notebooks/000_ESTIMATE_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#To-convert-immune-score-to-percentages-using-in-silico-simulation\" data-toc-modified-id=\"To-convert-immune-score-to-percentages-using-in-silico-simulation-1\">To convert immune score to percentages using in silico simulation</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To convert immune score to percentages using in silico simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "manifestpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Manifests/\"\n",
    "datapath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Data/\"\n",
    "plotpath <- \"/Users/anabbi/OneDrive - UHN/Documents/IPD2/Plots/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sims <- read.table(paste0(datapath, \"ESTIMATE/simulations/ESTIMATE_simulation_pure.txt\"),\n",
    "                  header = T, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 80 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Infiltration_percentage</th><th scope=col>StromalScore</th><th scope=col>ImmuneScore</th><th scope=col>ESTIMATEScore</th><th scope=col>simulation</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 4</td><td>-2649.727</td><td>-785.61380</td><td>-3435.34070</td><td>simulation_1</td></tr>\n",
       "\t<tr><td> 8</td><td>-2586.353</td><td> -83.17307</td><td>-2669.52646</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>12</td><td>-2535.314</td><td> 457.15530</td><td>-2078.15880</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>16</td><td>-2484.590</td><td> 884.67960</td><td>-1599.91020</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>20</td><td>-2436.727</td><td>1249.48200</td><td>-1187.24500</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>24</td><td>-2388.342</td><td>1552.98530</td><td> -835.35650</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>28</td><td>-2342.591</td><td>1822.99570</td><td> -519.59490</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>32</td><td>-2294.258</td><td>2061.46550</td><td> -232.79270</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>36</td><td>-2243.230</td><td>2265.15294</td><td>   21.92277</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>40</td><td>-2187.481</td><td>2451.91400</td><td>  264.43320</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>44</td><td>-2135.220</td><td>2618.03810</td><td>  482.81760</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>48</td><td>-2078.159</td><td>2773.95710</td><td>  695.79850</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>52</td><td>-2026.140</td><td>2915.46170</td><td>  889.32220</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>56</td><td>-1975.251</td><td>3045.21300</td><td> 1069.96200</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>60</td><td>-1921.179</td><td>3161.01100</td><td> 1239.83300</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>64</td><td>-1869.515</td><td>3268.09900</td><td> 1398.58400</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>68</td><td>-1812.557</td><td>3373.84500</td><td> 1561.28800</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>72</td><td>-1754.610</td><td>3466.21600</td><td> 1711.60700</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>76</td><td>-1695.013</td><td>3552.10200</td><td> 1857.09000</td><td>simulation_1</td></tr>\n",
       "\t<tr><td>80</td><td>-1621.880</td><td>3635.85700</td><td> 2013.97800</td><td>simulation_1</td></tr>\n",
       "\t<tr><td> 4</td><td>-2436.111</td><td>-508.95020</td><td>-2945.06160</td><td>simulation_2</td></tr>\n",
       "\t<tr><td> 8</td><td>-2354.957</td><td> 218.12560</td><td>-2136.83140</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>12</td><td>-2289.572</td><td> 748.56930</td><td>-1541.00230</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>16</td><td>-2233.501</td><td>1156.71800</td><td>-1076.78300</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>20</td><td>-2177.739</td><td>1489.48890</td><td> -688.25050</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>24</td><td>-2123.719</td><td>1771.51860</td><td> -352.20080</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>28</td><td>-2073.073</td><td>2016.27303</td><td>  -56.80018</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>32</td><td>-2028.317</td><td>2226.20220</td><td>  197.88540</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>36</td><td>-1978.302</td><td>2412.48780</td><td>  434.18610</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>40</td><td>-1929.954</td><td>2579.86460</td><td>  649.91020</td><td>simulation_2</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>44</td><td>-1848.456</td><td>2712.3038</td><td>  863.84810</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>48</td><td>-1804.206</td><td>2851.1040</td><td> 1046.89800</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>52</td><td>-1758.027</td><td>2978.9600</td><td> 1220.93300</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>56</td><td>-1716.495</td><td>3094.6540</td><td> 1378.16000</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>60</td><td>-1673.796</td><td>3203.2680</td><td> 1529.47200</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>64</td><td>-1626.953</td><td>3301.9350</td><td> 1674.98200</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>68</td><td>-1584.055</td><td>3395.7410</td><td> 1811.68700</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>72</td><td>-1543.596</td><td>3481.8130</td><td> 1938.21800</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>76</td><td>-1493.782</td><td>3562.1530</td><td> 2068.37100</td><td>simulation_3</td></tr>\n",
       "\t<tr><td>80</td><td>-1443.174</td><td>3642.7810</td><td> 2199.60700</td><td>simulation_3</td></tr>\n",
       "\t<tr><td> 4</td><td>-2254.544</td><td>-636.1168</td><td>-2890.66080</td><td>simulation_4</td></tr>\n",
       "\t<tr><td> 8</td><td>-2190.826</td><td> 110.3405</td><td>-2080.48540</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>12</td><td>-2141.064</td><td> 654.6227</td><td>-1486.44120</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>16</td><td>-2091.593</td><td>1081.7780</td><td>-1009.81500</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>20</td><td>-2037.878</td><td>1437.2718</td><td> -600.60620</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>24</td><td>-1989.993</td><td>1726.9429</td><td> -263.05050</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>28</td><td>-1938.092</td><td>1984.7355</td><td>   46.64332</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>32</td><td>-1897.247</td><td>2203.5404</td><td>  306.29360</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>36</td><td>-1845.898</td><td>2402.0230</td><td>  556.12500</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>40</td><td>-1794.460</td><td>2572.4606</td><td>  778.00080</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>44</td><td>-1744.340</td><td>2727.8890</td><td>  983.54900</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>48</td><td>-1702.636</td><td>2872.5980</td><td> 1169.96100</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>52</td><td>-1656.427</td><td>3004.6160</td><td> 1348.18900</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>56</td><td>-1615.384</td><td>3124.1340</td><td> 1508.74900</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>60</td><td>-1576.440</td><td>3233.3370</td><td> 1656.89800</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>64</td><td>-1533.068</td><td>3331.5170</td><td> 1798.44900</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>68</td><td>-1496.021</td><td>3425.1530</td><td> 1929.13200</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>72</td><td>-1457.100</td><td>3508.8120</td><td> 2051.71200</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>76</td><td>-1419.109</td><td>3588.0870</td><td> 2168.97700</td><td>simulation_4</td></tr>\n",
       "\t<tr><td>80</td><td>-1377.923</td><td>3664.9890</td><td> 2287.06500</td><td>simulation_4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 80 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Infiltration\\_percentage & StromalScore & ImmuneScore & ESTIMATEScore & simulation\\\\\n",
       " <int> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t  4 & -2649.727 & -785.61380 & -3435.34070 & simulation\\_1\\\\\n",
       "\t  8 & -2586.353 &  -83.17307 & -2669.52646 & simulation\\_1\\\\\n",
       "\t 12 & -2535.314 &  457.15530 & -2078.15880 & simulation\\_1\\\\\n",
       "\t 16 & -2484.590 &  884.67960 & -1599.91020 & simulation\\_1\\\\\n",
       "\t 20 & -2436.727 & 1249.48200 & -1187.24500 & simulation\\_1\\\\\n",
       "\t 24 & -2388.342 & 1552.98530 &  -835.35650 & simulation\\_1\\\\\n",
       "\t 28 & -2342.591 & 1822.99570 &  -519.59490 & simulation\\_1\\\\\n",
       "\t 32 & -2294.258 & 2061.46550 &  -232.79270 & simulation\\_1\\\\\n",
       "\t 36 & -2243.230 & 2265.15294 &    21.92277 & simulation\\_1\\\\\n",
       "\t 40 & -2187.481 & 2451.91400 &   264.43320 & simulation\\_1\\\\\n",
       "\t 44 & -2135.220 & 2618.03810 &   482.81760 & simulation\\_1\\\\\n",
       "\t 48 & -2078.159 & 2773.95710 &   695.79850 & simulation\\_1\\\\\n",
       "\t 52 & -2026.140 & 2915.46170 &   889.32220 & simulation\\_1\\\\\n",
       "\t 56 & -1975.251 & 3045.21300 &  1069.96200 & simulation\\_1\\\\\n",
       "\t 60 & -1921.179 & 3161.01100 &  1239.83300 & simulation\\_1\\\\\n",
       "\t 64 & -1869.515 & 3268.09900 &  1398.58400 & simulation\\_1\\\\\n",
       "\t 68 & -1812.557 & 3373.84500 &  1561.28800 & simulation\\_1\\\\\n",
       "\t 72 & -1754.610 & 3466.21600 &  1711.60700 & simulation\\_1\\\\\n",
       "\t 76 & -1695.013 & 3552.10200 &  1857.09000 & simulation\\_1\\\\\n",
       "\t 80 & -1621.880 & 3635.85700 &  2013.97800 & simulation\\_1\\\\\n",
       "\t  4 & -2436.111 & -508.95020 & -2945.06160 & simulation\\_2\\\\\n",
       "\t  8 & -2354.957 &  218.12560 & -2136.83140 & simulation\\_2\\\\\n",
       "\t 12 & -2289.572 &  748.56930 & -1541.00230 & simulation\\_2\\\\\n",
       "\t 16 & -2233.501 & 1156.71800 & -1076.78300 & simulation\\_2\\\\\n",
       "\t 20 & -2177.739 & 1489.48890 &  -688.25050 & simulation\\_2\\\\\n",
       "\t 24 & -2123.719 & 1771.51860 &  -352.20080 & simulation\\_2\\\\\n",
       "\t 28 & -2073.073 & 2016.27303 &   -56.80018 & simulation\\_2\\\\\n",
       "\t 32 & -2028.317 & 2226.20220 &   197.88540 & simulation\\_2\\\\\n",
       "\t 36 & -1978.302 & 2412.48780 &   434.18610 & simulation\\_2\\\\\n",
       "\t 40 & -1929.954 & 2579.86460 &   649.91020 & simulation\\_2\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 44 & -1848.456 & 2712.3038 &   863.84810 & simulation\\_3\\\\\n",
       "\t 48 & -1804.206 & 2851.1040 &  1046.89800 & simulation\\_3\\\\\n",
       "\t 52 & -1758.027 & 2978.9600 &  1220.93300 & simulation\\_3\\\\\n",
       "\t 56 & -1716.495 & 3094.6540 &  1378.16000 & simulation\\_3\\\\\n",
       "\t 60 & -1673.796 & 3203.2680 &  1529.47200 & simulation\\_3\\\\\n",
       "\t 64 & -1626.953 & 3301.9350 &  1674.98200 & simulation\\_3\\\\\n",
       "\t 68 & -1584.055 & 3395.7410 &  1811.68700 & simulation\\_3\\\\\n",
       "\t 72 & -1543.596 & 3481.8130 &  1938.21800 & simulation\\_3\\\\\n",
       "\t 76 & -1493.782 & 3562.1530 &  2068.37100 & simulation\\_3\\\\\n",
       "\t 80 & -1443.174 & 3642.7810 &  2199.60700 & simulation\\_3\\\\\n",
       "\t  4 & -2254.544 & -636.1168 & -2890.66080 & simulation\\_4\\\\\n",
       "\t  8 & -2190.826 &  110.3405 & -2080.48540 & simulation\\_4\\\\\n",
       "\t 12 & -2141.064 &  654.6227 & -1486.44120 & simulation\\_4\\\\\n",
       "\t 16 & -2091.593 & 1081.7780 & -1009.81500 & simulation\\_4\\\\\n",
       "\t 20 & -2037.878 & 1437.2718 &  -600.60620 & simulation\\_4\\\\\n",
       "\t 24 & -1989.993 & 1726.9429 &  -263.05050 & simulation\\_4\\\\\n",
       "\t 28 & -1938.092 & 1984.7355 &    46.64332 & simulation\\_4\\\\\n",
       "\t 32 & -1897.247 & 2203.5404 &   306.29360 & simulation\\_4\\\\\n",
       "\t 36 & -1845.898 & 2402.0230 &   556.12500 & simulation\\_4\\\\\n",
       "\t 40 & -1794.460 & 2572.4606 &   778.00080 & simulation\\_4\\\\\n",
       "\t 44 & -1744.340 & 2727.8890 &   983.54900 & simulation\\_4\\\\\n",
       "\t 48 & -1702.636 & 2872.5980 &  1169.96100 & simulation\\_4\\\\\n",
       "\t 52 & -1656.427 & 3004.6160 &  1348.18900 & simulation\\_4\\\\\n",
       "\t 56 & -1615.384 & 3124.1340 &  1508.74900 & simulation\\_4\\\\\n",
       "\t 60 & -1576.440 & 3233.3370 &  1656.89800 & simulation\\_4\\\\\n",
       "\t 64 & -1533.068 & 3331.5170 &  1798.44900 & simulation\\_4\\\\\n",
       "\t 68 & -1496.021 & 3425.1530 &  1929.13200 & simulation\\_4\\\\\n",
       "\t 72 & -1457.100 & 3508.8120 &  2051.71200 & simulation\\_4\\\\\n",
       "\t 76 & -1419.109 & 3588.0870 &  2168.97700 & simulation\\_4\\\\\n",
       "\t 80 & -1377.923 & 3664.9890 &  2287.06500 & simulation\\_4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 80 × 5\n",
       "\n",
       "| Infiltration_percentage &lt;int&gt; | StromalScore &lt;dbl&gt; | ImmuneScore &lt;dbl&gt; | ESTIMATEScore &lt;dbl&gt; | simulation &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "|  4 | -2649.727 | -785.61380 | -3435.34070 | simulation_1 |\n",
       "|  8 | -2586.353 |  -83.17307 | -2669.52646 | simulation_1 |\n",
       "| 12 | -2535.314 |  457.15530 | -2078.15880 | simulation_1 |\n",
       "| 16 | -2484.590 |  884.67960 | -1599.91020 | simulation_1 |\n",
       "| 20 | -2436.727 | 1249.48200 | -1187.24500 | simulation_1 |\n",
       "| 24 | -2388.342 | 1552.98530 |  -835.35650 | simulation_1 |\n",
       "| 28 | -2342.591 | 1822.99570 |  -519.59490 | simulation_1 |\n",
       "| 32 | -2294.258 | 2061.46550 |  -232.79270 | simulation_1 |\n",
       "| 36 | -2243.230 | 2265.15294 |    21.92277 | simulation_1 |\n",
       "| 40 | -2187.481 | 2451.91400 |   264.43320 | simulation_1 |\n",
       "| 44 | -2135.220 | 2618.03810 |   482.81760 | simulation_1 |\n",
       "| 48 | -2078.159 | 2773.95710 |   695.79850 | simulation_1 |\n",
       "| 52 | -2026.140 | 2915.46170 |   889.32220 | simulation_1 |\n",
       "| 56 | -1975.251 | 3045.21300 |  1069.96200 | simulation_1 |\n",
       "| 60 | -1921.179 | 3161.01100 |  1239.83300 | simulation_1 |\n",
       "| 64 | -1869.515 | 3268.09900 |  1398.58400 | simulation_1 |\n",
       "| 68 | -1812.557 | 3373.84500 |  1561.28800 | simulation_1 |\n",
       "| 72 | -1754.610 | 3466.21600 |  1711.60700 | simulation_1 |\n",
       "| 76 | -1695.013 | 3552.10200 |  1857.09000 | simulation_1 |\n",
       "| 80 | -1621.880 | 3635.85700 |  2013.97800 | simulation_1 |\n",
       "|  4 | -2436.111 | -508.95020 | -2945.06160 | simulation_2 |\n",
       "|  8 | -2354.957 |  218.12560 | -2136.83140 | simulation_2 |\n",
       "| 12 | -2289.572 |  748.56930 | -1541.00230 | simulation_2 |\n",
       "| 16 | -2233.501 | 1156.71800 | -1076.78300 | simulation_2 |\n",
       "| 20 | -2177.739 | 1489.48890 |  -688.25050 | simulation_2 |\n",
       "| 24 | -2123.719 | 1771.51860 |  -352.20080 | simulation_2 |\n",
       "| 28 | -2073.073 | 2016.27303 |   -56.80018 | simulation_2 |\n",
       "| 32 | -2028.317 | 2226.20220 |   197.88540 | simulation_2 |\n",
       "| 36 | -1978.302 | 2412.48780 |   434.18610 | simulation_2 |\n",
       "| 40 | -1929.954 | 2579.86460 |   649.91020 | simulation_2 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 44 | -1848.456 | 2712.3038 |   863.84810 | simulation_3 |\n",
       "| 48 | -1804.206 | 2851.1040 |  1046.89800 | simulation_3 |\n",
       "| 52 | -1758.027 | 2978.9600 |  1220.93300 | simulation_3 |\n",
       "| 56 | -1716.495 | 3094.6540 |  1378.16000 | simulation_3 |\n",
       "| 60 | -1673.796 | 3203.2680 |  1529.47200 | simulation_3 |\n",
       "| 64 | -1626.953 | 3301.9350 |  1674.98200 | simulation_3 |\n",
       "| 68 | -1584.055 | 3395.7410 |  1811.68700 | simulation_3 |\n",
       "| 72 | -1543.596 | 3481.8130 |  1938.21800 | simulation_3 |\n",
       "| 76 | -1493.782 | 3562.1530 |  2068.37100 | simulation_3 |\n",
       "| 80 | -1443.174 | 3642.7810 |  2199.60700 | simulation_3 |\n",
       "|  4 | -2254.544 | -636.1168 | -2890.66080 | simulation_4 |\n",
       "|  8 | -2190.826 |  110.3405 | -2080.48540 | simulation_4 |\n",
       "| 12 | -2141.064 |  654.6227 | -1486.44120 | simulation_4 |\n",
       "| 16 | -2091.593 | 1081.7780 | -1009.81500 | simulation_4 |\n",
       "| 20 | -2037.878 | 1437.2718 |  -600.60620 | simulation_4 |\n",
       "| 24 | -1989.993 | 1726.9429 |  -263.05050 | simulation_4 |\n",
       "| 28 | -1938.092 | 1984.7355 |    46.64332 | simulation_4 |\n",
       "| 32 | -1897.247 | 2203.5404 |   306.29360 | simulation_4 |\n",
       "| 36 | -1845.898 | 2402.0230 |   556.12500 | simulation_4 |\n",
       "| 40 | -1794.460 | 2572.4606 |   778.00080 | simulation_4 |\n",
       "| 44 | -1744.340 | 2727.8890 |   983.54900 | simulation_4 |\n",
       "| 48 | -1702.636 | 2872.5980 |  1169.96100 | simulation_4 |\n",
       "| 52 | -1656.427 | 3004.6160 |  1348.18900 | simulation_4 |\n",
       "| 56 | -1615.384 | 3124.1340 |  1508.74900 | simulation_4 |\n",
       "| 60 | -1576.440 | 3233.3370 |  1656.89800 | simulation_4 |\n",
       "| 64 | -1533.068 | 3331.5170 |  1798.44900 | simulation_4 |\n",
       "| 68 | -1496.021 | 3425.1530 |  1929.13200 | simulation_4 |\n",
       "| 72 | -1457.100 | 3508.8120 |  2051.71200 | simulation_4 |\n",
       "| 76 | -1419.109 | 3588.0870 |  2168.97700 | simulation_4 |\n",
       "| 80 | -1377.923 | 3664.9890 |  2287.06500 | simulation_4 |\n",
       "\n"
      ],
      "text/plain": [
       "   Infiltration_percentage StromalScore ImmuneScore ESTIMATEScore simulation  \n",
       "1   4                      -2649.727    -785.61380  -3435.34070   simulation_1\n",
       "2   8                      -2586.353     -83.17307  -2669.52646   simulation_1\n",
       "3  12                      -2535.314     457.15530  -2078.15880   simulation_1\n",
       "4  16                      -2484.590     884.67960  -1599.91020   simulation_1\n",
       "5  20                      -2436.727    1249.48200  -1187.24500   simulation_1\n",
       "6  24                      -2388.342    1552.98530   -835.35650   simulation_1\n",
       "7  28                      -2342.591    1822.99570   -519.59490   simulation_1\n",
       "8  32                      -2294.258    2061.46550   -232.79270   simulation_1\n",
       "9  36                      -2243.230    2265.15294     21.92277   simulation_1\n",
       "10 40                      -2187.481    2451.91400    264.43320   simulation_1\n",
       "11 44                      -2135.220    2618.03810    482.81760   simulation_1\n",
       "12 48                      -2078.159    2773.95710    695.79850   simulation_1\n",
       "13 52                      -2026.140    2915.46170    889.32220   simulation_1\n",
       "14 56                      -1975.251    3045.21300   1069.96200   simulation_1\n",
       "15 60                      -1921.179    3161.01100   1239.83300   simulation_1\n",
       "16 64                      -1869.515    3268.09900   1398.58400   simulation_1\n",
       "17 68                      -1812.557    3373.84500   1561.28800   simulation_1\n",
       "18 72                      -1754.610    3466.21600   1711.60700   simulation_1\n",
       "19 76                      -1695.013    3552.10200   1857.09000   simulation_1\n",
       "20 80                      -1621.880    3635.85700   2013.97800   simulation_1\n",
       "21  4                      -2436.111    -508.95020  -2945.06160   simulation_2\n",
       "22  8                      -2354.957     218.12560  -2136.83140   simulation_2\n",
       "23 12                      -2289.572     748.56930  -1541.00230   simulation_2\n",
       "24 16                      -2233.501    1156.71800  -1076.78300   simulation_2\n",
       "25 20                      -2177.739    1489.48890   -688.25050   simulation_2\n",
       "26 24                      -2123.719    1771.51860   -352.20080   simulation_2\n",
       "27 28                      -2073.073    2016.27303    -56.80018   simulation_2\n",
       "28 32                      -2028.317    2226.20220    197.88540   simulation_2\n",
       "29 36                      -1978.302    2412.48780    434.18610   simulation_2\n",
       "30 40                      -1929.954    2579.86460    649.91020   simulation_2\n",
       "⋮  ⋮                       ⋮            ⋮           ⋮             ⋮           \n",
       "51 44                      -1848.456    2712.3038     863.84810   simulation_3\n",
       "52 48                      -1804.206    2851.1040    1046.89800   simulation_3\n",
       "53 52                      -1758.027    2978.9600    1220.93300   simulation_3\n",
       "54 56                      -1716.495    3094.6540    1378.16000   simulation_3\n",
       "55 60                      -1673.796    3203.2680    1529.47200   simulation_3\n",
       "56 64                      -1626.953    3301.9350    1674.98200   simulation_3\n",
       "57 68                      -1584.055    3395.7410    1811.68700   simulation_3\n",
       "58 72                      -1543.596    3481.8130    1938.21800   simulation_3\n",
       "59 76                      -1493.782    3562.1530    2068.37100   simulation_3\n",
       "60 80                      -1443.174    3642.7810    2199.60700   simulation_3\n",
       "61  4                      -2254.544    -636.1168   -2890.66080   simulation_4\n",
       "62  8                      -2190.826     110.3405   -2080.48540   simulation_4\n",
       "63 12                      -2141.064     654.6227   -1486.44120   simulation_4\n",
       "64 16                      -2091.593    1081.7780   -1009.81500   simulation_4\n",
       "65 20                      -2037.878    1437.2718    -600.60620   simulation_4\n",
       "66 24                      -1989.993    1726.9429    -263.05050   simulation_4\n",
       "67 28                      -1938.092    1984.7355      46.64332   simulation_4\n",
       "68 32                      -1897.247    2203.5404     306.29360   simulation_4\n",
       "69 36                      -1845.898    2402.0230     556.12500   simulation_4\n",
       "70 40                      -1794.460    2572.4606     778.00080   simulation_4\n",
       "71 44                      -1744.340    2727.8890     983.54900   simulation_4\n",
       "72 48                      -1702.636    2872.5980    1169.96100   simulation_4\n",
       "73 52                      -1656.427    3004.6160    1348.18900   simulation_4\n",
       "74 56                      -1615.384    3124.1340    1508.74900   simulation_4\n",
       "75 60                      -1576.440    3233.3370    1656.89800   simulation_4\n",
       "76 64                      -1533.068    3331.5170    1798.44900   simulation_4\n",
       "77 68                      -1496.021    3425.1530    1929.13200   simulation_4\n",
       "78 72                      -1457.100    3508.8120    2051.71200   simulation_4\n",
       "79 76                      -1419.109    3588.0870    2168.97700   simulation_4\n",
       "80 80                      -1377.923    3664.9890    2287.06500   simulation_4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "simplot <- \n",
    "ggplot(sims, aes(x = ImmuneScore, y = Infiltration_percentage))+\n",
    "geom_point(size = 5) +\n",
    "geom_smooth(method=\"nls\",size = 3,\n",
    "            formula=y ~ (a*exp((x)*m)),\n",
    "            method.args = list(start=c(a=1, m=0)),\n",
    "            se=F) +                                                               \n",
    "theme(axis.title.x = element_text(size = 28),\n",
    "      axis.title.y = element_text(size = 28),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text = element_text(size = 28)) +\n",
    "theme(panel.grid.major = element_blank(),\n",
    "      panel.grid.minor = element_blank(),\n",
    "      panel.background = element_rect(fill = \"transparent\",colour = NA),\n",
    "      panel.border=element_blank(),\n",
    "      legend.key = element_rect(fill = \"white\", \n",
    "                                colour = \"white\"),\n",
    "      plot.margin = unit(c(0.2,0,0,0),\"cm\")) +\n",
    "scale_y_continuous(breaks = seq(0,100, by = 10), limits = c(0,100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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KIrBTtC5I2Gf/PTzqXjemo2vyaWo2k/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9VVVVVUhF0EZAYAcAAAB0NIWlaM0pxKoU\nXZKfrQkK3DFHnr6wrKxMVlZWVlaWShWRzxW0NRDYAQAAAB0KjqPNZ1FqNrFainS0cw5SV1NC\nCNFotGbpGWh+sHkCAAAA6FDOBKAX4YRrrZ+K9DSaoTegZUFgBwAAAHQc76PQibuEa/06AA3o\n2gy9AS0OAjsAAACgg8jMQ2tPI74jJ8TSyRgtGdM8HQItDtbYAQAAAG0Uh8N5/fr17du33759\nm5qaWlpaqq+vb2lpOXz48OHDh6uoqHBL5ubm3r5z//RLlwrZToReQaeibbMQBcKBjqLl/0uO\n9cPxFn8pAAAA0M48fvx45cqV4eHhGIYhhHAcRwhlZGSEhYVdvXpVQUFh9erVy5Yt43A4e/fu\n3bNnj3qXnVr2xKI6hNCaychYu+k7D1oLhOgAAABA28LhcNasWbN79+6q817x2gMiHA4HIVRS\nUrJu3boLFy5UVlb+/PlT1Wy8lv1ioi8a3RcN7d5UvQZtAtHAjlNRmJtXVFJWzmITGHdT0LXQ\nkSf4JgAAAEAK4Tg+ffr0CxcuoJoYrqFiCKEfP34ghGgqNsb9ThN9kZUB+mN8IzoK2iSxArvK\nrE83fM9cD3geGhaTVsgkPpM65jruN5ZwLQAAAEDq7Nq1qyqqExNJRt588A2yrCKht9Bp6O85\niCpLsHOgzRMZ2DHjbq6ZNPvA21xYGAcAAAA0r7i4uA0bNmAYhou7Hh0z7neapmpL8D14N417\nJjrehPsH2jwR6U6ybs7sPXo/RHUAAABAC9i0aVNlZaXYUR3S6bxSzfxXom9JD99zYtdE8d8C\n2hGhgV32tXmzLqa3VFcAAAAAaVZWVubn5yd+vKWoP1Cv23aibylivEh7/1dJSUlQUBDRuqDt\nEzYVm37t39t5fNeYWtexM6aOHeZma6Clqa5EFT+5MQV2TgAAAADCBQUFlZWViVmYomBsNvAK\nRiK2CZJVlhH/dBLOqUQIHT16dMCAAYR7Cdo2Id8QebdvvuDtxqE6Lr0buHewDrkFOgUAAABI\nn+joaDFLkmTo5p63ZGjEznbFOZVxT8azStOIvg60I0ICu4S4OF5c57jm4r7BOnAAGQAAANBM\n0tPFXf1k5HaMrt6FaPup79YUM15yL3Nycoi2ANo+IaEa/zeYw4SJ9hDVAQAAAM2HRqOJU0zb\ncYW61TSijecn3smI2Md/R1YWkp10QEKiNRkZ3nCesbFxC3QGAAAAkF66uroiyyjqD9R33UG0\n5YrC2ISgqQjV2pahqalJtB3Q9gkJ7AwMDLif2Wx2C3QGAAAAkF7dunUTXoCiYGw28DLRDRMc\ndnnck/FsZkGd+3369CHWP9AeCAnszDp35qaxTkpKaoneAAAAAFKra9eu+vr6GIYJfEqSoZt7\n3JShER5mS3o5tzT7c/37S5cuJdxF0OYJCeyowyaNVq7+/P3e3Z8t0h8AAABASmEYtnDhwgby\n2GHGfU/RNboSbTPz66GcGAEHlFlbW8Mqqw5J2I4I6pCFsy2q/m7gvD2053l5y3QJAAAAkFI+\nPj56enpkct3cYjpd16hZTCLaWnH6q5TQlQIfETqOFrQjQre6ynTbeHqpVdVUfsrJyZP/l8wR\nVhwAAAAAjUGn069du0YikUgk3i9oZcOhei5biDbFLE7++XgMzmHWf7Ro0SKR6/lAOyUih4li\nv933TozUxRBCeJr/TPex+0Nz4Gg5AAAAoLn07t373LlzZDK5atyOpmprOvAKhhE7H4DDLo97\nPKayLLP+I09Pz8OHDzdNX0Hbg4lxJh0r9sp875lnosoQQoikbOU5cfLw3g7mBlqqinKyZJLg\nRZ61qJh2MVEWXQyguLg4c3NzhFBwcLCbm1trdwcAAEDrePXq1bhx47Lz2Z1+eUdRNCFaPeH5\n9Jzoc3VuYhi2bNmyffv2CawCOgYhgZ3/BJnxN2oucA6bI/lQ3ZjruN9YiWtLEwjsAAAAVCko\nLJmwLju7gvAWh8yvh5JDfPjvYBjWpUuXK1euWFlZNV0HQVskJBcOhy192evwzCebfQ59KkEI\nIWQ6+cQ/E0Qni0QIIVSeHvHiafCnr1E/U7MLisrZMlR5ZU09I3M7p+5ufVzNlOGIXQAAAMQc\nvSOfXSFPtJaKTCKp8qqsmVlFRYWCgoKVlVWfPn0WLFggL0+4KdAeEUty2MGxU27uPVEd1Ymv\nNP7x2SNnA2OK+Ec02aUFmYkFmYnfPzzzO6vlMmru/F9dteCLDQAAQDyXniD/YMK1tFXRhb+M\n1RRfN0OPQPsgJNaQkWuy8F6uPYQ0zOiLuy9EVRCqg+e+/3fjroBEAXuO+BrO/HBtm0/E+PXr\np9gqCisIAAAAIIRCI9E/N0QXq4Mii/bMR2rwi0a6CYm4Rl0oLm65jrS20nDfPTcSiE09V0Sf\n27gjILFSnLIlUdc2baPv3T7aqD0EuQAAAFpLQjpacwqxiecXWzsZ2Zo0fX9A+wJRBkIIoYI3\nR/YHZBDbHVIRfmaXP39UR9VxGjxy+IAuJjqaSqTi7PTk768f3goISSyubrf8+/k9VxwPTrGA\nBXcAAAAEKihBy46iolLCFX/zQMN7NkOHQHsDgR1CeFbggcOv8ohV4iT4n3qYxb3EVLrM2LBm\nlIVczQ01PTM1PbPO7oOe71138HVW1V9enET/0w+H7Bym0TQdBwAA0JaVlJR8/vw5LS2tuLhY\nW1vb3NzcxsZGSPnyCvaCvaXJmYQnU3vaocW/NKKjoAOBwI6ddHPPqU/Vk850Or20VJw/lCre\n+d9N4o7wYVpeK9eOsqDVL0fWdV+xKuHnCv/0quvKyLv3Y72mWYiR/A8AAEB7FRQUdODAgcDA\nQCaz1iJsY2PjiRMnrlixQl1dnf9+XFzchg0bQzOGqpgRPjfMRAftmINIIg4cANJCsm8EdmHq\nj4gPIUGBd/1v3Hrw+EXIh4iY9NL2eN5Y1Y6Jqh87TNtz4ThLsaoVBQe84i1AVOo3Z5qDgKiu\nCtlq3KTuvKdpj+5/lrYsMgAAIDWysrKGDRs2YMCA+/fv14nqEEJJSUk7d+40MzM7efJk1R02\nm71mzRorK6ugH5YSRHVK8ujAQqQgJ7okkBKERuzKE5+ePXbu9tMXr8OSiuoFJ2RFAzuXHn2G\nTJgxfaSzlmzT9bH5lIaf2nMjqepfQjYau3JO559bxar3PjSCt7hOvb9XtwbDOoQQku/p7kJ5\n+6r657vo48co5GQnaZ8BAAC0Vd+/fx86dGhSUhJCiMMRMNxRdShAUVHRvHnzwsPDd+zYMXbs\n2MePH6tb/qbrtJ7o62TIaPc8ZKjV+I6DjkPcEbuiT6fm9DS1HPT77gsPPwqI6hBC7KKUiCC/\no6vGuRjoO0/d9zxdrN2irajg9ZH9gdU7Jig2v/05yYoiXkXW5w/hvK+AVp++NiK+jFRnV0fe\njon8sPBEwp0FAADQtmVmZnp6eiYnJ4s8q7OqwLFjx7p16/b48WMFnT7GfU8hRHiRzooJyMVa\nwt6CjkqcwI4Ze3VOD7e5p0PTWeI1ysr6dGFFfxvnGecjyxvVu2aEZ/DtmFBwmrPyFyOxN6vG\nR/Glu6PZ2pqJrEGzsjbkXSVHRRHf8AQAAKBNmzx5ckpKisCBuoZER0dTlczMPW5gZCrR143v\nj8b2I1oJdHyiA7vse/MHTjwdWSb4KYY1+BdGQcTZae7ehyIaqNmqqnZMVEdXqm6Ll3lqiv+X\nUlFcXDbvytLaRoyIUNfcgs67Smeki/02AAAAbV9AQMCTJ09EjtXVIUNVsxgaIEPTJPq6YsbL\nzmrPiNYC0kBUYBd/6tcp/yXxfaOS1Rx/8dl59n7Il4SsojIWm1PJLCnIiA9/ef9/+//8bYCV\nSq0Ws574DJp+M7cZOt4YzOgLuy9E1+yY8Fq+qKcykeqpqSm8C5qOrooYdTBtLb6f20wGA/ZP\nAABAB7J7924SwY2pGIliNtiPpmxF9F0VRfFxT8bu27uTaEUgDYR/FxbdXLfmaQH3Ut5+mm94\nYrj/wVXTvHraG2so0GQwRJKlK2mZOPbxmrxs1/mnP1KiA/+Z1pkvB0/WtWXrnrWlUbvST6f2\n3KzZMWEyduXMznQRNWpjZ2fn8640NcX7Q0tdgy95HSc3N7/hogAAANqV3Nzc4OBgQpOwCGEm\n7mcU9foTfRe7Ii82wItVlhUUFJSXRzAFK5ACQgO7mGNbruTUXMg4/Pno7dmZdgrCG5Q391hy\n9m3Y+QlG3FuJJzb4pjayn00m7/WRAzU7JmhEdkxw5efl8Y1gqqqqilWLrqjIN2NbXt5mFx8C\nAAAgKCQkhM0mNhGj77pDzWIy0RfhHFbck/Hl+VEIocrKynfv3hFtAXR4wtKdxN28EVbz5wfm\nsOby9l7iDm1RzX7zvfT+o9vhWIQQQpw3d+5nL5rb+uct4BmB+w+/qh6CVHCau/IXA+LHe9UO\nyihUMQNDCpWCUM3IZUVFRa2Hfn5+t2/frnrQqVOnHz9+EO4WAACAVpKWlkaovGaneTpdVknw\nouTXiwtTn3AvU1JShBQG0knIiF3u48cfucXcZs+1I5TzTr73srmuNRecFwGBrb8PlJ3kt+dU\neM2OiX6Llw0isGOCh8Xk2xyMUSniBnayfAXrJq3Mysr6/v379+/f4+Li6HS6kC0pAAAA2pr8\nfALLa5SNvQ3djkrwlvTPf2d9P8F/p6CgoKHCQGoJCeziY2O5ywWsBw0yINq06dCh3PQ6zNjY\nZKL1mxgz6sLuSzU7JnS9li8gtmOCh4PzraIgy4gb7taqJitTO4Fz165dp02bNm3atJEjR6an\npxNcqAEAAKA1aWtri1lSXqu72cArGEZ4tigv7nrqh7oZjMV/L5AeQsKSzMxM7md9fX3ibevr\n6yNUPaeYlZWFUCumUSz5dGpvzY4JGZOxK2d2lvj8FfWzB6YAACAASURBVCqVL9sQu1LcNMzs\nSr71F7XaQAj16NGjR48eqPq4wA2Sdg0AAEArMDExEacYTcXGYsh9kgyxHXsIoZKMNwlBUxFe\n929+U1NTok2BDk/MvdkUcScc+cnJ8YKn3NzWzHmS9/rQgcDMmh0T0/6cZCHBP6cGjcZ3fhjO\nZIqZtbmMf2lerTYAAAC0az179lRQELG1UJauazk0QIamTrRxZlHCz0e/cNh199ypqKi4uroK\nrAKkmZDAjn+INz1dgoy6/EN+KiriZHtrHiXBJw6/qV6HIO8yd+UICXZM8OGPVxEqKioSq1Zp\nSTHvTy1MXV28vbQAAADaPgqF8ssvvwhZHk2WVbQYcp+iaEK0ZTazMDbQm1WWUf/R6NGjZcRe\nDQSkh5DvCUNDQ4Tiqz5/efkyb6UpsWCkNDT0C/eiNRcClGRkcndulHw4NGvkIfHqxV+cN+Ii\n77LXyjur+yCE6JpaCggVV9/NzctFSE10Y3m5fNmGNLS1GxVbAgAAaFvWrVt3+fLlSkHLczCS\nrNng63SNrkTbxDmsuMdjynK/1n8kKyu7bt06SToKOjohI3aagwd3rvnMenTqvwRiB6VkX78Q\nwB04VnZyMpegd22Tvp4e7yI7NY3ZcFEeBv+Yp462TlN3CgAAQCuysrJasWKFoCeYcT9fJQNP\nCdpMCp7Pn9yE35o1a2CBHRBI2Bo7m1GjeNtaX2+Yvu9bhZDSteCZ/j6r79WMaiGa5/CBHWe8\nWF1fn7dCjpOQkCRGnaykRF6+Fx1LK0UhZQEAALRD27dvHzZsWJ2bBj32qFv+JkFrjI+bs3+c\nEfjI09MTttmBhgjdPGG/aP0o7tK4khcre7uvuBknOh9d8bdz03tPvMQbodKbPGcY4U1AbRfW\nybYT7+vGiIjIFlml/Nu3OO4FzdbWrFk6BgAAoPWQSKRr165NmDCh6jNCSKfrWm3HPyRoKjvK\nN+3jJoGPJk6ceOvWLTIZFvQAwYSPo2lM2rvleOCS19UHJhSE7httc6nX1IXzp4wf0cdSuc63\nFV6e9v7uJd9D+8+8YvCtMlAYtn3roNaM67TG7r8zVryi7OCdv+wJqb4wnXzinwm6AkrJO3W1\nQp+jqq9iQkNzRg8Xus+p7P3bCG6yE1n7LnbwAwkAAB0QnU6/fPmyu7v7hg0bOKpe+t22SdBI\nQXJA0qv59e8rKyvv3bt39uzZje4m6MhETJBi5otvXP/iNupUbE2kxmKE+K4L8V2HyWlZONhZ\n6qorKdKwssL8rOSoiK/x+XVzf5BMJp85OU1QdNSeabq6mv4XFV+16BCPenA/1muqRcODn5mB\nd0O5s9g01wE95Ju/iwAAAFoDhmHz58837Tp93RkKjggfI1SW8ynuyXicwxseIZFI9vb28+bN\nmzZtmrw8/P4AIohe+aY97N/HlzHvGSe/FvPfxssyY95lxghv3HDUscD/xul1vPOx9Dy9na4c\n+li9bSLl7un7g//21hUY2rFT7x++EsUdr1N0G+QKOewAAKADexeFNp+nEdtwiBBCyEATndnj\npEDLSU9Pr6ioIJPJ2traEMwBQsRJUEwyGXviQ/jN5W5aBGYQaRbj/3kd7j/HSlZ02XZI0X30\nIF72l4rIs9uOf8it/1PMTAn6ZzP3fFqEyBajxzk3IjkyAACAti0yEa04jpjinkrEo6KA/lmM\n1JQQhUIxMjKytLQ0MzODqA4QJebJE4hqNmpfcGLc89N/jnHWkRM2BEfT7TZxw/m38d+uLnFV\n7XhjdTVkHCbO6MHb28pKDty6ZOXR22+i0wuZ7Mqygsy4T4Hn/l66/MDzdG5iYpLBqHneHW1a\nGgAAQI2kTLT0CCqte0iEaFRZtH8BMoajX0GjEUpCQjPqN2tXv1m7WHmxn968ef81ISM3Ly83\nv4RNVVRVVVXTNrXv1qu3i5W6dAxJKbsvXBYWt/VZ9VFlCC+MDvTdEejbUHmazZQVk62l42sD\nAADSJzMfLTyIcgsJVySR0LZZyLHjpHsFrUmi7HKyqhbdvSy6ezV1Z9obZZdFm33KNhx6k1X3\nYOZ66FZj/9ow1qzjJPMDAADAJ68ILTyIGDmEK2IYWj0J9Sd8LAUAgkGk0Sgy+gNWH9C7feLI\nlVdJpQ0slJXRcBwxe8HkXnodc7UhAAB0CDExMbdu3Xr58mVKSkpeXp6urq6RkZGnp6e3t7em\npqaQipWVlUf/PX/+bW+Mbi2kWEPmj0Cj+0jaaQDqwXBcgo07oA68OPH98+chH7/EJDCyCkqZ\nSJauqKJtZG7TuUdf956dNMSegI2LizM3N0cIBQcHu7m5NWOXAQAAIIQQ+vHjx9q1a2/evInj\nOJlMZrPZCCEMwzAM43A4FApl8eLFa9euVVMTcC74tm3b/t6532jgbQUdSaKzce5o1cTG9h8A\nftjXoKCs6s+adu52fH+VZH17/i1LYCXC6rQMGgSBHQAAtKTLly/PmDGDyWQKH+bQ09O7e/eu\nk5MT9055eXn37t2/RsZYDLmvqNdfgld7dkNbZyFSx91lCFqFzMb+/W9Ufx5zHffjO6Hhxcb+\n424IrERYnZYBAACA1nf06NHFixdjmOjJq/T09N69ez99+rRXr14IIRaLZWZmlp6Rbe7hL1lU\n59oJbZwOUR1oeuKmOwEAAAA6ksePH/v4+CCEOByRG+AQh8NhMpkjR45MTk5GCPXt2zc9PdN0\nwCVlo+ESvNrRDO1fgCiwyh00AwjsAAAASJ2KiorZs2fjOC7+QnMOh5OTk/PHH39cv349NPSt\nUZ8TqmaSzEWZ6uIHFyEaJL8CzUNGjpfWWk6mwUeNJAd/lgAAAGg7Tpw4kZSURLQWjuN+fn5P\nnjw1cjuqYTNLgvcyi5NdVYOU5KdKUBcAcchcKC5u4NGohh8BAAAA7diFCxdIJJI4k7D1yVuv\n0bT9XYKKrLKMmAeD/OPUVvpAYAeaC0zFAgAAkC4MBuPjx4+SRXV63bZrO66QoCKbWRgbMLQ8\nP/rt27eZmZkStACAOCCwAwAAIF2io6Mly+Gq57JFp8saCSpyKktjHw4vzf6MEMJxPCYmRoJG\nABAHBHYAAACkC4PBkKCWnvMmXaf1ElTEOcy4x2OL04O5d9LS0iRoBwBxCAns/MZiXGP9CLfc\nyOoAAABAs6BSqUSraHdeqeu8UYJ34Tg7/tmUguQA/psUCuyJBc2l2UbsJFu8AAAAADQzXV1d\nQuV1uqwx6L5bghfhODsh6Le8uOt17uvp6UnQGgDiaLbALiMjo7maBgAAACRnZ2cn/piZtuNy\nfde/JXoPnvRqQW7s5Tp3qVSqra2tRA0CIBo3wRyrNL+EWetRKYv3mVWan58vdqMV2d9vbzgQ\nyr2mEB/1BgAAAJqHoqLiwIEDAwMDRc4taTksNeixT7K3pIT+mf39ZJ2bJBLJw8OjybLEAlAP\nN7C7PVVVyMmwd6apqkr+Em1tbckrAwAAAE3s999/DwgIEF5Gy97HsOcBydpPe78uI2Jv/fsc\nDuf33yXJgQeAmFpiV6xyv35dWuA1AAAAgHi8vb3d3NxIpAZ/CWo7LjfsJWFUl/HlAOPz9vr3\nMQzr37//0KFDJWsWAHE0f2Cn3GfrX96w/wcAAECbcv78eRUVFYGxnU6X1QY99iGESdBs5tfD\nKW+WC3ykrq5+9uxZCdoEQHzNGNjJKJn2nrTR/93DxTaQLQ8AAEDbYmpqevv2bQUFhTqxnU6X\nVfquOyRrMzvqdHKIj8BHSkpKd+7cMTIykqxlAMTEXWPndTw+vvZygIAFpgtqFiAMPRZ/jMDY\nMUaiyKmoaypRJflrBwAAAGgJbm5u7969Gzly5I8fP6qOjtVz3iRZvjqEUPaPM4nB8xAScKZF\np06dbt++bWlp2bj+AiAaN7Cja5qYaNZ6pEnnfaZrmpiYtFSfAAAAgJZhbW395cuXEydO7Ny5\nExks0en8p2TtZEf5JgbPRXjdbbYaGhpbtmyZPXu2rKxsozsLgGgyoosAAAAAHZesrOzChYuY\n2osuPZGwBQ9nppxheiC7Z0JCQllZGYVC0dbWHjFixMiRI52cnDAMZq9AyxES2DlM2bHDpfqz\njUOL9AYAAABoYTiO9l1DV55JWN3DBW2bRSGR/lq/7q8m7RcAkhAS2FmPWr26UW1XML5+TixG\nCCEkp+/Y2ZAuojwAAADQsjg42nkR+QdLWH2IK9oyAzWcNQWAltacU7EVAct7znqMEEJI1utM\n1v0Zys34MgAAAIAYDgdtvYDuhkhYfaAT2gxRHWhjmvP7Ece5W4NYoaGfmvFNAAAAADHMSrTy\nX8mjukHO6O85iAxRHWhjxB6xq8z5EvT0Q0xyel4ZW8BW7no45anPz/FWLBQUFEjSPQAAAKCu\n/Pz8yMjI9PR0Nputq6trbW2tqakpuhpCCCEcxyMjI6OiE3yfd8osN5OsA4Nd0NaZENWBtkic\nwK7k24U/Z/1x6m0WS/L36OnpSV4ZAAAAQDiOX79+/eTJky9evKisrOTeJ5FIPXv2nDFjxrRp\n02RkGvy9lpiYuGfPnsuXLxcUcyyG3FPQkTCq8+gGUR1ou0R+Y5Z92Dqo+9RjjYrqkGzn2dO6\nNaI+AAAAKff9+/du3bpNmDAhKCiIP6pDCHE4nDdv3syePdve3j40NLR+XTabvW7dOktLy6NH\njxaWyVgNf6ag01uybozohbbNgqgOtF0ivjeLHy0duSG0pBEvwOjGnqtv3vurM6TxAQAAIJlH\njx5179798+fPCCEOp24SYO7NmJiYfv36/e9//+N/VFxc7O3tvX37dhaLJUvXtRr2lK7RVbJu\njHJD66YiEvw+A22Y0KlY/NueP06ncS9JyjYDh7nbqLFSQu7c/pTFQQghiqmbt5M2QgjhrKL0\npLjoqNjs8qrimN7wnSfWT+jXzVgRfggAAABI6NOnTyNHjmQymQJDOn4cDqeysnLatGkaGhpD\nhgypujN58uSAgACEEEXRxGrYE6qSuWTdGN0XrZ4EUR1o64QFdpzXp05/rfkpIpnO8H91aqQe\nGSGE2IlnRzvNuJOLEDOb4rnfbw73UOPK7E83Dqz02fEsA8fT7u8/NsTbw7U5+w8AAKAjKykp\nGTFihDhRXRUOh0MikSZMmBAVFaWrq7tr1647d+4ghOTUHC29HsrSdSXrxqSBaNk4BEdIgLZP\n2FTs25s3ucN1qhP2Hq6O6hBCZOPfFo1SQwghVPRsy54XvMUOMhpOE7Y/fH1ulC5CCM8IWDxs\nwYO8Zug3AAAAabB///7U1FQxo7oqHA6nqKho06ZNmZmZ27dvxzBMXquH1fBnEkd1v3mg5eMh\nqgPtg5DALufjx6Saz3Sv8cPl+R+SHRw6VX9MuXQ+qNYyViRr/tupg+PUEEIIj/edufRuflN1\nFwAAgPRgMpl79+6V4KxVHMd9fX337t1bUlKibDzCyjtIhqYuWR+wjP98xkhWFYBWICSwi46J\n4X7u1KULpfZTbWNjWvXH3Jcvv9WtrDF+62L7qo8Z5//Y8ZnA31oAAAAAQgg9f/68sLCQL9s9\nAWw2+9KlSxrW08wG+ZHINNEVBEkP2/XxzqyUlBTJqgPQ8oSsscvP5w206ejo1HmKmZgYIRSN\nEELo57dv5ahznR8b64kTOm/+Go4QQjH/Hry/+Zy3hD9XzYGV++PNi9cfIqLikxlZBSXllRhF\njq6goKJpaGphYdO5Vx9nY0Wy6GZ4ytMjXjwN/vQ16mdqdkFROVuGKq+sqWdkbufU3a2Pq5ky\nocYAAAAghFBwsKRnuCJEIpFYqmONex1ASLI5VDz5zfLMLwerujFx4kSJewJASxIS2DGZTO5n\nWVnZuo+NjY1rAjs8JuYnQnZ1Clh7eBiuD09GCKHC2zeesL2Ht4nopjz5+fljZwK+5bNr3y4u\nKC8uyE5P/P7++d3Lp1VtBk6eN22wuYLo/x+Uxj8+e+RsYEwR/5+U7NKCzMSCzMTvH575ndVy\nGTV3/q+uWs15MC8AAHQ8qampJBKJ0AK7ahjJoOcBTbslkr0X51QmvpyVE32+6hJG7EA7ImQq\nVklJifu5sLCw7mM1ExPFms8/o6MF/NgZGhrWfCx4/z5a4j42oYJPJ1f9sf9e3aiunsq8qMAj\nfyzcHpBcKbQcnvv++KqVhx/WjurqYGZ+uLbNZ+3/Iosk6DEAAEivvLw8CRbYYSQZk76nGxHV\nMeOfTeRGdaj2BBYAbZyQwI4vLkPfIyPrPTc2Nq75WBYZGV+/ARUVFe7n5ORkCXvYdNiJflt3\n3osvF7c8J+/dv+sOvG54U29F9LmNOwISmQ0W4FMSdW3TNv8k4XEiAAAAPlpaWkSH60iyChZD\n7qlbz5DsjZzK0tiHI/Li/Op0Q7LWAGh5QmYHzZ2clFB41Ugd497td/t6u9YKA00sLMjoa9XQ\n19eQkCJkrli7gcTERO7n8nKx46lmgiffPHQxmq8XJBUbd8+Bbq72JlqqygqyrKKslOiwkMd3\nH7xN5ZbC84JPnhvotNRJrn6DFeFndvkn8kVqVB2nwSOHD+hioqOpRCrOTk/+/vrhrYCQxOLq\n0bzy7+f3XHE8OMWiTUxJAwBAm2dsbExo54QsXcdiyD26hrNkr+OwimMDRxalPatz38TERLIG\nAWh5QkbsMHevodwUJ7HHlx+MrD00RXV0tK75XPns+s2cOvVj792L4l6oq0u40byplL+/ejOG\nNwGr3GXG7qO7lk72dLHU11Cmy5Jl6Sp6Vq5e0/86cmz9cDO+fR55z+++FjCHyknwP/Uwi3uJ\nqXSZueOfTXOHuVjoayhQKHQ1PbPOA39bdXDfst6a3C8yJ9H/9MPs5vkHAgBAh+Pp6Sl+YaqS\nhfWIYImjusqK3Oj7g+pHdbKysu7u7pK1CUDLE5agmD5s+liNmovi1yt6u87Ycz34R05NfGcz\nYIBezePyu+v/eMAf2mXd/GvvW24gRXZ0rLu3omWVvnnEC88w3eF/rv7FSvBJZ2SNbnM3zHem\nc29wvnwMqzfbWvHO/24S989ITMtr5dpRFgKG9ci67itWjeJtKa6MvHs/VpKN+wAAIH2cnJyM\njY1JJBHHmiOEFHT62PzylqpkIdmLWCWpP267lWS+rXOfRCJ5enoqKytL1iwALU/oT4vckHWr\ne3Ina/H88LN/ju9rM+9O9Q2sz5RJ3LPEUNK50a7D1p64+eTF83tnN/3Se9K1DO4zmT4jhvLW\n27UCdtiHcG6USXGZMMmBLqy4mvuIPrydI8y0tLqjkUXBAa+KuVdK/eZMc2gwmQvZatyk7ryn\naY/ufxaxdQMAAABCCGEYtmXLFpHL7FRMR1t6BcpQ1SR7S0Vh3I+7/crzv9d/hOP4xo0bJWsW\ngFYh4s8gi6XnDw1u8EeF1P2PdR68CKki7sGO+aMHu/f3nrH5Vgzfajbd6X/NNGh0VxsjNT6+\nouYzZt/DVUFEeZKxEW/nCCooLKj9uPR9aARvcZ16f69uQnP0yfd0d+Hldy76+DFKSGEAAAA8\nU6ZM6dWrl5C9sdqOf5gPuk6SETBnIo6SrPdRt3pUFP4U+HTWrFkuLi6StQxAqxA1vk22+P3G\n8zNznFUE/0zpzD55xFNVaAsyVj6X9g2S8CeuqWRk8IYPNc1MRcV1CLFYLN4FhVL72A3WZ77x\nP6TVp6+NiC8j1dnVkbdjIj8sPFFIYQAAAFwkEsnf319PT69+bIdhZMNehwx67EWY6LlagYrS\nnsXcH1RZnlX/EYZhPXr0OHLkiGQtA9BaxPhhUHSYcfJtfNST/7YtnjzKs6+LKf9aA8x4xvXH\n+wZrN9COkvPyO0H73ZUEP20x7NJSXpympiZ6H0dlYlIa9wLT1q690z0+Koo7/odotrZmItuj\nWVnzjQAmR0WViqwCAAAAIYSQtrb2y5cvO3XqxH+TJEM3G3xdy36xxM3m/rwS82Aom1kvTStC\nCKFBgwY9ePCASqVK3D4ArULMwxDIKlYDp/81cLqgZ4rOywOjBl85sOe0X+Dr75kVOEKIpGji\n6jF6+oo/Z/XQbgPnLZD7rbvTj0D5vOcPQrhL6DDzLo61hviK4uL4drZaWtuIkb5E19yCjhJq\nwrl0RjpCosNBAAAACCGEzMzMQkND//rrr3///ZfFYlEV9E0H35TX7CZxg1nfjiaFLEG4gNV7\n8vLy69atW7lyJZkMyalA+9M0URem4jBx8/mJmxHOKsnLK8UU1VTl2uvPAzsv4sL2k5+4SwTp\n3UYO1qtVIjWV73AZmo6uONtCMG0tTYRqZmAzGQw2MmuvXyEAAGgFioqKhw4d8vHxOeL78GXa\nLxhVT3SdBmR92Zv0ZmWdmyQSqVOnTtOmTZsxY4aGhobAigC0fU08nIbJyqtpyYsu18bgrLKi\nwoLctJiw96+fPn6TWFKTkART7b3g9361Z5LZ2dl8h8toamqK9Qp1DQ1eYMfJzc1HqJUz+wEA\nQPuTWW7+qWQhJukEKYmEVk9Eo/uuKCycm52dzWazq7bcKisra2trS3B8GQBtjZDArigtKrVm\n6YGSvo2eYsNF2624c3OX3kgX/AxTMPdatGZ2r7rxV35eHl8iOlVV4XtHatAVFckI1Wy5qHsQ\nR2ho6Pv37xFC+fn5+vr6aWlp9RoA/2fv3uOaqv8/gL/PNu53uQoICMhVlEDxhvdbec+0Mi3T\nLO1evzQzUzNN7WaWlqZZmllmfi1veUfLGyoo3rjfb0PuMC7b2HZ+f4AwFMauMMbr+YcPzs45\nn/Nhc+O187kBQFf3+1n6+gCpuMZYE1Nj+nQBDe9LRGRtbS2/IDqAwVAQ7E6+FTjzfw0/P/Un\ne2BGu1RIX5j5PvnOB89FOD3aXto8lBmbGD9yRIuMTYyJahs2RCJRs503b97cvXt3/c8uLi4F\nBa2ETQCALkkmo6/20x/n1C/B2oI2vkahak5gDNBpKBgVayIXWcRipVa6NyC1qQfXLnxl2c4r\nBZKH9tSJ5WZCYUyMlQ12RnIHPvx8Wltbu7m5ubm5OTk5iUQildZGBAAwbDVCem+rRqnOzYF+\nXopUB12Cgjt2Hh4eRKn1P9+/f5+oR+vHGiZp6b1DGxZnLtrw8RPuTXfuZPKjqLg8ZXspNjvN\niGfUbOfs2bNnz55NROnp6T4+PupXGQDAsOQV07vfUboG/VNCvGnja2RniN2JAB6lIJaEPP64\n64Yt9W+muP/+E7w72/DeFt5ztx+eS6xULBLWVhbzczOTb8dc/O9SYnHjbTq28tYP6/b4fvti\nrwdPVbNpjaSSh2/otUYqkVtIDFMjAYChqa6uPnny5PHjx9PT0/Pz8+3s7FxcXAYPHjxt2jRf\nX2XvlclksitXrhw+fPjevXsZGRnVjJ9d+HauqXKD1FoyvC99uoBMlWxaAej8FAQ7zvB33h/0\n4ztXhEQk/mfLD+mzFxvo1GsM19jUwtjUwsbJMyBs+JQ5c2/++dWX++4JGtpDZbmHdp2c9OnE\nhtHvpqZy64exYnEdkdGjZT6iVr5rXrMyAAA6tdra2s2bN69bt66iooLD4RCRTCZjGIbD4fz1\n119LliyZMmXK+vXrg4KCFJdz+PDhpUuXJiYmEhHDMPYBL3sM3sJwlPmEbdm0SFo2m7hqLksB\n0Ckp/P/u8+bur8fWz9Imjl49Z831rrFeAs/hsVmrV013axr2Lr1z9t/CBxtmZvLrowkEAqUK\nramuamqKZeztlRtLCwCg57KysiIiIpYuXVpZWUlEMpmsfgIRlmWl0oaGiqNHj4aGhu7YsaO1\nQkQi0bx586ZOnZqcnExEDIfnPugbz6E/qJ3qGIZemUQfPY9UB12O4v/ynF6LDhxdMdCKiKjq\nysrRQxb+fLtc3YHmnYqx39NP95f7QElPSHgw3MHc0UluIYrSslKlCiwrLWvacHB2xuzEAND5\npaam9u/fPz4+nogUjPqSyWRSqfSVV15Zs2bNo3vFYvETTzyxa9eu+iN5po5+k6I0WSvM2IjW\nLaBXJqtdAEAnpqApVlReUCYk8nnt1wMmb7+85li2SBC3fX7o7+vHzpg+blCIh5ON0qtLdO83\nKdxFK/VtP2ahj/nTtbsNW7LiklKi+t/BzdWVKLlhR3Fevph82+6+wZefwcTFubM9GwAAD6us\nrJw0aVJJSYlMiZnl6o9ZtWpVYGDgjBnNps964403zp1rGPJqZt/Xd/whY0tPtWvVzZq+epVC\nDLTnEECbFAS7Iwu6N85j14QVpJz6+bNTP6t0lY6dBi9772tv/PFgFTD3Z775fnZPZU6z7WbP\nIXrwcVVX1zjJib2bmyklN/SYk2VmZtOwNvsFF2VnNbVju/TyM7xxKADQ1XzyySdJSUkqncIw\nzMKFC8eOHWtjY1P/SFRUVGMTrZ33DK8Ruzg89Zcv8nGlr98gV6zrA11YV+h9YGUlF6MK8/lK\ntiXLpBK5dgUba5sHPzKBQYFNzxv/9u3iNssS3ruX3rhhGhSE75IA0Lnl5ORs3rxZ1bNkMllp\naemXX37Z+Mj777/P4XCIGJfQpd6j/9Ak1Q0IpJ3vI9VBV9cVgp2Ns3PTGFRxSnKmcqfl5uQ0\nBTtjB4emdGgR9phf03Ep0dElbRRVe/3q7cbJTox6hwajhx0AdG779+9Xb+Z6hmF27dpV3yEv\nISEhNjaWY2Tba8Jxt4gNxKj/J8ms6vg3b5KlWdtHAhg2BU2x1m7+/v7auYpbhy7IxwkKDmDO\nxzWktIKrVzLnenu1tdKzNPXsv9mNW9yQPsFyZzhGRPT8OTGjvkQ28Z9jqRNe8G39A6nw5JHo\nxiXETCNGDVT/GykAgF44fPgwh8NRpnfdQ1iWzc3NvXXrVmho6JEjR8zs+/qMPWhirX47BstK\n868vL43/WrS9lGeBj1fo6hQEu3HfJCa2X0V0ySp8QAAnLqHh8yfv2C9nH18xxl5RtBMm//bd\n0aaJzrm9I8LN5fe7jp8ctu/b2IZvq7lHfjw2dt3k7i1GO2nesc37Ehvv11lFjonAHHYA0NnF\nx8erkeoaJSYmhoaGRifbBEy9zOGZt31CK2R1No8cxAAAIABJREFUVelRz1VkHSGijIyM3r17\nq10UgGHoCk2xRA6jpwxu+uCoitm6+oerhXWtHCwpvLZr5cd/pjUtKeEw7pnRD807ZzVi+pim\nh0Txu9ZujSl9dKi/OPfcN6t33GocN8H1nT4zHDOgA0DnJpFISkuVm+qpFbl5BV/+QdnchZqk\nOrEgM/HQ4PpUR0R5eXmaVAnAMCi70mknZzrouWf9rv2U3HCLrS7zn09fj+s3btywiNCAHo42\n1uZGklpBKT898da1qJNRt++L5M61HfrSs70fCWO8kFnzBl7YGN0wPXFdzsk1b2WMm/nU2AHB\nXo7m0qpSftrNCycOHYvObVpwguM+beHk7rr8PQEA2gGHw2GYtjq0tM7IzPlc7sz7yW0fqYCA\n/2/66RkSYdPoNSMj9deoADAYjIIpJQ0Lm3fko3d33BG2faQ8Y59nPt0w27/llV0rYja9tyaq\nUMkn0DTghQ3rZngrjtLp6ek+Pj5EdOHChcjISNUqCwDQXlxdXfl8vhonWjoP9h6z38jCTZOr\nF8Vvy7n8Fitr1vKSkJAQEBCgSbEABkBBU+yBGUyjGQdULlnD07WOcZv80ZrnQ1SYQI7r0P/F\ntWtaS3VEZNPvjdVvD3JUpjnb3G/GipVtpToAgM4iPDy8fllYlTgEvuI3+ZwmqY6VSfKufZB9\n8dWHUp2VlZW3N2aSAtBdHztNetXqipn/zI+/WjEn0tOirSYE0+5hk1//bNNH0wMsFR7Hcxv1\nwdcb5g/1MG+9RJ5Dn+kfbFz/QojiogAAOpGpU6eq9DHPNbLyHvOH59AfGI76/YwlwpKUf8YX\nxH320OMMw0ycONHYGD2YAXTXx+7+/fu6KloTRi79n36/38TcW9di4m7fjk/ll1ZWCqpqxIyJ\npaWlpa1jD5/AoOCQ8IjQHpZK9h9hrAOmLdk85unr589fjr2TkskvqqgRk5G5la2zh09A34HD\nRgwKdMCnDQAYlhkzZixZsqSiokKZ/jzm9qHeY/80sW5zkR5FakvvpJ2cKhJkPLqLZdmFCxdq\nUjiAwWgMdnU15dXN55qskbvNXVdTXl6udKGi4oRDK7+Obtw2Nmm1NbNDMBbuoSPdQ0dO016J\nlp4Rk+ZGTNJagQAAes3W1nbFihXvvfdem0fa+73gEblVk9GvRFSRfSwj6jmpuPLRXQzDPPHE\nEyNGjNCkfACD0RjsDr1g18LKsA8cnmtn1+rONjk7O6t/MgAA6KO33nrr+PHjZ8+ebe2mHYdn\n7hH5vb3fXI0uw8ryb3zCj/2EqOWrODo6bt++XaNLABiQ9pjHzmb48NB2uAwAALQjHo+3f//+\nkJCQFvea2gYGTIvWMNVJxeWpJ6fwY1e3luqsra2PHj3q5qbRGFsAQ6L7YGczdM3yyehjBgBg\neOzs7C5fvjxz5kwikh8ka+/3QuCT18y6tZz5lCQsT0z8e3BF9rHWDvD394+Nje3fv78mVwEw\nMDoMdjzrnkOeW3Xw2ok3A7rG+hYAAF2PhYXF/v37T548GRoaSkRcI6ueI/d4jdjNMdJoJoCy\ntD8SDvYTlie0uNfOzm7Lli137tzx9dVoQAaA4WmcoLimKLOwutmu46/1fO14w89PfJ/x/RMq\nlMoxNrO1d7Q2UX9m8i4KExQDQOd19krep79ZVIptNSmEw6HInrdMy/fn5uYUFRUVFRVVVlbK\nZDIOh+Ps7BweHj5r1qz+/furMYseQFfQOHjC3NHLy7HZLke5IUzmjl5eXu1VJwAA6HQOXqAv\n97mJJW0fqYCNBa1/mSIC+xL11VK9ALoWLIUAAAAaqaym1bvp31ualhPiTRteIWcN5mAAAAXB\nLmTO+vX9Gn4O0KgHLAAAGKgbKbRiJ90v07Scp0fQOzPJGHcbADSj4D3kP+2DD9qvIgAA0KlI\nZbTzGP34D2m4gqS5CS1/nsZjbCuANuDLEQCAYSotLU1JSbl//76xsbG7u3uvXr1M1F0GqKys\nLDU1taCggMfj1Rd1J6lkza/G+ZWObZ+skJcLfb6QvF01LAYAGvBijx7l6/wq3ftNCnfR+VUA\nAIDq6up+/PHHX3/99cqVK/JrQlhaWk6YMOH1118fNmyYkkVJJJLdu3fv2bPnwoULMrn7cg7+\nc3sM3qLhhCZENGEALZtNZvq16CRA58Y8RdT6SmLa8tSf7IEZOr+KIcB0JwCgiaioqAULFmRk\nZHA4HNkjTaT1D06ePHnHjh1trvV46dKl+fPnJycnyxfFNbbxiNzazXeWhvU05tGb02nWaA2L\nAYCHYR4gAAADsXXr1nHjxmVlZRHRo6mu8cGjR4+Gh4ffuqVoFOvu3btHjhyZmpoqX5SV68ig\nmXc1T3XujvTzB0h1ADqBYAcAYAh+//33119/XSaTtRjp5LEsy+fzx44dm5OT0+IBhw4dmjdv\nnlQqbSyKwzV1H/iV38QzxhbuGtZzXD/6dTn599CwGABoGc/N399f51dxs9b5JQAAuq6EhIR5\n8+YxDNNmqqsnk8lKSkqmT59+9erVh5ZwyMjIeO655+SLMusW0nPkHjN7TWcMNjaiN5/EjToA\n3eJ9k5jY0XUAAACNLFu2rK6uTslUV08mk8XExOzdu/f555+Xf3zFihW1tbX1oy4YDs+5z3uu\n/T5hOMYa1tDLhda/TL00vd8HAG1g5MdMQYfD4AkAUFV8fHxwcLAaJ3I4nF69eiXKfb3Pysrq\n2bNn/d8FE2tvrxG7LV208EE0YSAtew6jXwHaA+axAwDo3P766y/1TpTJZElJSUlJSY1dcv7+\n+2+WZYkYh8CXewzayOFZaFg3Dgk/ecn08QgNiwEAZWHwBABA5/bvv/8+1E9OJefOnZMvyszG\n22/iac+hP2ie6mqKYvhnRyLVAbQn3LEDAOjccnJyVOpd95Dc3Nz6H1iWcoT9/afv5hpZaVwp\ntvDu5tzoJRxGyrIswzAaFwgASuHdO3++qOFnx+ARwXKrwxTdO3+vqMWTVPZQyQAAoDUlJSVq\nn8swTGlpKRHll9DaPcT2WMbVuD7iqpzM8y8I8s8TkZSoqqrKykrzpAgASuGtGjnywcoTD60P\n8e+qkTO1tCgFVp4AANAVZ2fnoiI1v4azLOvk5HzwAm06QDVCLVSmPONg1n8vS0Sl9ZtGRkZI\ndQDtCX3sAAA6Ny8vL7X72JlY9YyreWXdr1pIdTJJTc7ld9JOP9WY6ojIzc1N03IBQBUIdgAA\nndvYsWPV6WPHcJx6vxU0805uRXfN61BdeDX+QN/Cu9889PiUKVM0LxwAlMczs7B4MPDJjNfq\nLg2ZYYwGAICOTJs27d1331Up25naBXkO22HpPFjzq7OstCBuAz/2Y1YmeXTv9OnTNb8EACiP\nt6eqqpVd01rfBQAA+sLDw2P+/Pk7d+5UZsJ5hmPk3Of/XMNXM1wtzBcsEmRknn+xiv9fi3sH\nDRo0fPhwza8CAMpDUywAQKe3evVqOzs7LreNIa0WzoMCn7rpFrFBG6mOLU7YHn+gT2upjsvl\nfv/99xpfBQBUg2AHANDpubq6Hjp0iMPhtDaKgsMzd4vY4D/lgpmdOouPPaSupiD1xJSsCwtl\nda027Gzbti00NFTzawGASnjRBw40zE1J7oNmDJQbv5QXfeBKK7sAAEC/REZGHj58+Jlnnqmq\nqnqov52NxwSPyK3Glh5auVBZ+p/ZF1+VCFudPI/D4Xz33XcLFizQyuUAQCW8L2fObGUeuyut\n7wIAAL3z+OOPX79+/Y033jh9+jTDMCzLGpm79hi8yc57plbKlwhLsi++Wpb+p4JjPDw89u3b\nN2jQIK1cEQBUxeNyiBq+2tXV1REZdWx9AABAfX5+fqdOnTp37tzevb9H3XWwCfyAa2ytlZIr\nso5kXVhYV8MnIhMTE7FYLD9Wg8vlhoSELFmyZNasWVhADKAD8ayticrrf05MSCDq07H1aTds\nVc6NS5dj4u6mZOffL6msFkk4JpZW1lY29j169Q7pExoREeykQudiYcHtf89euHE3MS2vuEIg\nlPJMLGwcXT18gsMGRA6N8LbRfJEeAABlufqOrPMZ2U1LHzxW5vTmdBobOiI396yxsbGbm5up\nqSkR5eXlJSQkMAwTHBzs4uKinYsBgGZ4bm6NwS5509znjV8ZF9jDuZu1hYlRctPc4VSZc/fu\nXfWvYuPRu4d2vjNqg5gfvW/HT0diCkTNH6+pKKmpKCnITb935eQfO20DxsycP2dSgE1bXz1r\nMk7v2rLrZIpAfp4BaU1FYVZFYVZCTNSBXU79pr2y6NkIJ8zmBwA6VlVL2w7T/vOkxozFLYoM\noQ/nkJMtEVkFBgbK73Jzc8PCEgD6hjn5crfxO0rbPlAj+tNFjy278dPqzw6l1yp1NNdl6Nur\n3x3RvdVExpZe37bqs+NZ4jaLsgh4esWKOUFtLZmYnp7u4+NDRBcuXIiMjFSqlgAARER0KoY2\n7qfiCu2UZmtJS56l8f21UxoAtA/OqJfm9+zoSrQbYdJvH69TNtURkbTgwtfLt8QIWtktSt69\nar0yqY6IqhP3f7z2YHYLE7MDAGgqu5De+pY+3KG1VDe0D/2+AqkOoPPh8QZ89POSs098cVPp\ntNNpiRP3bNyfIRfDGBufyDHjRg4K8XDqZmfFE1cW5abcvnLq8D/X8hqXw2aLo77ZErZl2TCb\nh4sT3frps4NZcknNxCVs7NRJo0K9XBytOVXFBTkJl078ffxyVlVDG60w4Zcv9vXZNMcXHe4A\nQFtqRPTjUfrtLEmk2inQzoref5bG9tNOaQDQznhENsM//+/GkG3f/3biyu20vJIqsaw+iIgF\nJYIHKcjYyt7KWP2rWGth5RqNFZ/e8w+/qR+cVdDM95fO6WvX1IXOyM7NP8LNP2Ls45e2fvLV\n6dwHma3iyi//Sxoy379ZHpNlHtxxoqhxk7ENnbdy2TRfswcPdHP17ubq3XfEmPNffrTpUlF9\nbxdZ1sEfTzy+YaKDTn5BANATSUlJf//9940bN/Lz8yUSiZOTU2Bg4KRJkwYNGtTm4hAtSk1N\n/fvvv2NjY/Py8sRisbOzs7+//5gxY64mmRyJC5TxnLRV8zHhtHQW2bXVaQQA9BbT+tqCB2Yw\nhjSPXc7e11//I+fBVrcRKzb/X/9WP7zYwpOr3/ruRs2DbdMh7/+6NFIu2oqiN85bd/7BlOuM\n08S1WxaGmLZYljR516uLDxY82HR9auPWub6tjchAHzuATu369evvv//++fPniYjL5dZPFMzh\ncKRSKRH17Nlz3bp1zzzzjPITgsTFxS1duvTUqVP15bAsy7Isl8s1svLtMfhba/dx2qq5qz0t\nm02DtLAsBQB0pC6zpFjBteuNqY5MBr74cuupjogYp3HzJ/do2hbejLkr38whuHD8YtNCOtbD\nX57bSqojIq7fzOcGNO3NP3XsppZaTABAf7As++mnnw4YMOC//xrWTpVKpfU5rD7VEVFWVtas\nWbOmTp0qELTWdbeZr776Kjw8/MyZM/WbMpmMZVmusU33iC+CZtzVVqrjcOi5MfTHKqQ6AEPQ\nVYKdMCEho3HDuP/IIW21NDAeg+QXUatJSy2Q27oefbupc539yAn9W411REQWg0b0a7rbJ4iN\nTVSu0gDQeSxatOijjz4iIlnrE43U7zpy5MiQIUMqKysVF/juu+8uXryYZdmmAhmOvd8Lwc8k\nOYe8y3C0M3+Srxv99D7930wy04ceMwCgMQUfDRFv79kzreFnz4h2qY3u5ObkNDU59woKUmKB\nDVc3V6K8B1vl5eVEDUmv7mbMraabbk5DhwW0kY9NwiP6cC/GNJxTHncri4I9la88AOi5r776\navv27UTUeueWZu7cufPss88ePXqUw2n502Pr1q2bNm2SL9Cq+3D3wZvM7UO1VGUyNqIFE+mF\nccTDeC4AA6Ig2HkMnTNnaPvVRLdKS5rWqzZ3dX1kiGsLjIzk0x9X7rMvIzGxaW5j06Ag7zbL\nMvXz70ExmQ1bOYmJNeRprkQdAED/paWlLVu2rH5tVuXPOn78+O7du+fNm/fortzc3Hfffbex\nQGMrL/cBn2trvdd6AwJp6XPkobVBFwCgL7pIU6y0urppmhNra6VWwSjgyzW+kp2t3YMfBenp\nxU07evkHKPF1t7uPr1yQa140AHRqK1eulEgkKqU6IuJwOMuXLxeJRI/uWr16df1KrByeuWv4\nx8Ez47WY6uytafU8+u4dpDoAw6RqLw2ZqLK0TFBdK6yTqvAhZtnd18VCxStpFXfkR4dHqnZK\nweUrTb3yyC4gsPFDMC8vt2mHqUt3WyVKY5ydHImyGrYK+XwpeaP5A6DzEwgEBw4cUDXVEZFM\nJuPz+WfPnp0wYYL840Kh8LfffmOJsfeb69Z/rZGF1tbs4jD01HB6bSpZob0AwHApFewkRTf+\nt/OnP4+fj45Lya8Uq/wB1glnSyk7/9NfaU2/qN2gwQEPZieQFheXNx3o6OioVIH2Dg5NwU5W\nWlpOZK+dqgJABzp16pRYrNTyM49iGObw4cMPBbtz585x7QYFPvGlFrvTEZGo7Pbvn4X07qns\nNCsA0Em1GezE6X8te27B11dLVY9znVZN0m9rv4tums7EKPiZp0IaPw7Ly8rkngs7OztShrmV\nFZfowZALoVDYbG9ubm5+fj4R8fl8a2trJadCAIAOl5CQoMnp8fHx8puZBbTjrI/fxDOaVaoZ\nmaSaf2PN/dtfdd9agC+UAAavjWBX9Nf8IdP3dqUOYeKccz9s+P50jtzoiMDZi8bL3ZZrHsqM\nTZRckcPYxJjowbptD/erOXLkyM6dO+t/7tWrV1xcnOo1B4AOkJ+fr+qwiUYsy+bkNEywWVpJ\n247QoYsklflpsXplaX/kRL9XV51HRHl5efb2CHYABk5hsCvev/ClLpTqpKV3/v5hy+9X+HLN\nKlyX0f/34XRP+e5wdeK6pg3GxFjZYGckF+zUbroBAP2i3hJh8qcLxfTbGdp1kmqEbR+vvNqy\nezmX3hTkn5O/ljYvAAB6SVGwK9i/7VCZ3DbT7bEZ816YMTEyyN3J0d7aRPkRtcYdOnJCCXVF\nNw7v+vGPC7nNPlgt/KYtXj4v/KG5UWSs3OyjXJ6yw0+anWbEaz6R3uzZs6dMmUJEOTk5I0aM\naJynHgD0nKurq3q364iIyzN27v3qkyuoqLztg5Unk1Tfv/Ul/+Y6VtbsG6Srq6s2LwMAeklB\nLCk79Ne/TVnEpM87R05+OdbF4L7xCXMu/bnz50M3Cpt9AnK7hUx/4+3n+jk9+vuamMhN0C6V\nSB45oGVSiVxYa1YGEVlbW9dPwiISiVqc/gAA9FOfPn3UOo+x837Ktd9aka2/VlMdW5qyN/fq\n+3U1/Id2uLi4KNsjGAA6MwXBLjM9vSnX9Vm296uxLoY16x1bmXLmtx9/PZFQ1mwBII5N4BNz\nX31+jFcrMwKYmsqtH8aKxXVESqxjQbXyXfOalQEAndfo0aPNzc1ramqUP8XSebDbgM8sXSK1\nWxNheWLOpTcr81oeeDFzpjbnNwYAvaUg2BUUNHWvC3lmVm+DSnU1mWd2b/n5RLKgWROKsVPY\npLnznx7qoWiWJzMzM7ktgUBA1E2JC1ZXNcVHxt4e35wBDIKpqencuXO3bt2qzMHm9qFuEeut\nezyu3TpIhCX5MSuLE7ezspabEBiGeemll7R7UQDQTwqCHU+u+5inpwEtbVqVePDLz/bcKJHv\nx8ZY+YyY+eKcSX0d2+wzZ+7oZEn0YDKU0rJSpYJdWalcd0UHZ2eDa9IG6LJWrly5a9cukUgk\nk8laO8bUxq97v9XdvJ8mRpvfkVlZXVH81vzYj6WiMgWHzZ49u2/fvlq8LgDoLQUxxt3dnahh\niiWD6c0v5Z//5uPN5/lyA1u5dr0nz3v52eE9zZWcudPN1ZUouWGjOC9fTL5tj4zly93/JBdn\nF+WrDAD6zcXFZceOHXPmzGlxr7GlR/fHltv7z2c4qq700wZB3tmcK+/Ult5VfFj37t03bdqk\n3UsDgN5S8EHj3bevFcXXz5SbnZ1NFNBOddIZtjR684pN5wsbv1QzlgGTFr45Z3gPM0WnPcze\nzc2Ukht6zMkyM7NpmG9b5xRlZzV1wXHp5WelygUBQM/Nnj07LS1t1apVHA6n8b6dkblr98c+\ndAh8meEoOSuSsoTlSbnR71VkH2vzSCsrq9OnT2P6OoCuQ0GjgMnE56Y/mOkj4eiRtHapjw6x\n9//54quoplRn7jvto2/Wv6xiqiMiJjAosOl549++XdzmKcJ799IbN0yDgrxVvCQA6LuVK1fu\n3r3byMiIYRieqYP7wC96P5vqGPy6dlOdVFyeG/1e/IEQZVKdr6/v3bt3g4ODtVgBANBzinp7\nmDz++gLf+uZJ2dVvvziv1ckz213Fue177jVOJGIe+MLaT+f3d1Srq5tF2GNyU8OnREeXtHFC\n7fWrtxvbso16hwajhx2AAXrhhRdu3EodP+/v3rPSnfss5vBU/daoCCurK7z7bcKf/sX3viG2\njXmWLC0tv/7664SEBA8PDy3WAQD0n8I+H7z+q35858iYr5MlRLnbZ8+OjP5zTo9OOjg258Rf\n1xsbQ+1GvPPhDF/1P3MdIyJ6/pyYUT+ilk3851jqhBd8W39iCk8eiW6MlKYRowbq+3zNAKCy\naiEd+Jd2nXAX8Ny1/tUtrJfkMfuodNscfs+xVVVVPXr08PX1nTRpUlVV1datW2NjYwsLC1mW\n7datW+/evV9++eWhQ4dyOJ30wxoANNJGZ16r4Z8f/SF9+IJDfJbNPzh/xIzCvTveHWiv5CgD\nPZJ89mzWg5/NIua+NNBG0dFtch0/OWzft7ENcxrnHvnx2Nh1k7u3+DEqzTu2eV9i4/06q8gx\nEZjDDsCQVFbT3jO0L4qqddCsEexF78ygx3rxiMYTjX/0gG3btmn/qgDQabU5SovXa/6f/5kv\nmjz/p8TauvS/3hsS9cP4WbMnDQnxcXeyszIz4nKUSHm2PUO9NEtSGsq5fr1xUKpl5IThGlfG\nasT0MXti/2mYYEAUv2vt1u5rXuvX7aEnQ5x7bssnO2413irk+k6fGa7lbtQA0FEqq2lfFP0e\nRQIV5idWlrMdvTSRpkWSMh+yAAD1mNZXOTz4DO/p/z3YYGVSmZrLIRLRU3+yB2aofbbmBCeW\nz/7+jkZF+C346cspDvKPVJxf99rGaEHjNmPtN27mU2MHBHs5mkurSvlpNy+cOHQsWm75WY77\nU59tmuuvKNilp6f7+PgQ0YULFyIjtTwxPQBoS5mAfjtL+8/p5C6dtQUtmEgzhpOxlidIAQDD\np+BjQyY1mNnrMjIytF+ozYjX341LXxNV2BB42crkkzvXn9zZ2vGmAXMWz1aY6gBA/5VW0t4z\n9Mc5EorbPlhVpsb0zEh68XGyUrT8DQBAq7rE98EqfkFV20epzqbfG6vfrl357ZWiVqebf8Dc\nb8bylTO8u8TTDdBeCgsLc3JyiouLra2te/To4e7urnmZtbW1GRkZBQUFDMO4uLh4e3ubmJjU\n78ovpt0n6fAltk6q/cZRLoemDqGXJ5GjrdbLBoAuRNGSYmYWFloavmnWsYGmvLxcRyXz3EZ9\n8LXroR+27LuYXdNKUzXPoc+UBa/NHuxqpKNKAHQxlZWV33333f79++Pi4uQf9/Lymj59+jvv\nvNOjRw81ij1y5MhPP/104sQJobCpedXMzGzChAnTZ72ZVhV5+DKxxCXSfqqLCKT/m0m+blov\nGAC6HAV97EB5bFXW9fPnL8feScnkF1XUiMnI3MrW2cMnoO/AYSMGBToo3QCLPnYAiv38889L\nliwpKSmRX+OhHsMwRGRkZLR48eLVq1fLL3etWFJS0ksvvXTp0qVHyzR37Nc99ANbrye1u8Zr\no/4B9MaTFOyli7IBoCtCsNMvCHYArZFKpe+8886WLVsYpu0PrlGjRh04cMDOzq7NYk+fPj1j\nxoyqqqqHIp2lS6RL6FIbj0kaVbp1vXvS/Ak0rI+OigeALgqdvgCgc3j//fe3bNlCRMp8HT13\n7tz06dNPnTplZKSoE8T169cnT55cV1fXmOoYhmvrPcOl7/vmDmFaqfaj/HrQq1NoKCIdAOgA\ngh0AdAL79+/fuHGj8sezLHv+/Pnly5d//vnnrR1TUVExZcoUiURSn+o4XFN7/xed+yw2sfbR\nQo1b0rM7LZxMo8OIwdR0AKAbCHYAoO9EItHixYsf7QDXpk2bNi1atMjb27vFvZ9//nlBQQER\n8UwdHYNfcwp6jWfmpIXqtsTNgV6ZTE9EEBb6AgCdQrADAH23d+/enJwcNU6USCRffPHF1q1b\nH91VXV399ddfm9r4Oga/6RCwgMPT1cRx3e3pudH0FGYbBoB2wbtz5sx9nV/Fuc+YEF19EQYA\nQ3fw4EE1btfV+9///vfdd99xHrlRtmPfDbdhf9h4TNTRcFcicnWglybQxIHE4+roCgAAD+Ot\nHjv2f20fpqGOXlIMADqzqKgo9VIdy7JFRUWJiYlBQUH1j0ikdCaW9p6hhKyhNp5araUckSDD\nx/z8gU/mIdIBQDtD2wAA6LWysrLa2lpNSsjJyQkKCioT0OHLtP8c3S/TVtVaIK7Kun/766KE\nbQ5DB/G483R4JQCAliDYAYBeKy0t1bCExGzZ1V/oxDUS12mlRi0TViQX3FxfmrqXldURUVmZ\nLvMjAEArEOwAQK85ODioeSbDsfWc4tT7rT0xI7Vao4fVlt4uuLm+NH0/sU3txfb29jq9KABA\ni3hv/PyzriZWb+LVX+eXAADDZGNjY2lpWVVVpfwpXGNbe7+5TiFvm1j11F3FiKimJK7g5rqy\n9ANED8+ZXL+EDABAO+ONePHFjq4DAIAi48aN+/vvv5UZP2HuEO4Y+Eq3XnN0N31JvaqCSwW3\nPqvIOvpopKu3YMECnVYAAKBFaIoFAH331FNPHTx4UMEBDNekm88zjkGvWzhF6LgubEXW0YJb\nn1UVXFJwkIWFRUSErmsCANACBDsA0HeWb7R7AAAgAElEQVRPP/306tWr09LSpFLpQ7tMrH0c\nAl52CJjPM3XUaR1YWV1p6u/3b31eW3avzYM/+OADnVYGAKA1CHYAoO94PN6mTZsmTpzIMAzL\nskREDMfadZRD4Cu2PaczjG4ni5NJqosTd96//ZW4KluZ452cnJYvX67TKgEAtAbBDgA6gSee\neGL16tUrV640sfK091/gEDDfyNxV1xeVCIsK724piv9OIixR8hQej3f58mWGYXRaMQCA1iDY\nAUAnIJPRsCkfTSh+pqDWR9e36IhIWJFceHtjScovMokKcyObmZmdO3cO42EBoAMh2AGAXiss\no+PX6MC/xC9hiPx0fS+squBS4d1vyjMOsqyUiOrvvbEsy+PxJBKJghP79u175swZ9WfdAwDQ\nBl2tfg0AoAmxhM7E0mubaOIy2nyQ+Mq2haqJw1DvHmX+7Iay6Oll6X/WpzoicnZ2fuWVV65d\nuyYWi1euXOnp6flQM6uRkVH//v3Pnj0bFxeHVAcAHe5BT2TQD+np6fXtOBcuXIiMjOzo6gB0\ngJRcOnSJ/rlKldXtcTkrc5o6hJ4ZSd0fLBVRVFTE5/MZhunevfujWU0ikdy9ezcxMdHCwuKx\nxx5zd3dvj1oCACgHTbEAoBcENXQ6lo5doVtp7XTFHk709AiaGknmJs0ed3R0dHRsdfIUHo8X\nGhoaGhqq8/oBAKgOwQ4AOpKMpZhEOhpNZ2NJVNdOF+3rQ7NG06jHiIPeKABgWBDsAKBjpPPp\n6GX65yoVV7TTFc1MaOJAenYUebm00xUBANoZgh0AtKvSSjp5nf65SglZ7XdRDyeaMZwmDyYr\n3S4hCwDQwRDsALq05OTkQ4cO3blzJz8/XyaTubm5BQYGTpkypXfv3mqXWV1dfeLEiaioqNzc\n3NLSUnt7e09Pz+Ejxlr3GHfmhvH5OJI8vDCYrnAY6hdA04ei1RUAugoEO4Au6urVq++///5/\n//1HRJwHqYdhGKlUunz58vDw8M8//3zUqFEqlVlRUfH5559//fXXtbW1DMNwOBwZS9auI+3K\nQ/+rHMY1Ntb+r9EKGwuaOoRmDCdXzEACAF0Jgh1AlyOTyVasWLF+/frGKdlkMtlDx9y8eXP0\n6NGLFi369ttvjYyMlCk2NjZ28uTJ9ROFEJGZQ3g33+e6+TzTDmt/yQv2oqeG0+P9yVipWgMA\nGBQEO4CuhWXZOXPm/P777/U/t3ZYfdTbtm1bWlrasWPH2sx2UVFREydOFIvFxlZe3Xyesfeb\nZ2rrr92aK2ZsRGPC6bnRFODRnpcFANAvCHYAXcuqVavqU52STp8+/eabb27btk3BMSkpKc88\n/5ZdwJu23s+YO4RrXEfV+PWgp4bRExFkbtrOVwYA0DtYeUK/YOUJ0KmbN2+Gh4er8a4/ffr0\nmDFjHn28qJzO3qCNu+7KzIKJdLyMa3OmxjSuH00fRr17tudlAQD0Gu7YAXQhH3zwAcOo/HWO\nw+EsWbLk5s2bjY9UVNPFO3Qmli7fJamMyEz9IbRq8O5OEwfRk5FkbdGelwUA6AQQ7AC6iry8\nvNOnT6txu04mk8XFxcXFxXl4h56Lo7OxFJNMjwy30DlLM3bCQGbaEPLr0d6XBgDoLBDs6glO\nrHj++1syIqJ+bx9YOVrVWRmEBbf/PXvhxt3EtLziCoFQyjOxsHF09fAJDhsQOTTC24argzoD\nqObo0aPqdb0wsnCz6zl96U67sjqSdUDfDVaQ/29x0s6fN86ZMml8+18eAKATQbAjIqKamGv3\n1L3/UJNxeteWXSdTBPJ/8KQ1FYVZFYVZCTFRB3Y59Zv2yqJnI5zwZEOHunfvnkrtsMaWnnbe\nT9n1nGHhPJCIKRHrtHYtEFfllKT8UpK0S1SZSkRJCX0IwQ4AQCFkDSIi/omjsRJ1TmRLr29b\n9dnxLIV/8cSFMfvXvn376RUr5gRZqVdBAC3Iz8/ncDhSaRvLPphYe9t4TLbznmnpMridx0PU\nY6Wi8qzDJSl7KnOOs7Kmd2ZeXl77VwYAoHNBsCO28OTW/SnqtC+JknevWn88S6lIWJ24/+O1\n5l9+Ot0DTzl0kMbpiFvax7FwjLDt+aSd15MmNr3asVLNVN+/UpK8uzRtn1Rc8eheRfUHAAAi\n6urBjhXybx798bu9cTVqnCy69dNnB+VTnYlL2Nipk0aFerk4WnOqigtyEi6d+Pv45ayqhtAo\nTPjli319Ns3xRYc76BCurq4PrTDBcE2sXEfaek619ZrSzutDyBMLMktSfi1N/VVYnqTgMDc3\nt3arEgBAJ9XFgp1MLCgtKiosyM3KTE9NuBN3O7VIqF5fcFnmwR0niho3GdvQeSuXTfM1e/BA\nN1fvbq7efUeMOf/lR5suFdX/NZVlHfzxxOMbJmLxSugIISEh9R3seCbdrNxG23hOtvWcyjW2\n7qj6SMUV5VmHS5N/qcw7S9T2GzEkJKQdagUA0Kl1rWCX++eS1/ZmaKMk0bWDR7Ib/xIxThOW\nfDjNt4Vp77ndRyxempm2+GBB/bYk/six1AlzfdGkBO0vbNA0l9AUG49JFs6DGabDbhyzsrrK\nnBMlKXsqso7IpEIlzzI3Nx85cqROKwYAYAC6VrDTGsGF4xerGresh788N6TVxYy4fjOfG/DP\nxqsNf8DyTx27OeftMDTHQruok9DNFLp4hy7eoexCB7eIzzquLmxVweXS1N/K0vdLhMWqnjxv\n3jxjY1WnIQIA6HIQ7NRRcz36dlPnOvuRE/orXKPSYtCIfsZXLzYMnRXExiZSWLBOKwhdXUkl\nXbpDF+9QdALVKHtTTFdqS++Upv5Wmvq7uCpLvRLMzMxWrFih3VoBABikrhXs3J/55vAzjz4s\nPPXx01tuKF9M3c2YW00zRjgNHRbAUXyCSXhEH+7FmIZzyuNuZVGwp/LXA1CGTEb3MunSXbp8\nlxKyqcNXgRZVppWl/VGa9ntt6V0Ni9qxY4ezs7NWagUAYNi6VrDTkozERFHjhmlQkHebZ5j6\n+fegmMyGrZzExBryNNdN5aCrKa2kK/F06S5djaeK6o6uDZG4Orc8/UBp2r7qwqstHqDqYrUr\nV66cPXu2lmoHAGDgEOxUJ0hPl+sg1Ms/QIn+ct19fM0p88GsKgX8AqK24yBAKyRSupNOV+Lp\nyj1K1IObc0Rkb02hXiUHf1yUeusgQ2yL0a0+0vXq1WvkyJE//PBDm2Vyudzt27fPnz9fB/UF\nADBMCHaqy8vLbdowdeluq8Q5jLOTI9GDDkaFfL6UvDF+AlSUXUjR8XQ1nq4ndXzPuXrdrGjk\nYzS2H4X1Ig7HftW83d98E7Zhw4bKykoi4nK5LMsyDFO/3IWdnd2HH374xhtvmJiYvPLKKwsW\nLLh582aLxTIMM378+O+//75nz57t+vsAAHRyCHYqkxYXlzdtOTo6KnWWvYNDU7CTlZaWE9lr\nv25geCprKCaJouMpOp7yVR5LqiuOtjQylEaH02O+xJHrYmpubr5s2bL33nvv3LlzZ8+ezcvL\nKywsdHZ2dnd3HzNmzPDhw42MjOqPDAsLu3HjRnZ29o4dO86cOZOXlycSiczNzT08PKZMmTJ3\n7lwHB8z3CACgMgQ7lZWXlck1MtnZ2Sl1lrmVFZfowZALoVA/breAfpLKKDmHriXS1QS6kUyS\nNhZ3bT/OdjTyMRrWh8L9idv6kCFjY+Px48ePHz++zQI9PDzWrFmzZs0abdYSAKALQ7BTWfNQ\nZmyi5NRaxibGRLUNGyKRqNnOn3766ddffyUimUwWGhp6+/ZtbdQUOhOZjJJy6FoiXUuguFQS\n1XV0heR4ONPIUBoVRkGehPVaAQD0GYKdyurEcn9yGRNl50w1NpILdmKxuNlOkUhU3yeJiLhc\nLhY77yJYltLyKSaJrifSzRSqVGfNYh0K9KSRoTQilLw7bBVZAABQDYKdymSs3DLqXJ6yz2Cz\n04x4Rs12Dh8+vH6aruLi4uXLlz+0UjsYmMwCik2mmCSKSaIyQUfXpjkOhx7zpRGhNPIxcunW\n0bUBAAAVIdipzMTEpGlDKpG0fmQzUvmOUs3KIKKgoKCgoCAiSk9PX7hwocZ1BO3Iz8/n8/nV\n1dUuLi4eHh6mpgpXGGkdy1JGAd1Iptgk9nqirLxa70ZEm5vQwGAa3peG9CZby46uDQAAqAvB\nTmXN/rqzYnEdkVHrRzeqle+ap3ZCgPaQlJT03Xff/fXXX7m5TTPbGBsbjxo1atasWbNnz+Zy\n205mMpYy+BSTRDeS6WYKlTbcmWOI9CjVOdnR0BAaHkr9/MkYHwYAAJ0fPstVZmZmJrclEAiI\nlGixqqmuampeZeztlRtLC+2spqZm8eLF27dvf7Q1XCwWnzp16sSJE+vXr9+2bdvw4cMfPV0i\npfgsikuluBSKS6NKPVgH4lEMQ4EeFBlCQ/tQgAcGQwAAGBQEO5WZOzpZElU1bJWWlSoV7MpK\ny5o2HJyd9eiuDTTIy8ubNGlSXFxcawfUp73k5OQxY8Z8++23r776KhHVCOlOBsWl0o1kupdJ\nQnFrZ3cwc1MaGEhDQigyhOytO7o2AACgGwh2qnNzdSVKbtgozssXk2/bI2P5BQVNGy7OLrqp\nGqhNIBCMHz8+ISGhzSNlMpmRhftHGy9c5Q+vMw5KySN9HuvSw4kiQygyhMJ6kRHe7gAAhg6f\n9Kqzd3MzpeSGHnOyzMxsGubb1jlF2VlNU1m49PKz0lntQD1z5869d+9ea3sZDs+sW19Ll8EW\nzoMtXSKNLdyJ6O79dqyfKkyNKdyPhvSmQcHUw6mjawMAAO0IwU51TGBQIOfczYa7NPzbt4vJ\nt43Fj4T37qU3bpgGBXnrsHqguvPnz//1118PPWhk5mzhPNDCaZCl8yBzx34cnnmH1E15Hs40\npDcNDqYwPzJRZkAPAAAYHAQ7NViEPeZHNxMbtlKio0umT1K48Gvt9au3Gyc7MeodGowedvpl\n7dq1HA6HJZ65Q6iF0wALpwEWToNMrDtB/paKKwX5UYK803//snRQmEdHVwcAADoYgp06HCMi\nev6cmFG/ZCyb+M+x1Akv+La+cmbhySPRjUuImUaMGmih+yqCcnIKKfpOVYpwqt+UT83tQxmu\nSdvndDSWldYUXa/MPV2Ze6q6MJqVSYjo8jnvQWHvdXTVAACggyHYqcV1/OSwfd/GNgyAzD3y\n47Gx6yZ3bzHaSfOObd6X2Hi/zipyTATmsOtAJZUUn0nxWXQvk+5lUEU1EVk6Br/Z0fVqm6gy\nTZB3tjLvdGXeWamoTH4Xh8OJiop67z0EOwCArg7BTj1WI6aP2RP7T8NfV1H8rrVbu695rV+3\nh+YEE+ee2/LJjluN4ya4vtNnhiu5uCxoR2UNJWbRvUyKz6L4TLpf1vYp+kMiLBLkRVXmnRHk\nnRUJMlo7TCaTZWZmtmO9AABATyHYqYkXMmvewAsboxvWE6jLObnmrYxxM58aOyDYy9FcWlXK\nT7t54cShY9G5TQtOcNynLZzcvYMq3HXUJ7mEbErMpoQsyi3q6AqpSFZXJSi4IMiLEuSdqSm5\nRcQqc1ZxcbGuKwYAAPoPwU5tNiNefzcufU1UYcPfXbYy+eTO9Sd3tna8acCcxbP9cbtO+8qr\nKCmnIcYlZFFeJ0w4MkltdeEVQV6UIP9cddG1+m5zKnF0dNRFxQAAoHNBsNOATb83Vr9du/Lb\nK0VtTlBr7jdj+coZ3ni6taKglJJyKCmbEnMoKbuTta42YqWi6qJrgvxzgvxzVfevsFJR2+e0\ngsPheHp6arFuAADQSSFpaITnNuqDr10P/bBl38XsmlZazHgOfaYseG32YFfMLKYeiZTS+ZSS\nS8k5lJJLiTl6ugarMmRSYXXh1ar88wL+v9WF0TJJrXaKlcnGjh2rlaIAAKBTY1hWqR48oBBb\nlXX9/PnLsXdSMvlFFTViMjK3snX28AnoO3DYiEGBDko3wKanp/v4+BDRhQsXIiMjdVhlPVZe\nRcm5lJrXEObS+VSncsukHpFJqqvvRwv4/wn452sKr8mkwrbPURHDMKmpqd7enWDiPQAA0Cnc\nsdMKxtIzYtLciEkdXY/OqE5CGQWUmteQ5FJyqbiio+ukMRsLsjPOvnj826qCizXFsWr0mVMe\nwzBz5sxBqgMAAEKwg3YmY4lfQql5lJZPqbmUlk9Z90kibftE/edqT6G+FOpLob2opwvJZG4D\nzpy/UXRDpzfFGYaxsrJau3at7i4BAACdCIId6FZBKWXwKS2f0vmUmkcZfKpVf5CAfuEwbIAH\n09eX+nhTX19ysm22l8vlHj58ODw8vLCwUCZrc3gNcTgcIlLmyEYMwxDR3r17PTywmBgAABAh\n2IF2FZRSZgGl5T8Ic/lUrf0eZR1JIiqtKbxaU3R1wbP9lr4xyUzhCmSurq6nT5+ePHlyVlaW\n4vt2HA7H3Nz8t99+u3r16rp165S5yccwjImJye7duydNQhcAAABogGAH6isqbwhw9f+m5FGN\nYcU4ImJZqag8qbo4tqrgYm3hlerSe8FBgT9t26bk0JbevXtfv379rbfe2rdv34MCm4U2Docj\nk8kGDx78ww8/BAUFTZ48OSws7M0338zPz1dccnh4+Pbt2x977DH1fi8AADBIGBWrX/R2VGyN\nkDLvU1YBZRRQVgFl3afs+yTuzINVFbCxkPKEiblJJ3KSjtcUXpPWCYjIxMRkzJgxs2bNevbZ\nZ7lcrqplxsbGbt++/dChQ/fv32980MLCYsKECS+88MJDd92EQuHOnTt37doVGxv70DuUx+ON\nGzfupZdeevLJJ+ubYgEAABoh2OkXfQh2MhnxSynrPmUVUHZhww+ddBJgJZkYUYAH9e5JwT2p\nd09ytW94/P79+3w+XyAQdO/e3d3d3dTUVMMLyWSygoKCvLw8sVjs7u7u6upqZKRofsOampqs\nrKyEhASJRGJpaRkYGOjm5mZsjPVLAACgZQh2+qX9g11pJWUXUnYhZd+n7PuUeZ9yCw32Vlwj\nLod83CjIk4K8KNiLfN2Iy+noOgEAAGgMfey6nNMxlJpHOYWUU0TZ9w1tcENrOAz1cKJgLwry\noiBP8vcgE6wEAgAABgfBrsvZdYKScjq6ErpXn+QCPSnQkwI9yN+DLDRtRwUAANB3CHZdjoez\nYQY7Loe8XMjfgwJ6UIAHBXiQOZIcAAB0MQh2XUV+fv7hw4fj4uKSBKPJcmZHV0cLeFzycKIA\nTwr0oEBPCvAgUwwqAACArg3BzvDFx8cvW7bsyJEjLMsyDOPgX+cxrFMGu25W1Mud/HtQL3fy\n60FeLhjxAAAA0AyCnYHbuHHjkiVL6MG8uCzL1pYndXSllMLlkKcz+bqTnzv5uVMvd3K0bfss\nAACArgzBzpC99dZbmzdvZphmk9oIK1I6sEoKdLNqiHG+buTrRt6uZIz/ngAAAKrAX06D9c03\n32zevJkeWcNKUlsoFVdwjW06qF4NWKlIVJlWXRwrLLs3frjfpx/Nd3Po2BoBAAB0egh2hik1\nNXXJkiUP3atrJKpIMXfs1571kUmFwrIEYXl8bcnt2rJ4Ydk9kSCTqKFuv9xili4c5OYQ2J5V\nAgAAMDwIdoZpxYoVEomktWVFhDoOdjKpUFieKCyLry27JyyLry29KxZksKy0teNZln3xxRev\nXr2quyoBAAB0BQh2Bqi8vPzAgQMKFosTVWqzm51UXCGqTBVVpjcmOWF5ooIY16Lr168XFxc7\nOKA5FgAAQH0Idgbo+PHjEomi1V41GD/BiquyheXJwvJEYXmCsDxJWJ5YV5Ovbmly5bLsgQMH\nFi1apHlRAAAAXRaCnQG6e/eu4gNEygU7WV2VsCJZWJEsqqhPcknC8iSZpFobdWzBxYsXEewA\nAAA0gWBngPLz8zkcjkwma+2AR4MdK6sTCTJEFcnCihQ7s8rMxP9EFcni6lwd17SZrKys9rwc\nAACA4UGwM0AKetfVk4hKy9IP1NXwRZUpwooUUUWKuCqLlTW03loFBgryE3RfzYe1WW0AAABQ\nDMHOALm6uiq4XVcv/UzLq4pxuVxPT8+kpKQ2S9A6Dw+Pdr4iAACAgcFamwYoODhY7XOlUmlI\nSEhAQACH097/N4YMGdLOVwQAADAwCHYGaMKECTye+vdip0yZMm3atHa+Y8cwzNNPP92eVwQA\nADA8CHYGyM7O7sknn2QYRtUTORyOv7//4MGDn3/+eS6Xq4u6tSYsLMzR0bE9rwgAAGB4EOwM\n09q1a7lcrqrZTiaTffbZZxwOJyAgYP78+aqezuFwLCwsVDqlHsMwu3fvVuNEAAAAkIdgZ5j8\n/PzWrVun6jjT559/furUqfU/b9iwoWfPnsr3tONwOEZGRocOHbK0tFStrkRvvPGGJv0CAQAA\noB6CncFasmTJyy+/TERK3ngbOnTo9u3bGze7det29OhROzs7ZbIdh8PhcDh79uwZPXr0v//+\nq1IPv5EjR37zzTfKHw8AAACtQbAzZD/88MO6deuISEE4q9/14osvnj592tTUVH5XYGBgTExM\n/b00xfHO1tb21KlTM2fOJKKwsLC7d+/a2dkpU8P58+efPXtWje6AAAAA8CgEO0PGMMyyZctu\n3rw5ZsyY+kc4DzSOjejdu/fJkyd//vlnExOTR0vw8vKKiYnZtGlTfVBjGKa+615jBz5jY+O3\n3347KSlp5MiRjWf5+/vz+fxFixYpGIHh4eFx7ty5nTt3ItUBAABoC4Pp/vVKenq6j48PEV24\ncCEyMlKLJWdmZh4+fPjGjRt8Pl8sFru7u/v7+0+ZMqVPnz7KnF5XV3f+/PmTJ09mZmYWFhba\n2tq6ubmNGDHi8ccft7Gxae0ssVj8888/79mzJycnp7Ky0sjIqFu3bgMHDnzvvfdCQkK098sB\nAAAAEYKdtggLbv979sKNu4lpecUVAqGUZ2Jh4+jq4RMcNiByaIS3jbJTh+gu2AEAAIDBw5Ji\nGqvJOL1ry66TKQL5hCytqSjMqijMSoiJOrDLqd+0VxY9G+GEJxsAAAB0CX3sNMKWXt+6dMnm\nE81T3UPEhTH717794a/xgvarGAAAAHRBCHYaECXvXrX+eJZYmWOrE/d/vPZgtkTXdQIAAICu\nC62DahPd+umzg1lySc3EJWzs1EmjQr1cHK05VcUFOQmXTvx9/HJWVcPdPGHCL1/s67Npjm+7\nrtUFAAAAXQaCnZpkmQd3nChq3GRsQ+etXDbN1+zBA91cvbu5evcdMeb8lx9tulQkqz8p6+CP\nJx7fMNGh/esLAAAAhg9NseoRXTt4JLuxXx3jNGHJh3Kprgm3+4jFS6e5NG5L4o8cS8U4ZAAA\nANAFBDu1CC4cv1jVuGU9/OW5IaatHcv1m/ncgKa9+aeO3ZTqtnYAAADQNSHYqaPmevTtps51\n9iMn9G811hERWQwa0c+4cUsQG5uou6oBAABA14Vgp4a6mzG3mm66OQ0dFtDG02gSHtGnacRE\nedytLF1VDQAAALowBDs1ZCQmiho3TIOCvNs8w9TPv0fTVk5iYo0u6gUAAABdG4Kd6gTp6cVN\nW738A5SYvqS7j69501YBv0D71QIAAICuDsFOdXl5uU0bpi7dbZU4h3F2cmzaKuTzMX4CAAAA\ntA3BTmXS4uLypi1HR8fWD5Vj7yA3eZ2stLS89UMBAAAA1IIJilVWXlYmNxGdnZ2dUmeZW1lx\niR7cpxMKhc32njp1Kioqioiqq6u9vb0zMzO1UlUAAADoUhDsVNY8lBmbGLd6ZDPGJsZEtQ0b\nIpGo2c60tLQzZ87U/2xnZ5eVhWGzAAAAoDIEO5XVieuaNhgTY2WDnZFcsBOLxc12uru7R0RE\nEFFtbe2lS5dYFotTAAAAgMoQ7FQmY2VNG1yess9gs9OMeEbNdk6ePHny5MlElJ6e7uPjo3Ed\nAQAAoCvC4AmVmZiYNG1IJZLWj2xGKpEbCNusDAAAAACtQLBTmamp3PphrFi+YVaRWvmuec3K\nAAAAANAKBDuVmZmZyW0JBAKlzqqprmpqimXs7ZUbSwsAAACgPAQ7lZk7Olk2bZWWlSp1Vllp\nWdOGg7OzEqtVAAAAAKgEwU51bq6uTRvFefni1g9twi+QW0XMxdlF25UCAAAAQLBTnb2bW1MP\nOVlmZrYS5xRlZ9U0brj08rPSQb0AAACgi0OwUx0TGBTY9Lzxb98ubvMU4b176Y0bpkFB3jqp\nGAAAAHRtCHZqsAh7zK9pKyU6uqSNE2qvX73dONmJUe/QYPSwAwAAAO1DsFOHY0RET+bBBpv4\nz7FUmaLDC08eiW5cQsw0YtRAC11WDgAAALoqBDu1uI6fHNa0lFjukR+P8VuLdtK8Y5v3JTbe\nr7OKHBOBOewAAABAFxDs1GM1YvqYpqnoRPG71m6NKX10gVdx7rlvVu+41Thugus7fWa4kovL\nAgAAAKgGa8WqiRcya97ACxujG6Ynrss5ueatjHEznxo7INjL0VxaVcpPu3nhxKFj0blNC05w\n3KctnNy9gyoMAAAABg/BTm02I15/Ny59TVRhw406tjL55M71J3e2drxpwJzFs/1xuw4AAAB0\nBU2xGrDp98bqtwc5KvMcmvvNWLFyhjdyNAAAAOgOgp1GeG6jPvh6w/yhHuZM68c49Jn+wcb1\nL4RYtnoIAAAAgBbgFpKmGOuAaUs2j3n6+vnzl2PvpGTyiypqxGRkbmXr7OET0HfgsBGDAh3Q\nAAsAAAC6h2CnFYylZ8SkuRGTOroeAAAA0JWhKRYAAADAQCDYAQAAABgIBDsAAAAAA4FgBwAA\nAGAgEOwAAAAADASCHQAAAICBQLADAAAAMBAIdgAAAAAGAsEOAAAAwEAg2AEAAAAYCAQ7AAAA\nAAOBYAcAAABgIHgdXQFo2bZt244ePdrRtQAAAIDOhGFZtqPrAE3u37//5ptvRkVFyWSympoa\nkUjU0TWCttna2jIMg9ers8Dr1bnY2dkREV6vzgKvV4dDsNNH/fr1I7wxOg8Ehc6l/vWqrq4W\ni8UdXRdoG4JC54LXq8OhKVYfmacbtrgAACAASURBVJmZ1dbWfvLJJzNmzOjoukDbhgwZIhKJ\n1q5dO3369I6uC7Rt8ODBYrH4008/xevVKQwaNKiurm7dunXTpk3r6LpA2wYOHCiRSNavXz91\n6tSOrksXhcETAAAAAAYCwQ4AAADAQKCPnT6KiYmRyWReXl5OTk4dXRdo2/Xr11mWxevVWeD1\n6lzwenUu165dI6KePXs6Ojp2dF26KAQ7AAAAAAOBplgAAAAAA4FgBwAAAGAgEOwAAAAADATm\nsVOb4MSK57+/JSMi6vf2gZWjjVU8X1hw+9+zF27cTUzLK64QCKU8EwsbR1cPn+CwAZFDI7xt\nuAZTmkHCU6QleB/pObYq58alyzFxd1Oy8++XVFaLJBwTSytrKxv7Hr16h/QJjYgIdjJRvjh9\nfoYN4PWqK0268u+lmNuJGTn8oopqoYQxNjO3tLR17NHT1zeg7+Ch4Z5WKv0a+vwMG8DrpSMY\nPKGumnOfzPk6RkJEqv9Bqsk4vWvLrpMpgtaee2OnftNeWfRshJMywVufSzNIeIq0CO8jPf5P\nIuZH79vx05GYAkULCHBtA8bMnD9nUoAN00Zx+vwMG8DrJcw5/8v3Px2/Vy5VcBDPLmD07IVz\nx/pYtvVq6fczbACvly5xP/74446uQ6fEP7Jlx43Shg3XgU8P91by2wFben3b8lV7Y4sULWYk\nrc6/99/ZW9KggX0cFX4Z1ufSDBKeIu3C+0hf/5OwZTd++mjFj5eyqxTlBCJihcVpN6Ku5Ln0\nG+Bl1WrfHn1+hg3h9aq4sX35yt3X+MI2btTIhMVp105Hpdv0G9zLRkFPLH1+hg3h9dIxBDt1\nsIUnv/jyREHdg23l/yCJknd/+PGRrLq2jySqK753McFkwMjAVt9/+lyaQcJTpFV4H+ntfxJh\n0m8frf5fhtJLfbJV2dHRRb1GDXRt8Y+oPj/DBvB6SbMOrFj1Z5pQ2eNZYV7slXyPEUM8zFo+\nQJ+fYQN4vXSvS96m1AQr5N88+uN3e+Nq1DhZdOunzw5mSZoeMHEJGzt10qhQLxdHa05VcUFO\nwqUTfx+/nFXV8LVLmPDLF/v6bJrj29JfO30uzSDhKdIevI/0+T+JOHHPxv0ZcvdDGBufyDHj\nRg4K8XDqZmfFE1cW5abcvnLq8D/X8hrTBFsc9c2WsC3Lhtk8XJw+P8MG8HqxOX99uzdZLtVx\nbANGjB8dGdHby8nOxtKoTlCUmxx3+fSRf67KvVplF7bvHh32TlgL0U6fn2EDeL3aA/rYtUUm\nFpQWFRUW5GZlpqcm3Im7nVr0yN1u5foGyTJ/f+vt37MfnMzYhs5buWya78NvLCn//JcfbbpU\nJGvY5gUt3L5hokNnKs0g4SnSCN5HqpfWUYqPLX/5hzuNLbBWQTPfXzqnr92jnbIkBZe2fvLV\n6dymv7NO07744f/bu8+AKI42AMBzHr33bqOJFAUpYgURIbFExBobSqJGYyyJ7bNiN4lJLEnU\n2DVGYwUblqgogggqChZEKdIOEI7ey30/5Hb34CrcHcPyPr/Yu9nZvXcG7mVndja4F893KM4R\npkN7Vcfu/GrLgzLuprbz7HXLA2w1W7ZWQ0Hc4S0/X00lkrsurotObvDVbFYM5wjTob3kopNd\noJRc1rnl04LnL1m1cee+4xdvx75r+W0krprYi1eIHokYRiOXr27ZIxFCTFPvZSsDTIjt+tdX\nrr1vcVCca6MlCFGbwO+R5LW1l8yb18msDul5L1kzg19WhxBSMBm0MGRePzXylfwboY94Zz7h\nHGE6tFflo1tRRFbHMB29YtU4flkdQohp4D53/TeuZGs1Jj593mKaGs4RpkN7yQckdvJSFhn+\nsJzY0vKaE+SkIqgs03bi1P7kuzm3rsU3m8CMc220BCHCBM49nx6dJDc2LpPYUPacNce9+TUd\nKoaRX/CYruR2dfyTl9QPgnOE6dBeDc+fvCBORMlt8lQnNWHF9by/GKJFbNXm5BQ2K4BzhOnQ\nXnICiZ2cVMbFJJADFvrDRroL7JIIIaQ+wNuNHJMqe/o0qcPURksQIkzg3PPp0Umq37xJIzaU\n3IcNEpbWIYQQo9sAT3NyszLlfS5lC+MI06K9stPIO1wYjp4eGiLKd+nejZKGl5SW8L6Nc4Rp\n0V5yAomdCBaTd1/m4+zCfhJVUxdP+b8KGQ0Zaici8squHn3ImSrFz1986CC10RKEqK3g90jC\n2tpNVmYmOWhlY2+vKHoXM3MzylZxcTHxM84Rpkd75eXlET8bWvYUldchVFdHuZ9USYl3SivO\nEaZHe8kJJHbykZaURK4coGJvbylyDxXbXpT/rDKTkii3D+JcGy1BiDCBc8+nSSdhF5Kjc2pm\nZi1uceVDUZGa/TEVyG9TnCNMi/ZqqKwk8zQ9PX2RO9R/yMghNhjGxkY87+IcYVq0l7xAYicX\nZampBeSWTS87MW69NrWypsyWyGWR4xs410ZLECJM4NzzadJJGioqyPn0WlpaQooSeE9cV0eX\n+yPOEaZHezG91pLXv38arSuqfFHE9WhimhrDyrkPzyU+nCNMj/aSF0js5CI7O4vcUDEx1RFj\nH4axkSG5lc9iEZehca6NliBEmMC559OkkzCHUTKFv4KsxdglN/oROSsP6dr1Jq4C4RxhmrSX\nBBqKEo5t/esZsdqJmvvYEWY8JXCOcOdrr7aAxE4eGgoKyGknyNDQUHBRCn0DysI7jWw2twqc\na6MlCBEmcO75nbaTFEUcuZRCzsrTHTDQjrvYBs4R7iTtxamrKi3MTU+MDD2yY8mCdReJdYwZ\nuoMWzPfivSCLc4Q7SXtJCzx5Qh6Ki4ooi+jo6oq8YI4QQkhNU5OJEPd/jOpq7q8kzrXREoQI\nEzj3/M7ZSSrf/rPljxhyCQpFh8njnYhF1HCOMN3bK/X43CUXBAw9MjSsRi7839cDm8/IwznC\ndG8vKYMrdvLA26GUlEUurs+nYE0Nd+YozrXREoQIEzj3/M7XSWoz7+1dvvbMO8qM9t7TvvGn\nXErBOcKdr724VK3HLfnf7IFGLeeo4RzhztterQKJnTzU1VLuMGcoK4nbKRUpBWtruXOaca6N\nliBEmMC553eqTtLATryw/bulv93OJL8pmSbDv18d2J2aLuAc4U7VXjyq3l/cMm/u/w4/yq1v\n9g7OEe687dUqMBQrD42cRnKDqSBu0Hl2U1RQ5PcyZrXREoQIEzj3/M7SSeo+Prt87NC/kVk8\no1rqtgHL1sx2bbY2Cs4R7iztxV8D+1XYjmXp3+wI+dyCTMVxjnDnbi+JdZrErvpOyKTdz4QW\nMZ+0a9900YvjtIKysjK50VDf/B8lQRrqKTfxkHXgXBstQYgwgXPP7wSdpDoz6tzho2HP8nmu\nezD1nAIXLp7qxmdoD+cI0729LIP+uhyEOA21NdVVpQWsrPTkhCcPH0QlFRCflFP64sC2k9Z7\nZtlwswCcI0z39pKyTpPYtSsVFcqzTzi1tXUIifOvQxV1WgFZB8610RKECBM493xadxJO6bv/\n/jn09403RY3Ul7to9/48aP4M3x4CHk+Kc4Rp3V4EBlNJRV1JRV3bqLtdP68vpgfFn/tl55lX\nZU33ITRmhR27OXrrqKZbR3GOcOdoL6mBOXbyoKqqStkqKysTa6/KinLyzyhDX597HxDOtdES\nhAgTOPd82naSyvT/9i2fv2zvdZ6sTsmoX+DyPQd+nCcwq0N4R5i27SWMgoHLlxs3BJoT9y2j\nhsQ79/O5GzhHuFO2V+tBYicPaoZGlAW+2UVssfYqYheRGwbGxtyhDpxroyUIESZw7vm07CTl\nSRdDvl26Jzy5jFxqgqFpNSx48779IbOGdBOc0yGE8I4wLdtLHEq2kya5Uy52pb55wx1axznC\nnba9WqfTDMWqDA+5PLzdjm5uZoZQctNGQXZOLbIWfVcPK5eyDJGJsUmHqI2WIESYwLnn062T\nNLAidofsjWBRbkZk6jqOmT1nildPNYbg/ahwjjDd2kt8qs4uvVDsy6atxoJCNkKfPgnOEe68\n7dUacMVOLvTNzcnR/cb09Awx9vmY8YF8ZrGJja1mh6iNliBEmMC559Oqk3DYMXvX7aJkdQwN\nuzE/7Nm/Ldhb7KwO4R1hOrRXxqkFXxAWnEoTvQdCCCEdPX3KF39dHdHKOEeYDu0lP5DYyQWj\nt31vMtSshIQCIYU/qX71KpXYULG3p9yui3NttAQhwgTOPZ9GnYSTd/3nX+7mE7OT1KwD1u7e\nPserq6qwvfjAOcJ0aC9NTUqqkp/DahRclKqxoZ7yFAdtLWKVGpwjTIf2kh9I7ORDvZ+LLbn1\nLiamUMQOVXGPE4gbtRUdnR2oswNwro2WIESYwLnn06aTlNz76+QrYuVhtd4zt2wNdjds1cnh\nHGEatJe2sTF5Fav2XXK6eLtlZWaSiZ2SgQGZHeIcYRq0l/xAYicnhh4ePYkhDE7S9Wvvhf53\nlX/zSgzxp1XFw8dTvcPURksQIkzg3PNp0kkyb1yKIwawdL2XrJ5gLemVOhLOEe747dXF3sGO\nHBjPffwonSOkdJOG93fuk+OYTKc+DpSxdZwj3PHbS34gsZMXM/8x/cjJnllXDl0TeOG8Ifva\n3jNJxP8amoN9PZqvv4NzbbQEIcIEzj2fFp0k+c6dD9yfVT2CvvLUFlZaJJwj3PHbS9O1vx35\nHZ597cSdQhGpXXXyP39czSE2mY4erjz3NuMc4Y7fXnIDiZ3caHoH+pLL6NS8PrZl3xN2y9/C\n2qx7uzcefEH8y8y0Dpzo2vL2H5xroyUIESZw7vk06CSZcXHEjYQag0d6tS2tQ3hHuOO3l8Hw\nLwaSiVn5k30bDzzOrxNQuD4/9tj6kHMp5GMbDPwmD2+2thvOEe747SUvDA5HjIu3oLnqWyGT\nfieeUOa2+Pz64WJ0nJKIbQt+jSGXVmRo2fpNHD+iv0MPQ7WGcjYrJT7yRti1GMpjGLtYjP9x\nV1AvvnXjXBstQYikDn6PMOskZTfWTPszsU1V2H59ZOcXBtRXcI5wB28vhBqzQlcsOZJMecyb\nspmbn99QD2e7robaWmqK9VVlbFZq0ovYuzfvJuTVUHbVGbJy9/JBLRftxTnCHb695AMSu9Zp\n3RcSQiVPdv2w+W6+mDFXsZu5Y9sES4GLDeJcGy1BiKQMfo8w6yQJ+6auDS9vUxUtEzu8I9yh\n2wshhDjZV9YuPZhYLboklZLV5K07pvXi//RUnCPc4dtLHpghISHtfQ4dUX1KxLlYFnfLzHOS\nl6VYt9yomLm5GmfExmZViuyXarYT1m740k7YzACca6MlCJGUwe8RXp2k/PnV0+RQbOvo9xvr\n16vZMylwjnBHbi+EEEIMrV4D+yi8jUnIrxVdGCGEENPAPWjd6sl2Ap8cgnOEO3x7yQMkdq3T\n2i8khLpo9Rw8zFnt49u3mSWC5kIoGPQJWLR26WjRd6PhXBstQYikCn6P8Ook+U9DryUWt60O\nfokd3hHuuO3FpWjgMHSQtVLRhxTBn+ATFdN+nwd9v2z2YHP+1+q4cI5wx28vmYOh2PbCKf8Q\nFxER/TTxXTrrY0llLVJU09Qx7mZl19dzqPeA3gaSTQnAuTZaghBhAueeD52kJZwj3PHbi1OR\n9SL2yfOEhNfvWezS0rLyylqGsoaGhoaOYVer3vYOTq4ezl01xH90CN4R7vjtJTOQ2AEAAAAA\n0AQsdwIAAAAAQBOQ2AEAAAAA0AQkdgAAAAAANAGJHQAAAAAATUBiBwAAAABAE5DYAQAAAADQ\nBCR2AAAAAAA0AYkdAAAAAABNQGIHAAAAAEATkNgBAAAAANAEJHYAAAAAADQBiR0AAAAAAE0o\ntPcJAAAAwExj8ev/rtx8EBUV/eR1Rj6bXVRUVquooa2jo2fc09HV3d1jiP9Yf2dDxfY+TwBA\nCwwOh9Pe5wAAwEL5sdGas69xt7Tn3S7e79ue5wPaQePH6L+2bNx55FZKufCCikbOY2YtWfe/\nmc46DPmcGgBAHDAUCwAAACGEGvMiNvr3HTJ/j8isDiFUl//84k+zXG085598VSWHkwMAiAcS\nOwAAAAix/1vo5RfyH6tRkp0aC2L3zxzivzm6RFanBQCQDCR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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_nls = nls(Infiltration_percentage ~ a*exp(ImmuneScore*b), \n",
    "              sims, start=list(a=1,b=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Formula: Infiltration_percentage ~ a * exp(ImmuneScore * b)\n",
       "\n",
       "Parameters:\n",
       "   Estimate Std. Error t value Pr(>|t|)    \n",
       "a 8.095e+00  1.286e-01   62.93   <2e-16 ***\n",
       "b 6.267e-04  4.956e-06  126.45   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 1.136 on 78 degrees of freedom\n",
       "\n",
       "Number of iterations to convergence: 9 \n",
       "Achieved convergence tolerance: 4.202e-07\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(fit_nls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "    pdf(file = paste(plotpath,\"ESTIMATE_simulation_fit.pdf\", sep = \"\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(simplot)\n",
    "    dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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