notebooks/000_IHC_plots.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=\"#Dependencies\" data-toc-modified-id=\"Dependencies-1\">Dependencies</a></span></li><li><span><a href=\"#Functions\" data-toc-modified-id=\"Functions-2\">Functions</a></span></li><li><span><a href=\"#Paths\" data-toc-modified-id=\"Paths-3\">Paths</a></span></li><li><span><a href=\"#Main\" data-toc-modified-id=\"Main-4\">Main</a></span></li><li><span><a href=\"#Box-plots-for-cells-H-score\" data-toc-modified-id=\"Box-plots-for-cells-H-score-5\">Box plots for cells H score</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(reshape2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "source(\"~/OneDrive - UHN/R_src/ggplot2_theme.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load raw scoring spreadsheets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_raw <- read.csv(paste0(datapath, \"IHC/Alberto/CD4_raw.csv\"),\n",
    "                  header = TRUE, stringsAsFactors = F, na.strings = \"\")\n",
    "\n",
    "cd8ihc_raw <- read.csv(paste0(datapath, \"IHC/Alberto/CD8_raw.csv\"),\n",
    "                  header = TRUE, stringsAsFactors = F, na.strings = \"\")\n",
    "\n",
    "cd19ihc_raw <- read.csv(paste0(datapath, \"IHC/Alberto/CD19_raw.csv\"),\n",
    "                  header = TRUE, stringsAsFactors = F, na.strings = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1159 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>organ</th><th scope=col>ID</th><th scope=col>Column</th><th scope=col>Row</th><th scope=col>X.</th><th scope=col>Intensity</th><th scope=col>type</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NA        </td><td>NA</td><td> 1</td><td>1</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td>NA        </td><td>NA</td><td> 2</td><td>1</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td>NA        </td><td>NA</td><td> 3</td><td>1</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 4</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 5</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 6</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 7</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 8</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> liver    </td><td>NA</td><td> 9</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> liver    </td><td>NA</td><td>10</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> liver    </td><td>NA</td><td>11</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>pancreas  </td><td>NA</td><td>12</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>pancreas  </td><td>NA</td><td>13</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>pancreas  </td><td>NA</td><td>14</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>NA        </td><td>NA</td><td>15</td><td>1</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td>bladder   </td><td>NA</td><td>16</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>bladder   </td><td>NA</td><td>17</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>bladder   </td><td>NA</td><td>18</td><td>1</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> cartilage</td><td>NA</td><td> 1</td><td>2</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td> cartilage</td><td>NA</td><td> 2</td><td>2</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td> cartilage</td><td>NA</td><td> 3</td><td>2</td><td>NA</td><td>NA</td><td>NA    </td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 4</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>epidid    </td><td>NA</td><td> 5</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> ovary    </td><td>NA</td><td> 6</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> cervix   </td><td>NA</td><td> 7</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>breast    </td><td>NA</td><td> 8</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>liver     </td><td>NA</td><td> 9</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>liver     </td><td>NA</td><td>10</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>liver     </td><td>NA</td><td>11</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td> pancreas </td><td>NA</td><td>12</td><td>2</td><td>NA</td><td>NA</td><td>normal</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>143</td><td>NA                  </td><td>17</td><td> 8</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>144</td><td>NA                  </td><td>18</td><td> 8</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>145</td><td>PARNYR              </td><td> 1</td><td> 9</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>146</td><td>PASAYM           FP1</td><td> 2</td><td> 9</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>147</td><td>PATGXY              </td><td> 3</td><td> 9</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>148</td><td>PATUFA              </td><td> 4</td><td> 9</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>149</td><td>PAPSKR              </td><td> 5</td><td> 9</td><td> 0</td><td> 0</td><td>High Grade Glioma</td></tr>\n",
       "\t<tr><td>150</td><td>PARCLS              </td><td> 6</td><td> 9</td><td> 0</td><td> 0</td><td>High Grade Glioma</td></tr>\n",
       "\t<tr><td>151</td><td>PAVDYT    C1        </td><td> 7</td><td> 9</td><td> 0</td><td> 0</td><td>High Grade Glioma</td></tr>\n",
       "\t<tr><td>152</td><td>PANDBY      A2      </td><td> 8</td><td> 9</td><td>NA</td><td>NA</td><td>ATRT             </td></tr>\n",
       "\t<tr><td>153</td><td>PAPHZW              </td><td> 9</td><td> 9</td><td> 0</td><td> 0</td><td>ATRT             </td></tr>\n",
       "\t<tr><td>154</td><td>PAVXUM              </td><td>10</td><td> 9</td><td> 0</td><td> 0</td><td>ATRT             </td></tr>\n",
       "\t<tr><td>155</td><td>NA                  </td><td>11</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>156</td><td>NA                  </td><td>12</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>157</td><td>NA                  </td><td>13</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>158</td><td>NA                  </td><td>14</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>159</td><td>NA                  </td><td>15</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>160</td><td>NA                  </td><td>16</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>161</td><td>NA                  </td><td>17</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>162</td><td>NA                  </td><td>18</td><td> 9</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>163</td><td>PARWLK              </td><td> 1</td><td>10</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>164</td><td>PARWLK              </td><td> 2</td><td>10</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>165</td><td>PATNLD              </td><td> 3</td><td>10</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>166</td><td>PATNLD              </td><td> 4</td><td>10</td><td> 0</td><td> 0</td><td>Ependymoma       </td></tr>\n",
       "\t<tr><td>167</td><td>NA                  </td><td> 5</td><td>10</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>168</td><td>NA                  </td><td> 6</td><td>10</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>169</td><td>NA                  </td><td> 7</td><td>10</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>170</td><td>NA                  </td><td> 8</td><td>10</td><td>NA</td><td>NA</td><td>NA               </td></tr>\n",
       "\t<tr><td>171</td><td>PAPHZW              </td><td> 9</td><td>10</td><td>NA</td><td>NA</td><td>ATRT             </td></tr>\n",
       "\t<tr><td>172</td><td>PAVXUM              </td><td>10</td><td>10</td><td> 0</td><td> 0</td><td>ATRT             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1159 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " organ & ID & Column & Row & X. & Intensity & type\\\\\n",
       " <chr> & <chr> & <int> & <int> & <int> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t NA         & NA &  1 & 1 & NA & NA & NA    \\\\\n",
       "\t NA         & NA &  2 & 1 & NA & NA & NA    \\\\\n",
       "\t NA         & NA &  3 & 1 & NA & NA & NA    \\\\\n",
       "\t breast     & NA &  4 & 1 & NA & NA & normal\\\\\n",
       "\t breast     & NA &  5 & 1 & NA & NA & normal\\\\\n",
       "\t breast     & NA &  6 & 1 & NA & NA & normal\\\\\n",
       "\t breast     & NA &  7 & 1 & NA & NA & normal\\\\\n",
       "\t breast     & NA &  8 & 1 & NA & NA & normal\\\\\n",
       "\t  liver     & NA &  9 & 1 & NA & NA & normal\\\\\n",
       "\t  liver     & NA & 10 & 1 & NA & NA & normal\\\\\n",
       "\t  liver     & NA & 11 & 1 & NA & NA & normal\\\\\n",
       "\t pancreas   & NA & 12 & 1 & NA & NA & normal\\\\\n",
       "\t pancreas   & NA & 13 & 1 & NA & NA & normal\\\\\n",
       "\t pancreas   & NA & 14 & 1 & NA & NA & normal\\\\\n",
       "\t NA         & NA & 15 & 1 & NA & NA & NA    \\\\\n",
       "\t bladder    & NA & 16 & 1 & NA & NA & normal\\\\\n",
       "\t bladder    & NA & 17 & 1 & NA & NA & normal\\\\\n",
       "\t bladder    & NA & 18 & 1 & NA & NA & normal\\\\\n",
       "\t  cartilage & NA &  1 & 2 & NA & NA & NA    \\\\\n",
       "\t  cartilage & NA &  2 & 2 & NA & NA & NA    \\\\\n",
       "\t  cartilage & NA &  3 & 2 & NA & NA & NA    \\\\\n",
       "\t breast     & NA &  4 & 2 & NA & NA & normal\\\\\n",
       "\t epidid     & NA &  5 & 2 & NA & NA & normal\\\\\n",
       "\t  ovary     & NA &  6 & 2 & NA & NA & normal\\\\\n",
       "\t  cervix    & NA &  7 & 2 & NA & NA & normal\\\\\n",
       "\t breast     & NA &  8 & 2 & NA & NA & normal\\\\\n",
       "\t liver      & NA &  9 & 2 & NA & NA & normal\\\\\n",
       "\t liver      & NA & 10 & 2 & NA & NA & normal\\\\\n",
       "\t liver      & NA & 11 & 2 & NA & NA & normal\\\\\n",
       "\t  pancreas  & NA & 12 & 2 & NA & NA & normal\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 143 & NA                   & 17 &  8 & NA & NA & NA               \\\\\n",
       "\t 144 & NA                   & 18 &  8 & NA & NA & NA               \\\\\n",
       "\t 145 & PARNYR               &  1 &  9 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 146 & PASAYM           FP1 &  2 &  9 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 147 & PATGXY               &  3 &  9 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 148 & PATUFA               &  4 &  9 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 149 & PAPSKR               &  5 &  9 &  0 &  0 & High Grade Glioma\\\\\n",
       "\t 150 & PARCLS               &  6 &  9 &  0 &  0 & High Grade Glioma\\\\\n",
       "\t 151 & PAVDYT    C1         &  7 &  9 &  0 &  0 & High Grade Glioma\\\\\n",
       "\t 152 & PANDBY      A2       &  8 &  9 & NA & NA & ATRT             \\\\\n",
       "\t 153 & PAPHZW               &  9 &  9 &  0 &  0 & ATRT             \\\\\n",
       "\t 154 & PAVXUM               & 10 &  9 &  0 &  0 & ATRT             \\\\\n",
       "\t 155 & NA                   & 11 &  9 & NA & NA & NA               \\\\\n",
       "\t 156 & NA                   & 12 &  9 & NA & NA & NA               \\\\\n",
       "\t 157 & NA                   & 13 &  9 & NA & NA & NA               \\\\\n",
       "\t 158 & NA                   & 14 &  9 & NA & NA & NA               \\\\\n",
       "\t 159 & NA                   & 15 &  9 & NA & NA & NA               \\\\\n",
       "\t 160 & NA                   & 16 &  9 & NA & NA & NA               \\\\\n",
       "\t 161 & NA                   & 17 &  9 & NA & NA & NA               \\\\\n",
       "\t 162 & NA                   & 18 &  9 & NA & NA & NA               \\\\\n",
       "\t 163 & PARWLK               &  1 & 10 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 164 & PARWLK               &  2 & 10 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 165 & PATNLD               &  3 & 10 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 166 & PATNLD               &  4 & 10 &  0 &  0 & Ependymoma       \\\\\n",
       "\t 167 & NA                   &  5 & 10 & NA & NA & NA               \\\\\n",
       "\t 168 & NA                   &  6 & 10 & NA & NA & NA               \\\\\n",
       "\t 169 & NA                   &  7 & 10 & NA & NA & NA               \\\\\n",
       "\t 170 & NA                   &  8 & 10 & NA & NA & NA               \\\\\n",
       "\t 171 & PAPHZW               &  9 & 10 & NA & NA & ATRT             \\\\\n",
       "\t 172 & PAVXUM               & 10 & 10 &  0 &  0 & ATRT             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1159 × 7\n",
       "\n",
       "| organ &lt;chr&gt; | ID &lt;chr&gt; | Column &lt;int&gt; | Row &lt;int&gt; | X. &lt;int&gt; | Intensity &lt;int&gt; | type &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| NA         | NA |  1 | 1 | NA | NA | NA     |\n",
       "| NA         | NA |  2 | 1 | NA | NA | NA     |\n",
       "| NA         | NA |  3 | 1 | NA | NA | NA     |\n",
       "| breast     | NA |  4 | 1 | NA | NA | normal |\n",
       "| breast     | NA |  5 | 1 | NA | NA | normal |\n",
       "| breast     | NA |  6 | 1 | NA | NA | normal |\n",
       "| breast     | NA |  7 | 1 | NA | NA | normal |\n",
       "| breast     | NA |  8 | 1 | NA | NA | normal |\n",
       "|  liver     | NA |  9 | 1 | NA | NA | normal |\n",
       "|  liver     | NA | 10 | 1 | NA | NA | normal |\n",
       "|  liver     | NA | 11 | 1 | NA | NA | normal |\n",
       "| pancreas   | NA | 12 | 1 | NA | NA | normal |\n",
       "| pancreas   | NA | 13 | 1 | NA | NA | normal |\n",
       "| pancreas   | NA | 14 | 1 | NA | NA | normal |\n",
       "| NA         | NA | 15 | 1 | NA | NA | NA     |\n",
       "| bladder    | NA | 16 | 1 | NA | NA | normal |\n",
       "| bladder    | NA | 17 | 1 | NA | NA | normal |\n",
       "| bladder    | NA | 18 | 1 | NA | NA | normal |\n",
       "|  cartilage | NA |  1 | 2 | NA | NA | NA     |\n",
       "|  cartilage | NA |  2 | 2 | NA | NA | NA     |\n",
       "|  cartilage | NA |  3 | 2 | NA | NA | NA     |\n",
       "| breast     | NA |  4 | 2 | NA | NA | normal |\n",
       "| epidid     | NA |  5 | 2 | NA | NA | normal |\n",
       "|  ovary     | NA |  6 | 2 | NA | NA | normal |\n",
       "|  cervix    | NA |  7 | 2 | NA | NA | normal |\n",
       "| breast     | NA |  8 | 2 | NA | NA | normal |\n",
       "| liver      | NA |  9 | 2 | NA | NA | normal |\n",
       "| liver      | NA | 10 | 2 | NA | NA | normal |\n",
       "| liver      | NA | 11 | 2 | NA | NA | normal |\n",
       "|  pancreas  | NA | 12 | 2 | NA | NA | normal |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 143 | NA                   | 17 |  8 | NA | NA | NA                |\n",
       "| 144 | NA                   | 18 |  8 | NA | NA | NA                |\n",
       "| 145 | PARNYR               |  1 |  9 |  0 |  0 | Ependymoma        |\n",
       "| 146 | PASAYM           FP1 |  2 |  9 |  0 |  0 | Ependymoma        |\n",
       "| 147 | PATGXY               |  3 |  9 |  0 |  0 | Ependymoma        |\n",
       "| 148 | PATUFA               |  4 |  9 |  0 |  0 | Ependymoma        |\n",
       "| 149 | PAPSKR               |  5 |  9 |  0 |  0 | High Grade Glioma |\n",
       "| 150 | PARCLS               |  6 |  9 |  0 |  0 | High Grade Glioma |\n",
       "| 151 | PAVDYT    C1         |  7 |  9 |  0 |  0 | High Grade Glioma |\n",
       "| 152 | PANDBY      A2       |  8 |  9 | NA | NA | ATRT              |\n",
       "| 153 | PAPHZW               |  9 |  9 |  0 |  0 | ATRT              |\n",
       "| 154 | PAVXUM               | 10 |  9 |  0 |  0 | ATRT              |\n",
       "| 155 | NA                   | 11 |  9 | NA | NA | NA                |\n",
       "| 156 | NA                   | 12 |  9 | NA | NA | NA                |\n",
       "| 157 | NA                   | 13 |  9 | NA | NA | NA                |\n",
       "| 158 | NA                   | 14 |  9 | NA | NA | NA                |\n",
       "| 159 | NA                   | 15 |  9 | NA | NA | NA                |\n",
       "| 160 | NA                   | 16 |  9 | NA | NA | NA                |\n",
       "| 161 | NA                   | 17 |  9 | NA | NA | NA                |\n",
       "| 162 | NA                   | 18 |  9 | NA | NA | NA                |\n",
       "| 163 | PARWLK               |  1 | 10 |  0 |  0 | Ependymoma        |\n",
       "| 164 | PARWLK               |  2 | 10 |  0 |  0 | Ependymoma        |\n",
       "| 165 | PATNLD               |  3 | 10 |  0 |  0 | Ependymoma        |\n",
       "| 166 | PATNLD               |  4 | 10 |  0 |  0 | Ependymoma        |\n",
       "| 167 | NA                   |  5 | 10 | NA | NA | NA                |\n",
       "| 168 | NA                   |  6 | 10 | NA | NA | NA                |\n",
       "| 169 | NA                   |  7 | 10 | NA | NA | NA                |\n",
       "| 170 | NA                   |  8 | 10 | NA | NA | NA                |\n",
       "| 171 | PAPHZW               |  9 | 10 | NA | NA | ATRT              |\n",
       "| 172 | PAVXUM               | 10 | 10 |  0 |  0 | ATRT              |\n",
       "\n"
      ],
      "text/plain": [
       "     organ      ID                   Column Row X. Intensity type             \n",
       "1    NA         NA                    1     1   NA NA        NA               \n",
       "2    NA         NA                    2     1   NA NA        NA               \n",
       "3    NA         NA                    3     1   NA NA        NA               \n",
       "4    breast     NA                    4     1   NA NA        normal           \n",
       "5    breast     NA                    5     1   NA NA        normal           \n",
       "6    breast     NA                    6     1   NA NA        normal           \n",
       "7    breast     NA                    7     1   NA NA        normal           \n",
       "8    breast     NA                    8     1   NA NA        normal           \n",
       "9     liver     NA                    9     1   NA NA        normal           \n",
       "10    liver     NA                   10     1   NA NA        normal           \n",
       "11    liver     NA                   11     1   NA NA        normal           \n",
       "12   pancreas   NA                   12     1   NA NA        normal           \n",
       "13   pancreas   NA                   13     1   NA NA        normal           \n",
       "14   pancreas   NA                   14     1   NA NA        normal           \n",
       "15   NA         NA                   15     1   NA NA        NA               \n",
       "16   bladder    NA                   16     1   NA NA        normal           \n",
       "17   bladder    NA                   17     1   NA NA        normal           \n",
       "18   bladder    NA                   18     1   NA NA        normal           \n",
       "19    cartilage NA                    1     2   NA NA        NA               \n",
       "20    cartilage NA                    2     2   NA NA        NA               \n",
       "21    cartilage NA                    3     2   NA NA        NA               \n",
       "22   breast     NA                    4     2   NA NA        normal           \n",
       "23   epidid     NA                    5     2   NA NA        normal           \n",
       "24    ovary     NA                    6     2   NA NA        normal           \n",
       "25    cervix    NA                    7     2   NA NA        normal           \n",
       "26   breast     NA                    8     2   NA NA        normal           \n",
       "27   liver      NA                    9     2   NA NA        normal           \n",
       "28   liver      NA                   10     2   NA NA        normal           \n",
       "29   liver      NA                   11     2   NA NA        normal           \n",
       "30    pancreas  NA                   12     2   NA NA        normal           \n",
       "⋮    ⋮          ⋮                    ⋮      ⋮   ⋮  ⋮         ⋮                \n",
       "1130 143        NA                   17      8  NA NA        NA               \n",
       "1131 144        NA                   18      8  NA NA        NA               \n",
       "1132 145        PARNYR                1      9   0  0        Ependymoma       \n",
       "1133 146        PASAYM           FP1  2      9   0  0        Ependymoma       \n",
       "1134 147        PATGXY                3      9   0  0        Ependymoma       \n",
       "1135 148        PATUFA                4      9   0  0        Ependymoma       \n",
       "1136 149        PAPSKR                5      9   0  0        High Grade Glioma\n",
       "1137 150        PARCLS                6      9   0  0        High Grade Glioma\n",
       "1138 151        PAVDYT    C1          7      9   0  0        High Grade Glioma\n",
       "1139 152        PANDBY      A2        8      9  NA NA        ATRT             \n",
       "1140 153        PAPHZW                9      9   0  0        ATRT             \n",
       "1141 154        PAVXUM               10      9   0  0        ATRT             \n",
       "1142 155        NA                   11      9  NA NA        NA               \n",
       "1143 156        NA                   12      9  NA NA        NA               \n",
       "1144 157        NA                   13      9  NA NA        NA               \n",
       "1145 158        NA                   14      9  NA NA        NA               \n",
       "1146 159        NA                   15      9  NA NA        NA               \n",
       "1147 160        NA                   16      9  NA NA        NA               \n",
       "1148 161        NA                   17      9  NA NA        NA               \n",
       "1149 162        NA                   18      9  NA NA        NA               \n",
       "1150 163        PARWLK                1     10   0  0        Ependymoma       \n",
       "1151 164        PARWLK                2     10   0  0        Ependymoma       \n",
       "1152 165        PATNLD                3     10   0  0        Ependymoma       \n",
       "1153 166        PATNLD                4     10   0  0        Ependymoma       \n",
       "1154 167        NA                    5     10  NA NA        NA               \n",
       "1155 168        NA                    6     10  NA NA        NA               \n",
       "1156 169        NA                    7     10  NA NA        NA               \n",
       "1157 170        NA                    8     10  NA NA        NA               \n",
       "1158 171        PAPHZW                9     10  NA NA        ATRT             \n",
       "1159 172        PAVXUM               10     10   0  0        ATRT             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd4ihc_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up column ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_raw$ptid <- gsub(\" .*\", \"\", cd4ihc_raw$ID)\n",
    "cd8ihc_raw$ptid <- gsub(\" .*\", \"\", cd8ihc_raw$ID)\n",
    "cd19ihc_raw$ptid <- gsub(\" .*\", \"\", cd19ihc_raw$ID)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load aggregated files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc <- read.csv(paste0(datapath, \"IHC/Alberto/agg.df.CD4.csv\"),header = TRUE, stringsAsFactors = F)\n",
    "\n",
    "cd8ihc <- read.csv(paste0(datapath, \"IHC/Alberto/agg.df.CD8.csv\"),header = TRUE, stringsAsFactors = F)\n",
    "\n",
    "cd19ihc <- read.csv(paste0(datapath, \"IHC/Alberto/agg.df.CD19.csv\"),header = TRUE, stringsAsFactors = F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 147 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>X</th><th scope=col>block</th><th scope=col>type</th><th scope=col>H.score</th><th scope=col>Subtype</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&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> 1</td><td>PAPHZW</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 2</td><td>PAPRJE</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 3</td><td>PARPCH</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 4</td><td>PARUCK</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 5</td><td>PATKWM</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 6</td><td>PATMWL</td><td>ATRT          </td><td>27.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 7</td><td>PATUIN</td><td>ATRT          </td><td>15.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 8</td><td>PAUETI</td><td>ATRT          </td><td> 5.25</td><td>ATRT          </td></tr>\n",
       "\t<tr><td> 9</td><td>PAVXUM</td><td>ATRT          </td><td> 0.00</td><td>ATRT          </td></tr>\n",
       "\t<tr><td>10</td><td>PANCDU</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>11</td><td>PAPBNC</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>12</td><td>PARMFT</td><td>Ependymoma    </td><td> 3.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>13</td><td>PARNYR</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>14</td><td>PARWLK</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>15</td><td>PASAYM</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>16</td><td>PASCZJ</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>17</td><td>PASZBK</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>18</td><td>PASZKN</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>19</td><td>PATBSS</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>20</td><td>PATEPY</td><td>Ependymoma    </td><td> 7.50</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>21</td><td>PATGWN</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>22</td><td>PATGXY</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>23</td><td>PATNLD</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>24</td><td>PATUFA</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>25</td><td>PATXDH</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>26</td><td>PATZTI</td><td>Ependymoma    </td><td> 3.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>27</td><td>PAUGVK</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>28</td><td>PAUZKS</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>29</td><td>PAWDJH</td><td>Ependymoma    </td><td> 0.00</td><td>Ependymoma    </td></tr>\n",
       "\t<tr><td>30</td><td>PAKMPZ</td><td>Hepatoblastoma</td><td> 0.00</td><td>Hepatoblastoma</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>118</td><td>PAMIGI</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>119</td><td>PAMZEL</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>120</td><td>PANBMS</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>121</td><td>PAPIBB</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>122</td><td>PAPSKA</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>123</td><td>PARNAR</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>124</td><td>PASIWW</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>125</td><td>PASUYB</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>126</td><td>PATJAE</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>127</td><td>PATMPW</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>128</td><td>PATRVB</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>129</td><td>PATWVI</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>130</td><td>PATXHB</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>131</td><td>PAUAES</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>132</td><td>PAWGKN</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>133</td><td>PAWRKF</td><td>NBL stage 3</td><td>0</td><td>NBL stage 3</td></tr>\n",
       "\t<tr><td>134</td><td>PALHHT</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>135</td><td>PALTAL</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>136</td><td>PAMHMK</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>137</td><td>PAMLMY</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>138</td><td>PANFLJ</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>139</td><td>PANLGS</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>140</td><td>PASMRP</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>141</td><td>PATKVF</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>142</td><td>PATNEC</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>143</td><td>PATXII</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>144</td><td>PAUKAW</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>145</td><td>PAWFMD</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>146</td><td>PAWHCY</td><td>NBL stage 4</td><td>0</td><td>NBL stage 4</td></tr>\n",
       "\t<tr><td>147</td><td>PAUYSJ</td><td>Wilms      </td><td>0</td><td>Wilms      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 147 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " X & block & type & H.score & Subtype\\\\\n",
       " <int> & <chr> & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t  1 & PAPHZW & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t  2 & PAPRJE & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t  3 & PARPCH & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t  4 & PARUCK & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t  5 & PATKWM & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t  6 & PATMWL & ATRT           & 27.00 & ATRT          \\\\\n",
       "\t  7 & PATUIN & ATRT           & 15.00 & ATRT          \\\\\n",
       "\t  8 & PAUETI & ATRT           &  5.25 & ATRT          \\\\\n",
       "\t  9 & PAVXUM & ATRT           &  0.00 & ATRT          \\\\\n",
       "\t 10 & PANCDU & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 11 & PAPBNC & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 12 & PARMFT & Ependymoma     &  3.00 & Ependymoma    \\\\\n",
       "\t 13 & PARNYR & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 14 & PARWLK & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 15 & PASAYM & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 16 & PASCZJ & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 17 & PASZBK & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 18 & PASZKN & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 19 & PATBSS & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 20 & PATEPY & Ependymoma     &  7.50 & Ependymoma    \\\\\n",
       "\t 21 & PATGWN & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 22 & PATGXY & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 23 & PATNLD & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 24 & PATUFA & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 25 & PATXDH & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 26 & PATZTI & Ependymoma     &  3.00 & Ependymoma    \\\\\n",
       "\t 27 & PAUGVK & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 28 & PAUZKS & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 29 & PAWDJH & Ependymoma     &  0.00 & Ependymoma    \\\\\n",
       "\t 30 & PAKMPZ & Hepatoblastoma &  0.00 & Hepatoblastoma\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 118 & PAMIGI & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 119 & PAMZEL & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 120 & PANBMS & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 121 & PAPIBB & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 122 & PAPSKA & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 123 & PARNAR & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 124 & PASIWW & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 125 & PASUYB & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 126 & PATJAE & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 127 & PATMPW & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 128 & PATRVB & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 129 & PATWVI & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 130 & PATXHB & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 131 & PAUAES & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 132 & PAWGKN & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 133 & PAWRKF & NBL stage 3 & 0 & NBL stage 3\\\\\n",
       "\t 134 & PALHHT & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 135 & PALTAL & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 136 & PAMHMK & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 137 & PAMLMY & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 138 & PANFLJ & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 139 & PANLGS & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 140 & PASMRP & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 141 & PATKVF & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 142 & PATNEC & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 143 & PATXII & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 144 & PAUKAW & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 145 & PAWFMD & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 146 & PAWHCY & NBL stage 4 & 0 & NBL stage 4\\\\\n",
       "\t 147 & PAUYSJ & Wilms       & 0 & Wilms      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 147 × 5\n",
       "\n",
       "| X &lt;int&gt; | block &lt;chr&gt; | type &lt;chr&gt; | H.score &lt;dbl&gt; | Subtype &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "|  1 | PAPHZW | ATRT           |  0.00 | ATRT           |\n",
       "|  2 | PAPRJE | ATRT           |  0.00 | ATRT           |\n",
       "|  3 | PARPCH | ATRT           |  0.00 | ATRT           |\n",
       "|  4 | PARUCK | ATRT           |  0.00 | ATRT           |\n",
       "|  5 | PATKWM | ATRT           |  0.00 | ATRT           |\n",
       "|  6 | PATMWL | ATRT           | 27.00 | ATRT           |\n",
       "|  7 | PATUIN | ATRT           | 15.00 | ATRT           |\n",
       "|  8 | PAUETI | ATRT           |  5.25 | ATRT           |\n",
       "|  9 | PAVXUM | ATRT           |  0.00 | ATRT           |\n",
       "| 10 | PANCDU | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 11 | PAPBNC | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 12 | PARMFT | Ependymoma     |  3.00 | Ependymoma     |\n",
       "| 13 | PARNYR | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 14 | PARWLK | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 15 | PASAYM | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 16 | PASCZJ | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 17 | PASZBK | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 18 | PASZKN | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 19 | PATBSS | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 20 | PATEPY | Ependymoma     |  7.50 | Ependymoma     |\n",
       "| 21 | PATGWN | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 22 | PATGXY | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 23 | PATNLD | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 24 | PATUFA | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 25 | PATXDH | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 26 | PATZTI | Ependymoma     |  3.00 | Ependymoma     |\n",
       "| 27 | PAUGVK | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 28 | PAUZKS | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 29 | PAWDJH | Ependymoma     |  0.00 | Ependymoma     |\n",
       "| 30 | PAKMPZ | Hepatoblastoma |  0.00 | Hepatoblastoma |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 118 | PAMIGI | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 119 | PAMZEL | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 120 | PANBMS | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 121 | PAPIBB | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 122 | PAPSKA | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 123 | PARNAR | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 124 | PASIWW | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 125 | PASUYB | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 126 | PATJAE | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 127 | PATMPW | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 128 | PATRVB | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 129 | PATWVI | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 130 | PATXHB | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 131 | PAUAES | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 132 | PAWGKN | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 133 | PAWRKF | NBL stage 3 | 0 | NBL stage 3 |\n",
       "| 134 | PALHHT | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 135 | PALTAL | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 136 | PAMHMK | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 137 | PAMLMY | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 138 | PANFLJ | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 139 | PANLGS | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 140 | PASMRP | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 141 | PATKVF | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 142 | PATNEC | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 143 | PATXII | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 144 | PAUKAW | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 145 | PAWFMD | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 146 | PAWHCY | NBL stage 4 | 0 | NBL stage 4 |\n",
       "| 147 | PAUYSJ | Wilms       | 0 | Wilms       |\n",
       "\n"
      ],
      "text/plain": [
       "    X   block  type           H.score Subtype       \n",
       "1    1  PAPHZW ATRT            0.00   ATRT          \n",
       "2    2  PAPRJE ATRT            0.00   ATRT          \n",
       "3    3  PARPCH ATRT            0.00   ATRT          \n",
       "4    4  PARUCK ATRT            0.00   ATRT          \n",
       "5    5  PATKWM ATRT            0.00   ATRT          \n",
       "6    6  PATMWL ATRT           27.00   ATRT          \n",
       "7    7  PATUIN ATRT           15.00   ATRT          \n",
       "8    8  PAUETI ATRT            5.25   ATRT          \n",
       "9    9  PAVXUM ATRT            0.00   ATRT          \n",
       "10  10  PANCDU Ependymoma      0.00   Ependymoma    \n",
       "11  11  PAPBNC Ependymoma      0.00   Ependymoma    \n",
       "12  12  PARMFT Ependymoma      3.00   Ependymoma    \n",
       "13  13  PARNYR Ependymoma      0.00   Ependymoma    \n",
       "14  14  PARWLK Ependymoma      0.00   Ependymoma    \n",
       "15  15  PASAYM Ependymoma      0.00   Ependymoma    \n",
       "16  16  PASCZJ Ependymoma      0.00   Ependymoma    \n",
       "17  17  PASZBK Ependymoma      0.00   Ependymoma    \n",
       "18  18  PASZKN Ependymoma      0.00   Ependymoma    \n",
       "19  19  PATBSS Ependymoma      0.00   Ependymoma    \n",
       "20  20  PATEPY Ependymoma      7.50   Ependymoma    \n",
       "21  21  PATGWN Ependymoma      0.00   Ependymoma    \n",
       "22  22  PATGXY Ependymoma      0.00   Ependymoma    \n",
       "23  23  PATNLD Ependymoma      0.00   Ependymoma    \n",
       "24  24  PATUFA Ependymoma      0.00   Ependymoma    \n",
       "25  25  PATXDH Ependymoma      0.00   Ependymoma    \n",
       "26  26  PATZTI Ependymoma      3.00   Ependymoma    \n",
       "27  27  PAUGVK Ependymoma      0.00   Ependymoma    \n",
       "28  28  PAUZKS Ependymoma      0.00   Ependymoma    \n",
       "29  29  PAWDJH Ependymoma      0.00   Ependymoma    \n",
       "30  30  PAKMPZ Hepatoblastoma  0.00   Hepatoblastoma\n",
       "⋮   ⋮   ⋮      ⋮              ⋮       ⋮             \n",
       "118 118 PAMIGI NBL stage 3    0       NBL stage 3   \n",
       "119 119 PAMZEL NBL stage 3    0       NBL stage 3   \n",
       "120 120 PANBMS NBL stage 3    0       NBL stage 3   \n",
       "121 121 PAPIBB NBL stage 3    0       NBL stage 3   \n",
       "122 122 PAPSKA NBL stage 3    0       NBL stage 3   \n",
       "123 123 PARNAR NBL stage 3    0       NBL stage 3   \n",
       "124 124 PASIWW NBL stage 3    0       NBL stage 3   \n",
       "125 125 PASUYB NBL stage 3    0       NBL stage 3   \n",
       "126 126 PATJAE NBL stage 3    0       NBL stage 3   \n",
       "127 127 PATMPW NBL stage 3    0       NBL stage 3   \n",
       "128 128 PATRVB NBL stage 3    0       NBL stage 3   \n",
       "129 129 PATWVI NBL stage 3    0       NBL stage 3   \n",
       "130 130 PATXHB NBL stage 3    0       NBL stage 3   \n",
       "131 131 PAUAES NBL stage 3    0       NBL stage 3   \n",
       "132 132 PAWGKN NBL stage 3    0       NBL stage 3   \n",
       "133 133 PAWRKF NBL stage 3    0       NBL stage 3   \n",
       "134 134 PALHHT NBL stage 4    0       NBL stage 4   \n",
       "135 135 PALTAL NBL stage 4    0       NBL stage 4   \n",
       "136 136 PAMHMK NBL stage 4    0       NBL stage 4   \n",
       "137 137 PAMLMY NBL stage 4    0       NBL stage 4   \n",
       "138 138 PANFLJ NBL stage 4    0       NBL stage 4   \n",
       "139 139 PANLGS NBL stage 4    0       NBL stage 4   \n",
       "140 140 PASMRP NBL stage 4    0       NBL stage 4   \n",
       "141 141 PATKVF NBL stage 4    0       NBL stage 4   \n",
       "142 142 PATNEC NBL stage 4    0       NBL stage 4   \n",
       "143 143 PATXII NBL stage 4    0       NBL stage 4   \n",
       "144 144 PAUKAW NBL stage 4    0       NBL stage 4   \n",
       "145 145 PAWFMD NBL stage 4    0       NBL stage 4   \n",
       "146 146 PAWHCY NBL stage 4    0       NBL stage 4   \n",
       "147 147 PAUYSJ Wilms          0       Wilms         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd4ihc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(cd4ihc)[colnames(cd4ihc) == \"block\"] <- \"ptid\"\n",
    "colnames(cd8ihc)[colnames(cd8ihc) == \"block\"] <- \"ptid\"\n",
    "colnames(cd19ihc)[colnames(cd19ihc) == \"block\"] <- \"ptid\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean the raw csv files using IDs from aggregated files. Convert NAs to zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_raw_clean <- cd4ihc_raw[cd4ihc_raw$ptid %in% cd4ihc$ptid,]\n",
    "cd8ihc_raw_clean <- cd8ihc_raw[cd8ihc_raw$ptid %in% cd8ihc$ptid,]\n",
    "cd19ihc_raw_clean <- cd19ihc_raw[cd19ihc_raw$ptid %in% cd19ihc$ptid,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_raw_clean$X.[is.na(cd4ihc_raw_clean$X.)] <- 0\n",
    "cd4ihc_raw_clean$Intensity[is.na(cd4ihc_raw_clean$Intensity)] <- 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd8ihc_raw_clean$X.[is.na(cd8ihc_raw_clean$X.)] <- 0\n",
    "cd8ihc_raw_clean$Intensity[is.na(cd8ihc_raw_clean$Intensity)] <- 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd19ihc_raw_clean$X.[is.na(cd19ihc_raw_clean$X.)] <- 0\n",
    "cd19ihc_raw_clean$Intensity[is.na(cd19ihc_raw_clean$Intensity)] <- 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H score for comparison with aggregated file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_raw_clean$Hscore <- cd4ihc_raw_clean$Intensity * cd4ihc_raw_clean$X.\n",
    "cd8ihc_raw_clean$Hscore <- cd8ihc_raw_clean$Intensity * cd8ihc_raw_clean$X.\n",
    "cd19ihc_raw_clean$Hscore <- cd19ihc_raw_clean$Intensity * cd19ihc_raw_clean$X."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregate by mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggcd4 <- aggregate(cd4ihc_raw_clean[, c(\"X.\",\"Intensity\" ,\"Hscore\")],\n",
    "         by = list(cd4ihc_raw_clean$ptid),FUN = mean)\n",
    "colnames(aggcd4)[colnames(aggcd4) == \"Group.1\"] <- \"ptid\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggcd8 <- aggregate(cd8ihc_raw_clean[, c(\"X.\",\"Intensity\" ,\"Hscore\")],\n",
    "         by = list(cd8ihc_raw_clean$ptid),FUN = mean)\n",
    "colnames(aggcd8)[colnames(aggcd8) == \"Group.1\"] <- \"ptid\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggcd4 <- aggregate(cd4ihc_raw_clean[, c(\"X.\",\"Intensity\" ,\"Hscore\")],\n",
    "         by = list(cd4ihc_raw_clean$ptid),FUN = mean)\n",
    "colnames(aggcd4)[colnames(aggcd4) == \"Group.1\"] <- \"ptid\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggcd19 <- aggregate(cd19ihc_raw_clean[, c(\"X.\",\"Intensity\" ,\"Hscore\")],\n",
    "         by = list(cd19ihc_raw_clean$ptid),FUN = mean)\n",
    "colnames(aggcd19)[colnames(aggcd19) == \"Group.1\"] <- \"ptid\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4_merge <- merge(aggcd4, cd4ihc[, c(\"ptid\", \"type\", \"Subtype\", \"H.score\")], by = \"ptid\")\n",
    "cd8_merge <- merge(aggcd8, cd8ihc[, c(\"ptid\", \"type\", \"Subtype\", \"H.score\")], by = \"ptid\")\n",
    "cd19_merge <- merge(aggcd19, cd19ihc[, c(\"ptid\", \"type\", \"Subtype\", \"H.score\")], by = \"ptid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cd4ihc_subset <- cd4_merge[cd4_merge$type != \"Wilms\" &cd4_merge$type != \"Hepatoblastoma\",]\n",
    "cd8ihc_subset <- cd8_merge[cd8_merge$type != \"Wilms\" &cd8_merge$type != \"Hepatoblastoma\",]\n",
    "cd19ihc_subset <- cd19_merge[cd19_merge$type != \"Wilms\" &cd19_merge$type != \"Hepatoblastoma\",]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd8ihc_subset$type <- as.character(cd8ihc_subset$type)\n",
    "cd4ihc_subset$type <- as.character(cd4ihc_subset$type)\n",
    "cd19ihc_subset$type <- as.character(cd19ihc_subset$type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_subset$type[grepl(\"NBL\", cd4ihc_subset$Subtype)] <- \"NBL\"\n",
    "cd8ihc_subset$type[grepl(\"NBL\", cd8ihc_subset$Subtype)] <- \"NBL\"\n",
    "cd19ihc_subset$type[grepl(\"NBL\", cd19ihc_subset$Subtype)] <- \"NBL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd4ihc_subset$type[ cd4ihc_subset$type == \"Ependymoma\"] <- \"EPN\"\n",
    "cd4ihc_subset$type[ cd4ihc_subset$type == \"Medulloblastoma\"] <- \"MB\"\n",
    "cd4ihc_subset$type[ cd4ihc_subset$type == \"High Grade Glioma\"] <- \"pedHGG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd8ihc_subset$type[ cd8ihc_subset$type == \"Ependymoma\"] <- \"EPN\"\n",
    "cd8ihc_subset$type[ cd8ihc_subset$type == \"Medulloblastoma\"] <- \"MB\"\n",
    "cd8ihc_subset$type[ cd8ihc_subset$type == \"High Grade Glioma\"] <- \"pedHGG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd19ihc_subset$type[ cd19ihc_subset$type == \"Ependymoma\"] <- \"EPN\"\n",
    "cd19ihc_subset$type[ cd19ihc_subset$type == \"Medulloblastoma\"] <- \"MB\"\n",
    "cd19ihc_subset$type[ cd19ihc_subset$type == \"High Grade Glioma\"] <- \"pedHGG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  ATRT    EPN     MB    NBL pedHGG \n",
       "     9     20     64     33     13 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(cd4ihc_subset$type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box plots for cells H score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$ATRT\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   0.000   0.000   1.389   2.000   7.500 \n",
       "\n",
       "$EPN\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   0.000   0.000   1.025   1.625   6.000 \n",
       "\n",
       "$MB\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.0000  0.0000  0.0000  0.1846  0.0000  7.5000 \n",
       "\n",
       "$NBL\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  0.000   0.000   3.000   9.091  12.000  45.000 \n",
       "\n",
       "$pedHGG\n",
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.0000  0.0000  0.0000  0.9231  0.0000  7.5000 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tapply(cd19ihc_subset$H.score, cd19ihc_subset$type, summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ptid</th><th scope=col>X.</th><th scope=col>Intensity</th><th scope=col>Hscore</th><th scope=col>type</th><th scope=col>Subtype</th><th scope=col>H.score</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&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><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>5</th><td>PALHHT</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 4</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>PALNFP</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 3</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>PALTAL</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 4</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>PALTLX</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 3</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>PALWKV</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 3</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>12</th><td>PAMHLN</td><td>0</td><td>0</td><td>0</td><td>NBL</td><td>NBL stage 3</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & ptid & X. & Intensity & Hscore & type & Subtype & H.score\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t5 & PALHHT & 0 & 0 & 0 & NBL & NBL stage 4 & 0\\\\\n",
       "\t7 & PALNFP & 0 & 0 & 0 & NBL & NBL stage 3 & 0\\\\\n",
       "\t9 & PALTAL & 0 & 0 & 0 & NBL & NBL stage 4 & 0\\\\\n",
       "\t10 & PALTLX & 0 & 0 & 0 & NBL & NBL stage 3 & 0\\\\\n",
       "\t11 & PALWKV & 0 & 0 & 0 & NBL & NBL stage 3 & 0\\\\\n",
       "\t12 & PAMHLN & 0 & 0 & 0 & NBL & NBL stage 3 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | ptid &lt;chr&gt; | X. &lt;dbl&gt; | Intensity &lt;dbl&gt; | Hscore &lt;dbl&gt; | type &lt;chr&gt; | Subtype &lt;chr&gt; | H.score &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 5 | PALHHT | 0 | 0 | 0 | NBL | NBL stage 4 | 0 |\n",
       "| 7 | PALNFP | 0 | 0 | 0 | NBL | NBL stage 3 | 0 |\n",
       "| 9 | PALTAL | 0 | 0 | 0 | NBL | NBL stage 4 | 0 |\n",
       "| 10 | PALTLX | 0 | 0 | 0 | NBL | NBL stage 3 | 0 |\n",
       "| 11 | PALWKV | 0 | 0 | 0 | NBL | NBL stage 3 | 0 |\n",
       "| 12 | PAMHLN | 0 | 0 | 0 | NBL | NBL stage 3 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "   ptid   X. Intensity Hscore type Subtype     H.score\n",
       "5  PALHHT 0  0         0      NBL  NBL stage 4 0      \n",
       "7  PALNFP 0  0         0      NBL  NBL stage 3 0      \n",
       "9  PALTAL 0  0         0      NBL  NBL stage 4 0      \n",
       "10 PALTLX 0  0         0      NBL  NBL stage 3 0      \n",
       "11 PALWKV 0  0         0      NBL  NBL stage 3 0      \n",
       "12 PAMHLN 0  0         0      NBL  NBL stage 3 0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(cd4ihc_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotcd4 <- ggplot() +\n",
    "geom_boxplot(data = cd4ihc_subset, aes(x = type ,y = H.score), size = 1, fill = \"light grey\") +\n",
    "theme(legend.position = \"bottom\", legend.title = element_blank()) +\n",
    "theme(axis.title.x = element_blank(), axis.title = element_text(size = 35),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(angle = 45, hjust = 1, size = 35, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 35, color = \"black\")) +\n",
    "myplot +\n",
    "scale_y_continuous(breaks = seq(0,30, by = 5)) +\n",
    "labs(y = \"H score CD4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wjPOvVbTJ3uLr+EJPbwhFGbn+VzjEZG8JoBw3bJr7KwH/XzuCraHxoAACh+8gl2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1qdxq8Iytl98r3K4jr+3o+Lsscn+9OLFq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\nP7QDJ4l0GyM3AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcd4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotcd8 <- ggplot() +\n",
    "geom_boxplot(data = cd8ihc_subset, aes(x = type ,y = H.score), size = 1, fill = \"light grey\") +\n",
    "theme(legend.position = \"bottom\", legend.title = element_blank()) +\n",
    "theme(axis.title.x = element_blank(), axis.title = element_text(size = 35),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(angle = 45, hjust = 1, size = 35, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 35, color = \"black\")) +\n",
    "myplot +\n",
    "scale_y_continuous(breaks = seq(0,60, by = 10)) +\n",
    "labs(y = \"H score CD8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAlEOwAAAAApASCHQAAAICUQLADAAAA\nkBIIdgAAAABSAsEOAAAAQEog2AEAAABICQQ7AAAAACmBYAcAAAAgJRDsAAAAAKQEgh0AAACA\nlECwAwAAAJASCHYAAAAAUgLBDgAAAEBKINgBAAAASAkEOwAAAAApgWAHAAAAICUQ7AAAAACk\nhFxjDwAAflyVlZUJCQlpaWnGxsaWlpZycvg3BgDADw3/mgYA7h4/fvzHH398+fLla9HU1PTX\nX3/t3Llz444KAAD4wKNYAOAiLCxs8eLFGRkZtTWpqakLFy588+ZNI44KAAD4Q7ADAC727dtH\nCGEymbU1TCaTyWQeOHCg8QYFAAACINgBACcmkxkdHc1iserWR0RENMqQAABAGAh2AMCpqqqK\nOldHVVlZyesrAABodAh2AMBJUVHRwMCAwWBw1MvIyLRs2VJGBv/eAAD4QeFf0JLGKkm6c3jj\nksmDu1m3NtZTV5KXV26m3byldbcBY2av2ns5/Et5Y48QQAijR4/m+ijWy8urUcYDAADCkP5g\n99jHlFEfJoufCXur8sSgVZ7tjC36Tlux49j1Z28/pmUXVVRXlxfnZX56++zW2f3r5w51bNHC\nafLmG8mVDfk3A9TbpEmTPD09v07aycjIfP1vg5eX18iRIxt7aAAAwJPU72NXnpCQ+j3uUxEX\nMN1zfuD7MkENq7NeHvMddClw6t6zeydYKX6PsQGITkZGZvXq1R4eHvfv309NTTU1NXV1de3Q\noUNjjwsAAPiR+mCXmJDA+ThJ8kqjtnr0+yU0S/griqIPT+zxMf3WlZ/sVRtuXAD1ZGdnZ2dn\n19ijAAAAYUn7o1hmfHxSQ98jO2iW51J6qmNodhyz8uD1sITMovKqypLclJiH5/0XD7RQpl13\n9+chk8+lNfTwAAAAoKmQ9mD3KSGB/Tab/PiL1SxRpfh15XuHtKNTJ5/4TJ0V1OmxOjQu8vS6\n/w10NNdTU5STV9Eybu8ycuGO6zFvzs1sTw13Xy5M8977WbJ/MgAAADRV0h7sEhIS2QUzS0tZ\nCfdfdN132dVCSoWS/YqbN9b21uf6wyqYjdp/+4hXc0pV8Z3lPqezJTwqAAAAaJKkPNhlxcfn\n1xYYFhbmEu4/xu/XExmUsrzj2sC1jnzfmjPy2v2npwalIv/i5v3xEh4XAAAANEVSHuwSEhLY\nBWNLS2XeTcVQ/WD3vtfUh7BG0zYtshI4Kag7ftk0E0qZFbV396NqiY4MAAAAmiLpDnYVCQkp\n7JKlpaVEey+9sv84bemD1cwFfYXZv0TW2WskNdmR1MP7rlZIdGwAAADQBEl3sEuMj2cfaqll\nYaEtyc6rQy5dK6ZW2IwbZy3ktZ0HD9Kjlgtv3Xza8LuyAAAAgHST6mDHTEj4wC5JeMKO+fDa\nzQJqhcXgwW2EvVimV3/63F72nTuvJTUyAAAAaKKkOth9jo9nH8yqYGnZQpKdv75zh7Z1XbOe\nPTsJf7WCnR19di8hNBTbngAAAEC9SHWwo+110trCQpJ7nRSEh9N3Pu5oZydK/607ddKgVURH\nR0tgWAAAANCESXOwy0lIyKstMCwtJbrXSWRkJO2lOG1rawOROrC2bk8rFyUminAkGQAAAEAd\n0nxWbHw8ZXs4k697nZSnvgwJDr1///6TyKT0nJyc3PwyhpqOfnN9gxbWzm79+vXv38vWQElw\n33mxsRm0CjMzM9FG19zISIYQ9toOkpiYSIge7wsAAAAA+JPiYFeZkEB5a83CVO7m5gkrtp2N\nyOLcMi7vy8e8Lx/fvXoWfHL7MjmDbpN9166a5d6Sb7xLSUmhlRkmJsaiDU/W0FCfkHR2RdqH\nDxWkqzDbpQAAAABwI8WPYpMSEijzYc9WdB/oe7JuquNUnf7078X92liP3BlZyKdZamoqrayl\nry9qRjY0NKSVWQUFRSJ2AQAAAEAhvcGOFR9PWTpBysrKeTatqzLp4kJnpwkn4iq5f1+SkVFC\nq9DS0hJ1gMrKHOdglJSUcG8JAAAAIAzpDXYpCQncopycvuP4X3dffPDqY1ZhWVVlcU5qQmTI\nsS2LPa01GPSW5e9PTu4/LegLt42DS0tL6RVqamqiDlBJieNhb51OAQAAAEQhve/Y0Y6J/Urd\nftbuwG0TrVQpddpG5tpG5p3cJv20Kvzw0skL/46hpCtmcuD4IeYPn6x14Hj1rbycIzQqKor8\ncpwwwW7GjBmJiYmEkIqKCgsLi5ycHFHvAgAAAE2H1M7Y5cXH51LLchZTzkQ+3UdPdVQy2g7T\nDz174t+PvjC1LGLjXP/3nLN2dYKdnJzIEbnOJTIydf5plJSUFBYWFhYWVlRUcPkaAAAAgEJq\ns0I8dcJOxtjr6O2/vcwUBF2lZrvw3IWf2slT66pfbFx6hmOmTF5enl7BYol81GtVVRW9QkVF\nhbPN2LFjFyxYsGDBAnt7++zsbFFvAQAAAE2K1D6K7bwlibVFnAvVe2zaM/Ncn92f2FUFlw+c\nSR87l7IBsaoqx8RfRUWFqDeqruZYoVtnNQUhnp6eXz/k5+cfPHhQjCUaAAAA0HRI7YxdPcj3\n9v3ZmfbDsB5euZZPragT7Coreayf5a24uJhewWXGDgAAAEB4CHbcmHh42NEqqu+FPqQ+a1VW\nVaWvoa2T0gQrKqJvWyenr4/5OAAAAKgHBDuuWvXoYUKrKE9Lo67FYOjq6tC+z8zMFPUenEtc\nW7RuLStqHwAAAABsCHbcGRtznBCWkUE7G9bS0pL2dWlGhohTdqyMjCxaRatWrUTrAQAAAIAG\nwY47bW1tegXHGlYjS0v6W3bJycmi3SE1OZm2eELTzAxPYgEAAKA+EOy4q7NRnZ4efX87S0sL\nWjkhJka05RNfNx6uxXBychTpegAAAAAOCHbcZWXRn5MSXV1dWtnSyUmDWq5+8+adKDdIjYqi\nbUtn5eKiw6stAAAAgDAQ7LgqjYiIo1W0sbGh724s29PdlXaKWPy9e6nC36DyxYtX1LK2i0s7\nUQcJAAAAQCONwa4s0EOWQSHrcbRQtB6qHt19THulTqd37w4cbZT79nOh/XrPgm8XESGxHgaH\nlFHKWkM8ujN4tgYAAAAQhjQGO2Vr69bUMvPZ0xcidVD0T8A52lYkym596+Yu7UEetG2Mq26f\nOp/D2Yi7qtDTF6nbo+iN8R6oyLM1AAAAgFCkMdgRK1tbWkzKvnzmTjmvxnWw4vf8eZG2d4nB\npNlDuRwK0WLKfE81SrkyZPeBuLrN6so85X+KmutMxnn3kdqz3QAAAOC7kcpgpzRk7FDaZiRf\njq7d94HFqzldasDC9S+oz2EZdgt8+ihwa6oxasm0lpQyM/xP3zNZ3FpSZV32WX6lhF2W6+az\nsJtU/nMAAACA70s6A0WzodO9aItYqx76ei59UCDwwqKnqzwX3qRN1xlP3jjPikdz2R4+v7go\nUSryguZPC/hUw/sG1Qk7x3qfpK6yMJ62Ya65wIEBAAAACCSdwY4o9Pt9y2DadiQVb7YOcZmy\n93kuz4m7kpiTi9wGr48opVYaTz+0Y4AGr0sIaTV7zwoHeUpF9tVZrhMDYku5tGVl3v/Nvfui\nUGq+NJy8d1MfJS6NAQAAAEQlpcGOEJOpB3cNo+8pXPTm6FxnS5tBs9YF3HwRk5iaU1pZWZyd\nmhj96MJfyyY4t7Wf8FdYHvUCuTbzj2wfoMn3PrI2voF/9qFGv+rE09MdOvT32RX0LCGzqKKq\nNOdTbNiNfUtH2rZzW3svkxIs5drMO/qXh3adPgEAAADEwWCxhHz37D+o4v3hSQNmnvtQLbhp\nXfJm44+EHhvfUlaItjmhPm4efq+4TdPxxDAeceTxWW+h+ifE399/8eLFWlpaubm5otzlP8nT\n0zMtLe3rZyMjo8uXLzfueAAAAP4rpHbGjhBCFNtMPfM0+I/RbVUFt6Vf2MpzU8iLE8KlOkKI\njuuOeyGbBpgKvbRVvbPP5edCpzoAAAAAYXznYMeqKslLT076mJpTUvVdZgoZzfssOxsdF7pj\ndj9LdSH+WCWTHtO2XIuJ+ce3p45IOwZrdvO9Ef08YJFrS/5vzMnqOHjvuPv20fYhxkh1AAAA\nIFHfZfu0mpyIcwcCLt66c+/Zu6yKfysZcip65k59BgwYMGTUqL4Wanx7qB954z6L9/ZZvC35\n4ZXLIY+ePnvxKj41Kzc3v6iSoaisqq5raNrSrJ2tYxcXt8EDnVupiR12Neyn+t2ZuPJ18IUL\nV0KeRETHJn3JLSypllVV19IzbdvRoUuPgSPHDnEyxF7EAAAA0BBEfceuIiPq9qXLoZEJKWkZ\n+TXKmtp6pu0duvToN8jFTI3bDBcz/c7GGbM3X00o5vLlN7K6jt6+69fM6y9gtqsJwzt2AAAA\nIJDwM3bVXx7sXrZobWBUHpPjm0OEMJpZDpi9we+30W2oJzQUv9g6cuiy4HTOCzjVZL88/POA\n84Fzj13yG9ZSXkBrAAAAAOBGuMeOrIxbPztb9Vp8vG6q+7dBUfyNP71sO009/eHb7rzMhP0j\nB/wiONXVKorcM8LBbfOLEsFNAQAAAKAOIYIdK/nUeOch28IKBbYsjz8yyX3+rVxCSE3s1nGL\ngvMEXsJxq5yHvkMnnU6R3h1YAAAAABqMwEexlVHrR087nSTsVnDVifsmzXB9szNnwdqXFRzf\nKeq379anj3PHls11dJrJlOblZH56/ST0zuPozHJKq/RL04Zvsnm+3Fqqd2IBAAAAkDgBwa76\nxRqvtWHlnNUyzUys2rTUVarIS/sQ9yGnkvZl1sUFgz7KhVN365U17rNw/eZfJznp1d3iozrz\n+eF1S5bvepL9rabs5R+rz8y+MA5HMgAAAACIgP+0WN7J9bviaUfaa3aauOmf6MyczzEvH91/\nFPY6KSvv89MTv3rSFk1khEdQjrlXtp0f9CJk+xRuqY4QIqffZcbOu4+PebVgj6Xo0rrtr4V+\nOQ8AAAAAiIBgl3Bgx1XKNiXKHef9E/niuK9nBx32ylWGiknXCRv/iQw7PN6c2/SfzpA9V/8a\nYiTguapCm0lHzy61qe2AFRt4KlK4vwAAAAAACCH8g13GrZtR7GUMhuOO39zl2YrHZiQq7acc\nu7VnoBZHtaLrpgNTTIQ6wkGpy5rtk5vXFj/euZMkzGUAAAAA8BWfYFf54MHz2oKy2+ptIw35\ndiVrPmPPSmcFapXehJ+9+V9EpdR3wQyr2lLE/fv8NjUGAAAAADo+wS4pJqbs22fZvt7jhUho\nrab9ry/lRTqlvoNdRTo+q6Obm963zzWfPqXyawsAAAAANHyCXU5OTu1nm+7d1YXpTrNnTxt2\nqYODg2jHojKsrNpwvT8AAAAACMIn2OXlsfcXbt68Oe+GVAYGBuyCnp4e75Zc6ejo1H5GsAMA\nAAAQBZ9gJyvLfqqqoKDAuyEVbZ2EjIyomwyXlLAPFFNUFG26DwAAAKBp45O8qLNn2dnZvBtS\nfU5JYReysrJEHA71Ptra2KEYAAAAQHh8gp2urm7t55ioKKEOFcu6dy+GXYp+WedYMf5Kw8Ji\nud4fAAAAAAThE+xa29vXTpkV3rgYUudgsbri9u0JZe98R8qCLwtzVa2KOzfv1R5Ppm1v31KE\nawEAAACaOj7BjuHSu0ftURDpR5f/+Zr/9BsrcffcLfSDwLIDNx1MFnosH/dtPJVbOzDn7l2F\n2tgYAAAAAAgh/E+eUOs/1FXpW6E6ck3/EX9F5PNoy0q7Msfjp1DOLYWrHq+YtDW2SoiBVLzZ\nOuW3Z7UtZbq4uwq1wQoAAAAAfMV32areBN9pJrUlVvr1RV3a95q748qrjHL2A9fqvPd39s51\n6TR8fyy3Gb2ih74DRu96zfcQCWb24z+G9l96n50aVYfM8TYV9m8AAAD4/qqrhXr7HOB74r8f\niUKfZWv6UifOqr882LvEs5OBpm4ra7vOXR1t2hjr6LftO3fvk6ya2kaM9vOWeTarLdZ8+mdB\nN7shvx5/nlEn+bGKE2/6z+nRodevt9IoL+c1n7TQS1P8PwoAAKChvH37du7cuT169HBxcRk7\nduyNGzdYLJbgywC+C4bA/zhm/jPZYfixFOH/Q6szNigx0GSztdOmdxwXMZQNO3br0r6Fvo6G\nYlVBdlpi5JPn73Iq63Qw4mTshXGi7m0s3fz9/RcvXqylpZWbmyu49X+cp7DaT/4AACAASURB\nVKdnWlra189GRkaXL19u3PEAANS6devWqlWrCCFMJpMQwmAwWCzWsGHDVq5c2dhDAyCEEDmB\nLfSH7r/6V86gxdfSagS2JUSmxZTD/kM1ZMjK/XPP9N6dRIt2rLIvr0KDXvHvQcvjr91IdQAA\n8MMpKyvbtGkTi8WqnRP5+iEoKGjQoEH29vaNOjoAQgQ9iv1KyXb+5bCrS7vqCGosZ+K5++p+\nD31CCFHpueXs+i5qIg5H3mrB2SPjDQQ3BAAA+M4iIiKKi4vrPuliMBh3795tlCEBcBDyzC8Z\nowGbnyREBfp6tFWX5dZArnnnyX53w4Nm29SePabisPzatTUuOkJvWqLZedn1e/59cd4EAAD8\niHidYS4jIyP0AU0ADUvwo9haDE2b8Zsuj/895+2Dm8FPYj59Sc/Mq1TQ1DU0s+3u1t/V3kip\nziU6PX8Ljei6ceHCLf/El/LrWqPDqF+2b1/qbiIv+p8AAADwPfA6EammpkZPD+8QwQ9BhGD3\nL3md9m4T2rsJ3bzFgDVBMXMirwYeO3Pl7tPwmE+F/y4PZyhoGFnYOLm4Dho1watvGw1sRwwA\nAD8ye3t7dXX14uLirysnqFxdXRtlSAAcRA924pDXtxvuYzfchxDCrCwuyC+uIAqqGprNFIV8\nEgwAANDolJSUVqxY4evrKyMj8zXbff0wevToTp06NfboAAj5XsGOQkZBTUtf1DUVAAAAPwI3\nN7eTJ0/u3r07KiqqsrLSwsJi8uTJbm5CP8YCaGDfPdgBAAD8l1laWvr5+RFCWCwWg4HXiODH\ngmehAAAA4kCqgx+QZGbsakrSE2KiY+I+pHz5kp5VUFJWVl4to6Sq1qxZMzV1HWOzNm3btm1r\nbqyhgP8OAAAAADSU+gS7ivTwG2fPXrpx5+6jyM/FnCuE6t5K07KLq7u7u3v/Qf26tlCpx40B\nAAAAoC6xgl115vNA/y3b9gVF5wqMc5Sr8uMfX4x/fHHPbzIabdwnzJgxc8pQW1285AcAAAAg\nGaK+Y1edErLZy9ai65SNF0VKdTTMgve39vwyqlOr9sPXnH9XJGYvAAAAAEAlSrCrTDq3wNnG\n3ffcWwllsZL4oN9Hd2jjujo4tUYyPQIAAAA0XUIHu4LnGwd0HbMrLF/SI6hJv7tugG2v5bez\nJN0zAAAAQJMi3CtuZZHrB7qvesproo6hrNvayrpDhw4dOli10tNs1kytWbNmampKjPLiouLC\nnLSPSYmJCe+iw19ExGWVs7h0wMp5vMmjT8GFO7sGN8fCWQAAAACxCBPsMs5MHsQt1cnpWLsO\nGzlq1Ojhbh105YW5W1Xe+yc3r1y+dDrwn5cZlfTvKmL2jBig/ejpOiclYboCAAAAADrBj2JT\njkyffS6dXsfQ6jh+y434lDe3Dq2dMUDIVEcIkddq02vcT9vOhn3+9PzoUveWHBGuMmrjmCUh\nBUJ2BgAAAABUgoJd8bVfl16jvVen1GbivrDEyMBfBrQSf2pNvnln783B76JPz3VQp9YzP+yd\nvfZFtdj9AgAAADRdAoJd3K41J6mLGtQ6r7nz4vgsBy2JHEWmZDFm95OwgFHG1N4S964MSJNE\n7wAAAABNC9+Axgo/dDCcslmdydRT139z1pDoABTaTD11d/cATXZN+e1dh95L9B4AAAAATQHf\nYPfi9Okkdslgyr6/hug0wBjkLGcf2T1cl10RfeZMbAPcBwAAAECq8Qt28aGhKeyGXZb9Nlit\noYbRfPy2X51ka4tvr19PbqhbAQAAAEgpPsGuKizsVW1Bvu/0SS0bciCtZyz2VK0tvY6KEve8\nMgAAAIAmik+wS4qPZx/05TRgQEM8haVoNmhwj9rNiUujo5P4NQYAAAAATnyCXVoae3GqQefO\npg09FE07u1a1hawsnDAGAAAAIBI+wa64uLj2s5mZWcOPxdSUHR4LCrBPMQAAAIBI+AS7ykr2\noV+ampq8G0qKujp7s+KSkpKGvyEAAACANOFzViyLxf6srKzc8GNRVFTkfvf/FFZJUujZ09fu\nPX768u2nzJyc/DKGUjN1bUOzdh1snXoPGjasn4MhTsMFAACABiCREySkQvqZ4foMtiFHykXu\nojwxaJVnO2OLvtNW7Dh2/dnbj2nZRRXV1eXFeZmf3j67dXb/+rlDHVu0cJq8+UZypeDuAAAA\nAESCYEcIIYT16cCU2UH1Wa9RERcw0dZm+PorcQX85xqrs14e8x1k4zgt8F1FPe4HAAAAwAnB\njhDCjPOf5HMrX/wOSqO2DuoxPfB9mdBXFEUfnthj4LYIvEkIAAAAEoNgR6qiN03wfVAqfgfZ\nQbM8l4bSpvsYmh3HrDx4PSwhs6i8qrIkNyXm4Xn/xQMtaG8qZt/9ecjkc2kEAAAAQCKafLAr\nD1s57rfwejwUTTs6dfKJz9THrzo9VofGRZ5e97+BjuZ6aopy8ipaxu1dRi7ccT3mzbmZ7anh\n7suFad57P4t/cwAAAAC2Jh7siu/+MmFrTLX4HRRd9112tZBSoWS/4uaNtb31uf6wCmaj9t8+\n4tWcOoI7y31OZ4s/AAAAAIBvmnSwy7u+0HtXfH0OpY3x+/VEBqUs77g2cK2jKs/2hBAjr91/\nempQKvIvbt4fX48xAAAAAHzVhINd5oWZUw+n1KeH6ge7972mPoQ1mrZpkZWsoMt0xy+bZkIp\ns6L27n5Uj1lDAAAAAEJIEw52KQFTZ57PrC2qGBg0E7WL0iv7j9OWPljNXNBXkVdrCllnr5HU\nZEdSD++7ir1PAAAAoJ74nDxB9e7C+vXvGnYkhJCYhr/FV8yEnd6LrufWljX77fDvsHHM9iJR\nOqkOuXStmFphM26ctZDXdh48SM//AHshbeGtm09Zw3ozRLk/AAAAAJ2QwS7m9KpVDTuQ76g6\n5s8JS++yM5nu0L1HZrbYtlG0XpgPr90soFZYDB7cRtiLZXr176t44BR7li77zp3XpLetaEMA\nAAAAoGp6j2IrwteMX/WCfV6Ykffff481FL2f13fu0Laua9azZyfhr1aws6PP7iWEhmLbEwAA\nAKiXphbsSh8sm/DH66pvRYbZ7CO7PHXE6KggPDyJVtHRzk7gsgmK1p06adAqoqOjxRgGAAAA\nQK2mFezyby2e9Fdc7f4mslY+J7a5i7xoghBCSGRkJO1MWG1rawOROrC2bk8rFyUm1uesWgAA\nAICmFOyyg2ZPOfipNo4pdFp9clM3FfH6youNzaBVmJmZidZDcyMj+o+fmJgo3lgAAAAACCF8\nF0/0XHP79uzvNxIOzTtKuMO0o9P+dya9tqjsvCFwhZ2CuL2lpNB3wGOYmBiL1oOsoaE+IewB\nkbQPHypIV2G2SwEAAADghk+w07fp29fm+42kQbGS9k5ecCWntqzutjXwp/aivBPHITU1lVbW\n0tcXcoFxLUNDQ1qwYxUUFBGCYAcAAADiahKPYqtjt034KYS9SZ3OkD1H57aqz6ZxJRkZJbQK\nLS0tUftQVlbm6LSkhHtLAAAAAGGIOs30H1QZ9fv4Fc/KassG4w8GTBDxuSmn0tJSeoWampqo\nfSgpKQnolJDLly/n5eURQmJiYrS1tVksFmcLAAAAgG+kPtiVPV4+fmNU5bcio+X/Du8Zrlff\nXsvLy+kViooiP0MVJtidPn36/fv3Xz/r6upmZWHhLAAAAPAk5cGuMGTJxO2xNd+Ksm0WnvAb\noMHvCuHUCXZyciL/knUukZGp82BcX1//6wPagoKC3Nxczq8BAAAAKKQ62OVcmTN538fap5fy\nNstPbnZRlUTP8vLy9AoxHpJWVVXRK1RU6uy94ufn9/WDv7//4sWLxXiTDwAAAJoOsYNdTf6H\nyJfhUVGvFPqu8xbuLK2Lk/R94+zs7O3su7oNG+7WVqNhV26kB86YfjKttqjU5feTqx0ktOhU\nVZUjH1ZUVHBvyVt1dTW9os5qCgAAAABRiJ6tSpND/145rmsLAzMn99Ezfll/OUHYK5llWfFh\nwWf3b/ad2s/KwMRp9M977qdWCr5OHKyPB6bMvcR+I61Z7y2BSztIbH6yTrCrrBT5DykuLqZX\ncJmxAwAAABCeSMGuMvnG74Ot27r9b8Pp52kiz1DRlX95eX7bvN5trIevDUqos2ignmri/Cf6\n3CqsLWsN3Hl8gbkEJwiVVVXpu6XUSWmCFRUV0cpy+vp40AoAAAD1IHTYqYw/NqWT9aA11z/W\nM9HRlSYE/Ta8g+24I7FlghsLqSp6/QTfx+ywqOe1//BkE4l1TwghDF1dHVpFZmamqH3k5OTQ\nyi1at67HjskAAAAAwgW7gucbBjhPPhrbQBvoViScntrVxefq5xrBbQX7vG/++nB2+jSdcnj/\n6OaS6JjK0tKSVi7NyBBxyo6VkUHfu6RVq1b1HBQAAAA0bUIEu8JHS3u7rrybzeNrhtAnOJj1\nHO5ipcdj+UJhhN+wXv+7lFb/HXgLcnKoqxI+HxmixRCs27ZkWi/XpipztHDxo5z/ZWRpSX/L\nLjmZfr1AqcnJtMUTmmZmeBILAAAA9SEo2LGSD40d9mdUnZfgZDTbe8z/4/C18JSjI4S9l/3C\niw9jM/PTwi/vXjGpu4kS5/c1H46MG7gmTHLPZBuSpaUFrZwQEyPa8onExERqkeHk5Fj/UQEA\nAEBTJiDYJe2ZuvgG/VUwhkanqX63E1JiLu9cNmWQvbGKiGeuKhnae8xdf+zRh88vT/zavxU9\n3lW83jBm8Z1CHpf+SCydnGgbHVe/efNOlOtTo6Joc6BWLi46vNoCAAAACINvsMu/sGzVXep7\ndTIG7hvvvnsZsKhv63rv8yun6zBh483YmHMLaQmJ+eHg3N+fNtAmKBIk29PdlfZUOf7evVTh\nL6988eIVtazt4tJOMgMDAACAJotfsIvbs/FCHrsobznpzNPrv/YykOTaTSWzUf6Pnh0abswe\nCOv9X0sPpojfZYff3rBEF7/JgdbL4MNlHC0eLTagNlDu28+F9us9C75N37+ED9bD4BDqI2et\nIR7dRZz5BAAAAODAJ9hF/H0ogr2UQdX5j6CAUa0a4ggyBavpZ4O39WpWW1H1aMdfL5gNcCeJ\n0h7k4Uz9+apunzqfw7M1TVXo6YvU7VH0xngPlNCZGAAAANB08Q52ry5d+lBbkHNZf2JJ+4Y7\nWFau/eJT/oPYj2QTTxx78sMnuxZT5nuqUcqVIbsPxAlzYeYp/1PUXGcyzruPVB/aCwAAAN8F\nz2CXHBLCPitMZ6zvzNYNPBLDKduX2dY+jvxy6dLzBr5h/WmMWjKtJaXMDP/T90wWz+b/yrrs\ns/wK5c1FuW4+C7s17LG5AAAA0CTwChQ1ERHsl/v1R08Z1PDHmDLazprnLv+tlPbwYVKD37K+\nZHv4/OJCXdebFzR/WsAnPvssVyfsHOt9krrKwnjahrnmDTVAAAAAaEp4BbuU9+9rX+6Xc+nZ\n7bu82a/tOdSl9kZRYWHV/Br/GFrN3rPCQZ5SkX11luvEgFhup9+yMu//5t59UWgBpc5w8t5N\nfers6AcAAAAgBl7BLjWVPavU1s6u4efrCCGENHdyMv32uSop6fP3uWu9yNr4Bv7Zh7phS3Xi\n6ekOHfr77Ap6lpBZVFFVmvMpNuzGvqUjbdu5rb2XSTlaQ67NvKN/eWh/9zEDAACAdOL1zn5B\nAXteSUfnu22da25uTsinr59TUlIIaeg3+yRAru2icxc/unn4vWJP05V9CPZbEOzH7zKG8Yi/\ng/3d1Rt8fAAAANBU8JqxKy8vr/2spfXdDjFVV2cHneLi4u912/rScd1xL2TTAFOhl7aqd/a5\n/Pysd0tJbgkIAAAATR2vYCcvz35xrKqq6vsMhn4rarb88Wl2870R/TxgkWtL/m/Myeo4eO+4\n+/bR9iHGSHUAAAAgUbzmmFRVVQmp+Po5OzubEFMeDSWL+gRYSel7Liqw8H3J8q1nHxr2U/3u\nTFz5OvjChSshTyKiY5O+5BaWVMuqqmvpmbbt6NClx8CRY4c4GWIvYgAAAGgIvIJd8+bNCcn9\n+vl9XByLmH6XdbGJiYm1nzU0NPi0/GHJ63YcPKvj4FmNPQ4AAABocng9ijUzN69NcvkPH775\nPqNJCw9Pry20aNHi+9wVAAAAQCrwCnZKNjYWtYW3Fy8KdVRWfeXfvPmstqDerp3x97gpAAAA\ngJTgeZSVbc+e7AWqbw7uul/R4GNJCzwcXLt2guHUpfN3efoLAAAAICV4BjvZHgP7qdaWUg/8\nujOR2aAjKQ9dv+UR+zAu+wED9Br0fgAAAABShvfh86pDvEey96+rfLp68ra3DXfGV8mjlXMP\nfGIPy8lrZKsGuxkAAACANOId7IjiwPkzKKfTlz3+1WP2lQwW7wvEVv3hxMQx2+PY03WKA+ZM\n/Q+cOQEAAADwI+ET7Iic0y8rB6uxyzVJf4/qMe1MkmS3Ky5+tW9U7ylBaZTEaDZn1UQ8hwUA\nAAAQDb9gR4iut9+G7iqUisr4I2MdXBaefCeR074qkq9vGGXvPOefTzWUWr3xW1d2led5EQAA\nAABwxT/YERmLBUf+dKMfVJ//YucEG0uXOX/diC8S98FsxZcXp1YNaWc1eOWF+FLaN4bj9vkP\n1xGzWwAAAIAmTECwI4RhMffc2TltOU6oqE5/vG/RoLZGZj3H/rQ98Hbkp6Ia7pfTVOZ9iLh9\nfMPMAVYGpl3Gr7/2gfMwWEWbpRcOjtAVfvgAAAAA8A2vI8WotPrvDD5Z6j7x6PtK+hes4o8P\nz2x/eGY7ITIq+q3btGlj0drEQF9PT1tdWUlRUZ5RVVJYUFBQUFCQm54UExUZ8yGvkvstCCFE\nsd3MS8F/dFPl3QIAAAAAeBMm2BEi22L04UeahsPG/PEkj3sLZmlmYlRmYtQjMceh47zs9KVN\nffWxJzEAAACAmAQ+iv2Goee+6X7kP79015eV9BB0u/8UFHHvD6Q6AAAAgPoQOtgRQohcS88t\nD9+FHZrXw1BBIndXMO698Gh4/MOtQ1tgGSwAAABA/YgU7AghhKFlN33Xg6SE0D0+w2z1xIxj\nDNUWLhPXBL5I/nDX37uTJmbqAAAAAOpPuHfs6lAy7TNne585m7Pf3L127UZwyMNnEW8+5Fbw\n2/1ERsXAqpODg1PXnu6Dh7jZGSiJd2cAAAAA4E7MYPcved0O/SZ36Dd5GSGkuijtQ2Ly59S0\njNyi0rLyihqGgpKysrKyqoaekYmpiamJgaaiyPODAAAAACCs+gU7Wk/NjCw7GVl2kliHAAAA\nACAKzKEBAAAASAkEOwAAAAApgWAHAAAAICUQ7AAAAACkBIIdAAAAgJRAsAMAAACQEgh2AAAA\nAFICwQ4AAABASiDYAQAAAEgJBDsAAAAAKYFgBwAAACAlEOwAAAAApASCHQAAAICUQLADAAAA\nkBIIdgAAAABSAsEOAAAAQEog2AEAAABICQQ7AAAAACmBYAcAAAAgJeQa/A41FUVFRSUlpWUV\nVTU1KgaWhmoNfksAAACApkjiwY5Z9PFFyO17z15GRkXHvk9KTssqrGTWfjvyHOv8qH8/P/Gb\nfrmmp5vHiL5tmjEkPQwAAACAJkdywa464+mJv/z2BASFpVcKdUHao4DNFwI2/7Kw7ZAZS1cv\nn+KojefCAAAAAOKTSJZiZtzfMtLawnnqxrPCpjo2VmHclW3Tu7R1nnfkVZEkRgMAAADQNNU/\n2BU939zfxnXZxffF9emFmf18z9Ru3Wdf/FRd7xEBAAAANEn1DHblL9f36+sbksUU3FQIZdH7\nR3WfeDqNJZHeAAAAAJqWegW77Muzhq1+VnemTrF5B7cxs5dt3H38wvW7z7e4c7/abvwvXp2N\nleiVrJQzU4ZuiMK0HQAAAICo6hHsCq77zD6WSp1dk9GyG7f62MPk3C/RIaf3/vHr3IkjBvbu\n3Fqd+/XmI7acef4pPerYwm561GFUvFz3v22xkpkDBAAAAGg6xA52zIgtP534wi4rWXrteZ4Y\nfnLtJJcWKsLvXiKjYTvJ//G7R2t7arErK8M3rwsqEXdkAAAAAE2TuMGu+J/Nu9/VlhQ6zL/2\n9MwcRy3x9qNjaHdbfT1kTReV2pq8c7tPZok5NAAAAICmScxgV3T5+OWCbwWFzuvP+7nq1G8g\nqvarD692UPhWrH4QdDWvfj0CAAAANC3ibVBcE3ojuPxbQX/S2kVtZes/FJl2S34b94fH0XxC\nCCHVjx48YU0dLMkjKapzYu5cvXzzzpPImLiET5n5RcUVRLmZhoaGhpZ+K2t7RweHzr0HDXAy\nUhLcFR+skqTQs6ev3Xv89OXbT5k5OfllDKVm6tqGZu062Dr1HjRsWD8Hw/rdAQAAAIAr8YJd\nXHh47StwmkO93BX4NRaevPtIz2ZHj33dprgwOjqZDG4lkY6Z2U/2rVq++ej9T2Uc3xTnZRbn\nZaZ+jH/z4vYZQoisVoeBE+cuXT6zh4HoWbU8MWiDj+/Oq3EF9A1bivPKi/MyP719duvs/vUL\n9RzH//T77z4DW0roVwMAAAD4SrxHsampqbWfbTp1ksB03VeKNjZtagspKSmS6JOVEeLbo53L\nvH11Ux0XNXlvru6c26t9tzlHY0RavlERFzDR1mb4+iucqY5TddbLY76DbBynBb6rEOUGAAAA\nAAKIF+xKStihR1tbW1KDIcTAwKD2c2FhYf07TP9nivOgzU+yRdvzmJUXtm9KjyFbXwsRBQkh\npDRq66Ae0wPfC9mcEFIUfXhij4HbIrD2FwAAACRGvGCnosJewJqbmyupwRBSWlpa+7mqqqqe\nvdW83Tx8wrEkWjcM1Va9Jq08dO1JVFxyZnFFRVH257cPL+xeNryDFsdvkXfvl76TTmUKvk12\n0CzPpaG0RbwMzY5jVh68HpaQWVReVVmSmxLz8Lz/4oEWyrTr7v48ZPK5NHH/OgAAAAA68YKd\noaFh7ef4uDjJHQH25Qt7azx1dR47GwuJFe8/Y80z6pQYQ6f70htxCfeOrZs+qJttmxZ6qgoK\najom7VxGzP3j4uuEJ/6jzenvvWVd+GllsIAzcNOOTp184jP1J9DpsTo0LvL0uv8NdDTXU1OU\nk1fRMm7vMnLhjusxb87NbE8Nd18uTPPe+7lefyYAAADAv8QLdpbW1vLfPqefPRlaI6HRfLl3\n731twczMrD59FV9evf4J9S22Zr23P723ub8xjzcCGdpdFp55cHZKa9pP8uXv3w7we9Wv6Lrv\nsqvUR8ZK9itu3ljbW5/rD6tgNmr/7SNezanDvLPc53Q2/z8FAAAAQBjiBTulXr261BbSj67y\nj5NItPt86uTD2qPEmnfu3LIefSX/vfUcdSM8jX5+xxZZClgEzDAaunvfdBNqFfP5lWu8t0qO\n8fv1RAalLO+4NnCtoyq/exh57f7TU4NSkX9x8/54/uMCAAAAEIKYGxQbj/Ryrp35Knu6YvyK\npwX82gsj7ei835/UBkTNQYOd69PZhTOPqWHTcs7GqabCbIqn0m/BtDbUCuajkNBy7m2rH+ze\n95r6ENZo2qZFVgLXCOuOXzaNGh5ZUXt3P6oWYmwAAAAA/Ih7pJiJ98LhmrWl8ojN7n0W3UgV\nO50w064vGjjvCjsctvCe0V9R3N4Iybtx4zk1cVmOHecg7F7HNr1709b5VqekZHBtWHpl/3Ha\n0germQv6CjNoWWevkbRpwdTD+65i7xMAAACoJ3GDHdHwWrfckbLUoCTyr0HWjtP3PckQcS0r\nq+DNyaX9Og/96zV7nYPKgNXLutVnd7xXkVFMSlGrVy8b4S+mLg0hhJDMTK5LY6tDLl2jLayw\nGTfOWshbdB48SI9aLrx186nklqAAAABA0yR2sCMMqyVHNzjT9u8oeBUwp3uLFp3HL9994X5M\nRhm/qMIsTX977+SmOYOtW3ea8Ocd6mSfas+Nu6cZiT0wQkj2u3e09Qjt2rcX4WyysjL6fnQs\nFrc/hPnw2k3a42eLwYPbcGnHlUyv/vS5vew7d14LP0IAAAAALsQ7Uuwr2fY/XwyM7jbq2Afq\n7FhletipTWGnNhEip6pnYNjcgMHewYS83Dl+1NHs7My0hLfvUou5rbhgGI0+cmqhWf3OiM3K\noq13UGjZ0pBXUy6oe64QQoiOjg6XVq/v3KHdpFnPnp2Ev4eCnZ01ORXBrkgIDf28ztZUhGEC\nAAAA0NUn2BFCmg8PuH9Grt/EAG7HY1WXZKUkZNE2C0l+cCqZT3cM/QF7bh8fZVS/WEdIUVER\ntaglyuEYrOd379Nm7NRtbVvXbVYQHp5Eq+hoZyfKw+PWnTppkAjKlF90dDQhCHYAAAAgPvEf\nxf5L1nTU32EvAmZ00hDcli+GXo+l115ent2+Hmsmvum8JYlFkb6rt9CXlocEnPpErVBxdXfm\n8itFRkbSHtBqW1sb1G3Fh7V1e1q5KDGR97YqAAAAAILVO9gRQohax6kHXkTfXDO0bTPx5tpU\nzT3WBr++u3mgqbzgxg0p79bCGQdTqTWG3vNHcDkBIy82lr5UVuTtlJsbGdF//MTERNF6AAAA\nAKCRSLAjhBB50/6/BcWmvr+5c/GQ9ppCPpRkqLTsPWPLpZiU95dX9zWozzJYCSh+/feU7iMP\nJlMn4pqP3vW7G7ewmZJCP4+CYWJiLNrtZA0N9WkVaR8+YMsTAOAtLi7u+vXrDx48yMnJaeyx\nAPwoysrKwsLCrly5EhERUVlZ2djDaXz1fMeOA6OZRf/5O/rP31qcEv388ZMnT55FxKVk5eXl\n5ebl5ZfUKKlraWlpaWlpG7Tu2NnZ2bm7s4OljoLgbhsKq6ayrDAnJeltxOPbQcePXHxJ36pF\nu+eGoIMj9LhemppKm9cjWvr6ov6UhoaGhKRTRlNQUESIBB5EA4C0+fTp07p16yIjI78W5eXl\nJ02aNGvWLFnZRv4fxACNKzg4eOvWrbm5uV+LBgYGvr6+Li4ujTuqxiXZYPeNrJpJJ7cxndzG\nzG2Q7uup/MQw5Un/8G0i27zbjO0BO8ZbKXH/viQjo4RWoaWlJeow5Q29zAAAIABJREFUlJWV\n6RUlJSWE6IraTX2VlZXFxsZWV/9AR19UVFRQP7948aIRB0MlLy9vbGysr68vuCmA5BQXF8+c\nOZM6S1dVVRUQEFBRUeHj49OIAwNoXI8ePVqxYgWDwX4JLDMzc8mSJQcPHrS1tW3EgTWuhgl2\n/3E6PVcGnvR1M+aR6gghpaWl9Ao1NTVR76KkxNF/nU4JKSkpqampIYRUVFQ0xP80f/v27cKF\nC/Pz8yXes6Tk5OTMnfsD/e8DGRmZxYsXjx8/vrEHAk3IpUuXsrOz69afOnVq6tSpmpqadb8C\naAr279/PYDCYTPaWa0wmk8FgHDx4cNeuXY04sMYlsXfspEnOg7V9W7bsPvdgRB6PFuXlHMfH\nKiqK/AxVmGA3Y8YMV1dXV1fXs2fPmpubi3oLgfbu3fsjp7ofEJPJ3LVrF5d/VAANJjo6WkaG\ny7+rmUxmTEzM9x8PwI+gqqrq3bt31FT3FYvFioqKapQh/SDEC3ZXZxhQzbgq4VH9AGoyn+yd\n6dxlwskEbkek1Ql2cnIiz33WuYTrv7obFudezCCEyspKrtMnAA2kqornQY18vgKQbtXV1dzP\nhSKkpqaG11dNgXiPYivyMzIou33klfNu+gNSmhjEmkgIYVaVFhUUFWR9iH39OuLR9fPnbkRk\n0NfTVMSf9B6govP8YH+Osyfk5TmWyorxn6E6/0ZWUVHhbLNo0aKvmy1fv3790KFDYsRHAPiv\nMzc3f/jwIa+vvvNgAH4QysrKBgYGGRkZHP//V0ZGxtzcnPriXVMjXlDQ0NAghH1qQkFBASH1\n3aD4+5ORV9HQVdHQbd6iXdf+Y2b+6pf1/ODKhSsPvsih/IekJvHQZB+P+GOezaiXqqqq0vui\nvu8vpDrrFeqspiCkS5cuXz/ExMQUFRWJsURDeMrKyh4eHg3X/39aeHg49hmExjJs2LDAwMCa\nmhrqUycGg9G1a1dTUxxXA03XuHHjduzYwVHJZDLHjBnTKOP5QYgX7MzNzQlhH3Sampr6Xwx2\nnGT1usze/8i161jX/11MpTy1zzjhu32p55oOlKZ1gp0Ye+cUFxfTK7jM2H1PSkpKgwYNaswR\n/MAyMzMR7KCxmJiYbNq0ae3atUVFRQwG4+v8hK2t7e+//97YQwNoTOPGjUtNTT1//jyTyZSR\nkfn6f6dMmdLEJynEC3at+/WzWB6R8K34LiQkdXV7ETfo/UHJt5kaeCysrdteyrlirNjDAU9W\nb3dmT+wqq6oyCKFM7NVJaYJxHGhL5PT1G3A+DgD+w3r37m1vb3/16tXExER1dfVOnTr17Nmz\nKT9sAiCEyMjILF261NPT88GDB2lpaaampn369BH5HCipI+Y7Ww5zFnXftuDxvy+JsZ4cDng3\nf5WVlCyxVXJdu6LPgVl3a9hVybdvvyfObWvLDF1dHUIoL9BnZmaKehvOveNbtG6NrUYBgAd1\ndXXsswNQl5WVlZWVVWOP4gcibhZrMWvnSofaQyNqov6Ytzuhht8F/yl6Q4Z0ptfERkXRXqKz\ntLSkfV+akSHilB0rIyOLVtGqVSvRegAAAACgEXuSTd5uxdm9Q5p/K5aG+rhNOJ4gLYe0GXXt\nSn8luSYzM5fWwNKS/pZdcnKyaLdITU6mLZ7QNDPDk1gAAACoj3o8PZU1m3Y2eLv7t+OVaj6d\n8bZznLL7SXojp7vHPqYMKvNlkaJ3oqvLcbZXWVkZrWxpaUErJ8TEiPZ3c7yLz3BychTpegAA\nAAAO9dsXTbmjz9WnRou9Zu8Lz2cRQoqjj87vfmp91+HjhvXr1dWmdXMdTTUF8bKjio6Jdp3d\nP4Sira1NSAq7nJqSQoidiJ00a9aMXqGjQ9/KztLJSYO8Ym/5Uv3mzTvi1VHoG6RGRdH2uLVy\ncdHh1RYAAABAGGIHu+BFVgtv/fuZqaRCSMm3byrTn53Z8ewM59YyIhp5jnV+lFhXck62VSQk\nfCZExM2eOI8WUNDXp+/nItvT3VXx0CX2i3fx9+6lko7CLg2ufPHiFbWs7eLSTrQRAgAAAHAQ\n+1FsYWpcrfj0EsEXfDd6traGtIroqChR13VUJSR8olVYWFhwNFHu28+F9us9C75dRITEehgc\nQn20qzXEozs2LgAAAID6kZIdSmgce/SgPcQtCw19JloPzEc3b9MWuZr261dnQk17kIcz9eer\nun3qfA5nI+6qQk9fpG6PojfGe6CiaEMEAAAA4CSNwU6hV79etJiUcuHMk2perbnICvQ7SduK\nRHPAwK51m7WYMt9TjVKuDNl9IE6Y/jNP+Z+i5jqTcd59cAgsAAAA1Jc0BjuiPW7uGNrWIZ8D\n1hxKZvFqziH72k8rLlPn62Ss5y/oy23vYI1RS6a1pJSZ4X/6nsni0pAm67LP8iuUZ9dy3XwW\ndpPKfw4AAADwfYk9UTT0WF7eIUmOhIOCquA2PKkMXjzT8tjm+NqKkpAlo/9wfvRrRwU+VxFC\nSFH4uuHjj3+mVmmNXfezDffgJdvD5xeXA/MflX+ryAuaPy2gW9C0FrzOkKhO2DnW+2QqpcZ4\n2oa55gJGBQAAACAEsYOdvIqmZqOeWc+XjN3yPbNOue9nr4AoC1vRp0fGvhN/jLZU4n5NTdaL\nQ4u9F5+MK6fWNh/mv3m4BvcrCCGtZu9ZcdhhVXjVt4rsq7NcJ5Irf09rV+fXYWXeXzvG6/d7\nBZQ6w8l7N/XhMSAAAAAAkUjtI0D1vlsPzWpFXWnKyn3h79XR0nnMz37n7ke++/glr6yqqiQv\nPfnt40v718/u37ZVl9kcqU6x49Kg45NM+N1H1sY38M8+1ORXnXh6ukOH/j67gp4lZBZVVJXm\nfIoNu7Fv6Ujbdm5r72VSngjLtZl39C8PbUn8uQAAAAD13KD4h6bm7nf5z/e9fr6bR6ksT3l6\ndtvTs9uEuF6ri0/gxU1d1QS1k2u76NzFj24efq9Ka+vKPgT7LQj243cZw3jE38H+7upCjAQA\nAABAGFI7Y0cIIUo2P11/enSqtciv6zHU7eeefflg+0Aj4X4fHdcd90I2DTAVOiard/a5/Pys\nd0ter+IBAAAAiE6qgx0hRKmtd0Dk2+Bt01xMhNooTr55F+8NF6OTX+4ebSZooQWNZjffG9HP\nAxa5tuT/xpysjoP3jrtvH20fYoxUBwAAABLVcI9ia4rSPyZ/Tk3LyCsuK6tkKahpaGpqampo\n6hi3aqGt+D2PWZBv4b7kb/dF294/uHrl9sPnL1+/S/qUmplXXFrBlFfT1NbW1tbRN7Vy7N6z\nZ89ePbu01REp0FFp2E/1uzNx5evgCxeuhDyJiI5N+pJbWFItq6qupWfatqNDlx4DR44d4mSI\nvYgBAACgIUg42FVlR10/e+HKjZAHz6MSssq5bx0nr9mqg72jo2PX3h5ew11Mlbk2kjhZzTZ9\nJv7UZ2JD30det+PgWR0Hz2ro+wAAAABwkFiwK08O2b129dbAp+mVgppW5X+MDP0YGXr+4Jal\n6hauXpNnzJ832lYLZ6UCAAAA1IdE3rEreLlnQqf27j8fFiLV0TELE0IOrRpj38Z5zsHwHKYk\nBgMAAADQRNU72LHS/pnt0nPeybhSwW15YmY/2zezcxvnX66l1NR3QAAAAABNVD2DXV7IInev\n/W/KJDEUZu7zrZ6dRwXEV0uiNwAAAICmpl7v2H06OnnMzrecT19ltdv17Ova06WLbVuz1q1M\nm2uoKqsqydeUFxcXFxdkfUp8/z7+XfTzB6F3n8ZmcV7L/BI0w32eUeT+AVr1GRgAAABAE1SP\nYJd+dPbCK7nUmmbtRv+8eunM4Y4GXDb0UFXV0Glu3NK8vWOfrxWs0pTn/xwL+PvwmdCEQvb6\nWWbygSnzBsee9ES0AwAAABCF2I9iK0N/X3Gj8P/s3XVcFOkfB/BZOqVBAQFBLGxERbHBU7C7\n++w4Ts8866yzuz0Vf7ZiJwoqmFgoYgEGCkpJ98L8/kBhhu3ZgVl2P+8/fO0ME19m2dmPM888\nT+m0ccuZlyNenFw0SGiqE4anZ9ty8Pw9N99/fn74D3czykOx8cdmr36MxnYAAAAAMmEa7FJP\nbjgQWzKl67bw5s113tU1mWyKZ9xo6MbgJ0cH2JUW8273lmu5DEsDAAAAUE0Mg92Ps8eulwQv\nrZYrji1xlXlAVhoNh0F+p+fUKykn9cyxa3lybREAAABAxTALdvzgwOCSZ1fNhy6c7MRCf3g6\nbvOW9DP6NZV9K+iR/NsEAAAAUB3MAtmHly8zf7028OnjxdLgp4bdB/no/ZqICwtLYGezAAAA\nACqBWbCLiYkped2gaVNGTeuE0XF1dSmZ+PjxI1vbBQAAAFABzIJdZmbJBTvC3NycrWIIwsrK\nquR1SkoKexsGAAAAUHrMgp2GRmn/dzk5rIw7USw/r/SJCVY3DAAAAKD0mAU7E5PS3oO/fPnC\nVjEEERsXV/JaV1eXvQ0DAAAAKD1mwc7R0bHk9YdHj5LYqiYjLCy6ZKJq1apsbRcAAABABTAL\ndtXc3Gx+vS4I2OcXI25h6aX5H75c0jueep06zuxsFgAAAEAlMOx/rkWXLiU3Y4serp5x+Asp\nbnGpZIUsXUUZbqJJmzby9XkMAAAAoFoYBjtNr9FDS67ZEYnnxvdZEJIsT7bjf/X/fdim96UD\nxDbs28dJju0BAAAAqBymI0aot541p61WyWTOk1UdGnb553psPoNt8b8H/ePlOuBYTGky1Os8\ndTRuxAIAAADIgvlQYPYTt85rXBrtiMK4gMVdHGu0Hrva//n3XNHrUZBZn0MOzPKu49Rp8e2E\notL56nX/XDnKSvR6AAAAACBIQ/Iiomg2/Pv4xuCWU26lls7Lj7u/f+79/fP0bJt16tqpVROX\nenXr1bK3NDY0MDTQVednZ2RkpCXFRr0Of/Xy2d3rFwOexgn2VafdZIHfQlfWRrMAAAAAUBFy\nBDuC0Kg9+czF77/5LAtNp/+AzP76+OLexxdl36S6/aCDF5a4aUleEgAAAABomN+KLWbs8U9g\n8N4h9Vh5gLVKk8knQw4PsuWxsTEAAAAAFSNvsCMIwqDRuCPPws/O6WClznwjGtadFl4Of7S9\nT3U5NgIAAACgylgIdgRBENo1ev0b+O7VpU3TfOoYybZNXbs2Y1aceBZx4x9vOzSsAwAAAGBM\nrjZ2dDyjOj4ztvjM+Pdz8PnzN+89fPToYeiLj6kFwpbVMq3ZuEVL95Yenn0GdK1vgqt0AAAA\nAHJjMdj9omffdvD0toOnEwRB5qXFJySlpKSmpqakpuep6xsZm5iYGJtaVqtqpI2WdAAAAABs\nKodgR8HTNqpa3ahq9XLdCQAAAAAQBGtt7AAAAACAa2wGu7yEl1f2Lpsxqo9ni7o2VQb7i1vW\nf5h5zea/DZr896ZTD2OlG6cCAAAAAMRh5VYs/1vI7iWLNxy78yGjdGAwF1LMGgSZmxz9OCD6\nccCJnStmGtXqOGjS3EVTOlnjsVgAAAAApuS+Ypf5dHN/l5ptp+65RU11MilKe39zt69n7cYD\nN9xLlLceAAAAAFUlX7BLvj27Q4c/Tr/PZqOUzNcnZ3ZqM/7cV4b5EAAAAEC1yRPs4vaP7LP2\nSYaIn/LEdmci4qd57/b2b/9HkKhtAgAAAIBIzIPdt0MTfS+nlJmpZ99m6F8bjt14EpX0vz7i\n1u598Mvz64fXTuvRyLxMuzp+9NZRfwSkMa4LAAAAQEUxDnavtq26mE6Z1rTpOOfo8y/RwYfX\n+A7ydHUy0xF7xU5N37Zx56GztpwP+xJ1Y93wRlWoP/yyf+qqJ2KfvQAAAACAshgGu8JbO/e9\nLZ00brXo9qvAfwc3NpV9cDAdO8+Zh0Kfnfy9rnbpzMi9my6z0nAPAAAAQGUwDHYvbtxIKJkw\n6//fxaWtjOUpQ8up/+4bfgOqlsz4ccrvYo48WwQAAABQNcyCXdKDB5ElEw1nLO1jKn8lPJuB\nW1Z0Nfg1mX/3bqj8GwUAAABQHcyC3aePH0teO3h712WpGKvBo711fk18f/QohqXtAgAAAKgC\nZsHux48fJa8dHR3ZKobQdXdvVDLx/ft31jYMAKAMcnJyCgsLua4CABQXsyHFsrNLn2wwNDRk\nqxiCsLS0LHlNTY8AACqMz+efPHny6NGj379/19TUbNCgwYwZM1xcXLiuCwAUDrMrdnp6eiWv\nk5KS2CqGIFJSSnvGy8/PZ2/DAACVFEmSM2fO3LBhQ3x8PEEQBQUFYWFho0ePDgoK4ro0AFA4\nzIKdnZ1dyeuwR4/YC2AfKY33jIyMWNsuAEBlFRQUdO/ePYIgSPJn/55FRUUEQaxcubKgoIDL\nygBA8TALdjWbNCl5fDXr7P4TZQegYIi8f9I/tnQnNWuys1kAgEosODhYTa3subqoqCg1NTU8\nPJyTkgBAYTFrY6fRvnNHrcMXiq/UZV2Y53vR+2B3M3lriTvyr9+nkinbli1t5N0iAEClR22i\nIv2PoFz5+/tnZmYWvzYwMOjbty+39QCUYBbsCKM+o3tPu3Di54iusX5DvW0uXlrezkLsMGJi\nZYUt7z/xYunjElY9e7ZgvDEAAKVhaWlZchNW8EcVXAwU8/Pzi4uLK35tbW2NYAeKg+lYsfo9\n5v/RsHTljNCVXk27LjwXxWQcMDL12d6hzVovvJ9VWpbLhEntGYZOAABl4uXlJRjs1NTUqlat\nigdjAaAMpsGOUGv41/YpzpTVC75eX967jl2TgXO3X3j2LU+abfB/vA3cN7uni32z8UffUiNh\n1VFrZpbL6YqfHHHzyLrZY/t2dq9fo5qZkb62ppaeiZVdzXpNWnToNXbepqM3w7/nCP+vsZTI\nrA+BB1b+OdLH3aWGjUUVHU1NXUNTK3sX9y4DJy7ceeHpt1y2fhkAUAktWrTo06cPQRAlLe14\nPJ6mpuY///wj2PYOAFScHFfF9D3WHF/2qO2C0NILbURhctjJ1VNPrp6mZ1WnafPmro1q17Cz\nq25tYayvq6urrcbPyczISEuOjX775vWrpyG3HkSnCfa0qeY44eAG7yrMCxMq79PljUtWbD3+\nIE4gc6YmfElN+BL9Jiz09vn9/xI848aD56xYNt3bUU/YhsTIjT63wnfu1kvv0ujRMDMlNzMl\nIeb1w+sndy+fbtFsyMx//vHtaq8lz+8DACpk/vz5rVq1Onr06Pv37w0NDV1dXSdMmFCtWjWu\n6wIAhSPX7U6dpvMvnknw7L05vOwdWDI7/s3di2/uXpR1kzwrnx3Xtv7GbkcnmWE7x/SfdUrK\n+8RkatjReT6ntvqsvnjCt6m+lPvIe7d/bI+pR97nSFqQn/jk0Fzvs0dG7zy5c2gdbSk3DwAq\nrn379u3bt+e6CgBQdPJexrfsvCk4aGMvRx3Ji0qmX3/c0QdnJzhrsrGxX1KCZnt4TJY21ZUo\niLv8ZwfPBbelGv0iO2ydd5uxUqS6EhnhB4a16br+WZbkRQEAAACkw0L7DOMWf5wNf35kmrsZ\n82diCa3qvy26HPF076AarKY6/pstPfusfUGPT5pV3Ucs2n3+3qsvyZl5BfkZSbFRT68f+HeK\np0OZeJr+cGXPIXs+SWpyl3RuQo/ZQYnUWTzjhgP/3nvlcVRCRm5BftaPrxEhpzf/0bWmLm29\nW7O6jTwVx/y3AwAAAKBiqeGtXp0hW+6+f3F23eQutarItk09h3ZjV/qHRVxb6s12q7PCt2tH\nzQ5Jo84yaDLx2KsP9/2Wju/RysXWVF9LQ9PAzNqpaedRc7bdeBd5ZW4rY9om0q//OXxbZJGY\nncT5jR55+As1/Jm1WRT07vnxZeO6NnOyMNDW0NQzsann0Xf6xisRr06Nr0cNd9/8x4zY+UX+\n3xQAAACAtWBHEAShZtqg18ztV9/FfbhzbMvi6cO8W9a20BFxEU/DyMG18+CpS3Zdeh0XfXvf\nvD51Ddkr5JfkY3OXh1IfldBsNOvKrZ2DaumKWEHLtuuqoOA1bWnt6rLuLph9TGQfoBlX5s65\nlE6ZodN0wbWrS9tbCj2wWo79dt84OMCKMiszcL7vcRZH2wUAAADVVQ59xenbtx00re0ggiAI\ngp+VFJ/4IzU1LS01NS2zQN3A2NTExMTMytraVLe8n9KP3Ln+IrVhnWazxcf/bSPpsQztBn8d\n23yr7rirpWkt4/yqrRFDFrkICakRm+YdjqftY+mRpc3EPnBhPWD72iPXR1wouZCYemb17shB\nC5wlFAYAAAAgQTmnKw19cxuHWi6N3Vq19+razbtz+1bNGtV1si3/VEcQr44cDqPeQrUYsezP\nOurSrGk9aulER+oMMmLP3ntCFuQHb9/1knoT1nrMqhmS92E+ZM4YW+rmw3Zuv8uXpjIAAAAA\nMZS2c8uPV668o07bDh7bWdQt2LLU3YYMqEmbE3vjxhuBxbIv7v4f7dGHOuOneUrTf4l6qwF9\nqcmOiD2w65JUXToDAAAAiMZNsMv9EfPuxaPgW3cehr39nCTfSA/C5d27/4w6XaWTVwsZftcG\nHh70HpJfh4SU7fiEf/Ps5UzaSoMHSztcRnMfbwvqdPr1aw/K4SgAAACASmE12JG5idGvnj16\nJ/JZADLt2cHZ/dztzc3t6zRu2a5je/cmdR0sTG2adpvw77n3bPbp9jYigjamhWvz5rL8qmq2\nttb0OSXDPf9SFHL5Gu2B25o+PrWk3n673+jX9pICA1/KUB8AAACAIHaCXWrE2X/He9W1MLKs\n2cC15YLbQhfKerymc93mo9f6P4zJol2dyv32/PKeeb3r12wz88zHAlYKKoyK+kidNqxZ01Km\nDVSpUmZMs6SkMnH1ZWAgres6w7ZtG0u/fa0mTehX96KCgtDtCQAAAMhF7mCXH336z7Z1GvWZ\nt/fm2+R8kYuRH//r7zPn5jfBoWFLFHy/u6Ff8y4r7qfKWxNBpP/4QduTzGMqJicn02fo6dFH\njk17+vQDbUbDJk2kejTjpxqNG9Mf0A0PD5elQAAAAICy5At22U//9WrRf2NIvJi8RhAEQcT/\nb4bv1UQJCxEEQSYF/e0zZO9HedubpaXR7pIKXoCTIPP16zLXz6ysrGjTz58/pxVp6uJSVaZd\nuLjUo01nREdLcYAAAAAARJIn2H07PNR7XnCy5AWJV1tXXcwQmKttYutU08HSsMwgYqlXp/Vb\n/UrcYA+SOcx6QlI9nmkvy+rZ1y8G0a8+GtSuTWt0l/LmTTxtAUdHWg8pkllZW9MPfnR0tGxb\nAAAAAKBhHuwSjk6Zei6BPo+nY17Tzat5dfrcwju7/3tL36t15xWXo5KSv0RFfoxP+/Hhzp6J\nrpShvPKerVl5js1HKWQUuW3NWfrutTp16UDLn1+/fqUtwLO1tZFtJ+rVqtGb/cV9/IguTwAA\nAEAOTIMdGbZhyVnK7U6ehduotZffJydGhgbMbkFbtOjOidPfqTM0Xf+5dnG+t5NB8VAOPIMa\nbX/feT9kVSuDkkVSTm04QE9OFaYwetfElaH0e8s6nXt3oQ8nERsbS5s2sbSUdRCPsu3+yLQ0\nwauaAAAAAFJjGOwKQ/b7RZZOVu22+969A7O8a+oJLkre8z9Ly3VmQ5fOaqBVdjGt+nMPLW1e\nclWMf//ydRYeopBZSvBfff4IojfQI5wmLxpqTpuTFR9Pv6RnYmIi6650dct0mJyVxeFVSgAA\nAKj8GI4V+/zKldKwZtxny6HfnTVFLXr1Gi3XWQ4a2UX48AxOk+b0W9r3WPEgreTDBw/JsV2E\njM9abvgxF6Z3H7LzZZkbomYDVs51K3OcsrOz6TMMDAwIGeno6EjYKEFs3rz527dvBEFERkZa\nW1vn5OTIuhcAAABQHcyC3Y/Q0NKG/jXGze4n+nJV9I0btG5BzHr0biOqWxBdn77e2seOFwer\n1OfPPxFdajCqT3b8mCuLh41aFZJY5nlc9VqTj+8ZYFF28dzcXPoMbW1pxhKjkSbYPXr06P37\n98WvDQwMEOwAAABADGbBjvoAp7Gnl5vo62opN2/ShvbS9+oiMtcRhLara33i+NPiieTkZIKo\niGCXHn5wzhjfXU8Eb/1adt50bpOnkeAqAsFOQ0PmIymwipqawI3x+vXrGxsbEwQRExMTHx9f\n9scAAAAAFMyCHXUYhjouLqIb6uXdCrxH7bhErV3nTgLN6yiovcWlpKQwKk4WOZHnlk+aujYw\nVnDAC62ag3Zf9htVS+gtZk3NMrNJUuau9woKyuy0bB/IBEHMnz+/+MXmzZsvX77MoCUfAAAA\nqA5mD09QL1iZmpqKXI58eOs27eaha6eOxqIWJgiC4PFKL/4JuTPJprxPV5d2c2nQe6WQVKdp\n33vj3SfHRtUSFUL19ekPyRJ5eTJ3VcLn8+kzBJ6mAAAAAJAFs2BHfVRAXV30rdVXQUG00RRq\nduokvqPgxMTS5WUdLUJ6BV9v/Nuvvov3kstCeo6r0mj0f49fnvnDTcgd2BICwS4/X/RwaiJk\nZmbSZwi5YgcAAAAgPWbBjnpLkHpbtowv1669pk6bd+rUUPyGP3+OKXld3LiMZUXfgzcOblS3\n8zz/KMHrgbqOPZddffV4/5hGkiKlrr4+vV2hQEqTLCOD3m2dhqUlbrQCAACAHJgFO4capQ81\nhD97JthAjSAIgvhw7Phj6rSup5eH+O5LIgIDS7tGsbAQeBhVPnkf/Ge2bdD+z+NvBEOYtoP3\n3xdeRZz7u0t1Uf22UPHMzc1oMxISEkQsKlJyMn00NrsaNURf+wQAAACQiFmwM2/duvav15nn\nj1wQdrUq+8bqrc+oTxRotO/qqSNkwRL8Z4eOvSqZsmrWzJZRcULlvTs82rVRvw33kgQectB1\n7LbkfMSby8u6O4otj8bZ2Zk2nR0fL+MlOzI+nnabmnBwcJBtCwAAAAA0DEeeaOjlVfL4auqJ\nWdMvlemJg/x2ZsLYvbRBwTQ79ekmrtlaYeRW3y3vSibV3d3dmNUmKOX+Cq9Www9GCEQvvZq9\nl11+/fri4h5O0mc6giAIwtrZmd7K7vPnz7JtIfbzZ9rDE8ZWD3Z0AAAgAElEQVSOjrgTCwAA\nAPJgGOzU2k2Z1ODXbVXy04G+LbotPHz3fUJGdkbC+5BD87o0G3D4C+3amI73sL6iH5/Nf+83\npNNfwaXP2mp36OalL3JxWaQEzWzj+XfIjzKzdZx6LL32+tWZv70dZO5amCAIwtm5Jm06KiJC\ntscnqH0BEgTBc3NrxqQOAAAAgF8YDilGEHUmL+y/dcDJn83E8j9fXj788nLRi1cdPn2AkAtS\n+cmRj29fOblv695r0dR+UayGzxpmybQ0Cv67Hf36bogoM16DUdPJOw6vHVJXnodQnd3cjIgX\npYPK8l+9eksMkPBsCEVsWBjtoZM6Hh5mopYFAAAAkAbDK3YEQVj037FvqLV0y+p5LlnYsWyf\ncFfHm+vrmdfy6PfHFnqqI6q0XzL3N0bX0ejyQ+f3mhFEH1BCt96Yk8/ub5cv1REEod7WqyOt\nxMjbt2NlKS30BXXa1MOjrnwFAQAAgMpjHuwIwqzX7svrvazEP+hKEETVPnsOjK8uMDvrR3J2\noZCKaow7dmqikxx1/US+2zht01taOza9xjOv3f2vvyMLoZHQ9ezsQTt6DwNuZIhaWKC2kICb\n1Cxr0q17a4nHEQAAAEAseYIdQeg3/vPSvWNT3S1E9tOhbttl3aVDQ22lSy0805azzt/e6W0u\nV1XF0k7NWxFK7YhFzXGM/411bVl7QsHUu3sr6uEruHHsdLLIpWkKgo6foXaPYjFwRFc2wiYA\nAACoNPmCHUEQWk4Dt96PDDv5z+/ejatSHy1VN3Xxmb4zOOzKTFdpnoJQN3Mduur68+C13ewY\nt/uj+npo+wXqBTQNl7nHtnVhIzGWsBs1tYcBZTr/5vY970QuTZFwbPMxaq6zHTyiAyu/NAAA\nAKg0dvKEUf3+C/f0X0jmpX6LjYtPSOMbWFW3r17VUFJfv2o6xtVq1G/p6e3Ts3//TjUNJCwu\ng8iD/wVT7/Pa/r5lUXO2h2I16vfnGPtzW0r6OSl6unbuiXFnB4rvWDnxgu/8i1ml0xruvtPd\n5Q7YAAAAAKxeKOJpG1s7Gls7Srd094MZuQYG0ozzILuPZ89Qn03gtZw1r2M53OtUb+P7l8ee\nqXdLumlJOTd1zH73c2PsRN2b5kdtHTTiKPUpC5sxKyaz0KIQAAAAgMMrRdrlleoIIu7S5eeU\nSZ77gP6CD2+wwmHijgWu1F8j6dKEjsP2vxEciJYgyIQ7S7xazwhKo8yrNnLnqg4ydo4MAAAA\nIJRS3gLkP7hPG6SWvP+nDU9O7XclCd2XeoO5R9Z2oA6pwY8+Pta1/m++2849jErIyCvITo55\n8/jqrtl9G9XttPR2AqXbZo1aU/y2dBfdbTMAAACALJSyzf7rp09zJC/FFo3aM06d+dSp+6YX\npZfpcj4GbJoWsEncajybPv8FbPaqUu71AQAAKC1/f//MzJ9DhhoYGPTt25fbejinjMGuKCrq\nY8Xu0azjxts3rQYPXHjtC1/y0gRBVGnue+TM2m42IruJAQAAAMn8/Pzi4uKKX1tbWyPYKeOt\n2O9fv0oXr9hk7D73avij/TM62otvMadu5jpi463Xdzcg1QEAAADLlPGKnfX0++R0LnZs1HT0\npsBhf78M8Pe/ePP+s/A3H779SM/iq+tXMbGoXruha4s2XfsO6uZWDX0RAwAAQHlQxmDHLU3z\nhj4TGvpM4LoOAAAAUDnKeCsWAAAAQCUh2AEAAAAoCQQ7AAAAACWBYAcAAACgJBDsAAAAAJQE\ngh0AAACAkkCwAwAAAFASCHYAAAAASgLBDgAAAEBJINgBAAAAKAkMKQYAAIorKSlp0aJFT548\nKSoq4roW4eLi4po1a8Z1FaXs7Oz+/vvvpk2bcl0IcANX7AAAQHFt2LAhNDRUYVOdAoqJiZk7\ndy6fz+e6EOAGgh0AACiud+/ecV1C5fPjx4/ExESuqwBuiLkVm534KSGr4iopQ9/SwUKPs70D\nAIBCIEmS6xIqJVzjVFligt2VSTX6+1dcJWX0PUWe7sfZ3gEAQOFoamo6OztzXYWC+vbtW0pK\nCtdVAPfw8AQAAFQOhoaGs2fP5roKBXXgwIE7d+5wXQVwD23sAAAAAJQEgh0AAACAksCtWAAQ\n6c6dO3fu3ImNjbW1te3UqVOrVq24rkh1HThw4L///svNzVVTUzMzMztw4EDVqlW5LgoAFI6Y\nYFfdY+BAGXPf13sn7n39+dq29cDWtozrIlpWZ74uAMgrPz9/zpw5ISEhPB6PIIjnz5+fP3++\nc+fOy5YtU1dX57o6lePj4xMfH1/8uqioKDExsVu3br6+vkOHDuW2MABQNGKSW4s/jh+XcWun\n+5UGuxZ/HD+Ox1oBKqkDBw6EhIQQvzqbKP43ICCgbt26w4cP57g4FbN+/fqSVEe1adMmBDsA\nKANt7ABAiLNnzwrO5PF4QudDuRJ1zEmS3LZtWwUXAwAKDsEOAMrKy8tLSkoSnE+S5NevX9Hx\naQXLy8sT9aPQ0NCKrAQAFB+CHQCUpampKaohnZaWlpoazhsVSswBNzIyqshKAEDx4QQNAGWp\nqak1bdpUME+oqam1aNGCk5JUmZWVlagfTZkypSIrAQDFh2AHAEJMmTJFXV2dmu3U1NQ0NTUn\nTpzIYVWqae/evULnm5iY1KlTp4KLAQAFh2AHAELUr19/9+7djo6OJXPq1Kmzd+9ejNRZ8ays\nrNavX1/m5ri1tfWNGze4KgkAFBY6KAYA4Ro2bHjs2LG4uLi4uDgbGxtra2uuK1Jd7dq1e/To\nUWho6KlTp6pWrTpy5Ehzc3OuiwIARYRgBwAi8Xg8GxsbGxsbrgsBgiCI5s2bN2/enOsqAECh\n4VYsAAAAgJJAsAMAAABQEgh2AAAAAEoCwQ4AAABASSDYAQAAACgJBDsAAAAAJYFgBwAAAKAk\nEOwAAAAAlASCHQAAAICSQLADAAAAUBIIdgAAAABKAsEOAAAAQEloiP7Rp8B9Nz/KtrWnn2ir\n70tlVBNBEARRw3NcJwfmqwMAAACoHDHB7snO33/3Z77lp7t+/5352kTfUwh2AAAAALLArVgA\nAAAAJYFgBwAAAKAkEOwAQIKCggKuSwAAAKmIaWMHACotLS1t9+7dQUFBSUlJFhYWXl5e48eP\nNzAw4LouAAAQSUywc5t64EC3iqukDAc3znYNAERCQsKIESOSk5NJkiQIIjEx8ejRo7du3fLz\n8zM1NeW6OgAAEE5MsLNvP2pUhdWhPMisD0Enj1++fe/Bk9cxCcnJqTk8HcMqptUc69Zv5Nbe\nu1evzq7VdLguEkCSbdu2laS6Et+/f9+zZ8/cuXO5qgoAAMRT9TZ2P/Z1Uef9Un/JW7k2lht9\nbmGPujY1Pccs2HjoysPXn+KSMvL4/NzMlISY1w+vn9y9fHLPZnZ2biNXX/2cz9IvAFAOSJIM\nCgoqk+qK5wcGBnJSEgAASEPF29jlBlwLLmJlS3nv9o/tMfXI+xxJC/ITnxya6332yOidJ3cO\nraPNys4B2JWbm5ubmyv0R6mpqUVFRWpqqv5/worn7++fmZlZ/NrAwKBv377c1gMAikm1g13G\npdMBEpOYFLLD1nXv/FdQogx7Dj8wrM2n79cvzmyqz0IBAKzS1dXV1dXNyRHy6TA1NUWq44Sf\nn19cXFzxa2trawQ7ABBKpU/QXw5uPZch/2aSzk3oMZue6njGDQf+vffK46iEjNyC/KwfXyNC\nTm/+o2tNXdp6t2Z1G3kqTv4CANjn5eXF4/EE53t6elZ8MQAAICUVDnaJ/n8uDy6UezNxfqNH\nHv5CbYtk1mZR0Lvnx5eN69rMycJAW0NTz8Smnkff6RuvRLw6Nb4eNdx98x8zYucXuWsAYN3k\nyZOtrKzKzKxevfqECRM4qQcAAKShqsEu9dHyARNOJ8i9nYwrc+dcSqfM0Gm64NrVpe0thR5Y\nLcd+u28cHED9tswMnO97PEnuOgBYZm5ufuLEiREjRlhbW6urq9va2o4dO/bo0aNVqlThujQA\nABBJ5drY5SdF3Dy2c926fbdi8uTfWsSmeYfjKdOazZYeWdpMbKs56wHb1x65PuJC2q8ZqWdW\n744ctMBZ/moAWKWvrz99+vTp06dzXQgAAEhL2YNdUV5K3OdPnz59iIx48fzZ00chwU8+Z7Lz\nHCxB8IO373pJvQlrPWbVjDrqklYzHzJnzPwLG7/+mibDdm6/O2eTh7K/FwAAAFDOlDxMZB7q\nazr6cjltPPvi7v/RHn2oM36apzT9l6i3GtDXduPmkmRHxB7YdWm1Ry/0fQIAAADyUNU2dizg\n3zx7OZM6o8HgwS5Srtvcx9uCOp1+/dqDsn3BAgAAAMgGwY6popDL19KoM2r6+NSSdmW1dr/R\nr+0lBQa+ZKsyADbl5uZ++vQpL4+FNqkAAFDeEOyYehkYSOu6zrBt28bSr63VpAn96l5UUBC6\nPQHF8unTp2nTprVt27Zfv34eHh5//vnn169fJa8GAADcUfJgZzDqEilSwf96Mt9y2tOnH2gz\nGjZpIvGxCYoajRsb0WaEh4czrwaAbR8+fBg+fPijR4+KiooIgiBJMiQkZNiwYch2AACKTMmD\nXfl5/vw5rVGcqYtLVZk24OJSjzadER0tw5BkAOVs27ZteXl5xamuGEmS2dnZu3bt4rAqAAAQ\nD8GOmZQ3b+JpMxwdHWXbgpW1Nf3gR0dHy1sVADuKiooePHhATXUl8+/evctJSQAAIA0EO2bK\n3o/i2drayLYF9WrVLGkz4j5+RPN0UAz5+fkFBQVCf5SdnS0Y+AAAQEEg2DETGxtLmzaxtJS1\nS8Bq1arRpsm0tAw5qwJgh46OjrGxsdAfWVpaqqnhvAEAoKBwgmYkKz4+izbDxMRE1m3o6uqW\n2WhWlvAlASqet7e30Pndu3ev4EoAAEB6Sj7yRHnJzs6mzzAwMJB1Gzo6OhI2ShAfPnwo7j8s\nOTlZYHmAcjRhwoSXL1++evVKTU2tqKio+F9XV9dRo0ZxXRoAAIiEYMdIbm4ufYa2tszjgUkT\n7P7+++/3798Xv7azs0tMxIOzUEH09fX3799/7ty5oKCg2NjY6tWre3l5+fj44D4sAIAiQ7Bj\nRCDYaWjIfCQFVsE3JigWNTW1Pn369OnTh+tCAABAWgh2jGhqatJnkKTMQ70KPHWop6dXdplN\nmzYVL3bw4MGlS5cyuOELAAAAqgPBjhF9fX36DAYjafL5fPoMgacpCMLS8meXKAYGBqK6nwAA\nAAAohpt/jAgEu/z8fFm3kZmZSZ8h5IodAAAAgPQQ7BjR1dfn0WYIpDTJMjLo3dZpWFrK3GUK\nAAAAQCkEO0Z45uZmtBkJCQmybiM5OZk2bVejhrp8VQEAAIBqQ7BjxtnZmTadHR8v4yU7Mj6e\n3neJg4ODnEUBAACAakOwY8ba2Zneyu7z58+ybSH282fawxPGjo64EwsAAADyQLBjyNm5Jm06\nKiJCtscnoqOjqZM8N7dm8lcFAAAAqgzBjiFnNzcj6jT/1au3sqwfGxaWRJ2u4+FhJmpZAAAA\nAGkg2DGk3tarI20Uscjbt2OlXz0/NPQFddrUw6MuO4UBAACAykKwY0rXs7MH7eg9DLiRIWrh\nssiQgJs5lGmTbt1b80QuDQAAACANBDvGTL27t6IevoIbx04ni1yapiDo+Blq9ygWA0d01Ra5\nNAAAAIBUEOyYsxs1tQd17Nb8m9v3vJNmxYRjm49Rc53t4BEdMLYbAAAAyAvBTg5G/f4cY0+Z\nLnq6du6JRJGL/5R4wXf+xazSaQ133+nueB8AAABAbggU8lBv4/uXhw5lRsq5qWP2xxSKXoMf\ntXXQiKPUpyxsxqyY7FReBQIAAIAqQbCTj8PEHQtcNSkzki5N6Dhs/5tsIcuSCXeWeLWeEZRG\nmVdt5M5VHXSELAwAAAAgKwQ7Oak3mHtkbQdql3b86ONjXev/5rvt3MOohIy8guzkmDePr+6a\n3bdR3U5LbyeQpUtq1Jrit6W7aYXXDAAAAMoJbfblplF7xqkznzp13/Si9DJdzseATdMCNolb\njWfT57+AzV5Vyr0+AAAAUBW4YscGs44bb99c1aW61DG5SnPfC49OjrBXL8+qAAAAQMXgih1L\njN3nXg3vfGDxX0t3B33OFb2cupnr0L/XrZzS3kZT9EIAAAAKiM/nR0RE5OXlcV1IKWoxeXl5\noaGhHBZTho6OTv369dXUKvQimioHO41h58hhbG7QqOnoTYHD/n4Z4O9/8eb9Z+FvPnz7kZ7F\nV9evYmJRvXZD1xZtuvYd1M2tGvoiBgCASic7O3v8+PFv38o0MnqFSk5Onjx5MtdV0DRu3HjH\njh1aWloVtkdVDnblQ9O8oc+Ehj4TuK4DAACATZcvX1bkVKeYwsLCAgMDu3btWmF7RBs7AAAA\nkCwxUWIX/CBEBR83BDsAAACA8kKSpOSF2INbsQAAACCzTp06mZmZcV2FIvr27VtISAhXe0ew\nAwAAAJm1atXKyQlDYgoRHh7OYbDDrVgAAAAAJYFgBwAAAKAkEOwAAAAAlASCHQAAAICSwMMT\n8BOfz3/9+jXXVSiolJQUrksAAACQDMEOfsrIyFizZg3XVQAAAABzuBULAAAAoCQQ7AAAAACU\nBIIdAAAAgJJAGzsAEMnf3z8zM7P4tYGBQd++fbmtBwAAxEOwAwCR/Pz84uLiil9bW1sj2AEA\nKDjcigUAAABQEgh2AAAAAEoCwQ4AAABASaCNHfxkaGg4adIkrqtQUNevX3/x4gXXVUDF4fP5\nAQEBiYmJXBdSquQpluLXfn5+HBZTBo/Ha9asWb169bguBAAQ7OAXDQ0NnJdFCQ0N5boEqFCz\nZ88ODg7mugqR0tPTt27dynUVNDweb/Xq1R07duS6EABVh1uxAAA03759U+RUp5hIkjx58iTX\nVQAAgh0AAF1GRgbXJVRK1JvFAMAV3IoFAIDKgc/nv379musqFFRqairXJYBCQLADABCnWbNm\naDomypYtW3Jzcytsd+np6WvWrKmw3QFURgh2AADimJmZ4bkiUdTU0J4HQLHgMwkAAACgJBDs\nAAAAAJQEgh0AAACAkkCwAwAAAFASCHYAAAAASgLBDgAAAEBJINgBAAAAKAn0YwegKN6+fXvh\nwoUfP35wXUiplJQU6uu5c+dyWEwZFhYWPXv2rFmzJteFQMWpUqXKxIkTua5CQV2/fv3Fixdc\nVwHcQ7ADUAifPn0aO3ZsXl4e14WIlJOTc/PmTa6roDl//vzx48etra25LgQqiIaGBjqLFuXR\no0dclwAKAbdiARTCrVu3FDnVKabs7Ozg4GCuqwAAUCAIdgAKoSIH3FQmOTk5XJcAAKBAcCsW\nAAAAZPbx40fcZxAqJiaGw70j2AEooilTpjg4OHBdhSJ6//793r17ua4CAIjDhw9zXQIIgWAH\noIhMTEwsLCy4rkIRJSQkcF0CAIDiQhs7AAAAACWBYAcAAACgJBDsAAAAAJQEgh0AAACAkkCw\nAwAAAFASCHYAAAAASgLBDgAAAEBJoB87AAAAkNmwYcOsra25rkIRffz48dSpU1ztHcEOAAAA\nZFajRg0nJyeuq1BEhYWFHO4dwY5tZNaHoJPHL9++9+DJ65iE5OTUHJ6OYRXTao516zdya+/d\nq1dn12o6XBcJAAAAygjBjkW50edW+M7deuldGkmbn5mSm5mSEPP64fWTu5dPt2g2ZOY///h2\ntdfiqEwAAABQUnh4giV57/YPa9Sg9/KLZVNdWfzEJ4fmejdoNubI27wKqg0AAABUA4IdG7LD\n1nm3GXvkfY7Ua2SEHxjWpuv6Z1nlWBUAAACoGAQ7+SWdm9BjdlAidRbPuOHAv/deeRyVkJFb\nkJ/142tEyOnNf3StqUtb79asbiNPxVVssQAAAKC80MZOXnF+o0ce/kK9/WrWZtHp04vbW5aG\nZhObeh5963n0nTjt9LTuI/a8Lrmy981/zIidLW9Oql6RFYuQmpr6119/cV2FgsrMzOS6BAAA\nAMkQ7OSTcWXunEvplBk6TRdcu7q0mb7QpbUc++2+UZTadODJ+F+zMgPn+x7vf3qQeXlXKhFJ\nkomJiZKXAwAAAEWFW7Fyidg073A8ZVqz2dIjolLdT9YDtq/tYUSZkXpm9e7I8ilPInV1dY72\nXLlpaOB/RAAAoIgQ7OTAD96+6yX1Jqz1mFUz6kiMSuZD5oyxpUyTYTu33+WzX54UGjVqxMl+\nK7WqVataWVlxXQUAAIAQuPDAXPbF3f+jPfpQZ/w0T20pVlRvNaCv7cbNX0tmxB7YdWm1Ry9p\n1mXXjBkzCgoKHj58mJenQH2vZGZmFhUVFb9WU1MzMDDgtp4S6urqjo6OM2fOxJVOAABQTAh2\njPFvnr1Ma1HfYPBgFynXbe7jbbF5T2mDtvTr1x6Qvdrz2KxPGvr6+osXL67ovUrSo0ePuLif\nkblq1aoXLlzgth4AAIDKArdimSoKuXwtjTqjpo9PLWlXVmv3G/3aXlJg4Eu2KgMAAAAVhWDH\n1MvAQNojpIZt2zaWfm2tJk3oV/eigoK+sFIXAAAAqCwEO4bSnj79QJvRsEkTWdpd1Wjc2Ig2\nIzw8nIWyAAAAQIUh2DH0/Plz2piwpi4uVWXagItLPdp0RnQ0OpEDAAAAeSDYMZPy5k08bYaj\no6NsW7CytqYf/OjoaHmrAgAAAJWGp2KZ+fr1K22aZ2trI9sW1KtVsySI76Uz4j5+zCNaVnyX\nJ6CQPn78mJ+fz3UViujz588VvMfk5OTXr19X8E4ri5KeiSpGQUEB3gtRfvz4wXUJoBAQ7JiJ\njY2lTZtYWsp6KKtVq0YLdmRaWgZBINgBQRAEceTIEa5LgJ+ePHny5MkTrqsAgiCIjIyMNWvW\ncF0FgELDrVhGsuLjs2gzTExMZN2Grq5umY1mZQlfEgAAAEAauGLHSHZ2Nn0Gg9ERdHR0JGwU\nAEDlValShesSKh81NTVDQ8Py3suyZcvKexfAAIIdI7m5ufQZ2toy30OVJtjNnz//y5cvBEEk\nJCTY29unp6fLuhcAgEpt6NChCxYsqODGfJVdz549EYhVFoIdIwLBTkND5iMpsIqamsCN8U+f\nPr1//774NYPsWEn169cvLe3nqB5GRkbiF1YampqaXJdQKWlpaVWKbaqCcjpuXl5e9vb2oaGh\nhYWF5bF9Zp49e1byPaCjo9O0aVNu6ymhra1ta2vbunXr8tg4g286ICr89I43iRGBd4kkSeFL\nilZQUECfoaenV3aZjh071q9fnyCIV69eBQUFybqLSmrEiBFcl8CBVq1a7dmzB5clZKKhodGy\nZUvWN1u9enU7O7uYmBjWt6zcyilMEARRq1atWrWkHrKxQowcOZLrEjjQqlWrffv24TQlk3I6\nTYnbY0XuTHno6+vTZ+Tl5cm6DT6fT58h8DQFQYwbN674xebNm48dO8bgEQ2oLOrVq7d+/fpN\nmzZVfHcelZS9vf3s2bOdnJxY37K6uvq2bdv27Nnz/Pnzsg/AgzC2tra9e/cePnw414VA+apf\nv/66devOnTsncNcKhNPV1R04cKDMHd3KB8GOEYFgx6DLsczMTPoMIVfsQKW0adPmzZs3e/bs\n4bqQyuG3335r0aJFOW3c2tp6yZIle/bswdshDW9vb9W8gqWC2rZt27ZtW66rAHHQ3Qkjuvr6\nPNoMgZQmWUZGBm1aw9IS1+MAAABADrhixwjP3NyMIJJKZyQkJMi6jeTkZNq0XY0a6nIXBpWc\nj49P48aNua6icrCxkXG0F9nh7ZBSBbwXACAlBDtmnJ2dacEuOz4+kyBk6cuOjI9PpM1wcHBg\npTSozGxsbPAdqTjwdgBApYNbscxYOzvTW9nJ3OI99vNn2sMTxo6OuBMLAAAA8kCwY8jZuSZt\nOioiQrbHJ6Kjo6mTPDe3ZvJXBQAAAKoMwY4hZzc3Ws+5/Fev3sqyfmxYWBJ1uo6HhxkbdQEA\nAIDqQrBjSL2tV0faSBCRt2/L0N9VfmjoC+q0qYdHXXYKAwAAAJWFYMeUrmdnD9rRexhwI0PU\nwmWRIQE3cyjTJt26t+aJXBoAAABAGgh2jJl6d29FPXwFN46dTha5NE1B0PEz1O5RLAaO6Koq\nA8ECAABAuUGwY85u1NQe1A5O8m9u3/NOmhUTjm0+Rs11toNHdEC/MwAAACAvBDs5GPX7c4w9\nZbro6dq5JxJFLv5T4gXf+RezSqc13H2nu+N9AAAAALkhUMhDvY3vXx46lBkp56aO2R9TKHoN\nftTWQSOOUp+ysBmzYjL7o5gDAACACkKwk4/DxB0LXDUpM5IuTeg4bP+bbCHLkgl3lni1nhGU\nRplXbeTOVR10hCwMAAAAICsEOzmpN5h7ZG0Hapd2/OjjY13r/+a77dzDqISMvILs5Jg3j6/u\nmt23Ud1OS28nkKVLatSa4relu2mF1wwAAADKCW325aZRe8apM586dd/0ovQyXc7HgE3TAjaJ\nW41n0+e/gM1eVcq9PgAAAFAVuGLHBrOOG2/fXNWlutQxuUpz3wuPTo6wVy/PqgAAAEDFINix\nxNh97tXwR/tndLQX32JO3cx1xMZbr+9u6GaDVAcAAACswq1YFhk1Hb0pcNjfLwP8/S/evP8s\n/M2Hbz/Ss/jq+lVMLKrXbujaok3XvoO6uVVDX8QAAABQHhDs2KZp3tBnQkOfCVzXAQAAACoH\nt2IBAAAAlASCHQAAAICSQLADAAAAUBJoY1eZkCSZkpLCdRUAAADAGTU1NSMjI1E/RbCrTFJT\nU01NMVIFAACA6nJ0dIyOjhb1UwS7ymHUqFGWlpZDhgzhupCKoKamVq9ePYIgvnz5kpaWJnF5\nKFf29vaGhoZpaWlfvnzhuhZVZ2JiYmNjQxDEq1evuK5F1ZWcpmJiYtLT07kuR9U5ODgYGBjg\nNFWMR5Kk5KVAASQmJt6+fZvrKioCn89fv349QRA9e/asU6cO1+WoOn9//6ioqDp16vTs2ZPr\nWlRdeHj4lStXeDze7Nmzua5F1RUUFGzYsIEgiN69e/ZZk6wAACAASURBVNeqVYvrclTdqVOn\nPnz4ULdu3R49enBdS0XQ19f39vYW9VNcsas0LCws+vfvz3UVFSEvL6842Lm7u3t6enJdjqq7\nf/9+VFRU9erVVeTPT5Hp6OgUBzu8F5zLyckpDnbu7u4dO3bkuhxVFxIS8uHDBzs7O3w0CDwV\nCwAAAKA0cMUOFI6amlrxhTorKyuuawGiUaNG2traDRo04LoQIKytrT09PXk8HteFAKGurl58\nmrK0tOS6FiAaN26sp6dXv359rgtRCGhjBwAAAKAkcCsWAAAAQEkg2AEAAAAoCQQ7UDyJwf63\nE7guAgAAoPJBsAMFk3hjWqcu/bp2nH4d2Q4AAEA2CHagSBJvTOvUc1t4DpEbsbUXsh0AAIBs\nEOxAYZSkumK5ESd2BcRxWpFKS7x7+VEq10UAAIBsEOxAMZRJdQRh+dvWW8eHWXNYkipLvDGt\nU+dunp0X3UO2AwCoTBDsQAEITXXnp9bT5rAmFVbydmQ+XtYF2Q4AoDJBsAOuIdUplDJvB7Id\nAEClgmAHnEKqUzCfzq8/THk7CALZTjGQhQUFRVwXAQCVAIIdcAepTvE4jPMPWNnWuMxcZDtO\nkOlvruyYN7xzU0czHXUNLS0tPbMazbqOmLP9amQGhoIEAOEwVixwBKlOcWU9XuXdeX5w2Rxn\n4LbwWsA/rcumPigH/NigDTNnrDr5KlXoCZpn1GjUqj3rJjY35VV0ZQCg4BDsgAtIdYoO2Y47\n+R9P/9lv7PZn6RKW06jRf9eVQ2Pr6FRIVQCKoSD53cM7d+4+fhUdE5uYWaimqWdm61irXlOP\nTh2bOxlrcF2eIkCwgwqHVFc5INtxIfX+ku7dl979IeXilt32PTw3toZ6udYEoADItNcXD+7Z\nf+DI5RdJfKFLqJs18Bnx+6SpY7s46lVwcYoFwQ4qFlJdZYJsV7ESb8zw7LXlZbYs62jWm337\n6epWuGwHyiv/S9COhbOX/+9psjQPEGlWdR+zcP3yie7mqvoQAYIdVCCBVEcQTtPuPN/S1pCz\nkkACZLuKIuTToevgOXLyyJ5tGtgZFiZHhl47unPH6ZcCre7U6y95+nxxI9yDAmWUEb5v6uAZ\nhyJk+u8OQahZuM89eWVFe9U8RZEAFSMhYGoDXSF/gkatlj9K57o4ECMzVPA5WYIgDNwW3k3h\nujZlIfDpULfutOjyp7wyixUmBq/qWk3gOoThwFPZnJQNUJ4yn+/s68TsYrSh+5L7Kvu9git2\nUCGEXI2gMGq1PODagua4bqewcN2uPAl8OoxbzDp5brVXVaF3kgo/HRvWZtjxr9SbUjq9TySd\nGaBf3oUCVJzEoHk+vf99XOYZIn27Fh0927dzb1TdRE9PM+975KvwsPtXL9yKTKd8IAzdl1y/\nvthddb9RuE6WoAJEXaujwnU7RYfrduVD4NNh6bnueab4dfLDljenX8ewn/O4YspVagkBf/ab\ndvlbEdd1QNHXs2OdtWh/4jxT11H/+r/6UShs+ezPwX7zujvpEoSKX6srhmAH5Uzwe6vT0N9s\nhF2KQLZTdMh2bBP8dPy2NSJXihX5LxfUo3Zhpzn8fLkXq+R+vRfadccj23ErOfAP+uN0Os59\n14TE8yWtl/c5YM3sTaqe6kgEOyhfIr638iMP9kK2q5SQ7djDONWRJEmS33d0pCQ744k3y7NU\npUd/L5DtOMSP3NGhCuVTwTPvuOJ+qvzbLYo9M+ePQ1H58m9J8SHYQbkR+72FbFdpIduxQb5U\nR5Ik+WKhc+najVZFlv6kMC8jPUfi5Q34RVhbEWQ7buQ9W9iYcrGOZ93nwPuyjxAxUBR7ZnQt\nTYJQs+2jCtkOwQ7KhxTfW8h2lRaynXzkT3UkSUb927hkfevp93O/hfpv/muYVxN7I02CIAg1\nHWObhp3HLNob+DGnXH4JpZH9ZJV7FUIQsl2FKwxf1pTSbY++x+qXsn4shPiV6oqpQrZDsINy\nIPX3FrJdpYVsxxQrqY4kC6+OLU0jlrVczEX2Y6fj0GXB2UgWviCVV9rDf5DtuPd1dwfKJ8O4\ny94v8h97eqpTkWyHYAdsk/F7C9mufCXcXPPX4m3Hb4Z9zRT6OJkckO1kx1KqI8m8K2MsRCU5\nYYxbzw9MREQRDdmOa/x7vjVKj7tWiw1Rch92YamumHJnOwQ7YBWj7y1ku/JCezs0jByadR3+\nx4rd/nci4nPY+apCtpMFa6mOLIre3ErmUfg0ncddTGT7d1ImyHZcyjk3iNLvnOnw8/L2uS06\n1RVT4myHYAcs+rCxNcPvLWQ79ontPlDLzLlVj9Gz1+y/cP99slyNk5HtpCPk7dBqMP1GEoNN\nFUZt7Wgk7F3laRtXtbG20FcX9bYbtF7zQjm/yliCbMeVrFMDKR1sm08KlO/pH4FUp2ZkZCDw\ntiprtkOwAzZRT4uyXo1AtmOTNJ1C/8LTtXJp12fCgo3/u/r4U1qB7DtDtpMs/tzI6kL+unUb\n+8qa7dKD/2pE77qV0LBoNnz5kbvRacVfhkU5cY9P/zu8iSlPcIeazda8ZfuevHJBtuNC0dWx\nJqXHWm/weQbnodKNCaQ6295+kUnBs5sIDk+mlNkOwQ5YVnxaZHaPCdmOHbKkOoHznL5t406D\npi7ZdvzmCxma5SHbScSPPjxA7myX/Xxte/rFOoPGY/eGCe3mix8XsMDDRGB/Bj0OJbP3Wykl\nZLsK93pRPcqBbr4mhvGWhKe64uj27eRAGyHnPKXLdgh2wL608MfvmD6Dh2wnP1HdN8hOw6hG\ns67DfVfs9g+W3CwP2U4iObNddthGLzPqelpOQw++FtsUKeflBoG7tuqtt35l6zdSWpxku/x8\npYoXMig6O5ByFZrX5yjDG7HiUh1JkiSZ9XCWi5Dnx5Us2yHYgcJBtpOfqK8lg4bD5s4Z26N1\nbXOZm94XN8sbM3vN/ov33/8Q3iwP2U4ixtlOINXpNZhxTZqIkXptnH2ZnbXdEsvW76PEKjjb\npT9c1qpG+3XPsljfciXwbWtr6p/2iAtMNiIx1ZEkSZJ5Txc3EPZIhTJlOwQ7UETIdvIT8bX0\nM0AU5Sa8Dj6ze8Wfw7u6ORiJ7AJNFJ6ulUu7vhMWbDxctlkesp1EDLKdYKprNCtQ6vu36ZeG\nWdL2pD/sHAamkEaFZbv0h8vci6+smrZTxWwXvboJ5fBqDDwl8xakS3UkSZJkXsgMR6GnNaXJ\ndgh2oKCQ7eQnPtuVKsyMDbt5fNuSKQM9G9kYCDvoYqnp2zb2HDR1yfafzfJUPtslREWniV9C\npmwnX6ojSZIkwxfXp+2n6coPMq2vNIr4+fmyPTtSAdmuNNUVM+1+5BsrG6486MGOaLM5XqbV\nZUh1JEmSZPL/fPREnMyUItsh2IHiQraTn7TZrhQ/9ePjK4c2zB/fp01dKx0hz1WKp2FUw63r\n8PG9XfSF/FAVsl1CwNQGulVaLLzPTrZjIdWRJEk+nVeDuhGnOc9l3kSlVZT2+vL2ucO8mtQw\n1eYRBKGua+rg2mX47G1X3qdLlczKNduVTXWEofuS+yp3dkve3YF6DMzHB8lyXIvebHSn9GUi\nIdWRJEnmX//dStQpTAmyHYIdyCPv+4uAY7vWLJo9c+bMuf9s3Hf2/qdMVhufINvJT/ZsVyov\n6d3d8//9O2tUt5Y1TcT09Sk95c52lOeR2ch2LKU6kiRz/udD3U79JW+ZbKXSKfgauHpgfWNR\n/z3hGTUaveNRshSnrHLKdkh1v1wYQmv167zwhWzrpwQvcDUgCKlSHUmSJD9gvKnos1Rlz3YI\ndsBIzueb26Z6OhkKfivpOXjO2PMggb2uspDt5CdPtitRlP09/NapHctmDOnc1M5QZB+4khi6\nr3xSjr8qhwR6mZEv210IZCvVkSSZf7QHZUu8LvuU/5OT9+HUlKZSPB2uUaP/vjc5krfHerZD\nqqOIWt6YdlRrL5D5mnJK8ILmdaRKdSRJklmHvMU2OlGzHXHxh8y/hoJAsANZ5X++tqxbDfEP\nVapZtZ1z9Str7bOR7eTHSrYrxc+IeRpwZPPCif071K+mJ/UNW+X96hLRd6Ac2Y5OnlRHkuTb\n5dRGdnVlvR5S6aTcW+wh5opMWZbd9n2Q4nTFYrZDqivj2jh6l4s2k+7IPiIOny/9l86DGaVd\n2qkP3P1oz5Q2VTWU5O1AsANZFMSc920udDAjQeq2PfdGSPEfYekg28mP5WxXKv9H1MOLB9bN\nGduzlbh+VCr3uVIcsT1Cs5Dt5Ex1JPl5QytK+HaY81ipe9lNCJjeUETbeJE0682+J83ZipVs\nh1QnKPN4P/qwENqeu8q1u8Xnc5xKP6PjA0iSJDPfnVvcu7Z+5X87EOxAaumhKztYyNSYnmfd\n5+hn1m7KItvR8ZNfXdoyZ3SvDq41LQ21tbQNzO3qt+k9adnBkBiR31Dllu1K5Sa8Dj6zZ4Xv\niK5uNYyU5X/AYvGv/24h/oMgV7ZTrz1VvlRH5j/yLf0OI9QaLH0j1+YUm5CQrevgOXHN/64+\nCIt49TT47M75/RsKa3WnXn9JmFTjWMmZ7ZDqhEs/0r3Mfwqthl0SOqQKOy4PLw3/TpQbvwVx\n76Ir+9uBYAfSSQyc6Vp2DGUNE+fWPgOGDR/co119CxEXarSaLGFx1HFkO5IkSbLox9N9E9tY\ni7w2plWt3Yz/RWQIX7kCsl2JX/2oTJu6UZm/uviPKf/5F4G952RlVfByuRvluRejASdV4uGV\nYurWnRZd/lT2nl5hYvCqrtUEDrXhwFNih/EoxTjbIdWJlnV2aNm75zUm3RRxGpNb/PY2pftp\ntzOxnHbDDQQ7kILA6VLNvPmk3SFxlHHDChKfHprZvqqQJvUaTVe8YvHGj6pnu4KPZ/70sJTi\n0QWdWoMPiBhuqiKznWpI+K+rsO5d6DjJdj9uTqtN6X9av/22T4y2UwkInKaMW8wK+CbqjgH/\n49FBtmUOtU7vE5nS7o1BtkOqE68wdHaNsofTesT5cjkn5V4cWfpWWE4OLo99cAfBDiQRPF22\n/Ot6rNA2qoXfA+e3EGyCZzL4PKv/7VLdbJf2eI2XlQx3w03brH2KbFcRCh7Oon0nqTu0di8b\nG4gKz3bpD5e2NKTuvv2OSPaeV1coAqcpS891zyWktPyw5c3p7brs5zyWYZ8yZTukOinE/9e1\n7H0hwsRz13upbpHLIu/csNI3Q7vXYSUb6wPBDsQSPF3+tvGl2A9BxsOFTcq2I9cddJq1pyiK\nich2hu6rnsr+KFUlkRAwvZGsTcIJnnW/k1+FXz9AtmNV/J7OtL/7GlP8dve2EUzhFZbtyiYJ\nnvXQM9/l/CUVlJDT1NaIXMnrkfyXC+pR3yLN4edl27OU2Q6pTkqF4f80ELgdoVF70o1kVneT\ncqJ36bum3d1P2U54CHYgGtPTZeyJAVXpn8zqvvdYr04g2yn1uVJIk3AD567T1h8LeBT+9m34\nw+uH10z2rEG//PCTbuv1USLuhSPbsSj/3h92tKPYef+XyH09rDnJdgJJwqTjxpfSRJ3Kh3Gq\nI0mSJL/v6Eh5g4wn3pR5/xKzHVKdLHIf/FVX8A/fwG3BA9YOWW7IDEqbWLORV1m+7MA9BDsQ\nQa7TZYJfd9qZTK3f8fK4A0TJdkp9rhR4LzTsfFYFfhW4P5H36eK81uZCgkSvwwmito1sx564\nnZ1o0dpp5gN+3pvtXS0Fj2+5ZjshSWLpg/JqhM4t+VIdSZLki4XOpWs3WhVZ+pPCvIz0HKn6\nRROT7Y6fV61Ul5/0Nth/98q508YO6dOjR89efQePnbFg9W7/e1Ep0t5PzQ2dXU9D8GAat1r8\ngI2HZPPDlzcp3bx6o39eKl/fPwh2IIzcp8ui+3/aUNfvdZi13orp8iMP9rIxVuZzpcB7YeKx\nMCRR5Lko771fH1uBbFd7wUvRe0C2Y0te8DRb6iHU7+qXQJI54Rs9zQSPbzllOxW6PiR/qiNJ\nMurf0iEPrKffz/0W6r/5r2FeTeyNNAmCINR0jG0adh6zaG/gR7HXdURlOzrlfS+KUiPOb5rR\ns5G5kEhWTN2sQQ/fLVejpWjOlv96UxtDIZswaOob8F2+SwSJV8c6Ue71OkwJlvJB6EoFwQ4E\nsHK6LLw6kvLJNB53rVxKJUmSJPOTk5XzagQp9L3Y9FLSmSj3xepWZZsgO4kfoQfZjiVft7Wn\n9UJTa04onyTJrGcr2xgLHl/Wsx1SnawKr44t/cO3rOUiOpjoOHRZcDZSzB4kZTtlfS/yYgI3\njnQ1kzh4SjHNqu4Ttt9PlBTP4s+NFNqto4Zdz+3PmR7FnBerPCjvEK/6uCuVdtQwsRDsgI6l\n0yVJ3plE6bTVff3n4rmFBfl8JX0sj31yvBdf93Yp0/1G05Xvxa+CbMeK3KDJ1tTjZ9DtcPHh\nS3+0uKWQ48titkOqk13elTESOpemM249P1D05XIx2U5J34v0l3tHuMj8SBehZuE+/5aE7hTz\n3+7yFv7e6NQctP1xiqw3UFMfLmtL7SdPs8Gc+8p4tY4kEeyAhrXTJUmmHvit9IJ31cHr9i0Y\n2blR9SqaPILQtnDpMmV7cCzrj7ArFTnfi8Lwv+vRToYGoy5JXAnZjg0xm9poUQ9f3flPf/5f\nJvX+guZC7jCxku2Q6hgoit7cSvyo10JoOo+7KKY7W2EfIuV8LzKf7+zrJPR5LYmkPCDZz9Z2\nMBG+BfVq7WYefyXhY1Mi4+W+IbVpfzNmXtvZ70RFYSDYwS8spjqSTD3aR+TYmT8Zu005Lu7G\nhipj4714Nr8W7Xi33yZNOEO2k1/2jQm0p8INex4rueGTeq88st1HFU51BKHVYDqjP87CqK0d\nhQ58zdM2rmpjbaEvshtwg9ZrxI2nQ/8QKed7kRA4103wRKFv16L7mDnr9h49cfrcxfMn9q5b\nOH1YJ+cq9D9bmQ5ITviObtVEdd2pbt581L9nX/0QdxMo8/2ZhT729Phu0HxRqNI23yFJBDv4\nidVURxa9W95EqhYXZm0WBbPbQZESYOm9SN/vTTvW0gU7EtmOBZ/Wt6IM4kXwXBaGld44Yj/b\naWpS96akSaKYiKZXDP4404P/aqRF34qGRbPhy4/cjU4rftKrKCfu8el/hzcxFZIrNJuteSsu\nTvz6ECnle1H09exYZ/qx45m6jvrXX0TEyv4c7Devu5Muwz/OguiT48Xe7tW1bdn/z3WHrj35\nkJzzs4CCjG+v7xzf4NvbxaTsn4t+Y185x15WfAh2wHaqI4uitnaQPL7SLzp1f78Qi2Z3v7D3\nXnzZ2Jq6Hb1h56RvlIJsJ6esa+NofZwY9T1FbVHEfrYroZRJgoqV3puzn69tT79YZ9B47N4w\nob1p8OMCFngI3hA06HFI/P9J0x6unrJKCd+L5MA/6tEugOk4910TEi+x24O8zwFrZm9ieEAy\nXuzu7yTFXXOelqGZmZmJvqaIH1t2WPGQjT5TFByCncoTvLWh3W7HF+bby3ryj5uMjWk1agw8\nEiXmzobKYDNhZx/pTT0P8jrtjJdpfWQ7+XxY04L6fCWv4ZJwWrAul2yn9KmumJzZLjtsoxet\n+xktp6EHRQyr/FPOyw0Cd23VW2/9ytZvVGnwI3d0oJ4WeOYdV9xnISkVxZ6Z88ch8d8CGRF+\nk9xMZBhRkU7NrPXsS1/LqdstBYNgp+Ki1rkLaQun22DaDTHNg0XLj9znXaY3VjUjF5/f56zY\nsmv7mr+nD2xuJfy/UmrVvHeGK+sjStJh97rptz0dqblOs+Me2XIdSSLbySfz0ihz6mEzGXCm\nTGJjOdupSKorxjjbCaQ6vQYzrgkM7CpE6rVx9mV21nZLLFu/T+WQ92xhY8pphWfd58B7FoZw\nLIo9M7qWJkGo2faRkO1I/vf7uyZ5VBPZJ41wPJNGw7bcT1CdG0MIdqouJXiBq8CwywSjbJcf\n6debPu65fv0Re578oJ00c77cXN3TTmi6M2276pG0TzkpG8HrpubDLjFv3psVNJk2JH21sVeZ\njXKNbCeHyFXNqM3v1Roviyi7CGvZTqVSXTEG2U4w1TWaJX17q/RLw+j/a9Ufdk41LgAVKwxf\n1pSSqPQ9VrMxSN2vVPfzQyI525EkmRUdsMO3h4ux5Ibc2jbuQ5eeeJmqfINLiIVgB+xkO4FU\np27Xa8fLTOELJ9//19NK2CV1g6azAr6r2GeQJPOfLmwoeN1Uza7/oQ+MHshPvTnJgXp0jb0P\nyXFlQXW6byBJkky4feDg3W8stQvIOD+cFiNMB58XPGosZDvlfTskkCnbyZfqSJIkyfDF9Wn7\nabryA2u/i8L7ursD5Sxl3GXvF/nP1PRU9/PEJ1W2I0mSzP3+4ur+ldOHdHWvZ2NYEjl5mlVs\n6rXuNmLW2sOBb3+oUvIuhWAHJCl3thNIdZq1Rp6MEfuRyo86MthJ2IU7Tfs+256q2LdU/rv/\negl7pJ9JtsuL2Erv+smy71F5mwKpQvcNJFl63VTHvuO0nbe/snCT6d3yJrSLds1WvRWylFzZ\nbmz/pcr6doRceiihE1upsx0LqY4kSfLpPNqVcKc5YsdzUSb8e76UX12rxYYouWOdsFT383Mi\ndbajbC0/KyUhNjY+LU/lrgwIQrCDnxhnOyGpbvSZWCk+XEXfr09vLGyXhJad16yDIZ8yCsmc\njEzl7UWSip1sJ/Be6DSaHcxKh01K3X0DSZJC7oZr27adtOXmZ7EjhEqUdmYILWabD7sk9DI2\n82ynpIrfDgO3hXflz3YspTqSJHP+50PdTv0lwnK6Mso5N4jy52k6/Ly8DaJFp7qfJz4G2Q5+\nQbBTMQl3Tt8S2YqeQbZjnOp+Sgtd2cFcyC4JgiAInrqGmlqdhc9V5Gq6vNlOyHsx7vx39upT\n1u4bSFJot7e/DmK1Vr9vuPqB8fdY0eslDWlNFJqvETG2G7JdCerbIW+2uxDIVqojSTL/aA/q\nCarLPuX8OAjIOjWQ0oOV+aRA+c7JAqlOzchI8JsH2Y4xBDtVUny61HGZdo2dbCdvqiuW++6/\nfvYi+nhX3utDwjHPduy8F6pJdKoroWHZfNSai5Ei2oyKl3JqIK2nDMuRIp9kQbYjhb0dcmU7\nOnlSHUmSb5dTG9nVXfiC+aYqk6KrYynXnfUGn5fnNopgqrPt7ReZFDy7ieDwZMh2zCDYqQzq\n6ZKNbMdmkiiMPT9J8KtV1VJdMSbZDqmOOSlSXQl186bDVpx9my7jkS16tdCF+o5quK+PFrmw\nyme7j3t/Mxb8/VnJdnKmOpL8vKEV5Y10mPNYRT5jrxdRB55uviaG8ZaEp7ri6Pbt5EAbwTcN\n2Y4BBDvVINhHWq//iXlQUmK2K4ckkXL3n3ampRtUzVRXTLZsh1QnDxFDVImjZtJw0NKTr2Tp\nQeHH8X60B4urjr0h5t6uqme7zNCVbdnPduq1p8o5klT+I18nyp9Bg6Vv5Npc5VF0diBlADFe\nn6MMb8SKS3UkSZJk1sNZLkK6qEO2kxWCnQpg1POtmGx35cHB8kkSeZHHJzQ0JJQ51SUETGvX\ndd0ziZ3KSZvtkOrkJiIKaFZtPWCAe1WRzbt5RvX6LTwaJnb88VKFL+bXpb6dmh6bPopbXni2\nM/xt72c2fmXFVx7ZTs7OFwteLnej/DUYDTgpsRJl8W0rdXRCvREXmGxEYqojSZIk854ubiDs\nM4dsJxMEO2Unx3gGorIdHctJouD7wyOnQpU11f18L0zbsZLtkOpYIiIKGP2/vfsOaBpt4Dge\ntsp2oYIo50DFrag4DregguPc+9wDTxHHqXfuvTgV3Asnnq84cAAqzlNUnCgqigMXooAMZZP3\njwJt2qZN03Tm9/nLlLYEYtsvGc/jtuTy44iNE9rZm0p8rYiBpXOvufvvfZW/C+Pbwd6UF1OV\n8ZGyX4bibae/f+1Ip11tl3JxqrPo4LztA96yeh6dFL+6icgv0XjgMYWfgVnVkSRJkjnXp/0i\n9bWGtmMOYafXlJ6lSl7boSQYE9sWZdsHPpNbA7La7sUzVB2HaFLAssWCG6lk9vvIQJ8OjpLn\ndhczr9lj5p7biTJPKc+/P6e26GNM3TfL2/0mbDu+VZ2AtrRdetTiVqKNbdV+y0v+TE8lHnZE\nu42KTU+oQNWRJEmSyQd60Mw2jrZjCGGnvziae1RG2zkNQ0kww35b0LadiQmqjls0KWDedM7l\nbyRJkrmfru+Y3sWJ5jOHIIjS1btN3/7fJ9pxjZOCvMxF7+8w6arcMZC//ze/hT0vq05A822X\nHrXUTfSqZoMqQ0M4HERIFyRv7yD6+ys/PlKRt5rCZ/5uIp8gcqqOJEkyN3ycHd2LDG3HBMJO\nT3E6ozyH88nykbLbgq7tSqDquEKTAqUb/BFWPNNd3pdbe2Z51qTfj13KsZNP4JX30jZw/t2Z\nNUXvatZpK4NJQbKzOZiRU4dpsu3Eq46w7ejPxQSpOub0EDPRX0ItRYd5KfkEYVB1JEmS+RHj\nyxK00HZyIez0EadVJ4C2Y4mbbSGj7Yxroeq4RJMCprXHnngvPPyW/y16/1zvOpa0vW1q/+vE\njRffik9b8WVPd8r/BkefG/iEkk8zbSdRdZZui29xMo+Lrnm1rDHld+c8X+Gp1FKvzW9Rh1HV\nkSRJ/tjfXea16oYOI0JTFP4x+ANhp3dUUHUCaDuFcbktuJxPFmSiSQFjp2HBbyjnRRakPDzy\nd18Xa9q8M6nUesz6sHiRC2XyoqZTZhst1XXHJ3X/fDpJ3W0nper4e0CcDBtLmRiPsGdwFoGE\n/Hzmw6TcmiYc0s5o4PbbO6a0q2SMbcEUwk6/qKzqBNB2CuB+W6Dt1IYmBYwcBx16Lf7pVPg9\n5tiSgY3K0u5iMK7QcuSqM3FF+3o+7exGuQrDafotbDtG1Nd2qDoxmcH9qJcOmXXexuAsAvYe\nzBEOGWg1PoIkSTLzxcmFfZzNeb8tmEDY6REVV50A2o4RVW0LtJ3aKNJ2JEkWpj87sWJo0/I0\nk+MRhFG5pkOXnXiWVph7c2o1ykvHczfPTsZnrNIuFgAAH1BJREFUTx1tx6OqS7q448hTRrve\n0g95mVF/bXbDznxX3ZqdHS68SKmGyIHfvE8v4vVzW3AKYacvpMyMVGPqVZW8BNB2cqi2sNF2\naqNg25EkSWbGnVk7sqWdlOHzi7aSbaMBi/6Z0owyCGsNvzvYcEyptu34VHURPg1KEwZ2PbYy\nabsfJ4aKX8/gNOmiqs44/BLYTvh93LfiY0VBCDu9QDffpXXrZbfRduqljv2maDu1YdF2JEn+\nfB3mP7ZNFdppK8SV6bkvSY0/lK5TVdu94VvVCTBru4I7s53Ef2lVRpxSbpY2GtmhI4XboeLk\na6r4HnoNYaf7ZM9ijrZTJ7UcDSdJEm2nRuzajiTJ7HeXNk90dzCT3EqSas2+z3ICTn5SRduZ\nmFBKnB9VJ8Co7b7s9pR4x7ftvC2O87ebnJPDhF1n1vug3Fl6QAzCTsfJrjoBtJ16qK/qBNB2\nasO67UiSzPlwddvUTtXpp60QqDLtOp8mM+CAKtquhP5WHZlzhXqOZxEGbVcQs6SBxDmkxs6T\nLiRzuoKpR/tYlTy9mVeQSnYK6jeEnS6TLIlOQ7vZS3u7QtupmrqrTgBtpzbKtB1Jkrmfb+7y\n9ahBM22FHmcESSZdXDNrYUDwxYcfMrlOVxW1nV5vDpIks55s6lZRys8tv+2yb82qK/k7s3Cd\nf4uz31f29WnCC2KJciPPi48FCfIh7HQWTUnkvtzXG22nZlxW3c+H/oMG+T9i/G6GtlMbJduO\nJMm8L3eCZvWsTX256HVGUF4axtbVm3sOn758+/GrT79kcTOqNudtp9ebowTrtsu+M7uelEuD\nbFovvMXFRbK5McuaCJ/eqNGSxxh8nQWEnW6SWRJoO7XiuOq6lCMIgijX5R+0nRZSvu1Ikiz4\ndv/QvN71rAz0PSNknihiWq5Wa+/fZ6/Zc/pmXLLio92K4LDt9HpziGHbdrmx/7SzlPI4i6a+\nEYnK7ZP9en5MDZFjvdWnXPup1PPxFsJOBzEoCbSdmqik6gTKddkcw3i6KbSd2nDSdiRJFqQ8\nPrphN3eHsLQNk9N/ixmUtnNx7zthvv+B83ffprH4/8pJ2/Gp6gTYtt2XkyOlHsU2duwV+IDt\nrzDr0cq2ViLrUHXsOcwaxhLCTtcwLgmtaTvLTlteqeLbaZ4Kq44gyjSaeUmhs4bRdmrDVdvp\nL0WqTuI/rLlD406DfBYFBF98pMBpeUq23Zj+i3lWdQIs2y73+bbuFaRuvlI1BwXeTVX0AOr3\nqKW/io6TZ9Jgzk3srWMNYadTFCwJLWg7/f0jWLuqTgBtpzZoO1l+Rq90s5L8f8iCsbVTc8/h\nvsu3H78m/7Q8ZdqOx1i23c/7azvYSnkYQRBGld39gp+kMfz+GY93DXGmvJeW6xLI/SAqfIKw\n0x2sSkKjbYeqY4KrqhOgazubAf9yOyYBoO1kSYtaIrXtLBoO+3POGO82zuUZje9HYVquVmvv\n0bPX7Am9GZcivTjQdqywbLusmC09pb3bCF4I5VuMWnXiSYqsfa6ZcSF/96hG/Z9g0WLBHVXN\naMEXCDtd8dq/DcuS0FDb8ajqiNp+d7Si6gQk205/t4WGoe1koWm7ovm7CrOTYq+FbF8+Y7in\na3Vr2jnY6BiUtnNx/23CfP+D4qfloe1YYdl2efH/jnehGcVHsLkdWvWfsW5/WPTr5KyixsvL\n+Bx7NXiDbx8XW/EXj3ljX2Xf/wBhp0NE3yUV3T+kgbZ7904/SyL33t8NJU8esnFfFa348Oiq\nqLqitRRpO1SdSqHtZJHddkIFmR8fXgwOWDRlYOdG9hayRg6WytDcoXHnQT6LAotOy0PbscL2\nWoqMR9v712CwA9bA1LJcuXK25jRT7RlU7LA8iosxUwBhp0sE75Lsjvqpv+30FO2xTgXbTnVV\nJ7qeqDo1QNvJwrTthPK/v7l7bv+GeeP7tqtrV4ruSB8tY2snV8/h4/u4mEv5ItpONtbj22U8\nDZrkaqvwxipmWK7N7DMf8GrhCMJOx6TF3H3Bdj4DtB1HlG87VVdd0Xq+uv8E21Ut0HayKN52\nQjnfXtw4tXvVzFE9W9W0pdnVoxi0nWzs56XIT7y5bVLbygoeVjewbTRs080kTKjHIYQdv6Dt\nOKJM26mn6kDN0HayKNN2JQp/JsZcPrZl6bQhXZs6WkrMWsqUpduKaJXP9qfTEvd0k3raHIP5\nZEnyR3zEFl9vFxv5B9TN7N2GLj76+Dsml+Aawo530HYcYdd2qDo9RjNF1eJbuMiP5KjthPIz\nEu5FHNr498T+HepXLsP4GCBOTpDn+82/WkqbWUKAUduRJElmJz46v2fFH0M83erZW5bsxDMw\nsbKv16bniJlrD156noI/eFQEYcdHaDuOKNp2Kqm69Kf34xnPUAEqxuMpqhjguO2EclNeRYXu\nXTdnTK/WssZRweaQR3bVKdh2QoW5P1KTPn78kpaDvXPqgLDjKbQdR5i3nWqqLmqpm7VBFe+d\ncWg7bcHjKaoYUFnbCWUnxV4L2bHcd4Snq5PIOCrYHPJIVJ1NW58ZLM+3A01C2PEX2o4jTNpO\ndVVX9EaLttMmPJ6iigE1tF2J4nFUpvr46+nmSIrwcfuVg8tBpFTd0rsZylxLARqDsOM1tB1H\nZLedqquuaJv9duQrJz8MgKqps+30Wclg6Upe6ktXdQJoO12DsOM7tB1HaNvOrWcb1VcdDjOB\njkHbKUtsChzWbSe76gTQdjoFYacd8vI0OOUx2o4jdG2HqgOQAm3HnuTEhuzajknVCaDtdAfC\nTgskRfg0KFu90+TNEa8Vn5aKG2g7jshsO1QdABXajp2C9yHDa0gbr1mhtmNedQJoOx2BsNM0\nyh9eRuWbDlp89EGyJob3QdtxhLbt7MeGZSr31Kg60D9oO3YK3h9Xqu0UrToBtJ0uQNhplNTd\n6QRhXqPb9K2RCVnqXh20HUe4mk+WAlUH+gptxw77tmNXdQJoO62HsNMcmqorYVKxxYjlx5+k\nqnUOPbQdRzhuO1Qd6De0HTts2k6ZqhNA22k3hJ2mpB3oJavqhK8Uy9peM3fe+Ki+uQ1p2s7S\nbeU9vGIVwVnboeqAD9B27CjWdspXnQDaTosh7DQmcZcH/dQ3Ekwrtxm9+vSzNPVMyCLRdigJ\ndjhoO1Qd8AfaTrq8PNnnXTNtO66qTgBtp60QdpqTfWGMHYOko7xgrF36/BkU9VkNUwyItB1K\nQhlKtR2qDvgGbScuKcKnsfOgQ6+VbTtuq05AetvhbUrDEHaaFLu0EeUT36juhI3rRraoaCSn\n78yquo/3P/dSyUss5cp9ua+3vQ1eokpj2XaoOuAntJ1QyanYRo5Ktd2Zc/M5rzoB8bbD25Tm\nIew06tv+7mUoL8BKY85nkT9enlk9vHl5eXlnWLZh/wWHor+qcmjj3ORkTl77vKdw26HqgM/Q\ndiQpcYGdMm0n9r7DVdUJCNsOb1NaAWGnWbmRk+0pLziTtgEJgi9lvDi1cmiTctKuUKUoU72T\nz+aIN5oa2hiYUqDtUHUAvG+7vKjZtcXf/7loO46rTiDryaZuNVvjbUo7IOw07eXqptTXrqPP\nFZFT6NKfnVg+qFFZuXlnXL7p0CVHH2lkaGNgilHboeoABPjednnx+36rym3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Srr5DWUvjjPj7tyKPbw0IfpAikpRWbVZcvTC3cWkF1wd4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\nAEBP/B+DFaMKINwSnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcd8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotcd19 <- ggplot() +\n",
    "geom_boxplot(data = cd19ihc_subset, aes(x = type ,y = H.score), size = 1, fill = \"light grey\") +\n",
    "theme(legend.position = \"bottom\", legend.title = element_blank()) +\n",
    "theme(axis.title.x = element_blank(), axis.title = element_text(size = 35),\n",
    "      axis.line = element_line(color = \"black\"),\n",
    "      axis.text.x = element_text(angle = 45, hjust = 1, size = 35, color = \"black\"),\n",
    "      axis.text.y = element_text(size = 35, color = \"black\")) +\n",
    "myplot +\n",
    "scale_y_continuous(breaks = seq(0,60, by = 10)) +\n",
    "labs(y = \"H score CD19\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/u2UPIr3bp1S/SA1qlWLaXHcxSqVq2aonJyWFgxnZYLAACMhIpgl5WVJfvs4FDg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\ntlOIQbaTTHU1Jl1Uuv029WSwM+2drIKPYmIKZWg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Rs2tTEpVVowVsTFL3M9X9uEgUZzuSJDNiwg/rUMEAACAASURBVNdO6ORjr7gjt5lb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\nSq+fE21kbmttkpOel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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotcd19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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 = paste0(plotpath,\"CD19_H_plot.pdf\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(plotcd19)\n",
    "    dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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 = paste0(plotpath,\"CD8_H_plot.pdf\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(plotcd8)\n",
    "    dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "    pdf(file = paste0(plotpath,\"CD4_H_plot.pdf\"),\n",
    "        width = 10, \n",
    "        height = 10,\n",
    "        useDingbats = FALSE)\n",
    "    print(plotcd4)\n",
    "    dev.off()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 4.0",
   "language": "R",
   "name": "ir40"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "284px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}