\\u003cbr\\u003enatural killer cell: -2.197\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.167\\u003cbr\\u003eactivated type II NK T cell: -3.866\\u003cbr\\u003emature NK T cell: -4.028\",\"value: -5.931\\u003cbr\\u003eplasmacytoid dendritic cell: -0.390\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.705\\u003cbr\\u003edendritic cell: -2.955\\u003cbr\\u003emyeloid dendritic cell: -5.020\\u003cbr\\u003eB cell: -5.106\",\"value: -5.693\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.336\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.528\\u003cbr\\u003enatural killer cell: -3.540\\u003cbr\\u003egamma-delta T cell: -3.561\\u003cbr\\u003einnate lymphoid cell: -3.718\",\"value: -5.470\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.926\\u003cbr\\u003enatural killer cell: -2.437\\u003cbr\\u003egamma-delta T cell: -2.906\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.018\\u003cbr\\u003emature NK T cell: -3.580\",\"value: -5.587\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.774\\u003cbr\\u003enatural killer cell: -1.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.096\\u003cbr\\u003emature NK T cell: -3.727\\u003cbr\\u003egamma-delta T cell: -3.985\",\"value: -6.287\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.733\\u003cbr\\u003eT follicular helper cell: -1.862\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.295\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.676\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.023\",\"value: -5.844\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.847\\u003cbr\\u003enatural killer cell: -1.992\\u003cbr\\u003emature NK T cell: -3.135\\u003cbr\\u003egamma-delta T cell: -3.393\",\"value: -5.276\\u003cbr\\u003eT follicular helper cell: -2.009\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.386\\u003cbr\\u003eT-helper 22 cell: -3.303\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -3.454\\u003cbr\\u003emature NK T cell: -3.503\",\"value: -5.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.982\\u003cbr\\u003egamma-delta T cell: -2.952\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.156\\u003cbr\\u003enatural killer cell: -3.197\\u003cbr\\u003emature NK T cell: -3.508\",\"value: -5.727\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.419\\u003cbr\\u003enatural killer cell: -1.844\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.280\\u003cbr\\u003emature NK T cell: -4.214\\u003cbr\\u003egamma-delta T cell: -5.048\",\"value: -6.512\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.446\\u003cbr\\u003enatural killer cell: -1.557\\u003cbr\\u003emature NK T cell: -3.901\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.964\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -5.038\",\"value: -6.395\\u003cbr\\u003eCD14-positive monocyte: -0.742\\u003cbr\\u003eclassical monocyte: -2.242\\u003cbr\\u003emonocyte: -2.963\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.272\\u003cbr\\u003epromonocyte: -4.346\",\"value: -4.993\\u003cbr\\u003eclassical monocyte: -1.515\\u003cbr\\u003eCD14-positive monocyte: -1.861\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.758\\u003cbr\\u003emonocyte: -3.630\\u003cbr\\u003emacrophage: -3.960\",\"value: -4.565\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.140\\u003cbr\\u003eT follicular helper cell: -2.661\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.746\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.829\\u003cbr\\u003eT-helper 22 cell: -2.905\",\"value: -6.084\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.175\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.532\\u003cbr\\u003enatural killer cell: -1.629\\u003cbr\\u003eactivated type II NK T cell: -4.065\\u003cbr\\u003egamma-delta T cell: -4.421\",\"value: -5.742\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.661\\u003cbr\\u003enatural killer cell: -1.311\\u003cbr\\u003emature NK T cell: -3.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.743\\u003cbr\\u003eactivated type II NK T cell: -4.751\",\"value: -4.328\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -2.691\\u003cbr\\u003eT cell: -2.714\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.252\\u003cbr\\u003eB cell: -3.416\\u003cbr\\u003eregulatory T cell: -3.546\",\"value: -5.501\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.519\\u003cbr\\u003enatural killer cell: -1.482\\u003cbr\\u003emature NK T cell: -3.954\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.991\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.845\",\"value: -6.392\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.995\\u003cbr\\u003egamma-delta T cell: -3.097\\u003cbr\\u003enatural killer cell: -3.215\\u003cbr\\u003emature NK T cell: -3.990\",\"value: -5.499\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.588\\u003cbr\\u003enatural killer cell: -1.484\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.859\\u003cbr\\u003emature NK T cell: -3.915\\u003cbr\\u003eactivated type II NK T cell: -5.092\",\"value: -5.560\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.696\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.002\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.519\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.944\\u003cbr\\u003eT-helper 22 cell: -3.009\",\"value: -6.558\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.351\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.688\\u003cbr\\u003enatural killer cell: -2.290\\u003cbr\\u003egamma-delta T cell: -2.887\\u003cbr\\u003emature NK T cell: -3.630\",\"value: -6.691\\u003cbr\\u003eT follicular helper cell: -1.811\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.176\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.252\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.500\\u003cbr\\u003eT-helper 22 cell: -3.255\",\"value: -5.920\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.778\\u003cbr\\u003egamma-delta T cell: -2.762\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.091\\u003cbr\\u003enatural killer cell: -3.094\\u003cbr\\u003emature NK T cell: -3.749\",\"value: -4.923\\u003cbr\\u003ehematopoietic precursor cell: -1.245\\u003cbr\\u003ehematopoietic stem cell: -2.963\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -3.161\\u003cbr\\u003emast cell: -3.943\\u003cbr\\u003eprogenitor cell: -4.184\",\"value: -6.747\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.380\\u003cbr\\u003enatural killer cell: -2.146\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.277\\u003cbr\\u003emature NK T cell: -4.149\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.089\",\"value: -6.779\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.997\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.701\\u003cbr\\u003enatural killer cell: -2.122\\u003cbr\\u003eactivated type II NK T cell: -4.030\\u003cbr\\u003emature NK T cell: -4.222\",\"value: -7.064\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.117\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.364\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.289\\u003cbr\\u003eT follicular helper cell: -3.297\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.731\",\"value: -6.034\\u003cbr\\u003eclassical monocyte: -2.051\\u003cbr\\u003eCD14-positive monocyte: -2.120\\u003cbr\\u003enon-classical monocyte: -2.850\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.433\\u003cbr\\u003emature NK T cell: -3.821\",\"value: -6.082\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.372\\u003cbr\\u003enatural killer cell: -2.006\\u003cbr\\u003emature NK T cell: -3.929\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.059\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.899\",\"value: -5.171\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -1.514\\u003cbr\\u003emucosal invariant T cell: -2.057\\u003cbr\\u003egamma-delta T cell: -2.267\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.310\\u003cbr\\u003emature NK T cell: -3.067\",\"value: -5.887\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.459\\u003cbr\\u003enatural killer cell: -1.582\\u003cbr\\u003emature NK T cell: -4.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.348\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.973\",\"value: -5.831\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.345\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.363\\u003cbr\\u003emature NK T cell: -4.426\\u003cbr\\u003eactivated type II NK T cell: -4.922\",\"value: -5.696\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.957\\u003cbr\\u003enatural killer cell: -1.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.945\\u003cbr\\u003emature NK T cell: -4.110\\u003cbr\\u003eactivated type II NK T cell: -4.206\",\"value: -5.239\\u003cbr\\u003eplasmacytoid dendritic cell: -0.223\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.829\\u003cbr\\u003edendritic cell: -3.137\\u003cbr\\u003eB cell: -5.483\\u003cbr\\u003eplasma cell: -5.542\",\"value: -5.545\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.758\\u003cbr\\u003enatural killer cell: -1.875\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.214\\u003cbr\\u003emature NK T cell: -3.534\\u003cbr\\u003egamma-delta T cell: -4.444\",\"value: -6.117\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.265\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.018\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.230\\u003cbr\\u003eT follicular helper cell: -3.229\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.265\",\"value: -6.230\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.438\\u003cbr\\u003enatural killer cell: -1.757\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.981\\u003cbr\\u003emature NK T cell: -4.070\\u003cbr\\u003egamma-delta T cell: -5.021\",\"value: -4.219\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.023\\u003cbr\\u003eT-helper 22 cell: -2.282\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.689\\u003cbr\\u003eT follicular helper cell: -2.881\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.258\",\"value: -6.741\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.978\\u003cbr\\u003enatural killer cell: -1.119\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.247\\u003cbr\\u003emature NK T cell: -4.144\\u003cbr\\u003eactivated type II NK T cell: -4.213\",\"value: -5.108\\u003cbr\\u003edendritic cell: -1.602\\u003cbr\\u003edendritic cell, human: -2.508\\u003cbr\\u003eplasmacytoid dendritic cell: -2.523\\u003cbr\\u003econventional dendritic cell: -2.654\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.719\",\"value: -6.234\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.003\\u003cbr\\u003egamma-delta T cell: -2.463\\u003cbr\\u003enatural killer cell: -2.530\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.556\\u003cbr\\u003emature NK T cell: -2.712\",\"value: -6.090\\u003cbr\\u003eT follicular helper cell: -1.803\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.257\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.586\\u003cbr\\u003eT-helper 22 cell: -3.217\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.484\",\"value: -5.150\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.933\\u003cbr\\u003enatural killer cell: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.840\\u003cbr\\u003emature NK T cell: -3.207\\u003cbr\\u003egamma-delta T cell: -3.778\",\"value: -5.432\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.203\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.390\\u003cbr\\u003enatural killer cell: -2.481\\u003cbr\\u003egamma-delta T cell: -3.085\\u003cbr\\u003emature NK T cell: -3.471\",\"value: -6.817\\u003cbr\\u003eclassical monocyte: -1.292\\u003cbr\\u003eCD14-positive monocyte: -1.487\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.246\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.379\\u003cbr\\u003edendritic cell, human: -3.775\",\"value: -5.806\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.579\\u003cbr\\u003enatural killer cell: -1.571\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.617\\u003cbr\\u003emature NK T cell: -4.190\\u003cbr\\u003eactivated type II NK T cell: -4.608\",\"value: -6.475\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.736\\u003cbr\\u003enatural killer cell: -2.353\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.473\\u003cbr\\u003egamma-delta T cell: -3.536\\u003cbr\\u003emature NK T cell: -3.675\",\"value: -6.679\\u003cbr\\u003eCD14-positive monocyte: -0.860\\u003cbr\\u003eclassical monocyte: -1.377\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.681\\u003cbr\\u003emonocyte: -4.225\\u003cbr\\u003emacrophage: -4.285\",\"value: -5.542\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.723\\u003cbr\\u003eT follicular helper cell: -2.235\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.396\\u003cbr\\u003eT-helper 22 cell: -2.420\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.034\",\"value: -5.656\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.089\\u003cbr\\u003enatural killer cell: -1.501\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.457\\u003cbr\\u003egamma-delta T cell: -3.256\\u003cbr\\u003emature NK T cell: -3.839\",\"value: -5.839\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.194\\u003cbr\\u003enatural killer cell: -1.837\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.183\\u003cbr\\u003egamma-delta T cell: -2.836\\u003cbr\\u003emature NK T cell: -3.430\",\"value: -5.401\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.700\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.677\\u003cbr\\u003enatural killer cell: -2.827\\u003cbr\\u003egamma-delta T cell: -3.518\\u003cbr\\u003eactivated type II NK T cell: -4.201\",\"value: -4.027\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.023\\u003cbr\\u003enatural killer cell: -1.055\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.012\\u003cbr\\u003emature NK T cell: -3.911\\u003cbr\\u003egamma-delta T cell: -4.397\",\"value: -6.528\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.413\\u003cbr\\u003enatural killer cell: -1.841\\u003cbr\\u003emature NK T cell: -4.099\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.768\",\"value: -6.568\\u003cbr\\u003eclassical monocyte: -1.156\\u003cbr\\u003eCD14-positive monocyte: -1.775\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.858\\u003cbr\\u003emonocyte: -3.816\\u003cbr\\u003edendritic cell, human: -4.010\",\"value: -5.584\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.412\\u003cbr\\u003enatural killer cell: -1.864\\u003cbr\\u003emature NK T cell: -3.962\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.092\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.094\",\"value: -6.127\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.829\\u003cbr\\u003enatural killer cell: -2.575\\u003cbr\\u003egamma-delta T cell: -3.359\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.966\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -4.187\",\"value: -5.896\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.649\\u003cbr\\u003enatural killer cell: -1.786\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.490\\u003cbr\\u003eactivated type II NK T cell: -4.384\\u003cbr\\u003emature NK T cell: -4.464\",\"value: -5.353\\u003cbr\\u003enatural killer cell: -0.811\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.339\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.863\\u003cbr\\u003emature NK T cell: -4.167\\u003cbr\\u003eactivated type II NK T cell: -4.542\",\"value: -5.952\\u003cbr\\u003eT follicular helper cell: -1.765\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.823\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.070\\u003cbr\\u003eT-helper 22 cell: -2.397\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.623\",\"value: -6.214\\u003cbr\\u003eCD14-positive monocyte: -1.103\\u003cbr\\u003eclassical monocyte: -1.355\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.061\\u003cbr\\u003emonocyte: -3.740\\u003cbr\\u003emacrophage: -4.170\",\"value: -6.485\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.518\\u003cbr\\u003enatural killer cell: -2.013\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.555\\u003cbr\\u003emature NK T cell: -4.388\\u003cbr\\u003eactivated type II NK T cell: -4.546\",\"value: -6.145\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.945\\u003cbr\\u003enatural killer cell: -1.218\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.626\\u003cbr\\u003emature NK T cell: -3.795\\u003cbr\\u003egamma-delta T cell: -4.598\",\"value: -6.415\\u003cbr\\u003eclassical monocyte: -2.102\\u003cbr\\u003eCD14-positive monocyte: -2.390\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.526\\u003cbr\\u003edendritic cell, human: -2.725\\u003cbr\\u003enon-classical monocyte: -2.869\",\"value: -6.343\\u003cbr\\u003eclassical monocyte: -1.408\\u003cbr\\u003eCD14-positive monocyte: -1.637\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.429\\u003cbr\\u003emacrophage: -3.677\\u003cbr\\u003emonocyte: -3.921\",\"value: -3.768\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.551\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.295\\u003cbr\\u003eT-helper 22 cell: -2.866\\u003cbr\\u003emature NK T cell: -3.187\\u003cbr\\u003eT follicular helper cell: -3.378\",\"value: -3.592\\u003cbr\\u003enatural killer cell: -0.804\\u003cbr\\u003elymphocyte: -3.317\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.433\\u003cbr\\u003eT cell: -3.837\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.954\",\"value: -4.813\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.839\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.857\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -1.944\\u003cbr\\u003eT follicular helper cell: -2.810\\u003cbr\\u003eT-helper 22 cell: -2.903\",\"value: -7.674\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.202\\u003cbr\\u003egamma-delta T cell: -3.341\\u003cbr\\u003enatural killer cell: -3.428\\u003cbr\\u003einnate lymphoid cell: -3.627\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.718\",\"value: -5.596\\u003cbr\\u003eplasmacytoid dendritic cell: -0.207\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.940\\u003cbr\\u003edendritic cell: -3.225\\u003cbr\\u003edendritic cell, human: -5.549\\u003cbr\\u003emyeloid dendritic cell: -5.662\",\"value: -6.192\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.793\\u003cbr\\u003enatural killer cell: -2.396\\u003cbr\\u003egamma-delta T cell: -2.626\\u003cbr\\u003emature NK T cell: -3.145\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.474\",\"value: -6.092\\u003cbr\\u003eclassical monocyte: -1.551\\u003cbr\\u003eCD14-positive monocyte: -1.694\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.756\\u003cbr\\u003emonocyte: -3.975\\u003cbr\\u003edendritic cell, human: -4.325\",\"value: -4.854\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.165\\u003cbr\\u003enatural killer cell: -2.110\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.737\\u003cbr\\u003emast cell: -3.610\\u003cbr\\u003einnate lymphoid cell: -3.646\",\"value: -6.681\\u003cbr\\u003eclassical monocyte: -1.055\\u003cbr\\u003eCD14-positive monocyte: -1.525\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.558\\u003cbr\\u003emacrophage: -3.751\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.889\",\"value: -6.587\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.542\\u003cbr\\u003enatural killer cell: -1.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.385\\u003cbr\\u003emature NK T cell: -4.288\\u003cbr\\u003eactivated type II NK T cell: -4.833\",\"value: -6.025\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.488\\u003cbr\\u003enatural killer cell: -1.572\\u003cbr\\u003emature NK T cell: -4.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.699\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.870\",\"value: -7.184\\u003cbr\\u003eclassical monocyte: -2.037\\u003cbr\\u003eCD14-positive monocyte: -2.286\\u003cbr\\u003edendritic cell, human: -2.388\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.865\\u003cbr\\u003econventional dendritic cell: -3.027\",\"value: -6.046\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.542\\u003cbr\\u003enatural killer cell: -1.437\\u003cbr\\u003emature NK T cell: -3.795\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.927\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.096\",\"value: -4.342\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.742\\u003cbr\\u003emucosal invariant T cell: -2.796\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.855\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -3.108\\u003cbr\\u003egamma-delta T cell: -3.172\",\"value: -6.135\\u003cbr\\u003eCD14-positive monocyte: -1.252\\u003cbr\\u003eclassical monocyte: -1.336\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.327\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.564\\u003cbr\\u003emonocyte: -3.843\",\"value: -6.863\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -2.318\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.725\\u003cbr\\u003emature NK T cell: -3.352\\u003cbr\\u003egamma-delta T cell: -4.366\",\"value: -4.844\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.669\\u003cbr\\u003enatural killer cell: -1.289\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.902\\u003cbr\\u003emature NK T cell: -4.150\\u003cbr\\u003eactivated type II NK T cell: -4.849\",\"value: -5.119\\u003cbr\\u003econventional dendritic cell: -1.413\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.102\\u003cbr\\u003edendritic cell: -2.113\\u003cbr\\u003edendritic cell, human: -2.831\\u003cbr\\u003emyeloid dendritic cell: -2.894\",\"value: -6.990\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.551\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.015\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.686\\u003cbr\\u003eT follicular helper cell: -3.237\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.875\",\"value: -6.002\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.032\\u003cbr\\u003enatural killer cell: -2.019\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.043\\u003cbr\\u003egamma-delta T cell: -3.703\\u003cbr\\u003eactivated type II NK T cell: -4.203\",\"value: -5.210\\u003cbr\\u003eclassical monocyte: -1.435\\u003cbr\\u003eCD14-positive monocyte: -2.293\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.761\\u003cbr\\u003emonocyte: -3.487\\u003cbr\\u003emacrophage: -3.768\",\"value: -4.513\\u003cbr\\u003enatural killer cell: -2.025\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.122\\u003cbr\\u003egamma-delta T cell: -2.227\\u003cbr\\u003emature NK T cell: -2.890\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.014\",\"value: -5.295\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -2.051\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.256\\u003cbr\\u003emature NK T cell: -3.784\\u003cbr\\u003egamma-delta T cell: -4.207\",\"value: -4.529\\u003cbr\\u003enatural killer cell: -0.501\\u003cbr\\u003elymphocyte: -2.693\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.579\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.729\\u003cbr\\u003eT cell: -3.935\",\"value: -6.976\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003enatural killer cell: -1.841\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.618\\u003cbr\\u003emature NK T cell: -3.964\\u003cbr\\u003eactivated type II NK T cell: -4.317\",\"value: -4.749\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.375\\u003cbr\\u003emature NK T cell: -3.544\\u003cbr\\u003egamma-delta T cell: -3.729\",\"value: -5.478\\u003cbr\\u003egamma-delta T cell: -2.053\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.258\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.929\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.591\\u003cbr\\u003enatural killer cell: -3.608\",\"value: -5.133\\u003cbr\\u003eplasmacytoid dendritic cell: -0.243\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.562\\u003cbr\\u003edendritic cell: -3.444\\u003cbr\\u003emyeloid dendritic cell: -5.808\\u003cbr\\u003edendritic cell, human: -5.820\",\"value: -6.712\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.516\\u003cbr\\u003enatural killer cell: -2.128\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.893\\u003cbr\\u003emature NK T cell: -4.329\\u003cbr\\u003eactivated type II NK T cell: -4.664\",\"value: -5.401\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.057\\u003cbr\\u003enatural killer cell: -1.567\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.420\\u003cbr\\u003emature NK T cell: -3.933\\u003cbr\\u003egamma-delta T cell: -4.262\",\"value: -5.349\\u003cbr\\u003enatural killer cell: -1.343\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.550\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.487\\u003cbr\\u003emature NK T cell: -3.252\\u003cbr\\u003egamma-delta T cell: -3.992\",\"value: -5.902\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.492\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.158\\u003cbr\\u003emature NK T cell: -4.231\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.820\",\"value: -5.911\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.651\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.808\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.508\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.605\\u003cbr\\u003eT follicular helper cell: -3.104\",\"value: -7.115\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.899\\u003cbr\\u003egamma-delta T cell: -2.760\\u003cbr\\u003enatural killer cell: -3.240\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.516\\u003cbr\\u003emucosal invariant T cell: -3.906\",\"value: -5.382\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.757\\u003cbr\\u003enatural killer cell: -1.564\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.300\\u003cbr\\u003emature NK T cell: -3.774\\u003cbr\\u003eactivated type II NK T cell: -4.615\",\"value: -5.417\\u003cbr\\u003eplasmacytoid dendritic cell: -0.383\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.681\\u003cbr\\u003edendritic cell: -2.767\\u003cbr\\u003emyeloid dendritic cell: -4.783\\u003cbr\\u003eB cell: -5.032\",\"value: -4.912\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.960\\u003cbr\\u003enatural killer cell: -1.378\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.925\\u003cbr\\u003emature NK T cell: -4.064\\u003cbr\\u003egamma-delta T cell: -4.093\",\"value: -5.870\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.561\\u003cbr\\u003enatural killer cell: -2.210\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.014\\u003cbr\\u003egamma-delta T cell: -3.538\\u003cbr\\u003emature NK T cell: -4.001\",\"value: -6.134\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.962\\u003cbr\\u003enatural killer cell: -1.484\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.849\\u003cbr\\u003emature NK T cell: -4.013\\u003cbr\\u003eactivated type II NK T cell: -4.470\",\"value: -6.296\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.390\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.140\\u003cbr\\u003enatural killer cell: -2.592\\u003cbr\\u003egamma-delta T cell: -2.799\\u003cbr\\u003emature NK T cell: -3.968\",\"value: -5.495\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.583\\u003cbr\\u003enatural killer cell: -1.857\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.158\\u003cbr\\u003emature NK T cell: -3.631\\u003cbr\\u003egamma-delta T cell: -4.241\",\"value: -5.994\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.400\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.650\\u003cbr\\u003enatural killer cell: -2.044\\u003cbr\\u003emature NK T cell: -3.804\\u003cbr\\u003egamma-delta T cell: -3.874\",\"value: -6.568\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.534\\u003cbr\\u003enatural killer cell: -2.378\\u003cbr\\u003egamma-delta T cell: -2.810\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.034\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.384\",\"value: -5.706\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.385\\u003cbr\\u003enatural killer cell: -1.998\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.216\\u003cbr\\u003egamma-delta T cell: -4.709\",\"value: -6.538\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.465\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.494\\u003cbr\\u003enatural killer cell: -2.244\\u003cbr\\u003egamma-delta T cell: -3.584\\u003cbr\\u003eactivated type II NK T cell: -3.973\",\"value: -5.758\\u003cbr\\u003enatural killer cell: -1.598\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.234\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.840\\u003cbr\\u003egamma-delta T cell: -2.919\\u003cbr\\u003emature NK T cell: -3.248\",\"value: -5.749\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.668\\u003cbr\\u003enatural killer cell: -1.434\\u003cbr\\u003emature NK T cell: -3.773\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.287\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.702\",\"value: -6.590\\u003cbr\\u003eclassical monocyte: -1.016\\u003cbr\\u003eCD14-positive monocyte: -1.345\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.491\\u003cbr\\u003emacrophage: -3.821\\u003cbr\\u003emonocyte: -4.327\",\"value: -6.777\\u003cbr\\u003eclassical monocyte: -1.768\\u003cbr\\u003eCD14-positive monocyte: -1.969\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.007\\u003cbr\\u003edendritic cell: -3.106\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.126\",\"value: -5.149\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003enatural killer cell: -1.693\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.725\\u003cbr\\u003emature NK T cell: -3.726\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.606\",\"value: -6.438\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.833\\u003cbr\\u003enatural killer cell: -1.999\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.033\\u003cbr\\u003egamma-delta T cell: -4.267\\u003cbr\\u003emature NK T cell: -4.287\",\"value: -6.015\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.852\\u003cbr\\u003enatural killer cell: -1.611\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.325\\u003cbr\\u003emature NK T cell: -4.086\\u003cbr\\u003egamma-delta T cell: -4.386\",\"value: -5.905\\u003cbr\\u003eclassical monocyte: -0.891\\u003cbr\\u003eCD14-positive monocyte: -1.791\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.143\\u003cbr\\u003emacrophage: -3.673\\u003cbr\\u003emonocyte: -4.310\",\"value: -5.120\\u003cbr\\u003enatural killer cell: -0.757\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.360\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.096\\u003cbr\\u003emature NK T cell: -3.733\\u003cbr\\u003egamma-delta T cell: -4.710\",\"value: -7.276\\u003cbr\\u003eCD14-positive monocyte: -1.379\\u003cbr\\u003eclassical monocyte: -1.538\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.772\\u003cbr\\u003emonocyte: -3.383\\u003cbr\\u003edendritic cell, human: -3.545\",\"value: -6.676\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.552\\u003cbr\\u003enatural killer cell: -2.222\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.473\\u003cbr\\u003eactivated type II NK T cell: -4.379\\u003cbr\\u003emature NK T cell: -4.659\",\"value: -4.774\\u003cbr\\u003eclassical monocyte: -1.664\\u003cbr\\u003emacrophage: -1.690\\u003cbr\\u003eCD14-positive monocyte: -2.011\\u003cbr\\u003emonocyte: -3.510\\u003cbr\\u003ealveolar macrophage: -3.748\",\"value: -5.572\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.795\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.530\\u003cbr\\u003emature NK T cell: -2.631\\u003cbr\\u003enatural killer cell: -2.658\\u003cbr\\u003egamma-delta T cell: -2.827\",\"value: -6.149\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.187\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.765\\u003cbr\\u003enatural killer cell: -2.199\\u003cbr\\u003egamma-delta T cell: -3.295\\u003cbr\\u003emature NK T cell: -3.770\",\"value: -6.330\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.629\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.641\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.131\\u003cbr\\u003eT follicular helper cell: -2.336\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.789\",\"value: -6.212\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.753\\u003cbr\\u003enatural killer cell: -2.405\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.579\\u003cbr\\u003emature NK T cell: -3.508\\u003cbr\\u003egamma-delta T cell: -3.829\",\"value: -5.586\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.637\\u003cbr\\u003enatural killer cell: -2.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.429\\u003cbr\\u003emature NK T cell: -3.731\\u003cbr\\u003egamma-delta T cell: -4.000\",\"value: -5.182\\u003cbr\\u003eclassical monocyte: -0.895\\u003cbr\\u003eCD14-positive monocyte: -2.118\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.328\\u003cbr\\u003emacrophage: -3.438\\u003cbr\\u003emonocyte: -4.113\",\"value: -6.240\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.667\\u003cbr\\u003enatural killer cell: -1.171\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.591\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.105\",\"value: -5.937\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.729\\u003cbr\\u003egamma-delta T cell: -2.424\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.903\\u003cbr\\u003enatural killer cell: -2.922\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.993\",\"value: -6.172\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.461\\u003cbr\\u003emature NK T cell: -4.172\\u003cbr\\u003eactivated type II NK T cell: -4.710\",\"value: -5.050\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.192\\u003cbr\\u003enatural killer cell: -1.785\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.735\\u003cbr\\u003emature NK T cell: -3.038\\u003cbr\\u003egamma-delta T cell: -3.190\",\"value: -6.765\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -2.213\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.093\\u003cbr\\u003emature NK T cell: -4.431\\u003cbr\\u003eactivated type II NK T cell: -4.596\",\"value: -6.416\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.703\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.975\\u003cbr\\u003enatural killer cell: -2.464\\u003cbr\\u003egamma-delta T cell: -3.949\\u003cbr\\u003emature NK T cell: -4.036\",\"value: -6.661\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.511\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.226\\u003cbr\\u003enatural killer cell: -2.290\\u003cbr\\u003emature NK T cell: -4.382\\u003cbr\\u003eactivated type II NK T cell: -4.483\",\"value: -6.487\\u003cbr\\u003eCD14-positive monocyte: -1.063\\u003cbr\\u003eclassical monocyte: -1.179\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.149\\u003cbr\\u003emonocyte: -4.302\\u003cbr\\u003emacrophage: -4.380\",\"value: -5.563\\u003cbr\\u003enatural killer cell: -1.401\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.471\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.422\\u003cbr\\u003egamma-delta T cell: -3.121\\u003cbr\\u003emature NK T cell: -3.133\",\"value: -5.566\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.465\\u003cbr\\u003enatural killer cell: -2.018\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.664\\u003cbr\\u003emature NK T cell: -4.602\\u003cbr\\u003eactivated type II NK T cell: -4.758\",\"value: -5.256\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.257\\u003cbr\\u003egamma-delta T cell: -2.661\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.112\\u003cbr\\u003enatural killer cell: -3.152\\u003cbr\\u003emature NK T cell: -3.250\",\"value: -6.642\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.584\\u003cbr\\u003enatural killer cell: -1.419\\u003cbr\\u003emature NK T cell: -4.099\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.740\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.815\",\"value: -5.179\\u003cbr\\u003eclassical monocyte: -1.213\\u003cbr\\u003eCD14-positive monocyte: -2.153\\u003cbr\\u003emacrophage: -3.334\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.337\\u003cbr\\u003emonocyte: -3.484\",\"value: -6.031\\u003cbr\\u003eclassical monocyte: -1.718\\u003cbr\\u003eCD14-positive monocyte: -1.725\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.945\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.277\\u003cbr\\u003emonocyte: -3.572\",\"value: -4.882\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.030\\u003cbr\\u003enatural killer cell: -1.685\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.394\\u003cbr\\u003emature NK T cell: -3.244\\u003cbr\\u003egamma-delta T cell: -3.431\",\"value: -5.725\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.704\\u003cbr\\u003enatural killer cell: -1.800\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.666\\u003cbr\\u003eactivated type II NK T cell: -4.284\\u003cbr\\u003emature NK T cell: -4.300\",\"value: -6.461\\u003cbr\\u003econventional dendritic cell: -2.012\\u003cbr\\u003edendritic cell, human: -2.172\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.233\\u003cbr\\u003edendritic cell: -2.260\\u003cbr\\u003emyeloid dendritic cell: -3.206\",\"value: -6.705\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.507\\u003cbr\\u003enatural killer cell: -1.717\\u003cbr\\u003emature NK T cell: -3.737\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.273\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.847\",\"value: -6.330\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.532\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.839\\u003cbr\\u003enatural killer cell: -2.373\\u003cbr\\u003eactivated type II NK T cell: -3.532\\u003cbr\\u003emature NK T cell: -3.962\",\"value: -6.705\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.144\\u003cbr\\u003enatural killer cell: -2.315\\u003cbr\\u003emature NK T cell: -4.039\\u003cbr\\u003egamma-delta T cell: -4.638\",\"value: -6.878\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.569\\u003cbr\\u003enatural killer cell: -1.561\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.790\\u003cbr\\u003egamma-delta T cell: -4.062\\u003cbr\\u003emature NK T cell: -4.070\",\"value: -5.809\\u003cbr\\u003eplasmacytoid dendritic cell: -0.351\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.711\\u003cbr\\u003edendritic cell: -2.785\\u003cbr\\u003edendritic cell, human: -4.780\\u003cbr\\u003emyeloid dendritic cell: -4.996\",\"value: -5.587\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.072\\u003cbr\\u003enatural killer cell: -1.615\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.563\\u003cbr\\u003emature NK T cell: -3.263\\u003cbr\\u003egamma-delta T cell: -3.849\",\"value: -6.430\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.009\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.790\\u003cbr\\u003enatural killer cell: -1.822\\u003cbr\\u003emature NK T cell: -3.628\\u003cbr\\u003eactivated type II NK T cell: -4.363\",\"value: -4.000\\u003cbr\\u003egamma-delta T cell: -1.741\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.491\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.947\\u003cbr\\u003emature NK T cell: -3.067\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.309\",\"value: -5.678\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.174\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.870\\u003cbr\\u003enatural killer cell: -2.137\\u003cbr\\u003emature NK T cell: -3.127\\u003cbr\\u003egamma-delta T cell: -3.183\",\"value: -6.095\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.725\\u003cbr\\u003enatural killer cell: -1.293\\u003cbr\\u003emature NK T cell: -3.657\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.067\\u003cbr\\u003egamma-delta T cell: -4.817\",\"value: -4.415\\u003cbr\\u003eplasmacytoid dendritic cell: -0.256\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.611\\u003cbr\\u003edendritic cell: -2.967\\u003cbr\\u003emyeloid dendritic cell: -5.365\\u003cbr\\u003econventional dendritic cell: -5.533\",\"value: -6.321\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.017\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.502\\u003cbr\\u003enatural killer cell: -2.453\\u003cbr\\u003egamma-delta T cell: -3.768\\u003cbr\\u003emature NK T cell: -4.046\",\"value: -5.126\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.981\\u003cbr\\u003enatural killer cell: -1.140\\u003cbr\\u003emature NK T cell: -3.336\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.487\\u003cbr\\u003egamma-delta T cell: -4.261\",\"value: -6.808\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.924\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.808\\u003cbr\\u003enatural killer cell: -2.315\\u003cbr\\u003emature NK T cell: -3.718\\u003cbr\\u003egamma-delta T cell: -3.818\",\"value: -3.955\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.078\\u003cbr\\u003emature NK T cell: -2.712\\u003cbr\\u003eT cell: -2.796\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.991\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.066\",\"value: -4.517\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.092\\u003cbr\\u003eT follicular helper cell: -2.768\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.914\\u003cbr\\u003eT-helper 22 cell: -2.965\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.119\",\"value: -4.110\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -2.669\\u003cbr\\u003eT cell: -2.851\\u003cbr\\u003emast cell: -3.053\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.404\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -3.730\",\"value: -6.932\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.196\\u003cbr\\u003egamma-delta T cell: -3.106\\u003cbr\\u003enatural killer cell: -3.379\\u003cbr\\u003emature NK T cell: -3.734\\u003cbr\\u003einnate lymphoid cell: -3.867\",\"value: -6.511\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.720\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.981\\u003cbr\\u003enatural killer cell: -2.517\\u003cbr\\u003emature NK T cell: -3.956\\u003cbr\\u003egamma-delta T cell: -4.248\",\"value: -5.266\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.779\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.233\\u003cbr\\u003enatural killer cell: -2.347\\u003cbr\\u003egamma-delta T cell: -2.806\\u003cbr\\u003emature NK T cell: -3.367\",\"value: -5.860\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.862\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.806\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.877\\u003cbr\\u003eT follicular helper cell: -3.551\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.976\",\"value: -6.709\\u003cbr\\u003eclassical monocyte: -1.336\\u003cbr\\u003eCD14-positive monocyte: -1.864\\u003cbr\\u003edendritic cell, human: -3.104\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.266\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.314\",\"value: -5.924\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -1.475\\u003cbr\\u003emature NK T cell: -4.096\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.519\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.737\",\"value: -6.907\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.312\\u003cbr\\u003enatural killer cell: -2.327\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.058\\u003cbr\\u003emature NK T cell: -4.548\\u003cbr\\u003eactivated type II NK T cell: -4.834\",\"value: -6.788\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.682\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.535\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.220\\u003cbr\\u003eT follicular helper cell: -3.939\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.306\",\"value: -6.389\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.800\\u003cbr\\u003enatural killer cell: -1.488\\u003cbr\\u003emature NK T cell: -3.491\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.947\\u003cbr\\u003egamma-delta T cell: -4.526\",\"value: -5.511\\u003cbr\\u003eT follicular helper cell: -2.086\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.120\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.417\\u003cbr\\u003eT-helper 22 cell: -2.576\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.152\",\"value: -5.855\\u003cbr\\u003eclassical monocyte: -1.404\\u003cbr\\u003eCD14-positive monocyte: -1.924\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.616\\u003cbr\\u003emonocyte: -3.123\\u003cbr\\u003emacrophage: -3.896\",\"value: -5.689\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003enatural killer cell: -2.108\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.272\\u003cbr\\u003egamma-delta T cell: -3.899\\u003cbr\\u003emature NK T cell: -3.973\",\"value: -5.789\\u003cbr\\u003eplasmacytoid dendritic cell: -0.203\\u003cbr\\u003edendritic cell: -2.975\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.032\\u003cbr\\u003emyeloid dendritic cell: -5.420\\u003cbr\\u003edendritic cell, human: -5.696\",\"value: -5.684\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.672\\u003cbr\\u003enatural killer cell: -1.741\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.010\\u003cbr\\u003emature NK T cell: -3.658\\u003cbr\\u003egamma-delta T cell: -3.931\",\"value: -5.162\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.888\\u003cbr\\u003eT follicular helper cell: -2.480\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.637\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.913\\u003cbr\\u003eT-helper 22 cell: -2.924\",\"value: -5.389\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.095\\u003cbr\\u003enatural killer cell: -1.592\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.875\\u003cbr\\u003eactivated type II NK T cell: -4.033\\u003cbr\\u003egamma-delta T cell: -4.290\",\"value: -6.487\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.230\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.459\\u003cbr\\u003enatural killer cell: -2.547\\u003cbr\\u003egamma-delta T cell: -3.285\\u003cbr\\u003emature NK T cell: -3.785\",\"value: -5.225\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.655\\u003cbr\\u003enatural killer cell: -1.806\\u003cbr\\u003egamma-delta T cell: -2.828\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.923\\u003cbr\\u003emature NK T cell: -3.212\",\"value: -6.232\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.575\\u003cbr\\u003enatural killer cell: -2.113\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.132\\u003cbr\\u003eactivated type II NK T cell: -4.407\\u003cbr\\u003emature NK T cell: -4.619\",\"value: -6.025\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.574\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.528\\u003cbr\\u003emature NK T cell: -3.633\\u003cbr\\u003egamma-delta T cell: -4.125\",\"value: -6.567\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.560\\u003cbr\\u003egamma-delta T cell: -3.003\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.611\\u003cbr\\u003einnate lymphoid cell: -3.743\\u003cbr\\u003emature NK T cell: -3.752\",\"value: -6.252\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.585\\u003cbr\\u003emature NK T cell: -3.995\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.527\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.646\",\"value: -6.896\\u003cbr\\u003eT follicular helper cell: -1.310\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.231\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.685\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.960\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.566\",\"value: -5.374\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.025\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.842\\u003cbr\\u003enatural killer cell: -1.863\\u003cbr\\u003emature NK T cell: -3.952\\u003cbr\\u003eactivated type II NK T cell: -3.985\",\"value: -4.185\\u003cbr\\u003eclassical monocyte: -1.271\\u003cbr\\u003eCD14-positive monocyte: -1.608\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.114\\u003cbr\\u003emacrophage: -3.253\\u003cbr\\u003emonocyte: -3.260\",\"value: -6.870\\u003cbr\\u003eT follicular helper cell: -1.419\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.083\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.448\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.865\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.983\",\"value: -5.665\\u003cbr\\u003eplasmacytoid dendritic cell: -0.179\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.874\\u003cbr\\u003edendritic cell: -3.942\\u003cbr\\u003emyeloid dendritic cell: -5.934\\u003cbr\\u003ehematopoietic precursor cell: -6.076\",\"value: -6.513\\u003cbr\\u003edendritic cell, human: -2.476\\u003cbr\\u003econventional dendritic cell: -2.492\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.509\\u003cbr\\u003edendritic cell: -3.174\\u003cbr\\u003eCD14-positive monocyte: -3.225\",\"value: -5.732\\u003cbr\\u003enatural killer cell: -0.647\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.113\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.733\\u003cbr\\u003egamma-delta T cell: -3.856\\u003cbr\\u003emature NK T cell: -3.982\",\"value: -6.117\\u003cbr\\u003eclassical monocyte: -1.625\\u003cbr\\u003eCD14-positive monocyte: -2.123\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.936\\u003cbr\\u003econventional dendritic cell: -3.188\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.220\",\"value: -6.089\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.926\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.194\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.480\\u003cbr\\u003emucosal invariant T cell: -2.569\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.851\",\"value: -4.193\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -1.893\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.327\\u003cbr\\u003emature NK T cell: -2.597\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.019\\u003cbr\\u003egamma-delta T cell: -3.521\",\"value: -5.850\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.798\\u003cbr\\u003enatural killer cell: -1.775\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.003\\u003cbr\\u003emature NK T cell: -3.688\\u003cbr\\u003egamma-delta T cell: -4.274\",\"value: -6.395\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.463\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.855\\u003cbr\\u003enatural killer cell: -2.167\\u003cbr\\u003egamma-delta T cell: -3.332\\u003cbr\\u003eactivated type II NK T cell: -3.797\",\"value: -6.615\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.887\\u003cbr\\u003enatural killer cell: -1.640\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.058\\u003cbr\\u003eactivated type II NK T cell: -4.060\\u003cbr\\u003emature NK T cell: -4.218\",\"value: -5.887\\u003cbr\\u003eclassical monocyte: -1.278\\u003cbr\\u003eCD14-positive monocyte: -1.831\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.921\\u003cbr\\u003emonocyte: -3.775\\u003cbr\\u003emacrophage: -3.888\",\"value: -5.535\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.987\\u003cbr\\u003enatural killer cell: -1.205\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.264\\u003cbr\\u003emature NK T cell: -3.632\\u003cbr\\u003eactivated type II NK T cell: -4.559\",\"value: -6.248\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.436\\u003cbr\\u003enatural killer cell: -2.188\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.151\\u003cbr\\u003emature NK T cell: -4.631\\u003cbr\\u003eactivated type II NK T cell: -4.665\",\"value: -3.966\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.924\\u003cbr\\u003egamma-delta T cell: -2.488\\u003cbr\\u003enatural killer cell: -2.659\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.804\\u003cbr\\u003emature NK T cell: -2.859\",\"value: -5.199\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.372\\u003cbr\\u003enatural killer cell: -1.446\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.788\\u003cbr\\u003egamma-delta T cell: -3.878\\u003cbr\\u003emature NK T cell: -4.078\",\"value: -5.459\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.460\\u003cbr\\u003enatural killer cell: -2.149\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.250\\u003cbr\\u003egamma-delta T cell: -3.946\\u003cbr\\u003emature NK T cell: -3.967\",\"value: -6.638\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.688\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.978\\u003cbr\\u003enatural killer cell: -2.557\\u003cbr\\u003emature NK T cell: -4.234\\u003cbr\\u003egamma-delta T cell: -4.271\",\"value: -5.649\\u003cbr\\u003eB cell: -1.228\\u003cbr\\u003enaive B cell: -1.308\\u003cbr\\u003emalignant cell: -2.962\\u003cbr\\u003ememory B cell: -3.455\\u003cbr\\u003eclass switched memory B cell: -3.474\",\"value: -5.990\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.702\\u003cbr\\u003enatural killer cell: -1.582\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.084\\u003cbr\\u003emature NK T cell: -3.793\\u003cbr\\u003egamma-delta T cell: -4.453\",\"value: -6.559\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -1.454\\u003cbr\\u003emature NK T cell: -4.114\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.546\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.850\",\"value: -7.299\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.252\\u003cbr\\u003eCD14-positive monocyte: -2.254\\u003cbr\\u003eclassical monocyte: -2.473\\u003cbr\\u003edendritic cell, human: -2.738\\u003cbr\\u003econventional dendritic cell: -2.765\",\"value: -6.264\\u003cbr\\u003eclassical monocyte: -1.202\\u003cbr\\u003eCD14-positive monocyte: -1.935\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.291\\u003cbr\\u003edendritic cell, human: -3.903\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.910\",\"value: -5.344\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.526\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.310\\u003cbr\\u003emature NK T cell: -3.870\\u003cbr\\u003eactivated type II NK T cell: -4.870\",\"value: -5.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.297\\u003cbr\\u003enatural killer cell: -2.330\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.087\\u003cbr\\u003emature NK T cell: -4.450\\u003cbr\\u003egamma-delta T cell: -4.945\",\"value: -4.921\\u003cbr\\u003eB cell: -0.811\\u003cbr\\u003enaive B cell: -2.575\\u003cbr\\u003emalignant cell: -2.917\\u003cbr\\u003eclass switched memory B cell: -2.988\\u003cbr\\u003ememory B cell: -3.532\",\"value: -6.864\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.897\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.417\\u003cbr\\u003emature NK T cell: -3.895\\u003cbr\\u003eactivated type II NK T cell: -4.357\",\"value: -6.715\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.806\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.873\\u003cbr\\u003enatural killer cell: -2.047\\u003cbr\\u003eactivated type II NK T cell: -4.116\\u003cbr\\u003emature NK T cell: -4.176\",\"value: -6.561\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.937\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.348\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.464\\u003cbr\\u003emucosal invariant T cell: -2.634\\u003cbr\\u003eT follicular helper cell: -2.719\",\"value: -5.844\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.986\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.887\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.188\\u003cbr\\u003eT follicular helper cell: -3.307\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.488\",\"value: -5.359\\u003cbr\\u003eplasmacytoid dendritic cell: -0.300\\u003cbr\\u003edendritic cell: -2.723\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.796\\u003cbr\\u003eplasma cell: -4.646\\u003cbr\\u003eB cell: -4.866\",\"value: -5.786\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.826\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.924\\u003cbr\\u003emature NK T cell: -4.751\\u003cbr\\u003eactivated type II NK T cell: -4.761\",\"value: -6.155\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.397\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.841\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.915\\u003cbr\\u003eT follicular helper cell: -2.502\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.231\",\"value: -5.788\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.268\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.507\\u003cbr\\u003enatural killer cell: -1.721\\u003cbr\\u003eactivated type II NK T cell: -3.867\\u003cbr\\u003egamma-delta T cell: -4.343\",\"value: -5.581\\u003cbr\\u003eplasmacytoid dendritic cell: -0.234\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.860\\u003cbr\\u003edendritic cell: -2.940\\u003cbr\\u003emyeloid dendritic cell: -5.401\\u003cbr\\u003edendritic cell, human: -5.518\",\"value: -6.642\\u003cbr\\u003eclassical monocyte: -1.428\\u003cbr\\u003eCD14-positive monocyte: -1.650\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.283\\u003cbr\\u003emacrophage: -3.356\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.890\",\"value: -5.884\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.537\\u003cbr\\u003enatural killer cell: -2.202\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.996\\u003cbr\\u003emature NK T cell: -3.828\\u003cbr\\u003egamma-delta T cell: -4.337\",\"value: -5.177\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.102\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.347\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.545\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.610\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.033\",\"value: -5.993\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.959\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.965\\u003cbr\\u003eT follicular helper cell: -2.425\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.555\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.751\",\"value: -5.649\\u003cbr\\u003eclassical monocyte: -1.253\\u003cbr\\u003eCD14-positive monocyte: -1.329\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.815\\u003cbr\\u003emonocyte: -3.608\\u003cbr\\u003emacrophage: -4.101\",\"value: -5.487\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.284\\u003cbr\\u003enatural killer cell: -2.003\\u003cbr\\u003einnate lymphoid cell: -3.464\\u003cbr\\u003elymphocyte: -3.669\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.921\",\"value: -6.958\\u003cbr\\u003eCD14-positive monocyte: -1.463\\u003cbr\\u003eclassical monocyte: -1.610\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.723\\u003cbr\\u003emonocyte: -3.472\\u003cbr\\u003edendritic cell, human: -3.607\",\"value: -6.917\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.451\\u003cbr\\u003enatural killer cell: -1.691\\u003cbr\\u003emature NK T cell: -3.910\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.444\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.012\",\"value: -6.166\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.594\\u003cbr\\u003enatural killer cell: -1.255\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.050\\u003cbr\\u003emature NK T cell: -4.180\\u003cbr\\u003eactivated type II NK T cell: -4.918\",\"value: -6.131\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.758\\u003cbr\\u003enatural killer cell: -2.127\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.826\\u003cbr\\u003emature NK T cell: -3.286\\u003cbr\\u003egamma-delta T cell: -4.097\",\"value: -5.088\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.999\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.204\\u003cbr\\u003eT follicular helper cell: -2.335\\u003cbr\\u003eT-helper 22 cell: -2.782\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.403\",\"value: -5.770\\u003cbr\\u003eplasmacytoid dendritic cell: -0.660\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.426\\u003cbr\\u003edendritic cell: -2.435\\u003cbr\\u003edendritic cell, human: -4.569\\u003cbr\\u003econventional dendritic cell: -4.935\",\"value: -5.333\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.960\\u003cbr\\u003enatural killer cell: -1.619\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.215\\u003cbr\\u003emature NK T cell: -3.320\\u003cbr\\u003egamma-delta T cell: -4.224\",\"value: -5.821\\u003cbr\\u003eclassical monocyte: -1.263\\u003cbr\\u003eCD14-positive monocyte: -1.325\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.785\\u003cbr\\u003emonocyte: -3.580\\u003cbr\\u003emacrophage: -3.953\",\"value: -5.902\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.420\\u003cbr\\u003enatural killer cell: -1.512\\u003cbr\\u003egamma-delta T cell: -2.730\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.881\\u003cbr\\u003emature NK T cell: -3.112\",\"value: -4.980\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.935\\u003cbr\\u003enatural killer cell: -1.920\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.972\\u003cbr\\u003emature NK T cell: -3.349\\u003cbr\\u003egamma-delta T cell: -3.846\",\"value: -6.442\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.607\\u003cbr\\u003enatural killer cell: -1.305\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.861\\u003cbr\\u003emature NK T cell: -4.336\\u003cbr\\u003eactivated type II NK T cell: -4.906\",\"value: -4.678\\u003cbr\\u003eT-helper 22 cell: -2.163\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.182\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.293\\u003cbr\\u003eT follicular helper cell: -2.487\\u003cbr\\u003emature NK T cell: -3.434\",\"value: -6.512\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.754\\u003cbr\\u003enatural killer cell: -1.854\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.390\\u003cbr\\u003emature NK T cell: -3.943\\u003cbr\\u003eactivated type II NK T cell: -4.222\",\"value: -5.725\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.789\\u003cbr\\u003enatural killer cell: -2.155\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.690\\u003cbr\\u003egamma-delta T cell: -3.528\\u003cbr\\u003emature NK T cell: -3.673\",\"value: -6.256\\u003cbr\\u003eclassical monocyte: -1.174\\u003cbr\\u003eCD14-positive monocyte: -2.170\\u003cbr\\u003edendritic cell, human: -3.187\\u003cbr\\u003enon-classical monocyte: -3.208\\u003cbr\\u003emacrophage: -3.208\",\"value: -5.359\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.772\\u003cbr\\u003enatural killer cell: -1.763\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.253\\u003cbr\\u003eactivated type II NK T cell: -4.154\\u003cbr\\u003emature NK T cell: -4.459\",\"value: -6.893\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.170\\u003cbr\\u003egamma-delta T cell: -3.259\\u003cbr\\u003enatural killer cell: -3.679\\u003cbr\\u003einnate lymphoid cell: -4.083\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.128\",\"value: -5.969\\u003cbr\\u003eplasmacytoid dendritic cell: -0.208\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.087\\u003cbr\\u003edendritic cell: -3.156\\u003cbr\\u003emyeloid dendritic cell: -5.339\\u003cbr\\u003edendritic cell, human: -5.496\",\"value: -6.112\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.628\\u003cbr\\u003enatural killer cell: -1.598\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.908\\u003cbr\\u003emature NK T cell: -4.300\\u003cbr\\u003eactivated type II NK T cell: -4.592\",\"value: -5.934\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.319\\u003cbr\\u003enatural killer cell: -2.379\\u003cbr\\u003emature NK T cell: -3.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.158\\u003cbr\\u003egamma-delta T cell: -4.554\",\"value: -5.554\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.873\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.832\\u003cbr\\u003emature NK T cell: -4.241\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.800\",\"value: -5.152\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.803\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.871\\u003cbr\\u003emature NK T cell: -4.047\\u003cbr\\u003egamma-delta T cell: -4.659\",\"value: -4.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.800\\u003cbr\\u003enatural killer cell: -2.593\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.478\\u003cbr\\u003egamma-delta T cell: -3.574\\u003cbr\\u003einnate lymphoid cell: -3.692\",\"value: -4.413\\u003cbr\\u003ehematopoietic precursor cell: -2.168\\u003cbr\\u003ehematopoietic stem cell: -2.334\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.435\\u003cbr\\u003eprogenitor cell: -3.569\\u003cbr\\u003ecommon myeloid progenitor: -3.981\",\"value: -5.708\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.974\\u003cbr\\u003enatural killer cell: -2.490\\u003cbr\\u003egamma-delta T cell: -2.855\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.950\\u003cbr\\u003emature NK T cell: -3.689\",\"value: -6.730\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.891\\u003cbr\\u003enatural killer cell: -1.527\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.630\\u003cbr\\u003emature NK T cell: -4.054\\u003cbr\\u003eactivated type II NK T cell: -4.144\",\"value: -5.399\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.079\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.790\\u003cbr\\u003eT follicular helper cell: -2.703\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.028\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.430\",\"value: -5.252\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.830\\u003cbr\\u003enatural killer cell: -1.681\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.850\\u003cbr\\u003emature NK T cell: -3.462\\u003cbr\\u003egamma-delta T cell: -4.020\",\"value: -5.238\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.640\\u003cbr\\u003enatural killer cell: -1.778\\u003cbr\\u003emature NK T cell: -3.326\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.474\\u003cbr\\u003egamma-delta T cell: -4.414\",\"value: -4.953\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.096\\u003cbr\\u003ehematopoietic precursor cell: -2.555\\u003cbr\\u003ehematopoietic stem cell: -3.318\\u003cbr\\u003eerythroid progenitor cell: -3.579\\u003cbr\\u003emast cell: -3.651\",\"value: -6.191\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.566\\u003cbr\\u003emature NK T cell: -3.694\\u003cbr\\u003eactivated type II NK T cell: -4.629\",\"value: -5.228\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -1.781\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -1.986\\u003cbr\\u003egamma-delta T cell: -2.698\\u003cbr\\u003emature NK T cell: -2.778\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.581\",\"value: -6.458\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.985\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.486\\u003cbr\\u003egamma-delta T cell: -2.998\\u003cbr\\u003emucosal invariant T cell: -3.310\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.354\",\"value: -5.126\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.796\\u003cbr\\u003enatural killer cell: -1.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.796\\u003cbr\\u003emature NK T cell: -3.777\\u003cbr\\u003eactivated type II NK T cell: -4.643\",\"value: -5.618\\u003cbr\\u003eplasmacytoid dendritic cell: -0.250\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.695\\u003cbr\\u003edendritic cell: -3.062\\u003cbr\\u003edendritic cell, human: -5.432\\u003cbr\\u003emyeloid dendritic cell: -5.552\",\"value: -6.476\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.515\\u003cbr\\u003enatural killer cell: -2.189\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.669\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003egamma-delta T cell: -4.428\",\"value: -6.473\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.157\\u003cbr\\u003egamma-delta T cell: -2.574\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.578\\u003cbr\\u003enatural killer cell: -3.079\\u003cbr\\u003emature NK T cell: -3.663\",\"value: -5.712\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003enatural killer cell: -1.442\\u003cbr\\u003emature NK T cell: -3.504\\u003cbr\\u003egamma-delta T cell: -4.736\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.788\",\"value: -6.099\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.821\\u003cbr\\u003enatural killer cell: -2.295\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.648\\u003cbr\\u003emature NK T cell: -3.608\\u003cbr\\u003egamma-delta T cell: -3.895\",\"value: -6.498\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.570\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003emature NK T cell: -3.620\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.343\\u003cbr\\u003egamma-delta T cell: -4.569\",\"value: -5.945\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.647\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.232\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.361\\u003cbr\\u003eT follicular helper cell: -2.409\\u003cbr\\u003eT-helper 22 cell: -2.451\",\"value: -6.548\\u003cbr\\u003eclassical monocyte: -1.292\\u003cbr\\u003eCD14-positive monocyte: -1.399\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.179\\u003cbr\\u003emacrophage: -3.693\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.935\",\"value: -6.136\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.940\\u003cbr\\u003eT follicular helper cell: -1.971\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.270\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.757\\u003cbr\\u003eT cell: -3.864\",\"value: -6.052\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.688\\u003cbr\\u003enatural killer cell: -1.590\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.073\\u003cbr\\u003emature NK T cell: -4.097\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.654\",\"value: -4.776\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.866\\u003cbr\\u003eT follicular helper cell: -2.235\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.437\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.619\\u003cbr\\u003eT-helper 22 cell: -2.623\",\"value: -5.413\\u003cbr\\u003enatural killer cell: -1.109\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.380\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.751\\u003cbr\\u003emature NK T cell: -3.774\\u003cbr\\u003egamma-delta T cell: -4.195\",\"value: -5.652\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.028\\u003cbr\\u003enatural killer cell: -2.228\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.321\\u003cbr\\u003emature NK T cell: -3.433\\u003cbr\\u003egamma-delta T cell: -3.479\",\"value: -4.903\\u003cbr\\u003eplasmacytoid dendritic cell: -0.163\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.228\\u003cbr\\u003edendritic cell: -3.342\\u003cbr\\u003econventional dendritic cell: -5.501\\u003cbr\\u003eplasma cell: -5.955\",\"value: -5.869\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.502\\u003cbr\\u003enatural killer cell: -1.740\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.362\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eactivated type II NK T cell: -4.826\",\"value: -6.513\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.540\\u003cbr\\u003enatural killer cell: -1.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.388\\u003cbr\\u003emature NK T cell: -4.329\\u003cbr\\u003eactivated type II NK T cell: -4.559\",\"value: -5.755\\u003cbr\\u003ehematopoietic precursor cell: -1.621\\u003cbr\\u003ehematopoietic stem cell: -3.053\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -3.203\\u003cbr\\u003ecommon myeloid progenitor: -3.514\\u003cbr\\u003eprogenitor cell: -3.545\",\"value: -6.200\\u003cbr\\u003eT follicular helper cell: -1.663\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.169\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.233\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.616\\u003cbr\\u003eT-helper 22 cell: -3.091\",\"value: -5.095\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.731\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003emature NK T cell: -2.693\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell, terminally differentiated: -3.128\",\"value: -5.466\\u003cbr\\u003eclassical monocyte: -1.120\\u003cbr\\u003eCD14-positive monocyte: -1.819\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.680\\u003cbr\\u003emonocyte: -3.726\\u003cbr\\u003emacrophage: -3.980\",\"value: -6.917\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.749\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.881\\u003cbr\\u003enatural killer cell: -2.390\\u003cbr\\u003emature NK T cell: -4.027\\u003cbr\\u003egamma-delta T cell: -4.369\",\"value: -6.303\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003enatural killer cell: -1.770\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.827\\u003cbr\\u003emature NK T cell: -3.718\\u003cbr\\u003eactivated type II NK T cell: -4.761\",\"value: -4.682\\u003cbr\\u003eB cell: -0.837\\u003cbr\\u003enaive B cell: -2.014\\u003cbr\\u003emature B cell: -3.249\\u003cbr\\u003eclass switched memory B cell: -3.459\\u003cbr\\u003ememory B cell: -3.596\",\"value: -6.391\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.980\\u003cbr\\u003eT follicular helper cell: -2.026\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.575\\u003cbr\\u003eT-helper 22 cell: -2.942\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.111\",\"value: -5.563\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.860\\u003cbr\\u003enatural killer cell: -1.622\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.280\\u003cbr\\u003eactivated type II NK T cell: -4.086\\u003cbr\\u003emature NK T cell: -4.403\",\"value: -6.054\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.172\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.009\\u003cbr\\u003enatural killer cell: -2.016\\u003cbr\\u003egamma-delta T cell: -3.578\\u003cbr\\u003emature NK T cell: -3.878\",\"value: -5.535\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.364\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003egamma-delta T cell: -3.568\\u003cbr\\u003emature NK T cell: -3.902\",\"value: -5.983\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003enatural killer cell: -2.118\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.260\\u003cbr\\u003emature NK T cell: -3.976\\u003cbr\\u003egamma-delta T cell: -4.398\",\"value: -4.119\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.041\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.504\\u003cbr\\u003emature NK T cell: -2.595\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell, terminally differentiated: -2.936\\u003cbr\\u003egamma-delta T cell: -3.119\",\"value: -6.425\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.976\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.356\\u003cbr\\u003eT follicular helper cell: -2.402\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.928\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.936\",\"value: -6.280\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.385\\u003cbr\\u003enatural killer cell: -1.878\\u003cbr\\u003emature NK T cell: -4.151\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.275\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.988\",\"value: -6.023\\u003cbr\\u003eT follicular helper cell: -1.807\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.851\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.230\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.537\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.299\",\"value: -5.553\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.411\\u003cbr\\u003enatural killer cell: -1.924\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.427\\u003cbr\\u003emature NK T cell: -2.529\\u003cbr\\u003egamma-delta T cell: -3.057\",\"value: -5.928\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.782\\u003cbr\\u003eT follicular helper cell: -1.790\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.413\\u003cbr\\u003eT-helper 22 cell: -2.626\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.848\",\"value: -4.169\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.194\\u003cbr\\u003enatural killer cell: -1.489\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.511\\u003cbr\\u003egamma-delta T cell: -3.002\\u003cbr\\u003emature NK T cell: -3.360\",\"value: -6.163\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.012\\u003cbr\\u003enatural killer cell: -2.317\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.959\\u003cbr\\u003egamma-delta T cell: -3.564\\u003cbr\\u003eactivated type II NK T cell: -3.924\",\"value: -6.088\\u003cbr\\u003eclassical monocyte: -1.246\\u003cbr\\u003eCD14-positive monocyte: -2.262\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.965\\u003cbr\\u003emacrophage: -3.612\\u003cbr\\u003emonocyte: -3.942\",\"value: -5.342\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.067\\u003cbr\\u003eT follicular helper cell: -2.378\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.873\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.118\\u003cbr\\u003eT-helper 22 cell: -3.296\",\"value: -5.514\\u003cbr\\u003edendritic cell: -1.571\\u003cbr\\u003econventional dendritic cell: -2.061\\u003cbr\\u003edendritic cell, human: -2.787\\u003cbr\\u003emyeloid dendritic cell: -2.885\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.968\",\"value: -5.409\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.745\\u003cbr\\u003enatural killer cell: -2.318\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.236\\u003cbr\\u003emature NK T cell: -3.266\\u003cbr\\u003egamma-delta T cell: -3.743\",\"value: -6.132\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.450\\u003cbr\\u003enatural killer cell: -1.961\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.170\\u003cbr\\u003emature NK T cell: -4.361\\u003cbr\\u003eactivated type II NK T cell: -4.852\",\"value: -5.974\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.421\\u003cbr\\u003enatural killer cell: -1.902\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.543\\u003cbr\\u003emature NK T cell: -4.478\\u003cbr\\u003eactivated type II NK T cell: -4.809\",\"value: -6.129\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.623\\u003cbr\\u003enatural killer cell: -1.412\\u003cbr\\u003emature NK T cell: -3.859\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.871\\u003cbr\\u003egamma-delta T cell: -4.694\",\"value: -5.710\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.055\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.228\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.267\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.317\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.724\",\"value: -4.635\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.861\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.381\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.796\\u003cbr\\u003emature NK T cell: -2.974\\u003cbr\\u003eT-helper 22 cell: -3.005\",\"value: -6.772\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.431\\u003cbr\\u003enatural killer cell: -2.104\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.179\\u003cbr\\u003emature NK T cell: -4.320\\u003cbr\\u003eactivated type II NK T cell: -4.762\",\"value: -5.372\\u003cbr\\u003eplasmacytoid dendritic cell: -0.268\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.673\\u003cbr\\u003edendritic cell: -3.437\\u003cbr\\u003econventional dendritic cell: -5.254\\u003cbr\\u003emyeloid dendritic cell: -5.642\",\"value: -4.995\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.316\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.136\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.528\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.781\\u003cbr\\u003eT-helper 22 cell: -3.104\",\"value: -5.439\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.555\\u003cbr\\u003enatural killer cell: -2.265\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.590\\u003cbr\\u003emature NK T cell: -3.731\\u003cbr\\u003egamma-delta T cell: -3.999\",\"value: -5.848\\u003cbr\\u003eplasmacytoid dendritic cell: -0.238\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.849\\u003cbr\\u003edendritic cell: -3.006\\u003cbr\\u003edendritic cell, human: -5.127\\u003cbr\\u003emyeloid dendritic cell: -5.169\",\"value: -5.172\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.563\\u003cbr\\u003enatural killer cell: -2.037\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.748\\u003cbr\\u003emature NK T cell: -4.030\\u003cbr\\u003egamma-delta T cell: -4.596\",\"value: -4.746\\u003cbr\\u003enatural killer cell: -0.705\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.382\\u003cbr\\u003elymphocyte: -3.979\\u003cbr\\u003emature NK T cell: -4.218\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.429\",\"value: -5.641\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.615\\u003cbr\\u003enatural killer cell: -1.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.577\\u003cbr\\u003egamma-delta T cell: -4.140\\u003cbr\\u003emature NK T cell: -4.298\",\"value: -5.292\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.606\\u003cbr\\u003enatural killer cell: -1.716\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.793\\u003cbr\\u003emature NK T cell: -4.436\\u003cbr\\u003eactivated type II NK T cell: -4.598\",\"value: -4.472\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.022\\u003cbr\\u003enatural killer cell: -2.289\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.283\\u003cbr\\u003einnate lymphoid cell: -3.443\\u003cbr\\u003elymphocyte: -4.144\",\"value: -5.945\\u003cbr\\u003eplasmacytoid dendritic cell: -0.206\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.952\\u003cbr\\u003edendritic cell: -3.292\\u003cbr\\u003edendritic cell, human: -5.473\\u003cbr\\u003emyeloid dendritic cell: -5.752\",\"value: -6.390\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.535\\u003cbr\\u003enatural killer cell: -1.719\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.013\\u003cbr\\u003emature NK T cell: -4.609\\u003cbr\\u003eactivated type II NK T cell: -4.619\",\"value: -6.170\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.494\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.137\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.279\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.011\",\"value: -6.140\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.477\\u003cbr\\u003emature NK T cell: -3.960\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.432\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.802\",\"value: -4.513\\u003cbr\\u003enatural killer cell: -0.433\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.652\\u003cbr\\u003elymphocyte: -3.119\\u003cbr\\u003eT cell: -4.321\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.454\",\"value: -5.738\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.641\\u003cbr\\u003enatural killer cell: -1.652\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.797\\u003cbr\\u003emature NK T cell: -3.510\\u003cbr\\u003eactivated type II NK T cell: -4.892\",\"value: -5.688\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.877\\u003cbr\\u003enatural killer cell: -1.490\\u003cbr\\u003emature NK T cell: -3.159\\u003cbr\\u003egamma-delta T cell: -3.683\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.823\",\"value: -6.111\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.520\\u003cbr\\u003enatural killer cell: -1.660\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.396\\u003cbr\\u003emature NK T cell: -4.077\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.908\",\"value: -6.357\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.609\\u003cbr\\u003enatural killer cell: -1.432\\u003cbr\\u003emature NK T cell: -3.758\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.266\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.832\",\"value: -6.717\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.675\\u003cbr\\u003eT follicular helper cell: -1.821\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.228\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.553\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.608\",\"value: -5.484\\u003cbr\\u003eclassical monocyte: -0.838\\u003cbr\\u003eCD14-positive monocyte: -1.560\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.276\\u003cbr\\u003emonocyte: -4.038\\u003cbr\\u003emacrophage: -4.428\",\"value: -5.313\\u003cbr\\u003enatural killer cell: -1.311\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.413\\u003cbr\\u003emature NK T cell: -3.249\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.515\\u003cbr\\u003egamma-delta T cell: -3.694\",\"value: -5.908\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -1.354\\u003cbr\\u003emature NK T cell: -3.570\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.344\\u003cbr\\u003egamma-delta T cell: -4.716\",\"value: -6.575\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.216\\u003cbr\\u003emucosal invariant T cell: -2.491\\u003cbr\\u003egamma-delta T cell: -2.497\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.969\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.066\",\"value: -5.003\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.850\\u003cbr\\u003enatural killer cell: -2.701\\u003cbr\\u003einnate lymphoid cell: -3.622\\u003cbr\\u003egroup 3 innate lymphoid cell: -4.261\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -4.292\",\"value: -5.845\\u003cbr\\u003eclassical monocyte: -1.184\\u003cbr\\u003eCD14-positive monocyte: -1.567\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.709\\u003cbr\\u003emonocyte: -3.736\\u003cbr\\u003emacrophage: -3.990\",\"value: -6.901\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.483\\u003cbr\\u003enatural killer cell: -1.466\\u003cbr\\u003emature NK T cell: -4.246\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.501\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.016\",\"value: -6.286\\u003cbr\\u003eCD14-positive monocyte: -1.068\\u003cbr\\u003eclassical monocyte: -1.213\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.182\\u003cbr\\u003emonocyte: -4.005\\u003cbr\\u003emacrophage: -4.101\",\"value: -5.569\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.836\\u003cbr\\u003enatural killer cell: -1.742\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.868\\u003cbr\\u003emature NK T cell: -3.406\\u003cbr\\u003egamma-delta T cell: -4.061\",\"value: -6.122\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.721\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.050\\u003cbr\\u003enatural killer cell: -2.191\\u003cbr\\u003egamma-delta T cell: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.364\",\"value: -5.113\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.749\\u003cbr\\u003enatural killer cell: -1.301\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.563\\u003cbr\\u003emature NK T cell: -3.836\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.679\",\"value: -6.396\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.167\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.820\\u003cbr\\u003enatural killer cell: -2.864\\u003cbr\\u003egamma-delta T cell: -3.026\\u003cbr\\u003eactivated type II NK T cell: -4.013\",\"value: -6.004\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.202\\u003cbr\\u003enatural killer cell: -1.913\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.072\\u003cbr\\u003emature NK T cell: -3.165\\u003cbr\\u003egamma-delta T cell: -3.312\",\"value: -3.840\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.505\\u003cbr\\u003ehematopoietic precursor cell: -2.592\\u003cbr\\u003ehematopoietic stem cell: -2.706\\u003cbr\\u003eprogenitor cell: -3.583\\u003cbr\\u003eserous secreting cell of bronchus submucosal gland: -4.019\",\"value: -6.438\\u003cbr\\u003eclassical monocyte: -1.483\\u003cbr\\u003eCD14-positive monocyte: -1.535\\u003cbr\\u003emonocyte: -2.986\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.006\\u003cbr\\u003emacrophage: -3.873\",\"value: -6.047\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.523\\u003cbr\\u003enatural killer cell: -1.784\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.706\\u003cbr\\u003emature NK T cell: -4.427\\u003cbr\\u003eactivated type II NK T cell: -4.487\",\"value: -4.906\\u003cbr\\u003econventional dendritic cell: -1.909\\u003cbr\\u003edendritic cell: -2.056\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.468\\u003cbr\\u003eplasmacytoid dendritic cell: -3.103\\u003cbr\\u003emacrophage: -3.284\",\"value: -4.887\\u003cbr\\u003enatural killer cell: -1.027\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.213\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.105\\u003cbr\\u003emature NK T cell: -4.272\\u003cbr\\u003egamma-delta T cell: -4.449\",\"value: -6.467\\u003cbr\\u003eclassical monocyte: -1.259\\u003cbr\\u003eCD14-positive monocyte: -1.412\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.421\\u003cbr\\u003emonocyte: -3.529\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.865\",\"value: -5.997\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.480\\u003cbr\\u003enatural killer cell: -1.779\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.524\\u003cbr\\u003emature NK T cell: -4.264\\u003cbr\\u003eactivated type II NK T cell: -4.793\",\"value: -5.186\\u003cbr\\u003eplasmacytoid dendritic cell: -0.208\\u003cbr\\u003edendritic cell: -2.930\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.140\\u003cbr\\u003emyeloid dendritic cell: -5.163\\u003cbr\\u003edendritic cell, human: -5.735\",\"value: -6.723\\u003cbr\\u003edendritic cell: -1.257\\u003cbr\\u003econventional dendritic cell: -2.002\\u003cbr\\u003edendritic cell, human: -2.342\\u003cbr\\u003emyeloid dendritic cell: -2.444\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.473\",\"value: -6.687\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.319\\u003cbr\\u003enatural killer cell: -2.475\\u003cbr\\u003egamma-delta T cell: -3.979\\u003cbr\\u003emature NK T cell: -3.989\",\"value: -5.940\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.164\\u003cbr\\u003enatural killer cell: -1.667\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.782\\u003cbr\\u003eactivated type II NK T cell: -3.786\\u003cbr\\u003emature NK T cell: -4.745\",\"value: -5.346\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.784\\u003cbr\\u003enatural killer cell: -2.889\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.352\\u003cbr\\u003eactivated type II NK T cell: -4.004\\u003cbr\\u003ehepatic pit cell: -4.372\",\"value: -5.511\\u003cbr\\u003enatural killer cell: -0.633\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.894\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.569\\u003cbr\\u003emature NK T cell: -3.996\\u003cbr\\u003egamma-delta T cell: -4.279\",\"value: -5.060\\u003cbr\\u003eclassical monocyte: -1.394\\u003cbr\\u003eCD14-positive monocyte: -2.017\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.683\\u003cbr\\u003emonocyte: -3.789\\u003cbr\\u003emyeloid leukocyte: -4.092\",\"value: -5.956\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.330\\u003cbr\\u003enatural killer cell: -2.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.037\\u003cbr\\u003emature NK T cell: -4.177\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.122\",\"value: -4.160\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.880\\u003cbr\\u003enatural killer cell: -2.245\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.432\\u003cbr\\u003egamma-delta T cell: -3.142\\u003cbr\\u003emature NK T cell: -3.835\",\"value: -5.603\\u003cbr\\u003eT follicular helper cell: -1.913\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.482\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.513\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.721\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.856\",\"value: -4.925\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.945\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.321\\u003cbr\\u003eT follicular helper cell: -2.351\\u003cbr\\u003eT-helper 22 cell: -2.787\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.429\",\"value: -5.391\\u003cbr\\u003eplasmacytoid dendritic cell: -0.368\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.662\\u003cbr\\u003edendritic cell: -2.868\\u003cbr\\u003emyeloid dendritic cell: -4.662\\u003cbr\\u003edendritic cell, human: -5.043\",\"value: -5.392\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.435\\u003cbr\\u003enatural killer cell: -2.242\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.105\\u003cbr\\u003egamma-delta T cell: -4.002\\u003cbr\\u003emature NK T cell: -4.088\",\"value: -6.036\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.718\\u003cbr\\u003enatural killer cell: -1.340\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.091\\u003cbr\\u003emature NK T cell: -4.017\\u003cbr\\u003eactivated type II NK T cell: -4.745\",\"value: -5.300\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.915\\u003cbr\\u003enatural killer cell: -1.560\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.563\\u003cbr\\u003emature NK T cell: -3.418\\u003cbr\\u003egamma-delta T cell: -3.830\",\"value: -6.012\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.513\\u003cbr\\u003enatural killer cell: -2.188\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.369\\u003cbr\\u003emature NK T cell: -4.376\\u003cbr\\u003eactivated type II NK T cell: -4.440\",\"value: -6.143\\u003cbr\\u003eclassical monocyte: -1.130\\u003cbr\\u003eCD14-positive monocyte: -1.411\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.023\\u003cbr\\u003emonocyte: -3.533\\u003cbr\\u003emacrophage: -4.270\",\"value: -6.241\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.423\\u003cbr\\u003enatural killer cell: -1.709\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.589\\u003cbr\\u003emature NK T cell: -4.533\\u003cbr\\u003eactivated type II NK T cell: -5.043\",\"value: -6.487\\u003cbr\\u003eclassical monocyte: -1.380\\u003cbr\\u003eCD14-positive monocyte: -1.995\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.741\\u003cbr\\u003emonocyte: -3.354\\u003cbr\\u003edendritic cell, human: -3.783\",\"value: -5.565\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.659\\u003cbr\\u003enatural killer cell: -1.562\\u003cbr\\u003emature NK T cell: -3.295\\u003cbr\\u003egamma-delta T cell: -3.935\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.256\",\"value: -6.633\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.612\\u003cbr\\u003egamma-delta T cell: -2.854\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.244\\u003cbr\\u003enatural killer cell: -3.548\\u003cbr\\u003emature NK T cell: -3.950\",\"value: -6.566\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.848\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.086\\u003cbr\\u003enatural killer cell: -2.439\\u003cbr\\u003emature NK T cell: -3.831\\u003cbr\\u003egamma-delta T cell: -3.875\",\"value: -7.337\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.512\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.384\\u003cbr\\u003enatural killer cell: -2.729\\u003cbr\\u003emature NK T cell: -4.367\\u003cbr\\u003eactivated type II NK T cell: -4.506\",\"value: -5.259\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.294\\u003cbr\\u003emature NK T cell: -3.590\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.348\\u003cbr\\u003egamma-delta T cell: -4.620\",\"value: -5.491\\u003cbr\\u003enatural killer cell: -0.991\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.166\\u003cbr\\u003eT cell: -3.360\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.631\\u003cbr\\u003emature NK T cell: -3.971\",\"value: -5.697\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.763\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.902\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.329\\u003cbr\\u003eT-helper 22 cell: -2.604\\u003cbr\\u003eT follicular helper cell: -2.844\",\"value: -5.529\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.470\\u003cbr\\u003enatural killer cell: -1.824\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.891\\u003cbr\\u003emature NK T cell: -4.075\\u003cbr\\u003eactivated type II NK T cell: -4.964\",\"value: -6.955\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.922\\u003cbr\\u003enatural killer cell: -2.409\\u003cbr\\u003egamma-delta T cell: -3.721\\u003cbr\\u003eactivated type II NK T cell: -4.025\",\"value: -6.522\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.757\\u003cbr\\u003enatural killer cell: -1.496\\u003cbr\\u003emature NK T cell: -3.242\\u003cbr\\u003egamma-delta T cell: -4.180\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.521\",\"value: -5.698\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.367\\u003cbr\\u003enatural killer cell: -2.012\\u003cbr\\u003emature NK T cell: -3.728\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.095\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.818\",\"value: -5.856\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.678\\u003cbr\\u003enatural killer cell: -2.280\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.144\\u003cbr\\u003egamma-delta T cell: -3.195\\u003cbr\\u003emature NK T cell: -3.341\",\"value: -6.785\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.453\\u003cbr\\u003eT follicular helper cell: -2.116\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.251\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.391\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.547\",\"value: -5.907\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.164\\u003cbr\\u003emature NK T cell: -4.163\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.317\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.764\",\"value: -5.980\\u003cbr\\u003eCD14-positive monocyte: -1.424\\u003cbr\\u003eclassical monocyte: -1.488\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.273\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.532\\u003cbr\\u003edendritic cell, human: -3.824\",\"value: -7.101\\u003cbr\\u003eCD14-positive monocyte: -1.627\\u003cbr\\u003eclassical monocyte: -1.793\\u003cbr\\u003edendritic cell, human: -3.051\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.279\\u003cbr\\u003edendritic cell: -3.587\",\"value: -6.389\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.546\\u003cbr\\u003enatural killer cell: -2.436\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.854\\u003cbr\\u003emature NK T cell: -3.844\\u003cbr\\u003egamma-delta T cell: -4.095\",\"value: -6.628\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.637\\u003cbr\\u003enatural killer cell: -1.345\\u003cbr\\u003emature NK T cell: -3.889\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.903\\u003cbr\\u003egamma-delta T cell: -4.802\",\"value: -4.997\\u003cbr\\u003edendritic cell: -1.819\\u003cbr\\u003eplasmacytoid dendritic cell: -1.861\\u003cbr\\u003econventional dendritic cell: -2.283\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.922\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.050\",\"value: -5.388\\u003cbr\\u003eplasmacytoid dendritic cell: -0.476\\u003cbr\\u003edendritic cell: -2.101\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.984\\u003cbr\\u003eB cell: -3.757\\u003cbr\\u003eplasma cell: -3.832\",\"value: -4.807\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.177\\u003cbr\\u003enatural killer cell: -2.917\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.993\\u003cbr\\u003egamma-delta T cell: -3.423\\u003cbr\\u003emature NK T cell: -3.687\",\"value: -4.964\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.768\\u003cbr\\u003eT-helper 22 cell: -2.700\\u003cbr\\u003eT follicular helper cell: -2.700\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.866\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.272\",\"value: -5.742\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.409\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.426\\u003cbr\\u003enatural killer cell: -2.011\\u003cbr\\u003egamma-delta T cell: -3.543\\u003cbr\\u003emature NK T cell: -4.114\",\"value: -6.968\\u003cbr\\u003eCD14-positive monocyte: -1.607\\u003cbr\\u003eclassical monocyte: -1.991\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.785\\u003cbr\\u003edendritic cell, human: -2.988\\u003cbr\\u003econventional dendritic cell: -3.023\",\"value: -6.732\\u003cbr\\u003eclassical monocyte: -2.083\\u003cbr\\u003eCD14-positive monocyte: -2.249\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.868\\u003cbr\\u003edendritic cell, human: -3.231\\u003cbr\\u003econventional dendritic cell: -3.469\",\"value: -6.597\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.980\\u003cbr\\u003enatural killer cell: -2.178\\u003cbr\\u003eactivated type II NK T cell: -4.413\\u003cbr\\u003emature NK T cell: -4.637\",\"value: -6.406\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.695\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.950\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.990\\u003cbr\\u003eT follicular helper cell: -2.444\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.771\",\"value: -6.093\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.909\\u003cbr\\u003enatural killer cell: -1.901\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.409\\u003cbr\\u003emature NK T cell: -3.721\\u003cbr\\u003egamma-delta T cell: -4.552\",\"value: -5.969\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.599\\u003cbr\\u003enatural killer cell: -1.580\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.517\\u003cbr\\u003emature NK T cell: -3.830\\u003cbr\\u003egamma-delta T cell: -3.993\",\"value: -5.523\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.930\\u003cbr\\u003enatural killer cell: -1.590\\u003cbr\\u003emature NK T cell: -3.384\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.541\\u003cbr\\u003egamma-delta T cell: -3.593\",\"value: -6.719\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.666\\u003cbr\\u003enatural killer cell: -1.702\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.443\\u003cbr\\u003emature NK T cell: -3.984\\u003cbr\\u003eactivated type II NK T cell: -4.438\",\"value: -7.773\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.803\\u003cbr\\u003egamma-delta T cell: -3.533\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.642\\u003cbr\\u003enatural killer cell: -3.680\\u003cbr\\u003einnate lymphoid cell: -3.807\",\"value: -6.237\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.470\\u003cbr\\u003enatural killer cell: -2.025\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.832\\u003cbr\\u003emature NK T cell: -3.828\\u003cbr\\u003egamma-delta T cell: -4.854\",\"value: -5.598\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.286\\u003cbr\\u003enatural killer cell: -1.537\\u003cbr\\u003emature NK T cell: -3.118\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.374\\u003cbr\\u003egamma-delta T cell: -3.577\",\"value: -5.715\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.559\\u003cbr\\u003enatural killer cell: -1.837\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.540\\u003cbr\\u003emature NK T cell: -4.036\\u003cbr\\u003eactivated type II NK T cell: -4.735\",\"value: -6.376\\u003cbr\\u003eclassical monocyte: -1.140\\u003cbr\\u003eCD14-positive monocyte: -1.604\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.359\\u003cbr\\u003edendritic cell, human: -3.502\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.587\",\"value: -5.743\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.714\\u003cbr\\u003enatural killer cell: -1.265\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.711\\u003cbr\\u003emature NK T cell: -4.214\\u003cbr\\u003eactivated type II NK T cell: -4.799\",\"value: -5.871\\u003cbr\\u003eclassical monocyte: -2.399\\u003cbr\\u003edendritic cell, human: -2.584\\u003cbr\\u003edendritic cell: -2.990\\u003cbr\\u003eCD14-positive monocyte: -3.161\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.261\",\"value: -7.066\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.869\\u003cbr\\u003egamma-delta T cell: -2.867\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.143\\u003cbr\\u003enatural killer cell: -3.283\\u003cbr\\u003emucosal invariant T cell: -3.971\",\"value: -6.366\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.078\\u003cbr\\u003enatural killer cell: -2.861\\u003cbr\\u003egamma-delta T cell: -3.065\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.181\\u003cbr\\u003emature NK T cell: -3.698\",\"value: -5.882\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003enatural killer cell: -2.273\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.234\\u003cbr\\u003emature NK T cell: -3.432\\u003cbr\\u003egamma-delta T cell: -3.632\",\"value: -6.543\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.615\\u003cbr\\u003enatural killer cell: -2.172\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.634\\u003cbr\\u003emature NK T cell: -4.021\\u003cbr\\u003eactivated type II NK T cell: -4.421\",\"value: -6.511\\u003cbr\\u003eT follicular helper cell: -1.824\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.150\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.257\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.705\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.945\",\"value: -5.949\\u003cbr\\u003eclassical monocyte: -1.103\\u003cbr\\u003eCD14-positive monocyte: -1.654\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.929\\u003cbr\\u003emonocyte: -3.879\\u003cbr\\u003emacrophage: -4.135\",\"value: -5.780\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.617\\u003cbr\\u003enatural killer cell: -1.675\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.387\\u003cbr\\u003emature NK T cell: -4.049\\u003cbr\\u003eactivated type II NK T cell: -4.578\",\"value: -6.107\\u003cbr\\u003eCD14-positive monocyte: -1.364\\u003cbr\\u003eclassical monocyte: -1.604\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.236\\u003cbr\\u003emonocyte: -3.405\\u003cbr\\u003emacrophage: -3.938\",\"value: -5.910\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.583\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.669\\u003cbr\\u003enatural killer cell: -2.586\\u003cbr\\u003egamma-delta T cell: -2.620\\u003cbr\\u003emature NK T cell: -3.451\",\"value: -6.097\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.835\\u003cbr\\u003enatural killer cell: -2.420\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.755\\u003cbr\\u003egamma-delta T cell: -3.826\\u003cbr\\u003eactivated type II NK T cell: -4.251\",\"value: -5.749\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.792\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.928\\u003cbr\\u003eT follicular helper cell: -2.589\\u003cbr\\u003eT-helper 22 cell: -2.698\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.119\",\"value: -6.087\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.746\\u003cbr\\u003enatural killer cell: -2.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.449\\u003cbr\\u003emature NK T cell: -3.547\\u003cbr\\u003egamma-delta T cell: -4.019\",\"value: -6.461\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.997\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.904\\u003cbr\\u003enatural killer cell: -2.109\\u003cbr\\u003egamma-delta T cell: -3.890\\u003cbr\\u003eactivated type II NK T cell: -4.085\",\"value: -6.881\\u003cbr\\u003eclassical monocyte: -1.564\\u003cbr\\u003eCD14-positive monocyte: -1.636\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.207\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.276\\u003cbr\\u003edendritic cell, human: -3.492\",\"value: -5.614\\u003cbr\\u003eplasmacytoid dendritic cell: -0.212\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.769\\u003cbr\\u003edendritic cell: -3.396\\u003cbr\\u003emyeloid dendritic cell: -5.676\\u003cbr\\u003edendritic cell, human: -5.786\",\"value: -5.518\\u003cbr\\u003eplasmacytoid dendritic cell: -0.218\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.815\\u003cbr\\u003edendritic cell: -3.313\\u003cbr\\u003edendritic cell, human: -5.441\\u003cbr\\u003emyeloid dendritic cell: -5.502\",\"value: -6.203\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.064\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.982\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.996\\u003cbr\\u003eT follicular helper cell: -3.204\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.211\",\"value: -5.985\\u003cbr\\u003eclassical monocyte: -1.175\\u003cbr\\u003eCD14-positive monocyte: -1.722\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.870\\u003cbr\\u003emacrophage: -3.671\\u003cbr\\u003emonocyte: -4.156\",\"value: -7.054\\u003cbr\\u003eT follicular helper cell: -1.710\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.845\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.951\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.507\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.807\",\"value: -5.855\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.690\\u003cbr\\u003enatural killer cell: -2.106\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.404\\u003cbr\\u003egamma-delta T cell: -3.757\\u003cbr\\u003emature NK T cell: -4.023\",\"value: -6.783\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.329\\u003cbr\\u003enatural killer cell: -2.012\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.120\\u003cbr\\u003emature NK T cell: -4.364\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.045\",\"value: -5.929\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.695\\u003cbr\\u003enatural killer cell: -1.415\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.127\\u003cbr\\u003emature NK T cell: -4.177\\u003cbr\\u003eactivated type II NK T cell: -4.666\",\"value: -4.183\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.360\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.675\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.785\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.887\\u003cbr\\u003emature NK T cell: -3.288\",\"value: -6.576\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.605\\u003cbr\\u003enatural killer cell: -1.431\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.894\\u003cbr\\u003emature NK T cell: -3.915\\u003cbr\\u003eactivated type II NK T cell: -4.842\",\"value: -5.609\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.627\\u003cbr\\u003enatural killer cell: -2.145\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.746\\u003cbr\\u003emature NK T cell: -4.046\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.374\",\"value: -5.681\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.276\\u003cbr\\u003enatural killer cell: -1.754\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.771\\u003cbr\\u003egamma-delta T cell: -3.216\\u003cbr\\u003emature NK T cell: -3.416\",\"value: -6.185\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.955\\u003cbr\\u003enatural killer cell: -2.617\\u003cbr\\u003egamma-delta T cell: -3.033\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.566\\u003cbr\\u003emature NK T cell: -3.676\",\"value: -5.670\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.835\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.155\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.167\\u003cbr\\u003eT-helper 22 cell: -2.564\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.722\",\"value: -5.938\\u003cbr\\u003eplasmacytoid dendritic cell: -0.281\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.624\\u003cbr\\u003edendritic cell: -3.046\\u003cbr\\u003emyeloid dendritic cell: -4.863\\u003cbr\\u003edendritic cell, human: -4.983\",\"value: -3.629\\u003cbr\\u003egamma-delta T cell: -1.868\\u003cbr\\u003emature NK T cell: -1.996\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.127\\u003cbr\\u003enatural killer cell: -2.364\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.554\",\"value: -5.410\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.181\\u003cbr\\u003enatural killer cell: -2.186\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.576\\u003cbr\\u003egamma-delta T cell: -2.852\\u003cbr\\u003emature NK T cell: -3.494\",\"value: -6.518\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.372\\u003cbr\\u003enatural killer cell: -1.831\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.883\\u003cbr\\u003emature NK T cell: -4.259\\u003cbr\\u003eactivated type II NK T cell: -5.147\",\"value: -5.365\\u003cbr\\u003eCD14-positive monocyte: -1.496\\u003cbr\\u003eclassical monocyte: -1.719\\u003cbr\\u003emonocyte: -2.433\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.294\\u003cbr\\u003epromonocyte: -4.017\",\"value: -6.243\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.388\\u003cbr\\u003enatural killer cell: -1.992\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.766\\u003cbr\\u003emature NK T cell: -4.178\\u003cbr\\u003eactivated type II NK T cell: -5.048\",\"value: -5.971\\u003cbr\\u003eplasmacytoid dendritic cell: -0.367\\u003cbr\\u003edendritic cell: -2.705\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.708\\u003cbr\\u003emyeloid dendritic cell: -4.921\\u003cbr\\u003edendritic cell, human: -4.969\",\"value: -6.002\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.768\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.829\\u003cbr\\u003eT-helper 22 cell: -2.206\\u003cbr\\u003eT follicular helper cell: -2.476\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.948\",\"value: -5.550\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.220\\u003cbr\\u003egamma-delta T cell: -2.585\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.886\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.645\\u003cbr\\u003emature NK T cell: -3.666\",\"value: -4.753\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.757\\u003cbr\\u003egamma-delta T cell: -3.216\\u003cbr\\u003eT cell: -3.417\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.441\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.631\",\"value: -4.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.664\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.721\\u003cbr\\u003enatural killer cell: -2.782\\u003cbr\\u003egamma-delta T cell: -3.842\\u003cbr\\u003einnate lymphoid cell: -4.169\",\"value: -5.243\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.160\\u003cbr\\u003enatural killer cell: -1.492\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.797\\u003cbr\\u003egamma-delta T cell: -3.325\\u003cbr\\u003emature NK T cell: -3.457\",\"value: -4.777\\u003cbr\\u003ehematopoietic precursor cell: -1.908\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.385\\u003cbr\\u003emast cell: -2.871\\u003cbr\\u003ehematopoietic stem cell: -3.383\\u003cbr\\u003egranulocyte: -3.655\",\"value: -5.834\\u003cbr\\u003eclassical monocyte: -1.046\\u003cbr\\u003eCD14-positive monocyte: -1.519\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.903\\u003cbr\\u003emonocyte: -4.004\\u003cbr\\u003emacrophage: -4.173\",\"value: -5.850\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.409\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.756\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.465\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.638\\u003cbr\\u003eT-helper 22 cell: -3.424\",\"value: -5.988\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.552\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.326\\u003cbr\\u003emature NK T cell: -3.827\\u003cbr\\u003eactivated type II NK T cell: -4.749\",\"value: -6.125\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.954\\u003cbr\\u003enatural killer cell: -2.220\\u003cbr\\u003eactivated type II NK T cell: -4.461\\u003cbr\\u003emature NK T cell: -4.588\",\"value: -5.919\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.680\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.717\\u003cbr\\u003eT follicular helper cell: -1.779\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.633\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.382\",\"value: -6.132\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.878\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.003\\u003cbr\\u003eactivated type II NK T cell: -4.026\\u003cbr\\u003emature NK T cell: -4.231\",\"value: -6.159\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.554\\u003cbr\\u003enatural killer cell: -1.692\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.568\\u003cbr\\u003emature NK T cell: -3.929\\u003cbr\\u003egamma-delta T cell: -4.686\",\"value: -6.322\\u003cbr\\u003eCD14-positive monocyte: -1.355\\u003cbr\\u003eclassical monocyte: -1.653\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.855\\u003cbr\\u003emonocyte: -3.679\\u003cbr\\u003emacrophage: -4.037\",\"value: -5.728\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.090\\u003cbr\\u003enatural killer cell: -2.736\\u003cbr\\u003egamma-delta T cell: -2.907\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.113\\u003cbr\\u003emature NK T cell: -3.764\",\"value: -6.414\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.500\\u003cbr\\u003emature NK T cell: -4.075\\u003cbr\\u003eactivated type II NK T cell: -4.478\",\"value: -6.489\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.411\\u003cbr\\u003enatural killer cell: -2.121\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.292\\u003cbr\\u003emature NK T cell: -4.299\\u003cbr\\u003egamma-delta T cell: -4.658\",\"value: -5.549\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.831\\u003cbr\\u003enatural killer cell: -1.593\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.556\\u003cbr\\u003emature NK T cell: -3.665\\u003cbr\\u003egamma-delta T cell: -4.281\",\"value: -5.000\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.864\\u003cbr\\u003enatural killer cell: -2.006\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.550\\u003cbr\\u003egamma-delta T cell: -3.881\\u003cbr\\u003eactivated type II NK T cell: -4.369\",\"value: -6.346\\u003cbr\\u003eclassical monocyte: -1.240\\u003cbr\\u003eCD14-positive monocyte: -1.246\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.107\\u003cbr\\u003emonocyte: -3.447\\u003cbr\\u003emacrophage: -4.109\",\"value: -5.484\\u003cbr\\u003eplasmacytoid dendritic cell: -0.229\\u003cbr\\u003edendritic cell: -2.847\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.922\\u003cbr\\u003emyeloid dendritic cell: -5.164\\u003cbr\\u003econventional dendritic cell: -5.492\",\"value: -4.969\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -1.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.818\\u003cbr\\u003emature NK T cell: -3.298\\u003cbr\\u003egamma-delta T cell: -3.769\",\"value: -5.774\\u003cbr\\u003enaive B cell: -1.205\\u003cbr\\u003eB cell: -1.324\\u003cbr\\u003emalignant cell: -3.164\\u003cbr\\u003eclass switched memory B cell: -3.462\\u003cbr\\u003ememory B cell: -3.486\",\"value: -6.092\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.314\\u003cbr\\u003enatural killer cell: -1.853\\u003cbr\\u003emature NK T cell: -4.542\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.748\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.051\",\"value: -6.254\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.651\\u003cbr\\u003enatural killer cell: -1.410\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.942\\u003cbr\\u003emature NK T cell: -4.012\\u003cbr\\u003egamma-delta T cell: -4.812\",\"value: -6.429\\u003cbr\\u003eCD14-positive monocyte: -1.241\\u003cbr\\u003eclassical monocyte: -1.335\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.892\\u003cbr\\u003emonocyte: -3.541\\u003cbr\\u003emacrophage: -4.069\",\"value: -6.247\\u003cbr\\u003eplasmacytoid dendritic cell: -0.323\\u003cbr\\u003edendritic cell: -2.524\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.801\\u003cbr\\u003emyeloid dendritic cell: -4.884\\u003cbr\\u003econventional dendritic cell: -4.898\",\"value: -5.620\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.900\\u003cbr\\u003enatural killer cell: -1.874\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.243\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003egamma-delta T cell: -4.081\",\"value: -5.029\\u003cbr\\u003eplasmacytoid dendritic cell: -0.327\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.508\\u003cbr\\u003edendritic cell: -2.740\\u003cbr\\u003emyeloid dendritic cell: -5.244\\u003cbr\\u003edendritic cell, human: -5.278\",\"value: -6.296\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.887\\u003cbr\\u003egamma-delta T cell: -2.940\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.402\\u003cbr\\u003enatural killer cell: -3.415\\u003cbr\\u003emature NK T cell: -3.669\",\"value: -5.923\\u003cbr\\u003eplasmacytoid dendritic cell: -0.401\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.354\\u003cbr\\u003edendritic cell: -2.972\\u003cbr\\u003edendritic cell, human: -4.869\\u003cbr\\u003econventional dendritic cell: -5.090\",\"value: -5.004\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.103\\u003cbr\\u003enatural killer cell: -1.530\\u003cbr\\u003emature NK T cell: -2.907\\u003cbr\\u003egamma-delta T cell: -3.573\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.802\",\"value: -5.306\\u003cbr\\u003eplasmacytoid dendritic cell: -0.237\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.840\\u003cbr\\u003edendritic cell: -3.126\\u003cbr\\u003edendritic cell, human: -5.680\\u003cbr\\u003emyeloid dendritic cell: -5.757\",\"value: -6.505\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.016\\u003cbr\\u003enatural killer cell: -1.598\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.920\\u003cbr\\u003emature NK T cell: -3.889\\u003cbr\\u003egamma-delta T cell: -4.138\",\"value: -5.468\\u003cbr\\u003eplasmacytoid dendritic cell: -0.330\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.358\\u003cbr\\u003edendritic cell: -3.110\\u003cbr\\u003edendritic cell, human: -5.422\\u003cbr\\u003emyeloid dendritic cell: -5.470\",\"value: -5.732\\u003cbr\\u003eplasmacytoid dendritic cell: -0.161\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.185\\u003cbr\\u003edendritic cell: -3.511\\u003cbr\\u003emyeloid dendritic cell: -5.856\\u003cbr\\u003edendritic cell, human: -6.104\",\"value: -6.693\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.493\\u003cbr\\u003enatural killer cell: -1.758\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.088\\u003cbr\\u003emature NK T cell: -3.991\\u003cbr\\u003eactivated type II NK T cell: -4.980\",\"value: -6.289\\u003cbr\\u003eclassical monocyte: -1.438\\u003cbr\\u003eCD14-positive monocyte: -1.728\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.985\\u003cbr\\u003edendritic cell, human: -3.638\\u003cbr\\u003emacrophage: -3.677\",\"value: -5.946\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.671\\u003cbr\\u003enatural killer cell: -1.977\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.588\\u003cbr\\u003eactivated type II NK T cell: -4.103\\u003cbr\\u003emature NK T cell: -4.615\",\"value: -5.957\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.549\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.777\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.095\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.271\\u003cbr\\u003eT-helper 22 cell: -3.237\",\"value: -6.327\\u003cbr\\u003enatural killer cell: -0.793\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.615\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.378\\u003cbr\\u003emature NK T cell: -4.023\\u003cbr\\u003eactivated type II NK T cell: -4.598\",\"value: -5.664\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.541\\u003cbr\\u003enatural killer cell: -2.008\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.780\\u003cbr\\u003emature NK T cell: -4.052\\u003cbr\\u003egamma-delta T cell: -4.616\",\"value: -5.795\\u003cbr\\u003eplasmacytoid dendritic cell: -0.266\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.727\\u003cbr\\u003edendritic cell: -2.977\\u003cbr\\u003edendritic cell, human: -5.224\\u003cbr\\u003emyeloid dendritic cell: -5.230\",\"value: -6.334\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.656\\u003cbr\\u003egamma-delta T cell: -3.199\\u003cbr\\u003enatural killer cell: -3.531\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.698\\u003cbr\\u003emature NK T cell: -3.795\",\"value: -4.588\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.748\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.909\\u003cbr\\u003enatural killer cell: -2.206\\u003cbr\\u003emature NK T cell: -2.517\\u003cbr\\u003egamma-delta T cell: -2.630\",\"value: -5.583\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.643\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.281\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.335\\u003cbr\\u003eT follicular helper cell: -2.948\\u003cbr\\u003eT-helper 22 cell: -3.106\",\"value: -4.423\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.947\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.318\\u003cbr\\u003eT follicular helper cell: -2.375\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.906\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.020\",\"value: -6.448\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.682\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.157\\u003cbr\\u003enatural killer cell: -2.342\\u003cbr\\u003emature NK T cell: -4.257\\u003cbr\\u003eactivated type II NK T cell: -4.340\",\"value: -5.674\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.582\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.947\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.548\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.009\\u003cbr\\u003eT-helper 22 cell: -3.031\",\"value: -5.910\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.952\\u003cbr\\u003enatural killer cell: -2.258\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.617\\u003cbr\\u003egamma-delta T cell: -3.460\\u003cbr\\u003emature NK T cell: -3.786\",\"value: -6.455\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.181\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.362\\u003cbr\\u003enatural killer cell: -2.485\\u003cbr\\u003egamma-delta T cell: -2.595\\u003cbr\\u003emature NK T cell: -3.283\",\"value: -6.644\\u003cbr\\u003eclassical monocyte: -1.074\\u003cbr\\u003eCD14-positive monocyte: -1.557\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.119\\u003cbr\\u003edendritic cell, human: -3.584\\u003cbr\\u003emacrophage: -3.737\",\"value: -5.456\\u003cbr\\u003enatural killer cell: -1.305\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.310\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.182\\u003cbr\\u003emature NK T cell: -3.723\\u003cbr\\u003egamma-delta T cell: -4.367\",\"value: -5.349\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.434\\u003cbr\\u003enatural killer cell: -1.662\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.910\\u003cbr\\u003emature NK T cell: -4.353\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.870\",\"value: -6.275\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.718\\u003cbr\\u003enatural killer cell: -1.178\\u003cbr\\u003emature NK T cell: -3.749\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.491\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.583\",\"value: -6.168\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.554\\u003cbr\\u003enatural killer cell: -3.282\\u003cbr\\u003egamma-delta T cell: -3.505\\u003cbr\\u003einnate lymphoid cell: -3.778\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.836\",\"value: -5.484\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.516\\u003cbr\\u003enatural killer cell: -2.579\\u003cbr\\u003egamma-delta T cell: -3.527\\u003cbr\\u003eactivated type II NK T cell: -4.162\",\"value: -5.632\\u003cbr\\u003eclassical monocyte: -0.966\\u003cbr\\u003eCD14-positive monocyte: -1.115\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.500\\u003cbr\\u003emonocyte: -3.830\\u003cbr\\u003eneutrophil: -4.261\",\"value: -6.418\\u003cbr\\u003emature NK T cell: -1.443\\u003cbr\\u003eCD14-low, CD16-positive monocyte: -1.577\\u003cbr\\u003enon-classical monocyte: -1.914\\u003cbr\\u003eCD14-positive monocyte: -3.926\\u003cbr\\u003eretinal cone cell: -4.151\",\"value: -7.350\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.944\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.829\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.002\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.173\\u003cbr\\u003eT follicular helper cell: -3.822\",\"value: -5.533\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.938\\u003cbr\\u003enatural killer cell: -1.249\\u003cbr\\u003emature NK T cell: -3.524\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.777\",\"value: -6.738\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.796\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.366\\u003cbr\\u003enatural killer cell: -2.107\\u003cbr\\u003eactivated type II NK T cell: -4.216\\u003cbr\\u003emature NK T cell: -4.856\",\"value: -5.399\\u003cbr\\u003eplasmacytoid dendritic cell: -0.233\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.847\\u003cbr\\u003edendritic cell: -2.923\\u003cbr\\u003emyeloid dendritic cell: -5.406\\u003cbr\\u003edendritic cell, human: -5.535\",\"value: -6.425\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.892\\u003cbr\\u003enatural killer cell: -1.281\\u003cbr\\u003emature NK T cell: -3.678\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.105\\u003cbr\\u003egamma-delta T cell: -4.272\",\"value: -4.902\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.594\\u003cbr\\u003enatural killer cell: -2.034\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.697\\u003cbr\\u003egamma-delta T cell: -3.310\\u003cbr\\u003emature NK T cell: -4.174\",\"value: -6.446\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.184\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.639\\u003cbr\\u003enatural killer cell: -2.646\\u003cbr\\u003egamma-delta T cell: -3.079\\u003cbr\\u003emature NK T cell: -3.478\",\"value: -7.820\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.674\\u003cbr\\u003egamma-delta T cell: -2.332\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.991\\u003cbr\\u003emucosal invariant T cell: -3.355\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.849\",\"value: -5.806\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.385\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -1.684\\u003cbr\\u003egamma-delta T cell: -3.850\\u003cbr\\u003eactivated type II NK T cell: -3.872\",\"value: -5.987\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.979\\u003cbr\\u003enatural killer cell: -2.617\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.760\\u003cbr\\u003egamma-delta T cell: -2.923\\u003cbr\\u003emature NK T cell: -4.232\",\"value: -5.596\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.773\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.602\\u003cbr\\u003emature NK T cell: -3.993\\u003cbr\\u003eactivated type II NK T cell: -4.792\",\"value: -6.596\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.703\\u003cbr\\u003egamma-delta T cell: -2.192\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.609\\u003cbr\\u003enatural killer cell: -2.933\\u003cbr\\u003emature NK T cell: -3.196\",\"value: -5.593\\u003cbr\\u003eplasmacytoid dendritic cell: -0.191\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.741\\u003cbr\\u003edendritic cell: -3.658\\u003cbr\\u003edendritic cell, human: -5.882\\u003cbr\\u003emyeloid dendritic cell: -6.016\",\"value: -5.876\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.809\\u003cbr\\u003enatural killer cell: -1.953\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.227\\u003cbr\\u003emature NK T cell: -3.329\\u003cbr\\u003egamma-delta T cell: -4.604\",\"value: -7.100\\u003cbr\\u003edendritic cell: -1.601\\u003cbr\\u003edendritic cell, human: -2.083\\u003cbr\\u003econventional dendritic cell: -2.182\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.515\\u003cbr\\u003emyeloid dendritic cell: -2.655\",\"value: -5.106\\u003cbr\\u003enatural killer cell: -1.602\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.729\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.612\\u003cbr\\u003emature NK T cell: -2.987\\u003cbr\\u003egamma-delta T cell: -3.480\",\"value: -5.209\\u003cbr\\u003eplasmacytoid dendritic cell: -2.626\\u003cbr\\u003emast cell: -3.074\\u003cbr\\u003edendritic cell: -3.121\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.164\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.385\",\"value: -5.358\\u003cbr\\u003eplasmacytoid dendritic cell: -0.171\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.887\\u003cbr\\u003edendritic cell: -3.676\\u003cbr\\u003emyeloid dendritic cell: -6.036\\u003cbr\\u003edendritic cell, human: -6.117\",\"value: -6.640\\u003cbr\\u003eCD14-positive monocyte: -1.137\\u003cbr\\u003eclassical monocyte: -1.496\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.179\\u003cbr\\u003emacrophage: -3.740\\u003cbr\\u003edendritic cell, human: -4.073\",\"value: -5.946\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003enatural killer cell: -1.643\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.670\\u003cbr\\u003emature NK T cell: -3.855\\u003cbr\\u003eactivated type II NK T cell: -4.712\",\"value: -5.142\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.247\\u003cbr\\u003enatural killer cell: -1.358\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.870\\u003cbr\\u003emature NK T cell: -3.452\\u003cbr\\u003egamma-delta T cell: -3.882\",\"value: -5.786\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.810\\u003cbr\\u003enatural killer cell: -1.811\\u003cbr\\u003egamma-delta T cell: -3.262\\u003cbr\\u003emature NK T cell: -3.462\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.539\",\"value: -6.039\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.478\\u003cbr\\u003enatural killer cell: -1.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.562\\u003cbr\\u003emature NK T cell: -3.919\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.828\",\"value: -5.817\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.878\\u003cbr\\u003eT follicular helper cell: -2.070\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.154\\u003cbr\\u003eT-helper 22 cell: -2.571\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.983\",\"value: -6.674\\u003cbr\\u003eclassical monocyte: -1.843\\u003cbr\\u003eCD14-positive monocyte: -2.152\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.602\\u003cbr\\u003edendritic cell, human: -2.665\\u003cbr\\u003econventional dendritic cell: -3.183\",\"value: -5.469\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.435\\u003cbr\\u003enatural killer cell: -1.907\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.081\\u003cbr\\u003emature NK T cell: -4.065\\u003cbr\\u003eactivated type II NK T cell: -4.938\",\"value: -5.647\\u003cbr\\u003edendritic cell: -1.609\\u003cbr\\u003econventional dendritic cell: -1.671\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.548\\u003cbr\\u003edendritic cell, human: -2.595\\u003cbr\\u003emyeloid dendritic cell: -2.797\",\"value: -5.929\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.498\\u003cbr\\u003enatural killer cell: -1.888\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.474\\u003cbr\\u003emature NK T cell: -4.257\\u003cbr\\u003egamma-delta T cell: -4.676\",\"value: -6.625\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.457\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.901\\u003cbr\\u003egamma-delta T cell: -2.372\\u003cbr\\u003enatural killer cell: -2.731\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.514\",\"value: -5.428\\u003cbr\\u003eplasmacytoid dendritic cell: -0.638\\u003cbr\\u003edendritic cell: -2.145\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.242\\u003cbr\\u003econventional dendritic cell: -3.861\\u003cbr\\u003edendritic cell, human: -4.276\",\"value: -6.048\\u003cbr\\u003enatural killer cell: -0.747\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.162\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.760\\u003cbr\\u003emature NK T cell: -4.092\\u003cbr\\u003eactivated type II NK T cell: -4.741\",\"value: -5.644\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.660\\u003cbr\\u003enatural killer cell: -1.435\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.008\\u003cbr\\u003emature NK T cell: -4.108\\u003cbr\\u003eactivated type II NK T cell: -4.856\",\"value: -6.202\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.670\\u003cbr\\u003eT follicular helper cell: -1.973\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.990\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.361\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.721\",\"value: -6.763\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.539\\u003cbr\\u003enatural killer cell: -1.505\\u003cbr\\u003emature NK T cell: -3.883\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.730\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.875\",\"value: -5.916\\u003cbr\\u003eclassical monocyte: -1.001\\u003cbr\\u003eCD14-positive monocyte: -1.532\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.228\\u003cbr\\u003emonocyte: -3.695\\u003cbr\\u003emacrophage: -3.926\",\"value: -5.330\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.679\\u003cbr\\u003enatural killer cell: -1.394\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.518\\u003cbr\\u003emature NK T cell: -4.198\\u003cbr\\u003eactivated type II NK T cell: -4.436\",\"value: -5.709\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -2.996\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.184\\u003cbr\\u003egamma-delta T cell: -3.472\\u003cbr\\u003emature NK T cell: -4.009\",\"value: -4.828\\u003cbr\\u003eclassical monocyte: -1.244\\u003cbr\\u003emacrophage: -2.122\\u003cbr\\u003ealveolar macrophage: -2.983\\u003cbr\\u003eCD14-positive monocyte: -3.298\\u003cbr\\u003enon-classical monocyte: -3.550\",\"value: -6.397\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.565\\u003cbr\\u003enatural killer cell: -1.815\\u003cbr\\u003emature NK T cell: -3.367\\u003cbr\\u003egamma-delta T cell: -4.501\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.627\",\"value: -5.870\\u003cbr\\u003eplasmacytoid dendritic cell: -0.510\\u003cbr\\u003edendritic cell: -2.330\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.557\\u003cbr\\u003edendritic cell, human: -4.748\\u003cbr\\u003emyeloid dendritic cell: -4.855\",\"value: -4.862\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.869\\u003cbr\\u003enatural killer cell: -2.737\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.081\\u003cbr\\u003egamma-delta T cell: -3.215\\u003cbr\\u003einnate lymphoid cell: -3.954\",\"value: -6.522\\u003cbr\\u003eclassical monocyte: -1.312\\u003cbr\\u003eCD14-positive monocyte: -1.677\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.174\\u003cbr\\u003edendritic cell, human: -3.475\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.585\",\"value: -5.143\\u003cbr\\u003eT-helper 22 cell: -2.137\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.223\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.245\\u003cbr\\u003eT follicular helper cell: -2.436\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.836\",\"value: -6.402\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.570\\u003cbr\\u003enatural killer cell: -1.521\\u003cbr\\u003emature NK T cell: -3.958\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.054\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.638\",\"value: -5.317\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.163\\u003cbr\\u003enatural killer cell: -1.812\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.516\\u003cbr\\u003emature NK T cell: -2.695\\u003cbr\\u003egamma-delta T cell: -2.916\",\"value: -6.618\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.551\\u003cbr\\u003enatural killer cell: -1.474\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.086\\u003cbr\\u003emature NK T cell: -4.151\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.917\",\"value: -6.844\\u003cbr\\u003enatural killer cell: -1.136\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.235\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.963\\u003cbr\\u003emature NK T cell: -4.115\\u003cbr\\u003egamma-delta T cell: -4.250\",\"value: -5.503\\u003cbr\\u003eplasmacytoid dendritic cell: -0.309\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.667\\u003cbr\\u003edendritic cell: -2.946\\u003cbr\\u003econventional dendritic cell: -4.285\\u003cbr\\u003emyeloid dendritic cell: -4.849\",\"value: -6.009\\u003cbr\\u003eCD14-positive monocyte: -1.066\\u003cbr\\u003eclassical monocyte: -1.130\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.265\\u003cbr\\u003emonocyte: -3.836\\u003cbr\\u003emacrophage: -4.274\",\"value: -6.083\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.968\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.647\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.785\\u003cbr\\u003eT follicular helper cell: -2.901\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.185\",\"value: -5.027\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.044\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.990\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.403\\u003cbr\\u003eT-helper 22 cell: -3.548\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.562\",\"value: -5.834\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.996\\u003cbr\\u003enatural killer cell: -1.204\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.152\\u003cbr\\u003egamma-delta T cell: -3.621\\u003cbr\\u003emature NK T cell: -3.653\",\"value: -6.603\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.944\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.291\\u003cbr\\u003egamma-delta T cell: -2.668\\u003cbr\\u003enatural killer cell: -2.817\\u003cbr\\u003emature NK T cell: -3.503\",\"value: -5.809\\u003cbr\\u003eT follicular helper cell: -1.834\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.894\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.392\\u003cbr\\u003eT-helper 22 cell: -3.054\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.124\",\"value: -5.451\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.595\\u003cbr\\u003enatural killer cell: -1.610\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.771\\u003cbr\\u003emature NK T cell: -3.863\\u003cbr\\u003egamma-delta T cell: -4.919\",\"value: -6.440\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.726\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.102\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.463\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.468\",\"value: -6.891\\u003cbr\\u003eCD14-positive monocyte: -1.183\\u003cbr\\u003eclassical monocyte: -1.438\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.110\\u003cbr\\u003emonocyte: -3.532\\u003cbr\\u003edendritic cell, human: -3.801\",\"value: -6.176\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.687\\u003cbr\\u003enatural killer cell: -1.390\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.945\\u003cbr\\u003emature NK T cell: -4.336\\u003cbr\\u003eactivated type II NK T cell: -4.523\",\"value: -6.577\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.959\\u003cbr\\u003egamma-delta T cell: -2.114\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.329\\u003cbr\\u003emucosal invariant T cell: -3.398\\u003cbr\\u003emature NK T cell: -3.486\",\"value: -6.352\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.861\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.192\\u003cbr\\u003enatural killer cell: -2.212\\u003cbr\\u003emature NK T cell: -3.263\\u003cbr\\u003egamma-delta T cell: -3.812\",\"value: -6.209\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.780\\u003cbr\\u003enatural killer cell: -1.119\\u003cbr\\u003emature NK T cell: -3.701\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.006\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.889\",\"value: -4.699\\u003cbr\\u003ehematopoietic precursor cell: -1.604\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.703\\u003cbr\\u003ehematopoietic stem cell: -2.708\\u003cbr\\u003emast cell: -3.702\\u003cbr\\u003eerythroid progenitor cell: -4.103\",\"value: -4.552\\u003cbr\\u003emast cell: -2.371\\u003cbr\\u003ehematopoietic precursor cell: -2.411\\u003cbr\\u003ehematopoietic stem cell: -2.999\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -3.351\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.810\",\"value: -5.157\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.185\\u003cbr\\u003enatural killer cell: -2.291\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.415\\u003cbr\\u003egamma-delta T cell: -3.967\\u003cbr\\u003einnate lymphoid cell: -3.968\",\"value: -5.399\\u003cbr\\u003eplasmacytoid dendritic cell: -0.247\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.701\\u003cbr\\u003edendritic cell: -3.328\\u003cbr\\u003emyeloid dendritic cell: -5.094\\u003cbr\\u003edendritic cell, human: -5.301\",\"value: -6.468\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.654\\u003cbr\\u003enatural killer cell: -1.925\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.372\\u003cbr\\u003eactivated type II NK T cell: -4.280\\u003cbr\\u003emature NK T cell: -4.684\",\"value: -6.754\\u003cbr\\u003eclassical monocyte: -2.090\\u003cbr\\u003eCD14-positive monocyte: -2.153\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.541\\u003cbr\\u003edendritic cell, human: -2.688\\u003cbr\\u003econventional dendritic cell: -3.141\",\"value: -4.770\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.575\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.698\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.472\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.630\\u003cbr\\u003eT-helper 22 cell: -2.637\",\"value: -5.401\\u003cbr\\u003eplasmacytoid dendritic cell: -0.445\\u003cbr\\u003edendritic cell: -2.821\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.110\\u003cbr\\u003eplasma cell: -3.686\\u003cbr\\u003eB cell: -4.213\",\"value: -4.897\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.801\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.758\\u003cbr\\u003enatural killer cell: -2.363\\u003cbr\\u003egamma-delta T cell: -3.877\\u003cbr\\u003emature NK T cell: -4.341\",\"value: -5.973\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.764\\u003cbr\\u003enatural killer cell: -1.323\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.559\\u003cbr\\u003emature NK T cell: -3.620\\u003cbr\\u003egamma-delta T cell: -4.568\",\"value: -5.737\\u003cbr\\u003eclassical monocyte: -0.967\\u003cbr\\u003eCD14-positive monocyte: -1.260\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.248\\u003cbr\\u003emonocyte: -3.855\\u003cbr\\u003emacrophage: -4.528\",\"value: -6.258\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.165\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.517\\u003cbr\\u003enatural killer cell: -2.797\\u003cbr\\u003eactivated type II NK T cell: -3.791\\u003cbr\\u003egamma-delta T cell: -3.874\",\"value: -5.364\\u003cbr\\u003eplasmacytoid dendritic cell: -0.332\\u003cbr\\u003edendritic cell: -2.432\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.821\\u003cbr\\u003emyeloid dendritic cell: -4.616\\u003cbr\\u003econventional dendritic cell: -4.695\",\"value: -6.973\\u003cbr\\u003eclassical monocyte: -1.213\\u003cbr\\u003eCD14-positive monocyte: -1.432\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.368\\u003cbr\\u003edendritic cell, human: -3.617\\u003cbr\\u003edendritic cell: -3.788\",\"value: -6.475\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.407\\u003cbr\\u003enatural killer cell: -2.004\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.257\\u003cbr\\u003emature NK T cell: -4.129\\u003cbr\\u003eactivated type II NK T cell: -4.788\",\"value: -5.568\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.528\\u003cbr\\u003enatural killer cell: -1.915\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.562\\u003cbr\\u003emature NK T cell: -4.038\\u003cbr\\u003egamma-delta T cell: -4.590\",\"value: -6.732\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.376\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.197\\u003cbr\\u003enatural killer cell: -2.682\\u003cbr\\u003egamma-delta T cell: -3.105\\u003cbr\\u003emature NK T cell: -3.129\",\"value: -6.333\\u003cbr\\u003eclassical monocyte: -1.161\\u003cbr\\u003eCD14-positive monocyte: -1.703\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.089\\u003cbr\\u003emonocyte: -3.677\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.845\",\"value: -4.846\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.404\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.056\\u003cbr\\u003enatural killer cell: -2.294\\u003cbr\\u003egamma-delta T cell: -2.456\\u003cbr\\u003emature NK T cell: -3.506\",\"value: -4.934\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.446\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.566\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.153\\u003cbr\\u003eT follicular helper cell: -2.764\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.763\",\"value: -5.541\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.516\\u003cbr\\u003enatural killer cell: -1.703\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.793\\u003cbr\\u003emature NK T cell: -4.429\\u003cbr\\u003eactivated type II NK T cell: -4.820\",\"value: -5.949\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.673\\u003cbr\\u003enatural killer cell: -2.775\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.041\\u003cbr\\u003egamma-delta T cell: -3.889\\u003cbr\\u003einnate lymphoid cell: -4.138\",\"value: -5.937\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.448\\u003cbr\\u003enatural killer cell: -2.019\\u003cbr\\u003emature NK T cell: -3.739\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.960\\u003cbr\\u003egamma-delta T cell: -4.722\",\"value: -4.480\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.132\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.489\\u003cbr\\u003eT follicular helper cell: -2.718\\u003cbr\\u003eT-helper 22 cell: -2.895\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.659\",\"value: -4.126\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.009\\u003cbr\\u003enatural killer cell: -2.003\\u003cbr\\u003egamma-delta T cell: -2.690\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.102\\u003cbr\\u003emature NK T cell: -3.157\",\"value: -5.374\\u003cbr\\u003eplasmacytoid dendritic cell: -1.337\\u003cbr\\u003emast cell: -2.344\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.931\\u003cbr\\u003edendritic cell: -3.380\\u003cbr\\u003ehematopoietic precursor cell: -3.580\",\"value: -5.866\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.642\\u003cbr\\u003egamma-delta T cell: -2.669\\u003cbr\\u003enatural killer cell: -3.061\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.352\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.382\",\"value: -4.754\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.713\\u003cbr\\u003enatural killer cell: -2.410\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.419\\u003cbr\\u003einnate lymphoid cell: -3.774\\u003cbr\\u003egamma-delta T cell: -3.905\",\"value: -5.580\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.386\\u003cbr\\u003enatural killer cell: -1.959\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.579\\u003cbr\\u003emature NK T cell: -4.297\\u003cbr\\u003eactivated type II NK T cell: -5.035\",\"value: -6.046\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.625\\u003cbr\\u003enatural killer cell: -1.408\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.864\\u003cbr\\u003emature NK T cell: -3.941\\u003cbr\\u003eactivated type II NK T cell: -4.937\",\"value: -5.852\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.499\\u003cbr\\u003enatural killer cell: -1.669\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.520\\u003cbr\\u003emature NK T cell: -4.129\\u003cbr\\u003eactivated type II NK T cell: -4.678\",\"value: -7.029\\u003cbr\\u003enon-classical monocyte: -1.883\\u003cbr\\u003eCD14-low, CD16-positive monocyte: -2.070\\u003cbr\\u003emature NK T cell: -2.093\\u003cbr\\u003eCD14-positive monocyte: -2.343\\u003cbr\\u003eclassical monocyte: -3.337\",\"value: -6.146\\u003cbr\\u003emast cell: -2.266\\u003cbr\\u003eplasmacytoid dendritic cell: -2.493\\u003cbr\\u003ehematopoietic precursor cell: -2.817\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -3.018\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.486\",\"value: -5.458\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.692\\u003cbr\\u003enatural killer cell: -1.719\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.814\\u003cbr\\u003emature NK T cell: -4.079\\u003cbr\\u003eactivated type II NK T cell: -4.241\",\"value: -5.893\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.298\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.800\\u003cbr\\u003emature NK T cell: -4.511\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.118\",\"value: -5.455\\u003cbr\\u003eplasmacytoid dendritic cell: -0.371\\u003cbr\\u003edendritic cell: -2.472\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.607\\u003cbr\\u003emyeloid dendritic cell: -4.795\\u003cbr\\u003econventional dendritic cell: -4.868\",\"value: -6.711\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.368\\u003cbr\\u003enatural killer cell: -2.115\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.533\\u003cbr\\u003emature NK T cell: -4.403\\u003cbr\\u003eactivated type II NK T cell: -5.027\",\"value: -5.602\\u003cbr\\u003eCD14-positive monocyte: -1.102\\u003cbr\\u003eclassical monocyte: -1.608\\u003cbr\\u003emonocyte: -3.069\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.159\\u003cbr\\u003epromonocyte: -4.018\",\"value: -6.272\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.431\\u003cbr\\u003enatural killer cell: -1.789\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.341\\u003cbr\\u003emature NK T cell: -4.264\\u003cbr\\u003eactivated type II NK T cell: -5.052\",\"value: -5.906\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.781\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.873\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.132\\u003cbr\\u003eT follicular helper cell: -2.669\\u003cbr\\u003eT-helper 22 cell: -2.674\",\"value: -5.735\\u003cbr\\u003eCD14-positive monocyte: -0.927\\u003cbr\\u003eclassical monocyte: -1.240\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.192\\u003cbr\\u003emonocyte: -3.660\\u003cbr\\u003eneutrophil: -4.345\",\"value: -6.637\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.834\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.809\\u003cbr\\u003enatural killer cell: -3.097\\u003cbr\\u003egamma-delta T cell: -3.377\\u003cbr\\u003emature NK T cell: -4.136\",\"value: -5.160\\u003cbr\\u003eCD14-positive monocyte: -1.024\\u003cbr\\u003eclassical monocyte: -1.989\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.772\\u003cbr\\u003emonocyte: -3.049\\u003cbr\\u003epromonocyte: -4.124\",\"value: -5.689\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.924\\u003cbr\\u003enatural killer cell: -3.272\\u003cbr\\u003egamma-delta T cell: -3.449\\u003cbr\\u003einnate lymphoid cell: -3.812\\u003cbr\\u003emature NK T cell: -4.016\",\"value: -4.576\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.190\\u003cbr\\u003enatural killer cell: -1.365\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.920\\u003cbr\\u003emature NK T cell: -3.436\\u003cbr\\u003egamma-delta T cell: -3.848\",\"value: -6.707\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.544\\u003cbr\\u003enatural killer cell: -1.399\\u003cbr\\u003emature NK T cell: -4.069\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.420\\u003cbr\\u003eactivated type II NK T cell: -4.895\",\"value: -6.405\\u003cbr\\u003eCD14-positive monocyte: -1.245\\u003cbr\\u003eclassical monocyte: -1.419\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.297\\u003cbr\\u003emacrophage: -3.909\\u003cbr\\u003edendritic cell, human: -4.084\",\"value: -4.599\\u003cbr\\u003emast cell: -1.103\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.579\\u003cbr\\u003edendritic cell: -3.890\\u003cbr\\u003ehematopoietic precursor cell: -3.904\\u003cbr\\u003egranulocyte: -3.918\",\"value: -5.485\\u003cbr\\u003enatural killer cell: -0.932\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.214\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.580\\u003cbr\\u003eactivated type II NK T cell: -3.817\\u003cbr\\u003emature NK T cell: -3.996\",\"value: -6.347\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.031\\u003cbr\\u003enatural killer cell: -1.833\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.798\\u003cbr\\u003emature NK T cell: -3.687\\u003cbr\\u003egamma-delta T cell: -3.934\",\"value: -5.273\\u003cbr\\u003eplasmacytoid dendritic cell: -0.374\\u003cbr\\u003edendritic cell: -2.278\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.897\\u003cbr\\u003emyeloid dendritic cell: -4.314\\u003cbr\\u003edendritic cell, human: -4.840\",\"value: -4.661\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.478\\u003cbr\\u003eT-helper 22 cell: -2.509\\u003cbr\\u003eT follicular helper cell: -2.693\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.872\\u003cbr\\u003emucosal invariant T cell: -2.903\",\"value: -4.863\\u003cbr\\u003eplasma cell: -1.414\\u003cbr\\u003eB cell: -1.606\\u003cbr\\u003eplasmablast: -2.719\\u003cbr\\u003eIgA plasma cell: -3.658\\u003cbr\\u003eIgM plasma cell: -3.685\",\"value: -7.067\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.708\\u003cbr\\u003eT follicular helper cell: -1.849\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.966\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.280\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.044\",\"value: -5.875\\u003cbr\\u003eclassical monocyte: -1.143\\u003cbr\\u003eCD14-positive monocyte: -1.153\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.086\\u003cbr\\u003emonocyte: -3.990\\u003cbr\\u003emacrophage: -4.008\",\"value: -4.838\\u003cbr\\u003eclassical monocyte: -1.165\\u003cbr\\u003eCD14-positive monocyte: -1.718\\u003cbr\\u003emacrophage: -2.932\\u003cbr\\u003emonocyte: -3.422\\u003cbr\\u003eneutrophil: -3.794\",\"value: -6.717\\u003cbr\\u003eclassical monocyte: -1.626\\u003cbr\\u003eCD14-positive monocyte: -1.711\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.986\\u003cbr\\u003edendritic cell, human: -3.299\\u003cbr\\u003emacrophage: -3.703\",\"value: -5.805\\u003cbr\\u003econventional dendritic cell: -1.620\\u003cbr\\u003edendritic cell: -1.782\\u003cbr\\u003edendritic cell, human: -2.247\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.374\\u003cbr\\u003emyeloid dendritic cell: -2.502\",\"value: -6.991\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.529\\u003cbr\\u003enatural killer cell: -1.459\\u003cbr\\u003emature NK T cell: -4.095\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.703\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.803\",\"value: -5.757\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.648\\u003cbr\\u003enatural killer cell: -1.218\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.848\\u003cbr\\u003emature NK T cell: -4.250\\u003cbr\\u003eactivated type II NK T cell: -4.911\",\"value: -4.697\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.968\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.315\\u003cbr\\u003eT follicular helper cell: -2.611\\u003cbr\\u003eT-helper 22 cell: -2.999\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.299\",\"value: -5.833\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.263\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.880\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.940\\u003cbr\\u003eT-helper 22 cell: -3.216\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.219\",\"value: -3.918\\u003cbr\\u003edendritic cell: -2.661\\u003cbr\\u003elymphocyte: -3.291\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.604\\u003cbr\\u003eclassical monocyte: -3.628\\u003cbr\\u003emacrophage: -3.742\",\"value: -6.127\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.890\\u003cbr\\u003enatural killer cell: -1.127\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.483\\u003cbr\\u003emature NK T cell: -4.185\\u003cbr\\u003eactivated type II NK T cell: -4.568\",\"value: -4.594\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.032\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.210\\u003cbr\\u003enatural killer cell: -2.382\\u003cbr\\u003egamma-delta T cell: -2.769\\u003cbr\\u003emature NK T cell: -3.270\",\"value: -4.965\\u003cbr\\u003eplasmacytoid dendritic cell: -0.172\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.918\\u003cbr\\u003edendritic cell: -3.667\\u003cbr\\u003eB cell: -5.801\\u003cbr\\u003eplasma cell: -6.013\",\"value: -5.144\\u003cbr\\u003eclassical monocyte: -0.973\\u003cbr\\u003emacrophage: -2.498\\u003cbr\\u003eCD14-positive monocyte: -2.795\\u003cbr\\u003emonocyte: -3.040\\u003cbr\\u003ealveolar macrophage: -3.332\",\"value: -6.119\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.509\\u003cbr\\u003enatural killer cell: -1.714\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.373\\u003cbr\\u003emature NK T cell: -4.167\\u003cbr\\u003eactivated type II NK T cell: -4.898\",\"value: -4.738\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.248\\u003cbr\\u003enatural killer cell: -1.424\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.911\\u003cbr\\u003emature NK T cell: -3.188\\u003cbr\\u003egamma-delta T cell: -3.540\",\"value: -5.106\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -1.729\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -1.772\\u003cbr\\u003egamma-delta T cell: -2.495\\u003cbr\\u003emature NK T cell: -2.887\\u003cbr\\u003emucosal invariant T cell: -3.220\",\"value: -6.219\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.389\\u003cbr\\u003enatural killer cell: -1.891\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.624\\u003cbr\\u003emature NK T cell: -4.833\\u003cbr\\u003eactivated type II NK T cell: -4.968\",\"value: -5.892\\u003cbr\\u003eclassical monocyte: -1.868\\u003cbr\\u003eCD14-positive monocyte: -2.599\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.614\\u003cbr\\u003edendritic cell, human: -2.666\\u003cbr\\u003econventional dendritic cell: -2.955\",\"value: -5.101\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.759\\u003cbr\\u003enatural killer cell: -1.500\\u003cbr\\u003egamma-delta T cell: -3.484\\u003cbr\\u003emature NK T cell: -3.589\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.873\",\"value: -5.045\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.452\\u003cbr\\u003egamma-delta T cell: -2.466\\u003cbr\\u003enatural killer cell: -2.614\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.663\\u003cbr\\u003emature NK T cell: -2.992\",\"value: -5.398\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.439\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.327\\u003cbr\\u003eT follicular helper cell: -2.451\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.675\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.729\",\"value: -6.619\\u003cbr\\u003eclassical monocyte: -2.061\\u003cbr\\u003eCD14-positive monocyte: -2.266\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.431\\u003cbr\\u003edendritic cell, human: -2.641\\u003cbr\\u003edendritic cell: -3.007\",\"value: -6.001\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.578\\u003cbr\\u003enatural killer cell: -1.494\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.593\\u003cbr\\u003emature NK T cell: -4.131\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.862\",\"value: -6.703\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.445\\u003cbr\\u003enatural killer cell: -2.257\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.695\\u003cbr\\u003emature NK T cell: -4.536\\u003cbr\\u003eactivated type II NK T cell: -4.624\",\"value: -4.948\\u003cbr\\u003eclassical monocyte: -0.850\\u003cbr\\u003eCD14-positive monocyte: -1.974\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.111\\u003cbr\\u003emacrophage: -3.515\\u003cbr\\u003emonocyte: -3.616\",\"value: -5.899\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.256\\u003cbr\\u003egamma-delta T cell: -3.350\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.765\\u003cbr\\u003emature NK T cell: -3.861\\u003cbr\\u003enatural killer cell: -3.869\",\"value: -6.629\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.469\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.010\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.204\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.048\\u003cbr\\u003eT follicular helper cell: -3.156\",\"value: -6.182\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.331\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.444\\u003cbr\\u003enatural killer cell: -1.995\\u003cbr\\u003emature NK T cell: -4.045\\u003cbr\\u003eactivated type II NK T cell: -4.058\",\"value: -6.405\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.612\\u003cbr\\u003enatural killer cell: -1.956\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.289\\u003cbr\\u003emature NK T cell: -3.745\\u003cbr\\u003egamma-delta T cell: -4.688\",\"value: -5.862\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.172\\u003cbr\\u003egamma-delta T cell: -2.565\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.497\\u003cbr\\u003emature NK T cell: -3.555\\u003cbr\\u003enatural killer cell: -3.660\",\"value: -6.537\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.658\\u003cbr\\u003enatural killer cell: -2.584\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.209\\u003cbr\\u003egamma-delta T cell: -4.080\\u003cbr\\u003eactivated type II NK T cell: -4.254\",\"value: -5.874\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.027\\u003cbr\\u003enatural killer cell: -2.553\\u003cbr\\u003egamma-delta T cell: -2.746\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.439\\u003cbr\\u003emature NK T cell: -3.475\",\"value: -4.670\\u003cbr\\u003eB cell: -0.826\\u003cbr\\u003enaive B cell: -1.732\\u003cbr\\u003eclass switched memory B cell: -3.411\\u003cbr\\u003emalignant cell: -3.438\\u003cbr\\u003emature B cell: -3.604\",\"value: -6.934\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.722\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.848\\u003cbr\\u003enatural killer cell: -2.077\\u003cbr\\u003emature NK T cell: -3.894\\u003cbr\\u003eactivated type II NK T cell: -4.512\",\"value: -5.819\\u003cbr\\u003eplasmacytoid dendritic cell: -0.367\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.483\\u003cbr\\u003edendritic cell: -3.067\\u003cbr\\u003edendritic cell, human: -4.865\\u003cbr\\u003econventional dendritic cell: -5.022\",\"value: -5.774\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.543\\u003cbr\\u003enatural killer cell: -1.639\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.091\\u003cbr\\u003eactivated type II NK T cell: -4.657\",\"value: -5.954\\u003cbr\\u003eCD14-positive monocyte: -1.184\\u003cbr\\u003eclassical monocyte: -1.294\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.451\\u003cbr\\u003emacrophage: -4.004\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -4.144\",\"value: -6.578\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.977\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.603\\u003cbr\\u003enatural killer cell: -2.379\\u003cbr\\u003emature NK T cell: -3.960\\u003cbr\\u003egamma-delta T cell: -4.178\",\"value: -6.741\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.567\\u003cbr\\u003enatural killer cell: -1.753\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.109\\u003cbr\\u003emature NK T cell: -4.195\\u003cbr\\u003eactivated type II NK T cell: -4.754\",\"value: -5.439\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.817\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.455\\u003cbr\\u003enatural killer cell: -2.497\\u003cbr\\u003egamma-delta T cell: -3.111\\u003cbr\\u003emature NK T cell: -3.856\",\"value: -4.442\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.689\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.952\\u003cbr\\u003eT-helper 22 cell: -2.587\\u003cbr\\u003eT follicular helper cell: -2.748\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.972\",\"value: -6.796\\u003cbr\\u003eclassical monocyte: -1.672\\u003cbr\\u003eCD14-positive monocyte: -1.917\\u003cbr\\u003edendritic cell, human: -2.935\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.112\\u003cbr\\u003emacrophage: -3.482\",\"value: -5.098\\u003cbr\\u003eclassical monocyte: -0.881\\u003cbr\\u003eCD14-positive monocyte: -2.022\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.352\\u003cbr\\u003emacrophage: -3.920\\u003cbr\\u003emonocyte: -4.005\",\"value: -6.259\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.696\\u003cbr\\u003enatural killer cell: -3.045\\u003cbr\\u003egamma-delta T cell: -3.518\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.819\\u003cbr\\u003emature NK T cell: -4.146\",\"value: -5.371\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.006\\u003cbr\\u003enatural killer cell: -1.944\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.995\\u003cbr\\u003egamma-delta T cell: -4.104\\u003cbr\\u003eactivated type II NK T cell: -4.330\",\"value: -4.753\\u003cbr\\u003eB cell: -0.660\\u003cbr\\u003enaive B cell: -2.369\\u003cbr\\u003eclass switched memory B cell: -2.894\\u003cbr\\u003emalignant cell: -3.204\\u003cbr\\u003ememory B cell: -3.548\",\"value: -4.979\\u003cbr\\u003eclassical monocyte: -1.228\\u003cbr\\u003eCD14-positive monocyte: -1.363\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.029\\u003cbr\\u003emonocyte: -3.201\\u003cbr\\u003emacrophage: -3.962\",\"value: -4.959\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.822\\u003cbr\\u003enatural killer cell: -1.651\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.918\\u003cbr\\u003emature NK T cell: -3.801\\u003cbr\\u003egamma-delta T cell: -4.005\",\"value: -5.858\\u003cbr\\u003eCD14-positive monocyte: -1.135\\u003cbr\\u003eclassical monocyte: -1.293\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.007\\u003cbr\\u003emonocyte: -3.811\\u003cbr\\u003emacrophage: -4.356\",\"value: -6.497\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.557\\u003cbr\\u003enatural killer cell: -1.797\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.880\\u003cbr\\u003emature NK T cell: -4.000\\u003cbr\\u003eactivated type II NK T cell: -4.580\",\"value: -5.971\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.074\\u003cbr\\u003eT cell: -3.324\\u003cbr\\u003emast cell: -3.603\\u003cbr\\u003emature NK T cell: -3.603\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.668\",\"value: -4.723\\u003cbr\\u003eplasmacytoid dendritic cell: -0.377\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.455\\u003cbr\\u003edendritic cell: -3.249\\u003cbr\\u003econventional dendritic cell: -4.739\\u003cbr\\u003ehematopoietic precursor cell: -4.916\",\"value: -5.674\\u003cbr\\u003eplasmacytoid dendritic cell: -0.304\\u003cbr\\u003edendritic cell: -2.591\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.861\\u003cbr\\u003econventional dendritic cell: -4.751\\u003cbr\\u003emyeloid dendritic cell: -4.854\",\"value: -6.421\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.487\\u003cbr\\u003enatural killer cell: -1.802\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.723\\u003cbr\\u003eactivated type II NK T cell: -4.518\\u003cbr\\u003emature NK T cell: -4.705\",\"value: -5.718\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.782\\u003cbr\\u003enatural killer cell: -1.211\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.754\\u003cbr\\u003emature NK T cell: -4.030\\u003cbr\\u003eactivated type II NK T cell: -4.619\",\"value: -5.748\\u003cbr\\u003eclassical monocyte: -1.078\\u003cbr\\u003eCD14-positive monocyte: -1.656\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.768\\u003cbr\\u003emonocyte: -3.917\\u003cbr\\u003emacrophage: -3.976\",\"value: -6.638\\u003cbr\\u003edendritic cell: -1.006\\u003cbr\\u003edendritic cell, human: -1.999\\u003cbr\\u003emyeloid dendritic cell: -2.557\\u003cbr\\u003econventional dendritic cell: -2.691\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.001\",\"value: -6.262\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.175\\u003cbr\\u003enatural killer cell: -3.306\\u003cbr\\u003egamma-delta T cell: -3.354\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.495\\u003cbr\\u003einnate lymphoid cell: -3.952\",\"value: -6.554\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.571\\u003cbr\\u003egamma-delta T cell: -3.573\\u003cbr\\u003enatural killer cell: -3.615\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.753\\u003cbr\\u003einnate lymphoid cell: -4.262\",\"value: -5.539\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.952\\u003cbr\\u003enatural killer cell: -2.807\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.916\\u003cbr\\u003egamma-delta T cell: -2.968\\u003cbr\\u003emature NK T cell: -3.994\",\"value: -5.676\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.997\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.743\\u003cbr\\u003egamma-delta T cell: -2.905\\u003cbr\\u003enatural killer cell: -3.010\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.666\",\"value: -5.783\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.154\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.878\\u003cbr\\u003enatural killer cell: -2.675\\u003cbr\\u003egamma-delta T cell: -2.696\\u003cbr\\u003emature NK T cell: -3.555\",\"value: -5.502\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.297\\u003cbr\\u003enatural killer cell: -2.209\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.191\\u003cbr\\u003emature NK T cell: -4.297\\u003cbr\\u003egamma-delta T cell: -5.226\",\"value: -6.864\\u003cbr\\u003eCD14-positive monocyte: -1.118\\u003cbr\\u003eclassical monocyte: -1.483\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.689\\u003cbr\\u003emonocyte: -3.065\\u003cbr\\u003emacrophage: -4.484\",\"value: -4.549\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.248\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.771\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.597\\u003cbr\\u003eT follicular helper cell: -2.778\\u003cbr\\u003eT cell: -3.386\",\"value: -6.857\\u003cbr\\u003enatural killer cell: -0.890\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.001\\u003cbr\\u003emature NK T cell: -4.040\\u003cbr\\u003egamma-delta T cell: -4.741\",\"value: -6.685\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.436\\u003cbr\\u003enatural killer cell: -1.803\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.971\\u003cbr\\u003emature NK T cell: -4.313\\u003cbr\\u003eactivated type II NK T cell: -4.856\",\"value: -4.964\\u003cbr\\u003ehematopoietic precursor cell: -1.720\\u003cbr\\u003ehematopoietic stem cell: -2.879\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -3.170\\u003cbr\\u003ecommon myeloid progenitor: -3.490\\u003cbr\\u003emast cell: -3.898\",\"value: -6.513\\u003cbr\\u003eCD14-positive monocyte: -1.058\\u003cbr\\u003eclassical monocyte: -1.218\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.335\\u003cbr\\u003emacrophage: -4.106\\u003cbr\\u003emonocyte: -4.227\",\"value: -6.232\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.481\\u003cbr\\u003enatural killer cell: -1.596\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.700\\u003cbr\\u003emature NK T cell: -4.249\\u003cbr\\u003eactivated type II NK T cell: -4.965\",\"value: -5.450\\u003cbr\\u003enatural killer cell: -1.272\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.493\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.754\\u003cbr\\u003eT cell: -3.431\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -3.515\",\"value: -4.787\\u003cbr\\u003eB cell: -0.952\\u003cbr\\u003enaive B cell: -1.565\\u003cbr\\u003emalignant cell: -3.002\\u003cbr\\u003eclass switched memory B cell: -3.557\\u003cbr\\u003eimmature B cell: -3.734\",\"value: -5.706\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.422\\u003cbr\\u003enatural killer cell: -2.007\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.209\\u003cbr\\u003emature NK T cell: -4.727\\u003cbr\\u003eactivated type II NK T cell: -4.741\",\"value: -5.604\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.970\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.019\\u003cbr\\u003eT follicular helper cell: -2.137\\u003cbr\\u003eT-helper 22 cell: -2.689\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -3.207\",\"value: -6.219\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.493\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.859\\u003cbr\\u003emature NK T cell: -4.163\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.926\",\"value: -4.469\\u003cbr\\u003emast cell: -1.717\\u003cbr\\u003eplasmacytoid dendritic cell: -3.087\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.461\\u003cbr\\u003edendritic cell: -3.529\\u003cbr\\u003ehematopoietic precursor cell: -3.558\",\"value: -5.466\\u003cbr\\u003eplasmacytoid dendritic cell: -0.217\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.750\\u003cbr\\u003edendritic cell: -3.267\\u003cbr\\u003emyeloid dendritic cell: -5.930\\u003cbr\\u003ehematopoietic precursor cell: -6.075\",\"value: -6.458\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.985\\u003cbr\\u003enatural killer cell: -2.078\\u003cbr\\u003eactivated type II NK T cell: -4.213\\u003cbr\\u003emature NK T cell: -4.272\",\"value: -5.182\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.582\\u003cbr\\u003enatural killer cell: -1.661\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.573\\u003cbr\\u003emature NK T cell: -3.747\\u003cbr\\u003egamma-delta T cell: -4.858\",\"value: -5.797\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.689\\u003cbr\\u003enatural killer cell: -1.504\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.548\\u003cbr\\u003emature NK T cell: -3.907\\u003cbr\\u003egamma-delta T cell: -4.337\",\"value: -5.632\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.705\\u003cbr\\u003enatural killer cell: -1.214\\u003cbr\\u003emature NK T cell: -4.027\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.146\\u003cbr\\u003egamma-delta T cell: -4.974\",\"value: -6.252\\u003cbr\\u003eclassical monocyte: -1.376\\u003cbr\\u003eCD14-positive monocyte: -1.840\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.967\\u003cbr\\u003emacrophage: -3.292\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.744\",\"value: -6.728\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.840\\u003cbr\\u003enatural killer cell: -2.626\\u003cbr\\u003egamma-delta T cell: -4.037\\u003cbr\\u003emature NK T cell: -4.130\",\"value: -6.348\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.276\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.541\\u003cbr\\u003enatural killer cell: -2.823\\u003cbr\\u003egamma-delta T cell: -3.412\\u003cbr\\u003emature NK T cell: -3.766\",\"value: -4.698\\u003cbr\\u003egamma-delta T cell: -2.388\\u003cbr\\u003emature NK T cell: -2.577\\u003cbr\\u003enatural killer cell: -2.618\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.757\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -2.855\",\"value: -4.727\\u003cbr\\u003enatural killer cell: -1.298\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.375\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.026\\u003cbr\\u003emature NK T cell: -3.898\\u003cbr\\u003egamma-delta T cell: -3.985\",\"value: -6.745\\u003cbr\\u003eclassical monocyte: -1.352\\u003cbr\\u003eCD14-positive monocyte: -1.462\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.909\\u003cbr\\u003emonocyte: -3.615\\u003cbr\\u003edendritic cell, human: -4.076\",\"value: -5.602\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.504\\u003cbr\\u003enatural killer cell: -1.859\\u003cbr\\u003emature NK T cell: -3.736\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.860\\u003cbr\\u003egamma-delta T cell: -4.450\",\"value: -6.164\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.234\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.256\\u003cbr\\u003enatural killer cell: -2.391\\u003cbr\\u003egamma-delta T cell: -4.006\\u003cbr\\u003eactivated type II NK T cell: -4.088\",\"value: -5.737\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.504\\u003cbr\\u003enatural killer cell: -3.063\\u003cbr\\u003egamma-delta T cell: -3.139\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.617\\u003cbr\\u003emature NK T cell: -3.644\",\"value: -6.106\\u003cbr\\u003eclassical monocyte: -1.135\\u003cbr\\u003eCD14-positive monocyte: -1.142\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.260\\u003cbr\\u003emonocyte: -3.693\\u003cbr\\u003emacrophage: -4.162\",\"value: -6.560\\u003cbr\\u003eT follicular helper cell: -1.724\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.747\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.246\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.666\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.771\",\"value: -5.994\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.730\\u003cbr\\u003enatural killer cell: -1.617\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.760\\u003cbr\\u003emature NK T cell: -3.766\\u003cbr\\u003egamma-delta T cell: -4.067\",\"value: -5.582\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.455\\u003cbr\\u003enatural killer cell: -1.808\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.919\\u003cbr\\u003emature NK T cell: -4.421\\u003cbr\\u003eactivated type II NK T cell: -4.875\",\"value: -6.271\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.433\\u003cbr\\u003enatural killer cell: -2.164\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.062\\u003cbr\\u003emature NK T cell: -4.370\\u003cbr\\u003egamma-delta T cell: -4.685\",\"value: -5.719\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.720\\u003cbr\\u003enatural killer cell: -1.698\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.551\\u003cbr\\u003emature NK T cell: -3.531\\u003cbr\\u003egamma-delta T cell: -4.535\",\"value: -6.234\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.381\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.873\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.955\\u003cbr\\u003eT follicular helper cell: -2.848\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.925\",\"value: -6.959\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.623\\u003cbr\\u003enatural killer cell: -2.231\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.343\\u003cbr\\u003emature NK T cell: -3.984\\u003cbr\\u003eactivated type II NK T cell: -4.452\",\"value: -5.468\\u003cbr\\u003eT follicular helper cell: -1.897\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.224\\u003cbr\\u003eT-helper 22 cell: -2.904\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.905\\u003cbr\\u003emature NK T cell: -3.580\",\"value: -6.194\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.787\\u003cbr\\u003enatural killer cell: -1.652\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.307\\u003cbr\\u003emature NK T cell: -3.950\\u003cbr\\u003egamma-delta T cell: -4.503\",\"value: -5.123\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.272\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.815\\u003cbr\\u003enatural killer cell: -2.077\\u003cbr\\u003egamma-delta T cell: -3.363\\u003cbr\\u003emature NK T cell: -3.751\",\"value: -6.374\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.640\\u003cbr\\u003enatural killer cell: -1.365\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.166\\u003cbr\\u003emature NK T cell: -3.990\\u003cbr\\u003eactivated type II NK T cell: -4.459\",\"value: -6.081\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.258\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.600\\u003cbr\\u003enatural killer cell: -2.044\\u003cbr\\u003emature NK T cell: -3.209\\u003cbr\\u003egamma-delta T cell: -3.663\",\"value: -4.107\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.673\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.691\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003egamma-delta T cell: -2.955\\u003cbr\\u003emature NK T cell: -3.267\",\"value: -6.681\\u003cbr\\u003eCD14-positive monocyte: -1.214\\u003cbr\\u003eclassical monocyte: -1.406\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.330\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.419\\u003cbr\\u003emacrophage: -3.810\",\"value: -5.691\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.410\\u003cbr\\u003enatural killer cell: -2.190\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.640\\u003cbr\\u003egamma-delta T cell: -4.258\\u003cbr\\u003emature NK T cell: -4.423\",\"value: -6.526\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.026\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.775\\u003cbr\\u003enatural killer cell: -2.172\\u003cbr\\u003egamma-delta T cell: -3.475\\u003cbr\\u003emature NK T cell: -4.026\",\"value: -6.184\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.565\\u003cbr\\u003enatural killer cell: -2.688\\u003cbr\\u003egamma-delta T cell: -3.653\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.968\\u003cbr\\u003einnate lymphoid cell: -4.246\",\"value: -6.714\\u003cbr\\u003eCD14-positive monocyte: -0.986\\u003cbr\\u003eclassical monocyte: -1.299\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.484\\u003cbr\\u003eneutrophil: -3.896\\u003cbr\\u003emacrophage: -4.347\",\"value: -6.649\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.734\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.991\\u003cbr\\u003enatural killer cell: -2.321\\u003cbr\\u003egamma-delta T cell: -3.541\\u003cbr\\u003emature NK T cell: -3.798\",\"value: -6.930\\u003cbr\\u003eT follicular helper cell: -1.509\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.013\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.587\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.824\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.200\",\"value: -5.816\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.448\\u003cbr\\u003enatural killer cell: -1.850\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.779\\u003cbr\\u003emature NK T cell: -4.109\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.927\",\"value: -6.464\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.662\\u003cbr\\u003enatural killer cell: -1.352\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.017\\u003cbr\\u003emature NK T cell: -4.242\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -4.727\",\"value: -5.886\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -1.556\\u003cbr\\u003emature NK T cell: -3.605\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.498\\u003cbr\\u003egamma-delta T cell: -4.844\",\"value: -5.744\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.649\\u003cbr\\u003enatural killer cell: -1.274\\u003cbr\\u003emature NK T cell: -3.796\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.333\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.580\",\"value: -5.915\\u003cbr\\u003eclassical monocyte: -1.314\\u003cbr\\u003eCD14-positive monocyte: -2.076\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.117\\u003cbr\\u003edendritic cell, human: -3.264\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.332\",\"value: -5.630\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.601\\u003cbr\\u003enatural killer cell: -1.521\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.437\\u003cbr\\u003emature NK T cell: -4.107\\u003cbr\\u003eactivated type II NK T cell: -4.760\",\"value: -5.928\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.562\\u003cbr\\u003enatural killer cell: -1.475\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.555\\u003cbr\\u003emature NK T cell: -4.057\\u003cbr\\u003eactivated type II NK T cell: -4.890\",\"value: -6.190\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.546\\u003cbr\\u003enatural killer cell: -1.956\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.845\\u003cbr\\u003emature NK T cell: -4.144\\u003cbr\\u003eactivated type II NK T cell: -4.606\",\"value: -4.565\\u003cbr\\u003eclassical monocyte: -0.975\\u003cbr\\u003eCD14-positive monocyte: -1.744\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.518\\u003cbr\\u003emonocyte: -3.665\\u003cbr\\u003emacrophage: -4.412\",\"value: -6.652\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.266\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.383\\u003cbr\\u003enatural killer cell: -2.299\\u003cbr\\u003emature NK T cell: -3.343\\u003cbr\\u003egamma-delta T cell: -3.616\",\"value: -6.220\\u003cbr\\u003eclassical monocyte: -1.112\\u003cbr\\u003eCD14-positive monocyte: -1.277\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.221\\u003cbr\\u003emonocyte: -3.900\\u003cbr\\u003emacrophage: -4.120\",\"value: -6.610\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.590\\u003cbr\\u003enatural killer cell: -1.646\\u003cbr\\u003emature NK T cell: -3.692\\u003cbr\\u003egamma-delta T cell: -3.972\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.032\",\"value: -5.954\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.213\\u003cbr\\u003enatural killer cell: -1.461\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.855\\u003cbr\\u003emature NK T cell: -3.661\\u003cbr\\u003eactivated type II NK T cell: -4.224\",\"value: -6.690\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.634\\u003cbr\\u003enatural killer cell: -1.551\\u003cbr\\u003emature NK T cell: -3.645\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.969\\u003cbr\\u003egamma-delta T cell: -4.576\",\"value: -5.614\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.744\\u003cbr\\u003enatural killer cell: -2.740\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.468\\u003cbr\\u003egamma-delta T cell: -3.661\\u003cbr\\u003einnate lymphoid cell: -4.038\",\"value: -4.624\\u003cbr\\u003eclassical monocyte: -1.349\\u003cbr\\u003emonocyte: -2.523\\u003cbr\\u003eCD14-positive monocyte: -2.712\\u003cbr\\u003emacrophage: -2.715\\u003cbr\\u003edendritic cell: -3.300\",\"value: -4.695\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.344\\u003cbr\\u003egamma-delta T cell: -3.057\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.063\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.069\",\"value: -6.767\\u003cbr\\u003eclassical monocyte: -1.268\\u003cbr\\u003eCD14-positive monocyte: -2.058\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.242\\u003cbr\\u003emacrophage: -3.424\\u003cbr\\u003edendritic cell, human: -3.457\",\"value: -6.015\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.537\\u003cbr\\u003enatural killer cell: -1.785\\u003cbr\\u003emature NK T cell: -3.917\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.093\\u003cbr\\u003egamma-delta T cell: -4.734\",\"value: -6.523\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.305\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.575\\u003cbr\\u003enatural killer cell: -2.073\\u003cbr\\u003emature NK T cell: -3.957\\u003cbr\\u003eactivated type II NK T cell: -4.035\",\"value: -6.758\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.482\\u003cbr\\u003enatural killer cell: -1.546\\u003cbr\\u003emature NK T cell: -4.012\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.710\\u003cbr\\u003egamma-delta T cell: -4.817\",\"value: -6.312\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.918\\u003cbr\\u003enatural killer cell: -1.858\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.181\\u003cbr\\u003emature NK T cell: -3.711\\u003cbr\\u003egamma-delta T cell: -4.275\",\"value: -6.170\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.855\\u003cbr\\u003enatural killer cell: -1.842\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.624\\u003cbr\\u003emature NK T cell: -3.712\\u003cbr\\u003eactivated type II NK T cell: -4.375\",\"value: -5.961\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.486\\u003cbr\\u003enatural killer cell: -1.899\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.246\\u003cbr\\u003emature NK T cell: -3.876\\u003cbr\\u003egamma-delta T cell: -4.936\",\"value: -5.704\\u003cbr\\u003eclassical monocyte: -1.275\\u003cbr\\u003eCD14-positive monocyte: -1.852\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.819\\u003cbr\\u003emonocyte: -3.272\\u003cbr\\u003emacrophage: -3.941\",\"value: -5.982\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.813\\u003cbr\\u003egamma-delta T cell: -3.197\\u003cbr\\u003enatural killer cell: -3.459\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.551\\u003cbr\\u003einnate lymphoid cell: -3.987\",\"value: -5.425\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.820\\u003cbr\\u003egamma-delta T cell: -3.120\\u003cbr\\u003enatural killer cell: -3.715\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.730\\u003cbr\\u003einnate lymphoid cell: -3.861\",\"value: -6.054\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.137\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.482\\u003cbr\\u003enatural killer cell: -2.663\\u003cbr\\u003egamma-delta T cell: -3.683\\u003cbr\\u003eactivated type II NK T cell: -4.227\",\"value: -6.197\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.471\\u003cbr\\u003enatural killer cell: -1.780\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.652\\u003cbr\\u003emature NK T cell: -4.458\\u003cbr\\u003eactivated type II NK T cell: -4.834\",\"value: -4.662\\u003cbr\\u003eT follicular helper cell: -1.995\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.758\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.804\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.869\\u003cbr\\u003eT cell: -3.370\",\"value: -6.245\\u003cbr\\u003enon-classical monocyte: -1.535\\u003cbr\\u003eCD14-low, CD16-positive monocyte: -2.371\\u003cbr\\u003emature NK T cell: -2.418\\u003cbr\\u003eCD14-positive monocyte: -2.788\\u003cbr\\u003eclassical monocyte: -3.001\",\"value: -5.289\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.564\\u003cbr\\u003enatural killer cell: -1.660\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.630\\u003cbr\\u003emature NK T cell: -4.085\\u003cbr\\u003eactivated type II NK T cell: -4.954\",\"value: -6.277\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.566\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.660\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.049\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.719\\u003cbr\\u003eT follicular helper cell: -2.782\",\"value: -5.852\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.236\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.941\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.980\\u003cbr\\u003eT follicular helper cell: -3.383\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.387\",\"value: -5.081\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.256\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.256\\u003cbr\\u003egamma-delta T cell: -2.282\\u003cbr\\u003emature NK T cell: -2.669\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.245\",\"value: -6.774\\u003cbr\\u003eCD14-positive monocyte: -1.055\\u003cbr\\u003eclassical monocyte: -1.069\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.291\\u003cbr\\u003emonocyte: -3.732\\u003cbr\\u003emacrophage: -4.260\",\"value: -6.163\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.576\\u003cbr\\u003enatural killer cell: -1.712\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.173\\u003cbr\\u003emature NK T cell: -4.199\\u003cbr\\u003egamma-delta T cell: -4.636\",\"value: -5.837\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.466\\u003cbr\\u003enatural killer cell: -1.886\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.238\\u003cbr\\u003emature NK T cell: -4.160\\u003cbr\\u003eactivated type II NK T cell: -4.993\",\"value: -6.481\\u003cbr\\u003eCD14-positive monocyte: -0.960\\u003cbr\\u003eclassical monocyte: -1.434\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.094\\u003cbr\\u003emonocyte: -3.484\\u003cbr\\u003emacrophage: -4.402\",\"value: -6.194\\u003cbr\\u003eplasmacytoid dendritic cell: -0.226\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.913\\u003cbr\\u003edendritic cell: -3.105\\u003cbr\\u003edendritic cell, human: -5.293\\u003cbr\\u003emyeloid dendritic cell: -5.448\",\"value: -4.667\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.093\\u003cbr\\u003eT follicular helper cell: -2.961\\u003cbr\\u003eT-helper 22 cell: -3.076\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -3.279\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.298\",\"value: -5.480\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.501\\u003cbr\\u003enatural killer cell: -1.543\\u003cbr\\u003emature NK T cell: -3.713\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.688\\u003cbr\\u003egamma-delta T cell: -5.044\",\"value: -6.049\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.847\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.806\\u003cbr\\u003enatural killer cell: -2.209\\u003cbr\\u003egamma-delta T cell: -3.754\\u003cbr\\u003emature NK T cell: -3.846\",\"value: -6.104\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.429\\u003cbr\\u003enatural killer cell: -1.850\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.253\\u003cbr\\u003emature NK T cell: -4.571\\u003cbr\\u003eactivated type II NK T cell: -4.819\",\"value: -5.298\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.659\\u003cbr\\u003enatural killer cell: -1.793\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.616\\u003cbr\\u003emature NK T cell: -3.767\\u003cbr\\u003egamma-delta T cell: -4.293\",\"value: -6.675\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.574\\u003cbr\\u003enatural killer cell: -1.468\\u003cbr\\u003emature NK T cell: -3.926\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.820\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.061\",\"value: -5.025\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.258\\u003cbr\\u003enatural killer cell: -3.552\\u003cbr\\u003elymphocyte: -3.808\\u003cbr\\u003eprogenitor cell: -4.126\\u003cbr\\u003einnate lymphoid cell: -4.154\",\"value: -6.356\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.409\\u003cbr\\u003enatural killer cell: -1.695\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.956\\u003cbr\\u003emature NK T cell: -4.457\\u003cbr\\u003eactivated type II NK T cell: -5.131\",\"value: -6.086\\u003cbr\\u003eCD14-positive monocyte: -1.155\\u003cbr\\u003eclassical monocyte: -1.550\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.785\\u003cbr\\u003emonocyte: -3.268\\u003cbr\\u003emacrophage: -4.483\",\"value: -6.175\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.721\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.151\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.303\\u003cbr\\u003eT follicular helper cell: -2.456\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.903\",\"value: -5.840\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.481\\u003cbr\\u003enatural killer cell: -1.849\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.053\\u003cbr\\u003emature NK T cell: -4.130\\u003cbr\\u003eactivated type II NK T cell: -4.680\",\"value: -6.450\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.392\\u003cbr\\u003enatural killer cell: -2.783\\u003cbr\\u003emature NK T cell: -2.913\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.006\\u003cbr\\u003egamma-delta T cell: -3.290\",\"value: -5.967\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.789\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.917\\u003cbr\\u003enatural killer cell: -2.300\\u003cbr\\u003egamma-delta T cell: -2.620\\u003cbr\\u003emature NK T cell: -3.070\",\"value: -4.949\\u003cbr\\u003edendritic cell: -1.736\\u003cbr\\u003eplasmacytoid dendritic cell: -1.816\\u003cbr\\u003econventional dendritic cell: -2.443\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.064\\u003cbr\\u003emyeloid dendritic cell: -3.284\",\"value: -6.009\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.954\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.004\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.268\\u003cbr\\u003eT follicular helper cell: -2.823\\u003cbr\\u003eT-helper 22 cell: -2.982\",\"value: -6.553\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.259\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.659\\u003cbr\\u003enatural killer cell: -2.152\\u003cbr\\u003egamma-delta T cell: -3.717\\u003cbr\\u003eactivated type II NK T cell: -4.018\",\"value: -6.383\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.781\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.543\\u003cbr\\u003emature NK T cell: -3.886\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.808\",\"value: -4.878\\u003cbr\\u003eplasmacytoid dendritic cell: -0.200\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.889\\u003cbr\\u003edendritic cell: -3.683\\u003cbr\\u003emast cell: -5.459\\u003cbr\\u003eB cell: -5.963\",\"value: -4.631\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.908\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.274\\u003cbr\\u003egamma-delta T cell: -2.507\\u003cbr\\u003enatural killer cell: -2.540\\u003cbr\\u003emature NK T cell: -3.656\",\"value: -6.453\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.749\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.943\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.116\\u003cbr\\u003eT follicular helper cell: -3.350\\u003cbr\\u003eT cell: -4.211\",\"value: -5.455\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.760\\u003cbr\\u003enatural killer cell: -2.478\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.923\\u003cbr\\u003egamma-delta T cell: -3.516\\u003cbr\\u003emature NK T cell: -4.061\",\"value: -4.224\\u003cbr\\u003ehematopoietic precursor cell: -1.628\\u003cbr\\u003eCD34-positive, CD38-negative hematopoietic stem cell: -2.770\\u003cbr\\u003ehematopoietic stem cell: -2.796\\u003cbr\\u003emast cell: -3.707\\u003cbr\\u003egranulocyte: -4.169\",\"value: -6.236\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.569\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.563\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.530\\u003cbr\\u003eT cell: -4.413\\u003cbr\\u003eT follicular helper cell: -4.551\",\"value: -6.714\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.427\\u003cbr\\u003emature NK T cell: -3.981\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.585\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.792\",\"value: -4.850\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.003\\u003cbr\\u003eT follicular helper cell: -2.121\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.571\\u003cbr\\u003eT-helper 22 cell: -2.725\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.508\",\"value: -6.419\\u003cbr\\u003eclassical monocyte: -1.300\\u003cbr\\u003eCD14-positive monocyte: -1.626\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.066\\u003cbr\\u003emacrophage: -3.148\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.464\",\"value: -5.773\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.619\\u003cbr\\u003emature NK T cell: -3.663\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.246\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.083\",\"value: -5.855\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.611\\u003cbr\\u003enatural killer cell: -1.982\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.206\\u003cbr\\u003emature NK T cell: -4.022\\u003cbr\\u003egamma-delta T cell: -4.344\",\"value: -6.017\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.769\\u003cbr\\u003enatural killer cell: -1.105\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.875\\u003cbr\\u003emature NK T cell: -4.223\\u003cbr\\u003eactivated type II NK T cell: -4.804\",\"value: -6.126\\u003cbr\\u003eclassical monocyte: -1.847\\u003cbr\\u003eCD14-positive monocyte: -2.110\\u003cbr\\u003enon-classical monocyte: -2.825\\u003cbr\\u003emacrophage: -3.420\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.458\",\"value: -6.179\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.630\\u003cbr\\u003enatural killer cell: -1.600\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.083\\u003cbr\\u003emature NK T cell: -3.568\\u003cbr\\u003egamma-delta T cell: -4.356\",\"value: -7.006\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.191\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.373\\u003cbr\\u003enatural killer cell: -2.416\\u003cbr\\u003egamma-delta T cell: -3.292\\u003cbr\\u003emature NK T cell: -3.979\",\"value: -5.565\\u003cbr\\u003eclassical monocyte: -1.422\\u003cbr\\u003eCD14-positive monocyte: -2.533\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.959\\u003cbr\\u003emonocyte: -3.934\\u003cbr\\u003emacrophage: -3.983\",\"value: -5.532\\u003cbr\\u003eplasmacytoid dendritic cell: -0.208\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.940\\u003cbr\\u003edendritic cell: -3.417\\u003cbr\\u003edendritic cell, human: -5.861\\u003cbr\\u003emyeloid dendritic cell: -5.876\",\"value: -4.382\\u003cbr\\u003eplasmacytoid dendritic cell: -0.935\\u003cbr\\u003edendritic cell: -2.613\\u003cbr\\u003eB cell: -3.401\\u003cbr\\u003eplasma cell: -3.437\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.452\",\"value: -5.386\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.653\\u003cbr\\u003enatural killer cell: -1.392\\u003cbr\\u003emature NK T cell: -3.644\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.179\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.565\",\"value: -3.686\\u003cbr\\u003eT cell: -2.240\\u003cbr\\u003emature NK T cell: -3.126\\u003cbr\\u003egamma-delta T cell: -3.262\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -3.301\\u003cbr\\u003eT follicular helper cell: -3.336\",\"value: -5.192\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.373\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.496\\u003cbr\\u003egamma-delta T cell: -2.602\\u003cbr\\u003emature NK T cell: -3.305\",\"value: -5.145\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.089\\u003cbr\\u003eT follicular helper cell: -2.264\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.492\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.528\\u003cbr\\u003eT-helper 22 cell: -2.765\",\"value: -6.529\\u003cbr\\u003eclassical monocyte: -1.253\\u003cbr\\u003eCD14-positive monocyte: -1.834\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.866\\u003cbr\\u003edendritic cell, human: -3.772\\u003cbr\\u003emacrophage: -3.877\",\"value: -6.337\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.562\\u003cbr\\u003egroup 3 innate lymphoid cell: -2.882\\u003cbr\\u003einnate lymphoid cell: -3.454\\u003cbr\\u003enatural killer cell: -3.735\\u003cbr\\u003ehematopoietic precursor cell: -4.125\",\"value: -5.596\\u003cbr\\u003eplasmacytoid dendritic cell: -0.342\\u003cbr\\u003edendritic cell: -2.551\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.782\\u003cbr\\u003emyeloid dendritic cell: -4.820\\u003cbr\\u003edendritic cell, human: -4.831\",\"value: -5.949\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.381\\u003cbr\\u003enatural killer cell: -1.751\\u003cbr\\u003emature NK T cell: -4.391\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.407\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.985\",\"value: -5.477\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.383\\u003cbr\\u003egamma-delta T cell: -2.124\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.611\\u003cbr\\u003enatural killer cell: -2.794\\u003cbr\\u003emature NK T cell: -2.807\",\"value: -6.147\\u003cbr\\u003eplasmacytoid dendritic cell: -0.324\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.647\\u003cbr\\u003edendritic cell: -3.113\\u003cbr\\u003emyeloid dendritic cell: -5.368\\u003cbr\\u003edendritic cell, human: -5.479\",\"value: -5.876\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.423\\u003cbr\\u003eT follicular helper cell: -2.574\\u003cbr\\u003eT-helper 22 cell: -2.856\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -3.350\\u003cbr\\u003emucosal invariant T cell: -3.415\",\"value: -5.485\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.046\\u003cbr\\u003egamma-delta T cell: -3.416\\u003cbr\\u003enatural killer cell: -3.512\\u003cbr\\u003einnate lymphoid cell: -3.569\\u003cbr\\u003egroup 3 innate lymphoid cell: -3.853\",\"value: -7.126\\u003cbr\\u003eclassical monocyte: -2.038\\u003cbr\\u003eCD14-positive monocyte: -2.087\\u003cbr\\u003edendritic cell, human: -2.422\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.820\\u003cbr\\u003econventional dendritic cell: -3.061\",\"value: -6.139\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.604\\u003cbr\\u003enatural killer cell: -1.753\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.559\\u003cbr\\u003emature NK T cell: -4.321\\u003cbr\\u003eactivated type II NK T cell: -4.330\",\"value: -6.282\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.835\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.637\\u003cbr\\u003enatural killer cell: -2.054\\u003cbr\\u003emature NK T cell: -4.223\\u003cbr\\u003eactivated type II NK T cell: -4.501\",\"value: -6.257\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.708\\u003cbr\\u003enatural killer cell: -1.480\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.574\\u003cbr\\u003emature NK T cell: -4.219\\u003cbr\\u003eactivated type II NK T cell: -4.509\",\"value: -6.293\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.364\\u003cbr\\u003enatural killer cell: -2.038\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.222\\u003cbr\\u003emature NK T cell: -4.401\\u003cbr\\u003eactivated type II NK T cell: -4.886\",\"value: -5.718\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.596\\u003cbr\\u003enatural killer cell: -1.482\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.571\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003egamma-delta T cell: -4.881\",\"value: -5.611\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.528\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.005\\u003cbr\\u003egamma-delta T cell: -3.256\\u003cbr\\u003eT follicular helper cell: -3.420\\u003cbr\\u003emucosal invariant T cell: -3.682\",\"value: -5.543\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.960\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.465\\u003cbr\\u003egamma-delta T cell: -2.639\\u003cbr\\u003enatural killer cell: -2.934\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.604\",\"value: -6.064\\u003cbr\\u003eclassical monocyte: -1.263\\u003cbr\\u003eCD14-positive monocyte: -1.779\\u003cbr\\u003emacrophage: -3.264\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.298\\u003cbr\\u003edendritic cell, human: -3.631\",\"value: -6.073\\u003cbr\\u003eclassical monocyte: -1.243\\u003cbr\\u003eCD14-positive monocyte: -1.549\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.103\\u003cbr\\u003emonocyte: -3.504\\u003cbr\\u003ealveolar macrophage: -3.988\",\"value: -5.063\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.207\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.417\\u003cbr\\u003eT follicular helper cell: -2.647\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.863\\u003cbr\\u003eT-helper 22 cell: -2.984\",\"value: -6.190\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.396\\u003cbr\\u003enatural killer cell: -2.307\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.063\\u003cbr\\u003egamma-delta T cell: -4.378\\u003cbr\\u003emature NK T cell: -4.548\",\"value: -6.533\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.519\\u003cbr\\u003enatural killer cell: -2.279\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.497\\u003cbr\\u003emature NK T cell: -4.174\\u003cbr\\u003eactivated type II NK T cell: -4.706\",\"value: -6.640\\u003cbr\\u003eCD14-positive monocyte: -1.791\\u003cbr\\u003eclassical monocyte: -1.948\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.138\\u003cbr\\u003edendritic cell, human: -3.299\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.315\",\"value: -4.215\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.531\\u003cbr\\u003eCD4-positive, alpha-beta T cell: -3.245\\u003cbr\\u003eT cell: -3.452\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.881\\u003cbr\\u003eerythrocyte: -3.967\",\"value: -6.617\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.388\\u003cbr\\u003enatural killer cell: -2.193\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.129\\u003cbr\\u003emature NK T cell: -4.036\\u003cbr\\u003egamma-delta T cell: -4.951\",\"value: -4.760\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.291\\u003cbr\\u003enatural killer cell: -1.604\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.523\\u003cbr\\u003egamma-delta T cell: -4.019\\u003cbr\\u003emature NK T cell: -4.191\",\"value: -5.438\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.643\\u003cbr\\u003enatural killer cell: -1.804\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.814\\u003cbr\\u003emature NK T cell: -4.074\\u003cbr\\u003egamma-delta T cell: -4.517\",\"value: -6.851\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.215\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.108\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.370\\u003cbr\\u003eT follicular helper cell: -2.464\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.849\",\"value: -5.211\\u003cbr\\u003eclassical monocyte: -1.289\\u003cbr\\u003eCD14-positive monocyte: -1.904\\u003cbr\\u003emacrophage: -2.821\\u003cbr\\u003emonocyte: -2.886\\u003cbr\\u003ealveolar macrophage: -2.992\",\"value: -4.968\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.591\\u003cbr\\u003enatural killer cell: -1.435\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.769\\u003cbr\\u003emature NK T cell: -4.022\\u003cbr\\u003eactivated type II NK T cell: -4.882\",\"value: -6.816\\u003cbr\\u003eclassical monocyte: -1.556\\u003cbr\\u003eCD14-positive monocyte: -1.646\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.949\\u003cbr\\u003edendritic cell, human: -3.411\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.442\",\"value: -5.699\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.828\\u003cbr\\u003enatural killer cell: -1.094\\u003cbr\\u003emature NK T cell: -3.668\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.577\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.751\",\"value: -5.883\\u003cbr\\u003eCD14-positive monocyte: -1.449\\u003cbr\\u003eclassical monocyte: -1.816\\u003cbr\\u003emonocyte: -2.538\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.981\\u003cbr\\u003emacrophage: -3.806\",\"value: -5.229\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.821\\u003cbr\\u003enatural killer cell: -2.079\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.524\\u003cbr\\u003egamma-delta T cell: -3.141\\u003cbr\\u003emature NK T cell: -3.431\",\"value: -5.031\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.660\\u003cbr\\u003enatural killer cell: -1.622\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.849\\u003cbr\\u003emature NK T cell: -3.965\\u003cbr\\u003elymphocyte: -4.606\",\"value: -5.395\\u003cbr\\u003enatural killer cell: -1.235\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.268\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.801\\u003cbr\\u003emature NK T cell: -3.130\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.419\",\"value: -5.879\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.056\\u003cbr\\u003enatural killer cell: -1.883\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.396\\u003cbr\\u003emature NK T cell: -3.262\\u003cbr\\u003egamma-delta T cell: -3.385\",\"value: -5.928\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.107\\u003cbr\\u003enatural killer cell: -1.664\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.683\\u003cbr\\u003eactivated type II NK T cell: -3.851\\u003cbr\\u003emature NK T cell: -4.635\",\"value: -5.842\\u003cbr\\u003eclassical monocyte: -1.303\\u003cbr\\u003eCD14-positive monocyte: -1.427\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.026\\u003cbr\\u003emonocyte: -3.778\\u003cbr\\u003epromonocyte: -4.267\",\"value: -7.551\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.612\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.641\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.299\\u003cbr\\u003eT follicular helper cell: -4.095\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.478\",\"value: -5.449\\u003cbr\\u003enatural killer cell: -1.001\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.705\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.087\\u003cbr\\u003eactivated type II NK T cell: -3.739\\u003cbr\\u003elymphocyte: -3.930\",\"value: -7.179\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.652\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.506\\u003cbr\\u003egamma-delta T cell: -2.662\\u003cbr\\u003enatural killer cell: -2.996\\u003cbr\\u003emature NK T cell: -3.602\",\"value: -5.172\\u003cbr\\u003eclassical monocyte: -1.244\\u003cbr\\u003eCD14-positive monocyte: -2.245\\u003cbr\\u003emacrophage: -3.013\\u003cbr\\u003emonocyte: -3.677\\u003cbr\\u003enon-classical monocyte: -4.016\",\"value: -5.720\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.664\\u003cbr\\u003enatural killer cell: -1.527\\u003cbr\\u003emature NK T cell: -3.428\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.892\\u003cbr\\u003egamma-delta T cell: -4.535\",\"value: -5.790\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.621\\u003cbr\\u003enatural killer cell: -1.835\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.594\\u003cbr\\u003emature NK T cell: -4.491\\u003cbr\\u003egamma-delta T cell: -4.621\",\"value: -6.343\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.414\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003emature NK T cell: -4.285\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.288\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.020\",\"value: -6.002\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.922\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.120\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.144\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.482\\u003cbr\\u003eT follicular helper cell: -3.078\",\"value: -5.093\\u003cbr\\u003eclassical monocyte: -1.015\\u003cbr\\u003eCD14-positive monocyte: -2.188\\u003cbr\\u003emacrophage: -2.669\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.034\\u003cbr\\u003emonocyte: -3.391\",\"value: -6.305\\u003cbr\\u003eclassical monocyte: -1.186\\u003cbr\\u003eCD14-positive monocyte: -1.294\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.696\\u003cbr\\u003emonocyte: -3.695\\u003cbr\\u003edendritic cell, human: -4.192\",\"value: -5.564\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.195\\u003cbr\\u003eT follicular helper cell: -2.483\\u003cbr\\u003eT-helper 22 cell: -2.702\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.780\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.426\",\"value: -5.736\\u003cbr\\u003eclassical monocyte: -1.481\\u003cbr\\u003eCD14-positive monocyte: -1.615\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.878\\u003cbr\\u003emacrophage: -3.871\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -3.873\",\"value: -5.319\\u003cbr\\u003eplasmacytoid dendritic cell: -0.210\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.927\\u003cbr\\u003edendritic cell: -2.997\\u003cbr\\u003emyeloid dendritic cell: -5.449\\u003cbr\\u003econventional dendritic cell: -5.538\",\"value: -5.712\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.490\\u003cbr\\u003enatural killer cell: -1.677\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.944\\u003cbr\\u003emature NK T cell: -4.148\\u003cbr\\u003egamma-delta T cell: -4.675\",\"value: -6.225\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -1.773\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.114\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.285\\u003cbr\\u003eT-helper 22 cell: -2.527\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.920\",\"value: -6.113\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.008\\u003cbr\\u003enatural killer cell: -2.885\\u003cbr\\u003egamma-delta T cell: -3.341\\u003cbr\\u003emature NK T cell: -3.850\\u003cbr\\u003einnate lymphoid cell: -4.024\",\"value: -5.366\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.751\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.854\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.196\\u003cbr\\u003eT-helper 22 cell: -2.339\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.038\",\"value: -5.598\\u003cbr\\u003eclassical monocyte: -1.075\\u003cbr\\u003eCD14-positive monocyte: -1.302\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.036\\u003cbr\\u003emonocyte: -3.887\\u003cbr\\u003emacrophage: -3.908\",\"value: -6.462\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.138\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.678\\u003cbr\\u003enatural killer cell: -1.679\\u003cbr\\u003eactivated type II NK T cell: -4.004\\u003cbr\\u003emature NK T cell: -4.247\",\"value: -6.162\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.491\\u003cbr\\u003enatural killer cell: -1.845\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.092\\u003cbr\\u003emature NK T cell: -4.428\\u003cbr\\u003eactivated type II NK T cell: -4.688\",\"value: -6.028\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.519\\u003cbr\\u003eT follicular helper cell: -2.009\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.013\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.468\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.735\",\"value: -6.195\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.112\\u003cbr\\u003enatural killer cell: -1.640\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.274\\u003cbr\\u003egamma-delta T cell: -3.350\\u003cbr\\u003emature NK T cell: -3.676\",\"value: -6.693\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.234\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.928\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.351\\u003cbr\\u003eT follicular helper cell: -2.460\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.404\",\"value: -6.230\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.200\\u003cbr\\u003enatural killer cell: -1.440\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.527\\u003cbr\\u003eactivated type II NK T cell: -4.328\\u003cbr\\u003emature NK T cell: -4.541\",\"value: -6.238\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.600\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.085\\u003cbr\\u003emature NK T cell: -4.055\\u003cbr\\u003eactivated type II NK T cell: -4.633\",\"value: -4.672\\u003cbr\\u003egamma-delta T cell: -2.684\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.742\\u003cbr\\u003emature NK T cell: -2.839\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.962\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.164\",\"value: -6.403\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.494\\u003cbr\\u003enatural killer cell: -2.138\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.655\\u003cbr\\u003emature NK T cell: -4.088\\u003cbr\\u003egamma-delta T cell: -4.570\",\"value: -6.144\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.814\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.725\\u003cbr\\u003enatural killer cell: -1.885\\u003cbr\\u003eactivated type II NK T cell: -3.990\\u003cbr\\u003emature NK T cell: -4.699\",\"value: -5.717\\u003cbr\\u003enatural killer cell: -1.625\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.931\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.059\\u003cbr\\u003egamma-delta T cell: -3.488\\u003cbr\\u003emature NK T cell: -3.817\",\"value: -6.229\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.466\\u003cbr\\u003enatural killer cell: -1.501\\u003cbr\\u003emature NK T cell: -4.211\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.583\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.890\",\"value: -6.007\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.295\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.506\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.193\\u003cbr\\u003eT follicular helper cell: -3.333\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.688\",\"value: -4.252\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.532\\u003cbr\\u003enatural killer cell: -3.109\\u003cbr\\u003egamma-delta T cell: -3.331\\u003cbr\\u003einnate lymphoid cell: -3.704\\u003cbr\\u003emature NK T cell: -3.744\",\"value: -6.337\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.540\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.805\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.217\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.357\\u003cbr\\u003eT follicular helper cell: -3.048\",\"value: -5.090\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.917\\u003cbr\\u003enatural killer cell: -2.111\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.697\\u003cbr\\u003egamma-delta T cell: -2.740\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -3.396\",\"value: -6.070\\u003cbr\\u003eplasmacytoid dendritic cell: -0.403\\u003cbr\\u003edendritic cell: -2.290\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.630\\u003cbr\\u003eplasma cell: -4.264\\u003cbr\\u003econventional dendritic cell: -4.606\",\"value: -6.668\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.301\\u003cbr\\u003edendritic cell, human: -2.317\\u003cbr\\u003econventional dendritic cell: -2.439\\u003cbr\\u003eclassical monocyte: -2.451\\u003cbr\\u003eCD14-positive monocyte: -2.711\",\"value: -7.318\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.896\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.994\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.175\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.112\\u003cbr\\u003eT follicular helper cell: -3.593\",\"value: -5.845\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.618\\u003cbr\\u003enatural killer cell: -1.648\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.939\\u003cbr\\u003emature NK T cell: -4.077\\u003cbr\\u003eactivated type II NK T cell: -4.365\",\"value: -4.970\\u003cbr\\u003egamma-delta T cell: -1.947\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.076\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.512\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.773\\u003cbr\\u003emature NK T cell: -2.922\",\"value: -6.512\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.098\\u003cbr\\u003egamma-delta T cell: -2.727\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.184\\u003cbr\\u003emature NK T cell: -3.563\\u003cbr\\u003emucosal invariant T cell: -3.978\",\"value: -5.396\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.969\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.043\\u003cbr\\u003eT follicular helper cell: -2.208\\u003cbr\\u003eT-helper 22 cell: -2.626\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.979\",\"value: -5.286\\u003cbr\\u003eplasmacytoid dendritic cell: -0.438\\u003cbr\\u003edendritic cell: -2.368\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.539\\u003cbr\\u003edendritic cell, human: -4.768\\u003cbr\\u003emyeloid dendritic cell: -4.867\",\"value: -4.521\\u003cbr\\u003eclassical monocyte: -1.334\\u003cbr\\u003eCD14-positive monocyte: -2.265\\u003cbr\\u003emonocyte: -2.777\\u003cbr\\u003emacrophage: -2.892\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.464\",\"value: -6.227\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.856\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.564\\u003cbr\\u003enatural killer cell: -2.401\\u003cbr\\u003egamma-delta T cell: -4.139\\u003cbr\\u003emature NK T cell: -4.167\",\"value: -5.843\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.626\\u003cbr\\u003enatural killer cell: -2.208\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.431\\u003cbr\\u003emature NK T cell: -3.733\\u003cbr\\u003egamma-delta T cell: -4.028\",\"value: -5.684\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.182\\u003cbr\\u003econventional dendritic cell: -2.274\\u003cbr\\u003eclassical monocyte: -2.414\\u003cbr\\u003edendritic cell: -2.501\\u003cbr\\u003edendritic cell, human: -2.790\",\"value: -6.836\\u003cbr\\u003eCD14-positive monocyte: -0.876\\u003cbr\\u003eclassical monocyte: -1.519\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.966\\u003cbr\\u003emonocyte: -4.050\\u003cbr\\u003emacrophage: -4.203\",\"value: -5.768\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.775\\u003cbr\\u003enatural killer cell: -2.092\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.494\\u003cbr\\u003emature NK T cell: -3.583\\u003cbr\\u003eimmature NK T cell: -4.534\",\"value: -4.790\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.826\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.460\\u003cbr\\u003eT-helper 22 cell: -2.511\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.599\\u003cbr\\u003eT follicular helper cell: -2.728\",\"value: -6.033\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.409\\u003cbr\\u003enatural killer cell: -1.559\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.825\\u003cbr\\u003eactivated type II NK T cell: -3.807\\u003cbr\\u003emature NK T cell: -4.023\",\"value: -6.131\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.888\\u003cbr\\u003egamma-delta T cell: -2.407\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.709\\u003cbr\\u003enatural killer cell: -2.941\\u003cbr\\u003emature NK T cell: -3.924\",\"value: -5.456\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.629\\u003cbr\\u003enatural killer cell: -1.578\\u003cbr\\u003emature NK T cell: -3.707\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.037\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.846\",\"value: -5.979\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.796\\u003cbr\\u003enatural killer cell: -2.420\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.847\\u003cbr\\u003egamma-delta T cell: -2.888\\u003cbr\\u003emature NK T cell: -4.199\",\"value: -4.982\\u003cbr\\u003enatural killer cell: -1.949\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.983\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.615\\u003cbr\\u003egamma-delta T cell: -3.748\\u003cbr\\u003elymphocyte: -3.759\",\"value: -4.534\\u003cbr\\u003eplasmacytoid dendritic cell: -0.406\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.624\\u003cbr\\u003edendritic cell: -2.984\\u003cbr\\u003econventional dendritic cell: -4.887\\u003cbr\\u003ehematopoietic precursor cell: -4.923\",\"value: -6.242\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.109\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.279\\u003cbr\\u003enatural killer cell: -2.054\\u003cbr\\u003egamma-delta T cell: -4.093\\u003cbr\\u003emature NK T cell: -4.178\",\"value: -5.892\\u003cbr\\u003egamma-delta T cell: -1.792\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.006\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.423\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.800\\u003cbr\\u003emature NK T cell: -3.074\",\"value: -5.464\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.899\\u003cbr\\u003enatural killer cell: -1.458\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.688\\u003cbr\\u003emature NK T cell: -4.173\\u003cbr\\u003eactivated type II NK T cell: -4.405\",\"value: -5.901\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.497\\u003cbr\\u003enatural killer cell: -1.565\\u003cbr\\u003emature NK T cell: -3.643\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.945\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.818\",\"value: -6.282\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.826\\u003cbr\\u003enatural killer cell: -2.096\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.576\\u003cbr\\u003egamma-delta T cell: -3.589\\u003cbr\\u003emature NK T cell: -3.697\",\"value: -5.156\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.015\\u003cbr\\u003enatural killer cell: -1.912\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.605\\u003cbr\\u003egamma-delta T cell: -3.130\\u003cbr\\u003emature NK T cell: -3.194\",\"value: -6.309\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.974\\u003cbr\\u003enatural killer cell: -1.101\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.582\\u003cbr\\u003emature NK T cell: -4.052\\u003cbr\\u003egamma-delta T cell: -4.304\",\"value: -6.227\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.707\\u003cbr\\u003enatural killer cell: -2.132\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.495\\u003cbr\\u003emature NK T cell: -3.480\\u003cbr\\u003egamma-delta T cell: -3.595\",\"value: -5.801\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.444\\u003cbr\\u003enatural killer cell: -1.828\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.193\\u003cbr\\u003emature NK T cell: -4.624\\u003cbr\\u003eactivated type II NK T cell: -4.861\",\"value: -6.095\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.533\\u003cbr\\u003enatural killer cell: -2.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.834\\u003cbr\\u003emature NK T cell: -3.843\\u003cbr\\u003egamma-delta T cell: -4.060\",\"value: -5.424\\u003cbr\\u003eplasmacytoid dendritic cell: -0.389\\u003cbr\\u003edendritic cell: -2.476\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.919\\u003cbr\\u003emyeloid dendritic cell: -4.678\\u003cbr\\u003eB cell: -4.779\",\"value: -6.554\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.450\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.068\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.677\\u003cbr\\u003eT follicular helper cell: -3.471\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -4.084\",\"value: -5.355\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.198\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.615\\u003cbr\\u003enatural killer cell: -1.667\\u003cbr\\u003emature NK T cell: -4.031\\u003cbr\\u003egamma-delta T cell: -4.116\",\"value: -5.636\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.625\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.910\\u003cbr\\u003eT follicular helper cell: -2.517\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.562\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.671\",\"value: -6.093\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.291\\u003cbr\\u003egamma-delta T cell: -2.928\\u003cbr\\u003enatural killer cell: -2.962\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.794\\u003cbr\\u003einnate lymphoid cell: -3.983\",\"value: -5.929\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.847\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.165\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.903\\u003cbr\\u003eT follicular helper cell: -3.981\\u003cbr\\u003eT cell: -4.045\",\"value: -6.791\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.987\\u003cbr\\u003enatural killer cell: -3.156\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.183\\u003cbr\\u003egamma-delta T cell: -3.503\\u003cbr\\u003eactivated type II NK T cell: -4.118\",\"value: -6.433\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.985\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.041\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.064\\u003cbr\\u003eT-helper 22 cell: -2.581\\u003cbr\\u003eT follicular helper cell: -3.173\",\"value: -5.964\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.459\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.732\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.213\\u003cbr\\u003eT-helper 22 cell: -2.391\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.055\",\"value: -5.973\\u003cbr\\u003eT follicular helper cell: -1.696\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.866\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.364\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.389\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.637\",\"value: -5.737\\u003cbr\\u003edendritic cell: -2.687\\u003cbr\\u003emast cell: -3.000\\u003cbr\\u003ehematopoietic precursor cell: -3.054\\u003cbr\\u003eplasmacytoid dendritic cell: -3.073\\u003cbr\\u003edendritic cell, human: -3.344\",\"value: -6.761\\u003cbr\\u003eclassical monocyte: -2.199\\u003cbr\\u003eCD14-positive monocyte: -2.282\\u003cbr\\u003eCD1c-positive myeloid dendritic cell: -2.581\\u003cbr\\u003edendritic cell, human: -2.976\\u003cbr\\u003econventional dendritic cell: -2.988\",\"value: -5.603\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.108\\u003cbr\\u003egamma-delta T cell: -2.734\\u003cbr\\u003enatural killer cell: -2.786\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.208\\u003cbr\\u003emature NK T cell: -3.472\",\"value: -6.022\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.027\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.034\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.942\\u003cbr\\u003eT cell: -4.058\\u003cbr\\u003egranule cell: -4.734\",\"value: -5.351\\u003cbr\\u003eplasmacytoid dendritic cell: -0.354\\u003cbr\\u003edendritic cell: -2.520\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.564\\u003cbr\\u003econventional dendritic cell: -5.115\\u003cbr\\u003emyeloid dendritic cell: -5.294\",\"value: -4.785\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.077\\u003cbr\\u003eT follicular helper cell: -2.563\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.567\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.964\\u003cbr\\u003eT-helper 22 cell: -3.161\",\"value: -6.956\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.314\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.729\\u003cbr\\u003enatural killer cell: -2.498\\u003cbr\\u003egamma-delta T cell: -2.721\\u003cbr\\u003emature NK T cell: -3.578\",\"value: -6.199\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.596\\u003cbr\\u003enatural killer cell: -1.539\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.346\\u003cbr\\u003emature NK T cell: -3.980\\u003cbr\\u003eactivated type II NK T cell: -4.639\",\"value: -4.939\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.443\\u003cbr\\u003enatural killer cell: -1.874\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.699\\u003cbr\\u003egamma-delta T cell: -3.533\\u003cbr\\u003emature NK T cell: -3.926\",\"value: -6.016\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -0.671\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.994\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.112\\u003cbr\\u003eT follicular helper cell: -3.401\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -3.981\",\"value: -6.200\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.551\\u003cbr\\u003enatural killer cell: -1.662\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.677\\u003cbr\\u003emature NK T cell: -4.385\\u003cbr\\u003eactivated type II NK T cell: -4.654\",\"value: -5.516\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.151\\u003cbr\\u003enatural killer cell: -1.294\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.450\\u003cbr\\u003emature NK T cell: -3.621\\u003cbr\\u003eactivated type II NK T cell: -4.289\",\"value: -6.035\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.328\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.493\\u003cbr\\u003enatural killer cell: -2.429\\u003cbr\\u003egamma-delta T cell: -3.118\\u003cbr\\u003emature NK T cell: -3.513\",\"value: -6.079\\u003cbr\\u003eCD14-positive monocyte: -1.143\\u003cbr\\u003eclassical monocyte: -1.467\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.929\\u003cbr\\u003emonocyte: -3.001\\u003cbr\\u003epromonocyte: -4.337\",\"value: -5.852\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.484\\u003cbr\\u003enatural killer cell: -2.041\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.028\\u003cbr\\u003egamma-delta T cell: -4.078\\u003cbr\\u003emature NK T cell: -4.173\",\"value: -5.328\\u003cbr\\u003eT follicular helper cell: -2.168\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.785\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.892\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -3.605\\u003cbr\\u003eT-helper 22 cell: -3.813\",\"value: -6.403\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.217\\u003cbr\\u003egamma-delta T cell: -3.222\\u003cbr\\u003enatural killer cell: -3.324\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.716\\u003cbr\\u003einnate lymphoid cell: -3.772\",\"value: -5.427\\u003cbr\\u003eplasmacytoid dendritic cell: -0.187\\u003cbr\\u003eplasmacytoid dendritic cell, human: -3.083\\u003cbr\\u003edendritic cell: -3.182\\u003cbr\\u003emyeloid dendritic cell: -5.648\\u003cbr\\u003edendritic cell, human: -5.957\",\"value: -5.761\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.374\\u003cbr\\u003enatural killer cell: -2.095\\u003cbr\\u003emature NK T cell: -3.514\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.711\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.990\",\"value: -6.540\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.638\\u003cbr\\u003enatural killer cell: -1.288\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.036\\u003cbr\\u003emature NK T cell: -4.111\\u003cbr\\u003eactivated type II NK T cell: -5.103\",\"value: -4.610\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -2.582\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.971\\u003cbr\\u003eT follicular helper cell: -2.979\\u003cbr\\u003eT-helper 22 cell: -3.337\\u003cbr\\u003eT cell: -3.479\",\"value: -5.689\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.841\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.014\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.145\\u003cbr\\u003eT follicular helper cell: -2.636\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.705\",\"value: -5.846\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.979\\u003cbr\\u003egamma-delta T cell: -2.083\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -2.477\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -3.231\\u003cbr\\u003emature NK T cell: -3.292\",\"value: -5.160\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.037\\u003cbr\\u003enatural killer cell: -1.099\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.671\\u003cbr\\u003emature NK T cell: -4.372\\u003cbr\\u003eactivated type II NK T cell: -4.797\",\"value: -6.847\\u003cbr\\u003eclassical monocyte: -1.375\\u003cbr\\u003eCD14-positive monocyte: -1.645\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.279\\u003cbr\\u003edendritic cell, human: -3.354\\u003cbr\\u003emacrophage: -3.504\",\"value: -5.986\\u003cbr\\u003enon-classical monocyte: -1.240\\u003cbr\\u003eclassical monocyte: -2.492\\u003cbr\\u003eCD14-low, CD16-positive monocyte: -2.531\\u003cbr\\u003emature NK T cell: -2.637\\u003cbr\\u003eCD14-positive monocyte: -2.841\",\"value: -5.756\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.369\\u003cbr\\u003enatural killer cell: -2.108\\u003cbr\\u003emature NK T cell: -4.089\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.263\\u003cbr\\u003eastrocyte of the cerebral cortex: -5.010\",\"value: -5.904\\u003cbr\\u003eCD14-positive monocyte: -0.874\\u003cbr\\u003eclassical monocyte: -1.304\\u003cbr\\u003epvalb GABAergic cortical interneuron: -3.295\\u003cbr\\u003emonocyte: -3.902\\u003cbr\\u003eneutrophil: -4.553\",\"value: -4.495\\u003cbr\\u003eplasmacytoid dendritic cell: -0.226\\u003cbr\\u003eplasmacytoid dendritic cell, human: -2.837\\u003cbr\\u003edendritic cell: -3.208\\u003cbr\\u003eplasma cell: -5.566\\u003cbr\\u003emyeloid dendritic cell: -5.621\",\"value: -7.401\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.895\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.237\\u003cbr\\u003enatural killer cell: -2.663\\u003cbr\\u003eactivated type II NK T cell: -3.709\\u003cbr\\u003egamma-delta T cell: -3.981\",\"value: -5.704\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -0.766\\u003cbr\\u003enatural killer cell: -2.628\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.397\\u003cbr\\u003egamma-delta T cell: -3.499\\u003cbr\\u003emature NK T cell: -3.729\",\"value: -6.064\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -1.224\\u003cbr\\u003enatural killer cell: -1.352\\u003cbr\\u003egamma-delta T cell: -2.917\\u003cbr\\u003emature NK T cell: -3.157\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.831\",\"value: -6.604\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -1.690\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.775\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -1.996\\u003cbr\\u003eT follicular helper cell: -1.996\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.961\",\"value: -5.661\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.839\\u003cbr\\u003enatural killer cell: -1.518\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.977\\u003cbr\\u003emature NK T cell: -3.875\\u003cbr\\u003eactivated type II NK T cell: -4.484\",\"value: -6.683\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.578\\u003cbr\\u003enatural killer cell: -2.209\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.401\\u003cbr\\u003emature NK T cell: -4.269\\u003cbr\\u003eactivated type II NK T cell: -4.788\",\"value: -5.360\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.526\\u003cbr\\u003enatural killer cell: -2.232\\u003cbr\\u003emature NK T cell: -3.549\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.741\\u003cbr\\u003egamma-delta T cell: -4.025\",\"value: -5.895\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.582\\u003cbr\\u003enatural killer cell: -1.867\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.369\\u003cbr\\u003emature NK T cell: -3.711\\u003cbr\\u003egamma-delta T cell: -4.356\",\"value: -5.113\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.004\\u003cbr\\u003egamma-delta T cell: -2.754\\u003cbr\\u003enatural killer cell: -3.092\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -3.329\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.493\",\"value: -5.555\\u003cbr\\u003ecentral memory CD4-positive, alpha-beta T cell: -1.778\\u003cbr\\u003eeffector memory CD4-positive, alpha-beta T cell: -2.230\\u003cbr\\u003enaive thymus-derived CD4-positive, alpha-beta T cell: -2.433\\u003cbr\\u003eT follicular helper cell: -2.516\\u003cbr\\u003enaive thymus-derived CD8-positive, alpha-beta T cell: -2.672\",\"value: -6.597\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.392\\u003cbr\\u003enatural killer cell: -2.268\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.005\\u003cbr\\u003emature NK T cell: -4.756\\u003cbr\\u003eactivated type II NK T cell: -4.768\",\"value: -5.805\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.710\\u003cbr\\u003enatural killer cell: -1.348\\u003cbr\\u003emature NK T cell: -3.609\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -4.349\\u003cbr\\u003egamma-delta T cell: -4.488\",\"value: -7.175\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.589\\u003cbr\\u003enatural killer cell: -2.469\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.510\\u003cbr\\u003emature NK T cell: -3.860\\u003cbr\\u003eactivated type II NK T cell: -4.319\",\"value: -5.336\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -1.158\\u003cbr\\u003enatural killer cell: -2.958\\u003cbr\\u003egamma-delta T cell: -3.043\\u003cbr\\u003emature NK T cell: -3.703\\u003cbr\\u003eeffector memory CD8-positive, alpha-beta T cell: -3.799\",\"value: -2.369\\u003cbr\\u003enatural killer cell: -1.689\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -2.158\\u003cbr\\u003egamma-delta T cell: -2.250\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.553\\u003cbr\\u003emature NK T cell: -2.935\",\"value: -5.913\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.605\\u003cbr\\u003enatural killer cell: -1.607\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.036\\u003cbr\\u003emature NK T cell: -4.282\\u003cbr\\u003eactivated type II NK T cell: -4.633\",\"value: -6.613\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.847\\u003cbr\\u003enatural killer cell: -2.615\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.743\\u003cbr\\u003egamma-delta T cell: -3.021\\u003cbr\\u003emature NK T cell: -3.239\",\"value: -3.556\\u003cbr\\u003eCD14-positive monocyte: -1.463\\u003cbr\\u003eclassical monocyte: -2.271\\u003cbr\\u003emonocyte: -2.289\\u003cbr\\u003emacrophage: -2.670\\u003cbr\\u003edendritic cell: -3.516\",\"value: -5.288\\u003cbr\\u003eCD8-positive, alpha-beta T cell: -1.624\\u003cbr\\u003emature NK T cell: -2.335\\u003cbr\\u003eeffector CD8-positive, alpha-beta T cell: -2.656\\u003cbr\\u003enatural killer cell: -2.892\\u003cbr\\u003egamma-delta T cell: -3.198\",\"value: -6.937\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.668\\u003cbr\\u003enatural killer cell: -2.633\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -2.864\\u003cbr\\u003emature NK T cell: -3.277\\u003cbr\\u003egamma-delta T cell: -3.564\",\"value: -5.873\\u003cbr\\u003eclassical monocyte: -0.808\\u003cbr\\u003eCD14-positive monocyte: -2.356\\u003cbr\\u003ealveolar macrophage: -3.781\\u003cbr\\u003emonocyte: -3.802\\u003cbr\\u003emacrophage: -3.991\",\"value: -5.710\\u003cbr\\u003eCD16-positive, CD56-dim natural killer cell, human: -0.524\\u003cbr\\u003enatural killer cell: -1.633\\u003cbr\\u003emature NK T cell: -3.704\\u003cbr\\u003eCD16-negative, CD56-bright natural killer cell, human: -3.844\\u003cbr\\u003eastrocyte of the cerebral cortex: -4.811\",\"value: -5.166\\u003cbr\\u003eclassical monocyte: -0.957\\u003cbr\\u003eCD14-positive monocyte: -1.780\\u003cbr\\u003epvalb GABAergic cortical interneuron: -2.776\\u003cbr\\u003emonocyte: -4.060\\u003cbr\\u003emacrophage: 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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"UMAP_1\"},\"showgrid\":false},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"UMAP_2\"},\"showgrid\":false},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"value\"}},\"colorscale\":[[0.0,\"rgb(5,48,97)\"],[0.1,\"rgb(33,102,172)\"],[0.2,\"rgb(67,147,195)\"],[0.3,\"rgb(146,197,222)\"],[0.4,\"rgb(209,229,240)\"],[0.5,\"rgb(247,247,247)\"],[0.6,\"rgb(253,219,199)\"],[0.7,\"rgb(244,165,130)\"],[0.8,\"rgb(214,96,77)\"],[0.9,\"rgb(178,24,43)\"],[1.0,\"rgb(103,0,31)\"]]},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"UMAP Scatter Plot\"},\"plot_bgcolor\":\"white\",\"width\":800,\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('8ba31993-e85f-4fa9-b74c-38b9ecc5b4f9');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generate_interactive_plotly_for_metadata(adata, 5, \"cell_type\", True, \"disease_score\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb
index 9d59f5ac..479c9780 100644
--- a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_annotate_genes.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -123,7 +123,7 @@
        "             'unprocessed_pseudogene': 2})"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
        "Name: count, Length: 528, dtype: int64"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +281,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +300,9 @@
      "output_type": "stream",
      "text": [
       "Number of autosomal genes: 34194\n",
-      "Number of sex genes: 1235\n"
+      "Number of sex genes: 1235\n",
+      "Number of X genes: 1128\n",
+      "Number of Y genes: 107\n"
      ]
     }
    ],
@@ -309,14 +311,22 @@
     "    model_genes_df['contig'].isin({str(i) for i in range(1, 23)})]['ensembl_gene_id'].values.tolist()\n",
     "sex_gene_ids = model_genes_df[\n",
     "    model_genes_df['contig'].isin({'X', 'Y'})]['ensembl_gene_id'].values.tolist()\n",
+    "x_gene_ids = model_genes_df[\n",
+    "    model_genes_df['contig'].isin({'X'})]['ensembl_gene_id'].values.tolist()\n",
+    "y_gene_ids = model_genes_df[\n",
+    "    model_genes_df['contig'].isin({'Y'})]['ensembl_gene_id'].values.tolist()\n",
+    "\n",
     "print(f\"Number of autosomal genes: {len(autosomal_gene_ids)}\")\n",
     "print(f\"Number of sex genes: {len(sex_gene_ids)}\")\n",
+    "print(f\"Number of X genes: {len(x_gene_ids)}\")\n",
+    "print(f\"Number of Y genes: {len(y_gene_ids)}\")\n",
+    "\n",
     "assert set(autosomal_gene_ids).intersection(sex_gene_ids) == set()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,48 +337,16 @@
     "\n",
     "with open(\"/home/mehrtash/data/data/cellariumgpt_artifacts/sex_gene_ids.txt\", 'w') as f:\n",
     "    for gene_id in sex_gene_ids:\n",
+    "        f.write(f\"{gene_id}\\n\")\n",
+    "\n",
+    "with open(\"/home/mehrtash/data/data/cellariumgpt_artifacts/x_gene_ids.txt\", 'w') as f:\n",
+    "    for gene_id in x_gene_ids:\n",
+    "        f.write(f\"{gene_id}\\n\")\n",
+    "\n",
+    "with open(\"/home/mehrtash/data/data/cellariumgpt_artifacts/y_gene_ids.txt\", 'w') as f:\n",
+    "    for gene_id in y_gene_ids:\n",
     "        f.write(f\"{gene_id}\\n\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6957"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "8192 - 1235"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8192"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "6957 + 1235"
-   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_metadata_prediction_debug.ipynb b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_metadata_prediction_debug.ipynb
new file mode 100644
index 00000000..3d306e37
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/metadata_benchmarking/xx_metadata_prediction_debug.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# key_order = list(ctx.gpt_pipeline.model.metadata_vocab_sizes)\n",
+    "\n",
+    "# ctx.metadata_ontology_infos = {\n",
+    "#     k: ctx.metadata_ontology_infos[k] for k in key_order\n",
+    "# }\n",
+    "\n",
+    "# ctx.predict_tokenizer.metadata_vocab_sizes = {\n",
+    "#     k: ctx.predict_tokenizer.metadata_vocab_sizes[k] for k in key_order\n",
+    "# }\n",
+    "\n",
+    "# ctx.predict_tokenizer.ontology_infos = {\n",
+    "#     k: ctx.predict_tokenizer.ontology_infos[k] for k in key_order\n",
+    "# }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata = sc.read_h5ad(REF_ADATA_PATH)\n",
+    "\n",
+    "ref_adata = adata.copy()\n",
+    "\n",
+    "adata = adata[adata.obs[\"cell_type\"] != \"unknown\"]\n",
+    "adata = adata[adata.obs[\"development_stage\"] != \"unknown\"]\n",
+    "adata = adata[adata.obs[\"sex\"] != \"unknown\"]\n",
+    "adata = adata[adata.obs[\"tissue\"] != \"unknown\"]\n",
+    "adata = adata[adata.obs[\"disease\"] != \"unknown\"]\n",
+    "\n",
+    "adata = adata[:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c18029c9d799486e8878dc993f371811",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "batch_size = 100\n",
+    "\n",
+    "logits_dict_chunks = []\n",
+    "\n",
+    "rng = torch.Generator().manual_seed(1)\n",
+    "\n",
+    "for i in tqdm(range(0, adata.n_obs, batch_size)):\n",
+    "    start = i\n",
+    "    end = min(i + batch_size, adata.n_obs)\n",
+    "\n",
+    "    # metadata_prompt_masks_dict = {\n",
+    "    #     \"cell_type\": False,\n",
+    "    #     \"tissue\": False,\n",
+    "    #     \"development_stage\": False,\n",
+    "    #     \"disease\": False,\n",
+    "    #     \"sex\": False,\n",
+    "    # }\n",
+    "    # metadata_prompt_masks_dict = {k: metadata_prompt_masks_dict[k] for k in key_order}\n",
+    "\n",
+    "    tokens_dict, context_indices = ctx.generate_tokens_from_adata(\n",
+    "        adata=adata[start:end],\n",
+    "        obs_index=None,\n",
+    "        query_var_names=[],\n",
+    "        n_rand_prompt_vars=4096,\n",
+    "        rand_prompt_var_names_sublist=ctx.model_var_names.tolist(),\n",
+    "        fixed_prompt_var_names_sublist=[],\n",
+    "        rng=rng,\n",
+    "        # metadata_prompt_masks_dict=metadata_prompt_masks_dict\n",
+    "    )\n",
+    "\n",
+    "    # convert to cuda\n",
+    "    tokens_dict = ctx.gpt_pipeline.transfer_batch_to_device(tokens_dict, ctx.device, 0)\n",
+    "\n",
+    "    # get model predictions\n",
+    "    with torch.inference_mode():\n",
+    "        logits_dict = ctx.gpt_pipeline.model.predict(\n",
+    "            gene_tokens_nc=tokens_dict[\"gene_tokens_nc\"],\n",
+    "            metadata_tokens_n=tokens_dict[\"metadata_tokens_n\"],\n",
+    "            prompt_mask_nc=tokens_dict[\"prompt_mask_nc\"])\n",
+    "        \n",
+    "    # transfer to numpy and cpu and keep track\n",
+    "    logits_dict.pop('gene_value', None)\n",
+    "    logits_dict_numpy = {k: v.cpu().numpy() for k, v in logits_dict.items()}\n",
+    "    logits_dict_chunks.append(logits_dict_numpy)\n",
+    "\n",
+    "# concatenate all chunks\n",
+    "logits_dict = {k: np.concatenate([d[k] for d in logits_dict_chunks]) for k in logits_dict_chunks[0].keys()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'True label')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x640 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 984,
+       "width": 980
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metadata_key = 'cell_type'\n",
+    "metadata_token_ctx_idx = context_indices[f'query_{metadata_key}']\n",
+    "\n",
+    "# metadata_token_ctx_idx = tokens_dict['gene_tokens_nc']['gene_id'].shape[-1] + 4\n",
+    "\n",
+    "logits_nk = logits_dict[metadata_key][:, metadata_token_ctx_idx, :]\n",
+    "\n",
+    "assert logits_nk.shape[-1] == len(ctx.metadata_ontology_infos[metadata_key]['labels'])\n",
+    "                       \n",
+    "mapper = {label: idx for idx, label in enumerate(ctx.metadata_ontology_infos[metadata_key]['labels'])}\n",
+    "mapper[\"unknown\"] = np.nan\n",
+    "labels_n = adata.obs[metadata_key].values\n",
+    "codes_n = np.asarray([mapper[label] for label in labels_n])\n",
+    "good_indices = np.where(~np.isnan(codes_n))[0]\n",
+    "codes_n = codes_n[good_indices].astype(int)\n",
+    "labels_n = labels_n[good_indices]\n",
+    "pred_codes_n = np.argmax(logits_nk, axis=-1)[good_indices]\n",
+    "pred_labels_n = np.asarray(ctx.metadata_ontology_infos[metadata_key]['labels'])[pred_codes_n]\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'codes': codes_n,\n",
+    "    'labels': labels_n,\n",
+    "    'pred_codes': pred_codes_n,\n",
+    "    'pred_labels': pred_labels_n\n",
+    "})\n",
+    "\n",
+    "\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "top_n_expected = 10\n",
+    "top_n_pred = 10\n",
+    "expected_codes_set = set(df[\"codes\"].value_counts().head(top_n_expected).index)\n",
+    "predicted_codes_set = set(df[\"pred_codes\"].value_counts().head(top_n_pred).index)\n",
+    "all_codes = sorted(expected_codes_set.union(predicted_codes_set))\n",
+    "other_code = np.max(all_codes) + 1\n",
+    "\n",
+    "_df = df.copy()\n",
+    "_df[\"codes\"] = _df[\"codes\"].apply(lambda x: x if x in all_codes else other_code)\n",
+    "_df[\"pred_codes\"] = _df[\"pred_codes\"].apply(lambda x: x if x in all_codes else other_code)\n",
+    "\n",
+    "y_true = _df[\"codes\"].values\n",
+    "y_pred = _df[\"pred_codes\"].values\n",
+    "labels = np.asarray(ctx.metadata_ontology_infos[metadata_key][\"labels\"])[all_codes]\n",
+    "labels = list(labels) + [\"other\"]\n",
+    "\n",
+    "cm = confusion_matrix(y_true, y_pred, labels=all_codes + [other_code], normalize='true')\n",
+    "\n",
+    "# plot the confision matrix using matplotlib\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "cax = ax.matshow(cm, cmap='Blues')\n",
+    "ax.set_xticks(np.arange(len(labels)))\n",
+    "ax.set_yticks(np.arange(len(labels)))\n",
+    "ax.set_xticklabels(labels, rotation=90)\n",
+    "ax.set_yticklabels(labels)\n",
+    "ax.set_xlabel('Predicted label')\n",
+    "ax.set_ylabel('True label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['nucleus', 'cell'], dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ref_adata.obs['suspension_type'].cat.categories"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/cellariumgpt_inference/xx_mean_expression_debug.ipynb b/notebooks/cellariumgpt_inference/xx_mean_expression_debug.ipynb
new file mode 100644
index 00000000..e3dcfc35
--- /dev/null
+++ b/notebooks/cellariumgpt_inference/xx_mean_expression_debug.ipynb
@@ -0,0 +1,1785 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Does CellariumGPT recapitulate empirical mean for a given cell type?\n",
+    "\n",
+    "Can we turn this into a quantitative benchmark?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import warnings\n",
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sc.set_figure_params(figsize=(4, 4))\n",
+    "\n",
+    "DEVICE = torch.device('cuda:0')\n",
+    "\n",
+    "from cellarium.ml.utilities.inference.cellarium_gpt_inference import \\\n",
+    "    CellariumGPTInferenceContext, \\\n",
+    "    GeneNetworkAnalysisBase\n",
+    "\n",
+    "# reset matplotlib params\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_PATH = \"/home/mehrtash/data\"\n",
+    "CHECKPOINT_PATH = \"/home/mehrtash/data/100M_long_run/run_001/lightning_logs/version_3/checkpoints/epoch=5-step=504000.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt\"\n",
+    "# CHECKPOINT_PATH = \"/home/mehrtash/data/mb_checkpoints/98M_001_bs4608/epoch=6-step=63560__updated.ckpt\"\n",
+    "\n",
+    "REF_ADATA_PATH = os.path.join(ROOT_PATH, \"data\", \"extract_0.h5ad\")\n",
+    "GENE_INFO_PATH = os.path.join(ROOT_PATH, \"gene_info\", \"gene_info.tsv\")\n",
+    "\n",
+    "ctx = CellariumGPTInferenceContext(\n",
+    "    cellarium_gpt_ckpt_path=CHECKPOINT_PATH,\n",
+    "    ref_adata_path=REF_ADATA_PATH,\n",
+    "    gene_info_tsv_path=GENE_INFO_PATH,\n",
+    "    device=DEVICE,\n",
+    "    attention_backend=\"mem_efficient\",\n",
+    "    verbose=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load validation cell type table\n",
+    "val_adata = sc.read_h5ad(\n",
+    "    os.path.join(ROOT_PATH, \"data\", \"cellariumgpt_artifacts\", \"cell_types_for_validation_filtered.h5ad\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GABAergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5527.708887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>glutamatergic neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10683.399862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>astrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5380.963698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5083.603161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>oligodendrocyte precursor cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6609.696174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>microglial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3645.962305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>cerebellar granule cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3935.779702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3115.828938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3922.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>non-classical monocyte</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8436.318182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2923.925418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>CD4-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3285.603705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CD8-positive, alpha-beta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3720.503504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CD8-positive, alpha-beta cytotoxic T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5061.393152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5221.671616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5649.398766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6333.568573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4984.057779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6689.483383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5216.285765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4950.552077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>conventional dendritic cell</td>\n",
+       "      <td>10x 5' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>blood</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>14936.870968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>enterocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8772.175057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>endothelial cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10069.068281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>enteroendocrine cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>10636.632432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>pericyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4604.858974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>intestine goblet cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>small intestine</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7435.644048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>kidney collecting duct principal cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6444.458460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>kidney loop of Henle thin descending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4345.735849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>kidney loop of Henle thin ascending limb epith...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5964.088479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>kidney loop of Henle thick ascending limb epit...</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4911.535804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>epithelial cell of proximal tubule</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4491.198843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kidney collecting duct intercalated cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4906.310584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>renal interstitial pericyte</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>kidney</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3130.350171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>hepatic pit cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2761.142458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Kupffer cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6850.818612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11532.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>periportal region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4790.702890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>centrilobular region hepatocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9280.724561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>endothelial cell of pericentral hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2734.614608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>endothelial cell of artery</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3987.627119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>fibroblast</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>7274.262295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>intrahepatic cholangiocyte</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6998.971698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>endothelial cell of hepatic sinusoid</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3836.788282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>hepatic stellate cell</td>\n",
+       "      <td>10x 3' v2</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>liver</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>4090.862637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8422.037378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>type II pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6109.480329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3094.649239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>ciliated columnar cell of tracheobronchial tree</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5754.694686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2744.064915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>epithelial cell of lower respiratory tract</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5956.795231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>pulmonary interstitial fibroblast</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>5294.602205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>9484.199912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>pulmonary artery endothelial cell</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2321.547941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>epithelial cell of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>15015.278925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>fibroblast of lung</td>\n",
+       "      <td>10x 5' v1</td>\n",
+       "      <td>cell</td>\n",
+       "      <td>lung</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>6561.739316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>cardiac muscle myoblast</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>11938.346771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>fibroblast of cardiac tissue</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>3099.779698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>cardiac endothelial cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2268.612916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>cardiac muscle cell</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>8017.024743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>cardiac neuron</td>\n",
+       "      <td>10x 3' v3</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>heart</td>\n",
+       "      <td>male</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>2238.708571</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            cell_type      assay  \\\n",
+       "0                                    GABAergic neuron  10x 3' v3   \n",
+       "1                                glutamatergic neuron  10x 3' v3   \n",
+       "2                                           astrocyte  10x 3' v3   \n",
+       "3                                     oligodendrocyte  10x 3' v3   \n",
+       "4                      oligodendrocyte precursor cell  10x 3' v3   \n",
+       "5                                     microglial cell  10x 3' v3   \n",
+       "6                             cerebellar granule cell  10x 3' v3   \n",
+       "7                                    endothelial cell  10x 3' v3   \n",
+       "8                                  classical monocyte  10x 5' v2   \n",
+       "9                              non-classical monocyte  10x 5' v2   \n",
+       "10                                natural killer cell  10x 5' v2   \n",
+       "11                    CD4-positive, alpha-beta T cell  10x 5' v2   \n",
+       "12                    CD8-positive, alpha-beta T cell  10x 5' v2   \n",
+       "13          CD8-positive, alpha-beta cytotoxic T cell  10x 5' v2   \n",
+       "14             CD8-positive, alpha-beta memory T cell  10x 5' v2   \n",
+       "15  naive thymus-derived CD4-positive, alpha-beta ...  10x 5' v2   \n",
+       "16  naive thymus-derived CD8-positive, alpha-beta ...  10x 5' v2   \n",
+       "17                                       naive B cell  10x 5' v2   \n",
+       "18                                      memory B cell  10x 5' v2   \n",
+       "19                                  regulatory T cell  10x 5' v2   \n",
+       "20                                 gamma-delta T cell  10x 5' v2   \n",
+       "21                        conventional dendritic cell  10x 5' v2   \n",
+       "22                                         enterocyte  10x 3' v2   \n",
+       "23                                   endothelial cell  10x 3' v2   \n",
+       "24                               enteroendocrine cell  10x 3' v2   \n",
+       "25                                           pericyte  10x 3' v2   \n",
+       "26                              intestine goblet cell  10x 3' v2   \n",
+       "27              kidney collecting duct principal cell  10x 3' v3   \n",
+       "28  kidney loop of Henle thin descending limb epit...  10x 3' v3   \n",
+       "29  kidney loop of Henle thin ascending limb epith...  10x 3' v3   \n",
+       "30  kidney loop of Henle thick ascending limb epit...  10x 3' v3   \n",
+       "31                 epithelial cell of proximal tubule  10x 3' v3   \n",
+       "32           kidney collecting duct intercalated cell  10x 3' v3   \n",
+       "33                        renal interstitial pericyte  10x 3' v3   \n",
+       "34                                   hepatic pit cell  10x 3' v2   \n",
+       "35                                       Kupffer cell  10x 3' v2   \n",
+       "36                                         hepatocyte  10x 3' v2   \n",
+       "37                       periportal region hepatocyte  10x 3' v2   \n",
+       "38                    centrilobular region hepatocyte  10x 3' v2   \n",
+       "39   endothelial cell of pericentral hepatic sinusoid  10x 3' v2   \n",
+       "40                         endothelial cell of artery  10x 3' v2   \n",
+       "41                                         fibroblast  10x 3' v2   \n",
+       "42                         intrahepatic cholangiocyte  10x 3' v2   \n",
+       "43               endothelial cell of hepatic sinusoid  10x 3' v2   \n",
+       "44                              hepatic stellate cell  10x 3' v2   \n",
+       "45                                alveolar macrophage  10x 5' v1   \n",
+       "46                                 type II pneumocyte  10x 5' v1   \n",
+       "47                         capillary endothelial cell  10x 5' v1   \n",
+       "48    ciliated columnar cell of tracheobronchial tree  10x 5' v1   \n",
+       "49                                  type I pneumocyte  10x 5' v1   \n",
+       "50         epithelial cell of lower respiratory tract  10x 5' v1   \n",
+       "51                  pulmonary interstitial fibroblast  10x 5' v1   \n",
+       "52                                          club cell  10x 5' v1   \n",
+       "53                  pulmonary artery endothelial cell  10x 5' v1   \n",
+       "54                            epithelial cell of lung  10x 5' v1   \n",
+       "55                                 fibroblast of lung  10x 5' v1   \n",
+       "56                            cardiac muscle myoblast  10x 3' v3   \n",
+       "57                       fibroblast of cardiac tissue  10x 3' v3   \n",
+       "58                           cardiac endothelial cell  10x 3' v3   \n",
+       "59                                cardiac muscle cell  10x 3' v3   \n",
+       "60                                     cardiac neuron  10x 3' v3   \n",
+       "\n",
+       "   suspension_type           tissue   sex disease  total_mrna_umis  \n",
+       "0          nucleus            brain  male  normal      5527.708887  \n",
+       "1          nucleus            brain  male  normal     10683.399862  \n",
+       "2          nucleus            brain  male  normal      5380.963698  \n",
+       "3          nucleus            brain  male  normal      5083.603161  \n",
+       "4          nucleus            brain  male  normal      6609.696174  \n",
+       "5          nucleus            brain  male  normal      3645.962305  \n",
+       "6          nucleus            brain  male  normal      3935.779702  \n",
+       "7          nucleus            brain  male  normal      3115.828938  \n",
+       "8             cell            blood  male  normal      3922.025000  \n",
+       "9             cell            blood  male  normal      8436.318182  \n",
+       "10            cell            blood  male  normal      2923.925418  \n",
+       "11            cell            blood  male  normal      3285.603705  \n",
+       "12            cell            blood  male  normal      3720.503504  \n",
+       "13            cell            blood  male  normal      5061.393152  \n",
+       "14            cell            blood  male  normal      5221.671616  \n",
+       "15            cell            blood  male  normal      5649.398766  \n",
+       "16            cell            blood  male  normal      6333.568573  \n",
+       "17            cell            blood  male  normal      4984.057779  \n",
+       "18            cell            blood  male  normal      6689.483383  \n",
+       "19            cell            blood  male  normal      5216.285765  \n",
+       "20            cell            blood  male  normal      4950.552077  \n",
+       "21            cell            blood  male  normal     14936.870968  \n",
+       "22            cell  small intestine  male  normal      8772.175057  \n",
+       "23            cell  small intestine  male  normal     10069.068281  \n",
+       "24            cell  small intestine  male  normal     10636.632432  \n",
+       "25            cell  small intestine  male  normal      4604.858974  \n",
+       "26            cell  small intestine  male  normal      7435.644048  \n",
+       "27         nucleus           kidney  male  normal      6444.458460  \n",
+       "28         nucleus           kidney  male  normal      4345.735849  \n",
+       "29         nucleus           kidney  male  normal      5964.088479  \n",
+       "30         nucleus           kidney  male  normal      4911.535804  \n",
+       "31         nucleus           kidney  male  normal      4491.198843  \n",
+       "32         nucleus           kidney  male  normal      4906.310584  \n",
+       "33         nucleus           kidney  male  normal      3130.350171  \n",
+       "34            cell            liver  male  normal      2761.142458  \n",
+       "35            cell            liver  male  normal      6850.818612  \n",
+       "36            cell            liver  male  normal     11532.500000  \n",
+       "37            cell            liver  male  normal      4790.702890  \n",
+       "38            cell            liver  male  normal      9280.724561  \n",
+       "39            cell            liver  male  normal      2734.614608  \n",
+       "40            cell            liver  male  normal      3987.627119  \n",
+       "41            cell            liver  male  normal      7274.262295  \n",
+       "42            cell            liver  male  normal      6998.971698  \n",
+       "43            cell            liver  male  normal      3836.788282  \n",
+       "44            cell            liver  male  normal      4090.862637  \n",
+       "45            cell             lung  male  normal      8422.037378  \n",
+       "46            cell             lung  male  normal      6109.480329  \n",
+       "47            cell             lung  male  normal      3094.649239  \n",
+       "48            cell             lung  male  normal      5754.694686  \n",
+       "49            cell             lung  male  normal      2744.064915  \n",
+       "50            cell             lung  male  normal      5956.795231  \n",
+       "51            cell             lung  male  normal      5294.602205  \n",
+       "52            cell             lung  male  normal      9484.199912  \n",
+       "53            cell             lung  male  normal      2321.547941  \n",
+       "54            cell             lung  male  normal     15015.278925  \n",
+       "55            cell             lung  male  normal      6561.739316  \n",
+       "56         nucleus            heart  male  normal     11938.346771  \n",
+       "57         nucleus            heart  male  normal      3099.779698  \n",
+       "58         nucleus            heart  male  normal      2268.612916  \n",
+       "59         nucleus            heart  male  normal      8017.024743  \n",
+       "60         nucleus            heart  male  normal      2238.708571  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show all\n",
+    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
+    "    display(val_adata.obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell_index = 3\n",
+    "adata = val_adata[cell_index, :].copy()\n",
+    "\n",
+    "metadata_prompt_masks_dict, metadata_dict = ctx.process_user_metadata(\n",
+    "    assay=\"Smart-seq v4\",\n",
+    "    # assay=adata.obs['assay'].values[0],\n",
+    "    suspension_type=adata.obs['suspension_type'].values[0],\n",
+    "    prompt_metadata_dict={\n",
+    "        'cell_type': adata.obs['cell_type'].values[0],\n",
+    "        'disease': adata.obs['disease'].values[0],\n",
+    "        'tissue': adata.obs['tissue'].values[0],\n",
+    "        'sex': adata.obs['sex'].values[0],\n",
+    "        # 'development_stage': adata.obs['development_stage'].values[0],\n",
+    "    },\n",
+    "    # total_mrna_umis=adata.obs['total_mrna_umis'].values[0])\n",
+    "    total_mrna_umis=100_000)\n",
+    "\n",
+    "obs_df = pd.DataFrame({key: [value] for key, value in metadata_dict.items()})\n",
+    "adata.obs = obs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renormalize counts to total_mrna_umis\n",
+    "adata.X = adata.X * adata.obs['total_mrna_umis'].values[0] / adata.X.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.var.set_index('feature_id', inplace=True)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>tissue</th>\n",
+       "      <th>development_stage</th>\n",
+       "      <th>disease</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>assay</th>\n",
+       "      <th>suspension_type</th>\n",
+       "      <th>total_mrna_umis</th>\n",
+       "      <th>cell_type_ontology_term_id</th>\n",
+       "      <th>tissue_ontology_term_id</th>\n",
+       "      <th>development_stage_ontology_term_id</th>\n",
+       "      <th>disease_ontology_term_id</th>\n",
+       "      <th>sex_ontology_term_id</th>\n",
+       "      <th>assay_ontology_term_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>oligodendrocyte</td>\n",
+       "      <td>brain</td>\n",
+       "      <td>embryonic stage</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>male</td>\n",
+       "      <td>Smart-seq v4</td>\n",
+       "      <td>nucleus</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>CL:0000128</td>\n",
+       "      <td>UBERON:0000955</td>\n",
+       "      <td>HsapDv:0000002</td>\n",
+       "      <td>PATO:0000461</td>\n",
+       "      <td>PATO:0000384</td>\n",
+       "      <td>EFO:0700016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         cell_type tissue development_stage disease   sex         assay  \\\n",
+       "0  oligodendrocyte  brain   embryonic stage  normal  male  Smart-seq v4   \n",
+       "\n",
+       "  suspension_type  total_mrna_umis cell_type_ontology_term_id  \\\n",
+       "0         nucleus           100000                 CL:0000128   \n",
+       "\n",
+       "  tissue_ontology_term_id development_stage_ontology_term_id  \\\n",
+       "0          UBERON:0000955                     HsapDv:0000002   \n",
+       "\n",
+       "  disease_ontology_term_id sex_ontology_term_id assay_ontology_term_id  \n",
+       "0             PATO:0000461         PATO:0000384            EFO:0700016  "
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tpm_q = adata.X[0]\n",
+    "# tpm_q = 1_000_000 * tpm_q / tpm_q.sum()\n",
+    "# keep_q = tpm_q > 10\n",
+    "\n",
+    "# adata.var['keep'] = keep_q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adata = adata[: , adata.var['keep']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# protein_coding_genes_set = set(\n",
+    "#     ctx.gene_info_df[ctx.gene_info_df[\"Gene Biotype\"] == \"protein_coding\"][\"ENSEMBL Gene ID\"].values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adata = adata[:, adata.var.index.isin(protein_coding_genes_set)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 1 × 28680\n",
+       "    obs: 'cell_type', 'tissue', 'development_stage', 'disease', 'sex', 'assay', 'suspension_type', 'total_mrna_umis', 'cell_type_ontology_term_id', 'tissue_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'sex_ontology_term_id', 'assay_ontology_term_id'\n",
+       "    var: 'feature_name'"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c880ffc065e4959b117b344c936f5ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/225 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "from more_itertools import chunked\n",
+    "\n",
+    "query_var_names = adata.var_names\n",
+    "query_chunk_size = 128\n",
+    "\n",
+    "rand_adata = adata.copy()\n",
+    "\n",
+    "# highly expressed genes\n",
+    "n_high = 4096 - 5\n",
+    "rand_adata = rand_adata[:, rand_adata.X.sum(axis=0).argsort()[::-1][:n_high]]\n",
+    "# rand_adata = rand_adata[:, np.random.choice(rand_adata.var_names, 4000, replace=False)]\n",
+    "# rand_adata.X = rand_adata.X.astype(int).astype(np.float32)\n",
+    "\n",
+    "gene_logits_chunks = []\n",
+    "chunks = list(chunked(query_var_names, query_chunk_size))\n",
+    "for query_var_names_chunk in tqdm(chunks):\n",
+    "\n",
+    "    # Tokenize\n",
+    "    tokens_dict, context_indices = ctx.generate_tokens_from_adata(\n",
+    "        adata=rand_adata,\n",
+    "        obs_index=None,\n",
+    "        query_var_names=query_var_names_chunk,\n",
+    "        metadata_prompt_masks_dict={\n",
+    "            \"cell_type\": True,\n",
+    "            \"tissue\": True,\n",
+    "            \"development_stage\": False,\n",
+    "            \"disease\": True,\n",
+    "            \"sex\": True,\n",
+    "        }\n",
+    "    )\n",
+    "    \n",
+    "    with torch.inference_mode():\n",
+    "        gene_logits_nqk = ctx.get_gene_value_logits_from_tokens(tokens_dict, context_indices, 2000)\n",
+    "        gene_logits_chunks.append(gene_logits_nqk.cpu().numpy())\n",
+    "\n",
+    "gene_logits = np.concatenate(gene_logits_chunks, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_logits_nqk = torch.tensor(gene_logits, device=DEVICE)\n",
+    "gene_logits_qk = gene_logits_nqk[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max_counts = 2000\n",
+    "# total_prob_mass = 0.5\n",
+    "# symmetric_range_pad = 10\n",
+    "\n",
+    "# # first, find the mode of the counts distribution for each gene\n",
+    "# gene_logits_mode_q = torch.argmax(gene_logits_qk, dim=1)\n",
+    "\n",
+    "# # symmetric lower and upper counts about the mode for each gene\n",
+    "# x_lo_qm = torch.clamp(\n",
+    "#     gene_logits_mode_q[:, None] - torch.arange(0, max_counts, device=DEVICE)[None, :], min=0)\n",
+    "# x_hi_qm = torch.clamp(\n",
+    "#     gene_logits_mode_q[:, None] + torch.arange(0, max_counts, device=DEVICE)[None, :], max=max_counts - 1)\n",
+    "\n",
+    "# # compute the CDF of counts for each gene\n",
+    "# pdf_qk = gene_logits_qk.exp()\n",
+    "# cdf_qk = pdf_qk.cumsum(dim=1)\n",
+    "# q_indices = torch.arange(cdf_qk.size(0), device=DEVICE)\n",
+    "# symm_prob_mass_qm = (\n",
+    "#     cdf_qk[q_indices[:, None], x_hi_qm]  # add total prob mass at the right point (inclusive)\n",
+    "#     - cdf_qk[q_indices[:, None], x_lo_qm]  # subtract total prob mass at the left point (inclusive)\n",
+    "#     + pdf_qk[q_indices[:, None], x_lo_qm]  # add back the prob mass of the left point\n",
+    "# )\n",
+    "# mask_qm = symm_prob_mass_qm > total_prob_mass\n",
+    "# range_q = torch.clamp(mask_qm.float().argmax(dim=-1) + symmetric_range_pad, max=max_counts - 1)\n",
+    "# x_lo_q = x_lo_qm[q_indices, range_q]\n",
+    "# x_hi_q = x_hi_qm[q_indices, range_q]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fixed_gene_logits_qk = gene_logits_qk.clone()\n",
+    "# kill_mask_qk = torch.zeros_like(gene_logits_qk, dtype=torch.bool)\n",
+    "# counts_qk = torch.arange(max_counts, device=DEVICE)[None, :].expand(gene_logits_qk.size(0), -1)\n",
+    "# kill_mask_qk[counts_qk > x_hi_q[:, None]] = True\n",
+    "# kill_mask_qk[counts_qk < x_lo_q[:, None]] = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# id = np.where((adata.var['feature_name'] == \"LINC00486\"))[0].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gene_logits_qk[kill_mask_qk] = -1000000\n",
+    "# gene_logits_qk = gene_logits_qk - torch.logsumexp(gene_logits_qk, dim=-1, keepdim=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gene_marginal_mean_nq, gene_marginal_std_nq = ctx.calculate_gene_mean_std_from_logits(\n",
+    "    gene_logits_qk[None, :, :],\n",
+    "    max_counts=2000,\n",
+    "    use_logsumexp=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr_q = gene_marginal_mean_nq[0].cpu().numpy()\n",
+    "var = adata.var.copy()\n",
+    "var['expr'] = expr_q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var.sort_values('expr', ascending=False, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature_name</th>\n",
+       "      <th>expr</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000112137</th>\n",
+       "      <td>PHACTR1</td>\n",
+       "      <td>933.556641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000182601</th>\n",
+       "      <td>HS3ST4</td>\n",
+       "      <td>912.605713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000128512</th>\n",
+       "      <td>DOCK4</td>\n",
+       "      <td>868.948181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000048740</th>\n",
+       "      <td>CELF2</td>\n",
+       "      <td>862.757935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000273079</th>\n",
+       "      <td>GRIN2B</td>\n",
+       "      <td>846.653931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000169744</th>\n",
+       "      <td>LDB2</td>\n",
+       "      <td>797.360840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000198108</th>\n",
+       "      <td>CHSY3</td>\n",
+       "      <td>785.042908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000153707</th>\n",
+       "      <td>PTPRD</td>\n",
+       "      <td>747.490417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000114757</th>\n",
+       "      <td>PEX5L</td>\n",
+       "      <td>744.050537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000150672</th>\n",
+       "      <td>DLG2</td>\n",
+       "      <td>730.407104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287684</th>\n",
+       "      <td>ENSG00000287684.1</td>\n",
+       "      <td>712.800781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287568</th>\n",
+       "      <td>ENSG00000287568.1</td>\n",
+       "      <td>685.608765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000185046</th>\n",
+       "      <td>ANKS1B</td>\n",
+       "      <td>678.247803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000155093</th>\n",
+       "      <td>PTPRN2</td>\n",
+       "      <td>671.461365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000234630</th>\n",
+       "      <td>ENSG00000234630.1</td>\n",
+       "      <td>671.198608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000081189</th>\n",
+       "      <td>MEF2C</td>\n",
+       "      <td>668.634094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286729</th>\n",
+       "      <td>ENSG00000286729.1</td>\n",
+       "      <td>668.498779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000185774</th>\n",
+       "      <td>KCNIP4</td>\n",
+       "      <td>636.418823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000108684</th>\n",
+       "      <td>ASIC2</td>\n",
+       "      <td>630.713745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000227248</th>\n",
+       "      <td>NALF1-IT1</td>\n",
+       "      <td>625.888306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000214548</th>\n",
+       "      <td>MEG3</td>\n",
+       "      <td>620.759277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000169933</th>\n",
+       "      <td>FRMPD4</td>\n",
+       "      <td>595.040466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000033122</th>\n",
+       "      <td>LRRC7</td>\n",
+       "      <td>591.428345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287616</th>\n",
+       "      <td>ENSG00000287616.1</td>\n",
+       "      <td>588.034180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000078328</th>\n",
+       "      <td>RBFOX1</td>\n",
+       "      <td>586.310303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287821</th>\n",
+       "      <td>ENSG00000287821.1</td>\n",
+       "      <td>582.569702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000034510</th>\n",
+       "      <td>TMSB10</td>\n",
+       "      <td>573.550781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000205542</th>\n",
+       "      <td>TMSB4X</td>\n",
+       "      <td>572.757812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286461</th>\n",
+       "      <td>ENSG00000286461.1</td>\n",
+       "      <td>571.133057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000198947</th>\n",
+       "      <td>DMD</td>\n",
+       "      <td>561.990906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286684</th>\n",
+       "      <td>ENSG00000286684.1</td>\n",
+       "      <td>555.422363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000145864</th>\n",
+       "      <td>GABRB2</td>\n",
+       "      <td>555.040649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000151474</th>\n",
+       "      <td>FRMD4A</td>\n",
+       "      <td>549.056335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000283154</th>\n",
+       "      <td>IQCJ-SCHIP1</td>\n",
+       "      <td>543.716797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000183117</th>\n",
+       "      <td>CSMD1</td>\n",
+       "      <td>529.593628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287873</th>\n",
+       "      <td>ENSG00000287873.1</td>\n",
+       "      <td>523.450317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000114279</th>\n",
+       "      <td>FGF12</td>\n",
+       "      <td>504.906281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286395</th>\n",
+       "      <td>ENSG00000286395.1</td>\n",
+       "      <td>499.850067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000174473</th>\n",
+       "      <td>GALNTL6</td>\n",
+       "      <td>489.139435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000287556</th>\n",
+       "      <td>ENSG00000287556.1</td>\n",
+       "      <td>486.758545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000186487</th>\n",
+       "      <td>MYT1L</td>\n",
+       "      <td>480.096588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286107</th>\n",
+       "      <td>ENSG00000286107.1</td>\n",
+       "      <td>480.032288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286067</th>\n",
+       "      <td>ENSG00000286067.1</td>\n",
+       "      <td>479.153809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000021645</th>\n",
+       "      <td>NRXN3</td>\n",
+       "      <td>468.873474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000248905</th>\n",
+       "      <td>FMN1</td>\n",
+       "      <td>468.267639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000186094</th>\n",
+       "      <td>AGBL4</td>\n",
+       "      <td>464.565582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000286361</th>\n",
+       "      <td>ENSG00000286361.1</td>\n",
+       "      <td>459.196228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000264853</th>\n",
+       "      <td>ENSG00000264853.1</td>\n",
+       "      <td>451.637268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000174482</th>\n",
+       "      <td>LINGO2</td>\n",
+       "      <td>445.615906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ENSG00000251562</th>\n",
+       "      <td>MALAT1</td>\n",
+       "      <td>439.513245</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      feature_name        expr\n",
+       "feature_id                                    \n",
+       "ENSG00000112137            PHACTR1  933.556641\n",
+       "ENSG00000182601             HS3ST4  912.605713\n",
+       "ENSG00000128512              DOCK4  868.948181\n",
+       "ENSG00000048740              CELF2  862.757935\n",
+       "ENSG00000273079             GRIN2B  846.653931\n",
+       "ENSG00000169744               LDB2  797.360840\n",
+       "ENSG00000198108              CHSY3  785.042908\n",
+       "ENSG00000153707              PTPRD  747.490417\n",
+       "ENSG00000114757              PEX5L  744.050537\n",
+       "ENSG00000150672               DLG2  730.407104\n",
+       "ENSG00000287684  ENSG00000287684.1  712.800781\n",
+       "ENSG00000287568  ENSG00000287568.1  685.608765\n",
+       "ENSG00000185046             ANKS1B  678.247803\n",
+       "ENSG00000155093             PTPRN2  671.461365\n",
+       "ENSG00000234630  ENSG00000234630.1  671.198608\n",
+       "ENSG00000081189              MEF2C  668.634094\n",
+       "ENSG00000286729  ENSG00000286729.1  668.498779\n",
+       "ENSG00000185774             KCNIP4  636.418823\n",
+       "ENSG00000108684              ASIC2  630.713745\n",
+       "ENSG00000227248          NALF1-IT1  625.888306\n",
+       "ENSG00000214548               MEG3  620.759277\n",
+       "ENSG00000169933             FRMPD4  595.040466\n",
+       "ENSG00000033122              LRRC7  591.428345\n",
+       "ENSG00000287616  ENSG00000287616.1  588.034180\n",
+       "ENSG00000078328             RBFOX1  586.310303\n",
+       "ENSG00000287821  ENSG00000287821.1  582.569702\n",
+       "ENSG00000034510             TMSB10  573.550781\n",
+       "ENSG00000205542             TMSB4X  572.757812\n",
+       "ENSG00000286461  ENSG00000286461.1  571.133057\n",
+       "ENSG00000198947                DMD  561.990906\n",
+       "ENSG00000286684  ENSG00000286684.1  555.422363\n",
+       "ENSG00000145864             GABRB2  555.040649\n",
+       "ENSG00000151474             FRMD4A  549.056335\n",
+       "ENSG00000283154        IQCJ-SCHIP1  543.716797\n",
+       "ENSG00000183117              CSMD1  529.593628\n",
+       "ENSG00000287873  ENSG00000287873.1  523.450317\n",
+       "ENSG00000114279              FGF12  504.906281\n",
+       "ENSG00000286395  ENSG00000286395.1  499.850067\n",
+       "ENSG00000174473            GALNTL6  489.139435\n",
+       "ENSG00000287556  ENSG00000287556.1  486.758545\n",
+       "ENSG00000186487              MYT1L  480.096588\n",
+       "ENSG00000286107  ENSG00000286107.1  480.032288\n",
+       "ENSG00000286067  ENSG00000286067.1  479.153809\n",
+       "ENSG00000021645              NRXN3  468.873474\n",
+       "ENSG00000248905               FMN1  468.267639\n",
+       "ENSG00000186094              AGBL4  464.565582\n",
+       "ENSG00000286361  ENSG00000286361.1  459.196228\n",
+       "ENSG00000264853  ENSG00000264853.1  451.637268\n",
+       "ENSG00000174482             LINGO2  445.615906\n",
+       "ENSG00000251562             MALAT1  439.513245"
+      ]
+     },
+     "execution_count": 296,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var.head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff8eb040610>]"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 283
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# renormalies\n",
+    "model_mean_q = gene_marginal_mean_nq.cpu().numpy().flatten()\n",
+    "model_mean_q = model_mean_q * adata.obs['total_mrna_umis'].values[0] / model_mean_q.sum()\n",
+    "\n",
+    "plt.scatter(np.log1p(adata.X[0]), np.log1p(model_mean_q), s=1)\n",
+    "plt.plot([0, 6], [0, 6], color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'logits')"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 300,
+       "width": 356
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gene_index = np.where((adata.var['feature_name'] == \"MT-CO2\"))[0].item()\n",
+    "# gene_index = 4045\n",
+    "gene_logit = gene_logits_qk[gene_index, :].cpu().numpy()\n",
+    "\n",
+    "plt.plot(gene_logit)\n",
+    "plt.xlim((0, 2000))\n",
+    "plt.gca().set_xlabel('counts')\n",
+    "plt.gca().set_ylabel('logits')\n",
+    "# plt.ylim((-10, 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7982217"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(np.exp(gene_logit)[:500])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoffs = np.arange(10, 1000, 50)\n",
+    "means = []\n",
+    "for cutoff in cutoffs:\n",
+    "    probs = np.exp(gene_logit)[:cutoff]\n",
+    "    probs = probs / probs.sum()\n",
+    "    mean = np.sum(probs * np.arange(0, cutoff))\n",
+    "    means.append(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff869240d30>]"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 320x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 321
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(cutoffs, means)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/run_all.sh b/scripts/run_all.sh
new file mode 100755
index 00000000..c6ce5df7
--- /dev/null
+++ b/scripts/run_all.sh
@@ -0,0 +1,7 @@
+#/bin/bash
+
+# python metadata_prediction_analysis.py --output_path ./test/10M --checkpoint_path /home/mehrtash/data/mb_checkpoints/10M_001_bs1536/epoch=1-step=29161__updated.ckpt --n_cells 10000
+# python metadata_prediction_analysis.py --output_path ./test/19M --checkpoint_path /home/mehrtash/data/mb_checkpoints/19M_001_bs2048/epoch=1-step=28244__updated.ckpt --n_cells 10000
+# python metadata_prediction_analysis.py --output_path ./test/30M --checkpoint_path /home/mehrtash/data/mb_checkpoints/30M_001_bs2560/epoch=2-step=43129__updated.ckpt --n_cells 10000
+python metadata_prediction_analysis.py --output_path ./test/59M --checkpoint_path /home/mehrtash/data/mb_checkpoints/59M_001_bs3072/epoch=3-step=53770__updated.ckpt --n_cells 10000
+python metadata_prediction_analysis.py --output_path ./test/100M --n_cells 10000
diff --git a/scripts/run_metadata_prediction_analysis_deep.sh b/scripts/run_metadata_prediction_analysis_deep.sh
new file mode 100755
index 00000000..c1e3ae55
--- /dev/null
+++ b/scripts/run_metadata_prediction_analysis_deep.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+# run_metadata_prediction_analysis.sh
+
+# Usage check: All three arguments are required.
+if [ "$#" -ne 3 ]; then
+    echo "Usage: $0 <GPU_ID> <CHECKPOINT_PATH> <OUTPUT_PATH>"
+    exit 1
+fi
+
+GPU_ID=$1
+
+# ----------------------------
+# Default parameters (CLI args)
+# ----------------------------
+CHECKPOINT_PATH=$2
+REF_ADATA_PATH="/home/mehrtash/data/data/extract_0.h5ad"
+GENE_INFO_PATH="/home/mehrtash/data/gene_info/gene_info.tsv"
+ONTOLOGY_RESOURCE_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/ontology"
+FIXED_PROMPT_VARS_SUBLIST_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/empty_gene_ids.txt"
+RAND_PROMPT_VARS_SUBLIST_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/autosomal_gene_ids.txt"
+VAL_ADATA_ROOT_PATH="/home/mehrtash/data/data/cellariumgpt_validation"
+VAL_ADATA_FILE_LIST="/home/mehrtash/data/data/cellariumgpt_validation/deep_study_files.txt"
+OUTPUT_PATH=$3
+RNG_SEED=42
+#N_CELLS=2000
+N_GENES=8192
+GENE_SELECTION_METHOD="highly_expressed"
+CHUNK_SIZE=32
+METRIC_STYLE="hop_k_call"
+
+# ----------------------------
+# Infer the total number of non-empty lines in VAL_ADATA_FILE_LIST
+# ----------------------------
+TOTAL_INDICES=$(grep -cve '^\s*$' "${VAL_ADATA_FILE_LIST}")
+echo "Total number of validation files: ${TOTAL_INDICES}"
+
+NUM_GPUS=1
+
+# Calculate the start and end indices for the given GPU.
+# Indices range from 1 to TOTAL_INDICES (inclusive).
+START_INDEX=$(python3 -c "import math; print(int(math.floor(${GPU_ID} * ${TOTAL_INDICES} / ${NUM_GPUS}))+1)")
+END_INDEX=$(python3 -c "import math; print(int(math.floor((${GPU_ID}+1) * ${TOTAL_INDICES} / ${NUM_GPUS})))")
+
+echo "GPU ${GPU_ID} processing validation files from line ${START_INDEX} to ${END_INDEX}"
+
+# ----------------------------
+# Loop over the assigned lines and run the Python CLI tool
+# ----------------------------
+for i in $(seq ${START_INDEX} ${END_INDEX}); do
+    # Read the i-th non-empty line from VAL_ADATA_FILE_LIST
+    file=$(sed -n "${i}p" "${VAL_ADATA_FILE_LIST}")
+    val_adata_path="${VAL_ADATA_ROOT_PATH}/${file}"
+    echo "Running metadata prediction for file ${val_adata_path} on GPU ${GPU_ID}"
+    python3 metadata_prediction_analysis.py \
+        --cuda_device_index ${GPU_ID} \
+        --val_adata_path "${val_adata_path}" \
+        --checkpoint_path "${CHECKPOINT_PATH}" \
+        --ref_adata_path "${REF_ADATA_PATH}" \
+        --gene_info_path "${GENE_INFO_PATH}" \
+        --ontology_resource_path "${ONTOLOGY_RESOURCE_PATH}" \
+        --output_path "${OUTPUT_PATH}" \
+        --rng_seed ${RNG_SEED} \
+        --n_genes ${N_GENES} \
+        --gene_selection_method ${GENE_SELECTION_METHOD} \
+        --fixed_prompt_vars_sublist_path ${FIXED_PROMPT_VARS_SUBLIST_PATH} \
+        --rand_prompt_vars_sublist_path ${RAND_PROMPT_VARS_SUBLIST_PATH} \
+        --chunk_size ${CHUNK_SIZE} \
+        --metric_style ${METRIC_STYLE}
+done

From a7b215a158d65eaf2d677d79fe669f8689be6398 Mon Sep 17 00:00:00 2001
From: Neil Mallinar <nmallinar@gmail.com>
Date: Wed, 12 Mar 2025 13:43:30 -0500
Subject: [PATCH 62/64] checkin

---
 notebooks/embeddings/run1.sh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/notebooks/embeddings/run1.sh b/notebooks/embeddings/run1.sh
index aac629d7..2760aa35 100644
--- a/notebooks/embeddings/run1.sh
+++ b/notebooks/embeddings/run1.sh
@@ -7,13 +7,13 @@
 #SBATCH --nodes 1
 #SBATCH --tasks 1
 #SBATCH --tasks-per-node 1
-#SBATCH --cpus-per-task 16
-#SBATCH --mem 32G
+#SBATCH --cpus-per-task 24
+#SBATCH --mem 96G
 #SBATCH --gpus-per-node 4
-#SBATCH --time 05:00:00
+#SBATCH --time 06:00:00
 
 source /u/mallina1/envs/torch_jax2/bin/activate
 cd /u/mallina1/research/cellarium-ml/notebooks/embeddings
 
 fname=$1
-python 02_extract_cell_type_activations.py --train_fname ${fname}
\ No newline at end of file
+python 02_extract_cell_type_activations.py --train_fname ${fname}

From 5e0554f69e6ebff8643145a4332c1231bcbdfb5b Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Wed, 12 Mar 2025 23:21:57 +0000
Subject: [PATCH 63/64] updates for linear response analysis

---
 .../inference/cellarium_gpt_inference.py      | 31 ++++++++++---------
 scripts/linear_response_analysis.py           |  7 +++--
 2 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
index 62bfa374..ab8d04ac 100644
--- a/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
+++ b/cellarium/ml/utilities/inference/cellarium_gpt_inference.py
@@ -339,12 +339,12 @@ def generate_tokens_from_adata(
             
         # total mrna umis
         prompt_total_mrna_umis_nc = torch.tensor(
-            adata.obs["total_mrna_umis"].values,
+            adata.obs["total_mrna_umis"].to_numpy(),
             dtype=torch.float32, device=cpu_device)[:, None].expand(n_cells, n_prompt_vars)
         if query_total_mrna_umis is None:
             # the same as prompt
             query_total_mrna_umis_nc = torch.tensor(
-                adata.obs["total_mrna_umis"].values,
+                adata.obs["total_mrna_umis"].to_numpy(),
                 dtype=torch.float32, device=cpu_device)[:, None].expand(n_cells, n_query_vars)
         else:
             query_total_mrna_umis_nc = torch.tensor(
@@ -355,12 +355,12 @@ def generate_tokens_from_adata(
         # convert assay and suspension_type to codes
         assay_nc = torch.tensor(
             pd.Categorical(
-                adata.obs["assay_ontology_term_id"].values,
+                adata.obs["assay_ontology_term_id"].to_numpy(),
                 categories=self.gene_ontology_infos["assay_ontology_term_id"]["names"]).codes,
             dtype=torch.int64, device=cpu_device)[:, None].expand(n_cells, n_total_vars)
         suspension_type_nc = torch.tensor(
             pd.Categorical(
-                adata.obs["suspension_type"].values,
+                adata.obs["suspension_type"].to_numpy(),
                 categories=self.gene_ontology_infos["suspension_type"]["names"]).codes,
             dtype=torch.int64, device=cpu_device)[:, None].expand(n_cells, n_total_vars)
 
@@ -384,11 +384,11 @@ def generate_tokens_from_adata(
         
         # generate metadata tokens dicts; `PredictTokenizer` will convert these to codes
         metadata_token_n_dict = {
-            "cell_type": adata.obs["cell_type_ontology_term_id"].values,  # categorical
-            "tissue": adata.obs["tissue_ontology_term_id"].values,  # categorical
-            "development_stage": adata.obs["development_stage_ontology_term_id"].values,  # categorical
-            "disease": adata.obs["disease_ontology_term_id"].values,  # categorical
-            "sex": adata.obs["sex_ontology_term_id"].values,  # categorical
+            "cell_type": adata.obs["cell_type_ontology_term_id"].to_numpy(),  # categorical
+            "tissue": adata.obs["tissue_ontology_term_id"].to_numpy(),  # categorical
+            "development_stage": adata.obs["development_stage_ontology_term_id"].to_numpy(),  # categorical
+            "disease": adata.obs["disease_ontology_term_id"].to_numpy(),  # categorical
+            "sex": adata.obs["sex_ontology_term_id"].to_numpy(),  # categorical
         }
 
         # apply the mask on metadata tokens
@@ -500,15 +500,16 @@ def generate_gene_tokens_by_metadata(
         )
 
         # The first cell is control unperturbed, so we will ensure that the first gene is marked as queried  (not perturbed)
+        CONTROL_CELL_INDEX = 0
         PERTURB_GENE_CONTEXT_INDEX = 0
-        tokens_dict['prompt_mask_nc'][0, 0] = False
-        tokens_dict['gene_tokens_nc']['gene_value'][0, PERTURB_GENE_CONTEXT_INDEX, 1] = 1  # Mark as queried
+        tokens_dict['prompt_mask_nc'][CONTROL_CELL_INDEX, PERTURB_GENE_CONTEXT_INDEX] = False
+        tokens_dict['token_value_nc_dict']['gene_value'][CONTROL_CELL_INDEX, PERTURB_GENE_CONTEXT_INDEX] = -1  # Mark as queried
 
         # In subsequent cells, prompt genes are set to 0 sequentially
         if perturb_gene_ids is not None:
             assert all(gene_id in self.var_name_to_index_map for gene_id in perturb_gene_ids)
-            tokens_dict['gene_tokens_nc']['gene_id'] = tokens_dict['gene_tokens_nc']['gene_id'].clone()
-            tokens_dict['gene_tokens_nc']['gene_id'][1:, 0] = torch.tensor([
+            tokens_dict['token_value_nc_dict']['gene_id'] = tokens_dict['token_value_nc_dict']['gene_id'].clone()
+            tokens_dict['token_value_nc_dict']['gene_id'][1:, PERTURB_GENE_CONTEXT_INDEX] = torch.tensor([
                 self.var_name_to_index_map[var_name] for var_name in perturb_gene_ids])
             if perturb_gene_values is None:
                 self.log("Perturbed gene values are not provided, assuming in silico deletion (set to 0) ...")
@@ -516,8 +517,8 @@ def generate_gene_tokens_by_metadata(
             else:
                 self.log("Perturbed gene values are provided, injecting the provided values into the prompt ...")
                 assert len(perturb_gene_values) == len(perturb_gene_ids)
-                tokens_dict['gene_tokens_nc']['gene_value'] = tokens_dict['gene_tokens_nc']['gene_value'].clone()
-                tokens_dict['gene_tokens_nc']['gene_value'][1:, PERTURB_GENE_CONTEXT_INDEX, 0] = torch.tensor(
+                tokens_dict['token_value_nc_dict']['gene_value'] = tokens_dict['token_value_nc_dict']['gene_value'].clone()
+                tokens_dict['token_value_nc_dict']['gene_value'][1:, PERTURB_GENE_CONTEXT_INDEX] = torch.tensor(
                     perturb_gene_values).log1p()
 
         return tokens_dict, context_indices, pert_adata, metadata_prompt_masks_dict
diff --git a/scripts/linear_response_analysis.py b/scripts/linear_response_analysis.py
index d22ec526..424b5dc5 100644
--- a/scripts/linear_response_analysis.py
+++ b/scripts/linear_response_analysis.py
@@ -88,12 +88,13 @@ def main():
         verbose=False
     )
     
-    all_query_gene_ids = val_adata.var['feature_id'].values
+    all_query_gene_ids = val_adata.var['feature_id'].to_numpy()
     print(f"Total number of query genes from validation AnnData: {len(all_query_gene_ids)}")
     
     if args.max_query_genes is not None:
-        query_gene_ids = all_query_gene_ids[:args.max_query_genes]
-        print(f"Limiting to {args.max_query_genes} first query genes for linear response analysis.")
+        highly_expressed_gene_indices = np.argsort(val_adata.X[args.cell_index, :])[::-1]
+        query_gene_ids = all_query_gene_ids[highly_expressed_gene_indices[:args.max_query_genes]]
+        print(f"Limiting to {args.max_query_genes} highly-expressed query genes for linear response analysis.")
     else:
         query_gene_ids = all_query_gene_ids
         print(f"Using all {len(all_query_gene_ids)} query genes for linear response analysis.")

From 07896ed2a332f0dcebc1f3221fb6828d5adc8830 Mon Sep 17 00:00:00 2001
From: Mehrtash Babadi <mehrtash@broadinstitute.org>
Date: Fri, 21 Mar 2025 17:18:57 +0000
Subject: [PATCH 64/64] small things

---
 scripts/metadata_prediction_analysis.py          | 9 +++++++++
 scripts/run_metadata_prediction_analysis_deep.sh | 2 +-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/scripts/metadata_prediction_analysis.py b/scripts/metadata_prediction_analysis.py
index e2c6f788..aa64832d 100644
--- a/scripts/metadata_prediction_analysis.py
+++ b/scripts/metadata_prediction_analysis.py
@@ -159,6 +159,15 @@ def main():
 
     os.makedirs(args.output_path, exist_ok=True)
 
+    output_prefix = os.path.splitext(os.path.basename(args.val_adata_path))[0]
+    meta_adata_output_file_path = os.path.join(
+        args.output_path, f"{output_prefix}_metadata_prediction_scores.h5ad"
+    )
+
+    if os.path.exists(meta_adata_output_file_path):
+        logger.warning(f"Output file {meta_adata_output_file_path} already exists. Exiting ...")
+        exit(1)
+    
     logger.info("Loading ontology resources ...")
     ontology_benchmarking_resource_path_dict = {
         'cell_type': os.path.join(args.ontology_resource_path, 'cl_benchmarking_resource.pkl'),
diff --git a/scripts/run_metadata_prediction_analysis_deep.sh b/scripts/run_metadata_prediction_analysis_deep.sh
index c1e3ae55..e0985154 100755
--- a/scripts/run_metadata_prediction_analysis_deep.sh
+++ b/scripts/run_metadata_prediction_analysis_deep.sh
@@ -19,7 +19,7 @@ ONTOLOGY_RESOURCE_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/ontology
 FIXED_PROMPT_VARS_SUBLIST_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/empty_gene_ids.txt"
 RAND_PROMPT_VARS_SUBLIST_PATH="/home/mehrtash/data/data/cellariumgpt_artifacts/autosomal_gene_ids.txt"
 VAL_ADATA_ROOT_PATH="/home/mehrtash/data/data/cellariumgpt_validation"
-VAL_ADATA_FILE_LIST="/home/mehrtash/data/data/cellariumgpt_validation/deep_study_files.txt"
+VAL_ADATA_FILE_LIST="/home/mehrtash/data/data/cellariumgpt_validation/all_files.txt"
 OUTPUT_PATH=$3
 RNG_SEED=42
 #N_CELLS=2000