EPICv2_tutorial/index.html at main · clark-lab/EPICv2_tutorial · GitHub

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<meta name="author" content="Braydon Meyer ([email protected]), Ruth Pidsley ([email protected]) &amp; Tim Peters ([email protected])" />

<meta name="date" content="2024-07-09" />

<title>EPICv2 Workflow: From .idats to DMRs - Using DMRcate to identify Differentially Methylated Regions from EPICv2 data</title>

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<div class="header-title-wrap">
<h1 class="title toc-ignore entry-title">EPICv2 Workflow: From .idats to
DMRs - Using DMRcate to identify Differentially Methylated Regions from
EPICv2 data</h1>
<h3 class="author">Braydon Meyer (<a href="mailto:[email protected]" class="email">[email protected]</a>), Ruth Pidsley (<a href="mailto:[email protected]" class="email">[email protected]</a>) &amp; Tim Peters (<a href="mailto:[email protected]" class="email">[email protected]</a>)</h3>
<h3 class="date">2024-07-09</h3>
</div><!-- /.header-title-wrap -->
</header>


<div class="entry-content">
<div id="TOC" class="toc">
<ul>
<li><a href="#introduction" id="toc-introduction"><span class="toc-section-number">1</span> - Introduction</a></li>
<li><a href="#install-and-load-packages" id="toc-install-and-load-packages"><span class="toc-section-number">2</span> - Install and load packages</a></li>
<li><a href="#download-data" id="toc-download-data"><span class="toc-section-number">3</span> - Download data</a></li>
<li><a href="#read-in-epicv2-.idats-and-generate-beta-value-table" id="toc-read-in-epicv2-.idats-and-generate-beta-value-table"><span class="toc-section-number">4</span> - Read in EPICv2 .idats and generate
beta value table</a></li>
<li><a href="#quality-control" id="toc-quality-control"><span class="toc-section-number">5</span> - Quality control</a>
<ul>
<li><a href="#identifying-sample-mix-ups-with-snp-control-probes" id="toc-identifying-sample-mix-ups-with-snp-control-probes"><span class="toc-section-number">5.1</span> - Identifying sample mix-ups with
SNP control probes</a></li>
<li><a href="#using-the-detection-p-value" id="toc-using-the-detection-p-value"><span class="toc-section-number">5.2</span> - Using the detection p-value</a>
<ul>
<li><a href="#extracting-detection-p-value-and-filtering-probes" id="toc-extracting-detection-p-value-and-filtering-probes"><span class="toc-section-number">5.2.1</span> - Extracting detection p-value
and filtering probes</a></li>
<li><a href="#filtering-of-samples-based-on-detection-p-value" id="toc-filtering-of-samples-based-on-detection-p-value"><span class="toc-section-number">5.2.2</span> - Filtering of samples based on
detection p-value</a></li>
</ul></li>
<li><a href="#removal-and-handling-of-snp-cross-hybridising-control-and-duplicated-probes" id="toc-removal-and-handling-of-snp-cross-hybridising-control-and-duplicated-probes"><span class="toc-section-number">5.3</span> - Removal and handling of SNP,
cross-hybridising, control, and duplicated probes</a></li>
<li><a href="#high-level-survey-of-the-data" id="toc-high-level-survey-of-the-data"><span class="toc-section-number">5.4</span> - High level survey of the
data</a>
<ul>
<li><a href="#density-plots" id="toc-density-plots"><span class="toc-section-number">5.4.1</span> - Density plots</a></li>
<li><a href="#multi-dimensional-scaling-mds-plots" id="toc-multi-dimensional-scaling-mds-plots"><span class="toc-section-number">5.4.2</span> - Multi-dimensional scaling
(MDS) plots</a></li>
</ul></li>
</ul></li>
<li><a href="#differentially-methylated-regions-dmrs-with-dmrcate" id="toc-differentially-methylated-regions-dmrs-with-dmrcate"><span class="toc-section-number">6</span> - Differentially methylated regions
(DMRs) with DMRcate</a>
<ul>
<li><a href="#basic-usage-with-dmrcate-plot" id="toc-basic-usage-with-dmrcate-plot"><span class="toc-section-number">6.1</span> - Basic usage with DMRcate
plot</a></li>
<li><a href="#remapping-cross-hybridising-probes-and-improving-dmr-calling-with-dmrcate-and-epicv2remap" id="toc-remapping-cross-hybridising-probes-and-improving-dmr-calling-with-dmrcate-and-epicv2remap"><span class="toc-section-number">6.2</span> - Remapping cross-hybridising
probes and improving DMR calling with DMRcate and epicv2Remap()</a></li>
</ul></li>
<li><a href="#session-info" id="toc-session-info"><span class="toc-section-number">7</span> - Session info</a></li>
</ul>
</div>

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<div id="introduction" class="section level1" number="1">
<h1><span class="header-section-number">1</span> - Introduction</h1>
<p>A link to the RMarkdown script can be found here: <a href="https://github.com/clark-lab/EPICv2_tutorial/blob/main/EPICv2_Tutorial_Final.Rmd" class="uri">https://github.com/clark-lab/EPICv2_tutorial/blob/main/EPICv2_Tutorial_Final.Rmd</a></p>
<p>In this tutorial we will demonstrate an easy and reproducible
workflow for methylation data from the Illumina EPICv2 array, using the
updated version of DMRcate to discover Differentially Methylated Regions
(DMRs). This version allows for precise control over the probes to be
included in analyses based on user preference, including removing,
merging, or selecting duplicated probes and rescuing cross-hybridising
probes, as explored in our recent paper on the new EPICv2 array (<a href="https://doi.org/10.1186/s12864-024-10027-5" class="uri">https://doi.org/10.1186/s12864-024-10027-5</a>). This
tutorial will take raw .idat files as input, apply our recommended
quality control measures and finish by identifying DMRs. The steps
include:</p>
<ul>
<li>Setting up the R environment and necessary packages<br />
</li>
<li>Downloading publicly available data from GEO for use in this
tutorial<br />
</li>
<li>Reading in of raw .idat files and normalisation using the SeSAMe
package<br />
</li>
<li>Filtering for poor performing or flagged probes and samples through
new DMRcate functions that use our enhanced version of the EPICv2
manifest<br />
</li>
<li>Plots to survey the data at a high level: Determine variance,
distribution of data, and sample mix-ups according to control SNPs<br />
</li>
<li>Using DMRcate to discover and visualise differentially methylated
regions</li>
</ul>
</div>
<div id="install-and-load-packages" class="section level1" number="2">
<h1><span class="header-section-number">2</span> - Install and load
packages</h1>
<p>Major package citations (Please cite these packages if you use any of
this tutorial in your work):</p>
<p>EPICv2 Paper and Manifest: Peters T.J., Meyer B, Ryan L,
Achinger-Kawecka J, Song J, Campbell EM, Qu W, Nair S, Loi-Luu P,
Stricker P, Lim E, Stirzaker C, Clark SJ, Pidsley R. Characterisation
and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA
methylation profiling. BMC Genomics. 2024 Mar 6;25(1):251. doi: <a href="https://doi.org/10.1186/s12864-024-10027-5" class="uri">https://doi.org/10.1186/s12864-024-10027-5</a>. PMID:
38448820; PMCID: PMC10916044.</p>
<p>DMRcate: Peters, T.J., Buckley, M.J., Statham, A.L., Pidsley, R.,
Samaras, K., Lord, R.V., Clark, S.J., Molloy, P.L. De novo
identification of differentially methylated regions in the human genome.
Epigenetics &amp; Chromatin 8, 6 (2015). <a href="https://doi.org/10.1186/1756-8935-8-6" class="uri">https://doi.org/10.1186/1756-8935-8-6</a></p>
<p>SeSAMe: Wanding Zhou, Timothy J Triche, Peter W Laird, Hui Shen,
SeSAMe: reducing artifactual detection of DNA methylation by Infinium
BeadChips in genomic deletions, Nucleic Acids Research, Volume 46, Issue
20, 16 November 2018, Page e123, <a href="https://doi.org/10.1093/nar/gky691" class="uri">https://doi.org/10.1093/nar/gky691</a></p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="do">## Install Bioconductor if you do not already have it</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(<span class="st">&quot;BiocManager&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>))</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>  <span class="fu">install.packages</span>(<span class="st">&quot;BiocManager&quot;</span>)</span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="do">## Install required packages for this tutorial</span></span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>BiocManager<span class="sc">::</span><span class="fu">install</span>(</span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>  <span class="fu">c</span>(</span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a>    <span class="st">&quot;pacman&quot;</span>,</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a>    <span class="st">&quot;DMRcate&quot;</span>,</span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>    <span class="st">&quot;ggplot2&quot;</span>,</span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>    <span class="st">&quot;GEOquery&quot;</span>,</span>
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a>    <span class="st">&quot;sesame&quot;</span>,</span>
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a>    <span class="st">&quot;patchwork&quot;</span>,</span>
<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a>    <span class="st">&quot;ggthemes&quot;</span>,</span>
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a>    <span class="st">&quot;limma&quot;</span>,</span>
<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a>    <span class="st">&quot;plyr&quot;</span>,</span>
<span id="cb1-17"><a href="#cb1-17" tabindex="-1"></a>    <span class="st">&quot;data.table&quot;</span>,</span>
<span id="cb1-18"><a href="#cb1-18" tabindex="-1"></a>    <span class="st">&quot;tidyverse&quot;</span>,</span>
<span id="cb1-19"><a href="#cb1-19" tabindex="-1"></a>    <span class="st">&quot;ggrepel&quot;</span>,</span>
<span id="cb1-20"><a href="#cb1-20" tabindex="-1"></a>    <span class="st">&quot;GenomicRanges&quot;</span>,</span>
<span id="cb1-21"><a href="#cb1-21" tabindex="-1"></a>    <span class="st">&quot;Gviz&quot;</span>,</span>
<span id="cb1-22"><a href="#cb1-22" tabindex="-1"></a>    <span class="st">&quot;RColorBrewer&quot;</span></span>
<span id="cb1-23"><a href="#cb1-23" tabindex="-1"></a>  )</span>
<span id="cb1-24"><a href="#cb1-24" tabindex="-1"></a>)</span>
<span id="cb1-25"><a href="#cb1-25" tabindex="-1"></a></span>
<span id="cb1-26"><a href="#cb1-26" tabindex="-1"></a><span class="do">## Load packages</span></span>
<span id="cb1-27"><a href="#cb1-27" tabindex="-1"></a>pacman<span class="sc">::</span><span class="fu">p_load</span>(</span>
<span id="cb1-28"><a href="#cb1-28" tabindex="-1"></a>  GenomicRanges,</span>
<span id="cb1-29"><a href="#cb1-29" tabindex="-1"></a>  sesame,</span>
<span id="cb1-30"><a href="#cb1-30" tabindex="-1"></a>  tidyverse,</span>
<span id="cb1-31"><a href="#cb1-31" tabindex="-1"></a>  limma,</span>
<span id="cb1-32"><a href="#cb1-32" tabindex="-1"></a>  ggplot2,</span>
<span id="cb1-33"><a href="#cb1-33" tabindex="-1"></a>  GEOquery,</span>
<span id="cb1-34"><a href="#cb1-34" tabindex="-1"></a>  ggthemes,</span>
<span id="cb1-35"><a href="#cb1-35" tabindex="-1"></a>  DMRcate,</span>
<span id="cb1-36"><a href="#cb1-36" tabindex="-1"></a>  plyr,</span>
<span id="cb1-37"><a href="#cb1-37" tabindex="-1"></a>  data.table,</span>
<span id="cb1-38"><a href="#cb1-38" tabindex="-1"></a>  ggrepel,</span>
<span id="cb1-39"><a href="#cb1-39" tabindex="-1"></a>  patchwork,</span>
<span id="cb1-40"><a href="#cb1-40" tabindex="-1"></a>  Gviz,</span>
<span id="cb1-41"><a href="#cb1-41" tabindex="-1"></a>  RColorBrewer</span>
<span id="cb1-42"><a href="#cb1-42" tabindex="-1"></a>)</span>
<span id="cb1-43"><a href="#cb1-43" tabindex="-1"></a></span>
<span id="cb1-44"><a href="#cb1-44" tabindex="-1"></a><span class="do">## Initialise sesame - Only needs to be done once</span></span>
<span id="cb1-45"><a href="#cb1-45" tabindex="-1"></a><span class="fu">sesameDataCache</span>()</span></code></pre></div>
</div>
<div id="download-data" class="section level1" number="3">
<h1><span class="header-section-number">3</span> - Download data</h1>
<p>For this tutorial we are using the publicly available LNCaP and PREC
cell lines (500ng concentration) from Peters <em>et al.</em> (2024) from
GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE240469" class="uri">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE240469</a>).
We will download this data in the chunk below, so you don’t have to
manually get it from the above link.</p>
<p>If you would like to use your own data, please create sample table
and variables of interest in the same format, with your own .idats,
samples and variables.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="do">## Create data table for idat sample download - You can have your own sample_table here instead.</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>sample_table <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a>  <span class="at">Accession =</span> <span class="fu">c</span>(</span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>    <span class="st">&quot;GSM7698438&quot;</span>,</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a>    <span class="st">&quot;GSM7698446&quot;</span>,</span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>    <span class="st">&quot;GSM7698462&quot;</span>,</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a>    <span class="st">&quot;GSM7698435&quot;</span>,</span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>    <span class="st">&quot;GSM7698443&quot;</span>,</span>
<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a>    <span class="st">&quot;GSM7698459&quot;</span></span>
<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a>  ),</span>
<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a>  <span class="at">Name =</span> <span class="fu">c</span>(</span>
<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a>    <span class="st">&quot;LNCAP_500_1&quot;</span>,</span>
<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a>    <span class="st">&quot;LNCAP_500_2&quot;</span>,</span>
<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a>    <span class="st">&quot;LNCAP_500_3&quot;</span>,</span>
<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a>    <span class="st">&quot;PREC_500_1&quot;</span>,</span>
<span id="cb2-16"><a href="#cb2-16" tabindex="-1"></a>    <span class="st">&quot;PREC_500_2&quot;</span>,</span>
<span id="cb2-17"><a href="#cb2-17" tabindex="-1"></a>    <span class="st">&quot;PREC_500_3&quot;</span></span>
<span id="cb2-18"><a href="#cb2-18" tabindex="-1"></a>  ),</span>
<span id="cb2-19"><a href="#cb2-19" tabindex="-1"></a>  <span class="at">Type =</span> <span class="fu">c</span>(<span class="st">&quot;LNCaP&quot;</span>, <span class="st">&quot;LNCaP&quot;</span>, <span class="st">&quot;LNCaP&quot;</span>, <span class="st">&quot;PREC&quot;</span>, <span class="st">&quot;PREC&quot;</span>, <span class="st">&quot;PREC&quot;</span>)</span>
<span id="cb2-20"><a href="#cb2-20" tabindex="-1"></a>)</span>
<span id="cb2-21"><a href="#cb2-21" tabindex="-1"></a></span>
<span id="cb2-22"><a href="#cb2-22" tabindex="-1"></a>sample_table<span class="sc">$</span>Name2 <span class="ot">&lt;-</span> <span class="fu">paste</span>(sample_table<span class="sc">$</span>Accession, sample_table<span class="sc">$</span>Name, <span class="at">sep =</span></span>
<span id="cb2-23"><a href="#cb2-23" tabindex="-1"></a>                              <span class="st">&quot;_&quot;</span>)</span>
<span id="cb2-24"><a href="#cb2-24" tabindex="-1"></a></span>
<span id="cb2-25"><a href="#cb2-25" tabindex="-1"></a><span class="do">## Define your variable of interest and convert to a factor</span></span>
<span id="cb2-26"><a href="#cb2-26" tabindex="-1"></a>variable.of.choice <span class="ot">&lt;-</span> <span class="fu">as.factor</span>(sample_table<span class="sc">$</span>Type)</span>
<span id="cb2-27"><a href="#cb2-27" tabindex="-1"></a></span>
<span id="cb2-28"><a href="#cb2-28" tabindex="-1"></a><span class="do">## Create standard colour pallet first</span></span>
<span id="cb2-29"><a href="#cb2-29" tabindex="-1"></a>myColors <span class="ot">&lt;-</span> <span class="fu">gdocs_pal</span>()(<span class="fu">length</span>(<span class="fu">unique</span>(variable.of.choice)))</span>
<span id="cb2-30"><a href="#cb2-30" tabindex="-1"></a></span>
<span id="cb2-31"><a href="#cb2-31" tabindex="-1"></a><span class="fu">names</span>(myColors) <span class="ot">&lt;-</span> <span class="fu">levels</span>(<span class="fu">factor</span>(<span class="at">levels =</span> <span class="fu">unique</span>(variable.of.choice)))</span>
<span id="cb2-32"><a href="#cb2-32" tabindex="-1"></a>colScale <span class="ot">&lt;-</span> <span class="fu">scale_colour_manual</span>(</span>
<span id="cb2-33"><a href="#cb2-33" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;Cell Line&quot;</span>,</span>
<span id="cb2-34"><a href="#cb2-34" tabindex="-1"></a>  <span class="at">values =</span> myColors,</span>
<span id="cb2-35"><a href="#cb2-35" tabindex="-1"></a>  <span class="at">drop =</span> T,</span>
<span id="cb2-36"><a href="#cb2-36" tabindex="-1"></a>  <span class="at">limits =</span> force</span>
<span id="cb2-37"><a href="#cb2-37" tabindex="-1"></a>)</span>
<span id="cb2-38"><a href="#cb2-38" tabindex="-1"></a></span>
<span id="cb2-39"><a href="#cb2-39" tabindex="-1"></a><span class="do">## If you would like to view the new manifest, you can bring it into your environment through annotation hub</span></span>
<span id="cb2-40"><a href="#cb2-40" tabindex="-1"></a>ah <span class="ot">&lt;-</span> <span class="fu">AnnotationHub</span>()</span>
<span id="cb2-41"><a href="#cb2-41" tabindex="-1"></a>EPICv2manifest <span class="ot">&lt;-</span> ah[[<span class="st">&quot;AH116484&quot;</span>]]</span></code></pre></div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="do">## Download specific idats for this tutorial</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>gsmlist <span class="ot">&lt;-</span> <span class="fu">sapply</span>(</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>  sample_table<span class="sc">$</span>Accession,</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a>  getGEOSuppFiles,</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a>  <span class="at">makeDirectory =</span> T,</span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a>  <span class="at">baseDir =</span> <span class="fu">getwd</span>()</span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a>)</span></code></pre></div>
</div>
<div id="read-in-epicv2-.idats-and-generate-beta-value-table" class="section level1" number="4">
<h1><span class="header-section-number">4</span> - Read in EPICv2 .idats
and generate beta value table</h1>
<p>The SeSAMe package has some wonderful functions that make it easy to
find your raw .idat files and a single-line code for quality control,
normalisation and beta value generation. At this point you can use
SeSame functions to mask particular probes as defined by Zhou <em>et
al</em> (2017). However, for this workflow we will apply our own quality
control to retain some probes that would otherwise be masked, for use or
removal through user options with the DMRcate functions.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="do">## Find downloaded .idat files</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>idats <span class="ot">&lt;-</span> <span class="fu">searchIDATprefixes</span>(<span class="fu">getwd</span>())</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="do">## Use sesame to prepare idats and extract beta values</span></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a><span class="do">## The prep = &quot;CDPB&quot; function will allow for technical corrections to be made.</span></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="do">## Specifically, C = Infer infinium I channel, D = dyeBiasNL, B = Noob Normalisation</span></span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>beta_v2 <span class="ot">&lt;-</span> <span class="fu">openSesame</span>(idats, <span class="at">prep =</span> <span class="st">&quot;CDB&quot;</span>, <span class="at">func =</span> getBetas)</span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a></span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a><span class="do">## Reorder beta table to be same order as sample_table</span></span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a>beta_v2 <span class="ot">&lt;-</span> beta_v2[, <span class="fu">match</span>(sample_table<span class="sc">$</span>Name2, <span class="fu">colnames</span>(beta_v2))]</span></code></pre></div>
</div>
<div id="quality-control" class="section level1" number="5">
<h1><span class="header-section-number">5</span> - Quality control</h1>
<p>In this section, we will determine if we have sample mix-ups through
the use of SNP control probes on the array. We will also determine which
probes have a poor signal based on the detection p-value of probes.
Users can apply their own discretion as to how stringent they want this
QC step to be by altering the threshold for the percentage of failed
samples tolerated per probe. Finally, we use the detection p-value to
assess the overall quality of each individual sample, setting the
threshold for removal of a sample as detection p-value &gt; 0.05 in &gt;
10% of probes.</p>
<div id="identifying-sample-mix-ups-with-snp-control-probes" class="section level2" number="5.1">
<h2><span class="header-section-number">5.1</span> - Identifying sample
mix-ups with SNP control probes</h2>
<p>There are 65 probes on this array designed to target SNPs for QC from
which we can determine if sample mix-ups have occurred. Below we can see
that PREC and LNCaP samples have very different SNP profiles, but are
the same for samples of the same cell types. <em>Note: If all of your
samples are genetically identical then this step is unnecessary and the
code will also fail (unless filter.nonvariant is set to FALSE in
sesameQC_plotHeatSNPs).</em></p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>sdfs <span class="ot">&lt;-</span> <span class="fu">openSesame</span>(idats, <span class="at">prep =</span> <span class="st">&quot;&quot;</span>, <span class="at">func =</span> <span class="cn">NULL</span>)</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">sesameQC_plotHeatSNPs</span>(sdfs)</span></code></pre></div>
<p><img src="data:image/png;base64,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CCAAAIIIIBAvgUIQPPdvuwdAggggAACCCCQOgEC0NQ1CRVCAAEEEEAAAQTyLUAAmu/2Ze8QQAABBBBAAIHUCRCApq5JqBACCCCAAAIIIJBvAYbizHf7sncIINBMgV7N3DjbRqCVBNpaaWfZVxPgDChvAwQQQAABBBBAAIFYBQhAY+WmMAQQQAABBBBAAAECUN4DCCCAAAIIIIAAArEKEIDGyk1hCCCAAAIIIIAAAgSgvAcQQAABBBBAAAEEYhUgAI2Vm8IQQAABBBBAAAEECEB5DyCAAAIIIIAAAgjEKkAAGis3hSGAAAIIIIAAAggQgPIeQAABBBBAAAEEEIhVoM3bFF2J70a3KbbURIF+Tdw2m45GYGo0m2ErTRWY2rZIU7fPxhFAAIE0CfT230RWHc6ARkbJhhBAAAEEEEAAAQTqESAArUeJZRBAAAEEEEAAAQQiEyAAjYySDSGAAAIIIIAAAgjUI0AAWo8SyyCAAAIIIIAAAghEJkAAGhklG0IAAQQQQAABBBCoR4AAtB4llkEAAQQQQAABBBCITIAANDJKNoQAAggggAACCCBQjwABaD1KLIMAAggggAACCCAQmQABaGSUbAgBBBBAAAEEEECgHgEC0HqUWAYBBBBAAAEEEEAgMoHekW0pbGjmaDfH1hBoWYEBLbvn7DgCCCCAQP4FOAOa/zZmDxFAAAEEEEAAgVQJEICmqjmoDAIIIIAAAgggkH8BAtD8tzF7iAACCCCAAAIIpEqAADRVzUFlEEAAAQQQQACB/AsQgOa/jdlDBBBAAAEEEEAgVQIEoKlqDiqDAAIIIIAAAgjkX4AANP9tzB4igAACCCCAAAKpEiAATVVzUBkEEEAAAQQQQCD/AgSg+W9j9hABBBBAAAEEEEiVQMQjIX2Xqp2jMghkV8Bnt+rUHAEEEEAAgRoCnAGtAcTLCCCAAAIIIIAAAtEKEIBG68nWEEAAAQQQQAABBGoIEIDWAOJlBBBAAAEEEEAAgWgFCECj9WRrCCCAAAIIIIAAAjUECEBrAPEyAggggAACCCCAQLQCBKDRerI1BBBAAAEEEEAAgRoCBKA1gHgZAQQQQAABBBBAIFoBAtBoPdkaAggggAACCCCAQA0BAtAaQLyMAAIIIIAAAgggEK0AAWi0nmwNAQQQQAABBBBAoIZAxENxMnxgDe+UvDwtJfWgGl0L8NuwaxteQQABBBDIugDfcllvQeqPAAIIIIAAAghkTIAANGMNRnURQAABBBBAAIGsCxCAZr0FqT8CCCCAAAIIIJAxAQLQjDUY1UUAAQQQQAABBLIuQACa9Rak/ggggAACCCCAQMYECEAz1mBUFwEEEEAAAQQQyLoAAWjWW5D6I4AAAggggAACGRMgAM1Yg1FdBBBAAAEEEEAg6wIEoFlvQeqPAAIIIIAAAghkTCDikZA+yNjut2p1I272VmVs6n4zWlVTeSPaeNtsEW2IzTRVwI9v6ubZeAQCbXNEsBE2kSkBzoBmqrmoLAIIIIAAAgggkH0BAtDstyF7gAACCCCAAAIIZEqAADRTzUVlEUAAAQQQQACB7AsQgGa/DdkDBBBAAAEEEEAgUwIEoJlqLiqLAAIIIIAAAghkX4AANPttyB4ggAACCCCAAAKZEiAAzVRzUVkEEEAAAQQQQCD7AgSg2W9D9gABBBBAAAEEEMiUAAFoppqLyiKAAAIIIIAAAtkXIADNfhuyBwgggAACCCCAQKYEIh6TsW+mdr51K9uvdXc9M3v+ZWZq2soVZYjHVm599j1KAf9plFtjW1kQ4AxoFlqJOiKAAAIIIIAAAjkSIADNUWOyKwgggAACCCCAQBYECECz0ErUEQEEEEAAAQQQyJEAAWiOGpNdQQABBBBAAAEEsiBAAJqFVqKOCCCAAAIIIIBAjgQIQHPUmOwKAggggAACCCCQBQEC0Cy0EnVEAAEEEEAAAQRyJEAAmqPGZFcQQAABBBBAAIEsCBCAZqGVqCMCCCCAAAIIIJAjgYhHQuqfI5o878qUPO9cTvaN0apy0pDsBgIIIIBABQHOgFZAYRYCCCCAAAIIIIBA8wQIQJtny5YRQAABBBBAAAEEKggQgFZAYRYCCCCAAAIIIIBA8wQIQJtny5YRQAABBBBAAAEEKggQgFZAYRYCCCCAAAIIIIBA8wQIQJtny5YRQAABBBBAAAEEKggQgFZAYRYCCCCAAAIIIIBA8wQIQJtny5YRQAABBBBAAAEEKggQgFZAYRYCCCCAAAIIIIBA8wQIQJtny5YRQAABBBBAAAEEKghEPBTnzyoUwaz0CXyXvipRow4C0zo85ykCCCCAAAL5EeAMaH7akj1BAAEEEEAAAQQyIUAAmolmopIIIIAAAggggEB+BAhA89OW7AkCCCCAAAIIIJAJAQLQTDQTlUQAAQQQQAABBPIjQACan7ZkTxBAAAEEEEAAgUwIEIBmopmoJAIIIIAAAgggkB8BAtD8tCV7ggACCCCAAAIIZEKAADQTzUQlEUAAAQQQQACB/AgQgOanLdkTBBBAAAEEEEAgEwIRj4Q0JRM7TSX7QZB6gb6pryEVlMAEGBBAAAEEGhDgDGgDaKyCAAIIIIAAAggg0LgAAWjjdqyJAAIIIIAAAggg0IAAAWgDaKyCAAIIIIAAAggg0LgAAWjjdqyJAAIIIIAAAggg0IAAAWgDaKyCAAIIIIAAAggg0LgAAWjjdqyJAAIIIIAAAggg0IAAAWgDaKyCAAIIIIAAAggg0LgAAWjjdqyJAAIIIIAAAggg0IAAAWgDaKyCAAIIIIAAAggg0LgAAWjjdqyJAAIIIIAAAggg0IBAxENxftVAFVglfoFv4i+SErsp0Keby7M4AggggAAC2RHgDGh22oqaIoAAAggggAACuRAgAM1FM7ITCCCAAAIIIIBAdgQIQLPTVtQUAQQQQAABBBDIhQABaC6akZ1AAAEEEEAAAQSyI0AAmp22oqYIIIAAAggggEAuBAhAc9GM7AQCCCCAAAIIIJAdAQLQ7LQVNUUAAQQQQAABBHIhQACai2ZkJxBAAAEEEEAAgewIEIBmp62oKQIIIIAAAgggkAsBAtBcNCM7gQACCCCAAAIIZEcg4qE4e2Vnz1u6pjO19N5nY+e/y0Y1qSUCCCCAAAINCHAGtAE0VkEAAQQQQAABBBBoXIAAtHE71kQAAQQQQAABBBBoQIAAtAE0VkEAAQQQQAABBBBoXIAAtHE71kQAAQQQQAABBBBoQIAAtAE0VkEAAQQQQAABBBBoXIAAtHE71kQAAQQQQAABBBBoQIAAtAE0VkEAAQQQQAABBBBoXIAAtHE71kQAAQQQQAABBBBoQIAAtAE0VkEAAQQQQAABBBBoXCDikZD6N14T1oxR4OsYy6KoxgT6NLYaayGAAAIIIJABAc6AZqCRqCICCCCAAAIIIJAnAQLQPLUm+4IAAggggAACCGRAgAA0A41EFRFAAAEEEEAAgTwJEIDmqTXZFwQQQAABBBBAIAMCBKAZaCSqiAACCCCAAAII5EmAADRPrcm+IIAAAggggAACGRAgAM1AI1FFBBBAAAEEEEAgTwIEoHlqTfYFAQQQQAABBBDIgAABaAYaiSoigAACCCCAAAJ5EiAAzVNrsi8IIIAAAggggEAGBCIeivObDOwyVXTOg5B2gQnfpr2G1E8C/ITPxvtgWjaqSS0RaCUBDp+t1NrsKwIIIIAAAgggkAIBAtAUNAJVQAABBBBAAAEEWkmAALSVWpt9RQABBBBAAAEEUiBAAJqCRqAKCCCAAAIIIIBAKwkQgLZSa7OvCCCAAAIIIIBACgQIQFPQCFQBAQQQQAABBBBoJQEC0FZqbfYVAQQQQAABBBBIgQABaAoagSoggAACCCCAAAKtJEAA2kqtzb4igAACCCCAAAIpEIh4JKSpKdglqlBTYBojIdU0SnqB/klXgPLrEmCEnbqYWAgBBBDoKMAZ0I4iPEcAAQQQQAABBBBoqgABaFN52TgCCCCAAAIIIIBARwEC0I4iPEcAAQQQQAABBBBoqgABaFN52TgCCCCAAAIIIIBARwEC0I4iPEcAAQQQQAABBBBoqgABaFN52TgCCCCAAAIIIIBARwEC0I4iPEcAAQQQQAABBBBoqgABaFN52TgCCCCAAAIIIIBARwEC0I4iPEcAAQQQQAABBBBoqgABaFN52TgCCCCAAAIIIIBAR4GIh+LsuHmep1Lgo1TWikqVCszLR7OUI72Pv0tv1agZAgggkGIBzoCmuHGoGgIIIIAAAgggkEcBAtA8tir7hAACCCCAAAIIpFiAADTFjUPVEEAAAQQQQACBPAoQgOaxVdknBBBAAAEEEEAgxQIEoCluHKqGAAIIIIAAAgjkUYAANI+tyj4hgAACCCCAAAIpFiAATXHjUDUEEEAAAQQQQCCPAgSgeWxV9gkBBBBAAAEEEEixAAFoihuHqiGAAAIIIIAAAnkUYLiVPLZqrX2al98dtYiSf33G5KtADWoKtO0yueYyLJACgTlSUAeqUFWgja+lqj55fJEmz2Orsk8IIIAAAggggECKBQhAU9w4VA0BBBBAAAEEEMijAAFoHluVfUIAAQQQQAABBFIsQACa4sahaggggAACCCCAQB4FCEDz2KrsEwIIIIAAAgggkGIBAtAUNw5VQwABBBBAAAEE8ihAAJrHVmWfEEAAAQQQQACBFAsQgKa4cagaAggggAACCCCQRwEC0Dy2KvuEAAIIIIAAAgikWIAANMWNQ9UQQAABBBBAAIE8CkQ7FOcnPo9G+dunOQfmb5/ytkfTvsjbHuVzf97L527lbq/eyt0e5W6H/EK52yV2qIYAZ0BrAPEyAggggAACCCCAQLQCBKDRerI1BBBAAAEEEEAAgRoCBKA1gHgZAQQQQAABBBBAIFoBAtBoPdkaAggggAACCCCAQA0BAtAaQLyMAAIIIIAAAgggEK0AAWi0nmwNAQQQQAABBBBAoIYAAWgNIF5GAAEEEEAAAQQQiFaAADRaT7aGAAIIIIAAAgggUEOAALQGEC8jgAACCCCAAAIIRCvQ5m2KdpNsDQEEEEAAAQQQQACBrgU4A9q1Da8ggAACCCCAAAIINEGAALQJqGwSAQQQQAABBBBAoGsBAtAONuPHj3cTJ04szr355pvdzjvv7Pbee283duzY4nweIIBA1wLfffedu/zyy90hhxzirrzySjd16tROC++7777uqaee6jSfGfEJfPDBB+7kk092Rx11lHv44Yc7Ffzf//7X7brrrp3mMwMBBDoL6Dj34YcfFl944YUX3BFHHOFGjBjhRo0aVZzPg3YB9QFl8t7eOH777bf3vXr18v369fMnnniity9O9Y/1SyyxhB80aJDv06ePt4AULgQQqCGw/vrrh8/OkCFDwt/lllvOv/7662VrzTrrrP7aa68tm8eT+ATeeOMNP8ccc/j+/fv7ueaaK7TTbrvt5qdMmVKsxF133RXmF2fwAAEEKgpYgOl1TFPMMHz4cP/222+HuEGxg2IIzd9pp50qrtuqM3u3x6Et/+e0005zDz30kLv00kvduHHjnAWgTmdxrr76arf11lu7b775xh144IFujz32cJtssknLeyUB8O6777ojjzyyrqJXWGGF0FZ1LcxCkQrceeed7l//+pd78skn3U9+8hP37LPPhrZYa6213COPPOIWWGCBSMtjY40JHHvssW7xxRd3t9xyi7MvTnfjjTe6XXbZxekq0FVXXeXa2toa2zBrRSbAMS8yyqZu6NVXX3UWXLpDDz3ULbXUUu64445zP/7xj92GG27oLrzwQmc/8pyOixtssEFYbpVVVmlqfbKycQLQ9payX/ruoIMOctttt12Y88UXX4Tg89e//nV4rjfQ4Ycf7s477zyng8KCCy6YlTbOTT31Jfnaa6+5MWPGhMBm3nnn7XLfvvzyyy5f44XmCjz//PPuF7/4RWgjlbTsssuGgNTOioYDsNpPbcmUrMBzzz3n9txzTzd48OBQka222soNGzbMrbnmmu7//u//3CmnnJJsBSk9fE445qX/jXDfffe5VVdd1R199NGhsvphZ1d9nLoZKXbQpONf4Uc4AWggcQSg3zu42WabzX399dftz5xT4Km+oKVnAb799ttwVrR3b9iKUDE+mGWWWdy9997rtthiC/fZZ5+FMza0RYwNUGdRSy+9tLvkkkvKlh4wYIC76aab3M9+9jOnQOe2224re50n8Qsss8wy7s033ywrWD8WbrjhhnDmZujQoeEMadkCPIlVgGNerNwNF2aX2cviB5391Mmsueeeu2ybX331lbOufGXzWvpJq/Y96Ljf1113nZ9nnnn8ueee6y3Q7Piyf+yxx7z9evHqy8aUrIDdOBH61lxwwQXJVoTSKwpMnjzZ2w86v99++/mXXnqpbJn33nvPL7zwwt7OkPoZZ5yRPqBlOvE+sR8Boe+njnl22b2s8Ouvvz70ebfuRqHvWtmLPIldgGNe7OTdKnDSpEl+pplm8nbG09sV0k7r2o1J3q6wejth4l988cVOr7fqDO6Cb//5sdlmm7kDDjjAHXbYYW7ChAmdfpTo1Ll1KuZOtk4y8c+wHwpOXSYWWWSR+AunxJoC+oV/xRVXhK4SBx98cNny888/f7gc/9FHHzmdDWBKTkDdJPbaa69wB/ytt95aVpEtt9zSXXbZZe7BBx8sm8+TZAQ45iXjXm+p9mM6XOF54IEHnDLndJzUve/00093Z599trMbkjq+3LLPGQmpQ9Or76cue3ScHn300dCpuNCfo+PrPEcAgc4CSvNTqa+ubvDTjS7LL7986LTfeU3mxCWgrkX6MaDLiB0npZRR+hj1CWVCAIHqAnYm09nZUDdw4MCyBe2sp5t99tk7XZIvW6gFnxCA9qDR9avmpJNO6sEWWLXZAspj+Pjjj7sddtih2UWx/R4IKBj94Q9/GH7k9WAzrNpkAY55TQaOYPMc8yJAjGETHPOc4xJ8D95oOp3OlG6Bt956y91+++3priS1c/fcc49TKhOmdAtwzEt3+6h2HPPS30aqIcc8AtBsvFOpJQIIIIAAAgggkCMBzoDmqDHZFQQQQAABBBBAIAsCBKBZaCXqiAACCCCAAAII5EiAADRHjcmuIIAAAggggAACWRAgAM1CK1FHBBBAAAEEEEAgRwIEoDlqTHYFAQQQQAABBBDIggABaBZaiToigAACCCCAAAI5EiARfYfGfOGFF0LeSI3g8sknn7jBgwe7oUOHusUXX9xtsMEGboYZpsfsNj68W2mllTpsgafNFtAoOsrtOXbsWKeRWiZOnOgWXHDB0E4rrriiW3bZZYtV+Pzzz0M7Dhs2rDiPB/EIyP66665zb775ZmgnGwfZ2TjwoZ00DOScc85ZrMhrr70WRuLR540pXgGOefF6N1Iax7xG1OJfh2Ne98wJQNu9pkyZ4n7zm9+466+/PnxJzjfffG622WYLQ9T973//cxpK66c//am7+OKL3ZJLLtk9ZZaOTOCVV15x66+/vnv33XdDOyhg0Ti848ePd++//75755133B577OFOPPHEMD+ygtlQtwQuvPBCt/feezsNXbvooouGYei0gc8++8y9/vrr4YfcmWeeGT5z3dowC0cmwDEvMsqmbohjXlN5I9s4x7wGKG3sUiYTOOqoo/xiiy3mn3766YoedjbU23COfu211674OjPjEbDhGv3vfvc7//HHH1cs8JlnnvF2ttr/7W9/q/g6M5sv8Nxzz/l+/fr5Sy+91E+ePLlTgZqn17SMncHu9Doz4hHgmBePc09L4ZjXU8Hmr88xrzFjzoC2B+2rr756OHO27bbbdhnGf/PNN+GSvM6ycamwS6amvaDL7UOGDHETJkxwAwYM6LKca6+91l1wwQVhqLMuF+KFpgn89a9/dffdd5+78cYbq5ax1VZbuXXXXdftvPPOVZfjxeYIcMxrjmuUW+WYF6Vm87bFMa8x2+kdGhtbPzdrqW/amDFjqu6P+oQqCFVfNqb4BQYNGhQCT/W9rTaNGzfO9erVq9oivNZEgYUWWsg9+eST4bNSrRh1mSjtU11tWV6LXoBjXvSmUW+RY17Uos3ZHse8xlx7HW1TY6vmay27HOgOOuggp87es88+ewhgNO+rr74KN1Dccsstbtddd3U/+9nPnF0CztfOZ2RvFPi//PLLzi6vu4EDB4Y+uprXp08fp87fb7zxRjjzaZcW3ZFHHumWXnrpjOxZvqqpg7H64CoI1Y1G6gfat29fN23atHBDmObrs6abyE477TQ388wz5wsgI3vDMS/9DcUxL/1tpBpyzGuwnRq7cp/Pte666y5vZwW8UXb6ZwGP33fffb1d/s3nzmdkr9R/cOTIkaH/YKV2Uv9Pu1EsI3uT32raTWJ+o4026vQ5Upu1tbX5DTfc0FsAml+AjOwZx7z0NxTHvPS3kWrIMa/77UQf0A6BuxG6jz76KKSO0Z3WOtM2//zzhzt59ZgpHQK6g1d9cZXiR1kK5pprLqfMBUqXxWXddLSRavHll1+GNnrrrbfc1KlTQxvpbMG8886bnkq2eE045mXjDcAxLxvtxDGv/nYiAK3fiiURQAABBBBAAAEEIhDgbpoOiN1JytxhVZ7GJNCdpMwxVYliKgh0JylzhdWZFZMAx7yYoHtQDMe8HuDFuCrHvO5hcwa03YukzN174yS1NEmZk5LvXrkkZe6eVxJLc8xLQr37ZXLM675ZEmtwzGtAvfvdRvO5BkmZs9GuJGVOfzuRlDn9baQacszLRjtxzEt/O3HMa6yNOAPaHrSTlLmBXy8xr0JS5pjBGyyOpMwNwsW8Gse8mMEbKI5jXgNoCazCMa8xdBLRt7uRlLmxN1Cca5GUOU7txssiKXPjdnGuyTEvTu3GyuKY15hb3GtxzGtMnET07W4kZW7sDRTnWiRljlO78bJIyty4XZxrcsyLU7uxsjjmNeYW91oc8xoUb+zKfT7XIilz+tuVpMzpbyPVkKTM2WgnjnnpbyeOeelvI9WQY17324k+oB0CdyMkEX0HkzQ+JSlzGlulc51IytzZJG1zOOalrUUq14djXmWXtM3lmFd/i3AJvt3q2WefdZ9++qmbe+65w+hHCy64YBgD/vLLL3fXX399GLd6scUWc3PMMUf9uiwZuYCdsXHqF6VRqWaffXY3ZMgQd++997qrrrrK3XHHHWF0pOWWWy6MPR554WywLgGNJPboo4+6RRZZxOkyb+EzpTb6+9//7h566CHXq1cvt+SSS9a1PRZqjgDHvOa4Rr1VjnlRi0a/PY55DZp2/6RpPtfYeuut/U477VTcOcvpFcaxtmAnjGk988wz+z59+vgHH3ywuAwP4hewYTa9LhsWpg022CC0kwUzfs011/R6XWlLvvjii8Ii/I1Z4IYbbvCzzDJLsdS3337b63NkQacfPny4X3TRRUOb7b///sVleBC/AMe8+M0bKZFjXiNq8a7DMa8xb+6CrxC4T5s2zR144IFuxx13DGdBb731Vvfxxx+73//+927EiBEV1mBWEgI6M6CznjfddJP773//60aPHu2sH46zPlPumGOOSaJKlFlBYOTIkW6mmWZy48aNc//617/ca6+95m677TZ32mmnuSeeeKLCGsyKW4BjXtzijZXHMa8xt7jX4phXnzgBaAUn5V4bP36822+//YqXcvv37++OOOII995777kPPvigwlrMilvgpZdecksssYTbbLPNikXPN998bp999nGPPPJIcR4PkhVQO+nHnC7FF6YNN9zQrbHGGrRTASThvxzzEm6AOovnmFcnVMKLccyrrwEIQCs4KYhZfPHFi8FnYZFvv/3WaUxeuyRSmMXfBAXsknunNlJ1Jk2aRBsl2C4di67WTuoLypS8AMe85NugnhpU+yzxvVSPYDzLVGsnjnnT24BIarqFGzVqlFtxxRXd7rvvHm42Ouyww5z1bAhL6IyazuIss8wyZWdySlbnYUwC2267rVtvvfXctdde69544w135ZVXhpJ16f3iiy92f/nLX8LrMVWHYioIWB/c8CNum222Cd1YzjnnHGd9QcOSuoJwwAEHuGeeecZZn9AKazMrLgGOeXFJ96wcjnk984tjbY553VcmAG03U9CigGajjTYKX5j6krz99tuLoltuuaXTZfhCsFN8gQexCjz99NOh76DdaOT+/e9/OyVqVj9QTY8//rj74x//6BT02A0usdaLwqYLrL/++u6BBx4IbTFgwICQQUIZJhRwajrhhBPcjTfe6C677DLuhJ/OFvsjjnmxkzdUIMe8hthiXYljXmPc5AGt4qZ8Xnb3e1jilVdeccOGDStb+qCDDnInnXRS2TyexC8wceLEkJZpwoQJTt0k5pprrmIldHOSAtMddtihOI8H8Qvo7LSuJigt05tvvumU5kw/HgqTUjTpR8WPf/zjwiz+JiDAMS8B9AaK5JjXAFrMq3DMqw3OGdAqRoXgU4t0DD417+yzz9YfpoQFlBNU06yzzloWfGreW2+9VXYmW/OY4hfo27dvCD5V8tChQ8uCT82755573KuvvqqHTAkKcMxLEL8bRXPM6wZWQotyzKsNTwBa24glEEAAAQQQQAABBCIUIACNEJNNIYAAAggggAACCNQWIACtbcQSCCCAAAIIIIAAAhEKEIBGiMmmEEAAAQQQQAABBGoLEIDWNmIJBBBAAAEEEEAAgQgFCEAjxGRTCCCAAAIIIIAAArUFCEBrG7EEAggggAACCCCAQIQCJKKvgKnx3kvHa33//ffdnHPO6fr06VO29GOPPeZWWmmlsnk8iUegYxupVOX8XHjhhcsq8Pnnn7tPPvmkYh7XsgV50hSBju309ddfOyU7Lx0sQAW/9tprbtCgQW7w4MFNqQcbrSwwZcoU98QTT4QhbTUIwI9+9KPKC7bP5ZhXladpL3722Wfu4Ycfdmqv1VdfPXwfdVUYx7yuZJo/X7mM9Xmae+65ncaDLx1so2PpHPNMxEYnYWoX+PDDD/0vf/lLf/PNN5eZjBgxwtsbyttoLWXzeZKMwOjRo/2yyy7r7WBcrIAeWxJtbz8IvH2wi/N5kIzA1KlT/Yknnuh33nnnsgrceuut3hI0+913393bqFVlr/EkXgELavyqq67q9TVQ+LfPPvvEWwlKqynw4IMPevtxVmyjWWaZxdsw0TXXY4F4BY488shiG+nztMwyy3gbgjjeSmSsNC7B2ztF07PPPhuGAtS4u/PMM8/3M9v/33777cMZm9/+9rfuuuuuK3uNJ/EKnHLKKW7ttdd2CyywQBh2s7T0ww8/3L300ktulVVWcRp7nCkZAZ311K9/OyB3Guvdfji4Lbfc0p177rlOnyum5ATOP/98N3bsWHf11Vc7DVl74IEHujPOOMONGTMmuUpRcieBfffd1y266KLu3//+t7v//vvdcsst53bbbTdnP/I6LcuMZARef/11N3LkSLf33nuH76CLLrrI2QmtMC+ZGmWk1IwFzE2rrs586uyZjbFbsQz7UvWbb765X2SRRSq+zszmC4wfP97PNNNM/k9/+lOXhdnl9nAm9JBDDulyGV5oroCuFMwwwwz++eef77KgUaNGhbMFTz31VJfL8EJzBdZdd12/xx57lBWy9NJL+z//+c9l83iSnMCECRPC50RXfQrTiy++GOZZ0FOYxd+EBS688EJvJ6684oTCZAGpX3755QtP+VtBgDOg7T8UHn30Ubfttts6C3Aq/nSwL1S34447hr5S6lPIFL/ACy+84CZNmuR22mmnLgufY4453KabbhrOFnS5EC80VUD9BFdeeeWq/Qm32morZ5cSaaemtkT1jdsPOjfvvPOWLWQ/sN0777xTNo8nyQmojTSVtpPOhmp69913w1/+S15A7aR+7YoTCpPaiTYqaFT+O12r8ustM7etra3sQ15px3X5UJMuMTLFL6A26tevX80bVXQzBW0Uf/uUljjffPOVPu30WO34gx/8wE2bNq3Ta8yIR0CXcDveJGH9c93kyZPjqQCl1BTQTUeaSm+KLdwMa32oa67PAvEIqJ06fpbUTnyWqvsTgLb7rLHGGu6uu+6qqqX+N8OGDevUR7TqSrwYmcBPf/rT8AvzgQceqLpNtZP6IDIlIyD7e++9t+qPgC+++MKpv7U+d0wIIIAAAq0n0Lv1drnyHlt/KLf//vs73Wj085//vNNCurnluOOOq/hap4WZ0RQBnZ1RcHPMMcc4nY3ueLOYCr3sssvcPffc4/bbb7+m1IGN1hZQUKmuEkcddVTohF96WUprf/PNN+EmCl2CtztFa2+QJZomcNNNN4X0ZYUCrE+u0+dMN7mUTrppjCk5gSOOOCJ0WSmtwZlnnun+8Y9/FGf95Cc/cZZ1ovicB/EKqOtK6efmjTfecEo7VzpPNdpll12c9Q2Nt3IpLY0AtL1hdthhh3BG5he/+IX7/e9/H/J7KqfkW5Zb8j//+Y+7+OKLQ582uwEmpU3ZGtVSgLnOOuuEANRS+YTMBQpkdBev7hK9/vrrnd2AxA+FBN8OyuVpqcyc3djndLZ6iy22cEsssYSzG/zCZ0lBjw7W11xzjVO3CqZkBIYOHershhb30EMPFSvQv3//8Lh0XvFFHsQuoB8DP/zhD0OWltLCNe/NN98M/wrzZ5xxxsJD/sYsoHsP1Ae04+dmscUW6zRPx0Wm7wVIRN/hnWC5C8MXo93BG9Jc6AtSnYk32WQTZ3eHhrMDHVbhacwCSsps+Qqd5ccr3jChPoU6m6ZUMr/+9a9jrhHFVRJQ8Hnssce6xx9/3OmSuyYdqDV4g9L9FG6mqLQu8xBAAAEE8i1AANpF+3711Vchn5e+JGedddYulmJ20gIffPBByPm55JJLduoEnnTdKP97Ad1opC4sAwcOdEOGDIElYwJqv47dKDK2Cy1RXdopG81MO01vJ25CarewFFXTVeyRLmeon0Zp8Kkzb7rBhSk5gY7tpPQklruwLPhUsKNuE0zJCZS2k4IXXTLsGHzqchUpzZJro1olf/TRR+6EE05wSs3ElF6BJ5980v3hD38IXcfSW8vWrpnukr/22mvDICql/XZbW8U5AtD2d4DusNYl9sKk/jW77rpr2Wg7+qBXukGpsA5/myug0SYUzOhvYdLIVH/9618LT8PfU0891dFXt4wk1if6HOnzVDqpX+4jjzxSOit0axk9enTZPJ4kL6CuE9tss41bcMEFnUYXW3zxxZOvFDUoE9AVOt2XsMIKK4R/Cm4KaQLLFuRJogJvv/12+Azps7T11lu79957z6nvNdP3AgSgXbwT9OtfQ9UV8rB1sRizExbQAAJ33HFHwrWg+FoCV155pXv55ZdrLcbrCQnYiDvhh5zOVK+11lru7rvvdocddli4yUVZJZjSIaCrOxqac/755w8Dcijf8aWXXurUFYnMH+loI11iv+2228IPbF09OP7448ONs/ph98p+mkRjAABAAElEQVQrrzhlK2D6XoAAlHcCAggg0KICSruky7caOODggw8O3Y623377cAb76KOPdgsttFCLyqRnt0sv36qvuy7h7rnnnqFbizKB/O53v+tyBL/07EX+a/Lxxx+HLiu6b2TjjTd2n3/+eUjdqD0/++yzQ85jsn6Uvw8IQMs9eIYAAgi0hICGQ1VGAqWaU07JDz/80F1xxRWcoUlR66tNdPlW3cGUFlBno9Veyi4x22yzkcYsJW2lz43aSWejR4wYEc50jhkzJjxOSRVTWQ0C0FQ2C5VCAAEEmiugcar1pan+uhoWdeaZZ25ugWy92wIablNn1jS8sPp4qj8uGQm6zdj0FdRlT8OlrrzyyuHMdK2hiJteoYwUQACakYaimggggECUAvfdd5/TZXYN4LD66qs7Jc0eOXKkU2DKlA4BdYHQDbEaAe70008PN7DoRtjLL788jCiWjlpSiwMOOMDdeeedAUID2WiUPv3VWVCmrgXIA9puo47B6p+hlD6alB5GHYk1NGefPn3CvHHjxoVLIKUpZsIL/BeLgO5+15fklltuWTxb88QTT7jx48c7jWBVmB5++OFwxkB3hjLFL6C74E8++WS36aabFgtXWyy33HJld1TrxiT906VgpmQFXn31VXfJJZeEoWzff/99p/RmGkpVgzoMGjQo2cpRehDQzS333ntvuPv9xhtvDBlaFIz+3//9Xzj+6QwcU/ICGnRDo7xddNFF7rHHHgsVUhvtuOOOTn14maYLEIC2WyjQ1LB09Uxjx46tZzGWiVhAKSxKg5pqm1dAqlGtmOIXUCDTMTVWV7U46aSTwh2iXb3O/HgFdFe1zuQoxc8tt9wSLvdutNFG7oYbboi3IpRWVUA3uFx11VUhyHn66afDGbcjjzzS6aYkpvQIaIhofZZGjRoVulLoRJcCU1Jmfd9GBKDpea9SEwQQQCARAQWepWfQdBZU09VXXx2+QF944YVE6kWh0wU6tpFe0Q1JugKkIGfy5Mnu3HPPnb4CjxIR6NhOX3/9tdMgNhqSWO2kS/OMB/9909AHtJtvUf16YUq3gA7I119/fborSe2cztwoDRBTcgK6eWKLLbZwt99+e1klDj300JCSSZfiCT7LaBJ5ohySOns2derUYvl6vMwyy7jddtvN7bPPPgSfRZlkHijw1BWdjmeh1dda+UCVwUBXEgg+p7cPAeh0i5B8WZfidUDW5Sf1uSlM6iO19tprh5x5hXn8jV9AbXLaaae5ddddNyRe7jjkpnLkKZk2/T/jb5vSEt955x23//77u+HDh4cUP7pkWJh0RkA5J1dccUX3xhtvFGbzN2aBZ599NnxWCpdwS4tXLtC55por9IHXaGNMyQmccsop4btngQUWKBuZTzXSSFVKTr/KKqu4Tz/9NLlKtnjJCj51o5i6QXTs56nL7bpvQWen9bliKhGwG2qYTMB+YXq72cjPPffcfrXVVvOW6sLbCAbB5rLLLvP9+/f3Ni68P+ecc/BKUMBG+/D29vV2NsDbDUm+X79+3s7QeAtqvH24w2v2gfc2bGqCtWztoq0TvreDcPg82U0S4XNjqX68nbHx9mXpLeVPaCfrlO8tMG1trAT33s7EeMsD6idOnFixFval6jfffHNvZ28qvs7M5gvY1Rw/00wzeRtauMvC7IZZbym0vA132+UyvNBcAeuPG2KG559/vsuCrB9oOO7ZVZ8ul2m1F1yr7XBX+7vzzjt7BS6Wdy0sYnfyehvuzJ9xxhnhTWM3v3i7C76r1Zkfk4AlX/ZHHHFEKM1GCAlfoHvttZe3X5++b9++4UeD5jMlJ2DDOIbPzHPPPRcqYWepww8F674SglFLqO3tbt7kKkjJQcAur3tL7VNV45///GdoS8tFWXU5XmyOgKXxCf52A2bVAuzKXTgGVl2IF5smYF0g/Kqrrlp1+998842fZZZZ/FlnnVV1uVZ6kUvw7WeDdSlQd3taEBPm6JS50i7Zr8owJrwdiMNwdSUnj3kYs4Au46p/p9pGU+/evcNd8bq0odyFSsmkvmuaz5ScgPIWKmF2IaWZukTospT6qq233nrOAlOn9DFMyQoo7Zz6eFabCnfr6hIjU/wCaiO7yuMGDx5ctXAlqqeNqhI1/cVayefVjhrwobRrX9MrlfICCEDbG0jBjToKF6bCm0m58OzsaGE2fxMU0J2EmoYOHVqshdpJH2iljlGHfKbkBdROpW2kGqmdlAdUqWMYcSf5NlIN1lhjDXfXXXdVrcz999/vhg0bFtL8VF2QF5sioFGqNPKRbkKqNqmd7CpQtUV4rYkCsleO1mo/ApQfVP2t9blj+l6AALSLd4J+eWriQ90FUEpm6+Cs4EZn3JjSI1D4/BRqpHayvtVlqX4Kr/E3GQHdyKeb9nSXbqVJN7ccd9xxbq211qr0MvNiENAVOX0HHXPMMU7jwlea7B6FcIc17VRJJ555CionTZoUBm+odIbTLr+HK0B2CZ4TJSVNwrXKEoxKDwujIFV6jXnpEOCSezraoVYtaKdaQvG+vsMOO4QzMhq0QbkJ7YYkt/DCC4fcksouoZyFP/rRj5zdABNvxSitTEAB5jrrrBOSlyvFj7q0KJBRknMNo6qUc+oqRreWMrZYn6iLxM033xxSLOlstTLpLLHEEs5u8HP6LN10001OmUE0QlLHH+exVjRlhZGIvr1BlGNtwIABoY+GZinHmsbb1cg7c8wxR1mzkQu0jCO2J4WhOLfbbrtiX12lx9JlDQ0ZWDrp0lXHfGylr/O4eQIaitM62of+noVSlAPPbiBzK6ywQmFW+Ks2UlsxJSegEcP0xWh38Ibjnr4gF110UbfJJps4tWWhX3xyNaRkdWtRrs8HH3wwBDISUZ9CdTs68MADOx3/EEtGQMHnscceG5LO65K7JsUP+nFnNzSHz1UyNUtnqQSg7e2y7bbb1j0U5zPPPJPO1sx5rTQU58Ybb1zXXuryopICM8UvoDNnZ555Zl0Fa8x4nYFjSl7gq6++CjklFXxayrnkK0QNKgp88MEHIeenbuzjqkJFosRn6jK8urAMHDjQDRkyJPH6pLUCBKBpbRnqhQACCCCAAAII5FSAPqAlDWv5I0MqH6VkUloL9X9iSp+ALkc9/PDDTu21+uqruznnnDN9laRGTt0jlBrLBncIN1JwtiZdbwr1G1TKrHomXaJnil9AQ6XuvffedRW88sorh9Hh6lqYhSIVUJpGZfioZ7LBVJzaislSKYLwvYDSMOny7iOPPFIkUZ8bS9RcfM6D5AUeeught9lmmzm1lyZ1xr/66qvdBhtskHzlqEFRQOnLRo4cWXyuvmq627pWPsPiCjxouoCGRNVNEl1NuoSoH+Pqa8iUjIAu5VZrI0taHlI0qfsEfXWTaSOVOnny5KrtNGHChHDSRMvqHgamdoFWyrpfbV818pGG27Rgxtvdhd46docRKCzgqbYar8UssPzyy3sN62h3f3rLfectRYm3Pjae0Y9ibogqxb322mvhs2NnbsLQmxoBycYV93rOlH4B+zL19gPC2xlrbzeNeY1kxZQ+Abur2ltfd283jfk99tijyyFV01fz1qrRHXfc4RdYYAE/aNAgr+E4maYLMBRnu4U+yPoQl042kotXYMqUDgH7FRkCm9GjRxcr9OKLL4Z5dod8cR4PkhW48MIL/TzzzOM1lnhhsrOhXj8emNItYDdYhiGJ7WxaGNbWsoGku8ItWjt9xjSso6XN8nZloUUV0r3b+r7aaaedwveTZZTw77//frornEDtSETffiZYQzx2HJZOIyMpdxdTOgTURppK20l37GrSUJxM6RBQO9kZzzCCS6FGaifaqKCRvr/qT618n0qTpdzHY8eODcPa9urVK32VbeEaKROIuhtpdD4b/z2kzlp77bVbWCSdu3733XeHe0g00IPSOSpHaOn3VjprHX+tCEDbzZX3s+NNEupTo74dTOkQ0JekptIvxcJAAd9++206Kkktws1hHT9Laic+S+l8czz33HMhT+Hxxx/vjj766JDcfKmllkpnZVu4VpdcckkIauyqTxj56G9/+1tI89PCJKnb9S+//NLtsssuIQfysssu61544QW3/fbbp66eaakQAWhaWoJ6IIAAAjEK6Ee3hngsjDf+1FNPucMOO6zTD/EYq0RRFQTGjRvnNtpoozBa1TbbbBPOeg4fPrzCksxKUkCDbShzznXXXecuvfRSd8stt4RhopOsU9rLJg9oewtpJCSdpVH6pcKkN5TOgnYcD/7cc88tLMLfGAUKIyFp0ADd/V6YzjvvvHCAto7ehVlO7anLVEzxC2j0nFNPPdVtueWWxcJ1N7VGCdlxxx2L8/RAZwusb2jZPJ7EI6BsEro0qJFaNBRnx7PWpbXQmPBM8QsoDZOGdFS3llVWWcVVu9xu9yw4BahM8QvYzUVuxIgRoduRznjOP//8XVZC31+kePyehzRM7W+ToUOHhpGQlOanMNld8eFh6bzCa/yNX0A/BjQO8rPPPltWuOYpn2FpTsMZZ5yxbBmexCeggEZ9QDt+bhZbbLFO8375y1/GVzFKKhOYYYYZ3LBhw8I8jVVdbSIArabTvNfUtUifJf373//+F8Z976o0pWsiAO1Kp7nzldKs8FmyDC1VC1PuagLQ74k4A1r1rcKLCCCAAAIIIIAAAlEL0Ae0B6IHHXRQD9Zm1TgELKdr6I8TR1mU0biARhGxFECNb4A1EUAAAQQyJUAA2oPmOvvss3uwNqvGIfDWW2+522+/PY6iKKMHAupvraE7mdIlMGbMGLf44ounq1LUppPA4YcfXveQnZ1WZkZsAurvbonpYysv7QXRBzTtLUT9EEAAgYQENMSjjWyVUOkUW6/Axx9/7DTcI1O6BXQzZrWhVdNd++hrxxnQ6E3ZIgIIIIAAAggggEAVAQLQKji8hAACCCCAAAIIIBC9AAFo9KZsEQEEEEAAAQQQQKCKAH1Aq+DwEgIIIJBXgTPPPNNplJ1qk27iY0pO4LPPPnMnnnhizQo8/vjj7gc/+EHN5VigOQKjR4+u6+aib775pjkVyOhWCUAz2nBUGwEEEOiJgLJDaKzqWlO1UV1qrcvrPRPQ2OJXXnllXRuZffbZ61qOhaIX+M9//lNXO2mQjgEDBkRfgYxukQA0ow1HtRFAAIGeCNx5553dXl25j0866aRur8cKjQkstNBC7r333uvWysp9rDOiO+ywQ7fWY+HGBfbcc0+nf92ZlPtYo/iVDv/dnfXzsCwjIXVoRZ0V0IH5/fffd5MmTQpjjmuMcY3Bu/HGG5ct/dhjj7mVVlqpbB5Pmitw1113uZ/+9Kdu8ODBoaCpU6e6v/71r+6+++5z3333nVtttdVCPryBAweG1z///HP3ySefFIdJa27t2HqpgNrm008/dfPMM0+YrbNt11xzjXv77bfdL37xC6cxkwuTUv0MGjSo2K6F+fxNl8BMM80UjovpqhW1KRXQd9ill17qrr322tLZPE6ZwI477ug23HBDt9VWW6WsZvFVh5uQ2q2nTJnidt99d7fRRhu5u+++O/TnULCjL9Enn3zSbbHFFm7zzTcP4/EWmofgsyAR3199YJ966qligZtuuqnbf//93euvv+6Us/CII44IPwp06UqTgprCGL3FlXjQdIErrrjC6XLTvPPO69ZZZx33zjvvuDXWWMNp8IYnnnjCjRgxwv3hD38o1kPjxBd+VBRn8gABBBBAILcCBKDtTXvhhReGs2gvvviie+mll8JZM40uoeS+DzzwQOisP23aNLfLLrvk9s2QtR3TDwSNKnHTTTc5XXZSR/B3333XTZ482R1zzDFZ253c1FcjGu20005u3333DWdhdBZUl5n040FXFvQZU7tddNFF7t///ndu9psdQQABBBCoX4A+oO1Wt956q9tvv/3cEkssEebMOOOM7pBDDglncBSQar4uayy88MJOd7L179+/fmWWbIpAoV0222yz4vbnm28+t88++7irr766OI8H8QqoO8Sqq67qjj766FCwhnJcbrnlQkBa+Nysv/76bq211nKPPPKIW2WVVeKtIKUhgAACCCQuwBnQ9iZQf0+dRSud/ve//4XLuoVO4G1tbeGSvC71MiUvsOaaa7q+fft2qojacoYZeGt3golphro9fP3118XSdPZzu+22c3PPPXdxnh7oc9SnT5+yeTxBAAEEEGgNAb6l29tZZ9F0SfDkk08O/dUefvhht+uuu7pZZ53VrbDCCu6VV14JlxXVj410F8l+OLbddlu33nrrhcu7Glu3kKZEl94vvvhi95e//CW8nmwtW7d03aynG450RaHw423UqFFuwQUXDCgfffSRO/jgg93YsWPduuuu27pQ7DkCCCDQwgIEoO2Nr8u2CmyUZkSpL3Q3tb4gdSlXQei5557r1Af0tNNOa+G3S/K7/vTTT4c2UPoK9R/s3bt3MQGwUo/88Y9/dNtss024MSn52rZmDdR9Rf1y1Xf65ptv7oSgz9jpp58ebkgqdHnptBAzEEAAAQRyLUAapg7Nq3Qwzz//fPHM58wzzxyWoN9nB6gUPZ04caJT2iXdMPbtt9+6ueaaK0W1a92qeO9Dyp5CSqyChG5C0lWEjpfkC6/zN70CpGFKb9sUakYapoJEuv+Shsk5AtB0v0epHQIIIJAaAXIfp6YpuqwIuY+7pEnVC+Q+JgAtviGfffZZ16tXL/ejH/2oOO/ee+8NfQo/+OCDMM6uLtNzybDIk+gDpfNRgnPdbKQDrrpKjBkzJrTT3nvv7WabbbZE60fhlQX++c9/umeeecYdddRRlRdgbqwC6lZ0//33u+HDh4dylU/3/PPPD+nMll12Wae+8RzzYm2SioV98cUXoV/10ksv7XRVTld9lGtXbae+1epvzZCpFelim6mBUHQ/gm5mVjttvfXWoYtYaQWUmk4DcPzkJz8pnd26j+0yGZMJ2JvFW+7CooXlBfX2rvB2R6+35PTePvTe7tj1Dz74YHEZHsQv8Oabb/oll1wytI1dwvXWJ9TbAAHefjx4+1CHv5Yqy1sGg/grR4lBwH6weUs2X/GfDVcX2qnw+meffYZaQgJvvfVW+CxZn/dQg4ceesjbONXeUmX5n//8595+xHnrPuEt325CNaRYCVhaM29dVsIxz7J+eOtb7S2rRPg+shHF/CyzzOJtEAdvgz0AlqCApZYLbTRkyJDw11LPeftBV1Yju5/E2whVZfNa+Ylr5Z0v3ffSANR+yYSDr/XR8NanMCxmaWW83RXvFdwwJSdgw3B6S2jub7nlFm+/JMMPBBttx1sC+lAp/bUblLydEUiuki1eso0VHg7A+gFX6x8H4+TeLDZYg1999dW99Z0OldBnSwGNnW0Lzy2rhLdsBX7o0KHJVbLFS7Yz1N7OcPrf/va33q4c+BNOOCF8N+mYZ9kkgo6lnfOWd9fbICktrpXc7tvAGt5uiPU2amKohNpKbaK2K3w36QUC0PI2IgBt9ygNQMeNGxe+OJ977rkyLUspE95kdvm3bD5P4hHQWU2dhdYZtsJkQzh6u6u68DT8teEevQ2TWjaPJ/EJ2A173i41ebthxZ911lnexn4v/lNAY4nni8/15cmUjIDa4aqrrioWrvbSD7vSafz48V5n3Uq/REtf53FzBXSWWse80is6ugJkg6SUFWyDpHhLF1g2jyfxCehH9wYbbFBWoOU59pa20Vu3Pq/PkSYC0DIiTxqmCr0vNJqORm/pmORcd1irnwdJziugxTCrX79+Tv/UDoXpgAMOCMM8Fp7rrxKcKz0TUzICaiPlYr3xxhudnbFxxx13XBjn3S5Nhb65el2P9U8pm5iSEZD/U089VSx8+eWXd/aju/hcD9TXWv1E7RJv2XyexCOgrANTpkxxH374YShQxz49LuTXLdRCg28UMrYU5vE3PgH1+XzrrbfKCrTuLCEdnWKGrbbaKrRj2QI8cQSgJW8CJcteccUV3e677+7mmGMOd9hhh+kMcVhCQwYqbcIyyyxD+pgSszgf6mCs8cTVidv64oaid9ttt5C/VU+mTp3qLrvsspAn1Prtxlk1yqogYJdzQ0CjEcV0QwvjvldASnCWcuaeeeaZzvrlhtRzf/7zn8NgHHY2J9xIccMNNzgNmfqb3/zG6cuUKX4BfQ+tvfbabtNNN3VHHnmk0+hvGlnMLvk6u08hpJ4bPXq0+9Of/uTsDFz8FaTEIKCb+HSz8v777+9efvnloopGhbvnnnvcq6++6vSdpB8TTCUC5SdEW/eZLqtb8mxv41eHm1rU11Od8dUHR5Pdce1t1BZvI7y0LlIK9lwd7dX3UzdJdJx0+dDe2n6LLbbwugzMlB6BSy65JPRdW2qppbyNAZ+eirV4TdR3TX0/9bmxKzvezk6Hx3quS7+6MdPuuG5xpWR3X12OfvWrX3m7yz18N33yySf+jDPOCO2ldtK/X/7yl159dpmSE7j11ltDNwjLHNGpEpZyydvJq9BW9HufzkMeUPv0djV9+eWXxcsaGopz2LBhXS3K/JgFlJbE7v4sK9XuqHbWfzekwCh7gSepENCwqXvttVdIF6NUP0zpEdDgAPbjLnx+lI5ugQUWcPZjIaQ6S08tqUmpgAWizjIXhJH7SOtTKpPsY50JtZvEOlVCl+Ktz7VTVxd9tpico6NclXdBoU+Ndfh2dsaNALSKVdwvdQw+Nb64gpuLLroo7qpQXp0CiyyyiLM7dUMe0DpXYbGYBOzGFqd/hUnHPH1hKtcuUzoEOuY+vv7664u5jxdddFFyH6egmdSHujDCm0ZP1BDedmd8CEgVQ6j7GNN0AQLQdgslj9XNK5UmJc/WpOEDNdklq/CX/+IVUAd8DZNaaVKn/Ouuuy4EOEpCr743OigzxS9Q6SaJQi3UH+rRRx8N/aE0T22ktmKKX4BjXvzmjZSom1vU911nqRXc3HnnnaHPp3U5Cv1Br7nmGmddXMINZYXvqEbKYZ2eCYwYMSIkotf9Ijp5ZZlYwuABihcs77E75ZRTwoABp556qmtra+tZYXlZe/rV+NZ+VOgHZe1a7APV1ePWlkpu75WEvqs26Tjf7jpMrqItXjJ5QLPxBuCYl512IvdxutvqpZdeCv2nC6kbTzzxxDCog92QVKy4jdQX+u0WcoUWX2jhB5wBtchFk4ZytNElnOXrcuedd56zm5DCfP2nu9e22WYbTp8XRZJ5oDbRZSddxtXdubrzs5Aqy3JNOsu55mxkpHCmmvQ+ybSRStVQqLpceMEFFzg7ELtNNtmkWJm//e1vIYOBPm+adJcvUzICHPOSce9OqerXrmGi1T9X3SE23njjkCLQbrQM/XS1LfXXVSaDyy+/vDubZtkIBW6//fZwllrpmDRZQBpSL5XeN/Kzn/0sLKMMLvTZ/R6fNEzfO4RLgerQrfFb7Y5Cp9QWhVyFCnJ0aaPwvH0V/iQgsOWWW4bL8OqAr9xqulFM7VIYB1kHYz0nsEmgcdqLJA9ocvbdKVndHzjmdUcs/mX1WdI/ch/Hb9+dEvWdo+5hlokgrGbDcHbKqWsnOp1OlBT6iHZn+3ldlgC0pGWVvNzSMLnbbrvNjRw50lnqC6cchkzpEtBAATY+tfvd737n9KtSSc+VLJspXQLkAU1Xe1SqDce8SirpmUfu4/S0RbWa6IqcpS1zNrqYe+CBB9zmm2/u1HY777xzyH+sH3o6a60z2lqW6XsBAtAK74SVV1453Klrw2Y5G0bL2TB0FZZiVpIC6sRtwz2Gu0BtGDqnM6NM6RPQlQN1m/h//+//hctPV1xxRfoqSY0cx7z0vgl084q6H6nLUcdJNyTtsMMOIfBREnSmZAQUbGpwgB/+8IdunXXWCVfhxo4dGwYLsDHhQ/cwZWrRjWPcKDa9jcgDOt2i4iMNJ3j88cc7G288XPKtuBAzExXQ5SkFOPfee6+77777SEeSaGt0XTh5QLu2SdMrHPPS1BrT60Lu4+kWaX6kQFN5w5WZRV3EdMVOPyB0MoupXKCXXXI+unxW6z5T3rtnnnnGKX9XITWMUmDoxiS9oQYOHOiUy5ApWQEdiDWGtdItqX+U2ksfds1Xh30l+e2YJzTZGrdm6ervpCHolJRZZ6zVb1ddWtRWStasjvg2+k5r4qR4r5UPVD8W9JdUZulpKB3rSicFOrr5SF2ROr5WuhyP4xVQnLDQQguFAVF0jFP6rPvvv9/ZCHDxViQDpXEGtL2RPv/88zCW7mOPPRbm6HKGxt21YbXCF6V+yejL9OKLLw6XPDLQtrmsoj7I6purvjS6OUx5JXW3tXLh6QOu9lNfHN0Nv+CCC+bSIAs7dfjhh7sTTjjBqeO9AhldKtRlXvXV1XPdCaqxq234uizsTi7rWC0PqBJm69inz5omch8n8xaolftYN8zqGEju42Tap1BqtdzH6iKm3MdKSq+J3McFNfvbwimoynZ9v/3280sssYS3D7PXmK52N7W3PqD+hhtuKC533HHHeeu/URwfvvgCD2IRsODFW1Dpf/vb33o7U+0twAnji9sZNm9nA0IdJk2a5K3PjbdUTbHUiUI6C1giZm/DOfpzzjnHWz8ob2nMQjtZoubieNUW/IRl9HljSkaAPKDJuHenVHIfd0cruWXJfdyYPQFou9uKK67obRjHouJRRx3l7ZdK8bke2FlSb2fdvA4KTPELWHcIb2c3vV3GLRZuZ9O8DRtYfK4H9ovTr7DCCmXzeBKfwMknn+ztTs9igS+88EIYQMC6TRTn6YECUx24mZIReO2117ydlQ4/vO3OXW9dJor/rL+aP/bYY4vPk6khpUrAbuILJz5+85vfeLsKV2wTu4oQPld2tSfMsy4ugCUkYN3AvN0U6+1mJH/WWWcV20ifqYMPPtjb3fHFeTpJwvS9AB2w2s8GK3F56d3u6vup/mpff/118XyxcnxNmTIlDLNVnMmD2AR0p6H8dblDky5PVbr0YR9w2ii2VulckNqp9LOkvqCa1PezdKKdSjXif0we0PjNGymR3MeNqMW7jvrgKh2gbuBT1yO7WuoGDx4c7oYv3KtQyCPOICnT24YAtN3i17/+tbMzN26vvfYKIyLdfffdzi7Jh1Fd1CFfAanGerUza+GNNZ2QR3EJKLn82muv7TbddFN35JFHhj66P/7xj0P6iwsvvNBNmDAhDCCgdCXqX8iUjIBGDrOrBKEN7Oy022mnncIILnZVIWQpUDvZmc+QH2/48OHJVJJSgwB5QLPxRiD3cTbaidzH3WwnTgV/L2B3wPtjjjnG213uXpfjLdG5tzvfQz9QIw2XOoYOHep1OZEpOQG7e9rbjRHeRj7yluzX67LTGWecEcbYLbSTdcwv9jVMrqatXbKlxPLqY6jPjP1Y8Pp82Y+H8DlSO9kZA3/++ee3NlLK9t5utPS///3vvQ356Oecc05vw6amrIZURwI24o5fZpllwj99lrj0ns73xSWXXBL6vltWFm83yKazkgnXirvg7RNcbdJld41soEmj7nD6vJpWcq8pxY9Gm1D6C8bZTa4dapVsP+BCSjP7kVccy7rWOrwerwB5QOP1bqQ0ch83ohb/OuQ+rm5OAFrdh1cRQAABBBBAAAEEIhboHfH2Mr+522+/PeQsfP/9951uklBCc0vJFPoebrzxxpnfvzzsgNpl1KhRIeenbhRTbkl19NYwaBqDV3+Zkhd4+eWX3dVXX+1eeuml0D9XHfXVj1f9qLfZZhsGC0i+iZwG39DgDRp4w7pLhBppSEGdBZ06daqzlGeOfrrJN5QG2dDVg6WXXjrcYDlx4kSnYW2VF1n5ji2NoLNuSclXtMVroBsuP/744+IgGxrA5sorrwzJ6FdbbTW3xx57OPW7ZmoXSLgLQGqKt0vtfrfddgt91H7wgx94u9TubfQWv8UWW/g11lgjpP+xxMz+008/TU2dW7EiSoGl/k9Kx6S+NWqjueee22+11VbhudJk2djJ5GpN+M2h/LlKSaK+hMrdaocbv9JKK/kNN9zQzzXXXN6GpvMPP/xwwrVs7eJtMIfQJmob/bPBN/w///nP8Ni6sfhhw4aFz5f6sjElJ2DDC4c0TGojHd+UMmu77bYLx0C76cXbSRJvd1z7d955J7lKUrI/7LDDwudF7aT0gErBpBzV+n5SH1Ab9S2knoNqugB5QNst1OFeB1wbNivMsbNs3sYX9/brPzy3XzV+k002CQHpdD4exS2gD/If/vAHry9PTZbuJ+RYs1EmwnMbCSl86P/+97+H5/wXv4AGBdDBVvkL7Sxa+DGgnIW6uUV5DPVjTzeO6UeencmJv4KUGAQYfCP9bwQG30h/G6mGDL7RWDsRgLa76cyMRm4pnfTlOPPMMxeDUiVA13PLDVq6GI9jEhg/frzv37+/t9yfZSUq0FHi7MJ03nnnebvEW3jK35gFNBCA7njvOG299dZ+5MiRxdnrrLNO2UhjxRd4EIsAg2/EwtyjQiz9H4Nv9EgwnpUZfKMxZ/KA2vlyTepXqLGRSyf1L/zqq6+KCbTtcm/oF6V5TPELKCOBjTjh7CxaWeFKej5u3LjiPI0Rb6NWFZ/zIF4BfZZslJ0waEBpyXaJsPhZ0nzrRkE7lQLF/FgZPUoHDFCuYwbfiLkRahTH4Bs1gFLyMoNvNNgQjcWt+VvrtNNOC5cENTSg+m6MGTMmDCeosd915s1uqPDKL7neeuvlb+cztEcaOlB9QHWJXfnvrCO+t5vEimc8b7rpJr/YYot5S0yfob3KV1V1mV1nqtVPTf08dRZHQ9vaISqc8dSVBRstJHze6FOdXNur25H66f7xj3/0drNR6Lqiz5e6uLz++uthyGEd73SmlCk5AV1N0LDQRxxxROizq+fq83nBBReE7ya7ESn0q9ZZOKZkBBQzqEuRhiDW0JvqbmQ3LXsbKMUrJ7JiiBNPPDHkP1Z+cabvBbgE3/5OUKJsG7ElfEnqi1L/dLOEjYgUllB/Kd2E9J///If3ToICGr962WWXLWsny8/q1UdXkzp/H3roofQtTLCNVLR+IMw666zFdurVq5e3UZFCrRSU2ihj/pprrkm4lq1dPINvZKP9GXwjG+3E4BvdbyfygHY4c6xLhzbShLMvz5Auxvp8hiV06dfO6nRYmqdJCCg9zFNPPeV0SVepY5R4Xt0jNNFOSbRI5TLVDeLJJ58M3Vg0ZKoGCdA0ZcoUZwGpsxuVKq/I3EQFGHwjUf66C2fwjbqpEl2QwTe65ichVYmN8kkqj5ddag9z7TKUO/bYY0M/KTvr5uwMaBgfvmQVHiYgYEMGhgDGUi+F0m+77TZ36623Oo0Osuqqqzq72SXkykugahRZIqD+hdZdwlkakvDD4PTTTw8BqaUmcT//+c/DWPEli/MwIYFquY/VT5QpeYFquY/takLyFaQGQaBa7mPlFGfqIND9k6b5XEP91HT51s7ShB20YR39gAEDQl82+7IMY7oOHDgwjBGfT4Fs7JUl9Q157w488MBQYfW3sbe0n2+++fzqq6/udal3+eWX73SnfDb2Lj+13H777UMqpkcffdQrpZmyFKidND680p3psbq1KM0MUzIC5D5Oxr27pZL7uLtiySxP7uPuu9MHtN3smGOOCQHMhAkTwhx9USrJr41AEZ7rYK1gxy75dl+ZNSITsJFA/PHHHx8CF90ooUT0f/nLX7z6s2nSjUnq+H3QQQdFViYb6p6AjXwUOts/99xzYUV1vtePOd3IV5h0k59yhdol+sIs/sYsQO7jmMEbLI7cxw3CxbgauY8bwyYAbXdbZZVV/FVXXVVU1N2ht9xyS/G5HuhONo1EoeTnTPELvPfeeyEnns6oabJLh+HMtPX7LKvMZZdd5nU3L1MyAsoooYwRhWnHHXf0I0aMKDwt/tVdolqWKRkBch8n496dUsl93B2t5JYl93Fj9twF0N4lYciQIeHGlvanzi7jOjuDU3ga/mp8ePUTtRQYZfN5Eo+AZSUIN67oJjFNurFFYySr327ppHyGlgajdBaPYxTQZ0ltpJtZNC233HKdPkt2uArtpv6hTMkIqF8huY+Tsa+3VH2GyH1cr1Zyy+mzRO7jBvwbi1vzt5b6fGp88T322MPr0qFSxSyyyCIhd5dSL2m0HftirXgmJ38a6d2jAw44IKT3ufjii0OeQrtJLOTGU0ofC3rC5Xm1Y8ez1+ndo/zVTHk+1Z9afXFHjx4dxqhWqizll9SQdRqWc/PNN/fzzz+/1+hiTMkIkPs4Gffulkru4+6Kxb88uY8bM+cSfInbHXfcEW6SsDg+9E/r169fuFlCzxXUKE8oY1eXgCXw0FL4eAWdc845Z2gbJTxX+xT+KQHw+eefn0DNKLJUQDf1KRF97969Q9uoD2ihjfRXXV7Gjh1bugqPYxYg93HM4A0WR+7jBuFiXo3cx90HJw+ofRt2nF588cWQY1LDOypfoY2045Zaaiku63aESvD5119/7ezMdBja0TqAOxuxytkZtdB1gnytCTZMh6LVNjbyR2gnpc+ybAVu4YUXdnZXfIcleZqUALmPk5Kvv1xyH9dvleSS5D7unj4BaLvXn//8Z2cjTrjjjjvOWbql7imydGwCG220kfvVr37l7MaW2MqkoO4J2B3uzjITuKOPPtpZ1oLurczSsQqoT7sN5eiGDx8eylXuY7uCQO7jWFuhdmEKbNQ2lp0lLEzu49pmSSyh+0bUr72Q+/jcc88l93G1huj+SdN8rmFJzcMlQvX7VP9PpnQK2Hs5tJPuslbKJab0CVx33XWhjdSF5dRTTyXXZ/qaKNSI3McpbZgO1SL3cQeQlD4l93H3G4a74Euic42gs+6667o111zT2c1IzvIZlrzKw7QIWFATukjo7NoZZ5zhLHdrWqpGPdoFNOqHzqRZft3wedIZG51tY0qPwKhRo9wcc8xRzFBgAwO41VZbzX388cfOxrUOf/fcc0+32267pafSLVgTXZ075JBD3EknneTeeOON8FdXGDTSmN3Q5z788MPw2bKbylpQJx27rBGQrr32WvfMM8+4lVZayZ111lnhjLXmP/HEE05/dWVI31fW9z0dlU5BLQhASxpBl97POeccp6Hp9M/u5HXrrLOOu/HGG8MwjyWL8jBBAfUftBF2wo8ESzgf+n7uuuuu7umnn9ZNdQnWjKJLBSz3p9M4yDPPPLOznJ9uscUWcyeffLJTv1Cm5AV0jNt9991dYYhA9X3fe++9i8PY2o2X7tBDD3XqC285eJOvcAvWQPY6EbLPPvs4G3QjBDKzzjpraDcbyCGI6EeEfjwoGGVKRkCfJcurW+xypDbTUNE26luxQpYJJCxDOxVJHAHodIviIxsBKeT0+sc//uEs8Xzoc2iJ6cM48Op/yJS8gN1d7Y444v+3d+YxUlRdH76BT/8wJgpoUJAoUdCIggoisggajGAUosYNMXEBNQouiFFRA6KCChIC7oCIG5AoETQu4AJuuCFRYwwugLhvqGhccKnv/s7bVW/POF3T3W9NVc30c5OZru5by+3ndFWduvee37nGbo7XXnutXXyl3Spnp3fv3m7WrFnZN5IW2MOBej/VM6ALtOZYS6NV+eB1nvlMSFDKiADaxxmBr+CwaB9XACvDVdE+rg4+DmgJbnJw/DxD6wmVsLnXnbT3XgaoxBZ8nAUBPf17bVCn3pvVq1c7n/bRHXzwwc7LZWXRHI5ZgkCPHj1sWEqBfkuXLrWeNjmiEnCmZENgzJgx9qCmYXYlDtBQ77x582yIVwL1Pre1GzJkiBsxYoTzMlrZNLLGj6peaNnpqKOOcvPnz7eRuMmTJzuNLmjIVyMMU6dOdV6aznmZwBqnld3X13kiW3l5Obdq1SrndY6dOq1Gjx5t9yWvM+6OP/54t3nzZjunsmtpzo5c+bTRlrmFgpCk80nJNwF/+gRPP/10vhtZ461TEJIf1q1xCs3j66N9nH87oX2cfxuphWgfV24nZJgKDwQaIlSvJ/qEOXtCqtecp556yqRI1PNJyScBBUWoZ0bzpynNgwDax/m3E9rH+beRWoj2cfl2wgEtnxVrQgACEIAABCAAAQgkQOD/EthHi9jFokWLTHaknC+jSFFK+gQkt7RgwYKyDtylSxc3dOjQstZlpWQJKLDI53wva6cKTFJ0PCV9AiTfSJ95NUck+UY11NLdhuQb1fGmB7TArWfPnqbPpSjqxsqWLVsaW4X6JiCgTCByVjRVorGgCAWQleusNkFTa3qXcmwk36OUqJqYH1fuu+8+m7Aftw51TUPgpJNOcn6+rvPJN5w0Qfv27ds0B2Kv/xMByS+p6JombV2mH/1POJtk44cffthkl3zyDTdlyhSTxQrt1iQHbCE7JQq+YEjpFLZr185J9kI9nIqolqPZ0F8LsX2z+xpt2rSxXs3WrVs7PTBMnz7dbdiwoUEb4XxmZ17ZRnOp5Xzqprl48WKL/mzoXFK0KCU7AiTfyI59JUcm+UYltLJZl+QblXPHAS0wk5akgidmz55tGSbUG9C9e3eTuJCTQ8meQNu2bU0WS+LMp556qtO0iY4dO5rI+QMPPID0UvYmshZI31OyPnqIkz6eHuik+ynRc4kw+1jJnLSUZpB8o3n8Bki+0TzsRPKNyuyEA1rES0O7mjeo3jOlo5NTquh4pXyUvpfSaEnHkJItAfVUn3POOe65556zHlClT7399ttd+/btnYYVyVyVrX3Co3fr1s1ScX744YdO6gXSxRs5cqQ5pePHj3dr1qwJV+U1YwIk38jYAGUcXvcnkm+UASrjVdQpQvKN8ozAHNAyOEksWz1syserYcS///67jK1YJW0Cn3zyic2/mTNnjtPQ4sKFC9NuAsdrhIB6PyXKrNSByokssXMJNFPSJ6CHNQ0bzp07918HV57x559/3hI8SJReyQMo2RDQXEKvfez0oF2/KCWxHuRkIzk+EyZMqL8K71MgoDmgSgSgQNn6RfJZK1ascO+9957ZadSoUW7gwIH1V6vJ90TBx5j9hx9+cI8++qhN1H/mmWdsGFE/Hkq+CCjvroIp9Cf9SfVWkzI1XzbSQ5scGmVvUQ/1r7/+ag8JGl2g5I9Ap06dLNtO/lpGi4oJ9OnTx+mPkl8CCpgdNmyY/eW3ldm0jB7Qetx//PFHczp1o5TTufPOO1t0m3rUdKIT2VYPWEZv161bZ86MnE7NN1T6TdnIZ7SyId6MmsVhiwjI6Vy5cqXZacmSJTZHV7JLspOC/rbbbruitVlMmwDJN9ImXt3xSL5RHbc0tyL5RnW0cUAL3DQUqHzv6ipXtLV60HSj7N+/v2vViqmy1f28kt1KvWYzZsywns533nnHHXDAAWYjDSVKSoaSDwIaDpw1a5aT06khKeWx1rmkXoByZM7y8S1afivQPs6/jdE+zr+N1EK0j6uzEw5ogZukY9auXev69evnDj/8cCepn1Jl4sSJpar4vAkJhDqgitw99thj3d57713yaPvuu6/1hpZcgYomIxDqgO6xxx6m8bnjjjuWPJZ6rGUrSvoE0D5On3mlRwyveWgfV0ou3fXDax7ax5VxZw5ogdcuu+ziNO9p06ZNjQqY44BW9iNLam1dhCXro/Lyyy/bX6l9K3uInBtK+gQU2CI7/fPPP9YLGteCXr164YDGAWrCOk2DUOCeHhBOOeUUkzaTcgElPwRC7WMpfuiBQfJzJ5xwgmlW56eVtCTUPtb5JO1jnU9SltA9i1KaAD2gpdlQAwEIQKBFE/jrr79s2pGG4xVwufvuu5uToxto586dW/R3b05f7vvvvzfFCNlJaW4HDx5sTo4SOWhEiJIPAop0l430p3gSTeXTQ8OAAQOIH2nARDigDUCp/5F0DNW1rh5SSj4J6Eb69ttvu3322cf0JvPZSlqli/LGjRtdjx49uCDn7Ofwxx9/WKIH3TylYyiFAjmimmOtRAKUfBCQFrWCL2UnXfM02iMnRwF+SgVJyQcByWNJClABzZKf0zx42Um9pZQCAQ+GUiCwbNmywGutBV4uxj7xQS+Bz4iktC3257vUA6/pBa8MCXhHM5g0aZLZJWzG66+/HvihKrORf1AIpk2bFlbxmhGB9evXB2eeeWbgtXOjFvjEDoGfW2128g5N4OdcR3Us5IvAL7/8Etx5552BH54PfBBmvhpHayIC/mEu8Ek5Aq/OEviHhehzFvJDwE9FClatWhUcdNBBdu3zAc/5aVzGLSG8u+CIa06h5tZITkH54FUklq3AJIkw+x+Q++CDD9zYsWMLW/CSBYErr7zSTZ48OeqNVpIACZl37drVIhGVM/nyyy834eYs2scxncktKeJdQ7pdunQxJJI08w8Odk5J/kdDUlrn559/BlmOCEj7eP78+TZ/Wtc6zecdN25cjlpIU0RA2sfXXXedBWMq8Qbax/n7XUiGTte9c88913wL2Uy9oGgfF9kqYwc4N4f3UdWBl4sJ9LSiot5PLyAb+MwSURt9d7r1tEUfsJAqAe9sWm+Mv+BGx/XDG/ZUWdyb5h3SYPTo0dE6LKRL4P777w+22Wab4Ntvv40O7OdCBV61IPAXZftMPWzqrV6+fHm0DgvZEPBOZ+CdzsCnITa7dejQIbjooosCP9cwuh5m0zKOWkzAOzCBf/gOvANj1zyvfRxMnz498IEvxauxnCEBjdB5p9N6pXfaaSe7xul+tHjx4kD3L0pdAoRoFZxx6UoqP3UoNK+J3kqhNXz48MhdP/DAA516CBTppsn6lHQJKMuR//maiHl4ZD1hKuJamqBhkZ38dIrwLa8pE9C8NPXI+AuwHVk2UxakM844I9LUVV549Y6qN1TRopT0CTSkfazzCe3j9G1R6ogNaR+PGDHC5uWifVyKWvqfN6R9PHPmTLSPGzEFDmgBkJzN4swsuhDvsMMOdSYM+x4dc1Dbtm3bCFaqm4KAbKQHBAWEheXZZ591RxxxRPjWXmWn0PmpU8GbVAjITko/FxZNY1EUL3YKieTjdcqUKf/SPtaDgv7qF6Tn6hNJ570Cjq655hqLdFcAi7SPFSzmRxn+1QC0j/+FJLUP1OFx1113OWkfn3baaSZt9tFHH1nilPqNQPv4v0RwQAssFJWrC+9ZZ51l+oXKV62emWJB+ieffNJ6Psnm8t8fUJpLspG0JV944QV7slRPm4SadSMNy59//mnzbhQZSsmGgOy0YMECJ1v4oXjTAtXD3aBBg6IGyXaab73ffvtFn7GQLgG0j9PlXc3R0D6uhlr626B9XCXzuiPytfvuscces/mFmq/hMyEF2267bbB69WoD4ntvgtmzZ1sE7w033FC7kHLwzf0wbuBP9uC8884L/NB74J/6g99//91a5jXYAt9LEHhnJ/BDuzlobW024bvvvots4x/oAi8NE/jAMIPhndLA57YO/PB70Lt372Dr1q21CYlvDQEIQKDGCaADWuS4a1hDEYX+N+EuvfRSSyOo6ssuu8xyW6t39I477ijagsW0CXhn01111VXOB6+Y5qefhB/Nx9Xwh4Z/ZSNFxlOyI/Duu+86/7DmNG9XaVO17OV83BtvvOG84+m8JIl7/PHH0ZfMzkQcGQIQgECmBHBAy8CvoCPN+2TovQxYGa6iieASopejQ8knAclmaY6uHhYo+SVA8o382iZsGck3QhL5fiX5Rmn7cKcuzSaqUcQ7zmeEI7cLmoSP85lb81jDFP2O85kfG/mpR87Lz7mVK1daozSC0K9fP9PVlbqET8zhNOpAyY6A9CR9EgezS9gKjSRIr7pXr14WcKmRIEq2BDZs2GAxJNKqDos0qxUQK2WWjh07mupHWMerczigRb8CDb3rgixJJhWfCcROcJ3k+hszZkzR2ixmRWDLli02JUL2UlG+3dBGepUAOiV7AhJeVspAFUWEFttITo4i4ynZESD5RnbsKzkyyTcqoZXNul7X2AJjSb5RIf8anwMbfX0JyHqZGEtpFgqde/mLwEeKmqi5114z8V8fKR9tw0L6BCTGrPSAXiIr+PTTT60BEjhXylSJz/fp08cCYJRIgJIdgVmzZtm5NGTIEGuEf6iz88dLlJidfO9NcP7552fXQI4ckHwj/z8Ckm/k30ZqIck3qrMTPaAFh33GjBnO5xR3a9ascaNGjYrc+L322svdfffd7sEHH3T+ZurQw4vQZLIwcuRIJ5mfTZs2ud12283aIG1QnwfZ7LRkyRK3efNmN2/evEzax0GdW7dunQXuSR7riSeeqINEQ4U6n6ZOnWq6eV988UWdet6kR0AjPcccc0zZyTfSaxlHCglUknxDSR0o2RAolXxD51c4Law4+UY2rczfUXFACzbR0Lsi3zVXIyxhVqTw/emnn275xqVFSUmfwNdff238b7vtNstR3VALdt11Vzd48GB7mGions+ansCKFSucHtyuuOKKyLmpfy5JVFtFD3yUbAhovifJN7JhX+5RZSOSb5RLK7v1ZCeSb1TOHwe0wMxrSDqfW7cOQfWwdevWLfpMPW9KjaaoeEr6BGQjPUUq2Ki4KA2nRLXDIju9//774VteUyagXpuePXvWOaocHT89woTpVaGLtbK6YKc6mFJ9o/MkzHqkh2qSb6SKv6yDyUZh8g1tECbfGDp0aLR9mHyDpA4RktQXZCfNqZYtVDQSR/KNxs1AJqQCI0WoKc97cfFzCp3+wvLZZ59ZGkitS0mfQIcOHUznU1G5xU+bCxcurNMY2WnPPfes8xlv0iOg8+PNN9+sc0DlrfaJHaLPdFPV8Lt6SinZELjwwgvd8OHD7aFa1z5F8d57773WGE1jeeihh9xNN93kFMlLyYaAn+/ufPINp9E35YDXlBY9gMtuKpKeu/76693GjRstBWQ2reSo0p2WHdQZogdtTdm7+OKLzQmVXJZSRo8dO9Y0kLUO5T8E6AEt/BIUlbtq1arY34WkSiSi7bMkxa5HZdMQkMan9FhffPHFkgeQYyM7DhgwoOQ6VDQtgf79+9sUCA1LlSqaryZ9PJ13lGwIaH6aHE7psqrnZvHixXbzVGs0R1dTkvQAPmHChGwayFGNgBJrKC7hpZdesvuPnFCfXczqjj76aHNulERFvXCUbAi0a9fOEmvsv//+7rXXXnOXXHJJlCJ67dq1Fj8iKUdFySs9MeU/BBCiL/wSli1b5o477jj7gShzS/2i4amTTz7Z3XjjjW7cuHH1q3mfEgH1BLz66qvmZKpHtLjI+Tz77LPdokWLbKiqa9euxdUsp0RA01Q6depk2ah081Q+6+Ly+eefW1546eMV94oWr8NytgRIvpEt/3KPTvKNcklltx7JN0qzxwEtYqObpbQ+9VR56KGH2hw1ZQSR6K+ClLxsjJs5c2bRFiymTUDD79L9VG/AiSeeaEFj6hV96623zClVCsilS5e6I488Mu2mcbwiApqvKxvINjqfNOwkrTydS+rBkfOpYSm9UrIj4MVTrOdGyTa6d+9u2sdz586NGqThxFtvvTV6z0I2BKR9rN5qDeMqKEnXQA27h+Xqq6+OUkeHn/GaPgFpH+sepHuTtI+lzhIW9Vqro0u9pZQCgerUm1ruVo888kgg7cI2bdqYbqH/0ZjG5LRp01rul25m38wPFwY+H3zgg8YCP5xhdvKOTDBs2LAAndb8GPPjjz8OvGxW4Od/mo38JSeQZqvvpQ78sG9+GlqjLUH7uHkYHu3j5mEntI8rt5OrfJPa2MIP5wa6gfoet9r4ws30W0pwfv369c209bXTbC+hhdOZM3PffPPNwfbbbx/40YOoZUq+4efwRu/1MH7YYYdF71lIn4DPIBYMHDgw+Omnn6KD+/nwgRweFR/MZ3acPXt2VM9CugT0kKDOKj93OpDvoBIm3/jyyy/tvdemDlq3bh34KUj2nn9BQBBSib5wDXMocjec7F1iNT7OmICi4Tt37pxxKzh8YwSUt5rh9sYopVuP9nG6vKs5GtrH1VBLfxu0j6tjjgNaHTe2DHHA2QAABeZJREFUggAEINCsCaB9nH/zoX2cfxuphWgfV2enuuGp1e2DrSAAAQhAoJkRQPs4/wZD+zj/NlIL0T6uzk70gFbHja0gAAEINGsCaB/n33xoH+ffRmoh2sfV2QkHtDpubAUBCECgWRNQOsd77rnHJOYa+iLSPr7llluirDsNrcNnTU/AB4K5Cy64wDKH1T9aqH2s7G9Iz9Wnk977Qw45xG3dutUpu5gyH9Uv0j6Wjrhkzdq3b1+/umbfowNas6bni0MAArVOAO3j/P8C0D7Ov43UQrSPK7cTDmjlzNgCAhCAQIshsGTJEjdnzhxLIaic8FL+6Nmzp2WGGz9+fIv5ns35i6hXbdKkSW758uVOaWyVOlWqEn379rW0j4MGDWrOX6/FtN1LArqJEye6V155xWlZxWsf2xC9siiiBFLX1DigdXnwDgIQgEBNEvC6hG7Dhg0WUIH8XH5/Ar/99pv76quvkJ/Lr4msZd98841r1aoVTmeMnXBAY+BQBQEIQAACEIAABCCQPAGCkJJnyh4hAAEIQAACEIAABGII4IDGwKEKAhCAAAQgAAEIQCB5AjigyTNljxCAAAQgAAEIQAACMQRwQGPgUAUBCEAAAhCAAAQgkDwBHNDkmbJHCEAAAhCAAAQgAIEYAjigMXCoggAEIAABCEAAAhBIngAOaPJM2SMEIAABCEAAAhCAQAwBHNAYOFRBAAIQgAAEIAABCCRPAAc0eabsEQIQgAAEIAABCEAghgAOaAwcqiAAAQhAAAIQgAAEkieAA5o8U/YIAQhAAAIQgAAEIBBDAAc0Bg5VEIAABCAAAQhAAALJE8ABTZ4pe4QABCAAAQhAAAIQiCGAAxoDhyoIQAACEIAABCAAgeQJ4IAmz5Q9QgACEIAABCAAAQjEEMABjYFDFQQgAAEIQAACEIBA8gRwQJNnyh4hAAEIQAACEIAABGII4IDGwKEKAhCAAAQgAAEIQCB5AjigyTNljxCAAAQgAAEIQAACMQRwQGPgUAUBCEAAAhCAAAQgkDwBHNDkmbJHCEAAAhCAAAQgAIEYAjigMXCoggAEIAABCEAAAhBIngAOaPJM2SMEIAABCEAAAhCAQAwBHNAYOFRBAAIQgAAEIAABCCRPAAc0eabsEQIQgAAEIAABCEAghgAOaAwcqiAAAQhAAAIQgAAEkieAA5o8U/YIAQhAAAIQgAAEIBBDAAc0Bg5VEIAABCAAAQhAAALJE8ABTZ4pe4QABCAAAQhAAAIQiCGAAxoDhyoIQAACEIAABCAAgeQJ4IAmz5Q9QgACEIAABCAAAQjEEMABjYFDFQQgAAEIQAACEIBA8gRwQJNnyh4hAAEIQAACEIAABGII4IDGwKEKAhCAAAQgAAEIQCB5AjigyTNljxCAAAQgAAEIQAACMQRwQGPgUAUBCEAAAhCAAAQgkDwBHNDkmbJHCEAAAhCAAAQgAIEYAjigMXCoggAEIAABCEAAAhBIngAOaPJM2SMEIAABCEAAAhCAQAwBHNAYOFRBAAIQgAAEIAABCCRPAAc0eabsEQIQgAAEIAABCEAghgAOaAwcqiAAAQhAAAIQgAAEkieAA5o8U/YIAQhAAAIQgAAEIBBDAAc0Bg5VEIAABCAAAQhAAALJE8ABTZ4pe4QABCAAAQhAAAIQiCGAAxoDhyoIQAACEIAABCAAgeQJ4IAmz5Q9QgACEIAABCAAAQjEEMABjYFDFQQgAAEIQAACEIBA8gRwQJNnyh4hAAEIQAACEIAABGII4IDGwKEKAhCAAAQgAAEIQCB5AjigyTNljxCAAAQgAAEIQAACMQRwQGPgUAUBCEAAAhCAAAQgkDwBHNDkmbJHCEAAAhCAAAQgAIEYAjigMXCoggAEIAABCEAAAhBIngAOaPJM2SMEIAABCEAAAhCAQAwBHNAYOFRBAAIQgAAEIAABCCRPAAc0eabsEQIQgAAEIAABCEAghgAOaAwcqiAAAQhAAAIQgAAEkieAA5o8U/YIAQhAAAIQgAAEIBBD4P8BJKKD+bbggbYAAAAASUVORK5CYII=" /><!-- --></p>
</div>
<div id="using-the-detection-p-value" class="section level2" number="5.2">
<h2><span class="header-section-number">5.2</span> - Using the detection
p-value</h2>
<div id="extracting-detection-p-value-and-filtering-probes" class="section level3" number="5.2.1">
<h3><span class="header-section-number">5.2.1</span> - Extracting
detection p-value and filtering probes</h3>
<p>This method compares the total DNA signal (Methylated + Unmethylated)
for each position to the background signal level. The background is
estimated using negative control positions, assuming a normal
distribution. A p-value is calculated for each position for each sample
to help determine which probes are of good quality. P-values &gt; 0.05
should typically not be trusted.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="do">## Extract the detection p-values for each probe</span></span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>pvals <span class="ot">&lt;-</span> <span class="fu">openSesame</span>(idats, <span class="at">func =</span> pOOBAH, <span class="at">return.pval =</span> <span class="cn">TRUE</span>)</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>pvals[pvals <span class="sc">&gt;</span> <span class="fl">0.05</span>] <span class="ot">&lt;-</span> <span class="cn">NA</span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="do">## Reorder pvals table to be same order as sample_table</span></span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>pvals <span class="ot">&lt;-</span> pvals[, <span class="fu">match</span>(sample_table<span class="sc">$</span>Name2, <span class="fu">colnames</span>(pvals))]</span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a></span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a><span class="do">## Use detection p-value to determine if any probes are performing poorly and should be removed. We used 20% in this case which allows for one sample failure per probe (normally we would use 10% as we would have larger data sets to be working with)</span></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a>row_Ps <span class="ot">&lt;-</span> <span class="fu">apply</span>(pvals, <span class="dv">1</span>, <span class="cf">function</span>(x) {</span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>  <span class="fu">sum</span>(<span class="fu">is.na</span>(x))</span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a>})</span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a></span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a>probes_to_remove20 <span class="ot">&lt;-</span> <span class="fu">which</span>(row_Ps <span class="sc">&gt;</span> ((<span class="fu">ncol</span>(pvals) <span class="sc">/</span> <span class="dv">100</span>) <span class="sc">*</span> <span class="dv">20</span>))   <span class="do">## &lt;- Change this &#39;20&#39; to whatever threshold you would prefer. We normally use a 10% threshold.</span></span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="fu">print</span>(</span>
<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a>  <span class="fu">paste</span>(</span>
<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a>    <span class="fu">length</span>(probes_to_remove20) ,</span>
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a>    <span class="st">&quot;Probes failed by each having a detection P value &gt; 0.05 in more than 20% of samples&quot;</span>,</span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a>    <span class="at">sep =</span> <span class="st">&quot; &quot;</span></span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a>  )</span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## [1] &quot;78204 Probes failed by each having a detection P value &gt; 0.05 in more than 20% of samples&quot;</code></pre>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="do">## Now we can remove these probes from the beta table and the detection p-values</span></span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>beta_v2.detP <span class="ot">&lt;-</span> beta_v2[<span class="sc">-</span>probes_to_remove20, ]</span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>pvals2 <span class="ot">&lt;-</span> pvals[<span class="sc">-</span>probes_to_remove20, ]</span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="do">## Finally, we can set all failed probes that have not been removed to NA in the beta table</span></span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>beta_v2.detP[<span class="fu">is.na</span>(pvals2)] <span class="ot">&lt;-</span> <span class="cn">NA</span></span></code></pre></div>
</div>
<div id="filtering-of-samples-based-on-detection-p-value" class="section level3" number="5.2.2">
<h3><span class="header-section-number">5.2.2</span> - Filtering of
samples based on detection p-value</h3>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="do">## Use detection p-values to determine if any samples are poor quality based on a 10% cut-off and therefore should be removed</span></span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="do">## It&#39;s important to note that we should ideally remove repeatedly poor performing probes first before removing samples, as we do not want to penalise and remove samples due to probe-specific technical problems.</span></span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>detec <span class="ot">&lt;-</span> <span class="fu">apply</span>(pvals2, <span class="dv">2</span>, <span class="cf">function</span>(x) {</span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>  <span class="fu">sum</span>(<span class="fu">is.na</span>(x))</span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>})</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a></span>
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a>keep_samples <span class="ot">&lt;-</span> <span class="fu">which</span>(detec <span class="sc">&lt;</span> <span class="fu">nrow</span>(pvals) <span class="sc">/</span> <span class="dv">100</span> <span class="sc">*</span> <span class="dv">10</span>) <span class="do">## All samples survive a 10% cut-off</span></span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="fu">print</span>(</span>
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>  <span class="fu">paste</span>(</span>
<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a>    <span class="fu">length</span>(keep_samples) ,</span>
<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a>    <span class="st">&quot;samples passed QC by each having fewer than 10% of all probes fail detection P value (&gt;0.05)&quot;</span>,</span>
<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a>    <span class="at">sep =</span> <span class="st">&quot; &quot;</span></span>
<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a>  )</span>
<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## [1] &quot;6 samples passed QC by each having fewer than 10% of all probes fail detection P value (&gt;0.05)&quot;</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="do">## Remove any failed samples. In this example, all samples passed QC!</span></span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a>beta_v2.detP2 <span class="ot">&lt;-</span> beta_v2.detP[, keep_samples]</span></code></pre></div>
</div>
</div>
<div id="removal-and-handling-of-snp-cross-hybridising-control-and-duplicated-probes" class="section level2" number="5.3">
<h2><span class="header-section-number">5.3</span> - Removal and
handling of SNP, cross-hybridising, control, and duplicated probes</h2>
<p>We can use DMRcate functions to remove other probes that overlap
common SNPs, as well as the <em>rs</em> and <em>nv</em> associated
probes. Within this function you also have the option to remove the
<em>XY chromosome probes</em> and <em>cross-hybridising probes</em> too
based on <strong>in silico</strong> analysis (Peters <em>et al.</em>
2024).</p>
<p>We can also choose how to handle replicate probes, a new feature of
the EPICv2 arrays. We have multiple options available to us, in that we
can collapse replicate probe sets by taking the <strong>mean</strong>,
or selecting one probe per replicate probe set depending on which is
more <strong>specific</strong> or <strong>sensitive</strong>, or even
selecting <strong>randomly.</strong> We use <strong>mean</strong> as a
default option in this tutorial.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="do">## Using functions from DMRcate, we are able to remove SNP and CH associated probes from the beta table.</span></span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a>beta_v2.clean <span class="ot">&lt;-</span> <span class="fu">rmSNPandCH</span>(beta_v2.detP2)</span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a></span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="do">## Note: If you would like to keep all of the SNPs then you can set parameters in the above code as follows. rmSNPandCH(beta_v2.detP2, dist=0, mafcut = 1)</span></span>
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a></span>
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a><span class="do">## We can also remove the replicate probes and collapse them based off preference. Mean, sensitivity, specificity and random are available options. We will use mean in this scenario</span></span>
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a>beta_v2.clean <span class="ot">&lt;-</span> <span class="fu">rmPosReps</span>(beta_v2.clean, <span class="at">filter.strategy =</span> <span class="st">&quot;mean&quot;</span>)</span></code></pre></div>
</div>
<div id="high-level-survey-of-the-data" class="section level2" number="5.4">
<h2><span class="header-section-number">5.4</span> - High level survey
of the data</h2>
<p>A good first pass of the data will consider global trends. Generally,
methylation data has a binomial distribution with the majority of probes
being either fully methylated or unmethylated. The density plot below is
used to illustrate this distribution and check for differences between
samples or groups. We can also look at the variability of our data
through MDS plots.</p>
<div id="density-plots" class="section level3" number="5.4.1">
<h3><span class="header-section-number">5.4.1</span> - Density
plots</h3>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>x <span class="ot">&lt;-</span> data.table<span class="sc">::</span><span class="fu">melt</span>(beta_v2.clean)</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>x<span class="sc">$</span>Variable <span class="ot">&lt;-</span> variable.of.choice[<span class="fu">match</span>(x<span class="sc">$</span>Var2, sample_table<span class="sc">$</span>Name2)]</span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a></span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(x, <span class="fu">aes</span>(<span class="at">x =</span> value, <span class="at">color =</span> Variable)) <span class="sc">+</span></span>
<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>  <span class="fu">geom_density</span>() <span class="sc">+</span></span>
<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a>  colScale <span class="sc">+</span></span>
<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Density Plot by Group Average&quot;</span>)</span>
<span id="cb13-11"><a href="#cb13-11" tabindex="-1"></a></span>
<span id="cb13-12"><a href="#cb13-12" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(x, <span class="fu">aes</span>(<span class="at">x =</span> value, <span class="at">color =</span> Var2)) <span class="sc">+</span></span>
<span id="cb13-13"><a href="#cb13-13" tabindex="-1"></a>  <span class="fu">geom_density</span>() <span class="sc">+</span></span>
<span id="cb13-14"><a href="#cb13-14" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb13-15"><a href="#cb13-15" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Density&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-16"><a href="#cb13-16" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-17"><a href="#cb13-17" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;Density Plot by Sample&quot;</span>)</span>
<span id="cb13-18"><a href="#cb13-18" tabindex="-1"></a></span>
<span id="cb13-19"><a href="#cb13-19" tabindex="-1"></a>p1 <span class="sc">/</span> p2</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAYAAAD7V9WfAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAMAAAAAAHdDvQgAAEAASURBVHgB7J0HfBzF+fd/13RqtuXee+8Y3DDY2BgMGNOb6e0fCIHQwxt6TwgJvQcSEnpoBgymuoJ7A2Pj3nDvVbo7Xdn3edasdKdinaQ76crv8Ue+3dnZ2ZnvjHTPPjPPMzZDBBQSIAESIAESIAESIAESqCEC9hp6Dh9DAiRAAiRAAiRAAiRAAiYBKqAcCCRAAiRAAiRAAiRAAjVKgApojeLmw0iABEiABEiABEiABKiAcgyQAAmQAAmQAAmQAAnUKAEqoDWKmw8jARIgARIgARIgARKgAsoxQAIkQAIkQAIkQAIkUKMEqIDWKG4+jARIgARIgARIgARIgAooxwAJkAAJkAAJkAAJkECNEqACWqO4+TASIAESIAESIAESIAEqoBwDJEACJEACJEACJEACNUqACmiN4ubDSIAESIAESIAESIAEqIByDJAACZAACZAACZAACdQoASqgNYqbDyMBEiABEiABEiABEqACyjFAAiRAAiRAAiRAAiRQowScNfq0GD1sx44duOeeeyJKc7vdaNSoETp37oxzzz0XLpcr4nptnDz44INo0KAB/vjHPxY9fufOnWY9ixKqcFBW++12O+rUqYPmzZvjnHPOQZs2bYpKvvvuu83za6+9tigt2oOK6jt37ly89tprsJ4RbbmVzWcYBpYsWYIffvgB06dPx6+//oqePXviqKOOwmWXXZYQ/V3ZNln5Fy1ahBdeeAHdu3fHzTffbCXzkwRIgARIgARSloBNvtiNZGvdqlWrTEVTFa569eqZ1Q8EAti7dy+8Xi/atm2Lf//73zj++ONrtWm9evVC69at8eWXX8Lv9+O0007D0KFDTWWtOhUrq/1a3v79+80fVUZffPFFWApn+/btTUXtww8/jPqx0db33XffxUUXXYT58+fjyCOPjLr8ymTcvXs3LrnkEpNjw4YN0adPH1Ox//nnn7FixQoMGDAAWo+OHTtWptiEyft///d/ePPNN836bNy4EY0bN06YurEiJEACJEACJBAPAkk9BX/DDTdgw4YN5s+WLVtw8OBB/O9//8O2bdtMpUgthbUpH3/8sakIah08Hg++/vrrmFYnvP3KYd++fVi8eLGpiOk1tRJWVeJR36rURd+PLrjgAnzzzTd46KGHsHXrVkyaNAmqTC9fvhzKeNmyZbjmmmuqUnyt35Ofn2+O2VtuuQWhUMh8car1SrECJEACJEACJBBnAkmtgJZk43A4cP7555uK3vbt23HTTTeVzIJgMIhffvkFX331FdatW1fq+qZNm7Bnzx4zXRU4VRrLyqcZDhw4gO+//x7ffvst1HJVUnJzc5GdnY3CwsIiZVDL1vJ8Pp/5qUpjSdFyNY/WtbKi09LabrUI63T14USVdq377NmzoYqQJWXVNxpDubbtu+++w9KlS01lKrw8bU9V2vrZZ5+ZZd5+++2499574XRGrho566yzoNdUKV24cKH5SG2LPk/rrAq5TnGHi7ZP86plWvOVFH2R0XS1AoeLWtjD8+tYUeusPsdaHqDcKyMffPCB+eJ06aWXYuTIkXjllVci2KlFX5+pdSopVn1UcbUk2vGt5U2ePNkcw9a9+qkK/pQpUzBx4kTzxS78Wvix9qX2tb74qGzevBm7du0Kz1Lh71pEZp6QAAmQAAmkFwH58kw6WblypS4bMO68885y6z5ixAhD1oRGXJdpYqNbt27mvaKsmp8nn3yyIV+6RflkHZ7xhz/8wRDFxryuz9Gfiy++2BCFpCifKA5G3bp1I8o69dRTDfkSLsojyqCh5YuyE1GWlqf59P6zzz67KL91IGsajRYtWhiizFhJEZ8Vtf+ll14yn/fPf/7TvK9du3aGrAstKqOgoMC46qqrDJvNZlgcZDmDYeUvq756T1nyzjvvmM+67bbbjMzMTEMURPNcpuONNWvWmLfovVVtq1gGzfLCuZashyhihkzHFyW/9dZb5j2yrtL8VN7PPvuseV0sqYby0DSr7SeccIIhilTR/f/5z3/M6+Fl6sW77rrLTJeXBzOvjhXtq2OPPdaQNceGvGwYsiSk6FlFBR7mQO+Vdctmjv/+979m+RMmTCi6QxRcQ9Y3G9dff31RmnUwatSoons1LZrx3alTJ7MsrbsyaNKkiTmuRYE0Ro8ebabJEg7zU6/r74EowdYjDVHezTGr/Wzxu+6664wePXoYV199dVG+aOpSlJkHJEACJEACaUdArTdJJxUpYNogVYj0C1Sm4832qQIj6wcNWT9ofPrpp+YX6bRp04yWLVsaYnkqYqBfzPrlesYZZxh6XSybxuWXX26WJdO+Zj6ZnjaysrLML+L169cbqmBZSsv9999fVJalgKoiuXbtWrMMVWLEscfMI9PGpnIhlsOie8QyZeTk5Bh33HFHUVrJg8O1X5WFvn37msqlWP7MW0sqoGIhNRWml19+2ay7WIuNK664wqyfsimvviXroeeWAqoK7Pjx4817xUnIEGcoQ5VQsc6Zt1W1rYMHDzbatm1rlhHtf5YCqi8gshbWeOaZZ8x+1L5SRfiYY44xZPreVLzEEm4qYf379zfEemg+wurLaBRQHWNidTfEam2OqT//+c+mYqaKbkWiddD7H330UTOr9r1YzQ1ZKxxxqyxBMF+mVPmzRBVGVQCte6Md36qAZmRkGKeffroh62YNWXtqFqkvKPoCoTzEgmyIFdt8SdH6qWJsiSrCOj71d0FfyH766SdT+dR8lgIabV2sMvlJAiRAAiSQfgRSVgH9y1/+Yn65yzS02aviNW+ey5R6RC8//fTTZvrUqVPNdFVAVVENt/iphVS/YC2Lq0yJmucPPPBARFnvv/++MWfOnKI0SwHVBJmyNO955JFHiq7PnDnTTLMsj3rhjTfeMNNkmUBRvpIHlgKqiqY4sJg/+uV/5plnmoqf1lWtuJaEK6CquKjls6RFTZXOrl27GuI4Zd5WVn2t8sI/LQXUUj6sa6+//rrZDpmmNZOq0lZVcFRZUstcSRk3bpzx/PPPG88991zRj0wpm9ksBTSctV74/e9/byr84dZOTVclTJlZLxiVUUBVyVXl0xJVYnUMnXjiiVZSuZ+qrKq1UZZ6FOW58sorTcUyPE2VQq2fKviW/OMf/zDvtdoS7fhWBVSVcFUyLdE6yzIGQ19IwkXL1udKhAMzWa2xeq7PChcd85pujYFo6xJeBo9JgARIgATSi0Dkgjr5FkkV0bVsKuo1rbJgwQKIhcdcq6drQC2x8uk6wWHDhpnJoohBLJxWFjRt2tQM82OtwxOLHMSKBlFAoWsUTznlFPNHwx+pB3q0ItY9yJIAiMKE3/3ud+ZtooBi4MCBZkieisoRq6rp+a/5RKmETKeaXvZjx46Fro0sS3StogxxiFIXcVmsaTjppJPMcEC6RrKyUrI8KwKBWMjMtY1Vaauu91SPcLFclqqOWDXNtYrhF2QqGMOHDy9K6tevX9GxHqjXvHrQt2rVKiJdlk6Y51pX7cPKiEY10LW+lmj/a5o6Rx1OdK2mWBYhLxHm2lhrfaxyEuUd8lKChx9+2CxClFmzzm+//TbGjBljpuk4sdI1obLjW9cmW6J1/vvf/26OC1k2Ya6RVgevWbNmmVl0HaqKtcZW16qGi0YhyMvLK0qqTF2KbuIBCZAACZBAWhFIWQV09erVplIm1j+zQ9VJSJU0jVlZUmT9mukUZKXXr1/fOiz61C9pVdwsUa9smf6EWD3NTz1WxVQsghgyZIiVrcJPsXhBLGGmk5IqgepMI1a9Cu/TDGLFhFh6o8prZbKcpVRZLSmq7KlipOGcxPJY8vJhz1VxChdL8dc4opZUpa2q6KvHuzoWydSvVRQ+//zzIictvSZrZouuWQcaEzVctO0aGqukaH+rsitTxyUvRZyH9791QV9OSorGftWyNJJA+ItMeD51gFInMP3p3bt3+CXzWMfpfffdZ7746NiTZSB48sknTachffHQFyZZzlF0X2XGd0kuWog6o4nVHBriS/terLhFL2RWu/W5KlboM/Pkt/+0zZZUpi7WPfwkARIgARJILwIpqYCqVVOm1DFo0CDT6qldqlYv/QLVwOnh1p+qdreWoUqn/qjVSBUiVRg0ZJBMXUZdrHo/qyKhiqwqoBpA/8ILL4z6/spm1LikKuq1XFLUq1sVKg3or0poZUQ9ssOVQKt86wVAy6pKW1UBVTZqaQ7nEq6MqvJYlugLR7ho2616hadr2C5ZglCkCKrCp2JZ/qy8Zd2r0RZKiubTmKTlKZ+aX+PU6nUdpyWVfY0J+sQTT+CTTz7BeeedZxavyruONU1TS64qzbJOuejRlRnfJbloG2T5hql0apB/WQ9r1kl/j/RlyFJArc0NVMEMty6rF344m8rUpagBPCABEiABEkgrAtHPFycJFlUadOchnS4XR56iWus0oYY3krWDRWl6oNOdqizIOraI9MOdzJs3DxrcXZUilQ4dOuDGG280lU8rHmnJ+y2lRhWdcFFrlHjKm/USByCIc4ipXITnieWxWtt01yhV6sJFuWl7LMWivPqG3xN+rBa9cFGrpSo64ZbRqrRVg9yr1VIVMPEOD39E0bEqZdGIjgG1HGrc0HDR2LEqVttljaR5Hv4iof2mwfZLioYs0nFliawdNkN8qRJXnqjCpy8sqvRpnXQaPvxHY4Iqf1mTWVSEjlFdIqJT+xq6SZVxXVJiSXXGt4bh0npr7Fi13lsKsSrHKmoVV9EXOlV8ZY2seW79995770Uo69Wpi1UmP0mABEiABFKcgFg3kk4sJxz5QjZknZz5o97lsluOIVYa0yFCnXAsD2xtoDrVNGvWzBALn5lfvXzV2UQdMjR8kiXqQBJ+bqVrKBz5gjZPtVz5kjW6dOliehLLtKWhji8yDWloSB9Lwp2Q1NFDvtgNWd9pyFo+wwrlo3k1pJMoHKbzyRdffGHdXu6n1X7LKarcjL9dECtkRBgmdSoR5dB0qtKyRKE2ZP2n6d0scU3Nuw5X3/DnWU5IGoLoX//6lyFWZuPVV181owRo6KqSUtm26v0ayUBZKj/tm7/+9a+m97068Wg/yK+oGY5IlEHzcZYTkoaTChf19pe1iqazlUw5G+pMpg5g6nmu4bDUEUtFn6fP0v7V/tBy9breq8+y+k7Hip4rO404oD+yHth0YtMxUZ6IddO8LzzcUsm8Wo6WLcpy0SV17NJxounhzm6aIdrxrU5IGuEhXPR3QceDKJ+GrN80xBJu6LPU21097UX5L8quEQX0+aIAG6K4G7LdrKEREPR+dYhTibYuRYXygARIgARIIO0I6PRa0omlgOkXofWjoZNkitXQ2Ihi3SuzTaoUaHxQ/bLU+1QhVc9dmVIsyh+NAqqZVWnT2IfW81Vh0ZiJ4SGVwhVQvUcVP82n92j4GktUoVHPe62PpQRZ18r6tNpfVQVUn6FRAlTx0rqokqGK3OTfvMitZ5ZXX+u6floKqCrV2gYtT/tCnKoiIglY91S2rdZ9qhRqaCpVkiyGGntTLLrG3/72tyKlUPOXp4DqNXHCMo4++uiifpN1r6Z3fHiII82nypW+UFjt0VBLqljrebgCqi9BqpzqmNIf5WhFXtByyhIdF/oiFB5XtmQ+9cjXZ8ne8EWXrDBNVqSCogu/HUQzvstSQPV2bZtYgIu46NiWYPSmQi3W14hH6Yubhq1SxVOsoqaCrn0iswBF+aKpS1FmHpAACZAACaQdgaTcC16+mKslOt2o6x3ly9icJq5OYbpOTnfD0bLkS7jCotTDXJcHhDttaJqum9NpZlGmKiwjVhl07Z6ui1XnI2vauWTZZdW3ZB7rXMtT5y9tS3nrH2PRVl0uoPXWaelomFv1C//UNa46Fa5liOIYfqnoWP4amO3RpQPha06tDOq8pssvdDpdd4HStpXllGTlr6nP6oxvXd+pfWc5kYXXWdsnVmOzzeHrbnUJgo4f3SpVd6sKl+rUJbwcHpMACZAACaQWgbRUQBOtC2XnInP9nYa+UUU2lSWV2hqugKZyn1lt03WwqohrqCqN9mCJrrXWME66La3s7GQl85MESIAESIAEyiVQtvtwudl5IZYE1Nqp4XbUaqhOTKmsfKZTW2M5RhKpLLV63nrrrXjsscfMuKPqaKWOWerUpeHAqHwmUm+xLiRAAiSQ2ARoAa3F/lGLkXo6y7pB03NfwzClqqRiW2WbT9MrPDw8VKr2X3i7VqxYAdlRDOo9r1ZgDbyvPxQSIAESIAESiJYAFdBoSTEfCZAACZAACZAACZBATAikXBzQmFBhISRAAiRAAiRAAiRAAnEjQAU0bmhZMAmQAAmQAAmQAAmQQFkEqICWRYVpJEACJEACJEACJEACcSNABTRuaFkwCZAACZAACZAACZBAWQSogJZFhWkkQAIkQAIkQAIkQAJxI0AFtARa2R4Re/fuhe7qQ4kkIFtQQoORUyIJcMxE8gg/0zGjfCiRBDhmInmEn3HMhNMoPrbGTDAYLE7kkUmAY6bsgZDoY4YKaIl+019uj8cD3YaREklABzP/+EUy0TOOmdJMrBQdM3yZs2gUf1pjhmyKmVhH+pJLLhaN4k9lwu+mYh7hRzpm+N0UTuTQcaKPGSqgpfuMKSRAAiRAAiRAAiRAAnEkkFAKqO6Frj8UEiABEiABEiABEiCB1CWQMAqorru84447MHPmzNSlzZaRAAmQAAmQAAmQAAkgYRTQxx9/HA0aNGCXkAAJkAAJkAAJkAAJpDgBZyK0b/z48ahfvz5atWoVVXXi6SBkla2f1nFUlUqDTBYTconsbIuHxSfyanqfWUwsRulNo3TrLT6lr6RvisWEYyZyDFg8LD6RV9P7zGJiMUpvGsWtt3hYfIqvxPbIZrNVqcBaV0A3btyIjz/+GC+++CJef/31qBqxdevWqPJVJ9OOHTuqczvvTUMCHDNp2OnVbPLOnTurWQJvTzcCHDPp1uPVb288x0zjxo3hdFZNlazaXdXnYZagoRMeffRR3H777cjKyoq61Hr16kWdt7IZNWxMQUEB6tSpA7s9YVYoVLYZccnv9XrNgVbVwRaXSiVAoRwz5XcCx0zZbDhmyuaiqRrT0eFwVPlLrfySk/uKNWZyc3NNPsndmtjWXseMfl+7XK7YFpzkpamOlZ+fj3iOmeroSbWqgK5btw5Lly7FLbfcYnaz/oKpKVe1dVVKy5Ps7OzyLlU7Xb8wVQFVhVj/CFKKCWhMMf0Fd7vdxYk8AsdM+YOAY6ZsNtaYyczMpKJVApFOF+pLLv/ORIJRJcv6bqIRIJKNjhn9vtbfJ0oxAR0zqoCqPpOIY6ZWFdBOnTphypQpRbR0Gl6VyyuuuKIojQckQAIkQAIkQAIkkKgECleswIF/vwZDFL7sMache+QJiVrVhKoX55gTqjtYGRIgARIgARIggWQh4J05A9suOA++efMQEJ+WXTffiP2vvZos1a/VetaqBbRky//whz+UTOI5CZAACZAACZAACSQcAVU4d4rC6e7XD41fegXIyMDexx/DvueegXvAQLj79k24OidShWgBTaTeYF1IgARIgARIgASSgsCevz4KW1Y2Gj39LGziG6E+LHm33g5Xp87Y88hDSdGG2qwkFdDapM9nkwAJkAAJkAAJJB0B7+xZ8E6birw/3QF73bpF9beJo269G2+Gf9lS6PQ8pXwCVEDLZ8MrJEACJEACJEACJFCKwP5/vQZnh47IHn1qqWuZw4aZ1w688d9S15hQTIAKaDELHpEACZAACZAACZDAYQkULl8On1g36155lTntXjKzTsXnjr0Q3uk/IMjNJkriKTqnAlqEggckQAIkQAIkQAIkcHgC+R++D3teHrJPLW39tO7MPnGUeVjw7TdWEj9LEKACWgIIT0mABEiABEiABEigLAIa6zN/whdmvE+bK6OsLGaao1EjuPsPgOfrr8rNk+4XqICm+whg+0mABEiABEiABKIi4Jk8Ecb+/cg5+9wK82eJFdS3YD5Ckp9SmgAV0NJMmEICJEACJEACJEACpQgUfPklXF26IqNz51LXSiZkHj0EkG1CvXPmlLzEcyFABZTDgARIgARIgARIgAQqIBA6eBCe76ch++RTKsh56LKrXTs4mjaFb/bMqPKnWyYqoOnW42wvCZAACZAACZBApQl4Jk8C/H5RQE+O+l73oMHQmKGU0gSogJZmwhQSIAESIAESIAESiCDgmfgdXF27wdm6TUT64U4yRQENrF3LcExlQKICWgYUJpEACZAACZAACZCARcAoLIR3xgxkHX+8lRTVZ8YRR5j5Chf9FFX+dMpEBTSdepttJQESIAESIAESqDQB76yZMDwFyBpeOQXU1aatGTPU99OPlX5mqt9ABTTVe5jtIwESIAESIAESqBYBz9QppkNRRo8elS4no09f0AJaGhsV0NJMmEICJEACJEACJEACRQS833+PzKHDis4rc+DuKwro4sUwAoHK3JbyeamApnwXs4EkQAIkQAIkQAJVJeBfsxrBLZuReczQKhWR0bsPDK8X/tWrq3R/qt5EBTRVe5btIgESIAESIAESqDYBtX7C6UTm0UdXqSxX90PT9v5lS6t0f6reRAU0VXuW7SIBEiABEiABEqg2Ac/0H+A+oh/sOTlVKsuRlyfrR5uhcCkV0HCAVEDDafCYBEiABEiABEiABH4joOGXCmU/98whsq1mNcQlzku0gEYCpAIayYNnJEACJEACJEACJGAS8Inyafh8cOu+7tWQjG7dUShT8IbsDU85RIAKKEcCCZAACZAACZAACZRBwDtzBmx16yKjR88yrkaflNG9O4z8fAQ3boj+phTP6UzG9uVLJ8ZL/LLPq0pBQQHsdurn4ZwLZSoiGAwiwFAS4Vhka2COmQggYSccM2Ewwg6tMePxePh3JoyLHvrE2qR/Y/h3JhKMxYNjJpKLnunfGZvNZn4/lb5avZSCGdPhPKo/CuR3tToSbNXavP3AokXIaNCwOkVFfa81ZlSfcTgcUd9XmYxZWVlV/huWlApovEAqdFWwVPQZVEBNFEX/KQ/9iSf/oocl0QHHTPmdxTFTNptQKGRe4O9TaT4cM6WZaArHTNlcNFWVT/1eivV3U+jAAQSXL0fW6WdWu2x7q1aAKGuhdWulrBPKb0wMr1hjJh5sYlHNpFRAMzMzY9H2cstQC6vb7a72gCv3AUl6Qd+mXC6XySZJmxC3anPMlI2WY6ZsLlaq/p1xSngXSjEBfaFTJsqGUkxAlSwVjpliJtaRjhlVsmKtG3jE+imLNpF7zLFwxUDvyOjYCcbadTGvp8Wh5GeijxnOMZfsMZ6TAAmQAAmQAAmkPQHvnNmwN2oEV4cOMWHh6twZgdUrY1JWKhRCBTQVepFtIAESIAESIAESiCkB3+xZyBw0OGZlusQC6l+3DsZvfgMxKzhJC6ICmqQdx2qTAAmQAAmQAAnEh0Bwzx74V62Ce8DAmD1ALaDiXYfAr+tjVmYyF0QFNJl7j3UnARIgARIgARKIOQHfvLlmme4BA2JWtlMsoCqq2FIAKqAcBSRAAiRAAiRAAiQQRkAVUEeTJnC1aRuWWr1DZ9OmsGVlyzT82uoVlCJ3UwFNkY5kM0iABEiABEiABGJDwDd3Tkyn361aOdu3Q2AtFVDlQQXUGhX8JAESIAESIAESSHsCwX174V+5Eu7+sZt+t6C62rWnBfQ3GFRArVHBTxIgARIgARIggbQnUDh/vsnALTsgxVqc7TvQAvobVCqgsR5dLI8ESIAESIAESCBpCXhl/ae9YUO42rePeRtc7drBkK0xg9u3x7zsZCuQCmiy9RjrSwIkQAIkQAIkEDcCvvnzEA/rp1ZYLaAqdETiGlBzIPA/EiABEiABEiABEggdPAj/smVxWf+pdJ1tD3nV0xGJCih/20iABEiABEiABEjAJOD7cSEQCsF95FFxIWKXPeUdTZvBz2D09IKPywhjoSRAAiRAAiRAAklHQKffbXXrwtWlS9R19/iMqPNqRmebNrIb0q+VuicVMztTsVFsEwmQAAmQAAmQAAlUloBPPODd/Y6EzWar8NbZywJ4+hMvVm0OoX6uDRcOz8DlJ2TAbj/8vToN71uwoMLyUz0DnZBSvYfZPhIgARIgARIggQoJGD4fChf/LA5IFU+/vz+tEDe8WIBstw3/77xMDO3lxIuf+3DPfz0wjMNbRE0L6IZfYchUfzoLLaDp3PtsOwmQAAmQAAmQgEnA9/MicU/3V+gB/91CP/7+oRfnHOvCHedmFlk8j+zkwANvedGpZSGuGuUul6qztTgiyXOC27bC2bxFuflS/QItoKnew2wfCZAACZAACZBAhQQKF8yHTZyEMrr3KDfvxh0hPPKuB8f1dpqWz/Dp9lMHZmDscRl47SsfNu4s37pZ5Am/fn25z0mHC1RA06GX2UYSIAESIAESIIHDEvDOm4eMPn1hc7nKzffX/3mQk2nD/RdnlblO9PenulE3y4bnPvWWW4azdWvzWro7IlEBLXeI8AIJkAAJkAAJkEA6EDCCQRT+9ONhp9+/nu/HnBVB3H5OJupkl+1opMrp1Se7MXlRAOu3B8tEZ4ZiatIEgQ0byryeLolUQNOlp9lOEiABEiABEiCBMgn4ly41t8gszwHJHzDw4ngvBndzYETf8i2kWvhpg1ymFfSdyYVlPksTHa1aI7CRCmi5gHiBBEiABEiABEiABFKdgE/Wf8LpNKfgy2rruBl+bN5t4IbTM8u6HJGWmWHDWce4MGGuH+XFCHW2akULaAS1Wjrxer2YOnUq5sn6i0AgUEu14GNJgARIgARIgATSkYBvgaz/7NED9qysUs1X6+d/v/PhhH5OdG3lKHW9rIQxYgX1igF00k/+si7DqRbQTRvLvJYuibU+Bb9jxw5cfPHFWLx4MT744APceeed6cKe7SQBEiABEiABEkgAAmYA+iP7l1mTz+f4sX2vgSsPE1qp5I1tmzjQs60DE+TeskQtoIbsOx/cu7esy2mRVutxQCdOnIhzzjkHF110EYKyCPj000/HeglN0FZ2CqCQAAmQAAmQAAmQQDwJ+NesRkgUwbLWf2pQ+bcmFuKYHk50aRmd9dOq6+gBLjzxkRd7D4aQlxtp7yvyhJd1oI68POuWtPqsdQV07NixRcAXLlxoKqGNGzcuSivr4MCBA2UlxyTNWgJwUN5M7PbIAROTByRxIYWFheYSCf2kFBPgmClmUfKIY6YkkUPn1pjJz8/n35kSiPwSoFt/+HcmEgzHTCSP8DMdL7p1pn5WRXzTfzBvK+zcBYES+sWsZQZ+3QHceFoIldU9jmpvICSbIn077yBOPirSaz6UV9985sGVK1HYtl1Vql3hPTUxZnJycqr8N6zWFVCL4P33349JkybhuuuuQ3Z2tpVc5qf+0Y6XWFtoFRQUlBnjK17PTYZylU00++MmQ1tiWUeOmfJpcsyUzYZjpmwumsoxUzYbjpmyuYSPGZ9spVkVCYj/Cdp3gMchFs4S+sX/vnejXRMbujbzlrxU4aNyxFm+Y9NMTFscwtBuJQw3EvAebje869bCX+KZFRYcZYaaGDNZsma2qsa6hFFAH3zwQVxzzTW47bbb0Lx5c4wYMaJcxM2aNSv3WnUvqEPUnj170ERidDl0MFKKCKhV2CUBet3yS0MpJsAxU8yi5BHHTEkih86tMaOzPU7xvKUUE1ADgzLh35liJnqkytXu3bvBMRPJRc90zOj3daYqdVWQzUuWIEt0jvoldIt124L4aV0+7rkwE82aVW2afOSRPtOBqUHD+shwRVpBt4gjklum/huUeG4VmlDmLYk+Zmp9jnmJdPyuXbtMeC1btsTw4cNNb/gyaTKRBEiABEiABEiABGJEILBpk7knu/uo0g5IH/3gR644xZ901OHjfh6uKkNk7ahPVgb8tLZ0UHqn6DyBzZsPd3tKX6t1BXTu3Ll47bXXTMhqFZg5cyY6deqU0tDZOBIgARIgARIggdon4Jsv0+8i7v6RCqjPb+DzOYUYI/u7a1zPqkr31nbUESV29rLSISZVAQ2KApyuUusK6Lnnngudprvqqqtw7bXXYujQoaYnfLp2CNtNAiRAAiRAAiRQMwR8sv7T2aYtHI0inZ8n/ujHQQ9w5pCqWz+1BXa7Df07OzFneVkKqASj37IFRihUM41NsKfU+uKj3NxcPPzww1CnH12/UdXFrAnGldUhARIgARIgARJIcAK+eXPhHjCgVC0/kZ2PerdzoGPz6vuCDOzqxOMferG/wEDdsD3kHWIBRcCP4PbtcMZpHWiphiVQQq1bQC0W6vlO5dOiwU8SIAESIAESIIF4EgjKRjiBDb+Wmn7fsCOEhauDOOPo6lk/rbof1dkh0R1kHeiaSCuos4UooCLBzek5DZ8wCqjVUfwkARIgARIgARIggXgT8M2faz6ipAPS+FmFsu4TOPHI2Cig7ZraUU8snz+uiXRE0jWgKum6JScVULP7+R8JkAAJkAAJkEA6EfDOnQdH8xawLJHa9pBEjtetN0/s50K2u+rOR+EcNX52nw4OsYBGKqD2evVgk0DugU3p6QlPBTR8lPCYBEiABEiABEggLQj45s4pNf0+Z3kQO/YZGDMoNtZPC+QRooD+8msQheJdHy6HPOE3hielzTEV0LTpajaUBEiABEiABEhACQQl/nhg7RpxQBoYAURDL7VsaEO/jtV3PgovuK8ooH5ZArp0Q6QV1CHrQNM1FigV0PARwmMSIAESIAESIIGUJ2DF/8zsX+wBf9BjYMqiAEZL7M9YbzvdrbUDDtG4lqyPVECdLVpQAU350ZZEDdRfgpJm+iSqPqtKAiRAAiRAAglNQKffHU2bwtm6dVE9Nfan7lp06sDYTr/rA9yyDWenFvZSCqiuQQ1u3ZqWsUBrPQ5oUc+n+UFQFj6/N6UQ708rxObdBlzSM0O6O3HTmZlo3ZiG6jQfHmw+CZAACZBADAl4ZRfGktPvX4jzka7VbNkwPt+5Pds6Su2IZHrCSyzQ0M6dcDRpEsMWJn5R8aGc+O1OqBqqxfO65wrw9Cc+9JLAt/ddnIlrTnGba0UufOwgZpexg0JCNYCVIQESIAESIIEkIRDcvRuB1asiFNDNuw7F/hwdB+unhUW/3zftMrA3v3jnI6dYQFXScR0oFVBrZNTSp+43e+NLBVi+MYjnr8/Go1dk47RBGbjiRDf+d2euuRPDra8UlDLb11J1+VgSIAESIAESSGoCOv2ukhnmgPTlPD8yZOZRwy/FS3q2OeTYFL4O1CFrQFUCaRiMngpovEZalOU+8q4Hy0T5fPa6bAyS7brCJTfLhqeuzUb7Znbc+XoBDsg2XhQSIAESIAESIIGqEzi0/rNZxPrPL+f6MbSXE/q9Gy/RgPQa4H75hmILqKNBA9jcbtkNKf1igVIBjddIi6LcLyTcw1fzAvjz+Zno2yFS+bRuz8yw4bErs7HnoIHnx3utZH6SAAmQAAmQAAlUgYB3zmy4Bw0qulMtkuu3hzB6QPysn/owu92GLi0dWFYqFJN4wm+hAlrUITyIL4Ed+0L4+4dejDzCidMHyyvRYaSVOCH97mQ3xs3wY6kEsqWQAAmQAAmQAAlUnoC5//vatcgcWKyAqjGoXo4NQ3qUbQiq/FPKv6NrK7s56xmeQ9eB0gIaToTHcSXw5MdeMyaYWj+jkYtGZJjBcV/8nFbQaHgxDwmQAAmQAAmUJKDWTxXLAhoIGvhmQQCjjnTC6Yjf9LtVD40HukUi3ezLL15Sp+tAA1u2WFnS5pNT8LXQ1fNXBvDdwgBuOD0TebnRdYH+YvyfWEFnLQti0VrZToFCAiRAAiRAAiRQKQK+2bNl7WcbOJs1N++buTRgKoOnSvD5mhBVQFXU8dgSZ/PmtIBaMPgZPwKGYeDZT71mQNozBlduvcnJ/V1o2ciGN74rjF8FWTIJkAAJkAAJpCgB7+yZRdZPbaLG/mwjy9w0RmdNSAdxKtY43+EKqEMUUMNTgNC+fTVRhYR5RnTmt4SpbvJXZNJPAfzyawg3npFpLkiuTIscsoB57HEZmLY4gI07i73oKlMG85IACZAACZBAOhIIbNhgWhozBw02m68xuL+X79N4xv4syVlnM1UJXbmp+Dtcd0NSSTdHpPivuC1JP43P1fr56pc+8Xh34GjZ5UhF03zz5sEzeSIKlyxBaM8e2OvWgbvfUcg57zy42rSNIKYxQl/+wocPvi/ELWdFt340ogCekAAJkAAJkEAaEvDOnmW22v2bAqpbbxbKirZTZHaxJqVzC4e50Yz1TCsYfVDXgXbrbiWn/CctoDXYxVMWBbB6S8jc5cgIhZD/yThsPf1U7LjqchR88Tkc9fPEM2+gbMfVFAc/fF+ujcG+55+N2CM2J9Nmhor4YrYf/kDxIuYabAYfRQIkQAIkQAJJR8A7ayZcXbvJd219s+7W1pst4rT1ZnmAOre0Y922UNF3uO5JD5st7RyRaAEtb4TEIf2NiYXo0caOvt4l2Hbe/fCvWIHMY45F/Tvvgfvoo2X8FXvghQoKsP+Vl7D/n6/Av2YNGv7t77C5Dr2lnTkkQyygfuh0/klH1eybWxywsEgSIAESIAESiCsBc7ZRLKA5Z5xlPsfaevPusTU/k6ixQIMyA79mawhdWzlgczrNfeDTLRQTLaBxHfLFhS9cLWs/1/hwy+5XsP3Si8wLTf7zBhq//E9kDhkSoXzqRXt2NvJuuQ0NH/87PJMmYs/DDxUVpoO3e2s7PptFZ6QiKDwgARIgARIggXII+JcuRWjvXvP7VrNYW2+eEMetN8upCjqJBVRl5aZiT3iHeOUHtqZXKKaktIDu3r27vH6tdnoweGhA7JWBGm6RrG7B740vwMNL70bDnfPhuvRyuC6/Avny1pNfUVsGDkbGzbci/4m/w9+hA1ynn2FWZUQvG176ShYyr9+NhnWqW7vo7g8EAuI4ZUd+fn50N6RJrniNmVTAxzFTdi9aY2afeL3G8u9M2U9LrlSOmbL7KyTLtlQ4ZkrzsX6fCmTmsDwpnPgdILOI+e07oEC+dz+b6cDgrgYKPXuw21PeXfFLb1jHgZ/XFGBI54PmQ4KNGkGdpGKp31hjRvUZ/e6Oh9SrVw8OR9UiCCSlAtpA9k6Nl3i9XuwRR6C8vLwqQy1Zty2/7sHJ79+IdoUb0OiFl5B17NCSWQ5/fsWV2LV8OTwvPo+GI08w9689a2gIr3x9ELNX5+CS492Hvz9GVw8ePCi/vy64Zd9aSjGBeIyZ4tKT+4hjpuz+s8aM/vF2yosopZiAvuAqE/6dKWaiRz6fz1ROOGYiueiZjhlVgjIzy59O3/7jQriPPAoNJeTRT2sC2Ly7AHecl40GDWpnGVvnlvnYtAfy/ByzQXvbtkX+Tz/Keez0G2vMqD6TiH9n4qMSlx4faZtiFBZi2x+uR3PPRuS9/K/KK5+/kat/192w5eZiz2N/MVM0gL160n81z5+2bNlwEiABEiABEqiIQMjjgW/hgqLpd3U+aljHhsG/RaOp6P54XO8knvCrNkeGYgrt3AnDnz5L66iAxmNkhZW5+5GHUe/XxZh2wZOo379P2JXKHdrr1DHXhHqnTYV3xnTzZnVAWr4xhPXbi9eRVK5U5iYBEiABEiCB1CbgmzsH8PtNp1+fX7fe9EM3dtHY2rUlHZvbsWt/8ZacuhuSSnDrttqqUo0/lwpoHJEXfPM1CsZ9hNc73YzBFxwKfFudx2WPOQ2unj2x99mnzWKG9ZZpKpk90G09KSRAAiRAAiRAAqUJqNHG3rARXF26QsMh5nuBMYNqZ+rdqp1aQFVWbT5kQFInJJV0ckSKiQK6f/9+Exz/KyYQkvWSe/7yCNa1OwbL+p+PPu2rv85LnRXq/eEG+CVgvWfKZGS7bRgiUwjfytschQRIgARIgARIoDQB7/QfiqLNjJ9diG4SRcZSAEvnrpmUdk3t4oAIMza4PrHIAqrB6NNEYqKA3n777RgoAdRffPHFmHpwJXMf7BOHIVVC/97yNjNwfKzakjXsONMKuv9fr5lFjpQQEhrcntPwsSLMckiABEiABFKFQGDTJgTWrUOmOP9u2xPCnOVB6I6CtS2ZGTa0bmSX7+9DFlB73bqwSfjFdNqOMyYK6D333IPTTjsNTz/9NFq0aIHzZAvJL774AlZohNru6Jp+fmDbNhx8711sPv4ybMloYa41iWUd6lx+JQrFo8+3+Gcc29MJlxhXJ0tQegoJkAAJkAAJkEAxAe8P08xdhjKHHAN1PnLKzLeu/0wE6SDrQNeIAckS3RPe3I7TSkjxz5gooG3atMG9996LFbKzz+TJk9G4cWNcffXVaNWqFf70pz9h2bJlKY4xsnkHXvsnbBkZeLvRWBzZyYFm9WOCuegh2SeOgm7ddfDtt6Bbcw7q6sQk2dOWQgIkQAIkQAIkUEzAM20aMnr3hl1Cnn0m0+/DxXeibnbtOR8V1wxo30wU0K3FTsQ6DU8FNJxQJY+Pli0l7777btx5553mnWoV7du3L0488USsEzN4qktIAkvnj/sYjnMuxqxNORh1ZOzftHTbrpxzz4c6OQX37cWIvk4s3RAypxdSnS/bRwIkQAIkQALRENAwiOoBn3nsMMxfGcSmnQbOOLr2p9+tunds7sB+iZ2/c/8hK6hDFNAA14BaeKL/VEek119/HSNHjoRaRF999VXo2tCNGzdig0T3b9asGUaNGhV9gUma86Aon4bsGDSryznQdyxVDuMhOWedLe5yARR89hmGyjS8Lmae+jOn4ePBmmWSAAmQAAkkHwGvKJ+GxADNGjoMn8rW1c3q2zCwa9V27YlH6zuIBVRlrewJr+LUKfg02o4zJnPDTz31FJrKlPCtt96Krl27YtasWVi8eDFuu+02M71Jkyb44x//iFWrVkF3AEllOfi/95B1/Eh8ua6BOf3eoE5MEJdC5hTemfJLlf/xh6gvz+jb3iEKKKfhS4FiAgmQAAmQQFoS0LjZGn7J076HLFML4PTBGQm19W3bJnZoKFJrHahaQFVhDsrWmekgMdGO6kiQdLV+bhHTsXrCDxgwoBS7bt26mQro4bbKKnVTkiXoTgvBjRuA0WfLVl9BsX7Gfvo9HEnOmWfBL0p94dJfcFwfpznFcNBjhGfhMQmQAAmQAAmkJQGPKKBZw4bJjoEB+GWp5WmD4/udXFnIGS4bWoV5wheHYtpc2aKSMn9MFNAdO3YgKyur1D6sqpC2bt0aBQUFqCshBjp06JCUkKKtdP7nn8vbVkPMzBmAkOiBGig+npJ13HGw162H/E8/xbBeTgTFij9jKafh48mcZZMACZAACSQ+Af+a1WIQ2ohMCV04bkahGTM71g7BsaCgnvDWFLxaQFXSxRGpyhqSrvl89tlnTVifigI0e/Zs/Pzzz+a59d/SpUuxfft2OMVpJtVF1316vv4S2WNOx9TFBrq2ssfc+70kQ5srA1knn4KCryag9Z/ugJrzv1/sj4vjU8ln85wESIAESIAEEpWARyLywOXCqhaDzFjZ153qTsiqtpeA9B+vPrR8ztGkqRkyKl0ckaqsGapFc+vWreZ6z/Xr10OtoOpwZInu2tNQrIHPPPMMMiQkUaqLb8F8qAd8xgmjMPu9AC4dWTODPWf0aOS//x58c2aLFfRIfDKzUCyhRq3ucZvqfc32kQAJkAAJJDYBz+RJyBw4CB8vcKFxvQCOlVnCRJT2zRzYl1+IPQdC4s/hhEN8ZmgBjaKnnn/+eTPXk08+aTofnXrqqVHclZpZdGtMe14eFuf0hrfQi2N61MxgzzjyKDMmaMGXX2Lo5QPx5qRCc/3pkZ1q5vmp2ZtsFQmQAAmQQLISCO7ejcJFPyHzT/fgu4V+XDYyI2GNMhoLVGXtNlVA7dA94dNlP/gqaylr166F/hx//PEYLVa4TbLd1cSJE8scrxqaKdXFO2WKudZkxrIQ6uXY0F32mq0JUUtztkzDa/in3nffKwF2gR8WB8QDv8pdWxPV5jNIgARIgARIIC4E1CAEw8DU3GPhF7eIRIr9WbLBuie8iq4DPbLToT3hA5s3lcyWkudV1lLeeOMNPPLII/D7/VALqMb9LE8MGQipLH4JsB/Y8Cvq3XwLZoq33dHdHbBrbIUakqxRJ+HAf/8D/7w58uwj8f2SAG48s4YezseQAAmQAAmQQAIR8Ez8Dq6evfDu4voY2suOpjHejTCWTdU94Zs3sIU5IrWAd/68WD4iYcuqsplO93/Pz883G6ahl3w+X7k/Cdv6GFXMN3umuXD4QPeB5iAa3K3Ken2VapTRu49MwzeDR3ZGGtrLhXViyt+4o3h/2SoVyptIgARIgARIIMkIhAry4Z01E7v6jMCv8j143tDE90HRdaBrf9uSUz3hQzt3whDjXqpLlRVQh8NR5FykXu5r1qzB6tWrzbTp06fjwgsvhCqpqR54XgeIVwLvu3r0wLwtOeZ4GSh7s9ek6DR8lmx16pk0EYO72GSti0zD/5L6g7cmGfNZJEACJEACiU/A+8MPgGzB+Yl9mBkZJpF2PiqPnnrC6xpQFTMWqMwaB7dtKy97yqRXWQENJzBnzhz07t0bX331FXbt2oUzzzzTXBOq4Zmuuuqq8Kwpd2yEQuKBLnvNDhqMOcuD0PUcjevFBGulWGWfcCJCsnuCe9kC9JFdkb6XdaAUEiABEiABEkgnAp5vvwHatMenW1rj3KGuhNr5qLx+UL1hxz4D+V6JYCPbcaqkQyimmGhKuv7zyiuvxM033wxVOnU6/ptvvjGdkvQ8la2g/uXLENq/z1RA564IYECX2tlnNqPfkbDXrw+POIJpuIkFq4Io8KX22tvyfpmZTgIkQAIkkH4EDNE9PFOnYln7kciSmfcxAxN/+l17yfKE1+Vz6bQbUkwUUJ1+V294nQr+UsIB6bHGCW0uaxncbjf2VrCvqToy6f7xP/30kziuJZfS5FuwAOJxhO0t+5hvMAO61Oz0u/UnxiZ1yBp+vDkNf6yEgArItmOzl9EKavHhJwmQAAmQQGoT8M6YLnupF+Dt4DCMGeRCblbNOQNXh2y7pocMV+u2BWV3w7qwZWeLBXRzdYpMintjooC2adMGU+WtQ3c9UsvnmDFjzMaPGzcOrVq1QrNmzcqFocHsx44dixkzZmD8+PG4+OKLTQtquTck2AXd/90l+9wv2HQo8Hy/TrVjAVUsWRLuKiiDttW+FWghXnU/iDc8hQRIgARIgATSgUDB11/B27g1lri64IJhyWH91H7R0I15uTbTgVjPdRo+HYLRx0QBvfHGG/HOO++gZcuWptXzkksuwdtvv206Il133XXKs1z54IMPoAHsb731VtNpqVOnTvj222/LzZ9oF3QHJHe/o7BwdRAdJKBsXk5MkFapmZmDj4YtMxO6BZlOw08XBTTZLMpVajhvIgESIAESSGsC5vS77H40rdEJOLanE22a1J4xqCodYToiSSxQFWeLFghsTn0LaEzmi/v164cVK1Zg8eLFGDRoEHJzczFgwAAsWbIEXbp0OWxfXHvttRGLhPfJdpYej+ew9yTKxYAE3w/JFqRuaf+CGbLVlwz62hSbLHdwHz0EnimTMPSBa/D+ND9++TWEnm2T6xexNhny2SRAAiRAAslHoHD6DzAKCjAhdyTuHJE81k+LtDoiqe+GioZi8s2dY11K2c+YaUxNmzaF/lhSkeJp5QvfJ37SpEnmfvKnnHKKdbnMzy1btpSZHstEXU5QkYR0twWR1XVaYeseA+0bHsCWLXsrui2u10PijBR68h9o4f0Fma62+HLWHjTIYEimuEL/rfBoxkxN1IPPSB4CO+QFlkIClSHAMVM2reDn47G9Xls4OrRHyzo75Lu47HyJmtogy4kNOzKwYeMW2HNyERILaKx0nXiOmcaNG0NDcVZFqnZXOU9SWBqcvuS0b+fOncu5ozj5s88+w1tvvWXuqqQW1MNJnTp1Dne5WtfUIUq99nNycsS36PDT6QVrVqOwcROsMdrKMw0M6JaFOnVqd9FzaOQJ2PfUE8hZ/CP6d26H+Wsy8PtTM6vFxLpZoxto/NeqDjarnFT7DAQCptU+mjGTam2vqD0cM2UT4pgpm4umFkoMR/3by78zkYw4ZiJ5hJ/59uxBvjgyf9fqClw43Cnfw/HTEcKfG8vjLq0NhMQJ+0BhLpq1bYt8+b7NCYpTUl5elR9TE2OmIj3pcJWPiQKqiueoUaPMKfiyHlZSIS2Z580338TXX3+N5557LsKKWjKfdV6Rgmrlq8qnKp+WAqrK1uEkX0IwZfbpjeWbnWiaF0D7lgkw6EV59/TqjZDsBDHi0rF4+B0vCgLZaJJ3eGX6cO0Mv+ZyuczIBuFp6X6s40WXjagCWtGYSUdWHDOle90aM9ni7UpFK5KPGjGUiUZQoRQT0Jc5/TvDMVPMxDoqnPAFbP5CLO10Em47OhdOR+0agqx6Veazeztd/3kQ2w5komOHDtB9JjNlSWKGOHJXVRJ9zMREK3n00UehmrY6Dy1fvhwrV66M+DkcvAkTJpjxQl966aWolM/DlVWT1wxpb+HSpcjo1QeL1gbN4O81+fzDPStr+HD4Zs/CMZ0OecHTG/5wtHiNBEiABEggmQnsHz8BK+r2xKjTOiSl8qnsm9W3we2C6Qmva0BVUj0YfUwsoKpw3nDDDTjhhBNMaJX579///je2yZZT6glvyTnnnIObbrrJOk3IT//KFeZ2X0a3Xlj1YciMOZYoFc08bjj2PfcsspfMQa92AzD15wDOPib5FmUnCk/WgwRIgARIIDEJBER/sP84DzN73obbhiTv95zGUVdHJA1G72gi/jQyA6thFVNZYqKAqhf83Llzq8Tpww8/rNJ9tX1ToXj8q6yu0x1BsZz3aR8TlDFpVkbXbnA0bSY7QkzBMAnN9NpXMnUjuyJluZNvWiImQFgICZAACZBAShLY8dFnCNocaHLmqKT/jmvbRBXQIHRjGYc4dQc2b0rJPrMaFROt6ZprrsHo0aNx3333YeDAgeZaOOsB+jlixIjw05Q4Llz6C5xt2mLxziy4nD50aRmT1QwxY5M5bBi806Zi2O/uxYuf+zB7eQDD+4h9n0ICJEACJEACKUJg94efYHmjITjjxIZJ3yLdEclaMqexQGkBjaJLH3vsMXPt58MPP1xm7oqckMq8KcET/b/8Alf3HliyPijKp3iGJ9ii5yyZhs//4H203r8SLRu2xNRFVEATfEixeiRAAiRAApUgsGv2j6i7Yy085//B3E2oErcmZFbdE77AB2zfK9PwshuSf9XKhKxnrCoVE7PdCy+8YHrnqYdeWT+xqmyilGM6IMka0Izu3U0FtGfbmGCMafPcAwcBGRmHrKC9XfhedkUKhoyYPoOFkQAJkAAJkEBtEfjlnx9hb0YDDL3qmNqqQkyfq2tAVXQdqGkBTfHdkGKiOWmYlUzZAnL27NlQZXTKlCnYLOAOHjxopse0hxKgMP/ataYDkrdtN2yTAPSJuNOQPSsLmYMGm+tAh/dxYl++gZ/WHNplIQEQsgokQAIkQAIkUGUCe3cVoMn8L7HlqFPRMC81lpe1aWyXnSF/84SXKfiQhGEKye5OqSoxUUCDEiz1zDPPxPDhw/HAAw9g2rRpphLaXSyEX375Zcqx88v6T5WV2V3Mzx5tDh8v1MxUC/9lDjsOhYsWoVf9/eb0xBSZhqeQAAmQAAmQQLIT+P658cgOFqDvH89P9qYU1T/DZUOLhjbTEckpU/AqqbwONCYK6LPPPmtaPzUc0/XXX29Cu/zyy/H73/8e5a0LNTMl6X+Fy5aaHmpL9tVDjmwypJ5riShZ4ogk21LBP+MHDOvlxJRF3JIzEfuJdSIBEiABEoiewN78EHImfoTtbfqhae+O0d+YBDnbNXEcCsUkFlCVQApPw8dEc5o8ebIZt7NTp05F3as7wtx9991YJBa4XbtaKX8aAABAAElEQVR2FaWnwoF/xQq4unTFsg1BdG3lEJN5YoY3crZoCZf0iWfqVIzo68SW3YZZ51ToA7aBBEiABEggPQl8+tZidN67GC2vGJtyANQRyVwDallAUzgUU0wUUN0y7cCBA6UGwurVq1Eg6xdSbas5/4rlooB2wdJfDymgpRqeQAkalN4rFtCB8pKYJTF6J/9EK2gCdQ+rQgIkQAIkUAkCu/aHEPrkPfiy89D0zJMqcWdyZFVHpB37DOQHXbA3bozAptQNRh8TBfS0007DP//5T7z77rsoLCyUWV8DM2bMMK2igwcPRr169ZKj56OoZXDnDoT27IGvdRfs3G+ge+vEXP9pNUXDMRnycmAs/hHH9nRi0k9cB2qx4ScJkAAJkEByEfjvZ7tw7JavkXv22bC5xKqSYmJ5wq/ffsgTPpWn4GMSiP6yyy7D/Pnzcckll5jKp3rFa2zQrl274tNPP02p4aHT7yrrcw6tO+neJiY6fNwYZfTpC7u8AHhlV6SRI4/Atws9WL0liI7NE1txjhsQFkwCJEACJJCUBDbvCiF/3Di4Qz40uvTCpGxDRZVu3+zQd/ParUE0k2V0gY0bK7olaa/HTHt65plnsHTpUrz11lt46qmn8NVXX+Hnn382ldCkpVNGxQtl+h2iYC8JtUGmvHxp2IREFpusxc0cOkzWgU7GMWIBdUu0iok/0gqayH3GupEACZAACZQm8NLnHpyy8UNkHDdC4mS2LJ0hBVLqZtvQoI4Na3VP+JaigKbwGtBqW0A11udrr71mKpsrxDqo0+0dO3ZE//79RU9Ljdhc4WPav1wckDp0xLItNnRu4YDdnpgOSOF1zjruOBR8Ph6OLetFCW2CiQv9uOYUd3gWHpMACZAACZBAwhJYvjGIXV9PRtOCjci77JGErWcsKqbT8IeC0bdESJy4Q14v7BJrPdWkWua7WbNmoW/fvrjlllswadIkNGnSBD6fD+PERD5o0CBcccUV0BihqST+1Svh6twZyzfJFpytqoWvxrBkHjMU4gkm0/BTMaqfC2u2hrBqc2r1S43B5INIgARIgARqnMCzn3hx7pZ34ezaDZkDBtb482vygaYCKt/Tjt9CMQW3bKnJx9fYs6qsQamz0bnnnouGDRtii8BZK7sDffTRR/j222+xfv16vPnmm/jggw/w3HPP1Vhj4v0gIxRCYM0aGG07YdNOwwzBFO9nxqJ8e506cB95FDyTJ5nT8OoN/80CesPHgi3LIAESIAESiC+B6b/4sWuehF7auRB1L78ivg9LgNI1FNPGnSGgqRULdFMC1Cr2VaiyAjp+/HjsEW/wL774As2aNYuomcbFVIekG2+8EW+88UbEtWQ+CWz4FYZYeLfmdTCb0aVl8jjyZI0YAd/CBcjw7Mew3k58SwU0mYci604CJEACaUEgEDTw9Dgfrtj5JhzNmiP7lNEp3+72TR0IGcBmV3OzrcEUdUSqsgK6YcMGtGvXDo0lTlV5outA14jFMFXEv3KV2ZRVGR2gSz87tagyvhpHkjX8eNnTKwjP99Nwcn+XvF0ZWLyOzkg13hF8IAmQAAmQQNQEPp7uh2/tOvT4dTLqyA6LNllOluqiFlCVtbudv8UCpQU0os/379+Ppk2bRqSVPNE1ofv27SuZnLTnuv7TlpWNnz1N0VYWCbtl39ZkEWerVod2RZoyGYO7OZGXa8OEuZyGT5b+Yz1JgARIIN0I7Ms38MoEL248+AYc4uCcc855aYGgSZ7d3OZ7rawDdbZsJcHoUzMUU5VNeBpsPlG3oIzXCFULqEs8/FdtCYkHfJXRxat6FZabOWIkvGIBdYT8OLGfTMMvDECnNygkQAIkQAIkkGgEVPmss38zui77EnUuuwL2rKxEq2Lc6tNOpuE1Fqgaj1JVAa2WLVu32tT93ssTnaZPJQmsEQW0Ry8J5B6SvdWTL8RU9vEjceDVV+CdPRunDhyCD773Y/qSAI7rk3xtSaVxxbaQAAmQAAlEElghkWY++sGPZ3xvwJ6Ti9yLLo7MkOJn7WWWdekGUUAlFqgajlJRqqyA1q9fX2Jg2s3tNw8Hpn379oe7nDTXDFk/6V+3Dr4Rp8O3EklpAXX17AmHLJvwTJqInvcNha4zGT/bTwU0aUYhK0oCJEACqU9AZ1j/9r4Xvdyb0XLqeNS5/gZRQnNSv+FhLWzf3I6vxVnY3lVigcpSxlB+fsoxqLICqh7u+pMuElBrrt+PTbmHFOrOSeQBb/WRLpnIGnE8CiRUlnHPfRgzyIUXx/uw+0BIdl5IviUFVrv4SQIkQAIkkDoEPp/jx6K1Qbzp/xc0jGDuxZekTuOibEkHMRD5xU94T25LqLeJbsmZIdubp5JUWQGtTQgejyduj9f4pipe3XlALLyW+Jb+Yh7+glaoI8tQ6rh9iGM1rMfG/NMu23KG3nsXB+bMxsjefUUBBT6ZXoALj6s4pJRfFPCQxELVH0oxAeWiUnLMFOdI3yOOmbL7PnzMOBwV/+6VXUpqpiob3cCEf2ci+zddxsz+AgPPjAvgzKarkfO/Cci66Rb4bPJdfJgvXGvMqOU0VaR53qG2rAo1QmdpVMHaNQi2aVOp5tXEmHG73RG6UmUqmJQKaE0MMn1G+HM0AD1kK6yfPc3Qsbkt4lplgNd2Xme/I2GrWw8+mYav36cvjuluw+dzQhg7rFjZrqiO4VwqypsO1y0eJcdMOrQ92jZajKLNn+r5wnmEH6d6u6Npn/V7RC6RtCweFp/Iq6lz9uIXQRSK5e/S1S/A3qQp3OecW+H3rcXEYpQKNJqJAuoW94yVhY3R2eGExgKtavssPonGJSkV0Ozs7LhxVCtWQUEBssTbLtwy4ZEg9K4OHbB2uw1DezmRnZ283ni+44+Hd8oUZN91D84ZGsBNLxdg6aYMHNX58MNBLRIulwv6xkMpJlDemCnOkb5HHDNl9701ZjLlpdaZBnENy6ZQdqp+WSoT/p2J5KPbXFvfTak6ZuavDGDCvAI82ONH2F6Ygby/PIacvLxIEGWc6ZjR72v9fUolad/sIDbutcMpW3Lat20VvaNyuo+OmXxZO6r6TCKOmejNXqnUq1Voi188/m3tOmLLbkMC0Cf3lFnWiaMQ3LIZvsU/S0xQB1o2tIlH/KGlB1VAw1tIgARIgARIoFoEfH4Df3nPix4tDfT75km4undH9pjTqlVmst/coZkDayTqjrN1a5h+KMneoBL1pwJaAkhZp/p2FVi3Fnsb/uaAlIQxQMPblXn00bDJwm7P11/L2g0bzj4mA1MWBbBzH9d2hnPiMQmQAAmQQM0QeGWCD5t2hXB/3hcIrFyB+v/vzrSLNV6StDoirdsWgkNjgW4UR+gUEyqgUXRocNMmGDI1vynnkALaoXlyW0BtrozfvOG/Nlt/+tEuOGQkfDSdVtAohgOzkAAJkAAJxJDAkvVBvD2pEL8bUgD3m88h++RT4D6qfwyfkJxFdZBQTLoeNr+BWEA3bYaRYg7AVECjGJd+8T5TWe5oa05XZ7s1KEJyS/ZJJ0MVa9/Pi5CXY8cpA1xm0N9CmQahkAAJkAAJkEBNENCp9wfe8phxqc9Y/AKMQj/q3X5HTTw64Z9hGbu2ZLWUOEwSHULWgaaSUAGNojd1/SecLvzoaY6OSb7+02pu5tFDYJe9dQsmfGEmjT0uA3sOGvhy3qGQQlY+fpIACZAACZBAvAi89LkPG3aE8FC/pfCM+wj1JOi8UzZMoQAtGtiQmQGstbUwcaTaOlAqoFGMcg3B5GzbFqu22dBJTOKpIDbxZldnpIKvvzLN+upYpQ5Jb8k0iK55pZAACZAACZBAPAmo1/s7U2Tq/QQb6rz4IFzduiP3kkvj+cikKls3j9F1oL8EqIAmVcfFsrL+NasRat0e+wuQMhZQ5ZM9+lSEduyAb+4cE9elI93mgudpi2XRCYUESIAESIAE4kTgoMfA/TL13rOtA2evfBWBX9ejwUMPw8aNGSKIq3Fo+U4J9t6wkcko4mKSn6SGOS/OnaBrQC0P+FSxgCoyd/8Bsjd8M+SP/8wkOLCrE91b2/Hvr31xJsriSYAESIAE0pnAX97zYH++gYeOXI6D//k36l79O2R075HOSMpse0eZdTU94WUXpMD69WXmSdZEKqAV9Fxw504Y+/djY047OMX5vU2T1EGm5v3sMWPg+fYbhMTLX+Xqk9z45dcQZi+jFbSCocHLJEACJEACVSDw6cxCfLswgD+fJiGG/nYnXF26ou7vr6tCSal/S0eJuhOUCInexqKAyoY4qSSpo03FqVeKPODt7dCuqexI4Eh+D/hwVDmnnwFDdn7yfPetmTystxOdJc6pxmSjkAAJkAAJkEAsCazZEsTfP/Ti5P5ODPrqMVkGth0N//Y41C+BUpqAWkBVdtWVUEy//ppSPhpUQEv3d0RKQNZ/qiwsbCU7IKUeLleHjsjo1Rv5n4wz26lW0WtHu/HzuiCmL6FHvAmF/5EACZAACVSbgMdn4P/924OmeXbckvUVCj7/DHl/vku2ue5Y7bJTtYBG9eyol23Dr67WZjzy4PbtKdPU1NOoYtw1/rVr4WjZEit3ZkBN4akoOWeeBd/sWQhs3mQ277g+LvRoY8cL430IhegRn4p9zjaRAAmQQE0TeORdj2xnHcLfhv+Kgr89bDrC5p57Xk1XI+me16mlHUtDEgtURK2gqSJUQCvoSfWAD7RsD58YA1PRAqrNV294W2Ym8j/+qIjGjWdkYuXmEOOCFhHhAQmQAAmQQFUJvDPZh28WBHD3aC+yH71Jtpdsjfr3P1jV4tLqvk5i/Frg+S0Uk0QLSBWhAlpBTzoaN8Hudv3MXKlqAbXLvvBZsjNS/riPYQSDZluP6uzEsT2dphVUp00oJEACJEACJFAVAnOWB/DMJz6cdzRw1Ft/Qkgcexs9+xzs2dlVKS7t7uksFtD1B7Jg01BMKeQJTwW0gqHc8NG/Ym7/q5DtBprVTy0HpPCm6zSIri3xTptalHzzWW7sPmDg9W/okFQEhQckQAIkQAJRE9Bdju58vQB9Ozhw1eLH4Fu4AI2efBquNm2jLiPdM2osUJXC5u3gX7c2ZXBQAY2iK1eL155aP9VBJ1XFfUQ/CYXRBQfefaeoiW2bOHDR8Ay8Kbsjrdt2yDJadJEHJEACJEACJHAYAvskzufNLxegTpYND/tfheezT1D/vvuROVhMoZSoCagnvKofu/PaIUAFNGpuKZFxlayFtEIhpESDymlE7tiL4Js5Q96w1hXl+N0pbjSsY8Nj7x+KE1p0gQckQAIkQAIkUA6BQr+BP71WgN0HQ3i60Yco/O8/Ufe665F79rnl3MHk8ghkZtjQupEdGzI1FugGGIHUiNNNC2h5Pf5buv4S6RSCZQKvIHtSX84ecxpssh704DtvFbUjy23DHedlYv7KID6fw7WgRWB4QAIkQAIkUCYBjZ5y75seM5zf8y0/g+PlvyP34ktQ7w/Xl5mfiRUT6NJK9oQ3WosbfACBTRsrviEJclABraCT1m4LQSMRpaoHfHjz7VlZyD33fIkJ+om5SNy6Nqy3C6OOdOKlLw1s3UMl1OLCTxIgARIggdIEdMZs8k8BPNvkY9R99S/IOf8C5P2/O0tnZErUBLq0dGC+TxRQkUDYLGXUBSRgRiqgFXTKqs2H1j6mwxS8osi96CIYhT4c/PD9CDJ/EitoZgbw6P/8jA0aQYYnJEACJEACFoFnP/Fi3Aw/ns78D5q98zhyL7wI9e+5L6V9KKy2x/NTLaCbnM1hOJ3Q+OSpIFRAK+jF1bL+U9dA5uWmBypns+bIPvkUHHjzDVFEC4vo5OXYccfZNixcbeC/3xWnF2XgAQmQAAmQQFoTeHG8F29/V4DnPI+j9YSXUed316L+XfdQ+YzBqFALaMjmQGETWQe6dk0MSqz9IhJKq/r2229leUNiLa5dvUUckFJwC87DDb06V/0fQjt3Iv/TTyKyDepqw/lDHeY+8T+uTqx+iqgoT0iABEiABGqUwPOfefG/Cbvw8pbb0XLOJ8i7+17k3XhTjdYhlR/WWLbkzMu1YWf99vCvoQIa077+6KOP8NBDDyH4WyD0mBZejcIa1bOhvwRlTyfJkHBMmccNx/5/vVrK2+660Q50a+3An1/3YOf+UDphYVtJgARIgARKEDAMA49/4MHET5fjlWVXo/Gmn9HouRdQZ+yFJXLytLoEusk0/Dq3xAKVHRpTQWrdAqoWz7vuugvffPNNQvK896IsXDlKotCnmdT7/R8Q3LQJ+Z99GtFyl1NCMl2VJetAISE2PNAoARQSIAESIIH0I+APGLj7Px5s++AzPPXjlaiXY0PTd95D1rDj0g9GDbRYjT+LjHYwZCep4M4dNfDE+D6i1k17avEcPHgwRo8ejREjRkTV2vz8/KjyVSWT3y+bvosUFBTAbq91/bwqTYjNPe3bwzXkGOx7+UXg+JGwuVwolDWh2l91MgJ48GIbbn0tiPveOIh7LrCl9Rofjpnyh5w1ZhJtaU35Na6ZK9aY8Xg86f13pgzcOmZ0vHDMRMKxeCTKmNlfYOChfx/AkO+ewPCtE5Ah3xM5d9+DwpxcFMbxOzqSyqEzHTO6UUyizaCWVdfqpLVvYmCys51ZxIElS+DqP+CwxVljRvUZh+PQbkqHvaEKF7Mkek5VdaVaV0DdbjdOP/30SjV7v2j/8ZaDBw/G+xEJX75x6eUIXXcN9r3zNuxnnR1R33YNgD+e4sRTn7tRN9OHK48/pLhHZEqzE46ZNOvwGDSXYyYGENOsiEQYM+t32PDBS0vwu/mPomFwD+w33oyQxJE+EJSpsRr4fi6vy1U5T2VpXteGTdltYdjsyF/6C+xdukbV3Hga7VSHS1oFNCp6JTI1a9asRErsTr1eL/bu3YvGjRvH7Y0hdrWNc0nCedfoU+F79200ueRSFMgbpkssoTrgVC6UbvDJvxc/B5o3zsUVJ6bfUgXlwDGjFMoW/bIMHzNl50q/VJ/Phz179qBRo0ZwSlgVSjEB/bJUJtbfmeIr6X2UKGNm4vTd2PjoE7hpozipduuNZn97DS6ZMatN0TGjFr7MzMzarEbcn62qT3buAeQ3aImmO3agfgW6UE2MmepsUZ6Uf/mq0+CKRohVtn5axxXdk8rX8266GVu++xYH/vkynLIutCSXK0dl4kABRAkthNtlw0Uj0k8JtcZJSTapPC6ibZvFxGIU7X3pks/iky7tjaadFhOOmUhaFg+LT+TV+J/5ZL3/uIc+Ro8JT6FDqADZN96OhlddDlucpnYr0yKLicWoMvcmW95urRzYUqc9Gq5eXaGOYvGw+CRaW5NSAU00iKlcH2eLlqhz+ZU48Pq/UFfig7o6dynV3BvPzIRf4vU/Nc6HgHxedkL6KaGloDCBBEiABFKEwIpJi7D14b/imJ0/YVfPoWjzxH1wtmyZIq1Lrmb0bOvAUmdHdFn5UXJVvIzaprGXTRk0mFQmgbq/uwaOxk1Q8PfHy7yuibedk4mLR2Tguc980HhwFBIgARIggeQm4Fm3AbMuvQ2ZN41Fjm8PvA88jyPee4XKZy12aw9RQFe4O5qe8IFt22qxJtV/dEIpoN9//z3X/VS/T2Negu4RX/+uuxFYMB+eT8aVW/7NZ2Xi96e6zZ2S7n/TI9ZQhmgqFxYvkAAJkECCEghs3Ypltz2ArWecijpLZmDRqX9Cz+8+Q+dzjk/QGqdPtdQCuj6nk9lg/8oVSd1wTsEndffVXOWzho+Aa+QJOPj0k8iVY2fTpmU+/OqT3Ggknnp//Z8XW/eE8DeJGZou25iWCYSJJEACJJAkBAIbN2LLi68hOGEcxOUUU3tficH3Xo0xXesmSQtSv5q6I1JhszYIOjPgX7EcWccOTdpGJ5QFNGkppknFs2/7E8SlGbvvvQu6+0V5csbRGXj2umys3BTEZf/Ix7INsjCUQgIkQAIkkJAEChcvxuabb8Om0Sfj4Jdf4ouOl2PlP77C5W/ejG5UPhOuz3q0y8D2urIl58qVCVe3ylSICmhlaKV5Xnv9+qh7z33wzZyJA//9z2FpDOzqxBu35yLbbcPVT+Xj4+mFh83PiyRAAiRAAjVHwJBNVwq+moBNF12MbReej+0/zMO7na/H9Psm4Mp3bsGYExpV6GVdc7Xlk8IJ9Gmv60A7oHD58vDkpDvmFHzSdVntVth93HDkXngR9j3zFNxH9JOfI8qtUKvGdvznthw89r7XnJKfvUy2XR2bZW7XVu5NvEACJEACJBA3AoENG3Dw44+w/6OPYNuzCyvr9sRXfR5C67NPxrWjslG/Du1ScYMfo4J7iwL6WU5XDFv9DQzdBSojI0Yl12wxVEBrlndKPC3v9jvgW/QTdt56E5q9/yEcjRqX267MDBseuCQLA7s68PgHXoz9q2zdeWEmjunpKvceXiABEiABEogdgZBsxeiReM4HxYnUN3cO/M5MTGt8In449hwMOq0P7jouA3k5VDxjRzy+JXWXPeGfq9sFtlDQnIbP6Nkzvg+MU+lUQOMENpWL1betRk8/i23nn4udf7wBTf7zBmy/7Y5UXrtHD8hAv45OPPCWBze/4sHoAQHccrabf/TKA8Z0EiABEqgGAZ1i986YgYIJn6Ng0iTZss2DNQ164auuf8aWvifhjJH18UJ/F9RIQEkuAtpnji7dYCwACpctBRXQ5Oo/1raaBJzNmqPRs89j+1VXYNf/kx0xnni6wh0xmjew4+U/ZuOjH/wSL9SL6b8EcOMZbpw2yMW1RtXsD95OAiRAAqbSOXsWPN98jfyJ35n7su+u0xLfNb0QM1udjF7HdsTYIS707UDbU7KPli5d62Fndgvk/vILcE5ytoajMDn7LSFqrWtAGz72OHbdfiv2PHg/6j/4cIWKpG4Jdu7QDAzr7cQ/PvTi4Xe8GDfDj9slkL3GN6OQAAmQAAlETyAk+6B7f/genkkTUTB1KpB/EPtzm2Jq/VPxQ5cTUKdfH5lxcuH3/VzIyaS1M3qyiZ1TZxRXyjrQxot/QYPErmq5taMCWi4aXoiGQPaokxC6/0HseeA+iPaJ+nJss1e8lqhJnh2P/182Zi4N4ImPvLjiiXycItNB141xQy2lFBIgARIggbIJ+Nevg/f7afBMmwrv3LmwBQLYWbctpjU6G7O6HYfsvn0w8ggnnhalU//WUlKPwBEdHHi1TjcMXvlvGNL/NmfyqXPJV+PUG0dJ36Lcc84FgkHsefhBGB4PGjz6V9gkXmg0cnR3J967Mwcf/lCI174sxHc/+nHusRm44sQMNKA3ZjQImYcESCDFCYQOHIBvxnQEp0zGlvmy8G/LJoRsDqxq0Acz216HH5sei5ZHdDRnlp6T2aVGEqycktoEtI/3tuoB+xof/KtWIqNb96RrMBXQpOuyxKxw7vkXwCZbdu6+7x4Ed+9Goyefhr1udLtnOB02jD3OjTEDM/DGdz68O7VQpuULcf6wDFwi+8szLEhi9jlrRQIkEB8CIXmRL5RII95ZM3FwxiyEli6BzQhhb1ZjzMsbjIU9r8eOzoPQr08ejpOX+Nu7OOlMFJ+uSOhS6x3ZG8Y0cUT6+WcqoAndU6xc3AnknHY6HA0bYudtt2DbRReg0TPPwdWxU9TPzc2y4Q+nZWLs8Az859tC/E8UUf05+5gMXCyKaNP6fKuPGiYzkgAJJA2B4N69KPxxIQrmzcf+WfNgX/kLAk43/HBgcd0jsKjTLdjYZgAadm+FIb2ycWf3DC5VSprejV9Fe/fKw+asNnAuWITc886P34PiVDItoHECm67FZg45Bk3ffhc7b7xBdte4APXvvhc5Z5xZKRw69X7r2Zm4/IQMvD2p0Jyef39aIU6WNaKqiHZuSWelSgFlZhIggYQhoJ7q/hUrcGDhIuyZ8xNCS35E1vZfzfrtd9XD0np9sbTD9TjY5Ug06NcDvTuKA5F4rTfM9WO3zC41blwXTidfxhOmQ2uxIgM6O/Ft3R5o+OOiWqxF1R9NBbTq7HhnOQRcHTqi6XsfYPcD92P3PXeZC+VVEXU0qJyvXsO6dtx4ZiauGOXGh98XQpXQL+b40b+zAxdI4OShvZxw2OnVWU43MJkESKCWCehUesGyFdg2bykO/rwUthVLUGfLSjhDfoRgw7bsdlhR7wjsGHAZ7L2OQHNZx9lDooGc2sqBLNnGOFx8vvAzHpMA0Ewcdje0HoAeWzYjVJAPe3ZOUmGhAppU3ZU8lbXn5qLRP55A/rBh2PPXR7H1jDGod+vtyDnzrApDNZVsZd1sG646yY1Ljs/ANwv8eE+m5f/0mgdN82w44+gM+aGnZ0lmPCcBEqg5AiGvFzuXrMGORauR///ZOw/AOIrz7T+nU++yJNuS5d6NCzYYG4zpGAg1dEPoJX8glQ6hhoQPkkBCC5DQEkhoCYTeTDMGFwzuBfciy1bv/U77ve/Ie76TTrIsn8rdPQPr3Z2dnZ35zejuuXdm3v1hA5o2bUR83nqklOciAhZ0SWZ9XA62p45GxcHHwjFmPFKnjMeI4UmYne3k/M3ua6qQe1LKxLHou/g+NCxbhthDDwuq+lGABlVzBV9hE047HbHTD0Xp/b9DqSxQqn7tVaTeeBNiDjp4nysTHeXAKdOizbZsk8s4tH/hk3o882E9pstEfHVor1bRGEnHQAIkQAKBJNBYVYNdq3NRsn4bqjduhWvbNkTu3IbE4q1Ird4l9kxAl11GRCZhV/Iw5A+YCvdhFyBu7Bj0nTIGw4cm4RAZ1WEggUASGHfUGNT+Ix5FX3+HHArQQKJlXqFAwNm3r3l1Z524ESn744MouPRixB4+E8nXXIuYiZM6VUV9k4duN4gD+w8WN+CdBY247flacbQM8X8XhePF/93Bo5zQFfYMJEACJNAegUZXE4q2FaN0005UbMtH3fY8uHftRETBTsSW5iG5YieS60tMFjqRKFlcIBXLW2jK0waifMwx2DloMOJHDUPfCSMweExfjG0xfN7es3mNBPaHwOSR0fgqeTyGLvweOfuTUQ/cSwtoD0AP10eaBUr//R9q3nkb5U89iYILZyNm6iFIuuRSxM48okMO7FuyS0loduGkbpzW73Djg28bzTD92yJIU2ToXt+4dOTESEwbTTclLdnxnARCmYBlWSgrqkbp9iJU5hWjemcx6vKL4C4qglVSBGdZEWIqipBYU4iUuiJEWm4kCRDddH5meWwGqpL6o7ZPDqrHHoLCATlIGJqDPqMHIXvsAAyN49dnKPefYKlbamIEigZNwrjVL8ESf9wOZ/As0uVfULD0shApp74lSVfFx598Cmo+/ACVzz+Hop9dC2dODhLPPleunQ5nRmanaqur43X7ubxffvlmNz5b5sIXyxrxzsJGREtPP0gWL6njexWjw7KC54+0UzB4EwmEGIGmJgsVtSIqyxtRkVuEqtwC1O0qRGNBIayiQqC0CFEVxYipLEZ8bQmS6+S4qd7Mv1Srpb0EsiYyEVXx6ahLzICr3wCU9TkQVX37ISa7L5IG9kef4VnoM6QvBsdEhxhBVidUCcQdfDCil/0ddStXIm5S50YVe4KNQ34lWj3x4N76zDqZTF5aWoq+MmzsDKJfEt3Bs6qqClHyhqOYmJiAPq7u20WoeuVl8y5jNDVBLaUqUOOOPgYRCfu/qm9DnhtfrXThm9UurNjihrsJSE9yYMoIJw6U9+lOHKrCNaLTK+rZZ9ruDl3VZ9p+YnBcsftMZmamuNQJPzuAW8RkZY2IyWoL5bLZ++qCUtTk5omgLEKEWilLCxFTXoD4ygIk1xahT30BUhpKzcIe75aujk5GdVw66hPT0ZiSDqSmI0J8EkdnpiOufwYSstKRmpOBtIEZiAjw55d3ObryuF6WwTe7YQrPPtMe2+rqavN9HRsrc7DCMHy/uhops2eg8cJrMObmn3oI9PY+E36ffJ6m4UFvIRArw/C66RuUat57F9Xvvo2S224BoqObxeixxyH2iCP32Y2TXb8RsspUt8vEnVOVWFC+3+DC4vVus/90aR3ku1BWoQJjxPXJ2EFOjM6JwCixpA7pF4GoSM4htTlyTwL+CNTWi4gUMamCssJLVKqw1K26pAKuolL5+y5GRFkxnBUliK0qRmpDiWzFSGsoQlp9MQbKcZTV6POIhugE1CZniqjsi6YhI+DOnIHKfn0RK9bKxJy+SJEtNisTDvmsYCCBcCUwaUw85qRNQMbX8wXBHgHa23kEpQVUfwV2VXDLHAqXyyXaJ3qf3QV1VZl6S77KJUKG0HXr6tC0fTtc8t5j95dfoGn9OkhjIGLsODinTYfzkEMQMXpMQOa61IhvvbW5DqzdAfyww4H1eQ4UVzaLzogICwNk3G5QpoUcMapk97GQlSa+19IsZMhEMdtAzj7Tdm/ozj7Tdil635VA9hkZNECDq3lrlH2je/ex7F27t0a3A/LRZq7pCICJl73GyfobzzWN1/sbXQ6TZ73kVy+aULcG/WORURBHVSWcVRWIqKlApGwJDZVIaixHYmMFkhvLZF8u+3KkuMqQ1FAmcyslkxahMS4JruQ0NIml0iGWykixVMb0FYtlvww0paUhIlNGoMQ67IiPb3Fn+J42SUM3ihN7fje17gP696QhnEct59z6Ig5a9AJSZWqb/YPM7jM6ctlV39spKSmd5h6UArR19wtcjD00xiH41kx7ajjVJatR60SI1s6di3oZrrfEubND/IzGHDzVWE51HzV6dEAEqda6tLJJhGgTdOh+864mbMlvwrbCJpRU7pmtov7v05MdyExxIC3RQkJUA4YNiBfn0RHQhVFJ8lrRJFkElRjrQLxsujo/PtoBdSUVTqGn+kxvYKzDzLbAM8LOZYmwaxaHFdXiN7K4EnEJqWhsioBqO7Uk1jZYqKmDnMteNklm4mskvtYrTW2DikILdbLXvDsS1Pl5nKsaCa5KxLt1X4V4OdZ9QqOISPeeLVHiEl0Vcl3SiLCMF5EZ6ZaH+QmWMxLuBHFAlJyCiJRURPZJQ3SfVERl9EFEapoZuYiQl1A4+8iwuO7T+8AR1bbFUodTdVpCoKf6+Cl6UEX19uHUnoQZ7kPwyn7ua99h6H0XAX96BgNPOMw0R2/vMxSgLf5qKEBbAPE67Q1iwmpsQP2SJahbsAD1ixagYdUq+QZ2GUtJtLh0ipEJ2NETdJvQ6SF7ryr7HFbXWdhR3ISdsu0qtZBfJq5byi0UlLlQWOYWy4RTBKuIDrEotRWcYjyOixExKm5a4kSQ6tC/7jWu+dz7WKYGiGCN3Z1O/ZvGiEfraNliZGpAlEygid69j5Q1VZGiinWvllkVyLp3iuVY/pdfv81x5ljOdb/nWE66KOxvn3G5LY8VzyWCTpp6t4VOxJyx1Km1bvexObeM5c5YAiVeLYPNoq/5uEFEm8apeLPFYIOmUwvf7vS2WNS9Wgd175ZyaLvqplM2zF4O9Fhn0eu5vbfj2kI6WeYeL9kgmfoJ2ibxu/tHirMeqRE1SI6oFbc/dbI6uwaJqEVcUy3iZYtz1yDGVYtoVw2iG2sR1ViDyHrd5I0odTVw1sm+ViyWtTWiaGUvfztthkixkCQnISJJNhWSZp8Mhx5rfHJyc7zZy7FYPUycCM5AzNP2LhcFqDeNPce9XUzsKWn3H1GAApXVbmybeTjKDjsVMx6/3TRCb+8znAPa/X8rfOJ+EFDLSewh08wG/FJeP1Yjb4BYKqL0e9QvXYLKf4kriqqnzBOc/fojatw4RI8Zg6hRuo1EZM7ATrl70gwTxJKpc0N18w7NP1qqPAvXdJ5p5e5NRWvz1mzVUiuXsXYZi9Zuq5YeizbQhRi1JU3muG53nO51+LO7gi1K7efZQlXPbZmqcS2DHWdf8twn4szhENOdbN7X7PtVtMn/RrzpULKKN7PtFnt2uv3dqxcEFewq4s1ehLsKeY1XEd983SGuu5rTRcm7to2ol6ZWYW/EvIh4/QFhxL3u5UCPta5qXYxsUCEowk/3Dc2CMKqhGlFyrtfMXsRhZFW0LKLJlX5ajji44KyvFcFYbYSiJWLRkj6tVn4DZS8Vd8iCGh2m1k2FoCNu9z5DrI0Jg+BISEREosTLiEGEHiclyrEISt3kOCJJRKaKzbi4vTyJl0mABHozgaQEJ7YPm4G+S+ZKMZsFaG8ur5aNArS3txDL1y6BCPni1deP2a8gU6cOrs2b0CDuKBpWr0LDmjWo/Oc/YMmwngkytzdq6DDZhiJyyBBEDhrcvOXkyNCgTPQMQEiU4XfdsgKQl52FPdyqVjozH2+3Jc/b+qfWQh3ytS11zVY6FXTNljoVeN6CT4+9rXWazlxXRbg72On1VI/NvnknEfaB7zXvdPX1DWbukVOGac29XvfouS1UVZzqsRF4u0WevkRAzz1CUM5VPEaJKIzafazXdKGYCkg9ti3CHuvw7uv6LH0vtwo7FXj6w6X5uNrsPefiBUNFYFO57iVtnW5yLPummj3H5ppcN3lKXsY0qw9pJxixKCIxZvIUNORuhTs6BjFqSUyTldnxIhZtISnvc95z3CwuHSIQ9T3PRmjuTmfSdMN87HaqxEskQAK9iEC8+NPu8+xHKNqcj4yh/XpRyfwXhQLUPxfGBikBh6iYqGHDzaavAdWgotSdl4fGDetl24BGeU+za/Nm1MqbmayKCk9N9UvemZWNyKwsOPv3h1pQnbLiVv2S6oIIZ7oIBZnD5ugBtzn28LvINE95g+GgqqpRXHf5n89nNTSIgNstBGubRV6z2JNjcTljyWRITWOO9bxBNhG0Jk6OYeK8ruu5iEV9L3edCMYaIyZVcDYLSY+Cbg+cmDq1HzjEnYsjVkRfnOzNeXOcGXbWc88mFkc99ohHEYxqiZRzj2Dcfe7tINqe6tNH+lU4umFqrwl4jQRIoHMEDrpsFmrOORIZ2Smdy6Cb76IA7WbgfFz3E1BRGjlggNnijjzKpwDusjJ5p7O81zk3F+4dO+DK2wH3zp0ynL8U7vxdMkxa5ZNeTxwyD86pK3VTZf6bzIGzZHjTLZbVSrGmOnVIVIY8dbhThUjzsOhuwSKCxhEr18XyZaxh3WS9stT0qaZREeL6pgxYu8e3Jd5c0+t6bMyncr3JDUsmPlo64dLtat7L6ltr9wZbFNr7ehGKRkCq+NttJVTRJ+KyUVdNSxzqRByqxbCm2eKox/rMDgdpQ2Wmrrl0hafhpxz12GYqolHbpFk87hGORiDuFolqMddhao8l0Y43e2mfdhbHdLisTEgCJEACPUAgITkWugVLoAANlpZiObuEgFMEi25tvZNeh1ibigrhLpStuBhN4svQXVyCprJS2cqgAtYt1lSrvAy1K1fAtWVLh4ZjTWXU2ibuMcQE1mxV1YmGIkr1bVHyT/OYtF1rHdc2Y9siIo1g1PNmYWneJaHi0UyeFOG4W1Aa0Wnf35V7LXOMCj61GuperH9mL4JORGOEvGUmUkS6sRKK0GsWgSrKVQzK+W5ro+d+zUtFpdnLsYpM5cRAAiRAAiQQMgQoQEOmKVmRriCg1rOIgYMQKVtbwR5OtV136RCwWk6NtU/nGuqmQ8MiZs1wsl4X66E49ROrogwhy17Folod1fromZhpBOfup+oESbPJ+W6B6tAVMBG7RatOllTRuntvhns1vYpcr3Sah7nmFWcEr0mn98tHguRh0tjCWPciAI0I1L0RhLutkGrxbUcc7u8q+LaYM54ESIAESCC4CVCABnf7sfS9kIBa9CCbExm9sHQsEgmQAAmQAAn0PAExeTCQAAmQAAmQAAmQAAmQQPcRoADtPtZ8EgmQAAmQAAmQAAmQgBCgAGU3IAESIAESIAESIAES6FYCFKDdipsPIwESIAESIAESIAESoABlHyABEiABEiABEiABEuhWAhSg3YqbDyMBEiABEiABEiABEqAAZR8gARIgARIgARIgARLoVgIUoN2Kmw8jARIgARIgARIgARKgAGUfIAESIAESIAESIAES6FYCFKDdipsPIwESIAESIAESIAESoABlHyABEiABEiABEiABEuhWAhSg3YqbDyMBEiABEiABEiABEqAAZR8gARIgARIgARIgARLoVgIUoN2Kmw8jARIgARIgARIgARKI7A0I6uvrsXjxYjgcDkydOhVRUVG9oVgsAwmQAAmQAAmQAAmQQBcQ6HEBWltbi8svvxwHHHAA8vLy8Prrr+Phhx82YrQL6sssSYAESIAESIAESIAEephAjw/Bv/rqq5g2bRruuOMO/PWvf0VNTQ0WLlzYw1j4eBIgARIgARIgARIgga4i0OMW0A0bNmDWrFme+k2ZMgWrV6/G9OnTPXEtD/Lz81tGBezcsiyTV2FhIa2wLagqG50mweBLgH3Gl4f3GfuMN409x3afKSoq4t/UHizmiH2mBZDdp+wz/rlorN1nysvL204Uhle6o8+kp6cjMrJzUrJzdwWwIXft2oXk5GRPjnqcm5vrOfd3EBcX5y86IHEulws6JzU2NhYRET1uIA5InQKVSUNDA5xOp9kClWco5MM+03Yrss/4Z8M+45+LxjY2NprPXv2sYdhDgH1mD4uWR9pn1DjSWSHUMr9QOXe73airq+tSPbM/OqnHBah+yCgkO+gf2d4Eprdgte8L1F4bSwVoUlIShVYLqFVVVWaBWExMTIsr4X3KPtN2+7PP+Gdj95nExER+abZAVF1dbZjwc8YXjH4v6cY+48tFz7TPqJZQwxHDHgLaX/Szprf2mR438WVkZKCkpMRDTI+zs7M95zwgARIgARIgARIgARIILQI9LkBnzpyJDz74wKh0nQ/1zTffYPLkyaFFmbUhARIgARIgARIgARLwEOjxIfjjjjsOX3/9NWbPnm3m/Zx//vkYOnSop4A8IAESIAESIAESIAESCC0CPS5AddLwfffdh8rKSjP3k5OIQ6uDsTYkQAIkQAIkQAIk0JJAjwtQu0C66IeBBEiABEiABEiABEgg9An0+BzQ0EfMGpIACZAACZAACZAACXgToAD1psFjEiABEiABEiABEiCBLidAAdrliPkAEiABEiABEiABEiABbwIOeVVT87snvWPD+Fid4uvbW9ShLV876dsR9CUByoRvKPHlwj7jy8P7jH3Gm8aeY/aZPSxaHrHPtCTSfG73GXXQvz9vn/Gfe3DHss/4b7/e3mcoQP23G2NJgARIgARIgARIgAS6iACH4LsILLMlARIgARIgARIgARLwT4AC1D8XxpIACZAACZAACZAACXQRAQrQLgLLbEmABEiABEiABEiABPwToAD1z4WxJEACJEACJEACJEACXUSAArSLwDJbEiABEiABEiABEiAB/wR6zas4/RcvsLH19fVYvHixcSU0depUREVF+X1Ae+l++OEHbN26FVOmTEFGRobf+xkZOgTa6wvetWxsbMR3332HuLg4TJw40ePCa9u2bcjLy/MkTU9Px8iRIz3nPAhNAh35nFiyZAm0f9lh1KhR6NOnjzntyP32fdyHBoG9tfnKlStRWVnpU1n9DtLPE37O+GAJq5O1a9cat5FDhgzxW+/2vsOKiorM95beO3r0aL/3d2Wk8x4JXfmA3pJ3bW0tLrvsMlRXV+Prr7/G559/jhNOOMEjFOxytpfuz3/+M/73v/8ZP6FPPPEEZsyYgZSUFPtW7kOMQHt9wbuqu3btwuWXX2760qpVq/D000/jlFNOQWRkJB566CHMmzcPW7ZsgX5QqL86FagMoUugI58T2g8uueQSlJeXm36hfWPo0KHo168fOnJ/6NILz5p1pM1ff/11LFy40NNf9Fz9PB566KH8nAnPboPNmzfjV7/6FYYNG+bXsNHed5j+ANZ7U1NTzXeW+j4fO3Zs95JUR/ThEJ5//nlL/sg9Vb366qut+fPne87tg7bSSUNbP/7xjy35gzdJX375Zev++++3b+M+BAm01RdaVvXRRx+1nn32WU/0nXfeab3zzjvm/Pzzz7fEYu65xoPQJtDRz4n169dbIkBbwejo/a1uZETQEuhMm4s11DrzzDMt+QFj6s3PmaBt/k4XXIxh1umnn27Nnj3bev/99/3m09532MUXX2wtXbrU3CdGFEuMJpZYS/3m01WRYTMHdMOGDWbY3Jb3OoS+evVq+9Szbyvdpk2bjOXKfgNFW/d7MuJB0BNoqy+0rNhPf/pTXHTRRZ5otWrpL8+amhqUlJSgsLAQL730EnJzcz1peBCaBDr6OSECFDk5Ofjwww/x1ltvmb6iRDp6f2jSC89a7Wub65CqGD9wyy23IDk5mZ8z4dltzHQvMXwYy2dbb21s6ztMR2D0+8gejdORl/j4eOzYsaNbaYaNANVhUv1jtYMeFxcX26eefVvpdu7c6TPc3tb9nox4EPQE2uoLLSsWHR3tmU/82WefmT/sk046CRs3bjRz/L799lszVKbDHe+9917L23keQgQ6+jmxbt066Jw/ndOn+3PPPRc6H6uj94cQsrCvyr62+Zw5c8x30SGHHGLY8XMmPLvQrFmzoGsKNIiF0i+Etr7DCgoKkJCQ4DMFUacTqsGkO0PYLELS95frfBk76C8AXTDSMrSVrq34lvfzPHQI7Gubv/3228bS+fDDDyMxMdHMp3nzzTeRlpZmoIwYMQLPPfccTj755NCBxJr4EOhon7nqqqugm1odNKhVS62hOm+4I59TPg/lSVAT6GifsSupnzNnn322fcrPGQ8JHrQk0Fbfahmv96km0nmg3RnCxgKqqwW91b0eZ2dnt2LdVrrMzMxW92dlZbW6nxGhQ6CtvuCvhi+++CJee+01PPbYYxg8eLBJUlZWhtLSUk/yQYMGIT8/H01NTZ44HoQWgY5+TuhQl4pOO2jfUEtYR++37+M++AnsS5vranftO0cddZSn4vyc8aDgQQsCbX2HqeVUF2R7fwa1pYlaZBnQ07ARoDNnzsQHH3yAuro6M9T1zTffYPLkyQamDn3ppqGtdOq2Sd1gbN++3fxSkEUmsIdAzI38J+QItNUXtKLefUYmgOPTTz/Fk08+aVYx2yB0Luivf/1rMx9Uh0jeffddHHnkkbDnEdvpuA8dAu19Tnj3mblz5+Kpp54yFde5wjp14+ijj0Z794cOJdbEm0B7be7dZ/Qe9ZYwZswYz5QfjePnjFJgsAl495m2vsN0pGXatGlQa7oG/TzSkTp7tM7Oq6v3YTMEf9xxxxn3S7JizAgAWTVo3J4oYFkpZv6gdY5ee+lk5TyuvPJK46tPrVwXXHBBV7cP8+9BAu31Be8+o8Pqatn0Hlo/66yz8Mtf/hKyUhXab3R4Q+cN33fffT1YIz66qwloG7f1OeHdZ8455xw8+OCDxjWcLlI7/vjjzSJJ/XHS1v1dXXbm3zMEOtpntHTqzk1d7niH4cOH83PGG0iYH3t/zrT3HXbttdfi5ptvhk4T08+du+66q9vJOXR5fbc/tQcfqJP+de6n/gJoL7SVTh2Oq9la5/gxhAeBtvpCR2uvQ+5VVVU+i+A6ei/TBSeBjn5OqPVT52PFxMT4VLSj9/vcxJOgJrC/bc7PmaBu/i4tfHvfYTqFQ32B9kQIOwHaE5D5TBIgARIgARIgARIggT0EwmYO6J4q84gESIAESIAESIAESKAnCVCA9iR9PpsESIAESIAESIAEwpAABWgYNjqrTAIkQAIkQAIkQAI9SYACtCfp89kkQAIkQAIkQAIkEIYEKEDDsNFZZRIggZ4joF407r33XuNTuOdKwSeTAAmQQM8S4Cr4nuXPp5MACYQZAXUcrm5Pvv76axx22GFhVntWlwRIgASaCdACyp5AAiRAAiRAAiRAAiTQrQQoQLsVNx9GAiQQ7AQWLFiA6667Dm6326cq+iY1fcWvhk2bNkHPTzzxRPOWmj/+8Y9oaGjwSW+f/OUvf8Frr71mn5r9nXfeaV7vakcuXboUV111FWbNmmXyzcvLsy9xTwIkQAJBSYACNCibjYUmARLoKQL66sO///3vPgJRh9OfeOIJ6LXNmzdj4sSJKCkpwUUXXYSRI0eaV7Decccdfov8/vvvY/78+T7XVJCuXLnSxOl74g899FDzNi19hefChQtN/hShPsh4QgIkEGQE2n8fZZBVhsUlARIgga4mkJmZiVNPPRUvvfSSsUjq81588UWcdNJJ6NevHz788EOcd955RqTqO5YvvPBC6Pve1XLamXDjjTeavF9++WVzu1pCp0yZgvvvvx+PP/54Z7LkPSRAAiTQ4wQoQHu8CVgAEiCBYCNw2WWXYfbs2bDf5a4Wy2effdZUQ4fdjzrqKKjlcs2aNVi9ejXmzJmD/v3773M1dcX8smXLkJWVhVtvvdVzv74/fvHixZ5zHpAACZBAsBGgAA22FmN5SYAEepyAiszExES89dZbiI6OhgrCU045xZRr+fLlOOGEE0z8zJkzMWPGDDN8/sMPP7RZbsuyfK7Z80UrKirQ1NRknqXWVDscf/zxSEtLs0+5JwESIIGgI0ABGnRNxgKTAAn0NIHIyEhcfPHFePXVV6HHP/nJTxAVFWWKdc8992DMmDHG6qnCVMO8efNaLVoyF+SfmJgYI1Dtc5fLhdzcXHOqw/3JycnIzs42Q+52mo8//tjzPDuOexIgARIIJgJ7flIHU6lZVhIgARLoYQI6DK9CUBcRXXrppZ7S6FC7LkCqra2FWjbVSvqf//wHOpzuL4waNQrvvvsuNmzYALV43nDDDUas2lbRa665xswxffvtt0383Llzcfrpp6OoqMhfdowjARIggaAgQAtoUDQTC0kCJNDbCKiVc9KkSUZY6t4O119/vVnBrguSYmNjMXnyZKgbpltuucUITDudvVfBqRZSXS2v6VXYHnfccXA4HCbJ3XffbSykZ599trG2ar433XQTdEU8AwmQAAkEKwG+CSlYW47lJgES6NUEiouLzTzQpKSkDpUzPz8fmjY+Pt5v+sbGRmianJwcv9cZSQIkQALBRIACNJhai2UlARIgARIgARIggRAgwDmgIdCIrAIJkAAJkAAJkAAJBBMBCtBgai2WlQRIgARIgARIgARCgAAFaAg0IqtAAiRAAiRAAiRAAsFEgAI0mFqLZSUBEiABEiABEiCBECBAARoCjcgqkAAJkAAJkAAJkEAwEaAADabWYllJgARIgARIgARIIAQIUICGQCOyCiRAAiRAAiRAAiQQTAQoQIOptVhWEiABEiABEiABEggBAhSgIdCIrAIJkAAJkAAJkAAJBBMBCtBgai2WlQRIgARIgARIgARCgAAFaAg0IqtAAiRAAiRAAiRAAsFEgAI0mFqLZSUBEiABEiABEiCBECBAARoCjcgqkAAJkAAJkAAJkEAwEaAADabWYllJgARIgARIgARIIAQIUICGQCOyCiRAAiRAAiRAAiQQTAQoQIOptVhWEiABEiABEiABEggBAhSgIdCIrAIJkAAJkAAJkAAJBBMBCtBgai2WlQRIgARIgARIgARCgAAFaAg0IqtAAiRAAiRAAiRAAsFEgAI0mFqLZSUBEiABEiABEiCBECBAARoCjcgqkAAJkAAJkAAJkEAwEaAADabWYllJgARIgARIgARIIAQIRIZAHVgFEiABEmiXwHuLGrB8s7vdNJ25GB3pwA1nxXbm1l57j7ukBOVPPNYl5Ys/8STETj2kS/JmpiRAAsFFgAI0uNqLpSUBEugEge/Wu/HRd43IyQjcoE9plYXaBivkBGhTVSWqX3sVzqxsOOLjO0Hb/y2ujRsQNWw4Bah/PIwlgbAjQAEadk3OCpNAeBIY2j8CL92cGLDKv/BJPZ79qD5g+fW2jNLuvgdxMw4PWLG2TxgXsLyYEQmQQPATCJw5IPhZsAYkQAIkQAIkQAIkQALdQIACtBsg8xEkQAIkQAIkQAIkQAJ7CFCA7mHBIxIgARIgARIgARIggW4gQAHaDZD5CBLoLQTcbjcWLlyIBx98V8KvFQAAQABJREFUEJdccgnuvPNObNiwYZ+K9+KLL+Kjjz4y93z//fd4+OGH27z/D3/4A77++mu/17Us9957LzZv3uz3OiNJgARIgARClwAXIYVu27JmJOBDoLGxEWeddRY+/PBDzJgxA6NHj8bLL7+M//f//h8++eQTHH300T7p2zpRAXrAAQfghBNOwHfffWcE6PXXX+83uQrQm2++2TyvZQIVoL/73e8wc+ZMDB06tOXlkDl/4403cNBBB2Hw4ME+daqpqcFLL72Ec845B2lpaZ5rCxYsQGRkJA4++GAT19TUhKVLl+LLL79Ev379cNJJJ/mk99zY4uCdd97Bzp07TazT6UR6ejqOPfZYJCUltUjZe05XrVrl+cHicDiQmJiIQw45BMOHDzeFXLNmDb766itPgWNjY03fmT59OqKiolBfX49//OMfnuveB1dffbXntKqqyjxHf0DpvdoHlTkDCZBA9xGgBbT7WPNJJNCjBH7yk59g0aJF2LJlCz7//HM89dRTxvp5wQUX4IorrkB1dXW3li86Ohoqio855phufW53P0xF9vLly1s9trS0FD/96U/xy1/+0ufa66+/jrffftvEqfjU9rn88stRXFxsROioUaPMDwafm/yc/PGPf8Rrr72GJUuWmHZ/4oknjFjbV4u3n6y7LOqzzz7DAw88YMqs4vDdd9/FpEmT8PTTT5tnfvHFF9B6qdVcNxXrl112mRHllmWZPqxMv/32W5OH1t3e7EJv3boVBx54IB5//HEo34ceegjKtLKy0k7CPQmQQDcQ4E++boDMR5BATxNQ0aliRIVNdna2T3H+/Oc/4/bbbzfCVC2bGtTipoJFv6zHjRtnrJgt7/PJpBMnLpcL11xzDW644QaMGTPGCIKRI0dix44dppxq3bryyitx3HHHeXKvqKiAWlUXL16MzMxMXHrppcaq50kQZAdqtZszZ46p72mnndaq9HfddRdyc3ONpVmtmBrUAqrWPLUGKiO1JKsYUzE1bNgwH0ue/rCYPXu2J1+1gD/zzDNG5Hkie9mBisMnn3zSU6pXXnkFv/nNb4xY10jtK2q1t0NZWRlycnKwbNkyDBo0yEQ/8sgjiG/Dh+msWbNw7bXXwttqryJf/wYee6xrHPDbZeWeBEhgDwFaQPew4BEJhCwBtRSpgDn++ONb1VGHZtXCZItPtUIdeuih0GFKHR7WOaMTJ05EXl5eq3v3J0IFk4ohFZwadF6pWq9eeOEFIypVWOkwvw7LalALrQ5lf/DBBzj99NON0Dr55JOhUwKCNagAVUv0//3f/6FE3kDUMrz33nu45557TNvZ18444wxj1VPxqWwmTJiA6667DmeffTbGjh3rNx/7Xh3WTk1NtU+DYh8REYGUlJQ2y6r9VENWVlabaewL69evR2FhIX7961/bUWb/7LPPGkuoTyRPSIAEupQALaBdipeZk0DvIKDDmQMHDjQWs72V6MYbbzRWNp0fquGqq67ClClTcP/99xsr5d7u35/rCQkJ0GFWFR0qqvr27YtPP/3UiONHH33UzGlU66cKErWe6tDpTTfdBJ1eoOIqGINaPnXY/Re/+IWZE2rXQeczrlixwlj87Dh7b4tIZaUWZLV0apg6darhpT8cNKh1Va3GdXV1+Oabb8z8X7UO9uag1lz9QaRD6iqw//vf/5rFcnaZdXhdf4Bo0LrpPNff//73Zn6sLeL1h4r2ITvo3FftP9p3dO5zy74SFxdnJ+WeBEigmwhQgHYTaD6GBHqSgA6fFxQUmGFa7y/mlmVS0aNDmWpNuvXWWz2X1XqqX95dHXThjV0+3Q8YMMBYYvW5On+1f//+PsOvaj3Nz883w9QqsIM1qDhSC/Rbb73lqUJMTAySk5ONxU6HmP0FFeFqEVZvBjptQi18KjbtsH37dvOjQxfYqChToaZMe3MoLy/3zJnVaRbPPfccTjzxRE+RdcHaz372M2g6tQ7rNI2W82hfffVVnx9buphJg/6gKSoq8uTFAxIggZ4jQAHac+z5ZBLoNgJqwdRV1zoXVOcJege1NN1yyy3mS16Hc3VoXL+wbSGoaXXo3nultvf9gTxWC6h3sOc9apwu2tF5fd7lUtF52223+cR53x8sx8pWrX46t1OnHdgctN10CsTkyZM9VdHFSLpwS4fn1Z2WWkl1kdKlMh/25z//ubEc2ol1gY73HFA7vjfvdQ6ozj9uK2RkZHimkuhUEe2zKqq96zlixAi/c0CVo1pY9ceYilE7qGBVS6vOk2YgARLoHgIUoN3DmU8hgR4loEJGrUn33Xcfnn/+eZ+y/Pvf/zYri9VCpmnU6qYWUx1yt8PHH39s3NzY5z2xV1Gh5dBV5bYI3bhxo3Gno+UO9nDqqadCF8ioayZdEKNBFyGdeeaZRoBOmzbNrITXxTMqutQqOm/ePPz2t7+F3quLlXR4WueChktQ4anWY11UpG7E1LNCe6FPnz5GpJ977rlGcOr8Zx3m1x8xf/nLX9q7lddIgAQCTGDPJJkAZ8zsSIAEeg8BtWjqPEMVN2olU6Gybds248NT53yqH0QVOhp0WFcX9uiKeV0INHfuXDPnrrNDl2pxUt+N3tu6dev2GY4uUFKRpc7r1Rqqc/8uvPBC46pnb8Jjnx8W4Bt0zqJac+2trXmYKoLU16cdjjjiCGhaFVhq7R0/frxZfKWugzTo/FdtP/UUoNZOFWG92c2SXa9A7nX+r/oK1X5tB/V1arO29+qOScOf/vQnM91Bf3Ap0/POO88sAvPnhcDOj3sSIIEuICDDbwwkQAJhQkBEqCVDvJYIUks+TixZhW3JIiNLFm94CMhQvSULgMw1WZxhDRkyxJI5hp7rMhxv/epXvzLnf/vb3yyxQnmutTwQC5N5jj7Le7vooossmW9q4mShjLntlFNOsUT8+mQhQ6aWzFv0xIm11pKhU0vmR1piqbVENFu7du3yXG/r4N6XaqwLH6xs63Kn4p//uM46/IbyTt3bmZtkzqMlrqta3So/EiwZlm8V39mIhq1brG3jx1o1877qbBZ+79M8K1560e+1nooMJLeeqgOfSwLBSsChBe8CXcssSYAEejEB9cGpC1R0GFddAfkL6iReF/i0tQDG3z3dFaeLj3TYvaOWz9/+qxbrdrjx0s3Ni1ECUc4XPqnHsx/V46s/JQciu16TR+O2rdh18knIeOpviJtxeMDKtX3COKTeejuSLvxJwPJkRiRAAsFLgHNAg7ftWHIS6DQBXRW9t9dfqjDtjeJTK93bV3J3umF4IwmQAAmECQHOAQ2ThmY1SYAESIAESIAESKC3EKAA7S0twXKQAAmQAAmQAAmQQJgQ4BB8mDQ0q0kC4U6gtMrCvz+vDxiGpRvdAcurN2ZU++kcuDZt6o1FY5lIgARCgAAFaAg0IqtAAiTQPgF9K2NxhYVH3wqcANUnxsW0/9ygvSouo6rf+G9giy95igPXwObJ3EiABIKWAFfBB23TseAkQAIkQAIkQAIkEJwE+HM0ONuNpSYBEiABEiABEiCBoCVAARq0TceCkwAJkAAJkAAJkEBwEqAADc52Y6lJgARIgARIgARIIGgJUIAGbdOx4CRAAiRAAiRAAiQQnAQoQIOz3VhqEiABEiABEiABEghaAhSgQdt0LDgJkAAJkAAJkAAJBCcBCtDgbDeWmgRIgARIgARIgASClgAFaNA2HQtOAiRAAiRAAiRAAsFJgAI0ONuNpSYBEiABEiABEiCBoCVAARq0TceCkwAJkAAJkAAJkEBwEqAADc52Y6lJgARIgARIgARIIGgJUIAGbdOx4CRAAiRAAiRAAiQQnAQoQIOz3VhqEiABEiABEiABEghaAhSgQdt0LDgJkAAJkAAJkAAJBCcBCtDgbDeWmgRIgARIgARIgASClgAFaNA2HQtOAiRAAiRAAiRAAsFJgAI0ONuNpSYBEiABEiABEiCBoCVAARq0TceCkwAJkAAJkAAJkEBwEqAADc52Y6lJgARIgARIgARIIGgJUIAGbdOx4CRAAiRAAiRAAiQQnAQoQIOz3VhqEiABEiABEiABEghaApHBWPLCwkLccccdPkWPiYlBRkYGRo4cibPPPhtRUVE+13vi5N5770WfPn3w85//3PP4oqIiU05PRCcO/NU/IiICSUlJyMrKwllnnYVBgwZ5cv7Nb35jzn/605964jp6sLfyfvvtt3jmmWdgP6Oj+e5run/961/4+uuvsXLlSvTr1w+TJk3Cueeei1GjRu1rVt2S3ubRGebdUkA+hARIgARIgAR6kIDDktCDz+/Uozds2GCEpgqulJQUk4fL5UJZWRnq6uowePBgPPfcczjmmGM6lX+gbho/fjwGDhyIDz74AI2NjTj11FMxc+ZMI9b25xn+6q/5VVRUmE3F6F//+lfY4mfo0KE46KCD8J///KfDj+1oeV9++WVccMEF+O677zBlypQO59/RhDU1NbjkkktM2Q844AAMHz4cq1evhjJITk7GSy+9ZLh2NL/uStcZ5t1VNj6HBEiABEiABHqaQFAPwf/sZz/D9u3bzbZz505UVVXh1VdfRX5+vhFFainsyfDGG28YIahlqK2txUcffRTQ4njXXzmUl5cbC6GKNL22bdu2Tj+vK8rbmcK89tprRnz+8Y9/NHV76623sH79emzcuNFYfGfPnm1+dHQmb95DAiRAAiRAAiTQMwSCWoC2ROZ0Os2wrAq9goIC/PKXv2yZBG6321jQPvzwQ2zZsqXV9R07dqC0tNTEq4DTvPyl0wSVlZX46quv8MknnyA3N7dVXomJiYiPj0dDQ4NHDGreml99fb3Zq2hsGTRfTaNl3degVkKtt1qE582b1+7tKtq17AsXLkR1dbUnrb/ydsRQrnWbM2cO1qxZg6amJp/8tD6dqauWT6dXqKD2DsOGDcPNN99syv355597XzJsdaj+vffew9KlS6FWVO+gddXyaJ3UYj537lysXbvWOwl++OEHLFq0yCdOT7R/lJSUmHtXrVplGCvrjga9V8u7YMGCVuXqaB5MRwIkQAIkQAJBT0CH4IMtiAVMpw1Yt912W5tFP/rooy2ZE+pzXYaJrTFjxph7Raya/Yknnmjt2rXLk27s2LHWtddea/34xz821/U5ul144YWWDEt70r3++uuWDAH75HXyySdbxcXFnjQiBi3Nf8mSJT55aX6aTu8/88wzPentg4svvtjKzs62RNjYUT77vdX/ySefNM/729/+Zu4bMmSIJfNCPXmIILMuv/xyy+FwWDYHmc5g2en9lVfv8Rf+/e9/m2fdcMMNVmxsrBUZGWnOZTje2rRpk7lF7+1sXX/729+a/GRKRavHi0C3RMj7xP/jH/+wUlNTzT0yFcHstR+8//77nnRiJTfxr7zyiiU/EMyxtolMj7Bkzqt1xBFHeOJE6FoyhcJzr/YPbZ/DDz/cknnG5n6ZBmI9+uijnjR60JK5iHrr1ltvtbRMuil7ZfLPf/7T5z6ekAAJkAAJkEA4EFBLTtCFvQkwrZAKIhUVMhxv6qeCLz093Zo4caIlw7iWCgKxfFkDBgywjj32WA8DFRgqok4//XRzXSyblsxBNHnJHEqTToanrbi4OCMet27daqnAeuGFF0yau+++25OXLUBVSG7evNlcv/32243I0URXX321JdY9SyyHnntkGoGVkJBgiXXPE9fyoL36i0XPkgU6RuAsX77c3NpSDImF1Iinp556ypRdrMXWpZdeasqnbNoqb8ty6LktQFXAvvPOO+ZeWSxkyWIoS0WoWELNbZ2tq/K3fzTInFrrxhtvNIJQrMStiqP11TaXYXlLhugtsXSattZ2lzmZnvS2ANUyv/nmm1ZeXp51yy23mHtVvKo4FyuupT9YtB7ajnbQ/qHPkAVQlpbBFpYq5D/++GM7WSsBKgvSzH36o0n7jLa5WHVNnPZDBhIgARIgARIIJwIhK0Dvv/9+8+Uuw9CmPWXVvDmXIXWf9v3LX/5i4r/88ksTrwJDBYu3xU8tpCo6bIurDN+a83vuuccnL5mvaMmwrSfOFqAaIcPP5p7f/e53nuvz5883cbblUS+oRUyfJQttPOlaHtgCVIXmlVdeabYrrrjCOuOMM4xg0vvVimsHbwGqYkutb9ddd5192exVdI4ePdpSkafBX3nNhRb/2AJUn+8dnn/+eVMPGZI30Z2tq94sw9ZGrMnqd5On1i86OtoSbweeHxia7tNPP7Uuu+wyS+b+6qknaNn0HtuCbQvQ3//+9540KgjVMqk/LFS42kEWchmxrtZWDdo/1KLqLYD1msYff/zx9m0+AlRZar6HHnqo57oeaHk0L7XWM5AACZAACZBAOBEISjdMIib2GkQ0mjQiJs3++++/hwwRQ+ft6SpqO9jpxHoGGXo10SLEIILBTmLc/qhbJ13kpEFX2c+YMQMiQPH222/jpJNOMpu6PxIR47lvbwfTp0+HWPfMSu6rrrrKJBcBikMOOQQiaPZ2O8Sqalb+a0IRlejbt69ZZX/++edDphD4vV/rLx0cP/rRj3yu6/zZE044AU888YSZs+pzsQMnLfOzPRAsW7YMYmHG/tQ1LS0Njz32GGSYG9pOn332Gd59912zOEnnUuq8U20zfaZuOodW43Vep84F1fQadL6nzsu1g3opsINYPqF9RdnrvF07ZGZmGg8GItAhotdEqycD73y0zTVOF535CzqfVBd1afvIDx6fJGKBN3XyieQJCZAACZAACYQ4gZAVoLpKWkXZkCFDTBPqIiE9V5+VLcO4cePMwhU7XgVPy6AiQ4WbHWS4FWJBg67S1r1uKkzFIojDDjvMTrbXvVjsIHMDzSIlFYEqllRsdSSIFRNi6e1IUk8ae7GUiqGWQcWWLnxSd0622GqZpq1zFZjewRb+6kfUDvtaV13IpAulVBBqm2j7qf9P3X79618bwaeiX9tUV8nrwq5rrrkGL774olmEpf5CDz74YOMDVcW6d/tpmfr3728XzbO3y21H6DNbBs23ZVB/rzLNwwhN7x8vms5mrguixHrd8lZTDhXH+gOJgQRIgARIgATCgUDHzXVBREOtmjKkjmnTpnm+1HNyciBzO6GO09UK2HKTOaP7VEMVRSo6VVCo2H3kkUeMNfK8887bp3wuuugiYzVVIaubWlrVtVBXBfVLqkGG4ls9Qld4q7iSYeFW1/YWoT5YvYOdv/0DQK/ta13Vkqn+NGXerHfWnmNZwAXNX32QalDn7zIXFw888IBxzaX9QC2lamXW0FKA7ou12mSw+x/1sNAyaH3V/VVL8anptO9pkKkArfqd9kO10lJ8GkT8hwRIgARIIEwIhJwAVUuSvnlIh8vVTY8dpk6daoZmZdGJHWX2MlfRCAd/Lnd8EnqdLF682AgjHX7XoC6BfvGLX0DFp+2P1Cu5ObTFjg7legd9c5GslIeWS31cnnbaacba550mkMcTJkwwbo1U7HoH5ab1mTx5soluq7ze93gfq7N976BO79V66G0Z3de66ksGdFqEllVW5ntnb47V3ZO6yjryyCPNubo30jdA6Y8JW/SpQ/1vvvnGXFfrbiDCF198YfqSnZfMF4a69VJrq7+gAliH7HV6hXfQYXntl7LIzTuaxyRAAiRAAiQQ8gSCegheX80oi3pMI+mXuYoR9emoe1mEA1mU42nA66+/Hk8//bQRJzocq6/rVP+Xv/rVr8z8PZ3719GgbxXS4eqbbrrJ+HJUEaFzDlV0yYISn/mBdp5qMdVhbXEH5HldqD3MLSvQjXhVwWaLWvu+QO916F1WkpuhexVrsuLb+OdU66H66dS9hvbK669Md911lxHOOgdT52Ted9995mUA4nXAJ/m+1lXcMBlRrtMa9F5lr2XTuaD6tietg8Zr0Ndyimsl0846NK9vS1JrqD3nVy2q/qZXmJv34R/15XnOOeeYYX+9TVbQm6kLdl9smZW+sevOO+806fRtWPoDSVww4eGHH4bOTX788cdb3sJzEiABEiABEghtAjIsGXTBXgUuLWNWN+teXSfJ8LI1a9YsSyxmfuskgsSsOBahZ+6TOYCWrpBWVz920NXM6s+zZVB3Seo2xw5iBbVk7qjn+SImje9Qb5dK3qvg9T4Rd2b1tpZXFufYWRlfljL30NLy6Gr0vQW7/vaq/L2ll2FqHz+g+gz1EiBWOVN+dSEkItoSC6JPVm2V1zuRvQpe3tVuvAfYbSGLqnw8Cdj3qN/Ofamr3qculbRd9T67zdX3pq76VxdSdtB2lNeCWnpN02mbiWXaEmf25lzmhpqk9ip4mY5h32r28qPC+Pj0jhThaO61/Y1q/1A/oeq/VfuRbsrO9rZg39uSubqjkmkaHh+lylz9jooF3r6FexIgARIgARIIGwJB+S54ERf7FXTIVOc7jhgxwgwT709mOs9QLWKal23RbC8/fcuQTg/QRSt20DgdMtZFOg8++KAd3eV7XeSj1mC15qpFzl/wV15/6TRO89P5sFoXf3MhNc3+1lWnOOibjHS+pVqM/QV9hvjaNNMkdN5vIIMuWNMpFzq3VH5smPr4W5TU3jO1bGoV9e4D7aXnNRIgARIgARIINQJhKUB7WyPKm4vMqybVXY8K2VAOwV5XbwEayu3EupEACZAACZBAVxIIrHmoK0sagnmrtVNdCKnVUBcxhbL4DKe6hmBXZZVIgARIgARIIKAEKEADinPfMtOFNbqYRv156sKUUA6hUleZBxyQhUyh3NasGwmQAAmQAAnsjQCH4PdGiNdJgARIgARIgARIgAQCSiDk/IAGlA4zIwESIAESIAESIAESCDgBCtCAI2WGJEACJEACJEACJEAC7RGgAG2PDq+RAAmQAAmQAAmQAAkEnAAFaMCRMkMSIAESIAESIAESIIH2CFCAtkeH10iABEiABEiABEiABAJOgAK0BVJ5B5Z5o4/uGXwJKBNy8WWiZ+wzrZnYMewzNgnfPfuMLw/vM/YZbxp7jtln9rBoecQ+05JI83lv7zMUoC3aTd75jfz8fCNCW1wK+1N9Baa+5pLBlwD7jC8P7zP2GW8ae47tPuN2u/dE8sgQ0Fcl83OmdWdQJvrdxD7Tmo32Gf2bYvAl0Nv7DAWob3vxjARIgARIgARIgARIoIsJUIB2MWBmTwIkQAIkQAIkQAIk4EuAAtSXB89IgARIgARIgARIgAS6mAAFaBcDZvYkQAIkQAIkQAIkQAK+BChAfXnwjARIgARIgARIgARIoIsJUIB2MWBmTwIkQAIkQAIkQAIk4EuAAtSXB89IgARIgARIgARIgAS6mAAFaBcDZvYkQAIkQAIkQAIkQAK+BChAfXnwjARIgARIgARIgAQ6TKBQnOAvLy9HLV8s0WFmmjByn1IzMQmQAAmQAAmQAAmQgCHw5/Ub8NC6jXDJq6rTo6Pw+/HjcEZ2Ful0gAAtoB2AxCQkQAIkQAIkQAIk4E3ggbXr8eAPG3DFkMH4z/SpmJKaimu/X4YPduV7J+NxGwRoAW0DDKNJgARIgARIgARIwB+BOfmF+MuGjbh+5HDcPHqkSTK9Txou/vZ7XL9sJabJcZ/oaH+3Mm43AVpA2RVIgARIgARIgARIoIMEamSu520rVxuR+YvybJQ/XoT6t8vhrLHw0MTxaGhqwv1r13Uwt/BNRgEavm3PmpMACZAACZAACewjgWc3b0VhZT3uenUQ6m7fhYr3KlHzaBFKr85F34II/GLEMLyyfQd21tbtY87hlZwCNLzam7UlARIgARIgARLoJIFqlwtPrt+Cp94bh8Gra/DdiZlYc/9QPHzCQBRVO1B+205ckp6D6IgIPLNlayefEh63UYCGRzuzliRAAiRAAiRAAvtJ4F/bcnHW3CGYvrkeRRdk4tgbkzFrMnD9NdH454z+aCxxI/LpMpyXMwCatlGG4xn8E6AA9c+FsSRAAiRAAiRAAiTgIWCJq6W3F5Xjp0scyD8gAWMuS/ZcS0sEZp8fjf8M64PGT6twkasfyhob8VlBoScND3wJUID68uAZCZAACZAACZAACbQi8EVhMX7ySQaiYGH4XRmtrh8wCHAfn4KSuEjkvN6A4QkJ+O+Ona3SMaKZAAUoewIJkAAJkAAJkAAJ7IXAm3OqcOy2OtSflIrIdP9eLH8804F3B6XCvagGl8Rk4eP8Ar4hqQ2uFKBtgGE0CZAACZAACZAACSiB8oZGHPppDFyRDuRckdImlIFiGK2fkYQGSXfS4njUyRzQeUXFbaYP5wsUoOHc+qw7CZAACZAACZDAXgk8O7cMR+XWouLIBEQkOttNf9TBEfi6XxISPq9HdnQM5nAeqF9eFKB+sTCSBEiABEiABEiABJoJxL7vRozbwsjL+uwVyUEjgOWDExFR2YQrKvpB35rE0JqA/0kMrdP1qpimLnRrYOete4fD0avq3dOF0RWAutmMero8veX5ykQD+0zrFrH7C/uMLxu7z9h8fK+G95nNhH3Gtx/YPHRvH/umCN8z7TN2v+kKCqt3NeLIjQ3YNDISUzKde+WvymHAjBiUfe/EsWvicd/kOmyorMQwWZTUncHuJ13ZZ1QndVYrBaUALS8v77I2dMsrtjRUSmfpLNQuK1wPZ+wSB7z19fVwOtsffujhYnb749ln2kaufUb/jurq+EYQb0p2n6moqECEOKxm2EOAfWYPC+8ju8/odxP7jDcZoKv7zEf/bcQV1Y0o/FEsOqo/xudEYmFmIo79tgKOA4HPcvOQ3r+vb8G7+Kw7+kxycnKnNUFQCtC0tLQuazb9oiwtLUVKSkqnoXZZ4Xo446qqKkRFRSEmJqaHS9K7Hs8+03Z7sM/4Z+PdZyIjg/Jj2H/FAhBbXV0NZcLPGV+Y+uO/pKTEfDexz/iy0T6jhpHY2FjfCwE4c4sf+RHf7UJZnAMjfpQNh7NjI6OHyDqlOQMiccL2cpxakYZloi2u7kLt4q+qvb3P8Ke3v1ZjHAmQAAmQAAmQQNgTWLTOhYN31WLTFGeHxadCc4q6Sj04FvUiWE/LS8X84tKwZ9kSAAVoSyI8JwESIAESIAESIAEhsPydSiS4mjDo9D1vPeoomMmjHFidFofxa6ORJxbQbTU1Hb01LNJRgIZFM7OSJEACJEACJEAC+0Kg0QUMWl6PnfKazRFT9n3q34QhwIr0eKRsa0JyXQS+K+269Sv7Uq/ekpYCtLe0BMtBAiRAAiRAAiTQawgsXd+EAwtqsFEWEXUmpMTLgubhcXCIo5RjC5PxfVlZZ7IJ2XsoQEO2aVkxEiABEiABEiCBzhJY9WEl4sT3Z/qJnXeflD0pGlXRETi+IFksoBSg3m1BAepNg8ckQAIkQAIkQAJhT0BXv2csrUO+WDEPnJrZaR4TBgNrU2Qe6JZYrBS3aw1d6Me804XsoRspQHsIPB9LAiRAAiRAAiTQOwms2mJhkgy/rxjrRtx++L4ePQBYlxqLjB0WnPXAqorK3lnhHigVBWgPQOcjSYAESIAESIAEei+BtZ/XIqmxCc4jo/arkElxQPngOESIRXXCrlis6MIX6exXQXvgZgrQHoDOR5IACZAACZAACfReAo5vq1El2nPsYen7XcjkcdFojHDg6JIkLC+v2O/8QiUDCtBQaUnWgwRIgARIgARIYL8JbC0ADsirxbeDXRiTuu/+P1sWYPRgB7YkRWPqrgSxgFKA2nwoQG0S3JMACZAACZAACYQ9gRXfNiK7phEFU90BYTEqG9iYHIuh2yOxprISjVyIZLhSgAakezETEiABEiABEiCBUCBQOa8aKj37zth/66fyyMkAtveJRUIV0KfciXWVcsAAClB2AhIgARIgARIgARIQApW1wIBNNVid2YTpg0U5BiDI9E80DY8xOY3PjxF3TFwJrzAoQAPQuZgFCZAACZAACZBA8BNY/kMTxpbWYcmoOmTFxQasQn1HR6EmKgLTihOwmgLUcKUADVj3YkYkQAIkQAIkQALBTGDnvDpENVlwT3MGtBrD+wNbEmMwuTDBzAMNaOZBmhkFaJA2HItNAiRAAiRAAiQQOAKWvLPduawapTJaPvLAtMBlLDmNyAK2JsVgyM5IWkB3k6UADWgXY2YkQAIkQAIkQALBSGCLuF8ak1+LBQPrcVjG/vv/9GaQJXo2Ly0aidUy97HEjcJ6eS1SmAcK0DDvAKw+CZAACZAACZAAsOb7RmSJ+6W14+rQJzo6oEgcshDJNaR5IdK4glis4TxQLkIKaA9jZiRAAiRAAiRAAkFJoGpBLWQUHjGHyPszuyCkjoxCg9OBsUUxWEtXTBSgXdDHmCUJkAAJkAAJkEAQEahvFB+dG2qwIc3C5CF9OlRyy+WCe8N6uLdshtUB5/JD+zuwPSEaU0oSKECFcGSHKDMRCZAACZAACZAACYQogZVbLIwprsV/DqjGJX0GtVtLS1YrNc75GA3/fR2oFcehEhwZmYi5+FJETjqwzXuH9gVWJ0ZjTEEMnq4qajNduFzgHNBwaWnWkwRIgARIgARIwC+BbQvqkehqwuYJDUhrZ/6nWjrrn/0bGl76JyKnHIy439yF2JtuRURGBur+/Cc0fvO13/w1clCmvBFJXDFlljqwtURWI4V5oAU0zDsAq08CJEACJEAC4U7A/X0t6sX1Z+qBie2iaHj9VbjmfYWSS87GY+nL8emW69BoNeKw46biZ2kjMfS5vyNi0GA4c3Ja5RMTBdRkRSNiHdCvIAK76urQPzZwzu5bPbCXR/QKC2idNMKXX36JxYsXwyVzKhhIgARIgARIgARIoDsIlIkxckBuDZb3dWNav7bnf7qWLUXj++/i6x9PwDF1D+DdvI8xq/9ROCvnFCwuW4rTB76LecMssZA+DR2m9xeihjevrh9dGBP274TvcQFaWFiICy+8ECtXrsTrr7+O2267zV+bMY4ESIAESIAESIAEAk5AX785oqwOXw2pxPR0/w7oLTGU1b/wLOYdnIorI17GQWmTMO+Yd/HAxDtwzwE34Yuj/ocpaRNw3YRV+KFwNVwLF/gtZ9ZAJ0pjnRhZEo0fqqr8pgmXyB4XoJ9++inOOussXHfddbj//vuxevVqbN26NVz4s54kQAIkQAIkQAI9SKBgvrx+UwyW2w9obNP/Z8P/3sDGpgL8fNhiHJoxFS9OewLpMXvEalJUIp6b+gj6x/fHr4/IR80br/m1guo80Nz4aEyQlfDrK8N7HmiPzwE9//zzPd1uyZIlcLvdyMyUFmonlJSUtHN1/y7p8zWUlZXBoZ5jGTwElI1Ol6iuDu8/Gg+Q3QfsMy2J7Dlnn9nDwvvI7jPl5eX8nPEGI8fKRj97+TnjC6Zpt5sf9hlfLnpm95mamprWFzsQE7HcjQoZGU+R4XG/+qK0BI5PPsT1JxchJSoZfxh+B6rKKv3m/Lvht2B29TV4IXklLv5qLqzxE3zSpURHYIW4YppWGIU1Zfn+n+dzR+dP7D6jeiYiomvsjSkpKXA6ZfJsJ0KPC1C7zHfffTc+++wzXHPNNYiPj7ej/e7bmlvhN3EnI7vjGZ0sWo/dZjOx9z1WkF76YHJp3TA2E3vfOkV4x5BL6/a3mdj71inCM8bmYe/Dk4L/WttM7L3/VP5jd5VFYHh+HRZnN2JqcpJfq2XkB+/h72OKsCqyAP8Y8QiSnIl+0+kTJiaNw4/7/ghPjPsE53z1MWIOGO/z4L5JbuxKikLadgcKyurbzMfnpk6eePPwPu5kdgG/rdcI0HvvvRdXX301brjhBmRlZeHoo49us7Lp6YF9R6v3g9TCV1pairS0tE6reu/8Qum4SuarREVFISam+XVioVS3/akL+0zb9Nhn/LOx+0xqaioiI3vNx7D/wnZzrFo+lQk/Z3zB18u7w9U6xz7jy0XPtM+oFS62EyvKv1/txsFVJXh+ajluH3wg0mN9v9+aCguwZeV8/PVHu3DR4HNwqPgIzV3/LGqqdyAxZTgGjz4fsfHi4NMr3B73K7xT8CFecizEjWLRj+jre71xQJ04BBXH97siEJWcjGT5Xu2K0Nv7TNfYZPeB5KpVq1BcXGzuGDBgAI466iizGn4fsmBSEiABEiABEiABEthnAmXza6GT7baNdaFvC/GpmTW8+w4emlggP4qi8aOSQrz/jwOwauHvsHPLR1g271a8+9wIbFz5jM9zB8Zn4+zsU/DCyCJUz/vM55qeRA+NNq/8HFEcjQ1V4TulrccF6LfffotnnmluPLUKzJ8/HyNGjGjVYIwgARIgARIgARIggUARaJKFR3Fra1GQAAwZntIq26aKcqxb+jHeHFCIUxqiULDiGYw/9G6c8dOdOOXStTjtii0YOPJsLP70Wqz59g8+91818mKUxLjw7vo3feL1JCs7QlbCR2KErIQPZwHa42M/Z599Nh588EFcfvnlZiLxzJkzcdppp7VqMEaQAAmQAAmQAAmQQKAIbM4HRhXWYP7Aehzqx/1S45xP8MiYPCQjAgftWIrDT30D2UNP8jxeh96nnfAcYuIzsfybO5CUNgo5I84w1w9IGYODYkbipcwtOHfzJjiHDvPcNzAD2BEfhbEl8Vgcxot6e1yAJiYm4r777oOuXtP5G121UsvT8jwgARIgARIgARIIewI/LJE3GNW68NXQMtyTPtSHhyUvxVm/4B28P6MUZ5QU4IhZz/uIT+/Ekw5/ABUla7D4s2uROeBwxMSJwpRw0eif4Ff1d2Pj4g8xaui1nlty5PJ8WQk/RSygr1b5X03vSRzCBz0+BG+z1ZXvFJ82De5JgARIgARIgAS6kkD1olqT/ZbRLuTExfk8yrVoIR4ZsgbxTW5cOvIyM9Tuk8DrRN2GHXzsk2hyN2DZ17/xXDkl50TEWE68sesjT5weZIn70PzEKKRXOrCjtHOuo3wyDNKTXiNAg5Qfi00CJEACJEACJBBkBBrF5XfKhlpsSwXGDpJ/WoRtX/wX7w+oxgnuOEw97PctrrY+jU8cgLFTb8GW1f9EZdkGkyAxMgHHx03G22m5aMrL89wUKW4z6/tHm8VPyG3sUldMnof2wgMK0F7YKCwSCZAACZAACZBA1xFYtwMYXVyLeTk1mN7H9/3v7u3b8Ejqx4iQ97nfNOMJGZ3t2GzFkZOuRXRsGlYvesBT8DNGnYNNyfVYtfRDT5weOAc1vxN+YHEU8mQBdjgGCtBwbHXWmQRIgARIgATCmMCmRQ1IaXBjrsz/PLTPnldqKpL8T/+Nd7PcmBU7FEOzDu0wpcioBIw88GfY9sPLqKuWFU4Sjsk5BtFNEfhw56c++WTkOFEVFYGhMg90U3V4DsNTgPp0CZ6QAAmQAAmQAAmEOoH672rgFgegm4e7MSxR/DDtDlZDA56ufg414tj+hkP2WDLt63vbDx9/FeRdsuIb9O8maXxkHI6IGIWPI9fDkpcJ2GGAvE9np6yEH14cIwI0PH2BUoDavYF7EiABEiABEiCBkCdQ2wBkbK7Fxkxgcrbv/M/8L1/Au32bcFBkFsb18X2Pe0fAxIpLpkEjzxUB+qzM7Wwyt5yYMwsrUmuwY/UCTxY5IkB3xUdjZFksNoapM3oKUE934AEJkAAJkAAJkECoE1i92cLo0lp8mVPZav7nm+v/hB0xsbhm0k2dxjD0gEtRW7UD+dua34J07NjTxSoKfLlxzzxQtYDuEgtoTpkTm6s4BN9p2LyRBEiABEiABEiABIKBwPYF9YhzWzL/s1wc0O9ZgLRz5Zv4IKUWmVYsTso+ttNVyRwwEwnJQ7BlzYsmj6y4fhjVkIYvq5Z48kyIBSrSohAtq/Grdu4ZmvckCIMDWkDDoJFZRRIgARIgARIggWYCTUtr0CCukLYNcWF0UqIHy6Jv7sKShGRcNPQCOB2SoJNB/YIOHnMBcje+BZer2dfoEfETMS9+F5qqqjy5OnKaV8JH5bnQJCvuwy1QgIZbi7O+JEACJEACJBCmBMpkvU/29lqszbJwcOYe62dB7peyUCgfLhGPF46cvd909B3xblcNdm1pHnY/esjxKIl1YcXqTzx5xw+Kgs4SHVgShR214eeKiQLU0xV4QAIkQAIkQAIkEMoEVq5vwojyOswZ6Dv8vnruPViYmIajYidgQFzWfiNIzRgv74Yfje3r/2vymj7yeEQ2OfD19rmevPv3daA4LhJDSqOxRV5HHm6BAjTcWpz1JQESIAESIIEwJVDwTR0iZbRb53/O2D3/s6TgeywqX4zc2Cj8RBYQBSoMHHEm8ja/B7e8ojNBfISOb8jANzWrPNlniwF2V1wUhpXEYnMYumKiAPV0BR6QAAmQAAmQAAmEMgHnihpUxziQn21hzO75n2sXP4TFCelIb4rDrOxjAlb97GGnwtVYjUIZ3tdwWOw4LIrLh7tR/EBJyN7timl4WbQIUFpADRT+0/0EmsRfWH5dIUoayrr/4XwiCZAACZAACYQ4gZ2lwNCdtVgx0I3pGWniL96B6oqt2LjhDSxOTMTZ6cchKiIqYBT69DsIsfH9jBVUM52RMxMVsux91Q9fmGf0ExekBQlR6FsVgW3lFKAGCv/pPgIqPP+28Z+Y8slxmPTx0Rj34eGY/v5MPD/nN3At+R5NZRSk3dcafBIJkAAJkECoEli1woWBVQ34cFApDts9/L5uyaNYFZ+CGnnd++xJVwS06ipws4acJAL0fZPvtDEnyvvlgQVbmy2iUbLQvj4jysTV5YWfKyYOwQe0u+1bZlWuapw7/yrcteoPGF5bjUsL8nBB4S7EVuTitpq3cOaK87Dt9gtR9/STsLxcN+zbU5iaBEiABEiABEigbH6t+oPH3CEVMv8zHY31Fdi06nksic3EpKZsjEkeEXBI2UNPEivrFlSU/oDE+FSMqU3BtxUrPc9x5jRbXCPzxCFomAUK0B5q8HqZlDx7wf/h+5KluK6kHBflbcMlGybhzuWn4r3UP+PulBPwXUo0rjhiNbZseBM1996Jpvz8HiotH0sCJEACJEACwUugSSyPCWtqUZQcgdp0B8bK/M9Nq55DYVMDlidZOG/QGV1Sub4Dj5ah/gjkb/3U5D/VOQyLnbmeZyUMihTXT0BWSaRMwwsvV0wUoJ5u0L0Htyz/LZaWrsD/FeRjqpWGmcuPxuDEo5B63yNIOPEMXHPkQ/j7QQ9hTXwC7pm4GgUx21D7h//HIfnubSY+jQRIgARIIAQIbNwJjCqswdLBDZienmbe075u6RNYkzQCUVYEzprwky6pZXRMKvr0Oxi7ts0x+U9Nn4ydsfXYnr/OnGeJGC6UlfBDjSumZqf1XVKQXpgpBWgPNMpbOz7EK9v/hwtqmjDWisG0pQchru8wxN14CxzJyZ4S/SjnRNw25pf4IiUVLw6bj2p3Ier++hisJnVdy0ACJEACJEACJNARAusWNSC93o23BhXicBl+z9v0Nmoqt+KbGGCWYxxSovZ893Ykv31J02/QcSjI/QJNTS5MG36suXXR+mZBmpXW/E74oaWx2BJmK+EpQPelFwUgbWlDOX6z4n5MjczEwQUbMbXsNEQ1RiP2l9fDESsvh20RfjbySkxPOxAvp6Vi4eT1cK9bi8Z33mqRiqckQAIkQAIkQAJtEahfVAO3DHXPH1SDGRl9sG7JYyhIGYXt8S6cL6/N7MrQXwSoq7EKJfmLkTNoIvrVRWNx4XfmkVniC7RALKCDy6PCzhk9BWhX9jo/eT/0w5OoclXhlE2LMCbrIiQuL0DsZVcgos+eV4J536ar6P48+X5URUbhVUcucmdGo+Ht/6FpZ553Mh6TAAmQAAmQAAn4IVArbjczNtUit18EYpMiRQBuQmHePCxzpiGzMQZHjzrZz12Bi+rT/xA4nbFiBf3SuH46sKEvljRsNA/omwIUiiumTHHFtD3MXDFRgAauj+01p81V2/DClldwUoMTOYmDMWQu4Jw0GRFjDkLj9zVo+KoKrrV1sNwyW9orDE0chCuHXYS5qen4xvUBGlNjUP/SP71S8JAESIAESIAESMAfgZUbLYwuqcXCoTXG/dJ6mfsZISvfv4gvx5mx0+F0iD+kLgxOZzTSs6YbAaqPmRw3CqtjitHQ1AinqLCGTHHFJPH1ueHlikk8XwVfKOtC35hud7MrhIqKCvNLJZB0/vjD40hwROPQ3GUYlfwLWCUNqIz+MerO2gKH97TOVBknOD0O+JEMyTvVaQRwWb/z8NKW1/BBUizGHViMCXOqUb5gPqwxYwNZxHbzamxsRH19PWprw2uidLtQ5KLdZ8rLyxERoR8jDDYBl8vFPmPD8NrbfUY/Z9hnvMDIofYZHfnh54wvF/YZXx7eZ3afqWtjFfmWzx0YKcvg/yvzP0+LTsbWH17BtvTDURW1DaeMOBddqSnsciZnTMPmlY+hpKQQ45PHo6H2Myxc8xkmDJgGq59MRNWQ5wpoWbqjzyQlJYl1t3MCPigFqFa4q4IKrIYGeW9rQkKnoforW25NHt4q+Ag/rhPHtBkHwXr/IDQUTEVNiRMfD03BivR4VEU7kVFfjVnlJTj4HzVwLnIj7u6+iEhzIkn+u2rYxXh0/dNYkfsJRg67FHEfvIfYqYf4e1yXxFXLu2qjoqIQHR3dJfkHa6Z2n0mUN2l09g8xWOu+t3LX1NQgMjKSfaYFKPaZFkC8TrXP6N9RTMzuL2Wva+F8aPcZ/W7SvymGPQT21meiV5WiNtqBFf3qcEPlEpRZLsxvqsH42jRMGXH4noy68GjAsOOwYemDcNWux9SxxyHiu0expuA7HDbmOKQMkR9eYmtKLxYDRmwckqIC076qZWw901V9Zn9+QAemll3YaP6y7soveRumPiOQz3l2678R54jCQXlrELnuG/TdlYivc+Lw28NLEZ1VhBmZqchsEL9kG+PxRN4QDO5TjJtWlKHpVzuR/OcBiMiMxNXDL8LTm/6JL1IzcGDGLox7rxjWyhWInHSgP0wBj1M2ugWSS8AL2QMZdlWf6YGqBPyRaslin2mN1e4zZOOfTaA/f1s/Jfhi7M9dsmnddvp31NbfUoG8THDojlpsGCLGn/hY1K99HPEDZ+EbxwbcHXtWt32fZWZNkzJGoWTXAow56HqM/DIeS5tWmudnZcg8UF2IJO+EzxUj2PjYwPz4sj9nemuf4Xhh674c8JgaVy1e2fY/zGiIQtymLzD5h0S8Mt6NW2avxW9+lI6vTj4YDxwyAncd3h8vXJKM+8UdWaEI0rsnZ6O4sgFlt+yQNyG5kRqdggsHn4UFSclYW/gBGoZkoIEr4gPeXsyQBEiABEggNAisWNb8+s1Ph5ViSkyVuF7ajpVWinn95VlTr+q2SjojY5HWdwqK8r4xz5zo7o/lrq3mWF0x6Ur4oaUx2CojAOESKEC7oaXf2PEeKlyVGL/4Zsxa1QdvjarDa6ftxKdHHobTsvu3KsG4gQ789UoZtsyOwUMTs1G/sxElD+4y6a6SxUh18v74BUkp2HJACZrWr4N7Y/NqulYZMYIESIAESIAEwphA+Vc15vWbb+QUY1DRx0jLnIz3Gtbh6JqByOiT061kMrIPQ9HOZgE6KX4kNkaXo9pVAxWg+fFqAQ0vV0wUoN3Q/f619T+YUXAZzlw2BT+ku/D8MRvw5szD0M+P30+7OKkJwIM/caIxKw7PHJAG5/w6VL1ThoHx2Tgx6xjM75OFzcUfwJ2ejMaP3rdv454ESIAESIAESEAIuGRNcfLaGhRkOFGQ6Eb/Ha+gccDxWB9XiXOzT+l2RpnZM1BfW4TK0nWYJNZQS+Z9LstdhL6pMuopArRvtRPbK8JnkS8FaBd3wbUVG7C6oBQXzpuNRFndedcJG/GPMcPlrQtRe31ycjxw7/kRWCWear/MiUT1U0VoKnThsiHnY2dTLVY63cg7SP7IFn/LV3TulSYTkAAJkAAJhBOBtdssjC2S78rhLgx2VCAjyokvS3ORVh+JE6Zd0u0o0rMONc8s2jkf44cdhkjxfrN0+zfNrpgyIuEUD4zVO8LnffAUoF3cBV+VV24ev/ZeHJVbi78eUoJrS1Zi8PjxHX5qtvin/9mPHHh9WA70d9HmR3fgcHHnMCxhML7rPwqbG+dCfJbANfeLDufJhCRAAiRAAiQQ6gQ2z61DgqsJbwzeheHlczHogMvwtnsZTm8Yg+i4xG6vfmx8JhJShqF450LEZg/G6Ip4LCtdacoRkd1slLLEFVO4BArQLmxpy7Lw0epiXL5sEPKS3NjZ/3ucPGHiPj/xcHH1OXliBN4Z2gd95jeienWtWYz0vUjSvMpNKJvSB41ffM53xO8zWd5AAiRAAiQQqgSsxTX/n733AIyruL6Hz/airVr1XixZki3JvTdMx4YYQg0EQggBQgKEkEbJlx+EkkIv+QcSINTQOzYYF2zjXmXJliWr97oq2+t35wmBZEu2LKvsrmbgeV5/M2eu9p135xY4ZSJsiLEg07oH5fJYdMjcuJJif45XiYiZi7bGnRCR5/5UbxQK3dVCU9SJMrBw4OpmP7zEHSZC4QR0FEd5a+tuzNv3E+SY7Xh6UQP+ULQXsoXDizn2s3NE2JFiQJ1GjMpn63F5wkVkPyLCgYhkVJlK4W9rhfdQ0Sj2ht+aI8AR4AhwBDgCwYFAaxeQUmNDZZqYJgk9WBKbig9rv8LkbjXyZ140bp0wEQHtbCuk3PBWCreUhnJph+CIFB0hQptSigSzDHUTJNkLJ6CjKIav763AFSUalIZ7kKTYj9icHIgoWPlwip7sQa9aJsZnyZFIKBHBVyjDWdFLsEtvQn3LBrijdXwafjjA8ms4AhwBjgBHIOQQKNhL712LC5tSG5FmL0JMzpXYKC7DZeJZEI1jIH8TxQP1UySb9qbdyDflCY5Iha0F33nCp3SwUEwTwxGJE9BR+rPzkYBJtuVjcocDL8+pw8/2kfZzwfC0n71NPG8GUJkVhlrSgta+2CRoQSs9nagmw+rGXC88e3bDT9mKeOEIcAQ4AhwBjsBERqBzkw1sIvu1BDOmiRqxrr5QCMd0ed7YOx/1HQdDRB7EEoUwDZ+TTMHpqZEHq3cghkIx9Qajr54gsUBHhICyfMa89EfgvaJS/OCIHvUU+iEydj/CFEpI8vL7n3SKWxIarauXivFFognpJRJMaZ0Jg0xHXvJTUe3fzZIow7NzxynelZ/OEeAIcAQ4AhyB0EHARX484YetaIoSoUkjwor0GXireTXOaItAZM7Ypa8eCFGxREaxSKcRAd2FsOQMpHcrUEAa0GgKxdREwejjusWosnAN6EDYDbjvrrvuwpw5c/Dcc8+hvb19wHMm2s7NX5PjUKsVb0xrwg37j0I6a/aIqP0XkkNSzRQN2pUi1LzeQoHsz8MOqRft5ElnzSZnpG82TzSoeX85AhwBjgBHgCPwHQKFR33IarNjb3IddN4OGGLTcVRqxpX65WQPSsE3x7mER8+iKfhdEFEs8BzKR1/kKIecEqPbw2WQ+UTorOcEdMhDdO+99+LCCy/EE088gbi4OFx22WX47LPP4PVSFNgJWMwWYOY+EzykWzdnboWxuRPSOXNHBAn2t/PDJWJsiDMia58E5youQJvXigptBOqSezIj+VqaR+RZ/CYcAY4AR4AjwBEINgRqNtih8Pnxbkoj5qmc+N/BVxHhkOKscYj9ORB24TGzYbfUwW5txBRZEkrEbXD73BDFEgul4ql3DXRZyO0bkSn4pKQk3HfffSgpKcGGDRsQGRmJG264AQkJCfjtb3+L4uLikAPuRB16a2cLFjdY8HmGBRc76wByPJJk55zoklM6tiALKMrR0jUieD/XIU4Zg0PR2aixbIZfKoFn27ZTuh8/mSPAEeAIcAQ4AqGCgGyPDd0qH7bGa3Fu2jR8bNuGS8wpUCQmB0QXTdGzhXYwLehUfTbcYh9KKaSiMqknFqi0kQVkCv0yIgS0L0zz58/HPffcgz/+8Y/CbqYVzc/Px9lnn43Kysq+p4bsOjmlI9zpxfaMXZhGdijS6TMhkkhGrL/MFiXeo4IAAEAASURBVPTsJVLsjNIglab6V0afjx3ogsXWCHOeFp7tPblmR+yB/EYcAY4AR4AjwBEIAgQqmoDsBisK42ogpsiaDtchWMUeXJW0KmBarzFMgkyuR3vjbuTGUzpDKgfrdiEySowOuQSRbWJ0u0M/IP2IEVDmiPTSSy/hzDPPBNOIvvDCC2C2obW1taipqUFMTAzOOeecgBGA0WrI0QZgTrkPtRR4Pj5yE5RNDkhn9AjYSD5zeS6wLVWHSModm12yHN1eOyrCKeB9ZB18dbXw1lSP5OP4vTgCHAGOAEeAIxDwCBz52kmpNr1YO6kG0zRyfFj2Oma1hmHygosDpu3MDtUYPQPtzXtgSs5BjE2GwsZ9QiimZnJESjYrKRSTLWDaO1oNGREC+vjjjyM6Ohp33nknJk+ejO3bt6OwsBC/+c1vhP1RUVH41a9+haNHj8LhCO08pxv22DGtxYqPs1twnpO+YORySKYSWxzhoiBN/eTlKtSFyRC5ToEkdQIOR6ajvnMLvBTM1rNj+wg/kd+OI8AR4AhwBDgCgY2Ae6sVTokPH0yKwOxIA3b5K3GFJx9ivT6gGh4eNRPm5r0QRUYhm4LjF1pKEEOe8IyAJnXKOQEd6mhptVpB+9nQ0CB4ws+e3WPf0Pf6rKwsgYAqyesrVAvLntW1ucf4uTxxHeJqKfQSkU8RkdDRKOdSXNCv43SYXibHctll2O5th93VjfY8FYVj4gR0NDDn9+QIcAQ4AhyBwERAyH5UYUVpdC3sFB+71bwWGrcYP8j7UcA1ODx6Jpz2Vtgstcjxx+Cwt16IBdqsliKxUzIhgtGPiAa0paUFKpUKx5JLRkgTExNhI1WyTqdDWlpawAnBSDaolKbf86tdKDN6MUn+JfRHnZDmTx/JR/S7VxR90NnmaSjYrghZu6fDStPwVZEZqDdUwd/UBG9VZb/z+QZHgCPAEeAIcARCFYED291ItLqwLbOBnHMVWN/2Pn5QHwntjAUB12VjFGmQqJhpGj5bnYYOiROdviZ062VQe0RobQr9Kfgen/9hDA2z+XzqqaeEKz/66CPs2LEDBw8e7Henw4cPo7m5GdJxTHvVr0GjvLG7yIcz22z4z+xmLJIqiRaKIMmfNqpPPWOhFAWr1cjb7kFCXgqKScGcWboZU1SrhGl4SXLKqD6f35wjwBHgCHAEOAKBgEDXOjO8Igq/lGPApDA7NlFKy6sNPxy1WcjT6bNGnwq5Mpzige5BjonM9NyrUWg+BH/MfOG2jrrQD8U0bALKNJqNjY2CvWdVVRWYFpQ5HPUWZmRrMpnw5JNPkhnk6ExB9z4rUOrmzd1C7LGS+I24pCMS4qRkiI2UX2sUy/R04IlUDX66y4Yzun+MDxWP4jyPHW15MsTs2gnF5VeO4tP5rTkCHAGOAEeAIzD+CFjIvSSx1IXKyBZUycVQdaxBXrsK+Yt+OP6NG6QFxqjppAHdh3kZl0NRKsKhuj2QJy4RzhY1hL4X/LAJKEPomWeeEYB67LHHBOejFStWCNsT8Z9GM5BW6URjmA/xso8oDVg6JEtHV/vJcBZTYPr4czSw7WvFlN2T8eo8G2qjcpDorUJ0c5QwDc+1oBNRInmfOQIcAY7AxEFg745uzO5y4pM5VqgkIhS7N+CR9mxIJmUELAjGyOmoOPQyZMuTkbFHiUPtRTgjVgKbVIywZj985FgiDoDMTaMF4IjYgDLv94lMPtng7C3zI59Sb65L68AcmKHukkA6Ct7vAwnCsuki7IoKw6KDGkTLs1EckYzGju3wqCkoPWlBeeEIcAQ4AhwBjkAoI2BeayZ/CJp+n6pAjKyNnI/8uHjy5QHdZaYBZY5IDnEXsmw6HLJX9DgikSd8glmBhhCPGjRsDWhFRQXYsnz5ciHTUV0dZfwZpLDYoCcqbrcbe/bsERyZ8vLyAiJX64naO9Cxmu1OLHD5sC+lALNEk0AeWRCP0ZdXNIVuaMgNw9IvunFmy4+w2vQ3nO11omWKBHFsGv7SwP4jHAhPvo8jwBHgCHAEOAJDQaDbakVKpRr1Me3YCdKA2tbjsmoTtNcvH8rl43ZO+HeOSPuQLYnHB6J9MOncaFBJkUIEtIpsWOPJwTtUy7AJ6CuvvIK//OUvYOSRTcGzwPODFT+LTzRIYXakt956KxYuXCh4y//1r38VQjopFIpBrgi83S4W7vOgFQ6JH279RzDVs9SbUyAaQ+erzLPV6NwgwbS96XjjjG40Rk9Fg6sKsY0ueMk2V0JpUXnhCHAEOAIcAY5AqCGwee1enNmRgW8uklH2Iz/sOIAfK86H2EDamQAuYfo0yoiko4D0+5CjzSAHqr2wySspFmg4FjZJsJ0iCC0whQdwD06vacOegr/33nthpa8OVp577jk4nc5BlxM18Z133hGm79k0PrvnpEmTsHbt2hNdEnDHDtcAU1sd2B3nwlzLVhjLQfE/p45pO+dni7A7OgxLStQwSvJxJCIVTZ074VFSvvhdO8a0LfxhHAGOAEeAI8ARGAsEvF4XXDt6nH3fyuqGWlSLec1iZAdQ5qPBcGDO2obIaTC3EAGNzhdOq7IVwKyVwegQo67dPtilIbF/2BpQCeU2ZwsrLMxScXExmKYzOzsbGzZsEByU0tPTBVJ5Ii/4m266qd+Ue2dnJ+z2E4POyO5oFabRZcXlckEsHho/LywS4fxOB97Lr8JimREKtwK+SZkCIR+tdh57X9ZSyywVDB904aymS/F17ONY5nOhKUeEeCKg/gtWHnvJKW97PKHvlXfKoNAFvTLD5LL3b2I49wnFa7jMDDyqvTLDfme8Xu/AJ03QvUxmTjRrNkFhEd5JrO9cZvpLQHHBm8isOxPNCS6sc7XD79uJaxpT4JmcBe8ocoX+rRj+lo5CMNWVvQ8d8aXIFikO1u6GKfI84YaWKsrqlDV8vjMWvzOM3zEiPZwybALa92E7d+4UptD/9re/gaXdXLVqlUBEWTrO8vJyvPvuu31P77fel5yuX79eCOV0/vnn9zvn2I329vZjd434dkdHx5Dv2bVTRWp/4GjcdlxoT4CPQlSZmQnBGLSzbyPjZsth/lyC2fsm4Z0IM5pM2ah1VCC+zgdzSQn8ERF9T+frI4wA+3jihSNwKghwmTkVtPi5DAEuM9/Lgd/nxdaN1biy04VNy+jDhf6LdBRjcdL1MJ/CO/z7O479mlSZDoe1Ac0SOyZ3kSe8qhgLo3rMFj21LqIRp893RlNmIiMjhx3rfUQIKLP/vP7663HHHXcI9ptME/Tll1+CBatPTU0V8r8fmyXp2GH++OOP8dprrwn2pBqN5tjD/bYjRpFIsbZ3d3fDSPE7h6LNYrHHoqo70Kz2wyhdjai2GEhzpmI029gPjD4bi8lUZHWMD0vKvdCK8lAW40Lc4dXwyJOgryiDlNKhnk5hGa2YtrvvR8Pp3C9Urj1VmQmVfg+lH1xmBkaJabHY7+NQf2cGvkto7mUzYOy3l//O9B9fLjP98WBbNaVvQ1d+LnykgHs9sxkiVyV+XKaE8acXQBRuOv6CANwjEy3CEQpWI5G1IMthxEe+elyYqgRlEIW6TXRaXKJXZgxkCztaCYGGwpMGg31ECCjTct54442CGnb16tWCZzwLVB8WFgbmTMS0iTExMYO1Aa+++iq++OILPP3004iOjh70vN4DMpmsd3XE697pMPaMoQBbUgHkkJ3G9gQL5nQXwliTANmZUzGabRys0wwV77wwGN7qwvKWS6lNz2IhTcO3sGn4vXsgu2jVYJcOaT/DgwnxePRtSA0cp5NOVWbGqZnj8lguMwPD3isz7O9ptF4MAz858Peylyb/nTl+nHw+n7CTy0wPNsxM48Cu/2Bq43/RmibCZrcFYt8BXK1eCnn04HzjeGTHd0941FSIJQp0mwuRpUjEv8U1MBjova2UIaJNChdNb4fR78RwSq/MsHd2IP7ODM3I8SQ9T0pKwtdffy2k3WSaz5Ure+wNP/jgAySQ9/WJyOfnn3+OdevW4Z///OeQyOdJmjLmh48WeRBrc2NHylGKPRYJpUcFSVbOmLej94E556jQKZdg3v5MVDua0RWViwZjLXwV5fC1tfaexmuOAEeAI8AR4AgELQJ1ZR+irmIRkrtdKJ7roMl34NxaSsCyrId/BEvHxGIpDBG5lBFpP7L1k4Vm2+VVaFLLkEyhmKopFFOolhEhoLfddhveeOMNxMfHIzY2Ftdccw1ef/11XHXVVbjllltOiN2LL76IsrIywRN+yZIlYAtL3xksxbHXJjS1JnYnTL5kiMg8QEw2EeNVspNEOBirpml4JRSiqSiJykSTZS88Ui88u3eNV7P4czkCHAGOAEeAIzBiCBTteAhxDT+CWyrCE3FHAH85bm2KgyQ3b8SeMVY36vGE34/JsfkQE5Nu9RxECwWjT+qQUSzQHo4xVm0Zy+cMT697TAunT5+OEnJyYU5Hc+fOBbPhnD17NoqKipCZmXnM2f03T+Sg1P/MwNuykv1nRJUDdRofInxfwdgSPa7aT4YQc0bzz9fA9L9uLG29BNsTX8QsFpQ+i3nD74T83BM7eAUeyrxFHAGOAEeAI8AR+B6B2rKPUNvUhSV1KrTmynHYI0Zq92HkzrsYoiFGr/n+buO/ZqRQTOWF/4EsJhop1QqUd22FNOwcRNeIsKWLCGjwWBScEpgjogFlT2S2mzk5OWCB5UtLSwV7UOaaz9ZDtRyi+J+TzQ7sirdghuUIjHVSIqCn5+gzElhNPlcFC+WSXVCQhVJbLRyRNA1vqoPvaCl8QeIZOBI48HtwBDgCHAGOQGghwGw/i7b/BRLrg+Tx7sG6GfQi9vvwq+JWyJYsDcrOGiJZDFA/uhRtgid8saUUrkg5RdcRoaOGNF0hWkaEgDY0NCA3NxdxcXHIyMgQtJ5M89m7hCh2qDjsQYzdjZ1JRxEHJcKcWkgmZ497dzMTRTgUq8LSchWk4myUxGajybqXvOrc8O7ZPe7t4w3gCHAEOAIcAY7AcBBgtp8drQcQV7EA1jAJntKXQ+U9ioviF0Ok0Q7nluN+DbMBpflLctguxmS3CUe89ZDE90xQu+qGHwd03Dt2kgaMCAF98MEHwYIHswxGR44cEbSeTPPZu5ykDUF72Lavxzi4Mm4/wkUplPbLCDHFQQ2E4p+rRSwlqprfsQo7/N3wMW/4yT54eFakQBge3gaOAEeAI8AROEUEmPazcPsD8GpuQW6dAzUzSWvoM2BlVTkky88+xbsFzulSmRpaYyY6Wg4gW5kCm4iURTEOsLgHkobQTVQxIjagjGj+8pe/xFlnnRU4IzrKLXGTTGgrHWQo7IdWvBGGDj0kmeM//d7b7fTzVXC+L8LCA7l4RPcwPFH5aLDVI26zAn6KPyiiMFm8cAQ4AhwBjgBHIFgQqC55G51thVBbPiStpxvPZG+jmWsNfm8xQJSYFCzdGLCdRpqGZyk5s8OvpePr4QlrRKtSAV2zCD4i3mLm4BFiZUQ0oMwJadeuieVhXdYATCL7z72xDuR2F8FQHxj2n73yOSlZjOIYmoYvU5PgTkZp7BQ02/bTNLwTnr18Gr4XJ15zBDgCHAGOQOAj4KOsR4Xb74cx/jzEHZCgJU6Gr5Ri5HSUwrAseLWfvcgzO9DOtiIkx+RARU5VHf5DlOCGPOHbFah3hKYd6IgQ0J///OfYvn07/vSnP+HTTz8VcsGzfPC9Sy/AoVQfLfch0eLCroQKJLucMFhp+j2jJ4ZXoPTTMysMqV1+TLdfiG3+LpqGd6Mp00PT8JR2gReOAEeAI8AR4AgECQKVh/4LC5FNv/RxZLQ7sG56MZlNGnFHdReQx5x4grswAuqjiDVWgxsZXQrUOXaiiUIxJXdSKCZraIZiGpEp+EceeUSw/XzggQcGlABmtxFqpYPsPxl7L0k4jDMdcZDKtRBT0P1AKikXhMHzSQsW7Z+OJ+f9Hf7YWWjorEPC1iL4Ld1Ba7AdSBjztnAEOAIcAY7A6CLg9ThQuOMviEu7ENZ1Bor92YlHkwugcSfi3JkL4AnC0EvHImaM6CHRXaIGZHapUBC+C61hUiytF6PAYsPCiOBILXpsv060PSIa0GeffRYsf+9gy4kaEKzHxCUO2Ii+u7RbYbRHQTJpUsDFH0ufJEFZhBJLysJoGn4SSkm132IvgFNsp6D0fBo+WGWPt5sjwBHgCEwkBEoP/BN2Sx3CMx5Bdkk3DmZ3oVs2GZfXVEO+bFlIQKEMi4ZSHY0OcxEm+yJR5m+EM4JSaPpFaK8NzWxII0JAWZ5RpVKJHTt2gJHRjRs3or6+HhaLRdgfEtLRpxOtpPFPbHHiUIQHmd27oW+SQ5Jx4oD7fS4f01X7DA2y2/zI9qzEN74OMG10YwbZge7cPqbt4A/jCHAEOAIcAY7AqSLgcnbi8K5HkJJ9DWrWxkLv8uKxKavpNlL8LI6yDxL3CJXCpuE7WguQrUqFV+SHI9ojdM1eE5qhmEaEgHq9XqxatQrL6Evkz3/+MzZt2iSQ0OzsbKxezQQltMqRWj/Su5zYE9+MDLsZhk4d2X8GJgFNoGl45js3v2A29ncVQ5SwAA3h1fAePgR/d3doDQzvDUeAI8AR4AiEFAKHd/+NwjxakTHzzwjf3InaSC/2REdhdms9Us46J6T6KhBQForJNEXoV1d0mxCKSVzfQ0RDqrPUmREhoE899ZSg/WThmG699VYBo+uuuw4333wzBrMLDWYgGw66ofb4cCD+CGJ9UmicOkjS0gOyS5OmSFFllGNpKQXJF6WhODoLbfbDZD7QzZ2RAnLEeKM4AhwBjgBHgCFg665Fyb6nkJF/Kwp3RSOTnI/+m7uenI/i8VOFkmJvG0IKKBaKyWlvhTbShHCHFJ2SMpgVUqibQs+Phg3ciBBQ5u1+++23YxLZQfYWiUSCe+65BwUFBWhra+vdHRK1o7DHHqMx+gCMSKCMBYkQqVQB27fufA2mNfkwSbwCm9xNEItlaMh08Gn4gB0x3jCOAEeAI8AROLj1T5BK1ciZ/Qe4PuyEVQ68nd1G5MyGFWeeFXIAMQ0oK13qDiElZ4tvP5ooFFNCmwLtLlfI9XdECKhCoUD3ANO5ZWVlsNlsJEDkrRMixUupCXRkj1Gr9SPctwv6DvJ+n5QR0L2LPT8MMvqAml0wD4XdJfAnn4l6bRm8xYcpN7w5oNvOG8cR4AhwBDgCEw8Bc/M+VBa/hpw5d6OsSofc8m58nFUAtyQP17gckEdHhxwoWkMGJFIVOr21REBVKPfvFghoslmGyhAMxTQiBPTCCy/E888/jzfffBMuYunM0WXr1q2CVnTevHnQ6/UhIyi1rUBKhxOFkQ5MthyBoUUpeMAHcgfTZ8jRqJFh8REdFOI0HIxIRpezCp30leXZwZ2RAnnseNs4AhwBjsBERGDfpt8iTJ+GSfm3oOrVLvIG9+OFGbvIp0GC6+bPD0lIRCIxWF74zvaDmIxoVMuKKRi9FHEWMSotlFs7xMqIqCavvfZa7NmzB9dcc41APplXPIsNOnnyZHz00UchBVlZlQ/5Vhdej6vCJKcDBls4EdDA1oCyDF7m3DDM2dmJVPkKrLd/jOlyPeonWWDcthXyc88PqTHineEIcAQ4AhyB4EWg9uiHaKnbhIUr3kZbhwyZ+zqxOakOHZppON9uRXxqavB27iQtZ9PwTTUbkK25hGxdC2Gl8J+yUhFaasj0L/EkFwfZ4RHRgLI+P/nkkzh8+DBee+01PP7441izZg0OHiQWTyQ0lErbfickNJ19KL4YUX4tlLIIiGJiA76LkedqKHeuH7kH56LMWgVH+nmoVxbDW1EGX2NjwLefN5AjwBHgCHAEQh8Br8eJ/Zt/j8j4JUiYtAr7XrMg3OnFv2avJedZHX6WnxvSIBgip1HGp6OYZEoHRWKCOapH82mvDr1QTCOiAe2VhszMTLAllIvnCAVxJ41id/g+GKqiyfs9DSKmYgzwkr5QgQqVBIsO67EmOxN7deGY7W5HS3gr5Fu3QHHJpQHeA948jgBHgCPAEQh1BEr2PUne71VYuPJtWEjpF7exAwcjO1EXkYppTjvmZWWFNATME54Vj8GLJIox3qCvpFBMpOSqd4dcv09bA8qCzT/xxBO44YYbsHjxYqxcuVKw/dy5M/Tyjbu9gKnBhTKjH0m2vdC1qSBOnxQUQsE4cmtOGBbUAWmqlVjTuR8qwyTUpZnh2fpNUPSBN5IjwBHgCHAEQhcBu6Ueh3Y9jLSpN4ARsR1v25DY7cKLs9egWRmHWyaHtoKLjazeNJX+FaFL1orMTiWaJYVoV1IopsbQC8V0WgR0+/btyM/Px69//WusX78eUVFRcDqd+OCDDzB37lz85Cc/AQtSHyqlupkckCgAfWFUN9Jt9RSAnmJrBgkBZWNgoml4rduHtIKZaHW2oTV1ORr9h+Bsr4W35EioDBPvB0eAI8AR4AgEIQL7t/yBwgTKkTv/fjhJ4af9zIxKnQNHYg1I9LixckpOEPbq1JoslamhNU5Gh7MCk7vDUCM+gEYKxRTXIkeXO7S0oMMmoMzb/dJLL4XJZEJDQwMqKirw3nvvYe3ataiqqsKrr76Kd955B08//fSpoR/AZ1eUexFl92BfXBmSKAyE3mYkAhqYAegHgjF1qRJdCgkWFukQr5qOLXIf/PCinjI6ubdsHugSvo8jwBHgCHAEOAKjjkBz7SZUH/kfchfcD4XKhO0fOYTA86/O+grlmlTcmp5GyVQC39xtJIBi2t+O1gPIksajTVEqhGJK6ZCjIsRCMQ2bgH7yyScwm8347LPPEBMT0w9zZhPJPOJvu+02vPLKK/2OBfNGxwGH0PyjMcWI8BuhMFEAeo02aLoklpAnXY4ai2qBZMVKfNW6DRqKCVoTUyOEY/KHYKDboBkc3lCOAEeAIzBBEfD5PNi78Xaadp+O9Nwb4abMk/L32lAf5kJRnAiRfh+uzAlt28++Q8884TvbipClTYdL2ojmMCliLSJUdoVWKKZhE9CamhqkpKQgMjKyL2791mfNmoXy8vJ++4J5w1vqgEMCuPT7oXeYyAEpOOw/+2LOpuENLh8iD+TATX/0xbG56PLUokNcB8/uXX1P5escAY4AR4AjwBEYdQRYuk1GuGac8RQ59Yqx7WM7slsceGvmRhSYJuEXpP1UUnbFiVKYBtTndSIqnBRdPhHaTSz6jgitVbaQgmDYXvBdXV2IPkkmAmYT2tnZOeKAuUfRDsLjoU8vKuwZPh+lPfq2eMiUNbLRhdJwH1KtREDbDPDnpgjn9Z4TDHXcIima5GIsLNSiNv9cfG4txa/UMahKbYZxI+XYnT1n0G4we16m3RaLh/3dMui9g/nAYDITzH0aqbazvyGGD5eZ/oj22sYzbFjiDl6+R6AXGy4z32PC1np/Z0JNZli+98LtDyAl+3roI2bAandD9jbTfrpRGOsGy/Z+dXrqCd+1vTIzmtyg/2iM7pbGOEV4QKfajIxmJRqMLFSiFtYqO9zzhm4H2iszDJfR+p1hmS6HGwlo2ASUdWa4Dz3dobPbe3Kxn+59Brq+d8AcDke/l2ZFsxjJ5IC0dlIXUu0t0FvoDyIuHu5RbMtA7RuJfc1ZSiw64sQa0RlY370GjrQVqC96Fdn7DsJbUw1EDKzV7iXlvX/sI9GWULjHYDITCn073T4wmWHywmWmP5JcZvrj0XeLyQzDh8tMX1S+J6DHvpv6nxV8W3u/vgMSiRLp0+8Ge7dv+cCFBa1O/GvRJmyNycDvkin6OpmHneitH3oyo4GCFEPt3hpkdapwNKyIwj/Og6jOI2A01FHu/RtizuEMo9EoGo1m2Fxw2ASUdYTler/nnnsG7RObph+NotPpRuO2wj3ZHzcbLK2WPNz7qPzbDniQTMFw98cexTw6rneaoM3OgUguH7W2jNaNo1dKoSlogHZfKkyZRmzXaTAfHtRF1yBzz24oLrtiwEezkFssy5VCoRjw+ETdOZjMTFQ8+vaby0xfNL5f75UZ9uPNNAi8fI+A1WoVMOG/M99jwtbYe4ktoSQztWUfobl6Neae8yLpPZJhp1jr0Z9Uo0rnxN44LyLFItw8JRuqPu/i/qj0bDGZYe9rpVI50OGg3BcePQM25glv1WKTvgjNqsXQNYpxKvyHyQv7rQlUmRn2L5/RaBQ0hCz/+4lKaoikzOoo6MlCUEEOSCs7w6GIoywFQUg+2VjFL1Wh9lExFhWGQbboKnzc8DLOTzkfVdiF1M0bIb/4hxDxl+KJxJof4whwBDgCHIHTQMDt7MLeDbcjOnE5Tb9fI9zpixcasazDjZcXbsTXcWl4hMIunYx8nkYTAvpSY0Q+Sg88h2zlZbApytEYJkMChWLqcJFZglwW0G0fauOGTUCZhztbJkrxltphJxtot+4A9A0GiNPSg7brIvKGZ7nhFxfYsMs9B1bvcziSMA1RFZ+hxVcC5d49kM6ZG7T94w3nCHAEOAIcgcBG4MA3f4TL0Y5Zy58VGtpm9iPrSyeKIruxLkWNVJptuyYpIbA7MYqtM0ZNg9vViVRDDOyKfWhQyzGvSYpy0vbOkDPL2OAv3JtkCGPoJV+kiCZ3jwNS9z7o2tVgKTiDuUSv1JI3vJfif2qwLHIh3u2kfoVnozK5Fu71XwVz13jbOQIcAY4ARyCAEWiu24yygy9g6vw/Q2PoUeZ89Wglom0UjiljPXZFxuC+vKmQTmCHV5YTnhWJzgeTy45mLdUOMaparQE8sqfWNE5Ah4BXdQvFzSQHpKIockCiLzYWgD6YNaCsy1ELlOik3PDLi9SYF3EFjnQfhWXyKjTLK9BZvh2+utohIMNP4QhwBDgCHAGOwNAR8Lhp5m3tzxEePROZ028XLtxX2IVFu4FtKQ14bUoS5uq1uCA2eug3DcEzNfpUyBQGdMpbBEekemOb0MuOitAJxcQJ6BAEt7rUg3ByQDoQW4YEMurVe6IhJg/4YC4iMu62zCTno3ovyppjkBKWiE/9rZArwlERXwHXV2uDuXu87RwBjgBHgCMQgAgUbL0Xtu5qzDn73+RHIqHwQEDd422U/dyH/Sk7UBOmwUN5uQHY8rFvkpG0oB2uSmR3qHDEUCo0wFnV448y9q0Z+SdyAjoETHsdkMpjDyHCR4FhkydDFAJTAwmXaKHx+CDZIMV1ydfgy6ZNCJv6Y9QaKmDd9iX8ZGvCC0eAI8AR4AhwBEYCATb1Xrr/GUyZex/0pp5Yl2+9UYaFVT5syziA5/OycC2FXZqiH71INyPRj7G6B7MD7Wg/iGyPCTXaQ7BIxZDUecfq8aP+HE5AhwCx+6hdyIDk0RZQ/E89JKlpQ7gq8E/R5ynQppfi3GI1whULoZVq8JVaARGFs6gIPwT31xsCvxO8hRwBjgBHgCMQ8Ai4XRbs/PIGmnqfhaxZvxXaW99uRc47EtToXHg3twNauQJ3Z2UGfF/GqoEsNanD1oh0bRw5IlWggTzhTfVMaxwaySs4AT2JJPlonE0Nbhw1+pHavR+6Tg3Zf4YGAWVd9y7WYlazC2sKrLgu5XK807AGppyrURVdAdvaj+GngNC8cAQ4AhwBjgBH4HQQ2LfpN3BYGzH33JeEqXd2r3UP1SDB4sXuSRuxIS4OD+RNgY6833npQYBpQFkx6ZXwSCsFT/jkdjnqKbZnKBROQE8yivXtQJLggNRNDkhtggOSJDV4QzAd293ky7SQEstO3qzGqvgr4PV7sc2UCK/Igwr5bni2bz32Er7NEeAIcAQ4AhyBISNQV/YxKopeQt6ih6EzThaue+fzYpxXIMcuirzy9/kJOCcqAqviYod8z4lwopawkkjVsKo7kWJvR4NGgsQuMcq6LCHRfU5ATzKMVUe9MDEHpJjyHgckUTzElOM+VIoiXobmBAUuOCrF+iYHrkhchf/Wf4qo7KtQQU5Xts/fDxl1f6iMGe8HR4AjwBEIFgTs1gbs/OomxCSdjYz8XwjNru+g+Jb/UaJD4cVbM8rgUyrwNwq7xEt/BEQiMQQ7UH8NpnQqUaPvgNwnQkNlaPhncALaf7yP2zIX9Ki6y+IKEeE1QJmcddw5wb5Dc4EOmR0ubN/sxC8n3QCLx4o9MVnwiN0o934N7+5dwd5F3n6OAEeAI8ARGGME/H4ftn/xEyFX+Jxz/vNdzvCv/q8O6Z1ebMnajM+T4/Awkc+YEEqjOZIwMztQc9chZHeF4YixQri1tYJPwY8kxgF7L2cJ5YYnms4ckHQWXdDH/xwI6LgLNXBKRThjtxbNzjBcmrAS/679ELE5P0Z5LMUH/eRNrgUdCDi+jyPAEeAIcAQGReDwrr+huWYDhVz6D1RhMcJ5/3l9P1YUKrAjuQb3L4rGqtgYXBIfN+g9JvoBI+WEt1vqkCmPJufgw3CLAF+NOyRg4RrQkwxjEoUiKjF5kGzdD32XLmQ84Pt2W6QSo2O6BmdVefBKURPuzLwZXR4LtsdkwCcBSjxr4d1DUYJ54QhwBDgCHAGOwBAQaKnbgsLtfxaCzcelni9csb24GkveNKBO68Y/ljUhQq2iqfeecExDuOWEPCU8aobQ7ziDBjYl5YSnlJyaGmKhIVA4AT3JINoUXdiR2IQ0e+u3GZDSTnJFcB6Ov1InpOaUrpfBKI/BVUkX4/nqdxE77RZURZWh4+MXIUQMDs7u8VZzBDgCHAGOwBgh4LA2Yevqq4WQS/kLHxKe2mG3w/qAGxq3F2/M2UnTyXq8MHsm93o/yZhojVnkiKSCS2NDuLca9RSKKaFFBmsIRKjhBPQkg3/Xyir8Y/FWwQHJIE+F2GA8yRXBeViTr0S7SYYfHFbirep6/CbzFrh9bqzVGyCT63BI/AXAbUGDc3B5qzkCHAGOwBgh4PN5BfLp87qw4II3IZb0hFX66L5yzGwCVk/dj5enRuHB3BxMM+jHqFXB+xiWLYplROoU0zS8pVUgoKkdEpRagt8RiRPQk8jldD05H+FrmLx6KFJCzwGpb/flK/XIa3Vg/dc2xKqicWPaNXip+h2Ez/sdGoy1aPvyOR4XtC9gfJ0jwBHgCHAE+iFwYMsf0EIZj+af9yrU2kTh2IvPHcSF+5XYnFqP+5apcU1iPGU8Sup3Hd8YHAFj9EyYLUcwtVOOSn0XwsgQtLqqe/ALguQIJ6AnGagq616kuxu+tf8MnfifA3U7nlJzeiQinLNDh29a23B75s9pekSHlz3VMOhzUKjfCM/6Lwe6lO/jCHAEOAIcgQmOQOXh11Gy70nkLXgAMclnCWh8vPoozv8oDIcjbbjr/A7MIa3nw7nc7vNURIVlj7Lb6pHpN6DUVClc2llmO5VbBOS5nICeZFiemf4gzmqqFFJwitNDm4CKwsTomqfF2VUuvLi3BRppGO7Ovh2fN66Da/7tsKi6Ubb9r/BbQiMI7kmGnh/mCHAEOAIcgSEi0Na4E7vW3YzEjMuQPft3wlW79jdhyrMStKs8+O2KMkRqwvDS3NmQiTn1GCKswmnhpAFlJUEfhjLTYXjIB8lD7+lgL1wKTjKCJnsXolw2GGxGSFJST3J28B9OvE4PjceHpHUq1NjsuCrxYsww5uHhWnJISr0SJREH0P72s8HfUd4DjgBHgCPAERgRBKxd1djyyQ+hN03BnHP+LdyzotoCxf/ZIfH58Mfzj8AersL/Fs+HQc5TbZ4q6FpDJqRyLURaJ6SSCjSpZVBXB78nPCegJ5GE9uY9whl6TRZEYWEnOTv4DyvS5GhJVeKSIyL8q7hWCBz8t7w/ocpWiw2JWZDL9Nhb/xQ8lT0BcYO/x7wHHAGOAEeAIzBcBFzOTmz66CKIxFIsvvB9SMlju6nJidbftCLC5sW955TgaLwUby9ZiASVariPmdDXiUQiIaKAmRyRUuw15IgkR3yjFE6vL6hx4QT0JMNnbtqDMI8RytTQdkDqC0PUtQYkWtwwr5ZQViQPpuqzcFPatXih9n8IW/QntGtbUPzmL+CnL1teOAIcAY4AR2BiIuD1OAXNp81SgyUXfQSVJg5trV5U3NaAJMp0dN/Z5did5sc7ixciQ6OZmCCNUK9N0bNhth1BXlc7qjVSpJklONod3I5InICeRDjam3YLDkjitNC2/+wLg26RGuZwKa48oMDLZbXCod9m3YoEJaVMM69DQtwqHFZuQNtnPVMtfa/l6xwBjgBHgCMQ+giwcEvbv7gWbQ3bsGjFOzBE5qG1yYejt9YSOfLi7rOrsS3DhXeXLsQUvS70ARnlHobHzIbLaUamV4EKgxlqMgStquQEdJRhH9/bL85/Bll1UyGZQASUqfvDrjIit92BPWs8cJGmUyVR4m85f8Lh7lKsz55F2ybsOHQP3A3V4ztA/OkcAY4AR4AjMKYI+P1+7PrqJtQe/RDzzvsvopOWo67Ki5pba5Dc4cFvzqvF3gwLPjxjCc2g8VifIzE4TAPKSoJWjSMRZcK6uTS4PeEDSgO6du1aeAIsur+0th0qnxbi5BRhwCfKP5EXatEdJsGVO8lwvKpO6PYMfS5uTbsez5W/CumZD8Km6MbO1y/meeInilDwfnIEOAITHgFGPvesvxWVh1/B7DP/SV7vl6K0wI2u22oRafHg5pX1qErpwKdnLUemTjvh8RopAJh5gyosDtDa0KorhlMsgq88uHPCBwwBfe+993D//ffD6/WO1HiNyH18ZWUQJyZBJJtYnnsiqQjSy4yY0+TAV6sd8Hxr73lH+s8x05iP39W+jOTJd6JWdlCwBx0RsPlNOAIcAY4ARyBgEegln2WF/8aMZU8hbepPsfsLB+R/qIXc68HVl9ZBHtuCT1ach3gKucTLyCJgip0HM6oR56wSHJG0Qe4JP+4ElGk87777bnz5ZYAGOKdIB5LsnJGVoiC5W8zlWljUYvzomzC8WVUvtFpKno7/mvV3IU3nI7oGJMoX42Dzi6jf+XqQ9Io3kyPAEeAIcAROFQFm87lz7c/QSz7T827G+me6kfRoPVpVXlzyo0osMrTgjUtWQa9QnOrt+flDQMAUMxedtqOYQk5ftRo5kpqkQZ0TXjqEPo/qKUzjOW/ePFxwwQU444wzhvSs1tbWIZ03nJN832r62tvbhRBEuPwq4Tbdo/jM4bRzrK5xr1Ri7ts2/PpjL864XAK5RAKFWIa/ZtyHmw/9Dm9PW4EVmyuw7ZubMDMsBpro/LFqWsA85ziZCZiWjX9D2N83sykW88DT/QajV2bMZnPP70y/oxN7g8vMwOM/njLj9TpQtOUXlGLzS2TPexQS3aVY96smzC62YFMC2XyuKMH/J3LivGXL0EHvzrEuvTJjCfEkKVLVZPh8bmR7LajU+bCgUYId5TXICx/Y1GEsZMZopBjpxAuGU8adgCroS+miiy46pbZLpaPXbCbIbGHPYC/OiV7CL5ag83Mnrt+mxP+WdOD6pChB2JZEzMPv027Fw+VPI3XhjUjf8ij2r70cc3+4ASrtxMrx2ysz7I+QE63+fzFsyo5hMtwfqP53C50tLjMnHkv228tlpj9G4yUzLkc79m+4Dp2t+5C/9AV0mC+C77ddmNnhwrOzbHh3xgG8HR6L1JmL+zd4jLcmgswYo2cQL5Eihvwz1pnqIfHr0VrpgjRqYE40XjIz1KEfuNVDvXqczjMYDKP2ZIfDAZfLBZ1Ox38Av0XZfb0U+U83493VCvhvV8LwbUD+2w03oc7bhEeq3sBTM++EfN9fsefjlTjzJzvIWDp21MYo0G7cKzN68vbkL83+o8M0EjKyn2Yfmrx8j0CvzLDfmdH8oP7+icGzZrVaBUy4zPQfM6fTCTYzN5Yy09VejF1rLobT0Yb5K9eg6IN85H/RAhu5RPx0VSOMqp34ZvYPoE5N69/YMd5iMsN+e5VK5Rg/eewfZ4zKh6e7C3WiI/TwOfBU+2E4Z2BONB4ycyqIjLsN6Kk0lp87PghEXKRBa7QcN+6U4omDPR7xvS15OO8enBOzDL9xvANp4o1w283Y8PoS2K2NvafwmiPAEeAIcASCDIG68k/x1VuLQCo3xE/djao/pWPe583YGefFRdfsxFXyrXjxihvHnXwGGayn3dyI2AVo95TBgFK0KqWQlQVvQhhOQE9bHEL/BmxqI/rXEUii7Ejad5Sosn4fe0wikuBfM/+B2cbp+KVmIxTqy+HsbsL6N5eA5QfmhSPAEeAIcASCBwHmbFTwzb2U4egSKEwr4K3ZgoS/eJHSasfdy9vwyNmv4CudApffeBdEPLvRmA9sRNx8OFytyLKVU0YkOaJrJEEbCpET0DEXn+B8oHaWCg15SlxT5MWDa2r7dUIpUeCVuc8g15CNW5MPkF3KBfB0NGPd/xaho7Ww37l8gyPAEeAIcAQCEwFrVyU2vLscB3c9Dbd0HaLffhBLNpqxM96N866l7fAXsWXxjYi94GLuIzFOQ8g0oKzkSCjeqlaC9FYJqvsohcapWcN6bEAR0M2bN3NbsWEN49hcFHeHBmKRHxd9GIZP65r6PTRMqsab8/6F6YZc/GpyCVze5ZB0ObD+7aVorF7X71y+wRHgCHAEOAKBhUB50Uv4/LXZOFS9FIaCYlzwQRzUbicFli/D/53zIF5Xe/Cnnz0DCcXF5mX8EGAB6dXaZJg0TpSY2hBGKTlLSzvHr0Gn8eSAIqCn0Q9+6RggIAmXwHWVAYsaHNjwkg3dbk+/pzIS+vq8f2JJ1Hzcnn0UNf550HXrsOnDlSjZ/2y/c/kGR4AjwBHgCIw/ApbOCmx8fyU++HQL3AV7cenan2BGvQ3PzGnDedc9jkz9S9i2+AHk/+AWiEYxAs34IxE8LYiMW4BOWT0aDMwRCWgvsgZP4/u0NCi94Pu0n6+OMQLRl6tRucGGX2714M8bKvHoOZP6tYDljH9p9pP4XcH9+DPex/VVmVjRpce+r3+N9qbdmLX8WUhl6n7X8A2OAEeAI8ARGFsEvB6K7bn7CazdUg8jhdO7vAJQeVz4MMuCxxa+iTjvdnwSfwfyKbA8L4GFQGT8YlQdeRMaUTE65dMgKnEGVgOH2BpOQIcIFD+tBwHmkJTyQDQ6flaDs15R4svcFpwTG9kPHpYt6bFp9yMtLBkP4gmUtBvw26rFqCl5F+bmfVhwwevQm6b0u4ZvcAQ4AhwBjsDoI8Bi85Yc+hCffFEOU8nVuKzKgzC3D2snWfHYgrdgUX2FP+ouwtVnfgUx13iO/oAM4wmR8QuFq7Jch1CpVSCqMjgJKJ+CH8bgT/RLlAkyuH8agfmNTux62ooWik83UPllxg14de6zOBjpwi+yO6FpWwqf04Yv35wnTMmzH0JeOAIcAY4AR2BsENhfsBkP/f1T7Ht0Pq7+4mJcVObC1pQOrLr6X/jDuT/BD0wW7FixBj8+9x5OPsdmSIb1FF14NhSqCExRVaBUL4HMKUGDzT6se43nRZyAjif6QfzsxMt1qMlT4+e7fXjw9Wr4BiGTZ0Uvwdpl78IUkYQbp1ehqC0ZCaazhCn5je+fS6GaKoMYBd50jgBHgCMQ2Ai4PX58+vVuPHBfEZofycDNa3OwtN6KNVl1OO/HD+F351yHxbFW7Dz7c9yz4klo1cbA7hBvnYBARNxCuNQtqDS2IKdNhOLijqBDhhPQoBuywGnwlL9Eo10nwS/fp9zwG8mAaJCSHJaIz5a9hetTrsRjWQ140F6PxKhfgGXZWP3qNBzZ9xTlt/UOcjXfzRHgCHAEOAKnikBVkwN/ffkg/nVHM1Iei8avKJ1ykrULL83ajcU/vRkPLbsNFyZFYvf56/DQec8iShNzqo/g548jAlEJS9HprkK79rDQivaDlnFszfAezW1Ah4cbv4oQEKvFiP9HHDpuqcWZz0rxbnwDLs2MHRAbuViGB/LvwfKYpbhj+29xtXc97rQuwexUGfZvugtVxW9g5hnPwBQza8Dr+U6OAEeAI8ARODECXZQj5J1tdajb4MecChmub1FD6rNgX5wZTy/8GB9P/hDJlD/8dylX4UdTr4NGGnbiG/KjAYsAI6B+eBHv340W5Wz4D3ECGrCDxRs2OghoUmTouicGqf9Xj477ge2PtWNeVPigDzsjehE2X/Al7vv6d3hQuhl57VrcPf0h2KtepbRvC5E25afIXXA/lOr+jk2D3pAf4AhwBDgCExgBO5ngv7uvA4c3WJFXIsfyJjeMLi8aNRa8k7cD/572Glq0jThHOQNv5j2LpTGLeBD5EJAXvWkq5EoTshyFqKDMVIlB6IjENaAhIIjj3YW4RSocvikSs/5fCzb9oRuHH5Uh26gdtFk6mRZPnvVP/PDoGvx+75/xI/PLuFo9HZdmXIbqvY+hpvQd5My5BxnTboVEIh/0PvwAR4AjwBGYiAiYrT68ua8L1VusmHpUgTlNVlxo98Ais+Gb5EN4Pfcd7EzYj3xxIn6RfgUuSf8hTApu2xlKssIi0jAtaLd7N0oNPlxRKkF5uwVp4Zqg6SYnoEEzVIHd0OxLddjT6cOSN9uw6a52yB8TIV1/4j+EJZPOw4akpXjm89/jGdEGfFxXiNvzfo1ptioc2PIHHC34J2lDH0BS5uX8iz2wh5+3jiPAERhlBAoa3Hh3lxuyg63Ir5bg3BYbTE4PbFIbtiWV4M85H2BL8m5kSaKxMukCPJ3xMJj9PS+hi0B04hmoPfoBao3VkPojcXS/GWnLT/zeDSQ0OAENpNEI8rbMvMGAbeRxueSdduy6rRWeR/2YHDG4JpR1VylX4a5VT9HX2278Zcs9uF/6FlK8Bty29O8Iq/gc29f8GEf2PIbchQ8gNvmcIEeIN58jwBHgCAwNAbPViw8PW3FwdzcSiuXIb/LijnYPVF43OhRubEo5iE8nf4bdCQWYrUjD2Snn4e+pDyBBPbAd/tCeys8KJgSik86k5vrhV+2AW7QS7WSKgeXB89HBCWgwSVsQtHX+TUZsUYgw4/U2HL21DXse8mBm+smnfhIzZuH/pX2On33xbzzY9l/cWf08cqSxuHHZw8Chd4R0nizsRO78/6NphyVBgARvIkeAI8ARGDoCVqcfX5TYsPNAN8IKRUhs81OsZRcutruJYthRGt6Fl6dvxoa0r2GPaMNi4yzcmHktXo6eD7VUNfQH8TNDBgGtYRLlhU8iO9BdqNRdCsPhgWNyB2qHOQEN1JEJ4nYt+okB240SJP6zFbY7OvDFnU6ce8bJQ3yIJBLMvuAmvN9xOdZ/9A/8w7cWv/a/hCm6BPw44z44Dr+LDe+dJdi9TJl7r1AHMUy86RwBjsAERqCly4+1pTYU7e+C5ogIGc1iTDY7cYbVBRHh0qB20tT6YXyTsh51ceXIMiUjL2IWXsp6FvEaruWcwKLTr+sxSWfB370GJQYpFtXLYfVQZqsgyWAlomw0PB1Nn+F0OBwwm82IiooiBxhJnyN81WKxQCaTQaFQDAmMwt12+B9sQYzFjc3nSbDqzmSIyXB6qMVbXYW1nz2Gp5TbsNdkQ5o4ClfQD3Bq6RrYKYZoRNwC5Mz+A2Jp6mk8C5eZwdE/VZkZ/E6hdaRXZiIjIyENkpfFWI2A1WoVMBnq78xYtet0nmN3AQW1Hmw90IquIhciGxTIMPuR1ukgO86eGMjNahd2x1WQ89BGdCXUITklEXNTl2J2zDzBgchJGefa29vBZeb4kWAyw97XSqXy+IMhvqeu7BNs/ewqHKnZjl/uU6H4HxrMmxYt9DrQZYZrQENcOMeze1NnqdDyfDx23t2M5avJUP5ABdIfiEJM8tCMpCVJyTjvlidx9tFSbFrzHP4p346HvZ8jXB+GVck/QW7tXrR+dBH0EbnImnGn4KwklsjGs8v82RwBjsAER6Crw4GDxe3Yd9gCS4UYhjY1kjolSO524jqrGyz7i1vswlGjBV9mlKA6dick6V5kZOdhevxcXK57CFIxfzVPcDEacvcjySSNxQN1arZRvRxl2xq/I6BDvsk4ncilfJyAnyiPjYwkb83/F4v/Pd2OxV92AT9vwuYLO7DolniIJEPThkomZeCMXz6OJRUVKPryFfzHsR6vJ22Dh2IoLzGtwBxLK8xfXo+CrfciI+8WpE29gfLkmiYKxLyfHAGOwBgj4CeNm6WuGWWVFhRWuNHaIIG0Q4PILjUSuv1IoGn0Ka6eGTS3yIZKgxN74+tQE1MIcUo7YmdnIzcmD9frfgCl5PIxbj1/XCghIFfoERE7HynOjajRnAdNQfAkuORT8MdIYu/UGJ+CPwYY2jzd6dStBxxofcKMxTU2NOpFUN9qQPLywYPWH9+Cnj2+tja0rv8E/6t8D2/E1aNS60S0PwyLRWpk1e1FtNdH2tArMCnvJsqsNHuw24zYfi4zg0N5ujIz+J2D+0ivzPDp1OPHMRCm4BnB9LW1wtJgRk2DFaWNftQ3SeC2qKG1aBFlVSDW4keMzQ2Nxyd0gtmyNat9KDd0oyqiDh1kt6nL9CMlLws5kTlIVieeVji5QJ9OPX4kx27PRJ6CZygf3vU3HPjmIYgPlGBmSxcSPkqDQiJGoMsMJ6DH/I30vhg4AT0GGNocCTLhdAPPv2jGvC+tyOh0oiZOjMRfmGCYpzv+gSfZ4ydja+/ePdiy6y285duFNfGdsEt9yPCFIY+0olM7G5BqyKbsStcjefKVpBWNOMkdh3eYy8zguI2EzAx+9+A90isznIAeP4ajSUD9ZEfp7+ykpQP2ti60NNtRZfagkiZnGrvF8Fi1UNvCEG5TEcmUItrmQyR5oevcPSSTtdZBis1arQe1egsawptgiamHNtWB1KmJyIqfiUR1HNm6j7wWKtDJxPEjOXZ7JjoB7WgtxBevz0B5zV78fL8Kluf1SE6L4AR07ERwZJ7U+2LgBPR4PEeSTBQ3ePDJ8x04d58dSRYXmmLEiL42HPrlOoikQ5ua79tCX4cZXdu+xmelH+BjRQm+ie6Gh94BqW4xsi0tyLXbMSvuDCRNvgJxqSshkw/NDrXvMwZb5zIzGDIj89Ey+N2D90ivzHACevwYngoBFXxorRb4u7q+W2xmC1rNbiKVflQ7pKh2S2B2KSFyahBm18BoV8JklxKx9MPk8AhLONV9KWOX3I8GjRsNOitaDW2wRbZAmdiN5IxwZGRMQ7J+EiSisXVS5QT0eFnp3TPRCSjD4dOXMnGg7hYU4VY8/RsXTCoNJ6C9AhIsde+LgRPQ40dsJAlo7903HHFg6+tWnHXIgclkvN8dJoJshQZRPzBCHD08hyI2dda6axO+qPgMX4iKsTm6C06JHwaaKsuydyPL6cKiiDnISbsYsakXQBV28hBRve0dqOYyMxAqPftGQ2YGf1rwHOmVGU5A+4+Z3+eDtaUZUrsDMqeDwsswYtndU3d3o63ThVKbFId9clRCBbNPD7EnHGqnDga7CuF2OcId9LdOnuVGyhLUWyt8/YO9dCi8aAlzUo50Czr1nXBHdEAVS6Y8STokZ6YhIW4KeVUHlosEJ6D9ZaXvFiegwL5Nd2Hfnq2wdT+Ka++aD50anID2FZJgWO99MXACevxojSaZWF/ixFcfWbGgwEN5jS2Q0gujI0uKuAuNUCzSQBTWVz9xfNsG2yM4CxTuxaYjn2O9ZS82GptQF+ah5BF+xLpdSHfYkSsOx4L45ZiafL4Q2kkmP3H2pmOfxWXmWES+3x5Nmfn+KcG31iszoUxAmYkM+/uDhQgk1X5WUyi37xYilMI+qlsdZhwQuXFIoUO1KhoWUSzkzhhoHCYYbRqaElcIWkuD0ycQS73LS+SSZQXqTyw9Ij/MKjfa1TZ0Ebl06LsgohBumigxwuONiExKQlRaGiTKwCKXQ5FgTkAHR4kTULI/rqMkBe+eiXkly5D44FsQG4ycgA4uMoF5pPfFwAno8eMzFmTiEOU7fmmdFQk7/FhUZ0V6lxM0gwbnNAWiztZDOk8NsWb4U1++xkZUHNqMLbVfY7PrIHbo2tGi7HmJ6eiFmURal0xyaMrVTsb0xGVk07UMuvDEKZRhAABAAElEQVRsnCi8E5eZ42Wld89YyEzvs4Kp7pWZQCKgjDDCTUbatPjp4wwuF/y00FsMzHayp3aQESRtk0mLnz7eYLf1rNuopsVr64SLyKSdljq5DeVaCcrDjKhTRqFNFgWfOx5qRzT0NhNpKzUwka2liTSaJrsIBpdPIJVMa3msxtJNxLJD5UEnBWe3aK3w6K2QmRwwxMhhiA2HKT4eqvgoiCkBhugUYg0Hk8xwAjr4aHECynQqPnz0QiIS4i/ArBUvCGAFuswEpRMSC8Y7WsXr9cJDP8RyuTxkf8iGix3DRSwWC8tw7zHU6xxuP94t8uHIXimml/gxu9mKZLIV9ZJ5aPckMXTz5MB0WhKGT0aFtpBDQmPFPuxv3IHdjv0okDeilDSkzm9DRKlIHpimNMEjQ5LIiGRVIlLDpyI1cgZM+kzIlRHgMjP4qI6lzAzeisA7MmSZYXlCGAl09RBAESOC35JCVn+3zQgjHRMxAsmOC0SSjrto29NDKoV99DcsnMPIJttPNSObXp8THpELDqmHHPk8sEndsMt6ahvV7WQN00oJKJqVJiKSJnRJI2AVm+D3RkJtj4DGaYDBpoXBoSYbSzmMZHtpIK5qpIVpK3XfLsfOY7jEjFj60KWmZ2lc8OndUJr8iI5XQhtngDiCUkwa6I9eS0uIEsuhSqePzBPcNK783XQ8YuzviZWJnjzm8I7fo7lmNZb8cD/9uYjRKzMsgQx7d49G0ev1w8Y9KAnoaIDYe89ezQTXgPYi8n09Xtqs+g4vXt1jReV+P3KP+jC91YYMsheV0rvZSuGc5BTwXj9HA+k0FcSm059a87S1oKxiFxl0b8OB7v04ggZUyuyg5CXwir93kFLTj56RXuDhXpreI5u0JCm9jGUqRJK2J0ITjwh9MiKMaYigXL3hmoQJma95vGTme6kd/hqzR+wlbX5G6L4jbGydXnheRuKIwLGXHyN0wraXttl6zz5Bq8iOsfPpej+7h1wJT2sLXGw6mqI22D122N122Lw2WljNFgfsPgesfjusjBASCbRLvUQMveSF7SOiSIGnqXaKfXCRfTMjcmymwE01c74TFtIasg82D72IPCIF6LMaPmhJU2KCyBtOC9lZ+9hCLxCvHnoHkUgHc9IJI+JIf1MOGS1S6J0S6Ij3aumjUEtaSo3bKyzMM1x2jG0lQ5v8/tBNswoWlR8OjZ9IpRjKCCVpKlUwxCkQFi2hv1NamLZygNkMps1i2aFCKRPS8KXw+ysDXZv1fUvHfo1rQHswZ9PwR/Y+gTlnPS/Ewg50meEE9Ji/FU5AjwGkz2YgkIlWiw/vFFixr8iLeMqfnNfiQk67DdF2eslT6TKJIctTIHwmEdIcJcSJshHTZHvI0762rgilDbtR1lGESmcNGnxmNIltaCdC4KcXfq1CTPX3JLUPfBCTNktJL2ylXwSVjxa/BGq/lFwpZFCDXsxEEtQiFTRiNS0aaCQaaCUUFkaihlqsFBYV1SqJghaVUCvFCiiktFAtk5BGmL5yRSyFLPvaZQsLByOsU5tov+i4/b3H6TrWbuEaVtPCrmX7+i59OzTQOtPYsYUVqq1k3yeVEZmQUdt6j1HNpovAyAsjeWw/q4X1nlogdb37GJmjRdj37bpA8Ng+Inc+0uC5vbR4WE0mG27S5lHtYtse2mbHvl1ctO302eAigicsfjrPR8f9bHELi9fvgZvoGgX5og8OerTIRwSOyBxbp1wjHiJ5ArHrremYh2DqJX2MBDIS5qKFTR2zmsUkZ7WTllS3AgUqej5dI2ArgHXsP4QXWFpB+urxq8gmWge5V0eEj8xQfFoijLSw2qchAhlGC8m7R40wF8WpdCqhccnJflJG61IijTRjQEpRDT1QQ6RRTc54YVQzIsnqMCLHrKamHVdoNGCnJthVIjjVIvg0Ioj1UijDSWZNbKF1SjYhMZBs0SLWUz1Me+3eh3MC2otE/zrQyUT/1o7tFiegA+Md6DLDCegx48YJ6DGA9NkMBALapzkCf9lR5cAX5EnfXOxHcoUI2e0uZJodiKHYfaw46OVpSZJCl0valylqSCYpII6T0nufvf1HpvTKTKTRCBE5WrS316LJXIqWzgq02RooBEwrOt1mdPooNR/TaIE0W+RwYRORNkvshZ0Ii5MIn4va5CIC6CTi5xK2aQrlFNpJf8yQEZeTErntqWmdbdNCSjLaz2oRmNEC26bHCnXPul/YT3zpu5oRErYt8KQ+6/1RExEl6yl+OpOtCzVdxNZ93+6jJvVsU3/YOiM2Qk0P6F1npM5HZ7H9wjodY8Svd9v7HeHrOS6QQnbfU8Cop6VD/JfwZBiwDwehprYJmAl133UiZfQRISYNI1tIJ081yZmfPoD84RCTplHiN0DkY4uOPiRUZCdJznUuDVSuMCjcKijdcqplVBOhI7aqIjZLvjRU04cKfVspyNlGyRYijwpKtNC7rmLrtE+oj3HI6dtLhqFLKYJbJYaXEUSNGBKdBHIijQoDfSCE92yLdPQhQvtF2m9rOk/UR+vf956jtc4J6MDIBjqZGLjVY7OXE9CBcQ50mTn9+cqB+833cgRGHQH2XpyfohQWnEvkhRjP3lonPi6To6bMh/AyP9KbSePU5oTiEyts73cLbXKR1JujiIYlk/PDZCX06TR1n0AEIpa0pcOIQdrbURHZ2UgiIhHJFkzv3T2k2kuaOZezHW5nB9UUFubb2k51l6MNFnLqsLq7YHNZYPNYadrWRlO0pM0jTZ+DtHcunwdkyQeX30vaO7JjJtJEE76Cxo6ROdIh9izEPdg6I7aMHDJiQlaB35JARiB7CWIvmWQEr4c8MipKukv6r6fQbiq0Rf/3XMnWe2iosC3sJwJHJzAi20vietbZeT0kWErHhWPCtd8Svm+vlXy7j9WkX6PYi2yRQEYLy5ctE8kgYzVpf2WkGZaJZZCTNlhO6z2LEnKpkrKCqKmmcabaSxmzPOIw0miymsggaRpdRBqdfhlNfcsJWxkctNi9ErhsEnKuEUFEi9j+/7d3HnBWFdcfP8DuUkVQEEFUsFcUYkOD4l/E2P7GhqBiiyVqjL3EbkxMjLGLJUbNP5oYS2LD3hVULLFEMSpiQwSld9iF+Z/fgbncd/fe+96+fe/tvre/+Xzeu3Xm3vnOnJkzZ8rVZ+tkmapFqmYuVjVTf+3UpNmuVpVEtTJ2gLKIfd22U6WxfZ2TGs2UUBiXK5DLt19p1/OmPyxvIBlC+9MI61ss/608q68gdTWaJrCst9X3UAWydQclodbI1h00v6mCWK3KYhtVKmF5DH5QHHGsXdytsN8BhOlIgARIoPkQoALafNKCb9JIAlBIt1m7rf1k8PLAFixxMuarBXLPN/q95s/rpPPXImv9ILL2nDrp9YGqaG/M1nF2s+1mKGOzdbLDfO3GdzpOrYMqpauvo13jPdWa1a3KflAAiuHaaDd6+6qeuiZpz2IE3yRhLlu2VObOmanj+dQiW62KnCrJTs/B5oktNFdbSHzF22HQPH6LNSF0+owsVoVziWst87Q/e75aBudqWs7TZXjwm79Yu/f1t0QXE6/VdR9rdbtwgf500ovT8zqfRlrruMUq3bbRXw2GXuoWiuFy5RDKopPO+hrtVFGElRFWxeXWRVUU9Vw7VRyTUxsKI37aPa831Wocl1S3krpqtVpDYVRro6xQGKtUUaxWBbCmoyrNus7tlt212F2qFnAdCtC5u46/7KgWedzfTuOv21bYIp9hf8VkuBWIuCEBEiCBiiFABbRikpIRiSPQQZWBoRt21J9e/Z+Vd0yaVSdvTloko6fUyrwvl0q7ybp49XSRNfSTfGvMrZNu39dJh7cWqKIwX7vMV7qFOhsYi+UvUEW1trPa+bqoorCqqiEdNZw+06TTqjqOs6uOjetSI1W6BI3o2LlCdvevfJOm21usitlCHX85V2c2z9PfXB1LOE/HEM7TCSrz9Isyi3Sc7pL5OmNXP2O4eI5qfmo1bKXWwlaqHLZWZbGNbqEYVuuYxGps1WpYg25mVQhrNNwaVQr9r1r75zHRpZv+1tQf1ofFMX4YWtAQh65+KIq1piiqsqh5Yyksi6ocOlX6WuOnil+d/pZizKNaENvotSpYGdXiWKOWxhpN9xpYFL2CqNt8rOYYtrF05hKp6t7BJtw0JB68lwRIgAQqgQAV0EpIRcahwQR667i33l30c5xbZHrVnl75bvZS+fj7JfLGjDqZNlUnpEzR2b7T1Eo2Wy1m81TfVEvbqouWyapzdHmZifpTBUxXkZFp7RZI+0U6s1mD9EorlB4orQtV6Vmsys9i3V+iPyhC2ssrS2EM0/1lqqvqnCRRg5/+VLHVbWB+U+OY9VfrxgLHZoXyFd7avp7H1n4r7lddzfw57Uv39yAcHKM7HP3wy7f6GD1urccY66gTrHWSy/Ifxo/iV61WQq8UonsZx910slCvFdZDjD3NxanOKUs03kvUwmdKIXiAhTKB9RBWRO0RlyW6xa+NWhPb6LZKr9Wo0lijCmLb9jpWUrujq/VcNRR9vW5WR70uut8KFkj9BZZFnKcjARIgARJoFgSogDaLZOBLNBcCmM/SSydm9OrSXnZLean5asWbowbSqfPq5LMZi+RbXae0Sr83XTtLu5Xn6OhKtQK2UUW1Wi1+Nfqr1u5js/DByqfKKs61nw9lTi15avGrUs2wje77CUFqkzNlUF/HnCmIoffBuEzvvM7nt4GyuuIGO2/3L78D/4inP4+tjfPEOfxUT8MYUdtiXxXjZaooOp2YvQzKsiqG6GrGT4dOapexjp1V5a6NWhCroRCq1bCtdjdX669W+8Hbda5Sq7DO1FfL4XLroSqG7Fr2ycctCZAACbRIAlRAW2SyM9KNJdBRV8jBr+dqVbLJGlUys8cyWWONTnkvyBt+H1gpYbU0K6ZpibofusF0Sf3DFookxr5i2xzdPFXEsQhy27ZYVoiOBEiABEiABJYToALKnEACzYwAlEkaCJtZovB1SIAESIAECkqAg6IKipOBkQAJkAAJkAAJkAAJZCNABTQbIV4nARIgARIgARIgARIoKAEqoAXFycBIgARIgARIgARIgASyEaACmo0Qr5MACZAACZAACZAACRSUABXQguJkYCRAAiRAAiRAAiRAAtkIUAHNRojXSYAESIAESIAESIAECkqACmhBcTIwEiABEiABEiABEiCBbASogGYjxOskQAIkQAIkQAIkQAIFJUAFtKA4GRgJkAAJkAAJkAAJkEA2AlRAsxHidRIgARIgARIgARIggYISoAJaUJwMjARIgARIgARIgARIIBsBKqDZCPE6CZAACZAACZAACZBAQQlQAS0oTgZGAiRAAiRAAiRAAiSQjQAV0GyEeJ0ESIAESIAESIAESKCgBKoKGlqegS1evFjefvttadWqlWy77bZSXV2dZ0j0RgIkQAIkQAIkQAIk0NwJNLkCunDhQjnmmGNk8803l8mTJ8sDDzwg11xzjSmjzR0e348ESIAESIAESIAESKDhBJq8C/6+++6T7bffXi688EK5+eabZcGCBTJu3LiGx4Q+SIAESIAESIAESIAEyoJAk1tAJ0yYIEOHDg1gDRgwQMaPHy877LBDcC66A6tpsdySJUss6EWLFknr1k2unxcrmnmFW1tbK8uWLbNfXgFUqCdwgWOeqZ/AzDP1meBMOM+0adMm/qYWehZsli5dynImkv7MMxEgoUOwqaurE+dc6Cx3S5Fn2rZtm7eu1OQK6JQpU6Rz585BTsH+pEmTguO4nVmzZsWdLui5OXPmFDQ8Blb5BJhnKj+NCx3DuXPnFjpIhlfhBJhnkhMYPah09QkUM8907969fBVQtP7R2vUOrZj27dv7w9htjx49Ys8X4iSsWLNnz5Zu3boJLROZROfPny9VVVWCFg/dSgKYRIdGEfPMSiZ+j3nGk8jcMs9k8ggfQYlA2ctyJkxFxOeZ1Vdf3crhzKst+wh5Bj2W7dq1a9kgIrEvRZ7B5PF8XZNbQFFpz5gxI3h/7K+99trBcdxOMbvGfdjY+v24d2iJ55DRyKV+ynsBJJt4NuQSzwVnvUzVv6PlnvFMWP5m5gHPg/KUyQVHzDP1meBMc88zTT7IcdCgQfLkk0/a+Llp06bJa6+9Jv3794+nybMkQAIkQAIkQAIkQAJlT6DJLaBDhgyRsWPHyogRI0xbHz58uPTt27fswTICJEACJEACJEACJEAC8QSaXAHFmMLLL79cMEgWYz9xTEcCJEACJEACJEACJFC5BJqNtrfKKqtULmXGjARIgARIgARIgARIICDQ5GNAgzfhDgmQAAmQAAmQAAmQQIsgQAW0RSQzI0kCJEACJEACJEACzYcAFdDmkxZ8ExIgARIgARIgARJoEQSogLaIZGYkSYAESIAESIAESKD5EGil307lx1Mj6QEkfnHxyCUekkAsAeaZWCw8mUKAeSYFDi/FEmCeicXCkykEmnOeoQKaknC8RAIkQAIkQAIkQAIkUHgC7IIvPFOGSAIkQAIkQAIkQAIkkEKACmgKHF4iARIgARIgARIgARIoPAEqoIVnyhBJgARIgARIgARIgARSCFABTYHDSyRAAiRAAiRAAiRAAoUnQAW08EwZIgmQAAmQAAmQAAmQQAqBZvMt+JR3LNilxYsXy9tvv21LLG277bZSXV0dG3bafZ988ol89dVXMmDAAOnWrVusf56sHAJpeSEcy9raWnnnnXekffv20q9fv2AZr6+//lomT54c3Lr66qvLhhtuGBxzpzIJ5FJOvPvuu4L85d1GG20kq622mh3m4t/747YyCGRL8w8//FDmzp2bEVnUQShPWM5kYGlRB//973+lXbt20qdPn9h4p9Vh06ZNs3oLfjfeeONY/8U82eZSdcV8QHMJe+HChXL00UfL/PnzZezYsfLiiy/KHnvsESgK/j3T7rv22mvl4YcfliVLlsioUaNkp512klVXXdV75bbCCKTlhXBUp0yZIsccc4zlpY8++khuu+022WeffaSqqkquvvpqGTNmjHz55ZeCgqKurs4U1LB/7lcWgVzKCeSDI488UmbPnm35Anmjb9++0qNHD8nFf2URY2xySfMHHnhAxo0bF+QXHC9dulQGDhzIcqaFZqEvvvhCTjvtNFlvvfViDRtpdRgawPDbpUsXq7OgxG666aalJamLlLYId9dddzkV8iCuxx9/vHv99deDY7+TdJ8mtNt///2dCrzdeu+997orrrjCe+O2Agkk5YVoVG+44QZ3xx13BKcvuugi99hjj9nx8OHDnVrMg2vcqWwCuZYTn332mVMFtB6MXP3X88gTZUsgnzRXa6g74IADnDZgLN4sZ8o2+fN+cTWGuf3228+NGDHCPfHEE7HhpNVhRxxxhHvvvffMnxpRnBpNnFpLY8Mp1skWMwZ0woQJ1m3u1Xt0oY8fP94fBtuk+yZOnGiWq9atlyNL8h8ExJ2yJ5CUF6IRO+GEE2TkyJHBaVi10PJcsGCBzJgxQ3744Qe55557ZNKkScE93KlMArmWE6qASu/eveWpp56SRx55xPIKiOTqvzLptcxYNTTN0aWqxg8599xzpXPnzixnWma2seFeavgwy2fSlxuT6jD0wKA+wnAxOPS8dOjQQb799tuS0mwxCii6SSGs3mF/+vTp/jDYJt333XffZXS3J/kPAuJO2RNIygvRiNXU1ATjiV944QUT7D333FM+//xzG+P31ltvWVcZujsef/zxqHceVxCBXMuJTz/9VDDmD2P6sB02bJhgPFau/isIWYuPSkPT/LnnnrO6aLvttjN2LGdaZhYaOnSoYE4BnFooYyEk1WHff/+9dOzYMWMIIoYTwmBSStdiJiG1adPGlAAPFy0ATBiJuqT7ks5H/fO4cgg0NM0fffRRs3Rec8010qlTJxtP89BDD0nXrl0NygYbbCB33nmn7L333pUDiTHJIJBrnjnuuOMEP1gd4GDVgjUU44Yxrs+7pHLKX+e2/Ankmmd8TFHOHHTQQf6Q5UxAgjtRAkl5K3oe/lDWYBxoKV2LsYBitmBYu8d+r1696rFOuq979+71/Pfs2bOef56oHAJJeSEuhnfffbfcf//9cuONN8q6665rt8yaNUtmzpwZ3L7OOuvI1KlTZdmyZcE57lQWgVzLCXR1Qen0DnkDlrBc/Xt/3JY/gYakOWa7I+8MHjw4iDjLmQAFdyIEkuowWE4xITtcBiXpRJEgC3rYYhTQQYMGyZNPPimLFi2yrq7XXntN+vfvbzDR9YUfXNJ9WLYJy2B888031lLQSSbiu0DMI/8qjkBSXkBEw3lGB4DL888/L7fccouNpfEgMBb09NNPt/Gg6CIZPXq07LLLLuLHEfv7uK0cAmnlRDjPvPLKK3LrrbdaxDFWGEM3dt11V0nzXzmUGJMwgbQ0D+cZ+MFqCZtsskkw5AfnWM6AAp0nEM4zSXUYelq23357gTUdDuUReup8b50Pq9jbFtMFP2TIEFt+SWeMmQKgswZt2RMA1pliJtAYo5d2n86cl2OPPdbW6oOV69BDDy12+jD8JiSQlhfCeQbd6rBshrvWDzzwQDn11FNFZ6oK8g26NzBu+PLLL2/CGPHRxSaANE4qJ8J55uCDD5Yrr7zSlobDJLXdd9/dJkmicZLkv9jvzvCbhkCueQZvh+XcsORO2K2//vosZ8JAWvh+uJxJq8NOOukkOeeccwTDxFDuXHzxxSUn1wrT60v+1CZ8IAb9Y+wnWgBpLuk+LDgOszXG+NG1DAJJeSHX2KPLfd68eRmT4HL1y/vKk0Cu5QSsnxiP1bZt24yI5uo/wxMPyppAY9Oc5UxZJ39RXz6tDsMQDqwF2hSuxSmgTQGZzyQBEiABEiABEiABElhJoMWMAV0ZZe6RAAmQAAmQAAmQAAk0JQEqoE1Jn88mARIgARIgARIggRZIgApoC0x0RpkESIAESIAESIAEmpIAFdCmpM9nkwAJtDgCmMR42WWX2ZJuLS7yjDAJkAAJrCDASUjMCiRAAiRQQgJYtxGzTseOHSs77rhjCZ/MR5EACZBA8yFAC2jzSQu+CQmQAAmQAAmQAAm0CAJUQFtEMjOSJEAChSLwxhtvyMknn5zxzXaEjQ9Z4AtrcBMnTrTjn/zkJ7ZI+FVXXSVLliyxa9G/6667zj7jGj5/0UUX2de1/Ln33nvPvh0/dOhQC3fy5Mn+ErckQAIkUJYEqICWZbLxpUmABJqKAL48c/vtt2coiOhOHzVqlODaF198If369RN8W3nkyJGy4YYb2hewLrzwwthXxqdcX3/99Yxr999/v336Fyfxmc6BAwfaxwzwBaVx48ZZ+FRCM5DxgARIoMwIpH8OqMwiw9clARIggWIT6N69u+y7775yzz33CCyScHfffbfsueee0qNHD3nqqafkkEMOMSUVn7g77LDDBJ/bhOU0H3fWWWdZ2Pfee695P+644+yznVdccYXcdNNN+QRJPyRAAiTQ5ASogDZ5EvAFSIAEyo3A0UcfLSNGjBD/KU1YLO+44w6LBrrdBw8ebJbLjz/+WMaPHy/PPfecrLnmmg2OJmbMv//++9KzZ08577zzAv/4fOfbb78dHHOHBEiABMqNABXQcksxvi8JkECTE4CS2alTJ3nkkUekpqbGvue+zz772Ht98MEHsscee9j5QYMGyU477WTd55988kniezvnMq758aJz5swRfOMbz4I11bvdd99dunbt6g+5JQESIIGyI0AFtOySjC9MAiTQ1ASqqqrkiCOOkPvuu0+wf/jhh0t1dbW91qWXXiqbbLKJWT1hqYQbM2ZMvUlLdkH/2rZtawqqP66rq5NJkybZIbr7O3fuLL169RJ0uXv3zDPPBM/z57glARIggXIisLJJXU5vzXclARIggSYmgG54KIKYRHTUUUcFb4OudkxAWrhwocCyCSvpgw8+KOhOj3MbbbSRjB49WiZMmCCweJ555pmmrHqr6IknnmhjTB999FE7/8orr8h+++0n06ZNiwuO50iABEigLAjQAloWycSXJAESaG4EYOXcaqutTLHE1rszzjjDZrBjQlK7du2kf//+gmWYzj33XFMw/X1+C4UTFlLMlsf9UGyHDBkirVq1slsuueQSs5AedNBBZm1FuGeffbZgRjwdCZAACZQrAX4JqVxTju9NAiTQrAlMnz7dxoGussoqOb3n1KlTBfd26NAh9v7a2lrBPb179469zpMkQAIkUE4EqICWU2rxXUmABEiABEiABEigAghwDGgFJCKjQAIkQAIkQAIkQALlRIAKaDmlFt+VBEiABEiABEiABCqAABXQCkhERoEESIAESIAESIAEyokAFdBySi2+KwmQAAmQAAmQAAlUAAEqoBWQiIwCCZAACZAACZAACZQTASqg5ZRafFcSIAESIAESIAESqAACVEArIBEZBRIgARIgARIgARIoJwJUQMsptfiuJEACJEACJEACJFABBKiAVkAiMgokQAIkQAIkQAIkUE4EqICWU2rxXUmABEiABEiABEigAghQAa2ARGQUSIAESIAESIAESKCcCFABLafU4ruSAAmQAAmQAAmQQAUQoAJaAYnIKJAACZAACZAACZBAORGgAlpOqcV3JQESIAESIAESIIEKIEAFtAISkVEgARIgARIgARIggXIiQAW0nFKL70oCJEACJEACJEACFUCACmgFJCKjQAIkQAIkQAIkQALlRIAKaDmlFt+VBEiABEiABEiABCqAABXQCkhERoEESIAESIAESIAEyokAFdBySi2+KwmQAAmQAAmQAAlUAAEqoBWQiIwCCZAACZAACZAACZQTASqg5ZRafFcSIAESIAESIAESqAACVRUQh2YXBbdwgcgyV/j3at1aWrVvX/hwGSIJFInArCWzixJyTesa6VBFWSgKXAZacAJOq4P5iwoerAVYUy1Sw5q8OHAZalEJtHLqivqEFhj4gvPPkWXfflvwmLfZdDNpf94FBQ+XAZJAsQisO3qALF62pODBH7bOgXL11pcVPFwGSALFIDB9rsixNxUjZJGRg0UOGFicsBkqCRSTANtNRaLbZrPNpWrnXQoWeu3TTxUsLAZEAqUksP9ae8nuPQonC7/9+NpSvj6fRQIFI7DvtiIb9CxYcHLj6MKFxZBIoNQEqIAWiXirHj2keuBOBQu97q23RBbML1h4DIgESkVgy1U3lQN6712wx9044Y6ChcWASKCUBDZfR2T7jQr3xFFPFC4shkQCpSbASUilJs7nkQAJkAAJkAAJkEALJ0AFtIVnAEafBEiABEiABEiABEpNgApoqYnzeSRAAiRAAiRAAiTQwglQAW3hGYDRJwESIAESIAESIIFSE+AkpFITL8HzFi9eLG+//ba89tpr0l7XDd15552lX79+GU/+73//K88995zMmzdP+vfvL3vssYddnzt3rtx7772yySabmL+wp2effVZ++OEHOfTQQ+XLL7+U8ePHhy9Lq1atZM8997RzX3/9tbzyyiuyxRZbyNZbb51x34cffiifffaZDBkyRFZZZZXg2vTp0+2dBg0aJL169QrO+51Zs2bZ9YMOOsifkkmTJsmLL74oAwYMkM033zw4jx08Z8KECfKTn/xE2rVrl3EtejBnzhx54IEH5Gc/+1n0koXz8ccfy8EHH5xx7e9//7uFvdpqq9l5sBw7dqz8+9//lh122EEQj6qqTBGbNm2a/Otf/5JDDjlEVl111SC8xx57TL777js7btOmjay++uqy2267ZfAJbo7svPzyy7Jw4cLgLNKzh06Cg0OYzz//vGy22WbGyN+0aNEiQXq21rVld999d6mpqfGXsm6RP8AiGresHpvghlxk4emnn5Z33nlHunTpIrvuuqtsuumm9qa5ysLrr78uM2fOzIgd8q/P90mysHTpUnnqqacE+Wf77be3tPCBZJMF5DE45Hvvkp6D6x988IF8+umnJp8dO3b0XmK3abLw6KOPylprrSU/+tGPAr/ff/+9vPTSSzJs2LDgXC6y8Oabb8o333wjBx54YOAPO8WSBTCDHP/P//yP9Oy5cip6PrKwYMECS7vOnTvL4MGDK14W3n//fRk3bpzstdde0rt37yC9li1bJnfeeaeV8yjzssnCG2+8YWXy0KFDZY011gjCSZOFbOV4nCz4gB988EGrZyDbcA1Nt48++sjKdPhF/dapUyfZbrvtZP3118cpy0+vvvqq7eMP9Uzfvn2t/K+urhaUP//3f/8XXA/vHH/88cFhLvIS3Bza+eSTT+SLL74Izqy55ppBuZNU9uPmJFkIAorZaUgZEuM9/hTWAaUrLIH5vzrbLbzrzwUNdMH117oFv/tN1jC1InRbbbWVU0XQXXHFFe6cc85xWhm6P/7xj4FfLeDtnCpb7txzz3WqnDhVKu36xIkTnQqaUwU0uB87dXV1TpUat/baa9v5hx9+2O2zzz7BT5U/u46LWhk5FUJ34YUXuh//+Mfu5JNPNj/4+9///V+34447ulNPPdVtuOGG7quvvrJrWrHZMfzg/UeNGhX48TsjRoxwW265pT90f/7zn+34oosusve94447gmvHHHOMU8XKnX322W6dddZxn3/+eXAtbkcVVde2bdu4S+7aa691Kj0O7xh2WhA7FUo7pQq500LJePzmN79xe++9tzHQyjzsxf3ud79z3bt3tzDDF1RZdapwup///OdOCyanyrlTJdSpoh6+rd7+kiVLXIcOHYJ0QJqoEmz3qWLutJB3p59+utOCyd188812Xgs7Y33EEUe4nXbayZ6lFUm9sONO3HjjjcZCC/K4y/XOrfNYfzfqszvrnW/MicEv7u/OePfirEHkIgvHHXec5SHIwdFHH215+L777rOwc5WFs846K4O/KpTuF7/4hYWRJAtIV+QXPPOnP/2p/XwaZJMFbQRaev76178OGCQ9BzdoZe9UcTBZxzNV4Q78xe2kyQJkV5U3N2PGjMCrKh1u4403Do5zlQVVno23LwN8AMWQBaTHNtts44499lhjpw1we1w+sqCNXisHke777ruvsfXvnrSdpsXAT69w7o1Pku7I7/ywPzj3z9ey+22sLFx55ZVWL6CsDTuUMSgbzzjjDDudJguQMW1ouQsuuMD4acPY/KTJQrZyPE4W/PtpQ9/eTRsddiqfdLvhhhusHEe5jB/qSW3AuVtvvdXCRJm6wQYbuPPOO89+J554osk1ynLIszYk7R2Q73wYfuvfM1d58feHt2oIMKa+LkY6wSWV/biWJAu4luQaWoYkhRM9L9ETPG48gaZUQNWC48IVE2KDDK7WNjd16lSLnFo73W233RZEFEIMoUJBgEpXW/VOrUCBcoUb1VpqiqFXQAPPuqPWN7vfK2goZJ588km7BYqKWkacWhmctqJdnz59Aq833XSTU2umHUNZ/Oc//2n77777rltvvfWC+7Bzzz33OLXsBQoohBtxVSus3ffMM884tbbaPuKD+OK94M4//3yrfO0g4S+t0oUCCiU2WvGGFdCNNtrIXX311RmhQ2H2ioi/AKX7r3/9a0aFjWuodNWi6m+z7QEHHJD1vd977z1LlwyPKw6grKsV2o5QyUPxRTogf5xyyimBF7VcBOkVnIzs1NbWmpKEe1HhlIMCmk0WUCmr5dchv3in1grLZzjORxbUwmN5BRUPXJIsoBJG48g77aVw999/vx1mkwUorGpdzZDzpOdoL4hD49A7tfKbQuqP47ZpsgAFFLJw+OGHB16jCmgusgAZh7yigQwWYVdoWUAZAVlVK5s9BnIKxQYuH1mA/z/8QTU/dWiYo6HolRw7GfPX1ApoY2UBig1kP9zQQDRPOOEEK3+8AhqOelgWIEso+7UXy2554YUXrDGOgyRZyKUcj5MFhDl58mR7L5R5Pm3ySTcooPvvvz+CDJz2AAX1ExRQKH9hh3IF9SnyuFdA58+fH74lYz8XecnwEDpAfaJW0NCZ5btJZX+aLNQLZMWJfMqQpLCi5zkGNN4wXJZn0b07ZswY0Yot4/3XXXdd6y73XR44VmVP0L0A161bN5k9e7ZoSy7why5irRCD43/84x+iClVwHN65+OKLRS1potYAQfcdumvQRfe3v/3NugfUoiRqXRR0VahFNPCK7kN0r8CpwinoCkW3J7oltZIL7kPX4lVXXWU/fxLdIVqIWXepWgGt2w7dzHDobsHz0MWMrkx0C4XD82E0ZKsFuHWJ//KXv6znTRV3G5qglsaMa2qRFS30gnOIP7qtR44ciYafvX9wMWYHcfRdRzGX7ZQqoJZud999t2ijwoZU4IJWjDbMwfNG/NFdqMqFpQ/i4x320TWW5lQBte5bxAHv1dxdLrKA4R9gAm5a2VmUkDYYvhJ2ucoCunKPOuoouf32261bPU0WIKc+bfAsyMJ//vMfe2yaLKjV34ZXYNiEd2nPUcXUuk79veCilaE/zGv729/+1oZ1aIOznv9cZQHdttrAsuE8kBPkrzTXGFkAV7DGcBO4cH5HWdVQWVBlKyhj1dJksoZhCc3VFUoWMJwK5ZfPpyhjEH90p0ddVBYwDAh1BIZMoazCMCG1Gpq3JFnIVo7HyQICRNmqDQzRXj8JDzcpVLohH4WHT0Xjju50uPAwj+g9/jhXefH3h7d4jhqVjOnvf/97K9txPa3sT5OFcNjh/WKUIT58KqCeRAVsMUZHW0TBeEdUNFDqoNBh/AaEH+6aa64RKKEoEFBwotLE+LCwQ6WLMZFwqBwwnhNjNqMOCp52lwuUUDg8A+NktFVoSg3Gham1za7hnFoqBWPG1IIpGEPpx6+oJUKeeOIJq1wvv/xy0Zal+cF9eD9tiZqyYCdDf9oVaGOSUIldcskldgWF5J/+9CfZb7/9RC22Ng5Hu1pDvvLbxTugIH3kkUcyAoDCopaBeooZxt+Gx1ai0j3yyCPNL+J0yy23ZISDMblQhq6//nobI4p0g0KU5rSVbeMXwQHjGDH+CGN/MLYOhWRYWcS4UhRYahG3MaY+XIxBnDJlij+M3SIuGLNUDuM+EYFcZAFjbZGe4A45wJhN5CFUnmGXqywgP4Oxr5DTZAGNNYylhWyh8Td69OissoDGA2Qt3KjBe6Y9B5Wlr4Rxnw7LER3mEo5eg/eRX5BP1folyHdhl4ssYFwcWOkQENHhNoJxazqkJxyMpUmhZAH5HY1s78L5PR9Z8OEMHz7c0loteDmN1fb+Sr0ttCx4wwTkZpdddsko43zcorLwrX6aGnMGMMYeBgG10Fm9hPuTZCGtHE+SBYSnw4RErYqx9RWuNzTdUEchL2q3u9Vlp512moTrk7f0IzGoa/BDYwb1JBppfhw+ngmDDOYo+J83ZOQiL/Af51CnY0wr6lSMocVzUcdkK/uTZCHuGThXjDLEP4sKqCdRAVsUrFDuvEPlAGHUrm5T4qDQwKFCgoKGe2GlxPHAgQMDayTuwSQkWBGRyZHBMSEmTvnQ7mSbwARFDw6CgBa3dlHZs1HYaPe5YAIRlF4IL6w9KCAwYNsLAwoy7Y6zyhiFFywWUKBh+dSxW/UmRNnD9A9xhlKFVjVaavADqyAKCGwRRxR2aBE31nXt2tUKIh3Dk1HxwrKMOKc5WHah0OM+NACguEORDSt+KDjAW7urrMDC+2ezrOh4U/OjY2otTTHhCoPekVZoCYcdlB2kNQbHh6/hPBoNleRylQVMzoMVR7vs5Fe/+pVgQtfgwYMzUOQqC7B86njnwG+aLKAhgsYVrJ2onJD/tbvQ/MbJApRU+EGjxSuU/kFpz/H3oPLHZEQo2MgjjXVQGmCF9RWpDy8XWUC+h/KPLWQBZUC0MVZMWQjn98bIAnqFYMFCWYrJLs3VFVIWwo2xtF6xOFlAWYdJmjAuoH5AYwouSRaSynGUXUmygF69v/zlLwKLYJJraLpB9lAuw3qIBiaUPB3rGQSPRr8OtTJjAXpSdLynoDwOO/QCPvTQQ8HPW39zkZdwOOF9TIaCYo+4ohGEuh5M08r+6LWwLITDjtsvdBmCZ2RO0Y17Ks+VDQFUZDDLo1sb3SVQCmFZgUPrDA7WHVQesDigIkNlix9aUo8//njGTFZf2MBKEJ6xZwGt+Lvrrrusq8OfwwxJWN0gHHDo5sQ5CDAqQAgeukJQaWJGKmbCovLH76STTrKKyVsucQ2FCSw3aH2iwsZsb3Rt4J2gOKAyxfPQnacD4M3qivjrJKBg5j+eB2tvIZyveL1VF2GiOwmtZCi7fpgDzqPQwVAHWAzwg3UarWLEA1YfKD/oRvIWKZ2QkjjMAeHFOVgCMOParyYAZQlsED66ZpHefgUAVAAoLHF/WPHFPhoEleRykQVYP9EoQyMHShDyO1ZYQIWNRkDYepxNFjAbFWkRntGdTRZQEaJyg6Uayi/kNUkW0HiBPHjrKqyIeD/cD6tmmszBQqNj5ayCgpwUysFSD4sO4uldLrKA3grILeQADooselCwMgfyL1whZQFWqfBMZeT3Pn362HPykQVYFNGYhoxhNjTSHNbA8OocFngz+SuELPiooJxAbwjyFDhAGYMchV2cLKAhDUMCGh5wyCcwRsBB9uJkAY3BuHIcilCSLKARh3rFWx9RH+K5WHkEcpZPuqFnxCvL9sKRP7y/HxIDQw4MHohveMgahrfpZNGIz9zqjnqeVpxAfQMjk69zIDsYrobjpLIfdUOSLCQ9B+eLVYZwElJ0VGwBjptyEhImzKiQOc0wNgtPWzhOu7adCoTDBAs4THjA4HG1FtqxWhtscDlmx/qJF7igFarTAscmMWhl4bRFGsyCx3VMRNECxWlmx2HgVKG1Geo4oS1Sm6WtFkALDzMG8U5wmIWNQd5wmBGrBZrtq5DYTNXoRBctdDImIakCF8zo1a4Mp4qWU2XV6XACe2ctfCw8rSjtWXaQ8Ie4ps2C1xZ34FOF3lYRQNxVsbbzWpg6tVw5tXDasSrBNntSrTx2rAWTU2U6CAM7qpQaT23Rx05Cyrg54UArbptJjctIBww+17Gvdrcqyw5xh9PWt01cwT4mQakiY+mvLWibtYl0ytWpslMWk5CyyQLyvVp+nVqvggkq2gizSSXIow2RBUySw8oTUZckC6r0BjOHkWcwuQN5EC4XWcAEpvBkw6TnIH21QrKVKaLvlnScJguYhARG3mFFDchBeHJKmixoBelUgcmYRY+wMJsX/uDiJiHZhSx/SbKAiSBYwQPlAlaNgCxrF6mFlo8sgDtmNcOhDIXMYTJKmmvqSUiNlQVMQvJxxkoeWFHApxcmEWkjP4h+nCxA1pAGfsWDSy+9NJjckyQLuZbjUVkIXkR3tKERTELKJ93iJiGFw4+bhKS9cU7HrzodCpXTJKQ0eQk/K7qPukcbQQ71HOpnTOjDCipwSWV/mixEw/fH+ZQh3m+2LRXQbITyuN6UCiheF0tEYJasWnJM+cMyS9qtHcREW05WWaoF1GZ2awvOaVeYXQ9XujihrWd35pln2rWoAopZflB2ow5LnGApJfjFzHEs2eQdlvFAgb3ttts6LIEDwYHT1q69ExQ1zOiFchZ1YQXU+8H9eA4qLa/s4RqWnVKrpxWUmMWIwizNodLVhp7T8S4ZPyi0KLxRaYUdKl7c7xVQKJFqwTUeahEyBdgviYFZmFgqKaqoqxXLlB28d76VLgoUzJZHeiMdtUs0UKbwXCy7g0YEVjXQtd8sCqiEsfoAGiWY0XzdddeFo5Z1v1wUUEQkmyyoFd34qAXZ0gKc0HiDa4gsYKWF8Kx2C0D/kmRBLZ9O16M0OYDCiZm13uUiC9FKN+k5uC+aryGTaS5NFqIKKMJBQzKsgKbJApQA5Neow6oZOsTFGjbFkAXtabDVPTBrGEvBQQbg8pEFzOSG/KgVz8oylGmIc5pragUU79YYWQgroFjSDnnKGwyiCmiSLEDZVyu/MUOeh4IGlyYLuZTjUVmwQFf8hRXQfNItHwUUj0YDH3kE5XNU/nwd48vjNHkJxyVuX+dLWF2DfI1VCqDowyWV/biWJAu4FufyKUPiwok71wonFRBdAQksOP8cab3RxtLuqJ8VLNSFN1ynq+jOl/bnXZBzmDDDo8sjOmbMB4BucIxZQVdSMRzCRvcEugfDDt2HyHa+azh8De+MmckNcb4bM+oHz0AXjO+ejl4v1jG6RdCNW0qHeGJ8TxxTjDtFOkQdxuVi7Cf8FcutO3qAnLvJKXLSBkcX7BG7vnSADOiypVy99WU5h5lNFjB2GOOhsq06kPMDIzcmyUJS3oX3fGQh6TmR1ynZYXOSBVU2bQhP3AzmfGQBModuVUzSyOamzxU59iaR83TN/e0LONrlkKtEDtGFRQ4YmO0NVl5vSllAnQPWGEsZdUmyUOhyvCHpFn3HYh/nIy8YygKmcXVOUtmfJgvFjmM4/OLVPOGncL9JCGRT5KCcFkv5RIT9pIpo5DG5Kclle+c4f3EVCu6D4htWPjFuCDPF4xy+BIUJIYVwcQVBY8LFbGkUMFGH+GFCFFzaJKI45RN+ospWLs+Bv3J02fJVUiOtUHFNkoWkvIvnZnvnuHdLek703lKldSFlAZUzxlXHOYyx8+PwkmQBK1KEV6UIhxOWhcY+Jxxuc9zPlq+KKQuoc+KUT3BKkoVoOd5YptH8USpZyOW9o/KSy7uhART155+VVPZHZSGX5/gwC7mlAlpImgyrWRNAwZpUQacpxU0dKRQumK1bbFeq5xQ7Hgw/O4FyTGsoL0nym02pyk5k5R2les7KJ3KvKQk0Z1ko1buV6jnRdKYCGiXC44ol4NdgK7cIwjpbCleq55QiLnxGOoFyTGtYyEox07xUz0lPIV4tFYHmLAulerdSPSeaptkHr0R98JgESIAESIAESIAESIAEGkGgbC2gL730kn12UmfcxQ4CxxqROstMdAZ33ngwuBcL52I9Mp3tLbpUSOwkj7gH1I15Vea99WbcpfzO6fqXbXRiEx0JlBuBP35ys9w04Y6CvfasJXNsElLBAmRAJFAiAtc/potvL18KsyBPXJL5rYmChMlASKBUBMp2FjwWwdVlB2wRXF3OpB4vfBUHC8Jioe98HD5niAV8MctRlwWxz0SiawbfzMZ4iTRX+/yzskxncxfatdZFdqt/vHOhg2V4JFA0Atd+eqvULVta8PD7ddlM9lhz14KHywBJoBgEFi4WeaSA9ojwO27VV2TTld8CCF/iPgk0awJlq4CCKhRMfHEHX9YIO3wJQde+NGURimg+Dl8mGTNmTPDVACzTgi9+4FN7+PwhHQmQAAmQAAmQAAmQQH4EynoM6M9+9jP71CE+Nxh2+BY2PkUZVj514XD7Hji+aa4LJ4sufBx40cVm7ZN8+GTjYYcdJviUGJbvwfdVvcMManzSC59cpCMBEiABEiABEiABEsifQFkroIcffrgtLAzl0juM29RPgYl+Nsyfsu+4HnroobbOo37Rxr57vtdee9n3TXETvgeNb53r5xxt4XKsE4axpfhesXdTp04VjDsNK7X+GrckQAIkQAIkQAIkQAK5EyjrLnhEc9iwYYJV/fVzjxbrZ555RvRTazJ58uRgrOYll1wi+tlBUzJxk376ytaT08/CySmnnCJQRvXzYjJ+/Hj7cpAFFPrDl3v0O8+CrwpASU1azDjkhbskQAIkQAIkQAIkQAIJBMp2FryPD7rhoXD6T1ih+12//R0on7jvsssuE0wq+te//iX6zWR5//33zXIa7rrHhCMsQBx1CPenP/2p6Ddd5dlnn6XyGQXEYxIgARIgARIgARJoIIGy7oJHXPH5NXxO8oEHHrBvJz/00ENy3HHHZWC47rrrpG/fvvK73/1Ovv/+e1vMeI011si4J+7zYLCi/vjHPzYL6yuvvCK9evXK8MMDEiABEiABEiABEiCBhhMoewsovoN69NFHyz/+8Q9bo7Nnz56y664rl2dZqOtnnnvuufLHP/7RutuBaOnSpTJy5EjBeNEk980338guu+wi/fr1E3wntX379km38jwJkAAJkAAJkAAJkEADCJS9Aoq4QgG94oorBF3q6JJv1apVgKCqqkpg3ZwyZYopnLjn7LPPFozrDHfBBx5W7Jx44ommqJ566qk27tNfxxqg+KRjmnvr+ZNk/pwv027J61qXbv1k60G/z8svPZFAUxAY+eY7siSloZfvO+22Rnc5fr0++XqnPxIoKYFls5fKgiumFuWZNXt2lprBnYoSNgMlgWISqAgFFF8pGjx4sLzwwgu2LFMYWHV1tfzhD38QTES67bbbrDsdyuXw4cPl3XffDd8a7E+cOFEef/xxO44uco+Z8eElnAJPoZ1pk1+Tutp50qXblqGzjdudMfVtcctqGxcIfZNAiQm8Mm26rNuhvfTRDzoUyo2dPkN6a5h0JFA2BGqd1L2Dr9m1lVar1Z9rkG886t5cIFX9KQv58qO/piVQEQooEGL2e5LDck34TZo0ycaLwioadk888UT40JZrcs5lnGvoQc91h8o2u93cUG+J948ZPUxqF89MvM4LJNBcCQxfu7ectL5+rqVAbteXxxQoJAZDAqUl0PawrlKzU8eCPXTW3hMLFhYDIoFSE8jUxEr99BI/D18yoiMBEiABEiABEiABEmhaAmU/C75p8fHpJEACJEACJEACJEACDSVABbShxHg/CZAACZAACZAACZBAowhQAW0UPnomARIgARIgARIgARJoKIEWNQa0oXDK9X4sMYVPhr722mu2funOO+9s65mG44MvQj333HMyb9486d+/v+yxxx52ee7cubbu6SabbCLwF3b4EtQPP/wghx56qHz55Zf26dLwdSx/hU+Wwn399deCxfu32GIL2XrrrcO3yYcffiifffaZDBkyRFZZZZXgGr42hXcaNGhQ7KL/s2bNsusHHXRQ4MfvPPjggxZely5d/Cl54403ZMKECTJ06FCJfngguGnFzpw5c+xjBljGK+rwvh9//LEcfPDBGZf+/ve/C1ZFwNJccGA5duxY+fe//y077LCDxSM64Q2fc8UXuQ455BBZddVVg/Aee+wx+1oXTuCLXFg6bLfddsvgE9wc2Xn55Zfty17+NNKzR48edogvgD3//POy2WabyYABA/wttgQZ0hPr6OJjDrl8XhafvEVYnTp1sg80hJc7CwJuZju5yMLTTz8t77zzjiDvYA3hTTfd1GKRqyy8/vrrMnNm5gRBfLTC5/skWcB6xE899ZTln+23397SwuPLJgvIY3DhNMUkyxdffNHOhZeK++STT+SLL77wQdtETP9uwcnQDtZOvvvuu4Mzbdu2tQ95QC6R5pCFV199Nbjerl07u448j1VH4LCKyLfffhvc43cg8+utt54d5iIv3l94mxYfLK2XlK/BDO+OlU2wXnSuDmVpx44dg3yRq7/mdl9jZAFfDxw3bpx9tjo8lwJrad95551WziP9s8lCUpmcJgsof1GOo6xFXou6qCzMnj3b3iN8H/zCpT0nfL/f/+ijj6xMxzHyPsq+7bbbTtZff327JZssgDm+zhjnjj/++OB0MWQhqezHQ/ORBaTdV199ZfVSt27dgndv1I7O9qYrMIEn/rqVe+u5Ewsa6quPHexeeHBI1jC1InRbbbWVU0XQ6dqo7pxzznFaGTpdiD/wq8qOnVNly+ki/U6VE6dKpV3XJaicCppTBTS4Hzt1dXVOlRq39tpr2/mHH37Y7bPPPsFPKzy7josvvfSS0y9PuQsvvNDpl6TcySefbH7wp59NdTvuuKPT9VXdhhtu6DRD27VHH33UjuEH7z9q1KjAj98ZMWKE23LLLf1hsFWFDksWOC0MgnOIl1bq7oILLrB3VsUpuBa3owWc04o27pK79tprLXy8Y9hpQew++OADO6UKudNCyXj85je/cXvvvbcxUMU27MXp17hc9+7dLczwBa3cnSqc7uc//7nTgslpRe1UCXWqqIdvCnkUngAAFHhJREFUq7evSqHr0KFDkA5IE1WC7T5VRpwq3u700093+rUwd/PNN9t5LeyM9RFHHOF22mkne5ZWJPXCDp9A/JD2SEv9iIPbeOON3YIFC8K3xO6v8/jTbtSEibHX8j05+KVX3Rnv/yer91xkQb+aZnkK+UXXE7Y8fN9991nYucrCWWedlcFfGyTuF7/4hYWRJAtIV+QXPFM/9Ws/nwbZZEEbgZaev/71rwMGf/7zny0eF110kcnuHXfcEVzThpPJgpfXK6+8MrgWt6MVl+V35EPkx2OOOcbyizZSnVbglo822GADd95559lPl7WzuCD/+jiokudUmTf/CMP/VNG3R+YqL3HvlxSftHyN9Nhmm23csccea+y0AR4XdL1zqoCYHP7lL3+pd60hJ5b+UOtm7jbBLR4zryHest47c6/P3cJ7Z2S/L4d6IU0WkGdQLyB/hR3KGJS9Z5xxhp1Ok4WkMjlNFpD3tIHsdO1ut84667jPP/88/HgXJwv6VUQrq3x+33fffc1P2nMyAg0d3HDDDVaO+/yLelIbI+7WW2+1u1CmpsmCNiSND/KdD8Nv/WOKIQtJZT+emY8sqALvDjzwQKtLUG5lq5d83LJtJdsNvN5wAk2pgKLQD1dMeHtkcLW2ualTp1pkUJHomqhBxCDEECpkKlS6nTt3dmoFCpQr3KiWSVMMvQIaeNYdtZjY/V5Bg+Kna6XaLVBShg0b5tQy4bQV7fr06RN4vemmm5xaM+0Yhcw///lP29f1WZ1aSYL7sHPPPfc4tezVU0D1c6n2XlDqvAKKOOA5ajG1MHR9WFP8MgKMHGRTQFH4qdXEzZixsrAPK6AbbbSRu/rqqzNChcLsFRF/AUr3X//6V1Pg/DlsoYCqRTV8yh1wwAHWQMg4GTl47733LP6R03YIZV2t0LYPRR+MkA7IH6ecckrgRS0XQXoFJyM7UGIvvfTS4CzSFEpPNteUCmg2WYCCqpZfq8R8PNRaYfkMx/nIgloJrKJExQOXJAtoGKFS9U57G9z9999vh9lkAQqrWjADOYfSh7iOHz/e/OuSdE57HnzQpjyq1TA4zrbjFdDa2trgVrUGO/0anIMCiUoXlXvYgSXKEMguHBTQNKUtV3kJP8PvQ4bi4pOUr8EFsgrlGQ5yCsUmm4OSgcY73jUtLtnCwfWmVkAbKwtQQFFOoOEZdieccIKVP14BDV8Ly0JamZwkC6iXUG+hfoE7//zz65WHUVnw98EIEHVJz4neFz6GArr//vuHTzn9MmJQP2WTBa+Azp8/PyOM8EExZCGp7M9HFtAIgzx7B2U6qmP4aw3dcgxoo+zHzcszunfHjBljX3oKvxkW6teMF3RD41iVPUH3AhzM6ei20JZc4A1dxFohBsf41KkqVMFxeOfiiy8WtaSJtjQFXdnorvnRj34kf/vb36zrTy1Kgm48dNupRTTwiu5DdK/AoVsOXaHo9kS3pCp8wX3owrzqqqvsF5zUHc3sohWJfWYVXWTeoZsY74MuSXQloktarTX+cl5bLcCt6+GXv/xlPf+quNvQBFXSMq6pFUq0sgvOIf7oksdnYPHu+HBCmkOXT3hIQdy9qoBauiGe+NACunLg1GJtwxw8b/DUhoV1ZSF9EB/vsI/ulTSnllv51a9+FdyCvOafFZxsRju5yAKGf4AJuGllZ2+PtEGXa9jlKgvo/j3qqKPk9ttvt271NFmAnPq0wbMgC//5z3/ssWmyoEq/Da/AsAnvkE+QlzB0AMMkMJwDQy7gkEba8DRZ+P3vf2/p7/01ZIv8iqEh4BXnfF7IpWs7V3lJek5SfJLyNbiCNYabwOWS33EfulsxNAPlBxiXqyuULGA4Fcovn09RxqilzYY4RdlEZSGtTE6SBZR9qDcwpAJDUtC9H64X4mQB74EyEcObUGfAL/IuXNJz7GID/pCPwsOnol6bWhbSyv58ZAFlCdIPDnkJQ+v8MKVo3Bt6TAW0ocSa8f0Yo6PWgWCcjLa6TKmDQqddxVYJ4fWvueYagRIKJW2ttdaySvPTTz/NiBkqXe3KsHNqCbFMh/FbUYeCQbvLBUooHJQ+FNxqITGlRi1lotY2u4Zz+GDA999/b59FxRhKPzZNW1SCDwJg7OLll18u2rI0P2rdsffTlmi9yu/GG28UbT3a2E+7ecUfxp5B4cZ4ThRa2ho0pTZ8Tz77eAcI4iOPPJLhHQqLWgbqVVJqMcoYW4mxUkceeaT5haJyyy23ZISD8a9Qhq6//nobI4p0g0KU5vA1L1SSapm1rQ59sLGk33zzjRWS4YoT40pReatF3MaY+nAxhhWfqk1zqAj8OFE0TDAmC3Fori4XWYBChfQEd8gBxkXii2moPMMuV1lAfgZjjDmGS5MFNNbUkiKQLTT+Ro8enVUWwByyFm7UhN8TeQDj89DwQTzgIPfaC2Fyh/FvkGHkw1zcn/70J8uPkE340y7soJH61ltvyX777Wc/KHS4/tvf/jYYe4zw1WJlny3GeFT/Q+WYq7zEvWNafJLyNc6Hx6zlkt/x7MMOO8zGy2LfKzHYLzdXaFnwhgnIzS677BKUC2EuUVlIK5OTZAHKLvIg8pn2vNnns3WYgD0mTRaggOJrhSh/kSd1OJT5SXpO+L3j9lFHoVxWi7jVZaeddpr498D9ucgCDDJeBrD1hoxiyEK2sj8fWUA8YVBCAwB1NFgWwnESUiEoNpMwULBCufMOFRKUNDhkdO2WEDWf24B6CLZ2gdtEJSiaAwcOtBaityRiEhKUDhT4yNCYEBOdUINwtTvZJjChgIBDJYdWEiYFoXCCRROZFkollF4IL6w9GEyuXRtBxYB7dbyq6PgYU0RhsYDlE++Iig8ToiDo3sF6q91iwQBxfx5bvAMUKrw3lAxMnkDF7Qeih+9tyH7Xrl2tIMLgcYTpHSY4Ic5pDhzAGZ+BRQMAijsUWbynjs80r3hfcAFnFFgoPKEYpTmkKdj6yVyYQIJB76g8UdmHHZQdpC8mioSv4TwaDbk4WPdgDYW1Os0KkEtYxbwnV1no16+fWXGQflBG0fCB4v/mm28Gr5erLICNr1jgOU0W0BDBM2DthJwNHjw4aGDFyYJ2YVrjBY0WL6PBC67YQZzRwHjooYcEk5qQtzBhApW/n4SH+F566aXWcxD1Hz2GtQTWHjwP5Qa+JucdGjo6vMSUZ4SH6zqu21+2LVhAxsMOeTsXeQn7Ce+nxScpX+OZ+eb38LPLdb+QsoDGmI7jtzIHvWLIx946FuYTJwtJZXKSLECRhKKHLeREx5davr3rrrtSZQETbHS4keVd+Ef5CoU16Tnh947bRwMR9SAcwkUDLlyX5CIL6AUMT6Dy5W0xZAG9I+H8jvf2ZX9jZAF1CtIePX0wjPiGCMLP11EBzZdcM/QHpQXmf3Rro7sESiEsK3BoRcLBuoPWi04isooFFR9+sJJg5ioslt55yw8sCOEZe/46tigMdIJTcAoWGFjdUFHAQTHCOQgwlEh0het4IaucMYMQlbCO47TfSSedZAqjb/HiGpRMWJLQ+oQ1FAoWuvlQsMC/n+2NeENRxQxzKG3Yh/IJhy40KL6FcGAHAfdWXR8+WslQ/n1Fj/ModDDUAYKKH6zTeF/EA4UilAF0I+nEK3s1nZCSOMzBboj5Q8GKGddeAYWyhPRC+OgCRnr7gg8VAApL3I9977APS3I2B8UTXf06sSajKyybv6a4nossoOKEsomuOlgFkN+xwgIqbCh8YetxNlnAzGykhQ7UD6KbTRZQgaNygyKP4Q2Q1yRZQOMF8uCtq5hdi/fD/dddd50p0agUcU7HDltljWEV6CpDQ9TnS+QPNOyQB32XdPDCkR00XlFhxTnw8sMA0HhFLwPkLjxMB3kwLl9BHrPJS9wzcQ4ylhSfpHyNdw3P2kd+79OnT9IjKu58IWTBQ0F6wrIIYwAsq1DGogponCyklclInzhZwOoesF6inIRDvYFeO/RupckCVnjx9QIadzB8YPY2hpjFPcfHLWmLnhEYMJJcLrKAZ+tk0XpBFEMWIOtJZT/qhobKAgwjGKIEwxHqGRhRfDlUL0INPaFdC3QFJtCUk5AwY1sFzmkBYTNSteXjtGvbaQHgMMECDhMeMHjcD4zWDGaDy9WqFUy8wH1aodoAfO0ysLC0JRrMgsd1TDBSJc9pZsdh4FShDSaoYAAzZmljEgPCw4xBvBMcZmFjkDecZm6nBZrtq5DYTFWEH3Za6NSbhOSva4USTEJCfLQACmbYq4Wm3kBy789v8W5aWPnDjC2Yaus5OKcVoE1OQNxVsbbzquA6tVw5taTZsTYCbPakWjntWCvpehMZVCk1ntpajZ2EFDwwZUeHPthMatyCdMDgcx33ZD5UWXbanW/7ahWziSs4wCQoLUAs/dU6ZjOYkU5pThsaNtkAcW+Ia8pJSNlkAflELRFOrfXBBBVthNmsZ+RRPwkJ8c0mC5gkh5Unoi5JFlTpDWYOI89gcgeeAZeLLGACk58IoMqk00raQX7htLfDaaPDacPN8qcqgk4baCbDWBUDs9vTXNwkpPD9cRMvtGHidMyeg1+4bJOQsslL+HnhfchbUnyS8jUmgqA80GFGDqtGQJa1dyEcbOq+Nj7qyW6qh5iLTT0JqbGygElImHwCh5U8tIHvkIZwmNwTnoQUJwtpZXKSLCC9UPcg78KhLEOdEXVRWcCqLqjz4FBnYPIl5DnpOdHwwsdxk5DC17PJAvKe6mRBXRv26/eLIQtJZX8+soC6AfqD1xew+oUOu/Gv36gtxrbQFZhAUyqgiApmb2KWrFpyTPmDQIZnWKsVwSpL7Vqzmd3agnPaLWwUwpUuTmjr2Z155pl2LaqAYsYrlN2owxInWEoJfjFzHEs2eYdlPKAkbbvttk6tmFYp4pq2du2doKhh5jCUs6jLVQGFP1RGalGyZ6FC9xVjNEx/jMofBYVahTJ+qMSjCij8YCkr3O8VUCiRasE1Hmr5soLTL3eD2flQwqOKulqxTNmBkho3C96/W9oWBQpmyyO9kY7a7RkoU3gulsxQq4WtUqBdUxYUKmGsPoBCRYdHOLWgpT3CriGd1bqWwQbPyuaaUgHFu2WTBSxXAj7asre0wOoPaLzBNUQWMEM3PKvdAtC/JFlQy6cpaZAD5E/MrPUuF1kIV7rwBz+QHcgc8pJv+OCaDtGw/IjZ45jJDGUgzeWjgCI8NGr8qhZQQKP5BbKlll57dJq8pL0briXFJy1fY8UGrO4BBlgKDvfm6ipBAUVcGyMLYQUUSyGh7PMGg6gCmiQLSWVymixg+UC1eprCi9noUEqjLioLapG1fK7WRVM+/eosac+JhumP81FA4dfLgldAo/UKjn15XAxZSCr78W75yAIaHVhZA/WMdsVb2YiwGutaIQDNTHQFJPDk3VtL9147yja7LZ9IU4igx4weJrWLZ8quBz6bc3Aww6MbOmnMGMaowbSOrrJiOISN7gl0C4Ydug+R7XzXcPga3jlppm34vlz2ET8sXo+JIaV06CJEN24pHYYgoLs0jinGNyIdog5sMBYpqZs1en8+x+s+8Yycu/GGctL6ffPxHutn15fHyICuXeTqflvEXo87mU0WMGFPrSRZVx2ICzuXc0my4Lvg48LIRxaSwkOXO9K71PkyLl7Rc/nIS1p8kvK1Kp02hKcpxi4vm1Ync4Z/JR0uW1NqduoYRZD38ay9J0q7kV2l3fCuOYfRlLKQViYn5V3UFSjf/DCjXCOqyp/l92j9k/ScXMMt5n2FloWksj8fWYDMYaieH79aCA7xA3wKETLDaHIC2RQ5KKfFUj4ReQzYjnMYl5Pksr1zkr+484hfWPnEuCHMGI9z+BIUBroXwhW6ksdsaVSqUYeCFZO24NIKhTjlE36iSzzl8hz4K0eXLV8lNdIKFdckWUhThrK9c9y7JYWnFpcM5bM5pXVUXnKR02h8wiyi+dpfwyoOfiUHnMvlOd5vJW2z5atiykK0TA5zTcq7KOcaqnwi3HDZn/ac5iwLubxbmiwklf1RWcj1OWn1TJhxrvtUQHMlxfvKngAK1iRFIE0pbuqIo4L2nzgs5ruU6jnFjAPDzo1Ac07rUslpqZ6TW4rwrqYi0JxloVTvVqrnRNOYCmiUCI8rloBfh63cIgjrbClcqZ5TirjwGekEmnNal0pOS/Wc9JTg1aYm0JxloVTvVqrnRNO6dfQEj0mABEiABEiABEiABEigmARoAS0S3WnfvS7/fqkwa0/iFWdN+0A6rrJ8sfcivTKDJYGiEHhyylT5VtdvLZSbsmhxoYJiOCRQUgJLHp8jde8uKNwzazmHuHAwGVKpCVABLQLxjp3XlbmzJsh3X+U+Yz3ba7Rq1UY6dKICmo0TrzcvAn07dpDpOvv4xR/SvxTVkLfuWlMt3WuSJ7I1JCzeSwIlIaDfxGjdu1qWTa61X6Ge2bpXtbRaZfkHNwoVJsMhgVIR4DJMpSLN55AACZAACZAACZAACRgBjgFlRiABEiABEiABEiABEigpASqgJcXNh5EACZAACZAACZAACVABZR4gARIgARIgARIgARIoKQEqoCXFzYeRAAmQAAmQAAmQAAlQAWUeIAESIAESIAESIAESKCkBKqAlxc2HkQAJkAAJkAAJkAAJUAFlHiABEiABEiABEiABEigpASqgJcXNh5EACZAACZAACZAACVABZR4gARIgARIgARIgARIoKQEqoCXFzYeRAAmQAAmQAAmQAAlQAWUeIAESIAESIAESIAESKCkBKqAlxc2HkQAJkAAJkAAJkAAJUAFlHiABEiABEiABEiABEigpASqgJcXNh5EACZAACZAACZAACVABZR4gARIgARIgARIgARIoKQEqoCXFzYeRAAmQAAmQAAmQAAlQAWUeIAESIAESIAESIAESKCkBKqAlxc2HkQAJkAAJkAAJkAAJUAFlHiABEiABEiABEiABEigpASqgJcXNh5EACZAACZAACZAACVABZR4gARIgARIgARIgARIoKYH/B1ZlDSldlwAfAAAAAElFTkSuQmCC" /><!-- --></p>
<p>Here we find that all of our samples show the standard bi-modal
distribution as expected. We also find that our PREC cells show an
increased density of probes at the higher (right) levels of methylation
compared to LNCaP cells, indicating global hypermethylation in the LNCaP
line. Looking at samples individually shows that there is little
variability between samples of the same cell type.</p>
</div>
<div id="multi-dimensional-scaling-mds-plots" class="section level3" number="5.4.2">
<h3><span class="header-section-number">5.4.2</span> - Multi-dimensional
scaling (MDS) plots</h3>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="do">## MDS Plot for Variation of data</span></span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>plot <span class="ot">&lt;-</span> limma<span class="sc">::</span><span class="fu">plotMDS</span>(</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>  beta_v2,</span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>  <span class="at">top =</span> <span class="fu">nrow</span>(beta_v2),</span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>  <span class="at">cex =</span> <span class="fl">0.5</span>,</span>
<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a>  <span class="at">main =</span> <span class="st">&quot;All Samples&quot;</span>,</span>
<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>  <span class="at">labels =</span> <span class="fu">colnames</span>(beta_v2),</span>
<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a>  <span class="at">plot =</span> F</span>
<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>)</span>
<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a></span>
<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a>toplot <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a>  <span class="at">distance.matrix =</span> plot<span class="sc">$</span>distance.matrix,</span>
<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a>  <span class="at">x =</span> plot<span class="sc">$</span>x,</span>
<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a>  <span class="at">y =</span> plot<span class="sc">$</span>y</span>
<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a>)</span>
<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a></span>
<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a>  toplot<span class="sc">$</span>distance.matrix,</span>
<span id="cb14-19"><a href="#cb14-19" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb14-20"><a href="#cb14-20" tabindex="-1"></a>    <span class="at">x =</span> toplot<span class="sc">$</span>x,</span>
<span id="cb14-21"><a href="#cb14-21" tabindex="-1"></a>    <span class="at">y =</span> toplot<span class="sc">$</span>y,</span>
<span id="cb14-22"><a href="#cb14-22" tabindex="-1"></a>    <span class="at">colour =</span> variable.of.choice,</span>
<span id="cb14-23"><a href="#cb14-23" tabindex="-1"></a>    <span class="at">label =</span> sample_table<span class="sc">$</span>Name</span>
<span id="cb14-24"><a href="#cb14-24" tabindex="-1"></a>  )</span>
<span id="cb14-25"><a href="#cb14-25" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb14-26"><a href="#cb14-26" tabindex="-1"></a>  <span class="fu">ggtitle</span>(<span class="st">&quot;MDS - All Probes&quot;</span>) <span class="sc">+</span></span>
<span id="cb14-27"><a href="#cb14-27" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">3</span>, <span class="at">alpha =</span> <span class="fl">0.8</span>) <span class="sc">+</span></span>
<span id="cb14-28"><a href="#cb14-28" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb14-29"><a href="#cb14-29" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Dim 2&quot;</span>) <span class="sc">+</span> <span class="fu">xlab</span>(<span class="st">&quot;Dim 1&quot;</span>) <span class="sc">+</span></span>
<span id="cb14-30"><a href="#cb14-30" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>)) <span class="sc">+</span></span>
<span id="cb14-31"><a href="#cb14-31" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb14-32"><a href="#cb14-32" tabindex="-1"></a>  colScale <span class="sc">+</span></span>
<span id="cb14-33"><a href="#cb14-33" tabindex="-1"></a>  <span class="fu">geom_text_repel</span>(<span class="at">min.segment.length =</span> <span class="dv">0</span>, <span class="at">box.padding =</span> <span class="dv">1</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>This MDS plot, using all probes on the array as input, shows that our
samples cluster by cell type, as expected. There is one LNCaP sample
that is clustering alone, so it’s worth keeping a note of this sample if
any downstream analyses return questionable results.</p>
</div>
</div>
</div>
<div id="differentially-methylated-regions-dmrs-with-dmrcate" class="section level1" number="6">
<h1><span class="header-section-number">6</span> - Differentially
methylated regions (DMRs) with DMRcate</h1>
<p>Peters <em>et al</em> (2015) created the DMRcate package for
identification of regions of differential methylation. Regions are of
particular interest compared to differences at individual CpG sites as
multiple coordinated changes are more likely to be a true change and may
have more functional relevance. Here we show some of the updated
functions that DMRcate offers with EPICv2, including options for
removing replicate probes and for remapping of probes that have a closer
affinity to their off-target location as defined in our new paper
(Peters <em>et al</em>, 2024)</p>
<div id="basic-usage-with-dmrcate-plot" class="section level2" number="6.1">
<h2><span class="header-section-number">6.1</span> - Basic usage with
DMRcate plot</h2>
<p>Our first example shows the default options in DMRcate to derive DMRs
with all replicate probes and off-target, cross-hybridising probes
removed in QC.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="do">## Create M values for DMRcate analysis</span></span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>M <span class="ot">&lt;-</span> <span class="fu">BetaValueToMValue</span>(beta_v2.clean)</span>
<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a></span>
<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a><span class="do">## Create design for your comparison.</span></span>
<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a>design <span class="ot">&lt;-</span> <span class="fu">model.matrix</span>( <span class="sc">~</span> variable.of.choice)</span>
<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a></span>
<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a><span class="do">## Annotate probes with appropriate position and probe weights.</span></span>
<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a><span class="do">## We have the potential here to remap v2 probes that bias towards off-target areas of the genome, however we removed these earlier so we will set this parameter to FALSE and proceed with the basic analysis.</span></span>
<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a><span class="do">## If you have multiple variables in your model, remember that the &#39;coef&#39; call in cpg.annotate will find DMRs based on the column number selected as found in your design variable.</span></span>
<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a>myannotation <span class="ot">&lt;-</span> <span class="fu">cpg.annotate</span>(</span>
<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a>  <span class="at">datatype =</span> <span class="st">&quot;array&quot;</span>,</span>
<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a>  <span class="at">object =</span> M,</span>
<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a>  <span class="at">what =</span> <span class="st">&quot;M&quot;</span>,</span>
<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a>  <span class="at">arraytype =</span> <span class="st">&quot;EPICv2&quot;</span>,</span>
<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a>  <span class="at">epicv2Remap =</span> F,</span>
<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a>  <span class="at">analysis.type =</span> <span class="st">&quot;differential&quot;</span>,</span>
<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a>  <span class="at">design =</span> design,</span>
<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a>  <span class="at">coef =</span> <span class="dv">2</span></span>
<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## EPICv2 specified. Loading manifest...</code></pre>
<pre><code>## loading from cache</code></pre>
<pre><code>## Your contrast returned 538081 individually significant probes. We recommend the default setting of pcutoff in dmrcate().</code></pre>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="do">## We can alter how permissive we are with the statistical cut-off for individual CpGs here.</span></span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="do">## Considering our initial analysis returned &gt;500,000 differential probes, we are choosing to focus our DMR approach to the most significant probes.</span></span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>myannotation <span class="ot">&lt;-</span> <span class="fu">changeFDR</span>(myannotation, <span class="fl">1e-10</span>)</span></code></pre></div>
<pre><code>## Threshold is now set at FDR=1e-10, resulting in 33224 significantly differential CpGs.</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="do">## Run DMR analysis</span></span>
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>dmrcoutput <span class="ot">&lt;-</span> <span class="fu">dmrcate</span>(myannotation)</span>
<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a></span>
<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>results.ranges <span class="ot">&lt;-</span> <span class="fu">extractRanges</span>(<span class="at">dmrcoutput =</span> dmrcoutput, <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>)</span>
<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a><span class="fu">length</span>(results.ranges)</span></code></pre></div>
<pre><code>## [1] 23605</code></pre>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="do">## Plot the top DMR</span></span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>cols <span class="ot">&lt;-</span> myColors[<span class="fu">as.character</span>(variable.of.choice)]</span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a></span>
<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a><span class="fu">DMR.plot</span>(</span>
<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a>  <span class="at">ranges =</span> results.ranges,</span>
<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a>  <span class="at">dmr =</span> <span class="dv">1</span>,</span>
<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a>  <span class="at">CpGs =</span> beta_v2.clean,</span>
<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a>  <span class="at">what =</span> <span class="st">&quot;Beta&quot;</span>,</span>
<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a>  <span class="at">arraytype =</span> <span class="st">&quot;EPICv2&quot;</span>,</span>
<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a>  <span class="at">phen.col =</span> cols</span>
<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="remapping-cross-hybridising-probes-and-improving-dmr-calling-with-dmrcate-and-epicv2remap" class="section level2" number="6.2">
<h2><span class="header-section-number">6.2</span> - Remapping
cross-hybridising probes and improving DMR calling with DMRcate and
epicv2Remap()</h2>
<p>Here we will show how DMRcate can remap particular cross-hybridising
probes from the new manifest to their off-target location based on
empirical evidence. Users may want to use this option to improve
accuracy of their results.</p>
<p>Here, we provide an example of how a remapped probe has led to the
identification of a DMR that was not present in our initial DMRcate
analysis. For this tutorial, we use a 30% beta value difference
threshold and require minimum three probes to identify DMRs that did not
exist under default conditions.</p>
<p>This method can add data in support of already existing DMRs, or
discover new DMRs through the insertion of a probe that was previously
incorrectly mapped. The “min.cpgs” and “betacutoff” variables in the
dmrcate() function calls can be set according to user preference.</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="do">## Here, we need to alter the beta table to maintain the cross-hybridising probes in order for us to remap them appropriately in the cpg.annotate() step used later.</span></span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a>beta_v2.clean2 <span class="ot">&lt;-</span> <span class="fu">rmSNPandCH</span>(beta_v2.detP2, <span class="at">rmcrosshyb =</span> F)</span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>beta_v2.clean2 <span class="ot">&lt;-</span> <span class="fu">rmPosReps</span>(beta_v2.clean2, <span class="at">filter.strategy =</span> <span class="st">&quot;mean&quot;</span>)</span></code></pre></div>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="do">## Create M values for DMRcate analysis</span></span>
<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a>M <span class="ot">&lt;-</span> <span class="fu">BetaValueToMValue</span>(beta_v2.clean2)</span>
<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a></span>
<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a><span class="do">## Create design for your comparison.</span></span>
<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a>design <span class="ot">&lt;-</span> <span class="fu">model.matrix</span>( <span class="sc">~</span> variable.of.choice)</span>
<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a></span>
<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a><span class="do">## Annotate probes with appropriate position and probe weights.</span></span>
<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a>myannotation.remap <span class="ot">&lt;-</span> <span class="fu">cpg.annotate</span>(</span>
<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a>  <span class="at">datatype =</span> <span class="st">&quot;array&quot;</span>,</span>
<span id="cb25-10"><a href="#cb25-10" tabindex="-1"></a>  <span class="at">object =</span> M,</span>
<span id="cb25-11"><a href="#cb25-11" tabindex="-1"></a>  <span class="at">what =</span> <span class="st">&quot;M&quot;</span>,</span>
<span id="cb25-12"><a href="#cb25-12" tabindex="-1"></a>  <span class="at">arraytype =</span> <span class="st">&quot;EPICv2&quot;</span>,</span>
<span id="cb25-13"><a href="#cb25-13" tabindex="-1"></a>  <span class="at">epicv2Remap =</span> T,</span>
<span id="cb25-14"><a href="#cb25-14" tabindex="-1"></a>  <span class="do">## This is the formative variable that changed!</span></span>
<span id="cb25-15"><a href="#cb25-15" tabindex="-1"></a>  <span class="at">analysis.type =</span> <span class="st">&quot;differential&quot;</span>,</span>
<span id="cb25-16"><a href="#cb25-16" tabindex="-1"></a>  <span class="at">design =</span> design,</span>
<span id="cb25-17"><a href="#cb25-17" tabindex="-1"></a>  <span class="at">coef =</span> <span class="dv">2</span></span>
<span id="cb25-18"><a href="#cb25-18" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## EPICv2 specified. Loading manifest...</code></pre>
<pre><code>## loading from cache</code></pre>
<pre><code>## Remapping 11409 cross-hybridising probes to their more likely offtarget...</code></pre>
<pre><code>## Your contrast returned 543398 individually significant probes. We recommend the default setting of pcutoff in dmrcate().</code></pre>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>myannotation.remap <span class="ot">&lt;-</span> <span class="fu">changeFDR</span>(myannotation.remap, <span class="fl">1e-10</span>)</span></code></pre></div>
<pre><code>## Threshold is now set at FDR=1e-10, resulting in 25357 significantly differential CpGs.</code></pre>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a><span class="do">## Run DMR analysis</span></span>
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>dmrcoutput.remap.nothreshold <span class="ot">&lt;-</span> <span class="fu">dmrcate</span>(myannotation.remap)</span>
<span id="cb32-3"><a href="#cb32-3" tabindex="-1"></a></span>
<span id="cb32-4"><a href="#cb32-4" tabindex="-1"></a>results.ranges.remap <span class="ot">&lt;-</span> <span class="fu">extractRanges</span>(<span class="at">dmrcoutput =</span> dmrcoutput.remap.nothreshold, <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>)</span></code></pre></div>
<p>Here we find that by using the ‘epicv2Remap = T’ variable, we have
increased the number of significant probes from 538,081 to 543,398. This
does however come at a cost of increased multiple-testing burden, thus
when thresholding by FDR, we find ~8,000 less significant probes in the
remapped dataset. However, below we will demonstrate how the remapped
data results in more DMRs by having probes being mapped to their
intended genomic location.</p>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a><span class="fu">print</span>(</span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a>  <span class="fu">paste</span>(</span>
<span id="cb33-3"><a href="#cb33-3" tabindex="-1"></a>    <span class="st">&quot;There are&quot;</span>,</span>
<span id="cb33-4"><a href="#cb33-4" tabindex="-1"></a>    <span class="fu">length</span>(results.ranges),</span>
<span id="cb33-5"><a href="#cb33-5" tabindex="-1"></a>    <span class="st">&quot;DMRs initially found using the default settings, however, by including the remapped probes, despite having an increased multiple-testing burden, we find that there are&quot;</span>,</span>
<span id="cb33-6"><a href="#cb33-6" tabindex="-1"></a>    <span class="fu">length</span>(results.ranges.remap),</span>
<span id="cb33-7"><a href="#cb33-7" tabindex="-1"></a>    <span class="st">&quot;DMRs using this method.&quot;</span></span>
<span id="cb33-8"><a href="#cb33-8" tabindex="-1"></a>  )</span>
<span id="cb33-9"><a href="#cb33-9" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>## [1] &quot;There are 23605 DMRs initially found using the default settings, however, by including the remapped probes, despite having an increased multiple-testing burden, we find that there are 23913 DMRs using this method.&quot;</code></pre>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="do">## For the sake of showing &quot;new&quot; DMRs, extract ONLY the DMRs with three probes. </span></span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a><span class="do">## We also use a betacutoff of 0.3 to reduce the amount of DMRs we find for easier demonstration in this tutorial.</span></span>
<span id="cb35-3"><a href="#cb35-3" tabindex="-1"></a>dmrcoutput.remap <span class="ot">&lt;-</span> <span class="fu">dmrcate</span>(myannotation.remap,</span>
<span id="cb35-4"><a href="#cb35-4" tabindex="-1"></a>                            <span class="at">min.cpgs =</span> <span class="dv">3</span>,</span>
<span id="cb35-5"><a href="#cb35-5" tabindex="-1"></a>                            <span class="at">betacutoff =</span> .<span class="dv">3</span>)</span>
<span id="cb35-6"><a href="#cb35-6" tabindex="-1"></a>results.ranges.remap2 <span class="ot">&lt;-</span> <span class="fu">extractRanges</span>(<span class="at">dmrcoutput =</span> dmrcoutput.remap, <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>)</span>
<span id="cb35-7"><a href="#cb35-7" tabindex="-1"></a>results.ranges.remap3 <span class="ot">&lt;-</span> results.ranges.remap2[results.ranges.remap2<span class="sc">$</span>no.cpgs <span class="sc">==</span> <span class="dv">3</span>, ]</span></code></pre></div>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a><span class="do">## Lets find DMRs of 3 CpG length that are uniquely found in our remapped dataset</span></span>
<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a>outer <span class="ot">&lt;-</span> <span class="fu">subsetByOverlaps</span>(results.ranges.remap3,</span>
<span id="cb36-3"><a href="#cb36-3" tabindex="-1"></a>                          results.ranges,</span>
<span id="cb36-4"><a href="#cb36-4" tabindex="-1"></a>                          <span class="at">invert =</span> T,</span>
<span id="cb36-5"><a href="#cb36-5" tabindex="-1"></a>                          <span class="at">type =</span> <span class="st">&quot;any&quot;</span>)</span>
<span id="cb36-6"><a href="#cb36-6" tabindex="-1"></a></span>
<span id="cb36-7"><a href="#cb36-7" tabindex="-1"></a><span class="do">## We can also define the new probes using the cpg.annotate() data</span></span>
<span id="cb36-8"><a href="#cb36-8" tabindex="-1"></a><span class="do">## Let&#39;s get a list of probes in a genomic range that are different between our two analyses. This should exclusively be the probes that are cross hybridising.</span></span>
<span id="cb36-9"><a href="#cb36-9" tabindex="-1"></a>remap.gr <span class="ot">&lt;-</span> myannotation.remap<span class="sc">@</span>ranges[<span class="fu">setdiff</span>(<span class="fu">names</span>(myannotation.remap<span class="sc">@</span>ranges),</span>
<span id="cb36-10"><a href="#cb36-10" tabindex="-1"></a>                                              <span class="fu">names</span>(myannotation<span class="sc">@</span>ranges)), ]</span>
<span id="cb36-11"><a href="#cb36-11" tabindex="-1"></a></span>
<span id="cb36-12"><a href="#cb36-12" tabindex="-1"></a><span class="do">## Finally, let&#39;s see if any of our unique DMRs are because of an insertion of a remapped probe</span></span>
<span id="cb36-13"><a href="#cb36-13" tabindex="-1"></a>new.dmrs <span class="ot">&lt;-</span> <span class="fu">subsetByOverlaps</span>(outer, remap.gr)</span>
<span id="cb36-14"><a href="#cb36-14" tabindex="-1"></a><span class="fu">length</span>(new.dmrs)</span></code></pre></div>
<pre><code>## [1] 7</code></pre>
<p>Here we find 7 DMRs that have been created through the remapping of
probes to their correct locations. Let’s take a closer look at the DMR
near <em>LINC01666.</em> This was previously not considered a DMR
because there were only two probes in this region, which were too far
apart to be called by DMRcate. Now another probe has been remapped in
between these two probes, we find a new DMR!</p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a><span class="do">## Extract the DMR in question from dmrcate output</span></span>
<span id="cb38-2"><a href="#cb38-2" tabindex="-1"></a>x <span class="ot">&lt;-</span> new.dmrs[<span class="dv">2</span>]</span>
<span id="cb38-3"><a href="#cb38-3" tabindex="-1"></a></span>
<span id="cb38-4"><a href="#cb38-4" tabindex="-1"></a><span class="do">## Extract the probes in this DMR</span></span>
<span id="cb38-5"><a href="#cb38-5" tabindex="-1"></a>probes <span class="ot">&lt;-</span> <span class="fu">subsetByOverlaps</span>(myannotation.remap<span class="sc">@</span>ranges, x)</span>
<span id="cb38-6"><a href="#cb38-6" tabindex="-1"></a></span>
<span id="cb38-7"><a href="#cb38-7" tabindex="-1"></a>betas <span class="ot">&lt;-</span> beta_v2.clean2[<span class="fu">rownames</span>(beta_v2.clean2) <span class="sc">%in%</span> <span class="fu">names</span>(probes), ]</span>
<span id="cb38-8"><a href="#cb38-8" tabindex="-1"></a>betas <span class="ot">&lt;-</span> betas[<span class="fu">order</span>(<span class="fu">match</span>(<span class="fu">rownames</span>(betas), <span class="fu">names</span>(probes))), ]</span>
<span id="cb38-9"><a href="#cb38-9" tabindex="-1"></a>melt <span class="ot">&lt;-</span> <span class="fu">melt</span>(betas)</span>
<span id="cb38-10"><a href="#cb38-10" tabindex="-1"></a></span>
<span id="cb38-11"><a href="#cb38-11" tabindex="-1"></a><span class="do">## Plot</span></span>
<span id="cb38-12"><a href="#cb38-12" tabindex="-1"></a><span class="fu">colnames</span>(melt)[<span class="dv">2</span>] <span class="ot">&lt;-</span> <span class="st">&quot;Sample&quot;</span></span>
<span id="cb38-13"><a href="#cb38-13" tabindex="-1"></a></span>
<span id="cb38-14"><a href="#cb38-14" tabindex="-1"></a>p <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(melt, <span class="fu">aes</span>(</span>
<span id="cb38-15"><a href="#cb38-15" tabindex="-1"></a>  <span class="at">x =</span> Var1,</span>
<span id="cb38-16"><a href="#cb38-16" tabindex="-1"></a>  <span class="at">y =</span> value <span class="sc">*</span> <span class="dv">100</span>,</span>
<span id="cb38-17"><a href="#cb38-17" tabindex="-1"></a>  <span class="at">group =</span> Sample,</span>
<span id="cb38-18"><a href="#cb38-18" tabindex="-1"></a>  <span class="at">colour =</span> Sample</span>
<span id="cb38-19"><a href="#cb38-19" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb38-20"><a href="#cb38-20" tabindex="-1"></a>  <span class="fu">geom_point</span>(</span>
<span id="cb38-21"><a href="#cb38-21" tabindex="-1"></a>    <span class="at">size =</span> <span class="dv">3</span>,</span>
<span id="cb38-22"><a href="#cb38-22" tabindex="-1"></a>    <span class="at">alpha =</span> <span class="fl">0.8</span>,</span>
<span id="cb38-23"><a href="#cb38-23" tabindex="-1"></a>    <span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> <span class="fl">0.1</span>, <span class="at">seed =</span> <span class="dv">123</span>)</span>
<span id="cb38-24"><a href="#cb38-24" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb38-25"><a href="#cb38-25" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="at">position =</span> <span class="fu">position_jitter</span>(<span class="at">width =</span> <span class="fl">0.1</span>, <span class="at">seed =</span> <span class="dv">123</span>)) <span class="sc">+</span></span>
<span id="cb38-26"><a href="#cb38-26" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb38-27"><a href="#cb38-27" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Beta (%)&quot;</span>) <span class="sc">+</span> <span class="fu">xlab</span>(<span class="st">&quot;CpG Site&quot;</span>) <span class="sc">+</span></span>
<span id="cb38-28"><a href="#cb38-28" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb38-29"><a href="#cb38-29" tabindex="-1"></a>  <span class="fu">ggtitle</span>(</span>
<span id="cb38-30"><a href="#cb38-30" tabindex="-1"></a>    <span class="fu">paste</span>(</span>
<span id="cb38-31"><a href="#cb38-31" tabindex="-1"></a>      <span class="st">&quot;Line Plot by Sample - New DMR - &quot;</span>,</span>
<span id="cb38-32"><a href="#cb38-32" tabindex="-1"></a>      x<span class="sc">$</span>overlapping.genes,</span>
<span id="cb38-33"><a href="#cb38-33" tabindex="-1"></a>      <span class="st">&quot; (&quot;</span>,</span>
<span id="cb38-34"><a href="#cb38-34" tabindex="-1"></a>      <span class="fu">start</span>(x),</span>
<span id="cb38-35"><a href="#cb38-35" tabindex="-1"></a>      <span class="st">&quot; - &quot;</span>,</span>
<span id="cb38-36"><a href="#cb38-36" tabindex="-1"></a>      <span class="fu">end</span>(x),</span>
<span id="cb38-37"><a href="#cb38-37" tabindex="-1"></a>      <span class="st">&quot;)&quot;</span>,</span>
<span id="cb38-38"><a href="#cb38-38" tabindex="-1"></a>      <span class="at">sep =</span> <span class="st">&quot;&quot;</span></span>
<span id="cb38-39"><a href="#cb38-39" tabindex="-1"></a>    )</span>
<span id="cb38-40"><a href="#cb38-40" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb38-41"><a href="#cb38-41" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>, <span class="dv">100</span>) <span class="sc">+</span></span>
<span id="cb38-42"><a href="#cb38-42" tabindex="-1"></a>  <span class="fu">annotate</span>(</span>
<span id="cb38-43"><a href="#cb38-43" tabindex="-1"></a>    <span class="st">&#39;rect&#39;</span>,</span>
<span id="cb38-44"><a href="#cb38-44" tabindex="-1"></a>    <span class="at">xmin =</span> <span class="fl">1.8</span>,</span>
<span id="cb38-45"><a href="#cb38-45" tabindex="-1"></a>    <span class="at">xmax =</span> <span class="fl">2.2</span>,</span>
<span id="cb38-46"><a href="#cb38-46" tabindex="-1"></a>    <span class="at">ymin =</span> <span class="dv">0</span>,</span>
<span id="cb38-47"><a href="#cb38-47" tabindex="-1"></a>    <span class="at">ymax =</span> <span class="dv">100</span>,</span>
<span id="cb38-48"><a href="#cb38-48" tabindex="-1"></a>    <span class="at">alpha =</span> .<span class="dv">2</span>,</span>
<span id="cb38-49"><a href="#cb38-49" tabindex="-1"></a>    <span class="at">fill =</span> <span class="st">&#39;forestgreen&#39;</span></span>
<span id="cb38-50"><a href="#cb38-50" tabindex="-1"></a>  )</span>
<span id="cb38-51"><a href="#cb38-51" tabindex="-1"></a></span>
<span id="cb38-52"><a href="#cb38-52" tabindex="-1"></a>melt<span class="sc">$</span>Line <span class="ot">&lt;-</span> sample_table<span class="sc">$</span>Type[<span class="fu">match</span>(melt<span class="sc">$</span>Sample, sample_table<span class="sc">$</span>Name2)]</span>
<span id="cb38-53"><a href="#cb38-53" tabindex="-1"></a></span>
<span id="cb38-54"><a href="#cb38-54" tabindex="-1"></a>melt2 <span class="ot">&lt;-</span> melt <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">group_by</span>(Line, Var1) <span class="sc">%&gt;%</span> dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">mean =</span> <span class="fu">mean</span>(value) <span class="sc">*</span></span>
<span id="cb38-55"><a href="#cb38-55" tabindex="-1"></a>                                                                     <span class="dv">100</span>, <span class="at">sd =</span> <span class="fu">sd</span>(value) <span class="sc">*</span> <span class="dv">100</span>)</span>
<span id="cb38-56"><a href="#cb38-56" tabindex="-1"></a></span>
<span id="cb38-57"><a href="#cb38-57" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(melt2, <span class="fu">aes</span>(</span>
<span id="cb38-58"><a href="#cb38-58" tabindex="-1"></a>  <span class="at">x =</span> Var1,</span>
<span id="cb38-59"><a href="#cb38-59" tabindex="-1"></a>  <span class="at">y =</span> mean,</span>
<span id="cb38-60"><a href="#cb38-60" tabindex="-1"></a>  <span class="at">group =</span> Line,</span>
<span id="cb38-61"><a href="#cb38-61" tabindex="-1"></a>  <span class="at">colour =</span> Line</span>
<span id="cb38-62"><a href="#cb38-62" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb38-63"><a href="#cb38-63" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb38-64"><a href="#cb38-64" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb38-65"><a href="#cb38-65" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> mean <span class="sc">-</span> sd, <span class="at">ymax =</span> mean <span class="sc">+</span> sd), <span class="at">width =</span> .<span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb38-66"><a href="#cb38-66" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb38-67"><a href="#cb38-67" tabindex="-1"></a>  <span class="fu">ylab</span>(<span class="st">&quot;Beta (%)&quot;</span>) <span class="sc">+</span> <span class="fu">xlab</span>(<span class="st">&quot;CpG Site&quot;</span>) <span class="sc">+</span></span>
<span id="cb38-68"><a href="#cb38-68" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;bottom&quot;</span>) <span class="sc">+</span></span>
<span id="cb38-69"><a href="#cb38-69" tabindex="-1"></a>  <span class="fu">ggtitle</span>(</span>
<span id="cb38-70"><a href="#cb38-70" tabindex="-1"></a>    <span class="fu">paste</span>(</span>
<span id="cb38-71"><a href="#cb38-71" tabindex="-1"></a>      <span class="st">&quot;Line Plot by Cell Line - New DMR - &quot;</span>,</span>
<span id="cb38-72"><a href="#cb38-72" tabindex="-1"></a>      x<span class="sc">$</span>overlapping.genes,</span>
<span id="cb38-73"><a href="#cb38-73" tabindex="-1"></a>      <span class="st">&quot; (&quot;</span>,</span>
<span id="cb38-74"><a href="#cb38-74" tabindex="-1"></a>      <span class="fu">start</span>(x),</span>
<span id="cb38-75"><a href="#cb38-75" tabindex="-1"></a>      <span class="st">&quot; - &quot;</span>,</span>
<span id="cb38-76"><a href="#cb38-76" tabindex="-1"></a>      <span class="fu">end</span>(x),</span>
<span id="cb38-77"><a href="#cb38-77" tabindex="-1"></a>      <span class="st">&quot;)&quot;</span>,</span>
<span id="cb38-78"><a href="#cb38-78" tabindex="-1"></a>      <span class="at">sep =</span> <span class="st">&quot;&quot;</span></span>
<span id="cb38-79"><a href="#cb38-79" tabindex="-1"></a>    )</span>
<span id="cb38-80"><a href="#cb38-80" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb38-81"><a href="#cb38-81" tabindex="-1"></a>  <span class="fu">ylim</span>(<span class="dv">0</span>, <span class="dv">100</span>) <span class="sc">+</span> colScale <span class="sc">+</span></span>
<span id="cb38-82"><a href="#cb38-82" tabindex="-1"></a>  <span class="fu">annotate</span>(</span>
<span id="cb38-83"><a href="#cb38-83" tabindex="-1"></a>    <span class="st">&#39;rect&#39;</span>,</span>
<span id="cb38-84"><a href="#cb38-84" tabindex="-1"></a>    <span class="at">xmin =</span> <span class="fl">1.8</span>,</span>
<span id="cb38-85"><a href="#cb38-85" tabindex="-1"></a>    <span class="at">xmax =</span> <span class="fl">2.2</span>,</span>
<span id="cb38-86"><a href="#cb38-86" tabindex="-1"></a>    <span class="at">ymin =</span> <span class="dv">0</span>,</span>
<span id="cb38-87"><a href="#cb38-87" tabindex="-1"></a>    <span class="at">ymax =</span> <span class="dv">100</span>,</span>
<span id="cb38-88"><a href="#cb38-88" tabindex="-1"></a>    <span class="at">alpha =</span> .<span class="dv">2</span>,</span>
<span id="cb38-89"><a href="#cb38-89" tabindex="-1"></a>    <span class="at">fill =</span> <span class="st">&#39;forestgreen&#39;</span></span>
<span id="cb38-90"><a href="#cb38-90" tabindex="-1"></a>  )</span>
<span id="cb38-91"><a href="#cb38-91" tabindex="-1"></a></span>
<span id="cb38-92"><a href="#cb38-92" tabindex="-1"></a>p <span class="sc">+</span> p2</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>Finally, let’s have a look at this probe using the Gviz package, to
show where it lies in a genomic context and compared to other probes
surrounding it.</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a><span class="do">## Create gviz tracks for chromosome on top and bar for length of area</span></span>
<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a>gtrack <span class="ot">&lt;-</span> Gviz<span class="sc">::</span><span class="fu">GenomeAxisTrack</span>()</span>
<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a>ideoTrack <span class="ot">&lt;-</span> <span class="fu">IdeogramTrack</span>(<span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>, <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x))</span>
<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a></span>
<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a><span class="do">## Create track to extract genes from UCSC</span></span>
<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a>refGenes <span class="ot">&lt;-</span> <span class="fu">UcscTrack</span>(</span>
<span id="cb39-7"><a href="#cb39-7" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb39-8"><a href="#cb39-8" tabindex="-1"></a>  <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x),</span>
<span id="cb39-9"><a href="#cb39-9" tabindex="-1"></a>  <span class="at">track =</span> <span class="st">&quot;NCBI RefSeq&quot;</span>,</span>
<span id="cb39-10"><a href="#cb39-10" tabindex="-1"></a>  <span class="at">start =</span> <span class="fu">start</span>(x),</span>
<span id="cb39-11"><a href="#cb39-11" tabindex="-1"></a>  <span class="at">end =</span> <span class="fu">end</span>(x),</span>
<span id="cb39-12"><a href="#cb39-12" tabindex="-1"></a>  <span class="at">trackType =</span> <span class="st">&quot;GeneRegionTrack&quot;</span>,</span>
<span id="cb39-13"><a href="#cb39-13" tabindex="-1"></a>  <span class="at">rstarts =</span> <span class="st">&quot;exonStarts&quot;</span>,</span>
<span id="cb39-14"><a href="#cb39-14" tabindex="-1"></a>  <span class="at">rends =</span> <span class="st">&quot;exonEnds&quot;</span>,</span>
<span id="cb39-15"><a href="#cb39-15" tabindex="-1"></a>  <span class="at">gene =</span> <span class="st">&quot;name&quot;</span>,</span>
<span id="cb39-16"><a href="#cb39-16" tabindex="-1"></a>  <span class="at">symbol =</span> <span class="st">&quot;name2&quot;</span>,</span>
<span id="cb39-17"><a href="#cb39-17" tabindex="-1"></a>  <span class="at">transcript =</span> <span class="st">&quot;name&quot;</span>,</span>
<span id="cb39-18"><a href="#cb39-18" tabindex="-1"></a>  <span class="at">strand =</span> <span class="st">&quot;strand&quot;</span>,</span>
<span id="cb39-19"><a href="#cb39-19" tabindex="-1"></a>  <span class="at">fill =</span> <span class="st">&quot;#8282d2&quot;</span>,</span>
<span id="cb39-20"><a href="#cb39-20" tabindex="-1"></a>  <span class="at">stacking =</span> <span class="st">&quot;squish&quot;</span>,</span>
<span id="cb39-21"><a href="#cb39-21" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;NCBI RefSeq&quot;</span>,</span>
<span id="cb39-22"><a href="#cb39-22" tabindex="-1"></a>  <span class="at">showId =</span> <span class="cn">TRUE</span>,</span>
<span id="cb39-23"><a href="#cb39-23" tabindex="-1"></a>  <span class="at">geneSymbol =</span> <span class="cn">TRUE</span></span>
<span id="cb39-24"><a href="#cb39-24" tabindex="-1"></a>)</span>
<span id="cb39-25"><a href="#cb39-25" tabindex="-1"></a></span>
<span id="cb39-26"><a href="#cb39-26" tabindex="-1"></a><span class="do">## Create granges object that extracts positional information of each CpG and beta values from samples</span></span>
<span id="cb39-27"><a href="#cb39-27" tabindex="-1"></a>granges <span class="ot">&lt;-</span> <span class="fu">GRanges</span>(</span>
<span id="cb39-28"><a href="#cb39-28" tabindex="-1"></a>  <span class="at">seqnames =</span> probes<span class="sc">@</span>seqnames,</span>
<span id="cb39-29"><a href="#cb39-29" tabindex="-1"></a>  <span class="at">ranges =</span> probes<span class="sc">@</span>ranges,</span>
<span id="cb39-30"><a href="#cb39-30" tabindex="-1"></a>  <span class="at">data =</span> <span class="fu">round</span>(betas <span class="sc">*</span> <span class="dv">100</span>, <span class="dv">3</span>)</span>
<span id="cb39-31"><a href="#cb39-31" tabindex="-1"></a>)</span>
<span id="cb39-32"><a href="#cb39-32" tabindex="-1"></a></span>
<span id="cb39-33"><a href="#cb39-33" tabindex="-1"></a><span class="do">## Create track for data visualisation</span></span>
<span id="cb39-34"><a href="#cb39-34" tabindex="-1"></a>dTrack <span class="ot">&lt;-</span> <span class="fu">DataTrack</span>(</span>
<span id="cb39-35"><a href="#cb39-35" tabindex="-1"></a>  granges,</span>
<span id="cb39-36"><a href="#cb39-36" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;Beta&quot;</span>,</span>
<span id="cb39-37"><a href="#cb39-37" tabindex="-1"></a>  <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">100</span>),</span>
<span id="cb39-38"><a href="#cb39-38" tabindex="-1"></a>  <span class="at">groups =</span> variable.of.choice,</span>
<span id="cb39-39"><a href="#cb39-39" tabindex="-1"></a>  <span class="at">col =</span> myColors,</span>
<span id="cb39-40"><a href="#cb39-40" tabindex="-1"></a>  <span class="at">type =</span> <span class="fu">c</span>(<span class="st">&quot;a&quot;</span>, <span class="st">&quot;confint&quot;</span>),</span>
<span id="cb39-41"><a href="#cb39-41" tabindex="-1"></a>  <span class="at">aggregation =</span> <span class="st">&quot;mean&quot;</span>,</span>
<span id="cb39-42"><a href="#cb39-42" tabindex="-1"></a>  <span class="at">legend =</span> <span class="cn">TRUE</span>,</span>
<span id="cb39-43"><a href="#cb39-43" tabindex="-1"></a>  <span class="at">na.rm =</span> T</span>
<span id="cb39-44"><a href="#cb39-44" tabindex="-1"></a>)</span>
<span id="cb39-45"><a href="#cb39-45" tabindex="-1"></a></span>
<span id="cb39-46"><a href="#cb39-46" tabindex="-1"></a><span class="do">## Create a heatmap for each DMR</span></span>
<span id="cb39-47"><a href="#cb39-47" tabindex="-1"></a>dTrack.heatmap <span class="ot">&lt;-</span> <span class="fu">DataTrack</span>(</span>
<span id="cb39-48"><a href="#cb39-48" tabindex="-1"></a>  granges,</span>
<span id="cb39-49"><a href="#cb39-49" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;Beta&quot;</span>,</span>
<span id="cb39-50"><a href="#cb39-50" tabindex="-1"></a>  <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">100</span>),</span>
<span id="cb39-51"><a href="#cb39-51" tabindex="-1"></a>  <span class="at">gradient =</span> <span class="fu">brewer.pal</span>(<span class="dv">5</span>, <span class="st">&quot;Reds&quot;</span>),</span>
<span id="cb39-52"><a href="#cb39-52" tabindex="-1"></a>  <span class="at">showSampleNames =</span> <span class="cn">TRUE</span>,</span>
<span id="cb39-53"><a href="#cb39-53" tabindex="-1"></a>  <span class="at">cex.sampleNames =</span> <span class="fl">0.4</span>,</span>
<span id="cb39-54"><a href="#cb39-54" tabindex="-1"></a>  <span class="at">type =</span> <span class="fu">c</span>(<span class="st">&quot;heatmap&quot;</span>),</span>
<span id="cb39-55"><a href="#cb39-55" tabindex="-1"></a>  <span class="at">na.rm =</span> T</span>
<span id="cb39-56"><a href="#cb39-56" tabindex="-1"></a>)</span>
<span id="cb39-57"><a href="#cb39-57" tabindex="-1"></a></span>
<span id="cb39-58"><a href="#cb39-58" tabindex="-1"></a><span class="do">## Add a bar to distinguish where the DMR is</span></span>
<span id="cb39-59"><a href="#cb39-59" tabindex="-1"></a>DMRbar <span class="ot">&lt;-</span> <span class="fu">AnnotationTrack</span>(</span>
<span id="cb39-60"><a href="#cb39-60" tabindex="-1"></a>  <span class="at">start =</span> <span class="fu">start</span>(x),</span>
<span id="cb39-61"><a href="#cb39-61" tabindex="-1"></a>  <span class="at">end =</span> <span class="fu">end</span>(x),</span>
<span id="cb39-62"><a href="#cb39-62" tabindex="-1"></a>  <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x),</span>
<span id="cb39-63"><a href="#cb39-63" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb39-64"><a href="#cb39-64" tabindex="-1"></a>  <span class="at">col =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb39-65"><a href="#cb39-65" tabindex="-1"></a>  <span class="at">fill =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb39-66"><a href="#cb39-66" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;DMR&quot;</span></span>
<span id="cb39-67"><a href="#cb39-67" tabindex="-1"></a>)</span>
<span id="cb39-68"><a href="#cb39-68" tabindex="-1"></a></span>
<span id="cb39-69"><a href="#cb39-69" tabindex="-1"></a><span class="do">## Add lines to distinguish where CpGs are</span></span>
<span id="cb39-70"><a href="#cb39-70" tabindex="-1"></a>CpGbar <span class="ot">&lt;-</span> <span class="fu">AnnotationTrack</span>(</span>
<span id="cb39-71"><a href="#cb39-71" tabindex="-1"></a>  <span class="at">start =</span> <span class="fu">start</span>(probes),</span>
<span id="cb39-72"><a href="#cb39-72" tabindex="-1"></a>  <span class="at">end =</span> <span class="fu">end</span>(probes),</span>
<span id="cb39-73"><a href="#cb39-73" tabindex="-1"></a>  <span class="at">chromosome =</span> <span class="fu">seqnames</span>(probes),</span>
<span id="cb39-74"><a href="#cb39-74" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb39-75"><a href="#cb39-75" tabindex="-1"></a>  <span class="at">col =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb39-76"><a href="#cb39-76" tabindex="-1"></a>  <span class="at">fill =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb39-77"><a href="#cb39-77" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;CpGs&quot;</span></span>
<span id="cb39-78"><a href="#cb39-78" tabindex="-1"></a>)</span>
<span id="cb39-79"><a href="#cb39-79" tabindex="-1"></a></span>
<span id="cb39-80"><a href="#cb39-80" tabindex="-1"></a><span class="do">## Plot everything</span></span>
<span id="cb39-81"><a href="#cb39-81" tabindex="-1"></a><span class="fu">plotTracks</span>(</span>
<span id="cb39-82"><a href="#cb39-82" tabindex="-1"></a>  <span class="fu">c</span>(</span>
<span id="cb39-83"><a href="#cb39-83" tabindex="-1"></a>    ideoTrack,</span>
<span id="cb39-84"><a href="#cb39-84" tabindex="-1"></a>    gtrack,</span>
<span id="cb39-85"><a href="#cb39-85" tabindex="-1"></a>    CpGbar,</span>
<span id="cb39-86"><a href="#cb39-86" tabindex="-1"></a>    DMRbar,</span>
<span id="cb39-87"><a href="#cb39-87" tabindex="-1"></a>    refGenes,</span>
<span id="cb39-88"><a href="#cb39-88" tabindex="-1"></a>    dTrack.heatmap,</span>
<span id="cb39-89"><a href="#cb39-89" tabindex="-1"></a>    dTrack</span>
<span id="cb39-90"><a href="#cb39-90" tabindex="-1"></a>  ),</span>
<span id="cb39-91"><a href="#cb39-91" tabindex="-1"></a>  <span class="at">from =</span> <span class="fu">start</span>(x) <span class="sc">-</span> <span class="dv">200</span>,</span>
<span id="cb39-92"><a href="#cb39-92" tabindex="-1"></a>  <span class="at">to =</span> <span class="fu">end</span>(x) <span class="sc">+</span> <span class="dv">200</span></span>
<span id="cb39-93"><a href="#cb39-93" tabindex="-1"></a>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a><span class="do">## Extract the DMR in question from dmrcate output with a wider window (+- 5000bp)</span></span>
<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a>x2 <span class="ot">&lt;-</span> x</span>
<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a><span class="fu">start</span>(x2) <span class="ot">&lt;-</span> <span class="fu">start</span>(x2) <span class="sc">-</span> <span class="dv">5000</span></span>
<span id="cb40-4"><a href="#cb40-4" tabindex="-1"></a><span class="fu">end</span>(x2) <span class="ot">&lt;-</span> <span class="fu">end</span>(x2) <span class="sc">+</span> <span class="dv">5000</span></span>
<span id="cb40-5"><a href="#cb40-5" tabindex="-1"></a></span>
<span id="cb40-6"><a href="#cb40-6" tabindex="-1"></a>probes <span class="ot">&lt;-</span> <span class="fu">subsetByOverlaps</span>(myannotation.remap<span class="sc">@</span>ranges, x2)</span>
<span id="cb40-7"><a href="#cb40-7" tabindex="-1"></a></span>
<span id="cb40-8"><a href="#cb40-8" tabindex="-1"></a>betas <span class="ot">&lt;-</span> beta_v2.clean2[<span class="fu">rownames</span>(beta_v2.clean2) <span class="sc">%in%</span> <span class="fu">names</span>(probes), ]</span>
<span id="cb40-9"><a href="#cb40-9" tabindex="-1"></a>betas <span class="ot">&lt;-</span> betas[<span class="fu">order</span>(<span class="fu">match</span>(<span class="fu">rownames</span>(betas), <span class="fu">names</span>(probes))), ]</span>
<span id="cb40-10"><a href="#cb40-10" tabindex="-1"></a></span>
<span id="cb40-11"><a href="#cb40-11" tabindex="-1"></a><span class="do">## Create gviz tracks for chromosome on top and bar for length of area</span></span>
<span id="cb40-12"><a href="#cb40-12" tabindex="-1"></a>gtrack <span class="ot">&lt;-</span> Gviz<span class="sc">::</span><span class="fu">GenomeAxisTrack</span>()</span>
<span id="cb40-13"><a href="#cb40-13" tabindex="-1"></a>ideoTrack <span class="ot">&lt;-</span> <span class="fu">IdeogramTrack</span>(<span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>, <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x2))</span>
<span id="cb40-14"><a href="#cb40-14" tabindex="-1"></a></span>
<span id="cb40-15"><a href="#cb40-15" tabindex="-1"></a><span class="do">## Create track to extract genes from UCSC</span></span>
<span id="cb40-16"><a href="#cb40-16" tabindex="-1"></a>refGenes <span class="ot">&lt;-</span> <span class="fu">UcscTrack</span>(</span>
<span id="cb40-17"><a href="#cb40-17" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb40-18"><a href="#cb40-18" tabindex="-1"></a>  <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x2),</span>
<span id="cb40-19"><a href="#cb40-19" tabindex="-1"></a>  <span class="at">track =</span> <span class="st">&quot;NCBI RefSeq&quot;</span>,</span>
<span id="cb40-20"><a href="#cb40-20" tabindex="-1"></a>  <span class="at">start =</span> <span class="fu">start</span>(x2),</span>
<span id="cb40-21"><a href="#cb40-21" tabindex="-1"></a>  <span class="at">end =</span> <span class="fu">end</span>(x2),</span>
<span id="cb40-22"><a href="#cb40-22" tabindex="-1"></a>  <span class="at">trackType =</span> <span class="st">&quot;GeneRegionTrack&quot;</span>,</span>
<span id="cb40-23"><a href="#cb40-23" tabindex="-1"></a>  <span class="at">rstarts =</span> <span class="st">&quot;exonStarts&quot;</span>,</span>
<span id="cb40-24"><a href="#cb40-24" tabindex="-1"></a>  <span class="at">rends =</span> <span class="st">&quot;exonEnds&quot;</span>,</span>
<span id="cb40-25"><a href="#cb40-25" tabindex="-1"></a>  <span class="at">gene =</span> <span class="st">&quot;name&quot;</span>,</span>
<span id="cb40-26"><a href="#cb40-26" tabindex="-1"></a>  <span class="at">symbol =</span> <span class="st">&quot;name2&quot;</span>,</span>
<span id="cb40-27"><a href="#cb40-27" tabindex="-1"></a>  <span class="at">transcript =</span> <span class="st">&quot;name&quot;</span>,</span>
<span id="cb40-28"><a href="#cb40-28" tabindex="-1"></a>  <span class="at">strand =</span> <span class="st">&quot;strand&quot;</span>,</span>
<span id="cb40-29"><a href="#cb40-29" tabindex="-1"></a>  <span class="at">fill =</span> <span class="st">&quot;#8282d2&quot;</span>,</span>
<span id="cb40-30"><a href="#cb40-30" tabindex="-1"></a>  <span class="at">stacking =</span> <span class="st">&quot;squish&quot;</span>,</span>
<span id="cb40-31"><a href="#cb40-31" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;NCBI RefSeq&quot;</span>,</span>
<span id="cb40-32"><a href="#cb40-32" tabindex="-1"></a>  <span class="at">showId =</span> <span class="cn">TRUE</span>,</span>
<span id="cb40-33"><a href="#cb40-33" tabindex="-1"></a>  <span class="at">geneSymbol =</span> <span class="cn">TRUE</span></span>
<span id="cb40-34"><a href="#cb40-34" tabindex="-1"></a>)</span>
<span id="cb40-35"><a href="#cb40-35" tabindex="-1"></a></span>
<span id="cb40-36"><a href="#cb40-36" tabindex="-1"></a><span class="do">## Create granges object that extracts positional information of each CpG and beta values from samples</span></span>
<span id="cb40-37"><a href="#cb40-37" tabindex="-1"></a>granges <span class="ot">&lt;-</span> <span class="fu">GRanges</span>(</span>
<span id="cb40-38"><a href="#cb40-38" tabindex="-1"></a>  <span class="at">seqnames =</span> probes<span class="sc">@</span>seqnames,</span>
<span id="cb40-39"><a href="#cb40-39" tabindex="-1"></a>  <span class="at">ranges =</span> probes<span class="sc">@</span>ranges,</span>
<span id="cb40-40"><a href="#cb40-40" tabindex="-1"></a>  <span class="at">data =</span> <span class="fu">round</span>(betas <span class="sc">*</span> <span class="dv">100</span>, <span class="dv">3</span>)</span>
<span id="cb40-41"><a href="#cb40-41" tabindex="-1"></a>)</span>
<span id="cb40-42"><a href="#cb40-42" tabindex="-1"></a></span>
<span id="cb40-43"><a href="#cb40-43" tabindex="-1"></a><span class="do">## Create track for data visualisation</span></span>
<span id="cb40-44"><a href="#cb40-44" tabindex="-1"></a>dTrack <span class="ot">&lt;-</span> <span class="fu">DataTrack</span>(</span>
<span id="cb40-45"><a href="#cb40-45" tabindex="-1"></a>  granges,</span>
<span id="cb40-46"><a href="#cb40-46" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;Beta&quot;</span>,</span>
<span id="cb40-47"><a href="#cb40-47" tabindex="-1"></a>  <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">100</span>),</span>
<span id="cb40-48"><a href="#cb40-48" tabindex="-1"></a>  <span class="at">groups =</span> variable.of.choice,</span>
<span id="cb40-49"><a href="#cb40-49" tabindex="-1"></a>  <span class="at">col =</span> myColors,</span>
<span id="cb40-50"><a href="#cb40-50" tabindex="-1"></a>  <span class="at">type =</span> <span class="fu">c</span>(<span class="st">&quot;a&quot;</span>, <span class="st">&quot;confint&quot;</span>),</span>
<span id="cb40-51"><a href="#cb40-51" tabindex="-1"></a>  <span class="at">aggregation =</span> <span class="st">&quot;mean&quot;</span>,</span>
<span id="cb40-52"><a href="#cb40-52" tabindex="-1"></a>  <span class="at">legend =</span> <span class="cn">TRUE</span>,</span>
<span id="cb40-53"><a href="#cb40-53" tabindex="-1"></a>  <span class="at">na.rm =</span> T</span>
<span id="cb40-54"><a href="#cb40-54" tabindex="-1"></a>)</span>
<span id="cb40-55"><a href="#cb40-55" tabindex="-1"></a></span>
<span id="cb40-56"><a href="#cb40-56" tabindex="-1"></a><span class="do">## Create a heatmap for each DMR</span></span>
<span id="cb40-57"><a href="#cb40-57" tabindex="-1"></a>dTrack.heatmap <span class="ot">&lt;-</span> <span class="fu">DataTrack</span>(</span>
<span id="cb40-58"><a href="#cb40-58" tabindex="-1"></a>  granges,</span>
<span id="cb40-59"><a href="#cb40-59" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;Beta&quot;</span>,</span>
<span id="cb40-60"><a href="#cb40-60" tabindex="-1"></a>  <span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">100</span>),</span>
<span id="cb40-61"><a href="#cb40-61" tabindex="-1"></a>  <span class="at">gradient =</span> <span class="fu">brewer.pal</span>(<span class="dv">5</span>, <span class="st">&quot;Reds&quot;</span>),</span>
<span id="cb40-62"><a href="#cb40-62" tabindex="-1"></a>  <span class="at">showSampleNames =</span> <span class="cn">TRUE</span>,</span>
<span id="cb40-63"><a href="#cb40-63" tabindex="-1"></a>  <span class="at">cex.sampleNames =</span> <span class="fl">0.4</span>,</span>
<span id="cb40-64"><a href="#cb40-64" tabindex="-1"></a>  <span class="at">type =</span> <span class="fu">c</span>(<span class="st">&quot;heatmap&quot;</span>),</span>
<span id="cb40-65"><a href="#cb40-65" tabindex="-1"></a>  <span class="at">na.rm =</span> T</span>
<span id="cb40-66"><a href="#cb40-66" tabindex="-1"></a>)</span>
<span id="cb40-67"><a href="#cb40-67" tabindex="-1"></a></span>
<span id="cb40-68"><a href="#cb40-68" tabindex="-1"></a><span class="do">## Add a bar to distinguish where the DMR is</span></span>
<span id="cb40-69"><a href="#cb40-69" tabindex="-1"></a>DMRbar <span class="ot">&lt;-</span> <span class="fu">AnnotationTrack</span>(</span>
<span id="cb40-70"><a href="#cb40-70" tabindex="-1"></a>  <span class="at">start =</span> <span class="fu">start</span>(x),</span>
<span id="cb40-71"><a href="#cb40-71" tabindex="-1"></a>  <span class="at">end =</span> <span class="fu">end</span>(x),</span>
<span id="cb40-72"><a href="#cb40-72" tabindex="-1"></a>  <span class="at">chromosome =</span> <span class="fu">seqnames</span>(x),</span>
<span id="cb40-73"><a href="#cb40-73" tabindex="-1"></a>  <span class="at">genome =</span> <span class="st">&quot;hg38&quot;</span>,</span>
<span id="cb40-74"><a href="#cb40-74" tabindex="-1"></a>  <span class="at">col =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb40-75"><a href="#cb40-75" tabindex="-1"></a>  <span class="at">fill =</span> <span class="st">&quot;forestgreen&quot;</span>,</span>
<span id="cb40-76"><a href="#cb40-76" tabindex="-1"></a>  <span class="at">name =</span> <span class="st">&quot;DMR&quot;</span></span>