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Diffstat (limited to 'vignettes/mkin.html')
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1 files changed, 112 insertions, 107 deletions
diff --git a/vignettes/mkin.html b/vignettes/mkin.html index bda45abb..3e5a004e 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -40,27 +40,27 @@ display: none; </style> <style type="text/css"> -code{white-space: pre-wrap;} -span.smallcaps{font-variant: small-caps;} -span.underline{text-decoration: underline;} -div.column{display: inline-block; vertical-align: top; width: 50%;} -div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} -ul.task-list{list-style: none;} -</style> + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + </style> <style type="text/css"> -code { -white-space: pre; -} -.sourceCode { -overflow: visible; -} + code { + white-space: pre; + } + .sourceCode { + overflow: visible; + } </style> <style type="text/css" data-origin="pandoc"> pre > code.sourceCode { white-space: pre; position: relative; } -pre > code.sourceCode > span { line-height: 1.25; } +pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode > span { color: inherit; text-decoration: inherit; } @@ -71,57 +71,58 @@ div.sourceCode { overflow: auto; } } @media print { pre > code.sourceCode { white-space: pre-wrap; } -pre > code.sourceCode > span { display: inline-block; text-indent: -5em; padding-left: 5em; } +pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } } pre.numberSource code -{ counter-reset: source-line 0; } + { counter-reset: source-line 0; } pre.numberSource code > span -{ position: relative; left: -4em; counter-increment: source-line; } + { position: relative; left: -4em; counter-increment: source-line; } pre.numberSource code > span > a:first-child::before -{ content: 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<script> // apply pandoc div.sourceCode style to pre.sourceCode instead @@ -155,26 +156,25 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } <style type="text/css"> - +/* for pandoc --citeproc since 2.11 */ div.csl-bib-body { } div.csl-entry { -clear: both; -margin-bottom: 0em; + clear: both; } .hanging div.csl-entry { -margin-left:2em; -text-indent:-2em; + margin-left:2em; + text-indent:-2em; } div.csl-left-margin { -min-width:2em; -float:left; + min-width:2em; + float:left; } div.csl-right-inline { -margin-left:2em; -padding-left:1em; + margin-left:2em; + padding-left:1em; } div.csl-indent { -margin-left: 2em; + margin-left: 2em; } </style> @@ -372,27 +372,26 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Short introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4> +<h4 class="date">Last change 18 May 2023 (rebuilt 2025-02-16)</h4> <div id="TOC"> <ul> -<li><a href="#abstract" id="toc-abstract">Abstract</a></li> -<li><a href="#background" id="toc-background">Background</a> +<li><a href="#abstract">Abstract</a></li> +<li><a href="#background">Background</a> <ul> -<li><a href="#derived-software-tools" id="toc-derived-software-tools">Derived software tools</a></li> +<li><a href="#derived-software-tools">Derived software tools</a></li> </ul></li> -<li><a href="#unique-features" id="toc-unique-features">Unique -features</a></li> -<li><a href="#internal-parameter-transformations" id="toc-internal-parameter-transformations">Internal parameter +<li><a href="#unique-features">Unique features</a></li> +<li><a href="#internal-parameter-transformations">Internal parameter transformations</a> <ul> -<li><a href="#confidence-intervals-based-on-transformed-parameters" id="toc-confidence-intervals-based-on-transformed-parameters">Confidence +<li><a href="#confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters</a></li> -<li><a href="#parameter-t-test-based-on-untransformed-parameters" id="toc-parameter-t-test-based-on-untransformed-parameters">Parameter +<li><a href="#parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters</a></li> </ul></li> -<li><a href="#references" id="toc-references">References</a></li> +<li><a href="#references">References</a></li> </ul> </div> @@ -411,36 +410,42 @@ this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> <details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary> -<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="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span> -<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</span> -<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</span> -<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>),</span> -<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="at">use_of_ff =</span> <span class="st">"max"</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a></span> -<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a></span> -<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span> -<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span> -<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span> -<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span> -<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a> <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span> -<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a> <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span> -<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span> -<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a> sampling_times)</span> -<span id="cb1-17"><a href="#cb1-17" tabindex="-1"></a></span> -<span id="cb1-18"><a href="#cb1-18" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> -<span id="cb1-19"><a href="#cb1-19" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> -<span id="cb1-20"><a href="#cb1-20" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot"><-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span> -<span id="cb1-21"><a href="#cb1-21" tabindex="-1"></a> <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span> -<span id="cb1-22"><a href="#cb1-22" tabindex="-1"></a> <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</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 class="co"># Fit the model to the dataset</span></span> -<span id="cb1-25"><a href="#cb1-25" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-26"><a href="#cb1-26" tabindex="-1"></a></span> -<span id="cb1-27"><a href="#cb1-27" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span> -<span id="cb1-28"><a href="#cb1-28" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</span></code></pre></div> +<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>),</span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="at">use_of_ff =</span> <span class="st">"max"</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span> +<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span> +<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span> +<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a> <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span> +<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a> <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span> +<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span> +<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a> sampling_times)</span> +<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> +<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> +<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot"><-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span> +<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a> <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span> +<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a> <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span> +<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span> +<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span> +<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</span></code></pre></div> </details> -<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwAAAAKgCAIAAACjiI2DAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdeVxM+/8H8DNtWqa9VLRIe1STbnsRIiRRpETRtdxSlN0lN1zLtX5xu/ZdLimJFvciKmtJJVRcosmIlFbtM78/5vvt9gtRZjrTzOv5x/dx5tOnz3lN5nvu+5w55/OhsFgsAgAAAECQCJEdAAAAAKCnoQACAAAAgYMCCAAAAAQOCiAAAAAQOCiAAAAAQOCgAAIAAACBgwIIAAAABA4KIAAAABA4KIAAAABA4KAAAgAAAIGDAggAAAAEDgogAAAAEDgogAAAAEDgoAACAAAAgYMCCAAAAAQOCiAAAAAQOCiAAAAAQOCgAAIAAACBgwIIAAAABA4KIAAAABA4KIAAAABA4KAAAgAAAIGDAggAAAAEDgogAAAAEDgogAAAAEDgoAACAAAAgYMCCAAAAAQOCiAAAAAQOCiAAAAAQOCgAAIAAACBgwKIP7148WLixIn+/v779+8nOwsAAADPobBYLLIzAOctW7bMx8fH3Nx86NChaWlpZMcBAADgLbgCxJ/Wrl1Lo9FKSkqUlJS4uqPU1NRJkyb15C9yyocPH16+fEliAAAAIBEKIP4kISERExMTHBz8xx9/kJ2FYw4ePGhoaKiqqjpjxoz6+vq29vXr14v+j4iIiIWFBUEQ27dv19TUVFdXDwsLY7FYr169srW1NTQ03L17N/u3Vq9e3djYSM47AQAAsqEA4k+JiYl3796Ni4tTVVX9xl9pbm7+7HbnPXtMVlbWxo0bb9y48fTp04qKii1btrT9KDw8vPl/Fi1atHDhwtzc3O3bt2dnZxcWFt68eTMuLm7Tpk0RERGPHz8+fvx4dXX106dPCYIwMDDo+TcCAAC8AAUQP1i8eLG+vn5GRgZBEDExMUZGRrGxsYWFhV5eXjNmzOjQec+ePQMHDtTT01u8eDGTybx165avr6+Pj8+6devabxMEsWHDBh0dnYEDBy5btqxDz08zdBh2zJgxFy5cYP/I0tIyLS2tQ4euvsfi4uL58+erqqrKyMh4enr+888/n/a5f/9+QUGBn5+fkJCQqKiohISEmJiYuLi4kJCQrKzskydP6HR6Q0ODqKjohg0bVq1a1dUMAADAP1jAF2JjY11cXD5+/KilpZWcnPylbjdu3DAzM3v37l1tba23t/evv/568+ZNWVnZgoICFovVfvvy5csGBgZVVVUNDQ0ODg4nTpxo/9P2A06cOPHTYY8ePTpjxgwWi/X8+fOBAwdev369Qwf2L7YfauzYsVqfePjwYYe3UF5ebmlpGRcX9+m7c3R0fPz4MXt7yZIlEhISsrKy7L1UVVXNmzfP2dn577//zszMXLlyZTf+yAAAwDdEyC7AgDMmTZq0YsWKwMBAU1PTMWPGfKnbtWvXqqqqvLy8CIIoKyurqalxcnKi0WhtXwa1baekpEyePFlGRoYgCB8fn5SUlIEDB7bv2fmwUVFRK1asaG5uPnv2rL+/f0pKSocODg4OHQZJSkr66tuMioqKiIiIiIiYOHFihx8lJyf37dvX2NiYIIhbt24lJCSkpqZKS0vPnj07KirK19d337597J5Tpkw5ePDgpUuXIiMjTUxMfv311z59+nx11wAAwE9QAPEJCoUye/bs8PDwx48fd9KNSqXOmzdvxYoVBEE0NDQ0NTXl5eVJS0u3dWjbZrFYFAqFvS0sLNza2tr+p18dVkZGxsbG5vr169HR0efPnz937lyHDtnZ2R0GcXFx+TR8UlKSqakpQRBMJnPmzJlVVVXp6emfvbHp1KlTnp6e7O2EhAQPDw9LS0uCIObOnXvhwgVfX1/2j/7++28rK6vW1taIiIi4uLhDhw5FRkYuWrSokz8aAADwH9wDxD+ys7OlpaU1NDQ66TN69Ojjx4+XlZU1NjZ6enreunXrSz1HjBgRGxtbW1vb1NQUFRU1YsSIrg7r7e29adMmeXl5bW3tb9nvX3/9VfIJdvVDEER8fDyDwbhw4UJb9cNisQoLC1taWgiCaGxsvHz5ctulLxMTk6SkJPalptjY2LZBWCxWZGRkSEhIXV2dvLy8hoaGiYlJeXl5J28NAAD4EgogPnHr1q3U1FQ7O7uYmJhOutFotNDQUFtbWx0dHR0dnbFjx36p55gxY6ZNm2Zubm5kZGRlZeXn59fVYd3c3DIyMvz9/bu03y9JT09PT0+XkZGRlpaWlpaeOXNmU1OToaHh69evCYL4+++/zczM5OXl2Z19fHzGjx9vYWFhaGiooqKydOlSdnt0dPSECRPExcU1NTUdHBwGDRq0Z8+eBQsWdDUMAAD0dpgJmh8wmUwrK6vg4OABAwYsWbLk7t27IiL4chMAAOCL8J9JfnDs2DEKheLv70+hUIyMjLS1tdPS0rS1tcnOBQAAwKNwBQgAAAAEDu4BAgAAAIGDAggAAAAEDgogAAAAEDgogAAAAEDgoAACAAAAgcPFx+CZTGZubm5paWlLS0v//v1pNJqQEOotAOgJVVVVMjIyFAolKysrPz/fwsLCyMiI7FAAwEO49Rj89evXAwMD1dTU+vfvTxAEg8Gg0+mHDx8eOnToN45w69atS5cucSMbAHSPpqZmUFAQ2Sm+7sCBAzt27MjLy9u6deuhQ4ccHR1v3LixevXqOXPmfOMIOP4A8BqOH3+4VQCZmJhcuHBBR0enrYVOp0+ePPnevXvfOMKyZcseP3787QUTAHBVVVXVyZMn6XQ62UG+TktLKy8vT0ZGRkNDIzc3V0FBoayszM7O7tmzZ5/tP3bs2A6Hppqamv79+wcGBvZIXgD4Cm4cf7j1FVhra6uamlr7FhUVla4WW05OTm2rOAEAuUpKSk6ePEl2im+irKzc1NREEIScnBz7m3dhYeFO+sfFxdXX17dvcXZ2VlNTW758OVdzAsA34sbxh1sFUHBwsIWFhbu7u7q6OoVCYTAYFy9eDA4O5tT4dDp9/fr1JSUl1tbWWlpa2traw4YN49TgANCrbdiwwc7ObsKECfr6+k5OTqNHj05MTAwJCflSf3FxcXFx8fYtWE0PgO9x6//kQUFBbm5uycnJDAaDIAhNTc2kpCQNDQ2ODE6n0w0MDERFRcXExC5fvqypqSknJzdw4MCYmBjcZw0ALi4uGRkZ8fHxcnJyhoaGysrKsbGxhoaGZOcCAB7CxbOc/v37W1patj0Fxr4bmiOWLFmipKSUlZVlaGh4+vTpadOm5efnjxgxIjk52dXVlVN7AYDeS05Ozt/fn+wUAMC7uFUAff9TYJ148eKFubl5cXGxtra2t7e3r6/vy5cv7e3tCwoKUAABAADAV3GrAFqwYEFiYuK3PwWWlpb29u3b9i3Z2dmDBg36bGcbG5sTJ04sXLjw0aNHoaGhFAplwIAB165d27p1KwffAkAnHj9+nJCQQHYKDhg2bJiNjQ3ZKQAAehqvPAV27dq1/Pz89i0PHjz4+PHjZzv/9ttvFy5ccHFxaW1t3b17t4GBgZGRkYuLi7OzM0fCA3xVTEzMlStXHBwcyA7yXR4+fPjs2TMUQAAggHjlKbC1a9d2aLG2tpaXl/9sZ0lJyRcvXly8eDEtLe3MmTO//vqrkZGRsbExJ98AQKdYLJazs3NERATZQb7L4cOH79y5Q3YKAAAS9MqnwAiCEBUV9fT09PT0LC0tffTokaenJ6dGBgAAAL7HxafANDQ05s6dy73x2bZv325mZubr66urq8vtfQEAAAB/6N2z5qSlpTk4ODQ3NxsbG//xxx9kxwEAAIDegVtXgPbt25eamvpp+59//smpXTQ2Nvr4+Jw4ccLR0dHY2Hjt2rU2NjZDhgzh1PgAAADAr7hVAM2YMePevXvV1dXcWzs6Pz9fRUVl5MiRBEHs27fP09Pz6tWrKIAAAL5Ffn7+f/7zn3fv3o0cOfKnn37C6h8gaLj1iZeSkgoLCztz5gy7QOEGFRUVBoPR0NAgLi7u7OwsLS199+5dLu0LAKAn1dXVnT59+t27d8OHD7ezs+P4+M+fPx8xYsSSJUvGjh0bGRn55MkT3EUAgoaLJb+pqampqSn3xldTU3N1dR01atSUKVOysrKkpaXT09MLCwsNDAy4t1MAAG6rqqqysrIyNzfX1dWdMWNGSEhIaGgoZ3cRFRU1a9asxYsXEwTh7Ozcr1+/nTt39unTh7N7AeBlvfsm6IMHDwYFBT1//tzc3DwzM3PNmjXz5s3rZLpFgJ7U0NBQXFzMZDLJDkIQBFFdXX3w4EGyU8A3OXLkiIODA3uSs7S0tIiIiNbWVs7uoqamRlFRkb0tJSUlIiLS0NDA2V0A8LjeXQAJCQn5+Pjs2rUrNDSUSqXOnz+/oaFh+/btqIGAdNu2bVNVVXV0dNTW1k5LS+Pqvpqbm7/ap6am5sSJE1yNAZxSXFxsYmLC3u7fv7+YmFhFRQVnd+Hq6rpv375Hjx41NDSsW7fO1NRUVlaWs7sA4HG9uwDqYNu2bY8fP16+fPmAAQNu375NdhwQXHfu3Nm/f39BQcGrV6+OHj3q4+PzLTVKe7t27Zo6daq+vr6uru7mzZsJgmCxWLNnz9bT09PS0mIve/fXX3/99NNPo0aNSkhIiIiI0NXVpdFox44dIwji2rVrHh4e9vb2xsbGP//8M0EQy5Yte/To0YYNGzj/boHTbG1tz507V1dXRxBEYmKirKyssrIyZ3fh5OS0YsUKV1dXBQWFrKysU6dOcXZ8AN7HP7f9p6enHz58+J9//tm1a9etW7e8vb2LioqEhYXJzgV8bteuXSdPnuzQWFpa2traOn78ePbLDx8+0Gg0CQmJ9n1ERUV3795taWn5pZEzMjLy8vIIgjA3Nx81ahSVSm1tbX369Gl1dbWRkdHSpUsJgrh06VJOTk5mZubNmzcfPnzY2NhoY2NDo9EIgrhy5UpxcTGVStXT0wsLC9uyZUtxcfGqVas4+/aBG6ZMmZKWlqalpaWqqlpbW3v27Flu7OXHH3/88ccfuTEyQK/AVwXQlClTVFRUwsPDzczMWCzWP//8gxuigdt8fHw+XRI1KSkpJSVl27ZtBEF8/PjRzc1tz549n37FMHjw4E5GHjduHJVKJQhiwoQJ6enpoaGhP//885EjR7Kzsz98+MDu4+joqKysnJ6eXlpaOnXqVIIg6uvrs7OzNTU1hw8fzl5NT09Pr7q6WlxcnEPvGP6LxWJdvHgxIyNDS0trxowZHQrc70GhUH7//fc1a9aUlZXp6+uLiopyamQAaMM/BZCqqmpSUtLRo0dLSkrmzJmzYsUKPNEAPaBv3759+/bt0GhkZHTu3LkdO3bQaLRz5875+fmNGDGiqyNTKBT2hrCwcGtra0pKSlhY2MKFC3/66aeEhAT2j6SlpQmCkJCQWLx48axZswiCqK2t7dOnT1paWtstrnDgwAFuLMsTHBx8//59d3f3K1eu7N+//9atW5ytMj/70QIATuGfAsjNzW3+/PmZmZkGBgY3b95UVlZeu3bt0aNHyc4FgkhSUvLOnTtHjx4tLi7++eef3d3duzFIcnIy+y6Q+Pj4EydOpKamTp06NSAg4M6dO+/evWv/cNnw4cNXrVrl6+tbX19vZmaWnp7+2QE5/iQRz0pKSiorK2t7uW7dOvbpkL+/P6d28fbt23Pnzr18+VJSUpIgiHHjxl28eNHLy4tT4wMIjhcvXpw6daqxsdHT07MnZzPmnwIoOTnZ2dnZ2dmZTqf7+fnNnz8/NTU1OTl57NixZEcDQSQlJRUcHPw9I9BoNGdn5/Lycn9/f2tra3V19ZkzZ8bHx5uamg4fPnz16tXDhg1j93R0dBw1apSpqWlzc/OqVas0NDSePn3aYTQlJaXa2to1a9asW7fue1L1Co8fP169erW3t7eCggJBEA0NDTk5OcSXC6D9+/cXFRW1bykpKen8cg6DwdDQ0GBXPwRBGBoavn79mjPpAQRJTk6Oi4vLrFmzpKSk3Nzc/vjjj+6dMXYD/xRAxcXFNBpt4cKF7JcbN25ctmzZTz/99PDhQzzeCb2RnZ0de546tv79+1+5cqVDHxcXF/ZGeHh4eHh4W/vIkSPbJmFv+62HDx9yMS4vWbp0qZ2d3cqVK/38/EaOHHn16tWdO3d20l9aWpp9v1QbISGhtq8gP8vIyIjBYNy8edPBwaG0tDQ+Pv7MmTOcSQ8gSLZt27Z+/Xr2l9QODg7Lly9HAdRltra2CxcuXLx4sZyc3M2bNysrK6dPn56RkREaGoovwgAEjb29fUJCQkhISEJCQmNjY+edp02b1qHl/Pnz7DvQv0RcXPzkyZPe3t7i4uIVFRWrV6/u5IE+APiSN2/e6Onpsbf19fUZDEaP7Zp/CqCRI0dOmjRJR0enX79+FRUVJ0+eFBER2bZtG41Gi42N9fT0JDsgQBcEBgZiPs/vJCMjc/z48TNnzhQUFHBjfGdn55cvX7569UpNTa3tuzAA6JKhQ4ceOHDAzs5OVFT0999/b/tmvwdwsQBiMpm5ubmlpaUtLS39+/en0WhCQtydd3H9+vVhYWEMBkNPT499z6OkpOTx48c9PDwcHBxUVFS4uncADhITEyM7Ap/w9vb29vbm0uAiIiI6OjpcGhxAECxfvtzf319VVVVUVNTY2Dg6OrrHds2tAuj69euBgYFqamr9+/cnCILBYNDp9MOHDw8dOpRLe2RTUFBg3/bYxtbW1t/fPzAw0MPD48qVK0pKSvPnzx84cCBXYwAAAMBXiYuLnz179sOHDy0tLRyf8bxz3CqAFixYkJiY2P7ciE6nT548+d69e1zaYyfWrl2rqal5584dY2Pjly9f2tjYZGRkDBgwoOeTAAAAQAcdnkLoGdz6Tqq1tVVNTa19i4qKCln3NIiIiFRWVr5//37o0KF2dnb19fW7d+8mJQkAAADwAm5dAQoODrawsHB3d1dXV6dQKAwG4+LFi985LUq31dbWNjc3b9y4MTY29vbt25mZmVgqFb7fhw8fXrx4QXaK7/L+/XuyIwAAkINbBVBQUJCbm1tycjL7kTZNTc2kpCQNDY0v9Y+KiiopKWnfwmAwOLW2jpCQEPv+6759+y5evPjJkycWFhYcGRkE1oABA06ePNm2JEXvheUwAUAwcfEpMAUFBV9fXykpKQaDkZ6eXlFR0UkBVF9f37a+IxuTyeTUV2bS0tKmpqZHjhwpKSm5fPmyvLz87NmzOTIyCKyZM2fOnDmT7BQAANBN3CqATp48GRISIiIiEhERsXPnTltb22XLli1duvRL34J9WpFcv36dvdAjR5w7d27GjBlv3rwRFxdvamqytrbm1MgAAADQ63CrAIqIiCgoKBAXF9fQ0Lh8+bK9vf379++trKzIug1IR0fn9u3btbW1EhISERER06ZNu3r1KrfnJQIAAADexK0KgMlkKigoyMjISElJKSkpEQRBpVI7X1unB1CpVGFh4V9++aWlpWXLli3khgEAAACycOsKkJeXl7W1tZiY2KhRo2bOnOnr65ucnDxmzBgu7a5LRERETp8+bWVlZWtr25OzbgMAAACP4FYB9Ntvv7m6ugoLC9vb21+7di05OXnixIkBAQFc2l1XqaurR0VFTZs2LTMzs1+/fmTHAQAAgB7FxafA2la9GDly5MiRI7m3o+4ZPnz4Tz/9NGXKlBs3boiKipIdBwAAAHqOQN8FvGrVKllZ2VWrVpEdBAAAAHqUQBdAQkJCp06dio2NPXv2LNlZAAAAoOdw8SuwXkFBQSEuLm7kyJF6enpDhgwhOw4AAAD0BIG+AsRmamq6f//+yZMnY10kAAAAAYECiCAIwsPDY/LkyT4+Pi0tLW2NSUlJDg4OBgYGP/30U0VFBYnxAKCrsrKy4uPjq6qq2lr4YOE2AOAgFED/tXnzZgkJibCwMPbLrKysuXPnrl69+tKlS0JCQn5+fuTGA4Bvt2nTJg8Pj+joaBqNlp2dzW6cM2cOuakAgKcI+j1AbYSEhKKiouzs7Pbu3RsYGHj+/PnAwED2zI179uxRVFSsqanh4NpkAMA9kZGRubm5ioqKeXl5np6eWVlZnf+f9+PHj42Nje1bWlpaOLUYMwDwJhRA/5KWlo6Pj7e3t9fX1xcWFm77OozJZBIEgYXDAHoLaWlpKSkpgiBMTEwCAwNDQ0MPHz7cSX8vL6/bt2+3b6mpqSF96R4A4Cr8R/3/GThw4NmzZ6dNm2ZhYbF///4///wzMzNz1qxZLi4u7OMpAPA+Hx8fe3v7AwcOEAQRGhpaUVHh7e398ePHL/VPSEio+P+GDBmiqqrag5EBoKfhClBHQ4cO3b17d1hY2L59+w4cOPDu3buRI0diskSAXmTNmjX29vbl5eUEQVAolJiYmOjoaHl5ebJzAQAPQQH0GVOnTi0sLNywYcP169clJSXJjgPA/wIDAz/bvnfv3u4N2H75HWFhYR8fHx8fn+4NBQB86TMFUE5OTk5Ojq+vb2lpqYaGRs9n4gXh4eFFRUXe3t5xcXHCwsJkxwHgc+PHjyc7AkCvwWQycVvq9+v4Fzx27NjUqVPXrFnT2Njo6Oh46NAhUmKRjkKhHDp0SEhI6KeffiI7CwD/c/3E2LFjb926RXYuAN5y7tw5bW1tcXFxGxub3NxcsuP0bh0LoJ07d969e9fMzIxKpebk5GzevLnbQzOZzOzs7OTk5EuXLj148ID9LFUvIiwsHBUVlZubGxERQXYWAIGwZcuWfv36SUhIDBgwgEql4vgO0N6TJ0/CwsJiY2Pr6+uDg4MnTZrUfvJe6KqOBVBdXZ24uDh7W1JSUlRUtHvjXr9+3djYeNGiRVFRUWfPnl2yZImBgUFaWtp3he1Z1dXVR48eHTZs2OHDhyMjI8mOA8D/fv/99/z8/ICAgMTExGvXrvXv35/sRJ9XU1Ozd+/eiIiI3nVMg97u2rVrnp6eQ4YMERYWnj59urS0dH5+PtmherGOBZCvr++ECROKioqOHDni7Ozs6+vbvXEXLFiQmJh4/fr1U6dOnTp1KiUlJSUlZenSpd8duId8+PBhyJAhmZmZ0tLSYmJi4eHhR48eJTsUAJ/r06ePjIyMubn5vXv3bG1tefMKUGVl5ZAhQ27fvk2hUGbPnr1161ayE4GgkJWVfffuHXu7paWlvLxcTk6O3Ei9WseboNeuXZuYmHjz5s3Xr19v3LjRwcGhe+O2traqqam1b1FRUelFM6seOXJk5MiR+/fvJwhizpw5+vr6q1evlpWV9fDwIDsaAN8aOnRoQEDAypUrp0yZ8vbtW1lZWbITfcbRo0ednJwOHjxIEMTcuXMNDQ3DwsJERPBELXDdhAkTIiIili9fbmFhcebMmR9++EFgH1TiiI7/py0vL7exsbGxsWl7qaio2I1xg4ODLSws3N3d1dXVKRQKg8G4ePFicHDw9+btKa9fvzYyMmJvq6mpSUhIHDt2bPr06XJyciNGjCA3GwC/OnTo0NOnT/X19Xfs2JGVlcWbl11LSkqMjY0JgmCxWOyDQ0VFRd++fTv/LSaT+erVK3l5eZyyQ7fJycndvn17586dsbGxjo6OQUFBZCfq3ToWQOzVrwiCqKqqevbsmZmZWU5OTjfGDQoKcnNzS05OZjAYBEFoamomJSV1Uqtu2rTp5cuX7VtevnxJ4kmVvb391q1bAwICZGRk4uLiFBQURo0aFRcXN2nSpOjo6GHDhpEVDICPVVRUKCkplZeX02g0Go1GdpzPs7OzW7p06ZYtWyoqKoyNjSUkJL5a/eTm5np5eTU3N3/48GHGjBm7du3COhvQPaqqqr/99hvZKfhExwojMzOzbTsvL+97VkGXkZGZM2cOhULJysrKz8+vra3tpLOFhUWHS01XrlyRkJDo9t6/k6en5+3bt7W0tJSUlFgs1tmzZwmCsLOzO336tJeX16VLl6ysrMjKBsCvOHUCxlUKCgofPnxgMpkDBgwoLi7u16/fV3/Fz88vIiLCx8enpqZm7NixZ86cwayMAORjdWrQoEGdd/iS/fv3GxgYNDU1bdiwQVtb28/PT1NT88CBA98+gpWVlaura/f2zikVFRXPnj1jrwvdJjExUUVFJSMjg6xUAKSg0+nq6uo9truHDx/SaLQe210HnRx/li1btmXLlvLy8oKCgubmZkVFxfLy8k6GqqqqkpWVbXsZGRkZFBTE4bgA/I4bx5+OV4DCwsLatgsKCgwMDLpXV23YsCEvL09UVHTv3r25ubkKCgplZWV2dnZz5szpfrHW4+Tl5T9dP2jcuHHHjh1zc3OLj4+3trYmJRgA3zMxMWlubiY7xWewn8RRUFBQUFCora1tamrqfMEcKpUqJCRUUlKirq5OEER+fj57AwDI1bEAav/Yl6ur6/Dhw7s3rrKyclNTE0EQcnJy7Bm7+WlBiTFjxhw5csTd3R01EAAHceoEjKumT59uY2MjJyenq6u7f/9+Pz+/trnTPktISGjNmjXDhw/38/N7+fJlSkpK+zsNAIAs/xZA9+7dIwiiw6nJ/fv3u/cf+A0bNtjZ2U2YMEFfX9/JyWn06NGJiYkhISHfGZd3jBs37ujRoxMmTIiJiXF0dCQ7DgA/4NQJGFdpamqmp6fv2rUrLy/P29v7xx9//OqvhIaGDhky5MqVK6amptu3b8eDYAC8gML639w87JvyGAxGenq6qakplUq9d+8e+6Gn7g1dWVkZHx9Pp9Pr6+uVlZXHjBljaGj47b9ubW2trKyckJDQvb33jNTU1KlTp544cWL06NFkZwHgrpKSEltbWzqdzo3B2SdgnyLrCmuvOP4ACA5uHH/+vQL0559/EgQxatSo/Px89pXnoqKiuXPndntoOTk5f3//74/Iy4YNGxYbG+vh4XHw4MEJEyaQHQegt/rPf/5DcPQEDACgcx3vAXr16pW+vj57e8CAAexZfKAT9vb2SUlJbm5ulZWV3zNrAIAg4/gJGABA5zoWQLa2tpMnT/b19WWxWH/++aelpSUpsXoXCwuLlJSUMWPGvH37thetd9syQ20AACAASURBVAbAa/j1BKyhoWHbtm1XrlxRUlJavHixnZ0d2YkA4JMC6MCBA/v27Tt9+rSQkNCwYcNwBvaNDA0N09PTXVxcysvLN23ahGleAbqBgydgTCYzNze3tLS0paWlf//+NBqN/TgqKYKCgj58+PDrr7++ePFi8uTJly9fNjU1JSsMALD9WwCFhISsX78+PDycIAj2OqZPnz5dsmTJnj17SEvXq2hoaNy6dcvd3d3Ly+vkyZOdPxkLAJ/i1AnY9evXAwMD1dTU+vfvTxAEg8Gg0+mHDx8eOnQoR/N+k6amppiYmLdv30pISDg6OpaUlJw5cwYFEADp/i2AnJ2dxcXF26aih26Ql5f/66+/pk2b5ubmFhsbKyMjQ3YigN6BsydgCxYsSExM1NHRaWuh0+mTJ0/+0rNmq1atevbsWfuW/Pz858+fe3l5tcVjz3aRmJh4/Pjx9rG/2n7kyJH6+no/Pz8KhRISEsJePLUb46Ad7YLcvm/fvqqqKoKj/i2A3N3dCYLQ0dG5devWjz/+uHr16hs3bmzevJmz++N7EhISMTExoaGhDg4OCQkJmpqaZCcC6AU4ewLW2trKrqLaqKiotE358amxY8d2WHs1MzNTWlp6ypQp7Jd6enrsjcGDB7c1fmO7t7f3q1evampqJk6cWFFRsXv37lOnTmloaHR1HLSjXZDbXV1dv3QC030dlsYYPXr0mTNn7t27N2zYsPT09DFjxnB26Y1vxwtrgX2P/fv3q6urY8kw4Bs9sBZYfn7+oUOHWCzWqlWr7O3t09PTuzdOZGSkoaHh8uXL9+zZ8/vvv//888+DBw/et2/ft4/A2ePP+/fvZ82apa6ubm5uHh0dzalhAQQHN44/He8KLC0tnTp16vnz56dPn+7g4PDixQsOF1wCY+7cuXv37h0/fjx7JfkvKSgoSE9Pr6mp6bFgADxr4cKFVCo1IyPj5s2bmzdv3rBhQ/fGCQoK+vvvvwcOHPj+/fuysjJNTc2kpKR58+ZxNu23U1RUPHLkCJ1Of/DgQfsTXAAgUcenwAwMDBYvXhwdHZ2Zmblz507cxfI9xo8ff/Xq1YkTJ2ZnZ2/cuLHDQyitra0+Pj6ZmZkaGhrPnj2LiooaMWIEWVEBeAH7BGzFihXffwKmoaGBh1gBoBMdrwBFRkb279//woULqqqq5eXl7NnJoNtMTEzu3r17584dT0/PDpd5Tp48WVVV9ezZs7S0tOjo6FmzZpEVEoBHsE/AoqKixo8fjxMwAOCqjgWQsrLyiBEj8vLympub582bp6urS0osfqKsrHzlyhVVVVVra+vCwsK29uzs7PHjx4uIiBAE4ejoWF9fX1ZWRl5MAPLhBAwAekzHAujYsWNTp05ds2ZNY2Ojo6PjoUOHSInFZ8TExPbu3btkyZKhQ4deuHCB3aijo9N2T/uzZ8+YTKaSkhJ5GQHIhxMwAOgxHQugnTt33r1718zMjEql5uTk4DF4DgoICEhISAgNDV22bFlLS8vs2bOfPHkyatSowMBAJyenHTt2YP5oEHA4AQOAHtOxAKqrq2ubwlhSUlJUVLTbQzOZzOzs7OTk5EuXLj148IDJZHY/Jr+wtLTMysp69OjRiBEjKisr7927FxgYaGZmdu3aNSykCoATMADoMR2fAvP19Z0wYcKbN2+OHDly7NgxX1/f7o3LU1PR8xRFRcWEhIRNmzb98MMPhw8f9vDwIDsRAK/g4AkYAEDnOhZAa9euTUxMvHnz5uvXrzdu3Ojg4NC9cbs6Fb1AERISWrVqlbOz87Rp08aOHbts2TJMGA1AcO4EDADgqzoWQARBuLq6urq6srdramqkpaW7MW5Xp6IXQNbW1hMnTty9e/eBAwe0tLTi4uIGDx5MdigAMnHqBAwA4Kv+LYAePHiwcuXKioqKcePGhYSErFy5sri4ODc3t7S0tBvjBgcHW1hYuLu7q6urUygUBoNx8eLF4OBgziXv9S5cuJCamvr27duEhITg4GAXF5eSkhLcBw0CjiMnYAAAX/VvATRr1qxJkyY5OzsfP37c1NR07ty5vr6+AwYM6N64QUFBbm5uycnJDAaDIAj2VPQaGhpf6j9//vynT5+2bykoKOjernuL1NTUGTNmKCgo+Pn5WVtbDx482MXF5fjx4x2unAEIAs6egAEAfNW/BdCHDx8iIiIIgjAyMoqPj2dvf4/2U9EfOHCgk+qHIIj58+ezS6U2wcHB8vLy35mBl6moqPzzzz/sbXl5eUlJSRsbG3Nz8507d/r4+JCbDaCHcfYEDKBNTU0NlUrFxXX41L8FkLCwMHtDUVFRSkrqO8dNSkpqP6/xunXr+vTpQxCEv7//Z/sbGxsbGxu3b5GVlWXPksyvAgICLC0tmUymrq7ukSNHlixZEh4e7u7u7u/vHxMTExkZqaqqSnZGgB7C8RMwgOzsbH9//5cvXwoLC2/YsCEoKIjsRMBbOs4DxCmPHz+eO3duSkpKTk5OTk5OQ0MDe4NLu+uN+vbtm5WVpaGhQafTf/vtt/DwcIIgLCwssrKyjI2NzczMTpw4QXZGgB7C2RMw+Bbnz5+3sLDQ0NCYOXPm+/fvyY7DYUwmc/LkyatXr66urs7Jydm+ffudO3fIDgW85d8C6NWrV9L/0367e+MuXbo0JSWlqKiIvaihmprazp07d+7cyaHYfEJJSWnFihU7duwYN25cW2OfPn3Wr1//119/7dq1a/To0c+fPycxIQDwpbt374aFhbHLAjk5Of6biLWoqEhISMjLy4sgCC0tLR8fn5SUFLJDAW/5twCqr69//z/tt7s9tL29fUJCwokTJ8LCwhobGzmRVoDQaLR79+6NGTPG1tZ28+bNzc3NZCcC4CLOnoARBJGVlRUfH19VVdXWkpCQwImkX1RcXLxw4UJPT88dO3bw/hHvwoULQUFBTk5O6urqO3bsuH37dk1NDdmhOElRUbG8vPzjx4/sl8XFxcrKyuRGAl7zbwHU5wu+Z3QZGZnjx49bW1u3nxERvpGIiMiiRYsyMzNv375tZmZ2/fp1shMBcAtnT8A2bdrk4eERHR1No9Gys7PZjXPmzOFc3o7evXtnb2+voKAwbdq01NRU3r+gIiYm1tDQwN5ubm5msVh8ds+lnJzclClTnJycAgICJk2adPPmTfbVIIA2PfGJ9/b29vb27oEd8SUtLa2LFy9evHgxICDA1tZ2y5Yt6urqZIcC4LDvPNfqIDIyMjc3V1FRMS8vz9PTMysri9vzCZ0/f97FxeWXX34hCMLNzU1dXb2srIyXLzl4e3uPHDlSS0tLV1f3P//5j7u7u4SEBNmhOIxGo0VHR1dVVdXV1enr61OpVLITAW/h1k3QwFkTJkx4/Pixjo4OjUbbuHFj26kbAHxKWlqafSe1iYlJYGBgaGgot/dYVVWlpKTE3hYTE5OVla2urub2Tr+HsbHxhQsXLl26tHLlShMTk/3795OdiMMaGhp+/vnnvLy8wsJCOp0uJCQUGxtLdijgLXx1zZO/SUpKrl+/PiAgYPHixcbGxps3b54yZQomtwD4lI+Pj729/bx58+bOnRsaGurh4eHt7d12O8innjx50mEesqqqKjk5uW/fo4uLi5ubm5eXl4mJyaFDh8TExAYOHNj9N9AjrK2t+bgmePXqlaqqKvt6OYVCcXBw4PvJdaGrUAD1Mtra2ufPn79x48bixYt37dq1bds2W1tbskMB8JY1a9bY29uXl5cTBEGhUGJiYqKjozuZWPWPP/4oLCxs3/LmzZsuFUA0Gm3btm1Tpkyh0+kODg7nz5/HyQm5tLW1y8rK8vPzjYyMmpub//7772XLlpEdCngLhWcXKLW2tlZWVub2gxu9F5PJPHnyZHh4uKWl5caNGw0MDMhOBHyupKTE1taWTqeTHaQnfMvxh8VinTt37u7duwMGDPjxxx8xfRGviY6ODgkJsbCwKCwstLOzO3HiBKrS3osbxx/cA9RbCQkJ+fv7FxYWWltbOzo6zps3r6SkhOxQALzIycmJG8POnTt3165dGhoaGRkZtra29fX13NgLdJuXl9fDhw+Dg4Pj4+NPnjyJ6gc6QAHUu0lISCxbtqywsFBRUZFGoy1atOjdu3dkhwLgLa2trRwfs7S09OLFi1evXg0LCzt16pSWllZ8fDzH9wLfSUVFZdy4cYMHDyY7CPAiFED8QF5efuPGjY8fP25tbTU2Nl66dCnKIIA23JiV582bN/379297dFxPTw8L1wP0LiiA+IeKisquXbsePnzY1NRkbGy8aNGiDg+2AAgmbkyBaGxs/PbtW/bqCiUlJXFxcY6OjhzfCwBwDwogftOvXz92GUShUNiToGA1MQCO69Onz+nTp2fNmqWhoWFiYrJ48WILCwuyQwFAF6AA4k/9+vXbvn17YWGhkpKSra2tt7f3gwcPyA4FwFeGDRtWVFR0586dt2/fBgcHkx0HALoGBRA/U1JSWr9+/YsXL6ytrSdOnDhixIiEhAQmk0l2LgA+ISQkpK6uLiYmRnYQAOgyFED8j0qlhoWFPX/+fM6cOWvXrjUyMtqzZw+frfwMAADQJSiABIWoqKiPj09mZubhw4fT09MHDBiwcOFCzA0PANC7PHnyJCkpCc+4fD8uFkBMJjM7Ozs5OfnSpUsPHjzANy88wsHBITo6Ojc3V0ZGZsSIEc7OzjExMc3NzWTnAgCAr5g3b964ceMiIyPNzc2PHj1KdpzejVtrgV2/fj0wMFBNTa1///4EQTAYDDqdfvjw4aFDh3Jpj9Al6urq69evDw8PP3/+fGRkZEhIyMyZM3/88UddXV2yowEAwGdcuXIlIyOjsLCwT58+RUVFP/zww+TJk6WlpcnO1VtxqwBasGBBYmKijo5OWwudTp88efK9e/e4tEfoBjExMW9vb29v76dPnx48eNDR0VFfXz8gIMDT05NKpZKdDgAA/vXw4cORI0f26dOHIAhtbW1tbe2nT59i/oVu49ZXYK2trWpqau1bVFRUeHbhVdDX19+6dWtxcfGiRYvOnz+vqanp7+9/7do1fHEJQIq7d+96eno6ODj88ssvHz9+JDsO8AQDA4Pbt2+zD8tv374tKipqf5UBuopbV4CCg4MtLCzc3d3V1dUpFAqDwbh48SKmyuBxoqKi7u7u7u7uZWVlp0+fXr58+Zs3b3x8fHx9fc3NzclOByAonjx5MmnSpM2bN+vq6u7evXv27NmnT58mOxSQb9y4cUeOHBkyZIi5ufnVq1dXr14tJydHdqhejMK9qzJ0Oj05OZl9p7qamtq4ceM0NDS+1NnV1fXOnTvtW2pqaszMzO7fv8+lePAt8vPzT58+ffr0aVFR0alTp3p5eQ0aNIjsUECOkpISW1tbOp1OdpCeYG1traysnJCQQMreIyIiWCzW2rVrCYJoampSVlZ+8+aNpKQkKWGA16SmphYVFVlZWRkbG5Odpedw4/jDrStABEG8e/dORUVl6tSpsrKy7JaEhITx48d/tvO5c+caGxvbtzg7O6uoqHAvHnwLIyOj9evXr1+/PjMz88yZM2PHjqVSqZMnT/b09DQzMyM7HQB/ampqaruzVUREREhIqKWlhdxIwDuGDRs2bNgwslPwA27dA7Rp0yYPD4/o6GgajZadnc1u7GRJQklJSfn/T0REhEKhcCkedJWlpeX27dtfvXp15MiR+vp6Dw8PHR2dpUuX3rx5s7W1lex0AHxl4sSJe/fuTUlJodPpixYtsrGxkZGRITsUAL/hVgEUGRn54MGDqKioixcvTp06FfMO8wcKhWJjY7N169bnz5+fP39eSkpqwYIFampqAQEBsbGx1dXVZAcE+IwDBw6QHaFrrKys9uzZs3z5cnt7++rq6pMnT5KdCIAPcesrMGlpaSkpKYIg2AuSh4aGHj58mEv7AlKYmZmZmZlFREQUFxdfunTp4MGDAQEBlpaWY8eOHTNmDG4VAhIlJSWVlZW1vVy3bh37yWF/f3/yQnUN+3EEslMA8DNuXQHy8fGxt7dnn3iFhoZWVFR4e3vjYU6+pKmpOX/+/MuXLzMYjIULFz5//tzNzU1TU3P27NnR0dHl5eWlpaWYaRp60uPHj+fOnZuSkpKTk5OTk9PQ0MDeIDsXAPAQbl0BWrNmjb29fXl5OUEQFAolJiYmOjpaXl6eS7sDXiAlJeXm5ubm5kYQxNOnT//+++/ff//d29ubQqEICQlNmzYtMjIS8ytCD1i6dKmdnd3KlSv9/PxGjhx59erVnTt3kh0KAHgLFx+D/07kPoYK34/JZMrJyfXr12/cuHEpKSkPHz4UFxc3NzcfNmzY0KFD7e3tMYN779LrHoOvrq4OCQlRUFBITEx8+vRpJz39/f0fP37cvqWgoMDIyCgzM5PLGQHgm/Syx+BBwOXm5tbV1WVlZbXdDaatrR0WFpaamrply5b79+/r6ek5ODjY29vb29uz14wD4CAZGZnjx4+fOXOmoKCg855r165lX65uM3PmTGVlZW6mAwCSoQAC7mp/iVFYWHj48OHDhw8nCKKpqen+/fu3bt2KiooKDg6WkJCws7OzsbGxtrY2Nzdn37IK8P3Yq9113mfAgAEDBgxo3yIpKSkkxK1bJAGAF6AAAm4xMzOjUqkWFhbBwcF37twpLCzcvn1720/FxMTs7Ozs7OyWLl1KEMSzZ8/u3Llz7969EydOFBQUDBo0yMrKytLS0sLCwsjISFhYmLz3Ab2ek5PTjRs3yE4BALwFBRBwi5CQ0NWrVydOnLh8+XKCINavXz969OgvddbT09PT0/Pz8yMIor6+Pjs7OzMz88qVK5s3by4pKTExMTH/n8GDB+P6EHQJ5uoEgE+hAAIusrS0fP369bt37xQUFEREvvXDxv46zM7Ojv2ypqYmOzs7Ozs7PT19z549z54909XVNf2fwYMHq6urc+0dAD9gF9bcdu3atV9++YVOp9vb22/duhW3tQHwOBRAwHV9+/b9nl+XlpYeOnTo0KFD2S+bmpoePXr08OHDvLy87du3P3r0qKGhYfDgwYMGDRo0aJCxsbGRkVG/fv04ERz4RCeL8HBKYWHh9OnTDx06NHjw4MOHD3t4eNy7d4/bOwWA74ECCHoZMTGxIUOGDBkypK2lvLw8Ly8vPz//0aNHcXFxT548aWhoMDAwMDIyMjAw0NfXZ3+/JiEhwZEA2dnZa9euffnypbW19bp167BkLxAEkZiY6Ovr6+rqShDEunXrTpw48erVKy0tLbJzAcAXoQCCXk9RUdHJycnJyamt5cOHDwUFBQUFBYWFhadOnXr69OmLFy/69u2rq6urq6uro6PD/t+BAwd2dS6i169fu7q6btiwYciQIadOnXJ3d79z5w4frNpbU1OTlJTU1NQ0evRolHTdICIi0tjYyN5mMpnNzc2ioqLkRgKAzqEAAj4kLy9va2tra2vb1tLa2lpcXPzPP//8888/z58/v3Pnzj///PPixQspKamBAwdqa2uzH4TW0tJi/++XLhclJSW5urrOmjWLIAgzMzM9Pb1//vlHT0+vZ94XlxQXFzs4OFhYWEhKSi5dujQ+Pt7a2prsUL3MxIkTra2tTUxMTE1NDx8+PHjwYHwPC8DjUACBQBAWFtbW1tbW1h41alT79tLS0qKioqKiopcvX2ZlZcXGxr569aq4uFhGRkZDQ0NDQ0NTU1NDQ0NdXV1dXV1DQ6Opqan9M/lCQkJMJrPH3w2H/frrr0FBQStWrCAIIjY2dvny5XhovKs0NTUvX768cePGo0ePOjg4bN26lexEAPAVKIBAoKmqqqqqqra/VsRWWlpK/5/i4uL79++XlJTQ6fQ3b960trZevnxZW1u7qqqqpqYmJSWlsLBQRUWlX79+ffv27Y2P6D979szX15e9bWdnFxISQm6eXsrMzOzs2bNkpwCAb4UCCOAz2IWRpaVlh3YWi3X58uVff/312bNnmpqa06ZNy83NvXz58tu3b9kP/FOpVBUVlb59+6qqqvbt21dZWblv374qKipK7ZDyjjpBo9EuXrw4bNgwgiDi4+PNzc3JTgQAwHUogAC6gEKh1NbWMhiMd+/eqaurz5gxw8zMrH2H8vLyt2/flpWVMRiMsrKysrKy+/fvl5WVvX//nv2yurpaSUlJ8XMUFBQUFBTk5eUVFBTk5OSoVGrPvKnw8PBRo0bRaDQqlcpgMC5fvtwz+wUAIBEKIIAuePLkSVhY2MWLF83MzP78889JkyY9ffq0/RyP7FKmkxFaWlrev39fXl7+/v37iooK9kZZWVlhYWHF/3z48KGysrKpqUlOTk5eXl6uHXl5eRkZGVlZWVlZWRkZGRkZGWlpaXl5eWlpaWlpaXFx8W68KQUFhYyMjPv37zc2NlpZWXVvEACA3gUFEEAXXLt2zdPTkz0L0fTp07du3Zqfn29iYvLtI4iIiLC/X/tqz6amJnYl9KnXr19XVVVVV1dXV1fX1NRUVlayN5hMJpVKlZeXp1KpUlJSUlJS8vLyUlJSkpKS0tLSMjIyEhISUlJSMjIy4uLiVCpVWlq6T58+MjIykpKS+vr6cnJyfPBIPwDAt+BiAcRkMnNzc0tLS1taWvr370+j0bC6MvR2srKy7969Y2+3tLSUl5fLyclxaV9iYmIqKipdmpWnqamptra2srKytra2tra2rq6usrKyrq6urq6utra2qqrq/fv3dXV1VVVVDQ0NdXV11dXVjY2NNTU1Hz9+bGxsDA8PDwsL49LbAQDgKdwqgK5fvx4YGKimpsZeEIfBYNDp9MOHD7ctaADQG02YMCEiImL58uUWFhZnzpz54YcfNDQ0yA71LzExMfaNRGQHAQDgddwqgBYsWJCYmKijo9PWQqfTJ0+e/KX1cRoaGurr69u3tLS0cCkbQLfJycndvn17586dsbGxjo6OQUFBZCcCAIDu4FYB1Nraqqam1r5FRUWFxWJ9qf/EiRMzMjLat9TU1OBmTOBBqqqqv/32G9kp4CuqqqpkZGQoFEpWVlZ+fr6FhYWRkRHZoQCAh3CrAAoODrawsHB3d1dXV6dQKAwG4+LFi8HBwV/q/+mTt8uWLVNWVuZSPADgYwcOHNixY0deXt7WrVsPHTrk6Oi4atWq1atX98Cy8ADQW3CrAAoKCnJzc0tOTmYwGARBaGpqJiUl8dTdEgDArzZs2JCXlycqKrp3797c3FwFBYWysjI7OzsUQADQhotPgWloaMydO5d74wMAfJaysnJTUxNBEHJycuyHT9uv4AYAQGAeIADgPxs2bLCzs5swYYK+vr6Tk9Po0aMTExM7WePs8uXLxcXF7VvKysqkpaW5nxQASMO7BZCwsPDu3bs7WVywtrb2xYsXoqKiPZmqE01NTWJiYmSn+C9eCyMqKsojM+w1NzcLCwvzyJRULS0tFAqFRy5OtLa2UqnUAQMGfKlDU1MTj0T9KhcXl4yMjPj4eDk5OUNDQ2Vl5djYWENDwy/1z8nJKSoqat8iJCT04MGDH3744Uu/guNPJ3gtDI4/n4XjD6WTJ7PIVVlZ+fz58046PHjwYNu2beHh4T0WqROVlZXh4eF79uwhO8h//fjjj3v37uWRY9DKlSvnz5+vrq5OdhCCIIidO3cOGzaMPZUz6aKiohQVFceMGUN2EIIgiCtXrlRXV69evbqTPvLy8gMHDuyxSCTC8ed74PjzJTj+fAk5xx9Wr5Wenu7g4EB2iv9iMBhqampkp/iXpKRkXV0d2Sn+a/DgwXl5eWSn+C93d/cLFy6QneK/wsLCduzYQXaK/9qzZ09wcDDZKbhi2LBhHB8Tx59O4PjzJTj+fAkpxx+euBAHAMA9ra2tZEcAAJ6DAggA+Jyfnx/ZEQCA56AAAgA+h+l/AOBTKIAAAABA4AhHRESQnaGbqFSqoqLi4MGDyQ5CEAQhLi4uLy9vYWFBdpD/UlBQsLW15ZEnP6lUqq2tLY88EiIhIfHDDz/IyMiQHYQgCEJSUtLY2FhFRYXsIARBEFQqVUtLq5PHUKE9HH86gePPl+D48yWkHH949zF4AAAAAC7BV2AAAAAgcFAAAQAAgMBBAQQAAAACBwUQAAAACBwUQAAAACBwUAABAACAwEEBBAAAAAKntxZAf/zxh7Gxsamp6fXr18nKsH79emNjYw0NjU2bNvFOqq1btyYlJZGe56+//rK0tNTS0jpw4ADpYVasWKGtra2jo3Pq1CkSw9TV1e3bt6/t5acZejJVhzC8+WHmWbzwx+HNfzIcfz6F489Xw5D2Ye7h1ec5ori42NDQsK6u7tmzZwYGBq2trT2f4erVq0OGDKmvry8rK9PU1MzKyuKFVDdv3hQTEzt27BiL1L/Shw8f9PT0ysrKqqqqDAwMqqqqSAyTmppKo9EaGhrevHkjKytbW1tLSpiHDx8GBAR4e3uzX36aoSdTdQjDmx9mnsULfxze/CfD8edTOP58NQyJH+ZeeQUoOTnZ3d1dUlJSV1dXVVU1Nze35zOUl5fPmzdPXFxcSUnJwcGBTqeTnqq6unrlypXe3t7slyTmiY+PnzBhgpKSkoyMTF5enrS0NIlhhIWFxcTEREVFxcXFRURECJL+Mn/++WdlZWXby08z9GSqDmF48MPMy3jhj8OD/2Q4/nwWjj9fDUPih7lXFkBv3rxRV1dnb6urq5eWlvZ8Bi8vr7lz5xIEkZeXd/v2bScnJ9JThYSE/PLLLwoKCuyXJOah0+kvX740MTHR1NTctm0bhUIhMYy9vb2+vn6/fv00NDQiIiKkpKRICbNx48agoKC2l59m6MlUHcLw4IeZl/HCH4cH/8lw/PksHH++GobED3OvLICYTGb7ZfZaWlpIicFisXbu3Onp6Xn+/HlZWVlyU509e1ZBQWHkyJFtLSTmqa+vf/bsWVpaWk5OzsGDB7OyskgMk5KS8uzZsxs3biQlJe3cuZPBYPDC5+fTDOSm4qkPM4/jkT8OT/2T4fjzJTj+TwoxZAAAIABJREFUfAuyPswiXBqXq/r161dcXMzeZjAY/fr16/kMra2tU6ZMkZOTu3//PntpX3JTHTt27Pnz51evXn3z5s25c+caGxtJzKOsrOzi4iIvL08QhKOjY0FBAYlhkpKSpk+fbmhoaGhoaGNjk5aWxgufn08zkJiK1z7MPI4X/ji89k+G48+X4PjzVWR+mLl0bxFXvXr1avDgwY2NjXQ6XVdXl5Tb/U6fPj116lReS8VisUJDQ9k3IZKYJz8/f9CgQeXl5e/evdPU1Hzy5AmJYQ4cODBu3Li2O+yys7PJCnP16tW2+/4+zdDDqdqH4dkPM2/ihT8Oz/6T4fjTAY4/Xw1D4oe5V14B0tTUnD9/voODA0EQhw4dEhIi4Yu89PT05ORkNTU19suDBw+OHz+e9FTtkfhXMjQ0nDt3rrW1NYvF+vnnn42MjAiCICtMQEBAdna2sbExQRBLly6l0Wgkhmnz6b8Oif9evP9h5ik4/nwLHH/YcPz5KhI/zBQWi8WloQEAAAB4E07sAAAAQOCgAAIAAACBgwIIAAAABA4KIAAAABA4KIAAAABA4KAAAgAAAIGDAggAAAAEDgogAAAAEDgogAAAAEDgoAACAAAAgYMCCAAAAAQOCiAAAAAQOCiAAAAAQOCgAAIAAACBgwIIAAAABA4KIAAAABA4KIAAAABA4KAAAgAAAIGDAggAAAAEDgog/vTixYuJEyf6+/vv37+f7CwAAAA8h8JiscjOAJy3bNkyHx8fc3PzoUOHpqWlkR0HAACAt4iQHQC4Yu3ateLi4iUlJUpKSmRnAQAA4Dn4Cow/SUhIxMTEBAcH//HHH1zdUWpq6qRJk3ryF7ukpaXl/fv3n/3Rhw8fXr58ye0AAADAm1AA8afExMS7d+/GxcWpqqqSnYVjsrOzbWxs+vXrN3v27A4/MjAwEBERERUVFRUVPX/+fFv7qlWrwsLCCIJ49eqVra2toaHh7t272T9avXp1Y2Njj4UHAACeggKIP8XFxRUWFnp5ec2YMeMbf6W5ufmz25337DENDQ0eHh4HDx4sKioqLi6+fPly249YLFZFRUVLS0tzc3Nzc7OHhwe7PTU19ciRI+ztTZs2RUREPH78+Pjx49XV1U+fPiUIwsDAoOffCAAA8AIUQPxg8eLF+vr6GRkZBEHExMQYGRnt3bs3ISHh3LlzJ0+e7NB5z549AwcO1NPTW7x4MZPJvHXrlq+vr4+Pz7p169pvEwSxYcMGHR2dgQMHLlu2rEPPTzN0GHbMmDEXLlxg/8jS0jItLa1Dh66+x4SEBCsrKxMTkz59+ly6dMnJyantR69fv1ZTU+vQv7KycunSpWvWrGG/lJWVffLkCZ1Ob2hoEBUV3bBhw6pVq7qaAQAA+AYKIH6wffv2zZs3r1mzpr6+fsmSJTt37hQVFf1sz9TU1MOHD9+7dy8nJ4fBYGzatIkgiMTExIiIiPXr17ff/uuvv06ePJmdnZ2fn3/nzp2oqKgOPTsf1tvbm/1V1IsXLyoqKphM5qf77WDcuHEDPpGXl8f+aVFRUWNjo4WFhaqq6qxZs1pbW9t+saioqLKy0tnZWVVVNSAgoL6+niCIoKCg8PDwvn37svusWrWqsLBwzpw5//nPfx4/fty/f/9+/fp9318dAAB6MTwFxicmTZq0YsWKwMBAU1PTMWPGfKnbtWvXqqqqvLy8CIIoKyurqalxcnKi0WhtXwa1baekpEyePFlGRoYgCB8fn5SUlIEDB7bv2fmwUVFRK1asaG5uPnv2rL+/f0pKSocODg4OHQZJSkrq5A3W1dVlZGTcvHmzb9++fn5+W7ZsWbt2LftHsrKyoaGhISEh1dXVvr6+W7Zs0dXVpVKpbm5uZ8+eZfeRkZHZt28fe3vKlCkHDx68dOlSZGSkiYnJr7/+2qdPn6/+hQEAgJ+gAOITFApl9uzZ4eHhjx8/7qQblUqdN2/eihUrCIJoaGhoamrKy8uTlpZu69C2zWKxKBQKe1tYWJh9xaV9z86HlZGRsbGxuX79enR09Pnz58+dO9ehQ3Z2dodBXFxcPg2flJRkampKEISqquro0aMHDhxIEISnp2dMTExbH2Nj40GDBgkLCysqKvr6+sbFxeXk5Ny7d09bW7uuru7jx4+urq6JiYnszn///beVlVVra2tERERcXNyhQ4ciIyMXLVrU2R8XAAD4Dr4C4x/Z2dnS0tIaGhqd9Bk9evTx48fLysoaGxs9PT1v3br1pZ4jRoyIjY2tra1tamqKiooaMWJEV4f19vbetGmTvLy8trb2t+z3r7/+KvkEu/ohCGL8+PFXr1598eJFbW3tmTNn7OzsWCxWYWFhS0tLZGSkq6trQ0NDdXX1uXPnHBwc4uLiGAxGUVHRnj17Jk2a1Fb9sFisyMjIkJCQuro6eXl5DQ0NExOT8vLyr/5tAQCAz6AA4hO3bt1KTU21s7Nrf2nkUzQaLTQ01NbWVkdHR0dHZ+zYsV/qOWbMmGnTppmbmxsZGVlZWfn5+XV1WDc3t4yMDH9//y7t90vU1dV37Njh4uJiZGSkoaGxcOHCpqYmQ0PD169fz58/n0ajDRo0aPDgwbq6ugsWLPjSINHR0RMmTBAXF9fU1HRwcBg0aNCePXs66Q8AAPwKS2HwAyaTaWVlFRwcPGDAgCVLlty9e1dEBF9uAgAAfBH+M8kPjh07RqFQ/P39KRSKkZGRtrZ2WlqatrY22bkAAAB4FK4AAQAAgMDBPUAAAAAgcFAAAQAAgMBBAQQAAAACBwUQAAAACBwUQAAAACBwePcx+I8fP5aWlpKdAgD+RaVS29aX5W84/gDwGo4ff3jlMfjhw4ffuHGjQ6O4uDiW7AbgES0tLcLCwi9evCA7SE/4+eef9+/fLycnR3YQACAI7hx/eOUK0PXr1zu0WFtbKysrJyQkkJIHoBOsxqa6m1nUkbZkB+lRJSUltraC8pZbWlpWrFixdOlSsoMAAEFw5/iDe4AAuqzqwpX3e6Ma8p6SHQQAALoJBRBA17S8/1BzOV1hpmf54WhWK5PsOAAA0B0ogAC65sPx89Kuw2TGDxdRlK+9cpPsOAAA0B0ogIDraq7cYtZ+JDsFZzQWvGh89lLWbSRBEAqzPCvPJTNr6sgOBQAAXYYCCLirIf95+YEzH87wxc3sLFbF0Rh5fw9KHzGCIETVVSVtaJUxyWTHAgCALuOVp8CAP7FYH47FKs6dWnk2UXq0g5hm757UoPHZy8bnxWXbD5e1axQS7yPvN4kiLExaLAAA6DoUQMBFNdduEyIi0s72RCuz4miM6i8LyE70Xfroaw+I+Z3sFAAAwAH4Cgy4hVnfUBmdpBAwmaBQpEc7MKtqP2Y8JDsUAAAAQaAAAu6pPJcsOWRwHx1NgiAIISGFWZMrjp9nNTeTnQsAAABfgQGXsFh1aZmtlf/H3p3HRVXufwB/Zh9mGPZtGMAFF9wQtxBRcivczaWCLC1zCUPDym6W7aUtt+jesuvVXKhMU3FlSVNxT1QExAVxFxiQZTaYfTm/P+iH3FFQiZkzzHzer/6YeTic5wNMx+855znPo6rbf/xuI4OhL77B79ONvlgAAACEoAACW2EwQn9cTncIgHbMotUZrpfye3WlOwiAc8ItMAAAR6T4LfPOZz+YauR0BwFwTja8AmSxWAoLCysrK00mk0QiiYqKYjJRbwEAPJix/I76yGnR6CHyn3f4L55NdxwAJ2SrAignJycpKUksFkskEkKIVCotLS1du3ZtXFycjXoEh0IZjGaFyqxQmZX1lnq1uU5tqVNbNFqLRktp9Ra9waLWUgYDZTRRej1lNBNCLDodaWZpLQaPy2CzCSFMNx5hsRgcNoPLZbrxGBw2043PEPCZPB5TwGe48ZlCN6ZQwHIXMN2FTHcBUyRk8nl2/ckB2oJsQ7rn1HjRk0PLUz7VXbzC74kbYQBtzFYF0KJFizIzM8PDwxtbSktLp0+fnpuba6MegRZmRZ3pTrWxssZUXWuulptq5CaZwixTUEYTy8uD5eXB8nBnioRMkZDlLmT7ejGFbgw+n8nnMoVuDA6HweUweFwGh00IYfB5zU0nSOkNlMlECLFo9cRspgxGymi0aHSU0WTR6S0aLaUzWLRai7LOJK0yqzUWtcZSp2kovIjFwhS5szyELC8PlqeI6eHO9vFieorY3p4sbw+WjydT4GbXXxnAg2jyzpuqakVj4xgslveMybJ16cFfvkVwBR2gTdmqADKbzWKxuGlLYGAgRVE26g7sgaJM1TLDLamxtMJYVmGUVhnL7zDYbHaQHzvIjx3gy+3aQRDTj+3rxfLxZAoFbdgzg8dtWH2iFbuljEazst6iqjcr68yqOouy3iRTWG6UmuRKs1xlrlVQFgvb14vl7cn282b5erN9PFn+Pmx/H7avN1OI2gjsjTKZ5Wk7fF6a1nA+IIztX7f3aN3BP0WjY+mOBuBUbFUAJScnDxgwYPLkySEhIQwGQyqV7t69Ozk52UbdgU1QlLGiWn/1luHqLcPNMsONMqbQjRMWzA0T8yMjROOGc8QBjl8iMDgctp838fNubgNKbzDVyM0ypUkmN9coDLel5vyLpmqZqVpGGAy2vw87wIcd4MsO8GMH+LIDfTmBfg3VGIAtaE4VGiuqan/c0thi0WhNO/9AAQTQtmxVAC1YsGDixInZ2dlSqZQQEhYWlpWVFRoa2tz2N27ckMlkTVvUarW3d7P/aIGNUEaT/spN/aVruuJr+pIbTKGA17UDN7yD16A+3E6hTPe2vK7jIBg8LkcSyJEE3vsli1pjqpKZqmWmqhrTnWrduWLjnRrTnVqmkM8O9OcE+bPFfpwgf3aQP0fs37YXvcBlCaKjQr7/wKqRgaFsAG3Nhk+BBQUFzZw5k8/nK5XK06dPG1ucAvijjz46f/5805abN28KBPgXxS4sFv31Um3BJd35EsO125zQIF5EuOiJoX7JL7A8RXSHoxNTKOB2EnA7hVi1m2VKY2W16U6NsaJac+qcsbLaVFHNYLPZYn+OJJAjDuAEB7CDAzjigIbhTUCv1atXz5s3j+4UD4vBYrID/ehOAeD8bHV0/uWXX5KSkjw8PF5//fXVq1d36dLl7Nmzn3766csvv3zf7Tds2GDVEh0d7e/vb6N4QAix1Kk1+Re1Z4q0RZfZPl5ufSM8J43i9QhnuvHpjuboWD6eLB9P0rNL00azos4ovWOqqDZW3Kk/csoorTJV1bJ8PDmSIK4kkC0J5EqCOCFBTnkVzdFkZWVVV1c3vv344495PB4hZNasWfSFAgDHYqsC6L333issLOzQoUN0dPSbb745d+7cmpqamJiY5gogsBtTjVxzqlBzstBwo5Qf2V3Qv7fPS9NY3p5052r3WF4ilpeoaVVEmS2mqlpjWaWx/I7+8o36AyeM5XcYHDYnRMyRBHFDgzghQZywYJaHO42xndKFCxeWLVuWkJDg4+NDCNHpdAUFBaT5Amj8+PF//vln05b6+vqYmJglS5bYIS0A0MJWBRCbze7cuTMh5MUXXxw9ejQhxMvLy0Z9wcMwK1TqE/nqY2dMFdVuA3t7TB7lFtmdweHQncuZMVhMjtifI/Yng/o0NpplSkNZpbGs0nBLqj6eZyitYDCYnA7BnJAgblgwJ0zMDRVjONHftGTJkiFDhixdunTmzJmjRo3av39/ampqC9tv3bpVr9c3bRk9erS7OwpTAGdmqwIoLi5u8uTJb731VsOTXxcvXlyxYsWQIUNs1B00hzIaNafO1R/K1ZfcFAzs4/X0WH5kBIOFCUVow/LxdPPxdIvs3thiVqiMpRWG2xWG66X1h04aSyuZAj4nVMztIOGEiblhEk5IkB3GEil3/mGsqPJLmmHrjuwjNjY2IyNj4cKFGRkZVsXNvQQCgdWIQzabzWAwbBkQoJV0xdeqv1kvSX3X8R/CdXC2OqquWbNmx44dFstfE/tev3594MCBr7zyio26g3sZbkvr/zhef+wMr3OY+/DogCVzGVxc72kblNmsO3/FrW9Em+ytYcZIfp+7JZGpqtZYWmG4LdUWXFLtOmCsrGYH+HI7BHM7SDhhwdwOEra/T5t0fbfHWoVq1wHCYeuLr/MiOrftzuni4eGRlpa2efPm4uJiurMAtBGKkq3bxnIXKLZm+bw4je407ZutCiAmkzlt2t2/zYQJE2zUEVihzGbNyQJV9mFTlUw0Kib4q7fZzU+BA61Tl31ElrY9+Iu3uJ2bndnh72AH+LIDfN0G9G54S5nNxrJK422p4Za0bu9Rw61ySm/gdgjhdgzmdJBwO4ZwQ8V/s7qV/7xTNDaOIw6oXbsl+Mt/ECe6+JGQkJCQkEB3CoC2UXfwTwaHE/hecnnKp6JRQzih4gd/DzQDz+g6D0u9pm7fUdXvRzjBgZ4TR7kN7INbXbZgrqtXbt/rOXGkbP22oI9T7FArMFgsbgcJt4NEOOxuBuPNcsPNcv2la3XZR4zSO+wAX27HEG5HCbdjCLdjCMvrEeYv0Jfc0Bdf83p6LGUwMvm8+kO57iMG2+QncToWnb7yna/9kp/ndg6jOws4P4tWp/gtM+Af85kioeeUJ2XrtwW+v5DuUO0YCiBnYKqRqzIO1h/KFTzWN/DdBdwOEroTOTPFrxnCuEHeLzxV8fZX6hP5wtj+9s/AErmz+nRvvGtGmc3G0krDrTLDzXLlrj8MN8oYbDa3Uwi3Uwi3Uyi3YwgnyK/ZQo2iZOu2eT8/uXb1ZlO1zD/lxaqvfhRE98USaQ9DuX2fxWCoXbdN/MliZ7psBo5JuTVb0K8XLzyMECIa+3jdgRPavPONl4rhUaEAat9MVbXK9L3qU4WikTGSb95l+Tje0+wWS3Xqeq9nxjnHpVpjaYXmVKHk22WEwfCZPb06db1gYG/aV8ZgsFjcjhJuRwl5/K8WU43ccKPMcLNMfeyM/Oedljo1t4OE2zmE2zGE2ymUEypmsP9ad7b+UK6x/I4274JReocpEiq2ZDE4bOWOP7xnTKLt52knTHdq6v84Lv7n29VfrVEfyxMOG0h3InBmpjs1qqxDwmGD5L/samhheXnI0nZI+vXEQrmtgwKovTLVyBVbs7SnikRjhoV894HDTq9Xd/BPbdFlc70m6ANnuFQr25Du9ex4pkhICOF178zr3km5a7/XM+PozmWN7efN9vMW/P/j9xa1xnCjzHCjTHe+RJWRY6ys5kgCuZ1CuZ1CiNksGjm4LifXrX8vptBNnZMrHBHtHNWqrcl+2uExcSTb18tn9vTqr9cKHoukvRQGJ8bgcrwS/mc0rVvfCAabjUuPrYYCqP0xK+uU6Xvrj572iI+TrPzAkW9VWLQ6xZaswHcX1K7apMktFET3pTvR36IvuaEtLDZKq5S79je0UHq9Nu+C57T4hoW7HRZTKOD37sbv3a3hLWUwGm5LDTdKDddLDTfK9DfLmFwOYRBh7ACmu8BcI3ePG0RvYMenO19iuFHmn/IiIYTXrROvRxfHLIXBabC8PT2feoLuFE4FBVB7QhmMqowc1Z6DwscHSf71nuPPIKzYkiUY0JvXtaPPi9NqVv3q1r9nu556kde1Y8jKD60aGRy2g1c/92JwObwuHXhdOhBCzDJF+euf+7/2gllRx+ByvKbGl7/2ia74Gj8inO6YDoyiZOvTOSFBqsxDDQ0skVC58w/3kTF46BKgvUAB1G6oj5+V/7yD17WjeMWb7KB2sFaisbJafehUcOo7hBB+n27cDsGqjBzPKU/SnetvYDCcb5VK1e9HKL2+9setjS0WtVa1JwcFUEsoSvBYJGU0Weo1DQ0MHtdj3HBaMwHAo0EB1A4Ybktl67ZZ1Bq/lBfb0T9L8vXpvO6ddJeuNbzlde2o3L7PfXg01h1zKF7TxohGWU/R3jDICZrFZHo9O57uEADwt6AAcmiU3qDYklV/KNfrmXGiJ2Lb01B/imKKhJTeoDlxtrHNLaqHWVGHAsihMHhc57usBQDwQCiAHJc2/2Ltmt/4PcKDU991/OE+1hgMv+QX6A4BAABwfyiAHJFFrZGtS9cVX/N95bmmq2YCAABAm0AB5HC0eedr/rtZGN1X8s07mFYEAADAFhylANqzZ09FRUXTlqqqKqHQBUZiUpRFo2UKBYQQSm+QpW3XFlzyT5nF79mV7mQA8GB1+44J4wYx+Ty6gwDAo3GUAujKlSuXL19u2qLRaIxGI1157KZu71Hljn2Sf79vLK+s/jaN17VD8NdLmW58unMBwINpCy/VrvnNVCPzfu5BK4dQ1J0VqzzGPu7Wr6ddogHAAzhKAfT6669btRQUFHh6OvnjQpZ6jWJrNkcSWPXVGsP1Up+Xn6FlZU0AaAXKbJGn7fB9JVHxy273ETEcsX8LG6uP5xlulsvWbQ1OXda4EBsA0Kj9PFbtjBS/ZQoGRTJ4XF3R5YA356D6AWhH6n4/wvQUiUYN8Zg4onF9yvuiDEb5xt3+i1/iSIJUmTl2SwgALUABRBtjWWX9sTO6c8UccYDH1HhV9mG6EwHAw7LUa5Q79vq8NI0Q4jFptPF2ubbwUnMbK3fs40V05vcI93lxmmrnfrNcacekAHB/KIBoU/3temIweT4zzmf2dK+pT+qv3dZdvEJ3KAAnkZeXt2vXLqXybqmRkZHRhvuXb9ojHDKAGxZMCGGwWd7PT5atT6fMlnu3NNUq6n4/2jBIiB3k5z5ysHxzWyYBgNZxlDFAroWiqr9ZZ7hZLojuq798XX/5OiGE6S6Q/7RT/PkSusMBtHsrVqxYtWrV0KFDU1JStm/f3q9fP0LI3LlzrR41bTXKbFYfOW3R6lRZh+62MhiGa7d43TpZbSz/aQdbEqAt+Ov6ENNTpMw46BEfx+0c2iZhAKB1UADZm0Wnr/l3mlmm9J41temjs9zOYSwvDxqDATiNlStXFhYW+vr6FhUVTZs2LS8vTyQStbC9VqvV6XRNW0wmE0VRzW3PYLHCfv7nQ4bhSAKZAr7h+u3GFtHIGMpkeshvBwAbQQFkV6YaedWKVbxunfzfeJnBwpMgADYhEokaZhHr06dPUlJSSkrK2rVrW9h+6tSpubm5TVvq6uraKozXM+PaalfQCkbpHY44gDAYdAcBh4MxQPZjuFle+e7X7iOifecnoPoBsJ3ExMTY2NjVq1cTQlJSUmQyWUJCgkajaW777Oxs2f/q37+/WCy2Y2SwCbNMIV3yRd0fx+kOAo4IV4DsRFd0ufrbDT6zpwtjBzRtN1ZWa07ke059kq5gAM7n/fffj42Nra2tJYQwGIxt27Zt2bLF29ub7lxgb/Jfdgn691JsyRTGDmAK3eiOA44FBZA9qI+dkW1I918yhx8RbvUl2dqtuqLL/J5deBGdackG4JRGjRrV+JrFYiUmJiYmJtKYB+xPX3JDd+GK5N/vy9K2K7Zm+bw4je5E4FhwC8zm6vYdk/20M/C95HurH23eeVNVre8rz9Wu3UKaH3EJAACPhqJk67Z5Pz+ZweN6PzdJffSMsbRtngEEp4ErQLalTN9bn3NS/OlidoCv1Zcos1mWtsPnpWluUT3qD5yozznpPjKGlpAAtEtKSrpv+3/+8x87JwHnUH8olzAZwqEDCSFMd4HnlCdl67cFvr+Q7lzgQO5TABUUFBQUFMyYMaOysjI0FDNVtJ580x7t6aKgTxff9/n2usxDbLF/w8qIPrOfvrP8P4LBUUwB7lKDK5owYQLdEcCJUJR8UwbTjXfnk+//ajBZdBev6C5e5ffsQm80cBzWBdCGDRtWrFih1WqnT58+bNiwZcuWzZkzp3W7tlgshYWFlZWVJpNJIpFERUUxmS50x02xOUObdyHwo0Uskfu9XzUr6xTpe/1TXjTdqSGEMAV8XudQ5fa93s8/ZfekAPQbP368VYvFYlm2bNm97QAPxmAEvPmyRWdo2uZJxmDySWjKugBKTU09efLkzJkz3d3dCwoKBg4c2LoCKCcnJykpSSwWSyQSQohUKi0tLV27dm1cXFwbpHZ4srTtugtXgz5cxHQX3HcD/ZWbTHdB7Y9bmjZa9Ib7bgzgIr788stvv/1WLpcHBgZWVVWNGDGC7kTQXt07JTeAFesCSK1W8/n8htcCgYDD4bRuv4sWLcrMzAwPvzvst7S0dPr06VazjTk4bcEl5Y59QR8ueqRJtOQbd+suXA36YGELT10KBvYRDOzTFhkBnMf3339/6dKld955Z8GCBSqVav369XQnAgB70OadN9XIRfHD7Nmp9T2pGTNmTJo06caNG+vWrRs9evSMGTNat1+z2Ww1jVhgYGALU8s7IMpslq3bZqqqrTv458N/l2JLljbvfNB7r2LOCYBHxePxPDw8+vXrl5ubGxMTU1hYSHciALA5Sm+oXfOb/Nc9pmqZPfu1vgL00UcfZWZmHjt2rLy8fPny5UOHDm3dfpOTkwcMGDB58uSQkBAGgyGVSnfv3p2cnPy3A9tPwyBl74QJdz77QRjT72GGJyt37VefOBv00WtMkdAOCQGcTFxc3OzZs5cuXfr000/fuXPH09OT7kQAYHPKHX/wenThBAfIf97p//psu/VrXQDV1tYOHjx48ODBjW99fa2f334YCxYsmDhxYnZ2tlQqJYSEhYVlZWW18EzZsmXLSkpKmrZcvXqVRd96EWZlnXLnH0GfLuYEBwoG9FZu+9175pSWv6XuwIm6fcfEnyxmeba07CIANOfHH38sKSnp1q3bN998k5eXh1tgAE7PVCOv23tU/NU/WB7u5Smf6i5e4ffsap+urQugMWPGNLxQKpVXrlzp27dvQUFB63ZdVVUVGBj47LPPNp7GZWRkNPew65gxY/r27du0JT8/XyC4/whiO1BsyhAOj+YEBxJCvJ6bKF38mfvoWE5wQHPba06fU2zODPpoEcsH56wArSSTyfz8/Gpra6OioqKiouiOAwA2J0/bLhr/ONvPmxDiPWOybF168JdvEbs8M25dAJ1F0wf9AAAgAElEQVQ+fbrxdVFR0cyZM1u33xUrVqxatWro0KEpKSnbt2/v168fIWTu3LkVFfefi/Pee23//Oc/G4dj25nhZnldzp/eiZMal9DjdAiW/7Qj4O35991ed76kdtWmwPdebSiYAKB12vAEDAAcn+7iVW1hsWj8cMP124QQdpAfZTDUHfxTNDrWDr23NBN0nz59jEZj6/a7cuXKwsJCX1/foqKiadOm5eXliUTt58YQRYlGxpjuVDc2cIL87zuZISHEWFpR/e16/zdmczuG2CsfgHNqqxMwAGgXDLfK2UH+snXpjS0MPt8krbJP79YF0OLFixtfFxcXd+/evXX7FYlEQqGQENKnT5+kpKSUlJS1a9e2OqWdcTuF+M5/qHUTzXLlnRWrvF+YYrd7lgAu4u+cgAFAu+Ax9nGPsY/T1bt1AdT0VtT48eNbPRFZYmJibGzs/Pnz582bl5KSMnXq1ISEBI1G0/qkjsei099Z/h/Rk0PdH3+M7iwAzqCtTsBoQ1GmGjnb34fuHADwYHcLoIYpCkNC/uc+zpkzZ6Kjo1ux3/fffz82Nra2tpYQwmAwtm3btmXLFm9v77+X1pFQVM23G3hdOng+9QTdUQCcRFudgNGlPuekbN02yb/fY/l40Z0FAB7gbgH07bffEkKkUunRo0cjIyPd3d1zc3OnTJmyZcuW5r+9JaNGjWp8zWKxEhMTExMf6r5SuyDfuMui1vi/2cqF0gCgqbY9AaOFRauTb8rg942Qb9zttxCjlwAc3d0CaNOmTYSQJ5544tKlSw1Xnm/cuDFv3jzaojmw+sOn1H8WiD9/k8GmbaYiAGfS5idg9qfcmu3Wv6fv7KfLX/tEV3yNHxH+4O8BAEIIIYabZWZlvVvfCHt2av2o/a1bt7p169bwumPHjg3TGEJT+pIb8p93BC595b7LvANAK2zatGnTpk1cLvfSpUsFBQXHjh0rKSmRy+V053pYpsqa+pxc74QJDB7XK3GibF06+dsr/1jq1JrT59okHoBDs1hqvv+l5t9pFrXWnt1aF0AxMTHTp0/fvn17enr6008/PWjQIHumcXxmRV31N+v8kmZwQoLozgLgbNrwBMxiseTn52dnZ+/Zs+fs2bMWi6WNMt6fbEO6x1OjWd6ehBD3uEFMLqf+8Km/uU/5xt3VX6812uuRYAC61O0/weTzBIMiFduy7dmv9VNgq1evXrVq1a+//spkMh9//HHcAmuKMluqv1nnPnqI24DedGcBcEINJ2AzZsygKGrTpk2tPgHLyclJSkoSi8USiYQQIpVKS0tL165dGxcX16Z5/6K/fF1zpsii0eoKLzW0mFX18l93uz/+GGEwWrdPw80yzZkiz6eelKVtD1z6StuFtUaZzQz6Fh0CsGh1iq3ZgUvns/x8pK99KhoZwwkVP/jb2sLdAmjhwoWffPLJe++9RwhpWMi9pKTkzTff/O677+wTxfHJ09KZAr7XtDF0BwFwTm11ArZo0aLMzMzw8LujcEpLS6dPn94w1PpeN27ckMn+ZxlqlUrFYrHy8vIa3kZERDRMbKZQKK5du9a4WUM7JyyYv3jW9dLSxvZu8cNEnh6Ewbjv9s3tp2l77apNbgN69Rs3TL3sX9r8i/pOwa3bT8vtoQqtcc+h4H8uVdbX2WL/aEf7A9tVew526dON2zmMEELihxxa/q3PnGfu3b6oqMhkMpE2dbcAGj16NJ/Pb5yKHqyoj+dp8y+Kv3ir1ad0ANCctj0BM5vNDTtpFBgYSDU/KOejjz46f/5805YbN26UlpbOn//X6jcff/zxuHHjCCGZmZmpqamNmzW0M934B26WWLdHRzW3/QPbLfUas0LFyQ/6uEPg8FlTZBvScwZ2/PZf/3rU/TygnaIWBXYbHdGnLvtwpqqi9ftBO9pb204ZjcbyquX/+a5hpfTDRuVX239hHc5kCvhW23/xxRcqlYq0KYbVQaG4uPj48eMvv/zysmXLDh069Pnnn9+7Spd9REdH+/v7Z2Rk0NJ7I4tayxS6GSurK9/9JnDZq9xOWO8CXFRZWVlMTExpk+scbWjXrl3x8fEHDhywah8/fnwr9vbDDz989913kydPDgkJYTAYUql09+7dycnJjQXNA9ni+GO4fpsdHMjk81rejDIYy1M+9Ut+vnF++Tuf/eDWt4fHhDaeFUmxOcNUVev5zLjKpV8Hp77T3Go/ALaj2Jyh2Pa7VaMwpp//Gy9bNdri+GM9Bui1116bPXv2qVOnjh079vnnn3/22WfZ2XYdlORQ6vYdU6b/Hvz10uqv13olTED1A2AjkydPJoSEh4dbnYC1bm8LFiyYOHFidnZ2wzDqsLCwrKys0NDQtkz8iMzKusoPv3MfOdjnxWktb6k5U2SqllV9saaxhTKZjBVVbVsAmWrkdXuPib/6B9vPWzj8McVvmQ+5/g9AG/JKmOCVMIGu3q0LoMrKymefffbtt99+/vnnhw4dev36dVpiOQKLWqPYkskRB9z5+HuOJEj0hD0WpwVwZW14AhYaGupQz3Aoft0jiO6rPnJa9MRQjiSwhS2FQ/rfOxsKg8Np2zzytO2i8cPZft6EEK9nxpW/9on7tdu88LC27QXAkVk/Bt+9e/c33nhj48aNEyZMSE1N9fBw3Yuiit+yBNH9hHGDDDfLPKc+SXccAOfXcAK2fft2JzsBM9wo0+Sd93lpmufUJ2VrHzy1I1MosPqPwW3LAkhXfE19ssCiqpf/skv+yy5l+l62j5f8551t2AWA47MugFauXCmRSHbu3BkUFFRbW9swPbQLMpZVqo+dcX8yVvFbpnDUEOVW170PCGA3znoCJlu/zStxAlPgJho73CRXafPOP/h7bInlIfKeMYnl48l0FzT8Jxgc5T4Ms76Ba7G+Bebv7z9y5MiCgoLIyMj58+fTe9ecRrK07Z7T4uUb0j0mjPQYN7w85VPdxSuNYxIBwBZWrlz5888/O9kJmPp4nkWrE40YTAhhsJg+L02Xrd4cHBnB4Fgffu2GExyAVZwBrP8P3LBhw4oVK7Ra7fTp04cNG7Zs2bI5c+yx3ue0adMKCgqatpSXl/fuTc98g9rCYm3BJYuyzqRQcYL8Zeu3Md0F8rQd4i/eoiUPgItwyhMwxbbfzTJl2cKPG1tMVbXqP/Pd43DFBYBO1gVQamrqyZMnZ86c6e7uXlBQMHDgQPsUQGvWrFEoFE1bpk6dGhjY0lBB2+GGij2nxtdlHfKYFs8SCggh3M5hLE8RLWEAXAddJ2A2FfTBIkqvt2pk+XjSEgYAGlkXQGq1ms/nN7wWCASctn70oDk+Pj4+Pj5NW3g8HoOmKQeZIqH29DnfeQnCYQNpCQDgmug6AbMplpeIEJw+ATgc60HQM2bMmDRp0o0bN9atWzd69OgZM2bQEoteis0Z7CA/VD8AdkbXCRgAuCDrK0AfffRRZmbmsWPHysvLly9fTtc00DTSX75ef/hU8NdL6Q4C4HIaTsAqKirWrVu3YcMG1zwBAwD7uM9jCOPHj2+cfr6urk4kcqGLt5TBWLPyF9/5CRjxA2B/OAEDALu5WwCdPXt26dKlMpls3LhxCxcuXLp06e3btwsLCysrK2nMZ2eK3zK5ncMEgyLpDgLgolz5BAwA7OnuGKCXXnopJiYmNTVVKpVGRkZKJJKlS5eePHmSxnB2ZrhRVn/4lM9LD1ipBwDa3NmzZ+Pj4wcNGvTBBx/U1NTMnTs3Pj6+a1fMvAUAtnL3CpBcLv/www8JIT169Ni1a1fDa9dBmS01P/zi8+JU3PwCsL+XXnppypQpo0ePTktLi4yMnDdv3owZMzp27Eh3LgBwWncLIBaL1fDC19dXKBTSlIc2yh372L7ewqF48guABi5+AgYA9mf9GLxrMlZWqzJzfGZPpzsIgIty8RMwALC/u1eAbt261TjeUK1WN76uq6ujIZd9ydb85jUtnh3gS3cQAAAAsIe7BZBWq6Uxh33If9kpHDKA2/l/FhhSHz1jVtSJxj5OVyoAcOUTMACgxd0CiMfj0ZjDDnTnS1TZR3QXr4o/e4P8/yIbFo1W/stO/zdeZvz/FXgAsL82PwHLy8srKysbPny4p+dfq25lZGRMmDChbXsBgPbLhmOALBZLfn5+dnb2nj17zp49a7FYbNfXw6SRrU/3XziTUKT+yOnGZvmve9wGRvK6daIxGgDwmtG6va1YsWLq1KlbtmyJiorKz89vaJw7d27b5QWAdu8+M0G3iZycnKSkJLFYLJFICCFSqbS0tHTt2rVxcXE26rFldX8cZ4qEgsFRLB/P6q/XCqL7Mvk8w22p5mS+JHUZLZEAwEZWrlxZWFjo6+tbVFQ0bdq0vLw8TKgIAFZsVQAtWrQoMzMzPDy8saW0tHT69Om5ubk26rEFFrVGsTU78L1XCSG8bp34vbqqdv7hlTBB9uNWr4QJTBEeOQFwKiKRqOFRsj59+iQlJaWkpKxdu5buUADgWGxVAJnNZrFY3LQlMDCQoigbddcyxZYsbkcJMZsN128TQgQx/au/Xc8UCS06nWhkDC2RAMB2EhMTY2Nj58+fP2/evJSUlKlTpyYkJGg0mua2P3r0qNWaPzKZDBeNAJybrQqg5OTkAQMGTJ48OSQkhMFgSKXS3bt3Jycn26i7lpnlKrNKXbNqc2MLWxyoTP894O1XCBMzIQE4m/fffz82Nra2tpYQwmAwtm3btmXLFm9v7+a237dv3+XLl5u2yGSygIAAmwcFAPowbHdVprS0NDs7WyqVEkLEYvG4ceNCQ0Ob21gmkykUiqYtU6dOlUgkmZmZtsgm37THXKvwS37BFjsHcEplZWUxMTGlpaV0B7GH6Ohof3//jIwMuoMAACG2Of7Y6goQISQ0NHTevHkPufHcuXMLCgqatpSXl7PZNolnqlXU7zsu/uofttg5ADia4cOHHzp0iO4UAG3GVC1j+/vQnaLds2EB9EjS09OtWhrOwGzRl/ynHaLxj7P9mr0eDgDOxGw20x3BUVksZoWK5eNFdw54BOrjeTXf/yL51zKsXvA32aoAWrVq1eHDh+9t37Rpk416fBiU0WS4Uaq/fN1vwQwaYwCAPc2cOZPuCA5KmZGj2rVf8t37TIEb3VngoVB6g/znnW79e8p/3un/xst0x2nfbFUAvfDCC7m5uSqVasGCBTbq4lFRZrN0yQrKZPJKnMjgcemOAwB2gikQ78usqFPt+IPbJUyx7XefmVPojgMPRblrPy8i3O/VGeWvfaotLHbrG0F3onbMVgWQUChcvHjx5s2bR40aZaMuHlVd9hFKbzTJlIKBvenOAgBAM/mvu91HDfaYOEq6+DPR6CGc4EC6E8EDmGoVddlHxF++xeBwvF94SrYhPfifSxksPMvcSjb8xUVGRi5fvtx2+38klnqNcsc+Qii3yAhl+l664wAA0Mlwo0ybf9FzajzLU+Tx1BPytB10J4IHk/+0QzQ2rmH4szCmH8vDvf7gCbpDtWOOMgja1uQbd3NDxYTN8kt+Xrr4M3ec7gCAC5Ot3+adMKFh6I/HuOHS/Se0+Rfd+vWkOxc0y3CzTH3iLK97J33xtYYWs7JOsSnDfWQMFvNuHZcogIylFercAgaTGfhOUuPpTsDSV+jOBQBAA83pIt2la0wPd23Bxb+aWEz5xl0ogBwZRxwQ+J71ZMIMNgvVT6u5RAEkW7+NwWIxhQL1ibPqE2cpk1lz9oL23GW3yO50RwMAsDdeRCf/12c3bREQwvJ0pysPPAwGj4t/s9qWSxRAbtGRuss33B8fxHQXNLR4z5jE9sPUFwDgilgid2FMP7pTANDMJQogc5Xcffhg7+efojsIAAAAOATnf3zOrFDVHTjhOfVJuoMAAACAo3D+K0DK7fvchw9m++KGF9Bm27Ztn3/+Od0p7u+ll1569dVX6U4BAGBvTl4AmWVK9dEzwanv0h0EXNr58+cHDBjw8GsD282uXbvy8/PpTgEAQAMnL4AUW7PdRw9heYnoDgKuTiwWDxgwgO4U1goKCqRSKd0pAABo4MxjgEw1cvXJfM9JjrIWBwAAADgIZy6AlDv2iZ6IZYqEdAcBAAAAx2KrAshsNje+zs/P37hx4+XLl23REWU2V33xX2NltXUAuVJ94qzH+BG26BQAAADaNVsVQEFBQQ0vUlNTJ0yY8Pvvv8fHx//0009t3lFd9mHdpWvytO1/vacoo/QOabj8MzKG5YnRPwCubvXq1XRHAACHY/NB0F9//fWZM2fEYnF1dXVsbOzMmTPvu9nRo0crKyubtshkMpHoAeWLWVWv3L4v6OOU6n/+qC245BbVo+7ACdn69KBPX68/fFryLR7+AnBFWVlZ1dV3rwp//PHHPB6PEDJr1iz6QgGAY7F5AeTn5ycWixtesJpfs23fvn1W98hkMllAQEDLO1ds2iN8/DFuWLD3C1NkG9KDPklRbM4U9O9V8+169xHRLG/PNvkRAKB9uXDhwrJlyxISEnx8fAghOp2uoKCAoAACgCZsdQuMw+H06tVr7NixKpUqLS2Noqg5c+YMHTq0ue0/+eSTLf+rS5cu3t7eLXRhuFmmOV3kOfmJyg/+xRH7s/28q79eJxjUx+fFacaKKn6Prjb4sQBsxXSnpjp1PbFYWvG9ubm5DAYjIyOjseWTTz5hMBh6vb7hrVqtXrVqVdsEbQ+WLFly8ODBGzduTJgwITU1VSwWp6ampqam0p0LAByIra4ASaVSrVZ769atmzdvBgUFURTVuXPn119/vQ27kK1L5/cIl6WlG26WVX+7gdczvD7riO+cZ+oOnHDr2VWxLUvwWB/CYLRhjwC2I0vbrisqqTvwp+iJ2FZ8u5+f37Zt2yZMmNDwNiMjo/EOclFR0bfffqvRaF555ZU2i+vwYmNjMzIyFi5cmJGR0VgINmfjxo1lZWVNW6RSqZubmy0DAgDNbPgYvJubW0RExJgxY6KiophM5rvvvtuGBxTKbGb7eVFGk+bPAl7XjqaqGvWR05wOwZTRVLfvqM/cZ5l8Xn3OybbqDsCmdEUlhlvSwPeTFb9lWtTaVuyhf//++fn5RqOREHL9+nV/f3+h8K8JIDZt2qRQKNoybjvh4eGRlpYWHR0dHh7e8pZarVb+v7y8vDp16mSfnGBT8l92mWrkdKcAR9ReZ4JmsFh+i2bVfP+zx6SR3s9NUmbmyNenM0PF1d+sI4TI1m4xq+rlmzLcRwzGRSBwHJTeYFaorFstlto1mz2fGs3ycOf37ir7abvX1HjrbZhMtp93Cx9mJpM5YsSIgwcPxsfHb9++fdq0aXl5eQ1fWr58+YEDB3788ce2/Enaj4SEhISEhJa3mTNnjlWLxWLx9/e3WSiwE01uoSrjoKm61n/xbLqzgMNprwUQIcRw/ba2sFjy7/cIIaJRQzTHzrIkQUZpldcz49jiAEIIk8dB9QMORZWZU3fgT6tGi0ZL6Q3KnfuVO/dTFspSK9MWFjPueWLAf+FMXkTnFnY+ffr0tLS0+Pj4Xbt27dy585133mnj9O3W8OHDDx06RHcKsDfKZJb/stP/jZdl69N1F6/we2JgKPyPdlsAUVTtj1v5vbtqCy41NAgG9lak/87r2lEUP4zeaADN8Zwa7/m/V3cs9ZqyhR95PzeJExbc0FJ/8E+LRhv4TtKj7nzIkCFJSUm3b9/m8Xi+vr5tk9gpNJ2XFVyHavcBTliwYFAkZTDJ1qUHf/kWYTrz4gfwqNprAURZKI4kkNLpNSfO/n8TxWCzhSNiaM0F8GhMNXJepxDt2fPas+cbGxlcLrFYHvVgzWQy4+LiXnvttalTp7Z1zPatuenHwImZFXWqPQeDlr9BCBHG9q/be6Qu56Ro1BC6c4EDaa8FEIPF9Hv1+aYt2vyLRmmV6PFBdEUCaAVuR0ng+wvbam/Tp08fNWrUypUr22qHzmHu3Ll0RwB7k2/cxe/dldJqDddvE0KEcYMUm/YIY/oxBXi4D/7SXguge6n2HPCYPBqDfsAFRUdHZ2dnE0JGjBhh+f+ZhCoqKho3GDVq1KhRo+gJB2B/FGW6U2vR6WtWbW5sY/l4G29XtDyQDlyKkxRAhttSQ9mdgNgBdAcBAAC6MRhBH79GdwhwdE4yIky164DHuMcZ7GaX2gAAAABo5AwFkFmm1Jwpwug2AAAAeEjOUACp9h5xjxvEFAnpDgIAAADtQ7svgCijqf7An6KxcXQHAQAAgHaj3RdA6mNnuJ1DOcGBdAcBAACAdqPdF0Cq7CMeYx+nOwUAAAC0JzZ8DN5isRQWFlZWVppMJolE0rAmfNt2obt4ldJq3aJ6tO1uAdrc8ePHv/jiC7pTWDt9+rSXlxfdKQAAaGCrAignJycpKUksFkskEkKIVCotLS1du3ZtXFxbDtapyz4sGjcckx+Cgxs7dqxOp5PL5XQHsdalS5eRI0fSnQIAgAa2KoAWLVqUmZkZHh7e2FJaWjp9+vTc3Nz7bv/NN99cvny5acvNmzd5PF4LXZjlSm3RZd8FM9okMIDtREdHR0dH050CAADuslUBZDabxWJx05bAwECKoprbvmvXru7u7k1bzp8/36tXrxa6YLjxA96Yw3Tj/82oAAAA4GpsVQAlJycPGDBg8uTJISEhDAZDKpXu3r07OTm5ue0nTpxo1XL16lV/f/8WumDyefw+3domLgAAALgSWxVACxYsmDhxYnZ2tlQqJYSEhYVlZWWFhobaqDsAAACAh2fDp8BCQ0PnzZtnu/0DAAAAtI5DrwZ/9erV/fv3N/dVk8l04sQJq5FDNJLL5d7e3nSn+IujhfHy8mI4xsN6KpVKKBSyWA6xbq5Go2GxWC0P9rcbg8EgkUhauExbU1Njzzy0w/Gn1RwtDI4/94XjD6OFgcn02rx589q1a1vYoLq6+uLFiyKRyG6RWkBRlFKpdJwpVRzqAKRUKt3d3R3k//n6+noej8fhcOgOQgghGo2GyWTy+Q4xkF+v1/P5/H79+rWwTefOnf/73//aLRKNcPz5O3D8aQ6OP82h5/hDtVtHjx4dOnQo3Sn+IpVKxWIx3SnuEggEarWa7hR/6d27d1FREd0p/jJ58uSdO3fSneIvixcv/uabb+hO8ZfvvvsuOTmZ7hTtBo4/LcDxpzk4/jSHluNPu18KAwAAAOBRoQACAAAAl4MCCAAAAFwOCiAAAABwOe24AOLz+Q4yfJ0QwuVyBQIB3SnuEolEDvLUA3Gwv5SjhXFzc6M7xV/c3Nwc5zfj+Bzqg4TjTwsc6i/laGFc/PjjuI/BPwy9Xu8gcxgQhGkewjTHaDQymUwH+afCYrGYzWYHeUC3XXCozxLCNAdhmoPjT/sugAAAAABaoR3fAgMAAABoHRRAAAAA4HJQAAEAAIDLQQEEAAAALgcFEAAAALgcFEAAAADgctprAfTDDz/07NkzMjIyJyeHrgyffPJJz549Q0NDV6xY4Tipvvrqq6ysLNrz7N27d9CgQR06dFi9ejXtYd5+++1OnTqFh4f/8ssvNIZRq9WrVq1qfHtvBnumsgrjmB9mh+UIvxzH/JPh+HMvHH8eGIa2D7OdV59vE7dv346IiFCr1VeuXOnevbvZbLZ/hv379/fv31+r1VZXV4eFheXl5TlCqmPHjnG53A0bNlC0/pbkcnnXrl2rq6uVSmX37t2VSiWNYQ4fPhwVFaXT6SoqKjw9Pevr62kJc+7cudmzZyckJDS8vTeDPVNZhXHMD7PDcoRfjmP+yXD8uReOPw8MQ+OHuV1eAcrOzp48ebJAIOjSpUtQUFBhYaH9M9TW1s6fP5/P5/v5+Q0dOrS0tJT2VCqVaunSpQkJCQ1vacyza9euSZMm+fn5eXh4FBUViUQiGsOwWCwul8vhcPh8PpvNJjT9ZjZt2qRQKBrf3pvBnqmswjjgh9mROcIvxwH/ZDj+3BeOPw8MQ+OHuV0WQBUVFSEhIQ2vQ0JCKisr7Z/hmWeemTdvHiGkqKjoxIkTw4cPpz3VwoULP/jgAx8fn4a3NOYpLS29efNmnz59wsLC/vnPfzIYDBrDxMbGduvWLTg4ODQ09MMPPxQKhbSEWb58+YIFCxrf3pvBnqmswjjgh9mROcIvxwH/ZDj+3BeOPw8MQ+OHuV0WQBaLhcFgNL41mUy0xKAoKjU1ddq0adu3b/f09KQ31W+//ebj4zNq1KjGFhrzaLXaK1euHDlypKCgYM2aNXl5eTSGOXjw4JUrVw4dOpSVlZWamiqVSh3h83NvBnpTOdSH2cE5yC/Hof5kOP40B8efh0HXh5lto/3aVHBw8O3btxteS6XS4OBg+2cwm81PP/20l5fXmTNnPDw8aE+1YcOGa9eu7d+/v6KiYuvWrXq9nsY8/v7+8fHx3t7ehJBhw4YVFxfTGCYrK+v555+PiIiIiIgYPHjwkSNHHOHzc28GGlM52ofZwTnCL8fR/mQ4/jQHx58HovPDbKOxRTZ169at3r176/X60tLSLl260DLc79dff3322WcdLRVFUSkpKQ2DEGnMc+nSpV69etXW1lZVVYWFhV28eJHGMKtXrx43blzjCLv8/Hy6wuzfv79x3N+9GeycqmkYh/0wOyZH+OU47J8Mxx8rOP48MAyNH+Z2eQUoLCzs1VdfHTp0KCHkxx9/ZDJpuJF39OjR7OxssVjc8HbNmjUTJkygPVVTNP6WIiIi5s2bFx0dTVHUO++806NHD0IIXWFmz56dn5/fs2dPQsiSJUuioqJoDNPo3r8OjX8vx/8wOxQcfx4Gjj8NcPx5IBo/zAyKomy0awAAAADHhBM7AAAAcDkogAAAAMDloAACAAAAl4MCCAAAAFwOCiAAAABwOSiAAAAAwOWgAAIAAACXgwIIAAAAXA4KIAAAAHA5KIAAAADA5aAAAgAAAJeDAggAAABcDgogAAAAcDkogAAAAMDloAACAAAAl4MCCAAAAFwOCiAAAABwOSiAAAAAwOWgAAIAAACXgwIIAAAAXA4KIJFd+eoAACAASURBVOd0/fr1p556atasWf/973/pzgIAAOBwGBRF0Z0B2t5bb72VmJjYr1+/uLi4I0eO0B0HAADAseAKkHP66KOPoqKiysrK/Pz8bNrR4cOHp0yZYs9vfCRKpbK5L8nl8ps3b9o6AAAAOCYUQM7Jzc1t27ZtycnJP/zwA91Z2sw//vGP0NDQ4ODgN998s6Fl586dERER4eHh//73v+/dXqVSxcXFNby+detWTExMRERE45bLli3T6/X2SQ4AAI4GBZBzyszMPHny5I4dO4KCgh7yW4xG431ft7yl3ezbt+/gwYMlJSUXL17MyMjYu3evTCZbuHDhvn378vPzf/jhhwsXLjTdfvny5dHR0SqVquHtihUrPvzwwwsXLqSlpalUqpKSEkJI9+7d7f+DAACAI0AB5AzeeOONbt26nTp1ihCybdu2Hj16pKenX758+ZlnnnnhhResNv7uu+86d+7ctWvXN954w2KxHD9+fMaMGYmJiR9//HHT14SQzz77LDw8vHPnzm+99ZbVlvdmsNrtmDFjdu7c2fClQYMGHTlyxGqDR/0Z3d3dP/nkEzc3Ny8vr969e2s0mszMzOHDh4eFhXl4eCQmJm7durXp9vHx8cuXL2986+npefHixdLSUp1Ox+FwPvvss3ffffdRMwAAgPOgwCmkp6fHx8drNJoOHTpkZ2c3t9mhQ4f69u1bVVVVX1+fkJDw6aefHjt2zNPTs7i4mKKopq9///337t27K5VKnU43dOjQn376qelXm+7wqaeeune369evf+GFFyiKunbtWufOnXNycqw2aPjGprsaO3Zsh3ucO3fO6sccN27chAkTdDpdQxHT0L5u3bo5c+ZY/bBlZWUdO3ZseK1UKufPnz969Oh9+/adPn166dKlrfs9AwCAc2DTXYBB25gyZcrbb7+dlJQUGRk5ZsyY5jY7cOCAUql85plnCCHV1dV1dXXDhw+PiopqvBnU+PrgwYPTp0/38PAghCQmJh48eLBz585Nt2x5txs3bnz77beNRuNvv/02a9asgwcPWm0wdOhQq51kZWU98MeMiYmxWCyffvppTk6O1WWklm/MeXh4rFq1quH1008/vWbNmj179qxcubJPnz6ffvopj8d7YNcAAOBMUAA5CQaDMWfOnPfee89qKIwVd3f3+fPnv/3224QQnU5nMBiKiopEIlHjBo2vKYpiMBgNr1ksltlsbvrVB+7Ww8Nj8ODBOTk5W7Zs2b59+9atW602yM/Pt9pJfHz8veGzsrIiIyMJIQ3jmWJiYqZPn3779u1du3ZFR0cfOnSoYbOysrLQ0NCH+D2Rffv2PfbYY2az+cMPP9yxY8ePP/64cuXK119//WG+FwAAnAbGADmP/Px8kUjUch3w5JNPpqWlVVdX6/X6adOmHT9+vLktR44cmZ6eXl9fbzAYNm7cOHLkyEfdbUJCwooVK7y9vTt16vQw/e7du7fsHg3VDyGkvLz83XffValUKpUqKyurR48e48eP379/f3V1tU6n++2336ZMmUJR1OXLl00mU3M5KYpauXLlwoUL1Wq1t7d3aGhonz59amtrW/jRAADAKaEAchLHjx8/fPjwkCFDtm3b1sJmUVFRKSkpMTEx4eHh4eHhY8eObW7LMWPGPPfcc/369evRo8djjz02c+bMR93txIkTT506NWvWrEfqtzkLFiyIjIyMjIzs1atXRETEggUL/P39U1NTn3jiiYEDB86ZM6d///4GgyEiIqK8vLy5nWzZsmXSpEl8Pj8sLGzo0KG9evX67rvvFi1a9KhhAACgvcNM0M7AYrE89thjycnJHTt2fPPNN0+ePMlm4+YmAABAs/DPpDPYsGEDg8GYNWsWg8Ho0aNHp06djhw50qlTJ7pzAQAAOChcAQIAAACXgzFAAAAA4HJQAAEAAIDLsVUB1DBtTIP8/PyNGzdevnzZRn0BAAAAPBJbFUCNa3CmpqZOmDDh999/j4+P/+mnn2zUHQAAAMDDs9UgaH9//+rqakJISEjI6dOnxWJxdXV1bGxswyrcAAAAADSy+RggPz8/sVjc8ILFYtm6OwAAAIAHstU8QBwOp1evXmFhYSqVKi0tbebMmXPmzLl3/csW7N+/f+vWrTaKBwCt0KFDh3feeYfuFPaA4w+Ao2nz448N5wHSarW3bt26efNmUFBQZGTkihUrXn/9dTc3t/tuPG3atIKCgqYtFRUVISEhb775po3iAcAjkcvl33//fWlpKd1B7OGtt966fv36k08+SXcQACDENscfR5kIUS6Xy+Xypi1Tp06VSCSZmZl0RQJwKZfqz8kMNbE+za56W1ZWFhMT4zoFkL+//5IlS+gOAgCE2Ob44yhLYXh7e3t7ezdt4fF4DAaDrjwArkaqK63US2NJswUQAIAzsVUBtGrVqsOHD9/bvmnTJhv1CAAAAPCQbFUAvfDCC7m5uSqVasGCBTbqAgD+vuL6ojv6CkLIVXWx0qQ4XLuPECJie/T3HEx3NAAAG7JVASQUChcvXrx58+ZRo0bZqAsA+Psq9dJb2muEkFpjtdasuam9Sgjx4wbQnQsAwLZsOAYoMjIyMjLSdvsHgL9vuG98w4sDNZmVeukMyVx68wAA2AcWQwUAAACX4yhPgQEAvTzYXlqzhu4UAAB2ggIIAAghZJBXLN0RAADsB7fAAAAAwOWgAAIAAACXgwIIAMARSXWla26n0p0CwGmhAAIAcET15rpaQzXdKQCcFgogAAAAcDl4CgwAwIGcr8vPU/5JCFGa5Hf0FWllPxBCeEz+NPHzHAaX7nQAzgMFEACAA/Fge3V060IIuWOoqNCWNbxmMpgswqI7GoBTQQEEAOBAwtw6hbl1IoSUqC9eV19+3PdJuhMBOCeMAQIAAACXgwIIAJzc6tWr6Y7QGt4c3+7uvelOAeC0cAsMAJxNVlZWdfXdB8g//vhjHo9HCJk1axZ9oR6ZPzdwStBzdKcAcFoogADA2Vy4cGHZsmUJCQk+Pj6EEJ1OV1BQQJovgGbPnn3u3LmmLdeuXYuOjl6yZIkd0gIALVAAAYCzWbJkyZAhQ5YuXTpz5sxRo0bt378/NbWlKZWXLVsml8ubtrSva0UA0AoogADACcXGxmZkZCxcuDAjI0Ov17e8cefOna1ahEIhm43DI4AzwyBoAHBOHh4eaWlp0dHR4eHhdGcBAIeDAggAnFlCQkJ2djbdKQDA4aAAAgAAAJeDAggAAABcDgogAAAAoJOZMhssD3hYoc2hAAIAAAA6HZMd2F650c6dogACAAAAOpkoo5ky27lTFEAAAADgcjDTF4Crq9JXXNeUXNdc6e7ea4BnDN1xAMBVbJFuKK4vIoSoTEoTZbymLiaEBPKC53d4ww69owACcDlas+a6puS6puSapuS6poTH5HUWdOss6NbBzXpCZAAA2xntP+Ex72GEkNOKYzWG6rEBUwghbkw3+/SOAgjAJVTpK65qiq+qi6+qi6sNdzq4hXcRdo/zeeKlkFc9Od50pwMAV+TD8fPh+BFCrqmLDRZDRze7TtqOAgjAOVkoy23djSv1F0vUF6+qi5kMZldhjy7CiDifJ8LcOjEZLLoDAgDQCQUQgPMwUaYbmisl6gsl9Revaop9OH5dhT37e0YnBM/25frTnQ4A4P66u/cW80Pt3CkKIID2zUyZr2tKiuuLLqsvXFeXBPEl3YW9RviNmStY7M4W0Z0OAODBQvgd7N8pCiCA9sdCWW5pr12qL7pUf+6a+nIQXxIh7P2k38SuHXq6sQR0pwMAaAdsWABZLJbCwsLKykqTySSRSKKiophMTDsE0HoV+rKLdYWX6s9drr/gxfHp6d53lN+4pA5LBCwh3dEAANoZWxVAOTk5SUlJYrFYIpEQQqRSaWlp6dq1a+Pi4mzUI4BTqjfVXawvvFBXcKGugMFg9HTvO9AzdmZIkgfbi+5oAADtmK0KoEWLFmVmZoaH332krbS0dPr06bm5uTbqEcBpWCjLNc3l83X55+vyK/Xl3YW9e4n6jg2YEsST0B0NAMBJ2KoAMpvNYrG4aUtgYCBFUTbqDsAJKI3yorqzRXVnL9YV+nID+oj6PyN+sYswgoVH1p2CiTKxGRh2CeAobPV/Y3Jy8oABAyZPnhwSEsJgMKRS6e7du5OTk23UHUA7RRHquqbknCrvnCqvxnCnlygqUjRghmQu7nD9TXl5eWVlZcOHD/f09GxoycjImDBhAo2Rfrj55fiAaeHC7jRmAIBGtiqAFixYMHHixOzsbKlUSggJCwvLysoKDW32KX+9Xq/RaJq2mEwmG2UDoJ3Ooj1fl1+oOnNOdcaT7R3pMTBR8nIXQQSTgQcF2sCKFStWrVo1dOjQlJSU7du39+vXjxAyd+7ciooKGlMZKL3eoqMxAAA0ZcPrsaGhofPmzXvIjSdPnnzq1KmmLXV1dTYIBdAGNGb1Junal0MXPeo3yow1BcpT+apT1zUl4YLuUR6DJgcm+HEDbBHSla1cubKwsNDX17eoqGjatGl5eXkiUUtTIn399dclJSVNW27evMnlcm0cEwDo5Cg3pH///XerlujoaH9/zF0LjqhGX3VacfzhC6Ay3a18ZW6+MrfGWN3XY+Bw3/jkjm/zmHybhnRlIpFIKBQSQvr06ZOUlJSSkrJ27doWtu/evbtVhbRv3z4Oh/P3k5go47rS78yUmRBSpru1586Ww7J9hJAoj0Ex3sP//v4BoNVsVQCtWrXq8OHD97Zv2rTJRj0C2I2e0pmpB9yipQh1VV18VnkyX5lrIdQAz8EJktldBD1wk8sOEhMTY2Nj58+fP2/evJSUlKlTpyYkJFjdZG/q3rFBa9euFQjaYEpJNoMT4z1cZ9YSQir0Zd2EvULcOhBCOgm6/P2dA8DfYasC6IUXXsjNzVWpVAsWLLBRFwB2VqEvy1eeIoTUGKoslCWrajshhMPgDPMdzWe6NWxjoczF9efPKP/MV+WKWJ4DPAe/2vHtULeONMZ2Qe+//35sbGxtbS0hhMFgbNu2bcuWLd7e9Cx630fUv+HFYdm+7u69eor60hIDAKzYqgASCoWLFy/evHnzqFGjbNQFgJ3d0FzNV54khOgsWkJIw2uKMCJFAzk87qX6c6cVx/NVp/y5gQM8Y5Z2WRHADaI5sQtreuRhsViJiYmJiYk05gEAR2PDMUCRkZGRkZG22z+AnXUWdFV4DiaE1BiqKnRl/TwHU5Sl1li9p2rLOdWZQH7wQM8hkwKfxbrrrZCUlHTf9v/85z92TmI7/T2iA3nBdKcAgL/cpwAqKCgoKCiYMWNGZWVlCw+uA7iaIJ5kXMBUQsjl+gtHZPuqDZVnlScDeOJBnrFTxTN8OH50B2zH6J2hxz5G+o2jOwIA3GVdAG3YsGHFihVarXb69OnDhg1btmzZnDlzaEkG4GgoQl1Xl+QqjpyUH6UIFcgLfr/r17je0ybGjx9v1WKxWJYtW3ZvOwBAm7B+ICU1NfXkyZN9+/Z1d3cvKCj4/PPPaYkF4FDKdbfTK375x6X568u+F7E93+j8Yaz3yDH+T6H6aVtffvllcHCwm5tbx44d3d3dCwsL6U4EAE7L+gqQWq3m8/+ankQgELTJTBgA7ZTcWJurOPqn/JDaVP+Y97CFHd9pfJ5rtmAhrdGc0/fff3/p0qV33nlnwYIFKpVq/fr1dCcCAKdlXQDNmDFj0qRJFRUV69at27Bhw4wZM2iJBUAjnUWbp/jzT/mh29obA7xiZkjmdhX2ZBAG3bmcH4/H8/Dw6NevX25u7uzZs1NSUuhOBABOy7oA+uijjzIzM48dO1ZeXr58+fKhQ4fSEgvA/iyU5WJ94Qn5oXOqM93de4/wG9vXYyCbgYug9hMXFzd79uylS5c+/fTTd+7caVzH1DlkVG17zDM2gCemOwgAEHJvAVRbWzt48ODBgwc3vvX19bV7KgC7kupKj8sP/ik/7MPxG+I9/LngOe7slpaOAhv58ccfS0pKunXr9s033+Tl5TnZLbDi+qLObl1RAAE4COsCaMyYMQ0vlErllStX+vbtW1BQYPdUAPagMatzFUePyQ4ojLIY7+FLwj8W80LoDuXSZDKZn59fbW1tVFRUVFQU3XEAwJlZF0CnT59ufF1UVDRz5kz75gGwOYpQl+qLjtb+UVR3treo31NBib3co7BEV43hTr2priOta1Q53wkYRag/5YeNFgMhRG6sPafKqzbcIYR0FfYI5mOWNQA6tTQTdJ8+fYxGo92iANiazFhzTHbgmOyAkCUa5jPq+ZD5QpY73aEcRaHqTKVeSm8B1B5PwDZL1z0VlNi4GJwVM2W+qblipIyEEJ1FW6kv11M6QoiI7YkCyD6uqos7CbqyGCy6g4DDsS6AFi9e3Pi6uLi4e/fu9s0D0PbMlLlAdepI7R83NFeiveOSOy4Nc+tEdyh4gPZyAnZWeXK03wQ+9/4FEJvBfk4yt+H1P69/8KT/JCyGamdpZT+80uFNCT+M7iBtSWvWuLEEdKdo96wLoKaPfY0fP37EiBH2zQPQlqr0FUdkfxyX5Yj5kjifJ5I7vc1hcOkO5ViMlMFgMRBCDBa9kTKqzfWEEDaDzWPy7R8GJ2AAD6Qwyr68tmx5xA90B2n37hZAubm5hJCQkP8ZBHrmzJno6Gh7hwL4v/buPCyq6+4D+Jl92Hdk2FRAFjUUt4qCRqMJabTaKEatvqYaxWgk2mapqFETo6aNLXniq7VaU33aNzYmoS4sEcENYtSIiAQ3VAR0GJhhZ5j1zn3/GF/kRUGLzJw7M9/PX3eOlztfZi7H350595xnY2SNxc3nT9cfu6etGus98fcRmwOwCGU3/nHvr8XN5wkhBlbPsqSo6SwhJEgaujpii/XD2MoFmN6kP990xsSaCCFak+Z8Y4H5tsGhbsN6mBzcU+jlLMRXrvCsWgzNDQYV7RT24GEB9NlnnxFC5HJ5QUFBbGysq6vr+fPnX3311YMHD9KLB/CfUeprT9fnft9wQiYNnuCTNNwjXsjraaAbLApJJSGphJB8VZZCJ5/3f9/XWJltXYBpTe0V7eUsYQkhRpPxnu6u1OBECAmUhvRQAC0OxbyOVtJibHrvaoqB1ZsffnDjbfPG68HLn/d5iV6uvqFjtQzL0E5hDx7+33DgwAFCyIsvvnjt2jXzJ88VFRUpKSnUogE8NRNrutJadFKVc1dza6zXxN9HfBwgCaIdCv4DtnUB5i70XBC8zLxd1np5ZsB/+Yr9CSE6k/Zs48mxXhz94MpxuAs9/xr74Mz54Mbb9jEGqKTl4mHFAUKI1qQ1scxHN98hhAj4wmX93/MW+dJOZ5O6XhxXVlZGRkaatwcMGCCXy60eCeA/0GJsPtNw/HT9MU+R90SfX2CUT6/xeQKKy33YxwVYvV6ZXZeBAggsQcKXBDn1J4S0GJtrdXLzNiFE2OPd3NCDri/cmDFjkpOT582bx7LsgQMHRo0aRSUWwBPdUl8/UZ9d2nJppOfY1AFrcGPXM4r3HK836ehm6MMLMJPJVFJSolAojEZjUFBQXFwcn2+RqZ68Rb5UBozDU7KbVfy8RD7mmVpFPDGPEPO2kCeUCHD69VLXAmj37t27du368ssv+Xz+888/b3NXYGD3zONP81XZepN2os8v5gctdRa40A5lD5wEztRvrO2rC7CTJ08uW7ZMJpMFBQURQuRyeXV19d69e8ePH9+neQkhZHXEFp1Je1dzmxCi1NUaWL15W8QT2cHXLnZgUUiqzC6+EO8nCXzFfwYhpFx9raDhuHkbnsXDAig1NXXTpk0ffPABIUQmkxFCbt68+e67727fvp1aOoBOVPq6k/U5hQ354c5Rs2QLBrv9zG6u7cCsry7A3n777aysrPDw8I6W6urq5ORk81DrR+Xm5t69e7dzi1wub2lp2b17t/nhlClTzLXUrVu3Tpw40bGbuf1yy48Hftx351wVwzKtTMs14ZuRE8I8AtzfHrBGWVn/6P7dHQftaH+adleBu/4+s/vcbo7ksU77t99+q9VqSZ96WABNnjxZKpV2TEUPwB3X20rzVFk328oSvF9YN+hTP3E/2omgj/XtBRjDMOaDdOjXrx/Lst3tX1RU1KUAamtrMxgMRUVF5oexsbHmjriioqKjsaN9tOe4FqL95t437UxbTWtdjGdYsmhh/KB4QsiFiouP7t/dcdCO9qdp9xR5+TUGcyePddpLS0v7fGZUXpdO4fr1699///0bb7yxbt26U6dOffLJJ51n5rCm0aNH+/n5ZWZmUnl24AIDqz/XeOa4MtNEmMm+U8Z4TcBgC4ru3bs3ZsyY6upqSxz88OHDSUlJ+fn5XdqnTJnSi6Pt3Llz+/bt06dPDw4O5vF4crn8yJEjK1asWLp06VMeoXf9j1xbvbPyjx9H2cyn5hqmvVx9LdZ9BO0gAE9gif6n66jAlStXurq6XrhwobCw8JNPPtm8eXMfPhnAU2oyNGQo/ue9qymXms/NCVy4KerzCT4vo/qxY9OnT5dKpeHh4QqFYsqUKT/88MPWrVs9PDx6d7Tly5fn5uaGhYWpVCqlUhkaGpqdnf301U+v8XkCPrGlNaeqtRXZdRm0UwDQ0XUQtEKhmD179urVq+fPn5+YmHjnzh0qscCe/OH22jdCVponSnmiu+23clVHS1uKxng9vyZiq79E9uSfAXuxcuXKRYsWdb4Ay8nJ6d2hQkJCrH8PR4Ak8P3wj6z8pADQO10LoKioqHfeeefgwYM//vhjenq6u7s7lVhgT9qMrTrTEwavsYQtbj6fqzxSb1BO9p36X0FLqd+RBNZnBxdgrkIb6DP1Jn2zsZEQ0mRoNLB6pb6WECLhS9yFnrSjAVhP1wJox44d//jHPw4dOhQQEFBfX2+enQzAcnQmbWFD/nFVppvA/SW/aSM84vk8W/oSAfoQLsCs41T9d/mqLEKIgdWrjeptt9cTQoQ80frIbfb3RfOeqvRZstc9Rd60gwDndC2A/Pz8XnjhhcuXL8fGxi5dujQkJIRKLLB1aqbtf+7vYVgjIaTBoPpK/nfzJzrPe7802O1n5n2aDY15qqzTDblRLkMWh6yMcImmmRg4ABdg1vGS37SX/KYRQm6qyzJqvlwdYc9jPas0FWqmDQUQPKprAbRv376tW7dqNJrk5ORx48atW7du8eLFVJKBTZPynUZ4xJuXy76tvjHELc68Wo2/JIAQcl9b9Z3y0OXmC/Fez3+A29rh/+ACDACspmsBlJ6efu7cuQULFri6ul6+fHnkyJEogKAXBDzBCI8x5u0jtV8NdRtmnhX3WtuVf9z7a5W2YpLvlE9idrkIXKnGBG7BBZiV8Qifz7PD2UR1Jm12XYb5E+gWY1Ou8oib0J0QMtIzYYBT+JN+GhxF1wJIrVZLpQ++A3Z2dhaJRFaPBHbIxJouNBV+V/dvPatP8pue6pUm5OHUgq5wAWZl4c6Ri0NX0U5hEU58JxNhCSE8wncSODsLXAnBzPHw/3QtgObNmzdt2rSampovvvhi37598+bNoxIL7IbepPMR+X1esdlH7D89YG6s+wisXwHdwQWYlfF5AvN303ZGwpe+7P+qefuHxlPjvCdjXTZ4VNcC6MMPP8zKyiosLLx///6WLVtoTQMNdqDN2HqiPvuEKmeQS8ybA94Nd46inQi4DhdgAGA1XQsgQsiUKVM6pp9vbW11c3OzbiSweQ0G1THl4bMNJ0d4jFkdsSVAEkg7EdgGXIBBnwtzjnQV4H8xeIyHBdClS5fS0tIaGhpeeeWV1NTUtLS0qqqqkpIShUJBMR/YFrm2Okf575LmHxN9Jm+K+tx862m9Xukt9sU3X/A0cAEGfWthyAraEYCjHq4FtnDhwjFjxqSnp8vlcvMSrGlpaefOnaMYDmzI7fYb2+9u/fT2+n6SwE9idr3Waeaxz+9uVmjv040HHHfp0qWkpKRRo0Zt2LBBpVItWbIkKSlp0KBBtHMBgN16+AlQY2Pjxo0bCSExMTGHDx82bzs4rUlzt/1WtOtztINwWlnr5ay6b1X62pf9frU09B0xX9xlBxNrMhETlWxgKxYuXPjqq69Onjx5//79sbGxKSkp8+bNGzBgAO1cAGC3HhZAAsGD9Qd8fHxcXFwo5eGWKk3FIcWB1REogB6DJeyl5nNZdd8aTPpf+M+I9xyHJSyg13ABBgBW9phB0H2CYZiOiqq4uPjq1asjR46MisJ9QPaAYZnzTWey6r6V8p2m+s8a5vHzR8f3tBibtt5aY2IZQkiDoT79zocCnpAQMq3f7ATvFyiEBm7DBRgAWNnDAqiysrJjvKFare7Ybm1t7cVxAwIClEolISQ9PX3btm0vvPDC2rVrP/roowULFjxzZotjWEZr0hBCtCYNwzJqpo0Qwid8rE9uZA0FDfk5df/2E/ebF5Qy2DW2uz3dhZ7vhn1o/ubrz3c+nBe0pJ8kkBDiKfKyXlwAAIBuPCyANBqNJZ7gT3/608WLF2UymVKpTEhI6K4AqqysVKlUnVva29tNJjoDR/JUmZm1XxNCGMIYGP3qa28SQqQCp4+jttvfUslPSWfSnqo/lqs80t8p/M3+74Q5Rz7xR3zEfuYNIU/oJfLBgl/Qg769ACOEFBUV3bt3b8KECR4eHuaWzMzMqVOnPntUALAPDwsgiURiiSfw9fWVyWTmjY5PuR+1fv36srKyzi0VFRUdc8JaWZLf9CS/6YSQm+qrGTX/XB2xhUoMjlAzbSdU2fmqrBjX2FVhH4RIB9BOBHaoby/Atm7dumvXrsTExFWrVmVkZAwbNowQsmTJkpqamj58FgCwaZYaAyQSiYYMGRIaGtrS0rJ///4FCxYsXry4h2nN9u/f36Vl9OjRfn5+FooHT6PV2JKrPHK6/tgwj9FpEVv79XY+w1cDfu0nDujbbGBn+vYCbMeOHSUlJT4+PqWlpTNnziwqKsJ8QgDQhaUKILlcrtFoKisr7969GxAQlH6CjQAAFNxJREFUwLJsWFjY7373Ows9nYXwCI9nj0slP1GLsTlXefh0fe7P3EeuG/RHf4nsWY423CO+r4IBPA03NzfzSOrnnntu2bJlq1at2rt3L+1QAMAtliqACCFOTk7R0dHR0dHmh2vXrrXcc1lImHPk68Fv0U5hVSp9XU5dxoWmwkTvSR9HbffAmGWwQXPnzk1ISFi6dGlKSsqqVatmzJgxZ86c9vb27vavq6tra2vr3KLVammNQQQA67BgAWQHBDyB46xjVauTZ9V9e7n5wgSfpC3RO92E7rQTAfTS+vXrExIS6uvrCSE8Hu+bb745ePCgl1e31XxqaurFixc7t9y/f18s7jqlJwDYExRAQO5rq7LqvrnaWjLJd8onMbucBZiFBWzepEmTOrYFAsHcuXPnzp3b3c5fffVVlxaMQQSweyiAHFqV5s7R2q9vqa+/5DdtQfAyKd+JdiKAvjdhwoRTp07RTgEA3IICyEHdab95tPZglabiZf9fLQldJeZbZBIEAC5gGIZ2BADgHBRADuemuuxo7de1Ovkr/jPeGvB7IU9EOxGAZdnEBPQAYGUogBxIWevlo7VfNxsbp/jPHOM1QYC1S8ExLFmyhHYEAOAcFED2jyXslZaio7UHtSbNVP/kn3uO4/P4tEMBAADQhALInrGEvdR87mjt14SwU/vNGuEx5tFl2wEAABwQCiD7xBK2pOXiIcUBQtgp/skjPcei9AEAAOiAAsjeGFnjD42nsusyPIRes2QLhrjF0U4EAADAOSiA7IfepC9oOP6d8pBMErwwZEWky2DaiQAAADgKBZA90Jm0J1U5x1RHwpwjl/d/f6DzINqJAAAAOA0FkG1TM235qqwTquwY19h3wz4MkobSTgQAAGADUADZqmZjY67ySEF93jCP0WsiPvGXyGgngm5duXIlJyeHdorHS0hISExMpJ0CABxanV6hNrZa+esLFEC2R6Wvzan794WmwjFez2+MSvcW+dJOBE+QkZGRl5fHwTrjypUr5eXlHAwGAA6ltKVIoZOjAIJu3ddWZdd9W9pyaYJP0uboHe5CD9qJ4GlNnjx548aNtFN0tXfv3h9++IF2CgAAClAA2YZb6uvZdRkV7eUv+k2dH7TUSeBMOxEAAMAz0TDtbUwrIaSNadUw7Up9LSHEWeDiInC1wrOjAOI0lrClLUXZdRlNhoaX/X+1bMC7Ip6YdigAAIA+8HXN/rLWy4QQDdPOEKZcfZUQ4i+RvRO20QrP7lgFUFHzD5EuQ9yE7rSDPBnDMuebCr6r+zefJ/iF/6ujPBKwgBcAANiTBcHLzBv5qiyFTj4vyKrrFjtWAXSy/jsnvvNgt5/RDtITrUlzuj43T5XZTxI4O3AhpnIGAADoc45VAHFco6E+T5VZUJ83xC0udUBaqFMY7UQA9mD37t0pKSm0UwBAt/g8gfUXrHSIAkihu681aQkhGpOmVid3FroSQvqJZdwZSlylqchVHilp+XGs18T1kX/yFfvTTgRgw7Kzs5VKZcfDjz76SCKREEJef/11eqEAoFtjvSYYTHorP6n9F0BG1rC36nOGNRJCFHr5ceVRaYMTIWSs98TJvlPpZjOPcc5VHqnR3Z/sO/XXQYudBS50IwFFbcZWV6Eb7RT2oKysbN26dXPmzPH29iaEaLXay5cvExRAAFwl4UslfKmVn9T+CyAhT7R20B/M29vubHjFbwYXxgDpTNqzjaeOK4/yeXwhT/jHmN0CnoB2KKBJpa9Nr9i0Oeq/e/Gz58+fj4+PP3r06NSpD2r6TZs2rV+/XqvVSiSSTZs2HThwoLW1dfny5WlpaX2amqPee++9sWPHpqWlLViwYNKkSXl5eenp6bRDAQC32H8BxDUqfd0JVXZh44kolyELQ1awxJRR8yWqH2BYxsSaev3jvr6+33zzTUcBlJmZ6ebmRgjJz88/dOjQpUuX2traRowYkZSUNHz48L5JzG0JCQmZmZmpqamZmZk6na7nnV9++eULFy50bmltbR0xYoQlAwIAZY5VAAmIgE+p1GAJe631yon67Jvqa4nekzYM+pOP2I8QclNdRiUP2Jnhw4cXFxcbDAaRSHTnzh0/P7+qqipCSH19/dKlS6VSqVQqTUxMrK6udpACiBDi7u6+f//+f/3rX9evX+95z0OHDmk0ms4tGzduDA4OtmQ6AKDMsQqgN0LfdrP68hEapv1s48kT9TkCIpzk+8qS0N9K+NILTYUHa/YRQtqYZrn23l8qPyWECHiC3wSvEPMx1aHdOqY8fL7xTOcWNdPWYmgihLCENbCGZVdmE0JYHuknDhDw/t+f5/zgpWHOkd0dmc/nT5w48cSJE0lJSRkZGTNnziwqKiKEvPbaa+YdSktLz549u3Pnzj7/pThuzpw5c+bM6Xkfc4HYucU8aBoA7JhjFUDuQk9rPt1dze3T9ccuNp0d6jbs9eBlkS5DOv5pgHOE+Za/Gl11k755pMdYQohU4CTii6yZEKws0XtSlOvQzi0Gk15pqCOENBsac5WHZwX+hhAi4gl9xf063xTKJ/xgaWjPB09OTt6/f39SUtLhw4cPHTq0Zs0aczvLsp999tlf/vKXjIwMDw9HXD9uwoQJp06dop0CALjFsQqgE6rsn7mPMn/3ZDlak+Z8Y8Hphtw2Y8t4n5c2R//3o4WXvzjAXxxACLmpLrvaWjrKM8GikYAjXASuLk5d17gZRGIIIbU6+Zn63LGez/f64GPHjl22bFlVVZVEIvHx8TE3Mgwza9YsT0/PixcvurvbwBzolsAwDO0IAMA5jlUAXWo5HyAJslwBdFt940zD8UvN56Jch74a8OuhbsOsP7MTOCw+nz9+/PiVK1fOmDGjo/HgwYNisfiLL76gGIy6BQsW0I4AAJzjWAWQhTQZGs42nvq+4QRL2HHekx/7kU93/MQBwz1GWzQe2AQvke9U/+RnPEhycvKkSZN27NjR0VJQUJCTkyOTycwP9+zZ03GnmONYssSqCwwBgE1wiAKotPWSltEQQlqMTTfaytRMGyFkoHOEr7jfsxxWZ9IWN58/23iqor18pOfYhSErIlyi/9ODeIl8XvKb9iwxwD6I+eIE7xd697OjR4/OyckhhEycONFkenAvfU1NDSFk586dDjjwGQDgieyhAFIzbS6CruMqOhhZww+NpxiWIYS0GJtvqssU+vvm9t4VQAzLlLUWn2squNJyMcIlJtF7UurANBEPt24BAADYDHsogDaVv/f78I+9RD6P/VchT5QS+jvz9rPMBM2wzLW2Kz82fV/cckEmCRrtOX5u4BtuQgcdVQoAAGDT7KEAMrGM+QMeS9CbdD+1Fl9qPlfSclEmDR7lkfCrgLndFVsAAABgE+yhAHp6T7/cWr1eWdp6qaTlx5vqq2HOkcPcRyfLFniKvC2dEAAAAKzAVguger1yR+UfCMsSQpoMDZ9XbBbyhISQJP9faRnN8z4vPfanlvd/v4dVt3Qm7Y22n8raSspaL7cZW4a6DRvrNTEl9HdOAmcL/RYAAABAha0WQN5i398Ev2UiJkLI5xWbZ8jme4q8+YTHsmRPdXp3BdCj1Y+Gab/Vfv1GW9lNdVm15m6Yc2SMW+zikJX9ncMxhQ8AAIC9smABZDKZSkpKFAqF0WgMCgqKi4vj8/l9dXAe4YU6DTRvC3nCYGl/X7E/IUSure75B/Um/T3t3UrNnbvtt26332jQqwY6D4p0HTwjYF6YcxTW4QILaWxsvHPnDu0UXalUKtoRAADosFQBdPLkyWXLlslksqCgIEKIXC6vrq7eu3fv+PHjLfF0Gqb9ruY2IUSpqzWwevM2n/A8RT4qfW2tTq7QyWu09+5pKxsMSpkkpL9T2ADniEm+U4KlobTWhwfHERERsWHDhszMTNpBHuPNN9+kHQEAgAJLFUBvv/12VlZWeHh4R0t1dXVycvL58+cfu39hYaF53rYODQ0NPS9dZGQNJS0XTawpynVovirrp5ZLJmIysoZ2pv2Pt9YxrJEhRie+i58koJ9Y5i+RjfJMmC6dEyAJ6mEYEIAlzJ8/f/78+bRTAADAQ5YqgBiG6Zh936xfv34sy3a3f05OTnl5eecWjUbj6trt9IaEELWx7cem71nCSvlOPB7v517jJAKp0WQ823hycehKT6G3j9hPzJc84y8CAAAA9sdSBdCKFStGjBgxffr04OBgHo8nl8uPHDmyYsWK7vbfvHlzl5b333/fz6+nVUs9RF5v9n+3S6NcW13ccj7GNbbXyQEAAMDuWaoAWr58+S9/+cucnBy5XE4ICQ0Nzc7ODgkJsdDTdRDyhOb74QEAAAC6Y8FaISQkJCUlxXLHfyx/iez34V0/TAIAAADojNMfljQ0NPR85/DNmzednJyslqdnarXaxcWFdooHEKY77e3tTk5OPB4nJnnS6XQCgUAo5MSfIcMwHh4eXl5e3e2gUCismYc69D+9hjDdQf/THSr9Dyd+88caOHDgtm3bDh482N0O7e3ttbW1HHnzWJZlGIYjYQghBoNBKBRy5M/MaDQKBALuhOHz+X04JdWzYBiGx+NxJIzJZBKLxV3uXegiNtZRRteh/3kW6H+6g/6nO3T6H9ZmFRQUJCYm0k7xgFwul8lktFM85OzsrFaraad4YOjQoaWlpbRTPDB9+vRDhw7RTvHAb3/72z//+c+0Uzywffv2FStW0E5hM9D/9AD9T3fQ/3SHSv/DidIPAAAAwJpQAAEAAIDDQQEEAAAADgcFEAAAADgcGy6AhEKhQMCVVb04FYYQIhKJODK2nxDCnTstCcJ0j2vnMMdx6uXiVBiC/qd7CNMdKucwj+1+fS6OM5lMCoUiMDCQdpAH7t+/HxQURDvFA1wLExgYyJHbUGtra318fDjyZ9/Y2CgWizkySUl7e7tWq/X29qYdxDag/+kB18Kg/3ks9D82XAABAAAA9A5XPqUEAAAAsBoUQAAAAOBwUAABAACAw0EBBAAAAA4HBRAAAAA4HBRAAAAA4HBstQDauXPn4MGDY2NjT548SSvDpk2bBg8eHBISsnXrVu6k+vTTT7Ozs6nnOXbs2KhRo/r37797927qYVavXj1w4MDw8PB//vOfFMOo1epdu3Z1PHw0gzVTdQnDzZOZs7jw4nDzLUP/8yj0P08MQ+1ktvLq832iqqoqOjparVaXl5dHRUUxDGP9DHl5ecOHD9doNEqlMjQ0tKioiAupCgsLxWLxvn37WKqvUmNj46BBg5RKZXNzc1RUVHNzM8Uwp0+fjouL02q1NTU1Hh4ebW1tVMJcuXJl0aJFc+bMMT98NIM1U3UJw82TmbO48OJw8y1D//Mo9D9PDEPxZLbJT4BycnKmT5/u7OwcEREREBBQUlJi/Qz19fVLly6VSqW+vr6JiYnV1dXUU7W0tKSlpc2ZM8f8kGKew4cPT5s2zdfX193dvbS01M3NjWIYgUAgFotFIpFUKjVPwEolzIEDB5qamjoePprBmqm6hOHgycxlXHhxOPiWof95LPQ/TwxD8WS2yQKopqYmODjYvB0cHKxQKKyf4bXXXktJSSGElJaWnj17dsKECdRTpaambtiwoWMqcYp5qqur7969+9xzz4WGhm7bto3H41EMk5CQEBkZGRgYGBISsnHjRhcXFyphtmzZsnz58o6Hj2awZqouYTh4MnMZF14cDr5l6H8eC/3PE8NQPJltsgAymUydF3YxGo1UYrAsm56ePnPmzIyMDA8PD7qpvvrqK29v70mTJnW0UMyj0WjKy8vPnDlz+fLlPXv2FBUVUQxz4sSJ8vLyU6dOZWdnp6eny+VyLpw/j2agm4pTJzPHceTF4dRbhv6nO+h/ngatk5kTS7L9pwIDA6uqqszbcrmcynqEDMPMmjXL09Pz4sWL7u7u1FPt27fv9u3beXl5NTU1X3/9tU6no5jHz88vKSnJy8uLEDJu3Ljr169TDJOdnT1//vzo6Ojo6Oj4+PgzZ85w4fx5NAPFVFw7mTmOCy8O194y9D/dQf/zRDRPZguNLbKoysrKoUOH6nS66urqiIgIKsP9vvzyy9mzZ3MtFcuyq1atMg9CpJjn2rVrQ4YMqa+vr6urCw0NvXr1KsUwu3fvfuWVVzpG2BUXF9MKk5eX1zHu79EMVk7VOQxnT2Zu4sKLw9m3DP1PF+h/nhiG4slsk58AhYaGvvXWW4mJiYSQv/3tb3w+hS/yCgoKcnJyZDKZ+eGePXumTp1KPVVnFF+l6OjolJSU0aNHsyy7Zs2amJgYQgitMIsWLSouLh48eDAh5L333ouLi6MYpsOj7w7F94v7JzOnoP95Guh/zND/PBHFk5nHsqyFDg0AAADATbiwAwAAAIeDAggAAAAcDgogAAAAcDgogAAAAMDhoAACAAAAh4MCCAAAABwOCiAAAABwOCiAAAAAwOGgAAIAAACHgwIIAAAAHA4KIAAAAHA4KIAAAADA4aAAAgAAAIeDAggAAAAcDgogAAAAcDgogAAAAMDhoAACAAAAhyOkHQBs3owZM4qLi7VabVNTU0BAACFk7ty5ra2t27dvpx0NAOwc+h/oNR7LsrQzgD04ffr0xx9/fPz4cUKIVqvVarWenp60QwGAQ0D/A72Ar8Cg7+Xn53/wwQf5+fmTJ09+7rnnQkJCPv3002nTpsXExGzatMm8z8aNGyMiIuLi4vbt20c1LADYFfQ/8JTwFRhY0I0bN65evXrt2rX4+Piffvqpf//+4eHha9asOXbsWGFh4ZUrV3Q6XXx8fFxcXFxcHO2wAGBX0P9Az1AAgQVNnDjRzc0tKioqMjJy8ODBhBAvLy+j0VhQUKBQKGbPnk0I0Wg0xcXF6IAAoG+h/4GeoQACCxIIBOYNkUjUud3Jyemdd95ZuHAhIaStrU0ikVAIBwB2Df0P9AxjgICCiRMn/v3vf9fr9c3NzUOHDlUoFLQTAYCjQP8DZvgECCgYN27ciy++GBsbazAY1q5dGxISQjsRADgK9D9ghtvgAQAAwOHgKzAAAABwOCiAAAAAwOGgAAIAAACHgwIIAAAAHA4KIAAAAHA4KIAAAADA4aAAAgAAAIeDAggAAAAcDgogAAAAcDgogAAAAMDhoAACAAAAh4MCCAAAABwOCiAAAABwOCiAAAAAwOGgAAIAAACH8789C6vgIiDp6AAAAABJRU5ErkJggg==" /><!-- --></p> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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CDcYAGuN2OUAUAvbdK/s5Z5NFudUN1jAaCmNEwut66G6RbrHkoEAChPNQkGEImu/RggsgDMUJupitWAMscvMIBAtO3HAJFtAQ6dTACACrBrMIBIdO3HAJEEgINJK8mvyovdgD1lORhAoATtx/gpNhbBDqRYAJi8FQPfQK/aciVgQIjC7segtBx9T7VmEEuhvscAQLjl0IlvgvuM4jowIFjh9mNQW44+uIVQO+TgiAWAcMuhaeiDNg3IPWDAI9m6bLcKfSV4OXqLOKB1If5gQOiF+PGzeCnwoifkesY/T61Hcyjccmg6bsLGbEu1AMDf2qwLtyc4eDm6oTAg9RL+sIrQReHj51/CouIsU+j1jH8udkRzKNxyaHpGIbj8GwDgce/9X9TiJpgnpLgc/bML08FSoFi6QGGXQ9MzDPfa8ef4gD3DOBhw4PtpMiwTDRo8Xo6eoPgDACEKtxyaHgDQIi/3bmhjTKw2qioy3oAllPsJ6ZmLPwBAsgmmzQCnaeD2i//HwQYyKDPeALGdv2oVAQBJAyBsE0ybAe2Wth38yR9YZ5f2S9ijGuA8KUAt2wBA0gBIRhO85viMkKYKoW2hJOMNYGweBgBgTxN8o5Rt1LvklvRfIBTVAAbnYQAASk1w3JnhIulo2Tgx4y3eAwOYnIcBAIjUXa5pJbgcd2a4yDrbx/rBAMT0PAwAEKLBErtMfxJ6Pd7McFE1xLPVggFMz8MAAMHizSM03xh6Pd7McNFlFwhKFsEAhudhAIAgreAA1BPcBMebGS6qPotGr9cxGAUKaBwASFL8NZrvPL+/5CvBS0FT8RZ5QHNPE/araTUI7QjkExltwPGB6IOz8kAFACQNgDuFefmvtFiXbwJqPEjYrzZiQ+hqbkvly2QDlooMlVhR50UqAlC/kQYAoFXZ4WHuFVHXtAEX1tBAmwFXSotVMY0KhmdXMxcA/NUjB4W3Yw8Atab8dbMk5QHYxgYNZdNE96LlpzqtVqjV0meAc0QbWAdfveW4zlgA1L//MAuA18WPe+20fERy5P4v5QGY2gw8viZaC1RmrEAKRr6BWl3ylwAAcwAM8Ttz/MvxvcdG/gxCDbqUB0B924hJCD3w6LR6ZprgjRPHyExGAtCzvr4e+EPwUsFmYP/o68jxb9fM2rKp/k7mEYTO5uLci+HA71umZSkPQMltvdsIsxjOt8xUH/Riqq0zEwGo/i2CMQLlF/UxQqrI8f+e5Z3GqA6kfSjC/+O5pvg+WLleJ1VtpzwAlc9uPwwDeYGi6ZdampN5AIQXdoQW9eNRAPAs4OjYrBTf+jKQQ+V7nB/sxZxe2ehJ+fh3ubJOTrKa+1gwClG/JdyvAwDu9Q8eC+v7KADcvnvRIOWbgFnjYLwpVPTdCHGmUz/+Y4Vmcy/506VoHYbL/qrulQAAdxp0cZFkQDr38LVNOA/TbG3/pkzGETwbp2cIffyevvFPBgAIdbrc3QDAnSp68a8nn+X+R9vdTPzK40Iv5QJ1csYiFf1I8qUcAEiwzhr4szsAAKn4N5VqP54l7YOVqQ4UpiYAIOFqeubg5AAAdzJFiH+1SGo1VSXvk3VN3kgQAECD+sxC7RUAEJAxQvw35TgE/GwESjcAEOrwC2VXYMCjxYgh8S8O3B6k2y0TAPBbE36hdgIMiBT/Rf4F3hGfhzqelgDgCNiFlpdgQIT4cxf4/HdQxdMVAIScWw7pMRgQPv4zOWdQw9MYAITmrfya12dgQLLiDwAk3YAZn0ivGQcDAIAMBQCXQSz074EBaQfAhuFNPavjT2tmOGofYgs7LJwBANIKgNFmUQ3/ksXxpzkzHDW1+2pU5m0AII0AsNsR+iFncfxpzwxHUTsylalhHgBIFwAOKxByOlgcf/ozw1G+Id7cXdBVnAEAJNQ96/7A7kNTLUUIeY0sjj/tmeFikVMjwuzHAEA0Xaoaylh+bvYEL0uq+snm+AdlhstaCEggSnbg9rZ4jR8uAYCIKvrA/nOz670+J6vjH5SVoL4vIBcb+qA3dn7bVisAEF63eR8WMu5U8oTGn1xWgiRJ0i3j8WSGMQCAWD/5Fa/izfyT6QCQykqQTLUWuRzvvZ60BoAb603wpoj/Hs7Njiv+pLISJFm1pQdzhyeXaWkA7blZAYDICs5KwFID6l9b1PzK3qPVmTQzgP7crABAZAVlJWCxATulxp4egW4+vQxgLjcrAJDwmzCGdbbeqG3uOXeb+8fTyIDHuVl1ooDyRVCnAYAQHffUI6SY6iyqWXf5e3PSxoC/c7NeFASkgKEdAIAIALz7oxjA/9ZeVr7rUEo13dXpaQB0gQAAoi7QnF4jFUze/7h9bXHl/8/WsDiW6gaYAAAAgIxusPPzJ4+uNJVpqv5Zd1s+FVSnsAFGAAAAIKcrohPbxvM0uqXzpapn/TmpaUDUibDV70p3P1RzACCC2o9/2OWqBSNHUzGR8gYEx9+Ye9GPLUI9BwCi6ajzraVGNfdUN+L9Fjal6/FuM6cjlQDoUEkQatmHeg4AkFNf91ruwPC58pDzzNsaMmeQg621ylbiz/g67vuvgxkAjhz4fb69HOo5AEBFY9nv3toHFPmOFam5uH/voUHgBk6KKol763Etf+CLaoiZLtCUrntNBQlyAYCYtNr9cZ8j1587RAP+cp/hopSDX9THfTwBZwQhr4kZADy5bnEBVHMAID4Qbrwau9StFPQoa1zY219Dr+IaOq3JQ2gCYwYAEACQOEn63Zjbztld4Z8K20xS23TxZ8Me9VX1WjtCxVkAAACQcipUC1butloeXeb5NFuVu279gkA1bByolD3715v3LYfMitNRV7NbvQMAAACppkvseOy5OnhGbWP1Ms/7aUT2fsCkwPIFDv3TAzEnd7/0+a/Ogmwn0cGuY0N5dQgAAABSTRo//rAUeVqpvWNnaNJX/MRsqZoyuXiOU8ECb8ktF+ssuftWru/zm6HW7NU+CQIAUh4A9uQGZUrXUvz7HntF7R+1j2Z/K3tX+KP0iVnLke7KnzbzVAtWACDlAWBVblBm1KWW9X7dZXUTDAAwFv/g3KB17MkLRJtWZWJrNQAAAAQUnBv04C4znBtiypABHgkqeL4HACQr/sS5QWWw7oQhA7iNM/tqnZILACSrBQ7KDQoAMGuA0oP4o8hZAgCwqQsKADBmgMiJFF1oFAAAADLTgEm9fbZZNqy5/7mfG5D6EEKaVADMfNG9GgX5lERz8XM6i5+eUisufIjSkjLWUHc1TLdY/9wEj3ACKlmIJf7np3R8UgoxpFKUfJjPRdEVe/zDuFLwIK7+ORWVLlAq/vx0jVJx/gil4i4tpeLLVZSK6wb+hCmbBfHnmEgXnRKTLiryky6KPSFdtOeabEmrqoCE6NssYaB2vs3DAmGSyqd2vIOC2sbY3U1KxbVWSsVLbQmKsSkx8V97T76JHyFd9KuXdNFh8gmMBRtkS2bzktu7AgDoB8AIAAAAmQwAFwAAADIZAAQAAAAAAADASgBGfZSKz3Cpvf1baulJrqmdDrBJLVWcgRpel/9n71x/2rjSMH4iLrlnmxs4XFNC1wESIEAChNyaJZs0bdKskpQtyrXdQJRQZBKliulaY1vFbuxtE2KklWwsy1GNBYHIxlGFvf2AbPMlEusPCAnJSAik7odou3/DzowxuYiQM75fnp9k64znPfbM855nzjme8fhkYun/41fUoZ/Q/wntf+k1bKQ/mp2njqz8OwEAAAAAAAAAAAAAAAAAAIgG55vvUJ/uudK85qKwGsU7BMRf2sL9dJY6fK046wPq8Jq8pX2lqcGHC9/dKOovaGOoZRcgObXc9FLHTuV3s+9AzY9NY3SxOzsrO3aNCqlR9Esj/SecqutQNCmow9cfLvvueg1l+O0bucF9panBhwvf3SjqL2hjqGUXIDm13PRSx07lFchcS8hBytPmn7EHi4c/CKihaKhtpP+ExpuEXM2hDi9aN3ZqYw1l+NFrucF9panBhwve3WjqL2Rj6GUXIDm13PRSx07lFThXQMhu+v8nvC1WCKhRu7O6kf4TrlwrYTtD+rfvL/2+gDp8Z25wX6lqcOGCdzfK+lNvDL3sQiSnlpte6pipvALfcJ9cTxmcc7puVECNkWrCZoI6/tidM1+IN1OH77z/ZH/Wl7ThnNaBWKoafGoE7m509affGAGyC5CcXm56qWOm8grkHSOkgvLPeVoe3VAIqXG3rmRjeSZ1/OlC9uB1jzr8JqvZwxHacE7rQCxVDS5c6O5GVX8BGyNAdgGS08tNL3XMVF6BPSUnVudTzj5GcoXW4A5F1PFPLnzRsesJdXhmBjt7+pg2nNM6EEtVgwsPYXejp7+wjaGVXYDk9HLTSx0zlVc8BO3du54y9Pj10tLSeiE1uEzQx1fl5+fRh7ccF4sLqMP53jYQS1ODCw9hd6Onv7CNoZadXnJ6uemljp3KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABgZbL7avOgAkhbCkdJA1QAacuhnNWPoAJIX7b1fbdywPrPaV56nZ6OxcKZF9AXJDY7br7vr4Lfa4DR++Uf8YW6DRs2cP/rXNhPXtxvqiLk+GMIDBKbjzK29r++fHXx8WrpfQaoFN8u+5C7dWrOrcW1t/rJqkstnYrHx6EvSNzpb2clqVoVXKoS598cK3qY+4B7kJ+zxIX8UqC18yuHfiJky3q++IYBtm0lpKySLay+wy+f2lvVTwpPv7hwqPZLqAwSl9ojlfmrg8f0wx01uT8XXX9MuMelJkVZw+9ciTdAYOWv/SQ76yRf5A2w4z4La6Divs7S3Br2hf1rnpbeOERy60f6iWLVh1/97yg0BgnMJ9+eXhssPxBXVFzIKDpICPcoPEbI+VquxBsgsFJRevXiuUDxjR5ge3n214++YQv/fH712dC5e6vISGBQtfVU/VBhGXQGiUqT+Eyw+IeL7GBeUXSZNQD7KGSHPnn9XIk3QGAl6bt0ODtQfKMHyKsl5B73Qk8LIb8/+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/><!-- --></p> </div> <div id="background" class="section level1"> <h1>Background</h1> @@ -618,7 +623,7 @@ parameter estimators.</p> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0"> +<div id="refs" class="references csl-bib-body hanging-indent"> <div id="ref-bates1988" class="csl-entry"> Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience. |